diff --git a/binder/overview.ipynb b/binder/overview.ipynb
index a1a93c8c91..219387ccc3 100644
--- a/binder/overview.ipynb
+++ b/binder/overview.ipynb
@@ -39,10 +39,7 @@
     "* [Efficient spline interpolation](../doc/examples/example_splines/ExampleSplines.ipynb)\n",
     "\n",
     "  Shows how to add annotated spline formulas to existing SBML models to speed up AMICI's model import.\n",
-    "\n",
-    "* [A real-world application of splines](../doc/examples/example_splines_swameye/ExampleSplinesSwameye2003.ipynb)\n",
-    "\n",
-    "  An illustration of how to apply AMICI's spline functionalities to parameter estimation for a reaction network.\n"
+    "\n"
    ]
   }
  ],
diff --git a/doc/examples/example_splines/ExampleSplines.ipynb b/doc/examples/example_splines/ExampleSplines.ipynb
index de5125aedb..f5aab2534f 100644
--- a/doc/examples/example_splines/ExampleSplines.ipynb
+++ b/doc/examples/example_splines/ExampleSplines.ipynb
@@ -6,7 +6,9 @@
    "source": [
     "# AMICI Python example \"splines\"\n",
     "\n",
-    "This is an example showing how to add spline assignment rules to a pre-existing SBML model."
+    "This is an example showing how to add spline assignment rules to a pre-existing SBML model.\n",
+    "\n",
+    "A full application example of using splines to parametrize time-dependent unknown inputs followed by parameter estimation with pypesto can be found in the [pypesto documentation](https://pypesto.readthedocs.io/en/latest/example.html#application-examples)."
    ]
   },
   {
diff --git a/doc/examples/example_splines_swameye/ExampleSplinesSwameye2003.ipynb b/doc/examples/example_splines_swameye/ExampleSplinesSwameye2003.ipynb
deleted file mode 100644
index 86cfb94763..0000000000
--- a/doc/examples/example_splines_swameye/ExampleSplinesSwameye2003.ipynb
+++ /dev/null
@@ -1,2408 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "c10af447-e4f1-46e1-bf60-910dc67f5d77",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Spline implementation of JAK2-STAT5 signaling pathway\n",
-    "In this notebook a practical example of the usage of AMICI spline functionalities is shown.\n",
-    "The model under consideration is the JAK2-STAT5 signaling pathway ([Swameye et al., 2003](https://doi.org/10.1073/pnas.0237333100)),\n",
-    "in which the dynamics of the system depend on a measured input function (the quantity `pEpoR` in the model).\n",
-    "\n",
-    "Following the approach of ([Schelker et al., 2012](https://doi.org/10.1093/bioinformatics/bts393)), a continuous approximation of this input function is estimated together with the other parameters.\n",
-    "As in the original paper, we will use a spline with logarithmic parameterization in order to enforce the positivity constraint.\n",
-    "\n",
-    "The model of the signaling pathway will be implemented in SBML using AMICI's spline annotations, experimental data integrated using the PEtab format and parameter estimation will be carried out using the [pyPESTO](https://pypesto.readthedocs.io/) library."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7323a4fb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# %pip install pypesto\n",
-    "%pip install pypesto git+https://github.com/dweindl/pyPESTO.git@amici100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "78dbeefe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install fides"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "5f94310d-d95c-4fb5-bd1e-a29bb1c92a30",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import copy\n",
-    "import logging\n",
-    "import os\n",
-    "\n",
-    "import amici\n",
-    "import amici.importers.sbml.splines as splines\n",
-    "import libsbml\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import petab\n",
-    "import pypesto.petab\n",
-    "import sympy as sp\n",
-    "from amici.importers.utils import amici_time_symbol\n",
-    "from matplotlib import pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "02162090-1008-4eb7-b954-c10370aae9c5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Number of multi-starts for MAP estimation\n",
-    "n_starts = 50\n",
-    "# n_starts = 0 # when loading results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c2a366ac",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Set default pypesto engine/optimizer\n",
-    "pypesto_optimizer = pypesto.optimize.FidesOptimizer(verbose=logging.WARNING)\n",
-    "pypesto_engine = pypesto.engine.MultiProcessEngine()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "21eca425",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# If running as a GitHub action, just do the minimal amount of work required to check whether the code is working\n",
-    "if os.getenv(\"GITHUB_ACTIONS\") is not None:\n",
-    "    n_starts = 5\n",
-    "    pypesto_optimizer = pypesto.optimize.FidesOptimizer(\n",
-    "        verbose=logging.WARNING, options=dict(maxiter=10)\n",
-    "    )\n",
-    "    pypesto_engine = pypesto.engine.MultiProcessEngine()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "ff552b79-96a1-42b4-a5cb-a9f2143f10bb",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# A dictionary to store different approaches for a final comparison\n",
-    "all_results = {}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3ab1206d-f0dd-4b77-a668-be3076c28fa3",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Spline approximation with few nodes, using finite differences for the derivatives\n",
-    "As a first attempt, we fix a small amount of nodes, create new parameters for the values of the splines at the nodes and let AMICI compute the derivative at the nodes by using finite differences."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5684adb0-4c66-48d3-9076-270839a77a5f",
-   "metadata": {},
-   "source": [
-    "### Creating the PEtab model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "c796cf8b-d7e9-473a-a368-b8919ef93efe",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Problem name\n",
-    "name = \"Swameye_PNAS2003_5nodes_FD\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c684a2e4-e4e8-4f4a-aa06-c1c9378a3bce",
-   "metadata": {},
-   "source": [
-    "First, we create a spline to represent the input function `pEpoR`, parametrized by its values at the nodes.\n",
-    "Since the value of the input function reaches its steady state by the end of the experiment, we extrapolate constantly after that (useful if we need to simulate the model after the last spline node)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "68562fc1-7213-4dac-9177-61fba454f9d4",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Create spline for pEpoR\n",
-    "nodes = [0, 5, 10, 20, 60]\n",
-    "values_at_nodes = [\n",
-    "    sp.Symbol(f\"pEpoR_t{str(t).replace('.', '_dot_')}\") for t in nodes\n",
-    "]  # new parameter symbols for spline values\n",
-    "spline = splines.CubicHermiteSpline(\n",
-    "    sbml_id=\"pEpoR\",  # matches name of species in SBML model\n",
-    "    evaluate_at=amici_time_symbol,  # the spline is evaluated at the current time\n",
-    "    nodes=nodes,\n",
-    "    values_at_nodes=values_at_nodes,  # values at the nodes (in linear scale)\n",
-    "    extrapolate=(None, \"constant\"),  # because steady state is reached\n",
-    "    bc=\"auto\",  # automatically determined from extrapolate (bc at right end will be 'zero_derivative')\n",
-    "    logarithmic_parametrization=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4a3de7bd-3157-4950-a264-cf8ff8f2250e",
-   "metadata": {},
-   "source": [
-    "We can then add the spline to a skeleton SBML model based on the d2d implementation by (Schelker et al., 2012).\n",
-    "The skeleton SBML model defines a species `pEpoR` which interacts with the other species,\n",
-    "but has no reactions or rate rules of its own.\n",
-    "The code below creates an assignment rule for `pEpoR` using the spline formula, completing the model.\n",
-    "The parameters `pEpoR_t*` are automatically added to the SBML model too (using nominal values of `0.1` and declaring them to be constant)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "118feb66-b3d3-46d9-9b4b-5a3f43586a64",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Add spline formula to SBML model\n",
-    "sbml_doc = libsbml.SBMLReader().readSBML(\n",
-    "    os.path.join(\"Swameye_PNAS2003\", \"swameye2003_model.xml\")\n",
-    ")\n",
-    "sbml_model = sbml_doc.getModel()\n",
-    "spline.add_to_sbml_model(\n",
-    "    sbml_model, auto_add=True, y_nominal=0.1, y_constant=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dedc2418-dc8b-4f54-84f3-22dc26f2846b",
-   "metadata": {},
-   "source": [
-    "A skeleton PEtab problem is provided, containing parameter bounds, observable definitions and experimental data.\n",
-    "Of particular relevance is the noise model used for the measurements of `pEpoR`, normal additive noise with standard deviation equal to `0.0274 + 0.1 * pEpoR`;\n",
-    "this is the same choice used in (Schelker et al., 2012), where it was estimated from experimental replicates.\n",
-    "\n",
-    "However, the parameters associated to the spline are to be added too.\n",
-    "The code below defines parameter bounds for them according to the PEtab format and then creates a full PEtab problem integrating them together with the edited SBML file.\n",
-    "The condition, measurement and observable PEtab tables do not require additional modification and can be used as they are."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "5c2f1c0b-72fe-4df4-b25c-525e36f66514",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Extra parameters associated to the spline\n",
-    "spline_parameters_df = pd.DataFrame(\n",
-    "    dict(\n",
-    "        parameterScale=\"log\",\n",
-    "        lowerBound=0.001,\n",
-    "        upperBound=10,\n",
-    "        nominalValue=0.1,\n",
-    "        estimate=1,\n",
-    "    ),\n",
-    "    index=pd.Series(list(map(str, values_at_nodes)), name=\"parameterId\"),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "f832bfe0-ddda-48fc-9a73-6e62db19b445",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Create PEtab problem\n",
-    "petab_problem = petab.Problem(\n",
-    "    sbml_model,\n",
-    "    condition_df=petab.conditions.get_condition_df(\n",
-    "        os.path.join(\"Swameye_PNAS2003\", \"swameye2003_conditions.tsv\")\n",
-    "    ),\n",
-    "    measurement_df=petab.measurements.get_measurement_df(\n",
-    "        os.path.join(\"Swameye_PNAS2003\", \"swameye2003_measurements.tsv\")\n",
-    "    ),\n",
-    "    parameter_df=petab.core.concat_tables(\n",
-    "        [\n",
-    "            os.path.join(\"Swameye_PNAS2003\", \"swameye2003_parameters.tsv\"),\n",
-    "            spline_parameters_df,\n",
-    "        ],\n",
-    "        petab.parameters.get_parameter_df,\n",
-    "    ),\n",
-    "    observable_df=petab.observables.get_observable_df(\n",
-    "        os.path.join(\"Swameye_PNAS2003\", \"swameye2003_observables.tsv\")\n",
-    "    ),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c69bc77d-3efc-4ac5-bde4-c0e43219f560",
-   "metadata": {},
-   "source": [
-    "The resulting PEtab problem can be checked for errors and exported to disk if needed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "3c10e6ad-c891-43ff-b1d7-199943bdfa4b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Check whether PEtab model is valid\n",
-    "assert not petab.lint_problem(petab_problem)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "0f2ec7eb-b4af-46ac-a96e-6e484f774883",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Save PEtab problem to disk\n",
-    "# import shutil\n",
-    "# shutil.rmtree(name, ignore_errors=True)\n",
-    "# os.mkdir(name)\n",
-    "# petab_problem.to_files_generic(prefix_path=name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7ebcbcc9-82d9-44cf-a0ad-2a80711cb382",
-   "metadata": {},
-   "source": [
-    "### Creating the pyPESTO problem\n",
-    "We can now create a pyPESTO problem directly from the PEtab problem.\n",
-    "Due to technical limitations in AMICI, currently the PEtab problem has to be \"flattened\" before it can be simulated from, but such operation is merely syntactical and thus does not change the essence of the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "5a46a020-c670-4f52-9bc7-a1dd74faedd0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Problem must be \"flattened\" to be used with AMICI\n",
-    "petab.core.flatten_timepoint_specific_output_overrides(petab_problem)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "fab8bd39-eb26-4462-b158-0fae8a8ba027",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Check whether simulation from the PEtab problem works\n",
-    "# import amici.petab_simulate\n",
-    "# simulator = amici.petab_simulate.PetabSimulator(petab_problem)\n",
-    "# simulator.simulate(noise=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c6741bce012b6ffa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import PEtab problem into pyPESTO\n",
-    "pypesto_problem = pypesto.petab.PetabImporter(\n",
-    "    petab_problem, model_name=name\n",
-    ").create_problem(\n",
-    "    # re-sample optimization startpoints in case of simulation errors\n",
-    "    startpoint_kwargs={\"check_fval\": True, \"check_grad\": True}\n",
-    ")\n",
-    "\n",
-    "# Increase maximum number of steps for AMICI\n",
-    "pypesto_problem.objective.amici_solver.set_max_steps(10**5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "715a8a22-878c-4c37-98e8-7f3898a2a2dc",
-   "metadata": {},
-   "source": [
-    "### Maximum Likelihood estimation\n",
-    "Using pyPESTO we can optimize for the parameter vector that maximizes the probability of observing the experimental data (maximum likelihood estimation).\n",
-    "\n",
-    "A multistart method with local gradient-based optimization is used and the results of each multistart can be visualized in a waterfall plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "bf1e3a52-8f72-466e-b63c-2f15fbe59137",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Load existing results if available\n",
-    "if os.path.exists(f\"{name}.h5\"):\n",
-    "    pypesto_result = pypesto.store.read_result(\n",
-    "        f\"{name}.h5\", problem=pypesto_problem\n",
-    "    )\n",
-    "else:\n",
-    "    pypesto_result = None\n",
-    "# Overwrite\n",
-    "# pypesto_result = None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d6e20c36-d6fe-4264-b366-c23e707246ff",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Parallel multistart optimization with pyPESTO and FIDES\n",
-    "if n_starts > 0:\n",
-    "    if pypesto_result is None:\n",
-    "        new_ids = [str(i) for i in range(n_starts)]\n",
-    "    else:\n",
-    "        last_id = max(int(i) for i in pypesto_result.optimize_result.id)\n",
-    "        new_ids = [str(i) for i in range(last_id + 1, last_id + n_starts + 1)]\n",
-    "    pypesto_result = pypesto.optimize.minimize(\n",
-    "        pypesto_problem,\n",
-    "        n_starts=n_starts,\n",
-    "        ids=new_ids,\n",
-    "        optimizer=pypesto_optimizer,\n",
-    "        engine=pypesto_engine,\n",
-    "        result=pypesto_result,\n",
-    "    )\n",
-    "    pypesto_result.optimize_result.sort()\n",
-    "    if pypesto_result.optimize_result.x[0] is None:\n",
-    "        raise Exception(\n",
-    "            \"All multistarts failed (n_starts is probably too small)! If this error occurred during CI, just run the workflow again.\"\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "0592af7c-227a-4e52-b383-d43061a4c52f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Save results to disk\n",
-    "# pypesto.store.write_result(pypesto_result, f'{name}.h5', overwrite=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "7762a31f-55e8-4e12-8107-3f3e6f0af5e5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Print result table\n",
-    "# pypesto_result.optimize_result.as_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "2cfbcf68-6d13-49eb-b18f-26d9d32e79ae",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 650x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualize the results of the multistarts\n",
-    "pypesto.visualize.waterfall(pypesto_result, size=[6.5, 3.5]);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b58b02a-d09c-4974-862a-0fbd29ce624b",
-   "metadata": {},
-   "source": [
-    "Below the maximum likelihood estimates for `pEpoR` and the other observables are plotted, together with the experimental measurements.\n",
-    "\n",
-    "To assess whether the noise model used in the observable is reasonable, we have also plotted 2-sigma error bands for `pEpoR`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "6464be7c-1365-4bf6-8625-cc7cf5a9c988",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Functions for simulating observables given a parameter vector\n",
-    "def _simulate(x=None, *, problem=None, result=None, N=500, **kwargs):\n",
-    "    if result is None:\n",
-    "        result = pypesto_result\n",
-    "    if problem is None:\n",
-    "        problem = pypesto_problem\n",
-    "    if x is None:\n",
-    "        x = result.optimize_result.x[0]\n",
-    "    if N is None:\n",
-    "        objective = problem.objective\n",
-    "    else:\n",
-    "        objective = problem.objective.set_custom_timepoints(\n",
-    "            timepoints_global=np.linspace(0, 60, N)\n",
-    "        )\n",
-    "    if len(x) != len(problem.x_free_indices):\n",
-    "        x = x[problem.x_free_indices]\n",
-    "    simresult = objective(x, return_dict=True, **kwargs)\n",
-    "    return problem, simresult[\"rdatas\"][0]\n",
-    "\n",
-    "\n",
-    "def simulate_pEpoR(x=None, **kwargs):\n",
-    "    problem, rdata = _simulate(x, **kwargs)\n",
-    "    assert problem.objective.amici_model.get_observable_ids()[0].startswith(\n",
-    "        \"pEpoR\"\n",
-    "    )\n",
-    "    return rdata[\"t\"], rdata[\"y\"][:, 0]\n",
-    "\n",
-    "\n",
-    "def simulate_pSTAT5(x=None, **kwargs):\n",
-    "    problem, rdata = _simulate(x, **kwargs)\n",
-    "    assert problem.objective.amici_model.get_observable_ids()[1].startswith(\n",
-    "        \"pSTAT5\"\n",
-    "    )\n",
-    "    return rdata[\"t\"], rdata[\"y\"][:, 1]\n",
-    "\n",
-    "\n",
-    "def simulate_tSTAT5(x=None, **kwargs):\n",
-    "    problem, rdata = _simulate(x, **kwargs)\n",
-    "    assert problem.objective.amici_model.get_observable_ids()[-1].startswith(\n",
-    "        \"tSTAT5\"\n",
-    "    )\n",
-    "    return rdata[\"t\"], rdata[\"y\"][:, -1]\n",
-    "\n",
-    "\n",
-    "# Experimental data\n",
-    "df_measurements = petab.measurements.get_measurement_df(\n",
-    "    os.path.join(\"Swameye_PNAS2003\", \"swameye2003_measurements.tsv\")\n",
-    ")\n",
-    "df_pEpoR = df_measurements[\n",
-    "    df_measurements[\"observableId\"].str.startswith(\"pEpoR\")\n",
-    "]\n",
-    "df_pSTAT5 = df_measurements[\n",
-    "    df_measurements[\"observableId\"].str.startswith(\"pSTAT5\")\n",
-    "]\n",
-    "df_tSTAT5 = df_measurements[\n",
-    "    df_measurements[\"observableId\"].str.startswith(\"tSTAT5\")\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "1fca8d58-1d37-48cb-8880-e9bacf256117",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 650x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot ML fit for pEpoR\n",
-    "fig, ax = plt.subplots(figsize=(6.5, 3.5))\n",
-    "t, pEpoR = simulate_pEpoR()\n",
-    "sigma_pEpoR = 0.0274 + 0.1 * pEpoR\n",
-    "ax.fill_between(\n",
-    "    t,\n",
-    "    pEpoR - 2 * sigma_pEpoR,\n",
-    "    pEpoR + 2 * sigma_pEpoR,\n",
-    "    color=\"black\",\n",
-    "    alpha=0.10,\n",
-    "    interpolate=True,\n",
-    "    label=\"2-sigma error bands\",\n",
-    ")\n",
-    "ax.plot(t, pEpoR, color=\"black\", label=\"MLE\")\n",
-    "ax.plot(\n",
-    "    df_pEpoR[\"time\"],\n",
-    "    df_pEpoR[\"measurement\"],\n",
-    "    \"o\",\n",
-    "    color=\"black\",\n",
-    "    markerfacecolor=\"none\",\n",
-    "    label=\"experimental data\",\n",
-    ")\n",
-    "ylim1 = ax.get_ylim()[0]\n",
-    "ax.plot(\n",
-    "    nodes,\n",
-    "    len(nodes) * [ylim1],\n",
-    "    \"x\",\n",
-    "    color=\"black\",\n",
-    "    label=\"spline nodes\",\n",
-    "    zorder=10,\n",
-    "    clip_on=False,\n",
-    ")\n",
-    "ax.set_ylim(ylim1, ax.get_ylim()[1])\n",
-    "ax.set_xlabel(\"time\")\n",
-    "ax.set_ylabel(\"pEpoR\")\n",
-    "ax.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "75c999c0-af7b-4ee2-9e14-52c1c7d2e5bc",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 650x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot ML fit for pSTAT5\n",
-    "fig, ax = plt.subplots(figsize=(6.5, 3.5))\n",
-    "t, pSTAT5 = simulate_pSTAT5()\n",
-    "ax.plot(t, pSTAT5, color=\"black\", label=\"MLE\")\n",
-    "ax.plot(\n",
-    "    df_pSTAT5[\"time\"],\n",
-    "    df_pSTAT5[\"measurement\"],\n",
-    "    \"o\",\n",
-    "    color=\"black\",\n",
-    "    markerfacecolor=\"none\",\n",
-    "    label=\"experimental data\",\n",
-    ")\n",
-    "ylim1 = ax.get_ylim()[0]\n",
-    "ax.plot(\n",
-    "    nodes,\n",
-    "    len(nodes) * [ylim1],\n",
-    "    \"x\",\n",
-    "    color=\"black\",\n",
-    "    label=\"spline nodes\",\n",
-    "    zorder=10,\n",
-    "    clip_on=False,\n",
-    ")\n",
-    "ax.set_ylim(ylim1, ax.get_ylim()[1])\n",
-    "ax.set_xlabel(\"time\")\n",
-    "ax.set_ylabel(\"pSTAT5\")\n",
-    "ax.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "226588b1-32b4-4aba-beac-fd70f0e79697",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 650x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot ML fit for tSTAT5\n",
-    "fig, ax = plt.subplots(figsize=(6.5, 3.5))\n",
-    "t, tSTAT5 = simulate_tSTAT5()\n",
-    "ax.plot(t, tSTAT5, color=\"black\", label=\"MLE\")\n",
-    "ax.plot(\n",
-    "    df_tSTAT5[\"time\"],\n",
-    "    df_tSTAT5[\"measurement\"],\n",
-    "    \"o\",\n",
-    "    color=\"black\",\n",
-    "    markerfacecolor=\"none\",\n",
-    "    label=\"experimental data\",\n",
-    ")\n",
-    "ylim1 = ax.get_ylim()[0]\n",
-    "ax.plot(\n",
-    "    nodes,\n",
-    "    len(nodes) * [ylim1],\n",
-    "    \"x\",\n",
-    "    color=\"black\",\n",
-    "    label=\"spline nodes\",\n",
-    "    zorder=10,\n",
-    "    clip_on=False,\n",
-    ")\n",
-    "ax.set_ylim(ylim1, ax.get_ylim()[1])\n",
-    "ax.set_xlabel(\"time\")\n",
-    "ax.set_ylabel(\"tSTAT5\")\n",
-    "ax.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "65bcf2fe-0189-4f92-bee1-d4229717c535",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Store results for later\n",
-    "all_results[\"5 nodes, FD\"] = (pypesto_problem, pypesto_result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec09ff5d-d370-4fac-ab90-6806a2d5945a",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Spline approximation with many nodes, using finite differences for the derivatives\n",
-    "Five nodes is arguably not enough to represent all plausible input choices. Increasing the number of nodes would give the spline more freedom and it can be done with minimal changes to the example above. However, more degrees of freedom mean more chance of overfitting. Thus, following (Schelker et al., 2012), we will add a regularization term consisting in the squared L2 norm of the spline's curvature, which promotes smoother and less oscillating functions. The value for the regularization strength $\\lambda$ is chosen by comparing the sum of squared normalized residuals with its expected value, which can be computing by assuming it is roughly $\\chi^2$-distributed."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f0e8d4ef-33f4-4e4a-8754-539251aa4d11",
