diff --git a/notebooks/example.ipynb b/notebooks/example.ipynb index 17d3c0f..e7f8162 100644 --- a/notebooks/example.ipynb +++ b/notebooks/example.ipynb @@ -2,33 +2,23 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], + "outputs": [], "source": [ "from deepdiagnostics import models\n", - "from deepdiagnostics import data\n", + "from deepdiagnostics import data as data_modules\n", "from deepdiagnostics.utils.config import Config\n", - "from deepdiagnostics.utils.register import register_simulator\n", + "from deepdiagnostics.utils.simulator_utils import register_simulator\n", "\n", - "from deepdiagnostics.plots import CDFRanks, CoverageFraction, Ranks, TARP, LC2ST\n", + "from deepdiagnostics.plots import CDFRanks, CoverageFraction, Ranks, TARP\n", "\n", - "import yaml\n", - "\n" + "import yaml" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -88,50 +78,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'common': {'out_dir': './DeepDiagnosticsResources/results/',\n", - " 'temp_config': './DeepDiagnosticsResources/temp/temp_config.yml',\n", - " 'sim_location': './DeepDiagnosticsResources/simulators',\n", - " 'random_seed': 42},\n", - " 'model': {'model_engine': 'SBIModel'},\n", - " 'data': {'data_engine': 'H5Data',\n", - " 'prior': 'normal',\n", - " 'prior_kwargs': None,\n", - " 'simulator_kwargs': None,\n", - " 'simulator_dimensions': 1},\n", - " 'plots_common': {'axis_spines': False,\n", - " 'tight_layout': True,\n", - " 'default_colorway': 'viridis',\n", - " 'plot_style': 'fast',\n", - " 'parameter_labels': ['$m$', '$b$'],\n", - " 'parameter_colors': ['#9C92A3', '#0F5257'],\n", - " 'line_style_cycle': ['-', '-.'],\n", - " 'figure_size': [6, 6]},\n", - " 'plots': {'CDFRanks': {},\n", - " 'Ranks': {'num_bins': None},\n", - " 'CoverageFraction': {},\n", - " 'TARP': {'coverage_sigma': 3},\n", - " 'LC2ST': {},\n", - " 'Parity': {},\n", - " 'PPC': {},\n", - " 'PriorPC': {}},\n", - " 'metrics_common': {'use_progress_bar': False,\n", - " 'samples_per_inference': 1000,\n", - " 'percentiles': [75, 85, 95],\n", - " 'number_simulations': 50},\n", - " 'metrics': {'AllSBC': {}, 'CoverageFraction': {}, 'LC2ST': {}}}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from deepdiagnostics.utils.defaults import Defaults\n", "Defaults" @@ -148,50 +97,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "usage: diagnose [-h] [--config CONFIG] [--model_path MODEL_PATH]\n", - " [--model_engine {SBIModel}] [--data_path DATA_PATH]\n", - " [--data_engine {H5Data,PickleData}] [--simulator SIMULATOR]\n", - " [--out_dir OUT_DIR]\n", - " [--metrics {,CoverageFraction,AllSBC,LC2ST} [{,CoverageFraction,AllSBC,LC2ST} ...]]\n", - " [--plots {,CDFRanks,CoverageFraction,Ranks,TARP,LC2ST,PPC,Parity,PriorPC} [{,CDFRanks,CoverageFraction,Ranks,TARP,LC2ST,PPC,Parity,PriorPC} ...]]\n", - "\n", - "options:\n", - " -h, --help show this help message and exit\n", - " --config CONFIG, -c CONFIG\n", - " .yaml file with all arguments to run.\n", - " --model_path MODEL_PATH, -m MODEL_PATH\n", - " String path to a model. Must be compatible with your\n", - " model_engine choice.\n", - " --model_engine {SBIModel}, -e {SBIModel}\n", - " Way to load your model. See each module's\n", - " documentation page for requirements and\n", - " specifications.\n", - " --data_path DATA_PATH, -d DATA_PATH\n", - " String path to data. Must be compatible with\n", - " data_engine choice.\n", - " --data_engine {H5Data,PickleData}, -g {H5Data,PickleData}\n", - " Way to load your data. See each module's documentation\n", - " page for requirements and specifications.\n", - " --simulator SIMULATOR, -s SIMULATOR\n", - " String name of the simulator to use with generative\n", - " metrics and plots. Must be pre-register with the\n", - " `utils.register_simulator` method.\n", - " --out_dir OUT_DIR Where the results will be saved. Path need not exist,\n", - " it will be created.\n", - " --metrics {,CoverageFraction,AllSBC,LC2ST} [{,CoverageFraction,AllSBC,LC2ST} ...]\n", - " List of metrics to run.\n", - " --plots {,CDFRanks,CoverageFraction,Ranks,TARP,LC2ST,PPC,Parity,PriorPC} [{,CDFRanks,CoverageFraction,Ranks,TARP,LC2ST,PPC,Parity,PriorPC} ...]\n", - " List of plots to run.\n" - ] - } - ], + "outputs": [], "source": [ "! diagnose -h" ] @@ -209,17 +117,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: Simulator not loaded. Can only run non-generative metrics.\n" - ] - } - ], + "outputs": [], "source": [ "! diagnose --model_path ../resources/savedmodels/sbi/sbi_linear_from_data.pkl --data_path ../resources/saveddata/data_validation.h5 --plots CoverageFraction" ] @@ -232,8 +132,7 @@ "\n", "In order to run some of the metrics and plots (including `LC2ST`, `PPC` and `PriorPC`), you must supply a simulator. \n", "If you do not supply a simulator and try to run these options in CLI a warning will be raised and the other metrics/plots will run as expected. \n", - "In standalone mode, when trying to initialize metrics and plots that require a simulator, without a simulator, a `SimulatorNotFoundError` will be raised. \n", - "\n", + "In standalone mode, when trying to initialize metrics and plots that require a simulator, without a simulator, `LookupTableSimulator` will be used instead, which may cause inaccuracies. \n", "\n", "Simulators are all subclasses of `data.Simulator`, and need to be registered with `register_simulator` to use during runtime. \n", "`data.Simulator` is an abstract class that requires a `generate_context` \n", @@ -241,26 +140,20 @@ "This can either be loaded in from a specific file, or a random distribution.) \n", "and `simulate` method \n", "(which takes a context and parameters of the model )\n", - "See below for an example with typing, simulating a 2d case where the model being fit is a linear model. " + "See below for an example with typing, simulating a 2d case where the model being fit is a linear model. \n", + "\n", + "Please note that the simulator file **must** be saved a python file, so it can be imported and used by metrics and plots. If you write you simulator in a notebook, it cannot be registered. Using the line magic `%%writefle your-sim-here.py`, you can save it from your notebook. " ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting my_simulator.py\n" - ] - } - ], + "outputs": [], "source": [ "%%writefile my_simulator.py \n", "\n", - "from deepdiagnostics.utils.register import register_simulator\n", + "from deepdiagnostics.utils.simulator_utils import register_simulator\n", "from deepdiagnostics.data.simulator import Simulator\n", "import numpy as np \n", "\n", @@ -293,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -303,17 +196,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: Cannot load config from environment. Hint: Have you set the config path by passing a str path to Config?\n" - ] - } - ], + "outputs": [], "source": [ "from my_simulator import MySimulator\n", "\n", @@ -326,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -334,13 +219,13 @@ "my_config = {\n", " \"model\": {\"model_path\": \"../resources/savedmodels/sbi/sbi_linear_from_data.pkl\"}, \n", " \"data\": {\n", - " \"data_path\": \"../resources/saveddata/data_validation.h5\", \n", + " \"data_path\": \"../resources/saveddata/data_test.h5\", \n", " \"simulator\": \"MySimulator\"}, \n", " \"metrics_common\": {\n", " \"use_progress_bar\": True,\n", - " \"samples_per_inference\": 1000,\n", + " \"samples_per_inference\": 100,\n", " \"percentiles\": [75, 85, 95],\n", - " \"number_simulations\": 50}, \n", + " \"number_simulations\": 10}, \n", " \"metrics\": {},\n", " \"plots\":{}\n", "}\n", @@ -350,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -360,202 +245,55 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100%|████████████████████████████████████████| 100/100 [00:00<00:00, 725.57it/s]\n" - ] - } - ], + "outputs": [], "source": [ "# We can do a similar thing by passing specific kwargs \n", "# Here we're just calculating the coverage fraction \n", - "! diagnose --model_path ../resources/savedmodels/sbi/sbi_linear_from_data.pkl --data_path ../resources/saveddata/data_validation.h5 --simulator MySimulator --plots CoverageFraction TARP" + "! diagnose --model_path ../resources/savedmodels/sbi/sbi_linear_from_data.pkl --data_path ../resources/saveddata/data_test.h5 --simulator MySimulator --plots CDFParityPlot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This produces a image of the coverage fraction from our model and data, shown below. \n", - "\n", - "\"Coverage" + "This produces a image of the coverage fraction from our model and data, shown below. " ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "We can do a similar thing with metrics, or with plot metrics or plots. " + "import os\n", + "import matplotlib.pyplot as plt\n", + "\n", + "for plot in os.listdir(\"./DeepDiagnosticsResources/results/\"):\n", + " if not plot.endswith('.png'):\n", + " continue\n", + " print(f\"Showing saved plot: {plot}\")\n", + " plot_path = \"./DeepDiagnosticsResources/results/\" + plot\n", + " plt.imshow(plt.imread(plot_path))\n", + " plt.axis('off')\n", + " plt.show()" ] }, { - "cell_type": "code", - "execution_count": 9, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - } - ], "source": [ - "! diagnose --model_path ../resources/savedmodels/sbi/sbi_linear_from_data.pkl --data_path ../resources/saveddata/data_validation.h5 --simulator MySimulator --metrics LC2ST" + "We can do a similar thing with metrics, or with plot metrics or plots. " ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[K12656823, 0.025117976419460897, 0.3210162208447511, 0.07601853392691404, 0.06380:\u001b[K3287, 1.0, 1.0, 0.3491858283608714, 0.11148460600402221, 0.1705053499709257, 1.0], [0.18886563228152808, 0.999801565848368, 0.06420094428578094, 0.003829460887442848, 1.0, 0.0, 0.05521830659929361, 1.0, 1.0, 1.0], [0.3723571672317456, 0.04190096758672124, 0.05917814671785937, 1.0, 0.5279380279033324, 1.0, 0.3939235158158101, 1.0, 0.3331214449554769, 0.14278821453735335], [0.16794501054145394, 1.0, 0.2836227076540423, 1.0, 0.08725893205243684, 1.0, 1.0, 1.0, 0.2898180261624975, 1.0], [3.4497582568349117e-10, 0.9999999999997726, 2.2475195038396123e-09, 0.9999999860851057, 0.0, 0.01498136204169076, 0.9999999999999996, 1.0, 1.0, 5.1250789168122424e-08]], \"lc2st_null_hypothesis_probabilities\": [[[0.2790388229755212, 0.2735488578185722, 8.397499558887578e-05, 0.002544143029782342, 0.4337491005580647, 0.5281736584242632, 0.2941875894651199, 0.8471557950221957, 0.491588056016956, 0.14904058563692402], [0.9298861402948934, 0.5799854862294562, 0.3862690266799903, 0.3032391090498666, 0.7976109456527416, 0.581394460307604, 0.6422201235768321, 0.9667699549263491, 0.4300242827109272, 0.603196699074676], [0.7458949626530249, 0.4908091805562187, 0.3295099292259487, 0.9048431566532888, 0.4475631382010551, 0.37204375915039756, 0.37192176501806684, 0.2599461309841308, 0.12187398110432535, 0.28911814717222706], [0.9715750712017363, 0.9302578076591757, 0.280485962197362, 0.7789217374607884, 0.5768391118918303, 0.6121690665788355, 0.8770606717117648, 0.5942009041559981, 0.6127097617959039, 0.9487840622625356], [0.5326295893366368, 0.7700871663694474, 0.6654461398938305, 0.8528700301216272, 0.8800724382630881, 0.6637915760733999, 0.8461285354871109, 0.8827762176374074, 0.2243871033069973, 0.6636540545834573]], [[0.049262331605416376, 0.0071008394:\u001b[K\u0007\u001b[H\u001b[2J\u001b[H\u001b[H\u001b[2J\u001b[H{\"lc2st_probabilities\": [[0.1983312341145994, 0.19218918728498446, 1.0, 0.20351917821653287, 1.0, 1.0, 0.3491858283608714, 0.11148460600402221, 0.1705053499709257, 1.0], [0.18886563228152808, 0.999801565848368, 0.06420094428578094, 0.003829460887442848, 1.0, 0.0, 0.05521830659929361, 1.0, 1.0, 1.0], [0.3723571672317456, 0.04190096758672124, 0.05917814671785937, 1.0, 0.5279380279033324, 1.0, 0.3939235158158101, 1.0, 0.3331214449554769, 0.14278821453735335], [0.16794501054145394, 1.0, 0.2836227076540423, 1.0, 0.08725893205243684, 1.0, 1.0, 1.0, 0.2898180261624975, 1.0], [3.4497582568349117e-10, 0.9999999999997726, 2.2475195038396123e-09, 0.9999999860851057, 0.0, 0.01498136204169076, 0.9999999999999996, 1.0, 1.0, 5.1250789168122424e-08]], \"lc2st_null_hypothesis_probabilities\": [[[0.2790388229755212, 0.2735488578185722, 8.397499558887578e-05, 0.002544143029782342, 0.4337491005580647, 0.5281736584242632, 0.2941875894651199, 0.8471557950221957, 0.491588056016956, 0.14904058563692402], [0.9298861402948934, 0.5799854862294562, 0.3862690266799903, 0.3032391090498666, 0.7976109456527416, 0.581394460307604, 0.6422201235768321, 0.9667699549263491, 0.4300242827109272, 0.603196699074676], [0.7458949626530249, 0.4908091805562187, 0.3295099292259487, 0.9048431566532888, 0.4475631382010551, 0.37204375915039756, 0.37192176501806684, 0.2599461309841308, 0.12187398110432535, 0.28911814717222706], [0.9715750712017363, 0.9302578076591757, 0.280485962197362, 0.7789217374607884, 0.5768391118918303, 0.6121690665788355, 0.8770606717117648, 0.5942009041559981, 0.6127097617959039, 0.9487840622625356], [0.5326295893366368, 0.7700871663694474, 0.6654461398938305, 0.8528700301216272, 0.8800724382630881, 0.6637915760733999, 0.8461285354871109, 0.8827762176374074, 0.2243871033069973, 0.6636540545834573]], [[0.049262331605416376, 0.0071008394:\u001b[K" - ] - } - ], + "outputs": [], "source": [ - "! cat DeepDiagnosticsResources/results/diagnostic_metrics.json | less" + "! diagnose --model_path ../resources/savedmodels/sbi/sbi_linear_from_data.pkl --data_path ../resources/saveddata/data_test.h5 --simulator MySimulator --metrics CoverageFraction" ] }, { @@ -569,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -577,370 +315,89 @@ "Config(\"./my_config.yaml\")\n", "\n", "model = models.SBIModel(\"../resources/savedmodels/sbi/sbi_linear_from_data.pkl\")\n", - "data = data.H5Data(\"../resources/saveddata/data_validation.h5\", simulator=\"MySimulator\")" + "data = data_modules.H5Data(\"../resources/saveddata/data_test.h5\", simulator=\"MySimulator\")" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/maggiev-local/repo/DeepDiagnostics/src/deepdiagnostics/plots/cdf_ranks.py:43: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", - " thetas = tensor(self.data.get_theta_true())\n", - "/Users/maggiev-local/repo/DeepDiagnostics/src/deepdiagnostics/plots/cdf_ranks.py:44: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", - " context = tensor(self.data.true_context())\n", - "Running 10000 sbc samples.: 100%|██████████| 10000/10000 [01:49<00:00, 91.56it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "plot = CDFRanks(model, data, save=False, show=True)()" + "plot = CDFRanks(model, data, save=False, show=True, run_id=\"my_run_42\")()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling from the posterior for each observation: 100%|██████████| 50/50 [00:00<00:00, 78.32 observation/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "plot = CoverageFraction(model, data, show=True, save=False)()" + "plot = CoverageFraction(model, data, show=True, save=False, run_id=\"my_run_42\")()" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/maggiev-local/repo/DeepDiagnostics/src/deepdiagnostics/plots/ranks.py:43: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", - " thetas = tensor(self.data.get_theta_true())\n", - "/Users/maggiev-local/repo/DeepDiagnostics/src/deepdiagnostics/plots/ranks.py:44: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", - " context = tensor(self.data.true_context())\n", - "Running 10000 sbc samples.: 100%|██████████| 10000/10000 [01:45<00:00, 94.46it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "Ranks(model, data, show=True, save=False)()" + "Ranks(model, data, show=True, save=False, run_id=\"my_run_42\")()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:00<00:00, 