diff --git a/README.md b/README.md index 40ffa77ad..28954775a 100644 --- a/README.md +++ b/README.md @@ -79,7 +79,7 @@ PACKAGE CONTENTS wind_data VERSION - 4.1.1 + 4.2 FILE ~/floris/floris/__init__.py diff --git a/docs/_config.yml b/docs/_config.yml index e3c7524c1..93b289c5c 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -11,7 +11,7 @@ only_build_toc_files: false # See https://jupyterbook.org/content/execute.html execute: execute_notebooks: auto - timeout: 360 # Give each notebook cell 6 minutes to execute + timeout: 420 # Give each notebook cell 7 minutes to execute # Define the name of the latex output file for PDF builds latex: diff --git a/docs/_toc.yml b/docs/_toc.yml index 4b78b0821..2dd0f99e1 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -12,8 +12,9 @@ parts: - caption: User Reference chapters: - file: intro_concepts - - file: advanced_concepts - file: wind_data_user + - file: floris_models + - file: advanced_concepts - file: heterogeneous_map - file: floating_wind_turbine - file: turbine_interaction diff --git a/docs/advanced_concepts.ipynb b/docs/advanced_concepts.ipynb index c13513c79..45fb9c017 100644 --- a/docs/advanced_concepts.ipynb +++ b/docs/advanced_concepts.ipynb @@ -9,7 +9,7 @@ "# Advanced Concepts\n", "\n", "More information regarding the numerical and computational formulation in FLORIS\n", - "are detailed here. See [](concepts_intro) for a guide on the basics." + "are detailed here. See [Introductory Concepts](intro_concepts) for a guide on the basics." ] }, { diff --git a/docs/dev_guide.md b/docs/dev_guide.md index d5ce55d42..4a7c5dd76 100644 --- a/docs/dev_guide.md +++ b/docs/dev_guide.md @@ -41,8 +41,8 @@ developer's guide, so please read on to learn more about each of these steps. ## Git and GitHub Workflows -The majority of the collaboration and development for FLORIS takes place -in the [GitHub repository](http://github.com/nrel/floris). There, +The majority of the collaboration and development for FLORIS takes place in +the [GitHub repository](http://github.com/nrel/floris). There, [issues](http://github.com/nrel/floris/issues) and [pull requests](http://github.com/nrel/floris/pulls) are managed, questions and ideas are [discussed](https://github.com/NREL/floris/discussions), diff --git a/docs/floris_models.ipynb b/docs/floris_models.ipynb new file mode 100644 index 000000000..763014b7e --- /dev/null +++ b/docs/floris_models.ipynb @@ -0,0 +1,390 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(floris_models)=\n", + "\n", + "# FLORIS Models\n", + "\n", + "This notebook provides information on the provided FlorisModels. [Introductory Concepts](intro_concepts) introduced `FlorisModel` as the base class for all models in the FLORIS package. This notebook introduces the `ParFlorisModel`, `UncertainFlorisModel`, and `ApproxFlorisModel` classes, which are subclasses or compositions of `FlorisModel`.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parallelized FLORIS Model\n", + "\n", + "The `ParFlorisModel` class is a subclass of `FlorisModel` that parallelizes the FLORIS calculations. This class is designed to \n", + "have an interface that is the same as `FlorisModel`, but the calculations are parallelized. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiation\n", + "\n", + "The `ParFlorisModel` class can be instantiated in the same way as the `FlorisModel` class, or else it can be instantiated by passing a `FlorisModel` object to the constructor. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from floris import FlorisModel, ParFlorisModel, TimeSeries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fmodel = FlorisModel(\"gch.yaml\")\n", + "\n", + "# Instantiation using yaml input file\n", + "pfmodel = ParFlorisModel(\"gch.yaml\")\n", + "\n", + "# Instantiation using fmodel\n", + "pfmodel = ParFlorisModel(fmodel)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameters\n", + "\n", + "The `ParFlorisModel` class has additional parameters the define the parallelization. These parameters are:\n", + "\n", + "**interface**: The parallelization interface to use. Options are `\"multiprocessing\"`,\n", + " `\"pathos\"`, and `\"concurrent\"`, with possible future support for `\"mpi4py\"`\n", + "\n", + "**max_workers**: The maximum number of workers to use. Defaults to -1, which then\n", + " takes the number of CPUs available.\n", + "\n", + "**n_wind_condition_splits**: The number of wind conditions to split the simulation over.\n", + " Defaults to the same as max_workers.\n", + "\n", + "**return_turbine_powers_only**: Whether to return only the turbine powers.\n", + "\n", + "**print_timings** (bool): Print the computation time to the console. Defaults to False." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Alternative parameters\n", + "pfmodel = ParFlorisModel(fmodel, max_workers=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Usage\n", + "\n", + "The `ParFlorisModel` class can be used in the same way as the `FlorisModel` class. The only difference is that the calculations are parallelized. \n", + "\n", + "```python" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Set to a two turbine layout\n", + "layout_x = [0, 500]\n", + "layout_y = [0, 0]\n", + "fmodel.set(layout_x=layout_x, layout_y=layout_y)\n", + "pfmodel.set(layout_x=layout_x, layout_y=layout_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "wind_directions = np.arange(240, 300, 0.5)\n", + "time_series = TimeSeries(\n", + " wind_directions=wind_directions, wind_speeds=8.0, turbulence_intensities=0.06\n", + ")\n", + "fmodel.set(wind_data=time_series)\n", + "pfmodel.set(wind_data=time_series)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fmodel.run()\n", + "pfmodel.run()\n", + "\n", + "farm_power = fmodel.get_farm_power()\n", + "pfarm_power = pfmodel.get_farm_power()\n", + "\n", + "# Show the results are the same\n", + "fig, ax = plt.subplots()\n", + "ax.plot(wind_directions, farm_power, label=\"FlorisModel\", color='k', lw=5)\n", + "ax.plot(wind_directions, pfarm_power, label=\"ParFlorisModel\", color='r', ls='--', lw=2)\n", + "ax.set_xlabel(\"Wind Direction (deg)\")\n", + "ax.set_ylabel(\"Farm Power (kW)\")\n", + "ax.legend()\n", + "ax.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## UncertainFlorisModel\n", + "\n", + "The `UncertainFlorisModel` class is a composition of `FlorisModel` that adds uncertainty to the input conditions. Its interface is meant to made similar to `FlorisModel`, but with the addition of uncertainty in wind direction." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiation\n", + "\n", + "The `UncertainFlorisModel` class can be instantiated in the same way as the `FlorisModel` class, or else it can be instantiated by passing a `FlorisModel` object to the constructor. Alternatively a `ParFlorisModel` object can be passed to the constructor which ensures the underlying calculations are parallelized according to the `ParFlorisModel` parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from floris import UncertainFlorisModel\n", + "\n", + "# Instantiation options\n", + "ufmodel = UncertainFlorisModel(\"gch.yaml\") # Using input yaml\n", + "ufmodel = UncertainFlorisModel(fmodel) # Using a FlorisModel object\n", + "ufmodel = UncertainFlorisModel(pfmodel) # Using a ParFlorisModel object" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameters\n", + "\n", + "To include uncertainty into the wind direction, the `UncertainFlorisModel` class, for each findex run, the result for a wind direction is provided by performing a Gaussian blend over results from multiple wind directions nearby wind directions. To reduce the total number of calculations required, a resolution of wind direction, wind speed, turbulence intensity and control inputs are specified and repeated calculations are only calculated once. See the class API for complete details but some key parameters are:\n", + "\n", + "**wd_resolution, ws_resolution, ti_resolution, yaw_resolution, and power_setpoint_resolution**: Define the granularity of calculations for wind direction, wind speed, turbulence intensity, yaw angle, and power setpoints, respectively.\n", + "\n", + "**wd_std**: The standard deviation of wind direction, used in the Gaussian blending.\n", + "\n", + "**wd_sample_points**: Specific wind direction points to sample for expanded conditions.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the uncertainty to have a wd_std of 5 degrees and blend over 10 degrees\n", + "ufmodel = UncertainFlorisModel(fmodel, wd_std=5, wd_sample_points=[-5, -4, -3, -2, -1, 0, 1, 2,3, 4, 5], wd_resolution=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Usage\n", + "\n", + "Usage of `UncertainFlorisModel` is similar to `FlorisModel` however the results will now include the effects of Gaussian blending\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ufmodel.set(wind_data=time_series, layout_x=layout_x, layout_y=layout_y)\n", + "ufmodel.run()\n", + "\n", + "# Get the power of the downstream turbine\n", + "f_power = fmodel.get_turbine_powers()[:,1]\n", + "uf_power = ufmodel.get_turbine_powers()[:,1]\n", + "\n", + "# Plot the two powers\n", + "fig, ax = plt.subplots()\n", + "ax.plot(wind_directions, f_power, label=\"FlorisModel\", color='k', lw=5)\n", + "ax.plot(wind_directions, uf_power, label=\"UncertainFlorisModel\", color='r', lw=2)\n", + "ax.set_xlabel(\"Wind Direction (deg)\")\n", + "ax.set_ylabel(\"Turbine Power (kW)\")\n", + "ax.legend()\n", + "ax.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ApproxFlorisModel" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " The ApproxFlorisModel overloads the UncertainFlorisModel with the special case that the `wd_sample_points = [0]`. This is a special case where no uncertainty is added but the resolution of the values wind direction, wind speed etc are still reduced by the specified resolution. This allows for cases to be reused and a faster approximate result computed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiation\n", + "\n", + "`ApproxFlorisModel` can be instantiated in the same way as the `UncertainFlorisModel` class" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from floris import ApproxFlorisModel\n", + "\n", + "# Instantiation options\n", + "amodel = ApproxFlorisModel(\"gch.yaml\") # Using input yaml\n", + "amodel = ApproxFlorisModel(fmodel) # Using a FlorisModel object\n", + "amodel = ApproxFlorisModel(pfmodel) # Using a ParFlorisModel object" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage\n", + "\n", + "`ApproxFlorisModel` is used in the same way as `UncertainFlorisModel` but with the special case that the `wd_sample_points = [0]`. This means that while the resolution of the values `wind_direction`, `wind_speeds` etc are still reduced by the specified resolution, no uncertainty is added. It is intended for quickly processing large sets of inflow conditions (e.g., when reproducing SCADA records), when approximate solutions are sufficient.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate with a wind speed resolution of 0.5 m/s and a wind direction resolution of 1 degree\n", + "amodel = ApproxFlorisModel(fmodel, ws_resolution=0.5, wd_resolution=1.0) " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "amodel has an n_findex of 40, however, the number of unique cases to run at this resolution is 9.\n" + ] + } + ], + "source": [ + "# Show approximation for a time series including smaller increments of wind speed\n", + "wind_speeds = np.arange(6, 10, 0.1)\n", + "time_series = TimeSeries(\n", + " wind_directions = 270.0,\n", + " wind_speeds=wind_speeds,\n", + " turbulence_intensities=0.06\n", + ")\n", + "amodel.set(wind_data=time_series)\n", + "\n", + "print(f\"amodel has an n_findex of {amodel.n_findex}, \",\n", + " f\"however, the number of unique cases to run at this resolution is {amodel.n_unique}.\")\n", + "\n", + "amodel.run()\n", + "farm_power = amodel.get_farm_power()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show the farm power over wind speeds\n", + "fig, ax = plt.subplots()\n", + "ax.plot(wind_speeds, farm_power, label=\"ApproxFlorisModel\", color='k', lw=2)\n", + "ax.legend()\n", + "ax.set_xlabel(\"Wind Speed (m/s)\")\n", + "ax.set_ylabel(\"Farm Power (kW)\")\n", + "ax.grid()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "floris", + "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": 2 +} diff --git a/docs/intro_concepts.ipynb b/docs/intro_concepts.ipynb index c72e8e0f0..60d8e96ea 100644 --- a/docs/intro_concepts.ipynb +++ b/docs/intro_concepts.ipynb @@ -5,7 +5,7 @@ "id": "86e53920", "metadata": {}, "source": [ - "(concepts_intro)=\n", + "(intro_concepts)=\n", "# Introductory Concepts\n", "\n", "FLORIS is a Python-based software library for calculating wind farm performance considering\n", diff --git a/docs/layout_optimization.md b/docs/layout_optimization.md index aa835fdee..f249ae9b1 100644 --- a/docs/layout_optimization.md +++ b/docs/layout_optimization.md @@ -79,3 +79,27 @@ shading indicating wind speed heterogeneity (lighter shade is lower wind speed, higher wind speed). The progress of each of the genetic individuals in the optimization process is shown in the right-hand plot. ![](plot_complex_docs.png) + +## Gridded layout optimization +The `LayoutOptimizationGridded` class allows users to quickly find a layout that fits the most +turbines possible into the specified boundary area, given that the turbines are arranged in a +gridded layout. +To do so, a range of different rotations and translations of a generic gridded arrangement are +tried, and the one that fits the most turbines into the boundary area is selected. No AEP +evaluations are performed; rather, the cost function $f$ to be maximized is simply $N$, the number +of turbines, and there is an additional constraint that the turbines are arranged in a gridded +fashion. Note that in other layout optimizers, $N$ is fixed. + +We envisage that this will be useful for users that want to quickly generate a layout to +"fill" a boundary region in a gridded manner. By default, the gridded arrangement is a square grid +with spacing of `min_dist` (or `min_dist_D`); however, instantiating with the `hexagonal_packing` +keyword argument set to `True` will provide a grid that offsets the rows to enable tighter packing +of turbines while still satisfying the `min_dist`. + +As with the `LayoutOptimizationRandomSearch` class, the boundaries specified can be complex (and +may contain separate areas). +User settings include `rotation_step`, which specifies the step size for rotating the grid +(in degrees); `rotation_range`, which specifies the range of rotation angles; `translation_step` or +`translation_step_D`, which specifies the step size for translating the grid in meters or rotor +diameters, respectively; and `translation_range`, which specifies the range of possible +translations. All come with default values, which we expect to be suitable for many or most users. diff --git a/docs/references.bib b/docs/references.bib index a6316d82d..da217dd4f 100644 --- a/docs/references.bib +++ b/docs/references.bib @@ -273,6 +273,19 @@ @Article{bay_2022 DOI = {10.5194/wes-2022-17} } +@article{Pedersen_2022_turbopark2, +url = {https://dx.doi.org/10.1088/1742-6596/2265/2/022063}, +year = {2022}, +month = {may}, +publisher = {IOP Publishing}, +volume = {2265}, +number = {2}, +pages = {022063}, +author = {J G Pedersen and E Svensson and L Poulsen and N G Nygaard}, +title = {Turbulence Optimized Park model with Gaussian wake profile}, +journal = {Journal of Physics: Conference Series}, +} + @article{SinnerFleming2024grs, doi = {10.1088/1742-6596/2767/3/032036}, url = {https://dx.doi.org/10.1088/1742-6596/2767/3/032036}, diff --git a/docs/wake_models.ipynb b/docs/wake_models.ipynb index 669d172ad..78919191a 100644 --- a/docs/wake_models.ipynb +++ b/docs/wake_models.ipynb @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "tags": [] }, @@ -64,10 +64,11 @@ "\n", "NREL5MW_D = 126.0\n", "\n", - "def model_plot(inputfile):\n", + "def model_plot(inputfile, include_wake_deflection=True):\n", " fig, axes = plt.subplots(1, 1, figsize=(10, 10))\n", " yaw_angles = np.zeros((1, 2))\n", - " yaw_angles[:,0] = 20.0\n", + " if include_wake_deflection:\n", + " yaw_angles[:,0] = 20.0\n", " fmodel = FlorisModel(inputfile)\n", " fmodel.set(\n", " layout_x=np.array([0.0, 2*NREL5MW_D]),\n", @@ -96,20 +97,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "model_plot(\"../examples/inputs/jensen.yaml\")" ] @@ -134,20 +124,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "model_plot(\"../examples/inputs/gch.yaml\")" ] @@ -168,20 +147,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "model_plot(\"../examples/inputs/emgauss.yaml\")" ] @@ -198,22 +166,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "model_plot(\"../examples/inputs/cc.yaml\")" ] @@ -225,11 +182,24 @@ "source": [ "## TurbOPark\n", "\n", - "TurbOPark is a velocity deficit model. For model details see the following references:\n", + "The TurbOPark model is designed to model long wakes from large wind farm clusters. It was originally presented as a “top-hat” model in {cite:t}`nygaard2020modelling` and was updated in {cite:t}`Pedersen_2022_turbopark2` to have a Gaussian profile. For the latter, Ørsted released the [Matlab code with documentation](https://github.com/OrstedRD/TurbOPark), which allows the verification of the implementation in FLORIS.\n", + "\n", + "The first implementation, the [`TurboparkVelocityDeficitModel`](https://github.com/NREL/floris/blob/main/floris/core/wake_velocity/turbopark.py), was released in [FLORIS v3.1](https://github.com/NREL/floris/releases/tag/v3.1). The second implementation, the [`TurboparkgaussVelocityDeficitModel`](https://github.com/NREL/floris/blob/main/floris/core/wake_velocity/turboparkgauss.py), was released in FLORIS v4.2 and shows a near-perfect match to the predictions of Ørsted’s Matlab implementation. As such, we will emphasize the use of the `TurboparkgaussVelocityDeficitModel` going forward, and suggest that new users use this model (by setting the `velocity_model` field of the FLORIS input file to `turboparkgauss` instead of the `TurboparkVelocityDeficitModel` (`velocity_model: turbopark`)) if they are interested in testing the TurbOPark model.\n", "\n", - "- https://github.com/OrstedRD/TurbOPark\n", - "- https://github.com/OrstedRD/TurbOPark/blob/main/TurbOPark%20description.pdf\n", - "- Nygaard, Nicolai Gayle, et al. \"Modelling cluster wakes and wind farm blockage.\" 2020" + "The `TurboparkgaussVelocityDeficitModel` implementation was contributed by [Jasper Kreeft](https://github.com/JasperShell).\n", + "\n", + "Note that the original top-hat TurbOPark model ({cite:t}`nygaard2020modelling`) is _not_ available in FLORIS.\n", + "\n", + "The wakes as predicted by the `TurboparkgaussVelocityDeficit` model are demonstrated below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_plot(\"../examples/inputs/turboparkgauss_cubature.yaml\", include_wake_deflection=False)" ] }, { @@ -267,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "tags": [] }, @@ -341,32 +311,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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", 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YPEVi0vnV8NR5kZcnoSn93++i5rBQp5p1TucJP6kvEQ0zBiUiIqIhJCXQJGvQBU+OEwoAmS/qjUPR4sPN2lGIJtTCL6vhmpyPwjIn9H7WpkmebuCpCyxqBAsXO1Dz2WpYKn1wuWLb1X4CjSrS98V7gCzCeC9OcnpnhhgiGvsYlIiI6JQTlDackEWIZvkYzB5eMlNShpv1woH905aizVKNo10FCMv4c6QPKRN9z6RH9bQpoZFSh9RdUgJ5XhXnnK2g/opy9OaXG45QFT1j7aWMDU1LfRynCb3vWEDrfwbsnMRfixDI8rqIiMY+BiUaMlJKBPs+mi1SQUg4DfstCEETHN9NREMr9b3HBgEdKnrghoTIOv3zITkZu3yfQVOwCoFIXmLhTdOZEz/Fr/WzX/Qny0op4NEdmDXbgUXnlyHgKofNDugS0OILfJqeThXGACMT22XGKaFb/AL2oihCRRKa6TYitZ9FOc1lE9sZZoiI0jAo0ZDpkcDX9D0AgK/hQfQIn+FbUCt6UY29qBM7USYOwym6RqeiRDShBCHx99HYe893sBL7sAgnUGIqJVPu0QHCsKDcY8eieheKF9ei016YDEPxmJISUKRM9hoZcosEFCUZZqQU0FQJR5GG4hJAVQFfSvGsQSXbdjXzdodLQoZTh7IREdFQY1CiIdMNd+Ln05bPx+yLJkOI5DenHTs/wYdv1+CN7YsROdoJB+JBKT60JNP0r8mbjstwCLViJ8rEEThE93C/HCIah+TZ52JK3QIUnl4Bm1M1rJ6jQo/11kjA5RbodriQlw9YrYDddB5rxk9HFZqa3vOUaRsQC0lERDR+MSjRkEm9abl2thN1Z+cjFLUmC5zpw9TLgUBrBD1b9qKtNWUISqRviliZuioGIPtGo3QFJD7ZPhUf7Z6GLUeOw4ZeZApYSoZ7AIw1NF7QxMsr0KGKKArRgiliO4MY0Tg1e76K0BQnnHNc6M0wkYKmJrd5R7JiREQ07jAo0YjSNKDQpwF/OwM1WWZRyjSDEjo7sffVMI7U1aGjdzp0XUmstZEarjJN/CQhEl8pS90YpFLH/uu6jsP7/WjeVoVC0YJMN3KnDc3pR3wtEQ1hlOII8kVr5tdGRERENEF19FjRfDwPxzsciERj44UTE77Ev/ROTACT4dpLGMskjotGUral3FNqOlfqV2bJbYGc6s6gROOD240pX52Nmf1MBtFfCOlvLQ5FRtDVJXDsQBCH3lGxtbgALSFjFBLo64+Sxn+oEkj7zjp+pN63snuoK4Rt2/aiGH4UyWbk41jGemQLX9lDWXqPmoAOjwjAhQBnmiIiIjrF6LrAX7ZXJhZzzhoyYj8Y9qWGFAEAkVDfzJXpE9vEyxvPazyPlAL+NhcUIVHs6UZZJWBxW4FIctmA+Gik1C+uzfsSX3bLDGUjkYznkeZjUuotpYCeZc03MwYlOuUJAbjdEu65VtTOrcHibwLBYPIfkCKNIcs8A5USDafsM/7DCwYF9m7oQss2G/ZurcXO9thNC/m2AFxa74B1y6XfKlU4GMXxHQFU4CB8aEIRmvtKDc+aJf2dV0DCiU72ohEREZ0szQJpd+RUVEQi+MxZAQTDyVle0gNEyi0OpnlrpBRAJJxynOgrIwYXXoBYGIJAYXE3JlX0AHke6HYXopbMr0XRP92syCLlWiwXga7cbiJlUCIysVoBqzX5L16V/d33BKh6//uVRXbUzZ+Es/pOuWe/HdWVIUyuDSbKaHooLYDFxd+EMr2JmA+xdLRi4ysnsOujQuz9eBb2RqIZe71SGdZnyVYm49TJsSP0cObXHwlK4EQXJsvt8IkmeHCin1rE6zK4QDe48v2XzXQuAcmeOSIaVqGwQPPR2P285m//Rd/PIipMa1OZhysl38tVPXu5RA9CynubKqXhPPF6mKfEj/ceGOqSqc7DuHZWr7Rj7yde7D/igqLIxOdi/DnNQ60MPSUp9RKRsKHNUl9DbHv6cLDYa4+kt6WhrVLOA+OxAKBEjfU1H68oElMqArBoElG3FxG7BxGrEyHNCU0P5dRGAsnJaU42fCiDDB0DHXdyZxsbGJSIhll5qfEtItCpQVGMM2Kp/X6o9M38l+kGLBPF68a8v/Ng2iVKxm5sIPlGnTyz6dn6Nip6NG1bpnIAIExvxlICsrsHG94IonW/D1t2hdCxvzO1RNbXkKkpBgpE/e/PPGRgoPPZ0Q0XOjAbm5An2nM8kkablLF7FTsDQG98qGz8okUAlpRPvfg+i4bERVTigib+ayOSF6sQgFSG7yJwsE60CrT4jcNohABURaS8buNFsKYK48Vk3zGaSL7GeNnERbpIvl+ZL8yFAPSUthOmdjwVHG/VcLjJYnjdFmm88E6VeuGsSsB/1Irmo1bkeyLoDQl0dGgoLAgbhnwDNoi+924phWF4UaYhTKll49uylUc0auxRiJfP9raq5z5KIP67Fh+ZkRpAMt1HYggPiVCRrEh3xA5nsRNV86zIK7cjCi35WZTSOxJ7ncnXoiLSfxtIYxvFA0Za+8FYDoh9pqaufRY/Tk+pl+HcMmqoc2pdDx62wlLQhcICjsQYKxiUiCYYh12Hwz5AL1eO3xYN5tsoc7e30tuDRYtVhBeVQteN44gzLaCZJiXQ6Xr6UIHYueJ/m65EIvFjjdtTz5M8X3KbDgCREDp6rGjdfwLbtwv8pbEW9tajUGD8oDf/bKyRUeap77M/znS8uYyGMKwimFauf4MLqIM792CP63/Y5mDOHZQRALF1lA60eBC02RDwA70ZrjWU1OAU35a6FlH8QldNPjZ8W68aw4JmAZzOZLn49lhgSTm+b5+mGr/tFn3BS+17EmE6j6ambEvZ3hkAThyVaD0mIOIXyInXaPr9il8LytgCtmn/syWgKsk1ouIX6KknVRQ1tXkgAKgaYHdIKCL7EBatb585QMW//VeFbgxnpiCmimhab0dqOwkBqFAN+w3PJQBVqmmBxfxcqlQy1C/9wl7R1WTvggACHQq6uhVs2eqMHStj7atErX3nSlkwWCT+09fQEorUoeuAyw20iiK0hNzoVS0omhqFx5u1WaGJ/t/jAUDNMmGSoUwOQ6OlBDREDI/NYQESifdIaSiXHtSAvuCUNuwrPQTGA058m81tgdWWrJu9n/uPje8xClTk/nmmysEFFVUfXJ+JmuWzteUYL8vHGv4fIaJhodtdqJyRuiU5VXxuY4mTb08inNuQg+T5TV8x5nJMJPWDqxfyPAdaO2xo2mfDocPl0MPpH5yJb3AT3+RmCmKmxxHTPW+m4zKdI1Gu73l6Qxosqo7zp+9J7I+GBvfBnhYuzftN9dZD8XHrg3iObK8l23bD2Phczh/7uyccAn7Rt23aDLSFS9B07ASikY5YCOhbtTU+1l5Vlb7nif1H0bTYN7tAYkkCRVVTvu0FNE2NfUMcv2iTAKAiGgUmzYxdzGkWxXhBKAFNUxLPE39+TVUMF5gSfeVMF5OqIpLHInm+RN3tavLCUxGGb67jx6fWJX4OVRVp9ZSIBTtzT7SiCsNr1lN+jvQAs6YBFmvKeWRKyEx5LbEwJhN1Tz2fqiSfNL4vsT+lTjKlUDz0pV5EG9pO6IbzGJ4XfcfqxvrFL9Q1JZp8nPLE8fMpiRvV+44vS864Gtuvxy7fU86ZWl6NL1wsk68p6lDh9KqYMg3YtlEirAsEs1zXq4qOCPpfaVhT9MTsYlnLCB1R9H+fRjxshVLLpYR2IBm29AyXlKnB2mKYjCl73YzhzdJv/YiGG4MSEVEGQgBFniCKTheYd7qOrH0ukZMcfX2yxwFo7bBh865CVM4uPOlzIBwcuEwOZPjT3YCbOE8kDMA2YLlMuoLJiynNZUOkHdAjAYR7DgNIjo9XLbFy8RprfePw4iOJNIsl2eugA5rNeJGmpBwDAJGwQIffjfbjPcnnt6RfeKqW9I9aczlLhhVu084lAKGkX2DGw1Nfkdj5tMwXwJol0/GZf7ct1gxlU8YJb98ItLbFgpIlyzmynVtLfbnRZBlNFYYL8QiSP2umJopfTmdbHDh2vuzRPvs+FVmaL3ms0s95lf57e/qrExGNLf1/3UBERERERHQKYlAiIiIiIiIyYVAiIiIiIiIyYVAiIiIiIiIyYVAiIiIiIiIyYVAiIiIiIiIyYVAiIiIiIiIyYVAiIiIiIiIyYVAiIiIiIiIyYVAiIiIiIiIyYVAiIiIiIiIyYVAiIiIiIiIyYVAiIiIiIiIyYVAiIiIiIiIyGTdB6ec//zlqa2tht9uxePFibNiwYbSrREREREREE9S4CEq//e1vccstt+Dee+/Fpk2bMH/+fCxduhQtLS2jXTUiIiIiIpqAxkVQ+tGPfoTrr78e11xzDWbPno1HH30UTqcTv/71r0e7akRERERENAGN+aAUCoWwceNGLFmyJLFNURQsWbIEDQ0No1gzIiIiIiKaqLTRrsBAjh07hmg0itLSUsP20tJS7NixI+MxwWAQwWAw8TgQCAxrHYmIiIiIaGIZ8z1KJ2Pt2rXwer2JP9XV1aNdJSIiIiIiGkfGfFAqLi6Gqqpobm42bG9ubkZZWVnGY+644w60t7cn/hw6dGgkqkpERERERBPEmA9KVqsVCxcuxPr16xPbdF3H+vXrUV9fn/EYm80Gj8dj+ENERERERJSrMX+PEgDccsstWL58ORYtWoSzzjoLP/nJT9DV1YVrrrlmtKtGREREREQT0LgISl//+tdx9OhR3HPPPfD7/Tj99NPxyiuvpE3wQERERERENBTGRVACgJUrV2LlypWjXQ0iIiIiIjoFjPl7lIiIiIiIiEYagxIREREREZEJgxIREREREZEJgxIREREREZEJgxIREREREZEJgxIREREREZEJgxIREREREZEJgxIREREREZEJgxIREREREZEJgxIREREREZEJgxIREREREZEJgxIREREREZEJgxIREREREZEJgxIREREREZEJgxIREREREZEJgxIREREREZEJgxIREREREZEJgxIREREREZEJgxIREREREZEJgxIREREREZEJgxIREREREZGJNtoVoIlDQCZ+PvhxN4qntMJSUwZ33ihWiohOKZEwEGgFerpKYHcWQ1FFbIeM/VFUBZBIvFtZrArsrijaj+8DoI9SrYmIaCxiUKIh40Rn4ufNT3yIv/yfY6iY48SCsyyoPbcSelUlAEBKQEE0UVbTAG++DkueY8TrTEQTy8E/7Ue4tx32UGzARGr0EUIiith7kBCAvTgP9opCdAbtCJwogdUehaqqscISQCD2l6KphnOpihoLWzoQjQoAtsRzRMKxc0u9E5pVgoiIxi8GJRoyDgH8X3UqAECT/w0/JmF/27loeKESf/p9F4BdhvISAr4aOybPcWHyZ8qQP88JAFAgkmX6rjPceRJ2+4i8DCIaZ5xWC3b88Hs40WWHpmyEEAIyEjX0cgOxoATE3ntOdDmw/YgPmw5WInDCA7dqhyKTsUr0/UfK+IPYXwLSsM0udESPRhLHSQBSCBSUO1AyrwSy0IF9+5shFCC1OoqWfJ+DFNCsKT1fiD2vmlqmb5+qiUQZoQAWq4TbO8gGIyKinDAo0ZARQsCeuKKQKJeNcH3SidlSpJVVhEQnPGg+UYGtm+fizSf9KKjbnTg2rqwMmLUoDxVnVaB8UQmQfioiOsUJIVBdFEV1UReALgCADIcHPK5+6iH8Q3gLDh7z4mjAHTtX4pymkKVIIBw1Ps6isbUAjZGp2LcxjJ6gBqEDSl++iZ9fmg6PGHMSoOuI9JWPb0sEPyGQV6jBUVuEKFzo7e6EWUQ1beg7VLUApvwINaWsogJ2Z6xXjIjoVMegRMNGFToKcCxruCnAMVSLfZgv38UxWYreA07D/mMl01CQPxv7NkfwfkMYFZWNmPcZLzxnTEJRiYCa8tsrYl/1EhENit0SwYzy45hRfnzAsjIcGbAMAEwqasPulgBmOuyI6OlzJgkR+7LIcO5IBEIY38ZSw5qABAQQ1QVau1w4cLwIzW/nodfhg+pJGbacSGJI9oil9mRJac5JBoqiw1NXAFeFC522KBzuWM9VXJRTQBHRKYRBiUadRYRRLg6nbfceO4FjLx+EG044pIpD703FxucrYSk5hKqZHtjzLJB67CNf04AZp1lRWV+O/GonXC7eG0BEo6PM24Eyb8egjsmlByxVd8iCg60F2NZUjq6g1fg9kelLo0TgisZ7xGTa90rxHqSWDg/aDk+Cv7kaQCxUZctGQgGiShBCSERg7CUz95jFFU92Qi/IR1gKWCz9v0YiotHGoERjVqE4ikJxNPH4DDSgW7rQfKwSR/5ci2jfr6+ARCcceHPrLJRs6ISluBBOawQWS/J+A5ctjNqzS1AwrQAFhToUfitKROOY0xrGrLIWzCpryfkYPYcesV0tPmz75DiOduxGT8QCAUDJ1AcVH2W9VSaCWOpwPRmJpBWP6gr2bvbBXeuDLCuFyymhan33jcVHFSZu0urrCEsNXzqAlImAYNoPxIYRejw6rCVeKEWuAV8vEVF/GJRoXHGKLtSJXagzTQwBANsaF6LlYDnaZR4isCL1E7QHLrzzahXKp+ShdGYRXHkS1niQCkdi35oqEjaLRJFPoOq8cthsaU9BRDShTfe1YLov9/CVjR4MpW3rDlnw1r4ZOHCoBIE9u3G8bxyfyNDDBZEMXsZ90rBBiW/rUzS1CL3V5Wg64UF+YSBWpm+SjtRMla33K3VCj/g+XQJWC2AtccNVoMHB/EV0ymBQogljjtiIOVnuUwpKGzbvq4d/Xw0O/TEP0ZTBJPFvMPPq8lAz3YVj08txvLcbdrtu+ADVJWC36vC4o6hekMebnYmIBsFpDWPpjK2QKRP8mCfNMBvM++yWpmps/6QETbv9CPQ6cAIiMTmQYQZEkfhP+j6kBzMhgEhUBdz5KJxSgIIZBQjb8xJfpumm0GXogUv8KAyfJwJRw7M68q0oLFW47iDRGMOgRKcEmwjiTLwJCSXDpA8SUWhoPlCJ/Qdm4uNXpyCI2Fzkom+MvuqxoXRaHibPdsA7rRytqg2qBqh6GELYUOqLoKgwyiF9RET9EGLgcHSy5pYdxmRv8n7X/kKWORyl7TfVsSPowDsHp+Lw3iLs+ygPUV0kP0pSJ9AAAD1q2C6lSDxfsk6p5xeIWPPgLC1A8XQvwvlF6O7S0j5P4kFLVdK3mX/WlMz3ibnzwM8pokFgUKJTRmyWqWjGfSpCqBb7UY39CEsLQikLSApIdHXmoWVTBXZsmo0WWQFHhSuxT5cKnAU2TJttwdxzPcibUQqbLfkJpegC8dEcigK0nlBRWW4c3kFERJ+Oqki4rIObFCNXdq0D59XtyjpJBRALQdEB7gPLFNAkBFq6PNh7vAw7/1yBjqAT+p8FVGm6zytbwDRmrozntzitqJqXD+cZhYiWlce2i7RbvGLbUzaqmQKX6XE8d6kaYLVmriLReMWgRGRiEWFYYPywdaILJcKPmXIzOoUXEX9yuqaoVOC3LkLrvtPw8s5OdEdaDR+GekRCtQhUlkUw5fQ8WGsqAZgXOSEiorGswNE1YJmoltsU8pnOPaP4E/zt1A9xoseNiK7k3POWqeMsfqweiiKsq2hs8+HAGz7sft0LR8mBrOfKcLdY6s6+GTWMQwuFAMqqBOxVPpy+rBDd0rjURzxoSdNj8/7Up8m0T9O4vheNPAYlokFQhQ4vThg3CsB+6G0UHfoIYZl5vtvuWafB7qjDoS29aPqzjgrPcWy09STPq0iU+6I46yIXnPOmQlf4T5OI6FRjUaPwuduH7Hx6KBbcqjzHcWblLhzt8qArbItNoGEKHea1vYD0YJKpR+yQnAHNUYmD2wP4ZHsgsb2/3reMosmRFqmhyqLpKK+1oGRWAUrnFQzypESfDq/GiIaAR7TBg7asi95+siOIju27oMKB6r5tPX0fBRIKWlGCrY5S7NyhY8qc3Ynj4h8WVi0CryOIMz7rhl43ddheBxERTUwWNYoKz4mBCw6SqyuIo40HMDOqQZfGSTIy9QApIhmIouGIIXwppvIRqaCpoxhtJwpwZN9kOBuSIVJKY2wTUT3W6ZXowTJNy2Ea8W63RVE91428qUUoKIhyXS/KaNiC0v33348XX3wRmzdvhtVqRVtbW1qZxsZG3HTTTXj99dfhdruxfPlyrF27FpqWrNYbb7yBW265Bdu2bUN1dTXuuusuXH311cNVbaJhUS4OoVwcyro/KlW09RSiqWESPmzIB5D85i4IO8Kwwju/Av5jIZTV7U8cJyBRNsmGvDnVcDp43xMREY2sYlcAxa7AwAUziIYGvqdsVtEhHOvx4Ej7AQSPJ9OMQKb7tiQkBDY3TzFMogH0TSSS8jgUtWDzehvc3iPwTXajeJIz5SzGoCUl0m/oigeyaAQCgK/GiqJpHhQVRWCzctH7iWLYglIoFMJll12G+vp6/Pd//3fa/mg0imXLlqGsrAx//etf8cknn+Cb3/wmLBYLvv/97wMA9u/fj2XLluHGG2/Ek08+ifXr1+Mf//EfUV5ejqVLlw5X1YlGnCqiKBJHUYSjGfd3SC9atpTjwy112IDkm7kOBd1wI6/WjxlzNZy22Am3zw1pmoo2TlN1eKcUwePmWG8iIhr7LGoU5e4TKHfn1hsW0RV0hRw4s2InLEr2e8Z6I1b4Owuwt60CTduL4N9mvCQ2DzOMT/uu63ra4JGoVHDIA6hFJSidZIe1OB9OZxRSAkI3TiKlG27CMp6ps7MTYW87gl0RCD0ZIqUUCAJwOyKw23T05JX23wg0ZISUgx5FOiiPP/44Vq1aldaj9PLLL+MLX/gCmpqaUFoa+x/+6KOPYs2aNTh69CisVivWrFmDF198EVu3bk0cd/nll6OtrQ2vvPJKznUIBALwer14Rp0Cp+BN9DSx9EoHjslSHEEtWlCJaMpEEeb1QHSocNXm4bS5YUxfmI/SBVWGEhZNh7dYhdp3CkXP/cZkEc19tqnBlAUAEU5fvHIozw8AInJyN2EjcpKzbJ3scQBaO2zYvKsQfzM/ey/lgMLBkz82hRxglq+cz/Mp2iPj+cJDP/vZUL3WzOcentnaUunDWP+058qw4OyIPO8ItGN/Bpr1bqTF71Eaq3LpURqsiK7g9QOn44JJW2BRM890a9YVsqErbM/pC8RM/462HauDTQ1h14lqBCMW9ESSM+cqGe7rksh8v5cQgNvaY+gpk1LAY+1C6SQnfFPzMb/eiqCzKGPdMl3RxzcpGT4XzWuaufpGpujqyY1DzPQcOR87iOuNTAb7uR/o6kblRZejvb0dHo8na7lRu0epoaEB8+bNS4QkAFi6dCluuukmbNu2DQsWLEBDQwOWLFliOG7p0qVYtWpVv+cOBoMIBpMXAYHAyXUJE40HdtGDKnEAVTgAXQrohqBkfNeMQsXh4ALsemcS3vtLFAqOGfbrUFBaZ8f8MyyYdZYbrjpfxueUUiDPFYFF4/ACIiIa31zWIFzW3L48yhSU5pXsQ2fYgYq84zk/50BrecW1dBegK+SA0taOhj8qaPgjANNnd+KcpsWUY/p/HkXoqCzqgr20GJ/5GwX2SaVZ1+cynDXDdj2SnjS1cX6dMGpBye/3G0ISgMRjv9/fb5lAIICenh44HI6M5167di3uu+++Yag10dimCAkF2b+VURFFjX8jSrEt4/4QbGjeVIW3N9Xixf8qRP60VgDJN3QpAc1uxby5OqYu8GLauclvtURUwKJlvnmXiIhooipyBFDkGJ4v5X3ONhxsj10Ln1V6IvOcUSf5uauHI2jsKENdYRc+OpKHpx/TABzu95h+p5DPoKIkiCmzNFTM8sJV4T25io6iQQWl22+/HQ8++GC/ZbZv346ZM2d+qkp9WnfccQduueWWxONAIIDq6up+jiA6dWgiAg2dGfe50IkCcRwzsQXd0oWePS7DfgngsJyCTVtLseHtCuT/X+O6IkUFUcw9Q0PtwmIUlWrj/pskIiKi0WTXQphR9CmGWfcjGopNRHHMb4dP3wVdV4wFMk4Zn/lzPVN8agvmoduvYB+KsHFTF6xqIK20ofcqwzmMk2oYn8VlDWHabAUlk/NQUGFP3DYwlAYVlFavXj3gjHOTJ0/O6VxlZWXYsGGDYVtzc3NiX/zv+LbUMh6PJ2tvEgDYbDbYbLas+4loYE7RBSfSF1gsFi0ISwtOHC5C8LAj0dukQ8ExlOH//bEGekEbyqflIa/QOM7Z+IYoYmOvbd0oqbTj/IstcJQYgxkRERENn2kFwxPCAOBYjxd72qoR9XdDi9hi08dnCl8pfw+00HLqkMVjIRf273YC6EWBz4ry6lisSYSrSPIeNfMUHHYtt2GSgwpKJSUlKCkpGcwhWdXX1+P+++9HS0sLfL7YfRDr1q2Dx+PB7NmzE2Veeuklw3Hr1q1DfX39kNSBiE6ORYThE/607TXYh6h8B4G2AhzbUIYwrH17Mr/xdaAArbDCf/pktB51w+aKJr4xslvCmDrHitJZBci3hTikj4iIaBwpdrSj2DF0CyibSQm0h9w43uPFoQ4fDn7oBpDSX9X3Q6ZJTVxa+jVMJsN2j1JjYyNaW1vR2NiIaDSKzZs3AwCmTp0Kt9uNiy++GLNnz8ZVV12Fhx56CH6/H3fddRdWrFiR6A268cYb8R//8R+47bbbcO211+K1117DM888gxdffHG4qk1En5IqdBTgOArEwN/WSAl0wovjm/fh0OYKSIjEG1wPHHinrg5ldSH4at0ozg9BEdK4wCAkXLYwnE5gzjl5w/J6iIiIaOwRAsi3dSLf1okp+Ueylgv3pM+I15njDJnDFpTuuecePPHEE4nHCxYsAAC8/vrruOCCC6CqKl544QXcdNNNqK+vh8vlwvLly/Gv//qviWPq6urw4osv4rvf/S4efvhhVFVV4b/+67+4hhLRBCEEkId25Il21GJ32v6DB6bCv78KzciDhJK2P39eCU6fF8InhdOhb+7rYo9E0NeBnygnZWxdKUWRUBWJAncvKqZweC4RERFlN+zrKI0FXEeJaGLaJheiQ3pxHKVZZuJJvr3ZCi3Ir83HrFkS1vJSzJinQUSN3fHxYX/F3l54XDmuycB1lIbmPFxHadjOHcd1lIYf11EanLGyjtJgjOS/o+EWHeO/H0MlW4/SGc/+aeyuo0RE9GnNwiZIkd7TlE4CJwQOnZiMjzdVwI9qvFluM0QrqVpQUmXDrNkSnhofpk5Lf3vM+LVSVEOxp5drShEREU0wDEpENG4pQgLI/RvDWuxGrdiNXumA7jcGrCDsOHR4Cv76jg+YHILVHt9v7KlKneWvujKKsike1M7NR1VJcsp1uyUKq0U/qddEREREYwODEhGdcuyiJ22bE10oEMcRlhZ07vdC77snKtvq6a0ogf/jUhz6uAxbNlZAUZLlNA2YMUvBjLlW+Mp0uOzJ4Q2JXqlIMqhZNIYqIiKisYZBiYgohUWEUYBjA5YrxFHUYScCTQXoaXIa9gVkPra2noF9+3199z1lujcyua2sOIJZCywoL9dRlNcLJZfRhERERDSsGJSIiE6SKqIowDEUmLZXiEYc3+FH+45CSAnoKaHI3EOlQ8H+klrs2j4Z5bUOQHFDU6KG/QBgUaIo8IRRUO7ErJmjc6M8ERHRqYRBiYhoGBSJFhShxXyLU2bHt2LPsdno2WpHSNpMa0UBYVhgm1GGwhlR7A/OxuEmOwS60eJuSzuVTYvAO6l4iF4FERHRqYtBiYhoDJgqPo79kCVYHdvlw6GdxfCjExIKLAihET19hySjVRhWlM5rx9w5UcycZ4O1zJeY9twsdRY/q+iFYrFAWPixQEREBDAoERGNC8WiBcWiBdPwcb/lolLB0UPT8PqBarz+YhDAwGstFc3Ix7QZAmeeGYTDlz80FSYiIhrnGJSIiCYQVegoC+yET+7KWsZ8n1TrRh82bZyEvzzpQv607kSpTCQEHEVuXHKOH77pPigOZ8ZyRERE4x2DEhHRBBRbYyo3RYjdTxWCFZE9FgDZp0V3TrJhf+QivPrnQtj/2gmg07BfQoHNqeFvzzkBV10VhGY56ddAREQ0mhiUiIgIAGAVIVjR/4x6srEbJQdfQQi2zPuhYIeci6OHHaia1AqLTUvZZ+TQgqib4UDFzIKc5rwgIiIaSQxKREQ0KC7RCZepJynVmXgDnbu9aNvt7vc8PXBhgyyCvTaISbU6SiqshgkmzEpKBaqmumB1ZFqXioiIaGgxKBER0ZBShIQHbfCgbeDCAug+6MKJg8VoSfRSpaclCQW9eUVwTLejpjaK6vKwab+xT0pTdJRPsoG3UBER0cliUCIiolHlFF1womvggp1Ay8Zy7Nrow+6M91Alt0VgBWp8KKmyYv5pEXiL7f2e2uEAvIXKIGtOREQTGYMSERGNGz7xCXz4JKeykUYNexrn4KW/uKBAB5B9koogbPDNdaJ2MjBvdhj28lLISMQ0FDB9YgqrFoVF1Qf7MoiIaBxgUCIioglJExHMxJasi/im0qVAMFCJD96dgw/e1SBwDED2YAUArjI3ymssOH9BAK6SzPdjSQkIzlRBRDQuMSgREdEpTxESjsOHcToO53xMd4sL+z6chV0bimDzhLOWs1gFSotDmDxFxZSZFtgcHOJHRDQeMCgRERGdBKfowly8j3CLBXqLkrX3KQILQlVl2NwzF+99aIFd7c1YzjwhhWKz4/zT/Cid7B3yuhMR0cAYlIiIiD4Fi8jemwQAVoTgPLIXrsON6IGr37LxsFW4wIv9lnPx1odlyNvRBSn7H78nIeBwAOcuaIe1gMGKiGgoMCgRERGNAIsIw5LLlOkAwpvb4ZEB9MKBTvR/r5SAhA4Vh6aegXDQhTyvsWxyQgoVipAoLZWoqBSchIKIaAAMSkRERGOQR+S4FlUf9552tO4pwtEMH+3xoBWFiq2lZbBWlcFXrqCmPAg9HE0rnymWlbraUFzd/yLCREQTCYMSERHRBJDzelQtexBozkcTKtDUT7F4uMqb4oQsKsfRshJM1WLDDLP1b+UXqMizBwdXcSKiMYpBiYiI6BQzqN6qfUDXXjcOoAY74ACQfcb1qK8AhZVWVFfZMHUaYLMlS5rvsxJCIs/Wy+nTiWjMYlAiIiKifrlEJ6bi4wHL6S0CorAKez+agq1bHNBSJrow32cVhQKby4HayUBNjY7y4syTYmSbyCJ1lkCLEoWiZL+Pi4joZDAoERER0ZBQhAR2HsJkHBqwrJSAXlOCYFMNXttbBcB6Us9pdVpQVxNE/ek5DDskIhoEBiUiIiIacUIA6qGjcBw6iqlyEyRSF+LN3DuUafa/dhRh69EzceiwJ6feJ0VTsHDGcdRVRz5V/Ylo4mNQIiIiolGlCAkgffa9XBTgGPIa1yHUaMtaJjVgtU+ZjU3RSuxqjD2WWUbsRSNRKJoGi03B4ul+5HnVk6ofEY1fDEpEREQ0rmkiAg259RDZ9m5Ex9690JEafNLTkrvahoLaPOzuOgMNOyvhtPTkXJ/UHiyPM4S5kwM5H0tEYweDEhEREZ0yFCHhxYmBCx4Gug8DTnkc3bCjO4dzp/ZceaY7YS0rwYHwdOCgpd/j8t1h+PK7YFVPrleNiIYHgxIRERFRFgXi2MkduBuI7NqPIJqxI+uE6rHep968IqhVPpQUR+Cr0FCQL7OuVZV6H5YMp/eiVRUFYGHgIhoSDEpEREREw0ATEdRg78AFO3cDzS4cbSrD9i1eCMPgvXTmSS1UlwJPlRtdtnJ0hBwoy+8EEAtS/Z1JETqKnR25vBSiUxKDEhEREdFoO9GFEuxFyUkc6iyyw9qrYd+Jyfj4cBF2iMw9SqkBy13qRthZjHOnH0rrgco2e2Bif9/fNjUMm8bZA2niYlAiIiIiGse6D/SiG0AhPkRhjsfI/cDu0vPwR3+BIUBlmoI9lRCx/dY8J4pLJBZPPpS118ocuDgkkMYbBiUiIiKiU4wQwPSWt4GWwR1nLbTAM9mFbkspPjo4G4cPVuZ0nC3Pjjm1rZjqaz2J2hKNjmELSgcOHMD3vvc9vPbaa/D7/aioqMA3vvEN3HnnnbBak6tvf/jhh1ixYgXee+89lJSU4Nvf/jZuu+02w7meffZZ3H333Thw4ACmTZuGBx98EJ///OeHq+pERERElEGoNYxjrW0A2jBJDnz/lavWhrxKN5ptM/HRnjLs3OfK2GeVqVcqzxFCVXkIxY4A8my5T89ONFSGLSjt2LEDuq7jl7/8JaZOnYqtW7fi+uuvR1dXF374wx8CAAKBAC6++GIsWbIEjz76KD766CNce+21yM/Pxw033AAA+Otf/4orrrgCa9euxRe+8AU89dRTuPTSS7Fp0ybMnTt3uKpPRERERP3QxMD3JwUPRhA82AXIYyiEM+vQPvN2CYEueLBjShXCBVWwOCywq0EoQk8ckUlUKgiGeyAjYUBVBvV6iMyElNnWpB56P/jBD/DII49g3759AIBHHnkEd955J/x+f6KX6fbbb8fzzz+PHTt2AAC+/vWvo6urCy+88ELiPGeffTZOP/10PProozk9byAQgNfrxTPqFDgFV9YmIiIiGi90KRBAQWKR4Gxhy5KvIW+SE16fDTV1g7/eUywD9x/oGaZkH6+ioYnzWvoT7gmnbesMh3HGs39Ce3s7PB5P1mNH9B6l9vZ2FBYmbzNsaGjA+eefbxiKt3TpUjz44IM4ceIECgoK0NDQgFtuucVwnqVLl+L5558fqWoTERER0ShRhEQ+cri3qR2QHwJtiP0ZrPy5buTXeOEqyYPFZT+JM9BEM2JBac+ePfjZz36WGHYHAH6/H3V1dYZypaWliX0FBQXw+/2Jball/H5/1ucKBoMIBoOJx4FAYCheAhERERFNUKHOCFo+Pg7g+KCPtbo1uEtdcBa5Yfc6hr5yNCoGHZRuv/12PPjgg/2W2b59O2bOnJl4fOTIEVxyySW47LLLcP311w++loO0du1a3HfffcP+PEREREQ0MXQf6D35Y/v+7mzuyvkYza7B7XPBVZIH1cqJqMeiQf9fWb16Na6++up+y0yePDnxc1NTEy688EKcc845+NWvfmUoV1ZWhubmZsO2+OOysrJ+y8T3Z3LHHXcYhusFAgFUV1f3W2ciIiIiopPVtrVz0McECrvgmdwFzT64S3JXcSxg0fAadFAqKSlBSUlu60YfOXIEF154IRYuXIjHHnsMimKcfaS+vh533nknwuEwLBYLAGDdunWYMWMGCgoKEmXWr1+PVatWJY5bt24d6uvrsz6vzWaDzWYb5CsjIiIiIho5yenWByd/bi+6jnVBs538JGWaTYPVbYfFYTnpc0x0w9bPd+TIEVxwwQWYNGkSfvjDH+Lo0aOJffHeoH/4h3/Afffdh+uuuw5r1qzB1q1b8fDDD+PHP/5xouzNN9+Mz372s/j3f/93LFu2DE8//TTef//9tN4pIiIiIqJTQaz3avA9WHHxhYPdAEKdJzfkULVqE/5+rGELSuvWrcOePXuwZ88eVFVVGfbFZyT3er344x//iBUrVmDhwoUoLi7GPffck1hDCQDOOeccPPXUU7jrrrvwL//yL5g2bRqef/55rqFERERERHQS4j1Zx05ifsB4yLJ7bRnvrepv2vHxFqxGdB2l0cJ1lIiIiIiIRpe10AJnhS0xQ+Bgh/2lromU67HjZh0lIiIiIiI6NYVawwi1hvuGDjbDWji4oKR5VDiL7XD7XAByD0sni0GJiIiIiIhGXKg1vbdnoPLdB3pxDG1w1g5+UWCrW4M93w7pzW3SNwYlIiIiIiIaV05m3avYeledkDW59UQpAxchIiIiIiKaGHoO5hayGJSIiIiIiIhMGJSIiIiIiIhMGJSIiIiIiIhMGJSIiIiIiIhMGJSIiIiIiIhMGJSIiIiIiIhMGJSIiIiIiIhMGJSIiIiIiIhMGJSIiIiIiIhMGJSIiIiIiIhMGJSIiIiIiIhMGJSIiIiIiIhMtNGuwEiQUgIAuqU+yjUhIiIiIqLRFM8E8YyQzSkRlI4fPw4AuFrfP8o1ISIiIiKisaCjowNerzfr/lMiKBUWFgIAGhsb+20M+vQCgQCqq6tx6NAheDye0a7OhMa2Hjls65HDth5ZbO+Rw7YeOWzrkTNe21pKiY6ODlRUVPRb7pQISooSuxXL6/WOq/+J45nH42FbjxC29chhW48ctvXIYnuPHLb1yGFbj5zx2Na5dJ5wMgciIiIiIiITBiUiIiIiIiKTUyIo2Ww23HvvvbDZbKNdlQmPbT1y2NYjh209ctjWI4vtPXLY1iOHbT1yJnpbCznQvHhERERERESnmFOiR4mIiIiIiGgwGJSIiIiIiIhMGJSIiIiIiIhMGJSIiIiIiIhMJkxQOnDgAK677jrU1dXB4XBgypQpuPfeexEKhQzlPvzwQ3zmM5+B3W5HdXU1HnroobRzPfvss5g5cybsdjvmzZuHl156aaRexrj285//HLW1tbDb7Vi8eDE2bNgw2lUad9auXYszzzwTeXl58Pl8uPTSS7Fz505Dmd7eXqxYsQJFRUVwu9346le/iubmZkOZxsZGLFu2DE6nEz6fD7feeisikchIvpRx54EHHoAQAqtWrUpsY1sPnSNHjuAb3/gGioqK4HA4MG/ePLz//vuJ/VJK3HPPPSgvL4fD4cCSJUuwe/duwzlaW1tx5ZVXwuPxID8/H9dddx06OztH+qWMadFoFHfffbfhs/B73/seUudtYlufvLfeegtf/OIXUVFRASEEnn/+ecP+oWrbXK5VJrr+2jocDmPNmjWYN28eXC4XKioq8M1vfhNNTU2Gc7CtczPQ73WqG2+8EUII/OQnPzFsn7BtLSeIl19+WV599dXy1VdflXv37pW///3vpc/nk6tXr06UaW9vl6WlpfLKK6+UW7dulb/5zW+kw+GQv/zlLxNl/vKXv0hVVeVDDz0kP/74Y3nXXXdJi8UiP/roo9F4WePG008/La1Wq/z1r38tt23bJq+//nqZn58vm5ubR7tq48rSpUvlY489Jrdu3So3b94sP//5z8uamhrZ2dmZKHPjjTfK6upquX79evn+++/Ls88+W55zzjmJ/ZFIRM6dO1cuWbJEfvDBB/Kll16SxcXF8o477hiNlzQubNiwQdbW1srTTjtN3nzzzYntbOuh0draKidNmiSvvvpq+e6778p9+/bJV199Ve7ZsydR5oEHHpBer1c+//zzcsuWLfJLX/qSrKurkz09PYkyl1xyiZw/f75855135J///Gc5depUecUVV4zGSxqz7r//fllUVCRfeOEFuX//fvnss89Kt9stH3744UQZtvXJe+mll+Sdd94pf/e730kA8rnnnjPsH4q2zeVa5VTQX1u3tbXJJUuWyN/+9rdyx44dsqGhQZ511lly4cKFhnOwrXMz0O913O9+9zs5f/58WVFRIX/84x8b9k3Utp4wQSmThx56SNbV1SUe/+IXv5AFBQUyGAwmtq1Zs0bOmDEj8fhrX/uaXLZsmeE8ixcvlt/61reGv8Lj2FlnnSVXrFiReByNRmVFRYVcu3btKNZq/GtpaZEA5JtvvimljH04WCwW+eyzzybKbN++XQKQDQ0NUsrYG56iKNLv9yfKPPLII9Lj8Rh+9ymmo6NDTps2Ta5bt05+9rOfTQQltvXQWbNmjTzvvPOy7td1XZaVlckf/OAHiW1tbW3SZrPJ3/zmN1JKKT/++GMJQL733nuJMi+//LIUQsgjR44MX+XHmWXLlslrr73WsO0rX/mKvPLKK6WUbOuhZL6gHKq2zeVa5VTT38V73IYNGyQAefDgQSkl2/pkZWvrw4cPy8rKSrl161Y5adIkQ1CayG09YYbeZdLe3o7CwsLE44aGBpx//vmwWq2JbUuXLsXOnTtx4sSJRJklS5YYzrN06VI0NDSMTKXHoVAohI0bNxraTVEULFmyhO32KbW3twNA4vd448aNCIfDhraeOXMmampqEm3d0NCAefPmobS0NFFm6dKlCAQC2LZt2wjWfnxYsWIFli1blvbvnm09dP73f/8XixYtwmWXXQafz4cFCxbgP//zPxP79+/fD7/fb2hrr9eLxYsXG9o6Pz8fixYtSpRZsmQJFEXBu+++O3IvZow755xzsH79euzatQsAsGXLFrz99tv43Oc+B4BtPZyGqm1zuVahdO3t7RBCID8/HwDbeijpuo6rrroKt956K+bMmZO2fyK39YQNSnv27MHPfvYzfOtb30ps8/v9hgsaAInHfr+/3zLx/ZTu2LFjiEajbLchpus6Vq1ahXPPPRdz584FEPv9tFqtiQ+CuNS2zuX3nGKefvppbNq0CWvXrk3bx7YeOvv27cMjjzyCadOm4dVXX8VNN92E73znO3jiiScAJNuqv/cQv98Pn89n2K9pGgoLC9nWKW6//XZcfvnlmDlzJiwWCxYsWIBVq1bhyiuvBMC2Hk5D1bZ8Xxm83t5erFmzBldccQU8Hg8AtvVQevDBB6FpGr7zne9k3D+R21ob7QoM5Pbbb8eDDz7Yb5nt27dj5syZicdHjhzBJZdcgssuuwzXX3/9cFeRaFisWLECW7duxdtvvz3aVZmQDh06hJtvvhnr1q2D3W4f7epMaLquY9GiRfj+978PAFiwYAG2bt2KRx99FMuXLx/l2k0szzzzDJ588kk89dRTmDNnDjZv3oxVq1ahoqKCbU0TUjgcxte+9jVIKfHII4+MdnUmnI0bN+Lhhx/Gpk2bIIQY7eqMuDHfo7R69Wps37693z+TJ09OlG9qasKFF16Ic845B7/61a8M5yorK0ubsSr+uKysrN8y8f2Urri4GKqqst2G0MqVK/HCCy/g9ddfR1VVVWJ7WVkZQqEQ2traDOVT2zqX33OKvfm3tLTgjDPOgKZp0DQNb775Jn76059C0zSUlpayrYdIeXk5Zs+ebdg2a9YsNDY2Aki2VX/vIWVlZWhpaTHsj0QiaG1tZVunuPXWWxO9SvPmzcNVV12F7373u4leU7b18BmqtuX7Su7iIengwYNYt25dojcJYFsPlT//+c9oaWlBTU1N4rPy4MGDWL16NWprawFM7LYe80GppKQEM2fO7PdPfLzjkSNHcMEFF2DhwoV47LHHoCjGl1dfX4+33noL4XA4sW3dunWYMWMGCgoKEmXWr19vOG7dunWor68f5lc6flmtVixcuNDQbrquY/369Wy3QZJSYuXKlXjuuefw2muvoa6uzrB/4cKFsFgshrbeuXMnGhsbE21dX1+Pjz76yPCmFf8AMV+snsouuugifPTRR9i8eXPiz6JFi3DllVcmfmZbD41zzz03bZr7Xbt2YdKkSQCAuro6lJWVGdo6EAjg3XffNbR1W1sbNm7cmCjz2muvQdd1LF68eARexfjQ3d2d9tmnqip0XQfAth5OQ9W2uVyrUDIk7d69G3/6059QVFRk2M+2HhpXXXUVPvzwQ8NnZUVFBW699Va8+uqrACZ4W4/2bBJD5fDhw3Lq1KnyoosukocPH5affPJJ4k9cW1ubLC0tlVdddZXcunWrfPrpp6XT6UybHlzTNPnDH/5Qbt++Xd57772cHjwHTz/9tLTZbPLxxx+XH3/8sbzhhhtkfn6+YTYwGthNN90kvV6vfOONNwy/w93d3YkyN954o6ypqZGvvfaafP/992V9fb2sr69P7I9PWX3xxRfLzZs3y1deeUWWlJRwyuocpM56JyXbeqhs2LBBapom77//frl792755JNPSqfTKf/nf/4nUeaBBx6Q+fn58ve//7388MMP5d/93d9lnFZ5wYIF8t1335Vvv/22nDZtGqesNlm+fLmsrKxMTA/+u9/9ThYXF8vbbrstUYZtffI6OjrkBx98ID/44AMJQP7oRz+SH3zwQWKmtaFo21yuVU4F/bV1KBSSX/rSl2RVVZXcvHmz4fMydVY1tnVuBvq9NjPPeiflxG3rCROUHnvsMQkg459UW7Zskeedd5602WyysrJSPvDAA2nneuaZZ+T06dOl1WqVc+bMkS+++OJIvYxx7Wc/+5msqamRVqtVnnXWWfKdd94Z7SqNO9l+hx977LFEmZ6eHvlP//RPsqCgQDqdTvnlL3/Z8IWAlFIeOHBAfu5zn5MOh0MWFxfL1atXy3A4PMKvZvwxByW29dD5wx/+IOfOnSttNpucOXOm/NWvfmXYr+u6vPvuu2Vpaam02Wzyoosukjt37jSUOX78uLziiiuk2+2WHo9HXnPNNbKjo2MkX8aYFwgE5M033yxramqk3W6XkydPlnfeeafh4pFtffJef/31jO/Ry5cvl1IOXdvmcq0y0fXX1vv378/6efn6668nzsG2zs1Av9dmmYLSRG1rIWXKct1EREREREQ09u9RIiIiIiIiGmkMSkRERERERCYMSkRERERERCYMSkRERERERCYMSkRERERERCYMSkRERERERCYMSkRERERERCYMSkRERERERCYMSkRERERERCYMSkRERERERCYMSkRERERERCYMSkRERERERCb/H1lqoGNZ3no1AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "combination_plot(\"fls\")" ] @@ -384,30 +333,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "combination_plot(\"sosfs\")" ] @@ -476,7 +383,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.1" + "version": "3.10.6" }, "orig_nbformat": 4 }, diff --git a/docs/wind_data_user.ipynb b/docs/wind_data_user.ipynb index d16e896bf..a7ae7a7b8 100644 --- a/docs/wind_data_user.ipynb +++ b/docs/wind_data_user.ipynb @@ -35,7 +35,12 @@ "outputs": [], "source": [ "from floris.wind_data import WindDataBase\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import warnings\n", + "\n", + "warnings.simplefilter(\"ignore\")\n", + "\n" ] }, { @@ -66,7 +71,6 @@ "outputs": [], "source": [ "from floris import TimeSeries\n", - "import numpy as np\n", "\n", "# Like FlorisModel, TimeSeries require wind directions, wind speeds, and turbulence intensities to be of the same length.\n", "N = 50\n", @@ -75,7 +79,11 @@ "turbulence_intensities = 0.06 * np.ones(N)\n", "\n", "# Create a TimeSeries object\n", - "time_series = TimeSeries(wind_directions=wind_directions, wind_speeds=wind_speeds, turbulence_intensities=turbulence_intensities)" + "time_series = TimeSeries(\n", + " wind_directions=wind_directions,\n", + " wind_speeds=wind_speeds,\n", + " turbulence_intensities=turbulence_intensities,\n", + ")" ] }, { @@ -99,7 +107,9 @@ "outputs": [], "source": [ "# Equivalent to the above\n", - "time_series = TimeSeries(wind_directions=270.0, wind_speeds=wind_speeds, turbulence_intensities=0.06)" + "time_series = TimeSeries(\n", + " wind_directions=270.0, wind_speeds=wind_speeds, turbulence_intensities=0.06\n", + ")" ] }, { @@ -125,7 +135,12 @@ "outputs": [], "source": [ "# Including value for each indices\n", - "time_series = TimeSeries(wind_directions=wind_directions, wind_speeds=wind_speeds, turbulence_intensities=turbulence_intensities, values=np.linspace(0, 1, N))" + "time_series = TimeSeries(\n", + " wind_directions=wind_directions,\n", + " wind_speeds=wind_speeds,\n", + " turbulence_intensities=turbulence_intensities,\n", + " values=np.linspace(0, 1, N),\n", + ")" ] }, { @@ -159,7 +174,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -174,8 +189,8 @@ "\n", "fig, ax = plt.subplots()\n", "ax.scatter(time_series.wind_speeds, time_series.turbulence_intensities)\n", - "ax.set_xlabel('Wind Speed (m/s)')\n", - "ax.set_ylabel('Turbulence Intensity')" + "ax.set_xlabel(\"Wind Speed (m/s)\")\n", + "ax.set_ylabel(\"Turbulence Intensity\")" ] }, { @@ -209,7 +224,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -225,8 +240,8 @@ "fig, ax = plt.subplots()\n", "ax.scatter(time_series.wind_speeds, time_series.values)\n", "ax.grid()\n", - "ax.set_xlabel('Wind Speed (m/s)')\n", - "ax.set_ylabel('Value (normalized price/MWh)')" + "ax.set_xlabel(\"Wind Speed (m/s)\")\n", + "ax.set_ylabel(\"Value (normalized price/MWh)\")" ] }, { @@ -269,14 +284,14 @@ "source": [ "from floris import WindRose\n", "\n", - "wind_directions = np.array([270, 280]) # 2 Wind Directions\n", - "wind_speeds = np.array([6.0, 7.0, 8.0]) # 3 Wind Speeds\n", + "wind_directions = np.array([270, 280]) # 2 Wind Directions\n", + "wind_speeds = np.array([6.0, 7.0, 8.0]) # 3 Wind Speeds\n", "\n", "# Create a WindRose object, not indicating a frequency table indicates uniform frequency\n", "wind_rose = WindRose(\n", " wind_directions=wind_directions,\n", " wind_speeds=wind_speeds,\n", - " ti_table=0.06 #As in Time Series, a float indicates a constant table\n", + " ti_table=0.06, # As in Time Series, a float indicates a constant table\n", ")\n", "\n", "wind_rose.freq_table" @@ -337,15 +352,17 @@ "source": [ "from floris import WindTIRose\n", "\n", - "wind_directions = np.array([270, 280]) # 2 Wind Directions\n", - "wind_speeds = np.array([6.0, 7.0, 8.0]) # 3 Wind Speeds\n", - "turbulence_intensities = np.array([0.06, 0.07, 0.08]) # 3 Turbulence Intensities\n", + "wind_directions = np.array([270, 280]) # 2 Wind Directions\n", + "wind_speeds = np.array([6.0, 7.0, 8.0]) # 3 Wind Speeds\n", + "turbulence_intensities = np.array([0.06, 0.07, 0.08]) # 3 Turbulence Intensities\n", "\n", "# The frequency table therefore is 2 x 3 x 3 and the sum over all entries = 1\n", - "freq_table = np.array([\n", - " [[2/18, 0, 1/18], [1/18, 1/18, 1/18], [1/18, 1/18, 1/18]],\n", - " [[1/18, 1/18, 1/18], [1/18, 1/18, 1/18], [1/18, 1/18, 1/18]]\n", - "])\n", + "freq_table = np.array(\n", + " [\n", + " [[2 / 18, 0, 1 / 18], [1 / 18, 1 / 18, 1 / 18], [1 / 18, 1 / 18, 1 / 18]],\n", + " [[1 / 18, 1 / 18, 1 / 18], [1 / 18, 1 / 18, 1 / 18], [1 / 18, 1 / 18, 1 / 18]],\n", + " ]\n", + ")\n", "\n", "# The value table has the same dimensions as frequency\n", "value_table = np.ones_like(freq_table)\n", @@ -355,7 +372,7 @@ " wind_speeds=wind_speeds,\n", " turbulence_intensities=turbulence_intensities,\n", " freq_table=freq_table,\n", - " value_table=value_table\n", + " value_table=value_table,\n", ")\n", "\n", "# Demonstrate setting value again\n", @@ -411,9 +428,9 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -422,16 +439,13 @@ ], "source": [ "# Generate a wind rose with a few wind directions and speeds\n", - "wind_directions=np.array([260,265,270, 275, 280, 285, 290])\n", - "wind_speeds=np.array([6.0, 7.0, 8.0, 9.0])\n", + "wind_directions = np.array([260, 265, 270, 275, 280, 285, 290])\n", + "wind_speeds = np.array([6.0, 7.0, 8.0, 9.0])\n", "freq_table = np.random.rand(7, 4)\n", "freq_table /= freq_table.sum()\n", "\n", "wind_rose = WindRose(\n", - " wind_directions=wind_directions,\n", - " wind_speeds=wind_speeds,\n", - " ti_table=0.06,\n", - " freq_table=freq_table\n", + " wind_directions=wind_directions, wind_speeds=wind_speeds, ti_table=0.06, freq_table=freq_table\n", ")\n", "\n", "wind_rose.plot()" @@ -454,9 +468,9 @@ }, { "data": { - "image/png": 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fz+23347ZbPa+brPZuPfee4mNjUWn0zFr1qxe5wMOuyETEhLQarVceOGFtLa2DuV0c80113DBBRfw9NNPExkZicFg4De/+Q1Op5P77ruPkJAQ4uLieP/994/Z9l//+hc/+clPANi7dy9LliwhMDCQoKAgpk+fzo4dO4Y0Jn9FFDeRQVFRUcFNN91ESkoKxcXFfPfdd7z//vsoFAr2799PaGgop59+OtOnTyckJGRMRFWJnBxLlixhzZo13udr1qzx1vr0LLdYLGzdutUrbkfjEbo333yT999/n5UrV1JTU8PLL7/MhAkTkMlkPProo7z33nu88cYbbNiwgba2Nv75z38ed2y33norNpuNdevWsX//fp577rljupnfd999vPjii2zfvp3w8HDOO+88b/pDeXk5Z555JhdffDH79u1jxYoVbNiwgdtuu827/W233cbmzZv529/+xr59+7j00ks588wzKS0tBWDr1q388pe/5LbbbmPPnj0sWbKE3/72t4M/0f9l9erV1NfXs27dOl566SWeeOIJzj33XIKDg9m6dSs33XQTN954I7W1td5t3G4333zzDeeffz4AV155JXFxcd4OGg8++OD4uwEVREQGwN69e4UrrrhCUCqVwpVXXins3r1bKC0tFb7//nvhP//5j1BVVSW4XC5fD9PvsdvtwpdffinY7XZfD2XAWCwWoaCgQLBYLH2+/s477wg6nU5wOBxCV1eXIJfLhaamJuHTTz8VFi5cKAiCIKxatUoAhKqqKkEQBGHNmjUCILS3twuCIAjvv/++AAhlZWWCIAiC2+0WXn31VSEiIkJob28XzGazEB0dLTzyyCPCrl27hMLCQqGlpUWIi4sTzj///H7HPmnSJOHJJ5/s8zXPGP72t795l7W2tgoajUZYsWKFIAiC8Mtf/lL4v//7v17brV+/XpBKpYLFYhGqqqoEmUwm1NXV9VrntNNOEx566CFBEAThiiuuEM4+++xer1922WWCXq/vd9yVlZUCIOzevbvX8quvvlpITEzs9VvLzMwUFixY4H3udDoFnU4n/PWvf/Uu27hxoxAREeHdLjAwUPjggw/6Pb7nWP2d2xN9J/wBcc5N5LgUFhbyyCOP8N1333Httdeyb98+BEGgoqICnU7H5MmT/b7GnMjIsnjxYsxmM9u3b6e9vd3b8XvRokVce+21WK1W8vPzSUlJOW6pK61WS2pqKnA4ujEuLo7m5mYCAgK83ccXLlzIhAkTaG1tpaamhszMTBwOh7em5NHcfvvt3Hzzzfzwww8sW7aMiy++mMmTJ/daZ86cOd7/exrYFhYWAofdd/v27eOTTz7xriMIAm63m8rKSioqKnC5XN55RA82m807V1ZYWMiFF154zDG///77gZzeYzi6/VBkZCQTJ070PpfJZISGhnpLZMFhl+S5557r3e7uu+/m+uuv56OPPmLZsmVceuml3nM/XhDdkiJ9UlNTw3XXXce0adOIiYnh4MGD3HLLLZSWltLa2sqMGTNYuHAh0dHRorCd4qSlpREXF8eaNWtYs2YNixYtAvBWKdm0aRNr1qxh6dKlx93P0W4xiUSCIAje5rHwv9qGQUFB5OTkoFAo6OnpoaSkpNc8mIfrr7+eiooKfvGLX7B//37y8vJ47bXXBvzeTCYTN954I3v27PE+9u7dS2lpKampqZhMJmQymbcPnudRWFjIK6+8MuDjDIa+zlNfy9xut/f5V1995Z1vA3jyySc5ePAg55xzDqtXryYnJ+eELt6xhihuIr1obW3l3nvvJTMzE6vVyq5du7j55pspKCigp6eHefPmMW/ePG8dQRERODzv5umxd2QKwMKFC/nuu+/Ytm1bv/NtA0Gv1xMdHc2+ffsIDAxEIpHQ1dXlzY/T6XQUFxdTXl5+TAub+Ph4brrpJr744gvuuece3nnnnV6vHxn00t7eTklJCdnZ2cDhvnAFBQWkpaUd81AqleTm5uJyuWhqajrm9aioKACys7PZunVrv8ccaUpLS6mqquL000/vtTwjI4O77rqLH374gYsuuqjPIJSxjOiWFAHAbDbzyiuv8Pvf/57Zs2ezdu1a9Ho9ZWVlGAwG5s+fL5YqEumXJUuWcOutt+JwOLyWG8CiRYu47bbbsNvtJyVuAHfccQfPPvss6enpZGVl8eKLL9LZ2YnL5fJ2imhsbOTgwYOEhoYSExPD/fffz1lnnUVGRgbt7e2sWbPGK1wefvOb33hTDh555BHCwsK44IILAHjggQeYPXs2t912G9dffz06nY6CggJ+/PFHXn/9dTIyMrjyyiu56qqrePHFF8nNzaW5uZlVq1YxefJkzjnnHG6//XbmzZvHCy+8wPnnn8+///3vIbskh8K//vUvli1b5q0HabFYuO+++7jkkktITk6mtraW7du3c/HFF4/amEYD0XI7xXG73bz77rukpaXxr3/9i88//5y33nqLtrY2GhoamDFjBnPnzhWFTeS4LFmyBIvFQlpaGpGRkd7lixYtoru725sycDLcc889/OIXv+Dqq69mzpw5BAUFceGFFyKXy71NVyMiIsjJycHlcnHgwAE6Ozu59dZbyc7O5swzzyQjI4M//vGPvfb77LPPcscddzB9+nSMRiNff/21tzvB5MmTWbt2LSUlJSxYsIDc3Fwef/xxYmJivNu///77XHXVVdxzzz1kZmZywQUXsH37du/84uzZs3nnnXd45ZVXmDJlCj/88AOPPvroSZ2LwXBkCgAcnpNrbW3lqquuIiMjg5/+9KecddZZ/PrXvx61MY0GYrPSU5gdO3Zw66230tjYyPPPP8+cOXMoKioCICcnR5xPGwHGQrPSo7FarVRWVpKcnOzXydROpxOr1YrL5UKlUuF0OqmtrcVmsxEfH09wcHCv77Mn7669vd3vbt485bd2797N1KlTh7yflpYWoqOjqa2t7XXTMRCOV9psLHwnRMvtFKS1tZWbbrqJhQsXsnz5cjZt2kRkZCQHDhwgLS2NpUuXEhMTIwqbyJhCLpcTEBCAVqvF4XDgcrlISUkhNjaW6upqSkpKsFgsvh7moJg7dy5z584d8vZtbW289NJLgxK29evXExAQ0CtCdCwizrmdQrjdbv785z/z4IMPMnv2bHbs2IHD4WDXrl2kpqaSnp7eb58tEZGxgkKhQC6XY7fbsVgsqNVqcnJyMBqNFBQUEBkZ6dcdpAHi4uK8SeCebglDISMj45g0hRORl5fnLdR8dML7WEK8kp0ibN++nVtvvZXm5mbee+89pkyZQmFhIQaDgcWLF4/pL7GIyNFIJBJUKhUKhQKr1UpPTw/h4eGEhoZSU1PDgQMHmDx5Mm632y89FHK5/IQ1IEcKjUbjs2MPJ6JbcpxjNpu54447WLRoEWeddRabNm0iODiYkpISpk6dyuzZs0VhExm3SKVStFotAQEB3k7hHldlTU0NpaWlx/SHExkfiJbbOGb9+vVce+21REZGsn37dpxOJzt37hRdkCKnHJ75uCNdldnZ2dTX13Pw4EHi4uIICwvzSytOZGiIlts4pKenhzvvvJMzzzyTW265hb///e/U1tZiNptZvHgx2dnZorCJnHJ4XJWBgYHeQs1RUVEkJydTX18vWnHjDPEKN87YsGED1157LREREWzduhW73c7u3buZOHEiCQkJ4p3pCCMIAna7Hbfbjdvt9tYhFAQBQRDo7u4GoLOzE4VCgUQiQSKRIJVKvX+VSmWv2oHjgaamJoxGIw6HA61WS0JCgrek1tE0NzfT2trqjWzUarXExcX1Wr+ysvKYtjFBQUEDCp7wuCqVSiU9PT3IZDKys7Opq6vj4MGDxMfHExoaKv5WxjiiuI0Tenp6eOSRR3j77bd56qmnuPzyy9m/fz9BQUEsXboUjUbj6yGOadxuNzabDavVitVq7fX/I5fZbDZvj7GjhUsikXgtg+3bt3sF70gB9KBUKlGr1ahUKtRqda/HkctkMplPzsdg8DSuTUxMRKfT0djYSElJCRMnTuwz16+7u5uQkBACAgKQSCQYjUZKSkqYMGGCN7kaDotZcnKy9/lgxUgulxMYGIjFYqGnp4fo6GiCg4Opqqqivb2dxMTEXscTGVuI4jYO2LNnD5dffjkhISFs3boVh8MhWmtDwOVy0dXVRWdnJ52dnfT09PQSLfif6BwpNEFBQccs8wja0XiSuJctW9bnhd0jokeLp81mo7u7u9dzQRBQKBResdNoNAQFBWEwGNDr9X6TJN7Y2Eh4eDhhYWEAJCYm0tnZ6U0wPpqUlJRez5OSkti9ezddXV3efcBhC+xk36NEIjmuFZeYmEhISMhJHUPEN4jiNoYRBIE333yT++67jwcffJDrr7+effv2idbaAPAIWUdHB52dnXR0dNDV1YVcLkev12MwGIiKiupTtEYSqVSKRqM54WfncX8eKYAWi4XW1lZv8WCdTofBYPCKncFgGHXBc7vdmM1mbxFhOCwoQUFBfVbx728fDQ0NdHd3ExgYCEBdXR3d3d1s374dmUyGTqcjPDz8pOaSPSW8goKCSE9Px2AwcOjQIbq7u4mPjx93ruLxjihu/+Waa67hww8/5JlnnuHBBx/0Lv/yyy+58MIL6e7uJjg4mI8++ojLL7/c+/rll1/OihUrqKysJCkpybs8KSmJX/ziFzz11FMjMt6Ojg6uv/56Nm/ezLfffkt4eDg7d+5k0qRJorV2FC6Xy2uNecTMI2SeC7/nYqbVasfEufMER6hUKvR6/TGv22w2Ojo66OjooLW1lYqKCiwWi1fwPGKn1+tH1PXmdDqBY9u0ePLPBsLWrVu59NKLsVhswz6+vtBq1WzbtoPExESys7OprKyksLCQ1NTU45aaqqur44EHHuC7776jp6eHtLQ03n//ffLy8vrdJj8/n7vvvts71/foo4+yaNEiWltbiY2N7WXZtre3U15eTl5eHt3d3RQXF/e5zylTpviN1e5LRHE7ArVazXPPPceNN95IcHBwr9cCAgLIy8sjPz+/l7jl5+cTHx9Pfn4+11xzDXB4sruqquqE/auGyrZt27jsssvIzs5m06ZNVFVV0dTUxKJFi7x3tqcybreblpYWGhsbaWlpobu72ytkBoOBjIwM9Hr9mBGyoaBSqYiMjOxVdulIwWtvb6eyshKLxYJWqyUkJISoqCgiIiL86sLY0NDAoUOHsFhs/OX1SLLTR3YOrLDUzlW3NWKxWJBIJNhsNlJTU2lqaqKgoIDExERvE9IjaW9vZ968eSxZsoTvvvuO8PBwSktLj7mOHEllZSXnnHMON910E5988gmrVq3i+uuv589//jMTJ07EaDSe0BqdOHHiMfOuYiT0YcSzcATLli2jrKyMZ555ht///vfHvL5kyRK++OIL7/PCwkKsVit33HFHL3HLz89HpVL16vA7HAiCwB/+8Acee+wxfv3rX3P55Zezb98+YmNj+/ySn0rY7XYaGxsxGo00NTUhl8uJiooiMzMTg8GARqMZt0I2UPoTPM/8V3FxMTt37iQsLIyoqCiioqK8bVKGiudC63A4ei13OBwnFFGj0YjRaCQxMRGA7HQl0yaPTpFeT0Sl3W6np6eHsLAwAgMDqayspKuri4SEhF6/t+eee474+PhePdGODHbpi7feeovk5GRefPFF4HDftw0bNvDee+/x7rvvYrPZMBqNxMXF9bsPuVwuilk/iGflCGQyGU8//TQ/+9nPuP3224/5Ui1ZsoRnnnmGhoYGoqOjWbNmDfPnz2fp0qX86U9/8q63Zs0a5syZM6zVstvb27nqqqvYv38/P/zwA4GBgezdu5fc3Nxe7TdOJUwmk/cC2NbWRlBQEFFRUaSnp6PX6095MRsIKpWKiIgIb6sYs9mM0WikoaHB2wjU05h2KA1EpFIpOp3O69aHwzdpXV1dRERE9LudZwzp6emUlJQM+f2dDB7Xr0wm8wabZGVlcejQIa+b0jM3+tVXX7F8+XIuvfRS1q5dS2xsLLfccgs33HBDv/vfvHkzy5Yt67Vs+fLl3H777UgkEmJjY6moqCAiIkKM2hwC4gzpUVx44YVMnTqVJ5544pjX5s2bh1KpJD8/HzhsoS1atIjp06fT0tJCZWUlAGvXrj3pxoxHUlhYyMyZM5FIJKxfvx6Hw0F7ezuLFy8+pYRNEARaW1s5ePAgq1atYs2aNTQ3NxMbG8vpp5/O4sWLycrKwmAwiMI2RHQ6HampqcybN8/b5NMTvWkymejp6cFutw9K6CIjI2lubqalpQWLxUJ1dTVut9sb+VhZWUltba13/YaGBurq6khKSvK2rvElnpQBj5syJSWF4OBgioqK6OjoAKCiooI333yT9PR0/v3vf3PzzTdz++238+GHH/a7X6PReEy1/sjISEwmE1arleDgYLRaLfX19f3uY9++fezatcv7OHDgwLC85/GAaLn1wXPPPcfSpUu59957ey3XarXMmDGD/Px8rrjiCtauXct9992HXC5n7ty55OfnIwgC1dXVwyZuK1eu5Gc/+xm33XYbt9xyC7t37yYxMZGcnJxTInrL7XbT2NhIQ0MDjY2NwOELQHZ2NuHh4X41PzTeUCgUxMbGEhoaSkVFBWq1GolE4i1ELJfLUSgUKBSK434XQ0JCcDqd1NfXe5O409PTvZ/d0VVBmpubEQSB8vJyAJ9ZbkfiSRnwuClDQ0PRaDRUVFQQHR2N2+0mLy+Pp59+GoDc3FwOHDjAW2+9xdVXXz3k48bFxVFcXNxvy5qsrKxe5168qfsforj1gafP2UMPPeSdR/OwZMkSVqxYwcGDB7FYLEybNg043HF4zZo1uN1utFots2bNOqkxCILACy+8wK9//Wveeecdpk+fzq5du8jNzSU2Nvak9j0WMJvNVFVVUV1djVQqJTY2lpkzZxISEiL+gH2ARCJBoVB40yLcbjcOh8Nbq9GTbyeTyfr8fDyuz77IzMzs9Xzy5Mm9nvvLTdyRbkqz2YxKpSIzM5Py8nLCw8PJzs7utX52djaff/55v/uLiory3rB5aGxsJCAgwDulERgYiF6vp66urs9AFqVSKc659YN4Vvrh2WefZerUqcf88JYsWcJvf/tbPv30U+bPn++dVF64cCFvv/02giB43ZdDxWq1csMNN7BmzRpWr16NVCrl0KFDzJ8/3+86Bg8nLpeLhoYGqqqqaGtrIzIykmnTpnnnfET8A4lEgkwmQyaToVarcTqd2O12TCaTt3zYeCwh5sHjpvTk6WVmZpKbm8uePXuw2+3e335JSYk3GKYv5syZw8qVK3st+/HHH703zB5iY2MpKCjw247X/ooobv0wadIkrrzySl599dVey+fOnYtKpeK1117jkUce8S6fOXMmTU1N/Otf/+Khhx4a8nHr6+u58MILkUqlrF+/nqqqKmQyGQsXLhy3X+6enh4qKyuprq5GoVCQmJjI9OnTx+37HW94IvY0Gg12u92bXO6x5obLsigsHfmixgM9hlQqJSAgAIvFgtVq5eGHH2bRokXcc889XH/99Rw8eJC3336bt99+27vNQw89RF1dHX/5y18AuOmmm3j99de5//77ue6661i9ejWfffYZf/7zn3sdS6vVEhoaeoyVB4fzCI+e/5TJZOP2xmIwiOJ2HH7zm9+wYsWKXsvUajWzZ89m7dq1LF682LtcpVIxe/Zs8vPzhzzftmfPHs455xxOP/10nn76afbt20dkZCSTJ08ed2H+nuCQ8vJympqaiIqKIi8vT2w7MoY5MrHc5XJ5g1BkMpm3cehQPtuwsDC0WjVX3XbsxX0k0GrVvcp89YdEIkGj0SCTycjJyeGzzz7j4Ycf5u233yYxMZGXX36ZK6+80rt+Q0MD1dXV3ufJycl8++233HXXXbzyyivExcXx7rvvsnDhQlwuV69jxcTE0NbWdswY+gogycrKEns0AhJhKPG9IsNOfn4+F1xwAQ888ABXXnkl+/btIycnh+Tk5HF1sXe5XNTW1norZiQmJpKcnHzS+VRjBU9tybPPPnvMBMNYrVYqKytJTk4etDXtdru91pwgCKhUqiG5LKurq2lpaRnUNkMlLCyMhISEQW3jcDjo6elBoVDgcDgoLy8nNja230CQsc7JfCdGC9Fy8wP+8Y9/cPXVV/Paa68xf/58Dhw4wMyZM4+bBzTWcDgcVFRUUFFRgUqlIiUlhbi4OHEyfJwjlUq9dTmdTqe3ILRSqfQGZwyEhISEQQvOaKJQKAgICMBsNiOTycjIyKCsrAyHw0FsbOy4ukEdK4hXFh/jKXz8t7/9jcTERCoqKpg3b16f9QLHIi6Xi6qqKoqLiwkICGD69OligMgpiCfaUqFQ4HK5sFqtdHd3e92Y42GOSCaTeQUODgealJaW4nQ6SUxMFL/zo4wobj5CEASefPJJXnvtNb777jvUajVNTU0sXLhwXLjoBEGgtraWoqIiZDIZubm5REZGij9wEW8Vf6fTidVqpaury2vdjfXvhyfQxGw243A4yMjIoLy8nLKyMlJSUsbd3Lk/I4qbD3C5XNxyyy2sXLmSNWvWeCs/LFiwAJVK5evhnRSCIHiLzDocDrKysoiPjx/zFy2R4UculxMQEIDD4fD2qFOr1SiVyjH9fZFIJOh0OiwWCzabjfT0dCoqKigpKSE9PV10xY8S4lkeZex2O1dccQWFhYWsXr2auro6lEol8+bNG/Nf+ra2NgoKCuju7iYjI4OkpCTxTlXkhCgUCuRy+TEiN9ToSn/AE0kpkUiwWCykpKRQXV1NUVERGRkZYq3IUWBsX03HGDabjUsuuYSGhga+//57SkpKCA4OJjc3d0zPOXR1dVFYWEhzczOpqanMmjVrzEQCivgHEokEpVKJQqHwVj3xiJxcLh+TInekwPX09JCQkEBdXR3FxcVkZmaKAjfCiOI2SlitVi666CJaW1v55z//SUFBATExMUycOHFM/nABLBYLRUVF1NbWkpiYyLJly/w2LFhkbODJlVMqldhsNm81fo/IjUXUajVSqZSenh5iY2Opr6+nuLiYjIyMMT8N4c+MzW/LGMNisXD++edjMpn4/PPPOXDgAAkJCWRnZ49JYXM6nRQXF1NZWUlUVBRLly5Fp9P5elgi4wiJROKdf/MkgysUCjQazZj0cnistJ6eHmJiYmhoaPBacKLAjQyiuI0wVquVCy+8EKPRyB/+8Af2799PUlISWVlZY1LY2tra2LVrF0qlctzXuhTxPVKpFI1Gg0qlwmKx0NXVhUajGZNBJ0cKXFhYGHa7XRS4EUQUtxHEZrNx8cUX09HRwb/+9S/2799PYGDgmBQ2l8tFYWEhhw4dIisri9TU1DH3HkTGLrW1tbS0tOB0OrFYLF5X5UhYcUOpUDJQlEqlt9B0XFwcTU1NlJSUiHNwI4AobiOE3W7n0ksvpbm5mS+++IIDBw6QkpJCY2Mju3btYtq0aWNGHI601hYtWkRgYKCvhyRyClFdXU1mVhZWi2VUjqfWaCguKhqUwLlcLp588kk+/vhjjEYjMTExXHPNNTz66KO9fuc2mw273Y5arcZqtVJSUsJdd91FSUkJ8fHxPPbYY8e02RIZGqK4jQBut5urr76a2tpa/vWvf3HgwAESExPJysoiLS2NjRs3jgmBO9Jay8zMJC0tza/HKzI+aWlpwWqxEP7zn6EY4VqNjsZGmj/+lJaWlkGJ23PPPcebb77Jhx9+yIQJE9ixYwfXXnster2e22+/HTgsbBaLhYCAAORyOVVVVVxwwQXccMMN/OEPfyA/P5/rr7+e6Oholi9fPlJv8ZRBFLdhRhAE7rrrLnbs2MGPP/5IQUEB8fHxXlekWq1m3rx5fi9wbW1t7N69G4VCIVprIn6BIjISVXycr4fRJ5s2beL888/nnHPOASApKYm//vWvbNu2DThW2ADee+89kpKSePLJJ9FqtSQlJbF7925eeuklUdyGgbEXduTnPPfcc6xYsYKvvvqK8vJyoqKiyMnJ6SVgHoHr6Ohg165dx/Rj8iUul4uDBw+yadMmEhISmD9/vihsIiInYO7cuaxatYqSkhIA9u7dy4YNGzjrrLP6FDaAzZs3c/rpp6PVaunp6SExMZH58+ezadMmv7omjFVEy20Yef/993nmmWdYtWoVzc3NBAYGMnny5D4tM3+04DzWmlwuF601kRPS1NSE0WjE4XCg1WpJSEjoNyWkubmZ1tZWLP+dN9NqtcTFxfVaXxAE6uvrvYEjAQEBx+1k7U88+OCDdHV1kZWVhUwmw+Vy8bvf/Y5LLrmkT2EDMBqNREZGolQqcbvdWCwWJkyYgMlk8kZR+vqaMJYRLbdh4ptvvuFXv/oVX3zxBQ6HA5lMxvTp04/75fQXC+5oa23BggWisIkcl7a2NmpqaoiJiSEnJweNRkNJSQkOh6PP9bu7uwkJCSEzM5OsrCyUSiUlJSXY7f/rfG00GmlqaiIxMZHs7GxkMhmlpaW43e7ReltD5rPPPuOTTz7h008/ZdeuXXz44Ye88MILvPfee30K29F4cvo856+rq4v6+vrRGPq4RbTchoFNmzZxxRVX8MEHH2AwGOjq6mL+/PkDqqvoawvOarWybds23G43CxcuJCgoaNSOPR4QBMHbo8zz1/NwOBwIgoAgCLjdbm9RaYBt27Yhk8mQSCRIpVIkEok3vN3zUKlU3hwvf0tcbmxsJDw83NuxOjExkc7OTlpaWoiOjj5m/ZSUlF7PPfNLXV1dhIWFec9NdHS0N3cyKSmJvXv30t3dPeLv52S57777ePDBB7n88ssByMjIoLi4mFdeeYWbb765z22ioqJobPxfd3G1Wk1LSwtBQUFMnDiRkpISFArFuOrrOJqI4naSFBYWcu655/L888+TnZ1NfX09CxYsGFRtRV8JXEdHB1u3biUsLIypU6eKRY77QBAEenp66OjooLu72ytintqHNpsNQRBQKBReQfKIk6eaxpECptPpvJVdpFKpV/QEQfC2gOno6OglmHA4P+pI0VOr1eh0OgwGA4GBgaMqfm63G7PZTFRUlHeZRCIhKCjI28tsIPsQBMFr0djtdhwOR6+bK7lcjk6nw2g0Du8bGAF6enq8n4Hn+6HRaI5rdc6ZM4eVK1d6n0skEtatW8fMmTNxu92kpaVRUlKCXC4nJCRkxN/DeEMUt5Ogra2N8847j5tvvplly5ZRWlrKggULhlRfcbQFrq6ujt27d4sh/kcgCAJms5mOjg46Ozvp6Oigo6MDl8tFUFAQQUFBqNVq798jhWagNwYOh4PKykoSEhIGdAPkdrv7tQzb2to4cOAAbreboKAgDAYDBoMBvV5PUFDQiAme0+kEOGb8CoUCq9U6oH3U1taiVCq9YuZxxx3tvlMoFN7jOY6wckaKoR7jvPPO43e/+x3R0dGkpKRQUlLCyy+/zHXXXedd56GHHqKuro6//OUvANx00028/vrr3H///Vx33XWsXr2azz77jG+++QZBEJBKpaSkpHi714sl7gaHKG5DxOl08tOf/pRJkyZxyy23sHv3bubNm0dAQMCQ9zkaAicIAoWFhVRWVpKXl9fr7vtU4kghO1LMjhSK2NhYJkyYMKJCcSI85ac0Gk2frx/9Pmpra48RPL1e7/3rDzcxDQ0NtLW1kZmZOaDzGhISglqjofnjT0dhdIeTuD3u1oHy2muv8fDDD/OrX/2KlpYWYmJiuPHGG3n88ce96zQ0NFBdXe19npyczLfffstdd93FK6+8QlxcHO+++y5nnnkmbrcbk8mESqUiJiaGsrIycnJyxG4bg0AiiDGnQ+L2228nPz+f77//nt27d5Obm0tMTMyw7NtqtbJx40YMBsOwCpzD4WDXrl10d3cza9asUy5oxO1209raitFoxGg0YrVaR9XigcOfwcqVKzn77LNH7ELlEbwjrc+Ojg6kUimRkZFERUURHh4+4ONbrVYqKytJTk5GrVbjdrvZtWsXqampBAcHe9errKzE5XKRlpbW776MRiMNDQ1kZGT0skRsNhv79+8nJyenVyf6oqIi7/OWlpbBnopeuN1urFYrbrcbjUbTr7U9lPJb/YX7nwwulwuTyYRaraa+vh6r1TrgG4KR5ujvhD8iWm5D4J133uHTTz9lw4YNHDx4kLS0tGETNhgZC85sNrN161bUajULFy48ZerYORwOGhsbvZF4UqmUqKgoJk2aRFhY2Jhto3I8JBIJAQEBBAQEEBsbCxwWvPb2dhoaGigsLGTnzp2EhYV5xe5IQTkRUqkUnU5Hd3e3V9wEQaCrq+u4wQ8eYUtPTz/Gxebp5dbV1eUdi8vlwmw2ExERQUhIyLDUexQEAavVit1uR6vVDssNxkgIG4BMJkOr1WI2m4mNjaW8vJyqqiqSkpL8wgL3d8bfL3uEWb9+PXfeeSdff/01ra2tGAwGMjIyhv04wylwzc3NbN++nfj4eCZMmOAXd34jidls9lpnra2tBAYGEhUVxZw5czAYDKfkhUEikRASEkJISIg3l6qxsZGGhgYOHDhAUFCQV+gGco4iIyOprKxEq9Wi0+loamrC7XZ73XmVlZUoFAri4g5XFGloaKC+vp6UlBRUKpV3jk0qlXqjRiMiImhoaPCGxdfX16NUKoe184SngahUKsVsNnvnTYf6nRgpYfPgCVSyWCwkJydTXFxMY2PjKTudMBhEcRsEVVVVXHzxxbz44ouEhobS1tbGrFmzRuxiebICJwgChw4d4uDBg0yaNGnMJMQOBYvFQnV1NXV1dZhMJsLCwoiOjiY3N3dQVsmpgseyS01NxW63exOyN23ahFwuJzo6moSEhH6FJSQkBKfTSX19vTeJOz093WsJHZm/BodvsARBoLy8vNfymJgYr9cjKioKt9tNVVWVN4k7PT19RG7GVCoVMpkMs9nsdVMO9nc80sLmQaVS4Xa7cTgcpKamUlJSgkajQa/Xj9gxxwPinNsA6enpYe7cucydO5f77ruPoqIiFi1aNCoXzqHMwbndbvbv309DQwMzZswgNDR0xMc52rjdbhobG6mqqqKpqYmIiAji4+OJiIjw24n30ZhzOxk885K1tbXU1dUREBBAfHw8NpuNlJQUv51fGSqetAYAnU43YCEdLWHzIAgCJpMJqVSK1Wqlurqa7Oxsn30e4pzbOOL2228nMDCQxx57jJ07dzJnzpxRswgGa8E5HA62bduGw+Fg4cKF485ysVgsVFZWUl1djVQqJTExkSlTpvQbUSgycKRSKeHh4YSHhzNx4kTq6uqorq4mICAAi8WCXC4fV/OUUqmUgIAAenp66O7uJiAg4IRpHaMtbIA3R7K7uxudTkdYWBjl5eVkZ2eP+2mGoTJ+vqUjyCeffMKXX37Jli1b2Lt3LxMnThx1S2igAudwONi8eTNyuZz58+ePmwuRIAi0tbVRUVGB0WgkIiKC3NxcIiIiTsk5tNFAoVCQlJREVFSU151oMpmQyWSoVCoUCsW4OPcSiQStVovVasVkMh1X4HwhbB48gTwmk4nIyEhMJhM1NTXjerrhZBgfV74RpKSkhJtvvplPP/2UpqYmwsPDffZlOpHA2e12Nm3ahFqtZsaMGeOi4ojL5aK+vp7y8nLMZjOJiYksXbpUTGgdZTz5dkqlErvdjsViwWKxoFKpUCqVY9568LSjgsMCrtPpjhEvXwqbB7lcjkaj8QaYFBYWEhgYKFYw6QNR3I6D1Wrlsssu4//+7//IyMigurqaGTNm+PRutT+Bs9lsbNq0CZ1OR15e3pi/2AiCQE1NDUVFRd5KDfHx8X45T3UqIZVKvRGGDofDWzVFpVL5ZQ3MweCJpJRIJJjN5l4C5w/C5kGpVOJ0OnE6nSQlJXHo0CF0Oh0qlcqn4/I3RHE7Dvfeey9KpZJ77rmHnTt3Mn/+fL+4uB4tcDk5OWzatAm9Xs+0adPG9AVGEASMRiOFhYW4XC6ys7OJi4sbF+6v8YREIkGpVHovtBaLBbvd7hW5sfx5HWnBBQQE4HK5/EbY4H8ibDKZ0Gg0hIaGUl5eTlZW1pj+7Q83vv+k/JQvvviCjz/+mC1btrBv3z5ycnKGNd/mZPEI3Pr161m9erU37H0sX1RaW1spKCjAZDKRmZlJUlKS+GMdA8jlcgICAnqJnFqtHtY5uerq6pOuUDJQPBVKJBIJJpMJwG+EzYNUKkWr1WIymYiKiqKsrIy6ujri4+N9PTS/wX8+LT+isrKSX/7yl7zzzjt0dHQQHBxMcnKyr4d1XMZCz6v+6OrqoqCggJaWFtLS0pg9e7ZfWMgiA0cikaBQKJDL5d45OavVikajQS6Xn5TIVVdXk5mZhdVqGcYR949araG4uIjIyMgBb7Nu3Tqef/55du7cSUNDA//85z+54IILvK8LgsATTzzhvabMmzePN998k/T09OPu94033uD555/HaDQyZcoUXnvtNWbOnAkcvqk4MsHbM//mTzfhvkQUt6Nwu91cddVVXH755UybNo3y8nIWL17sdxaRZ44tJCTE65b0l47eA6Wnp4eioiLq6upISkoiNzdXnDcY40gkEm+Qic1m87aC8YjcUGhpacFqtTCBmegY2XqoZro5aN1GfX09QUFBXrekx0XZ33swm81MmTKF6667josuuuiY13//+9/z6quv8uGHH5KcnMxjjz3G8uXLKSgo6DdPbMWKFdx999289dZbzJo1i5dffpnly5dTXFzsLXOmUqlwOp243W4SExM5dOgQEyZMEG8OEcXtGF599VUaGhpYsWIFu3btYu7cuX5Xh9Fut7N582YCAwPJzc1FKpX6tOHpYHE4HBQXF1NZWUlMTAynnXbauMvFO9XxRB96RM5kMnkj/YYaxasjkCBJ8IlXPBn+W9LCZrN5xUwul3uLUfcVRQlw1llncdZZZ/W9S0Hg5Zdf5tFHH+X8888H4C9/+QuRkZF8+eWX3ganR/PSSy9xww03cO211wLw1ltv8e233/Lee+/x4IMPAv9LY/Dk6AUGBlJdXU1KSopfXwNGA3FC4whKS0t59NFHeffddykpKSE1NdXvQmw9eWwajYbp06d756Q8c3AdHR3s2rULfy0809TUxOrVq+ns7GThwoVMnz5dFLZxjMdq83Rb8DR89dfvp4ejLU1PhKjZbMblcg1qX5WVlRiNRpYtW+ZdptfrmTVrFps3b+5zG7vdzs6dO3ttI5VKWbZs2THbeObfLBYLcXFxdHd3097ePqgxjkdEcfsvLpeLa6+9ll/+8pdER0cjCAKZmZm+HlYvnE4nW7ZsQalU9hnu788C53A42LNnD9u3byczM5O5c+eKtfFOITwXYJ1Oh91ux2QyDVokRpO+rDOPJTrYsXs6iR89hxcZGdlvl/GWlhZcLteAt1EoFCiVShwOBwkJCVRXV3uLU5+qiOL2X1599VWMRiMPPPAApaWlXnefvyAIAjt27EAqlTJz5sx+XTv+KHBNTU2sWbOGnp4elixZIrbsOIVRKBQEBgYik8no7u7GZrP5xXd0oHgEzlNw2Z9Qq9W4XC50Oh2BgYFUVVWNqXM73PjP1duHlJSU8Oijj/LnP/+Z4uJiUlNT/S7iqLCwEJPJNKDKI/4icEdaaxkZGaNaj1PEf/HME+l0Ou98nD9bcUfimUuUyWT09PQM6LflaU/T2NjYa/nxWteEhYUhk8kGtc3R7kmTyXRKuydPeXHzuCNvuOEGrwvA39yRtbW1VFZWMmvWrAEHt/ha4ERrTeREjFUrziPObrcbi+XE6QnJyclERUWxatUq77Kuri62bt3KnDlz+txGqVQyffr0Xtu43W5WrVrV7zYguieP5JSPlnzttddoamrigQce8FYh8Sd3ZHt7O3v27GHGjBkEBg4uDHokOnqfCIfDwcGDB6mrq2PChAkkJiaKoibSLx6hUCgU3gRwrVbbp3fCTLc3mnGkMNM9oPU8VfpNJhM2mw2Hw0FZWZn39crKSvbs2ePtIH7nnXfy29/+lvT0dG8qQExMTK9cuNNOO40LL7yQ2267DYC7776bq6++mry8PGbOnMnLL7+M2Wz2Rk/2h1qt9nYP8Lgn09LSBn8yxjintLjV19fz2GOP8eWXX1JUVERaWppfuSOtVivbtm0jKytrUAmlRzKaAtfe3s727dvR6XQsWbJEdEGKDBhPArjFYqG7uxutVuv1UoSFhaFWazho3TYqY1GrNd6O4sdDJpN5BW7Hjh2cfvrp3tfuvvtuAK6++mo++OAD7r//fsxmM//3f/9HR0cH8+fP5/vvv++V41ZeXt6rCstll11Gc3Mzjz/+OEajkalTp/L999+f8FrgcU+azWbi4uIoKCigs7PzlAvgOqWblf7sZz8D4KmnnuLQoUMsWbLEb6w2l8vFxo0bCQgIGJayWkNpeDoYamtr2bNnD5mZmaSlpYnWWj/4e7PSvhjtxpQOh4Oenh6USiVqtRqJRDKs5bfsdjs2m63fxHJP+a2B4ikePZBecKOJpwmr2WymsbGRCRMmDNv1TWxW6sesWbOGb775hj179lBYWMjMmTP9RtgEQWDPnj0ATJkyZViEYqQsOEEQKCwspLKykhkzZgzZwhTxPccr9XQ0FouFuro6enp6sNvtxMfHH/PZ19fXU19f32uZWq1m4sSJxx2HQqEgICDAm1Om1WpJSEgYlOD0x0hU91epVLhcLsxmM4GBgX5zY6fRaOjq6sJgMNDS0oLRaCQmJsbXwxo1/ONqPso4HA5uu+02Hn/8cdrb24mMjCQ8PNzXw/LicU8cL+R/KAx3kImn43d9fT0LFy4UhW0M4yn19MQTT7Br1y6mTJnC8uXLaWpq6nN9t9uNSqUiLi7uuBaoRqNhypQp3sdAg7VkMhkBAQEAwxZNOZJtazQaDVKpdMARlKOBJ4HearUSHx+P0WjEZrP5elijxikpbq+++ioAV155JQ0NDUyYMMHHI/ofjY2NFBUVMXPmzBEx94dL4MxmM+vXr8flcrFw4cJBB7uI+BdHlnrKycnhrbfeQqvV8t577/W5vk6nIz4+npCQkBNaKgqFotdjoHg6TysUCkwm00lF/Y10PzZPYIzL5cJqtQ77/oeKUqn0FrUOCQmhpqbG10MaNU45cauvr+fXv/41r776KoWFhWRmZqLRaHw9LAC6u7vZsWMHU6dOJTh45GronazANTc3s3btWiIiIpg9e7bf1d4UGRyDKfU0WGw2G3v37mX//v1UVFQM2nLw9C7TaDSYzeYhle4arUajHjG22WzY7fYRO85g8Jw/q9VKdHQ03d3ddHZ2+npYo8IpJ2733nsv55xzDklJSQiCQGpqqq+HBBy+wGzdupXk5GTi4uJG/HhDEThBEKioqGDr1q1MmDCBiRMn+s08pb8hCAI2m42uri46Ojpob2+ntbWVuro6ANra2mhra6Ojo4POzk6f1lscaKmnwY5Pp9ORlJREeno6CQkJ2Gw2iouLh+RiVCqVBAQEeIVqoGMZ7Q7angjKnp4enE7niB9vIMjlchQKBU6nk9jYWKqrq0+6uoq/uF6PxykVULJhwwa+/vpr9uzZ43X9+cPFWRAEdu7cSUBAANnZ2aN23MEEmbjdbvbt24fRaGTu3Ll+V1B6tLHZbHR2dnr7lnkensg5m82G2+1GJpMhk8mQSCRIpVJv0q8nYEgQBFwul/dCqFKpUKvV3seRz/V6PRqNZtQDFjyuxJ6enkF5OY4OPdfpdOzfv5+2trYhzXHL5XICAwMxm82YTCZ0Ot1xf7+jLWweFAoFarXaG2DiD9eYo4NLGhsbiY6OHvL+enp6APw64veUETdBELj//vu577776OzsJCIiwm+CSA4dOkR3dzdLliwZ9QvXQATO5XKxfft2rFYrixYt8hs37mhhs9no6OjwWlkdHR1YLBa0Wi1ardYrPofzsXoL0tEXVU8qwNKlS3tdGFwul1cYjxRJq9XqPZ7JZEKhUKDX6zEYDBgMBvR6PVqt9qS+Nycq9SSTyTAYDN7gkqOPJwgCDodjQHNNCoXCe9EfKp58uLa2tn5b6NjtdqxWK1qtFqfTOepWlCAIOJ1OOjo6/CbfUyqV0tXVRUREBNXV1QQGBg5a9AVBoKenh6amJgwGg1+lPhzNKSNuX331FWVlZVx//fXs3LmTpUuX+npIwOHAjIMHDzJr1iyf3QUdT+CcTifbtm3D5XIxb948v75TGw4EQcBkMtHY2Ehra6vXOtPpdBgMBm9Xdr1eP6xzjTKZzCuW/eFyubxuzo6ODkpKSujq6kIul6PX6wkODiYqKorg4OBBid2RpZ48FTM8pZ481TI89Qz7ip5samrCarXS3X386h5ut5v6+nr0ev2AyladCLvdjsvlQqVS9bKOnE4nDofjmOWjjSAIWK1WlEqlX4iAx1Uul8vp6upi3759Q57bNxgM/da49BdOiSRul8vF5MmTuemmm5g5cyZarZZJkyb5elgIgsCmTZsICAhgypQpvh7OMYneTqeTrVu3IpFImDVr1qi6dkYTt9tNW1sbRqMRo9GIxWIhPDycsLAwr5U0XKI+3EncRwpeW1sbjY2NSCQSIiMjiYqKIiIiYkCf24oVK7j66qv505/+5C319Nlnn1FUVERkZCRXXXUVsbGx/Pa3v8XhcGC32ykvLwfgxhtv5Nxzz+W8885Dq9WSmJgIwHPPPceSJUuIjY2lqamJ1157jcLCQr799tthcWsLgkBpaSlNTU3k5uai0+moqamhvLycqVOn+kW1IaPRSGlp6aDqwo4kjY2NlJaWotfr+cUvfsF33303aPekQqHwC7E+EePzanUUH330ERaLhUsuuYSdO3f2KpPjSyorK+np6WHWrFm+HgrQ24LbsWMHFosFuVzOzJkzx52wORwOmpqaMBqNNDY2IpVKiYyMZMKECYSHh4+Z9yuTyQgODvZalB6hbmxspKCggJ07dxIWFkZUVBRRUVH9upRPVOqpuroaqVTqnUM0Go1MmzbNu/327dt54oknWLRoEfn5+cDhThZ/+tOfaG1tJTw8nPnz5/OPf/xjWBOJJ02aRGFhIdu3bychIYHKykrmzJnjN3PCiYmJNDY2UlJSQl5enq+HQ0JCAocOHcJgMJCbm8vvfvc73n33XV8Pa0QY95ab1WolIyODp59+msTERMLDw8nKyvL1sDCbzaxZs4ZZs2b5zdyfh+7ubvLz81EqlZx22mlj5kJ/IgRBoLm5mUOHDmE0GgkICPBe9Afryhsqo11+q7u7m8bGRoxGI21tbQQHB5OYmEhMTMy4+ly3bNlCU1MT06ZNIz4+3tdD6oXVamX16tVMnTrVLyqENDc3s337dpKSkpg+fTq7du0a1UC20WJ8fLuPwx//+EdCQkJYunQpe/fuZfbs2b4eEoIgsHv3buLj4/1O2JxOp7eaucViYe/evaPSTWAksVqtVFVVUVVVhdvtJiEhgaVLl3orYIxnAgMDCQwMJC0tDZvNRm1tLWVlZezfv5+4uDiSk5MJCgry9TBPisrKStra2khISODgwYMEBwf71WerVquZPHkye/fuJTQ0FJVK5dPxhIeHYzAYEASB6667jkceeYQvvvjCp2MaCca15dbZ2UlKSgofffQRarWapKQkUlJSfD0sKioqKC8vZ8mSJX5193z0HJvD4RjRYssjTXt7OxUVFdTX1xMWFkZiYiJRUVE+DTLwh8LJgiDQ3t5OVVUVdXV1BAcHk5KSQlRU1Jj7jCsqKigsLGTOnDkEBwdTWFhITU0N8+fPR6fT+Xp4XgRBYPv27UgkEmbMmOHr4dDR0cGGDRuYMGECkyZN4j//+Y9f3PgPJ75PwBhBXnrpJSZNmsSECRNwuVwkJSX5ekiYzWYKCgrIzc31K2FzuVxs27YNQRC8NS193fB0KAiCQENDA+vWrWPjxo0oFAqWLFnCnDlziImJ8YucI18jkUgICQkhNzeXM844g/DwcPbt28d//vMfysvLx0xX7COFzVMGLDs7m5iYGDZt2uTNxfIHJBIJkydPpqWl5Zhi0r7AE+3Y2dnJXXfdxSOPPOLrIQ0749Zy6+7uJiEhgX/84x+43W6ysrJ87osXBIGNGzcSFBTE5MmTfTqWIxEEgW3btmGz2ZgzZ84xFsVIt8sZLlpaWigoKKCnp8dbFcPfUhf8wXLrC7fbTUNDAyUlJTgcDu/vxV8/66OF7UgEQWDfvn00NzezYMECn7sBj6S2tpb9+/ezdOlSn4/LZDKxZs0acnNzyc7O5j//+Y/fBLcNB+P2NvZPf/oTmZmZpKenI5FIiI2N9fWQqKiowGKxkJOT4+uh9KKgoACTycTs2bP7vOD6uwXX2dnJ5s2b2bp1K5GRkSxbtozU1FS/Eg9/RyqVEhsby+LFi8nOzqa4uJg1a9ZgNBr97vM+nrDB/6wkvV7P9u3bT7rU1HASGxtLaGgo+/bt8/VQCAgIICYmhtbWVm666SaeeeYZXw9pWBmXlpvNZiM5OZk//vGPBAUFkZKS4nOXpMlkIj8/n9mzZw+oy+9oUVNTw/79+1m4cOEJJ+H9zYIzm80UFRXR0NBAUlISGRkZfpFLdDz81XI7GrfbzaFDhygpKUGn05GTk0NoaKivh3VCYTsSp9PJhg0bCA4O9os8Ug9Wq5U1a9YwefJkn990d3V1sXbtWnJycpg4cSLbt28/Yb+9scK4tNw+/PBDQkJCmDFjhreRoi/xNB9NSEjwK2Frb29n79695OXlDSi6zF8sOJvNxr59+1i9ejVSqZSlS5cyceJEvxe2sYRUKiUlJYXTTjuNiIgItmzZwpYtW+jq6vLZmAYjbIA3R7OhoYHKyspRGOHA8ERP7tu3z+f91YKCgoiIiMBqtXLVVVfx3HPP+XQ8w8m4Ezen08nvf/97HnjgAcrLy0lLS/N5Nn1NTY3fuSMtFgtbt24lOzubiIiIAW/na4Grq6tj9erV9PT0sGjRInJzc/2mdt94RKFQkJmZybJly9DpdKxbt47i4uJRd/UNVtg8aLVaZsyYwcGDB2lubh7BEQ6OmJgYQkNDKSgo8PVQSE9Pp7KykjvvvJPPPvvMr24EToZxJ27/+Mc/cLlcnH766ZhMJm8pIF/hcrkoKioiOzvbb6IjPZGRkZGRQ0qN8IXA2Ww2tm/fzr59+5g8eTKzZ88e8/lZYwmVSsWkSZOYP38+dXV1rFu3btSsuKEKm4fQ0FAmTZrEjh07MJvNIzDCwSORSMjJyaG2ttan1jBASEgIwcHBSKVSLrroIl544QWfjme4GFfiJggCzz77LPfddx8VFRUkJyf7fF6jsrIShULhc9+6B4+L1DPpPtR5s9EUuPr6elavXo0gCCxdutRvzuWpiMFgYNGiRURGRo6KFXeywuYhMTGRuLg4tm7delIdvYeTgIAAEhISKCws9PVQSE9Pp6KignvvvZf333//mA4RY5FxJW7ff/89RqORCy+8kLa2Np8nbDscDkpKSsjJyfF58IWHsrIyWlpavLlsJ8NIC5zHWtu7dy+TJk1ixowZPg+fFjlczzI7O5t58+aNqBU3XMLmYcKECajVar+K+M3MzKS5uZm2tjafjiM8PByNRoPBYGDp0qW8/PLLPh3PcDCuxO2VV17h1ltvpba2lsTERJ9fCMvKyrwTtv6A0WikuLiYWbNmoVarh2WfIyVwR1prS5YsIS4uzm9uEEQOExwczKJFi4iIiBh2K264hQ0OB8nk5eXR3d1NUVHRsOzzZFGr1aSmplJQUOBTwZVIJGRkZFBWVsZdd93F22+/PSxtiXzJuBG34uJi8vPz+cUvfoHRaPS51Wa1WikvL/cbq627u5udO3eSm5s77K1AhlPgBEHg4MGD7N6922utDZcQiww/MpmMnJwc5s2bR21t7bC4/UZC2DwolUpmzZpFRUUFdXV1w7rvoZKWlkZ3d3efvfJGk+joaCQSCVlZWURHR/PXv/7Vp+M5WcaNuP3xj3/kkksuwWKxEBERMeC6cl1dXSMSjltcXExERIRftN5wu93s2rWLpKSkEZuvGg6BczgcbN26FaPRyKJFi0RrbQwRHBzMwoULkUgkrF279oSNS/tjJIXNQ2BgINOmTWPPnj1+YZ0oFArS09P9wnpLTk6msrKS2267jddee81v3LdDYVyIW3d3Nx988AG33HILVVVVJCcnD2i78vJyoiKjiI+L5+mnn6a9vX1YxmMymaiurvabNhJlZWW4XK4Rb/VzMgJnMplYt24dgiAMKKFcxP9QKBTMmjWL6Oho1q9fP2hLZDSEzUN0dDTR0dHs2bPHLy7gycnJOBwOamtrfTqOxMRE2tvb+clPfkJlZSWbNm3y6XhOhnEhbp9++ilpaWnExcWhUCgG3EbmtddeQ3CAsiWQxx97gtiYWO68804OHTp0UuMpKioiLi6OwMDAk9rPcNDV1UVJSQm5ubmjku83FIFrampi3bp1REVF9VsCTGRsIJFIvJXmt23bRllZ2YC+A6MpbB4mTZpEV1cX1dXVo3K84yGTycjKyqKoqMin5cKUSiVxcXE0NTVxzTXX8Oabb/psLCfLmBc3QRB46623uPnmm6mqqiIpKWlArqzOzk7eefsdolyJZEumMdd9JlHWJN56/U+kpqZx+eWXs2vXrkGPp6OjA6PR6BcNUT3uyJSUFIKDg0ftuAMVOEEQKC8vZ9u2bUycOJEJEyaIbshxQnx8PPPmzaO8vJzdu3cft9OAL4QNDluaU6dO5cCBA37hnoyPj0cmk530zfXJkpycTG1tLddddx3/+Mc/aGlp8el4hsqYF7ft27dTXl7O2WefTWdn54BLbf35z3/GarURTyoAKomaVMkE5rrOJN09iW8/X8n06dNZvGgx33333YBdFwUFBSQnJ6PRaIb8noaL0tJS3G43mZmZo37sEwmc2+1m7969lJaWMnfuXBISEkZ9jOOdN954g6SkJNRqNbNmzWLbtm39rnvw4EEuvvhi781hf6Hgg9mnZx6uu7ubjRs39jm37Sth8xAZGek37klPy57i4mKf5uIZDAZvk9tZs2bx4Ycf+mwsJ8OYF7e33nqLX/ziF7S0tBATEzOg+oJOp5M/vPQHIolFJektQjKJnHhJGrOcZzCJ2ezbeJCzzz6bCTkT+OCDD7Db7f3ut7m5mfb2dtLT00/6fZ0sXV1dlJaWjpo7si/6Ezi3283OnTtpa2tj0aJFfhF0M95YsWIFd999N0888QS7du1iypQpLF++vN95sJ6eHlJSUnj22WeJiooaln0CaDQa5s+fj1qtZuPGjVitVu9rvhY2D/7knoyKiiIgIICKigqfjiMpKYlDhw5x44038qc//cnnwj8UxrS4mUwmVqxYwbXXXktNTc2AK///61//oraulnihfxGSSCRESuKY5lrEdBbRUtLJtddeS2xMLJdccglVVVW91hcEgYKCAtLT031ewNdX7si+OFrgnE4n27dvx2w2M2/ePL+wcMcjL730EjfccAPXXnstOTk5vPXWW2i1Wt57770+158xYwbPP/88l19+eb/5oYPdpweZTEZeXh56vZ6NGzdisVj8RtjAv9yTnrJcZWVlPi2qHBsbS09PD0uWLKG1tZUNGzb4bCxDZUyL25dffklqaipRUVGo1eoBX8hfefkVQmQRBElOvL5EIiFYEs4UYS6zOYPONhOff/45SSkp5OXlsXXrVuBwgrTFYvF5fh341h3ZFx6Ba29vZ9WqVVitVubOnevzJPvxit1uZ+fOnSxbtsy7TCqVsmzZMjZv3uyTfUqlUqZNm0ZISAj5+fkUFBT4hbB5iIyMJCYmxi/ck6GhoYSGhlJWVuazMcjlcmJjY2lqauKyyy7jk08+8dlYhsqYFrePP/6YK6+8ktra2gF3DS4rK2P9hvVEu5IGfTwXDhyCldDLLkW/ZDG7Duxn9uzZJCcns2XLFlJSUnxeHLmzs5PS0lKmTZvm824IR6JUKtFqtdjtdrRarRgROYK0tLTgcrmIjIzstTwyMhKj0eizfUokEoKCgnA4HCgUCr+z2idOnOg37sn09HSqqqpwOp0+G0N8fDz19fVcccUVfPbZZ8edkvFHxqy4NTY2smrVKi6++GKampqIi4sb0HYffPABSpmKCGIGfcwqSSmKkFACZ80k5LxzSPj1E4RccD5SnQ6Hw8Gy00/nmWee8Vkor9vtZvfu3aSmpg57FZKTwe12s2PHDux2O4sXL6arq8uv6vuJjA4VFRUUFRUxb948IiIi2LRpk8/7mR2JP7knQ0JC0Gq11NTU+HQMMpmMtLQ09Ho93333nc/GMhTGrLj97W9/Y968eUilUu8X4US4XC7e+/N7hLtikEkGZ2FZhR6ahDqClixEIj182qRqNfrFC7n0gfvZWl9Ho8PBww8/jC4ggJtvvpmenp4hvbehUlNTg9PpJCMjY1SPezwEQWDXrl2YzWbmzp1LYGCgXzQ8Hc+EhYUhk8mOqeze2NjYb7DISO/zyDm20NBQpk6disFgYNOmTX5lEURGRhIVFeXzSv0SiYSUlBQqKip89huRSCTExcVRV1fHlVdeyccff+yTcQyVMStun3zyCT//+c+pra0dsNW2atUqGowNxJA06OPVUIZUqSRg5oxey3VSKdMDAtmiVBBz/z1E3XIjkoR43nrrLQL1es4777xRqTpwZN84f3JHFhQU0NHRwdy5c72BNr5ueDpSuFwuurq6aGpqorq6mpKSEvbv38/27dvZtGkTGzduZOXKlQBs2bKFTZs2sXXrVvbu3UtxcTFVVVUYjUY6OjpOKhRcqVQyffp0Vq1a5V3mdrtZtWoVc+bMGfV99hU8IpFIyM3NRafTsW3bNp8mLh9NVlYWdXV1Pu+zFhsbi8Ph8GmT1bi4OBoaGvjpT3/K119/TWdnp8/GMlj8o3vmICkpKWHv3r2ceeaZ7N69m5iYgbkY33vvPYLkBoKcg5vEdglOaqWHCJw3F+lRQRBzAoM4ZLPS4LAjkUjQZGSgycjAXt9Ax+o1fPPtt3yTmMjMvDzeeOMN8vLyBnXsgVJRUYFKpRrwuRgNqqurqaqqYuHChccEj3gEbuPGjezatYtp06aNqQRul8tFZ2cnHR0d3r/d3d1IpVLUanWvR3BwMAqFAolEgsFgoKyszDtH7HQ6sVqtmM1mWltbsVqtWK1WHA4HWq0Wg8GAwWBAr9djMBgGHIl79913c/XVV5OXl8fMmTN5+eWXMZvNXHvttQBcddVVxMbG8swzzwCHA0Y8XaHtdjt1dXXs2bOHgIAA0tLSBrTPvjheVKQnyGTDhg3s27ePKVOm+MV3QKfTkZiYSGFhIbNmzfLZOGQyGUlJSZSXl/uss0hQUBCBgYEYDAYmTJjA559/znXXXeeTsQyWMSlun3zyCeeeey4mk4mIiIgB/eA7Ojr45xf/JMGZOegfUBN1uNx2AufN7bVcCswL1POP1mPvrJQx0UT8/Gc4zz2brnXr2b5hEzNmziQlJYXfP/ccF1988aDGcDwcDgelpaXk5eX5xcUBoK2tjX379jFz5sx+60SONYEzmUwYjUaMRiNtbW0oFAqv8GRmZmIwGNBoNMd9Dw6Hg7KyMmJjY48bVGOz2byi2d7ezqFDh+jp6SEoKMjrOgsODu73WJdddhnNzc08/vjjGI1Gpk6dyvfff+8NCKmurkYq/Z/jpr6+ntzcXO/zF154gRdeeIFFixaRn58/oH0ezUDC/eVyObNmzWLt2rUEBQX5RbQxHO6z9uOPP9La2kpoaKjPxpGUlERpaSnd3d0+K+cXFxdHbW0tP//5z/nkk0/GjLhJhDHmExIEgfT0dJ577jn0ej05OTkDslbee+89rv/l9czn7GMSt0/EDtZhTQog+o5bey2fotVxXnAov6ur5kQn0W210r15C52r83F1dxMaFsZDDz7IXXfd1esiMxQKCgpob29n3rx5J7Wf4cJisbB27VrS09NJTU094fpWq5WNGzdiMBj8SuAEQaC9vZ2GhgaMRiM9PT2EhYURFRVFREQEWq120GN1OBysXLmSs88+e9ARo3a7nebmZoxGI42NjUilUq/QRURE+JU7erB5bG1tbWzatIlZs2YNuDbsSFNUVERzczPz58/36Xdy586dKJVKJk2a5JPjWywWfvzxRyZOnEhmZiaHDh0ase4iw8mYm3M7ePAgDQ0NzJ07F5vN1u9d49F8/NHHhMgiBi1sFsFMh9BEwJyZx7w2JzCIzaauEwob/Df4ZMli4p98jLArr6BLLuPee+9FFxjIr371q16VGwY1vv8mxObk5Axp++HG5XKxdetWIiMjB3wX7m9zcDabjdLSUlatWsWWLVuw2WxkZ2dz5plnMmfOHJKTk9HpdKN+wVMqlcTGxjJ9+nTOPPNM8vLyUCgUHDhwgB9++IEDBw4MudXMcDKUBO2QkBAmTZrkTfD3B1JTUzGZTMcE0Yw2SUlJ1NTUHLc+50ii0WgIDQ1FEATmzp3L119/7ZNxDJYxJ25fffUVp59+Op2dnQO+WzUajaxdu5YI18ACT46kgSqkCiW6KZN7LQ+Ry0lVa9hmGtzFRCKTETgjj9gH7iPypv+DmBhef/11AoKCuOiiiwadh1RcXExkZKTPK5HAYUtn9+7dyGQyJk+ePKiLvz8InOfYP/zwA01NTWRlZbF8+XKmTZtGTEyMX+XmSaVSwsLCmDhxIsuWLSMvLw+r1Up+fj6bNm3CaDT65ByeTOWRxMREEhIShqXh6XCgUCjIyMjweZ+1kJAQVCoV9fX1PhtDVFQURqOR8847j6+++spn4xgMY07cvv76a37yk59gNBoHHNb82WefARIiGJwpLQgC9dIqtLlTjwkkmRUQREGPme4h3k1JJBK0WZlE33YzMffejXrSRP755ZfExMYyd+5c9uzZc8J9dHd3U1NT4zd940pLS2ltbWXGjBlDcpH5SuA6OjrYtGkTGzZsQCaTsWjRIubNm0dcXJxfufr6QyKREB4eTl5eHmeccQahoaHs2bOH1atXU19fP2rncThKauXk5KBWq9m5c6fPLXg4bDW5XC6f5ptJJBISExOPKfk3mkRFRdHa2spZZ53FqlWrMJlMPhvLQBlT4mY0Gtm+fTtLly6lq6trwC7Jj/7yEWGSSBSSwdV8bKcZq9tM4Oze4f9SYGZAIJsHabX1hyouloirfk78448QuHABW3bsIHfaNDIyMo7rAigqKiI+Pt4vGnu2tbVRUlLCrFmzUKvVQ97PaAqcyWRix44dbNiwAb1ezxlnnMGUKVMICgoasWOONCqViszMTM444wxSUlLYt28f69evH/G2JcNVK1IqlZKXl0d3d7fPiwdD7z5rvnILwuFqIe3t7T5zO+t0OgICAjAYDCQnJ/Pjjz/6ZByDYUyJ27fffsuMGTMQBIGQkJABRUlWVFSwY+cOItwDa4VzJI3UINcHozqqs3e2RosbgWLL8CZpy4ODCb3gJ8T/+gmCzzmb8oYGfvKTnxARGckrr7zSKxeovb2dxsZGv6gf6XK52L17NxkZGcNSGWWkBc5qtbJ3717WrFmDXC7ntNNOY8KECT4veD2cSKVSkpOTWbZsGVFRUWzdupXNmzePSJ7ScBdBViqV5ObmUlhY6BcWgqcJsi/7rKlUKqKjo31aGmysuSbHlLgNxSX5+eefI5cqCCd6UMdyC24apXXoZhwbvTcnMIht3d0DCiQZCjKtBsOypSQ8+RhhV1xGBwJ33nkngUFB3HXXXVitVoqKivymb1xRURFyudybDzUcjITACYJAVVUVq1atwmazsXjxYqZOneoX53CkkMvlZGRkcPrppxMYGMj69es5cODAsFkhI1XdPywsjMTERHbv3u1z96Snz1pJSYlPrbfExESqq6t9lvAeFRVFY2Mj5557Lt98841Pz8VAGDPiZrFY+OGHHzjrrLNoaWkZsLh98/U3BBM+6HJbrRhxuu0ETMvttVwnlZKl0bJ1mFySx0MilxM4ayaxDz1A5P9djzsqkpdffpnM7GwaGhrQ6/UjPoYT0dbWRmVlJbm5uSed0nA0wylwFouFLVu2UFRU5E1C9lXekC9QKpVMnDiRRYsW0dbWRn5+Pm1tbSe1z5FuW5OdnY3NZvML92RkZCQqlWpUqg31R1hYGHK53GfRm8HBwUilUjIzM3G73d6OKP7KmBG31atXExUVRXh4OFqtdkDzTJ2dnWzctJFQ98Dm5o7ESA3KiEiUMb0tvhyNjlq7jQ7X6FXrlkgkaHOyif7VrcTccyc/ufJK1q5dS0JiIgsWLODAgQOjNpYj8bgjMzMzR2ye6mQFzmOtrV69GrVazdKlSwc8VzseCQwMZMGCBSQkJLBp0yYOHjw4pDvw0ejHJpfL/cY9KZFISE1Npby83Ke1HqOjo4fc2WE4jh8ZGUlLSwvnnHOO37smx4y4ffvtt5x77rmDKv76ww8/4HK5CGVwxWJdgpNmSQO6GdOOeW2CVsvBHt/l4RgSE1kwfTrboiIJnD+PjVu3MGnyZLKzs0e9arfHHTmQRO2TYagC53A42Lp1q9day83N9atwfl8hkUhIT09n4cKFtLS0kJ+fP6hAhdFsNBoaGuo37sm4uDhsNtuIB+ccD49r0FfnwjPvdu655/Ltt9/6ZAwDZcyI25o1a1i6dCnNzc0DrrO2cuVKguTBaCS6QR2rBSNuwYkut7dLUi6RkKXRcnCUq/0fyeyAIGrtNhq1akIvuoD4J58g+OwzKamp4eyzzyYqOpo//vGPI+6X97gjp02bNuzuyL4YrMCZTCbWrVuHIAgsWbLklLbW+iMoKIgFCxYQHR3NunXrBuTu8kUHbY97sry8fFSO1x8ymYzExESfuklDQkJwu920t7f75Pjh4eH09PQwa9YsDh486NOizidiTIib0WikpKSEvLw87Hb7gBKW3W43X3/1NcHOwZfyaaEBZUQUirDeNeXS1GrMbhf1Dt+06JAC84OCWNf1v4g3mU6L4fRlhyufXP5TWp1Obr31VoL0Bu6///4RaSfi6RuXmZk5qvNWAxW4pqYm1q1bR1RUFLNnzx5XUZDDjVQqJScnhylTprB9+3bKysr6Pa++EDb4n3uyqKjI59VLkpOTaWpq8tk4PCXXfOWalMvlGAwGJBIJEyZMYN26dT4Zx0AYE+K2du1apkyZgsvlIjg4eEDdrisqKmhta6WbToxCDXZhYE0RBUGgRdqIZtKxidETNDqfWm3ZGi0SJBzowy0qVSgInD2LuIcfIPKGX+IMD+X5559HFxjIFVdccdLBA0fiCUceaXdkXxxP4ARBoKysjG3btjFp0iQmTJjgN3Uq/Z24uDjmzZtHeXk5u3btOmYezlfC5iE0NJSYmBiKiopG/dhHotFoiIqK8mlagMc16SvCwsJoaWlhyZIlrFmzxmfjOBFjQtzy8/NZvHgxLS0tA67QnZqayhNPPEFkVggH2Mp6vmGnLJ9y4QAdQgtuoW+3XRftONxWtH3Uapyg1fl0vm12YBDbTF0cz+EokUrRTsgh5o5fEXPXHSizs/jbihWERUSwZMmSk27C6HQ6vX3jRsMd2Rd9CZwgCOzfv5+ysjLmzp1LfPzg8xpPdYKDg1m0aBFms5lNmzZ5S2D5Wtg8ZGVlUV9f7/OeYomJidTU1PgsJD8iIoLu7m6fWY8ecVu8eLG3Y4Q/MmbEbdGiRbS0tBAWFjagbSQSCU8++SQFhQXU1tby5/f+zOkXLaUtyMgO8tkg+5Z9bKZWqMAi/O9L0kIDUpUaVVJir/3FKpVopFLKrL5pP6+XyQ6nIAxi4l+VmEDktVcT98iDBMyZTf6GDeRMmMDEiRN7NZ0cDBUVFWg0GqKjB5c3ONwcKXA7d+5kz549NDY2snDhQp9egMc6nvMql8vZtGkTpaWlfiFsAFqtluTkZJ93yQ4PD0cmk/nMNahQKAgNDfXZ8UNCQrBareTl5VFQUEBTU5NPxnEi/F7cGhoaKCkpYdq0adjt9iH9wGJjY7n22mtZsWIFrW0tbN26lUefeITUWQmUSveyke/YKvuRImE3RmktmuwsJEfVFJyo0VFk6cFXaYszAwIptVhoH0IKgiIsjLBLLiLh149jOHM5hZWVLFu2jJjYWN59990B78dut1NaWkpOTo5fuPvUajVz586lsbGR+vp65s2bh1ar9fWwxjwymYyZM2ficrkoKCggLy/P58LmIT09ndbWVp9GLPpLrUdfuSblcrk37mHixIl+O+/m9+LmmW9zu90EBwefdCFbzw/3scceY9PmTbS0tvD5559zxXWXIcRYsWDGvG8fDa/9kY5Vq7HVHS4868sUAAmHCzVvNp1c23uZTkfw8tOJf/JxQn96Cc02GzfccANBej0PP/wwTufxhbO0tJSQkBC/6bclCAIlJSUolUqUSiWFhYU+DxcfL1RVVdHT00NISAjFxcV+UaUfDpehSktL83ml/oSEBJqbm+nx0Rx8VFQULS0tPvtcQkND/d416ffiduR820BdkoPBYDBw0UUX8ac//Yma2hpW/fgjF11wAQFdXbR/s5L651/E/NIrxCiUbN+yBZcPkknT1BqUUsmwiatUqSBo7hziHnmQiOuuwR4czDPPPIM2IICrrrqKjo6OY7axWCxUVlb6TQcCwOsSmT9/PgsWLPCbfnBjHc8c29y5c5k7dy5yuZytW7f6Tbml1NRUenp6fOaWg8Neg8jISJ/VevQUMvaVS3AszLv5vbht2LCBBQsWDCqYZKhIJBKWLl3K559/TnNTE50dHfz+97/norPPpqS4mEN/fp/qR5+g7vcv0vbtSizl5Qij8IOfpNWxz2w+biDJUJBIpegmTyLmrtuJvuNXKDLS+eijjwgJC2PZsmWUlpZ61y0qKiIqKmpYCiMPB9XV1VRVVTFnzhw0Go1f9IMbDd544w2SkpJQq9XMmjWLbdu2HXf9v//972RlZaFWq5k0aRIrV67s9fo111yDRCLp9bj00ku9c2weT4fT6WTfvn1+cV499TILCgp8FtQBh6c7fCmwnoRqX+CZd5s+fToHDx70Wd7d8fBrcTObzRQWFjJp0iSsVuuoX1iDgoK47777uOaaa7jkkkvYvXs3119/PVEKBV2r8zG+9keqHnwE4zvv0bVhE46W1hEZxwStlgPD3IHgaNTJSUT+8trDwSezZrIqP5+MzEwmT57MmjVrqK2tJSsra0THMFDa2trYt28fM2bM6FWGbbwL3IoVK7j77rt54okn2LVrF1OmTGH58uX93r1v2rSJK664gl/+8pfs3r2bCy64gAsuuOCYcm1nnnkmDQ0NbNmyhY8++oi///3vvebY5HI5s2bNorGx0S/qPMLhPmtut5u6ujqfjSEiIoKuri6fuiYbGxt9IvByuZygoCDkcjmJiYns2rVr1MdwIvxa3Pbu3UtoaKjXBPdF6SSn00ljYyM3/t9NvPLKKyxcuJBNGzdit1r58MMPOW3RIuQ1tbT+43Nqf/s0Nb/+LS3/+IKeAwdx2waWW3c8YhRKdFLZqEVpKsLDCfvpJSQ8+RiGM07nYFkpn376KVu3buXzzz8flTEcD4vFwrZt28jJyelz7m88C9xLL73EDTfcwLXXXktOTg5vvfUWWq2W9957r8/1X3nlFc4880zuu+8+srOzeeqpp5g2bRqvv/56r/VUKhU9PT20tLRw9tlnk5KScsy+NBoNM2fOpLCw0C+i46RSKWlpaT6t9ahUKn0ategpZDycOayDQa/X09HRwbRp09i5c6dPxnA8/Frcdu3axfTp0+ns7PSZO6y5uZnu7m42rt3Elx9/xVVXXUVcXBxZGVls3ryZG2+8kYryMmpqanjwwQfJiI6mZ+s2Gt99j6qHHqXh1dfp+M8qbLW1CEO4w5qg1VJk6cE5yj9gWWAgwWctJ/2pX7Ns+XI+/+47rrnmGvQGA48//vgJg09GAqfTydatW4mKiiL5qB57RzIeBc5ut7Nz506WLVvmXSaVSlm2bBmbN2/uc5vNmzf3Wh9g+fLlx6y/evVqpk2bxh133MEjjzxCa2vfHoiQkBAmT57Mjh07fF7IGA438Ozp6fHZxR18G7XoKWTsq+MbDAY6OzuZPn26aLkNlp07d3rFzVftXdrb29m/fz8R7ljyXEtZyHlMYja2SoFP/vxXLr30UsLDw8mZMIEffviBiy++mIqyMn744QcuuegiAk1m2r/9jvoX/kD1o0/Q9NEnmHbsxDXAfLUJWh0HR9gleTxmh4RidLmw/vwKIq69GmtQEE899RS6gACuvfZaurpOLoJzMBw8eBCZTMbkyZNPmIow3gSupaUFl8t1TI3M45ViMhqNJ1w/NzeXO++8k2+++YYXXniBtWvXctZZZ/UbPJKQkEBCQgLbt2/36XwX4HWJ+bLmpK+jFkNCQnw232UwGETLbah4xK2jo8NnlpvRaGT//v0Ec9gFppSoiJTEkSWZxizn6cznbCKIxWQ2s7eslKeeeorExEQuvOgi6uvrue/eezlUWcmLL77IjEmTcBUW0fzxp1Q/9iR1z71A29ffYiktQ+jDEgqSyYhTqijwYQrC/EA967o7DwefTJlMzN13EH37rcjSUvnggw8IDglh+fLlVFZWjuhYmpqaqKmpGVSh5vEmcMNNRUUFaWlp3HnnncyfP58LLriAb775hu3btx83Ai7nv9V7SkpKRmmk/ZOcnExjY6PP5r10Oh1ardZnBYQ9AuOL73ZQUBBOp5OcnBzKysp8XjnmaPxW3CwWCwUFBUyYMAGr1eoTy00QBDo7OykrK8NA32kIaokWp8SJOj2d+F8/QdwjDxH600sQ0lLZsns3999/P0nJyfzmqacICAjg5ZdeYuvWrdx4443EqFR05a/F+MabVD30KMa336Vr/QYczYcTVHM0WqpsVsw+ukNOV2tQSyXsM//PBSWRSFCnpBB1wy+Jfeh+tDPy+GHVKlJSU5k2bRobN24c9nE4HA727NnDhAkT0OkG1+FhvAhcWFgYMpnsGBfU8VpA9eUy86zfX0mtlJQUwsLCKCsr63csUqmU3Nxcv7igabVaIiIifBaSD76NWgwMDEQQBJ+4iWUyGYGBgSgUCuLj4/3ONem34rZ3716Cg4MJCgpCp9P5JJjEYjkcxNFe34VSoupzHbfgpoNW1GkpSCQSFOFhBM2dQ+Q1V5Hw9FPEPnAvIRf8BHtsDKvXr+emm25i1uzZ/OOLL5g4cSIfvv8+H374IacvWYKirp7Wz/9J7e+eofrJp8jo6GJ3RQVuq3U037aXw7Usu/utyqKMjCT88p8eDj45/TT2FBQwf/58EhMT+etf/zps4zh48CA6nY6kpKQhbT8eBE6pVDJ9+vReZdPcbjerVq1izpw5fW4zZ86cY8qs/fjjj0yYMKHfklq1tbW0traesLyawWAgLS2NXbt2+dw9mZiYSHV1tc8+1+joaJ/1WJNKpd7ADl/gsRynT5/ud67JE5fX9xFHzrf5yiXZ0dGB0WhEbQ447KPrg246cAtO1H1UyJdIJCijo1FGR6NftBDB5cJeV4elpAxzcQnffP8933zzDRKZjKjISJafcQbnnXcehYWFfP/99+QkJPDnu++mtq4OVVIi2pxsNFmZKGNjkYxw0WKdVMokrY7n6mpOuK4sMJDgs89Cf9pSurduo271Gn72s59xy623cc/dd/Hwww8PuchyU1MTdXV1LFmy5KRKfnkEbuPGjezatYtp06b5RQmxwXD33Xdz9dVXk5eXx8yZM3n55Zcxm81ce+21AFx11VXExsbyzDPPAHDHHXewaNEiXnzxRc455xz+9re/sWPHDq688krmzJmDUqnkvvvu4+KLLyYqKory8nLuv/9+0tLSWL58+QnHk5GR4S2P58s0kYiICARBoKmpySd9+zylqNra2kY8F7cv9Ho9nZ2dPikWbjAYaGhoEMVtMBw4cIApU6bQ2dk5oP5tI0FzczOFhYUE0f/xO2hGIpejio874f4kMhmqhARUCQkYli1FcDqxVlVhLSmjrbiEv3/+OX//+9+RyuWctXw5Drude++9l82bN5Ofv5bG7/5N+7ffIdVo0GRnocnKQpOVgTwoaDjfNgAzAgKptFppcQ58olyqUqFfuICgeXMx79tP539W89hjj/Hb3/2OX/z85/zhD3/olZd2ItxuN/v27SMnJ2dYakb6SuBsNhs9PT243W7vHXZbWxtKpRKVSoVGoxnQOC677DKam5t5/PHHMRqNTJ06le+//957Qa+uru51EzF37lw+/fRTHn30UR5++GESExN5+OGHueKKKwgJCcFisbBv3z4+/PBDOjo6iImJ4YwzzuCpp55CperbU3EkUqmUadOmsW7dOuLi4gb12Q4nUqmUhIQEDh065BNx80QtGo1Gn4ibwWCgpubEN6EjgV6vp6ioiClTpvC3v/3NJ2PoD4ngpz6a0047jSuvvJKkpCRycnL6nVcYSVauXMkbb7xB60o7gRJDn+vsFTbRnaIm+vZbT/p4bpsNa0Ul1tIyrpqeh6mlhffffx+5UklqcjKLFi3CYDCwfv16du/di/W/k+iKqCivVadOSUYygH53J+KOqFi2mLrYahp4F4KjEQQBa1k5navXYCksQiqXc+YZZ/Dmm2+SkJBwwu0rKio4dOjQSVttR2O1Wtm4cSMGg2HYBc5ms9HR0UFHRwednZ10dHRgsVhQKpXIZDKvq1uj0eB2u7Hb7SgUCvR6PQaDAYPBgF6vR6vVDuu4RrJtze7du3G5XOTl5Q3rfgeDyWRizZo1nHXWWQPq9zjc1NfXU1hYyGmnnTbqx+7s7GTDhg2cffbZo+6NcDgcrFy5ktTUVPLy8ujp6fFZK6yj8VvLrbi4mPT0dDo6OnxyRygIAlarlYqKSmLov55il7QTVWL6sBxTqlKhzc5Cm53F5NgE/lFfS8R112AtLaOiqJjit98GQKlWk5WRwYwZM7BarWzevJlDa9fRuXoNErkcdXoamuwstFlZyMPDBv2FD5DKiFep+HPTyUWgSSQSNOlpaNLTsDcY6VyzlpXff09icjLTc3N54403mDVrVp/bOp1OSkpKmDJlyrD/YIfbgnM4HNTV1VFdXU17ezs6nQ6DwUBwcDDJyckYDAbvnLHnYrBkyRIUCgUul4uuri6vIJaUlNDV1YVGoyEhIYH4+PiTtlpHuh9bVlYWq1at8mlUc0BAAFqtlqamJmJiYkb9+OHh4Wzfvh2r1YparR7VYwcGBuJ2uzGZTAQGBo7qsRUKBSqVioiICJxOJ9XV1UOeGx9u/FLcTCYTdXV1xMfH097e7pM2JhaL5XD2f3UHcZK+70Qcgh2bYCYoNnZYj62WSglXKKiTgG7yJHSTJwHg7OrCWlqGpbSMg0XF7Nu3DwCNVse03FxSU1Npbm5m567dtBUV0yZ8iUyvRzshB01WJpr0NKQazQmPn6PVUmOzYXIPX91MZXQU4T+7jOBzzqJr3Xp2bdjA7NmzSUpK4rnnnuOnP/1pr/XLy8vR6XQjZrEPh8CZTCbKy8upqakhMDCQhIQEZs+ejVKpHPA+ZDIZwcHBvVzvLpeLxsZGqqurKS4uJjIyktTUVEJDQwc9xtFoNKrRaEhOTqagoIC5c+eOyDEGgic61BfiplAoCAgIoKOjY9S9TFKplKCgIDo7O0dd3OBwOoTNZiMlJYWSkhJR3I5HWVkZer0ejUaDVqv1iZnb0dFBU1MTsh7VcYNJAJRxwytucUolbU7HMSkA8qAgAqZPI2D6NAAcrW1Yy8qwFJeyq7CQHTt2gERCYGAgs2fNQq/XU1tbS8mOXXRv2gwSCarEBDQ52WizMlHGxfUZmDJRq+OgZWRy6+T6IELOOwfD6afRvWUbNavXcNlll3HzLbdw3733cv/99+NwOCgrK2P27Nkj6mYZqsBZrVb279+P0WgkJiaGBQsWDGuqikwmIyYmhpiYGG83hm3btqHT6ZgyZcqAraPR7KCdnp7Ojz/+SHNzs89aIkVFRbFt2zYEQfBJsJCnYocvplA8UYtxcSee+x9uAgICMJlMZGRkUFJSwhlnnDHqY+gLvxS3kpISMjIyMJvNPpuk7ujooLCwkED6v2h104FEJkcxzD/mOKWK2gHUpVSEhqAInUngrJkIgoCjqemwZVdSytY9exCsVpBICA4OJi11MlKplENVVTR+/wMdK79Hqlb/NzAlE01WJnK9HoVEQqZaw8r2kS1pJFWr0S9eSNCCeZj37KXzP6t56KGHeOLJJ3nwgQdYtmzZqEzOD0bgBEGgtraW/fv3ExkZybJly9AMwBI+GTQaDTk5OWRkZFBWVsaGDRtITU0lIyPjuL0NR1PY4HCqQnp6OoWFhT4Tt+DgYARBoL293SfNVfV6fb+ly0Yag8FAbW2tT44dEBBAZ2enV9z8Bb8WN5PJ5DNxa2hooLS0lAD6j0Q00YkyOvqYrt0nS7xSRY19cEWXJRIJyshIlJGRBM2fh+B2Y6+vPyx2xaXs2LMHweEAqZTw0DCio6Ow2+1UlZTSsnsPAIrICOaccw5d4VHU95iRjkJuoUQmI2D6NHTTcrEUFGD+29/IzMzk5pv/D5Dx+eefk5mZOaJjGIjAWa1W9u7dS3t7O7m5uSfMAxtu5HI5WVlZREdHs3v3bhoaGpg2bVqfVtxoC5uH5ORkSktLaWtr84m4SKVSIiMjaWho8MnxDQaDz0qBGQwGDhw44BOrVafTUVdXR0ZGBl9++eWoHvt4+EdYy1EcabkNtiLFcODJ+C8rK0N3HHHrlnahiBv+i1ycSkXtIMXtaCRSKaq4OPRLFhN10w0kPvs7ou+4DcOZZ9AdFMC+gwcpKirCarUSGRlJRkYG4TI5E9UaNq9adbhiyltv07l2HfZRSFCVSCQ4OzpZuGAROnULudl1FBUdZMKELGJiYvj+++9H9PjHS/Suq6tj9erVyGQyli5dOurCdiR6vZ6FCxcSGxvLhg0bjuk+7ithg8PzTgkJCT5ti+PLQsZ6vR6r1YptGLqBDBZPUInZPPql+o52S/oLfi1uvrLcrFYrgiBQX9OAkr4jnwRBoEfoQhk5vP51tURKhEJJzTD/QCQyGerkZILPOJ3oX91K4jO/I+qWGwlauph2pZKS0lIaGhrIy8ujrKyMhJgY5PUNtH35FXXP/J6aJ35Dy4q/Y967D1fP8LffEQSBnk0buPSSc5mavpkPX4uiYlsSd90YTFenkbPPPougoCBefvnlYT+2h74Erry8nD179jB16lTy8vIGFSwyUkilUjIzM1mwYAF1dXXs3LkTt9vtU2HzkJKSQn19PVYfVdWJiIjAZDL5pByVQqFAp9P5pFqIJ6jEF8fW6XS4XC5vrqEvxL0v/FLcDh06RGJiIj09PT6x3Lq6ujCZTCjsqn5NfCs9uAUXisiIYT12rEpJex/BJMONVKlAk5FByDlnE3vPnSQ+81um3X0nmoAAtpWVUV1dTXdXFzK5nLCwMIKVSiw7d9H0/odUP/IY9S+9Qvv3P2A9VDWkVj5HY6+tJSkohODgYGJC9wIQF6PgucfCqNmdzHOPhaFV93DXXXehVqu45ZZb+q1cfzJ4BK69vZ01a9ZQUlLC3LlzfRKBdyL0ej0LFizAZDKRn59PQUGBT4UNDl/owsLCfFbrUaFQEBYW5rOec57ADl+g1+tHtUuHB5lMhkaj8aa8+LLO55H4nbg5nU6ampq8wQSjnTMChy235qZm1M7+hdXM4S+RImp4KyLEK1XDbrUNBKlaTUZ2NrUuF1H33U3Cb39NxDVXoZ05gw7BTUtLC067HYVSSUhwMIq2djp++JGGl1+l6qFHaXz/Q7q3bMU5xB+2adt2zjtvOYkRO5FJe3dI0AfJuOfmYA7tSOaDVyNJTYQ333wTjUbJmWeeOex36Wq1mujoaEwmE8HBwT7L3RoIKpWK2NhYuru7vYngvsbXtR6Dg4N9XmvRF6jVap9ZzGq1GpvNRlRUFA0NDT4Zw9H4nbh5CpDq9XqUSqVP0gCsViv1DfVo6T9nxEwXErkC+TBfTOKUKmrt9mHd50CJP2KuTxYQgG7qFMIuvZj4xx4h/snHCLvyClRTp9DpdGAydYPbjVKpJECpxFFUTMvfPqPmyaeo/d2ztP7zX/QUFuG2n7h8l+By4So4wKyZs0mM3NHvekqlhF9cGsS+/AS++2sM82eq+Pe//01wcBCTJ08etsn8iooKqqqqmDNnDmaz2a+LLVdUVFBSUsKcOXNwuVx+Mdbo6GjsdrvP+4z5Ak+dR1/gERhfHdtqtRITE+M34uZ30ZINDQ2EhYUhCIJPrDaA7u5umpubUdN/8ngPJpTh4cNewDhepWKn2TddjuOUKvK7+v5hyg0GAmfkETgjD0EQcLa0YiktxVJSirm4BPd/f1QKpRK5yUT3ho10rV0HMhnq1BS02YfLgymiIo+NRCwrIzMxFaW8hyBt/QnHKZFIOGOxjjMW69i938qLb3aw4l/7ycxMIyoqlr/85S8sXbp0SOegpaXFm4wcEhLi18WWj55j0+v1rF+/ntLSUjIyMnw2Lk/UotFo9FnUYnd3N06nc9RLcRkMBiwWCzabbUD1OYcTX1puKpUKq9VKdHS034ib31luDQ0NREdHY7VaR/3L4aGzs5O2tjbU9J/DZMWCLHR4f7gqiWREgkkGggyIGWB+Xb+tfS48H3l6GlaXC+G/82EKmQzXoSravvqauueep+bxX9P81xWYdu/BZT5c3su8/yCLF88iPqKAwWpH7iQ1H/8xivKtSdx+vYG21npOX3Yaer2eN998c1D7cjqd7N69m+zsbO9F2V/b5fQVPKJSqZg2bZq3hJcv8WXUolqtRqlU+uQceIJKfGG9eQTGF3iEVRS343CkuPnKcuvp6aGtrQ3V8cRNZkEePLwNVIPlCqxu97CWvRooUUolTkEYVBcAD57WPvpFC4m64ZckPvNbYu6+g+Bzz0aWmIjD7QZBAIkEicVCz85dNH/4EdWPPk7tCy9h27OT6dNnEKYvGPL4E+IUvPBkOLV7kvndw6GoFGZuueUWNBoVd9xxx4CCTwoKClCr1aSkpPRa7m8Cd7yoyJCQEJKTk9m9e7dP+6xFRETQ3d3tk9B0iUTiU9ekTqfzyftWq9XY7XaffO6iuA2AhoYGYmJifCpuDoeD9vb244ubYEGmNwzrcYNkMrpczhOvOAIcnuuzMRyXbU9rH8Oy04i+9SaSnv0dUb+6BcMZpyOLi8V9hNC46htIiolFIlEzce6PXPLLBt75uJPq2sGLLIBBL+P+20Ko3pXMn1+OJDFW4NVXX0WjUXLuued6q/IfTUtLC9XV1eTm5vbpevQXgRtIuH9WVhZOp/O43bRHGoVCQWhoqM86VPs6sMMXc18eT5cvju15z/405+Z34lZfX090dDQ2m80n4ubJ8O/q7KKJWtqEJsxCFw7B7r2gOQUnLrcduWF4LbfD4jb6VhuMbJSmRC5Hk5pK8FnLibnzdhKf/R2RN96Afsli5NFR5ObmsXv3bjo6HXz1bzM33ddE8oxDZMw5xF2PNfPdKjM9PYO7G1UqJVxzWRAH1yfyzccxzJ6u4ttvvyUoSMe0adOoqqryrnukO/J4eZW+FriB5rHJZDK/cE/6OqH6VItalEqlPnNNHjnnVl9/4nnz0cAvA0omTpzYr+UmCAIPPfQQRUVFhIWFER4eTlhYWJ+PoKCgQQcAOBwOpFIpJrOZWrb1ek0ulaOWaZG5ZeAC8569ONrakAcGIgsK+u8jEFlAwJBKcgXJ5HQ5fWS5qVSs6yeYZLg5srUPnEtuWAQ7yssJWjAfS1ExruZmAMoPOfjjBx28+m4HCgXMn6nhrNN0nLFYy8Qs5YA+W4lEwlmn6TjrNB079lh58a12/v7VblJTk4iJiefjjz8mMjISlUp1jDuyL3zV8HSwCdqedjsHDx5kzpw5Iz6+vggPD/dWUBntQBxfBpWoVCqfRkz6QtzUajUOh4O4uDi/sdz8TtxaWloIDw/HZrP1WQ2ira2N5557jkAMyOVynBI7NrcVu+vY8Hm5XE6wIYSw0FAiIiOIiIw4oSA6HA7kcjnt7W2YTCYaGhpoaGjAaDR6/9bV1fGfVatoK6+gs7DIGzxxJFKtFllgIHKDAZn+f8In9wjgf8VQesR71Mt9Z7lFKZTU+yoFQaNlZXgYoRdfCPRu7WM6WADd3TgckL/JwtrNFu7/DYSHyTj7NC1nLNZx+kItoSEnvpnIm6rmr29F88wjDl7+UwfvfFLLGWcs4+233wVg4cKFAxrvaAvcUCuPpKWl8cMPP9DZ2TmsXQsGiscK7u7uJmgEusUfD41Gg0KhwGQyjXrunz9ELfriuHDYYm5vb8flch23sPdo4Hfi1t3dTWBgIE6n09vg8Ug8uTPpTCbE9b/qIG5cOLBjx/a/v04bjhY73S022oorOCAtwiVz4sCG1WnBJRwrJDNnzuSXv/wlU6dMJTIykvCI/wlheHg4eXl5nHnmmTzwwAOEhYURGhqK3W6noKCAgoICSktLqaqqora2lqamJlrb2uiqq8Nis+FyOg8HVhyBRKFAFhiITK9Hef31VLa20FFSgiwoCLk+EFngYTGU6nQjdgFVS6SopFI6fDDfFyKXo5ZKaXD8T1iPbO0TTu/WPuaCArBaaW5x8cnn3Xy4ohuJBKZMVHHOf6262dPVyOX9n6ukeAUv/zacx+8J4bPvJtPd3cFtt93BjTf+H7fd9iuef/75E457tATuZEpqqVQqEhISKC8vZ9q0acM+thNxZJ+x0RY3iUTiUyvG11GLo41EIkEmk3m9bSaTySc3VEfid+LmqSfZ2dnZpzvB40eX01v4pBIZKjTHDQJBAP57/RYEARcuHNhwYMOOHQc2UnRZdDebadnXRT0tuGUOnFIHduGwIAp9hFwEBgQRFhpKeEQ4EZERXhE8UhTDwsIICQnBbDZTU1NDSUkJFRUV1NTUYDQaaWlpIVguZ+vefXSsWXNsSSupFJlOd9jiM+j/awH2YREGBiIZpBsmSC7D7nZj9UGUVZxSRYPdjvM481d9tfbpOXCQ9q+/RSYFlxv27Lexv8DG715uQ6eVsGyhluVLdCxfoiUpvu/uBiHBMnImn0ZM8BbefjGC37/ezgsvvMArr/yBc845j08//fS4LW1GWuCGo1ZkcnIya9euZeLEiT6pi+kJ7IiPjx/1Y/sqsMNzXF+4Y32ZyC2Xy70WnChufdDd3Y1Wq6Wjo6NPs9ZjuSk4uR+qRCJB/t9/Gv5XZishJBl3m4RsyfTDC9z/fQACAk4c/7UOj7AQTXYcJhs1VY1UUINb7sQhsWMXbNicx95FyaQyDHoDoWGhRERGolQq0Wq1REREkJmeTmZ6uvdi5nQ6MZvNNDU10dDQQFNTE+1NzXRXVmG223D3MUcnVasPC51ej1yv7+UGlf13flCuD0KiOlw705eBLJ4ozYHiae3jaDxcO7BkSxLtHS7WbLTw/epu8jfZMPcIfPOjma/+bUYQIDlBwTmnH3ZhLp6rQac9HEfV1RNFtyWSxKx9/PJneq69PIhv/2Pm96+38+WXXxIUpGPq1Ol8+eWXxPbTbX2kBG64iiAHBQWh1+upq6sjOTn5pMc1WAwGg89qDfrKijkyanG0g+LUarXP5vs8xohWq/3/9s47Kq7zzP+fKTADDMzQi+gSTUJIoC5ZQpKLvIlLsnYSx7F3HccliXfPib2xT9bJL23TE6/jrL127BR745Jux1Ys26qoISEEqkj0DjPUYZhh+r2/P9BMkIQkEDB3gPs5Zw4IwbzPtPd7n+d9CsPDw5LYMJagEzer1Up4+GhnkPE8t+kStysRER2GbXD8dHGFQkEIoRfWvso49zE6ISDgvuAV+kKmbsGJa9CFfdBJQ30LPcpuCNcQHh7O33fsoH1MJt+YxVGq1YSGhBCm1ZIYH4deryc6OhqtVotSqcTr9eJ2u7Hb7VitVgYHBxky9WBzOPC43ZeFRFGrUel0ZG/dSn9pKf1/+etFAnhRgswMtUFL1Wg4OzL5miBnewdxcSFkpo3eipdqeeKL0bjdIsdOONh70M7b7w9TfcZFc5ubl14b4vlfD6FWw/pVYXzsxnBWrbuV5OjThKhHN0ClUsHtt+i4/RYdR6sc/Ox/B3n7/UoyMlJJS8vkrbfeYu3atZfZMt0CN93d/dPS0mhvb5dM3E6dOiWJFyPV+ZNKpSIkJESSciYpC7nVajUej4fIyEhJpjJcZo/UBozF7XZfdLVzJc9NgQLVDJkeEROGqa5v2u5PqVCiQYvmCqNzAPaI75D8T/+ERqNB9x9fIcPhQLDZ8FpteG02//eCbfTfTqsV07CVLmM33sZGBPsICOOE9ZRK1Go1oaGh6CMj0el0hISE+J9XQRBwu92kREQw1NOD41gVbrfr8gQZhQJleDjqqEhUBoNf+NR+j/DC18golKGTG3Aao1bT5558TZu7s4M1hZdf4ISEKFi/Koz1q8L4+uMx2O0Chysd7Cob5vfv2Gjr9HLgiJ2DR+386Edp7N2znbhII7dsDufmTeHEx42+r9aUaPnTr5JpbHHx85fN/PqNVjZsWIfBEMNzzz3Hfffdd9G60yVwMzG2JikpiVOnTkmSORgeHo7X68XlcknSjqqvb/o+y5Nde76d9/nETafTyZ7bpfiekLCwMNRq9bibg9lsJlStQeGdmatAjS4UuyVwMWtBFBDwEB0T7T/3UoaGogwNRR0dPaH7EEUR0eHAO1YErVa/GHptNkasNqzDw3gHBvCOjCCO+QBs2bIFk9GIy2FHoVKhCgkh9MJNpVKhVCpHzyjtDtzDHbg8Hjwez7ghUUVo6GiWqF6PyqAf50zwQpZoeNiUQqJij5GlpdcW0rAwJTduDOfGjeH88BtgGfay/4idv++0k5WZybMn6+ntHeZ3fxpNTFm6WMPHL2RhrlupZWFmKP/zgwS+/dVYXnzVzM9fMXP//ffzyCMP8/jjT/D973/fv9ZUBW6m5rFptVo0Gg0WiyXgvR7VajVqtXre9VqUau3Q0FBcEmU9j/XcZHG7BJ8rGxoaesUrTIfDgVoxc2YrVUoEb+ASK9yMvhENej3D13nupVAoUISFoQwLIyQubkJ/I3q9CHY7XquNlMws2gcGiP303Rd5iS6rFe+wdVQoR0YQr+RhKZWoLtyUSiXYbIhWK872DgRRRBC8ME6CTHhMDGG/+hUNr/0fdrX6EiEckyBziQcvejzY+83kLZr8LL2oSBW33axj4/pcDtcIVH8UTtmRJF79/RB7Dtg5ddbJ2fNOfviLQcLDFGzdGM6tW8PZtjmCbzwRy398KZrf/XmYn74wyA9+8AN+9rMfc8cdn+SNN94gNDT0ugVuJgeNKhQKf1GzFI2MfRt9oDMmpe6SL8XavgtRKRjruc2qsORLL73Ek08+yeDgoF94fPOuNmzYwL59+/y/u2/fPrZs2UJDQwMLFy6csDHDw8PodDoEQbiiuHm9XhTMXOxeqVIieAInbh5GBUMbHo5TDNy6CpUKlU6HSqcj2mCgXqUiav3Vi30FtxvBNnIhVGod4yWOCZ8OW/EOD49+PzJyuaj5EEX0oaG43W76jleBQjHqgY5XMxgWNnoOaBhNkFGEqEGE5lY3+8vtJCeqSEpQE6mb+LngkG0B+ohO4uOU3H1bJHffNnqG2tHl5sO9Vp7/zRBnz7t5f5eNv++0IYq9ZKSq+fjNo+UGx3emseegnZ+8MMif//xn3nnnL6xcuYa3336bpKSkSQlcICZoS92OSsoQnRTnfSqVakaG6V4L3+OU6jFPl+f2wAMP8Nprr132823btvHBBx9M6D4mLG5btmzBarVSWVnpP1Q/cOAASUlJHD169KLD071795Kenj4pYQOw2+2EhYVdtQDQ4/HMvLiNd341QwgXsk9UavW4x2aBIFypxDaBZs3KkBCUBv2E246JoojodF50bjj2+5ToaMw2GyFpaf4wqjhO70fBbkew23H39KBQKhEBtRp++ItBfviLf8wM02oVJMSpSElUk5qiJjlBTVKCiqRENckJowKYnKgiLkaF2ZaKPqLjsrVSU0L4wuei+cLnohFFkcYWN2/8xcKv3rDQ2uHh5d8N8b+/HUKtgrUrw7hzWwT33x3Jh/tG2P7REdLSksnIWMgf/vCHCQlcIIQNRsVNqj6PUoboRFG8Ys3sTKK4cLEWaHzzLwVBCHgRtU/Qw8PDGRkZmfL93Xrrrfz2t7+96GeTCW1PWNzy8vJITk5m3759fnHbt28fd955J3v27OHIkSNs3rzZ//MtW7ZM2AgfvhfkalcdXq93Rq9IlCoFgjdwb0pf3ZxKqcI7LW2LJ48KBd4Z+CAqFAoUWi1KrRYuTFYfS1p4BCNhWlK+8u/+n4mC4A+X+s4MhUvOEr1WGy6zGYXVijgyAheukB0OkbYOD20dHpTVoFIpEAURzyW6rVTCT38aS3XlRwz0dpGcoCI58YIQXhDApAQ1SfEqFmWF8q2vxvGtr47OGDx51s5PXzDz910jHKqwc/iYHUGAGIOSVcs1nKt309jYyOrVK4mOjuWFF14A8AvcWAIlbDDaOWJ4eFiSzhFSZfCN9WICjVKplKQ7v5SP2bf2dIVGNRoNSUlJ1/33kzq82rJlC3v37uVrX/saMOqhPfXUU3i9Xvbu3cvmzZux2+0cPXqUBx98cNLGCIJwzcnbo2HJmev3rFQpEAN45uYXN5VSMs9NqbioeiFghCqUuC550ApfsXpExBX+6nJEjwfvyAjCJZ6h73vX4ADe3n68FguC04ng9aLV6jh1tpdTp2yo1QoUjIrgpZ/JSJ2ShHgVqUlqFiSPCuCKZWF8/GYd8bEqzje6+O2bQ5yrc1N12okvx0YQoL+/n8/ecw+JSUn8/Oc/B2Dp0qUAtLS0+CdoB+IcTKPR+L0YKa7oPRL0TB3rxQQahUIxL8VNFEV/WZLUTFrcvvKVr+DxeLDb7VRXV1NaWorb7eall14CoLy8HKfTOWOem8fjAXHmPDeFShlgz230A6BSqhAk8tyUKBCkuLpVMC2PWaFWo46KgkkkLIQnJGC4715STDf6Q6IuiwVPdzfunt5RIbQ7GB5xMdzsprHZjUo1WgsneEUuvf4JDYFog4revn98qNUq8HjBaDTy6KOP8r3vfY+dO3dSUFAQUGGDf2z0Umw689WLkTIsKdVj9jko0/F6b9++/bIpHU8//TRPP/30hP5+UuK2efNmbDYbx44dY3BwkNzcXOLj4yktLeXzn/88DoeDffv2kZ2dTXp6+mTuGhgVN4VCgcfjGW2zdIXsvLCwMEI8MxNDV6mVqEKUhIQFJkYfIoYQRhga9ejVtCbAh8AAKoUCtSLwa4deWE+KxxyiVBISqSNKM7FmAKPlFs7RhJoRG65uI672duwNDXjNQ+jCFYzYRcLCQKEYPRP0eCDkwh7j8bj4/ve/z3PPPQdAUVERkZGRV3yPzwRKpRKn0xnw8ydBEPwNBqTA5XIFvL7P5yUH+jH7RMXlcgU8ocT3OsP0XERt2bKFF1988aKfTeZiUCFOUuLT0tL40pe+xODgIDabjf/93/8FICcnh1/+8pd861vfIj8/n1deeWUydwvAoUOHuPvuu/1eoIyMjIzM7OLnP/8527Zt8x9fXQ8PPPAAZrOZd95557rvY9KXM1u2bGHfvn0MDg7y5JNP+n++adMmduzYQUVFBV/60peuyxjlhY4axcXFNDc3s379+st+5+mnn+Z3r7xBiXvzda1xLT71k1s58voJ2k8FJrPMIg5QxX4+/l/f4YbUdF4yBX7Q35Mpafylv5cmZ2AP/VdE6FgeoePXPTPzXF/qbQkjI6MJKSMjfPfOT/Lqjh2cq61FsFrxWofxjtgRr1SbpFSOumSCMHowp1CAKKLVjDZgFkURu13EbPnHtaJaBSGhWvLy8nnsscfQ6/UUFxdTWVlJWFgY0dHRLFu2LCBX2IIg8MEHH7B169aAt4Sqq6vD4XBQVFQU0HWlfMznz5/H4/FQWFgY0HU9Hg8fffQRN998c8A9dN/svoSEhGvmTgSC6xK3xx57DLfbTWlpqf/npaWl/Nu//Rsul+u6ztsA/0GkL4RwpRfHbrfPmLsvuAUEr4DbHphwgkt0Y8eO3e0BBTgliJV7EfES+LVdF85WJ7qu4HJdnCxitV6eQHKhzs5rsyHY7ZdniAAolYyUbsHR0MBAVdVFP1eEhIzO4tPpUEdHo06IJyQhnpCYGH+XFUVoKI7WdozP/QK7HZzO0ZCkSgVqdSg5Obncf//9fOUrXyE0NPSirMjIyNF6uvXr13P06FFOnz4dkIGnvs+LRqORJC3e128xkPhCY6GhoZI8ZrVaHfB1fYSEhAR87bFZktMhbk6n87LyFbVaTdwEG1Vcl7jZ7Xby8/NJTEz0/7y0tJTh4WF/ycD14DuIvNphrFqtHnfszHQhCAIKZeBi1b6aPUEUkGq0nyCOJncEEtHrxWK1Eh6hw17fcHkdnG20O4owPDz6c7sdxsu4UyhGvSoYnX0zzntDERqKUqdDbdCjjosjJCaaAYWC/E/eSffG9aMF4lcZFSSKIp7ePuz19djr6nHU1SPY7ahUoFKpSVmQyd13381//ud/XtaF49J0/7EiE8iBp1arVZJNHkaFVYpxO749JNBnT761pVjXd+Ymhefke8zTVWP3wQcfXKYleXl5nD9/fkJ/P2lxy8zMHFd4MjIyppyho1arcbvdVxU3XzblTCF4BJSqwL0xlBfKGjxeL0oJPgwAblEgVHH9j3m0Ns3xjzq0cZo9e61WvJbhUW9rZATR5UKRlUXkd7+L8YUxh8Yq1ajgi8LlM+3GolZfECwDIdGGixo4q8dOOg8LG3eiQbcuguxoA4fDx5/X5hkcxF7fgKOuHvv5WrwX2gnpIqNYv2IFn/zkJ3nwwQevOuX5WnVsgZzobTab0ev1kmy4DofD77EGEp/nJsVEaF9KvBTrgjSC7uss5Xa7p5zA8+qrr/Lqq69O6T6CqrekryeZr0fZeKhUqhn23ESUqsC9MXzTDbxOJ6oZ7LxyNSxeL5EXNgBRFBFdrgvCZL3Em7L5a8m8lmF/Cr3gcFwx/KdUqi6ItgDihQ/fhd8dGBggMjKSkJAQv0ej1GhGBcrXdFkfNRoOjLx4AoFyip5Ah8vJyjFpxt7h4VExq2/Afr4Wz4XRStrwcJYXFHD77bfz8MMPk5KSMqH7n2iBdqAEzmw2X1WIZxIpRr/41h07BSOQXK2F4Ewipbh5PB60Wq1/4LTUBJW4RUZG4nK5EAThiuKmVqsRmLmaGdErBtRz882lGx4eJmIG1hU9njHTAqzjdv4wbrsV5cAAbW++ORr+Gy+NV6FAdaHDe2hICOEXQlzqC/Fvj8eD0+5k2Db8j/NQQUAQBIQLfSzV+qgxI3MiCYmKwisI5D35HwxpNKh0EZc1SZ4pWs1m4hKSsG1/n8HTp/3DT0M0GvJzcvinhx7i4YcfJicnZ9L3PdnOI4EQuKGhIXJzc6f1PieKlOIW6EkEPlwulyQbvO9YRwpx8+VLDA8PS+KpX0pQiZvvzeBwOK4oblFRUXi8MzfSwePyoNYG7mkZ9dwUDA6aiVSpUcIVpVsUhAsZf7aLxtlc1JljeHhCnfyVFw67tRoNjv5+FsTFkZeejkajGT3XvFBnaLOOYDabGRwcwOt243W7cdrtDANadRgahRa1N5QQQUMkkcSSigYt55TVRGxch2HbLSjDtFf8sA0LAnFpqdhmuIO64HLhbG7GXteAvbYOV2cnA6+8QmJPL/ooPTf981089NBDl7XImizX21JrJgXO6/VisVgk8dxEUZRk3A1IJ6pSru31eiXLVPTNC5Q9t3GIiIhAoVBgt9tHr/jHacdlMBhwC24EBJRTOCe6ErZBOxGG8c9hphNRFPHiwYUTlTKE3tra0S4hRysY6On1n1ONJlRYEWwjo+G/cVCoVKjVajQaDfqICKIiI9GnpBAREUF4eDhardYfmvF4PPT399Pd1Y3J1MOQ2UxbWxtJSUnU1NSgVCgJU4cTKmpReULQoCWSOOJIJfTC0FXfV6V3jJd1yT7crKhD9HpQXeFMy4fF68GgUtPK9Iqb6PHgbGvzi5mztRUEAaVKRWpqKpvvv5+kpCT+9Mc/kpeXNy1rTrVX5EwJnNlsJiQkhLCwmX9fX4rT6UQURck8t/kmblI+Zp+4yZ7bOCgUCnQ6HfYLneHHuwqJvjDA043rqtOtr5eRQTvh0ZPfBLyiFzdOXDhx4xrnexdelRuv0o0LJw6PHcE34kahYuhoBZahIRTHq7G2tKAJCUUXHkZUVBSGzCzi4uJISEggJSWF9PR0MjMzMZlMtLS00NfXR3d3Nx3tHXR3d9Pa0orTdbFYhKhCCVOGoxZCCfGGEoqWBNJIJQfDQBKpMRls4nZCxFAUngsb6hT21TBvGLa+gWv+ntHtIjk0lJMjtutfjFGv1tXZib2+Afv5OpxNTYgeDwqlksTEBDZ88pPcf//93H777f73VHt7O/X19eTm5k5ZRKarCfJMCFxHRwfJycmShKosFgsRERGSnHtJtdH7vFUp1pZqXbjYc5PFbRzGitt4oyp84uaZIXGzDdiJzTDgFB24L4jTqEiNCpTvZx6FC6/agxsnDq8Dj3h5+C80JJTo6Bji4+NJSMwkISGBuLi4i27x8fFERkaiVqsZGBhg/759EyqlGBgYICEhETUqwpQRqD2hhIgaNESQQewYDyuMULSoBfU/4p2X7HEqs5bIGB2hiukLHUUQyVC36Zq/1+F0kh8WPun7F0URt8nkT8231zeMFmArFMTExLDl5pu59957+fSnP33FNPTk5GROnTqF2Wz2v6+uh+nu7j+dAuf1euno6GDduqvP6pspfFmaUuB0OiUJj7ndbgRBmHehWF9Tbt9cTqkJOnGLjIzEarVesZP4Pzy3iRVZi6KIB/dF4nSpR+XBOSpUChfOoR4WRN/BAbZfdD9KpZJofTSxcbGkJ6STmJhwmUhdKly+MOtEKS8vn/BokMjISATBS7a4jAVC1ugPr/PC3DZoJ1yvndZxPxFE4R6qQ3A6UV7lQ97hcnGTYWLC4u7rx1FffyHUWIswMgIKBVFRUWxev567776bf/mXf5nwB0utVrNgwQJaW1uvW9xmamzNdAlcZ2cnWq12SuI9FaZ64TAVpEoocTgckhSt+9aWKonG4/Hg9Xr9A0ulJmjFLSwsbFxx8x2KWxjAI7ov8a5GBcur8uBRunCJo16VOM6E60hdFLExMaQmxJOQmOAXp7S0NNLS0vjb3/52kVAZDIYZP6idzGj6kJAQ4mLjcfZdPtxzstjNdkRBJMwQhq1/6kMGAXSMFjO7jSY0GVduot3pcqJTqtCrVAxdkqXpGRoaTc2/kJ7vHRoCIFynY3VREXfeeScPPfTQhDsWjEd2djZlZWUUFBRMelOY6XlsUxU4URRpbGxk4cKFkoQkYTRLMzMzU5K15+O5l8PhkKzkwzctBpDFbTyioqIwm81ERUWN22IrNjYWg95A3dBJ/8+0Wi0x0bHExcWRmLiQ+IT4K3pTcXFxxMbGXvGqamRkhJ07d3L77bcHfEOY7MTitNRUjH2D1/7FayB4RawDIxiSIqdN3CLQAwqcHZ1XFTeXKNLjdpMaqmGgrw9Hwz/EzNPXB4AmLIyleXl87GMf49FHH72uiRNXIioqiri4OJqamigoKJjw3wVq0OhUBK63txeHw0FaWtqM2Xc1XC4XIyMjkmy2Xq8Xh8NBePjkQ95TRUpxk+rMzTfJZWRkhJCQEMm8x7EEnbglJSVhNBrJzc0dd6MPCwvj9JnT9PT0+MVqOt/AvhdFijeJRqNh6IJ3MhEysjJoPdU1XsepSdPbNED8whg6z177nGwiqBQqIhRRuDo6rvg7gtOJo7GJhhEnsW1ttF2YBqEODWVRdja33ncfDz30EEuWLJkWm65Ebm4u5eXlpKenEzGBIamBnKAN1ydwXq+XM2fOsGjRIkmSOQAGBwcJDw+XpPXW0NAQarVasgzR+ZilqVAo6O/vJykpSbJIwViCTtxSUlLo6uq66mj61NRUUlNTZ2R9X6xcijfJZD23vLw8dqp2wTQMOe5tHCBh4fRu1Hohhr6GJv+/BbcbZ0vr6LnZ+Tqc7e0gilSUlvKZT3+akIce4sEHHwx48kNsbCzp6elUV1ezYcOGq34wAy1sPiYrcLW1tSiVShYuXBgwGy/FZDKRkJAgydpDQ0MYDAbJ2o1JlaUp1ZmbT9C7u7sn3MVnppF+LsElJCcn093dPemNfjqZzNnXdK87mce8ePFirO7hcTM1J0tP4wDxC2OnfD9jMRCLq7eHwR0f0v38/9L6ta9jfOFFhnbvJU4Q+Ow99/DBBx/w4QcfkJqaynPPPSdZVt/ixYux2+00NTVd8XekEjYfPoEzm81UVVVdscfq4OAgTU1NlJSUSFbQK4oiRqPxupuoTxWp241JITC+hA4pu8F0d3dL9ppfiixu4yDV2j5RneiIdt8ZkY3hKa/d29hP9IKoKU0gF0WRYdFMq1jHScVh6pUnQanEvHMXkZZh7rztNv7whz/gcjjoaG/nzTffZNu2bWi1WmJjYy8bbxFIfHMEz507h/VCk+SxSC1sPq4lcF6vl6qqKnJzcy+bUBBILBYLLpeL2NjpvWCaKFKLm1QCo1QqJcvS9HluwSJuQReWHCtuUnhPwFVDojNJWFiYv8J/IrVB+fn5ANiwoGdqG+6I2YG1f4T47Gi6zvZM6G9EUWQEK4P0MKjoZUjdj8NjRxOqYf369dx082OsW7eOdevWXfPD7jtrzc7OntLjmApxcXGkp6dz4sQJ1q9f7/d6gkXYfFwtRHn+/HnUajWLFi2S1Eaj0UhCQoIk531er3fCn6HpRhRFLBaLJBcWvtCglKHY7u5uyd97PoJS3Lq6uggNDZ13nptCoUCv10+48FWn05GeloG1feJJKFdj9Nwt9qri5hBHGKCHQUbFbMQzWpO4csUqbtn2ebZu3cratWsnfeWalJTE2bNncbvdkg14hNHw5P79+zlx4oR/InwwCZuP8QSutbWVlpYWNm7cKPkkZKPRSFZWliRrWywW1Gq1JJmSVqsVQRAkSYWXugRBp9PR1dXFxo0bJbHhUoJO3FJSUnA6nTgcDhwOhyRD/7RaLX0X0tADjcFgwGw2k5GRMaHfX7lqBWVdh67cbXkS9DT2E5998QbuEh0M0MsgPVhCBhh2D6FQKChaWsTnbvkUW7du5YYbbpjyhzkiIoLIyEi6u7unNdV/sqjVatavX8+BAwc4dOgQZrOZ9evXB5Ww+RgrcAcPHmRoaIh169ZJGo4EsNlsWCyWi4YZBxJfSFIKD2ZoaIioqChJLi6kLOB2Op3ExcUFVUJJ0IlbZGQk4eHhDA4OIgiCJFN8pTzvMxgMV01quJTi4mL+/rf3p+UioLdpgJyNGfSKXQzQg0Xdz5BntI4ud1Eun9h2H1u3bqW0tHRGzlLS09NpbW2VVNxg9PVPS0ujtraW5ORkyTpsTAStVktWVhanT58mLi4uKES4tbWVxMREyTZaKVt+zcezvrFrB9OZW9AllCgUChYsWIDRaEStVvsr3gNJeHg4NpttRid+Xwm9Xs/Q0NCEk0qKi4txeh04uL7ia6/ooV80US+e5oP6d9AnR9IZV4eY4uCuf/kkr7/+Ol1dXdTW1/L888/zz//8zzOWJJCWlobZbGZ4eOoJMlOhqamJxsZGVq1ahcVioaqqasKvR6DxnQeuXLkSh8Nx1SzKQCAIAm1tbZJ1JRFFkd7eXskSWXwlCFJgs9kkCcWKouif5DIwMMCCBQsCbsN4BJ3nBpCTk0NDQwOFhYVYrdaAX4VFRUX5W8kE+s2i0+lQKpUTPhBfuXIlAEMMEMa1C5AFUWCI/tEzM1Ufg0I/guglLjaOm2+5GUEQ2LFjh/9+A0loaCgpKSk0NzdTVFQU8PXh8uSRmJgYysvLOXDgAMXFxZKH/Hy4XC5OnTpFb2+v39bY2NgZn+h9Lbq7u1GpVMTHxwd8bRgd+utyuSRZXxRFzGYzhYWFAV8bRr1GKeoanU4nHo8Ho9FIVFSUZK/9pQSd5wajHSPq6urQ6XTjpmXPNCqVisjISMxmc8DXHptUMhESExNZmL0IM73j/r8oiljEAVrEWk4oDrJf+R7HKaMvsoONt6/nuV/8nLNnz9LT28Obb77J8uXLx217Fiiys7Npa2uTxIbxsiK1Wi2bNm0iLi6O/fv3U1dXJ7kX19XVxZ49e/B6vWzZsuUiWydSBzeTNDY2kp2dLVmHiu7ubuLj4yXJ0rTZbJIlk/jyFKQIx/p6ATc2Nk7L+KjpIig9t9zcXN59910iIiKw2aY25+t68SV2SHE4ajAYJtWGa+uNW/hj+5/BMypmNiwM0ItZ0cuQqh+n1+HfpG+66Sa2bt3K8uXLx90AkpKSOHfunGRZi9HR0URFRdHa2hrQlOKrpfurVCqWLFlCcnIy1dXVdHd3S+LFuVwuTp8+jclkYunSpaSmpl62kczkRO9rMTAwgMVikawQH0a7okw0GWu68fXElSKZxGw2ExERIcln1mazodPpqKysJDc3N+DrX4mgFTef59bTM7Gaq+lGr9dLVlSs1+tpbm6e8O+XlpbyyiuvcJojWNQD2D0jhKhDWLt2LTfd/Ahbt25l9erVE0rM0el0hIeH09vbK1nWU25uLtXV1WRkZATkwzrROraYmBg2b97M+fPn2b9/P+np6SxcuHBC/SingsvloqWlhcbGRmJiYti6detVEwekErjz58+TlZUlWSmHw+HAbDazevVqSdaX8rxNykQWq9VKREQEdXV1srhdi9zcXFpaWggJCZEkLAmj3lNtba0kpQgGgwGLxYIgCBO6CrzppptYnL+YsPAwbr7lPm688UbWr19/3eeFvoJqqcQtMTERnU5HY2Ojv1B9pphsgbbPi0tNTaWhoYE9e/aQmJhIRkYGCQkJ0/ZeEUWRoaEh2traaGtrw2AwUFxcTGJi4oTWCLTA9fT0YDabJTmr9WEymTAYDJJlDJrNZsmSKcxms2RJNFarlbi4OOrq6ti2bZskNoxHUIrbggUL0Gg0mEwm3G43Lpcr4OUAer0et9stWVIJjL5pJhL6SkxM5Oy5s9O2flJSEhUVFZIIO4yeOy5ZsoTDhw+TlZU1YynlU+k8otfrWbFiBQUFBTQ3N1NdXY1CoSAlJYXo6GgMBsOkh9Xa7XaGhoYwm810d3djs9lISUlhw4YN11WOECiBE0WRmpoacnJyJJkA4MNoNJKUlCTJ2r5kkpmeYHElhoaGJOvuY7VayczMlD23iaBUKsnJyaG5uRmNRoPVag14/c7YpJJAi5tCocBgMNDf3y9Jdp7vuR4YGJDsajAmJob4+Hhqa2tnJHNyulpqhYeHs2TJEgoKCujt7cVoNNLY2IjFYkGpVKLX6/1Cp1Kp/EkeHR0dKBSKiwTN6XSi0+kwGAwsXLiQlJQU1OqpfUQDIXBdXV04nU5JW6d5vV56e3snNZNvOhkeHkYURcnabtntdknCkqIoYrPZcDgc9Pf3k5OTE3AbrkRQihuMhibr6+tZu3atJOIG/6g5kyI8l5iYiMlkkqSFkUKhIDExke7ubsnEDUYbQ5eVlU37udZM9IpUKpUkJib6u3IIgsDw8DBmsxmz2YzRaEQQBP90+Y6ODtRqNRqNhvj4eHJyctDr9VMWs/GYSYETBIFz586Rn58v2dw4GA2LajQaySZAG41G4uPj510yycjIaH1tZ2cnSUlJQVMqA0FaCgCjTYHPnj1LZGQkFotFEht8GZNSkJSURG9vr38zDDRpaWm0t7fj9XolWR9G6w3T0tI4efLktKW1B6oJss9ry8jIYNmyZaxbt44NGzawfv16ANauXcu6desoKSlh4cKFxMbGzoiw+ZipMoH6+npUKpVk0759+DrbSJWGbjKZJAuJSplMYrFY0Ol0nD9/nry8PElsuBJBK27FxcVUVVVNOi1+OvGJmxT1QjqdjrCwMHp7x69fm2ni4uIICQmRdAwNwJIlSxgeHqa1tXXK9xVs3f0DzXQL3NDQEPX19RQXF0vaqNlut9PT0yNZ2zan08ng4KBkvTSHhoYkbzd2/PhxSkpKJLHhSgStuK1YsYKzZ88SGhoqmcBERUXhcrkkmxDgy1qUAoVC4e/1KCUhISEUFxdz9uxZfwjkepjvwuZjugROEASqqqpYuHChZF6Dj9bWVhISEggLC5NkfZPJhF6vlzRLU6rXwFf+cPz4cVasWCGJDVciaMUtPT0dg8FAS0sLgiBIUsytVqsl61QCo6FJk8kkWa/A9PR0+vv7JSuk95GQkMCCBQs4ceLEdT0XsrBdzHQIXF1dHYDkoShfL0upCrdB2ixNqZNJzGYzOp2OkydPyuI2URQKBStWrKC6ulpSgZHy3C0mJgZBEBgcHJRkfa1WS1JS0qSmFMwUS5YswWq10tLSMqm/k4VtfKYicGazmYaGBsnDkYA/siFVSNDr9dLT0yNZJ/yhoSHJkkkcDgdOp9OfHBVMZQAQxOIGo6HJ48ePB8W5mxT4MvCkPPdauHChZL0exzI2PDkwMDChv5GF7epcj8A5nU4qKirIycmRPBwJo69xVlaWZCLb19dHaGioZFmag4ODkp63RUZGcvLkyaC40LmU4LLmElasWOFPKpFKYOLi4ujv75csa1DKczcY7fWo0+lob2+XzAYf8fHxFBQUUFFRcc1RSLKwTYzJCJwgCBw7dozo6OiguEr31QcGQ0hSqizNnp4eybrw+xJZqqqqgi4kCUEubiUlJZw5cwatVitZUklkZCQajUayrMWEhASsVqtk514KhYLs7GyampoknRPmIzs7m8TERCoqKq54wSEL2+SYqMCdPn0aj8dDcXFxUHR+b2xsJC0tTbKuKKIoSn7eJmWWZjBnSkKQi1tmZiaRkZG0tLTg9Xol2eClzloMCQkhLi5OUu9twYIFiKJIW1ubZDb4UCgUFBUVoVQqx00wkYXt+riWwDU3N9PV1cXq1atntB5vogwPD9PZ2SnJ/DIfQ0NDeDwe4uLiJFnfl6UpRZbo2GSSEydOyJ7bZFEoFKxevZqKigqio6MnfNYy3fjETSrPRerQpFKppKCggNraWkmLun2oVCpWrVpFX18ftbW1/p/LwjY1riRwJpOJs2fPsnr1akkmPY/HuXPnSE9P9/dhlQKj0UhCQoJkZ01Seo02mw232017eztqtVryrNnxCGpxg9FxLmVlZcTGxtLX1yeJDbGxsXi9XsnO/VJSUujv759SnddUWbBgAaGhoZMaxTOTaLVa1q1bR3NzM/X19bKwTROXClxPTw/Hjh2juLhY0lZsYxkYGKCnp0fSDdUXyUhNTZVkfV+WplTi1tfXR3R0NAcOHGDjxo2Stl67EkEvblu2bKGsrIyYmBj6+vok8Z6kzlrUarUkJiZKWlCtUCgoKCigrq5O8sxJH1FRUaxbt47z589z9uxZWdimCZ/A9ff3c+TIEYqKiiQb5XIpoihy7tw5Fi5cKFnRNEBvby+CIEh23tXf309oaKhkvRz7+/uJi4tj3759bN68WRIbrkXQi9uKFStwOBx0d3fjcDgk816kDg1mZmbS1taGIAiS2ZCQkIBer6ehoUEyGy5lYGAAhUKBUqmkv79fanPmDBaLxT9qqre3NyiSiWA0O9BisQR0Svt4tLS0kJ6eLmlIcqKz/aYbURTp6+vDYDCwf/9+Wdyul5CQEG644QYOHjxIdHS0ZKHJhIQEhoeHJRNX3yBMqSaTwz+8t8bGRklakl2KLxS5fv16NmzYQENDA+fOnQuajXi20t3dTUVFBcuWLWPz5s3T3mz5ehk7N06qad8wWrxsMpkkK0GQOkvTZrPhcrno6OhAEASKi4slseNaBL24AWzevJm9e/f6a86kIDQ0lNjYWMl7PU62Q8d0ExMTQ0JCwkWJHFJw6RmbwWBgw4YNdHR0UFlZKdk0hdmMKIrU1dX5U7vT0tJmbJrA9dDZ2Ynb7ZZkDNRY2tvbiYmJmdYxTJPB51VLlaXpO2/bv38/GzduDIrs2fGYNeJWVlbm99zma9Zieno6PT09kiaWwOictba2NoaHhyVZ/0rJI1FRUWzatAmn08nBgwclf55mEx6Ph+PHj9PS0sLGjRsvmmEYDALn9XqDYm6cKIq0tLSQmZkpmQ2+LE2pnofZcN4Gs0TcVqxYgcvloqurS/Jzt76+PskSKsLDw0lKSpI8YzEyMpKsrCyqq6sDvtFdKytSo9Gwfv16oqOjKSsrk8/hJoDdbufgwYPY7XZKS0vHbecktcCdO3cOjUYj+dw439BZqXpJ+myQKiQ5W87bYJaIm+/c7cCBA0RHR0vWLSQiIgKdTifZ+jDaoaO1tVXysFtBQQEul4vGxsaArTnRdH+lUsmyZcvIz8+nvLychoYGyc+LghWTyURZWZk/rKvRaK74u1IJXH9/Py0tLUHRGaWpqYnMzEzJEknsdjtDQ0OSZWlarVZcLhft7e2Iosjy5cslsWMizApxA7jpppv44IMPSExMxGQySWZHUlIS3d3dkq0fGxtLeHi45L0eVSoVJSUlnD9/PiDhyeupY8vKymLdunW0tLRw8OBBrFbrDFs5e3C73VRVVVFZWUlBQQHLli2b0IYdaIHzeDxUV1eTn58vWXNiHxaLhYGBAUlDkiaTiejo6KtehMwkRqORuLg4PvroI7Zs2RK0520wi8TtjjvuYM+ePX7PScpGxiaTSbKU/GDq9RgTE0NmZuaMhyenUqAdGxvL5s2bMRgM7Nu3j8bGRsmfN6kxmUzs2bMHp9PJ1q1bycjImJRHFEiBO3/+PBqNRtI2Wz6amppITU2VTFhA2pAkjL53kpKSeO+997jjjjsks2MizBpxy8nJISsri/LyckkbGUdHR6NSqSRNyV+wYAEul0vS8KiPgoIC3G73jNW+TUfnEbVazdKlS/0dTQ4ePIjFYplmS4Mfh8NBdXU1lZWV5Ofns3bt2uvuSxgIgQumcKRvbll2drakNvT29kp23udyufwtECsrK/n4xz8uiR0TZdaIG4x6b9u3b5c0a9GXki9ltxCVSsXChQuDoqZLpVJRXFxMbW3ttIcnp7ulls+L8yWbVFVVzYuMSrfbzfnz59m1axcul+u6vLXxmEmBGxuOlLJ/pI/6+npiY2Mlm50G/yhBkOr5MJlMREVFsWfPHlatWiXZud9EmVXidvvtt7N9+3bi4+MlbWSckZGByWS65kyxmSQ7Oxu73S7p+Z+PmJgYsrKyqKqqmrZw7Uz1ilSr1RQWFnLjjTcCsHv3bk6fPo3T6Zy2NYIFr9dLY2Mju3btoq+vj/Xr17NmzZpp7SI/UwLny44MhnDkyMgIzc3NLF68WDIbfCUIwTC77t133w36kCTMMnFbt24doijS0NAgaSPj8PBw4uPjJR0B4+vEfe7cOUlbcvnIz8/H6/VSU1Mz5fsKRBPk8PBwSkpKKC0txWazsWvXLmpqaiS9YJkuPB4Pzc3N7N69m7a2NkpKStiwYcOMPZfTLXBdXV20tbUFRTgSRs/9UlJSJPXa+vv7cblckoUkfY2a9Xo9O3fulMVtulGr1XzsYx9j+/btkjYyhlHvra2tTdKwYEZGBqIoSp45CaPhydWrV9PW1jalkG2gu/tHRUWxdu1a1q5di8ViYdeuXVRWVtLf3y95yHeyWK1Wzpw5w4cffkhrayuLFy9m8+bNAelBOF0CNzQ0RFVVFSUlJUERjrRYLHR2dpKfny+pHa2traSlpUlauK1Wq6msrCQpKUlSL3aizCpxg9Fzt/fee0/ybiFJSUl4vV5Jkzp8c9bOnz8fFHPWdDodq1at4vTp09c1e0/KsTWxsbGsXbuWLVu2oNFoOHr0KHv27KGhoSGoz+V8NUeHDh1i79692O121q5dS2lpKampqQH1fKYqcE6nk6NHj5KTkyNpkfRYzp07R0ZGhmSttgB/A4tgCElu376dO+64Iyg86msx68Rt27ZtNDQ0YLFYsFqtktUuKZVK0tLSJE0sgdFZbxqNhqamJknt8BEfH8/ixYupqKiYVIgvWOax6XQ6li5dyrZt28jNzcVkMrFr1y727t3LuXPnGBwclNyjs9lsNDY2cujQIT744AMaGhpITEzklltuYdWqVcTGxkq2+VyvwAmCwLFjx4iJiSE3N3eGrZwY/f399Pb2Sm5Pe3s7BoNBsvE2oijS1dVFQkIC7733HrfffrskdkyW4K3AuwKRkZH80z/9E3/+85+59dZb6ejokCxkkJmZyZ49e7Db7ZKMeofR7M3FixdTWVlJRkYGoaGhktgxlqysLCwWC0ePHuWGG264ZqFnsAjbWFQqFWlpaaSlpeFyuejp6cFoNHL48GFUKhWJiYkYDAb/pjNT4SJRFBkeHmZoaAiz2Uxvby9Wq5W4uDiSk5MpLi4OmunYPnwCd+jQIX+I8WpiK4oip06dwuPxsHz58qDwCnwTCBYtWiTp3DhRFGlubpZ0MKsvOlVTU4PX66W0tFQyWybDrBM3gM997nN8/etf5wtf+ALnzp0jLy9Pkg9EREQECQkJkmdSJSQkYDAYqK+vZ8mSJZLZ4UOhUFBUVMShQ4c4ceIEK1asuOLrE4zCdimhoaGkpqaSmpqKIAj+K/ru7m7OnTuHx+MhMjLSL3RarRaNRoNWq0Wr1V5T3L1eL06nE4fDgcPhwOl0YrVaMZvNDA0NAaNngwaDgby8PBISEiQd+TIRJiNwzc3NGI1GSktLg6bjhclkwmq1snbtWknt6OnpwePxXNTIOtB0dHSQmprKf//3f3PPPfcEzWt0LRSi1DGW68But5OUlMSHH35If3+/pBtjb28vlZWV3HLLLZJ2KzebzRw8eJAbb7xRMi/yUpxOJ2VlZWRmZo4b2pkNwnYtRFHEbrdjNpsxm81YrdaLREoQBNRqNSEhIf72Vjabze9teTweXC4XwEWCGB4e7vcMdTqdZL0Mp4rD4eDQoUMYDIZxBa63t5ejR4+yfv36oHkPiKLI3r17ycjIkLwUoby8nOjoaMmiUx6Phw8++IAVK1aQn5/Prl27WLVqlSS2TJbZIcGXEBYWxl133cVbb73F5z//eTo6OiT7YMTFxaHRaOjo6JD0wNdgMJCYmEhtbW3QNDPVaDSsWbOGgwcPEhoaelFPvrkgbDDqpYaHhxMeHn7Z1bUoirjdbhwOB263G0EQcLvdHDt2jMLCQkJDQ1GpVH5PLxjCcdPN1Ty4gYEBKioqKCoqCqr3QEdHB16vV9IekgDDw8P09fVJOgzUZDIRFhbGgQMHSExMZOXKlZLZMllm5+UgcN999/H73/+epKQkOjs75V6PjLbC6ujokKz+bzz0ej1r167lzJkz/rrAuSJs10KhUBAaGkpUVBSxsbHEx8cTHx8PjF4UxcbGYjAY0Gq1c1LYfIyXZGI2mzly5AgFBQWkp6dLbaIft9tNTU2N5HPjYPRzsmDBAknP/Nrb20lNTeXNN9/kvvvum1Xv01krbqWlpYSEhFBdXY1SqZS012Nqaip2u52+vj7JbIDRTL9FixZRXV0dFIXdPmJjY1mzZg2nTp2iqqpqXgibzMWMFbijR49SXl5Obm6upL0ax+PMmTNERkaSmpoqqR1ut5v29nbJe1n29PQQFhbGjh07+NznPieZLdfDrBU3lUrFZz/7Wd544w1SU1Pp6OiQzBa1Wk1mZmZQpOPn5uaiUCiora2V2pSLiI+PJy0tjfb2dhYuXCgL2zxEq9WydOlSenp60Gq1kp9nXYrJZKKrqysoMjZbW1vR6/UYDAbJbOjq6iI6Opr333+fkpKSoHu9rsWsFTcYDU2+/fbbREdHYzQaJZuQDaPp7729vQwODkpmA4zW3xUXF9PY2BhU4cmmpiY6OjooLCykoaFB0tZlMtIwODhIZWUlixYtQhAESSZ6Xwm3282JEydYsmSJ5KUVbreb+vp6cnJyJLXDF5J84403uO+++yS15XqY1eJWVFTEwoUL+fDDD4mMjJS0DVVYWBhZWVnT0ltxquj1enJycqiqqgqKziVjz9gWLlzI2rVrOX36NC0tLVKbJhMgBgYGOHz4MHl5eSxevFiSid5XwxeOlDIpzEdjYyM6nU7SrvtDQ0NYLBZGRkaoqKjg05/+tGS2XC+zWtwUCgWPPvoov/zlL8nMzKS1tVXSD0pOTg5DQ0OSnv+NtUWpVFJXVyepHeMlj8TFxbF27VrOnj3L+fPng2Jzk5k5uru7OXz4MIsXL/aHtgI90ftq+MKRwdCo2el00tDQwOLFiyW1paWlhQULFvDb3/6WT37yk/5EqNnErBY3GA1Nnj59mt7eXkZGRiQNC4aGhpKTk0NNTY3kG7ZSqaSkpITGxkbJnpOrZUXGxsayceNG2tvbOXbsGB6PRxIbZWYOURSpra3l+PHjlJSUkJWVddH/B4PA+cKRhYWFQVEfWldXR3x8PLGxsZLZ4EtmSUpK4tVXX+WLX/yiZLZMhVkvbnq9ns9+9rP8+te/Ji0tjebmZkntyc7Oxul00tXVJakdMNrVIicnh+rq6oCHJyeS7h8VFcWmTZtwu90cOHAgqBsUy0wOj8dDZWUlra2tbNy48YodNqQWuDNnzhAVFRUU5Qg2m42WlhYKCgoktaOzsxOdTsfu3buJi4ubNe22LmXWixvAF7/4RV5//XViY2Pp6uqSdPCkSqUiPz8/aOas+cKTgcyenEwdm0aj8f9eWVmZ5OUUMlNnZGSEgwcP4nQ6KS0tveYcNKkELpiyI2F0btyCBQska5AM/+hlmZWVxUsvvcQjjzwSFM/N9TAnxG3lypUsXryYt99+m9jYWMkTFdLS0lAoFJJPDIB/hCebmpoCEp68ngJtpVLJsmXLKCgo4MiRI5K/fjLXT39/P2VlZRgMBtavX49Go5nQ3wVa4IItHDk0NERXV5fkc+P6+/ux2+309PRQXV3N5z//eUntmQpzQtwA/v3f/53nn3+ejIwMWlpaJPWafHPWamtrg+IsKSoqitzcXI4fP+7vYzgTTLXzSGZmJmvXruXcuXOcPHkyKDxfmYkhiiKtra2Ul5eTn5/PsmXLJt0PM1ACJ4oi1dXVQROOhNG5cZmZmZKXITQ3N5ORkcFLL73EfffdR3R0tKT2TIU5I26f+tSnGBwc5OzZsyiVSrq7uyW1Jzk5mbCwsKAo7IbR8GRkZCSVlZUzIhrT1VLLF+MfHBykrKwsqGr1ZMbH4XBQUVFBTU0Na9asISsr67pDWYEQuNraWoaGhq46rSKQ9Pf309/fL/ncOLvdjtFoJDIykjfffJN/+7d/k9SeqTJnxE2j0fDII4/wi1/8guzsbBoaGiTNWPTNWauvr59Rb2ky9pSUlOBwODh79uy03vd094oMDw9n06ZNJCcnc/DgQc6fPy97cUGIKIp0dHSwZ88eVCoVW7dunZaU8ZkUuM7OThobG1mzZk1QzD4URZGzZ8+yaNGiCYdwZwrf0Ns333yTNWvWsHTpUkntmSpzRtwAHnvsMXbv3o3FYsFms/mH7ElFfHw8MTEx1NfXS2qHj5CQENasWUN7e/u0nQfOVBNkpVJJfn4+GzdupLu7m7KyMv9sMxnpcTgcHDt2jNOnT7N8+XJWrlw5rZvzTAic2WymurqaFStWSJq0MRaj0cjIyIjkra2cTietra2kpqby85//nK9+9auS2jMdzClxS0pK4vOf/zw/+9nPWLhwYVCISkFBAc3NzdjtdqlNAUYHrK5atYrTp0/T398/pfsKRHd/vV5PaWkpycnJHDhwQPbiJEYURTo7O9m7dy8KhYKtW7fO2CDN6RQ4X+g0NzeXpKSkabTy+hFFkXPnzpGbmyv5ANDm5mZiYmL429/+RkJCAh//+McltWc6mFPiBvDkk0/yl7/8BVEUGRwclLzXo8FgICkpifPnz0tqx1ji4+NZvHgxx44du+7askCOrfF5cTfccAPd3d3s379fPouTAJ+3durUKZYtW8aqVatmPJQ2HQInCALHjh0jJiZG8n6NY2lrawuKuXFut5umpiaysrL46U9/yte+9rWgOIucKnNO3LKysrj77rt57rnnyMzMlLz9FIx6b52dnUFVw5WVlUVycjIVFRWTzuiUah6bwWCgtLSUpKQkDh48yPHjx7HZbAFbf77im3G2a9culErljHpr4zEVgRNFkZMnT+L1eoOivZYPp9NJTU0NS5YskXzKemtrKxEREezevRuFQjEr+0iOx5wTN4Cvfe1rvPrqq4SHh9PT04PFYpHUnoiICAoKCqiurg6K0gAYTTBZunQparWa6urqCW8YUg8a9XlxW7duRalUsmfPHk6dOoXD4Qi4LXMdr9dLQ0MDO3fuZHBwkA0bNkz72dpEuV6Ba25uxmQysWbNGsmHj47l1KlTxMbGkpycLKkdXq+XxsZGFi1axI9//GOefPJJyUOk08WcFLfCwkJuvvlmXnrpJdLS0mhoaJDaJLKzs9FqtUExNcCHUqlk1apVDA4OTsjDlVrYxhIeHk5xcTGlpaXY7XZ27drFuXPnJB17NFcQBIHW1lZ27dpFR0cHK1euZP369ZLXPE1W4Hp6eqipqWH16tVBUajtwxfFWbZsmeSeZHt7OyEhIRw/fpy+vj4eeOABSe2ZTuakuAH853/+Jy+++CKxsbF0dnZK3rdQoVBQXFxMW1tbUIUnNRoNa9asob6+/qoDX4NJ2MYSFRXFmjVrWL9+Pf39/ezatYvGxsagGPUz2xBFke7ubvbu3Ut9fT1LliyhtLSUhIQEyTdhHxMVOIvFQmVlJUVFRUH1fnU6nZw6dYqioiLJU/8FQaChoYFFixbxox/9iCeeeAKtViupTdOJQpS6ff0MsnnzZrZu3crHP/5xf52X1DQ2NtLU1MSWLVuCyv3v6emhoqKCkpKSy85TglXYLkUURf/VutPpJDMzk8zMzKD6wLrdbt5//30+9rGPERISIrU5wGiT446ODpqamnC5XOTl5ZGRkSH5WdDVcDgcHDp0CIPBQElJyUXiOzw8zMGDB8nOziYvL09CKy9GFEWOHTuGQqFg1apVUptDa2sr9fX1KJVK7rrrLtra2oiMjJTarGkjeN+908C3vvUtnnnmGeLj4+ns7JT87A1Gw5NhYWFBFZ4ESEhIYOXKlVRVVWE0Gv0/ny3CBqPecWJiIps3b6a4uJjBwUF27tzJ8ePHGRwclHwMUbAxMjJCTU0NH330Ec3NzSxcuJCbb76ZrKysoBY2uLIHZ7VaOXToEJmZmUElbABdXV309/dTVFQktSl4vV7Onz9PXl4e3/jGN3jiiSfmlLDBHPfcAG699VYKCwv5l3/5F+x2O2vWrJHaJKxWK/v27WPt2rXExcVJbc5FdHV1UVVVxerVq7FarbNG2K7E8PAwTU1NtLe3o9PpyMjIIDU1VTKvSWrPTRAEjEYjra2t9Pb2kpiYSHZ2NnFxcUETepwMYz24/Px8Dh06xIIFCyQf9nkpDoeDvXv3UlRUxIIFC6Q2h/r6ejo7OzGbzXzxi1+koaEBnU4ntVnTypwXt+rqatavX8/Jkyepq6tj3bp1kg4C9BGs4UmAjo4OqqurAdiwYcOsFbaxeDweOjs7aWlpYXh4mOTkZJKTk0lISAjo8y+FuAmCwMDAAEajkY6ODlQqFenp6aSnpwdVosX14nA4OHDgAE6nk/T0dJYuXRpUwhZs4Ui3283OnTspLi7mpptu4stf/jKPPfaY1GZNO8G1q84AxcXFfOITn+BHP/oRTz31FDU1Ndxwww2Sv/mzs7Pp7u6mpqYmKMIUYxnbCzNYShemilqtJiMjg4yMDIaGhujo6ODcuXMcP36cuLg4kpKSSEpKmhObPYxuYD09PRiNRkwmE0qlksTEREpKSoiPj5f8/T+deL1eRFFEoVAERR/XS+ns7KS/v5+tW7dKbQow6rXp9Xp27NiBw+Hg4YcfltqkGWHOe24w2hC0sLCQo0eP0tXVRXFxcVC04LHZbOzdu5c1a9ZMS8PZ6WDsGdvIyAgnTpxg5cqVQfF8zQRWqxWj0YjRaGRgYICoqCi/0On1+mkXgZn03Gw2m/+x9Pf3o9Pp/I8lOjp6TgmaD98ZW0pKCosWLeLw4cPjJplIRbCFI+12O7t372bFihWsWbOGH/3oR9x7771SmzUjzAtxg9Gmyl1dXfzsZz+jra2NzZs3B8Wbv6mpicbGxqAIT46XPOI7gxsvi3Ku4XK5MJlMGI1Genp6gNGuKHq9HoPBgMFgICIiYkrvm+kSN7vdztDQEGaz2X9zuVzExsb6BS0iIuK67382YLFYOHz4MGlpaf4ztqtlUQaaYAtHApw8eRKXy8X+/ft5/fXXOX78eNAnD10v80bcjEYjixYt4oMPPsBut5OXlxcUgwpFUeTQoUNERkaybNkyyey4Wlak0WiksrKSZcuWkZaWJpGFgUUQBCwWy0UCYrFYUCqVfrHT6/VERESg0WjQarUT6oAxGXETBAGn04nD4fCLmc8ep9OJTqfzi65er0ev1wdNecFMMzQ0xOHDh8nKyiIvL+8iEQsWgevo6OD06dNs3bpV8po2GE2u2rdvHyUlJRQVFfHGG29w6623Sm3WjDFvxA3g//2//0dZWRlvvPEGZ8+e5cYbbwyKzcAXnpQq/DeRdP+enh6OHTvGwoULL9tM5guCIDA8POwXu6GhIex2O06nE1EUCQkJQavV+sXOd1MqlSiVShQKBYIgcPLkSYqKilAqlYiieJGIORwO//dOpxOA0NBQtFotUVFRF4mZ1J6+VPiiCXl5eVdshCy1wNlsNsrKyli+fHlQRDxEUeTIkSNERETw+9//nvLycvbs2TOnP8fzStwsFgt5eXk888wzZGRkoNfrKSwslNosYPTQ+cSJE2zcuDGgs6YmU8dmsVioqKggKiqKkpKSebu5XoooihcJkk+kfP8WBAFBEPxCNjAwQGxs7EWip9FoLhJFjUZDWFgYGo1mzoaNJosoitTV1dHQ0EBJSck1+zJKJXBut5sDBw4QHx8fNAM/u7q6OHnyJOnp6axatYoDBw6wYsUKqc2aUeaVuAG88cYbPPnkkxw7doyqqipKS0uDZnDhuXPn6OzsZNOmTQGZEnw9Bdoul4tjx47hcrlYs2YN4eHhM2zl3ELqOrfZisfjobq6GrPZzJo1ayb8mQ20wImiSEVFBV6vl7Vr1wbFhYnH42HPnj3k5uby5S9/mYyMDF588UWpzZpxpH/mA8y9995LTk4Ozz77LFlZWZw6dSpoOlfk5+cTGRnJsWPHZnwg5/V2HgkNDfXXCpaVlQVVn0yZucnIyIi/jm3Tpk2TuhidiYneV+P8+fMMDw+zcuXKoBA2GE3912q1VFdXU1FRwfe//32pTQoIwfHsBxCFQsELL7zACy+8gMfjwWaz0dnZKbVZwKhtK1aswOVycfbs2RlbZ6ottZRKJUVFRRQUFHDkyBFaWlqm30gZGaC/v5+ysjJiYmJYv379dSVmBErgOjs7aWpqYvXq1QGJvEwEq9XqH2nz+OOP86Mf/WhONGWYCPNO3GB0JM6XvvQlHn/8cRYvXsyZM2eCZlSKWq1m9erVdHR00NraOu33P529IjMzM1m3bh3nzp3j5MmTM+5tyswvWltbKS8vJz8/n2XLlk3JE5ppgTObzVRXV7NixYqgOeYQRZHTp0+TlpbGiy++SGJiIg8++KDUZgWMeSluAN/+9rc5f/48Bw8eJDIyktraWqlN8hMREcGqVas4ffo0/f3903a/M9EEOTY2ltLSUgYGBigvL/dn+MnIXC+CIHDq1ClqampYu3YtWVlZ03K/MyVwDoeDo0ePkpeXF1TNDrq7uzGbzYSEhPDMM8/wwgsvBE2oNBDMn0d6CVFRUfzsZz/jq1/9KpmZmbS0tDA0NCS1WX7i4uJYsmQJFRUV0zKLbia7+4eHh7Nx40ZCQkLYv3//tAqyzPxiZGSEw4cP09fXx6ZNm6a9sfh0C5zX66WiooK4uDgWLVo0TVZOHbfbzZkzZygoKOCrX/0q//qv/8rKlSulNiugzFtxg9HkkkWLFvGzn/2MhQsXUl1dHVShtaysLFJSUjh69OiUejwGYmyNWq1m1apVZGdnU15ezunTp+dMX0qZmUcURZqbm9m7dy86nY5NmzbNWIeV6RI4URQ5efIkoiiyfPnyoKoZq6mpISIigsrKynmVRDKWeS1uCoWCX/7yl7z88sv09vb662iCiaVLlxIaGnrdH8JAzmNTKBQsXLiQzZs3Yzab2bdvn+zFyVwTn7dWX1/PqlWrWL58+YzXUE6HwDU1NdHT08Pq1asn1J0mUPT09NDR0UFaWhqPPfYYzz333LxJIhnLvBY3GE2//853vsMXvvAF8vLyaGhoCKrwpFKpZOXKlQwNDU1aeKUaNKrT6bjhhhvIysqSvTiZKyKKIi0tLezdu5eIiAi2bNlCQkJCwNafisD19PRw7tw5Vq9eHVSTJNxuNydOnGDx4sU89dRTrFu3bs42Rr4W817cAB5//HHi4+P56U9/yqJFi6iqqgqq8KRGo2HNmjU0NDTQ1dU1ob+ReoK27MXJXI2RkRHKy8upq6vze2tSFLVfj8ANDw9TWVlJUVFR0HlEZ8+eJSIiguPHj7Nz505eeumloAqXBhJZ3ACVSsWrr77Kyy+/jMlkAgi68KSv5VVVVZW/Y/2VkFrYxuLz4jIzMykvL+fMmTOyFzePGeuthYeHB9xbG4/JCJxPlDMzM4Oi8fpYenp66OzsJDU1lS9/+cs8//zzQZW9GWhkcbtAbm4u//Vf/8VDDz1Efn5+0IUnAZKTk1m+fDkVFRVX7AwSTMLmQ6FQsGjRIn/JgOzFzU98wlBbWyuptzYeExE4u93OoUOHSEpKoqCgQAIrr4wvHLlkyRKeeuopbrjhBu655x6pzZKUeddb8mp4vV5KS0tZuXIlX/ziF+nu7mbTpk1BdVgMo8WtZ86cuUzAglHYLkUURRobGzl//jyJiYkUFBSg0+mkNitgzMfeki6Xi/r6epqbm1mwYAGFhYVB+9iv1IvS4XBw8OBB4uLiWLZsWdCF+k6cOIHNZqOrq4svfelLnD17lsTERKnNkhTZcxuDSqXiN7/5Da+88gpGoxGFQkFNTY3UZl1GRkYGixcvpry8nMHBQWB2CBv8w4vzjRvau3cvJ0+exOFwSG2azDTj8Xior69n165dDA0NccMNN1BcXBy0wgbje3BOp5PDhw8THR0dlMLW1dVFV1cXqampPPbYYzz//PPzXthA9tzG5Re/+AXPPPMMBw8e5NSpUxMaryEFjY2N1NbWkpGRQUtLS9AL23gMDw9z/vx5TCYT2dnZ5OTkBPXmN1Xmg+cmCAJtbW3U1tai1WpZvHgx8fHxUps1KXweXFRUFFarFZ1Ox4oVK4Kuw4fNZmPfvn0sW7aMRx55hIiICP74xz8GnQBLgSxu4yCKInfeeScqlYpf/OIXnD59ms2bNwfleJejR49iNBopLi4OugPuyTA4OEhNTQ1DQ0Pk5uaSlZUVdOHg6WAui5soinR3d/ujHQUFBaSkpMzajdZisVBWVkZoaCg33XRT0L0fBUHgwIEDREdH89FHH/HCCy9QXV2NwWCQ2rSgQBa3K9Df38/y5ct56qmn2LhxI8PDw2zYsCGortx8ocjU1FQ6OztZt24d0dHRUpt13YiiSG9vLzU1NTidTvLz80lLSwuq53yqzFVx871udrud/Px80tPTZ/Xr5gtFarVabDYb0dHRkkz0vhpnzpyhr6+P0NBQbr75Zvbs2cOaNWukNitokEcpX4HY2Fjeeusttm3bxt69e3G73Zw/f57FixdLbRpw+RmbTqfj8OHDrF27ltjYWKnNuy4UCgUJCQnEx8fT2dnJ+fPnaWhoIC8vj5SUlFm9Wc5FRFGkv7+furo6BgcHycnJITs7e9ZPaLfb7Rw+fBi9Xk9JSQkul4tDhw5RVVUVNAJnNBppbW2lqKiIjRs38t3vflcWtkuQPbdr8MMf/pBf//rX7Nu3jxMnTrB69WrJ63KulDzS0tLCmTNnWLNmzaw74xgPQRBobW2lsbERr9dLVlYWGRkZ1zXTK1iYC56b1+v1zy4bGRkhMzOTRYsWBc0Ms6ngawUWExNDcXHxRdmSgZzofTXsdjt79+5l6dKlfOUrX8HpdPLuu+/KF3+XIIvbNRAEgVtvvZWYmBh+/OMfU1NTw5YtW9BqtZLYc62syLa2Nk6dOhUUIjxdiKKIyWSisbGRgYEBUlNTyc7ORq/XS23apJnN4uZwOGhubqalpYXQ0FAWLlxIamrqrPfUfNhsNg4dOkRiYiJFRUWXCVgwCJwgCBw6dMgfqfnhD3/IiRMnpn16wlxAFrcJYDKZWL58Od/5zndYuXIlNpuN9evXB/yAeaLp/h0dHZw4cYLly5eTmpoaQAtnHovFQlNTE+3t7ej1ejIyMliwYMGs2WBnm7iJokhPTw8tLS2YTCYSEhLIzs4mPj4+KMJz08XQ0BBHjhwhJSWFwsLCKz42qQXu9OnT9Pb2EhkZyebNm/nggw/YuHFjQG2YLcjiNkH27NnDHXfcwe7du3E4HERFRQV0zMVk69hMJhOVlZVkZWVRUFAwpzYiGC0M9k0rHxkZITU1lYyMjKDPFJst4jYyMkJbWxttbW0IgkBGRgbp6ekzNoZGSjo7O6muriY3N5ecnJxrflakErjW1lbOnj1LYWEhW7Zs4fOf/zzf+MY3ArL2bEQWt0nwzDPP8Oyzz7J//37q6urIyclh4cKFM77u9RZoDw8Pc/ToUSIjIykpKQnqzfR6EUURs9lMa2srHR0dhIeHk5SURFJSEtHR0UEn6sEsblarFaPRiNFoZGBggISEBDIyMkhMTJyT5zmiKFJbW0tjY+Oka1kDLXD9/f2Ul5dTUlLC/fffj16v589//vOcfF2mC1ncJoEoijzwwAPU1tby17/+laqqqhk/25pq5xGXy0VlZSUOh4M1a9bMyStvH263m56eHoxGIyaTCaVSSWJiIklJScTHxwdF6DKYxE0QBAYHB/2CNjIyQlxcnP/iIJhGuUw3Ho+HqqoqhoaGWLNmDVFRUZO+j0AJ3MjICPv37ycvL4///u//pqysjMOHD8+rtnXXgyxuk8ThcLB582Zyc3P53ve+x5kzZ9i0adOMvNGmq6WWIAicPXuWjo4OVq5cOScyKa+FIAgMDAz4N2673U58fDxJSUkkJiZKtnFLLW5ut5ve3l66u7v90yV8FwAJCQlBcQEw04yMjHD06FFCQ0NZtWrVlLI8Z1rgPB4PBw8eJDo6miNHjvCNb3yDY8eOkZmZOa3rzEVkcbsOuru7WblyJU888QS33norRqORTZs2TetmNRO9IltbWzl9+jRLliwhKytrWu5zNiCK4kUht8HBQfR6PYmJicTExKDX6wNWXhBocfN4PAwNDWE2m+np6aGvr88fuk1OTg7K0O1M0tfXx7Fjx/wNnKcjrDdTAieKIpWVlTidTjweDx//+Md5//33KS0tnZb7n+vI4nadHDt2jC1btvDHP/7Rv0GsWbNmWt7YM9kEub+/n2PHjpGcnMzSpUvnZcze6XRiMpno6enBbDZjs9kICwtDr9djMBj8t5kQvJkUt7FCZjabGRoaYnh4GI1Gg8FgID4+nsTExHkbzvLVgRYWFk675zMTAldbW0trayuZmZls2LCB733vezz66KPTYO38QBa3KfDmm2/y5S9/mQMHDtDb20tsbOy49TGTIRDd/UdGRqioqECtVrNq1apZXRQ9Hbjdbr8Y+ITBZrOh1WovErvIyEi0Wu2ULgimQ9x8neqtVutFNlutVr+Q+W56vX5On51NBEEQOHPmDJ2dnaxatWrGasKmU+Da29s5deoUy5cv52Mf+xg33HADL7zwwjRaO/eRxW2K/Od//id/+tOf2LVrl79Df35+/nXdVyDH1ng8HqqrqzGbzaxevXpWFkTPJG632y8cvq9WqxWA0NBQtFotWq0WjUbj//7Sn41XB3k1cRMEAafTicPh8H/13cb+2+l0IoqiX3zHepxSNRcIVlwuF8eOHcPlcrFmzZoZb34+HQJnMpk4duwYK1as4JFHHsFms/Hhhx9KnoA025DFbYoIgsD999/PuXPnePfddzl16hQFBQWTPtOSYh6bKIrU1dXR0NBAcXExKSkpAVl3tjJZ8VGpVCgUChQKBUql0v+93W5Hq9UiiiKiKCIIAqIo4vV6gamJp8w/sFgsHD161N8jMlDJMlMRuIGBAQ4fPszy5cv55je/SWVlJfv37w/6+s1gRBa3acDlcnH77bfj9Xr53e9+x/HjxykpKZmwWEg9aLSrq4vq6mr/OZx8hTg1fGFDt9t9mYC53W6OHDnCunXrCAkJuUj41Go1Go1mXp6DTieCINDQ0EBdXR2LFi0iLy8v4Ekz1yNww8PDHDx4kLy8PH71q1/x5ptvcvjwYfmi8zqRxW2asFqtbN26laysLJ599lmqqqpYu3btNeP7UgubD7vdzokTJ7BYLCxfvlye5DtDSF0KMNexWCxUV1fj8XgoLi6W9DM1GYGz2+0cOHCAtLQ0PvroI/7rv/6LQ4cOkZeXF0CL5xbyJeI0odPp+Pvf/051dTU//OEPKSws5OjRowwNDV3xb4JF2ADCwsJYu3Yt+fn5VFZWUlVVhdvtltQmGZmJIggC9fX17N+/n7i4ODZv3iz5Z0qr1bJhwwbMZjNVVVVcyY9wuVyUl5cTHx/PiRMn+MY3vsH7778vC9sUkcVtGomPj+ejjz7iL3/5C2+++SY5OTmUl5f7ExHGEkzC5kOhUJCRkcHWrVtxOp3s2bMHk8kktVkyMlfFF85ra2tj/fr1LFmyJGjOI68lcB6Ph6NHjxIREUFPTw8PPfQQf/rTn1i9erVEFs8dZHGbZjIzM/nggw/4yU9+QllZGWlpaRw6dAibzeb/nWAUtrFc6sVVV1fLXpxM0CGKIvX19ZSVlREbGxsU3tp4XEngfMLmO2P91Kc+xS9/+UtuvfVWKc2dM8jiNgMUFRXx7rvv8vjjj1NVVUVKSgqHDh1iZGQk6IXNx1gvzuFwyF6cTFAxPDzMgQMHaG1tDTpvbTwuFTiPx0NFRQWiKKLRaPjYxz7Gd77zHe677z6pTZ0zyAklM8iuXbv4xCc+wSuvvMKSJUvo6OhAEATWr18f1MJ2KaIo0tbWxpkzZ/zzruRkiOtDTiiZGqIo0tDQQG1tLZmZmRQUFAS1qF2Kw+Hg4MGDeDweIiIiiIiI4JZbbuGrX/0qX/va16Q2b04he24zyE033cRf/vIXHn74YcrKyvB4PKjV6llXaOvz4rZs2YLdbmfPnj3+prsyMoFirLe2bt06CgsLZ5WwAYSEhKDVanG73XR2dnLrrbfyla98RRa2GUD23ALA+++/z913383zzz/PypUrMRqNbNiwYVaOnxFF0T80MT4+noKCAiIjI6U2a9Yge26Tx+VyUVdXR0tLC5mZmeTn58/K6QW+MzZBENBqtdx666089thjfPe735XatDmJLG4B4v333+fTn/40r7zyCoWFhXR1dbFhw4ZZ28TW4XBQW1tLW1sbaWlp5OXlzfsehhNBFreJ4/F4aGxspKGhgZiYGBYvXjxr28T5hE0URcLDw9m2bRuPPfYY3/72t6U2bc4ii1sA+fDDD7nrrrt48cUXKSkpob29nbVr187q1jpWq5Xz589jNBrJysoiJydnSvOx5jqyuF0bQRBobW2ltraW8PBwFi9ePGPNjgOB0+nkyJEjqNVqQkND+ad/+ie+8pWv8I1vfENq0+Y0srgFGF+SyX//93+zZcsW6urqWL169awfIGo2m6mpqcFsNrNo0SKys7NnZehoppHF7cqIokhXVxfnzp1DoVBQUFBAcnLyrJ43NzIywuHDh9Hr9TidTu644w6eeuop+YwtAMjiJgEHDx7k9ttv5/HHH+df//VfOX36NMXFxSxYsEBq06ZMb28vNTU1OBwO8vLySE9Pl3sljkEWt8sRRdH/vnE6neTn55OWljbr3zcWi4Xy8nKSk5NpbW3ls5/9LD/+8Y957LHHpDZtXiBfWkvADTfcwIEDB9i2bRsmk4mvf/3rVFVV4XQ6yc7Oltq8KREfH8+mTZv8V+ANDQ0sXrx41l+By8wMg4OD1NTUMDQ0RE5ODtnZ2bMuA3I8+vv7OXr0KAsXLuTw4cP827/9G6+99hp333231KbNG2Rxk4jCwkIOHz7Mtm3b6Onp4bnnnuPEiRP+K9fZLAQKhYIFCxaQnJxMW1sbp06dor6+nsWLF8/68KvM9GC1Wjl37hwmk4ns7GxWrVo1Z85qu7u7OX78OEuWLOGPf/wjP/jBD9i+fTtbtmyR2rR5hRyWlJi+vj5uu+02wsLC+N3vfkdNTQ0JCQkUFRXN+rCMD4/HQ1NTE/X19URFRbFw4UKSkpLmzOObDPM5LCmKIoODgzQ2NmI0Gudklm1LSwtnzpyhuLiYn/3sZ7z11lvs2LGD4uJiqU2bd8jiFgTYbDY+9alP0d3dzV//+leam5vRarWsXLlyzlzNwmi9UltbG01NTQBkZWWRkZExpx7jtZiP4iYIAp2dnTQ1NWG1WklPTyc7O3tW1nleCVEUOXv2LG1tbRQXF/Mf//EfVFRU8NFHH836o4bZyvy7dL4Kvb29fOlLXyI9PR2NRkNSUhLbtm3j0KFD3HPPPZc1NP3ggw9QKBSX1ap8+9vfJj09fcLrRkRE8Le//Y2lS5dy4403EhMTg0qlYv/+/QwPD0/HQwsKQkNDWbRoETfddBOFhYWYTCY++ugjTp48icVikdo8mWnG6XRSW1vLRx99RG1tLWlpadxyyy0sXbp0TgmbbwCtyWSisLCQ+++/n9raWg4dOjQlYbvafgSjTdp9093H3n70ox9N10Ob1chnbmO46667cLlcvPbaa2RnZ2Mymdi9ezf9/f1s2bKFr371q/4WWgB79+4lLS2Nffv2XXQ/e/funXR8PSQkhFdffZXvfOc7lJaW8rvf/Y6cnBz279/PypUr59TwUKVSSUpKCikpKZjNZpqamigrKyMmJoaMjAySk5PnRFLBfEQURfr6+mhtbaW7u5vY2Fj/8NvZfI58JYaHhzl69Cg6nY74+HhuvPFGFi9ezNtvvz3lzj1X2498fPe73+Xhhx++6O/kjkEXEGVEURTFwcFBERD37ds37v/X1taKgFheXu7/2erVq8UXXnhB1Gq1ot1uF0VRFO12u6jRaMTf/va3123Ln/70J1Gn04k/+MEPxPb2dvG9994T6+rqREEQrvs+gx2HwyHW19eLO3fuFP/+97+Lp06dEoeGhqQ2a9pxuVziO++8I7pcLqlNmVbsdrtYV1cn7ty5U3z//ffF06dPixaLRWqzZhSTyST+/e9/F8+cOSO+9957YlRUlPj1r39d9Hq9U77va+1HoiiKGRkZ4rPPPjvlteYqcljyAjqdDp1OxzvvvIPT6bzs/3Nzc0lJSWHv3r3A6BVbVVUVn/rUp8jMzKS8vByAw4cP43Q6p5QZdffdd3PgwAFeeuklnnzySUpKSmhqaqKqqgqv13vd9xvMaDQaFi1axI033sjq1atxuVyUlZVx4MAB2tra5HlyQYggCJhMJioqKti5cye9vb0UFBRwyy23UFhYOGc9CPHCZIKKigoKCwv5+9//zj333MPLL7/M9773vWlJlLrWfiRzbeSEkjH4Ovjb7XZKSkooLS3lnnvuoaioCID77ruP3t5ePvzwQ95//32efPJJzp49y6OPPkpSUhLf+c53+OY3v8nrr7/uT5qYCj09PfzzP/8zDoeD3//+93R1dSGKIqtWrSI8PHzK9x/suFwu2tvbaWtrY3h4mLi4OJKSkkhKSpq1j3+2J5S4XC56enowGo2YTCbUajVpaWlkZGTMqXO0K+HxeDh16hS9vb0sXbqUp556irKyMt555x1WrFgxrWtdaz/KzMyku7v7svfRjh072Lhx47TaMhuRxe0SHA4HBw4c4MiRI+zYsYOKigp+9atf8cADD/CrX/2Kr3zlKwwODvL1r38dm83GCy+8wJtvvskvf/lLysrK2LRpEzk5Ofz617+eFntcLhdf/vKX2b59O3/+85/R6XR0dXVRXFxMcnLytKwxG7DZbBiNRoxGI/39/URGRvqFzmAwzJrznNkobpc+91FRUSQmJs66536qWCwWKisrCQkJITk5mXvvvRe1Ws1f//pXkpKSZmTNq+1HmZmZ3HfffTzwwAMX/c2CBQvmVHnFdSNlTHQ28IUvfEFMT08XRVEUGxoaREA8dOiQuHLlSvEPf/iDKIqi2NHRIWo0GrG/v18MDQ0VX3/99Wm1QRAE8Re/+IUYHh4uPvfcc2JbW5u4fft28dSpU9MS359tOJ1Osb29XTx27Ji4fft2cceOHWJ1dbXY3d0tejweqc27KrPhzE0QBLG/v188c+aMuHv3bvHdd98VDx8+LDY2Noo2m01q8wKOIAhiS0uL+N5774k1NTXihx9+KCYmJooPPPCA6HA4AmrL2P1IPnO7OnK25DVYvHgx77zzDgALFy4kLS2Nd999lxMnTlBaWgqMXiktWLCAZ555BpfLNe2dCBQKBf/+7/9OSUkJ99xzD3v37uW5556jqamJAwcOsHLlynkREvIRGhpKamoqqampCIJAf38/RqOR06dP43Q6iY+PJykpicTExFk3GFYqPB7PReFGgMTERPLz84mPj581XuZ043a7/WHIkpISXnzxRZ599lmee+45HnzwwYB7rWP3I5mrI4vbBfr7+/nUpz7Fgw8+SFFREZGRkVRWVvKTn/yEO++80/97W7Zs4X//939ZtGjRRen5paWl/M///I8/8WQm2LBhAydOnODzn/88Gzdu5I033kCv17Nv3z6WL18+JxovTxalUkl8fDzx8fEUFhYyPDyM0WiktbWVkydPEhkZicFgQK/X+7/O9zIDQRCwWq2YzWaGhoYwm82YzWbCw8NJSkpi9erVREdHz8sOMmMxm81UVlYSFhbGokWLuOeee+jv7+fIkSMUFhbO6NoT3Y987/exhIeHExUVNaP2zQZkcbuATqdjzZo1PPvsszQ2NuJ2u0lLS+Phhx/m6aef9v/eli1b+L//+z82b9580d+Xlpby29/+lnvvvXdG7YyNjeVvf/sbzz33HNu2beNb3/oWn/vc5zh58iR9fX0UFhbO281boVAQFRVFVFQUubm5OJ1O+vv7MZvNGI1GamtrcbvdREZG+sXOYDAQFRU1Z8fzjBUy381XMK/X69Hr9WRkZFBcXDxrB+dON6Io0tzcTE1NDTk5OdTX13PnnXdy5513smPHjoBESSa6H33zm9/km9/85kV/++ijj/LSSy/NuI3BjpxQMouprKzkM5/5DDk5Obz00ku0t7fjdDopKSkhJiZGavOCDlEUsdvtl3ksLpdrXA9vJgRvJhNKBEFgeHjY/7iGhoYuEjKfmOv1eiIjI+dNIshksNvtnDhxAovFQlFREc888wwvv/wyL774Ip/73OekNk9mEszNy9V5wsqVK6mqquLhhx9m/fr1/OY3v2HRokUcPnyYrKws8vPz560XNx4KhYLw8HDCw8P9oWNRFHE4HH5B6Onpob6+HqfTiUajQavVXnS79GcajWbGw3eiKOJyuXA4HJfdnE7nRf9WKpV+AcvKysJgMKDT6WQhuwaiKNLW1saZM2dISUkhMjKST3ziE7hcLiorK8nNzZXaRJlJIntucwBRFPn1r3/NE088wWc+8xm+/e1v+0MZxcXFshc3SXyCZ7fbrygiTqfTX1wbGhp6meCpVCp/rz+lUun/XhRFTp8+TWFhof/foigiCIL/66VC5nQ6EUWRkJCQawpuRESELGSTZKy3tnTpUt566y2++c1v8uijj/KDH/xATkqapcjiNodobW3loYceora2lldeeYXs7Gxqa2tlL26GEAThItHzfe90OvF6vZcJlyiKeL1e+vr6iI+Pv6IAjhWrsd/Lr9/0cqm3FhoayiOPPEJPTw+//e1vueGGG6Q2UWYKyOI2xxBFkZdffpknn3ySe+65h29961s0NDTg8XhkLy4ImI1F3HORS7213//+9/y///f/eOSRR/j+978/azvgyPyD+Z3rOwdRKBQ8+uijnDp1iqamJtatW4fD4SA9PZ3Dhw/7p33LyMxHBEGgvr6e3bt3o9FoWLBgAZ/+9Kd58cUX+eCDD3j22WdlYZsjyOI2R8nMzGTnzp08/fTT3H333fz4xz+mqKgIp9PJ7t27aWlpQXbaZeYTvb297N27l46ODlasWMHu3btZu3YtK1eu5OTJk3I/xjmGLG5zGIVCwRe/+EVOnTpFd3c3xcXFnDx5kmXLllFfX09ZWRmDg4NSmykjM6PY7XaOHTtGRUUFmZmZeL1ebrzxRl599VV27NjBz3/+c9lbm4PI4jYPyMrKYvv27fz2t7/lhz/8IZ/4xCeIjIwkOTmZQ4cOyaFKmTnJ2BCkUqkkPz+fr3/96/zzP/8zDz30ECdOnJC9tTmMLG7zBIVCwR133EFNTQ0f+9jH2Lp1K8888wxLly71hyqbmpoQBEFqU2VkpoQoihiNxotCkHv27GH58uWoVCrOnz/PE088ISf0zHFkcZtnhIWF8Z3vfIfTp0/T1dVFSUkJJ0+eZPny5bS0tLB7927a29vl8ziZWcnAwAAHDx6kurqarKwsPB4PW7du5bXXXmP79u288cYbM9b7VSa4kMVtnrJw4UK2b9/Oq6++yg9/+EM+/vGP43Q6yc3N5dy5c+zbtw+TySSLnMyswGKxcPToUQ4fPkx8fDzJyck88cQT3HXXXTzyyCNUV1f7p3jIzA9kcZvn3H777Zw7d4777rvPP/hQp9ORnp5OVVUVhw4dYmBgQGozZWTGZWRkhKqqKsrKyggPDycvL48f//jHrFu3jrS0NOrr63n88cflEOQ8RBY3GbRaLf/xH/9BY2Mj69ev5+abb+bJJ58kNTWV2NhYDh8+zNGjR/1NeGVkpMbhcHDmzBn27NmDKIoUFRXx6quvUlRUhMvl4uzZs/zP//zPRWOpZOYXsrjJ+DEYDHz/+9+noaGBxMREVq1axTPPPEN+fj5hYWGUlZVx5MgR2ZOTkQybzcbJkyfZuXMnNpuNkpISduzYQVFRETU1NZSXl/PGG2+QnZ0ttakyEiOLm8xlJCcn8+KLL3L69GmGh4f9V8WLFy8mMjKS8vJyDh48SE9Pj3wmJxMQLBYLx48fZ8+ePXg8HlasWMHhw4cpKSnhvffe429/+xs7duxg+fLlUpsqEyTI4iZzRXJycvjDH/7AgQMHaGlpYcmSJfz85z8nKyuL+Ph4jh8/TllZGZ2dnbLIycwIAwMDHD16lLKyMtRqNcuXL2f79u0sXbqUN998k5deeokjR45cNjxYRkYWN5lrsmLFCv76179SVVWFy+WipKSEb37zm8THx5Oens7Zs2f9Lb08Ho/U5srMcnx1agcPHqS8vBydTsfixYv5v//7PwoKCti/fz9/+MMfqKio4BOf+IQ84kdmXGRxk5kwixcv5rXXXqO2tpaEhAQ2bdrEY489hkajITc3l5aWFj788EPOnDmDzWaT2lyZWYbL5aKhoYFdu3Zx8uRJEhISyMrK4rnnnqOwsJCmpib27NnD7t27uemmm2RRk7kqsrjJTJqMjAz+53/+h5aWFkpKSrjzzju555576O7upqSkBIfDwZ49eygvL6e7u1vueiJzRURRZHBwkOrqaj766COMRiMFBQWEhoby9NNPU1JSgtPp5Pjx47z99tusXr1aapNlZgnyPDeZKWOxWPjNb37DCy+8gNVq5Ytf/CL3338/breblpYWYFQQMzIyCAsLk9ZYiZHnuY3idrvp7OykpaUFq9VKWloa8fHxbN++neeff56Ojg4eeughHnvsMTIyMqQ2V2YWIoubzLQhCAIffvghzz//PLt27eKOO+7gkUceYcmSJbS1tdHT00NsbCxpaWkkJyfPy819PoubIAj09vbS3t6O0WhEp9ORmZmJ2WzmN7/5Df/3f/9HZmYm//7v/85nP/tZuVO/zJSQxU1mRmhsbOSVV17hN7/5DXq9nkcffZS77roLr9dLR0cHw8PDJCUlkZaWRkJCAkrl/IiQzzdx84UdOzo66OzsRKlUkpqaSnR0NLt37+bll1+msrKSz3zmMzz66KOsXbtWPkuTmRZkcZOZUZxOJ2+//TYvv/wyBw4cYOvWrdx3331s3bqVoaEhOjo68Hq9pKSkkJaWRnR09Jze3OaLuFmtVjo6Oujo6MDpdLJgwQKSkpKorq7mzTff5O233yYzM5OHHnqIf/mXfyEmJkZqk2XmGLK4yQSMjo4O3nrrLV5//XXq6+v5xCc+wb333svKlSsxmUz+K/ukpCSSkpKIj49HpVJJbfa0MlfFzeehGY1GjEYjNpuNxMREFixYQHt7O2+99Ra///3vUavV3Hvvvdx3330UFRXN6QsZGWmRxU1GEk6fPs0bb7zBG2+8gcPh4DOf+Qyf+cxnyMnJobe3F6PRiNPpJD4+nqSkJBITE9FqtVKbPWXmkrh5PB56enowmUwYjUZEUSQxMZHExEQGBwd5++23ef311zGZTNx1113cd999lJaWzrkLFpngRBY3GUkRBIEDBw7w+uuv89e//hWlUsltt93Gbbfdxrp167BarRiNRsxmMwaDgcTEROLj4zEYDLPynG42i5soigwPD9PX14fJZKKvr4+wsDCSk5OJjY3l3LlzbN++nXfffZf29nZuvfVWPve5z3HbbbfN+yxZmcAji5tM0ODxeCgvL+e9997j3XffpaWlha1bt3L77bdz8803ExISQk9PD319fXi9XmJiYoiLiyMuLm7WiN1sErexYtbf33/R8x4fH09YWBiHDh3ivffe4/333yckJITbbruNO+64g5tuuomIiAipH4LMPEYWN5mgpa6uzi90hw4doqioiG3btlFaWkpRUREOh8O/8Y4Vu5iYGPR6fVCKRzCLmyAIWCwWBgcH6evru0jMYmNjMRgMNDY2sn//fnbt2sX+/fvJy8vjjjvu4Pbbb2f16tWz4gJDZn4gi5vMrKC/v58dO3awZ88e9u3bR3t7O6tWrWLz5s2UlpaydOlSv9gNDg7icDjQ6XQYDAb0er3/q9SCEizi5vV6sVgsDA0NYTabMZvNDA8Po1QqiY6OJjY2Fr1eT3NzM/v372ffvn0cPHiQ0NBQNm/ezObNm7ntttvIysqS7DHIyFwNWdxkZiUtLS2UlZWxb98+9u7dS2dnp1/sVq9ezZIlS9DpdFgsFv/m7XA4iIiIwGAwEBkZiU6nQ6fTERERgVqtDojdgRY3QRAYGRnBarX6b2azGYvFglqt9gu/wWDA6/VSW1vrn/bgE7PS0lK/oBUWFsremcysQBY3mTlBS0sL+/bto6ysjMrKSmpqaoiNjWXFihX+25IlS4iKimJoaOiizd7j8aDVav1i5xO8sLAwtFotoaGh05ayPt3iJooiHo8Hh8OB3W7HZrNhs9n8j21kZASFQkFERIT/sen1erxeL+fPn6eqqorjx49z/PhxmpubyczMZMWKFWzcuJHNmzezdOlSWcxkZiWyuMnMSUZGRjh58qR/466qquLs2bNER0dTUlJCfn4+ubm55OTkkJGRQUxMzEUejs1mw2634/F4UCgUaDQatFrtRTeNRkNISAhqtfqym0qlQqVSXSaKVxM3r9eL1+vF4/GMe3M4HDidThwOh//mdDrxer0olUq/QPuELCIiArvdTnt7O/X19dTV1VFbW8uJEydoaWkhKyvrIvEvKSkhNjY2kC+TjMyMIYubzLzBbrdz6tQpqqurqa2tpa6ujrq6OpqbmwkJCWHRokXk5uaSm5vLokWLWLBgAfHx8cTGxhIREYHL5bpIVBwOx7giNPYjpVarUSgU/huMdm0JDQ31/47P+7r07y69+QR27FeXy8XAwIC/NrCxsdH/uOrq6hgaGmLBggX+x5Wbm8uyZcsoKSkhOjo6cE++jEyAkcVNZt7jcrlobm6+SBQaGhro6uqiq6vLfz6VlJREcnIyKSkpJCcnk5SURFRUlP/8LjIyksjISMLCwggPDycsLAyNRoNKpUKpVKJQKBAEgfLyctasWYNKpUIURbxeLy6XC7vdzsjICHa7HavVyvDwsP/r8PAwPT09dHd309XVRXd3N93d3djtdn+tWXJyMtnZ2RcJ2aJFi9DpdFI/xTIyAUcWNxmZazAyMuIXk7HiYjKZsFgsl4mQ73uXyzWpdVQq1WVCOfb7+Ph4v7COven1ermNlYzMJcjiJiMzQ7hcLmw2G16vF0EQLrr5vDnfLSIiAo1GI4uUjMw0IYubjIyMjMycQ87xlZGRkZGZc8jiJiMjIyMz55DFTUZGRkZmziGLm4yMjIzMnEMWNxkZGRmZOYcsbjIyMjIycw5Z3GRkZGRk5hyyuMnIyMjIzDlkcZORkZGRmXPI4iYjIyMjM+eQxU1GRkZGZs4hi5uMTIDo7e3lS1/6Eunp6Wg0GpKSkti2bRuHDh2S2jQZmTmHWmoDZGTmC3fddRcul4vXXnuN7OxsTCYTu3fvpr+/X2rTZGTmHPJUABmZAGA2m4mOjmbfvn2UlpZKbY6MzJxHDkvKyAQAnU6HTqfjnXfewel0Sm2OjMycRxY3GZkAoFarefXVV3nttdcwGAxs2LCBp59+mlOnTkltmozMnEQOS8rIBBCHw8GBAwc4cuQIO3bsoKKigl/96lc88MADUpsmIzOnkMVNRkZCHnroIXbu3Elra6vUpsjIzCnksKSMjIQsXrwYm80mtRkyMnMOuRRARiYA9Pf386lPfYoHH3yQoqIiIiMjqays5Cc/+Ql33nmn1ObJyMw5ZHGTkQkAOp2ONWvW8Oyzz9LY2Ijb7SYtLY2HH36Yp59+WmrzZGTmHPKZm4yMjIzMnEM+c5ORkZGRmXPI4iYjIyMjM+eQxU1GRkZGZs4hi5uMjIyMzJxDFjcZGRkZmTmHLG4yMjIyMnMOWdxkZGRkZOYcsrjJyMjIyMw5ZHGTkZGRkZlzyOImIyMjIzPnkMVNRkZGRmbOIYubjIyMjMyc4/8DpxIyQj/sFyQAAAAASUVORK5CYII=", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -464,8 +478,8 @@ } ], "source": [ - "# The downsample functions of WindRose/WindTiRose allows for \n", - "# aggregating the data into larger bin sizes \n", + "# The downsample functions of WindRose/WindTiRose allows for\n", + "# aggregating the data into larger bin sizes\n", "wind_rose_aggregated = wind_rose.downsample(wd_step=10, ws_step=2)\n", "wind_rose_aggregated.plot()" ] @@ -487,9 +501,9 @@ }, { "data": { - "image/png": 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", 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", 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", 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", 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LtWvX0qdPH5KTk5u8NjIykqioKFQqFTqdjpSUFGpqaoJdzxvLPNXX11NRUcGFF17I0KFD2bNnD/X19aSnp+P1ehFFEUEQqKysJDo6mqioKADuuusu/u///o9t27Zx/vnnc+mllzJ06NAmcxg/fnzw98YGtllZWUCD+2737t188MEHwX3EQxZ3fn4+eXl5+P1++vfv32RMt9sdXCvLyspi9uzZzY753XffndT5PrL9UGxsLIMHDw7+3Zhf2HjuoMElecEFFwRfN3/+fP7617+ybNkyZsyYweWXXx5cC+0pSOIm0SLFxcU89thjfPTRR9x4443k5eWdUhVEPB4PtbW11NXVBYUsEAgEL/AJCQlkZGS02wW+O6BSqYiIiCAiIqLJ9sOF3Ww2U1JSwt69e5ucj/DwcKKiok6q712/fv1ITExkzZo1mEwmpk6dCkB8fDxJSUls2LCBNWvWNHEfBgIBgCYdHZRKJSEhIcH1LEEQWlxTUygUyOVyYmJiUCqVOBwODhw4QEJCAna7vUmgyk033cSgQYNYvXo1O3fuZMGCBbzwwgvcfvvtx/XebDYbN998M3fccUez55KTk9m9ezdyuZxt27Y1q9RxpPuzvTjSfSgIQovbGs8xwJdffskzzzwT/Pvxxx/n6quv5uuvv+bbb7/lscce4+OPP24mwqcykrhJNKG2tpYFCxbw+uuvc/HFF7N3795T4o6uUcxqamqora2lvr4eg8FAZGQkiYmJPU7ITgRBEDAYDBgMhuAazJGCV1BQwM6dO9FqtURFRQUtquMVu+nTp7N27VpMJhP33HNPcPuUKVP49ttv2bx5M/PmzQtubwyQObIMl1KpxOVyNRs/NDSUXr16sWnTJqZMmRIcY9++fYwYMQK9Xs+BAweC7+1wUlJSuPzyy3n00Ud54IEHWLJkSRNx+/3334MWpclk4sCBA8HKOSNHjiQzM7PVCMMRI0bg9/upqqpi8uTJLe4zcOBANm3a1GTb4YE2HU1OTg6FhYWcffbZTbb379+f/v37849//IOrrrqKd955RxI3iZ6H3W7nlVde4bnnnmPcuHGsX7+eESNGdPW0goiiyN69e1m5ciUpKSnMmTOHmpoaqqqqqKmpCYpZVFQUaWlpREZGnrbh48dDS4J3eImsw8UuMjISpVLZxBI4kunTp3Prrbfi9XqDlhvA1KlTue222/B4PMFIyZPlzjvv5JlnniEtLQ1BEHj++ecxm80IgkBiYiLh4eFkZWWRn5+PxWIhPj6ee++9l9GjRxMbG4vT6WTNmjXNSr49+eSTwZSDhx56iKioqGDy8n333ce4ceO47bbb+Otf/xoMSvnxxx959dVX6d+/P3PmzGHu3Lm88MILjBgxgurqalavXs3QoUOZOXMmd9xxBxMnTuTf//43F110Ed9///1JuyRPhpUrVzJjxozgjYrT6eSee+7hsssuo3fv3pSUlLBlyxYuvfTSTptTZyCJ22lOIBDg7bff5pFHHiE5OZkvvviizReh9sLv9/Prr7+ycuVKvvj8C+pMdYwaOYozxpxBeHh4sKSTJGbtw+ElsuAPsauqqsLn82Gz2QgEAigUimYNK6dPn47T6WTAgAHB10ODuFmt1mDKQCONrz0yUdvr9bYatXfXXXdRXl7OddddhyiKXH311cyePZv6+nrgj2r1SUlJWK1W9u7dS319PQ899BAVFRWEhoZy3nnn8dJLLzUZ95lnnuHOO+8kJyeH4cOHs2rVqmAHg6FDh/LLL7/w0EMPMXnyZERRpG/fvlxxxRXB17/zzjv885//5K677qK0tJSoqCjGjRvHBRdcAMC4ceNYsmQJjz32GI8++igzZszg4Ycf5qmnnjqB/87Js3LlSq677rrg33K5nNraWubOnUtlZSVRUVFccsklPPHEE50yn85CalZ6GrN161ZuvfVWKisr+fe//82ll17a5QEUoiiye/duli1bxrL3liGTy5g0bjJjR4+l78A+VBfUsWbLT8ycOZM5c+Z0+XxPlFOxWanL5SI/P5/k5GTkcnkwPUEmk6FUKoNCd6L/i6ysLPR6fdAl2Pi/j4mJOeb67u7du5sIcUtj2u12iouLsdlsRERE0Lt37yZzbMy7M5lMRy2F1RUUFBTQu3dvduzY0abyW43BOSUlJc3O1bG4/vrrMZvNwfSBw2n8TPTu3bvb3lSefgsQEtTW1nLLLbcwZcoUzj33XDIzM7nsssu6VChKSkp47rnnyBiUwbRp08g7mM9D9z7Cq4teY/KQaZT+Vst7N6/gk7u/4/tPfmLFihWnnLCd6sjlcjQaDQaDgZCQEDQaDYFAALvdjsViwel0nlCyeWxsLNXV1dTU1OB0OikqKiIQCAQjHfPz8ykpKQnuHwgEcDgcOBwORFHE4/HgcDiarNEdPqZMJkOj0SCTybBYLBw4cACn09l+J6QTmDBhAhMmTDjp19fV1fHiiy+ekLCtW7cOg8HQJEL0VERyS55GBAIB3nrrLe6//37GjRvH7t27j1mKpyOxWCx89tlnvLf0PTZt3sT4ceO55oq5DBjSn7LMKg58X8D3GzfgcXibvC7cH8t3336Hx+ORGmB2ETKZDJVKhUqlQhRFfD4fHo8Hq9WKXC5HqVSiUqmOGsATERGBz+ejrKwMr9eLTqcjLS0taNF6PJ4m+3u9XjIzM4N/V1ZWUllZidFoJD09vdUx+/fvj0ajoaysjMzMTGJjY7t95G9iYiI5OTkAbeqW0Bg0ciKMHj2anTt3Ah0X8dkZSG7J04QtW7Zw6623Ul1dzSuvvMKsWbO6zPLZtm0br7zyCp/87xMyMjI4e/q5jBwzgvoyKwd+LeDAugLstY5WX28VzWziJ3766SfOOuusTpx52zmV3ZLH44IKBAJ4vV68Xi8+ny+4PqdSqbqFpe1wOCgqKsLtdpOUlER4eHi3mNephuSWlOhy7HY7d955J1OnTuVPf/oTmZmZXHjhhZ3+hfZ6vSxfvpzx48YzY8YM/O4Ary96g3/cNh9ltY7P7v2e5fO/YceKzKMKG4CBUHQKA19//XUnzV7ieJHJZKjV6qDrUqFQ4Ha7u02NTJ1OR3p6OomJiRQXF5OTk9PMQpToGUhuyR7MunXruOGGG4iNjWXnzp0n7J5oD6qrq/nPf/7DooWLiIyKZPYFl3D33XdTuqeSjW/tomBrKWLgxJwHgiAQ7otm5RcrefHFFzto5hJtpXHNS61W4/f7cbvdQbelWq1GqVR2idUkCEKwC3hJSQn79u0jMTGRqKgoyYrrQUji1gNxOBw8+OCDLFmyhKeeeoo777yzWfWEjmbHjh0sXLiQTz/5lEkTJ/HIA48SGRVJ1k+5fHj7V1gqbW0aP4pe7CrYwIEDB7pEtCWOH0EQgkWkA4EAHo8Hl8uF0+lEpVKhVqu7JLleoVCQmppKeHg4BQUFmEwmUlNTpXXcHoLkluxh/PbbbwwbNowtW7awfft25s+f36nC9ssvvzBt6jQmT56Mx+Fl8WuLufRPl5P9dRFL//oFG97b0WZhA4ggBoVMwVdffdUOs5boLBqtOaPRiE6nw+/3Y7FYutRlGRoaSkZGBkqlkn379lFTU3NC3cAluieSuPUQHA4H//jHPzj33HOZN28ev/76azCCrDP49ddfmTZ1GhdffDH9+6Tz1pK3mTB0Mj+/sonl878la3UuPk/7XbzkgoJwYvhy5ZftNqZE59FYD9FgMGA0GhFFEavVit1u7xKRUygU9O7dm969e1NaWsrBgweltbhTHMkt2QPYuXMnV155JREREWzfvr3TRe3RRx5lf/Z+rrjkSm679TaKd1Xw1ZNrqMiu6dBjRwZi+W39b9TX1582DVJ7InK5HL1e32RdTqFQoNFomtWe7GjCwsIwGAwUFxezb98+UlJSmhWiljg1kMTtFEYURd544w3uuece7r//fh588MFOc0GuW7cuKGrXXjWX22+/ndwNxXx67/fUFpk7ZQ5R9GK/fwc//PADl19+eaccU6LjkMvl6HQ6NBoNbrcbm82GXC5Hq9VSVlZGTU3H3iw1EhUVRe/evTGZTBQUFGC1WklKSjoti26fykjidojrr7+epUuXsmDBgmBHYYAVK1Ywe/ZsrFYr4eHhLFu2jCuvvDL4/JVXXsny5cvJz88nNTU1uD01NZVrr722w+rHmc1m/vrXv7Jx40a+/fbbYKX0jub333/nwQceZPuO7Vx75Vxu/b9byVlX2C5BIieKRtARKo/gq6++ksStByGTydBqtajVajweD1lZWYwbNwaHo3m3gI5Ap9OQlZVNcnIyOp2OvLw8srKy6Nu371Fzul577TWef/55KioqGDZsGIsWLWLMmDGt7v/JJ5/wyCOPUFBQQFpaGs8++yznn38++fn51NbWkpCQ0CTZ3GQykZuby+jRo7FarWRnZ7c47rBhw06ZHMqORBK3w9BoNDz77LPcfPPNhIeHN3nOYDAwevRo1q5d20Tc1q5dS1JSEmvXruX6668HGsoGFRYWNulf1Z5s3ryZK664goEDB7Jz506io6M75DiHU1BQwH333ceqVau44pIrueXmeZTsquB/d3+HqaS+w4/fGuG+aFZ9uQq/39/pEaESHUtj8Inb7cbhcPHeq7EMTOvYSMasHA9zb6ukpqaG5ORk1Go16enpweomKSkpwSakh7N8+XLmz5/P4sWLGTt2LC+//DLnnnsu2dnZxMTENNt/w4YNXHXVVSxYsIALLriADz/8kIsvvpjt27ej1+sRBIGKigqio6OP6podPHhws899Z7tyuyvSWTiMGTNmcPDgQRYsWMBzzz3X7Pnp06fz+eefB//OysrC5XJx5513NhG3tWvXolarm3T4bQ9EUeSll17ikUce4YknnmD+/Pkd7iqxWCwsWLCAha8s5Lxz/sR/F/8Xc7GNVU/83OFrasdDFL0oMGezefPmdj/fEt2Dxs/4wDQVI4d2fjUMmUxGYmIiRqMx2E6nsYh0Iy+++CI33XQTN9xwAwCLFy/m66+/5u23327iCWrklVde4bzzzgv2vnvqqaeCbXTuu+8+QkJCcLvdVFRUBFsStURjioVEcyQn8mHI5XKefvppFi1a1KRgayPTp08nOzub8vJyANasWcOkSZM488wzWbt2bXC/NWvWMH78+HYtS2MymbjwwgtZuHAhq1ev5u677+5QYQsEArz77rv07dOP39at59VXXuNP02by88JNfPHIj91C2ABCiUSj0EopARIdTmhoKIMGDcLtdpOVlRUswuzxeNi2bRszZswI7iuTyZgxYwYbN25scayNGzc22R/g3HPPDe4vCAIJCQlUVlZKUZsniSRuRzB79myGDx/OY4891uy5iRMnolKpgkK2du1apk6dyqhRo6ipqSE/Px9oyPVqz55oWVlZjBkzBkEQ2LFjB+PGjWu3sVti69atjB83nocffpi7br+LeTfNY/vHWSy/6xsKt5d16LFPlIZqJTGsXLGyq6cicRqgUqlIT08nPDyc/fv3Yzabqampwe/3N6u8HxsbS0VFRYvjVFRUHHP/8PBwdDodZWWtf+d2797N9u3bg4+9e/e24d31LCRxa4Fnn32WpUuXkpWV1WS7TqfjjDPOCIrbL7/8wrRp01AoFEyYMIG1a9eSl5dHUVFRu4nbN998w/jx47niiitYsWJFs7XA9sRkMvG3v/2NKVOmMHTQMF5+8WU8JfDBravYvyYPumleayRx7MvcR3FxcVdPReI0oNGqSklJIS8vj8rKyg47VmJiYrAlUEsMGDCAQYMGBR9paWkdNpdTDUncWqCxz9kDDzzQ7Lnp06ezZs0a9u3bh9PpZOTIkUBDx+E1a9awZs0adDodY8eObdMcRFHk+eef589//jOLFy/mn//8Z4e6IVeuXEl6/3Sysw7wxqLFjEgfzaf3f8+6t7Y2aznT3YgkFkGQSYWUJTqViIgIBgwYQCAQQC6XB5crGqmsrCQuLq7F18bFxTUTxZb2NxqNhIaGUlpa2uI4KpUKjUYTfLSlPU5PQxK3VnjmmWdYtWpVM5/59OnTycnJ4cMPP2TSpEnBReUpU6bwyy+/sHbt2qD78mRxuVzMnTuXV155hV9++aVJdGZ7U1NTw5VXXsktt9zCP26fz8033syW9/fyxUM/Ultg7rDjtidKQUWELJovv1zV1VOROM3Q6XQMHTqUjIwMPvvss+D6WCAQYPXq1a0GOY0fP57Vq1c32fbjjz+2uH9CQgJmsxm73d7+b6AHI4XZtMKQIUOYM2cOCxcubLJ9woQJqNVqFi1axEMPPRTcPmbMGKqqqli5cmWLFt/xUlZWxuzZs5HJZGzdurXVO7/24JNPPmHeLf/HpAkTeeWlhWT/nMcHz63q9pZaS0T4Y/l59WocDgc6na6rpyPRAWTldHxgxckcQ6lUcv/993P99dczYMAA/vSnP7FkyRLsdnswenLu3LkkJCSwYMECgGAbqhdeeIGZM2fy8ccfs3XrVv7zn/80G1+n0xEZGdmi+9Pn8zWrgymXy6WEcyRxOypPPvkky5cvb7JNo9Ewbty44HpbI2q1mnHjxrF27dqTXm/buXMnM2fO5Oyzz2bx4sUd1gSwsrKS/5v3f2zYuIF7599HbFQs3/zrF0r3ddzaQUcTRS9yPLv5+eefueCCC7p6OhLtSFRUFDqdhrm3dc7nU6fTEBUVdUKvueqqq6ipqeHZZ5/lqaeeYsiQIXz33XfBoJGioqImgjNhwgQ+/PBDHn74YR588EHS0tJYsWIFgwcPDgamHU58fDx1dXXNtrcUQDJgwIBTuoN2eyF14u4mrF27losvvpj77ruP+++/v0P6SomiyIcffsjtt93OlMlTufrqq8lek8/GZTvwunztfrzORBRFNit+4uobr2Tx4sVdPZ1W6emduDuKoqKikyq/JYoiXq8Xt9sdrFd5rO9WVFQUycnJJztV6uvryc3NJSEhoVlEZE+hO3wmjoVkuXUDPv30U6677jpeffXVoBujvamvr+fGv9zIb+t/44G7HyQ2Oo5vnv6F0r2nrrV2OIIgEOaL5suVX/LGG29ITSd7GMnJyW0SHL/fj8PhQBRFtFpth95YhIaGkp6eTk5ODl6vl4SEBOnz2AVIjtku5o033uD6669n+fLlHSZsW7duZeiQodSbLSx8eSHu4gAf//3rHiNsjUTRi/KKcnbv3t3VU5HoZsjlcgwGAyqVCrvdjtPp7NCebXq9ngEDBlBXV0dhYaHUH64LkCy3LkIURR5//HEWLVrEDz/8wIQJEzrkGAsXLuThhx/mlhvnMXLkSL7/93qKulkidnsRTjRKuYqvvvqKYcOGdfV0TjuqqqqoqKjA6/Wi0+lITk5Gr9e3un9dXR1lZWW43W40Gg2JiYlNWhf5/X5KS0sxmUz4fD7UajUxMTEt1mo8HgRBQKPRoFQqsdvt+Hw+dDpdh9Uk1Wg0DBgwgJycHA4ePEifPn2k+qediGS5dQF+v59bbrmFt99+m99++61DhM1kMnHxxRfz0ksv8e8FL9A7ui/L53/TY4UNQCbICPNH88XnX3T1VFqlp97B19XVUVxcTHx8PIMGDUKr1XLgwAG83pYjb202G3l5eURFRTFo0CDCwsI4ePBgk2Tl4uJi6uvr6dOnD4MHDyY2NpaioiLMZnOb5iqXyzEajcjlcmw2W4eWt2qsaOL3+zlw4AA+36m9tn0qIVlunYzH4+Gqq64iKyuLDRs2kJSU1O7H2LRpE5ddehlDhwzluWeeZ9eX+9nyvz2IgZ55YfWLPuqoopoyTEIVNTvK2bdvHxkZGR1zvENNNV0uFy6Xq8nvjX+73W4CgQCBQABRFBFFkUAgEBzjm2++QSaTIQhC8CGTyVCpVKjV6iaJuY3JuY2/d8dCuZWVlURHRwejDFNSUqivr6empqZJ25bD9w8NDQ2muiQkJGCxWKiqqiIlJQUAu91OZGQkRqMRgOjoaKqrq7Hb7YSFhbVpvoIgoNPp8Hg8OBwOfD4fWq22Q9bGFAoF/fv3Jy8vj/3799O/f/825cFKHB/d71vSg3G73Vx22WWUl5ezbt26FltntIXGrgGPPfYYt99yBxmDMvhmwS+U7ulZa2sAATFAHZWUU0Q1ZQTwo9NGERc9hpKi9bz88sssWbLkpMf3er3U19djNpuxWCxNhKvxTr8lEQoLCwuKkUwmayJgMpkMt9vNr7/+yvTp05HL5c3Ez+PxNBHK2traJn+LoohCoWhy7MYqFo3H7mwCgQB2u71JTqYgCISEhLSaeGy325tFEoaEhDSxyvR6PWazmaioKJRKJVarFZfL1a43hCqVCrlcjsPhwGazdZibUiaT0bdvXwoKCsjOziY9PV0SuA5GErdOwuVycckll1BbW8tPP/3U5jvPI3E4HFx33XVs2LCBF559EVeVj+Xzv8FZ727X43QloihiwUQFRVQIxXhFN3pdDKnxM4iOzkCnb+hrV28q4Ntvvz3ucb1eL2azOShmjdUgGsUqJCSEiIiIJiKmUqlOKlG2MUrvZCL2RFHE4/E0sxStVislJSXYbDbUajVhYWHBR2hoKFqt9oTneSI0utqOfD9KpRKXq+UGo16vt5kFqlQqm7gxk5OTKSwsZPfu3UGLKiUlJWjJtReNwSaN51Kv13dINKUgCKSmplJYWEh2djb9+/eXymV1IJK4dQJOp5OLLroIm83GDz/80GTRvD0oLy/nggtmIRdkvPD8i2R+d5DfP9zVY9yQTtFOOUVUCEU4RCsqpYG4XmOI7TUcg6FXM1dSVPQgCvJXY7PZmiWzNgrZ4WJ2uJCFhYWRlJREaGhot8vfEQQBtVqNWq0mJCSk2fON1mbj+yotLW0meI0WXkcLXntQVVWF3W6nX79+qFQqbDYbRUVFqFSqFt9/WxAEAa1Wi1wuD34e1Gp1u7spBUEgJSWFoqKioAUnCVzHIIlbB+NyuZg9ezYVFRW8+uqr7f6l3LlzJ+f/6Xwmjp/En/98OWvf2MyBdQXteoyuQBRF6qiimIPUUI5cpiQ6ZjBpvUYQHtEXQWjdaoqMGkB+3o+8+eabzJ8/H5vNRkVFBRUVFdTV1aHVaoMX+qSkJMLCwnrEBUapVBIVFdWkuobX68VisQQFvaysDKvVisFgIC4ujri4OCIiItp0EW+0wI4MHvF6va1aQEqlsllwxeH7BwIBSktL6du3b9DLodPpcDgcVFRUtPv3qJFGi9xut+P3+9HpdB0icLGxsXg8HkngOhBJ3DoQt9vNpZdeitls5qeffmLPnj1s376dkSNHtssX5ssvv+Taa67lb3+5mWFDhrHikZ+oym1eoudUwif6qKCQIiEXh2jBoI8jPfkSYuOGIZcf3xpFSGgvhg0bTU1NDatXr8bpdBIdHU1SUhKjR48+JayW9kKpVBIZGdlkfdfr9VJdXU1FRQWbN28GGnqJxcXFER0dfcIuOZlMhl6vx2q1BlsyiaKIxWJpNWxfr9djsViarLtZLJagpd24Dtn4PWmsUFJeXo7H48Fms53QHE+EqKgoEhMTsdvt2Gw29Hp9u9ZqbHQrp6amUlZWxoEDB6Q1uA5AErcOwuPxcPnll1NdXc2PP/5IaGgoEydOZP369W0WOFEUeeGFF1iwYAGPP/wEWgx8cu/3OEwt93w6FXCINkrIpYwCfPiIjhpEevJEQsNSj+s8qVQyUpL19OltJDXFgNvzd7Zu2URGRgbR0dHdMsKwq1AqlcTHxxMfH99gIdfVUVFRQVZWFtu2bSMqKipo1R3vjUBsbCz5+fnodDr0ej1VVVUEAoGgFZmfn49SqSQxMTG4f3Z2NhUVFYSGhmIymXA4HKSmpgJ/hOuXlJRQWlrK6DNG4XS0vH7X3mh1WvZn7ScpKQmn0xlchzvaZ+jxxx/niSeeaLItPT2d/fv3N9nmdrtxOp0YDAa++OILHnnkEfLz80lOTubf//43F110UYe8p9MR6RvfAQQCAa677jpKSkpYvXp1cI1No9G0WeA8Hg/z5s1j9erVvPjcS5TvruH7xT/i9waO/eJuiFU0k0cW1ZSikGuJT5xAQtI4NJqwY77WYFDQt4+RPr2NJMTrMJnc5BXYWLGqiMx9O9i98z1mzpzJueee2yFzt1gsFBUVUVhYSHV1dXC96/B1L5PJhNVixefz4/f7USoVPPLoI0ycMBGv14dMJsNg0BMRGUFoaGjw0eg2jYiIICkpiZSUFKKiojrERRYZGUlERAQZGRlBF25paSl79uwhJCSEmJiYoCXVGhEREfh8PsrKyoJJ3GlpaUEr8MhcMoPBQO/evSkrK6O0tBSNRkO/fv2aiGmfPn0oKSlh06ZNOB0uLnx6NFF92jeY5Ehq8qx8+eBWampqSE5ORqvVIpPJgpGUR7OuMjIy+Omnn4J/HymGhwvb5s2bueqqq1iwYAEzZ87kjTfe4LLLLmPLli0MHz68o97eaYUkbu2MKIr84x//YOvWraxfv75Z5+y2CFx9fT0XzrqQ2to6nvnXM+xamc3WT0/NtvI2sZ48MqmiFI06jPTes4ntNfyYrkeZDHqnGhmcEUZSop7SMgd5+VZW/1yOxfrHmk9YeF8EQc7rr79+0uImiiIlJSXs3r2bffv2UVhYSGFhIXm5eZSUlGC1WZvsr5ApUclVKFAiF5XIAnLkATlylMgQAAGttiFIpW6fDZfThYiInyr8Qg4BuR+/4MOHF2/AjcffVBDUKjUJ8Qn07tub1NRUkpOTGTBgAEOHDqVfv34nbZ1mZWVxzoxzSEpJ5pJLZnPhhRcyadIkPB4PlZWVVFRUBMPlZTIZSqWyRTfd0aqHpKenN9sWERFBREREq/NSKpX07t0bk8kEQFQfI3EDw07qPZ4sjVVNGgNNRFFsdX1MoVC02qLqcGFTKBS88sornHfeedxzzz0ALFy4kHXr1vHMM8/w4YcfSi1r2gFJ3NqZZ599luXLl7Nhw4ZWv+gnI3BVVVXMmHE2IXojjz3yKL+9s53MHw92xFvoUBpELYsqStCowxjQ51Jie41AJjt6blFoqJLBg8IYOCAMvz/A3kwzP/1cjt3ecsUHuVxFeHhffv113XHNy+FwsGvXLnbv3s2ePXvYuWMnu3fvwWqzAKCSq9DJDCh9GtSillhSSUGHBh0atKjQIBNlcOR0jvi3KoUGS6a/MByvcET1Dn/TP0VEvHhw4Wh4eBy4ChzsKdjPdsUuXDhw+hxAg/ANSB/AiFEjGDJkCEOHDmXEiBHHlUv58ccfU1VZjac8wAObHuSee+4hvX86c6+by5w5cxgyZAh5eXkolUo8Hg9OpxOlUolarUYul58WRYGVSiUGgwG73U4gEGixu0BOTg7x8fFoNBrGjx/PggULSE5ObiZsABs3bmT+/PnB1wqCwKxZs/jkk0/Iy8ujb9++p8V57UgkcWtH3nnnHRYsWMAvv/xCnz59jrrviQhcUVERZ04/kwH9B3Ddddfz48vryd9c0hFvocM4UtTS+1xCXK+RRxU1QYDUFAPDh0UQH68lP9/GDz+VUVR8fB2Jo6IHcCD7AEVFRS1WlP/pp5/4+uuv+fWXX9m1exd+vx9BkBGiCEXjNRBDEn0IxUgYar8WIXDo/9NJ1xxBEFChRoWaEJp6ABqF0IMbG/XYPPVU76lnZdZXvB/4AF+gQTj79unH1GlTmDx5MnPnzm3RIli5YiURgRgGMxZ/wEctVVTllPDYI4/z0EMPcekll3LvffcecqEa8Pl8waAOmUyGWq1GpVL1+IuxQqHAYDBgs9mC3QUa3/PYsWN59913SU9Pp7y8nCeeeILJkyezbdu24OsOt6wrKiqaJbH36tULk8mE2+2msLCQlJSUHn9OOxJJ3NqJr776ittvv51Vq1Ydt8/8eATuwIEDTJs6jTOnnsWsWbNY9dTPlGdVd8A76Bjcootc9lFGARp16HGJmkopY9CgUIYNjUAhF9i9x8T3P5TicPpbfU1LREYNgOwvefnll3nxxRebPFddXc15552HWtAQ4oskjaGEEIFeDEHuk3eagLUVlaAmghgiOOQl8De4Ux3YsGCiPq+Gz4q+4O233yYqKqpZI9eysjJ27d7FYMaAAHJBQQzxxBCPL+ClijJydxRSV1dHdnY2YWFhREZGBpPDPR5PsKqKSqUKVvzoqTQGuthsNux2O3q9HkEQ+NOf/hTcZ+jQoYwdO5aUlBQ++ugj5s2bd0Iu47S0NPbv309ZWRkJCQkd8TZOCyRxawc2bNjAVVddxdKlS0+4C/exBO7TTz/lglkXMHncVL546Edqi8ztPPuOwS/6KSKHAvYjyJWk9b2A+MQxyGStf+RCQ5UMHxrBoIGh1Na52fh7NQdzLQROMlZGow1Hp4tm5cqVzcQtIiICrUZLrD2F3sLAkztAN0UQBPQY0WOkF8mYfNVs45cWy1Z98803CAhE0nytSCEoiSeFaDECNVqUogqL2YrJZEKlVBETG0NUVFQTa85qtQZdlj01QrXRgj1aqoBWq6Vv376UlJS0eB7i4uKorGxaFq+yspK4uDhUKhVpaWlkZ2ejVCpPugvC6Y60atlGsrKyuOCCC3j++ee59NJLT2qMRoEzm81s3749GJUmiiLTp09nzJgxPPzQQ5QUlrbn1DsEURSpEkvZKPxAHpn0ShrLuEl3k5g8oVVh0+sVnDU9jmuu6oNGI+fzFUX879NCDuScvLA1EhWTQUFBYbNoPblczqRJk6iX1bbtAKcAJqoxGowMHjy42XOrvlxFuDwapXD0QB4BASVq9GKDaIpeKCkpYdeuXRQWFuL1etHr9RiNxmB0oc1m67FV8BsFThCE4DpcI263m+rqagoKCppYXna7nT179lBQUMD48eNZvXp1kzF//PFHxo8fDzSIY79+/SgpKaGu7tTOXe0qeuatVSdRV1fHrFmzmDdvHrfcckubxjrSghs+fDg7d+7E4XAwY8YM/vXPf7FLsZ4Rvsmohe6ZhGwXLWSzizoqiQhPY1j6rGC9x5ZQq2WMHhnJsKER5OVbef+jPOrrW26RcrJERQ2gqGAtS5cu5aabbmry3NRpU1n9088EAgFkR6l4cqpjkdUxefLkZu5Cl8vFDz/+QKK/3wm5YeUo0KJAjRav6KGuto6amhqMBiMxsTHBii9utxubzYZSqQxGHLaFmjzrsXdqIydyDEEQ0Ov1OBwO/v73vzN79mzi4+MpKCjg+eefRy6Xc9VVVwFw9dVXo1aruf3WO6hx13DFFVdwxRVX8MILLzBz5kw+/vhjtm7dyn/+85/g+AaDgT59+pCXl4darT5qbzyJ5kjidpL4fD7+/Oc/M2TIEJ566ql2GbNR4H777TdWr16NQqFg4sSJaDQa1qxdw4TxE9hVs57hvsmohO5TricgBihgP/nsR60JZUj6tURGDWx1MVyhEBg2NILRIyOprHLy6eeFVFV3TIJuSGgSCoWGd999t5m4TZ48Ga/fgw0zIbQekn4qExAD1MtqmTJ1SrPn1q5di8vlIormLWmOBxky1GhQiWp8eHHanOTactFoNMTHxxMeHo5arQ4WJFapVGg0mhMOc4+KikKr0/Llg1tPap4nilanbVLC7Gg0ts4pKyvj6quvpq6ujujoaCZNmsTvv/9OdHQ0FouF7OxsEnoloMeIFw/Jycm8+uqrvPjiizz44IOkpaWxYsWKZtZ1WFgY8fHxHDx4kEGDBnVIQeeeiiRuJ8n8+fOpqqpixYoV7ZqTolarMRqNVFVVNal5mJqaypq1a5g4YRK76zcw3D8JhdD1H3SLaCJT2IZdrCc5dRopvacjl7c8L0GAQQPDGDcmCrvdxzfflVBc4ujQ+QmCjMioAWzfvqPZc6NHj0alVGHy1vRYcbNixuv3Mnny5GbPffXVVxgUIeh9bavT2OCyVKFEhQ8fHpeLvLy8FkXOYrEEiz8f7/cmOTmZ/Vn7qampadM8j5eoqKgWo2tbQxAE3n//fVwuV9Bd2fjezGYzubm5vPXm22jRI9AQASsiMmrUKH799ddmUZNHEhsbi9Pp5ODBg6Snp0s5cMeJJG4nwZIlS/jwww/ZsmVLs6rzbUEURbZv347NZmPKlCls3bq1SZBJeno6q3/+iSmTp7DbsYFh/onIha75FwZEP3lkUUg2el0sozKuxRgS3+r+/foamTCuwUX5y7pKDuZ2vIupkciogVRW7GTTpk2MHTs2uF2tVjN27Fiy1+d12lw6GzM1qFVqRo8e3WS7KIqsXLGSMF90u4abK1CgwNCqyPn9/qAl1yhyx3P85OTkExKczqSxVqTBYMDtdgejKE0mU0PZMVRo0CEc5vtVoUFEpLi4GJVK1azYw+E0dhLIzs6msLCQ1NTjK0l3uiPdApwg69at4+9//zuffvopvXv3brdxRVFk586dmM1mJk6cGKxFeWSQybBhw/jhxx9wqezskf2OXzyx8Pj2oF6sY5OwmkLhAKl9zmLU2FtbFbbEBB1XXJ7K1CmxbN9Zx/sf5XWqsAFERKYBAq+88kqz56ZOm4pFXnfU0lKnMvVCLWPHjm1WNiozM5OS0pKTdkkeCwUKdBjQYcTn8pOXl8e+ffuCF36dThfsWOB2u0/Z839kgrZOp0Mmk2E2myksLGxR2KBhiVONFiUq8vLyWm3q2khjs1OLxdIsylKiZSRxOwEKCwu59NJLeeGFF5g2bVq7jp2ZmUlNTQ0TJkwI9hFrLYpy7NixfP3N11gVJvbJNhMQO6euZEAMkCPuZgtrkBmMjB57O6l9zmoxClKnlTPzTwlccH4iublWli7LZe8+c5ujH08GpVJLaFgKP/20utlzU6ZMweVzYqdzBbczEEURi7yOadOnNXvuq6++QiFTEk7rAT/twZEil5OTQ05ODj6fD4PBgFarxe12Y7VaT7nIypYqjwiCgNVqxel0Et8rHq2gbyZsjQiABh0yUc7BnIPNInqPRKVS0a9fP8rKyqivr2/vt9PjkMTtOHE4HFx00UVcdtllbY6MPJKDBw9SVFTE+PHjm1Vhb03gpk2bxudffE6tUEGmsLXD73ydop2twlqKhIP06XcOI8+Yh8HQch29/mkhXDOnD36/yNJluWzdXovP17V35lHRg6iurm62bjN+/HjkMjlmOmc9pzOxY8Hlc7a43rZy5UoixBjkQuckXCtQoMWAFj02q519+/ZRVFSETCbDaDSiVCqx2Ww4nc5TwoprSdhEUaSsrIySkhJMFWZkyAmJPfqyhYCAFj1+v5+cnIP4/Uf3xOj1elJSUsjLy2u1y7lEA5K4HSd33HEHRqOxRddWWyguLiY7O5vx48e3un7XmsCdf/75fPTRR1RRwn62d9hFoUosZRM/4VEFGHnGPFJSp7VYYaTRWps6OZaf11Tw3Q9lOF2d7zZtiaioAYDIokWLmmw3GAwMGzasR4qbmRrkcnkwd6qR2tpaNv2+iUix5ZuTjkIAlKjQi0bUaKmprmHPnj1UV1ej0WgwGAx4vd5ub8W1JmwlJSWUlZWhRotK1FBfYUWhkmOMPnoIvwwZWtGAy+kkPz//mN/jyMhIoqKiyM3NbZJfJ9EUSdyOgw8++IAVK1bw0UcftWsobmVlJbt27eKMM84IdhtujdYE7vLLL+ett9+ilHxy2N2uAhcQ/ewXd7CbjYRH92f0uDsICUlscd/+aSFcc3WDtbbsw85fVzsWWl0Uak0Yn376abPnpk2fhlVp6vxJdTBmahg+bHiz/KjvvvuOgBggqoWqJJ2BgIAaDXpCkPnlFBUVkZmZhcfj6fZWXGvCVlRURGVlJRp0qGlYVhD9IuYyKyqdEl340XNT5cjRoA92Sz8WCQkJyGQyiouL2/6meiiSuB2DAwcOMG/ePJYuXRpstNge1NXVsWXLFkaMGHHc5XVaE7jrr7+eRYsWUUQOeWS2y/wcoo0twlpKhQLS0i8kY+gclMrmX1Dt4dba2gZrzdVNrLXDEQSB6OhBZGcfaHa3O2XKFOxeK07x+AoynwqIoohFYWL6mc3Lwb3zzjtoZXpEulY4ZMjQokePEY/LTWZmZoPlo1Z3SyuuNWHLz89vsD7RoaJp/mnAF8BcbkUXpkETcvTcVCVK1GgpLy8/ZtqDTCajT58+1NXVSRVMWkESt6Pgcrm44oor+Nvf/sbMmTPbbVyr1cqmTZsYNGjQCRdGbU3gbrvtNp5++mnyyaJQPNCm+TW4IVfjU8OoM+aRmDS+xdDjtH5Grr26D4EAvN8NrbUjiYwaiN/va2a9TZo0CaBHuSZdOHD4bM3W21wuFz//vAZnwM5vfMMmVpMnZuIQbV0004aKJzrRiBoN5eXlZGZm4na7u5UV15KwBQIBcnNzqaurQ4u+mbA14vf4MZdbMUTpUOuPXuZMhQYlagoLC7Faj/59UqvVpKamUlhYiNvtPrk31oORxO0o3H333ahUKp5++ul2G9Pj8bBp0yZSUlKO2RanNVoTuAceeIAHHniAHHZTIp547pYoiuSL+9nNRiKi0xk97g6MIc3FV6uVc/55CUybGsfPayv49vvSbrO2djTCwlORyZQsWbKkyfbIyEgG9B/Qo8TNdOi9NAp3I0uWLEEUAwwbcSODBl+FNiaJQtlBNvAdW8Q1lIi5eMTOv1A2uCq16AnB6/aRlZVFaWlpEyuuq2pVtiRsfr+fgwcPUm+uR4cBJUcXLZ/Lh6XSjjFWj0Ldem5qQwSlFrmoIDc3F6/36OXowsPDiYyMlNbfWkBK4m6Fzz//nPfff58dO3YctbX8iRAIBNi6dStGo5GBA9tWib61bgL/+te/sNlsvLroVeSinF5CyvHNTfSTyXYqKCS191mk9jmrRWstNcXA2TN6UVLi4P0P8k4JUWtEJlMQEdmfjRs3NXtu2pnT+DD/4+bNRk9RzFQzMH1gs07Xy5YtQ6nUER7RB0GQERs3FL/fQ011JhVlO8iu20k2u4gU4+hFMtG0npjfEciRoxMNeHBTUVGByWSiT58+mEwmSktL8Xq9wdY6HZXIfHiFkpaEzefzcTDnIHa7HS16FBzfOrzH7sFRJyM0zoCppJ6Av2VLVEBAgw6H30peXh79+/c/6ntNTExk//79lJaWttj54XRFErcWyM/P58Ybb+S///1vuyZq79u3D5fLxeTJk9vli9mawL388stYLBaWvbcMuaggRji669MjutnFRqyCiUEZVxAbN7zF/UaNjGTM6Eh+XltB9gFLm+ffFURFD2R/5j6ysrKa3GBMmTKFxYsX48GFStB04QzbB6vSzMVnnd9s+65de4iKHoxwWKFouVxFbNxwYuOG43FbqarcTUXZDvbYfkctaDlDnNip63ONAScKlLjcDtauXcull1+G29k5oe8arZbs/fuDZa+OFLYD2QdwOp3oMCA/dAndsn0zby1bwt79+6iuqeK1599gxrSzg2OKosjCN1/hkxXLsdqsnHHGGTw8/zFSk1JbnIMMGRpRx1tvvcVHH31EdXU1w4YNY9GiRYwZM6bpvofW3zIzMzEajccMTjtdkMTtCAKBAHPnzuXKK6/ksssua7dxCwsLKS4uZurUqe0acdmSwMlkMt566y3sdjtffP4F8sB4IoWWI+NsooWdwnr8chg+/CZCw5pbenK5wIwze5EQr+PTzwuprjn1/Pter4Pammyqq/aCTMbdd9/N119/HXy+cW3KRA2xtF/gUFfgFl1YveZm620///wzHo+roYlrK6jURhKTJ5KYPBGbrYLSog2UCvm4ceLGiRpN8ILe0ciRo8OAzWTD7XQRfdtlqBI6treZp7SK6lc/paysjJCQkCbC5vF4OHDgAG6XBx1G5PyRDuNwOknvP5BLL7yc2+79v2bjLnnvPyxbvpRnHn+OxIQk3nj3VW668y989dG3wfqxR/LDDz/w8ssvc//99zNr1iyWLFnCueeeS3Z2drMgNI1GQ0pKCgUFBWRkZEgFlpHErRkLFy6kvLyc7777rt3GrK2tZc+ePYwdO7ZD2la0JHByuZwPPviAiy+ezY/f/8iwwATChabVKGrFCnazCY02nBEjrkejbV7fTq9XMOv8RPwBkY//l3/C3bC7Er/fS21NFhXlO6irPYAoBtDGJaHQh7BmzZom+yYmJpKUmIy55NQXt8a1wyPF7bXXXkMQZIRH9Du+cUz51NXlEBcXgSAI+AUfdtGKHAWqQ5bV4f4HLx7cOAkQQIYcDdpmLjs/ftw48eEDROTI0WJA1sryf0Oh4QZLWpUQg7p357hJ3W53E2Fzu91kZ2fj8/oOWWxN8zynTpzK1IlTWxxLFEXe++hd5v3lVmZMbbDmnn7oGcafPZZfN6/l7Mnntvi6dz58m8svvoJLLrwUFLBo0SK+/vpr3n77be6///5m+0dGRmI2mykqKqJPnz6nff1JKaDkMHJycnj44Yd5++23202EHA4HW7ZsISMjg+jojit11FKQiUql4rPPPmXipAnskW/EIv4RMlwhFrGT9YRG9GbkmHktCltsrIYr/5xKTa2bz78oOiWETRQDmOryyMr8jPXr/sW+PR/hUDqIPesi0m59lN7X/4Pw4eNwulyYzeYmr51+5jSsilM/381MDclJKc0icdeu/YWwsN4oFMdul1RZsZuDB74mtc9ZDBl2DTJZg0hpNOEgl+HEhh0LXryIgA8fTuwNDU0JQYkSBzb8/PGZCeDHgRUZcvQYMBCCiu7Zm1Cr1QaFzeVysX//fnxePzrR2EzYjkVJaTHVtdVMGDMhuE2vNTBs8DB279+FStfcyvJ4Pezbv5eJYyaiQYff56ewsJAZM2awcePGVo+VnJyM1drQLf10RxK3Q/j9fm644QZuvPFGpkxp3vvqZMfcvHkzcXFxpKamtsuYR6MlgdNqtaz6ahXDRgxjl3wDNrGeYjGXvWwmttcIhgyfi0LRfI1pQHoIl1yUwtZttfz0czn+QPdKpj0Sj8dGQf4aNm54np3bl1BryyN8zBT6/u1+el/3dyJGT0ZpDAPA0HcQiCIPPPBAkzEmT55Mva8On9i+DVM7G6uijulnTmuyraysjLq6OqKijy+QqbhoHfEJZ9ArfjRaXSRKpQ4QEEU/Ol00Ol0UglweFDk3ThQoD7kt5ajRIkeBhz9c2G5cKFA2RAOiQIYcJcpWrbaupFHYHA4H+/fvJ+ALoBNbtzCPRnVtgyUdGdm0R1xEeCRlxWWExBqQKZqOazKb8Pv9REZEHsoH1GGxWNDr9VRUVLR6LKVSSXJyMkVFRceMtOzpSG7JQyxcuJCKiop2DfvPzGxIqB4yZEinuQhaclEajUa+/+F7pkyeyrastXgDXhKTJtKv//lNAgugoefahPExDB4UxtffllBU3L0Tm62WMkqKN1BZuQsECB00kl7DxqJNaL0tiCY2AbnOwOeff84bb7wR3D5lyhRERMzUdFi1/I7GJ3qp95ma3aA1lI0Tj7re1kgg4MNmLSMldVqT7XK5Cr/fAwLIFWp08mj8fjdutwW/34OADB/eoCtSgQIvDRdYEfDiRY36kEXnQ4bsUF5X+0Qjtzd2u50DBw6AXziq67Qt+L0BXDZPQwRlqYXW4nYUh/oL2Gy2Y9afjIiIwGQyUVhYSN++fU9b96QkbjRUIXn44Yf57rvv2s0dWV5eTlFREdOmTUMu75zitI20JHDh4eGs/vkn+vTpS8AlkJQyqZmwqVQy/nROAiEhSpZ/ko+5vnve+YligOqqfRQXb8BiLkBpDCN6yrmEDRuHQnvs/58gCBj7D6Z695Ym29PS0oiMiMRcd/LiJlPI0Idr0YVr0YdrURtUyOQyZHIBQS4QP6ihMeWQ8/rjdXkJBEQCvgBelw+HyYnd5MRhcuJ1nVxOgpkaRMRm621ffPEFWm0kWl3kMcfweh2IYgCVqmmtU0GQ4Q8cNq/DRM5qLUWQyXAEbIcsOC0CMkQacq8afoq4caFGixptQ/du7AjIUHSzS5Hdbic7OxtZQIYWQ6uV/Y+H6EMWW21tDTFRfwSC1NbWMKD/IGw1dsITQ9FH6LDXNjTvDQ8LRy6XU1tXG9xfjQZTnQmj0YjP5wtaly2RnJzMvn37MJlMzdJBThe61yeqC2h0R950000tVk8/GRwOBzt27GD48OZ1/TqLlgQuJiaGNWt+ZsKEiezYtoRRo29BpTYCEBaqZNYFSdTXe1n+aQEeT/dLCBXFAFWVu8nP/xmnvRpdUl8Sp1+PMS0DoYVCzkfD2DcD887fefvtt/nLX/4CNIjetOnTWLNiHbTy9lU6JdF9I4hMDkMfoW0iZLpwLdoQDQF/AGe9G7vJicvqJuAPIPpFAoEAScMaRDNuQMMFTyZrEL7G+oP6cC1ypRyPw4Pd5GoiePY6B6YyK9W5tdjrnC3Oz0wN0VHR9Ov3R9CIz+cjNzePhKSJJ3SOjptD132V2oiAgMtVj120tBhVqUQVrL0oR44fH17c3U7cioqKyEgfHOye3RYSE5KIjoxm45YNDEwfBIDNZmXXvl1cddkcEMFSYSM8KQSv04vH4UWlVJExYDAbt2wIphSIAZEtW7Zw+eWXU1JSctSljsPdk42VXk43utcnqgtYtGgRVVVV7eaObEzUjo+PP+HSWu1NSwJ3xhln8P3333H22eeyY/t/GTn6ZuLiwrnkomSysutZv6GKblartpmoGfoOovekOWh7nXxnZn1KP5DJeemll4LiBg2uyRVfrMAv+tEZtET3iSC6bwQxfSOI7htJWC8j1mo7tYUmbLUO6itslGVVHxKfQ0JkduINePDgIkAAERGRAAECKLUK7v/odj556Uu8Ti8CMmTIEJChRIUKNdoQNfpwXYPYRfwhnrH9oxh4Vl/CE0Jx1Luozq2jKq+24WduHfZaBxZ5HWdOm9rEFfXBBx8QCPgPdUY4NkqlDkGQ4fE0LckligFkQitRjYIcUQygVBkwKDR4PHbc7oaeYx7cQdej7IhgDNkhgTsePKVVx7VfW2g8hkyUn5Cw2R12iooLg3+XlBWTlZ1JaGgY8XHxzL3qet54+3VSklJJTEjilcUvERMVG4ye9Hv9zJp5IRdePItLz/0zAV+AG67+C/c9cQ+DBw5haMZQln70Lk6nk0tnXU5NTQ0RERGEhIS0OqfD3ZOH3+ycLpzW4lZWVsYjjzzCl19+iU6na5cxs7Ky8Pl8DBkypF3GaystCdyZZ57JF198xuyLL8Fc+yO33nI3O3bWsWVb7bEH7EQaRG0vBfmrcdirMPQZSO/JbRO1RmQqNfqUfuzPzj50LBGbzcawYcOYf9d8BvbNICIuDEuV7ZB41JK1OpeqvDoc9U48uHDhwIkD1+EPwYlLdOCnZZeuFi1wOzv5DSfNrS8ZctRWHRqLFk2hDg1aNOjQojtUcV6HRqsmMjWcmEOi23dcMuEJIbisbsbnDqJfv77U1tYSEdEQwv/OO+8gl6sJCT2+8yaTKTAY4zHV5RIdkxHc7vd7mrkqG5ErVPh9blAZQBBQqQ14vQ5AxBVw4MWDDDkBmq4XBfAfcy0rPCwCtUZD9avNOzp0BGqNhriw+BOy2PZm7WHuLdcE/17wUsPN8uyZl/DM489x09y/4XQ6efTph7HYLIwaNpr/Lny7SY5bYWEBVRXVhMQaMJdaOP+cmdSZa1n45stU11YzsP8g/rvwbXpFJuDERkF+ARmDM4667JGcnMzevXupr68nNDT0JM7GqYsgdreeEp3I1VdfDcCHH37YLuNVVVWxefNmpk6ditFobJcx2wuXy8X69esJCwsLVjL56KOPCAQCrFr1E+VVvZHLu8/CvtlcwMEDX2O1lGDoM5DoSeegjT++UmLHfYydG4go2c+rr76K1+vF6XQSFRXFokWLMGU5UOQZsFjqqaeOemqpx4RDsOEWnRy+8q+Qa9FoQtFow1FrwtBow9BowlCpjMhkCgRBjiCTIRPkqNUq5v99HC+8tB6320tA9CMG/AREP16PA7fLjMttxuU043aacLnMeLxNLSiloEGLjhAxnFAiCSUCozoUdSr4+9n4+9//HoyUi42NZf78u8jLt9Gv/6XHfW4qK3azP/MT+g+4mLT+Gdz+f1OIiAhFr49DkMlwOU0Ighy1psFy8Ps8OBzVqNWhKBQavD4HHrcVvT4GURRxOU0ExAYLTYMOBQp8+HDhQIfxmG7JsooyTOZjV7/34sWDC6PRSEJCAoFAAKfTiVqtDpbR83g8uN3uJuH+xcXFWK1WBATiw5JIiOsir4sAEUmhuG2eVl3P0HBTYMdKVHQUKSlH/15UVVVRWVlJRkYGMln7BMW4XC7y8/Pp3bs3Gk33rOhz2orbmjVruOiii9i/fz/x8W1PDPV4PPz8888MHDjwmB+2ruJwgUtJSWHTpk3U1NRw/fU3EBHRjyHD5yKTda0x73KZOZjzDdWVe9DEJBI74yL0yX3bbXy1XKB/mI6B4Tr6h2rx2G1UVlZy7bXXEh0djUKh4Kwzz2L9mg3IBAXOQ9XyVUoDIaHJ6A0xaDThqDWhaDRhqDVhx5U31ohKKWPezem88WY2Hu/xrWsGAj7crnpcLnPw4XTUYDEX43Q1WNtKQYNSVILOj8ViQSaTUVdXx65du8jMzKRXr3hKSp3k5VvJz7dhsx/bFVhSvIGiwnX0iotg8eLFJCT2OZQSAA57DTKZvEl+pM/rxO22EAj4kMmUqDUhwTQTURTxuC1NXJ2yQykDyuOszXi8ePHgxE5ISAj9+vUjEAhgs9mCXe6PLKmVk5NDfX09SqUOn9eJ7FBllLautZ0scpWc8MRQzGUWfEcJLGrwHjhJT08/6s20KIpkZWURFhbWLtc6ODXE7bR0S3q9Xm677TYeffTRdvtn79mzh7CwsGDB1e6E3+9HEISgi/KXX36htLSUoUOHkpqaSmVlJffddz+Zez5m0JCrWuyy3fFz9FJc+CuFhb8gU2uIn3kVoYNHNYvoPBmUMoGhkQaGRelJNWqpcXnJMtl5N7uSX15+CpXbzr333hvcX6fX4cZFQsJ4QsNSCQ1LRq0O7bKQaplMgVbXcqSj12PHYinBUl9ESfEGokLCgm6qyMhIli5dynvvLWPWhU+S1j+G/mkhTJsSR02Nm5xcC5lZ9TgcLV9AE5MmkJg0gehoHWq1EbnsDxHS6aOa7a9QalG00PMPGoJ11JpQFEptgxUX8KFE1SGBJEpU+PBhsVjwer3BzgI2W4OwNgqbKIpkZ2djs9lQqY2oVSH4lXocjhqc2NslmORk8Hv8OExOQmL01BXXt5oeoESDDy/5+flkZLTunhQEgeTkZA4cOEBkZGSr5b56GqeluC1cuBCAO++8s13Gq6iooLKykunTp3e7nJJffvmFc885lzPOOIOvv/kar9cbrKxeW1tLSkoK9957L2azmQULFrA/6zMGDrqsXUTleDGbC9if+Skul5mIM6YQNeFs5Oq23w3G61WcERPCsEgDJreXbdVWvsirweT+42Ju6DeI2s1rcblcwTvQCy64gK+++oq4+JGtdh7vLihVeiKj0gmP6EdR4a+MGDGiyfPff/8DIaFJ2BwKduysY8fOOtRqGakpBgYOCGXcmGgKCm3s3WeisMje4cFEcrkKnT4Gj9uK22PFhxct+nbNIfPgxoubuLi44IX88Nwwv9+PXC4nMzPzkMsyFJW6YS1RrlCh1UXgdNTiwokGXZfYbw6TE7Ve2SQ94EgEQI0Oh8dKWVnZUTsCGAwGIiIiKC4uPm2CS7pfaYAOpqysjCeeeIJXX321XcJjPR4PO3fuZPDgwUG3R3dh27ZtzDx/Jlqfkc2/b2HOnGvYuHEjgwcPZtq0aU0qmTz99NPcdtttVJbv4ED2qk5pDOn3eziQ/SU7tv4Hwgz0+cvdxE6f1SZhUwgCI6MN3Do4gZsGxSMD3s4qZ9GeUjZUWJoIG4CxXwYEAjz55JPBbXPmzAEE6k0FJz2PzsZmLSMQ8HHxxRcHt5nNZiorK4mKHtRkX7c7QPYBCyu+LGbp+7nU1Lo5a3ovbpjbjzNGRaLRdKzl3mDFhaDTRRMQAtix4GslAOdEaVhxcxATExOMVj68bY3BYMDpdJKfn4/T6WxYG1U3DZJRKDRoNGF4ceOlczoRtISlyo42VINC07oNIkeOCg2VlZVBy7Q1EhISsFqt1NfXt/dUuyWnnbjdfffdzJw5k+nTp7fLeI3uyO7WR2n//v2cffY5qNxahgcmcU7yxVx5xRWs+GIFGo2mxVJdixYt4tprr6Ws5Hfycr/v0PmZ6nLZ/PvLlJVvJfasC0m95nbUUbEnPZ5BKeesxHDuHZHElF5hbK22sGBbIV/k11Bib72LgTY+BZlaw7Jly/4Yy2AgNDQUs+nEG752FWZzASAEg6SgoVByQ1WS9FZfZ7V6+X1TNW8vPcjaXytIStRz4/X9OGt6HJGRHeu+kitU6PWxyBUqHNhw4WxTa53GtbbIyEiSkpIQBKFZPzZBEKisrCQsLIyIiDiUqpbzUJUqPSqVARfOdhPeE+Vw9+TRzEfVoU4NBQUFR70pVSqVJCQkUFRUdFo0Nj2t3JK//fYbq1atYv/+/e0yXnl5ebd0R1ZVVXHO2efgtwYY7p9ITHIksx8/m98/386Hqz5mzZo1rFm7huTk5GZpAu+99x4Wi4WVK1cil6tJ7d0+NwGN+P0eDh74hrLSTeiS+tL3/FtQhZ98QekQlZwzE8IZEW0gt97F/3KrOVjfepTZkQgyGYZ+GZTt39lk+9ChQ9i4cQuiKHar/21rmE35hIWGYjD8YYUsX74ctToEvf7YNw2iCHn5NvLybURGqhk+NJwrLkulrNzB/uyjWwRtQZDJ0Gqj8HisDWW88J2Um7Kx2kl4eDipqaktCpvP52Pv3r34fD7kch0REQasVi8ud8vlrNTqUAIBHw6fHT0nXjC5PThe96QGLXaXlerq6mbtcA4nOjqampoaKisr6dXr1Cwxd7ycNpabKIrce++93HPPPe2SXO3xeNi1a1e3c0e6XC4uuvAiaipqGeIbT3hUOBc9PoPd32Szb0UeI3yTqSyuZsL4CeTk5LRowa1YsYLp06eTn/sDJUXr221udlslW7e8RkXlduLOuZSUq+edtLBp5TLOS45g/rAkNHIZr+4u5b3sihMStkaM/QYR8PtZtWpVcNsFF1yAz+fCYe/4xOG2IooBzKY8hg0fFtwWCATIytpPVPSgExbn2lo3q9dU8PbSg1RXuzj37Hg0WjkyeQeJvNBQ3aTBTSlix3rcid3wR0eC0NBQevfu3aKweb1e9uzZg8/nQ6uLwh9QUG/xYDQqUSlbuQwKoNFGIJMpcGAj0FrZmg7m+NyTDUXPSktKj1owWRAEEhMTqaiowOfrIW3nW+G0Ebcvv/ySgwcPMn/+/HYZLysri9DQ0G7ljhRFkRtvvJEtW7Yy2DcWozqEmfdPpWBrCVv+twcAnWBghG8yliobE8ZPZM+ePS0K3E8//cTo0aPJOfAV5aVb2zyv8tKtbN3yGn61jN7X/4OIkRNPKmhFKROYEh/K3SOSiNOp+E9mGR8frKLadfKuI0PvASAIPPXUU8Ftc+fOBRrdfd0bh70av9/NzJkzg9tWrVqFz+c9rkLJreFy+Vm/sZoVXxaDCGGhKoxGJTJZx4hcg5syBplMgR0rXjzHfI0fH05sGAwG+vbti0wmayZsbrebPXv24PcH0Omig6kbHk8Aq81LaKgKeSvCLQhCQ5SqAE7sndqRvJFG96Qx+uil/NRoCAQClJaWHnW/kJAQ9Ho95eXl7TnNbsdpIW5+v58HH3yQRx55pInb5mQxm80UFxd3arX/4+Hpp5/mww8/ZGBgFKFCJGfeNh6fx88vS5oWCNYIOkb4JuM1+5kyeQpbt25tJnCCILBp0yYGDhzE/qzPqKrcfVJz8vncZO37hP1ZnxGSMYLe1/8ddVTLXcGPhgw4I8bI/OFJZETo+eBAJe/ur6DMfuwL4LGQa7RoE1LZteuP9xgXF4dOp8dsym/z+B2N2dwwx+uuuy64bfHixchkCsLC+7R5fIfTh8vlx1zvQSYIREaoMegVHfLZF2QytPooFEotTuy4cbUqJ378OLCj0+nol9avRWFzOp3s3buPgCii00chVzQtVOBy+XG6/ISFqlp9PzKZHK0uEj9+XDi6QN7AYXY2CG1o68FWMmSo0VJTU4PdfvRuHomJiVRXV+PxtP370105Ldbcli1bhtPp5Oabb27zWKIosnv3bvr27dsuQtlefPLJJzz88MP0YRBxQhKjLs0gLj2KT+79loCvuTtFJWgY7p/EbtsGpk2dxrfffcvkyZObrcHt3r2LtLQ09u35GJlMedz9wADs9ir27H4ft7uehFlzCM0YdVLvbXCEnrOTGpKFvy6oZW9d+7fhMaYNpqq0gOLi4qA1PnDgAPbsye72625mUz56vaHJWsv69RsIj0hDLm+/BOmAX6Te4kGpkGEwKIiKVGB3+HA6fO16wRcEAa0mAresYR0ugJ+6CjNm8x8NOAMEcAkOVCoVKSkp7Nq1q1nlEafTSUFBAQAaTRiCrKbVYxoNDefJavMSHh5BfHzTFBC5XIVGG4bLaTrUibyTc8VEsNXYCYk14LK5Ef0tn3ElKryCm+LiYtLT01v93Op0OsLCwigrK+uUXpNdQY8XN5fLxaOPPsqCBQuC5XfaQlFRES6Xi7S0tHaYXfuwZcsWrr3mWuKEZHqLA+k9JpFRlw7m8wd/wFnfeqSgUlAxzD+JPa6NnH32OaxcuYJzzz23mcBlZWXRp3cf9u7+gGEjbiA84tgVQ2prD7Bvz4coQsPpffV81JGtL3K3RqhKzuw+0cTpVKwuMbGtytphqx7GfoOoWrOK+++/nw8++ACAc845h23btuFymdG20Km8OyCKIiZTHsOG/uF+zM3NxWq1kJ54Vocc0+sLYDJ7UKlkGPRKtBo5FosXbws3USeNAGp1Q/mygvwsLrvsMtzuzgnLV2s0fPvtr80ETqnU4fe5cXkdyFE0CTDZsn0zby1bwt79+6iuqeK1598IVvMHuP/xe/ni68+bjDdp3GTeWvTOUefywf+W8db7/6W6tpoBaQN5ZsEChg8bgbWq5Rs8AQG1qMVms2E2mwkPb/1zGx8fz759+4iNje1WcQPtRY8Xt9dff52IiAiuuuqqNo/l8XjIzMxk2LBhR+2l1JlUVVUx64IL0fmNDBRHEZkSxtl3TmD1wg3UFBy71bxCUDA0MIG93k1ccMEFLF++nEsuuaSZwO3P3k+fPn3YvfNdho/8K6FhrZcYKy3eyIEDX2Ho3Z+Ei+aeVN7aqGgjM1Mi2Vtn5+OcElz+jl3MV0XEoAwJ59tvvw1uu+GGG1iwYAH15vxuK24ulwmvx8Z5550X3HbnnXeCIFBSshGvx0FUzCD0+hO/uTgWHk+AOo8bnU5BWJgKp9OP3e5tVytOqdTicoHb7SJh1hxUkSefLnI8eGorKV31ASZTXTNxA1BrwvD7PTgDDRGUjRVMHE4n6f0HcumFl3Pbvf/X4tiTx09hwaPPBv8+1s32Nz98zYKXn+aJ+59i2OBhLP3oXa6ccyWbt2xGqdG0WppLgRIFSoqLiwkNDW21nqRGoyEqKorS0tIemdjdPa7QHUR9fT3/+te/eP/999ulYGhjfbbuEkIbCASYM2cO9bX1nOE/E32IlpkPTGPnl/vJ/b34uMeRC3KGBMaRKW7h8sv/zNKl73LNNdc0F7j9++nbty+7drzNiNE3YzQ2LV0WCPg5mPM1pcUbiRg9mdgzLzzhPmshh1lrHx+s5ID5xKMfT4bGBqam7RuCFSzS0tJQqzWYTQXE9RrZKfM4URoTza+//vrgtjVr1qAMCUceF0tB7hrycr9Hq48mJmYw8Qlj0GjC2nUODocPt9tPaIgKtVrd7lacTN5wmVJFxqKN65yKMQp5a619BLTaSOz2Klw40NIQ5DF14lSmTpx61DFVKhXRUccfHfzOh2/z54uv4NILLwPgiQeeYu36tbz736XMv2s+ppLWk7HVaLF7LFRVVREX1/oad3x8PHv27MFms3WrZZb2oEcHlLz44osMGTKkyV3tydIdg0ieffZZVv+0moH+UWgVOs67Zwo1+SY2/+/Egz9kgowMcQy9xCTmzp3L4sWLmwWZREZGsm/fPtRqJTu3/Rf7YWHyPp+L3buWUlqyibhzLyNuxuwTFraR0QbuHJqIzePnlV0lnSZsjRj6ZSAG/Lz88svBbf369cVsym3z2KIoEgj48QcaojoDor9dqsCYzfmo1Vr69m1wFVutVhxOF6GDR5M4+zr63/kkSZfeiKp3KsWlG9n423Ps2f0BZlN+u1ah8ftF6kzuhuCMMFVDwEm7jd75GAyKVqNCZXLFoQomHjzHEdHZyOZtmxh/zhjOvfRsHnvmUUzm1j0rHq+Hffv3MmHMHw1mZTIZE8ZMYPPmzQiyoweXyJGjRE15eXmT0mNHolQqiY2NPWaE5alIj7XcrFYrCxcu5PPPP28XMdq3bx+9e/fuNnc3v/32Gw8//AgppBMpxDHpL6PRGNR8/fTaVgutHgtBEBggjkKGgnnz5mG1WrnnnnuaWXA7d+5gyJCh7Ny2hJFnzEMuV7Fr5zs4XHUkX/E3DKn9T+i4ISo5s3tH00uv4n8Hq8juZFFrRJ/UB0GhZPHixdx1110ATJ8+nX37XsXjtga7lh+O3+/B5TI3tKo5VLnf7TI1/O4243HbEAN+RLHhAqPVarlt3kf89stTOJ1OBEGGIJOjUGrRaMLQqBs6DWgOezR0HtC0+Dk21eWSlvbHGuhDDz0EYgBjv4aSWzKlCmNaBsa0DAKeSzDv3Ypp6zp2bPsPemMvkhInEBM3rN0CTxwOHx63n5AQFWp1+6zFdUXfEq83QFioCpPJ3eLXSanS4fc3rr/Jj5ngPXnCFM6efg6JCUkUlxTx4uv/5qY7b2T525+0WPDYZDbh9/uJjGhaLDsyIoq8gryG4JIYAy6rGzHQ8glSo8Hmr6eqquqo3qbY2Nhg+a7ucn1rD3qsuL355pukp6czbdq0No9VVVVFfX09Z5xxRtsn1g7U1NRw+WV/JkyIpA+DyDgnjbSJKfzvnm/xHqVFxvEgCAL9xWHIUXDvvfditVp54oknmgnc779vZMyYsWzfshiZQoVPdJM651Y0sSeWID84Qs/sPlFkmRy8vKvj19aOhiBXYOgzgLzcrOC2uXPn8uqrr2IyFRASkoDFUkS9uQiLtRin04TPc/jCvoBSH4IiNBxlXDj6kBRC9SEICgWCrEHEtJqGKLte516G0+1G9Df0c/M77HgtZuwWE2ZzKV6rGTHwxx23XKFGrQkjxJhASGgyIaFJKJV6XC4TZ555bXC/Tz75BLlWh6YF951MpSZi5ETCR4zHXpBD3dZ17M/6nIMHvyU+YQwJiWPbxWXpO2TF6Q+txTmcPuzH0WKnRUQRj9vc5jmdKA6HD1EUMRqVWKwt51C2tv7WEjPPuSD4e3q/dNL7pTNj9pkN1tyYCSc8P4/Di8/rRxemabXvmwwZKtRUVFQQExPTatcAhUJBdHQ0FRUVPWrtrUeKm9vt5sUXX+SNN95os9UmiiKZmZmkpaW1S7RlWwkEAlx77VzMtWZG+8+kV3oMk24YxaqnfsZa3T4h8oIg0I/BKEQFTz31FDabjRdeeKGZwK1Z8zOTJ09G9IikXnP7CQmbAJyVGM6EuFA+za0i09RyaaHOxtgvA+uBPWzYsIEJEyZwxhlnIJPJ2b//UwK+BheUKjwabWIK4ZFDUIaEowwJQxkSgdIYinCUrsjQ0E8OIDRjJJpWwrmhoeqIz27DW1+H12LGazHhNddiLiuiInsniAEEmQIEoUl+W2VV9TFbBQmCDEPvdAy90/GYqqnbtp6S3b9TVPgr0dEZJKdMJiS07cUJ7A4fbneA0FAlSoWMeov3xFyhIjidJvz+zq/tKAL1Fi8R4Sp0WgUOZ3NxFgQBjTYCh70KD27UHH/gVFJiMuFh4RSWFLYobuFh4cjlcmrraptsr62rISqyod2QrdZBWLwRR72r1dQAFRpsfstxWW979uzB6XT2mMjJHiluS5cuJSIiglmzZrV5rLKyMtxuN336tD0Ztj148cUX+e67bxnOJPRqAzPumMDm5bspy2z/MlGpwgDkooKXXnoJi8XCm2++2UTgJkyYwJIlS/jrTX+j/PtPSZ1zG3LNsb8YKpnAn/vFEKtTsXhfKVXOrilM2xKGvg15fA899BBr1qwBQK/X4fD6SZo9F218Cgrt0StFtAeCIENpCEFpCIEj7hkCXg/OihKq1n6Fq7yYkSMbgl0++OADxIAfQ99BLYzYMqrwaOJmXEz05POo37uVuq3r2LbldWJih9Kn37lotRFteh8+f4A6k4fQECUR4SrMZg/+VtxoTRAbIkF9PieRkc372HUGgYBIfb2XsDAVbo8ffwsCIpcrUamMuD1WFCiPu/5kRWU55noz0ZEtB5iolCoyBgxm45YNwZSCQCDAxi0buObyBkvd5/LhdfjQh2ux1bR8c9hgvamoqKgINuNt8XgqFVFRUZSXl3eba11b6XHi5vP5eO6553j88cfbHCHZUJ8viwEDBrRq0ncmWVlZPPjAg6TQnyghjnFXD8Ntc7Pzy6xjv/gkSRL6IRcVvP3229jtdt57770mAveXv/yF+vp67rr7HoqWv0nKVfOQqVpPcA1XK7g2PQ6718/re0txtmduVDug0BvRxCayadOm4LYhQ4awYcMGdAm9j0u8OxqZUoU+qQ8BtwuDXhfc/vzzz4MgQ3+Ca54AcrWGiFGTCB8xAfOezVT/+h3VG18kMXE84eFtu0kURRFzvQeDQUlEhJr6es/Ru5CLDR3Zvd6G1jWN32NPbWWb5nE8HHkMry+A0+UnxKjEZG45eESlNmKx1HKwcAcaGj4fJWXFZGVnEhoaRmhIKK8uWcS5Z55LVGQ0xSVFPL/oWVKSUpg8fnJwnOvmXcvZ08/mmj83lH674eq/cN8T9zB44BCGZgxl6Ufv4nQ6uWTWZcHX2OocRCSG4qh3EWjlnB5uvR2tOXNsbCz79u3D7Xb3iIamPU7cPv30U/x+P1deeWWbxyosLEQQhG5RP9Lv93P9ddejQUcfMogbEM3gc/uz/O5vWl1Qbi/ihVTkooL/Lf8Em83GJ5980kTg/vGPf2Aymfjnv/5F0advkfznm5Apmgco9A7RcHVaLLtqbHxTVEsHT/ukMfYfTPVv31NfX09oaCg33XQTGzZswFFagLHv8Vdo6Uj8LifumgrOmDQpuG1fZhb65L5t6ocnyGSEDxtH6MAR1G75hdLf16DcUYXPP7bNc7bZvPh9AUJDVdjsPpwtuPoQwe224PU2tK5JTk7G7Xaj0WgpXfVBm+dwPKg1GsLD/7BY7TYvERFqdDpFi13LBUHg4MEibrjhj1zaBS89DcDsmZfw+P1PcuDgflZ8/TlWq5WY6Bgmjp3Enbf8A9VhN4LFpUVNIijPP2cmdeZaFr75MtW11QzsP4j/Lnw76JaEhrqTLpsbQ4QOS2XL3RsarbfKykpiYmJatd40Gg3h4eFUVFSQktJ6HuupgiB2RlfKTkIURUaMGMHf/vY3/u//Wk6kPF58Ph8//fQTQ4cOPerdTmfx4osvcvdddzOKqUSpY7nypZns+/EgO1ZkdtocasRy9so2MXnKZL5c9SUKhYL169cTFhbGyJEjueOOO3j1tdcw9BlA0iV/abL+NCbGyPkpkXxVWMvWKmunzflkcFaUkP/ui8ybN4/XX38dv9+PQqUicsw0YqddcOwBjoJaLvDYGb15Yks+7qOsuR0La24mxZ/8l6VLlzJ37lwOHDhAevoAYmdcROToKW2a4+H47FbU2dt47h/ziI6JQ6sNRy5v29qzUiEjNLTB1Wc9IljD424ouRUWFka/fv2CtSLr6uowmUwUFBTgdDpRKvUolbqj9jk7WVoqv6VUyAgLU1FncrfongRwu+rxeGzoCenU9jgyhYzI5DBMpfX4WmnfEyCADQvx8b2Oej1zOBzs37+fIUOGHLWZs8vlIj8/n969ewc72Hc3epTl9t1331FRUcENN9zQ5rEKCgrQarXdImE7JyeHBx94kET6EiZEMW7OcFzWjnVHtkSU0IuhgQn8tm49Z884m2+/+7aJBbdw4UIsFgvvvbeM0lUfkHDhNchlMi5IjWRwpIF391dQYO26zsbHiyY2AbnOwOeff87rr7+OXC5Hq1bjKGp7vlt74SjOA5nsUNdwuO+++wAR4wmstx0PCr2RmKl/QqE3gtDQgUCp1KNWhyCcpNvf6wtQZ3ITFqoiPExFfb2XgCji8dhwuy0YjcYmwtbYPNbhcJCSkoJaE4pK1bkh60H3ZIgSk6k192QIPq8Ll+hAh7HT8vwCvgBOiwtduBZLxTGst4qjW286nQ6j0UhlZSWJiZ2TMN9R9Kgk7ldeeYVbb721zdE+fr+f3Nxc+vfv3+UJ24FAgBuuvwFlQE0/BtNrYDQZ56Tx08KNHe6ObIkIIYbh/ons2LqTyZOmUFNT0yTR+9133+XCC2dh2b+Tqh8+48/9YugdouX1PaWnhLDBoWolaYOpqv6j0G7//v1xVhQT8HaPKuqOwoNo1ergWvDPP/+MKjwKVXhUi/vXbfuNnNefIuv5e8lb+jLOssKjjm/Zv5OD/3mGrOfvpejTtxBFEWV4JMqQMLw+B3Z7JX5fQ91Sl9OM1VKKx3P8TU0DARGTyU0gAGHhKvx+J25XPTqdjv79+zcRNplMxt69e3G5XGg0YZ0ubI3YbV5kgoBO17IwNERPhuPHh5fWa7p2BA6zC7VOhVzVusWoQoM/EKC6uvqoY8XGxlJTU3PKd+vuMeKWnZ3N2rVr+dvf/tbmsYqLi1EqlUctW9NZvP7666zfsJ503wjUajVn3TaeTR/twlxm6bI5hQqR9PMPYe++vfTt24/MzMwmArdixQrOPPNM/vanaYT6HCzZV4rZc2o1RjT2G4QY8PPuu+8CcPXVV0MggLO8qGsnxh/Rkunp6UBDzVOLzYYxbXCL+9dn7aDy55VETzqXPjfMRxMTT+Hy/+Czt+wedpTkU7LyfcKGjaHPDXehT+mP32EDvx+5zog6uheCUonDUYPTWYff70EQTtwN1xBu78Hr8RIVaUCv1zNw4EA8Hk9Q2ARBYM+ePQ3rbtoIlKqOj1Q92nwtFi96naLV/m9yhQqlUocbZ6f2fgv4ArisbnRhR2+Jo0RFVVXVUYXLaDSiVCqpq6vriKl2Gj1G3F5//XUuu+wyYmPbVlg1EAhw8OBB0tLSutxqKyoq4t577iWRvoQL0YybMxxnvYtdX+3v0nnZRQsHhN0YjQmAirFjx7Fly5agwG3bto1//vOfJCYm8tDd8ylY802Xzvdk0KekgUzOCy+8AMDtt98OggxHcdf3d3OWFYEY4JprrgEa+vgRCGDo17JLsnbzL4QNG0fY0DGoo+Lodd5lyJRKzLs3t7h/3dZ1GPoMIGrsmaijYok8YzKCXI7P2ZBHKcjkqCKiUeiN+LxOaMP3xOd1UVZWjMfjISEhAZfLFRQ2v9/Pzp078Xq9aLURKJVdH6n6h3uy9XVHtToUEXDRuZV2HGYnGoMamaL1y7oKNV6vF7PZ3Oo+giAQHR1NVVVVu5Zo62x6hLhZrVbefffdhgtQGykrKyMQCJCQcGKVNjqC+f+YDz5ZE3fk6le7xh3ZiEt0sF34DZU2lGEj/8KoM+ahUOiZPv1Mfv75Z8aNG0dFRQVms5m//vWvJCQkULPhR2o2/dxlcz4ZZCo12vhksvZnAw1ls1RKBY6ig108M3AU54IgC37e3333XWQqNbqE3s32Ff0+XBUlTdIDhEPpAo7SgpbHLytAn9q0pZOgUCJ6mwZ/BLwe5DoDouhDFP0EAidmnft8bpzOWpRKJfHx8QiCgNvtRqfT4fV62bN37yHbR8DrdZx0Wbn2psE9SevuSZkMtToEL278tF7Xsb3xewO47R504a3fBMiRo0BJZeXR82IjIyNxu93HbHranekR4vbhhx/Sr18/xowZ06ZxRFEkJyeHtLS0duki0BZWr17NZ59/Rh9fBhq15jB3ZNdFGnpFDzuE30CpZNjIv6BUatFowxl5xjzU6nBmz76E77//HoPBgFKpZM+ePWRlZZGUlETVmq+o276+y+Z+PIiiiKuylKp135H79vM4S/Lx+31s2dLQyTw1NRVHaX6Tslhdgb0oF5VSGayYU1xSiqHPwBaro/gcdhADDQEhh6HQG1t1S/ps1mb7C4KsyftueK2AMiTsUGd1Aa/HjtttOS4R8vs8OB21KBQKBg8ejNfrxe/3o1QqcTgcZGc33FSoIqJRhUfi87lwuUzdQuCOxz2pUhmQyRSd3rnbbnaiMaoRWpkXNDQ0tdttRxUuuVxOZGQkVVXtXxyiszjlxU0URRYvXsy8efPa7EasqqrC7XaTnJzcTrM7ObxeL7f+361EyGOII4kxVw7F0cXuSL/oZyfr8ch9DB91I2pNaPA5tTqEUWNu4Z577ic/P5/c3FwmTZoUXIPLy8sjKiqKih8+w7x3a5e9h9YIeD2Ydv1O3jv/Ju+dF6jb/ivKtBii/noRiCLz5s0D4OKLL0b0+XBVdl0FddHvx1laQO/eqQD88MMPBPw+jGkZnTcHrwef3YoyLAIQQCZHkMuQqTV43FYcjpqj3gD4/V4cjhrkchkZGRn4fL4mrkiLxUJ8fDzaqFhkSjUytRZlaARer6NBPLsBXl8Ap7MhubtFBFBrQvHjw0fnVeDxu/14nV50oa1bbwqUyATZMYUrOjoak8mE19t9KgidCKd8KsCWLVvIzc1tl6TtnJwc+vTp0+XVSBYvXsyBAwc4gzMJiTEw9Px0Prn/uy51Rx5gN1bBzIgRN6PTNy8ZdMH5/QgPV/H4Y09QXp6N2Wzm1ltvZf369ezatYvy8nIiIyMp++ojZEoVIelDu+BdNMVbb6Jux3pMu34n4HKiG5lO3A1nox3SB+FQqHT9NxvYtWsX0BBu/9zzz+MozkPbq2tugFyVJYh+H7NnzwZo6F4gCLhNNXhM1ajCm/5vFDo9CLJmVprP3tw6C77G0NyqE8VAsIVRwOOGQAB3VVnTffx+kMkIiD7s9iq0ushmOXEBvw+nowaZTGDQoEEEAoGgsFmtVnJzcxFkMuISk4gx6Kh2eQmIINfqIRDAYzUjyGRdFjF5OHa7l8hINWq1HHcL+WUKuQa5XI3b70BByFELK7cnDpOT0F5GHGZni9cMAQGlqKauro7ExMRW89m0Wi16vZ7a2tpuEVx3opzy4rZ48WKuvfbaNrdqqK+vx2w2t9m12VZMJhOPPvIovUglRAhnzJVDyf29mNoCc5fNqVIsoZRc+qdf3GJB3fHjoomN0fK/zwrol34VTud73H33PVitVu6///4mAhcdHU3JivdIvvxGDH06v9qHKIo4inOp27oOa85eZBo1xjNHEXLOWJSxzeso6s8YiHnVb7hcLiIiIlDIFdiLcokcM63Jflq5jGitkhCVghCVHKNSgVElx6iUY1QpUMkEZIJAmLrhK/f3YUn4AyI+UcTm9WP1+LF6fYd++rF4fNS6vNR7ml407cV5IAjcc889AGRmZSHTqqndtJqa375HHdOLkPThhA8bi8IQgiBXoIlLxF6QQ0j/IYfOQQB7YQ4RIyfRErr4VOwFOUSe8UfzTdHnQzh0EZRrdciOqILiqatGrtUh1+oRZDI8phoc9hq0ukgUioYqHIGAH4ejGhAZNKjB0mwUNrPZTEFBQUOwSmQMZk+ACI2MCLWSWpeXstISTHV1+B1W/E4HGk0YsnZq03MkLSVxt4QI2O0+DHpFi+KGABpNKHZ7FV48qOicklZelw+/14/GqMZZ33L6jQo1HtFFTU3NUXN5o6OjKSsrIzY2tssD7E6UU1rcbDYby5cvZ/36tq/l5OXlkZiY2OWV/5966insVjtDmEREcihpE1P44I5VXTYfh2gjk23ExAwhPqG58PdPC2HYkHCWf1qA0+lHLlcxdMT17N39IU888QQWi4Wnn36a9evXk5WVRWlpKbFxcRR/9jbJV9yCPrlvC0dtf0RRxLJ/JzUbf8JdVY4yPpqov8zCMHkYMk3rFx3dyHTMK3/llltu4d133yU+vhdmUyX9QtQkGLQk6FXE69VEaJTUe3xYPH8IVK3LS4HFhdXrw+MX8YsioSo5V/WP49PcKvwBUMoEDEo5RpWcEKWCRIM6KIjhagUOX4Ayu5tSu5sym5vNtoZ1qoiICAoKCgiIASIumkLIueNw7s7BvimTmk0/Ub3hB0IGDCdy9GQix0yl7KuP0PZKQtsrmdqtvxDweAgb2vD/LF31IQpjSLD6SsToyRR8+Bq1m9Zi6DeQ2uwSxJGpfxSMlsmbN6IVGqIohUNl11QR0XjMtTgdNWi0ESjkKhz2BmEbOHAggiAEha22tpbi4mIEuQJVZDSCTIEImNw+ojRKbNXlzJo2Cberc/Ik1WoN337363EJnNPlR6dToNXIcbqaC5xMrmxIDfC6UKLqNOvNWe9GF65pVdwEBBSoqKqsIjY2ttUYg/DwcIqLi7HZbBiNLVv63ZVTWtxWrFhB3759GT58eJvG8Xg8lJaWMmVK+5UtOhny8vJYtGgRKYF01IKGcXOGs+/Hg1iruiZiKSD62SNsQqU2kD7okmZ3bjHRGs6a3otvvy9pUrVBJlMweOgcsvZ9wksvvYTZbG7I11u/ntzcXAry80lOSaHok/+QevWtHe7isxfnUbnmS1xlRWiHpRF30/VoB/c9rjtRdVoScr2WrKws9u7dy9NPP43BYKDG7qLc5aPE7mZzlZVSu/u4ikA3trwpsbmPWX5LKROI06lI0KuJ16s5M1HHlfffhcPhYPv27bz55ptoVGp0I9ORaVTox2SgH5OB3+bE+st2LN/9jmXpNrQJKYQMGkHVum/x262oYxJIvuJvQbek12JqEs6vS+xN4oXXUPXrt1T9+jX9hoxArjOA/AQuF4IMVXgU3vo6XM66hoAUUWTAgHTkcnlQ2KqqqigrK0NQKFFHRMNhohkQoc7txW4243a5GJjxZ/T6mOOfw0lgt1eRte9/mEx1xyVuDa/x8d//LmLFilXk5R1Eo9EwYsRo7rr7Ifr06YdKZcTrdWBzW3nx5Rf45sev8Xg8TBo3mcfue6JJrcgjEUWRhW++wicrlmOxWRg5dBSP3/8kqcmpR52Ty+ZGH6lDqVXibaXrhgo1dp8Fs9lMRETL3R9kMhnh4eHU1dVJ4taZvP/++8HyQ22hqKiIsLAwQkJC2mFWJ89TTz2FEhXJpBE3IJrEIXEsm7eiy+aTwx5sWBg1dB4KRVM3lE4n54KZiWzaUk1BYXPxlcnkDBr8Z+QKNe+88w4Wi4X3338/aGVnZWaSPnAghR8tJvWa29DEtH/9To+phoo1X2I7sBdV73h6PfIXtBnH185Dg4wBMgMZyhDS//sWPrcHr9dLRkYGkyZNwjjpT0SMOPEmkyeCNyBSbHNTbDtUCaS6nJJlrzB//nxGjBjBsGHDWLZsGQdlLvYFrOzzW6jHh9ygJWzmREL/NB7H9mws326kfs8WFIYQoiacTfiICSh0f7jxU+fc2uzYIQOGEzJgOAC9jNoWC2Efjjq6pf+fgDIkHL/LhSgGSEtLQ6lUBoWtrKyMqqoqZEoVqohoaKEHnS8AlkMFAPT6GIwhXZ+icyQut5/16zdwww030j99CH6/j5defIa/3ngVX339CzqdDqVSx1PPPMqG3zbw8oJFGA1Gnnr+cW679//4+K3/tTr2kvf+w7LlS3nm8edIjE/ilcUvcePtN/DN/747euV+EVwWF7pQDfWtiFtDWoCCmpqaVsUNGtICcnJySEpK6vIo8hPhlBW3yspKVq9ezZIlS9o0jiiKFBQUMGDAgHaa2cmRm5vLe+8to19gMHJBwYRrR7BzZRbO+s4t49NIlVhKMQdJ6z+r2QVFLhO44PxESkocbN/RehUDQZCRPuBiFHI1n332GVarlZUrVwYFbvvWrYwYNYrCj94g9drbUUe0z115wOuh5vefqd30M/IQPTG3XY5+wpBj1kIUgH4yPePk4QyRhVAtetgbsLBo3yY2/2sx77zzDtdddx1Olxt5UW6Hi9uROIrz8Hg8zJs3j/j4eAYPG0r/S85l4p8vZLg8lNmKXuSLDn731bE7YMEnk6EfPRD96IF4iiup//53an5ZTe3mtUSNO4uI0ZORKTvQDS8G8NRVgxggMTERjUYTFLbi4mJqa2uRqdQNJcOO0lzV013bRxzGW299SGioippaN6IosuCZl5kwfgj79u3mjDPG4faIrFy5kmf/+RzjzxgPwNOPPsv5l5/Lzj07GD5kRLMxRVHkvY/eZd5fbmXG1Iaebs898W8mnDuWn375sUl375ZwWtxEJmuRKWQEWvEqKFBhsVjweDytLsno9XrkcjkWS0NB61OFU0eGj+Djjz9m4sSJbW5HU1NTg9fr7fICyU899RRqmZoE+pA6OoHwhBB2dHJh5Eacop1MthIdnUFC4vhmz585PQ4BgdVryo85liAI9E37E6l9zuKHH35g+vTpTJgwAbPZjM/n49e1awm4XRR++Dqe+raX+7HmZpH79vPU/L6a0JkTSHzxTgyThh1V2DTImCKP5AFVGtcokzCLXp73HOR5z0G+9VVRkRqBCDz++OMAREdFYi/M6fTqDY7iXGRyBcnJyTz22GPgD2DpG80v/lpe9+TzuHs/+/wWzlZE87g6nVmKOMKFQ2tgSbFE//Uikl+7G8P04VSt+5aDS57BvGcLotgBNQRFEY+phoDXQ69evQgPDw8KW0FBQYOwqTUN0Z1HEbZTBY83gNcbQK9rcKtarQ0pC6GhYUCDp8Ln8zFyzMhgWa6+qX2Jj4tn554dLY5ZUlpMdW01Ew7r1G00GBmWMYwdu1t+zeEEfAE8Ti8aY+sWnpIGQTtaqS1BEIiMjKS2trbVfbojp+yn6oMPPgiWH2oLBQUFJCcnd2n4/8GDB1m27H2SfGko5ArGXzOcLZ/uadVX3pEExAB7hE0o1HoGZFzWbF1qxLAIkpP1fPVtSautP45EEAR695lB37Tz+f333xk7dizjxo3DbDaj1WpZ9eVK/A4bhR++js92cnlMAZ+P8h8/p/iTJSjiQ0l87jYirjoHmaZ1yyQMJbMVvXhMnc4weQjf+Cp50p3NKl8l1eIfa4hygxZ1/2QKixvqSk6ePBm/w4a3HcT4eBFFEXvhQWKiG9Zn/vOf/yAoFWgG/VGVxI6fX/y1POv5f/bOOzyKcm3jv5nZvkl20ztJKKH3ItJB7N2jogJ2xXr0HD2KXcSux97g2BV7RREVBRSl916SENJJ3U227+zM98eSQEwh2V3K+a5z58o/uzPvvjs7897v0+4nj7d8RcQKWu7R9eBKbSaZQrDuSYoxk3DlWWT+++/oe6VTtuBjCj98GXdFSSRni89WjeLzkpSUREJCQhOx5efnY7PZkAymAxbbf1cGXntwOGWMRg2g8vjjDzFkyHByc4MeoarqSrRaHVHR5maiyvFxCVTVVLc6XuPr8X+JycXHJ1Bd0774cSPc9R6MMW2Tm4CAFt1hiSsuLq5pQ/rfgv9Kt+Tu3bvZtGkTF1544eEPbgder5fy8nImTZoUoZmFhkcffTRotSldyR2XjdagZeuPe47JXPLYQgM2hgxsGWfLSDcx8oREvvpmH05n52/yLlljkUQdW7Z8Q0ZGBnl5eWzcuJGUlBQ+/ugjLrnsMgo/fo3sabcezMzrAHx11ZR8+z6e6nLirzqLmFNOaDdZxITESZoERkvxbFMaeM1XSLHavg6geVhvvLuLyMvLY+bMmXzxxRe4igvQWeOBYPNQv70Wf70Nf31d8/8GO6rsR1UCGHQ6eP99dr/6CB6fF0HSoDFHo4mORRcTizYmFq0lFm2MFa0lDskUFA/222sJuByMP/tMAKrrajENykXUtR4L26u62Ot3YUXLWE08N+ly2KE0sPAAcWtTE0j+56W4t++l+q3v2Pve88QOHk3SuNPD7Dau4rfVong9xMfHk5yc3ERsu3fvxuVyIRnNaC2xHJFmbMcQsqzg8yk89th97Nmzk48++qbFMQczJ/VHJXPS5/RDAuhMWnyu1jfLGnS43Q7cbnebHVWMRiNGoxGbzUZCQtsJMMcT/ivJbd68eZx11llh+3/LysqIjY0Nu0YuHDRabd2Vfmi1Wk64dCCrPtrUpo/8SKJKLaOIPXTvcRYxMX9p1qgVOfmkVP5cUcn+ytBTsj3eYKdhm91OZmYmBQUFbNu2je7du/PaK69w0823UPTJG2RddnOHOkrX79xI2cLPEC0m0mfPQJ/TdmKKFoFxUjyTNInsU1y87CugVO3YdzENyaV23o9ce+21LF26FESRuo0rcO7bg7t8H75Dd9KShCbOgibRgqZbHPr4bASdFkEjYTzwnSznjkHn9aD6ZQK2BuRqOw3lu5C32lC9h1qN0RhTs5qktWbOnMmvv/4KAQXTkJ6HnbcNP9/JFfwmV3OqJok7dd1ZG7Dxk1xJPTLGPjlkPHUz9h9XUPf5Yhr2bCHjnOmYMjuWeNMcKn57HQGPC6vVSlpaWhOxbd26FVmWkUxmtDH//4itEf/6179YvPhnPpz3NSkpB+/FxIQk/H4fHq+CJCpNdW81tdUktpEt2fh6TU01SQkH49E1NdX0yu143z6Pw4chWt8OuWkQBIGampp2e7g1uib/R25HCKqqMm/ePJ5++umwxyouLg47ZhcugrE2A+lKV/qflovP5Wf3ssKjPg9Z9bNT2EB8XE8yMlsmSowZlYS93s/mLXUhf0bxvj8oKvyN5EnnoLMmUPzNu3TJymb3rp3k5+czYsQIHn/sUe697z6KPptL1iU3tJnwoMh+9i/+lrr1yzGP7Efi9echmlonQwE4QYrlVE0SNtXPO/4i8pTOlVdo0xLRJFhYuXIlAEa9HndpIYrej2FYNyzdxqFNTUCTYEWymNuM8ekPRAIsp5+IgZYbGFVVUZxu5CobclUd3oIyPHuK8ezaB6LIoEGD6N8/WIxtGnx4cmtEPTKfy2UsDVRzuiaZe/S5/BGoYZFchU8D1rPGEDWyH5Uvf07hx6+RNPY04kdOQuhwPExFrrcRcDuJjo4mMzMTt9uN2Wxmy5YtBAIBEARU2Q+q+v/KHQnB32327Pv4ZdGPfPnVN3Tv1g17/cFNSt9+A9BqtaxatZJxY0fik70UF5ZSVlHWajIJQEZ6JonxiaxYs5zePYNk5nA0sGnbJi69sONZ4p4GL7EZFgRRaEexJOiaTE9Pb9PrERsbS0lJCT7f8dHT8HD4ryO3bdu2UV5ezumnnx7WOE6nE5vNxsiRIyM0s86jvLycjz76iBy5D1qtliEX9GXJ66uOiczWXnbgx09ur3Nb3NyZGSZ69Yxh3seht3upKN9A3p4FxI+c1KTu0eWi6yj+4i169Mhl06aNlJeXc8opp2Cz2Xj6mWco/vJtMi+8FvEvXYO9tVWUfPsevppKEq45h+jJw9t8IOMFHZdo07Gi5Wt/OZuV0GJ6giBgGtaH+kWrkGWZHj16sHnzZpLvmo7GGjnLXxAEpCgTUpQJfU4a5hFBJY+iW/+NVO8CYMeOHei6pKCJ63zpSpXq431/MRmCgXO1qfxL151P5VLyFCeaBCupD15N3eeLqfx2Ic6ifNLPugyiD++mlB31yC4HJpOJ7OzsZsSmKAqauBgEgx5/eTU+W82BpqqdIzin88iL+Ib6GY/Mupfvv/+aV197B1E0YrNVYbP5MJqiMBiMREfH8Le/XcpTTz5MdNQzaDQyzz3zHIP7D25GbqddeAp33HwnJ088BUEQuPzSK3n97dfIyswmIz1YCpCUkNyUPdkRBHwBAv4AerMOT0Pr2dcadLj8De0Wa+t0OsxmMzab7ZiXTXUE/3XkNn/+fE4++eSwu22XlJSQnJx8TBVJ5syZA4pAGtn0GJ2Fz+WncG0kA/sdg0O1U8QesruejMEY2+w9rVZk8qRU/lheSX1DaAkuNlshO7d/gbX/cJLGn9n0elROT7IuuYF9n82l/4CBrFyxHJvNxsUXX4zdbmfO3LmUfvs+Gedf0aSI4SzKp/irt5AsZtIenYE+u/UsVwEYLcVxpiaZNQEb/5H34WvFUuoMTEN6Uv/jCmbOnMkdd9zBFVdcgWfXPqJOOLKixbKtAbmqjr4DB2Kz2QioCtHDw5MuK1E9vObby2gpjqu1XVgbsPG9vB+fBHGXnIyhdzaVr3xBwTv/JuGKm4HWrQsA2VmP7KjHYDDQrVs33G43JpOJzZs3o6oqmgQLUkxwA6BNjsNfUYPfXovWckB4+TCIjYtDbzCwY1vb9WCRhF5vIDa27bqv1vDxx+8BcPn0vzV7/fEnnueCC6YAcM+9DyOKAv/4x014vR5GjRzFI3c/1uz4vfsKaHAc1PW87vLrcbvdPPj4/dQ76hk6cBhvvvR2+zVurcDb4EUf3Ta5SWgQBYmampp2i7WtVit2u/2/gtwE9b+sG92JJ57Iddddx9VXXx3yGKqqsnjxYnr16nXM+rZ5vV4y0jMx1ETTSxjCxc+czo7F+WxZuPuozkNVVdbxO16DwohRtyOKzfc7kyakYLXo+Orb0DpQ+31OVq9+GU18HFlTb2op2wS4K4rZ9/HrqH4/vyz6GVVVsVqtPPvss3zyySdY+g4l7axLcezZTsn899H37ELKHZe16YaME7Rcos0gFm2TVRIJqH6ZwmseIzYqmpqaGgSNRMzJJ5Bw5ZmHP/kA9Ig8YejDPZ7teDtIto6VW6l84RPmzZvH119/zRdffEHaYzdg6NYxBY3D4VDr9hO5lPwD10uurafy5c9JdqvMfftdUnK6ImqabwYDLgf++jp0Oh09e/ZsSkrYsnULqKBJjEWKNjU/x+FCrqxDE21BY+7YIll+QFvyrzBrRfSiSJ1XPpBgH0xoQQWjITak0F5HtSXbg0YSiY09WPf2V/h9TjweG1FYEI9C0rqoEYnPslJTWIfSRpazFzd+0cegQYPaLNb2eDxs27aNXr16UVRURE5ODgbD4WPjxwL/VZZbRUUFa9as4ZtvvglrHJvNhsfjOaZK159//jnVNVWMZDApvRKxpEazc0nBUZ/HfoqxUcXA3le1ILYumWZyc0N3R6qqwvbtnxPAT9a501slNgBjSibZ0/7Ovo9fY/LJJ/P1V18BcOedd2K321m4cCG++jrcJXsxj+hD0i0XIWhbv3WHiVb+pk1lbcDGmxGw1g6FoNVgHNiD2vXB1kMaQcSz7cj/Zp6dhSCJXHbZZVx//fWIB1yWkUKN6uM1317GSHFcq+3CikAd38sVaOJiSH3gKqKWbEXxuvHX29DHJjbFywJuJ/76OjQaDbm5ubjdbvR6PVu2bAFAkxyHZG7pYZGiTKg+GdlmD7a00R3eCklNzyA1vSXhCECiUYsnoFB/QGRa8Xvx1VRiMFjR6jqedRtJyAEFWVYwGiVcrpaZxRqtCTx2fHgxcOQ7jCuygt8to49qW0xZgw6v4qGhoQGLxdLqMQaDAb1ej8PhOJLTjQj+q+rcFixYwPDhw0lOTg5rnJKSElJTU49ZbZuqqjz37+dIEFOIEmIYeFYvtv+Sh99zdGtIAqrMHmELiYl9iYvPbfae7oA78s8/K2kI0R1ZXPQHtdW7SDt7KtoYa7vHGhJTyJl+K5I5mvMuuKCpm/fs2bMZM2YM7tK96DKTSLptSqvEJgLnaFI4T5vCu/5ivpTLI0psjTAN6QkBhR9//JHs7Gx8xftRXEdW0Ne9tQBto1vW7cI4sPth1VY6CxVYFqjl3758eopRXKfNxoSEIIpYTjsRMcaM6vfhqwsqjigeF357LZIk0atXLzweD1qtlm3btoEA2pT4VomtEZrYaASDDr+tBsJo/toosBylldCKQdIVtXokoxmP146qHP2s40a43AFMxtbXGEEQ0OmCNW/qUWpn6nF42y3oFpEQBQmbzdbuOBaLhYaGY9c0uaP4ryK37777jnPOOSesMVRVpaysrN2U1yONVatWsWHjBtKVrkTFm+g6IoPNP+w66vPYx258+OiWe0aL98aMTqLO5mPLNltIY9tt+yjI+4n4EyYS3a1j8SFdbCI50/+ONiaWaZdfzs6dO7HZbLzwwgukp6bhK9qP7evfWpxnQORabRa9xChe8BWwSzlyu0rT4OAm4J///CfXXHMNAJ5doblsO4KA042/pJLs7GwefPBBUFVcK7ZQ/tjb1P+8ioDDFdHPq1Z9vOQrwI/CbbquJAnBxVDUadEkWlFkH97q/fhsNYiiSO/evfF4PIiiyI4dO0AQ0KYktOkyboIgoE2KQ0U9oEwT+gLvV4Jtg2L1miYvpDbaAgjHtLmp1xtABfT61glOqzOjouLn6GQfeh0+NHoJUdP6si8AGlWDrc7WrvqO1WqloaHhqCv0dBb/NeTmdrv5+eefOfvss8Max263I8sy8fHxEZpZ5/Hiiy8SpYkhgVT6ndaDfevLjrryv1f1sI/dZGSOwmhsHjyPj9PTu5elQ/JarcHvd7Ft2ycYUruQNK4lcbYHbUwsOdP/jj4ukVtu/TurV6/GZrPx7bffEh8fT93nv2L/YXnT8YmCjtt13Qig8qKvgGr1yC4UGms0uuxUdu/ezZ133gmSGHQbHiF4DxDnjBkzeOmllxBEgZPu7EeSsYGa976n+KanqZr7Db6iish9Jgrv+IvYoNi5TdeVbmIwZibqtGjiLKgBGRCaiA2CwgqIAtq0BERjx5IdBI2ENikWxetp0Ry1s2jwBRAEMGkPLGmihDYqBr/fiRI4dqoa7nasN1HUIEn6o0ZuqqLid/vRm9tOotOgwy/7cbna3jQ11gUf7yUB/zXktnjxYlJSUujbN7zMtIqKCpKSko6ZS7K6upovvviCVDkbURLpPakbW38++mokBWxHkDRk5Uxs8d6oExPZss1GfX3n3ZGqqrJj+xfIipeMc6c3FR93BhpzNNlTg50C7r3vPhYtWoTNZmPhwoXB/l/v/0D94rX0EM3cruvGZqWet/1FHU7QCBfm4b0JqEpQRgoB9/bQSyQOB/eBeNttt91GfUM9XYYlMGJqd6b+Zwx/X3Q6Y67rQWDzNkrueoXy2W/hXLM9Iq44FfhRruRzfynnalIwCCKKz49cYwegd+9eeL1eFEUhLy8PRBFtWiKivnPZx6LJgGSNQm6wBzt8hzHfel+AaK2mqYROMkUhSBq8XnvI44YLjzuAVisiSa1ntmi1JgLIKEfp3vU6/ejMLVVtVFRk/MgHiLa9mJogCERHR7dLgMcD/mvIbcGCBZx11llhd4OtqKg4pokkn332GUpAIZUssoelE5AVijeGZiGFCodaTyl7yeo6Ca22eVwkLdVIRrqJNWtb17s7HEqK/qSmagdpZ112QGIpNEhGM1mX3YQpPYennn6aL7/8kvr6ehYvXozBYCBnQylXiRl85S/nB3n/UYpaBGEa0gsUlWuvvZaUlBS8+aUoviOjA+rZvhcJgTVr1qCqKj3GH7x3zfF6xlzfi1t/PIVznxxOrGRn/78/ouT256hfsi4iJLdRqecjfyl6RMw+BVSVnj174vf78fv9FBQUgCSiS09sUwrscNDExgTjb/bw4m9uWUFRVaI0BzZUgoAm2oIsewjIx6a7hqKqeL0BjIbWE6A0WiMgHD3XpMuHzqBFEAUUFHz4cOPEgR0XDlSNQkJCQrstcCBovbnd7UvWHWv815DbkiVLOOmkk8Iaw+12U19fH3ZCSjh49933iBdS0Al6+p7cnR2/5h/1ou08tmLQW8jIbKn4P/rEJNZvqMXt7vwi01BfRn7ej8QNH090j/BrvyS9gS5Trsecncvrr7/R1Bfuxx9/5I477uCFfz/H76tXhP05nYUuOxUxxsyiRYuC+qaKgjcv8vWJis+Pt6CM1NRUrrjiClChx7iWGzNJK9L39Ayu/GA8V300gW4DzVTP+Zqyma/g2hS+V6B4fzC5J8ocRY8ePQgEAng8Hvbt24egkdClJ7aZvdohCALapFhUNfz4m90nE62TEButN4MRUavD47WHM2xYcLsDGAxtJ5ZotAb8+I749FQ4sCmRUU0BHNjx4ERr0pCalkqfPn0YMHAA2dnZaLXtb1TMZjN+v7/dbgLHGv8V5FZRUcHu3bvD7pRdUVFBXFzcMSvczsvLY82a1SQrmZjjTWQOTGXHr/lHdQ5OtZ5qysjuNrlF6n9OdhRWq471Gzt/w6qqwq5d36CLTyR5Qsfrvg4HUaujy4XXEJ3bn3nz5vHJJ59gs9lITk5m7dq17H/+Y1xb8iL2eR2BIAiYh/XC5fXw6KOPgijg2VEY8c/x7ikGRWHKlCnk5eURm2kmNrN9NZTUvrFc8OwIrvhgPAlxASqeeI/9z36IvzI02TR/VR1Vr39BwC8jSRKqquL3+ykpKUHQatCmJSJowq8oEjSapvhbwBV6/NkbUPEFFKK1jWQioIm2ogT8+OVjY2n4/AqqqradWKI1oRBAIfKxwUZ3oxsXTsGOk3qcLgcx1miys7MZOHAgffr0IS0tDZPJ1GHPmFarRavVsnbt2ojPOVL4r6hz++233xg4cCCxsaG7ueDYuyQ//PBDdJKOxEAafU7qRvGmchw1R9dvXUw+Wo2Z5JSBzV4XhGCsbfXaavz+zruzysvW0WAvJmvqLQhSZG8rQdKQNPFMcmONTJ48mXnz5jFjxgyWLF7MhEkT2f/0h6TefxWGnlkR/dz2YBrSi4bF63jllVcQEHDv2EssLeOX4cCzsxBEkXvvvZfnX3iO3Ekd7zmYPiCOy98dy46fS1n07FZK73wR698mYjl7bIfLCOS6BsofeZv0KGuTWK7D4cBgMBCXmIDDpEWQIrc/Fk0GxGgTfoc92JngkLrItoq4W4NWFLDqNdR6/DTWK/vrayGQj9FoPez5kSji/is8ngBGg4TX29IjopEMCIKIX/UhRWBJVlAOxM/8BJBRUdHpdCRYE7BarRiNRlwuFzExMWGFeQwGA4sXLw47g/1I4b+C3JYuXcqECRPCGkOWZaqrq5tEZ482VFXlvXffIyGQhiRI9JrYlT/eWXdU5yCrfsrZR2bmuBZWW89cC1qtyNattk6P6/c5yc//EUu/YZhDUpNvH7KzAdOGJdz5z38w99fvWfz552zYsIE33niDRT/9zORTTqH88fdIe/jaiBY3twdjv64gibz44ovEx8VRs6sINRAIKYGmLbi3FyIA11xzDUpApfu4zjXUFQSBPqdm0G1MMn+8sZNVHyzCvSWfxFsuPqweZqDBRfnst5Br7Dz+wiuIokhDQwN1dXXoo8ykJScjqDIOQo+RtQZNXAw+pwd/g/2APFeQ2M4ePwav98jWEzZCrzOw8KffI0pwbk8As1mDKAoofw1DCEHrze9zosfY6VY4KhBADpKZIBNQgxZgVFQUVmsyFosFg8HQRGSNKfyyLB/W/dgeDAYDq1evDvn8I43/GnJ76qmnwhqjpqYGg8FwzNrbrFy5ksJ9hQxhHPFZVkxWI0Ubyo7qHMooREEhLeOEZq9LosCJJySwfGUVgRDif/n5P6OgkDwxvDKN1qD4vDh/+YJ7Z97Jd85i9k7qibVyHHnf/s7VV1/N22+/zcIFCzj9zDMpn/02aY9cjy4j6fADhwnRoMfYtysVW/KZetllzJs3D29hecQksVQ5gHd3EQnx8SxcuBCdSUPGwM7pHTZCb9Zy0h396TommW9mrqNs5ssk3nIxxn7dWj1ecXkof+wd/BW1vPTCC8TFxeFwOFAUBdGoR02IoVr1kSjo8atqRLNUBUlCExeDXG1DMpoRdXrqamvxej30ZQRm2tY9jAScNLDNt5q6utpOkducOS+z6OcfKCjIw2AwMHjwMO648z66du0OgKKonHXWWSxfvrzZeVOmTGfWI0+h0Zrw+RwEkNFwkHBUVeWlOS/y+TefUu+oZ8iAoTw88xGyumQ1WWeyIKOqCpIkYbFYsFqtxMTEoGnDXSwIAhqNJmxy0+v15OXlUVlZSVLSkX/mOovjntzKy8vZvXs3Y8eODWuc6urqY9qH6MMPP8SkiSJWTiR7eAZFG8sIhOD+CxWqqlIs5JOU1B+9vrmeX9++Vnw+hV27O1/w6mgop7x0DcmTz0VjjuzCo6oq1Yu/5d7rr2aL2sBKsxcBgbhLT0Ew6Cj69BemTZvGhx9+yPxvvuGc886j7JG3SH/kerQpR66O0Ve8H8eKLcgl+xElOP/885n30Ud4du6LGLl5C8tR/TKnn346H8z7gF7j05AO1HCt/aSAVe/twVHtITnXwikzB5DWv23i2/FzKb+9uh17mQtruglJK1L+2DtYL5iI9dxx1H2+GNfG3ciVtUHyCigoDhdPPvEkXbt25dNPP2XKlCmY46zB6yoI+FCpU/3ECzoqVS9yBNMhpGgTgQYn/oY69PEHk7/MRBMjhBeaOCxC/BprVq/gsqlX0r//IAIBmeefe5Jrr7mU7xf8hskUrBNUFJg2bTozbrij6bxGAXhJ1CIIErLqb0Zu/3l/Lh98+h5PPvw0aWlpvPjGC1x96xV8+tmn6PV6DAYD8dYkrFYrZrO5w25GjUYTdp2aJEn06NGD33//PezG0UcCx31CSaTibceS3BRF4fPPPidBTkUQBHKGZ1C45uiq/1dTjlt1kN6lea82QYDBA+NYs679NvNtIW/PD+hiE4gbPDoS02wG2+bVXHXyWGSrifmG5rVKsedPIP6KM6moqODSSy9Fr9fzxWefgctL2SNvNdVjRQpqIIBz9TbKH3mTkn+9jGvRcroPi0GRVe6++24EQcDTRr2bqijItfVBNfzSYFNTf3kNcrUNVW49icCzoxAEgb59+6IGDpYAbP+xhF+f3cKYGb24+pOJJPW08MmNy3HWtJ7qXrKxhm9mrmHQ+Vlc8+lEek1Op7qggaEX52D/eikVT7yLN7+E2AsmkDZ7Bpo4C0qDi9SUVPr168fzzz/PkiVLEPRatPHWZn3YXARwIpMg6CLbelQQ0MRbUf1+Au7ju5aqEW++9REXXDCFHj160qtXX5548gXKykrZtm1z0zGKomI2m0hKSiYxMYnExCSiog5sCAUOZE36m/hVURXe+/gdrrv6OkZOOIGMHmk8/MhDVFVXsXXrVvr370+/fv3IyMggKiqqU/EzjUZDIBAIW2VkxIgRwea9xyGOe8stEvE2v9+P3W4/ZuS2Zs0aqqqrGEofTFYDSd3iWLCu9KjOoZh8oqPTiYlp3pw1OysKjUYgL7/zVltNzW7qavPIuOCqiMaaADxV5YyJgdz+fXlJV06glS215fQTEQw6quZ+zZRLLuGTjz9m3gcfMHX69CYLTrKE54YOOFw0LF5Hw88r8VfbyRicwPBnR5A7IRVJK7J/9yIKCgqIjo6mYfteXJt2480rxV9RQ6C6jkCNDX9NA2ogaKUbjUb4eDyl974WrBMSQGuNQkqwIMXHokmKRd8tA9em3QiCwLPPPgsCdB0dtGBWf5DHoAuyGXheMHnm9PsHkfd7BZu+KWTUNS2bl66Zl0+3UUmMvDIoGzb+lj7sXVmJElC5dM5ovrxjNVit6HtkUPPuD3j3ljF16lTmzZvHQw89xLp16+hzyVlBZf9W1k67KqMVROIFXUTVYUSDDtFsRG6wg3rs9CFDRUND8HmyWKxNr6nAF198wWeffU5CQiITJp7MTTfdjtEYtOw0GiN+nxMfbhQUCkv3Ul1TzYhRI4hPiMNisRAdHc3IkSPZvn17p9veHApJkhAEIWzX5IgRI7j33ntDPv9I4rgntz/++IPZs2eHNUZtbS0mkynsHnChYv78+Rg0RixyPNnDMti/uxq3/egVlTrUemrZT+8uF7fY3Q0aGMfmLXV0tt5XVRXy9yzElJ5DdI9+EZxtMM6WWLCeC2Zcx0v+vTjbSVqImTgU0aCj8uXPmHLJJXz80Ue8+/bbXHn11ZTNfpu0h69Diur87674/NQvXIH9299Q/X76np7B8MuGkNLb2uy4nhNTWfV+HkOGDOG3Zb9R8cT76KJ1JHaLxpJtxHJiMjGp2cQkG9GZNOg0Qc3Fi18dhdfjpqHSjb3CTX2ZC1vFfmpWFmCfvwwIJgRUVVeR1j8WU6yegF+hfIeNE685KHItiAI5IxMp3dx6JmHp5lpGTO/e7LWuo5LZvaSM0x8YzBXvjeWjGcspu+tlAi4ft9xyS1NG8bp167CcOZrYCyfRXu+YGtVHsqDHImiwq5FLZ9fExeAr3k/gOC8W/isUReHxxx9iyJDh5Ob2anr9rLPOp1vXLqSmprJ+/RaeffYxCvfm8+KLbyDLHvz+4Pf04gm6Mg9c8rFjx5KWdjBRKjk5mYqK8OXWIhF3GzZsGNu2baOuri5s71qkcVyTm9PpZMeOHQwfPjyscaqrq4+pluSinxeBLFBBMWcOH8/etUfXaishD53WTFJy80zR+Dg9aalGfvyp8/PZX7EJp6OC7AtuC1s15q+w/76Au266mi/qC9gfc3jWjTqxP6JeR8W/53HJpZfy0bx5zHn9dWbceAMVT7xL6v1Xd1jvUFVVXGu2U/v+D8h19Qy5KIfR1/ckKr51IeDu41JY8c4e/H4/qqIy9sbejLm+J4LY+jURFS3sg8zB8Shi66ome1dV8vH1f9K1a1e2bN1M7oRglqSrzosaUDHHN/8u5ngDNXtbl0tyVHtaOV6Pozq4uYrPiSZnZCJb5hdx5ZVXMmnSJKZOnQpA7EWTsF4w8bC/r0pQcDlZ0OMmELFyZEGrQbJEEfAcXd3VcPHIrHvZs2cnH330TbPXp0yZhlYrYonRkZySicVi4vrrr2bXrvVkZGSg1WqJi0sgLS0NnU7XpM4f6eerEZGIu8XFxZGVlcX69evDFtmINI7rmNumTZuIj48Pu6HosU4mue/+++g7pDd5uk1kDEzhh7Vfs1fdQYPavvp2JOBXfZRRRFrGyBbp//37Wdm9px63p3Pp3KqqUlz0B+acnpjSIltbZtuyhotHD6Uk4GJDB4itEaYhPUm95wrsjgYunTqVlJQUXn7xJfz7Kqh4+oMOyWPJ1Tb2P/Ue+5/7mC69jVz/1Umces/ANokNgvVk+igNa9asQRAFavY2tElsHUVtoQOEYENbVaHTJQAdhaqqLHlhG1vmF3HRRRdx3nnnccUVV+B2u4mdMpnYv03q8MIqo1KvysRFOP4mWY9sdmSk8cgj97J06SLef+8LUlIOWluKEsDvc1JvrwIUVMVJ7949gKBy0uDBgxk4cCDZ2dlNIhONFvT+/fubfcb+/fsjUq8bqbjbkCFDWLfu6JY1dQTHNbmtX7+eoUOHhrVzOdbxNoBzzz2XtevWsnHjRjQaDT2GdKPcuJdV/MIKzU/sUNdTrZYTUCNbMwTB9H9VUEjLGNHsdY1GoFdPS0gtbez2fTgayogbFp5izF/ht9eRXlvIiBNH8nV059vWGPt1I/XBa6h3O5l2+eVkZGTw72eexbe7mP3Pfdxm8gaAc91OSme+glhexoUvjmTKKycSn334hVXUiHQfl4I/4EcSJPatqQp7sShaW40gCuTl5RGVaCCxe3Aeplg9giS0SB5x1ngwJ7RumUYlGFo53ktUgp4/5+5i5bt7OP/887nsssuYNn0aDQ0NxE07ndjzJ3R63g3IKKjECJFzCAmSiGg+Pjs9HwpVVXnkkXv5ZdGPvPve52RkdCEg+/B66nE69uN0VODx2FDVoCbnoS23+vbt26qQe05ODikpKfz6669Nr9XX17Nq1SpOPLGldF5ncWjcLRwMHTqU9evXhz2fSOO4Jrd169YxdOjQsMaw2WwYDIZjFm87FF6vlx49evDVV19RW1fLokWLuO7ma9BkBtjInywTv2eTsJwSNR+PGn6WmKqqlAp7SUzq1yL9v0f3GBocfioqOh/PKClejs6aQFTXlgkM4aBh+U/cNGMG38gV2EOUIjL0yCTt4eto8Hm4/MorycrK4sknnsC7JZ/Kl79oISasyjI1Hyxk/zMfkjM0lus+n9jkBuwouo9LRQ2oxMXF4azxYi8N/bdTVZV9a6tRAyqqoJA7KbVpcydpRVJ7WylcVXXweEWlcFUV6QNaLwVIHxDX7HiAvSsr0Ro1/P7aDs455xymT5/O9OnTcTQ4SLjmHKxnhZ75Wqv6iUJDJO23xqanThqoV+uO6L+T0FrvPDLrXr6b/xVPPfUcGkmmsHAzRUU7qK+vQquVcLvdfPfddyiK0qSPesUVVzBu3DgGDBjQNE6vXr34+uuvgaA78vbbb+fRRx9l/vz5bNmyhcsvv5y0tDTOO++8sK8rHLTewsHQoUOPS8vtuI65rVu3LmxpF5vN1mbL9KMJVVWpqKhoImu9Xs/kyZOZPHkyzz//PLt27eL7779n/rfzWb58OTuVDVikOGLlJBJJJYa4TluwTupxqQ10Sx3c4r1+fa1sDcFq83jsVFVuI+WkcxGEyO2NGvJ3cMnEURTLTtYZwoux6LNTSXvkesofeYurrrmGN+fO5ZFZs3jwwQepmvMNiTPOQxDFoG7ii5/gKyxn8r/6M3xqt5C8BF1HJyGINKmkF62vxpphDmnutlIXrlovVqsVm83WQih5xPTufPfAOlL7WknrF8vqD/PxuwMMOJA9Of++tUQnGZl4W1C4evjUbnx4zTJWvbeHbuNS2P5jCWVb6lAPFBVfccUVXH755UFZrX7dMA3vjWwLLvBSlLHTupGHuif3q96IRN9ikxPR6/Rs8x0dNQy9zkBsbMcK5pWAjCx7+Pjj9wC46qrLmr3/1ltvcfXVV1NcXMzSpUt55ZVXcDqdpKenc/755wcb0B6CXbt2YbcfLGO56667cDqdXH/99dhsNsaMGcOPP/6IwRAZa1aSpIhYbnl5edjt9uNirW3EcUtubreb7du3h2252e12rFZrZCYVBmw2G4FAoNXEFkEQ6NWrF7169eLOO++krq6On3/+me+//57v5n9PYf1ODBoTsXIiCaQSTzIa4fAZTpWUIYk6YmObK1HExupISjQw//viTn+PspKViBotlv7hJfkcCkWWsRZt4YQLb+dJJTK90XTpiaTNvp6yWW9x7fXXM+f113nggQeYPXs2NQYd0ROHsv/JdzGZ4JL3xpHWL/RML2OMjvQB8ZRsrkHUCBSvr2HAOUGyEVQRo8+CRjEgqhJmTyIAVlc6AdFHQPTj0tWhiMEFpnhdsNWQ2+1G0olkDU9s9ll9TsvAVefl99d24Kz2ktzTwpTXRjXFBesr3M1ifhmD4jn3ieH89sp2lr68HVOcDlVROeOMM7j22mv55z//2dS7y7M1n6IbDioBpT5wNca+nZdTa0DGhEQUGhoiIAacmpHBN8t+pXr3XrQxsQja0ITP4/Qa3AEFtxy03v31dQgBFcNf9Cbb1ZZUVQIBX1N2o3oglLBhwwaio6NJTk5uVQUpMzOT33472EXe4XCg0WhakNRfXdqCIPDII4/wyCOPdPbrdgiSJOH1hpe5nZiYSGZmJuvXr2fixMjqq4aD45bcNm3aRGxsLJmZmYc/uB3YbLawx4gEKioqSE5ORuyAaG1sbCxTpkxhypQpBAIBVq5c2WTVbdmxElEQiRUSiVOSSSQVk9B6bKhKKCM+oSeS1JwIu3eNpnCfE6+3c/n/gYCf0tLVWAeMQNJHLg5St/4Pbj//PBY7ymgwRy7uqE2KI332DMpnv8WMG2/ktVde4d577+Xxxx/HsWQtCTlRXPL6ie0mjHQUuRNT2L/dTpfMLLK1vcmuHonZG4/RF4siBvBLbhRBxuwLbm5S7f0RVRFJ0aMNGPBo63Hqaqj1xNBvgI+C/AIyh8WiaUVJftil3Rh2aevSWdPeaqnk0/uUdHqfks7upeV8+Y+VnHHGGVx33XXcPfNuisv3Mf3dcWQOjufX57aw6r08Uu6ejmlweC5nm+onQdDhVOWIiHOld88hUW9GkLTorKHFz6O1EnpJoNoTJNyA24nfXktUVGq7YtKqoiDLnqb/YH6ogMGgJzY2iaSkpE6n02u1Wnw+X8QssFDR2OlBUZQOrU1todE1+T9y6wAa423hJpM4nc7jwnKrrq4mK6vzmYWSJDF69GhGjx7NE088wb59+1iwYAHz589nyeIl7PFvJlqyECsnkUAqVhIQBRG36qSBOjKTTmsxZk5ONFu3db4FSuX+Tch+F7FDx3T63LYQcDvJ8taQ3iWT98V9ERu3EZq4GNJmXU/5o29z0y0388pLL3PnnXfy7LPPkjsxJWxiE1QBizudi8eN5sZRWXi9XvLz83F73dRbtuLU1+LVNDTVLImKlmH7LmNH6o9NpQBa2YDJF4/ZG0+iPpPb/z6WuLg49rtK8DSUUGMubLNsoKPYu7KSr+5YxemnBS22mffMpLC0gGlvjyW1T9BqnXR7P6oLHBS+9gXpT96CJj50F5MXBR8K0RGrfROQLFFBVZeAHFLnCXcgQIxOhyiAogb7BfoBWXaj1R3iRlZBUfwHrDMPihJMlxdFkejoKBITE4mNjQ1rbdJoNLjdblRVPWKp/h2BKIqIokggEIgIuR1POG7JbevWrQwcOPDwB7YDu92OwWAIq5I/ElBVNWLu0aysLG666SZuuukmnE4nv/76KwsWLODbb75lfeUedJKeWCUREBAEifiE5jtwk0kiOcnAdws6l42oqiolJSuI6tobfVzi4U/oIKpX/MrN06fws1yJV3dklCikGDOpD11LxRPvccvfb+XF51/gH//4B88//zwmq65NK6g9aAIGkut7ktjQE5UA1dZ8HvjHTHZtKkBVVc5/dgS9T+5YCYtf48GuKaXUmc9LDy1EkiSio6O5Z941ZNT3oUvNCGrMBVRYtuPRdV5WrGRjDZ/duoLTTjmdq6++mnvuvYfi/Xu5/N1xJHY/mGgkiAJnzx7CmxcvoerlT0l54JqwlGdsqp8kQY8DOSK9A8QoI9TaCbgcaKKtnT5fVkBWVfSSGHRNihKiTo8se9BoTQRk7wHrzI16QBWlsfYsOTk5rKQ0WZZxOBw0NDTQ0NCA1+Mlp2sOgUCgTYHjowVJkggEAmEVcw8cOJBPPvkkgrMKH8dttuTu3bvp2TNM14jNdlxYbY0xjUh3JDCbzZxzzjnMmTOH8opy1q9fz/0P3UfG4GT2U4yqBti4/k0K9y7B0VCOqqrkZEdTsd/d6U7bdvs+HPVlxA0LT8D6UMjOBgZGSxhjLazSHVkNQclsJPX+q/CmxnLb7bfTp08fbrvtNn55eiubv+24xaiVjWRXj2RQ8d8wexPZm/AHmzK/pDRuI1G9VYQDXNAYO+sMitYHzwkoAbSJCvaMAralf8/2tB8A6Fd2NrkVkzB7Oy5IULHDxsc3/smpk0/jqquu4r7776Osdh9XfDC+GbE1whSr5/ynh+PZU0zdZ7+2MmLH4UfFQ4CYDsSHOwJBFJGizchuZ8iSXB5ZwXCgB50akBFEEVn24Ggow+2uQZZdGI0GMjIymtWedZbYfD4ftbW17Nu3j61bt7Jx40by8vKoqqzC75JRlWDT13AzFSOBSCSV9OzZk7y8PJTOSh0dQRy35LZr1y5yc3MPf2A7OF7IzWazERMTE5bZfzgIgsDgwYN54IEHWLtuLXv27OGaa64hOkqhsOAX1qx6ieXLHicxoYGt2/YSCHTOzVVRvh5tTBzmnPB+k0PRsGUVl1xyCT+q1a1qR0YaokFPyszL8STFcMe/7iA3N5fbbruNH2ZtZMeiw6i0qJDQ0I3+peciKTq2pS1gd8ov2E1lTS7H7uNSUOTg9yhc03lyK14fFK8WRIHciQfLEdy6OgoTV7Ap4wtcOhu9yk8jo3YIgtr+/VSVX89H1/3ByRNO5corr+T+B+5nv6OEKz4Y325H78zB8Yy/uTe2b3/HtWkPatNf52FXg8klmgiVBkgxZlAUAp5QJLlUXB4vBlHAW12Bt6qcgMeNIAhYLBZ69OjBkCFD6NOnDykpKa3WnrU6qqri8Xiorq5m7969bN68mc2bN1NQUEBNdS0Bj4oBM1FYMKsWjJjRoMXtdh835NbZefw18SUnJwdZlikqKork1MLCcemWdDgclJaWhk1uDQ0NYaubRAKRIFlVVdmyZQu5ubkdCkJ3796dN998EwgWfr7xxht8/vnn9OqZxZv/uZOSkjJi47qTkNib+ISeGAxtz09VFaqrdhAzaFjE0v/VgEwfI8gagY3ao6cdKFfWIZdV4hYCzJw5kyeeeILb/n4bL93zElqDRPexLZUfgtbaiZh98RQk/oHN1HpHhy7DEtDoRWSvQnV+PZ56H4aYjmf27VsdrEdTA2qLEgAIui9L4tZTE1VA16rRWF2Z7E38A6e+ZUeHuhIn8679g0njTuby6ZfzwIMPUOMv4/L3x3UoznjiVbnsW1tN8RtfEvt8Dn5FQfX6Qdc5K0xGxUWAaEFDnRpe3BCCklyCQUfA40IydqDcQlUIeD0oXjcBjxuPqpKQk4NRp0VrNpGent7UkqajUFUVt9vdzM3YaPlIggZJlTBiRkKD2MYGREKDy+UK22KKBEJJKnG5gp6WRlemVqula9eu7N69m+zs7CM11U7huCS3vLw8LBYLiYmhx3ZUVcXhcByz5qSHwm6306VLl7DGeOKJJ7jvvvvQarQMHzGCCRPGM27cOEaNGkV0dPtKGjExMdx1111cfvnlbNmyhTfeeIPXX3+dZcv+YPfOXQCYzUkkJPYlPrEXMTEZzUjMbi/C73MQnRu5Lub1uzZz7eSTWOavRj1K9fUBh5v9T7+PNVnH9LfHsOSFbdxzzz08/vjj/P2Wv/PKHS9z8asjD6bfqxDv6EpW7QhsxhK2pH9LQGpbi0+jk+g6Kpk9v1egBlRKNtW2SpatwVPvo3pvsL7MYNGS2rft0gS3zsb2tB9IsfejV/lp7I/ZTmnsJlQh6BKq3+/mw6uXMeHEk7h8+uU89PBD2KX9TJ87DqOlY2QriAJnPjSYN879lbLPfmbJbbmcqdFiBQS9tlN2WD3BujdUf0Tsc9GgI2BzoPi90GKzpUIggOLzEvB5Uf0Hfy+NRoPVakUQBNLS0po2iR5P+x2+FUXB7XbjcrlwOp24nC6UA25RCQkRCQMmJCRQD14ZFZVAG9FGAQGPx4PH40Gr1R7TpBIIJt+5XK7Dxv9UVcXlclFZWYnVam1m3ebm5rJ7925OOeWUIz3dDuG4JLfdu3eTm5sb1g/udrtRFAWzObRi2khBVVVsNlszFYJQ8OPCH4kVEkmU08hbsY/1q1/g8ccfRxRFBvQfwISJExg3bhxjxoxpc1NQUVFBSkoK/fv354wzzgBg3759PPfcc8yf/x1FRb+zr3AJGo2R+IReJCT2Ji6+B1WVW9GYojGmR05HMtFWSkramWwQIlPXdjioqkr1nK8Q3C6mvDsBk1XPGQ8N5lfzFu69914ee+wxbrnpVl699RUunTuKjH4JdK0eS7QniYKEP7GZ268JVAIqReuq8dT7EFEJEIy7dZTcSjbWggqiRiB3Quph9SlVQaXcugWbqZiuVWOwujLYnfIrdfY6PrxmGWOHTWT6tOk89PCDuEw1TH1lDHpz56yumBQTY2f0ZMnLK/h0whAYCBNlP1pRROikm7FB0OBHwROB9jVqQCFQV4/kdCNqdYCKIsuosh/F7z8kHieg02mxWCxN1lmjxeX3+9v0gCiKgs/nayIfn8/X5IYTkRCb/sJr8+Ql6JY0GAxHNGTREXg8HjQaTYeTW6xWawt9y0ZyO15wXJNbOHA4HJjN5mN+00QimcTv97N6zRq6qLl0EXrQhR6osooLBzaliopN1by5/S1eeOEFAHp0z2XipAmMHTuWcePG0aVLlxYKKY3IysrixRdf5MUXX8Tn8/HOO+/w3nvvsX79RvZXbAAERI0WfVIqvrpq9HHht5N3lxdx6tBBrKgtxhd/dALQ9T+uxLlmB397/gSs6cENjyAInHRnf3Tmndx33308+uij3HzDLbz9j7f499zH0Bn1bE2fjyy1XeTq9wRY92kBa97fQ0O1l4x0LY3hi31rOx53KzqQgKLIKt1bcUm2haAVt4CsmhPoVXoasx+fzagB45k2dRoPPfwQcoKdS54bjdYQ2kI8Ynp3Nn5dRM2HC/mqSwo/2PYRq9F3mtxyRTMnaRJ5w1cYEett/78/QvBr0JijcBXvRZX9IIrEREUxZswY7rjjjjZDEn6/nz/++IMRI0ZgNpux2+2sX7+etWvXsmrlKnbs3EEgEECn0RMdsBKtWokmFjMxiBG0sHaI67j9wVsZM2bMMQ+fbNu2DUmSyMnJOeyxWq221Xhkbm4u33zzzRGYXWj4f0tuTqfzmFttEJlkko0bN+L1erByMEtOEATMRGMmmnS6ggweXNRRjS2vmk/3fc7cuXMBSE9N59zzz2Xy5MlNHRJas4p1Oh0zZsxgxowZAPz555+8/PLLzP/uO9zlReTPfRKtJY7o3P5Ed+uDKTMnpHoj77a1nHj3bfxbLgjxinTy8/JLqZ23kOHTutFzUlqz9wRBYNxNvdGZJO6//34ee+wxXn7uFQq376Wi3wqsUus+UyWgsunrQv6cswNXrZfLL47h2qlJjBisZ9gpxWzc6qViuw2/J9AhYmkkQkESyBnZuQ2EKqjsMizD9X0q9/39YQRB4KGHH0LIcHDRUyORtKHfe5JWZNLtffjyn6twbdoDA3tQ7u98ZmsFTiYQS1RAYJfSeVFsVVXxFZbjWr8L59od+PaWgRS0n7KzunD9jTdy5513digJZPv27VRUVHDXXXcxb97H+HxeVFXBpIkiRo7FSgJWEjAT0/SceAjgofO1oe2hWrWxceNGBg0adMyLuaOjo6mvrw9rHj179vyf5XY47N69u8ltFiqOp3hbuMkkGzduREAgivbHMQgmUulCKl1ABh9ebFRjK6+maHcJOzN2csEFFxAXG8f4AzG7cePGMXDgwFYXhcbicYC9e/cyc+ZMfv75Z2rXLaN2zW8IWh1RXXsR3b0vUd16ozEd/nrLLgdD0+IotFdTFRV+gsHhoPplql79jOSeFibd3nZT1ZFX5mKKMuF2u4mPj8dWa+f965Zy+Xvjmiy9RuzfZefHR9ZTutXGJedHM+tfKXTPORjLOvc0M1t2eAkEVMq21LaQ0Por/G6Ziu02EKDL0Hj0UZ1zH/o9AT77+0r6J49g4okavF4vqcOjOfEfvRE14Xsucielkj4onupPfsY4oHtI4QIFWBOwMVKK7TC5KV4f7q0FuNbvxLV2JwG7A0QBURDJzc3lmWeeOaz2rKIoLFmyhHnz5rFs2TIKC4uQZR/Tpk0jLi4JoykVr7eAEUwiWg6vMLuziMYSEf3cSCAqKoqysrKwxsjNzaWwsBCv13vMa4vhOCW3wsLCDpnH7cHhcESk51G4iIT81+bNm4nRWpHkzrmWdIKeJNJJIp0ROcPw5isMZgx1ddX8Pv9P5n/7HQFFxmwyM2r0aCZMGM/o0aMZNGhQCwHUnJwcPv30UwACgQDPPPMMc+fOZV/edhp2bQbAkJJJdG4/orv1QZ+U1upC0bBnK2MmjGad9sjWtTXC9v0f+PfXcvbLk9q1YARV5KLRV+Gq8fH3v9/Kgw8+xHXTbuTt6+ZyxXvjiEo0oCoqy97YyfI3d9Gzu45P5mcwanhLy+7MyWZmPRvsjF20ruaw5FZ6QMgYAXp0siNBwK/w5R2r6J88nKlTp3L//fdz3lWncdOUf7CdhfgJ/zoLgsDYGT355MbleHftw9ArO6RxVgfq+JeuOzpEfG2Icvmr6nBt2I1r7U7c2wtADoAkYtTpmXjGGbz11lvtPteyLPPNN9/w2WefsWLFCsrLKggowYxEszmJ5NShWK05aPUD6dc/k959NKz482m8eIg5ykkdUVjIz99FfX192PJX4cJsNuN0OsNSTElLS0Or1VJUVESPHj0iPMPO47gjN1mWqaysJDU1vAaNx4NbslGZpH//8LIMN23chMFvJpxSocRu8Wz9aTfxQgrxpIACihqgnjrqXNVs+HULvy1eil+RUVWVmJgY+vfvx+mnn85VV13VrM29JEnMnDmTmTNnArB06VIefPBB1qxZQ9WyH6n6fSEaczTRPfoR1b0P5qweBwL/IJbvpXv3KczzH3n3hVxjx/71UkZM69ZqwfKhyKwdhkbRUdpnCePu6cYDDz3A7Fmzufri63n/2je5dM4oFj+3lR0/l/LQnXHcfUscOl3rP8jg/noS4yWqagJNsbT2UHygeBs1aLl1FEpA5dt71tIzeghTp05j1qxZJIwF6bQi7DUZ9Ng/MVgALoQf5co5MYnY7GjsP64ImdyqVB81qp+eYhRblHogqNvo3VOCa/1OnGt34C+tAgEEQSQ5MZFbb72V++67r80xGxoa+PTTT/n6669Zu2YtVdXVqKqCIIhER6eTnjkaS2w2FksWWu3BjUhtnUB8nB6TORadNgq7v5ZE0tr8nCMBMxbKy4PiCvX19ce0JtdsNiPLMl6vN2TXpCAIpKSkUF5e/j9yaw379+9HVdWwrK7GdNVjTW4OhwNFUQ6bqt8eVFVl06bNJNKGSnkHIIgCCTmxVOXXNntdFKSm+AIq+BUfvzGfpOQBKEqAVavW8+eff3L//fdjNJrp3bsnkydP5sorr6R3795N40yYMIHff/8dgNraWmbOnMk333xD9ebV1G1cgSBJmLNyMeXkMiAplr22KhwRFEhuC7Wf/oLeLDHm+l7tHhftTiaxoTtb079DEWV6npSG1iDx0IMPMuuhR7j8/Gt548JXUXx+PvtPKhec2b77VRQFzj7VzPuf1VOysYaAP2ileOr9KO5garqzxoMmSkVr1DRLPHl/2m90HZ3MCVfmkjm4baJTFZUFs9aToxnAtGlBYks7TcOoa4KqPvviV9G37GxS7f0ot27p0PVqD4IgMOKyrvz05GbkahuaBGtI42xT6umjmFix/E+c63fiXr8LxeUBUUQjigwePJiXXnqJMWNa1y8tKiri8ccf58cff6S0rCxYJ6aCKErEWLqQlTMAqzWHGEsGktR22UN9vR+/XyExwUCMpQv26s4X3YcLjaAhShOD3W4/5t1LGjsUuFyusOJuaWlplJeXR3BmoeO4I7fy8nISEhKaWq2HgsbU3WMdpG20HsNxN5SWllLfYCeHviGPEZsRAyrUlda3e1wDNgCyu56E2ZwULFZ1VWOz7cVWV8jWbXmsX7+ep59+Gp3OQNeu2UycOJHp06c3dQaOi4tj7ty5zJ07l0AgwNtvv83zzz/Pnj15OAp2MPyee1j1+x/U2osxDemJvntGu4rsocJXvB/Hsg2ces/AdmNYoqIhp3o0JbHr8WoPNqrsOjqZM56WmHXPQzx0/yxuuPYmlix6nVMndqzg98zJZt7+qB5QePuiX6gtcRHwqxiNRk76+DreOHsRbrebhC4mqotcaDQw7cIYBvbR8dbHdXxw5e90H5vMxNv7tbA6VVVl0dObyfD1bSK2rPMNDL/soEamKirsTfyTXuWnYjMV49bZOnX9WkP/s7uw+MXt1C9aTdylHa9lUlUVf3k1rvW7+K3Kyz8vu5LqVz5HESDKaOLcqVN54403Wo2Rb9y4kSeffJLffvuNyqpqlEDQxSjq9JiyclFlP56SfYweez9SJ5ObKqs8JCUaiLF2YV/17mMiYmyQzZSWljZlVR9L6PX6w9b8HQ6pqan/I7e2UF5eHrZLsrEwsqPyOUcKHo8nbILdvDkYz4oidIX2pG7xVO+tDcZ12oGdGjSSHpMp2FJEEARM5kRM5kTS0kcA4PHYsNXtxW4rZF9RAa+//jqvv/46GklLZpcMxowZwymnnMKll16KJElcd911XHfddQBN+noff/wxtqIibN/8hmg2YBrSC9OQnpgG9kA0RWZDYl/wJ1GJRgZdkN3ucZm1Q/FpnOyP2dnivS5DEzjz3/156PYHmPXQbDj5Rs6/ai7z30vFYGifkCePM6HRgCxDVozMQ48kkJ6qwWg04QE+mZtKdY2TBYucfFUUPG76RdFMGGXilmusfPG9g/ueqOXtSxYz8spcRl/Xs6n9zW+vbCfR1jNIbI/MosfUKAae17IG0amvZn/MdrpWjWZ72g+oYbondSYNg8/vwrpv12D920TEdtRKVFnGvaMQ1/pduNbsQK62gSBglySk6dfw5ptvctVVV7U4b9GiRbzwwgusXLkSm72+icwkUxRRuf0wZXbDlNkVfUIygiDi2LuLosI5eD11mMydE32orHSTlGQgJiaTADJO6sN6zkKBXjVSWloaNqlEAgaD4X/kdiQRKXI71lYbEJb/uhHbtm1DK+kwBDonEXQoErvGUfkXl2RrsFNLdExmuxJbBoOVlNTBpBzo7u3zObHbCrHZ9lJVVcAHH3zABx98wPQrrsCg09GjRw8uvvhi7rjjDlJSUqiqqqKgoACn08mMGTOYP38+juWbcSzbCKKAoWcWpmG9MQ3uiS4ttL5dgXonjj83Mf7Gnu0mkUS7U0hwdGNr+vw245l7l1dSW23jqScf5O6ZjwDXc8kNb/L5f1LQatve5UeZRSaMMrH4DxcpSRpuvNIKgF/W8+M6OHWCCa1GoqY2wFcLHESZBUYfSE4RRYGLz4nm3FPNPPlyHU+8vJu9f1Rw4Suj2PztPiyl3Zg2bRqPzH6Evtda6X1K2zVSpbGbsLoySYmQe3LIlK6s/jAf1/pdRI1snn0q2xy4N+7CuW4X7k17UH1+kER0koaxY8fyn//8h549e7J+/Xr0ej2BQICPP/6YOXPmsHnzZhocTlQl6K7WWuKI6Tukicy0ltY70RtTg8o/9fbizpNblYdhQxIwmYP3mRvnUSc3Ayb2Fe77f0Vux0s5wHFJbocmL4QCj8dzXKSiRoJkCwsLMYtRCEro7pKkbnFsW5R32OPsQh1p1t6HPe5Q6HRmEpP6kpgUdJtuWPcfPAY/UTk9ce7LZ8u27Wx54AEeePAhbphxPWazmYULF3LPPfcwb968pnHmzp3LY489RvGuIjw791H7wUI0ibGYhvXGPKQnht5ZCB1UT6hfvBZRoF2rTVAkcqpHURy3Hq+2dZfQth+K+f21Hcy+O56Lzoni3Csf4pa/zwKu5arb3uK9l5ORpLZ/l3NONfPrMhe//O5EUVTEVlRHfl/hRpLg9JPMLchSrxd56M54zj0tirOml/HW335h3ImTmTZtGrMffYSBN8cdtuBbFRrdk6dRZyoKqWXOoYjrEkVirgXn6m2YT+jbsvYMQBSJs1qZPn06zz77bJPqhcfj4amnnmLLli0MHz6cfgMGwAEVeX1CCrGDB2DK7IopoyuaqPYTgBohGYzo45Kx1xeRkjakU9+lstJDfLweoyEaQRDxqEcng/dQGDBRsb+iSavxWMJgMOB2h6fzmpaW1qzj+LHEcUduZWVl/28sN4/HE1YyCQTlsTRy6EQdTCaJozK/pbjuoZBVP348mDu5+z0UqqrQ0FBKXP+TSBw1GQBF9uMpL8ZZXED3Xn2Y/83X/L50Cc+/8AKSpCE1JZmTTz6ZmTNncv311wNBa/WGG25g9erV1P+8kvqFyxF0WoyDcjEP7YlpUC6SpfWkDlUO4Fi0kn5nZmCKbfu6JTi6ogh+KqNbuiMBHFUefn58I5deEM09twXrn378KIZzrnyY6294GNRruPHud5jzTGKbcZozJpv5+31VeLywfbePfr2az0dRVH5a6iQQgLNObjv5aVA/Pf+43sqSNcOZNm0ajz4xmyH/TCR7RMd+K6e+huqofNLs/SlI/KND57QFn0smqXs0OxZtp/D6J1AbXM1qz55++mnOPfdcAKqqqrjmmmtYuHAh1TU1QWUSARJi47ngggtIGXUS2pQsTBk5SIbQBUYN6V2o39e6mHV7sB9IKklINKLXWfB4jz7BGDFRXVt43FhudXXhFaqnpqaGXS8XKRx3LW/+P7klIzGPgvwCDGroLkmT1YDWoMFW1tDucZ4D9VAGQ9uCvYeDy1lFQPZiSjsY/xE1WkyZXUkeNZmsLhmo484l58p/kjzpHExde1FWXcM777xDz549kTRakpOTefDBB3nmmWfwer34PV7+/ve/YzVH4Vq7g6rXv2LfjCcpufd16r5agrewvFn7DdemPfhrGhg6pWvbE1Uhpb4v5ZZtbbojf3l6MyadykuPHiSvLhlafvwohvfenkXXbt2ITryKf82qadH+oxE5XbTkdgvGpZatarkj3r7bh98PggCnTWyb3L74vqGJ2B5/6lFG3J3cYWJrRIVlG3HObLRy5+8le5mLdZ8W8MmNf/Lc2O/Z9kMJqAp6v8JZZ51FeWkZAVlm/vz5vPnmm1itVkRJIik5mffff59qWx363tlYzhgFAQXDmDPwKDDgtPOI7t4nLGID0MUm4vGEtijbbD4sMVoMRisejl53ikYYMFFXV4eiKMe8/c3/EkqOMKqrq8PqBgDBWNfxoE4SCfdoSUkJyWSHfL451ojb7kGR29dwbCQ3fTutbw6HensxIGBIbVm0nmTSoahQ61MwpmRgTMkgfvj4oKxSbSWu4gJcxQXU7svjq6++4quvvkKQJCzR0QwbNox3332Xc845h6+++op77rmHvPx86grLqPvsVyRLFKbhwTidc+VWrF2iSe7VduzE6s5AUrTURhW2+v7eFZVs/7mUD15NJi62eVJSSpKGhfNiOP/q2UyZ+gD5+Vcw+7kPePCOuFbHOve0KJ6fU8evv9m58Qprs/eWrQwupsMHG0iIbz356Ydfnbz71WCmTZvGE88+xsj7U0nu2fm4kFfbgM1YQkp9b4rj1rV7rBJQKd1cS/6yCnYtKaemoOFA7ZmA2WTm2muv5fnnn+f333/n/vvvp3v37jjd7iYXo2DUYxzUA0OfHAy9stFnpyJogm1VHL9vxFNZSqnTS7pZT4mjbd3OjkIbY0X2uQgEfO2m/7cGh1PGbNZgMFpx249+LzIdBhwNweJpr9fb6fY7kUQkYm6JiYnU1dURCASOeULfcUduDQ0NYbvyjgf5l8abNRzLzW630+BoIIswLLdYI866w+9IPbgQENHrQ7/2bncN2mgLkr7ld04z6yhzeluI5gqCgD4+GX18MrGDguUEfnsdrpICnMX5uPbl8csvv/DLL7+AIKLVSGRkZPDwQw9x1VVXMWPGDJYsWULDknU0/LIGUSMSk2Jk/ed76T42BUtqy2uX2JBLZfTuphYxh0JVVX57aSsnDDVw6fmtX4u4WInv3o/i4hmPcs4F95OfP53n35jHP26wtjj2zMlmnnm1jvk/+1pYePN/siOKcO6prVttS5e7eH3eQKZNm8bTLzzBqIfSSMgJ/ffZH7OT7pXjKIld3yJz0lPvo2B5JXt+Lyfvt/14HX4ESQAlqE4zZ84c8vLymDNnDq+//jovvPwyjQrRksWM+YS+GHtnY+idjTY9sdXyDkEQ0Odm4irbR5nTS7o59HKfQ6GNCXobPB4bZnPndDldLhmzSYPeEEsduyIyn85AEASMoqmpC8GxJrfGMqpQSyIa126Hw9FC5eho47gjt0hoQsqy3OHWDUcKkai1a+xqawiD3MyxRlwdJDe9PiasZqQej61pofkr0s16Sp1t90I7FFpLLBbLUCx9gx0MZJcDV3EBNWt/x12yl72FhTz44IM8OOthRDW4Wzxz7Fh69uzJK6+8gr2snp8e38RP6iYSukaTOzGV7uNSSOsfh141YnGlsS9uVaufvevXMsq22/nwy/R2H/CYaImv3oxi6i2PcfIZ95GfP5W5H3zM9dObJ0KcOMxATJRIvUNhb5FM5oHERhWVX373oShwZivxtlXrPfz7rQFMnTaNZ199ktEPp2HNCE+UoMFQgSIGsLgyqDMVUVPoIO/3CnYvKad0Uw2qEmy5oxG1nHTSSQwYMIB58+ZRWFjIaWeciiKriBoBMdZKdL+uGHplYeiVjSap45qM+q5p2LetoNTpY2K6Nazv0witJXRyczplrFYtBoMVr+pCQUGMUEPejkKr6nC5XMc87ta4Zsqy3NSEtLNoFM74H7m1gkhYbscDuUWi1q60tBQAA6HHJDpjuYXjkoTg4qJJa909l2bWs2p/+0XkbUFjiiKm5wDsOzZg6JlFyl3T8OwpxrOzEPe2vVQVFPPFF18Ee6CpwR1o9+7dqa2tpWxvGSv27WH5W7vRR2m58pbLSBlQTL27DoO2peWw+r09TBxjYvyow28oTCaRT143M+2WWUw89SF2F1zKvC8/YerfDt6/Go3AGSeb+OI7B8tWubnsguDGbV+xjKJAarJEv17N57F5u5fHXu3H1KnTeH7O04yZlU50UvgdXQOywh7bZrT7Enn10TepL3chiKCqEGWOIjc3l4KCAux2O0t+W8yvv/4aTADJiSZrRCKosO7TAjKfuBEpKrQNlyYpFsXtoqSugeTuSWgEAbmNmGVHoY2ygCDg9dg6fa7TJZOeZsRgCC7EXtwYObrKRmJAwuFwHHNya1yrwiE3SZIwmUw0NLQf4z8aOO7IzeFw/L8ht3BdozabDQANod1oAOY4I87aw5ObW3BjMIYu8QXg8dqIsnRr9b04vYZqT3hdANxl+zCP74doMmAa2APTwB7INXaKbn6GcTf3RhAFitZWU7Khhi1btiAIBLM1lKC7xO12k6TrwoKPfmHBDz+QMTie3PEpdB+fQlxWFPXlbko21/HEq8kdnpNeL/Lx61au/edsRox7gA35l2Be+BnnnX5wgTzr5Cg++drBC3NrueyCYDx56fJ6NBo47/SoZlbPrjwf9/27L1OnTuPRxx5h+MzksIjNUeMhf9l+9vxWTsHySjJTN/Poo4/SUOHGZDIFVWg8bpxuJ+vXr0cQBVL6WskalkDmkHgyBsdjjAmSb22Rg3WfFuDNK8E0KLSWVJp4KwCVVVWodCNGJ1HrlUP+fgCCJKE1x+Bx2zp9rtPpx2TWNG3sPLiOOrlJqgZbne2Yk5soikiSFJQ0CwPR0dHHheLKcUVufr8/Iskgxwu5hZspabfbERCQwviZzLEmaosOX9vkwYU1jExJVVXweuqJbcUtKQJmrUSDL/SHRvH7kOtt6DKbE49nT7BD9qDzs4lKNDD62p4oskLlnnqK1lVTtK6afWuqgztJAbp17cbnn32OgEDJhhpK1lfz63NbsaSZiE42IEntZy62Bo1G4O0XYphx16MMHnk/f269GJPpc04ZH7RuTp1gQhRh87aD5P7cnAZkORiTa0RhsZ87n+jD1KnT+PyTJ1D8e1n4UClXfTYJja5jHgBVVdm/097kbqzYYWv2fmlpKTqdjtS0FMpKy5F0Il2GJRwgswTS+8eiNbZ+v8VmmtGatfj2VYRObglBC8lfX0eDXyZGpwmb3CDomvSEYrk5ZaLMQbckHEysOprQoKW6pgavN/zkmnARCXKLior6n+X2VzRekHAsN0VRUBTl/wW52Ww2dJI+rALujrglFVXBi7vpAQ8FXm8DqAramJZjRGklREHA4Q891dlfH0z1/qtgr7+8Gn2MjqjEg9da1Iik9LaS0tvKiGndUVWVmkIHtZt9xMTEUOUoR1GaJ5PYy1zUl7tQVcgaupfTJpk48+QozjjJRFLC4e8lURSY+0w0t977OH2G3ssvay7GaPiCsScYiIuVOGGogZVrPVRWBxeO4pIAep3AhFFBq6x8v8ytD/dm6tRpfPf1k7z/gpu9RSkMPbmIle/sYcyMtsWffS6ZwtVVTYTmqm2+SAqSEJReUwGtQkVNKWffPB5PZjkpva0dbmYqCAKWNBO+mtALwTWxMSAIQXLzBYjuIGkfdlxLLJ5yW6fPc7pkDAYJnU6PVmPCIx8bcqvaX3nMLTcIxt0iYbn9j9z+gkZTNpyMocYf5linofr9/rDEnyFouWlFHW20vuoQzLGGw5KbFzegojeEHgBurDNqLaEkWifh9AcIhBFa8dfbgJbkJlfbiGklI/JQCIJAQk403ZMy8dTVM2PBJOzlLorX1xyw7KqoK3LSGPpxuVW+/dHJVwucCAIMGaDn3FOjOPNkMwP76tpMnhAEgZcfN3P7g0/Sc+BMvv/jQoz6Lxk2SM+5p5pZtc7DvC/r6T0MJAkmjTZiNIpU1wS4/t5eTJ06jV8WPs1bz7gwGET69dLz92utvPrubgZflIM57qCb217mIm9ZBXuWllO4ugpFPnhxGzUtNRrQxejJGpZAlwNuxsRuMehrvQwS+1Ec13lLwZJqpLwq9EJfQSOhiY05YLkFiNZG5jnVxsTi2FvY6fPc7gCKomIyadDrLXjko1/rFrTcqvH5OpZwdUTnotGEXW8XFRV1XLglO5wW9MYbbxAdHd2M1R0OB1qtlgkTJjQ7dunSpQiCQH5+fqcm09DQQFRUVFgq+o3zO9aWWyQUxu12e1jxNgCj9fDZkj6CO0a9vmOSR62hMZjfKrlpNdSH4ZKEA5abIKCJaz5HudqONa1jMSmzLx6XPqjUYkk10e/MTM54cDA3fncKty05g789dwLDp3UjqaeFwIENharCuk1eZv27hqEnF5E2cC83/Gs/3/3swOVquesQBIEXZ5vYufFJsrK68uXiv7F1p48zJ5tRFHhuTpAYFAXOOiUKe32Aq+7qydSp0/hzydO8/rizmSjzzFvjEFWVDV/spXhDDUte3MbLZyzg1dN/4qfHN1GwvBLpkLT+rEwN0y6MYcp5USAK3PLTqZz/zAiGTulKUg8Lgijg1NVg9na8b9yhsKaaCNTYQjq3EVKCBb/dRoNPJloXmedUGxOLz2Nvs6C+LagquNzBWjdJoyNA+C7SzkKDFofT0em5H5G5HCeW25VXXokgCC3+TzvttA6P0eE7a+LEiTgcDtauXcvIkSMBWLZsGSkpKaxataqZG27JkiV06dKFbt1aTy5oC263G6MxvKwwWZaRJOmot674KyLhGrXb7UhK6DtbUSMiaUT8h0nkUA6YhqIY+ny93gZEja7VGrcYnURDGC5JCNa+SdZoBE3z66HU1GHp2zGL0+yNx25sXRrIHKen50lp9DwpqGvqrPXwysk/MnGUEadbYcWa4AagsirAO5/U858P69FpYeIYE2efaubMk8x0yTi4EXnpURO33PcEfQbfw8c/XsCV53xNRpqGmgNGj6rChFFGpv0jSGzrVzzLCw87m2lV2uwBflnmItqs8PurO4AdQNAig2CuTK8eOiaNMTLmBCNjTzCSmhx8849Vbj79xkFNQQPJvazNvqtTX0Nm3bCgm7KTj0lMqhG5prRzJ/0FmgQr/uJaGvwBEgzhbd4aIWp1qKpyoFFp554Zv19BqxEQBAmV8JKeQoEGLT6f75grlEBkyM1kMkVEK/O0007jnXfeafZaZ5L0Orya9ezZk9TUVJYuXdpEbkuXLuXcc89l8eLFrFy5ssmCW7p0KRMnTuzwJBqhKEpE3InHmtggaLmF2zbe4XAgBMSQO3CLBxZK5TD+wEZyE8TQr72qBNoUNjZrg27JcOCvtzUlIzR9pqrir7ZjSe1YY1uj3xKU3OoAGvZ7CMgqD/8rjhOHGfF4FNZs9PLHKjdLl7tZvMyFzw+LfnPx81IXt6hV9M7Vce6pZs482cwJQwy88piZG+9+nP7D7+X9789n2MD/8OsfwevQLVvLHY8Hk0d2bXqOp+4N7nR37vHxwWd1vPpOPQ3OIIFJB24jSQp2+Z442sSYEwyMHm4k1tr6bzaoX3ARqNxT34Lc3DobkqJFGzDh13RuEYpJMRFwelFcnpDbE0lRRry+ahr8AbKiIyOT11g0rqoBoHP3saIENVhFUULl6Cd1iEgEAt4WceBjAUEQwrYgRVGMiBWq1+vDalrdqa36xIkTWbJkCTNnzgSCFtpdd91FIBBgyZIlTJgwAbfbzapVq7j66qs7PRlFUcImhGPRcPBIzSN4s4c+hnhgVTyc9JbaSG6d3PEeCkUNtEmOkiAgH6aX3GHH93kQjc0XQtUvo3j87QokHwpR0RIQOxbXqCt2AtCrezBuajCIjB1pZOxII/fcBrKssmlbkOzmfWVn3SY/O3b72JPv48mX67DEiJx1spmzTjHz9Q+PMXbyfbg91xL4bS4AXXtMYMqUKRRsew6LuZDE3rXU2VUab39FAYNeYOQwA+NPDFpmI4cYMJk69nxEmUUMBgFPfUtLRBUUFEFGUjSdtlOMMUFLKxxyQxJRlQABRUWK0LMqHNgUK0qAzu6PFUVFFAQEUWra6B1tBAKB48ItGSlyOy6s0M4cPHHiRG6//XZkWcbtdrNhwwbGjx+P3+/njTfeAGDFihV4vd5jZrn9fyI3WZYRwiK3A5bbYYhFPSCKFY4yg9oOuYlCWDkxwfEVBeGvyQcHAmOipmPzFlUJRejYQ+f3BF0zUebWx9ZoBIYONDB0oIHbro9FVVV27vGz8Fc7T7xkp9am8Om3Dcz7sgFRhF//uJ+HH36Um266CYBLLrmEhx9+mPz83ShK0E0ZHSUybqSBcSeaGDvSyJD++nZ7xh0ORqPY9D3+ClUIIKidf9Yar7UaCP0XFSQJFAVFVWmlC1BoOHDvBS23ziHYjghE4diQm4CAoijHBblFAqIoRsQK/f7771uUhd17773ce++9HTq/U+Q2YcIEnE4na9asoa6ujtzcXBITExk/fjxXXXUVHo+HpUuX0rVrV7p06dKZoYEguQmCgN8fut+70V8czhiRQCAQQFGUsOYhSRIGoxGtEFpcQmcKWh0anabJimv1OFWLESN6vQZtB9PC/wq9ToPRZELfSn8zrRik6Nbe6ygMWg2qQYf+kByogAJGoxG9Vo+oHP4aCaoEqtChY/FpiIoygmDA38EQRPeuBm7tGs2t1wWL4YtL/fzr4SoWLXPjdvt47LHHePHFFwF4/vnnKS4upm9vE9dPi2fUcCO9emgR/7Ih6uhntwar1Ywga1r9voqgoFE6dt0OhU6rD15zBbQhNhUxGgyoWg2SKKARhbDui6Yx9cF5abUCus7ewyrotCJGkwm/UR/y8xYqDKoen8Yd9noRCaiqiizLYc8jEpbbxIkTef3115u9FhfXugJSaxDUTm4XMjMzufHGG6mrq8PpdPLaa68B0KNHD+bMmcNDDz1Er169+M9//tOZYQH4888/ufDCC5uswP/hf/gf/of/4b8LL7zwAqeeempT+CoUXHnlldhsNr755puQx+h0etzEiRNZunQpdXV1/Otf/2p6fdy4cSxcuJDVq1dz4403hjQZURTRaDScccYZIZ0PUF9fz6pVqzj55JNDHiMS2LRpEyaTiR49eoQ8xkUXXcT6n7fQTzghpPN1Zi1X/ecC3rrqC2Rv2zupWrWCzazkhFF3oNeFVkBfuHcJ5dUb6Daj5Q09MT0Ws0bk+33tN0xtDyXffkjA7CP5H5c2vaZ4fRTd8BRnPjyYXqccXjpsUPGF7Er+BbfOdthjd/5cwoKHN1C2KQejsW1LQFFVduX5Wb7GzfI1Hn5f6aa6JnitG+vN9Ho46aSTueCCKTz//PPcf//9lJaWsnfvXkr3vs2+EoWNW7yoKkgaCMgQaxG44Qoz40+0MLi/Dp2u81ZS5pC9DL2yF8Mua5m1PKjoQnamLOp0Z+59q6v44vaVZDxza4uaw46i9uOf8awr4Nx/3s+QhGje21UR0jiHomH3Vkq//4gTx9yFVtO5OtkL/5bFmrXV/LDgLdTqOgYIJ4Y9n86gWi1D7evk0UcfZdKkSUf1s/+KdevWER8fT3Z2dshjfPTRR2HnTkQCIZHbzTffjN/vZ/z48U2vjx8/nltuuQWfzxdSvA0OBiJDFe2Eg/Vt4YwRCWg0GgRBCGseqqridrvxCyG6CA4Y5QFfAL+77TF8qh83brxeP0IrbWA6Ap9fweV04m0lM9OvBKN6rb3XUXjlAH6HE+8hMRFVIwR1EZ0uFPHw10gRZEDt0LFSVPDa19pcZEcf/A39fpUNW70sW+nm9xVufl/ppr5BQRBAFINdYHQ6uOgcMw/8I4HUZA33P9eXkaOnMHv2LIqKgnJhjzzyCPfffz+BwOVcP/0zeneLZeFiF98vcvLjYidlFSqP/NuFLFcB0LeXyPmnWxlzgoEThxnbjAU2XRtVpbbWiaqTW/2+oioRkLwduhaHwuN143a78YoQCDE+5fZ48Pr8KCrIqhrWfdE0ptcXfFb8AqrauXkJAvh8Ci6XC8ntC/15CxEe1Ysky4iieMzXLQiuXeHOIxLk5vV6qahovvHRaDQkJCR06PyQyM3tdtOrVy+Skw/q/I0fP56GhoamkoFQEIlAZCSyfSKBSMxDp9Ohigdkk0JAYyJJe/E2AOFA7ERVQveTC4LY5vnegIJBCu9h0ZijcFc0r1ETJAltbBQN+zsmW+SX3OgCZpxUH/bYpNxg2cGfq90UFvtZttLNbyvcrFjrweNpntUoCHDyeD3XTo3l5PEmYqKDyQ11tgB3PdWHcROm8NILs8lMLqE4yG04HPUkG1+Crn/n26UXoxU/5fKLY7j84hh8PpU/VrtZsMjJNz86KCyS2bFbYfvuWhrX7cH99UwYFczeHDPCSHxc8+SQvUUygQCt9rPTygYERPxS59U4HFUeEAUkS+jiwqpfRpA0GCQRXwSIDQ7eu6G0bBJFAVU9UM4SYhwxHKiox0VtbqQQqZKuH3/8sQWX9OzZk507d3bo/E6TW3Z2dquLdlZWVtiLuUajCTuQebykoUaC3GJiYlBEGUL8Oo0lANJhsgnFAw+0EkKmWSM0GgMBnzeY1fiXXVtQiSI8pXVNTCzyVluLLFQp3oq9vGO1Wo3KHHXmfW0e46n3UbKxlqJ11ej0Apffsj/4OcEEvyaJrm7ZcPE5cZx5spnhg/SIf0n7q6yWuf+5fkyafAmvvjKbnz/2E9/LhU4XFCkQANlnI077EnT7O18snoKifMLoE6LR6QQmjTExaYyJf89KZHe+jx9+cTL/Jyd/rHYTCMCWHV42bPHy/BwbALndtEwcHcyyHHuCgXWbg4Sf0sfa4juafPF4tPXBe6uTsJe70MZHN6XehwK5th6NOToiyjWNUA8882IItZqiKKAoKqoaQAwjOzlUBJCbFPmPNSLRQdvv94ctYPHuu+/y7rvvhjXGcaUtGQlNMo1Gg6qqEamZCweRKGS0WCwEhNAfflVR8Tp9GGL07epLNnYdkP2h6+oZDBZQFWRHfQvx5EhoCOossaheH4rTgxR1UMVGSojFXt6xmI1LX0uss3kWr6Pac4jGZDXVBfWgBpt2HqrXqATgjMkmzjolKKackda2JVpS5ueB5/tx6umX8Pqrs/ntC5nvfnYhy3DC0GAGa0CBO2fVULzRylsfv0C3brfz1dJL8Pg+4aSxzeOeud105HbTcfuMWOz1AX7+zcWCRU6++9mJza6gkWB3vp/d+XbmvB+MoRkNAgazRN5vFWQOiScu62BrHbM3HqcutPhnfbm7qW1NqJCr7RiSehCtk6gJsw1SIwIuBxqtMSTLzWiUcHsCKMfIcpPxYzLFHhdxqkh0VIlEw+lI4Lgit+joaHw+Hz6fL2TR4UO7yYYrXBwORFEM2wq1Wq3IYcoBuercmGON1OyztXlMY6dvj8dOqNLJjf2w/PV1LcktAurvmgOalXKNrRm5aRIs2Dbs7dAYDl016TWD2Dx/H8XraihcU4W9NGj1tSAzWSU1WeLc04KCyRNHGdtNLGlEwT4/D73QjzPPvpQ5b8xm8WcyoiiwYJETjQaeeiCJajlYoF22P2hZX3OpyMtvPU+3bv/gh+WX4Jc/57SJrSdFWGIkLjo7movOjiYQUFmz0XPAfelk+y4fghB0k7o9KhDgh1kbADBadHQZnkCXIQn87YQxOOIP75ptDbYyN1J8WkjnNiJQY0Pbw0q0VqKwPjJK+P76upCa7Wo0AnqdhNMp4/c2oCMxIvPpDGT8WGIs/2/ILRINpyOBY381D0Ej24djvR3aTfZYQq/Xh93CwmKx4AuEpxTurHNjim1fr1Mr6JDQhtTJuBGGQ8jtr6j3y2hFEcNhYn/toVGQWa6yNXtdk2DFUeEi4G8Zq1UVlco99az7rICv71rNs+d8giBrWP1yCVu+L2oiNgAUFUGAEUMMPHZPPBsXd6F4Qw6vPpnEGSeZO0RsO3YHG42eefalzJ0zm58+lJGkoHt6/k9OZBkG9Q1uuIYNMiAIkF8Y/H1vvUbCXv5vunXrxqLVF/H1D87Dfp4kCYwcamT2zAS2LM2icG02rzyRyGkTTegOGJaN65Tb7mPXL2UsemYzmjoL7/zzOz65aTnL39pFycaaVq9fa7BXuEPOkoSgsoni9qKNiSVGp6EhnEK+Q+C312HQd35eZrOGQEDF7fbj8dU3bfSOJmT8JCUnhd0iKyJz+Z/ldmRgNpsRBIGGhoZOFesdCkEQIiL+GS4MBkNEyE1W/CgoIauHOGuDltvhYBBMITV7bIRGY0DSGlolN29AxRdQiNFJeNyhJQxpzFEIGg1yZfPx9V3Tg81Jd9tJyrWwf6eNogNuxuJ11XgdMggHtAMRKSkpoVu3blTXBLMQtUaJ7BOTKPi9gkfviuPuW0O77zZs8fDEG/254G+XMfeN2Sx4z9+Uvr9hi5eqmgAmQ9BSABhzgoEly+Cz723cc0sSADNv0XD/U8/Qrfu/+GPLxThdnzDtwo7vgDPTtdxwhZUbrrDiciksWR5MSvn2RwcVlQEkEaJiLMTFxZG/ey9Ol5O9KypRFRVJK5LaL5asA9Zd+sA4dKbmy4PPJePY7yI+ObRrBOA/sDnRxsQSrZOo90UmPu6312Ex5nT6PLNJg9Mp4/M5UdXAMSO3xMTETokCH7G5HBCeDwfHi+V2XJGbIAgRi7sdD+QWbmddiyXoJJTxoyO0G99V58Yc1wFyU41NPdlChcFgbeq79lfU+wLE6DRUtlOS0B4EQcSQnI43v6TpNcXnR3F5ECSBb+5eQ/1+NwGfQnAfIKDT6khPT6aurg63x01ADbB7924GjxiA0qOK7uNSyBgUj6QV+eL2FXz0dR133RLb6ay15Wvc/PvtAVx00VTmvPYI89+Vm1l6C35xIklwzdSDu9kThxmRA/D86/VN5CYIAo/ereP2B5+i58C72ZB/CY73PuKGKzrvLDaZRM6cbObMyWZefTKRzdt9LFjkZFdxH8rKSnE4HEgakEQd8fHx1NXVUbKxhtLNtfw5dxeCCEk9LWQPTyRzSDwZg+Op2lOPqqgYemR2ej6NkKttwflZ4zBpwu8W0Qh/gw1DrLXT55nNGpwuuclrcazILS4u7phbbqqqEggEImK5/Y/cWkEkWpRHolV6uGi03MLRmExMDPr/fXhDJjdnnZuUnoevCzFiwua2hfQZjTDorXhtta2+t9/tI9WkI88eRtJKUjr127ZQ+/HPuLcV4C0oA0VBkATqSpyYjCZGjhmJw+Fgw4YN+AM+SktLESSBLsPi6TE+ldgxbgYJpxDbpQr1kD5oQ6d04+Mb/uT3FW7Gj+r4AvfL7y5e/XAgUy6ZyhuvzeLrN2Wio5rvfL/90UkgAOeefpCkhg/UI0lQU6vS4FCIjgqSoSAIPD/LwHV3PsHQUfewJ/8ynn1tHnfeZA35ugmCwMC+egb21bN696lo2Mbj98Zz7+M1oJMpLy9H1AgICPTo1oPevXuzbNkyKnfWUbWnnlXv5wWvf4wWUSvhLSxHNBnQxHeedOUaO0gSmUkJOPyBsLqzNyLgcaP4PCHF3MzmoOXmOYbkpkgBYmJijjm5NepbhkNuqqo29eU81jjuyC0Sje6OB8tNr9ejqio+ny9kd0OjPqcHF1GE1ki0MaHkcDBgwuNpvddZR2EwWHHUt55mX+rwkm7u3HWQnQ24igtwFefj3JeHt7oCBLB9/wcSAumpqUyZMoWRI0dyxx13UFJSwpLflqAGVAwxWnpPyKT7+BRyRiahjwoGoVTVjlIcwOJOx2Y6aAVmj0wkqWsUL75p7zC5ffezgzc/H8Sll07l5Zdm8dV//MRamz9SFZUyG7YELfgTBh9cvIxGkcH99azd6GXRby4uOPPgYiCKAnOeNjHt1scZd9K95OdP5eFnPuChOztvVR4Kr99Mpa0XkwbMZ5leQKMVuOXn06jcU0/e7xXsXlLO7t272bNnNwgCFouFO++8E4DXXnuN8vJyVKDq1S8AkOItGPvmYOiVjaFXNtrU+MPOz19ahc4aR3qUgVJnZNrLNLrCDUZrp881mzS4XEFyk9CE3Rw4FAREmaioqGNObpFo9Oz1epFl+X+WW2uIjo4O2y2p0+mOuQCpRqNBo9Hg8XhCJre0tDQkUcKjhN74ryMJJRAkNzngQZa9aDShzVdvsOLfv6FVa7XU6WVIYts3vKqq+O21B8isAOe+PfjtQStQECWizCaGjR7N9OnTufzyy/nnP//JBx98wPMvPB8suVAhsUcMuRNS6T4uhbR+sQitSc4LKtXR+SQ29GhGboIgMHRad+bP3simbV4G9m3/GnzyTQMffTeYy6ZO46UXZvHp6z6SE1sujD/8GkwM6ZIhYDKJzYSQJ4wysWmrl69/qGtGbhCMzb33gomLZzzGqWfdR37+dO58+D2effjwBNIWiquGERdViFFfy9x59XQbm4IpVk/2iESyRyQy+c7+1O5zkLesgt1LyyleV8P999+PqBGQBA0TJ07kyy+/ZPHixTzxxBNs3boVx5+bcfy+EQAxyoShTw7GPkGy03VJblHz6N1djDE1i3SznjJHhMktRMvNZvPj9dgwCOawunCECo/qwmAwHHNy8/l8iKIYVtZm49r9P3JrBTExMdhstrDGiESmYiQQbtxNo9GQnJyCp4NFyq3BWevGHHd4S6TRHeP12NBEJR/m6NYRFZWC4vfiq6tGH9c8pbrM6SXBqEUvCXgDKqqq4K2uxFWc30RmAVfwwRAkDbGWGEacdhq33347p556Kvn5+Vx88cXcfPPN3HTLjShyMAmi6+hkciek0G1sCjHJHeviXhm9m4ElF2Dwx+DR1je9PuCcLNa+v4d/PlTNL5+ntUkib86z8+3ioUydOo1/PzOLT173kpneetnJ9z8H421TzrW2eG/sSAPPvgZffOfhvZfVFoXger3Ix6+ZOe/qRznvwvvJz7+C6//1Dm88ldisY3dHoCgie/ePoV/Wtyz+w83O3T6m3tVSczIuK4oRWd0ZMa07ngY/e1dWkvd7BXuWlrN48WLiEuJACQqof/HFF5x55pmsWLGC++67jzVr1uBYtwPXmu2gqggGHYaeWRj7BK07XWYS3n3lxJ58ImlmHTvqDp8R2hH47XUIgoRO13lXmNmspbTUhcdjw6AawmmfGBICqowqBRU9jnVCicfjwWAwhOUdsNlsaLXaY/5d4Dgkt5SUlBZ6Yp1FJDIVI4FIzCMrqwt7y0tDPt9Z60KjkzDE6PHUt020B2vdbJhDJLcYS9CN6i7b14LcHD4/dS4PUft2sGfln7iK8lF8wWsjShoSE+IZf9YZ3HXXXQwdOhSAOXPmcPnll1NZVYkggKpAVIKB3ElB6yxreCJaQ+czu/waF7XmvSTb+7AvYWXT65JWZNJdA/n0puV88o2DS89vuft8cW4di1YOZ9q0aTz1xCzmveKhW3brD7LXq/DzUheBAIwd2ZJ4Rw83BnUN/bBus5fhg1ru3E0mkc/nmjlr+mwunf4A+VzF9Fve4r2XkjvV662sdiCS4Ccldhs3vGkjuUc0XYa1H4s1RGvpfXI6vU9ORwmolG+ra3JfFuUVcdbZZyEIAiajiSuvvJKffvoJrVZLXl4ed999N0uWLMG2JR/35rygtIsogKISqK4gyail2NZAZ7tmtwZ/fR16oyWkAu7oKA0Op4zHVYflGMTbPLiIjQ2WuRzLulwIuhTDtR4rKipISUk5LqTEjjtyS0tLo6ws3NiPgerq0IpUI4lIkFtO1xx2rckPudun3yNTv99BYk4sxZva3jToMCAg4HG3nhDSEWi1RozmRNxl+4jpOQB3eVHQzViUj6t0L7vuvJO4hv14CneTkZbKqaeeyj333ENOTjCF2+PxcNttt/H+++/j9XtRAyoIkNYvltyJQUJL7B4TkQen3LKdPmVnUG7Zik970A3ebXQyfU5J56aZFYwcaiCnS2OsTuXxF+pYsWUE06ZN47FHZ/HeC2769Gx7Mfh9pftAMTWMHt7yuFirRK8eOnbsDmYytkZuECzc/uadKE6fOosrr3kIuIaLrn2TT+YkYzAcfkEPKBJ7Sk+ia+oy8gu9LFjk5PQHB3fqOoqSQPqAONIHxDH+lj7UV7jIW7afPb+VU7iyildffZXX57yGoIqMHDmSTz75hIyMYKcGm83GXXfdxfz586mpqcHaUImjoYFVT96FISkdU1Z3zJldMWXkIBk7L9Pm2V+KydT54mutVsBq1VFd48XrtWGga6fHCBceXMTFxaHVao85ITRabuGgrKyMtLTwivwjheOO3FJTU1mxYkVYY0QiDT8SiIzlloVPdIfVyroyv4bEbvHtkpsoiJix0NAQ2sZClj3YbYWIgoht8yrqNqwImlqCiE6rJbdbN1JSUjj99NOb9WjavHkzJ554IuvWrcOvBJq6a+ecmET/szLpOjoZU2zkXRxuXR3VUXl0rR7FzpSfm7mjTntgEO9espgpMyr449sMtFq457EathaMZNq0acx+ZBZzn3YxZED7btAFi1xoNJCdCVZL6xbKxNFG8vb6+PZHBw//K77NseLjJL59N5rTLnuYGTc+DFzL2dPn8s17qZhN7RPcntLJiKKfLomrmHpTDdGJevqefvgWQe0hJsXEkItyGHJRDn5PgH1rqsj7vYKtC4pZsXI5mVldEAWBrjldeeqpp5g7dy5z584FYMeOHWzcuJFhQ4eyfft26tYuo3b1UgB0cUmYs7pjyuyGKTMHbbS13XmoqoK7rIikzHGd/g6JCQZcrgD19S78svuYZEp6cJEQH4/ZHJ72akTmEkZ+QCPKy8tDFs6PNI5LcisvLw9rjOPFLanX66mtDd0SgmATWKffQQAZSQjt56rKryWp2+ELby1qHHV1bYsKHwqftwGbrRC7rZC62nyczkpARRAkjEYDubk9uPTSS7n11lsxGoMk0NDQwNKlS3nllVd48sknKasoR1VUUFU0yXFYhvXGOKAblc9/TJdhCfQ7q/Pd3DuD4rh19C89h6SGnlTG7Gp63RCj45ynT+CDK3/j5nsq0WqgqGo006ZN49FHZ/HiI05Gj2h/IVRVla8XOpBlOHVi2ynzY04w8to7djZv91FaLpOe2vZvnJqsYf670Zx22UPcetss4HpOvngOP8xLbZM8bY4M8ivGMbbvK6zZ6OKzbx2cOWsIWmPkHn2tQaL72BS6jUlm99IKtAP7oOuSjGvtDvLy8vjb3/4Gkog1OoZLL72UKVOmMGrUKC69NNibLxAI8NVXX/Haa6+xYcMGbJtWUbdhOQCaaCvmrB6YMrtiyuyKLjahmYXjq6lC8XmIsXS+9i4p0UBlleeYlgG4cZGZ2fuYJ5NAkNxiYkLLym7E/8itHUSS3MKpMYsEImFBDhgwABUVB/VYCE0ZojK/lj4ndz/scRbiKHUX4Pe70WoPWiWqquLx2LDX7cV2gMw8niBpazQ6srIyGTv2dC677DJOOumkFtlWNpuN6667joULF/L000/z4YcfUlpejqF3NuahvTAN6Yk25aDVYhyUy85fyxh9bc+Qvm9HoYgyBQnLyd0/CbuxFO8h7sm0frGceu8g3p61gdNPPz1osc2exeN3NzB53OETF3bu8VNSFkyNbC3e1oixJxx874dfnVw3rf3asaxMLd++G80Z0x7kn3c8Asxg4gWv8/OnaSQmNH+cA4rExoIp9Ehbgllfxk0zK0nrbaH/2Udm01C+zYaj0k3qjGEY+3Yl9rzxBOqduDbuxrVuF/aNu/joo4+YPHkyF154Ibm5ucydO5e+ffty0UUXcdFFFzWNtXTpUp5//nmWL19O7Y4N2LeuAUAymjF16YY5sxumzK64K4oBgZiYzluiSUkGKivdOA54K8whltuEAw8uumRmHhfkFomYW3l5Od27H36tORo4LsmtrKwsLGKKRI1ZJGA0GnG5Qs90BOjTpw+iKOJQ7CGTW1VBDZaUaPRROryOtrUqLQQJpqG+GL3egs1WiK1uL7a6fHy+4MKv1xvo3r0bkyZN5fLLL2fYsGGtjrVq1Squu+46tm/fTkBVQVEQzUY2lu9j3JVT2D/jZERT6w+SeURf9r+0jdp9DuKyjmwxaIOxguqofHKqR7Mz5acm96TsC5C3rKKJ2B55ZBYP3FbPuad1LMV5wS9ORDHYJudQAvsr0lI0dMnQUFQi8/2iw5MbBLsEfP12NGdNf5C7Zz4Cp9zIuPNe59cv0khLOfhI7y49GVGQ6Z66mOfn2Ni4xcsVH4xE7GSmZUex4+cSpGgjhl5ZTa9JMWaixw0metxgVDnA4GqFvdX7sUkKy5cvp1///giCQHpaGjNnzuTmm28GYMKECUyYMKFpnK1bt/LUU0+xePFi9udvp2HX5oOfoTVQWrIKqzWb6Jh0RLFjy1pSopG8/Abs9iKMQnTIQgnhwCe5SUtPa/JuHEtEKuY2duzYCM0oPBx35JaWlobX68VmszVlEXUWjZ1k3W73MSW3mJgYPB4PXq835HkYjUZysnNwFNhDnoenwUd9pYPErnGUbG4Zd1NUhQZs1FGFiMTmje81dTM2m6IYMKAXp5xyCldeeSU9evRo9TMCgQCvv/46r7zyCnn5BQTkYJ2hNiOJ6BF9MA3phb5rGnulKK7QdeErr6vNHqymYb3RxBhZ92kBJ981IOTv3VEUx62jX+k5JNf3Zr9lB363zBe3r6J3XDDdf9asWfzzOhuXnt9xRY5vf3KgqpCVqSElqf3HbNIYEx9+Uc+i31x4PEqHkkT699bz+X9Uzr/6Ae67bzZnnH0jY855jcVfppGdqaXOkUlBxVjG9n2Z1Ruc3Pt4NSOv7EH6gNB1IduD3y2z8esiosYNb7PXm6CRGJyZwy7FTZcX/4m/vBrXht041+2kZEcht9xyC7fc9neijCbOOOMM5syZg9VqBaBfv3588MEHTWOVlpby5JNP8sMPP1BVXc3e/J8ObIglYixdiI3NwWLNwWLtgiS1zELUaARiY3VUVnqotxVhUWOPehmAqqo4sJOcnBy2OzASc3G73RGx3P6XUNIGoqOjMZlMlJeXh0xuEBRhdjqdTQ/HsYBWq8VsNmOz2Zp1Le8shgwdwuJ9v4eVVNIYdyvZXEFADVBPLXVUUS/WYhdr8Af86HV6+uT2JjY2lrPOOovLL7+clJSUNsesrKzk7rvv5rvvvqO2zhbsZCxpMOfkoo9PoWbVYhKuOQdj7+ymcwpVFyKQJRgpVFuX4hJ1WqJOGsHGr5cz7ube6M1HVjVCEWUKEv+gZ8VknLKd1+74hAGpI5qIbcZltVw3reP3Yp0twMq1HlQ1SFyHw5gTDLz7ST2yrLJ0uZvTJnUsuWDEYAMfvaowZcb9PPjQo5z/t5sYfdarLPx0EOXOq+mdsZCAr5RLbthPat9Yxt/Sp8PfobPYtrAEb4OPpFNPaPMYLQI9xSgWysEGsNrUBCypCVjOGIXi8uDako9r/U5ca3fy2Wef8dkXXyAJAn379uXll19m3LiDSSPp6em8/PLLvPzyy0DQ6vj000/56quvWLVqNUX7fkPZuxgQiI5OxRrXDas1G4s1G63WRGKCAY8ngL3ejaOhnHQGHrFr0xY8uJB0wfq2Y7lOQfD6BQKBsBNb/hdzaweCIJCenk5JSQl9+oT+MEZCgDkSsFqt2O32sMhtwIABzP/6u5BdtbLqpzC/kLiuFtaLv2FXawkoAaKjohk7dizjxo9j7NixDBs27LC1Nhs3buSZZ57hyy+/wuvzBpNBomKwDhpJVLc+mLt0Q9TqUFUF+7a1uNZsb0ZuCrBNaWCQZKFQbltnMubkE7B9+ztb5hcx7NKWxcaRhsNQyXbTYnpUjee0oV4mTpzIrFmzuOzcam6f0XYWY2v4aakL5cBGZEw7LslGHOq2XPCLs8PkBjB+lIl3X1S5/Nb7eXjWo1xx1T9ZX9CdnJTlZCUt44Kr91NTD1e9OQJJe2Q6XKmqyup5BZiH9kKb1LZl2FuMpl71U6G2jEOLJgNRJ/Ql6oS+qIqCN78U1/pduNbuYPPmzYwfPx5EkcT4eG644QYefPDBZjJRBoOBK664giuuuAIISkktWLCATz/9lOXLl1NSsoLifcsAMJkSOe/8iyku1lFTvQsVhZgQXf7hwIGdnJwctFrtMY+5OZ1OTCZTWB0BXC4XtbW1pKenR3BmoeO4IzcIZgju2bOHU045JeQxjhdys1gs1NWFp7bfv39/vLIHLx1LV/aqHmxUY6OaBk0tdrkOW95Abjr5JiadP55x48Yxbtw4+vXrd9ibWZZlPvroI95++23WrF6Ly31QVSJu2FisA0eiT2hZtCkIItE9+tGwegdx009v9v5quY6rdF34Xt6P3IZzUhMXg3lEX9Z8tJehU7q2LqUVQTiqPcy5/kOmnqPlzDPP5M033+Tskyq5/x+HF53+K75f5ECjAVmGce0kkzSiW7aWxHiJqpoA3yx08NJjiZ3axJw2yczrTync+9QLPPnkU+zfv5/zLpnD5HFGFvzi5KIXT8SSeuQyAYvWVlOdZyf1/gvaPW6kFMuqgO2w4wmiiKFHJoYemcRNmYxcY8e1YTeudTup2pLH7Nmzmf34Yxi0OsaPH8+bb77ZVFPXCI1Gw7nnnsu5554LBEWB//zz/9g77+ioyvwPP3daejLpvZMQkkBCJ3SUooCIK4gI2MC6NnRta1/dtXeRtS2KBVZRw36jkwAApKRJREFUFKRJTUKooSWk9z7pkz79/v6IRCLpM0jwt885OR6TO++8M8zcz/vtSXz55ZfEx8fj4S7nzJmDpKV+g4BAMTk4i+44444Ndn9IIlozDYyKih7weC+L7qW52WyrLTc3F0dHx46G75eaQSlu4eHhZGdnm7WGvb09lZWVFtrRwFEqlRQU9G1SdHeMGzcOgAbqLhA3URTR0Er9OTGT19OkVwMQ4BfAtVdew5QpU5g0aRLZ2dmsX7++1+C1SqXinXfeYfPmzeTm5mEyGZFKFbi4DiUwZBhOyiCOHn4LuaMz1u7duyAcwodTf+oQ2tzSTmNS8sVWmkUjIySOnDR1H0t0ujqO8ufOkn+4itBJA7d8e6OhvJWvViYyaeR0JoxvL0C+7babGR8hRxDS+rWWwSCybXcrBgO4u0kJDuj9KyYIAtMm2rB5ezPlKiNpWTqiI/oXo519RSg4rmLfvn0MGzaM21fez3vvvcfY5UMYMrV717IlOP5NPlb+7lhHdV8E7SzIGSKxY4O+/912ZK5OOM4ci+PMsZh0ejRpBbSeyqLleDq7du3CPyAAiaS9pu6tt97immuuuWANiUTClClTmDJlCqIosmfPHhwdHfHy8qKiooKzqWc5m3YSURSxkdnhaHRGKbqhxA17nC6K2DXTwMiRsZfcJQmWGTCanZ1NeHj4JS9GP8egFbctW7aYtYadnR3Nzc2XvBxAqVTS1taGTqcbcHsdb29v/P0CUJfW4CH60kITaqpRU0uTrI4WQ7uFGhEewbVXLu34Ev/+NFtTU4NKperoCHI+8fHxvP/+++zff+DX2jwRGxtXfP0n4uoWgZNTIBLJb1aei0sYTVmpuI6b3u2+7QLDkCtdaNx15IIZYEeMdcTJXDip617crMIDsAr25uj63IsmbnVFzXy1MpGp465g+bLlvPji8zTU5/Hqk62czl9Oi3Yvod7xSIS+BTyPnNDQ2NR+bWOjiatvKmf0cCuCA+XY2rS3qpK6wPfbmmltbaWyysjJVC3xh9s49zHdvqelX+KmqoviePYift6+jS27NiO2WfHSSy/xwIMPsObDDwga637RBE5d2kLOgQpcb1/Q4/dsgtSZDFMzTZg3rUOikGM7MhzbkeG43jYffUklLSezaE3OJDc3lwULFiCRygjw92PlypU8/vjjyOWdY7ZNTU1otVrGjBnD+PG/xQjVajWHDh0iMTGRA/sPkHwiGYPBgJXUGkfRBSeTK8644YDzgIcHn0+bvJnAwMBBIW4tLS1mW1znxG2wMGjFzRKWm16vv+TlAOcnlXh4eAx4nWnTp/Lfr7+lWlqGxtCGVCIlNjaW6TMWM3XqVCZNmoSra8+xIS8vLyorKwkODkaj0fDJJ5/w1Vdfcfp0CjqdBkGQ4KQMYkh4HK5uQ7G17d4l5+4RSWb6DxhampDZdZ0eL0gkuIyaTGX8NlyWX4VM+dt1x41qrpJ54itYUyZ2XXAvCAJOC6dT+PYGCo5WETx+4O9fV1TlNPD1qoPMmDSTFctX8Pzzz9Eoq6K6TuTW+/bwyduNFFXfhKoumtjQ/+JgU9Xrmtv2tHS4JP3GeVBogqMb1TRWt8eZbGxs2LABVq2upK2tDRsHGZ4RTvhMcqZ2awkAP+1q4bH7endV6Qw2pBZcS3HlUN7/4GNKtBncvmkmpzYV8PTTT/PPf/6T++69n/dWv8eCl0czbLblYyHxazKQOtpiP6X7hAw5AnFSF77Ul1j0uQVBQBHghSLAC6fZ4ym882Ucw2MQjUaK8jN45plneOa553BycGDmzJm8/vrrBAcHo1KpcHd3v8Alr1QqmTt3LnPnzgWgra2No0ePtovdgQMcPnSYXE0qMokcJ8EFR6MLzrjhhGu/GywYRSN6qRZbW9tBIW7Nzc1dHnr7w//ErQ+Eh4dTWFhoVgq9TCbD2tqalpaWS96h2snJyWxxW716NY0NjcTExjB16lQmTJjQbzeCTqejvLycsLAw8vLyEUUTMrkNbm5RuLlH4OwS1udxN65uw0AQaMxKwWXUpG6vU44YT1XiDpr2HMd50RUdv2/FyAmjmikyVzb24KqyGxeJdbg/e99MY+VGd4vF3srP1rPhroNcOW02N6+4meeefxatsp4VH0ylPLWeH/92lMnzkvhmbRGufgtJPPsg4b67CfWORxC6K2KAn3a2dyUBmPFQNB5h7Sneoihi0JowtQmghgf2XI1gY+qoORNNIln7KtC1GDh6QkNtnRFXl+7joar6SE7lXU9WZg7/fOV+om/04oaVk5BIBeJuC0fXYuCpp57iX//6Fw/c/wDvPvEuulYDMQsDu12zv1Sk1ZO2vQS3O65FYtW9V2KUVEmTaCDHZJkpAF3RejobDEY8ps1F4eSCaDTSWlpAU146TdmpfP/993z/ww/I5XLeevNNvL29O1ltXWFjY9NRb/fMM8+g1+s5deoUiYmJJCQkEH8ggYLGDCSCBCepC44GF5S0uzLlQs9emibqCQ4JRiqVXvJkEpPJREtLi9kxt+zsbObMmWOhXZnPxUmfMhNfX1+srKzIz883ax1LTPW2BEql0uwxPqNGjeKnLT/xj3/8g5kzZ/ZJ2EwmE1u2bGHu3Lk4OSmJiopCpVLh6zeEgKDpjB57L5OnPs2wqEW4e0T3a46bQmGHm1sE9SeS2uepdYPU2gZl9FgafzmG+LsBsgnGWkZKnFD2MCBSEARcll1NVZaa1J+L+7y/nig+UcPXKxO5ctpsbrn5Fp57/jmarWpY/O4EZAopAaPduP3bK7EJUDJrcSE/bFrP2LB/U1w9loNpf6WpretDSmGJnqzc9vo+hZ0M99DfLFVBEJBbS7Fxan+P5bayTsXUgkQgYLQbgkRAFGHn/q6FQGew4UTOjRzJuIEP1qzjhddf4+rXopl8Z0Sn9ab+dRjh8934+9//Tnh4OA8+8CA7XjjN8W/yzH7/oF2s97x5Fis/dxymj+r2OgkwXepKgrHWIs/bHS3H0rH28kPh1G7xClIpdoFD8LpiAWF3P0XonU/iecUCXMMi8fHxYeXKlUhkMnx8fHj44Yf7lHwml8sZN24cjzzyCD/99BN19bWcPXuWD9Z8wFWLZ6H1aOQMh0hgK8dl+8gUT6ESS9B2UfKippaIoRG4ufU/YcnStLS0tE93sDUv6WiwWW6DUtwkEglhYWFmuybPWUyXGicnJxoaBl6E3R/UajX//Oc/GTFiBFZW1lx77bXs2rUbucKbocOuo6xcYPbsGwkJnYWjk/+AxoScw89/ItpaFa1FuT1e5zJ6MsbGZpqPnO30+ypRS4qpkatkPVu01kMDsI+LZu9baWgau++w0hfykyrZcFcSs66Yw6233Mqzzz1LvahiyZqJnfot2rtbc+PHk5l4ZwTPv1HH7EVJWOtextWxgITU1ZzIWUZ9c+c44vY9LR1xs3NC1R8CRrvCr9mjP+/uLG6tWiVnC+ezK/nv7EkUueOO+6h0KGDlppkEdjG6RhAEZv5tOIHTnXjiyScYOnQoDz30EHtfP0vSp1kXXN9f0neWUXKiBuflV3dbtA0wRqpEisBxo3kZwz1h0ulpPZWNQ/jwbq+xcnHHdew0Zt3+V8patDjMWIhj5Cgq69W8/fbbODg6YW9vz5w5czh9+nSfnlcikRAVFcU999zDN998Q7mqnPz8fNZ9vo5FN1+HIkjkLEdJZBtHZbtJE5MpFwtpFZtpFOoYO26sWbW8lqKhoQFHR/OmbdTW1lJbW9ttk4dLwaB0S0K7azInJ8esNZRKJXl5ljmpmruP1tZWs5JKeqK8vJynnnqKnTt2oqqsBEQUCgc8vUbj6haB0jkEqbTdOioq0THvKo/2+WjdG1x9Qukcgq29J3XJCdgFdf+htnLzwi4onMYdR3CYHNvpbzsMlTyuCGO/sYbKLuqfzuGyYi6lD79D/JoM5jw5sILbzD1lbH7sOFfPuZrbbruNp595mhpNGSu+mIq1w4XWo0QqMPWeYQyZ7Mkv/zrNlAUFLF7wKc8/loBBNp3DGXfhaFtBgPsxfFxT2LLrV3ET6HVWWlf4j3Lj18Yw7NjTgk4vUN88lALVGKrUkaSeTebLL5/B4FzPwg9j8Rqm7HE9QSIw97lR/PjEcR5/4nFef+11Vq9ezdtvv42uxcD0ByIHdEPTtujZ/cZZ7MYOwza2+5O6HIE5Mk+26lXm9B/olbbT2YhaHY49iNs5hjnbkqHW4BgRg2NETPtUgYoSmnPTaco5yy+//MIvv/yCVCYnbEgoDzzwAHfeeWef6r8EQSA4OJjg4OCOejuVSsXBgwdJSEjgwP4DnE070TE5PigoCCenvne9uVio1Wqz4345OTl4eXld8k4r5zNoxS0iIoK0tP6lYf8eJycnGhsbMZlMZo1ONxeFQoGtra3ZcbdzmEwmTp48yc8//8xPP/7E6TOnAXBw8CFkyGxc3SKws/Ps8sZVUdGGIAEvTxsqVN0XUfcFQRDw959IVsaP6NS1KJTdJ7S4jJlCyabP0OSUdMqcrBP1HDXWM1fmyTp9925HmYsjykVXcPKbXUTP8+93G6nUrcX8/OwJrr5qLitXruTvT/0dVWMJt6yfhq2yZ3esz3AXbv5qBqlbitj1YTrfTz3JtVdnc//KrXi7TKKgchKphdcSM/YoVg7Z5ObmEjSy/9md3hHOhISFEBIYQmhoKNuOxmEwGPll9wG27/gQp2ESxj0eSsCYqD6LkkQqcO2/xrDpoSM89vijvPnGWzzyyCO8+eab6Fr0zH4ipt8WZsKaDDSNevxumdfjdZOkLrSIBs70UO5hCRp2HsHGLwgrt54zQmWCwBAnG3aX/DapQxAk2PoEYusTiMfUq9E3qmnOz6ApJ42snGzuvfde/nrf/bg4K1mwYAGvvvpqv7IKvby8WLRoEYsWLQJ+y8jMysrCxsZmUCSTqNVq/P37P1XhfNLT0xk69OI2Ou8vgthTwOQS8sMPP/DCCy9w5syZAa8hiiLbtm1jypQpl/yElJycjL29PREREQN6fHNzM7t372bbtm1s+WkL1TXVKKRWuJg8cBRdyeY0kdFL8PSK7XWt2TN9aG7Rc+hw9YD2cj5Go45DB1/BceR4vK5Y0O11omgi95NXkAe74fXo8k5/s0fKU1bhfKQr7LYlF4BoMFLxwifIm+tY9e0MbBz7ZgWf+DafXf88w9y5c1m1ahVP/v1JymqLuGX9NJx8+hdn0LcZSNlazMlv8qguaGZ4pBV3rXDEytafvYfDCQsLIyQ0BKXSiTZ5I61WtbQoatFLWzH9Wk4QXnUFOe4HQACZUYGtzgU7nSu2Ohd0Gj25ubkU5Odx4sQpCsoyiZjrx+glISh9Bx7wN2iNbLz3EI15Bt5+620yMzN5/fXXiZrvx7znRiKR9e3wl5ug4tv7D+Oy4mqU87pPJLJGwtNW4XypLyXLdPGaKeiKVZQ+9gG+167AadjIHq8dqrThmiA33jjdt6xNk15HS3Fuu1WXfRZDSyNIpHh7ejBnzhxWr17NiBED631aXV3NyZMnmT179iUtVRJFke3btzN58mSz7pF//etfsbKy4q233rLg7sxj0IpbUVERoaGhNDU1mdUx++DBg/j7+xMYaLkssYFQUlJCXl5ep07nvZGfn98uZlu2EH8gHr1Bj6NMibPBAze8ccK1o97mKHuxcfcjasRNva47JNSBuAnufPm1eQk758jL2UFpxTHC73sOiaJ7K6gh/SRlW77C+7mV2AzrnHZ8lcyDUIkda3Q9F7zrq+spf3INIWOcWfTO+F5vDEc+z2bf22nMmzePVatW8cSTT1CsKuDmL6biGtS3Dv9dIYoihUeqSf4ml7ykKkxGsb0EwNjukrzt33Ow07pip3PBVuuK3GSFIEqRmGTYGJxolddjkhgwCnpaFfUdIrj7P8c4+HEmolHEwdOa+3ZeZbEMUV2rga/vOEhbuch777xHZmYmr7z6CmHTvVj46the23M1qlr5ZPF+pEOC8Hx0RY/v/dUyD4IEW9bqCy2y9+6o/vhH2o5nE3b3Mz3G/gCuC3FDbxT5uaj/yS2iKFLy3Sfoy1VYKRxobCwFRGxt7Rg3biwrV67kxhtv7NQSrCdSU1MxGo3Exsb2ey+WpLm5mf379zNv3jyzvFsTJkzg/vvvZ9myZRbcnXkMyoQSgICAAJRKJSkpKb1f3AODJanE09OTxsZG2tq6t0wMBgMJCQk8/vjjRAyNIDQ0lIceXM3pfWcJNkQykasYZ5xJmDACZ8G9UyGpu+hNbU0WRqO+170UFDZjayPD28syYzZ8/SZg0mtRp53o8TrHYbFYe/tT9+VORFPnKMx+Qw3ugoLRkp5Pj3J3Z9zuvp6cAxU9Zv6Jokj8mnT2vZ3GNddcw5133skTTz5BQUkeyz6ZbJawwa/xlTgPFr8/kQf2XY3CVoooShEkAoFj3NHLWlHblVDmfIYcr32k++wgzfdn0ny3AZDus4N0n+1kee+mxDWZWvsCNIpG/Ee5IhrF9kzfSg3qMsulzytsZSxdOxG5q4kHHnqAqKgonn7qaXIOqPjuwSPoNcZuH2vUm9j8eDImhTXu9yzqUdg8BCumSt342XBxOwQZm1ppTjyD88hJvQqbQiIwwtWeUzUDy54WjUZaSwrw8R3H6HH3MmnqUwyLugE7+xASEw+xYsUKrKxsGDp0KE8++SQqVfdT70VRRKVS9diU/I9CrVbj6OholrAZDAbOnDnD6NGjLbgz8xm04iYIAqNHj+bEiZ5vmL1xrnHxpUahUODi4nJBS7C6ujq++eYbli5diquLG9OmTeP9tz6gMVvLCOKYKs4nVpxMgBCGrdB9+r8X/hhNOmprMnrdi9EokpndQHSU0tyXBYC1jTNu7pHUH0/ssSxAECR4zrgGbX4ZLb/LnNRiYpO+nOvk3jj0Egq2GzMMp7kT2fdWGuVnL8zCE0WRPW+kkvRxFtdeey133HEHjz3+GHmFudz00SQ8wi3rom6saEPXasTZ2RnRKOI/qn+Nls/Hd7gzwrmUfgFyEywrENaOCm76eDKCvZ6/3v9XoqKieOH5Fyg6Ws3Ge5LQtnR9OEr4MIOylDrc778RqUP3rlwBWCr35ZCxjpIeXMyWoGn/CRBFnGPjer12hKs9tRo9ZS0Dy7ZtLc7BpNfi5j4MaC+F8fIeSfSIm5g87VliR92Br18cJSU1vPLKK3h7++Dm5sbixYtJSEjovO9fO6QMhh6MlkgmSU9PRyaTDaoyABjE4gZYVNyMxu5PpX8Unp6eHSe69evXM2nSZNzd3Fm2bBk7N+3GtcmHsVzBRMPVRAlj8BB8kQl9G/diKzjgKLigKj/Zp+vT0tSEDXFEobDMR8DPfyLaukqa89J7vM4uYAj2YVHUfbULk6bzjeasqYkMUzOL5b3Pg3K5aTaKIG9+ePQ4beeVB5iMIttfOMXxr/K4/vrrWbVqFY8+9ii5eTksWTMRn+GWb1KbE69CkAgdVnlOfAVZe8upzGqgrVHXo+BDexyvpqCJ/KRKjn+Th9xa2lF7lH3AvKn0XWHnYsXyT6dgUmi56567GDZsGP986V+Up6j5etVB2ho6/7vkJVVy+D/ZuNw4G+uhPU/xni51wxopOy+y1SYajTTuOopjRCwy295rPsd6OnC8auA1rw3pp7Cxc8PO7sJEIYlEirNLCEPC5zJh0qOMn/gIQ8LnYTQ58f33PzBt2jSsrW0YP348H3zwAaWlpV12SLkUWELcTpw4wciRIy9p0l5XDNpsSWgXt5deesmsNezt7ZHJZKjV6l7bU11svLy8yMzMRKPRcNeddyHXWTNUjMUVb6yNNmYPS/QWA8iuTUGna0ah6PkLX1OrpbZWS0S4EyldWD/9RakMxsk5mKr927APiUCQdP/F9bpyIXmfvkr9D/txvalzR4PN+goesxrCaImSEyZ1t2sIMhnuDyyh/O8f8t2DR1m6diISqcCWp5LJ2FXGkiVLWL58OY8++ihZmVlMuSeiy3owS5B9oALE9nZNEjtrTm1Tcfyr31ymcls5Dl42WDvIsbO1YczjN/HN3Uk0qVtoqmxDo/6tBEJio0DqYI/Y1oCttQ3FJ2rQtugtPtPOwdOG5Z9N4fMV8ay6cxXrPlvH66+9zqOPP8qXtyVw0yeTsXe1piqnkR+fSMY2Ngyn+d0nkEC7O3K2zIN/6wrQdzuK1jK0nsjEUKvG5Zrepz572SrwtFFweoAuSZNOS1NmCoEBU/uU/GFr64ZtgBv+AZMwGDTU1eZSW5PJyZOpHDt2P6+++ionT57Ex8eHp59++pJNBTAajdTX15sd9zt58uSgc0nCILfcRo0axdmzZ9Fouu492BcEQcDNzY2amhoL7mxgODg4YGNjQ319PWPHjsVasMVXCMFasEzsyxN/QKSqsm9xytS0eoYPV1rkuQVBYEjYXLS1KtSpx3u8VqF0xW3ClTT8nISurHO/xlaMfPere9Kxl7OX3MMFz8dupvysmh8fP86m1UfJ+KWMFStWsGLFCh5//HEyMzOxCvYm6ZNs0neWmv06f09zrYbKjPZhtCYBHK4ci/+HTxCw9nF8XrwLjweX4HDdlRjCI2lUBqC2bp+i0OTgh8Y/DJs5U3C/93q8n12J/3sPE/jZ07jeeg2YROLi4hCNIgWHe+9pORCUfnYs/2wyGkMLt628jSFDhvDu2+9SX9zK+psTKDlVy4Z7DoGzMx4PLEHo4WQu4Zw7spaii+yOBGjYcRgbvyBsvHpPYR/v6UhqbQta48AEtynnLCaDDk/vnrMxu0Ims8bDM5phUYuYPO1ppk5/iCFDwoiPP87bb7+Nu5s7UyZP4bXXXiM9Pb1XK9+S1NfXd/S+NYcTJ04walT3XWouFYNa3IKCgnBwcCA1NdWsdQaLuEG79aZSqVh43ULqqMIgmtcl/XwUghWueFFZfqpP12dlN2JnK8PP1zKzvhwd/fDwiqE6fgcmXfcF2QCuE65A7uRMzadbL0guSTM1kW5q4ka5b6/GrHV4AG5/XUROfAX5SSruWHUHS5Ys4cknnyQ9PR33e/6Cz4t3YztxBD8+cZz4D9IxGSxXUpyX2O5+mzFjBhhN2EQEIQgCMmcHrMP8sY8bjvKaybjdOh+Pe67H7a7rAHBbdS3uq67F+brpOEwdiU1kMHIPFwSJBKtfXX8lJSVIZELHc1wM3EIcuenjybRomrj5lpsJDg7m3x/+m6ZKDV/emoDOJMfziVuR2Pbc/3Cm1P1Xd+TFEeLz0RZVoMkoxGX01F6vtZZKGOlmzyHVwOPu6rMncFQGYWNjnoUlCBKGDw+nqlqLtU0QMuSEiTFkHcrnqSefIioqiqCAIB544AF++eUXtNqev0PmUlNTg5ubm1mlCAaDgdOnT//PcusvgiAwbtw4Dh8+bNY6rq6u1NfXD4q42zlxW7BgAUaTgTose+PyJoDGplJaWnq/yRiNImfT1MTGWM4tEhI6B6OmlZqj+3u8TiKT4zPnBjSZhah/Srjg7z/oy3EWFMyT9VwMbWxpo2HrQQAefOAhrr32Wp555hlSU1Nxvf0aHKaNQpBJcb/7LzjfMJOkz7L56o4kmqosY13kxFcgSAWSk5MBOoTJHKR2Nsj9PCgoKEAqyMg+UIFoungneq9hSpb+exLNrY0sW74MPz8/1n22DolEgkGrx9Tc2uPjoyQOTJe58aW+5KK7IwHUm+OROTr1qSPJWA8Hylq0VLQOLJHE0NJES2EWXn2oH+0LIcEOFBQ0UVOVjite+AuhxDCRKaZriGUSplI5/1n7OXPmzMFZ6czCaxfy2WefUVFh+djrOXEzh9TUVGQy2aAr4IZBLm4A06ZNIz4+3qw1HBwcOuJulxoXFxdEUcTFxYXh0cOpFCw7BsQdH+SCNWUlR/p0fUpqPYGBdjg5WiamY2PjjH/AZGqP7EOn7rmeyC4oDLe4mdR/u5e29M71bRpM/EdfxASpS7flAcbGFipe+BRtfhn/eKG9ofSLL77IqVOncFk6G6fZv3V9FyQSnK+bjvczt6Mq1PLJ4v3kJ5l3sDDojOQfqsJaYU1BQQFyPw+kdpZxMdtEhaA3mYiLi6NNraMiXW2RdbvDL8aVG96fSGNTA0tvWoqnpyebNm1C0Ogpf+4TtPldT27wEqxYJvdjo76M8m5GF1kSTU4JLUfO4jG5556W0H5zm+DlyKGKgVttDeknEQQJHp4DK9Y+H6lUIMDfjozMcppbVHjwW+KUVJDiJngTIYxkgmE2E5iFryaUxG2HuOOOO/Dx8WHUyFH861//sojr8ly8zVxxO3DgAFOmTBkUyTG/Z9CL24wZM4iPj8dkGrgrSRAEXF1dB4VrUiKRdGRN3nLrLdQIKvSiec2AO60vSPETg1GVn8Bg6P1m09xiIC+vyaLWW1DwFSjkdqh2b+71WvfJc7D1D6HqvW8xNnau6aoWdXypL2GR3IeA38UlDXWNlD/3MbrSat58/Q1GjRrFa6+9xtGjR1FeNw3ltV27rGyGBeP7yn1IgvzZeO8h9r+bhkE3MIu+5EQtBo2RRYsWYRBN2PQwibq/WEcEgtHIzTffjCAVyE3ovm7KUgSNd+f6t8bT0NDAkhuX4ODgwJYffwSdgfIXPkOTWdjpelukrJQHcsBQQ4qp8aLvTxRF6r7aiZWHN07RY3q9PtKlPZaUXt+z5dnT86nPHMPVLQK53PxDi7+fHa1tBtLTjyJBiitd17kJgoC94ESQEMEo0zSmiPOJYizFp9t7yJ461bewQ0/U19ejUCjMjrcdOHCgX40p/kgGvbiNHj0ajUZjdp/JwRh3W7p0KaJoooru55kNBD9CMJn0VJT3rYwi+UQtUZFKHOwtkzwrlSoIC59Pc157M9qeECQSfK9ZDjoTVR9suiD+lmVqZoehitsUAR0JJvqqOsqf/Rh9ZT0ffvABQ4cO5b333iMhIQHHq+JwvmFmz/tztMPzsZtxWTqbI1/k8vH1+8g/1H8rLjdRhUQmcOWVV4LRhFVI7yUMfcUqtH2w6Jo1a8AE2fst75bqCrcQB2xdrGhsauCmm27C2tqandu3IzGaqPjn57SmtE+AkAC3yP0pFdvYbTS/jVtfaD2egSarCM8ZC3pMbjm3v5n+ziSUqwfsKG0tzkNbU4GfX+91dH0hJNie/IJmVOUnccO7z2U+CsEKbyEQJW7YWNsQGRlp9l4sEW8zGo0kJCT8T9wGilwuZ/LkyRw4cMCsddzc3KirqxsUcTcPDw+am5txcnJixhUzqJJaNovPSrDBAz/KipMQxd4t3ppaLXn5TUwYb7miUjf3KFxcw1Ht3txrconcwQnfa5bTlppLw9bEC/6eYKwly9jM7YoAhPI6yp/5GENdI1+sW0dAQACffvopv/zyC/bTR+F689V9+sIKEgnKa6fi+8pf0dq7sfGeQ2xafZS6or71QRRFkay95WASWLduHQhQvfYHSh56h8p3NlL71U4adh6mJTkDbUE5xoZmTK0aTLr2ImlTmxZjUyu6sipaT2fTuOc4df/dTdWaTZQ9uZaSh94B2rut+/j4UJXdQFPlxc1CVGWo+XxFAnqFHU6LZ1FdXc2KFSuQSCTs2b0biQiqV9fTkpzB9TIfbAUpG/Slf0CUDUSDgbpvfsEuOBz74N7jOyPdHZAgmFXbVpeciK29J0pny1jkwUH2pKcX0dJSiTf9j83WySq5eu7VFhluWl1dbXZpVEpKCiaTiZEj+59F+kcw6MUNYPr06ezf33OCQm/Y29tjbW1NdfUfc8rsCblcjqenJyUlJdx8883UGitpEy07pTiAIbRp6qmt7dtMvMNHqgkPc8TFxTIjeQRBIHzoAoytLVTs2tRrnMA+eChuE66k7r97L3B/AXxnKKelrY3bte5ItQa+3bgRV1dXvvnmG3786UfsJkTjfufCXk/0v0fh74nXMyvxuG8xBSnNfHTdHn555QzNNT27dGsLm2lUtREZGdkeVLd3wveaZdj7DEWs0NB6KJO6L3dS+cbXlD35IUV3vULh7S9RfNcrABTf+xpFd/yL0kfeQ/XKemo+20Lz3jMYC9RY2/ngNfM67IdE0dLaxrvvvgtA7sGL55o8u62E9bclIrq44v38nThfOxXXm+dSUVHB7bffjtFo5MD+/cgkUubWWRGut+IzXTG6P0Ta2lP/9ZW1eM7ovjn3OWSCwEw/Z3aX1jHQPByduo6m3LP4+020SGPjAH87JBKBY8fikf+a1dwftGIb9YYaFi5caPZedDod9fX1eHr2f3LF+ezfv58pU6b0uZ/mH83g3NXvmD59Oq+//rpZo2sEQehwBw6Gnm6BgYGcOXOG6667jrvuuguVpoRgBjYxoCscccFRcKGkMBE3t97XbWzSczZNzcQJHvy83TKWpI2tKxER15F+9r/YBgzBOWZ8j9e7T5lDS2k+le/8F9+X7kLmpuz4W0tOMc+//jLPPP4k3/33v0gkErZu3co3GzZgExOOx32L+y1s5xAEAfvJMdiOi6RxxyFO/ZTAye8KGTbbl7E3hXTZ1SQ3QYUggQ0bNjAiNhanqNEdP+cQRRPG1hb0jfUYmhoxGQ1Y/9pay2fejWiNIlJbe+SOSuQOThcUvktkcppz0wgLC2uPu8WrGHl954bT5qJvM7Dr1RRSNhdhPyUGt5ULkFi3N792mjsRk0ZL8bd7ueeee/jwww/Zvm0bpaWl/H31Ixjmj8dx5liL7qcrDLUN1G/aj8voyVh79O76jfNypFlv5GztwA+M9aeSkMms8fSOHfAa5xMVqSQ9Q42q7ASeol+nvrB9oZpypBIpc+fONXsvlZWVODo6mtWQHgZ3vA0uE8tt9OjR6HQ6zp7tOX7TG+fEbTAMQjg3162lpYXrrruOalmpRfclCAKBYjhqdT5qdWGfHnM8uQZ/P1uLNVQG8PSKxdtnLKrdP6Cp7tnyECRS/BasQBBlVLy8HuOvKehtZ/Oo+MdnaJtbmRgXhyiK1NTU8Pn6L7AeFoTnw0sRZOZna0kUcpTXTsPv/b+hXDqH7OQmPl8ez7pl8aRtL8Go/83Fm72/AlEErVaLaDRi6x964esRJMjsHLDxDsAhPBqnYbE4DosFwHHoCByHjsDOPwSFk0uXHV1s/dvdYa+88go2VjbkH67CoLWcW70mv5H/LIvn7LYy3O++Dvd7F3UI2zmU103H6Zop5OTksHPnTtra2hg6dChqtZqaT39CvS3JYvvpjtr1O5DIrXCffFWv11pLJUzzVbKrpG7ANqVJp0V9+gjePmORSs33ZNhYSwkJsSfpcApaXdOAXJJV0lJmzZ5tkS5LKpUKb29vs9YY7PE2uEzE7VzczVzXpKurK0ajcVCUBEgkEgICAigqKuKWW26h0aCmgf6P4ugJD3yxF5QU5u3p0/WtbUZOna5jUpz5A1XPJ2zofGytXSjb/EWf4m+BN9yFqb4F1atf0nzkLBUvfwFGE4cOHqS1tZXU1FRKS0t5/qWXCHzsZiQKy7amktrZoJw3Cb93Hsbz0eWoBSU/PZnMe7N2seOl02TtK6fsTC0uzi689tprwG9CZEnkSlektvYcOHCAm266CaPORFGyZZKiUrcW85+l8TRpFPj88x4cpo/u0v0mCAIuN83mnheeYtiwYfz73/+mra2NhIQEHBwcqPtyB/Wb9l20A2NrSi4tR8/iNWMBUuveD11TfZyoaNGR2zDw+KQ69ThGnQZfvwkDXuN8hg1zoqKijYy0g9gI9jjSv8zkVrGZOmM1t9xys9l7MRqNVFZWmu29OnPmDKIoXvKRPT1xWYgbwMyZM9m5c6dZa5yfhj8YCAwMpLKykilTphASHEKp0P0Il4EgCAIh4jDq6/P6bL2dPFWHs4uCoMDem9H2FalUQdTwm9A31FHxy/e9Xm/l6kHA4jvQFVRQ9c5GJAgkHz9OXV0dOTk5PPPcc7yy7iPwd+d+xwiUWFbcziFIJNiNjsD7qdvxe/1+FHGjOBtfz/erjyKa4KmnniIhIaHdtehk+f6AgiBgFzCEyupqPvzwQySydtekOTRVtbH5sWNsffoE1uOH4/PPe1H4dx97kSOwQuHPmOEx/HPzV+zYuYNXXnmFxsZG9u/f394gYdM+6r7aaXGBM7VqqPl0C7YBoThG9t7eyUEuZaKXEzuL63q9ttvnNBioPbwXD88YbGycB7zO+URFKklJraa68izeYkC/Y3gqirGztWPBgt7jjb1RW1uLQqHA0dHRrHV27NjBjBkzBm28DS4jcVuwYAH79u2judm8qb7nXJODAVtbW9zc3CgtLeWh1Q9RRRkacWA1Od3hjs+v1tvuPl2v05s4fryGSXHuWHJAsJ2dB0OHLqThbHKvvScBtNUqRIMBhUJBypkzVFRUUFlZyepHHkHm4YzbEyv4j6mUElMbq61CCbJQf87uUPh74nrzXPzefQTr2HCQCDz88MNUVddgFzjkok1TtvUPwWQwtHeoMAlk7S8fkIgY9SaOrs/h39fuIedwPR73LcbjnuuRWHfvdnNCxn2KEJwFOe/q8uCmGdiOiWTP3r2sXbsWtVrNrl278PX1pWFbEjWf/nRBKcdAEUWR6k9+wtTQgs/VN/Tp/b3Cz5ksdStlLQNvW6VOOYq+pZHg4CsGvMb5+PjYYGMjJelgPEaTHh+C+vV4URSplpWx+IbF2Nqa3yZPpVLh6elp9ud169atFhHbi8llI25hYWEEBwfzyy+/mLWOh4cHTU1NtLZaVkQGSlBQEIWFhdx8883Y2NhQimWmY5/jN+stH3V9z1Ouz5F6Vo1cISEqUmnRvXj5jMLLezSqXZvQ1nR/wKhLTqR8+0aUSiVnz56lsLCQtrY2Vt5xB1JnB3yeXYnU0Q4jIt8ZyvnFUMVdimDGSi273y4RRbRZRTjY2VNQUIDJaOgy3mYpzo+7jRgxguYqDdW5fU9vF0WRnAMVfLJ4P3vfTsNm8mh8316N/eSYHh8XKNiw2iqUcpOGD3WFNGNEkErxfPAGbIaH8v0PP/D111+jVqv56aefCAsLo2lvMlVrNiFaoNymaW8yLYdT8b5qCQrn3ktUvGwVjHK3Z3fJwCdcmAwGag7twdMzBls7y5TFxAx3ISNDTWF+Iu74YC30T6AaqaPJ0MDy5cvN3su5IanmxttUKhXJycnMmzfP7D1dTC4bcYN2623r1q1mraFQKHB1db0ovdoGwrkPWktLC6vuWIVKWoRRtGwt3jnrLT/3lz6d+o0mkX0HVEye5IGDg2XdDuERC7C2dqb0hy8wtl2YzVZ9aA+qPZvx9PQkOTmZ7OxspFIpi5csQepgg89zK5G5dHapJBnr+ExfxAKZFwtkXhf1Q63NKUFs07Jw4UJeeaU9rf9ixNvOYeXuhURhxY4dO9i4cSOCBPISe/c8iKJI6elavlp5kO8ePEKbnSu+/7wHt9uv6bVF2BiJkrsVwew1VPNfQxnG81IzBJkMz0duwircn3VffM7WrVtRq9Vs2LCBUaNG0XIohcq3NiDqB94QXFukovaLbShHxuH0awJOT0gFWBTqTmJ5AzWa3ifRd4f6zBEMLY0EWchqs7eXERJiT0LCKVrbaghgSL/XqKAYL08viyRuqNVq9Hq92Ukp27ZtY+zYsWaXElxsLitxu+aaa/j555/NLsT28/OjpMSyPR0HiiAIhISEkJeXx3333YfWpKESy+5NEATCxGgaGgqpqe55mOg5iotbyMlpZOYM8055v0cqVTB8+HKMTU0Uf/cZJn176zFRFKk88DPVCdsJCgoiKSmJzMxMlEolV8+bh8RGgfezK5F7dB3byjW18I4unwiJPXfLg3DuY/eH/tJ6MgskEj788EN27dqFRGGNldvF+5ILggRb/1BKy8p+bU7b8wBTg87I2Z+LWbcsnvW3JFBVKeD1+Aq8nrodq+Ce0+gVSLhe5s1CuRfr9EUkGruOXUmsFHg/fjOKQG/eff99EhISUKvVfPzxx0ybOo3Wk1lUvLr+gmG0fcHUpqXq7Y0onD3wunJhnx4z3dcZAdhfZo7Vpqfm8F6LWm0jhjtTVNRCasoe7AUlSvrXx9EkmqiRlbPi5hUW6d1YWlqKt7e32Wtt2bJl0Lsk4TITt7hf08CPHj1q1jo+Pj40NTXR1DTw7gWWJCAggIaGBtzc3LjqqqsplxVYPDjvKnjhihd52dsxmfp2qk48WIWzsxXRUUqL7sXWzp2Y2FvRVpZTuvkLRKMB1e4fqD2yj+joaPbu3UtmZia+vr5MnjoVQSHD++nbUfj2nMVZK+p4V5dPlajjUcUQJkpdzJ3/egEtxzOQSSTY29tTVl6ObUAIQj9rlvqLbUAIBoOBhoYGXF1cKUupo1XdOa7UXKMhYW0G78/5hS1PnaBR6oLno8vxee1+bEcO7TXGMkRix6OKIXgKVrylyyPb1HONmMTWGu+nbkXu7co/XnqJtLQ01Go1b775JtcuWIAmrYCKf63D1Nr3ZsqiKFLz6RaMdU34LbwZiaz3A4q3rYIp3k5syqtmgOPaAKg/fdiiVptMJhAdpeTIsQJqa7MIEPsfl61BhcbQxooVK8zej8lkoqysDH//3uff9URbWxu7d+/+n7hZGplMxty5c9myZYtZ65zrEFJaavnhlQNBoVDg7+9PXl4eq1c/hNpQSx2Wn4sVxnA0mnpKiw/16Xqd3sSefRW/uictawk5OvkzfMQyWgqyyP34ZepPJjFhwgR++uknMjIyCA0NZdSY0QhSCd5P3tqr1XEOLSY2GcpZpy/mCpkbd1nQitNX16Mvr2bEiBHU1dVhMBiw8++/q6m/2PqHgijy2muv8fzzz4MI+UntNW858RX8+MRxPpizi0Pr8pCPHoHfGw/g9ffbsBsd0WthuwIJf5F5s1IewAFjDWv1hdSJfXPtSe1t8X5mJTI3Jx5cvZrS0lLUajXPPPMMK5YvR5tbSvkLn13QELs7mvafoDnpDN5XLcbKpfdylA53ZEXDgEfaAO0jmg7+gpf3aItZbUPDnWhuNnA4aRdywerXQcL9o1ySz+hRYxg+vPfRPr1xrq+uuVMA9u7di5eXl0X6W15sLitxA8vE3QD8/f0pLbVs4bQ5hISEUF5ezuTJkxk9agxF0kyL781ecMKPUArz96LV9q2Le3FJC9k5jcy8wrLuSQClcwj29j7omxqYPXs2X3/9NRkZGcTGxhIVHY0IeD22AusBzEjLMbXwujaX2l+tuDips9lWXOup9lZma9as4fXXXwdRxNbfsh1DusLG0w9BKuOHH37gr3/9K4JUYP+7abw9fTvfPXCE3DNtKJfOwf/Dx3BbuQCFX9/qFM9Za94Sa97Q5ZFk7H/hs0xp357g42THrbffTnNzM2q1mgcffJAH738AXUkl5c9/gqG+Zy+JrlhF7bqfUcZMwKkPaf/Q7o4UgQNmuCMBqpN2I+oNhITONmud84mNcebkqSoqypPxFYORCv1zBTaLjdSYVDz8yGqL7KekpAQ/Pz+LZUlerOxgS3LZiducOXPIzc0lJyfHrHU8PDzQ6/XU15v3xbAUDg4OuLu7k5eXx4sv/YM6Y/VFsd5CiEQiCuRl7+jzYw4erELppGC4Bd2TRqOe1NPraWoq4+9PPMHatWvJyMhgzJgxREREYEJE7umKInDgxaZaTHxnKOdzfQlXytx5WBFKhGTg9XutyZkIUgkTJkxg8+bNCDIZ1p5+A16vrwhSKTa+QeTnt2e7Wiusaa7VYjd3Kn6v34/vmw+hnDepz7PkvAQrbpcHcLs8gHhjDR/qCqg1Y+ySzE2Jz3OrkNhZs/Avf0EqlaJWq1mxYgUvPPccelUd5c9+hL666++aqU1L5Tv/ReHsjtfMhX16znPuyO/NdEdq66qpP3GQwKDpWFk5DHyh8wgNccDGWsa+vTsxGfX40f+EoxJycXdzZ9GiRWbvx/BrKYmfn3mfVaPRyNatW7nmmmvM3tMfwWUnbg4ODlx99dVs2LDBrHWkUik+Pj6DJrEEYNiwYRQUFDB16lTGjL441ptcUDBEjKay8jR1tX07IOj0Jvbub3dPOlrAPWkwaDlz8j/U1eXyyisvs3LlSjIyMhg/fjzDhg2jubkF98lXYWpoo/y5TzDUqM16vmxTM69oc0g2qlkm9+NeRTCB/ayLM2l0tKXl4enebhUVFBRi4xPU68BMS2EXEIpOr0en03HTTTchGkzYRAWj8O97zZIzcm6U+7JaEUq9qOdf2mwODsBa6wq5lys+z65EsJYz48orcXR0RK1WM2/ePN575x0MtY2UP/MRuvLOHVZEg4HKN7/BWNuI37U3I5H33u7KUu5IURSp3LMZhZUj/gGTB7zO+QgCTJzgzuGjFeTn7seX4H6n/+tFHZWSEu67/z4UCvPbf6lUKmxtbXFy6nrob1+Jj4/HaDQybdo0s/f0R3DZiRvAsmXL+Oqrr8y+8fv5+VFeXm7WIFRL4uTkhI+PD1lZWbz40osXzXrzIQhnPMhK/x6DoW8Fr8UlLWRlNzJ7pg8D7E8MgF7fyukTn9DYWMyHH65h8eLFHcI2fPhw6uvr8b76BtzjZhK8/H5oM1H2zMfoSsycmo1IvLGWf2qzKTC1cLciiNvkAXgIVr0/mPb+lhhNPPjgg+h0OnR6PXYBF6++7ffY+oWAaOKDDz7ggw8+AImkPXOzD9ghZYHMiyeswpAi8Kouh82GCpqxbMmJwt8Tn6duR5BLGT9hAr6+vqjVaiZOnMhX69djbGyl/NmP0Ba1lzKIJhNVa75Hk1GI/19WYuXat6zTmf4uFnFHNuem05yfSVjYPKRSy8RlIyOckEgEdu7cjsGgJYjex/P8nnIKQSJy1113WWRPpaWlFnFJfvXVV9x4442DuivJ+VyW4jZ//nwqKytJTk42ax1XV1dkMtmg6VgCEBERQVlZGXFxcYwZM/aiWG+CIBDJaHTaFvJz+u6eTEyqRGElYerkgaW+67RNnEz+iJZmFV9+uZ45c+aQkZFBXFwc48ePb++eMHNhx/QAhbM7QcsfQCa3p+zpj2g6eHpAz3s+GkzsMFTxL20OjaKevylCWSkPIEzS80Ti1pNZIJXwxBNP8P7774NouqjF27/HxjcQBIGvv/4aa2tr5FIpLcczenyMp2DFYpkPz1gNxVOw4l1dPl/rS/ucMDIQrEJ98XriFkSJQGRUFOHh4ajVaiIiIti2dSumVi3lz3+CJreU2s+303LkLL4LVmAX2LfEnBGudoz3cOS/OVVmuSNNOi2q3Ztxdg3Dzd0yyRFSqcD48e4cPFROYd6BAVltoihSLivgxhtvtEgdWVtbG1VVVWa7JNva2vj+++8tUkz+R3FZipuNjQ3XX389X331lVnrCIJAYGAghYWFltmYBbCzsyMwMJDMzExefPHixd5sBDuGEE1Z2VHq6/rWFUWvF9m6rZSwMMd+lwdoNGpOJP8bTVsdm3/8gbi4uA5hmzZtGvn5+bhPvRrXMVM7PU5u70jw8vtxDBtB9QebqP5oMybtwF1R52jCwPeGCv6pzaZc1LBC7s+jiiFMkbpgS2dXoyiKtCRnYKNot/LaP3cCVh6WT7LpDkEqxcrVi4yMdkEbOXIkBlUtelXnZttSBGIkjtwtD+JhRShSBN7X5fOJvohyse9p+eZgMywIr0eXIwoQHBxMbGwsarUaT09PEuPjEbV6yp/9mMZfjuA9ZxGOQ0f0aV1fOwXXhbizMbeSajOKtQGqErZjbGkifKjlkiNiRjjT2mpg/74dGI0Ds9qqqaDF0MQDDzxgkT0VFxfj4eFhduuun3/+GU9PT8aMGWORff0RXJbiBrB8+XI2btyIwTDwTgjQXmNWW1trds9KSzJ06FCqq6sZM2YM48aOp0CaflGyOv0JRYkbmembMBr7JhhNTXq27Shl6mRPfHz6Frdqba3h5PG16HVN7N69i+jo6A5hmz9/PmfPnsV1whW4xc3s8vEShRW+82/CZ+6NNB9Moeypj9CVWUbwGzCww1DFi9osDhhriJU68ZzVUJbJ/RgucUCBBF1hBabGFq688kqAXw9DItnvPUf+f96iYtcm6pITaco5i6ayDKOmdcD/XiadFm1NJc35mdSfPkzl/q0Ufr2GrLf+jramAo1Wh8lkYu3atSC0Z3BKgFDBlgUyL561Gso8mSfZpmZe1Gax0VBG2R8kaudjGxOG54M3YjSZ8PL2YsyYMajVamxsbEg9c6a9b6kgIHNQ9mk9B7mU5eFe7CutJ1tt3kTy1tIC6pIPEhIyG1tb81Ljz6FQSBg72o3Eg2UUFSQMyGoDKJPmMXbsOMaONX9OnslkorCwkKCgILPX+vrrr1m+fPllkSV5DkEcLLnw/cRoNBIYGMinn37KVVf1PuepJ44fP46trS1RUVEW2p35ZGZmUl1djSAITJkyhUjG4iMEWvx5WsUmjrAHb7+xhEdc2+fHRUcpiZvgzsZvC2lq6v4U3dys4vSJTxEwcDApEVdX1w5hu+mmm9i1axfOIyfiNfv6Pn1xtDUqSn78An1jHW4rr8FhWt/SxvuDp2DFWKmSaIkjzoKcjPJikrbu5JOPP8bX1xeZTI6n90icHANoaCimsamEttZaRNNvMSyJ3Aq5gxK50hmZvSOCVIYgkWBtbcOHj/6Ve199nzatBtFoxNjajL5Bjb6xDqPm/J6nAlY2Tjg4+OHk5I8oiuTn7mTv3r1MmTKFGTNmMGHWDEbHxGIE0o1NnDCpyTO1/EHzsXunKfE01Ws2YW1tTU1NDUeOHEGpVOLj40NwcDBanR6/a1fgGNF9r0uZILAq0ptajZ7v8qrN2o9JryP/P29iLdowavRdFivAnxjnjqeHDW+/8wWFeXuYxFX9Frd6sZoTxLNp0yauv/56s/dUUVFBamoqs2bNMkuUamtr8fHxIT09ndDQP84Vby6XR2SwC6RSKUuXLuWrr74yW9yCg4M5duwYERERFmlzYwlCQ0MpKChg5MiRLF68mG2bt+Np8EUqWPafzFZwIEwcTlbpEZxdQnH3iO7T486mqXFzteKaeX58930hev2Ft9OmxjJOnfwUuUwgOfkENjY2HcJ2zz33sGvXLpyiRuM1+y99/vJZuXkRcvNDVOz+geq1P9CWVoDb7df02N2+v1SKWn42VPIzlbgLCvySzzJt6lROnTrF6dOnufXWW9AbfGhtc6Fefa59mAmdrgWNph6tpgGNRo1GU4+mqQFdbQWiaMRkMsKv+9Tm5qLT6JBIpMhltjhY+2DlG4m1tbLjR2HliOTXIab2djJcXCSMH+tMfn4+zc3NLFu2jEOHD/NxiD0lCsOgEbTzcZgSi6jVUfPpFry9vamqqiIpqX3AqUqlwtfXl9If1+Mz70aUw7u2Vq4NcUMQYHO++bPsqg/uQt9QT+z45RYTNjs7GbEjXNjwbSYlhfEDttqKJFlERURx3XXXWWRfBQUFBAYGmm1tfffdd4waNeqyEja4jMUN2l2TEydOpLm5GXv7gdcvubq6Ym1tTWlpKYGBlreOBoJcLic8PJz09HReeeUVftz8I4VkEYrlrUs/Qqmnmsy0Tdg7+GBj07fZZAkHK1m4IIDZM33YtqOs09/U6kLOnFqHjbWClJQzAB3C9sQTT/Dtt9/hED4cn3k39vsmI1FY4TtvKXaBYVTs+g5tbinud103oGLv3qior+PouvWcHTqUlJQUXnzxReRyOWPHReLhboPJJFJdo6WqSkNNrYaWFi9aWgy0tBho01yYjaiQt7/WMWPuRafvnKWrUEiws5NhZyvDzk6Gi7MVHu7WeHhYY20tpa5eS3paJQcPHmTNmjX8+9//5qsvv8TT/Sbsxg3ejhGOM8dhatNR9/VO/P39KSkpISkpiby8PKqrq/Hx8aF82wZMeh0uoyZ1euwkLyfCnGxYk1qGwUwnU1t5EbXHDhASOhs7O8sN5B0/1o2CwmaOHt6CaDQRQv//LerFampEFR+9uAaJOenIv9LU1ERtbS2jR482e61zLsnLjcvWLQntgf6YmBhWr17NbbfdZtZahYWFFBYWMm3atEHjVzYajezbt4+hQ4fy73//m9defZ0JplnYCD1n9g0Eg6jniLAHub2SUWPvRiLp27nH2lrKjYuDyMhq4Oix9pN1XW0OKae/wNHRgfT0NDQaTYewvfLKK7z+xhvYBYYTsHglgtS885W2tpKyn79BU1GC/bRRuC6bg9TRcu9P04ETVP97M1u3bmX+/PmEhYVRUdHEuLiHEARwdbHCw8MaD3drXFysOoTJykqK0SjS2mqgpdWAXm/CZBJBhMBAe4pLWhAAqUzA1rZd0ORyCQaDqUMc1Q06qqo1VFVpqK7RYDCIZGduoaryBLpfJ5oLMikOU2Jxv/svFnvNF4uqD7+nOeEUUVFRJCcnk5SUhFKpJCYmBl9fX6qqqvCYPg+3Ce2xzTAnG24K9+Sz9ApKzZjRBmDUaihY9xZWJitGjbmnwyI2F6VSwbIbg/n0P8ns2v4yoUQTJPQ/keSUJBHvCHfOpJyxiLilpKSg1+vNFrfs7GyGDx9OaWkp7u6WaU32R3FZW26CIHDXXXfx0UcfmS1ufn5+pKWlUVdXZ/ZICEshlUqJiIggIyODJ554gnX/WUdudQrDxTiLP5dMkDNCHM/xpgPk5e4kLHx+nx6n0RjZuq2UxdcH0tJiYP/+JNJSv8HV1ZWsrEzUanWHsK1du5bX33gDG58g/K+/zWxhA7By9SR4xYPUnzlCVcI2Wo+n43LjLBxmju21r2JfaDnRXgIwf377+1FYWIynd/sNQxShplZLTa2W9IyGTo+TyYQOobOzkyGTSZBIBOQygcBAe/Lzm9AbTBiNYruYtbYLmk7Xc82l0jmIstLDnD59mtjYWGytrGlJzsTNZLLI671YtBxLp+VQKjInZ9LS0pgzZw67du0iKSmJM2fOUFZWRmhoKMUHtmHSahk1dyFLwzz5Mb/GbGETRZGKnd9hbG4ictwtFhM2aC/YTs9Qc+zIJqwEWwLE/vcarRerqTVW8smLay0ibHq9npKSEuLizL9PfPzxx1x33XWXnbDBZZwteY7ly5eTmprK6dOnzVpHJpMRFBRkdlsvS+Pn54dcLqeqqoq333mbSlMZteLFqctzFFwIYzilxUlUV6X1+XG1dVq2/FzC5IluuLuW4+PjQ0FBfidh27BhA08/8yzWHr4E3HBHnzpR9BVBIsFl5ESG3PEkDkNGUPOfrZQ++gGtp7LMyjIVDQbazuTg4qQE4MSJExgMOpTK3vtJGgwiDY16yivayMltIiOzgbR0dYcIZmQ2kJ7RQFZ2I6VlrdTX63oVNgAnZRAAX3zxBQBXXXUVpuZWtAXlA3uRfwCN+5KpfHsDDmHRhK56AmXMBBISEli8eDGTJk1CrVZz5swZCgsLiYiIwLEyn1uGuPFLSS1nas3PYlafOUJjximGRvzFYtmRAF5eNgQF2rNz1xHq6rIJF0cg6WcPSYBCaSbRUdFce23fE7p6XK+wEEdHR1xc+hZe6A6NRsPnn3/O3XffbZF9/dFc9uLm5OTE0qVL+eijj8xeKzQ0lOrqahoaGnq/+A9CEASGDx9OZmYm8+bNY/q06WTLzmAQzSuB6A5/huCBLxln/0tzU98HuiYfj+ell/7B3XffxcGDiVRVVXUI286dO7nvgQdQuLgTeONdSK2sL8reZbb2+M69keBbViOXOqB69UsqXlo34Bt/W0Yhok7fMXJk/fr1ACh/FZhLgZWVI9bWSvbu3Qu0n6z7063kj0QUReo3H6Dm4x9xHjkR3wXLkcrl7bVtw0by88/bWLlyZYfAnTx5kiNHjvDSSy/x7cYNfP+fTxDN7B6kqSpHtXsz3r7j8PTqWz1dX5BKBWZd4c2RY5WcTP4OFzxxp2+TK87nnNX24ksvWsRqMxqN5OXlERYWZvZa33//PW5ubpdNu63fc9mLG8Ddd9/NV199RWNj3zrdd4e1tTUBAQGDznpzc3MjMDCQ06dP88mnn2CU6ckl9aI8lyAIRDEWW9GelFOf92l6QHFRIlmZmwGYPHkyKSkppKWlERcXx+HDh1m+YgVyByVBN92L1Mby8cLfY+PtT+DSe/G/fiUmVTNlT35IxcvraT2d3a+b5bmuJG+88QYA+/btw9raBYWFGuwOFGeXIeRk5wLtyVBSQaA1ueduJX80xpY2Kt/aQP1/9+A+eQ5es/7SkTgkSCT4zr8J+yGRfLNhA3/729+YNGkSdXV1JCQkMGrUKJqammhIPUbpT18iGgd2kDPptJRu/gJbW7c+u9n7yoRxbuh0Jn788Ue0GjVDiel3rF4URQqkGQyPHm4xq624uBgrKyuLdDdZu3Ytd95556DJQegvfwpxGzNmDJGRkR0na3NoTxiooKWlbzOo/iiGDRuGVqtFEAReeeUVSsmjXjSv5qc7pIKMGHEi6Ns79xuNXdexiaJIQd4e8nK2M3HiRE6fPo1Go+n4Mpw8eZJrFlyL1NaeoJv+iszujxMFQRBwCIsidOWj+MxbiknVguqV9ZQ+/B4NO49gaus5jiOKIq3HM1BIZR299HJy8nB26X+Hd0vjpAxCo22joKB9SkBERAS6IhWGOvMOd5ZCk1tK2eMfokktwO8vt+E+ec4FN0hBKsVv4c3Y+oey5sMPeeONNzAYDEilUpqbm9mzZw8LFy6kKSuFku//0zGxva+Iokj5zm8xNDYQHX2TxXpHAnh5WhMzwoUtP6dRkLuHAMKwExz7vU415dQZq3j1tVctIiAmk4nc3FzCwsLMXu/UqVOcOnXK7FyGS8mfQtwA7r//fj744AOzmyDb2tri4+NDbm6uhXZmGWQyGSNHjiQjI4PbbruNuAlxZMlOYbxI7klrwYYYcSItTZVkpH2LKHZ+X0VRJC9nO4UFe5k9ezZJSUkUFhaSkZHBpEmTcHd3p6amhitnzSZo2V+ROzlflH32hiCRohw+lpBbHyZo2X1YK32pXb+d4ntfo+aL7Re0rzqHvqIGQ42aCRMmAJCXl4dW29aneNvF5twe1q1bB8Dbb78NQOvp7Eu2J2j/TDRsP0T5c58gUzgQcusjOIZ3P2hTIpMTsGglUeMnERgUREVFBVdccUWHi/KHH37glltuprkgi+JvP8ak63tiSc3hPTSmn2JY5PUWG0AKv7ojr/Th6LFqkhLWY4XNgFL/TaKJfFkas2bO4uqrr7bI3srKyhAEAR+f/rtHf88HH3zA8uXLcXa+NN9bS/CnEbfFixdTX1/fEYswh7CwMIqLi9Fo/vi2RT3h6upKYGAgZ86cYd3n69BLtOTR98SP/uIoOBPNWKqrzlKQt7vj96JoIitjMyXFB1m0aBG7du0iPz+/I8ZWV1fHpMlT+Ndrr7Nq1UomD7301o4gCNj6h+C/8BbC7n4K59jJtMSfpmT1O6he/ZLWk1mI+t8OCq0ns0AQ+PTTT4H2Wh+A+vp8qqvOotX88XFZg0FDXW0OKtUpQGDPnj0AzJo1C6QSWk9k/uF7OoexuZXKN76hdv12XEZPIXjZfSiUvSc0BLo48vTfVrN5xy5WrrqDL7/8slMMbt26dTz4wAO0lhZQuOHD33Vw6ZrGzNNUJ+wgOGQmHp6Wi7MBTBjvjk5nYuvWn2loLCZSHD2gxgol5NJqbOatt9+yyL5EUSQnJ4chQ4aYHburra3lm2++4b777rPI3i4Vl3UpwPlYWVlx55138u6777Z/2c3A0dERDw8P8vLyBlVLLmh3Tx44cACpVMqLL73I4489jqfoh5NwccoXPARfhojDyS08gFxhj6/fBDLSvqOq8gy33347n332WSdh02q1DIuMRJQINI+YxudZldwS4YXSSsbukvpB0UVD7uiM57R5uE+cRWPGaWqTE1C99iUSGytsRg7FblwkLUfTEAShIzBfV1eHVJBRr8pAVXECACuFI7Z27lhZK7G2ae8qYnVeh5G+1gqew2QyotM1odGo0WrU7V1O2n79b2sNrW3tVqZMUKDAiqam36Zbuzm7UJuSi0mnR6KwnAuuL2hySqh657+YWrX4X78Sh7C+fWeGu9jxl1B3dhbXkeYWjkLpyp13341SqeSaa64hKSmJkydP8vbbb6NUKnnhHy9S+PUaAm+8u1sXd1tFMWVbv8HDK4bA4Css+TLb3ZHDnfn8i1PkZO/Ej1Cchf5bhTpRS7E0i1UrVxEd3beOQL2hUqnQ6XT4+/ubvdYnn3zSMYLqcuayLuL+PSqVqqOVlrn/MPX19SQlJTFz5kysrS9Odt9Aqa2t5fDhw0yZMoWZV84k60wOow3TkQkX76aWI6ZQRDYODr40NZWxevVq3nrrrU7CJpFI8PT0Qm80EnDj3dj5t1ts7tZyVgz1pLpNz7d5VWjNmVVyERBFEW11BU3ZqTTmpKKtbM+uDAgIoKioCICxY8ZSeqKSaGE8WrGNBupooI42WtAIrWhoQyd2buirkNtjZeWEVCpHkEgRJFJsbWz54IPnufevz9LW2vprWy4DOk0jWl0j4nnyLxMUWGOLtWiDDXY4oMQJV2yxp5R8cqUpNDY2Ymtry+OPP85rr72G15O3YBtjfqZcX9+3hm1J1H3zCzbe/vgtuLlP7mcBuNLPmYleTvw3t4osdbs1pm9qoPDL9zA0N7Br506mTJnSUeg9atQo3n33XVY//AgKpQuBS+9F7qjstK6+sZ6Cz9/BVuFM7MhVFo2zSaUCN90YTFp6PZ998iqahiomiLOQDcBqyxBP0ORQS25erkXqx0RRJD4+Hj8/P4YM6X+d3fm0tbURHBzMp59+2lHbebnypxI3gHvvvZeGhoYON5I5HD16FGtra2Jium/qeqlITU2lvr4eLy8vRsaOxFHjRpQ49qJlNhlEPUf4Ba2g5bnnnuW5557rJGw2Nja4urnRptEQsPgO7IM7d2mwlkpYGuaBo0LGl1kq6rQXJ1ZoCWqTE6jc8yM//PAD1113HS0tLTg5ORFmHIGf0H1/PZNoREMbGlo7/ZgwYkJExITCRsGaDe9w/9KH0bZpEZAgQYIV1u1Cdt5PT4eVZrGBI+xm7969XHHFFRgMBuTWVjjOHIfbbRf/pqRX1VLzn59pS8nBdfwMPKbO7dNUcoVE4IYhHnjaKvgyS0VVW+dkJZ26joIv38XU1sqhpIPExsZ2ErgvvviC21auRGbnSNCyv6JQtnssjFoNRV99gNjcypgx91o8o3XyRA98fGx5591vycrYzEim4Cr0PyOxUazjOPt59713uf/++y2yt9LSUtLS0pg5c6bZvXHXrl3L2rVrOXPmzGWbJXmOP03M7RyPPvoo33//Pfn5fZtR1hORkZEUFxcPqnE45xg2bBg6nQ6JRMKnn32KSixun+B7ETCIelKkhxBsYO/ePRcIm6OjI8OGDaOttRXn2LgLhA1AYzTxRaaKnIZW7o32JdRxcFnD59NWUYJEKu1oYHvkyBGMRiNKei4AlghSbAV7XAQPfIQgQoRIIoUxRAvjGSFMIEaYyAihvWtE++/iGC6MJ0oYyxBhOH5CKG6CN/aCU69WuB2OWEmtSUxMBNoTjqxkclqOX5zxSOcw6fTUfbeX0r+9j76oGv/Fd+A545o+CZuzlYy7o31RSAU+TC27QNgAFEoXgm66F0FhxaTJk8nNze0Ug7vlllv4YdMmjC2NFKx/F21NJSa9jpJNn6Gvr2NEzC0WFzYvLxtGDHdm67YMcrO24UPwgIRNFEVypCkMGzaMe+65xyJ7M5lMZGRkWKTpu8Fg4PXXX+eJJ5647IUN/oTiFhwczKJFi3j99dfNXsvBwQE/Pz8yMy9doL47zmVPZmZmMn/+fO644w5yJGdoFi2b6KATtZyWHsRgq2Xfvn3MmDGjk7AplUrGjBlDUVERTspg6k8mUXv0QJdrmYDtRXVsL65lxVAvJnj2P336YiOaTDTnpuHj/dsg0oSEBKxlNtgxePYrCAJOJhcO7D/Q8bvJkydjrGtEX2r54bbQnmRT+rf3Uf+YgOvY6QxZ9QQOocP69NhgR2vujfYlv6GNzzNVtBm7z2q2cvUkaOk9IJURO3IkKpWqk8AtXLiQPbt3Y9K0UfDVe5Rs+gxNeTEjYm/B3t7LUi8XaHdHzr7Sm8NHK0k88BlyUU44A0tSKSOfemMNH679sKO8xFyKioqQSCQWibX997//RRAEbrjhBgvs7NLzpxM3gCeeeILPP/+cioq+d9jojoiICFQqFWq12vyNWRhXV1eCgoI6gu5DI4aSJjtuse4lWrGN07JEpE6QkJjAhAkTOgmbi4sLU6dO5cyZM4RHLGTk6DsICJpO5f4tVO7f2q0FcbK6mf9kVDDDV8l1wW5IB9Ehsa28CJNWw7Jlyzp+F38gHkejy6A7zTqJrhw5egS9vt0C+vTTT0EQLN6tRFdSScXL61G99iVWtq6E3v4oHtPm9rmF2nhPB24Z6sWukjp+LqrF1AfD0trTF6foMRgNBmJiYtBqtZ0EbsaMGRw/dhT0OlqKcggOmXVROsdMnuSBRmvku2+/prmpghHi+AHFttvEFvKkaaxatcpiHT8MBgNZWVlERkaanSFpMpl45ZVXePTRRy0mvJeaP6W4RUdHM2vWrI76H3OwsbEhODiY9PR0C+zM8gwb1n5yzsrKYtP3mzDItGQJp8x2TbWJLZySJWLnZkPSoSRiYmIuELarr76apKQkQsOuxtdvPIIgEDpkDkPC51F7dD8V2zd2GuB5PsXNWj48W4aPnRV3RPrgbv3HZvh1R1NeOkgkPPfccwDodDqOHD2Kkzg4mmmfjxJ3NBoNJ0+eBCAoKAiJREKLhbqVGOoaqf5oM6WPfYChuBa/hbcQsORurFz7Ni7GWiphUag7V/q5sC6zguSqpt4fRLv7rvLAz9SfTMLTK5bGxmaGDo3AZDJ1ErhRo0aRnZWFg4MjBXm/UFdn2drUqGFODA135JsNCZQUHySM4TgK/e/XKIoiWZJTuLm7dnS7sQR5eXnY2tri5WW+tbpt2zZqamq49dZbzd/YIOFPKW4ATz75JGvXrqW+vt7stcLCwqivr6e6+uJ0BDEHqVTKuHHjqKqqQi6X88mnn1AhFlFGwYDXbBGbOCVLxNXHmUOHDzF06NALhG3JkiXs3LmToOArCAic2unx/gGTGRZ1Aw1nT1D87ScYWruOWTbojHyUVk5Rk4a/DvdlsrcTl9o2aso+i72tLTY2NkB7s2StVtNrvO1S4IASmUROQkJCx+8CAwLQ5pZibOq9Hqw7jE2t1H27h5KH3qb1aAaeV17LkFWP4xjR9xZTQ5U2PBjjh51MygcppRQ19a0A22QwUL5tA7VH9hEaNpfI6CUMj11BZWUlI0eORKFQdBK4kJAQ8vPzcHZWknJqHTXVlhF2by8bpk314scfMzl88HPc8cGfgWUillNIjUnFZ//5DCcnJ4vsT6vVkpubS2RkpNkeBVEUefnll3n44YcHXWa4OfxpxS0uLo7Ro0fz/vvvm72WQqEgLCyMtLS0ixqsHyg2NjaMHz+ejIwMZs2axT333EO2cJo6sf+xlyZRzSlZIv4hvhw6fIigoKALhO3OO+/k22+/xS9gEkEhM7tcx8t7JCNib0NXXkb+Z2/QUpzX5XUGUWRHcR3rMisY5+HInVE+uF4iK07XUIeutrKT2ygxMRG5VI4Dykuyp56QCBKUuJIQ/5u4Pf/88yCKtJ3pf39UbZGK6o82U3zvazRsOYjLyMkMufMpXMdM7fN4ImuphOtD3blhiAe7S+r4IktFo75r6/33GFqbKd6wlsb00wyLuoGAwCkACAhIBAk5Obncc889WFlZdRI4V1dX8vPz8PLyIvXMl1SqzvT7tZ+Pg72M+XP9SDioYse2j5CZpEQyZkAiohHbyJOe5eabb7ZYJxJo99S4urri5mb+oSs+Pp6MjIzLtvt/d/zpSgHOZ//+/SxcuJD8/HyzZ7QZjUb2799PaGgowcGXvgVTV5SUlJCamsrEiRNZtGgRBw8kMco4DTuhb9ljDWItKdJDREQPY8+e3bi5uV0gbH/7299488038fYZy9Bh1/X6hddqG0k7u5EGdSHuU67CLe7KbidvyyUCs/xdGOfhwO6Seg6pGv7Qou+6k0mofvme1NTUjuLaeXPncWzXSWLFyRZ5DrmNnLu+WcJHN/0XfRfZgv0lX0ynxqGMenVdR9xFkEmxGx+F5wNLen28aDTSeiKLhh2H0WQUIHNwwnnkJJxjJyCz7d90+3ClDdeFuKNq1bE5v5pGXd9EDUBbo6L4209Bo2P4iOU4KQMBqFSdIePst7jihRteZHKSRx55hNdffx2tVtupTECr1RIZGUlBQQFDh/0FH9+x/do/tM/hW3x9ECpVG598+h9Kiw4yhukDapIgiiIpksPgYiArO9NirawaGhpITExk+vTp2Nv379+oqz1OmjSJq6++mmeeecYi+xss/GktN4AZM2YQFxfHyy+/bPZaUqmU6OhoMjIy0GrNG554sfD39ycwMJATJ06wceNGAoL9OSs7gl7svelsnVjFaWkSo8aOIj7+QJfC9o9//IM333wLD88Yhg5b2KeTrJWVI7EjVxIYNJ3qhJ0Ub/wYQ0vXsRe9SWR7US3rMisY7+nIHZF/rBXXlJOGTC7vEDaj0UhiYiKOJvPmYl1MlLjR2NRAWtpvbdgc7ezb24kZuxcXY3Mr6q2JlDz4NpVvfYPQJOJ77c2E3f007hNn9kvYrKUSrg9x58YhnuwpqeeLTFW/hK05P5OC9e8hN8kZPfZenJSBiKJIYcF+0s9uxBN/RjABPyGEocTy5ptv8o9//ANra+tOFpyVlRXZ2dlERkaSlfEDJcUH+7yHc8y60gedzsh3m36hpCiRIQwfcPcfFcVUm8r55NOPLSZsoiiSkpJCSEiI2cIGsGXLFvLz81m9erUFdje4+FOLG8DLL7/MmjVrKC4uNnstLy8vXF1dB21yCbTX5tnZ2ZGTk8P2HduR2Us4Kz2KSew+9bpGrCBFcogpUyeze89unJycLhC2d999l+eeex5XtwiGRS3u1vrqColESkjobGJG3o6uorzdTVnUvdusqEnL+6mllLVoue/XWNzFzqg06bS0FOUwNDy843dnz56lqbkJZwbvFGInXJAI0k5xt0WLFiFqdGiyL/zM60oqqf7kR4rvfZ36jXuw8xpC8K2rCV7+AE7DYvtUr3Y+Ec62PDjCDweFlHdTSjhR3bekEWjvUVpzZB/F332K0iGA0WPuxsbGGZPJQEbadxTk/UIIkUQxBsmvnzd/YQihRPP888/z1ltvXSBwUqmU1NRUxo0bR272Ngrz9/U5lDB2jCuentZs/O8R0lK+xYtAAhhYtxeN2EquNJUbb7zRYuNsoN0709bWRvh5n9OBYjQa+fvf/84zzzxjEaEcbPzpxW3kyJEsXLiwPRZhAaKjoykrK6Ours4i61kaQRAYPXo0ra2taLVaftryEw1CbbcZlJViKSnCEa6aexXbtm/Dzs7uAmFbt24dq1evRukcTNTwpUgkAysWdXEdwtjx92Nv5U7Rxn9TfXBXt/PV9CaRbUW1fJGpYrS7A6tj/Ilxtb9oCSctRblgMnY6wSYmJiIRpDgyeC03qSBDKXHpJG5r1qzpGGAqiiLagvL2BJG/vU/po+/Tdiwbt/EzCbv3WXzn34SNV/9rpALsrbgz0pu/hLizp7SezzNVNPTDWjO0NlPy3adUHfiZgIDJDI+5GZnMGp2umdPJn1CtSiGacYQIFyZMBAsRBDGURx55hI8//vgCgRMEgcOHDzNz5kwK8neTn7uzV4ELDXFg9ChXvv8hnaOH/oOD6EwkowYUZzOJJtKlybi4O/PBBx/0+/HdodfrSU9PJzo62iLp+uvXr0ej0XDHHXdYYHeDjz91zO0cubm5REdHc/LkSSIj+z+e4vdkZWVRUVHBtGnTBl3t0zmam5tJSEggOjqaffv2cdtttzGEaIKEiI5rysVCMoQTLFmyhPXr1yOXyy8Qtk2bNnHDDUtwcPAldvQqpNK+1Tb1hCiaKCzYR2H+PmwDQvFbsByZffcF0gIQ62bPTH9n2gwmfimpI1vd1u31A6F857c0pBzHdN5gzMWLF7NvczyjTJabRGzpmBtAjphKm3s9qkpVx+dRoVBgtJIhsbbGUFOPxNoGh9BIHIaOwCE0st8W2jk8beTM9nchxMmGxHI1Bysa0PWlcO08WkvyKf3pS9DqiYxajKtb+2eypbmSlFOfY9RpiBXjenQHiqJINmcoFfL48ssvWbZsGRqNplMMThAErrvuOn788Ud8fMcTHrGgS4+Dq6sVi68PZOeuIr7d8AqmtlbGijNQCFb9e3N+JVdMpViSS2JiAhMnThzQGl2RkpJCc3MzcXFxZt93NBoN4eHhvPLKK9x0000W2uHg4k9vuQEMGTKElStX8tRTT1lsPb1eT2FhoUXWuxjY29szZswYUlJSWLBgAc8++yy5nKVUbG9LViLmkk4yK1eu5KuvvupS2Hbt2sWSJTdiZ+dBzKjbLSJsAIIgIThkJrGjVqKvrCT/szdozu++C4wInKpp5u3TpZyqbmZxqAerIr3xtx/YzeeC9UWRpuyzuLu5dvrdueLtwY4zblRVV3VqORcXF4epTYutRzABS+5i6P3/wPeaZTiGDx+QsCkVMq4Pdefe4b7Uaw28caqYfWXqfgmbKJqoObyXwm8+xFbmzNjxD3QIW1VlCieOfYhMJzJOnNFrnEsQBMKJwZtAbr75Fn788ccLLDhRFNm8eTO33HIL5WVHyUj7DtPv6i6traVcM8+PEydr+Wnzv9G01hMjThywsNWKKgrJ4l//+qdFha2hoYHi4mKGDx9ukQP1mjVrcHV15cYbb7TA7gYn/y/EDeCZZ55h9+7dHD582Oy1pFIpw4cPJyMjY9DNfDsfDw8Phg0bxrFjx3jssce47777yOIUqeJRsjjN6tWr+fjjj5FKpRcIW1JSEvPmzcfa2pmYUauQySxf/+LsEsq4cffjYONF8bcfU/rjevRN3bcPM4giSaoG3jhdTGGjhtuHebMs3NPsAnBtVTnG1uaOXpLQbu1X11QPyvq23+OEKwJCJ9fkJ598AkYTtj6B2AcPHbClZiuTMDfQldWxfkiAd86U8nNRLS2G/g0F1qlrKfpmLVXx2wkMnMrIUauwtnbCaNSTlbGZtNQNuJo8GCNOx0aw69OagiAwTByNu+jDDYtvYPfu3V0K3Oeff84DDzxApeo0aanfYDK1W+dSqcC8q32prNTwzddfUF+XwwjGYz+AqdrQnvafIT3BnDlzePTRRwe0RleIosiZM2cICQnBwcH8vpkNDQ3861//4uWXXza7s8lg5s/7yn6Hl5cXq1ev5vHHH7dIrZqXlxceHh6cOXNmUNa+nSMkJAQfHx8OHTrU7oJYdhOVlPDss8/y5ptvIgjCBcJ2+vRppk+/ArncntjRd6BQ9O1mMxAUVg7EjLyNiMhFtOXnkPfxK9QeO9Bjpp/WKLKntJ43T5fQqDNw3whfFoe642M3MMuyKTcdBIF//etfHb9LTExEQLgsxE0uKHCSOXc0UQYIDw9HJpfTlHN2QGs6KaTM8Xfhb7EBuFnLWXu2nO/yqqnv5zQHUTRRezyB/E9fw1hTR8yo2wkZMgeJREprSzUnj32IquwEEYxiOP1vbSUIAlHiWJQmd665ZgEHDx7sUuDeffddnn76aWqqM0g5vR5R1DPvaj8kEoF16zZRWpJEODG4CgPr9tEeZzuOk6sTX375pUVFIzc3F51Ox9ChFzYkHwivvvoqI0aMYM6cORZZb7Dy/yLmdo7GxkaGDh3KW2+9xdKlS81eT6fTsW/fPqKioizSuPRiIYoip0+fpq6ujri4OFpbWzuKP38vbFlZWcTExCKKckaNvQcbmz9uzLxe30ZB3i+UlR3FytUTr9nXYxfQ/YiZc7hYyZjk7cQodwcqWnQcUjWQXtdCX22L/M/fRmioobX1t64et956Kz99/TNjjDMG+Gq65mLE3ACyxNNIAgwUFv3WmWbEiBGkpqUR8dA/kSj65mYLsLdikrcTw5ztyFK3crBC3efuIr9HW1tFxfaNtJYV4usXR8iQOchk7fuoVJ0mK30zVqIVw8XxOAjKAT3HOYyikRTpIbTWLRyIP8Do0aO7jMG9/vrrPPnk33nyyWfw8xvC2n9v5czpjQQxlCHCwGdA5olnKRKy2X9gP1OnTu39AX2kqamJ+Ph4Jk6ciIuL+S7ynJwcYmJiSExMZPTo0RbY4eDl/5W4AXz99dc8+uijZGVlWcTEr6io4NSpU8yYMaOjZdNgRBRFTp48SUNDA5MmTcLKyuoCYSsuLiYiIhK9QWT0mLuxtbs0KfBNjWVkZf1EU0MJjkNj8Jg+D4Vz7xaUtVTCGA8HJng6IhEEkqsaSa5u6rHmytDSRPb7zzF16lTi4+M7fh8YEIRYImeoEGuJl9TBxRK3SrGUVI5QVlaGj48PAF999RUrVqzA7y+34Rje/Y1bIRGIcbNnnIcjLtYykquaOFLZ2G8r7Ryi0UDt8QSqE3diZeXEsGHXo3Rub3yg17WQk/UzlZWn8SKACEZabMiuQdRzRpoEDkYSDyYSFRV1gcBBe/d7lUrFP//5NjU1JfgSTAQjBxzLqhErOMMhXnzpRYvF9aH9O5uYmIiLi4tFJnaLosi8efMIDAxk7dq1Ftjh4Ob/nbiJosj06dMZO3asxZqYnjhxAr1ez/jx4wdt9iS0d/4+ceIELS0t+Pr6kp2d3SFsVVVVDBkSRmurllFj7sLewbv3BS8iomhCVXGK/Pxf0OtacB41EbdJs5HZ9O4iFWivvxrr4cgQJxtyG1o5XtVEVn3rBdacOuUY5ds3Eh8f33HiLisrw8/Pj+FMwFPws+jruljiphU1JPIzGzduZMmS3zqTSKQynKLH4DP3wm4lfvZWjPVwYISrPTVteo5XNXK6prnf2Y/n01yQheqXH9Cpa/D3n0Rw6KyORKSqyrNkZ/6IqNf/mgwSYPHvi17UcVqaiMJZzqHDSQwZMqRD4JycnBAEgYaGBiorK1mx4hYkJoHx4iyshYEdTJvFBk5I45k1ZyZbt261qDsyJyeH4uJipk+fbvasNoAff/yRVatWkZ2dbRErcLDz/07coL04d+zYsSQnJxMVFWX2eufck5GRkQQEBFhghxcPk8nEgQMHaGpqYsKECXh6etLY2EhQcDAN6iZiR6/CyWnwvAajUUdJcRLFRfGIEgG3STNxGT0Fiaxvp30nhZTR7g6M8XBEJhHIrG8ls76F3IY2dCaRkh/W0ZKXgdHwm9Bs3LiRpUuXMoX5WAmWTaS5WOIGcFS2m5vvXN6ptsrLy4uaxmbCH3gBqSAhwMGaYc62RDjb4iCXcaa2meOVjZS39t7Fpie0ddVU7dtCU24aTs7BhIdf03FA0umayc78ieqqs7jhwzBGYjVAMenTXkQNp2WJOHk6cOjwIfz9/Wlra2Pfvn0AXHHFFdjY2JCRkcGM6VfQWtdGjGES1oJtv55HJ2o4IYsnKDyAw0cOW8QTdI7GxkYSEhIs5o5sbW0lMjKSp59+mlWrVllgh4OfP8fgnn4SHR3NPffcw3333ce+ffvMPj0qFApiYmI4deoU7u7ug9o9WVhYSGtrK66urmRkZGBtbU1ERATq+gZiRt4+qIQNQCpVEBQ8Ax/fsRTm76X8wHbqjsbjPGoiziMnIrPr+YbSoDOyr0zN/jI1gQ7WRDjbMifAhSVWMvLUrRwY4k+lXeevwdGjR5EIUirEQpxEVxxxRioMzq+KSTTRTAMN1GHQGziYmNTp7zfeeCPJycnM8HUg0tsNE5BZ38rO4jpyG9rQm2GlARjbWqg+tIf6EwdRKOyJjF6Ch2f79ABRFKmqPENO5hYwmohmPJ74XXTvhpVgTYxhEqcqE5kxfQYHkw5SVlaGlZUVoiiSnp7OqFGjGDZsGIcOJzF92nROqRKIMUzCto99WI2ikVTpUWycrNm+Y7tFhc1kMnHq1CmCg4MtZmG9/PLLeHp6cvvtt1tkvcuB/5eWG/yWXPL2229brNajfTyK1iJFlheD30/QTk5O5uTJkzz55N/xD1qAm1tE74tcYlpbaygpPoiq4iSiaMJxWCwuY6Zi492/hB43azkhYgthCiORkZEolUq8vLzw8vIiPz+fRx5+hGPHjtHa1oogSHCSKrEzKLHDAWtsO37kKPr1bz1Qy80g6tHQ2vHTRgtNUjWNYh0GkwGpVErMiBgefexRrrnmGiorK1GpVNTU1FBYVMTZ8mqK7b0obdZapBm1sa2F2uMJ1CUngslEYOB0/AMmI5W2W9TNzSpyMregVhfggR8RxKKwsBXcG61iMylWh3j86UcZP348U6a0Txn4fZJJWVkZM6bPoLSwjBjDJOyFnsfSiKJIunCcOnkl8QnxjB8/3qL7zszMpKyszGLuyNzcXEaMGEFCQgJjxoyxwA4vD/7fihu0J5c89thjZGZmWuTkpdfriY+PJyAgwCK93yzJ75NHTCYTI0aMYObMmUQPH81PWytpabHMBO8/Ar2+jYryZMpKD6Npq8fGJxCXMVNxHDqizzVdqj0/Un/yIC3NzdTU1KBSqaiqqkIURZycnHB0dKSlpYXMzEySkpJIOniIwsICtLrfsgdlEhm2UnvkRmusTDZYY9MhfFbYIEGC0D60BQkSrGwU3LthOWuWfom2TYsJERETIiZ06DoJmFZoQy/T0GZqRWv8rZ5SKpHi7e3D+AnjmDJlCiNGjMDV1ZWWlhbUajV6vR5XV9cOsXZ3d0d0cCHk9kfMft+NmlZqj8W3i5rRiK/fBAICpqCwcvj136WVwvy9lJUcxkawJ1yMwW2A6fXmIpFJmPW3OGTu8MMPP7Bt+zYcHBy6zKKsqqpi5sxZZKdnE2Oc2ONQ0nwxg3zSLohvWoLq6mqOHj3KlClTLDL77f9bEsn5/L8Wt3PJJaNGjbLI1G4AtVrNwYMHiYuLM3vMjqXoStjGjx9PcnIyw6IWseymefj52fHDj8U0NVk2DnSxEUUTtTWZlJQcQl2Xh8zOsd1lGRvXo8tSFEVy176EnUTsNNDWZDLR3NyMWq1GrVbT0NBAQ0N7YbmTkxNOTk5IpVIaGxupqKiguLiY4uJiioqKyM8roLi4iLr67vuO2tjYsGHDBpYuXUpbW9ctxOzt7PH39yc4JJjAwEACAwMJCAjAx8cHFxcXBEGgsbGRhoYG9Ho9Dg4OKJVKnJycUCqVODo6duo9eOWVV7Jv3z7C7nseeQ9tznrC0NpMXXLib6LmO4GAwN9EzWQyUFZ6hMK8vYhGI8EMJYDwjobHfzQyhZSrH5+KlZ2Cb/6xmSOavUyYOJ6du3ZiY2PTpcCp1WrmzJ7D6ZOnGW6Mw1m4MFu4UiwhlaO88MILPPvssxbds1arZf/+/URERBAUFGSRNb///nvuuuuu/zdJJOfz/1rcoN0FMHr0aHbt2sXkyZaZ2ZWfn09OTg4zZsxAobBMyypz9nK+sEF7QH3//v2EDb0GP//2FkFTJ3syNNyRbTtKKa+wbN/GP4rmZhVlJYdRqU61uywjR+IyZkqXjYG1tVXkffIKS5YsYePGjT2uK4oiTU1NHWKnVqtpbGzEYDAgCAJWVlZYW1t3/EilUlpaWmhqasJkMmEwGDr9WFlZYTAYkMlkHT9SqRRbW1vs7e0RBAGNRtPxo9VqMRqNSCQS7O3tUSqVHWJ2Tmx74siRI8TFxeF99Q04x0zo13vapiqlLjmRxoxTCAhdiJqRStUpCvP3odGo8SWYECItnojTH+xdbZn75DT0bQZ+/tcB9G161GINpyVJXDnrCrZs2YJCoehS4Jqbm7lm/jUcTExiuGl8p6LuerGaM5IkFt2wiG+++caioQdRFDly5AgymYwxYwY2GPX31NTUEBUVxVtvvcWyZcsssMvLi//34gbwxhtv8NFHH3HmzBlsbfuXMdUVoihy7NgxRFG8pOUBXQnbwoUL+emnnwgJnUNg8PRO1w+PVjJlkifxCSrSMrpvgzXY0etbqShLprTsMNo2NVYuHu0Ng8OHY+3VntBQe/QAlQe2UlRYOOAMV4PBgFarvUCIzv9/vV6PKIqYTCZEUUQURYxGIzKZDEEQkEgkCIKAVCrtJJDW1tYXiOa5xwwEmVyOTdBQAhat7PVa0WSkKTuVuuOJtJYVYGXthK9fHD4+Y5D/2q2mXdROU5S/lzZNPe74Ekpkr/Gqi43XUDeufnwahcmlxH98HNN5bcLyxDQKyCAxMbHjINuVwGk0Gq6//np27thFtDgOD8GXRrGe09KDTJoyke07tmNtbVnxzsnJobCwkOnTpyOXW6bu78Ybb0Sj0bB58+ZBmQNwsfmfuNE+12jKlCmMHz/eYu5JnU7HgQMHCA0NJTS09y4blqYrYbv99ttZt24dAUHTCR3SdesdP19b5l7lS2Z2I4kHK7mcPx0mk5G62myqq9KoqcnAoG9Fbq/EYWg0LSX5iOpatNo/tjeoXq9n+/btzJ0712I3sb4QHh5ObkEBQx/6Z7dlFNqaShrSTtCQehx9cwNOzsH4+U3EzX1Yx5ijrkQthGFmdxixBBEzQph25zgOrT9J6o7sTn9TicWkC8nMmz+Pb7/9tpM4dSVwer2e5cuXs+m7TYSIkZTK8oiOiWLf/n0WzYwEqKur49ChQ0yaNMliQ02///577rzzTtLS0vDyujQxz0vN/8TtV7Kzsxk5ciQ7d+7syKoyl3MfWkvVqvSVroTtm2++Ydmy5QiCQOyoVR0dI7rC0VHONfP8aG0xsH1XGVpt/5rkDkZMJiMN6kKqq9Korj6LTtte52eJRtr94VKJ2zvvvMPq1asJuOFO7EN+y4o1tDTRkH6ShrMn0FSWIpVb4+kxAh+/8Tg4+Jy37zYqyo9TVnwYjVY9qERNkAhMumUUETNC2Pl6IqWpqk5/LxHzyOY0K25ewWeffdblLLSuBM5oNHLHHXewbt06hoYPJelQksXj6BfjEFxdXU1UVBTvvvuuRdoMXq78T9zO46233mLt2rUWc08C5OXlkZuby9SpU/+Q+reuhO2nn37iL3+5Hg+TLxraaBTqiY5Z1jFupCvkcglzZvng6mLF1m0l1NWbV+Q7mKhUnSH97EYOHTpEXFzcH/rcl0rcjEYjMoUVLiPjcJs4i6bcNJqyUmguyEZAwNU9Ai+vWFzdIpBIfrv5tzRXUlpyCFXFKUSTEU/8CSRsUIgagJWdgjmPTMbO1ZZtLx+gUdXc8TdRFMkhhWJyeOihh3jzzTd77CDSlcCda3oQHR2Nh4eHRfduMpk4fPgwcrmcsWPHWsx1uGTJEvR6Pd9///3/S3fkOf4nbudhNBqZNm0aY8aM4Z133rHImqIocurUKZqampg8ebJF6la6oyth2717N/PmzsPV5EWUaRwmTKRylFpUREQtwst7ZI9rThjvTuwIZ3btLqegsLnHay8X0s9+S4M6k7a21t4vtjCXStwAXF1dqVM3wK/Tz52UgXh6xeLhORy5/LfDnMlkoLYmi7LiQ9Sr81EINviJwfgSckkTRX6P0seReX+fjrq8kV/eTupUN2gQ9aRJjlMrqnjn3Xe4//77+7RmVwJ3sUhJSaG2tpYpU6ZYZLI2wKZNm7j77rtJS0vD09PTImtervxP3H7HOffkjh07LNbd22g0kpSUhJ2d3UX7wnQlbAcPHmTWrFk46FwYbprQkZZtEk1kcIIKiggMvoLgkCu7nFB8jrAhDsy8wodjyTWcOFlr8b3/kYiiiYPxLzJu3CiSkpJ6f4CFuZTitnr1at555x2CQ2bh4zcOhcK+42+iKNLYUIyq4hRVqhQMxjYcBRcCxCF44HfJUvq7I2CUD3MenkzqjmyObjiDeF6nFY3YSqrsMAaFju82fcfVV1/dr7X/CIErLCwkIyODadOmWcxLdM4d+d577/2ph5D2lf+JWxe89957vPnmm5w6dcpisTKNRkN8fDyhoaEMGTLEImueoythO3HiBNOnTcdKY8cI40SkQmeLURRFCskij7N4eAwnImpxR3eJrnB3t+aauX6UlbeyZ18FRuPl+bFpUBdxMvnffPTRR9x5551/+PNfSnFTq9U4O7sQMmQOgUHTAGhtqaZSdRpVxSk0mnqsBFu8RH+8CbjkmY/dMfLaYYxdMoL9Hx4l52Bhp781iLWclR3FzcuV7Tu2D7ib/sUUuJqamo7yDEvF8EwmE/Pnz8fOzo5vv/32/7U78hz/E7cuEEWRa6+9FqlUyg8//GCxD8q5Au+xY8dazGXQlbClpaUxZfIUaJISY5zU40iRSrGUNI5j5+DN8NibsbLqPhPM1kbKvLl+yGUSftlTTk3twOZ8XUrycndRWpxIW1vrJalBvJTiBuDl5U1zC7i5RVBTlU5LaxVS5HjgizcBOOM+aG+MNk5WTL9rPJ5hrmx/JZ6qvM7F8pViCRmSE4wZO4aftvxkdozsYghca2sr8fHxDBs2zGKF2tBezrRmzRpOnTqFUqm02LqXM4PL1zBIEASBdevWkZyc3KnDurkolUpiY2NJTk6mqanJ7PW6Era8vDyumHEFpmYYYZzY66wsT8GPMUxD21zLiaMf0NxU0e21rW1Gvt9cRH5hMzcsCmL8WDcutyn1NVXpBAUFXvLi+kvF/PnzaGlWUVZ0CIdWG4YzganMJ0oYg4vgMWiFbcjEAG567xpEUeS/f9veSdhMookcMYVUjrJk6RL2H9hvkeSPriZ6m4PBYODo0aP4+vpaVNiOHj3Kc889x8aNG/8nbOdxmd2a/jhcXV3ZsGEDTzzxBCdOnLDYun5+foSEhHDkyBE0moHXWHUlbCUlJUyfNp22ei0jDJOQC327gTsKLowTZ6DQSTh5fC011endXmsywZGj1Wz6oYghQxxYsigYN9e+TXm+1Gg0alpbq1iwYMGl3sol44UXXgBgqBhDtDAWT8HvApf1YMLGyYqrHp3CtLvGEf/RMXa+nkhbw28egzaxhVPSBEqlebz55pt8+eWXFi2wtpTAmUwmjh8/jkKhsMjg0XPU19ezZMkS/vGPf1i8gfPlzv/ckr3w8ssv89lnn3Hy5EkcHQfWl+/3nMugbGhoYPLkyf12T3UlbFVVVUyMm4iquIqRhin9nk0FYBQNnOUY1ZQTEDSN4JBZHcW7XSGVCIwb68bIWBeST9SSfLLmXCLeoKSs9AjZmT9RWFhIYGDgJdnDpXZLAsTGxFKZWkc0g/tmGDoxgOl3jqMsrZL4j491EjWAKrGcLOkJ3L3c2fT9pot6czfHRSmKIidPnqSpqYlJkyZZ7N9dFEUWLVqEVqtly5YtFh2U+mfgf+9GLzz++OOEhIRw5513mu2WOIcgCMTGxmJjY8OxY8cwGo19fmxXwlZfX8+VV1xJebFqQEMXzyEVZIwgjiFEU1KYwOkTn6DRdN+Gy2gSOXy0mk2biwgLc+CGRUG4DmIrrqYqA6WT8pIJ22BhwbULqJdUYxIH50nE2tGKOX+bwvS7xhH/yfELrDWTaCJLPE0Kh5gzbw4pqSkX3Woxx4JLT0+nrq6OCRMmWPRA8+GHH3L06FE+//zz/wlbF/zvHekFiUTCl19+SXx8PJ9++qlF1x0zZgxGo5ETJ0706cvSlbA1NTUxe/YccjPzfh22aN/LKj0jCAJBQgSjmYamsZrkI+9SW5PZ42OqqjRs/G8hRcUtLFkUxNgxroMuFmc06qivz2PqNMuUd1zOzJ8/H61RQwODr6wjNK49tiaRCHzzwM/kJhV1+nu7GzKeClkh7777Lj/++KPFWlb1xkAELjc3l+LiYuLi4izqLj116hSPPfYYGzZswM3NzWLr/pkYZLegwYmnpydff/01q1evJjk52WLrymQyJkyYQFNTEykpKT1+WboStra2NubPm0/KqRRGGCdiL1jGbQqgFNwYL16Jo0FJyukvyMvZgcnUvYVpNIkcPtJuxYWHObZbcS6Dx4qrr8tDFI3ce++9l3orl5wxY8bg6uJKDd0nD/3RWDtYMeeRycy4ZzyJnx5nx2sJtDX8FpMWRZFSMY9j0r04+Nhx+PBhHnjggT88AaY/AldSUkJWVhZxcXHY25t36Dyf2tpaFi1axJNPPmmxVoF/Rv4nbn3kiiuu4IUXXmDhwoVUVFjupqBQKIiLi0OlUpGdnd3lNV0Jm06n4y9/uZ7DSYcZYYzDUbD86VUhWBHLRMIYTklRIqeSP0ajUff4mHNWXHFxC0sWBzFhnBty+aX/mNXWZCKXK5gzp+uG0f+fkEgk7a5JWfWl3gqCRCBiRgg3vTcfiUzC1/dvJedgZ2utVWzmtPQgmZzitpW3kno29ZJOlO6LwFVWVnLmzBnGjh1r0QxGvV7PDTfcQExMDH//+98ttu6fkUt/17mMePjhh7nyyiu57rrrzMp0/D22trbExcWRl5dHXl5ep791JWwGg4Fly5ax+5dfiDZNQClcPLeEIAgECkMZzXR0TTUcO/Q25WXHejyxGk0ih3614vz97Lh1RSgxI5yRSi5NmrkoilRXpxMdHXVJnn8wMn/+fBoN9bSKl66lWtAYX258ay7jlowg4dNkdrx6obVWLOZwXLIXW28Fe/bs4aOPPrJYYpc59CRwNTU1HD9+nNjYWIv3o3z44Yeprq5m/fr1/4uz9cL/3p1+IAgCH330EYBFE0wAHB0diYuLIzMzk/z8fKBrYTOZTKxatYofvv+BKNN4XIU/pn+cUnBlvDgTT5MPWRmbOXPyU9raup84De1W3Hc/FLFnXwXRUUpWLA9haPgff2Nqbq5Ar2v+f90h/ffMmjULmUxGDareL7Yw3sPc+cu/ZnPFfXGk/ZLLV/dtuSC21iI2cUqaQDZnuPuvd5Oekc6VV175h++1J7oSuNraWo4ePcrw4cPx8/Oz6PN9/PHHbNy4kS1btljUzfln5X+lAAOgoqKCMWPG8PDDD/PII49YdO26ujoOHz6Mh4cHVVVVnYRNFEXuv/9+PlzzIVGMxUsY2JBNc6kVVWQIJ9ELekLCrsLXb0KPvSkBBAEihjoxYZw7Wp2RQ4erKCxq+UP2W5i/j8KCvTQ2Nlzym8JgKAU4x5VXXknKgXRiRctMoO8N1wAlE5bH4hvtyakf0zm9JQO9xtDpGpNopIgciiSZ+Af488X6LwZ9XOlcmYCNjQ319fVERUVZtEgbICEhgauvvprt27czbdo0i679Z8Uyraj/n+Ht7c2PP/7IjBkziIyM7Hdj1p5wcXEhICCA/Px8QkNDO/W2fOqpp1izZg3DGHXJhA3AVfBigjiLHDGVnKytVKlSiIhahK1t9+5RUYSMzAaysxsZPlzJ7Jk+1NZpSTpcjUrVdlH3W1Odjo+P9yUXtsHGggULiD8Qj0E0IBMu3q3Awd2O8UtHMGRiIGm7c9m35vAFNWsANWIFubJU2sQWHnroIf7xj39YrKnwxcTa2prIyEiOHTuGUmn5UpOioiKuv/563nrrrf8JWz/4n1tygIwdO5aPP/6YpUuXkpnZc6p8f8jPz6e4uJiYmBiKioo6XJQvv/zy/7V353FV1fkfx18XLpvsAsoiq7impqCiUgKSoSZqLmmGpqXNzE9Nm8oplxmzxa2xzHKYZnJ00rKZambErdxwQU0RccPADdlBdi7b3c7vD4c7krvejcv3+Xichzzw3nM+V3mcN+e7smzZMjrRCz9ZiN6u97DkMhu6ycIIYzDK6jJOHF3DtewDaLXqu75Po5VIP13Bhr9fJj+/jmdHBTByRAfauhtmOSylUkFNTb5efwGxFCNHjkSj1VBOsUHOb+9ixxMvhfPC2nhAxuZXkzj0ReotwVYr1XBadoR0Uuj3RDhnzpzhww8/bBHBBjdGL6alpdGtWzfUarVelupqolAoGDVqFM899xy/+tWv9HLO1kI0Sz6it99+m3/+85+kpKQ88mLIv+xja2qivH79OtOnTyeE7oTIuuupcv3RSGouc55cLmHv0JZOXeLx8OxyX+9t42BN/36ePNbdjcysalJPllJZpbr3G+9TYcFJfs74lnPnzvHYY6YfUGJOzZIAnUM7U3tZRXeZ/kYf2jvb0nN4F/qM7kZBRglHN6VTdq3yltepJCVXyCBfdgVfX18+XvMxY8eONdv1LW+naYX/Hj16EBQUpNfFltVqNc8++yy1tbX88MMPZvHz0pKIcHtEWq2WKVOmcOHCBZKTkx96JNftBo/AjdVHdu3cxc7tu8j7qhprAzYfPSqFVMXPpFPJdTw9u9Kx8zN3baq8mauLDRH9PenUyYXc3DrST5eTk/vofXLnTm+mri4bheLRF6rWB3MLtzfffJPPPv4Tg9TDHjlU2ga48vjIrnQeHExxVinHt5yhIKPkltdpJDV5XCHHOgu5nTWLf7+YuXPn6nWSszEUFhZy8uRJevbs2awpUh8BJ0kSL7/8MqmpqRw8eFAsiPwQRLjpgVKpJD4+Ho1Gw/bt27Gze7DJy3cKtiZ79uwhJyeHsyfPkZ54Bblk+pvinUiSRAn5ZMnOoKQBP/+BBAXHYmPjcF/vd3SU07OHOz17uFFfpyH9TDk/Z1ahVj/4j6lWq+ZQ8lJiYgazZ8+eB36/IZhbuB04cIDo6Gj6MwQX2UPsXSiDoHA/Hh/ZFZ+uXmQdyub09p8py6685aVNoZYnv4RSauTll19m6dKlLXLH6GvXrnH27FnCwsLw9fW95e8fNeAWLFjAV199xZEjR257fuHeRLjpiUKhYMiQIQQHB/P111/f9xyUewVbk2PHjpGWlkZRXhGHPjyNXG3eW7ZoJA05ZJFNJjJrG4I6DsHXL+KuG6LezNpaRpfOLvTu1RZnZxsyfq7k3PlKKiqU911DefklTqd9wZYtW5g4ceLDfhS9MrdwU6lUeLT1wFPRgY6y+2+2dXC1p9uQELoP7YTc1pqzu7I4/+NFGqpvHSjyy1CbNm0aCxcuJDg4WJ8fxSgkSSIrK4tLly4RERFx16WvHjbg1qxZw7vvvktKSgpdutxf875wKxFuenT9+nUiIyOJi4vjk08+uecP8/0GW5OzZ8+y+8fdqJQqdr1/GHmd+TfjNEoNXOY8BWRja+NIYHAMPn797jvkAHx9Hej5mDuhHZ0pKWngXEYlFy9V3/Np7mLmNgoLfkKpbDSbCa/mFm4AEydO5Mfv99JXE3P3F8og4HEfug8NJbhfBwoySji/+xJXjuWgvc3O7BpJQz5XyJVfpFHbyLRpL7Jw4UJCQkw/IOphSJLE2bNnKSgoYODAgbi63nun8gcNuC1btjBz5kz27t1L//799VV6qyTCTc+ys7MZNGgQs2fPvuvyOA8abE2uXr3KN1u+oY1jG75dshO7ypYxvL1OquEqP1NIDrY2TgSGROPj+2AhZ2dnRbcurjz2mBvOTjb8nFnFz1nVFBfXc7uf4qOHVxAc7ENGxp33pzM2cwy3TZs2MWXKFJ7kGexktzYfu/m6EBoZSPenOmItt+bCvstk7L1EddHtVzdplOrJ4wpF8ms0aht58cWpLFq0qMWGGoBGoyEtLY2qqioGDRr0QCM57zfgdu/ezZgxY/juu+8YNmyYvkpvtUS4GcCZM2cYPHgwH374ITNmzLjl7x822JpUVFTw5z//GQ8PDza/9y02uaZfjuh+1Uk1XOFnih4h5AC8vR3o0d2NkBAnJC1czVZw5WoNObm1qNUSdbXX+enoahYtWsS7775roE/z4Mwx3EpLS2nfrj1dpD74yYKRWcnw6epFcL8OBPXrgLOXI7mnC7mw9zLZqXm3fUoDqJLKyZNdoph87OxsmTZ9Gm+88UaLDjW40ad+4sQJ1Go1AwYMeOA+dbh3wKWmpjJkyBDWrVtHQkKCvkpv1US4GcjBgwcZMWIEiYmJzX5YHzXYmqhUKj799FO8vb3Z9PE3NBy3xuoeq4SYkxshd4EicrGzdcIvIBJf377Y2Do+0HlkMvDxcSAk2JmQYCecnWzIzasj9eQpdm7fQGbmz2a1JYg5hhtAdFQ0bTTORPUbQlC4H1qtRHZqHleP55F3puiWlUSaaCUtJeSRb32FCk0pgQGBzHttHtOmTbOIEX7V1dUcP34cZ2dnwsPDkcsffrTynQIuPT2d2NhYFi1axGuvvaav0ls9EW4GtGfPHsaMGcNf//pXJk2apLdgu9n69etp06YNu77/kWv/LEcuM58b5v2olWrIJpNickEmo71Pbzr4D8LJ2eehzufmZktIsBPtPBWEhgbRtm1bvL298fb2xtXV1eRzqMwp3Orq6igqKqKoqIiSkhJyc3MpPl7FtdR8ii+WIWnvfGtQSNUUcY1ieS716jpih8Qy77V5DB8+HGvrO+/e3pIUFRVx8uRJQkJC6Nq1q15+dn4ZcOfOnSMmJoY33niDt956Sw9VC01EuBnYDz/8wLhx41i+fDnBwcF6DbYmu3btIj8/n0uZlzm65hw2KvPZR+1+KaVG8rlCnuwKjVI9bm7BdAiIxNOr2z3XrfwllaqewwfeZerUKbz33nsUFRVRXFyMjY0N3t7euLu74+bmhpOTk9EHmpgq3CRJor6+nsrKSioqKigpKaGmpgYPDw+8vb2pqqoiPDycPjyBh8z7tudolBooIofr8nwq1WW4urgy+YXJzJ49m+7dzW9xgYclSRKXLl0iMzOTPn364Ofnp9fzNwVcWVkZs2fP5tVXX2XRokV6vYYgws0oduzYwfjx4/nss8+YPn26Qa5x5swZdu/ejZXMiqRle7Euu795ZeZGK2m5Tj45XKaKUuztXPH1H0B77z7Y2997dBpASfEZzp/9miNHjjBw4EDgxoCAsrIyiouLqayspKqqCrixG4Obmxuurq64ubnh7Oxs0MAzRrhJkkRdXR2VlZW6z1pZWYlarcbFxQVXV1e8vLxo164dtra2uvf4d/BHVmBPV1kf3bk0kpoSCiixyqVUKkJuLWdk/EimTJnCiBEjHqr/yZxpNBrS09MpKyujf//+BmtaTUtLY+jQocyaNYulS5ca5BqtnQg3I9mxYwfPPfecronSEEpKSkhMTCQgIICvVn2LNsPe5M1wj6JaqiCHS5SQhxYNbm4hePv2watdD+TyO0+DyDj3D6oqf6a+vu6Or5EkCYVCcUsASJKEq6urLuz0HXj6DrdfBlnTZ2kKspuD28XF5a5NhrNmzeLvn28iTB1FKUWUyQoptypBpVEycOAgpk17kfHjx+u95cFc1NXVceLECaysrOjXr5/BVkw5c+YMsbGxzJo1iyVLlhjkGoIIN6NqaqL85SATfVKpVKxbtw5vb292fvcDV78txZaW/du1WlJRQj6F5FBBCVYyOZ5e3WjvG0bbtp2wsvrfDVuStBw+8C79+vXhyJEjD3SdpsBrCrqmoNBoNNjZ2WFvb9/s+OX37Ozs7vnLxP2GmyRJqFQqGhoamh2NjY23fE+SJF2Q3RzID9r3tXPnTkaMGIFMJkOSJPqG92XsuLFMmjSpRU64fhCFhYWcOnUKX19fevbsabB+w1OnTjF06FDmzZsnmiINTISbkTUNMlm9ejWvvPKKwa6zc+fOGwMEikrYs/oIttUPNgrRXDVIdRSRS6Esh1qpCht5G7y8e+Lp1R139xBqqvNJS00kMTFRL6uoNz0Z3SlYmr6vVN5YOaUp8Ozs7LCyssLKygqZTIZMJsPKygpJksjNzaVDhw66ENFqtbo/lUql7pxarRa5XI6dnR0ODg63DVN7e3scHR318mSpUqn45JNPcHFxIT4+Hm/v2/e9WRKNRkNGRoZuJw59bzB6s6NHj/LMM88wf/58MXjECES4mcDhw4eJj4/ntddeY/HixQZrOszNzeWrzV/R3rs9Wz76HvUZ2xbdTHkzSZJQUEUh1yiRFdAg1WJtZYutvQsN9RU0NNTp+pOMQaPRNAu/pnCSJKlZgGk0Gi5dukRoaChyubxZ+MlkMuzs7JqF2KMMPRfurra2lhMnTgDQt29fg+73t23bNiZNmsSKFSuYNWuWwa4j/I8INxM5d+4ccXFxjBkzhk8++cRgzSBNzZTt27dnz7Z9XPy6CFupZTdT/pIkSdRSTQkF5Miy6NO3D8eP/2Tqsm7LnKYCtGb5+fmkp6cTEBBA9+7dDTp9YcOGDcyaNYuNGzcyfvx4g11HaK7lzPq1MD169ODIkSPs3buXSZMm0dDQYJDr2NjYMHfuXNq2bUt4ZB+GvzcIjZdhd742NplMhpPMFV8CUUsq3njjdVOXJJgplUpFeno6p0+fJiwszKD9a5IksWLFCubOncu2bdtEsBmZCDcTCgwM5PDhw+Tm5jJ8+HDd8HRDePrppxk1ahRqjZpX1iTgGWeHivtfYb8lKKUQa2trnn76aVOXIpih69evs3//fhQKBdHR0fj4PNxCAfdDq9Xy29/+lo8++ojk5GRiYu6xKLWgdyLcTMzT05O9e/fi4OBAdHQ0RUVFBruWn58fCxcupLGxkafGxzB0aX9UXrdf/LYlKrMqJnJQpEUs+yToj1qt5vTp0/z000+EhoYSGRn5QAsfPyilUklCQgJJSUkcOXKEPn363PtNgt6JcLvJ9evX+c1vfkNAQAB2dnZ4e3sTFxdHSkoKkyZNumWl7l27diGTyW6Zq7JkyRICAgLu+7qOjo785z//oWfPngwaNIjz58/r4+Pclkwm48UXXyQ2NhaZlYxffTyVtnG2KKVb9+FqSTSSmgpKGDV6lKlLEcxI09NaTU0NMTExhISEGHRQVXl5OSNGjCAzM5OUlJRHWjT6bvcjgKCgoGaDkZqO5cuX6+vjtGgi3G4ybtw4Tp06xcaNG8nKymLr1q1ER0dTVlZGTEwMKSkpqNX/W0B2//79+Pv7k5yc3Ow8+/fvf+BmCBsbGzZs2MCUKVMYNGgQSUlJ+vhIdxQUFMTbb7+NVqslbnwsQ5dG0OhZY9BrGlI5Jai1akaOHGnqUgQzcPPTWseOHYmMjMTR0bDTYS5cuEBERASOjo4kJyc/8g7jd7sfNVm6dCmFhYXNjjlz5jzqR7EIYrTkf1VWVuLu7k5ycjJRUVG3/H1WVhZdunTh6NGjDBgwAICIiAhefPFFXn/9dSoqKrC3t6ehoQE3NzcSExOZNm3aQ9Xy7bffMn36dBYsWMBbb71l8OH7eXl5bN68mcDAQHb+4wdyksqRa8x7p+9fuiClYRsocfnqZbOe7iBGSxqWJEkUFhZy7tw52rRpQ58+fQweagDbt29n8uTJzJkzh6VLlz7yvMN73Y/gxi+o8+bNY968eY90LUslntz+y8nJCScnJ/7973/T2HhrE13nzp3x9fVl//79ANTU1JCWlsaECRMICgri6NGjABw5coTGxsZH6kAeP348hw4dIjExkcmTJ1NXd+dlpPShQ4cOzJ8/H5lMRv8hfZnwcRy2PTVoJa1Br6svkiRRIS9h9LOjzTrYBMNSKBQcO3aM06dP06VLF6M8rUmSxMqVK5k4cSKff/457733nl4m1N/rfiTcmwi3/5LL5WzYsIGNGzfi5uZGZGQkCxYs4MyZM7rXxMTE6JogDx06ROfOnfHy8mLw4MG67ycnJxMcHExgYOAj1dO7d29OnDhBbm4ugwcPJi8v75HOdy8ymYyJEycyfsJ4KiorSHhrAhFvdkXd1vynDSiook6tEE2SrZRarSYjI4Pk5GQcHR2JjY0lMDDQ4L/o1NfXM2XKFNauXcuBAweYOHGi3s59P/cjgN/97ne6IGw6Dh06pLc6WjIRbjcZN24cBQUFbN26lWHDhpGcnExYWBgbNmwAIDo6mpSUFFQqFcnJyURHRwMQFRXVLNz0Ney3Xbt27Nu3j969e9O3b1/d06EhtW/fnoULFxIcHEwbFwd+9WkCvmOcaLQ235ArpRDHNo48+eSTpi5FMCJJkigoKGDfvn2UlpbyxBNP0KtXL6OsTJOfn09UVBRXrlzhxIkThIeH6/0a97ofAbz55pukp6c3O/r27av3Wloi0ed2DzNmzGD37t1cu3aNy5cvExoaSkpKCnPnzuXNN9/kueeeIz8/n44dO1JQUICPjw/r16/nhRde0FsNkiTx6aef8tZbb7Fs2TLmzJljlOY3rVbLV199RVVVFTJk7PpyHzU/qc1uQ9STVgeIGf0k3333nalLuSfR56YflZWVZGRkUFVVxWOPPYa/v7/RmqT37NlDQkICw4cPJzEx0ajb/tx8PxJ9bncnntzuoXv37tTW1gLQsWNH/P392bp1K+np6bqOXj8/P/z8/PjjH/+IUqnU+4RNmUzGnDlz+PHHH1m1ahVjx46lvLxcr9e4HSsrKxISEkhISEClVjHuN/GMWPEEtt3Npz9OKTVQqSklPj7e1KUIRqBQKEhNTeXw4cO4uroSGxtLQECAUYJNrVazePFixowZw/vvv8/69euNvp/dzfcj4e7Eqqz/VVZWxoQJE3jppZfo1asXzs7OpKamsnLlSkaPHq17XUxMDOvWrSM0NLTZUN+oqCjWrl2rG3hiCJGRkaSnpzN9+nT69OnDli1bdJtxGpKrqytz587l6tWrbNq0iRcWjOdS1mUObvwJKdsOqwfcKVufSikCGQwfPtxkNQiG19DQQGZmJjk5Ofj7+xMbG4uDg/E25M3Ly2Py5MmUlZVx7NgxevToYdDr3e/9qKam5paFH9q0aYOLi4tB62sJxJPbfzk5OREREcFHH33E4MGD6dGjB4sXL2bmzJl8+umnutfFxMRQU1Oj629rEhUVpZsoakgeHh785z//4bXXXuOpp55i5cqVaLXGeYoKDg5m8eLFdOrUCSu5jFdWTCX8t6E0eN3Y5NMUymRFhIeFP/KcIsE8qVQqLly4wJ49e2hsbCQ6OprevXsbNdi2b99O79696dSpE8ePHzd4sMH9349+//vf4+Pj0+yYP3++wetrCUSfWwuWmprKxIkT6dSpE3//+99p166dUa//008/ceDAgRtTIQ4dI+27DGwKnYzW96GVtBy23sbiJYtbzMaPos/t/iiVSq5cucKVK1dwdXWlW7duRt8BXKlUsmDBAj7//HP+9Kc/6bUfXTA80SzZgvXt25e0tDRmzpxJ7969+dvf/kZcXJzRrh8REUFERATHjh0j2z+b2WsGknbiFCe/PYf2quH3jqukFKVGyTPPPGPQ6wjGU19fz+XLl8nOzsbd3Z1+/frh6elp9PmLmZmZTJ06FaVSSWpqKp07dzbq9YVHJ5olWzhXV1e++eYbli5dyoQJE5g5cybV1dVGrWHAgAHMnz8ff39/nFwdmbE8gcF/eBybbirUkvreJ3hIpRTSvl17evfubbBrCMahUChIT09nz5491NXVERkZSWRkJF5eXkYNNo1Gwx//+EfCwsKIjIzk6NGjIthaKBFuFkAmkzFjxgzOnj1LdnY2PXr04McffzR6HQMGDGDhwoU3JtBaQcLC5xi2fCC2fVXUy/Q/wqvCpoTRY8SqJC1ZRUUFqamp7N+/H61WS3R0NP3798fd3d3otWRlZTF48GASExP54YcfWL16Nfb29kavQ9APEW4WJDAwkB9//JGFCxcyfvx4XnnlFaM/xcGN5srFv1/MwIEDcXCwZ9yvRzH1T2PwHuNIvVOVXgaf1Eo11KiqxKokLZBGoyE3N5eDBw+SkpKCra0tsbGxhIWF4ezsbJJ6Vq9eTZ8+fejfvz+nT5/miSeeMHodgn6JASUWKjs7mxkzZpCVlcUXX3zB0KFDTVaLQqHg+++/p6ysDD8/P35KOc7ZnVmoL1pjLXu4bt9rUhY5tpmUV5QbdG8ufWvNA0pqa2u5du0aOTk5yOVyQkJC8Pf3N+m/Q1ZWFi+99BLFxcWsX79erHJjQcSTm4UKCgpi9+7dLFiwgLFjxzJjxoxmW2UYk5OTE1OnTmXevHl4eHjQzseL6e88z6jVUXg8bUedw4M/zZVbFxMzZEiLCrbWSKvVUlhYyNGjR9m7dy8KhYKwsDBiY2MJCQkxWbCpVCpWrVpFWFgYffv25fTp0yLYLIx4cmsFrl69yuzZszl27BjLli3j5Zdfxtra2qQ1Xblyha1bt2JlZYWPjw+nTqZzdv/PVKTV46C5+0ruaknFQVkSaz9dy//93/8ZqWL9aA1PbpIkUVFRQW5uLgUFBVhbWxMYGEhAQIBR56fdyb59+5g9ezYymYzExEQRahZKhFsrIUkSSUlJzJ07F09PT9atW0e/fv1MXRZarZYDBw5w8uRJ3FzdsHewJ/XISS4kX6bhZwlbbu3QL5byOMsxsrOzH3n3BWOz5HCrqakhLy+PvLw8VCoVvr6+dOjQAQ8PD7MY9JOfn8/rr7/Ojh07WLJkCXPmzLG4/wPhf0S4tTL19fUsX76cVatWkZCQwAcffICnp6epywJu1LZjxw4uXrxIhw4dqK+vJ/VQGleP5tN4GWy5sY7feekE7t0cOZ9x3sQVPzhLC7fa2loKCwvJz8+nurqa9u3b4+/vT7t27UzeOtBEqVSyZs0ali5dyqhRo1i1apXBlsgTzIeYxN3KODg48M477zB16lTmzp1Lly5dzKap0sHBgXHjxgFw/fp1kpKSCOwaQNSwJ2lsbOTsyXNc/ukaiowKpo2ZbNJaW6umJseioiKKiopQKBR4enoSFBSEr6+v2QX23r17mT17NtbW1mzbtu2Ou1oLlkc8ubVySUlJvPrqq7i5ubFs2TLi4uLMognpZoWFhezevZucnBzatWuHm5sbLi4udOvWjfbt27eouUgt8clNrVZTUlJCcXExRUVFSJJE+/bt8fHxwcvLyyw/x/nz51m4cCH79u3jnXfeYfbs2WZZp2A4ItwEGhoa+Oyzz/jggw/o1asXy5cvJyIiwtRl3ZZCoaCgoACAoqIiKioqcHZ2xsPDA09PTzw9PY2+DcmDaAnhplarKS8vp7S0lLKyMioqKmjTpg0+Pj54e3vj7u6OlZV5DrTOycnhD3/4A1u2bGHGjBksWrRILKrdSolwE3QqKytZtWoVH3/8MXFxcXzwwQd07drV1GXdlVKppKysjNLSUkpLS6murm4Wdh4eHmb1ZGeO4Xa7MLO3t9f9suDp6Wn2Uy5KS0tZtmwZ69atY+zYsbz77ruEhISYuizBhES4CbcoLCxk6dKl/O1vfyMhIYElS5bQoUMHU5d1X5RKpe4m3RR2Dg4OuLq64ubmpjtM9XRn6nBTq9VUVVVRWVmpOxQKBQ4ODi0qzJrU1tby0UcfsWrVKgYNGsSyZcvEWqMCIMJNuIuLFy+yaNEikpKS+PWvf83rr7+On5+fqct6ICqVisrKymY39NraWuzt7XVB5+TkhJOTE46Ojsjlhh1jZaxw02q11NbWUltbi0Kh0H1+hUKBnZ1ds6B3dXU1i/lnD0KhUPCXv/yFFStWEBgYyIoVK27ZY1Fo3cRoSeGOOnXqxDfffMPJkyd5//336dixI1OmTGH+/Pl06tTJ1OXdFxsbG7y8vPDy8tJ9T6VS6W72VVVVulF/arUae3t7XdA1/eng4IC9vT12dnZmM9hGkiRUKhUNDQ00NDToQkyhUFBbW0tdXR0ymUz3GVxcXPDz88PNzc2smmkfVHl5OWvXruWTTz4hJCSExMRERo8Wi2cLtxLhJtxTeHg433//PRkZGaxYsYKePXsyevRo3n777RbZBGRjY6NrfmsiSRJKpbJZQJSXl5Obm0tDQwNKpRIAOzs77O3tdYednR22trbI5XKsra2Ry+W3HFZWVrqbr1qtbvanJElIkoRarb7r0dDQQGNjoy7MGhsb0Wq1yOVy7OzsdGHs7e2t+9rBwcFibvr5+fmsXr2aP//5z0RERPDNN98QGxtrMZ9P0D/RLCk8sGvXrvHhhx/yxRdfEBUVxYIFCyx+CSONRtMsXG4OG5VKpQshjUbTLJS0Wu19nV8mkzULxF8G5S8DtelrQzejmtqlS5dYuXIlX375JcOGDePtt9+mf//+pi5LaAFEuAkPraSkhDVr1vDZZ5/RvXt3Xn31VcaOHYutra2pSzMbWq0WrVarWxhapVKxe/duhg4dio2NDTKZDJlM1uzprrWTJImDBw+ydu1atm3bxsSJE/nd735H9+7dTV2a0IKY52QVoUVo164d77//Pjk5OTz33HMsXryYwMBA3nnnHQoLC01dnlmwsrJCLpdjY2OjOwDd101PaSLYbox8/Pzzz3n88cd59tlnCQkJITMzk40bN4pgEx6YCDfhkbm4uDBv3jwyMzNZv349x48fJygoiAkTJrB37977bpoTWqfz58/z6quv4ufnx7p165g7dy55eXmsXLmyxS2MLZgPEW6C3lhZWTF8+HC2b99ORkYGHTt25Pnnn6dLly58+OGHFBUVmbpEwUzU1tby5Zdf8uSTT9K3b1+qq6vZuXMnp06d4uWXX24x8+wE8yX63ASDamxs5F//+heff/45hw4dYsiQISQkJDBmzBicnZ1NXZ7RmXoStymp1Wr27NnD5s2b+de//kVQUBAzZsxg6tSptG3b1tTlCRZGhJtgNHl5eXz99dds2rSJixcvMmbMGF544QWefvrpVnOjb23hJkkSqampbNq0iS1btiCXy5k8eTIJCQn06tVL9DUKBiPCTTCJs2fPsnnzZjZv3kxDQwMTJ07k+eefZ8CAASbfeseQWkO4SZJEZmYm//jHP9i0aRPFxcWMGzeOhIQEoqKiLPr/VzAfItwEk9JqtRw6dIhNmzbx/fffY2VlxciRI4mPj+fpp5/GycnJ1CXqlaWGm1qtJiUlha1bt7J161Zyc3MZNmwYL7zwAiNHjmxxy3sJLZ8IN8FsqNVqjh49SlJSElu3biU7O5shQ4YQHx9PfHx8i1m8+W4sKdyqqqrYtWsXSUlJ7NixAxsbG0aOHMmoUaN46qmncHR0NHWJQismwk0wW1lZWbqgS0lJoVevXsTFxREdHU1kZGSLfKpryeGmUqk4efIkycnJ7Nmzh4MHD9KlSxdGjRpFfHw8/fv3N9t93oTWR4Sb0CKUlZWxc+dO9u3bR3JyMrm5ufTr14/o6Giio6MZNGhQiwi7lhRuKpWKtLQ0kpOTSU5O5vDhw9ja2ur+zUeOHElwcLCpyxSE2xLhJrRI2dnZHDhwgOTkZPbv309+fr4u7Pr37094eDgdOnQwu9F45hxu5eXlpKWlkZqayoEDB3RhFhUVpQu0Hj16iKczoUUQ4SZYhOzsbJKTkzlw4ACpqalkZGTg4eFBeHh4s8Pf39+kgWcu4VZWVkZaWhonT57UHVevXiUoKIjw8HCefPJJoqOj6dmzpwgzoUUS4SZYpLq6Ok6fPq27caelpXH+/Hnc3d0JCwuja9eudO7cWXf4+/sb5SZuzHCTJInr16+TlZWlOzIzM0lPTyc7O5vg4OBmwR8WFoaHh4dBaxIEYxHhJrQa9fX1nDlzhlOnTpGZmam74V+9ehUbGxtCQ0N1YRcaGoqfnx8+Pj74+vri4eGhl/DTZ7hJkkR1dTUFBQUUFhZSWFjI5cuXm4VZVVUVfn5+zYL88ccfJywsDHd390f+PIJgrkS4Ca2eUqnk6tWrzULh0qVLFBQUUFBQQHV1NXK5HG9vb13Y+fj44O3tjYuLC87Ozjg5OeHs7HzL146OjrqV/62srNBoNOzcuZO4uDisra3RarVoNBrq6uqoqamhpqYGhUJx269LSkooLCxsFmb19fU4ODjg4+ODj48PISEhzYIsNDS0RQy0EQR9E+EmCPdQV1enC5Obw6W4uJjq6uo7BlLT7t33y9ra+q5B6eXlpQvWmw9XV1ezGzgjCKYmwk0QDESpVFJbW4tGo9FtWtp0ND3JNR2Ojo7Y2dmJkBIEPRHhJgiCIFgcMcZXEARBsDgi3ARBEASLI8JNEARBsDgi3ARBEASLI8JNEARBsDgi3ARBEASLI8JNEARBsDgi3ARBEASLI8JNEARBsDgi3ARBEASLI8JNEARBsDgi3ATBSK5fv85vfvMbAgICsLOzw9vbm7i4OFJSUkxdmiBYHLmpCxCE1mLcuHEolUo2btxISEgIxcXF7N27l7KyMlOXJggWR+wKIAhGUFlZibu7O8nJyURFRZm6HEGweKJZUhCMwMnJCScnJ/7973/T2Nho6nIEweKJcBMEI5DL5WzYsIGNGzfi5uZGZGQkCxYs4MyZM6YuTRAskmiWFAQjamho4NChQxw7doydO3dy/Phx/vrXvzJt2jRTlyYIFkWEmyCY0IwZM9i9ezfXrl0zdSmCYFFEs6QgmFD37t2pra01dRmCYHHEVABBMIKysjImTJjASy+9RK9evXB2diY1NZWVK1cyevRoU5cnCBZHhJsgGIGTkxMRERF89NFHXL58GZVKhb+/PzNnzmTBggWmLk8QLI7ocxMEQRAsjuhzEwRBECyOCDdBEATB4ohwEwRBECyOCDdBEATB4ohwEwRBECyOCDdBEATB4ohwEwRBECyOCDdBEATB4ohwEwRBECyOCDdBEATB4ohwEwRBECyOCDdBEATB4vw/CA83mkOiUssAAAAASUVORK5CYII=", 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", 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Qur/n5+cnPz+/EuM+Pj5ue3O7c238hj57Bn32DPrsOfTaM1zdZzNrmf60VHR0tCZNmqSwsDANHz5c6enpZpew69ixozIyMhzGvvnmmxK/OA4AAOAs0+Fm3rx5+vHHH7VkyRIdO3ZMnTt3VsuWLfXcc8/p6NGjptaaMGGC0tPTNWvWLB04cECJiYlauHChRo8ebZ8TFxenwYMHOxy3e/du7d69W3l5efrpp5+0e/du7du3z+xLAQAAFlSu77mpUqWK7rrrLq1Zs0Y//PCDBg4cqGnTpikyMlJ33nmntmzZ4tQ6N9xwg1avXq1ly5apdevWevLJJzVv3jwNGjTIPic7O1tZWVkOx0VHRys6OlqfffaZEhMTFR0drdtuu608LwUAAFhMhT4KvmPHDi1ZskTLly9XnTp1NHToUB0+fFh9+/bVqFGj9Nxzz11yjb59+6pv376l7l+6dGmJMX6lHAAAlMZ0uDl27JjefPNNLVmyRPv371e/fv20bNky9erVSzabTZI0dOhQ9e7d26lwAwAA4Eqmw039+vXVpEkT/f3vf9fQoUMVGhpaYs4111yjG264wSUFAgAAmGE63GzevFk333xzmXOCgoKUlJRU7qIAAADKy/QDxdOnT9epU6dKjOfm5uqWW25xRU0AAADlZjrcpKSk6OzZsyXGCwoKtHXrVpcUBQAAUF5O35b68ssvJf36SaV9+/bpyJEj9n3nz5/Xhg0bVK9ePddXCAAAYILT4ebaa6+VzWaTzWa76O2ngIAAvfTSSy4tDgAAwCynw82hQ4dkGIYaN26sHTt2OHxKytfXV3Xq1FHlypXdUiQAAICznA43F37vqbi42G3FAAAAVJRT4Wbt2rXq06ePfHx8tHbt2jLn/uUvf3FJYQAAAOXhVLi58847deTIEdWpU0d33nlnqfNsNpvOnz/vqtoAAABMcyrc/P5WFLelAADA5axcvwr+Rxf7Uj8AAABvMB1unnnmGa1YscK+HRsbq1q1aqlevXras2ePS4sDAAAwy3S4WbBggSIjIyVJmzZt0scff6wNGzaoT58+evTRR11eIAAAgBmmfzjzyJEj9nDz/vvv65577lHPnj3VsGFDtW/f3uUFAgAAmGH6yk3NmjX1/fffS5I2bNig7t27S/r1Zxn4pBQAAPA201du7rrrLg0cOFBXXXWVTpw4oT59+kiSvvjiCzVt2tTlBQIAAJhhOtzMnTtXDRs21Pfff69nn31W1apVkyRlZ2dr1KhRLi8QAADADNPhxsfHR5MmTSoxPmHCBJcUBAAAUBGmw40k7d+/X0lJSTp27FiJL/V74oknXFIYAABAeZgON4sWLdLIkSMVEhKisLAw2Ww2+z6bzUa4AQAAXmU63Dz11FOaOXOmpkyZ4o56AAAAKsT0R8FPnjyp2NhYd9QCAABQYabDTWxsrDZu3OiOWgAAACrM9G2ppk2batq0aUpPT1ebNm3k4+PjsH/s2LEuKw4AAMAs0+Fm4cKFqlatmlJSUpSSkuKwz2azEW4AAIBXmQ43hw4dckcdAAAALmH6mZsLzp49q4yMDJ07d86V9QAAAFSI6XCTn5+vYcOGKTAwUK1atVJWVpYk6eGHH9bTTz/t8gIBAADMMB1u4uLitGfPHiUnJ8vf398+3r17d61YscKlxQEAAJhl+pmb9957TytWrNBNN93k8O3ErVq1UmZmpkuLAwAAMMv0lZuffvpJderUKTF++vRph7ADAADgDabDTbt27fTBBx/Yty8Emtdee00xMTGuqwwAAKAcTN+WmjVrlvr06aN9+/bp3LlzeuGFF7Rv3z6lpqaW+N4bAAAATzN95aZTp07avXu3zp07pzZt2mjjxo2qU6eO0tLSdP3117ujRgAAAKeZvnIjSU2aNNGiRYtcXQsAAECFmb5yU7lyZR07dqzE+IkTJ1S5cmWXFAUAAFBepsONYRgXHS8sLJSvr2+FCwIAAKgIp29Lvfjii5J+/XTUa6+9pmrVqtn3nT9/Xp988olatGjh+goBAABMcDrczJ07V9KvV24WLFjgcAvK19dXDRs21IIFC1xfIQAAgAlOh5sLvwberVs3rVq1SjVr1nRbUQAAAOVl+tNSSUlJ7qgDAADAJUyHm/Pnz2vp0qXavHmzjh07puLiYof9W7ZscVlxAAAAZpkON+PGjdPSpUt1++23q3Xr1vyeFAAAuKyYDjfLly/XO++8o9tuu80lBRw+fFhTpkzR+vXrlZ+fr6ZNm2rJkiVq165dqcckJydr4sSJ+uqrrxQZGampU6dq6NChLqkHAABc2UyHG19fXzVt2tQlJz958qQ6duyobt26af369QoNDdX+/fvLfFj50KFDuv322zVixAi9/fbb2rx5s/7xj38oPDxcvXr1ckld5fX77/kp7fuAUHH02TPos2fQZ8+h155xOfTZZpg885w5c3Tw4EG9/PLLFb4l9dhjj+nTTz/V1q1bnT5mypQp+uCDD7R371772L333qtTp05pw4YNlzw+NzdXNWrUUE5OjoKCgspV98VcrBf8w+N69Nkz6LNn0GfPodee4c4+m/n3t+krN9u2bVNSUpLWr1+vVq1aycfHx2H/qlWrnF5r7dq16tWrl2JjY5WSkqJ69epp1KhRGj58eKnHpKWlqXv37g5jvXr10vjx4y86v7CwUIWFhfbt3NxcSVJRUZGKioqcrrUspX0zs81m09mzZ11yDtBnT6HPnkGfPYdee4a7+2zm39mmw01wcLD69+9v9rCLOnjwoObPn6+JEyfq8ccf186dOzV27Fj5+vpqyJAhFz3myJEjqlu3rsNY3bp1lZubqzNnziggIMBh3+zZs5WQkFBinY0bNyowMNAlr6MsH374odvPAfrsKfTZM+iz59Brz3BFn/Pz852ea/q2lCv5+vqqXbt2Sk1NtY+NHTtWO3fuVFpa2kWPadasmR544AHFxcXZxz788EPdfvvtys/PLxFuLnblJjIyUsePH3fZbamyflOL/1fgOvTZM+izZ9Bnz6HXnuHuPufm5iokJMQ9t6VcKTw8XC1btnQYu/rqq7Vy5cpSjwkLC9PRo0cdxo4ePaqgoKASwUaS/Pz85OfnV2Lcx8enxC218jIMg/u5HkCfPYM+ewZ99hx67Rnu7rOZf2c7HW6io6OdeoD4888/d/rkHTt2VEZGhsPYN998o6ioqFKPiYmJKXF5a9OmTYqJiXH6vO7wx/9S+YfGPeizZ9Bnz6DPnkOvPeNy6bPT4ebOO+90+cknTJigDh06aNasWbrnnnu0Y8cOLVy4UAsXLrTPiYuL0+HDh/XGG29IkkaMGKGXX35ZkydP1t///ndt2bJF77zzjj744AOX12fW2bNn9eGHH7rsO4BwcfTZM+izZ9Bnz6HXnnE59NnpcDN9+nSXn/yGG27Q6tWrFRcXpxkzZqhRo0aaN2+eBg0aZJ+TnZ2trKws+3ajRo30wQcfaMKECXrhhRdUv359vfbaa17/jhsAAHB58OozN5LUt29f9e3bt9T9S5cuLTHWtWtXffHFF26sCgAAXKkqebsAAAAAVyLcAAAASyHcAAAAS6lQuCkoKHBVHQAAAC5hOtwUFxfrySefVL169VStWjUdPHhQkjRt2jS9/vrrLi8QAADADNPh5qmnntLSpUv17LPPOnzVcuvWrfXaa6+5tDgAAACzTIebN954QwsXLtSgQYNUuXJl+3jbtm313//+16XFAQAAmGU63Bw+fFhNmzYtMV5cXGzq58gBAADcwXS4admypbZu3Vpi/N///reio6NdUhQAAEB5mf6G4ieeeEJDhgzR4cOHVVxcrFWrVikjI0NvvPGG3n//fXfUCAAA4DTTV27uuOMOrVu3Th9//LGqVq2qJ554Ql9//bXWrVunHj16uKNGAAAAp5Xrt6Vuvvlmbdq0ydW1AAAAVJjpKzc7d+7U9u3bS4xv375du3btcklRAAAA5WU63IwePVrff/99ifHDhw9r9OjRLikKAACgvEyHm3379um6664rMR4dHa19+/a5pCgAAIDyMh1u/Pz8dPTo0RLj2dnZqlKlXI/wAAAAuIzpcNOzZ0/FxcUpJyfHPnbq1Ck9/vjjfFoKAAB4nelLLc8995w6d+6sqKgo+5f27d69W3Xr1tWbb77p8gIBAADMMB1u6tWrpy+//FJvv/229uzZo4CAAD3wwAMaMGCAfHx83FEjAACA08r1kEzVqlX14IMPuroWAACACitXuNm/f7+SkpJ07NgxFRcXO+x74oknXFIYAABAeZgON4sWLdLIkSMVEhKisLAw2Ww2+z6bzUa4AQAAXmU63Dz11FOaOXOmpkyZ4o56AAAAKsT0R8FPnjyp2NhYd9QCAABQYabDTWxsrDZu3OiOWgAAACrM9G2ppk2batq0aUpPT1ebNm1KfPx77NixLisOAADALNPhZuHChapWrZpSUlKUkpLisM9msxFuAACAV5kON4cOHXJHHQAAAC5h+pmbC86ePauMjAydO3fOlfUAAABUiOlwk5+fr2HDhikwMFCtWrVSVlaWJOnhhx/W008/7fICAQAAzDAdbuLi4rRnzx4lJyfL39/fPt69e3etWLHCpcUBAACYZfqZm/fee08rVqzQTTfd5PDtxK1atVJmZqZLiwMAADDL9JWbn376SXXq1Ckxfvr0aYewAwAA4A2mw027du30wQcf2LcvBJrXXntNMTExrqsMAACgHEzflpo1a5b69Omjffv26dy5c3rhhRe0b98+paamlvjeGwAAAE8zfeWmU6dO2r17t86dO6c2bdpo48aNqlOnjtLS0nT99de7o0YAAACnmb5yI0lNmjTRokWLXF0LAABAhTkVbnJzc51eMCgoqNzFAAAAVJRT4SY4OPiSn4QyDEM2m03nz593SWEAAADl4VS4SUpKcncdAAAALuFUuOnSpYu76wAAAHAJ0w8Uf/LJJ2Xu79y5c7mLAQAAqCjT4aZr164lxn7/PA7P3AAAAG8y/T03J0+edPhz7NgxbdiwQTfccIM2btzojhoBAACcZvrKTY0aNUqM9ejRQ76+vpo4caI+++wzlxQGAABQHqav3JSmbt26ysjIMHVMfHy8bDabw58WLVqUOr+oqEgzZsxQkyZN5O/vr7Zt22rDhg0VLR0AAFiI6Ss3X375pcO2YRjKzs7W008/rWuvvdZ0Aa1atdLHH3/8W0FVSi9p6tSpeuutt7Ro0SK1aNFCH330kfr376/U1FRFR0ebPjcAALAe0+Hm2muvlc1mk2EYDuM33XSTFi9ebL6AKlUUFhbm1Nw333xT//znP3XbbbdJkkaOHKmPP/5Yc+bM0VtvvWX63AAAwHpMh5tDhw45bFeqVEmhoaHy9/cvVwH79+9XRESE/P39FRMTo9mzZ6tBgwYXnVtYWFjiPAEBAdq2bVup6xcWFqqwsNC+feGnJIqKilRUVFSumktzYT1XrwtH9Nkz6LNn0GfPodee4a4+m1nPZvzxEowHrV+/Xnl5eWrevLmys7OVkJCgw4cPa+/evapevXqJ+QMHDtSePXv03nvvqUmTJtq8ebPuuOMOnT9/3iHA/F58fLwSEhJKjCcmJiowMNDlrwkAALhefn6+Bg4cqJycnEv+jmW5ws3mzZs1d+5cff3115Kkq6++WuPHj1f37t3LV/H/O3XqlKKiovT8889r2LBhJfb/9NNPGj58uNatWyebzaYmTZqoe/fuWrx4sc6cOXPRNS925SYyMlLHjx93+Y98FhUVadOmTerRo4d8fHxcujZ+Q589gz57Bn32HHrtGe7qc25urkJCQpwKN6ZvS7366qsaN26c/vrXv2rcuHGSpPT0dN12222aO3euRo8eXb6q9esPdDZr1kwHDhy46P7Q0FC99957Kigo0IkTJxQREaHHHntMjRs3LnVNPz8/+fn5lRj38fFx25vbnWvjN/TZM+izZ9Bnz6HXnuHqPptZy3S4mTVrlubOnasxY8bYx8aOHauOHTtq1qxZFQo3eXl5yszM1P3331/mPH9/f9WrV09FRUVauXKl7rnnnnKfEwAAWIvp77k5deqUevfuXWK8Z8+eysnJMbXWpEmTlJKSom+//Vapqanq37+/KleurAEDBkiSBg8erLi4OPv87du3a9WqVTp48KC2bt2q3r17q7i4WJMnTzb7MgAAgEWZDjd/+ctftHr16hLja9asUd++fU2t9cMPP2jAgAFq3ry57rnnHtWuXVvp6ekKDQ2VJGVlZSk7O9s+v6CgQFOnTlXLli3Vv39/1atXT9u2bVNwcLDZlwEAACzKqdtSL774ov3vLVu21MyZM5WcnKyYmBhJvz5z8+mnn+qRRx4xdfLly5eXuT85Odlhu0uXLtq3b5+pcwAAgD8Xp8LN3LlzHbZr1qypffv2OQSN4OBgLV68WFOnTnVthQAAACY4FW7++MV9AAAAlytTz9wUFRWpSZMm9u+3AQAAuNyYCjc+Pj4qKChwVy0AAAAVZvrTUqNHj9Yzzzyjc+fOuaMeAACACjH9JX47d+7U5s2btXHjRrVp00ZVq1Z12L9q1SqXFQcAAGCW6XATHBysu+++2x21AAAAVJjpcLNkyRJ31AEAAOASpp+5AQAAuJyZvnLTqFEj2Wy2UvcfPHiwQgUBAABUhOlwM378eIftoqIiffHFF9qwYYMeffRRV9UFAABQLqbDzbhx4y46/sorr2jXrl0VLggAAKAiXPbMTZ8+fbRy5UpXLQcAAFAuLgs3//73v1WrVi1XLQcAAFAuTt+WmjFjhh555BF16tTJ4YFiwzB05MgR/fTTT3r11VfdUiQAAICznA43CQkJGjFihO644w6HcFOpUiWFhoaqa9euatGihVuKBAAAcJbT4cYwDElSfHy8u2oBAACoMFPP3JT1/TYAAACXA1MfBW/WrNklA87PP/9coYIAAAAqwlS4SUhIUI0aNdxVCwAAQIWZCjf33nuv6tSp465aAAAAKszpZ2543gYAAFwJnA43Fz4tBQAAcDlz+rZUcXGxO+sAAABwCZf9/AIAAMDlgHADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAsxavhJj4+XjabzeFPixYtyjxm3rx5at68uQICAhQZGakJEyaooKDAQxUDAIDLXRVvF9CqVSt9/PHH9u0qVUovKTExUY899pgWL16sDh066JtvvtHQoUNls9n0/PPPe6JcAABwmfN6uKlSpYrCwsKcmpuamqqOHTtq4MCBkqSGDRtqwIAB2r59uztLBAAAVxCvh5v9+/crIiJC/v7+iomJ0ezZs9WgQYOLzu3QoYPeeust7dixQzfeeKMOHjyoDz/8UPfff3+p6xcWFqqwsNC+nZubK0kqKipSUVGRS1/LhfVcvS4c0WfPoM+eQZ89h157hrv6bGY9m2EYhkvPbsL69euVl5en5s2bKzs7WwkJCTp8+LD27t2r6tWrX/SYF198UZMmTZJhGDp37pxGjBih+fPnl3qO+Ph4JSQklBhPTExUYGCgy14LAABwn/z8fA0cOFA5OTkKCgoqc65Xw80fnTp1SlFRUXr++ec1bNiwEvuTk5N177336qmnnlL79u114MABjRs3TsOHD9e0adMuuubFrtxERkbq+PHjl2yOWUVFRdq0aZN69OghHx8fl66N39Bnz6DPnkGfPYdee4a7+pybm6uQkBCnwo3Xb0v9XnBwsJo1a6YDBw5cdP+0adN0//336x//+IckqU2bNjp9+rQefPBB/fOf/1SlSiU//OXn5yc/P78S4z4+Pm57c7tzbfyGPnsGffYM+uw59NozXN1nM2tdVt9zk5eXp8zMTIWHh190f35+fokAU7lyZUnSZXQBCgAAeJFXw82kSZOUkpKib7/9Vqmpqerfv78qV66sAQMGSJIGDx6suLg4+/x+/fpp/vz5Wr58uQ4dOqRNmzZp2rRp6tevnz3kAACAPzev3pb64YcfNGDAAJ04cUKhoaHq1KmT0tPTFRoaKknKyspyuFIzdepU2Ww2TZ06VYcPH1ZoaKj69eunmTNneuslAACAy4xXw83y5cvL3J+cnOywXaVKFU2fPl3Tp093Y1UAAOBKdlk9cwMAAFBRhBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGApXg038fHxstlsDn9atGhR6vyuXbuWmG+z2XT77bd7sGoAAHA5q+LtAlq1aqWPP/7Yvl2lSuklrVq1SmfPnrVvnzhxQm3btlVsbKxbawQAAFcOr4ebKlWqKCwszKm5tWrVcthevny5AgMDCTcAAMDO6+Fm//79ioiIkL+/v2JiYjR79mw1aNDAqWNff/113XvvvapatWqpcwoLC1VYWGjfzs3NlSQVFRWpqKioYsX/wYX1XL0uHNFnz6DPnkGfPYdee4a7+mxmPZthGIZLz27C+vXrlZeXp+bNmys7O1sJCQk6fPiw9u7dq+rVq5d57I4dO9S+fXtt375dN954Y6nz4uPjlZCQUGI8MTFRgYGBFX4NAADA/fLz8zVw4EDl5OQoKCiozLleDTd/dOrUKUVFRen555/XsGHDypz70EMPKS0tTV9++WWZ8y525SYyMlLHjx+/ZHPMKioq0qZNm9SjRw/5+Pi4dG38hj57Bn32DPrsOfTaM9zV59zcXIWEhDgVbrx+W+r3goOD1axZMx04cKDMeadPn9by5cs1Y8aMS67p5+cnPz+/EuM+Pj5ue3O7c238hj57Bn32DPrsOfTaM1zdZzNrXVbfc5OXl6fMzEyFh4eXOe/dd99VYWGh7rvvPg9VBgAArhReDTeTJk1SSkqKvv32W6Wmpqp///6qXLmyBgwYIEkaPHiw4uLiShz3+uuv684771Tt2rU9XTIAALjMefW21A8//KABAwboxIkTCg0NVadOnZSenq7Q0FBJUlZWlipVcsxfGRkZ2rZtmzZu3OiNkgEAwGXOq+Fm+fLlZe5PTk4uMda8eXNdRs9AAwCAy8xl9cwNAABARRFuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApVxWvwruCRe+3Tg3N9flaxcVFSk/P1+5ubn84qwb0WfPoM+eQZ89h157hrv6fOHf2878SsGfLtz88ssvkqTIyEgvVwIAAMz65ZdfVKNGjTLn2Iw/2Q81FRcX68cff1T16tVls9lcunZubq4iIyP1/fffKygoyKVr4zf02TPos2fQZ8+h157hrj4bhqFffvlFERERJX5U+4/+dFduKlWqpPr167v1HEFBQfyD4wH02TPos2fQZ8+h157hjj5f6orNBTxQDAAALIVwAwAALIVw40J+fn6aPn26/Pz8vF2KpdFnz6DPnkGfPYdee8bl0Oc/3QPFAADA2rhyAwAALIVwAwAALIVwAwAALIVwAwAALIVw4yKvvPKKGjZsKH9/f7Vv3147duzwdkmWEx8fL5vN5vCnRYsW3i7rivfJJ5+oX79+ioiIkM1m03vvveew3zAMPfHEEwoPD1dAQIC6d++u/fv3e6fYK9il+jx06NAS7+/evXt7p9gr2OzZs3XDDTeoevXqqlOnju68805lZGQ4zCkoKNDo0aNVu3ZtVatWTXfffbeOHj3qpYqvTM70uWvXriXe0yNGjPBIfYQbF1ixYoUmTpyo6dOn6/PPP1fbtm3Vq1cvHTt2zNulWU6rVq2UnZ1t/7Nt2zZvl3TFO336tNq2batXXnnlovufffZZvfjii1qwYIG2b9+uqlWrqlevXiooKPBwpVe2S/VZknr37u3w/l62bJkHK7SGlJQUjR49Wunp6dq0aZOKiorUs2dPnT592j5nwoQJWrdund59912lpKToxx9/1F133eXFqq88zvRZkoYPH+7wnn722Wc9U6CBCrvxxhuN0aNH27fPnz9vREREGLNnz/ZiVdYzffp0o23btt4uw9IkGatXr7ZvFxcXG2FhYca//vUv+9ipU6cMPz8/Y9myZV6o0Br+2GfDMIwhQ4YYd9xxh1fqsbJjx44ZkoyUlBTDMH59//r4+Bjvvvuufc7XX39tSDLS0tK8VeYV7499NgzD6NKlizFu3Div1MOVmwo6e/asPvvsM3Xv3t0+VqlSJXXv3l1paWlerMya9u/fr4iICDVu3FiDBg1SVlaWt0uytEOHDunIkSMO7+8aNWqoffv2vL/dIDk5WXXq1FHz5s01cuRInThxwtslXfFycnIkSbVq1ZIkffbZZyoqKnJ4T7do0UINGjTgPV0Bf+zzBW+//bZCQkLUunVrxcXFKT8/3yP1/Ol+ONPVjh8/rvPnz6tu3boO43Xr1tV///tfL1VlTe3bt9fSpUvVvHlzZWdnKyEhQTfffLP27t2r6tWre7s8Szpy5IgkXfT9fWEfXKN3796666671KhRI2VmZurxxx9Xnz59lJaWpsqVK3u7vCtScXGxxo8fr44dO6p169aSfn1P+/r6Kjg42GEu7+nyu1ifJWngwIGKiopSRESEvvzyS02ZMkUZGRlatWqV22si3OCK0adPH/vfr7nmGrVv315RUVF65513NGzYMC9WBlTcvffea/97mzZtdM0116hJkyZKTk7Wrbfe6sXKrlyjR4/W3r17eTbPzUrr84MPPmj/e5s2bRQeHq5bb71VmZmZatKkiVtr4rZUBYWEhKhy5colnrQ/evSowsLCvFTVn0NwcLCaNWumAwcOeLsUy7rwHub97XmNGzdWSEgI7+9yGjNmjN5//30lJSWpfv369vGwsDCdPXtWp06dcpjPe7p8SuvzxbRv316SPPKeJtxUkK+vr66//npt3rzZPlZcXKzNmzcrJibGi5VZX15enjIzMxUeHu7tUiyrUaNGCgsLc3h/5+bmavv27by/3eyHH37QiRMneH+bZBiGxowZo9WrV2vLli1q1KiRw/7rr79ePj4+Du/pjIwMZWVl8Z424VJ9vpjdu3dLkkfe09yWcoGJEydqyJAhateunW688UbNmzdPp0+f1gMPPODt0ixl0qRJ6tevn6KiovTjjz9q+vTpqly5sgYMGODt0q5oeXl5Dv9P6tChQ9q9e7dq1aqlBg0aaPz48Xrqqad01VVXqVGjRpo2bZoiIiJ05513eq/oK1BZfa5Vq5YSEhJ09913KywsTJmZmZo8ebKaNm2qXr16ebHqK8/o0aOVmJioNWvWqHr16vbnaGrUqKGAgADVqFFDw4YN08SJE1WrVi0FBQXp4YcfVkxMjG666SYvV3/luFSfMzMzlZiYqNtuu021a9fWl19+qQkTJqhz58665ppr3F+gVz6jZUEvvfSS0aBBA8PX19e48cYbjfT0dG+XZDl/+9vfjPDwcMPX19eoV6+e8be//c04cOCAt8u64iUlJRmSSvwZMmSIYRi/fhx82rRpRt26dQ0/Pz/j1ltvNTIyMrxb9BWorD7n5+cbPXv2NEJDQw0fHx8jKirKGD58uHHkyBFvl33FuViPJRlLliyxzzlz5owxatQoo2bNmkZgYKDRv39/Izs723tFX4Eu1eesrCyjc+fORq1atQw/Pz+jadOmxqOPPmrk5OR4pD7b/xcJAABgCTxzAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwA6CE5ORk2Wy2Ej8uaNbQoUOv6J9p6Nq1q8aPH3/JeZ07d1ZiYqL7C/qde++9V3PmzPHoOYErBeEGsLAFCxaoevXqOnfunH0sLy9PPj4+6tq1q8PcC4EmMzNTHTp0UHZ2tmrUqOH2GhctWqS2bduqWrVqCg4OVnR0tGbPnu3287rK2rVrdfToUd17770uWe9///d/1alTp0vOmzp1qmbOnKmcnByXnBewEsINYGHdunVTXl6edu3aZR/bunWrwsLCtH37dhUUFNjHk5KS1KBBAzVp0kS+vr4KCwuTzWZza32LFy/W+PHjNXbsWO3evVuffvqpJk+erLy8PLee15VefPFFPfDAA6pUyTX/c7pmzRr95S9/ueS81q1bq0mTJnrrrbdccl7ASgg3gIU1b95c4eHhSk5Oto8lJyfrjjvuUKNGjZSenu4w3q1bN/vff39baunSpQoODtZHH32kq6++WtWqVVPv3r2VnZ1tP/78+fOaOHGigoODVbt2bU2ePFmX+um6tWvX6p577tGwYcPUtGlTtWrVSgMGDNDMmTPtcy7c2kpISFBoaKiCgoI0YsQInT171j6nuLhYs2fPVqNGjRQQEKC2bdvq3//+t8O59u7dqz59+qhatWqqW7eu7r//fh0/fty+//Tp0xo8eLCqVaum8PBwp275/PTTT9qyZYv69evnMG6z2fQ///M/6tu3rwIDA3X11VcrLS1NBw4cUNeuXVW1alV16NBBmZmZDscVFBRo48aN9nDz6quv6qqrrpK/v7/q1q2rv/71rw7z+/Xrp+XLl1+yTuDPhnADWFy3bt2UlJRk305KSlLXrl3VpUsX+/iZM2e0fft2e7i5mPz8fD333HN688039cknnygrK0uTJk2y758zZ46WLl2qxYsXa9u2bfr555+1evXqMmsLCwtTenq6vvvuuzLnbd68WV9//bWSk5O1bNkyrVq1SgkJCfb9s2fP1htvvKEFCxboq6++0oQJE3TfffcpJSVFknTq1Cndcsstio6O1q5du7RhwwYdPXpU99xzj32NRx99VCkpKVqzZo02btyo5ORkff7552XWtW3bNnt4+aMnn3xSgwcP1u7du9WiRQsNHDhQDz30kOLi4rRr1y4ZhqExY8aUeJ316tVTixYttGvXLo0dO1YzZsxQRkaGNmzYoM6dOzvMv/HGG7Vjxw4VFhaWWSfwp+OR3x4H4DWLFi0yqlatahQVFRm5ublGlSpVjGPHjhmJiYlG586dDcMwjM2bNxuSjO+++84wDMNISkoyJBknT540DMMwlixZYkgyDhw4YF/3lVdeMerWrWvfDg8PN5599ln7dlFRkVG/fn3jjjvuKLW2H3/80bjpppsMSUazZs2MIUOGGCtWrDDOnz9vnzNkyBCjVq1axunTp+1j8+fPN6pVq2acP3/eKCgoMAIDA43U1FSHtYcNG2YMGDDAMAzDePLJJ42ePXs67P/+++8NSUZGRobxyy+/GL6+vsY777xj33/ixAkjICDAGDduXKn1z50712jcuHGJcUnG1KlT7dtpaWmGJOP111+3jy1btszw9/d3OG748OHGpEmTDMMwjJUrVxpBQUFGbm5uqeffs2ePIcn49ttvS50D/BlV8V6sAuAJXbt21enTp7Vz506dPHlSzZo1U2hoqLp06aIHHnhABQUFSk5OVuPGjdWgQYNS1wkMDFSTJk3s2+Hh4Tp27JgkKScnR9nZ2Wrfvr19f5UqVdSuXbsyb02Fh4crLS1Ne/fu1SeffKLU1FQNGTJEr732mjZs2GB/jqVt27YKDAy0HxcTE6O8vDx9//33ysvLU35+vnr06OGw9tmzZxUdHS1J2rNnj5KSklStWrUSNWRmZurMmTM6e/asQ/21atVS8+bNS61d+vWKl7+//0X3XXPNNfa/161bV5LUpk0bh7GCggLl5uYqKChIhmFo3bp1eueddyRJPXr0UFRUlBo3bqzevXurd+/e6t+/v0MfAgICJP16VQ3Abwg3gMU1bdpU9evXV1JSkk6ePKkuXbpIkiIiIhQZGanU1FQlJSXplltuKXMdHx8fh22bzXbJZ2qc1bp1a7Vu3VqjRo3SiBEjdPPNNyslJaXM22QXXHj4+IMPPlC9evUc9vn5+dnn9OvXT88880yJ48PDw3XgwIFy1R0SEqKTJ09edN/v+3XhweyLjRUXF0uSduzYoXPnzqlDhw6SpOrVq+vzzz9XcnKyNm7cqCeeeELx8fHauXOngoODJUk///yzJCk0NLRc9QNWxTM3wJ9At27dlJycrOTkZIePgHfu3Fnr16/Xjh07nAoSpalRo4bCw8O1fft2+9i5c+f02WefmV6rZcuWkn59wPeCPXv26MyZM/bt9PR0VatWTZGRkWrZsqX8/PyUlZWlpk2bOvyJjIyUJF133XX66quv1LBhwxJzqlatqiZNmsjHx8eh/pMnT+qbb74ps9bo6GgdOXKk1IBjxpo1a3T77bercuXK9rEqVaqoe/fuevbZZ/Xll1/q22+/1ZYtW+z79+7dq/r16yskJKTC5weshCs3wJ9At27dNHr0aBUVFdmv3EhSly5dNGbMGJ09e7ZC4UaSxo0bp6efflpXXXWVWrRooeeff/6SXwI4cuRIRURE6JZbblH9+vWVnZ2tp556SqGhoYqJibHPO3v2rIYNG6apU6fq22+/1fTp0zVmzBhVqlRJ1atX16RJkzRhwgQVFxerU6dOysnJ0aeffqqgoCANGTJEo0eP1qJFizRgwABNnjxZtWrV0oEDB7R8+XK99tprqlatmoYNG6ZHH31UtWvXVp06dfTPf/7zkh/vjo6OVkhIiD799FP17du3Qv1bu3atZsyYYd9+//33dfDgQXXu3Fk1a9bUhx9+qOLiYodbZVu3blXPnj0rdF7Aigg3wJ9At27ddObMGbVo0cL+/If0a7j55Zdf7B8Zr4hHHnlE2dnZGjJkiCpVqqS///3v6t+/f5lfMte9e3ctXrxY8+fP14kTJxQSEqKYmBht3rxZtWvXts+79dZbddVVV6lz584qLCzUgAEDFB8fb9//5JNPKjQ0VLNnz9bBgwcVHBys6667To8//rikX2/Bffrpp5oyZYp69uypwsJCRUVFqXfv3vYA869//ct++6p69ep65JFHLvkFeZUrV9YDDzygt99+u0LhJjMzUwcOHFCvXr3sY8HBwVq1apXi4+NVUFCgq666SsuWLVOrVq0k/fqx8ffee08bNmwo93kBq7IZrrppDgBuMHToUJ06dUrvvfeet0u5qCNHjqhVq1b6/PPPFRUVVa41nn/+eX388cf68MMPnT5m/vz5Wr16tTZu3FiucwJWxjM3AFABYWFhev3115WVlVXuNerXr6+4uDhTx/j4+Oill14q9zkBK+PKDYDL2uV+5QbA5YdwAwAALIXbUgAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFL+D33PKiu3qYUbAAAAAElFTkSuQmCC", + "image/png": 