-   "metadata": {},
-   "source": [
-    "### Creating the PEtab model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "9ea0c348-88d1-4d0e-845f-dd1dc1a43edc",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Problem name\n",
-    "name = \"Swameye_PNAS2003_15nodes_FD\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "cefd1af3-dac6-4e0d-92a0-33016dce168d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Create spline for pEpoR\n",
-    "nodes = [0, 2.5, 5.0, 7.5, 10.0, 12.5, 15.0, 17.5, 20, 25, 30, 35, 40, 50, 60]\n",
-    "values_at_nodes = [\n",
-    "    sp.Symbol(f\"pEpoR_t{str(t).replace('.', '_dot_')}\") for t in nodes\n",
-    "]\n",
-    "spline = splines.CubicHermiteSpline(\n",
-    "    sbml_id=\"pEpoR\",\n",
-    "    evaluate_at=amici_time_symbol,\n",
-    "    nodes=nodes,\n",
-    "    values_at_nodes=values_at_nodes,\n",
-    "    extrapolate=(None, \"constant\"),\n",
-    "    bc=\"auto\",\n",
-    "    logarithmic_parametrization=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0851dd99-83e8-4e7c-8678-7f0914d2bf01",
-   "metadata": {},
-   "source": [
-    "The regularization term can be easily computed by symbolic manipulation of the spline expression using AMICI and SymPy. Since it is very commonly used, we already provide a function for it in AMICI. Note: we regularize the curvature of the spline, which for positivity-enforcing spline is the logarithm of the function.\n",
-    "\n",
-    "In order add the regularization term to the PEtab likelihood, a dummy observable has to be created."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "525ed7b4-999d-40ad-9383-8f5c1157088f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Compute L2 norm of the curvature of pEpoR\n",
-    "regularization = spline.squared_L2_norm_of_curvature()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "46182f2e-a8a4-4a7f-8072-e8034ed09c1a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Add a parameter for regularization strength\n",
-    "reg_parameters_df = pd.DataFrame(\n",
-    "    dict(\n",
-    "        parameterScale=\"log10\",\n",
-    "        lowerBound=1e-6,\n",
-    "        upperBound=1e6,\n",
-    "        nominalValue=1.0,\n",
-    "        estimate=0,\n",
-    "    ),\n",
-    "    index=pd.Series([\"regularization_strength\"], name=\"parameterId\"),\n",
-    ")\n",
-    "# Encode regularization term as an additional observable\n",
-    "reg_observables_df = pd.DataFrame(\n",
-    "    dict(\n",
-    "        observableFormula=f\"sqrt({regularization})\".replace(\"**\", \"^\"),\n",
-    "        observableTransformation=\"lin\",\n",
-    "        noiseFormula=\"1/sqrt(regularization_strength)\",\n",
-    "        noiseDistribution=\"normal\",\n",
-    "    ),\n",
-    "    index=pd.Series([\"regularization\"], name=\"observableId\"),\n",
-    ")\n",
-    "# and correspoding measurement\n",
-    "reg_measurements_df = pd.DataFrame(\n",
-    "    dict(\n",
-    "        observableId=\"regularization\",\n",
-    "        simulationConditionId=\"condition1\",\n",
-    "        measurement=0,\n",
-    "        time=0,\n",
-    "        observableTransformation=\"lin\",\n",
-    "    ),\n",
-    "    index=pd.Series([0]),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "c8214d30-1cb8-4575-9fef-2e485cabf319",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Add spline formula to SBML model\n",
-    "sbml_doc = libsbml.SBMLReader().readSBML(\n",
-    "    os.path.join(\"Swameye_PNAS2003\", \"swameye2003_model.xml\")\n",
-    ")\n",
-    "sbml_model = sbml_doc.getModel()\n",
-    "spline.add_to_sbml_model(\n",
-    "    sbml_model, auto_add=True, y_nominal=0.1, y_constant=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "a75cf7db-918e-4048-a77b-082cab06dcb8",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Extra parameters associated to the spline\n",
-    "spline_parameters_df = pd.DataFrame(\n",
-    "    dict(\n",
-    "        parameterScale=\"log\",\n",
-    "        lowerBound=0.001,\n",
-    "        upperBound=10,\n",
-    "        nominalValue=0.1,\n",
-    "        estimate=1,\n",
-    "    ),\n",
-    "    index=pd.Series(list(map(str, values_at_nodes)), name=\"parameterId\"),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "4fdd82f8-adc5-471f-aa80-6856834e6913",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Create PEtab problem\n",
-    "petab_problem = petab.Problem(\n",
-    "    sbml_model,\n",
-    "    condition_df=petab.conditions.get_condition_df(\n",
-    "        os.path.join(\"Swameye_PNAS2003\", \"swameye2003_conditions.tsv\")\n",
-    "    ),\n",
-    "    measurement_df=petab.core.concat_tables(\n",
-    "        [\n",
-    "            os.path.join(\"Swameye_PNAS2003\", \"swameye2003_measurements.tsv\"),\n",
-    "            reg_measurements_df,\n",
-    "        ],\n",
-    "        petab.measurements.get_measurement_df,\n",
-    "    ).reset_index(drop=True),\n",
-    "    parameter_df=petab.core.concat_tables(\n",
-    "        [\n",
-    "            os.path.join(\"Swameye_PNAS2003\", \"swameye2003_parameters.tsv\"),\n",
-    "            spline_parameters_df,\n",
-    "            reg_parameters_df,\n",
-    "        ],\n",
-    "        petab.parameters.get_parameter_df,\n",
-    "    ),\n",
-    "    observable_df=petab.core.concat_tables(\n",
-    "        [\n",
-    "            os.path.join(\"Swameye_PNAS2003\", \"swameye2003_observables.tsv\"),\n",
-    "            reg_observables_df,\n",
-    "        ],\n",
-    "        petab.observables.get_observable_df,\n",
-    "    ),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "43e47476-8f75-4d63-a57d-09b9b55f086b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Check whether PEtab model is valid\n",
-    "assert not petab.lint_problem(petab_problem)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "e4f9f7a8-dcab-431e-8f80-e10605bdb69c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Save PEtab problem to disk\n",
-    "# import shutil\n",
-    "# shutil.rmtree(name, ignore_errors=True)\n",
-    "# os.mkdir(name)\n",
-    "# petab_problem.to_files_generic(prefix_path=name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "784aebee-b501-4fa8-808e-ff6277c9432c",
-   "metadata": {},
-   "source": [
-    "### Creating the pyPESTO problem"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "19654099-93d0-469e-bb03-82f1d9319419",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Problem must be \"flattened\" to be used with AMICI\n",
-    "petab.core.flatten_timepoint_specific_output_overrides(petab_problem)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "fd8e3502-f9de-457e-9652-95bea94c0972",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Check whether simulation from the PEtab problem works\n",
-    "# import amici.petab_simulate\n",
-    "# simulator = amici.petab_simulate.PetabSimulator(petab_problem)\n",
-    "# simulator.simulate(noise=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "a7104b34-d33c-46d8-afa5-1278070d6af1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Import PEtab problem into pyPESTO\n",
-    "pypesto_problem = pypesto.petab.PetabImporter(\n",
-    "    petab_problem, model_name=name\n",
-    ").create_problem(\n",
-    "    # re-sample optimization startpoints in case of simulation errors\n",
-    "    startpoint_kwargs={\"check_fval\": True, \"check_grad\": True}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c890937-c9e8-4617-992f-65d39479ed55",
-   "metadata": {},
-   "source": [
-    "### Maximum Likelihood estimation\n",
-    "We will optimize the problem for different values of the regularization strength $\\lambda$, then compute the sum of squared normalized residuals for each of the resulting parameter vectors. The one for which such a value is nearest to its expected value of $15$ (the number of observations from the input function) will be chosen as the final estimate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "116f6d10-9aba-4db7-88fb-f503b1b5d408",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Try different regularization strengths\n",
-    "regstrengths = np.asarray([1, 10, 40, 75, 150, 500])\n",
-    "if os.getenv(\"GITHUB_ACTIONS\") is not None:\n",
-    "    regstrengths = np.asarray([75])\n",
-    "regproblems = {}\n",
-    "regresults = {}\n",
-    "\n",
-    "for regstrength in regstrengths:\n",
-    "    # Fix parameter in pypesto problem\n",
-    "    name = f\"Swameye_PNAS2003_15nodes_FD_reg{regstrength}\"\n",
-    "    pypesto_problem.fix_parameters(\n",
-    "        pypesto_problem.x_names.index(\"regularization_strength\"),\n",
-    "        np.log10(\n",
-    "            regstrength\n",
-    "        ),  # parameter is specified as log10 scale in PEtab\n",
-    "    )\n",
-    "    regproblem = copy.deepcopy(pypesto_problem)\n",
-    "\n",
-    "    # Load existing results if available\n",
-    "    if os.path.exists(f\"{name}.h5\"):\n",
-    "        regresult = pypesto.store.read_result(f\"{name}.h5\", problem=regproblem)\n",
-    "    else:\n",
-    "        regresult = None\n",
-    "    # Overwrite\n",
-    "    # regresult = None\n",
-    "\n",
-    "    # Parallel multistart optimization with pyPESTO and FIDES\n",
-    "    if n_starts > 0:\n",
-    "        if regresult is None:\n",
-    "            new_ids = [str(i) for i in range(n_starts)]\n",
-    "        else:\n",
-    "            last_id = max(int(i) for i in regresult.optimize_result.id)\n",
-    "            new_ids = [\n",
-    "                str(i) for i in range(last_id + 1, last_id + n_starts + 1)\n",
-    "            ]\n",
-    "        regresult = pypesto.optimize.minimize(\n",
-    "            regproblem,\n",
-    "            n_starts=n_starts,\n",
-    "            ids=new_ids,\n",
-    "            optimizer=pypesto_optimizer,\n",
-    "            engine=pypesto_engine,\n",
-    "            result=regresult,\n",
-    "        )\n",
-    "        regresult.optimize_result.sort()\n",
-    "        if regresult.optimize_result.x[0] is None:\n",
-    "            raise Exception(\n",
-    "                \"All multistarts failed (n_starts is probably too small)! If this error occurred during CI, just run the workflow again.\"\n",
-    "            )\n",
-    "\n",
-    "    # Save results to disk\n",
-    "    # pypesto.store.write_result(regresult, f'{name}.h5', overwrite=True)\n",
-    "\n",
-    "    # Store result\n",
-    "    regproblems[regstrength] = regproblem\n",
-    "    regresults[regstrength] = regresult"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "5d3ef681-81f0-423d-9b78-18f3b3939adb",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Target value is 15\n",
-      "Regularization strength: 1. Statistic is 6.794369874307712\n",
-      "Regularization strength: 10. Statistic is 8.435094498146606\n",
-      "Regularization strength: 40. Statistic is 11.83872830962955\n",
-      "Regularization strength: 75. Statistic is 15.030926511510327\n",
-      "Regularization strength: 150. Statistic is 19.971139477161476\n",
-      "Regularization strength: 500. Statistic is 32.44623424533765\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Compute sum of squared normalized residuals\n",
-    "print(f\"Target value is {len(df_pEpoR['time'])}\")\n",
-    "regstrengths = sorted(regproblems.keys())\n",
-    "stats = []\n",
-    "for regstrength in regstrengths:\n",
-    "    t, pEpoR = simulate_pEpoR(\n",
-    "        N=None,\n",
-    "        problem=regproblems[regstrength],\n",
-    "        result=regresults[regstrength],\n",
-    "    )\n",
-    "    assert np.array_equal(df_pEpoR[\"time\"], t[:-1])\n",
-    "    pEpoR = pEpoR[:-1]\n",
-    "    sigma_pEpoR = 0.0274 + 0.1 * pEpoR\n",
-    "    stat = np.sum(((pEpoR - df_pEpoR[\"measurement\"]) / sigma_pEpoR) ** 2)\n",
-    "    print(f\"Regularization strength: {regstrength}. Statistic is {stat}\")\n",
-    "    stats.append(stat)\n",
-    "# Select best regularization strength\n",
-    "chosen_regstrength = regstrengths[\n",
-    "    np.abs(np.asarray(stats) - len(df_pEpoR[\"time\"])).argmin()\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "6e362c17-4222-48ce-8db1-9f9956078e2b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 650x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualize the results of the multistarts for a chosen regularization strength\n",
-    "ax = pypesto.visualize.waterfall(\n",
-    "    regresults[chosen_regstrength], size=[6.5, 3.5]\n",
-    ")\n",
-    "ax.set_title(\n",
-    "    f\"Waterfall plot (regularization strength = {chosen_regstrength})\"\n",
-    ")\n",
-    "ax.set_ylim(ax.get_ylim()[0], 100);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "9a4fb86c-4b74-43f8-97d8-eba60529abaf",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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kJJx1HGBLu4Bra2ubrC94anvWoKAgOnbsCDTs2DVr1iwGDhzI4MGDeeWVV7BYLMyePbsFTyY8SRJAcdGq6h14OSow6jTgHQrA0fKGrokewT3QahomchQVFTFmzBhSU1Px8vLiN7/5DXfddRdRUVFkZ2fzySef8Kc//Ym8vDzWvFJMz2HRhIa8yS8mPSmtgEKIdmnDhg18/fXXpKamNjmflJTU6kvB7N69mzFjxjS+nj9/PgCzZs1q3Jlr+vTplJSUsHjxYgoLC+nbty+bNm06bWKIuPIpajvbcqG6uhp/f3+qqqrw85MJBq3hrW2ZBOZ+zXV+efh1vwZ77HDeTHkTt+rmjh53EGQKwmazMXr0aLZv3050dDSff/45iYmJp9VVW1vLM888w8svv4zb7SY0OoB/fbSGcQPHeeDJhBBXE6vVSmZmJvHx8ZhMJk+HI0SLnOtz3JwcRyaBiIvidqtU1zsbuoB/aAHMrs7GrbrxN/oTaAwEGrYw2r59OwEBAXz99ddnTP4AfHx8ePHFF/nssw34+3tRklvJLddP4YtvvricjyWEEEJc1SQBFBelxurEraqYXVUYdBowB5NVnQVAvH88iqKQnJzMK6+8AsC//vUvunbtes46VVXlhtHX8eYffkvHTsHUVdczecJk1q1bd4mfRgghhGgfZAyguChV9Q60Lis+ih0FM6opkNyaXABi/WJRVZVHH30UVVW55557Ggcx/y+31Yr1SCr2zAycJSWoDied86v597VDWO1zmB3Hi3lg+nQKXnmFhx9++HI+ohBCCHHVkQRQXJSqegdezipMeg0YfalwWahz1qFVtISbw/nggw/YtWsX3t7e/OEPfzjtetXtpv7AAep27kK125u85x0QhqPYyG0DutHRx4+ctAK+fOopqo8e5Td/+hMajTRgCyGEEC0hCaC4KFX1DkzOqoYt28xB5NXkARDhHYEGDUuXLgXgiSeeOG2WmNtqpXrTJhw5DS2GupBgTD17oo+JQevrS4DDSdpaf0Kyk+kTH4LJzxd2HSNv9XusOJHBr95bjV4m8gghhBDNJk0o4qI0LAFThVGvAa8g8iwNCWAHnw589tlnHD58GF9fXx599NEm17ktFirXrcORk4ui1+EzZgwBd9yBV+/e6AIDUXQ69F4mTEmjcUUE0bF7DAFP3ULgtGG4FDi5cydvz7wTR1mZJx5bCCGEaNMkARQX5VQLoOmHFsAiS8MWQZE+kY0TP+bOnUtAQEDjNW67napPP8VVVo7G25uAadPw6tXzjGv9dY6OpMIcR12dm9G+EUTNnYD2kRupcLk4sH07m59+GldV1eV4VCGEEOKqIQmguCjV1h9bAOv0XlgcFhQUqnKr2Lx5M4qiNJm0oaoqNV9+ibOkFI3ZC/9bpqALCTlr/TFBZioDe+NwqfgXZjMwJInEyUOw3TGCUpeTLz7+hBNvv4P7f8YPCiGEEOLsJAEULWZ3uqm3OX8YA6ihSHEBEGgK5O033wZgwoQJxMbGNl5jPXAAe0YmaDX4TZqELjDwnPfQahTCY7pSpw+mvNrCQJuDjn4dGX3feI51jqDCauXDN9+kesuWS/WYQgghxFVHEkDRYjVWBwaXBYPiQqfVUeKyAhDiFcKaNWsAmDNnTmN5V3U1lu3bAfAZORL9BW4d1C3Cjxz/AZRZ7Ki5e7g+Yhj+Xv5Mfe5OvsNFbl4uW995B1tGZis/oRBCCHF1kgRQtFiN1YmXs7JhAWivQIrqSwAoPVZKdnY23t7eTJgwAWjo+q395htUhxN9VBSmpKQLvk90oBf2gE6UawLZ8H0K6//2MoZMAwFhAQyZN4l99fVs2fJfCj75BHdd3SV5ViGE8LQnn3wSo9HIzJkzPR2KuApIAiharMbqxORo6P5VvQIprisGYNumbQBMnDgRLy8vAGzHjmPPzkHRafG5bswZJ3ycjUajUHLoO+769avc/NQ7zHzsee6YdAe/m/I7TH4mSuIiyKuzsHnjRizJya3/oEIIcQVYuHAhK1asYPXq1aSnp1/Se73wwgsoisKvfvWr095buXIlcXFxmEwmhgwZws6dOy9pLOLSkARQtFi11dE4/q/aYMLmsqFRNHzzxTcA3HLLLQCoLhd1Oxq6fs0DB5533N//WrduHYse+TnhnRL405KHKVn/NMmr/8iAPgN489dvkjimF1tqa9m3fx+ZmzfjKCpq3QcVQogrgL+/P3PmzEGj0XDo0KFLdp9du3bx+uuv07t379PeW7NmDfPnz2fJkiXs3buXPn36MH78eIqLiy9ZPOLSkARQtFjNqRnAOi3FP3ySlGqFwymHURSFcePGAWA9koqrqhqN2YxX377NuofL5WL+Y/O5/tpx/PrhP6EPvI19x2LwqQlk5aKXGHndKLat30b8uIEctVrZvPlrav/7X1RVbeWnFUIIz3M6nZjNZlJSUi5J/bW1tdx555288cYbBJ7hy/pLL73E/fffz+zZs0lMTGTVqlWYzWbefPPNSxKPuHQkARQtVm1tmAFs0GkoUZ0AnNx9EoBBgwYRFBSE6nRSt3s3AOaBA1D0+guuv6bcyr9fX8/J7JPcceP9RBkNODBT5Aqipk5H8YFU7h43j7K8MnoMS2CXzUZq+nEydu3ClpbW+g8shBAe9vTTT1NbW3vOBHDZsmX4+Pic88jOzj7jtQ8//DATJ05k7Nixp71nt9vZs2dPk/c0Gg1jx44lWYbftDmyFZxosdp6G9HOGow6X4rdDTOAD247CMANN9wAgPXwYdy1tWh8fDD17HlB9bpdbnLTKig8UcXJjBwABg3rT1RcCCUn9JRXKPjUfIPRZaNLWLeGi/K9GXLLWPZ8uoXNm78mrlcSxq5dUbTa1n1oIcRVQ1VVnG6nR+6t0+iaNRYaYM+ePaxatYqJEyeeMwGcO3cut99++znrioqKOu3ce++9x969e9m1a9cZryktLcXlcp22rWd4eDhHjx69gCcQVxJJAEWLuN0qdksVCip6vZ5ShwWA/Tv2A3D99dejut3UHzgA/ND6pzv/x81udXJ8VxGWShsAnXvEAVCnKyIkOp4hRoWP9zs54e7AJP9Mso80tDiG+kbSM6E/L36xl6NZWRzft48+/fvjldSrlZ9cCHG1cLqdvHHoDY/c+/6k+9FrL7xHxO128+CDDzJv3jyGDBnCXXfdhcPhQH+GXpWgoCCCgoKaFU9OTg6PPvooX375JSaTqVnXirZJuoBFi9TanRgcVWgUcJh9sbvtVBRVkJebh1arZfDgwdhPnsRVVY1iMmLq0eO8ddrqHKR+X4Cl0obOoKXroHCmz55MXFwcy5Ytw+12Ex/iTYivkWzvJHKrXfx59TriYzow/trxeJm8mXrLA6QqXmzd+i11u3ejOj3z7V4IIVrTq6++SmlpKUuXLiUpKQmHw3HWVreWdAHv2bOH4uJi+vfvj06nQ6fT8d///pc///nP6HQ6XC4XISEhaLVaiv5nol1RURERERGX7NnFpSEtgKJFaqxOjM4aDFoN5Tod4KbocMMvhb59++Lt7U3VwYbuYFNC4nnH/tnqnaR+X4C93onRrKf70AhM3g3XrFixgmnTpjFlyhQWLlxIYmgs7+44wu3//oTdu9NYu+w+rr0uiZyNOXTu3YWM/aNIzf2Ok6lHMKekNHviiRCifdBpdNyfdL/H7n2h8vLyWLRoEatXr8bb25uuXbtiNBpJSUkh6QxrqrakC/j6668/bWbx7Nmz6dGjB7/5zW/QarVotVoGDBjA5s2bmTJlCtDQMrl582bmzZt3wc8jrgySAIoWqbE6MDmrMeg0lGs1gJucQw3j9YYPH46zogJ7dg4oynm7YV0ON8d2FGKvd2LyMdBjaAQGrx8/mlOnTmXt2rUsWLCA4cOHN54Pjohm1ZMzmDokHsoPM/SaBDZ/sYf4nl0pczvY+t12Yrt3x5SYiGIwXJKfgxCi7VIUpVndsJ7yyCOPMGHCBCZOnAiATqcjISHhrOMAW9IF7OvrS69eTX9Xe3t7Exwc3OT8/PnzmTVrFgMHDmTw4MG88sorWCwWZs+e3cynEp4mCaBokep6JyZnNUadlgLcAKTtbZh5O2LECKw/fJM0xMWh9fc/az2qWyV9bzH1NXb0Rh3d/yf5O2Xq1KncfPPNbN26lYKCAqw6X8p9OxPmOIpLTUWbtY1ugx9gd889xJXEkHs0m0yLjfyMTLwOHsQ8cOAl+CkIIcSltWHDBr7++mtSU1ObnE9KSrpkS8Gcy/Tp0ykpKWHx4sUUFhbSt29fNm3adNrEEHHlkwRQtEiN1YHRWYPRW0M5Tmz1Do4dPgbAsKFDsX31FQBevc498zfvWAVVxXVotBq6DQ7HeIbk7xStVsvo0aMBcLlV3tqWSVZ9dwqt6XRQatAUHmRgx/5UDfov4dsjKc/S8t2eLKLi9+PVu7e0Agoh2pxJkyZRUVFx2vl33333kt97y5YtZzw/b9486fK9CsgkENEiNfUNXcB6rUKF287JwydxuVxER0cToSi46+rReJnQx8SctY7q0nry06sAiO8TgneA8YLvr9UoDIoLQlV07FJ74FZVyE6mq288/oHedLjenzKnk+xab3KzS6n3wDdlIYQQ4kolCaBokbq6WrRuO/VaJy6tnqwDWUDD+D/b8eMAGDp3Pus6fA67ixP7SkBVCe3oS3AHn2bH0DPKD2+jlix9F4rsRrDVoi06RL+wfoQN9EMfrpLvdLIjtQrLnn2oDkeLn1cIIYS4mkgCKJpNVVVstQ1dEhaDHjTaxgkgI4YPx57VsDafsUuXs9aRc7gch7Vh0kfHnsEtikOn1TAgNhBV0bLL3b1h+7fsZLr7d8Zbb6bzLWHk1VVzsqiKjBy7tAIKIYQQP/B4Arhy5Uri4uIwmUwMGTKEnTt3nrP8K6+8Qvfu3fHy8iImJobHHnsMq9V6maIVADanG621IQGsMepRVZWMlAwAhiUk4K6rQ9Hr0Z9hpXmAqpI6SnNrQFHo1DcEra7lH8OkDgGY9FoydJ0odjS0AuqKDtMnrA+dBsRTZc4n127jcEYlJcmHpBVQCCGEwMMJ4Jo1a5g/fz5Llixh79699OnTh/Hjx1NcXHzG8v/3f//Hk08+yZIlS0hNTeUf//gHa9as4amnnrrMkbdv1VYHJmcNeq1CpRYqiyqpLKtEq9XS1aehK1cfHX3G7l+Xy03WwTIAwuP88Am8uBXnDToN/ToGoCo69qjdUWloBewZ0B2jzkj/6b3YX5hKVl4eWflgOXT4ou4nhBBCXA08mgC+9NJL3H///cyePZvExERWrVqF2WzmzTffPGP577//nhEjRjBz5kzi4uIYN24cM2bMOG+roWhdDYtANywBU46b7NSGFeV79uwJBQUAGGJjz3htQXoVtjoHBi8d0d0DWyWevjEBGHQajmk6UeYwgq0GQ3EqSSFJJF2bRKUun+NVJWTnFZO55ajsDiKEEKLd81gCaLfb2bNnD2PHjv0xGI2GsWPHkpycfMZrhg8fzp49exoTvoyMDD777DNuvPHGyxKzaFBd78DkqkGrhWrc5BxtGP83qG9fHIWFABhiO552na3eSeGJhlm/HROD0epb5+Nn0mvpG9PQCrhbTWhsBUwKSsSgMzDqrpFsTv+WjJwcSspUSnccOn+lQgghxFXMY+sAlpaW4nK5Tls8Mjw8/Kz7G86cOZPS0lJGjhyJqqo4nU7mzp17zi5gm82GzWZrfF1dXd06D9COnWoBtJmdqDojeUfzABge3wncKtqgQLR+fqddl3u0HLfLjW+wicBIc6vG1K9jAPuyKziqxjHIeZRgajCXHiMhKIH6G+v5z6r/sCv7AN3iO2HYouGgpYzishIiIyMZNWoU2rPMVhZCCCGuRh6fBNIcW7ZsYdmyZfz1r39l7969rFu3jo0bN/Lcc8+d9Zrly5fj7+/feMScY106cWFq6m0YnRasGifovMg9mgtAr5CG2byGjqd3/9ZW2CjLrQVFoWNiMIqitGpMZoOO3tENrYB7+WEs4Mlk+gT3wmA0cM2Ma9ickcwnOzdx2x9/ydjxNzBz5kzGjBlDly5dWLduXavGI4QQQlzJPJYAhoSEoNVqKSoqanK+qKiIiIiIM16zaNEi7r77bu677z6SkpK45ZZbWLZsGcuXL8ftdp/xmoULF1JVVdV45OTktPqztDe2mnJApU6rUlVRT3lxOYqiEGNsWMjZ0PH0JDv3aDkAIR18mrXgc3P0jw1Ep1E4rMZT6TKBrQa/8gy6BXRj5K0jUfUK7x3cRIeAcFY+sJyinGKSk5NJSkpi2rRpkgQKIYRoNzyWABoMBgYMGMDmzZsbz7ndbjZv3sywYcPOeE1dXR0aTdOQT3Xdqap6xmuMRiN+fn5NDnFxHLUNyVytUU9uWkPrX//u3dFZbaBR0EdGNilfXVpPdWk9ikahQ/eASxaXj1FHrw7+P8wI7tFwMns7/UKSMHoZUXQKCjC1yyASwuIp+P4kQ4YMYf369UyaNInHH38cl8t1yeITQoiWyMrKQlEUvLy8GDlypEf2ABZXH492Ac+fP5833niDd955h9TUVB566CEsFguzZ88G4J577mHhwoWN5SdPnsxrr73Ge++9R2ZmJl9++SWLFi1i8uTJMobrEnK5XGzZsoXVq1ezefPXOGpKAbDotY0TQK5JSABAFxraZM9dVVXJTWtYMzC0oy9Gs/6SxjogLhCtRiFFjaPKbQRrNUGVuViOWbDWWtHotHy082NqqiuoPJZLUWYlGo2GhQsXkpmZydatWy9pfEII0VxGo5HBgwdjNBrZtm0bN998M85WXs3gVJJ5tuOZZ55pLPvMM8+ctVxrxyUuHY9NAgGYPn06JSUlLF68mMLCQvr27cumTZsaJ4ZkZ2c3afF7+umnURSFp59+mry8PEJDQ5k8eTLPP/+8px7hqrdu3ToWLFhAVlZW47mw0BAeu20YgZ16kJPakAD2+2FspT6y6eLPVSX11JZb0WgVoroGXPJ4/Ux6EiP9OJRXxV56MIYDkJ2MX70vAINuHMTuT7Zz/MgW+g+9hZPfpRMU2Y9evXoBUPDDMjZCCHGliIyMZMeOHaSlpZGQkEBGRgYffPABM2bMaLV7GI1GhgwZ0uRcZWUlaWlpjTH8r5CQEDp37tzkXGuP7xaXjscngcybN4+TJ09is9nYsWNHkw/gli1bePvttxtf63Q6lixZQnp6OvX19WRnZ7Ny5UoCAgIuf+DtwLp165g2bRpJSUkkJydTU1PD+k3fEBcTwVOvfcqebRmNLYDdAhrW9NN3+DEBVFWV/OOVAITF+mEwXZ7vG4PigtAoCgdccVSrJrBWk+DbcO/EEYm4FIV/ffcpLms51uw8sg6VcOhQw9IwZ/olJ4QQV4Lu3btzzTXXAPDuu++2at2RkZFs3769yXFqmbbAwEDuvPPO066ZOHHiaddIb1zb4fEEUFyZXC4XCxYsYNKkSaxfv56hQ4fi4+NDl6R+/O6XdzC0bzwfrtxMWX4ZJkUh0scboMn4v5pya2PrX0Rn/8sWu79ZT49I34YZwWp3AEZF2ojpGM2ujbvoM6YPh611pKV8jmqzUnzoJM898zzx8fGMGjXqssUphPAsVVWxWCweOc42bv188ebmNoy7/uqrr866a9a5umhPHT/t1TmTsrIy3nrrLQAeeughfH7Y5emnPvzwQ7y8vIiMjGTSpEns27ev2c8kPMejXcDiyrV161aysrJYvXp1k2746joHXmot06b0YcGS9QD0i47Gy+SFNigQjZdXY9mC9IZFn0OifS9b698pQ+KDOFpQw35XHH05SoCjlpeffpTbHvw1nfp2wgH849svmRsaxztrXyI5bTdr3ntfvr0K0Y7U1dWdMbG5HGpra/H29m7WNZ9//jknTpwAwOl08t577/HII4+cVi46Ovq07tz/ZTSeezWGv/71r9TV1WE0GvnlL3952vtarZaIiAh0Oh1Hjx5l48aNfPXVVyQnJ9OvX79mPJXwFGkBFGd0aizcqbFxp9RZqtG6HYTG+jaeG/LDGBB91I/dv5YqG1XFdaBc3ta/UwLMBnp18GvYHcTVsC7grb3M/OWtP1NZVAnAvro6Hvznn8gqzOJ3v3iBgT1GX/Y4hRDiQr322mvAj+Ps/vWvf52x3H333Xda1+z/Huca7mKz2Vi5ciUAd91112lLs82cOZPi4mKOHz9OamoqmzZtOu06ceWTFkBxRqd+OaSkpDB06NDG87aacsxAWsGPO6r0+mHSjj6qQ+O5woyG1r+gSG9M3pd25u/ZDI4P4kh+NYed8fR3pBJEFXPHjsN3wMv8Z81/WP271XQzGvnXQ49Q592d4qwqgjt44xfsdf7KhRBtntlspra21mP3bo7s7Gw2btwIwOOPP84f//hHdu3axbFjx+jWrVuTsn//+9/5+9//fs76Pvroo7Mmge+++y5FRUUoisKCBQtOe/9/7zd+/HiCg4MpKysjOzu7OY8lPEgSQHFGo0aNIi4ujmXLlrF+/frGbmB7bTlut8rHH+1HZ9Ch2p3E+wcAoI9s+JbosLsoz7cAENHp8rf+neJr0tMnJoA9JyvY6erGeFLQZO9gQJdrqLm1hrTv0zj0zT6Skzdz7Q3R1JaUknXQSK9rotBopXFciKudoijN7ob1lL/97W+4XC569OjB888/z1tvvUVpaSn/+te/WLp0aZOyubm57Nix45z1/XSL1J9SVZUVK1YADZM8En5Y4uunfv/73zNjxgw6dmzY8/3LL7+krKwMgLi4uOY+mvAQ+Ssnzkir1bJixQo2bNjAlClTSE5Oprq6miP7dvDrles5vOskikYhVKcjPCwUjdmMxrehW7gstxbVrWL2N+IdYDjPnS6tQXFBGHQa0pQulNr1YK2iu92Gn8GPnz34Mxyqyj+2b0exZEJBDvVV9Y0zl4UQ4krgcDgaW/TmzJmDXq9vXALm3//+92nln3nmGVRVPedxtkTt008/bVz65Yknnjhjmddee424uDhiY2NJTExk/PjxAHh7e/OrX/3qIp9WXC6SAIqzmjp1KmvXruXQoUMMHz4cf39/fvvUYk7klXL7ozfgsDroYDIRHByCLjwcRVFQVZXikzUAhMX6enxNKC+Dlv4dA3FrdGx3dsOtqmhPfs+g0H506NaBgeMHkmK1smnrFsKN5TgKCyk4UUVdtd2jcQshxCnr1q2jqKgIvV7PPffcA8CsWbMAyMjI4Pvvv2+1e7344osADB48uHHJmf/11FNPcf311+NwOMjIyCA2NpY777yTPXv2kJiY2GqxiEtLuoDFOU2dOpWbb76ZrVu3cuTESWozttOpi5PNxQ1dvP1iY9FqtegjGsYB1lbYsNba0Wg1BEd5Znbd/xoQG0hKXhUn1K7kWtPpqNTStaaEfaYgJjwwgb1f7uWNHdsZM2gQXk5vnGFhZB0sJWFEpMcTWCGEmD59OtOnT29ybsCAAS1aSuZ8vv322/OWeeCBB3jggQda/d7i8pIWQHFeWq2W0aNHM3bSVIZ0DcOuc5OX2TDeo9cPg4h1P0wEKfmh9S8oyhut/sr4eBl0GkZ2DUFVdHzn7o3d5UaTs4vBgQlEdIpg5K0jSbfbWf3554SbKnAV5FNbYaUsz+Lp0IUQQohL4sr4Cy3ahGpLPQZXHVatg/zMErwUhdjgYFAUdOHhuFxuygsbkqbQjr7nqe3y6hHhS6S/iSJDR9KsgeB2El+SQaR3JBMfmoiPnw9r0o6ScmAPAZZM3FYruUfLcbncng5dCCGEaHWSAIoLVl9TBqhYtCp56QWE6/SEh4ejDQxAYzBQVVyP2+nG4KXDJ/Dci4xeboqiMLp7GCgK3yt9qbE5UUqOMtK3E76BvvzswZ+R73Tyzldf4q2UQ0E29nonRT8sZyOEEEJcTSQBFBfMVlOOCzfVKhSfLCZcryMsPBz9D4uEnlr6JSjK+4ocOxfhbyIxyo86Qwh77B1RVZXQnN0kBHbnmtuuIapTFJ8XFvLd1m8JtuXgqqkhP70Ku9Xp6dCFEEKIViUJoLhgjpoyLDgoLbLidrnp5O+Pr68vurBwXE43lUV1AFfM5I8zGdklBJNeyyFDX3ItQG0xg10avExeTJk/hQqXi7f++1/s1Tnoik/icrrJS6vwdNhCCCFEq5IEUFwQVVVx1lVSi4Oi3IaJHklRHVBQ0EeEU1lUh9vlxmjWY/b37Np/5+Jt1HFtt1CcWi+2uvtQZ3diztnF8MCe9BzRkz6j+5BcU8unn31GqDsPV3k5JTm1siyMEEKIq4okgOKC2JxutLYqarBTnFNFoFZLh7BQFL0ObXAw5QU/dP92uDK7f38qIdKX+BBviry6cKAuCLfLQULRMTp4R3LrE7fiMun5KDWV9CN7MJdlobqlFVAIIcTVRRJAcUGq6x2YnFU/zAAuJUKnIzw8HF1oGKoKVcX1QMPev1c6RVG4PiEMk0HHXtMwsqqcKNX5jHbrCY8OZ8IDE9hnrefjz7/A25aNq6SEikILtRVWT4cuhBBCtApJAMUFqa53YHTWYNU4yc8oJkynIywsHF14ONVlDWMC9SYdZr8rt/v3p3xNesb1DMeu82GrZjDlFjv++YcY6R3LdXdeR0inCL4pKWHrN5/jXZGJ6nKRe7Tikiy8KoQQQlxukgCKC1JbW4miOimxWKgqrSZCpycsLAx9RHhj619AmNcV3/37U51DfegfG0i5uRPbrHHU2Z0k5B2iq3cId/z2DlJtVjbv2omtKAVXUSHVpfVUl9Z7OmwhhBDiokkCKC6ItbqMepwU5FvQAZ1DQzAYDGjDwhpn/waEmz0bZAuM7BJCh0AvTvgNZlelLzarldEl2fTt04khNw8jua6OTRvX411xArfdIa2AQgiPefLJJzEajcycOdPToYirgCSA4oLYqsuoxU5xvoUQnY7I8HA0ZjN2xYitzoGiUfAL9vJ0mM2m1ShM7h1FoI8X+wNuYHu5GZvFxg2Vpdxy30iKzXoO5OVxYv9m3AV5WCptVBTWeTpsIUQ7tHDhQlasWMHq1atJT09v9fq//fZbJk+eTFRUFIqisH79+tPKPPPMMyiK0uTo0aPHaeVWrlxJXFwcJpOJIUOGsHPnzlaPV1wcSQDFBXFYyhuWgMmpJuzUBJCwMKpLGiZG+Aabrpi9f5vLy6Dl1gHRBPn7cCBwPMkVvlQW2Rhor2PS7KF8X2fh2/9+hSvvEJaqWlIPFJNTZiGr1MLJMgs1VoenH0EI0Q74+/szZ84cNBoNhw4davX6LRYLffr0YeXKlecs17NnTwoKChqP7777rsn7a9asYf78+SxZsoS9e/fSp08fxo8fT3FxcavHLFpO5+kARNvgslRQq9gpyq6ge+MEkDAqi3/o/g1re92/P+Vj1DF9YAzfpZdwSHMjNVV76VCxhxE9w/m+SwBpeRa+2vgvYtVIaiPj2VVjQQ34ccJLiK+RvtEBJEb5odW0nXGQQoi2xel0YjabSUlJ4ZZbbmnVuidMmMCECRPOW06n0xHxww5QZ/LSSy9x//33M3v2bABWrVrFxo0befPNN3nyySdbLV5xcdpmk424rOxONxprJdVuG4VZpY17AGuCQqkpa2gBbOsJIIBBp+G6HuHMGhFPh743UNplGkmmBG67eyA7bfWU5GdC+ma8bBa8K52EeBsI9jGgURRKa2x8lVrEe7uyqbDIotFCtAWqqqLa7Z45WjiW+Omnn6a2tpaUlJSzllm2bBk+Pj7nPLKzs1v6Y+P48eNERUXRqVMn7rzzziZ12e129uzZw9ixYxvPaTQaxo4dS3JycovvKVqftACK86qxNiwBk1taiWJ3ERJoJDAokDqdH6q7CqO3HpOP3tNhtpoAs4HhXUKgSwi4B9Mv+ztOHC7i8MfHMO/YyKT4bnh1HEVsRAARnQKwOlwczq9mZ2Y5xdU23tuVwy39OhDhb/L0owghzsXhoPT1v3nk1iEPPgCG5i2btWfPHlatWsXEiRPPmQDOnTuX22+//Zx1RUVFNevepwwZMoS3336b7t27U1BQwLPPPsuoUaNISUnB19eX0tJSXC4X4eHhTa4LDw/n6NGjLbqnuDQkARTnVW2xoLprKcitIEynIzQ0FH1AAFVVbuDqaP07K40G/7hreHHFvxn73Si619ZzePeX9AvwJb8um1DvvpjCuzAgNpDuEb58eiCfwior6/fnMX1gDIHebWNdRCHElc3tdvPggw8yb948hgwZwl133YXD4UCvP/3Ld1BQEEFBQZckjp92Effu3ZshQ4YQGxvL+++/z5w5cy7JPcWlIQmgOK+6ytKGLeDyLYTrdISHhaMNDaPy1Pp/4W1v9m9zdYlIYPGrf+Cvd8zDcGgv4Z37E+NjpOi/m4jqFQNdx+NjNHFr/2jW7smlqNrKhkMFzBgUg04rIy2EuCLp9Q0tcR66d3O8+uqrlJaWsnTpUrKzs3E4HBw9epSkpKTTyi5btoxly5ads74jR47QsWPHZsVwJgEBAXTr1q1xVnJISAharZaioqIm5YqKis45blBcfvKXSZyXtaaMWhwU59US+sMMYIdvKA6rE41Wg29Q++jqnD15NpGTr8XidnFg97fUWg0UlPrizEuF3W9CTSEGnYab+0ZhNmgprbGx7USZp8MWQpyFoigoBoNnjmYsmp+Xl8eiRYtYuXIl3t7edO3aFaPReNZu4Llz57J///5zHi3tAv5ftbW1nDhxgsjISAAMBgMDBgxg8+bNjWXcbjebN29m2LBhrXJP0TqkBVCcl72m/IcZwJUM1ukIDw/DovgB4BdqQtNOWrgUReEff32HyYmJeOemcWTfHgbcfAcFNRCjy4f9/4ak2/AO6Mi4nhGs35fH/uxKEiP9CPU1ejp8IUQb9cgjjzBhwgQmTpwINMzCTUhIOGsC2NIu4Nra2ibrC2ZmZrJ//36CgoIaWwsff/xxJk+eTGxsLPn5+SxZsgStVsuMGTMar5s/fz6zZs1i4MCBDB48mFdeeQWLxdI4K1hcGSQBFOflsJRTaqvDVlyDT2AwYeER1DqNgPPqHv93BmHBYTz4x9+z+dHHydr1FX7de6IfOJJw804MddlwYA30vo34kDi6hPmQXlzLd+kl3NIv2tOhCyHaoA0bNvD111+Tmpra5HxSUtI5J4K0xO7duxkzZkzj6/nz5wMwa9Ys3n77bQByc3OZMWMGZWVlhIaGMnLkSLZv305oaGjjddOnT6ekpITFixdTWFhI37592bRp02kTQ4RnSQIozsttKedEbgmhWj3ePt6YIztQXOMCwL+dJYAAs+/+OZ//4y1sh9PI/X4rgV06EdpxLHFe30JZOqSsgwH3ck3XUDJKLGSV1lFUbSXcr310lQshWs+kSZOoqKg47fy7777b6vcaPXr0eZenee+99y6ornnz5jFv3rzWCEtcIu2j7060mNPlxmktJ+eHGcDh4WFYzeGgqpj9DBi92t93CEVRWP7Wu5QqUJq2nbTvvuNERiHW+Eng3wGcNji0Fn+9i+4RvgDsyio/rR6Xy8WWLVtYvXo1W7ZsweVyXe5HEUII0U5JAijOqbbeht1ZTklOFeF6HRHhkVi0/gD4h7e/1r9TOnXqxLC5D+GoK6F6/37SMg+ReawMek4Foy/UlcGxTQyIDQTgRLGFWpuz8fp169bRpUsXxowZw8yZMxkzZgxdunRh3bp1nnokIYQQ7YgkgOKcaqrKqMVGSW5NwwzgiHBqXQ3LvrS38X//6xeLF+GICKcm7wCl21LYe+QwFqseek0FRQPFqYTacogKMOFWVQ7nVQENyd+0adNISkoiOTmZmpoakpOTSUpKYtq0aZIECiGEuOQkARTnZKkspUq1U5dXg5eiISAsHrfehM6gxSegfc9s1ev1zPnTn3BYK6g/cYScw0fZvucg+EVBzKCGQsc20TuiIWFOLajG6XSyYMECJk2axPr16xk6dCg+Pj4MHTqU9evXM2nSJB5//HHpDhZCCHFJSQIozslaXUJOWQWBTtBoNZgiu6FoNPiFeKFoLnwdq6vV4OuvJ27s9dQVp1CzO53UtBNkFxRA3CjwCgRbDZ3r9qLTKFTUOfj0i6/JysriqaeeQqNp+s9Po9GwcOFCMjMz2bp1q4eeSAghRHsgCaA4J3tNKek5JYTpdASHBFNvaFhbKqAdj//7X/e/8gp6o4qrOJ38nUfZkrwTJwp0GweAofAAXfwaWvQOpGUB0KtXrzPWdep8QUHBpQ9cCCFEu9XqCWB9fX2zyq9cuZK4uDhMJhNDhgxh586d5yxfWVnJww8/TGRkJEajkW7duvHZZ59dTMjiHGw1JeTklBOu0xMe2RG71gyKgn/Y1b/924UKiIhg7MMPU1d8GPfxPAqOl/D9kd0Q1AkC48DtIsl1GIB6XcOs4LOt33Xq/KlV9YUQQohLodUSQJvNxooVK4iPj7/ga9asWcP8+fNZsmQJe/fupU+fPowfP57i4uIzlrfb7dxwww1kZWWxdu1a0tLSeOONN+jQoUNrPYb4CVVVqbYUUpJbTahOR0hENzQ+PvgEGNEbtJ4O74oy7pe/pGPXWNxlx8n/9jCH9mVSbCmBuJEARNSfwAsrYV37EtMxlmXLluF2u5vU4Xa7Wb58OfHx8YwaNcoTjyGEEKKdaFYCaLPZWLhwIQMHDmT48OGsX78egLfeeov4+HheeeUVHnvssQuu76WXXuL+++9n9uzZJCYmsmrVKsxmM2+++eYZy7/55puUl5ezfv16RowYQVxcHNdeey19+vRpzmOIC2S12al3lGPPq0WvKHiHxKGYTASES+vf/9IYjUxbsgRnTSbm8gpy9+by9e7vUf06gF8kOlz00WSi0Wp54IklbNiwgSlTpjSZBTxlyhQ2bNjAiy++iFYrCbYQQohLp1kJ4OLFi3nttdeIi4sjKyuL2267jQceeICXX36Zl156iaysLH7zm99cUF12u509e/YwduzYH4PRaBg7dizJyclnvOaTTz5h2LBhPPzww4SHh9OrVy+WLVt2zhmTNpuN6urqJoe4MNUVJRRbazFW2kHRYAyNQ1GUdrn7x4WIHTOGkeNvwFFyhOp9GRSmVnO4+Ch0GAhAF+cxFNVFh36j+eCDDzh06BDDhw/Hz8+P4cOHk5KSwtq1a5k6dapnH0QIIcRVr1kJ4AcffMC7777L2rVr+eKLL3C5XDidTg4cOMAdd9zRrFaL0tJSXC7XaXsDhoeHU1hYeMZrMjIyWLt2LS6Xi88++4xFixaxYsUKfve73531PsuXL8ff37/xiImJueAY27u6yhLS8xomgPiFxGAICEJv0mH2M3g6tCuSotUy/tdP4KOrxb+ukszt6STvOYA1KB6MPgTpHIRaM6msczDmZ5NJT0/nm2++4f/+7//45ptvOH78uCR/QoizevLJJzEajcycOdPToYirQLMSwNzcXAYMGAA0zFY0Go089thjKMrlWQ7E7XYTFhbG3/72NwYMGMD06dP57W9/y6pVq856zcKFC6mqqmo8cnJyLkusV4P66lIycksbZgB36IbG25uAMK/L9v+7LTJ368bYO6ZTX3wI1/ECyo/U8X3Wbojqj1ajkOA6BkBmqQWtVsvo0aOZMWMGo0ePlm5fIcQ5LVy4kBUrVrB69WrS09Nbvf5nnnkGRVGaHD169Dit3IVM3mzuBE9x+TUrAXS5XBgMP7b+6HQ6fHx8WnTjkJAQtFotRUVFTc4XFRURERFxxmsiIyPp1q1bkz+UCQkJFBYWYrfbz3iN0WjEz8+vySEuTH11CTnZ5YTodASEdkbj7S3dvz842z6+iqIw5IH7ie/gj29dMRnfp3L0YA6VQXGgaIhUyjE5KskstXj2AYQQbY6/vz9z5sxBo9Fw6NChS3KPnj17UlBQ0Hh89913Td6/kMmbzZ3gKTyjWQmgqqrce++9TJ06lalTp2K1Wpk7d27j61PHhTAYDAwYMIDNmzc3nnO73WzevJlhw4ad8ZoRI0aQnp7eZPbksWPHiIyMbJKYitZRVZOHM68Wg8EX74BwtGYv/ENkAsj59vHVR0Uxfs7PsZamYCqqpOhAJd+dOAzBnQkwGwizpJFXUY/NKbt9CCGax+l0Yjabz7qU1MXS6XREREQ0HiEhIU3ev5DJm82d4Ck8o1kJ4KxZswgLC2scT3fXXXcRFRXVZIydv7//Bdc3f/583njjDd555x1SU1N56KGHsFgszJ49G4B77rmHhQsXNpZ/6KGHKC8v59FHH+XYsWNs3LiRZcuW8fDDDzfnMcQFqqgtQF9cj94nEp+wMHyDTGj17Xvt8Avdx7fz1KkM7N8LU1UWOcmpZB4spsg3Ci+9lo72DNxuF9lldR5+GiHaN1VVcTndHjlUVW1RzE8//TS1tbXnTACXLVuGj4/POY/s7OwzXnv8+HGioqLo1KkTd955Z5NyFzJ5syUTPIVn6JpT+K233mrVm0+fPp2SkhIWL15MYWEhffv2ZdOmTY0TQ7Kzs5tslxUTE8Pnn3/OY489Ru/evenQoQOPPvroBc88FhfO5bBxIv8kwW4NJr8o/CMi2v3uHy6Xq8k+vqc+m6f28Z0yZQqPP/44N998M7rAQG548AEOPvwIAbUdOLkrl+/iA7hVbyLUaMHfmk9maQBdw309/FRCtF9ul8qe/2R55N4DJsSh1TVvPPWePXtYtWoVEydOPGcCOHfuXG6//fZz1hUVFXXauSFDhvD222/TvXt3CgoKePbZZxk1ahQpKSn4+vqec/Lm0aNHgXNP8DxVRlwZmpUAnklubi4A0dHRLbp+3rx5zJs374zvbdmy5bRzw4YNY/v27S26l7hwtRXFpGYXE2k04RcSg87Pr93v/rF161aysrJYvXr1WffxHT58OFu3bmX06NGEXHstY8aO4atvDlB+0EROz2jyh8QQaK4hrCqNrLJYVFWVSTVCiPNyu908+OCDzJs3jyFDhnDXXXfhcDjQ6/WnlQ0KCiIoKKjZ95gwYULjf/fu3ZshQ4YQGxvL+++/z5w5cy4qfnHlaVEC6Ha7+d3vfseKFSuora0FwNfXlwULFvDb3/72tD+Oou2pLS8iN7OEJN8ofPz88Qr1x+R9+i+a9uTU/rwXuo+vxtubEXPmsH3rbFRLBSf3Z5Ec05NbzDqCi09yot5KSa2NMF/T5XkAIUQTGq3CgAlxHrt3c7z66quUlpaydOlSsrOzcTgcHD16lKSkpNPKLlu2jGXLlp2zviNHjtCxY8dzlgkICKBbt26NM44vZPJmSyZ4Cs9oUab229/+lr/85S+88MIL7Nu3j3379rFs2TJeffVVFi1a1NoxCg+oqsjDnl2FwScS76AgAjv4tfuWqlP78zZnH1+f/v25Zvw4XMUHqT2SQ06mhUKXH4FGhcD6kzIOUAgPUhQFrU7jkaM5v0/z8vJYtGgRK1euxNvbm65du2I0Gs/6u2ju3Lns37//nMeZuoD/V21tLSdOnGj8nXYhkzdbMsFTeEaLWgDfeecd/v73v3PTTTc1njs1Ju8Xv/gFzz//fKsFKDyjuOIk2qJ6DN0iCIiMIkCWf2HUqFHExcWxbNmyJmMA4ez7+CoGA0Pvu49tX21GW5lN5v4TbA/sxFDvYoLrMsgq683AuOZ31Qgh2o9HHnmECRMmMHHiRKBhpm5CQsJZE8CWdgE//vjjTJ48mdjYWPLz81myZAlarZYZM2Y0lpk/fz6zZs1i4MCBDB48mFdeeaXJ5M0LLSM8r0UJYHl5+RkXh+zRowfl5eUXHZTwvONZqYTrg9DqvQjpGI1vsHRTarVaVqxYwbRp05gyZQoLFy6kV69epKSksHz5cjZs2MDatWtPW9DZq2dPrr1pMmvf+he21EhO9oqhl86HQGs2WeWV2J1RGHQybEIIcboNGzbw9ddfk5qa2uR8UlJSqy8Fk5uby4wZMygrKyM0NJSRI0eyfft2QkNDG8ucb/LmhZYRnqeoLZiLPmTIEIYMGcKf//znJud/+ctfsmvXrit6kkZ1dTX+/v5UVVXJotBn43bxy0dvwPCli9C4AdyycB7dr+3k6aiuGOvWrWPBggVkZWU1nouPj+fFF1886zqYtoxMXp56C8V13jiHXsfoodF09MogNXAUI0aMplNoyxZUF0JcOKvVSmZmJvHx8ZhM8qVWtE3n+hw3J8dpUQvgH/7wByZOnMhXX33V2KefnJxMTk4On332WUuqFFcQR20ZhccL6e7XBy9fH4I6hZ7/onZk6tSp3HzzzWzdupWCggIiIyMZNWrUObdyM8THce1tt/HvV/6MMzWWkz0jCFeCCbGc4GT5YEkAhRBCXFYt6ne69tprOXbsGLfccguVlZVUVlYydepU0tLSmox/Em1TTXkhjgIbOlMA3iEhMv7vDJq7j6+iKAy87z5iO8bgVXKIk4fyybOY8a4pIreo9DJFLYQQQjRo8TqAUVFRMtnjKlVSmoWvNQj8ISI+Ar3x3MmNuDD68HBGzpzBv5e/gCPlEPk9gjG7g9GUpVNV3xV/r/a9zI4QQojLp8UJYEVFBf/4xz8aB6YmJiYye/bsFs08EleWI+mHCPWKRAG6D070dDhXlUE//zlbV68mL/8Q2SndCU2Kxbc0h5NlFnpHB3g6PCGEEO1Ei7qAv/32W+Li4vjzn/9MRUUFFRUV/PnPfyY+Pp5vv/22tWMUl9nenXvw9YlEbzIQlRTn6XCuKrrAQEbdey8uey2WA7sotNbgLnGTU1Di6dCEEEK0Iy1KAB9++GGmT59OZmYm69atY926dWRkZHDHHXfw8MMPt3aM4nJSVUqPV6FoNGi8dZiDvD0d0VVnyOzZRMfHYShLI+dIHtVOharDabjdLdscXgghhGiuFiWA6enpLFiwoMnAd61Wy/z58xu3jBFtlLUSTWXDjFRf2f3jktB4e3Pt/Q+gup1Y931PUV01SkEl2SUWT4cmhBCinWhRAti/f//TFqUESE1NpU+fPhcdlPCcsuIsfNQQAHoN7+3haK5eQ+6dRUR8LLrqHHIOp6E6qkndddLTYQkhhGgnWjQJ5JFHHuHRRx8lPT2doUOHArB9+3ZWrlzJCy+8wMGDBxvL9u4tSURbsuWrrXhpDKhuG0MnXe/pcK5aGoOB6+f9kn8veBz7vu/JT+wB6bnUlHfGN0gWqBVCCHFptSgBPLUv4K9//eszvqcoCqqqoigKLpfr4iIUl9WxnScAsFGBUXZKuaSG3nUXn698ldKMk2Qe2EW4XwfSD5TQ99poFI10vQshrnxZWVnEx8ezb98++vbt6+lwWuTtt9/mV7/6FZWVlRd8zdXw3C1KADMzM1s7DnEFUN0q9UVqw7iAQE9Hc/VTtFrGz3+cf8/7Je79OyjrM4DCvHKKTvoREe/v6fCEEGfhcrmatRPQ1SwmJoaCggJCQkI8HQrPPPMM69evZ//+/Z4O5YzuvfdeKisrWb9+vadDAVqYAMbGxrZ2HOIKUF1SAxY3bqedsL7dPR1OuzB0+nQ+felFqjNOcnzn10THDCT3qD+BEd4YvVq8TKcQ4hI5017gcXFxrFix4qx7gV+t7HY7BoOBiIgIT4ciWqBZk0B+8YtfUFtb2/h69erVWCw/zlysrKzkxhtvbL3oxGWVc/AoGpsLe00egyfe4ulw2gVFUZj05FMAqCkHySvcg8vp5mRKKaoqy8IIcSVZt24d06ZNIykpieTkZGpqakhOTiYpKYlp06axbt26S3Jft9vN8uXLiY+Px8vLiz59+rB27VoAVFVl7NixjB8/vvF3Rnl5OdHR0SxevBiALVu2oCgKGzdupHfv3phMJoYOHUpKSkqT+3z33XeMGjUKLy8vYmJieOSRR5r8jY+Li+O5557jnnvuwc/PjwceeICsrCwURWlsdTt1r88//5x+/frh5eXFddddR3FxMf/5z39ISEjAz8+PmTNnUldXd0HP+NN6N2/ezMCBAzGbzQwfPpy0tDSgoRv32Wef5cCBAyiKgqIovP322wC89NJLJCUl4e3tTUxMzGm5zIXYuXMn/fr1w2QyMXDgQPbt29fkfZfLxZw5cxrj7969O3/6058a33/mmWd45513+Pjjjxvj27JlCwC/+c1v6NatG2azmU6dOrFo0SIcDkez4msRtRk0Go1aVFTU+NrX11c9ceJE4+vCwkJVo9E0p8rLrqqqSgXUqqoqT4dyRXG73eq7i15Xl4yaqz7Utbdab3d6OqR2Zf7AvurDwSHqL4b1Vb95/6C645MTallerafDEuKqUl9frx45ckStr69v9rVOp1ONi4tTJ0+erLpcribvuVwudfLkyWp8fLzqdLb+787f/e53ao8ePdRNmzapJ06cUN966y3VaDSqW7ZsUVVVVXNzc9XAwED1lVdeUVVVVW+77TZ18ODBqsPhUFVVVb/55hsVUBMSEtQvvvhCPXjwoDpp0iQ1Li5Otdvtqqqqanp6uurt7a2+/PLL6rFjx9Rt27ap/fr1U++9997GOGJjY1U/Pz/1xRdfVNPT09X09HQ1MzNTBdR9+/Y1udfQoUPV7777Tt27d6/apUsX9dprr1XHjRun7t27V/3222/V4OBg9YUXXrjgZzxV75AhQ9QtW7aohw8fVkeNGqUOHz5cVVVVraurUxcsWKD27NlTLSgoUAsKCtS6ujpVVVX15ZdfVr/++ms1MzNT3bx5s9q9e3f1oYcearz3W2+9pfr7+5/1519TU6OGhoaqM2fOVFNSUtRPP/1U7dSpU5Pnttvt6uLFi9Vdu3apGRkZ6r/+9S/VbDara9asaazj9ttvV3/2s581xmez2VRVVdXnnntO3bZtm5qZmal+8sknanh4uPr73//+rPGc63PcnBynWQmgoihNEkAfHx9JAK8S1WX16ot3L1EXD5+j3t2/h6fDaXe2fvqx+nBwiDovNER9Z8Wf1B2fnFD3fn5SdUgiLkSruZgE8FQCkpycfMb3v//+exVQv/nmm4uMsimr1aqazWb1+++/b3J+zpw56owZMxpfv//++6rJZFKffPJJ1dvbWz127Nhpsb/33nuN58rKylQvL6/GBGXOnDnqAw880OQeW7duVTUaTePPKzY2Vp0yZUqTMmdLAL/66qvGMsuXL1eBJvnCgw8+qI4fP/6Cn/FM9W7cuFEFGuNbsmSJ2qdPn7P9KBt98MEHanBwcOPr8yWAr7/+uhocHNzkc/Paa681ee4zefjhh9Vbb7218fWsWbPUm2+++bzx/fGPf1QHDBhw1vdbKwGUQUYCgLK8WqxlVdhr8vHr09XT4bQ7IyfdxDsxoRhzSjj4/uv0eX4KtjonuakVxPX2/OBqIdq7goICAHr16nXG90+dP1WutaSnp1NXV8cNN9zQ5Lzdbqdfv36Nr2+77TY++ugjXnjhBV577TW6dj399/iwYcMa/zsoKIju3bs3rul74MABDh48yL///e/GMqqq4na7yczMJCEhAYCBAwdeUNw/XQIuPDy8sXvzp+d27tzZrGf833ojIyMBKC4upmPHjmeN5auvvmL58uUcPXqU6upqnE4nVquVuro6zGbzeZ8lNTW1sev8lJ/+LE9ZuXIlb775JtnZ2dTX12O32y9ohvCaNWv485//zIkTJ6itrcXpdOJ3GVbhkARQ4HarlGSU4rLYqK86Sbcxsp2fJ9z09BI2PvAwtoxiKgr/i9lvBMUnqwmK8sYvxMvT4QnRrp1KNlJSUhrXv/2pU+PpTpVrLafGqm3cuJEOHTo0ec9oNDb+d11dHXv27EGr1XL8+PEW3efBBx/kkUceOe29nyZX3t4Xtj2oXq9v/G9FUZq8PnXO7XY33hvO/4xnqhdorOdMsrKymDRpEg899BDPP/88QUFBfPfdd8yZMwe73X5BCeCFeO+993j88cdZsWIFw4YNw9fXlz/+8Y/s2LHjnNclJydz55138uyzzzJ+/Hj8/f157733WLFiRavEdS7NTgAXL17c+AOz2+08//zz+Ps3LFnx0wGdou2oKq4j5/ARnPZ6iuqKuGv0zZ4OqV2aNPV23n5mIZEFNWxd+SJ3/eUGSvLqyDxQSq9rOqDVt2jjHiFEKxg1ahRxcXEsW7aM9evXo9H8+O/xpxMYRo0a1ar3TUxMxGg0kp2dzbXXXnvWcgsWLECj0fCf//yHG2+8kYkTJ3Ldddc1KbN9+/bGZK6iooJjx441tuz179+fI0eO0KVLl1aN/0Jc6DOej8FgOG3t4T179uB2u1mxYkXj/7P333+/WfUmJCTwz3/+E6vV2tgKuH379iZltm3bxvDhw/nFL37ReO7EiRPnje/7778nNjaW3/72t43nTp68PLtCNSsBvOaaaxpn3AAMHz6cjIyM08qItqUsz0JRZjq2qhzsEd5EBwd7OqR2SVEUrp37ELsXLUc5nk/lsc0YQ6/BVucg+0gZ8X1CPR2iEO2WVqtlxYoVTJs2jSlTprBw4UJ69epFSkoKy5cvZ8OGDaxdu7bV1wP09fXl8ccf57HHHsPtdjNy5EiqqqrYtm0bfn5+zJo1i40bN/Lmm2+SnJxM//79eeKJJ5g1axYHDx4kMPDHRV2XLl1KcHAw4eHh/Pa3vyUkJIQpU6YADTNRhw4dyrx587jvvvvw9vbmyJEjfPnll/zlL39p1WdqyTNeiLi4ODIzM9m/fz/R0dH4+vrSpUsXHA4Hr776KpMnT2bbtm2sWrWqWfHNnDmT3/72t9x///0sXLiQrKwsXnzxxSZlunbtyrvvvsvnn39OfHw8//znP9m1axfx8fFN4vv8889JS0sjODgYf39/unbtSnZ2Nu+99x6DBg1i48aNfPTRR82Kr8XOO0rwPNxut+p2uy+2mstGJoE05bS71J0bMtQ/3vS4+miHruqMKSNVm8N1/gvFJZGWV6re0DNafTg4RH1x1CC1IqtY3fFphrrjkxNqRaHF0+EJ0aZdzCSQUz788EM1Li5OBRqP+Ph49cMPP2zFSJtyu93qK6+8onbv3l3V6/VqaGioOn78ePW///2vWlxcrIaHh6vLli1rLG+329UBAwaot99+u6qqP06g+PTTT9WePXuqBoNBHTx4sHrgwIEm99m5c6d6ww03qD4+Pqq3t7fau3dv9fnnn298PzY2Vn355ZebXHO2SSAVFRWNZc40yeJ/J2yc6xnPVu++fftUQM3MzFRVtWEyya233qoGBASogPrWW2+pqqqqL730khoZGal6eXmp48ePV999990mdZ1vEoiqqmpycrLap08f1WAwqH379lU//PDDJs9ttVrVe++9V/X391cDAgLUhx56SH3yySebPGNxcXHjz5efTBh64okn1ODgYNXHx0edPn26+vLLL58zntaaBKKoassWG/vHP/7Byy+/3DjWoGvXrvzqV7/ivvvuu8iU9NKqrq7G39+fqqqqyzLI8kpXklND+rYsPv/LHylM/Qy/BbP4w1OXfuyBODOrw8Xy5b/i4Ip/Em3Q88Bzz+A3ajpFGdXojTp6je6A3tA+dxwQ4mJZrVYyMzOJj49vMqC/udraTiBbtmxhzJgxVFRUEBAQ4OlwxEU61+e4OTlOiyaBLF68mJdeeolf/vKXjTNhkpOTeeyxx8jOzmbp0qUtqVZ4QGlOLeXZ2VhKsyhwOxkzdIKnQ2rXTHotg66ZwaYP1xGRZ2PTW28y7/rxVPuaqK+xk7m/hK6DwhsHPwshLj+tVsvo0aM9HYYQF6VFo8pfe+013njjDZYvX85NN93ETTfdxPLly/nb3/7GX//619aOUVwiVouDmrJ6itPTsFVmURfmRUx0kqfDavci47pz2x1jOWCzcvJENvve/AfxvQLQaBUqi+ooyqz2dIhCCCHauBYlgA6H44xrAQ0YMACn03nRQYnLoyy3FtXppCrnCG5nPabuUYQHXtgUf3HpdA7zJbbzWDSDI6hyufh8/Ue4U/YQk9gwOScntZzaCpuHoxRCtBWjR49GVVXp/hVNtCgBvPvuu3nttddOO/+3v/2NO++886KDEpeeqqqU5tbirKykMPMgpS4n8QP6E+7X8nExonUEmA2o4T2ZddMwdrhtlJWUse2dtwk01REY6Y3qVjmxtxinw3X+yoQQQogzaPFC0P/4xz/44osvGhfE3LFjB9nZ2dxzzz3Mnz+/sdxLL7108VGKVldTZsVW56A8K4PasmxycTG697X4GGVt8CtBdIeO+J/sSeLkFI59fAKvrzfTZ8NG4mbeRV2VHVudg4x9JXQdGI6ikfGAQgghmqdFf+1TUlLo378/8ONChyEhIYSEhDSuhg7IQPUrWGluLaqqUnFiN6guXLE+REf18XRY4gedw3z40rcnd1w7kF9vyaRDjYWvPlrH1O7d6DJgMKnf51NZVEfusQpiegR5OlwhhBBtTIsSwG+++aa14xCXkcvpprzAgrumhvzjO6h3uwlP7EBwcOtuYSRaLszXiC2oGzGVoUybPYB1v9+Kz85d9P3Pf+gRH09c71Ay9hVTcLwSs5+B4CgfT4cshBCiDZG9pdqh8gILbqcbakrJzzpGlsNO7759CPOX8X9XCkVR6BoZRKl3D25K6EnoyDgO1dfx8SefUPXFFwSF6ono3LAFY+b+UixVMilECCHEhZMEsB0qzalBBWoy9uF2uSkxKUTHDpAJIFeYHhG+FPkkYLLqmXfvIPZq3BzLy2fbF19S+/XXRHcPxD/UC7fLzbGdRdjqZQa+EEKICyMJYDvTsPafFdVqJf/Id7hQ0XUNwOyfIBNArjChvkZ8AkOpMMYw1C+amx8YxZe1NXz1zdfkfP891oMH6DwgDC9fAw6rk2M7CnHaZWawEKKp0aNH86tf/arxdVxcHK+88orH4rkYWVlZKIrC/v37PR1Km3dFJIArV64kLi4Ok8nEkCFD2Llz5wVd995776EoSuNm1uL8SnNrATC7qjlx/Ah5Dgc9+3TELyzOs4GJ0yiKQvdwX/L9+uKu03Dfdd0JG9ad/1ZXs27dOqq/3YpaXEi3IREYvHTU19g5vrsIt8vt6dCFEFewXbt28cADD3g6DOFhHk8A16xZw/z581myZAl79+6lT58+jB8/nuLi4nNel5WVxeOPP86oUaMuU6Rtn+pWKc2pAcCZf5iKskpOOhz07dOXiEDZF/lK1CPCj2pjJLnuQAboQ7hv/g1kmXR8m53NV19+QfWmz9G5bHQbHIFWr6GmzMqJfSWo7hZt8S2EOIdnnnmG55577ozvPffcczzzzDOXN6AWCg0NxWw2ezoM4WEeTwBfeukl7r//fmbPnk1iYiKrVq3CbDbz5ptvnvUal8vFnXfeybPPPkunTp0uY7RtW2VxHfZ6Jxpc5O/7FhUVW0dvggITiQqQ8X9XIn+znqhAL/J8+1JV6+QmX1/uXHQH/62tZeO2baTs3kX1xo14mWhcE7CiwELGgRJUVZJAIVqTVqtl8eLFpyWBzz33HIsXL0ar1V6S+65du5akpCS8vLwIDg5m7NixWCwWAO69916mTJnCs88+S2hoKH5+fsydOxe73X7W+v63C1hRFP7+979zyy23YDab6dq1K5988kmTa1JSUpgwYQI+Pj6Eh4dz9913U1paetZ7vP322wQEBPD555+TkJCAj48PP/vZzygoKGgs43a7Wbp0KdHR0RiNRvr27cumTZua1LNz50769euHyWRi4MCB7Nu377R7nS+2c/382jOPJoB2u509e/YwduzYxnMajYaxY8eSnJx81uuWLl1KWFgYc+bMOe89bDYb1dXVTY72qiS7ofUvQKkk7dgRCh1OopPCUcxdiZAJIFesXh38qfCK5aTVTKxiYuqInoy+9wb+U13NmvXrKTxyhOovvsQ3yEiXAWEoGoWy3FqyDpVJEihEK1q0aBFLly5tkgSeSv6WLl3KokWLWv2eBQUFzJgxg5///OekpqayZcsWpk6d2uTf9ubNmxvfW716NevWrePZZ59t1n2effZZbr/9dg4ePMiNN97InXfeSXl5OQCVlZVcd9119OvXj927d7Np0yaKioq4/fbbz1lnXV0dL774Iv/85z/59ttvyc7O5vHHH298/09/+hMrVqzgxRdf5ODBg4wfP56bbrqJ48ePA1BbW8ukSZNITExkz549PPPMM02uv5DYLuTn126pHpSXl6cC6vfff9/k/BNPPKEOHjz4jNds3bpV7dChg1pSUqKqqqrOmjVLvfnmm896jyVLlqjAaUdVVVWrPUdbYK1zqDs+zVB3fHJCzX73PfWR8HC1j8mkPvnsRPWD5OOeDk+cg93pUv/6Tbr69vpNavmGZ9T6r5ep/9i9Uu06sKsaodOpi3okqLkvrlBr/vtfVVVVtTS3pvH/ddahUtXtdnv4CYS4MtTX16tHjhxR6+vrL6qepUuXqoBqMBhUQF26dGkrRXi6PXv2qICalZV1xvdnzZqlBgUFqRaLpfHca6+9pvr4+Kgul0tVVVW99tpr1UcffbTx/djYWPXll19ufA2oTz/9dOPr2tpaFVD/85//qKqqqs8995w6bty4JvfNyclRATUtLe2Mcb311lsqoKanpzeeW7lypRoeHt74OioqSn3++eebXDdo0CD1F7/4haqqqvr666+rwcHBTf5/vfbaayqg7tu374JiO9/Pry061+e4qqrqgnMcj3cBN0dNTQ133303b7zxBiEhIRd0zcKFC6mqqmo8cnJyLnGUV6bS7BpQVXx8NWTv+h6X00Wpj4ausT2ICA7wdHjiHPRaDT2j/Cj3iiPTEYQJGK/x5ufLf059gDerM07w0UfrsOzfT93evQR38CG+d8O/j6LMqoaWQBkTKESrWbRoEQaDAbvdjsFguCQtf6f06dOH66+/nqSkJG677TbeeOMNKioqTivz0zF9w4YNo7a2tll/73r37t34397e3vj5+TWOxT9w4ADffPMNPj4+jUePHj2AH3cDOxOz2Uznzp0bX0dGRjbWWV1dTX5+PiNGjGhyzYgRI0hNTQUgNTWV3r17YzL92EM1bNiwJuXPF9uF/PzaK48mgCEhIWi1WoqKipqcLyoqIiIi4rTyJ06cICsri8mTJ6PT6dDpdLz77rt88skn6HS6M34QjUYjfn5+TY72RnWrlPww+cOfSo4dS6PY6SA8KQyjqYuM/2sDekf7g6KwR98fq8NFh8p8xsQlMeePc8hS3by5dy+fb/qc2m3bqD94kNCOvsT3DQVFoeRkdcOYQEkChWgVzz33XGPyZ7fbzzoxpDVotVq+/PJL/vOf/5CYmMirr75K9+7dyczMbNX76PX6Jq8VRcHtblhRoLa2lsmTJ7N///4mx/Hjx7nmmmuaVafayl2v54vtcv382iKPJoAGg4EBAwawefPmxnNut5vNmzefluUD9OjRg0OHDjX5n3zTTTcxZswY9u/fT0xMzOUMv82oKqnHXu9EZ9DiVXGSY8ePkWG3k5gUhcPcmagAL0+HKM4jwGwgPsSbWkMoR5U4AAaV5TF6xCjuee4e9tXX89p/t7Bj+3Zq//st9YcPExrjS+d+oY1jAtP3FuOWJFCIi/LTMX82m+20MYGXgqIojBgxgmeffZZ9+/ZhMBj46KOPGt8/cOAA9fX1ja+3b9+Oj49Pq/1N7N+/P4cPHyYuLo4uXbo0Oby9vVtUp5+fH1FRUWzbtq3J+W3btpGYmAhAQkICBw8exGq1Nr6/ffv2Zsd2vp9fe+XxLuD58+fzxhtv8M4775CamspDDz2ExWJh9uzZANxzzz0sXLgQAJPJRK9evZocAQEB+Pr60qtXLwwGgycf5YpVnN0w8SUozEDhgX3UVNVw0u1gYI9Y9MFxmPSXZuaaaF2D44MA2ObuQ71iQlNXxljVxOjJo7nlsVvYXlfHHz7+mMOHD1P7zRasqakEd/BpnBhSUWDh+K4iXE5ZJ1CIljjThI8zTQxpTTt27GDZsmXs3r2b7Oxs1q1bR0lJCQkJCY1l7HY7c+bM4ciRI3z22WcsWbKEefPmodG0zp/4hx9+mPLycmbMmMGuXbs4ceIEn3/+ObNnz8blavni80888QS///3vWbNmDWlpaTz55JPs37+fRx99FICZM2eiKAr3339/47O9+OKLzYrtQn5+7ZXHt36YPn06JSUlLF68mMLCwsZp4OHh4QBkZ2e32oe4PbLXO6ksavhmGEA5+4+mUeZyEtg9mCCfzkQF+Xs4QnGhogK8iA/xJrMUdhsHM8r6LV75+5nQYwLWe62U5Zfx7ZpvWbr6/3j2rrvooaqoDgeBvXvTbXA4x3cVU1VcR+r3BXQbHI7B5PF//kK0KS6X64yzfU+9vphk6Gz8/Pz49ttveeWVV6iuriY2NpYVK1YwYcKExjLXX389Xbt25ZprrsFmszFjxoxWXZPwVEvdb37zG8aNG4fNZiM2Npaf/exnF/X3+ZFHHqGqqooFCxZQXFxMYmIin3zyCV27dgXAx8eHTz/9lLlz59KvXz8SExP5/e9/z6233nrBsV3Iz6+9UtTW7pC/wlVXV+Pv709VVVW7GA+Yd6yCvLQKfINNRJXt5a+Lnmb9seME39qFu296gkHXTqFLmI+nwxQXqKjayv/tyEZR4OfBh/GrPAoGb3K7j+OT7K95e/Hb7Nq4i9F+fjx39910794D85DBmAcNwlJp49iuIpw2F0aznm6Dw/HylVZz0X5YrVYyMzOJj49vMrGgLbv33nuprKxk/fr1ng5FXCbn+hw3J8eRprWrmNutNq79FxJuoPzwYfLy8jhutzGobzx15jhigmT8X1sS7meiS5gPqgpfOvugeoeC3UJ0VjLjYq9j1rOzGPizgWyprubX775LWloadTt2Ytm6FW9/A4kjojD56LHVOUj9voDqsvrz31QIIcRVRxLAq1hFoaVh8odRi9lSyNHUVEodTnw7+dMlKBLfsBiMOhn/19Zc0zUUvVYhu9JJasgNoPeCmkK65KcwKnoE9zx3DwPGDSC5poZH3nqL/fv3UX/gINUbP8OgV0kYEYVPkAmn3UXa9sLGLwlCCCHaDxkEdBUrzmqY/BEW64fj8F6OpB7huN1G9/6dMBk6Exvi6+EIRUv4m/UM6xzMt8dK+TrLTlSPyQQcXwelx+mtaFBjRsHzoDPo2LFhB7/41794wWJhFCruDz/Eb9Ikug+NIGNfCRUFFjIPlGCpstGxZzAajeLpxxNCNMPbb7/t6RBEGyUtgFcpS5WNmjIrikYhOFhD9YkTnMw6yXGbjd59O+Lw6kJssGwG3lb17xhITJAZh0tlXYaGum43g0YLJWn0KU7nuphR3L30bsbNHke63c7DH37Ix59/jq24mMr338dVkE+XAWF06B6Iy+XiP59+wcvPvcZXX26+JAPZhRBCXFmkBfAqVZTZ0PoXGOGNmpPBsbSj5Nls+MT40iMsDKt/PGG+Rg9HKVpKURRuTIpgza4cKuscfJRt5LYeN2M4+gmUHqenrQZt9FA0j2jwC/Hjwxc/ZMF//kN2cTFzbr0V9/qP8R4ymO3Z2cx/bAHZOScb647tGMtLL7/E1KlTPfiEQgghLiVpAbwKOewuyvNrAQiP98N27DhHUlM5ZreROCAKP20HIsPCUBTp7mvLzAYdN/WOIOfwLjZ9/CHPfriPym5TG8cE9jixjRuDkhh/93geeOkBHF4GXti1i9+sep2C/HzWvP46t912G72TevLNV9/y/YZU/v6HtXSM6MK0adP48MMPPf2IQlwS7WzxC3GVaa3Pr7QAXoVKsmtwu1TM/kZM1FGcm0t6+glO2GyM7t8BvBKJC2nZ6u3iyrFu3ToWLFhAVlZW47nXn4tm2dLFPNDHGyylxJ7Yyi1h3TGOHUJoTCh/W/A33s/KJPXVP1MM3NCzJ2/eeiv+3ePRhEcSEh5Iz259+PWyufzqkfnc+LNJeHlLS7G4Opzamqyurg4vL1kBQbRNdXV1wOlb7TWXJIBXGbdbbZz8ER7vh/14Kunpx8my1uMd7k1CRATV3nHESwLYpq1bt45p06YxadIkVq9eTUzn7rzx8RbW/uNV5j74IFl/eI2F45LwLTtEcHEat+qNfB4fyRPvPs47i97l0LcpAHTRaqkrLYWP1uPVty+dhw7BP9SLe29/iDmPT+OfKz9i6sxJBHfwlhZj0eZptVoCAgIoLi4GwGw2y+datBmqqlJXV0dxcTEBAQFotRe3iockgFeZ8vyGpV/0Rh1BkWaqvknjyJFUjtlsJF4bS6g2GG1ER9n+rQ1zuVwsWLCASZMmsX79+saV+J+692auGTGMR35+J6+v+B3Bvb4gyTuQPtZdBLst3Gy3sksP5t/fyTvPf8TODTv524EDFJaXM//mm+kG2LNPEjh6NBPvGA2PQ1FxIRn7iikvMBOXFCK7h4g2LyIiAqAxCRSirQkICGj8HF8M+W1+FVFVlcITlUBD65+7qJC6kmJS0o6SYbMxekA0WmMPuoTL8i9t2datW8nKymL16tVNtmEy6DRclxDBH3+3mBvHjibz8G40vYdwRB1HdMUButsPE2VS8NXbyLwmhp0bduIbEci6nBz2vP46C0Zdw03jxuFa9xGHXE4AEvp2RtEoVBbWcagsl5iEIEJjfFE8uFyMy+Vi69atFBQUEBkZyahRoy76m7BoPxRFITIykrCwMBwOh6fDEaJZ9Hp9q/2+kwTwKlJdWk9dtR2NTkNYrC/13+4m7WgaqbUWAqP8SIyOoMa7E51DZeu3tqygoACAXr16nfH9kYP7A9AvGHrEB3GipJYcZQDFzm50qNpLWMUxbgyM4u0QHwKjfOh1bU+++2Abv/7ma75KTeWBG8by5z17iQ0JYVx8EJpuwWSl1lBXZSPrYCkl2TXE9grBJ/Dyjw0807jHuLg4VqxYIbOWRbNotVr54iDaNZkFfBUpPFEFQGiMLxpc2NLTOXToIKk2K72HRROmBOAV3hlvo+T9bVlkZCQAKSkpZ3z/1PnOcTGM6BLCPcPimD0ijmt6d8HQcxLH42ZQ653I/NuuI31fDkVH0xl771DMHYL4pCCfqf/8J18cTuHePn2o270b67r3iNNnE93ZjFavwVJp48i2fDIPlOCwXb41A0+Ne0xKSiI5OZmamhqSk5NJSkpi2rRprFu37rLFIoQQbZ2itrP58M3ZKLktsVTZOPxtHopGofeYaNSs4xR+8imL/vgH/lVezqN/GMfoDpOIGXEnA2KDPB2uuAgul4suXbqQlJTUZAwggNvtZsqUKaSkpHD8+PEztnCoqkplnYOC/Bw+e+dlXnr9nxSUVv9YQFFAVelkMDA2IoqxgwfTr19f/P390cR3ocwrjopaHQqg1WuI7BxAeCc/tNrTv0+qqopbBZdbbThUFY0CRp0WbTO6kS/2mYUQoj1oTo4jTUFXicKMhta/wAhvjGY9lampHD58mCP19XTsGkzn4FCqvbvQVcb/tXlarZYVK1Ywbdo0pkyZwsKFC+nVqxcpKSksX76cDRs2sHbt2rMmQoqiEOhtILBrZxJ/9xd+9etFfPH+G6Qc3kmeqQ6iDOz7NpfdX6bzt+wsvirMZ8BXX5IYE0905+5EdYzHFRhDlTkOmymQvZkVoFdQwky4/fW4+UnC5z7790uDToPZoCXI20Cg2UCQt4HoQC/8vfSnzcw827hHAI1Gw8KFCxk+fDhbt25l9OjRF/sjFkKIq54kgFcBq8VBeb4FgMjO/jgrKnDkF3Ao5RBHrTauG9qNCPxQIrvjZ7q4dYPElWHq1KmsXbuWBQsWMHz48Mbz8fHxrF27tlnj4XR+4dx439PcWF+BenI7J7OT2R4ZwdEbO7H3u2x2f5PFB5kVhNbU0DvzGF2NRoKCQwmJisUvoieayL44/UNxVPri8vHCHW4Cb21DS+L/0CgK7h86HexON3anm8o6B2BpLONr0tE51Ieu4T50CPBCUZTzjns8df5UOSGEEOcmCeBVoCC9EtWt4h9mxjvAiOX7PVRUVLDtRAb1ikrCwCiMxu7ERUnX79Vk6tSp3Hzzza03I9YrEKXHBOK6XE9caRrVBQc41uEAJ24u43B2Ebu2nuRAci5bc8vpXFtLt8J8Ouh3oyj/IiSmJ8EdeuPl74tvsD8d473pmqDF3w80Gi2KRoNGo0Gj0eFGwYkOm9ZMveJFpdtMCQHk2cwU1tipsTrZn1PJ/pxKgrwN9I72JyQsHGgY3zh06NDTQj817vHU+EghhBDnJglgG2erc1Ca27DtW1TXAFSXC2vqUVJSDpFqs9I1KYJYn2BqfLvSNUy6f682Wq229bs8dQaISMIvIomB7hkMrC2itvIkOSNSyavKIi33JDt2Hid1fz77jpURWOWk04m9ROWm4h2aiCmwC2n7NHxl0GLRleAILiS8hy+9u0US6e+LWdHhrdHjregI1BgIUzR0A9DocPqHUqyNIN0ZxqEaX8otdrakleCliyYquiPPP7+Mjz8+fQzg8uXLiY+PZ9SoUa37sxBCiKuUJIBtXEF6FapbxS/UC98gE7bjx3HVWdhx8CCZdjtTBkcRiR/6Dgl4GWRwvGgmjRb8ovDxiyKh4zASgLHAAzMclFmKqLaWk52fxa6duzm8az+5KRkYck4Q69cD74B4zPYgsARSciCTv+R9QrHZBZ1CiewWRnTnEOK6BtExwJtQp4swxUC0o5ZITT5RisJwow8ntfEk18dQ4vRi3M+f4J3nHmH8xMksXfx0s8Y9CiGEaEoSwDbMVu+kJKcGaGj9A6g/lEJuTg5bsnPQmXT06B+FzpRAtw4hHoxUXG30Wj0RftFE+EXTLaw3Y/veBA/8+H52diZ7PvuK/D152MscGL1MDAqIw1p+nJq0VHIP5rPVZudtu42gLpEkDO1B4pAudE30wt9dSUe7nW5OC520NcQrh8gxdiRo5BBY9Gc+ef2Fix73KIQQ7Z0kgG3YqbF/fiFe+AV74Swrw5GXx569+zhss9Ln2jiiDQFYAhLoHCp7/4rLp2PHeDrOvR+A6tI6Tu48ScmxPCpzcqgrL6a26AhFWbspKi4mq7CGY2u+Y8s/v8YU4E3f6/syYGw/uvbUEWArpbvDTU+Dk5mak/QdFUu/QetJO5oK9RVMGJTIhBvGSMufEEI0kySAbZSt3knp/7T+WVNSsNlsbNy/H4vbTe+RMUQoQQTGJ6I7wxptQlwOfiFmek3oQdWAWHJSy7GUVOOqGEnvqql41+dQnp/CifQTpBxLY3dZOYc+Smbbh9sI6xjGqNtHMfRnvdnrLiXB4WKA0UmMPofNPZLINQwm36SjvM5JqK8kgEII0RySALZReWkVuF0qvsFe+AabcNvtWI+mkZKSwp7qKsKiA+kYG4zi1ZNeMTL7V3iWoigEhJvxD/WiLN+f3KM+2Osjqa/vgm90b65LymaSo5yTJ7NIOXyYTQcOsKOgnA9f/JANKzcw8raRjJsxjKP2fPpiYoLBxv7abPaqI/lgTw639OtApL+Xpx9TCCHaDEkA26C6anvjzN+YhEAURcGadgzVbufb/fvJdTiYcG1HYhQ/tJG9CPG5/Hu2CnEmikYhJNqXoEhvik/WkH+8EqeXFyVEY3LWEB0RT6dOnfjZ+PEcOnSQz3ftZv3x42x+dzPbPtzG6BmjqbklkTRrFqO8rZirP2OH9xg+2ge3D4yRz7oQQlwgSQDboNy0clBVAiO98Qk0oaoq9YcOUlxcxMa0o2i0GnoMiSJcG0XHzgmeDleI02i0GiI6+RMa40tBRhWFGVXY8CVP1xOfsERCHLkM9DbTf8AAbjuezkdbvmHN0aNs+vsmtn+8nVsfm0h1z3x6edUwtOozdqrjWL9Pw+2DYmSxcyGEuACSALYxNeVWKgvrQFGI7hEIgCMnB1dZObv3H+CozUbi0Fg6ePtj9etDt4irZ79jcfXR6jVEdw8kLNaX/OOVlGTXUGuBWmII7NOJYGs23fQGnujahZuOHuWdL7/iw4wT/OOpf9NzRAJ33NeHHkH19Kz4lCPqJD7ep2H6oI4YdDLmVQghzkUSwDZEVVVyUssBCI3xwcvHAED9vn04nA7W7NqFXVXpOzyKjqo/QZ36o1VUtmzZ0jo7RQhxiRhMOuKSQojo5E/u0QrK82upKHVQqYkibGgnAiqP00Oj4bkuXRm37Tte/fJLkrelsvzQSe751UgG9bXTqfxjjmum8eURIzcmRZy2n7AQQogfSQLYhlQU1FFbbkWjVejQraH1z1lSgj07h0MpKXxXUkxQhB9xCWF4GbuReWg3s6fcQFZWVmMdcXFxrFixQtZME1ckk7eeLgPCsHTxJze1nKqSeopy6qnwiqPD2K54pe1hjE5HUs+e/PPjj3nr8GFWLf2CzNv7MvlWJ9El68hUprPX38iAWJn8JIQQZyP9JG2Ey+km+0gZABGdAzB4NeTudfv2oaLy4Y6d1LjdDL6+Ex0UX7afqGHOPTNJSkoiOTmZmpoakpOTSUpKYtq0aaxbt86TjyPEOXn7G+k+NJKug8IxmvXY651kHqunKHwghuvGE9Yxlkfunc0fxo9nmNnMF+/v5+2VOzmmPUFkyUd8n1ZEYZXV048hhBBXLEVVVdXTQVxO1dXV+Pv7U1VVhZ9f2xkfl3O0nILjlRjNenqN7oBWq8FVU0P5u+9yMjOLG195hSq9wrwXxzLS1J1fPruO/n37sH796fumTpkyhZSUFI4fPy7dweKK53K5KTheScGJhm0PNVqFiFhv/PIPYjt6lAMH9vPaunVsqqwkqmcYM+cNpZvfNdhjpnDn0DgZDyiEaDeak+PIb8Y2oL7GTuGJKgA69gxC+8OizvUHDoJb5fP9+yhxOel7bRdCvXxJyzORl5PNU0891ST5A9BoNCxcuJDMzEy2bt162Z9FiObSajVE9wgi6dpo/EO9cLtU8jNqyTEloBs1lr4DB/HrWfdyd3gElqNlvPH8Fg6VfI2r8Hv+e6zE0+ELIcQVSRLAK5zbrZKxvwTVreIfZiYg3Nxw3mrFevgwVdVV/GPbNgD6XxNFnOqPy6th399evXqdsc5T5wsKCi7DEwjROkw+eroNiaBTv1B0Bi2WShvpeUbqBk2g84AB/PK+OdwVHY1PkZW3/7CVo7nvcSx9P+nFNZ4OXQghrjiSAF7hCtIrsVTa0Oo1xPcOaZzZWL//AKrdzneHDpFptdK5TwxhEX4EGrswoG9fAFJSUs5Y56nzkZGRl+UZhGgtitKwkHSvazsQEGFGdasU5jsp7DCKsAHDmHPvbG7tEEV0uYN3/vBfTp5YxVeHjlNnd3o6dCGEuKJIAngFq62wkX+8EoDYXiGNEz/cViv1Bw7gcDj4yzffADDwuo7E4IdPpyHccN1o4uLiWLZsGW63u0mdbreb5cuXEx8fz6hRoy7r8wjRWgwmHV0HhtO5fxg6g5a6WicnDQl49R/Dvffey7iICHrXwFt/+IrMo3/h61Rp7RZCiJ+SBPAK5bC7SN9ThOpu2PEjuIN343unWv+2px1lT3ExQZEBxCUG00ETTtfEgWi1WlasWMGGDRuYMmVKk1nAU6ZMYcOGDbz44osyAUS0aYqiENzBh57XdGjYD9ulUkgH3H1uZNbsOQwJDWGQReGfv/+QrXtel65gIYT4CUkAr0CqWyVjbzH2eicmHz3xfX7s+j3V+udyu3j5888BGD6xBxEab7wiBhMW0JAoTp06lbVr13Lo0CGGDx+On58fw4cPJyUlhbVr18o6gOKqYfTS0WNoZMPamIpCjSYIS4+J3DFrLr0C/BlcBe8vf41/b/0Aq8Pl6XCFEOKKIAtBX4HyjlVQVVKPRquhy4BwdPofW+pOtf7ty8jku5Mn8QvyJXFwCLFqAHG9RzSpZ+rUqdx8881s3bpVdgIRVzVFo9CheyC+ISYy9pVgxw97t59xy91aPnhrBfqSelYv+S1xkV2YNfwaT4crhBAed0W0AK5cuZK4uDhMJhNDhgxh586dZy37xhtvMGrUKAIDAwkMDGTs2LHnLN/WlBdYGsf9xfUOwexnaHzPXV9P/YEDqKrKK19+AcCQST3x05oIDOxNbGT4afVptVpGjx7NjBkzGD16tCR/4qrmF+xFr2saJohofHwhaTwTZj5BlJeZ/nlWVjw6m/25Jz0dphBCeJzHE8A1a9Ywf/58lixZwt69e+nTpw/jx4+nuLj4jOW3bNnCjBkz+Oabb0hOTiYmJoZx48aRl5d3mSNvfbUVVjL2NTx3WJwfIdE+Td6v27kT1W7nSEE+X6Sm4uXjRdLQUDqrgUQmjpS9T4UAdAYtXQeGE5MQhNbHB9/B47nu1scI8w6kT3oFc++9hSprrafDFEIIj/J4AvjSSy9x//33M3v2bBITE1m1ahVms5k333zzjOX//e9/84tf/IK+ffvSo0cP/v73v+N2u9m8efNljrx1WS0Oju0swu1SCQg3E9szuMn7zvJy6lNSUFF58auGZx00oRd+Rj2Rpjg6d0nwRNhCXJEURSGySwA9hkZgDPAlfPQkhvzsPkIDO9J9bxZ3z74dl1vGAwoh2i+PJoB2u509e/YwduzYxnMajYaxY8eSnJx8QXXU1dXhcDgICjrzxu82m43q6uomx5XGYXeRtqMQp92Fd4CRzv3DUDRNW/Ms330HbpW0Wgsbd+9Cb9DT75oI4tUAghOuRaeTrl0h/pdfiBc9r4nCP8qf+PE303P4zURE9iPki1089uu5ng5PCCE8xqMJYGlpKS6Xi/DwpmPXwsPDKSwsvKA6fvOb3xAVFdUkifyp5cuX4+/v33jExMRcdNytyeVyc3xXETaLA6NZT9dB4Wj/Z+9S2/Hj2E9mg0bD4nUfAjB4Yl/8zDpidFF06TnIE6EL0SYYTDq6D40kqmcEvSbdRHTiICLjRmP952e88sfFng5PCCE8wuNdwBfjhRde4L333uOjjz7CZDKdsczChQupqqpqPHJyci5zlGenulUy9pVQW25FZ9DSbXA4BlPTidmuWgs1W7YAsN9mY8vu3Ri9jAy6IZqO+OHfeQQmo+EMtQshTtFoFDomBtP12h4MuPE6/GNiieg8jvS/fMCaN//i6fCEEOKy8+gyMCEhIWi1WoqKipqcLyoqIiIi4pzXvvjii7zwwgt89dVX9O7d+6zljEYjRqOxVeJtTaqqkn2knIoCC4pGocvAMLx8DaeVqf3ma1SrDW1ICE8sXwbAiFsGYTaqxCmhdO4z0hPhC9EmBUV60//Wa7BbCtm6aR8h2tHsenENIb7BXH/bDE+HJ4QQl41HWwANBgMDBgxoMoHj1ISOYcOGnfW6P/zhDzz33HNs2rSJgQMHXo5QW11hRjVFmVUAdOobil+w12ll6vftx551EkWn5eOiQg4cOoTZx0y/66LpiC/+MYPx9/U57TohxNmZ/EyMvGcCg0ZH4fLR4xPSi6//+CF7N3zm6dCEEOKy8fhC0PPnz2fWrFkMHDiQwYMH88orr2CxWJg9ezYA99xzDx06dGD58uUA/P73v2fx4sX83//9H3FxcY1jBX18fPDxaRvJUFleLTlHygCISQwmuMPpcduzs7F8/z0A1h49eOyWWwC4YfZojIqVLmo4nfuNvkwRC3F10fmFMnjyCKzqp2z/ohIToWz44zoMihe9Jo7xdHhCCHHJeTwBnD59OiUlJSxevJjCwkL69u3Lpk2bGieGZGdno9H82FD52muvYbfbmTZtWpN6lixZwjPPPHM5Q2+R6tJ6MvaXABDeyZ+ITn6nlXFWVFD9+eegqpgSE3jk9depqqqic6/O9BoRRkeLG9+oAQQFh17u8IW4agR1G0Zin8NY2U/ypzUEqt588qf14NDR82ZZV1MIcXVTVFVVPR3E5VRdXY2/vz9VVVX4+Z2efF1KddV2Ur/Px+VwExTlQ+f+oaf9kXHVWqha9yGuqmp0EeF8rapMmz4djUbDr/72EF5KHtepcfSa9GvCwiMva/xCXG0cdZUcWLeCPXlZ7PygkhhzNN4+3lx/1xR6TxuGziDLKwkh2o7m5DhtehZwW2Krd5K2oxCXw41vsIlOfUNOS/7cNhvVGz7FVVWN1t+fku7dmX3ffQDc+PMb8fW3Ek8A5v9v787jo6rPxY9/zjmzT5bJvpCFyA5hURAMSlGh4loRrVTt1bb3/u7VYt27WKu1eiveqr1q60/r9Vf1ahXrWsGlIgKiIqsIYQ0QtpCF7JPMPuf7+2OSIRFERSYh5Hn3NT0z3/Odc57zEGee+Z4td6wUf0IcA1aXhwETL2FMZg6DZ1rZWLuGdq+XpfMWsPq5T2hrCvZ2iEIIkRBSAPaAaNikYmUN4UAEZ7KNIRNy0I3uqTeDQVr+8Q8iB+rRXS70s8/i/Fmz8Hq9DBoxiO9cMQHd28xQlU7huGm9tCVCnHjyhp6CY0ApU/NKKLmqgI+3/5PGuv2sfus91s1bQdW2JpTZr3aUCCH6ASkAE0yZih3r6vC1hrDaLQydlHvIbqV48Vdbh+awsxDFgGHD2LFjBwA7Nu/gvh88QPsqL1rOaPLzC3pjU4Q4YQ2bcinYUrhowECGXV/Gws3zqdrxGRsWf8DO99axeXk1QV+4t8MUQohjptdPAjnR7dvSRHOND93QGHJqDnZn95SbgQAt/3iTSF0dutPBIsPgip/8BACr1coNf76BQKCWZX9dwgNPvMewSVdyhhycLsQx5XInk3nqTBo+fo7v5+UQ/vmFvH3fq0xvq0FTJiMjUXwtgygZm3XYs/aFEKKvkZNAEujAXi+VHWf8Djol+5Avji8Wf+4LLyRn2LD4/Yp/8edfUDgxh7YtqykL5PCbZz6lqrqWiooKDEMOThfiWFJK8fE7L2JUryWa5OB/d1bz0b0vMC01m9Kyyxh+2jQcQwaTUeSheHQGVjlBRAhxnJGTQI4D3sYAu9bXA5A/xHP44u+Nf8SLv6SLLuLSf/3XePF34+9vpHhyEa27N3FyKAMtKY975/6ByspKli1b1uPbI8SJTtM0Rp1xMSGbB6MtwLWTTmHyXVcxv6mGtUv/l63LXiOwaRP1lQ1sWLKPhv1tvR2yEEIcNdkFnAAhf4SK1bUoU5GW52bAsLRu8+PF34ED6C4nrgsu4Mc338zbb8fuRHDL729h0PmD8FZvZ1SLFbeWROEZV5Du8QBQXV3d05skRL+QluIm5ZTL8K/4K9Hdu7j5/PMwlcnrv32O4MevoYXqKeUKgieNYMeaKI372ykuzTjkHt5CCHG8k0+tBLDaDTLyk/A2BjhpXPdr/ZnBYLfiz3HeefzgP/6DN998E13XMU0TvUAn2FJLSW072SRjG30hxYWFLF++HIC8PLkEjBCJMnbEMBZWnUXK3kVom9Zw8w+uRKF45a7n8K9YSqCtnqkX/Ji23FIaAW9DgKJR6WQMSJKLRwsh+gzZBZwAmq5RXJrBiMl5GJaDKTZDoY5LvcSKP8s553Dx1Vfz5ptv4nA4uPMvd5KRn8G7T85nYGUdhdFkwrnjGDd+MqZpMnfuXEpKSpgyZUovbp0QJzZd1zh18tk0JQ2mpT1Exua1/Ozqf+OqB/6NN/1tvL1hPf989f+SVbccS3Ul4UCYnZ8dYMvyanytod4OXwghvhYpABOoa/GnQiFa588nUhs75o8zz+K82bNZtGgRSUlJ3PHUHWRPyObS62ew8ZOt/PHh91hZa2XIxPNYueJTZs6cyYIFC3jwwQflBBAhEiw9yU7exEvwWz3sqz3A8KpK/vXyH3Pt/72eJSrMvM2bmff8U6TVr8JTsx6iYbwNATYuq2J3eQORcLS3N0EIIY5IzgLuASocpmX+AsJVVWh2O9EpZ3DulVeybt060tLS+Plffk7qkFQsAS+lew6wdEkFj7zyMbUHGuLLKCkp4cEHH2TWrFk9ErMQ/Z1Sijc/WU/m1hdJtcHwSTNY5Xbw5rI3eWzOY3haA8wuKuJHV1xJ2oBiWgom4FXJAFjsBoUj0skckISmy25hIUTP+CY1jhSACaaiUVrfeovQ7j1oNhv+SZM454ofsGXLFrKys7j1yVtJLk7GEfIzoaqe9sYwTa6BDJl6JVXb1lNdXU1eXh5TpkyRkT8heliLP8y7i96nqHYRAzxOCk+fzQrVxj9X/5PH5jxGoKqBizOzuPbyyxk6dCjh4pHUu04i4DcBcKXYKBiRTmqWU44PFEIknBSAR9CTBaBSCu/77xPcshXNaqFtwgSmzZ7Nzp07KSgo4Gd/+RlJ+Um4I2Gm1NZTV+On0VlETtmVjC/JTGhsQoivp6LWy/oP3yC/9XNGDEjDU/Yj1gRq+GDLBzz186fYvmobE11ufvm9i5g8eTJ6igf/yDOoa9CIhmOFYEqmk8IR6bg99l7eGiHEiUyuA3ic8C1fTnDLVtA1vGPGcPbll7Nz504Glgzkxv93I0n5SaSaihlNXurrAjQ7CrCNuZRTBmb0duhCiA5DcpLxjJpOk7OY7TXNBD9/mfGpg5kxcgbXP3Y9ZZeezqe+dua88irPvvwy/rpaHCveZpB9LzlFbjRdo7Xez8ZlVWxbWUNbU7C3N0kIIeQyMIniX78e35q1AHiHDWPaVVexa9cuBp40kOueuA5XlotMzco5zfVU7m+lyZZH25DvccnIfNlVJMRx5jvDcvh787nYt7/Mjn21DLf/nTGn/BDHYAeW31goGFrAqw++yp0ffcTnVfv5xezLyVOK5JSdpE86gwP+FOqr2miu9dFc6yM120X+EA/J6Y7e3jQhRD8lu4ATINraSuPzz0PUJDBkMGfOmRMb+Rs0kOsev46krCTyDBfnNR6gsqqRPWYGNcXf4/JJg3DK7aWEOC41tAV5bfkmhla9TnGSycCThsLYK9jvP8A7le+wbf02/vrLv1JfVc8gp4v7Lr2UqadOQNd07EMGY4ydSG1tlIaqNpQZ+9hNyXSSOyhVjhEUQhwTcgzgEfTUMYDBnZU0btnMOb/4BeUbN1I0sIifPvlTkrOSKbR5OLehhrr6Zja1JVGRdwGXnzaYzCQ5PkiI49n2ujYWrdrAqLr5DM2wkj1wFJReSlOolbd2vkVNfQ1/+93fWPfBOqzANRNO5baLLiI9LQ0MHeeYsRgjx1Kz10/9voOFoDPZRk5JChkFSRiGHJkjhDg6UgAeQU8VgO3t7Xz3u99l+fLlZOdmc8P/u4G0/DSKHBmc21CDr7WFlQ02NmVdwLTRRYzKT01YLEKIY2f5jgbKN2+k9MDblOa5SC4cAyO+R8AM8f6e99ndspsV81fw6gOv4mvzUeB2M3f2D5g+ejSGYaA57LgmTEA/aTi1+3zU7/XGTxax2Ayyi5PJLk7B5pQjdIQQ34wUgEfQEwVgKBTioosu4r333iPVk8oNT91A7qBcSly5nNNQjdnezMoDBmvSzmdoQTYzRuXI7h8h+gilFPPXV9OwexOjmxZSmpeMY8AoGPE9lKazunY1q2tWU7+/npfueYmNKzYCcNaw4dw3ezaDMmNn+OsuJ85x47AMH0VjbZDaylaCvnBsJZqGJ9tJVlEynmyXXEtQCPG1SAF4BD1RAFZXVzN16lT2Ve3jhiduoGh0ESXufM5prEX3NVDeZLDUNYPkFA9XTCzCZpFdPkL0JcFIlJdX7yNSu5UxrYspzUvCljMMRs4Ew8Le1r18sPcD2kJtrHpnFfP/ez6N9Y1owP+ZNp2fTTubnKTYRaM1ux3nmNE4RpXS4tWo3dWCtyEQX5fNaSGzMJmsomTsMioohDgCKQCPoKd2AW/atYlnFz9L8cnFFLjzOL+lGYu3htqQlVfNs4jYUvjBxEKyk+UsQCH6orZghJdW7UVv3MEp3sWMynVhyRwMoy4Biw1/xM/SfUvZ2bwTn9fHe0+8xwcvfUA0GsXQNH528cX82+TJZDucsQUaOvbBg3GOGUPYnUb93jbq97YRCXXcVk7TSM10kFGQRFqOG8MqPxyFEN1JAXgEPVEAtoXaeGXbK/giPnKd2VwUjGJt2ElQs/FCaCrNWgqTB2Uw6SS53p8QfVlTe4iXVu/F1rKbie2LGZHjxEjJhdHfB3sySim2NW3jk/2f4I/4qd1dy5KnlvDhgg/jy/jRtGn8x9QzOcmTikZsV68lOxvHqFFYTxpES1OEA3u8tNb74+/RDZ20XBcZA5JIyXKiyy5iIQRSAB5RTxSApjJZuncpB/wHuFhLxl61DqXpLLSdzcb2VLJT7Pzg1CIM+dAWos+raQnw6tp92Nqrmdj+AaWZVgxnCoy5HJKyAQhGg6yqWcWG+g0opdi3ZR8fPfcRH7/7MaYZOwFk2sknc/2Mc5mYm4NVj10OSrNasJ10Eo4RI4im5dC4v52GqjYCbeH4+i02g/R8N+l5bpLTHXK8oBD9mBSAR9BTu4CVUoT2fIp95xIAdmSdzZvVaRi6xhUTi8hKlku+CHGiqGr288ZnVWiBZk5rW8SY9AgWq53o4HNYtq0xfk/vERNGsPbAWipbKgGo31vPqr+v4v1X3yfgjx33l5WSwpzzz+fiMWPIT0qKjwrqyUk4hg7FOmgQQVtqvBiMBKPxOCw2A0+Oi/Q8NymZDnS5pIwQ/YoUgEfQY/cCrq+A8ldBKXyFZ/DMvjyCYVN2/QpxgqppCfDaZ/uIBHyc6vuQvZ9/wi+ffJddNU3xPgMHDuShhx5iyrlTWFWzil2tuwBoa2pj3YJ1fPTaR+zdtTfef/Kw4Vw9ZQrfKS4mM/XgpaKM1BTsgwdjPWkQPi2JphofTTW+g8cLAoZVx5PjIi3HTWqWU44ZFKIfkALwCHqkAAy0wMonIRpB5Y3hTf9Ydtb7yElx8INTC+V4HSFOUHWtAV77rIoVi97i2f+8ifMmDuXOfzmL0tJRlEdKuO+Rv7BgwQJeeeUVZs2aRWOgkQ0HNrC1aSsRM4Jpmuxcu5N1C9bx8bsfEwqGADCAGaWlXF5WxqTCItK7HC9opKZgGzQIW3ExflsaTbV+mmp8hAOReFyarpGc4cCT7SI124nDbZVLTwlxApIC8Ah6pABUCvatgsZKNmZ8l/c212PoGldOKpK7fQhxgqtv9TNi+FByiofw8zvu4CLbWtKtEdA0zLxTmHnrI5Rv2kxFRQWGETvWLxAJsLVpK1sbt1LvrwfA3+Zn04ebKF9UztoP1xKJxAo6K3DG4MFcMn48kwqLKBowIL4czWHHVlSMtbiYcGouzY0Rmmp9BNvD3WK0u63xYjA5wyF3HxHiBCEF4BH02C5gwOsP8dyKPQTDJmcMyeTUgekJXZ8QovctWbKEs846i//86xu4CkZgM31Ms5YzlD3omsbyLdVM/umfWLzwXc6cPuOQ99f769nWuI2K5graw+0AtLe0s37xejYu2siGFRuIhA8WgyPT07l4wgQml5QwuLDo4OearmHNy8c2sBgzIw9vxEnLAT/ehkD8FnQQGx1MSnOQmuUgOcOJ22OXvRRC9FHfpMaRq4omiFKK97fUEQyb5KU6GF+U1tshCSF6QHV1NQDXX3o2q6t8rN8H70QnUmEZyFRLOaVFwVi/D5+FQgUDxkNSVvz9mc5MMgdkUpZfRq2vlsqWSirtlbhnuimbWUagPcCWT7ew5aMtbPhoA5/XN/L5e++hAbkWC1MGDeas4cMoLSig2O/HUVUFgM3lJH9AAXrhAALubLxtGi0H/IT8EbwNfrwNfqAJ3aKTkuEgJdNJUpoDV6pNCkIhTkBSACZIeVUru+p9WHSNc0blygeoEP1EXl4eAJs3bWTaaadRlO5i4eZatoez2RE5E+e+RbF+aW7Y/1nskZwD2SMhazg4PQBomkauO5dcdy5l+WU0BZqobKlkX9s+3NPdjJs2jsvNy9mzaQ8blm5g+8rt7Czfyd+3buHvW7eQouuU2B2cOXwYE0tKOKmwiILmZuwVFQCkejxkFAxAlQzAb03D6zXxNgSIhKI01/porvUBoBsarhQ77jQ7SR47bo8du8vSq8cQRqNRli1bFj+7esqUKfHd4EKIr0d2ASeANxDmf5fvJhQx+c7QLMYXy+ifEP1FNBpl8ODBjB49mjfeeANd1/EGwny8vZ6NVS08ffdPqd5VwbyXX2acsZOMwB50unwMuzMhvQTSSsBTBIb1kHWEzTA1bTXsbdvLPu+++HGDPq+PitUVbFmxhYqVFVRXxkYjdWKjg0UOB6cPGsS4oiIGFhVRWFSEy+kCwEhPw5KXRyQ1D78tlTafTntzsNuZxZ0sNgNnsg1nshVXig1Xig1nsg2jB25r+dprr3Hrrbeya9eueFvn2dWzZs1K+PqFOJ7JLuBe5rRo2Oo2s33HblqMUUQLviO/ToXoJwzD4KGHHuKyyy5j5syZ3H777ZSWlpLq3cU/H/5PNq1YwjV3Pkq5P51y0vFYxjLWUctAczeeUA16ez2018PeVaAbkJwLKfmQnB+bOlKx6lYKUwopTCkEwBf2UdNew/72/RTnFDPurHEoFE01TWxZuYVtK7exc91OPq2q59PPP8e2fj35FitFNisTiooYXVRMcXExBYUFpKamYkMjKyWZ3OxcounpBO0eApobn0/haw0RCUW77DbuoGnYnRYcSdbYwx17OJOsWB3GMRkxfO2117jsssu48MILefHFFyktLaW8vJz77ruPyy67LH52tRDiq8kI4DEmv06FEHD4z4KSkhIefPBBpnz3AjZUNbO1po1A+OAIm0sLMtTeSCG1ZIX3k4wP/YuFk80FyXngzordacSdBa6MWLHYIRQNUdtey/72/VS3V1PbXktURWmsbmT7Z9vZsXYH29dup6ayBgCnppFntZJnsTIsPZ0xhYUUDMhnwIAB5OTm4na70dDQk5PQ0zKJuDwErUmEDTcBZScQgHAwwpfRDR1HkhW704LNZcHutGB3WbB1TC3Wr/6BfLiR1U6maTJz5kzKy8u7nV0tRH8jZwEfQSILwK6/Tn/96193+3Xa9dpfQoj+4auOVYuaisr6dipqvexu9OHvurtVKdxmK4WWZnJpIMNsIDXahMuqYf3iZVt0A1zp4O4oCN2ZsaLQ4QFdJ2JGqPfXU+urpba9ljpfHa2hVryNXnasixWDO9buYF/FPsyIiVXTyLVYyLJYyDIsFKemMCg7h+zsLLKyssnMzMCTlkZqaiqGbqA7HahkDyFbCmFrEmHDQQg7IdNKyDSAI4/+GVYdu8uKzWlgc1iwOSxYHQY2hxF/vuyjDzn77LNZvnw5p5122iHLWL58OZMnT2bx4sWceeaZR/+P1o/JsZV9nxSAR5CoAlB+nQohvg3TVNR5g+xp9FHd4qemJYDvC8ff6WYEV7ieDNVMjtFKOi2xolCP4rAZ2A29+67WzsLQlQGujqLQnQnOdHxmKFYQdikKfX4f+7buY1f5LnZv3M3u8t0c2HsApRQ2TSPTsJBuMUgzDDyGQbrFQkFaOmlpHjyeNDweDykpySQnp5CcnExySjJ2h5OoPYWw3U3U4iJiOAnrdsKajbCyEMWCZrOBYRyxTFy4bAG/eeBGVi2sIC09BavDwGq3YLXrWKwG/qCP/OIsnn/uea764VWJ+Uc6gcneqxODHAPYC5YtW8auXbt48cUXuxV/ALquc/vttzN58mSWLVsmv06FEIfQdY3cVAe5qQ4gdimpVn+EmtYADW1B6ttDNLQFafHn0qZy2d35RovCHm3D5WsgOdpEpuYlTWslVbXiNBROXwCHtbb7qKGm4XJ4KHFnUuLKAHcJZtZ4GnWdusHNNJzdQGOgkYZAA61trdTuqqV6ZzXVO2KPir0HaKhqINwaxtrYSGpHQZhsGCTrOim6QYqhk6wbOGzWWDGYlExycjJutxu324XL5cLlcuN0ubE5U7G6PViS0jGtTqKGg4hhI4KVCFZMzcBjdQKwbtVqRo+cgPaFz9kNW9YC0LpHY/Xbu7DYdCw2A4tVx7DoGJ1Ti4ZhNTqmsTaLVUc3dHRDQzc0jI7nWj+5eoMcW9k/HRcjgI899hgPPPAANTU1jB07lj/96U9MnDjxS/u//PLL3HnnnezatYshQ4bwX//1X5x//vlfa12JGgF88cUXufLKK/F6vSQlJR0y3+v1kpKSwgsvvMAVV1xxzNYrhOhfQhGTJl+I+rYgTe1hmnwhmnwhmn1hol0u8IxS2KNenOEmnOFmUlUrGZoXD6249QgOq47TauCwGt2PM7Qnx0YKnR6ULQm/xUYDUVpRtKgwrdEAraFWWoIt1NfW01DVQH1VPfX76mmsbqTlQAvNB5ppOdBCe3M7Lk0j2TBw63q3h6vLc4cWK+Y0TcPhdHQUh06cThcupxOH043V4eaGdxYwMCOb3114FTZXCrozGYszGd3q5JfP3s+O6j28+NvnsXSOKFosoOuxYrHjoRnGwTbjyCenxApC/QtTDcPQ0C06uh4rEjuLRb3jEW/TDs7TdA7O+8J7uk41PXZxbk3T0DQSfrkd2Xt1YulTI4AvvfQSt9xyC0888QSTJk3i4YcfZsaMGWzdupXs7OxD+n/yySdcccUVzJ07lwsvvJAXXniBmTNnsnbtWkpLS3thC2I6r/1VXl5+2ONTysvLu/UTQoijYbPo5KQ4yElxdGs3TYU3EKHJF6LRF6LZF6Kp3U2TL53qQITqzo5KYY36cEaacbY344o0k0YraXhJwo/d6sdmqcdu0bEaOjZDo0DTDhYimgYWO1hdBDxZtKXn4Buj0a4p2lUUn4rSriK0m2GaA35q65poqG+hpb6NloY2vM0+2prbqW/24W1uo62pDX9TG3pbAKeu42jRceoaLl3HqemxNl3DpemMQrF4zw6u+5/7Ge90km4xaIxEWeP3sysc4nuZGSz880+xOdxYbW6sdhcWmwOr1YHFasdidWCxOjAsdgyLDc2woRlWMGxgWNEMK5puQTMMdENH0w104wsPS2yq6Qaarnc819EMHV3X0XUDdC2WJ7SOidb98WXtHTnWurzWdR3N0GLr0LWOuLoUjYaGZsQK2m5FZMe/ma5roHHoPF1D12H5yo/ZtWsXjz30FPV72w4Wnx0F6/X/cTMzLjybdxe8z3emTO02jy4FqqZ33J1aI168xud3tnWZL3pfr48ATpo0iVNPPZU///nPQOwXR2FhIT/72c/41a9+dUj/2bNn097ezoIFC+Jtp512GuPGjeOJJ574yvXJMYBCiP4mFDFp9sdGCZvaQx2jhrHRw2DYjPczzGDHiGEL9mgbtkgbtmg7drMdl+nDroUxdA1d63wQm+oaRscXu945ckVsHhpElUlYMwkRJaiisSlRQpgEMAlqCn80SmNbkMZWH80tAZpafXi9QQK+ML72EP72EP62EHV7mzhQ1UQkcjBuC5BntZJtsWDTNOyajl3TsOkaVk3DioZFiz23aGDVNPQjHXGodRZ4FjQtNuULrzXdQNOMWLumx96j6bHnncWhbkHXLR3FW2yZeud7O9vi6+p8P8QLxEOmQLyQOnQam2goDTQOFpQHC8wu7R3LXbNnA/+7/FUe+P4d2G2OQwrUQCTEz1+4mx9NvYIJg07uVpxq8b5avP2QqX7wdSxPscIz3qZ3FLeog0Ws1rldsW1T8c3okocu2wzqkKIyXmDHG7rOUB156Dq747XWtXuXPl37fnFdR/xb6n4K1Gkzy8gqStxAUJ8ZAQyFQqxZs4bbb7893qbrOtOnT2f58uWHfc/y5cu55ZZburXNmDGDN95447D9g8EgwWAw/rq1tfXbB34YX3btr/LycubOnRs/C1iKPyFET7NZdLKTHWQndx81VErhD0dpbI8Vh62BMK3+LLyBMN5ABH84SqhLoaWpCBYzhMUMYokGYlMziMUMYDFD6GYYwwxjqAi66piaYQwVRjcjGCqMoRSaAgMDJ+DsEk8JgKvjkQcmijAmEcyOaZQwJkEzQvm2ahpa23Gl2Mgv9OALhPD6Qvj8IYKhMIFghFA4TDAUpT0UJhiMEgqFCYeihENRooEoREy0iIkeBS1qQlRBxIRoGKKheJsKd5kfVWhKYahY0asTmxrECmKNWNuXPidWg+gdrw95rukYHcWkoenoHUWloevoWscu69h+4lhBRWdhpR8yjT3Xus+j++tweyMA6z9bQkFSRiySLsvZ194AQKT2ADWB9d3XES8yDxaDaEcohgSR5CYu+fG/9nYYQC8XgPX19USjUXJycrq15+TksGXLlsO+p6am5rD9a2pqDtt/7ty5/O53vzs2AX+FWbNm8corr3DrrbcyefLkeHtJSYkcRCuEOO5omobLZsFls1DwJTcsipqKQDgae0RMwhGTiGkSMRWRqCIcPfi8s10pRdQEUykipiKkYs87H9GoQplhNDOCGYmAGQEzjIpGUCqKFg2DGUFTUTCjYEbQVQSisb42Iow/NYqhYn30joemzNhIEiZ0Pu8yRUUBEzAxlQlEUEphojr+Z2KiYvM7WzrmE++j0OjcDpOoGcU0Y9tuRqNElSJqmkRNhdkxPfi647kyiUZNTLNjOaZJNBrrbyoVb1ed8xWElUJFo6iOXBJV0NFHdeQcBUqZYJqYZhRlxvqgOuabQLxvx/JNE1e1lfebP+f0wgGxEbd4X5OPd+7HZbfg8+xkMztBgdZlfbF0da6HjnWBpkBTHWNjSkNTBwtDrfO1FusHOvGb4aguw3Bd/k5j8ztGBDvb44Vn13d09O3WHvv/7tfU1L4w0bpNuy7r0Jq2S8MXdqKqbr0OLYYnuMsOaestvX4MYKLdfvvt3UYMW1tbKSwsTNj6Zs2axcUXXyzXUhJCnBAMXcNtt+C2H19fF6qj0IgVlR3lWcfr+BRQ5sHnnQXo0a/0K4M62PEwhYH2VQtQ6ou1zyG+anztK9eBOmQZC+a/yY+vuQavvYSbbrqZESNHsmXzZh7+7z9S07yDp599losu+t6Xvv/QVaivGAhUR95t+jXyAEcYbOzI/dcZi/yqM70PnfsNRjgPE6DDeehJor2lV/+LzszMxDAMamtru7XX1taSm5t72Pfk5uZ+o/52ux273X5sAv6aDMOQS70IIUQCdR4PdsRj+cTXcs2//AvJbje33nor5507I94ue69ObIm/c/cR2Gw2xo8fz6JFi+JtpmmyaNEiysoOP0xaVlbWrT/AwoULv7S/EEIIIY5s1qxZbN++ncWLF/PCCy+wePFiKioqpPg7gfX6mP4tt9zCNddcw4QJE5g4cSIPP/ww7e3t/PjHPwbg6quvZsCAAcydOxeAG2+8kalTp/LQQw9xwQUXMG/ePFavXs2TTz7Zm5shhBBC9Gmy96p/6fUCcPbs2Rw4cIC77rqLmpoaxo0bx7vvvhs/0WPPnj3dLqkyefJkXnjhBX7zm9/w61//miFDhvDGG2/06jUAhRBCCCH6kl6/DmBPS9R1AIUQQgghetM3qXF69RhAIYQQQgjR86QAFEIIIYToZ6QAPIbuvvtu7r333sPOu/fee7n77ruPm+VKrBJromJNlL4Ur8SaGH0p1r5E8poYx3tepQA8hgzD4K677jrkH/zee+/lrrvuOuqLQSdiuRKrxJqoWBOlL8UrsSZGX4q1L5G8JsZxn1fVz7S0tChAtbS0JGT599xzjwLUPffcc9jXx9NyJVaJNVGxJkpfildiTYy+FGtfInlNjJ7O6zepcfrdWcAtLS14PB727t2bsLOA//CHP/D73/8em81GKBTijjvu4Be/+MVxuVyJVWJNVKyJ0pfilVgToy/F2pdIXhOjJ/Paebvb5uZmUlNTj9i33xWA+/btS+i9gIUQQgghetPevXspKCg4Yp9+VwCapsn+/ftJTk5GO/Ldqo9aZ7XfqT+O/iQy1k59Idb+nNdE6UujFH0pt5JXIXlNjJ7Mq1IKr9dLfn5+t5tofFlncQx17t+/4447uk370/FfiYxV8to38poofek4pb6UW8mrkLwmxvGcVykAj6GuH5pdD8T8th+mX/b+b7PcRCyzJ2KVvB7/eU2UROU2EfpSbiWvQvKaGMd7Xnv9XsAnkmg0yj333MOdd95Ja2trvP3OO++Mz/+2y+3q2yw3EcvsiVglr8d/XhMlUblNhL6UW8mrkLwmxvGe1353DGBPCQaDzJ07l9tvvx273d7b4ZwwJK+JIXlNHMltYkheE0PymhjHY16lABRCCCGE6GfkTiBCCCGEEP2MFIBCCCGEEP2MFIBCCCGEEP2MFIAJ8thjjzFw4EAcDgeTJk1i5cqVvR1Sn/Lhhx9y0UUXkZ+fj6ZpvPHGG93mK6W46667yMvLw+l0Mn36dCoqKnon2D5k7ty5nHrqqSQnJ5Odnc3MmTPZunVrtz6BQIA5c+aQkZFBUlISl156KbW1tb0Ucd/w+OOPM2bMGFJSUkhJSaGsrIx33nknPl9yemzcf//9aJrGTTfdFG+T3H5zd999N5qmdXsMHz48Pl9yevSqqqr44Q9/SEZGBk6nk9GjR7N69er4/OPpu0sKwAR46aWXuOWWW/jtb3/L2rVrGTt2LDNmzKCurq63Q+sz2tvbGTt2LI899thh5//hD3/g0Ucf5YknnmDFihW43W5mzJhBIBDo4Uj7lqVLlzJnzhw+/fRTFi5cSDgc5pxzzqG9vT3e5+abb2b+/Pm8/PLLLF26lP379zNr1qxejPr4V1BQwP3338+aNWtYvXo1Z599NhdffDEbN24EJKfHwqpVq/jLX/7CmDFjurVLbo/OqFGjqK6ujj8++uij+DzJ6dFpamri9NNPx2q18s4777Bp0yYeeugh0tLS4n2Oq++u3rwI4Ylq4sSJas6cOfHX0WhU5efnq7lz5/ZiVH0XoF5//fX4a9M0VW5urnrggQfibc3Nzcput6sXX3yxFyLsu+rq6hSgli5dqpSK5dFqtaqXX3453mfz5s0KUMuXL++tMPuktLQ09dRTT0lOjwGv16uGDBmiFi5cqKZOnapuvPFGpZT8vR6t3/72t2rs2LGHnSc5PXq//OUv1RlnnPGl84+37y4ZATzGQqEQa9asYfr06fE2XdeZPn06y5cv78XIThyVlZXU1NR0y3FqaiqTJk2SHH9DLS0tAKSnpwOwZs0awuFwt9wOHz6coqIiye3XFI1GmTdvHu3t7ZSVlUlOj4E5c+ZwwQUXdMshyN/rt1FRUUF+fj4nnXQSV111FXv27AEkp9/Gm2++yYQJE/j+979PdnY2J598Mv/zP/8Tn3+8fXdJAXiM1dfXE41GycnJ6daek5NDTU1NL0V1YunMo+T42zFNk5tuuonTTz+d0tJSIJZbm82Gx+Pp1ldy+9U2bNhAUlISdruda6+9ltdff52RI0dKTr+lefPmsXbtWubOnXvIPMnt0Zk0aRLPPPMM7777Lo8//jiVlZVMmTIFr9crOf0Wdu7cyeOPP86QIUP45z//yXXXXccNN9zAs88+Cxx/311yKzgh+qk5c+ZQXl7e7dgfcfSGDRvGunXraGlp4ZVXXuGaa65h6dKlvR1Wn7Z3715uvPFGFi5ciMPh6O1wThjnnXde/PmYMWOYNGkSxcXF/P3vf8fpdPZiZH2baZpMmDCB++67D4CTTz6Z8vJynnjiCa655ppeju5QMgJ4jGVmZmIYxiFnTNXW1pKbm9tLUZ1YOvMoOT56119/PQsWLGDx4sUUFBTE23NzcwmFQjQ3N3frL7n9ajabjcGDBzN+/Hjmzp3L2LFjeeSRRySn38KaNWuoq6vjlFNOwWKxYLFYWLp0KY8++igWi4WcnBzJ7THg8XgYOnQo27dvl7/XbyEvL4+RI0d2axsxYkR89/rx9t0lBeAxZrPZGD9+PIsWLYq3mabJokWLKCsr68XIThwlJSXk5uZ2y3FraysrVqyQHH8FpRTXX389r7/+Oh988AElJSXd5o8fPx6r1dott1u3bmXPnj2S22/INE2CwaDk9FuYNm0aGzZsYN26dfHHhAkTuOqqq+LPJbffXltbGzt27CAvL0/+Xr+F008//ZDLam3bto3i4mLgOPzu6vHTTvqBefPmKbvdrp555hm1adMm9e///u/K4/Gompqa3g6tz/B6veqzzz5Tn332mQLUH//4R/XZZ5+p3bt3K6WUuv/++5XH41H/+Mc/1Pr169XFF1+sSkpKlN/v7+XIj2/XXXedSk1NVUuWLFHV1dXxh8/ni/e59tprVVFRkfrggw/U6tWrVVlZmSorK+vFqI9/v/rVr9TSpUtVZWWlWr9+vfrVr36lNE1T7733nlJKcnosdT0LWCnJ7dG49dZb1ZIlS1RlZaX6+OOP1fTp01VmZqaqq6tTSklOj9bKlSuVxWJRv//971VFRYX629/+plwul3r++efjfY6n7y4pABPkT3/6kyoqKlI2m01NnDhRffrpp70dUp+yePFiBRzyuOaaa5RSsdPp77zzTpWTk6PsdruaNm2a2rp1a+8G3QccLqeAevrpp+N9/H6/+ulPf6rS0tKUy+VSl1xyiaquru69oPuAn/zkJ6q4uFjZbDaVlZWlpk2bFi/+lJKcHktfLAAlt9/c7NmzVV5enrLZbGrAgAFq9uzZavv27fH5ktOjN3/+fFVaWqrsdrsaPny4evLJJ7vNP56+uzSllOr5cUchhBBCCNFb5BhAIYQQQoh+RgpAIYQQQoh+RgpAIYQQQoh+RgpAIYQQQoh+RgpAIYQQQoh+RgpAIYQQQoh+RgpAIYQQQoh+RgpAIYQQQoh+RgpAIYQ4CkuWLEHTNJqbm3s7FCGE+MbkTiBCCPE1nHnmmYwbN46HH34YgFAoRGNjIzk5OWia1rvBCSHEN2Tp7QCEEKIvstls5Obm9nYYQghxVGQXsBBCfIUf/ehHLF26lEceeQRN09A0jWeeeabbLuBnnnkGj8fDggULGDZsGC6Xi8suuwyfz8ezzz7LwIEDSUtL44YbbiAajcaXHQwGue222xgwYABut5tJkyaxZMmS3tlQIUS/ISOAQgjxFR555BG2bdtGaWkp99xzDwAbN248pJ/P5+PRRx9l3rx5eL1eZs2axSWXXILH4+Htt99m586dXHrppZx++unMnj0bgOuvv55NmzYxb9488vPzef311zn33HPZsGEDQ4YM6dHtFEL0H1IACiHEV0hNTcVms+FyueK7fbds2XJIv3A4zOOPP86gQYMAuOyyy3juueeora0lKSmJkSNHctZZZ7F48WJmz57Nnj17ePrpp9mzZw/5+fkA3Hbbbbz77rs8/fTT3HfffT23kUKIfkUKQCGEOEZcLle8+APIyclh4MCBJCUldWurq6sDYMOGDUSjUYYOHdptOcFgkIyMjJ4JWgjRL0kBKIQQx4jVau32WtO0w7aZpglAW1sbhmGwZs0aDMPo1q9r0SiEEMeaFIBCCPE12Gy2bidvHAsnn3wy0WiUuro6pkyZckyXLYQQRyJnAQshxNcwcOBAVqxYwa5du6ivr4+P4n0bQ4cO5aqrruLqq6/mtddeo7KykpUrVzJ37lzeeuutYxC1EEIcnhSAQgjxNdx2220YhsHIkSPJyspiz549x2S5Tz/9NFdffTW33norw4YNY+bMmaxatYqioqJjsnwhhDgcuROIEEIIIUQ/IyOAQgghhBD9jBSAQgghhBD9jBSAQgghhBD9jBSAQgghhBD9jBSAQgghhBD9jBSAQgghhBD9jBSAQgghhBD9jBSAQgghhBD9jBSAQgghhBD9jBSAQgghhBD9jBSAQgghhBD9jBSAQgghhBD9zP8HdjX3e2KFB58AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 650x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot ML fit for pEpoR (all regularization strengths)\n",
-    "fig, ax = plt.subplots(figsize=(6.5, 3.5))\n",
-    "for regstrength in sorted(regproblems.keys()):\n",
-    "    t, pEpoR = simulate_pEpoR(\n",
-    "        problem=regproblems[regstrength], result=regresults[regstrength]\n",
-    "    )\n",
-    "    if regstrength == chosen_regstrength:\n",
-    "        kwargs = dict(\n",
-    "            color=\"black\",\n",
-    "            label=f\"$\\\\mathbf{{\\\\lambda = {regstrength}}}$\",\n",
-    "            zorder=2,\n",
-    "        )\n",
-    "    else:\n",
-    "        kwargs = dict(label=f\"$\\\\lambda = {regstrength}$\", alpha=0.5)\n",
-    "    ax.plot(t, pEpoR, **kwargs)\n",
-    "ax.plot(\n",
-    "    df_pEpoR[\"time\"],\n",
-    "    df_pEpoR[\"measurement\"],\n",
-    "    \"o\",\n",
-    "    color=\"black\",\n",
-    "    markerfacecolor=\"none\",\n",
-    "    label=\"experimental data\",\n",
-    ")\n",
-    "ylim1 = ax.get_ylim()[0]\n",
-    "ax.plot(\n",
-    "    nodes,\n",
-    "    len(nodes) * [ylim1],\n",
-    "    \"x\",\n",
-    "    color=\"black\",\n",
-    "    label=\"spline nodes\",\n",
-    "    zorder=10,\n",
-    "    clip_on=False,\n",
-    ")\n",
-    "ax.set_ylim(ylim1, ax.get_ylim()[1])\n",
-    "ax.set_xlabel(\"time\")\n",
-    "ax.set_ylabel(\"pEpoR\")\n",
-    "ax.set_xlim(-3.0, 63.0)\n",
-    "ax.set_ylim(-0.05299052022388704, 1.126290214024833)\n",
-    "ax.legend()\n",
-    "ax.figure.tight_layout()\n",
-    "# ax.set_ylabel(\"input function\")\n",
-    "# print(f\"xlim = {ax.get_xlim()}, ylim = {ax.get_ylim()}\")\n",
-    "# ax.figure.savefig('fit_15nodes_lambdas.pdf')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "18f79f00-265a-4cb8-a462-e4606636612b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 650x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot ML fit for pSTAT5 (all regularization strengths)\n",
-    "fig, ax = plt.subplots(figsize=(6.5, 3.5))\n",
-    "for regstrength in sorted(regproblems.keys()):\n",
-    "    t, pSTAT5 = simulate_pSTAT5(\n",
-    "        problem=regproblems[regstrength], result=regresults[regstrength]\n",
-    "    )\n",
-    "    if regstrength == chosen_regstrength:\n",
-    "        kwargs = dict(\n",
-    "            color=\"black\",\n",
-    "            label=f\"$\\\\mathbf{{\\\\lambda = {regstrength}}}$\",\n",
-    "            zorder=2,\n",
-    "        )\n",
-    "    else:\n",
-    "        kwargs = dict(label=f\"$\\\\lambda = {regstrength}$\", alpha=0.5)\n",
-    "    ax.plot(t, pSTAT5, **kwargs)\n",
-    "ax.plot(\n",
-    "    df_pSTAT5[\"time\"],\n",
-    "    df_pSTAT5[\"measurement\"],\n",
-    "    \"o\",\n",
-    "    color=\"black\",\n",
-    "    markerfacecolor=\"none\",\n",
-    "    label=\"experimental data\",\n",
-    ")\n",
-    "ylim1 = ax.get_ylim()[0]\n",
-    "ax.plot(\n",
-    "    nodes,\n",
-    "    len(nodes) * [ylim1],\n",
-    "    \"x\",\n",
-    "    color=\"black\",\n",
-    "    label=\"spline nodes\",\n",
-    "    zorder=10,\n",
-    "    clip_on=False,\n",
-    ")\n",
-    "ax.set_ylim(ylim1, ax.get_ylim()[1])\n",
-    "ax.set_xlabel(\"time\")\n",
-    "ax.set_ylabel(\"pSTAT5\");\n",
-    "# ax.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "ac882963-7714-4536-b974-da0b642d79a9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 650x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot ML fit for tSTAT5 (all regularization strengths)\n",
-    "fig, ax = plt.subplots(figsize=(6.5, 3.5))\n",
-    "for regstrength in sorted(regproblems.keys()):\n",
-    "    t, tSTAT5 = simulate_tSTAT5(\n",
-    "        problem=regproblems[regstrength], result=regresults[regstrength]\n",
-    "    )\n",
-    "    if regstrength == chosen_regstrength:\n",
-    "        kwargs = dict(\n",
-    "            color=\"black\",\n",
-    "            label=f\"$\\\\mathbf{{\\\\lambda = {regstrength}}}$\",\n",
