diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..13bddf1 --- /dev/null +++ b/.gitignore @@ -0,0 +1,24 @@ +#GIT IGNORE FILE + +#Ignore settings.json + +.vscode/ +.vscode/settings.json + +# Ignore the pycache folder + +__pycache__/ +*.pyc + +# Ignore the imp_score and plt_data folders created in the unit tests +/tests/test_imp_score_global/* +/tests/test_imp_score_local/* +/tests/test_plt_data_local/* +/tests/test_plt_data_global/* +/tests/test_plots/* + +# Ignore the images,imp_scores and plt_data folders +images/* +imp_scores/* +plt_data/* + diff --git a/diffi_tests.ipynb b/diffi_tests.ipynb new file mode 100644 index 0000000..a1e7783 --- /dev/null +++ b/diffi_tests.ipynb @@ -0,0 +1,4713 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DIFFI Methods Tests\n", + "\n", + "In this notebook I will do some tests of the methods and produce some plots." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import pickle as pkl \n", + "import time\n", + "import matplotlib.pyplot as plt \n", + "%matplotlib inline\n", + "from sklearn.ensemble import IsolationForest\n", + "from pyod.models.iforest import IForest\n", + "from sklearn.metrics import precision_score, recall_score, f1_score\n", + "from sklearn.utils import shuffle\n", + "import shap\n", + "import interpretability_module as interp\n", + "import sklearn_mod_functions as sk_mod\n", + "import pyod_mod_functions as pyod_mod\n", + "from utils import *\n", + "from plot import *\n", + "import scipy.io\n", + "sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Glass Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(os.getcwd(), 'data', 'local', 'glass.pkl'), 'rb') as f:\n", + " data = pkl.load(f)\n", + "# training data (inliers and outliers)\n", + "X_tr = np.concatenate((data['X_in'], data['X_out_5'], data['X_out_6']))\n", + "y_tr = np.concatenate((data['y_in'], data['y_out_5'], data['y_out_6']))\n", + "X_tr, y_tr = shuffle(X_tr, y_tr, random_state=0)\n", + "# test outliers\n", + "X_te = data['X_out_7'] \n", + "y_te = data['y_out_7']\n", + "y_te=np.ones(shape=X_te.shape[0])\n", + "X=np.r_[X_tr,X_te]\n", + "y=np.r_[y_tr,y_te] " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lympho Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "path = os.path.join(os.getcwd(), 'data', 'ufs', 'lympho.mat')\n", + "data = scipy.io.loadmat(path)\n", + "X_tr=data['X']\n", + "y_tr=data['y']\n", + "X_tr, y_tr = shuffle(X_tr, y_tr, random_state=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ionosphere Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "path = os.path.join(os.getcwd(), 'data', 'ufs', 'ionosphere.mat')\n", + "data = scipy.io.loadmat(path)\n", + "X_tr=data['X']\n", + "y_tr=data['y']\n", + "X_tr, y_tr = shuffle(X_tr, y_tr, random_state=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Letter Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "path = os.path.join(os.getcwd(), 'data', 'ufs', 'letter.mat')\n", + "data = scipy.io.loadmat(path)\n", + "X_tr=data['X']\n", + "y_tr=data['y']\n", + "X_tr, y_tr = shuffle(X_tr, y_tr, random_state=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Synthetic Datasets - Xaxis" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(os.getcwd(), 'data', 'local', 'syn_train.pkl'), 'rb') as f:\n", + " data = pkl.load(f)\n", + "data.keys()\n", + "X_tr=data['X']\n", + "y_tr=data['y']\n", + "X_tr, y_tr = shuffle(X_tr, y_tr, random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(os.getcwd(), 'data', 'local', 'syn_test.pkl'), 'rb') as f:\n", + " data = pkl.load(f)\n", + "data.keys()\n", + "X_te=data['X_xaxis']\n", + "y_te=np.ones(X_te.shape[0])\n", + "X_te, y_te = shuffle(X_te, y_te, random_state=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train the IsolationForest Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "sklearn model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "hyperparams=fs_datasets_hyperparams('ionosphere')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "F1 score (on training data): 0.653\n" + ] + } + ], + "source": [ + "iforest_sklearn = IsolationForest(n_estimators=hyperparams['n_estimators'], max_samples=hyperparams['max_samples'], contamination=hyperparams['contamination'], random_state=0, bootstrap=False)\n", + "iforest_sklearn.fit(X_tr)\n", + "y_tr_pred = np.array(iforest_sklearn.decision_function(X_tr) < 0).astype('int')\n", + "f1 = f1_score(y_tr, y_tr_pred)\n", + "print('\\nF1 score (on training data): {}'.format(round(f1, 3)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PyOD Model" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "F1 score (on training data): 0.3\n" + ] + } + ], + "source": [ + "iforest_pyod = IForest(n_estimators=hyperparams['n_estimators'], max_samples=hyperparams['max_samples'], contamination=hyperparams['contamination'] , random_state=0, bootstrap=False)\n", + "iforest_pyod.fit(X_tr)\n", + "y_tr_pred = np.array(iforest_pyod.decision_function(X_tr) < 0).astype('int')\n", + "f1 = f1_score(y_tr, y_tr_pred)\n", + "print('\\nF1 score (on training data): {}'.format(round(f1, 3)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Adapt the code for PyOD\n", + "\n", + "\n", + "Try methods on sklearn_mod_functions on an IForest object and on a IsolationForest object\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With pyod -> No method _max_features" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.0167934 , -0.0167934 , 0.09140447, -0.18584581, 0.132566 ,\n", + " 0.12842896, -0.23575484, 0.05280761, 0.16003561, 0.132566 ])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sk_mod.decision_function_single_tree(iforest_sklearn,0,X_tr)[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-1.04393383, -1.04393383, -0.93573596, -1.21298624, -0.89457443,\n", + " -0.89871146, -1.26289526, -0.97433282, -0.86710482, -0.89457443])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pyod_mod.decision_function_single_tree_pyod(iforest_pyod,0,X_tr)[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.0167934 , -0.0167934 , 0.09140447, -0.18584581, 0.132566 ,\n", + " 0.12842896, -0.23575484, 0.05280761, 0.16003561, 0.132566 ])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "decision_function_single_tree(iforest_sklearn,0,X_tr)[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem 1 -> _max_features \n", + "\n", + "PyOD does not have the method _max_features" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "33" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iforest_sklearn._max_features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can obtain max_features in this way" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "33.0" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "int(iforest_pyod.max_features*X_tr.shape[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem 2 -> estimators_features\n", + "\n", + "Now the error is on the estimators_features attributes " + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iforest_pyod.estimators_[0].tree_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The solution would be to assume that we always have max_features=1 and so use np.arange(X.shape[1]) as the \"estimators_features_\" attribute for the PyOD object. The problem is that if someone wants to use max_features different from 1, this will not work because we have no way of finding the subset of features indeces used by each estimator. In the sklearn implementation we can do that with the estimators_features_ attribute. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another solution would be to insert the estimators_features_ method from sklearn (we can also copy the code from sklearn). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the source code of the IForest object it actually uses the IsolationForest from sklearn. The problem is that that sklearn object is used just inside the code and it is not returned. It only returns the estimators_ and estimators_samples_ from it. So we should add another property to the IForest object that returns the estimators_features_ from the sklearn object.\n", + "\n", + "Like this: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@property\n", + "def estimators_features_(self):\n", + " \"\"\"The indeces of the subset of features used to train the estimators.\n", + " Decorator for scikit-learn Isolation Forest attributes.\n", + " \"\"\"\n", + " return self.detector_.estimators_features_\n", + "\n", + "@property\n", + "def n_features_in_(self):\n", + " \"\"\"The number of features seen during the fit. \n", + " Decorator for scikit-learn Isolation Forest attributes.\n", + " \"\"\"\n", + " return self.detector_.n_features_in_\n", + "\n", + "@property\n", + "def offset_(self):\n", + " \"\"\"Offset used to define the decision function from the raw scores. \n", + " Decorator for scikit-learn Isolation Forest attributes.\n", + " \"\"\"\n", + " return self.detector_.offset_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Actually we should do somethig like this also for the n_features_in_ property so that I do not have to assume that n_features_in is equal to X.shape[1] in line 14 of pyod_mod_functions.py " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem 3 -> offset_\n", + "\n", + "The offset_ attribute is another attribute present in the sklearn object but not returned in the PyOD one" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'IForest' object has no attribute 'offset_'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Users\\lemeda98\\Desktop\\PHD Information Engineering\\Python Code\\DIFFI\\diffi_tests.ipynb Cell 37\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> 1\u001b[0m iforest_pyod\u001b[39m.\u001b[39;49moffset_\n", + "\u001b[1;31mAttributeError\u001b[0m: 'IForest' object has no attribute 'offset_'" + ] + } + ], + "source": [ + "iforest_pyod.offset_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also in this case the solution would be to add a property to the source code of IForest that returns the offset_ attribute from the sklearn object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@property\n", + "def offset_(self):\n", + " \"\"\"Offset used to define the decision function from the raw scores. \n", + " Decorator for scikit-learn Isolation Forest attributes.\n", + " \"\"\"\n", + " return self.detector_.offset_" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([10.5276071 , 2.3456453 , 5.96417261, 2.48230074, 3.46290833,\n", + " 2.9867716 , 1.74884253, 2.21993414, 2.29262178, 1.82246775,\n", + " 2.80516824, 1.64551369, 3.5170043 , 1.44307393, 2.27328164,\n", + " 2.15248895, 2.01305523, 2.14096036, 2.32798271, 1.96621092,\n", + " 2.30000947, 1.84162034, 1.81808767, 1.71246663, 2.13524375,\n", + " 1.79294545, 1.33222892, 1.82643637, 1.64688637, 2.62885146,\n", + " 1.97958874, 2.0220032 , 1.98839036])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fi,time=pyod_mod.diffi_ib_pyod(iforest_pyod,X_tr)\n", + "fi" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2.74637902, 2.07500701, 4.72127578, 2.47732838, 2.40862152,\n", + " 2.64975284, 1.57542783, 2.1763442 , 1.92312969, 1.7922453 ,\n", + " 2.17232727, 1.57128592, 3.45982766, 1.42313878, 2.23519336,\n", + " 1.82692197, 1.65484337, 1.91770655, 2.10541481, 1.74063398,\n", + " 1.58231424, 1.73614965, 1.8020169 , 1.67557289, 1.95368843,\n", + " 1.5974838 , 1.19121221, 1.61891422, 1.60640604, 1.86742374,\n", + " 1.93158492, 1.84148145, 1.93260796])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fi,time=diffi_ib(iforest_sklearn,X_tr)\n", + "fi" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "tree = iforest_sklearn.estimators_[0]\n", + "features = iforest_sklearn.estimators_features_[0]\n", + "leaves_index = tree.apply(X_tr)\n", + "node_indicator = tree.decision_path(X_tr)\n", + "n_samples_leaf = tree.tree_.n_node_samples[leaves_index]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "100" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train = [[1, 1], [1, 2], [2, 1]]\n", + "clf1 = IsolationForest(contamination=0.1).fit(X_train)\n", + "len(clf1.estimators_)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "clf1=IsolationForest(contamination=0.1).fit(X_train)\n", + "clf2=IsolationForest().fit(X_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the imp_scores and plt_data folders that will contain the data generated by the compute_local_importances and compute_global_importances methods" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "imp_scores_path=os.path.join(os.getcwd(),'imp_scores')\n", + "plt_data_path=os.path.join(os.getcwd(),'plt_data')\n", + "if not os.path.exists(imp_scores_path):\n", + " os.makedirs(imp_scores_path)\n", + "if not os.path.exists(plt_data_path):\n", + " os.makedirs(plt_data_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the tests folder that will contain the unit tests " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "tests_path=os.path.join(os.getcwd(),'tests')\n", + "if not os.path.exists(tests_path):\n", + " os.makedirs(tests_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LFI" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### compute_local_importances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Glass" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "name='glass'\n", + "dim=X_tr.shape[1]\n", + "imps,plt_data=compute_local_importances(iforest_sklearn,X_te,name,imp_scores_path,plt_data_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Lympho" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "imp_scores_path=os.path.join(os.getcwd(),'imp_scores')\n", + "plt_data_path=os.path.join(os.getcwd(),'plt_data')\n", + "name='lympho'\n", + "dim=X_tr.shape[1]\n", + "imps,plt_data=compute_local_importances(iforest_sklearn,X_tr,name,imp_scores_path,plt_data_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Ionosphere" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "imp_scores_path=os.path.join(os.getcwd(),'imp_scores')\n", + "plt_data_path=os.path.join(os.getcwd(),'plt_data')\n", + "name='ionosphere'\n", + "dim=X_tr.shape[1]\n", + "imps,plt_data=compute_local_importances(iforest_sklearn,X_tr,name,imp_scores_path,plt_data_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Xaxis" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "name='Xaxis'\n", + "dim=X_tr.shape[1]\n", + "imps,plt_data=compute_local_importances(iforest_sklearn,X_te,name,imp_scores_path,plt_data_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### plt_importances_bars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Glass" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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", 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d4QRBges35iMr847UpUjGytodtbyCpS7DpHG4B4d7GIrcf9b4c0akPZMNus+ePcOkSZNw9+5dODo6IigoCLNmzYK5uTkA4IsvvkB6ejqGDh2K5ORkvP766zhw4ECBlRpiY2OxdetWnD9/Xn3s/fffx/Hjx9GuXTvUqVMHmzZtMmTXivXk8W9ITbskdRmSsa3QAGDQ1TsO99B8uAeVjpx/1vhzRqQ9kw26vXr1Qq9evYp8XxAEhISEICQkpNjr+Pj45NsxDQAUCgXCwsIQFhamk1qJiIiISPdMcsMIIiIiIiIGXSIiIiIySSY7dIGIiEiOrK2tpS5BMnLtu621YeJcadtJS0tDXFyc+vWtW7dw4cIFVKxYUe97CzDoEhERmQhRFOHt7S11GZIqKzt26YIoilCJIlp62xusTVUptgA+d+4cOnf+d/nTiRMnAgD69u2L5cuX67S+/2LQJSIiMhGCIODIkSNITk6WuhRJODg4oGPHjlKXYTDi/2/HKxhwC2CxFEHXz88PaWlpeqqoeFoF3RcrFowYMQJOTk4lOufp06dYsmQJAGDKlCnaNE9ERET/cf36dTx48EDqMiTh6uoqq6ALlC54yolWQXfatGkQBAHvv/9+iYPukydP1Ocx6BIRERGRvnDoAhGRgch1ogwg774TkXQMHnRzc3MBQL1DGRGRHHCSkLwmCRGRcTB40H2xlW7lypUN3TQRkWQ4SUhek4SIyDhoFHTXr19f6PHdu3cjKiqq2HOzs7Nx48YNrF69GoIgoGXLlpo0TURU5nGSEIMuERmWRkH3448/LrCEhSiKmDx5comvIYoiFAoFPvvsM02aJiIiIiLSiMZbAL9YxuLlsVYvHyvuj7m5OXx9fbFnzx688cYbOu0IEREREdHLNHqiGx8fr/67KIrw9PSEIAiIiIgodpKFIAiwsrJCpUqVYGZmVvpqiYiIiEhNEASj3zBCShoF3erVqxd63M3Nrcj3iIiISkvOy5LJue9UMoIgwNrKEoLCcA8RRZUSmVnZZSbsarXqgkql0lUdRERE+XBJNi7JRsUTBCEv5G4fDDz6R/8NOtWGELQSgiCU+Gfz+++/x549e/DPP//AysoKrVu3RkhICGrXrq3nYvNwwwgiIjJKXJKNS7JRCT36B3jwl9RVFOrkyZMYOnQomjVrBqVSiWnTpqFbt26IioqCjY2N3ttn0CUiIqPFJdkYdKls27VrV77XP/74I2rWrImYmBi8/vrrem9fJ0H38ePH+N///offf/8dcXFxSE1NhVKpLPYcQRBw48YNXTRPRERERGVASkoKAKBixYoGaU/roLtt2zYMHTpUXXhJx2wYcoYgEemW3CfJyL3/RESloVKp8OWXX6JNmzZo0KCBQdrUKuiePn0affr0gUqlgiiKcHNzQ9OmTeHo6AiFQuMleomoDOAEoTycJEREpJlx48bh8uXLOHTokMHa1CrozpkzB0qlEtbW1ggPD0efPn10VReRwcj96Zym/Zf7BCGAk4TIuDk5OUldgmTk3HdjN378eBw4cAARERGoWrWqwdrVKuj++eefEAQBEydOZMilMolPJ/No+nRSzhOEAE4SIuOlUqkQFBQkdRmS4tKnxkUURQQHB2Pv3r3Yv38/atSoYdD2tQq6L57oBAYG6qIWIoPj00k+nSQyJQqFArn/JEDMyJG6FEkI5S1gXttF6jIMz8kwa9KWpp1x48Zh27Zt2LJlC2xtbZGYmAgAsLOzM8g3qloFXVdXV9y+fdvoJpa9WKftf//7HxISEuDm5oaPP/4YkydPVtcqiiKmTp2K8PBwJCcnw9fXF8uWLVM/3cvOzsbgwYOxe/duuLi4ICwsDP7+/uo25s2bh9u3b2PJkiWS9JF0h08n+XSSyJSoHqZCTMmSugxJCHZWgIyCriiKEFVKCEErDdemSqnRt4ArV+bV1qlTp3zHf/zxR/Tr10+ntRVGq6Dr7++P1atXIzo6Gi1bttRVTVqbM2cOli1bhnXr1qFBgwaIiorCwIEDYW9vjzFjxgAA5s6di8WLF2PdunWoWbMmvvnmGwQGBuLy5cuwsrLCihUrEB0djcjISOzfvx99+vRBYmIiBEFAfHw8wsPDERUVJXFPiYiISK5EUURmVrZBHziKoqhR0E1LS9NjNa+m1dIIn3/+OaysrPD9999L3pGX/fnnn+jWrRveeecd1KhRA++//z4CAgJw5swZAHn/T1q4cCEmT56Mbt26oVGjRli/fj3u37+vXtj4ypUrePfdd9GgQQOMHDkSDx8+xKNHjwAAw4cPx5w5c2BnZydVF4mIiIggiiJUKpXB/pS1FWe0Crp16tTBxo0bcf/+fXTs2BGXLl3SVV1aadu2LY4cOYJ//snb9/mvv/7CyZMn1Y/N4+PjkZCQkG8ogr29PVq1aoXIyEgAQOPGjXHy5ElkZmYiIiICrq6ucHJywsaNG2FlZYX33nuvRLVkZ2cjJSUl3x8iIiIi0j+thi4MGjQIAFC/fn2cPXsWjRo1QsOGDVG3bl2UL1++2HMFQcCqVau0ab5IEydOREpKCurWrQszMzMolUrMmjULffv2BQAkJCQAAJydnfOd5+zsrH5v0KBBuHDhAurXrw8nJyds3boVT58+xZQpU3D8+HFMnjwZW7ZsgZeXF1avXl3kUhmzZ8/G9OnT9dJPIiIiIiqaVkF37dq16nEhgiBAFEVcvHgRFy9eLPY8URT1GnS3bt2KjRs3YtOmTWjQoAHOnz+PsWPHws3NDQMGDCjRNczNzREaGprv2MCBAzFmzBjExMRg165d+OuvvzB37lyMGTMG27dvL/Q6kyZNwvjx49WvU1JS4O7uXvrOEREREVGJaBV0PTw8jG7FBQCYMGECJk6ciN69ewMAGjZsiFu3bmH27NkYMGAAXFzyZmQmJibC1dVVfV5iYiKaNGlS6DWPHTuGS5cuYeXKlZgwYQI6d+4MGxsb9OrVC0uXLi2yFktLS1haWuquc0RERERUIloF3Zs3b+qoDN3KyMgosAWxmZmZehHpmjVrwsXFBUeOHFEH25SUFJw+fRrDhw8vcL2srCyMHDkSGzduVA+FeDEYOzc3F0qlUr8dIiIiIiKNaTUZzVh17doVs2bNwr59+3Dz5k3s3LkTP/zwg3oCmSAIGDt2LGbOnIk9e/bg4sWL+Oijj+Dm5obu3bsXuN6MGTPQuXNnNG3aFADg6+uLHTt24MKFC1i6dCl8fX0N2T0iIiIiKgGtnugaqyVLluCbb77BiBEjkJSUBDc3N3z66aeYMmWK+jNffPEF0tPTMXToUCQnJ+P111/HgQMHYGVlle9asbGx2Lp1K86fP68+9v777+P48eNo164d6tSpg02bNhmqa0RERERUQiYZdG1tbbFw4UIsXLiwyM8IgoCQkBCEhIQUey0fHx9cu3Yt3zGFQoGwsDCEhYXpolwiIiKiUhEEwag3jJCaVkH39u3bWjXu4eGh1flEREREciUIAqytrCAoDDcSVVSpkJmVVWbCrlZBt2bNmqU+VxAEPH/+XJvmiYiIiGRLEIS8kBv7E5CepP8GbapA8PlAvaRsSYSHh2PlypXqh6P16tXDxIkTERAQoM9K1bQKumUlzRMRERGZrPQkIPW+1FUUqmrVqggJCYGXlxdEUcTGjRvxwQcf4I8//kD9+vX13r5WQXfNmjWv/Ex6ejr++ecfbN++Hffu3YOvry8GDx6sTbNUjPI2XlKXICm595+IiMiYdO7cOd/radOmYdWqVTh79qzxB92S7jIGAPPmzcO4ceOwbNky+Pr64rvvvtOmaSqEKCrh02CB1GVIThS5rjEREZGxUSqV2LFjB9LT0/Haa68ZpE2Drbpgbm6OpUuX4sqVK5g3bx7efPNNBAYGGqp5WRAEM9y+fRvZ2dlSlyIZS0tLTnIko+Xk5CR1CZKRc9+J5C42NhYdO3ZEVlYWKlSogM2bN6NevXoGadvgy4t9+umnOHbsGJYsWcKgqwfJycnIyMiQugzJlC9fnkGXjJJKpUJQUJDUZUjqxe6URCQvtWvXxp9//omUlBTs2rULQ4cOxYEDBwwSdg0edL29vQEAUVFRhm6aiHRE7k/nStN/hUKB3H8SIGbk6KEi4yeUt4B5bRepyyAiCVhYWMDLK28OTdOmTREdHY2wsDAsWbJE720bPOg+e/Ys3/8lorKFTybzlObppOphKsSULD1UY/wEOyuAQZeIkPffz5wcw/zSb/Cgu27dOgCAq6uroZsmIh2Q+5NJgE8nicjI2FQx2namTp2Kt956C+7u7khNTcW2bdvw+++/Y/fu3XoosCCDBd1r165h/vz5WLduHQRBKLDcBBGVHXJ+Mgnw6aQhyXmYjJz7TiUjiiJElQqCzweGa1Ol0mgfhYcPH2Lo0KFISEiAnZ0dfHx8sHv3bnTo0EGPVf5Lq6Dr6en5ys+oVCokJycjNTVVfaxKlSr4+uuvtWmaiIhMHIfJcAIfFU8URWRmZUEQBIO2qUnQDQsL02M1r6ZV0L1586bG57Rp0warV6/m0AUyGnJ/aiL3/pPxkvswGQ6RoZLQNHjKjd43jFAoFLC1tUXNmjXxxhtvoEmTJto0SaRTfGKUh0+NyFjJeZgMh8gQaU/vWwATGTO5PzEC+NSIiIhMl8FXXSAyNnJ+YgTwqREREZkuhdQFEBERERHpg06f6Obm5uLcuXOIjY3FkydPAACOjo7w8fFBs2bNYG5ursvmiIiIiIiKpJOgm5GRgRkzZiA8PBxPnz4t9DMVK1bE0KFDMXnyZJQvX14XzRIRERERFUnroHv79m34+/vjxo0bxS5v8eTJE8yZMwfbt2/HkSNHUK1aNW2bJtIJoYKV1CVISu79JyIi06VV0M3NzUWnTp1w/fp1AEDdunUxcOBAtGrVCi4ueZNbEhIScObMGaxduxaXL1/GtWvX0KlTJ8TExKBcOc6FI2mJogiLxu5SlyE5rsFIRFQ2CYJg1BtGSE2rpLly5UpcuXIFgiDgq6++wrRp02BmZpbvM7Vr14afnx/Gjx+PadOmYebMmbh8+TJWrlyJYcOGaVU8kbYEQUByRDyUT+S76oKZoxUcAmtKXQYREWlIEARYWlnCTGH26g/riFKlRHZWdpkJu1oF3W3btkEQBHTv3h0zZswo9rMKhQIhISG4dOkSdu7ciW3btjHoklHIvvoUuffTpS5DMuZuNgCDLpHJkPNwJLn1XRAEmCnMMPHERMQ9i9N7e572nvjO7zsIglDqoDt//nxMnToVI0aMwNy5c3VcYUFaBd3Y2FgAwKBBg0p8zieffIKdO3fi4sWL2jRNRERE/8HhWPIcihX3LA5XnlyRuoxXio6OxurVq+Hj42OwNrUKus+ePQMAuLm5lfgcV1dXAEBKSoo2TRMREdF/yH04FodiGa+0tDR88sknWLp0KebMmWOwdrUKuo6OjkhKSkJ8fDyaNm1aonPi4+PV5+pTjRo1cOvWrQLHR4wYgdDQUGRlZSE4OBhbtmxBdnY2AgMDERYWBmdnZwB5q0QMGDAAx44dg7e3N1avXp2vjyNHjoSnpyeCg4P12g8iIiJNyHk4FodiGa/x48cjMDAQb775pkGDrlY7ozVr1gyiKCI0NLTE54SFhUEQhBIH49I6e/YsHjx4oP5z6NAhAEDPnj0BAOPGjcPevXuxbds2/Pbbb7h//z569OihPn/WrFlITU3FuXPn0L59ewwZMkT93qlTp3D69GmMHTtWr30gIiIiKuu2bduG8+fPY/r06QZvW6ug++GHHwIAjh8/jkGDBiE9vejfIDMyMjB48GAcPXoUANCnTx9tmn6lypUrw8XFRf3nl19+gZeXF9544w08e/YMq1atwg8//IAOHTqgefPmWLNmDf7880+cOnUKAHDlyhX07t0btWvXxtChQ3HlSt7Yl9zcXAwbNgw//vhjgRUmCpOdnY2UlJR8f4iIiIjk4O7du/jiiy+wevVqWFkZfrKgVkG3b9++aNu2LURRxLp16+Dl5YUxY8Zg48aNOHToEA4fPoyNGzdizJgx8PT0xJo1awAAvr6+6Nu3r046UBI5OTn43//+h0GDBkEQBERHRyM3Nxf+/v7qz9StWxceHh6IjIwEADRu3BhHjx7F8+fPERERgUaNGgEA5s6di/bt26NFixYlanv27Nmwt7dX/3F3l/ckASIiIpKPmJgYPHz4EL6+vuosdPLkSSxbtgz29vZQKpV6bV+rMbqCIGDv3r145513cOrUKSQlJSE0NLTQoQwvZkG2adMGu3fv1qZZje3atQvJycn4+OOPAeRtYmFhYQEHB4d8n3N2dkZCQgIAYOLEiRg+fDi8vLxQo0YNrFq1CteuXcO6desQGRmJYcOG4eDBg2jRogXCw8Nhb29faNuTJk3C+PHj1a9TUlIYdqnMk9sSPv8l9/4TEZVU+/btcfr06XzHhg8fjtq1a2PcuHEl+nZcG1pvTVaxYkV1Mg8LC1N/xf9f9erVw8iRIzFs2DAoFFo9SNbYqlWr0KlTJ41Wh7C3t8emTZvyHevQoQPmzZuHjRs3Ii4uDlevXsWQIUMQEhKC+fPnF3odS0tLWFpaalU/kTHh8kV55LiEEREZJ097T6Ntx9bWFg0aNMh3rHz58nB0dCxwXB90sgevQqHAyJEjMXLkSDx48ACxsbF48uQJgLzVFXx8fNTLihnarVu3cPjwYezYsUN9zMXFBTk5OUhOTs73VDcxMVG9dfF/rVmzBg4ODujWrRt69OiB7t27w9zcHD179sSUKVP03Q0ioyH35YsALmFERMZBFEUoVUp85/edwdpUqpRl6hd9nQTdl7m6ukoWaguzZs0aVKlSBe+88476WPPmzWFubo4jR44gKCgIAHD16lXcvn0bbdq0KXCNhw8fIiQkBCdPngQAKJVK5ObmAsibnKbv8SVExkbOyxcBXMKIiIyDKIrIzsqGIAgGbVPboHvgwAEdVfNqOg+6xkSlUmHNmjUYMGAAypX7t6v29vb45JNPMH78eDg6OsLOzg6jR49GmzZt0Lp16wLXGTt2LIKDg1G1alUAeZPpNmzYgICAAKxYsQK+vr4G6xMRERHRC7oInqZMq6CbmZmJbdu2AQA6deqEypUrF/v5hw8fYv/+/QDyliYzNzfXpvlXOnz4MG7fvl3oFsULFiyAQqFAUFBQvg0j/isiIgLXr1/Hhg0b1MdGjRqFqKgotGrVCq+99hqmTp2q135owtraWuoSJCX3/hMREdG/tAq6W7duxcCBA1G1atUSrYtbsWJFfP3117h//z4sLCzQu3dvbZp/pYCAgCJ/y7GysipyhYiXBQYGIjAwMN+x8uXLY+vWrTqrU1dEUYS3t7fUZUiOv9kSERERoGXQ3bt3LwDggw8+yDc0oMjGypVD7969MX/+fOzatUvvQVduBEHAkSNHkJycLHUpknFwcEDHjh2lLoOoUHJelkzOfSci6WgVdM+dOwdBEODn51fic/z8/DB//nxER0dr0zQV4fr163jw4IHUZUjG1dWVQZeMEpdl47ctRGR4WgXdF4FKkw0QqlWrBgC4f/++Nk0TEZUpcl+WjUuyEZEUtAq6L3azyM7OLvE5OTk5APibPRHJj5yXZeOSbEQkBa22KHN2dgYAxMbGlvicixcvAsArV2ggIiIiItKGVk9027Zti/j4eISHh2Pw4MElOmf58uUQBKHQ9WqJiIheJudJbHLuO5WcIAhlbsMIQ9Iq6Pbp0wcbN25EVFQUPvvsMyxcuLDImy2KIsaOHYvo6GgIglCi5ciIiEi+OIGPw/yoeIIgwMraCgpBqy/oNaISVcjKzCozP5taBd1OnTqhQ4cOOHr0KJYuXYrIyEiMGTMG7dq1U28D/ODBA5w4cQJLlixRh1w/Pz9069ZNJx0gIiLTxAl8pZvAV65KeT1UUzbIre+CIEAhKHDk4S9Izn2s9/YczCuhY+UuEAShxEF31qxZmD17dr5j3t7eiImJ0UeJBWi9BfDWrVvRvn17xMbGIjo6GgMGDCjys6IoomHDhti+fbu2zRIRkQxwAp9mQVdUiajUu66eKiobRFXZeNKoS8m5j/EoJ0nqMopUr149/PLLL+rXLxYzMAStg66joyNOnz6Nr7/+GitWrEBGRkahn7OxscGnn36KGTNmcJtWIiIiPRAUAm5FnUJWaorUpUjCytYO1VtwDpCxKVeunHoBA4O3rYuLWFtb44cffsDUqVNx9OhRxMTE4NGjRwAAJycnNGvWDG+++Sbs7e110RwREREV4endW0h//EjqMiRhU8mJQdcI3bhxA7Vq1YKVlRVee+01TJ8+XaM9GLShk6D7gr29Pd577z289957urwsEREREZVBLVu2xI8//ojatWsjISEBs2fPRkBAAM6cOQNbW1u9t6/ToEtERERE9EJAQID67z4+PmjRogXq16+PHTt2FDuvS1cMtx4FEREREcmag4MDatWqhbi4OIO0xye6JsbJyUnqEiQl9/4TEREZs7S0NMTHx6N3794GaY9B14SoVCoEBQVJXYbkVCqV1CUQEREZjIN5JaNt56uvvkKnTp3g4eGBBw8eYNasWVAoFOjZs6ceKiyIQdeEKBQK5P6TADEjR+pSJCOUt4B5bRepyyAiItI7URShElXoWLmLwdpUiSqNdkW7d+8eBg4ciCdPnsDJyQlt2rTBsWPHULlyZT1W+S8GXROjepgKMUWeuwgBgGBnBTDo6p3cdh/6L7n3n4iMgyiKyMrMgiAIBm1Tk6C7bt06PVbzagy6RKQR7ryUR467LxGR8dE0eMoNgy4RaUTuOy8B3H2JiKisYNAlIo3JeeclgLsvERGVFQy6RERktOQ8HlrOfSfSFZ0G3Rs3biAyMhIJCQnIyMjAiBEjuK4pERGVCseDcyw4kbZ0EnTPnTuHsWPH4o8//sh3/P33388XdENDQzF9+nTY29vj8uXLMDc310XzRERkguQ+HpxjwYm0p3XQ/eWXX9CzZ0/k5OTkm/VX2FIXH330ESZOnIjHjx/jl19+wXvvvadt80W6d+8evvzyS+zfvx8ZGRmoVasW1qxZgxYtWgDIm6U4depUhIeHIzk5Gb6+vli2bBm8vb0BANnZ2Rg8eDB2794NFxcXhIWFwd/fX339efPm4fbt21iyZIne+kBEJHdyHg/OseBE2lNoc/KDBw/w4YcfIjs7G/Xr18f+/fuRmppa5OdtbW3x7rvvAgD279+vTdPFevr0KXx9fWFubo79+/fj8uXLmD9/PipWrKj+zNy5c7F48WL8+OOPOH36NGxsbBAYGIisrLw1aFesWIHo6GhERkZi6NCh6NOnjzrIx8fHIzw8HLNmzdJbH4iIiIhIO1o90V2wYAHS09NRvXp1/P7773BwcHjlOe3bt8fmzZsRHR2tTdPFmjNnDtzd3bFmzRr1sZo1a6r/LooiFi5ciMmTJ6Nbt24AgPXr18PZ2Rm7du1C7969ceXKFbz77rto0KABPD09MWHCBDx69AiVK1fG8OHDMWfOHNjZ2emtD0RkeuQ8uUjOfTe08g6OUpcgGTn2XRAEo94wQmpaBd0DBw5AEAQEBweXKOQCQN26eRML4uPjtWm6WHv27EFgYCB69uyJ3377DVWrVsWIESMwZMgQddsJCQn5hiLY29ujVatWiIyMRO/evdG4cWNs2LABmZmZiIiIgKurK5ycnLBx40ZYWVmVeNhFdnY2srOz1a9TUuQ51oxI7jixihOrDEFUqVC7/VtSlyEpUaWSugSDEQQB1paWEMzMDNamqFQiMzu7zIRdrYLurVu3AACvvfZaic958RQ0LS1Nm6aLFRcXh2XLlmH8+PH46quvcPbsWYwZMwYWFhYYMGAAEhISAADOzs75znN2dla/N2jQIFy4cAH169eHk5MTtm7diqdPn2LKlCk4fvw4Jk+ejC1btsDLywurV69G1apVC61l9uzZmD59ut76SkRlAydWcWKVIQgKBX7fsh4pSYlSlyIJuyrOaNf7I6nLMBhBECCYmeHe5xOQExen9/YsPD1R9ft5EARBo6B7//59fPPNNzh06BAyMjLg6emJH3/8Ec2aNdNjtXm0CrrPnz8HAKg0+O3p2bNnAIAKFSpo03SxVCoVWrRogW+//RYA0LRpU8TGxuLHH3/EgAEDSnQNc3NzhIaG5js2cOBAjBkzBjExMdi1axf++usvzJ07F2PGjMH27dsLvc6kSZMwfvx49euUlBS4u7uXsmekD3L/SlXu/TckTqxi0DWEm+ejkRR/Q+oyJFGlppesgu4LOXFxyLp8WeoyCvX06VP4+/vDz88PO3bsgJOTE27cuFHikQDa0irouri44ObNm4iLi0Pr1iX7D9iZM2cAAB4eHto0XSxXV1fUr18/37F69eqpw6iLiwsAIDExEa6ururPJCYmokmTJoVe89ixY7h06RJWrlyJCRMmoHPnzrCxsUGvXr2wdOnSImuxtLSEpaWllj0ifeHXyXn4lTIREenDggULULVqVfz444/qYzVq1DBY+1oF3Xbt2iE+Ph7btm1Dnz59Xvn5nJwcLF++HIIgoH379to0XSxfX19cvXo137F//vkH1atXB5A3Mc3FxQVHjhxRB9uUlBScPn0aw4cPL3C9rKwsjBw5Ehs3boSZmRmUSqX6kX1ubi6USqXe+qIpoYKV1CVIStP+y/3rZIBfKRMRkf7s27cP/v7+6NevH06ePAk3NzcMGTIEAwcONEj7WgXdjz/+GOvXr8eePXtw6NAhvPVW0QPgc3Jy8NFHH+HGjRtQKBTqiWH6MG7cOLRt2xbffvstevXqhTNnzmDFihVYsWIFgLwxLWPHjsXMmTPh7e2NmjVr4ptvvoGbmxu6d+9e4HozZsxA586d0bRpUwB5QXrChAkYOHAgli5dCl9fX731RROiKMKiMYdFaDpAXs5fJwP8SpmIiPTn5s2bWLlyJUaPHo0JEyYgOjoaEyZMgIWFBfr27av39rUKuu3bt8cHH3yAn376CV27dsVnn32GoKAg9fs3b95EcnIy/vjjD6xYsQJxcXEQBAHDhg1DgwYNtC6+KC1btsTOnTsxadIkhISEoGbNmli4cGG+G/rFF18gPT0dQ4cORXJyMl5//XUcOHAAVlb5nwjGxsZi69atOH/+vPrY+++/j+PHj6Ndu3aoU6cONm3apLe+aEIQBCRHxEP5JEvqUiRj5mgFh8Car/4gERER6Z1KpUKzZs0wbdo0AEDjxo1x+fJlrFq1yviDLgCsXbsWqamp+PXXX/H999/j+++/V6/n1rVrV/XnXjxl69GjBxYtWqRts6/UpUsXdOnSpcj3BUFASEgIQkJCir2Oj48Prl27lu+YQqFAWFgYwsLCdFKrLmVffYrc++lSlyEZczcbgEGXiIjIKLi4uKiXln2hTp062L17t0Ha12pnNCBvstUvv/yC5cuXw9PTU72Q8H//VKtWDWFhYfj5559hZsD13oiIiIhIGq1bt8Y///yT79j169f1uijBy7R+ovvCkCFDMGTIEFy+fBlRUVFISkqCUqlEpUqV0LRpUzRr1sygO3cQERERyYGFp6fRtjNq1Ch07NgR8+bNQ48ePRAdHY01a9ZgyZIleqiwIJ0F3Rfq169fYGkvIiIiItItURQhKpWo+v08w7X50spTJdG8eXNs3rwZU6dOxXfffYfq1atjzpw5+OCDD/RY5b90HnSJiIiISP9EUURmdrZBvzF/MSRVE506dUKnTp30VFHxGHSJiIiIyqjSBE850SrovmrFgsIIggArKyvY29vD29sbzZs3h52dnTZlEBEREREVoFXQnTZtmtaPy83NzdGtWzfMmjULtWrV0upaREREREQvaL282MuPzItaWqy4z+Tk5ODnn39GkyZNcOTIEW3LISIiIiICoGXQValUuHnzJlq1agVRFPHee+9h586duHPnDrKyspCVlYU7d+5g586d6N69O0RRRKtWrXDjxg08ffoUv//+O4YPHw6FQoGMjAy8//77ePz4sa76RkREREQyplXQTU1NRUBAAKKjo7Ft2zZs374d3bp1Q9WqVWFhYQELCwtUrVoV3bp1w44dO7Bt2zZERUUhICAAAODr64vQ0FD88ssvMDMzQ0pKCkJDQ3XSMSIiIiKSN63G6C5cuBD//PMPRo8ejaCgoFd+PigoCMOGDUNoaCjmz5+vnswWGBiIvn37Yv369di/fz+mTJmiTVlEGinv4Ch1CZIqTf95z+Tdf0OS872Wc9+JdEWroLtt2zYIgoD33nuvxOcEBQUhNDQUO3bsyLdqQ7du3bB+/Xpcv35dm5KINCKqVKjd/i2py5CcqFJp9FneM83uGZUOf9b4c0akLa2Cbnx8PABotDzYi8/eunUr3/Hq1asDAFJSUrQpiUgjgkKB37esR0pSotSlSMauijPa9f6oxJ/nPdP8nlHpyP1njT9nVBKCIBj9hhFS0irompubAwBiY2PRrFmzEp0TGxub79wXVP//W6uDg4M2JRFp7Ob5aCTF35C6DMlUqeml8T+mvGea3zMqHTn/rJX258yxqrseqikb5NZ3QRBgbWUFQaH1IlolJqpUyMzKKjNhV6ug26hRI5w4cQLff/89PvjgA1haWhb7+aysLMybNw+CIKBhw4b53rtxI+8/ZJUrV9amJCIiItlSqVR4Z/TnUpchKZWMhnsIgpAXco8cBpKf6r9Bh4oQOvpDEIQSB9369evj9u3bBY4PGTIECxYs0HWFBWgVdAcNGoQTJ07g0qVL8Pf3R3h4OOrWrVvoZ69cuYIhQ4bg0qVLEAQBn3zySb73Dx8+DEEQ0LhxY21KIiIiki2FQoFblxORnZErdSmSsCxvjur1naUuw/CSnwKPHkldRaF+++23fL98XL58GV27dtVofpc2tAq6H330EXbs2IE9e/bgzz//RIMGDdC0aVM0b95c/WT24cOHiI6ORkxMjPq8Ll264KOP/v065tmzZ9i2bRtEUcTbb7+tTUlERESylpyUjvRnWVKXIQkbeytUry91FfSy/35TP3/+fHh6eqJdu3YGaV+roAsAP//8M8aMGYPly5dDFEXExMTkC7UviKIIQRAwdOhQLFmyJN97z58/x+7duwEALVq00LYkIiIiIjIyOTk52LJlC0aPHm2wCXRaB91y5cohLCwMQ4cOxYoVK3D48OECS4R5eXmhY8eOGDp0aKGT1ipVqoQ33nhD21KIiIiIyEjt3bsXz549Q79+/QzWptZB94UmTZogLCwMAJCdnY3k5GQAeasovGqSGhERERGZtvXr1yMgIACurq4Ga1NnQfdllpaWcHaW4WBwIiIiIirg9u3bOHbsGDZt2mTQdg238BoRERERydKGDRtQuXJlgy86oJcnukRERERkIA4VjbodlUqF//3vf+jbty/KlTNs9NRZa8eOHcOuXbvw119/4dGjR8jMzCx2MWFBENSbRBARERGRZkRRhKhSQejob7g2VSqNd0U7duwY7ty5g/79++upqqJpHXSTkpLQu3dv/PbbbwBQZOf/u4uGPpeVmDZtGqZPn57vWJ06dfD3338DyNuhLTg4GFu2bEF2djYCAwMRFhamHlf85MkTDBgwAMeOHYO3tzdWr16Npk2bqq81cuRIeHp6Ijg4WG99IMOR25aR/yX3/hMRlVWiKCIzK8tgS3W9aFPToNuxY0ekpaXpqaLiaRV0c3Nz0alTJ5w/fx6iKKJJkyaoWrUq9u3bB0EQ0K9fPzx58gTnzp3DgwcPIAgCmjVrBh8fH13VX6QGDRrg8OHD6tcvPyofN24c9u3bh23btsHe3h6jRo1Cjx498McffwAAZs2ahdTUVJw7dw7Lli3DkCFDEBUVBQA4deoUTp8+jcWLF+u9D6R/3C4zj5y2zCQiMiWlCZ5yolXQXbt2LWJiYiAIAtasWYMBAwbg0qVL2LdvHwBg3bp16s/u2rULo0aNwuXLlzFx4kQEBQVpV/krlCtXDi4uLgWOP3v2DKtWrcKmTZvQoUMHAMCaNWtQr149nDp1Cq1bt8aVK1fQu3dv1K5dW70+MJAX7IcNG4aVK1fCzMxMr/WTYch9u0xAxltmEhGRydMq6G7fvh0A8Pbbb2PAgAHFfrZ79+5o2LAhWrRogY8//hiNGjWCt7e3Ns0X69q1a3Bzc4OVlRXatGmD2bNnw8PDA9HR0cjNzYW//7/jWerWrQsPDw9ERkaidevWaNy4MY4ePYrBgwcjIiICjRo1AgDMnTsX7du312j3tuzsbGRnZ6tfp6Sk6K6TpBNy3i4T4JaZhlTewVHqEiQj574TkXS0Crp//fWXeohCYV5s+/uCl5cXPvvsM4SEhGDRokVYunSpNs0XqVWrVli7di3q1KmDBw8eYPr06WjXrh1iY2ORkJAACwsLODg45DvH2dkZCQkJAICJEydi+PDh8PLyQo0aNbBq1Spcu3YN69atQ2RkJIYNG4aDBw+iRYsWCA8Ph729fZG1zJ49u8B4YSKSH1GlQu32b0ldhqREDpEhIgPTKug+efIEAFCzZk31MQsLC/XfMzIyYGNjk++cjh07IiQkBIcOHdKm6WJ16tRJ/fdGjRqhVatWqF69OrZu3Qpra+tXnm9vb19gQeMOHTpg3rx52LhxI+Li4nD16lUMGTIEISEhmD9/fpHXmjRpEsaPH69+nZKSAnd3/U3+KVelvN6uXRbIvf9kvASFAr9vWY+UpESpS5GEXRVntOv9kdRlEJHMaBV0LSws8Pz583zh1s7OTv33e/fuoXbt2vnOsbKyUr9nKA4ODqhduzauX7+Ot956Czk5OUhOTs73VDcxMbHQMb1A3hheBwcHdOvWDT169ED37t1hbm6Onj17YsqUKcW2bWlpabAtkEWViEq96xqkLWMmqjgon4zTzfPRSIqX57KKVWp6MegSkcFpFXQ9PDzw999/IzHx3ycUzs7OsLW1RVpaGk6fPl0g6MbGxgLQ7/Ji/5WWloYbN26gf//+aN68OczNzXHkyBH1hLirV6/i9u3baNOmTYFzHz58iJCQEJw8eRIAoFQqkZubN3EpNzcXSqXSYP14FUEh4FbUKWSlynccsJWtHaq3aC11GURERGQEtAq6zZo1w99//42YmJh8wwX8/Pywb98+LFq0CL169VI/0UxOTsacOXMgCALq19ff7JfPP/8cXbt2RfXq1XH//n1MnToVZmZm+PDDD2Fvb49PPvkE48ePh6OjI+zs7DB69Gi0adMGrVsXDEhjx45FcHAwqlatCgDw9fXFhg0bEBAQgBUrVsDX11dv/SiNp3dvIf3xI6nLkIxNJScGXSITIud1nkvbd2tbi1d/yETJue9UOK2CbseOHbFx40bs27cPX331lfr4sGHDsG/fPsTExKBRo0Z49913kZ6ejr179+LevXsQBAEffaS/r7Du3r2LDz/8EI8fP0blypXx+uuv49SpU6hcuTIAYMGCBVAoFAgKCsq3YcR/RURE4Pr169iwYYP62KhRoxAVFYVWrVrhtddew9SpU/XWDyIiOeM615qvcS2KImo3r6anasoGua0pKwiC0W8YISWtgm737t0xbdo03L17Fzdu3ICXlxcA4J133sGgQYOwevVqXLt2DT/88AOAf3/4AgICMHz4cC1LL9qWLVuKfd/KygqhoaEIDQ0t9nOBgYEIDAzMd6x8+fLYunWr1jUSEVHx5L7OdWnWuBYEAad230DKo0w9VWXc7Jys0bqbl9RlGIwgCLCytILCTGGwNlVKFbKys8pM2NUq6Do4OODmzZuFvrdy5Uq0adMGK1euxKVLl/D8+XN4e3vjo48+wmeffQaFwnD/TyEiorJJzutcl3aN61uxj/HojjTbrUrNyb2C7IKuwkyBQ6sv4cmDdL235+hqg7cGNYAgCCUOukqlErNmzcJPP/2ExMREuLq6om/fvvjyyy8N8iRaq6D7Kp988gk++eQTfTZBREREJGtPHqQb7S83P/zwA1auXIkVK1agXr16OHfuHIYPHw47OzuMGDFC7+3rNegSERERkXydPn0aXbp0wdtvvw0AqF69OrZt24bo6GiDtK/V+IEOHTqgY8eOuHXrVonPuX//vvo8IiIiIjJdrVq1wvHjx3Ht2jUAwMWLFxEZGYmAgACDtK/VE93jx49DEASkp5d8XEhmZqb6PCIiIiIyXcHBwUhNTUWzZs1gZmYGpVKJqVOn4oMPPjBI+xy6QERERER6sX37dvz0009YvXo16tWrh4sXL+LLL79UT0rTN4MH3RdPf19sBUxERES64+hqI3UJkpFz343V5MmTMX78ePTs2RMA4OPjg9u3b+P77783zaC7f/9+AEC1avJe0JqIiEjXVCoRbw1qIHUZklKpysb6rnKRmZlZYElZMzMzg63Dq1HQHTRoUKHHJ0+eDAcHh2LPzc7Oxo0bN3D27FkIgoA33nhDk6aJiIjoFRQKAThzGkhNkboUadjaQfFaK6mrMDhDPckuTTudOnXCvHnz4O7ujnr16uGvv/7CkiVL9LpD7ss0Crpr164tMIlMFEXs3r27ROe/SO+Ojo6YNGmSJk0TERFRSdy5DTx6JHUV0nByAmQUdEVRhEqpMuhTfJVSpdHT2O+//x4zZszAuHHj8PDhQ7i6umLQoEEGy4EaBV0PD498QffWrVsQBAGurq4wNzcv8jxBEGBlZQVXV1e0bdsWw4cPh5ubW+mrJiIiIpI5URSRlZ1l0JWsRFHUKOja2tpi7ty5mDt3rh6rKppGQfe/2/2+GHNx8OBB1K9fin0KiYiIiKjUNA2ecqPVZDQ/Pz8IggAbG85yJCIiIiLjovWGEURERERExkirLYCJiIiIiIyVztfRTUlJQWpqKpRK5Ss/6+HhoevmiYiIiIgA6CjoHjp0CGFhYTh58iSePHlSonMEQcDz58910Ty9pLyDo9QlSEru/ScyNda2FlKXIBk5951IV7QOumPGjEFoaCgAcNafxESVCrXbvyV1GZITVSqpSyAiHRBFEbWby3sXTf67SqQdrYLupk2bsHTpUgCAlZUVunfvjubNm8PR0bHAdm+kf4JCgd+3rEdKUqLUpUjGrooz2vU2zG4rcuZY1V3qEiQl9/4biiAIuPT7PaSn5EhdiiRs7CzQoF1VqcsgKtO0CrrLly8HALi7u+Po0aPw8vLSSVFUejfPRyMp/obUZUimSk0vBl09U6lUeGf051KXITkVvznQO5VKlH3QU6n4RJeKJwiCUW8YITWtgu6FCxcgCAKmTp3KkEskEwqFArcuJyI7I1fqUiRjWd4c1es7a3yenJ8El6bvCoUAnDkNpKbooaIywNYOChltZ0uaEwQB1lbWEBQGDLoqEZlZmRqF3dTUVMyYMQN79+7Fw4cP0bhxY8ydOxfNmzfXY6V5tAq6ubl5/9A1bdpUJ8UQUdmQnJSO9GdZUpchGRt7K1TXcDNIPgkv5VPwO7eBR490X0xZ4OQEMOhSMQRBgKAQ8E/0XWSm6n+Ij7WtBWo3rwZBEDQKuiNHjsTly5cRHh4OV1dXbNmyBV27dkVUVBTc3Nz0WLGWQbdGjRq4cuUK0tLSdFUPEZFJkvuT8NI+BSeiV8tMzTHahw+ZmZnYvXs3fvrpJ7z++usAgK+//hr79+9HeHg4pk6dqtf2tQq6PXr0wKxZs3DkyBG0a9dOVzWRFuT81SjA/pNxk/OT8NI8BSeisu/58+dQKpWwtLTMd9za2hqRkZF6b1+roBscHIwNGzZg4cKF6N27N+rWraurunTqu+++w6RJk/DZZ59h4cKFAICsrCwEBwdjy5YtyM7ORmBgIMLCwuDsnPfE4cmTJxgwYACOHTsGb29vrF69Ot8QjZEjR8LT0xPBwcFSdKlQ/Go0DycJERERGQdbW1u0atUKc+bMQd26dVGlShVs27YNp0+fNsj8Lq2Crr29PSIiItC1a1e0bdsWM2fOxIcffoiKFSvqqj6tnT17FsuXL0ejRo3yHR83bhz27duHbdu2wd7eHqNGjUKPHj3wxx9/AABmzZqF1NRUnDt3DsuWLcOQIUMQFRUFADh16hROnz6NxYsXG7w/xZH7V6NA6b4elfui7HLvPxER6Vd4eDiGDx8Ob29vmJmZoUmTJujZsydiYmL03rZWQdfT0xMAkJGRgeTkZIwePRpjxoyBk5MTypcvX+y5giDgxg39LoOVlpaGvn37Ijw8HDNnzlQff/bsGVatWoVNmzahQ4cOAIA1a9agXr16OHXqFFq3bo0rV66gd+/eqF27NoYOHYoVK1YAyJuAN2zYMKxcuRJmZmZ6rb805PzVKKD516NckD5PWVoqhoiIyhZPT09EREQgPT0dqampcHFxwUcffYSaNWvqvW2tgu7NmzfzvX6xtlpSUtIrzzXEmm8jR47EO++8A39//3xBNzo6Grm5ufD391cfq1u3Ljw8PBAZGYnWrVujcePGOHr0KAYPHoyIiAj1E+G5c+eiffv2aNGiRYlqyM7ORnZ2tvp1SopMl8kxUnJfkB7govREJsfBeL5VNTg5970MsLGxgY2NDZ4+fYojR45gxowZem9Tq6A7YMAAXdWhc1u2bMG5c+dw9uzZAu8lJCTAwsICDg4O+Y47OzsjISEBADBx4kQMHz4cXl5eqFGjBlatWoVr165h3bp1iIyMxLBhw3Dw4EG0aNEC4eHhsLe3L7SO2bNnY/r06TrvH+kGF6TPw0XpiUyDqFJB6Oj/6g+aMG4Db3wOHz4MURTh7e2NuLg4fP3116hduzb69++v97a1Crpr1qzRVR06defOHXz22Wc4dOgQrKysSnUNe3t7bNq0Kd+xDh06YN68edi4cSPi4uJw9epVDBkyBCEhIZg/f36h15k0aRLGjx+vfp2SkgJ3d64MYCxkvyA9wEXpiUyIoFAgacFC5N67K3UpkjCvWg1Vxo2VugyDM9Rci9K28+zZM0ybNg337t1DxYoV0a1bN0ydOhXm5uY6rrAgrYKusYqOjkZSUhKaNWumPqZUKnHixAksXboUERERyMnJQXJycr6nuomJiXBxcSn0mmvWrIGDgwO6deuGHj16oHv37jA3N0fPnj0xZcqUImuxtLQssKQGGRk5L0gPcFF6A5LzxD85993Q0n//HVmXL0tdhiSs6tcHZBR0RVGEqDLsXBNRpfkWwEFBQQgKCtJTRcUzyaDbsWNHXLx4Md+xgQMHom7duvjyyy/h7u4Oc3NzHDlyRH3jr169itu3b6NNmzYFrvfw4UOEhITg5MmTAPJC84td4XJzc6FUKvXcIyIq6zjxkZMeiXRNFPO24zXEvKeX2yxL/1vWadDNzMxEdHQ0EhISkJGRge7du8POzk6XTZSIra0tfHx88h2zsbFBpUqV1Mc/+eQTjB8/Ho6OjrCzs8Po0aPRpk0btG7dusD1xo4di+DgYFStmjeW09fXFxs2bEBAQABWrFgBX19f/XeKiMo0QRBwavcNpDzKlLoUSdg5WaN1N/2vmUkkN2UteBqaToLunTt38NVXX2Hbtm3qJ50A0KJFC9Sv/+9aT6tWrcLy5cthb2+PgwcPGvQ3kP9asGABFAoFgoKC8m0Y8V8RERG4fv06NmzYoD42atQoREVFoVWrVnjttdf0vn0dEZmGW7GP8eiOPLdMd3KvwKBLRAanddA9ffo03nnnHTx9+jTfbxSFhdiuXbti5MiRyM3NxcGDBxEYGKht8yV2/PjxfK+trKwQGhqK0NDQYs8LDAwsUGf58uWxdetWXZdIRERERDqkVdBNTk5Gt27d8OTJE7i6uuKbb75Bu3bt0LBhw0I/X6VKFXTq1Al79uzBvn37DBp0iUh35D6xqLT9d3S10XElZUep+y7ndVFL2XeL/9/MSY7k3HcqnFZBd/HixUhKSoKTkxMiIyPh4eHxynP8/f2xe/dunDlzRpumiUginFSVR9MxcSqViLcGNdBTNWWDpus1c01YzdeEFZVKVP1+np6qKRtEThCnl2gVdPfu3QtBEDB+/PgShVwAaNAg7z/0+t7+l4j0Q+6TqoDSTayS/ZrNpVivWVAoEJtyDhlKeY5rLm9WAT52zV79wZcIZmY48/QEUp8/01NVxs22nD1eq+gndRlkRLQKutevXwcA+PmV/IeqYsW8r2K4FS5R2SXnSVWAFhOr5LxmcynXa76adhGPcl69rbwpcrKoonHQBYA7mfGyvmcMuvQyrYJuVlYWAGi0s0V6ejoAwNraWpumqQgcOynv/hMREdG/tAq6VapUwd27dxEfH4+WLVuW6Jzz588DANzc3LRpmgrBsZN5uJ4gERHJhSAI3DCiGFoF3VatWuHu3bvYv38/evXq9crPi6KI8PBwCIKAdu3aadM0FYJjJ7koPRERyYcgCLCytITCzMxgbaqUSmRlZ2sUdk+ePIlFixYhJiYGCQkJ2Lx5M7p27ap+XxRFzJw5E2vXrsWzZ8/QunVrLFy4ELVq1dK6Xq2Cbt++ffHzzz9j48aN+Oyzz9CkSZNiPx8cHIy//voLgiBgwIAB2jRNReDYSS5KbwhyXiYLYP+JyDgIggCFmRn2LfkeT+7d0Xt7jlXd8c7ozyEIgkZBNyMjAz4+Pujfvz/69OlT4P0FCxbgxx9/xPLly1GjRg3MmDED3bt3R1RUFKysrLSqWaug261bN7z55ps4duwYOnbsiJkzZyIoKEj9/vPnz3H//n388ccfWLx4Mf78808IgoAePXqgbdu2WhVORNLgMll5NF0qi4hIX57cu4OkeONdzSogIAABAQGFvieKIkJDQ/HFF1+gS5cuAIAVK1bA09MTe/fuRc+ePbVqW+ud0bZv346OHTsiJiYGo0aNwqhRo9RjRZo2bZrvs6IoonXr1li7dq22zRKRRGS/TBZQqqWyiIiooJs3byIxMRFvvvmm+pi9vT1atGiBM2fOSB90HRwcEBkZienTpyMsLAzPnhW+dl/58uUxatQohISEwMKCM+OJyjQ5L5MFlHqpLCIiyi8xMRFA3gIHL6tSpYr6PW1oHXQBwMLCArNmzcJXX32F3377DVFRUUhKSoJSqUSlSpXQtGlT+Pv7w97eXhfNERERERG9kk6C7gs2Njbo3LkzOnfurMvLEhEREZEJcnZ2BgAkJSXBxcVFfTwpKQmNGjXS+voKra9ARERERFQKNWrUgLOzM44fP64+lpKSgqioKLz22mtaX1/rJ7q3b98GkJfILS0ti/1sVlYWkpLytiX08PDQtmki3XCoKHUF0pJ7/4mISK/S0tIQFxenfn3r1i1cuHABFStWhLu7O0aOHIm5c+fCy8sL1atXx8yZM+Hq6ppvrd3S0iroHjx4EJ06dUKFChVw8+bNVwbdjIwMNGjQAJmZmTh8+DDat2+vTfNEWhNVKggd/aUuQ3KiSiV1CUREVEqOVd2Nup1z587lG9Y6ceJEAHn7MSxfvhzjxo1Deno6Ro8ejWfPnqFNmzbYuXOn1mvoAloG3W3btkEURXTv3h0VK776qZCjoyOCgoKwfv16/PTTTwy6JDlBoUDSgoXIvXdX6lIkY161GqqMGyt1GfIg56fncu47kZ6IogiVUol3Rn9usDZVSqXGWwD7+fkhLa3ozawEQcA333yDb775RtvyCtAq6EZGRkIQhCIXAS5MYGAg1q9fj8jISG2aJtKZ9N9/R9bly1KXIRmr+vUBBl2947cH/OaASNdEUURWdrZ6/wJDtalp0JWSVkH35s2bAIDatWuX+JwX+xbHx8dr0zQRUZki928P+M0BkX6UteBpaFoF3efPnwMAzMzMSnzOi89mZWVp0zQRUZmTe+8ecuLk+ku+4Z44ERG9oFXQdXJywoMHDxAXF4dmzZqV6JwXs+4cHR21aZqIqEwRlUpU/X6e1GVISlQqpS6BiGRGq6DbpEkTPHjwAD/99BPef//9Ep2zZcsWAICPj482TVMRHF1tpC5BUnLvPxkvwcwMZ56eQOrzwrdJN3W25ezxWkU/qcsgIpnRKuh269YNv/76K3bs2IFt27ahZ8+exX5+69at2LFjBwRBQPfu3bVpmgqhUol4a1ADqcuQnErFsUpknO5kxuNRTpLUZUjCyaIKgy4RGZxWQXfAgAGYPXs2bt68iT59+uD06dP47LPP4O6ef521O3fuYMGCBViyZAkEQYC7uzsGDx6sVeFUkEIhAGdOA6kpUpciHVs7KF5rpdEpFp6eeiqmbJB7/4mIyHRpFXQtLCywY8cO9fpoCxYswIIFC+Dh4QFXV1cAwIMHD9S7p4miiAoVKmDnzp2v3FxCG8uWLcOyZcvUq0I0aNAAU6ZMQadOnQDkTYQLDg7Gli1bkJ2djcDAQISFhan3W37y5AkGDBiAY8eOwdvbG6tXr0bTpk3V1x85ciQ8PT0RHBystz6U2p3bwKNHUlchHScnQIOgy3GTeTh2koiITJHWWwA3adIEp0+fRr9+/RATEwMgb2u3l8PtC82bN8eGDRtQt25dbZstVrVq1fDdd9/B29sboihi3bp16NatG2JiYtCgQQOMGzcO+/btw7Zt22Bvb49Ro0ahR48e+OOPPwAAs2bNQmpqKs6dO4dly5ZhyJAhiIqKAgCcOnUKp0+fxuLFi/XaBzIMuY+bBDh2koiITJfWQRcA6tWrh+joaBw6dAi//PILYmJi8Oj/nyo6OTmhWbNm6Nq1Kzp27KiL5l7pv3sjz5o1C8uWLcOpU6dQrVo1rFq1Cps2bUKHDh0AAGvWrEG9evVw6tQptG7dGleuXEHv3r1Ru3ZtDB06FCtWrAAA5ObmYtiwYVi5cqVGS6qRcZPzuEmAYyeJiMoyQRC4YUQxtAq6L57aVqhQAY6Ojnjrrbfw1ltv6aQwXVEqldi2bRvS09PRpk0bREdHIzc3F/7+/+5QVLduXXh4eCAyMhKtW7dG48aNcfToUQwePBgRERFo1KgRAGDu3Llo3749WrRoUeL2s7OzkZ2drX6dkiLj8bNERESkM4IgwMrKCgqFwmBtqlQqZGVlaRR2T548iUWLFiEmJgYJCQnYvHlzvoeSu3fvxqpVq3D+/Hk8efIEf/75pzp7aUuroFujRg0IgoAlS5ZgxIgROilIVy5evIg2bdogKytLPS64fv36OH/+PCwsLODg4JDv887OzkhISAAATJw4EcOHD4eXlxdq1KiBVatW4dq1a1i3bh0iIyMxbNgwHDx4EC1atEB4eDjs7e2LrGP27NmYPn26PrtKWnIwryR1CZKSe/+JiMoqQRCgUCjwz/FDyEh+ovf2yjs4onb7tyAIgkZBNyMjAz4+Pujfvz/69OlT6Ptt2rRBjx49MGrUKF2WrF3Qtba2RlZWFlq2bKmrenSmTp06OH/+PJ49e4aff/4ZAwYMwG+//Vaic+3t7bFp06Z8xzp06IB58+Zh48aNiIuLw9WrVzFkyBCEhIRg/vz5RV5r0qRJGD9+vPp1SkpKgVUpSDoqUYWOlbtIXYbkVKJK6hKIiKiUMpKfIP2x8U5EDwgIQEBAQJHvf/jhhwDy5njpmlZBt2rVqrhx4waURjhj28LCArVq1QKQNwnu7NmzWLRoET744APk5OQgOTk531PdxMREuLi4FHqtNWvWwMHBAd26dUOPHj3QvXt3mJubo2fPnpgyZUqxdVhaWup1hQnSjkJQYPG5xbiXdk/qUiRTtUJVjGk2RuoyiIiIdE6roBsQEIBly5bh5MmTaN26ta5q0guVSoXs7Gw0b94c5ubmOHLkCIKCggAAV69exe3bt9GmTZsC5z18+BAhISE4efIkgLwxv7m5uQDyJqcZY8gnzZy8dxJXnlyRugzJ1HOsx6BLREQmSavRy5999hmsra3x/fff494943kiNmnSJJw4cQI3b97ExYsXMWnSJBw/fhx9+/aFvb09PvnkE4wfPx7Hjh1DdHQ0Bg4ciDZt2hQa1seOHYvg4GBUrVoVAODr64sNGzbgypUrWLFiBXx9fQ3dPSIiIiIqAa2Crre3NzZt2oSMjAy0bt0amzZtQk5Ojq5qK7WkpCR89NFHqFOnDjp27IizZ88iIiJCvSLEggUL0KVLFwQFBcHPzw8uLi7YsWNHgetERETg+vXr+SbajRo1Cp6enmjVqhVycnIwdepUg/WLiIiIiEpOq6ELL9ahrVy5MuLj49G/f3988skn8Pb2RsWKFYtda1YQBBw5ckSb5ou0atWqYt+3srJCaGgoQkNDi/1cYGAgAgMD8x0rX748tm7dqnWNRERERKRfWgXd48eP51ukWBRFZGdnIzY2tshzXixJYcjFjYmIqGyS8/J3cu47mZa0tDTExcWpX9+6dQsXLlxAxYoV4e7ujidPnuDu3bt48OABAOCff/4BkLf0q7Ozs1ZtaxV0/fz8GFiJiEgvuPwfl/6jkinv4GjU7Zw7dw6dO3dWv544cSIAoG/fvli+fDl+/fVXDBs2TP3+xx9/DCBvztXXX39d+oKhgye6RERE+iD35f+49B+9iiiKUKlUqN3ecLvSqlQqjbcA9vPzQ1paWpHv9+vXD/369dO2tEJpFXSJSKYcKkpdgbTk3n8DkvPyf6Vd+k/OQx7k1ndRFJGVlWXQb9dFUdQ46EqJQZeINCKqVBA6+ktdhuREFb9SJuPD4R7yG+5R1oKnoek86N69excJCQnIyMhAy5YtYW1tresmqDhyf9Ik9/4bgKBQIDblHDKURX8NZerKm1WAj10zqcsgKoDDPTjcg/LTSdBNTU3F3LlzsXbtWty/f199/OLFi6hfv7769ZYtW7Bjxw7Y29sjPDxcF03TS/ikLQ+ftOnf1bSLeJSTJHUZknGyqMKgS0aLwz0YdOlfWgfda9euoXPnzoiLi8v36Lyw8SKtW7dGv379IIoiBgwYgNdff13b5uklgkKBpAULkXvvrtSlSMa8ajVUGTdW6jKICiW38YMvk3PfiUg6WgXdrKwsvPPOO7hx4wZsbGwwcuRI+Pn5oUuXwscH1ahRA2+++SaOHj2KPXv2MOjqQfrvvyPr8mWpy5CMVf36AIMuGSGOnZTf2Ekikp5WQXfZsmW4fv06bGxs8Pvvv6NJkyavPKdTp044cuQIIiMjtWmaiKhM4dhJjp0kIsPTKuju2LEDgiDgs88+K1HIBYDGjRsDyBvyQEQkJxw7yaBLRIal0ObkK1fy/oMdEBBQ4nMqVcobp5WcnKxN00RERERExdLqie6LXS4qVKhQ4nOys7MBAObm5to0TUREMuBp7yl1CZKRc9+p5ARB4IYRxdAq6FaqVAkJCQm4efMmmjUr2VI7ly5dAgC4uLho0zQREZk4pUqJ7/y+k7oMSSlVSqlLICMmCAKsrawgKLT6gl4jokqFzKwsjcLuyZMnsWjRIsTExCAhIQGbN29G165dAQC5ubkICQlBREQEbt68CTs7O7z55psICQmBq6ur1vVqFXSbNWuGX3/9FSdOnECPHj1KdM769eshCALatGmjTdNERGTizBRmwPWDQOYTqUuRhrUjzGqVfGggyY8gCBAUCjze8jeeJ2Xovb1yVcqjUu+6EARBo6CbkZEBHx8f9O/fH3369Cnw3vnz5/Hll1+iYcOGSE5OxhdffIFevXrh999/175mbU5+//33sW/fPqxYsQLjx4+Hh4dHsZ9fuHAhTpw4AUEQ8OGHH2rTNBERycHjq0Dq/Vd/zhTZugEMulQCz5MykHs/XeoyihQQEFDkfC57e3vs3bs337H58+fjjTfewJ07d+Du7q5V21o96+7fvz8aNWqErKwstG/fHvv37y+waYQoijh79iz69u2L4OBgCIKAdu3aoVOnTloVTkRERESmJyUlBYIgwN7eXutrafVEV6FQqDd+uHnzJrp06YLy5curB0W3b98eqamp6glooijCy8sLW7du1bpwIl2R+4QPufffkOR8r0vdd5squi2kLJFz30m2srKy8M0336Bnz56ws7PT+npabwHs4eGB8+fPY/To0di6dSvS0/99dP7w4UP13wVBQK9evbBs2TJUrFhR22aJdIKTXfJwwov+8WdN858zUVRB8PlAT9WUDSJ3kyMZyc3NxUcffQRRFLFw4UKdXFProAsAjo6O2LhxI7799lvs27cPUVFRSEpKglKpRKVKldC0aVN07doVtWvX1kVzVAwLT/k+MQI077/sJ7sAnPBiILL/WSvFz5kgKIAjIUDybT0VZeQcPCB0nCJ1FUQGkZubi/79++P27dvYt2+fTp7mAjoKui9Ur14dI0aM0OUlSQOiUomq38+TugzJiUoNn07KebILwAkvhiTnn7XS/pxdPww8+Ev39ZQFro0BBl2SgRch98aNG/j111/Vm4vpQqmC7r59+3DgwAHcunULSqUSbm5uaN++PXr16sWNICQkmJnhzNMTSH3+TOpSJGNbzh6vVfSTugwiIiL6f2lpaYiLi1O/vnXrFi5cuICKFSvCxcUF/fr1w/nz5/Hzzz9DpVIhMTERAFCxYkVYWFho1bZGQTcxMRHdu3fHmTNnCry3evVqTJkyBbt27ULDhg21KopK705mPB7lJEldhmScLKow6BIRkayUq1LeqNs5d+4cOnfurH49ceJEAEDfvn3x1VdfYd++fQBQYI+FX3/9FX5+2v2bXuKgq1Qq8e677+Ls2bNFfiY+Ph6BgYG4cOECnJyctCqMiIiIiIomiiJElQqVetc1XJsqlcZbAPv5+SEtLa3I94t7T1slDrpbt27F2bNnIQgCvLy8MGnSJLz22mswNzfHxYsXMX/+fJw6dQqJiYmYP38+Zs+erbeiiXRK7kv4yL3/ZNycZDyJuZR95zJ28iGKIjKzstTLuhqqTU2DrpQ0CroAUKNGDZw5cwYODg7q92rXro3u3bvD398fv/32G7Zt2yZp0J09ezZ27NiBv//+G9bW1mjbti3mzJmDOnXqqD+TlZWF4OBgbNmyBdnZ2QgMDERYWBicnZ0BAE+ePMGAAQNw7NgxeHt7Y/Xq1WjatKn6/JEjR8LT0xPBwcEG7x/pDpcvysMljMgYiSolhKCVUpchKVHDJdm4jJ38lkssa8HT0EocdGNiYiAIAoKDg/OF3BfMzMwwffp0tG/fHvHx8UhNTYWtra0uay2x3377DSNHjkTLli3x/PlzfPXVVwgICMDly5dhY2MDABg3bhz27duHbdu2wd7eHqNGjUKPHj3wxx9/AABmzZqF1NRUnDt3DsuWLcOQIUMQFRUFADh16hROnz6NxYsXS9I/0h3ZL18EcAkjQ5Lz0/NS9F1QmOHS7VSkZ8sruLxgY2mGBh6a/TtqpjADLu8FMh/rqSojZ10JZvW7Sl0FGZESB90Xmz+0aNGiyM+8/N6jR48kC7oHDhzI93rt2rWoUqUKoqOj4efnh2fPnmHVqlXYtGkTOnToAABYs2YN6tWrh1OnTqF169a4cuUKevfujdq1a2Po0KFYsWIFgLwlMIYNG4aVK1fCzMzslbVkZ2erd4YD8ra1IyMj5+WLAC5hZCD89qB03xwkJufgWcZzPVRj/OzLl0MDD83OEVVKCDIPepo+BSfTVuKgm5mZCUEQUKFChSI/U778v7PxsrKytKtMh549y1tuy9HREQAQHR2N3Nxc+Pv7qz9Tt25deHh4IDIyEq1bt0bjxo1x9OhRDB48GBEREWjUqBEAYO7cuWjfvn2xgf9ls2fPxvTp03XcIyIqa2T/7QG/OTAIPgXX/Ck4mTadbhjxMmMZL6JSqTB27Fj4+vrCx8cHAJCQkAALC4sCQzCcnZ2RkJAAIG/pi+HDh8PLyws1atTAqlWrcO3aNaxbtw6RkZEYNmwYDh48iBYtWiA8PBz29vaFtj9p0iSMHz9e/TolJQXu7u766SwRGTc5f3vAbw4Mhk/Bpa6CjInegq6xGDlyJGJjY3Hy5EmNzrO3t8emTZvyHevQoQPmzZuHjRs3Ii4uDlevXsWQIUMQEhKC+fPnF3odS0tLWFpalrp+IiIiIiodjYNuWFgYqlR59aSCknxuyhT9/nY/atQo/PLLLzhx4gSqVaumPu7i4oKcnBwkJyfne6qbmJgIFxeXQq+1Zs0aODg4oFu3bujRowe6d+8Oc3Nz9OzZU+/9ID2T8/JFAPtPREQmS+Ogu2zZsmLff7GW26s+B+gv6IqiiNGjR2Pnzp04fvw4atasme/95s2bw9zcHEeOHEFQUBAA4OrVq7h9+3aBXTmAvIl4ISEh6qfCSqUSubm5APImpymV8hwLZQq4fFEeTt4gIiJTpFHQ1eW4W30ubjxy5Ehs2rQJu3fvhq2trXrcrb29PaytrWFvb49PPvkE48ePh6OjI+zs7DB69Gi0adMGrVu3LnC9sWPHIjg4GFWrVgUA+Pr6YsOGDQgICMCKFSvg6+urt76Qfsl94gbAyRtERGWZIAjcMKIYJQ66x44d02cdOvXiaXL79u3zHV+zZg0+/vhjAMCCBQugUCgQFBSUb8OI/4qIiMD169exYcMG9bFRo0YhKioKrVq1wmuvvYapU6fqrS+kf3KeuAFw8gYRUVklCAKsrawhKAwYdFUiMrMyNQq7J0+exKJFixATE4OEhARs3rwZXbv+uwzerFmz8PPPP+PevXuwsLBAkyZNMHXqVLRs2VLrekscdN944w2tGzOUktx8KysrhIaGIjQ0tNjPBQYGIjAwMN+x8uXLq3eKIyIiIpKCIAgQFAJy/roDMU3/y7oKFaxg0dgdgiBoFHQzMjLg4+OD/v37o0+fPgXe9/b2xg8//IAaNWogMzMToaGh6NatG/766y9UrlxZq5pNftUFuXEwryR1CZKSe//JyMl54p+c+06kZ2JaFsQU49m/4L8CAgIQEBBQ5Pu9evXK93r27NlYt24dYmNj8eabb2rVNoOuCVGJKnSs3EXqMiSnKsXuS0T6xomPnPRIRK+Wk5ODNWvWwN7eHg0bNtT6egy6JkQhKLD43GLcS7sndSmSqVqhKsY0GyN1GUQFyH3iIyc9ElFx9u/fj48//hgZGRlwcXHBnj174OTkpPV1GXRNzMl7J3HlyRWpy5BMPcd6DLpktOQ88ZGTHomoOH5+fvjzzz/x+PFjrF27Fh999BGOHTtWor0biqPQUX1ERERERKViY2MDLy8vvPbaawgLC0O5cuWwfv16ra/LoEtERERERkWlUiE7O1vr63DoAhEREVEZJlSwMup20tLSEBcXp35969YtXLhwARUrVoSjoyPmzZuHzp07w8XFBY8fP8aKFStw//59vPfee1rXzKBLRBqT+zJucu8/ERkHURQhqkRYNHY3XJsqzXdGO3fuHDp37qx+PXHiRABA3759sWjRIly9ehUbN27E48eP4ejoiObNm+PgwYOoX7++1vUy6BKRRriMXR4uY0dEUhPFvF3KjH0LYD8/P6SlpRX5/ubNm7Utq0gMukSkEYWgwPZ/tuNh5kOpS5FMZevKCKodJHUZRESlCp5ywqBLRBpRqpQMeci7D0REZNwYdIlII2YKM+D6QSDzidSlSMfaEWa1it7OkoiIjAODLhFp7vFVIPW+1FVIx9YNYNAlIjJ6XEeXiIiIiEwSn+iaGE97T6lLkJTc+28wNtptyVjmyb3/RERlBIOuCVGqlPjO7zupy5AcJwnplyiqIPh8IHUZkhO5vBgRkdFj0DUhnCQEThIyAEFQAEdCgOTbUpciHQcPCB2nSF0FERG9AoOuqeEkIU4SMoTk28Cjf6SugoiIqFgMukSkEVGlhBC0UuoyJCdyiAwRGQFBEIx+ZzQpMegSkUYEhRku3U5FerZ8g56NpRkaeNhKXQYRyZwgCLCysoJCYbhFtFQqFbKysjQKuydPnsSiRYsQExODhIQEbN68GV27di30s2PGjMHq1asxZ84cjBw5Uut6GXSJSGOJyTl4lvFc6jIkY1++HBp4SF0FEcmdIAhQKBTYvn07Hj16pPf2nJycEBQUBEEQNAq6GRkZ8PHxQf/+/dGnT58iP7dnzx6cPXsWrq6uuigXAIOu6ZH7skdy7z8REcnOo0eP8ODBA6nLKFJAQAACAoqfP3P//n18/vnn2LVrF95//32dtc2ga0K47FMeLvtERERUdqhUKgwePBifffYZ6tevr9NrM+iaEC77BC77REREVMb88MMPKFeuHEaMGKHza5ts0D1x4gTmzZuH6OhoPHjwADt37kT37t3V74uiiKlTpyI8PBzJycnw9fXFsmXL4O3tDQDIzs7G4MGDsXv3bri4uCAsLAz+/v7q8+fNm4fbt29jyZIlhu5a8a4fBh78JXUV0nFtDDDoEpkMW2uT/WfqleTcd5KPmJgYhIWF4Y8//tDL6hEm+7+i9PR0NG7cGIMGDUKPHj0KvD937lwsXrwY69atQ82aNfHNN98gMDAQly9fhpWVFVasWIHo6GhERkZi//796NOnDxITEyEIAuLj4xEeHo6oqCgJekZEJA+iKKKlt73UZUiqLC3jRFQaf/75Jx4+fIi6deuqjymVSkyaNAmhoaG4fPmyVtc32aDbqVMndOrUqdD3RFHEwoULMXnyZHTr1g0AsH79ejg7O2PXrl3o3bs3rly5gnfffRcNGjSAp6cnJkyYgEePHqFy5coYPnw45syZAzs7O0N2iYhIVgRBwLyIv3HnSYbUpUjC3bE8JgTWffUHicqw3r17o3379vmOde/eHR9++CH69eun9fVNNugWJz4+HgkJCfmGItjb26NVq1aIjIxE79690bhxY2zYsAGZmZmIiIiAq6srnJycsHHjRlhZWeG9994rUVvZ2dnIzs5Wv05JSdF5f4iITNXxqw9x6b48/7vZwM2OQZdKxMnJyajbSUtLQ1xcnPr1rVu3cOHCBVSsWBHu7u6oVKlSvs+bm5vD2dkZtWvX1qpeQKZBNyEhAQDg7Oyc77izs7P6vUGDBuHChQuoX78+nJycsHXrVjx9+hRTpkzB8ePHMXnyZGzZsgVeXl5YvXo1qlatWmhbs2fPxvTp0/XbISIiIpIdURShUqkQFBRksDZVKpXGQ2rOnTuHzp07q19PnDgRANC3b18sX75cp/X9lyyDbkmYm5sjNDQ037GBAwdizJgxiImJwa5du/DXX39h7ty5GDNmDLZv317odSZNmoTx48erX6ekpMDd3V2vtRMRkXzJeRKb3PouiiKysrKMfgtgPz8/pKWllfjz2o7LfZm8fiL+n4uLCwAgMTEx3+4biYmJaNKkSaHnHDt2DJcuXcLKlSsxYcIEdO7cGTY2NujVqxeWLl1aZFuWlpawtLTUaf1ERESF4QQ++U3gK03wlBNZBt2aNWvCxcUFR44cUQfblJQUnD59GsOHDy/w+aysLIwcORIbN26EmZkZlEql+ocqNzcXSqXSkOUTEREVihP4OIGP8jPZoJuWlobr16+rX8fHx+P8+fNwdHSEh4cHxo4di5kzZ8Lb21u9vJibm1u+tXZfmDFjBjp37oymTZsCAHx9fTFhwgQMHDgQS5cuha+vr6G6RUREVCxO4GPQpX+ZbNCNiorCm2++qX79YpzsgAEDsHbtWnzxxRdIT0/H0KFDkZycjNdffx0HDhyAlZVVvuvExsZi69atOH/+vPrY+++/j+PHj6Ndu3aoU6cONm3aZJA+EREREVHJmWzQbd++fbFjVgRBQEhICEJCQoq9jo+PD65du5bvmEKhQFhYGMLCwnRSKxERERHpnkLqAoiIiIiI9MFkn+gSERkbuS199DI5952IpMP/8hARGQCXfZLfsk9EJD0GXZI9uT9pKk3/ec807z+XfeKyT0RkePL+14pkj0/Z8mjypI33LE9pnk5y2ScGXSJdEwTB6HdGkxKDLsma3J+yAZo/aeM949NJIjIOgiDA2tra4EE3MzNTo7B78uRJLFq0CDExMUhISMDmzZvRtWtX9fuffvopNm7cmO8cf39/7Nq1S+t6GXRJ9uT8lA0o3ZM23jM+nSQi6b14mnvt2jVkZmbqvT1ra2t4e3tDEASNgm5GRgZ8fHzQv39/9OnTp9DPvPXWW/jxxx/Vry0sLLSuF2DQJSIiIirTMjMzkZFhvN+yBQQEICAgoNjPWFpawtnZWedtM+iaGqfaUlcgLbn3n4iIqAz6/fffUaNGDVSsWBF+fn6YMmUKKlWqpPV1GXRNiKhSQghaKXUZkhNVSqlLICIiohLy9/fHu+++i+rVqyM+Ph7Tpk1Djx49cPToUZiZmWl1bQZdEyIozHDpdirSs+Ub9GwszdDAw1bqMoiIiKiEevbsqf67j48PfHx80LBhQ5w4cQJvvvmmVtdm0DUxick5eJbxXOoyJGNfvhwaeEhdBREREZVWzZo1UalSJcTFxWkddBU6qomIiIiISGv37t3DkydP4OLiovW1+ESXiIiIqAyztrY26nbS0tIQFxenfn3r1i1cuHABFStWRMWKFTF79mx069YNzs7OiIuLwzfffAMvLy/4+/trXTODLhEREVEZ9GKXMm9vb4O3qYlz586hc+fO6tcTJ04EAPTt2xcLFy5EbGwsNm7ciGfPnsHV1RUdOnTAN998A0tLS63rZdA1MbbW8v5/qdz7T0RE8vFilzJj3wLYz88PaWlpRb6/e/dubcsqElOBCRFFES297aUuQ3JlaQ9uIiIibZQmeMoJg64JEQQB8yL+xp0nxrs7ir65O5bn1qxEREQEgEHX5By/+hCX7qdIXYZkGrjZMegSERERAC4vRkREREQmikGXiIiIiEwSgy4RERERmSQGXSIiIiIySQy6RERERGSSGHSJiIiIyCRxeTEiIiKiMkoQBKPfGU1KfKJLREREVAYJggArK0tYW1sb7I+VlaXGwfrkyZPo2bMnatWqhQoVKmDv3r0FPvP333+jV69ecHNzQ5UqVeDn54c7d+5ofY/4RNfAXvwWlJqaqtPrKhQKKJVKuFcAch3NdHrtssS9ApCSkoL09HSoVKpiP8t7lof3THOa3DOA9w3gPSsN3jPNaXrPNPHi321jepopCAIUCjPEXhqHjPQbem+vvI0XfBosgCAIGt2HjIwM+Pj4oH///ujTp0+B9+Pi4hAQEICPPvoIX3/9NWxtbXHlyhVYWlpqXbMgGtP/x2Tg7t27cHd3l7oMIiIiKoU7d+6gWrVqBm83KysLcXFxcHFxUQdAhUIBa2trnDnzLlLTLum9BtsKDfDaa3uQmZlZ6l8kKlSogM2bN6Nr167qYwMGDIC5uTlWrlxZomtkZ2cjISEBnp6esLKyKvazfKJrYG5ubrhz5w5sbW0NOqbGEFJSUuDu7o47d+7Azs5O6nLKBN4zzfGeaY73THO8Z5oz9XsmiiJSU1Ph5uYmdSkmRaVSISIiAmPHjkW3bt3w119/oUaNGggODs4XhkuLQdfAFAqFJL8JGpKdnZ1J/kdOn3jPNMd7pjneM83xnmnOlO+Zvb291CWYnIcPHyItLQ0//PADpkyZghkzZuDQoUPo06cPfv31V7Rr106r6zPoEhEREZEkXgyBeOeddzBq1CgAQKNGjXD69GmsWrVK66DLVReIiIiISBKVKlVCuXLlULdu3XzH69Spg7t372p9fQZd0hlLS0tMnTpVJ7Mk5YL3THO8Z5rjPdMc75nmeM+oNCwsLNC8eXNcu3Yt3/Fr167pZPI+V10gIiIiMnLFrbpg6OXFNF11IS0tDXFxcQCAtm3b4rvvvoOfnx8qVqwId3d37NmzBwMGDMAPP/wAPz8/HDp0CF9++SX279+Ptm3bFrieJqsuMOgSERERGbnCgu6LDSMUCsOtm6xSKZGVla3ROronTpxA586dCxzv27cvli9fDgBYv3495s+fj3v37sHb2xtff/01unTpUuj1GHSJiIiITEhhQReQ5xbAXEeXiIiISAaMIXgaM05GI4OIi4vDnj17pC6DiIiIZIRBlwxi//79uHz5stRlEBERkYww6JJOhYeHo1mzZvDx8cEHH3wAAPjtt9/wzTffYNWqVWjatCnS09MlrrLs8ff3L7D0ChWN96ug/36rsnv3bowdO1a6gsoA3jPN8Z6RsWHQJZ15+vQpQkNDcfbsWcTGxqpnUr7xxhto1KgRDh06hJiYGNjY2Ehcadlz7do1eHl5SV2GUVIqlQWO8X4V9N9vVS5cuIAmTZpIV1AZwHumOd4zMjYMuqSREydOoGvXrnBzc4MgCNi1a5f6vXLlyuHp06f44osvcOnSJTg4OKjfu337NmrUqGHweo1Z586dMWXKFPj6+sLT0xOxsbHq9y5duoRWrVqhUaNGmDVrFlxcXKBQ8H+uL7z77rsYMWIEWrZsiTVr1vB+vUJh36pcuHAB//zzT6E/f8R7Vhq8Z2SM+C8BaSQ9PR2NGzdGaGhogfdsbW0RGxuLJk2aoFevXuoQfPfuXbi5uRm4UunNnj0bLVu2hK2tLapUqYLu3bvj6tWr6vdjY2Ph4eGBP/74A2PGjMHu3bsB5C2b8sEHH2DlypW4cOECzp49i0aNGknVDUksW7YMjRo1gp2dHezs7NCmTRvs379f/f7FixdRp04dnD17Fv3795f9/fqv7777DoIgqL8yLuxblQsXLqBGjRoFfv7kZNq0aeqlmV78ebENKe9Z0e7du4d+/fqhUqVKsLa2RsOGDREVFcV7RkaJy4uRRjp16oROnToV+t61a9fg7e2N/v37IzIyEtnZ2QCAW7duwdXV1ZBlGoXffvsNI0eORMuWLfH8+XN89dVXCAgIwOXLl6FUKiEIAgYPHgwAyM3NVT8B37VrF9544w00bNgQAFCvXj3Z/aJQrVo1fPfdd/D29oYoili3bh26deuGmJgYeHh4QKlU4rPPPgPA+/VfZ8+exfLlywuE/Ze/VcnMzIRSqcTQoUMB5P/5k5sGDRrg8OHD6tflyv37zyLvWUFPnz6Fr68v3nzzTezfvx+VK1fGtWvXULFiRQC8Z2R8GHRJZ2bOnIlTp06hfPnyaNu2LXr27AkA8PHxQVxcHBo2bIiffvoJ9evXl7hSwzhw4EC+12vXrkWVKlUQHR2NcuXKoWXLlur3Ll68iEGDBqn//vKYtujoaLz99tsGqdlYdO3aNd/rWbNmYdmyZTh16hRSU1PzbQnJ+/WvtLQ09O3bF+Hh4Zg5c6b6+H+/VYmNjUWLFi3Ur1/++ZObcuXKwcXFpcBx3rPCzZkzB+7u7lizZo36WM2aNQHwnpFx4tAF0pl169bh6tWriImJQWhoqHqMpL29PaKjo3Hx4kXZhNzCPHv2DADg6OiI2NhYNG7cWP3exYsX1U8kX7wPAAcPHsThw4dl/VW8UqnEli1bkJ6ejjZt2uS7VwDv18tGjhyJd955B/7+/vmO//dblQsXLuS7h/+9p3Jy7do1uLm5wdPTE3379sXt27cB8J4VZc+ePWjRogV69uyJKlWqoGnTpggPDwfAeyYVQRCgUCgM9seQu7DpAoMukQGoVCqMHTsWvr6+8PHxQWxsrDqMPX/+HMnJyahUqRIAoF+/fjh58iSaNm2KDRs2oHr16uqvBeXk4sWLqFChAiwtLTFs2DDs3LkT9evXL/CPJe9Xni1btuDcuXOYPXt2gfde/lbl8uXL+QLIf3/+5KRVq1ZYu3YtDhw4gGXLliE+Ph7t2rVDamoq71kR4uLisGzZMnh7eyMiIgLDhw/HmDFjsG7dOt4zCQiCAGtrK1hbWxvwj5XGYffkyZPo2bMnatWqhQoVKmDv3r353q9QoUKhfxYuXKj9PRK5bxyVkiAI2LlzJ7p37y51KUZv+PDh2L9/P06ePIlq1apJXU6ZkJOTg9u3b+PZs2f4+eefsXLlSvz222+y/lagKHfu3EGLFi1w6NAh9S9Q7du3R5MmTXTyD4VcJCcno3r16vjhhx/wySefSF2OUbKwsECLFi3w559/qo+NGTMGZ8+eRWRkpISVmb6srCzExcXBxcUFlpaWAACFQgFra2skJPyK3Nwneq/B3NwRLi6dkZmZCZVKVeLzDh48iMjISDRt2hR9+vTB5s2b8w1RS0xMLPD5ESNG4MKFC+qhMS/Lzs5GQkICPD09YWVlVWzbHKNLpGejRo3CL7/8ghMnTjDkasDCwgK1atUCADRv3hxnz57FokWL1Osz07+io6ORlJSEZs2aqY8plUqcOHECS5cuRXZ2NszMzCSssGxwcHBA7dq1cf36dalLMVqurq4FftmsV68etm/fLlFFBAC5uU+QnZ0kdRlFCggIQEBAQJHvOzs753u9b98++Pn5FRpyNcWgSxpJS0vL949AfHw8zp8/D0dHR3h4eEhYmfERRRGjR4/Gzp07cfz4cZ38D1bOVCqVeiUPyq9jx464ePFivmMDBw5E3bp18eWXXzLkllBaWhpu3LiB/v37S12K0fL19c23TCIA/PPPP6hevbpEFZGpSUxMxIEDB7BixQqdXI9BlzQSFRWFN998U/16/PjxAIABAwZg7dq1ElVlnEaOHIlNmzZh9+7dsLW1RUJCAoC8yXnW1tYSV2fcJk2ahE6dOsHDwwOpqanYtGkTjh8/joiICKlLM0q2trbw8fHJd8zGxgaVKlUqcJz+9fnnn6Nr166oXr067t+/j6lTp8LMzAwffvih1KUZrXHjxqFt27b49ttv0atXL5w5cwYrVqzQWSgh2rRpE2xtbfHuu+/q5HoMuqSR9u3bg8O6S2bZsmUA8u7Zy9asWYOPP/7Y8AWVIUlJSfjoo4/w4MED2Nvbo1GjRoiIiMBbb70ldWlkQu7evYsPP/wQjx8/RuXKlfH666/j1KlTqFy5stSlGa2WLVti586dmDRpEkJCQlCzZk0sXLgQffv2lbo0MhHr169Hr169Xjn2tqQYdIn0hL8QlN6qVaukLqHMO378uNQlGL0tW7ZIXUKZ1KVLF3Tp0kXqMsgE/fHHH7h27RrWr1+vs2tyeTEiIiIiktz69evRtGlTna63zCe6RERERGWYubmjUbeTlpaGuLg49etbt27hwoULqFixItzd3QEAKSkp2LlzJ7799lud1PoCgy4RERFRGSSKIkRRBReXzgZsU6Xx0Lxz586hc+d/a5w4cSIAoG/fvuolI3/++WeIooiePXvqrlhwwwgiIiIio1fYhhFA3uZNhtyWNy9cSxsduWEEERERkQwYQ/A0ZpyMRkREREQmiUGXiIiIiEwSgy4RERERmSQGXSIiI3b8+HH1ZBNuAkFEpNmGTAy6RGRSXg6G//1Tvnx5uLu7o0uXLli9ejWys7OlLpeIqETMzMwAALm5uRJXIr0X/+02Nzd/5We56gIRyUZmZibu3r2Lu3fvYt++ffjhhx/wyy+/oEaNGlKXRkRUrHLlyqF8+fJITk5GuXLlDLqkmLEQRRHZ2dlITk6Gg4ODOvwXh0GXiEzW8OHDMWLECPXrpKQkxMbGYt68ebh79y4uXbqEd999FzExMSX6DyYRkVQEQYCbmxvi4uKQmJgodTmScnBwgKura4k+y6BLRCarSpUq8PHxyXesQ4cOGDhwIBo1aoSbN2/i4sWL2LlzJ95//32JqiQiKhkLCwvUrl0bOTk5UpciGXNzc40eTDDoEpHs2NraYvLkyRg8eDAA4PDhwwy6RFQmKBSKV+4GRv/iZDQikqWGDRuq/37nzp0C7+fk5GDv3r0YNWoUWrZsiYoVK8Lc3ByVKlVCq1atMG3aNDx69KjYNmrUqAFBEPDxxx8DAK5evYohQ4agRo0asLS0hLOzM9577z2cOnVKq748ffoUbdq0gSAIMDc3x8aNG7W6HhGRqeATXSKSJQsLC/XfC5u5O3ToUKxbt67A8SdPnuDMmTM4c+YMli5dit27d8PX1/eV7e3cuRP9+vVDRkaG+lhSUhJ27dqFvXv3YuPGjfjggw807sf9+/cRGBiI2NhYWFtbY9u2bXjnnXc0vg4RkSli0CUiWbpy5Yr674WtuvD8+XN4enrivffew2uvvQYPDw+UK1cOt27dwuHDh7F69Wo8fvwY7733HmJjY1GlSpUi27p48SJ++uknuLq6Ijg4GC1atIAoioiIiMB3332HrKwsDB06FB06dEDlypVL3IcbN27grbfeQnx8POzs7LB37174+flpdB+IiEwZgy4RyY5SqcS8efPUrwsbnzt9+nR4enoWWMKnRYsWCAoKwogRI9C2bVs8fPgQS5YswYwZM4ps79y5c2jevDmOHj0KOzs79fHWrVujVq1a6NevH1JSUvC///0P48aNK1EfLl68iICAACQkJKBy5co4cOAAmjVrVqJziYjkgmN0iUg2Hj58iKNHj+KNN95ATEwMgLyQ+/rrrxf4rJeXV7HrVDZs2FA9mW3Xrl2vbHv16tX5Qu4Lffr0gZubGwDg999/L0k38Oeff+KNN95AQkIC3N3d8fvvvzPkEhEVgk90ichkTZ8+HdOnTy/0vfLly2PYsGH47rvvSnStp0+f4smTJ8jKylJvP+ng4AAAuHz5MnJzc4vcpadhw4Zo1KhRoe8JgoCmTZvi/v37iIuLe2UdERER6NGjBzIyMlCnTh0cOnQI7u7uJeoDEZHcMOgSkSw1adIEY8aMKXYLyYsXL2LBggXYv38/EhISivycSqXC06dPixynW7du3WJrcXR0BACkpqYW+7mff/4Z4eHhyMnJQbNmzXDgwAGNxvQSEckNgy4RmayXd0Z7/vw57t69i59//hkbNmzAn3/+ifbt2+PMmTOFhsVVq1Zh2LBheP78eYnayszMLPK98uXLF3uuQpE3ikypVBb7udDQUACApaUldu3axZBLRPQKHKNLRCbrxc5oPj4+aNKkCbp06YK1a9di9erVAICbN2+qx9m+7O+//1aH3CpVqmDevHmIjo7G48ePkZOTA1EUIYoiVq1apT7nxXAGferRowcAIDs7Gx988MErnwATEckdgy4Ryc6AAQMQFBQEANizZw+OHj2a7/21a9fi+fPnMDMzw2+//YbPP/8czZo1g6OjY76hDk+ePDFo3aNHj8bcuXMBAJGRkejcuTPS0tIMWgMRUVnCoEtEsvTtt9+q90v/6quv8r136dIlAEDjxo2LHV8bFRWlvwKLMGHCBHz77bcAgJMnT+Kdd97JtwkFERH9i0GXiGSpdu3a6NWrFwDg9OnTOHTokPq9F+Ny09PTizz/wYMH2LNnj36LLMKkSZPU6/aeOHECXbp0KXaMMBGRXDHoEpFsffXVV+q1cmfOnKk+7u3tDQC4du0a/vzzzwLnZWRkoE+fPpKGy8mTJ2Pq1KkAgGPHjqFr167IysqSrB4iImPEoEtEsuXj44N3330XQN6T0ZMnTwIA+vfvDyBv2bB33nkH3377LU6cOIEzZ85g2bJlaNKkCY4fPw5fX1/JageAadOmYfLkyQCAI0eOoFu3bsjOzpa0JiIiY8KgS0Sy9vXXX6v//mI4QMuWLdUbTSQnJ+Prr7/GG2+8gVatWmHEiBG4du0agoODC12xwdBmzJiBiRMnAgAOHjyIHj16ICcnR+KqiIiMA4MuEclay5Yt8dZbbwHIC4pnz54FAEyZMgX79u1DQEAAKlasCAsLC1SrVg09evTAwYMH8f3330tZdj6zZ8/GhAkTAAC//vorgoKCGHaJiAAIoiEWfyQiIiIiMjA+0SUiIiIik8SgS0REREQmiUGXiIiIiEwSgy4RERERmSQGXSIiIiIySQy6RERERGSSGHSJiIiIyCQx6BIRERGRSWLQJSIiIiKTxKBLRERERCaJQZeIiIiITBKDLhERERGZJAZdIiIiIjJJDLpEREREZJL+D3FElWdT9l6SAAAAAElFTkSuQmCC", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "path=os.path.join(os.getcwd(),'images')\n", + "dim=X_tr.shape[1]\n", + "plt_importances_bars(imps,name,dim=dim,pwd=path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### plt_feat_bar_plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Glass" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt_feat_bar_plot(plt_data,name,pwd=path,save=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Lympho" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Cw8NDa6JTVlYGlUolaGNnZweZTAaFQtFuolNTU4PPPvsMISEhXU5yACNNdP7zn//ggw8+gFwuR0JCAn766ScsXboU5ubmiIuLa7dNcnIyNmzY0MOREkII6UxpaanOM1wcx6GhoQFKpRIMw3SpjeaSk5+fn6A8KSkJ69evF5RVV1eDZVk4OzsLyp2dnTtcK6NSqdpto8ut9Lt27cKqVaugVqsxdOhQZGdnw9y85+4WVqlUMDc3x4ABAwTlHfVHU96VMVi9ejV27NiBxsZGjBkzRjAL1hVGmei0trZi5MiR2Lx5MwDgiSeeQFFREVJTU7UmOvHx8YLsuqKiAn5+fkZ96Up64l+GDqHHsCyL4uJi+Pr60iUBkdCYio/GVOjcuXOIioqCt7c3/P39ddqHLpcDKyoqAABKpRJubm58+f2zOb3F888/jyeffBK///47tm3bhhkzZuCHH36ARCJpt/6iRYuwb98+fruxsRFRUVGC8bl58yYAYPjw4bh8+TIAYOzYsTh+/Lgee9K+lStXYv78+bh8+TI2bNiA2bNn48iRI11OXI0y0XFxcWmTiQ8bNgxffvml1jb3T0lqpistTRhYmRjlmm3Y2toaOoQew7IsrKysYGtrSycQkdCYio/GVMja2hrA3UtBDzIeDMPA1NS0y/vQ1LOxsen096SjoyNMTU3b3GFUVVXV4bN/pFJpt9toY2dnBzs7O/j4+GDMmDF45JFHcOjQIcyaNavd+hs3buTXxQDAhAkTsHXrVshksjZ1jx07htu3bwMALC0ttfalpaUFtbW1glmdjvqjKa+qqoLLPcsoqqqq2qxDcnR0hKOjIx577DEMGzYM7u7u+PHHHxEcHNzuvu9nlGfwP/3pTygpKRGU/fbbbxg8eLCBIiKEEGKMzM3NERQUhJycHL6stbUVOTk5HZ6Ig4ODBW0AIDs7u8snb204jgPHcYI1p/dzcnKCt7c3/zEzM4Obm5ugTGPw4MF82b2zW/cKCgpCv379BP0pKSlBeXm51v54eXlBKpUK2tTX1yM3N7fDMWhtbQWADvt3P6Oc0Xn11VcREhKCzZs3Y8aMGThz5gx2796N3bt3Gzo0QgghRkYulyMuLg4jR47E6NGjkZKSArVajblz5/J1Zs+eDTc3NyQnJwMAli1bhvHjx2P79u2Ijo7G/v37kZeXJzhP1dTUoLy8nH9ujOYPeKlUCqlUiv/85z84cOAAwsPDMXDgQFy9ehVbtmyBpaUlJk+e3GP9t7Ozw/z58yGXy2Fvbw9bW1ssWbIEwcHBgoXIvr6+SE5OxtSpU8EwDJYvX47XX38dPj4+8PLywrp16+Dq6oqYmBgAQG5uLn766SeEhobikUcewcWLF7Fu3ToMGTKkWwmhUSY6o0aNwqFDhxAfH4+NGzfCy8sLKSkpeP755w0dGiGEECMTGxuL69evIzExESqVCoGBgcjKyhIstC0vL4fJPcsgQkJCkJ6ejrVr1yIhIQE+Pj7IzMwUrEU6fPiwIFnS3ImkWRQtkUjw/fffIyUlBTdu3ICzszPGjRuH06dPw8nJqQd6/l/vvPMOTExMMH36dDQ3NyMiIgK7du0S1CkpKUFdXR2/rVlAvXDhQtTW1iI0NBRZWVn82iIrKyscPHgQSUlJUKvVcHFxQWRkJNauXdut9VJG+RwdMWieo3P+/Hmt03X6ostzH0jHaEzFR2MqPhpTocLCQowbN67XP0eH9G5GuUaHEEJI3yeVSrFmzRp6oSd5IEZ56YoQQkjfJ5VKkZCQYOgwSB9HMzqEEEIIMVqU6BBCCCHEaFGiQwghhBCjRYkOIYQQQowWJTqEEEIIMVqU6BBCCCHEaFGiQwghhBCjRYkOIYQQQowWJTqEEEIIMVqU6BDSx6hUKmzevBkqlcrQoRBCSK9Hr4DoxG+//Yba2toeP66ZmRmKi4sfaB/Dhw8XKRrSm6hUKmzZsgWTJ0+mdwARQkgnaEaHEEIIIUarTyY6p06dwtNPPw1XV1cwDIPMzEzB93PmzAHDMIJPZGSkYYIlhBBi9Hbu3AlPT09IJBLIZDKcOXOm0zYZGRnw9fWFRCLBiBEjcOzYMcH3Bw8eRHh4OBwcHMAwDAoLCwXf19TUYMmSJRg6dCgsLS3h4eGBpUuXoq6uTsyudcmtW7ewePFiODg4oH///pg+fTqqqqo6bMNxHBITE+Hi4gJLS0uEhYXhwoULgjpvvPEGQkJCYGVlhQEDBugUW59MdNRqNQICArBz506tdSIjI/H777/zn88//7wHIySEEPKwOHDgAORyOZKSklBQUICAgABERETg2rVrWtucPn0as2bNwvz583H27FnExMQgJiYGRUVFfB21Wo3Q0FBs3bq13X1UVlaisrIS27ZtQ1FREdLS0pCVlYX58+d3K35PT0+cPHmyW23u9+qrr+Krr75CRkYGvvvuO1RWVmLatGkdtnnzzTfx3nvvITU1Fbm5ubC2tkZERARu3brF12lpacFzzz2Hl19+WefY+uQanaioKERFRXVYx8LCQpT1C7du3UJTU9MD78cQ1Gq1oUPoNViWRVNTE9RqNUxNTQ0dzgPpqz+PhBirt99+GwsWLMDcuXMBAKmpqTh69Cj27NmDNWvWtNvm3XffRWRkJFauXAkA2LRpE7Kzs7Fjxw6kpqYCAF588UUAwKVLl9rdh7+/P7788kt+e8iQIXjjjTfwwgsv4M6dOzAz65lTfF1dHT7++GOkp6fjz3/+MwBg7969GDZsGH788UeMGTOmTRuO45CSkoK1a9fimWeeAQB8+umncHZ2RmZmJmbOnAkA2LBhAwAgLS1N5/j6ZKLTFSdPnoSTkxMeeeQR/PnPf8brr78OBwcHrfWbm5vR3NzMbzc0NAAAYmNj9R4rIbpobW0Fy7IGOz7LsuA4zqAxGBsaU/HpMqaaug0NDaivr+fLLSwsYGFhIajb0tKC/Px8xMfH82UmJiYICwuDQqHQegyFQgG5XC4oi4iIaLMUo7vq6upga2vbY0kOAOTn5+P27dsICwvjy3x9feHh4QGFQtFuolNWVgaVSiVoY2dnB5lMBoVCwSc6YjDKRCcyMhLTpk2Dl5cXLl68iISEBERFRUGhUGj9az45OZnPHAnpC0pLSw06O8VxHBoaGqBUKsEwjMHiMCY0puLTZUw1l5z8/PwE5UlJSVi/fr2grLq6GizLwtnZWVDu7Ozc4Z2zKpWq3TYP8tiI6upqbNq0CQsXLtR5H7pQqVQwNzdvs4amo/5oysUeg/YYZaJzbyY4YsQIPP744xgyZAhOnjyJSZMmtdsmPj5ekF1XVFTAz88PBw4cwMCBA/Uesz489thjhg6h12BZFsXFxfD19e3zl67OnTuHqKgoeHt7w9/f32BxsCwLpVIJPz+/Pj+mvQWNqfh0GdOKigoAgFKphJubG19+/2xOb1JfX4/o6Gj4+fm1Scbut2jRIuzbt4/fbmxsRFRUlGB8bt68CeDuY0ouX74MABg7diyOHz8ufvB6ZpSJzv0effRRODo6orS0VGuic/+UpGa6UiKRwNLSskfiFJutra2hQ+g1WJaFlZUVbG1t+/wJxNraGsDd6XFD94VhGJiamho8DmNCYyq+7o6ppp6NjU2nv0cdHR1hamra5g6jqqqqDteJSqXSbrfRpqGhAZGRkbCxscGhQ4fQr1+/Dutv3LgRK1as4LcnTJiArVu3QiaTtal77Ngx3L59GwC0ngulUilaWlpQW1srmNXpqD+a8qqqKri4uAjaBAYGdhh/d/XJu6666+rVq/jjjz8Eg0kIIYQ8KHNzcwQFBSEnJ4cva21tRU5ODoKDg7W2Cw4OFrQBgOzs7A7btKe+vh7h4eEwNzfH4cOHIZFIOm3j5OQEb29v/mNmZgY3NzdBmcbgwYP5sntnt+4VFBSEfv36CfpTUlKC8vJyrf3x8vKCVCoVtKmvr0dubm63x6AzfXJG5+bNmygtLeW3y8rKUFhYCHt7e9jb22PDhg2YPn06pFIpLl68iFWrVsHb2xsREREGjJoQQogxksvliIuLw8iRIzF69GikpKRArVbzd2EBwOzZs+Hm5obk5GQAwLJlyzB+/Hhs374d0dHR2L9/P/Ly8rB7926+TU1NDcrLy1FZWQngbvIA3J0NkUqlfJLT2NiIffv2ob6+nr8aMXDgwB6bFbSzs8P8+fMhl8thb28PW1tbLFmyBMHBwYKFyL6+vkhOTsbUqVPBMAyWL1+O119/HT4+PvDy8sK6devg6uqKmJgYvk15eTk/DizL8s8S8vb2Rv/+/bsUX59MdPLy8jBx4kR+W7O2Ji4uDh988AF++eUXfPLJJ6itrYWrqyvCw8OxadOmXn19lRBCSN8UGxuL69evIzExESqVCoGBgcjKyhIstC0vL4eJyX8vooSEhCA9PR1r165FQkICfHx8kJmZKVh3d/jwYUGypFl/qlkUXVBQgNzcXAAQzMIAdycAPD099dHddr3zzjswMTHB9OnT0dzcjIiICOzatUtQp6SkRPAww1WrVkGtVmPhwoWora1FaGgosrKyBLNSiYmJ+OSTT/jtJ554AgBw4sQJTJgwoUuxMRzHcQ/QN6N19epVuLu74/z581qn6/SFZVkUFRXB39+frtOLxJjGtLCwEOPGjcOpU6dEv5bdHcY0pr0Fjan4dBnTiooKDBs2DFeuXMGgQYP0HCHRt4dijQ4hxkQqlWLNmjX0Qk9CCOmCPnnpipCHmVQqRUJCgqHDIISQPoFmdAghhBBitCjRIYQQQojRokSHEEIIIUaLEh1CCCGEGC1KdAghhBBitCjRIYQQQojRokSHEEIIIUaLEh1CCCGEGC1KdAghhOidSqXC5s2boVKpDB0KecjQk5E78Y/Xf8Yj/SsNcuyfUfBA7We/NUqkSAgh5MGoVCps2bIFkydPpteXkB5FMzqEEEIIMVqU6BBCCCHEaBllosOyLNatWwcvLy9YWlpiyJAh2LRpEziOM3RohBBCjNDOnTvh6ekJiUQCmUyGM2fOdNomIyMDvr6+kEgkGDFiBI4dOyb4/uDBgwgPD4eDgwMYhkFhYaHg+5qaGixZsgRDhw6FpaUlPDw8sHTpUtTV1YnZtS65desWFi9eDAcHB/Tv3x/Tp09HVVVVh204jkNiYiJcXFxgaWmJsLAwXLhwQVBnypQp8PDwgEQigYuLC1588UVUVnZvOYlRrtHZunUrPvjgA3zyyScYPnw48vLyMHfuXNjZ2WHp0qXd2lfL7Vtovt2kp0j1S61WGzqEXoNlWTQ1NUGtVsPU1NTQ4RgFGlPxGfOYNjX1zd+jXXHgwAHI5XKkpqZCJpMhJSUFERERKCkpgZOTU7ttTp8+jVmzZiE5ORlPPfUU0tPTERMTg4KCAvj7+wO4+zs8NDQUM2bMwIIFC9rso7KyEpWVldi2bRv8/Pxw+fJlLFq0CJWVlfjHP/7R5fg9PT2RlpaGCRMm6NR/AHj11Vdx9OhRZGRkwM7ODq+88gqmTZuGH374QWubN998E++99x4++eQTeHl5Yd26dYiIiIBSqYREIgEATJw4EQkJCXBxcUFFRQVWrFiBZ599FqdPn+56cJwRio6O5ubNmycomzZtGvf8889rbXPr1i2urq6O/yiVSg4AfehDH/rQR8TPyZMnuRs3bvTYp7q6mjt16hRXXV3d5TZFRUUcAE6pVArOC7du3Wr3/DF69Ghu8eLF/DbLspyrqyuXnJys9ZwzY8YMLjo6WlAmk8m4l156qU3dsrIyDgB39uxZrfvT+OKLLzhzc3Pu9u3bndbVGDx4MHfixIku179fbW0t169fPy4jI4MvO3/+PAeAUygU7bZpbW3lpFIp99Zbbwn2Y2FhwX3++edaj/XPf/6TYxiGa2lp6XJ8RjmjExISgt27d+O3337DY489hp9//hn//ve/8fbbb2ttk5ycjA0bNvRglIQQ8vApLS3t0dkqjuPQ0NAApVIJhmG61ObatWsAAD8/P0F5UlIS1q9fLyhraWlBfn4+4uPj+TITExOEhYVBoVBoPYZCoYBcLheURUREIDMzs0sxalNXVwdbW1uYmfXc6T0/Px+3b99GWFgYX+br6wsPDw8oFAqMGTOmTZuysjKoVCpBGzs7O8hkMigUCsycObNNm5qaGnz22WcICQlBv379uhyfUSY6a9asQX19PXx9fWFqagqWZfHGG2/g+eef19omPj5e8ENXUVEBPz8/rJuRhgH9HXsibNHFbgw0dAi9BsuyKC4u5n8myIOjMRWfMY/puXPnEBUVBW9vb/7STE9gWRZKpRJ+fn5dHtOKigoAgFKphJubG19uYWHRpm51dTVYloWzs7Og3NnZGcXFxVqPoVKp2m3zIM8Zqq6uxqZNm7Bw4UKd96ELlUoFc3NzDBgwQFDeUX805V0Zg9WrV2PHjh1obGzEmDFjcOTIkW7FZ5SJzhdffIHPPvsM6enpGD58OAoLC7F8+XK4uroiLi6u3TYWFhaCH+L6+noAgHk/CSz6WfZI3GKztbU1dAi9BsuysLKygq2trdGdQAyFxlR8xjym1tbWAO7OdvR03xiGgampaZePq6lnY2PTZ36P1tfXIzo6Gn5+fm1mne63aNEi7Nu3j99ubGxEVFSUYHxu3rwJABg+fDguX74MABg7diyOHz8ufvCdWLlyJebPn4/Lly9jw4YNmD17No4cOdLlGTqjTHRWrlyJNWvW8FNfI0aMwOXLl5GcnKw10SGEEEK6y9HREaampm3uMKqqqurwwYhSqbTbbbRpaGhAZGQkbGxscOjQoU4v62zcuBErVqzgtydMmICtW7dCJpO1qXvs2DHcvn0bAGBp2f4f/VKpFC0tLaitrRXM6nTUH015VVUVXFxcBG0CAwMFdR0dHeHo6IjHHnsMw4YNg7u7O3788UcEBwd32E8No7y9vLGxESYmwq6ZmpqitbXVQBERQggxRubm5ggKCkJOTg5f1traipycnA5PxMHBwYI2AJCdnd3lk7dGfX09wsPDYW5ujsOHD/N3K3XEyckJ3t7e/MfMzAxubm6CMo3BgwfzZfdexrtXUFAQ+vXrJ+hPSUkJysvLtfbHy8sLUqlU0Ka+vh65ubkdjoHmPN7c3NxpPzWMckbn6aefxhtvvAEPDw8MHz4cZ8+exdtvv4158+YZOjRCCCFGRi6XIy4uDiNHjsTo0aORkpICtVqNuXPn8nVmz54NNzc3JCcnAwCWLVuG8ePHY/v27YiOjsb+/fuRl5eH3bt3821qampQXl7OPzempKQEwN3ZEKlUyic5jY2N2LdvH+rr6/llFwMHDuyxS4R2dnaYP38+5HI57O3tYWtriyVLliA4OFiwENnX1xfJycmYOnUqGIbB8uXL8frrr8PHx4e/vdzV1RUxMTEAgNzcXPz0008IDQ3FI488gosXL2LdunUYMmRItxJCo0x03n//faxbtw7/8z//g2vXrsHV1RUvvfQSEhMTDR0aIYQQIxMbG4vr168jMTERKpUKgYGByMrKEiy0LS8vF1xpCAkJQXp6OtauXYuEhAT4+PggMzNTsFD78OHDgmRJsxxDc/dXQUEBcnNzAUAwCwPcvavJ09NTH91t1zvvvAMTExNMnz4dzc3NiIiIwK5duwR1SkpKBA8zXLVqFdRqNRYuXIja2lqEhoYiKyuLn5WysrLCwYMHkZSUBLVaDRcXF0RGRmLt2rXtLgzXhuE4elxwe65evQp3d3ecP39e63SdvrAsi6KiIvj7+xvdgkRDoTEVH42p+Ix5TFUqFfbs2YN58+b16Es9dRnTiooKDBs2DFeuXMGgQYP0HCHRN6Oc0SGEENK7SKVSJCQkGDoM8hAyysXIhBBCCCEAJTqEEEIIMWKU6BBCCCHEaFGiQwghhBCjRYkOIYQQQowWJTqEEEIIMVqU6BBCCCHEaFGiQwghhBCjRYkOIYQQQowWJTqEGDGVSoXNmzdDpVIZOhRCCDEIegVEJ77/rQmOtY09f2AzH5wo7vpr6NsTNtxKpGBIX6VSqbBlyxZMnjy5R98vRAghvQXN6BBCCCHEaBltonPq1Ck8/fTTcHV1BcMwyMzMNHRIhBBCjNTOnTvh6ekJiUQCmUyGM2fOdNomIyMDvr6+kEgkGDFiBI4dOyb4/uDBgwgPD4eDgwMYhkFhYaHg+5qaGixZsgRDhw6FpaUlPDw8sHTpUtTV1YnZtS65desWFi9eDAcHB/Tv3x/Tp09HVVVVh204jkNiYiJcXFxgaWmJsLAwXLhwgf/+0qVLmD9/Pry8vGBpaYkhQ4YgKSkJLS0t3YrNaBMdtVqNgIAA7Ny509ChEEIIMWIHDhyAXC5HUlISCgoKEBAQgIiICFy7dk1rm9OnT2PWrFmYP38+zp49i5iYGMTExKCoqIivo1arERoaiq1bt7a7j8rKSlRWVmLbtm0oKipCWloasrKyMH/+/G7F7+npiZMnT3arzf1effVVfPXVV8jIyMB3332HyspKTJs2rcM2b775Jt577z2kpqYiNzcX1tbWiIiIwK1btwAAxcXFaG1txYcffohff/0V77zzDlJTU5GQkNCt2BiO4zide9ZHMAyDQ4cOISYmpsttrl69Cnd3d7x/4N9wGOiiv+D0aOIwWqOjwbIslEol/Pz8YGpqauhweswvv/yCiIgInDp1CoGBgaLum2VZFBUVwd/f/6EaU32iMRWfLmNaUVGBYcOG4cqVKxg0aFCn9WUyGUaNGoUdO3YAAFpbW+Hu7o4lS5ZgzZo17baJjY2FWq3GkSNH+LIxY8YgMDAQqampgrqXLl2Cl5cXzp492+l/xxkZGXjhhRegVqthZta1Zbienp5IS0vDhAkTulT/fnV1dRg4cCDS09Px7LPPAribpAwbNgwKhQJjxoxp04bjOLi6uuK1117DihUr+P04OzsjLS0NM2fObPdYb731Fj744AP85z//6XJ8tBj5/zQ3N6O5+b+LfxsaGgAAS2JDDRUSIaJpbW0Fy7Ki7pNlWXAcJ/p+H2Y0puLTZUw1dRsaGlBfX8+XW1hYwMLCQlC3paUF+fn5iI+P58tMTEwQFhYGhUKh9RgKhQJyuVxQFhER8cDLLOrq6mBra9vlJEcM+fn5uH37NsLCwvgyX19feHh4aE10ysrKoFKpBG3s7Owgk8mgUCi0Jjp1dXWwt7fvVnyU6Pyf5ORkbNiwwdBhEKIXpaWlos8QcByHhoYGKJVKMAwj6r4fVjSm4tNlTDWXnPz8/ATlSUlJWL9+vaCsuroaLMvC2dlZUO7s7Izi4mKtx1CpVO22eZBHQVRXV2PTpk1YuHChzvvQhUqlgrm5OQYMGCAo76g/mvLujEFpaSnef/99bNu2rVvxUaLzf+Lj4wXZdUVFBfz8/Pr0patxj1l0XukhwbIsiouL4evr+1BdEjh37hyioqLg7e0Nf39/Uff9sF4O1CcaU/HpMqYVFRUAAKVSCTc3N778/tmc3qS+vh7R0dHw8/Nrk4zdb9GiRdi3bx+/3djYiKioKMH43Lx5EwAwfPhwXL58GQAwduxYHD9+XPzgu6CiogKRkZF47rnnsGDBgm61pUTn/9w/JamZrrSQWEFiaW2osB6IrS2t0dFgWRZWVlawtbV9qE4g1tZ3f3ZNTEz00m+GYWBqavpQjam+0ZiKr7tjqqlnY2MDW1vbDus6OjrC1NS0zR1GVVVVHT67SiqVdruNNg0NDYiMjISNjQ0OHTqEfv36dVh/48aN/LoYAJgwYQK2bt0KmUzWpu6xY8dw+/ZtAIClpWW7+5NKpWhpaUFtba1gVqej/mjKq6qq4OLiImhz/zqkyspKTJw4ESEhIdi9e3eHfWuP0d51RQghhOibubk5goKCkJOTw5e1trYiJycHwcHBWtsFBwcL2gBAdnZ2h23aU19fj/DwcJibm+Pw4cOQSCSdtnFycoK3tzf/MTMzg5ubm6BMY/DgwXzZvbNb9woKCkK/fv0E/SkpKUF5ebnW/nh5eUEqlQra1NfXIzc3V9CmoqICEyZMQFBQEPbu3QsTk+6nLUY7o3Pz5k2Ulpby22VlZSgsLIS9vT08PDwMGBkhhBBjIpfLERcXh5EjR2L06NFISUmBWq3G3Llz+TqzZ8+Gm5sbkpOTAQDLli3D+PHjsX37dkRHR2P//v3Iy8sTzFjU1NSgvLwclZWVAO4mD8Dd2RCpVMonOY2Njdi3bx/q6+v5qxEDBw7ssVlBOzs7zJ8/H3K5HPb29rC1tcWSJUsQHBwsWIjs6+uL5ORkTJ06FQzDYPny5Xj99dfh4+MDLy8vrFu3Dq6urvwd0pokZ/Dgwdi2bRuuX7/O76s7M19Gm+jk5eVh4sSJ/LZm/U1cXBzS0tIMFBUhhBBjExsbi+vXryMxMREqlQqBgYHIysoSLLQtLy8XzEaEhIQgPT0da9euRUJCAnx8fJCZmSlYS3f48GFBsqS5E0mzKLqgoAC5ubkAIJiFAe7+ce/p6amP7rbrnXfegYmJCaZPn47m5mZERERg165dgjolJSWChxmuWrUKarUaCxcuRG1tLUJDQ5GVlcXPSmVnZ6O0tBSlpaVtbvPvzpNxHorn6OhC8xyd8+fPa52u0xd6lob4HtYxLSwsxLhx4+g5On0Ejan4euI5OqR3ozU6hBgxqVSKNWvW0As9CSEPLaO9dEUIuZvodPdx6YQQYkxoRocQQgghRosSHUIIIYQYLUp0CCGEEGK0KNEhhBBCiNGiRIcQQgghRosSHUIIIYQYLUp0CCGEEGK0KNEhhBBCiNGiRIcQQgghRouejNyJ81/9A3/YP9Ljx2UA/Fry8wPt4/EZs8UJhjxUVCoV9uzZg3nz5tGrIwghfR7N6BBCBFQqFbZs2QKVSmXoUAgh5IGJlujcvn0bSqUSSqUSzc3Nbb6/desWXnvtNbi7u8PS0hJ+fn54//33xTo8IYQQQkgboiU6hw4dwogRIzB+/Ph2v586dSpSUlJQUVGB5uZmFBcXY/ny5XjllVe6faxTp07h6aefhqurKxiGQWZmpuD7gwcPIjw8HA4ODmAYBoWFhTr0iBBCCOmanTt3wtPTExKJBDKZDGfOnOm0TUZGBnx9fSGRSDBixAgcO3ZM8H1n57KamhosWbIEQ4cOhaWlJTw8PLB06VLU1dWJ2bUuuXXrFhYvXgwHBwf0798f06dPR1VVVYdtOI5DYmIiXFxcYGlpibCwMFy4cIH//tKlS5g/fz68vLxgaWmJIUOGICkpCS0tLd2KTbRE5+uvvwbHcYiJiYGFhYXgu6NHj+Lrr78GAAwaNAhTp06Fm5sbOI7DBx98gNOnT3frWGq1GgEBAdi5c6fW70NDQ7F161bdOkMIIYR00YEDByCXy5GUlISCggIEBAQgIiIC165d09rm9OnTmDVrFubPn4+zZ88iJiYGMTExKCoq4ut0di6rrKxEZWUltm3bhqKiIqSlpSErKwvz58/vVvyenp44efJkt9rc79VXX8VXX32FjIwMfPfdd6isrMS0adM6bPPmm2/ivffeQ2pqKnJzc2FtbY2IiAjcunULAFBcXIzW1lZ8+OGH+PXXX/HOO+8gNTUVCQkJ3YpNtMXIBQUFYBim3RmdPXv2AAAee+wxnDlzBjY2Nqirq0NISAiKi4vxt7/9DSEhIV0+VlRUFKKiorR+/+KLLwK4mw0+qFstLWhq51JcX6BWqw0dQq/BsiyampqgVqthampq6HB6taamJkOHQEif8vbbb2PBggWYO3cuACA1NRVHjx7Fnj17sGbNmnbbvPvuu4iMjMTKlSsBAJs2bUJ2djZ27NiB1NRUAJ2fy/z9/fHll1/y20OGDMEbb7yBF154AXfu3IGZWc/cb1RXV4ePP/4Y6enp+POf/wwA2Lt3L4YNG4Yff/wRY8aMadOG4zikpKRg7dq1eOaZZwAAn376KZydnZGZmYmZM2ciMjISkZGRfJtHH30UJSUl+OCDD7Bt27YuxyfaKGgyV29vb0F5a2srcnJywDAMlixZAhsbGwCAnZ0dXnnlFSxevBgKhUKsMHTW3NwsWFvU0NAAAPjL2k2GCunBLX7N0BGQPqy1tRUsy2r9nmVZcBzXYR3SPTSm4tNlTDV1GxoaUF9fz5dbWFi0uWLR0tKC/Px8xMfH82UmJiYICwvr8NymUCggl8sFZREREW2WYnRXXV0dbG1teyzJAYD8/Hzcvn0bYWFhfJmvry88PDygUCjaTXTKysqgUqkEbezs7CCTyaBQKDBz5sx2j1VXVwd7e/tuxSfaSFRXVwMALC0tBeWFhYWor68HwzCIjo4WfOfv7w8AuHLlilhh6Cw5ORkbNmwwdBiE9BqlpaUdzn5xHIeGhgYolUowDNODkRkvGlPx6TKmmj/c/fz8BOVJSUlYv369oKy6uhosy8LZ2VlQ7uzsjOLiYq3HUKlU7bZ5kLsdq6ursWnTJixcuFDnfehCpVLB3NwcAwYMEJR31B9NeXfGoLS0FO+//363ZnMAERMdCwsL3Llzh094NE6dOgXg7tqcwYMHC77TzO70hr9e4uPjBdl1RUUF/Pz8kP76Ogx8ZIDhAnsAw56JNXQIvQbLsiguLoavry9duurEuXPnEBUVBW9vb/6PkfawLAulUgk/Pz8aU5HQmIpPlzGtqKgAACiVSri5ufHl98/m9Cb19fWIjo6Gn59fm2TsfosWLcK+ffv47cbGRkRFRQnG5+bNmwCA4cOH4/LlywCAsWPH4vjx4+IH3wUVFRWIjIzEc889hwULFnSrrWiJzuDBg6FUKpGbm4tJkybx5V999RUYhsG4cePatKmpqQEADBw4UKwwdHb/lKRmulJibg7LXvzD3RFbW1tDh9BrsCwLKysr2Nra0gmkE9bW1gDuTr93NlYMw8DU1JTGVEQ0puLr7phq6tnY2HT6e9TR0RGmpqZt7jCqqqrq8IGbUqm02220aWhoQGRkJGxsbHDo0CH069evw/obN27EihUr+O0JEyZg69atkMlkbeoeO3YMt2/fBtD2io2GVCpFS0sLamtrBbM6HfVHU15VVQUXFxdBm8DAQEHdyspKTJw4ESEhIdi9e3eHfWuPaHddTZw4ERzH4f3338f58+cBAIcPH+ZXck+ePLlNG83q8ns7SQghhPQV5ubmCAoKQk5ODl+mWZsaHBystV1wcLCgDQBkZ2d32KY99fX1CA8Ph7m5OQ4fPgyJRNJpGycnJ3h7e/MfMzMzuLm5Cco0Bg8ezJfdO7t1r6CgIPTr10/Qn5KSEpSXl2vtj5eXF6RSqaBNfX09cnNzBW0qKiowYcIEBAUFYe/evTAx6X7aItqMzpIlS7B7925cu3YN/v7+eOSRR3Djxg1wHIdBgwZh+vTpbdp88803YBgGjz/+eLeOdfPmTZSWlvLbZWVlKCwshL29PTw8PFBTU4Py8nJUVlYCuDvgwN0Mkh5pTwghRExyuRxxcXEYOXIkRo8ejZSUFKjVav4uLACYPXs23NzckJycDABYtmwZxo8fj+3btyM6Ohr79+9HXl6eYMais3OZJslpbGzEvn37UF9fz1+NGDhwYI/NCtrZ2WH+/PmQy+Wwt7eHra0tlixZguDgYMFCZF9fXyQnJ2Pq1KlgGAbLly/H66+/Dh8fH3h5eWHdunVwdXVFTEwMgP8mOYMHD8a2bdtw/fp1fl/dOZeLluj4+Pjg73//O+bNmwe1Ws1flhowYAA+//xzmJubC+qrVCpkZ2cDAH87Wlfl5eVh4sSJ/LZmbU1cXBzS0tJw+PBhwQ+YZvV2ewvJCCGEkAcRGxuL69evIzExESqVCoGBgcjKyhIstC0vLxfMRoSEhCA9PR1r165FQkICfHx8kJmZKVgX19m5rKCgALm5uQDa3vFcVlYGT09PfXS3Xe+88w5MTEwwffp0NDc3IyIiArt27RLUKSkpETzMcNWqVVCr1Vi4cCFqa2sRGhqKrKwsflYqOzsbpaWlKC0txaBBgwT74jiuy7ExXHdqd8G1a9dw9OhRqFQquLi4YMqUKe3eCvbNN9/g888/BwCkpKTAzs5OzDAe2NWrV+Hu7o7z589rna7TF5ZlUVRUBH9/f7pOLxIa064rLCzEuHHjcOrUqTbXyu9FYyo+GlPx6TKmFRUVGDZsGK5cudLmBEv6HtFvtHdychJkoNqEh4cjPDxc7MMTQh6QVCrFmjVr6DIvIcQo9NwThQghfYJUKu32I9YJIaS30lui09TUhPz8fKhUKjQ2NiImJoZudyaEEEJIjxI90bly5QoSEhKQkZHB33sPACNHjhQ8ZfLjjz/Ghx9+CDs7O/7uK0IIIYQQMYn2HB0AyM3NxRNPPIH09HS0tLSA4zitK6Offvpp/PLLL/jXv/6Fb775RswwCCGEEEIAiJjo1NbW4plnnkFNTQ2kUil27dqFc+fOaa3v5OTEv4H86NGjYoVBCCGEEMIT7dLVe++9h2vXrsHR0REKhQIeHh6dtgkLC8M///lPnDlzRqwwCCGEEEJ4os3oaN5pJZfLu5TkAHdfFgYAFy9eFCsMQgghhBCeaImO5pUM7b28U5tHHnkEwH9foEkIIYQQIibREp1bt24BQKdvTb2XWq0GoP2NqIQQQgghD0K0RMfJyQnA3fdrdFVhYSEAwNXVVawwCDF6KpUKmzdvhkqlMnQohBDS64m2GFkmk+Hq1as4fvw4ZsyY0Wl9juPw0UcfgWEYjB07VqwwRLd7926DPejw22+/1frda6+91oORkN5EpVJhy5YtmDx5Mr2mgRBCOiHajM7zzz8PjuPw2Wef8TM1HXnttdfw888/A7j71nFCCCGEELGJlug888wzmDhxIu7cuYNJkybhgw8+wLVr1/jv79y5g8rKSmRkZGDs2LF49913wTAMpk2bhpCQELHCIIQQQgjhifpk5C+//BJPPPEEbty4gVdeeQUuLi78qx2eeOIJuLu7Y+bMmTh9+jQ4joNMJkNaWlq3j3Pq1Ck8/fTTcHV1BcMwyMzM1Fp30aJFYBgGKSkpunWKEEII6cTOnTvh6ekJiUQCmUzWpefDZWRkwNfXFxKJBCNGjMCxY8cE3x88eBDh4eFwcHAAwzDtXi3ZvXs3JkyYAFtbWzAMg9raWpF61D0cxyExMREuLi6wtLREWFgYLly40Gm7jsatpqYGS5YswdChQ2FpaQkPDw8sXboUdXV13YpN1HddDRgwAAqFAhs2bMCuXbu0BmNlZYVXXnkFGzduhLm5ebePo1arERAQgHnz5mHatGla6x06dAg//vjjAy12bmlpQUtLi87t9UVzxxrpGpZl0dTUBLVaDVNTU0OH80CampoMHQIh5B4HDhyAXC5HamoqZDIZUlJSEBERgZKSEv5GnfudPn0as2bNQnJyMp566imkp6cjJiYGBQUF8Pf3B3D393xoaChmzJiBBQsWtLufxsZGREZGIjIyEvHx8TrFP2HCBMyZMwdz5szRqT0AvPnmm3jvvffwySefwMvLC+vWrUNERASUSiUkEkm7bTobt8rKSlRWVmLbtm3w8/PD5cuXsWjRIlRWVuIf//hHl2NjOG0vo3pAarUa3333HfLy8nDt2jWwLAsHBwc88cQTCAsLg52dnSjHYRgGhw4dQkxMjKC8oqICMpkMX3/9NaKjo7F8+XIsX75c636am5vR3NwsaH/vS0gJ6W1OnjyJgIAAgx2fZVkolUr4+fn1+eSxt6AxFZ8uY1pRUQF/f38olUq4ubnx5RYWFrCwsGhTXyaTYdSoUdixYwcAoLW1Fe7u7liyZAnWrFnT7jFiY2OhVqtx5MgRvmzMmDEIDAxEamqqoO6lS5fg5eWFs2fPIjAwsN39nTx5EhMnTsSNGzcwYMCALvVT40ETHY7j4Orqitdeew0rVqwAANTV1cHZ2RlpaWmYOXNmu+10GbeMjAy88MILUKvVMDPr2lyNaDM6n376KQBg6NChkMlksLa2xuTJkzF58mSxDtFlra2tePHFF7Fy5Ur+6cudSU5OxoYNG/QcGSHiKS0tNejJkOM4NDQ0QKlU8peoyYOhMRWfLmOqWV96/x+7SUlJWL9+vaCspaUF+fn5gtkUExMThIWFQaFQaD2GQqGAXC4XlEVERHS4FKO3Kisrg0qlQlhYGF9mZ2cHmUwGhULRbqKj67jV1dXB1ta2y0kOIGKiM2fOHDAMg88//xwymUys3epk69atMDMzw9KlS7vcJj4+XvBDp5nRWbx4scFuL+/I4sWLDR1Cn8KyLIqLi+Hr69vn/1I+d+4coqKi4O3tzU9xGwLNPoiPxlR8us7oAGh3Rud+1dXVYFkWzs7OgnJnZ2cUFxdrPYZKpWq3TV98PpYm5u70R5dxq66uxqZNm7Bw4cJuxSdaomNnZ4f6+nr4+PiItUud5Ofn491330VBQUG3/iK6f0pS81oKc3NzndYR6VtvTL56M5ZlYWVlBVtb2z5/ArG2tgZw968fQ/eFYRiYmpoaPA5jQmMqvu6OqaaejY2NUf6u3bx5MzZv3sxvNzU14ccff8Qrr7zClymVSnh4eCAqKgrff/89AGDw4MH49ddfezxe4O45OTo6Gn5+fm1m1TojWqLj5eWFn3/+GTdu3BBrlzr5/vvvce3aNcGLRVmWxWuvvYaUlBRcunTJcMERQggxKo6OjjA1NUVVVZWgvKqqqsMHekql0m63EcuiRYsED/Z9/vnnMX36dMHNPZqbeP72t7/xN0Boe8WTJuaqqiq4uLjw5VVVVVrXFHVn3BoaGhAZGQkbGxscOnSoW6+aAkS8vXzq1KngOA5fffWVWLvUyYsvvohffvkFhYWF/MfV1RUrV67E119/bdDYCCGEGBdzc3MEBQUhJyeHL2ttbUVOTg6Cg4O1tgsODha0AYDs7OwO24jF3t4e3t7e/MfS0hJOTk6CMs0aGDc3N75s8ODB7e7Py8sLUqlU0J/6+nrk5uZq7U9Xx62+vh7h4eEwNzfH4cOHtd7B1RHRZnSWLVuGPXv24IMPPsBTTz2FSZMmibXrNm7evMm/LR24uxCqsLAQ9vb28PDwgIODg6B+v379IJVKMXToUL3FRAgh5OEkl8sRFxeHkSNHYvTo0UhJSYFarcbcuXP5OrNnz4abmxuSk5MB3D1njh8/Htu3b0d0dDT279+PvLw87N69m29TU1OD8vJyVFZWAgBKSkoA3J1B0cx6qFQqqFQq/px47tw52NjYwMPDA/b29j3Sf4ZhsHz5crz++uvw8fHhby93dXUV3BE9adIkTJ06lb9E1tm4aZKcxsZG7Nu3D/X19fyykoEDB3b5UqRoiY6trS2ys7Px7LPPIjIyEnPnzsVf/vIXPP7443jkkUdEvYMgLy8PEydO5Lc1i4jj4uJ0egBhRxYuXChYjNYTWJZFUVER/P396To9IYT0crGxsbh+/ToSExOhUqkQGBiIrKwswULb8vJymJj89yJKSEgI0tPTsXbtWiQkJMDHxweZmZmCGwwOHz4sSJY0dy/de/dXamqq4I7hcePGAQD27t37QM/F6a5Vq1ZBrVZj4cKFqK2tRWhoKLKysgQzMBcvXkR1dTW/3dm4FRQUIDc3FwDg7e0tOF5ZWRk8PT27FJtoz9G594TMcVy3EhuGYXDnzh0xwhDN1atX4e7ujvPnz1OiYwSMaUxVKhX27NmDefPmGfSlnsY0pr0Fjan4dBnTiooKDBs2DFeuXMGgQYP0HCHRN9FmdO7Pl/T0HEJCHnpSqRQJCQmGDoMQQvoE0RKdpKQksXZFCCGEECIKSnQIIYQQYrREfXs5IYQQQkhvQokOIYQQQowWJTqEEEIIMVqirdHZuHHjA7VPTEwUKRJCCCGEkLtES3TWr1//QA8FpESHEEIIIWITLdEBuvfsHIZh6Fk7hBBCCNEr0dbotLa2dvq5efMmCgoKsGLFCvTr1w9/+tOfoFKp0NraKlYYhBBCCCG8Hl2MbGVlhcDAQLz55pv49ttvkZeXh4iICDQ3N/dkGISQ/6NSqbB582aoVCpDh0IIIXoh6qWr7ggNDcXLL7+Md999FykpKVi9erWhQunQZysXY4CVpUGOndvBdwv/9nmPxUGMl0qlwpYtWzB58mSDvjeLEEL0xaC3lz/11FPgOA779+83ZBiEEEIIMVIGTXTs7e0B3H11e3ecOnUKTz/9NFxdXcEwDDIzMwXfcxyHxMREuLi4wNLSEmFhYbhw4YJYYRNCCCECO3fuhKenJyQSCWQyGc6cOdNpm4yMDPj6+kIikWDEiBE4duyY4PuDBw8iPDwcDg4OYBgGhYWFbfaxe/duTJgwAba2tmAYBrW1tSL1qHt0Pe92NG41NTVYsmQJhg4dCktLS3h4eGDp0qWoq6vrVmwGTXRKSkp0aqdWqxEQEICdO3e2+/2bb76J9957D6mpqcjNzYW1tTUiIiJw69atBwmXEEIIaePAgQOQy+VISkpCQUEBAgICEBERgWvXrmltc/r0acyaNQvz58/H2bNnERMTg5iYGBQVFfF11Go1QkNDsXXrVq37aWxsRGRkJBISEnSOf8KECUhLS9O5PaDbebezcausrERlZSW2bduGoqIipKWlISsrC/Pnz+9WbAZbo1NbW4tNmzaBYRj4+fl1q21UVBSioqLa/Y7jOKSkpGDt2rV45plnAACffvopnJ2dkZmZiZkzZ3brWC13WDTfudOtNj1BrVYbOoQ+hWVZNDU1Qa1Ww9TU1NDh9BpNTU2GDoGQPu/tt9/GggULMHfuXABAamoqjh49ij179mDNmjXttnn33XcRGRmJlStXAgA2bdqE7Oxs7NixA6mpqQCAF198EQBw6dIlrcdevnw5AODkyZPidEYHup53Oxs3f39/fPnll3z9IUOG4I033sALL7yAO3fuwMysaymMaInOqVOnOq3T2tqKGzduIC8vD3v37kVVVRUAYM6cOWKFgbKyMqhUKoSFhfFldnZ2kMlkUCgUWge8ublZcPdXQ0MDAODNrO9Ei01M/3vQxdAhECPS2toKlmW71YZlWXAc1+12RDsaU/HpMqaaug0NDaivr+fLLSwsYGFhIajb0tKC/Px8xMfH82UmJiYICwuDQqHQegyFQgG5XC4oi4iIaLMUoy/Q5byr67jV1dXB1ta2y0kOIGKiM2HChG49GVnzsMCpU6fipZdeEisM/jZZZ2dnQbmzs3OHt9AmJydjw4YNosVBSF9SWlra7ZkujuPQ0NAApVL5QE9FJ/9FYyo+XcZUc+nk/qsNSUlJWL9+vaCsuroaLMu2e84pLi7WegyVStXt81Rvpct5V5dxq66uxqZNm7Bw4cJuxWewJyM//vjjWLx4Mf7617/2iv+g4+PjBdl1RUUF/Pz8sCpyPOysJAaMrH1x731s6BD6FJZlUVxcDF9fX7p0dY9z584hKioK3t7e8Pf371ZblmWhVCrh5+dHYyoSGlPx6TKmFRUVAAClUgk3Nze+/P7ZnL5q8+bN2Lx5M7/d1NSEH3/8Ea+88gpfplQq4eHhgaioKHz//fcAgMGDB+PXX3/t8XgBoL6+HtHR0fDz82uTbHZGtETnxIkTndYxMTGBjY0NPD09MWDAALEOLaB5FkhVVRVcXP57eaeqqgqBgYFa290/JamZrjQ3M4VFN6bIeoqtra2hQ+hTWJaFlZUVbG1t6QRyD2trawB3/9vUZVwYhoGpqSmNqYhoTMXX3THV1LOxsen0d62joyNMTU35pRgaVVVVHT6bSiqVdruNWBYtWoQZM2bw288//zymT5+OadOm8WWurq4AgL/97W/8Wr5+/fq1uz9dzrvdGbeGhgZERkbCxsYGhw4d0hqHNqKdwcePHy/Wrh6Il5cXpFIpcnJy+AGur69Hbm4uXn75ZcMGRwghxKiYm5sjKCgIOTk5iImJAXB3zVtOTo5ghuR+wcHByMnJ4RcTA0B2djaCg4P1HPHdR7toHu8CAJaWlnBycoK3t3ebuvfOaGmjy3m3q+NWX1+PiIgIWFhY4PDhw5BIun+FpfdNVXTBzZs3UVpaym+XlZWhsLAQ9vb28PDwwPLly/H666/Dx8cHXl5eWLduHVxdXfnBJIQQQsQil8sRFxeHkSNHYvTo0UhJSYFarebvJgKA2bNnw83NDcnJyQCAZcuWYfz48di+fTuio6Oxf/9+5OXlYffu3XybmpoalJeXo7KyEsB/H8kilUr5WQ+VSgWVSsWfE8+dOwcbGxt4eHgIkhl9YhimS+fdSZMmYerUqXwi09m41dfXIzw8HI2Njdi3bx/q6+v5qy0DBw7s8gydaInOn//8ZzAMgz179mDw4MFdalNZWYkXXngBDMMgJyeny8fKy8vDxIkT+W3N2pq4uDikpaVh1apVUKvVWLhwIWpraxEaGoqsrCydMkFCCCGkI7Gxsbh+/ToSExOhUqkQGBiIrKwswULb8vJymJj899F1ISEhSE9Px9q1a5GQkAAfHx9kZmYK1sodPnxYkCxp7l66d1F0amqq4EaacePGAQD27t0r6h3NnenKeffixYuorq7mtzsbt4KCAuTm3n0Z0v2zTWVlZfD09OxSbAzXnRXEHTAxMQHDMDh37lyXn4tz8eJF+Pj4gGGYXnc75dWrV+Hu7o7z5893aepOTCzLoqioCP7+/nSdXiQ0pu0rLCzEuHHjcOrUqQ7XsLWHxlR8NKbi02VMKyoqMGzYMFy5cgWDBg3Sc4RE3wz6ZGRCiGFJpVKsWbOGXuhJCDFaBl2jo3m6L11SIsQwpFLpAz06nhBCejuDzugcP34cAGhqkBBCCCF6ofOMzrx589otX7t2bafPyGlubsbFixfx008/gWGYXnNrOiGEEEKMi86JTlpaWpsnGnMch3/+859daq9ZA21vby941wUhhBBCiFh0TnQ8PDwEic7ly5fBMAxcXFw6fGohwzCQSCRwcXFBSEgIXn75Zf4JjIQQQgghYtI50bn/tfGa5wN88803Xb69nBBCCCFEn0S762rcuHFgGIZ/dw4hhBBCiKGJluicPHlSrF0RQgghhIiCHhhICCGEEKNFiQ4hhBBCjJboT0ZuaWnBZ599hszMTPz888+orq5GU1NTh20YhsGdO3fEDkUU16+nw8xsQI8fVyoFqqvPaP3e2XlBD0ZDxKZSqbBnzx7MmzePXr9ACCF6JGqi89tvvyEmJgYlJSUQ6V2hhBgllUqFLVu2YPLkyZToEEKIHomW6KjVakRFRaGsrAwmJiZ45plnMHDgQHz00UdgGAZr165FTU0N8vLykJubC4ZhEBwcjCeffFKsEAghhBBCBERbo5OamoqysjKYmprim2++wcGDB7F06VL++w0bNuD999+HQqFAfn4+hg0bhh9//BEODg5ISkrq1rFOnTqFp59+Gq6urmAYBpmZmYLvGYZp9/PWW2+J0VVCCCFEYOfOnfD09IREIoFMJsOZM9qXHmhkZGTA19cXEokEI0aMwLFjxwTfHzx4EOHh4XBwcADDMCgsLGyzj927d2PChAmwtbUFwzCora0VqUfdw3EcEhMT4eLiAktLS4SFheHChQudtuts3MTon2iJzldffQWGYTBjxgz8+c9/7rDuE088gRMnTsDJyQlyuRz5+fndOpZarUZAQAB27tzZ7ve///674LNnzx4wDIPp06d36ziEEEJIZw4cOAC5XI6kpCQUFBQgICAAERERuHbtmtY2p0+fxqxZszB//nycPXsWMTExiImJQVFREV9HrVYjNDQUW7du1bqfxsZGREZGIiEhQef4J0yYgLS0NJ3bA8Cbb76J9957D6mpqcjNzYW1tTUiIiJw69YtrW26Mm5i9E+0S1dKpRIAMHXq1Ha/b21t5Z+eDAADBw6EXC7HqlWrsGPHDuzdu7fLx4qKikJUVJTW7+9f8/DPf/4TEydOxKOPPtrlY2jcutWCxsbmbrfTN7VabegQ+hSWZdHU1AS1Wg1TU1NDh9PpAn1CSN/x9ttvY8GCBZg7dy6Au1c4jh49ij179mDNmjXttnn33XcRGRmJlStXAgA2bdqE7Oxs7NixA6mpqQCAF198EUDbNxHca/ny5QAM+yw7juOQkpKCtWvX4plnngEAfPrpp3B2dkZmZiZmzpzZbruujJsY/RMt0dFMJw0ePJgvs7Cw4P+/Wq2GjY2NoM2f/vQnAMB3330nVhhtVFVV4ejRo/jkk086rNfc3Izm5v8mNA0NDQCA8HDtmbRh6Z7dkt6jtbUVLMsaOgydsCwLjuP6bPy9EY2p+HQZU03dhoYG1NfX8+UWFhaC8xpw907j/Px8wcupTUxMEBYWBoVCofUYCoUCcrlcUBYREdFmKUZfUFZWBpVKhbCwML7Mzs4OMpkMCoWi3URH13HThWiJjpWVFRoaGgQv+hwwYAD//8vLyzF8+PB226pUKrHCaOOTTz6BjY0Npk2b1mG95ORkbNiwQW9xENKe0tLSXjHDpAuO49DQ0AClUin4757ojsZUfLqMqebSyf3vbUxKSsL69esFZdXV1WBZFs7OzoJyZ2dnFBcXaz2GSqVqt40+z4f6oom5O/3Rddx0IVqi4+XlhV9++QWVlZV8maOjI+zt7XHjxg388MMPbRIdzdocc3NzscJoY8+ePXj++echkUg6rBcfHy/IrisqKuDn54dvvlkNZ2c7vcWnKweH2YYOoU9hWRbFxcXw9fXtFYnFuXPnEBUVBW9vb/j7+xs6HJ2wLAulUgk/P79eMabGgMZUfLqMaUVFBYC7SzLc3Nz48vtnc/qqzZs3Y/Pmzfx2U1MTfvzxR7zyyit8mVKphIeHB6KiovD9998DuHvF5tdff+3xeB+UaInOyJEj8csvvyAvLw9TpkzhyydNmoSMjAy89dZbePbZZ2Fvbw8A+M9//oMtW7aAYRgEBgaKFYbA999/j5KSEhw4cKDTuvdPSWqmKyUSc1hZ9b4fbltbW0OH0KewLAsrKyvY2tr2ihOI5uW3JiYmvSIeXTEMA1NT0z7dh96GxlR83R1TTT0bG5tOf9c6OjrC1NQUVVVVgvKqqqoOn5EllUq73UYsixYtwowZM/jt559/HtOnTxdc+XB1dQUA/O1vf+PXFPbr16/d/WlirqqqgouLC19eVVWl9fyu67jpQrS7rp588klwHIfDhw8LyjW3mP/nP//BY489hueeew6TJ09GYGAgP/uzcOFCscIQ+PjjjxEUFISAgAC97J8QQsjDzdzcHEFBQcjJyeHLWltbkZOTg+DgYK3tgoODBW0AIDs7u8M2YrG3t4e3tzf/sbS0hJOTk6DMzOzuPIibmxtfdu8a3Ht5eXlBKpUK+lNfX4/c3Fyt/dF13HQh2ozOU089hXHjxoFlWVy8eBFDhgwBcHfBcWJiIjZu3IiamhocPHgQAPgnJ8+dOxd/+ctfunWsmzdvorS0lN8uKytDYWEh7O3t4eHhAeDuIGdkZGD79u1idI8QQghpl1wuR1xcHEaOHInRo0cjJSUFarWav5sIAGbPng03NzckJycDAJYtW4bx48dj+/btiI6Oxv79+5GXl4fdu3fzbWpqalBeXs5PCpSUlAC4O4OimfVQqVRQqVT8OfHcuXOwsbGBh4cHfwVF3xiGwfLly/H666/Dx8cHXl5eWLduHVxdXRETE8PXmzRpEqZOncpfIuvKuInRP1EXI2u7/Wv9+vUYO3Ys/va3v+HXX3/FnTt34OPjg9mzZ+v0bJu8vDxMnDiR39asrYmLi+OfBbB//35wHIdZs2Z1e/+EEEJIV8XGxuL69etITEyESqVCYGAgsrKyBAtty8vLBY9YCQkJQXp6OtauXYuEhAT4+PggMzNTsGbv8OHDgpO+5u6lexdFp6amCm6kGTduHABg7969mDNnjj66265Vq1ZBrVZj4cKFqK2tRWhoKLKysgTrYy9evIjq6mp+uyvjJkb/GI5eStWuq1evwt3dHefPnxcsRusJLMuiqKgI/v7+dJ1eJL1tTAsLCzFu3DicOnVKb2vU9K23jakxoDEVny5jWlFRgWHDhuHKlSsYNGiQniMk+ibaGh1CSNdJpVKsWbOGXuhJCCF6JurbywkhXSOVSh/okeaEEEK6Ri8zOjk5OXjxxRfh7e2N/v37w8zMjH9FhMapU6ewa9cu7Nu3Tx8hEEIIIYSIO6PT2NiIuLi4NndWtfc0SlNTU7zyyitgGAYymQw+Pj5ihkIIIYQQIu6MzowZM3Dw4EFwHIdRo0ZhxYoVWuv+6U9/4leXf/nll2KGQQghhBACQMRE58svv8SxY8cAALt378aPP/6IN998s8M206ZNA8dxen2pJyGEEEIeXqIlOpq3g7/wwgv461//2qU2QUFBAIDz58+LFQYhhBBCCE+0RCcvLw8MwyA2NrbLbTTvxLh+/bpYYRBCCCGE8ERLdP744w8A/30RWJcO/n9PiWxtbRUrDEIIIYQQnmiJjp2dHQDw7+ToirKyMgB332JKCCGEECI20RKdxx57DADw888/d7lNZmYmAOCJJ54QKwxCjIZKpcLmzZuhUqkMHQohhPRZoj1HJzo6GqdPn8b777+PV199VfAir/Z8//332L9/PxiGwdNPPy1WGKILey8XZrY9P+O07ym7Hj8m6V1UKhW2bNmCyZMn06siCCFER6LN6CxevBj29vaoqqrCs88+i5qamnbr3blzBx999BGeeuoptLa2wt3dvUffsEoIIYSQh4doMzq2trY4cOAAJk+ejOPHj8Pd3R3jx4/nv1+1ahVaWlqQl5eHuro6cBwHiUSCL774Av369RMrDEIIIYQQnqhPRp40aRL+9a9/wcPDA01NTcjKyuJf/3D8+HHk5OSgtrYWHMfB3d0dJ06cwOjRo8UMQWDnzp3w9PSERCKBTCbDmTNn9HYsQgghDy9dzjcZGRnw9fWFRCLBiBEj+Ifuahw8eBDh4eFwcHAAwzAoLCzUU/SdE7t/t2/fxurVqzFixAhYW1vD1dUVs2fP7tYNTV0l+tvL//SnP+HChQv4/PPP8dVXXyEvLw/Xrl0Dy7JwcHDAE088gSlTpiAuLg7m5uZiH5534MAByOVypKamQiaTISUlBRERESgpKYGTk1OX99N6uxmtLbf0Fqc2TU3mUKvVMDU17fFjGyOWZdHU1NSnxrSpqcnQIRBCukCX883p06cxa9YsJCcn46mnnkJ6ejpiYmJQUFDAvx5JrVYjNDQUM2bMwIIFC3SKbc6cOfD09MT69et17Z5e+tfY2IiCggKsW7cOAQEBuHHjBpYtW4YpU6YgLy9P51jbw3CaN292w6effgoAiImJga2tragBiUUmk2HUqFHYsWMHAPDrgZYsWYI1a9a0qd/c3Izm5mZ+u6KiAn5+fj0WLyHanDx5EgEBAYYOow2WZaFUKuHn59dnksfejsZUfLqMaUVFBfz9/aFUKuHm5saXW1hYwMLCok397p5vACA2NhZqtRpHjhzhy8aMGYPAwECkpqYK6l66dAleXl44e/YsAgMDu9QHDTESHX33T+Onn37C6NGjcfnyZXh4eOgc7/10mtGZM2cOGIbByJEj200Grl+/jg8++AAMw2DdunUPHGR3tbS0ID8/H/Hx8XyZiYkJwsLCoFAo2m2TnJyMDRs29FSIhHRZaWlprzzpcRyHhoYGKJVK/hI1eTA0puLTZUyvXbsGAG3Ob0lJSW0SBl3ONwCgUCggl8sFZREREfxjV3qLnuxfXV0dGIbBgAEDHjRsAdEvXQF3f0jWr19vsESnuroaLMvC2dlZUO7s7Izi4uJ228THxwv+UTQzOi5//RBmNg56jbc9f4u0ga+vb688wfVFLMuiuLi4T43puXPnEBUVBW9vb34quzeh2Qfx0ZiKT9cZHQDtzujcT5fzDXD38RHtteltz83qqf7dunULq1evxqxZs0S/UqSXRKcvun9Ksr6+HgBg0s8CJuYdPxNIH6ysrGBra0u/7ETCsmyfG1Nra2sAd/966q0xMwwDU1PTXhtfX0RjKr7ujqmmno2NTa9dnqHNZ599hpdeeonfbm5uBsMw2LZtG192/PhxjB07FosWLcK+ffv48ps3b/ZorBq3b9/GjBkzwHEcPvjgA9H3b5SJjqOjI0xNTVFVVSUor6qqogevEUIIEY2u5xupVKqXc9SUKVMgk8n47dWrV8PNzQ1Lly7lyzSzVBs3bsSKFSs63J+++6dJci5fvox//etfekksRb29vLcwNzdHUFAQcnJy+LLW1lbk5OQgODjYgJERQggxJrqeb4KDgwVtACA7O/uBz1E2Njbw9vbmPzY2NrC3txeUWVpaAgCcnJwE5T3dP02Sc+HCBXz77bdwcNDPMhGjnNEBALlcjri4OIwcORKjR49GSkoK1Go15s6da+jQCCGEGJGunG9mz54NNzc3JCcnAwCWLVuG8ePHY/v27YiOjsb+/fuRl5eH3bt3821qampQXl7OP1umpKQEwN3Zkp68OqGP/t2+fRvPPvssCgoKcOTIEbAsy6/fsbe3F/XxM0ab6MTGxuL69etITEyESqVCYGAgsrKy2iyO6sy3S2WCxWg9gWVZFBUV9egxCSGE6KYr55vy8nKYmPz3IkpISAjS09Oxdu1aJCQkwMfHB5mZmYIbDw4fPixIJmbOnAmg/bu/9Ekf/auoqMDhw4cBoM0t8ydOnMCECRNEi1+n5+iYmJiAYRicO3eu3dvLf/31V4wYMQIMw4BlWVEC7WlXr16Fu7s7zp8/b7BEx9/fnxYkiqQvjqlKpcKePXswb968Xrm2rC+OaW9HYyo+Xca0oqICw4YNw5UrVzBo0CA9R0j07YFmdHbt2tXuUxE1zyAA7i526orExMQHCYUQoyOVSpGQkGDoMAghpE97oESno9vANA9m6upD+CjRIYQQQojYdE50dLjipRU9AZQQQggh+qBTonPixAmx4yCEEEIIEZ1Oic748ePFjoMQQgghRHRG+cBAQgghhBCAEh1CCCGEGDFKdAghhBBitCjRIYQQQojRokSHEEIIIUaLEh1CRKBSqbB582b+pXSEEEJ6B6N9qadYCn+OQEVlzw+ThfknPX5MojuVSoUtW7Zg8uTJvfK9VIQQ8rCiGR1CCCGEGK0+meicOnUKTz/9NFxdXcEwDDIzM9vUOX/+PKZMmQI7OztYW1tj1KhRKC8v7/lgCSGEGL2dO3fC09MTEokEMpkMZ86c6bRNRkYGfH19IZFIMGLECBw7dkzw/cGDBxEeHg4HBwcwDIPCwkI9Rd+5vty/PpnoqNVqBAQEYOfOne1+f/HiRYSGhsLX1xcnT57EL7/8gnXr1kEikfRwpIQQQozdgQMHIJfLkZSUhIKCAgQEBCAiIgLXrl3T2ub06dOYNWsW5s+fj7NnzyImJgYxMTEoKiri66jVaoSGhmLr1q06xzZnzhysX79e5/ZA7+5fVzCcmG/nNACGYXDo0CHExMTwZTNnzkS/fv3w97//Xef9Xr16Fe7u7tibNgiOjoZYo7Mbfn5+MDU17fFjGyOWZaFUKvU2pr/88gsiIiJw6tQpBAYGir7/3ohlWRQVFcHf359+TkVCYyo+Xca0oqICw4YNw5UrVzBo0KBO68tkMowaNQo7duwAALS2tsLd3R1LlizBmjVr2m0TGxsLtVqNI0eO8GVjxoxBYGAgUlNTBXUvXboELy8vnD17ttu/X+bMmQNPT88HSnZ6c/+6wugWI7e2tuLo0aNYtWoVIiIicPbsWXh5eSE+Pl6QDN2vubkZzc3N/HZDQwMAYO6cq/oOWYtwAx2XPIjW1lawLGvoMHoEy7LgOO6h6W9PoDEVny5jqqnb0NCA+vp6vtzCwgIWFhaCui0tLcjPz0d8fDxfZmJigrCwMCgUCq3HUCgUkMvlgrKIiIh2l2IYkjH0z+gSnWvXruHmzZvYsmULXn/9dWzduhVZWVmYNm0aTpw4ofWFpMnJydiwYUMPR0uMTWlp6UPzlzjHcWhoaIBSqQTDMIYOxyjQmIpPlzHVXJLx8/MTlCclJbWZGamurgbLsnB2dhaUOzs7o7i4WOsxVCpVu2162yMqjKF/RpfotLa2AgCeeeYZvPrqqwCAwMBAnD59GqmpqVoTnfj4eEH2WVFRAT8/P4NdujLv9yF8fX0fmpOmvrEsi+LiYr2N6blz5xAVFQVvb2/4+/uLvv/eSN+XAx9GNKbi02VMKyoqAABKpRJubm58+f2zOb3RZ599hpdeeonfbm5uBsMw2LZtG192/PhxjB07FosWLcK+ffv48ps3b/ZorD3F6BIdR0dHmJmZtcnEhw0bhn//+99a290/JamZrpRITGBp2fNrti3MrWBra0u/7ETCsiysrPQ3ptbW1gDuTuk+TP9mDMPA1NT0oeqzvtGYiq+7Y6qpZ2NjA1tb2w7rOjo6wtTUFFVVVYLyqqqqDp+pJZVKu92mK6ZMmQKZTMZvr169Gm5ubli6dClfpkneNm7ciBUrVnS4v97WP130ybuuOmJubo5Ro0ahpKREUP7bb79h8ODBBoqKEEKIMTI3N0dQUBBycnL4stbWVuTk5CA4OFhru+DgYEEbAMjOzu6wTVfY2NjA29ub/9jY2MDe3l5QZmlpCQBwcnISlPeF/umiT87o3Lx5E6Wlpfx2WVkZCgsLYW9vDw8PD6xcuRKxsbEYN24cJk6ciKysLHz11Vc4efKk4YImhBBilORyOeLi4jBy5EiMHj0aKSkpUKvVmDt3Ll9n9uzZcHNzQ3JyMgBg2bJlGD9+PLZv347o6Gjs378feXl52L17N9+mpqYG5eXlqKysBAD+D3ipVNqjMyN9vX99MtHJy8vDxIkT+W3N2pq4uDikpaVh6tSpSE1NRXJyMpYuXYqhQ4fiyy+/RGhoqKFCJoQQYqRiY2Nx/fp1JCYmQqVSITAwEFlZWYLFuOXl5TAx+e9FlJCQEKSnp2Pt2rVISEiAj48PMjMzBWv8Dh8+LEgmZs6cCaD9RdH61Nf71+efo6MvmufonD9/XrAYrSfQszTEp+8xLSwsxLhx4+g5OuSB0JiKryeeo0N6N6Nbo0OIIUilUqxZs4Ze6EkIIb1Mn7x0RUhvI5VKkZCQYOgwCCGE3IdmdAghhBBitCjRIYQQQojRokSHEEIIIUaLEh1CCCGEGC1KdAghhBBitCjRIYQQQojRokSHEEIIIUaLEh1CCCGEGC1KdAjpBVQqFTZv3gyVSmXoUAghxKjQk5E7UfxDJWocW3v+wA49f0hiOCqVClu2bMHkyZPpNRKEECIimtEhhBBCiNGiRIcQQgghRstoE52GhgYsX74cgwcPhqWlJUJCQvDTTz8ZOixCCCFGaOfOnfD09IREIoFMJsOZM2c6bZORkQFfX19IJBKMGDECx44dE3x/8OBBhIeHw8HBAQzDoLCwUE/Rd64v989o1+j89a9/RVFREf7+97/D1dUV+/btQ1hYGJRKJdzc3Lq8n1vNTWi61ajHSNvX2tQEtVoNU1PTHj+2MWJZFk29eEybmpoMHQIhREcHDhyAXC5HamoqZDIZUlJSEBERgZKSEjg5ObXb5vTp05g1axaSk5Px1FNPIT09HTExMSgoKIC/vz8AQK1WIzQ0FDNmzMCCBQt0im3OnDnw9PTE+vXrde1er+5fVzAcx3F627uBNDU1wcbGBv/85z8RHR3NlwcFBSEqKgqvv/56mzbNzc1obm7mtysqKuDn59cj8RKicfLkSQQEBBg6jC5hWRZKpRJ+fn69Mnnsi2hMxafLmFZUVMDf37/NH8YWFhawsLBoU18mk2HUqFHYsWMHAKC1tRXu7u5YsmQJ1qxZ0+4xYmNjoVarceTIEb5szJgxCAwMRGpqqqDupUuX4OXlhbNnzyIwMLBLfdAQI9Hpzf3rCqOc0blz5w5YloVEIhGUW1pa4t///ne7bZKTk7Fhw4aeCI8QrUpLS/vMCY7jODQ0NECpVIJhGEOHYxRoTMWny5heu3YNANr8sZuUlNQmYWhpaUF+fj7i4+P5MhMTE4SFhUGhUGg9hkKhgFwuF5RFREQgMzOzSzH2FGPon1EmOjY2NggODsamTZswbNgwODs74/PPP4dCoYC3t3e7beLj4wX/KJoZnc93HsFAB+eeCp3HPlIDX1/fPnPS6+1YlkVxcXGvHdNz584hKioK3t7e/LRub0ezD+KjMRWfrjM6ANqd0blfdXU1WJaFs7PwPOHs7Izi4mKtx1CpVO226W3P0jKG/hllogMAf//73zFv3jy4ubnB1NQU/+///T/MmjUL+fn57da/f0qyvr4eACCxsISlxKpHYr5Xq9Ut2Nra0i87kbAsCysrq147ptbW1gDu/qXUG+PThmEYmJqa9qmYezsaU/F1d0w19WxsbGBra6vP0ET32Wef4aWXXuK3m5ubwTAMtm3bxpcdP34cY8eOxaJFi7Bv3z6+/ObNmz0aa08x2kRnyJAh+O6776BWq1FfXw8XFxfExsbi0UcfNXRohBBCjISjoyNMTU1RVVUlKK+qqurw4Z9SqbTbbbpiypQpkMlk/Pbq1avh5uaGpUuX8mWaWaqNGzdixYoVHe6vt/VPF0Z7e7mGtbU1XFxccOPGDXz99dd45plnDB0SIYQQI2Fubo6goCDk5OTwZa2trcjJyUFwcLDWdsHBwYI2AJCdnd1hm66wsbGBt7c3/7GxsYG9vb2gzNLSEgDg5OQkKO8L/dOF0c7ofP311+A4DkOHDkVpaSlWrlwJX19fzJ0719ChEUIIMSJyuRxxcXEYOXIkRo8ejZSUFKjVasH5Zvbs2XBzc0NycjIAYNmyZRg/fjy2b9+O6Oho7N+/H3l5edi9ezffpqamBuXl5aisrAQAlJSUALg7W9KTMyN9vX9Gm+jU1dUhPj4eV69ehb29PaZPn4433ngD/fr1M3RohBBCjEhsbCyuX7+OxMREqFQqBAYGIisrS7AYt7y8HCYm/72IEhISgvT0dKxduxYJCQnw8fFBZmam4GaEw4cPC5KJmTNnAmj/7i996uv9M8rn6Ijh6tWrcHd3x/nz57v1gEExsCyLoqIi+Pv704JEkfT2MVWpVNizZw/mzZvXZ17q2dvHtC+iMRWfLmNaUVGBYcOG4cqVKxg0aJCeIyT6ZrQzOoT0JVKpFAkJCYYOgxBCjI7RL0YmhBBCyMOLEh1CCCGEGC1KdAghhBBitCjRIYQQQojRokSHEEIIIUaLEh1CCCGEGC1KdAghhBBitCjRIYQQQojRokSHEEIIIUaLEh1CRKZSqbB582aoVCpDh0IIIQ89egVEJ/pnHoTNI4/0/IGHj+j5YxJRqFQqbNmyBZMnT+4z760ihBBjRTM6hBBCCDFalOgQQgghxGgZbaLj6ekJhmHafBYvXmzo0AghhBiZnTt3wtPTExKJBDKZDGfOnOm0TUZGBnx9fSGRSDBixAgcO3ZM8P3BgwcRHh4OBwcHMAyDwsJCPUXfub7cP6Ndo/PTTz+BZVl+u6ioCE8++SSee+65bu2nsaUF6uZmscPrVFNTE9RqNUxNTXv82MaIZdkeG9Ompia97p8Q0rscOHAAcrkcqampkMlkSElJQUREBEpKSuDk5NRum9OnT2PWrFlITk7GU089hfT0dMTExKCgoAD+/v4AALVajdDQUMyYMQMLFizQKbY5c+bA09MT69ev17V7vbp/XcFwHMfpbe+9yPLly3HkyBFcuHABDMO0+b65uRnN9yQ0FRUV8PPz68kQiZE5efIkAgICDB2G3rAsC6VSCT8/P0rIRUJjKj5dxrSiogL+/v5QKpVwc3Pjyy0sLGBhYdGmvkwmw6hRo7Bjxw4AQGtrK9zd3bFkyRKsWbOm3WPExsZCrVbjyJEjfNmYMWMQGBiI1NRUQd1Lly7By8sLZ8+eRWBgYJf6oCFGotOb+9cVRjujc6+Wlhbs27cPcrm83SQHAJKTk7Fhw4YejowYs9LSUqM+WXEch4aGBiiVSq3/XZHuoTEVny5jeu3aNQBo88duUlJSm4ShpaUF+fn5iI+P58tMTEwQFhYGhUKh9RgKhQJyuVxQFhERgczMzC7F2FOMoX8PRaKTmZmJ2tpazJkzR2ud+Ph4wT+KZkanZEMS3AYM0H+Q9/nB1w++vr5GfaLsSSzLori4uEfG9Ny5c4iKioK3tzc/RWuMaPZBfDSm4tN1RgdAuzM696uurgbLsnB2dhaUOzs7o7i4WOsxVCpVu2162/O3jKF/D0Wi8/HHHyMqKgqurq5a69w/JVlfXw8AsDI3h3U7P9z6ZmVlBVtbW/plJxKWZXtsTK2trQHc/avH2P/9GIaBqamp0fezJ9GYiq+7Y6qpZ2NjA1tbW32GJrrPPvsML730Er/d3NwMhmGwbds2vuz48eMYO3YsFi1ahH379vHlN2/e7NFYe4rRJzqXL1/Gt99+i4MHDxo6FEIIIUbG0dERpqamqKqqEpRXVVV1+MBQqVTa7TZdMWXKFMhkMn579erVcHNzw9KlS/kyzSzVxo0bsWLFig7319v6pwujvb1cY+/evXByckJ0dLShQyGEEGJkzM3NERQUhJycHL6stbUVOTk5CA4O1touODhY0AYAsrOzO2zTFTY2NvD29uY/NjY2sLe3F5RZWloCAJycnATlfaF/ujDqGZ3W1lbs3bsXcXFxMDMz6q4SQggxELlcjri4OIwcORKjR49GSkoK1Go15s6dy9eZPXs23NzckJycDABYtmwZxo8fj+3btyM6Ohr79+9HXl4edu/ezbepqalBeXk5KisrAQAlJSUA7s6W9OTMSF/vn1Gf/b/99luUl5dj3rx5Ou/jZsw0NNyzGK0nsCwLFBX16DEJIYToJjY2FtevX0diYiJUKhUCAwORlZUlWIxbXl4OE5P/XkQJCQlBeno61q5di4SEBPj4+CAzM1NwA8Phw4cFycTMmTMBtH/3lz719f49NM/R6a6rV6/C3d0d58+fF6y67wksy6KoqAj+/v60IFEkPTmmKpUKe/bswbx584z6pZ70cyo+GlPx6TKmFRUVGDZsGK5cuYJBgwbpOUKib0Y9o0OIIUilUiQkJBg6DEIIIXgIFiMTQggh5OFFiQ4hhBBCjBYlOoQQQggxWpToEEIIIcRoUaJDCCGEEKNFiQ4hhBBCjBYlOoQQQggxWpToEEIIIcRoUaJDCCGEEKNFiQ55qKhUKmzevBkqlcrQoRBCCOkB9AqITmTe+DsekQzo8eMOR8+/yv5hUFVVhS1btmDy5MlG/R4qQgghd9GMDiGEEEKMltEmOuvXrwfDMIKPr6+vocMihBBihHbu3AlPT09IJBLIZDKcOXOm0zYZGRnw9fWFRCLBiBEjcOzYMcH3Bw8eRHh4OBwcHMAwDAoLC/UUfef6cv+MNtEBgOHDh+P333/nP//+978NHRIhhBAjc+DAAcjlciQlJaGgoAABAQGIiIjAtWvXtLY5ffo0Zs2ahfnz5+Ps2bOIiYlBTEwMioqK+DpqtRqhoaHYunWrzrHNmTMH69ev17k90Lv71xVGvUbHzMzsgddhtDTdRnNji0gRdV1TUxPUajVMTU17/NjGiGVZNDU1gWVZQ4dCCDEyb7/9NhYsWIC5c+cCAFJTU3H06FHs2bMHa9asabfNu+++i8jISKxcuRIAsGnTJmRnZ2PHjh1ITU0FALz44osAgEuXLum/Ex3o6/0z6kTnwoULcHV1hUQiQXBwMJKTk+Hh4dFu3ebmZjQ3N/PbDQ0NAICkSW/1SKykZ7W2tlLS84BYlgXHcTSOIqIxFZ8uY6qp29DQgPr6er7cwsICFhYWgrotLS3Iz89HfHw8X2ZiYoKwsDAoFAqtx1AoFJDL5YKyiIgIZGZmdjnOnmAM/TPaREcmkyEtLQ1Dhw7F77//jg0bNmDs2LEoKiqCjY1Nm/rJycnYsGGDASIlhlBaWkqzZQ+I4zg0NDRAqVSCYRhDh2MUaEzFp8uYai7J+Pn5CcqTkpLaXAaqrq4Gy7JwdnYWlDs7O6O4uFjrMVQqVbttetujL4yhf0ab6ERFRfH///HHH4dMJsPgwYPxxRdfYP78+W3qx8fHC7LPiooK+Pn5YUPOSgyQ2vVIzPfyvToKvr6+dDIWCcuyKC4uxu3bt/HUU0/B29sb/v7+hg6rT2NZFkqlEn5+fvRzKhIaU/HpMqYVFRUAAKVSCTc3N778/tmc3uizzz7DSy+9xG83NzeDYRhs27aNLzt+/DjGjh2LRYsWYd++fXz5zZs3ezTWnmK0ic79BgwYgMceewylpaXtfn//lKRmutLcsh8srMx7JMZ7WVlZwdbWln7ZiYRlWVhZWfFT0iYmJjS2ImAYBqampjSWIqIxFV93x1RTz8bGBra2th3WdXR0hKmpKaqqqgTlVVVVHa4RlUql3W7TFVOmTIFMJuO3V69eDTc3NyxdupQv0yRvGzduxIoVKzrcX2/rny6M+q6re928eRMXL16Ei4uLoUMhhBBiJMzNzREUFIScnBy+rLW1FTk5OQgO1v7g1+DgYEEbAMjOzu6wTVfY2NjA29ub/9jY2MDe3l5QZmlpCQBwcnISlPeF/unCaGd0VqxYgaeffhqDBw9GZWUlkpKSYGpqilmzZhk6NEIIIUZELpcjLi4OI0eOxOjRo5GSkgK1Ws3fpQQAs2fPhpubG5KTkwEAy5Ytw/jx47F9+3ZER0dj//79yMvLw+7du/k2NTU1KC8vR2VlJQCgpKQEwN3Zkp6cGenr/TPaROfq1auYNWsW/vjjDwwcOBChoaH48ccfMXDgQEOHRgghxIjExsbi+vXrSExMhEqlQmBgILKysgSLccvLy2Fi8t+LKCEhIUhPT8fatWuRkJAAHx8fZGZmCtYOHj58WJBMzJw5E0D7i6L1qa/3j+E4jhNtb0bk6tWrcHd3x/nz5wWL0XoCy7IoKiqCv78/XacXiWZMWZbFhAkTcOrUKQQGBho6rD6Nfk7FR2MqPl3GtKKiAsOGDcOVK1cwaNAgPUdI9O2hWaNDCHD39sY1a9bQCz0JIeQhYbSXrghpj1QqRUJCgqHDIIQQ0kNoRocQQgghRosSHUIIIYQYLUp0CCGEEGK0KNEhhBBCiNGiRIcQQgghRosSHUIIIYQYLUp0CCGEEGK0KNEhhBBCiNGiRIf0SSqVCps3b4ZKpTJ0KIQQQnoxejJyJ/r9dBX9ym6Lus/boZ6i7u9hpFKpsGXLFkyePJle50AIIUQrmtEhhBBCiNGiRIcQQgghRqtPJjqnTp3C008/DVdXVzAMg8zMTMH369evh6+vL6ytrfHII48gLCwMubm5hgmWEEKI0du5cyc8PT0hkUggk8lw5syZTttkZGTA19cXEokEI0aMwLFjxwTfcxyHxMREuLi4wNLSEmFhYbhw4YKgTkFBAZ588kkMGDAADg4OWLhwIW7evClq37ri1q1bWLx4MRwcHNC/f39Mnz4dVVVVHbbpSv/eeOMNhISEwMrKCgMGDNAptj65RketViMgIADz5s3DtGnT2nz/2GOPYceOHXj00UfR1NSEd955B+Hh4SgtLcXAgQO7dazGW01Q32oUK3QAwG21usPvWZZFU1MT1Go1TE1NRT22sWhqajJ0CIQQAgA4cOAA5HI5UlNTIZPJkJKSgoiICJSUlMDJyandNqdPn8asWbOQnJyMp556Cunp6YiJiUFBQQH8/f0BAG+++Sbee+89fPLJJ/Dy8sK6desQEREBpVIJiUSCyspKhIWFITY2Fjt27EB9fT2WL1+OOXPm4B//+EeX4/f09ERaWhomTJig8xi8+uqrOHr0KDIyMmBnZ4dXXnkF06ZNww8//KC1TWf9A4CWlhY899xzCA4Oxscff6xTbAzHcZxOLXsJhmFw6NAhxMTEaK1TX18POzs7fPvtt5g0aVK7dZqbm9Hc3MxvV1RUwM/PT+xwichOnjyJgICATuuxLAulUgk/Pz9KHkVCYyo+GlPx6TKmFRUV8Pf3h1KphJubG19uYWEBCwuLNvVlMhlGjRqFHTt2AABaW1vh7u6OJUuWYM2aNe0eIzY2Fmq1GkeOHOHLxowZg8DAQKSmpoLjOLi6uuK1117DihUrAAB1dXVwdnZGWloaZs6cid27d2PdunX4/fffYWJy9wLNuXPn8Pjjj+PChQvw9vbuUn8fNNGpq6vDwIEDkZ6ejmeffRYAUFxcjGHDhkGhUGDMmDFt2nSlf/dKS0vD8uXLUVtb2+34+uSMTne0tLRg9+7dsLOz6/CEmJycjA0bNvRgZEQMpaWlXfrlxXEcGhoaoFQqwTBMD0Rm/GhMxUdjKj5dxvTatWsA0OaP3aSkJKxfv15Q1tLSgvz8fMTHx/NlJiYmCAsLg0Kh0HoMhUIBuVwuKIuIiOCXYpSVlUGlUiEsLIz/3s7ODjKZDAqFAjNnzkRzczPMzc35JAcALC0tAQD//ve/u5zoPKj8/Hzcvn1bEKuvry88PDy0Jjpd6Z9YjDbROXLkCGbOnInGxka4uLggOzsbjo6OWuvHx8cLfug0Mzq/7D4MV8f2px51dWv0oA6/Z1kWxcXF8PX1pb/qtDh37hyioqLg7e3NT/N2hP5SFh+NqfhoTMWn64wOgHZndO5XXV0NlmXh7OwsKHd2dkZxcbHWY6hUqnbbaJ4Npvnfjur8+c9/hlwux1tvvYVly5ZBrVbzM0i///57l/oqBpVKBXNz8zZraO6Ntb02mjpdbaMro010Jk6ciMLCQlRXV+Ojjz7CjBkzkJubq/V66f1TkvX19QAAK4klrCVWosZmbmvb4fcsy8LKygq2trb0y04La2trAHf/curqGDEMA1NTUxpTEdGYio/GVHzdHVNNPRsbG9h28vvakIYPH45PPvkEcrkc8fHxMDU1xdKlS+Hs7CyY5bnfokWLsG/fPn67sbERUVFRgvHRLGgePnw4Ll++DAAYO3Ysjh8/rqfe6I/RJjrW1tbw9vaGt7c3xowZAx8fH3z88ceC6UVCCCHkQTg6OsLU1LTNHUZVVVUdPsxUKpV22Ebzv1VVVXBxcRHUCQwM5Lf/8pe/4C9/+QuqqqpgbW0NhmHw9ttv49FHH9V67I0bN/LrYgBgwoQJ2Lp1K2QyWZu6x44dw+3bdx+aq7ks1l5fWlpaUFtbK5jV6WgMuto/MfTJ28t10draKlhsTAghhDwoc3NzBAUFIScnhy9rbW1FTk4OgoODtbYLDg4WtAGA7Oxsvo2XlxekUqmgTn19PXJzc9vdr7OzM/r3748DBw5AIpHgySef1HpsJycnfiLA29sbZmZmcHNzE5RpDB48mC+79zLevYKCgtCvXz9BrCUlJSgvL9c6Bt3t34PokzM6N2/eRGlpKb9dVlaGwsJC2Nvbw8HBAW+88QamTJkCFxcXVFdXY+fOnaioqMBzzz1nwKgJIYQYI7lcjri4OIwcORKjR49GSkoK1Go15s6dy9eZPXs23NzckJycDABYtmwZxo8fj+3btyM6Ohr79+9HXl4edu/eDeDu5bbly5fj9ddfh4+PD3/7taurq+Au4x07diAkJAT9+/dHdnY2Vq5ciS1btuj8zBld2NnZYf78+ZDL5bC3t4etrS2WLFmC4OBgwUJkX19fJCcnY+rUqV3uX3l5OWpqalBeXg6WZVFYWAgA8Pb2Rv/+/bsUX59MdPLy8jBx4kR+W7OIOC4uDqmpqSguLsYnn3yC6upqODg4YNSoUfj+++8xfPhwQ4VMCCHESMXGxuL69etITEyESqVCYGAgsrKyBAtty8vLBetmQkJCkJ6ejrVr1yIhIQE+Pj7IzMwU3FyxatUqqNVqLFy4ELW1tQgNDUVWVhb/jBkAOHPmDJKSknDz5k34+vriww8/xIsvvtgzHb/HO++8AxMTE0yfPh3Nzc2IiIjArl27BHVKSkpQV1fHb3elf4mJifjkk0/47SeeeAIAcOLEiS7fDt/nn6OjL1evXoW7uzvOnz+vdbpOX1iWRVFREfz9/WlBohYqlQp79uzBvHnzuvRSTxpT8dGYio/GVHy6jGlFRQWGDRuGK1euYNCgju+SJb1fn5zRIUQqlSIhIcHQYRBCCOnlHprFyIQQQgh5+FCiQwghhBCjRYkOIYQQQowWrdHRorW1FQBEfxR1V7Asi2vXrqGiooIWJIqExlR8NKbiozEVny5jqvm9rzkPkL6NEh0tNE+svPc2dkIIIQ+PqqoqeHh4GDoM8oDo9nIt7ty5g7Nnz3b6zhB9aGhogJ+fH5RKJWxsbHr02MaKxlR8NKbiozEVny5j2traiqqqKjzxxBMwM6P5gL6OEp1eqL6+HnZ2dqirq+vVL5TrS2hMxUdjKj4aU/HRmBJajEwIIYQQo0WJDiGEEEKMFiU6vZCFhQWSkpJgYWFh6FCMBo2p+GhMxUdjKj4aU0JrdAghhBBitGhGhxBCCCFGixIdQgghhBgtSnQIIYQQYrQo0SGEEEKI0aJEpwfs3LkTnp6ekEgkkMlkOHPmTIf1MzIy4OvrC4lEghEjRuDYsWOC7w8ePIjw8HA4ODiAYRgUFhbqMfreScwxvX37NlavXo0RI0bA2toarq6umD17NiorK/XdjV5H7J/V9evXw9fXF9bW1njkkUcQFhaG3NxcfXah1xF7TO+1aNEiMAyDlJQUkaPu3cQe0zlz5oBhGMEnMjJSn10gPYkjerV//37O3Nyc27NnD/frr79yCxYs4AYMGMBVVVW1W/+HH37gTE1NuTfffJNTKpXc2rVruX79+nHnzp3j63z66afchg0buI8++ogDwJ09e7aHetM7iD2mtbW1XFhYGHfgwAGuuLiYUygU3OjRo7mgoKCe7JbB6eNn9bPPPuOys7O5ixcvckVFRdz8+fM5W1tb7tq1az3VLYPSx5hqHDx4kAsICOBcXV25d955R8896T30MaZxcXFcZGQk9/vvv/OfmpqanuoS0TNKdPRs9OjR3OLFi/ltlmU5V1dXLjk5ud36M2bM4KKjowVlMpmMe+mll9rULSsreygTHX2OqcaZM2c4ANzly5fFCboP6Ilxraur4wBw3377rThB93L6GtOrV69ybm5uXFFRETd48OCHKtHRx5jGxcVxzzzzjF7iJYZHl670qKWlBfn5+QgLC+PLTExMEBYWBoVC0W4bhUIhqA8AERERWus/bHpqTOvq6sAwDAYMGCBK3L1dT4xrS0sLdu/eDTs7OwQEBIgXfC+lrzFtbW3Fiy++iJUrV2L48OH6Cb6X0ufP6cmTJ+Hk5IShQ4fi5Zdfxh9//CF+B4hBUKKjR9XV1WBZFs7OzoJyZ2dnqFSqdtuoVKpu1X/Y9MSY3rp1C6tXr8asWbMempcA6nNcjxw5gv79+0MikeCdd95BdnY2HB0dxe1AL6SvMd26dSvMzMywdOlS8YPu5fQ1ppGRkfj000+Rk5ODrVu34rvvvkNUVBRYlhW/E6TH0fvnCbnH7du3MWPGDHAchw8++MDQ4RiFiRMnorCwENXV1fjoo48wY8YM5ObmwsnJydCh9Tn5+fl49913UVBQAIZhDB2O0Zg5cyb//0eMGIHHH38cQ4YMwcmTJzFp0iQDRkbEQDM6euTo6AhTU1NUVVUJyquqqiCVStttI5VKu1X/YaPPMdUkOZcvX0Z2dvZDM5sD6Hdcra2t4e3tjTFjxuDjjz+GmZkZPv74Y3E70AvpY0y///57XLt2DR4eHjAzM4OZmRkuX76M1157DZ6ennrpR2/SU79TH330UTg6OqK0tPTBgyYGR4mOHpmbmyMoKAg5OTl8WWtrK3JychAcHNxum+DgYEF9AMjOztZa/2GjrzHVJDkXLlzAt99+CwcHB/10oJfqyZ/V1tZWNDc3P3jQvZw+xvTFF1/EL7/8gsLCQv7j6uqKlStX4uuvv9ZfZ3qJnvo5vXr1Kv744w+4uLiIEzgxLEOvhjZ2+/fv5ywsLLi0tDROqVRyCxcu5AYMGMCpVCqO4zjuxRdf5NasWcPX/+GHHzgzMzNu27Zt3Pnz57mkpKQ2t0L+8ccf3NmzZ7mjR49yALj9+/dzZ8+e5X7//fce758hiD2mLS0t3JQpU7hBgwZxhYWFgltMm5ubDdJHQxB7XG/evMnFx8dzCoWCu3TpEpeXl8fNnTuXs7Cw4IqKigzSx56mj//+7/ew3XUl9pg2NDRwK1as4BQKBVdWVsZ9++233P/7f/+P8/Hx4W7dumWQPhJxUaLTA95//33Ow8ODMzc350aPHs39+OOP/Hfjx4/n4uLiBPW/+OIL7rHHHuPMzc254cOHc0ePHhV8v3fvXg5Am09SUlIP9KZ3EHNMNbfpt/c5ceJED/WodxBzXJuamripU6dyrq6unLm5Oefi4sJNmTKFO3PmTE91p1cQ+7//+z1siQ7HiTumjY2NXHh4ODdw4ECuX79+3ODBg7kFCxbwiRPp+xiO4zjDzCURQgghhOgXrdEhhBBCiNGiRIcQQgghRosSHUIIIYQYLUp0CCGEEGK0KNEhhBBCiNGiRIcQQgghRosSHUIIIYQYLUp0CCGEEGK0KNEhhBBCiNGiRIfo5OTJk2AYBgzDYP369YYOh5B2qdVqpKamYvLkyXBzc4NEIoGFhQUGDhyIUaNGYd68efjoo49w5coVQ4dKCNETM0MHQMjDjGEYAEBSUhIljCJTKBSYOXMmysvL23xXXV2N6upq5OXlYe/evXB2doZKpTJAlIQQfaNEhxBidH777TdERESgoaEBADBlyhQ8++yzeOyxx2Bubo7q6mr8/PPPyM7OxokTJwwcLSFEnyjRIYQYnf/93//lk5y9e/dizpw5beo8+eSTWLFiBa5fv44vvviihyMkhPQUWqNDCDEqLMvi6NGjAICRI0e2m+Tca+DAgVi8eHEPREYIMQRKdIhe3LtY+eTJk+A4Dh9//DFCQ0Ph4OAAW1tbjB49Gn//+98F7VpaWpCamooxY8bA3t4eNjY2+NOf/tThX9yXLl3ij5WWlgYAyMjIQFhYGJycnGBpaQlfX1/Ex8ejtra209hbWlqwa9cuTJw4EQMHDoS5uTmkUikmT56Mffv2obW1VWvbOXPmgGEYeHp6AgB+//13rF69GsOHD4eNjQ0/Hp6envz6HADYsGED3wfN5/4T9O+//45du3bh2WefhY+PD6ytrWFhYQE3Nzc888wzOHDgQIex3f9vAgBffPEFJk2ahIEDB8LS0hJDhw7FqlWrUFNT0+k4AcCxY8fwwgsv4NFHH4W1tTUkEgm8vLwwffp0pKWlobGxUWvbgoICLFq0CEOHDkX//v1hbW2NoUOH4uWXX8Zvv/3WpeO35/r162hqagIAeHt767yfezU3N2P37t2Ijo6Gm5sbLCwsYG1tjeHDh+Ovf/0rvv76a3Ac127bmzdvYsuWLQgODoa9vT0sLCwwaNAgPPvsszhy5EiHx50wYQIYhsGECRMAABcuXMArr7wCHx8fWFlZgWEYXLp0SdDm1q1b2LFjByZNmgSpVApzc3M4OTkhLCwMH3/8Me7cuSPGkBDSd3CE6ODEiRMcAA4Al5SU1OH333zzDff000/z2/d/li5dynEcx9XU1HDjxo3TWu+NN95oN5aysjK+zt69e7l58+Zp3Yerqyt3/vx5rf0qKyvjfH19tbYHwIWGhnJ//PFHu+3j4uI4ANzgwYM5hULBOTo6tml/4sQJbvDgwR0eAwAXFxfH7/fOnTuciYlJp22efPJJrqGhodN/s5ycHO6FF17Quh9vb2/u999/1zpO1dXV3KRJkzqNZ+/evW3asizLvfrqqxzDMFrbmZmZcR9++KHW43fkjz/+4PcTEBCg0z7udfbsWc7Ly6vTvpaVlbVpW1BQwLm6unbYbtq0aVxTU1O7xx4/fjwHgBs/fjyXmZnJWVtbd3jcwsLCTn+2Ro0axalUqgceF0L6Ckp0iE66k+jIZDIOAPf8889zR48e5fLz87nPP/+cGzp0KF8nOzubmzJlCmdmZsa9/PLL3DfffMPl5+dzH3/8MX+iMDU15YqKitoc695EZ9SoURwAbvTo0dznn3/O5eXlcceOHeNmzJjB1/Hw8ODq6+vb7KehoYF79NFH+XoxMTHc4cOHuby8PC4jI4M/6QDgQkJCuDt37rTZhybRcXBw4FxdXbn+/ftz//u//8udPHmSO3PmDPfxxx9zxcXFXElJCXfu3Dl+fy+//DJ37tw5wefq1av8fm/fvs2ZmJhwf/7zn7m33nqLy8rK4vLz87mTJ09ye/bs4YKDg/l9zZ49u9N/s5CQEL6PBw8e5PLz87ljx45x0dHRfJ2ZM2e2ux+1Ws2NGDGCrxcUFMR9+OGH3A8//MDl5eVxhw4d4l599VXO1dW13UTnf/7nf/i248aN4/bs2cOPz0cffcQNHz6c//6f//xnuzF05t6T/ZYtWziWZXXaj1Kp5Pr378/va+rUqdyBAwe4n376ifvx/7d3/zFV1f8fwJ8g8yIQID8SL96ANtFMB5dbihYmLQltkAiKSSX+2PwDU0rbalMLy81NlzWqGVEkIyT6gZmTQEb+iAlxCYgb7UZRasSuBCgSciXu6/MHu+/vgXvP5V5+lNzv67Hd7cz3j/M+5149L9/n/aO6mvLz8+npp58mT09Pi0Dnjz/+oJkzZxIAcnFxoc2bN1NZWRlptVrKz8+niIgIUW9qaqrV85t/c2FhYeTl5UWBgYF06NAhqqqqourqasrOzqaOjg4iImppaSEfHx8CQN7e3vTyyy9TSUkJabVaKisro4yMDHJzcxN/J2/fvj2me8LYVMOBDhsTRwIdAPTmm29a5Glvb6e77rqLAFBgYCC5uLhQSUmJRb7GxkbRm2Hu/ZGSBjoAaPXq1TQwMGCR78CBAyLPiy++aJG+Z88ekb53716LdJPJRGlpaSLPu+++a5HHHOgAIC8vL2poaLDII2XrHo48d0tLi808+/fvFw/Vn3/+2SJ95Hfy+uuvWz1PXFyc6FW5du2aRZ7nn39e1JGRkUEmk8lqe4xGo0XPQXl5uSibm5trtdytW7fo0UcfJWCoZ8zadzmaI0eODLvW0NBQ2rlzJxUVFVFra6vd9URFRREAcnV1pRMnTsjm++uvv6ivr2/Yn6WkpNi81v7+foqNjRV5zpw5Y5FHGlwrlUq6fPmybBvMwatarRbBz0ilpaXi71JOTo5sXYw5Ew502Jg42qMj59lnnx31f7VEJF5pqdVqizRpoKNQKKitrc1qHYODg7Rw4UICQH5+fmQ0GkVaf38/+fr6EgC6//77rfbWEBHduHGD/P39CQAtWLDAIl0a6Bw4cED2eszsDXTs8c8//4hXZUeOHLFIl34nGo1GNkD5+uuvZXtUuru7ycPDQ9Qhd5/kmAOY5ORkm/mam5tFG8rLyx06B9HQd23rFeasWbMoNTWVTp06JXsfysrKRP7MzEyHzt/W1kbTpk0jABQfHy+b77fffhO9LKtXr7ZIlwY6+fn5svVcuHBB5Pvhhx9sts3cu7ls2TL7L4ixKYwHI7NJt2HDBtm0iIgIh/K1trbaPFdcXByUSqXVNFdXV2zatAkA0NXVhe+//16k1dXViYHK6enpmDZtmtU6vL29sX79egBAc3Mz2tvbZduSlpZms63jYTKZ8Oeff0Kv10On00Gn0+Gnn37CnDlzAACNjY02y2/cuHHYYGgpjUYjjkfe78rKSjHAeOfOnbL3yZqenh4xCDolJcVm3vvuuw8BAQEAhhb+c5Srqys++OADlJeXIz4+Hm5uw1fSMBgM+OSTT5CYmIjFixfj119/tahDOlA4MzPTofOfO3cOg4ODAICtW7fK5gsNDcXKlSstyow0ffp0rFu3TraeU6dOAQDmzZuHRYsW2Wzb8uXLAQC1tbU8MJn9v8CBDpt04eHhsmm+vr4O5TOvjSLnwQcftJm+ePFicdzU1CSOdTqdOF6yZInNOqTp0nJSXl5euPfee23W4ygiQkFBAWJjY+Hl5YXg4GDMnz8fixYtEp+GhgYAQyv/2jJ//nzZND8/P3E88n7X19eL45iYGIfaX19fL2aFPfXUUxazzEZ+zNcwnhWLV65cidLSUnR2duLMmTPIyspCQkICfHx8RB6tVouYmBiLoNV8rffccw9CQkIcOu9Yfk99fX2ygfzcuXPh7u4uW4dWqwUA6PX6Ue/rjh07AAADAwN2z65jbCrjBQPZpPPw8JBNc3V1dSifrenTAHD33XfbTJ81a5Y4lv4jLz0erY6goCCr5aSkAdxE6O/vx9q1a1FaWmpXfvP0ajn2ficjexikAdTs2bPtaovZtWvXHMpvZmuKur28vb2xatUqrFq1CsDQdPHCwkLs3r0b3d3daG9vx759+5CbmyvKmK/V0esEJv73NHPmTJt1/Jf3lrE7HQc6zKnIvY75t+tw5JWOPQ4ePCiCnEceeQQZGRmIiopCUFAQZsyYIYKT5cuX4+LFi7JruvyXpEHTe++9h2XLltlVbrSH/FgoFAps3rwZSqUS8fHxAIAvvvgCOTk5wwK9ifBv/J7M9zYiIgIFBQV21xscHDyudjE2FXCgw5yKwWCwO136ikZ6bDAYbL5Gk75KkZabLEQkehpiYmJQWVkp+zCe7FcR5nEzwNAChmFhYXaX9ff3F8ceHh5YuHDhhLZtLB5//HGoVCpcvXoV3d3d6OzsRGBgIID/u1Zb47DkjPw9qVQq2bwT8Xsy39ve3t474r4ydifhMTrMqdTW1tqdLn0gSI9ramps1vHdd99ZLTdZurq6xMNw3bp1skFOb28v9Hr9pLYlKipKHF+4cMGhspGRkaJ3o6qqakLbNR7SwevS3hfztV65cgWXL192qM6x/J48PDzGPK5LrVYDGBo8zruwMzYcBzrMqZSXl8v+D9xkMuH48eMAhl6HSB/aGo1GjKs5fvy47Figmzdviu0oFixYMKbxG1LmAaZGo1E2j3RmzN9//y2bLzc3d9Jn0cTGxsLT0xMAkJ2dLTtLyJrAwEBER0cDAAoLC9HR0TEpbXREX18fmpubAQyN45H2OiUkJIjjo0ePOlTvihUrxOumDz/8UDbflStXcPbsWYsyjkpMTAQw1Pv31ltvjakOxpwVBzrMqRiNRmzfvt3qA/jQoUNiptWWLVugUChEmkKhwLZt2wAMzZh57bXXLMoTEXbs2CEGqZpnr4yHOVCyNr3ZLDAwUARhJ06csBoU1dbWYt++feNuz2h8fX2xfft2AENT8jMzM2XHAw0MDFgMkt27dy+AoanmKSkpNvceMxqNeOedd9Df3+9QG3t7e7FkyRKcPn3a5uB1k8mE5557TswsS0xMHNaj89hjj4mp9tnZ2SgqKpKtq7Ozc9gAcKVSiaSkJABAaWmpCLClbt++jS1btmBgYADA+H5PcXFxYkbh4cOHR92NvampCV999dWYz8fYlPJfLuLDpi5HFgz85ptvZOvJy8sT+aztFWT2yiuviHwjSRcMfOCBB8QihUVFRVRXV0elpaW0YcMGkWfOnDl0/fp1i3p6enqGbQGRnJxMp0+fprq6Ovrss89oxYoVIm3p0qU2t4AICQmRvRYp80rLCoWCjh07Rk1NTdTS0kItLS1kMBhEvoyMjGHXWFhYSLW1tVRRUUEvvPACubu7U0BAAIWHhxMwtDfSSPZ+J0S2FzK0tgVETk4OXbp0ierq6ujLL7+kPXv2UHBwsNUtIHbt2iXKBgUF0auvvkoVFRVUX19P3377LX300Ue0detWsX2C3N5dcm7evCnqDw4OpoyMDCooKKCLFy9SQ0MDnTt3jo4ePTrsGnx8fKz+/kZuAbF27VoqLi4mrVZLNTU19PHHH9OmTZusbgFx9epVcQ2urq60bds2Onv2LGm1WiooKKDIyEhR7/r1661ei3Svq9H88ssv5OfnJ+pMSEiggoICqqmpEVuhHDx4kKKjowkA7d6926H7ythUxYEOG5M7NdDJy8uj9PR02RVxZ8+eTT/++KPseezZ1POhhx6ya1NPe9TX15NCobB6HummntevXx/2YBz58fPzo/Pnz9t8ME5UoENE1NHRYXMDVun3MZLJZKKsrCyxIrCtj6enp8XWCqO5desWBQUFjVq3+TN37lzSarWy9Wm1WlKpVKPWM9mbetpDr9eL1b9H+2RlZdlVJ2NTHc+6Yk4nLy8PcXFxyMnJQVNTE3p7exESEoI1a9bgpZdesjldOTQ0FI2NjXj//ffx6aefQqfToaenB35+flCr1UhLS8PGjRsnbApyZGQkLl26hMOHD6OqqgoGg8HqqykfHx9UVVXhjTfeQHFxMVpaWuDm5gaVSoUnnngCu3btEqsi/xsCAgJw/vx5lJSUoLCwENXV1ejo6ICLiwuUSiU0Gg3WrFmD5ORki7IuLi7Yv38/nnnmGRw7dgyVlZVobW3FjRs34OHhAZVKBbVajbi4OCQlJWHGjBkOtc3d3R1tbW2orq5GRUUFqqurodfrYTAY0N/fD09PTyiVSkRERODJJ59EcnIypk+fLlufRqOBXq9Hbm4uTp48CZ1Oh66uLri7uyMsLAxLly5FamoqQkNDLcqq1Wro9Xq8/fbbOHnyJPR6Pfr6+hAQEIDo6Gikp6cPGws0XuHh4WhoaEBxcTE+//xz1NbWoqOjA4ODg/D398e8efPw8MMPIykpadgYNcacmQvRHbjgBmMO+P3338U057y8PKSnp/+3DWKMMXbH4MHIjDHGGHNaHOgwxhhjzGlxoMMYY4wxp8WBDmOMMcacFgc6jDHGGHNaPOuKMcYYY06Le3QYY4wx5rQ40GGMMcaY0+JAhzHGGGNOiwMdxhhjjDktDnQYY4wx5rQ40GGMMcaY0+JAhzHGGGNOiwMdxhhjjDmt/wGnfTX4WsolCAAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt_feat_bar_plot(plt_data,name,pwd=path,save=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Ionosphere" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt_feat_bar_plot(plt_data,name,pwd=path,save=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Xaxis" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt_feat_bar_plot(plt_data,name,pwd=path,save=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### plt_importance_map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Glass" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "name='glass'\n", + "path=os.path.join(os.getcwd(),'images')\n", + "plot_importance_map(name,iforest_sklearn,X_tr,y_tr,30,pwd=path,save=True,feats_plot=(7,8))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Lympho" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Handling the IndexError Exception...\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "name='lympho'\n", + "path=os.path.join(os.getcwd(),'images')\n", + "plot_importance_map(name,iforest_sklearn,X_tr,y_tr,30,pwd=path,save=True,feats_plot=(7,8))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Ionosphere" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Handling the IndexError Exception...\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_importance_map(name,iforest_sklearn,X_tr,y_tr,30,pwd=path,save=True,feats_plot=(5,8))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Xaxis" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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/1/bPfvazPPfcczz11FNEo1E++clPcsstt/DOO++MRrMkSZIkCXAv2g9+4AGuvfRyfF4vAJPLJvCTb3yX5/7+MhNLSse4hWeuER/66erq4v3vfz//+7//S2bm4WlW7e3t/OIXv+B73/sel156KYsXL+ZXv/oVK1euZPXq1SPdLEmSJEnqkRGJcuvV1/UEKYf4vF5uvfo6MiIjN7QhHduIByoPPvgg1157LStWrOi1fcOGDZim2Wv7zJkzKSsrY9WqVUcfpkcqlaKjo6PXlyRJkiRJp6cRHfp54okn2LhxI+vWretzW11dHR6Ph4yMjF7b8/PzqaurG/CY3/zmN/nqV7863E2VJEmSJGkcGrEelcrKSj796U/z29/+Ft8wrpPwxS9+kfb29p6vysrKYTu2JEmSJEnjy4gFKhs2bKChoYFFixah6zq6rvPGG2/wox/9CF3Xyc/PJ51O03bESo0A9fX1FBQMPB/b6/USiUR6fUmSJElnOEdwVAFXaRwQ4FbWdU7+jzNiQz+XXXYZW7Zs6bXtgQceYObMmfzzP/8zpaWlGIbBq6++yq233grArl27qKioYNmyZSPVLEmSJOk0lGhuwzZNUBTcy6M0Hjiagm2aJJpbT/oYIxaohMNh5s7tPec8GAySnZ3ds/1DH/oQDz30EFlZWUQiET71qU+xbNkyli5dOlLNkiRJkk5DVjzBplfe4NwbrsLwB1GEQBnrRp3BBG6Q0hmLse/Z17DiyZM+1piW0P/+97+PqqrceuutvQq+SZIkSdKJeuvJZ0hpCudfdCGGrqMoMlQZK0IIbNNk37Ovsf3RPw3pWKMaqLz++uu9fvf5fDz88MM8/PDDo9kMSZIk6TQkhODVPzxD1a+fpTAUBVUGKmPGESSaW4fUk3KIXJRQkiRJOq2YiSSdrfGxboY0TOSihJIkSZIkjVsyUJEkSZIkadySgYokSZIkSeOWDFQkSZIkSRq3ZKAiSZIkSdK4JQMVSZIkSZLGLRmoSJIkSZI0bslARZIkSZKkcUsGKpIkSZIkjVsyUJEkSZIkadySgYokSZIkSeOWDFQkSZIkSRq3ZKAiSZIkSdK4JQMVSZIkSZLGLRmoSJIkSZI0bslARZIkSZKkcUsGKpIkSZIkjVsyUJEkSZIkadySgYokSZIkSeOWDFQkSZIkSRq3ZKAiSZIkSdK4JQMVSZIkSZLGLRmoSJIkSZI0bslARZIkSZKkcUsGKpIkSZIkjVsyUJEkSZIkadySgYokneYsBKuNNBZirJsiSZJ0wmSgIkmnuZ26xWveNLt0a6ybIkmSdMJkoCJJp7nd3QGKDFQkSToVyUBFkk5jaQT7dZvSkMN+3SYth38kSTrF6GPdAEmShk+z4vDnQAJbcX+3EZjAty9IcdcLfn4RiqHh3qgJuD7uY79uc7ZpoHdvlyRJGk9koCJJp5GgUPAKhUrFIcsnuHO6ycSIw4UlNt9anuRgh8qTuw1akgqlQqVas3nNmyYsFOZYxlg3X5IkqQ859CNJpxEfCnfH/ZyfNmhNKmxu0LhlqpubcstUi80NGq1JheVpg7vjfg7oNiDzVyRJGr9kj4oknWZUFC5Me/EJhVfr0jQmFCxH0JJUWF2ncVnKwzmmp3f+Spebz+KRwz+SJI0zMlCRpNNUvepQGnR4ZIvBo9s93D87TXHQ4W3SbPaax81feX86QI7QxvZBSJJ0xpOBiiSdhmwE+wyLREzlt9s9LDB1frMdLEBTBI5HcOeMY+evhIQcGZYkaezJdyJJOg1VaTYJIE8o3Bf3c3XKx31xP7lCwRYKranj56/45DCQJEnjgOxRkaTTUK6tsSLlYYFpYHQHHHmOxn2xAJsNkxSCt+oYMH9FkiRpvJA9KpJ0GgqgsMT09AQphxjd21tV0ZO/MvuxED/falASdGhQnTFqsSRJUv9kj4oknWGOlb/SbFjYKdGTVCtJkjTWZKAiSWeYI/NXrk/4yHM0Fps2f/EnaVQEVZrNBFu+NUiSND7IdyNJOsMcL38l15ZTkiVJGj9koCJJZ5hD+StHMwbYLkmSNJZkMq0kSZIkSeOWDFQkSZIkSRq3ZKAiSZIkSdK4JQMVSZIkSZLGLZlMK0nS4Og6Wkl+3+2K/LwjjS9VGTqdCVm8cKyF0w6Zw/B3kIGKdFpIG+402+EqrCoUhWRBBgCKNXJveMLQwe91f7HtETvPSVEU1Lxs1NJCtNJC1MJcULuDklQakUi6d8uKYlyyFPPNdePvMfTHtkHXIDMCseRYt6Zfiu2+5tKZQfR4aoxbc+pQhfs9rSk0huQ0+zEXQwYqkgSQ0hU6/RqKgEjcPnwxPUkC6JpWgBnxo1gOgcqm4Wlof6YUuxfNjhi0do7ceQZJjYYxygoxygrxlxWBt/d0ZdHeiV1Zi71jH6K5DXP1Zoxzz0KfMw01L5v0i28i2sf+cRzT3mqYMwnmTYHqRhBirFvUR6Cqmc5phcQn5OJt7kRLmmPdpFNCaZtJa0Bj/P1Fz0xBc3j+EjJQkU55TRH3ZRxO2HgtAUMsBZIoySaZFwUBkZ1V6In0MLSyL6GpiOmlACg7y8ekaL3i82CUFKKXFWJMKEKLhI64UYFUGruqFqeyDruytk8QYq3fglPfhOeK5ai5WfjuvJb0Kyux91WM8iM5AbvK3QAx5He/760a6xb14a1vJ5UbIZ0RpHNaIdEtFXJRg0Hw2VDYeQr06kknRAYq0inN1KA96HbxZndYQz5eKjtMbGIuAKF9dXja4kM+5oAmFYHHcIcfKhtG7jz98EybgG/RbPT8HDcg6SYcB6u2EbOiBk91PU5D83F7HJzKWlJPPIvnqgtRC/PwXH0R1rs7MN/ZCIzDPAHbgW37EYtn0jZvAuHyWnRzfF3cFCC0p47WRZMwowGSBRn469rGulmSNCZkoCKd0lrCOkKBYNIhkD5+N6ONYIffZlZC67Pwnhny0TmjCAB/TcuIXhiEoiBmlAGg7CpHGaXhB70wl8CFZ6MX5PZss1vaMStqMCtqMavqwHQDPl0f/BCaiCVI/elljKVnoS+ai37WLNT8HNRn38DpjA374xiyA7V0zCyhI8NPak4pBZsPjnWL+tBSJsGDjXRNySc2KQ9PawwtJYeApDOPDFSkU5atQGvoxHpTyr0OG4IWAUdhcupwsp3t0emYXYJQFTwtXQT3j3APx4QCN4k2mYaDdT2bLQRbvBbLHQ/6MHb2q9EwgfMX4pk2EQBhWiQ3bCO1bQ9O1zD1GjkO5spNOLWNeFacj1qQS8bd19H50tuYB6uH5xzDRQh27tpJwbkLUaeXwa5aSIy/pFVfbSup3AhmxE/X1AIi2yrlEJB0xpHzCqVTVmtIw1YVvKYglBzcEEO51+3ir/Ac7uoXqkLHnFIcj44eSxHeVTOiFwMBh3tTdlegOIfbvt9jsypgsksf+jAWgOL1ELjgbKL33ugGKUKQ2rqHtkf/RGLNu8MXpBzBPlBF8olncRqaUP1eojddRuC8hb2GmMaaiWBbaz1WVwspXcE5eyao46d9h7hDQLUojiCdGSSVFx3rJknSqDttelSeOPc6FN071s2QRotwyIlvRxMm7d4yVk3O7rnpG+893+8uaQS1HofSkEMNkJnWMFConV6EFvZhmDZNFRXMMBR0Y2j/Gn5t4IteqjiXrowgatoio7oOxXe4Z+c1j/upvtxrc5nuP/kGqCrMnY5y9rzDM3cqaxErN+JpaSMM4Dv2Y/RpQ/gck04h/voqyXMXoMybgXfpfDLK8nH+9jbEEyd/3JOgKQrNisMf/XHs7odkCYEJ3J25mSdSF1NXmkH7itns2LQJ1XK4JREgW7h3TjtjPIfEcQjUtNBUloM1tYDseHLc5dSMpq+e9/6xboI0TISVhFe/edz7nTaBinRm8VmtaMLEVgySema/92lRHP4STGB3xwwW7sXp2xekuOsFP78Kx8grLCSS6wPHIrS/nDeUGFHdyxzLGJF2O4ZOfO5UALz7q3rqZYAbSO3TbEpDDju6LFIIvCfTtzO5FGXpQoiG3d9b2hArN0Jl7XA8hEFTHAfnrXUotQ2olyxDKcpHu/1a7Jffgur6UW1LUCh4hUKFcMjyCe6cbjIx4nBlQRdV+1dSE12KouUwYeFCGtdvJiTGV+9KRm0rXVlhkiEvDZPyKdw9sr1+kjSeyEBFOvUIQdB0c0jiRu6AlVGDQsEjFKoU9+J0V/fF6cISm28tT7Itkcd2OwfTgURFFW2pLtBhl26NSKAigNhZ03F8HrTOOLF95fyfv6tXIJUWhwOpbxkdPf+gulB4wAqSxzGKWOXnoJy3CA4lysaTiLWbYef+Ma0VIvaWYze1oF55IUp2JtoNK3DWvovYsHXU2uBD4Z5EgLeMNG+TZnODxkOL3Gnnd5bW8Q/vrMMz8RxmZOex/Kxz8azeCs74mbGkAHn766icV0YsM0hXVohwS9dYN0uSRoXMUZFOOR67A91J4igaCSNnwPt5Ubgz5ue8lEFrUmFzg8YtU93cj3PKApQrZZiOQqSqiaurU1To7rDQft0mPQIlo9IleaSLc1EcQWjjTsKWwCsUGoXA9jrcMS/Nt5YnewKp2+elsLwODULgQyEy0L9rOAiXn49yy5VukGLbiPVbEL99BnbsGx8Fzdo6cf7wImLnPrfi7bkLUK+7FHyjN1yronCR6WVFysvqOo3GhEJ7ChriCq/vacdY9R7ZaYGdl0l82TzEUIa+RoA3kSazugWAxol5WLqsvCqdGcbXf6IkDcKh3pSEno1Qjv1mraKwPO3l4qSn5+JUHvfx55ZptKVVYq2t7Gip5dFwvGdYyAR+EYrxSPfXL4IxmpWhfbq2fV5i86cB4N9Vjt7WiQ+F+5MBLjY9fQKpW6ZabG7QaEsqXOV4+aQVwnd0Z7+uw7KFKHffgDJ1IgBi1343QFn3HljDk5A7bCwb5++rcP6+CiwbpawI7fZrUcqKRrUZdapNadDhkS0Gsx8L8fOtBiVBh8rWZgIr30OxbKzcDOJLx1+wklXTgjeexjY0mibmHn8HSToNjK//Qkk6Ds1J4bG7ECjusM8gNWhOz8Xp7nemsrfDQE13sb+qEuFzuGt+796MO+enET6HZtxej6HmLCSnlyEMHb21E9+ew1VbNRQutbxcme77KX91ncaNto+rHX+fmi8AnHsWyoLZbuJsdR3iqefh76sgNrrJqidK7NyH/YcXEG0dEAq4PSvZGaNybhvBXsOiMqby2+0eFpoGv9nuoSqmssewUFraewUr6cnFo9KuwVKEOwSEgM7sMGnfEMswS9IpYEQDlW9+85ssWbKEcDhMXl4eN910E7t27ep1n2QyyYMPPkh2djahUIhbb72V+vrRTbSTTh2KcHsJHNXAUQf3Jm0j2HfExalQ8VDdpbK/pgkDaBmgN6M1qbA8bXB33H9ySa1HcDxuzou3qr7f4m51qptEe/Sn/GrlGLM7/D4AxKbtiL+8Ck2tQ2rjqGpuw3nqiNlZPt+onLZStUkAeULhg/EA16Z9fDAeIE8oJLpv11s68Ox3676IURyaGixfLIWne+0f25DDP9Lpb0QDlTfeeIMHH3yQ1atX8/LLL2OaJldccQWx2OFKlZ/97Gf561//ylNPPcUbb7xBTU0Nt9xyy0g2SzrDVGs2SSBXKNwb8zPR1iixVDIcSAtYkNb77c24NOXhgrQXdTjnV/STLmIh2KVbVHa5gdTZlsFvuz/lb9VMrOPly4zydN9hY1qI5tENrnIdlStSXh6IB8kT7kU+T2g8EA9yRcpLrtP9ljge8nqOaby3T5KGz4jO+nnxxRd7/f5///d/5OXlsWHDBi688ELa29v5xS9+we9+9zsuvfRSAH71q18xa9YsVq9ezdKlS0eyedIZIsfRuDTp4SzTwEChEvCgcHXCx55kkmrtcM7Co9s93D87TUnQocEcnVkfhz7l56PwvqSffKFxGT4e02PUCocDisU0MTLTpc80QVTOsfr2xBko/W6XJGnsjer05Pb2dgCysrIA2LBhA6ZpsmLFip77zJw5k7KyMlatWtVvoJJKpUilDpe67ujoGOFWS6e6gFA42+x7EdJRWGgarPSlSXYPC52V1vnNdrCAZsPCTon+80OGUZ6jcnXay2LbwNN9riI0HrLCrFTSFArZvT/eWAg2eEwWp41hXepAkqS+Ri2Z1nEcPvOZz3D++eczd+5cAOrq6vB4PGRkZPS6b35+PnV1df0cxc17iUajPV+lpaUj3XTpNHb0sNCVKR/3xf3kducsVGkjXwE0iMoy29MTpBziQeFi4SUkc97HnV26xRveNLuHaakDSZIGNmrvgA8++CBbt27liSeeGNJxvvjFL9Le3t7zVVlZOUwtlM5Eh4aF7ukKkOt05yw4GvfFAqxIeci1ZW+G1Nceww1QdhsyUJGkkTYqQz+f/OQnefbZZ3nzzTcpKSnp2V5QUEA6naatra1Xr0p9fT0FBQX9Hsvr9eL1jr9MfOnUNNCwkIHCkn62S1IawQHdnaV1oMv9/ejeMEmShs+IBipCCD71qU/xpz/9iddff51Jkyb1un3x4sUYhsGrr77KrbfeCsCuXbuoqKhg2bJlI9k0SZKkQRnMmlGH8lQ0ATfE/GQJOVwnScNlRAOVBx98kN/97nc888wzhMPhnryTaDSK3+8nGo3yoQ99iIceeoisrCwikQif+tSnWLZsmZzxI0nSuHC8NaMOdqg8udugJalQIlSC42xBQ0k61Y1o2P/Tn/6U9vZ2Lr74YgoLC3u+fv/73/fc5/vf/z7XXXcdt956KxdeeCEFBQX88Y9/HMlmSZIkDdqx1ow6sjjgeSmDO2NDLw4oSVJvIz70czw+n4+HH36Yhx9+eCSbIknSacJE8Jaa5gLHgzFKQcGhNaO8QuG1ujSNCQXLEbQku4sDJj395jpJkjR0ciBVGluOjbdtJzjDNw3YQrDGkz5GRdfT5BPvafIwgBN6LO8pJs9qSd5TzJFrT7ejR3GOXDPqyKUOGrTRKQ4oSWciGahIY8qIV+FvfQ8jXjWo+yvdwYc4xpXteDUubN192av26F1chOF2XirDuKKxkuMWTiQWH7Zjjrout+1KQd6gd3lPcwOULerIBSpK2v07HbnWz9FrRrnFAd2lDvYZFvYolrVXbfdch17LknQ6k69yaUx5Y24dHE9scIGK2r0ooVAGHrU8Vo0LoYDldcvRG8n0CbV1KJyAu+iemkgd556DlBmBjAg4DlTUDs8xx4DYcxAAdcakY9+xWwrBLsWiNOSwU7VIjVBwoLV3AWBnhHq29Vcc8N7Y4eKA1aNQHPAQPe0GaZZHLq0gnf5GtYS+JPXiWBiJOkpCDlWxWmKOBeqxX5KqcC8GjjJAITbHOmaNC9NjIBRQHIFmjs6FRQCO3/1krsaTw3PQSWXusStrwRz5IZCRIvZXgmlBNAz5OVDf1Ov2BmweM+JYihuQWLgLSR6aGvwdT2fPm5guFD5gBshj6EX6tLZOwA0whaGjmFafNaMAch2Ne7oCvGuY5DijVxxQT7l/c9Mr38Kl0598lUujRjU7iTS8jUZ3r4gQOMLpvuioZNU8j6K4FwAbnY685ThGuNcxlO4eFaf7pdvfMY9V4+JKPRsAI2WOWoqH8BgITQUBanJ4elSUKd1LR+w/xSszWxZifwXKjMmoMybjHBWohFHxobBfuFODbz/G1ODJqESGqZNYsWzUeBIn4MPJCKE1th2zOOBoJ9IaKff1fqh3cCTJdY2ksSaHfqRR42hehGpgpxNE1Tj/MKuDby1P9lx0/mFWB1E1jp1OIFQDR/P1OcahoZ9DPSrHO+Zd89MIn0MzAo9Q0LurGhvJ0euF6Bn2SaVRnGEYqvAYcCg/5eDghszGM2fXfgCUqRNA7f2W5Efh42aQK2wvbQNMDW5LKlxhe/m4GcQ3jBdSrc0d/nEywse55+g7NPRjjkKgItc1ksaaDFSk0aN6aC+4lGTGbFqSChuPuuhsbNBoSSokM2bTXnApqH3fhFXc4ZqeHJXjHPPoGhd43U++o5qfMtzDPv7uAC5twjD10Iyp6nqIJcDnRZlQ1OdmDYWrHB/X2z5W12k0JhTaU9AQd6cGX2/7uMrxDfsq1z3DP9HQce45+ozUoRyVke8Ul+saSWNNBirS6FJUkplzSWSdxdqjLjpr6zQSWQtIZs4Fpf+X5uEeFX1Qx1xdp3FJ0sPytBcVBat7TP9Q1/losP3DnEh7KFAZwSDFRPASCczRmMkiBM6eAwAo0ycPeLdqxc09OnpqcI0yMrlGavuhHpXxF6jo3a9f29Bw1JEbjum1rpFukx7FmU2SdIjMUZHGhJZuozgkeGSLwaPbPdw/O01RSHAw3XrM/ZRjJNP2d8ySoEOT6eA5tFCLoaGi4LVtPCP4Bn/ksVNBHyoKnmRq0OfUjnE34fe6dUeSSY51OE05+ce3mTR/VBJkKhrnKie/CKjHGVwbxO4DsHA2TCpB83tRUm6PV/csXCwE2zWTeJfK77Z7OM/x8NvtYAItmolwGDB/wnOSH8f0ji6SCohIEMPQUIYwnd0eRPHLY+nztxQC3XZwNA3F50FNplhrpDnH9PQ8Dxaiz7bjaVYc/uiPY3c/Z9ZROV//F46hd7dFc+CWRIBsua6RNMLkK0wafcLGF6+iukvhsR1eUuHJPLrDS02Xgi9RDWLgT8j99qgcdczfbvew0DR6alzsOaLGha27AY5mjV4NFbt76EdLDNfQT3fgMFw9NP3Y1F2jZJMYnaElpaUNmttAU2FKWZ/bDygWcQFFqDxkhbnDCfCQFaYQlbhwbx9uaiqNmnT7EOzxOPzTXevF9Ors0Cz+7kmzUzv8PPS37RALwUoj1acoYlAoeIVCkxA4Xoc756d75XzdOT+N43VoEgKvUAjJdY2kUSADFWnU6clmHMdG8YRpL7qcRM7ZtBddjuIJ4dgWerJ5wH176qgc1Rl45DE/GA9wbdrHB+MB8rprXFSqbvBzOFAZvZoXh5JptfgwXfR9Izv0k0KwXTEpDTlsVUxSQ+wNGLTupFpm9B3+KRQaN9t+PmuFKeqeflyExkNWmJttP4Xi5KcGmwje0lP9DnMdqqcynvNUTK/BLsP9ead+OEm8v22HDBTE+FC4JxHggrTnmOsaXZD2cE8iINc1kkaFHPqRRp3tiRDPWkA6PLmnborjyaC18Ao8nfuxPZH+dxRiwKGfI4+Z1/oyAHlC44F4kE26Sa7jxuSHKnmOZqByqEdFHfYeleE5Xj02/6t1Ha5VInrXKvl3tbXnjcIQCh8lQv5AdWyGYs9BWLYICnIRkRBKR1fPTSFULhZ9h6A8KP1uH4iJYLWeZql1eJ2g7ZrFy0aaqFCZb/dO4Nbbu0jlZfUq/DZeHMqzSnh09mluHsm+Lkin3L/j0duOrCd0ZBAz96jHrKJwkenFJxRerkv1Wdfo8pSXcy25rpE0es6IQCWUrum5wEnjhM+P1+ynoqrPj9dqhH568lVh95TQP3roR2g+0tHpffYxUDin+03VURREd5KuNkrl8x1Dx/G459eGa6hmmHtUIqj4hcLeQdQqmaqoREboU7QSTyCq6qC0AJYuRLy3E+qb3eq7w6S/oGR7d0n+7ZrZN1DpnvljZ2fgeHTU9NjPfDmURxJUfWTpITrDDmYrKAqgCn4Y7kQIMIXSE2w+EuzqyS1RHWhRxIBBzCF1qt2zrtGROV/1o1QoUZIOOSMCFZ/ViuaM3nRUaWQ5ijHgrKBjaSx1i71ptj0q6/yYkSDtZ892z5lMophDv8iJ3CyY3J3DcUSPw1D4UfiME+Z5JcmLySSbGzQeWuT+v9wy1eIDL/ppSypch59r8Q8pSfe4du51A5UpZTClDCVtIarroLLW/WrvHNLhjw5K0oienoe9/VQy1lq7A5WQn64rl+E5WINnb9XwzeA6CYfySBqTSTJVwfTSMDcU67y+P0VFp3rcYNOjCqwBgphDCbIZQmGvYZHoXtfIzflyPz80GxZ2Wgz7dHBJGsgZEajEjVwUIVc3PV2ktOgJ79OSn0FLfiYA+QcbRvQtVgDxiUV0zZmCUFW0ZJLI+h1DPqcIBeGaS8DQ3Iv2MBZ701C4XvgJCIWn6xJ9uvtvVwKsUPzDdr4B7S13i75NKIaSQvB6UCaWwMQS9/bOmLtsQGUtVNdBavAfQNII9nYHJTtjgh9pndhK72GuhwNdvSoZ3550CKzaQmrWROyMMKkpJaQnFWNU1uHdXYEaG6bhvBNwKI/kLStFRVYHISOCmj+Z/52+jf/dbPOTdw02Nai9gs17X/TRkgSfJvjAbJPJ0f6DmDKhEhIKlapNAsgTCjcl/OQJjSWmwZ/9CRoUQaVqM9E5Iy4f0jhwRrzS4sbgV2aVBsGx8XbsIRWZBurorW9ysjoyQ9SX5QCQV9VEtGV4eiL6I3SNxKKZiOJcALz1zUQ27UQdYm+K8Bhw7SUQ8LmzY156C2UYh0QOqexe8O/I7v7SoENlzGI0PkArALsPwO4DCEDkZEFJIUppIRTmQjiIMnsqzJ7q7tDQjNhXAe/ugKOSfhuw+aUeo8tw8KJgIzCPCEo6VEHQEOSq8NXVHj63OEXMVHou2hO6L9pqfQt6fQt2biapGROwcqKkJxRiTihEr2rAu7sCrSM28k/OEQ7lkazeW89Bw6Aj28PTLTNZMqkWbVuaNXVKr2BzTZ3OZEvjADbvNWr80+LePWatSYXz0wYGoAG5jsoVKS8LrcPrGvWX8yVJo+GMCFSk4WXEq/C3voej+zFDE8a6OccUD/momZIPKGQ2tJFVe+w6LcdyvLoUVmaYxJLZOAEfhuMQ2rEf//7qofekqCpccSFkRd0Krs+9hjICCxFaCLaoh2uVXID3cK0SJY0lRM8QwWhQABpboLEFsWkb6DoU5UFpd+CSGYW8bJS8bER+DrzyDtiH8yfCqDgIOgUYPoc7BhgOaUwoTIzYvH+GySf+7l60LzY9XGR50FBId7dFb2xFb2zFyoqQnl6GWZCNWZKHWZKHXt+CUd2AXts85KD0RNRjYTbspypnKu/Eg5SEJjN3jgKpGD/Y3cIz2+LcPbWLkqBDqE1nRco7YIJsQCg840uS4WjMtY2e3K4jHZnzJUmjRQYq0gnzxtyF8DyxqnEdqKS9BlXTihCKSqgtRn5545CChkNTOiOO2mumhADSU0tIzZmMUBTUeJLMjTsw2oaWT3Ho2Fx4jpu3YVrw/GsosfiQj9uf/bi1SooVlQfsEGW6wSXCx8/ppEY47FMsZjDya8sMyLKgogYqatznJeiHyWUo5y1CmVwK112KeOF1d2kB3NybPKFSr9i0JumTe3NoOOSDc9J8ZmGajrR70b4q7eU8e+CLsd7Sgb56K3Y0RGp6GVZxLlZ+FlZ+FgqgNbWj1zZh1DYN37IJ/bARbh5JB9Sv38/8SC57s4L4A36EJ8RuO8Q50xVWpeKYoQ4q7HbsTnPABNm06vZI9TcTSJLGkgxUpBPjWBiJOkpCDlWxWmKO1TPFeDyxdI3KGUXYuoYvnqR4X+2Qezb6m9LpeHSSi2dh5ruLBBrVjfg37cJgmIZlFs6BWVPciOXlt1GaTr5H6HgK0bjN8bMcb09CaYmi80WRwdtKsqd+ybgRS8CWXYiWNpSrLoKiPJSbLkc8+xrEE6QQ7Oouu1/TpfasE3TkcEiGx8EWMP83oZ6Ldp1lwyAmtmjtXQTWbcfe7scqzcMszMGOhrByolg5UZLzpqC1d7m9LDWNqO1dwzp61iuPpMNDXnsXm2taeCnskBmNsCCQRTgUIsMToi7PTzo/Hyudxu7o4E/l7Sw0070SZE047kwgSRoL4+8KI40rqtlJpOFttO75wkIIHOF0j/OrZNU8j9I9HGCj05G3HMcY29VmHVWhanoRaa8HI21SursGdYirFh85O+TQG7maneEO9fg8KLaDb8tejIPdAZE+9Dd5MXUCLF3g/vL2OpTy6iEf81jCqFxK3xWrvYrCZYxCIu3Jqq5HPPMyqesuoSonTNf7LuH1556jraPjiERZX5/lFYqDDtUxt5Lx2dYRs1p0C8sUgy47r8USaDvL8e4sx/F7MYtysApzsHMysKMht6rtzAko8SR6TRN6bRNqczvKEAvp9ZdHMs3WSXSZ0NXJHCuJT9PpygiSmRnkYKafdo+HouwcsjPzaBQmkzraae5sp7WjCyHEMWcCyVL50lhRhBitspMjo6Ojg2g0SvSSf0bRT35NEmkAdppAxQsQb+w17fHeWRa/3qH3mjFAIJd42dWgjeUYtuBLiZU0hoMYtsOCA7UE0yeWz6FoKnWOxU+t9p4iaKYQNDuCx69OcNcLfhZMm8qEqdPcJXdiMZZt3U9J/PAMFN03tM8A6oR84hcsR2ga3j178b23ZUjHOxlDfQwA6djw59IMJOb3s3HpYpxQCMdMENjzV4qo447pFrMfC5KwFFRF4AgFFYGDgq4I/tWbxUTNQ5Vj8rN0O9W2zRd8mczsfh076ZOrG2IbOrGcLLrysohnZyC0wxd6zbQINrYQbGgh0NyGepzE6I+v+NeTasORrNYqwu1biOSWEFE60TsrwEpiqIKQ7jDJ18aVxXFW1mo0JRTW1WvETAXFl4EoOW/Uek5jIkAHYQbK3k62N45KO6SRJ8wkra98nfb2diKRAQp9IntUpOPRPMQnXo/RuImWxk1sbNB5aJGbI3HLVIt7XgzQklQwcxdi5i48qfomQ+JY6C3bsLLmgKpT5u+kUQ+iCsGcyvoTDlIOiSgqfkVhj233CtAWlvj451sX0a7lUhMT7K+oJr1rN9cp4WF77E4oSOq8ZQhNw6ipxTsGQcqpKJhIsOydtWxbdjbtkQje+TezQvyZ9RU1JCyFkCGImQo3GEGmqwa/MNtpdeBQFkmJavBlbzZvWgmKhuGirJkWkdoGIrUNOKpKPDuDWF4WsdwsbEOnoyiPjqI8FNsh0NJG1oEqfO0jOCPNE6Yj9zw6AIQgGCwj2rqBaLqeYNggGomyLhZFDUF1QiOQoRDwZ4M/C2gfsXYdLVdpJo6fWiefGMFRO680fslARTo+RcXMW4xQPaytW9NrnH9tnUq6YClW9twxaZrWcRBP/TqEHiQnv5B8bxxMmFndRMYQ1tYJKCqf1zP5KzGeS8bZ3GhwzdklvKDOIDtLo7zOZsOm7Syub+M6PTJsRdCEx0Pq0gvA40FrbcW/dp3MFDgBejrN/HfWsnXJQlpystledBN55kv844ID/Gizlzs9Ia4w3IvfN7TcPkGJV1G43AgMe7tUxyHU2EKosQWhQDIjQlduFrG8bEy/l1huFomsDIo2bsff1jHs5+9LIaaEiWVdTE3VSg5seYu7r/Rg6G4vypNVXsidC77J9LME0ohRcchU2gmQYIp6kE6C1Dr5JMfz0KM04mSgIg2ammymOESvcf6iEFQcYxHBkWZ0ugvZZTs1lPrd9Vgm17eQd1RdC1MIXnESrFD9GIMMKjRF4SYjRCgrk6qpk3kXH4YDoXQDf3l1DzekVS43hm8NGKGppC4+DxEOocXjBN5ZhWLLcuUnSrEsVr70EnkXnUN5eApvq9fhL3qT0j3bqUwdnjo8UkHJcdsnwN/agb+1g5zdB0mHAjRNn0g8O4OaRbMp2rAN/xAr8J6QrnqyjSSr9nTy6HaD+2ebKJ0GNU4eZJw3eu3oVi/yyFMayVZaCRMjrO6nTUSpE3mkkVOjz0QyO0oaHMfG03WQ6i54bIcPM3Mmj+7wUdMFns4D4IzBBdUx0buqmJzjY1KOF0U4NKQClDb3/US6QaT4gxNnoxh8L4ut69TNnoJy9lnkRIOUt5r88NmtrF+3jmyliwpn+PIvBJBeugQnNwdMk8DbK1FTY1em/Whpx+GZ1lbSI1BkDiAtBH/t6iA9DClzlhBsSMR4+vm/87fV28hWNVKlF1I4Zymb7STWOErLUwBvV5zCzTsItLTjaCo1i2aTjIzSIoiOjdayg+pOeGynFwoW8dhOLzVdoDXvGJP/awudGlHILmcqrcKtQp2htDND3UuRUotfTTOq3TzSmJM9KtKgqIl6HNtC8UWJF1+G8GVhZc0mWP0KTrLDvT1YNKJtUFLtBKr+hiYOzUByUDWNFQsmsLpBIVG/h9aqGv4/O4EOfEyNUtC9yu/G7rWeNjhpzlX7zmw5WldOJg2zpmAaBu1OioMHKtm/exfnODq/rfNgCmhRUgMWQUsLwcupLi73hvAMogfHmjcLe2IpCIH3zVVoXaP4iXoQ1sRi/Ka5hSxd54Lw8M/qWpeM80RnG1maxvn+oeUl7E6niDmCUkPnml37ybY95Cyci3fhQgoKCti/ejPTY+MnCARQHUHh5h3ULJxNIjNC1dlzyd11gEh1/cgO/XVUYlsmWjAbe+ZtEMzHKT4HbceT2PEW6KiEjIkj2YIBpfFQKUpoFNkUqg2E6SJHaSEzmMIWGp22h07LS6ftJeEYjErpZGlMyEBFGhTHm+nmomTO7Mn+F74suibdgt66E8ebOeJtELofoXqw4u1k+QS3zbBIZ04j5NNYlNmFh908rWjUOQrTVJ1o9xtXSgi2kqY05LA1liYlBN4Bggdb02iaNZmOInfZhURXF++8u4FgRyf/okcpMXQudQI8YrVT7djsdcye2SFHWm8meDLZSZaqscxz7OEFq7QYc/4cADxrNqLVNUBofBXcWt3lJnmu6eoasUDl0PehBiolusG9kQwuCYTwKiqpLTuIxBNMO3cRWUUlZN5UgrVpC9qO3Sjj6IO5ajsUbdpO7fyZxHMyaJg9hXh2Bnk79o3cSQO5MOVK7ILFoHW/5oL52As/BnUb3NvHWBI/B5wJBImRqzThFyaaYpOhJ8jQEwDYQqXLdoOWLtuDEDJoGQ8soZIWQw8zZKAiDY7u7z9hVtVHL5H2qBlIW8wylgQDeFWbB8t28eAujZakwiWKj90ixX86bQCkhUNawNfPS3Hf3/x8xWnpqTtxZM9LPDNK/dypWH4fioCMihoK9h3kKtPiQiOrJ7gpUXW+ZGTxpp2geIDZIevNRPf35DEDFScjSvq8JW5bdu1F33dgmJ6s4ZN0HHY6Ngsn55HwZ+NMm4EnGkEJh8GycKqrcSorEV0nN2Ml6Ti8l0pQGnJ4L5Yg6Tj41JMflY5oGlcFe0911PYdxF/XQP55S3CK8rGWLMCZUIr+zlrUjvHTe3UoWGmbUETz1Al05WeTjIbJNFK0miNQfsEThOKlfbdrRv/bx1CMIDERJNXlJ6CmCespQlqasJZCUxyieoJod+AijQ+N6RAVqaF/iJWBinRq6Z6BlBn20WXESDsWVwX2kUqlWVsXJF2wlJubdvNjx2KPY5HlEyzItfl7pU6nqfRZLXaaqhNWNRqnT6KtrBAAI5GkYPte/G2dgMLlet9Aw6v0vx3cHpwtVqr7wpskJRy8/UxdFl4PqYvPB11HrWvA2PDusD5VJ0TTUCIR1EiYtmCQv2sQzMggmplJOJrBQ14vi/JsNjZo7FOUnk52VYHJkybhU1REezt2ZSVOZSVObQ3pdJq/dnVwZTDca/ir1jL5YVsjFodr1KSOWCzwn5trehKedRQ+nZFLoT70HiYlFsd4+Q3saZPdQCUvm/QNV6Jv3oq2bdeQC7ANFwXILK/B39JO3fwZmAEfizOaOBAPsz8WRpzhQxwChZjjJZb29mwJqCZhPUVYS+FXzTP8GRo/rGEqEigDFemUE9BMJkQFPqCxoZ6rX7S4f7bRMwMpoKj8kxrlWeI8l0ywucH9Z3n+gM4PLk7ygRf9tCUVrlf9rMjMo2nudNJBd/pjtKqe3H3lqPbgk0b7Kw6XPuLC+69dDT2Fag2h8MlANvmGQerCZYhgAKWzC+9bq497oUwLwYtdHVwVigwq72VAioI2YQL6tCko0ShqJIISOBx05QrBYtPN8zBUQVFQ4DdscrUuJtNOQ1Mbb+1tp6qhjWmZUabMOwsKClCiUfRoFObOBcehrrycinffZWtDM4tihz/pRlWNgKKyK53uVaPm6MUCW5IKMzweosO4QrcC6Hv2o1XXYp63BKe4AGvxfKw5M9HKK1HLq1DrGsZF0OLrjFG2ejONMyej5ExjcqCTLCPFlo5Mko586z5MIe54iKc91DO2VbGlkSFf7dIpRVMcpgZa0RIJGjs6WXewtXsG0m6EcPBoB7CUKLqicKMWxBAKf07HKA05/L1So7zDXXjuDi3EounTqZlYAgroqTR52/YSbG5D0U7sU8BAxeH6u/BO1w2iqoq5eAFOXi5YFt43VqKk08c9z9pEjMfb28jWdM4PnEQeh9+HMXs2+tw5qP3kmQjTRHR0IDo6mNjezsrGBl6orGSi0cwPzq0lrKXJNuG+F/2sqdO4NTOTm7IyEQfLSXo8qMXFaCWlqKUlKKEwdlERV+Tnk6GqBNMWVkUldnkFgYpKvqTk86eudp7p6uDdoxYLPBRI3hKKcFMoOmw1ao6kxBMYr7yJM2Ui5tlngc+LPWMK9owpkEyjVVShllei1jUM+7lPhGo75G/by5aJ1zEr3EaGkWZZViPbOzOoT8naItKZ4YwIVIp8XWjKyEyrlEZXSDPxijjpVIrK+jbik2/qMwPpy0pLz0ycdhwcofDtC5Lc9YKfW/4aICcaon7eWawOR5igCHJqm8jduR/NOrmpmH2Kww1w4b3JG+J6Xxgxfw7m9CkAeN9eg9o+uAJfaxPx7u8xzg8EB93DohYUYMyfhzZ1Ckp37odIJrF27sSuq0d0dOK0t8NR06HP8+m0tLXxaFMzzXNNHC+0JBVW1Wncn5PNtRkZh++cTuMcOIBzwM2xSYfDPO31Mm/mZOziyUwIhTFmzcSYNROEwGlo5O6KCop27OBne3bSmEj2FBFcXadxTySDq4MDl9QeDgpu7oq6vxynIA9nQgn2hFLwebCnT8aePhlSadQDlaj7K1Bq61GGuGbUyapLBWgzPcyPtBI10syPtFCdDLCrM4otq0xIp7nTJlBRVBVlgC7iXG8CjwxUThuO42VPPJuuSeeB2p0WG8glNuV9aE1baTPrsFoqyPIJ7ju6d4MyqsILsIRKl2Lwl9C5NE3LgmmHj//rXT88qXZ9mGxyG1v4ZU1Dr+q9q+s0PjKhgBtKCmifNY9Udj4eILR/F6FwCmZm9TnW0evsJG2b92oqKA05vBtPEpgUYXNTO49Xt1FWFOXS/N4Ja0LT0aZNw5k4HRE5fJvS2oR6cDdKTQWKY4Mf9ys/2k8bPNSt76As3Hsxv9KQoC6gkjevoOe+lZ0JvrJ2B+nuuhtmtaA+YfJg5uvc8+sQC6ZPZsqUqUyYNJnc/HymTykiMr2U2ecs4GupBGsTtbyypY45+kHmT6ij0VYoPKsQMQx1W1Idg5yKnKxC7K4mHc0kmVNAMicPJxKGnNmwZDaKlcbX1ICvsQ5Pa/MJDQ99+rwvnmTrXfq+FiwMNsV9TPK3M9HXQUkgQZbXYmsshy77+IXQhDNKtVlGmJGWCbOnC0cfXAhy2gQqx9KQCqKNp3mI0pC0mj7i0ZK+N6gGdt5C/DPmkdr+PK3bn2dTg9rTu3HJtBDfb1mAnVJpaGhgb2Q5hPoGCUNxIJGkLNT3wl6re2levAzbHwTHIbJnO4G6qgGPUxVP8h/bDmAK90JtOoKUc3h124+s20Gn6QYF/727gulhPyUBHyIchckzEBOnYHu7kw1tC7X6IOrBPSjtLYN+LKbj8E5dM12mwuM7vVw/MY/f7Wwg7QjaUs2YjoPR3UOT6TMIGhr7mxN9hr/+c1mMgx3bePKV3bQkFZaU5PNvV16IKCimyvTgDQQwfaVce+EUGhNLuGUhOGaalNdBa21GaXO/SMZHPElSEQJvWwvethYi+3aQjmSSyM4jlVuAY3hIFJSQKChBsUx8TfUnFbQMhUBhfyKDVtPH7GAzAc1kSaSOvfEMKlMDL+QnSaeyMyJQqUvLBKtR4VjozVvd6cqjtNJqfxRVwzf3elBU1mx7lsaEQtzReVM7l7aUSrOSwzYnjBEpGdZOc9MRrO3oJGYrPL7Tw1V5mfxuZyulk6ZQdMnFmP4AeipB5rZNGJ3HHu7J9BgEdY1t7al+8172tKn8apvh9m4kdEITp+DMnAO5h3s5lK4O1PI9KJX7Uczj58AcbWtzJ12mw6SIj//v7JlMiQa5eXIhX1u3k4OdSbY2d7Iw1+2JCRk6P1g+n8d2VfLrXZW829h7+Ou+l9zhrw9MK+beqcVodRVs3raVb67ewbzifD5/4TkUFheT44+wR/ViGh46IyEiOUc8nmQcpa0F2ppQDgUw1sitzqwIgbe9BU9LE2LPdtIZWaRyC0j2E7R4mxsxOtsxOtvRY52oI7z0QavlY01HAbODLeQYcaYFWsnxJGgzfSQcnYSjkbB10kJDBi/Sqe6MCFSk0aG1H8CoW4MwgtgZ046/wwgTXY0Uh+GRLR62BZaxcFIIxYyz9eB+vIvvHfbz7YzHidmCCT6DL0yfwISgn+suupA9OYUkbIeOulqm79uBOoiLa1DX+M5Z0/hteR2Pl9ezulbj7xUa//OeByGgIaEQCkf5/gfnsUpbyr5oBFUB0dpB+Z49LGuvpdjsHNIlalIkwKfmTeK6ifn4dHdYdUo0yM8uXsCzB+uZFOk9PVtXFT44q4yQofHwloO9hr9W1Wp8YnYZ75tU2HP/iSE/D86ewLVlufg6GqCjgRwg5Dj8vcPGM3Mqak4uIiMHEYkifAFEQQAKDvemKV0dKI21qLveO6lgbLAU6OlpCe/ZjpmRRTK3gGRuPo7hJZlfRDL/UGVmgZaIY3R2uIFLVwe6IrCGuQiZJTTe68qh2NvFNH8rmXqSTD3Z6z6OUEg6OglHp8sQJGyduK2R6P6yh2n6qCSNJBmoSMNG73AraOrt+0c+UDlO742wLZyazVSbsNKcw7QJ06noaGbl2k2YyTT6grtQtOF9+Zf5vHy4KI8rsjPwZYRomb0AfzSLOUKwd8u7FDRUoRqDP6emKnxgUiEhXeNn+2qYn2Ozu0klO6Dw1Q9fQ9mshRSEYUmXQkdnO395YyNvrt5IqWJxxbwpKOHjLxVwLBleg/dN7bssgk/X+t1+yN62WL95Lfs64n2Of+ukgj77+1SVazJUqCtH1LjJuULTIZqJyMh2A5fMbEQghAhF3K+iCahb1qHWVgzpMQ+GAnjaWvAcClqimaQzsjDDEcxQFMfrw/YHsf1BknluYHZ1VhdxW6XdVGkzNdotlXZTIz3k4EWhOhWm1fSR64njVy38moVftfCqNqoiCGgmAc0kS+ub75N21J6gJWFrw1b3YiiStkZr2iDpyN4gySUDFWl4OCZ6VyXFIYfqrgpSjgnqyJWBP17vjd28H9tMMXH6PKafewmq4WOnWUJS2YRldmI370fPmz6sbYrqOtfl5ZAoLaN52nQcw4NiW2TveI/i1gY4gSDlSPu6EpSFBWfl2mxtMbjwihvwTJhPYdDB13GAWVWr+Pjj+1hdrXD3hHzunlCApo7NG/zReS3XleXyu52N3XktLZjOpJ68lhOh2Ba0NKK0NPZsEx4vIjMXZ9ZZiHAG9tkXIGrKUbesQ0mPzlo+CuBpb8XT3tqzzTY8WOEIZiiCGY5ghdzhsYDmENAcCn2HV3CO2yqt3cFLdVIn5ZxcoBB3DMqTvROiFQRe1Q1a/KqNx4nj1+yeL4/q9HxFjZEbQjtZCVujJe2hJe2lJe0h5QxfPR3p1CIDFemkKKk2/BUvHbFAoI3tON3JniqhPY+jdC8IaCs6ibIrEd6MYTv/8Xpv1EgBcy+/h4klBSiKSrUZocmXjW/Fl1D3v40aKeyzz1AIRSFVWExsynRsvw9VVdFjnWRs24SeiB//AAMwHYfVze10WQp/qYjy1Y/fj5ObT2tSwHtPEGjbTEtSYWV1kI9OKeKW0rxhfFQn7ui8lol+LzdNyOfrm/ZwsCvJttYuFmQPz7RjJZ1Cqa9CaazBmTYXZ+ocnKIJiKw81HdXozbUDMt5TpRmptFamvC2NPVse/H8ZUQNm6jukNH9Pag7PcFLsc9iejDNhnYfjeljvy0L28Ta/wb65ItQtIE/DAgUko5B0jFoBcxk7+NqioNfswn0BC8WYxTf9lAQhHSLsG7i12yK/QmK/e4sn5il05L2UGdaNCc1TEf2tpwpZKAinRSh+xGaByvW5iZ7zji6yFm6p8gZwTzEAOXmT8pxem80HOZmxMnOcYcnDqYzKE9nAKDoHrzTLx22pgggnV9AbOoMrKBbhE1LJglX7cdfVz3k2SDb22N0WQ5nlRbypY98mHBmJtua2/jTn37LQWsHj24P9Qyt7O8a+2mbR+e12GmLyZEAP1k+l+cqGpkYGv4iZYrjoO16D7WuCnvheYhwFPvcSxDle1G3bQDGfqVkUyg0pXWajkij0RVBhmGTadgUeS0ihsPSjAS7Yx52xTwMNOxh12zG2v4Mij8DveTsPrcPNpCxhUqXpdJlja8FMMENojIMkyxPiixPmohuEtQtgrpFkeE+iR1pleaESlNCoyWpYsuFCE9bMlCRTo7mJTHpRvSGDbQ0bGRjg85Di9yeg1umWtzzYoCWpIKZtxgrbxH0s9bNYJ1I743hD3LWvCBhXcdGYVcyh0Zr+OtHCMDMziE2dQZm1O1yV9MmgQN78VeWY3iHZ6x/QtDP5y84h4tuvBnN68WJdfHwI49woKaONYqHy/LdmUVpB1qT7b2mDI+FAfNaNK3ffJThpLS3oL35As7M+TiTZ+FMmIrILYC334DG+hE998mweoIXnX0xD3PDKSYETKaH0mQaNhvbfaT7yRkRtZsAcGo2QT+ByvECmVOBLVSa016au9fz0RWHTE+abE+KKCYhQxDxOEQ8DpOi7qpRbUmV5qRKa1LDltUoxoWkpRC3hv5+JAMV6eQpKlb+EoTmZW3tql6zPNbWqaQLl2HnzB/yaQbbe2N7M1h8zmICXoW00NiayKPTGVpCaX/MjExiU2eQznJrsCiWTaB8P/6DB1DtQ/kHwxMsRKfP5NKFS0FVoaWRHc//lQM1dUwMevmXWROZFPJzW2ke39xxkPJYiu3tMc7KPHOn4yuOjbZ9E0pdNc7CZYhACC65CnZtgy2bwBnZacMny0HhvU4fLabG/EiSXK/NRdlx1rf7aTUP52YIK4XTsIPSkEN1w3aElULRe6+qfLxA5lRkCZXGlI/GlI9UexqvJsjy2eT6bbL9Dn5dkOlzyPQ5gHXc40mjo7xDZ1vz8YsRHo8MVKQhUxNNFIfoNcujKAQViWaG5bJwVO/NpqNK1N/zYgBvZhFzz74QxZ9BTPjZmsgnJYbv5e32oOQSnzTlcIDiOPgrywns34c6zFNjBcDcRYgZc91zVR6E9e9QqsHHphZzdWE2vu41iSaF/Pxo0QxeqG1mQnD4h1ZStsMf9tVw65QivCe4DlLadvjjwTpumViA5wT3HQq1pQHl9edw5izGKZoEM+ZAYQmseRNaB1/0brRVJQ3aTJUlGUlCusOyUAsrX/wzFZvdlbWFY+PYVk9vov33r/VU5HY0D+rC+48byJwOUrZCbUynNub+j/t1h2yfQ47fJuJx5GShcSJlD88fQgYq0tA4Np7OA1Tb8NgOH1bWDB7dsatngUDTuRCGY/Xb7t4bJdXBmrq9Pb03zUmF1vAM5s85G+GJUNsJlRTiDFOPhlAUUvmFxCdNwepeyE9xBL6aKgL79qClksc5wknQdFh6MaK4zD3fjvdg+2YUIMOjc3NJbp9dfJra7/bh8GZNEz/bVk6u38vlpSd2jjfrWnhkZyW5Pg+XFeeMSPsGotgW2ntrsPbth3POh0gUVlwH296FHe/BOFghuT9dtsabLQHOiiQp8hgsWbyQrKCHd1e9xs1TYgPmgumahrPmJwMGMkIxYMH9KMGReZ2MpYSlUtWlUtUlL2mno7GfNC+d0tR4PY5tofqiJKbegll8IYmpt6D6oji2iRof3twALV5LccjhkS0Gc38T4f/aljJnxhSwTfY0w9oqhiVIEapKonQCLcsvpmP+Aqxw2B3iOXiArLdeI7x9y8gEKT4/yopr3CDFcVDWvoW5ZSNPVdSTtge/7k3adnhif+2g9jnefd+obgbcgOVEvVnbvW/dGPZi1FbBC3+GqnJQFJi7AC67BsIju+jhUNhCYWO7j61dATyF05k89xwuufZW9pj53DLVHdq4ZarFxkad1qT7qTWsW3x4ejPfWp7sCWQ+NK2JkN1COtYKug+8YzssKGwT58BrCHv8TYeWxq/TJvzUfCFUY/jzEaTj0Euwyy7BzJ2HdmiGgT9EKnIfauMW1IwSVGOYZvyYSXSri2pT5cl9Ic65+EpqNAvHdNixcx37cm9G92t4vCf/svaogp9e9wUmB028qvuJO+Uo7I8ZHLAMzOIlUHz84xTlBk/43BkeheWFOgEd5sUOENy3CT1L4fkGD7/YX8vkaSVcPbN0UMd6fmcF/7uriklTi7lm1oRj3ve57eX93lf3eYmnTdb9dRWlIYe1DW34p5US8Axulkg8bbL+r6soCTqsamhDnVBARmD0hyG+VHxfz89FXpibqWAUgTPnAna0Cw52HXv/2rahz6ZSYiczNKhQnvLSZuvM8RWQWaIRCoXYp71CWWIrrUmFtbUqnvm3gJmkbecLbGrsndR+70sBWlMqnllXo0y+fMCFW0eLXfUe5u7nMYI5aCeZP2N5hn94Uxobx1j0vZfTJlCRxogRwClY1He7ZvS//UQkWvDufQZVuJ++HDuNLRSmF4Qomnk2fp+DaTq8u3Edjc0tKOpORO6ckzqVXxfMzLSZEnXwe92ehbitsCfmoSKu44zwoHdRQGFpvo6uQkdaENq5Gq17ldhX91Z3f68adKDSs8/uquMGKq/uruy575z8LD73zNukbQsUhbTtkLQOz7C64X/+3JNrkhmN8o3rz2dC1L1wHGzu4LNPv0bKcj/xH9r3xtkWD7+ncuMvnyfavSq0R9P57o3LmZA1up/waxLQkhLMz1LI9cGcDIUCH+xoF7SP0w/57ZbGs5UK5yrVTMtSWe+/nP+3ayKTml6jOAz17dX4l9yHYvhZ894feiW1r6lV8Z51K55pl2Kmxj7J1Knd3PP9ZAMV6cxzRgQqWT4HVa6efOrxBDBycxGxJqJewYXFCp0ik/MXzqIyZpCIt/GXVRtpbE2AN4IID6Kr4ygRj2BWps2EsNNT7KrdVNkTM6hJ6ohRyMqbEVWZn62hKFAXF6yqt7gmnSBl2fx6425WHqyjNOSwsryehGnhP06F24Rpsaq8ntKQwzsHakmkLfye/vdJpK2e479zoJZ/uXQRIa/B5ppOcoMq9y4OM7W4iKVzsvjN3AIS3lxqyCeUmUtuNERRUQ6iqxVaasnRKgl5Pexp7Oi1kOIbVe65szxJ2pMqLUmFBUVZZAfHpgc06cDaJsGEIMyKKmT7YLlPIWFBXUJQl4CWkVs26KSkTJuX/vpraubM4pxzzmXmgqU0V+fRVfkSomYzwnk/dntVz9pWj243uH+26QYybZVj3XzAnbEkGre7ib6Np2+irzT8FCHGaUbZIHV0dBCNRsm+9j8HHPq5bEIKv35KP8wzlxCQbEVJtpLpg4W5NpoKWXYDP3thC6urHeyiZTjFS3tqtRTOmHncw2b7HGZnORQHD+dlNCQUdrRoxPwBhjptYDBDPwqwOFdjcsRt9952h5U7K9j522+SlWohlrZoTrgf8x+/OsFdL/gpCHl7ejQ8usZ3rl0GwOefW0XacudYpW2Huq7U4X3CPjya+3gURe3OIXUft2p4sbwhHr4+wn/vLWZycQE5OdkEM7IJR6Jk+mFhroOmgu3ApkaNthRMyIowITOMclTfrZOK8+aa9fzsmZfI6NjJf1/YwdInguT6BbUxBVso/MOy2Xxk2Wz0Uar3cuTQz9ECGsyIQr5P4chJSWn7cNCytT7B4LOD+td5UkM/h1WtXUvizR9gRPIpXfExLp6di1ckSDfu5/VX/0Z10TWYqx/BNlOoqoY2YRl2+Socx0YzvPiv/w4juNB0v5yuBpz1/4vquI9dOBZmorPndWn4wyjd63Q5qgf17I+gho5fWTnZ3jyi7ZZGj2MmqPvDZ2hvbycSGThn7IzoUelKK5jjs3yCdFwKqNko2HQ2VnNuhkmpUkdubCerqwPYZRfjFCwe9NHChuCcfItc/+HAtapLZUerWywKIOIf+V4UTYHlBTr5AQUhYHOzzZ52Bz2Uge4LUtFQ3Z0jozA32+41y+P3uwxaUwrzCzLIDvgQQMhjsLm5q1dPRn8zQ+YVZpKVnUvh3EWcs/gspuYFCOhQEhb8c6dC3ILamEraBp+h09CZIKnX4U00EGtr5JEXO7l2Sh4Tp+bCzjgYXsgqhKwCiOaSVD0EJ07nUw9OJZFK8YvdO5m3aBOfK1zHnX+BqE/nhR0HeGHHgTEb/jlS3IZNLaAiyPVBgR/y/QoeDcpCCmUhmBX2Uh2zqepyqIk5pIcatZwENVqI96z3YUxaTqPq4dm9ggtK/eQXzOKKW/LZUZfkrZUWRiQf49wPo0WLsadehLnmfzE7GrCb9kN08qi2WfGGwfCTbqk/7utSz5ro3l+S+nFGBCpraodecEYaW9q+rZSmtrOpNc1ntnu4f7ZOUQiq4g2DPoZHFVxcbBE0BI6Ag50qO1o0Os3RLbqgHhGkWA6sqreojbuBk+4LMufD32DaM//KYxt3URpy2Num0pKA5oTCpgaNtpTb3vfNn0LI6ya2PvK+i/jfNTv5xbqdvNvYu87MfS/5aUsp/Ov7ruK2669BySmiorWLirZOOtKCKb42tJZGijob+NnqDtbsbebi4ijbDlayefcO3i0ze+rj7K8weLm+jGuLL3YfTCoBtfuhdj9C1cCfwfaDnURLJpKTlcH02fO45Jw5zM68g6fP3c72dzfwk5d2UNeeHtPhn6M5QH3S/VJaBdleN2gp6A5aJkU0JkU0hICGhEN1zKGqy6E9PXBPrWOlaVjzPHnnXjPk9qneMJ5ph5d+SFgKLx+ABfkqc3OzmT3JpvhjX6BJLSBma3SmBV2BIjqv+Ffa97yNGi0cnppGJ0Ax/GjLPo3Y8xKte17qk+h7z0sBWpMq2vQr0aZdOeaJvtL4dUYEKtIpzrHR2/a4tVq2GzjhUh7dWY1wHIzkHmznikHUahGcV+gGKTFT4dUqnbg1+lWh8vwKi3I0Ih43SHm9xqIlJUg0VbPrt98Ey12XZl+6E0cofPuCJHe94OfCpwJ0dQdUPl0hYQle31fDdd2Jsrqq8vFlswl7Db731pbDCZUpHatkKY//6x3MmjKppx0TnA7eW/k3Hnr6b7x2cyuZXjf58odPBvHpKuvXCYRwsIXCYzs8oKjuc49C7e4KPvv0G3z75gswjhgvURybYKyZOzJMzv/3r5BXUMj8sxZw40ULITuXWXPmk8xdwL8sswl0NrLYm0Rrrh131WIF0JRyv7a2CaxkmpKQRklIJcOrkB9QyQ+oLMqFLlP09LQ0JBzMI3pb2ravpuaV3+CJZGNMPncE2qmwqV6hPiY4v0QhmltEtOcRHAqgNJzZF9FlKrQnHLpS0JmGzkPf04xob7OiahgzrsHS/azZ/qfe1atrVfQ5N6FPvmTkGiCdFmSgIo17SlcNjm2i6Qa2ZaLoXtKz78G776/YiVaUrhpE5NizYeZlOxQEBLaAt2q0UQ9S/Bqcla1RFnYv7EkbVtW5QQqA0T3k01ZRTZZPcFOfbnK1p5vcEQ7FQdFvcu3uxjbKwoJf7Y5SnnMJH7xhOR+9OAvFHwDHRtQeQFTsorq2jp89/wZ5nmTvisJBh5qYwC2xpKBH8+CCj0EoF/u9Z9F2vIQtFF7eWcE5336cKTlRHCHICvj42AXzOHtCAVuqm0nbgpqaGpT2Kl596Tne+UgGdvESdpvncdbUiUyb4Razw7ERbU3Q3ohoa4T25nEXuDQmBY1Ji01NENShJKRRHFIpCKiEDIWZmRozM93eltaUoCHhUB93qDvglrJv276K3BEIVA6p6VJ4Zg8UhRRCBoQ9gpAHQh4IGgJVhYhXEB5gVnnKcgOWrhS9Aq3hZKshIqUX8Wcc1parnFvgcOMVKh3+MFpJ7/t2pqGhCxpiyDV7JOAMSaaVTnFmHLVxG3r125QELarjBqmFDwKgNm7ByZ4JR9RqOTqZtjjocEGROzVzdZ3Gwc5j975Esodep+FQMq0CTI+qzMnS0FU3N3hvh8PWFrvPRUHYNhWvPUnV60+xtMDi0SsTBA2ImXDPiz7W1XVXGEXpN7nW0FTSniDnLr+Q889bRml2BvWdCdra21m38m2+NDuD6uY2HvrzW+xv6UJTBLZQULqPqeLgoLjb592I6vGjTlvea2aGtfUlxKY/9G64aoBw0LDQVYXMgI+EadGetHraqSuCkEeQtBQmlE3gJ5+4l5zJ01H8Ry0YKQSiswXaugOXtiawhpaIeqxk2sEYqI6KrkBBQKU4pFIYUAl7Dge/wnHo3LeZZHsjOw5Uk1pwKw0pjY5jDBUdS93BtpPaT0EQNNygxa/YhDwQ9kLY4375RmPhZCFwGncgHMdNvvZlQrIVIQSKqqHmzuy3oIbjQHMC6rqgvjtwSdkymfZ0IpNppVPbUTVUhLCxxeF6Hr6tP+9ZMdlpfJfU1BvBn9XnMGFDsLTADVJ2t6nHDVKGU65PYXGuO8wD0JQUbGy0aRvgYqVoGhNW3IVwLFa98cejFnnUCeiCD8zuPyExWjiZW6+7mpJpMwl5DWbmZRA04/j37uAzP/sNexraadxewDsH6nrOZwsFTVWwg7mQ6MCxkt2zexTUnIloxXP7tFGbeTHWlmfBG4RYq7vRsQCBUDVStkPaTHDXAD1CSVthf3kF6oEtiPo9iGAUMnJRMnLd714/SiQbItkoZW7AKWIdRwQujZCKD+r5T1k2v9u4B/u6NJox/HlqloCqmENVzCHZXEPzs/9NSUEOpcXFlBQXkZ2ZwbKpmXgjOajaQQgodIkYVTX1dJScQ6MSpTXpMJKfFAUKXSZ0mWCm+t6uq27AEuoOXkZiOSanow5792o0fxRl0iUodhYi7kMc+Dt2ogNtegZqpBBwA/tMPxQEIeCB3KD7NS/fPVZrAqoafdR2WNR12MROMvCTTi0yUJHGJyOI0L1YnS3HXDG5JamghAvB03c6sKYILiiyMFRoTChsahydICVgKJybpzGhe5gnZcO7zTYHOwfXr55saaCke5mAQ0MyxSGH6i6FzY1qT6LszdMcvlZxLh+8YgUXLphLYThAYyxJXrIV9b23EJ1NFAOP3XUZH/n9691Bitt/AipK6Vmw9B4MXxhhpnD2vo29/vfgDaNm918kTtG9EMiAZCfonp6cGgBH0cDjpzXZxaYGtd+E3gy/QVvCZG9TB0vKfBBrh1g7onovAMIX6B24BCIowQgEIyjFU9z7JONu4NLeHbjEOvpt6yu7q/jhm++xoGwlxYsvHtRzf7KMYJS4LdiweiUHume4BLM9zFpSQm3nFFKRSbT5Sgh4PcyaOQNvYS6KqpFyoD5mUxez2dNqETNH98JrOdCadL9GikiFsM3JaIXnoXR6oBMgC5FxA3bHSrS2EEo/nVYhD+SH3KAlPwRRnxvERAo9zC50A8+ulENtu0Vth01dh01bYgymZEkjTgYq0vike0nPvBO1ehUtNavZ1HDUjIEXA7QklT41VA4TLC2wiXgECRveqR354m2qAnNzDJYUeQn73Xol+7qHeQY7pdWxTDp2r6cppfLbne6b8a93GNhCwa8LVtfqNCYUzNLzSU67nqXteUzJjlAc9iNqD1BwcKd78Qfozl15dXclWw+tteOPQqIN8qagZJWhmCnwhVEML9qsyxA12xANu90gpB/CTEG8DXXOVaizVuDsfRtnw1PgDaHd+DVUTwB76wus2dQ7cXJVrcZnLzqL2xdM4Y/v7ac0I8Sv1u7k7kXT8OpHBJDJONSVI+rK3fMZXojmHA5cwpkovgAUTEApcIMpkYwj9r0H9eW92nqo4m7du2+PeKCi+YJMvf/r1L7xNPVvPd09w6WLQOtOLjF3cu+fg2xs8jDr6g8w8/wrKIxDfgC8KpSFNUpDKrO0RlZ3RjnQdXot/at4w+iTL+67Xff0u/2QrjR0tcC+7peuT4f8IGRpKQojOjkhlZBXZVqeh2nd5VeSpkNjl40l45VxobLVYmf90Av4nDaBiu7xoRpyDYjTzuQVCG+YNQdePmooREVMuhy1+Nw+SxCqmsKMDIeysIMjYGWdQRp10Is468aJ97wUBlXOK/aS5XNb05Z2a6M0x9JUvfMsJedfN6jhh9by3aRTSXKLy7jus1/j4Hvree2x/2ZSxOZAh0ae3+GN0A2E826i1BGIdJxnNtSQs+xazIxCWHBez7HuqX6KG7/5aw40tB4+QaLNDULScZztL+G8+xfUactRz7kLAKF7wUrj7H0HbealHM3Z+zZYKZS8aT3BDYCz/vfYq3+DeuE/INpruyuk9k7SfXR3M8F//AneC+Gnb77EM28+S/mim5m77IoBn4+uI8u+O6B1QNSrkenTyPBqRL0aWhTIn01bymZXS4qOtIOZTPDWD/9Eacihdud6DMdE955cDlvYN9i3SZ3I1e8nGA6z5rlfHlXKXqHg8rtx5l3J9g7Y3mGhYJHtU8gPaBSbdUSSDVyaH+RgboTVtSbWEZ0rXv/Qk0kU9dQOgBygNg0HYkFocoet8rt7WwrCCrkBCPhhgrwMjBtpVbD/GCO1Tnpww7inTaAincZidX1KgxeFoDrW/8rMeX6HBdnuBW5Tk05zauQqoPp1hXMLPUzLdP+VUpZgTV2amOoGJY1bV7LvhUfxRrMpWHjRcY8XzC9j2vUf5rrb78Dw+lj7l8cpjSicW+hwsENl3nmXE1l4E7rXzy/+8AwvvPAChsfgE4svR9N7X8xu+tZvjghSFFAU1LNvR516Poru7RnucTY+fXinqs2QO8XtJRGiJ5m2131VDdGwBwqmA6BOW46z+c9Qvh6n/QbUyk1Um/DrnX5QRM+0Zi1eSVN1OTnFE9i16jUAdq56jbkXDByoHM0W0JK0aUm6M4NUBSZEPBR7LGLluyh2HOo3beK555/HNE2+vcLNaXrlPx5A092/kWL4OPfD/0Y4v+RYpzppXTX7KYkoR5WyV4jVHex1P4Gbt9SUtHjp6f9jYVGQS6+JMq0oi3y/yuvVaZqTMgdjIJYD1Z3uF7UCVYGcgDs8dGqHZKePtmEaUpSBijS+OTZ6y06qLXhspxdRcBaP7nwX4TjoyZ1YzrW9aqiEvArn5btByoFOjb0dIxOkKMCcHJ3F+R48mltddmeLybq6NCkbSnLcQKVpy0oAGresHFSg4glFKV1+PYbXh22Z7Fv3JsmE4MkuLxdedAE333wTQsBzf32GzPNvJHPjZhqrK6netZWyOQt7jrN/0xr217cccWSBevYdvXpJeveIPOneZ8mdqNMvxFn7OM76J3HefQaCWdDV7PakTFyCaK2C5OG8EEX3uvfpqMfZ/GeEmUKP5sMFH8V582c4HQ1oiluT5a2nHuXaj32efZtWUxpy2LdxFelkAo/v5D4GOwIOtKc5kIpjbdvJjMkTWDRvNlctnkzHe89zQerF7pym+p6cptxJM/FFMk/qfMdtj2XSun016ZTgN7t8FJ5zGb/e8Cq2bWPsXoNjfxRV6/2266STdO7byN7qNOXl5dz35e8S8epcN8nLhgaLrc1jv5jgqcAR7syghthYt0QabjJQkca3jkpsy0QLZGPNuBWCeTiFS9B3PY0db4GOSsiYCLgzFq6YFcSrCVpTCusbNUbis1V+QGV5sZcsvxsENcZt3qlO03hUIp+dTtKya4M7/LBrPXY6ieYZ/PBD9a6tJBMJcovL+Icvf4O5hRn4gmGefOwX/PUvf+XOhZdzz7d+ybuv/pWc0ok9+6WTSZ7+5j8dPpCqgaqjTj2/Z5PobMI5sMYNODwB0NwpxurU81FUHW3pvSgTz8XZ8iwi2YmSPQHRdAAlnIeoeg+8h6cVCzPlBjLCQVRvQZl7Dcy72u0yiLUCAoGKqjjsevtFqret69Xb8cinbkc33N4gzePjli98i+yissH/QQC8AdRFV/Pym39lelQnOGMCZ196I7Hk+dyU/ST3/d8uWlMqs66+m1lX3Y2qjUxidUfFTtKpJOG8Yqbf9XlCBRMpWnYNO377/4g31RKv2oURyqT6D9/pSUR2LAvbsrpntO3n+//8ca695mpmTp/GHEUlP7eYP9cpcoaLdMaSgYo0vgVzEZOuwCpY6F5MAYJ5WGd9BOo2QTC3567Lp/jJDemkHYW36wwcMfxByjkFHubnGiiKO8yzti7NrhYLAaRbaql/5r/ASlGrKti2hdVzAVJZ918fRTv0adrwMefefyWQO/CKzzmlE7n0/n/kqhtvZV6+uw5KRadJwRX3cqmWQU7pRAyvj7Ovua3Xfr/47PvdHxQV9ezbEW01iMZ97hCOY7m9JXvedtfpCWZDrBnsNKi6O6xz9H0CmYjmcrBNnN1vgJXC2b8Kdd7VKKru5q3YJsq8axFbX0BsfR67dhtCNdzjRgugow5HKGT4BLeX1DFx9pEzuOp6ejvKZswhlJF9Un8bVdOZfMnNrH3+SV5//VF+9plr8QRzMBd/gqWxA/jbNXLOHvww08kI5JUx5boPUbjkCjSPW38mVDCRiR/8Ni2bXsWXU4qi6ajeAF2N1QOsgVPBk7//JTtmL+b6W26jNCvMB84O8MKOTg62jvLKgpI0DoyLQOXhhx/mO9/5DnV1dZx11ln8+Mc/5pxzzhnrZknjgRGE4n5eC5rRa3vUrzIz370wrKobmfL4ZWGNs/LcYGl3i8nqWneYp6dJwSiqN0CsqYYsn+D9fS5ADT0X5MwJ0/GEjz38EIhksuSa25iT7xazq+oy2dee7jc4OaRi2yY6m931j9Szb0ebeSn2e88hYs0IK+UO5+xbibrkjr65Khuewl79WxRVPeZ90AzoasJ66xco0QLE1ufdYERRIbMEWioQ7bUQyHITd9vrcFDAF6Y12d5n6vIH/hakNaVywe0PcMH77usZGrHSKdb/9fcsuPJWdI+338fbn8byvXTt3sDD39lAfck1fPDmS5k3cxrRDovtgz7KyfGEopScf32f7arhJeecw2v+TLj3azS89Qea3vlDnzVw7n3JfT604GRetiZxiakQNBSunxPmx2+39Dm2JJ3uRmed9WP4/e9/z0MPPcS//du/sXHjRs466yyuvPJKGhoGv9icJHWlHNLd9bZHqux2c9LB6S7kvLPF6hWkAGjeAEV3/jtZy26hNaWyqVHnlqlufsEtUy02NRq0plQmrriTsz76LXRf4OhT9CGAZPf0j7ajT9iPlX941P1B9/QM9aiTzgEzhb31JZw9b6PMvRrSCZyNf8R+7zlIdqLNugx18W2Ife/g7HkLdfFtaDMv7alKeyifRV18G9gmoEDFBsSW50Bz1wISW5+H1irUJXei3/ZdjBu/hv6+76KefYdbeTTZgVA9rKlzp1i3p6CyU2F1jcIl93yci+74UK/8jR2rX+ftx3/G3nVv9vtY3UDmd1jpw7VcbMvkwPo3qewQPLZNozyh8tLaXSQswd4t63Gs8dEjoaga+RfdTv5l97KmVu15Phri7gyh/BUfIP/C2+kwVXa1uq+hlvjILC0gbJPUzr8h7PHx3EjS0cY8UPne977HRz7yER544AFmz57N//zP/xAIBPjlL3851k2TTiG2A/ub3E/pE8MjU0QhZgp2tbgXjcUF/U81VlSNnOW3MeXaB/q9AE299oNMvvyuE8qRaO6e4ZLtO/4+iY7uWT6BrMNBRjgXddpyxJZn3YDivefcqcn1u3G2v4T15y9hr/41ypRlPfVTREslwumbxKlOWw66FxDu0NKSO9Fv+y/0iz8BjtPTi9NvgAPguMXrHtliMPuxEP++2r3fnvXv9DnXoZlBe9e+0e9j3bv2zT6BTM1uN68np7iUO7/+CNd89PNccvPdGF4fr736Kk37R7pP5cQk6w9SEnFntM1+LMTPt3ooDiskj5ghNDnq/t13NvRTWnYYWFWbSL33J6yqTSNyfEkaqjENVNLpNBs2bGDFihU921RVZcWKFaxatarffVKpFB0dHb2+JAlgd4MbqJSFHFRlZLpVNjeYOEJQHNJ6LiD96ao50DNF9cgLUGfN/hM+Z3PCDVSyBlHPQ1G77xNvQRxRNVY95y43aRaBuuQO9Pd9B+P6f0O/9TuoZ9+Os28lYuMf3JyV7AmIA6tx1j7e9/i61y0aB72CEufAWjC8vRJ2j6ROWw6aB02B6i6V3+z0sfDyG3mnxm1vze4t2ObhT/TpZKJnZlD55tWYyb6lS/eufb37++FAJrtkEhfd+ynu+PrPySmbQknIwPAHsAtmkHfejUQL+6+4OxYc2yK2ey1VHfDrXT4yF67gsV0+qjsFsd1rcGyLoOHWWRECdjUMbc2jgVhVG7q/bxyR40vSUI1pjkpTUxO2bZOfn99re35+Pjt37ux3n29+85t89atfHY3mSaeY2nabzpRDTlBQEnSo6Br+mR1dpmBjvcnZBR7OL/ZS3RXvMwQkbIuWbat6pqgWnbOCX294Bdu28WxfhWM9iKoPvoBXW8rGFgKvphA2VDqPscRtz9puRxdti7VCOo665M5jT1FWdXBMyJnsDgHNuRolnHP4sZkpiLX0GloC3NlDwexeCxj2apfuBV8YO5ZGUwRXf/yfmX3eZWx9/TmKvWmqugT/86nb0bqLklmm2Wtm0G/+6e6eOiiax8fln/gK5e+u6RXIGD4/gUgGC69+n3tOoCRs4DgO+9otpl1y06Cf86FyzDRVK5+l5LzrUAco9Bev2oWZShLIKaLo5ofw5ZWRffZV1Pzpuz0zhM5adBYAlW0mXYMtb3wChJXCrttOacihum4bwkoN+DeUpLEyLpJpT8QXv/hFHnrooZ7fOzo6KC0tHcMWSePJnoY0OVlBJoZHJlABeLfRZHKGTpZPZVmRl9cre3fJJ6p390xRnXX3FwgVTqTkvGvY8bv/R2dDDe3lO8mcMm/Q5xO4Rc5y/TrZfu2YgYpjW26wIRw38Ogu2ub2ePiO2ePhbP6zO2U2sxRaykHVcA6sQZt/7eHj733bXYQwVND7guaLQHfCbn8XOmGmINmBZnhQ7TSvP/oj3n78Z5im1R2M+Im1NpO2bLJ8gjunm71mBm1urOOPewwEoOkGz37n88cMZK757DeYMWUSXk2hLS2obB+Z3oiBNG5dxYEXH8MbzSZ/Qf/1c3w5pRRcfj9ZCy9DNdznzJdX1muG0JRhHvaxO+tJr/wfVLt7arRj49iHZ6YlX/wKanddIkfz4jnvY2jh/GMdcsQJ2yS95zU80y5B0UZjuWdpvBnToZ+cnBw0TaO+vneF0fr6egoKCvrdx+v1EolEen1J0iGHhn8KAg4+bWSGfxwBb1WlEAKmZeqUhHsHRJ6cEqZd/2EWfer7hAonAhAqnMiiT/2Aadd/mGD+CdYIARo6k7TWV5F5nCr8/kgmODYIB7In4qz/PdZTn8PZ/jfwZxy7xyOQ4QYcrVUoc64Cx8ZprwHcQMPe8apbmTaQ2XdoqTth19nbN9cE6JnCTDgf0wGP1cWdJXV8a3myJxi5f1YCnyZoTcKmBq1XIvLedne9I9NRCKgmd5fW99r3rpI66KinpaEBjz9EIJpFadi9qO1rSY7oCsX9ad76Tvf3lQPeRw9GyDnnmp4g5ZBDM4Rys6Jk+lQcIdjTODyBluqNgOEn1dVK0GrhQ1Obej2PH5raRNBqIdXVCoYf1Tf2768yh0Ya0x4Vj8fD4sWLefXVV7npppsAcByHV199lU9+8pMndCwzGUO1RyYrXjp1NCWhKVFAtk9QGrDZ1XbivSqOffwu9rpOhy2NaeblGiwv8vDUzhiHOjpUb4ii864DwHaOuETqnv639+OD+x7r9fuLO2poXnIVs50YD5S/jWL1f+Gau6yAD2x1E11pKUeZdy2k44jyDT3BxYA9HvE2lJmXIXb93V1jWfdA+XrMtmp3uMdMQc4kaHLzbI4cWjqUsHus0vvqtAtg4a3w1OdoTdlsbjJ4aJFbRvTQNOWUozLprCWseXdtr7Vy1tcpXPD+T5BOxFj7p8f63bc1pXLuLfdx7s33EvZ5yPJpCGBv69B6I1InuMKdnU7SunsjpSGHml0biMfj2M6Jvw4nRnSEEJR32CSO0Yt2IhSPH9/Fn0PZ/jytO17oZ2p0gNaUimf2NXhnX40y2AWyRtCROTTGBFm24kw05kM/Dz30EPfddx9nn30255xzDj/4wQ+IxWI88MADY9006RR1oEMl22czKeKcVKAyWOtqU0yK6oQ8CksKvaysHplZGQDPrd/KwoI55IYDFEbzUJqr+r3fOZOLiPg8dBiZqAXTcbY87xZtU7RBLDaYRpu6HKtqM6Tjbs9JRz2017mJuKoNTQcgWgSJtj5BibLwVmitxln/e5zNf3LL6sfbwEz1LHzo7HoDcDjvlvt45w+P9gpGVtcoXP7Ap6jbv5vSo9bKKYkoNFXs48qP/yveQIg3f/Nwn30vvPdBFl3tzi4qCbm9KU1xi/gwXeQHkmiqYd/j3+ypNGtbvQv9bf3hJ3qSnIXuofCWz+PNLjrucad2D/vsax/eD2CKquGbez2KEWDNu08ftXiiinfBrXinXzas5zxZ/eXQyJV8zjxjHqjccccdNDY28pWvfIW6ujoWLFjAiy++2CfBVpIGq6JTZVGuTYZXEPU4tKdHZoTTcuCNiiTXTvUzN9dgX6tJfXz4L4rxtMlbuysp2LuFlrIy0tm5+AYIVADuOW8OP3lzm7sA4ZyrcQ6swSlfD4heeSt9ezyWgy/s9p54AhBvQ521wi217w2D2YAy8zIUbxBn24sok5b2Xg/oUK9L2WKo3ARWGnXWCtTJ54EvjLPrDZwNT7FwxfV0NNX3G4zU7tvFvnVvkkgIfrvbz9xLr+Y3b76AZVn417+JbX2exvK9R+2bpiSi0HhwDwCaAkUh962tsnPka4MYoSiaL0h7xUCVZg8X+gsWT0MPZRz3mPkBlaChkLahsnNkeoqdtso+i30Wh6GhbeDX1kgbTA6NonTn0KgelEUfRgnljVl7pdEx5oEKwCc/+ckTHuqRpIGkHYWamEpJyGFSxGFz08ilYlV32exuMZmeZXBRmY+nd8U5zqjOccUbq7nht0+SMt0cjbTtkLQcbgpupEZcw9sNKb77vd/j2DZeQ+eH77+CSbkZPfvfvHg6P/n7pp7ek0PJsM62F1FKF3T3ePwZQtk9wUWvHg8z5eZ0WGmUKefD3rdRJy7BKV+HImzUSefgvPsXlKxStHn/eXjNIH8EdeK5OFXv4lRsgFgLzo5XcMo3QGcTOCb5E6dx+f3/yI8/fH1PMLLw8mv5zWvPYVkWDWteI51Kk1dSxk0PfZ1AwQQWXHEzL/7oKzTVVFG1YzMH1rsLNf52t5+S2fN4bPtGHERPIFOcEUBXFeKW01ODZiTpviAzP/ifVL3+FDVvPDVgpdmc5beSc/4tgxpOOdSbUt5pj0gBQ2FbODWbu1e59mFMXsZjO1fhODZ6chPCfj+KNvqXh54cmva6AYK+dE/Qp2dOAO/Y59BII2/MC75J0kg42Om+tCdEHBjhVMpV1SkSpiDDp7Io/zjZroPgCWcQ8nkob4nRGe/ixgntfGt5knN9+5jmb6csQyU7P5/ylhghn4fccO8Kt6VZEeYW5+CsfxJ7x6sIK+Umu1pplPzpKBOXuGvweAKos1ag3/SfqItvd3s8Nj4NOZMQ2150i8TV7QQzhVK6wA1qfJFe+ShO1buos1egnXMX6swVbpDSnY+iXfdvKLlTob22p7d+/iVXU7N3B4lEgrySMj747V9y7ce+wAe//UvyiktJp9KcfdWtPPDtX5E3YQoAOWVTuOM/f8EFd3+cnW/9rVdBN18giIOC3+8nkUhQs3trTxJt1Sj0phyiaBqll91J2VX391voL+/Se8m94LZBBSkqMCnqBgl72wa3crKwTRLbXhx0dVm7eT+2mcKI5ONf8UX8Z78f/4ovYkTysMwUdvOJ1/sZDodyaDyzrxmgurNOa1JFnXYVytJPoxiDX+RTOnWNix4VSRpuNTGFtAN+DfIDgvr4wOPawjZJ7HwV/8zLTmr6Y8qGd6pTrJjoY2G+h71DXDhO9wV57CM38JO/b+J/XtvIu92f0BWgLP4uG/0XMmH6XM7ND/PxSxaia30/b3znjku4+ntPHc4XCWSCouCsfxJl4a0omgexbyVOSyXOwXVunRU77a603LQfZcr5iHABontIyA1YkqiTzgW6C8jh1l5xNv/ZnTEUb3PzNFQNp24X7F/jHhNQ7DQChcrtm5l7wRVc8cCnWXj5DRhe90KTN2EKD3zn/9j08l+Yc8HlPdsPMbw+ghnZvPW7nzJz+RVc9qHPgRA9tVRqkybL7/oY06ZPJ+xRsQXUdI1+SfhY7eFCf4eHUxS66g8O+hjFIRWvBnFLUBsb3FBiumIjic1/RA1k4u3+Gx2LGinEu+A2PJOXo3RXI9YyivGt+BLq/rdRI4WDbu9wO14OjTb7JrRJF49Z+6TRJwMV6bTkCIXKTpUpUYeSoEN9fODOw1T5BuIb/4AayMQ3iDf5/uxvs6josCiL6Jxd6GVH7GRb7tI1lX+8fDERv4dvPbe65826vWIbrRMu5K4rLmRu68Dl4CflZnD7ubN5cu1O0H1uUqzuA1VFbHzKLZUfznEDlI76wytTA4RzERUbwFyJMuU8N2DZ8BSguD0o3fkt6uLbEb4IYsvzbjn9uVeh5E7D2fYiomZrr/aoqkpRwGbfxlUYXh/nXHd7nzYPtP2Q7W++AMD+da/TsHsztmX1qqWy9W9PcdnMQqqShazbuAlz2vlkFo5ejSXHMmnfsZqm7kJ/eWdfyq83/R3btjH2rEXYHx3UcEp+0O11qey0B90XaFWuB8Cs3DC4QMUXxju9b1K1onv63T4WBsqhqe0YuxwaaWzIQEU6bdXE3EClKCjY0Cjob7aAsE2SW58HwKzYcNKBCsDamjRlEZ3JGTo1pmA4aoztqGmmLAKPbDF4dLuHjy05wNkfBBGIIjp0FHvgoYF/u2U5AE+u2d496yfpBiihHIg1Q2ej24MSyoFUl1vnxPB3b9fdgKV8HVhpyJkMkfzDybOBTOhqBtvNY9GWvh9sG2fv24ia7WiKoLvILJrhJZlK9yREPvKp29ENNzDSPD5u+cK3yC46dm0ZM5mgdte77uyPWArRXs9dM3oXhXMmTiU7N4v2tjbeWLWWFWf3XcV4JHVWupVmQ7lFTL798wQKJlBw7jXsf/I7dDXWEK/aRXDCnOMeJ9fvBtWNicH1pggrhVmzzX1uaraeFtVlj5VDoyXfQzjW4eUipNOe/EtLp626uIIjIGgIwgb0l7KQ3L8ap6OG0pCgunqLW0+E469q3J+WpMO+VospmTpzMzXeqR9aImfasvn79gN0puDxXT5uP2c6v9ywm7yrGyksUDH9GXi6mgbc39A0vn7bRXz00oX8ecMu/rB2JzVtXdDV5AYogQw376SrCaV4HtqSu1DCOYjOpp4EWaejEWr///buO06uqz74/+fce6fPzmzvRb3bllwkyw2MjW1sh2abDnFwTDNJKAnGkAdIQn7mMQSSOCTAk4diY2JK8kAgYGxwx7Ilq7eVrLba3tv0W87vj7u70mp3pS2z2tnVeb9e+5J2ZvbOmb2zM9855/v9nn1u75S+pqEE3F4372SIbNiG1Xnk1PIRblaQ6YihhMjYGQmRbSMJkbUr1xLOLxo5lpVJs/XXP2PZhk384htfwEy7e/y4syenutgOZiQ/POihyC/5/n4P/3yTxsmNbyPlCI70pnnTXz40aifm8yFYWkPdrR+k9LI3onvdQCFYXsfqj/wDJ7c8ga9kcrM7xf6zByr2QDvJ57+JcIZ+17Y1qjIm/svPj8zcSM1L4Lr70CPzq4ry9Bwa75X3oudXYS97PZmXv4M50IHoPY4oWj7Xw1TOExWoKAuWLQUdSUF5UFIVdqjv1bEH2og/903E0BuqkxzAkYKHrk3x7t9oDP7350gMfdqXho/SN38Kb8Hk1+tfbUuzJN+gIigo9Al60tNP5N15sp3BtMXy0ihfe/eNrCwv5L2b1/L7tiZKSko4aXlZNonj1BRF+LObruDPbrqC45293P61n2DaDoQKEEs2IY9vQ7bsP7Wsk1fsliUfeREOP+fOxkgbEJBJustEgWII5EPHYQiXgO4Fv+kGPsDSsiI2La/hsRf3TNic7dp3/AnX3vnHowKKgy8/y9OP/iuBcB6+YJj2xgYK/ZJ3n9FS/8SAxk8Oe2gYKkUPXPRmkiKEFikmVHjRee9EC+AJRanYfPuYy3Wvj8Irbp3UMfK8Aq8OtoS+1PiPQvjzwBsg3XnuyhhvyWK0wPyrjDlbDo1T/ywiPHc5NMr5pwIVZUFriWuUB20qQ26gIvwRhCdAum/oRX7NxC/ywcplGMHolO6vPy053GOyvkJnXYHG823Tn1VZXlrA527fzF1XrCLgdf9UV5YXUhepoK2kgBIRgiMtUzrm4pICtv3dB/mjf/gJjR1HkD0n3YBDcqpsOVQ4sqxDpAKx6FJk4x7obXRfMYYTZwc73MBmwx3IY1twtv+USNDPz//qPdQUub+39qXX8dT3Hx63sdum2985ZnyHtjwDwNEdW3jf3/0rv3/8u+N2on3/EwF6U3BJsU2bp4auyhsIpAU/+/UTrHrz/G0WObzs0510mGjhR/MGCd34GbR9/0Pvvv+ZsLus/6LbCKy7LSe6y07V2XJoVCLthUeVJysLWkvcfYqXBCRarI3Ybx9EJvvQvX56U4KdHdqo8scdHTq9KYEeCFP0xnvRfFNfBtrelsGRUBoQlPqn30WzMBzgA1evGwlShvmSvVQXRvDlFyEDeVM+btDr4fcPvJcffvQtXLmomLDZCziAcPNRfCFE5RoAtDU3Ibxh6GsGNDd3ZaDdre7RDZz2w9j/+RmcV3/M4pIoL/ztPSNBCkDb8ddGmrOteSTMv+/zUh0RtB9/bcy4MqkkR3e+TE3Y4eiOLVimyeY7P8i17/0YL7eI0SW/bToSwf4+Hx/9ywdoThhseXUnz/3ix9jW+NU+tpnh0FM/xTbP7waFUzG87NOVOnt+itB0ghe/mcCGO8cph9YIXHoXwYvfPC+DFEU5k5pRURa0mCkYNAV5HklFUYT+02ZTVlbYbGk1Rn3a39rmvrD7CsrxhAuneZ+SY4MOyyIaaws1Olqy3ALdTCF6WpGFFTh1F6HVvzStpuIbl1byyNK3ANDYPcD3n9vNj7ceJNNxBIR7ROeVR0BK3M80DrqVoK60AF0T9MZTIAeprS3i0390DRuXVY86fsayObL1uXEbu3VvfZaOhvfwi2/8NXYmBYBlmqOqeL7zZ+9AMwxiA/1Uh51R1R+VIYduGeYT3/g+l65bSjIe47lv/tupXiprNox5vM27XmTvL/4vgfxiaq+4fhq/sdk3PKPSNclEWqu3kepxKmM6extnc5iKcl4tmEBFOjbSUZsSKmCfsYlc0wCsLpRURjw0vOGvEPv/h979v+a1PkltnsP/2evh+we83L0mQ2XIoTNQR9md/8vtBzLNNrP7e2wWhTUKfYIyP7QkpnYcK5k66/Xy0Ha47CZkIB8nWILoHPvGpPsn33yuMuTnc7du4nO3buLFw4189IdPUhFyaI5rlBeEWVQU5aM3XsrGJRPvUeOkEqO+3360hWQy6ebYvOMNrCgv5PDKP+LTP/49RzoH2LDjEV4VCXa0d1Lol7xrTB5KGz8+5MHOQFNK47HDHtZefys/fOHXWJbN1ddcyqZVi5ASmjIebvzLf6DimV9RULlo3E0fm3e+4P6764UpBSqGdv72limaQqAibQunaSdNQ5UxvmVX8Uj9S2532fQOpP3+OekuqyjZpp7FyoLXHBOsLoTKkERoOoGL3gy6n949P6NLCn5U7+HNS0weOeDBQeCxOodmEaYvZcNr/Q6rCjTWFeq0JCbXYXSyRDqJbKyHurWw5CJkT+tZS5XP5nhXP3/xo6fIWEMt+y0Hy2GoiiSAdGyae/r56588g89j8M8fuHlUy/6JLC8r4IFbN3HX5adybFaUF/KTj76Nn75az8U1Jfzgntv5t2d38a1nd440tgN3Ge6PfxukN+0GCcWV1bzxz/6W4polXHHrnURadrN8yWKcZIw+T5jj/Rk8Pj/rb7lz3LFY6RTtB191m8Md2IaVTmH4cqurab5P4NHcPaT6JpGEbXUdxTLTeKNl+K/+MEZBNdaK60n94dtk+tuxuo7iKVt5HkauKLNLBSrKgteZEJgOBAxJoR96UmC1HcCWgkK/Q09Ko9APxUFBj+XHTKdItRwmULNmRvdb32ezNKqR7xNUhwRN8SzXojS9BmV14A9D3Ro4tmdahykOB8jze9lxsv+cVSSX1pWOadk/kcJwgA9ctW7M5QGvMeryP7vhUvL8Xv73b14ZtQy3pVVj050fxOsPsvb62/H4/JQGDa7ftBaD1Qx0tnF0wKY5kxhT5dPb2sgT//h5HDOF7Uh3R+PTlpV+93d/gm64L3/C42fTn36RvLJq5tJwfkp3yplU1ZIerSR42TvxLbtmpG+KUVBN6Ja/xjjyInr03Ds0K8p8oJJplQXPQdA2lFRbFXbcKfPuowD0pg28S6/lkXo/HQmQ0qHw2vfgLZr5m1bGgUN97nLkRYV61jenF9KBo7vdbyqXIoPTK0PN83v5wT2387HrN9CXFuw+Y3+V3Z0GfWnBx2+8jEc/8hbCU1hSmqz61lON7dykWw+1edDf1sz6W+4kEPBzUbGfDaUBvJogYQsO2Pk0ZTzjvqkHowV4g2F6OjrQYx28r7adr1yTGgm+3lfbjh7roL+rE48/hD9SkPXHNFXFU2z0pvnz8K+6YUxzN2H48K+6Ac0/9URrRclFKlBRLgjNMTdMqApLrK6jOFYGb7SMvFv+mtDG9xG++fN4o6XYZgZf6SL0ab7pn+lwn0PadvtjLI2c/c/NMTM0Pf//cKZQlSJ626G7GRCw/FLkNPYqArdl/5/dcCl/dcsmtpxRRbKlVeP+2zfzZzddPu6+QjOVsWyePniCkwNDje0uX8mPDvk5OQid9dupCWlcXRWiMuw+tmP9GV5qjjOYmfgN3RcM87a//meueNsf05vW2NXlGb25XZeH3rTG6je9l2s/8Q94AqGsP66pmmoiraJcKNTSj3JBaI65XWqL/JJFNVUcvfQd+JZeO6qZVPCm/4V+9IVJzaY4VoaBXU8SWX8TmjHxDIMlob7X5pJinUtLdHQBh/rHfyPq2r+FE799BG+0CFZM4cEd2wMFZZBXCJfegKzfihjsmcIBTjl9ZuMHQwnGtXlQ39I9reNNxq6THaca273jDSwvL+RPbr+BZ3qgcskKqgMWfl0jYTns7UzRl55c0rymG1x5x5+QFj5e/q/vjN7crkVwyR0fYvn1b5u1xzUViyM6pUE3UGlPqEBlps5f+rNyNtla7FaBinJBSNmCAz0a64ocNtX66bTeQMYZ/XImDC/+lTegB8/96Tp+ZBs9Lz6OES4kvOqqs972UL9D0CNYHtW4pFgnaAh2do99s+3Z/5L7776XYMXkIxWRTiL3vgirNoIvCJe8DtlwENl5bEov2MMzG8Mt+9+5aSU/evUQGduhb/9xMtZ1eI3s9+VYVpbvJt1ecxm+ysU4JbXUeP2815HsTxgk8HCsO0VLzMSexitff9PRkV4uwyW81RFBX+PRrD+WqQoasCzfYH2JO1u0u9MiZs5FX935rzIPLioTVM2/RrwL1sFOyZYsVMqrpR/lgrGvS6M/IwgYkjfU2ni06b8hJF7bCkB86N9z2dlls7vLDU6W52tcXa5zetWrnUnRd3gHNWGHvsPbSWTGb1qWtmy++9J+0tboQEcM9sCO30FnIyCgbg3OmquR3sCkH9PpMxs//ehb+Ns7ruNnf/52lpVGGUiZ7Gxon/SxJktqOvl1y3nvPR/Ce8XNyKoV4PWDlcHoOM4+M8K2LpvGwekFKbZl0rbnDzQOSH542M/iq9/Eo4f9NA1I2vb+AWeC5nCzSQNq83TeWOvlXSsCXFHmwaNBW9xhe8fo8ThWhu6Xf4G0z/845wNNwLJCeNtqwS3LVZCyUF0QMyoBQw73r1IWgKQJchqTu7YUvNisc2OtTZFfckOtze9P6pjO1I7lmClSDe5Ovi0Nu3DMFJrHf8ZtMrRs+R8qN9+G5nGXhg71O8QtyaYyg3JPhuI9/48nn/yte3vLwrJObSz35m/9HO9QPojPMPjGndezqCjCUwcb+PrTOyiLBLlt3eJR9ylsCw5tQ/a2w7L1kFeEc9Hr0Y7vQvS0ci4jMxunlROvrCjiZ39+Bz/ZWs/ysuwlnErdQFYsRZYvheFeH1JCfyda50nobUNIh8FFYxu3TUXr4X2kUynyy6u54k8+T7RqMcuuezPbvvtl+tqb6Tp2gNIVl2ThEZ1b1CtYUWCwPF8nYJx6zrUnHA73Whztt8dMlQ/Wv0LHMz8idFUA3wx29l5ovDqsKoY1pYLgUFqW5cDhLsmBTkhntxuAMk3T+XAxngsiULmmxsZ/QTzSC0NfWvDiSW1awUpfWvC7k1MLVjK9rXT96hsIKw2AY1vYpwUVrT/49MjGetLwUXz7J+lqbh7JNym95LqRYzXFJVarxTVlHi5dfwmvvPAsye6ms5YEb6gupjjsBkJP1Te4/x5sGBOoDBMdJ5ED3bD2SggX4Cy/AtHRgGjYhzhLU8TC0ETlxB7++JqLzv6LnSSp6ciyxcjK5WAMvcOkE4iOk4iuk4jM2RvdTVVR9WIuueMjLL76Fgyv+zuMVi3m9fd/k+N/eIJoRV1W7+9MuoCasMbmEh9lwVMT2ClL8lqfzeE+66w9U2KHXgbAbNyuAhXcndAvqYaVxQJj6NeZMOFAh6S+CzKq5+eCdEG8fVuOm0SnzH+6Bvk+yYoiyaHu6U2TTTVYMYJRNG+ARHfLOfuMBCuWYgSj9Gz7KeDmm5weqAC0JSUnExp1dat590c+xTe+9Bl2dsoxzc760oKPXnsxH772IgxNI5Ex+cPRFmrCDi8ebSaRMQl6x6/yEak42oEXkVUrkZXLkaV1yLwitCOvIhID0/q9zYQUAllSh6xeAcOzT8kYWuNB6G2dteTHQCSf5de/dczlhtc/7uXZUuQTLI5o1IY1DM1toieBpkGbQ702jYP2hJsODnMyKeLHdlETdmhu2Ye00mNKkS8URQHJmiJJXUTi2O6zpTcJ+9olR3tBvbwvbBdEoPJMg9qYa6GoCDtcXuGwvNChPSboS89+sKL5gpTd9QV6X/k5vVt/PuFutfmb3krBprcibXMk36T18HbsTArdO3ppaHeXTWXQQ+3yldz24fv573/9/8Y0O7v3qnX8rv4E/7PvCAAZ2yFlOedcHhompEQ01SMHunCWXgqBMM666xAnDyLajp6XyggJyOJqZPUqN9EX3BmUpkOIrsYFVZ3h02BRnsbiiEbEe+qRxUzJng6T1/osztagON3dQut/fRVhueXpZ87cxX/5+ZGW+FLzErjuPvRI2aw+prklqc6DNUUOZaf1GGwZgL3tkubBuRuZcn4tmEBFWhkclRu84DX3QnkQqiOwvtTh2eNjP03JSX686k3CUyc03lhnU+iXvKHG5vcNGnLcj7o6BZvuQPMGeeWFx0aXurZqGME80vXP01b/PI49Ot9k1zc+OtIFFcPH0nc/gFVcyc4uiytKDS6tK+FQZYTv7DVHqlJq8iQNbd0ENY3XehPnnMm5pCyfQDxB3DyVdDmy109bNxw/jr5uM6KsGsqXI40w9p6X4CxLLcZMG7uVVMPytYjw0G7KgwM4R/chm48ywS95jD9u+8eZjSELPl72gbNeX+gTrCnQqQppI7lwpgONMZuj/Q6dKUn/JLZQMEL5aL4Q8a5zz9x5SxajBaaWOTrZv4u5pgvJ0gJYXSSJ+Nwx2xJO9AkOdAtau1UCykIx2f35Fkygolw49rRBcRDyfLC6BPZ3TP9YfWnBUw06b6yzKQpIbqhz2J6UWHL8z/qZzoZxd6ttsxyS/d0TvMF0jLzBRGtX4AnnA3B0wGFx2MKTHuTiq2/mh//1X5Re/gYe3fk0tm3Tl+7gl+++gUf3HON7u46wu1PnU5e6n7bd5aEAfWnBn25Yxp9sWIahnSVQNzPYO59D1CxDX3U5orgC45rbcVpPkOnp5P9t2c6bawvwZaP8uLAcsfQiRF4hUkowMzjH9yNPHoYFtnFoXVhjU5kxUsHVk3I4OuBwMuZgTrEdiu4PUvO+v6Hrxf+k+6X/mnDmzn/RbQTW3YbQsjNTbGiSkiCUBiWeOf6sp2tQFwGf4QYopi041AP13YKktZDm35SpUIGKkh2OhdG9D6toHWiz+7QybdjVClfWwNIiaBuE7uT0j3dmsHJ1MMkfugNjghVpW6SObaMpA48e8pO37joeOfg8jm1j+Cyil7+F3u3/Pc4bTIjetEbV9XdR/bq7EPqpN5hndtZzQ6HFxRsuJbb8enoD5ZRvupXBxz7P8b4EBzr7+dBlK8jzevjHVw6esTyk84krV/PuCZJqxyMbj2D1dGCsvxby8tHqVjJYVEd1/jIGogHKZRLZ343s74bUAKSn8IuNFiOWXozIL3G/ty2c4weQDfUwB2XAs21NgcbFRe5zvTnusKfbpj8zs1kLoemUXPcONH+IV37/yJiZu+Bld+FfdeOM7sOrSUpDUBaSlIWg0H+WqkgpceJdaKFizmfpZDzjzp4c6XVzDJULmwpUlKzQ+4/jaXsF6Qlh5y+f9ftrj0FDH9Tlw4ZKeObYzErhTg9W8oMOVxeNDVZSrYexMmn8hRUU3fLneItriFx8E91P/BOpnjYCtWvRA+FxloYEdW+6m4qr/mjM/abCZRxJNLFx5VqusQX/c9IkWF7Hw2+9lp/Xn2RJQRiAwz0D1ObJUR1ja/Ikr3VPIzE2PoC15QlEWTUiWsy+7jQyr5A+009FWTmiqBwATdOQ6SQM9kB/D3KwBwZ6wDqjxX843w1Qiirc7x0H2fwa8sRBZCI+9fHlOAFcXqKzNOoGnPW9NrvGaeA3E+n2E1RHxs7cdfZOvXuWT5eUBiRFhW6uR4FfAkMBiN8NQOIZQXvCraA5nd19HPPYi3iWXIteNPmAeCa6k4LGgem1IFAWJhWoKFlhDLhdPo3+Y+clUAHY1w4lIQh5YW2ZuyQ0E8PBym2rIN/jBisvdQcwh4IVb2E1hde9j7y116N53OoLb3ENZe/8ewb3P4O3sJrYgefHWRoSxFuPj3ufnlCUkyLKRY4g5BGsK9DZ02PjN3TeNTRTYtoOzze0EcsI/uOQj7euquZHh5rI2JK+dBumfRGeqe7B49jI1gYSJ4/ywP/9NWUhiZFfzQ8/dBe+whJEtAiihQhfAHxVUFw18rYhkzEYcAMXESlClNYMXSGRrceQx/dPbSZmHgkacHmJQWXI/X3v6LQ4PMGWCNMlbYvEa6/Qn3Zn7vLXv55H9jyL49gY6R1I+/0jSbWn/RRBw+0Z5TcgoLszJaVBh8hQqpFzWo5KX3cXTbufpTt6Cd3hVcTN8YOC1Eu/w2zZg6c5jX/zh7P6OBVlslSgosycY2LEGqkKOzTHTpJ2TNCmtzneVFgO7GyBq+tgcQHE0nCsd2bH7EsLXuwKcE1xknyPww2lCXb3+2hNGejBCNH1t4z5Gc3jI7r+llFLQz885B+Vb+KtfxnH+iiaMfb3YknY3mlzTYXBmkKdvjOWD/a09xLL2CwpCPF3129gWWGEu9bU8ddP7+B4X4I97b1cVlk0qcf3Wlc/H//FHwh4NIQQmEOVRF+5Ksm7f9PI2x785kjQEwoE+Pr7/ojKmhpEpAgihYhAGBEIQyCMKKsdOa5sb0Ae2wfJ2FR+3fPCcC+UJRGN0qGNA20JL7WZNMezn6CaaDqEmU4TKK6k/C2fxF9ai1N5Nak/fJtMfztW11E8ZStHbl8edNhQ4gzNlIyvLw2tA4L2uDtz0vf8z90ApKof/+bV4/6MtNLY7Qfc8ui2/Rd0ebQyt1SgokyZSPcROPlbdOlm30tpYzunymbDr/0HQrjT4rYwSNbejPTlz8pYuhJwsNNNqr2o3A1eZpoNMWDpvNAV4IqCNHmGw8aCFM1Jgz0DvjH7A53u9KWhFe/+DMHyOso33cqxn3yVWGcLg42HiC4e21ANoCnucLTfZmlUZ3OZgdlajqfHnSJaUhDmk1eu5q2ravEPJbsuK4zw/TOWhyZjX3sv3ckMJDlnZcn6UIBIegAaD490TJWG1w1YIoUQioJtIRsPQ7x/0mOYDyRghwvIFFfxtgrvSHMxgI6kw64um56zNGqbCV9JDWU3/jH5628YmbkzCqoJ3fLXGEdeRI9WAhD1SjaU2lSG3HFICUkbkpYgaUEsI2hPCjoTgowjMIfatU4UgDiD7WS2fAvhuEt70rFx7FPVa+knvziSwCs1L97NH0HLW8jl0UquUIGKMmXSCCB1L1a8z32zWznxmx2hUqQRPPdBp+q05N3DXQYeDZYVwfpKOGHE6LQm/+Y9nkFL59nOACvzMqwIm1QFLEp8NruGZlfGc/rSULDcbTcfLK9j9Uf+gY7tTxEcXiKZwLZOG10TLMrTSK24FHHoVYzeDgoCvpFloNOdvjx0urRl85P9J3j3ZSvGVPG81OAGP4sLwjT0xSasJLr3ipUjjeZGsTLQ04bsmeE6W45yvH4yRVWYxVU4Q31fDA3ipuTYgMPxQfusvVCywQhGKLzi1jGXC8OHf9UN+HXJxcU2S6MOQrjl+a/1aezr1kjbYwNpe6Cd5PPfBNvtrDxRACKlxEnHsC3rnEGsp2gRwq821lHODxWoKFOn+0gufgtGx3Z6Onawo2N0lcv7ngjSkxKYpZdhlV4KIvs1j2cm7+7vcEsbFxfASl8HjhR02+feBflsHAQHB93A5NL8U7MrLSmD3f1jZ1cmWhrSvT4qNt8+qft8pd1CFwbrdI3kyssJHNyK0d81pXE/c6KNf9l2iPL8MLesPBUcJU2LLSc73EZ0gwk+duUa/mXLgTGVRJ+6Zh3vWX+OcucFRGo6ZkEZZnEVVt5pS2iOhaenjd/LMjpTc9+DxBCSVYUOawqdkRmexkHBrk6dwQlyTACEPw+8ATKdbecMQIzCOjwlK+g9/LsJy6O9q9+Ed/WbslYerSjnogIVZXqEhlV2BVL3sbV1y6g3u61tGpmKzdjFF8/a3Y+XvLunzc0nWB6G1f4O9qfK6LVnPpvTZ56aXVkeNqn0W5T4LA4PejkW9+BksTpBAi+1WfyRvw2rqJzk6isIHNiKMdA96WM8c9yd7fjlwZN8f/sh0rZbkWKe0dn2e6/W49clt/08wGBG8OYlbqO5w10LaxlnPFJoWPklmAXlmPklo0rqjcFuPF3NeHrbEY5NZ9mlczhSAMmSqMMlxTaBoWF2JwU7OjU6k+cOJjVvkNCNn4Fdv6S3/jeTCkAyvgiv7PnPMeXRvkvuwLv8DbP5YBVlDBWoKDOiJbuoCjOqbLYyDCeT3cxaa6+zJO/uaoX88hAlRpw1/nb2JcvpdwIzv8vTZlc25KeJGA5rIxmWhEwODHppShqQpYBFAv7DO0ituhyroJTk6isIHngFffDcmcJJ0+LlJnfWZFdrFyuKoxzpiU3wSVob+iStURz08qvjAtOBvuMtmPaGBffiIDUdK1rizp6cEZxo6QSerma83c1oWd4YcSaKDJPVwRS+gPvXFDdhZ6fOyUHBVJ5vQtPxrb0d4QlMKgCx+5uoGqexYXvf1MujFWWmFtprkXI+OTbeweM02/DIQT9W4Up+cPAQUjp49eOYznWQhenhqSbvHqj5BBeVBCnUE6wNtLE3WcGg4z/bXUxan6nzTGeA2oDF6kiGgC65LD/N0pDJ/gEfXZnsTIcL6eCvf5Xk6iuw80tIrtlEYP/L6LG+Ubc72R/js7/bTmZo1iRjO6RsOfS7CdARSxDxGfSmLHZ2jM5Hed8TfnpTgjvWLuKvXncxJ3oG+dxvt3KsN87u1m6uXFaVlccyl6SmY0ZLsArLMaMlo56PWiaJ0dOGp7cNPd6fU1078nSbVcEkJR73OT+Ygn3dGof7NJwJuiZPxmQCEOlYyJZdNJvwaL0fz+LNPFK/Bcex0VO7kM57EbPc1FFRTrdgnm2OZSLUXj/nlRZvxbEthC9CvPJ6pL+QTHQFoZZncNIDMNiEE6rIwj0ZSM2DlZhE8m6wBOmLsD/pY12gnQI9xTp/G7uS5cSdyZVWPvTrL07qdlLXSS1ZQnrZcqRhcBXg6WoncGA/3szM+oh0FwwFVoea4bo3QkkZ6eqL4ZknoK/n1A0tG5/tcHQgddb8A4nglTb9jCU6gw8uruAdRVH69jWQD3x9zRKeaOsmv62P9r6ZN2uzUjPLPBVT7Q8DYHigshpqFkF51angZCAF8UHkyRPQeAJ6Jpf780Dyc1MfwxmcSe6z4/h8xJetJF1dAynAcQicbODPxQ1kbAfOuefyxNLJFLJ5J82WG4BotZt4pP6VkQAkFX8HQjNwul/DNtMYeaWw/k8QkUq06qvRdn4XK9ZJquUwWtHYXknSNnFOPI+26DqEPnvtCcwJKswMIcnz2oSMhbVNw3wWM3X6MhOHGY45udfJBROoKOef4ysgXbYRK3/lyDS69BcSW/QWjL5DOL6C7NyR7iVeeyuert30dO2aMHk3U7wes/gSIp4ADrAvWcbFgTaieppLAm3sSlaQcGa40d5phG0TeO01fA0NpFauIl1Xh1lWhllWir+pkeBrh9Ey6ZndiW3D87+D170Rikvh9Te5wUp/HwAhQ+d/r1vGfzS283hjx/hVPCnB4pAfW0uO7mwblpxMjF7m8Osab60qmdmY54LHA5U1pwUnpwU4gwOngpPeyef6nE9S10kuWkJy8VKkruNIOHrsKOubThBIpcgsn1nbfMANQKwMergEseGDaJFKnLprRwIQ0XscUbQcEa5AX/M2RO1VCN39e9EilchrPoN+8iVEePwPH07bbuz6/wZ/FL3q8hmP92w8mkPEYxP12kSGvoIqQMk5J2O+swYqk6UCFWX6DD9W4dqxl2vG+JfPhNAwSzYgNS9b27aOSd5Nl20cc58OGnuT5VwSbCVPy7jBSqKCpMzupz0tkyG4dw++Y0dJrlmDWV5BuraOdFUVgWNHCRw/hrBn8CJqW/DC7+B1N0NhEbz+Znj6CRh0P1nqQvC+2nJCus7/OdE6pornTxdX8HhjOzFL8B/1Xt5UXsiP6nvIONCb6sd0HDzzpcJH0yEYhEAQgiH335IyKKscE5zQeAKaTiC7cys4kYATCGJFIlh5EexIFDO/AOlxn5dGXx8Htm3lq7v38cmaCq7Lz1IZcHzo97DkjWgRtxfLeAGI8IXRF79+zI8L3Tvu5SNadwEg23ZBFgMVr7DJMzJEDJM8I0MoPEjAGH9mKWVpxCx9zI7qytwYzNJSuApUlJwn0v2Emn+PjoVtpqgMO6OTd0MOrZ3b0fv2Y2NgL6pDH2pEZaOxJ+EGK2HN5OJgK7sSFaSzHKwA6PE44W3bsAoLSV20Diu/gOTylaRqFxF87RC+pkaEnOYrqGnCc0/C9TdDfqH779O/gdjgyE2OxZNj9wMKS3b0DRKzHBaFfNy/qo7FoQBvrirmKwdP0JDIcGAgwSX5M+s7kxUerxt8nB6IhEKnApJAELxnWb4b6B8JToZnnOaa1DSscB5mKO9UYJIXQRpjX3r1RILg4YN429t4oaEZgC39g9kLVLrq3TF17IWaTSMXC92LZ/F1+DQbTUyvXaK0MtjJBspLg7QlGtBlHGFMb/YyqFtDgUmGPMPEp40O8h3LDVISls5Axv3qz+gMmDqmM08CbmVKLohApcibRM+lTDllajwCf1ERpPvR8NE5GONH9Wneu8rksXoPpiMwhIWVjkOwBO2MRlQWOnuSFawPtBLUTC4JtLE3WZ71mZVhRk8PkS1/IFNeQWLlaqxAgCMrVlFRt5i8QwfxdHZML3HTzMCzT8IbboFIPtz8FmhrhpZGzMYGXu4dID48a1J2atakR49z7+IKbqssxj+U87E4FOCfNqzkibZu6oLZSTSeNMNwZ0FKyhBFJRAKQyAE+jifvsb7Rdk2JBOQiLv/DvRDc4P77xxydAMrGsXKi4x82eE8pADOjE8dB2NwEGNwAH2gH2NwAKO/DyElKcdhZyxGTdj9N+XMdC8hid9J4HfaqF5WQUykCAY68HsEPs3Gr9l4tCneh5WG/pOM5MwIiXPVVVxa6rCjQ0MTL8FInyENorUwg/b7cdtgwPQyaHnpHkgymNHH7G6uLFwXRKBSHYjjneofopJbwqvQUj1oVoJKafPFxTvYVGbyrpUm7/p1gLgpMCNLyVReQ8QzthzZlDq7k+WsD7QS0Cw2BFvYGq/G4txTkxnH4Tf9/bwpGsU7ySUSAfjaWvG2t/FqcSkti5bgzY/C5RsJ7dmNv3maZZ6ZNDzzWzfBtqAQqmqhqpbYqosR2w9R52T4zIqhWZPqU7Mmy/KCI0HKsDnJRzEMxO13gn+CkvFMGpJxSCSQiTginYREwg1Ihi7HzIz/s3PIDgTp3XgVjm/sLIKWMUeCEX1gAGOgHz0RH5lda05n+OrJJsyh700pSTuMVG79+WvHyDT8NeC2rvds+vA5W9c7g+2Yr3wbHYuL1qyipKgQ57LLTgUSyd2QGh1I2Lp/0hVFEi/YEplJ4NUkdRGHkAcq/Q7pfI24CQ0DGhlHILwhwIuY5GxHytEZtLwMWB4GLS8xy4N9WqFEJj3TTTKU+eaCCFT6TS+GUIuW818pxXo72EmEx09/2qTAJ4mbAiuvlkzZxrN2wc1Ig93JCq4INeERDlE9Nanuta/E4/ywu4dCw+DavLwpjVhIydM7drBzyyu85w3Xc/XatWQqKqYfqACkU/DUL90loKpaWLKcvHCYv7zpei5uOZ4bsyYTqVnkBilmBtnUAF0d7kxIMuEGIc4ZuTzTqfo5zyQQW7UWx+dFS2fw9PW4MyQDAxiD/WjpNPIsSRP5hk5Q1zkYT01Q1abxk8PWlFrXC38EzRtk3aIiKoryqAymyCPGGn8CvGl6YmmeO2HSOZDC8hWRXnclthFgKr1ZJFXYJ3+LfO23bKxwePTmBCEJcT+877kg21p1xPKb0Ze9HtGnutgq03dBBConEmpPioXCL5op8Ul+fCTA47vlSIO5Bs0DxrnfjNPSoMsKUmbECWom3ZPIcX055u4I/EosNuVAJeU47IrHKQs6/G7XTq5cswYRiU7pGBPq63G/knGMy69i44YN0N4w6ia5VsUjFi0FQNbvg/2753g02ZEpKSNdUoJwJPnbtmAkxpZ1n+1jUkjX+bvFtfyko5ufdXSz+4x+N3/8RIDetIZn1ZvwrrplUq3rdY+PS255L/mpk6QG2ji4exv/56omQhI2FsL7tgWpHwkkbppWO3yh6RgrbsU2Arxy8OejE9xbNfQ1bz178q2iTNIFEagoC4S0cZLdpIwIv28NYeZX84ODR5DSwac3kJE2iHO/4LolynFCunnOrZZTjsPuZMLt9JpIkHIc/GdZ/mnJZPiH9jasobcm0zk1jf/+p3rYF0+gaYJftbRhp5L8VVUVleMsF0xJ4wm49EqIRN1ZltP7rOQSfwDKhkpbG47N7ViyKL58JQDBE0fHDVImQxeCd5cVE9I1vtfaObpyq03He/Hb8S6bXOt6DYeLQh0UGGlso5xdjSk6DvfRuWF2Agk50DymiVxlGNoGmmZ8bEUBFago84iW6CBjpkEzsMuvIGOVYRasHmkwpyU6JtVgLm67SbQhbWyugz3YzqcbT2JKN6fJkozKF/hU40mModlxj9D4dFk5ld5TgUZU1wlqGvXJtDuNv/rUNP6Xr4yRFl10UoQVySeUyZBvZGFK3DShpRGq62DRUtiVo4FK3RJAQHfnqGql+cwOBLHCYYQjCZyYefB1PJmmNjy2cqu1b3Jv+qeClBS2FOyJl9HT+uysBRLSsRDtu8dvIpfe416vutgqM5T7C8CKMsTxFZAMLcIKlOPxucs8ww3m0mUbJ91gbrjpW0AzOXNSXvNFCGoarRmbtGZxx+okX7kmNZIvcMfqJGnNojVjE9Q08s8oMw3pOl+qrOIdxYX0pQS7O3Tevsztzvr2ZRYnWrowbfijxYv427oaguNVukzHCXeTRmoXk609h7JN1C0BQA6PdQHIFLnLap6+XjRrZl14TUeybXCQkzHBjw56uakwymMHvTTGBLJ1F9I5+/E1HC4+LUjZHS+jzzSQrbtpHhwOJK7ikXo/LTEQ7XvOecxzkb3Hsa0MRl4p2tV/iXHRO91/wyXYZhrZe3xGx1cUUIGKMp8YfpKhRSDE6Cqu4QZzk8hRAUhKAweBjsQvRr9QC2+AL1VWcWdBwbiBxu4Onb6U4K6CAr5UWUVwnGUgXQjeXlzIhnCILUNt6/vT0JEQvHq8hxqfj4011egiiwFFa7NbMeMPQFl59o6bLXkRKCwGKeHkwnnzyhQXA+Dt6pzxsQ4lksRtSa3fw1eXLeKjVeV8ddkiav0ebDON3T3xjM1wkJJvpLClxu54GQO2H7v7GLaZxhOZnUBipIvt1X81qomcuOYz7uUTdLFVlKlQc3LKvDLc0MkrZlJuLkg4HsJahpCWIWWP7qeiC8E7iwoJ6Ro/aOseky9wd3ERt+Xnn/UetgzG2B6LUXPGNL6R7iTh2FjZSqgdJh04eQKWrYRFy6C9NbvHn6mhJFramt2qpQVACkGmcChQ6Z55oFLj9/LBilJuKoziGwqA6/w+Hlq6iA/ol6BHJn7TXxnoJt9IYUmNPfEyBmy3Z4keqcB78Z14Fl2NabvHnEw7/MmadhdbRZkCFago80pmKFBxG1RJxlvmOFsp6LC47SGkpQmIDNIJjrrOzrilQMeTKWrP6IJbE3Y4lkxhB89eLvSH3n50AY0xjR8d9HJjJMJjBwfA6GazZWMGg5joaJnxe4I4mWm03D/yGtrSlVBRg+MI5Eza9gO6d+bLUtJ2z4WodZd9nGNHwZ58q4CZPgYAZwr3Nx5zgo0VzaJiHKGhJVI4nT3nyss+pzCCWyMRsCQ2px63AXhXXn+Wn5QUedzN3Q4kSkaCFADhy8O7bOhn7VOPYz4HEvLMEnZl3prsuVRLP8q8Yjrum6cmJPoMeuOcSqgd/+3FkpLtyQQnhwKNG8LhoXwBje3JBNZZWuG7lUJJbCkwhOSGvAh/UlzEg9XVlGvQ3ddH3Laxsz2r0tWBHBxwN+irrs3usWeiqBiRFwHLgqaGc99+njBLhvJTpttpOEv8moUhHBwEvVaO9MtRlCxSMyrKvOIgsKVAFxKPcLDl9GLtuDMUqOjjByqH0inijqTGa/BnhaXUer3cHI7wzz0dNGUsDqVTrB3qrtpimvxjd/uozqKmhA+szvDIQS+/G+xnazKGTwh0BOXxOH5Nw86P4slCbsMoJ47CRRsQi5dBY27kgoz0TmlqcIOVBcIsLQXA6OyY03Hk6e6sXNz2IHM0kVpRZkIFKsq8Yzoaum7j1RxS00xVGa78CWoZxltCqvF4+UB+ITeEwyP5ArVeL39fWsnvYzFqPKdKkvN1nYDQOJnJuCXJK9yS5PevtlhT5Ax1FoXWlGBVwMc66SCFwAlM0EZ+BuTxo4iLNiAqqpA+/9zngwgBi5aMjG2hcPx+7Dy3kaSnM8vB5hQNByqD9vT30lGUXKaWfpR5JzO0/OPRpr9WnXQMHCnQxNjKH4CIrnNrJDISpAzzaRq3RiJETisrDmoaXyit4I5IdNxKoZ0dGr0peFt+Pl+qrCKQTgPum13WDQ4guzqHAoSl2T/+VJVXIvwBZCoFbS1zPZqsMUuGZlP6etHMud17Jk93n0+D9gwbBypKjlIzKsq8M7xr6sz2bxKkHYOA7m4jf2blz1TpQnB1KMzv44NsaWNUpdArbe6f2YvxQV5ODFJtaFy1fDlLNZ3wjO51Ai2NUFwC+ZPrKzOrCgrdf9taYMa7AOcOO+yeOaO3d45HAr6hgD0xw+ewouQqNaOiXPCytV1lvq7jSEnVUKXQmkfC/Ps+D7V5DpeWWiPN4nosiY7Ap81SPsFwf5ZcyAcZHkM2e8bkgrMkUyuKkl1qRkW5cGX5vdMrBDbQHNN49ICH0oDDjw55yNiCvrTAIwR9KcHb8/JY6ffjGdrsMOuMoU/WuRSoGAvrpUYMzQ7Js+zWfb4Mx0wiayG3ouSWuf8rU5Q5MvzCnq2X99MrhW7Pz6cjqfHvNyZZGrUZyAheadf5QHERr49G3Rhptj6VDwcF1tzmTowawwILVEbO3WzNik3BSKXP3A9FUWaFClSUC9bI67rMziv8cKXQl0sr6bdtasOS3zcaHO3X2VhmURmSHE+nT7/jrNzvGMMzKlloljZj1tAYFlqgMpxvk0NLWrkzEkXJLhWoKBe8bIULw5VCuhC8moiPbC53YyTCzg6DlrhgeyJ+qufobM3U5+SMysJK9BRDMypynL2ehmUch59395KZ5STi4aeRClSUhUoFKsq8NdP3+dl6YT+UTrmby/k8PFhdzYdLS3iwupoan4eYLWnKuG/eYtaXflSOyqyZxIzKy7E4P+zq5pVY/LwMSeWoKAvVAnv1UJSpGM5RyW7IUuPxcndxETee1oelzufjwapqfjcwQLF3eHbhQghU5n+OiqaP/TynCYEQAqHr414P8MpQsvQrsRjXRvJmbXyqG62y0KkZFeWCNVsv7xFd57b8/HGbxd2Wn09ouFnchZBMO5QnIxbY0g9yuOpn/GdRynHYFY9TE3bYGY+TmsXlH1X1oyx08/djjnLBEuP8bybHme2X94zj8Jv+ft4UjeLVtFP3PGs5KrlUnjwULOm6u0yyUPqPDO/QPRSMtqQzfLW5+dR+T44k7cBD16Z5928CfPJEA56hCiEDwV9WVlDpzU4n2eEZlRzK61WUrFIzKsqFS8zO0s+ZXonH+WF3D6/Eh3IVZr3qJ5eWfk4bwzxe/jmTkKNzVKKGTlDTaUlbpITJHauTfOWaFNdV23zlmhR3rE6SEiYtaYugppGvL5zfhaLMtlkJVE6cOME999zD4sWLCQQCLF26lC9+8YtkMplRt9uzZw/XXnstfr+fmpoaHnroodkYjqLMqZdPy1WA05YLLoQZFds+NYuygAKV4cc0fC5Dus7f1tXwjuLCcfd72t2h05cS3FVUwN/UVBOcIK9lWkMZ+ldNqCgL1ay8ctTX1+M4Dt/+9rdZtmwZ+/bt49577yUej/O1r30NgIGBAW666SZuvPFGvvWtb7F3714++MEPkp+fz4c+9KHZGJayQAy/MGtZeqc3mL38gZTjsDuZoCbssCuRcHMVZrlZ2MhR8/IgNjAr9zFpodN3M1o4b6VynKBLF4J3lRYT0nW+19Y5ar+nLW06l4WCvK2wAD3LazT6jPa8UpTcNyuByi233MItt9wy8v2SJUs4dOgQ//Zv/zYSqDz22GNkMhm++93v4vV6Wbt2Lbt27eLrX/+6ClSUs0raBpAmZJiQnv5x+i0/RZ4ExZ4Eg2lfVsbWYpr8Y3c75lAQZUlG5Sp8qvEklbbF65YtI6HpXJTJZC1XYZg8dgSxcjXisiuRv/n5nDV+k4C4YjMIgWxrgWRiTsYxG9KV1QB4urvGXHc8laI2LPnOXg8/OODl7jUZqsMO22NxXonFs1oBZAh7ZPfkfis7z2FFyTXnLUelv7+fwsLCke+3bNnCddddh/e0F+mbb76ZQ4cO0XuWHUnT6TQDAwOjvpQLy6DlLm2EjZlVtbSb7qf9Uk+MbK3D5Os6AaHRmrFJa9a4uQrtvW2kHEkkEiEaCGTlfk8nd7+KjMchHIF1G7J+/EmrrEFU1YBtI7dtmbtxZJkdCmEVFgISX1PTqOtMKdkWi400+3tjfpTHDnppimnoAl4aGMzqWAqNFAKI2x7ScgEtrSnKac5LoHLkyBEefvhhPvzhD49c1tbWRllZ2ajbDX/f1tY24bEefPBBotHoyFdNTc3sDFrJWfGhQMWrOXjF9GcLuq0AttTwa9bIp9KZCmoaXyit4M6CgglzFToGTIrTaVb6/XgLCs9xxGkwTeTWlwAQq9ZBQVH27+McpG7Ahk3u/w/uhYH+8z6G2ZKqdl9zPJ2d6OnUqOsOJZJusz+/h4cW1/HRyjL+rq4GQ0hsKU4t/02RtE0yh59C2qOD8yLDnaXqtoLTfDSKkvumFKh89rOfdZscneWrvr5+1M80Nzdzyy23cNddd3HvvffOeMAPPPAA/f39I1+NjY0zPqYyvzgIErb76XEmsyoOGt2WO6NRamSve6guBO8sKuT9xUVsadPpTAr609CRcHMVPlBcxPJUEgHY+dGs3e8oLY3Ik8dBCMSmq+F87/K75hIIhJCxQeS+3ef3vmeRFIJ0lbvs42s8Oeb6Gp+XO4oKkVLy1eZmPn70GP/Q3IIlBR9YncGU8MkTDfz5iRP8+YkTfOpEAy1nFBmMx2reSWbfz7Fadp0+Ggo9SQC6zezPzClKrpjSXOGnP/1p7r777rPeZsmSJSP/b2lp4frrr+eqq67iO9/5zqjblZeX097ePuqy4e/Ly8snPL7P58PnU2uxF7pB00NQtwgbJj2mf9rH6TDDlHrilHoSHE0Xks2EzxPp9JhchZqwuzHhDX19mJVVWPkFWbu/Mba/DOWVkF8Eq9bCwb2zd1+nkZEorFjj/n/by7mxOWKWmMUlSJ8fkcng7Wgfc33UMHhrcQEHkgkOJlIU+iXvWG2yKOLw/tUWa4ocTgxo/OSwh56UYFXAN6lSZadlJwB28048NVcAENHTeISDJTUGbPWaqCxcUwpUSkpKKCkpmdRtm5ubuf7667nsssv43ve+h3ZGl87Nmzfz+c9/HtM08XjcqfynnnqKlStXUlAwiy/eyoIQsz2UkSRvhnkqPVYAS2p4NYt8PUWfnZ1PpqaUvJqIE7dPbUz42MEBTAm9epx7hvKwZm1GBSCdQu7cith0LWLdBmTjCYhlN0fiTBJgw5XuDE7LSWhZWDOe6ZpaAHwtTRPu1TRcqvzTzm5+1tXD7g6dT13qzpq8fZnFHz8RGClVvrOo8JxVQCnHwW4/QE3Yobl9P9JKIwwfRUOzKT1WQLXRVxa0Wcm+am5u5vWvfz11dXV87Wtfo7Ozc+S64dmS97znPfzN3/wN99xzD/fffz/79u3jn/7pn/jGN74xrfuUjnNqozBlwRvM6BCUBHQT4dg403yhlgi6zBDl3kFKPXH67ABWaub9Rw4lTOK2pMZj8PHCEmo9Xt4YCPFwTydNpsVrJ1uo2ixxfAEyjoZ2xvS/mYUxaJqAA/XoVYsRlVXIDZuxn/z1pH9e2tNIMF6yDK2oDDImzpaXyMRnFkgKfeZvwNN6HKfJxNxz43i9pApLwHLQDh0duXwibw9G8OVLHm3rHVOq/P5oAbcGIjgJa0xxfKtp8o89HSOVY6aUOLYzVDmmkXnqSwhNp+CKS3HywnSwCkc/92OUzsKY2Vooj0OZ/LmclUDlqaee4siRIxw5coTq6urRAxv6FBKNRnnyySe57777uOyyyyguLuYLX/iCKk1WJiXj6GQcDa/mEDZMBqzpl/i2DwUqJUaC17JU/VPt8fCBaAFvCIURQvDfg/3cEo7w5ZIKno7HqBYCbXAQOy8Pp6AArX3sMkK22FtexHjrHYiKSsSyFcgjh2fnjrw+xAZ3WcLZuxMS52fX4PPFrKsFIdB7e9EnWb3TYGaoDTtnLP85NJgTBzlRXSegaZxMZ9yloxXu0tFw5diJgQy/aMgj6Pcg0emWs5CQrSg5ZFYy7O6++26klON+ne7iiy/mhRdeIJVK0dTUxP333z8bw1EWqNhwmbJnZrMPfbYf09ExNJsCI5mNoRHRdd6U5+6evDUZ5z/6+9iWTODTNN6UFyGi62i9fQDYBflZuc8JxQaxd24HQLtiEwRmp0JErL8M4Q8g+/ugfv+s3MdckUCmrg4A74mGSf2MJSXbkwlOxjR+dNDLDaEwjx300hjT2J5KYE2wdBTUNP5XcTlvz4uOWzm2o9PAEy1DC5cyGF2FbYSy8hgVJVepvX6UeWu4n8pM81RA0GG6L/bZrP4ZtnWo0dnW5Ohj68N5KuchJ0se2Ifs6kR4fWgbN2f/DopKEMtWuve19aUFtwxrFxTg5OWBY+NpbiIjHf57oJ+MnPhxHkqniDuSGq/Bl0sr+NOCIr5cWkG1xyDuSA6nJy6J14Xgrmg+740WjKkc29qqUbb2dYhwGd2mClKUhU8FKsq8FTNPL1Ge2ZJNh+W+4Bd7Ejha9v4sUo7DnlSSmrDD7lRyVA8N/XzNqABIif3SCyAdtEWLETV12Tu2EIiNV7kdaI+9Bh0T90Gar8xF7u/L09yCMC22JhL8qK+XrYmJu+3WeLx8IL+QL5dWUutxlyZrPV6+XFLBB6IFVA8VEZzNiZGlIy9rHgnz7/u81EQ0Cr3u0lGXpcqSlYVPtTJU5q2EbeAg0IXEr9uk7Ok/nQdsHynHwK9ZxKIRIkNBxFS1Wib/PNCNJU4lQqblqRb6f9XRjGeoysPf3c4nrrsGXyCA4/OhneUTdlb09uDs24N20Xq0K6/Cbm7MzszHkmWIwiJkOo3csW3mx8sxUgjMoVw774kTALwylH+zNZngmlH7GZ0S0XVujUTcYzinAunh5b9zsaRkRypB3NF4tN6DsehKHql/mcLCAjRzkLSjEbfPHewoynynZlSUeUs6DlaiH5AYM96YTRC33U+9tnf6L/5RTSeoCVpNm7Ruceea1KgW+neuSZHWLVpNG6/j4Bna4VhO4tN1Nji7dyKTSUQwhKiqPvcPTIJYshwAeWAvnNGpdSFwolGkYSAyGfSu7tGzZMnpdZqdjMPptLt05DHwvu4zeNe/G+/rPkN+aTXScejtj7GQNnpUlImoGRVl3jJiDehaJ8JrADPP88hGvU9Q0/hctJRfpGL8fLB/TA+NDwz10LgjL8rbIlHiQmSpzmiSbNvdtHDtRYily5HjdFedkmAQUVru7gh94kh2xphjOvLCnEinaGtu4vnWxjGzZH/Z1jQyS+YRgk8UlVGZhcDz9Mqxj0WrANCiVeSV/hGabCLp5MPMq9gVJeepQEWZtzyDJyASRLOyU6mTLcOJkCFNG9ND4+XhHhqTmPqfLc7Rw2hrL0KrqcXx+WAmS051bidq2dEGZ8nXmM+CRUXoAo61tZPWrXHKhU91ml3p85Kv61m53+HKsTHj8UiEUUwykb1dmBUll6mlH2V+ckw8iWbCHomwk+Dk3kfLE6f10HATIT3n7KFxXvT2Iru7QNPRb3sL2nXXI9Zd4i4FTbF0WSxaCoBsOD4bI80JemEhK7x+1iWT45YL7+rQ3VmySJQvlFYQzGIy9niCunvfSZWfolwg1IyKMi+ITD/hlmfQhue6HRvbcdhU7vBiB+Q1P4WedgOAVOsTeDd/BC2v7CxHnF2nJ0Ke6qERw5TQo7k9NIxztE6fTc6BfejXvh6RF0HkRWDx0pHrZCoJPT2IgV5kXw/09ri7H5+Zi5EXQRQWuZefXJiBitQ07EgEAbzRtLHyC8adJftAfgG3RmZxO4QhGqd2DE846uVbuTCoZ7oyL0g9gNQ82Mn+URu9ZUIOqwptLtNj/OJghp6UQAuXIPxzt7QCoxMhh1vo3xTOG2mhfzidZo1/+pspzpQ8dgSrtdkNNAqKEIWFiIJCRDQf4Q9AZRWiuvpUqqZtu43c+nqQvT3Q042oqnGP1do8s+WjHGbn5bnl15kMIpmcsNPsifM0SzY8m2JJDUtmZ4lJUXKdClSU+UH3Eqt5E97uPfR072bHUJLq471QEZS8fEynJyXIFF1C+HV/itDm9kX89ERI39BSwHAPjafjsUn10Jh1ySSyuQmam04l9Oo65BcgCovQS0qgoBAKChEejxvUFBaNqTORDcfO88DPHyuaD7jN+eyhTrMjs2Th8z9LFtDc5oZqNkW5kKhnuzJ/CI1M8Xqk5mFr26t0JgUZB9I27O3SSJdcgVm4ZsZBiiklv4oPclMwjHeabzwTJUJOtodGNjjONOqJHAs6O6GzE/naoVOXh8OIwiJEQREUFrr/D+chE3Hso8fBGn9zMceeWemuloU0Ojsz/U3sMqEIUkpEdy8H48mRWbKPRYup9Xi50R/mm/1dNJkWB+NJ1vhmZ5ZsePsRN1CRJGxjzJYk5zyG2sxPmadUoKLMO1q6h6qw5Dt7Pbxse1hapFEahP50T1aOf9jM8Hi8n0Jd5yr/7OyLM+/EYshYDHnytH1uPF6wrQXXLv90dr6bd6L39FJteHh/JJ/rg2F84tQs2d8WlfNMIka1MfuzZEHNXfpJqERa5QKiqn6U+UXa+OInaY4JHjnow/FGaYppdCTAF28AOfNPjYdNN99iW3phlttmjZlZ0EGK1DTsodkvrbePiK5zSygyEqQM8wmNW0LuRpOzLaC7Sz9JtfSjXEDUs12ZV/RkJ45tIXwR4hWvxw5KbFIIbxtOogM92TmzO3AcTlhuguSeeIqUdPALFc9fiOxI1E2kTacRydzo1XNqRkW9dCsXDvVsV+YV25tPuvQKzOgK0AygB6l5iFe8nrT3OLY3f0rHcwbbyWz5FsLJYK9bjVNUhIUY6Tr62d62ka6jhhT8eaSIivMwxZ/rMo7Dr7p6ub24AO8s9w2ZK1ZRIeAu++RCo3pDOHg1d8Yw6ajnoHLhWJivMMrCZfgxC9YMBSlgOe5TOOKT7uXG1JIZhT8CngCZWC8y3kNtKMM7NtdxRbUxZm+eoCaIDiXqZoYSbjNTTGg8k5Zy98axKspndJzz7aX+QX7Q2smW/sG5HsqsMMvKSK5aDYDeMcNZuixZFugF3CDFkuqlW7lwqGe7Mq+1p9zApNyfZDq75ghPAN/rPo139a3srj9KV3+C0oifF8WVvGWZM9J19G3BPD4XLR3pOrotneTxeD+vpme2JOA9/BoAmVUrz9vGhNkwHKAsxEDFLC0ldvkVoGl4WlrwvDb3exiVeBJU+txNCOvjhXM9HEU5r1SgosxrPRkfGUfHEA4lvunt3Cs0Hd/a29FWv5lfv7AdxzZpk4W84FzKy2067wlHuSMcRT+tVHk40XamCbeehpPog4M4Xi/p5ctmdKzzJWU77ByMURN22DEYIzXDEuRcYpaUELtioxuktLYS2rEdMcNZsxmPyethdagbgIZUhF5r7hoFKspcUDkqyrwmEbSmAtQFY5QHknSkp/8ibvc3UawnqN+7ndbCq6iN1LFxTZqGY+2jbpeSDnszqawk3Aop8e0/QOLKTWSWL8N79BhajnV5bU5n+N8nmjClG5CYUpJ2Tu0e/PHDR0/bPVjj/kXVlHgMftXVy83hvHmTw2KWlBDbuMkNUtpaCW1/dc6DFAk0LV2CIRwGLB/HkrPfpl9Rco0KVJR5rzPlpyqQwK/ZFHqn18pcOhayZRfNJjz26iA3LTlG84qlFNau5GBvgs92tGIPvd+aUpKWnDXhtto7+VJVo7nF7XxaUEBm1Ur8u/dM6zHMlnxDJ6hrHIxn3O0LVk68e/DqkJ8CQx/JYcmv0Lg2kvu7/JrFJcQ2Ds2ktLURenXugxSAzqoKEpEwttTYHy9C5kRar6KcX/Pjo46inIWDGMlVqQgkYBpvMHbXMWwzjSdSxt8WlPHuwSTrO3sJaBor1l1MKJJHq2mT1i3uXJPiK9ekRt6oJ0q4nSwB+PfuByCzZDFOMLeazIV0nb9fWsc7y4rG3T14d4dOb0pwV2khf7+0jqCuj+SuvDwYm8uhT4pZXExs00bQdDztbYRe3ZYTQUoiHKKzqhKAQ4lCVemjXLDUjIqyILSnAlQEkoR0i6TdT9LIn9LPa9EKfJfciWfxNdS89DUAVp9sxh8McjIS5rJNV3H45Vf4eV83u4b2GQL3jfoDTwRGEm7fGoqMymWZLKOzE6OjA6u0lPSa1QSP7p/yMWaTLgTvKS8hpOt8t6Vj1O7BW9rcwKzG70MXYlQOy65EnJTj4M/R5Z9MeQXxyy4dClLac2YmxdZ1mpYtAQHRrh7aikJzPaR5Q8OBaSTWK9knEcgszIcIOdUNI3LMwMAA0WiU8NV/gTB8cz0cZQ7VheKUB1Jc13KC1Udbp30cr3Yq0LANndar1mOGA3j7YtS/vJWfEecP74xT4HPfqK/5SYg7nQA3cCo/Rjem/sfplBRiv/VmkBB+5mn0/oFpP4ZsEPrYx/Ctvi4a9BjX11ojuwf/+rhBd0rg1wQRTceUki7L4T/elOTdvwlQpGt4hn6lhhR83F9AuT65z0giCwGOHKd7rhQCa/1FmOtWAaA3t+J99g+IcW6b7j8/OyOPjA3oWL+SWEUJnmSK6hd3kkmbMz7uJ2/6q5kPLgekY+NtlSHJ05KUG32EtdxozqdAlxWh0SqZ8HrHTNH9P5+nv7+fSGTiPdBy82OOokxDa9KPRNAfCRIPeLNyTN2yKXt1P1rGIpMfJn3JKmrCDt/Z62HNI2H+fZ+HmpBDk7BmfF9aZw/ieCMIyFy0Ngujzy5LSnakk5yMubsHvyEY5rGDXjqS7stI3GbcpbG71qbIGDZtlkNQaETneHZF+n1kbnzdSJBiHDyM99kXxw1S5kKsqpRYRQlCSkp3HUKbYMNHBUCSr8VY6W1mmbdVBSkLlFr6URaMjKPTnXYDlJayApafaD/HT0yOJ5GidMdBWjeuw6kswRdfwY8OHOEa6eWxA2ACPZqJZUuMGSY76tt2Yy2qxq6qxC4uQu/qzspjyIbDmTRxR+LXIKxpHDSThDTBoONgS/dx96ZgZ4c27tLYW7wh3uwLT2tpLFvskiIy112FDAYQpoVny1aMhqY5G8+ZMqEAXWuXAlDwWgP+voXXpyYbBJICPUaZ0YdfuM81B40uK49OO4op1VvbQqLOprKgtCb84IGe/BApr4E/M/OZDoBATz/pffXYFy9j3fLl3DEoWdTaw+uln+/qMVqkwzEsVjCzhEfRP4h26BhcvILMxWvxP/18ztR5VBse3pkXZUcqwWumSaFf8q7VJpUhB4nAdCS/P2mwpdUYlcPycpvOu3153OybuzwLCdirlmNedglS09D6B/A+9we0HGpYJ4Wg45KVOLpOoKef/KO5E0DlCoFDsd5PmdGHd2gW05YanXaUTiuKxexvDKmcfypQURaUhG2Qn0zQFwnSWprP4qaurB27uqmTnnCA8iWLkJesJmEdoKqzl/vtCC+SpiJLL5Lajn3IdUuxS0qwy8sw2rIzMzRTEV3nzeEot4Ui/L9YP7+IDbC7Q+dTt2QIeSBuwrd2e6kaWhobzmGpCTuczEKOxXRJAebVm7AW1wGgn2jEu2UbwspOEJstPSvqSEfD6KZJ6e5DOROg5gZJEd2U0I7mcZssWlKnw4rSaUdxVBbDgqYCFWXBqejooy8SpLsgnNVAJQ+Nq+qb6AiESVQU07uijmBnL14EbyB73UJFPIHx2jHMlcuxFtflTKAyTBeCO/PyCQmNH7b1jcyetMcFvWmBnRKn5bDEMCX0aGksKTHmYNnHqSjHWlyHcByMHXswDh7OuSBACkH/IrcUuXjvEYzU+U3gzXUeMlTQDEBKGrRb+XTbeVmpKFFynwpUlAXHN/TpXc7Cm6IAoieaSVQU4xiz9+ejDbhLElLP3ansBitD7WmzJzfXmdhSkKcJPldYRq3Hy/XCx7dSfTTbNq/ZGVbPQWXe8B5KWkcXnoOHz/v9T4oAOZRkHOzum9ux5CBtqNzYQeNAulo1vrvAqEBFUZQpG64AijunKoB+3+A2d7OlpNJwg4Ma3cMXgsU8l0lQpamGZcrMuH05VJByoVGBiqIoUzZcAVTjMfhYtJhaj5c3BsN8s7+LJtPicCbNGp+7HOYTgpvmMJFWUZT5TQUqiqJMWbXh4f2RfK4PhvENbchY6/Hyt0XlPJOIUW2o2RNFUbJDBSqKokxZRNe5JTS2k6RPaONeriiKMl0qZVpRFEVRlJylAhVFURRFUXKWClQURVEURclZKlBRFEVRFCVnqUBFURRFUZScpQIVRVEURVFylgpUFEVRFEXJWSpQURRFURQlZ6lARVEURVGUnKUCFUVRFEVRcpYKVBRFURRFyVkqUFEURVEUJWepQEVRFEVRlJylAhVFURRFUXKWClQURVEURclZKlBRFEVRFCVnqUBFURRFUZScpQIVRVEURVFylgpUFEVRFEXJWSpQURRFURQlZ6lARVEURVGUnKUCFUVRFEVRcpYKVBRFURRFyVkqUFEURVEUJWepQEVRFEVRlJylAhVFURRFUXKWClQURVEURclZKlBRFEVRFCVnzXqgkk6nWb9+PUIIdu3aNeq6PXv2cO211+L3+6mpqeGhhx6a7eEoiqIoijKPzHqg8pnPfIbKysoxlw8MDHDTTTdRV1fH9u3b+epXv8qXvvQlvvOd78z2kBRFURRFmSeM2Tz4b37zG5588kn+8z//k9/85jejrnvsscfIZDJ897vfxev1snbtWnbt2sXXv/51PvShD83msBRFURRFmSdmbUalvb2de++9l0cffZRgMDjm+i1btnDdddfh9XpHLrv55ps5dOgQvb29Ex43nU4zMDAw6ktRFEVRlIVpVgIVKSV33303H/nIR7j88svHvU1bWxtlZWWjLhv+vq2tbcJjP/jgg0Sj0ZGvmpqa7A1cURRFUZScMqVA5bOf/SxCiLN+1dfX8/DDDzM4OMgDDzyQ9QE/8MAD9Pf3j3w1NjZm/T4URVEURckNU8pR+fSnP83dd9991tssWbKEp59+mi1btuDz+UZdd/nll/Pe976XH/zgB5SXl9Pe3j7q+uHvy8vLJzy+z+cbc1xFURRFURamKQUqJSUllJSUnPN2//zP/8yXv/zlke9bWlq4+eab+fGPf8ymTZsA2Lx5M5///OcxTROPxwPAU089xcqVKykoKJjKsBRFURRFWaBmpeqntrZ21PfhcBiApUuXUl1dDcB73vMe/uZv/oZ77rmH+++/n3379vFP//RPfOMb35iNISmKoiiKMg/Nanny2USjUZ588kn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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_importance_map(name,iforest_sklearn,X_tr,y_tr,30,pwd=path,save=True,feats_plot=(0,1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### plt_complete_scoremap\n", + "\n", + "This method takes a lot of time in particular with the datasets with a lot of features (like the ones contained in the folder data)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_complete_scoremap(name,X_te.shape[1],iforest_sklearn,X_te,y_te)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GFI" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### compute_global_importances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Glass" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "name='glass'\n", + "pwd_imp_scores=os.path.join(os.getcwd(),'imp_scores')\n", + "pwd_plt_data=os.path.join(os.getcwd(),'plt_data')\n", + "iforest = IsolationForest(n_estimators= 100, max_samples=64, contamination=0.1, bootstrap=False)\n", + "global_imps,plt_data=compute_global_importances(iforest,X_tr,10,name,pwd_imp_scores,pwd_plt_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Lympho" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pwd_imp_scores=os.path.join(os.getcwd(),'imp_scores')\n", + "pwd_plt_data=os.path.join(os.getcwd(),'plt_data')\n", + "iforest = IsolationForest(n_estimators= 100, max_samples=64, contamination=0.1, bootstrap=False)\n", + "global_imps,plt_data=compute_global_importances(iforest,X_tr,10,name,pwd_imp_scores,pwd_plt_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### plt_importances_bars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Glass" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dim=X_tr.shape[1]\n", + "plt_importances_bars(global_imps,name,dim=dim,is_local=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Lympho" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dim=X_tr.shape[1]\n", + "plt_importances_bars(global_imps,name,dim=dim,is_local=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### plt_feat_bar_plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Glass" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "path=os.path.join(os.getcwd(),'images')\n", + "plt_feat_bar_plot(plt_data,name,pwd=path,is_local=False,save=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Lympho" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "path=os.path.join(os.getcwd(),'images')\n", + "plt_feat_bar_plot(plt_data,name,pwd=path,is_local=False,save=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Code to understand what to do in the pytest" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## _get_iic" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# start time\n", + "start = time.time()\n", + "# initialization\n", + "num_feat = X_tr.shape[1] \n", + "estimators = iforest_sklearn.estimators_\n", + "cfi_outliers_ib = np.zeros(num_feat).astype('float')\n", + "cfi_inliers_ib = np.zeros(num_feat).astype('float')\n", + "counter_outliers_ib = np.zeros(num_feat).astype('int')\n", + "counter_inliers_ib = np.zeros(num_feat).astype('int')\n", + "in_bag_samples = iforest_sklearn.estimators_samples_" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# for every iTree in the _sklearn\n", + "for k, estimator in enumerate(estimators):\n", + " # get in-bag samples indices\n", + " in_bag_sample = list(in_bag_samples[k])\n", + " # get in-bag samples (predicted inliers and predicted outliers)\n", + " X_ib = X_tr[in_bag_sample,:]\n", + " as_ib = decision_function_single_tree(iforest_sklearn, k, X_ib)\n", + " X_outliers_ib = X_ib[np.where(as_ib < 0)]\n", + " X_inliers_ib = X_ib[np.where(as_ib > 0)]\n", + " if X_inliers_ib.shape[0] == 0 or X_outliers_ib.shape[0] == 0:\n", + " continue\n", + " # compute relevant quantities\n", + " n_nodes = estimator.tree_.node_count\n", + " children_left = estimator.tree_.children_left\n", + " children_right = estimator.tree_.children_right\n", + " feature = estimator.tree_.feature\n", + " node_depth = np.zeros(shape=n_nodes, dtype=np.int64)\n", + " is_leaves = np.zeros(shape=n_nodes, dtype=bool)\n", + " # compute node depths\n", + " stack = [(0, -1)] \n", + " while len(stack) > 0:\n", + " node_id, parent_depth = stack.pop()\n", + " node_depth[node_id] = parent_depth + 1\n", + " # if we have a test node\n", + " if (children_left[node_id] != children_right[node_id]):\n", + " stack.append((children_left[node_id], parent_depth + 1))\n", + " stack.append((children_right[node_id], parent_depth + 1))\n", + " else:\n", + " is_leaves[node_id] = True" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from interpretability_module import _get_iic\n", + "lambda_outliers_ib = _get_iic(estimator, X_outliers_ib, is_leaves, adjust_iic=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "29" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lambda_outliers_ib.