759.05it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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GpEmTqpJBazWmxaGnc9fyPvXE4szILc741l9/fb755hv++c9/8sILL/DEE09w6623ctFFF1XK3iVLHikwJDVnzJgxvPLKK2y//fbdvvQWZJtttmGbbbbh8ssv56GHHuLII4/kkUce4cQTT+zzH6g111yTb775psvyr7/+urK+r/z73//mxx9/5LLLLuvS26O9vZ2TTz6Zp59+mqOOOooxY8YQhiFffvlll0S/DsaMGQPAF1980eUbXmcGDBhAKpXqsnz69OmstdZavRr7448/zq677spdd91VtTyVSjFo0KCqMb3//vt4nldJOuyOgQMHss8++/Dggw9y5JFH8vbbb/e60dGOO+6I67o8/PDDzJo1qyIkdtppp4rAWGeddSpCoycW56XVVzpEw6677srNN9/MueeeC5Tu61prrcWTTz5ZNZ6LL764X+fpeC6nTJnSZd2Cz/Oaa67JK6+8QjabrZrFWJxnvNasueaahGHIlClTqn5nmpqaSKVSNRvjwp6FeDzOYYcdxmGHHYbruhx88MFcfvnlnHfeebKfyVJChkgkNefnP/85QRAwceLELut836+8NNvb27t8G+l4KXeESToa9nT3ou2Ovffem8mTJ/Puu+9WluXzee68805GjRq10NBFT3SER84++2wOPfTQqp+TTjqJsWPHVmZrDjzwQFRV5bLLLusyQ9BxrXvssQfJZJJJkyZh23a320Dppf/ee+/hum5l2T//+U9mzpzZ67FrmtblHj/22GNdOlMecsghtLS0cPPNN3c5xoL7H3300Xz55ZecffbZaJrG4Ycf3quxjBs3DsMwuOqqqxg4cCAbbrghUBIe7733Hv/+9797NXsRj8e7DUMsKXbZZRe23nprbrzxxsrn1fHtuvO9ef/996ueu74wfPhwNttsM+67776qa3v55Ze75A3tvffeBEHQ5bO64YYbUBSFvfbaq19jqCV77703QBfxef311wOwzz771OQ8PT0Lra2tVf+ORCJssMEGCCFqlu8lWTRyBkNSc3beeWdOOeUUJk2axCeffMIee+yBYRhMmTKFxx57jJtuuolDDz2U++67j1tvvZWDDjqIMWPGkM1m+ctf/kJdXV3lD1Q0GmWDDTbg0UcfZZ111mHgwIFstNFGPeYvnHvuuTz88MPstddenHbaaQwcOJD77ruPqVOn8sQTT/S58ZTjODzxxBPsvvvuPX7r2X///bnppptobm5m7bXX5oILLmDixInsuOOOHHzwwZimyX//+19WW201Jk2aRF1dHTfccAMnnngiW221FUcccQQDBgzg008/pVAocN999wGlMr/HH3+cn/70p/z85z/n+++/54EHHqjMgPSGfffdl8suu4zjjz+e7bbbjs8//5wHH3ywywzIMcccw9/+9jfOPPNMJk+ezI477kg+n+eVV17hN7/5TVX/i3322YfGxkYee+wx9tprL4YMGdKrscRiMbbYYgvee++9Sg8MKM1g5PN58vl8rwTGFltswaOPPsqZZ57JVlttRSKRYL/99uv1PekPZ599Nj/72c+49957+dWvfsW+++7Lk08+yUEHHcQ+++zD1KlTuf3229lggw3I5XL9OsekSZPYZ5992GGHHTjhhBNoa2ur9HLofMz99tuPXXfdlQsuuIBp06ax6aab8tJLL/HMM89w+umn9+n5WFJsuummHHvssdx5552kUil23nlnJk+ezH333ceBBx7IrrvuWpPz9PQs7LHHHgwbNoztt9+eoUOH8tVXX3HzzTezzz77dMldkSxBlkXpimTlYsEy1Q7uvPNOscUWW4hoNCqSyaTYeOONxe9//3sxe/ZsIYQQH330kfjFL34h1lhjDWGaphgyZIjYd999xQcffFB1nHfeeUdsscUWIhKJ9Kpk9fvvvxeHHnqoaGhoEJZlia233lr885//7LIdvShTfeKJJwQg7rrrrh63eeONNwQgbrrppsqyu+++W2y++ebCNE0xYMAAsfPOO4uXX365ar9nn31WbLfddiIajYq6ujqx9dZbi4cffrhqm+uuu06MGDFCmKYptt9+e/HBBx/0WNb32GOPdRmbbdvirLPOEsOHDxfRaFRsv/324t133+1yDCGEKBQK4oILLhCjR48WhmGIYcOGiUMPPVR8//33XY77m9/8RgDioYceWtjt68LZZ58tAHHVVVdVLV977bUF0OVc3ZWp5nI5ccQRR4iGhoaqEs6e7kNHqeg999yz0LF1lF92VzYdBIEYM2aMGDNmjPB9X4RhKK644gqx5pprCtM0xeabby7++c9/imOPPbaqbLLj3Ndcc02XY3b3LD/xxBNi/fXXF6Zpig022EA8+eSTXY4pRKn0+owzzhCrrbaaMAxDjB07VlxzzTVdSoW7e8Z7GtPCnqPe3qfOeJ4nLr300srzNHLkSHHeeecJ27artuvt89zd59jTs3DHHXeInXbaSTQ2NgrTNMWYMWPE2WefLdLp9ELHLKktihBLIctIIpGsVJxxxhncddddzJ07t0++ExKJZNVB5mBIJJI+Yds2DzzwAIcccogUFxKJpEdkDoZEIukVzc3NvPLKKzz++OO0trYyfvz4ZT0kiUSyHCMFhkQi6RVffvklRx55JEOGDOFPf/pTj2W4EolEAiBzMCQSiUQikdQcmYMhkUgkEomk5kiBIZFIJBKJpOascgJDCEEmk1kqHgASiUQikayqrHICI5vNUl9fTzabXdZDkUgkEolkpWWVExgSiUQikUiWPFJgSCQSiUQiqTlSYEgkEolEIqk5UmBIJBKJRCKpOVJgSCQSiUQiqTmyVXg3CCHwfZ8gCJb1UFZoNE1D13UURVnWQ5FIJBLJUkYKjAVwXZc5c+ZQKBSW9VBWCmKxGMOHDycSiSzroUgkEolkKSIFRifCMGTq1KlomsZqq61GJBKR3777iRAC13WZN28eU6dOZezYsaiqjMhJJBLJqoIUGJ1wXZcwDBk5ciSxWGxZD2eFJxqNYhgG06dPx3VdLMta1kOSSCQSyVJCfqXsBvlNu3bIeymRSCSrJvKvv0QikUgkkpojBYZEIpFIJJKaIwXGKsIuu+zC6aefvtwfUyKRSCQrB1JgrCQcd9xxHHjggct6GBKJRCKRAFJgSCQSiUQiWQJIgbEIhBDk8/ll8iOE6NeY8/k8xxxzDIlEguHDh3Pdddd12cZxHCZMmMCIESOIx+OMGzeON954o7K+tbWVX/ziF4wYMYJYLMbGG2/Mww8/3N/bKJFIJJJVjGUqMP7zn/+w3377sdpqq6EoCk8//fQi93njjTf4v//7P0zTZO211+bee+9domMsFAokEoll8tPfbqJnn302//73v3nmmWd46aWXeOONN/joo4+qtvntb3/Lu+++yyOPPMJnn33Gz372M376058yZcoUAGzbZosttuC5557jiy++4OSTT+boo49m8uTJi31PJRKJRLLys0wFRj6fZ9NNN+WWW27p1fZTp05ln332Ydddd+WTTz7h9NNP58QTT+TFF19cwiNdccjlctx1111ce+21/OQnP2HjjTfmvvvuw/f9yjYzZszgnnvu4bHHHmPHHXdkzJgxTJgwgR122IF77rkHgBEjRjBhwgQ222wz1lprLX73u9/x05/+lL///e/L6tIkEolEsgKxTDt57rXXXuy111693v72229n9OjRlSn/9ddfn7feeosbbriBPffcc4mMMRaLkcvllsixe3PuvvL999/jui7jxo2rLBs4cCDrrrtu5d+ff/45QRCwzjrrVO3rOA6NjY0ABEHAFVdcwd///ndmzZqF67o4jiM7nEokEskKwmeffcbkyZM58cQTl8n5V6hW4e+++y677bZb1bI999xzoaWSjuPgOE7l35lMpk/nVBSFeDzep32Wd3K5HJqm8eGHH6JpWtW6RCIBwDXXXMNNN93EjTfeyMYbb0w8Huf000/Hdd1lMWSJRCKRLEAYBkxv+g5FMRk1bFTVunfffZe9996bVCpFY2MjBx100FIf3wolMObOncvQoUOrlg0dOpRMJkOxWCQajXbZZ9KkSVx66aVLa4jLnDFjxmAYBu+//z5rrLEGAO3t7Xz77bfsvPPOAGy++eYEQUBzczM77rhjt8d5++23OeCAAzjqqKOAkhHct99+ywYbbLB0LkQikUgkXRAiAJHF91v4rukTPpzyGZayXpXAeOWVVzjggAMoFApst9127LrrrstkrCt9Fcl5551HOp2u/MycOXNZD2mJkkgk+OUvf8nZZ5/Na6+9xhdffMFxxx1X5QmyzjrrcOSRR3LMMcfw5JNPMnXqVCZPnsykSZN47rnnABg7diwvv/wy77zzDl999RWnnHIKTU1Ny+qyJBKJZJVFiAARthN60xDOB2Ry/+bLppeY/P1kvNBj83U3r2z71FNPsc8++1AoFNhjjz146aWXaGhoWCbjXqFmMIYNG9blJdfU1ERdXV23sxcApmlimubSGN5ywzXXXEMul2O//fYjmUxy1llnkU6nq7a55557+OMf/8hZZ53FrFmzGDRoENtssw377rsvAH/4wx/44Ycf2HPPPYnFYpx88skceOCBXY4jkUgkktojhA8igwgyEDaDyGL7Bdq8Ire9GfDFjDq2H6my1sChjB5Wyqf729/+xgknnEAQBBxyyCE8+OCDy/T9p4j+NluoMYqi8NRTTy20G+U555zD888/z+eff15ZdsQRR9DW1sYLL7zQq/NkMhnq6+tJp9PU1dVVrbNtm6lTpzJ69GhpLV4j5D2VSCSS3jFfVKQgbAGRBUK8UCfl+7S4Gb6cneOCRwfiBir7bfAlp+6+PesN344///nPnHbaaUCps/Nf/vIXdH3ZziEs0xBJLpfjk08+4ZNPPgFKZaiffPIJM2bMAErhjWOOOaay/a9+9St++OEHfv/73/P1119z66238ve//50zzjhjWQxfIpFIJJLFQggPEbYRet8j3A8Q7kcQfA94hDSQCqL86GSZXWxlXsbmrtfjuIHK8ESG/7eex6jGdZk4cWJFXJx++uncddddy1xcwDIOkXzwwQdVySdnnnkmAMceeyz33nsvc+bMqYgNgNGjR/Pcc89xxhlncNNNN7H66qvz17/+dYmVqEokEolEUmuE8CDMIMJ2COeBKAAClBiogwGVfJCnzWki7+fxQoV0IeS1/wV8PSeKqoTsu9E3jB62Eeefezk33HADAJdeeikXXnghiqIs0+vrYLkJkSwtZIhk6SLvqUQikYAQbllUpMo5FeVOzUoclBiKUmoZYAc27W6KjJcGFDxPozmfoynlcOWzjWQcna2Gz+Hwbabx9G1f88B9TwBw4403Mn78+GVzcT2w7OdQJBKJRCJZCZkvKjrPVFASFergiqgA8EKftJcm5bbjhT5RLUbKdmjOZ3Ftn3/8N0rG0WkwHbYZNZ3bLnuBV5//EFVVueuuuzjuuOOWzUUuBCkwJBKJRCKpESVRkUaEbRC2LlRUlLYPyfo5Wt12in4BS7eIqxZN2Txtdh58+GZGyLvTkgDsNHwa91/5MJ++9z2GYfDII49w8MEHL+3L7BVSYEgkEolEshgI4ZRFRSuE7SDygFoWFUNQlO7rKQp+gXa3nYyXRVd16ox6vMBnVj5D2rYxhEpLusDjHzUCCuvWt/Dcn//ED19OJxaL8tRTT7PHHnss1WvtC1JgSCQSiUTSR4Sw589UBG1AHtBASYA6tEdRAeAGLikvRcpNIxDE9QS6qpJ3PeZksxR9DwudtnSef39lMTdnYWo+X/7tCn78ejp19TGef+6fbL/9sunQ2VukwJBIJBKJpBcIUSznVLSWRUWR0kxFApRhCxUVAEEYkPGztLltOIFLTI8RUXUQkCrazM3nCMKQqKrT2l5gblvIK1MGAJB762/M/vpbGhoTPPvP29h+m+VbXIAUGBKJRCKR9EhJVJTDH0E7UAD0cvVHfa9KQoUQ5Pw87W4bOT+PqZnUG3UoCoQhtBbzNOfz6JpKTDNoTeVwHJdnPx+IF6oEc79m9n+eYuDQBu597HS23eInS/y6a4EUGBKJRCKRdEKEhXJHzZZSTgVFSqIi0WtR0UExsEt5Fm4WVYU6ow61vL8fCprzOVoLBaK6gY5KSypH0fZwHUE67SACi6Z/3sTQ1Qdx+b2nsPXGm6PpjUvmwmvMSm92tiqgKMpCfy655JLKtuuttx6maTJ37twux9lll10q+1iWxTrrrMOkSZPo3Cpl2rRpVcdubGxkjz324OOPP14alyqRSCRLBBHmEcEcQvfzUkdN77NSaalqgTocRRuCosZ6LS680Gee08Kswo9k3DQxPUpCT1TEheP7zMqmaSnkSURMdFWlLZ2naLtowNcffMFnN5zCnPvOYFhdwPm3j2eTdQaTtEahKJEleCdqhxQYKwFz5syp/Nx4443U1dVVLZswYQIAb731FsVikUMPPZT77ruv22OddNJJzJkzh2+++YbzzjuPiy66iNtvv73Ldq+88gpz5szhxRdfJJfLsddee5FKpZbkZUokEknNEEIgwlxZVHyGcD9EeF+USkvVaFlUDEZRon2asRAiJONl+LH4I/PseaiqTl2kDl2dX56adz1mpjNkbId6y0JVIJUpkC86RHSVya99zPUX/ZVi3maNxpDfX/dbhq9Rz6BoHZax2pK4HUsEKTB6ScH1+/zjB2Flfz8IKbg+thf06rh9YdiwYZWf+vrS9F3nZYlEAoC77rqLI444gqOPPpq7776722PFYjGGDRvGmmuuyfHHH88mm2zCyy+/3GW7xsZGhg0bxpZbbsm1115LU1MT77//fp/GLZFIJEuT+aJiNsL7HOF+hHC/gLAN1CiKNhxFG9RnUdFx7IJfYFZxDrMKs/HDgDqjHkutnm1I2TYzs2nswKfBslAEpNJFsjmbqGnw9LPfcM+bOh4RNtx8Hc7448kkh9UzOC4YaA1HUetreUuWKDIHo5dscNGLfd7nliP+j302GQ7Ai/9r4tSHPmLc6IE8esq2lW12uOp12vJul32nXblP/wfbDdlslscee4z333+f9dZbj3Q6zZtvvsmOO+7Y7fZCCN566y2+/vprxo4du9BjR6NRAFy363VIJBLJskQIUepLIdKIYB6EaRAOKBFQEyjKwMU+hxu4tHsp0m4KASSMBNoCFSVCQGuxQHMuh6ap1JsWIEhnbTK5IlHL4F+PvsErTVuR2GwMg4cO4NR9o0QbEsTiKoNjKlFjFIpiLPZ4lxZyBmMV4ZFHHmHs2LFsuOGGaJrG4Ycfzl133dVlu1tvvZVEIoFpmuy0006EYVhx6euOVCrFxIkTSSQSbL311kvyEiQSiaRXlGYqsgj/R4T7cSn84f6vJC7UBIq+WnmmYvH8kYIwoM1pY2bxR1qdNiKaRbIbceGHgjm5LHOyWSKaTkyPAIJMziadKWKZBs/87SXuuOoB2l77K2Z+JqfsU4+ZsDDiOskoDLIGYhiDF2u8Sxs5g9FLvrys746tEW3+Q7bnhkP58rI9Kwk+Hbx1ztKpZb777rs56qijKv8+6qij2Hnnnfnzn/9MMpmsLD/yyCO54IILaG9v5+KLL2a77bZju+2263K87bbbDlVVyefzrLXWWjz66KMMHTp0qVyLRCKRLIgQIYhcqU9F0AwiA8ItJ2kmUJTaVV4IEZLz87S57eTLZacNkbput3WCgOZclnbbIVlO5gTI5R3a00V0XePRO57lkTufBeD/bdXIz/cp4DsCzTIwTJ1BMUgYQ0BZccIjIAVGr4lFFu9W6ZqKrnWdMFrc4/aGL7/8kvfee4/JkydzzjnnVJYHQcAjjzzCSSedVFlWX1/P2muvDcDf//531l57bbbZZht22223qmM++uijbLDBBjQ2NtLQ0LDEr0EikUgWZL6oSCOCptL/V0RFEkUxa37O6rJTparsdEEK5c6cBd+jzjLRykGDfMGhPV1AU+BvN/2dZx98GTXewEGHbseBR+6OXXTRLQMzGiEeV2k0FaLGml18TJZ3pMBYBbjrrrvYaaeduOWWW6qW33PPPdx1111VAqMziUSC8ePHM2HCBD7++OOqpKeRI0cyZsyYJTpuiUQiWZCSqMgignTZ9jwLwiuLirolVsLphV6lvXcQ+sT0eFVlyIKkbYe5+Sx+EFJvWSiU/n7ajktbOk8YBvzlygd59dm3ia27PUMPmMDQdVMEQRuqArppoJk6SQsGmI0o+oAlcl1LEpmDsZLjeR73338/v/jFL9hoo42qfk488UTef/99/ve///W4/ymnnMK3337LE088sRRHLZFIJPMRIkCEKUJverny40PwvwaKoNaXcirUgUtEXAgRknYzzCz+yDy7pWRKtkDZafX20FIoMCuTRgioM+eLC8dxaW3P47keN114F68++zZaNMmIA04nVAwKnoZb9NCjEYxoBCuq0hhRsPTBoHQfglmekQJjJefZZ5+ltbWVgw46qMu69ddfn/XXX7/bZM8OBg4cyDHHHMMll1xCGIY9bieRSCS1pCQq2sui4kOE+xEE3wI2qAPKomLAEpuxEEKQ9wv8WJjNrOJsgjCk3qjHVHs+34LJnHFj/rau59OaKpLP2Vxz9m28++qH6LrG9uMn4ipRBsVctl99HnpER4tE0CMqcVNhkGliGmss0udkeUQRnds0rgJkMhnq6+tJp9PU1VUrQtu2mTp1KqNHj8ayFi+7WFJC3lOJRNJbhPAXCH/kgACUKCjxpVai6QYubV47aTeNAOJ6rEtlSNd9Apq6SeYE8PyAtvYcrS1Zrvn9LXz96XeYVoSfnz2eN92dECicsPkshkfaiTbEiMRMrKTO8AEa6yUHkYjthKImF3L25ROZgyGRSCSSZYoQLsKfVWrNLbJAWBIV6kAUZem9poIwIO2lafdSOIFLXI9hqIs+f8H1mZPLUvDcqmROgMAPaUvlmTunjavO/DNTv51JPBHl1POP5w1/G4SrsPnwDCMTWRRFRzU09IhGzNIZaChEjSElD5QVECkwJBKJRLLMEMJGeN9CMBfUOKiNS1VUlMZQXXZqLaTsdEEyjsOcXBZvgWROgCAMaUvnmTltLpNO/xOzZzTRMLCO0y8+gZnmJjRNNYkZAT9dex6+65NoTJRCJKZK1FBoMAw0fbU+dxVdXpACQyKRSCTLBBHmEd43ELaANmSpCwuAYlCk3Wkn7WXRVHWhZaed6dyZU1U7OnPOJwxDUukCU76ezqQz/kzL3DYGD2/krIknoyUH8+9PBgGw19gWDOGgRQ3QVAxDI2bq1JsaUX3gCtf7ojNSYEgkEolkqSPCTElciBRoQ5d6j4eOstN2N00QBsT12ELLTjvjh4KWQp55+TxRXcfUq3NDhAhJZYp89tEUrjrzz6Tbs6w+ejhnT/oVlmHy2Hcj8EKV0QMKbDYsg10IidfHUTQN1dQwIzDAULCMYaDEl8TlLxWkwOiGVSzvdYki76VEIlkQEbaVxUUB1GFLNQQQipCsl6XVa8P2baJ6jIQe6/X+pWTOHO12kWTEqkrmLFHyF/nvO19w9YRbKOSKjFl/Tc656tcogcI32QFMaYujKYID1puH67iYMRN0FdPSMQ2VuKGS0M2SRfwKGh4BKTCqMIySCi0UChUDL8niUSgUgPn3ViKRrNqIYF5JXOCBuvReoEIICkGRNqeVnJ/H0AzqjXr6cvqi5zMnlyPvOtRZVlUyZ/ksZHI2/37pA64/7w5cx2XD/1uHc675DV7exVF0/jVlCAA7j25jYNTFtSEaNwkVFS2iYhkqdYZGVB8AK5BzandIgdEJTdNoaGigubkZKFmXr8jqcVkihKBQKNDc3ExDQwOatmK1uJVIJLVHBHNKCZ2KgqIuPeMuJ3DKbqdpoHu300WRLSdzukHQJZmzg1ze4V9PvsXNl9yF7wdsscPG/P6qX1HMOAgheH3GUHKuzqCYy86j2vFsFytqEmoalqmj6ypWRKVeV9H1ISjKiv1FVwqMBRg2bBhARWRIFo+GhobKPZVIJKsmQghEMBP870AxUZbSN/OOstM2rx038HpddtqZzsmciqpSb3b/0s8XHJ64/2XuvPJ+wlCw455bc/rEE7FzNm7RJZowWX9wnu/aYhyw3jwQIShgJSwCIdBNnYihETM04pqFog6qxS1YpshGWz0QBAGe5y3Fka18GIYhZy4kklUcIUKEPw2CqaVmWeqS7+kgREjWz9HupsplpxaW1nfjs87JnJauY+ndh3qLtsu9tz7N3258DIA9D9mJX19wDG7BJd2SxbCMitllEIKmQjHnEK2ziMQsIqaOEdNpTBgMjRmMjA5BNcctEbO2pYmcwegBTdPky1EikUgWAyF8hD+1JC7UAUtlyr8QFEg5KdJeBk3VqDPqUfsR6fbCgLnZUjJnImJhdEnmLGHbDrdd/QiP/fWfABx07E85/oyf4Tk+2fY8mq5Bp3CMpoLvB6iaQjRh4QUC09IxdJ2IrlCvqSja0BVeXIAUGBKJRCJZApS6c34Pwcxy86wl+8J0A5eUlyblpQlFQHwRbqcLo+j5zM3lyLkOdabVY76G7bhcc+HdPP/oawAc/buD+dkv9yEMBdn2PCIMyRPnjvdXZ9fR7Wy9ehpVAdf2SNTHCADL1BE6xCIaMd0gppko6sD+3oblCikwJBKJRFJT5nfnnFNuoLXkqsgqZaduG3ZQKjs11d6XnS5I1nWYk83i+j0nc0IpLDLxjJt5/bl3AfjVeUeyz+E/QYSQT+VxC6W8i9em1JNzdf7XHGfc6mk810c3dKyEheOHROsiCEUhYgiSGhjail890oEUGBKJRCKpGSLMI/xvIWguN9BaMq+ZUtlpgTanjayfJ6JF+lx2Wn1AaLOLzM3mUBSFeqvncE4x73Deb67j/dc/RtVUTr/sl+y677YA2HmbQqZIJBZBURR+OraVgVGPtQcWAfAcn7rGBAECM6IjNIjrBpaukdC1siBbMg6xSxspMCQSiURSE0SYLTfQagdt2BLrzukEDm1uOxkvA0DSSKItRkuBIIR5hdwikzkBstkCZ59wFZ++/yW6oXPONb9mm103B0qhj1yqgKZrlaROVYFtRpbG6To+hlmavSg6HnUNUUIgaipYqk5UtVC0lSM8AlJgSCQSiaQGiLC9LC5yoA5F6WOfid7ghz4ZL0Ob244XesT6UXa6IF4Y0JTN02YXSUTMHpM5AVLtGU4/+gq++ex7rKjJH276HZuO2wCAIAjJpQqEQYAVM/lfc5yxjQUiWqlQUwiB7/k0DK7DD0Iiho5mqEQ0BUOHOkNF0xpA6Z3J2oqAFBgSiUQiWSxE0FLuzumUxUVtGxR2lJ22ue0U/AKWZlHfS7fThVH0fJpyObKuvdBkToCWpjZ+d8REpk35kURdjEtuOYN1NxlTHiAUUgWcvEM0YTI9ZfHQZ8MZGPX47bgZmLrAtT0iVoRILELR9mhsiOOJkAYzSkRViasKqIOXieHbkmLluRKJRCKRLHVEMLeU0IlA0YbU/PiFoEC7kyLrZ1GV/pedLkjWdZibzeL4AXWWhdql7fd85vzYzG8Pv4zZM5oYMKieibefxZpjV6+sL+Zt8tlS3kUgFJ7+qtSldPSAAqYuEEIQhoJknUUQCAxdQ49oKAgMHaKqjqVFUbQBi39hyxFSYEgkEomkz5S6c/4I/hRQIihqQ02PP7/sNEUoBDEt3o2xWD8Q0G7bzM1lAYV6y4IeKkUApn33I787YiItc9sYstog/njnBIaPnC+kfMcn115A00p5F29MHUBz3iRu+Px0bCsATtHFikWwYib5oktDfQxXhNSbJpoKDboOSsNKFR4BKTAkEolE0kdK3TlnQPA9KDEUNVmzY4ciLOdZtGEHDjE9RkStTZlrEFLuzJkjoutEF5LMCfDN5z8w/qg/VuzW/3jHBBqHzp9lCIKQbCpPEAREYyatBYPXp5bW771OCzEjJAhDAKLJGJ4v0DWNqGlghz4xUyOialgaZeO3lau5oxQYEolEIuk187tzTgOlHmUxek5UH1eQD/K0O+1k/RymZi5e2ekC+GHI3GyONrtI3IgQWUSn5k/e/4qzjp9UsVu/9LYzqR/QSUiV8y7sXCnvQgh49uvB+KHKmIEFNh2WA0qVJdGYScQyKBRd6pNRfEUQi5joakhCi2KoForWUJsLXY6QAkMikUgkvaK6O+dAFMWqyXG90KfFaSHjpQGFpFG3WGWnC2J7PnPzObKOQ51pLtJJ9d3XP+bck6/BdTw22HwsF998OrFEdV+MYt6mkC0SiZb6XXw6N8F3bTF0NeSA9eahKOD7IYoC0booQRCW2oNHDQqBx+BYDPBJ6oBSD8qS92hZ2kiBIZFIJJJFIoSD8KZAMBu0QTVrBuUGLk12M1k/S0KPoy9m2emCZF2XpmyWou9TZ5kLTeYEeOUf73DJ+D8R+AH/t/3GnH/9qZhW9bV25F0omoquqxQ8lee+Kbmf7jK6ncZYySjTtV3i9TEipkG+4JJIWKCCqeoYeoipR4kqCoo2ZImU9S5rpMCQSCQSyUIRYaFTd84hNSuldAKHJruJnF+o+axF52ROATQsIpkT4NmHX+XKc+9ACMF2u2/F2ZNOQjeqrzUMBLlOeRcAL05pJO/pDIk77LhmO1AyNNN0jVjCIghDVFUhETUo+j5D4glCAuq1CIoagRonyC4vSIEhkUgkkh4RYQ7hfV3uzjm0ZomIxcCmyW7C9ovUGXU1KT3tIAxhXiWZUyOqL3q25cE7nuXmy+8HYLcDd+LUC49B1xeYVRBQSBdKVuuJkriY1m7xweySd8gB689DV0GIUu5FckAc3dTJFzwSsQiqrqH5IbGIhqJCTAtLviNKvHYXvxwhBYZEIpFIukWEqXJ3zmxNu3MW/AJNTjNO4JI06mqWyAkdyZx52u0CUSOCuYhkTiEEd177CPf++UkA9j9qT3551s9Ru1E8xYJDLlMgEjVQFAU/hKe/LpWsbjkizagGG6BiaBaNRwmCUifPeCxC0feoM00UxSeuxTEVyuGR2jYmW16QAkMikUgkXRBBK8L/BoRd0+6ceT/PXLsJP/RJ6omaigvbL9msZ3qZzBmGIddffDdP3PciAIf/6kCOOGU/lG7Ehe8G5NryqKqCrpdEy4ez65iXjxCP+Oy5dqnnhRAC3/WpG5REi6jkCx4xy8CI6NiOQ71p4guXpK6DYpQSPFdSpMCQSCQSSRVLqjtn1svSZDcTEpI0als1kXNd5mZz2L5HfS+SOX0/4PKzbuGFp95EURSOP+swDjhi927FRRgIcu05Ar/kM9LBlqtl8AKVessjZpT6XbiuT8TSsRJmafZCQCJuYvs+Ud1A10FRIkRVUarEUVfO8AhIgSGRSCSSMkIICGeXxIVi1LQ7Z9rN0OQ0owAJvYYvVQEp22ZuLkcoxCI7cwI4tstFv72R/7z0X1RN5dd/OIbdD9gRVetmPwGFzPy8i84zLpoKO6yZmr+pEAReQHJwHZqmUix6RC0dy9RJOQ5D6uL4ocvAyEB0xUfRBtXmHiynSIEhkUgkknJ3zpkQfFfT7pxCCFJemma7GV3ViWq16Z0BpWTOlmKe5nwOQ9NI6uYi98nnipxz4tV8+M4XGBGD8X88iR122wKtO3EB2AWXfHp+3gXA7GyEwTEPo+yU2oFju0SsCGbMJAxK/iOJhIUbhER0jZih4wmfhK4CsZW2eqQDKTAkEolkFUeIoFN3zmTNpu2FCGl3UzTbLUQ0A0tbtADoLX4Ylm3W80QNc5HJnADpVJazjp3E/z6eQjRmMeGqX7PF9hv2KC4CNyDXnkNR5uddFFyVez4aQcwIOHaz2QyM+QCEQiBCiNVHUTWFou1hlWcvso7DoHicAA9LixJVvHJFTu3E1vKIFBgSiUSyCiOEV+7OOR3Uxpq99IQIaXFbaXHasHQTU61NYy6Yn8yZdmzqF2Gz3kFLUzvjj5rID9/MJFkf59zrTmP9zcagad3vKwJBtj2P7wZY8fnCqLVooCkCTRHUW35ludthaBaNIEJBGAgS9RYCUBWFuoiJFxYYFBkIioOiNi72fVjekQJDIpFIVlGEcEv5FsGscnfO2swwhCKkxWmh1WkjqkdrZlYG85M5i75HwyJs1juYM7OZ3x1xGbOmN9E4uIHzbhzP6HVWxzB63jefLVLM2V3yLkbWO5y+3QyyjkaHNvGD+YZmiqpgOx6RiE7UMsi7Hgkzgq4JEAZxlVLfi5U8PAJSYEgkEskqiRDFsrhoKnfnrJVjacA8t4U2p424HseoVevvjmTOfI4wFL3qzAkw9duZnHbkRFqa2hm++mDO/9N4hq8+hEik55CKk3fJp/JVeRedsfQQSw8r//Zsj2jCwowaiLDkQdIwMAYqBCKk3oxihw51Rh2G6oI6rGat1pdnpMCQSCSSVYxSd85vQLTWtDunH/o0O/NIuSkSegJdrc1x5ydz5jE0laTZu5mWrz79njOOuZx0e5Y11x7BBTeNp2FQPabZ86svcANyqRyKQiXvAuDtGfXE9JDNhmerZjR8P0BRIZq0QCmXqRo6UStSKU2NGTqF0CWpxYHCKhEeASkwJBKJZJVChOmyuMiUv0nXpjunF3o02/NIe2kSehJdrc1x/TCkOZentZgvd+bs3Wvro/f+x9knXEUhV2S9Tdbi3Ot+RzQRI2r1PFMjyj4jruMTjc/PRWnJG7w4ZRCBUEiYPmMbi5V1ru2VDM0sAyHA8wMaG+JoqorrBgxPxvDxMLUIMTUoh0fq+n9DViCkwJBIJJJVBBG2lX1Fatuds+SI2kTWz5M0kr1KuuwNju8zJ5cj49jU9TKZE+DtVz/k/F9dh+t4bL7NBky46jdoEYNodOFhoEK2SCFnY8Xn510IAc98PZhAKIxtzLP2wPniwvd8NEMjlixZuXtuUCpHtUycwC+VzkYiFIMCQ63BqIoN6lo1C0ct70iBIZFIJKsAImgqd+cMatqd0wkc5tpNFPwCdUYStVYtxV2POdksRd+jvpfJnAAvP/s2l57+ZwI/YPufbMFpE09EoBCLGgttS+4UPHLpPBHLqLqGj+ck+aE9hqGG7L/evCrh4do+ycYEejmfw/V9BjbE0XSVrO3QGIuhqaALlZhmAj6KNqC/t2SFQwoMiUQiWYkpdeecU+7OqaGotese2dkRNVkrR9ROyZxBGPY6mRPg6Qdf5urz/4IQgt0P2J5fn38MbiCIRSMLFReBG5JL5YDqvIu8q/KvKaX79f/WamNgdH5Zquf66BG9Ekpx3RBD14hZEQIRlkpTTRM7dIhqMSzFByW5UnuPLIgUGBKJRLKSIoQod+ecAkoUpYax/4JfYK7ThBt4NXNEDUNosws05XLomkqd2fueHA/c9gy3THoAgIOO2p3jzjqMQtEjZkVYWGRFhIJcOo9re1V5FwAvTBlEwdMYmnDYfo3U/H06G5qVS11dz6OhLoaua2Rdh3gkQkw3yPpF6q06IAfq6jVLqF0RkAJDIpFIVkJK3TmnQTC1pt05AXJ+jia7uaaOqH4oaM7naC0UiOoGpt6715MQgtuvfpi/3fIUAMeceiA/O3l/cnmHqBXp3l+kE4WsTSFbxIxV97v4oS3KR3PqUBAcuN48Ovfjch2PSNTASpSqWTwvRNNUYrEIAkEQBjSYSVw8IppJVNUBHUVr6MstWeGRAkMikUhWMkrdOX8od+ccgKJEa3bsjJeh2W4mRNTMEdUNAubksqRtm2TE6nUFShiGXHfhXTx5/0sA/ObcI9jvyD1JZYtErUiPLcAr5y145FI5IqaO1im+4wUKz3w9GICtVs+wRoNdWSeEIPRDkgMSlS6gruuTTFhEdJ2i72LpOgnTJO/naDQHoCs2UAfKqlE90oEUGBKJRLISIYSL8KeAPwu0xpp154QOR9QmFFQSeqwmx+yoFMk6Tp+SOX3P548TbuXFst362ZefyP87YAdS6SJRS1+kuAi8kGxH3oVR/Sr8z7QBtBQiJCI+e4xprR6v7RKJRSq27UEQomoK8XikfD0Bw5NJFEWgoBDXExCmUIzRNSsJXlGQAkMikUhWEkrdOadAMBe0wTUrhxRC0O62M89pqakjqu37zM7myLs2dX0QF47t8offXM9br3yIpmtcdMNv2fYnW9CWKhKJ6D36i3QgwnK/C9sjGq8WYPPyBv+eVqr02HfdFqLG/I6dQSgQAmLJGEpZwNi2TyJhYhoGbhBgqBoJI4IdOER1i5iqgIisEq3BF0QKDIlEIlkJKHXn/BbClpp25xQipM1tp9luwdQjWGptZkSKns+sTAbb96i3oii9rBQp2a1fxYfv/I+IaXDF7Wex2bYb0tpewDDUhfqLdFDI2hQreRfzzysEPPPVEAKhsE5jno2G5Kr282wXK2piRkuzFUEQoigKifK/i77LADOGZeik3AKDzIEgCqXQiFKbcNKKhBQYEolEsoIjwky5gVa65uJiviOqhVkj07KC6zM72yEuel+Gmm7PcsYxl/PVp98TS0S55u5zWG+zsbS259A0ZaH+Ih24RY98Koceqc67AGgv6jTlI116XsB8Q7NYMlqpSrGdgHjMwDQNAlFaXx81cUMfQ9WJaTEQrSjG2jVrarYiIQWGRCKRrMDM785ZLLf+rs2LrMMRtcVpI6bHiNTItCzveszOpnGCsE/iYt7cNk4/6o/88O1M6gckueFvFzB6vZG0tucBFuov0kHgh2Tb8wgBRqTr9gNjPmdsO50fMxYDOvW8gLKhWdIiUu4GGgQCgETMBBSKvkfCMInpEfJBjqSeIKIKCK1VqvdFZ6TAkEgkkhUUETSXu3P6KNrQmh03CAOanXm0u+01dUTNuS6zshn8IKTeLL2Ye8Os6U2MP3Iis2Y0MWjoAP704IWsNnoYrW15wlAssgU4gAgp5V0UXaKJnsM8sUjIOoMKVct8P0BVS7MXHUN2HJ+YqWOZBiDwgoDh8QQoJXGWNJIQ5kAdWPIfWQVZtVJaJRKJZCVBBHMQ3leghCha7bpz+qFPk91Eu9tOQk/UTFxkXYcfsxmCQJQbaPVOXPzwzUx+dciFzJrRxIg1hnLHExNZfa3VaGsv4Plhr8QFQCFXpJixMeORLrM8U9stPm9KIET3+7q2RzQRxSjPkgSBQAhBPFG6Dtv3sXSNhBnBDW1MzSSmRgEXRRuySoZHQAoMiUQiWaEo9WGYURYXOoo6sGbH9kKPuXYTqYojam1yOdK2w6x0FiHotdU6wJeffsevf3YRLc3trLXuSG5/YiJDVxtMWyqH6/rEeikuXNsj355HMzS0BXpseIHCU18N4ZHPh/H+j137VPiej27oRJPze4m4ro9lGUTLgsP2fRqsKLqq4gQuSSOJqrhAdJXrfdEZKTAkEolkBaHUnXMq+N+CEkdRaxfbdwOXucW5ZLwsSaOuZnbrqaLNrEwGFEgYkV7v99G7/+N3h19KJpVjg83W5tbHLmXAoHraUjls21+kv0gHQRCSbcsjhCDSTZ6Gogg2HZZlgOWx2fDqqpEOQ7NY0qoYmoWBIAwFiZiJoqh4YYimqCQjJn4YoCoqcT0GIgfaQBS1Nv1CVkSWucC45ZZbGDVqFJZlMW7cOCZPnrzQ7W+88UbWXXddotEoI0eO5IwzzsC27YXuI5FIJCs6QvgI/3sIvge1vqatv53AYY49l5yfo85IotVoSr+9aDM7m0HTFOJ9EBdvvvwBZxxzOYW8zRbbbcSfHrqIZF2c9kyevO0StYyF+ot0IELIpwq4RbdSWrogugo/Waud07ebjqWHVes818cwdazE/L4fjudjmXolNFPwXJJmhKiuY4c2MS1KVIkAQU2N5VZElqnAePTRRznzzDO5+OKL+eijj9h0003Zc889aW5u7nb7hx56iHPPPZeLL76Yr776irvuuotHH32U888/fymPXCKRSJYele6cwXRQB9a09XcxKDLbnkvBL5I06mtjty6gpVBgdjaDrmnE9N6LixeffpPzTr4G1/HYaY+tuO7e84jGTdozhV77i3Rg520KmSKRWNe8i1BA0ElP6Au8DTsMzWJ1UbTyShGW8i8ScRNVUQkJEULQELUQlPJX6ow6wAFiq2Rzrc4sU4Fx/fXXc9JJJ3H88cezwQYbcPvttxOLxbj77ru73f6dd95h++2354gjjmDUqFHsscce/OIXv1jkrIdEIpGsqAhhl8pQ/ZmgDqpp6++CX2BOcS5u4FBnJGtmt95SLDA3l8XUNKJ673tnPHn/i1w6/s8EQchPD9qRy287E9MySGdssjm7V/4iHbi2R7Y9j6Zr6N109vxodpLbJo9kZrr7++naJUMzs1OnT8fzMCM6llVurOX5xIwIMd3EFS4RLUJUi5bDI7X9rFZElpnAcF2XDz/8kN12223+YFSV3XbbjXfffbfbfbbbbjs+/PDDiqD44YcfeP7559l77717PI/jOGQymaofiUQiWREQYR7hfllu/T2kZq2/AbJejjn2XLzQI2nUxhFVCGgu5JmTzWJpBmYfxMXfbnmKay74K0IIDjl2Ty684bfohkYqWySTLRK1jF6LiyAo97sIw27zLnKuxgtTBjEnZzI91XU2SAhBGIjS7EVZnAgBvh+SiJvlRFGBG/gMjEbRVHACt1x1owGipsm3KyrLrA9GS0sLQRAwdGh17fbQoUP5+uuvu93niCOOoKWlhR122KE0feX7/OpXv1poiGTSpElceumlNR27RCKRLGlEmEV434Bor2l3Tig5ojbZzQiomSNqGMK8Qp7mfI6oEcHUejdeIQS3Xfkg99/2DADH/e5gTp5wOIqikM0VSaeLmL3wF6kcryPvotBzv4t/fdtI0dcYnnDYdmSqy/qKoVl0/v6u6xMxdGJWh6mZj6nrJEyDQJTKVpN6otwaPAY1TMBdUVnmSZ594Y033uCKK67g1ltv5aOPPuLJJ5/kueeeY+LEiT3uc95555FOpys/M2fOXIojlkgkkr4jwnaE9z8QKVBr2fpbkHbTzC02lTw0auSIGobQXMjRnM8S64O4CIKAay74S0VcnHr+UZxy9i9QFIV8waYtXcQwNPRe+It0sLC8C4DvWqN8MrcOBcGB6zezoG4JQgEC4sloxdBMCPD8gGTcrAidou9Tb1oYqoYTOli6VQ6PFEAdgqL0Pu9kZWWZzWAMGjQITdNoamqqWt7U1MSwYcO63efCCy/k6KOP5sQTTwRg4403Jp/Pc/LJJ3PBBRegdlNWZZomZh/qriUSiWRZIoJ55e6cTllc1KaiY0k5ogYhNOWztBYKJCJWr8tbfc/nsjNv5uVn3kZRFM6ZdDIHHFEKmRdsl7Z0Ab2X/iIduLZHLlXoMe/CCxSe/XowANuMTLN6vdN1G9vFiplEOlWdeF6AoWvErNK7xA9DNEWhLlK6h27gMMwaikKpU5eiyfAILMMZjEgkwhZbbMGrr75aWRaGIa+++irbbrttt/sUCoUuIkIrK2XRUws2iUQiWUEQwVyE9yUoQU07QAoR0uq20WTPw9CMmokLPxTMzWZpKeRJRMxeiwvbdjjnpGt4+Zm30XSNy/48viIubMelrQ/+Ih0EQUguVSAMgm7zLgDemDaA1mKEpOmz25jWrtfjh6BAtC5aVQbrej7xuFmpJukoTY0ZOl7oo6s6sY7eF0pilW6u1Zll6kVy5plncuyxx7Lllluy9dZbc+ONN5LP5zn++OMBOOaYYxgxYgSTJk0CYL/99uP6669n8803Z9y4cXz33XdceOGF7LfffhWhIZFIJCsaQghEMBP870CJoNSwvFGIkHlOK61OK5YerZkjqh+GzM3maLOL1JkWWm8aUwD5bIGzf3kVH7/3JRHTYNIdE9ju//0fAI7r0dZeaorV2xbgAAgopAo4eafHvIvmnMGb0wYAsN+687D0rl9KPdsjWm8RMeef23VDdE0jXs7HCAkJhaDeKvmS2L5NQk9gqiaE7aCNQVGkzRcsY4Fx2GGHMW/ePC666CLmzp3LZpttxgsvvFBJ/JwxY0bVjMUf/vAHFEXhD3/4A7NmzWLw4MHst99+XH755cvqEiQSiWSxECJE+NMh+KHcnbM2SZdQMt2a57TQ6rQR12M18xXxw5A52Qzttt0ncZFqy3DmMVfw1Wclu/Vr7zmXzcdtAIDr+7SlCniBIB7rmwgq5m3y2Z7zLkIBT389hEAorDcozwaD812vyQ9QNYV4Ilplk+J5PvXJKIZe+hJr+z5R3SBmREq9NERYTpT1AQ1FG9Cnsa/MKGIViy1kMhnq6+tJp9PU1clpLIlEsuwodeecCsFUUBpq2la6syNqQo+j10hceGHA7GyWjO2QtEy0Xkbam+e2Mv6IiUz7bhYNA0t26+ttMqZ0TD+Y3wI81rsW4B34jk97c6n9QE+hkf/OquPpr4YQ0ULGbzuDBsvvsk0ha5NoiJFsnC/wfC/ECwKGDqojYuiAIGXbrJasozEWxQ4cQLBGbA00coCFEtm8phU/KzIrVBWJRCKRrCzM7845FdTGmooLbwFH1FqJCycImJUpiYu6PoiLH6fN5VeHXMi072YxeNhAbnt8Yvfiopf+Ih0EQUg2lSdYSN5FztF4YUojALut1dqtuHAdHz2iE0tWfwaO6xOLmmVxAY5fSvZMmOVS1cApm8LpEHZUj0hx0YEMFEkkEslSRggH4U2BYHa542PtShrdwKXZmUfGy5A0kr0OXywK2/eZk82Rc0viQu2luPj+mxmMP2IirfNSrD5qGH968EKGjxxSGqvv096ex3bK4qIvQy3nXdi5nvMuAJ6fMgjb1xietNlmZLrrYcotwesaE2iR+QMIghBVVUh0ygWxfY/GeBxT0/DDEFVRSehxhHBBMVC0hj5cwMqPFBgSiUSyFBFhAeF/C0FTuYFW7f4Mu4HLXHsuOb9A0qirmWmZ7fnMymYpei71loVC7477v0+mcMbRl5NN5xmz3hrc9MAfaBxSylFwPZ/W9nzZdr2P4oJS3kUhWyQS7T7vAkq5F/WWj66GHLj+vC49L6DUQMswdaxkdWWN7QQkYhHMcsJnIEJURaEuUhIzdmhjaVapIkekQKkvVZBIKkiBIZFIJEuJ6u6cw2o6nW4HNk12MwW/UPYVqY24KHo+szIZir7XJ3Hxwdufc86JV1PI22z0f2O59t7zqG9IAiVPj7b2Aq4b9DnnAkp5F7n2Aoqmoi/oUtYJVYE9125l+5EpEmbQZb0QgsALSA6uq+oUGgSl1MR4LEJHxmfB9YibEWJGSXD4occQcxCKoiICG8VYC6VGs0UrC1JgSCQSyVJAhKmyuMiWG2jV7mVUCAo02c04gUPSqKuNaRmll+rsbAY78GmwLOiluPjPS//lwlNvwHU8ttx+Y67669nE4iXPD8f1aG0v4PkBsZjRZ3ERBoJcKk/gB0TjvWui2J24AHBtl4gVwYxVH8d2fGKWgVWevQgJCURIvWmhKOCEHhHVIKbFEMIBxVzlnVO7Q8otiUQiWcKIoKXUQEvkay4u8n6BucW5OIFLUq+RIyqQc11+zKaxg4B6s/fi4oUn/8P5p1xbslvfcyuuvefc+eLCceeLi2jfxQUCCukCxZyDFes5b6UpF+HOD0YwJ9vzNkIIwhBidVaV/XsQlFqFJ+ImHdfcUZqaKIdHnMAmrieIaBEIsyXfESXex4tZ+ZECQyKRSJYgle6cwqtpd06ArJdljj0HX/jU1cgRFSDrOszKZvAD0Sdx8cTfXuTS00t263sdsjOX33YWZoc5mOPSmloMcQEUCw65TIFI1FjoffzXlEamp6K8/kPPLbudoosVi2AtMHvhuj5RSydqzU/udPyAhqiFriqEHcZmHSZxwq3557qyIEMkEolEsgQQQkAwq1SKqhg17c4JJUfUuXYzCpDQa5dcmHEcZmezCAHJXvo4CSG475anuOPqhwH42XF7cfolx1UaJXa0//b70USrA98NyLXlUVUFXV947srBGzTz4pRGdl+7rdv1QRgigGgyhtJpyicMBGEoSCTmiyonCIhoGolIWSh1GJupUYQogmKVEjwlXZACQyKRSGpMqTvnDAi+ByWGoiZreGxB2kvTbDejqjqxGvmKAKRsmznZbMlpNdK70lkhBLdc8QAP3vEsAMePP4STzjys8o3edlxa2/MEgSDWT3ERBoJce47AD7rMOHRHnRnws42ae1zv2h7RmIm5QDtyx/OxLB2rU08N23MZGI1h6eVeGIHLMGsImqohghyog1FUGR7pDikwJBKJpIYIEXTqzllf0wZanR1RDVXHqqG4aC/azMlm0DSVmN47cREEAdec/xeeebhkWnnahcfwi5P2q6wv2i5tqTxh2H9xgYBCppR3EU2YCw2tzM5GWC3pLvRwvh+glA3NOkd+RCgIA0Gi3kIt58gEIgRFoa7iouqjKxpRPVY22PRQtEH9u65VACkwJBKJpEaUunN+D8FMUAeiKLUTAEKEtLhttNgtmLqFpdaoOZeANrvInGwWQ1OJ9lJceK7HZWfczCv/eAdVVTjnylPY//CfVNYX7FJYRNBH47IFsAsu+fSi8y6+bYlx3yersemwLD/bsKlHIeLaHvH6GBGr6+xFJFKde1H0fBKRCHGjdE/s0Camx7FUE0QBiMnqkYUgBYZEIpH0ESF8EA7ggHAQYaFk1S3yEOZq3p1TlE3LWpw2YnqUSI0cURHQUiwwN5fF1HQsvXfHtYsOF/z6Ot557WN0Q+OSm8bzk323rawv2C6t7TmAqhd2XwncgFx7DkVZeN6FGyg8+81gABKRoEdx4Xs+mq4RS1QLPxGWrNobBsYqeSMCgR8GNJil5FkhSv4udVYSRVEQYQ601WoqIlc2pMCQSCSSHuhWSIQZoAjCA7zS20lRQYkAkXJ3zto10ArCgHluC+1OG3E9XjNHVCGgpZBnbi5HVDcw9d4dN5fJc/YJV/HJ5K8wrQiT7pzAtrtsXllfKLq0pnIoCpU+Ev0aXyDItufx3QBrEf0uXv9hIO1Fg3rT4ydrtXZ/PAGu7ZMcGEdfwLfE9Xwihk7Umi8Kbd/D6uw7IhwimklUiyFECIQoamO/r29VQAoMiUSyylMSEjbggrARYXHRQkJNAD23qa4FfujT7Mwj5abKpmW1ES5CQHM+T3M+R9QwMLXevQraW9OccfTlfPPFVOLJKNfdcx6bbr1+ZX2+4NCWzi+2uADIZ4sUc/Yi8y7m5iK8NaMBgP3Wm4epd28Q7rklQ7NouSdHB0KUDNcaG+Jo6vzODY4fMCyRQC8vc32XgeYADFUvzV4ocRkeWQRSYEgkklWG5VVIdIcXejTb80h76ZqKizCE5kKOefk8MSNCROvdcZvntHLakROZ/t0sBjTWccP9f2DdjUZX1ucLNm2pAqqqYPbgbNpbnLxLPpVfZN5FKODpr4YQCoUNB+dYf3Ch2+0qhmaDklWGZgCeGxDRNWLW/FkSNwjQVZVkpCO5M0RROpUDixxoa9Q0DLYyIgWGRCJZ6egqJAqljotVQkKAonQSEklg4S+0pYUbuDTZzWT9bE0dUYMQmvJZWgt54hELQ+3dcWdOm8NpR0xk7o/zGDK8kT89dCFrjhlRWZ8r2LS1F9C0xRcXgRuQK4dYFtXv4r+z6piZtjC1kH3Wndfjdq7jE7F0rG5cV13fZ2BDHK2Tp0nR92gwLawOm/bQwdKiRDULIUptx2V4ZNFIgSGRSFZYhPBKORLlPImuQsIFwXIrJLrDCRya7CZyfoE6o65mpmV+KGjK5mi1CyQjVmXqf1F899V0xh81kbZ5aUaOHs5ND17I8NUHl9cKcnmH1lQBQ1OJmIs3yyLKPiOu4xONLzx5MuNovPRd6SW/+5hW6q3u/UaEEAR+QHJAtaEZgOuGGLpGrFPuRUAICOqj88/vhS6DzMaSsVmYKYdH6vp5lasOUmBIJJLlnpVRSHRHMbBpspuw/WJZXNTmuH4omJvN0mYXqTPNXs+IfPHRt5x5zBVkM3nGbrAmN97/BwYObiivFWRzNm3pIoauEoksfginkC1SyNlY8YXnXQA8/+0gbF9jRJ3NuJHpHrdzbJdItKuhGYDreTTUxapmSoqeT8wwiZfLdd3Qx1ANYlo5d0PkQVsLRalRJc9KjBQYEolkuaH3QkIFxQBMUC1WNCHRHQW/wFynCTfwSBp1NfMV8cOQOdks7XaROtPqtbiY/OZnnHvS1RQLDhttsQ7X3XMedQ0dLcnL4iJVxDBqIy6cgkcunSdiGYuctfmmJcbnTUlURXDg+s09CrFQCEQIsbpolaEZgOeFpaZiVaZpAj8IGBZP0DHBYwc2dUYSUzNLoTdUFG1A/y90FUIKDIlEstSpFhKdky1tEC7zcyS0lU5IdEfezzPXbsIPfZJ67UzLvDBgTjZLynaosyy0Xvpb/vvFyVx46g14rs/WO27ClX85m2isI2QgyORs2lNFDEMjEln8/BDf8cm1Z4FF5124gcKzX5dCNNuNTC20c6fbYWgW7ZqM6bo+yYRFpFN5ru37RHSNZLk0tWRsFpLsnNypJKX3SC+RAkMikSwx+ick6lhZhUR3ZL0sTXYzIeF8h84a4AYBs7NZso5DvWWi9lJc/OuJf3P5hFsJgpBd9hrHpX8aT6RScipIZ4uk0kUMQ6+ZuEi3ZvHc3vmMvDF1ACnboMHy+H9rdW9mBuAHIQCxBQzNAIIgRNUU4vFq4WH7PkPi80tTndDB1E1iWrnduyiAtnpN+5yszEiBIZFIFpveCQkWKP+sB/RVRkh0R9rN0OQ0oaCS0GtnmOX4PnOyObKuQ10fxMVj9/yL6y++G4B9frYL5171q04zCoJUtkg6XSQS0TGMGooLx8eKLTrvAmCrERnm5ky2HpHusecFgOd4ROMWkW7alNu2TyJhYhrz1/lhiKao1Jmdy1VdBlmDSsZmwgN0FK2hL5e4SiMFhkQi6TWLFhJ+eUZCComFIYQgVXZE1VWdaA1Ny2zPZ3YuR74P4kIIwb1/fpI7r30EgJ+fsDfjLzq20jYbBKlMkXSmiBnR0ZeRuAAYEPU5ZrM5Cz92h6FZ0qoyNIPS7IWiKCQWCJvkPZd60yRaDpn4YYCmdBJ+IgdKXelH0iukwJBIJF2YLyRsqpMt5YzE4iJESLubotluIaIZWNqiwwK9pej5zMpksH2PestCWfDt2u14BH++/H4evvMfAJx4xs854fRDK5+jECHpjE06W8Q0dXR92YiLnKORMLsvRV2QngzNAGwnIB4zMDt1Gg0JEULQYM0XJHZoE9ViWGpZ/IVFFGMUSo16kqwKSIEhkUiqEGEa4X0NoogUErWl5IjaSovThqWbmLVyRAUKrsfsbBY7KImLLl/duyEIAq46707+8chrAIy/6DgOP3GfqvGmMjbpbIGoZXTpI9Ef5udc9F5cZByNm95dg/UH59lv3Z7bgUPZ0MzQiCWjXdYFQWm/RMyk8/0plaZGiBklsSdEaQajzqwrGZsJp/T8y9bgfUIKDIlEUkGIAOFPB5EFtREpJGpHKEJanBZanTaieoxIjUzLAHKuy5xsBjcIqTd7Jy481+OS8X/mtefeRVUVzrv61+z7810r65e4uIj2PiwypTWG46vMy0cwtJ7FRcXQrDGB3k3prOP4xEx9AZ8UgRsEDInH6bhEV7hENIOYXhYpYUd4pHZJuKsCUmBIJJL5hE0QzgV1sGwkVEOCMKDZmUe7215TR1SArOsyO5shCAR1Zu9yOeyiw3mnXMt7b3yCbmhc9ufT2XXvbSrrQxGSShfJ5OxlLi4Atlgty5C4S0QTC20+VjE0S3S9D0EgEEIQT1QLMMcPMHWNRGT+bJITOAyIDMBQy78DwkEx1pZiu49IgSGRSAAQoojwp4ESk+KihvihT7M9j5RXW0dUgKzjMCubJRSCpNm7XI5cJs+E46/k0/9+jRU1ufIvZzNup00r68MwpD1TILuciIsORtY7C10vhMD3fOoak1W+Ih24ro9lGUQX8Eop+h6D4/GK6VsgQoSgktwphA2KJXtf9AOZrSKRSBBCIPwZ5alg+Ye0VnihR5PdXBYXyZqKi5RtMytTak7V4fq5KNpa0px6+KV8+t+vSdTFuOnBCxciLiLLXFx8MCtJS753Ytd1PCKW0a2hWRgIwlCQiJlVSZql0lSFuk73zwkdorpFVOsUHlEbSv4jkj4hZzAkEgmEbRDMAm2gnAauESVH1Cayfp6kUYdWw/uaKtrMzmbRNIWY3rtE0abZLZx25ERmfD+bAYPqufH+C1hnw/l260EYkkoXyOY7xMXij9dzfDKtWfx+iIvZ2QjPfD0EVRGM32YGA2N+j9sKIQj9kOSARLeiyPF8LFMnukBPjKLnkTQjxDr1w3ADl4HWAFRFRQgBeCjaEPl70Q+kwJBIVnGE8EqJnSgoSu36MazKOIHDXLuJgl+gzkjWzBEVAe22zZxspuSj0UtxMXPqHE474jLmzmph6GqN/Omhi1hjrdUq6yviImcTjdZeXJh9FBehgKe/GkIoFDYYnFuouICyoVnM7LYTqAhL+ReJehO10+xFSEggQuqtaCUlwwt9dNUgXul9UQQs2fuin0iBIZGs4gh/DogWUIcu66GsFBSDInPtZhzfJllDR1QEtBQLzM3liGgaUb13oYMpX05j/FF/pL0lzRprlezWh40YXFkfhCHt6Ty5vFNbcdGSwfeCPosLgPd/rGdWxsLUAvZZt2Wh2wahQAiIJ6Mo3Yzd8TzMiI5ldW0LHtUNYsb85XZgk9QTRDrKh0UOtKEoaqxvFyABpMCQSFZpRJiDYDooSemvsBgIEeKGHk7o0OK04oYeSSNZM9MyIaC1WGBuNoul65i9FBefffANE46bVLJb33BUyW590PwcmyAIaUvlyRed2oZFFkNcpG2Nl79rBGDPsa3ULaK5lme7WFGTSDeGZkKA74fUD4ihqdWhE8f3WS1Zh15WgKEoVc8kjES590UIBCjqoL5dgKSCFBgSySqKECEimA7YKOrwZT2cFQYhBL7w8UIPN3SxA5tiaOOFPkHoo6lqTR1RhYB5hTxNuRxR3cDUe/dne/J/PuWck67BLjpssuW6XHvPeSTr5ycqlsRFjnzRXW7EBcBz3w7GCVRG1tlsNSKz0G0rhmZ1UbprsOm6PhFDJ7bA7IXj+0Q0nWSn0lQ3tDG1zsZmRSAmm2stBlJgSCSrKuE8COaWG2pJeiIIA1zh4YUuTuhQ9Iu4wsMPfAQCVVHRVR1LjaBpsZoJC4AwhOZCjnn5PFEjgqn1bpbpjX+9z0W/uxHP9Rm306ZMunNCJ7t1CPyyuLBdYlYEtQbiwrU9sq3ZxRIXX82L8b/mBKoiOHD95kWGlzzbI5q0um0JLgR4fkBjQ7xL4qftewyMxarEmhO4DLIa0Tt6lIgcaCNQlNq1cl/VkAJDIlkFEcIpJXYqBopSu3bVKzpChHihjytc3MCjGBRxQgc/9AhEySRLU3QMVccyrNolb3ZDEEJzPktLIU88YmGovSsZfe6x17ni7NsIQ8Gue2/DJTed1sluvSQuWlM5CsuZuHB8hX98XcoN2WGNFMOS7kK39/0AVaXUEryb83legKFrxKxqgdDxOdZ3akrmhwGqolaSO4UIAIEixfdiIQWGRLIKIvwfIUyBNmxZD2WZ4oU+XujihR7FSqjDIwhLVQuaqpVmJ7QYei9f8LXADwXN+RwthQLJiNXrcz9613PceOm9AOx72K6ce+UpaJ1mPXw/oDWVx7Y94tEISg0yUGshLgBe/WEgacdggOWx61ptvTpvoj6GYXb/GnM9n4b6WJemWwXPIx6pLk21Q4eoZhHtMDYTBVBiZd8dSX+RAkMiWcUQYQqCH0FtWKWcIUMR4oYubujihC5Fv1AJdYTlUIeh6phqBL3GoY6+4IeCudksbXaBOtNC68VnJITg7pse56/X/x2Aw0/ch9MuPLaqd4PnB7SVxUUsaixX4mJ2JsI7MxoA2G+9eUQW4jcC5Zbght6toRmA64bomkY8Wj17IRAEYUCDNT8Bt2Rs5lNvDp7/+yAKoK0pZ/cWEykwJJJViIqZGf5KXXrXEerwRCkRsxgUsYOOUEcAqOiqhq4aSzzU0Rf8MGRONku7bfdJXPxp4n088tfnADjprMM4/rRDuhEXOWzbJxaNdJsQ2VdqJS7cQOGpr4YgUNh4aJZ1BxUWur0QpWTSusYEWjeGZgCe51OfjGLo1euLvoel6yQ6de50hUdENSqdO0vhEVC0gf27IEkFKTAkklWJsAnCZlAHL3rbFYjOoQ47dCgGRbzQwxcBCLHMQh19oSQuMrTbDnWm2Stx4fsBV557B8/9/XUAzrjkeH5+wt5V27i+T3t7HttZ/sQFwIyUxZysiaUH7LPOwnteQGn2wjB1rG4MzQB8L0TVFGKxrrMPrh8wPJmslKZCqSlag1FHROvU+0JJyOZaNUAKDIlkFUGEhbKZWRRFWXF/9TuHOtzQmx/qCD1CIVDKoY6IGiGm6Mss1NEX3CBgTi5Lxrapsyy0XthEuY7HJafdxOv/eh9VVbjg2lPZ+9Cdq7fxfFrb87hubcVFpiVL4C++uABYu7HIYRvPpd4MSC6i54UQAt/1qR/cvaEZgOP6JBIWEaP6GXeCAEOtdk0NhECUe1/MP0kBtDEr9O/I8oK8gxLJKoAQAhGUzcy0FafnhRACL/SqQx2hgx/45VCHUgl1xHVzuQl19AUnCJiTzZJ1HOosC7UX4qJYsDnv5Gt5/z+fYkR0Lrv5dHb56bjq43oebe0FXDcgFovURGjVSlz4IRQ9rSIoNh6a7/X5I9EIZrz70tEgCFFVhUS0a9mq7bkMsGJYnUtTQwdLj1Z6XwjhARqKNqCPVyTpDikwJJJVgbAVgtmgNS7Xpk1+uUTUC3yc0KYY2OXQhw8KaIqKrhhYmlVTZ9Jlhe37zM7myLt2r8VFNp3nrOMm8fmH32BFTa766+/ZesdNqrZxXI/W9gKeHxCLGcuduHjos+HMyxv8covZNFgL9xnpQAhBGAiSjVaPLq+2E5CIRTDNaoERiBAUhfoFkj7dwGWYNWS+R4nIl0IjMjxSE6TAkEhWcoRwS6ER1OWqaVBHe+2OcEfBL+IKtxzqCFEUFV01MNQI0WVY1bGkKHo+szMZir5HvRVF6a6ZwwK0zUtx+jGXM+V/00jWxbnuvvPYeIt1q7ZxHJfWVLEkLqLLl7gAKHgazfkIWUejvaj3WmA4tosZj3RraAYlQzOAeCzCgo0xip5PwogQ7+Q7UjI204jpnZKdwwLoa8i2+TVCCgyJZCVH+HNLduzasjMzmx/q8DuFOmz8wMcXAQqgqaUGVjE9UVNr8+WRguszO5vB9j3qLYtuO0UtwNxZ8zjtiInMnDqHgYPruemBC1l7/TWrtimJiwKeHy4RcdHTy70v1JkBJ24xi7aCwegBdq/2CcIQBMSSsR7La23HJ2YZWAvMXggEfhjQYFW3b3dCm7gWx1RL1ySEW2o8p8neF7VCCgyJZCVGhNmSmZm6dM3MSu21y1UdQUdVRynUISiFOgx15Ql19IW86zE7m8YJwl6Li+nfz2L8kRNpmt3KsBGD+NNDFzFydHUuje24tLXn8QNBPNY7M7RFUStxEYQwK2uyRr0DQIPl93rmAkotwa2Y2W1LcCjPXghIxE0WvJ+272PpGglz/uxFKEpVO0krOT9kKHKg1IOS7NvFSXpECgyJZCVFiLDc88JBUZdc0lrPoQ6/HOpQKqEOS4vVzr58BSTnuszKZvCDkHqz68uwO77931ROP+qPtLdmWGPMavzpwQsZulq1w6ftuLS25wlDQayG4iI9L0sYLJ64CAU88eVQPm9K8PON5vY6obMD3w9BgWgPhmZQMjWLWjrRbgSI4/sMTSSqypNd4WBqpdDb/IHaKMZaq1TzuSWNFBgSycpK2Axhbc3MFgx12IGNHRbLTqKlUIdaCXXEV/pQR1/Iug6zslnCQFBndt/DYUE+/e/XTDh+ErlMgXU2Gs2N91/AgMbqKfyi7dKWKomLaDfVE/2hpuLif0P4dG4SVRH0x/bEsz2i9VaVn0rVOQJBGAoSia6zQV4YoqsqycgCyZ2+w0CzEaNsbCaEA4opnVNrjBQYEslKyHwzswiK0v+XTvehDq8c6hDlUIeOpVro+qoV6ugLadthTi6LAJJm717Y7/37E8496Roc22XTrdbj2nvOJVEXr9qmYJfCIoLlU1w89eUQPplbh6oIDttoLhsM6evsRYCqKcQT3RuaATiej2XpWN14khQ8lwbTItqpJ4YflmbVEnqnexlmS+JCiXc5hqT/SIEhkayECH8mhOl+9bwIwoC0l6bgF3DKoY4OB0pD1UvttbXoKh3q6Aupos3sbLbUn8HonbfFa8+9y8Wn3YTvBWy76+ZccftZWAuUWBZsl9b2HEC3oYH+UBEX4eKLi2e+GsxHc+pQEPx8oyY26mNoRAhwix7JAXH0HgzNRFgqXU3UW/NLTcsEhCBEl9JUJ7SxtChRrdMsknBRtCHLdQn3iogUGBLJSoYI2yGYBdqAfv3BbPPamWfPQy+LiZge61XbaklX2os2c7IZNE0lpvdOXPzj0de48pzbCUPBT/bdlotv/B1GpFpAFIourakcikKXqon+4hY90i1lcRHtv7gQAv7xzWA+mF2PguBnGzWx8dBcn4/juT56RCea6N7QDEqzF5FI97kXtucTi5jE9epr8UOPQeagSq6FEEVQrFKCp6SmSIEhkaxECOGXEzsDFKXnP8w9UfALtLvtRPUopiqdJPuNgNZigbm5HIamEdV7JwIe+etz3HTZvQDsf/hP+P2kk6rs1gHyBYe2dH65FRfPfTuIyT+WxMUhGzaz6bC+i4uOluAlQ7Puxa0ISwmgDQNjqF38ZQRu4DM0nqDzKjf0MFSDeFVyZw7UwSiqDI/UGikwJJKViYqZ2ZA+7xqEAa1OK0IIKS4WBwEtxQJzc1ksTcfshbgQQvDX6//O3Tc9DsAvTt6P311wdJcZqHzBpi1VQFUVzB7CBn2lluLi+W8H8e7MBhQEB2/QzObDs/0bk+sTsXSsZM/JsK7nEzF0olbXZ9XxfUxdJ7GAALMDm7pOxmZCCMBD0QZ1OYZk8ZECQyJZSRBhvpzYGe+XUVPaS5P18yQN2QegvwgB8wp5mnI5orqBqS/6cwjDkJsuvZe/3/MvAE45+3CO/e3BXcRFrmDT1l5A05ZPcfHCd428M7MBgAPWn8f/rdY/cSGEIHADkkPqemwJLkTJgr6xIY7WjTtu0fcZEk9gdOqxEgpRSrLVOz3fogDEZPXIEkIKDIlkJaBkZjaz1CxI7XtiZzEo0uq2YemmLC3tI2EITuBj+z451yVlF4kaBqa26D+vvh8w6fe38fzj/wbgrIm/5NBjf7rAVoJc3qE1VcDQVCJmbap1aikuXvq+kbeml3qtHLBeM1uNyPR/XLZbMjRbSJKp5wZEdI2Y1XUbPwzRFKVLaaoTlnpfxLROoUORA201FKV3ZcOSviEFhkSyMhC2lhI71b6bmYUipNVpIxAhcTW26B1WcYQoTcHbgU/B8yi4Lm4QEIhS2W4iYlY1deoJ1/G46Hc38u8XJqNpKhdc+xv2OmTnBbYSZHM2bekihq4SidRIXBQ80q2LLy46CMLSf/dbdx5br95/cREKQRhCsj6KupCmGa7vM7Ah3q1le8FzqTNNYl3s2l2GWoPRyrMaQoRAiFLDPjGSaqTAkEhWcOabmWn9MjNLuSmyXoaEDI10jwAnDHB9n4Lnk3McnCColO5GNJVYJNKnSptiwebck65h8pufYUR0Jt5yBjvvuXWXE2dzNm2pIoax/IoLRYG9xraywZA8oxp65y3S49iKLlYsghXtOQfIdUMMXSPWTe5FSEgoBA2WVdU3ww99dEUl1rn3hSiU+l7I8MgSo98C47vvvuP7779np512IhqNIoSQNcQSyTJA+HP6bWZmBzZtbjumZspS1E74YYjtzw972L6PFwQARHSNqGH0apaiOzKpHGcdP4kvPvyWaMzkqr+ew1Y7bLzAVoJMzqY9VcQwNCI9VFL0lVqKi8+b4qw/OI+ulkTG4oqLIAwRQHQhhmYArufRUBfrtrGb7ftEdYOYUX1tdugQ1WJYaqflIgfaGiiKTGheUvRZYLS2tnLYYYfx2muvoSgKU6ZMYa211uKXv/wlAwYM4LrrrlsS45RIJN0gwkzZzKyuz2Zmohwa8UOfusiqPXvhhwLH93ECn5zrUPR9XL8kKAxNw1A14oZBb7xDFkZrczunH3053301nWR9nBv+dgEbbj52ga0E6WyRVLqIYejLpbh4a3oD/5oyiLGNeY7edA495GL2bXy2RzRuYi6kI6nnhaWeIrHuRIHA8QNWr4tXjUcICEKfequu8iVYiNJnK8MjS5Y+PxZnnHEGuq4zY8YMYrH58drDDjuMF154oaaDk0gkPVMyM5sBeChqos/7p70MGS9TPW28ihCGUPR8UkWbH9NZpra3MjXVzqxMhpzroaFSb1k0WFHiRoSIprG44mLOj/P41aEX8d1X02kc3MCtj13arbhIlcVFJLJ8iguA4UkHQw1Zs96uibjwXB9VgWiy55bgUCpfjUdNIt1U5zh+KfEzEakWH45wiWgm0arkznw5PFK3+IOX9EifZzBeeuklXnzxRVZfffWq5WPHjmX69Ok1G5hEIlkEi2Fm5gQOrW4bES3S76n+FQoBduDj+AFF3yPnuLiBTyAEqqIQ0TSSZgS179+5esW070p2681zWhm2+mD+9NCFjBy1YLWPIJUpks4UMSM6ulGbsTgFj0wNxQXAmIFFxm87gwHR3luu94Tv+fh+QN3ARI927ABBEJZ8SeLdhzRs32NQPF4Wg/NxfIdGcwCG2unYIg/aWovl0yNZNH0WGPl8vmrmooO2tjbMXpr4SCSSxUMIu99mZkKEtLptuIFLfWTl/QbnBgFO4FP0fLJlQVEyuoKIpvU5MbO/fPP5D5x+9B9JtWVZc+0R/OnBCxkyvFoUChGSztiks0VMU0fvpjqiP9RSXHwwK8nIeoehCRegNuLCD/C9gOSABLHkwjvP2rZPImFiGl2f90CEqIpC3QKlqYEIUVCI6/Nn+ITwARVFG7DY45csnD4/xTvuuCN/+9vfKv9WFIUwDLn66qvZddddazo4iUTSPcL/sWRmpvT9j2TGy5H2MsRXstCIH4bkXJeWQoFpqRQ/tLczrT1Fcy6HLwIs3aDBilJvRonqS0dcfPL+V5x6+CWk2rKst/Fa3PbYpd2Ki1TGJpUtYFnLp7iY/GMdT301lLs+XI2MU5tqFt8P8ByfREOceN3CQyNBUHZA7aG6pOB6xM0IMWPBzp0OUd0iVmVslgMlCcrKK66XF/o8g3H11Vfzk5/8hA8++ADXdfn973/P//73P9ra2nj77beXxBglEkknRNgGwY/9MjNzA5dWt6Xsirpi26v7ocD1fZwgIOc6FHwPzw8QgF7DxMz+8u4bH3Peydfi2C6bjVufa+8+l3iyevZXiJD2dJFMrkjUMnrsXNlXOsSFqNHMxTNfl1rPbz48SzISLPb4OsRFckCceH1skR+R7QTEYwZmN94rISGBCGkwu4oUL/QYZA6sGJsBpfJUbfV+dbuV9I0+3+GNNtqIb7/9lptvvplkMkkul+Pggw/m1FNPZfjwvncQlEgkvadkZjYDEH02M+sIjdiBS72x4n176+iY6filBld5z8ENSn0PNEUlomlYlr7E8ij6wqv/fJdLxpfs1rf7f5tz+e1nYS3QdTIUIal0kUzOXmLiwlxMcfHR7CRPf1USF9uNTPHTsa0sbjcC3w9x7ZINe6xu0eIiCAQAiZhJdxt3lKbGF0judEMfQ9WJdTI2E8IDdBStYfEuQtIr+iXh6uvrueCCC2o9FolEsiiCuWUzs773vMj6OVJumoQeX+yXxFJBdLTg7pqYqSgKpqYRNyNoy4Gg6Myzj7zKVefeQRgKdttvOy664bdd7NbDMKQ9UyC7BMRFujUDYbjY4uKTOQme/HIIAoVxq6fYe52WxRcXQYhruyTqY8Tr4/QmSuU4PjFT79E51vUDhidj6Av0znACm6SewNQW6H2h1MnwyFKizwLjs88+63a5oihYlsUaa6whkz0lkiWACPOIYAYoiT73vHADlxa3FV3VluvQiBcGpQZXnk/W9XB9Hy/0UVBK3RuXUmJmf3nozn/w5z+WctQOOGI3zr78xC5269XiIoK2kJbYfaGW4uKzuQke/99QBApbjUiz77o1EhfFkrhIDOiduAgCgRCCeMKiu9kLJ/AxNI3kArMXoSjNEHUx7guLKMao6pCJZInRZ4Gx2WabdWpWUpq66hwHNgyDww47jDvuuAPLkgYyEkktKJmZzQCRQ9FW6/O+bV47tu8sd6ERPxTYvofjB2Rdp9IxU6GUR2HqOnF12eVR9BYhBHde9yj3/ukJAI761QH85rwju+TIBGFIKl0gm19+xcUXTXEeK4uLLVZLs/9681hIY81eEYQhbsEl3hAj0RBfaKfOzriuj2UZRHtwjy16Ho2xWBfXWje0MTVzgfCIA0pEtgZfivRZxj311FOMHTuWO++8k08//ZRPP/2UO++8k3XXXZeHHnqIu+66i9dee40//OEPvTreLbfcwqhRo7Asi3HjxjF58uSFbp9KpSr5HqZpss466/D888/39TIkkhWLsAWC2f3qeZHzc6TdNHE9tsxDI0EIBdenvWgzM53m+/ZWpqXamZ3NUPR8dFWj3rKoLze4KvXoWL7FRRiGXH/x3RVx8etzjuDU849aBuJCLLa4+F9znEe/GEYoFDYfnuHA9WshLgRO3iVWHyXZEEfp5XWHgSAMBcm41e2MQ6U0tZsZcydwSRrJirFZ6YAd4ZG+N6WT9I8+z2Bcfvnl3HTTTey5556VZRtvvDGrr746F154IZMnTyYej3PWWWdx7bXXLvRYjz76KGeeeSa3334748aN48Ybb2TPPffkm2++YciQIV22d12X3XffnSFDhvD4448zYsQIpk+fTkNDQ18vQyJZYSiZmU2nP2ZmXujR4rahKAqGuvSz5ivOo75PwffIuw5eEFacRyOahmUuH4mZ/cH3Ay6fcCsvPPkfACb88UQOOWbPLtsFYUh7Ok8u79RWXORd0m3ZsrhYPE+Nr+bFeOTzkrjYdFiWgzdoXmxxEQqBk3eI1UVJDkj0WlwAOJ6PZepYVvfPbcHziEcixI3q6/bDAFVRiesL9GsSDoqxtvTMWor0+S/O559/zpprrtll+Zprrsnnn38OlMIoc+bMWeSxrr/+ek466SSOP/54AG6//Xaee+457r77bs4999wu29999920tbXxzjvvYJTrnUeNGtXXS5BIViiEP7tsZjasb/sJQbvbTtEvUGfUL6HRLXjSkvOo43c0uHKqEjOXZoOrJY1ju1z0uxv5z4v/RdNU/nDdqfz04J26bBcEIW2pPPniEhAXrVkQiy8upqUsHv5sOKFQ2GRolkM2aFpscSGEwM45xJIWiYHxhdqvd9k3LOVfJOpN1G6eFYEgCAMazGSXWTk7tIlpUaLq/BC9EDYoFihL6fdAAvQjRLLeeutx5ZVX4rpuZZnneVx55ZWst956AMyaNYuhQxee5e66Lh9++CG77bbb/MGoKrvtthvvvvtut/s8++yzbLvttpx66qkMHTqUjTbaiCuuuIIg6Lku23EcMplM1Y9EsqJQMjObAWp9nxPT8kGedjdFTI8v9stiYfhhSNZ1mZfPMy2VYmpbO9NTKeblcwSExCKRcoMri6hurBTiopAvctZxk/jPi/8lYhpMumPCQsRFbrkWFwCrJR3WbCiy0ZAch27YtNj+IkIIijmHaMIi0Zjoc5WM43mYER2rG0t2KLUFt3SdhFm9XoiSNXudUVf9+xLmSrkXysrVXG55p88zGLfccgv7778/q6++OptssglQmtUIgoB//vOfAPzwww/85je/WehxWlpaCIKgixAZOnQoX3/9dbf7/PDDD7z22msceeSRPP/883z33Xf85je/wfM8Lr744m73mTRpEpdeemlfL1MiWebMNzNzUdSBfdrXD31anbZSW+wah0Y6nEdt3yfvORR8H6+T82hkBUnM7C/pVJazjp3E/z6eQixucfVd57DFdht12S7wy+LCdolZkT59g18YtRYXABFNcMxmc1AVURtxkXeIJkySg/ouLoQo9cqoHxBD68Enx/EDhiUSXXx0XOES0SJVxmalYgQPRRsiwyNLGUV0lIL0gWw2y4MPPsi3334LwLrrrssRRxxBMtl7y+fZs2czYsQI3nnnHbbddtvK8t///vf8+9//5v333++yzzrrrINt20ydOrVS+nX99ddzzTXX9BiScRwHx3Eq/85kMowcOZJ0Ok1d3fKVUS+RdEYEcxHeF6A29tlvZJ4zj3l2C3VG/eLH0cOOfhQ+edej4JcaXAkh0FUNQyvlUigrqaDoTGtzO+OP+iPffz2DuoYE1//tfDbcbEFH1JK4aE3lKNRYXNh5l0xrFhCYPXy77y0/tEWZnrLYZXR7zZJ/K+IibpJsTKL1o+254/goisLQQXXdihM3CPCCgFENDVhGtXjOeDkaIvUMs+Z/cRVhoZR/EdkCRe3qoyVZcvTrq00ymeRXv/rVYp140KBBaJpGU1NT1fKmpiaGDes+1jx8+HAMw6iqK19//fWZO3curusSiXT9hTNNU/blkKxwlMzMpoFi9llc5P0C7U6KmB7rt7iwyzMURd8n7zi4QdApMVNdos6jyytzZjbzuyMuY9b0JgYNGcCND/6BMeuu0WU73w9oTeWxbY94NNLrksxFYeddMi0ZUFhscZG2Nf72yXC8UKUh6rP58Oxij69DXJgxk+TA/okLIcDzAxob4j3OfBR9lwFmrIu4CESpZ0ZSX6BKRORAGyrFxTKg33OnX375JTNmzKjKxQDYf//9e7V/JBJhiy224NVXX+XAAw8ESuVer776Kr/97W+73Wf77bfnoYceIgxD1PLU2Lfffsvw4cO7FRcSyYqK8GdCmAGtb+33gzCg1WlBIIio/bOizroOs7JZPD8oJ2aqK01iZn+ZNuVHTjtyIvPmtrHayCHc9OCFrD5qwS9CAtcLaE8XsG2PWNRYLsUFQL0VsPuYVr5ri7HRkNxiH08IgZ13iUQj1A9MovXTat7zglJDNav7L4WBCAGo66Yc1wkdLN3qJjwSoKiD+jUeyeLRZ4Hxww8/cNBBB/H555+jKEqXZlsLS7hckDPPPJNjjz2WLbfckq233pobb7yRfD5fqSo55phjGDFiBJMmTQLg17/+NTfffDPjx4/nd7/7HVOmTOGKK67gtNNO6+tlSCTLLYtjZtbupcj5eer62VDLCwOasnnCUNBg9c3rZGXl68++5/SjLyfdnmX02NW56cELGTysIyemJCpc16dQdHE8nzAQxKKRXnWq7A21FhcdbL9mmm3XSNegWgTsoothGdQ3JtEi/b9w1/NpqI/1OPtR9H3ihklc73of3MBhmDW0uupEFICYbK61jOizwBg/fjyjR4/m1VdfZfTo0UyePJnW1tZe9b1YkMMOO4x58+Zx0UUXMXfuXDbbbDNeeOGFSuLnjBkzKjMVACNHjuTFF1/kjDPOYJNNNmHEiBGMHz+ec845p6+XIZEslwjhI7xp9MfMrOAXaHPbiepR1P4E1QXMyxco+q4UF2U+fv9LJhx/JYVckfU3GcMN919AXUMcx3FxvYCC7eK6AUEYomkqEUNDs2o301PMO2RbsqAomFb/ZqQ6+DFt8vL3jRy+8VyiRmkmoDbiwsGI6NQ3JtAj/W9D77ohuqYR77FZmMALAobHEyyY++mFPrqqE+vS+yIH2og+94+R1IY+J3kOGjSI1157jU022YT6+nomT57Muuuuy2uvvcZZZ53Fxx9/vKTGWhMymQz19fUyyVOyXBL6M8H/CtShffIbCcKA2cXZFIIiSaN/nQpTRZsfM2niEbNLdv6qyDuvfcR5p1yL63hsNm4D/njbGWiRCEXbw/UCEAJNVzB0vWblp52ppbiYlTG5+6PVsH2Ncaun2H+9lsUenxDgFB10Q6N+UB16D+28e0s+71KfjNJQ332uhO17CARrDRjY5fnMejkSeoLVosM7WVkEEM5DMTZF0QYv1tgk/aPPf0WCIKhUiwwaNIjZs2cDpUZb33zzTW1HJ5GsQogwB37/zMzSXpqsnyOm96/O3/Z9mgs5DF2T4gJ45dm3+f2JV+M6HlvvtCm/v/ZUcnZIOlMkDEMsUycej2CZxhIUF5maiIvZ2Qj3lMXFmvVF9ly7tSZjdIoOmq6RbEwutrhwHB9NU4nFeg4B2b5PgxXt8nyGopSbkTQS1SFFUQAlBqpsrrWs6PNTsdFGG/Hpp58yevRoxo0bx9VXX00kEuHOO+9krbXWWhJjlEhWekpmZjOBAorat8TOYlCkzW3D0i20foRGwhDm5fM45T/gqy4Czw958v6XuOmSexBCsP0eW/HrC4/HtAwMXa1Z0ubCmC8u1MUWF3NzEe75aARFX2Nkvc0xm8/G1PvcmaALdtFB1TTqBiWJLOYYXTcgCASNA2JEjO5fSV4Yoin/n73zDpOlLNP3XV2xc/fkkwiSkSTRxBoW17TBNaEgKGLCLCJBERQMwBJdFQzoii6ra9rwU5BdV1x1lSBZJQuHkyd1rFzf9/ujZuakmTmdJhyo+7q4lD5T1d9wuqueer/3fZ4UeWPnrQ5feFiqsV2wGRALDHVPFCUZAFgq2hYY559/Ps1mE4CLLrqIv/7rv+b444+nv7+f733vez1fYELCMwIxOhVm1p6hlpCCcW+CUEYUOhzDm3RtKq5L3pw9EvvpzfZNmt//xk/4zhd/BMBfve5FvPcTp7RtFNUNvRQXmxsG3/j9SuxAZVXB5W3P2YDVK3GRUin2RFwIgkDQV86Qzcydvm0HPkXTJK3tfMvyIo9+sx9tG0M5KeNhA0Vt7/uU0FvaFhjbhpztu+++PPjgg0xMTFAut9/xnpCQMB1mthbQ2m5Gq/hVakG9474L2w8ZtW1MTXvGjKFKKfD9cLsmzTCK+P7X/pMf/9NNALz+7a/i1A++blGvaU7DpT5eh1QK0+zuxr2lqXP9XStpBhor8y6nPWcDlia6XqPn+KRSKoX+PEa6uzWGgSAIQvpKGfLZuT/3gtjUrZS2dtK/oRCklBS5HbcGZSNOTVWSPrulpC2BEQQB6XSae+65h0MO2WqN29eXqMSEhE6R4bqOwszcyGUimMBSO/OoCIVki90gjCKKT/OtkUjENzPXD3Gc7Zs0NS3FP131XX76vV8AcOoHX8cbTn/1oq6vl+JitKlz/e9X0fQ1VuQ8Tjtyw8zUSDd4bgCKQqEvh5npbo1RJPD8kGIxTT43f+XMCUIyukFG21mEuMLFUi3S6g7VD2mDug+KsvgJwglbaeu/vq7r7LHHHm15XSQkJMyNFNXY86LNMDMpBRPeBGEUUDA6e0qbcGxqnkvRmrs0vTsTRQIvCHG9ENf1CQIBSDQthWXGkx9hEHLNhd/g1p/8FkVROOPjb+GVb3zJoq7TabjUxusoPRAX47bO9XetouFrjOQ83n7kejI9EBe+F4IUFAaKmNnuehqiSOC4AaV8hlI+zfzbchI/ChnO5mbNSAlFwJA5sN13R8oAUFHUclfrTOieth97PvGJT/Dxj3+ciYmJhVhPQsIzBikjZPgkcZhZe9MftaBOJaiR2dEWuUUavs9Ys0lGe3pZfgdhRNP2GJ9ssGm0yuhYnXrDBSCTjic/zClx4XsBl5z1ZW79yW9RNZWPfv6du7W4mLA1rv/9SuqexlB2SlwYvREXQkTk+/NYXYsLieMGFHJpioVd9/x4YYipaeRm+W/jiQAjpc/S3NkEJZ9sjywD2q4fffGLX+TRRx9l5cqV7LnnnmSz218Y77rrrp4tLiHhaY3YAmIztGlj7EUeY/44pmp0NFIaCsGWpg0omLM0ze1ebG3SdFwf1w+JonhfXtNUMmltVkdNu+nw2Q/9I/fd8SCGqXPOP5zBsS86YlFX3ktxISX84I/DVD2dwYzP6UduINtDcVHoy5POdVfpisWFTz5nUSqmW6rYuWHIQDaLntp5bNuLXIp6EUPdQfQIG7Q92h71Tug9bV9dpnNDEhISOkdKBxn+GZR0W2FmUgrG/Qn8yKfYydaIhDHbpuG7u+1I6mxNmpEQqKkUuq5imfq86aD1aoNPvfcqHn7gz6QzJp/8woc49JgDF+8XYKu4SKVUjC49JAAUBV7/7M38+5+GeN2zN5Mzu9/GDoMQEUUU+vOk8z0SF1mLciGzvZ33XO8vBClFoWDs/N5iOthsh+ZmKX1QdBQ18b5YDnQU1747kzh5Jiw1Ukpk+CiEj4O6sq1JhVpQY72zkayaQZvlqW6Xx3seT1WrpHUDfTcy1BJC4M/RpNmOk+b4lkkueM+VrH1sPflilk99+Uz2P2TvBV799vRSXESCWXsTuiUMQsIgIteXI1voToiKSGK7AdmMQV8pi9ri567muRRMgzWF0k47KU7kklJS7JFeg7rN90CKCSCHYhzRVk9TwsLQ0d9ApVLh61//Ouedd95ML8Zdd93F+vXre7q4hISnJXJyKsysry1x4Uc+Y944ekrrSFx4UcSWRoNUKrVbiIsoEtiuz0TVZtNojc2jDarVzp00N60b5dzTLmHtY+vpGyzx+W+cu1uLi5qn8sXb1vCn0d7GkIdhRBBE5Mo5svnuxIUUcc9F1tLpK7YuLgQCIWU83TTLX68X+RS1wnbiIj7QRVGHEnGxTGj7E37fffdxwgknUCwWeeKJJ3jnO99JX18fP/rRj1i7di033HDDQqwzIeFpgZRBR2FmUkrG/QncyKPYQVKqlDDWsHHCkNIynhoJwwjPD3G9ANcLCEMBioKupab6KTrzpVj72Ho++e4rmBitMLJ6kIu/chYjqxc3n6LX2yL/t7bElqbJTY8MsF//WuYIIG2LMIwIvJB8ORtXLrqwAZECbCfAsnT6Srm2DMvcMCSt6WT0nZtKQxGiKSrpHYLNpPRAMZPk1GVE25/yM888k7e97W1cdtllM5kkAK961as46aSTerq4hISnGzLaCHIMUsNtHVcP61T8KjktM29/wVxUXJcJ1yZvmiwvt87ZmjQlKUWZt0mzHR75w5+58L1XUa802GOfVVz8lY/SN1jqyepbxam71CZ6Jy4AXrbPOJFQeN4eld6JCzck15clU8h0Jy4k2I6PZWnxtkhbC5R4YciqfBFtFkHpCpeMlsVK7eCLIeqxuFA6y+NJ6D1tf9LvuOMOvvKVr+z0+qpVq9i0aVNPFpWQ8HRka5hZvq0Odz/yGfcn0FLqdnbIreIGcZCZoanLwq1z+ybNAD8IEZGIt250FctMdSSiZuP+Ox/i4g9eg9N02f+QvbnwSx+hUOpstLdTeiku3DCFoQpSStx78eoDuk9FBQhDQeAG5MpZsoVMV6IuFhcBhqFRLmXRtfa287wwwtA0cubO1Qsp4+TggpXfeXtRBlPbI8tJQD+zafvTbpomtVptp9cffvhhBgeTSNyEhNmQUiCjJwGnrTAzKSWTQQUndCjq7XfGRwJG7QZBuLRunbM1aUopUNUUhq6idplpMRu3//IeLv3YtfhewGHHHMgnrvkAmezi/jfopbiw/RTX37WKVQWP1xy0hV7lroWRwHd8cqUM2WJ34gLAcQJ0TaWvnMHoYAzaDQP6MhlMdZbRVOlhqCZpdcftESfeHlGS6ZHlRNsfpb/927/loosuIggCABRFYe3atZxzzjm87nWv6/kCExKeFogxiDZBqr+twxphk4o/SVbLdvRUPx1klrPayzjpBbtq0sxlTdKWviBhYr+86Xd87swv4XsBx734CC780kd2a3HhBCm+cfdKNjVMHhrLUPd64/EQCYFv+2RLGXLlbNdpsY4ToKkK/aU0pt6+aIxkPJpaNGfvE/JDn7yWQ9+xkicakOpr27AuYWFp+5t9xRVX0Gg0GBoawnEcXvSiF7HvvvuSz+f57Gc/uxBrTEjYrYnDzJ4gDjNr3QkxECHj/jiKktr5gtoCth8w1rRJazrqIrl1hvM4aaat7Z00F4qb/vUXXHHe14jCiBe/6rmce/l7Mbo0smqXXouLb969ko11i6wRcvqR6yla3ftcRELiNWNxkS/1QFy4AamUQl85iznL9kYr2H5AxjDIzCJOIilQFMhpO3pfSCBAUdszrEtYeNr+5BeLRf7rv/6LX//619x33300Gg2OPPJITjjhhIVYX0LCbk8cZjYJaptbI/4EdtikoJfafs84yKxJKAVZbSGnRiR+ONWk6SxMk2Y7/OAbP+Vb1/wAgFed+BLefe7JpBZ5JLfXPRf/dPdK1tcsMnrE6UduYCgXdL1GISVe0yNTTJMrZVG6FHyuF6+pXMpidSguBIJICkqWNWu1zo08LDU9e7AZmWR6ZBnS9qf/qaeeYs2aNbzwhS/khS984UKsKSHhaYMUFYieglSprdl8O7KZ9CtktFxHe+3jtk3N8yguwNbIYjZptr4myQ1f+CE/+MZPAXjDO17NKe9/7eI1/EkI/RDPDWhUGj0RF16o8K27V7CuZpHWI04/cj3DOb/7pUqJ2/DIFNLkyzlSXYoLzwuREvpLOTJW51kl06OpOWP2z2wgfAbM/p2/R7IxZVi3fMevn6m0/Q3Ya6+9eOELX8hb3vIWXv/611MuJ4l1CQmzEYeZrQXCtvaGQxEy5o2jKGB0sDVS9z3G7SYZvXdBZjs2aQZBhFjgJs121nbd577DTd+/FYDTPvIGXvu2Vy7Ke0ehwHd9vKaP73pEoUQ3NXSjB+LinpWsraaxtIi3P2c9I/neiAun4ZHJW+T6sl2LC9+PiCJJfzlDJt1dEJoXRqzMZ2YdTfVFiJ7Syajb99FIKQCB0mZvU8Li0PbV58477+TYY4/loosuYsWKFbzmNa/hBz/4AZ7nLcT6EhJ2X8QWEJsg1dfWYZPBJM2wSUZtf5wyEBGb601QlFm78NthuklzcpYmTXOBmzRbJQxCrvzE17np+7eiKArvv+CtCy4uRCTx7IDqeJ2JjRUqW2p4roeq62TyVtfiwo8Uvn3PSp6sxOLitCM3sLLQO3GRzlnk+tszvpp1nb4gCATlUoZsprvqgR36mJpKzphdpLiRS1bLYqo7VDekHfteJNsjy5KOs0iklNx6663ceOON/PCHP0QIwWtf+1q+8Y1v9HqNPSXJIklYDKR0kP7dxE9XpZaPs0ObdfZ6dNXATLVZFZCwoVFn3LYpWhZKh05JUSioNGxcd8pJk9hJU9NSXT/x9hLP9bn0Y9dyx//eG8etf/adHP+KYxfkvaSQBH6I7/i4TY8wiFAU0AwNVVV7tiUUi4sVPD6ZwVRjcbGm2P3Dm5QSp+mRzprkB/Jdi4swEHh+SF8pQz6369j1+XDCgDCKWFkozOoyK6SkHtRZnVlFXs9v92cy2gTqHqT0/Tt+/4SFoydhZ3fddRenn3469913H1HUfXfzQpIIjISFJg4zexjCJ9oKM4tExHpnPU7k7pQS2QoV12VdtUrWMDuKcY+RjFea1Bsuhq6ja6lFbdJsFbvp8JkP/iP33xnHrZ93xfs4+vjDevsm030VXoDb8Aj9AClA1VV0Q+15f0cQKXzn3hU8OpHBUAWnPWcDe5Tcrs87LS6srEmhP9+mq+bORJHAdUOKxTSlfJpuxIUXhrhRyKp8gXJ69iqIE7koisKe6T22DzaTEYhRFP2IZIJkmdJxLW/dunXceOON3HjjjTzwwAM873nP40tf+lIv15aQsHsiJiBaD2p/WzehSlChETYpdJA14oUho80Gmqp2IS6g3nBpNDzSlrGgo6TdUJ2s86n3XcWjf3iCdNbign/8EIccdUDPzh8FU30Vtofn+ohIomoqRtogtYBNo5saBk9ULAxV8NYeigu36WNmDArl3ogLxw0o5TPdi4sowgkDVuTzlOfJx/EjnwFrYOdgM9mc2h5JzLWWK20LjK985SvceOON/OY3v+HAAw/k5JNP5t///d/Zc889F2J9CQm7FVIGyPBJQGmrq92ObMb9SSwt3fZNTAjY0rRxw6irIDPP86nUHXRdXbbiYnzzJJ98zxU89fgGCuUcn/7ymex78F5dnzeKBIEb4jkenuMTBREpVUEzdLRF6jFZU/Q45fCNqCnJXj0RF+DaPrqlU+zLoxrdios4GbWQS1MsdLctEgiBE/gM53IMpOfOPQlFhKqkyGmzNElLG9S9UZSlazBOmJ+2BcZnPvMZ3vzmN/OFL3yBww8/fCHWlJCw2yLD9sPMIhEx7k4gpcBKtd+JX/VcKq7TVZBZFAkqNQcpwLB64xLZazat28L577qczevH6B8qc/FXPsqaZ63s+HxSSAIvjCsVjk/oR5AC3dAwDH1RRm0jAXVfo2SFAOzb7/TkvFKC63jopk5xII9qdNvwK3Fcn3zOolRMdxWHHklB0/cYzGYZzGTn/ci6wiWtZrBS2wtnKeP/XoqaTDEuZ9oWGGvXrk3CZBISZkGKBkTth5lVgyqNsE6ug60RJwjZ3GxgdhVkJqnUbRwvIJtefEvxVnjykXVccMYVTIxWWbFmiIu/chbDqzrYd5fEzZpugNt0CfwQJGi6ipU1FvXaFgn43gMjrK1YnH7Uegaz3RtoQSwuPMdDNzSK/Tm0XomLrEW5kCHVpbioeR4DmQyDmdy8Ik7KuIJRMAuzBJs1QCnE/yQsW9oWGIqiUKlUuP766/nTn/4EwMEHH8zpp59OsZjshSU8M+k0zMyJXCb8CUzVQm3z5ha7dTYIo+6CzBpNb6rvQl+WDZ0P3/84n3rfVdSrTfbcdxUXXdd+3HrkR/hegGt7+NN9FbqKlV5cUbEtXpRirKljByoVV+uZwPAcD01XKfTn0bo0+xJT2yLZjEmpmOnKFVUQi4s+K81QNseudp586WOo+s7OnTC1PbIaRenu90tYWNqeIrnzzjt5+ctfTjqd5thj45GwO+64A8dxuOWWWzjyyCMXZKG9IpkiSVgIZLQZGTwAqXLLeSNSCjY4m6gFdYpGftcH7MBos8nGRoOiZXZsqOV5PlsmGqQUBbPLm9FCcN/tf+IzH/oCju1xwKHP4sIvfZh8sbUJmygS+G6Ab/txX0UYoaoK6iL2VeyKpp9iU8Nkn77ebI24toeqqRQG8hhdmp9JIbGdgIyl01fuzjdDIKi5HiXLZEW+0FIjci2oUzbKDFtD269LBiAmUYwj2xoBT1h82hYYxx9/PPvuuy9f+9rX0KaieMMw5B3veAePP/44//u//7sgC+0VicBI6DVSekj/XsBFacNUa9KvsNHZRF7Ptb290fB91laqGKqK2UEkNsRJmmMTdVwvIptZfo1yt916N5d+7FoCP+Tw4w7iE1d/gPQuDJ1kNNVX4Xi4tk8UhCgpBc3Q0LSl7y0REp6YTPOsHgmKbXEdj1RKpTjYC3EBtuNjWToDpVxX0ycSSdV1yZsmqwp59B2nQWYhkoJG0GSP7GqyOzR4SjEJpGOBsRxLbgkztH1luvPOO7cTFwCapnH22Wdz9NFH93RxCQm7A3GYWQXUkZaP8SKPcX8cUzXaFhehEGxp2khkx+ICJNWag+MEZDPdWTwvBLf+5Ldc9cnrEZHguJc8h7Mvfc+ciahSQOAHBG68BTLdV6EbKnrWXDY9Y0LCj/84xF0bC/zdgVs4dnWtZ+f2psVFfw/EhZwWFxp9pWyXo62SmuuS0Q1W5HItiQsAT3ikNYu0OsvWn3BQ9L0ScbEb0PbVqVAosHbtWg488MDtXn/qqafI59sv8yYk7M7EYWbr2gozk1Iw7o0TRgEFo80qmoQx26bhu5S66Lto2h71hks6rXcd091rfvK9/+Ern/9npJS85K+fx4c+/XbUWaoPoR8ReAFO0yXwAmTEkvdVzIWQ8O9/GuSujQVSiiSt986Q0HN9SKUo9OcxuqxExeIiwDA0yqUsepdVn5rnYWk6K/P5tsSwH/n0WeWdGkql9EAxEmvw3YS2BcaJJ57I6aefzuWXX87zn/98AH7zm9/wsY99jDe/+c09X2BCwnIlDjN7kjjMLNPycbWgTjWo7VT6belY32PMtsnqZsdW4F4QUKk5aFpqSXNEdkRKyQ++8VNu+MIPAfjrN/0l7zznzds1FkaRwHf8rX0VURy4pps66iLHsreKlPCfDw5y54YiCpLXP3szhw43e3JuzwtAQrE/j9mDbS7HCdA1lb5yBqPj6lhM3fPQ1RQr83nSeuvnCkSIltJn/36I6emR9p1uExaftj9Bl19+OYqicOqppxKG8SyyruucccYZXHLJJT1fYELCskVsjgPNUoMtH+JHPmP+OLpqoLVYLt56bMSWRhM1lcLoMMgsEoJK1SGM5LLqu5BS8q2rf8AP/+kmAE58199w8ntfg6IoiG36KjzbJwzC2ARL1zC7TPBcaKSE//fQALev3youDh9p9OTcvhcihaAwUMDMdv/fwXECNFWhv5TG1Lv7bDQDn5SqsDJfIGO0dy43cslrOYzZPGGkh6Lvu+wqVAmz03EWiW3bPPbYYwDss88+ZDKtP8EtJUmTZ0IvkMJGBvfQTpiZlIKN7maqfpVim1sjUsKGWp0J155y6+zkAiuZrDpU6zbZtLFstkaiSHDtZ7/Nz374SwDefuYbec0pryDwA3w3wGu6BH4ECrHLqKYtiglWt0gJP314gP97qoSC5LUHb+HIlfWenNv3QoSIKPTnSee6SzIFcNyAlKLQX85imd2JFTv0EUKyqlCgYLbnqyIk1IMaqzIrd7LMl9IFYU9NjyQVjN2BlisYURTxhz/8gf322490Ok0mk+HQQw8FwHEc7rvvPg455JCu5qQTEnYHpJTI6Km4XKu27nlRDxtU/WpHWyNV12XCtbty62zaPrW6O+V3sTzu0GEQctX5X+d/b74dRVE447y38KJXPpeJTRVCP0AIiaZrWJnl11cxH1LCzY/2839PlQD4u4NGeyYuAj9ERL0TF64X+2+US92LCzcMiCLBynz74gLAFy6mapJRZ3lgFQ1I9cX5Iwm7BS2rgW9/+9u8/e1vxzB2/gDqus7b3/52brzxxp4uLiFhWTITZtbX8k3Pj3zGvHG0lN721