-    "            zorder=2,\n",
-    "        )\n",
-    "    else:\n",
-    "        kwargs = dict(label=f\"$\\\\lambda = {regstrength}$\", alpha=0.5)\n",
-    "    ax.plot(t, tSTAT5, **kwargs)\n",
-    "ax.plot(\n",
-    "    df_tSTAT5[\"time\"],\n",
-    "    df_tSTAT5[\"measurement\"],\n",
-    "    \"o\",\n",
-    "    color=\"black\",\n",
-    "    markerfacecolor=\"none\",\n",
-    "    label=\"experimental data\",\n",
-    ")\n",
-    "ylim1 = ax.get_ylim()[0]\n",
-    "ax.plot(\n",
-    "    nodes,\n",
-    "    len(nodes) * [ylim1],\n",
-    "    \"x\",\n",
-    "    color=\"black\",\n",
-    "    label=\"spline nodes\",\n",
-    "    zorder=10,\n",
-    "    clip_on=False,\n",
-    ")\n",
-    "ax.set_ylim(ylim1, ax.get_ylim()[1])\n",
-    "ax.set_xlabel(\"time\")\n",
-    "ax.set_ylabel(\"tSTAT5\");\n",
-    "# ax.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "c4d6fe0b-335f-4e69-ba72-6cc2b2b1af70",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 650x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot ML fit for pEpoR (single regularization strength with noise model)\n",
-    "fig, ax = plt.subplots(figsize=(6.5, 3.5))\n",
-    "t, pEpoR = simulate_pEpoR(\n",
-    "    problem=regproblems[chosen_regstrength],\n",
-    "    result=regresults[chosen_regstrength],\n",
-    ")\n",
-    "sigma_pEpoR = 0.0274 + 0.1 * pEpoR\n",
-    "ax.fill_between(\n",
-    "    t,\n",
-    "    pEpoR - 2 * sigma_pEpoR,\n",
-    "    pEpoR + 2 * sigma_pEpoR,\n",
-    "    color=\"black\",\n",
-    "    alpha=0.10,\n",
-    "    interpolate=True,\n",
-    "    label=\"2-sigma error bands\",\n",
-    ")\n",
-    "ax.plot(t, pEpoR, color=\"black\", label=\"MLE\")\n",
-    "ax.plot(\n",
-    "    df_pEpoR[\"time\"],\n",
-    "    df_pEpoR[\"measurement\"],\n",
-    "    \"o\",\n",
-    "    color=\"black\",\n",
-    "    markerfacecolor=\"none\",\n",
-    "    label=\"experimental data\",\n",
-    ")\n",
-    "ylim1 = ax.get_ylim()[0]\n",
-    "ax.plot(\n",
-    "    nodes,\n",
-    "    len(nodes) * [ylim1],\n",
-    "    \"x\",\n",
-    "    color=\"black\",\n",
-    "    label=\"spline nodes\",\n",
-    "    zorder=10,\n",
-    "    clip_on=False,\n",
-    ")\n",
-    "ax.set_ylim(ylim1, ax.get_ylim()[1])\n",
-    "ax.set_xlabel(\"time\")\n",
-    "ax.set_ylabel(\"pEpoR\")\n",
-    "ax.set_title(f\"ML fit for regularization strength = {chosen_regstrength}\")\n",
-    "ax.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "658cd182-8f00-4eb7-8d14-30b839cd9e38",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Store results for later\n",
-    "all_results[\"15 nodes, FD\"] = (\n",
-    "    regproblems[chosen_regstrength],\n",
-    "    regresults[chosen_regstrength],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3c33c2c6-b299-40a6-83b1-a3724d72dfc8",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Spline approximation with few nodes, optimizing derivatives explicitly\n",
-    "An alternative way to achieve higher expressivity, while not increasing the number of nodes, is to optimize the derivatives of the spline at the nodes instead of computing them by finite differencing. The risk of overfitting is still present, so we will include regularization as in the above example."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8ab36ca3-4555-4fd0-9a98-8ed8fc899fee",
-   "metadata": {},
-   "source": [
-    "### Creating the PEtab model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "ec220c5d-ec0b-44f3-bfae-129ffe3ee26b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Problem name\n",
-    "name = \"Swameye_PNAS2003_5nodes\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3ef383a-c34c-40cb-bdd9-4012554ba0fe",
-   "metadata": {},
-   "source": [
-    "We now need to create additional parameters for the spline derivatives too."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "e686b762-b485-4624-bbd6-27220bdb9c03",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Create spline for pEpoR\n",
-    "nodes = [0, 5, 10, 20, 60]\n",
-    "values_at_nodes = [\n",
-    "    sp.Symbol(f\"pEpoR_t{str(t).replace('.', '_dot_')}\") for t in nodes\n",
-    "]\n",
-    "derivatives_at_nodes = [\n",
-    "    sp.Symbol(f\"derivative_pEpoR_t{str(t).replace('.', '_dot_')}\")\n",
-    "    for t in nodes[:-1]\n",
-    "]\n",
-    "spline = splines.CubicHermiteSpline(\n",
-    "    sbml_id=\"pEpoR\",\n",
-    "    evaluate_at=amici_time_symbol,\n",
-    "    nodes=nodes,\n",
-    "    values_at_nodes=values_at_nodes,\n",
-    "    derivatives_at_nodes=derivatives_at_nodes\n",
-    "    + [0],  # last value is zero because steady state is reached\n",
-    "    extrapolate=(None, \"constant\"),\n",
-    "    bc=\"auto\",\n",
-    "    logarithmic_parametrization=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "8b0924bf-ec72-4631-a3db-3325945e3ccb",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Compute L2 norm of the curvature of pEpoR\n",
-    "regularization = spline.squared_L2_norm_of_curvature()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "80ca3be0-a3b2-446d-96fc-edbf405215db",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Add a parameter for regularization strength\n",
-    "reg_parameters_df = pd.DataFrame(\n",
-    "    dict(\n",
-    "        parameterScale=\"log10\",\n",
-    "        lowerBound=1e-6,\n",
-    "        upperBound=1e6,\n",
-    "        nominalValue=1.0,\n",
-    "        estimate=0,\n",
-    "    ),\n",
-    "    index=pd.Series([\"regularization_strength\"], name=\"parameterId\"),\n",
-    ")\n",
-    "# Encode regularization term as an additional observable\n",
-    "reg_observables_df = pd.DataFrame(\n",
-    "    dict(\n",
-    "        observableFormula=f\"sqrt({regularization})\".replace(\"**\", \"^\"),\n",
-    "        observableTransformation=\"lin\",\n",
-    "        noiseFormula=\"1/sqrt(regularization_strength)\",\n",
-    "        noiseDistribution=\"normal\",\n",
-    "    ),\n",
-    "    index=pd.Series([\"regularization\"], name=\"observableId\"),\n",
-    ")\n",
-    "# and correspoding measurement\n",
-    "reg_measurements_df = pd.DataFrame(\n",
-    "    dict(\n",
-    "        observableId=\"regularization\",\n",
-    "        simulationConditionId=\"condition1\",\n",
-    "        measurement=0,\n",
-    "        time=0,\n",
-    "        observableTransformation=\"lin\",\n",
-    "    ),\n",
-    "    index=pd.Series([0]),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "6c9af000-624f-46a6-907b-fa1875a87986",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Add spline formula to SBML model\n",
-    "sbml_doc = libsbml.SBMLReader().readSBML(\n",
-    "    os.path.join(\"Swameye_PNAS2003\", \"swameye2003_model.xml\")\n",
-    ")\n",
-    "sbml_model = sbml_doc.getModel()\n",
-    "spline.add_to_sbml_model(\n",
-    "    sbml_model, auto_add=True, y_nominal=0.1, y_constant=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "266ea27b-4eff-4fdf-98db-0009389cca81",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Derivative parameters must be added separately\n",
-    "for p in derivatives_at_nodes:\n",
-    "    amici.importers.sbml.utils.add_parameter(\n",
-    "        sbml_model, p, value=0.0, constant=True\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "223e47a3-6d7b-49af-94ef-f536aeea3e18",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Extra parameters associated to the spline\n",
-    "spline_parameters_df1 = pd.DataFrame(\n",
-    "    dict(\n",
-    "        parameterScale=\"log\",\n",
-    "        lowerBound=0.001,\n",
-    "        upperBound=10,\n",
-    "        nominalValue=0.1,\n",
-    "        estimate=1,\n",
-    "    ),\n",
-    "    index=pd.Series(list(map(str, values_at_nodes)), name=\"parameterId\"),\n",
-    ")\n",
-    "spline_parameters_df2 = pd.DataFrame(\n",
-    "    dict(\n",
-    "        parameterScale=\"lin\",\n",
-    "        lowerBound=-0.666,\n",
-    "        upperBound=0.666,\n",
-    "        nominalValue=0.0,\n",
-    "        estimate=1,\n",
-    "    ),\n",
-    "    index=pd.Series(list(map(str, derivatives_at_nodes)), name=\"parameterId\"),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "5afa675e-0f72-4ff4-b18a-538a8a5f1d4e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Create PEtab problem\n",
-    "petab_problem = petab.Problem(\n",
-    "    sbml_model,\n",
-    "    condition_df=petab.conditions.get_condition_df(\n",
-    "        os.path.join(\"Swameye_PNAS2003\", \"swameye2003_conditions.tsv\")\n",
-    "    ),\n",
-    "    measurement_df=petab.core.concat_tables(\n",
-    "        [\n",
-    "            os.path.join(\"Swameye_PNAS2003\", \"swameye2003_measurements.tsv\"),\n",
-    "            reg_measurements_df,\n",
-    "        ],\n",
-    "        petab.measurements.get_measurement_df,\n",
-    "    ).reset_index(drop=True),\n",
-    "    parameter_df=petab.core.concat_tables(\n",
-    "        [\n",
-    "            os.path.join(\"Swameye_PNAS2003\", \"swameye2003_parameters.tsv\"),\n",
-    "            spline_parameters_df1,\n",
-    "            spline_parameters_df2,\n",
-    "            reg_parameters_df,\n",
-    "        ],\n",
-    "        petab.parameters.get_parameter_df,\n",
-    "    ),\n",
-    "    observable_df=petab.core.concat_tables(\n",
-    "        [\n",
-    "            os.path.join(\"Swameye_PNAS2003\", \"swameye2003_observables.tsv\"),\n",
-    "            reg_observables_df,\n",
-    "        ],\n",
-    "        petab.observables.get_observable_df,\n",
-    "    ),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "acb52953-ab44-4beb-9378-3ab8f743c1f8",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Check whether PEtab model is valid\n",
-    "assert not petab.lint_problem(petab_problem)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "2b4d2215-0551-4f9c-a092-c22259405582",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Save PEtab problem to disk\n",
-    "# import shutil\n",
-    "# shutil.rmtree(name, ignore_errors=True)\n",
-    "# os.mkdir(name)\n",
-    "# petab_problem.to_files_generic(prefix_path=name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5466e058-089c-4bc7-a7bb-1f88649fb29f",
-   "metadata": {},
-   "source": [
-    "### Creating the pyPESTO problem"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "6fc63a63-d5f3-45a3-b8a2-284e07b229e0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Problem must be \"flattened\" to be used with AMICI\n",
-    "petab.core.flatten_timepoint_specific_output_overrides(petab_problem)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "4c55449b-b877-451d-8a40-0547c9f88e12",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Check whether simulation from the PEtab problem works\n",
-    "# import amici.petab_simulate\n",
-    "# simulator = amici.petab_simulate.PetabSimulator(petab_problem)\n",
-    "# simulator.simulate(noise=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "bbb28c44-11d3-4a74-996f-cc13870b091b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Import PEtab problem into pyPESTO\n",
-    "pypesto_problem = pypesto.petab.PetabImporter(\n",
-    "    petab_problem, model_name=name\n",
-    ").create_problem(\n",
-    "    # re-sample optimization startpoints in case of simulation errors\n",
-    "    startpoint_kwargs={\"check_fval\": True, \"check_grad\": True}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ca2f1ac5-4616-43bd-88ce-3e424461ccd2",
-   "metadata": {},
-   "source": [
-    "### Maximum Likelihood estimation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a745c509-6e28-4e98-aefc-12db835eb0ed",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Try different regularization strengths\n",
-    "regstrengths = np.asarray([1, 175, 500, 1000])\n",
-    "if os.getenv(\"GITHUB_ACTIONS\") is not None:\n",
-    "    regstrengths = np.asarray([175])\n",
-    "regproblems = {}\n",
-    "regresults = {}\n",
-    "\n",
-    "for regstrength in regstrengths:\n",
-    "    # Fix parameter in pypesto problem\n",
-    "    name = f\"Swameye_PNAS2003_5nodes_reg{regstrength}\"\n",
-    "    pypesto_problem.fix_parameters(\n",
-    "        pypesto_problem.x_names.index(\"regularization_strength\"),\n",
-    "        np.log10(\n",
-    "            regstrength\n",
-    "        ),  # parameter is specified as log10 scale in PEtab\n",
-    "    )\n",
-    "    regproblem = copy.deepcopy(pypesto_problem)\n",
-    "\n",
-    "    # Load existing results if available\n",
-    "    if os.path.exists(f\"{name}.h5\"):\n",
-    "        regresult = pypesto.store.read_result(f\"{name}.h5\", problem=regproblem)\n",
-    "    else:\n",
-    "        regresult = None\n",
-    "    # Overwrite\n",
-    "    # regresult = None\n",
-    "\n",
-    "    # Parallel multistart optimization with pyPESTO and FIDES\n",
-    "    if n_starts > 0:\n",
-    "        if regresult is None:\n",
-    "            new_ids = [str(i) for i in range(n_starts)]\n",
-    "        else:\n",
-    "            last_id = max(int(i) for i in regresult.optimize_result.id)\n",
-    "            new_ids = [\n",
-    "                str(i) for i in range(last_id + 1, last_id + n_starts + 1)\n",
-    "            ]\n",
-    "        regresult = pypesto.optimize.minimize(\n",
-    "            regproblem,\n",
-    "            n_starts=n_starts,\n",
-    "            ids=new_ids,\n",
-    "            optimizer=pypesto_optimizer,\n",
-    "            engine=pypesto_engine,\n",
-    "            result=regresult,\n",
-    "        )\n",
-    "        regresult.optimize_result.sort()\n",
-    "        if regresult.optimize_result.x[0] is None:\n",
-    "            raise Exception(\n",
-    "                \"All multistarts failed (n_starts is probably too small)! If this error occurred during CI, just run the workflow again.\"\n",
-    "            )\n",
-    "\n",
-    "    # Save results to disk\n",
-    "    # pypesto.store.write_result(regresult, f'{name}.h5', overwrite=True)\n",
-    "\n",
-    "    # Store result\n",
-    "    regproblems[regstrength] = regproblem\n",
-    "    regresults[regstrength] = regresult"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "efa1b2f7-347a-4fba-9e22-e375aeb06d30",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Target value is 15\n",
-      "Regularization strength: 1. Statistic is 9.638207938045252\n",
-      "Regularization strength: 175. Statistic is 15.115255701660317\n",
-      "Regularization strength: 500. Statistic is 19.156287450444093\n",
-      "Regularization strength: 1000. Statistic is 25.09224919998158\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Compute sum of squared normalized residuals\n",
-    "print(f\"Target value is {len(df_pEpoR['time'])}\")\n",
-    "regstrengths = sorted(regproblems.keys())\n",
-    "stats = []\n",
-    "for regstrength in regstrengths:\n",
-    "    t, pEpoR = simulate_pEpoR(\n",
-    "        N=None,\n",
-    "        problem=regproblems[regstrength],\n",
-    "        result=regresults[regstrength],\n",
-    "    )\n",
-    "    assert np.array_equal(df_pEpoR[\"time\"], t[:-1])\n",
-    "    pEpoR = pEpoR[:-1]\n",
-    "    sigma_pEpoR = 0.0274 + 0.1 * pEpoR\n",
-    "    stat = np.sum(((pEpoR - df_pEpoR[\"measurement\"]) / sigma_pEpoR) ** 2)\n",
-    "    print(f\"Regularization strength: {regstrength}. Statistic is {stat}\")\n",
-    "    stats.append(stat)\n",
-    "# Select best regularization strength\n",
-    "chosen_regstrength = regstrengths[\n",
-    "    np.abs(np.asarray(stats) - len(df_pEpoR[\"time\"])).argmin()\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "07e7e94c-2017-424a-a7ab-cd42d9b454b4",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 650x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualize the results of the multistarts for a chosen regularization strength\n",
-    "ax = pypesto.visualize.waterfall(\n",
-    "    regresults[chosen_regstrength], size=[6.5, 3.5]\n",
-    ")\n",
-    "ax.set_title(\n",
-    "    f\"Waterfall plot (regularization strength = {chosen_regstrength})\"\n",
-    ")\n",
-    "ax.set_ylim(ax.get_ylim()[0], 100);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "bd083fae-d3f6-4de8-bb57-fa9f94ca3157",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 650x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot ML fit for pEpoR (all regularization strengths)\n",
-    "fig, ax = plt.subplots(figsize=(6.5, 3.5))\n",
-    "for regstrength in sorted(regproblems.keys()):\n",
-    "    t, pEpoR = simulate_pEpoR(\n",
-    "        problem=regproblems[regstrength], result=regresults[regstrength]\n",
-    "    )\n",
-    "    if regstrength == chosen_regstrength:\n",
-    "        kwargs = dict(\n",
-    "            color=\"black\",\n",
-    "            label=f\"$\\\\mathbf{{\\\\lambda = {regstrength}}}$\",\n",
-    "            zorder=2,\n",
-    "        )\n",
-    "    else:\n",
-    "        kwargs = dict(label=f\"$\\\\lambda = {regstrength}$\", alpha=0.5)\n",
-    "    ax.plot(t, pEpoR, **kwargs)\n",
-    "ax.plot(\n",
-    "    df_pEpoR[\"time\"],\n",
-    "    df_pEpoR[\"measurement\"],\n",
-    "    \"o\",\n",
-    "    color=\"black\",\n",
-    "    markerfacecolor=\"none\",\n",
-    "    label=\"experimental data\",\n",
-    ")\n",
-    "ylim1 = ax.get_ylim()[0]\n",
-    "ax.plot(\n",
-    "    nodes,\n",
-    "    len(nodes) * [ylim1],\n",
-    "    \"x\",\n",
-    "    color=\"black\",\n",
-    "    label=\"spline nodes\",\n",
-    "    zorder=10,\n",
-    "    clip_on=False,\n",
-    ")\n",
-    "ax.set_ylim(ylim1, ax.get_ylim()[1])\n",
-    "ax.set_xlabel(\"time\")\n",
-    "ax.set_ylabel(\"pEpoR\")\n",
-    "ax.set_xlim(-3.0, 63.0)\n",
-    "ax.set_ylim(-0.05299052022388704, 1.126290214024833)\n",
-    "ax.legend()\n",
-    "ax.figure.tight_layout()\n",
-    "# ax.set_ylabel(\"input function\")\n",
-    "# ax.figure.savefig('fit_5nodes_lambdas.pdf')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "fb75829f-ff65-4d92-b4e6-7a7670fc829a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 650x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot ML fit for pSTAT5 (all regularization strengths)\n",
-    "fig, ax = plt.subplots(figsize=(6.5, 3.5))\n",
-    "for regstrength in sorted(regproblems.keys()):\n",
-    "    t, pSTAT5 = simulate_pSTAT5(\n",
-    "        problem=regproblems[regstrength], result=regresults[regstrength]\n",
-    "    )\n",
-    "    if regstrength == chosen_regstrength:\n",
-    "        kwargs = dict(\n",
-    "            color=\"black\",\n",
-    "            label=f\"$\\\\mathbf{{\\\\lambda = {regstrength}}}$\",\n",
-    "            zorder=2,\n",
-    "        )\n",
-    "    else:\n",
-    "        kwargs = dict(label=f\"$\\\\lambda = {regstrength}$\", alpha=0.5)\n",
-    "    ax.plot(t, pSTAT5, **kwargs)\n",
-    "ax.plot(\n",
-    "    df_pSTAT5[\"time\"],\n",
-    "    df_pSTAT5[\"measurement\"],\n",
-    "    \"o\",\n",
-    "    color=\"black\",\n",
-    "    markerfacecolor=\"none\",\n",
-    "    label=\"experimental data\",\n",
-    ")\n",
-    "ylim1 = ax.get_ylim()[0]\n",
-    "ax.plot(\n",
-    "    nodes,\n",
-    "    len(nodes) * [ylim1],\n",
-    "    \"x\",\n",
-    "    color=\"black\",\n",
-    "    label=\"spline nodes\",\n",
-    "    zorder=10,\n",
-    "    clip_on=False,\n",
-    ")\n",
-    "ax.set_ylim(ylim1, ax.get_ylim()[1])\n",
-    "ax.set_xlabel(\"time\")\n",
-    "ax.set_ylabel(\"pSTAT5\");\n",
-    "# ax.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "c68e4f72-bd06-48db-bebf-7079df51a616",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 650x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot ML fit for tSTAT5 (all regularization strengths)\n",
-    "fig, ax = plt.subplots(figsize=(6.5, 3.5))\n",
-    "for regstrength in sorted(regproblems.keys()):\n",
-    "    t, tSTAT5 = simulate_tSTAT5(\n",
-    "        problem=regproblems[regstrength], result=regresults[regstrength]\n",
-    "    )\n",
-    "    if regstrength == chosen_regstrength:\n",
-    "        kwargs = dict(\n",
-    "            color=\"black\",\n",
-    "            label=f\"$\\\\mathbf{{\\\\lambda = {regstrength}}}$\",\n",
-    "            zorder=2,\n",
-    "        )\n",
-    "    else:\n",
-    "        kwargs = dict(label=f\"$\\\\lambda = {regstrength}$\", alpha=0.5)\n",
-    "    ax.plot(t, tSTAT5, **kwargs)\n",
-    "ax.plot(\n",
-    "    df_tSTAT5[\"time\"],\n",
-    "    df_tSTAT5[\"measurement\"],\n",
-    "    \"o\",\n",
-    "    color=\"black\",\n",
-    "    markerfacecolor=\"none\",\n",
-    "    label=\"experimental data\",\n",
-    ")\n",
-    "ylim1 = ax.get_ylim()[0]\n",
-    "ax.plot(\n",
-    "    nodes,\n",
-    "    len(nodes) * [ylim1],\n",
-    "    \"x\",\n",
-    "    color=\"black\",\n",
-    "    label=\"spline nodes\",\n",
-    "    zorder=10,\n",
-    "    clip_on=False,\n",
-    ")\n",
-    "ax.set_ylim(ylim1, ax.get_ylim()[1])\n",
-    "ax.set_xlabel(\"time\")\n",
-    "ax.set_ylabel(\"tSTAT5\");\n",