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((29,), (10, 9))" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lambda_outliers_ib.shape, X_outliers_ib.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-1. , -1. , 0.66666667, 0.66666667, -1. ,\n", + " -1. , -1. , -1. , 0.83333333, -1. ,\n", + " 0.8 , -1. , 0.5 , 0.5 , -1. ,\n", + " -1. , 0.5 , -1. , -1. , 0.85714286,\n", + " -1. , -1. , -1. , -1. , -1. ,\n", + " -1. , 0.5 , -1. , -1. , -1. ,\n", + " -1. , 0.75 , -1. , 0.66666667, -1. ,\n", + " -1. , -1. , -1. , -1. , 0.98734177,\n", + " -1. , 0.94871795, 0.60810811, -1. , 0.2 ,\n", + " -1. , 0.75555556, -1. , 0.09090909, 0.75 ,\n", + " 0.66666667, 0.5 , -1. , -1. , -1. ])" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(0)\n", + "X = np.random.randn(100, 10)\n", + "# create an isolation forest model\n", + "iforest = IsolationForest(n_estimators=10, random_state=0)\n", + "iforest.fit(X)\n", + "is_leaves=np.random.choice([True, False], size=X.shape[0])\n", + "#adjust_iic=np.random.choice([True, False], size=1)\n", + "lambda_outliers_ib_false = _get_iic(iforest.estimators_[1], X, is_leaves, adjust_iic=False)\n", + "lambda_outliers_ib_true = _get_iic(iforest.estimators_[1], X, is_leaves, adjust_iic=True)\n", + "lambda_outliers_ib_false" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-1. , -1. , 0.66666667, 0.66666667, -1. ,\n", + " -1. , -1. , -1. , 1. , -1. ,\n", + " 1. , -1. , 0.5 , 0.5 , -1. ,\n", + " -1. , 0.5 , -1. , -1. , 1. ,\n", + " -1. , -1. , -1. , -1. , -1. ,\n", + " -1. , 0.5 , -1. , -1. , -1. ,\n", + " -1. , 1. , -1. , 0.66666667, -1. ,\n", + " -1. , -1. , -1. , -1. , 1. ,\n", + " -1. , 0.96052632, 0.61111111, -1. , -0.5 ,\n", + " -1. , 0.76190476, -1. , -0.125 , 1. ,\n", + " 0.66666667, 0.5 , -1. , -1. , -1. ])" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lambda_outliers_ib_true" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, False, False, False, False, False, False, False, True,\n", + " False, True, False, False, False, False, False, False, False,\n", + " False, True, False, False, False, False, False, False, False,\n", + " False, False, False, False, True, False, False, False, False,\n", + " False, False, False, True, False, True, False, False, False,\n", + " False, True, False, False, True, False, False, False, False,\n", + " False])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lambda_outliers_ib_false >= 0.7" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## local_diffi" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# create a random dataset\n", + "np.random.seed(0)\n", + "# local_diffi works on a single sample\n", + "X = np.random.randn(100,10)\n", + "# create an isolation forest model\n", + "iforest = IsolationForest(n_estimators=10, max_samples=64 ,random_state=0)\n", + "iforest.fit(X)\n", + "#Select a single sample from X at random\n", + "x=X[np.random.randint(0,X.shape[0]),:]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "fi,time=local_diffi(iforest,x)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.008695840835571289" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "time" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# start time\n", + "start = time.time()\n", + "# initialization \n", + "estimators = iforest.estimators_\n", + "cfi = np.zeros(len(x)).astype('float')\n", + "counter = np.zeros(len(x)).astype('int')\n", + "max_depth = int(np.ceil(np.log2(iforest.max_samples)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## local_diffi_batch" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
IsolationForest(max_samples=64, n_estimators=10, random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "IsolationForest(max_samples=64, n_estimators=10, random_state=0)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create a random dataset\n", + "np.random.seed(0)\n", + "# local_diffi works on a single sample\n", + "X = np.random.randn(100,10)\n", + "# create an isolation forest model\n", + "iforest = IsolationForest(n_estimators=10, max_samples=64 ,random_state=0)\n", + "iforest.fit(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((100, 10), (100, 10))" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fi_ib,ord_idx,exec_time=local_diffi_batch(iforest, X)\n", + "fi_ib.shape,X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.008002281188964844,\n", + " 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"execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exec_time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## compute_local_importances" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# create a random dataset\n", + "np.random.seed(0)\n", + "# local_diffi works on a single sample\n", + "X = np.random.randn(100,10)\n", + "# create an isolation forest model\n", + "iforest = IsolationForest(n_estimators=10, max_samples=64 ,random_state=0)\n", + "iforest.fit(X)\n", + "imp_score_path=os.path.join(os.getcwd(),'imp_scores')\n", + "plt_data_path=os.path.join(os.getcwd(),'plt_data')\n", + "name='test'\n", + "imps,plt_data,path_fi,path_plt_data=compute_local_importances(iforest,X,name,imp_score_path,plt_data_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Importances': array([0.00302778, 0.00578659, 0.0061828 , 0.00781217, 0.00918624,\n", + " 0.00938135, 0.00954048, 0.00968045, 0.01192269, 0.01221124]),\n", + " 'feat_order': array([3, 0, 7, 1, 6, 9, 8, 2, 4, 5], dtype=int64),\n", + " 'std': array([0.0123734 , 0.01245985, 0.01632168, 0.03286729, 0.0154525 ,\n", + " 0.01837945, 0.02232898, 0.02532521, 0.01537595, 0.03801821])}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pickle.load(open(path_plt_data,'rb'))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "plt_data_keys=list(plt_data.keys())\n", + "imp,feat_ord,std=plt_data[plt_data_keys[0]],plt_data[plt_data_keys[1]],plt_data[plt_data_keys[2]]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "imp_score_path=os.path.join(os.getcwd(),'imp_scores')\n", + "plt_data_path=os.path.join(os.getcwd(),'plt_data')\n", + "name='test_global'" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "nruns=np.random.randint(1,10)\n", + "fi,plt_data,path_fi,path_plt_data=compute_global_importances(iforest,X,nruns,name,pwd_imp_score=imp_score_path,pwd_plt_data=plt_data_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "plt_data_keys=list(plt_data.keys())\n", + "imp,feat_ord,std=plt_data[plt_data_keys[0]],plt_data[plt_data_keys[1]],plt_data[plt_data_keys[2]]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((10,), (100, 10))" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "std.shape,X.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## plt_importances_bars" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "pwd=os.path.join(os.getcwd(),'images')\n", + "imps_path=os.path.join(os.getcwd(),'imp_scores','imp_score_LFI_ionosphere.pkl')\n", + "fig,ax,bars=plt_importances_bars(imps_path,name='bar_plot_test',pwd=pwd)\n", + "#fig,ax,bars=plt_importances_bars(imps_path,name='bar_plot_test',pwd=pwd)\n", + "#plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with open(imps_path, 'rb') as f:\n", + " importances = pickle.load(f)\n", + "type(importances)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "importances.shape[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Rank'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax.get_xlabel()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Percentage count'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax.get_ylabel()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "x_tick_labels = [tick.get_text() for tick in ax.get_xticklabels()]\n", + "y_tick_labels = [tick.get_text() for tick in ax.get_yticklabels()]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['$1^{st}$', '$2^{nd}$', '$3^{rd}$', '$4^{th}$', '$5^{th}$', '$6^{th}$']" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_tick_labels" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['10%', '20%', '30%', '40%', '50%', '60%', '70%', '80%', '90%', '100%']" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_tick_labels" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "''" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "legend = ax.legend()\n", + "title = legend.get_title().get_text()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig1,ax1,bars1=plt_importances_bars(imps,name='bar_plot_test',dim=imps.shape[1],pwd=pwd,f=9)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "x_tick_labels1 = [tick.get_text() for tick in ax1.get_xticklabels()]\n", + "y_tick_labels1 = [tick.get_text() for tick in ax1.get_yticklabels()]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['10%', '20%', '30%', '40%', '50%', '60%', '70%', '80%', '90%', '100%']" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_tick_labels1" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(9, 9)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bars.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\n", + "from PIL import Image\n", + "import numpy as np\n", + "\n", + "# Save figure1 as an image\n", + "canvas = FigureCanvas(fig)\n", + "canvas.draw()\n", + "image1 = Image.frombytes('RGB', canvas.get_width_height(), canvas.tostring_rgb())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pdf2image in c:\\users\\lemeda98\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (1.16.3)\n", + "Requirement already satisfied: pillow in c:\\users\\lemeda98\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from pdf2image) (9.3.0)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 23.2.1 -> 23.3\n", + "[notice] To update, run: C:\\Users\\lemeda98\\AppData\\Local\\Microsoft\\WindowsApps\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\python.exe -m pip install --upgrade pip\n" + ] + } + ], + "source": [ + "! pip install pdf2image" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from pdf2image import convert_from_path\n", + "from PIL import Image\n", + "\n", + "# Specify the path to the PDF file\n", + "pdf_file = os.path.join(os.getcwd(),'images','GFI_glass_synt.pdf')\n", + "\n", + "# Convert the PDF to a list of PIL Image objects\n", + "images = convert_from_path(pdf_file)\n", + "\n", + "# Save each image as a PNG file\n", + "for i, img in enumerate(images):\n", + " img.save(f'GFI_glass_synt{i}.png', 'PNG')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PIL.PpmImagePlugin.PpmImageFile" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(images[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting opencv-python\n", + " Downloading opencv_python-4.8.1.78-cp37-abi3-win_amd64.whl.metadata (20 kB)\n", + "Requirement already satisfied: numpy>=1.21.2 in c:\\users\\lemeda98\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from opencv-python) (1.23.5)\n", + "Downloading opencv_python-4.8.1.78-cp37-abi3-win_amd64.whl (38.1 MB)\n", + " 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--------------------------------------- 38.1/38.1 MB 2.7 MB/s eta 0:00:01\n", + " --------------------------------------- 38.1/38.1 MB 2.7 MB/s eta 0:00:01\n", + " ---------------------------------------- 38.1/38.1 MB 1.3 MB/s eta 0:00:00\n", + "Installing collected packages: opencv-python\n", + "Successfully installed opencv-python-4.8.1.78\n" + ] + } + ], + "source": [ + "! pip install opencv-python\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figures are not visually similar.\n" + ] + } + ], + "source": [ + "import cv2\n", + "import numpy as np\n", + "\n", + "# Load the images\n", + "image1 = cv2.imread(os.path.join(os.getcwd(),'images','GFI_glass_synt_test.png'))\n", + "image2 = cv2.imread(os.path.join(os.getcwd(),'images','GFI_glass_synt_expected.png'))\n", + "\n", + "# Resize the larger image to match the shape of the smaller image\n", + "if image1.shape[0] * image1.shape[1] < image2.shape[0] * image2.shape[1]:\n", + " image2 = cv2.resize(image2, (image1.shape[1], image1.shape[0]))\n", + "else:\n", + " image1 = cv2.resize(image1, (image2.shape[1], image2.shape[0]))\n", + "\n", + "# Convert images to NumPy arrays for comparison\n", + "array1 = np.array(image1)\n", + "array2 = np.array(image2)\n", + "\n", + "# Compare the images for visual similarity (e.g., using mean squared error)\n", + "mse = np.mean((array1 - array2) ** 2)\n", + "\n", + "# Define a threshold for similarity\n", + "threshold = 0.1 # Adjust as needed\n", + "\n", + "if mse < threshold:\n", + " print(\"Figures are visually similar.\")\n", + "else:\n", + " print(\"Figures are not visually similar.\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## plt_feat_bar_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt_data_global_path=os.path.join(os.getcwd(),'plt_data','plt_data_GFI_glass.pkl')\n", + "plt_data_local_path=os.path.join(os.getcwd(),'plt_data','plt_data_LFI_glass.pkl')\n", + "\n", + "name_global='test_GFI_Glass'\n", + "name_local='test_LFI_Glass'\n", + "\n", + "plot_path=os.path.join(os.getcwd(),'images')\n", + "\n", + "ax1,ax2=plt_feat_bar_plot(plt_data_global_path,name_global,pwd=plot_path,is_local=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['2', '8', '3', '5', '4', '0', '6', '1', '7']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_tick_labels = [tick.get_text() for tick in ax1.get_xticklabels()]\n", + "y_tick_labels = [tick.get_text() for tick in ax1.get_yticklabels()]\n", + "y_tick_labels" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_tick_labels2 = [tick.get_text() for tick in ax2.get_yticklabels()]\n", + "len(y_tick_labels2)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, True, False, False, False, False, True, False, True])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yticks=np.array(y_tick_labels).astype('float')\n", + "yticks>5" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all(np.array(y_tick_labels).astype('float')>=np.array(y_tick_labels).astype('float'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## plot_importance_map" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "name='Glass'\n", + "# create an isolation forest model\n", + "iforest = IsolationForest(n_estimators=10, max_samples=64, random_state=0)\n", + "iforest.fit(X_tr)\n", + "plot_path=os.path.join(os.getcwd(),'tests','test_plots')\n", + "\n", + "_,_=plot_importance_map(name,iforest,X_tr,y_tr,30,pwd=plot_path)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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setR33cIdt7wPgKf/wE+dn6IlSZIkSZIkSZI0FMmwC5AkSZIkSZIkSZIkSdKpm8gH07m+3bHPRpNBCnu+CERO3CF7Yf9D3PHZPyP51u8jve5ZhKwJQMibpE96Nsm3vIg7PvtnLOzfd97qliRJkiRJkiRJ0vlnIFuSJEmSJEmSJEmSJGmDGMkgSwJVjCw9QVdnnVyTCoDZ4sRhbICvfeGThLxFcvXTT7g+ueYZhLzF177w52tSoyRJkiRJkiRJktYHA9mSJEmSJEmSJEmSJEkbxGQ+CA8vFGB/7LMRVztkzz1BILs7P0MY3braGfuxQtYkjE7TnZ9ZkyolSZIkSZLWs26vz2+9+2a6vf6wS5EkSVpzBrIlSZIkSZIkSZIkSZI2gCxAOxuEh+f6xrHPRk5NQqQmsFg9/natiS3EpYPEsnfC9bHoERcP0prYskaVSpIkSZIkrV8f+dRn+c/veBcf/Ytbhl2KJEnSmjOQLUmSJEmSJEmSJEmStAFM5IEALJeR0jz2WWmtdMfukQGP3yH7qqc9i1h0qe/63AnX13fdQix7XPW0Z69FmZIkSZIkSevaRz752ZXpZ4ZciSRJ0tozkC1JkiRJkiRJkiRJkrQBTDQG0/nCNPbZaq4GstMn3G58+8Xs/bbvpv7SH1Pd/uernbJj0aO6/c+p/+Z97P2272Z8+641r1mSJEmSJGk9We50+PTn/5rLxmr+4i//muVOZ9glSZIkrals2AVIkiRJkiRJkiRJkiTpiY1mkIZAWUeWymFXs9FFGlTA4Q7ZT+yfvvwNANzx2ZuJt36QMDpNXDxILHvs/bbvXl0vSZIkSZK0md19/wP8yM+9hV5vMGBdvyjo9gt+5dk9XvaRhGd/36to5DkAzWaT3/qVf8+Vuy8dZsmSJEnnlIFsSZIkSZIkSZIkSZKkdW48DwDMF0MuZBPIqEmI1AQKkpNun2QZT/+Bn+LJ3/VSvvaFP6c7P0N7Yporn/YsO2NLkiRJG0AC5If/9I/DrESSNrZt01sYHxvlH+75OtOtyIv3Flx+bc23X1rx1md0uXf+EW6+I+dQN/DUb76O7dNbhl2yVoSVqR+DOlOJR48kAQayJUmSJEmSJEmSJEmS1r1WOpgul174draOXIAajrp3cuPbd/GP/sW/XJOaJEmSJK2dyQYkAcoaevWwq5GkjWtibJT/8Vu/yn/93T/i7b/7R/zdgZyf/pYlAL7n6pJXfnyU2V7CT/7g9/OGV38/WZYOueKB+x/ax/s/+kkOHJpl2/QU13/ns9h98YU1yN7h7xYLPwd1hpphcPD04skHuJSkzcxAtiRJkiRJkiRJkiRJ0jqWJ5CGQB0ND0iSJEnS6RjPoJFAHWG2sDOoJJ2tLEv56df9ABPjo9zwa7/N/k6grCOHuoHPPxT4tz/5w7z2Zd8z7DIBKMqSf3PjO7jpAx8hyVskY1upFw/yX975h7z0Bd/FDT/7evJs88eqAoPPQoBuNdRStIG1k8EX0x0D2ZIucJv/LwdJkiRJkiRJkiRJkqQNrL3SwaZXGR2QJEnaCNoppGHYVUhKwpH/T80X4H+pJOncue2Or7FnMvDOrzT4g9tyXvUNBbsnArfd8bVhl7bq39z4Dm764McIT30J4eqnQ9YkFD3CXbdw04feB8Bbf/4nh1vkedBcyc9W0c9CnZlApBkGB0+3NpAt6cJmIFuSJEmSJEmSJEmSJGkda62keTp2sJEkSdoQWumRLoSShm+phF497CokafPoFwWf+PQtzC9Fblpu8/Lrn8N7//Tj9IqS2U/fQr/4SRp5PtQa739wHzd94COEp76E9LpnrS4PeZP0Sc+mAm76wM382Ctfwu6Ldw2v0POguTI4id2xdabaYfCHVBEDFY48JenCZiBbkiRJkiRJkiRJkiRpHWuvXjRpCxtJkqSNoFtBYfhTWhfKaABNks61L916G/NLXfZefilv/4//huuuvoJXft8LeP0v/AfuvO9BvnTrbfzTb33KUGt8/8c+SZK3Bp2xTyC55hnEWz/I+z/6Sd74mu8/z9WdP4EjAwU5OInOVCsZHDyd6KhTkmQgW5IkSZIkSZIkSZIkaZ3KAmRJIGKIQJIkaaPo+HebJGmD6fb6/N7NH+DVL34BrWZj2OWc0P0PPcz7P/pJDhyaZdv0FNd/57PYffHOYZd1Qdp75R7e8lM/ysuu/y7arRYA1119BR/6w//Ge9//EfZeuWfIFcKBQ7MkY1sha55wfciahLFpDhyaPb+FnWeHu2OXERzrUWfqcIfsbm0gW5IMZEuSJEmSJEmSJEmSJK1TrZWLJntVxGsmJUmSJEnSWvjIp27hP//G77Drom1c/9zvGHY5xyjKkl+88Te46YMfJclbJGNbqRcP8rb/99289PnfyS/97I+TZ0ZjzqetW6Z4zUtfeNzydqt1wuXDsG16inrxIKHsEU4Qyo5Fj7h4kG3TU+e/uPOodbg7tgMG6QylRPIw+Ga6a4dsScJ3QkmSJEmSJEmSJEmSpHWqlQbA7tiSJEmSJGntfORTnz1mup784o2/wU0f+jjhqS8hfO+vwD//t4Tv+RXCt76Ymz70cX7xxt8Ydolah65/7rOoiy71XZ874fr6rluoix7Xf+ezznNl508A8pXUWLceainawFrJ4ODpxUBNGHI1kjR8BrIlSZIkSZIkSZIkSZLWqfZKg6dOZX9sSZIkSZJ07i13uvzF57/IZWM1n/rLv2a50x12Savue3AfN33wo4Rv/T7S65612uk45E3SJz2b8C0v4qYPfpT7H3p4yJVqvdl9yS5e+oLvIv7NH1Pd/ufEsgcMOmNXt/858W/fx0tf8F3svnjXkCtdO810MC0j1H61qDPUDoNAdrc2gihJANmwC5AkSZIkSZIkSZIkSdDt9fn9m9/Pq158Pa1mY9jlaB1IAjSSlQ7Z5ZCLkSRJkiRJm8Ld9z/AD7/p39PrDYLX/aKk2y/4lWf3eNlHEp714lfTyAdRk2azxW//8lu4cvelQ6n1Ax/7FEneIlz99BOuT655BvHWD/L+j36SN77m5ee5Oq13N/zs6wG46QM3E2/9IGFsmrh4kFj0eOkLvmt1/WbVOtwduxpuHdrI4mqH7E40kC1JYCBbkiRJkiRJkiRJkqR14SOf+iz/+R3vYudF27j+uc8adjlaB9orXWz6daQebimSJEmSJGmT2Da9hfGxUf7hnq8z3Yq8eG/B5dfWfPulFW99Rpd75x/h5jtyDnUDT/3m69i+dcvQaj1waJZkbCusdMZ+rJA1CWPTHDg0e34L04aQZxlv/fmf5Mde+RLe/9FPcuDQLNu3buEFz/2OTd0ZGyAB8pX8bM9Ats5QTiQjEoGegWxJAgxkS5IkSZIkSZIkSZJ03oWyOG7ZR//806vTFz7728583/XGbKVcpGsTOd678NU12e9ae4CraKWD7tidjflPKkmStMkEkhCGXYSkNRTzEwc+tcEkGzMi8XDrkvP3ZKPw63/4J/zub/8mv/fbv8nfHcj56W9ZAuB7ri555cdHme0l/OCP/jivft2PsphlLJ6/6o7R3HkF9dInCGWPcIJQdix61IuHaO68gn2jV5zSPnctfO1cl6nHk6xhgLM+9e8S9+zYzk+88iXHLqw21xduBfGY+6MZxAj9+sINZNcxnnyjM1CtzW7XpdGkJgKdOqGMj/9/oaq+gH4okjaUtXh3cngKSZIkSZIkSZIkSZKGbLnT5S/+6otcNlbzqc//Ncud7rBL0jrQWumQ3b2QrvKTJEmSJElrLssyXvfjb+SN/+rNfP6hwP5OYK4Hjy4HPv9Q4I0/+2Z+6MfeQJYNN+D+3O9+HnW/S33X5064vr7rFmLR5Tv/+fPOc2XS+nb4e8XOBRrG1rnRTgYDH3Tsji1Jqzbm8E+SJEmSJEmSJEmSJG1gd9//ID/8C/+BXq8HQL8o6fZLfuXZPV72kYRnvewHaeSDU/rNZpPf/k//lit3n8dOSRq6ADQPd8j2wklJkiRJkrQG7vjqbeyeDLzzKw3+4LacV31DwWUTgTu/evuwSwPgkssu4/nf+3188H++jypGkmueQciag87Yd91C/Ns/4fnf+31cfOllwy5VWjfSAI2V/Gx3czUC13kVaYeVQHZtIFuSDjOQLUmSJEmSJEmSJEnSebZteorx0VH+4Z4HmG5FXry34PJra7790oq3PqPLvfOPcPMdOYe6gad+07Vs37pl2CXrPGulg1B2UUdskC1JkiRJks61oujzmU9+gsWlyHuX21z/ou/lPR/4E/pFyewnP05R3ECeN4ZdJj/3C/8WgA/+yc3Er3yQMLqVevEgsejy/O/9vtX1kgYOd8fu1VAPtxRtYM0QSQLUEXoxDLscSVo3DGRLkiRJkiRJkiRJknSeTYyNcvNv3Miv//57efvv38TfHcj56W9ZAuB7ri555cdHme0l/ORrXsYbX/lSsiwdcsU631or3bG7dseWJEmSJElr4NYv/y2LS8tceeUV3HDjr3P13mv5vpd9P7/4r97IPffcy61f/lu+9R8/bdhlkuU5v/CWG3jVa1/HR//0Qxw6eJCt27bx3O/+F3bGHqL7H3qY93/sUxyYmWXblimuf+53sPvincMuS0B75atku2PrbKx2x44Jg6FDJUlgIFuSJEmSJEmSJEmSpKHIspSf+cF/yeTYKP/h7e9ifydQ1pFD3cDnHwr82zf+ID/4kuuHXea6lCdQbPL2LqsXTtoeW5IkSZIkrYErr7qGn3rTL3D9976YVrsNwNV7r+X3/8f7ef+f3MyVV10z5AqPdfGll/GaH/6xYZdxwSvKkl98229y0wc/RpK3SMYGHcvf9q7/zkuf/1x+6Wd+jDwzqjQsaRh8dxqBjgM96iy0k5VAdp0MuRJJWl/8K0eSJEmSJEmSJEmSpCH6+zvvZs9k4J1fafAHt+W86hsKdk8Ebrvz7mGXti5taQzCyv0aZvqwGfPKEWiudMj2wklJkiRJkrQWtkxP89J/+crjlrfa7RMul4BBGPtDnyA89SWEq58OWZNQ9Ah33cJNH3ofAL/8pjcOucoL1+FBHvv14DtG6UwEIq3k6A7ZkqTDfFeUJEmSJEmSJEmSJGlI+kXBJz7zl9w3F7npzhYvf8F38t47W9w/H/n4Z/6SflEMu8R1pZ0euaiwkcD2JjQ34ZUPdZKTBKhi3PSdwCVJkiRJkrQx3PfgPm764McI3/p9pNc9i5A1AQh5k/RJzyZ8y4u46YMf4/6HHh5ypReu1krbzk453Dq0sTVDJABlDBQxDLscSVpXNuFpSUmSJEmSJEmSJEmSNoYvfeV25pe77L38Ej70O/+Vt/7cG/jQ7/xXrtlzCfNLHb70lduHXeK6kQSYzAfzSyUU9WDZ1iZMZMOt7Vyr0sHFrF27Y0uSJEmSJGmd+MDH/4Ikb5Fc/fQTrk+ueQZJ3uL9H/vUea5MAFmAPAw6Y/u9os7GSDI4gAbdsQ1kS9LRNtkpSUmSJEmSJEmSJEmSNo69V+zhLT/xOl7+/OfSbrUAuO6qy/nw7/5X3vPBj7H3ij1DrnD92JIPAthFDXMrjcMncxjNYCyHRgozfajicOs8F+q0AUCn3AQvRpIkSZIkSZvCgZlZkrGtsNIZ+7FC1iSMTXNgZvb8FiYAWulg2qsGoWzpTLVDDUCntg+sJD2WgWxJkiRJkiRJkiRpnYgE7hm9mGZd0KgKmnWfRl2Q1wVe8iBtTlu3TPLaF7/guOXtVuuEyy9Uoxk0U4hxELo+bK4YXGA41YBGAtub8GgX6uGVetYiUK0Esu1kI0mSJEmSNpper8fNf/SHvPj7f4Bm88TBXW1M27ZMUS8eJJQ9wglC2bHoERcPsm3L1Pkvbki6vT6/98cf4NXf9wJazcYx6+5/6GHe/7FPcWBmlm1bprj+ud/B7ot3rkkdaYCRlYRYx+8UT8tIqJhKy2P6QD9RT+jNHnYPRNKVH4CBbEk6noFsSZIkSZIkSZIkacjSuiKtK6okZSEfZeEx60OEvC4GQe2VkHazOjxfkpzl5R9pHEQXyyQj8sQXmpyuECtiyKiTnKTyKiBJp6+dDjphA8yX8Nim0d0a9vdgWwPSZBDc3tgXHQZiGFzo1t/IyXJJkiRJknRB+tQnPsY7fvX/4aIdO3nuP3/esMvROfSC5/xfvO1d/51w1+dIr3vWcevru24hFj2uf+53DKG64fjIX3yO//ybv8eui7Zx/XMGr7soS37xbb/JTR/8GEneIhnbSr14kLe967/z0uc/l1/6mR8jz85NnCsCVdZme3NwbqeKgwEsdWpGQsX2rDit82KbPZB9WLdOqM7pGUNJ2hwMZEuSJEmSJEmSJElDlhC55tCddLMWZXuMfpLTS3L6aYN+klOHQD/N6ac5MHLsY2PkqsWv0656Z/z8WSzIYkkZMrppi3bVPctXdNS+yy79fIwyb5Odw/1KujC0Utiy0lhmqRzcTqSKUERIz19p58WFcnGfJEmSJEnaPD71iY8C8MlPfNRA9iaz55JdvPT5z+WmD/0xVYwk1zyDkDWJRW8Qxv6b9/HS5z93zbpAr0cf+dRnV6a3rAayf/Ftv8lNH/oE4akvIVz9dMiahKJHuOsWbvrQ+wD45Te98ayfuw4JRXOCOm0QgF4Nc32/UzxVR4exF+uUhfrxv10++mdaxxMv32z60TC2JJ2IgWxJkiRJkiRJkiRpHUhjzWixTJYe27ogAmVI6SWNQSg7yVfne0mDOgQONSa4pLP/jJ87AK2qw2I2TicdObeB7KIzCGSnTSKBsKkvT5F0LrUSmF7pjL1cwlwx3HokSZIkSZL0xDrLy/zV5z7DZWM1f3XLZ+gsL9MeGTn5A7Vh/NLP/BgAN33wZuKtHySMTRMXDxKL3mr35wvFcqfLX/zVF7lsrOZTn/8iy50u+w/NcNMHP0Z46kuO6SIe8ibpk55NxeBn9+OvePEZB9cHXbFbFI1xCAFiZK4cfIeqU9N+TBj7QJXBKXaDrjzNJUkXNAPZkiRJkiRJkiRJ0joWgDxW5FUHqs4x6+azUe4du5i5fJyLO/tP8VKRE2uXg0B2N20ROdXLTk4uiSVJXVAnOWXWJi+Xz9GeJW1mzWSlM3aATgmzhrElSZIkSZLWnfvvvYc3/+SP0e8OvrvuFwXdXsGvPKvHyz6S8JLn/TMa+WDEvUarzVt/7TfZffkVwyxZZynPMn75TW/kx1/xYt7/sU9xYGaW7dNbeMFz/q9N3xn77vsf4Id//gZ6vcGgtv2ipNsv+ZVnD473Z730tfR6PbIkUt/xKeLF30iY2HHMPpJrnkG89YO8/2Of4o2vftlp1xBDQn+lKzZAUhXkvXmWgwMfnKp2qLjoDMPYkiQZyJYkSZIkSZIkSZI2qLFyiTTWlEnKUtpm7DGB7dPRrHsksaYKKf2kQbPun7M687JDr5FT5gayJZ1cI4HpxqC5S7eCGcPYkiRJkiRJ69L01m2MjY3zd1+7m+lW5MV7Cy7fW/Ptl1a89Rld7p1/iJvvyDnUDTzlKU9hetv2YZesc2T3xTvPKFC8kW2b3sL46Aj/cM/Xjxzv1x59vD/CzXfkFFUgbYxAa+K4fYSsSRib5sDM7Gk99+Gu2GVjnLjSFTsvlkiL5UGU2DzxKTk6jL1UJ4axJUmnLRl2AZIkSZIkSZIkSZLOTAJMFIsAzDXGzmpfAWhVg64O3bR9lpUdKy0HQfEqaVCH9JzuW9LmkgfYuhLG7lVw6NyNDSFJkiRJkqRzbGx8nN/8/ffw2h/5cWZ7CX93IOd7ri4B+J6rS/7uQM5sL+EHf/T1/Obvv4exsbP7HlsapomxUW7+zf+Hn3jNyx/3eJ/pBUKSwbN+gtA4/lxLLHrUiwfZtmXqlJ83hoSiOUnRnCCGQFIVNDuHyA6HsXVKHhvG3l/lGMaWJJ0uA9mSJEmSJEmSJEnSBjbZHwSy5/Nx4lnuq73SYbtzjgPZSaxJqx4AZXZu9y1p88gCbG0Owth9w9iSJEmSJEkbQpZlvO7H38gb/9Wb+fxDgf2dwFwPHl0OfP6hwBt/9s380I+9gSzLhl2qdNayLOVnfvBf8otv+METHu+vf+VLIVbEu//qhI+v77qFuuhx/XO/46TPFYEqbdJrT1NlzUFX7P4ije4MSazO8Svb3FqGsSVJ54iBbEmSJEmSJEmSJGkDGyuXSGNNkaQsn2WQulV1CESKJKcI5/biuGylS3aZt886OC5p86lDyrYmJAH6NRzs43uFJEmSJG0i/V6PP/7D36Hf6w27FElr5I6v3sbuycA7v9LgG/5wjHf97waXTQTu/Ortwy5NOuf+/o672fOY4333RGDfI/t56fOfS/zSH1Pd/ufEcvC5F4ve4P7fvI+XPv+57L545+PuOwJ1klE0J+m3JokhIalLu2KfoVao2LESxl42jC1JOksOMSRJkiRJkiRJkiRtYAkwUSwy05hgrjHGaKdzFvuKNKse3bRFN22TlwvnrM6s7NJvRuqQUSc5aV2cs31L2tjqkNBtTZMEKGo42DOMLUmSJEmbzS2f/Di/+/a3se2iHXzHd/6LYZcj6Rwrij6f+eQnWFyKvHe5zfUv+l7e84E/oV+UzH7y4xTFDeR5Y9hlSudEvyj4xGc+x/xy5KblFi9/wXN47599nF5RMfuZv+QLH/zvANz0wZuJt36QMDZNXDxILHq89PnP5Zd+5sdW9xWBmAzOm9RJtjKfQTgSGM76S2TFkhHiM9AK9TFh7EcNY0uSzpKBbEmSJEmSJEmSJGmDm+yvBLLzcXZ19p/VpSTtqkM3bdHJ2oyfw0B2IJKWXcqsTZm1SfsGsiUdCWPXSUJpGFuSJEmSNq3PffJjg+mff8xAtrQJ3frlv2VxaZkrr7yCG278da7eey3f97Lv5xf/1Ru55557ufXLf8u3/uOnDbtM6Zz40lduZ365y97LL+UdN/w81111Ba960b/gx3/xP3HnfQ9y6+138stveiM//ooX8/6PfYoDM7Nsn97CC577LC655BJimtNfCV7H5MSxrhAjoS7I+4skdXmeX+Hm0Aw1F2V9w9iSpHPKQLYkSZIkSZIkSZK0wY2Vy6SxpkhSltMWo1X3jPfVqjrAFvpJk4qElPqc1ZmVndVAdqM/72Uv0gUuElbC2ClJXXGgzzl8x5EkSZIkrRfdzjJf+svPctlYzRf/8jN0O8u02iPDLkvSOXTlVdfwU2/6Ba7/3hfTarcBuHrvtfz+/3g/7/+Tm7nyqmuGXKF07uy9Yg9v+YnX8fIXfCftVguA6666gg//3tt5zwc+yt4r9hBDwiWX7eZHX/da4mr365T+CfYXYk2oS5KqIKlLQl0SYuU5lLOQ1CU7sj4J0IkJ+w1jS5LOEQPZkiRJkiRJkiRJ0gaXEBkvlphtjDPXGGe0c+aB7CxWNOo+/aRBN20zWi2dszrTqkeINTEkVGmTrOqds31L2lgigU77cBi7ptU9RJ1MDLssSZIkSdI58MB99/Iff/b1FL0OAEVR0OsX/Mqze7zsIwk//L3fSZ7nAOTNNv/6xndw6Z7Lh1ixpLO1ZXqal/7LVx63vNVun3D5RhGHXcA6daHHWrdumeS1L7keGBwjMSTEJCcbH+UVP/BK6jSnG5ITPjbU1WroOqkHAWxifcH/TM+lpC5plkvUDMLYj5Y50Z+wJOkcMZAtSZIkSZIkSReIuqpZ3j877DIkSSeRj7TO6HGtdk110ZUcrBpMPjpzwktLWnMHT2lfzSKhP7qd5TLSmj+1xyQT06e0XRozyvYUfXKqsjvo8lCv3GIFtV0fTiY028MuYd1J8uawSzh9VbFmu47l2u37XIgEuuM7qWMglH2aC/sIdQmjw65MkiRJknQubNm6ldHxMf7+nruZbkVevLfg8mtrvv3Sirc+o8u98w9x8x05h7qBb3zyk9mydduwS5aG7uKZ29Zmx2v4HdRGVS/MnnSbGBKq0a2Uo9OQpGtf1IYSIUaoa0KsB2HilSmxJtRHzSdhsO3hbeoaYnzM4wbrj8TfA4QAISGG5Kj5wXR1Pg62O3abZGWbcMw8IVm7cP3hug6rI9SDPtih6hOKHknRJZTdwTTWj/dTPSvf3L3rLPewORRpk9mRHcSQ8EA5xd2dSOTcvg8mwbNYj7Vv7tQGik6IbM9LsuBwF5LO3CNFThlP7b24qs/9+42BbEmSJEmSJEmSJGkTGOnMk9QVZZrTbY7S7p15Z+tWb4GF0e10G2PUBM7lpUpZb4GyPUWdt6jzxwmfx/r4kPbK/OHlHB3ktk+JtGFEAr2xHdRpkxArWgsPD7rASJIkSZI2jdGxcX75t/877/md/8ZNv/Nb/N2BnJ/+lsF3Vd9zdckrPz7KbC/h+3/oR3nZa36ENPOSdknrQyRQjWyhHN8Gie9NJ7YSmE6PnDl43G/o1zK4GtfXeYFQHh287hHKLmGd1bjZFUljNYydl13u7iR2xl5HmqHmG0Y6jKUnHpRAkk7VTJlSxuENmONfiJIkSZIkSZIkSdImkBAZ68wxPzrNwsiWswpkZ1WXtCqo0pxec5x2b/7c1Vn2aCw8Qp23iCElJoMbK/PAoFtFmhDT/NR2GuNjOmxvwIuc6pqsO0ta9oZdibRmItAbu4gqaxFiTXPxEZLaLk2SJEmStBmlWcYrfvgNjI1P8M5f/WX2dwJlHTnUDXz+ocDrfupNvPDlPzDsMiUJGHxvVbcnKce3E9MGMAjYZgv7Sc7iu/ZNKXBU9+mVrtRJ8pgO1SvzWX5Mt+rH62Z9QqudtSNwbGftw+cEVuePXn7U/DFduNfqvEGEUBUb87zEJlIkDWZHd66GsaeWHyGGS4ZdllZMpSVPanfJkkhZBw5VwwtSStr4TrU79loxkC1JkiRJkiRJkiRtEuNLs4NA9ugU22ceOONx/wPQ7s6yOLqd5dbUOQ1kw6BLNr2F45ZHGFyIlaSrYW2Omj+8nKPnw6AbR0wz4gY//Vm1xkn6y+TLBw1ma9OJQG/0IqqsDQzC2GnVH3ZZkiRJkqQ19rV/uJ3dk4F3fqXBH9yW86pvKLhsInD3HbcPuzRJGgSxm6OU4zuIeQsYhGvTxf2ky7P21j2RCIHq1LbNTj7o6uHzAqvB7MNB6pM9sLbLrgZOFMY2IL9eRC5tFFzR7EGAhSrh9uU2vZgMuzBJOmMb+4oESZIkSZIkSZIkSatGuvMkdU2Z5nSbo2fVJXukN8fi6HZ6+