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", 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G6vLLL9eqVau8VRcAAIDPNTsI1dTU+KIOAAAA23ntHiG7rFmzRj169FDbtm01YsQI7dy5s8HtX375ZcXHx6tt27YaOHCg3nzzTZsqBQDvCwoK0uTJkxUUFOTvUoAWoUlnhFJTU5s84cMPP3zaxTTmxRdfVGpqqtauXasRI0Zo9erVSk5OVkFBgTp37lxr++3bt2vq1KlKS0vTVVddpfXr12vy5Mnas2ePBgwY4LM6AcAXHA5HrWXLsvxUDdAyNCkI/fOf/2zSZD/9I/W2hx9+WLNmzdKMGTMkSWvXrtUbb7yhdevW6a677qq1/R/+8AdNmDBBd9xxhyRp5cqVysrK0h//+EetXbvWp7UCgDfV9+8rYQg4M00KQjk5Ob6uo1HV1dXavXu3Fi1a5B5r1aqVxo0bp7y8vDr3ycvLq3U2Kzk5WRkZGfW+TlVVlaqqqtzLZWVlkn74BG2Xy3UG78DTqbm8OSfqRq/tQZ/9h557H8ezPXzZ56bOeVpfuuoP33zzjU6ePKmoqCiP8aioKO3fv7/OfUpKSurcvqSkpN7XSUtL0/Lly2uNZ2ZmKiQk5DQqb1hWVpbX50Td6LU96LP9uPfRdzie7eGLPldWVjZpu9MKQrt27dJLL72koqIiVVdXe6x79dVXT2fKs8aiRYs8ziKVlZUpLi5OSUlJCgsL89rruFwuZWVlafz48XI6nV6bF7XRa3vQZ9+qrq6u8wbpn/4bDO/geLaHL/t86opOY5odhF544QVNnz5dycnJyszMVFJSkj7++GOVlpZqypQpzS60qSIiItS6dWuVlpZ6jJeWlio6OrrOfaKjo5u1vSQFBwcrODi41rjT6fTJH4Ov5kVt9Noe9Nl3LMvyuFeIe4N8j+PZHr7oc1Pna/bj87/73e/0yCOP6PXXX1dQUJD+8Ic/aP/+/bruuuvUrVu3ZhfaVEFBQRo6dKiys7PdYzU1NcrOzlZCQkKd+yQkJHhsL/1w+q2+7QHgbFddXa2MjAzOBAFe0uwgVFhYqCuvvFLSD+GkoqJCDodD8+fP15///GevF/hjqampevLJJ/XMM89o3759uu2221RRUeF+imz69OkeN1PPnTtXmzdv1qpVq7R//34tW7ZMu3bt0pw5c3xaJwAACAzNvjR2zjnn6NixY5KkLl26aO/evRo4cKCOHDnS5BuTTtf111+vr7/+WkuWLFFJSYkuvPBCbd682X1DdFFRkVq1+v9sN3LkSK1fv16LFy/W3Xffrb59+yojI4PPEAIAAJKaEYT27t2rAQMGaNSoUcrKytLAgQN17bXXau7cudqyZYuysrI0duxYX9YqSZozZ069Z3Ryc3NrjV177bW69tprfVwVAAAIRE0OQoMGDdLPfvYzTZ482R0sfvvb38rpdGr79u265pprtHjxYp8VCgAA4G1NDkJbt27V008/rbS0NN1333265pprdPPNN9f5ic4AAACBoMk3S1922WVat26diouL9dhjj+nzzz/X6NGjdd555+n+++9v8EMKAQAAzkbNfmqsffv2mjFjhrZu3aqPP/5Y1157rdasWaNu3brp5z//uS9qBAAA8IlmB6Ef69Onj+6++24tXrxYoaGheuONN7xVFwAAgM+d9neNbdu2TevWrdPf/vY3tWrVStddd51mzpzpzdoAAAB8qllB6ODBg0pPT1d6eroOHDigkSNH6tFHH9V1112n9u3b+6pGAAAAn2hyEJo4caLeeustRUREaPr06frVr36lfv36+bI2AAAAn2pyEHI6nXrllVd01VVXqXXr1r6sCQAAwBZNDkKbNm3yZR0AAAC2O6OnxgAAAAIZQQgAABiLIAQAAIxFEAIAAMYiCAEAAGMRhAAAgLEIQgAAwFgEIQAAYCyCEAAAMBZBCAAAGIsgBAAAjEUQAgAAxiIIAQAAYxGEAACAsQhCAADAWAQhAABgLIIQAAAwFkEIAAAYiyAEAACMRRACAADGIggBAABjEYQAAICxCEIAAMBYBCEAAGAsghAAADAWQQgAABiLIAQAAIxFEAIAAMYiCAEAAGMRhAAAgLEIQgAAwFgEIQAAYCyCEAAAMBZBCAAAGIsgBAAAjEUQAgAAxiIIAQAAYxGEAACAsQhCAADAWAQhAABgrIAJQt99951uvPFGhYWFKTw8XDNnzlR5eXmD+yQmJsrhcHj83HrrrTZVDAAAznZt/F1AU914440qLi5WVlaWXC6XZsyYoVtuuUXr169vcL9Zs2ZpxYoV7uWQkBBflwoAAAJEQAShffv2afPmzXr//fc1bNgwSdJjjz2mK664Qg899JBiY2Pr3TckJETR0dF2lQoAAAJIQAShvLw8hYeHu0OQJI0bN06tWrXSjh07NGXKlHr3ff755/XXv/5V0dHRmjRpku65554GzwpVVVWpqqrKvVxWViZJcrlccrlcXng3cs/34/+F79Bre9Bne9Bne9Bne/iyz02dMyCCUElJiTp37uwx1qZNG3Xq1EklJSX17jdt2jR1795dsbGx+vDDD3XnnXeqoKBAr776ar37pKWlafny5bXGMzMzfXJZLSsry+tzom702h702R702R702R6+6HNlZWWTtvNrELrrrrt0//33N7jNvn37Tnv+W265xf37wIEDFRMTo7Fjx6qwsFC9e/euc59FixYpNTXVvVxWVqa4uDglJSUpLCzstGv5KZfLpaysLI0fP15Op9Nr86I2em0P+mwP+mwP+mwPX/b51BWdxvg1CC1YsEA33XRTg9v06tVL0dHROnTokMf4999/r++++65Z9/+MGDFCknTgwIF6g1BwcLCCg4NrjTudTp/8MfhqXtRGr+1Bn+1Bn+1Bn+3hiz43dT6/BqHIyEhFRkY2ul1CQoKOHDmi3bt3a+jQoZKkLVu2qKamxh1umiI/P1+SFBMTc1r1AgCAliUgPkfo/PPP14QJEzRr1izt3LlT7777rubMmaMbbrjB/cTYV199pfj4eO3cuVOSVFhYqJUrV2r37t36/PPPtWnTJk2fPl2jRo3SoEGD/Pl2AADAWSIggpD0w9Nf8fHxGjt2rK644gpdeuml+vOf/+xe73K5VFBQ4L45KigoSG+99ZaSkpIUHx+vBQsW6JprrtHrr7/ur7cAAADOMgHx1JgkderUqcEPT+zRo4csy3Ivx8XFaevWrXaUBgAAAlTAnBECAADwNoIQAAAwFkEIAAAYiyAEAACMRRACAADGIggBAABjEYQAAICxCEIAAMBYBCEAAGAsghAAADBWwHzFBgAAdgkKCnL//uOvb0LLwxkhAAB+xOFwNLiMloUgBADA/6kv9BCGWi6CEAAAMBZBCAAAGIsgBADA/6nvxmhumG65CEIAAPzIT0MPIahlIwgBAPAT1dXVysjIUHV1tb9LgY8RhAAAgLEIQgAAwFgEIQAAYCyCEAAAMBZBCAAAGIsgBAAAjEUQAgAAxiIIAQAAYxGEAACAsQhCAADAWAQhAABgLIIQAAAwFkEIAAAYiyAEAACMRRACAADGIggBAABjEYQAAICxCEIAAMBYBCEAAGAsghAAADAWQQgAABiLIAQAAIxFEAIAAMYiCAEAAGMRhAAAgLEIQgAAwFgEIQAAYCyCEAAAMBZBCAAAGIsgBAAAjEUQAgAAxiIIAQAAYwVMELrvvvs0cuRIhYSEKDw8vEn7WJalJUuWKCYmRu3atdO4ceP0ySef+LZQAAAQMAImCFVXV+vaa6/Vbbfd1uR9HnjgAT366KNau3atduzYofbt2ys5OVknTpzwYaUAACBQtPF3AU21fPlySVJ6enqTtrcsS6tXr9bixYt19dVXS5KeffZZRUVFKSMjQzfccEOd+1VVVamqqsq9XFZWJklyuVxyuVxn8A48nZrLm3OibvTaHvTZHvTZHvTZHr7sc1PnDJgg1FyfffaZSkpKNG7cOPdYx44dNWLECOXl5dUbhNLS0tyh68cyMzMVEhLi9TqzsrK8PifqRq/tQZ/tQZ/tQZ/t4Ys+V1ZWNmm7FhuESkpKJElRUVEe41FRUe51dVm0aJFSU1Pdy2VlZYqLi1NSUpLCwsK8Vp/L5VJWVpbGjx8vp9PptXlRG722B322B322B322hy/7fOqKTmP8GoTuuusu3X///Q1us2/fPsXHx9tUkRQcHKzg4OBa406n0yd/DL6aF7XRa3vQZ3vQZ3vQZ3v4os9Nnc+vQWjBggW66aabGtymV69epzV3dHS0JKm0tFQxMTHu8dLSUl144YWnNScAAGhZ/BqEIiMjFRkZ6ZO5e/bsqejoaGVnZ7uDT1lZmXbs2NGsJ88AAEDLFTCPzxcVFSk/P19FRUU6efKk8vPzlZ+fr/Lycvc28fHx2rhxoyTJ4XBo3rx5uvfee7Vp0yb961//0vTp0xUbG6vJkyf76V0AAICzScDcLL1kyRI988wz7uUhQ4ZIknJycpSYmChJKigo0NGjR93bLFy4UBUVFbrlllt05MgRXXrppdq8ebPatm1ra+0AAODsFDBBKD09vdHPELIsy2PZ4XBoxYoVWrFihQ8rAwAAgSpgLo0BAAB4G0EIAAAYiyAEAACMRRACAADGIggBAABjEYQAAICxCEIAAMBYBCEAAGAsghAAADAWQQgAABiLIAQAAIxFEAIAAMYiCAEAAGMRhAAAgLEIQgAAwFgEIQAAYCyCEAAAMBZBCAAAGIsgBAAAjEUQAgAAxiIIAQAAYxGEAACAsQhCAADAWAQhAABgLIIQAAAwFkEIAAAYiyAEAACMRRACAADGIggBAABjEYQAAICxCEIAAMBYbfxdAAAAMFNQUJD7d8uy/FIDZ4QAAIDtHA5Hg8t2IQgBAABb1Rd6/BGGCEIAAMBYBCEAAGAsghAAALBVfTdG++OGaYIQAACw3U9DD0+NAQAAo1RXVysjI0PV1dV+q4EgBAAAjEUQAgAAxiIIAQAAYxGEAACAsQhCAADAWAQhAABgLIIQAAAwFkEIAAAYiyAEAACMRRACAADGIggBAABjtfF3AWe7U18CV1ZW5tV5XS6XKisrVVZWJqfT6dW54Yle24M+24M+24M+28OXfT713+3GvsyVINSIY8eOSZLi4uL8XAkAAGiuY8eOqWPHjvWud1j++t77AFFTU6ODBw8qNDRUDofDa/OWlZUpLi5OX375pcLCwrw2L2qj1/agz/agz/agz/bwZZ8ty9KxY8cUGxurVq3qvxOIM0KNaNWqlbp27eqz+cPCwvgjswm9tgd9tgd9tgd9toev+tzQmaBTuFkaAAAYiyAEAACMRRDyk+DgYC1dulTBwcH+LqXFo9f2oM/2oM/2oM/2OBv6zM3SAADAWJwRAgAAxiIIAQAAYxGEAACAsQhCAADAWAQhP1mzZo169Oihtm3basSIEdq5c6e/S2pRli1bJofD4fETHx/v77JahG3btmnSpEmKjY2Vw+FQRkaGx3rLsrRkyRLFxMSoXbt2GjdunD755BP/FBvAGuvzTTfdVOsYnzBhgn+KDVBpaWn62c9+ptDQUHXu3FmTJ09WQUGBxzYnTpzQ7Nmzde6556pDhw665pprVFpa6qeKA1dTep2YmFjrmL711lt9XhtByA9efPFFpaamaunSpdqzZ48GDx6s5ORkHTp0yN+ltSj9+/dXcXGx++edd97xd0ktQkVFhQYPHqw1a9bUuf6BBx7Qo48+qrVr12rHjh1q3769kpOTdeLECZsrDWyN9VmSJkyY4HGMb9iwwcYKA9/WrVs1e/Zsvffee8rKypLL5VJSUpIqKirc28yfP1+vv/66Xn75ZW3dulUHDx7UL37xCz9WHZia0mtJmjVrlscx/cADD/i+OAu2Gz58uDV79mz38smTJ63Y2FgrLS3Nj1W1LEuXLrUGDx7s7zJaPEnWxo0b3cs1NTVWdHS09eCDD7rHjhw5YgUHB1sbNmzwQ4Utw0/7bFmWlZKSYl199dV+qaelOnTokCXJ2rp1q2VZPxy7TqfTevnll93b7Nu3z5Jk5eXl+avMFuGnvbYsyxo9erQ1d+5c22vhjJDNqqurtXv3bo0bN8491qpVK40bN055eXl+rKzl+eSTTxQbG6tevXrpxhtvVFFRkb9LavE+++wzlZSUeBzfHTt21IgRIzi+fSA3N1edO3dWv379dNttt+nbb7/1d0kB7ejRo5KkTp06SZJ2794tl8vlcTzHx8erW7duHM9n6Ke9PuX5559XRESEBgwYoEWLFqmystLntfClqzb75ptvdPLkSUVFRXmMR0VFaf/+/X6qquUZMWKE0tPT1a9fPxUXF2v58uW67LLLtHfvXoWGhvq7vBarpKREkuo8vk+tg3dMmDBBv/jFL9SzZ08VFhbq7rvv1sSJE5WXl6fWrVv7u7yAU1NTo3nz5umSSy7RgAEDJP1wPAcFBSk8PNxjW47nM1NXryVp2rRp6t69u2JjY/Xhhx/qzjvvVEFBgV599VWf1kMQQos0ceJE9++DBg3SiBEj1L17d7300kuaOXOmHysDvOOGG25w/z5w4EANGjRIvXv3Vm5ursaOHevHygLT7NmztXfvXu4ltEF9vb7lllvcvw8cOFAxMTEaO3asCgsL1bt3b5/Vw6Uxm0VERKh169a1njooLS1VdHS0n6pq+cLDw3XeeefpwIED/i6lRTt1DHN8269Xr16KiIjgGD8Nc+bM0d///nfl5OSoa9eu7vHo6GhVV1fryJEjHttzPJ+++npdlxEjRkiSz49pgpDNgoKCNHToUGVnZ7vHampqlJ2drYSEBD9W1rKVl5ersLBQMTEx/i6lRevZs6eio6M9ju+ysjLt2LGD49vH/v3vf+vbb7/lGG8Gy7I0Z84cbdy4UVu2bFHPnj091g8dOlROp9PjeC4oKFBRURHHczM11uu65OfnS5LPj2kujflBamqqUlJSNGzYMA0fPlyrV69WRUWFZsyY4e/SWozbb79dkyZNUvfu3XXw4EEtXbpUrVu31tSpU/1dWsArLy/3+H9on332mfLz89WpUyd169ZN8+bN07333qu+ffuqZ8+euueeexQbG6vJkyf7r+gA1FCfO3XqpOXLl+uaa65RdHS0CgsLtXDhQvXp00fJycl+rDqwzJ49W+vXr9drr72m0NBQ930/HTt2VLt27dSxY0fNnDlTqamp6tSpk8LCwvTf//3fSkhI0MUXX+zn6gNLY70uLCzU+vXrdcUVV+jcc8/Vhx9+qPnz52vUqFEaNGiQb4uz/Tk1WJZlWY899pjVrVs3KygoyBo+fLj13nvv+bukFuX666+3YmJirKCgIKtLly7W9ddfbx04cMDfZbUIOTk5lqRaPykpKZZl/fAI/T333GNFRUVZwcHB1tixY62CggL/Fh2AGupzZWWllZSUZEVGRlpOp9Pq3r27NWvWLKukpMTfZQeUuvoryXr66afd2xw/ftz69a9/bZ1zzjlWSEiINWXKFKu4uNh/RQeoxnpdVFRkjRo1yurUqZMVHBxs9enTx7rjjjuso0eP+rw2x/8VCAAAYBzuEQIAAMYiCAEAAGMRhAAAgLEIQgAAwFgEIQAAYCyCEAAAMBZBCAAAGIsgBAAAjEUQAnBGcnNz5XA4an0xZXPddNNNAf01HImJiZo3b16j240aNUrr16/3fUE/csMNN2jVqlW2viYQKAhCACRJa9euVWhoqL7//nv3WHl5uZxOpxITEz22PRV+CgsLNXLkSBUXF6tjx44+r/HJJ5/U4MGD1aFDB4WHh2vIkCFKS0vz+et6y6ZNm1RaWqobbrjBK/M988wzuvTSSxvdbvHixbrvvvt09OhRr7wu0JIQhABIksaMGaPy8nLt2rXLPfb2228rOjpaO3bs0IkTJ9zjOTk56tatm3r37q2goCBFR0fL4XD4tL5169Zp3rx5+s1vfqP8/Hy9++67WrhwocrLy336ut706KOPasaMGWrVyjv/9L722mv6+c9/3uh2AwYMUO/evfXXv/7VK68LtCQEIQCSpH79+ikmJka5ubnusdzcXF199dXq2bOn3nvvPY/xMWPGuH//8aWx9PR0hYeH6x//+IfOP/98dejQQRMmTFBxcbF7/5MnTyo1NVXh4eE699xztXDhQjX2tYebNm3Sddddp5kzZ6pPnz7q37+/pk6dqvvuu8+9zanLa8uXL1dkZKTCwsJ06623qrq62r1NTU2N0tLS1LNnT7Vr106DBw/WK6+84vFae/fu1cSJE9WhQwdFRUXpP//zP/XNN9+411dUVGj69Onq0KGDYmJimnTZ6euvv9aWLVs0adIkj3GHw6EnnnhCV111lUJCQnT++ecrLy9PBw4cUGJiotq3b6+RI0eqsLDQY78TJ04oMzPTHYT+9Kc/qW/fvmrbtq2ioqL0H//xHx7bT5o0SS+88EKjdQKmIQgBcBszZoxycnLcyzk5OUpMTNTo0aPd48ePH9eOHTvcQagulZWVeuihh/Tcc89p27ZtKioq0u233+5ev2rVKqWnp2vdunV655139N1332njxo0N1hYdHa333ntPX3zxRYPbZWdna9++fcrNzdWGDRv06quvavny5e71aWlpevbZZ7V27Vp99NFHmj9/vn75y19q69atkqQjR47o8ssv15AhQ7Rr1y5t3rxZpaWluu6669xz3HHHHdq6datee+01ZWZmKjc3V3v27GmwrnfeeccddH5q5cqVmj59uvLz8xUfH69p06bpv/7rv7Ro0SLt2rVLlmVpzpw5td5nly5dFB8fr127duk3v/mNVqxYoYKCAm3evFmjRo3y2H748OHauXOnqqqqGqwTMI7Pv98eQMB48sknrfbt21sul8sqKyuz2rRpYx06dMhav369NWrUKMuyLCs7O9uSZH3xxReWZVlWTk6OJck6fPiwZVmW9fTTT1uSrAMHDrjnXbNmjRUVFeVejomJsR544AH3ssvlsrp27WpdffXV9dZ28OBB6+KLL7YkWeedd56VkpJivfjii9bJkyfd26SkpFidOnWyKioq3GOPP/641aFDB+vkyZPWiRMnrJCQEGv79u0ec8+cOdOaOnWqZVmWtXLlSispKclj/ZdffmlJsgoKCqxjx45ZQUFB1ksvveRe/+2331rt2rWz5s6dW2/9jzzyiNWrV69a45KsxYsXu5fz8vIsSdZf/vIX99iGDRustm3beuw3a9Ys6/bbb7csy7L+9re/WWFhYVZZWVm9r//BBx9YkqzPP/+83m0AE7XxXwQDcLZJTExURUWF3n//fR0+fFjnnXeeIiMjNXr0aM2YMUMnTpxQbm6uevXqpW7dutU7T0hIiHr37u1ejomJ0aFDhyRJR48eVXFxsUaMGOFe36ZNGw0bNqzBy2MxMTHKy8vT3r17tW3bNm3fvl0pKSl66qmntHnzZvd9N4MHD1ZISIh7v4SEBJWXl+vLL79UeXm5KisrNX78eI+5q6urNWTIEEnSBx98oJycHHXo0KFWDYWFhTp+/Liqq6s96u/UqZP69etXb+3SD2fS2rZtW+e6QYMGuX+PioqSJA0cONBj7MSJEyorK1NYWJgsy9Lrr7+ul156SZI0fvx4de/eXb169dKECRM0YcIETZkyxaMP7dq1k/TD2ToA/48gBMCtT58+6tq1q3JycnT48GGNHj1akhQbG6u4uDht375dOTk5uvzyyxucx+l0eiw7HI5G7wFqqgEDBmjAgAH69a9/rVtvvVWXXXaZtm7d2uClulNO3Vj9xhtvqEuXLh7rgoOD3dtMmjRJ999/f639Y2JidODAgdOqOyIiQocPH65z3Y/7deqm87rGampqJEk7d+7U999/r5EjR0qSQkNDtWfPHuXm5iozM1NLlizRsmXL9P777ys8PFyS9N1330mSIiMjT6t+oKXiHiEAHsaMGaPc3Fzl5uZ6PDY/atQo/c///I927tzZpNBRn44dOyomJkY7duxwj33//ffavXt3s+e64IILJP1w8/IpH3zwgY4fP+5efu+999ShQwfFxcXpggsuUHBwsIqKitSnTx+Pn7i4OEnSRRddpI8++kg9evSotU379u3Vu3dvOZ1Oj/oPHz6sjz/+uMFahwwZopKSknrDUHO89tpruvLKK9W6dWv3WJs2bTRu3Dg98MAD+vDDD/X5559ry5Yt7vV79+5V165dFRERccavD7QknBEC4GHMmDGaPXu2XC6X+4yQJI0ePVpz5sxRdXX1GQUhSZo7d65+//vfq2/fvoqPj9fDDz/c6Acy3nbbbYqNjdXll1+url27qri4WPfee68iIyOVkJDg3q66ulozZ87U4sWL9fnnn2vp0qWaM2eOWrVqpdDQUN1+++2aP3++ampqdOmll+ro0aN69913FRYWppSUFM2ePVtPPvmkpk6dqoULF6pTp046cOCAXnjhBT311FPq0KGDZs6cqTvuuEPnnnuuOnfurN/+9reNPhI/ZMgQRURE6N1339VVV111Rv3btGmTVqxY4V7++9//rk8//VSjRo3SOeecozfffFM1NTUel+vefvttJSUlndHrAi0RQQiAhzFjxuj48eOKj493368i/RCEjh075n7M/kwsWLBAxcXFSklJUatWrfSrX/1KU6ZMafAD/8aNG6d169bp8ccf17fffquIiAglJCQoOztb5557rnu7sWPHqm/fvho1apSqqqo0depULVu2zL1+5cqVioyMVFpamj799FOFh4froosu0t133y3ph8uA7777ru68804lJSWpqqpK3bt314QJE9xh58EHH3RfQgsNDdWCBQsa/bDC1q1ba8aMGXr++efPKAgVFhbqwIEDSk5Odo+Fh4fr1Vdf1bJly3TixAn17dtXGzZsUP/+/SX98Kh9RkaGNm/efNqvC7RUDstbF+4BwM9uuukmHTlyRBkZGf4upU4lJSXq37+/9uzZo+7du5/WHA8//LDeeustvfnmm03e5/HHH9fGjRuVmZl5Wq8JtGTcIwQANomOjtZf/vIXFRUVnfYcXbt21aJFi5q1j9Pp1GOPPXbarwm0ZJwRAtBinO1nhACcfQhCAADAWFwaAwAAxiIIAQAAYxGEAACAsQhCAADAWAQhAABgLIIQAAAwFkEIAAAYiyAEAACM9b9ce9fOz1shqAAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -564,16 +578,13 @@ } ], "source": [ - "wind_directions=np.arange(0, 360, 10)\n", - "wind_speeds=np.arange(0.0, 30.0, 5.0)\n", - "freq_table=np.random.rand(36, 6)\n", + "wind_directions = np.arange(0, 360, 10)\n", + "wind_speeds = np.arange(0.0, 30.0, 5.0)\n", + "freq_table = np.random.rand(36, 6)\n", "freq_table = freq_table / freq_table.sum()\n", "\n", "wind_rose = WindRose(\n", - " wind_directions=wind_directions,\n", - " wind_speeds=wind_speeds,\n", - " ti_table=0.06,\n", - " freq_table=freq_table\n", + " wind_directions=wind_directions, wind_speeds=wind_speeds, ti_table=0.06, freq_table=freq_table\n", ")\n", "\n", "# Set value\n", @@ -634,16 +645,18 @@ "fmodel.set(\n", " wind_directions=wind_directions,\n", " wind_speeds=wind_speeds,\n", - " turbulence_intensities=turbulence_intensities\n", + " turbulence_intensities=turbulence_intensities,\n", ")\n", "\n", "# Is equivalent to the following (but now we'll include value as well):\n", - "time_series = TimeSeries(wind_directions=wind_directions, wind_speeds=8.0, turbulence_intensities=0.06)\n", + "time_series = TimeSeries(\n", + " wind_directions=wind_directions, wind_speeds=8.0, turbulence_intensities=0.06\n", + ")\n", "\n", "# Scale some of the default parameters to get reasonable values representing USD/MWh\n", - "time_series.assign_value_piecewise_linear(value_zero_ws=25*1.425, slope_2=-25*0.135)\n", + "time_series.assign_value_piecewise_linear(value_zero_ws=25 * 1.425, slope_2=-25 * 0.135)\n", "\n", - "fmodel.set(wind_data = time_series)" + "fmodel.set(wind_data=time_series)" ] }, { @@ -672,8 +685,8 @@ " 3507908.91825968]\n", "Expected farm power has shape () and is 3140313.447929242\n", "Farm AEP is 27.50914580386016 GWh\n", - "Expected farm value has shape () and is 74778713.97881508\n", - "Farm annual value production (AVP) is 655061.5344544201 USD\n" + "Expected farm value has shape () and is 74778713.9788151\n", + "Farm annual value production (AVP) is 655061.5344544202 USD\n" ] } ], @@ -731,25 +744,19 @@ } ], "source": [ - "wind_directions = np.array([270, 280]) # 2 Wind Directions\n", - "wind_speeds = np.array([6.0, 7.0, 8.0]) # 3 Wind Speeds\n", + "wind_directions = np.array([270, 280]) # 2 Wind Directions\n", + "wind_speeds = np.array([6.0, 7.0, 8.0]) # 3 Wind Speeds\n", "\n", "# Frequency matrix is 2 x 3, include some 0 frequency results\n", - "freq_table = np.array([\n", - " [0, 0, 1/2],\n", - " [1/6, 1/6, 1/6]\n", - "])\n", + "freq_table = np.array([[0, 0, 1 / 2], [1 / 6, 1 / 6, 1 / 6]])\n", "\n", "# Create a WindRose object, not indicating a frequency table indicates uniform frequency\n", "wind_rose = WindRose(\n", - " wind_directions=wind_directions,\n", - " wind_speeds=wind_speeds,\n", - " ti_table=0.06,\n", - " freq_table=freq_table\n", + " wind_directions=wind_directions, wind_speeds=wind_speeds, ti_table=0.06, freq_table=freq_table\n", ")\n", "\n", "# Set value and scale some of the default parameters to get reasonable values representing USD/MWh\n", - "wind_rose.assign_value_piecewise_linear(value_zero_ws=25*1.425, slope_2=-25*0.135)\n", + "wind_rose.assign_value_piecewise_linear(value_zero_ws=25 * 1.425, slope_2=-25 * 0.135)\n", "\n", "fmodel.set(wind_data=wind_rose)\n", "\n", @@ -841,11 +848,11 @@ " wind_speeds=wind_speeds,\n", " ti_table=0.06,\n", " freq_table=freq_table,\n", - " compute_zero_freq_occurrence=True\n", + " compute_zero_freq_occurrence=True,\n", ")\n", "\n", "# Set value and scale some of the default parameters to get reasonable values representing USD/MWh\n", - "wind_rose.assign_value_piecewise_linear(value_zero_ws=25*1.425, slope_2=-25*0.135)\n", + "wind_rose.assign_value_piecewise_linear(value_zero_ws=25 * 1.425, slope_2=-25 * 0.135)\n", "\n", "fmodel.set(wind_data=wind_rose)\n", "\n", @@ -869,12 +876,222 @@ "print(f\"Turbine power have shape {turbine_powers.shape} and are {turbine_powers}\")\n", "print(f\"Farm power has shape {farm_power.shape} and is {farm_power}\")\n", "\n", - "print(\"Expected farm power and value, AEP, and AVP are the same as before since the new cases are weighted by 0\")\n", + "print(\n", + " \"Expected farm power and value, AEP, and AVP are the same as before since the new cases are weighted by 0\"\n", + ")\n", "print(f\"Expected farm power has shape {expected_farm_power.shape} and is {expected_farm_power}\")\n", "print(f\"Farm AEP is {aep/1e9} GWh\")\n", "print(f\"Expected farm value has shape {expected_farm_power.shape} and is {expected_farm_value}\")\n", "print(f\"Farm annual value production (AVP) is {avp/1e6} USD\")" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# WindRoseWRG" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `WindRoseWRG` is a data object which is used to represent within FLORIS the information in a Wind Resource Grid (WRG) file. `WindRoseWRG` is a type of WindData object, like `WindRose` and `TimeSeries`, that\n", + "is used to store wind data in a format that can be used by the FLORIS model. `WindRoseWRG` is different that `WindRose` however because the internal data holds the information of the WRG file and then a `WindRose` object is created \n", + "for each turbine in a provided layout." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from floris import WindRoseWRG" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Read a WRG file (from examples)\n", + "wind_rose_wrg = WindRoseWRG(\"../examples/examples_wind_resource_grid/wrg_example.wrg\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WindResourceGrid with 2 x 3 grid points, min x: 0.0, min y: 0.0, grid size: 1000.0, z: 0.0, h: 90.0, 12 sectors\n", + "Wind directions in file: [ 0. 30. 60. 90. 120. 150. 180. 210. 240. 270. 300. 330.]\n", + "Wind directions: [ 0. 30. 60. 90. 120. 150. 180. 210. 240. 270. 300. 330.]\n", + "Wind speeds: [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.\n", + " 18. 19. 20. 21. 22. 23. 24. 25.]\n", + "ti_table: 0.06\n" + ] + } + ], + "source": [ + "# Print some basic information\n", + "print(wind_rose_wrg)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Aggregate the wind speeds and directions\n", + "wind_rose_wrg.set_wd_step(5.0)\n", + "wind_rose_wrg.set_wind_speeds(np.arange(0, 30, 5))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Set a turbine layout within the grid points of the WRG file\n", + "layout_x = np.array([0, 500, 1000])\n", + "layout_y = np.array([0, 1000, 2000])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up a FLORIS model with the above layout and wind_rose_wrg\n", + "fmodel = FlorisModel(\"../examples/inputs/gch.yaml\")\n", + "\n", + "fmodel.set(layout_x=layout_x, layout_y=layout_y, wind_data=wind_rose_wrg)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Within FlorisModel, the is set to a separate wind rose per turbine\n", + "fig, axarr = plt.subplots(1, 3, figsize=(15, 5), subplot_kw=dict(polar=True))\n", + "fmodel.wind_data.plot_wind_roses(axarr=axarr)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2296892.47124259 2297743.68483228 2342752.51651458]\n" + ] + } + ], + "source": [ + "# Can get the expected power for each turbine\n", + "fmodel.run()\n", + "print(fmodel.get_expected_turbine_powers())" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6937388.672589453\n", + "60771524771.883606\n" + ] + } + ], + "source": [ + "# Getting expected farm power and AEP weights each turbine by its own wind rose\n", + "print(fmodel.get_expected_farm_power())\n", + "print(fmodel.get_farm_AEP())" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using point 0 at (0.0, 0.0) as reference location\n" + ] + }, + { + "data": { + "image/png": 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pKdq1a4f79++L6/bt2wdXV1cMGTJEXGdhYaExrVtlWVlZlTqjRIEuXbrAz89PXBYEAb///jv69esHQRDw5MkT8RUQEICUlBRcuXIFQP7UqA4ODnjvvfeKtPvsVH7lsW/fPgDAjBkzNNa///77AFBkejw/Pz+89NJL4rKjoyMaN26s8b0GgIiICERERJS5/6ysLI0p/AqYmZmJ71fl9kSkP5gj8jFHVFzBZ/3z5oOytmcuIdJfzB35mDsK5ebmYvr06XjnnXc0vifFWbhwIb7++msMGzYMw4cPR1BQEBYsWIAzZ86U+dgI5g4iw8S8kY95o2SbN28GgGIfc8e8QWScmDvyMXcUmjRpEpKTk/Haa6/h6tWruHPnDqZNm4ZLly4BKPzM5/kqopqPOSIfc8TzUSgUaNSoEYYMGYKtW7di06ZNeOGFF/DGG2/g3LlzYlxWVhZMTU2LbaM8+YDX0o0DBz5VM4lEgg4dOojPND1z5gycnJzg7e0NQDMBFPxbngTg7u5e5MOvVq1aSEpKEpcfPHgAb2/vInFPP4e5stLT02FtbV1mnJeXl8ZyfHw8kpOTsXbtWjg6Omq8xo4dCwCIi4sDkP8M0caNG2s807kyHjx4AKlUKv4fFHBxcYGdnR0ePHigsf7pZ1EXePZ7XRHm5ubFPm82OztbfL8qtyci/cEckY85ouIKPuufNx+UtT1zCZH+Yu7Ix9xRaNmyZXjy5AnmzZv3XNtPnz4dEokEs2bNgpubGyQSCXbt2lUkTpu54+TJk+jXr1+p+xszZgwkEonGq3fv3uXex7Oio6Px+uuvo1GjRpBKpZg2bdpzt0VkSJg38jFvFE8QBGzZsgVNmzZF8+bNy7XN9OnTIZVKsXHjxlI/y7VdczB3EFUf5o58zB2FAgMDsWLFCpw8eRKtW7dG48aNsXfvXixYsABA/kABgOeriIwBc0Q+5ojnM2XKFPz111/49ddfMXz4cIwcORKHDx+Gq6srpk6dKsaZm5sjNze32BqgPPngea+ls+YwLNr5DaIK6dSpE/766y/8+++/4nNOC3To0AEzZ85EVFQUTp8+DTc3N9SvX7/MNmUyWbHrBUHQWr/L8ujRI6SkpBT5IC3Osx8garUaAPDGG29g9OjRxW5T3hMuz6u8o2a1/b12dXXFsWPHIAiCRh+io6MB5D+vuqztC2KfVt7tiUi/MEcwRzwPe3t7KBSK584Hrq6uGrHPbs9cQqTfmDuYOwqkpKRg/vz5ePfdd5GamorU1FQA+SfqBEFAREQELCws4OTkVGIb5ubmsLa2hoWFBRYuXIhBgwYVG/d07vDw8NB4Lzo6Gu3atSt3vzMyMtCiRQuMGzeuxP0BQO/evbF+/Xpxubi79QpERETAy8urxO9jTk4OHB0dMWfOHCxbtqzcfSWqCZg3mDdKcubMGTx48AALFy4s9zbm5uaoXbs2kpKS8PLLL5f4Wa7NvAEwdxBVN+YO5o5nTZkyBWPHjsW1a9dgamqKli1bYt26dQCARo0aASj7fFPB+aySaDt3EFHVYI5gjngeubm5WLduHWbNmgWptHCuHrlcjsDAQHz//ffIzc2FqakpXF1doVKpEBUVpVED5OXlISEhoVzX0qOiooqsL+vaCWsOw8KBTzpQMJL19OnTOHPmjMZIvTZt2kChUOD48eM4f/48+vTpo7X9enp64vr160UG2Ny+fVsr7W/cuBEAEBAQUOFtHR0dYW1tDZVKhR49epQa26BBA5w/fx55eXmQy+XFxlRk6j9PT0+o1WrcvXsXvr6+4vrY2FgkJyfD09Oz3G09j5YtW+Knn37CzZs3NaZBPH/+vPh+WdufOnUKarVaIzGcP38eFhYWYpFBRIaBOaIoY84R5SWVStGsWTNxSvGnnT9/HvXr1y/1zpSmTZvCxMQEly5dwrBhw8T1ubm5CAkJ0VhHRPqHuaMoY80dSUlJSE9PxzfffINvvvmmyPteXl4YMGBAsXeoFUhLS0NaWhqGDx+OV199tdiYnJwcHDp0CADw0ksvoWXLlli0aBG6du2Kx48f49GjRxWaWj4wMBCBgYFlxikUCri4uJS73dLUq1cPy5cvBwD8/PPPWmmTyFAwbxRlrHnjWZs3b4ZEIsHrr79e7m3S0tLw5MkTvPrqq5g/f36JcQXnvHr16oW8vDw0bdoUixYtQqNGjSqcNwDmDqLqxtxRFHMHYGlpCX9/f3H58OHDMDc3R8eOHQEAderUgaOjY7Hnqy5cuFCuax8AcOnSJY1BTs9TcxBR1WGOKIo5omwJCQlQKpVQqVRF3svLy4NarRbfK8gHtra2GjVHWFgY1Gq1+H5OTg4++eQTbN26FcnJyWLN0bJlSxw7dgypqamwsbERty/rWjxrDsPCR93pwAsvvAAzMzNs3rwZUVFRGiNfFQoFWrdujZUrVyIjI6Nc0/2VV58+ffD48WPs2LFDXJeZmYm1a9dWuu2jR4/iyy+/hJeXF0aOHFnh7WUyGQYPHozff/8d169fL/J+fHy8+PXgwYPx5MkTfP/990XiCkZHWlhYAACSk5PL3HdBkv3uu+801i9duhQA0Ldv33Idw7PCwsIQFhZWZtyAAQMgl8uxatUqcZ0gCFizZg3q1Kmj8fMRHR2NW7duIS8vT1w3ZMgQxMbG4o8//hDXPXnyBNu3b0e/fv1KHVVKRPqHOaIoY84RFTFkyBBcvHhR42TS7du3cfToUQwdOlQj9tatW4iMjBSXbW1t0aNHD2zatEnjmeUbN25Eenp6ke2JSL8wdxRlrLnDyckJO3fuLPLq1q0bzMzMsHPnTsyePRtA/nTeT3/mF/jyyy8hCILGtNxKpRK3bt0S74SbMmUK7t69i7p166J+/foYPHgwevfujbt372L16tWQSCQYMmTIcx1naY4fPw4nJyc0btwYkyZNQkJCgtb3QWQMmDeKMta88bS8vDxs374dnTp1KvZxFxXJG0D+HfJPz/CxatUqmJubw8HBAVevXsXQoUPRu3dvLFiwoMryBsDcQaQtzB1FMXdoOnv2LP744w+MHz8etra24vrBgwdjz549ePjwobjuyJEjuHPnjsb5pry8PI2aAwCaNGkCHx8frF27VuPCeFXWHERUccwRRTFHlM3JyQl2dnbYuXMncnNzxfXp6en466+/4OPjI86k9fLLL8Pe3h6rV6/WaOPAgQOwsLAQj2nKlCk4efIkFi1ahPPnz4s1R7t27aBSqTR+NnJycrB+/Xq8+OKLRWYVrCjWHPqBMz7pgKmpKdq2bYtTp05BoVCgTZs2Gu936NABS5YsAVC+55yW18SJE/H9999j1KhRuHz5MlxdXbFx40bxw7K89u/fj1u3bkGpVCI2NhZHjx7FoUOH4OnpiT///BNmZmbP1b+vv/4ax44dw4svvoiJEyfCz88PiYmJuHLlCg4fPozExEQAwKhRo/DLL79gxowZuHDhAl566SVkZGTg8OHDePfddzFgwACYm5vDz88Pv/32Gxo1agR7e3s0bdoUTZs2LbLfFi1aYPTo0Vi7di2Sk5PRpUsXXLhwARs2bMDAgQPRrVu35zqe7t27A8ifsq407u7umDZtGhYvXoy8vDy0bdsWu3btwqlTp7B582aNKQZnz56NDRs2IDw8HPXq1QOQf6G7ffv2GDt2LG7cuAEHBwesWrUKKpUK8+bNe66+E5HuMEcUz1hzBJA/+OjBgwfIzMwEkP9c6YK7Gt58803xDo13330XP/74I/r27YsPPvgAcrkcS5cuhbOzM95//32NNn19fdGlSxccP35cXLdgwQJ06NABXbp0wVtvvYVHjx5hyZIl6NWrV6WeSU1EVY+5o3jGmDssLCwwcODAIut37dqFCxcuaLwXExODVq1aYcSIEfDx8QEAHDx4EPv27UPv3r0xYMAAMTYxMRG+vr4YPXo0vvjiC6xfvx6RkZG4cuUK+vfvj4MHD8LLywuvv/46rly5ggkTJmjcVagNvXv3xqBBg+Dl5YWwsDB8/PHHCAwMRHBwcInTshNR8Zg3imeMeeNpBw8eREJCQokXdiqSNwDgvffew6VLlxAUFITIyEisX78e69atw9ixYzFp0iQMHz4cTk5OWLVqFSZOnKj1vAEwdxBpE3NH8Yw1dzx48ADDhg1D//794eLigtDQUKxZswbNmzfHV199pRH78ccfY/v27ejWrRumTp2K9PR0LF68GM2aNcPYsWPFuKioKLHmCAoKEtcvXrwY/fv3R69evTB8+HBcv34d33//fZXUHET0fJgjimesOQIo3zUNmUyGDz74AHPmzEH79u0xatQoqFQqrFu3Do8ePcKmTZvE9szNzfHll19i8uTJGDp0qDgL14kTJ7BgwQLY29uLNcfUqVPx5ptv4tixY/jggw9w4MAB8caL2bNnIy4uDt7e3tiwYQMiIiLEx7Q+L9YcekQgnZg9e7YAQOjQoUOR9/744w8BgGBtbS0olUqN944dOyYAEI4dOyau69Kli9CkSZMi7YwePVrw9PTUWPfgwQOhf//+goWFheDg4CBMnTpVOHDgQJE2i7N+/XoBgPgyNTUVXFxchJ49ewrLly8XUlNTi2zz2WefCc/+mAEQJk+eXOw+YmNjhcmTJwseHh6CXC4XXFxchO7duwtr167ViMvMzBQ++eQTwcvLS4wbMmSIEBYWJsacPXtWaNOmjWBqaioAED777LMS+5SXlyfMmzdPbM/Dw0OYPXu2kJ2drRHn6ekp9O3bt0i/u3TpInTp0qVI7LPf/5KoVCrhq6++Ejw9PQVTU1OhSZMmwqZNm4rEjR49WgAghIeHa6xPTEwUxo8fL9SuXVuwsLAQunTpIly8eLFc+yYi/cMcwRzx7PZPf2+ffj37//Lw4UNhyJAhgo2NjWBlZSW88sorwt27d4u0CaBInwRBEE6dOiV06NBBMDMzExwdHYXJkycX+39HRPqHuYO5ozSjR48WLC0tNdYlJSUJb7zxhuDt7S1YWFgICoVCaNKkifDVV18Jubm5YhwA4YcffhAACKNHjxb27NkjABAsLS0FS0tLwczMTJBKpQIAwdzcXJgzZ45w7dq1EnNXwevDDz8stq8AhJ07d5Z5TGFhYQIA4fDhw+I6Pz8/sV8WFhYa/bS0tBR69+5dbFtdunQRpk6dWvY3kqgGYd5g3njW8OHDBblcLiQkJBT7fnnzhiAI4v/R6NGjBUEQNHLH03kDgODn5yfk5uYKN2/eZO4g0nPMHcwdBRITE4UBAwYILi4ugqmpqeDl5SV8+OGHJZ5Dun79utCrVy/BwsJCsLOzE0aOHCnExMRoxISHh2vkjqft3LlTaNmypaBQKAR3d3dhzpw5RXIPEekWcwRzxLPbl/eaxubNm4V27doJdnZ2grm5ufDiiy8KO3bsKLbdtWvXCo0bNxa/B+PGjRPUarUgCIU1h1wuFwAIZmZmgqWlpWBiYiIMGzZMyMrKEsaOHcuaowaTCMJ/c6QRERERERERET1FIpFg586d4mxRv/32G0aOHInQ0NAid65ZWVnBxcUFubm5uH//fqnt1q5dG46OjmXurzSOjo6YP38+3n77bQD5d54XPBY8KioKXbt2xd27d8V4c3Nz1KlTp0g7Xbt2RcuWLYtMA09ERM+HuYOIiIiIiKoSaw56Fh91R0RERERERETl0qpVK6hUKsTFxeGll14qNsbU1FR8DFJVefToERISEuDq6iquK3j8KwCYmOSf7vD29q7SfhARUdmYO4iIiIiIqCqx5iAOfCIiIiIiIiIiUXp6Ou7duycuh4eHIyQkBPb29mjUqBFGjhyJUaNGYcmSJWjVqhXi4+Nx5MgRNG/eHH379tXq/urWrYv09HTMmzcPgwcPhouLC8LCwjBr1ix4e3sjICDguY8zJCRE3H98fDxCQkJgamoKPz+/526TiMhYMXcwdxARERERVSXWHKw5SsNH3RERERERERGR6Pjx4+jWrVuR9aNHj0ZQUBDy8vIwf/58/PLLL4iKioKDgwPat2+PefPmoVmzZlrfX1ZWFgYOHIirV68iOTkZbm5u6NWrF7788ks4OzsX22ZERAS8vLxQ2ikPiURSZJ2npyciIiIqfAxERMaOuSOiwsdARERERETlx5ojosLHYEw48ImIiIiIiIiIqBJWr16N1atXiyehmjRpgrlz5yIwMLDY+Ly8PCxcuBAbNmxAVFQUGjdujEWLFqF3795iTFpaGj799FPs3LkTcXFxaNWqFZYvX462bduKMcWdDAOAb775BjNnztTeARIRkVYxbxARERERUVUytppDWmUtExEREREREREZAXd3d3z99de4fPkyLl26hJdffhkDBgxAaGhosfFz5szBDz/8gBUrVuDGjRt455138Oqrr+Lq1atizIQJE3Do0CFs3LgR//77L3r16oUePXogKipKjImOjtZ4/fzzz5BIJBg8eHCVHzMRET0/5g0iIiIiIqpKxlZzcMYnIiIiIiIiIiIts7e3x+LFizF+/Pgi77m5ueGTTz7B5MmTxXWDBw+Gubk5Nm3ahKysLFhbW2P37t3o27evGNOmTRsEBgZi/vz5xe5z4MCBSEtLw5EjR7R/QEREVKWYN4iIiIiIqCrV5JrDpEpbryHUajUeP34Ma2vrEqfmIqrJBEFAWloa3NzcIJU+/0Rx2dnZyM3N1UqfTE1NYWZmppW2iKoCcwcZM+YNoufD3EHGTB9zhyAIRX4XFQoFFApFqdupVCps374dGRkZ8Pf3LzYmJyenSF4yNzfH6dOnAQBKpRIqlarUmGfFxsZi79692LBhQ6n9o5qDeYOMnTZyB/MG84axYe4gY6aPNQfPV5G+Y94gY6dvNUdBnypadxhFzSFQmR4+fCgA4Isvo389fPjwuX+PsrKyBFtHudb64uLiImRlZWnxN51Iu5g7+OKLeYOoopg7+OKr8rnDFGZa64uVlVWRdZ999lmJ+7927ZpgaWkpyGQywdbWVti7d2+JsSNGjBD8/PyEO3fuCCqVSvj7778Fc3NzwdTUVIzx9/cXunTpIkRFRQlKpVLYuHGjIJVKhUaNGhXb5qJFi4RatWox3xkR5g2++Mp/PW/uYN5g3jBGzB188VX5msPeUaa1vvB8Fek75g2++Mp/6UvNAVSs7jCmmoOPuiuHlJQU2NnZ4eHDh7CxsdF1d4iqXWpqKjw8PJCcnAxbW9vnbsPW1hZLT7aGuZWsUv3JSldhRucrSElJ4e8k6S3mDjJmzBtEz4e5g4yZNnNHJ/SBCeSV6o8SeTiNfUV+H0u7gy43NxeRkZFISUnBjh078NNPP+HEiRPw8/MrEhsfH4+JEyfir7/+gkQiQYMGDdCjRw/8/PPPyMrKAgCEhYVh3LhxOHnyJGQyGVq3bo1GjRrh8uXLuHnzZpE2fXx80LNnT6xYsaJSx06Gg3mDjF1lcwfzBvOGMWLuIGOmzZrj17NesLB6/lmjACAzXY3hHcJ5vor0GvMGGTt9qjmAitcdxlRz8FF35VAwVZiNjQ0/1MmoaWMaS3MrGcyt+NFDNR9zBxHzBlFFMXcQaSd3mEAOE0klTyb9d4tYRX4fTU1N4e3tDQBo06YNLl68iOXLl+OHH34oEuvo6Ihdu3YhOzsbCQkJcHNzw0cffYT69euLMQ0aNMCJEyeQkZGB1NRUuLq64rXXXtOIKXDq1Cncvn0bv/3223McLBkq5g2ifJXNHcwbZEyYO4i0U3NYWElhaV25G/WIDAHzBlE+vag5gArXHcZUc1RuODIRERERERERERWhVquRk5NTaoyZmRnq1KkDpVKJ33//HQMGDCgSY2lpCVdXVyQlJeHgwYPFxqxbtw5t2rRBixYttNZ/IiKqXswbRERERERUlWpyzcHb54mIiIiIiIiIKmH27NkIDAxE3bp1kZaWhi1btuD48eM4ePAgAGDUqFGoU6cOFi5cCAA4f/48oqKi0LJlS0RFReHzzz+HWq3GrFmzxDYPHjwIQRDQuHFj3Lt3DzNnzoSPjw/Gjh2rse/U1FRs374dS5Ysqb4DJiKiSmHeICIiIiKiqmRsNQdnfCIiIiIiIiIiqoS4uDiMGjUKjRs3Rvfu3XHx4kUcPHgQPXv2BABERkYiOjpajM/OzsacOXPg5+eHV199FXXq1MHp06dhZ2cnxqSkpGDy5Mnw8fHBqFGj0KlTJxw8eBByuebU6L/++isEQcCIESOq5ViJiKjymDeIiKiiTp48iX79+sHNzQ0SiQS7du0qc5vjx4+jdevWUCgU8Pb2RlBQkMb7KpUKn376Kby8vGBubo4GDRrgyy+/hCAIVXMQRERUbYyt5uCMT0RERERERERElbBu3bpS3z9+/LjGcpcuXXDjxo1Stxk2bBiGDRtW5r7feustvPXWW2XGERGR/mDeICKiisrIyECLFi0wbtw4DBo0qMz48PBw9O3bF++88w42b96MI0eOYMKECXB1dUVAQAAAYNGiRVi9ejU2bNiAJk2a4NKlSxg7dixsbW3xv//9r6oPiYiIqpCx1Rwc+EREREREREREREREREREpKcCAwMRGBhY7vg1a9bAy8tLfMyQr68vTp8+jWXLlokDn86ePYsBAwagb9++AIB69eph69atuHDhgvYPgIiIqApx4BMRVas0lRmUqsp99GSplFrqDRER6TvmDSIiIiIiIiIiqqlSU1M1lhUKBRQKRaXbDQ4ORo8ePTTWBQQEYNq0aeJyhw4dsHbtWty5cweNGjXCP//8g9OnT2Pp0qWV3j8REVF14sAnIiIiIiIiIiIiIiIiIqJySFFZIE8lq1QbmSoVAMDDw0Nj/WeffYbPP/+8Um0DQExMDJydnTXWOTs7IzU1FVlZWTA3N8dHH32E1NRU+Pj4QCaTQaVSYcGCBRg5cmSl909ERFSdOPCJiIiIiIiIiIiIiIiIiKiaPXz4EDY2NuKyNmZ7Kq9t27Zh8+bN2LJlC5o0aYKQkBBMmzYNbm5uGD16dLX1g4iIqLI48ImIiIiIiIiIiIiIiIiIqJrZ2NhoDHzSFhcXF8TGxmqsi42NhY2NDczNzQEAM2fOxEcffYThw4cDAJo1a4YHDx5g4cKFHPhEREQGRarrDhARERERERERERERERERkXb4+/vjyJEjGusOHToEf39/cTkzMxNSqealYplMBrVaXS19JCIi0hbO+EREREREREREREREREREpKfS09Nx7949cTk8PBwhISGwt7dH3bp1MXv2bERFReGXX34BALzzzjv4/vvvMWvWLIwbNw5Hjx7Ftm3bsHfvXrGNfv36YcGCBahbty6aNGmCq1evYunSpRg3bly1Hx8REVFlcOATEREREREREREREREREZGeunTpErp16yYuz5gxAwAwevRoBAUFITo6GpGRkeL7Xl5e2Lt3L6ZPn47ly5fD3d0dP/30EwICAsSYFStW4NNPP8W7776LuLg4uLm54e2338bcuXOr78CIiIi0gAOfiIiIiIiIiIiIiIiIiIj0VNeuXSEIQonvBwUFFbvN1atXS9zG2toa3333Hb777jst9JCIiEh3pGWHEBERERERERERERERERERERER6RfO+ERE1SpdrYBSLa9UG9nqPC31hoiI9B3zBhEREREREREREREREZWEMz4REREREREREREREREREREREZHB4YxPRERERERERERERERERETlkC6YQaWu3CXWLEGppd4QERERZ3wiIiIiIiIiIiIiIiIiIiIiIiKDw4FPRERERERERERERERERERERERkcDjwiYiIiIiIiIiIiIiIiIiIiIiIDA4HPhERERERERERERERERERERERkcHhwCciIiIiIiIiIiIiIiIiIiIiIjI4HPhEREREREREREREREREREREREQGx0TXHSAi45KqtECOUl6pNnKUeVrqDRER6TvmDSIiIiIiIiIiIiIiIioJZ3wiIiIiIiIiIiIiIiIiIiIiIiKDo9OBTwsXLkTbtm1hbW0NJycnDBw4ELdv39aIyc7OxuTJk1G7dm1YWVlh8ODBiI2N1YiJjIxE3759YWFhAScnJ8ycORNKpVIj5vjx42jdujUUCgW8vb0RFBRU1YdHRERaxrxBREQVxdxBREQVwbxBREQVxdxBZHxSVBZIruQrRWWh68MgHWLuICLSLp0OfDpx4gQmT56Mc+fO4dChQ8jLy0OvXr2QkZEhxkyfPh1//fUXtm/fjhMnTuDx48cYNGiQ+L5KpULfvn2Rm5uLs2fPYsOGDQgKCsLcuXPFmPDwcPTt2xfdunVDSEgIpk2bhgkTJuDgwYPVcpypqakIDw9HampqteyP6Fn8GaSawljyBsDfW9It/vxRTcLcQVQ9+PNHNQXzBlH14c8g1RTMHUTVgz9/VJMYS+7g7y3pGn8GjYdEEARB150oEB8fDycnJ5w4cQKdO3dGSkoKHB0dsWXLFgwZMgQAcOvWLfj6+iI4OBjt27fH/v378corr+Dx48dwdnYGAKxZswYffvgh4uPjYWpqig8//BB79+7F9evXxX0NHz4cycnJOHDgQJn9Sk1Nha2tLVJSUmBjY1OhY7py5Qr++usvcblPnz5o27YtAEAQBOTl5ZW4rVQqhYmJSYVjASA3N1crsRKJBHK5/Lli8/LyUNKPV1XFAoCpqelzxSqVSqjVaq3EyuVySCSSKo1VqVRQqVRlxj79MyiRSPDKK6+gdevWJW5XnMr8DjzbxrQz/aGwkpe9QSly0vPwXcc/K9Ufqhn0NW8A2ssdBb+3rVq1Ys7QcizAnFFc7KVLl7B3714AzBtUMzF3FGLuqHgswNxRXKy+5Y6uGAATSeVyh1LIw3HsZu4go8gbAM9XVXUswPzxbKw+nK9i3qCqYgy5gzUHc4Y2Yg255vj6YheYWZmUvUEpstOV+KjtCeYOAqC/uYM1B/NHRWP1KX/UtJoDYN1RmsplZS1LSUkBANjb2wMALl++jLy8PPTo0UOM8fHxQd26dcUP9eDgYDRr1kz8QAeAgIAATJo0CaGhoWjVqhWCg4M12iiImTZtWrH9yMnJQU5Ojrj8vCMAU1NTNT7QAWDfvn1o3LgxbGxskJmZiW+//bbE7Vu0aIGBAwcCyP9wWrhwYYmxfn5+GDp0qLhcWmzDhg3x+uuvi8vffvttiQnD09MTY8aMEZeXL1+OzMzMYmPd3NwwceJEcXnlypXi/+mzHB0d8e6774rLP/74I+Lj44uNtbW11fi/CgoKwuPHj4uNtbCwwMyZM8XlzZs348GDB8XGyuVyfPzxx+Lytm3bcPfu3WJjAeCzzz4Tv965cydu3LhRYuzs2bPFJLBnzx78888/JcZ+8MEHsLS0BAAcPHgQly5dKjF26tSpsLOzAwAcOXIEwcHBJcZOmjQJZmZm2LNnj7hOEATs2bMH3t7eRvVhePLkSSxevBiXL19GdHQ0du7cKf5uleT48eOYMWMGQkND4eHhgTlz5mj8LixcuBB//PEHbt26BXNzc3To0AGLFi1C48aNxZi3334bhw8fxuPHj2FlZSXG+Pj4aOwrKCgIS5cuxZ07d2BjY4OhQ4di5cqV2vwW1Fj6kjeAqssdBb+3bm5u+OGHH0rcjjkjH3NGoYrmDCcnJ6SmpoonkQDjzRsAc0dNxtxRiLkjH3NHIeYOoqKMIW8APF9VgPmjEM9XET0/Y8gdrDmYM57GmoOo8vQld7DmKMT8UcgQ8wdrDuOjNwOf1Go1pk2bho4dO6Jp06YAgJiYGJiamoo/xAWcnZ0RExMjxjz9gV7wfsF7pcWkpqYiKysL5ubmGu8tXLgQ8+bNq/QxJSQkFLt+y7URMHdOgipbDuDlEre/l7oPG+5+AwBQK2UAepQYG5F2FBvuLntqTUCJsY8yzmLD3e/FZaW6O0r6UYjJuooNd9eKy9mqbgBMi419kn0TG+52EJfTlZ0BmBcbm5wbrhGbnNsRgFWxsenKGI3YJ9ntAdgWG5utStaIjclqC8C+2FilOksjNjajNQDHYmMBaMTGpbUA4FJi7Oaw7pCa5I80jU9tCqBOibG/3e8DmVl+Uk1I8QVQt8TYHeGDILfKBgAkJjcC4FVi7O4HI6HKUUAQ2mqsFwQBiYmJRvWhnpGRgRYtWmDcuHEa04CWpGDqz3feeQebN2/GkSNHMGHCBLi6uiIgIP93q2Aa0rZt20KpVOLjjz9Gr169cOPGDTFJt2nTBiNHjkTdunWRmJiIzz//HL169UJ4eDhkMhkAYOnSpViyZAkWL16MF198ERkZGYiIiKiy70VNok95A6ja3CEIAv648TaAkkeiM2cUtMOcUaCiOcM0JQNZsfYAmDcA5o6aythyx9SuH8NvhGeJ2/294ThWDtoMAJDKJejwcZMSY09uD8ba17aJy50+a1pi7Pm9V7D+jZ3isv9sP8hMi3/K+rUTN9BzXOHJqRc/8IHcsvg8c/viPfR8uzD2hamNYGZXfJ6JCH2IntLC2FaTvGHpZFZsbOyDeI3YFhPqw7qORbGxKfGpGrHNRnvBtp5lsbHZGTkasX4jPGHfyLrYWAAasT5DPODQpPj8BQD9rN+AOi//rr2GA+rAuWWtEmOHOI+HMjM/z9Tv4wq3trVLjH3D613kpOTnmXo9neHeoeRcN7HZDGTG58C2niWajdbMMcaaO6jmMaa8AQDjWvwPKQ8yYGIhQ/uZviVuz/yRj/mjUEXzh9zShLmDaixjyh2CIGBq9znwG+ZR4naHfzuHH947BgCQmgDtxhR/3gUATv19HT99XTgjSYf+xecAADh37h7W/bgQKkV+TOd2efivZC7iauhD/Dgk/+K6SiFB9/qZMC3hylhoRAzavFV4zqy7SxosS2g3LCYBTWcVxvYyT4dtCV2OSkmFz7xlUP830UNvZTpK+lRNzMpC/eVLoZbnz3rROzkLriXEZirzUO+XRQAAqYkK3aOUcC8hFgCa7P5c/LpjtIA6KPl73O/Uh1D/d+wNYyzgXMI5PgCYdGUGLCzyBwPYxDvAEnYlxi6/PQESy1zYmmQiK9ELKKXHhx8OgVlaGhBnhWfPdTJvUE2iT7mDNUch1hyFWHOQIdCbgU+TJ0/G9evXcfr0aV13BbNnz8aMGTPE5dTUVHh4lPzHe0lq1y7ul1OA3Dr/D0CpIg91hx0ucXuJpHA6N4lMVWosJJpT1VUk1mPw8ZJjoRnrPuBkuWPr9D0NQFKuWNeA4HLHuvS4UEqsJueul8sd6/hSCCCUL9ahw7+AcL3E9yWywun1HNqFonbbm+WKtW99C7Va3SlXbK3md2HXLKzUWGmWEvnfv8Ljkkgk4ohxYxEYGIjAwMByx69ZswZeXl5YsmQJAMDX1xenT5/GsmXLxIvXz04BGhQUBCcnJ1y+fBmdO3cGALz11lvi+/Xq1cP8+fPRokULREREoEGDBkhKSsKcOXPw119/oXv37mJs8+bNn/tYjYk+5Q2ginOHRICpfSpzxn+YM/JpO2cAgNw6A8wb+Zg7aiZjyh2CWkB6dBbOfhVa4nbCU7NHq/OEcscCqFDs+W9L/lx75mMbF5ffLnfslZV3y5sO8M+PYeWO/TcovLzpAKGbI8ode3NbJCQlX1fQcHvnI9zZ/ajE9wtOIAHAvT2PEbav+Lv9no0NPxiDiEMx5Yp9cCQOkcfjyozNSsiBoBYgkTJ3UM1jbHkjKzH/zm5lpor54z/MH/m0nT9Ms1XMHVRjGVvuyIjOxvnlmrNJSMzNNGJgkn/hV60ELmzN0GzDrPCRL4IA4KnHD53bq4ZaoXkJS22aPxLn2Qf3nL6k+O99zQ9MtVxSJPZ4uKXG+09TPrN8JFHzxju1HJqeWj6SZQn1M9e4i8T/55DMEmpTzZ4JJcXamkECQJBrxktMiz4O6JirTBzKJDPVfASQ3EQz/kp9Fa4KhTHmcs0ZTxRPfSvvNcpEtK/mzCWWJoWPfBJkhX1L9XuCNN8EjfcBwFqW/V/HCvth1iwCZk0fwFaWpRkrzV8uOF9lbpMGnq+imkyfcgdrjqeDNRdZc+RjzUH6Si8GPk2ZMgV79uzByZMn4e5eOLrbxcUFubm5SE5O1hjRGhsbCxcXFzHmwoULGu3FxsaK7xX8W7Du6RgbG5ti74JQKBRQKBSVPi4bGxsEBgZi//79/60RUPvFUJhY5H+oSySAxKTk508+rSKxAMSZI3QbW/JzOPUyVlY1sRKZAAnK+f+s5VgTixzYv3ADiZcKR0K/8sorNWYk67NTbGrrd/d5po1+dhrSZ2VkZGD9+vXw8vIS/0g8dOgQ1Go1oqKi4Ovri7S0NHTo0AFLlix5rj8kjYm+5Q2ginNHu1DILXNK3e5pzBnVEFsDcwbAvFEZzB36z5hyh6AWcG/PY+SmKSvUztMnEAwiVmlYsYJKgFDO1KgXsWqhyAnB4uSmKRG2Pxrefd3EdTUpd5DxMva8oRef88wfAPQkJ2g5fzB3UE1ldLljXzRy05SQmmuO2JGU8hmnfuazpLTPFrUKUKs0r9iq1cVfwS1Y/+z7qmJuWnt6nfqZ99XPXCFW4dn3S6aCpMj7JcWrJBI8c2gl3l+nkuS/ITxzQfrpC7ni/qRP9eGZ96WyZ2Of2c+zM1s9tbkghTj7k7jOpIT/ZxkgQACeeV9SzPkxiUwAIEAq0/xBkEo1lxWWWWjw4kWEnW8nrmPeoJpC33IHaw4txepBHcGagzWHsSnn2L2qIQgCpkyZgp07d+Lo0aPw8tKcbqxNmzaQy+U4cuSIuO727duIjIyEv78/AMDf3x///vsv4uIKR/UdOnQINjY28PPzE2OebqMgpqCNqtSyZUvxa7dXTsG6QVSV75PoaVb1o8Wvp0yZgtatS35UVnXIVCqQUclXpjL/jy4PDw/Y2tqKr9Ke21sRZU39+azipiEtsGrVKlhZWcHKygr79+/HoUOHxGfb3r9/H2q1Gl999RW+++477NixA4mJiejZsydyc3OL7IeMI28AzB2kW8wbz4e5Q38ZY+64/P0dxF5Nqpb9EgFA3D+FP2/6kDuIKoN5g6h6MHdQTWKMuePK6jDEhaSUHEykZc4NwsWvmTeoJjCG3MGag3SNNYdx0emMT5MnT8aWLVuwe/duWFtbi88btbW1hbm5OWxtbTF+/HjMmDED9vb2sLGxwXvvvQd/f3+0b98eANCrVy/4+fnhzTffxDfffIOYmBjMmTMHkydPFkekvvPOO/j+++8xa9YsjBs3DkePHsW2bduwd+/eaj1eEwtejCLdsrYu+fmshujhw4caI3O1NWtHRZU2DenIkSPRs2dPREdH49tvv8WwYcNw5swZmJmZQa1WIy8vD//3f/+HXr16AQC2bt0KFxcXHDt2THw0EhUytrwBMHeQbjFvVB3mjupjjLkjN71iMz0RaVNNyx1kfJg3iKofcwcZOuYOourFvEE1gbHlDuYN0jXmjppPpwOfVq9eDQDo2rWrxvr169djzJgxAIBly5ZBKpVi8ODByMnJQUBAAFatWiXGymQy7NmzB5MmTYK/vz8sLS0xevRofPHFF2KMl5cX9u7di+nTp2P58uVwd3fHTz/9VC0XhuRyOaZOnYod4YPE5xETkXbY2NhUyZSEFZn6s6RpSAsUzCrSsGFDtG/fHrVq1cLOnTsxYsQIuLq6AoA48h4AHB0d4eDggMjISK0fV01gDHmDiKpOVeUNgLlDnzF3EBFRRRhL3ig4X/WG17sVemQDEREVZSy5g4gKpSjNkaOUlx1YimxlnpZ6Q4bIGHIHaw4iqk46HfgkCGV/yJmZmWHlypVYuXJliTGenp7Yt29fqe107doVV69erXAfK0sikcDOzg5yq+xq3zcRPR9/f/8inynPTv0pCALee+897Ny5E8ePHy8yDWlxBEGAIAjIyckBAHTs2BFA/vSkBRe+ExMT8eTJE3h6emrrcGoUY8gbRLomkargGhCMvh7rYGKi0z8VDQpzh/5i7iCqemqlgJAfw/D9+YXMHWTwjCVvFJyvyknhBTciosoyltxBpEtSmRot+x7AC647WHNQjWAMuYM1BxFVJ6muO0BEVavgAvaECROMtiBIT09HSEgIQkJCAADh4eEICQkRZ8aYPXs2Ro0aJca/8847uH//PmbNmoVbt25h1apV2LZtG6ZPny7GTJ48GZs2bcKWLVvEaUhjYmKQlZUFALh//z4WLlyIy5cvIzIyEmfPnsXQoUNhbm6OPn36AAAaNWqEAQMGYOrUqTh79iyuX7+O0aNHw8fHB926daum7w7po4I7Ier0P8HZAqnaSaSAonYq6tSpA6nUeP9UZO4gQ1OQOy5+d5t30VH1E4D0x1lGnzuIiKj8CgbNGvP5KiJDU1BzXFpxjzUHVTuJVIC1QyJrDiIiKjfWHMaFfx1UMZVKhb///huJVxtBUEl03R0yQryADVy6dAmtWrVCq1atAAAzZsxAq1atMHfuXABAdHS0xuOBCqb+PHToEFq0aIElS5YUmfpz9erVSElJQdeuXeHq6iq+fvvtNwD5I/FPnTqFPn36wNvbG6+99hqsra1x9uxZODk5ie388ssvePHFF9G3b1906dIFcrkcBw4cgFxeuWlyybA9PVughKmDSCeYO8jQ8C46IiKqiILzVfV6OkMiZdFBOsBBs0QGhzUHERFVBGsO0jnWHEaFQ9uqmEqlQnBwMAAv2DULgwScuYOounXt2rXUaUODgoKK3aa0qT/LmobUzc2tzOlFAcDGxgbr1q3DunXryowlIqoOgkqC1NueOBN3Bu3bt4dMJtN1l3SCuYOIqPwkUgnc2tfGmTPGnTuIDEnB+Sr3Do6IPB4HQa3rHhERERGVTK2S4vHNxjgTxZqDyFCw5iCi6sShbUQ1nKCSIOVGPZw5cwYqFQfeERkCzhZIuiQIUiSFNMbhw4eZN4gMCO+iI12SyACvni7MHUREVG4SqQR1OjjwfBWRARFrju5OkPDKElUzQS1B+OVWrDmIiKjcWHMYF874VI3SVQpIJBzOStVLUP53ARuH0bZtW53fCZGuksNUZVqpNnKZm6iGe3q2QBO/R5CAuYOqj6DSr7OXzBtE5cO76IiIiMiQPD1oVh/OVxFR2Qpqjjr+tRF5Mh6CuvRZjYmIiIh0iTWHcdGvK1tERERERERERERERERERERERETlwIFPRERERERERERERERERERERERkcDjwiYiIiIiIiIiIiIiIiIiIiIiIDI6JrjtARERERERERERERERERGQI0lTmyFXJK9VGjoqXaImIiLSFMz5VMblcjkmTJsG290VAptZ1d4iIiIiIiIiIyMgVnK+6suou1HmCrrtDREREREQ1DGsOIqpOHE5cxSQSCZycnGCSmKnrrhARERGVTaqGTbcQvFp3GUxM+KciERGVTa0U8G9QOL499jlzB5GBKDhflRmfo+uuEBEREZVJKlOjWcBhtHbZyJqDyECw5iCi6sQZn4hquv8uYI8ePZoFAZGB4GyBpEsSKSB3SkG9evUglfJPRSJDwbvoSKcEIOVBBnMHERGVW8GgWZ6vIjIcYs2xJow1B1U7iVSAnUscaw4iIio31hzGhX8dVDGVSoXjx48j87onBJVE190hI8QL2ESGR5wt0DYTEqYOIiIqB95FR0REFVFwvqpuFydIpCw6SAc4aJbI4BTUHFlPcnXdFSIiMgCsOUjnWHMYFf4PVzGVSoUTJ04gK7QeIPBDnShLpUCmsnKvLJVC14dBRFRjCWoJsu+64cKFC1CpVLruDvMGEZEBkEgB17b2epM7iKhsBeer6nZ1gkSm694QERERlU6tluDxrYZGXXOcPHkS/fr1g5ubGyQSCXbt2lXmNsePH0fr1q2hUCjg7e2NoKAgjffr1asHiURS5DV58uSqOQgyKqw5iKg6ceATUQ2nbxewiahsnC2QdEotQcaVhti/fz/zBpEB4V10pEsSmQQN+rgxdxARUblx0CyR4SmoOTw6O0DCK0tUzQSVFGHn2xp1zZGRkYEWLVpg5cqV5YoPDw9H37590a1bN4SEhGDatGmYMGECDh48KMZcvHgR0dHR4uvQoUMAgKFDh1bJMRARVSfWHMaFDzMkqukKLmBjP1q2bAmZjMOqifRdwZ0QQD2Y+zwEIOi6S0REpOeevovu0dl4CGpd94iIiIioZE8PmuX5KiLDINYcnR0RFZwAQc3zVUTVKTAwEIGBgeWOX7NmDby8vLBkyRIAgK+vL06fPo1ly5YhICAAAODo6Kixzddff40GDRqgS5cu2us4EZGOsOYwLhyXT0RERERERERERERERERUzVJTUzVeOTk5Wmk3ODgYPXr00FgXEBCA4ODgYuNzc3OxadMmjBs3DhIJZ5ImIiLDwhmfiIiIiIiIiIiIiIiIiIjKIUMpR57StFJt5Crz//Xw8NBY/9lnn+Hzzz+vVNsAEBMTA2dnZ411zs7OSE1NRVZWFszNzTXe27VrF5KTkzFmzJhK75uIiKi6ceBTNUpTm0Gi5jMnqHoJak7sRmTImDuoujFvEBERERERERERVY+HDx/CxsZGXFYoFDrpx7p16xAYGAg3Nzed7J+IiKgyOPCJiIiIiIiIiIiIiIiIiKia2djYaAx80hYXFxfExsZqrIuNjYWNjU2R2Z4ePHiAw4cP448//tB6P4iIiKoDb+mvYiYmJpgwYQJMul8HZJyxg4iIiIiIiIiIdKvgfFXIj2FQKwVdd4eIiIiItMzf3x9HjhzRWHfo0CH4+/sXiV2/fj2cnJzQt2/f6uoeGQHWHERUnTjjUxWTSqWoU6cOpCkZWm/bRKKAQmoBAWpkqzKghlLr+yAioppEAjOpJWRSOVTqPGSrMwCw4KBnSNUw6XQbQz0+h4kJ/1QkMnZyUxNY2llCIpEgIyUTudm5uu4S6SG1UkDolgjM/2s2cweRgSg4X5X+OEvrbVvYWMDMwhR5uUpkJGdCzUd3ExFRKWQmUlhZm0EqlSIjT4Wc7Dxdd4n0kFSmRpPux9Hc6QejrTnS09Nx7949cTk8PBwhISGwt7dH3bp1MXv2bERFReGXX34BALzzzjv4/vvvMWvWLIwbNw5Hjx7Ftm3bsHfvXo121Wo11q9fj9GjRxvt95aqRlXWHGaWZrCwNoNKqUZaUjrUKtYcRMaOGcyA2JvWQWPrjvC0bAFns/qwMLHVeD81Lx7RWXcRkXEVt1PPIkOVpKOekl7RswvYmUo55Ep5pdrI48hwquEK7oQICp9WqdkCpZChvlUbNLBqizoWvqht6gETaeHvn1Kdh6Tcx4jMvIa7aecRkRECASwQjJ1ECkhck9GoUSNddwUA8wZReRXkjikvzq7UXXQOdezx0uD2aPVyMzRsUx8Odew13k+MSUZYSARCjv2LkzvOISY8rrJdp5pAAJLuputN7iCi6iORSNCssy/8+7dF044+8GziDnNLM/H9vFwlHt+Lwc3zd3Bh31Wc33uFg2gJAAfNEhmigprjPf9PKlVzWNmao0PPpmjzUiM0au4BJzc7SKWFDydJS8nE/buxuHY5AqdO30V4GGsOAiRSAfbuj9GonvHWHJcuXUK3bt3E5RkzZgAARo8ejaCgIERHRyMyMlJ838vLC3v37sX06dOxfPlyuLu746effkJAQIBGu4cPH0ZkZCTGjRtXPQdC9Bwatq6Pjq+2Q7OXfFG/uSes7CzF91QqNWLC43Dn4j1cPBiCs7svIiMlU4e9JX3BmsO48H+4iqlUKpw7dw6qOFdIG8ZAIq14QeBp0QIdHIejnmWLUuNs5I6wkTuisU0H9HR5B7dST+PMk1/xJOfB83afagB9u4BNRGWr7GyBcokZXqjdHy/Y94eViX2JcSZSORzNPOFo5ok29v2QmvcEFxN34UriHigFXowgIjIklb2LrkHLenhjzhD4D2gLmazkJ6Lbu9jBvndLtO3dEhMXvYlLB0Owaf7vCD1z63m7TkREOlBwvqpOBwc8PpcAQV2x81UyExkCx7+MoR/0h1sDlxLj5KYm8PRzh6efO3qPfRmpienY+8Pf2P7tX0hLSq/sYZAh46BZIoMj1hzR2c+1vUvd2hg+rTdeHtAactOSL01Z21qgxQteaPGCF958uxtuhkZh6y9nEHzqzvN2nahG6Nq1KwSh5L/ZgoKCit3m6tWrpbbbq1evUtslel6VrTkkEgleGtIeIz56Fd6tvEqMk8mkqOPtgjreLug2ohOyM3NweOMJbFnwB+IfJVT2MMiQseYwKhz4VMVUKhUOHz4MoC6kDWKBCgx8spTVQoDru2hs07HIe+nKRMhgAnMTGwiCGmnKRJhKzWAmswIASCUy+Nl2gY9NJ1xK/BMn4n6BUsjR1mEREZGe8rZ6Eb1dJ8Na7qCxXi2okJgbhdS8OOSpc+Fl1QqmUnMIggCJRAIAsJE7oLvzBLxQqz/2R/8fwjOu6OIQSMcEtQTqyNoIyQ1Bs2bNIJPJdN0lIqpCZpZmeGvxm+j3Tq8i76UmpuPhrSikPkmDWq2GTW1ruDd2Qy2nwplnXwhoiRcCWuLoltNYOfVnpCakVWf3SU9IpIBjMzuEhDB3EBmKgvNVXj1dEH0xAUIFJn71bd8I7/80CZ5+7hrr1Wo1osNiERMRj6z0bJgqTODgXhsePnXEC9w29lYYMXsQ+r7VE2ve34BDv5zQ5mEREZEekplI8dqUXhg+pSfkCs1LUlmZOXh4Px6JT9KhVqthaWMONw97ODoX1hy+Tergi0XDcPVSOL5dsh8xMSnVfQikB9RqCeLv10NIMmsOIkNRmZqjTkNXfLDuXTTt5FPkvdgH8Yi+H4v05EyYyGWwd7FDXT93mFkoAABmFgq88nYv9HizCzZ9sR3bv/2Lj98mMgIc+KSn6pj7YbDHJ7A0qSWuS8h5hJCk/biTFozkvBgM8ZiLhtbtoYYa/yQfxOn4LXBSeMHHphNa1gqApUktSCUytKv9KjwtW+D3h18gJY/TwhobXsAmMjzPM1ugBFK87Dwe7Wq/Kq5TCyrcTjuL68lH8CDjH+T9NwBWChk+8N2Zv51EgpNxv8DFvCEaWr0IiUQKW1MnDPecjzPxv+Jk/EYAvOPHqKglUF1sgN3YDT8/P+YNIgPxPHfRuTdyw7xds1DXp4647klUIvb9dBinfj+PiOuRxW5Xp6ErOg16EX0n9oBrfWcAwMuvd0LTl3zw+auLcffKfe0cFBkMiUyCRgPdsXs3cwdRTffq1D54e/EoyEwKf88vH7qGg+uP4uKBEKQnF521Vq6Qo3lnX3Qf2RldXusAU4UcNrWtMStoClp2a4rv3v4BebnK6jwM0gMcNEtkeMSao709Hl9ILNcFbFsHa3y6/h00aVdfXJeemoW/d1zEyX3/4M7dWKifql2E/wbKOjjZwL+LDwIHtUGDRvkzC7Z6wQs//DgOC+b/iQvnw7R7cKT3BJUUd8744w7PVxHVeC8Nbo+ZQZM1HqF9++I97PvpCM79dQmJMclFtpHKpPB9sSG6Du+InqO6wNLGAmYWCkz4+g206t4c819bWmytQjUbaw7jwoFPeqi+ZRsM8pgDuTR/ZGqGMglHY9fhesoxlH7xWUBczn3Exd/H2Se/oW3tAejoMAJyqQLOZvXxZr0l2PLgIyTmRlXLcZCe4AVsIoNT0dkCJZCif51Z8LPtLK67l3YBR2J/LPYz317hDpmk8E+AK0l7kfUkDQ4KT/R0eRv1LFsCADo6DoeViT32RS8HBz8REem3it5F16BFPSw69ClsHWwAAFnp2djw2W/4c+WBMi8+R92Nxm+LdmH7t38iYGw3TPj6DdjYW8HJwwHLTn2J2b3n499TN7V1aEREpCfGLxyJ4R8OFJdvnr+Llf/7Gbcv3it1u7ycPFw+dA2XD13D+jlbMXHRG+g2ohMAoNforqjtZo+5AxYhN5uP2zYmHDRLZHgKao56PZwRfTmpzJstHFzt8PUf01GnvhMAQJmnwh8/n8Rva44hMz3/cXmCmbzYbZ/EpeKv7Rew+8+r6Ni5MSZN6wVnF1tYWZlh/oIh+HrhXzh65IZ2D5CoAtJVZpArTSvVRp6q5MfMExmrPhN7YOrqiZBK838/Ht15jFXT1uPigZBSt1Or1Ag9exuhZ2/jl8+24Y25QzDwvUBIpVK06dkcS098gVk95iE5PrUajoL0BWsO48KsqmdczRppDHoKT7+KH8Mm4XrKUVTkorNSyEHwk21Yf/9/SMh5BACwltfGcM8FsJTVKmNrIiIyJAGu74qDnlSCEgeiv8f2h5+XONDVSVFP/DpTmYIsVf5jiZ7kPMDWBx/jcMyPUAsqAECLWr3QzWls1R4AERFVK6e6Dlh44BNx0NP9aw/wTquZ+H3ZngrNuKFWqbH/pyP4OHAB8nLyAAAKc1Ms2Psx6jXxqJK+ExGRbgz9oL/GoKctX/2BaZ3mlDno6VnxjxLw1cjl+PK1pcjOzJ+Rtk3P5pi9ear4CG4iIjJ8FtZmmP/re+KgpyfRyXh/0HdY/+1+cdBTeZ05eRtvvfEDTh7Lv7lCJpPio9n98EJbL633m4iIdKfTq+0w/Ye3xUFPh345gUmtZ5U56OlZaUnpWD09CB90+xxJsckAAK9mdTF/z2wozCs3YJGI9BcHPukRM6kVBnl8Ig56upl6Ctsi5yJL9fyjTxNyH2JjxPuIycqf+tVW7oQB7rMA8GQSEVFN0My2J1rV6gMAUKrz8PvDL3A1aV+p2zia1YNKUEIQBMRmF30c0cXEndj16Gtx8FN7hyFobN1R+50nIqJqJzOR4dNt76OWsx0AIPTsbUzvPBePw2Keqz1nT0d8tuN9SKSFpaW5lRk++/0DmD01JTkRERmu5l38MOHrkeLy8klrsX7OVqhV5XjGUQlObg/GrB5fIDMtC0D+RY5hswZUuq9ERKQfpi19A54+bgCA6AdPMH3AMtwJKf5R2uWRmZmL+XN+x5+7rwDIH/z0yZwBcHSw1kp/iYhIt+p4u2DWhvfE5W3f/olvxnwv3izxPP49dRPTOn2K+EcJAIDGbb0x+f/GVbqvRKSfOPBJj7zsPB42ckcAwMPM6/jz0WKooap0u1mqNGyLnIvUvHgAgKdlC7Su1bfS7RIRkW5Zmdijp8vb4vK+6O8Qln6pzO2cFF6QQgo1VIjNDis25nbaGfwds1pc7u06BWZSq8p3moiIdGroB/3g084bABB1Lwaf9vsamamZz9WWs6cjlp38ArVcasFEnj9VdGJ0EgDAvZEbxnz5mnY6TUREOmNqZor3f5ok3nX9y7xt2PPDIa20ffPcHXw5dAlU/w2gGj3vNXg0dtNK20REpDudXmmFl/q3AQCkJWfikzdW40l0cqXbFQTg/5YfxJnTdwAANjbmmPFer0q3S0REujfjx0kwt8q/ge7wppP4cdZGrbT7OCwGHwcuQNZ/sw0Gju+ONj2ba6VtItIvHPikJ5wUXmhu1xMAkK3KwO5Hi6BG+R8zUZYMVRL+ivpWXO7s+AZMpeZaa5+IiKpfJ8eRUMgsAADXkg8hNOVYubZzNmsAiUQKmcQE8TkRJcZdTdqHW6lnAAAWJrbo4Di80n0mIiLdsXWwwesfDwYAqFRqfPX6d0hLSn+utoob9LR14R+Y3nmueDfewCmBcGvgop3OExGRTgx8r7f4WX7txA1s+mKHVtu/9Pc/2P7tnwAAuakJJnz9hlbbJyKi6iUzkWH8Z4PE5RUzNyM64onW2hcE4JtFe/EkPg0A4P+iN9q08tRa+0REVP06DmyH5l38AOQPVFr+zlqtth8R+hCrpq0Xl99eMlq8sYOIag7+VlcxExMTjB49GiZdbgCykqcAb+8wBBJJ/n/H6fgtSFMmaL0vkZn/4nryUQCAuYkNWtr11vo+iMqSpTRBplJeqVeW0kTXh0Gkc5YmtdDcrgeA/AGzR2PXlWs7hdQC1vLa4nJpA58A4HDMGuSp8y9gt6rVh7M+UbVj3iDSnv6TA8S75/b9eBh3LhU/619ZShr09PMnW/E4LAa/fbMLQP5FjyHv99NK34mISLsKzlf9GxQOtVIoPkZugsHT8z/H1Wo1Vkz5CYJQfGxlbPpiu/j4iQ4D2qKur7vW90FERNWjy+B2cKnrAAC4cuImTv15Rev7SE/Pxtq1hTf/vT6svdb3QURElVeemgMAXvtwoPj1mhkbKvV4u5Ic+Pkobp7LnzHQq2ldvNi3tdb3QUS6xatAVUwqlaJevXpIS1UBKotiY8ykFmhs3QkAkKFMxfH4Y1AKxccWsJHbw0xWeOHaWu6JLLU9ctXZpW53MHYXmtq9DABoatcbh+IOVuRwyBApJeDcXkSGK1VpDqBoUdCiVm/IJHIAQHDCQcTkKAGUnjvMpBbo5VL4DGu1oMb9jIRSc06KMgsXE4+ig0MgTKVmqGfVA+cT/36uYyEDoQak7R5gZN33YWLCPxWJagqJRILe4/LrAJVSha0Ld1Zoe6lUCt/2DaHMU+GzHe8XO+ipwB/f7cPQ9/vDwtocL7/eCWtmbEBudq72Dob0jlop4Ob2SHz62wzmDiIDUXC+KuVBRokxL/ZtDXsXOwDA6T8uICL0YZX0JScrF/vXHcGoz4YBAHqP64a1M7XzaAsiIqpeAW++JH69dem+KtvPsaM3MGp0J7i726NNq3pwcbZFTGxKle2PdE8qU8Onyyk0dVzOmoPIQJSn5qjXtC58X2wIAAgLiUDwX5eqrD+b5v+OBXtmAwB6j3u5SvdFRNWPMz7pgUbWrWAizb94fTXpJJRC2RcFRtSdDg+L/ESgVgtoadcJXR1eLXO7+JzHCM+4CQBwNvOAg6lrJXpOBkEqIKfdAwwZMoQFAZGBKLgTIqfTfUBW/J0QTWzaiV9fSjxarnbb1uqBNvZdoVTlAQCkEin6uY0rYyvN9v1s25ZrX2TApIC6TiqaNGnCKX+JDEhZd9E1bO0FJ4/8O68vHfwH8Q/L97gJQRCQKMTBq4cLvjs9H9+fX1jqoCcAyEzNxKkd5wAAljYWaPly08ocGhkCAUi4wdxBVNN0HFhYc+z/6XC5tyvIHTFCJBKFuGJnibK0tUDHge3w3soJ+CXse3HQEwB0Gdqhch0ng1AwaJbnq4gMh1hzbHxQbM1hVcsSTTs2BgA8uheL6+fulatdQVAjMf0BopNDkZgWAUEo+ckZBdRqAQcPXBOXO7b3LudRkKGSSAU41nvImoOohuk4sPB6w/51R8q9XXlqjmddOhCCJ1GJAIAXAlpAYW5a8Q6TQWHNYVz4P1zFVCoVLl++DNlje6jqJRY71MzL0lf8+lZa+aZ+vfhvMLza528nl+UPmlr9wWYoW0ejcY86pW57K/WyuE8vKz88SYwu1z7JQD11AZuIDEPBnRDq1OLvhDCRmMLdIv+ETlz2IyTkxpTZ5u3DUTh/6Cf03T8KJv/lDQCwyXYqc9vH2eFIyU2ArWlt1LPwgQRSCCj7JBQREVWfsu6ia9bZT/z63N7L5WozTojCbYQgB1l4d+goCIIAiURS6qAncR97LiFgbDcAQPPOvriwT/uPuCAioudXcL7Kta09Yi4norhrzE1fyj93lJ2Zg5BjoeVq9+ncAQC2trY4F3wOFnIrLJ/4I1p1b4a2AS3RsE19SKVSKPOUMJFrnp50quuA2m72SHicWLmDJP321KBZIjIMBTVH6oPMYt9v8qI3ZLL8CyAXDv9brjZjU27hZvQh5CjTxHUKuQ183APgWtuv1LNP54LvYfyErgCA5s088Pvu8tU5RERUPcpTczR76anzVXsqfr6qgALmaCy0hJOk5GvkarUaF/ZfQZ8JPWBqZorGbb1x7eSN8h8QGR7WHEaFw6KrmEqlwv79+2H6Tx1ALSk2xsXcU/z6YWbZd0HcPhyFib2nIyUlf+pWpVKJy5cvY8eWndj1wTncPhxV6vYPM+8W7tusbnkOg4iI9Iijwg0ySf5F56c/00ty+3AUdn1wDicOnkZCQgKA/PwUHx+PLq17lpk3AOBhVn5+UsjMYW9a9mApMmBqQBplg9DQUKjVHOBGVFPUa+ohfn3rfNm5I06IwjUEIwdZMDExwZAhQyCR5NczBZ8Nm+f/XuL2N88X1jVeTVlz1HgSoLYfcweRISk4X9WgjxsksqLnq8ytzODqlf93f9jVcCjzlGW2+XTuAIA6deogODgYPr4+qOvtjiXH5mHER6+i0QsNxJkapDIpVKr8z42cpx6L6tWMuYOIyNDUa+Iufn37SkSZ8THpdxDy8A+NQU8A0KKVD9b++gH+OPERHJ1sStw+PDweWf/ljvr1HJ+v02QwBLUE8REerDmIDEhZNQdQeL4qKTYZsQ/iy2zz2ZqjQA6ycA3BiBNKv9Zx68JT56uas+Ygqkk48EkP2MrtAQAZylTkqIu/W6KAWiXgyDf/IDUlFYsXL4YgCDAxMcGnn34K/DeL35Fv/oFaVfKUfkm5ceLXHR36Vv4ASL/xAjZOnjyJfv36wc3NDRKJBLt27Spzm+PHj6N169ZQKBTw9vZGUFCQxvsLFy5E27ZtYW1tDScnJwwcOBC3b9/WiMnOzsbkyZNRu3ZtWFlZYfDgwYiNjS2yr6CgIDRv3hxmZmZwcnLC5MmTK3O4VAOoVCpcuHABsvv2KO7WNjt5bfHrxNyiP1NPK8gbEPKnf929e7c4Y8ewYcMQHx9fZt4ANAfKNrFtV0okGTy1BIoLntixYweUyrIvcNVUzB1kaApyh2tbe0iKqfIc3R3Er2PC44oGPEUQBNxGiLjcvXt32NraAgDy8vLEi9U5WSU/ovvpWTra9WldnkMgAyY1kcB3aF2jzx1ENYlDHXvx6+j7pecNoGjuaNKkCS5evAhvb81HD8lMZFAp84ucvJw8hBwLxbrZm/FO65k4tuW0GNfvnV6VPALSexw0S2RwCmoOlza1iq05ns4dMQ9Kf7S2IKhxM17zkUbt2rXD/v37cf78ebRr1w7mFmZYFTQeW3dPFV+/bZ8ivn7dNgXmZvmPKfJwt4fpfzPTUs2kVklx68RLelNzpOcptPIiMmYyExlqu9YC8Hw1R3FuI6TUx94pcws/P96cO7R8HSXDxZrDqHDgkx4wlZoBALJVWWVEAo+uPEFabH7cihUrkJqaisuXL2P//v35AQKQFpuFR1dKLiyUgu7/KKRqxAvYyMjIQIsWLbBy5cpyxYeHh6Nv377o1q0bQkJCMG3aNEyYMAEHDx4UY06cOIHJkyfj3LlzOHToEPLy8tCrVy9kZBQ+Xmb69On466+/sH37dpw4cQKPHz/GoEGDNPa1dOlSfPLJJ/joo48QGhqKw4cPIyAgQDsHTgarrNkC5f/lDQDIVpeeO57OGwCwe/duSCQSfPHFFzh+/Hi58gYA2MgLT17VNnUp55EQGS7mDjI0Zd1FZ2ZZeEI1I6X0my2SEK9x51zBdNAZGRn49ddf0b9/f5iZmSFRKPukFBERGSZzq8KaIyO19LwBFM0dq1atgqurK+RyuUbcnX/uYcfSvzCrxzwMsBuND3t+ge3f/omwkAhY2FiIcd6tvbRwFKTPOGiWyPCINUegS/E1h8VTNUdadqltJWY9Qo4qXVx2d3fH+fPni9S2dnaWcHC0Lnw5aL6IiMhwKZ7KG5kVrDlMTU2xatUqDBgwQCMmB1lIQskzR5k8NUjW1qHkWQWpZmDNYVxMdN0BAtSCCgDExxaVJv1JYcGQmpoKR0fHYkeuPh33rIJHVBAZutTUVI1lhUIBhaLoXRKBgYEIDAwsd7tr1qyBl5cXlixZAgDw9fXF6dOnsWzZMrH4PnDggMY2QUFBcHJywuXLl9G5c2ekpKRg3bp12LJlC15++WUAwPr16+Hr64tz586hffv2SEpKwpw5c/DXX3+he/fuYlvNmzcvd1/JOKmhEr8uK3c8mw/+/PNPWFtbIzMzs9S4Z0nA3EGGr7x5A2DuoJpHpSzMHSamJsjLySsxNheaOWHZsmW4evUqzp49i5ycnBLjiIio5lDmaeaNsjybE+bNm4cRI0agQ4cOaNy4MWSy/LplcMuRcJEU/0gJqZQ1BxGRIVM/XXOUMftSjipDYzkpKQm//vorhg0bBqVSCblcDqVSCROT8l/CkkqlgKAqO5CIiPTC03lDJq9YzTF27FhMmjQJgwcPxoEDB8p9vkoi5ZwwRDUVf7v1QMZ/z7C2MrGFpIz/EisHM43lvLy8YkcoPhv3NEtZ4Z0QoSnnK9JVokrLVsq18gIADw8P2Nraiq+FCxdqpY/BwcHo0aOHxrqAgAAEBweXuE1KSgoAwN4+f1acy5cvIy8vT6MdHx8f1K1bV2zn0KFDUKvViIqKgq+vL9zd3TFs2DA8fPhQK8dBNVeGsnDwhrVJrVJji8sH6enpRab1LC1vAMDttCvi15eSjpWnm0RaYQh5A2DuIP2XEl+YOwqmES+JKTRzgiAIOHbsmMZJpOLinvb03d6RNx9VpKtERKQHUp6kiV/bu9iVGf9sTjh69CgmTpyIJk2awNraGh06dEDDhg1LzR0RNwr/ntkw97eKd5qIiHQqJaFwBid7p9Jn0VDILDWWMzIyMGLECDRt2hS///47AMDExATffbsN82ZvF1+fzf1d41VApVIju5SbO4iISP9kZ+YgOzP/XFNtV7sy4wtqCVNTU8ydOxeCIMDBwQETJ04sNq44WWmFs9Ru/Xrnc/SaiPQVZ3zSAwm50XA194SJVA5HhRvickq+MODe2gHWzuZIi8sCintEqQSwdjKHe2uHEttwNiu8s+5hZlhluk6kUw8fPoSNTWERXdKsHRUVExMDZ2dnjXXOzs5ITU1FVlYWzM3NNd5Tq9WYNm0aOnbsiKZNm4ptmJqaws7Orkg7MTExAID79+9DrVbjq6++wvLly2Fra4s5c+agZ8+euHbtGkxNTbVyPFTzPMmJFr92MfMsNVYbeQMAbOS1xa/jsnkBmwxTVeUNgLmD9F/UvRjxa69mdRETUfJj6mrBEQqYazyy6FkKmKMWHEt8v15TD/Hrm+fuVrC3RESka4nRSchKz4a5lRnqNy+95gBKzx1ZWVkIDg6GAubohBYltuHqVfi3VNg/Ec/VbyIi0p3HYbHi115+dXD15K0SY+3N3aGQWWk87g4Abt68iREjRuCLL76Ar09zpET7QSIpvFlcrSicScrGprDODr0ZpY1DICKiahZ1NxoNWtRDnYauUJibIicrt8TYgppjzNhRcHV1FZ9w9Omnn+LHH39ETk5Omeer3Bu7iV/fuxquvQMhIp3jjE96IDKz8EJAA6umpcZKZRJ0n/XfSaJnZwD/b7n7rBaQFvOM7QLeVs3Erx9l8SIEGS4bGxuNlzYvYFfE5MmTcf36dfz6668V2k6tViMvLw//93//h4CAALRv3x5bt27F3bt3cewYZ9ShkqUpk5CSmwAA8LRsBBNJyQMdtJE3zGWWqGNeHwAQm/0QOeqSL4QT6TN9yRsAcwdVv9sXCv/ub9W9WSmR+Y/GboyWpcY0RstSH6H99D5uXWDNQURkaARBwO2L9wAAzp6OcK3vXGp8ZXOHRCJBy25NAOTf+R1xnbNZEhEZmluX7otft+jUuNRYiUQKX8fuJb5/8+ZNhIWoNQY9Patlq8KBuTdvR5cYR0RE+qug5pCZyNCss1+psQU1x5o1a8S6QiKRwMnJCW+88QaAcpyvevmp81Xneb6KqCbhwKcqZmJighEjRiDHPwKQFjfVBnAnLUT8uoVdpzLbbNyjDgZ+2x7WTpozB1g7mWPgt+3RuEedkvsjMUUTm3YAgBxVFiIySr7rgshYubi4IDY2VmNdbGwsbGxsiszYMWXKFOzZswfHjh2Du7u7Rhu5ublITk4u0o6LiwsAwNXVFQDg51f4x5yjoyMcHBwQGRmpzUOiGuhO+lUAgKnUDH42bUuNrUzeAIBmtv6QSfLvqHs6ZxFRIeYO0ndXj15HXm7+I7I7D2kPmYms1HgnSR00hz8U0Pz5VcAczeEPJ0npuaPb8MK65tLBf56z10REVFUKzleFbomAWln8+apLB0PEr7uN6Fhmm5XJHc27+KG2W/7jf68e/hcqpaocR0FERPok4sYjxEclAgBadfGFbW2rUuNdrBqhpccgKEysNdYr5DZo4TUUzrV8S92+e/fCuvjCJc7aQUSkb8pTc1w8ECJ+3W14+WqOlXN/1FgXGxuLk4dOlVlzOHo4oEnH/IG5kTcfIS7ySTmOgogMBR91V8WkUikaNWoEdXZaiTEx2Q8Qk/UALuaeqGfpAw/zhnhYxkxMjXvUQcNubnh05QnSn2TDysEM7q0dSp2xAwDa1OoKc5P8giM09QKUAp97TfQsf39/7Nu3T2PdoUOH4O/vLy4LgoD33nsPO3fuxPHjx+Hl5aUR36ZNG8jlchw5cgSDBw8GANy+fRuRkZFiOx07dhTXF1z4TkxMxJMnT+DpWfajBMi4XU06hbb2PQAAnRz64lrKmVLjnzdvSCBFR4e+4nJI8unKd570m1RAbuuHGOoxBTJZ6QMjqBBzB+m7jJRMnN97BZ1ebYfabvbo+loHHNl8qtRtnCR14Ci4IQnxyEU2TGGGWnAs9c45IH+2J69m+Y/XDj17u9TH6lHNIKgE3Nn1CDPXT2buIDIQBeerku6mlxhz7NczGPfV65BKpeg/KQDbv/0LeTmln0d63twxeNor4tdHt5aen4iISH8d334OQ6f1gdzUBK+M7YLN3+4tNd7Z1gdONo2QlPEQOcp0mJrbopZV3VJnegIAN7da8O/QEACQkJiOkH8eaO0YSD9JZGo06hgMX4evWXMQGYjy1BwX9l1FWlI6rGtZoetrHfDzJ1uR8Dix1HZ3zf8bA0e9gjre+TeIrpixFnUjm5VZcwya2gdSaX5+ObqV1zmIahrO+KQnziTsF7/u4/omJEWeR1SUVCZB3baO8Av0QN22jmVevFZILdDdeWjhPp+UXnRQDfHfBewBAwYYbUGQnp6OkJAQhISEAADCw8MREhIizowxe/ZsjBo1Sox/5513cP/+fcyaNQu3bt3CqlWrsG3bNkyfPl2MmTx5MjZt2oQtW7bA2toaMTExiImJQVZW/iPAbG1tMX78eMyYMQPHjh3D5cuXMXbsWPj7+6N9+/YAgEaNGmHAgAGYOnUqzp49i+vXr2P06NHw8fFBt27dqum7Q/qoPLMF3s8IRXRWBACgrmUjNLftUGa7Fc0bAPCCfTc4m3kAACIybiEqK6zcx0EGSgqoPJPRsmVLo80bAHMHGZ7y3EW38/8K//4f/cVwKMxLflRqAYlEAnuJE1wkdWEvcSrzJJJUJsWEr994ap/7SommmkJQA3H/MHcQ1TRxkU9wdtdFAEBtN3sMfb9fubaraO5o3tkP/v1fyN/nwyc4/ceFynWcDELBoFljPl9FZGgKao4bvz4ssebY89NRcda+QZN6wN7Ztsx2JRIp7K084WrXBPbW9coc9AQAE9/uCpksP27XX1egUhffH6o5pFIBzt7hrDmIapjc7Fzs+/EwAMDUzBTj5o+ocBuWEpsyaw7X+s7o/24AACAnKxd71x6ueGfJ4LDmMC6c8amKqVQq/Pvvv1A+cEROndQSh5qdij+LlxwGwMnMFV5WfnihVn8cjz+g1b4M8nwLNvJaAIB/ki/iTloUAIVW90F6qk4WWrZsqete6MylS5c0LgbPmDEDADB69GgEBQUhOjpa4/FAXl5e2Lt3L6ZPn47ly5fD3d0dP/30EwICAsSY1atXAwC6du2qsa/169djzJgxAIBly5ZBKpVi8ODByMnJQUBAAFatWqUR/8svv2D69Ono27cvpFIpunTpggMHDkAul2vzW0AGpuBOiLT0XECtANTFx/35+He83eB9AEA/t/EITQlDqjJZa/2obeqIPq6jxeXdUTuQoWTeIOPA3EGGpjx30V07cQNXDl9D6x7N4erlhLcWv4kVU9ZptR+vfzIIjdrUBwCEhUTg5PZgrbZPRETaUXC+yqmFHeL/TYZQQs2x4bPf4D+gLWQyKUbOGYxzey7j/jXtzaphaWuB99dNEpc3fbkDyjyl1ton/fX0oFkiMgxizXGv5JojNjIBBzadQd8xnWFhZYbp372JuWN+hCBob2DSy9390LmzDwAgMSkDv+++rLW2icorS2mCPGXlzsMolSX8AUZUQ5S35tj+7V/o+1ZPWNlZoteYrjj1xzmc26O9z3aZiQyzgqbA1Cz/BsBdK/YhOS5Fa+2T/mLNYVw441MVU6lU2L17N6xDXAB1yaNNVYIKvz1cLy4PdH8dfjYttNaP7k590a52JwBAlioLvz/apLW2ifRd165dIQhCkVdQUBAAICgoCMePHy+yzdWrV5GTk4OwsDDxgnSB4toTBEEjzszMDCtXrkRiYiIyMjLwxx9/wMXFRaMdGxsbrFu3DklJSUhISMAff/wBDw+PKvguUE10PeUKriVfAgBYy23xVoMZUEjNtNK2ucwCbzV4H+YyCwDAhYRTuJd+Uyttk55TA/JYS9y5cwdqtfGegGHuoJpqxZR1yMnKBQD0f7c3Xnmnl9bafmlwe7w5N3+GWZVKjeWT1mr1AgfpMQlQq6GV0ecOIkNScL6q0UB3SEqZCTYi9CF+X7YHQP4d2PN2zUJtN3ut9EFuaoI5v06HW4P8v3VCz9zCwZ+PaaVtIiLSnaCFu5EYm39B+YWXm2D8nAFaa9vH1w0z3g8Ul7//4QgyM3O11j7pL0EtQeIjN9YcRAakvDVHypNU/PRR4XXrjzb+D/Wbe2qtH1O+H4+mnfIHzEaHx2Hz/N+11jYR6Q8OfNIjd9JCcSjmLwCATCLDhPrT0Nz2hUq328u5Pwa6vy4u/xr5E5Jyn1S6XTIQvIBNZHBUKhVCQkKgeGhT4mxPBbY8+BGJ/32me1o2wGTvj2BlYl2p/dvI7fBew4/hZp4/kCIuOxrbH26oVJtkQNQS2F6og61bt0Kp5N32RIaiIHc4tbBDaU+GeHTnMVb+r3CWp6mrJuLV//Wp9P5ffr0TPt4yFVJp/s5/+ew33Dx/t9LtkmGQmkjQ5PV6zB1ENVTQnK24ffEeAMClnhOWHJ8nDlZ6XuZWZvh85yy8ENASAJCakIav31zB8xbGhINmiQyOWHM0ty215khPzsTiyeuhUuX/bg9+qxve+uxVSKWlP4aoLC1ae2LRN6/B/L9Hdh88cA1Hj/MmPWOhVkkReqQraw6iGmrv2sM4ueMcgPxZYb85PBe+LzasVJsyExmm/fA2XnmrJwAgL1eJha9/h6z07Er3lwwEaw6jwoFPeuavx7/hatJ5AIBcaoqJDaZjYJ3XIZeYVrgtaxNbTKg/Df3qvFbYftRvuJJ0Tmv9JQOgZxewc1QmyFZW7pWj4lM6qWYr72yBAJChSseae98iQ5k/zbiXVUPM9JmPRtZNnmvfTWxa4UOfBfCw8AIApOalYHXYYmSrs56rPaLKYt4gKp/y3kUHAPvXHcVv3+wSl9/9biw+2vg/WNtbVXi/5lZmeO/78Zi9aSpM5Pm/awfWH8WWr/6ocFtERKSf8nKVmDtgER6HxQAA6ni7YOXFr/Hy652eqz2fdt74/sLXaBfYCgCQnZmDuQO/QUxEnNb6TPqPg2aJDE9BzdGwv1uZNUfIqdtYMXOLuPzqhK5YsPldONWpVeH9mphI8fqYTlj03UhYWeXPdH7lSgSWLT1Q4baIiEh/LR7zPW4E3wEA2DrY4Nvj8zBkxiuQyio+nKGOtwuWHPscfSf2AACo1WosHvM9b9IzMqw5jAsHPukZAQKCwlfiQsJpcV13576Y02QxOjl0L9cjjKxNbNHHdTA+bbIELezaiut3R23F37F/Vkm/iYhId6KzH2LF3a+QkpsEALA3dcB7DT/GxPoz4GVZvrsivK18ManBLLzj/QFs5HYAgIScePzfnS/xJCe2qrpOREQ68tNHm7Fp/g5xufvIlxB0ZwVGzH4VNrXLnjnQwsYCr07tg/W3/w/93+0trt/zw99YNvGHKukzERHpTmJMMj7o9jkiQh8CAKzsLDF701QsPfEF2vZuKc74V5oGLevho43/w4pzC1HXpw4AIC0pHR8HLkDomVtV2n8iIqp+BzefwbLpG6FSqgAALTs1wtoDH2DCh33h5GZX5vZyUxP0DGyOHze9jbFvdYXMJD/XnDt3D3M+3o68PFVVdp+IiKpZdmYOZgcuwNWj1wEApgo53v52NNZcXYxuwztCblr2za2u9Z3x7vKx+PH6MjTpmP94u9ycPCwcuRzHfj1Tpf0nIt3i7e96SA0VNj5YjUdZ4ejnNhxyqRz2pg54re44vOo+ErdTr+N+xh3EZUcjU5UBCaSwltvA2cwN3la+aGDVGDKJTGwvLS8VWyJ/xPWUKzo8KiIiqkpRWQ/wze05GF3vXXG2p+Z2bdDcrg3is2NwK+1fPMyMQFJuAvLUuZBLTWFv6gAPCy/42jRHbYWjRnuhKSHY9GAN0pVpujgcIiKqBhvm/obwfyMx/Ye3YWVnCRt7K4xb8DpGfT4M107cwL+nbiLy5iMkx6cCAGzsreDe2A1NOvigdY9mMDUrnJU2OzMHP7y/AXt+OKSrwyEioioW/ygBUzt8gqmr3xJne2r2ki+avfQJEh4n4uLBENy5FIbYiHhkpmXB1MwUDnXs0aBlPbTu0Rz1mnhotHfnUhgWjPhOnEmKiIhqnr+3nEX0oyTMXP4mHN1qQWEmx+AJXfDquJdwKyQS1y5HIOJeLJKepEOtVsPCzhJ16taGTzN3tGnfAJZWhTeCq1RqbNl8Fr9sOA21WtDhURERUVXJTM3E7N7zMX7h6xj6fn8AgFfTuvh4yzSkJaXj0sF/cPP8HZhZKMRtmnRoDK9mnmjZrSl82nlrtBd9PxZfvf4dbl24V63HQUTVjwOf9NixuAO4kfIPBrqPRFPb/Om/TaUKNLNrg2Z2bcrcXiUoEfzkBPZGb+eFayIiI5Cal4zv7y7Ei7U7o6/rENiZ2gMAHM1c4GjmUq42EnLisefxNlxKOluVXSUiIj1xcnswrp+6ifFfjUT3NztDJpPCRG6C1j2ao3WP5uVrY8c5/PjhRsSE8xFFREQ1XWZaFha+sRzHfj2NiYveQF1fdwBAbTd79B77MnqPfbnMNlKepGLzgt+x+/sDUKvUVd1lIiLSsX/PheGd7gvx+rTe6DfmJZgq5JBKpfBrXQ9+reuVq41/rjzAmv87hDsP4qu2s0REpHMqpQprZ27Eqd/P450lo+Hn3wgAYF3LCt2Gd0S34R014p+eibxAVkY2di7fh60LdyI7I7ta+k1EusWBT3ouNicaP4R9C3dzT3RweBkt7NrCRm5b6jbx2TG4knwOZ+KPIikvoZp6SkRE+kCAgHMJJ3Ap8Qxa1/JHO/tO8Lb21ZgJ8FlKtRJ302/gfMJJhCRfgErgVOFERMYkMSYZi8etxKb5O9D37Z7oMtQfLvWcSt0m4XEiTv1xHnvW/I0HNx5VU0+JiEhfnNtzGRf2XUX7fm0QMKYb2vRqAYW5aYnxarUaN4Pv4PCmkzi86RQvPhARGZnM9Bz8NH83dqw/jd6vtUPXV1rCs2HpN+mlpWQi+PRd7N19FTeu/1dzKEo+v0VERDXLzXN3MLXjJ2jexQ99JvRA+1fawNLWotRtwv6JwLGtp7F/3VGkJnBSECJjwoFPVczExARDhgzBz+H/B0iff/rVR1kPsO3hemx/GAQXszqoY+EJe1MHmMssIAhqZKoy8CQnDpGZ95GY+0SLR0BERIZIKShxIfEULiSegkJqBk/LBnBWuMFGbgsTqRx56lyk5aUgOjsKkZn3kavO0XWXiYhIx6Lvx+KnDzfhpw83wa2BC7xbe8G1vjOs7CwhkQAZKZmIiYhH2NVwRN6K0nV3iYioEgrOV3352lKolc93vkqtVuPs7os4u/si5Ao5GrWpD88mHnCoYw+FhQLKXCWSYpPx6PZj3LpwD+nJGVo+CiIiMjTJCen4ddVR/LrqKBxcbOHd2hOu7vawrWUJqVSCzBwl4mKSEX43FuF3Y6GU8xIWEZGh0kbNAQDXTtzAtRM3IJVJ4d2yHrya1YWjhwPMrMygVqmREp+KqLvRuH0xDEmxydo7ACIyKPyrsYpJpVI0adIEuTnpWmlPgIDo7EeIzuZd1UREVD456mzcSQvFnbRQXXeFDIFUQHrTWAz1GAOZjHdSEhmrx2ExeBwWo+tukIEQVALC9j3GlBXjmTuIDETB+aqEG6laaS8vJw+hZ28j9OxtrbRHREQ135OYFMQfvamxTjDlJSsqnkSmRoMXL6KR/VzWHEQGQts1h1qlxp3L93Hn8n2ttEdENYtU1x0goir23wXswMBAFgREBqLgTojUNo8rNVsg0XORAtleKWjXrh3zBpEBKcgdN7dHVuouOqLnIaiB6IuJzB1ERFRuBYNmeb6KyHAU1By3fn/EmoOqnVQqwM3nrt7UHJl5plp5ERFR1WHNYVw48KmKqdVqhIaGwvSxFaDWdW/IKPECNpHBEWcLdEtnpiYionLRuIuO1yCIiKgMBeeravvZABJd94aMEQfNEhkesea4mcaag4iIysSag3SNNYdx4eXUKqZUKrFjxw7YXHYD1PxUJ8rOlWvlRUREVUQA5E/MERERAbVa96O2mTeIiAyABLD1tNSb3EFEZSs4X+U7tC6kJjxfRURERPpNUEuQHOPEmoPIgLDmIKLqxIFPRDWdnl3AJqKycbZA0imVBLbBHtiwYQOUSqWue0NE5cS76EiXpCYSNBvjxdxBRETlx0GzRAZHrDl8rVlzULVTq6T492AP1hxERFR+rDmMiomuO2BMMlSmECScA5aql0QpgWewBzYEb8Ds2bNhasrnRhPpO3G2QLjhQa8HEEyYO6j6SFQSOOi6E0RUYU/fRXf2q1Co85g7iIiISH89PWiW56uIDENBzeEz2B3Bi26x5iAiIiK9xprDuHDGJyIiIiIiIiIiIiIiIiIiPXXy5En069cPbm5ukEgk2LVrV5nbHD9+HK1bt4ZCoYC3tzeCgoKKxERFReGNN95A7dq1YW5ujmbNmuHSpUvaPwAiIqIqxIFPRERERERERERERERERER6KiMjAy1atMDKlSvLFR8eHo6+ffuiW7duCAkJwbRp0zBhwgQcPHhQjElKSkLHjh0hl8uxf/9+3LhxA0uWLEGtWrWq6jCIiIiqBB91R0RERERERERERERERESkpwIDAxEYGFju+DVr1sDLywtLliwBAPj6+uL06dNYtmwZAgICAACLFi2Ch4cH1q9fL27n5eWl3Y4TERFVA874RERERERERERERERERERUzVJTUzVeOTk5Wmk3ODgYPXr00FgXEBCA4OBgcfnPP//ECy+8gKFDh8LJyQmtWrXCjz/+qJX9ExERVScOfKpiMpkMAwYMwJPmTyBIBV13h4iIiIiIiIiIjFzB+ao7ux5BUPF8FREREVFFZCvlyMqr3CtbKQcAeHh4wNbWVnwtXLhQK32MiYmBs7OzxjpnZ2ekpqYiKysLAHD//n2sXr0aDRs2xMGDBzFp0iT873//w4YNG7TSBzJurDmIqDrxUXdVTCaToWXLlkjPS9d1V4iIiIjKJEgFJPokYpj7MMhkMl13h4iIDICgAsIPxWDiojeYO4gMRMH5qrh/knXdFSIiIqIySaQCvNpcRYNaM2tczfHw4UPY2NiIywqFotr2rVar8cILL+Crr74CALRq1QrXr1/HmjVrMHr06GrrB9VMrDmIqDpxxieiGq7gAnaPHj30oiDIU0qRp5RV8sWPLqrZOFsg6ZQUSK2fio4dOzJvEBkQ3kVHuiSoBUSdfaI3uYOIiPRfwaBZfTlfRURlK6g57v75mDUHVTupTA33pjdrZM1hY2Oj8dLWwCcXFxfExsZqrIuNjYWNjQ3Mzc0BAK6urvDz89OI8fX1RWRkpFb6QESkS6w5jAuvAlUxtVqNO3fuwDzOHFDrujdklPTsAjYRlU2cLdA9nZmaiIjK5em76ATWHUREVIaC81W1GloBEl33howRB80SGR6x5riWwpqDyAD4+/vjyJEjGusOHToEf39/cbljx464ffu2RsydO3fg6elZLX2kmo01B+kaaw7jwsupVUypVGLr1q1wvuQMiZqf6kRERKTnBMA02RRRUVFQq3kmk4iIykECWLmZM3cQGZCC81VNXq8HqQnPVxEREZF+E9QSpD2xN+qaIz09HSEhIQgJCQEAhIeHIyQkRJydafbs2Rg1apQY/8477+D+/fuYNWsWbt26hVWrVmHbtm2YPn26GDN9+nScO3cOX331Fe7du4ctW7Zg7dq1mDx5crUeG9VMrDmIqDrpdODTyZMn0a9fP7i5uUEikWDXrl0a748ZMwYSiUTj1bt3b42YxMREjBw5EjY2NrCzs8P48eORnp6uEXPt2jW89NJLMDMzg4eHB7755puqPjQi/cEL2FTDGEPu4GyBpEsSlQRuZ93w008/QalU6ro7RJVmDHkD4F10pFtSEwlaTmzA3EE1hrHkDiKd4qBZqkGMJW+INYc3aw6qfmqVFCF7ext1zXHp0iW0atUKrVq1AgDMmDEDrVq1wty5cwEA0dHRGo+o8/Lywt69e3Ho0CG0aNECS5YswU8//YSAgAAxpm3btti5cye2bt2Kpk2b4ssvv8R3332HkSNHVu/BGSFjyR1EOsWaw6jodOBTRkYGWrRogZUrV5YY07t3b0RHR4uvrVu3arw/cuRIhIaG4tChQ9izZw9OnjyJt956S3w/NTUVvXr1gqenJy5fvozFixfj888/x9q1a6vsuIj0CS9gU01jDLmDswUSEWmPMeQNgHfRERFpk7HkDiJd4qBZqkmMJW8U1Bx+wz1YcxDpQNeuXSEIQpFXUFAQACAoKAjHjx8vss3Vq1eRk5ODsLAwjBkzpki7r7zyCv79919kZ2fj5s2bmDhxYtUfDBlN7iDSJdYcxsVElzsPDAxEYGBgqTEKhQIuLi7Fvnfz5k0cOHAAFy9exAsvvAAAWLFiBfr06YNvv/0Wbm5u2Lx5M3Jzc/Hzzz/D1NQUTZo0QUhICJYuXarx4U9ERIaBuYOIiCqCeYOIiCqKuYOIiCqCeYOIiCqKuYOISLt0OuNTeRw/fhxOTk5o3LgxJk2ahISEBPG94OBg2NnZiR/oANCjRw9IpVKcP39ejOncuTNMTU3FmICAANy+fRtJSUnF7jMnJwepqakaLyIiMhzMHUREVBG6yBsAcwcRkSFjzUFERBXBmoOIiCqKNQcRUfnpdMansvTu3RuDBg2Cl5cXwsLC8PHHHyMwMBDBwcGQyWSIiYmBk5OTxjYmJiawt7dHTEwMACAmJgZeXl4aMc7OzuJ7tWrVKrLfhQsXYt68eVo/nkylAgIErbdLVBqJktMOk3Fh7iCqHOYNMja6yhtA1eUOIiKqWjWt5iAioqrFmoOo5snJM4Esr3KXWFV5Ki31hmoi1hxERBWj1wOfhg8fLn7drFkzNG/eHA0aNMDx48fRvXv3Ktvv7NmzMWPGDHE5NTUVHh4eVbY/ImOizpMBebLKt0FUAuYOopqFeYOqmq7yBsDcQURkqFhzEBFRRbDmICKiimLNQURUMXr/qLun1a9fHw4ODrh37x4AwMXFBXFxcRoxSqUSiYmJ4jNPXVxcEBsbqxFTsFzSc1EVCgVsbGw0Xs9LJpMhMDAQ0T4pEKScsYOIqLoZYu4gIiLdqa68ATB3EBHVFIZYcxScrwrb9xiCiueriIiqE2sOIiKqKNYcRESlM6iBT48ePUJCQgJcXV0BAP7+/khOTsbly5fFmKNHj0KtVuPFF18UY06ePIm8vDwx5tChQ2jcuHGJ079qk0wmQ7t27ZBUN9PAvttERDWDIeYOIl0SpALi66ehS5cukMk4UxIZH+YNoooTVEDk8TjmDjJahpg7Cs5XRV9MhKCu8t0REdFTDDFvEOmaRCqgbot/WXOQ0TLE3MGag4iqk06H4qSnpyMkJAQhISEAgPDwcISEhCAyMhLp6emYOXMmzp07h4iICBw5cgQDBgyAt7c3AgICAAC+vr7o3bs3Jk6ciAsXLuDMmTOYMmUKhg8fDjc3NwDA66+/DlNTU4wfPx6hoaH47bffsHz5co1p+ohqMl7ApprGGHIHZwsknZIC8d7p6Nq1K/MG1QjGkDcA3kVHuiWoBUSeiGPuoBrDWHIHkS5x0CzVJMaSN8SaY38Maw6qdlKZGp4t/2XNQTWGseQ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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Use the get_heterogeneous_map method to generate a WindRose that represents\n", + "# the information in the WindRoseWRG, rather than a set of WindRose objects\n", + "# but as a single WindRose object (for one location) and a HeterogeneousMap\n", + "# the describes the speed up information per direction across the domain\n", + "# This will allow running the optimization for a single wind speed while still\n", + "# accounting for the difference in wind speeds in location by direction\n", + "wind_rose_het = wind_rose_wrg.get_heterogeneous_wind_rose(\n", + " fmodel=fmodel,\n", + " x_loc=0.0,\n", + " y_loc=0.0,\n", + " representative_wind_speed=10.0,\n", + ")\n", + "\n", + "# Pull out the heterogeneous plot to show the underlying speedups\n", + "het_map = wind_rose_het.heterogeneous_map\n", + "wind_direction_to_plot = [0.0, 10.0, 45.0, 75.0, 90.0, 180.0]\n", + "\n", + "# Show the het_map for a few wind directions\n", + "fig, axarr = plt.subplots(1, len(wind_direction_to_plot), figsize=(30, 5))\n", + "axarr = axarr.flatten()\n", + "for i, wd in enumerate(wind_direction_to_plot):\n", + " het_map.plot_single_speed_multiplier(\n", + " wind_direction=wd,\n", + " wind_speed=8.0,\n", + " ax=axarr[i],\n", + " show_colorbar=True,\n", + " )\n", + "\n", + " axarr[i].set_title(f\"Wind Direction: {wd}\")" + ] } ], "metadata": { @@ -893,7 +1110,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/examples/009_parallel_models.py b/examples/009_parallel_models.py new file mode 100644 index 000000000..60f20762c --- /dev/null +++ b/examples/009_parallel_models.py @@ -0,0 +1,95 @@ +"""Example 9: Parallel Models + +This example demonstrates how to use the ParFlorisModel class to parallelize the +calculation of the FLORIS model. ParFlorisModel inherits from the FlorisModel +and so can be used in the same way with a consistent interface. ParFlorisModel +replaces the ParallelFlorisModel, which will be deprecated in a future release. + +""" + +import numpy as np + +from floris import ( + FlorisModel, + ParFlorisModel, + TimeSeries, + UncertainFlorisModel, +) + + +# When using parallel optimization it is important the "root" script include this +# if __name__ == "__main__": block to avoid problems +if __name__ == "__main__": + # Instantiate the FlorisModel + fmodel = FlorisModel("inputs/gch.yaml") + + # The ParFlorisModel can be instantiated either from a FlorisModel or from + # the input file. + pfmodel_1 = ParFlorisModel("inputs/gch.yaml") # Via input file + pfmodel_2 = ParFlorisModel(fmodel) # Via FlorisModel + + # The ParFlorisModel has additional inputs which define the parallelization + # but don't affect the output. + pfmodel_3 = ParFlorisModel( + fmodel, + interface="multiprocessing", # Default + max_workers=2, # Defaults to num_cpu + n_wind_condition_splits=2, # Defaults to max_workers + ) + + # Define a simple inflow + time_series = TimeSeries( + wind_speeds=np.arange(1, 25, 0.5), wind_directions=270.0, turbulence_intensities=0.06 + ) + + # Demonstrate that interface and results are the same + fmodel.set(wind_data=time_series) + pfmodel_1.set(wind_data=time_series) + pfmodel_2.set(wind_data=time_series) + pfmodel_3.set(wind_data=time_series) + + fmodel.run() + pfmodel_1.run() + pfmodel_2.run() + pfmodel_3.run() + + # Compare the results + powers_fmodel = fmodel.get_turbine_powers() + powers_pfmodel_1 = pfmodel_1.get_turbine_powers() + powers_pfmodel_2 = pfmodel_2.get_turbine_powers() + powers_pfmodel_3 = pfmodel_3.get_turbine_powers() + + print( + f"Testing if outputs of fmodel and pfmodel_1 are " + f"close: {np.allclose(powers_fmodel, powers_pfmodel_1)}" + ) + print( + f"Testing if outputs of fmodel and pfmodel_2 are " + f"close: {np.allclose(powers_fmodel, powers_pfmodel_2)}" + ) + print( + f"Testing if outputs of fmodel and pfmodel_3 are " + f"close: {np.allclose(powers_fmodel, powers_pfmodel_3)}" + ) + + # Because ParFlorisModel is a subclass of FlorisModel, it can also be used as + # an input to the UncertainFlorisModel class. This allows for parallelization of + # the uncertainty calculations. + ufmodel = UncertainFlorisModel(fmodel) + pufmodel = UncertainFlorisModel(pfmodel_1) + + # Demonstrate matched results + ufmodel.set(wind_data=time_series) + pufmodel.set(wind_data=time_series) + + ufmodel.run() + pufmodel.run() + + powers_ufmodel = ufmodel.get_turbine_powers() + powers_pufmodel = pufmodel.get_turbine_powers() + + print("--------------------") + print( + f"Testing if outputs of ufmodel and pufmodel are " + f"close: {np.allclose(powers_ufmodel, powers_pufmodel)}" + ) diff --git a/examples/009_compare_farm_power_with_neighbor.py b/examples/010_compare_farm_power_with_neighbor.py similarity index 97% rename from examples/009_compare_farm_power_with_neighbor.py rename to examples/010_compare_farm_power_with_neighbor.py index c67465f31..6ea23abd8 100644 --- a/examples/009_compare_farm_power_with_neighbor.py +++ b/examples/010_compare_farm_power_with_neighbor.py @@ -1,4 +1,4 @@ -"""Example 9: Compare farm power with neighboring farm +"""Example 10: Compare farm power with neighboring farm This example demonstrates how to use turbine_weights to define a set of turbines belonging to a neighboring farm which impacts the power production of the farm under consideration diff --git a/examples/examples_control_optimization/001_opt_yaw_single_ws.py b/examples/examples_control_optimization/001_opt_yaw_single_ws.py index 533347a78..32fc42621 100644 --- a/examples/examples_control_optimization/001_opt_yaw_single_ws.py +++ b/examples/examples_control_optimization/001_opt_yaw_single_ws.py @@ -8,6 +8,8 @@ import matplotlib.pyplot as plt import numpy as np +import floris.flow_visualization as flowviz +import floris.layout_visualization as layoutviz from floris import FlorisModel, TimeSeries from floris.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR @@ -63,4 +65,27 @@ ax.legend() ax.grid(True) +# Visualize results for a single wind direction (270 deg) and wind speed (8 m/s) +fig, axarr = plt.subplots(2, 1, figsize=(10, 5), sharex=False) +ax = axarr[0] # Baseline aligned operation +fmodel.reset_operation() +fmodel.set(wind_directions=[270.0], wind_speeds=[8.0], turbulence_intensities=[0.06]) +fmodel.run() +horizontal_plane = fmodel.calculate_horizontal_plane(height=90.0) +flowviz.visualize_cut_plane(horizontal_plane, ax=ax) +layoutviz.plot_turbine_rotors(fmodel, ax=ax) +ax.set_title("Turbines aligned") + +ax = axarr[1] # Optimized yaw angles +optimal_yaw_angles = ( + df_opt[(df_opt["wind_direction"] == 270.0) & (df_opt["wind_speed"] == 8.0)] + .yaw_angles_opt.values[0] +).reshape(1,-1) +fmodel.set(yaw_angles=optimal_yaw_angles) +fmodel.run() +horizontal_plane = fmodel.calculate_horizontal_plane(height=90.0) +flowviz.visualize_cut_plane(horizontal_plane, ax=ax) +layoutviz.plot_turbine_rotors(fmodel, ax=ax, yaw_angles=optimal_yaw_angles) +ax.set_title("Optimized yaw angles") + plt.show() diff --git a/examples/examples_control_optimization/005_optimize_yaw_aep_parallel.py b/examples/examples_control_optimization/005_optimize_yaw_aep_parallel.py index 17e02412b..d1688cb0e 100644 --- a/examples/examples_control_optimization/005_optimize_yaw_aep_parallel.py +++ b/examples/examples_control_optimization/005_optimize_yaw_aep_parallel.py @@ -16,14 +16,14 @@ import numpy as np from floris import ( - FlorisModel, - ParallelFlorisModel, + ParFlorisModel, TimeSeries, WindRose, ) +from floris.