-    "# ax.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "7c0ed0ba-8870-470e-8009-0c69dc4a48df",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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cvHkTJpPJovfHPHxnnvVnD+7u7hgzZgzGjBkDpVKJt99+G4sWLWKDn5K+Rs1Drw4ODkW+NkrC/HgfPnxo0dOUmZlZoMfGHn9ThXnzzTdtej0uXLiwXOoi7dy5ExwOp0RD9eahR3tPMCBlR8EPqVBSqRQbN25EUlIS+vbtW6ltMc8IefWDsiR4PB7ee+89HDx4EElJSez097S0NLao46u9FWUVHByMRo0aYeXKlRgxYkSBb7kZGRnw8vICj8dDaGgoDhw4gOfPn7N5Pw8fPsSRI0csjnF2doanpyfOnTuH6dOns9v/+9//Ftse87f5/N/edTqdTccW5eTJk/jqq68wb9489O/fv9Brv9prsG7dugI9BOZaSrb8rsPDw3H8+HHs3buXzfsxGAxYt24dpFIpunbtWvIHY0VmZiY7aw3I+9to3LgxkpOTrbbbll5Sb29vdOvWDT/88AOmTJkCX19fi/vNr42SeOedd8Dn87Fx40a8++677Pb169cX2Lckz3NJ7dy5E2q1utj98ufe2Yter0dMTAzeeustq0N3Go0Ger0eTk5OFtuXLFkChmHQs2dPu7eJlA0FP6TCFZWr8qrLly/j66+/LrC9W7duBapFl5RYLEbz5s2xd+9evPbaa3B3d0eLFi3QokWLEp3n66+/xokTJ/DWW2/h008/BZ/Pxw8//ACtVotvvvmmTG20hsvl4scff0SvXr3w+uuvY8yYMahXrx5SUlJw5swZODs74/fffweQN536+PHj6Ny5Mz755BMYjUasX78eLVq0wPXr1y3O+9FHH2H58uX46KOP0K5dO5w7dw73798vtj2dOnWCm5sbRo8ejalTp4LD4eCnn34q87DJ8OHD4eXlhSZNmuDnn3+2uO/dd9+Fj48P+vTpg59++gkuLi5o3rw5Ll68iJMnT1oEFQDQpk0b8Hg8rFixAnK5HEKhED169IC3t3eB606YMAE//PADIiIicOXKFQQGBmLfvn24cOEC1qxZU+ADrrSaN2+Obt26ITg4GO7u7rh8+TL27dtnUT08ODgYADB16lSEhYWBx+MVmzuyYcMGvPXWW2jZsiXGjx+Phg0bIi0tDRcvXsSzZ89w48aNErXTx8cH06ZNw6pVq/D++++jZ8+euHHjBo4cOQJPT0+L3p6SPM8lZc+cn5s3b7J1jx4+fAi5XM6+z7Ru3brAF7Njx44hMzOz0No+qampaNu2LYYPH872UB47dgyxsbHo2bMn+vXrZ7e2EzuprGlmpHYobNr5qwqb6l7YbcmSJSW+5qtT3RmGYeLi4pjg4GBGIBAUO022sKnuDMMwV69eZcLCwhipVMpIJBKme/fuTFxcnE3tKox5qntMTIzV+69du8YMHDiQ8fDwYIRCIRMQEMAMGTKEOXXqlMV+p06dYtq2bcsIBAKmUaNGzI8//sjMmjWLEYlEFvupVCpm3LhxjIuLC+Pk5MQMGTKESU9Pt2mq+4ULF5gOHTowYrGYqVu3LvPZZ58xx44dKzDtuWvXrszrr79u9fG8OuW8qN+/+ZzZ2dnMmDFjGE9PT0YqlTJhYWFMQkICExAQYDEFm2EYZsuWLUzDhg0ZHo9ncQ5rU+zT0tLY8woEAqZly5YFygAU9Xoo7rXEMAzz9ddfM+3bt2dcXV0ZsVjMBAUFMUuXLmV0Oh27j8FgYKZMmcJ4eXkxHA6HnfZe1LUZhmEePXrEjBo1iqlTpw7j4ODA1KtXj+nTpw+zb98+dp/CXo/m113+35vBYGDmz5/P1KlThxGLxUyPHj2Yu3fvMh4eHszEiRMtji/sebb2N84wxZc4KA/mx27t9urrhmEYZtiwYYyDgwOTmZlp9XzZ2dnMBx98wDRu3JiRSCSMUChkXn/9dWbZsmUWv09SdXAYphyy2gghVVr//v3LPM2a1G4ymQxubm74+uuvMW/evMpuDiElQnV+CKnhXs2TePDgAWJjY21azJUQoOBrCMirwA3YtigwIVUN9fwQUsP5+voiIiICDRs2xJMnT7Bx40ZotVpcu3YNTZo0qezmkWpg27Zt2LZtG7usy/nz57F792689957OHbsWGU3j5ASo4RnQmq4nj17Yvfu3UhNTYVQKETHjh2xbNkyCnyIzVq1agU+n49vvvkGCoWCTYK2NhmBkOqAen4IIYQQUqtQzg8hhBBCahUKfgghhBBSq9S6nB+TyYTnz5/DycmpXEuxE0IIIaTiMAyDnJwc1K1bt8DCxK+qdcHP8+fP7b7WEiGEEEKqhuTkZNSvX7/IfWpd8GMuTZ+cnGz3NZcIIYQQUjkUCgX8/PxsWoKm1gU/5qEuZ2dnCn4IIYSQGsaWlBZKeCaEEEJIrULBDyGEEEJqFQp+CCGEEFKr1LqcH0IIqUlMJhN0Ol1lN4OQcufg4AAej2eXc1Vq8HPu3Dl8++23uHLlCl68eIHffvsN/fv3L3T//fv3Y+PGjbh+/Tq0Wi1ef/11LFq0CGFhYRXXaEIIqSJ0Oh0SExNhMpkquymEVAhXV1fUqVOnzHX6KjX4yc3NRevWrTF27FgMHDiw2P3PnTuHd999F8uWLYOrqyu2bt2Kvn37Ij4+Hm3btq2AFhNCSNXAMAxevHgBHo8HPz+/You6EVKdMQwDlUqF9PR0AICvr2+ZzlepwU+vXr3Qq1cvm/dfs2aNxc/Lli3DwYMH8fvvv1PwQwipVQwGA1QqFerWrQuJRFLZzSGk3InFYgBAeno6vL29yzQEVq1zfkwmE3JycuDu7l7oPlqtFlqtlv1ZoVBURNMIIaRcGY1GAIBAIKjklhBSccyBvl6vL1PwU637SVeuXAmlUokhQ4YUuk9UVBRcXFzYGy1tQQipSWiNQlKb2Ov1Xm2Dn127dmHx4sX45Zdf4O3tXeh+c+fOhVwuZ2/JyckV2EoC5H1DNX9LJYQQQipbtRz22rNnDz766CPExMQgNDS0yH2FQiGEQmEFtYzkp1QqIZfLoVarAeSN17q6usLR0bGSW0ZIzWU0Git09heXy7Xb9OPS4nA4xc4WJpYiIiIgk8lw4MCBCr/2tm3bMH36dMhksgq/tlm1C352796NsWPHYs+ePejdu3dlN4dYwTAMMjMzkZGRAQBs8KlQKKBQKODl5QV3d3eanUKInRmNRiQnJ1do3R+BQAA/Pz+bA6CoqCjs378fCQkJEIvF6NSpE1asWIGmTZuWug0vXryAm5tbqY8ntU+lBj9KpRIPHz5kf05MTMT169fh7u4Of39/zJ07FykpKdixYweAvKGu0aNH4/vvv0dISAhSU1MB5PUouLi4VMpjIJYYhsHLly+Rnp4OR0dHi2RMkUgErVaL1NRUGI1GeHt7U74CIXZkLnjI4/HA55f/27vBYIBOp4PJZLI5+Pnzzz8xadIkvPnmmzAYDPjyyy/x3nvv4c6dO6XuFa5Tp06pjquKGIaB0Wgs8PvT6XSlSm4v7XE1XaV+9b58+TLatm3LTlOfOXMm2rZtiwULFgDIi+afPn3K7r9582YYDAZMmjQJvr6+7G3atGmV0n5SkEKhQEZGRoHAx0woFMLJyQkZGRnIysqqhBYSUvPx+Xw4ODiU+600AdbRo0cRERGB119/Ha1bt8a2bdvw9OlTXLlypdBjdDodJk+eDF9fX4hEIgQEBCAqKoq9n8PhWAzfxMXFoU2bNhCJRGjXrh0OHDgADoeD69evAwDOnj0LDoeDY8eOoW3bthCLxejRowfS09Nx5MgRNGvWDM7OzhgxYgRUKpVF29966y24urrCw8MDffr0waNHj4p8vCaTCVFRUWjQoAHEYjFat26Nffv2sfeb23LkyBEEBwdDKBTi/Pnz6NatGyZPnozp06fD09OTLeb7559/on379hAKhfD19cUXX3wBg8HAnq+w4wqzePFieHl5wdnZGRMnTrToNSzu8SYlJYHD4WD//v3o3r07JBIJWrdujYsXL1pcY9u2bfD394dEIsGAAQOQmZlpcf+NGzfQvXt3ODk5wdnZGcHBwbh8+XKR7S6rSu356datGxiGKfT+bdu2Wfx89uzZ8m0QKROtVouMjAwIBIIiv2k4ODhAIpHg5cuXEIlElANESC0ml8sBoMiSJWvXrsWhQ4fwyy+/wN/fH8nJyYVOXlEoFOjbty/Cw8Oxa9cuPHnyBNOnT7e676JFi7B+/XpIJBIMGTIEQ4YMgVAoxK5du6BUKjFgwACsW7cOn3/+OYC8wrwzZ85Eq1atoFQqsWDBAgwYMADXr18vdBg/KioKP//8MzZt2oQmTZrg3Llz+OCDD+Dl5YWuXbuy+33xxRdYuXIlGjZsyA7hbd++HZ988gkuXLgAAEhJSUF4eDgiIiKwY8cOJCQkYPz48RCJRFi0aBF7rlePK8ypU6cgEolw9uxZJCUlYcyYMfDw8MDSpUtL9HjnzZuHlStXokmTJpg3bx6GDx+Ohw8fgs/nIz4+HuPGjUNUVBT69++Po0ePYuHChRbtGDlyJNq2bYuNGzeCx+Ph+vXrcHBwKLLtZcbUMnK5nAHAyOXyym5KjWIymZiUlBTm1q1bTEpKik23u3fvMklJSYzBYKjs5hNS7ajVaubOnTuMWq1mt+l0OubevXtMUlKSzX+HZbklJSUx9+7dY3Q6Xakeg9FoZHr37s107ty5yP2mTJnC9OjRgzGZTFbvB8D89ttvDMMwzMaNGxkPDw+L52XLli0MAObatWsMwzDMmTNnGADMyZMn2X2ioqIYAMyjR4/YbR9//DETFhZWaLsyMjIYAMy///5r9X6NRsNIJBImLi7OYvu4ceOY4cOHW7TlwIEDFvt07dqVadu2rcW2L7/8kmnatKnF87BhwwZGKpUyRqOx0OOsGT16NOPu7s7k5uay2zZu3GhxruIeb2JiIgOA+fHHH9l9bt++zQBg7t69yzAMwwwfPpwJDw+3OM/QoUMZFxcX9mcnJydm27ZtxbaZYay/7s1K8vlOGafELnJzcyGTyeDk5GTzMVKpFDk5OZWa8U8IqTyTJk3CrVu3sGfPHnbbxIkTIZVK2RuQNzPp+vXraNq0KaZOnYrjx48Xes579+6hVatWEIlE7Lb27dtb3bdVq1bs/318fCCRSNCwYUOLbeblFADgwYMHGD58OBo2bAhnZ2cEBgYCgEV6Rn4PHz6ESqXCu+++a/GYduzYUWC4rF27dgWODw4Otvj57t276Nixo0WuZOfOnaFUKvHs2bNCjytM69atLaqDd+zYEUqlku1Vs/Xx5n8ezctOmJ+3u3fvIiQkxGL/jh07Wvw8c+ZMfPTRRwgNDcXy5cuLHUq0h2o324tUPQzDIDs7G1wut0Q5AFwuF2KxGFlZWZBKpVSSgJBaZPLkyfjjjz9w7tw51K9fn90eGRmJ2bNnW+z7xhtvIDExEUeOHMHJkycxZMgQhIaGWuTOlEb+oRUOh1NgqIXD4ViUDejbty8CAgKwZcsW1K1bFyaTCS1atCh0dp1SqQQAHD58GPXq1bO479X3O2vD/6VNCbBXKoGtj/fV5xFAicotLFq0CCNGjMDhw4dx5MgRLFy4EHv27MGAAQPs8jisoeCHlFlubi5ycnJK1OtjZg5+ZDIZfHx8yqF1hJCqhGEYTJkyBb/99hvOnj2LBg0aWNzv7e1ttXCts7Mzhg4diqFDh2LQoEHo2bMnsrKyCuQKNW3aFD///DO0Wi0bYPzzzz9lbndmZibu3buHLVu2oEuXLgCA8+fPF3lM8+bNIRQK8fTpU4v8ntJq1qwZfv31VzAMwwYZFy5cgJOTk0UAaasbN25ArVaza2ZdunQJUqkUfn5+pXq8hbU5Pj7eYtulS5cK7Pfaa6/htddew4wZMzB8+HBs3bq1XIMfGvYiZcIwDGQyGTgcTqkLnUmlUmRnZ1vMqiCE1EyTJk3Czz//jF27dsHJyQmpqalITU1li6Fas3r1auzevRsJCQm4f/8+YmJiUKdOHbi6uhbYd8SIETCZTJgwYQLu3r2LY8eOYeXKlQDKtjSCm5sbPDw8sHnzZjx8+BCnT5/GzJkzizzGyckJs2fPxowZM7B9+3Y8evQIV69exbp167B9+/YSt+HTTz9FcnIypkyZgoSEBBw8eBALFy7EzJkzS1U3TafTYdy4cbhz5w5iY2OxcOFCTJ48GVwut1SP15qpU6fi6NGjWLlyJR48eID169fj6NGj7P1qtRqTJ0/G2bNn8eTJE1y4cAH//PMPmjVrVuJrlQT1/JAyUavVyMnJKVM3q0AggEqlQnZ2NsRiMdX+IaSM8k99rmrX2bhxI4C82b75bd26FREREVaPcXJywjfffIMHDx6Ax+PhzTffRGxsrNUPfGdnZ/z+++/45JNP0KZNG7Rs2RILFizAiBEjLPKASorL5WLPnj2YOnUqWrRogaZNm2Lt2rUFHserlixZAi8vL0RFReHx48dwdXXFG2+8gS+//LLEbahXrx5iY2MxZ84ctG7dGu7u7hg3bhy++uqrUj2md955B02aNMHbb78NrVaL4cOHs7PGSvt4X9WhQwds2bIFCxcuxIIFCxAaGoqvvvoKS5YsAQDweDxkZmZi1KhRSEtLg6enJwYOHIjFixeX6jHZisMwRcw1r4EUCgVcXFwgl8vh7Oxc2c2p9tLT05GRkVHm6qoGgwE5OTkICAhgkxwJIYXTaDRITExEgwYN2A/16lDhuTLs3LkTY8aMgVwuZ4d4SPVk7XVvVpLPd+r5IaWm1+uhUCjs8mbC5/PB4/GQnZ0NiURCS18QUgo8Hg9+fn61bm2vV+3YsQMNGzZEvXr1cOPGDXz++ecYMmQIBT6ERcEPKTWVSgWNRmO3NXUcHR2hUCjg6upaquRpQkheAFTVgpGKlpqaigULFiA1NRW+vr4YPHgwW7iPEICCH1JKDMNAoVDAwcHBbjk65vWIsrKy4OjoSL0/hJBS+eyzz/DZZ59VdjNIFUafLqRUtFotVCpVmRIIrXF0dIRSqURubq5dz0sIIYSYUfBDSkWtVkOv19t9/RVzocSsrKwKzVsghBBSe1DwQ0rMPORV1OKlZUG9P4QQQsoTBT+kxLRaLdRqtd2HvMzMs0dkMhlqWSUGQgghFYCCH1JiGo0GBoOhROt4lZSjoyNycnKo94cQQojdUfBDSiwnJ8fuuT6v4vF44HA4kMvl1PtDCCHErij4ISWi0+mgVqsrZAV2c92fotb8IYQQQkqKgh9SIhqNBjqdrtySnfPj8/lgGAZyubzcr0UIqTgRERHgcDiYOHFigfsmTZoEDofDrvMVERGB/v37F3quwMBAcDicArfly5eXU+tJTUDBDymR3NzcCq0eK5FIoFAooNFoKuyahJDy5+fnhz179lj07Go0GuzatQv+/v4lOldkZCRevHhhcZsyZYq9m0xqEAp+iM2MRiNUKlWF9PqYCQQCdg0xQkjN8cYbb8DPzw/79+9nt+3fvx/+/v5o27Ztic7l5OSEOnXqWNwcHR3t3WRSg9DyFsRmWq0WGo0GLi4uFXpdiUQCuVwOV1fXCg28CKlOGIaBSqWqlGtLJJJSLXMzduxYbN26FSNHjgQAREdHY8yYMTh79qydW0iIJQp+iM3MQ08VveaWSCRCVlYWcnJy4OHhUaHXJqS6UKlUkEqllXJtpVJZqp6WDz74AHPnzsWTJ08AABcuXMCePXtKHPx8/vnn+Oqrryy2HTlyBF26dClxm0jtQMEPsZlSqSz3Ke6FEYlEkMlkcHFxKdf6QoSQiuPl5YXevXtj27ZtYBgGvXv3hqenZ4nPM2fOHDZB2qxevXp2aiWpiehThNhEp9NBo9FU2rCTWCxGdnY2cnNzK3zYjZDqQCKRQKlUVtq1S2vs2LGYPHkyAGDDhg2lOoenpycaN25c6jaQ2oeCH2ITrVYLnU5Xad3qHA4HAoEAMpkMTk5OFT70RkhVx+FwqmWSb8+ePaHT6cDhcBAWFlbZzSG1BAU/xCZarbbSAw7ztPfKzG0ghNgXj8fD3bt32f9bI5fLcf36dYttHh4e8PPzA5BXdT41NdXifolEAmdnZ/s3mNQI9PWZFIthGCiVykqfaWUOvmjaOyE1i7Ozc5GBytmzZ9G2bVuL2+LFi9n7FyxYAF9fX4vbZ599VhFNJ9UUh6llCycpFAq4uLhALpfTtwIbabVaJCUlQSQSVVrCs5ler4darUZgYGC5rSpPSHWg0WiQmJiIBg0a0N8CqTWKet2X5PO9Unt+zp07h759+6Ju3brgcDg4cOBAscecPXsWb7zxBoRCIRo3boxt27aVeztrO51OB4PBUCDwMRqNiIuLw4EDBxAXFwej0VjubXFwcIDBYEBOTk65X4sQQkjNVKnBT25uLlq3bm1zhn9iYiJ69+6N7t274/r165g+fTo++ugjHDt2rJxbWrtZW1oiNjYWnTt3xuDBgzFp0iQMHjwYnTt3RmxsbLm3RywWQy6XQ6/Xl/u1CCGE1DyVGvz06tULX3/9NQYMGGDT/ps2bUKDBg2watUqNGvWDJMnT8agQYPw3XfflXNLay+GYZCbm2uxintsbCwmTJiAoKAgHDp0CPfv38ehQ4cQFBSECRMmlHsAJBaLodFokJubW67XIYQQUjNVq4TnixcvIjQ01GJbWFgYLl68WEktqvn0ej10Oh075GU0GhEZGYnQ0FBER0cjODgYjo6OCA4ORnR0NEJDQ7FkyZJyHwITCoXIzs6GyWQq1+sQQgipeapV8JOamgofHx+LbT4+PlAoFBYrA+en1WqhUCgsbsR2Op3OIviJj49HcnIypkyZUmDqO5fLxeTJk/H06VPEx8eXa7vEYjFUKlWlrWVECCGk+qpWwU9pREVFwcXFhb2Z60IQ22i1WosFC9PT0wEAQUFBVvc3bzfvV164XC64XC7kcnm5XocQQkjNU62Cnzp16iAtLc1iW1paGpydnSEWi60eM3fuXMjlcvaWnJxcEU2tMXJzcy1meXl7ewMAEhISrO5v3m7erzxJJBLk5ORYTcgmhBBCClOtgp+OHTvi1KlTFttOnDiBjh07FnqMUChkC2gVV0iLWDIYDNBqtRbBT0hICPz8/LBu3boC+TYmkwnr16+Hv78/QkJCyr19NO2dEEJIaVRq8KNUKnH9+nW2bHliYiKuX7+Op0+fAsjrtRk1ahS7/8SJE/H48WN89tlnSEhIwH//+1/88ssvmDFjRmU0v8Yzr+eVv7Izj8fDggULcPLkSYwdOxaXL1+GUqnE5cuXMXbsWJw8eRLz588vtEy9vdG0d0IIISVVqcHP5cuX2VLlADBz5ky0bdsWCxYsAAC8ePGCDYQAoEGDBjh8+DBOnDiB1q1bY9WqVfjxxx9pMbxyotPpwDCMRc4PAISHh2Pz5s1ISEhAv3790LRpU/Tr1w/37t3D5s2bER4eXmFtFIlENO2dEGIXSUlJ4HA4BdYRq062bdsGV1fXEh1TEx53SVXqwqbdunVDUatrWKve3K1bN1y7dq0cW0XM1Go1+HzrL5Hw8HCEhYUhPj4e6enp8Pb2RkhISIX1+JjlX+3d2dm50hdfJaS6MRqN+Ouvv/DixQv4+vqiS5cuFf53XFX4+fnhxYsX8PT0rOymYNGiRThw4ECVDUgiIiIgk8lsWpmhKqJV3YlVJpMJarW6yLW8eDweOnXqVIGtso5WeyekdPbv349Zs2YhKSmJ3RYYGIhVq1Zh4MCBldewSmAe4q9Tp05lN4VUAPqaTKzS6XTQ6/WVvpK7Lcy9PZT4TIjt9u/fj0GDBqFly5a4ePEicnJycPHiRbRs2RKDBg3C/v37y+W6JpMJUVFRaNCgAcRiMVq3bo19+/YByKsoHxoairCwMHZUICsrC/Xr12fTIc6ePQsOh4PDhw+jVatWEIlE6NChA27dumVxnfPnz6NLly4Qi8Xw8/PD1KlTLYbHAwMDsWTJEowaNQrOzs6YMGFCgeEf87WOHTuGtm3bQiwWo0ePHkhPT8eRI0fQrFkzODs7Y8SIERY1x4p6jPnPe+rUKbRr1w4SiQSdOnXCvXv3AOSNeixevBg3btwAh8MBh8NhR0JWr16Nli1bwtHREX5+fvj000+hVCpL9Dv4+++/0bZtW4hEIrRr167AaIrRaMS4cePY9jdt2hTff/89e/+iRYuwfft2HDx4kG3f2bNnAQCff/45XnvtNUgkEjRs2BDz58+vkjmZFPwQq8yLmZa1+9toNEKpVJZ7JWZz749Wqy3X6xBSExiNRsyaNQt9+vTBgQMH0KFDB0ilUnTo0AEHDhxAnz59MHv27HKp1B4VFYUdO3Zg06ZNuH37NmbMmIEPPvgAf/75JzgcDrZv345//vkHa9euBZA30aVevXps8GM2Z84crFq1Cv/88w+8vLzQt29f9kP20aNH6NmzJ/7zn//g5s2b2Lt3L86fP4/JkydbnGPlypVo3bo1rl27hvnz5xfa5kWLFmH9+vWIi4tDcnIyhgwZgjVr1mDXrl04fPgwjh8/jnXr1tn0GPObN28eVq1ahcuXL4PP52Ps2LEAgKFDh2LWrFl4/fXX8eLFC7x48QJDhw4FkPdlb+3atbh9+za2b9+O06dP47PPPrP5+VcqlejTpw+aN2+OK1euYNGiRZg9e7bFPiaTCfXr10dMTAzu3LmDBQsW4Msvv8Qvv/wCAJg9ezaGDBmCnj17su0zjwI4OTlh27ZtuHPnDr7//nts2bKlai5BxdQycrmcAcDI5fLKbkqVlpGRwdy6dYtJSUkp1S0mJoZ55513GJFIxABgJBIJExYWxuzfv7/U5yzu9u+//zIZGRmV/dQRUiHUajVz584dRq1Wl/jYM2fOMACYixcvWr0/Li6OAcCcOXOmjK20pNFoGIlEwsTFxVlsHzduHDN8+HD2519++YURiUTMF198wTg6OjL3798v0PY9e/aw2zIzMxmxWMzs3buXPd+ECRMsrvHXX38xXC6Xfb4CAgKY/v37W+yTmJjIAGCuXbtmca2TJ0+y+0RFRTEAmEePHrHbPv74YyYsLMzmx2jtvIcPH2YAsO1buHAh07p168KeSlZMTAzj4eHB/rx161bGxcWl0P1/+OEHxsPDw+J1s3HjRovHbc2kSZOY//znP+zPo0ePZvr161ds+7799lsmODi42P1sVdTrviSf75TzQ6zKzc0t1ZCXTqfD/Pnz8fPPP1tsV6lUOHbsGI4dO4YRI0YgMjKy0MKUpWWe9u7q6lpoojYhJG8mLQC0aNHC6v3m7eb97OXhw4dQqVR49913LbbrdDp21i8ADB48GL/99huWL1+OjRs3okmTJgXOlb++m7u7O5o2bYq7d+8CAG7cuIGbN29i586d7D4Mw8BkMiExMRHNmjUDALRr186mdrdq1Yr9v4+PDzukk3/b33//XaLH+Op5fX19AeRVx/f39y+0LSdPnkRUVBQSEhKgUChgMBig0WigUqkgkUiKfSx3795lhwvNrNXK27BhA6Kjo/H06VOo1WrodDq0adOm2PPv3bsXa9euxaNHj6BUKmEwGKpkfT36hCAFGAwG6HS6EgcQWq0WY8eOZcezR44ciYiICAQGBuLRo0fYsWMHdu3ahV27duHx48fYvn27XROURSIRsrOzoVKpquQfGyFVhfmD9tatW+jQoUOB+835M+b97MWcm3L48GHUq1fP4j6hUMj+X6VS4cqVK+DxeHjw4EGprvPxxx9j6tSpBe7LH1g4OjradL78Ez84HE6BiSAcDocd2rf1MVo7L4AiUwSSkpLQp08ffPLJJ1i6dCnc3d1x/vx5jBs3DjqdzqbgxxZ79uzB7NmzsWrVKnTs2BFOTk749ttvi12z8eLFixg5ciQWL16MsLAwuLi4YM+ePVi1apVd2mVPFPyQAsyLmZbkD4lhGMyYMQNnz56FWCzG5s2b0aNHD/b+Fi1a4JtvvkGfPn0wYcIEXLp0CRMnTsT27dvtNq3W/KYkk8ng5ORUoD4RISRPly5dEBgYiGXLluHAgQMWJSLyJ+t26dLFrtdt3rw5hEIhnj59iq5duxa636xZs8DlcnHkyBGEh4ejd+/eFu8nAHDp0iU2kMnOzsb9+/fZHp033ngDd+7cQePGje3aflvY+hiLIxAICuRcXblyBSaTCatWrWJ/Z+Y8HFs1a9YMP/30EzQaDdv7c+nSJYt9Lly4gE6dOuHTTz9ltz169KjY9sXFxSEgIADz5s1jtz158qRE7asolPBMCiisuGFRtm3bhoMHD4LP52Pr1q0F3qjM3n77bezevRsikQhnzpzBsmXL7NVsAHmJz0qlEmq12q7nJaQm4fF4WLVqFf744w/079/fYrZX//798ccff2DlypV2r/fj5OSE2bNnY8aMGdi+fTsePXqEq1evYt26ddi+fTuAvB6T6Oho7Ny5E++++y7mzJmD0aNHIzs72+JckZGROHXqFG7duoWIiAh4enqif//+APJmHMXFxWHy5Mm4fv06Hjx4gIMHDxZIeC4PtjxGWwQGBrKrHrx8+RJarRaNGzeGXq/HunXr8PjxY/z000/YtGlTido3YsQIcDgcjB8/Hnfu3EFsbCxWrlxpsU+TJk1w+fJlHDt2DPfv38f8+fPxzz//FGjfzZs3ce/ePbx8+RJ6vR5NmjTB06dPsWfPHjx69Ahr167Fb7/9VqL2VRQKfkgBWq22RG96ycnJbBCzYMGCYr8ttm3bls3+37RpE/7444/SN/YV5nbTtHdCijZw4EDs27cP//77Lzp16gRnZ2d06tQJt27dwr59+8qtzs+SJUswf/58REVFoVmzZujZsycOHz6MBg0aICMjA+PGjcOiRYvwxhtvAAAWL14MHx8fTJw40eI8y5cvx7Rp0xAcHIzU1FT8/vvvbJ5iq1at8Oeff+L+/fvo0qULu3JA3bp1y+UxleQx2uo///kPevbsie7du8PLywu7d+9G69atsXr1aqxYsQItWrTAzp07ERUVVaK2SaVS/P777/j333/Rtm1bzJs3DytWrLDY5+OPP8bAgQMxdOhQhISEIDMz06IXCADGjx+Ppk2bol27dvDy8sKFCxfw/vvvY8aMGZg8eTLatGmDuLi4ImfRVSYOwxRRYrkGUigUcHFxgVwup7wQKxiGQWJiIkwmk03DXgzD4IMPPsDZs2fRoUMHxMTE2FxlOSoqCuvXr4ebmxvOnDkDLy+vsjYfQF7wptfrERgYWC3qFBFSGhqNBomJiWjQoIFF8mpJVbcKz2fPnkX37t2RnZ1d4mUcSPVX1Ou+JJ/v1PNDLOj1ehgMhiIrO+d3+PBhnD17FkKhECtWrCjR8hKzZs1C8+bNkZ2djblz5xa51ElJCIVC6HQ6Wu+LEBvweDx069YNw4cPR7du3ap04EOIvVDwQyzo9Xro9XqbZnoZjUZ2rPjTTz8tcXKhQCDAmjVrwOfzceTIERw8eLBUbbbGPPOrPIq0EUIIqd4o+CEWdDodANiU7Hzw4EE8ePAArq6umDBhQqmu9/rrr2P69OkA8qqdZmRklOo8rxKJRFCr1RYl5wkh1Z95QWwa8iJlQcEPsaDRaGzq9jYYDFi9ejWAvOS4suRPTZ48GS1atIBMJsOSJUtKfZ78uFwueDwe5HK53YbTCCGE1AwU/BAWwzBQqVQ25fscOXIEiYmJcHNzY9ejKS0HBwesWLECHA4Hv/76K+Li4sp0PjPztHeNRmOX8xFCCKkZKPghLPNiprYEP1u3bgUAjBo1yi5Vmtu0aYMPP/wQAPDll1+yw29lwefzYTQaado7IYQQCxT8EJZ5pldxyc63b99GfHw8+Hw+G7CUhtFoRFxcHA4cOIC4uDjMnj0bnp6eePDgAX744YdSnzc/83pf5tWeCSGEEFregrBsDRC2bdsGAOjVq1ep1/6JjY1FZGQkkpOT2W1+fn54//33ER0dje+//x6DBg0q89pCIpEIWVlZyM3NpQRJQgghAKjnh+SjVquLTXZWKBTYv38/AGDMmDGluk5sbCwmTJiAoKAgHDp0CPfv38ehQ4cQFBSErVu3olGjRlCr1QWqjpaWUChEdnZ2kQsGEkIIqT0o+CEA8hYzVKvVxeb7HD58GBqNBk2aNEH79u1LfB2j0YjIyEiEhoYiOjoawcHBcHR0RHBwMKKjoxEaGsquihwTE4ObN2+W6vHkJxaLoVKpqOghITVAt27d2PIYQN4aU2vWrKm09pRFUlISOBwOrl+/XtlNqXUo+CEAbK/svG/fPgDAoEGDSrVqenx8PJKTkzFlypQC1aC5XC4mT56MtLQ0dn2wRYsWlXmqOpfLBZfLpWnvhNRA//zzT6nrjJHai4IfAsC2ZOfk5GRcunQJHA4HAwYMKNV10tPTAQBBQUFW7zdvf++99yASiRAfH4/Y2NhSXSs/R0dHmvZOSD6LFi0qtK7WkiVLsGjRooptUCl5eXnZtA4hIflR8EMA2Jbs/OuvvwIAOnXqhHr16pXqOt7e3gCAhIQEq/ebtwcFBbGrOC9duhRarbZU1zOjae+EWOLxeFiwYEGBAGjJkiVYsGBBua3xtW/fPrRs2RJisRgeHh4IDQ1lh6QjIiLQv39/LF68GF5eXnB2dsbEiROLLH3x6rAXh8PBjz/+iAEDBkAikaBJkyY4dOiQxTG3bt1Cr169IJVK4ePjgw8//BAvX74s9Brbtm2Dq6srjh07hmbNmkEqlaJnz5548eIFu4/JZEJkZCTq168PoVCINm3a4OjRoxbn+fvvv9G2bVuIRCK0a9cO165dK3Ct4tpW1PNHbEfBDwFQfGVnhmHYROdBgwaV+johISHw8/PDunXrCiQgm0wmrF+/Hv7+/ggJCcGnn34KHx8fPHnyhK0rVBbmae/2qCFESHU3f/58REZGWgRA5sAnMjIS8+fPt/s1X7x4geHDh2Ps2LG4e/cuzp49i4EDB1oMR586dYq9b/fu3di/fz8WL15coussXrwYQ4YMwc2bNxEeHo6RI0ciKysLACCTydCjRw+0bdsWly9fxtGjR5GWloYhQ4YUeU6VSoWVK1fip59+wrlz5/D06VPMnj2bvf/777/HqlWrsHLlSty8eRNhYWF4//338eDBAwCAUqlEnz590Lx5c1y5cgWLFi2yON6Wttny/BEbMbWMXC5nADByubyym1JlmEwm5uHDh8zDhw+ZlJQUq7eTJ08yABiBQMAkJCQUup8tty1btjAcDod59913mYMHDzL37t1jDh48yLz77rsMh8NhtmzZwu67evVqBgDj6urK3L17t0zXTUlJYf79918mKyursp9yQspMrVYzd+7cYdRqdZnOExkZyf5tA2AiIyPt1MKCrly5wgBgkpKSrN4/evRoxt3dncnNzWW3bdy4kZFKpYzRaGQYhmG6du3KTJs2jb0/ICCA+e6779ifATBfffUV+7NSqWQAMEeOHGEYhmGWLFnCvPfeexbXTU5OZgAw9+7ds9qurVu3MgCYhw8fsts2bNjA+Pj4sD/XrVuXWbp0qcVxb775JvPpp58yDMMwP/zwA+Ph4WHx+9q4cSMDgLl27ZpNbSvu+asNinrdl+TznXp+iE35Pua8m65du8LJyalM1wsPD8fmzZuRkJCAfv36oWnTpujXrx/u3buHzZs3Izw8nN130KBBeO211yCTybBp06YyXRf4v7o/tNo7IXnmz58PgUAAnU4HgUBQLj0+Zq1bt8Y777yDli1bYvDgwdiyZQuys7ML7JM/h6djx45QKpUWNcGK06pVK/b/jo6OcHZ2ZvMNb9y4gTNnzkAqlbI3c67ho0ePCj2nRCJBo0aN2J99fX3ZcyoUCjx//hydO3e2OKZz5864e/cuAODu3bto1aoVRCKRxWPLr7i22fL8EdtQ8ENsCn6OHDkCABaBSVmEh4fjwoULiImJwYYNGxATE4Pz588XOD+Px8Nnn30GANi8eXOZV30Xi8XQaDQ0Rk7I/7dkyRI28NHpdHZbXNgaHo+HEydO4MiRI2jevDnWrVuHpk2bIjEx0a7XeXXWKofDYYfZlUol+vbti+vXr1vcHjx4gLfffrtE52TsPNxUXNsq6vmrDSj4IdDr9WAYptCp648fP8bdu3fB5/Px3nvv2e26PB4PnTp1Qv/+/dGpU6dCc4569uyJtm3bQq1W4/vvvy/TNTkcDvh8PmQyGY2Tk1ovf46PVqstkANUHjgcDjp37ozFixfj2rVrEAgE+O2339j7b9y4AbVazf586dIlSKVS+Pn52eX6b7zxBm7fvo3AwEA0btzY4ubo6Fiqczo7O6Nu3bq4cOGCxfYLFy6gefPmAIBmzZrh5s2bFjNOL126VOK2Fff8EdtQ8EOKTXY2D3l17ty5UpaI4HA4mDt3LgDg559/xtOnT8t0PvNq7yqVyh7NI6RaspbcbC0J2p7i4+OxbNkyXL58GU+fPsX+/fuRkZGBZs2asfvodDqMGzcOd+7cQWxsLBYuXIjJkycXqAtWWpMmTUJWVhaGDx+Of/75B48ePcKxY8cwZsyYMg2Hz5kzBytWrMDevXtx7949fPHFF7h+/TqmTZsGABgxYgQ4HA7Gjx/PPraVK1eWqG22PH/ENpUe/GzYsAGBgYEQiUQICQnB33//XeT+a9asQdOmTSEWi+Hn54cZM2ZQ7ZYyYBgGKpWqyOKG5uma9hryKo3OnTuja9eu0Ov1Bd4wSsoc6CkUCns0jZBqyVxt/dUcH3MAVB55cc7Ozjh37hzCw8Px2muv4auvvsKqVavQq1cvdp933nkHTZo0wdtvv42hQ4fi/ffft2vNIXMPjdFoxHvvvYeWLVti+vTpcHV1LVOANXXqVMycOROzZs1Cy5YtcfToURw6dAhNmjQBAEilUvz+++/4999/0bZtW8ybN6/AEj7Ftc2W54/YhsNUYt//3r17MWrUKGzatAkhISFYs2YNYmJicO/ePbYeTH67du3C2LFjER0djU6dOuH+/fuIiIjAsGHDsHr1apuuqVAo4OLiArlcDmdnZ3s/pGpHr9cjMTERAoEAAoGgwP0vX75EmzZtwDAMrl69Ch8fn0poZZ6bN2+iV69e4HA4OHHiRJm+7eh0Omi1WgQEBFgkIBJSXWg0GiQmJqJBgwY15jUcEREBmUyGAwcOVHZTSBVV1Ou+JJ/vldrzs3r1aowfPx5jxoxB8+bNsWnTJkgkEkRHR1vdPy4uDp07d8aIESMQGBiI9957D8OHDy+2t4gUTq/XQ6/XF9rzc/r0aTAMg5YtW1Zq4APkzeDo06cPGIbBihUrEBcXhwMHDiAuLq7E31IFAgH0ej0VPSSEkFqo0oIfnU6HK1euIDQ09P8aw+UiNDQUFy9etHpMp06dcOXKFTbYefz4MWJjY4scjtFqtVAoFBY38n+KS3Y+efIkAFj8nirTnDlzwOVyceLECQwePBiTJk3C4MGD0blz5xIvgyGRSCCTyajoISGE1DKVFvy8fPkSRqOxQG+Cj48PUlNTrR4zYsQIREZG4q233oKDgwMaNWqEbt264csvvyz0OlFRUXBxcWFv9poxUFNotdpCx7n1ej3OnTsHIG8cviq4f/8+O2W1bdu2uH//Pg4dOoSgoCBMmDChRAGQSCSCVqul3h9Cqoht27bRkBepEJWe8FwSZ8+exbJly/Df//4XV69exf79+3H48OEiZyXMnTsXcrmcvZWkUFZtUFSy899//42cnBx4enqidevWFdyygswJmm+99RZ4PB6uXbuG27dvIzg4GNHR0QgNDcWSJUtKNAQmEokgk8lgMBjKseWEEEKqkkoLfjw9PcHj8ZCWlmaxPS0tDXXq1LF6zPz58/Hhhx/io48+QsuWLTFgwAAsW7YMUVFRBdaJMhMKhXB2dra4kTwGgwEajQaXL1+2mjtz+vRpAED37t3tNs20LOLj45GcnIzPPvsMw4YNAwB25heXy8XkyZPx9OlTxMfH23xOsVgMtVpNRQ9JtUX1qkhtYq/Xe6V9ogkEAgQHB+PUqVPsNpPJhFOnThUo+W2mUqkKfAibpy3TG0DJxcTEIDQ0FCNGjLCaO2Me8urevXtlNpNlLiUfFBSEadOmwcHBARcuXEBcXBy7Pf9+tuBwOBAIBMjOzi40gCakKjK/91HOGqlNzPXZiirPYovC1zOoADNnzsTo0aPRrl07tG/fHmvWrEFubi7GjBkDABg1ahTq1auHqKgoAEDfvn2xevVqtG3bFiEhIXj48CHmz5+Pvn37FlmkjxS0f/9+jBw5El27dsXGjRsRFBSEhIQErFu3DhMmTMDKlStx584dAMBbb71Vya3NYy5/kJCQgODgYIwYMQLbt2/HqlWr0LFjRyQkJFjsZyuJRAK5XI7c3Nwyr1tGSEXh8/mQSCTIyMiAg4NDleidJaS8mGvSpaenw9XVtcyf+ZVa5wcA1q9fj2+//Rapqalo06YN1q5di5CQEABAt27dEBgYiG3btgHIG6ZZunQpfvrpJ6SkpMDLywt9+/bF0qVLba48THV+8nJnGjdujKZNm+Lbb7+Fh4cHe5/JZMLYsWNx9epVZGZm4vXXX8fx48crsbX/x2g0onPnzggKCkJ0dDRSU1Px1ltvQavVYteuXdi6dSvu3buH8+fPl/gPIycnhy2cWdjMN0KqGp1Oh8TEROq1JLWGq6sr6tSpY/V9uiSf75Ue/FQ0Cn7yEse7d++OAwcOoFmzZpBKpRb3X758Gf369QMAfPLJJ/jqq68qo5lWxcbGYsKECQgNDcXkyZOxb98+/PTTT3BxcYFCoSiwKrytjEYjcnJy4O/vX+D5IKQqM5lMNPRFagUHB4civ9iW5PO9Uoe9SOV48eIFAKBBgwZWV3Jv2rQp+/8uXbpUWLtsER4ejs2bNyMyMpIN0ABALpdj+vTppV6Cg8fjgcPhQCaTwdHRkXp/SLXB5XJrTIVnQioKDRLXQr6+vgCAO3fuWE0aM8/ycnBwQPv27Su0bbYIDw/HhQsXEBMTgw0bNqBPnz4AgD///LNMie+Ojo5QKBS04CkhhNRwFPzUQl26dEFAQAB++OGHAj0cJpMJa9euBQC8+eabEIvFldHEYvF4PHTq1An9+/fH119/DbFYjGvXrlnMHiwpcy+YXC6n2YOEEFKDUfBTC/F4PERGRuLPP//E2LFjcfnyZSiVSly+fBljx45lZ029/fbbldxS23h5ebEzBFeuXFnm3h+5XA61Wm2v5hFCCKliKPippUJDQ7FmzRokJCSgX79+aNq0Kfr164eEhAQ2f6C6BD9AXmK2o6Mj/v333zLNTnNwcADDMJDL5XZsHSGEkKqEZnvVQgzD4PHjxwDyik3Gx8cjPT0d3t7e4PP5GDBgAFxdXXHz5s1qVT9p+fLlWLduHZo1a4bjx4+Xuu6JXq+HWq1GQEBAlR32I4QQYqkkn+/U81ML6fV6GAwG8Pl8i9yZTp064e+//wYAdOrUqVoFPgDw8ccfQyqV4u7duyVe4T0/BwcHmEwmyGQy+zWOEEJIlUHBTy2k1+thNBqtTnO/dOkSAKBDhw4V3awyc3Nzw/jx4wEAq1atKtECp6+i3B9CCKm5KPiphfR6PRiGKTDTy2AwsD0/1TH4AYDx48fD2dkZ9+/fx++//17q81DvDyGE1FwU/NRCOp3Oaj7MrVu3kJubCxcXF3aR0OrGxcUFEyZMAJDX+2MwGEp9LnPvD9X9IYSQmoWCn1pIpVJZLW5oHvJq3759tcv3ye+jjz6Cm5sbHj9+jP3795f6PPl7f2rZvABCCKnRKPipZQwGA/R6vdXg5uLFiwDsO+RlNBqh1Wqh1WrLlINTEk5OTpg0aRIA4LvvvivTukdSqZR6fwghpIah4KeW0ev10Ov1BXp+jEYjm+/TsWPHMl9Ho9EgKysLSqUSBoMBBoMBSqUS2dnZ0Gg0ZT5/cSIiIuDl5YWnT59i7969pT4Pn88HwzDU+0MIITUIBT+1jF6vh8lkKpDzc/fuXSgUCkilUrz++uulPr+5QKDBYECdOnUQGBiIBg0aoEGDBggMDISPjw8MBgNkMhlMJlNZH06hxGIxpkyZAgD4/vvvyxRwSaVSyGQy5Obm2qt5hBBCKhEFP7WMTqezumK5ecirffv2VqfA28JkMiE7OxsSiQR+fn7w9PSEWCwGn88Hn8+HWCyGp6cn/P394eTkBJlMVq5DYSNHjoSvry9evHiBnTt3lvo85npI2dnZ5RqwEUIIqRgU/NQyarW6yGTnkJCQUp3X3OPj4uKCunXrFlkZWSQSwdfXFx4eHpDL5eUWAIlEIkyfPh0AsG7dujLV7JFKpVAoFFAqlXZqHSGEkMpCwU8tYjQaodPpCvTsmEymMhc3lMvlcHR0RJ06dawGV6/i8/nw9vaGu7s75HJ5ufWoDB06FP7+/sjIyMC2bdtKfR4ulwsHBwdkZWVVWOI2IYSQ8kHBTy1inun1anDy8OFDyGQyiEQitGrVqsTnNfcm+fj42BT4mPF4PHh7e7NrsZRHQrGDgwNmzJgBANiwYQNycnJKfS5HR0colUooFAp7NY8QQkglsHvwQ8sBVF3mNb1eneZ++fJlAEDbtm0hEAhKdE6j0Qi1Wg0vL69SLQLK5/Ph4+MDsVhcpsCkKAMHDkSjRo2QnZ2NH3/8sdTn4XA4EIlEyMrKgl6vt2MLCSGEVCS7BT9arRarVq1CgwYN7HVKYmeFfWD/888/AIB27dqV+JwKhQJubm5wcXEpdbsEAgHq1KkDLpdbLsEzn8/HrFmzAACbN28u05IVEokEarWalr0ghJBqrETBj1arxdy5c9GuXTt06tQJBw4cAABs3boVDRo0wJo1a9ghBlL1qNVqqzO5zD0/JQ1+NBoNHBwc4OHhYXUGWUlIJBJ4e3tDq9WWS69K37590axZMygUCmzatKlM53J0dERWVhb1chJCSDVVouBnwYIF2LhxIwIDA5GUlITBgwdjwoQJ+O6777B69WokJSXh888/L6+2kjJgGMbqTK+srCw8fvwYABAcHFyi86lUKri7u0MkEtmljS4uLvDw8EBOTo7dE6C5XC5mz54NAPjxxx+Rnp5e6nMJhUIYjUZkZ2dT4UNCCKmGShT8xMTEYMeOHdi3bx+OHz8Oo9EIg8GAGzduYNiwYdV6PaiaTq/Xw2g0Fuj5Mff6NGnSBG5ubjafT6PRQCwWl2m461UcDgeenp5wcXEpl6TisLAwtG3bFmq1Gt99912ZzmUufEhT3wkhpPopUfDz7NkztnegRYsWEAqFmDFjRpmHPEj5Myc7Fxb8lGTIy9yL5O7uXqLZXbbg8Xjw8vKCg4OD3dfT4nA4mDdvHgBg165dbI9XSRiNRsTFxeGPP/7A5cuXkZ6eTlPfCSGkmilR8GM0Gi1mA/H5fEilUrs3itifXq8HwzAFAtXSBD/mXh8nJye7ttFMJBLB29sbOp3O7vk/HTt2xDvvvAODwYBvvvmmRMfGxsaic+fOGDx4MCZNmoTRo0eja9eu+Pnnn+3aRkIIIeWrROsYMAyDiIgICIVCAHkfghMnToSjo6PFfvv377dfC4ldaLXaAut56XQ63LhxA0DJgh+1Wo169eqVehkMWzg7O0OtVuPly5dwc3Oza+/i3Llzcfr0afz++++YOHEi2rRpU+wxsbGxmDBhAkJDQ7FhwwYEBQUhISEB33//PcaMGQOhUIhhw4bZrY2EEELKD4cpQcbmmDFjbNpv69atpW5QeVMoFGxRPWdn58puToVJSkqCwWCARCJht129ehV9+/aFq6srbt26ZVOAodFoYDKZEBAQYPchr1fp9Xo8e/YMOp3O7r1M06dPR0xMDDp16oRffvmlyMduNBrRuXNnBAUFITo62iKINJlM+PDDD/H48WM8evSoXANCQgghhSvJ53uJ3qmrclBDCldYZef8Q1629qyo1Wp4e3uXe+AD5FVn9vLyQnJyMrRaLdvjaA9z5szBoUOHEBcXhz///BPdunUrdN/4+HgkJydjw4YNBXrPuFwupk2bhgEDBuDo0aPo06eP3dpICCGkfJS5yOGzZ8/w7NmzUh+/YcMGBAYGQiQSISQkBH///XeR+8tkMkyaNAm+vr4QCoV47bXXEBsbW+rr1wZ6vR56vb7QZOc333zT5vPw+fxyy/WxRiqVwtPTE7m5uXadVl6vXj1EREQAAJYuXVrk1HrztPigoCCr9zdv3hxA3jIhOp3Obm0khBBSPkoV/JhMJkRGRsLFxQUBAQEICAiAq6srlixZUqL6LHv37sXMmTOxcOFCXL16Fa1bt0ZYWFihNVh0Oh3effddJCUlYd++fbh37x62bNmCevXqleZh1BrmZOf8vRYMw5Q42VmlUkEqldqtro+t3N3d4eTkZPfp75MnT4azszPu3LlTZJ6at7c3ACAhIcHq/ebtLi4uyMzMpNo/hBBSxZUq+Jk3bx7Wr1+P5cuX49q1a7h27RqWLVuGdevWYf78+TafZ/Xq1Rg/fjzGjBmD5s2bY9OmTZBIJIiOjra6f3R0NLKysnDgwAF07twZgYGB6Nq1K1q3bl2ah1FraLXaAsNaz549Q1paGvh8vk3Pn8lkgslksmtdH1vxeDx4enqCw+FAq9Xa7bzu7u6YPHkyACAqKqrQqfUhISHw8/PDunXrCgT3JpMJ69evh7+/P7p164bs7OxyW6OMEEKIfZQq+Nm+fTt+/PFHfPLJJ2jVqhVatWqFTz/9FFu2bMG2bdtsOodOp8OVK1cQGhr6f43hchEaGoqLFy9aPebQoUPo2LEjJk2aBB8fH7Ro0QLLli0rss6KVquFQqGwuNU2KpWq0Hyfli1b2rQgqUajgUgkskiYrkiOjo7lMvw1btw4+Pv7IzU1FRs3brS6D4/Hw4IFC3Dy5EmMHTsWly9fhlKpxOXLlzF27FicPHkS8+fPh1AohIODAzIyMmj4ixBCqrBSBT9ZWVlW8x+CgoKQlZVl0zlevnwJo9EIHx8fi+0+Pj5ITU21eszjx4+xb98+GI1GxMbGYv78+Vi1ahW+/vrrQq8TFRUFFxcX9ubn52dT+2oKc7JzYfk+ti5podFo4OrqWiDhtyK5ubnZffhLJBLhq6++AgD897//RUpKitX9wsPDsXnzZiQkJKBfv35o2rQp+vXrh3v37mHz5s0IDw8HkBekaTQaGv4ihJAqrFSfZK1bt8b69esLbF+/fn25DkGZTCZ4e3tj8+bNCA4OxtChQzFv3rwiF6qcO3cu5HI5e0tOTi639lVF5mTnV3t+SrKSu/n4V+s5VTR7DX+ZqzQfOHAAcXFxCAsLQ4cOHaDRaLB8+fJCjwsPD8eFCxcQExODDRs2ICYmBufPn2cDHzMnJydkZWVBLpeXuo2EEELKT6mKknzzzTfo3bs3Tp48iY4dOwIALl68iOTkZJtnXnl6eoLH4yEtLc1ie1paGurUqWP1GF9fXzg4OFisIdasWTOkpqZCp9NZVJ82EwqFdp0iXd2Y1/TK32OjVCpx9+5dALbN9FKr1XB0dKzwRGdrHB0d4eHhgbS0NAgEghIXP4yNjUVkZKRFEOzn54fRo0cjPj4e+/fvR0RERKE9YjweD506dSryGnw+HyKRCBkZGRAKhTYNKxJCCKk4per56dq1K+7fv48BAwZAJpNBJpNh4MCBuHfvHrp06WLTOQQCAYKDg3Hq1Cl2m8lkwqlTp9iA6lWdO3fGw4cPLZJO79+/D19fX6uBD8nLrXo1QLh27RpMJhPq169faKBpxjAMDAZDpSQ6F8bNzQ1SqbTEi4qaqzQHBQXh0KFDuH//Pg4dOoSgoCAsXbqUDWoWLlxY5lXlxWIx9Ho9MjIyaO0vQgipYkpdjrZu3bpYunRpmS4+c+ZMjB49Gu3atUP79u2xZs0a5ObmspWkR40ahXr16iEqKgoA8Mknn2D9+vWYNm0apkyZggcPHmDZsmWYOnVqmdpRkxWV7GzLkJe5uGBV6r3g8/nw9PTE06dPrQ7pWWM0GhEZGYnQ0FCLKs3BwcGIjo7G2LFjcefOHUgkEly7dg0HDhzAwIEDy9ROZ2dnyGQyCIVCeHt70wLAhBBSRZQ6+MnOzsb//vc/dvikefPmGDNmDNzd3W0+x9ChQ5GRkYEFCxYgNTUVbdq0wdGjR9kk6KdPn1oM1/j5+eHYsWOYMWMGWrVqhXr16mHatGn4/PPPS/swajSDwQCdTldo8GPLkJdGoymX1dvLSiqVwsPDAxkZGTat/VVclebJkyejX79+GD58OHbv3o2lS5ciLCysTHlOXC4XTk5OyMzMhEgkqlK9Z4QQUpuVatjr3LlzCAwMxNq1a5GdnY3s7GysXbsWDRo0wLlz50p0rsmTJ+PJkyfQarWIj49HSEgIe9/Zs2cLTJ3v2LEjLl26BI1Gg0ePHuHLL7+0yAEi/0ev1xcIfkwmE65cuQKg+J4fhmHAMAykUmm5trO03N3dIRaLC63Pk19xVZrN29u3b89Off/uu+/K3EYHBwcIhUKkpaXZ1E5CCCHlr1TBz6RJkzB06FAkJiZi//792L9/Px4/foxhw4Zh0qRJ9m4jKSW9Xg+TyWTR03H//n3k5ORAIpEUGgiYmWv7VKUhr/zMa3/pdDoYDIYi97W1SnP9+vXZ0glbtmwpdP+SEIvFMBqNSEtLo/o/hBBSBZQq+Hn48CFmzZpl0ePC4/Ewc+ZMPHz40G6NI2Wj1WoLDPGYp7i/8cYbxa5ArtVq4eTkVKV71pycnODm5lZsVWVbqzSHhITgnXfeQa9evWAwGPDll1/apV6Ps7MzcnNzkZ6eTgnQhBBSyUoV/Lzxxhtsrk9+d+/epaUmqpDc3NxSJzubA4TKquhsKw6HAw8PDwgEAqjV6kL3s7VKsznQW7x4McRiMeLj4xETE2OXdrq6ukImk+Hly5dUAJEQQipRqRKep06dimnTpuHhw4fo0KEDAODSpUvYsGEDli9fjps3b7L7tmrVyj4tJSVSWHFDW4OfqjjLqzBCoRCenp5ISUmBUCgstAq1uUpzZGQk+vXrx2739/e3qNIM5K36PnPmTCxduhRff/013n33Xbi5uZWpnVwuF87Oznj58iV4PB48PDxoBhghhFQCDlOKr6DFLXHA4XDAMAw4HE6V6+JXKBRwcXGBXC6Hs7NzZTen3KhUKiQmJsLV1ZX9gM3IyECbNm3A4XBw+/btImcfyWQyeHp6srkyVZ3JZEJKSgpycnLg6upa5L5GoxHx8fFIT0+Ht7c3QkJCrA7t6XQ6hIWF4f79+xg5ciS++eYbu7RVq9VCrVbD19e3zAEVIYSQPCX5fC9Vz09iYmKpGkYqjjmxNn/PgnmWV9OmTYsMfMyzvKr6kFd+XC4XHh4eyM3NZXutCmNLlWYgrxBnVFQU/vOf/2Dnzp3o168fOnfuXOa2CoVCMAyDFy9esMNhhBBCKk6pgp+AgAB7t4PYmUajKdBDZ+tiplV9lldhJBIJPD09kZqaWqqlL6zp0KEDPvzwQ/z000+YM2cOTp48aZegUCQSWQRAVAOIEEIqTokSnj/99FOLJQV2796N3Nxc9meZTFZgkUdS8RiGgUqlKrDkh63FDbVaLaRSaZWe5VUYV1dXODo6lnjpi6LMmzcPdevWxZMnT+w29AXkTYEXCAR4/vw5ZDKZ3c5LCCGkaCUKfn744QeLQm0ff/yxxcKkWq0Wx44ds1/rSKnodLoCyc5arZZNRC8q2ZlhGJhMpmo15JWfeekLo9EIvV5vl3M6OTlhxYoVAIAff/yRDSLtIX8AlJ2dTbPACCGkApQo+Hn1jZneqKsmc/CTv47Pv//+C61WCw8PDwQGBhZ5rFAorBIruJeWVCqFu7t7sbV/SqJHjx4YNGgQGIbB7NmzodFo7HZusVgMoVCIFy9eIDMzs8yLqhJCCClaqer8kKrN2kru+Ye8isqF0Wq1kEgkVW4tr5LgcDglWvrCVosWLYKXlxcePHiAb7/91m7nBfICIIlEgtTUVKSlpRVbsZoQQkjpUfBTA5VlJXeDwVBl1/IqCYFAAE9PT2g0