ogbhGy2GxhduHX6QUilZqNqLJu+C9fx+OxHvsj/3nw7qqbyvo+fwlHPPYTaWJ0oDNEtg0zOwjC13U5c3PJYP79+Mrax/rsDt3DMqt5MjIRBSBRG5PtypPPdiwvPC5ES+opZMlZ34sILQ7woZCSfpzRHMuouzxH55PUcWmr7Z9+40B6gqEO71WfhmU7LV5rrr7+es846C3WWvd/pMdWvfvWrPV1cQsJyQ0ofGT5BO2FmUkom/AncyCMz29jdPEQCRu0GQRiR0Tq7AcR9FzZhKLDmGPVcbOrVJhe+50ru/NV9GKbO+847lSOOezaqrpPOWZiWgbpMqizt8vPH+/jfJ2Jx8TcHjPZsHDUMI4IgIlfOkcl3PkE0je9HRJGkr5ghm+nOIt6fSkYdyeXp63C6KRQRKSU1e4VPOoCVWIPvZrS8RfLQQw/x3Oc+d84/P+aYY2aswxMSnq7IcBOIcVBbDzOrh3Uqfo2clmm7d2DStam4Lnmz06dVSb3uYrt+10+o3SKFJPBDxjZO8PmzvsQTj64jnTH5yKffwUGH77db9FXsiv95vMwv/hybrb16/1Geu6bak/OGYUTgheTLWbKFdDdBpgD4viAIBH3lDNldmJftcm1CYAceQ9k8/fMko+4KV3ikVYt0ajZr8Aaow21NayUsPS0LjGazSa02txKv1+vYtt2TRSUkLEekqEP0JKRaDzMLRMC4P0Eqldqp7LsrbD9grGljaXrHWyO2E1BtuFimTmopItglhH6I5wa4TY8tG8e46oKvs3HdFvLFLGd//j3svd+axV/XAjFddHnlfmM8f48eigs3JNeXJVvs/AY+c75AEAQhfaUM+Wx3lYtIChq+R38mTkbtVCTGwWYhRXNwJz+ZeHskQkl1EG6XsKS0fMXbb7/9+L//+z8OO+ywWf/817/+Nfvtt1/PFpaQsJyIw8zWAh5KqrWI6HhrZBIndCjq7QUBxkFmTUIpyGqdPWH6YUil2kRVQevKjbF9Il/ge34chT4VLja2ZZIrL/gaY5sn6Bssce4lZ7Byj9YrQcuNpp/i9xsK6CnJ86bExIv3nmSfPps1Ra8n7xGGcZ5KXLnoXlxEkcDzQ4rFNPlcp9NIU+dCUPNc+q0sw9k83fT3NyMbSzVmd+6UNpBJzLV2Q1r+SJx00kmcf/753HfffTv92b333ssFF1zASSed1NPFJSQsG8QWiDZCqr/lQxphk4pfIau1/2Q3btvUPJd8h139QgiqFZtgEfsuokjgNn2qY3XGN09SGa3huT6aoTM+OsGl513L2OYJhlcOcMFVH9ytxQXAnyfT/OzRAW59okwotr7eM3ERCXzXJ1fMkC1mu066jSKB4wYU82lK+TTdiAuBoO56lK00w/ldJ6POhxO5AAyaQxjqbN4XDVD7UZTuqi0Ji0/LFYyPfOQj3HTTTRx11FGccMIJM1bhDz74IP/93//NC17wAj7ykY8s2EITEpYKKT1kuBYUA0Vp7WYdiJBxfxxFUdDb3Bqp+z7jdpOMbnSYkiqpNVyai9B3IcWUCZY9HS4WoaRAMzQMQ0dR4NE/PsE/nP9VmnWbPZ61knMuOYNiefeKFfBChXs25dFTcmbc9KDBJvv3N3n2UG+Ms7YlnHIszRUz5Mq9EBdx7Hohl6ZY6K5yIZHUXI+8abIin0frohnXFR6hCBmxRsjrO3tbSBkBEqUNYZ+wfGjLaCsIAq666ipuvPFGHnnkEaSU7L///px00kl8+MMfnnWEdbmRGG0ltIsIHoXwcVBHWs4b2eKOMuaNUdCLtHP9DYXgyckKXhRNeV60j+36jI43MHUVTV+ArREZezH4jr9duJimq2i6ut0Y4QN3PcxVF34dz/XZ7+C9OOsz7yKb330a9TY1DG5bV+SejXn8KEXJCvjoC55s6++0XSIh8GyfbDFDvpRF6bJ3JhYXPvmcRbmY2Snfoz3iZNSsESejduooC+CLACd0GLaG6DNnTyGWog5IFONoFGX5318StqdjJ8/dlURgJLSDFJNI/z5Q0i13sDfDJuvt9RiqiZFqY3tCwoZGnTE7duvsJGskCCNGx+tEQpDuMlVzRyI/wvcCnKZH4MV9FaquYhiz+1T8/v/u5x8/80+EQcQhR+7Phz91OlZ6+Ze5QwEPbM5x+7oiT1a39gQMZHyOXV3luNVVFqqlJRISr+mRKaYplHO9ExfZKXHRlRGipOq5WKrO6mIBq4uskkCE2KHNgDXAgNE/p7eFjDaBuhcpfd+O3yth6eguzSYh4WmMlGG8NULUsriIRMSYN46E9sQFUPFcJhybvNFZkJmQsd9FEERkehTBHkVxk+FMuFgYoaoKmqGjzbPx/uv/uoOvXv4vCCE4+gWH8b6Pn4puLO/LzYStcfv6Ir/fUMAO4ifzlCI5eLDJsaurPKvsLOgorZBT4qKQJt8DcSGmxEU2bVLqWlxA3fMxVZVVhe7ERSgimqHNgNnHgDG3WV28PaKgqLNXNxKWP8v7G5+QsJSIzfE/qaGWD5kIJmmGzZ1yFHaFF4aMNhtoKRWtwxtBveHSdDzSltHdjVCC7wR4znRfRYiSUtAMDbOFqsgt//YrbvhSnIh6/MuO4R0ffdOsBn3LASHhobEMt60r8uh4Bjkl7IpmwNGrahy9qkbB7F20+lxIKXEbHpm8Ra4v2/VIsRRxz0XWMugrZbtOmm34PqqqsDJfbCsZdUciKWiEDUpGiQFzYP4tR9mIU1MTc63dlkRgJCTMghTNOIpdyaIorX1N7NBm0p8krWVItXGHlxJGmzZuGE0FmbWP4/pUay6moaN2eXNyGi61iTpSgG6o6FmzJXtmKSX/ceN/8f1/+ikAf/Wav+AtZ7xmWecTXf/7VTxR2boNsm+fzXGrqxww0OxqMqIdpJQ4TY90ziLXn+vayl2K2P/EsnT6St2fzw59FAVW5vNk20xG3RYhJfWgQVEvMmQO7roXRNqg7tPy9y9h+ZH8zSUk7MBMmJlsQKq1MLNIRIx74wgpMdvdGnFdJl2n4yCzMIyYrDooKdC7bOr0nYD6RINUSsVIt355kFLyL1/7D376/V8A8PenvJzXnvKKZZUbISU8UbFYU3Rneij277fZ3DQ4amWNY1fV6M8Ei7ymKXGRNckP9EBcSLAdH8vS4spFl80iznR4WaFA3ui8f0ZKqAd18lqWIWtwl6ZzUgaAiqK25jmTsDxJBEZCwo6IcYg2QGru5rMdqQZV6mGDfJtbI85UkJnZYZCZlIJK3SYIwq77LiJfUJ9sIJGYZuuXBhEJvnHNv3LrTb8D4OT3vIZXvu7FXa1lIfjm3St5bCLDiYds4rCReLT0uWsqPH+PCrq6+L3u24mL/nyPxEWAYWiUS1l0rbttKS8M8aOIVflCx5W16XXVwwZpLc2wNYzeigCXTVDyyfbIbk5LV5Ezzzyz5RNeeeWVHS8mIWGp2RpmlmrZ2MeObMb9CSwtjdrGE3skYMtUkFmpw4CoetOj0ey+70JEkvpkA98NSGdbv5mEQci1l36H2355D0pK4R0fOZEXvWLuzKLFZEPNYDjnz2x17FF0eapq0fC33nhNbWmG6KbFhZkxyfflu640ADhOgK6p9JUzGF00YQJ4U+FlK/J5yl2IC4BG2MBI6Qxbw7Mbac2GsEHbo2VL/oTlSUufwrvvvnu7f7/rrrsIw5ADDjgAgIcffhhVVTnqqKN6v8KEhEVEhhvjOPYWw8yEFEx4k0RSkE21V0GYdG2qrkfB6qz07Ho+1ZqDoXfZdyGhWWniNFzSObNloeK5Ptdc9A3uu+PBqbj1Uzn2+MM7X0cPCCKF+zfnuG1dkXU1i7ccvoGDBuOMpBfsUeGFe05iLZGomEZKiWv7GGmDYl8etQdeJY4ToKkK/aU0pt7deHIgBE7gM5zLMdBFeBlAM7RRUyoj1jBptdX0YR8UHUVtz14/YfnRksD4xS9+MfP/r7zySvL5PN/61rcol+P9scnJSU477TSOP/74hVllQsIiIEVtKsys0PKTU8WvUA9q5PT2nCkbvs+WRpOMpqN24NYZhYLJqgOAYXS5z95wadYdjLTR8paQ3XS4/Pyv8fADj2OYOh++8O0cdsxBXa2jG8aaOrevL3LXhjxOGP/dqYpkzDaAWGCkdTHPGRYHKcG1fXRTp9ifR+3y7w7AcQNSKYW+chazQ2v5aSIpaPoeg9k4vKwbceFELhLJiDlMRmvDXE02QCnGWyQJuzVtG22tWrWKW265hWc/+9nbvf7AAw/wV3/1V2zYsKGnC+w1idFWwmxIKZDBH0FsQlFHWjrGiVzW2+tJKSkstfUqRCgET1Vr2IFPoYMYdikFExWbetMlm2m94jAbvhNQGa2hKApGi30XtUqDS8+7jicfXUcma3HWZ97F/oc8q/NFdEgk4E+jWW5fX+Sxia03sLIVcOzqKkeurJMzFn7EtFWkBM/x0AyNYn8erY0+l7lwvQApob+c69oWPpKCmucxkMkwnM13NUXjCp8g8hmxhika7VUiZLgBRT8YRVvd+QISlgVtf8JrtRqjo6M7vT46Okq9Xu/JohISFh2xZcrzorXMAykFE94EgQgpGm08aUkYs23qvkuxw73tRtOj3nS77ruYaeqUArPFbZrxLZNceu61bHhqC4VSjnM+/x723HdxbwRVV+WO9UXuXF+g7seXMAXJ/gPxiOl+/faCWnl3woy40NWeiQvPC5EC+krZrsWFIBYXfVaaoWx34WW+CPBCl2FrqG0/GCk9UMwkOfVpQtuf8r//+7/ntNNO44orruDYY48F4LbbbuNjH/sYr33ta3u+wISEhUZKd8rzQm85zKwa1KgFNbLazgFN81H3PcZtm6xudhRk5no+lbqDoWtd9V100tS5ad0onz/ny4xvmaR/sMQ5l57ByjWLl4g6bmvc9MgAD41lETL+3bNGyNEraxyzqkY5HS7aWtrFczxUTSXfI3Hh+xFRJOkvZ8hmurNfFwhqrkfZMhnJ57oKLwtFiB06DFr9lI1y+2PKoh6LCyXb8RoSlg9tf9Kvu+46zjrrLE466SSCIJ4Z1zSN008/nX/4h3/o+QITEhYaGa4DUQW1Nc8LL/IY9ycwVKMt100/itjcaJJSUh2FREWhoFpzkAIMq4vu+g6aOtc+voFLz72W6mSdkdWDnHvpexkYWniPAimZWZ+pSR6eEhd7lxyOXV3l4KHGguWC9ArX8UipKoWBPEYP8mF8XxAEgr5yhmymuwmPbZNRR/L5jl1kIbYAb4RN+s3+efNF5l9QgKIOLSv/lITO6TjsrNls8thjjwGwzz77kM3uHooz6cFI2BYpJuIws1QWRdn1qKiUgo3OJqpBjaLR+udHSthQqzPhNqdGUtu9gErGK03qDZds2uwqvtupu1TH6+iGhtaCV8Ijf/wz//CJr2I3nEWLW590NH7+eB8NX+Vtz9k48/pdG/KsKngM5/wFff9e4ToeqZRKcSCPke5eXISBwPNDyqUMhVx3sevTyahp3WB1IY/ZxWhrJCX1oEbJKDFsDqGm2hfAUjogHBTjKJTU7nE/SZifjj9RGzduZOPGjfzFX/wF6XQaKWWiOhN2K7aGmcmWxAVALWhQDWpktfYugFXXZcK1yRmd3RTqDZdGY8rvopv9cTegPtlEVdWWxMUDdz3EVRdePxW3vjdnffadZHMLH7eeUiT3bsojpMKYrTMw5bB55Mrdp8/Lc3xSKZVCf2/ERRTF4qJYTPdAXEDN87A0nZX57sRFbAFen7EA70RcxCdqQGowERdPI9r+VI2Pj/PGN76RX/ziFyiKwiOPPMKznvUsTj/9dMrlMldcccVCrDMhofdEm+LmzlRrfQR+5DPuj6GrBlobF1E3jN06Da2zIDNvqu9C19Su+i6iQFCfaCCFwGxh3/6OX9/Hlz73LcIg4tCjDuBDF759QeLWNzcMbl9XoBmovOnQzQAUrYhX7TfG6qJLf3px7bt7gecGoCgU+nKYmd6IC8cNKOUzlPKdVMC2p+556GqKlfl8V+FlUkJjygJ80BzYpQX43OeRQICiDnS8loTlR9tXu4985CPous7atWvJZLY+yZx44oncfPPNPV1cQsJCIUUTGa2dCjPbtViQUjDuT+BGPulU6/veQsBos4kfRmS09jv9o0hQqTsIAYbZed+FjCSNqaZOM73rdfzvLbfzhYu/SRhEHHP84Zx50Tt7Ki5CAfdtyvG1O1fxhd/twe/WlXhgc44JZ+sN6nl7VFlT9BY0In0h8L0QpKDQn8fMdjfdARBFcTJqIZemWOi+ctEMfFIphZX5ApkuwssgtgC3tDRD1lDrLp2zIcZj34tkeuRpRdty85ZbbuFnP/sZq1dvP5q233778eSTT/ZsYQkJC0UcZrYWZANFXdnSMfWwQcWvkNOybd3wJlybiuuQ78DvAiSVuhPHbndzc5fQrNnYdRcrt+tk1J/9+Jd8+8s/BuAvXn4sp3/kxJ7FrU86GnesL3DnhgLNqRHTlCI5cKDJcaurlKzlOwmyK6SU+F6InBIXVs/EhU8+Z1EqpuePN28BO/SRUrKqUCBndLe+rRbgQ5ht+MDsiBQVIIWi7Y+idNe0mrC8aFtgNJvN7SoX00xMTGCavS+fJiT0HDE2E2bWCn7kM+aPo6W0trZGbD9kzLaxNL2jILNG06PRcElbendNnQ2XZtXGSOvzxshLKfm3f76FH37rJgBe8doXcdK7/67ruHUh4eHxDLevK/LwWAY59QSeN0OOWVnj6FVVitbyMcRql1hYBEShQDc0sqU86Vz3N8oZcZG1KBcyu4433wXudDJqvkChy2t1M3RIoTJsDZNWO8vRAZCiAdKPjbXU1r6PCbsPbQuM448/nhtuuIGLL74YAEVREEJw2WWX8ZKXvKTnC0xI6CVxmNmTgNpSmJmUkolgEjf0KLZhGhQKyRa7QSgismb7F2DP86nUHDQt1VXKpu8G1CtNUrto6pRS8s9f+Tdu/uEvAXjdqa/gNW95eVeN2w1f5ffrC9yxvsCku7UUv09fbIh14ECzK0OnpSYSksDzkQJ0UydftjAyRtepqBD7lDiuTzZtUipmuhZ5XhjiRSErcnlK6e7ET2wBLhixRsi2YwG+A1I6sS24tn/L7rkJuxdtC4zLLruMv/zLv+TOO+/E933OPvts/vCHPzAxMcFvfvObhVhjQkLPkOEGEJOgDrX0842wQcWrktUy7W2NODY1rzO3zkgIKnWXSEiyXTQIxk2dTWQ0f1OniATXX/09fnnzbQC85Yy/5xWvfVHH7zvNTx8e4N5N8ThrWos4cmWNY1fVGMjufk2b2xJGgtALkICVNrByaUxLR+kmcG4bpIh7LrKWQV8pi9qluPCnklFHcjn6091NALnCJxQBI9YI+Tbzd7ZFSm/qe7gvirqmqzUlLF/aFhiHHHIIDz/8MF/84hfJ5/M0Gg1e+9rX8r73vY8VK1ozKkpIWAriMLO1LYeZBSJgzJ8glVLQ2+iOr/s+Y80mGd3owK1TUq05U0+vne+Rb23q9Eln5xYXgR9y7SXf5vZf3YuSUnjnR9/MX/zVsW2/nxumuGdjnn36bAanBMSxq6tM2DrHrq5y6HADXV3aFNNuCcOIwA1RVLCyFumshd7l9tWOSAG2E2BZOn2lXNfVkFAI7MBjKJtjoMvwMl+EeJHHsDnYtgX4tkgZQDQO2p4o2p6JvcHTmLaNttauXcuaNWtm/VCsXbuWPfbYo2eLWwgSo61nJu2GmUkpGfVGGfPGKejFlrMtQiF4slLFC0PyHexzN22XsYkmlqV1fnOR0Kg0qU80sXLmnH0XruNxzUXf5P47H0TTVd533qkc02Hc+nfvH+b+zXmeu7rC3xw41tm6lyFSxiIs9ENUXSWdNTGzJoapdzvMMet72baPZWn0lXLoLfiUzEccXuYykMkyks3TTSEkFCGN0GbQmnbp7OxkUkYQbQZ1JYp+QMvW/Am7J21/Svbee+9Zw87Gx8fZe++9e7KohISeI7aA2NRyY2czajLpV8hq2daDsyRsaTZpBj65DmKzvSDoSd+F09x1U2ezYXPZeddx/50PYloGH734XS2LiyBSuHtDfruR0qNW1hjM+qzIex2vezkhpcTzApyGC0C+P0ffSIl8fy62+14IceEEGIZGuZTtXlwQi4t+K8tw1+JCTFmA93UpLkT8PVSHUPT9EnHxDKDtT8pcjp2NRgOrw3TIL33pS+y1115YlsVxxx3H7bff3tJx3/3ud1EUhde85jUdvW/CM4M4zOwJUMyWLmqhCBn3JlAU2toaqXoeE45N3jBR2rwDRUJQqTqEocTsIgzLdwMak/a8TZ3VyTqfO+tLPPyHP5PJpTnnkjM49KgDdnnucVvnpof7ufTXe/GDPw5z+7qtMdz79jl86LlrOXrV7uO0ORtSSjzHw2l6qGqK4mCevhUlcqUMmtGbUd3ZcJwAXVPpK2cwunDVhDi8rO66lK00w/nuklEjKWmEdUpGqStxES9sFJQyirZfSw3WCbs/LX+SzzzzTCCeGvnkJz+53ahqFEXcdtttHHHEEW0v4Hvf+x5nnnkm1113HccddxxXX301L3/5y3nooYcYGpq7Ee+JJ57grLPO4vjjj2/7PROeWcjwKRC1lsPMJvxJmmGTgl7c9Q9P4UURW5p1tFQnbp2SWt3tuu8iCuOmThFFWHM0dY5tmeSSc77MpnWjcdz6JWew5z6r5j6ngAfHsty+rsijE1u/8yUroLiNZ8Xuvo0eCUHgBkgJRlonn7Mw0r2ZCNkVjhOgqQr9pTSm3t1T/dbwMosV+XxXyahCQj2oU9AL3VmAAzIaAyWNou+fWIE/g2hZYNx9991ArPDvv/9+jG1MWgzD4PDDD+ess85qewFXXnkl73znOznttNOAOK31Jz/5Cd/4xjc499xzZz0miiJOPvlkPv3pT/OrX/2KSqXS9vsmPDOQYgKidaC2Fh3dDG0q/iQZLdPy1oiUMNpo4oYRpQ6qeE3bp1af9rvo7IYQN3U2523q3LhuC5ec/WXGRyv0D5U579L3MrJ6cNafrXlqbIi1vkjNiy8TCpL9+m2OXV3lgAG79a2jZUwYRgR+iAKYaZN0zsJId/730C6OG5BKKfSVs5gdbKttj6TmuuQMc0pcdC6OYgvwGjktw5A52LEFOEwbaako+oEoqaTv7ZlEy5+aX/ziFwCcdtppXHPNNT1pkPR9n9///vecd955M6+lUilOOOEEfvvb38553EUXXcTQ0BCnn346v/rVr+Z9D8/z8Lyt+8K1Wq3rdSfsHsRhZk/SaphZJCLGvTEkYKRaf5KsuC6TrjPV1NnejckPQio1G1Wjq6bOZt3BrjtY2dmdOp98dB2XnncdtUqDFWuGOPeSM+ifJW49iBT+/cHBmaAxgIwecfTKGsesrtKX3n2dNrclDEJ8L0RVFdK5eCJkIXor5sP14mmbcimL1QNxUfVcLE1nRT6H2aXzamPKAnzYGu7KAlyKehzBrh+Mkurrak0Jux9ty9Krr76aMNz5IjMxMYGmaW0Jj7GxMaIoYnh4+7Cp4eFhHnzwwVmP+fWvf83111/PPffc09J7fP7zn+fTn/50y2tKePogo43xvm+LYWYTwSSNsNHW1ogTbA0ya9etMxKCatUmDAXZLmylnaZLo9LEsGZv6nz4gce5/PyvYjdd9txnFWd//j2zxq0LCT/64xD3bY7/bM+Sw3Grqzx7qIG2GxtiTSOlJPAjQj9EMzRyxQzpnIXWRc9Lp3heiBTQV8qSsbq3FK97PqaqsqpQwOqyh6MRNtBTWvcW4NIBaaNoB6CorX0HE55etH3ZeNOb3sR3v/vdnV7/13/9V970pjf1ZFFzUa/XOeWUU/ja177GwEBrqXvnnXce1Wp15p+nnnpqQdeYsDyQ0oPoqZbDzOzQZtKfJK1l5rXT3pZIwKjdIOwoyExSr7s0XZ+01fm++9amztSsTZ333/kgl553HXbTZf9DnsXHL3/frOIC4Bd/LnPf5jwpRfLWIzbwrqPXc/jI7i8upJR4boDTjIPTCv05+objiZClEBe+HxFFknIpQ7aFVNtd0fB9VFVhRb7QVTIqbGsBPtKdBbj0QFRA3RvUuXt8Ep7etP1pvO2227jyyit3ev3FL34xn/jEJ9o618DAAKqqsnnz5u1e37x5MyMjO3sVPPbYYzzxxBP8zd/8zcxrQggANE3joYceYp999tnuGNM0k4yUZyAy2gyi0VJjZ7w1Mo6UEjPVulCYdG2qrkfeav/zZTsB1YaLZeqkOnSA3FVT5+2/upcvfe4GojDisGMO5EMXvB1znqflAwds7lhX5GX7jrP/gN3RmpYTQkp810dGoFsa+XK2Z1beneL7giAQ9JUz5DLd55XYoY8CrMznuw4vcyMXKQXD6eEuLcC3NdLaKzHSegbTtsDwPG/WLZIgCHAcp61zGYbBUUcdxc9//vOZUVMhBD//+c95//vfv9PPH3jggdx///3bvXb++edTr9e55pprWLMmsZxNmK5erINUtqWLWzWoUg8b5NtwJ2z6AaPNJmlNR22zEBiEEZVqk1QKtA7LA1JMNXU6PunczuLilzffxtev+i5SSI79iyN477lvQdvF0+2qgseHn/8klrabO25GgsALUADdMkjnLUzL6FjI9WxdgSAIQsqlDPl53FVbxZkOLysUyBvdnc8TPoEIGbaGunTpjCAaBW0VivasrtNfE3Zv2hYYxx57LF/96lf5x3/8x+1ev+666zjqqKPaXsCZZ57JW9/6Vo4++miOPfZYrr76aprN5sxUyamnnsqqVav4/Oc/j2VZHHLIIdsdXyqVAHZ6PeGZSzvVCydyGPcnsDQLtcUnrVBIRptNIinJtbnfLYSgUm0ShIJMpsMnTgnNmoNdczBnaeq8+Ue/5DvXxnHrL3rFczn9w28kNcdT+5amjhemWFOMG6F3Z3ERhhGBF6KkIJ0xsXJpjB5beXdKFAk8P6RYTFPIWXTbTeqFIX4UsSpf6GhyaVt8EeJOWYAX2+g/2pGtRlrDU14XiZHWM522BcZnPvMZTjjhBO69917+8i//EoCf//zn3HHHHdxyyy1tL+DEE09kdHSUCy64gE2bNnHEEUdw8803zzR+rl27tuskwYRnDnH1Yn1L1QshBePeBJGMyKZaLAlLGLOb1HyPYgdbI7WmS9PxSVtGx94Rju3RrDQx0jrqNuOUUkp+9O2b+fG3fwbAq17/Et78rr+d879Dw1e54Z6VNDyVU47YyD597VUglwNSQhROT4SkyBTSWAtk5d0pUSRw3IBSPkMpn6ZrcTEVXrYin6fcpbgIRYgd2gyY/ZSNUsfbGVLKWFzMGGl137iasPvTdhYJwD333MNll13GvffeSzqd5rDDDuO8885jv/32W4g19pQki+TpjQzXxZkj6sguy7MT3gSb3M3k9XzLEyB13+OpShVD09seBbRdn7HxBrquouudiebAC6lsqcX9Its0hwoh+Ofr/o2f/fh/AXjD217F3570snlvGF6o8N37Rxizdd5zzDqyhuhoTUvBjhMh6ZyJlTGXpGlzPqKp2PVCLk25mO56yyAUgobvMZzLMdRleFlsAV6nz+xj2Bzsam0yGp0y0no2SqrzlNWEpxcdCYzdmURgPH2R0kf6vwcilFRp3p91I5d19npSioKltvYUGIiIJyer+CJqe887CCNGx+tEQnQ8NRJFgsqWGqEXbNfUGUURX7/ye/zqlthi/9T3vY6/ek1rDreRgGagUjCjjta02Egp8d2490A3NNL5NFbGRO1QsC0k0+Iin7MoFzOkuhQXkRTUPY/BbJbhbK4r99RISupBjZJeYtga6s6lU0yCVFCMxOsiYXs6+sQ/9thjnH/++Zx00kls2bIFgJtuuok//OEPPV1cQkJbRFtA1EGZXzjKqa2RUAQtiwspYbRp44Q++Ta79YUUVGo2fhBimZ2Ji22bOs1t7MQDP+SLn/kWv7rldlKpFO8++6Rdios/T1pMP1aoKXYLcREJiet4uLaPqmuUBguUV5TIFtPLW1xkLcqF3oiLmufRn8kwmOlOXGxnAW51aQEu6iCjKQvwRFwkbE/bn/pf/vKXHHroodx222388Ic/pNFoAHDvvfdy4YUX9nyBCQmtIKWPjNZDKrPLUm81qFENamS0XMvnr7ouE7ZNzmi/Qa/ecGna3fVd2NNNnZmtTZ2u43HFJ7/GHb++D01X+eAFb+P4lx0773nuWF/g679fzb/9aZDdoXYZhgLX9vBdH9MyKA0V6Rsuks5bSzpuOh9iSlxk0yalYqbrHjJBLC7KVpqhbHfhZbEFeL1HFuB2bKSl74eizp0blfDMpe2P6rnnnstnPvMZ/uu//mu7PJKXvvSl/O53v+vp4hISWibaArK2y+qFF3mM+xOYqtFyVoMbhmyxG+ha+0FmjutTrbkYuoba4Zik2/RnnDqnmzqbdZtLz72WB+56CNMyOOsz7+LoFxw273keHU/zHw/G2SMFK1zWAWVhGOE0PMIgwMpa9A2VKA4WsbIGyhKPm86HFBLHDchaBn2lLGovxIXrUbZMVuRzXYWXQezSaWpW9xbg0gNZAXUfSK3sak0JT1/alq/3338/N954406vDw0NMTY21pNFJSS0w0z1Qpm/iU5Kwbg/gR95FI3WxvGEgNFmE6+DILMwjJisOigKGEbnTZ31iQZKSplx6qxO1rn03GtZ+/gGMrk0Z3/23ex78F7znmdzw+DG+0YQUuGIkRov3Xuyo/UsJFLGWz5hEKJqKtliGnOZTYTMh+8L/CAgbRn0lXJdV1i2JqOajHQZXgbQCJtTFuCDXVqAByDGQd0bRdsjMdJKmJO2BUapVGLjxo3svffe271+9913s2pVYgmbsASI0bh6sYvMkVrQoBrUyLaxNTLp2lRct+0gMykFlbpNEIQd+11EkaA+2SSKItJTTZ1jmye45Jxr2bR+lGI5zzmXnMEez5r/CbLuqdxwzwq8SGWvksPfH7xlWVUvpJT4fkgURGi6RqEvh5kx0YzuArsWi2lhoakqpXyGfNZC7dpfPU5GzegGK3I59C76JADsyCGFwrA1TEbtxqVzWyOtvRMjrYR5aVtgvOlNb+Kcc87h+9//PoqiIITgN7/5DWeddRannnrqQqwxIWFOpPSR4bpdVi/8yGfcH0dPaWgtXqxtP2TUtjE1re0gs3rTo9H0Ou67mG7q9JrejFPnhqc2c+k51zI+WmFguMy5l76XkVWzx61P40cK3753BRVXZyDjc/LhG5dNtoicsvKOIolh6eSKGcyM2YOb8+IQBALfD0mpCqV8hmzWRJ8lD6YTap6HpemszOcxuwwvcyMXIQQj6WGyWrbj88RGWpu3MdJaXiPBCcuPtj8hn/vc53jf+97HmjVriKKIgw8+mCiKOOmkkzj//PMXYo0JCXMjRkFWIbVzds0001sjbuRRbNEGORSSLXaDUEQUzfZCn1zPp1p3MHS9474Lu+bg1BzMrIGiKDzx6Doum4pbX7lmiHMufS/9g6V5zyEkfP+BYdbXLDJ6xKlHbCSjL73XRSQEgRsgJRhpnXzOwkybS27l3SphELtyplSFQj5NNmtgdCkCtqXue+hqipX5fNfhZZ7w8aOQkXS3FuDTRlp98cRIYqSV0AId+2CsXbuWBx54gEajwXOe85zdwmQLEh+MpxOx78U9gD+v70UtqLHe3khWS7fcNb+l2WRTo07Rski10QsdhYItE3XCMCKd7mwk1W36VMeqaJqKpms89MDjXP6Jr+LYLnvtt5qzP/ceCqVdb/Pc9HA/v15bRksJ3n7kBvYsuR2tp1eEYUTghyiAmTZJ5yyMtI7SZePiYhGGAs+LhUU2bZLLmhhdCoAdaQY+AKsLha7DywIRYkcOw+YgZaPcVa+EjLbEycT6s1FSrW8xJjyz6fjbsccee8yEiyVNPglLghiLO9nnqV7EWyMTaCm1ZXHR8H3Gmk0ymtGWuJjuu/D8oOMY7nC6qRMFTde4944/cc2nv4HvBRxw6LP46MXvJJPddUXltnUFfr22DMDrDt6ypOIiDKatvBXSOSsWFrtJ4ybEvTCuGwuLXM4ilzUw9d7nbNihj5SSVT0QF6GIsMMm/eZAVxbgAFJMAOaU10UiLhJap6PNzuuvv55DDjkEy7JmAsi+/vWv93ptCQlz0krvhZSSiWASJ3RabmwLhWBL0waUtve+G132XUSRoDbZJAojzLTBbb+8hysv+Dq+F3D4MQdx9ufe05K4eGgsw39OjaO+bJ9xDhtptL+YLpFS4nshTsNFCMgVM/SNlCkO5DGs3UNcRJGg2fTx/YhczmRoIE9/Kbsg4sKdSkZdkctTMLtLRo0twBuUzT4Gzf7uLMBFDaScEhflrtaV8Myj7QrGBRdcwJVXXskHPvABnve85wHw29/+lo985COsXbuWiy66qOeLTEjYiRaqF42wQdWvktWyrd3wJYzZNg3fpWS133dRqTvoutpR34UU0Kg08W0PK2ty602/4/qrv4cUkue++Dm85+yTdxm3DuCGCt9/YBiJwpErarxor8UdR50WFlEYoRsa+b4cVsZC7XBMdymIKxYRqRTkcia5jIlpLFwyqBeFeFHIilyeUrq78LJIShphnZJeZNAY6FJcNEE6KPpBKOr8zcQJCbPRdg/G4OAgX/jCF3jzm9+83ev/8i//wgc+8IFl74WR9GDs/mztvfDmfKqKRMQ6Zz2e8Mi12Dlf8zyeqlZJ6wZ6G54DUSgYm6zj+RGZTGc3ombVoTZex8wY/OxHv+TGr/w7AC951fM47YNvmDNufTYem0hz21NF3njopkWbGBFTEyEyAt3SyOTTGBlj2bptzsa0sFAUyGYMchkD09RZyHKLH0XYgc9ILsdgl+FlQsb9RgU9z7A1jN6NS6d0QUyCun/idZHQMW1/AoMg4Oijj97p9aOOOoowDHuyqISEeZmpXszte1ENqjTDZsud814UsaXRIJVKtSUuQFJp2DheQDbdWWnba/o0Kg10Q+XHN9zMv/3zLQC8+o0v5U3v+Ju2L+779DmLFr0eRoLQCwDQLYN03sK0jN1mIgTi3BDXi5tPc9nFERYQb2XYgcdQNsdAl+JCTuWLZKcswLsTFwGICVCfhaKtScRFQse0/XhxyimncO211+70+le/+lVOPvnkniwqIWEutvZeWCjK7J4DfuRTCSqYqkmqhYujlDDaaOKEIbk299frTY9GY6rvooOH9dALqU82kELyL1/7jxlx8ca3v7plcSEk/MeDA4w2F66MvyNhGOE0PUI/TnYtDZUoDxVJZ3efcdMokjTtANcLyWZ0hgZy9JezmKbBQouLSArqvkt/Jtt1eBlAPWxgaRYjXVuAh7GRlroGRdsrMdJK6IqOZO7111/PLbfcwnOf+1wAbrvtNtauXcupp57KmWeeOfNzV155ZW9WmZAwjRjfZfWiElSnPC9aswOvuC6TrtO2W6fn+VRqNrrWWd9FFAnqlSae5/OdL/+YX//3HQC89QOv52V/+8KWz3Prn8vctq7EH0dzfPT5T6KrC5NiJmU8ERL4IaqaIlNIY+1GVt7TTFcsADJpnXzWnEq5XZxfIkJQ81z6rSzD2TxdOoD30AJcxJk+6giKtk9ipJXQNW1/gh544AGOPPJIII5tBxgYGGBgYIAHHnhg5ueSslpCr5EyQIZPzVu9cCKXSlAlrWVaeip0gzjIzNDUttw6o0hQqTsIAelM++6NUkCzYlOfaPCNL/wrv/+/+0mlUrzrYyfxwhN