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR -# When using parallel optimization it is importat the "root" script include this +# When using parallel optimization it is important the "root" script include this # if __name__ == "__main__": block to avoid problems if __name__ == "__main__": @@ -33,19 +33,26 @@ ti_col_or_value=0.06 ) - # Load FLORIS - fmodel = FlorisModel("../inputs/gch.yaml") + # Load FLORIS as a parallel model + max_workers = 16 + pfmodel = ParFlorisModel( + "../inputs/gch.yaml", + max_workers=max_workers, + n_wind_condition_splits=max_workers, + interface="pathos", + print_timings=True, + ) # Specify wind farm layout and update in the floris object N = 2 # number of turbines per row and per column X, Y = np.meshgrid( - 5.0 * fmodel.core.farm.rotor_diameters_sorted[0][0] * np.arange(0, N, 1), - 5.0 * fmodel.core.farm.rotor_diameters_sorted[0][0] * np.arange(0, N, 1), + 5.0 * pfmodel.core.farm.rotor_diameters_sorted[0][0] * np.arange(0, N, 1), + 5.0 * pfmodel.core.farm.rotor_diameters_sorted[0][0] * np.arange(0, N, 1), ) - fmodel.set(layout_x=X.flatten(), layout_y=Y.flatten()) + pfmodel.set(layout_x=X.flatten(), layout_y=Y.flatten()) # Get the number of turbines - n_turbines = len(fmodel.layout_x) + n_turbines = len(pfmodel.layout_x) # Optimize the yaw angles. This could be done for every wind direction and wind speed # but in practice it is much faster to optimize only for one speed and infer the rest @@ -53,46 +60,25 @@ time_series = TimeSeries( wind_directions=wind_rose.wind_directions, wind_speeds=8.0, turbulence_intensities=0.06 ) - fmodel.set(wind_data=time_series) - - # Set up the parallel model - parallel_interface = "concurrent" - max_workers = 16 - pfmodel = ParallelFlorisModel( - fmodel=fmodel, - max_workers=max_workers, - n_wind_condition_splits=max_workers, - interface=parallel_interface, - print_timings=True, - ) + pfmodel.set(wind_data=time_series) - # Get the optimal angles using the parallel interface start_time = timerpc() - # Now optimize the yaw angles using the Serial Refine method - df_opt = pfmodel.optimize_yaw_angles( - minimum_yaw_angle=0.0, - maximum_yaw_angle=20.0, + yaw_opt = YawOptimizationSR( + fmodel=pfmodel, + minimum_yaw_angle=0.0, # Allowable yaw angles lower bound + maximum_yaw_angle=20.0, # Allowable yaw angles upper bound Ny_passes=[5, 4], - exclude_downstream_turbines=False, + exclude_downstream_turbines=True, ) + df_opt = yaw_opt.optimize() end_time = timerpc() t_tot = end_time - start_time print("Optimization finished in {:.2f} seconds.".format(t_tot)) - # Calculate the AEP in the baseline case, using the parallel interface - fmodel.set(wind_data=wind_rose) - pfmodel = ParallelFlorisModel( - fmodel=fmodel, - max_workers=max_workers, - n_wind_condition_splits=max_workers, - interface=parallel_interface, - print_timings=True, - ) - - # Note the pfmodel does not use run() but instead uses the get_farm_power() and get_farm_AEP() - # directly, this is necessary for the parallel interface - aep_baseline = pfmodel.get_farm_AEP(freq=wind_rose.unpack_freq()) + pfmodel.set(wind_data=wind_rose) + pfmodel.run() + aep_baseline = pfmodel.get_farm_AEP() # Now need to apply the optimal yaw angles to the wind rose to get the optimized AEP # do this by applying a rule of thumb where the optimal yaw is applied between 6 and 12 m/s @@ -102,9 +88,10 @@ # yaw angles will need to be n_findex long, and accounting for the fact that some wind # directions and wind speeds may not be present in the wind rose (0 frequency) and aren't # included in the fmodel - wind_directions = fmodel.wind_directions - wind_speeds = fmodel.wind_speeds - n_findex = fmodel.n_findex + # TODO: add operation wind rose to example, once built + wind_directions = pfmodel.wind_directions + wind_speeds = pfmodel.wind_speeds + n_findex = pfmodel.n_findex # Now define how the optimal yaw angles for 8 m/s are applied over the other wind speeds @@ -133,15 +120,9 @@ # Now apply the optimal yaw angles and get the AEP - fmodel.set(yaw_angles=yaw_angles_wind_rose) - pfmodel = ParallelFlorisModel( - fmodel=fmodel, - max_workers=max_workers, - n_wind_condition_splits=max_workers, - interface=parallel_interface, - print_timings=True, - ) - aep_opt = pfmodel.get_farm_AEP(freq=wind_rose.unpack_freq(), yaw_angles=yaw_angles_wind_rose) + pfmodel.set(yaw_angles=yaw_angles_wind_rose) + pfmodel.run() + aep_opt = pfmodel.get_farm_AEP() aep_uplift = 100.0 * (aep_opt / aep_baseline - 1) print("Baseline AEP: {:.2f} GWh.".format(aep_baseline/1E9)) diff --git a/examples/examples_layout_optimization/004_generate_gridded_layout.py b/examples/examples_layout_optimization/004_generate_gridded_layout.py new file mode 100644 index 000000000..649c474fa --- /dev/null +++ b/examples/examples_layout_optimization/004_generate_gridded_layout.py @@ -0,0 +1,37 @@ +"""Example: Gridded layout design +This example shows a layout optimization that places as many turbines as +possible into a given boundary using a gridded layout pattern. +""" + +import matplotlib.pyplot as plt +import numpy as np + +from floris import FlorisModel, WindRose +from floris.optimization.layout_optimization.layout_optimization_gridded import ( + LayoutOptimizationGridded, +) + + +if __name__ == '__main__': + # Load the Floris model + fmodel = FlorisModel('../inputs/gch.yaml') + + # Set the boundaries + # The boundaries for the turbines, specified as vertices + boundaries = [(0.0, 0.0), (0.0, 1000.0), (1000.0, 1000.0), (1000.0, 0.0), (0.0, 0.0)] + + # Set up the optimization object with 5D spacing + layout_opt = LayoutOptimizationGridded( + fmodel, + boundaries, + min_dist_D=5., # results in spacing of 5*125.88 = 629.4 m + min_dist=None, # Alternatively, can specify spacing directly in meters + ) + + layout_opt.optimize() + + # Note that the "initial" layout that is provided with the fmodel is + # not used by the layout optimization. + layout_opt.plot_layout_opt_results() + + plt.show() diff --git a/examples/examples_layout_optimization/005_layout_optimization_complex_boundary.py b/examples/examples_layout_optimization/005_layout_optimization_complex_boundary.py new file mode 100644 index 000000000..79bfcf2e7 --- /dev/null +++ b/examples/examples_layout_optimization/005_layout_optimization_complex_boundary.py @@ -0,0 +1,83 @@ +"""Example: Separated boundaries layout optimization +Demonstrates the capabilities of LayoutOptimizationGridded and +LayoutOptimizationRandomSearch to optimize turbine layouts with complex +boundaries. +""" + +import matplotlib.pyplot as plt +import numpy as np + +from floris import FlorisModel, WindRose +from floris.optimization.layout_optimization.layout_optimization_gridded import ( + LayoutOptimizationGridded, +) +from floris.optimization.layout_optimization.layout_optimization_random_search import ( + LayoutOptimizationRandomSearch, +) + + +if __name__ == '__main__': + # Load the Floris model + fmodel = FlorisModel('../inputs/gch.yaml') + + # Set the boundaries + # The boundaries for the turbines, specified as vertices + boundaries = [ + [(0.0, 0.0), (0.0, 1000.0), (1000.0, 1000.0), (1000.0, 0.0), (0.0, 0.0)], + [(1500.0, 0.0), (1500.0, 1000.0), (2500.0, 0.0), (1500.0, 0.0)], + ] + + # Set up the wind data information + wind_directions = np.arange(0, 360.0, 5.0) + np.random.seed(1) + wind_speeds = 8.0 + np.random.randn(1) * 0.0 + # Shape frequency distribution to match number of wind directions and wind speeds + freq = ( + np.abs( + np.sort( + np.random.randn(len(wind_directions)) + ) + ) + .reshape( ( len(wind_directions), len(wind_speeds) ) ) + ) + freq = freq / freq.sum() + # Set wind data in the FlorisModel + fmodel.set( + wind_data=WindRose( + wind_directions=wind_directions, + wind_speeds=wind_speeds, + freq_table=freq, + ti_table=0.06 + ) + ) + + # Begin by placing as many turbines as possible using a gridded layout at 6D spacing + layout_opt_gridded = LayoutOptimizationGridded( + fmodel, + boundaries, + min_dist_D=6., + min_dist=None, + ) + layout_opt_gridded.optimize() + print("Gridded layout complete.") + + # Set the layout on the fmodel + fmodel.set(layout_x=layout_opt_gridded.x_opt, layout_y=layout_opt_gridded.y_opt) + + # Update the layout using a random search optimization with 5D minimum spacing + layout_opt_rs = LayoutOptimizationRandomSearch( + fmodel, + boundaries, + min_dist_D=5., + seconds_per_iteration=10, + total_optimization_seconds=60., + use_dist_based_init=False, + ) + layout_opt_rs.optimize() + + layout_opt_rs.plot_layout_opt_results( + initial_locs_plotting_dict={"label": "Gridded initial layout"}, + final_locs_plotting_dict={"label": "Random search optimized layout"}, + ) + + plt.show() diff --git a/examples/examples_turbine/001_check_turbine.py b/examples/examples_turbine/001_check_turbine.py index 7291ca60c..52a879dab 100644 --- a/examples/examples_turbine/001_check_turbine.py +++ b/examples/examples_turbine/001_check_turbine.py @@ -43,6 +43,7 @@ for t in turbines: # Set t as the turbine fmodel.set(turbine_type=[t]) + fmodel.reset_operation() # Remove any previously applied yaw angles # Since we are changing the turbine type, make a matching change to the reference wind height fmodel.assign_hub_height_to_ref_height() @@ -80,6 +81,7 @@ wind_directions=wd_array, turbulence_intensities=turbulence_intensities, ) + fmodel.reset_operation() # Remove any previously applied yaw angles fmodel.run() turbine_powers = fmodel.get_turbine_powers().flatten() / 1e3 if density == 1.225: diff --git a/examples/examples_turbopark/001_compare_turbopark_implementations.py b/examples/examples_turbopark/001_compare_turbopark_implementations.py new file mode 100644 index 000000000..b462b3561 --- /dev/null +++ b/examples/examples_turbopark/001_compare_turbopark_implementations.py @@ -0,0 +1,190 @@ +"""Example: Compare TurbOPark model implementations +This example demonstrates a new implementation of the TurbOPark model that is +more faithful to the original description provided by Pedersen et al and uses +the sequential_solver, and compares it to the existing implementation in +Floris. +""" + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +import floris.flow_visualization as flowviz +from floris import FlorisModel, TimeSeries +from floris.turbine_library import build_cosine_loss_turbine_dict + + +# Note: "new" is used to refer to the new implementation of TurbOPark, which is +# more faithful to the description provided by Pedersen et al. (2022). "orig" +# is used to refer to the existing TurbOPark implementation in Floris (which +# was based on Ørsted's Matlab code, originally from Nygaard et al. (2020). + +### Build a constant CT turbine model for use in comparisons (not realistic) +const_CT_turb = build_cosine_loss_turbine_dict( + turbine_data_dict={ + "wind_speed":[0.0, 30.0], + "power":[0.0, 1.0], # Not realistic but won't be used here + "thrust_coefficient":[0.75, 0.75] + }, + turbine_name="ConstantCT", + rotor_diameter=120.0, + hub_height=100.0, + ref_tilt=0.0, +) + +### Start by visualizing a single turbine in and its wake with the new model +# Load the new TurboPark implementation and switch to constant CT turbine +fmodel_new = FlorisModel("../inputs/turboparkgauss_cubature.yaml") +fmodel_new.set(turbine_type=[const_CT_turb]) +fmodel_new.run() +u0 = fmodel_new.wind_speeds[0] + +col_orig = "C0" +col_new = "C1" + +# Get plane of points for visualization +rotor_diameter = 120.0 +x_resolution=1501 +y_resolution=201 +z_resolution=100 +x_bounds = [-5*rotor_diameter, 25*rotor_diameter] + +horizontal_plane = fmodel_new.calculate_horizontal_plane( + x_resolution=x_resolution, + y_resolution=y_resolution, + height=100.0, + x_bounds=x_bounds +) + +# Visualize the flows with a horizontal slice +fig, ax = plt.subplots(3,1) +fig.set_size_inches(7, 10) +flowviz.visualize_cut_plane( + horizontal_plane, + ax=ax[0], + label_contours=True, + title="Horizontal plane" +) +ax[0].set_xlabel("x [m]") +ax[0].set_ylabel("y [m]") + +# Get points and velocities, normalized by rotor diameter and freestream velocity +x_locs_norm = horizontal_plane.df.x1[:x_resolution]/rotor_diameter +y_locs_norm = horizontal_plane.df.x2[::x_resolution]/rotor_diameter +u_norm = horizontal_plane.df.u[150100:151601]/u0 + +# Plot downstream velocities +ax[1].plot(x_locs_norm, u_norm, color=col_new) +ax[1].set_xlabel("Downstream distance [D]") +ax[1].set_ylabel("Normalized velocity [-]") +ax[1].grid() +ax[1].set_xlim([x/rotor_diameter for x in x_bounds]) + +# Plot axial velocities at various downstream distances +for loc in np.append(251, np.linspace(350,750,5)): #range(200,1200,200): + u_norm = horizontal_plane.df.u[int(loc)::x_resolution]/u0 + alpha = 1.0 - (loc-250)/1000 + ax[2].plot(y_locs_norm, u_norm, label=str((loc-250)/50)+"D downstream", alpha=alpha, c=col_new) +ax[2].legend() +ax[2].set_xlabel("Radial distance [D]") +ax[2].set_ylabel("Normalized velocity [-]") +ax[2].grid() +ax[2].set_xlim([-2, 2]) + +### Look at the wake profile at a single downstream distance for a range of wind directions +# Load the original TurboPark implementation and switch to constant CT turbine +fmodel_orig = FlorisModel("../inputs/turbopark_cubature.yaml") +fmodel_orig.set(turbine_type=[const_CT_turb]) + +# Set up and solve flows +wd_array = np.arange(225,315,0.1) +wind_data_wd_sweep = TimeSeries( + wind_speeds=8.0, + wind_directions=wd_array, + turbulence_intensities=0.06 +) +fmodel_orig.set( + layout_x = [0.0, 600.0], + layout_y = [0.0, 0.0], + wind_data=wind_data_wd_sweep +) +fmodel_orig.run() + +# Extract output velocities at downstream turbine +orig_vels_ds = fmodel_orig.turbine_average_velocities[:,1] +u0 = fmodel_orig.wind_speeds[0] # Get freestream wind speed for normalization + +# Set up and solve flows; extract velocities at downstream turbine +fmodel_new.set( + layout_x = [0.0, 600.0], + layout_y = [0.0, 0.0], + wind_data=wind_data_wd_sweep +) +fmodel_new.run() +new_vels_ds = fmodel_new.turbine_average_velocities[:,1] + +# Load comparison data (generated by running Ørsted's Matlab code +# https://github.com/OrstedRD/TurbOPark) +df_twinpark = pd.read_csv("comparison_data/WindDirection_Sweep_Orsted.csv") + +# Plot the data and compare +fig, ax = plt.subplots(2, 1) +fig.set_size_inches(7, 10) +ax[0].plot(wd_array, orig_vels_ds/u0, label="Floris - TurbOPark", c=col_orig) +ax[0].plot(wd_array, new_vels_ds/u0, label="Floris - TurbOPark-Gauss", c=col_new) +df_twinpark.plot("wd", "wws", ax=ax[0], linestyle="--", color="k", label="Orsted - TurbOPark") + +ax[0].set_xlabel("Wind direction [deg]") +ax[0].set_ylabel("Normalized rotor averaged waked wind speed [-]") +ax[0].set_xlim(240,300) +ax[0].set_ylim(0.65,1.05) +ax[0].legend() +ax[0].grid() + +### Now, look at velocities along a row of ten turbines aligned with the flow +layout_x = np.linspace(0.0, 5400.0, 10) +layout_y = np.zeros_like(layout_x) +turbines = range(len(layout_x)) +wind_data_row = TimeSeries( + wind_speeds=np.array([8.0]), + wind_directions=270.0, + turbulence_intensities=0.06 +) +fmodel_orig.set( + layout_x=layout_x, + layout_y=layout_y, + wind_data=wind_data_row +) +fmodel_new.set( + layout_x=layout_x, + layout_y=layout_y, + wind_data=wind_data_row +) + +# Run and extract flow velocities at the turbines +fmodel_orig.run() +orig_vels_row = fmodel_orig.turbine_average_velocities +fmodel_new.run() +new_vels_row = fmodel_new.turbine_average_velocities +u0 = fmodel_orig.wind_speeds[0] # Get freestream wind speed for normalization + +# Load comparison data +df_rowpark = pd.read_csv("comparison_data/Rowpark_Orsted.csv") + +# Plot the data and compare +ax[1].scatter( + turbines, df_rowpark["wws"], s=80, marker="o", c="k", label="Orsted - TurbOPark" +) +ax[1].scatter( + turbines, orig_vels_row/u0, s=20, marker="o", c=col_orig, label="Floris - TurbOPark" +) +ax[1].scatter( + turbines, new_vels_row/u0, s=20, marker="o", c=col_new, label="Floris - TurbOPark_Gauss" +) +ax[1].set_xlabel("Turbine number") +ax[1].set_ylabel("Normalized rotor averaged wind speed [-]") +ax[1].set_ylim(0.25, 1.05) +ax[1].legend() +ax[1].grid() + +plt.show() diff --git a/examples/examples_turbopark/comparison_data/Rowpark_Orsted.csv b/examples/examples_turbopark/comparison_data/Rowpark_Orsted.csv new file mode 100644 index 000000000..439fb384c --- /dev/null +++ b/examples/examples_turbopark/comparison_data/Rowpark_Orsted.csv @@ -0,0 +1,11 @@ +wtg_nr,wws +1,1 +2,0.709920677983239 +3,0.615355749367675 +4,0.551410465937128 +5,0.502600655337247 +6,0.46316755609319 +7,0.430238792036599 +8,0.402137593655074 +9,0.377783142608699 +10,0.356429516711137 diff --git 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+308.7,1 +308.8,1 +308.9,1 +309,1 +309.1,1 +309.2,1 +309.3,1 +309.4,1 +309.5,1 +309.6,1 +309.7,1 +309.8,1 +309.9,1 +310,1 diff --git a/examples/examples_uncertain/003_uncertain_model_with_parallelization.py b/examples/examples_uncertain/003_uncertain_model_with_parallelization.py new file mode 100644 index 000000000..37f785c77 --- /dev/null +++ b/examples/examples_uncertain/003_uncertain_model_with_parallelization.py @@ -0,0 +1,85 @@ +"""Example: Uncertain Model With Parallelization + +This example demonstrates how to combined the parallelized model with the uncertain model +""" + +import matplotlib.pyplot as plt +import numpy as np + +from floris import ( + FlorisModel, + TimeSeries, + UncertainFlorisModel, +) +from floris.par_floris_model import ParFlorisModel + + +# Following the refactoring of ParFlorisModel, the UncertainFlorisModel can be +# parallelized by passing the ParFlorisModel as the model to be run. This example +# demonstrates the usage and shows that the result obtained from the UncertainFlorisModel +# with and without parallelization is the same. The results are compared to the nominal +# results. + +# Instantiate a FlorisModel and ParallelFlorisModel using the GCH model +fmodel = FlorisModel("../inputs/gch.yaml") +pfmodel = ParFlorisModel("../inputs/gch.yaml") + +# Use the above model to declare a serial and parallel UncertainFlorisModel +ufmodel = UncertainFlorisModel(fmodel) +pufmodel = UncertainFlorisModel(pfmodel) + + +# Define an inflow where wind direction is swept while +# wind speed and turbulence intensity are held constant +wind_directions = np.arange(240.0, 300.0, 1.0) +time_series = TimeSeries( + wind_directions=wind_directions, + wind_speeds=8.0, + turbulence_intensities=0.06, +) + +# Define a two turbine farm and apply the inflow +D = 126.0 +layout_x = np.array([0, D * 6]) +layout_y = [0, 0] + +# Apply to fmodel, ufmodel, and pufmodel +fmodel.set( + layout_x=layout_x, + layout_y=layout_y, + wind_data=time_series, +) + +ufmodel.set( + layout_x=layout_x, + layout_y=layout_y, + wind_data=time_series, +) + +pufmodel.set( + layout_x=layout_x, + layout_y=layout_y, + wind_data=time_series, +) + +# Run the models +fmodel.run() +ufmodel.run() +pufmodel.run() + +# Collect the farm power results from each model +farm_powers_nom = fmodel.get_farm_power() / 1e3 +farm_powers_unc = ufmodel.get_farm_power() / 1e3 +farm_powers_punc = pufmodel.get_farm_power() / 1e3 + +# Compare the results +fig, ax = plt.subplots() +ax.plot(wind_directions, farm_powers_nom.flatten(), 'k-', label="Nominal power") +ax.plot(wind_directions, farm_powers_unc.flatten(), 'bs-', label="Uncertain power") +ax.plot(wind_directions, farm_powers_punc.flatten(), 'r.--', label="Parallel uncertain power") +ax.grid(True) +ax.legend() +ax.set_xlabel("Wind Direction (deg)") +ax.set_ylabel("Power (kW)") + +plt.show() diff --git a/examples/examples_wind_data/001_wind_data_comparisons.py b/examples/examples_wind_data/001_wind_data_comparisons.py index 34009eade..d3887d9b5 100644 --- a/examples/examples_wind_data/001_wind_data_comparisons.py +++ b/examples/examples_wind_data/001_wind_data_comparisons.py @@ -56,7 +56,7 @@ # Plot the wind rose fig, ax = plt.subplots(subplot_kw={"polar": True}) -wind_rose.plot(ax=ax,legend_kwargs={"title": "WS"}) +wind_rose.plot(ax=ax,legend_kwargs={"label": "WS"}) fig.suptitle("WindRose Plot") # Now build a wind rose with turbulence intensity @@ -64,9 +64,9 @@ # Plot the wind rose with TI fig, axs = plt.subplots(2, 1, figsize=(6,8), subplot_kw={"polar": True}) -wind_ti_rose.plot(ax=axs[0], wind_rose_var="ws",legend_kwargs={"title": "WS"}) +wind_ti_rose.plot(ax=axs[0], wind_rose_var="ws",legend_kwargs={"label": "WS"}) axs[0].set_title("Wind Direction and Wind Speed Frequencies") -wind_ti_rose.plot(ax=axs[1], wind_rose_var="ti",legend_kwargs={"title": "TI"}) +wind_ti_rose.plot(ax=axs[1], wind_rose_var="ti",legend_kwargs={"label": "TI"}) axs[1].set_title("Wind Direction and Turbulence Intensity Frequencies") fig.suptitle("WindTIRose Plots") plt.tight_layout() diff --git a/examples/examples_wind_resource_grid/000_generate_example_wrg.py b/examples/examples_wind_resource_grid/000_generate_example_wrg.py new file mode 100644 index 000000000..0b68abcf3 --- /dev/null +++ b/examples/examples_wind_resource_grid/000_generate_example_wrg.py @@ -0,0 +1,227 @@ +"""Example: Generate Example WRG File + +This first example demonstrates the content and structure of a +Wind Resource Grid (WRG) file. + +WRG files are Wind Resource Grid files, and their structure is +defined here: +https://backend.orbit.dtu.dk/ws/portalfiles/portal/116352660/WAsP_10_Help_Facility.pdf + +In the script, a synthetic WRG file is derived using the WindRose class. + +""" + +import matplotlib.pyplot as plt +import numpy as np +from scipy.optimize import curve_fit + +from floris import WindRose + + +# Define a function given the distribution of wind speeds in one sector, +# compute the A and k parameters of the Weibull distribution +def weibull_func(U, A, k): + return (k / A) * (U / A) ** (k - 1) * np.exp(-((U / A) ** k)) + + +def estimate_weibull(U, freq): + # Normalize the frequency + freq = freq / freq.sum() + + # Fit the Weibull distribution + popt, _ = curve_fit(weibull_func, U, freq, p0=(6.0, 2.0)) + A_fit, k_fit = popt + + return A_fit, k_fit + + +################################################## +# Parameters +################################################## +# Top line parameters +Nx = 2 # Number of grid points in x +Ny = 3 # Number of grid points in y +Xmin = 0.0 # Minimum value of x (m) +Ymin = 0.0 # Minimum value of y (m) +cell_size = 1000.0 # Grid spacing (m) + +# Other fixed parameters +z_coord = 0.0 # z-coordinate of the grid +height_above_ground_level = 90.0 # Height above ground level +num_sectors = 12 # Number of direction sectors + + + +################################################## +# Generating data +################################################## +# The above parameters define a 3x3 grid of points. Let's start +# by assuming the point at (0,0) has the wind rose as +# defined in inputs/wind_rose.csv +wind_rose_base = WindRose.read_csv_long( + "../inputs/wind_rose.csv", wd_col="wd", ws_col="ws", freq_col="freq_val", ti_col_or_value=0.06 +) + +# Resample to number of sectors +wind_rose_base = wind_rose_base.aggregate(wd_step=360 / num_sectors) + +## Generate the other wind roses +# Assume that the wind roses at other points are generated by increasing +# the north winds with increasing y and east winds with increasing x +x_list = [] +y_list = [] +wind_rose_list = [] + +for xi in range(Nx): + for yi in range(Ny): + # Get the x and y locations for this point + x = Xmin + xi * cell_size + y = Ymin + yi * cell_size + x_list.append(x) + y_list.append(y) + + # Instantiate the wind rose object + wind_rose = WindRose.read_csv_long( + "../inputs/wind_rose.csv", + wd_col="wd", + ws_col="ws", + freq_col="freq_val", + ti_col_or_value=0.06, + ) + + # Resample to number of sectors + wind_rose = wind_rose.aggregate(wd_step=360 / num_sectors) + + # Copy the frequency table + freq_table = wind_rose.freq_table.copy() + + # How much to shift the wind rose for this location + percent_x = xi / (Nx - 1) + percent_y = yi / (Ny - 1) + + # East frequency scaling + east_row = freq_table[3, :] + shift_amount = percent_x * east_row[:5] * .9 + east_row[:5] = east_row[:5] - shift_amount + east_row[5:10] = east_row[5:10] + shift_amount + freq_table[3, :] = east_row + + # North frequency scaling + north_row = freq_table[0, :] + shift_amount = percent_y * north_row[:6] * .9 + north_row[:6] = north_row[:6] - shift_amount + north_row[6:12] = north_row[6:12] + shift_amount + freq_table[0, :] = north_row + + # Add to list + wind_rose_list.append( + WindRose( + wind_directions=wind_rose.wind_directions, + wind_speeds=wind_rose.wind_speeds, + ti_table=wind_rose.ti_table, + freq_table=freq_table, + ) + ) + +################################################## +# Show the wind roses in a grid +################################################## + +fig, axarr = plt.subplots(Nx, Ny, figsize=(12, 12), subplot_kw={"polar": True}) +axarr = axarr.flatten() + +for i, wind_rose in enumerate(wind_rose_list): + wind_rose.plot(ax=axarr[i], ws_step=5) + axarr[i].set_title(f"({x_list[i]}, {y_list[i]})") + +fig.suptitle("Wind Roses at Grid Points") + + +################################################## +# Demonstrate fitting the Weibull distribution +################################################## + +freq_test = wind_rose_list[0].freq_table[0, :] / wind_rose_list[0].freq_table[0, :].sum() +a_test, k_test = estimate_weibull(wind_rose_list[0].wind_speeds, freq_test) +print(f"A: {a_test}, k: {k_test}") + +fig, ax = plt.subplots(1, 1, figsize=(6, 3)) +ax.plot(wind_rose_list[0].wind_speeds, freq_test, label="Original") +ax.plot( + wind_rose_list[0].wind_speeds, + weibull_func(wind_rose_list[0].wind_speeds, a_test, k_test), + label="Fitted", +) +ax.legend() +ax.set_xlabel("Wind speed (m/s)") +ax.set_ylabel("Frequency") +ax.grid(True) + + +################################################## +# Write out the WRG file +################################################## + +# Open the file +with open("wrg_example.wrg", "w") as f: + # Write the top line of the file + f.write(f"{Nx} {Ny} {Xmin} {Ymin} {cell_size}\n") + + # Now loop over the points + for i in range(Nx * Ny): + # Initiate the line to write as 10 blank spaces + line = " " + + # Add the x-coodinate as a 10 character fixed width integer + line = line + f"{int(x_list[i]):10d}" + + # Add the y-coodinate as a 10 character fixed width integer + line = line + f"{int(y_list[i]):10d}" + + # Add the z-coodinate as a 10 character fixed width integer + line = line + f"{int(z_coord):8d}" + + # Add the height above ground level as a 10 character fixed width integer + line = line + f"{int(height_above_ground_level):5d}" + + # Get the wind rose for this point + wind_rose = wind_rose_list[i] + + # Get the frequency matrix and wind speed + freq_table = wind_rose.freq_table + wind_speeds = wind_rose.wind_speeds + wind_directions = wind_rose.wind_directions + + # Get the A and k parameters across all sectors + freq_table_ws = freq_table.sum(axis=0) + A, k = estimate_weibull(wind_speeds, freq_table_ws) + + # Write the A and k parameters + line = line + f"{A:5.1f}{k:6.2f}" + + # Add place holder 0 for the power density + line = line + f"{0:15d}" + + # Write the number of sectors + line = line + f"{num_sectors:3d}" + + # Get the frequency table across wind directions + freq_table_wd = freq_table.sum(axis=1) + + # Step through the sectors + for wd_idx in range(num_sectors): + # Write the probability for this sector + line = line + f"{int(1000*freq_table_wd[wd_idx]):4d}" + + # Get the A and k parameters for this sector + A, k = estimate_weibull(wind_speeds, freq_table[wd_idx, :]) + + # Write the A and k parameters + line = line + f"{int(A*10):4d}{int(k*100):5d}" + + # Write the line to the file + f.write(line + "\n") + + +# Show the plots +plt.show() diff --git a/examples/examples_wind_resource_grid/001_wind_rose_wrg.py b/examples/examples_wind_resource_grid/001_wind_rose_wrg.py new file mode 100644 index 000000000..1eccd1b4a --- /dev/null +++ b/examples/examples_wind_resource_grid/001_wind_rose_wrg.py @@ -0,0 +1,77 @@ +"""Example: WindRoseWRG + +`WindRoseWRG` is a type of WindData object, like `WindRose` and `TimeSeries`, that +is used to store wind data in a format that can be used by the FLORIS model. `WindRoseWRG` +is different that `WindRose` however because the internal data holds the information +of the WRG file and then a `WindRose` object is created for each turbine in a provided +layout. + +In this example the WRG file generated in the previous example is read in +using the `WindRoseWRG` object, and wind roses as points on the WRG grid, as will +as in-between interpolated points have wind roses calculated using the `get_wind_rose_at_point` +method. Finally, the wind roses are upsampled to 5 degree wind direction bins and plotted. + +""" +import matplotlib.pyplot as plt +import numpy as np + +from floris import WindRoseWRG + + +# Read the WRG file +wind_rose_wrg = WindRoseWRG("wrg_example.wrg") + +# Print some basic information +print(wind_rose_wrg) + +# The wind roses were set to have a higher concentration of faster north winds for +# increasing y, show that this is contained within the wind roses, even those interpolated +# between grid points +y_points_to_test = np.array([0, 500, 1000, 1500, 2000]) + +fig, axarr = plt.subplots(1, 5, figsize=(16, 5), subplot_kw={"polar": True}) + +for i in range(5): + wind_rose = wind_rose_wrg.get_wind_rose_at_point(0, y_points_to_test[i]) + wind_rose.plot(ax=axarr[i], ws_step=5) + if i %2 == 0: + axarr[i].set_title(f"y = {y_points_to_test[i]}") + else: + axarr[i].set_title(f"y = {y_points_to_test[i]}\n(Interpolated)") + +# Go through the axarr and delete the legends except for the middle +for ax in [axarr[0], axarr[1], axarr[3], axarr[4]]: + ax.legend().set_visible(False) + + +# Draw a horizontal line on each axis indicating the level of the lower wind speed +# bucket for the north wind from the first wind rose +for i in range(5): + axarr[i].axhline(y=0.036, color="red", alpha=0.5) + +fig.suptitle("Wind Roses at locations with increasing y. Note the location where the 5 m/s bin \ +transitions to 10 m/s for north wind at y = 0 is \nindicated by the red line to show \ +the increase in wind speed to the north as y increases.") + +# Since wind directions was not specified, the wind directions implied by the number of sectors +# in the WRG was used, however the wind directions can be set using the set_wind_directions method +# or passed in at initialization. Here we upsample from 12, 30-deg sectors, to 72 5-deg sectors +wind_rose_wrg.set_wd_step(5.0) + +fig, axarr = plt.subplots(1, 5, figsize=(16, 5), subplot_kw={"polar": True}) + +for i in range(5): + wind_rose = wind_rose_wrg.get_wind_rose_at_point(0, y_points_to_test[i]) + wind_rose.plot(ax=axarr[i], ws_step=5) + if i %2 == 0: + axarr[i].set_title(f"y = {y_points_to_test[i]}") + else: + axarr[i].set_title(f"y = {y_points_to_test[i]}\n(Interpolated)") + +# Go through the axarr and delete all the legends except for the middle +for ax in axarr: + ax.legend().set_visible(False) + +fig.suptitle('Wind roses with upsampling to 5-deg bins') + +plt.show() diff --git a/examples/examples_wind_resource_grid/002_set_floris_with_wrg.py b/examples/examples_wind_resource_grid/002_set_floris_with_wrg.py new file mode 100644 index 000000000..f763caf6b --- /dev/null +++ b/examples/examples_wind_resource_grid/002_set_floris_with_wrg.py @@ -0,0 +1,106 @@ +"""Example: Setting FLORIS with WindRoseWRG + +This example shows how to set a FLORIS model with a WindRoseWRG object. When a WindRoseWRG object +is set as the wind data in a FLORIS model, the wind roses for each turbine in the layout are +generated and stored in the WindRoseWRG object. The wind roses are then used to calculate the +expected turbine powers and farm power. +""" + +import matplotlib.pyplot as plt +import numpy as np + +from floris import ( + FlorisModel, + WindRoseWRG, +) + + +# Read the WRG file into a WindRoseWRG object +wind_rose_wrg = WindRoseWRG("wrg_example.wrg") + +# Print out some information about the +print(wind_rose_wrg) +print("=====================================") + +# Since we didn't specify the wind speeds or fixed ti, the default values are used +# and displayed in the above printout. Note the wrg file itself, does not specify +# turbulence intensity, or the wind speed bins FLORIS should use. These can be chosen +# at initialization of the WindRoseWRG object, or set later. We can update these values +# using the following methods +wind_rose_wrg.set_wind_speeds(np.arange(0, 26, 2.0)) # Use 2m/s steps +wind_rose_wrg.set_ti_table(0.07) # Use a fixed ti of 7% for all wind directions and wind speeds + + +# Select a turbine layout +# The original grid in example 000 was defined by having the wind rose rotate with increasing y +# and move to higher speeds with increasing x. Here we will define a turbine layout that +# has a line of turbine along the diagonals of the grid. +layout_x = np.array([0, 500, 1000]) +layout_y = np.array([0, 1000, 2000]) + +# Set the turbine layout in the WindRoseWRG object, note that until this is done, the +# wind_rose_wrg object contains no WindRose objects. +# Note that if the wind_rose_wrg is applied to a FlorisModel, the layout_x and layout_y +# will be set in the FlorisModel and the wind roses will be generated for each turbine +wind_rose_wrg.set_layout(layout_x, layout_y) + +# Plot the wind roses in the first figure +fig, axarr = plt.subplots(1, 3, figsize=(16, 5), subplot_kw={"polar": True}) +wind_rose_wrg.plot_wind_roses(axarr=axarr, ws_step=5) + +# Apply the wind_rose_wrg to a FlorisModel + +# Load the FLORIS model and set that layout +fmodel = FlorisModel("../inputs/gch.yaml") +fmodel.set(layout_x=layout_x, layout_y=layout_y) + +# Set the wind data as the wind_rose_wrg +fmodel.set(wind_data=wind_rose_wrg) + +# Run the model and get the expected turbine powers and farm power +fmodel.run() +expected_turbine_powers = fmodel.get_expected_turbine_powers() +expected_farm_power = fmodel.get_expected_farm_power() + +# Print the expected turbine powers, farm power, and the sum of the expected turbine powers +print("=====================================") +print("Expected turbine powers:", expected_turbine_powers) +print("Expected farm power:", expected_farm_power) +print("Sum of expected turbine powers:", expected_turbine_powers.sum()) +print("=====================================") + +# Now re-run using one of the turbine wind roses alone + +# Compare with the result if just using the first wind rose or the last wind rose +fmodel.set(wind_data=wind_rose_wrg.wind_roses[0]) +fmodel.run() +expected_turbine_powers_first = fmodel.get_expected_turbine_powers() + +fmodel.set(wind_data=wind_rose_wrg.wind_roses[-1]) +fmodel.run() +expected_turbine_powers_last = fmodel.get_expected_turbine_powers() + +# Print the results to show match of first and last turbine when using their respective wind roses +print("Expected turbine powers:") +print("All wind roses:", expected_turbine_powers) +print( + "First wind rose (1st power matches): (" + + str(expected_turbine_powers_first[0]) + + "), " + + str(expected_turbine_powers_first[1]) + + ", " + + str(expected_turbine_powers_first[2]) + + ")" +) +print( + "Last wind rose (Last power matches): " + + str(expected_turbine_powers_last[0]) + + ", " + + str(expected_turbine_powers_last[1]) + + ", (" + + str(expected_turbine_powers_last[2]) + + ")" +) + + +plt.show() diff --git a/examples/examples_wind_resource_grid/003_wrg_compar_layout_optimization.py b/examples/examples_wind_resource_grid/003_wrg_compar_layout_optimization.py new file mode 100644 index 000000000..2e30ba0e8 --- /dev/null +++ b/examples/examples_wind_resource_grid/003_wrg_compar_layout_optimization.py @@ -0,0 +1,217 @@ +"""Example: Layout optimization with WindRoseWRG comparison + +This example compares a layout optimization using a WindRoseWRG. In the example, two +turbine positions are optimized within a square grid. The optimization is run 3 times: + +1. Using a WindRoseWRG object generated using the example wrg file +2. Using a WindRose object created from the WindRoseWRG object +3. Using a WindRose object created from the WindRoseWRG object, but with the HeterogeneousMap + also generated by the WindRoseWRG object + + +Because the WRG file includes a speed-up for northern winds, optimizaitions (1) and (3) place both +turbines near the northern boundary of the grid, while optimization (2) places the turbines in the +furthest corners of the grid. The optimization illustrates that using the full WindRoseWRG object +produces a similar result to the WindRose object with the HeterogeneousMap, since they both +can represent the difference in resource for different locations. The HeterogeneousMap may have +advantage for larger cases since it is running with only 1 wind speed. For this example the results +and performance are similar. + +""" + +import matplotlib.pyplot as plt +import numpy as np + +from floris import ( + FlorisModel, + WindRoseWRG, +) +from floris.optimization.layout_optimization.layout_optimization_random_search import ( + LayoutOptimizationRandomSearch, +) + + +if __name__ == "__main__": + + # Parameters of layout optimization + seconds_per_iteration = 30.0 + total_optimization_seconds = 120.0 + min_dist_D = 3.0 + use_dist_based_init = False + + # Initialize the WindRoseWRG object with wind speeds every 2 m/s and fixed ti of 6%. Specify + # a wd_step of 4 degrees, which implies upsampling from wrg's 90 degree sectors to 12 + # degree sectors + wind_rose_wrg = WindRoseWRG( + "wrg_example.wrg", + wd_step=2.0, + wind_speeds=np.arange(0, 21, 1.0), # Use a sparser range of speeds + ti_table=0.06, + ) + + # Define an optimization boundary within the grid that is a square with a + # buffer to avoid the outer limits of the heterogeneous area + buffer = 100.0 + boundaries = [ + (buffer, buffer), + (1000 - buffer, buffer), + (1000 - buffer, 2000 - buffer), + (buffer, 2000 - buffer), + (buffer, buffer), + ] + + # Select and initial layout in the corners of the boundary + layout_x = np.array([500, 1000 - buffer]) + layout_y = np.array([900, 2000 - buffer]) + + ########################## + # Set up the FlorisModel + fmodel = FlorisModel("../inputs/gch.yaml") + + ########################## + # Use the get_heterogeneous_map method to generate a WindRose that represents + # the information in the WindRoseWRG, rather than a set of WindRose objects + # but as a single WindRose object (for one location) and a HeterogeneousMap + # the describes the speed up information per direction across the domain + # This will allow running the optimization for a single wind speed while still + # accounting for the difference in wind speeds in location by direction + wind_rose_het = wind_rose_wrg.get_heterogeneous_wind_rose( + fmodel=fmodel, + x_loc=0.0, + y_loc=0.0, + representative_wind_speed=9.0, + ) + + # Pull out the heterogeneous plot to show the underlying speedups + het_map = wind_rose_het.heterogeneous_map + wind_direction_to_plot = [0.0, 10.0, 45.0, 75.0, 90.0, 180.0] + + # Show the het_map for a few wind directions + fig, axarr = plt.subplots(1, len(wind_direction_to_plot), figsize=(16, 5)) + axarr = axarr.flatten() + for i, wd in enumerate(wind_direction_to_plot): + het_map.plot_single_speed_multiplier( + wind_direction=wd, + wind_speed=8.0, + ax=axarr[i], + show_colorbar=True, + ) + + axarr[i].set_title(f"Wind Direction: {wd}") + + # ########################## + # Run the optimization with the full WindRoseWRG first + fmodel.set(layout_x=layout_x, layout_y=layout_y, wind_data=wind_rose_wrg) + + # Set the layout optimization + layout_opt = LayoutOptimizationRandomSearch( + fmodel, + boundaries, + min_dist_D=min_dist_D, + seconds_per_iteration=seconds_per_iteration, + total_optimization_seconds=total_optimization_seconds, + use_dist_based_init=use_dist_based_init, + ) + + layout_opt.optimize() + x_initial, y_initial, x_opt_wrg, y_opt_wrg = layout_opt._get_initial_and_final_locs() + + # Grab the log array + objective_log_array_wrg = np.array(layout_opt.objective_candidate_log) + + # Normalize + objective_log_array_wrg = objective_log_array_wrg / np.max(objective_log_array_wrg) + + print("=====================================") + print("Objective log array (WRG):") + print(objective_log_array_wrg.shape) + print(objective_log_array_wrg) + + # ########################## + # Repeat using wind_rose_het + fmodel.set(layout_x=layout_x, layout_y=layout_y, wind_data=wind_rose_het) + + # Set the layout optimization + layout_opt = LayoutOptimizationRandomSearch( + fmodel, + boundaries, + min_dist_D=min_dist_D, + seconds_per_iteration=seconds_per_iteration, + total_optimization_seconds=total_optimization_seconds, + use_dist_based_init=use_dist_based_init, + ) + + layout_opt.optimize() + _, _, x_opt_het, y_opt_het = layout_opt._get_initial_and_final_locs() + + # Grab the log array + objective_log_array_het = np.array(layout_opt.objective_candidate_log) + + # Normalize + objective_log_array_het = objective_log_array_het / np.max(objective_log_array_het) + + # ########################## + # Repeat using single wind rose (without het) + wind_rose = wind_rose_wrg.get_wind_rose_at_point(0, 0) + fmodel = FlorisModel("../inputs/gch.yaml") + fmodel.set(layout_x=layout_x, layout_y=layout_y, wind_data=wind_rose) + + # Set the layout optimization + layout_opt = LayoutOptimizationRandomSearch( + fmodel, + boundaries, + min_dist_D=min_dist_D, + seconds_per_iteration=seconds_per_iteration, + total_optimization_seconds=total_optimization_seconds, + use_dist_based_init=use_dist_based_init, + ) + + layout_opt.optimize() + _, _, x_opt_wr, y_opt_wr = layout_opt._get_initial_and_final_locs() + + # Grab the log array + objective_log_array_wr = np.array(layout_opt.objective_candidate_log) + + # Normalize + objective_log_array_wr = objective_log_array_wr / np.max(objective_log_array_wr) + + fig, ax = plt.subplots(1, 1, figsize=(8, 8)) + layout_opt.plot_layout_opt_boundary(ax=ax) + ax.scatter(x_initial, y_initial, label="Initial Layout", s=80, color="k", marker="s") + ax.scatter( + x_opt_wr, y_opt_wr, label="Optimized Layout (Single Wind Rose)", s=60, color="b", marker="^" + ) + ax.scatter(x_opt_wrg, y_opt_wrg, label="Optimized Layout (WRG)", s=40, color="r", marker="o") + ax.scatter( + x_opt_het, + y_opt_het, + label="Optimized Layout (Single Wind Rose + Het)", + s=20, + color="g", + marker="h", + ) + ax.set_aspect('equal') + ax.legend() + + print("=====================================") + print("Objective log array (HET):") + print(objective_log_array_het.shape) + print(objective_log_array_het) + + fig, ax = plt.subplots(1, 1, figsize=(8, 8)) + for objective_log_array, label, color in zip( + [objective_log_array_wr, objective_log_array_wrg, objective_log_array_het], + ["WR", "WRG", "Het"], + ["b", "r", "g"], + ): + ax.plot( + np.arange(len(objective_log_array)), + np.log10(objective_log_array * 100.0), + label=label, + color=color, + ) + ax.set_xlabel("Iteration") + ax.set_ylabel("Objective") + ax.legend() + + plt.show() diff --git a/examples/examples_wind_resource_grid/wrg_example.wrg b/examples/examples_wind_resource_grid/wrg_example.wrg new file mode 100644 index 000000000..20707a2d0 --- /dev/null +++ b/examples/examples_wind_resource_grid/wrg_example.wrg @@ -0,0 +1,7 @@ +2 3 0.0 0.0 1000.0 + 0 0 0 90 9.5 2.25 0 12 116 106 273 86 93 228 61 76 220 54 74 220 66 79 220 121 98 244 177 107 279 84 89 232 43 70 195 36 75 188 53 100 201 98 111 267 + 0 1000 0 90 9.6 2.31 0 12 116 107 341 86 93 228 61 76 220 54 74 220 66 79 220 121 98 244 177 107 279 84 89 232 43 70 195 36 75 188 53 100 201 98 111 267 + 0 2000 0 90 9.7 2.36 0 12 116 106 409 86 93 228 61 76 220 54 74 220 66 79 220 121 98 244 177 107 279 84 89 232 43 70 195 36 75 188 53 100 201 98 111 267 + 1000 0 0 90 9.6 2.34 0 12 116 106 273 86 93 228 61 76 220 54 82 407 66 79 220 121 98 244 177 107 279 84 89 232 43 70 195 36 75 188 53 100 201 98 111 267 + 1000 1000 0 90 9.6 2.40 0 12 116 107 341 86 93 228 61 76 220 54 82 407 66 79 220 121 98 244 177 107 279 84 89 232 43 70 195 36 75 188 53 100 201 98 111 267 + 1000 2000 0 90 9.7 2.46 0 12 116 106 409 86 93 228 61 76 220 54 82 407 66 79 220 121 98 244 177 107 279 84 89 232 43 70 195 36 75 188 53 100 201 98 111 267 diff --git a/examples/inputs/turbopark_cubature.yaml b/examples/inputs/turbopark_cubature.yaml new file mode 100644 index 000000000..11805f0d4 --- /dev/null +++ b/examples/inputs/turbopark_cubature.yaml @@ -0,0 +1,59 @@ +name: Case TwinPark FLORIS v4.0 +description: Two aligned wind turbines with 5D spacing as currently implemented in v4.0 + +floris_version: v4.0 + +logging: + console: + enable: true + # Can be one of "CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG". + level: WARNING + file: + enable: false + # Can be one of "CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG". + level: WARNING + +solver: + type: turbine_cubature_grid # turbine_grid + turbine_grid_points: 6 + +farm: + layout_x: + - 0.0 + layout_y: + - 0.0 + turbine_type: # Constant CT turbine (TODO: implement in script?) + - nrel_5MW # Will be replaced in script + +# Configure the atmospheric conditions. +flow_field: + air_density: 1.225 + reference_wind_height: -1 + turbulence_intensities: + - 0.06 + wind_directions: + - 270.0 + wind_speeds: + - 8.0 + wind_shear: 0.0 # Needed to reproduce Pedersen et al's results with new model + wind_veer: 0.0 + +# Configure the wake model. +wake: + model_strings: + combination_model: sosfs + deflection_model: none + turbulence_model: none + velocity_model: turbopark # current TurboPark model in FLORIS + enable_secondary_steering: false + enable_yaw_added_recovery: false + enable_active_wake_mixing: false + enable_transverse_velocities: false + wake_velocity_parameters: # Parameters for the wake velocity deficit model + turbopark: + A: 0.04 + sigma_max_rel: 4.0 + wake_deflection_parameters: + none: + wake_turbulence_parameters: + none: diff --git a/examples/inputs/turboparkgauss.yaml b/examples/inputs/turboparkgauss.yaml new file mode 100644 index 000000000..f9274cb76 --- /dev/null +++ b/examples/inputs/turboparkgauss.yaml @@ -0,0 +1,63 @@ + +name: TurbOParkGauss +description: Three turbines using TurbOParkGauss model +floris_version: v4 + +logging: + console: + enable: false + level: WARNING + file: + enable: false + level: WARNING + +solver: + type: turbine_cubature_grid # turboparkgauss does not work with type: turbine_grid + turbine_grid_points: 4 # 4 is sufficient in nearly all cases + +farm: + layout_x: + - 0.0 + - 630.0 + - 1260.0 + layout_y: + - 0.0 + - 0.0 + - 0.0 + turbine_type: + - nrel_5MW + +flow_field: + air_density: 1.225 + reference_wind_height: -1 + turbulence_intensities: + - 0.06 + wind_directions: + - 270.0 + wind_shear: 0.12 + wind_speeds: + - 8.0 + wind_veer: 0.0 + +wake: + model_strings: + combination_model: sosfs + deflection_model: none + turbulence_model: none + velocity_model: turboparkgauss + + enable_secondary_steering: false + enable_yaw_added_recovery: false + enable_transverse_velocities: false + enable_active_wake_mixing: false + + wake_deflection_parameters: + none: + + wake_velocity_parameters: + turboparkgauss: + A: 0.04 + include_mirror_wake: true + + wake_turbulence_parameters: + none: diff --git a/examples/inputs/turboparkgauss_cubature.yaml b/examples/inputs/turboparkgauss_cubature.yaml new file mode 100644 index 000000000..05209fd92 --- /dev/null +++ b/examples/inputs/turboparkgauss_cubature.yaml @@ -0,0 +1,59 @@ +name: Case TwinPark new implementation +description: Two aligned wind turbines with 5D spacing under the new implementation + +floris_version: v4.0 + +logging: + console: + enable: true + # Can be one of "CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG". + level: WARNING + file: + enable: false + # Can be one of "CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG". + level: WARNING + +solver: + type: turbine_cubature_grid # turboparkgauss does not work with type: turbine_grid + turbine_grid_points: 6 # 4 is sufficient in nearly all cases + +farm: + layout_x: + - 0.0 + layout_y: + - 0.0 + turbine_type: # Constant CT turbine (TODO: implement in script?) + - nrel_5MW # Will be replaced in script + +# Configure the atmospheric conditions. +flow_field: + air_density: 1.225 + reference_wind_height: -1 + turbulence_intensities: + - 0.06 + wind_directions: + - 270.0 + wind_speeds: + - 8.0 + wind_shear: 0.0 # Needed to reproduce Pedersen et al's results + wind_veer: 0.0 + +# Configure the wake model. +wake: + model_strings: + combination_model: sosfs + deflection_model: none + turbulence_model: none + velocity_model: turboparkgauss # New model + enable_secondary_steering: false + enable_yaw_added_recovery: false + enable_active_wake_mixing: false + enable_transverse_velocities: false + wake_velocity_parameters: # Parameters for the wake velocity deficit model + turboparkgauss: + A: 0.04 + include_mirror_wake: true + wake_deflection_parameters: + none: + wake_turbulence_parameters: + none: diff --git a/floris/__init__.py b/floris/__init__.py index f3ef231a7..d97bb24eb 100644 --- a/floris/__init__.py +++ b/floris/__init__.py @@ -13,10 +13,12 @@ visualize_quiver, ) from .heterogeneous_map import HeterogeneousMap +from .par_floris_model import ParFlorisModel from .parallel_floris_model import ParallelFlorisModel from .uncertain_floris_model import ApproxFlorisModel, UncertainFlorisModel from .wind_data import ( TimeSeries, WindRose, + WindRoseWRG, WindTIRose, ) diff --git a/floris/core/core.py b/floris/core/core.py index 89af93bcf..d12005ba5 100644 --- a/floris/core/core.py +++ b/floris/core/core.py @@ -154,7 +154,7 @@ def steady_state_atmospheric_condition(self): vel_model = self.wake.model_strings["velocity_model"] - if vel_model in ["gauss", "cc", "turbopark", "jensen"] and \ + if vel_model not in ["empirical_gauss"] and \ self.farm.correct_cp_ct_for_tilt.any(): self.logger.warning( "The current model does not account for vertical wake deflection due to " + @@ -181,6 +181,10 @@ def steady_state_atmospheric_condition(self): self.wake ) elif vel_model=="turbopark": + self.logger.warning( + "The turbopark model has been superseded by the turboparkgauss model. We " + + "recommend using `velocity_model: turboparkgauss` instead." + ) turbopark_solver( self.farm, self.flow_field, @@ -251,7 +255,7 @@ def solve_for_points(self, x, y, z): if vel_model == "cc" or vel_model == "turbopark": raise NotImplementedError( "solve_for_points is currently only available with the "+\ - "gauss, jensen, and empirical_guass models." + "gauss, jensen, and empirical_gauss models." ) elif vel_model == "empirical_gauss": full_flow_empirical_gauss_solver(self.farm, self.flow_field, field_grid, self.wake) diff --git a/floris/core/farm.py b/floris/core/farm.py index 58b29637b..38235bc80 100644 --- a/floris/core/farm.py +++ b/floris/core/farm.py @@ -120,6 +120,10 @@ class Farm(BaseClass): internal_turbine_library: Path = field(init=False, default=default_turbine_library_path) + # Private attributes + _turbine_types: List = field(init=False, validator=iter_validator(list, str), factory=list) + _turbine_definition_cache: dict = field(init=False, factory=dict) + def __attrs_post_init__(self) -> None: # Turbine definitions can be supplied in three ways: # - A string selecting a turbine in the floris turbine library @@ -134,21 +138,28 @@ def __attrs_post_init__(self) -> None: # This allows to read the yaml input files once rather than every time they're given. # In other words, if the turbine type is already in the cache, skip that iteration of # the for-loop. - turbine_definition_cache = {} + for t in self.turbine_type: # If a turbine type is a dict, then it was either preprocessed by the yaml # library to resolve the "!include" or it was set in a script as a dict. In either case, # add an entry to the cache if isinstance(t, dict): - if t["turbine_type"] in turbine_definition_cache: - continue - turbine_definition_cache[t["turbine_type"]] = t + if t["turbine_type"] in self._turbine_definition_cache: + if self._turbine_definition_cache[t["turbine_type"]] == t: + continue # Skip t if already loaded + else: + raise ValueError( + "Two different turbine definitions have the same name: "\ + f"'{t['turbine_type']}'. "\ + "Please specify a unique 'turbine_type' for each turbine definition." + ) + self._turbine_definition_cache[t["turbine_type"]] = t # If a turbine type is a string, then it is expected in the internal or external # turbine library if isinstance(t, str): - if t in turbine_definition_cache: - continue + if t in self._turbine_definition_cache: + continue # Skip t if already loaded # Check if the file exists in the internal and/or external library internal_fn = (self.internal_turbine_library / t).with_suffix(".yaml") @@ -174,7 +185,7 @@ def __attrs_post_init__(self) -> None: f"The turbine type: {t} does not exist in either the internal or" " external turbine library." ) - turbine_definition_cache[t] = load_yaml(full_path) + self._turbine_definition_cache[t] = load_yaml(full_path) # Convert any dict entries in the turbine_type list to the type string. Since the # definition is saved above, we can make the whole list consistent now to use it @@ -184,23 +195,23 @@ def __attrs_post_init__(self) -> None: # types must be used. If we modify that directly and change its shape, recreating this # class with a different layout but not a new self.turbine_type could cause the data # to be out of sync. - _turbine_types = [ + self._turbine_types = [ copy.deepcopy(t["turbine_type"]) if isinstance(t, dict) else t for t in self.turbine_type ] # If 1 turbine definition is given, expand to N turbines; this covers a 1-turbine # farm and 1 definition for multiple turbines - if len(_turbine_types) == 1: - _turbine_types *= self.n_turbines + if len(self._turbine_types) == 1: + self._turbine_types *= self.n_turbines # Check that turbine definitions contain any v3 keys - for t in _turbine_types: - check_turbine_definition_for_v3_keys(turbine_definition_cache[t]) + for _, v in self._turbine_definition_cache.items(): + check_turbine_definition_for_v3_keys(v) # Map each turbine definition to its index in this list self.turbine_definitions = [ - copy.deepcopy(turbine_definition_cache[t]) for t in _turbine_types + copy.deepcopy(self._turbine_definition_cache[t]) for t in self._turbine_types ] @layout_x.validator @@ -285,7 +296,10 @@ def construct_turbine_correct_cp_ct_for_tilt(self): ) def construct_turbine_map(self): - self.turbine_map = [Turbine.from_dict(turb) for turb in self.turbine_definitions] + turbine_map_unique = { + k: Turbine.from_dict(v) for k, v in self._turbine_definition_cache.items() + } + self.turbine_map = [turbine_map_unique[k] for k in self._turbine_types] def construct_turbine_thrust_coefficient_functions(self): self.turbine_thrust_coefficient_functions = { diff --git a/floris/core/grid.py b/floris/core/grid.py index b20ca25ce..5e7353cd7 100644 --- a/floris/core/grid.py +++ b/floris/core/grid.py @@ -347,6 +347,17 @@ def set_grid(self) -> None: self.y_sorted = np.take_along_axis(_y, self.sorted_indices, axis=1) self.z_sorted = np.take_along_axis(_z, self.sorted_indices, axis=1) + # Now calculate grid coordinates in original frame (from 270 deg perspective) + self.x_sorted_inertial_frame, self.y_sorted_inertial_frame, self.z_sorted_inertial_frame = \ + reverse_rotate_coordinates_rel_west( + wind_directions=self.wind_directions, + grid_x=self.x_sorted, + grid_y=self.y_sorted, + grid_z=self.z_sorted, + x_center_of_rotation=self.x_center_of_rotation, + y_center_of_rotation=self.y_center_of_rotation, + ) + @classmethod def get_cubature_coefficients(cls, N: int): """ diff --git a/floris/core/wake.py b/floris/core/wake.py index fe2fa9c50..e58a85cb4 100644 --- a/floris/core/wake.py +++ b/floris/core/wake.py @@ -25,6 +25,7 @@ GaussVelocityDeficit, JensenVelocityDeficit, NoneVelocityDeficit, + TurboparkgaussVelocityDeficit, TurbOParkVelocityDeficit, ) @@ -53,6 +54,7 @@ "jensen": JensenVelocityDeficit, "turbopark": TurbOParkVelocityDeficit, "empirical_gauss": EmpiricalGaussVelocityDeficit, + "turboparkgauss": TurboparkgaussVelocityDeficit, }, } diff --git a/floris/core/wake_velocity/__init__.py b/floris/core/wake_velocity/__init__.py index dc1342f8a..07762a5cd 100644 --- a/floris/core/wake_velocity/__init__.py +++ b/floris/core/wake_velocity/__init__.py @@ -5,3 +5,4 @@ from floris.core.wake_velocity.jensen import JensenVelocityDeficit from floris.core.wake_velocity.none import NoneVelocityDeficit from floris.core.wake_velocity.turbopark import TurbOParkVelocityDeficit +from floris.core.wake_velocity.turboparkgauss import TurboparkgaussVelocityDeficit diff --git a/floris/core/wake_velocity/turboparkgauss.py b/floris/core/wake_velocity/turboparkgauss.py new file mode 100644 index 000000000..466684d3a --- /dev/null +++ b/floris/core/wake_velocity/turboparkgauss.py @@ -0,0 +1,142 @@ +# Copyright 2021 NREL + +# Licensed under the Apache License, Version 2.0 (the "License"); you may not +# use this file except in compliance with the License. You may obtain a copy of +# the License at http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations under +# the License. + +from typing import Any, Dict + +import numexpr as ne +import numpy as np +from attrs import define, field + +from floris.core import ( + BaseModel, + Farm, + FlowField, + Grid, + Turbine, +) +from floris.core.wake_velocity.gauss import gaussian_function +from floris.utilities import ( + cosd, + sind, + tand, +) + + +@define +class TurboparkgaussVelocityDeficit(BaseModel): + """ + Model based on the TurbOPark model with Gaussian wake profile. + For model details see: + Pedersen J G, Svensen E, Poulsen L, and Nygaard N G. "Turbulence Optimized + Park model with Gaussian wake profile." Journal of Physics: Conference + Series. Vol. 2265. No. 022063. IOP Publishing, 2020. + doi:10.1088/1742-6596/2265/2/022063 + """ + + A: float = field(default=0.04) + include_mirror_wake: bool = field(default=True) + + def prepare_function( + self, + grid: Grid, + flow_field: FlowField, + ) -> Dict[str, Any]: + + kwargs = { + "x": grid.x_sorted, + "y": grid.y_sorted, + "z": grid.z_sorted, + "u_initial": flow_field.u_initial_sorted, + "wind_veer": flow_field.wind_veer + } + return kwargs + + # @profile + def function( + self, + x_i: np.ndarray, + y_i: np.ndarray, + z_i: np.ndarray, + axial_induction_i: np.ndarray, + deflection_field_i: np.ndarray, + yaw_angle_i: np.ndarray, + turbulence_intensity_i: np.ndarray, + ct_i: np.ndarray, + hub_height_i: float, + rotor_diameter_i: np.ndarray, + # enforces the use of the below as keyword arguments and adherence to the + # unpacking of the results from prepare_function() + *, + x: np.ndarray, + y: np.ndarray, + z: np.ndarray, + u_initial: np.ndarray, + wind_veer: float, + ) -> None: + + # Initialize the velocity deficit array + velocity_deficit = np.zeros_like(u_initial) + + downstream_mask = (x - x_i >= self.NUM_EPS) + x_dist = (x - x_i) * downstream_mask / rotor_diameter_i + + # Characteristic wake widths from all turbines relative to turbine i + sigma = characteristic_wake_width( + x_dist, turbulence_intensity_i, ct_i, self.A + ) * rotor_diameter_i + + # Peak wake deficits + C = 1 - np.sqrt(np.clip(1 - ct_i / (8 * (sigma / rotor_diameter_i) ** 2), 0.0, 1.0)) + + r_dist = np.sqrt((y - y_i) ** 2 + (z - z_i) ** 2) + + # Compute deficits for real turbines and for mirrored (image) turbines + delta_real = (x_dist > 0) * gaussian_function(C, r_dist, 2, sigma) + if self.include_mirror_wake: + r_dist_image = np.sqrt((y - y_i) ** 2 + (z - 3*z_i) ** 2) + delta_image = (x_dist > 0) * gaussian_function(C, r_dist_image, 2, sigma) + delta = np.hypot(delta_real, delta_image) + else: # No mirror wakes + delta = delta_real + + velocity_deficit = np.nan_to_num(delta) + + return velocity_deficit + + +def characteristic_wake_width(x_D, ambient_TI, Cts, A): + # Parameter values taken from S. T. Frandsen, “Risø-R-1188(EN) Turbulence + # and turbulence generated structural loading in wind turbine clusters” + # Risø, Roskilde, Denmark, 2007. + c1 = 1.5 + c2 = 0.8 + + alpha = ambient_TI * c1 + beta = c2 * ambient_TI / np.sqrt(Cts) + + # Term for the initial width at the turbine location (denoted epsilon in Pedersen et al.) + # Saturate term in initial width to 3.0, as is done in Orsted Matlab code. + initial_width = 0.25 * np.sqrt(np.minimum(0.5 * (1 + np.sqrt(1 - Cts)) / np.sqrt(1 - Cts), 3.0)) + + # Term for the added width downstream of the turbine + added_width = A * ambient_TI / beta * ( + np.sqrt((alpha + beta * x_D) ** 2 + 1) + - np.sqrt(1 + alpha ** 2) + - np.log( + ((np.sqrt((alpha + beta * x_D) ** 2 + 1) + 1) * alpha) + / ((np.sqrt(1 + alpha ** 2) + 1) * (alpha + beta * x_D)) + ) + ) + + sigma_w_D = initial_width + added_width + + return sigma_w_D diff --git a/floris/floris_model.py b/floris/floris_model.py index 91b3c4cb1..09a5aa5d0 100644 --- a/floris/floris_model.py +++ b/floris/floris_model.py @@ -41,6 +41,7 @@ TimeSeries, WindDataBase, WindRose, + WindRoseWRG, WindTIRose, ) @@ -168,7 +169,32 @@ def _reinitialize( flow_field_dict = floris_dict["flow_field"] farm_dict = floris_dict["farm"] - # + ## Farm + if layout_x is not None: + farm_dict["layout_x"] = layout_x + if layout_y is not None: + farm_dict["layout_y"] = layout_y + if turbine_type is not None: + if reference_wind_height is None: + self.logger.warning( + "turbine_type has been changed without specifying a new " + +"reference_wind_height. reference_wind_height remains {0:.2f} m.".format( + flow_field_dict["reference_wind_height"] + ) + +f" Consider calling `{self.__class__.__name__}." + +"assign_hub_height_to_ref_height` to update the reference wind height to the " + +"turbine hub height." + ) + farm_dict["turbine_type"] = turbine_type + if turbine_library_path is not None: + farm_dict["turbine_library_path"] = turbine_library_path + + ## If layout is changed and self._wind_data is not None, update the layout in wind_data + if (layout_x is not None) or (layout_y is not None): + if self._wind_data is not None: + self._wind_data.set_layout(farm_dict["layout_x"], farm_dict["layout_y"]) + + # Wind data if ( (wind_directions is not None) or (wind_speeds is not None) @@ -185,14 +211,18 @@ def _reinitialize( self.logger.warning("Deleting stored wind_data information.") self._wind_data = None if wind_data is not None: - # Unpack wind data for reinitialization and save wind_data for use in output - ( - wind_directions, - wind_speeds, - turbulence_intensities, - heterogeneous_inflow_config, - ) = wind_data.unpack_for_reinitialize() - self._wind_data = wind_data + + # Set the wind data to the current layout + wind_data.set_layout(farm_dict["layout_x"], farm_dict["layout_y"]) + + # Unpack wind data for reinitialization and save wind_data for use in output + ( + wind_directions, + wind_speeds, + turbulence_intensities, + heterogeneous_inflow_config, + ) = wind_data.unpack_for_reinitialize() + self._wind_data = wind_data ## FlowField if wind_speeds is not None: @@ -225,15 +255,7 @@ def _reinitialize( flow_field_dict["heterogeneous_inflow_config"] = heterogeneous_inflow_config - ## Farm - if layout_x is not None: - farm_dict["layout_x"] = layout_x - if layout_y is not None: - farm_dict["layout_y"] = layout_y - if turbine_type is not None: - farm_dict["turbine_type"] = turbine_type - if turbine_library_path is not None: - farm_dict["turbine_library_path"] = turbine_library_path + if solver_settings is not None: floris_dict["solver"] = solver_settings @@ -532,7 +554,7 @@ def get_turbine_powers(self): turbine_powers = self._get_turbine_powers() if self.wind_data is not None: - if type(self.wind_data) is WindRose: + if isinstance(self.wind_data, (WindRose, WindRoseWRG)): turbine_powers_rose = np.full( (len(self.wind_data.wd_flat), self.core.farm.n_turbines), np.nan @@ -558,6 +580,80 @@ def get_turbine_powers(self): return turbine_powers + def get_expected_turbine_powers(self, freq=None): + """ + Compute the expected (mean) power of each turbine. + + Args: + freq (NDArrayFloat): NumPy array with shape + with the frequencies of each wind direction and + wind speed combination. freq is either a 1D array, + in which case the same frequencies are used for all + turbines, or a 2D array with shape equal to + (n_findex, n_turbines), in which case each turbine has a unique + set of frequencies (this is the case for example using + WindRoseByTurbine). + + These frequencies should typically sum across rows + up to 1.0 and are used to weigh the wind farm power for every + condition in calculating the wind farm's AEP. Defaults to None. + If None and a WindData object was supplied, the WindData object's + frequencies will be used. Otherwise, uniform frequencies are assumed + (i.e., a simple mean over the findices is computed). + """ + + turbine_powers = self._get_turbine_powers() + + if freq is None: + if self.wind_data is None: + freq = np.array([1.0/self.core.flow_field.n_findex]) + else: + freq = self.wind_data.unpack_freq() + + # If freq is 2d, then use the per turbine frequencies + if len(np.shape(freq)) == 2: + return np.nansum(np.multiply(freq, turbine_powers), axis=0) + else: + return np.nansum(np.multiply(freq.reshape(-1, 1), turbine_powers), axis=0) + + def _get_weighted_turbine_powers( + self, + turbine_weights=None, + use_turbulence_correction=False, + ): + if use_turbulence_correction: + raise NotImplementedError( + "Turbulence correction is not yet implemented in the power calculation." + ) + + # Confirm run() has been run + if self.core.state is not State.USED: + raise RuntimeError( + f"Can't run function `{self.__class__.__name__}.get_farm_power` without " + f"first running `{self.__class__.