GruVW3Bzc2Nzfn744QecP3/eLuc1EwgEcHZ2RmZmJp4/f27X3iVCCCH/p8SzvRYsWMDmg+h0OixdupSdqUILN1Y+g8EArVZrEfwwDGNT8GPOE6rOQ175OTs7w83NDXK53G7Tyd977z2MHDkSO3fuxLRp03Dy5Em71urh8/lwdXWFQqGATqeDl5cXnJ2dqRgiIYTYUYmKHHbr1s2mN+EzZ86UqVHlqaYXOVSpVEhKSoKzszM71f3Jkyfo1KkTBAIB7t69W2hwo1QqIRKJ4OfnV2M+bDUaDZKTk8Hlcu02dV+lUiEsLAyPHz9Gnz59sGnTpnJ5vlQqFXQ6Hdzd3eHu7l5g9h4hhJD/U25FDs+ePVtgmzl2qikfltWdTqcDwzAWNX7Mi5m2aNGiyF4dc09DTfpdikQieHh44Pnz50UufVESEokE69evx/vvv48//vgDMTExGDJkiB1aW/A6AoEAL1++RG5uLjw9PeHk5GSXx0AIIbVZqd9F//e//7EfpiKRCC1atMCPP/5oz7aRUlCr1QXq8/z9998A8lY2L4zRaASPx6sxQ175ubq6wsXFxa6zv1q3bo1Zs2YBAL766is8fPjQbufOj8/nw93dHQzD4NmzZ0hOTkZOTg7NtCSEkDIoVfCzYMECTJs2DX379kVMTAxiYmLQt29fzJgxAwsWLLB3G4mNTCaT1WRnc89PUcUNzVPci1oWoroyL33B5XKh1Wrtdt5JkyahY8eOyM3Nxfjx4y0KftqbRCKBi4sL1Go1kpOT8ezZM+Tm5lIQRAghpVCqhU29vLywdu1aDB8+3GL77t27MWXKFLx8+dJuDbS3mpzzo9FokJSUBEdHR7b3JysrCy1btgQA3Lx5Ex4eHlaPlclk8PLygpeXV4W1t6K9fPkSqampcHNzs9vQXkZGBsLCwpCWlob+/ftj/fr15T5saDQaoVQqwTAM3Nzc4ObmViN77AghpCRK8vleqp4fvV5vddZQcHAw1SepRDqdDgaDwWLYyzzLq3HjxoUGPuaFTKvbWl4l5ebmBqlUatfhLy8vL2zatAk8Hg8HDhzA9u3b7XbuwvB4PLi4uEAqlSIrKwtPnz5FZmam3ab0E0JITVeq4OfDDz/Exo0bC2zfvHkzRo4cWeZGkdKxtpipecirffv2hR6n0+kgEAhqfO8Bj8dje7bsOfzVvn17fPXVVwDyCiFeuXLFbucuCp/Ph5ubG/h8Pl68eIHk5GS7rmlGCCE1ValWdQfyEp6PHz+ODh06AADi4+Px9OlTjBo1CjNnzmT3W716ddlbSYrFMAyUSmWB6dDmZOei8n20Wi2cnJwslsOoqRwdHeHh4YG0tDS7rfwOAOPHj8fly5dx+PBhfPTRR/jjjz9Qr149u5y7OCKRCEKhEEqlEsnJyXB3d4eHh0et+H0SQkhplOrd8datW3jjjTcAAI8ePQIAeHp6wtPTE7du3WL3q0lTpqs6nU4HnU5nsSCpRqNhFzMtquenplR1tpWbmxtyc3ORk5Njt7wvDoeD1atX4/Hjx7h79y5Gjx6NAwcOVNjzyuFw4OTkBJ1Oh4yMDKhUKnh5edWq3yshhNiqVMFPVS5iWFtptVoYDAaLb/s3b96ETqeDt7c3AgICrB5nPqYmzvIqDJ/Ph5eXF5KTk9khP3uQSqXYvn07evfujbt37+KTTz7B1q1bK7QHRiAQwM3Nje0F8vT0hLu7e4HyB4QQUptRtbQawlq+T/4hr8J64bRaLTtsUpuYh7/Ms6bspV69eti2bRtEIhFOnz6NxYsX2+3ctjL3AolEIqSlpdE6YYQQ8goKfmqA4vJ9ikt2lkqltXKI0jz7S6FQ2PW8bdq0wbp16wAA0dHR+O9//2vX89tKKBTC1dUVOTk5SE5Ohlwup7pAhBACCn5qBHO+T/7gx2QysdPcCwt+GIYBh8Op8VPcC2Me/uJwOHad/QUA4eHhmD9/PgBg6dKl+Pnnn+16fltxuVy4urqCw+EgJSUF6enpNCWeEFLrUfBTA2i1WhiNRovckjt37kAul8PR0RHNmzcv9DiBQFDrhrzyc3R0hKenJ3Jzc2Eymex67okTJ2Ly5MkAgC+++AIHDhyw6/lLQiKRwNHRERkZGXj+/Lndgz1CCKlOKPipAdRqdYFhq7i4OAB563kVlnCr0+kgFotr/ZRoNzc3uLi42H34C8gLekaNGgWGYTB16lQcPHjQ7tewlYODA1xdXaFQKPDs2TOqCUQIqbWqRPCzYcMGBAYGQiQSISQkhM1VKc6ePXvA4XDQv3//8m1gFWYymaBUKgv03ly4cAEA0Llz50KPrW1T3AtjLn7o4OAAtVpt13NzOBwsXboUgwYNgtFoxOTJk/Hrr7/a9RolweVy4ebmBqPRiGfPniErK4vygAghtU6lBz979+7FzJkzsXDhQly9ehWtW7dGWFgY0tPTizwuKSkJs2fPRpcuXSqopVWTVqstkO9jMBhw6dIlAIUHP7VxintRRCIRvLy8oNFo7L5EC5fLxerVqzF8+HCYTCZMmzYNu3btsus1SkoqlUIgEOD58+eUB0QIqXUqPfhZvXo1xo8fjzFjxqB58+bYtGkTJBIJoqOjCz3GaDRi5MiRWLx4MRo2bFiBra16zPk++eu43Lx5E0qlEq6uroXm+9TkVdxLy8XFBe7u7lAoFHbvDeHxePjmm2/YIbA5c+bgu+++q9ReF5FIBGdnZzYPSKfTVVpbCCGkIlVq8KPT6XDlyhWEhoay27hcLkJDQ3Hx4sVCj4uMjIS3tzfGjRtX7DW0Wi0UCoXFrSbJzc0tkLNjHvLq2LFjocXtzEta1MYp7oXhcDjw9PSEo6NjueTDcLlcLFu2jE2CXrlyJebMmQO9Xm/3a9mKz+ezeUApKSlQqVSV1hZCCKkolRr8vHz5EkajET4+PhbbfXx8kJqaavWY8+fP43//+x+2bNli0zWioqLg4uLC3vz8/Mrc7qrCYDBArVYXqO9TXL6Pubehpi9kWhoODg7w8vICwzDlUhiQw+Fg7ty5iIqKApfLxe7duzFixAi8fPnS7teylXk6vEajQUpKSo37gkAIIa+q9GGvksjJycGHH36ILVu2wNPT06Zj5s6dC7lczt6Sk5PLuZUVx5zvk3/oSqvVsiu5Fxb81JZV3EtLKpXCy8sLKpWq3HJhRo0ahf/973+QSCSIi4tDz549ce3atXK5li04HA5cXFwAACkpKcjMzKREaEJIjVWpwY+npyd4PB7S0tIstqelpaFOnToF9n/06BGSkpLQt29f8Pl88Pl87NixA4cOHQKfz2cXWc1PKBTC2dnZ4lZTqNVqtlCh2dWrV6HRaODl5YUmTZpYPc68AGptn+JeFDc3N7i5uZVrVeT33nsPhw8fRsOGDfHixQsMHDgQmzdvtnu9oZJwdHSEUChEamoq0tPTK7UthBBSXio1+BEIBAgODsapU6fYbSaTCadOnULHjh0L7B8UFIR///0X169fZ2/vv/8+unfvjuvXr9eoIa3iFLakRf4hr8LyefR6PRwdHcu9jdUZl8uFl5cXJBJJudbDee211xAbG4tevXpBp9Nh8eLFGDp0KFJSUsrtmsURiUSQSqXIyMhAamqq3We/EUJIZav0Ya+ZM2diy5Yt2L59O7sSdm5uLsaMGQMgb3hg7ty5APLelFu0aGFxc3V1hZOTE1q0aGG31bmrA51OB41GU2h9n06dOlk9zjwzjGZ5FU8gELD5aPau/5Ofk5MTtmzZguXLl0MsFiMuLg49evTAli1bKi3wcHBwgIuLC7KysqgiNCGkxqn04Gfo0KFYuXIlFixYgDZt2uD69es4evQo+6Hz9OlTvHjxopJbWfWY69HkH7qSy+W4cuUKABRa/4imuJeMo6MjvL29oVaryzUQ4XA4+PDDD3H8+HEEBwdDqVRi0aJF6NmzJ1utu6LxeDy4ubkhJyeHZoIRQmoUDlPLshoVCgVcXFwgl8urdf5PSkoKcnJyLB7DH3/8gY8//hiNGjXCuXPnrB6XnZ0NLy8veHt7V1RTqz2GYZCeno6MjAy4urqCyy3f7wwmkwm7d+/GsmXLIJPJAABvvfUW5syZg3bt2pXrta1hGAYKhQIODg7w8fGBk5NThbeBEEKKU5LP90rv+SElp9froVKpCvTenDlzBgDQvXt3q8cxDAOGYWrtKu6lxeFw4OHhwf5RlTcul4uRI0fir7/+wujRo+Hg4IDz58+jX79+CAsLQ1RUFC5cuFBhVZnNM8FMJhNSUlIgk8loJhghpFqj4Kca0mg07IrsZgzD4OzZswCAHj16WD1Or9fTFPdS4vP58Pb2hkgkqrA6OO7u7li2bBn++usvdhjz1q1bWL9+PYYMGYK2bdvi999/r5C2AHklABwcHGgqPCGk2qPgpxpSqVTgcrkWs7nu3r2L1NRUiMVihISEWD1Oq9VCLBbDwcGhoppaowiFQtSpUwdcLrfU+S9GoxFxcXE4cOAA4uLibOq9+ffff3H+/Hl06dIF77//Pttzl5mZiYkTJ2LWrFkVlo8jFoshkUjYqfC0JhghpDqi4KeaMRqNVldxNw95derUqdCeHb1eT6u4l5GjoyN8fHyg0+lKPAMqNjYWnTt3xuDBgzFp0iQMHjwYnTt3RmxsbKHHGI1GREZGIjQ0FLt27cLGjRvxzz//YM6cOXB3dwcA7NmzB+3bt0d0dHSFrM8lFArh5OSEjIwMpKWl0VR4Qki1Q8FPNWMe8ios+ClsyMtkMoHL5dKQlx24uLjAx8cHubm5Nq/LFRsbiwkTJiAoKAiHDh3C/fv3cejQIQQFBWHChAmFBkDx8fFITk7GlClT2ERrNzc3TJ8+HX///TcmTpwIIC+Rff78+ejevTsOHTpU7kNS+afCv3jxghZFJYRUKxT8VDMqlQoMw1jMOMrJyWGXtCgs2dkcMNEUd/twd3eHl5cXcnJyih36yd97Ex0djeDgYDg6OiI4OBjR0dEIDQ3FkiVLrJ4nPT0dQF6Bz1eJxWLMmDEDQF7JCC8vLyQlJeGTTz5Bnz592NdEeeHxeBaLopZnLSRCCLEnCn6qEZPJBIVCUaD35q+//oLBYEDDhg0REBBg9VitVgupVFru07RrCw6HAy8vL3h4eEAulxe5DIS13hszLpeLyZMn4+nTp4iPjy9wrLkkQUJCgtVzm7cPGjQIFy5cwKxZsyCRSHD9+nX0798fM2fORGZmZmkfZrG4XC6cnJxw7tw5bNq0CUeOHKE8IEJIlUefhNWIWq22WtXZvDxIYb0+QF7gJJFIyrV9tQ2Xy4W3tzdcXV0hk8kKDYCK6r3Jv928X34hISHw8/PDunXrCpzfZDJh/fr18Pf3R0hICBwdHTFz5kxcuHABw4YNAwDs3bsXb7/9Nnbs2FEuQUlsbCzeeustREREYObMmQgPD0fDhg2xf/9+u1+LEELshYKfasQ8oyd/74HBYMDx48cB5C2UaY15FXca8rI/Ho8HHx8ftgaQtVwbW3tvrBWe5PF4WLBgAU6ePImxY8fi8uXLUCqVuHz5MsaOHYuTJ09i/vz54PF4FtdbtWoVDhw4gObNm0Mmk2Hu3Ll4//33cefOHXs8bADW85hiYmLQsGFDDBo0CL/++qvdrkUIIfZEFZ6rCaPRiKSkJACwKFIYFxeHwYMHw9XVFTdu3LC6UrtSqYRIJIK/v39FNbfW0ev1eP78OXJycuDm5mZRhsBoNKJz584ICgpCdHS0RfBqMpkwduxY3Lt3D+fPn7cIYvKLjY1FZGQkkpOT2W3+/v6YP38+wsPDC22XwWDA9u3b8e233yInJwd8Ph9Tp07FlClTyrQWXlGPSa1W46OPPsLjx4/x4MGDWrXmHiGk8lCF5xqosCGvo0ePAsjr9bEW+AB5PT+0JEH5cnBwgK+vL5ycnJCdnW3RA1Sa3ptXhYeH48KFC4iJicGGDRsQExOD8+fPFxn4AHnFGceNG4c///wTPXv2hMFgwOrVqxEeHo6bN2+W+vEWlcckFosxZcoUPH36FAcPHrR5RhwhhFQUCn6qidzcXACWQ14Mw+DIkSMAgF69elk9zmg00hT3CiIQCNgA6NUcoPDwcGzevBkJCQno168fmjZtin79+uHevXvYvHlzsUEMkBdEderUCf3790enTp2KDJZe5ePjgx9//BH//e9/4e7ujrt376JPnz6IioqCRqMp8WMtLo+pRYsWAIDExERaFZ4QUuVQ8FMNGAwG5OTkFFiT6+bNm3j+/DkkEgnefvttq8fSKu4VSyAQoG7dunB2drYaAJWm98ZeOBwO+vXrh7Nnz+L999+H0WjE+vXrER4ejtu3b5foXLbmMTVo0AC5ubl49uwZG8ATQkhlo+CnGlCpVFaHvMyF8Xr06FFoz45Wq4WzszNNca9A5iEwNzc3yGQyiwrIZem9sRcPDw9s3LgRP/74I7y8vHDv3j307t0bGzdutHlGmK2z0Dp06ABXV1fo9XqkpKRUyMKwhBBSHPpErAbMiar5k2iB/8v3KWzIi1Zxrzx8Ph916tSBh4cHFApFlayA3KtXL5w6dQphYWHQ6/X4+uuvMXToUDx79qzYY0uax2QOwFNSUvDy5UtaFJUQUqlotlcVp9Fo8OTJE4hEIosFSR88eIBu3bpBIBDg5s2bVhOaNRoNTCYTAgMDC02GJuXLZDIhMzMT6enpEIlEVTIQZRgGe/bswYIFC6BSqeDk5IRly5ZhwIABBQLuV5V0FppWq4VKpYKHhwe8vLwqpeeLEFIzleTznT4Rqzjz+lGvBjeHDh0CALz11luFzuTSarVwdXWlwKcScblceHp6gs/ns4uAVrWZdxwOB8OHD0fHjh0xZcoUXL16FVOmTMGJEyewfPlyuLi4FHpseHg4wsLCEB8fj/T0dHh7eyMkJKTQoEYoFILH4+Hly5fQ6/Xw9vamfDRCSIWjYa8qzGg0QiaTFcjnYRiGraA7cODAIo93dHQs1zaS4nE4HLi5uaF+/frg8/nIzs4ucjmMyhIYGIjffvsNs2fPBo/Hw6FDh/Dee+/h8uXLRR5X0jwmPp/PrglGidCEkMpAwU8VZk50fjX4uXr1KpKSkiCRSBAWFmb1WHNVZ5riXnVIpVLUr1+fnQlWFad/8/l8zJgxAwcPHkRAQACePXuGgQMHYu3atXZdHoPL5cLNzY1NhJbJZJQHRAipMBT8VGEKhQJcLrfATC1zr0/Pnj0hkUhgNBoRFxeHAwcOIC4uDkajEVqtFmKxmKrrVjFCoRB169aFj48PNBoNcnJyquSHftu2bXH06FH0798fRqMRK1aswPDhw5GammrX6zg7O4PH4+H58+fIyMigRVEJIRWCEp6rKLVajSdPnkAsFlskOuv1erRt2xbZ2dnYuXMnVCpVgYRTPz8/zJw5E2PGjCkyX4NULqVSiYyMDOTm5sLJycni91xVMAyDX375BfPmzYNarYa7uzu+++47hIaG2vU6Op0OSqUSrq6u8Pb2pqCdEFJitLxFDaBUKmEwGAp8IJ49exbZ2dnw8vJCTk5OgYUlDx06hKZNm2LmzJnsVHhSNZmHwby8vKBSqaBQKKpcLhCHw8HQoUNx9OhRvP7668jKysLo0aOxcOFCuw7bCQQCuLq6QiaTITk5mfKACCHlinp+qiC9Xo+kpCTweLwCOTuffPIJDh06hLFjx+LEiRNWF5aUy+WYOnUqHj16hAcPHtB04mpAqVQiMzMTOTk5EIlEkEgkld2kArRaLZYuXYr//e9/APKWsPjvf/+LRo0a2e0aDMNAqVQCyKsi7erqWux0e0IIAajnp9pTKpXQarUFAh+FQoHjx48DAF577bVCF5Y0Go2YM2cOEhMT8ddff1VYu0npmXuB6tevDw6Hg6ysLKjV6spulgWhUIjIyEhs27YNbm5uuHXrFnr27IlffvnFbnlLHA4HTk5O4PP5SElJQWpqKi2MSgixOwp+qhiDwYCsrCyrs7R+++03aDQavPbaa5BKpQAKLiyp0+ng4OCAN954AwDw4sWL8m80sQsejwdXV1f4+/ujTp06YBiGDYKqUgftu+++ixMnTqBjx45QqVSYMWMGpk6dipycHLtdQywWw9nZGZmZmUhJSYFKpbLbuQkhhIKfKkapVEKtVheoBMwwDH7++WcAwMiRI+Hj4wOg4MKSWq0WEokEDx48AAD4+vpWQKuJPTk4OMDT0xP+/v7s7y87Oxs5OTkW64RVJl9fX+zduxefffYZeDwe9u/fj549e+L69et2uwafz4ebmxvUajWSk5OrbH0kQkj1Q8FPFWI0GpGdnQ2hUFggz+HGjRu4c+cOhEIh/vOf/xS6sKRer4dUKkVUVBQaNGiALl26VPTDIHYiEAjg4eGBgIAA+Pv7QyKRQKVSISsrC7m5uZU+LZzH42HatGn49ddfUa9ePSQlJaFfv35Yv3693drG4XDg4uJiMQxWFddJI4RULxT8VCFKpRK5ublWk1137twJAOjduzfc3NysLiyZmZmJ27dv48MPP8Qff/yBlStXUrJzDcDn8+Hs7Iz69esjMDAQ9erVg4ODA5RKJbKzsyt9WOzNN9/E8ePH0bt3bxgMBkRFRWHo0KF4/vy53a4hFovh4uKC7OxsJCcn23WIjRBS+1SJ4GfDhg0IDAyESCRCSEgI/v7770L33bJlC7p06QI3Nze4ubkhNDS0yP2rC6PRiKysLKu9PjKZDL/99huAvCEvs/DwcGzevBkJCQno168fWrVqhWHDhuHOnTvYt29fkUtfkOqHw+FAJBLBzc0NAQEBCAgIYIc/K3tYzNXVFT/88ANWrVoFiUSCixcv4t1338Xhw4ftdg0ejwc3NzcYjUYkJycjPT29ygwDEkKql0oPfvbu3YuZM2di4cKFuHr1Klq3bo2wsDCkp6db3f/s2bMYPnw4zpw5g4sXL8LPzw/vvfceUlJSKrjl9pWTk1Nor8+ePXugVqvRrFkzhISEWNwXHh6OCxcu4JdffsGKFStw+PBhPHjwgAKfGo7D4UAikcDT09NiWEypVEImk1XK0BCHw8GwYcNw7NgxtG7dGjKZDBMmTMDs2bPtWrdHKpVCIpEgPT2d1gYjhJRKpdf5CQkJwZtvvon169cDAEwmE/z8/DBlyhR88cUXxR5vNBrh5uaG9evXY9SoUcXuXxXr/Oj1ejx9+hQMwxQIfgwGAzp37oxnz55h5cqVGD58uNVzmKdFBwQE0CrutRTDMFCr1VAoFFAoFNDr9RCLxZWyvptOp8OqVauwYcMGMAyDBg0aYMOGDWjdurXdrsEwDDv85enpyQ4HE0Jqp2pT50en0+HKlSsWpfK5XC5CQ0Nx8eJFm86hUqmg1+vh7u5eXs0sd3K53OoMLwA4fvw4nj17Bnd3d/Tv37/Qc2g0GrY+CqmdzL1BderUQUBAALy9vdnh1IquGSQQCDB37lzs3bsXderUQWJiIt5//31s2LDBbjO2OBwOnJ2dIRQKkZqaSpWhCSE2q9Tg5+XLlzAajWzegpmPj4/NCyh+/vnnqFu3bqFrDWm1WvabsPlWlWg0GmRlZUEikVitZLt582YAwAcffGA1OALyer84HA5b+4cQoVAILy8vBAQEwNfX16JmUEXq3LkzTp48ifDwcBgMBixbtgxDhw616zC1UCiEm5sbNBoNnj59SrlAhJBiVXrOT1ksX74ce/bswW+//VZo135UVBRcXFzYm5+fXwW3snDmDyS9Xm+1/fHx8fjnn38gEAgQERFR6Hk0Gg0kEkmhwRGpvfJPl88fBGk0mgprg5ubGzZv3oyVK1dCLBYjLi4OPXr0wJ49e+xaGdrZ2RlisRjp6el48uQJFApFlSoOSQipOio1+PH09ASPx0NaWprF9rS0NNSpU6fIY1euXInly5fj+PHjaNWqVaH7zZ07F3K5nL3lX/28suXk5EAmk8HJycnq/evWrQMADB06tEDvWH5arZbWQCJFcnBwgIeHB1s92jwcZs/FSYvC4XAwfPhwHD9+HMHBwVAqlZg1axZGjRplcy+vLQQCgcWMsBcvXlRooEcIqR4qNfgRCAQIDg7GqVOn2G0mkwmnTp1Cx44dCz3um2++wZIlS3D06FG0a9euyGsIhUI4Oztb3KoCvV6PjIwM8Pl8q3k6//77L86cOQMul4tPPvmk0PNotVoIhcIquRAmqXoEAgFbPdrb2xs6nQ7Z2dkVtn5Ww4YN8dtvv+Grr76CQCDA6dOn8c4772D//v127QWSSqVwcnJCdnY2nj59ipcvX9JQGCGEVenDXjNnzsSWLVuwfft23L17F5988glyc3MxZswYAMCoUaMwd+5cdv8VK1Zg/vz5iI6ORmBgIFJTU5GamsquBF0d5M+/cHR0tLrPmjVrAAD9+/dHQEBAoedSq9VwcnKCQCAoj6aSGkooFMLb2xsBAQFwd3eHSqWCXC6vkKrRPB4Pn3zyCY4ePYpWrVpBJpNhypQpGD9+PF6+fGm365iXx+Dz+UhNTcXTp08hl8tpiQxCSOUHP0OHDsXKlSuxYMECtGnTBtevX8fRo0fZYZ6nT59aLM65ceNG6HQ6DBo0CL6+vuxt5cqVlfUQSiwnJwcvX76Ek5OT1aEq83PA5XIxderUQs9j/qAqbNiMkOKIRCLUqVMH/v7+kEqlUCgUUCqVFZIr07RpUxw6dAizZ88Gn8/HkSNH0L17dxw8eNCu1zcXhjQPhaWkpCA3N5fygQipxSq9zk9Fq+w6P1qtFs+ePYPJZGJ7fYxGI+Lj45Geng5vb2+sW7cO586dw+DBg9keIGuUSiWEQiH8/f0p34eUmclkglKpRGZmJnJzcyEWiyssif7WrVuYPn067t69CwDo0aMHoqKiUL9+fbtex2QyIScnBwzDwNXVFW5ubjRRgJAaoiSf7xT8VCCTyYTnz59DLpfDzc0NABAbG4vIyMgCidg8Hg/nz5+Hv7+/1XMxDIPs7Gz4+fnBxcWl3NtOag+j0QiZTIasrCzodDo4OjpWyLCqTqfDhg0bsHbtWuh0OojFYsyZMwfjxo2ze/0qg8EApVIJLpcLV1dXuLq6VkoxSEKI/VSbIoe1CcMwyMzMhEwmY4OV2NhYTJgwAUFBQTh06BDu3r2Lhg0bAsj7ALp161ah59NoNBCLxYXmDBFSWjwej50Z5unpCY1GUyH5QAKBADNmzMCJEyfQoUMHqNVqREZGok+fPvj333/tei0+n88GPJmZmXjy5AnS0tJoZhghtQT1/FQQmUyG58+fQyKRQCAQwGg0onPnzggKCkJ0dDS4XC52796N2bNnw8nJCW+88QYSExNx/vx5qyX7s7Ky4OvrCw8Pjwp7DKR2ys3NRVZWFuRyOTuzsLyHWU0mE/bu3YslS5ZALpeDy+Vi3LhxmDVrVrnkuOl0OuTm5oLP57M1wWg4jJDqhXp+qhilUom0tDQIhUJ2+CA+Ph7JycmYMmUKuFwucnJysGLFCgDAjBkzMHPmTDx9+hTx8fEFzqfVaiEQCKiiM6kQjo6OqFevHvz8/MDlcpGdnV3uPSRcLhfDhw/Hn3/+if79+8NkMmHLli3o0qUL9u7da/cZW+b6QEKhkO0Jev78OSVGE1JDUfBTzlQqFVJTU8HhcCy+SZpXrQ8KCgKQN4U/IyMDDRo0wJgxY9jt1la3V6lUcHFxgVAorIBHQEheMOLi4oKAgADUqVMHBoMBMpms3GvneHl5YcOGDdi5cycaNmyIjIwMzJw5E++//z6uX79u9+uZgyCxWAyZTIYnT54gOTkZCoWiQsoAEEIqBgU/5UitVuPFixcwGAwFemm8vb0BAAkJCbh69Sq2bdsGAFi2bBkEAgESEhIs9jPT6/Xg8XhVplgjqV34fD5bJNHV1RVKpZKdPVWeunXrhlOnTmH+/PlwdHTEtWvX0KdPH8yePdvqF4SycnBwgKurK5ycnKBWq5GcnIykpCRkZmZWWFVsQkj5oZyfcqJSqfDixQvodDqrs7HMOT+vvfYaUlJSkJCQgEGDBuH777+HyWTC2LFjce/evQI5PzKZDG5ubvD19S23thNiC4Zh2KnxSqWywqbGp6WlYdmyZdi3bx8AQCKR4OOPP8bEiRPLbSiYYRio1WpoNBoIBAI4OTnByckJEokEXC59hySkKqCp7kWoiOBHqVQiNTUVer2+yGnosbGxGD9+PIC8QoUnTpxAWloa1q9fj5MnT2Lz5s0IDw9n99fr9VCr1fD396flLEiVYTQaIZfLkZmZCZ1OB6lUCgcHh3K/7uXLl7F48WJcvXoVQN5agTNmzMDIkSPL9fo6nQ4qlQoMw0AkEsHV1RUSiQQikYjqbRFSiSj4KUJ5Bz9yuZxdqLG4WSl///03Bg4cWGDIwN/fH/Pnz7cIfADq9SFVm1arZesDAXmv//LuFWEYBrGxsYiKikJiYiIAoEGDBvjss8/Qp0+fcr2+yWSCRqOBVqsFj8eDRCJhV5anfDxCKh4FP0Uoz+CHYRgkJiZCr9cXG/hkZ2ejV69eSE5OxqBBgzB06FC2wnNISEiB6e3mXp+AgACagkuqtMqYGq/X67Fz505899137PpgQUFBmD59Onr37l3uQZjBYIBGo4FOp4ODgwMbCIlEIggEAuoRIqQCUPBThIoIfhiGKTJAMZlMiIiIwKlTpxAQEIBjx47ZFCx5eHigTp06dm0zIeXBvIzEy5cvoVar2WGh8qZUKrF582Zs2bIFCoUCQN4aYjNmzKiQIAjIC8Q0Gg30ej0cHBwgFovh5OQEkUgEoVBIOUKElBMKfopQFYKf7777DitXroRQKMShQ4fQokWLIs+r1Wqh1+vh7+9PJfhJtWKeEp+VlQW9Xl9h+UByuRz/+9//CgRBn376Kd5///0KWa4DyAuEtFotdDodeDwehEIhGwiJRKIKeS4IqS0o+ClCZQc/v//+OyZOnAgAWLlyJYYPH17sebOysuDj4wMvLy+7tpeQiqLVapGVlQWZTAYAkEqlViuX25tcLkd0dDQ2b97MBkF16tTBRx99hJEjR1ZoyQij0cgGQiaTCQKBACKRCFKpFEKhEEKh0O5rmBFSm1DwU4TKDH4uX76MoUOHQqPRYNy4cYiMjCz2nCqVChwOB/7+/vQtkVRrDMNApVIhKysLCoUCDg4OcHR0rJB8GLlcjp9++gnR0dFIS0sDkBeAjRgxAuPGjbP76vHFYRgGer0eOp0Oer0eHA4HDg4OEAqFkEqlEAgEEAgEcHBwoHwhQmxEwU8RKiv4+ffffzFkyBAoFAqEhoYiOjq62G++JpMJMpkM9evXh6urq13bSkhlMZlMUCqVyMrKglKphEgkglgsrpAPea1WiwMHDmDTpk24f/8+gLzq1e+88w5GjRqFbt26VUpODsMwbCCk1+sB5BWUNAeI5qVxHBwcwOfzKSAixAoKfopQGcHP7du3MWTIEMhkMrRv3x4///yzTauxy+VySKVS1KtXj5IkSY1jNBqRk5ODzMxMqNXqCiuSCOT9rZ45cwabNm3ChQsX2O3+/v744IMPMGzYsEpfNNgcCOn1ehiNRrZ3iM/nQyKRQCgUwsHBgd1WEcOIhFRlFPwUoaKDn1u3bmHEiBHIzMxE27ZtsXv3bptWpdZqtdBqtVTQkNR4er0eCoUCWVlZ0Gq1bE9HRXn48CF27NiBffv2QS6XA8hb4ys0NBSDBw9Gt27dKixBuijmoTKDwQC9Xs8u7srj8dheIvPUej6fb3GjniJSG1DwU4SKDH7OnDmDjz/+GLm5uWjVqhX27NlTZMXn/OfJzs6mJGdSq+h0OsjlcmRnZ0On01V4EKRWq3Ho0CHs2LHDYtFUd3d39O/fH4MGDUKrVq2qXCBhNBrZ3iGDwcAGRVwuFzweDzwejw2MHBwc2G3moInL5VLPMqkRKPgpQkUFP7/99hu++OILdg2vLVu22BT4mNsoEolQv359mv1Bah1zpWjzqvGOjo6l6nkxGo2Ij48vsnhoYW7fvo1ff/0Vv/32m8XCqQ0bNkTv3r3Rp08fvP7661UuEMrPZDLBYDDAaDRa3ACAw+GwAZA5SDInWb+63RwcmW9V+TGT2o2CnyKUd/Bz584dLFq0iF10cdCgQfj2229tfvM2V4n18/OzKS+IkJpKo9FALpezQZA5z8UWsbGxiIyMRHJyMrvNz88PCxYsKLBsTFEMBgP++usv7Nu3D0ePHoVGo2HvCwgIQHh4OMLDw9GmTZtq13uSPyAymUwwmUzs/80BDofDYYMeHo8HDodjdUjt1eCosH8pcCLliYKfIpRn8HPr1i0MHDgQDx48AIfDwaxZszB9+nSb/+DNC0T6+vpWerIlIVVF/iBIr9cXOxwWGxuLCRMmIDQ0FFOmTEFQUBASEhKwbt06qwsG2yonJwenTp3C4cOHcfr0aYtAyMvLC927d0ePHj3QtWvXCq0fVJ4YhrEIisw/57+Z98v/PmctEAJg0ZOUv4cpf3BU3A1AkdvM/8//L6kdKPgpQnkFP+np6WjUqBGUSiW8vb2xfv16dO7cuUTnyMrKYhcurW7fIgkpbxqNBgqFAjKZDDqdzursMPMwc1BQEKKjoy3+jkwmE8aOHYt79+7h/PnzZZodpVKpcPr0aRw+fBinTp1Cbm4uex+Px0P79u3RtWtXdOrUCa1atapVNboYhmGDpPz/5r9Z25b/eKBg4FJUAJT/5/z7A2BfA/n/zb/vq/dbu+/V9hT2f1t+Lsm2st5Xln3L8zwcDqdcJhFQ8FOE8uz5mT9/Pv7880+sXLmyxEXTzEXf/Pz8qsTMEkKqKq1Wi5ycHMhkMmg0Gos6QXFxcRg8eDAOHTqE4ODgAsdevnwZ/fr1Q0xMDDp16mSX9uh0Ovz99984ffo0Tp8+jQcPHljc7+joiPbt26Njx47o1KkTWrZsSbl8pfBqoGQteHo1kHp1W2H/2nKf+QO/sP+X5ufCttlyn5mtgYg9AhZ7cXBwQL169ey+XBMFP0Uoz+DHYDAgKSkJHA6nRPVK1Go1DAYD6tevT3k+hNhIr9ezxRI1Gg34fD5OnDiBKVOm4P79+1b/lpRKJZo2bYoNGzagf//+5dKup0+f4vTp07hw4QLi4uLYJT3MHB0d0aZNG7Rt2xbBwcFo27ZtlZjVWZYEcVLxbP3oLutHvL1DBJPJBJVKhQYNGlRq8ENfP+zIPH5dkheLVquFRqNBvXr1KPAhpAQcHBzg5uYGZ2dn5ObmQiaTQSqVAsirqN6hQ4cCxyQkJAAAvL29y61d/v7+iIiIQEREBEwmE+7evYuLFy8iLi4Oly5dglwux4ULFyyKK9avXx9vvPEGWrVqhebNm6NZs2bl2sZX2StBnFSciurxsbeq0h7q+bETo9GIc+fO4caNG/Dy8sLbb79d7LcmvV6PnJwc1KlTBx4eHlXmRUFIdcQwDHJyctCiRQs0btwYa9asgUQigUgkApfLtWvOT2kZjUYkJCTg2rVr7O3+/ftWvzB5enqygVDz5s0RFBSERo0a2b0KdnkliBNijclkQk5OTqX3/FDwYwf79+/HrFmzkJSUxG4r7luTOfDx9vaGl5cXBT6E2Mn+/fsxaNAghIWFYdy4cfD398fDhw8RHR2N06dPV7kP85ycHFy/fh3Xrl3D7du3cffuXTx+/LjQHuS6deuiYcOGaNSokcW/devWLXFidUUliNdUNFRYchT8VBJ7Bz/mN9o+ffpg7ty5kEqluHfvHjZv3lzotyZz4OPl5QUvLy+a2UWInVn7QlK/fn3MmjUL4eHhEIvFVfpDSq1WIyEhAXfv3sWdO3dw584d3L9/H9nZ2YUew+VyUadOHdSvX9/i5ufnh3r16sHX17fAUjmVkSBeU9BQYelQ8FNJ7Bn8GI1GNG7cGC1btsSBAwfA4XDYCs9CodDqtyadTofc3Fx4enpS4ENIOTIajfjrr7/w4sUL+Pr6on379tDpdJDJZFCr1TAajRCLxRAKhdXm7zArKwuPHz/Go0eP8PjxY/aWmJgIrVZb7PFSqRReXl7w8fGBt7c3FAoFzp49i+XLl8Pf3x8eHh5wc3ODu7s7RCIRcnNzyz1BvDqiocLSo+Annw0bNuDbb79FamoqWrdujXXr1qF9+/aF7h8TE4P58+cjKSkJTZo0wYoVK2x+odkz+Dl79iy6d++OixcvokOHDgXW9nr1W5NarYZWq4W3tzfl+BBSSRiGgUajQW5uLuRyOTQaDTgcDoRCYbUKhPIzmUx4+fIlkpOT8ezZM6SkpBT4v0qlKtE5hUIhHB0dkZWVhebNmyMwMBBubm5wc3ODq6srpFIpnJyc4OjoCCcnJ/Zn8/9raskOGiosm6oS/FT6bK+9e/di5syZ2LRpE0JCQrBmzRqEhYXh3r17Vmc7xMXFYfjw4YiKikKfPn2wa9cu9O/fH1evXkWLFi0qtO0vXrwAgEKvGxQUBCCvAGJOTg4YhoGvry9cXV0p8CGkkphLUYjFYri7u0OtViM3Nxc5OTlQKBRgGAYikahaBUJcLhfe3t7w9va2OnzFMAyUSiXS09MtbqmpqdixYwdEIhG8vLyQlZWF7OxsGAwGaLVatjfJPPRWEgKBgA2IzInnYrG40H+tbRMKhXBwcICDgwMEAkGB/5vXI8u/rbxXsY+Pj0dycjI2bNhQ4PXB5XIxefJk9OvXD/Hx8TRUWIVVes9PSEgI3nzzTaxfvx5AXlTo5+eHKVOm4Isvviiw/9ChQ5Gbm4s//viD3dahQwe0adMGmzZtKvZ6ldHzEx0djbfffhs+Pj7sVFxCSNViNBqh0WigVqshl8uh1WrBMAwcHBzYD+GaKP8QzuTJk9G0aVNcu3YNGzZswIULFzBt2jQ0adIE2dnZ7E0ul0OpVLK3nJwcNoAsaQ9TecgfKL26kv2rK9rn/39h28xlTPh8Pp4/f45//vkH//nPf9iei/xLeRiNRvz000/o3r07GjdubHV9s/zroZn//+p5Xj2msPNYW9Yj//9t3ZZfWc5T3LmBvJy2kSNHwsfHp5DfYOlUm2EvnU4HiUSCffv2WYwnjx49GjKZDAcPHixwjL+/P2bOnInp06ez2xYuXIgDBw7gxo0bBfbP/+0FyHty/Pz8KiTnZ/To0bh37x7i4uJQp06dGtsNTEhNYzKZoNFooNFokJOTA41GA4PBAC6XC4FAUK16hWxhLXnX398f8+fPL3HuisFgQG5urkVgpFKp2MCyuH/z/1+n00Gv10Ov10Or1bL/f3U7qX4uX75stZeyLKrNsNfLly9hNBoLRH8+Pj5sMbJXpaamWt0/NTXV6v5RUVFYvHjx/2vv3oOiKv8/gL/XXRZFhCVRri5YoUYGmgSD5PgtmSidBkUbmtGR6g9Hg1FTpywTHRzFsbHxkoNZM+BURumEpqXlqGyX8QZq4iUviWEpkJWAXJfd5/eHnfPbhQVBV85Zzvs1s7N7znn27IcP8PDhnOc8xz0Bt6HX67F27VpMmzYNkydPxuLFi+Hr64vz589j8+bNsFgsKCgoQHh4eK/qKIl6uz59+sDHxwc+Pj4ICAhAS0sLmpubUV9fj4aGBtTW1sJutzudhvHk3/GJEyciJSXFLZdtGwwG+Pv7w9/f/wFE2p4QAjabzakoavtst9vR2trqdCd7x0dra6t8A1fpdUfrrFYrNm7ciEGDBjn90y7d6HXXrl34+++/MWPGDOh0uk7vcya9R3ot7aej93S0HykPjs/3ss7V8r3ut6P9S9+vtlce9jTFx/w8aG+//TYWLFggL0tHftwlLS0NO3bswMKFC51uZGo2m7Ft2za8/PLLbvssIup5joOh/fz8YLPZ0NzcLF+52djYKI8V0uv1cjHkaYNd9Xq9R45R0el0MBgMMBgMbp8AsiMRERGYNWsWfvnlF2RlZclXe33wwQe4evUqr/bqhOOAZyUpWvwEBgZCr9ejqqrKaX1VVRWCg4Ndvic4OLhb7aVO60FKS0tDamoqfvzxR1y8eBFhYWFISUnhzQuJeiG9Xi8fFTKZTE7FUENDgzyA2mazAbgz/sRgMMDLy8ujjw7R/5s4cSK2bNmCnJwcpKamyuvNZjMLHw+higHP8fHx2LhxI4A7VaHZbEZWVlaHA54bGhqwe/dued3YsWMRExPT4wOeiYjastvtaGlpkR/19fVOp190Op18VZI0GJc8E2d47j5e6v6fBQsWICMjA3FxcYiPj8e6detQX1+PV199FQAwc+ZMhIWFITc3FwAwb948jB8/HmvXrsWkSZNQWFiIkpISbNmyRckvg4gIwJ3xQn379pU79sDAQHmsiDQOpaGhAVarVT5CJJ0yk64skp45JYa6eeqpQlJB8ZOeno6//voL2dnZqKysxKhRo7Bv3z55UHNFRYXToeKxY8di27ZtePfdd/HOO+8gKioKO3fu7PE5foiIusrVmBRpIK3VakVraytaWlrQ2NiI1tZW+Vni6tJsFkZE907x0149jae9iEjNpKJIujrJccJBaVk6WiSR5q6R5o5xfE2kJjztRURE7UiFjKsLNTq6VFsaX2S1WuVl6TJq4M7lxY4T67WdYM/xQaQFLH6IiDyEVBh1RJoDRiqKpNdSIeR4ms1xHhtpuzR/jHRKTXrdlRmG27bjaTlSMxY/RES9hE6nu2uBJHGcZM9x8ry2y0IIp0LJsajqaBI+x8n3HD9PirFtzNJzV1539F5Xz3db19k+qXdj8UNEpEHdKZRccSxyXBU+nT0c399R8eR42q7tc9t9OMbk+Hy3dZ29lpYdi6G2y3fLT0dtOyoEu7rdVduutu/qvtyxv44+Qw3TAbD4ISKiblPq1FZ3budwtyKoozau2t3vuq5s68r27rbrqf10hzTXlZJY/BARkcfo6BQYUXdwaD8RERFpCosfIiIi0hQWP0RERKQpLH6IiIhIU1j8EBERkaaw+CEiIiJNYfFzn5YvX44VK1a43LZixQosX768ZwP6j1rjAtQdmxoxX0TkydTYh7H4uU96vR7Z2dntvrErVqxAdna2YjNZqjUuQN2xqRHzRUSeTJV9mNCYmpoaAUDU1NS4bZ85OTkCgMjJyXG5rBS1xuUqFjXFpkbMFxF5sp7ow7rz910nhAJzWyuopqYGJpMJ165dg5+fn9v2u2bNGqxcuRJGoxEtLS1YsmQJ3nzzTbftv7fFBag7NjVivojIkz3oPqy2thZDhgzBrVu34O/v32lbzRU/f/zxB4YMGaJ0GERERPQAXLt2DeHh4Z220VzxY7fbcf36dQwYMMCt94aRKlqJWv4rV/PRArXmTK2Yr+6T/hN095He3or56j7mrOsedB8mhEBdXR1CQ0PRp89dhjS77WSbhknnLpcsWeL0rPR4DDWPE1FrztSK+bo3D2KMX2/GfHUfc9Y1auvDWPzcJ8eCwvGXQOlCo6PPVzqutjGoKWdqxXzdO/5h6h7mq/uYs7tTYx9mcMehJi2z2WzIycnB0qVLUVtbK69funSpvF3puBwpHZf02WrMmVoxX0TkyVTZh/V4udWLNTU1iWXLlommpialQ/EYzFn3MF/dw3x1D/PVfcxZ96glX5ob8ExERETaxhmeiYiISFNY/BAREZGmsPghIiIiTWHxQ0RERJrC4seNNm3ahMjISPTt2xcJCQk4duyY0iGpwg8//IAXX3wRoaGh0Ol02Llzp9N2IQSys7MREhKCfv36ITk5GZcuXVImWBXIzc3FU089hQEDBmDw4MGYPHkyLly44NSmqakJmZmZGDhwIHx9fTF16lRUVVUpFLGy8vLyEBMTAz8/P/j5+SExMRF79+6VtzNXnVu9ejV0Oh3mz58vr2POnC1fvhw6nc7pMWLECHk789Xen3/+iRkzZmDgwIHo168fnnjiCZSUlMjble73Wfy4yRdffIEFCxZg2bJlOHHiBGJjY5GSkoLq6mqlQ1NcfX09YmNjsWnTJpfb16xZgw0bNmDz5s04evQo+vfvj5SUFDQ1NfVwpOpgsViQmZmJI0eOYP/+/bBarXjuuedQX18vt3njjTewe/dubN++HRaLBdevX0daWpqCUSsnPDwcq1evRmlpKUpKSvDss88iNTUVZ8+eBcBcdeb48eP48MMPERMT47SeOWvv8ccfx40bN+THTz/9JG9jvpz9+++/SEpKgpeXF/bu3Ytz585h7dq1CAgIkNso3u8reqF9LxIfHy8yMzPlZZvNJkJDQ0Vubq6CUakPAFFUVCQv2+12ERwcLN577z153a1bt4S3t7f4/PPPFYhQfaqrqwUAYbFYhBB38uPl5SW2b98utzl//rwAIA4fPqxUmKoSEBAgPv74Y+aqE3V1dSIqKkrs379fjB8/XsybN08IwZ8vV5YtWyZiY2NdbmO+2nvrrbfE008/3eF2NfT7PPLjBi0tLSgtLUVycrK8rk+fPkhOTsbhw4cVjEz9ysvLUVlZ6ZQ7f39/JCQkMHf/qampAQA89NBDAIDS0lJYrVannI0YMQJms1nzObPZbCgsLER9fT0SExOZq05kZmZi0qRJTrkB+PPVkUuXLiE0NBQPP/wwpk+fjoqKCgDMlytff/014uLi8NJLL2Hw4MEYPXo0PvroI3m7Gvp9Fj9ucPPmTdhsNgQFBTmtDwoKQmVlpUJReQYpP8yda3a7HfPnz0dSUhJGjhwJ4E7OjEYjTCaTU1st56ysrAy+vr7w9vbG7NmzUVRUhOjoaOaqA4WFhThx4gRyc3PbbWPO2ktISEBBQQH27duHvLw8lJeXY9y4cairq2O+XLhy5Qry8vIQFRWF7777DnPmzMHcuXOxdetWAOro93lvLyIVy8zMxJkzZ5zGF1B7w4cPx6lTp1BTU4MdO3YgIyMDFotF6bBU6dq1a5g3bx7279+Pvn37Kh2OR3jhhRfk1zExMUhISEBERAS+/PJL9OvXT8HI1MlutyMuLg6rVq0CAIwePRpnzpzB5s2bkZGRoXB0d/DIjxsEBgZCr9e3G91fVVWF4OBghaLyDFJ+mLv2srKysGfPHhw6dAjh4eHy+uDgYLS0tODWrVtO7bWcM6PRiEcffRRjxoxBbm4uYmNjsX79eubKhdLSUlRXV+PJJ5+EwWCAwWCAxWLBhg0bYDAYEBQUxJzdhclkwrBhw3D58mX+jLkQEhKC6Ohop3WPPfaYfKpQDf0+ix83MBqNGDNmDA4cOCCvs9vtOHDgABITExWMTP2GDh2K4OBgp9zV1tbi6NGjms2dEAJZWVkoKirCwYMHMXToUKftY8aMgZeXl1POLly4gIqKCs3mrC273Y7m5mbmyoUJEyagrKwMp06dkh9xcXGYPn26/Jo569zt27fx22+/ISQkhD9jLiQlJbWbnuPixYuIiIgAoJJ+v0eGVWtAYWGh8Pb2FgUFBeLcuXNi1qxZwmQyicrKSqVDU1xdXZ04efKkOHnypAAg3n//fXHy5Enx+++/CyGEWL16tTCZTGLXrl3i9OnTIjU1VQwdOlQ0NjYqHLky5syZI/z9/UVxcbG4ceOG/GhoaJDbzJ49W5jNZnHw4EFRUlIiEhMTRWJiooJRK2fx4sXCYrGI8vJycfr0abF48WKh0+nE999/L4RgrrrC8WovIZizthYuXCiKi4tFeXm5+Pnnn0VycrIIDAwU1dXVQgjmq61jx44Jg8EgVq5cKS5duiQ+++wz4ePjIz799FO5jdL9PosfN9q4caMwm83CaDSK+Ph4ceTIEaVDUoVDhw4JAO0eGRkZQog7lz0uXbpUBAUFCW9vbzFhwgRx4cIFZYNWkKtcARD5+flym8bGRvH666+LgIAA4ePjI6ZMmSJu3LihXNAKeu2110RERIQwGo1i0KBBYsKECXLhIwRz1RVtix/mzFl6eroICQkRRqNRhIWFifT0dHH58mV5O/PV3u7du8XIkSOFt7e3GDFihNiyZYvTdqX7fZ0QQvTMMSYiIiIi5XHMDxEREWkKix8iIiLSFBY/REREpCksfoiIiEhTWPwQERGRprD4ISIiIk1h8UNERESawuKHiDxScXExdDpdu3sqERHdDSc5JCKP8L///Q+jRo3CunXrAAAtLS34559/EBQUBJ1Op2xwRORRDEoHQER0L4xGo2bvmk1E94envYhI9V555RVYLBasX78eOp0OOp0OBQUFTqe9CgoKYDKZsGfPHgwfPhw+Pj6YNm0aGhoasHXrVkRGRiIgIABz586FzWaT993c3IxFixYhLCwM/fv3R0JCAoqLi5X5QomoR/DIDxGp3vr163Hx4kWMHDkSOTk5AICzZ8+2a9fQ0IANGzagsLAQdXV1SEtLw5QpU2AymfDtt9/iypUrmDp1KpKSkpCeng4AyMrKwrlz51BYWIjQ0FAUFRXh+eefR1lZGaKionr06ySinsHih4hUz9/fH0ajET4+PvKprl9//bVdO6vViry8PDzyyCMAgGnTpuGTTz5BVVUVfH19ER0djWeeeQaHDh1Ceno6KioqkJ+fj4qKCoSGhgIAFi1ahH379iE/Px+rVq3quS+SiHoMix8i6jV8fHzkwgcAgoKCEBkZCV9fX6d11dXVAICysjLYbDYMGzbMaT/Nzc0YOHBgzwRNRD2OxQ8R9RpeXl5OyzqdzuU6u90OALh9+zb0ej1KS0uh1+ud2jkWTETUu7D4ISKPYDQanQYqu8Po0aNhs9lQXV2NcePGuXXfRKRevNqLiDxCZGQkjh49iqtXr+LmzZvy0Zv7MWzYMEyfPh0zZ87EV199hfLychw7dgy5ubn45ptv3BA1EakRix8i8giLFi2CXq9HdHQ0Bg0ahIqKCrfsNz8/HzNnzsTChQsxfPhwTJ48GcePH4fZbHbL/olIfTjDMxEREWkKj/wQERGRprD4ISIiIk1h8UNERESawuKHiIiINIXFDxEREWkKix8iIiLSFBY/REREpCksfoiIiEhTWPwQERGRprD4ISIiIk1h8UNERESawuKHiIiINOX/AOxO4Toq8/C0AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 650x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot ML fit for pEpoR (single regularization strength with noise model)\n",
-    "fig, ax = plt.subplots(figsize=(6.5, 3.5))\n",
-    "t, pEpoR = simulate_pEpoR(\n",
-    "    problem=regproblems[chosen_regstrength],\n",
-    "    result=regresults[chosen_regstrength],\n",
-    ")\n",
-    "sigma_pEpoR = 0.0274 + 0.1 * pEpoR\n",
-    "ax.fill_between(\n",
-    "    t,\n",
-    "    pEpoR - 2 * sigma_pEpoR,\n",
-    "    pEpoR + 2 * sigma_pEpoR,\n",
-    "    color=\"black\",\n",
-    "    alpha=0.10,\n",
-    "    interpolate=True,\n",
-    "    label=\"2-sigma error bands\",\n",