23oKcj+NWV3lkPMML9qgsiLiQUhL4EaEfok03bqYNNHP3ugFFkcTzQqScEha5xRUWEIeX1V2XspVmON9dMipstQAf6doCfMpIKzUQp6MmRloJPaBjo63dlaTJc/dFRhuRwf2QGp5VYEgp2ehuouZXKRi7/ruNBGyoV6i6HsW2pkYk4xWbesMhmzY72hppVh1G141z7WXf5o/3PIKua7z//Ldx1PMPaf9kxNskvfariq284/FJw9SwcmmsjImq715l8+2EhaWRz1uLLiwgDi+rTuXarC4Uug4vcyOPUASMpEe6cumEaSOtHIp+cOJ1kdAz2n4EGR0dZXBw9pGl+++/n0MPPbTrRSUk7EhcvVg/b/WiGTWpBzUyWmtPcnGQmUfeaq+s3LA9Gg2XtKV3JC48O2DTU1v4x4v/iccefBLTMjjzonfw7Ofs3/I51tdMNjcMjlxZB3orLqSUeK6PFKCbOvk+a7ebCIHthUXa0sjnLNLW4guLmDi8LGeYrOhBMqonAvwoYLhLC3CYMtJSrMRIK6HntP0pP/TQQ/nJT36y0+uXX345xx57bE8WlZCwE2IM5AQos/dVCCmY9CYBpSXHTtsPGGvapDUNtY2vgecHVKoOmpbq6IYbeiHrHl3P5R//Co89+CTZfIbzLntvW+Ki4mrccM8KfvjHYe7akG97DfMhpcRpeuimTmmoSN9wkXTe2q3ERRRJHCfAdQMsU2NwIMfQQJ60tfDNm7MjqXoulqazIp/DVLsLRAtEiBs6DFr9lFrsM5pzZaIaG2lp+81rt5+Q0AltVzDOPPNMXve613Haaadx5ZVXMjExwamnnsr999/PjTfeuBBrTHiG00r1oh40aIRNcvqun8DiILMmkRRktdaNjSIhqNQcIiHJduB3EUWCPz/4FJec82W2bByn2Ffg3EvOYM3eK1o+hxsq3HDPChq+xnDW4+ChZtvrmAspwbV9DMug2FfYrcyxAEQk8fwQISSWFYeopU1tySch6p6PqaqsKhSwugxF29YCvK/bfBHRBOklRloJC0bbn/azzz6bl73sZZxyyikcdthhTExMcNxxx3HfffcxMjK3q2JCQseIcZCTkBqa9Y8DETIRTKCl9JYaNcdtm5rnUWxra0RSrTk4rk823X4DnBTw8L2P87mPfpHKRI3BkX7OvfQMhlcOtHyOSMB37x9hc8MkZ4Sc+pyNWFpv0lGlBM/x0E2N4kB+txIXIpJ4QYiIpoRF1iJtLb2wAGgEPqqqsCJf6DoZNZKxBXifUWagWwtw6YKsg7r/vG64CQnd0NEndN999+WQQw7hiSeeoFarceKJJybiImFBiKsX60Ax56le1HBCh7S660bNuu8xbjfJ6O0FmTVtj3rDm+q7aP+p8Q93PsRFH7qGykSNVXuO8MmrPtCWuJAS/vOhQR4Zz6KnBKccsZGS1TtjO8/x0HSVQn8ezeiuhL9YSCFx3ADHDTB0lYG+HIP9OTJpY1mICzv0USSszOe7Di8TUlIPpizAzUFSXYkLf8pIay8UbXUy8ZewYLT9Kf3Nb37DYYcdxiOPPMJ9993Htddeywc+8AFOPPFEJicXN/cg4RnAdPVijt4LL/KYCCaxVGuXjY6BiNhcb4KitLUP7gUBlZqDpikd9SLc/X9/5KIPXU2j1mTv/ddw/hXvp2+g1NY5frO2xB3riyhI3njIZlYXvLbXMRee45FSVfL9efTdYPR0Wlg0HR9DmxYWebIZs6sbby9xpsLLRvI58kZ34WViyqUzr+UZtIZQU50LwNhIa2zKSGvvZSHEEp6+tP3peulLX8qJJ57I7373Ow466CDe8Y53cPfdd7N27dpkgiShp0gZ7rJ6UQmq+FGAtSuDIQmjTRsnDNp6moyEoFJ1CEOJ2cHN97Zf3MPFH7oax/Y48LB9OO+y95Evttep/4ctWW5+pB+AV+431tO+C88NIJWi0D+VcrqM2VFYDPbll52wAPDCED+KWJEvULK6Cy+btgDPaBmGraEuLcCjHYy0do9KVcLuS9uf1ltuuYUXvehF2722zz778Jvf/IbPfvazPVtYQsLM5Mgcrp12aFP1qy2NpVY8lwnbJmeYKC1PEkjqdRfH9clY7Ze4f/nT33HV+V8nigRHHHcwH/zk2zDM9s7zVNWcSUc9bnWF5+9RbXsdc+F7IVIKiv1FzA5D2hYDKeIqUhhKTEOjr5ghbRmkurXAXAC8KMQJA1bk85S7FBcQJwJbmsmwNdSlBbgEMTplpLV/YqSVsCgkRlsJyxIpwzgxVTZR1P5Z/lywwdlEPWxQ2MXkiBeGrK1WiCRk9dYvrE3bY2yiiWWpbW+N3PyDX/Llz9yAlJLnveRI3n32yWhae0+Mk47GtXespulr7N/f5C2Hb+za+XGawI8j1Qv9edL57m+EC8FWYSEwjdh5M20ZqMtQWERS0PB9UsBANstQl+FlEFuAq4rKCmukZW+XuUiMtBKWgpa/qa961auoVrc+PV1yySVUKpWZfx8fH+fggw/u6eISnsGIsanJkdKsf9wIm9SCOpldNHYKAVuaNm4YkW0jyMwPQyo1G1WjbXHxo2/dzJcu/hZSSl7y6udzxjlvaVtcOEGKG+5ZQdPXGMl5vOnQTT0TF2EYEYUR+b7cshQXUoDnhTQdj5SiMlCOfSxyGWvZiQtBLCzqnkfRNNijVOqJuLAjFwWFYWuoB+JifMpI64BEXCQsKi1/W3/2s5/heVsbyz73uc8xMTEx8+9hGPLQQw/1dnUJz0ji3ov1gDHrPnEkIib8SdRUCm0XDW8Vz6HSZpCZEIJqxSYMxZSldKvrlnz7iz/im1f+KwCvev1LePuH3kCqA2XwyHiGLU2TvBly6hEbMLXeFBrDMCLwQnKlLJl8O/boC4+UW4WFoqToL+cYHsyTyy4/YQESO/SpuR6mprFHscTqQinu7+lSXLiRhxAhw9YQOa07QSBFBVBQtP1RUt2ZciUktEvLPRg77qQ8w3ZWEhaTmd6L2X0v6mEdO2yS34VFshOEbGk2MTWtjSAzSa3h0myz70IIwVcvuZGffO9/AHjtqa/gtae8ouXjd+SwkQaKson+tE/Rijo+z7aEkSBwA3LlLJlCZtkkoEoJvh8ShBGGrtFfypLJmMtQVMR4YYgdBqQ1ldWFInnTROuRV7snArzIZyQ93AML8AZIP94WUVsfiU5I6BXLfyYt4RlFXL3YAOizVi8CETDhT6KrJql55vcjAVvsBmEUtRVkZjsB1bqLZeikWoxgD4OQay78Jrf+5LcoisJJ7/o7XvG6F7f8njuue7rgcehwo6NzzH5egW/75EoZssVsRxkqvWY7YaGpsbBIm8vWljwUgobvoasqI7kc5bSF3sXI6I5MW4APWYPdW4BLB2QD1P1R1MSjKGFpaFlgKIqykyFLYtCS0HPE+FT1Ynbr4kpQwY1ciru4AFdcm5rrkm+jkz8IIyo1m1QKtBYTQ30v4LKzr+W2W+8hlUpx2gffyItfdVxLUfE7cv/mHL98osQph2/sWdUCYpMmr+mTLWXIlZZeXEgJgR/hh+FWYWGZqNryFBaRFDR9HwXoszL0ZdJdu3LuSCgimmGTAbO/ewtw6YOYBHUfFG1ND1eZkNAebW2RvO1tb8M0Y78B13V5z3veQzabBdiuPyMhoROkjKZ6L7RZqxdu5FL1q6S19Lw3cDcIGXVsTE1vOchMSEGlahMEEZlMa1sjdtPhsx/+Ivfd/id0Q+PdZ53M0X9xeEc3h1DAzY/0U3F17lxf5C/3mdj1QS0gpcRteGQK6VhctFiVWQikhCCI8IMQXVPpK2XJLmNhIRDYfkgoIgqWSX86Q07vvsdiR6YtwMtGmQFzoEsL8CkjLW0PFG2v5CEwYUlpeUz1tNNOa+mE3/zmN7ta0EKTjKkuX2S0GRncH8/qK9trXyklm9zNVPwKRWPuvzcpYV2tStV129oaqdRtKlU7HoNs4SZcrzb49Puu5qH7H8fKmHzgE2/joMP3w+jCCXPS0fjtUyVesd9YT+LXpZQ4DY90ziI/kFvSrQff3yosclmTXNpatsIibuAM8MKIrG4wkMmQN0wWoiVESEktqFHSiwxbw126dEYQbQJ1VTwxoixfb5OEZwaJD0bCskDKCOnfC7I2a0NaM7RZZ6/DUq153QwrjstTtSp502y5sdN2fcbGG+i6it7C1sjEaIUL3nMFTz66nnwxy0c+/Q72eNZKrHT7ltBS0tF2yq7PG8eup7Mm+f78kt3MfV/gBwG6ppLNmuTSZtsju4vJdAOnpakMZLIUTKtnDZw7EluA18hpWUbSK7p06ZQgNoHSj2I8G0Xpzp48IaEXJE2eCcuDmd6LncWFlIJJfxIJ816EvShi1G5gaGrL4iIIIypVByVFS+Ji07pRPvnuy9m0bpTyQJGzP/ce+gfLmG0ls8aEAm68bwXHrKpy0KDd9vFzMRO7njbIl5dGXEwLC01VKRUyZDMm+jIWFjMNnCmV4WzcwGm0kVfTLlJCI5y2AB/uSlwAILaAUpyqXCTiImF5kAiMhCUn7r1YR9x7sfNHsh42qAd1cvM5dkoYa8SGWq3mPwgpqNZtgiBsqe9i7WPr+eS7r2BitMLI6kHOueQMspkM6azZdhVCSvi3Pw3x0FiWJysWZ73gSdJ699Hr28Wu9y9+7Pq0sFDVFKV8hmx2eQuLCEHDix04F6qBczYaYQNT7d4CHEBGY6Ckp4y0sj1aYUJC9yQCI2Hpmad6EYpwylRrfi+Lqucx6cVZI6124dUbLo2mH0ew7+KQR/7wZy5871XUKw322GcVH7/ifaio6KbeUSPdL/5c5u6NBVKK5E2Hbu6JuICli10PAoHvh6RUZbcQFhJJMwgIo4Vt4JyNRmijpTSGzSEstcswNFEBUijaAYmRVsKyIxEYCUvKrqoXtaCGHdoU5hlLDUTEaLOJmlLRWuzEc1yfWt3F0LVdNnXef+dDXPzBa3CaLvs9e28+ceX7ibwIJZVC62D74Z6NOX7+eJyv8jcHjLJff2+2R9wliF0PA4E3JSwK+TTZrIGhLefLisSZauBMazorijkKhrUgDZyzEVuAw5DZAwtwUQcZoOgHzZrXk5Cw1CznK0HCM4F5qhd+5DMZVLBUc+6pCgnjtoMdBi2nV4ZhRKXmAGDsYgvhjv+9l0vO+jK+F3DYMQdy3j+8H8/xkHQW3/7nSYsf/TFOhz1+z0mOXV1r+xyz4bkByiLGroehwPNiYZHPWeSyJsYibC10gxeFOEGAqamsyOcpWekFa+CcDVd4RCJkRXqE/C4C+nZFbKRlg7ZfYqSVsGxZ3leEhKc1UkbIaD2gzlq9qAQVvMinNM9Yaj3wmbDtqZTUXd8spBRU6w6+v+u+i1/e9DuuOv96ojDi2BcdwccueQ9u3cV3A9LZ9kvbY02df75vBZFUePZQg7/ad7ztc8yG74UgBYVFiF2PIoHrxsIil7PIZQ3MNkLkloJQCJq+h5ZSGcxmKafTmAvYwDkbngjwQp9ha7B7C3DpTRlp7YuiJkZaCcuXRGAkLB1iIq5gpHYu79qRTSWoktHm9rIIhWS0aQNKyx3/jaZHvemStox5+y5u+v6tXPvZbyOl5MWvei4f+vTbceouTsMlnWu/qbPpx+moTqCyuuDy+mdv7onXReCHiCiiMJDHynbXLDgfM8IipZDLmeQyJqaxvIVFhKDp+QCUrDT9mcyiNHDuSCBCnNBh0BqgbJS7OpeUAUTjoO2Jou2ZGGklLGsSgZGwJGxfvdB3+DNBxasQCYGhzX0Tm3BsGr5HscURUdfzqdSdXfZd/PCbN/FPV38fgFe+8SW857yT8Zo+zbqDkW6/qTOIFP753hWMOwYlK+CUwzdiqN3bz2wXu55bmNj1WFhEKAq7jbCQSOwgIIgi8qZJfyZDfpEaOHdkWwvwAaOvSwvwCKJRUFeiaM+a1e02IWE5kQiMhKVhpnrRt9MfNUKbWtAgO08TXNMPGGvaZDSdVAt24FEomKw6SAGGNfuFWUrJDV/4IT/4xk8BeMPpr+aUD7yWwAupTzZRVbVtkygp4Ud/HOLJahpLi3jrERvImd3njIRhROCH5Bcodn07YZE1yGUMTFNn2USwzorECUPcMCCjGYwUchTMxWvg3JHeWoCL2OtCHULR90tcOhN2CxKBkbDoSCmmqhepnS6UQgom/Yk4cGwO2+RQSEbtJgKJ2cLEgpSCSsPG8wOyc7htCiG47nPf4abv3wrA2z78Bl532iuJfEF9ooEUAjPTvoHRzx/v477NeVKK5KTDNjGUC9o+x47MxK735cgWex+77nkhYSR3I2Ex3cDpY2gaK/MFSpbV8kTRQiCkpB7UKepFhsxBUl2JCwliFJQyirZfYqSVsNuQCIyExUeMz1m9qAd1GqFNXs/PeXjFdah5rW+NNJoejYYX913Mcp0Pg5CrL/gGv/zp71AUhfeefwqveP2LkZGkUWl03NQpJfhRfGP+uwO3sE+f0/Y5diQSAt/xyRUzZAu9FxeuFyAlDJSzZDOtNc4uJdMNnGoqxWA2tyQNnDsSW4DXyWs5hqzBrvJF4hOOTxlp7Z8YaSXsViQCI2FRiasXGwBlp+pFIELGgwkMVUedY6/aCULGbJu0prW0NeJN9V3oujpr34XvBVz6sWu5/Zf3oGoqZ37mHfzFK48DCc2ajd1wO3LqhDhj5FX7j3PocIM1xe7ThreLXS/3PnZ9Wlz0l3Jk0gvXMNoLIgS2FyCQlKw0fekMGWPpL2c7W4B3t5UhxSSgougHoqSS7KSE3Yul/0YmPLMQEyDGZq1eVIMqbuhSnMNUSwgYsxuEUUS2haTUKBRUanP3XdhNh8988B+5/84HMUydcy9/L8f8xeEAOA2XZtXGsNpv6qy4GjkjZNqDqxfiYqfY9R77NzhuvHXTX86RsZavuNi5gTNNXjeXRaElkpJm2MBQjd5YgIs6yBBFPxhllu9LQsJyJxEYCYvG1t6LnasXXuRR8StYmjVntaDquVRcj7zZytaIpNKwcbzZ+y5qlQafeu9VPPKHP5POWnzyCx/i0KMPAMB3A+qVJqkOmjobvsrXf7+Kkhly0uEbyfQkX2Rr7HquL0uqhTj5dthdxIUTBrhhSEbTGSnkyJsWS5hAvx1O5OJHHjktR7/V3wMLcBukHVuAq8M9WmVCwuKSCIyExWOO6oWUksmgQiACitrsZWA3DNnSRlJqfZ6+i/Etk3zy3Vfw1OMbyJdyXHTtmex78F4ARIGgPtFERp01dY7bOravohCXy7tlJnY9Z5IfyKH2+I7quAEKCv3lLOllKi68KJpq4FRZmc8veQPntgQipBnamKrBSHoFBS3fdc+FlB7ICqj7grqqNwtNSFgCEoGRsCjM13vhRA41vzpnNoOUMNa08cOIUgtbI67nU6nZs/ZdbFq3hfPfdTmb14/RN1ji4q98lD32iS/iMpI0Jhv4jk8611mn/p4ll3cdsw5VkWSN7qoXO8Wu91pcOAGpVCwuLHP5iYtICuqeh6qkYgdOy2ppamgxEFJiR02khD6zTJ9e7npLBLY10toLRdsrMdJK2K1ZHt/WhKc/YjIetdupeiGY9CtIQE/N/nGsei6TrtPS1kgUCqpz9F08+eh6LnjP5UyMVlmxZoiLrvsoI6sHpxYy1dRZd7FyZtsXdjtIzWyHjOT8to6djYWOXV/O4kIgaHoBAkHJsuhPZ5dFA+c0buThRh45LUOf2UdWzfZECMwYaWmrULS9u/LNSEhYDiyfb23C05b5ei/qYYNaUCOrzR7+5EcRY00bXd311oiUgkp99r6Lhx/4M59675XUq0323HcVF133UfoGSzN/7jSnmjrTOqk2bxZ3rC9wy6P9nHz4RvYquW0dOxfTsevFBYhdt+0AVV1+4mK2Bs6c3tkEz0IQTm2H6KrOiDVEUS92P4I6RWyktRnU4Smvi8RIK2H3JxEYCQvPHNWLSERM+pW5Y9YljNk2Tui3tDXSaHo0mjv3Xdx3+5/4zIe+gGN7HHDos7jwSx8mX9wqaHw3oDFpd9TU+eh4mv94cBAhFR6bSPdEYGwbu671MHZdyrhyoU2JC3MZiQs3DHCWaQOnkGBHTYSQlIwSfUYZU+2d2VVspLUFlL4pcbF8/l4SErohERgJC8p81YtaWKcZNudMl6z7HpOOQ9aw2NUcojuH38Xtt97DJR/7MoEfctixB3H+NR8gndna4R+FcVOniCKsNps6NzcMbrxvBCEVDh+p89K9J9s6fjY810dJpSj2OHZ9RlxoKfpLmWUjLvwowg589GXYwAlxxLobumS1LH1WHzmtN9sh2yHGQMmh6AegpOa2x09I2N1IBEbCwjJH9cKPfCb9CUzVnHVLIhSCLU0bRVHQd3HDCcOIyaoDEoxtthNu/envuOr8ryMiwXEveQ5nX/oeDHPrTTtu6mziuz7pbHviou6p3HDPCrxIZa+Sw2sP3tx1KT+OXZcU+gsYPYxdlxJsJ0DXVPpL6WUhLiIpaHgeKSXFQCZDXzq9bBo4IQ4ps8MmWkpnxBru6XbItsRGWvqUS+fc7rUJCbsjy+cbnfC0Q0o5Z/WiElRxI29OU61x26EZeLvcGpnuu/D9kExm643zp//6C6773HfiuPVXP48Pffo0tG2juiU06w523cHKttfU6UcK3753BRVXpz/jc/JhG2dMtTol8EOEiCj09zZ2fTtxUc4seRLqtg2cRcuiP50hs4zSWePtEJtIRBSNIn1GuWtPi7mIjbSixEgr4WlLIjASFg45OeV7UdruZSdyqQZV0lpm1qf+hu8zbjfJ6AbKLrZG6tv2XUz96Pev/wk3fOGHALz6xJfyrnNPIrVDFcRpujQqTQyrvaZOIeH7DwyzvmaR0SPeesRGMl2Oo4ZBSBTG4qKXsetSxg2dhqHSV85g6kt5I5fYYYAfhuQNK27gNJZPAyeAJ3yc0CGjZeizhshruQWb5JCiGRtp6QeiqEML8h4JCUtNIjASFgQpJTLcALBd05qUkkl/kkiEs8axh0IyZtsIwFTn/3g6rk+l6mDoOqqqIKXkW9f8gB9+8yYATnznX3Py+/5+p+rE1qbOVNtNnT97pJ8/juZQFcnJh2+kP9NdOmocux6R78stgLjwMQyN/nIWQ1+6r7oXBthTEeqrCyUKy6iBE+LtkGZoo6VUBq1BynoJbY6R6V4QG2nVQN0PUisX7H0SEpaaRGAkLAxyMu6M36F6YUc29aA2p6lWxXWoex6FXSSlBlN9F0oKDCNFFMVx6zf/4FYATjvzjbz2ra/Y6bhumjpvW1fg12vLALzu2Zu7nhgJQ4HvBuT7cmQL6Z7laUgBtuNjmRrlchZjiXobtm3gXJHLU06nl1UDp5zaDgllRFHPUzbLpNVdTyt1955BnI6q7o2i7ZEYaSU8rUkERkLPmat6IaRgwpsAlFmfEG2/taRUIQWVmk0QxH0XYRBy1Sev539vug1FUXjfJ0/l5a970c7rElNNnR04dT40luE/H4xNuU541jiHjzTaOn5HIiHw3d7Hrs+IC0ujr5RDb7NC0wsiKWj4HikU+qcaOK1l1MAJ4IkAJ7RJa2mGjYXdDplmq5HW6sRIK+EZwfL61ic8PZijelEPGtTDJnl9Z1OtSMC401pSaq3h0rTjvgvf87nkrC9z56/uQ9NUzvzcOzn+5cfOsiZo1hzsmoPZZlPnaFPnu/ePIFE4ckWNF3c5jhqJhYldX2pxIRA0/QAhBEXLpC+dJbuMGjghnk6yI5uUkmLQGqCkl+d0kO0l2xtp7YuiJJfehKc/yac8oadsrV7I7aoXoQiZCCbRU/qsjpxVz6HiuuSt+fsQbNenVnMxDR3Pcbnog9fwh98/jGEZnHfFezn6hYfNepxjezQrTYy0jtpm1Hk5HXDQYJO6p/J3B23pqjFRSonX7H3suhQS2wmWSFxsbeDMTTVw5pdZA6eU4AiHIAoo6AXKZomMujieE1uNtPrjcdTESCvhGUIiMBJ6yxzVi1pQwwltCrOMpbpByKjdxNQ01Hm2RoIwolK1UVJgN5p86r1X8ugfnySTS3PBP36IZx+5/+zHeSGNiSZKqv2mTgAtBW949mYCoXQ1jrpQsetbxYVOfynb0e/YKdMNnGlNY3WhRN400XokmnqFLwLs0MbSLFamV1LQF347ZDvEKCj52EhLWdgej4SE5UQiMBJ6xvbVi609Dl7kMRFMYqkWO9575JQd+K6SUoUQVKpNgiDCqTe54IzLeerxjRTKOT597UfZ96A9Zz0uigS1iUbbTZ1Cwt0b8zxnRZ2UAooChtp5/vpCxa5LIWk6PmnLYKCUQ+3WkKNFAiFo+h66qjKSy1NOW+gLYETVDZEU2KGNgsKA2U9JL/Uk8bQdpJgAzCkjrdnzdhISnq4kAiOhd8xRvagEVfwooGTsbAledVtJSpVx34XjUxmtcOEZV7Blwxj9Q2Uu/upZrNl7xexHbdvU2aZT508fHuC3T5V4bCLDGw/Z3NaxO61jKnbdzBjk+3oXuy4iie36ZCyD/kUSF3EDp08K4gZOK421hCOwsxFvh7j4kU9ey9Fv9s05tbSg6xA1kHJKXJQX/f0TEpaa5XVlSNhtiV07N7Jj9cIObap+ddYLvBdFjNoNDG3+pFTbCajWXTav3cyn338Vk2NVVuwxxMXXncXwqoE5FgT2dFNnpv349TVFlzvWCw4a7G5aRMo4vEw3NQp9eVS9t+Iiaxn0LYK4mG7gjISgZJmU0xlyutGz6ZdeEUwlnlqqwar0CvJ6ntQSTGvERloOin4Qijq46O+fkLAcSARGQm+QFYg2b1e9kFJQ8SsIJMaOnfoSxm0bN4wozdPY6YchlWqTx//0Zz734X+kUWuy1/6ruejaj1IemN1mHMCuOzNOne02dQIcPtJg77JDwYzaPnZbPMdD73HsehRJHNcnmzboKy60uJhu4IzIGiYDy7CBEyCSEjtsAtBv9lFegu2QaaR0p4y09ofU7NW1hIRnAonASOiauHqxARDbVS8aYZNqUJ/VsbPme0w4NjnDZK7H4EgIKhWbu3/3Ry4/51pcx+OAw/bhU1/6MLlCds71OA2X+kQDVWsvfn1T3SBjRDOioltxsRCx61vFhUlfKduz7ZbZ8MIQO/SnGjiLy7KBE2LreT/yyGk5+sx+Mmp6yQyspPRBTID6LBRtTWKklfCMZlk4vXzpS19ir732wrIsjjvuOG6//fY5f/ZrX/saxx9/POVymXK5zAknnDDvzycsArIC0RbYZp85EhET/iRqKoW2Q/NfKARbGk1URZ3H2VFSq7v87y13cOlZX8R1PA4/7iAu/spH5xUXXtOnPl4nparoRus39Yqr8U93r+S621cz2uzeu2EhYtenxUUua9JXXjhxEUlBxXUIpWAkl2evcply2lp24iIQIRW/BsBIegWrMqvIapmlExeiHmfvqGtQtL0SI62EZzxL/g343ve+x5lnnsmFF17IXXfdxeGHH87LX/5ytmzZMuvP33rrrbz5zW/mF7/4Bb/97W9Zs2YNf/VXf8X69esXeeUJsG31ItquelEP69hhc1avgTHbxg6DeU2YmrbPT75/K1d94qsEfsjzXnokF37xw6Qz82ynOAG1iQagYLRRMXBDhRvuWUHd1zA1Qb7LysV07HqxL9+z2PVtxUW5mEVdIMvtUAjqnkfZSrNXscRQNrvspkOElNSDBm7k0meWWZNeTdkoLUmvBYCUITLaBDIA9UAUbb/ESCshAVCklJ3P3vWA4447jmOOOYYvfvGLQDyOuGbNGj7wgQ9w7rnn7vL4KIool8t88Ytf5NRTT93lz9dqNYrFItVqlUJh56mGhPaQYhLp3wOpwozACETAU/Y6BJLMDlHXdd/nqUoFU9Mx1NlvXF4Q8O3r/pPr/+FfAHjp376AD174NtR5tjt8N6A2VieKIqx06xMjkYBv37uCR8az5IyQ9xyzjnI6bPn4ndbhbY1d71V42bS4yGctSsXMgokLLwpxgoDBbJbBTG5ZBZJN40YubuSR07L0mX1k1eySbkPEkyINSI3EVYtUck1JSJhmSWW27/v8/ve/57zzzpt5LZVKccIJJ/Db3/62pXPYtk0QBPT19c36557n4XnezL/XarXuFp0wQ1y92MSO1YtKUMWNPIr69hfbUEjGmjagzCkuwijia1f+gH/+0o8A+Os3/yXvPPvNO8Wtb3eMH1GfaBCF7XldSAn/76FBHhnPoqcEpxy+sStxEQYhIto9xYUTBvhRyEgux0Amu+yaOMOp6RBd1RmxhinqRdQlrKzMhJZhoWgHgzqSVC0SEnZgSZ9RxsbGiKKI4eHh7V4fHh5m06ZNLZ3jnHPOYeXKlZxwwgmz/vnnP/95isXizD9r1qzpet0JU8jq1OTI1t4LN3Kp+hUszdrpJjXh2NR9l6w5+7aBlIKrP/2tGXHxpnf/Le8656R5xUUUCKrjdQIvxGyjcgHwm7Ulbl9fREHyxkM2s7ro7fqgOZiOXc+Vexe7PiMuchblBRQXzcAnjASr8kUGl5m4EFLSCJvYoUvJKLEmvZo+s29pxYWoxr0WqREU43AUbXUiLhISZmEZFkFb55JLLuG73/0uP/7xj7HmGHU877zzqFarM/889dRTi7zKpydbfS/CmeqFlJJJv0IgQqzU9iOCth8wbttkNGPWpNQoivjMWdfyw3+6CYDTP3oiJ7/3NfOWv6NIUJ+o4zs+VsZo68b4hy1Zbn6kH4BX7jfGwUPN1g/egenY9Vw527PY9e3ERSEzr8jqHEnNc0GB1cUC5bS1rHwtXOFRC2qYKZNVmZWMWMOYansispdI6U995hUU/dmxx0Uqv2TrSUhY7iyp7B4YGEBVVTZv3t4pcfPmzYyMjMx77OWXX84ll1zCf//3f3PYYbMHXAGYpok5r0tkQkfMVC9KMy/ZkUMtqO1kqhUJ2GI3iaQgp+38dxH4ARd+8Av84qe/I5VSeP8Fb+Nlf3/8vG8fRYLGeGMq26M9I611VZPvPzCMROG41RWev0e15WN3JIx6H7seRQLHDcjn0pSL6QVpXpRIqq5LWtNZkc8vq9TTUETYYRMttVy2Q2T8eZcuqCtR1D0T2++EhBZY0gqGYRgcddRR/PznP595TQjBz3/+c573vOfNedxll13GxRdfzM0338zRRx+9GEtN2Iat1YsIRbGmXhNM+pNI2Cn+etK1qXverHbgruNx9jsu4xc//R2qpnL2ZWfsUlzIKLYAt+suVpviYtLR+Pa9KwhEiv37m7x6/7GOtwQiIfFtn2yxd7HrsbgIKSyguBAIqq5L1jBZVSgsG3EhZOyd0gxtCkaR1ZlVS78dIj0QG4EUin4IinZQIi4SElpkyTcOzzzzTN761rdy9NFHc+yxx3L11VfTbDY57bTTADj11FNZtWoVn//85wG49NJLueCCC7jxxhvZa6+9Zno1crkcuVzyxV8UZG2qerHVSbMeNqiHDXLa9h4VThAy5tiYmrbT1kij1uRjb7+Ue27/E4ap8/Er389RLzx0/rcW0Kg0sasOZtYg1YY6cIIUN9yzgoavMZLzeNOhmzqelFiI2PXpysW0uFgIH4VICmqeS9GyWJHLz9lsu9i4wscNHTJahj6rTF5b5MTTHYirFpNTo6drpqoWi59nkpCwO7PkAuPEE09kdHSUCy64gE2bNnHEEUdw8803zzR+rl27drv952uvvRbf93n961+/3XkuvPBCPvWpTy3m0p+xxL4X4Uz1YsZUS9k+U0QIGLObhGFEcYek1MnxKh855bM89MCfSWctPvmFD3Ho0Qfs4o2hWbNpVm2MjNF20+N/PdbHlqZJ3gw59YgNmFpnE9rTseuZfO9i16fFRTGfoVSwFuTmGghBw/foszKM5HPzmJwtHqGIaIY2Wkpl0BqkrJfQdrSVX2Sk9CAag1QJRd8fUkOJaVZCQgcsuQ/GYpP4YHSHFNUp34vsjMCY8CbY5G6moBe3i2OfdFzW1arkTXM74bFl4zgfPPlinnx0PYVynk9+4cMceNjeu3hjaNYc6hMNdFNrywJ8GjdU+PEfh3nRXhOsLPhtHw/bxK5nTfIDvUlGDUOB54UU82mKCyQuvDDECZePx4WQ4EQ2oYwoannKZh9ptTfTN50ipQAxCYSgrkLR9kRR0rs8LiEhYXaWvIKRsHsR914EM+LCj3wmgwqmam4nLrwwnDUp9ak/b+SDJ1/MpnWjDIz08fGrP8R+B+16dNhpuDQmG+hGe/ki22Jpkjcf1tr482xICW6zt7HrYSDw/JBiIU2pkGYhxjic0CeIBCvyefrTmSUfQ/VEgBPapLUMw8bQkm+HAEjpQDQZVy20vSA1mOSIJCR0SSIwElpGiupOvReVoIIXeRT1ra9JCWNNB2+HpNRH//QkH3rLxUyMVlm55zDnXfVB9tx712mTTtOjNhHni2h6ex/Z+zdnqTg6L9yz0tWNdSZ23dIp9ih2fTHERSPwkUKyMl+gbC3tGGooBHZkk1JSDFoDlPTyTg3Bi01ctZgABGh7TwWULW0lJSHh6UIiMBJaJq5e+ChK7B/hRA6VqbHUbW/eNc9j0t0+KfWBux7mzFM/R73W5FkH7MHZV7yfkZX9u5y88OyA+kQDRUm1lS8CMOFo/OAPw4QiRSkdcuhwo63jt1vHTOx6DrUHsetBIPD9kGIxTSm/EOJCUvM8NDXFymJh1gmexULKeIQ5FAEFvUDZLM2aUbP465quWvRNVS36k6pFQkIPSQRGQktIUdvO9yIeS60QiQhjG9+LQESMNpuo6tak1Nt/dR/nvvMyHNvj2c/Zj49edgbFUh51F82RvhtQG68jhWgrX2SavnTIy/cd5/HJNM8e6lxc9Dp23fcFQRBSKqYpLoC4kEhqrou1DDwufBFghzaWZjFkrqSgL4ftkGjK5lsBbZ+pqoWxy+MSEhLaIxEYCS0RT45srV40I5uaXye7ramWhHHbwQ6DuBwP3HrzbVzw/qsJ/JBjjz+MD3/u3aiahrGLKkDohdTH64iovXwRiBsIp/tBnr9HleetqXa8PeK5PqmU2rPY9VhcRJRLGQo5i16LiwhB3fXIGSYrcjmsNreUerYOKbBDGwWFAXOAslFCTy2934YUTRDVuFqh742Smj3DKCEhoXsSgZGwS3asXggpmPQmURTQtjFBqgceE/bWrZGf/uCXfO5jXyaKBC9+5XF86KLTcYOI9C5u1JEfUR1vEATtJaMC/HFLllufKHPaczaQ1gVAx+JiOna90J/rSey670cEgaCvlCa/EOJiyuOibFkML5HHhZTgCJcg8snrefqM8k7OrktBXLWYAFKg7TuVH5JULRISFpJEYCTskh17L+pBnXrYJK9vzWEIhWC06QAKeirF9795E1de+A0AXv2GF/O+T76VasMlbRnz9l1EoaA60cB3fdJZs2VxIGUcXnbzI/1IFH6ztsQJ+0x0+BtvH7tuZru/ES20uJj2uOi3sgznc2g9MP5qF1+E2GETS7VYmV5BXs8viBNpu0jRAFkHZbpqUd71QQkJCV2TCIyEeYmrF5tmqheBCJkIJjFUHXWbu/+E49DwPYqmyTeu+QFfu+J7ALzx7a/i3ee8mfFJG0PX5+27iCJBfbyBZ3tT4qK1m2Qk4D8fGuSO9fEky7Grqrxk787FRa9j130/IggXTlxMe1wMLZHHRSQldthEAQbMfkp6CUNd+uqAlOFUr4UO6v4o2koUZem3aRISnikkAiNhXnasXtSCKk7oUNhmLLXpB4w1bdKaxhc/823