__name__}.run`." + ) + + if turbine_weights is None: + # Default to equal weighing of all turbines when turbine_weights is None + turbine_weights = np.ones( + ( + self.core.flow_field.n_findex, + self.core.farm.n_turbines, + ) + ) + elif len(np.shape(turbine_weights)) == 1: + # Deal with situation when 1D array is provided + turbine_weights = np.tile( + turbine_weights, + (self.core.flow_field.n_findex, 1), + ) + + # Calculate all turbine powers and apply weights + turbine_powers = self._get_turbine_powers() + turbine_powers = np.multiply(turbine_weights, turbine_powers) + + return turbine_powers + def _get_farm_power( self, turbine_weights=None, @@ -591,36 +687,12 @@ def _get_farm_power( Returns: float: Sum of wind turbine powers in W. """ - if use_turbulence_correction: - raise NotImplementedError( - "Turbulence correction is not yet implemented in the power calculation." - ) - # Confirm run() has been run - if self.core.state is not State.USED: - raise RuntimeError( - "Can't run function `FlorisModel.get_farm_power` without " - "first running `FlorisModel.run`." - ) - - if turbine_weights is None: - # Default to equal weighing of all turbines when turbine_weights is None - turbine_weights = np.ones( - ( - self.core.flow_field.n_findex, - self.core.farm.n_turbines, - ) - ) - elif len(np.shape(turbine_weights)) == 1: - # Deal with situation when 1D array is provided - turbine_weights = np.tile( - turbine_weights, - (self.core.flow_field.n_findex, 1), - ) - # Calculate all turbine powers and apply weights - turbine_powers = self._get_turbine_powers() - turbine_powers = np.multiply(turbine_weights, turbine_powers) + turbine_powers = self._get_weighted_turbine_powers( + turbine_weights=turbine_weights, + use_turbulence_correction=use_turbulence_correction + ) return np.sum(turbine_powers, axis=1) @@ -660,7 +732,7 @@ def get_farm_power( farm_power = self._get_farm_power(turbine_weights, use_turbulence_correction) if self.wind_data is not None: - if type(self.wind_data) is WindRose: + if isinstance(self.wind_data, (WindRose, WindRoseWRG)): farm_power_rose = np.full(len(self.wind_data.wd_flat), np.nan) farm_power_rose[self.wind_data.non_zero_freq_mask] = farm_power farm_power = farm_power_rose.reshape( @@ -710,15 +782,21 @@ def get_expected_farm_power( n_turbines). Defaults to None. """ - farm_power = self._get_farm_power(turbine_weights=turbine_weights) - if freq is None: if self.wind_data is None: freq = np.array([1.0/self.core.flow_field.n_findex]) else: freq = self.wind_data.unpack_freq() - return np.nansum(np.multiply(freq, farm_power)) + # If freq is 1d + if len(np.shape(freq)) == 1: + farm_power = self._get_farm_power(turbine_weights=turbine_weights) + return np.nansum(np.multiply(freq, farm_power)) + else: + weighted_turbine_powers = self._get_weighted_turbine_powers( + turbine_weights=turbine_weights, + ) + return np.nansum(np.multiply(freq, weighted_turbine_powers)) def get_farm_AEP( self, @@ -758,11 +836,7 @@ def get_farm_AEP( The Annual Energy Production (AEP) for the wind farm in watt-hours. """ - if ( - freq is None - and not isinstance(self.wind_data, WindRose) - and not isinstance(self.wind_data, WindTIRose) - ): + if freq is None and not isinstance(self.wind_data, (WindRose, WindRoseWRG, WindTIRose)): self.logger.warning( "Computing AEP with uniform frequencies. Results results may not reflect annual " "operation." @@ -821,24 +895,26 @@ def get_expected_farm_value( float: The expected value produced by the wind farm in units of value. """ - - farm_power = self._get_farm_power(turbine_weights=turbine_weights) - if freq is None: if self.wind_data is None: freq = np.array([1.0/self.core.flow_field.n_findex]) else: freq = self.wind_data.unpack_freq() - + # If freq is 1d + if len(np.shape(freq)) == 1: + farm_power = self._get_farm_power(turbine_weights=turbine_weights) + farm_power = np.multiply(freq, farm_power) + else: + weighted_turbine_powers = self._get_weighted_turbine_powers( + turbine_weights=turbine_weights + ) + farm_power = np.nansum(np.multiply(freq, weighted_turbine_powers), axis=1) if values is None: if self.wind_data is None: values = np.array([1.0]) else: values = self.wind_data.unpack_value() - - farm_value = np.multiply(values, farm_power) - - return np.nansum(np.multiply(freq, farm_value)) + return np.nansum(np.multiply(values, farm_power)) def get_farm_AVP( self, @@ -892,6 +968,7 @@ def get_farm_AVP( if ( freq is None and not isinstance(self.wind_data, WindRose) + and not isinstance(self.wind_data, WindRoseWRG) and not isinstance(self.wind_data, WindTIRose) ): self.logger.warning( diff --git a/floris/heterogeneous_map.py b/floris/heterogeneous_map.py index ea6edb963..137998cd7 100644 --- a/floris/heterogeneous_map.py +++ b/floris/heterogeneous_map.py @@ -3,6 +3,7 @@ import matplotlib.cm as cm import matplotlib.pyplot as plt import numpy as np +import pandas as pd import scipy.spatial._qhull from scipy.interpolate import LinearNDInterpolator, NearestNDInterpolator from scipy.spatial import ConvexHull @@ -154,10 +155,20 @@ def __str__(self) -> str: else: num_dim = 3 + # Make a pandas dataframe of the data + df = pd.DataFrame( + data=self.speed_multipliers, + index=self.wind_directions, + columns=list(range(len(self.x))) + ) + return ( f"HeterogeneousMap with {num_dim} dimensions\n" f"Speeds-up defined for {len(self.x)} points and\n" f"{self.speed_multipliers.shape[0]} wind conditions" + + f"\n\n{df}" + ) def get_heterogeneous_inflow_config( @@ -361,6 +372,7 @@ def plot_single_speed_multiplier( show_boundary: bool = True, show_wind_direction: bool = True, show_colorbar: bool = True, + show_points: bool = True, ): """ Plot the speed multipliers as a heatmap. @@ -383,6 +395,8 @@ def plot_single_speed_multiplier( show_wind_direction (bool, optional): Whether to show the wind direction as an arrow. Default is True. show_colorbar (bool, optional): Whether to show the colorbar. Default is True. + show_points (bool, optional): Whether to show the points of the heterogeneous inflow + configuration. Default is True. Returns: matplotlib.axes.Axes: The axes on which the speed multipliers are plotted. @@ -490,8 +504,8 @@ def plot_single_speed_multiplier( ) # Plot the grid coordinates as a scatter plot - ax.scatter(x, y, color="gray", marker=".", label="Heterogeneity Coordinates") - ax.set_xlim + if show_points: + ax.scatter(x, y, color="gray", marker=".", label="Heterogeneity Coordinates") # Show the boundary if show_boundary: diff --git a/floris/optimization/layout_optimization/layout_optimization_base.py b/floris/optimization/layout_optimization/layout_optimization_base.py index 99977c2f5..608c75c71 100644 --- a/floris/optimization/layout_optimization/layout_optimization_base.py +++ b/floris/optimization/layout_optimization/layout_optimization_base.py @@ -51,8 +51,8 @@ def __init__( b_depth = list_depth(boundaries) boundary_specification_error_msg = ( - "boundaries should be a list of coordinates (specifed as (x,y) "+\ - "tuples) or as a list of list of tuples (for seperable regions)." + "boundaries should be a list of coordinates (specified as (x,y) "+\ + "tuples) or as a list of list of tuples (for separable regions)." ) if b_depth == 1: @@ -130,7 +130,14 @@ def optimize(self): sol = self._optimize() return sol - def plot_layout_opt_results(self, plot_boundary_dict={}, ax=None, fontsize=16): + def plot_layout_opt_results( + self, + plot_boundary_dict={}, + initial_locs_plotting_dict={}, + final_locs_plotting_dict={}, + ax=None, + fontsize=16 + ): x_initial, y_initial, x_opt, y_opt = self._get_initial_and_final_locs() @@ -140,6 +147,7 @@ def plot_layout_opt_results(self, plot_boundary_dict={}, ax=None, fontsize=16): ax = fig.add_subplot(111) ax.set_aspect("equal") + # Handle default boundary plotting default_plot_boundary_dict = { "color":"None", "alpha":1, @@ -148,9 +156,30 @@ def plot_layout_opt_results(self, plot_boundary_dict={}, ax=None, fontsize=16): } plot_boundary_dict = {**default_plot_boundary_dict, **plot_boundary_dict} + # Handle default initial location plotting + default_initial_locs_plotting_dict = { + "marker":"o", + "color":"b", + "linestyle":"None", + "label":"Initial locations", + } + initial_locs_plotting_dict = { + **default_initial_locs_plotting_dict, + **initial_locs_plotting_dict + } + + # Handle default final location plotting + default_final_locs_plotting_dict = { + "marker":"o", + "color":"r", + "linestyle":"None", + "label":"New locations", + } + final_locs_plotting_dict = {**default_final_locs_plotting_dict, **final_locs_plotting_dict} + self.plot_layout_opt_boundary(plot_boundary_dict, ax=ax) - ax.plot(x_initial, y_initial, "ob", label="Initial locations") - ax.plot(x_opt, y_opt, "or", label="New locations") + ax.plot(x_initial, y_initial, **initial_locs_plotting_dict) + ax.plot(x_opt, y_opt, **final_locs_plotting_dict) ax.set_xlabel("x (m)", fontsize=fontsize) ax.set_ylabel("y (m)", fontsize=fontsize) ax.grid(True) diff --git a/floris/optimization/layout_optimization/layout_optimization_gridded.py b/floris/optimization/layout_optimization/layout_optimization_gridded.py new file mode 100644 index 000000000..f730c2287 --- /dev/null +++ b/floris/optimization/layout_optimization/layout_optimization_gridded.py @@ -0,0 +1,212 @@ +from __future__ import annotations + +import numpy as np + +from floris import FlorisModel + +from .layout_optimization_base import LayoutOptimization +from .layout_optimization_random_search import test_point_in_bounds + + +class LayoutOptimizationGridded(LayoutOptimization): + """ + Generates layouts that fit the most turbines arranged in a gridded + pattern into the given boundaries. The grid can be square (default) + or hexagonal. The layout is optimized by rotating and translating + the grid to maximize the number of turbines that fit within the + boundaries. Note that no wake or AEP calculations are performed in + determining the maximum number of turbines that fit within the + boundary. + """ + def __init__( + self, + fmodel: FlorisModel, + boundaries: list[tuple[float, float] | list[tuple[float, float]]], + min_dist: float | None = None, + min_dist_D: float | None = -1, + rotation_step: float = 5.0, + rotation_range: tuple[float, float] = (0.0, 360.0), + translation_step: float | None = None, + translation_step_D: float | None = -1, + translation_range: tuple[float, float] | None = None, + hexagonal_packing: bool = False, + ): + """ + Initialize the LayoutOptimizationGridded object. + + Args: + fmodel: FlorisModel, mostly used to obtain rotor diameter for spacing + boundaries: List of boundary vertices. Specified as a list of two-tuples (x,y), + or a list of lists of two-tuples if there are multiple separate boundary areas. + min_dist: Minimum distance between turbines in meters. Defaults to None, which results + in 5D spacing if min_dist_D is not defined. + min_dist_D: Minimum distance between turbines in terms of rotor diameters. If specified + as a negative number, will result in 5D spacing using the first turbine diameter + found on the fmodel. Defaults to -1, which results in 5D spacing if min_dist is not + defined. + rotation_step: Step size for grid rotations in degrees. Defaults to 5.0. + rotation_range: Range of possible rotation in degrees. Defaults to (0.0, 360.0). + translation_step: Step size for translation in meters. Defaults to None, which results + in 1D translations if translation_step_D is not defined. + translation_step_D: Step size for translation in terms of rotor diameters. If specified + as a negative number, will result in 1D translation steps using the first turbine + diameter found on the fmodel. Defaults to -1, which results in 1D steps if + translation_step is not defined. + translation_range: Range of translation in meters. Defaults to None, which results in + a range of (0, min_dist). + hexagonal_packing: Use hexagonal packing instead of square grid. Defaults to False. + """ + + # Handle spacing information + if min_dist is not None and min_dist_D is not None and min_dist_D >= 0: + raise ValueError("Only one of min_dist and min_dist_D can be defined.") + if min_dist is None and min_dist_D is None: + raise ValueError("Either min_dist or min_dist_D must be defined.") + if min_dist_D is not None and min_dist is None: + if min_dist_D < 0: # Default to 5D + min_dist_D = 5.0 + min_dist = min_dist_D * fmodel.core.farm.rotor_diameters.flat[0] + if len(np.unique(fmodel.core.farm.rotor_diameters)) > 1: + self.logger.warning(( + "Found multiple turbine diameters. Using diameter of first turbine to set" + f" min_dist to {min_dist}m ({min_dist_D} diameters)." + )) + + # Similar for translation step + if (translation_step is not None + and translation_step_D is not None + and translation_step_D >= 0 + ): + raise ValueError("Only one of translation_step and translation_step_D can be defined.") + if translation_step is None and translation_step_D is None: + raise ValueError("Either translation_step or translation_step_D must be defined.") + if translation_step_D is not None and translation_step is None: + if translation_step_D < 0: # Default to 1D + translation_step_D = 1.0 + translation_step = translation_step_D * fmodel.core.farm.rotor_diameters.flat[0] + if len(np.unique(fmodel.core.farm.rotor_diameters)) > 1: + self.logger.warning(( + "Found multiple turbine diameters. Using diameter of first turbine to set" + f" translation step to {translation_step}m ({translation_step_D} diameters)." + )) + + # Initialize the base class + super().__init__( + fmodel, + boundaries, + min_dist=min_dist, + enable_geometric_yaw=False, + use_value=False, + ) + + # Initial locations not used for optimization, but may be useful + # for comparison + self.x0 = fmodel.layout_x + self.y0 = fmodel.layout_y + + # Create the default grid + + # use min_dist, hexagonal packing, and boundaries to create a grid. + d = 1.1 * np.sqrt((self.xmax - self.xmin)**2 + (self.ymax - self.ymin)**2) + grid_1D = np.arange(0, d+min_dist, min_dist) + if hexagonal_packing: + x_locs = np.tile(grid_1D.reshape(1,-1), (len(grid_1D), 1)) + x_locs[np.arange(1, len(grid_1D), 2), :] += 0.5 * min_dist + y_locs = np.tile(np.sqrt(3) / 2 * grid_1D.reshape(-1,1), (1, len(grid_1D))) + else: + x_locs, y_locs = np.meshgrid(grid_1D, grid_1D) + x_locs = x_locs.flatten() - np.mean(x_locs) + 0.5*(self.xmax + self.xmin) + y_locs = y_locs.flatten() - np.mean(y_locs) + 0.5*(self.ymax + self.ymin) + + # Trim to a circle to avoid wasted computation + x_locs_grid, y_locs_grid = self.trim_to_circle( + x_locs, + y_locs, + (grid_1D.max()-grid_1D.min()+min_dist)/2 + ) + self.xy_grid = np.concatenate( + [x_locs_grid.reshape(-1,1), y_locs_grid.reshape(-1,1)], + axis=1 + ) + + # Limit the rotation range if grid has symmetry + if hexagonal_packing: + # Hexagonal packing has 60 degree symmetry + rotation_range = ( + rotation_range[0], + np.minimum(rotation_range[1], rotation_range[0]+60) + ) + else: + # Square grid has 90 degree symmetry + rotation_range = ( + rotation_range[0], + np.minimum(rotation_range[1], rotation_range[0]+90) + ) + + # Deal with None translation_range + if translation_range is None: + translation_range = (0.0, min_dist) + + # Create test rotations and translations + self.rotations = np.arange(rotation_range[0], rotation_range[1], rotation_step) + self.translations = np.arange(translation_range[0], translation_range[1], translation_step) + self.translations = np.concatenate([-np.flip(self.translations), self.translations]) + + def optimize(self): + + # Sweep over rotations and translations to find the best layout + n_rots = len(self.rotations) + n_trans = len(self.translations) + n_tot = n_rots * n_trans**2 + + # There are a total of n_rots x n_trans x n_trans layouts to test + rots_rad = np.radians(self.rotations) + rotation_matrices = np.array( + [ + [np.cos(rots_rad), -np.sin(rots_rad)], + [np.sin(rots_rad), np.cos(rots_rad)] + ] + ).transpose(2,0,1) + + translations_x, translations_y = np.meshgrid(self.translations, self.translations) + translation_matrices = np.concatenate( + [translations_x.reshape(-1,1), translations_y.reshape(-1,1)], + axis=1 + ) + + rotations_all = np.tile(rotation_matrices, (n_trans**2, 1, 1)) + translations_all = np.repeat(translation_matrices, n_rots, axis=0)[:,None,:] + + # Create candidate layouts [(n_rots x n_trans x n_trans) x n_turbines x 2] + candidate_layouts = np.einsum('ijk,lk->ilj', rotations_all, self.xy_grid) + translations_all + + # For each candidate layout, check how many turbines are in bounds + turbines_in_bounds = np.zeros(n_tot) + for i in range(n_tot): + turbines_in_bounds[i] = np.sum( + [test_point_in_bounds(xy[0], xy[1], self._boundary_polygon) for + xy in candidate_layouts[i, :, :]] + ) + idx_max = np.argmax(turbines_in_bounds) # First maximizing index returned + + # Get the best layout + x_opt_all = candidate_layouts[idx_max, :, 0] + y_opt_all = candidate_layouts[idx_max, :, 1] + mask_in_bounds = [test_point_in_bounds(x, y, self._boundary_polygon) for + x, y in zip(x_opt_all, y_opt_all)] + + # Save best layout, along with the number of turbines in bounds, and return + self.n_turbines_max = round(turbines_in_bounds[idx_max]) + self.x_opt = x_opt_all[mask_in_bounds] + self.y_opt = y_opt_all[mask_in_bounds] + return self.n_turbines_max, self.x_opt, self.y_opt + + def _get_initial_and_final_locs(self): + return self.x0, self.y0, self.x_opt, self.y_opt + + @staticmethod + def trim_to_circle(x_locs, y_locs, radius): + center = np.array([0.5*(x_locs.max() + x_locs.min()), 0.5*(y_locs.max() + y_locs.min())]) + xy = np.concatenate([x_locs.reshape(-1,1), y_locs.reshape(-1,1)], axis=1) + mask = np.linalg.norm(xy - center, axis=1) <= radius + return x_locs[mask], y_locs[mask] diff --git a/floris/par_floris_model.py b/floris/par_floris_model.py new file mode 100644 index 000000000..bfb60b300 --- /dev/null +++ b/floris/par_floris_model.py @@ -0,0 +1,366 @@ +from __future__ import annotations + +import copy +from pathlib import Path +from time import perf_counter as timerpc + +import numpy as np + +from floris.core import State +from floris.floris_model import FlorisModel + + +class ParFlorisModel(FlorisModel): + """ + This class mimics the FlorisModel, but enables parallelization of the main + computational effort. + """ + + def __init__( + self, + configuration: dict | str | Path | FlorisModel, + interface: str | None = "multiprocessing", + max_workers: int = -1, + n_wind_condition_splits: int = -1, + return_turbine_powers_only: bool = False, + print_timings: bool = False + ): + """ + Initialize the ParFlorisModel object. + + Args: + configuration: The Floris configuration dictionary or YAML file, or an instantiated + FlorisModel object. The configuration should have the following inputs specified. + - **flow_field**: See `floris.simulation.flow_field.FlowField` for more details. + - **farm**: See `floris.simulation.farm.Farm` for more details. + - **turbine**: See `floris.simulation.turbine.Turbine` for more details. + - **wake**: See `floris.simulation.wake.WakeManager` for more details. + - **logging**: See `floris.simulation.core.Core` for more details. + interface: The parallelization interface to use. Options are "multiprocessing", + "pathos", and "concurrent", with possible future support for "mpi4py" + max_workers: The maximum number of workers to use. Defaults to -1, which then + takes the number of CPUs available. + n_wind_condition_splits: The number of wind conditions to split the simulation over. + Defaults to the same as max_workers. + return_turbine_powers_only: Whether to return only the turbine powers. + print_timings (bool): Print the computation time to the console. Defaults to False. + """ + # Instantiate the underlying FlorisModel + if isinstance(configuration, FlorisModel): + configuration_dict = configuration.core.as_dict() + super().__init__(configuration_dict) + # Copy over any control setpoints, wind data, if not already done. + self.set( + yaw_angles=configuration.core.farm.yaw_angles, + power_setpoints=configuration.core.farm.power_setpoints, + awc_modes=configuration.core.farm.awc_modes, + awc_amplitudes=configuration.core.farm.awc_amplitudes, + awc_frequencies=configuration.core.farm.awc_frequencies, + wind_data=configuration.wind_data, + ) + else: + super().__init__(configuration) + + # Save parallelization parameters + if interface == "multiprocessing": + import multiprocessing as mp + self._PoolExecutor = mp.Pool + if max_workers == -1: + max_workers = mp.cpu_count() + # TODO: test spinning up the worker pool at this point + elif interface == "pathos": + import pathos + if max_workers == -1: + max_workers = pathos.helpers.cpu_count() + self.pathos_pool = pathos.pools.ProcessPool(nodes=max_workers) + elif interface == "concurrent": + from concurrent.futures import ProcessPoolExecutor + if max_workers == -1: + from multiprocessing import cpu_count + max_workers = cpu_count() + self._PoolExecutor = ProcessPoolExecutor + elif interface in ["mpi4py"]: + raise NotImplementedError( + f"Parallelization interface {interface} not yet supported." + ) + elif interface is None: + self.logger.warning( + "No parallelization interface specified. Running in serial mode." + ) + if return_turbine_powers_only: + self.logger.warn( + "return_turbine_powers_only is not supported in serial mode." + ) + else: + raise ValueError( + f"Invalid parallelization interface {interface}. " + "Options are 'multiprocessing', 'pathos', or 'concurrent'." + ) + + self._interface = interface + self.max_workers = max_workers + if n_wind_condition_splits == -1: + self.n_wind_condition_splits = max_workers + else: + self.n_wind_condition_splits = n_wind_condition_splits + self.return_turbine_powers_only = return_turbine_powers_only + self.print_timings = print_timings + + def run(self) -> None: + """ + Run the FLORIS model in parallel. + """ + + if self.return_turbine_powers_only: + # TODO: code here that does not return flow fields + # Somehow, overload methods on FlorisModel that need flow field + # data. + + # This version will call super().get_turbine_powers() on each of + # the splits, and return them somehow. + self._stored_turbine_powers = None # Temporary + if self.interface is None: + t0 = timerpc() + super().run() + t1 = timerpc() + elif self.interface == "multiprocessing": + t0 = timerpc() + self.core.initialize_domain() + parallel_run_inputs = self._preprocessing() + t1 = timerpc() + if self.return_turbine_powers_only: + with self._PoolExecutor(self.max_workers) as p: + self._turbine_powers_split = p.starmap( + _parallel_run_powers_only, + parallel_run_inputs + ) + else: + with self._PoolExecutor(self.max_workers) as p: + self._fmodels_split = p.starmap(_parallel_run, parallel_run_inputs) + t2 = timerpc() + self._postprocessing() + self.core.farm.finalize(self.core.grid.unsorted_indices) + self.core.state = State.USED + t3 = timerpc() + elif self.interface == "pathos": + t0 = timerpc() + self.core.initialize_domain() + parallel_run_inputs = self._preprocessing() + t1 = timerpc() + if self.return_turbine_powers_only: + self._turbine_powers_split = self.pathos_pool.map( + _parallel_run_powers_only_map, + parallel_run_inputs + ) + else: + self._fmodels_split = self.pathos_pool.map( + _parallel_run_map, + parallel_run_inputs + ) + t2 = timerpc() + self._postprocessing() + self.core.farm.finalize(self.core.grid.unsorted_indices) + self.core.state = State.USED + t3 = timerpc() + elif self.interface == "concurrent": + t0 = timerpc() + self.core.initialize_domain() + parallel_run_inputs = self._preprocessing() + t1 = timerpc() + if self.return_turbine_powers_only: + with self._PoolExecutor(self.max_workers) as p: + self._turbine_powers_split = p.map( + _parallel_run_powers_only_map, + parallel_run_inputs + ) + self._turbine_powers_split = list(self._turbine_powers_split) + else: + with self._PoolExecutor(self.max_workers) as p: + self._fmodels_split = p.map( + _parallel_run_map, + parallel_run_inputs + ) + self._fmodels_split = list(self._fmodels_split) + t2 = timerpc() + self._postprocessing() + self.core.farm.finalize(self.core.grid.unsorted_indices) + self.core.state = State.USED + t3 = timerpc() + if self.print_timings: + print("===============================================================================") + if self.interface is None: + print(f"Total time spent for serial calculation (interface=None): {t1 - t0:.3f} s") + else: + print( + "Total time spent for parallel calculation " + f"({self.max_workers} workers): {t3-t0:.3f} s" + ) + print(f" Time spent in parallel preprocessing: {t1-t0:.3f} s") + print(f" Time spent in parallel loop execution: {t2-t1:.3f} s.") + print(f" Time spent in parallel postprocessing: {t3-t2:.3f} s") + + def _preprocessing(self): + """ + Prepare the input arguments for parallel execution. + """ + + # Split over the wind conditions + n_wind_condition_splits = self.n_wind_condition_splits + n_wind_condition_splits = np.min( + [n_wind_condition_splits, self.core.flow_field.n_findex] + ) + + # Prepare the input arguments for parallel execution + fmodel_dict = self.core.as_dict() + wind_condition_id_splits = np.array_split( + np.arange(self.core.flow_field.n_findex), + n_wind_condition_splits, + ) + multiargs = [] + for wc_id_split in wind_condition_id_splits: + # for ws_id_split in wind_speed_id_splits: + fmodel_dict_split = copy.deepcopy(fmodel_dict) + wind_directions = self.core.flow_field.wind_directions[wc_id_split] + wind_speeds = self.core.flow_field.wind_speeds[wc_id_split] + turbulence_intensities = self.core.flow_field.turbulence_intensities[wc_id_split] + + # Extract and format all control setpoints as a dict that can be unpacked later + control_setpoints_subset = { + "yaw_angles": self.core.farm.yaw_angles[wc_id_split, :], + "power_setpoints": self.core.farm.power_setpoints[wc_id_split, :], + "awc_modes": self.core.farm.awc_modes[wc_id_split, :], + "awc_amplitudes": self.core.farm.awc_amplitudes[wc_id_split, :], + "awc_frequencies": self.core.farm.awc_frequencies[wc_id_split, :], + } + fmodel_dict_split["flow_field"]["wind_directions"] = wind_directions + fmodel_dict_split["flow_field"]["wind_speeds"] = wind_speeds + fmodel_dict_split["flow_field"]["turbulence_intensities"] = turbulence_intensities + + # Prepare lightweight data to pass along + multiargs.append((fmodel_dict_split, control_setpoints_subset)) + + return multiargs + + def _postprocessing(self): + # Append the remaining flow_fields + # Could consider adding a merge method to the FlowField class + # to make this easier + + if self.return_turbine_powers_only: + self._stored_turbine_powers = np.vstack(self._turbine_powers_split) + else: + # Ensure fields to set have correct dimensions + self.core.flow_field.u = self._fmodels_split[0].core.flow_field.u + self.core.flow_field.v = self._fmodels_split[0].core.flow_field.v + self.core.flow_field.w = self._fmodels_split[0].core.flow_field.w + self.core.flow_field.turbulence_intensity_field = \ + self._fmodels_split[0].core.flow_field.turbulence_intensity_field + + for fm in self._fmodels_split[1:]: + self.core.flow_field.u = np.append( + self.core.flow_field.u, + fm.core.flow_field.u, + axis=0 + ) + self.core.flow_field.v = np.append( + self.core.flow_field.v, + fm.core.flow_field.v, + axis=0 + ) + self.core.flow_field.w = np.append( + self.core.flow_field.w, + fm.core.flow_field.w, + axis=0 + ) + self.core.flow_field.turbulence_intensity_field = np.append( + self.core.flow_field.turbulence_intensity_field, + fm.core.flow_field.turbulence_intensity_field, + axis=0 + ) + + def _get_turbine_powers(self): + """ + Calculates the power at each turbine in the wind farm. + This override will only be necessary if we want to be able to + use the return_turbine_powers_only option. Need to check if that + makes a significant speed difference. + + Returns: + NDArrayFloat: Powers at each turbine. + """ + if self.core.state is not State.USED: + self.logger.warning( + f"Please call `{self.__class__.__name__}.run` before computing" + " turbine powers. In future versions, an explicit run() call will" + "be required." + ) + self.run() + if self.return_turbine_powers_only: + return self._stored_turbine_powers + else: + return super()._get_turbine_powers() + + @property + def fmodel(self): + """ + Raise deprecation warning. + """ + self.logger.warning( + "ParFlorisModel no longer contains `fmodel` as an attribute " + "and now directly inherits from FlorisModel. Please use the " + "attributes and methods of FlorisModel directly." + ) + + @property + def interface(self): + """ + The parallelization interface used. + """ + return self._interface + + @interface.setter + def interface(self, value): + """ + Raise error regarding setting the interface. + """ + raise AttributeError( + "The parallelization interface cannot be changed after instantiation." + ) + +def _parallel_run(fmodel_dict, set_kwargs) -> FlorisModel: + """ + Run the FLORIS model in parallel. + + Args: + fmodel: The FLORIS model to run. + set_kwargs: Additional keyword arguments to pass to fmodel.set(). + """ + fmodel = FlorisModel(fmodel_dict) + fmodel.set(**set_kwargs) + fmodel.run() + return fmodel + +def _parallel_run_powers_only(fmodel_dict, set_kwargs) -> np.ndarray: + """ + Run the FLORIS model in parallel, returning only the turbine powers. + + Args: + fmodel: The FLORIS model to run. + set_kwargs: Additional keyword arguments to pass to fmodel.set(). + """ + fmodel = FlorisModel(fmodel_dict) + fmodel.set(**set_kwargs) + fmodel.run() + return fmodel.get_turbine_powers() + +def _parallel_run_map(x): + """ + Wrapper for unpacking inputs to _parallel_run() for use with map(). + """ + return _parallel_run(*x) + +def _parallel_run_powers_only_map(x): + """ + Wrapper for unpacking inputs to _parallel_run_powers_only() for use with map(). + """ + return _parallel_run_powers_only(*x) diff --git a/floris/parallel_floris_model.py b/floris/parallel_floris_model.py index ea235aaae..9b0b0b355 100644 --- a/floris/parallel_floris_model.py +++ b/floris/parallel_floris_model.py @@ -18,6 +18,12 @@ def _get_turbine_powers_serial(fmodel_information, yaw_angles=None): fmodel.run() return (fmodel.get_turbine_powers(), fmodel.core.flow_field) +def _get_turbine_powers_serial_no_wake(fmodel_information, yaw_angles=None): + fmodel = FlorisModel(fmodel_information) + fmodel.set(yaw_angles=yaw_angles) + fmodel.run_no_wake() + return (fmodel.get_turbine_powers(), fmodel.core.flow_field) + def _optimize_yaw_angles_serial( fmodel_information, @@ -64,9 +70,8 @@ def __init__( parallel computing to common FlorisModel properties. Args: - fmodel (FlorisModel or UncertainFlorisModel object): Interactive FLORIS object used to - perform the wake and turbine calculations. Can either be a regular FlorisModel - object or can be an UncertainFlorisModel object. + fmodel (FlorisModel object): Interactive FLORIS object used to + perform the wake and turbine calculations. max_workers (int): Number of parallel workers, typically equal to the number of cores you have on your system or HPC. n_wind_condition_splits (int): Number of sectors to split the wind findex array over. @@ -83,6 +88,11 @@ def __init__( print_timings (bool): Print the computation time to the console. Defaults to False. """ + self.logger.warning(( + "ParallelFlorisModel is deprecated and will be removed in a future version. " + "Please switch to ParFlorisModel instead." + )) + # Set defaults for backward compatibility if use_mpi4py is not None: warnings.warn( @@ -106,11 +116,19 @@ def __init__( from concurrent.futures import ProcessPoolExecutor self._PoolExecutor = ProcessPoolExecutor else: - raise UserWarning( + raise ValueError( f"Interface '{interface}' not recognized. " "Please use 'concurrent', 'multiprocessing' or 'mpi4py'." ) + # Raise error if uncertain model is passed in and refer to new parallel_floris_model_2 + if isinstance(fmodel, UncertainFlorisModel): + raise ValueError( + "UncertainFlorisModel is not supported in this version of ParallelFlorisModel. " + "Please use the new version ParFlorisModel (par_floris_model) " + "for UncertainFlorisModel compatibility." + ) + # Initialize floris object and copy common properties if isinstance(fmodel, FlorisModel): self.fmodel = fmodel.copy() @@ -157,11 +175,52 @@ def set( layout_x=None, layout_y=None, turbine_type=None, + turbine_library_path=None, solver_settings=None, + heterogeneous_inflow_config=None, + wind_data=None, + yaw_angles=None, + power_setpoints=None, + awc_modes=None, + awc_amplitudes=None, + awc_frequencies=None, + disable_turbines=None, ): """Pass to the FlorisModel set function. To allow users to directly replace a FlorisModel object with this - UncertainFlorisModel object, this function is required.""" + UncertainFlorisModel object, this function is required + + Args: + wind_speeds (NDArrayFloat | list[float] | None, optional): Wind speeds at each findex. + Defaults to None. + wind_directions (NDArrayFloat | list[float] | None, optional): Wind directions at each + findex. Defaults to None. + wind_shear (float | None, optional): Wind shear exponent. Defaults to None. + wind_veer (float | None, optional): Wind veer. Defaults to None. + reference_wind_height (float | None, optional): Reference wind height. Defaults to None. + turbulence_intensities (NDArrayFloat | list[float] | None, optional): Turbulence + intensities at each findex. Defaults to None. + air_density (float | None, optional): Air density. Defaults to None. + layout_x (NDArrayFloat | list[float] | None, optional): X-coordinates of the turbines. + Defaults to None. + layout_y (NDArrayFloat | list[float] | None, optional): Y-coordinates of the turbines. + Defaults to None. + turbine_type (list | None, optional): Turbine type. Defaults to None. + turbine_library_path (str | Path | None, optional): Path to the turbine library. + Defaults to None. + solver_settings (dict | None, optional): Solver settings. Defaults to None. + heterogeneous_inflow_config (None, optional): heterogeneous inflow configuration. + Defaults to None. + wind_data (type[WindDataBase] | None, optional): Wind data. Defaults to None. + yaw_angles (NDArrayFloat | list[float] | None, optional): Turbine yaw angles. + Defaults to None. + power_setpoints (NDArrayFloat | list[float] | list[float, None] | None, optional): + Turbine power setpoints. + disable_turbines (NDArrayBool | list[bool] | None, optional): NDArray with dimensions + n_findex x n_turbines. True values indicate the turbine is disabled at that findex + and the power setpoint at that position is set to 0. Defaults to None. + + """ if layout is not None: msg = "Use the `layout_x` and `layout_y` parameters in place of `layout` " @@ -183,7 +242,16 @@ def set( layout_x=layout_x, layout_y=layout_y, turbine_type=turbine_type, + turbine_library_path=turbine_library_path, solver_settings=solver_settings, + heterogeneous_inflow_config=heterogeneous_inflow_config, + wind_data=wind_data, + yaw_angles=yaw_angles, + power_setpoints=power_setpoints, + awc_modes=awc_modes, + awc_amplitudes=awc_amplitudes, + awc_frequencies=awc_frequencies, + disable_turbines=disable_turbines, ) # Reinitialize settings @@ -269,20 +337,26 @@ def run(self): "'get_turbine_powers' or 'get_farm_power' directly." ) - def get_turbine_powers(self, yaw_angles=None): + def get_turbine_powers(self, yaw_angles=None, no_wake=False): # Retrieve multiargs: preprocessing t0 = timerpc() multiargs = self._preprocessing(yaw_angles) t_preparation = timerpc() - t0 + # Set the function based on whether wake is disabled + if not no_wake: + turbine_power_function = _get_turbine_powers_serial + else: + turbine_power_function = _get_turbine_powers_serial_no_wake + # Perform parallel calculation t1 = timerpc() with self._PoolExecutor(self.max_workers) as p: if (self.interface == "mpi4py") or (self.interface == "multiprocessing"): - out = p.starmap(_get_turbine_powers_serial, multiargs) + out = p.starmap(turbine_power_function, multiargs) else: out = p.map( - _get_turbine_powers_serial, + turbine_power_function, [j[0] for j in multiargs], [j[1] for j in multiargs] ) @@ -316,7 +390,7 @@ def get_turbine_powers(self, yaw_angles=None): return turbine_powers - def get_farm_power(self, yaw_angles=None, turbine_weights=None): + def get_farm_power(self, yaw_angles=None, turbine_weights=None, no_wake=False): if turbine_weights is None: # Default to equal weighing of all turbines when turbine_weights is None turbine_weights = np.ones( @@ -338,7 +412,7 @@ def get_farm_power(self, yaw_angles=None, turbine_weights=None): ) # Calculate all turbine powers and apply weights - turbine_powers = self.get_turbine_powers(yaw_angles=yaw_angles) + turbine_powers = self.get_turbine_powers(yaw_angles=yaw_angles, no_wake=no_wake) turbine_powers = np.multiply(turbine_weights, turbine_powers) return np.sum(turbine_powers, axis=1) @@ -392,16 +466,17 @@ def get_farm_AEP( watt-hours. """ - # If no_wake==True, ignore parallelization because it's fast enough - if no_wake: - return self.fmodel.get_farm_AEP( - freq=freq, - cut_in_wind_speed=cut_in_wind_speed, - cut_out_wind_speed=cut_out_wind_speed, - yaw_angles=yaw_angles, - turbine_weights=turbine_weights, - no_wake=no_wake - ) + # This code is out of date, let's just thread it through + # # If no_wake==True, ignore parallelization because it's fast enough + # if no_wake: + # return self.fmodel.get_farm_AEP( + # freq=freq, + # cut_in_wind_speed=cut_in_wind_speed, + # cut_out_wind_speed=cut_out_wind_speed, + # yaw_angles=yaw_angles, + # turbine_weights=turbine_weights, + # no_wake=no_wake + # ) # Verify dimensions of the variable "freq" if ((self._is_uncertain and np.shape(freq)[0] != self._n_unexpanded) or @@ -438,7 +513,9 @@ def get_farm_AEP( ) farm_power = ( - self.get_farm_power(yaw_angles=yaw_angles, turbine_weights=turbine_weights) + self.get_farm_power(yaw_angles=yaw_angles, + turbine_weights=turbine_weights, + no_wake=no_wake) ) # Finally, calculate AEP in GWh @@ -523,6 +600,67 @@ def optimize_yaw_angles( return df_opt + def get_operation_model(self): + """Get the operation model of underlying fmodel. + + Returns: + str: The operation_model. + """ + return self.fmodel.get_operation_model() + + def set_operation_model(self, operation_model): + """Set the operation model of underlying fmodel. + + Args: + operation_model (str): The operation model to set. + """ + self.fmodel.set_operation_model(operation_model) + + # Reinitialize settings + self.__init__( + fmodel=self.fmodel, + max_workers=self._max_workers, + n_wind_condition_splits=self._n_wind_condition_splits, + interface=self.interface, + propagate_flowfield_from_workers=self.propagate_flowfield_from_workers, + print_timings=self.print_timings, + ) + + def get_param(self, param, param_idx=None): + """Get the parameter of underlying fmodel. + + Args: + param (List[str]): A list of keys to traverse the FlorisModel dictionary. + param_idx (Optional[int], optional): The index to get the value at. Defaults to None. + If None, the entire parameter is returned. + + Returns: + Any: The value of the parameter. + """ + return self.fmodel.get_param(param, param_idx) + + def set_param(self, param, value, param_idx=None): + """Set the parameter of underlying fmodel. + + Args: + param (List[str]): A list of keys to traverse the FlorisModel dictionary. + value (Any): The value to set. + param_idx (Optional[int], optional): The index to set the value at. Defaults to None. + """ + self.fmodel.set_param(param, value, param_idx) + + # Reinitialize settings + self.__init__( + fmodel=self.fmodel, + max_workers=self._max_workers, + n_wind_condition_splits=self._n_wind_condition_splits, + interface=self.interface, + propagate_flowfield_from_workers=self.propagate_flowfield_from_workers, + print_timings=self.print_timings, + ) + + + @property def layout_x(self): return self.fmodel.layout_x @@ -550,8 +688,3 @@ def n_findex(self): @property def n_turbines(self): return self.fmodel.n_turbines - - - # @property - # def floris(self): - # return self.fmodel.core diff --git a/floris/uncertain_floris_model.py b/floris/uncertain_floris_model.py index ba62c4ba5..4d9d691f5 100644 --- a/floris/uncertain_floris_model.py +++ b/floris/uncertain_floris_model.py @@ -1,21 +1,33 @@ from __future__ import annotations from pathlib import Path +from typing import ( + Any, + List, + Optional, +) import numpy as np from floris import FlorisModel +from floris.core import State from floris.logging_manager import LoggingManager +from floris.par_floris_model import ParFlorisModel from floris.type_dec import ( floris_array_converter, NDArrayBool, NDArrayFloat, ) -from floris.utilities import wrap_180 +from floris.utilities import ( + nested_get, + nested_set, + wrap_180, +) from floris.wind_data import ( TimeSeries, WindDataBase, WindRose, + WindRoseWRG, WindTIRose, ) @@ -35,13 +47,10 @@ class UncertainFlorisModel(LoggingManager): conditions from within the expanded set of conditions are run. Args: - configuration (:py:obj:`dict`): The Floris configuration dictionary or YAML file. - The configuration should have the following inputs specified. - - **flow_field**: See `floris.simulation.flow_field.FlowField` for more details. - - **farm**: See `floris.simulation.farm.Farm` for more details. - - **turbine**: See `floris.simulation.turbine.Turbine` for more details. - - **wake**: See `floris.simulation.wake.WakeManager` for more details. - - **logging**: See `floris.simulation.core.Core` for more details. + configuration (dict | str | Path | FlorisModel | ParFlorisModel): The configuration + for the wind farm. This can be a dictionary, a path to a yaml file, or a FlorisModel + or ParFlorisModel object. If dict, str or Path, a new FlorisModel object is + created. If a FlorisModel or ParFlorisModel object a copy of the object is made. wd_resolution (float, optional): The resolution of wind direction for generating gaussian blends, in degrees. Defaults to 1.0. ws_resolution (float, optional): The resolution of wind speed, in m/s. Defaults to 1.0. @@ -64,7 +73,7 @@ class UncertainFlorisModel(LoggingManager): def __init__( self, - configuration: dict | str | Path, + configuration: dict | str | Path | FlorisModel | ParFlorisModel, wd_resolution=1.0, # Degree ws_resolution=1.0, # m/s ti_resolution=0.01, @@ -98,13 +107,18 @@ def __init__( self.weights = self._get_weights(self.wd_std, self.wd_sample_points) # Instantiate the un-expanded FlorisModel - self.fmodel_unexpanded = FlorisModel(configuration) + if isinstance(configuration, (FlorisModel, ParFlorisModel)): + self.fmodel_unexpanded = configuration.copy() + elif isinstance(configuration, (dict, str, Path)): + self.fmodel_unexpanded = FlorisModel(configuration) + else: + raise ValueError( + "configuration must be a FlorisModel, ParFlorisModel, dict, str, or Path" + ) # Call set at this point with no arguments so ready to run self.set() - # Instantiate the expanded FlorisModel - # self.core_interface = FlorisModel(configuration) def set( self, @@ -272,7 +286,7 @@ def get_turbine_powers(self): turbine_powers = self._get_turbine_powers() if self.fmodel_unexpanded.wind_data is not None: - if type(self.fmodel_unexpanded.wind_data) is WindRose: + if isinstance(self.fmodel_unexpanded.wind_data, (WindRose, WindRoseWRG)): turbine_powers_rose = np.full( ( len(self.fmodel_unexpanded.wind_data.wd_flat), @@ -280,9 +294,9 @@ def get_turbine_powers(self): ), np.nan, ) - turbine_powers_rose[ - self.fmodel_unexpanded.wind_data.non_zero_freq_mask, : - ] = turbine_powers + turbine_powers_rose[self.fmodel_unexpanded.wind_data.non_zero_freq_mask, :] = ( + turbine_powers + ) turbine_powers = turbine_powers_rose.reshape( len(self.fmodel_unexpanded.wind_data.wind_directions), len(self.fmodel_unexpanded.wind_data.wind_speeds), @@ -296,9 +310,9 @@ def get_turbine_powers(self): ), np.nan, ) - turbine_powers_rose[ - self.fmodel_unexpanded.wind_data.non_zero_freq_mask, : - ] = turbine_powers + turbine_powers_rose[self.fmodel_unexpanded.wind_data.non_zero_freq_mask, :] = ( + turbine_powers + ) turbine_powers = turbine_powers_rose.reshape( len(self.fmodel_unexpanded.wind_data.wind_directions), len(self.fmodel_unexpanded.wind_data.wind_speeds), @@ -308,6 +322,80 @@ def get_turbine_powers(self): return turbine_powers + def get_expected_turbine_powers(self, freq=None): + """ + Compute the expected (mean) power of each turbine. + + Args: + freq (NDArrayFloat): NumPy array with shape + with the frequencies of each wind direction and + wind speed combination. freq is either a 1D array, + in which case the same frequencies are used for all + turbines, or a 2D array with shape equal to + (n_findex, n_turbines), in which case each turbine has a unique + set of frequencies (this is the case for example using + WindRoseByTurbine). + + These frequencies should typically sum across rows + up to 1.0 and are used to weigh the wind farm power for every + condition in calculating the wind farm's AEP. Defaults to None. + If None and a WindData object was supplied, the WindData object's + frequencies will be used. Otherwise, uniform frequencies are assumed + (i.e., a simple mean over the findices is computed). + """ + + turbine_powers = self._get_turbine_powers() + + if freq is None: + if self.fmodel_unexpanded.wind_data is None: + freq = np.array([1.0 / self.fmodel_unexpanded.core.flow_field.n_findex]) + else: + freq = self.fmodel_unexpanded.wind_data.unpack_freq() + + # If freq is 2d, then use the per turbine frequencies + if len(np.shape(freq)) == 2: + return np.nansum(np.multiply(freq, turbine_powers), axis=0) + else: + return np.nansum(np.multiply(freq.reshape(-1, 1), turbine_powers), axis=0) + + def _get_weighted_turbine_powers( + self, + turbine_weights=None, + use_turbulence_correction=False, + ): + if use_turbulence_correction: + raise NotImplementedError( + "Turbulence correction is not yet implemented in the power calculation." + ) + + # Confirm run() has been run on the expanded fmodel + if self.fmodel_expanded.core.state is not State.USED: + raise RuntimeError( + "Can't run function `FlorisModel.get_farm_power` without " + "first running `FlorisModel.run`." + ) + + if turbine_weights is None: + # Default to equal weighing of all turbines when turbine_weights is None + turbine_weights = np.ones( + ( + self.fmodel_unexpanded.core.flow_field.n_findex, + self.fmodel_unexpanded.core.farm.n_turbines, + ) + ) + elif len(np.shape(turbine_weights)) == 1: + # Deal with situation when 1D array is provided + turbine_weights = np.tile( + turbine_weights, + (self.fmodel_unexpanded.core.flow_field.n_findex, 1), + ) + + # Calculate all turbine powers and apply weights + turbine_powers = self._get_turbine_powers() + turbine_powers = np.multiply(turbine_weights, turbine_powers) + + return turbine_powers + def _get_farm_power( self, turbine_weights=None, @@ -337,29 +425,9 @@ def _get_farm_power( Returns: float: Sum of wind turbine powers in W. """ - if use_turbulence_correction: - raise NotImplementedError( - "Turbulence correction is not yet implemented in the power calculation." - ) - - if turbine_weights is None: - # Default to equal weighing of all turbines when turbine_weights is None - turbine_weights = np.ones( - ( - self.n_unexpanded, - self.fmodel_unexpanded.core.farm.n_turbines, - ) - ) - elif len(np.shape(turbine_weights)) == 1: - # Deal with situation when 1D array is provided - turbine_weights = np.tile( - turbine_weights, - (self.n_unexpanded, 1), - ) - - # Calculate all turbine powers and apply weights - turbine_powers = self._get_turbine_powers() - turbine_powers = np.multiply(turbine_weights, turbine_powers) + turbine_powers = self._get_weighted_turbine_powers( + turbine_weights=turbine_weights, use_turbulence_correction=use_turbulence_correction + ) return np.sum(turbine_powers, axis=1) @@ -399,7 +467,7 @@ def get_farm_power( farm_power = self._get_farm_power(turbine_weights, use_turbulence_correction) if self.fmodel_unexpanded.wind_data is not None: - if type(self.fmodel_unexpanded.wind_data) is WindRose: + if isinstance(self.fmodel_unexpanded.wind_data, (WindRose, WindRoseWRG)): farm_power_rose = np.full(len(self.fmodel_unexpanded.wind_data.wd_flat), np.nan) farm_power_rose[self.fmodel_unexpanded.wind_data.non_zero_freq_mask] = farm_power farm_power = farm_power_rose.reshape( @@ -449,15 +517,23 @@ def get_expected_farm_power( n_turbines). Defaults to None. """ - farm_power = self._get_farm_power(turbine_weights=turbine_weights) - if freq is None: if self.fmodel_unexpanded.wind_data is None: - freq = np.array([1.0 / self.core.flow_field.n_findex]) + freq = np.array([1.0 / self.fmodel_unexpanded.core.flow_field.n_findex]) else: freq = self.fmodel_unexpanded.wind_data.unpack_freq() - return np.nansum(np.multiply(freq, farm_power)) + farm_power = self._get_farm_power(turbine_weights=turbine_weights) + + # If freq is 1d + if len(np.shape(freq)) == 1: + farm_power = self._get_farm_power(turbine_weights=turbine_weights) + return np.nansum(np.multiply(freq, farm_power)) + else: + weighted_turbine_powers = self._get_weighted_turbine_powers( + turbine_weights=turbine_weights, + ) + return np.nansum(np.multiply(freq, weighted_turbine_powers)) def get_farm_AEP( self, @@ -497,10 +573,8 @@ def get_farm_AEP( The Annual Energy Production (AEP) for the wind farm in watt-hours. """ - if ( - freq is None - and not isinstance(self.fmodel_unexpanded.wind_data, WindRose) - and not isinstance(self.fmodel_unexpanded.wind_data, WindTIRose) + if freq is None and not isinstance( + self.fmodel_unexpanded.wind_data, (WindRose, WindRoseWRG, WindTIRose) ): self.logger.warning( "Computing AEP with uniform frequencies. Results results may not reflect annual " @@ -512,6 +586,146 @@ def get_farm_AEP( * hours_per_year ) + def get_expected_farm_value( + self, + freq=None, + values=None, + turbine_weights=None, + ) -> float: + """ + Compute the expected (mean) value produced by the wind farm. This is + computed by multiplying the wind farm power for each wind condition by + the corresponding value of the power generated (e.g., electricity + market price per unit of energy), then weighting by frequency and + summing over all conditions. + + Args: + freq (NDArrayFloat): NumPy array with shape (n_findex) + with the frequencies of each wind condition combination. + These frequencies should typically sum up to 1.0 and are used + to weigh the wind farm value for every condition in calculating + the wind farm's expected value. Defaults to None. If None and a + WindData object is supplied, the WindData object's frequencies + will be used. Otherwise, uniform frequencies are assumed (i.e., + a simple mean over the findices is computed). + values (NDArrayFloat): NumPy array with shape (n_findex) + with the values corresponding to the power generated for each + wind condition combination. The wind farm power is multiplied + by the value for every condition in calculating the wind farm's + expected value. Defaults to None. If None and a WindData object + is supplied, the WindData object's values will be used. + Otherwise, a value of 1 for all conditions is assumed (i.e., + the expected farm value will be equivalent to the expected farm + power). + turbine_weights (NDArrayFloat | list[float] | None, optional): + weighing terms that allow the user to emphasize power at + particular turbines and/or completely ignore the power + from other turbines. This is useful when, for example, you are + modeling multiple wind farms in a single floris object. If you + only want to calculate the value production for one of those + farms and include the wake effects of the neighboring farms, + you can set the turbine_weights for the neighboring farms' + turbines to 0.0. The array of turbine powers from floris + is multiplied with this array in the calculation of the + expected value. If None, this is an array with all values 1.0 + and with shape equal to (n_findex, n_turbines). Defaults to None. + + Returns: + float: + The expected value produced by the wind farm in units of value. + """ + if freq is None: + if self.fmodel_unexpanded.wind_data is None: + freq = np.array([1.0 / self.fmodel_unexpanded.core.flow_field.n_findex]) + else: + freq = self.fmodel_unexpanded.wind_data.unpack_freq() + # If freq is 1d + if len(np.shape(freq)) == 1: + farm_power = self._get_farm_power(turbine_weights=turbine_weights) + farm_power = np.multiply(freq, farm_power) + else: + weighted_turbine_powers = self._get_weighted_turbine_powers( + turbine_weights=turbine_weights + ) + farm_power = np.nansum(np.multiply(freq, weighted_turbine_powers), axis=1) + if values is None: + if self.fmodel_unexpanded.wind_data is None: + values = np.array([1.0]) + else: + values = self.fmodel_unexpanded.wind_data.unpack_value() + return np.nansum(np.multiply(values, farm_power)) + + def get_farm_AVP( + self, + freq=None, + values=None, + turbine_weights=None, + hours_per_year=8760, + ) -> float: + """ + Estimate annual value production (AVP) for distribution of wind + conditions, frequencies of occurrence, and corresponding values of + power generated (e.g., electricity price per unit of energy). + + Args: + freq (NDArrayFloat): NumPy array with shape (n_findex) + with the frequencies of each wind condition combination. + These frequencies should typically sum up to 1.0 and are used + to weigh the wind farm value for every condition in calculating + the wind farm's AVP. Defaults to None. If None and a + WindData object is supplied, the WindData object's frequencies + will be used. Otherwise, uniform frequencies are assumed (i.e., + a simple mean over the findices is computed). + values (NDArrayFloat): NumPy array with shape (n_findex) + with the values corresponding to the power generated for each + wind condition combination. The wind farm power is multiplied + by the value for every condition in calculating the wind farm's + AVP. Defaults to None. If None and a WindData object is + supplied, the WindData object's values will be used. Otherwise, + a value of 1 for all conditions is assumed (i.e., the AVP will + be equivalent to the AEP). + turbine_weights (NDArrayFloat | list[float] | None, optional): + weighing terms that allow the user to emphasize power at + particular turbines and/or completely ignore the power + from other turbines. This is useful when, for example, you are + modeling multiple wind farms in a single floris object. If you + only want to calculate the value production for one of those + farms and include the wake effects of the neighboring farms, + you can set the turbine_weights for the neighboring farms' + turbines to 0.0. The array of turbine powers from floris is + multiplied with this array in the calculation of the AVP. If + None, this is an array with all values 1.0 and with shape equal + to (n_findex, n_turbines). Defaults to None. + hours_per_year (float, optional): Number of hours in a year. + Defaults to 365 * 24. + + Returns: + float: + The Annual Value Production (AVP) for the wind farm in units + of value. + """ + if ( + freq is None and not isinstance( + self.fmodel_unexpanded.wind_data, + (WindRose, WindRoseWRG, WindTIRose) + ) + ): + self.logger.warning( + "Computing AVP with uniform frequencies. Results results may not reflect annual " + "operation." + ) + + if values is None and self.fmodel_unexpanded.wind_data is None: + self.logger.warning( + "Computing AVP with uniform value equal to 1. Results will be equivalent to " + "annual energy production." + ) + + return ( + self.get_expected_farm_value(freq=freq, values=values, turbine_weights=turbine_weights) + * hours_per_year + ) + def _get_rounded_inputs( self, input_array, @@ -718,6 +932,52 @@ def _get_weights(self, wd_std, wd_sample_points): return weights + def get_operation_model(self) -> str: + """Get the operation model of a FlorisModel. + + Returns: + str: The operation_model. + """ + operation_models = [ + self.fmodel_unexpanded.core.farm.turbine_definitions[tindex]["operation_model"] + for tindex in range(self.fmodel_unexpanded.core.farm.n_turbines) + ] + if len(set(operation_models)) == 1: + return operation_models[0] + else: + return operation_models + + def set_operation_model(self, operation_model: str | List[str]): + """Set the turbine operation model(s). + + Args: + operation_model (str): The operation model to set. + """ + if isinstance(operation_model, str): + if len(self.fmodel_unexpanded.core.farm.turbine_type) == 1: + # Set a single one here, then, and return + turbine_type = self.fmodel_unexpanded.core.farm.turbine_definitions[0] + turbine_type["operation_model"] = operation_model + self.set(turbine_type=[turbine_type]) + return + else: + operation_model = [operation_model] * self.fmodel_unexpanded.core.farm.n_turbines + + if len(operation_model) != self.fmodel_unexpanded.core.farm.n_turbines: + raise ValueError( + "The length of the operation_model list must be " "equal to the number of turbines." + ) + + turbine_type_list = self.fmodel_unexpanded.core.farm.turbine_definitions + + for tindex in range(self.fmodel_unexpanded.core.farm.n_turbines): + turbine_type_list[tindex]["turbine_type"] = ( + turbine_type_list[tindex]["turbine_type"] + "_" + operation_model[tindex] + ) + turbine_type_list[tindex]["operation_model"] = operation_model[tindex] + + self.set(turbine_type=turbine_type_list) + def copy(self): """Create an independent copy of the current UncertainFlorisModel object""" return UncertainFlorisModel( @@ -734,6 +994,37 @@ def copy(self): verbose=self.verbose, ) + def get_param(self, param: List[str], param_idx: Optional[int] = None) -> Any: + """Get a parameter from a FlorisModel object. + + Args: + param (List[str]): A list of keys to traverse the FlorisModel dictionary. + param_idx (Optional[int], optional): The index to get the value at. Defaults to None. + If None, the entire parameter is returned. + + Returns: + Any: The value of the parameter. + """ + fm_dict = self.fmodel_unexpanded.core.as_dict() + + if param_idx is None: + return nested_get(fm_dict, param) + else: + return nested_get(fm_dict, param)[param_idx] + + def set_param(self, param: List[str], value: Any, param_idx: Optional[int] = None): + """Set a parameter in a FlorisModel object. + + Args: + param (List[str]): A list of keys to traverse the FlorisModel dictionary. + value (Any): The value to set. + param_idx (Optional[int], optional): The index to set the value at. Defaults to None. + """ + fm_dict_mod = self.fmodel_unexpanded.core.as_dict() + nested_set(fm_dict_mod, param, value, param_idx) + self.fmodel_unexpanded.__init__(fm_dict_mod) + self.set() + @property def layout_x(self): """ diff --git a/floris/version.py b/floris/version.py index 627a3f43a..bf77d5496 100644 --- a/floris/version.py +++ b/floris/version.py @@ -1 +1 @@ -4.1.1 +4.2 diff --git a/floris/wind_data.py b/floris/wind_data.py index deb066fbe..b470fe515 100644 --- a/floris/wind_data.py +++ b/floris/wind_data.py @@ -1,14 +1,21 @@ from __future__ import annotations +import copy import inspect from abc import abstractmethod from pathlib import Path +from typing import List +import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np import pandas as pd from pandas.api.types import CategoricalDtype -from scipy.interpolate import LinearNDInterpolator, NearestNDInterpolator +from scipy.interpolate import ( + LinearNDInterpolator, + NearestNDInterpolator, + RegularGridInterpolator, +) from floris.heterogeneous_map import HeterogeneousMap from floris.type_dec import NDArrayFloat @@ -87,6 +94,21 @@ def check_heterogeneous_inflow_config(self, heterogeneous_inflow_config): if "y" not in heterogeneous_inflow_config: raise ValueError("heterogeneous_inflow_config must contain a key 'y'") + def set_layout(self, layout_x=None, layout_y=None): + """ + Default implementation the explicitly does nothing. Only WindData objects that depend + on layout need to implement this method. + + Included so that FlorisModel can call this method on the WindData object when the layout + is updated. + + Args: + layout_x (list, optional): List of x-coordinates of the turbines. Defaults to None. + layout_y (list, optional): List of y-coordinates of the turbines. Defaults to None. + """ + # No operation performed + return None + class WindRose(WindDataBase): """ @@ -692,7 +714,7 @@ def plot( color_map="viridis_r", wd_step=None, ws_step=None, - legend_kwargs={"title": "Wind speed [m/s]"}, + legend_kwargs={"label": "Wind speed [m/s]"}, ): """ This method creates a wind rose plot showing the frequency of occurrence @@ -711,7 +733,7 @@ def plot( ws_step: Step size for wind speed (float, optional). the current step size will be used. Defaults to None. legend_kwargs (dict, optional): Keyword arguments to be passed to - ax.legend(). Defaults to {"title": "Wind speed [m/s]"}. + ax.legend(). Defaults to {"label": "Wind speed [m/s]"}. Returns: :py:class:`matplotlib.pyplot.axes`: A figure axes object containing @@ -738,6 +760,8 @@ def plot( # Get a color array color_array = plt.get_cmap(color_map, len(ws_bins)) + norm_ws = mpl.colors.Normalize(vmin=np.min(ws_bins), vmax=np.max(ws_bins)) + sm_ws = mpl.cm.ScalarMappable(norm=norm_ws, cmap=color_array) for wd_idx, wd in enumerate(wd_bins): rects = [] @@ -755,7 +779,8 @@ def plot( ) # Configure the plot - ax.legend(reversed(rects), ws_bins, **legend_kwargs) + ax.figure.colorbar(sm_ws, ax=ax, **legend_kwargs) + ax.figure.tight_layout() ax.set_theta_direction(-1) ax.set_theta_offset(np.pi / 2.0) ax.set_theta_zero_location("N") @@ -1717,7 +1742,7 @@ def plot( color_map="viridis_r", wd_step=15.0, wind_rose_var_step=None, - legend_kwargs={"title": "Wind speed [m/s]"}, + legend_kwargs={"label": "Wind speed [m/s]"}, ): """ This method creates a wind rose plot showing the frequency of occurrence @@ -1744,7 +1769,7 @@ def plot( will be used if wind_rose_var = "ws", and a value of 4% will be used if wind_rose_var = "ti". legend_kwargs (dict, optional): Keyword arguments to be passed to - ax.legend(). Defaults to {"title": "Wind speed [m/s]"}. + ax.legend(). Defaults to {"label": "Wind speed [m/s]"}. Returns: :py:class:`matplotlib.pyplot.axes`: A figure axes object containing @@ -1778,6 +1803,8 @@ def plot( # Get a color array color_array = plt.get_cmap(color_map, len(var_bins)) + norm_wv = mpl.colors.Normalize(vmin=np.min(var_bins), vmax=np.max(var_bins)) + sm_wv = mpl.cm.ScalarMappable(norm=norm_wv, cmap=color_array) for wd_idx, wd in enumerate(wd_bins): rects = [] @@ -1795,7 +1822,8 @@ def plot( ) # Configure the plot - ax.legend(reversed(rects), var_bins, **legend_kwargs) + ax.figure.colorbar(sm_wv, ax=ax, **legend_kwargs) + ax.figure.tight_layout() ax.set_theta_direction(-1) ax.set_theta_offset(np.pi / 2.0) ax.set_theta_zero_location("N") @@ -2671,3 +2699,674 @@ def to_WindTIRose( value_table, self.heterogeneous_map, ) + + +class WindRoseWRG(WindDataBase): + """ + The WindRoseWRG class is a WindData object the represents a wind resource grid (WRG) file + to FLORIS. As a WindData object it can be passed to the FlorisModel.set method. A WRG file + represents a wind resource as a grid of points where each point has a separate wind rose define + by the frequency of each wind direction and the Weibull parameters for each wind direction. + + WindRoseWRG objects are provided the layout of a wind farm and computes a wind rose at + each point in the layout. The wind rose at each point is computed by interpolating the weibull + parameter in the WRG file to the point in the layout and using them to compute a WindRose + object. Each WindRose object shares wind direction and wind speed, only the frequencies differ. + + When running a FlorisModel with a WindRoseWRG object, most behaviors are the same + except functions which compute an expected value, use separate frequencies for each + turbine to weight the individual power bins. + + Args: + filename (str): The name of the WRG file to read. + wd_step (float, optional): Step size to use resampling the wind directions given by the WRG + file. If None, wd_step and wind_directions are set by the number of + sectors in the WRG file. Defaults to None. + wind_speeds (NDArrayFloat, optional): Wind speeds to use in the wind rose. Defaults to + np.arange(0.0, 26.0, 1.0). + ti_table (float, optional): Turbulence intensities table to use for each WindRose object. + As in the WindRose ti_table, this can be a single value or an array of values. If an + array of values is provided, it must be (len(wind_directions) x len(wind_speeds)). + Defaults to 0.06. + + """ + + def __init__( + self, filename, wd_step=None, wind_speeds=np.arange(0.0, 26.0, 1.0), ti_table=0.06 + ): + # Read in the WRG file + self.filename = filename + self.read_wrg_file(filename) + + # If wd_step is None, then use the wind directions in the WRG file + if wd_step is None: + self.wind_directions = self._wind_directions_wrg_file + self.wd_step = self.wind_directions[1] - self.wind_directions[0] + else: + self.wind_directions = np.arange(0.0, 360.0, wd_step) + self.wd_step = wd_step + + # Initialize the layouts which will need to be specified + self.layout_x = None + self.layout_y = None + + # Save the wind speeds and ti_table + self.wind_speeds = wind_speeds + self.ti_table = ti_table + + # Initialize the flat arrays, these will depend on the specified wind speeds + self.wd_flat = None + self.ws_flat = None + self.non_zero_freq_mask = None + + def read_wrg_file(self, filename): + """ + Read the contents of a WRG file and store the data in the object. + + Args: + filename (str): The name of the WRG file to read. + + """ + + # Read the file into data + with open(filename, "r") as f: + data = f.readlines() + + # Read the header + header = data[0].split() + self.nx = int(header[0]) + self.ny = int(header[1]) + self.xmin = float(header[2]) + self.ymin = float(header[3]) + self.grid_size = float(header[4]) + + # The grid of points is implied by the values above + self.x_array = np.arange(self.nx) * self.grid_size + self.xmin + self.y_array = np.arange(self.ny) * self.grid_size + self.ymin + + # The number of grid points (n_gid) is the product of the number of points in x and y + self.n_gid = self.nx * self.ny + + # Finally get the number of sectors from the first line after the header + self.n_sectors = int(data[1][70:72]) + + # The wind directions are implied by the number of sectors + self._wind_directions_wrg_file = np.arange(0.0, 360.0, 360.0 / self.n_sectors) + + # Initialize the data arrays which have the same number of + # elements as the number of grid points + x_gid = np.zeros(self.n_gid) + y_gid = np.zeros(self.n_gid) + z_gid = np.zeros(self.n_gid) + h_gid = np.zeros(self.n_gid) + + # Initialize the data arrays which are n_gid x n_sectors + sector_freq_gid = np.zeros((self.n_gid, self.n_sectors)) + weibull_A_gid = np.zeros((self.n_gid, self.n_sectors)) + weibull_k_gid = np.zeros((self.n_gid, self.n_sectors)) + + # Loop through the data and extract the values + for gid in range(self.n_gid): + line = data[1 + gid] + x_gid[gid] = float(line[10:20]) + y_gid[gid] = float(line[20:30]) + z_gid[gid] = float(line[30:38]) + h_gid[gid] = float(line[38:43]) + + for sector in range(self.n_sectors): + # The frequency of the wind in this sector is in probablility * 1000 + sector_freq_gid[gid, sector] = ( + float(line[72 + sector * 13 : 76 + sector * 13]) / 1000.0 + ) + + # The A and k parameters are in the next 10 characters, with A stored * 10 + # and k stored * 100 + weibull_A_gid[gid, sector] = float(line[76 + sector * 13 : 80 + sector * 13]) / 10.0 + weibull_k_gid[gid, sector] = ( + float(line[80 + sector * 13 : 85 + sector * 13]) / 100.0 + ) + # Save the x_gid and y_gid form for iteration in het map + self.x_gid = x_gid + self.y_gid = y_gid + self.weibull_A_gid = weibull_A_gid + self.weibull_k_gid = weibull_k_gid + + # Save a single value of z and h for the entire grid + self.z = z_gid[0] + self.h = h_gid[0] + + # Index the by sector data by x and y + self.sector_freq = np.zeros((self.nx, self.ny, self.n_sectors)) + self.weibull_A = np.zeros((self.nx, self.ny, self.n_sectors)) + self.weibull_k = np.zeros((self.nx, self.ny, self.n_sectors)) + + for x_idx, x in enumerate(self.x_array): + for y_idx, y in enumerate(self.y_array): + # Find the indices when x_gid and y_gid are equal to x and y + idx = np.where((x_gid == x) & (y_gid == y))[0] + + # Assign the data to the correct location + self.sector_freq[x_idx, y_idx, :] = sector_freq_gid[idx, :] + self.weibull_A[x_idx, y_idx, :] = weibull_A_gid[idx, :] + self.weibull_k[x_idx, y_idx, :] = weibull_k_gid[idx, :] + + # Build the interpolant function lists + self.interpolant_sector_freq = self._build_interpolant_function_list( + self.x_array, self.y_array, self.n_sectors, self.sector_freq + ) + self.interpolant_weibull_A = self._build_interpolant_function_list( + self.x_array, self.y_array, self.n_sectors, self.weibull_A + ) + self.interpolant_weibull_k = self._build_interpolant_function_list( + self.x_array, self.y_array, self.n_sectors, self.weibull_k + ) + + def __str__(self) -> str: + """ + Return a string representation of the WindRose object + """ + + return ( + f"WindResourceGrid with {self.nx} x {self.ny} grid points, " + f"min x: {self.xmin}, min y: {self.ymin}, grid size: {self.grid_size}, " + f"z: {self.z}, h: {self.h}, {self.n_sectors} sectors\n" + f"Wind directions in file: {self._wind_directions_wrg_file}\n" + f"Wind directions: {self.wind_directions}\n" + f"Wind speeds: {self.wind_speeds}\n" + f"ti_table: {self.ti_table}" + ) + + def _build_interpolant_function_list(self, x, y, n_sectors, data): + """ + Build a list of interpolant functions for the data. It is assumed that the function + should return a list of interpolant functions, length n_sectors. + + Args: + x (np.array): The x values of the data, length nx. + y (np.array): The y values of the data, length ny. + n_sectors (int): The number of sectors. + data (np.array): The data to interpolate, shape (nx, ny, n_sectors). + + Returns: + list: A list of interpolant functions, length n_sectors. + """ + + function_list = [] + + for sector in range(n_sectors): + function_list.append( + RegularGridInterpolator( + (x, y), + data[:, :, sector], + bounds_error=False, + fill_value=None, + ) + ) + + return function_list + + def _interpolate_data(self, x, y, interpolant_function_list): + """ + Interpolate the data at a given x, y location using the interpolant function list. + + Args: + x (float): The x location to interpolate. + y (float): The y location to interpolate. + interpolant_function_list (list): A list of interpolant functions. + + Returns: + list: A list of interpolated data, length n_sectors. + """ + + # Check if x and y are within the bounds of the self.x_array and self.y_array, if + # so use the nearest method, otherwise use the linear method of interpolation + if ( + x < self.x_array[0] + or x > self.x_array[-1] + or y < self.y_array[0] + or y > self.y_array[-1] + ): + method = "nearest" + else: + method = "linear" + + result = np.zeros(self.n_sectors) + for sector in range(self.n_sectors): + result[sector] = interpolant_function_list[sector]((x, y), method=method) + + return result + + def _weibull_cumulative(self, x, a, k): + """ + Calculate the Weibull cumulative distribution function. + + Args: + x (np.array): The wind speed values. + a (np.array): The Weibull A parameter values. + k (np.array): The Weibull k parameter values. + + Returns: + np.array: The cumulative distribution function values. + """ + + exponent = -((x / a) ** k) + result = 1.0 - np.exp(exponent) + + # Where x is less than 0, the result should be 0 + result[x < 0] = 0.0 + + return result + + # Original code from PJ Stanley + # if x >= 0.0: + # exponent = -(x / a) ** k + # return 1.0 - np.exp(exponent) + # else: + # return 0.0 + + def _generate_wind_speed_frequencies_from_weibull(self, A, k, wind_speeds=None): + """ + Generate the wind speed frequencies from the Weibull parameters. Use the + cumulative form of the function and calculate the probability of the wind speed + in a given bin via the difference in the cumulative function at the bin edges. + Args: + + A (np.array): The Weibull A parameter. + k (np.array): The Weibull k parameter. + wind_speeds (np.array): The wind speeds to calculate the frequencies for. + If None, the frequencies are calculated for 0 to 25 m/s in 1 m/s increments. + Default is None. + + Returns: + np.array: The wind speed frequencies. + """ + + if wind_speeds is None: + wind_speeds = self.wind_speeds + ws_steps = np.diff(wind_speeds) + if not np.all(np.isclose(ws_steps, ws_steps[0])): + raise ValueError("wind_speeds must be equally spaced.") + else: + ws_step = ws_steps[0] + + # Define the wind speed edges (not half-open interval in np.arange) + wind_speed_edges = np.arange( + wind_speeds[0] - ws_step / 2, wind_speeds[-1] + ws_step, ws_step + ) + + # Get the cumulative distribution function at the edges + cdf_edges = self._weibull_cumulative(wind_speed_edges, A, k) + + # The frequency is the difference in the cumulative distribution function + # at the edges + # NOTE: The probability mass associated to each discrete wind speed (ws) is taken as the + # cumulative mass under the continuous Weibull distribution from ws - ws_step/2 to + # ws + ws_step/2, where ws_step is the step between the provided wind_speeds. + freq = cdf_edges[1:] - cdf_edges[:-1] + + # Normalize the frequency + freq = freq / freq.sum() + + return wind_speeds, freq + + def get_wind_rose_at_point(self, x, y, wind_directions=None, wind_speeds=None, ti_table=0.06): + """ + Get the wind rose at a given x, y location. Interpolate the parameters to the point + and then generate the wind rose. + + Args: + x (float): The x location to interpolate. + y (float): The y location to interpolate. + wind_directions (np.array): The wind directions to calculate the frequencies for. + If None, use self.wind_directions. Default is None. + wind_speeds (np.array): The wind speeds to calculate the frequencies for. + If None, use self.wind_speeds. Default is None. + ti_table (float): The ti_table to use in the wind rose. + Default is 0.06. + """ + + if wind_speeds is None: + wind_speeds = self.wind_speeds + + # If wind directions is None, use the values stored + if wind_directions is None: + wind_directions = self.wind_directions + wd_step = self.wd_step + else: + # Calculate wd_step for these directions + wd_step = wind_directions[1] - wind_directions[0] + + # Get the interpolated data + sector_freq = self._interpolate_data(x, y, self.interpolant_sector_freq) + weibull_A = self._interpolate_data(x, y, self.interpolant_weibull_A) + weibull_k = self._interpolate_data(x, y, self.interpolant_weibull_k) + + # Initialize the freq_table + freq_table = np.zeros((self.n_sectors, len(wind_speeds))) + + # First fill in the rows of the table using the weibull distributions, + # weighted by the sector freq + for sector in range(self.n_sectors): + wind_speeds, freq = self._generate_wind_speed_frequencies_from_weibull( + weibull_A[sector], weibull_k[sector], wind_speeds=wind_speeds + ) + freq_table[sector, :] = sector_freq[sector] * freq + + # Normalize the table + freq_table = freq_table / freq_table.sum() + + # First build the wind rose using the wind directions in the wrg file + wind_rose = WindRose( + wind_directions=self._wind_directions_wrg_file, + wind_speeds=wind_speeds, + freq_table=freq_table, + ti_table=ti_table, + compute_zero_freq_occurrence=True, + ) + + # Now upsample or downsample the wind rose to the specified wind directions + if wd_step == (self._wind_directions_wrg_file[1] - self._wind_directions_wrg_file[0]): + # If the wind directions are the same, return the wind rose + return wind_rose + elif wd_step < (self._wind_directions_wrg_file[1] - self._wind_directions_wrg_file[0]): + # If the wind directions are smaller, upsample + return wind_rose.upsample(wd_step) + else: + # If the wind directions are larger, downsample + return wind_rose.downsample(wd_step) + + def set_wd_step(self, wd_step): + """ + Set the wind directions for the WindRoseWRG object. + + Args: + wind_directions (np.array): The wind directions to use for the wind roses. + """ + + self.wind_directions = np.arange(0.0, 360.0, wd_step) + self.wd_step = wd_step + + # Update the wind roses if the layout has been set + if self.layout_x is not None: + self._update_wind_roses() + + def set_wind_speeds(self, wind_speeds): + """ + Set the wind speeds for the WindRoseWRG object. + + Args: + wind_speeds (np.array): The wind speeds to use for the wind roses. + """ + + self.wind_speeds = wind_speeds + + # Update the wind roses if the layout has been set + if self.layout_x is not None: + self._update_wind_roses() + + def set_ti_table(self, ti_table): + """ + Set the fixed turbulence intensity value for the WindRoseWRG object. + + Args: + ti_table (float): The ti_table value to use in the wind roses. + """ + + self.ti_table = ti_table + + # Update the wind roses if the layout has been set + if self.layout_x is not None: + self._update_wind_roses() + + def set_layout(self, layout_x, layout_y): + """ + Set the layout for the WindRoseWRG object. + + Args: + layout_x (np.array): The x coordinates of the layout. + layout_y (np.array): The y coordinates of the layout. + """ + + # Confirm that layout_x, layout_y, and wind_roses are the same length + if len(layout_x) != len(layout_y): + raise ValueError("layout_x and layout_y must be the same length") + + # If the current layout is the same as the new layout, return + if self.layout_x is not None and self.layout_y is not None: + if np.allclose(np.array(layout_x), self.layout_x) and np.allclose( + np.array(layout_y), self.layout_y + ): + return + + # Save the layouts + self.layout_x = np.array(layout_x) + self.layout_y = np.array(layout_y) + + # Update the wind roses + self._update_wind_roses() + + def _update_wind_roses(self): + # Initialize the list of wind roses + self.wind_roses = [] + + # Loop through the turbines and get the wind rose at each location + for i in range(len(self.layout_x)): + wind_rose = self.get_wind_rose_at_point( + self.layout_x[i], + self.layout_y[i], + wind_directions=self.wind_directions, + wind_speeds=self.wind_speeds, + ti_table=self.ti_table, + ) + self.wind_roses.append(wind_rose) + + # Save also the wd_flat and ws_flat from the first wind rose as this could be needed + # for unpacking and non_zero_freq_mask + self.wd_flat = self.wind_roses[0].wd_flat + self.ws_flat = self.wind_roses[0].ws_flat + self.non_zero_freq_mask = self.wind_roses[0].non_zero_freq_mask + + def unpack(self): + """ + Implement the unpack method for WindRoseByTurbine by + calling the unpack method for each of the WindRose objects in wind_roses. + Mose of the variables can be passed as is but freq_table_unpack are combined + and stacked along the 1th axis + + Returns: + Tuple: Tuple containing the unpacked wind rose data. + """ + + if self.layout_x is None: + raise ValueError("WindRoseByTurbine must be initialized to a layout before unpacking") + + # Initialize freq_table_unpack + freq_table_unpack = np.zeros((len(self.wd_flat), len(self.layout_x))) + + # Loop over remaining wind roses and stack freq_table_unpack + for i, wind_rose in enumerate(self.wind_roses): + ( + wind_directions_unpack, + wind_speeds_unpack, + ti_table_unpack, + freq_table_unpack_0, + value_table_unpack, + heterogeneous_inflow_config, + ) = wind_rose.unpack() + freq_table_unpack[:, i] = freq_table_unpack_0 + + return ( + wind_directions_unpack, + wind_speeds_unpack, + ti_table_unpack, + freq_table_unpack, + value_table_unpack, + heterogeneous_inflow_config, + ) + + def plot_wind_roses( + self, + axarr=None, + wd_step=None, + ws_step=None, + ): + """ + Plot the wind roses for each turbine in the WindRoseByTurbine object. + + Args: + axarr (NDArrayAxes, optional): Array of axes to plot the wind roses on. + Defaults to None. Must have length equal to the number of wind roses. + wd_step (float, optional): Step size for wind direction. Defaults to None. + ws_step (float, optional): Step size for wind speed. Defaults to None. + """ + + if self.layout_x is None: + raise ValueError("WindRoseByTurbine must be initialized to a layout before plotting") + + # If axarr is not defined, create a new figure + if axarr is None: + _, axarr = plt.subplots(1, len(self.wind_roses), subplot_kw={"polar": True}) + + # Test that axarr is the correct length + if len(axarr) != len(self.wind_roses): + raise ValueError("axarr must have the same length as the number of wind roses") + + # Plot the wind roses for each turbine + for i, wind_rose in enumerate(self.wind_roses): + wind_rose.plot(ax=axarr[i], wd_step=wd_step, ws_step=ws_step) + axarr[i].set_title(f"Turbine {i}\n ({self.layout_x[i]:.1f}, {self.layout_y[i]:.1f})") + + def get_heterogeneous_wind_rose( + self, + fmodel, + wind_speeds=None, + x_loc=None, + y_loc=None, + representative_wind_speed=8.0, + ): + """ + Get the heterogeneous map at each location in the grid, with the speeds ups + defined relative the location indicated by gid_norm_index. + + Args: + fmodel (FlorisModel): The FlorisModel object to use to generate the power curve. + wind_speeds (np.array): The wind speeds to calculate the frequencies for. + Default is np.arange(0.0, 25.0, 1.0). + gid_norm_index (int): The index of the turbine to normalize the speed ups to. + Default is 0. + representative_wind_speed (float): The representative wind speed to use + in the power curve. + + Returns: + HeterogeneousMap: The heterogeneous map object. + """ + ############################ + # Compute the power curve for combining the wind speeds + ############################ + + if wind_speeds is None: + wind_speeds = self.wind_speeds + + # Get a local copy + fm = copy.deepcopy(fmodel) + + # Get the power curve for the turbine + # TODO: Maybe the power curve could be directly extracted + fm.set( + layout_x=[0], + layout_y=[0], + wind_data=TimeSeries( + wind_speeds=wind_speeds, + wind_directions=270.0, + turbulence_intensities=0.06, + ), + ) + fm.run() + turbine_power = fm.get_turbine_powers().flatten() + + ############################ + # Identify the point on the original wrg grid closest to the x_loc and y_loc + ############################ + + if x_loc is None or y_loc is None: + # Simply use the first point + gid_reference = 0 + + else: + # Find the closest point + gid_reference = np.argmin((self.x_gid - x_loc) ** 2 + (self.y_gid - y_loc) ** 2) + + # Assign x_loc and y_loc to this point + x_loc = self.x_gid[gid_reference] + y_loc = self.y_gid[gid_reference] + print(f"Using point {gid_reference} at ({x_loc}, {y_loc}) as reference location") + + ############################ + # Get the wind rose at this point + ############################ + wind_rose = self.get_wind_rose_at_point( + x=x_loc, + y=y_loc, + ) + + # Subset to the representative wind speed + + # Check the represenative_wind_speed is valid + if representative_wind_speed in wind_rose.wind_speeds: + ws_idx = np.where(wind_rose.wind_speeds == representative_wind_speed)[0] + else: + raise ValueError("representative_wind_speed must be in original set") + + # Create a new wind rose with only the specified wind speeds + wind_rose = WindRose( + wind_rose.wind_directions, + wind_rose.wind_speeds[ws_idx], + wind_rose.ti_table[:, ws_idx], + wind_rose.freq_table[:, ws_idx], + wind_rose.value_table[:, ws_idx] if wind_rose.value_table is not None else None, + wind_rose.compute_zero_freq_occurrence, + wind_rose.heterogeneous_map, + ) + + ############################ + # Calculate speed multipliers + ############################ + + speed_multipliers = np.zeros((self.n_sectors, self.n_gid)) + + for direction_sector in range(self.n_sectors): + for gid in range(self.n_gid): + _, freq = self._generate_wind_speed_frequencies_from_weibull( + self.weibull_A_gid[gid, direction_sector], + self.weibull_k_gid[gid, direction_sector], + wind_speeds=wind_speeds, + ) + + # Record the expected power + speed_multipliers[direction_sector, gid] = np.sum(turbine_power * freq) + + # Normalize the speed ups + speed_multipliers[direction_sector, :] = ( + speed_multipliers[direction_sector, :] + / speed_multipliers[direction_sector, gid_reference] + ) + + # Take the cube root of the speed ups to place in the frame of wind speed ups + speed_multipliers = np.cbrt(speed_multipliers) + + # Create the heterogeneous map + heterogeneous_map = HeterogeneousMap( + x=self.x_gid, + y=self.y_gid, + wind_directions=self._wind_directions_wrg_file, + speed_multipliers=speed_multipliers, + ) + + # Return the wind rose with the heterogeneous map + return WindRose( + wind_directions=wind_rose.wind_directions, + wind_speeds=wind_rose.wind_speeds, + freq_table=wind_rose.freq_table, + ti_table=wind_rose.ti_table, + heterogeneous_map=heterogeneous_map, + ) diff --git a/setup.py b/setup.py index b3532ffd5..7e3a11be4 100644 --- a/setup.py +++ b/setup.py @@ -28,6 +28,7 @@ # utilities "coloredlogs~=15.0", + "pathos~=0.3", ] # What packages are optional? diff --git a/tests/conftest.py b/tests/conftest.py index d31b7dee1..cde943043 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -490,6 +490,10 @@ def __init__(self): "A": 0.04, "sigma_max_rel": 4.0 }, + "turboparkgauss": { + "A": 0.04, + "include_mirror_wake": True + }, "empirical_gauss": { "wake_expansion_rates": [0.023, 0.008], "breakpoints_D": [10], diff --git a/tests/data/input_full.yaml b/tests/data/input_full.yaml index f3235b581..49c41273d 100644 --- a/tests/data/input_full.yaml +++ b/tests/data/input_full.yaml @@ -78,6 +78,9 @@ wake: kb: 0.004 jensen: we: 0.05 + turboparkgauss: + A: 0.04 + include_mirror_wake: True wake_turbulence_parameters: crespo_hernandez: diff --git a/tests/farm_unit_test.py b/tests/farm_unit_test.py index 3c8893998..81d20f8ae 100644 --- a/tests/farm_unit_test.py +++ b/tests/farm_unit_test.py @@ -115,6 +115,44 @@ def test_check_turbine_type(sample_inputs_fixture: SampleInputs): assert len(farm.turbine_type) == 5 assert len(farm.turbine_definitions) == 5 + # Check that error is correctly raised if two turbines have the same name + farm_data = deepcopy(sample_inputs_fixture.farm) + external_library = Path(__file__).parent / "data" + farm_data["layout_x"] = np.arange(0, 500, 100) + farm_data["layout_y"] = np.zeros(5) + turbine_def = load_yaml(external_library / "nrel_5MW_custom.yaml") + turbine_def_mod = deepcopy(turbine_def) + turbine_def_mod["hub_height"] = 100.0 # Change the hub height of the last turbine + farm_data["turbine_type"] = [turbine_def]*4 + [turbine_def_mod] + with pytest.raises(ValueError): + farm = Farm.from_dict(farm_data) + # Check this also raises an error in a nested level of the turbine definition + turbine_def_mod = deepcopy(turbine_def) + turbine_def_mod["power_thrust_table"]["wind_speed"][-1] = -100.0 + farm_data["turbine_type"] = [turbine_def]*4 + [turbine_def_mod] + with pytest.raises(ValueError): + farm = Farm.from_dict(farm_data) + + # Check that no error is raised, and the expected hub heights are seen, + # if turbine_type is correctly updated + farm_data = deepcopy(sample_inputs_fixture.farm) + external_library = Path(__file__).parent / "data" + farm_data["layout_x"] = np.arange(0, 500, 100) + farm_data["layout_y"] = np.zeros(5) + turbine_def = load_yaml(external_library / "nrel_5MW_custom.yaml") + turbine_def_mod = deepcopy(turbine_def) + turbine_def_mod["hub_height"] = 100.0 # Change the hub height of the last turbine + turbine_def_mod["turbine_type"] = "nrel_5MW_custom_2" + farm_data["turbine_type"] = [turbine_def]*4 + [turbine_def_mod] + farm = Farm.from_dict(farm_data) + for i in range(4): + assert farm.turbine_definitions[i]["hub_height"] == turbine_def["hub_height"] + assert farm.turbine_definitions[-1]["hub_height"] == 100.0 + farm.construct_turbine_map() + for i in range(4): + assert farm.turbine_map[i].hub_height == turbine_def["hub_height"] + assert farm.turbine_map[-1].hub_height == 100.0 + # Duplicate type found in external and internal library farm_data = deepcopy(sample_inputs_fixture.farm) external_library = Path(__file__).parent / "data" diff --git a/tests/floris_model_integration_test.py b/tests/floris_model_integration_test.py index 7b3f7d140..fa05d43d3 100644 --- a/tests/floris_model_integration_test.py +++ b/tests/floris_model_integration_test.py @@ -5,7 +5,11 @@ import pytest import yaml -from floris import FlorisModel, WindRose +from floris import ( + FlorisModel, + TimeSeries, + WindRose, +) from floris.core.turbine.operation_models import POWER_SETPOINT_DEFAULT @@ -390,6 +394,84 @@ def test_get_farm_aep(caplog): expected_farm_power = fmodel.get_expected_farm_power(freq=freq) np.testing.assert_allclose(expected_farm_power, aep / (365 * 24)) +def test_expected_farm_power_regression(): + + fmodel = FlorisModel(configuration=YAML_INPUT) + + wind_speeds = np.array([8.0, 8.0, 8.0]) + wind_directions = np.array([270.0, 270.0, 270.0]) + turbulence_intensities = np.array([0.06, 0.06, 0.06]) + + layout_x = np.array([0, 0]) + layout_y = np.array([0, 1000]) + + fmodel.set( + wind_speeds=wind_speeds, + wind_directions=wind_directions, + turbulence_intensities=turbulence_intensities, + layout_x=layout_x, + layout_y=layout_y, + ) + + fmodel.run() + + expected_farm_power = fmodel.get_expected_farm_power() + + # Assert the expected farm power has not inadvetently changed + np.testing.assert_allclose(expected_farm_power, 3507908.918358342, atol=1e-1) + +def test_expected_farm_power_equals_sum_of_expected_turbine_powers(): + + fmodel = FlorisModel(configuration=YAML_INPUT) + + wind_speeds = np.array([8.0, 8.0, 8.0]) + wind_directions = np.array([270.0, 270.0, 270.0]) + turbulence_intensities = np.array([0.06, 0.06, 0.06]) + + layout_x = np.array([0, 0]) + layout_y = np.array([0, 1000]) + + fmodel.set( + wind_speeds=wind_speeds, + wind_directions=wind_directions, + turbulence_intensities=turbulence_intensities, + layout_x=layout_x, + layout_y=layout_y, + ) + + fmodel.run() + + expected_farm_power = fmodel.get_expected_farm_power() + expected_turbine_powers = fmodel.get_expected_turbine_powers() + + # Assert the expected farm power is the sum of the expected turbine powers + np.testing.assert_allclose(expected_farm_power, np.sum(expected_turbine_powers)) + +def test_expected_farm_value_regression(): + + # Ensure this calculation hasn't changed unintentionally + + fmodel = FlorisModel(configuration=YAML_INPUT) + + wind_speeds = np.array([8.0, 8.0, 9.0]) + wind_directions = np.array([270.0, 270.0, 270.0]) + values = np.array([30.0, 20.0, 10.0]) + time_series = TimeSeries( + wind_directions=wind_directions, + wind_speeds=wind_speeds, + turbulence_intensities=0.06, + values=values, + ) + + layout_x = np.array([0, 0]) + layout_y = np.array([0, 1000]) + fmodel.set(layout_x=layout_x, layout_y=layout_y, wind_data=time_series) + fmodel.run() + + expected_farm_value = fmodel.get_expected_farm_value() + assert np.allclose(expected_farm_value,75108001.05154414 , atol=1e-1) + + def test_get_farm_avp(caplog): fmodel = FlorisModel(configuration=YAML_INPUT) @@ -700,3 +782,29 @@ def test_set_operation(): with pytest.raises(ValueError): fmodel.set_operation(yaw_angles=np.array([[25.0, 0.0], [25.0, 0.0]])) fmodel.run() + +def test_reference_wind_height_methods(caplog): + fmodel = FlorisModel(configuration=YAML_INPUT) + + # Check that if the turbine type is changed, a warning is raised/not raised regarding the + # reference wind height + with caplog.at_level(logging.WARNING): + fmodel.set(turbine_type=["iea_15MW"]) + assert caplog.text != "" # Checking not empty + caplog.clear() + with caplog.at_level(logging.WARNING): + fmodel.set(turbine_type=["iea_15MW"], reference_wind_height=100.0) + assert caplog.text == "" # Checking empty + + # Check that assigning the reference wind height to the turbine hub height works + assert fmodel.core.flow_field.reference_wind_height == 100.0 # Set in line above + fmodel.assign_hub_height_to_ref_height() + assert fmodel.core.flow_field.reference_wind_height == 150.0 # 150m is HH for IEA 15MW + + with pytest.raises(ValueError): + fmodel.set( + layout_x = [0.0, 0.0], + layout_y = [0.0, 1000.0], + turbine_type=["nrel_5MW", "iea_15MW"] + ) + fmodel.assign_hub_height_to_ref_height() # Shouldn't allow due to multiple turbine types diff --git a/tests/layout_optimization_integration_test.py b/tests/layout_optimization_integration_test.py index 18353a8f5..cf848f7bc 100644 --- a/tests/layout_optimization_integration_test.py +++ b/tests/layout_optimization_integration_test.py @@ -12,6 +12,9 @@ from floris.optimization.layout_optimization.layout_optimization_base import ( LayoutOptimization, ) +from floris.optimization.layout_optimization.layout_optimization_gridded import ( + LayoutOptimizationGridded, +) from floris.optimization.layout_optimization.layout_optimization_random_search import ( LayoutOptimizationRandomSearch, ) @@ -24,14 +27,13 @@ TEST_DATA = Path(__file__).resolve().parent / "data" YAML_INPUT = TEST_DATA / "input_full.yaml" +test_boundaries = [(0.0, 0.0), (0.0, 1000.0), (1000.0, 1000.0), (1000.0, 0.0), (0.0, 0.0)] + def test_base_class(caplog): # Get a test fi fmodel = FlorisModel(configuration=YAML_INPUT) - # Set up a sample boundary - boundaries = [(0.0, 0.0), (0.0, 1000.0), (1000.0, 1000.0), (1000.0, 0.0), (0.0, 0.0)] - # Now initiate layout optimization with a frequency matrix passed in the 3rd position # (this should fail) freq = np.ones((5, 5)) @@ -39,12 +41,12 @@ def test_base_class(caplog): # Check that warning is raised if fmodel does not contain wind_data with caplog.at_level(logging.WARNING): - LayoutOptimization(fmodel, boundaries, 5) + LayoutOptimization(fmodel, test_boundaries, 5) assert caplog.text != "" # Checking not empty caplog.clear() with caplog.at_level(logging.WARNING): - LayoutOptimization(fmodel=fmodel, boundaries=boundaries, min_dist=5,) + LayoutOptimization(fmodel=fmodel, boundaries=test_boundaries, min_dist=5,) assert caplog.text != "" # Checking not empty time_series = TimeSeries( @@ -56,34 +58,31 @@ def test_base_class(caplog): caplog.clear() with caplog.at_level(logging.WARNING): - LayoutOptimization(fmodel, boundaries, 5) + LayoutOptimization(fmodel, test_boundaries, 5) assert caplog.text != "" # Not empty, because get_farm_AEP called on TimeSeries # Passing without keyword arguments should work, or with keyword arguments - LayoutOptimization(fmodel, boundaries, 5) - LayoutOptimization(fmodel=fmodel, boundaries=boundaries, min_dist=5) + LayoutOptimization(fmodel, test_boundaries, 5) + LayoutOptimization(fmodel=fmodel, boundaries=test_boundaries, min_dist=5) # Check with WindRose on fmodel fmodel.set(wind_data=time_series.to_WindRose()) caplog.clear() with caplog.at_level(logging.WARNING): - LayoutOptimization(fmodel, boundaries, 5) + LayoutOptimization(fmodel, test_boundaries, 5) assert caplog.text == "" # Empty - LayoutOptimization(fmodel, boundaries, 5) - LayoutOptimization(fmodel=fmodel, boundaries=boundaries, min_dist=5) + LayoutOptimization(fmodel, test_boundaries, 5) + LayoutOptimization(fmodel=fmodel, boundaries=test_boundaries, min_dist=5) def test_LayoutOptimizationRandomSearch(): fmodel = FlorisModel(configuration=YAML_INPUT) - fmodel.set(layout_x=[0, 500], layout_y = [0, 0]) - - # Set up a sample boundary - boundaries = [(0.0, 0.0), (0.0, 1000.0), (1000.0, 1000.0), (1000.0, 0.0), (0.0, 0.0)] + fmodel.set(layout_x=[0, 500], layout_y=[0, 0]) layout_opt = LayoutOptimizationRandomSearch( fmodel=fmodel, - boundaries=boundaries, + boundaries=test_boundaries, min_dist_D=5, seconds_per_iteration=1, total_optimization_seconds=1, @@ -92,3 +91,199 @@ def test_LayoutOptimizationRandomSearch(): # Check that the optimization runs layout_opt.optimize() + +def test_LayoutOptimizationGridded_initialization(caplog): + fmodel = FlorisModel(configuration=YAML_INPUT) + fmodel.set(layout_x=[0, 500], layout_y=[0, 0]) + + with pytest.raises(ValueError): + LayoutOptimizationGridded( + fmodel=fmodel, + boundaries=test_boundaries, + min_dist=None, + min_dist_D=None, + ) # No min_dist specified + with pytest.raises(ValueError): + LayoutOptimizationGridded( + fmodel=fmodel, + boundaries=test_boundaries, + min_dist=500, + min_dist_D=5 + ) # min_dist specified in two ways + + fmodel.core.farm.rotor_diameters[1] = 100.0 + caplog.clear() + with caplog.at_level(logging.WARNING): + LayoutOptimizationGridded( + fmodel, + test_boundaries, + min_dist_D=5 + ) + +def test_LayoutOptimizationGridded_basic(): + fmodel = FlorisModel(configuration=YAML_INPUT) + + min_dist = 60 + + layout_opt = LayoutOptimizationGridded( + fmodel=fmodel, + boundaries=test_boundaries, + min_dist=min_dist, + rotation_step=5, + rotation_range=(0, 360), + translation_step=50, + hexagonal_packing=False, + ) + + n_turbs_opt, x_opt, y_opt = layout_opt.optimize() + + # Check that the number of turbines is correct + assert n_turbs_opt == len(x_opt) + + # Check that min_dist is respected + xx_diff = x_opt.reshape(-1,1) - x_opt.reshape(1,-1) + yy_diff = y_opt.reshape(-1,1) - y_opt.reshape(1,-1) + dists = np.sqrt(xx_diff**2 + yy_diff**2) + dists[np.arange(0, len(dists), 1), np.arange(0, len(dists), 1)] = np.inf + assert (dists > min_dist - 1e-6).all() + + # Check all are indeed in bounds + assert (np.all(x_opt > 0.0) & np.all(x_opt < 1000.0) + & np.all(y_opt > 0.0) & np.all(y_opt < 1000.0)) + + # Check that the layout is at least as good as the basic rectangular fill + n_turbs_subopt = (1000 // min_dist + 1) ** 2 + + assert n_turbs_opt >= n_turbs_subopt + +def test_LayoutOptimizationGridded_diagonal(): + fmodel = FlorisModel(configuration=YAML_INPUT) + + turbine_spacing = 1000.0 + corner = 2*turbine_spacing / np.sqrt(2) + + # Create a "thin" boundary area at a 45 degree angle + boundaries_diag = [ + (0.0, 0.0), + (0.0, 100.0), + (corner, corner+100.0), + (corner+100.0, corner+100.0), + (0.0, 0.0) + ] + + layout_opt = LayoutOptimizationGridded( + fmodel=fmodel, + boundaries=boundaries_diag, + min_dist=turbine_spacing, + rotation_step=45, # To speed up test + rotation_range=(0, 360), + translation_step=50, + hexagonal_packing=False, + ) + + n_turbs_opt, x_opt, y_opt = layout_opt.optimize() + + # Confirm that spacing is respected + xx_diff = x_opt.reshape(-1,1) - x_opt.reshape(1,-1) + yy_diff = y_opt.reshape(-1,1) - y_opt.reshape(1,-1) + dists = np.sqrt(xx_diff**2 + yy_diff**2) + dists[np.arange(0, len(dists), 1), np.arange(0, len(dists), 1)] = np.inf + assert (dists > turbine_spacing - 1e-6).all() + + assert n_turbs_opt == 3 # 3 should fit in the diagonal + + # Test a limited range of rotation + layout_opt = LayoutOptimizationGridded( + fmodel=fmodel, + boundaries=boundaries_diag, + min_dist=turbine_spacing, + rotation_step=5, + rotation_range=(0, 10), + translation_step=50, + hexagonal_packing=False, + ) + n_turbs_opt, x_opt, y_opt = layout_opt.optimize() + assert n_turbs_opt < 3 + + # Test a coarse rotation + layout_opt = LayoutOptimizationGridded( + fmodel=fmodel, + boundaries=boundaries_diag, + min_dist=turbine_spacing, + rotation_step=60, # Not fine enough to find ideal 45 deg rotation + rotation_range=(0, 360), + translation_step=50, + hexagonal_packing=False, + ) + n_turbs_opt, x_opt, y_opt = layout_opt.optimize() + assert n_turbs_opt < 3 + + # Test a coarse translation + layout_opt = LayoutOptimizationGridded( + fmodel=fmodel, + boundaries=boundaries_diag, + min_dist=turbine_spacing, + rotation_step=45, + rotation_range=(0, 10), + translation_step=300, + hexagonal_packing=False, + ) + n_turbs_opt, x_opt, y_opt = layout_opt.optimize() + assert n_turbs_opt < 3 + +def test_LayoutOptimizationGridded_separate_boundaries(): + fmodel = FlorisModel(configuration=YAML_INPUT) + separate_boundaries = [ + [(0.0, 0.0), (0.0, 100.0), (100.0, 100.0), (100.0, 0.0), (0.0, 0.0)], + [(200.0, 0.0), (200.0, 100.0), (300.0, 100.0), (300.0, 0.0), (200.0, 0.0)] + ] + + layout_opt = LayoutOptimizationGridded( + fmodel=fmodel, + boundaries=separate_boundaries, + min_dist=150, + rotation_step=5, + rotation_range=(0, 360), + translation_step=50, + hexagonal_packing=False, + ) + + n_turbs_opt, x_opt, y_opt = layout_opt.optimize() + assert n_turbs_opt == 2 # One in each of the boundary areas + + # Check they're inside as expected + assert ((0.0 <= y_opt) & (y_opt <= 100.0)).all() + assert (((0.0 <= x_opt) & (x_opt <= 100.0)) | ((200.0 <= x_opt) & (x_opt <= 300.0))).all() + + +def test_LayoutOptimizationGridded_hexagonal(): + fmodel = FlorisModel(configuration=YAML_INPUT) + + spacing = 200 + + # First, run a square layout + layout_opt = LayoutOptimizationGridded( + fmodel=fmodel, + boundaries=test_boundaries, + min_dist=spacing, + rotation_step=5, + rotation_range=(0, 360), + translation_step=50, + hexagonal_packing=False, + ) + n_turbs_opt_square = layout_opt.optimize()[0] + + # Now, run a hexagonal layout + layout_opt = LayoutOptimizationGridded( + fmodel=fmodel, + boundaries=test_boundaries, + min_dist=spacing, + rotation_step=5, + rotation_range=(0, 360), + translation_step=50, + hexagonal_packing=True, + ) + n_turbs_opt_hex = layout_opt.optimize()[0] + + # Check that the hexagonal layout is better + assert n_turbs_opt_hex >= n_turbs_opt_square diff --git a/tests/par_floris_model_unit_test.py b/tests/par_floris_model_unit_test.py new file mode 100644 index 000000000..9e56e4d8c --- /dev/null +++ b/tests/par_floris_model_unit_test.py @@ -0,0 +1,288 @@ + +import copy +import logging + +import numpy as np +import pytest + +from floris import ( + FlorisModel, + TimeSeries, + WindRose, +) +from floris.par_floris_model import ParFlorisModel + + +DEBUG = False +VELOCITY_MODEL = "gauss" +DEFLECTION_MODEL = "gauss" + +def test_None_interface(sample_inputs_fixture): + """ + With interface=None, the ParFlorisModel should behave exactly like the FlorisModel. + (ParFlorisModel.run() simply calls the parent FlorisModel.run()). + """ + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + + fmodel = FlorisModel(sample_inputs_fixture.core) + pfmodel = ParFlorisModel( + sample_inputs_fixture.core, + interface=None, + n_wind_condition_splits=2 # Not used when interface=None + ) + + fmodel.run() + pfmodel.run() + + f_turb_powers = fmodel.get_turbine_powers() + pf_turb_powers = pfmodel.get_turbine_powers() + + assert np.allclose(f_turb_powers, pf_turb_powers) + +def test_multiprocessing_interface(sample_inputs_fixture): + """ + With interface="multiprocessing", the ParFlorisModel should return the same powers + as the FlorisModel. + """ + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + + fmodel = FlorisModel(sample_inputs_fixture.core) + pfmodel = ParFlorisModel( + sample_inputs_fixture.core, + interface="multiprocessing", + n_wind_condition_splits=2 + ) + + fmodel.run() + pfmodel.run() + + f_turb_powers = fmodel.get_turbine_powers() + pf_turb_powers = pfmodel.get_turbine_powers() + + assert np.allclose(f_turb_powers, pf_turb_powers) + +def test_pathos_interface(sample_inputs_fixture): + """ + With interface="pathos", the ParFlorisModel should return the same powers + as the FlorisModel. + """ + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + + fmodel = FlorisModel(sample_inputs_fixture.core) + pfmodel = ParFlorisModel( + sample_inputs_fixture.core, + interface="pathos", + n_wind_condition_splits=2 + ) + + fmodel.run() + pfmodel.run() + + f_turb_powers = fmodel.get_turbine_powers() + pf_turb_powers = pfmodel.get_turbine_powers() + + assert np.allclose(f_turb_powers, pf_turb_powers) + + # Run in powers_only mode + pfmodel = ParFlorisModel( + sample_inputs_fixture.core, + interface="pathos", + n_wind_condition_splits=2, + return_turbine_powers_only=True + ) + + pfmodel.run() + pf_turb_powers = pfmodel.get_turbine_powers() + + assert np.allclose(f_turb_powers, pf_turb_powers) + +def test_concurrent_interface(sample_inputs_fixture): + """ + With interface="concurrent", the ParFlorisModel should return the same powers + as the FlorisModel. + """ + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + + fmodel = FlorisModel(sample_inputs_fixture.core) + pfmodel = ParFlorisModel( + sample_inputs_fixture.core, + interface="concurrent", + n_wind_condition_splits=2, + ) + + fmodel.run() + pfmodel.run() + + f_turb_powers = fmodel.get_turbine_powers() + pf_turb_powers = pfmodel.get_turbine_powers() + + assert np.allclose(f_turb_powers, pf_turb_powers) + + # Run in powers_only mode + pfmodel = ParFlorisModel( + sample_inputs_fixture.core, + interface="concurrent", + n_wind_condition_splits=2, + return_turbine_powers_only=True + ) + + pfmodel.run() + pf_turb_powers = pfmodel.get_turbine_powers() + + assert np.allclose(f_turb_powers, pf_turb_powers) + +def test_return_turbine_powers_only(sample_inputs_fixture): + """ + With return_turbine_powers_only=True, the ParFlorisModel should return only the + turbine powers, not the full results. + """ + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + + fmodel = FlorisModel(sample_inputs_fixture.core) + pfmodel = ParFlorisModel( + sample_inputs_fixture.core, + interface="multiprocessing", + n_wind_condition_splits=2, + return_turbine_powers_only=True + ) + + fmodel.run() + pfmodel.run() + + f_turb_powers = fmodel.get_turbine_powers() + pf_turb_powers = pfmodel.get_turbine_powers() + + assert np.allclose(f_turb_powers, pf_turb_powers) + +def test_run_error(sample_inputs_fixture, caplog): + """ + Check that an error is raised if an output is requested before calling run(). + """ + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + + pfmodel = ParFlorisModel( + sample_inputs_fixture.core, + interface="multiprocessing", + n_wind_condition_splits=2 + ) + + # In future versions, error will be raised + # with pytest.raises(RuntimeError): + # pfmodel.get_turbine_powers() + # with pytest.raises(RuntimeError): + # pfmodel.get_farm_AEP() + + # For now, only a warning is raised for backwards compatibility + with caplog.at_level(logging.WARNING): + pfmodel.get_turbine_powers() + assert caplog.text != "" # Checking not empty + caplog.clear() + +def test_configuration_compatibility(sample_inputs_fixture, caplog): + """ + Check that the ParFlorisModel is compatible with FlorisModel and + UncertainFlorisModel configurations. + """ + + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + + fmodel = FlorisModel(sample_inputs_fixture.core) + + # Allowed to instantiate ParFlorisModel using fmodel + with caplog.at_level(logging.WARNING): + ParFlorisModel(fmodel) + assert caplog.text == "" # Checking empty + caplog.clear() + + pfmodel = ParFlorisModel(sample_inputs_fixture.core) + with caplog.at_level(logging.WARNING): + pfmodel.fmodel + assert caplog.text != "" # Checking not empty + caplog.clear() + + with pytest.raises(AttributeError): + pfmodel.fmodel.core + +def test_wind_data_objects(sample_inputs_fixture): + """ + Check that the ParFlorisModel is compatible with WindData objects. + """ + + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + + fmodel = FlorisModel(sample_inputs_fixture.core) + pfmodel = ParFlorisModel(sample_inputs_fixture.core, max_workers=2) + + # Create a wind rose and set onto both models + wind_speeds = np.array([8.0, 10.0, 12.0, 8.0, 10.0, 12.0]) + wind_directions = np.array([270.0, 270.0, 270.0, 280.0, 280.0, 280.0]) + wind_rose = WindRose( + wind_directions=np.unique(wind_directions), + wind_speeds=np.unique(wind_speeds), + ti_table=0.06 + ) + fmodel.set(wind_data=wind_rose) + pfmodel.set(wind_data=wind_rose) + + # Run; get turbine powers; compare results + fmodel.run() + powers_fmodel_wr = fmodel.get_turbine_powers() + pfmodel.run() + powers_pfmodel_wr = pfmodel.get_turbine_powers() + + assert powers_fmodel_wr.shape == powers_pfmodel_wr.shape + assert np.allclose(powers_fmodel_wr, powers_pfmodel_wr) + + # Test a TimeSeries object + wind_speeds = np.array([8.0, 8.0, 9.0]) + wind_directions = np.array([270.0, 270.0, 270.0]) + values = np.array([30.0, 20.0, 10.0]) + time_series = TimeSeries( + wind_directions=wind_directions, + wind_speeds=wind_speeds, + turbulence_intensities=0.06, + values=values, + ) + fmodel.set(wind_data=time_series) + pfmodel.set(wind_data=time_series) + + fmodel.run() + powers_fmodel_ts = fmodel.get_turbine_powers() + pfmodel.run() + powers_pfmodel_ts = pfmodel.get_turbine_powers() + + assert powers_fmodel_ts.shape == powers_pfmodel_ts.shape + assert np.allclose(powers_fmodel_ts, powers_pfmodel_ts) + +def test_control_setpoints(sample_inputs_fixture): + """ + Check that the ParFlorisModel is compatible with yaw angles. + """ + + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + + fmodel = FlorisModel(sample_inputs_fixture.core) + pfmodel = ParFlorisModel(sample_inputs_fixture.core, max_workers=2) + + # Set yaw angles + yaw_angles = np.tile(np.array([[10.0, 20.0, 30.0]]), (fmodel.n_findex,1)) + fmodel.set(yaw_angles=yaw_angles) + pfmodel.set(yaw_angles=yaw_angles) + + # Run; get turbine powers; compare results + fmodel.run() + powers_fmodel = fmodel.get_turbine_powers() + pfmodel.run() + powers_pfmodel = pfmodel.get_turbine_powers() + + assert powers_fmodel.shape == powers_pfmodel.shape + assert np.allclose(powers_fmodel, powers_pfmodel) diff --git a/tests/parallel_floris_model_integration_test.py b/tests/parallel_floris_model_integration_test.py index 4b4d5aeec..21857b5b3 100644 --- a/tests/parallel_floris_model_integration_test.py +++ b/tests/parallel_floris_model_integration_test.py @@ -1,7 +1,9 @@ import copy +import logging import numpy as np +import pytest from floris import ( FlorisModel, @@ -17,11 +19,31 @@ VELOCITY_MODEL = "gauss" DEFLECTION_MODEL = "gauss" +def test_raise_deprecation_warning(sample_inputs_fixture, caplog): + """ + Test that a warning is raised when instantiating the ParallelFlorisModel. + """ + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + + fmodel = FlorisModel(sample_inputs_fixture.core) + + caplog.clear() + with caplog.at_level(logging.WARNING): + ParallelFlorisModel( + fmodel=fmodel, + max_workers=2, + n_wind_condition_splits=2, + interface="concurrent", + print_timings=False, + ) + assert caplog.text != "" # Checking not empty + caplog.clear() def test_parallel_turbine_powers(sample_inputs_fixture): """ The parallel computing interface behaves like the floris interface, but distributes - calculations among available cores to speep up the necessary computations. This test compares + calculations among available cores to speed up the necessary computations. This test compares the individual turbine powers computed with the parallel interface to those computed with the serial floris interface. The expected result is that the turbine powers should be exactly the same. @@ -76,7 +98,7 @@ def test_parallel_get_AEP(sample_inputs_fixture): assert np.allclose(parallel_farm_AEP, serial_farm_AEP) -def test_parallel_uncertain_turbine_powers(sample_inputs_fixture): +def test_parallel_uncertain_error(sample_inputs_fixture): """ """ @@ -88,53 +110,12 @@ def test_parallel_uncertain_turbine_powers(sample_inputs_fixture): wd_sample_points=[-3, 0, 3], wd_std=3 ) - pfmodel_input = copy.deepcopy(ufmodel) - ufmodel.run() - - serial_turbine_powers = ufmodel.get_turbine_powers() - - pfmodel = ParallelFlorisModel( - fmodel=pfmodel_input, - max_workers=2, - n_wind_condition_splits=2, - interface="multiprocessing", - print_timings=False, - ) - - parallel_turbine_powers = pfmodel.get_turbine_powers() - - if DEBUG: - print(serial_turbine_powers) - print(parallel_turbine_powers) - assert_results_arrays(parallel_turbine_powers, serial_turbine_powers) - -def test_parallel_uncertain_get_AEP(sample_inputs_fixture): - """ - - """ - sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL - sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL - - freq=np.linspace(0, 1, 16)/8 - - ufmodel = UncertainFlorisModel( - sample_inputs_fixture.core, - wd_sample_points=[-3, 0, 3], - wd_std=3 - ) - pfmodel_input = copy.deepcopy(ufmodel) - ufmodel.run() - serial_farm_AEP = ufmodel.get_farm_AEP(freq=freq) - - pfmodel = ParallelFlorisModel( - fmodel=pfmodel_input, - max_workers=2, - n_wind_condition_splits=2, - interface="multiprocessing", - print_timings=False, - ) - - parallel_farm_AEP = pfmodel.get_farm_AEP(freq=freq) - - assert np.allclose(parallel_farm_AEP, serial_farm_AEP) + with pytest.raises(ValueError): + ParallelFlorisModel( + fmodel=ufmodel, + max_workers=2, + n_wind_condition_splits=2, + interface="multiprocessing", + print_timings=False, + ) diff --git a/tests/reg_tests/turboparkgauss_regression_test.py b/tests/reg_tests/turboparkgauss_regression_test.py new file mode 100644 index 000000000..1548b7144 --- /dev/null +++ b/tests/reg_tests/turboparkgauss_regression_test.py @@ -0,0 +1,494 @@ + +import numpy as np + +from floris.core import ( + average_velocity, + axial_induction, + Core, + power, + rotor_effective_velocity, + thrust_coefficient, +) +from tests.conftest import ( + assert_results_arrays, + N_FINDEX, + N_TURBINES, + print_test_values, +) + + +DEBUG = False +VELOCITY_MODEL = "turboparkgauss" +DEFLECTION_MODEL = "gauss" +COMBINATION_MODEL = "sosfs" + +baseline = np.array( + [ + # 8 m/s + [ + [7.9736858, 0.7871515, 1753954.4591792, 0.2693224], + [5.3669227, 0.8968386, 526338.6265211, 0.3394063], + [4.7291434, 0.9398463, 342625.1907593, 0.3773687], + ], + # 9 m/s + [ + [8.9703965, 0.7858774, 2496427.8618358, 0.2686331], + [6.0385619, 0.8590958, 754925.9561188, 0.3123139], + [5.2198714, 0.9051982, 477269.3475684, 0.3460505], + ], + # 10 m/s + [ + [9.9671073, 0.7838789, 3417797.0050916, 0.2675559], + [6.7109723, 0.8285157, 1057233.8964038, 0.2929467], + [5.7609373, 0.8744397, 657816.5966079, 0.3228276], + ], + # 11 m/s + [ + [10.9638180, 0.7565157, 4519404.3072862, 0.2532794], + [7.4177796, 0.8004049, 1413470.6329668, 0.2766196], + [6.3467168, 0.8450814, 893468.8191848, 0.3032015], + ], + ] +) + + +yawed_baseline = np.array( + [ + # 8 m/s + [ + [7.9736858, 0.7841561, 1741508.6722008, 0.2671213], + [5.3686096, 0.8967427, 526901.4969868, 0.3393316], + [4.7296392, 0.9398058, 342737.3617937, 0.3773274], + ], + # 9 m/s + [ + [8.9703965, 0.7828869, 2480428.8963141, 0.2664440], + [6.0405714, 0.8590044, 755829.3886024, 0.3122531], + [5.2206194, 0.9051556, 477518.9548881, 0.3460159], + ], + # 10 m/s + [ + [9.9671073, 0.7808960, 3395681.0032992, 0.2653854], + [6.7133964, 0.8284054, 1058323.7446597, 0.2928801], + [5.7619351, 0.8743830, 658149.5244311, 0.3227875], + ], + # 11 m/s + [ + [10.9638180, 0.7536370, 4488242.9153943, 0.2513413], + [7.4229011, 0.8002352, 1416412.6499511, 0.2765247], + [6.3490875, 0.8449736, 894534.6529145, 0.3031330], + ], + ] +) + +full_flow_baseline = np.array( + [ + [ + [ + [7.88772361, 8. , 8.10178821], + [7.88772361, 8. , 8.10178821], + [7.88772361, 8. , 8.10178821], + [7.88772361, 8. , 8.10178821], + [7.88772361, 8. , 8.10178821], + ], + [ + [7.88772229, 7.99999863, 8.10178685], + [7.79725047, 7.90606371, 8.00885965], + [4.18190854, 4.15233328, 4.29539865], + [7.79725047, 7.90606371, 8.00885965], + [7.88772229, 7.99999863, 8.10178685], + ], + [ + [7.88768632, 7.99996148, 8.1017499 ], + [7.66326846, 7.7681154 , 7.87123883], + [3.69538982, 3.66849132, 3.79562999], + [7.66326846, 7.7681154 , 7.87123883], + [7.88768632, 7.99996148, 8.1017499 ], + ], + [ + [7.88740669, 7.99967377, 8.10146266], + [7.50793067, 7.6089272 , 7.71165714], + [3.64994795, 3.63535913, 3.74869 ], + [7.50793067, 7.6089272 , 7.71165714], + [7.88740669, 7.99967377, 8.10146266], + ], + [ + [7.88664826, 7.99889554, 8.10068331], + [7.44424308, 7.54429736, 7.64614946], + [4.32643439, 4.33927499, 4.44299895], + [7.44424308, 7.54429736, 7.64614946], + [7.88664826, 7.99889554, 8.10068331], + ] + ] + ] +) + +# Note: compare the yawed vs non-yawed results. The upstream turbine +# power should be lower in the yawed case. The following turbine +# powers should higher in the yawed case. + + +def test_regression_tandem(sample_inputs_fixture): + """ + Tandem turbines + """ + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["combination_model"] = COMBINATION_MODEL + + floris = Core.from_dict(sample_inputs_fixture.core) + floris.initialize_domain() + floris.steady_state_atmospheric_condition() + + n_turbines = floris.farm.n_turbines + n_findex = floris.flow_field.n_findex + + velocities = floris.flow_field.u + turbulence_intensities = floris.flow_field.turbulence_intensity_field + air_density = floris.flow_field.air_density + yaw_angles = floris.farm.yaw_angles + tilt_angles = floris.farm.tilt_angles + power_setpoints = floris.farm.power_setpoints + awc_modes = floris.farm.awc_modes + awc_amplitudes = floris.farm.awc_amplitudes + test_results = np.zeros((n_findex, n_turbines, 4)) + + farm_avg_velocities = average_velocity( + velocities, + ) + farm_cts = thrust_coefficient( + velocities, + turbulence_intensities, + air_density, + yaw_angles, + tilt_angles, + power_setpoints, + awc_modes, + awc_amplitudes, + floris.farm.turbine_thrust_coefficient_functions, + floris.farm.turbine_tilt_interps, + floris.farm.correct_cp_ct_for_tilt, + floris.farm.turbine_type_map, + floris.farm.turbine_power_thrust_tables, + ) + farm_powers = power( + velocities, + turbulence_intensities, + air_density, + floris.farm.turbine_power_functions, + yaw_angles, + tilt_angles, + power_setpoints, + awc_modes, + awc_amplitudes, + floris.farm.turbine_tilt_interps, + floris.farm.turbine_type_map, + floris.farm.turbine_power_thrust_tables, + ) + farm_axial_inductions = axial_induction( + velocities, + turbulence_intensities, + air_density, + yaw_angles, + tilt_angles, + power_setpoints, + awc_modes, + awc_amplitudes, + floris.farm.turbine_axial_induction_functions, + floris.farm.turbine_tilt_interps, + floris.farm.correct_cp_ct_for_tilt, + floris.farm.turbine_type_map, + floris.farm.turbine_power_thrust_tables, + ) + for i in range(n_findex): + for j in range(n_turbines): + test_results[i, j, 0] = farm_avg_velocities[i, j] + test_results[i, j, 1] = farm_cts[i, j] + test_results[i, j, 2] = farm_powers[i, j] + test_results[i, j, 3] = farm_axial_inductions[i, j] + + if DEBUG: + print_test_values( + farm_avg_velocities, + farm_cts, + farm_powers, + farm_axial_inductions, + max_findex_print=4, + ) + + assert_results_arrays(test_results[0:4], baseline) + + +def test_regression_rotation(sample_inputs_fixture): + """ + Turbines in tandem and rotated. + The result from 270 degrees should match the results from 360 degrees. + + Wind from the West (Left) + + ^ + | + y + + 1|1 3 + | + | + | + 0|0 2 + |----------| + 0 1 x-> + + + Wind from the North (Top), rotated + + ^ + | + y + + 1|3 2 + | + | + | + 0|1 0 + |----------| + 0 1 x-> + + In 270, turbines 2 and 3 are waked. In 360, turbines 0 and 2 are waked. + The test compares turbines 2 and 3 with 0 and 2 from 270 and 360. + """ + TURBINE_DIAMETER = 126.0 + + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["combination_model"] = COMBINATION_MODEL + sample_inputs_fixture.core["farm"]["layout_x"] = [ + 0.0, + 0.0, + 5 * TURBINE_DIAMETER, + 5 * TURBINE_DIAMETER, + ] + sample_inputs_fixture.core["farm"]["layout_y"] = [ + 0.0, + 5 * TURBINE_DIAMETER, + 0.0, + 5 * TURBINE_DIAMETER + ] + sample_inputs_fixture.core["flow_field"]["wind_directions"] = [270.0, 360.0] + sample_inputs_fixture.core["flow_field"]["wind_speeds"] = [8.0, 8.0] + sample_inputs_fixture.core["flow_field"]["turbulence_intensities"] = [0.1, 0.1] + + floris = Core.from_dict(sample_inputs_fixture.core) + floris.initialize_domain() + floris.steady_state_atmospheric_condition() + + farm_avg_velocities = average_velocity(floris.flow_field.u) + + t0_270 = farm_avg_velocities[0, 0] # upstream + t1_270 = farm_avg_velocities[0, 1] # upstream + t2_270 = farm_avg_velocities[0, 2] # waked + t3_270 = farm_avg_velocities[0, 3] # waked + + t0_360 = farm_avg_velocities[1, 0] # waked + t1_360 = farm_avg_velocities[1, 1] # upstream + t2_360 = farm_avg_velocities[1, 2] # waked + t3_360 = farm_avg_velocities[1, 3] # upstream + + assert np.allclose(t0_270, t1_360) + assert np.allclose(t1_270, t3_360) + assert np.allclose(t2_270, t0_360) + assert np.allclose(t3_270, t2_360) + + +def test_regression_yaw(sample_inputs_fixture): + """ + Tandem turbines with the upstream turbine yawed + """ + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + + floris = Core.from_dict(sample_inputs_fixture.core) + + yaw_angles = np.zeros((N_FINDEX, N_TURBINES)) + yaw_angles[:,0] = 5.0 + floris.farm.yaw_angles = yaw_angles + + floris.initialize_domain() + floris.steady_state_atmospheric_condition() + + n_turbines = floris.farm.n_turbines + n_findex = floris.flow_field.n_findex + + velocities = floris.flow_field.u + turbulence_intensities = floris.flow_field.turbulence_intensity_field + air_density = floris.flow_field.air_density + yaw_angles = floris.farm.yaw_angles + tilt_angles = floris.farm.tilt_angles + power_setpoints = floris.farm.power_setpoints + awc_modes = floris.farm.awc_modes + awc_amplitudes = floris.farm.awc_amplitudes + test_results = np.zeros((n_findex, n_turbines, 4)) + + farm_avg_velocities = average_velocity( + velocities, + ) + farm_cts = thrust_coefficient( + velocities, + turbulence_intensities, + air_density, + yaw_angles, + tilt_angles, + power_setpoints, + awc_modes, + awc_amplitudes, + floris.farm.turbine_thrust_coefficient_functions, + floris.farm.turbine_tilt_interps, + floris.farm.correct_cp_ct_for_tilt, + floris.farm.turbine_type_map, + floris.farm.turbine_power_thrust_tables, + ) + farm_powers = power( + velocities, + turbulence_intensities, + air_density, + floris.farm.turbine_power_functions, + yaw_angles, + tilt_angles, + power_setpoints, + awc_modes, + awc_amplitudes, + floris.farm.turbine_tilt_interps, + floris.farm.turbine_type_map, + floris.farm.turbine_power_thrust_tables, + ) + farm_axial_inductions = axial_induction( + velocities, + turbulence_intensities, + air_density, + yaw_angles, + tilt_angles, + power_setpoints, + awc_modes, + awc_amplitudes, + floris.farm.turbine_axial_induction_functions, + floris.farm.turbine_tilt_interps, + floris.farm.correct_cp_ct_for_tilt, + floris.farm.turbine_type_map, + floris.farm.turbine_power_thrust_tables, + ) + for i in range(n_findex): + for j in range(n_turbines): + test_results[i, j, 0] = farm_avg_velocities[i, j] + test_results[i, j, 1] = farm_cts[i, j] + test_results[i, j, 2] = farm_powers[i, j] + test_results[i, j, 3] = farm_axial_inductions[i, j] + + if DEBUG: + print_test_values( + farm_avg_velocities, + farm_cts, + farm_powers, + farm_axial_inductions, + max_findex_print=4, + ) + + assert_results_arrays(test_results[0:4], yawed_baseline) + +def test_regression_small_grid_rotation(sample_inputs_fixture): + """ + Where wake models are masked based on the x-location of a turbine, numerical precision + can cause masking to fail unexpectedly. For example, in the configuration here one of + the turbines has these delta x values; + + [[4.54747351e-13 4.54747351e-13 4.54747351e-13 4.54747351e-13 4.54747351e-13] + [4.54747351e-13 4.54747351e-13 4.54747351e-13 4.54747351e-13 4.54747351e-13] + [4.54747351e-13 4.54747351e-13 4.54747351e-13 4.54747351e-13 4.54747351e-13] + [4.54747351e-13 4.54747351e-13 4.54747351e-13 4.54747351e-13 4.54747351e-13] + [4.54747351e-13 4.54747351e-13 4.54747351e-13 4.54747351e-13 4.54747351e-13]] + + and therefore the masking statement is False when it should be True. This causes the current + turbine to be affected by its own wake. This test requires that at least in this particular + configuration the masking correctly filters grid points. + """ + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["combination_model"] = COMBINATION_MODEL + X, Y = np.meshgrid( + 6.0 * 126.0 * np.arange(0, 5, 1), + 6.0 * 126.0 * np.arange(0, 5, 1) + ) + X = X.flatten() + Y = Y.flatten() + + sample_inputs_fixture.core["farm"]["layout_x"] = X + sample_inputs_fixture.core["farm"]["layout_y"] = Y + + floris = Core.from_dict(sample_inputs_fixture.core) + floris.initialize_domain() + floris.steady_state_atmospheric_condition() + + # farm_avg_velocities = average_velocity(floris.flow_field.u) + velocities = floris.flow_field.u + turbulence_intensities = floris.flow_field.turbulence_intensity_field + yaw_angles = floris.farm.yaw_angles + tilt_angles = floris.farm.tilt_angles + power_setpoints = floris.farm.power_setpoints + awc_modes = floris.farm.awc_modes + awc_amplitudes = floris.farm.awc_amplitudes + + farm_powers = power( + velocities, + turbulence_intensities, + floris.flow_field.air_density, + floris.farm.turbine_power_functions, + yaw_angles, + tilt_angles, + power_setpoints, + awc_modes, + awc_amplitudes, + floris.farm.turbine_tilt_interps, + floris.farm.turbine_type_map, + floris.farm.turbine_power_thrust_tables, + ) + + # A "column" is oriented parallel to the wind direction + # Columns 1 - 4 should have the same power profile + # Column 5 leading turbine is completely unwaked + # and the rest of the turbines have a partial wake from their immediate upstream turbine + assert np.allclose(farm_powers[8,0:5], farm_powers[8,5:10]) + assert np.allclose(farm_powers[8,0:5], farm_powers[8,10:15]) + assert np.allclose(farm_powers[8,0:5], farm_powers[8,15:20]) + assert np.allclose(farm_powers[8,20], farm_powers[8,0]) + assert np.allclose(farm_powers[8,21], farm_powers[8,21:25]) + +# TurboParkGauss enables full_flow_solver +def test_full_flow_solver(sample_inputs_fixture): + """ + Full flow solver test with the flow field planar grid. + This requires one wind condition, and the grid is deliberately coarse to allow for + visually comparing results, as needed. + The u-component of velocity is compared, and the array has the shape + (n_findex, n_turbines, n grid points in x, n grid points in y, 3 grid points in z). + """ + + sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + sample_inputs_fixture.core["solver"] = { + "type": "flow_field_planar_grid", + "normal_vector": "z", + "planar_coordinate": sample_inputs_fixture.core["farm"]["turbine_type"][0]["hub_height"], + "flow_field_grid_points": [5, 5], + "flow_field_bounds": [None, None], + } + sample_inputs_fixture.core["flow_field"]["wind_directions"] = [270.0] + sample_inputs_fixture.core["flow_field"]["wind_speeds"] = [8.0] + sample_inputs_fixture.core["flow_field"]["turbulence_intensities"] = [0.1] + + floris = Core.from_dict(sample_inputs_fixture.core) + floris.solve_for_viz() + + velocities = floris.flow_field.u_sorted + + if DEBUG: + print(velocities) + + assert_results_arrays(velocities, full_flow_baseline) diff --git a/tests/turboparkgauss_unit_test.py b/tests/turboparkgauss_unit_test.py new file mode 100644 index 000000000..9561ad007 --- /dev/null +++ b/tests/turboparkgauss_unit_test.py @@ -0,0 +1,80 @@ +from pathlib import Path + +import numpy as np + +from floris import FlorisModel +from floris.turbine_library import build_cosine_loss_turbine_dict + + +TEST_DATA = Path(__file__).resolve().parent / "data" +YAML_INPUT = TEST_DATA / "input_full.yaml" + +def test_row_of_turbines(): + + fmodel = FlorisModel(configuration=YAML_INPUT) + + # Configure as turboparkgauss + fmodel_dict = fmodel.core.as_dict() + fmodel_dict["wake"]["model_strings"]["velocity_model"] = "turboparkgauss" + fmodel_dict["wake"]["model_strings"]["turbulence_model"] = "none" + fmodel_dict["wake"]["model_strings"]["deflection_model"] = "none" + fmodel_dict["wake"]["model_strings"]["combination_model"] = "sosfs" + fmodel_dict["wake"]["enable_secondary_steering"] = False + fmodel_dict["wake"]["enable_yaw_added_recovery"] = False + fmodel_dict["wake"]["enable_active_wake_mixing"] = False + fmodel_dict["wake"]["enable_transverse_velocities"] = False + fmodel_dict["solver"]["type"] = "turbine_cubature_grid" + fmodel_dict["solver"]["turbine_grid_points"] = 6 + fmodel = FlorisModel(configuration=fmodel_dict) + + # Define turbine + const_CT_turb = build_cosine_loss_turbine_dict( + turbine_data_dict={ + "wind_speed":[0.0, 30.0], + "power":[0.0, 1.0], # Not realistic but won't be used here + "thrust_coefficient":[0.75, 0.75] + }, + turbine_name="ConstantCT", + rotor_diameter=120.0, + hub_height=100.0, + ref_tilt=0.0, + ) + + # Set up problem and run + fmodel.set( + layout_x=np.linspace(0.0, 5400.0, 10), + layout_y=np.zeros(10), + wind_speeds=[8.0], + wind_directions=[270.0], + turbulence_intensities=[0.06], + wind_shear=0.0, + turbine_type=[const_CT_turb], + ) + fmodel.run() + + # Run and extract flow velocities at the turbines + velocities_row_normalized = fmodel.turbine_average_velocities[0,:] / 8.0 + + # Comparison data generated by running Ørsted's Matlab code + # https://github.com/OrstedRD/TurbOPark + velocities_comparison = np.array([ + 1.0, + 0.709920677983239, + 0.615355749367675, + 0.551410465937128, + 0.502600655337247, + 0.463167556093190, + 0.430238792036599, + 0.402137593655074, + 0.377783142608699, + 0.356429516711137, + ]) + + # Compare the results + print(velocities_row_normalized) + + np.testing.assert_allclose( + velocities_row_normalized, + velocities_comparison, + rtol=1e-2, + ) # Within 1% tolerance diff --git a/tests/uncertain_floris_model_integration_test.py b/tests/uncertain_floris_model_integration_test.py index cdf3374c4..e8e95d513 100644 --- a/tests/uncertain_floris_model_integration_test.py +++ b/tests/uncertain_floris_model_integration_test.py @@ -4,7 +4,11 @@ import pytest import yaml -from floris import FlorisModel, TimeSeries +from floris import ( + FlorisModel, + ParFlorisModel, + TimeSeries, +) from floris.core.turbine.operation_models import POWER_SETPOINT_DEFAULT from floris.uncertain_floris_model import ( ApproxFlorisModel, @@ -233,7 +237,7 @@ def test_get_powers_with_wind_data(): wind_directions=wind_directions, turbulence_intensities=turbulence_intensities, layout_x=[0, 1000, 2000, 3000], - layout_y=[0, 0, 0, 0] + layout_y=[0, 0, 0, 0], ) ufmodel.run() farm_power_simple = ufmodel.get_farm_power() @@ -243,7 +247,7 @@ def test_get_powers_with_wind_data(): wind_rose = WindRose( wind_directions=np.unique(wind_directions), wind_speeds=np.unique(wind_speeds), - ti_table=0.06 + ti_table=0.06, ) # Set this wind rose, run @@ -265,18 +269,19 @@ def test_get_powers_with_wind_data(): turbine_weights = np.array([1.0, 1.0, 1.0, 0.0]) farm_power_weighted = ufmodel.get_farm_power(turbine_weights=turbine_weights) - assert np.allclose(farm_power_weighted, ufmodel.get_turbine_powers()[:,:,:-1].sum(axis=2)) + assert np.allclose(farm_power_weighted, ufmodel.get_turbine_powers()[:, :, :-1].sum(axis=2)) -def test_approx_floris_model(): +def test_approx_floris_model(): afmodel = ApproxFlorisModel(configuration=YAML_INPUT, wd_resolution=1.0) time_series = TimeSeries( - wind_directions = np.array([270.0, 270.1,271.0, 271.1]), + wind_directions=np.array([270.0, 270.1, 271.0, 271.1]), wind_speeds=8.0, - turbulence_intensities=0.06) + turbulence_intensities=0.06, + ) - afmodel.set(layout_x = np.array([0, 500]), layout_y = np.array([0, 0]), wind_data = time_series) + afmodel.set(layout_x=np.array([0, 500]), layout_y=np.array([0, 0]), wind_data=time_series) # Test that 0th and 1th values are the same, as are the 2nd and 3rd afmodel.run() @@ -287,14 +292,180 @@ def test_approx_floris_model(): # Test with wind direction and wind speed varying afmodel = ApproxFlorisModel(configuration=YAML_INPUT, wd_resolution=1.0, ws_resolution=1.0) time_series = TimeSeries( - wind_directions = np.array([270.0, 270.1,271.0, 271.1]), + wind_directions=np.array([270.0, 270.1, 271.0, 271.1]), wind_speeds=np.array([8.0, 8.1, 8.0, 9.0]), - turbulence_intensities=0.06) + turbulence_intensities=0.06, + ) - afmodel.set(layout_x = np.array([0, 500]), layout_y = np.array([0, 0]), wind_data = time_series) + afmodel.set(layout_x=np.array([0, 500]), layout_y=np.array([0, 0]), wind_data=time_series) afmodel.run() # In this case the 0th and 1st should be the same, but not the 2nd and 3rd power = afmodel.get_farm_power() np.testing.assert_almost_equal(power[0], power[1]) assert not np.allclose(power[2], power[3]) + + +def test_expected_farm_power_regression(): + ufmodel = UncertainFlorisModel( + configuration=YAML_INPUT, + wd_sample_points=[0], + ) # Force equal to nominal + + wind_speeds = np.array([8.0, 8.0, 8.0]) + wind_directions = np.array([270.0, 270.0, 270.0]) + turbulence_intensities = np.array([0.06, 0.06, 0.06]) + + layout_x = np.array([0, 0]) + layout_y = np.array([0, 1000]) + + ufmodel.set( + wind_speeds=wind_speeds, + wind_directions=wind_directions, + turbulence_intensities=turbulence_intensities, + layout_x=layout_x, + layout_y=layout_y, + ) + + ufmodel.run() + + expected_farm_power = ufmodel.get_expected_farm_power() + + # Assert the expected farm power has not inadvetently changed + np.testing.assert_allclose(expected_farm_power, 3507908.918358342, atol=1e-1) + + +def test_expected_farm_power_equals_sum_of_expected_turbine_powers(): + ufmodel = UncertainFlorisModel(configuration=YAML_INPUT) + + wind_speeds = np.array([8.0, 8.0, 8.0]) + wind_directions = np.array([270.0, 270.0, 270.0]) + turbulence_intensities = np.array([0.06, 0.06, 0.06]) + + layout_x = np.array([0, 0]) + layout_y = np.array([0, 1000]) + + ufmodel.set( + wind_speeds=wind_speeds, + wind_directions=wind_directions, + turbulence_intensities=turbulence_intensities, + layout_x=layout_x, + layout_y=layout_y, + ) + + ufmodel.run() + + expected_farm_power = ufmodel.get_expected_farm_power() + expected_turbine_powers = ufmodel.get_expected_turbine_powers() + + # Assert the expected farm power is the sum of the expected turbine powers + np.testing.assert_allclose(expected_farm_power, np.sum(expected_turbine_powers)) + + +def test_expected_farm_value_regression(): + # Ensure this calculation hasn't changed unintentionally + + ufmodel = UncertainFlorisModel( + configuration=YAML_INPUT, + wd_sample_points=[0], + ) # Force equal to nominal + + wind_speeds = np.array([8.0, 8.0, 9.0]) + wind_directions = np.array([270.0, 270.0, 270.0]) + values = np.array([30.0, 20.0, 10.0]) + time_series = TimeSeries( + wind_directions=wind_directions, + wind_speeds=wind_speeds, + turbulence_intensities=0.06, + values=values, + ) + + layout_x = np.array([0, 0]) + layout_y = np.array([0, 1000]) + ufmodel.set(layout_x=layout_x, layout_y=layout_y, wind_data=time_series) + ufmodel.run() + + expected_farm_value = ufmodel.get_expected_farm_value() + assert np.allclose(expected_farm_value, 75108001.05154414, atol=1e-1) + + +def test_get_and_set_param(): + ufmodel = UncertainFlorisModel(configuration=YAML_INPUT) + + # Set the wake parameter + ufmodel.set_param(["wake", "wake_velocity_parameters", "gauss", "alpha"], 0.1) + alpha = ufmodel.get_param(["wake", "wake_velocity_parameters", "gauss", "alpha"]) + assert alpha == 0.1 + + # Confirm also correct in expanded floris model + alpha_e = ufmodel.fmodel_expanded.get_param( + ["wake", "wake_velocity_parameters", "gauss", "alpha"] + ) + assert alpha_e == 0.1 + + +def test_get_operation_model(): + ufmodel = UncertainFlorisModel(configuration=YAML_INPUT) + assert ufmodel.get_operation_model() == "cosine-loss" + + +def test_set_operation_model(): + ufmodel = UncertainFlorisModel(configuration=YAML_INPUT) + ufmodel.set_operation_model("simple-derating") + assert ufmodel.get_operation_model() == "simple-derating" + + # Check multiple turbine types works + ufmodel.set(layout_x=[0, 0], layout_y=[0, 1000]) + ufmodel.set_operation_model(["simple-derating", "cosine-loss"]) + assert ufmodel.get_operation_model() == ["simple-derating", "cosine-loss"] + + # Confirm this passed through to expanded model + assert ufmodel.fmodel_expanded.get_operation_model() == ["simple-derating", "cosine-loss"] + + # Check that setting a single turbine type, and then altering the operation model works + ufmodel.set(layout_x=[0, 0], layout_y=[0, 1000]) + ufmodel.set(turbine_type=["nrel_5MW"]) + ufmodel.set_operation_model("simple-derating") + assert ufmodel.get_operation_model() == "simple-derating" + + # Check that setting over mutliple turbine types works + ufmodel.set(turbine_type=["nrel_5MW", "iea_15MW"]) + ufmodel.set_operation_model("simple-derating") + assert ufmodel.get_operation_model() == "simple-derating" + ufmodel.set_operation_model(["simple-derating", "cosine-loss"]) + assert ufmodel.get_operation_model() == ["simple-derating", "cosine-loss"] + + # Check setting over single turbine type; then updating layout works + ufmodel.set(turbine_type=["nrel_5MW"]) + ufmodel.set_operation_model("simple-derating") + ufmodel.set(layout_x=[0, 0, 0], layout_y=[0, 1000, 2000]) + assert ufmodel.get_operation_model() == "simple-derating" + + # Check that setting for multiple turbine types and then updating layout breaks + ufmodel.set(layout_x=[0, 0], layout_y=[0, 1000]) + ufmodel.set(turbine_type=["nrel_5MW"]) + ufmodel.set_operation_model(["simple-derating", "cosine-loss"]) + assert ufmodel.get_operation_model() == ["simple-derating", "cosine-loss"] + with pytest.raises(ValueError): + ufmodel.set(layout_x=[0, 0, 0], layout_y=[0, 1000, 2000]) + + # Check one more variation + ufmodel.set(layout_x=[0, 0], layout_y=[0, 1000]) + ufmodel.set(turbine_type=["nrel_5MW", "iea_15MW"]) + ufmodel.set_operation_model("simple-derating") + ufmodel.set(layout_x=[0, 0], layout_y=[0, 1000]) + with pytest.raises(ValueError): + ufmodel.set(layout_x=[0, 0, 0], layout_y=[0, 1000, 2000]) + +def test_parallel_uncertain_model(): + + ufmodel = UncertainFlorisModel(FlorisModel(configuration=YAML_INPUT)) + pufmodel = UncertainFlorisModel(ParFlorisModel(configuration=YAML_INPUT)) + + # Run the models and compare outputs + ufmodel.run() + pufmodel.run() + powers_unc = ufmodel.get_turbine_powers() + powers_punc = pufmodel.get_turbine_powers() + + assert np.allclose(powers_unc, powers_punc) diff --git a/tests/wind_rose_wrg_test.py b/tests/wind_rose_wrg_test.py new file mode 100644 index 000000000..3b324a58a --- /dev/null +++ b/tests/wind_rose_wrg_test.py @@ -0,0 +1,218 @@ +import math +from pathlib import Path + +import numpy as np +import pytest + +from floris import ( + FlorisModel, + UncertainFlorisModel, + WindRoseWRG, +) + + +WRG_FILE_FILE = ( + Path(__file__).resolve().parent / "../examples/examples_wind_resource_grid/wrg_example.wrg" +) + +TEST_DATA = Path(__file__).resolve().parent / "data" +YAML_INPUT = TEST_DATA / "input_full.yaml" + +def test_load_wrg(): + WindRoseWRG(WRG_FILE_FILE) + + +def test_read_header(): + """Test reading the header of a WRG file. + + The header of a WRG file is the first line of the file and contains the + number of grid points in x and y, the minimum x and y coordinates, and the + grid size. + """ + + wind_rose_wrg = WindRoseWRG(WRG_FILE_FILE) + + assert wind_rose_wrg.nx == 2 + assert wind_rose_wrg.ny == 3 + assert wind_rose_wrg.xmin == 0.0 + assert wind_rose_wrg.ymin == 0.0 + assert wind_rose_wrg.grid_size == 1000.0 + + # Test x and y arrays + assert np.allclose(wind_rose_wrg.x_array, np.array([0.0, 1000.0])) + assert np.allclose(wind_rose_wrg.y_array, np.array([0.0, 1000.0, 2000.0])) + + # Test number of grid points + assert wind_rose_wrg.n_gid == 6 + + # Test number of sectors + assert wind_rose_wrg.n_sectors == 12 + + +def test_read_data(): + """Test reading the data of a WRG file. + + The data of a WRG file is the information about each grid point, including + the x, y, z, and h coordinates, the frequency of each sector, and the Weibull + parameters for each sector. + """ + + wind_rose_wrg = WindRoseWRG(WRG_FILE_FILE) + + # Test z and h values + assert wind_rose_wrg.z == 0.0 + assert wind_rose_wrg.h == 90.0 + + # Test the first and last gid for sector_freq, weibull_A, and weibull_k + assert wind_rose_wrg.sector_freq[0, 0, 0] == 116 / 1000.0 + assert wind_rose_wrg.sector_freq[-1, -1, -1] == 98 / 1000.0 + + assert wind_rose_wrg.weibull_A[0, 0, 0] == 106 / 10.0 + assert wind_rose_wrg.weibull_A[-1, -1, -1] == 111 / 10.0 + + assert wind_rose_wrg.weibull_k[0, 0, 0] == 273 / 100.0 + assert wind_rose_wrg.weibull_k[-1, -1, -1] == 267 / 100.0 + + +def test_build_interpolant_function_list(): + + wind_rose_wrg = WindRoseWRG(WRG_FILE_FILE) + + # Initialize the values + x = np.array([0.0, 1000.0]) + y = np.array([0.0, 500.0, 1000.0]) + n_sectors = 3 + data = np.ones((2, 3, 3)) + + # For the first sector, the point at (1000, 0) is 2.0 + data[1, 0, 0] = 2.0 + + # For the second sector, the point at (1000, 500) is 3.0 + data[1, 1, 1] = 3.0 + + function_list = wind_rose_wrg._build_interpolant_function_list(x, y, n_sectors, data) + + # Length of the function list should be n_sectors + assert len(function_list) == n_sectors + + # Test the interpolation in the first sector + test_value = function_list[0]((500, 0)) + assert test_value == 1.5 + + # Test the interpolation in the second sector + test_value = function_list[1]((1000, 250)) + assert test_value == 2.0 + + # Test using linear method + test_value = function_list[0]((500, 0), method="linear") + assert test_value == 1.5 + + # Test using nearest method + test_value = function_list[0]((1001, 0), method="nearest") + assert test_value == 2.0 + + +def test_interpolate_data(): + wind_rose_wrg = WindRoseWRG(WRG_FILE_FILE) + + sector_freq = wind_rose_wrg.sector_freq + + # Test that interpolating onto the point 0,0 returns the 1st row of the sector_freq + test_value = wind_rose_wrg._interpolate_data(0, 0, wind_rose_wrg.interpolant_sector_freq) + assert np.allclose(test_value, sector_freq[0, 0, :]) + + # Test the interpolating just out of bounds of 0,0 also yields the 1st row of the sector_freq + test_value = wind_rose_wrg._interpolate_data(-1, -1, wind_rose_wrg.interpolant_sector_freq) + assert np.allclose(test_value, sector_freq[0, 0, :]) + + # Test that value at x=500, y0, this is the average of the rows at [0,0] and [1,0] + test_value = wind_rose_wrg._interpolate_data(500, 0, wind_rose_wrg.interpolant_sector_freq) + assert np.allclose(test_value, (sector_freq[0, 0, :] + sector_freq[1, 0, :]) / 2) + + # Test that value at x=0, y=500, this is the average of the rows at [0,0] and [0,1] + test_value = wind_rose_wrg._interpolate_data(0, 500, wind_rose_wrg.interpolant_sector_freq) + assert np.allclose(test_value, (sector_freq[0, 0, :] + sector_freq[0, 1, :]) / 2) + + +def test_generate_wind_speed_frequencies_from_weibull(): + wind_rose_wrg = WindRoseWRG(WRG_FILE_FILE) + + wind_speeds_in = np.array([0.0, 5.0, 10.0, 15.0]) + wind_speeds, freq = wind_rose_wrg._generate_wind_speed_frequencies_from_weibull( + 10.0, 2.0, wind_speeds=wind_speeds_in + ) + + # Test that the wind speeds are the same + assert np.allclose(wind_speeds, wind_speeds_in) + + # Test that freq is the same length as wind_speeds + assert len(freq) == len(wind_speeds) + + # Test that the frequencies sum to 1.0 + assert np.allclose(np.sum(freq), 1.0) + + # Test the correctness of the frequencies by reversing the process + wind_speeds = np.arange(0.0, 100.0, 1.0) + A_test = 9.0 + k_test = 1.8 + wind_speeds, freq = wind_rose_wrg._generate_wind_speed_frequencies_from_weibull( + A_test, k_test, wind_speeds=wind_speeds + ) + + # Test that the mean value matches theory + mean_value = np.sum(wind_speeds * freq) + assert np.allclose(mean_value, A_test * math.gamma(1 + 1 / k_test), rtol=0.1) + + +def test_get_wind_rose_at_point(): + wind_rose_wrg = WindRoseWRG(WRG_FILE_FILE) + + wind_speeds = np.arange(0.0, 26.0, 1.0) + n_wind_speeds = len(wind_speeds) + + wind_rose = wind_rose_wrg.get_wind_rose_at_point(0, 0) + + # Test that there are 12 wind directions at n_wind_speeds wind speeds + assert wind_rose.freq_table.shape == (12, n_wind_speeds) + assert len(wind_rose.wind_speeds) == n_wind_speeds + assert len(wind_rose.wind_directions) == 12 + + # Test that freq table generated at (0, 0) is the same at that of (-1 , -1) + wind_rose2 = wind_rose_wrg.get_wind_rose_at_point(-1, -1) + assert np.allclose(wind_rose.freq_table, wind_rose2.freq_table) + + # Test that uneven spacing in wind_speeds is not allowed + with pytest.raises(ValueError): + _ = wind_rose_wrg.get_wind_rose_at_point(0, 0, wind_speeds=np.delete(wind_speeds, [2])) + +def test_wind_rose_wrg_integration(): + + wind_rose_wrg = WindRoseWRG(WRG_FILE_FILE) + + # Set a layout with two turbines + layout_x = np.array([0, 1000]) + layout_y = np.array([0, 2000]) + + # Apply the layout + wind_rose_wrg.set_layout(layout_x, layout_y) + + # Get a wind rose at the second turbine + wind_rose = wind_rose_wrg.get_wind_rose_at_point(1000, 2000) + + # Also take the second wind rose from the wind_roses list + wind_rose2 = wind_rose_wrg.wind_roses[1] + + # Show these are the same by compare the freq_table + assert np.allclose(wind_rose.freq_table, wind_rose2.freq_table) + +def test_apply_wrg_to_floris_model(): + fmodel = FlorisModel(configuration=YAML_INPUT) + wind_rose_wrg = WindRoseWRG(WRG_FILE_FILE) + fmodel.set(wind_data=wind_rose_wrg) + fmodel.run() + +def test_apply_wrg_to_ufloris_model(): + ufmodel = UncertainFlorisModel(configuration=YAML_INPUT) + wind_rose_wrg = WindRoseWRG(WRG_FILE_FILE) + ufmodel.set(wind_data=wind_rose_wrg) + ufmodel.run()