-    ")\n",
-    "ax.plot(t, pEpoR, color=\"black\", label=\"MLE\")\n",
-    "ax.plot(\n",
-    "    df_pEpoR[\"time\"],\n",
-    "    df_pEpoR[\"measurement\"],\n",
-    "    \"o\",\n",
-    "    color=\"black\",\n",
-    "    markerfacecolor=\"none\",\n",
-    "    label=\"experimental data\",\n",
-    ")\n",
-    "ylim1 = ax.get_ylim()[0]\n",
-    "ax.plot(\n",
-    "    nodes,\n",
-    "    len(nodes) * [ylim1],\n",
-    "    \"x\",\n",
-    "    color=\"black\",\n",
-    "    label=\"spline nodes\",\n",
-    "    zorder=10,\n",
-    "    clip_on=False,\n",
-    ")\n",
-    "ax.set_ylim(ylim1, ax.get_ylim()[1])\n",
-    "ax.set_xlabel(\"time\")\n",
-    "ax.set_ylabel(\"pEpoR\")\n",
-    "ax.set_title(f\"ML fit for regularization strength = {chosen_regstrength}\")\n",
-    "ax.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "809c240f-e3ca-45ec-906e-0e87d10b46dd",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Store results for later\n",
-    "all_results[\"5 nodes\"] = (\n",
-    "    regproblems[chosen_regstrength],\n",
-    "    regresults[chosen_regstrength],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6ae693f7-f3c1-43cd-8b0e-12b69b4ca6ef",
-   "metadata": {},
-   "source": [
-    "## Comparing the three approaches"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "71b57527-51f6-479a-8d6d-895e9b2f4d34",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 650x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot ML fit for pEpoR\n",
-    "fig, ax = plt.subplots(figsize=(6.5, 3.5))\n",
-    "for label, (problem, result) in all_results.items():\n",
-    "    t, pEpoR = simulate_pEpoR(problem=problem, result=result)\n",
-    "    ax.plot(t, pEpoR, label=label)\n",
-    "ax.plot(\n",
-    "    df_pEpoR[\"time\"],\n",
-    "    df_pEpoR[\"measurement\"],\n",
-    "    \"o\",\n",
-    "    color=\"black\",\n",
-    "    markerfacecolor=\"none\",\n",
-    "    label=\"experimental data\",\n",
-    ")\n",
-    "ax.set_xlabel(\"time\")\n",
-    "ax.set_ylabel(\"pEpoR\")\n",
-    "ax.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "214476c9-ecab-4201-a528-4507539b0b05",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 650x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot ML fit for pSTAT5\n",
-    "fig, ax = plt.subplots(figsize=(6.5, 3.5))\n",
-    "for label, (problem, result) in all_results.items():\n",
-    "    t, pSTAT5 = simulate_pSTAT5(problem=problem, result=result)\n",
-    "    ax.plot(t, pSTAT5, label=label)\n",
-    "ax.plot(\n",
-    "    df_pSTAT5[\"time\"],\n",
-    "    df_pSTAT5[\"measurement\"],\n",
-    "    \"o\",\n",
-    "    color=\"black\",\n",
-    "    markerfacecolor=\"none\",\n",
-    "    label=\"experimental data\",\n",
-    ")\n",
-    "ax.set_xlabel(\"time\")\n",
-    "ax.set_ylabel(\"pSTAT5\")\n",
-    "ax.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "5776b49f-a3ba-401d-88a5-0a7674e4b14b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 650x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot ML fit for tSTAT5\n",
-    "fig, ax = plt.subplots(figsize=(6.5, 3.5))\n",
-    "for label, (problem, result) in all_results.items():\n",
-    "    t, tSTAT5 = simulate_tSTAT5(problem=problem, result=result)\n",
-    "    ax.plot(t, tSTAT5, label=label)\n",
-    "ax.plot(\n",
-    "    df_tSTAT5[\"time\"],\n",
-    "    df_tSTAT5[\"measurement\"],\n",
-    "    \"o\",\n",
-    "    color=\"black\",\n",
-    "    markerfacecolor=\"none\",\n",
-    "    label=\"experimental data\",\n",
-    ")\n",
-    "ax.set_xlabel(\"time\")\n",
-    "ax.set_ylabel(\"tSTAT5\")\n",
-    "ax.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "c2aeab6e-828c-4748-b3c8-2494ae89ef43",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "### 5 nodes, FD\n",
-      "k1 = -0.012344171128634264\n",
-      "k2 = -1.11975626735931\n",
-      "k3 = 5.999999816644789\n",
-      "k4 = 0.22576351403212522\n",
-      "scale_tSTAT5 = -0.020792663448966672\n",
-      "scale_pSTAT5 = 0.1422550065768319\n",
-      "sigma_pEpoR_abs = -1.562249437179612\n",
-      "sigma_pEpoR_rel = -1.0\n",
-      "pEpoR_t0 = -2.6870875267006804\n",
-      "pEpoR_t5 = -0.7797622417853871\n",
-      "pEpoR_t10 = -0.11820562755751975\n",
-      "pEpoR_t20 = -0.9974218537437654\n",
-      "pEpoR_t60 = -6.90775527898212\n",
-      "\n",
-      "### 15 nodes, FD\n",
-      "k1 = 0.1543170078851364\n",
-      "k2 = -1.0042579083153138\n",
-      "k3 = -0.17925294344845363\n",
-      "k4 = 0.31486258696137254\n",
-      "scale_tSTAT5 = -0.03364700359730668\n",
-      "scale_pSTAT5 = 0.11013784140762342\n",
-      "sigma_pEpoR_abs = -1.562249437179612\n",
-      "sigma_pEpoR_rel = -1.0\n",
-      "pEpoR_t0 = -2.40456191981547\n",
-      "pEpoR_t2_dot_5 = -1.6438670641678346\n",
-      "pEpoR_t5_dot_0 = -0.80437214623219\n",
-      "pEpoR_t7_dot_5 = -0.1144993579909219\n",
-      "pEpoR_t10_dot_0 = -0.09849380649928209\n",
-      "pEpoR_t12_dot_5 = -0.30861764847405077\n",
-      "pEpoR_t15_dot_0 = -0.535565172217061\n",
-      "pEpoR_t17_dot_5 = -0.8808659628360864\n",
-      "pEpoR_t20 = -1.1184724117332843\n",
-      "pEpoR_t25 = -1.3245209689075161\n",
-      "pEpoR_t30 = -2.3651756746835053\n",
-      "pEpoR_t35 = -3.4734027477458524\n",
-      "pEpoR_t40 = -4.578132101040909\n",
-      "pEpoR_t50 = -5.7968417139258435\n",
-      "pEpoR_t60 = -6.268875988124801\n",
-      "regularization_strength = 1.8750612633917\n",
-      "\n",
-      "### 5 nodes\n",
-      "k1 = 0.2486924371230916\n",
-      "k2 = -0.9010429810987043\n",
-      "k3 = -0.3408591074551208\n",
-      "k4 = 0.3594353532480489\n",
-      "scale_tSTAT5 = -0.03395751814386045\n",
-      "scale_pSTAT5 = 0.1008121903144357\n",
-      "sigma_pEpoR_abs = -1.562249437179612\n",
-      "sigma_pEpoR_rel = -1.0\n",
-      "pEpoR_t0 = -2.8601663957890175\n",
-      "pEpoR_t5 = -0.7275787612811422\n",
-      "pEpoR_t10 = -0.08172482568007049\n",
-      "pEpoR_t20 = -1.02532663950965\n",
-      "pEpoR_t60 = -6.907755278982137\n",
-      "derivative_pEpoR_t0 = 0.026587630472163528\n",
-      "derivative_pEpoR_t5 = 0.17154606724507934\n",
-      "derivative_pEpoR_t10 = -0.05503215878900286\n",
-      "derivative_pEpoR_t20 = -0.016352876798592663\n",
-      "regularization_strength = 2.2430380486862944\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Compare parameter values\n",
-    "for label, (problem, result) in all_results.items():\n",
-    "    print(f\"\\n### {label}\")\n",
-    "    x = result.optimize_result.x[0]\n",
-    "    if len(x) == len(problem.x_free_indices):\n",
-    "        names = problem.x_names[problem.x_free_indices]\n",
-    "    else:\n",
-    "        names = problem.x_names\n",
-    "    for name, value in zip(names, x):\n",
-    "        print(f\"{name} = {value}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2ced065a-4b15-4403-91c6-6a46dc0b3e66",
-   "metadata": {},
-   "source": [
-    "## Bibliography\n",
-    "Schelker, M. et al. (2012). “Comprehensive estimation of input signals and dynamics in biochemical reaction networks”. In: Bioinformatics 28.18, pp. i529–i534. doi: [10.1093/bioinformatics/bts393](https://doi.org/10.1093/bioinformatics/bts393).\n",
-    "\n",
-    "Swameye, I. et al. (2003). “Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by databased modeling”. In: Proceedings of the National Academy of Sciences 100.3, pp. 1028–1033. doi: [10.1073/pnas.0237333100](https://doi.org/10.1073/pnas.0237333100).\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/doc/examples/example_splines_swameye/Swameye_PNAS2003/swameye2003_conditions.tsv b/doc/examples/example_splines_swameye/Swameye_PNAS2003/swameye2003_conditions.tsv
deleted file mode 100644
index 97ed387788..0000000000
--- a/doc/examples/example_splines_swameye/Swameye_PNAS2003/swameye2003_conditions.tsv
+++ /dev/null
@@ -1,2 +0,0 @@
-conditionId	conditionName
-condition1
diff --git a/doc/examples/example_splines_swameye/Swameye_PNAS2003/swameye2003_measurements.tsv b/doc/examples/example_splines_swameye/Swameye_PNAS2003/swameye2003_measurements.tsv
deleted file mode 100644
index 8813d0aae9..0000000000
--- a/doc/examples/example_splines_swameye/Swameye_PNAS2003/swameye2003_measurements.tsv
+++ /dev/null
@@ -1,47 +0,0 @@
-observableId	simulationConditionId	measurement	time	noiseParameters	observableTransformation
-tSTAT5_au	condition1	1.0000	0	0.084	lin
-tSTAT5_au	condition1	0.9275	2	0.046	lin
-tSTAT5_au	condition1	0.7923	4	0.038	lin
-tSTAT5_au	condition1	0.7778	6	0.032	lin
-tSTAT5_au	condition1	0.7053	8	0.033	lin
-tSTAT5_au	condition1	0.6522	10	0.037	lin
-tSTAT5_au	condition1	0.5894	12	0.039	lin
-tSTAT5_au	condition1	0.5894	14	0.040	lin
-tSTAT5_au	condition1	0.6377	16	0.030	lin
-tSTAT5_au	condition1	0.6425	18	0.028	lin
-tSTAT5_au	condition1	0.6908	20	0.030	lin
-tSTAT5_au	condition1	0.6908	25	0.031	lin
-tSTAT5_au	condition1	0.7585	30	0.032	lin
-tSTAT5_au	condition1	0.8068	40	0.040	lin
-tSTAT5_au	condition1	0.9275	50	0.046	lin
-tSTAT5_au	condition1	0.9710	60	0.082	lin
-pSTAT5_au	condition1	0.3315	2	0.050	lin
-pSTAT5_au	condition1	0.8645	4	0.066	lin
-pSTAT5_au	condition1	0.9635	6	0.070	lin
-pSTAT5_au	condition1	0.9279	8	0.065	lin
-pSTAT5_au	condition1	0.8162	10	0.051	lin
-pSTAT5_au	condition1	0.7553	12	0.053	lin
-pSTAT5_au	condition1	0.7680	14	0.051	lin
-pSTAT5_au	condition1	0.8416	16	0.040	lin
-pSTAT5_au	condition1	0.7680	18	0.040	lin
-pSTAT5_au	condition1	0.8010	20	0.048	lin
-pSTAT5_au	condition1	0.7832	25	0.052	lin
-pSTAT5_au	condition1	0.8086	30	0.054	lin
-pSTAT5_au	condition1	0.4888	40	0.055	lin
-pSTAT5_au	condition1	0.2782	50	0.044	lin
-pSTAT5_au	condition1	0.2553	60	0.071	lin
-pEpoR_au	condition1	0.01713	0		lin
-pEpoR_au	condition1	0.145	2		lin
-pEpoR_au	condition1	0.2442	4		lin
-pEpoR_au	condition1	0.7659	6		lin
-pEpoR_au	condition1	1	8		lin
-pEpoR_au	condition1	0.8605	10		lin
-pEpoR_au	condition1	0.7829	12		lin
-pEpoR_au	condition1	0.5705	14		lin
-pEpoR_au	condition1	0.6217	16		lin
-pEpoR_au	condition1	0.331	18		lin
-pEpoR_au	condition1	0.3388	20		lin
-pEpoR_au	condition1	0.3116	25		lin
-pEpoR_au	condition1	0.05062	30		lin
-pEpoR_au	condition1	0.02504	40		lin
-pEpoR_au	condition1	0.01163	50		lin
diff --git a/doc/examples/example_splines_swameye/Swameye_PNAS2003/swameye2003_model.xml b/doc/examples/example_splines_swameye/Swameye_PNAS2003/swameye2003_model.xml
deleted file mode 100644
index 1e8045a74c..0000000000
--- a/doc/examples/example_splines_swameye/Swameye_PNAS2003/swameye2003_model.xml
+++ /dev/null
@@ -1,282 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<sbml xmlns="http://www.sbml.org/sbml/level2/version4" level="2" version="4">
-  <model id="swameye2003">
-    <listOfUnitDefinitions>
-      <unitDefinition id="time" name="time">
-        <listOfUnits>
-          <unit kind="second" exponent="1" scale="0" multiplier="60"/>
-        </listOfUnits>
-      </unitDefinition>
-    </listOfUnitDefinitions>
-    <listOfCompartments>
-      <compartment id="cyt" spatialDimensions="3" size="1.4" constant="true"/>
-      <compartment id="nuc" spatialDimensions="3" size="0.45" constant="true"/>
-    </listOfCompartments>
-    <listOfSpecies>
-      <species id="pEpoR" compartment="cyt" initialConcentration="0.0" hasOnlySubstanceUnits="false" boundaryCondition="false" constant="false"/>
-      <species id="STAT5" compartment="cyt" initialConcentration="1" hasOnlySubstanceUnits="false" boundaryCondition="false" constant="false"/>
-      <species id="pSTAT5" compartment="cyt" initialConcentration="0" hasOnlySubstanceUnits="false" boundaryCondition="false" constant="false"/>
-      <species id="pSTAT5_pSTAT5" compartment="cyt" initialConcentration="0" hasOnlySubstanceUnits="false" boundaryCondition="false" constant="false"/>
-      <species id="npSTAT5_npSTAT5" compartment="nuc" initialConcentration="0" hasOnlySubstanceUnits="false" boundaryCondition="false" constant="false"/>
-      <species id="nSTAT5_1" compartment="nuc" initialConcentration="0" hasOnlySubstanceUnits="false" boundaryCondition="false" constant="false"/>
-      <species id="nSTAT5_2" compartment="nuc" initialConcentration="0" hasOnlySubstanceUnits="false" boundaryCondition="false" constant="false"/>
-      <species id="nSTAT5_3" compartment="nuc" initialConcentration="0" hasOnlySubstanceUnits="false" boundaryCondition="false" constant="false"/>
-      <species id="nSTAT5_4" compartment="nuc" initialConcentration="0" hasOnlySubstanceUnits="false" boundaryCondition="false" constant="false"/>
-      <species id="nSTAT5_5" compartment="nuc" initialConcentration="0" hasOnlySubstanceUnits="false" boundaryCondition="false" constant="false"/>
-      <species id="nSTAT5_6" compartment="nuc" initialConcentration="0" hasOnlySubstanceUnits="false" boundaryCondition="false" constant="false"/>
-      <species id="nSTAT5_7" compartment="nuc" initialConcentration="0" hasOnlySubstanceUnits="false" boundaryCondition="false" constant="false"/>
-      <species id="nSTAT5_8" compartment="nuc" initialConcentration="0" hasOnlySubstanceUnits="false" boundaryCondition="false" constant="false"/>
-      <species id="nSTAT5_9" compartment="nuc" initialConcentration="0" hasOnlySubstanceUnits="false" boundaryCondition="false" constant="false"/>
-    </listOfSpecies>
-    <listOfParameters>
-      <parameter id="k1" value="1.95" constant="true"/>
-      <parameter id="k2" value="0.11" constant="true"/>
-      <parameter id="k3" value="98400" constant="true"/>
-      <parameter id="k4" value="1.49" constant="true"/>
-    </listOfParameters>
-    <listOfReactions>
-      <reaction id="reaction1" reversible="false">
-        <listOfReactants>
-          <speciesReference species="STAT5" stoichiometry="1"/>
-        </listOfReactants>
-        <listOfProducts>
-          <speciesReference species="pSTAT5" stoichiometry="1"/>
-        </listOfProducts>
-        <listOfModifiers>
-          <modifierSpeciesReference species="pEpoR"/>
-        </listOfModifiers>
-        <kineticLaw>
-          <math xmlns="http://www.w3.org/1998/Math/MathML">
-            <apply>
-              <times/>
-              <ci> cyt </ci>
-              <ci> k1 </ci>
-              <ci> STAT5 </ci>
-              <ci> pEpoR </ci>
-            </apply>
-          </math>
-        </kineticLaw>
-      </reaction>
-      <reaction id="reaction2" reversible="false">
-        <listOfReactants>
-          <speciesReference species="pSTAT5" stoichiometry="2"/>
-        </listOfReactants>
-        <listOfProducts>
-          <speciesReference species="pSTAT5_pSTAT5" stoichiometry="1"/>
-        </listOfProducts>
-        <kineticLaw>
-          <math xmlns="http://www.w3.org/1998/Math/MathML">
-            <apply>
-              <times/>
-              <ci> cyt </ci>
-              <ci> k2 </ci>
-              <apply>
-                <power/>
-                <ci> pSTAT5 </ci>
-                <cn type="integer"> 2 </cn>
-              </apply>
-            </apply>
-          </math>
-        </kineticLaw>
-      </reaction>
-      <reaction id="reaction3" reversible="false">
-        <listOfReactants>
-          <speciesReference species="pSTAT5_pSTAT5" stoichiometry="1"/>
-        </listOfReactants>
-        <listOfProducts>
-          <speciesReference species="npSTAT5_npSTAT5" stoichiometry="1"/>
-        </listOfProducts>
-        <kineticLaw>
-          <math xmlns="http://www.w3.org/1998/Math/MathML">
-            <apply>
-              <times/>
-              <ci> cyt </ci>
-              <ci> k3 </ci>
-              <ci> pSTAT5_pSTAT5 </ci>
-            </apply>
-          </math>
-        </kineticLaw>
-      </reaction>
-      <reaction id="reaction4_01" reversible="false">
-        <listOfReactants>
-          <speciesReference species="npSTAT5_npSTAT5" stoichiometry="1"/>
-        </listOfReactants>
-        <listOfProducts>
-          <speciesReference species="nSTAT5_1" stoichiometry="2"/>
-        </listOfProducts>
-        <kineticLaw>
-          <math xmlns="http://www.w3.org/1998/Math/MathML">
-            <apply>
-              <times/>
-              <ci> nuc </ci>
-              <ci> k4 </ci>
-              <ci> npSTAT5_npSTAT5 </ci>
-            </apply>
-          </math>
-        </kineticLaw>
-      </reaction>
-      <reaction id="reaction4_02" reversible="false">
-        <listOfReactants>
-          <speciesReference species="nSTAT5_1" stoichiometry="1"/>
-        </listOfReactants>
-        <listOfProducts>
-          <speciesReference species="nSTAT5_2" stoichiometry="1"/>
-        </listOfProducts>
-        <kineticLaw>
-          <math xmlns="http://www.w3.org/1998/Math/MathML">
-            <apply>
-              <times/>
-              <ci> nuc </ci>
-              <ci> k4 </ci>
-              <ci> nSTAT5_1 </ci>
-            </apply>
-          </math>
-        </kineticLaw>
-      </reaction>
-      <reaction id="reaction4_03" reversible="false">
-        <listOfReactants>
-          <speciesReference species="nSTAT5_2" stoichiometry="1"/>
-        </listOfReactants>
-        <listOfProducts>
-          <speciesReference species="nSTAT5_3" stoichiometry="1"/>
-        </listOfProducts>
-        <kineticLaw>
-          <math xmlns="http://www.w3.org/1998/Math/MathML">
-            <apply>
-              <times/>
-              <ci> nuc </ci>
-              <ci> k4 </ci>
-              <ci> nSTAT5_2 </ci>
-            </apply>
-          </math>
-        </kineticLaw>
-      </reaction>
-      <reaction id="reaction4_04" reversible="false">
-        <listOfReactants>
-          <speciesReference species="nSTAT5_3" stoichiometry="1"/>
-        </listOfReactants>
-        <listOfProducts>
-          <speciesReference species="nSTAT5_4" stoichiometry="1"/>
-        </listOfProducts>
-        <kineticLaw>
-          <math xmlns="http://www.w3.org/1998/Math/MathML">
-            <apply>
-              <times/>
-              <ci> nuc </ci>
-              <ci> k4 </ci>
-              <ci> nSTAT5_3 </ci>
-            </apply>
-          </math>
-        </kineticLaw>
-      </reaction>
-      <reaction id="reaction4_05" reversible="false">
-        <listOfReactants>
-          <speciesReference species="nSTAT5_4" stoichiometry="1"/>
-        </listOfReactants>
-        <listOfProducts>
-          <speciesReference species="nSTAT5_5" stoichiometry="1"/>
-        </listOfProducts>
-        <kineticLaw>
-          <math xmlns="http://www.w3.org/1998/Math/MathML">
-            <apply>
-              <times/>
-              <ci> nuc </ci>
-              <ci> k4 </ci>
-              <ci> nSTAT5_4 </ci>
-            </apply>
-          </math>
-        </kineticLaw>
-      </reaction>
-      <reaction id="reaction4_06" reversible="false">
-        <listOfReactants>
-          <speciesReference species="nSTAT5_5" stoichiometry="1"/>
-        </listOfReactants>
-        <listOfProducts>
-          <speciesReference species="nSTAT5_6" stoichiometry="1"/>
-        </listOfProducts>
-        <kineticLaw>
-          <math xmlns="http://www.w3.org/1998/Math/MathML">
-            <apply>
-              <times/>
-              <ci> nuc </ci>
-              <ci> k4 </ci>
-              <ci> nSTAT5_5 </ci>
-            </apply>
-          </math>
-        </kineticLaw>
-      </reaction>
-      <reaction id="reaction4_07" reversible="false">
-        <listOfReactants>
-          <speciesReference species="nSTAT5_6" stoichiometry="1"/>
-        </listOfReactants>
-        <listOfProducts>
-          <speciesReference species="nSTAT5_7" stoichiometry="1"/>
-        </listOfProducts>
-        <kineticLaw>
-          <math xmlns="http://www.w3.org/1998/Math/MathML">
-            <apply>
-              <times/>
-              <ci> nuc </ci>
-              <ci> k4 </ci>
-              <ci> nSTAT5_6 </ci>
-            </apply>
-          </math>
-        </kineticLaw>
-      </reaction>
-      <reaction id="reaction4_08" reversible="false">
-        <listOfReactants>
-          <speciesReference species="nSTAT5_7" stoichiometry="1"/>
-        </listOfReactants>
-        <listOfProducts>
-          <speciesReference species="nSTAT5_8" stoichiometry="1"/>
-        </listOfProducts>
-        <kineticLaw>
-          <math xmlns="http://www.w3.org/1998/Math/MathML">
-            <apply>
-              <times/>
-              <ci> nuc </ci>
-              <ci> k4 </ci>
-              <ci> nSTAT5_7 </ci>
-            </apply>
-          </math>
-        </kineticLaw>
-      </reaction>
-      <reaction id="reaction4_09" reversible="false">
-        <listOfReactants>
-          <speciesReference species="nSTAT5_8" stoichiometry="1"/>
-        </listOfReactants>
-        <listOfProducts>
-          <speciesReference species="nSTAT5_9" stoichiometry="1"/>
-        </listOfProducts>
-        <kineticLaw>
-          <math xmlns="http://www.w3.org/1998/Math/MathML">
-            <apply>
-              <times/>
-              <ci> nuc </ci>
-              <ci> k4 </ci>
-              <ci> nSTAT5_8 </ci>
-            </apply>
-          </math>
-        </kineticLaw>
-      </reaction>
-      <reaction id="reaction4_10" reversible="false">
-        <listOfReactants>
-          <speciesReference species="nSTAT5_9" stoichiometry="1"/>
-        </listOfReactants>
-        <listOfProducts>
-          <speciesReference species="STAT5" stoichiometry="1"/>
-        </listOfProducts>
-        <kineticLaw>
-          <math xmlns="http://www.w3.org/1998/Math/MathML">
-            <apply>
-              <times/>
-              <ci> nuc </ci>
-              <ci> k4 </ci>
-              <ci> nSTAT5_9 </ci>
-            </apply>
-          </math>
-        </kineticLaw>
-      </reaction>
-    </listOfReactions>
-  </model>
-</sbml>
diff --git a/doc/examples/example_splines_swameye/Swameye_PNAS2003/swameye2003_observables.tsv b/doc/examples/example_splines_swameye/Swameye_PNAS2003/swameye2003_observables.tsv
deleted file mode 100644
index 63d12243d7..0000000000
--- a/doc/examples/example_splines_swameye/Swameye_PNAS2003/swameye2003_observables.tsv
+++ /dev/null
@@ -1,4 +0,0 @@
-observableId	observableFormula	observableTransformation	noiseFormula	noiseDistribution
-tSTAT5_au	scale_tSTAT5 * (STAT5 + pSTAT5 + 2 * pSTAT5_pSTAT5)	lin	noiseParameter1_tSTAT5_au	normal
-pSTAT5_au	scale_pSTAT5 * (pSTAT5 + 2 * pSTAT5_pSTAT5)	lin	noiseParameter1_pSTAT5_au	normal
-pEpoR_au	pEpoR	lin	sigma_pEpoR_abs + sigma_pEpoR_rel * pEpoR	normal
diff --git a/doc/examples/example_splines_swameye/Swameye_PNAS2003/swameye2003_parameters.tsv b/doc/examples/example_splines_swameye/Swameye_PNAS2003/swameye2003_parameters.tsv
deleted file mode 100644
index e3858d0119..0000000000
--- a/doc/examples/example_splines_swameye/Swameye_PNAS2003/swameye2003_parameters.tsv
+++ /dev/null
@@ -1,9 +0,0 @@
-parameterId	parameterScale	lowerBound	upperBound	nominalValue	estimate
-k1	log10	0.001	100000	1.95	1
-k2	log10	0.001	100000	0.11	1
-k3	log10	0.001	1000000	98400	1
-k4	log10	0.001	100000	1.49	1
-scale_tSTAT5	log10	0.01	100	0.95	1
-scale_pSTAT5	log10	0.01	100	1.25	1
-sigma_pEpoR_abs	log10	0.001	1	0.0274	0
-sigma_pEpoR_rel	log10	0.001	1	0.10	0
diff --git a/doc/python_examples.rst b/doc/python_examples.rst
index e433a656c6..14303da030 100644
--- a/doc/python_examples.rst
+++ b/doc/python_examples.rst
@@ -20,4 +20,3 @@ Various example notebooks.
    examples/example_jax/ExampleJax.ipynb
    examples/example_jax_petab/ExampleJaxPEtab.ipynb
    examples/example_splines/ExampleSplines.ipynb
-   examples/example_splines_swameye/ExampleSplinesSwameye2003.ipynb