+5Wv/D4B3fOSNnPr+v2dsMk4qNYy573wikjQmmjgNt63wMjdM8S/3jfDoRAYFySv2G+cFe3Q+kjodu57v603suu9FBJGgr5Qhn906VdMr7CX0uJASXOHiRR55LUef2U9GTS+LSQwp6nHVIjWMou2Fss1odUJCwuKQCIyEOZGivl3vhRd5TPoVLM2aaaIMhWTUbhJGEf/4yW/wn9/9HwA+fOHbeMPbX8lEpYnvh2Qycz/RSiHjfJGag9lG5WLS0bjhnhVsaZroKcEbD9nck9j1fF+uJ7HrnhcSRZL+UoZctvfeCo3ABylZnS/EfiOLeF8PREgztDFVgxXpFRT1wvLYDpHBVNXCBO1AFHUlipJc5hISloLkm5cwJ3H1wkNR+pBSMhlUCERAUdtqsV5xHcbrTa4556v8z0/iuPWP/8MZvPoNL6Fat2k0fdKWPueTtRTQrGybL9LaXfKpqsl37o2Dy/JGyClHbGRVwev4dw0jge/45Eq9iV2fFhd95Qy5TK/FhaTqeehqipX5xfW4EFLSDGMR12eW6dPLy2I7BKa282Rzm6pFEgWQkLCUJAIjYVbi6sUmUOLSshM51PzqdhMBThCybnySz77vGm7/5b1ousrFX/wIL37lcdiuT7XmYuja3H0XEuyaTaNqY1g6WovNAw9szvKDPwwTiBQjOY9TjthIyQo7/l1nYtdLvYld97wQIST95QzZHosLMTWGamk6K/N5MovoceFGLm7kkdOy9Jl9ZNXs8tgOkQGIMSCDoh8MqREUZXmkxCYkPJNJBEbCrMhoE+ChpPpmTLUkzFg7CwF/3riZs9/6ef541yNYaZNLvvYxjvuLwwnCiErVQUnN33dh1x0alWbL4WVSwq+eLPGzRwcA2L+/yZsO3dRxKmp8zt7GrrtegJTQV8qSbdO/Y1dMe1zkTZORXA5LW5yvbzi1HWKoBiPWMEW9iJpaHjdwKSogHUitmKpa5JZ6SQkJCVMkAiNhJ6RoQLRxpnpRDxvUghpZbevF+/F1m3jPGz/F439aS66Q4Yp/+jiHHX0AQgoqVRs/COa9wToNl/pkA1VrPbzs19uIi+eurvCq/ce6mpiYTkbtVez6tLjoL+XIpHu7bRB7XHiULZORfB59EW7wQkrsyEZKScko0WeUMdWlsxzfFin9uNdCyaLoz463RZKqRULCsiIRGAk7EfdeuCipPiIRMelXUFNbrb8ff3Ijb/m781j3542UB4pc853z2e/gvQBJreHSdLzY72KO+7XX9KmPN0ilVHSj9Y/gc1bUuX19geetqfL8Pard/Y7bxK7n+nNdJ6M6bgBAfzlHxuqtuPCjCDvw6bcyi+Zx4UYebuSS1bL0GX3ktOWyHSJBVkB6oK5EUfdCSWWXelkJCQmzkAiMhO3YWr0oAVAL6zTDJgU9bph77JF1nPh357Bp/RjDK/v5wo0XsMezVgJguwHVmotlzO134TsBtYk4F6SV8DI3TGFpsSNnzoz44HOfQlc73xKB+CbVy9j1hRQXO3pcpBZ4UENIaIYNVCW1/LZDpAfROKRyKPohkBpa8lyThISEuUkERsJ2xL0XcfXCj3wm/QlM1SSlKPzx/sd402vOY2KsyppnreAL//xJRlYNAuCHIZVKk1QKtDmizAMvpDZeR4jWLMDXTk2KvGLfcY5cWQfogbgA1/F7FrvuuAEKCv3lLOkeiws79AkX0eMiFIJG2CCrZRi0BpZF4ilMVy0mQQagrUFR90RJLY+1JSQkzE0i/xNmiKsXG2Z6L6phFTfysFIWd972B173qo8xMVZln4P35LofXDwjLoQQVCs2QSjmzBkJ/YjaeJ0ojDCt1vbxHx7L0vQ1bltXRHSnK2boZey64wSkFIWBvt6Li4bvIwWsyhcYyCy8uPBEQDOsUzKKrEyvWEbiwo0rapgo+iEo2oGJuEhI2E1IKhgJM8hoE0gXRe3DiVwqfpW0luZXv/g9p5/8aRzb4+Aj9+Pqb32cfHG64VNSq7s0XX/O7YEoENQm6gReiJVpPV/kpc+awNIEx6yq0ou2g17Grtt2gKrGlQvL7KW4iD0uDDXFinyevLHwTZXN0EHIiAFrkH6jb5kYZompcLIItD1RtD1RlN6blSUkJCwcS38lSVgWzPRepIqxqZY/SSRCfv7/budtJ16IY3scdfyhXPPPn9xGXEDT9qk2XCxTn3UKI4oE9Yk6nu1jZeZu/AQIBfzyiRJBFP9QSoEX7lnpagx1Gs/pXey6bQdoCyAuBIKK62KpGqvzpQUXF0JCLWiQUhRWpEcYMPqXibhwpjxYMij6YSja/om4SEjYDUkqGAnAdPXCQVHLNMMm9aDOT/71N5zzgS8ghOBFrzyOs684g1w2PXOMH4RUajaqCpq2840pigSN8QZOw9tlvogTpLjxvhEen8ywuWHyxkM29+x3870QkBT6uotdlxJsJ0CfEhdmD8VF7HHhkjetRfG4CEVEI2yQ07IMWoOk1fSuD1pgtlYtBGh7o2h7oCjLYyw2ISGhfRKBkbBD9UIw4U1yw1f+H5//xDcB+Ls3vZR3XHgK5ezWve9ICCarNmEoyM4SZy4jSWOyiV13sXYhLiZsjRvuWcmobWCogsNH6j373XoVuy5l3HOhayn6S5neiosZj4s0I/ncgntceMLHCV1KRolBcwA9tfQJo1LYICqQ6kPR9o7/dxmMxSYkJHROIjASkNHmmepF1a9y2ee/yZcu+1cA3nbGa3jzR16LqRsoMwEdcd+FM0ffhRTE4WVVBzNrkJrnRvFkxeI7967ADlSKZsApR2xkRd7vye8VBiFRFFHsMnZ9pnKhqfSX0j0VF7HHhcdAJstQduE9LpqhjZCCYWuQPqO85GOeUkZT4WQp0PZB0dagKMsj2yQhIaE7EoHxDEeKZjw5kirihz5nfuwf+Oa1/w7ARz9xKq95x6uwwwBT3fpU3bQ9anWXtDVL34WE5nS+SMZAnce44d5NOX70xyFCkWJl3uWUIzZSMKPufycp8RwfFMiXu4td305clDOYPcz+8MIAJwwZyuYZzGQX1ONCSEkjbKCndIatoRlfk6VEiiaIKqQGUPS9UFJ9S72khISEHpIIjGc4ce+FTSTzvP1dH+fG79wEwEWXvZe/ecsJbGzUKVpbb9CeH1CpOWiasrNBlYRmzaEx2Zw3vExKuPXPZf778X4ADhps8MZDNmN06XEB8ZZIGISYGZNcMYOR7rLnwg4wDJW+cgZT7524sEOfaMrjYiDdfXrrfEz3W+S1HAPWIGl1aRsmt1YtVND2Q9FWJVWLhISnIYnAeAYTVy824gUZTjr1o/zbv/8PqVSKK798Jq963UtYW62Q0QxSU8NGkRBUag5hJMnO0izpNFwakw10Y+58kVDAv/1piLs3xk/QL9hjklfsN971GGokBJ7to+kahf48mZyF0kW2iBRgOz6GodFfzmLovfuq1H2PFAor8wVK6YW92bvCxwtdykaZAXNgJqxuqZCiAbIGqcGpcLLykq4nISFh4UgExjMYGW2i2RjndW++lP/679+iGxpf/sbH+atXv4B19QqhjMhq00+WkmrNwXF9srMEeTlNj9pEnZSqos1xM7aDFP987wqeqKRJKZK/PmCU41bXuvsdpMR3A4SUZPJpsoV01x4X0+LCMjXK5SxGzyY6tvW4KJA3FvapvRHaIAXD1hBlo7Sk/RZShlOR6gaoB6BoK1GUpW8uTUhIWDgSgfEMRYomlfGH+Zu//yT/97v7yWQtrvvO+bz0pccw4TjUXJf8NlsjDduj3oj7LnaMNPfsgPpEA0VJzZkvMm7rfOueFYzbBqYqePNhm9iv3+7qdwjDCN8JMNI6+WIGK23S7T10RlxYGn2lHHqLSa+7QiCouR5pTWdlvkCmjZC3tt9LSupBA1PVGbRGyOtLG2EuRX2qajEyVbUoLul6EhISFodEYDxD2bThD7zyVe/n3vsfp1jMce13z+Mvnn80bhCyxW5iahrq1NaI5/lMVm00LbVT34XvBtTG60gh5s0XURSJG6QoWfGkyEiu80mR7Zs4s6SL6a4Dy2DhxEUkBXUv9rhYkcthLqDHRShCGmGTvJZj0BrEWsJ+CymDqV4LE7SDUNSVKEpyyUlIeKaQfNufgTz5xEO87GWv45FH1zE01Md1//pxjjz8YKSEUbtJEEaUrNh4KYoElbqDEJDObP9xCb2Q+ngdEUVYmfkNVoCsbQAAM+ZJREFUkfrSIW99zkYKZki+i0kR3/v/7d15mBT1tT/+d+3V62w9Kw7goLIIOG4gohcXErgqLjcRFUT0uaJG3OAx7oqJFzF8NdFE1KveuPwCwX2JCookGEVEZRMVUBZBkRmYvffuqjq/P6qnmWaGZYZeZuC8nmcemerq6s98HOjTnzrnfAwYMQMOtwZnnvOgu3K2IosQCseh6woK811pCy4My4I/Zve4KPd4klveZ0LEiiJqRFGoFcKnFkHOYb4FWc0AhQCxFIJ8JATRk7OxMMZygwOMw8yGDRvwi1+ciR9/3IHeleV44Y3fofxIH2RRQlM4gqZIBB6tNVggNPlDCEficO2xOmHGLDTXBxCPdRxcEAH/3FKACk8UA4vtWyG9vNEuj9swLcTCdhJnXrEHDtfBJXGmjLVNcFGU79prgmpnRU0T4Sz0uCACgmYQIOQ838JetagD4ISgDLJviwjdY7t3xlh2cYBxGFm1ahXGjPkldu2qw4D+ffDK23+EUGjCITkRNU3sCgWgyhKkxJuTPxhFIBCFQ1dTchtMw0JLox+xSAwOV8ebl6342YN/bi6CKlmYdurWLve3sJM4YyACnF4HXF4n5IPcBTXl+hYhGI7Boavw5bshddDyvCsiRhyRLPS4MIkQjPuhyhpKtGK45dzkW9hbqjcDFAHE8kSuRW5zPxhjucUBxmFi6dKlOPfcc9Hc3IwTqo/C228/hYgnBItkSBBQGwwhYpjITyR2RqIxNLWEoMgSpDYrBaZpwV8fQGQ/+4scX+7H1zvdOLYk2OXgom0SpyvPBd2pprVfhGUSQolupEVpDC6y1eNid76FByV6MTQpN/t2EMXsVQvBDUE51r4t0g02TWOM5RYHGIeB999/HxdddBHC4TBOH3kc3nr9/yHulNAUiSBPyUNLLIqGSAhuVQMgwDQsNLeEQRagOnevFlgmIdAQRDgQ6TC4aAzL8GoGJBGQRGBy9Y4D3pq9rZQkzkIXHN70JHG21RpcuHQVhWkMLvzRKERRQIXXmwzWMiFiRhG1YijSiuBTiyBleP+SjtirFk0ARQGpEoLUG4Loyvo4GGPdEwcYh7hXX30VEyZMQDwex9ixZ+CVv02D7CxAXfgn6LIDBpnYGQhCEiXIoggiy867iKbmXZBF9v4iLWForvbBxZZGHXPXlGNomR/j+tdBENCl4KK1E6fDld4kzrZMk5L9PArz3empQAGhJRqBJkko93jhzlCPi2S+BYBSrThn+RZEUcCsB0QPBOVoQCzhVQvGWAoOMA5hf/3rXzFlyhRYloXx43+FF569EaqioDbWhLhlIE9yoiYYQMiIJz9tB4JRBIKpeRdkAcHmEILJ/UVSI4dVP3vwxroSmCTg5xYdcUvodNvvtkmc+cVe6E4tbUmcbe0OLjQU5rvSElzYPS4icCoaKjweONLY9bMtkwiBuB+6rKNEK4ZLzv5qgb1q0QhQHJB7Q5D7QBByv9U7Y6z74QDjEPWnP/0J06dPBwBMmTIFc/58OyTahJClozneAqfshD8eQ0MoBFdip9RINIYmfxiK0ibvgoBQSwiBplC7/UWIgA83F2LJFnuTqsElfvz62J1QOhFcEBGikRiQSOJ0e52Q0pjE2VZrcOF2aSjIc+1zI7YDvmZiq3VvhntcxC0DQSMIr+JFsebLSb4FUQQwGwAxH4IywG73zVuqM8b2ggOMQwwR4f7778fvf/97AMCtt96KP/zhd0B8NQAXGmNNAAABEnYFAwAEqJIE07DQ2BwGCFDbvMGH/GEEmoJQNDmlfDNuCnj92xJ8VWv3NxjVtwGj+zV0ak+R3UmcKtx5TmhpTuJsKxPBhd3jIoIC3YlyjztjPS4iZgRRMwafVoSiHORbEFmA1QDAAuS+EOTeEITcbpjGGOv+OMA4hFiWhWnTpuHPf/4zAGDmzJm48847QcY2wArAb7ngNwJwSS40hEMIxKLI07Vk3kUsZsDp3J07EA5E4G8MQJJTNy8LxkT8bU05tjXbe4pcMGAnTurlP+BxtiZxCgLgKXLD4dHTnsTZVmtw4XHpyM9zpiW4sHtcxDLa48LOtwhAgIhyRxnyFG/W8xyIwoDZCIgFEOS+9tbqvGrBGDsAHGAcIgzDwNVXX40XXngBAPD4449j6tSp9huEtR0WnGiIN0ESJEQNE/WhEJyyAhEiWoJh+IMRO+8i8d4RDcXhrw9AFCUobfbN2BVU8OLqCjSEFeiyiQlDa9CvMHxAYyQC4jEDZtyAnsEkzrYyEVxEjDiihoFStxs+R2Z6XJhkIRAPQJcdiXwLZ/pfZB/sLdUb7G/kKghyJQQhN2WwjLGeiQOMQ0A0GsWll16KN998E5Ik4bnnnsOkSZMAAGTsBKwAmg0NISMEt+zFT4FmmERwyzLCkRiaW8JQFSWZdxEL2/uLAEjZvGxTgwPzvipDxJBQ4IhjcvXPKHbFD2iMqZ04vdBdWrtN09ItGVy4dRR4nRDTEAkE4zFYloUKrxeFuiMjt3Ra8y3ylDwUaz6oUmZ3Xd3T7lWLQrvNt1SU1ddnjB0aOMDo4QKBAC666CJ8+OGHUFUVL7/8Mi644AIAiaQ8azvipKHRaIYmaWiKhOGPRuHVNcQN0867AKCq9ptvPGqgpd4PyzJTNi9b8bMHb64rgUUCeueFcflxO+BSrf2Or20SpyvPCZfHkbEkzlZG3EI0bgJEaQwuCP5oDKIooJc3D3l6Zj7Nh80IYmY8J/kWRJTYnMwC5H6JVYvsBjeMsUMHBxg9WGNjI8455xx89tlncLlceOutt3D22WcnH29dvWgyZETNKFS4UBdqgi7LAAFNLSHE47vzLoyYiZZ6P0zDhNYmuAhEJby7oRgWCRha6sd/DTqwShEjbiAWMbKWxBk3DBiGBUkS4XaqcDpU6Jp80HkLBEJLJAJNzlyPCyIgYAQgCSIqHGXwKt6s5jrYfS3qErkWVRAkX9ZemzF2aOIAo4eqqanBmDFj8NVXX6GgoAALFizA8OHDk4/bqxc/IUIymowW6KIDu4IBGKYJl+5Akz+EYCiazLsw4xZaGvyIRw27B0Wb9za3ZuKSITX4sVnHWVX7rxSxEkmcoiBkNImTCIjHLcTjBgABuiYhz+OErilp2w3V7nERhVNRM9bjwiQL/ngAzkS+hTPb+RZWk72HiNzbviXCFSKMsTTgAKMH2rp1K0aPHo2NGzeirKwMH3zwAYYMGZJyjr164UdjXIBlmQibIpoiUXg0DaFIDC0tEWiqnXdhmhb8DX5EQ7s3LwtEJTRFZRyR2AG1vy+E/r7QPsfVmsRpd+LU4cxzZKwTZyxuwDQtKLIEj1uH06FAUw9+tSLldRI9LvJ0DWVuDzQp/bcrYpaBUI7yLYiMxB4iTt5DhDGWdhxg9DDr16/HL37xC/z000/o27cvPvzwQ/Tr1y/lnNbVi6Alwm/4IUPFjpAfqizBMglNzSEIIqAoor2/SH0A4TablzWEZPx1ZS9ETRHXnfwjipzGfsfVrhNnmpM4yQLihol43AQEAbomw5WnwqGqadtHpK3WHheFuhNlGepxETYjiJsGfJoPRWphdvMtrKC9+6lYZt8S4Z1PGWNpxgFGD7Jy5UqMGTMGdXV1GDhwIBYtWoRevXq1O4+MnSCrGY0xO0+iMRxDzDDhUTQ0NAUQj5twOlWQSfA3BRDyR6C32bzMrZlwKCYEgUC07yChfRKnE5KaxlUE00IsZsK0CIosIs/jgEOXoWkKMpXQETUNhONxFDtdKM5AjwsrkW8hCxLKHWXwKp6s5VvY5af1ACRA6g9B7gVB4H8GGGPpx/+y9BAff/wxzjvvPLS0tODEE0/EwoUL4fO1T8QjigLWTwgYAgJmEJapoDHSAreqwR+IIBiOwaGrAMHevKw5DM2lQhQEENkblKkS4YrqHRAF2melSEoSZ74LmkNJy3s+WYRY3IRhWBBFAboqw+nUoGtKRhtyAUDYiCNm2j0uip2uLm3Yti+GZSFg+OGSXSjWfFnNt9hdfloEQamCIBZk7bUZY4cfDjB6gAULFuBXv/oVwuEw/uM//gP/+Mc/4PV6OzyXzFoYZhPqDRMEEfWhMGRJQixqoNkfga4qkEQBgeYQAs0hqLoKQRCx8PsiqJKFs6oaAQAezdzreCwiREN2yaa3yA09TUmcpmkhGjVBRFAUCfl5Dui6Ak2RkbHykzZae1z08uShQNfT/pIxK46QEUK+kgdfFvMtUjcoq0q0+ubyU8ZYZnGA0c29/PLLmDhxIgzDwLnnnotXXnkFDkfHu1fapYY/IWASwmYEwYiAsBGDQ1JQ3xyAJAGyIiLYbO8vouoKLEHCy2tL8c1O+x78sSVBlLpje7k+EIvFYcUt6C4NrnwnFO3gfoUsk+zcCsOCJAlwOhU4HRo0VU5L180Dk+hxIWWux0XIDMO0TBTrPhSpRRCzlExJFEskcubxBmWMsaziAKMbe/bZZ3HNNdeAiHDppZfixRdfhKLsvSqDzFrEzQbUxw0YpoimSBgOWUFzUxhxw4LLpSLsjyDQGICiSAibKv6/NeXY3qJDEgj/Nah2r8GFYViIRWJQVBmeYs9BJ3G2bYalqjKK8nVougJVlpCN1YpWrT0udFlGmceT9h4XFgFBIwBJtPMtPHIW8y2sFoCCgNQLgnQkBDG75a+MscMbBxjd1COPPIJbb70VAHDttddizpw5kPZRJkkUBYyf0GSYCBsxNIcJAgSEg3GEIjE4dRWRYAwtDX6IkoT6qBMvri5HU0SBUzExcegO9C2IdHDdPZI4vU5IStc+fe+tGZamyVn7RN9Wa48Ll6qh3O1Oe48LwzIRMAJ2voXug1PKzhu8nci5C4CeKD8t4/JTxljWcYDRzRAR7r33XsycORMAcNttt+Ghhx7a/6decyciRh2aYgYiMQHBeASKJaLZH4FDV2BEDbQ0+CEIIrYGvPj7V6WImhJ8zhiuqN6BImf7PUXSkcSZjWZYXWGSheZoBPm6npEeF1ErjogRQr6aj2LNB0XM7KZurXaXn5Ymyk89WXldxhjbEwcY3YhlWbjpppswZ84cAMCsWbNwxx137Pd5RFGQ8SOajDgCRhzNEQMyRDS3hCHJgBk30VzvB1kW1tQX451E2+8j88OYcNwOOJXUSpFkJ05R7HISZ7aaYXWF3eMiiqIM9bgIGmFYZMKnF6NILczK6gyRlSg/FQDpaAjyERCE7AQ1jDHWEQ4wugnDMHDVVVfhb3/7GwRBwJw5c/Cb3/zmwJ5s7kIoXoP6mIFglGBYFmJBA4ZB0GQBzQ1+GIaJJdsrsHSbXZp4fHkLLhy4E217VBEBsWgclmnBkdhOvTNJnNluhtUVUcNA2IijxOVCsdONdFa9tva3UEQZpXr28i3s5N56e/dTpQqCWJjx12SMsf3hAKMbiEQiuPTSS/HWW29BkiS88MILmDhx4gE9lygG09iKBiOOYMxEMGbAiloIR2LQZAnN9QEEQyb+sbkP1tXZlSKjq+pxxpGNKT0eDMPuxKloMjyFHns/kgNM4sxFM6yuCBsxxEwTZW43fGnucdGab+GWXSjWS+CQMr+fh11+2gRQDJD7QJD7QhAys8srY4x1FgcYORYIBHDBBRfgn//8JzRNwyuvvIJx48Yd+AXMnQjEd2BX1F69sOIWAoEoVFlEoCmIaDiG+Rv6YVuzA7Jo4b8G7cRxZYHk01OSOPMPPImTLCAWN3LSDKsr7B4XlJEeF1ErhrARSeRbFEMRM//XiiieKD91tdlHpPsEc4wxxgFGDjU0NOCcc87B8uXL4Xa78fbbb+PMM8884OcTxRCLb0FDLIpA1EIoaiIaiEMUBYSawogEonC6NQw/ohn1IQUTj6tBn/zdlSLxmIF4zIDmVOHyHlgSZ66bYXUeoSUahSSJOCLPC6+W3k/4QSMEiyyU6sUoVAuykl9Clh+gACCW27ufiq6MvyZjjHUWBxg5smPHDvzyl7/E119/jcLCQixYsADDhg3r3EWsXfDHfkZN2II/ZMKImDBNE0YoBn9LFB6vvb9IdXkA/X0hOBLJnKZFiIVjEGUR3iIPHG4dorT34KB7NMPqPAKhORKBQ1ZQ7vHApaYv6dEiSuRbKCjTS+FRMl+tYZef1gFQAHkABKkCgpC7ShzGGNsXDjBy4IcffsDo0aOxadMmlJeXY9GiRTj22GM7dQ2iGCLRjdgZDcMfERGLmoiGY7Cicfz7Owc+r63EtSdvT7b8dihWuyROV54T8j6SOLtLM6wDYcGCYRJMMmFYBNOyQCC4VR0Vbjf0NPa4aM238MhuFOvF0LOSb9G6j4gPgtIPgpiX8ddkjLGDwQFGlq1btw6/+MUvsH37dhx55JH48MMPUVVV1enrkLkTzbGfUBMiBEJxxMNxWLE4mhsj+HJnJRojKlbu8GBU3yYAgGGYiIXjUPXWJE4dHa3md7dmWO3GRxYMy4JJFkyTYJK9KiMIAmRRhCwI8KgKdFmGIkpwaQqUNG6DHrFiiBoRFKgF8Gm+jOdbEFGi/JQAuR8EuZL3EWGM9QgcYGTRl19+ibFjx6K+vh6DBg3CokWLUFFR0enrEMUQin6P7aEgmoMijJAJIxxDxB+ByyXjiuN3YP0uF0b2brKTOMMxQADc+U4485ztSkZ3N8OyVzty3wyLYBLBMC0YiYCCyN56XhIESJIIGSLcugJNlqCIiS9ZgCxIad8BtVXACAFkoVQvQYGan/F8C7v8tA4QC+ymWVL73XMZY6y74gAjSz766COMGzcOfr8fJ598MhYsWICioqIuXYvMWtSHt2JHkBANmdhZb2BrrYqhFQZkWYJPjuO0Pk12EmfUgObS4PY6oe6RxNm+GZaW1WZYBIJpEQzLvq1hWLt3cJUEEbIkQJdkaLoMTZKgSCJkofW/Ytbu0lhE8McD0CQVxXoZPIo7469JVhNAEUDubSdyCpm/DcMYY+nEAUYWvPvuu/j1r3+NSCSCM844A2+//TY8nq4lBRLF4I+sx4+BAFqaJWz7EZi7uhcCMRke1884uihsJ3GGYhAVEV5fahJnLpphWbAQT+RHpN7WAGRBgiQCTlWGQ3ZAFSXIkmjf7hAlyAexoVo6GJaBgBGER3ajRC+BJmW2zwSRkSg/dUJQBgNiSc47nzLGWFdwgJFh8+fPx6RJk2AYBsaNG4eXXnppr9utHwjD2IGfA5uxvUnAVxtlvPZVKeKWhGJXDIWOOKKRGCyT4PDocHkdySTObDTDMsmCYVowYf/XStzWEAUBkmivOrg0GboiQxZFKIlgQhEkdMdilIgVRdSIolArhE8tgpzpfAsrkNhHpDyxj0jmV0oYYyxTOMDIoKeffhrXXXcdiAgTJkzA888/v8/t1veHKIaG4Nf4odGPxat9WLTeB4KAfoUhXDxwOyQjCkmR4Sl0QnfaS+rRaLqbYVGiSsOEQdRhfoQiyvAoEjRZTt7WUOXs3tY4GERA0AwChKzkW9jlp/UAJEAaCEGugCDwX03GWM/G/4plyOzZs3H77bcDAK677jrMmTMH4kF+TI/Gt2Nzw/f4vyWl+HKrvd/EiRXNGNP7J0gAnIkkTghAOBI/qGZYFiyYll2Sae6RH2HfvhCgyzJ0WYYmyZAlAYogQZZyf1vjYJhECMb9UGUNJVox3HJmVxFSy0+PhCAWZPT1GGMsWzjASDMiwt13341Zs2YBAO68807MnDnzoNs4E8Xw3c5VmPFWCb6rtXsgjD5yJ4aV7ITu1OD0OiBIIsJRo1PNsMw2/SP2VvbpUGVobfIjFFGELMpp3SisO4hbBoJGEB7ZgxK9OKP5FvY+Io0AGYBcBUHuzeWnjLFDSrd4i5gzZw769u0LXdcxfPhwfP755/s8/5VXXsGAAQOg6zqGDBmC9957L0sj3TfLsjB16tRkcPHQQw/hwQcfTMseERtqv8dv/mbhu9o8yKKFXx29DSOPaIC7wAXNo8MkQBRFFOU7UVrsha/ADaeuJoILO3CIGgaC8RiaoxE0RcJoioQRisZgkAkJIryajnKPB73z8lGVX4CqggL0KyxCZV4+Slwu5Dt0uFUVmnzoBRcRM4qQGUaRVoQKR3mGg4sYYO4AoEFQBkOQ+3FwwRg75OR8BeOll17C9OnT8dRTT2H48OF49NFHMWbMGGzYsAElJSXtzv/0009x2WWXYdasWTjvvPMwb948XHjhhVi5ciUGDx6cg5/AFo/HcdVVV2Hu3LkQBAFPPvkkrr322rRce82PuzD5uU1oCjnglOMYP2AbevsMKE4diq7AqdvNsFRVBiHRaTIeh2mZSKRHQBIEyJKYWvaZqNTIdtlnd5LMtwBQqpWgQM3LbL6F1QxQGJArIUh9IIjOjL0WY4zlkkCtGXo5Mnz4cJx88sl4/PHHAdirAJWVlbjxxhtxxx13tDv/kksuQTAYxDvvvJM8dsopp6C6uhpPPfXUfl+vpaUFeXl5aG5uhtfrTcvPEIlEMH78ePzjH/+ALMt48cUXcdlll6Xl2h98U4Ob/r4CEQMo0kMYP+gnlBVLcLp16A4VsiKmbKuuiHbZpyrL0CUFqtRaqSH2+PyIdDOJEIj7ocs6SrRiuOTMbRpmJ3LuAqBDUKoAsYzLTxljh7ScrmDEYjGsWLECd955Z/KYKIoYPXo0li1b1uFzli1bhunTp6ccGzNmDN58880Oz49Go4hGo8nvW1paDn7gbRARzj//fCxatAi6ruPVV1/Fueeem7brf1fbgIgB9C2ox/mDa1Fa7ILbpcOpqlBlu1KjtS22LIlQJKnbln12J635Fl7Fi2LNl9lbIlYwUX5amig/zfzGaIwxlms5DTDq6upgmiZKS0tTjpeWlmL9+vUdPqempqbD82tqajo8f9asWfjd736XngF3QBAEXH755Vi+fDnefvttjBo1Kq3XP6e/ip9qf8SZA5vhK3JCljVA0CBICiRBgCAIIDIhwoQpCCBLQFwQIZIAEQIEQYRo/ynxZyFjrbR7iogZQdSMwacVoUgtgpTGvUraIrIS5aciIB0DQe4FQUjfjq6MMdad5TwHI9PuvPPOlBWPlpYWVFZWpvU1rrjiCpxzzjnw+dK/V0RVr6Pw4CW9QdQC02wCmbWwrACIorAEGSZ0EKkwQTBhwLQse0dRMmDBBJGFOJmwYPersJtfJe6KCfYfBUGAKAgQILYJQpJhCURBhHgIRCVEQMAIQBRElDvKkKd4M3abwt5HpB4QCyEoVRDEwoy8DmOMdVc5DTB8Ph8kSUJtbW3K8draWpSVlXX4nLKysk6dr2kaNC2z7Z0BZCS4aCWKKgAfJMkHoiqAggC1gMx6wGoCEAYgAYITEBwQBPsTOZHdTdOCBYssWCAQ2TuRUusxsmCQCYtMGJYBExZM2N+biecTCFaifBUAINhv1q2BR2sQIiSCFDswEROPZ2xaOsUkC4F4ALrsSORbZCa5cnf5aRyQ+0KQ+0AQMv/7xxhj3U1OAwxVVXHiiSdi8eLFuPDCCwHYSZ6LFy/GDTfc0OFzRowYgcWLF+OWW25JHlu0aBFGjBiRhRHnniCIgOAB4IEg9QJZIYD8IKsBsBoBa5e9PiE4AMEJSVQgoXO3AFoDE5NMO7hAIlBJfG+SBSILBhkwyIRpmXZQAssOVBKP24GJvVqSWCwBBNghSGswIuxeLWldObGPp2/OWvMt8pQ8FGs+qFJmSkKJ4ol9RFwQlP6JfUS6SYTFGGNZlvNbJNOnT8fkyZNx0kknYdiwYXj00UcRDAZx1VVXAbBvP/Tq1SvZW+Lmm2/GqFGj8Mgjj+Dcc8/F/Pnz8eWXX+Lpp5/O5Y+RM3aZoxOCVGr3V7D89k6cVj1gNdqbZ4m6/aZ3gJ+kBUGEJKBLgYmZWBUhEEyYidsyrSsprasi9i2c1hUTIoIB0w5G2qya7I5KAAitAUjilg0EIJFfIu4jvyRsRhAz4/BpPhSphZnLt7D8AAUS+4gcCUHMXEUKY4z1BDkPMC655BLs2rUL9913H2pqalBdXY2FCxcmEzm3bduW0mL71FNPxbx583DPPffgrrvuwtFHH40333wzpz0wugtBUAGpCIJUBKK+APntgMPcZf+XGgBBBgRX4lZKej9dC0Kin0YnUCKYaF0Vab2dYx+zAxST7Ns2pmXaHUepNTBJ3N7ZM78kcQsHIKiiggpHGbyKNyOrCXb5aR0ABZAHQJAqkreoGGPscJbzPhjZlok+GN2dnRcQtG+lpORtiIm8DWePe1NMroy05pUgNUBpXTXRJQ3OTOVbWCF7LqXiRPlpXkZehzHGeqKcr2CwzBMEARDcANwQpHIQRRK3UhoTt1J2JW5JOBLBRvdvWy0IAiShszdx0sMuP20AQIB8FAS5kstPGWNsDxxgHIYEQQckHYJUbCcmkh9ktthL/VazfUxQAdEFQOdExTbs8tM6QCyw9xCRinI9JMYY65Y4wDjMCYICCIUQxEIQ9bYTFak1b6MFQCMIbfM2Dt8WoWQ1ARQB5D4Q5L52oMYYY6xDHGCwJLsE1gvA26YEtiVxK6URsHa2KYF1QRAOj1+f1PLTwYny08M30GKMsQNxeLxDsC7ZXQJb1kEJbEOXSmB7GrICiX1EyhOJnO5cD4kxxnoEDjDYAcl1CWy22eWnDQAkQBoIQa44bFZsGGMsHfhfTNZpgiABQj4g5gPSEe1LYKkZ1KNLYMOA2QiIPgjKkRDEglwPiTHGehwOMNhB6bgEtiVxK6UOsOpAsNrkbXTfck4iSqxamIBcBUHu3SNKdhljrDviAIOl1e4S2BIQHblHCWxTogRWA0QnulMJLFEsUX6aB0GuslcvusnYGGOsJ+IAg2VMTymBJasZoDAgVybKTx05GQdjjB1KOMBgWdG2BBZihf2GTi2JXWCbAKsWBCGrJbBERmIfER2CMggQy7j8lDHG0oQDDJZ1dt7GniWwLfZKglWXlRJYsoKJ8tPSRPmpJ+2vwRhjhzMOMFjO2SWwPgiSD0R9AAq0ydtoLYFVElUpB1cCa+8jUg9ABKRjIMhHcPkpY4xlAP/LyroVQZABIR+CmA+iyjYlsPY+KQdTAksUAcwGQCyEoFRBEAsz+JMwxtjhjQMM1m2lqwTW3q6+EaA4IB+ZKD89NDuPMsZYd8EBBusx9l4Cu3OPElgXAA2CIOyxj0j/xD4iXH7KGGOZxgEG65E6LIG1WkBWXaIEtgEECYAJSL0gSH0hiK5cD5sxxg4bHGCwHi9ZAit6AeoFUMhe3bAaIYjeRPlpz2pXzhhjPR0HGOyQYudtuAC4IEhluR4OY4wdtrirEGOMMcbSjgMMxhhjjKUdBxiMMcYYSzsOMBhjjDGWdhxgMMYYYyztOMBgjDHGWNpxgMEYY4yxtOMAgzHGGGNpxwEGY4wxxtKOAwzGGGOMpR0HGIwxxhhLOw4wGGOMMZZ2HGAwxhhjLO04wGCMMcZY2nGAwRhjjLG04wCDMcYYY2nHAQZjjDHG0o4DDMYYY4ylnZzrAWQbEQEAWlpacjwSxhhjrGfyeDwQBGGf5xx2AYbf7wcAVFZW5ngkjDHGWM/U3NwMr9e7z3MEav1If5iwLAs///zzAUVfB6qlpQWVlZX48ccf9zvh7MDwnKYfz2l68XymH89pemVyPnkFowOiKOKII47IyLW9Xi//pUgzntP04zlNL57P9OM5Ta9czScneTLGGGMs7TjAYIwxxljacYCRBpqmYcaMGdA0LddDOWTwnKYfz2l68XymH89peuV6Pg+7JE/GGGOMZR6vYDDGGGMs7TjAYIwxxljacYDBGGOMsbTjAIMxxhhjaccBxgGaM2cO+vbtC13XMXz4cHz++ef7PP+VV17BgAEDoOs6hgwZgvfeey9LI+05OjOnzzzzDE4//XQUFBSgoKAAo0eP3u//g8NNZ39HW82fPx+CIODCCy/M7AB7oM7OaVNTE6ZOnYry8nJomoZjjjmG/+630dn5fPTRR9G/f384HA5UVlZi2rRpiEQiWRpt9/fvf/8b48aNQ0VFBQRBwJtvvrnf5yxZsgQnnHACNE3DUUcdheeffz5zAyS2X/PnzydVVemvf/0rffPNNzRlyhTKz8+n2traDs9funQpSZJEs2fPpm+//ZbuueceUhSF1q5dm+WRd1+dndMJEybQnDlzaNWqVbRu3Tq68sorKS8vj3766acsj7x76ux8ttqyZQv16tWLTj/9dLrggguyM9georNzGo1G6aSTTqJzzjmHPvnkE9qyZQstWbKEVq9eneWRd0+dnc+5c+eSpmk0d+5c2rJlC73//vtUXl5O06ZNy/LIu6/33nuP7r77bnr99dcJAL3xxhv7PH/z5s3kdDpp+vTp9O2339Jf/vIXkiSJFi5cmJHxcYBxAIYNG0ZTp05Nfm+aJlVUVNCsWbM6PH/8+PF07rnnphwbPnw4XXvttRkdZ0/S2Tndk2EY5PF46IUXXsjUEHuUrsynYRh06qmn0rPPPkuTJ0/mAGMPnZ3TJ598kqqqqigWi2VriD1KZ+dz6tSpdNZZZ6Ucmz59Oo0cOTKj4+ypDiTAuO222+jYY49NOXbJJZfQmDFjMjImvkWyH7FYDCtWrMDo0aOTx0RRxOjRo7Fs2bIOn7Ns2bKU8wFgzJgxez3/cNOVOd1TKBRCPB5HYWFhpobZY3R1Pn//+9+jpKQE//3f/52NYfYoXZnTt99+GyNGjMDUqVNRWlqKwYMH48EHH4RpmtkadrfVlfk89dRTsWLFiuRtlM2bN+O9997DOeeck5UxH4qy/d502G121ll1dXUwTROlpaUpx0tLS7F+/foOn1NTU9Ph+TU1NRkbZ0/SlTnd0+23346Kiop2f1kOR12Zz08++QT/93//h9WrV2dhhD1PV+Z08+bN+Oc//4mJEyfivffew8aNG3H99dcjHo9jxowZ2Rh2t9WV+ZwwYQLq6upw2mmngYhgGAauu+463HXXXdkY8iFpb+9NLS0tCIfDcDgcaX09XsFgPc5DDz2E+fPn44033oCu67keTo/j9/sxadIkPPPMM/D5fLkeziHDsiyUlJTg6aefxoknnohLLrkEd999N5566qlcD61HWrJkCR588EE88cQTWLlyJV5//XW8++67eOCBB3I9NHaAeAVjP3w+HyRJQm1tbcrx2tpalJWVdficsrKyTp1/uOnKnLZ6+OGH8dBDD+HDDz/E0KFDMznMHqOz87lp0yb88MMPGDduXPKYZVkAAFmWsWHDBvTr1y+zg+7muvI7Wl5eDkVRIElS8tjAgQNRU1ODWCwGVVUzOuburCvzee+992LSpEm4+uqrAQBDhgxBMBjENddcg7vvvhuiyJ+PO2tv701erzftqxcAr2Dsl6qqOPHEE7F48eLkMcuysHjxYowYMaLD54wYMSLlfABYtGjRXs8/3HRlTgFg9uzZeOCBB7Bw4UKcdNJJ2Rhqj9DZ+RwwYADWrl2L1atXJ7/OP/98nHnmmVi9ejUqKyuzOfxuqSu/oyNHjsTGjRuTwRoAfPfddygvLz+sgwuga/MZCoXaBRGtwRvxFlpdkvX3poykjh5i5s+fT5qm0fPPP0/ffvstXXPNNZSfn081NTVERDRp0iS64447kucvXbqUZFmmhx9+mNatW0czZszgMtU9dHZOH3roIVJVlV599VXasWNH8svv9+fqR+hWOjufe+IqkvY6O6fbtm0jj8dDN9xwA23YsIHeeecdKikpof/5n//J1Y/QrXR2PmfMmEEej4f+/ve/0+bNm+mDDz6gfv360fjx43P1I3Q7fr+fVq1aRatWrSIA9Mc//pFWrVpFW7duJSKiO+64gyZNmpQ8v7VM9be//S2tW7eO5syZw2Wq3cFf/vIX6t27N6mqSsOGDaPPPvss+dioUaNo8uTJKee//PLLdMwxx5CqqnTsscfSu+++m+URd3+dmdM+ffoQgHZfM2bMyP7Au6nO/o62xQFGxzo7p59++ikNHz6cNE2jqqoqmjlzJhmGkeVRd1+dmc94PE73338/9evXj3Rdp8rKSrr++uupsbEx+wPvpv71r391+O9i6zxOnjyZRo0a1e451dXVpKoqVVVV0XPPPZex8fF27YwxxhhLO87BYIwxxljacYDBGGOMsbTjAIMxxhhjaccBBmOMMcbSjgMMxhhjjKUdBxiMMcYYSzsOMBhjjDGWdhxgMMYYYyztOMBgjOXMGWecgVtuuSXXw2CMZQAHGIx1E1deeSUEQWj3NXbs2FwPLUW2g4JYLIbZs2fjuOOOg9PphM/nw8iRI/Hcc88hHo9nbRyMsc7h7doZ60bGjh2L5557LuWYpmk5Gk3uxWIxjBkzBmvWrMEDDzyAkSNHwuv14rPPPsPDDz+M448/HtXV1Tkb2+G+Sypj+8IrGIx1I5qmoaysLOWroKAAALBkyRKoqoqPP/44ef7s2bNRUlKC2tpaAPbqwg033IAbbrgBeXl58Pl8uPfee1O2t45Go7j11lvRq1cvuFwuDB8+HEuWLEkZx9KlS3HGGWfA6XSioKAAY8aMQWNjI6688kp89NFHeOyxx5IrLD/88AMA4Ouvv8Z//ud/wu12o7S0FJMmTUJdXV3ymsFgEFdccQXcbjfKy8vxyCOP7Hc+Hn30Ufz73//G4sWLMXXqVFRXV6OqqgoTJkzA8uXLcfTRRyd/pptuugklJSXQdR2nnXYavvjiCwD2tuBHHHEEnnzyyZRrr1q1CqIoYuvWrQCApqYmXH311SguLobX68VZZ52FNWvWJM+///77UV1djWeffRZHHnkkdF0HACxcuBCnnXYa8vPzUVRUhPPOOw+bNm1Kea1PP/0U1dXV0HUdJ510Et58800IgoDVq1cnz9nf/DHW03CAwVgP0XprYtKkSWhubsaqVatw77334tlnn0VpaWnyvBdeeAGyLOPzzz/HY489hj/+8Y949tlnk4/fcMMNWLZsGebPn4+vvvoKF198McaOHYvvv/8eALB69WqcffbZGDRoEJYtW4ZPPvkE48aNg2maeOyxxzBixAhMmTIFO3bswI4dO1BZWYmmpiacddZZOP744/Hll19i4cKFqK2txfjx45Ov+9vf/hYfffQR3nrrLXzwwQdYsmQJVq5cuc+fee7cuRg9ejSOP/74do8pigKXywUAuO222/Daa6/hhRdewMqVK3HUUUdhzJgxaGhogCiKuOyyyzBv3rx21x45ciT69OkDALj44ouxc+dOLFiwACtWrMAJJ5yAs88+Gw0NDcnnbNy4Ea+99hpef/31ZHAQDAYxffp0fPnll1i8eDFEUcRFF10Ey7IAAC0tLRg3bhyGDBmClStX4oEHHsDtt9+eMpYDmT/GepyM7dPKGOuUyZMnkyRJ5HK5Ur5mzpyZPCcajVJ1dTWNHz+eBg0aRFOmTEm5xqhRo2jgwIFkWVby2O23304DBw4kIqKtW7eSJEm0ffv2lOedffbZdOeddxIR0WWXXUYjR47c6zhHjRpFN998c8qxBx54gH75y1+mHPvxxx8JAG3YsIH8fj+pqkovv/xy8vH6+npyOBztrtWWw+Ggm266aa+PExEFAgFSFIXmzp2bPBaLxaiiooJmz55NRESrVq0iQRBo69atRERkmib16tWLnnzySSIi+vjjj8nr9VIkEkm5dr9+/eh///d/iYhoxowZpCgK7dy5c5/j2bVrFwGgtWvXEhHRk08+SUVFRRQOh5PnPPPMMwSAVq1aRUT7nz/GeiLOwWCsGznzzDPbLeUXFhYm/6yqKubOnYuhQ4eiT58++NOf/tTuGqeccgoEQUh+P2LECDzyyCMwTRNr166FaZo45phjUp4TjUZRVFQEwF7BuPjiizs17jVr1uBf//oX3G53u8c2bdqEcDiMWCyG4cOHp/xc/fv33+d1qc2tnb3ZtGkT4vE4Ro4cmTymKAqGDRuGdevWAQCqq6sxcOBAzJs3D3fccQc++ugj7Ny5M/lzrlmzBoFAIDkHrcLhcMrtjj59+qC4uDjlnO+//x733Xcfli9fjrq6uuTKxbZt2zB48GBs2LABQ4cOTd5SAYBhw4alXGN/87fn/y/GegIOMBjrRlwuF4466qh9nvPpp58CABoaGtDQ0JC8TXAgAoEAJEnCihUrIElSymOtb24Oh6OTo7avO27cOPzhD39o91h5eTk2btzY6WsCwDHHHIP169d36bl7mjhxYjLAmDdvHsaOHZsMKAKBAMrLy9vlogBAfn5+8s8dzfW4cePQp08fPPPMM6ioqIBlWRg8eDBisdgBj21/88dYT8Q5GIz1IJs2bcK0adPwzDPPYPjw4Zg8eXLyE3Or5cuXp3z/2Wef4eijj4YkSTj++ONhmiZ27tyJo446KuWrrKwMADB06FAsXrx4r2NQVRWmaaYcO+GEE/DNN9+gb9++7a7rcrnQr18/KIqSMrbGxkZ89913+/x5J0yYgA8//BCrVq1q91g8HkcwGES/fv2gqiqWLl2a8tgXX3yBQYMGpVzr66+/xooVK/Dqq69i4sSJKeOvqamBLMvtxu/z+fY6vvr6emzYsAH33HMPzj77bAwcOBCNjY0p5/Tv3x9r165FNBpNHmtNQD3Q+WOsR8r1PRrGmG3y5Mk0duxY2rFjR8rXrl27iIjIMAw65ZRT6Fe/+hUREf38889UVFSUzDMgsvMj3G43TZs2jdavX0/z5s0jl8tFTz31VPKciRMnUt++fem1116jzZs30/Lly+nBBx+kd955h4iINmzYQKqq0m9+8xtas2YNrVu3jp544onkOKZMmUInn3wybdmyhXbt2kWmadL27dupuLiYfv3rX9Pnn39OGzdupIULF9KVV15JhmEQEdF1111Hffr0ocWLF9PatWvp/PPPJ7fbvc8cjEgkQqeffjoVFBTQ448/TqtXr6ZNmzbRSy+9RCeccEIyh+Hmm2+miooKWrBgAX3zzTc0efJkKigooIaGhpTrjRw5ko477jjyeDwUCoWSxy3LotNOO42OO+44ev/992nLli20dOlSuuuuu+iLL74gIjsH47jjjku5nmmaVFRURJdffjl9//33tHjxYjr55JMJAL3xxhtERNTc3EyFhYV0xRVX0LfffksLFy6kAQMGEABavXo1EdEBzR9jPQ0HGIx1E5MnTyYA7b769+9PRES/+93vqLy8nOrq6pLPee2110hV1eQb1ahRo+j666+n6667jrxeLxUUFNBdd92VkvQZi8Xovvvuo759+5KiKFReXk4XXXQRffXVV8lzlixZQqeeeippmkb5+fk0ZswYamxsJCI7ADnllFPI4XAQANqyZQsREX333Xd00UUXUX5+PjkcDhowYADdcsstydf2+/10+eWXk9PppNLSUpo9e3aHCaN7ikQiNGvWLBoyZAjpuk6FhYU0cuRIev755ykejxMRUTgcphtvvJF8Ph9pmkYjR46kzz//vN21nnjiCQJAV1xxRbvHWlpa6MYbb6SKigpSFIUqKytp4sSJtG3bNiLqOMAgIlq0aBENHDiQNE2joUOH0pIlS1ICDCKipUuX0tChQ0lVVTrxxBNp3rx5BIDWr1+fPGd/88dYTyMQHUAWFWOsRzjjjDNQXV2NRx99NNdDYfswd+5cXHXVVWhubu5SzgtjPQEneTLGWIa9+OKLqKqqQq9evbBmzRrcfvvtGD9+PAcX7JDGAQZjjGVYTU0N7rvvPtTU1KC8vBwXX3wxZs6cmethMZZRfIuEMcYYY2nHZaqMMcYYSzsOMBhjjDGWdhxgMMYYYyztOMBgjDHGWNpxgMEYY4yxtOMAgzHGGGNpxwEGY4wxxtKOAwzGGGOMpd3/D2ETxwQZrlzsAAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "TARP(model, data, save=False, show=True)(\n", + "TARP(model, data, save=False, show=True, run_id=\"my_run_42\")(\n", " coverage_sigma=5, bootstrap_calculation=True\n", ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rerunning Plots\n", + "\n", + "If you want to return plots you have already made and saved, instead of redoing the entire calculation, you can load an intermediate file with all the calculations needed for the plot. \n", + "The file is saved with a run ID in your \"results\" folder, so if you set a file called `{run_id}_diagnostic_metrics.h5`. \n", + "Supplying this path to the `plot` method of an plot will reload that data and plot it again. " + ] + }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "
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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "LC2ST(model, data, save=False, show=True)()" + "from deepdiagnostics.plots import Parity\n", + "\n", + "run_id = \"my_run_42\"\n", + "out_path = \"./DeepDiagnosticsResources/results/\"\n", + "\n", + "Parity(\n", + "\tmodel, data, run_id, save=True, show=False,\n", + "\tout_dir=out_path,\n", + "\tparameter_names=[\"$\\theta_1$\", \"$\\theta_2$\"], \n", + "\tparameter_colors=[\"#5ec962\", \"#fde725\"]\n", + ")(include_residual=True)\n", + "calculation_path = os.path.join(\n", + "\tout_path, f\"{run_id}_diagnostic_metrics.h5\")\n", + "\n", + "# Change the parameter names and colors, don't rerun metrics\n", + "# Return the figure and axes for further customization\n", + "figure, axes = Parity(\n", + "\tmodel=None, data=None, run_id=run_id, save=False, show=False,\n", + "\tout_dir=out_path,\n", + "\tparameter_names=[\"Parameter 1\", \"Parameter 2\"],\n", + "\tparameter_colors=[\"#1f77b4\", \"#ff7f0e\"]\n", + ").plot(\n", + "\tdata_display=calculation_path,\n", + "\tinclude_residual=True)\n", + "\n" ] }, { @@ -954,7 +411,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -981,252 +438,27 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sampling from the posterior for each observation: 100%|█| 50/50 [00:00<00:00, 11\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/maggiev-local/repo/DeepDiagnostics/src/deepdiagnostics/plots/plot.py:91: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", - " plt.tight_layout()\n", - "100%|████████████████████████████████████████| 100/100 [00:00<00:00, 767.85it/s]\n" - ] - } - ], + "outputs": [], "source": [ "! diagnose --config ./my_full_config.yaml" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['coverage_fraction.png',\n", - " 'local_c2st_corner_plot.png',\n", - " 'local_c2st_pp_plot.png',\n", - " 'cdf_ranks.png',\n", - " 'tarp.png',\n", - " 'diagnostic_metrics.json',\n", - " 'ranks.png']" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import os \n", "os.listdir(\"./DeepDiagnosticsResources/results\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "deepdiagnostics-081AeCAa-py3.10", + "display_name": "deepdiagnostics-THlyEMaS-py3.11", "language": "python", "name": "python3" }, @@ -1240,7 +472,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.13" } }, "nbformat": 4, diff --git a/src/deepdiagnostics/client/client.py b/src/deepdiagnostics/client/client.py index 721526a..80381d7 100644 --- a/src/deepdiagnostics/client/client.py +++ b/src/deepdiagnostics/client/client.py @@ -117,7 +117,7 @@ def main(): for metrics_name, metrics_args in metrics.items(): try: - Metrics[metrics_name](model, data, save=True)(**metrics_args) + Metrics[metrics_name](model, data, run_id, save=True)(**metrics_args) except SimulatorMissingError: print(f"Cannot run {metrics_name} - simulator missing.") diff --git a/src/deepdiagnostics/data/data.py b/src/deepdiagnostics/data/data.py index ad9722b..e06ace3 100644 --- a/src/deepdiagnostics/data/data.py +++ b/src/deepdiagnostics/data/data.py @@ -1,4 +1,4 @@ -from typing import Any, Optional, Sequence, Union +from typing import Any, Optional, Union import numpy as np from deepdiagnostics.utils.config import get_item @@ -43,7 +43,7 @@ def __init__( try: self.simulator = LookupTableSimulator(self.data, self.rng) except ValueError as e: - msg = f"Could not load the lookup table simulator - {e}. You cannot use generative diagnostics." + msg = f"Could not load the lookup table simulator - {e}. You cannot use online diagnostics." print(msg) self.context = self._context() diff --git a/src/deepdiagnostics/metrics/__init__.py b/src/deepdiagnostics/metrics/__init__.py index a713120..bbe4065 100644 --- a/src/deepdiagnostics/metrics/__init__.py +++ b/src/deepdiagnostics/metrics/__init__.py @@ -1,15 +1,27 @@ -from deepdiagnostics.metrics.all_sbc import AllSBC -from deepdiagnostics.metrics.coverage_fraction import CoverageFraction -from deepdiagnostics.metrics.local_two_sample import LocalTwoSampleTest as LC2ST +import importlib +import inspect +from pathlib import Path +from deepdiagnostics.metrics.metric import Metric +# Void is included as a placeholder for empty metrics def void(*args, **kwargs): def void2(*args, **kwargs): return None return void2 -Metrics = { - "": void, - CoverageFraction.__name__: CoverageFraction, - AllSBC.__name__: AllSBC, - "LC2ST": LC2ST -} +Metrics = {"": void} +__all__ = [] +for file in Path(__file__).parent.glob("*.py"): + if file.name.startswith("__") or file.name == "metric.py": + continue + module = importlib.import_module(f"deepdiagnostics.metrics.{file.stem}") + for _, obj in inspect.getmembers(module, inspect.isclass): + if issubclass(obj, Metric) and obj != Metric: + Metrics[obj.__name__] = obj + globals()[obj.__name__] = obj + __all__.append(obj.__name__) + +if 'LocalTwoSampleTest' in Metrics: + Metrics['LC2ST'] = Metrics['LocalTwoSampleTest'] + globals()['lc2st'] = Metrics['LocalTwoSampleTest'] + __all__.append('lc2st') \ No newline at end of file diff --git a/src/deepdiagnostics/plots/__init__.py b/src/deepdiagnostics/plots/__init__.py index f221674..621c332 100644 --- a/src/deepdiagnostics/plots/__init__.py +++ b/src/deepdiagnostics/plots/__init__.py @@ -1,28 +1,28 @@ -from deepdiagnostics.plots.cdf_ranks import CDFRanks -from deepdiagnostics.plots.coverage_fraction import CoverageFraction -from deepdiagnostics.plots.ranks import Ranks -from deepdiagnostics.plots.tarp import TARP -from deepdiagnostics.plots.local_two_sample import LocalTwoSampleTest as LC2ST -from deepdiagnostics.plots.predictive_posterior_check import PPC -from deepdiagnostics.plots.parity import Parity -from deepdiagnostics.plots.predictive_prior_check import PriorPC -from deepdiagnostics.plots.cdf_parity import CDFParityPlot +import importlib +import inspect +from pathlib import Path +from deepdiagnostics.plots.plot import Display +# Void is included as a placeholder for empty metrics def void(*args, **kwargs): def void2(*args, **kwargs): return None return void2 -Plots = { - "": void, - CDFRanks.__name__: CDFRanks, - CoverageFraction.__name__: CoverageFraction, - Ranks.__name__: Ranks, - TARP.__name__: TARP, - "LC2ST": LC2ST, - PPC.__name__: PPC, - "Parity": Parity, - PriorPC.__name__: PriorPC, - CDFParityPlot.__name__: CDFParityPlot -} +Plots = {"": void} +__all__ = [] +for file in Path(__file__).parent.glob("*.py"): + if file.name.startswith("__") or file.name == "plot.py": + continue + module = importlib.import_module(f"deepdiagnostics.plots.{file.stem}") + for name, obj in inspect.getmembers(module, inspect.isclass): + if issubclass(obj, Display) and obj != Display: + Plots[obj.__name__] = obj + globals()[obj.__name__] = obj + __all__.append(obj.__name__) + +if 'LocalTwoSampleTest' in Plots: + Plots['LC2ST'] = Plots['LocalTwoSampleTest'] + globals()['lc2st'] = Plots['LocalTwoSampleTest'] + __all__.append('lc2st') diff --git a/src/deepdiagnostics/utils/config.py b/src/deepdiagnostics/utils/config.py index a07a170..378603e 100644 --- a/src/deepdiagnostics/utils/config.py +++ b/src/deepdiagnostics/utils/config.py @@ -15,7 +15,7 @@ def get_section(section, raise_exception=True): class Config: ENV_VAR_PATH = "DeepDiagnostics_Config" - + _printed_warning = False def __init__(self, config_path: Optional[str] = None) -> None: if config_path is not None: # Add it to the env vars in case we need to get it later. @@ -31,7 +31,9 @@ def __init__(self, config_path: Optional[str] = None) -> None: self._validate_config() except KeyError: - print("Warning: Cannot load config from environment. Hint: Have you set the config path by passing a str path to Config?") + if not Config._printed_warning: + Config._printed_warning = True + print("Warning: Cannot load config from environment. Hint: Have you set the config path by passing a str path to Config?", flush=True) self.config = Defaults def _validate_config(self): @@ -40,7 +42,8 @@ def _validate_config(self): pass def _read_config(self, path): - assert os.path.exists(path), f"Config path at {path} does not exist." + if not os.path.exists(path): + raise FileNotFoundError(f"Config path at {path} does not exist.") with open(path, "r") as f: config = yaml.safe_load(f) return config diff --git a/src/deepdiagnostics/utils/defaults.py b/src/deepdiagnostics/utils/defaults.py index d82f619..cb984b7 100644 --- a/src/deepdiagnostics/utils/defaults.py +++ b/src/deepdiagnostics/utils/defaults.py @@ -38,9 +38,9 @@ }, "metrics_common": { "use_progress_bar": False, - "samples_per_inference": 1000, + "samples_per_inference": 100, "percentiles": [75, 85, 95], - "number_simulations": 50, + "number_simulations": 10, }, "metrics": { "AllSBC": {},