RhVyEhjeQ4rPbEAgwutqho4edfcwYVa4bjwNhvwMrkqb1G1xqkbI/QaIyvB7EOkZXfYpUlnLQL9ke1U+QgQaS0+Slo56IAkSZIkbXZF0ecLn/5fLC5F3rvc5rte+ELe8+H/Sb8omfmLT1AU/548bwy7TEkXqDpvU05cRN0YXVlQkS0dJF06uNKVWefD4fMC5md1JookZ3Zkx0oYu8ekYex1IyVybbvL1nxwfvHhfs7Xuk1OYbgFSVrXDGRLkiRJkiRJkiRJm0QSI2OdlS7ZI1NnFcjOqj550aHI23Rak4x1Dp7DSs+NwYVakRBLqNc+ML6Wst489fIhipFpg9naVAZh7K2UjVEg0lx81ONZkiRJki4Qt9/6ZRaXlrn8iiv4uf/0q1xx9V6e9+Lv55d//ie57957uf3WL/Pkb/0nwy5T0gWmThuDIHZrYrAg1qRLM2SLBwjxFDs/Sxq6MsmZHdlJTNKVMPbDJIax14WRpOIb2l3aaU2McFe3ycOFg/BI2hwMZEuSJEmSJEmSJEmbyPjS4UD2FrbPPHhW48yPdGeZy9sst6bWZSB7s0nqkubiowaztWlEoGhPUzbGAWgu7ScrO8MtSpIkSZJ03uy+8mp++Kd/nu984YtotdoAXHH1Xn7t3X/CR//n+9h95dVDrlDSRnP/vkf5n//rsxyYmWPblkle+H9/G7t3XXRKj61DStmapBzbxWC4z0i6PEe28Chhgw/4KV1ojg5jZ5Vh7PVkW1awt90lDdCrA7cvt1mo02GXJUnnjIFsSZIkSZIkSZIkaRMZ6c6T1DVlltNtjNDuL5/xvtq9eebGdlJmTYq0RV4ZBj4fjgtmN48NZjfKJdKqP+wytUnUISUma3MxVNUYpWgOOg01l/eTFWf+fiRJkiRJ2nimtkxz/ctecdzyVqt9wuWS9HiKsuQXf/13eO+ffpIkb5GMTVMvHuJtv3czL/vnz+KXfuK15NmJ4zExJJTNCcrmODEEKEuS7gLZwiMkpd+zShvN4TB2vRLGnlp6xDD2OhCIXN7sc2lz8L46W6Z8tdOiiMmQK5Okc8tAtiRJkiRJkiRJkrSJJDEy1pljfnQLC6NbziqQncSKVn+BbnOC5dYkk0sGss+no4PZ5cg05Uowu9saJy275N1Zg9k6IxGosxZFc4IqH1nz52ssHyTrL63580iSJEmSJGlz+sVf/x3e+2d/QXjqSwhXPx2yJqHoEe66hff+2fsA+OWf+eFjHhMJlM1xytYEMQwCgWnZIz1wP0nROe+vQdLZK5PsqDB2n6nlR0ioh13WBS8PNde1u0xlFQAP9Brc22sQCUOuTJLOPQPZkiRJkiRJkiRJ0iYztjTD/OgWFkem2D7z4Fld7jDSnaXbnKDTmmJi6REvnRiCpC5pLD5KdjiYPTJFlbWoxnYazNZpiRzuWj1JnTZWl4e6XJPnC0Sy7hx5f3FN9i9JkiRJkqTN776HHuG9f/pJwlNfQnrds1aXh7xJ+qRnUwHv/dOb+fGXv5Dduy5a+Q5sjKI1SUxSAJKqIO/MkpQdomFsaUM6Poz9MEk0jD1sed3n/xhdpplEqgh3dFocKPNhlyVJa8ZAtiRJkiRJkiRJkrTJjHbnSWJNkTXo5W1aZ3GBWbO/SFJX1ElKPx+hWZx5x22dncPB7LxcpmhNUjZGV4LZO2gv7CNZo1CtNo+yOU6/vXXlXk3WWyTvzXvsSJIkSZIkad16/5/fQpK3Bp2xTyC55hnEWz/IJ//mNl7+ouuoGiOrHbGTuiLrzJIWS6uDjcbzVLe0HkVguTFJkbWGXcpp66ctCMEw9jrSqjpsKeaYSyKdKuG2TovlOh12WZK0pgxkS5IkSZIkSZIkSZtMEiNZ2aeft6jTFIoz31cAsrJHvzFCHTy9uB4ksaLZOUTenaM7vpMY0tULDKUnEpPB73BaLNNcPkDwgjVJkiRJkqQLRgQIyeC7xJXp0fODaTjZbs67S67+Bl78/ZPwTXtOuD4EuO7J/56nXHsFZXNssKyuyHrzZL0F1t8rkoajChlzI9sp0+awSzljaVUYxl4PYmSsWmS8XATgUJHx1U6LyndcSRcAr5iQJEmSJEmSJEmSpA0oiRXEGoLdBnR6krowjC1JkiRJkrRB1SF9TKA6PCZYPbgfSYC4rsPWp+JJ3/xkmlt2EHZNnvi70FgR902QJoGsv0TaXyIpu8YCpaN0sxEW2tuIISHEmtHe7Ib7jjjESKNcJrHP/XDFyFQ5R7vqALCUjvL3nRJ815V0gTCQLUmSJEmSJEmSJEmSJEmSJEmStI7VSUbRmqTKWqf+oBMELkOsCTFCrAeBzGOmEdZZ2HE6LXjHn36YcOUBkou/8bj19YN/z6P/34f4Lz/5ShrLGytgKq21SGCxNU2nMQ5AXvaY6DxKGqshV6aNKIkVW4pZGnUfgLlskuVsBJgZbmGSdB4ZyJYkSZIkSZIkSZIkSZIkSZLOkakc8mTYVUiCQay0U0GnBGOa2qhiSCiaE1T5CDGEY8LUxwSpY02ghhiPrKv6g2m9cp+44XqY7mrDBB3e+463EL7lRSTXPIOQNYlFj/quW4h/8z5e9t3/F7t3bht2qdK6UiY58+3tlGkDgJHeHKO9mQ33HqD1IatLpotDpLEiknAon6KfNoddliSddwayJUmSJEmSJEmSJEmSJEmSJEmbTgBG0sGtX8NyBYXJbG0QESgbY5TNcWIYjPSRFh3y3hxJfYrdbati7Qo8j37pJ14LwHv/9GbirR8kjE0TFw8Six4v++fPWl0vafDe0c3HWGxNE0NCUldMdPbTqLrDLk0bVKPqMV3MEqipQsqhfJoyMZIo6cLku58kSZIkSZIkSZIkSZIkSZJ0jsxtjuybtCnkCbRTaCRHblUcdM3uVoPQmrTeRKDK2hStSWKSApBUffLuHGnVH25xQ5JnGb/8Mz/Mj7/8hfzP//VZDszMsX16iuuf/Qx277po2OVJ60ZNYLG9lW4+BkBedpjoHCCNpziIwwUoxEiz7lGHhCokVKQQ7CN+2Ei5zGQ5B0A/aTCTT1GHdMhVSdLwGMiWJEmSJEmSJEmSJEmSJEmSzhEDntL60a8HtzQMgtmtdDA/lsFoNghld6pBSFtaD6q0QdGapE4bAIS6Iu/NkRYdjAfC7l0X8ROv+N5hlyGtS0XSYL69nSrNIcJob4aR/pzvHU8kRrYWMzTrYwe7qENCGRKqkFKRDoLaIaUKKXFluulD2zEyUS4wWi0B0EnbzGaTm/91S9JJGMiWJEmSJEmSJEmSJEmSJEmSJG1aVYTFEpZKaKYwkh4JabdTKGqoigZJ3Te4pqGoQ0rRmqTK28CgY2vWWyDrL3hMSnpCEVhujLPYnIYQSOqSic5+GlVv2KWtbzEyXc7RrPvEEKhCQhorQoQk1jRiDZTHP27lTbnmSEi7PiqwfbjL9kYObYdYM1XM0aq7ACxkYyymYxv29UjSuWQgW5IkSZIkSZIkSZIkSZIkSZK06UUGXbG7FeQB2hk0E8gTKJrjhFiTll3Sskuw373Og0igzFrUY6PEECBCViyR9+YJsR52eZLWuZrAfD5JN+QANIplJroHSHz/eGIxMlkt0K66xAAH8yl6SRNiJBDJYkUaK1LqwTRWpHEwn1EDkYSaJNbksXjcp6lJqENy/l7XOXL4tUFgJp+km7aHXZIkrRsGsiVJkiRJkiRJkiRJkiRJkiRJF5QiQlFAwiCYPZ5EYkgo8xHKbIS06pGWHZJYDbtUbUIRqNImZdYCAtQladkl786R1CfoyCpJj9EPOXONqUEn5qpkrDdDuz+PPYxPbqxaYqxcBmAmmxyEsQFCIBIoQkJBfsLHJkBCJFkNaq+EtTn2/tGh7Y2oJuFQYwtF0hh2KZK0rhjIliRJkiRJkiRJkiRJkiRJkiRdkGpgqYRmfYh6JSAbk4wqa1JlTUJdkpVdkqpnyE1nLQJ1klNmbeJK19QQKxpLB0ir3nCLk7QhRGA5HWUxHycCWawYX9pHXveHXdqG0K46TJaLAMxl43ROt/tzCNQE6pBQPk5omxhXQ9sbNZBdJPnq55Qk6QgD2ZIkSZIkSZIkSZIkSZIkSZKkC1oA0qpHUvWIIaPKWlRpk5hkFI0xiCNkZZe07BHYmOEqDVcdUsqsTZ0cjnFEsrJDWvUNY0s6JRUJ840jHZ3bVYfxYh4MY5+SZt1jSzkHwEI2wmI2ujZPdFRoW5K0uRjIliRJkiRJkiRJkiRJkiRJkp5AWdfcO9sZdhmS1tA3JocAVrtgp0AMCWVznKo5TkwyypBT5hlpf5mk6DDoU6q1FmKEWBPqihCrwfywizoNMUkp2luoGiNQ11D1yHpzZJ05wsoxVB7ctzbP3Vlak/1KpyK01yjsCsSiWLN9r1f9fIT5sZ3UVSBUPcaWHqXVmycCfzL5zGGXt+48+PDCMffboeSqdIEekZnY4OudDPDv+3PhvgN+1khan8rq3P9/zUC2JEmSJEmSJEmSJEmSJEmSJEmPEWJN3p0j685R5yOUrQnqrEXVGKVqrF3QUCcXYkWoK6grwkpYm5Vlg3X1kftDCs5HAmV7krI5CWEQIU/7i+TLM4NguSSdgggstbfSaU8TgazqM7G4j6yyK/apalBxRbpAQmQh5jxQjcKGGtpDkrReGMiWJEmSJEmSJEmSJEmSJEmSJOlxBCAtlkmLZeo0p2qOUyf5sMu6cIRADAkkKTGkAMSQEtN00Mr8ZGK9GtoO8XCI+3Bou16bmpOUsjWxWm9SdsmXD5EYoJR0GqokY35sF0XWAqDdm2Nsaf/QBprYiDJqrswWyIh0Ysp91RjRMLYk6QwZyJYkSZIkSZKkdWrfvn3ceOONfOELX6Df7/PkJz+ZN7/5zVxzzTXDLk2SJEmSJEmSJOmClFQFyfKhYZdxwYoAISUmySCUnaQr99NjQtvxcHg7BAgJMSTE5PzHJ0JVkHdmSIpl43+STksvH2VhbCd1SEhizdjSI7T6i8Mua0NJiFyeLtKgpk/CPdU4te/GkqSzYCBbkiRJkiRJktahfr/P6173Oqampvit3/otWq0Wb3/723nlK1/Jhz/8Yaanp4ddoiRJkiRJkiRJknReBYBYEaoKKJ5w27jyiCOh7eSooPbhMPfaBfOSokPaWzD6J+mkIlAnGWXWoshalFmLftYGIK96TCzsI62f+D1PxwpE9qSLjISSksDd5TglybDLkiRtcAayJUmSJEmSJGkd+uIXv8gdd9zBZz7zGXbs2AHAjTfeyNOe9jQ++clP8qIXvWjIFUqSJEmSJEmSJEnr1yAIHQl1CZRQDbceSTqsDskx4esia1GH9LjtRrozjC4fcGCH0xUjl6ZLjIeCmsA91Th9jv/56vxq0ecyZsioh12KpA3sbrbRIx/a8xvIliRJkiRJkqR16JprruGd73znahgbIEkGozXPz88PqyxJkiRJkiRJkiRJknSKIlCmzWMC2GXaOG67AGRVj6zskpdd8qJjV+wz1O7Pk4U+EbivGqMTjc8N2wQd9vIIKXHYpUja4JIhv4/4iSJJkiRJkiRJ69D27dt55jOfecyyd7/73XS7XZ7+9KcPqSpJkiRJkiRJkiRJknQiEaiSfBC6Xg1gN4kn6HGd1gV52V0NYGdlj2BY9aw1+4s0i0UK4IFqlIU4vC6qGtjOAlcw6PS+QJOHmRh2SZI2sD7pUJ/fQLYkSZIkSZIkDcEDDzzAs5/97Mdd//nPf57p6enV+5/4xCd429vexqte9Squvfba81GiJEmSJEmSJEmSJEkbWiQQw/GB6HO172PD1y3qkBy3XRLro4LXXfKyQxLrNanpQpYXy7T7cwDsq9vMxOaQK7rQRS5jhosZ/JscYJS72X7CAQokaaMwkC1JkiRJkiRJQ7Bjxw7+7M/+7HHXT05Ors6/973v5YYbbuD5z38+P/dzP3c+ypMkSZIkSZIkSZK0wUUghuQkt3QwTRJDcjolq12cYzx+PsJKBJrQaAFxZR0r80c9/vARt/LY1a1CWHmWAGFlq8csq7N6cH81aD2YjwRiklKHlDpJqZPsvB/XgUhW9o7pfp3Uhb9daylG8qrLaG8GgF4+yv76+GC8zp8mBXs4xBaWAXiAKR5kCvxNkLTBGciWJEmSJEmSpCHI85yrrrrqpNvdeOONvOtd7+LVr341b3rTmwhrNHK3JEmSJEmSJOncaFKRhHjyDSWtuUigHxNqwz/aBCKBMm1QpE1iSKlDgJBQh4RIMgijHg5ZM5ivx/w80vCENF2zfcd6/XSXzqr+Md2vs6rnp855lFZ92v15sqoHQJG16TQmgcXhFnaBGqPLLubYwjKBwRgNd7OdA4wNuzRJOicMZEuSJEmSJEnSOnU4jP2mN72J17zmNcMuR5IkSZIkSZJ0CiaSkmaohl2GpKOUMaFHQj8m9GJCyaDTqrSe1SGhSFsUWWtl2uT0j9vB51EgEmJ94ltdE2K1cj8Chrj1BMLhXtfh2PnjulkDeXNwNK12t+bY7R6zn8GCI123Dx+P4TFdtkOMUFVH5h8zDbEmqUuSWJHU1crxvVbHtX3lhyXUFe3+PI1y+fASuvko3cbEsceUzoPIFpbZxRzj9FaXztLmAbawRHOItUnSuWUgW5IkSZIkSZLWoS984Qu8613v4hWveAXPe97z2L9//+q6kZERRkdHh1idJEmSJEmSJOnx9GMy7BIkrUiJZKEe3KgZXcln1Sudsw8HtPv4e6vhikCVZBRpeyWA3aRKG8dtl8SKvOySrAao69UAauAE95cXCbHG0KiGIbTX7px2LIo127fWuVjT6i/SKhY5PIBEP2vTbUxQJ8bkzqtY0+zM8BQeoEU5WAQcYIx9TNLh+M8xSdro/KSRJEmSJEmSpHXowx/+MADvfve7efe7333Mute//vW84Q1vGEZZkiRJkiRJkqSTmIu5zUWldSQQaVLTCDXNMJgmRFqhonVUN/sDjNOgJI8lDSoSagOsWjMRKEgpyOiHjP7EHuqQHrddWvXJqy552SWvuqR1eVrHZYzVyTeSpI0gRhrlMu3+/MpAE1AmDTrNyRMOYKG1E+qS1vJBmssHSeqSgpKShEcZ52EmKIwrStrEfIeTJEmSJEmSpHXohhtu4IYbbhh2GZIkSZIkSZIkSRtaJNAlpRvTlcESIjlxNZzdDBUpkTKklKQQmgAkMa4GtHNKcioD2jpjNYGClH7IVkPYR4uhhhjJq94xAexkJXQoSackRgKREGtCrEhiTYj1yvTI/RA33uhBg/oHg0zUIaPTnKBIWxD8dD5fkrJHa3k/zc7saii+Shvcyzj7GacmGXKFkrT2DGRLkiRJkiRJkiRJkiRJkiRJki4QgYJAEZPVbvYpNdcmj66GZYuQUodAl5xuyFcfOeieXZLHigw7D+vxRQLlagA7pTxB9+uwEvpvxJJ0cR9Z1TP0L+k4dYSShIpw4mkMjC8/uhq63swiCd3GOL181CD2+RIjWbFMa3k/jd786t9OZd6mO7qdfnOSRxZnhlujJJ1HBrIlSZIkSZIkSZIkSZIkSZIkSResioQWBa1YABAjgy7GpBQho09GDIFiJbBtalZnIo31atf1BiUp9eqhVFW9odYm6dyIEWoCNYengfiY+4eXVbCyLhx5TAyry6qVW30KHzppXRxbB4EYUmJIqEOyMj1ynzB4no2mShvEYBfm8yJG8t487aX9ZMXy6uJ+c4Lu6DZKQ/GSLlAGsiVJkiRJkiRJkiRJkiRJkiRJWhFg0LmYEmKPCFQxoU+2GsquNmCQTedXRk2+0gE7pyQ93FZ0CCKDDrsnvqWUIVntuCudTEI89hYH03TlfiCSUpNUOUlgdXlCJAlHP/7x85wxDo7bY8LNcTAfCVTxyPJ4dKD56NDzUduslcixoeq1+i0PDN5TUmoyImmIZIfnqbmztfeo8HVqUPZUxUizc4hGd5YQIwRWgurhuHlWjrXD8xCIR80f2X5lWTh8PIQNN5BLiDWNzixp1QcghkCvvYXuyDbqrDXk6iRpuAxkS5IkSZIkSZIkSZIkSZIkSedKXQ4CHZKGLoYEkvSs93M4CJfRh9g/+8Kkc6SOUBIoY0KxMh3cD4OwdWisBq+lc+W4gPPjhU3rk0eWjg5nD/Z9JFT9hGJ90n0P00pc95jXdySUPojtPjbYfqJlhwPXTxReBygNyZ6eGMn7C4ws7CMte8OuZt2qk5TeyFa67a3ENB92OZK0LhjIliRJkiRJkiRJkiRJkiRJks6RxtIhkrI77DIkrYhJTpW3qLMWdd6EYDBVG08dYTmmLMWMTkxXA9en09H6yMACNVmsV7vt5lSry1PWd8hVw3d0N+iT3kJz0M2aQB2P7l59pJPwqYSvHxtqDgESqpVg8/Gh53CCEPRaOqbj91HhaptUr09psczIwj7y/hIwCB13Ry+iShvA4N9wcMis9D8/ap64sv6obY7d/vDjDx9zhx+/8ZR5m15r6pwMbHNYo7/AxNI+QqzO2T4lXXgOTVxBNcSBSAxkS5IkSZIkSZIkSZIkSZIkSedIDGHQlVfS0IVYE+qCrFdAbwGAOmsOwtlZkzprmpjTulRF6KwEsJdjSiemqwHWxzrcRTcLNRmRfGWahUhaLq+ErSsefw/S2gjZiQP+cSW/ejicXROo4+DoTMJjw9UnfpuOsVirsrVJJVWf9uLDNDuzwOBv9u7INrqjFxHPYehYx0urHlML9zHaOTDsUiRtAkncwzCHdTCQLUmSJEmSJEmSJEmSJEmSJJ0jxdj2YZcg6bC6Iil7JGWXtOgS6nLlfm9lg7DaPbvbiDRTCAa0NQSnEsDOqRkNJSNJRYOaLEQy6kFX3sc5bCP9tS9eOk0hDDq2H+lcHXG0AK2VUFe0lh6ltXyAsNK5uteeojO2k3qlK7bWRqgrJpYeZGLpIUIcDNCw1L6IbmN8yJVJ2siqIb93G8iWJEmSJEmSJEmSJEmSJEmSnkA+e4Cr3/pDwy5D0lmqWm3KLdspp7dTTG8nNpqr6/4uS0iKPo25gzRmD5DPHSTtd4dYrTazOs0oJrZQTExTTEzTSUaOC6SG5WXSA/tJDzxKcmA/dWeZHnDoNJ5n5u7Zc1i1dHq2XDm1Zvvuzmy89+d8LF+zfX/r7j9Zs31vVE/ulhya2s6j23ZRpYP43OjyAjsffYCR3vIZ77euTtz5/UL2vEeP/XlG4NGxce6dmqafDX72k90OVx46wFjfgUIknT9bXvhHMHnxOd2ngWxJkiRJkiRJkiRJkiRJkiRJ0qaXdjuk++6nue9+IlCNTayGs+vtF1HnDbrbdtHdtguArLNEPneQxtxB8rmDJFU53BegDevYAPZWyrGJ1d7AAHQKwtLSIIB9cD/Jgf0knTMPDErShSoC8yOTPLhrB/1GC4Bmv8vORx9gfGnOZuxrbK7Z4p7prSw0Bz/7VllwxaGDbF1e8mcvaVMwkC1JkiRJkiRJkiRJkiRJkiRJuqAEIFucJ1ucp3X/18gnRyjGpygmt9Gf3Eo5NknZHqVsj9LZuXtl+7nV7tlZZ5FjE7XSETEJlKOTFJODDtjl6MRxh0vaXaYxf4h87hCdv7+bpNsZTq1AnedUoyNUreZxnbqlY9Q1Sb8g7fVJ+gWhLD1kgBgCMUmISUK9Mo1JQkzT1flkrEEMyYm3O/r+yjbESGtxgfbCPK2lRUL0Q+dklpojPDR9McutUWIdScuCHQceYnrugMfpGutmGfds2cqB0TEA0rpm9+wMuxbmSD12JW0iBrIlSZIkSZIkSZIkSZIkSZIkSRe0ECON+Rka8zOMfv3OlY7G0xSTW+lPbaNsj1KMTVKMTcKlVw27XG1AWWeJfP7Q6i3t91bX9c5DGLvOM8rRkWNvI22q0RHqzGiJzlCMpP2CpN8n6fVJioKk1yftF4TpnLQsScuCtCzIipKkOvMAdwyBKs2ospTueE6dZdR5NphmGVWWr84/dl1cqzhuYBCeDifff0hPv4alLdODx9b1aji7PT9Ha3GBxJDrql7W4OHpXcyOTgGDz/RtB/ex/eDDpLEebnGbXEng3qlpHpycog6BQGTHwjx7ZmZo1NWwy5Okc86/miVJkiRJkiRJkiRJkiRJkiRJOkpSlTRnHqU58ygAVaM5CGdPbqOY3ErVaA65Qq13qwHsuZUAdtE7+YPOUpWmFM0W/VaLfrPN3MQlVCvB6zrPn/CxabdH2umCIU89gZgm1I0GdSMnpimEQNVsUDUbMH7stp3RExxzkZWAdkm2EtROy5K0KCBAnWaroevD8/XK/Zgkq7upi/Ubsg11PbhVNaGuVu9naViZrwixJjm8XX3s/OH7VZbSHZugMzFJlWV0JibpTEzCJZcRYqS1uEh7YY72/DxjIbkgg8dlkvLo1A4OTGwjroTitywcYtfMwyRLy0OubnOLwEONEb7WnmS5GCyb6nS44tABxor+UGuTpLVkIFuSJEmSJEmSJEmSJEmSJEk6R+695CqW2mPDLkMSkNYVY0vzTCzNMbq0cFZhtbTfI93/EK39D53DCqXTVyUpRatFv9lambZX56vHdLoulopj7qe9PunSMtnRt+UO6dIySX3hhTl1dmKSUDVy6mZjNaRdNxpUjQZ1M6e5Y4IqyylXu1enEKDKc6o8p0/7jJ43KSuSbo+kLEmKcjBduaVlMVhfFkfWVYNQ9FoJjwlTP14f7HzsiQdFOLF9RKBotQeB7PEJOhMTlHmDzvg4nfFxuBgebeeM9JYZ7S4x2l1ktLtEtom7E9cEDk5u45GpHVRJCsBYZ4GLDz1Eu98FYPO++uE7lDW5oz3JYtoAoF10ueLQAaY7y2vVi16S1g0D2ZIkSZIkSZIkSZIkSZIkSdo09u3bx4033sgXvvAF+v0+T37yk3nzm9/MNddcc16ev04S6tRLdKX1oE4zZqa2MzO1nRBrRpcXmFicY3xxjkZp90ZtDHWSsDw2wfLEJMvjk/RGRp5w+6woyLtdGr0O/fsPki11yJaWSZc7JJURRZ07oa7Juj3onrj7+5Yrp465H0OgyjLKLKfKcqosG4SzV0LbAQah6qoiqUrSlTB1WpXHLA9Ad6a75q9vvQhAo9uh0e0w+ejDg4B2s0VnYoLO+CTd8Qlo5yw3R1hujrB/cjsArX6Hse4So51FxrqLmyKgHYG50Un2TV9MPxuEgVv9LrsOPcREZ2G4xV0AlpOUO9tT7M8HgylkseaK7jzTDz5KcpLHStJm4bc9kiRJkiRJkiRJkiRJkiRJ2hT6/T6ve93rmJqa4rd+67dotVq8/e1v55WvfCUf/vCHmZ6eXvMaLt13LzEYSZDWg36jycLoJPNjkxR5k8XRSRZHJ2EHNHudQTh7aY6RzuKwS5VW1SHQHR1bDWB3R8eI4dieo2lR0Oh1yXtdGt3uYL7bodHrHtPpeubu2fNcvfT4QoxkRUFWFCffWI8rAI1el8b+LpP7HwWgfcV2FttjLLXGWGqN0subdBttuo02Bya2AdAsuox1Bh20W0V3kG7eQMos5+EtO1luDgalyKqCnYceZnrxkF2Z11gRAve0Jvh6c4xIACKX9pa4sjtPI9YsDbtASTqPDGRLkiRJkiRJkiRJkiRJkiRpU/jiF7/IHXfcwWc+8xl27NgBwI033sjTnvY0PvnJT/KiF71ozWtolIaMpPWiWfQYX5pn16Nfp9dosTA2yfzoJMvtMXrNNvubbfZv3UlalUxVHSY680xski6i2jgi0B0ZZXl8chDCHhsnJscO7JH3eowszDEyP8fIwjyZnzWSjtKoCqYXZ5henAGgSDOWWqMstgYh7W6jRS8f3A5ObB1ytWcniTXbZx9l+9x+0lif/AE6YzXwUGOUr7UnKEIKwNayyzXLs4zV5Rnts1q5SdKZymGoA3EYyJYkSZIkSZIkSZIkSZIkSdKmcM011/DOd75zNYwNkKyE2ubn54dVlqQhC0Cr36V1qMv2Q49QJimLoxPMj02xODpBlWYcak5yaGSSAIz2lpnoLDDZmadV9uy8qXMqAr2syUJrlIXmGIutUbrbjg0VpkXByMI8IwtzjM7Pkfd7wylW0oaUVyVTS3NMLc0BUCbpkYB2e4wizYdc4ZmZWJ5j58zD5NWZhYF16g5mTe5oT7G0cqyM1AV7l2fZVp7Z51EPuC1vck+Wb7Tm7JLWmf+7u8zkEAfkMJAtSZIkSZIkSZIkbWL9rMUIi+dkX0XeotWf9wLUdag/spVQFSR1SVi5JXU1mPfSlgtOJBCTjDrJiGl+ZJo2hl3aeTGRQTuFsoYyQlU3CHVFiNW6ff+KBOo0p0oaF8y/0/BEGvZgkSRpU9u+fTvPfOYzj1n27ne/m263y9Of/vQhVSVpvcnqiqmFGaYWZojAcnuM5W3bmG9P0MmbLDZHWGyO8NDUDsa7S1y9/551+39KbSwRuGPHVSw12scsT6ojAeyR+Tka3Y7HnDalh3t9Pn1ojtmyZCrLeOb0JDubfh+21rK6YnJ5nsllByjSyX2tNcE9rQkA8lhxZXeeS3pLJGe4v6UQ+GRrhL6fbJI2AQPZkiRJkiRJkqTjHEoaPJiODrsMSUAg0o4Vo7FktC4YiSXpsIvShtDsd+nnLR7Zehn9vMm2mYfOeF9Z1aPPCIsj2+jlo0wtPERe2ZFlPUirPmWSUyc5JPmJI4ZDHCH8TAUgVAVp2SUtuyRV32D5Y0TCIGh9OHh99PQkl0UlZf88VXl+FTW0UkgTBp+VKx+YnWwbsHJc1eVg4IJ4ZNCCwf3zF9aOQEyylfB1TpU2Br/Dj9GrPebPtZyKKbo0GXTQKc74EkJJkjRMDzzwAM9+9rMfd/3nP/95pqenV+9/4hOf4G1vexuvetWruPbaa89HiZI2mACMdhbZMldxydwj9NKc+fY4c+0J5ltjLLRGqUNCugG/Y9H6UybZahh7vLvEeG+R8e4ixZ0PGVPTplbGyG99fR8fPzBLkjVJRqepZw7xR/v285xtU/zIZbvIgr8F0npwMG8CsLO/xLWdOfKz/BtoNknpE2jGyD/pd9lWO2CmpDM37L8WDGRLkiRJkiRJko7TDSn709awy5B0AgFoxZLRumTsqJC2QW091s6D95JVlzAzsZ2ZiYtYbo0zFhNa4fQvmphc3EdW9VgYvYgib7N/y1WMdQ4wtrSfxJDsUDWWD5Inc6tB3KNDuTHJiCGBsPEChxGIWZM6a1IwCTGSVj2SYwLam18E6iSnTvNjA9chIyZP8O9aR0KsV7qlr3ROr46axs15sdNCCcsVZGHllkAaIInl4PcBVn+OJ3I4mJ2s4cVgdZJSJw3iCS4uDbEirfokVUFa93mwvGzN6rjQJNRM0GOUgkCkJrBAkwXsviRJ0ka0Y8cO/uzP/uxx109OTq7Ov/e97+WGG27g+c9/Pj/3cz93PsqTtAk0q4Lti4fYujTDly/9xmGXo00qANfsv2f1/tzwSpHOi9/6+j4+cXCe5B+/lOTqpxOyJknRg7tu4RNf+mMAXr/74iFXKeloO4rOWYexjzYeay4yjC1pgzOQLUmSJEmSJEk6zmRdcG3hpR/SelADnZCxmOQshYwiJHRCRifNOHDUdgForwS1R2OxMh0EtTdeFFPnQhIjF808wEh3gYe37aHXaHNbOc7udJmtoc/pNJoIwFjnEO3ePHNjO+k2J1gc2UanOcnk4j5a/cU1ex16Ykd3/D2RGJJBKHsDqtImddaiylrEJKVamS8AYk1a9UiLLknVJWX4o6GfjQjEkFKn+WoAuz7c+fwJXlio60GI+JiuzyWh34FYb+ifyZmq4uDWAw63jL+6ePTIzzjJiEm6EmrPVu8f7jgOGdV5GOEkEEmqgqTqk9Z9kidaYSIAAQAASURBVKpP8pgL2xzu4lyIjNFngt7qACLL5MzRovIvJEmSNqw8z7nqqqtOut2NN97Iu971Ll796lfzpje9iWDHRUmSpKF4uNcfdMb+xy8lve5Zq8tD3iR90rOpgI9/8X/woh3b2Nl0AD1JkrR+GciWJEmSJEmSJB1nNJaMVicOdkkarj4JS0nGYshYXglpHw5qL4eM5TRjP0c63AciI7FitC4Gv9srQe22Qe0Lxlhnjj0P3c7D2/ZQTE5wbzXKfJKzO1kmC6cX90vrkun5B+g2xpgb20WV5hya3E2rN882AqnxwXUnxJpwDrsXnE9JXUKxtNrV+HAgu86axJBSZW2qrA1ACJBW/UFIu+wTYrluw8iD4G9OnTSo08aRDtiPEw4JsR4Ed48OXh+ePs7vXNyg/+ZrKTDoQJ1U1WpQ+7BBWDs5te7jZ1tHrAfh63r9HqObRZOSKbrkK//gfVJmadH3UhlJki4Ih8PYb3rTm3jNa14z7HIkSZIuaJ8+NEeSNUmufvoJ1yfXPAO+/H4+fWiOl+zafp6rkyRJOnWeZZIkSZIkSZIkSdpAGtQ06j5b6K8GyiJHgtpLIV+ZDm5lSAbz6bGnhRIirVgR4sYL0IajpoP5+Jhl8Zj1h1/jkW3iCR+7pgH1GEmJZETSWJMRyY6apkfdXwt5VXDpI3fR3zXNQ3WbQ3WDpZhxRbrIWKhOvoPHaPUXaRy6i4XRi1hqb6XbnGAfI0zSZYyeIUOdU6tdwPuL5P3FlYB2/piAdkaZtijTFjQGwdtBQHsQ0g6xOu/HZQTqJKNO8kGgfGVah8Ptl48NTYcIoS5I6mIlgD2YDqP2C83gs6KGqk9Kf9jlrBORFiVtSppszPB4uvI7VpEwT5Mlcp6w5fwpyGPBdDlHIxbnoEJJF6r5dIy5dHzYZUib2he+8AXe9a538YpXvILnPe957N+/f3XdyMgIo6OjQ6xOkiTpwjNbliSj04SsecL1IWuSjG5htuyd58okSZJOj4FsSZIkSZIkSZKkDS4ATWqadZ/pxwS1eyQsHe6kfVRguyKwHLKzzSVpDWQhDILaR9/i4fn6uHUtaiZOISwXgF1pl/Gk4J5qjF5M+IdygovTDjtDl8dpzvu4EiKTS48w0p1ldvxiytYoM7RZosE0yzQe24JWOkcGAe2CpF+Q9xcGAe3mGFXaoEqb1GmDGFLKrE250kE7iRVJ1VsNabM6+MFRB344ejiHo54twGOHdTg8iY/ZNobD3a8z6iTj8d5kk1iRlL0joeuVILZvyRqmsBrCLmhRkqzRICHnSySwSIN5mo/5XT19IUamqnm2VAuP25lekk5VGv07WVprH/7whwF497vfzbvf/e5j1r3+9a/nDW94wzDKkiRJumBNZRn1zCGSsnfCUHYsetRLh5gamRxCdZIkSafOQLYkSZIkSZIkSdImFYAWNa26x1Z6xwS1uyGlu9qldeOJHAlCxhPcB4gr4crDQcrDy+vH3I9HbbuWSgJlSKgeMz16+aC+QJ9wbI/Wk+TIdsUeT2KRU/kXHQsVT0rnub8e4VDd4MGqzULIuTxdpBFO/6eQVz22zd5Db3yEOdr0SXmYccbpMUlnbTuPSwx+PZK6IK0LKJZWOlM3qLKVgHaSU4eUOhuhzEbOc201SV2udLwuBwHsuhy8A1V22NXwJdSrnbBblMeEjUsSOuR0ydiIfdrrwW/gWe+nXXfYXs6SxxKA5aTFbDp+1iFvSReucgP/P0zaKG644QZuuOGGYZchSZKkFc+cnuSP9u2Huz5Het2zjltf33ULddnnmdMGsiVJ0vpmIFuSJEmSJEmSJOkCE4B2rGjbmW1dqYGKQBhpUxCoCBSsBLcJRy1bma6EuGfJ2BeaLJDxlDjPyErk/IlkIXJFssREKLi/GmE+ZtxWTXJ5ssRUcvoh0QCM02eEghlGWCZngSbL5GyhwwgGT3X+BCCt+6T9PrBIJFClOXXapEqbVEm+st3RwzIcnhwdsTy8/Oj+2Mdue+Q5j2x7OHSd1AUh1kY2te6EWDNKnzYFTarjQtjL5HTIKUg46Yggm1gaS7aVs4zVHWAQoDyQTrGUtCFcuD8XSZIkSZKk07Wz2eA526b4xBdvpoqR5JpnELLmoDP2XbcQv/THPGfbFDubjWGXKkmS9IQMZEuSJEmSJEmSJEnrQAIkRHJq2qfygJX83CFyvhLGWCTlC2GKb4oLbD+FAHQIsC30GQsld1djLMeUu6oxLopdLk06JGeQNUuJbGOJDhkzjFCScIBR2hRsYZlszfuQS8cLRLKqD1UfWCByIUdMdaFKYkVWF+R1n6wuaR81eEeflA45HTLKCzyEDUCMTNaLTJdzJEQiMJeOcyidIIaz77gtSZIkSZJ0IfqRy3YB8PEv/g/48vtJRrdQLx2iLvs8Z9vU6npJkqT1zEC2JEmSJEmSJEmStIFNU/C0OMetYZw5Mr4cJrgyLnMlnVOK1LVCzXXpPA/WbR6pWzxat1iMOVeki7TDybttn0ibkibzzNNigRYdcrpMMkWHMXoXetRPQ+bxpwtCjCSxJq/75HWfNFbHrO6R0SGjQ06FIePDWnWPbeUMzTgY2KSbNNifbqGf2J1JkiRJkiTpbGQh8PrdF/OiHdv49KE5ZsseW0Ym+fbpSTtjS5KkDcNAtiRJkiRJkiTpOMt1ZK46+XaS1l4ARhMYSSANxgh1Yi1qnhrnuINRvh5a3B1GmCPnm+MC+Sl0pU4CXJZ2GA8l99ajLMeU28sJLkuX2Rb6nMmhlwBTdBmhzwwj9MiYoc0SDbawTBM/aCTpnIqRNFYrIeyC5DEh7DLJKJIGRdLgYNf34KMlsWJrNcdEtQRAFRIOppMsJKOc0YegJEmSJEmSTmhns8FLdm0fdhmSJElnxEC2JEmSJEmSJOk4D/Xhr5ZOHuCTdP6EAKNJZCyBiTQwlsB4CuMJjKWDzgK6sCXAdSwxGUtuC6McJOevwhRPjgtMUp7SPqaSgm8Ic9xbjTIfc+6rRpkJDfakyzTPsFt2g5qLWGSJBrO06ZPyCOO0KEmIR91qwjH3j9wCkYCdjSXpODGSxpK8LsjrPkk89r26SHKKpEGZ5MRwdCdsA9kAxEjozLG7eJh05Wc3n45yMJ2kDumQi5MkSZIkSZIkSdJ6YiBbkiRJkiRJki4Q83Nd3vFfPntK2/a3jtPd48jk0rqQBOpmRkySJ96sX1It9Qid/vG36syCtBqOi1vn4BTe5Cj8/54MY23uqCP8zT/APQ/xf/+T20/p4REorrqKznXXMZ+kPFBVtL96O827737cUPRF37zz5PttNCmv/UdUF+9h+dRfDUmaQqwJZQFlccz0RMuIG29QkVCVJItzhH73pNu2t06ch4o2ltbWyWGXcNriGr4392YX1mzfa+mq/ANrst9D6RN/hq5X37h04veDCFRjUxRbdlJs2UlstI6srEvy2f1kM4+Qz+0n1CcOXncPzq9BxRtLrznCgZ1X0G2PM77cJ1taYOKOv6cxPzvs0iRp1ei3/D6M7Bp2GZIkSZIkSZIkDGRLkiRJkiRJkk6gcXCBxsGNGeSRNqMIxEZG3W5QtXLqdoO61Vi9H7OUupFR5ylMjRy/g6IidPokjw1q94rBzjeiGFd+MPHY+wymF3wn5bkl+F9/Df/kG+DibfDU62DrBHVYIDmFsHIAWl/7GvnDD7P8lH9EuXUrnW/8JvoXX8Lo332ZdOHMPiNCv0f+lS+Q3ncHcXSCmDcgzyFrELMc8nywLMuJ2eF1+cqDE2LehLy5YQ/bUxF6HZKFGZL5QyTzMyQLs48bqJR04XiiEHaoSrLZR8hnHiGbO0CIDsTyROok4dC2y5if3kUEkrpm/Gu3M/Lg/YQNOKCHJEmSJEmSJEmSzg8D2ZIkSZIkSZIkSetcAEK/JOmXZHPHroswCGS3GyzlGbHdILZzYnsQ2KaRQZ4S8zbVRHsY5Q/Pakib4wLc4XED3WtXSygq6BeEfknolSvTAlbuU9XnNkhelPC5W+FJe+Abr4IrLuauZs3l99xDoyhOaRfp0hJjf/k5+nv20PmGb6TasoX5b38mrbvupHXnnYT6zEJ/yfwMzM+c0rYRyMbHVoLaGTEdhLRjvhLiznJimhNXwtsxy9mIkfyYNwYh9Wabqtmm2nbxyopIsjS/EtKeIVk4RGQjvkLp7A0+8xrUWYOYpqTdZZLq1N7PNqIIVKNTFNNPEMI+9DDZ/EFD2KcgAkvjWzm443LKlcE+xhYOsvWR+1h64IHhFidJkiRJkiRJkqR1z0C2JEmSJEmSJEnSBhaAUFYkCx3S8vgwVkwTYitfCWo/5tbMN3eqM4SjXt+xL3QY/S9P+pxVJPQLDtQ1Sb8kXQnhp4+ZD6cb3L79Pji0AP/nN9KZGueOa69l9333MXGKXa4D0LzvPvJHHmH5m59MsXMn3b3X0r/4Ykb/9stks6cWrD5TAQh1Reh1oLemTzV0MUmpx6eoJ6apJ7ZQj28hNtvUY5PUY5Ow63IAijSQLc+RLg1u2fIcSbHJfzjatAYh65yYN6laI9R5YyV03RwEr/NBALvOmisDLhwrKfuknQXSzgLZyjTpdzbsx1sEqvYEvakddEe2niCE/Sj5zMN2wj5NRd7kwM4rWB6dAiDvd9n2yL2MLM0OtS5JkiRJkiRJkiRtHAayJUmSJEmSJEmSNrFQ1YSlHixtrrBmhJWM9UroOqxE78KJ78dT3XatBIiNbHBr5nDUfGxkkCWQBmK7QT85SR11JC2OhLSzTp+xhw6RnCCQv+qRQ/CJv6b9wv+TzsgI91x5FTse3seORx455dBi0u0y+tf/H8XFF7P8zd9MPTbOwrc9g+Y999C+/fZT/lHo8YW6Ip07SDp3cHVZ3WgdCWhPbKEemyI2WhTj0xTj06vbJf0u2VEB7XRpzrCmTioCMW8OOs+vhRCo00FH+zpvDoLWeXMlZN1c6XTfIK68/4ZTfB8OZUGIFXXeGoS1x7dSjG89sr6uSLuLqyHtrL9E2l1at78TqyHsyYvoT15EnQ9C2LGqoS7JZx8ddMI2hH3a6hCY3XoJs1svIYZAiJGpgw8ydfBBknhmw7MsNtt08uY5rlTShaTd7zLW7w67DEmSJEmSJEnSaTKQLUmSJEmSJEmSpA0nwEoqOx6Znmz7dSwmgdjMoJGzZaxJ1cioG9kx0yrPiFkCSaBq5lTNIwHKzvZJpr/6APkTBe+Xu1x91508eMklHNq6jUd27mJ5ZJTd999HVlWnVGcAGg89RLZ/P51v/Cb6l11G74orKXbuZHzpQUYW17Zb9oUo6XdJDjwEBx4CBkd689KLKUenqEYnKUcmqdpj1I0W/UYLtuwAIMRI0lkcdM/uLjOcvvBnIcYjwfJh17KJRALVyPjg+BnbQjk2Rb1OgqVJWZDUBaHok5R9krJ31HyfUPQG07IgrBzPMUkpW2NU7fGV2xhVa2ywfGTw+9GD1QE30t4S2eGgdneBtLNIUhVDeb2HQ9j9lRB2lR/VCbuuaCwcIHn464awz8Ly6CQHdlxBsdJlfGRpjm0P30NenFkIskhS7t+6k4Njk+eyTEkXoF2zBwxkS5IkSZIkSdIGZCBbkiRJkiRJkiRJGrJQR0KngE5Bu9d/3O1iCMeGtBsZS7u2ULVy9n/zHqbufoSRR+ce9/FJjFz2wAOMLC/z4KWXsjAxwZ1793L5PffQ7p56KCQpCka//Lc0HnyA5ac8hbo9wiM7vomxmUeZfuhrpFV5Wq9fpy4AaWeRtLMIBx4AWA2fVqOTlKODaZ03qUbGqUbGh1vwWUr6XRozD5PPPEy6PG84+zTFJKUcnaIcWwlgj04Sk/SYbUKMhLUKJUcIVUFSDMLVoeyRHD1dDVn3B4MI5Kd3CUOoK/LlOfLlI+97kUDdHKFsHxXUHpmgTnOq5ihVcxQmd6xun5Q9ss4iaXeBtLtI1lkg6XfW5FgbhLDH6U/ueNwQdmPuUfKFg4RYUywNL6wXgc7IBEsTW1kenSImydBqOVPVSuf3rCzY+si9jC4cPKN/1wgcGJvi61svokwyIDLRWTrjDtuS1C6eYBAlSZIkSZIkSdK6ZSBbkiRJkiRJkiRJ2iBCjGS9AnpHwpMjj84xs/dieltGmb16J/3xFpN3P0p4gqDY1kOHaHc63Hv5FfQbTe7cu5dLv/51pmdOr8N1vn8/E5/6FJ3rnkR8ypNY3HIRnfEtbH3wa4zM7Tc8e56EuiJfPES+eAgYhAdj3lztor1eOiCfjphklBPT1I0W3R2X091xOWlvmXzmYRozD5N0Fj2+TqDKmhTtCTrjlw5C2O3jA/mhKsmWZskWZ0kXZ8iW5jZVB+ZAJO0tkfaWYPaRwcI0IWYNyvY4VWuMsrXSTbsxQp016Y83YXzr+a/1BCHsYYpAd2SCxYmtLI1PrwaaN7LJQ/uYPvAASV2d0eM7eYN7t+1ioTUKwEi/y+UHHmKsZ2dbSZIkSZIkSZKkC42BbEmSJEmSJEmSJGkDS6qa6dsfYPHSrSzs3sbyjimK0RZbvvogWf/xO1WPdDrsveMfuH/PHhbGJ/j67j0sj4xy8UMPnlbXz1BVjPz9/2ZirMfBS/fSb43w6J7rGJm/iK0P3klWPH7Hb62NAISiR2P2kSOB1A0ohoRichvFll0Uk9upmiNUO6+ku/NK0u7SIJx96OFB8PYCFAlUzRGK9iRFe4KiPUG1Er6viyO/+0mvsxLAniFbmr0gw+wBCGWfxsJBWDi4ujwmKWVrjKo16KZ9eD6GtesGHWJFPn+A5gYIYadVyejCIUYXDpKVG++9PClLsjPs/l4T2De1lYemthNDIIk1l8zsZ+fcmXXZliRJkiRJkiRJ0sZnIFuSJEmSJEmSdJyZdsJDE+mwy5AEECGvoVFF8irSKCNj/Ui7PPXArDa/AIw/cJB8scvM3ospxlrs/0dXMP0PD9KcW37cx2VVxRV3380jO3byyM6dHNy2jc5Im8vvvZe8OL0QW2t5gYvv/BtmL7qMuYt2szwxTXf0qUzvu5uxQw8bYNNpC7GmMfsojdlHiUlKMbmd/pZdlJPbqFqjVLuuorvrKtLOAlUxT3vuYZL68Qch2AyK1hj90emVEPY4MTn277UQI1lvEQ4+SrY4OwhgF70hVbv+hboiX54jX55bXRYJxGztOkOHqhx6CBug2xplYfIiliYeJ4Q9f5D28jyBC+/vjYVmm3u2X0x3ZYCDyc4ilx/YR7M8s3C3JEmSJEmSJEmSNgcD2ZIkSZIkSZKk41QButnadQaUdHq6j7kfYuQp+/qkF15GSifRml1i+9/dw8x1l1KMNjl03SXs+Ou7SOrHP1gCsPORhxnpLHPfSpfsBy+5hMvvvfe0nz/EyJZH7md09gAHLttLb2ScA5deQ9rvMbI4c+YvTBe8UFc0Zh6mMfMwMc3oT15EMb2TcnwrVXucpclpuhMXMX3/l9dF2HUtLG+5hMWLrjxmWVJXZJ158s48eWeOvLNAiDW92YUhVbnxBSJhA3aDPh1LzTEe3L579X6yEsIeWzhIe+nCDGEf1k8zvrprDzEkZFXJnoMPM70076AikiRJkiRJkiRJMpAtSZIkSZIkSTreZLdm7/7NHUSRNowARRLop4F+Ftg/mhBDoEwgrYZdnNajrFey7db72PdP9xLThJgmUJ/8YJmYn+eyr9/PfZdfQT9vnFUNjd4yu+76Mvuu/kf0Rsap17DbrC48oSppHnqI5qGHqNOcYssOisufRNUYYXHrHsYP3DPsEs+pOslY2n45naldADQXD9JYmiHvzJP2lgyK6rQUac6D03ugjIwszTJ5aN8FH8I+2v7xKWJIGO11uPbh+8jqzTnAgyRJkiRJkiRJkk6fgWxJkiRJkiRJ0nHyGvK+oQxp/Tjy+3hwpEFt+k4nEeKZvYc/USft066BQddVaS0lVUHzwAO02hlzl3wjnS2X0Fw6RKMzN+zSzlodUjrTl9CZvpQ6SQEYe/Qe2jMPGMLWGakJPDi9hzpJaXbn2PHAP5Cc4efFZhSB/eNbANg5d9AwtiRJkiRJkiRJko5hIFuSJEmSJEmSJEmStKE1l2doz+6jM7WL+R17mb7/b0hOoTP8ehRDQmdqF8tbL6NOB93ls+4SY/vvobE8M+TqtJE9OnUx3cYISV2x48E7DGM/xuzIGP0sJ6tKtiwtDLscSZIkSZIkSZIkrTMGsiVJkiRJkiRJkiRJG97YgXvoj0xRNdosbr+KiUfuGHZJpyUS6E7uYHnbHqqsAUDW7zBy4F6aCwfsiq2zMtfewuzoVgAuPnQ/WdEbckXrz+Hu2NsW50gwrC5JkiRJkiRJkqRjGciWJEmSJEmSJEmSJG14IdZMPHIHM5c+me7ERTSWDtFaPDDssk4qAr2Ji1jauoeq0QIgLXqMHLyP1tyjBIOhOkvdrMXDWy4FYOv8I4z1FugOuab1ppflzI6MAXDRvJ3oJUmSJEmSJEmSdDwD2ZIkSZIkSZIkSZKkTSHvLjAy83WWp3ezcNHV5J150qo/7LJOKAL9sa0sbdtD2RwFICkLRg7eT3tuHyEaxNbZq0LCg1svJ4bAaHeBbQuPDLukdWn/+BQQGO8s0SrX53uGJEmSJEmSJEmShstAtiRJkiRJkiRJkiRp0xg9+HX6I9OUrTEWdlzD5EN/Txh2UUeJQNGeYnF6N0V7HICkKhk59ADtmQcJsR5ugdo0IrBvy26KrEFWFlx86P519buwXtQcDmTDRQt2x5YkSZIkSZIkSdKJGciWJEmSJEmSJEmSJG0agcjEI//AzGX/B/3RLXQnd9Kee3jYZQFQtMZZ3Ho5xcgkMUZCXTEy8xDtQ18nqathl6dN5tDYRSy2JyBGLjl0L2n0GDuRg2mLgpysKtmyND/sciRJkiRJkiRJkrROGciWJEmSJEmSJEmSJG0qWb/D6IF7WLzoKha3XUm+PEdWdIZWT9EYZWnbHv7/7P15kKR3YSd4f/Ouu6rvU91qtW4hc1gCazE2NhEeIsaz4bA3wnPAjuwRHrA98hojbPx6WBnDghEYM7sjPNjD2CtG4AMMY17NLuOYeb1jjQeQYTGS0NWSurv6qu6u+8qsysz3j241ElKhrr6yqvrzieioqiefevLb3flkVf7y931+jd71pza0W+kZO5yekwdTbC50LBdr10ytL8cHtyZJto4fSncHH/8r3eFyb9JMNk2Np9jpMAAAAAAAAKxYCtkAAAAAAAAArDndE0fS6NuQRs9QJrdem3UH/y6FtC9phsVKd2Y27Eq9f9OpDe12uiaPpXf0YIp1BVkujoVSJYfX70qSDM6MZXB2tMOJVq65QimjpVqSRjZNjXU6DgAAAAAAACuYQjYAAAAAAAAAa04hSf+xJzK66zVZ7OrP7Pqd6R09eEnuu1muZWb9rswPbE4KhSRJbep4ek8eOLNS96WthnO5aKeQQ+t3p1ksp7Ywny3jwyl0OtQKdqTckyQZmJtO16LV6gEAAAAAAFiaQjYAAAAAAAAAa1JpsZH+kacyue36zKzflerMWCr16Yt2f61SJTPrr8jc4NakUEySVKdH03tyfyqNmYt2v/CckcHtma/2pNhqZcfJZ1NU/V9SK8nhcm+SZPOk1bEBAAAAAAD43hSyAQAAAAAAAFaR+tjFKxSvVsXK0m99FydnUi73pzG0JePrr8rAE19Jod1a1vHbSVIspVWupl2uplWunP5YTbtcObN9sXco7UIxWWylPHMyPUf2pTw7kXaSxncfs7m8DMvRmJy9aMdejb7X42Mlq49PLWv/qcFNOVEZSOoL2XTg22lOj2VuiX3nTy7v2GvRia6+zJeaqbYaKTx2OONt5XVg5WktXLzfFwAAAAAAWJ7V+a4jAAAAAAAAAJylnkOPZbFvXZq1nsxuvyY9hx5/XsG68sKidel5RetKLe3SqdtTKJzVfZVnJ9N9dF/K06M5u++A89eo9eTE9r1JkqHjw+mZtuLzyznWO5gk2TI7kYIyNgAAAAAAAC9DIRsAAAAAAACANa3YXEzvgUcyddWrU9+wM43120+tZL1MhVYzheZCiguNFJqNFBcXUlhspLj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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "np.random.seed(0)\n", + "X = np.random.randn(100, 3)\n", + "#Assign at random the anomalous/not anomaoous labels\n", + "#Create a random array of 0 and 1 of shape=(100,)\n", + "y=np.random.randint(0,2,size=100)\n", + "name='test_complete'\n", + "# create an isolation forest model\n", + "iforest = IsolationForest(n_estimators=10, max_samples=64, random_state=0)\n", + "iforest.fit(X)\n", + "plot_path=os.path.join(os.getcwd(),'tests','test_plots')\n", + "\n", + "fig,ax=plot_complete_scoremap(name,X.shape[1],iforest,X,y,pwd=plot_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/images/GFI_Feat_bar_plot_glass.pdf b/images/GFI_Feat_bar_plot_glass.pdf new file mode 100644 index 0000000..ad1773c Binary files /dev/null and b/images/GFI_Feat_bar_plot_glass.pdf differ diff --git a/images/GFI_glass_synt.pdf b/images/GFI_glass_synt.pdf new file mode 100644 index 0000000..c5673bf Binary files /dev/null and b/images/GFI_glass_synt.pdf differ diff --git a/images/LFI_Feat_bar_plot_Xaxis.pdf b/images/LFI_Feat_bar_plot_Xaxis.pdf new file mode 100644 index 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b/imp_scores/imp_score_LFI_ionosphere.pkl new file mode 100644 index 0000000..9e8bedd Binary files /dev/null and b/imp_scores/imp_score_LFI_ionosphere.pkl differ diff --git a/imp_scores/imp_score_LFI_lympho.pkl b/imp_scores/imp_score_LFI_lympho.pkl new file mode 100644 index 0000000..aac0017 Binary files /dev/null and b/imp_scores/imp_score_LFI_lympho.pkl differ diff --git a/plot.py b/plot.py new file mode 100644 index 0000000..fa13ecb --- /dev/null +++ b/plot.py @@ -0,0 +1,386 @@ +import numpy as np +import pandas as pd +import os +import matplotlib.pyplot as plt +from matplotlib import colors +from matplotlib.pyplot import cm +from matplotlib.pyplot import * +from utils import * +from tqdm import tqdm +import pickle +from interpretability_module import * + +def compute_local_importances(model, X: pd.DataFrame,name: str,pwd_imp_score: str = os.getcwd(), pwd_plt_data: str = os.getcwd()) -> tuple[np.array,dict,str,str]: + """ + Collect useful information that will be successively used by the plt_importances_bars,plt_global_importance_bar and plt_feat_bar_plot + functions. + + Parameters: + model: An instance of the Isolation Forest model + X: Input dataset + name: Dataset's name + pwd_imp_score: Directory where the Importance Scores results will be saved as pkl files, by default the current working directory + pwd_plt_data: Directory where the plot data results will be saved as pkl files, by default the current working directory + + Returns: + imps: 2-dimensional array containing the local Feature Importance values for the samples of the input dataset X. The array is also locally saved in a pkl file for the sake of reproducibility. + plt_data: Dictionary containig the average Importance Scores values, the feature order and the standard deviations on the Importance Scores. The dictionary is also locally saved in a pkl file for the sake of reproducibility. + path_fi: Path of the pkl file containing the Importance Scores + path_plt_data: Path of the pkl file containing the plt data + """ + + name='LFI_'+name + fi,_,_=local_diffi_batch(model,X) + + # Save the Importance Scores in a pkl file + path_fi = pwd_imp_score + '\\imp_score_' + name + '.pkl' + with open(path_fi, 'wb') as fl: + pickle.dump(fi,fl) + + """ + Take the mean feature importance scores over the different runs for the Feature Importance Plot + and put it in decreasing order of importance. + To remove the possible np.nan or np.inf values from the mean computation use assign np.nan to the np.inf values + and then ignore the np.nan values using np.nanmean + """ + + fi[fi==np.inf]=np.nan + mean_imp=np.nanmean(fi,axis=0) + std_imp=np.nanstd(fi,axis=0) + mean_imp_val=np.sort(mean_imp) + feat_order=mean_imp.argsort() + + plt_data={'Importances': mean_imp_val, + 'feat_order': feat_order, + 'std': std_imp[mean_imp.argsort()]} + + # Save the plt_data dictionary in a pkl file + path_plt_data = pwd_plt_data + '\\plt_data_' + name + '.pkl' + with open(path_plt_data, 'wb') as fl: + pickle.dump(plt_data,fl) + + + return fi,plt_data,path_fi,path_plt_data + +def compute_global_importances(model, X: pd.DataFrame, n_runs:int, name: str,pwd_imp_score: str = os.getcwd(), pwd_plt_data: str = os.getcwd()) -> tuple[np.array,dict,str,str]: + """ + Collect useful information that will be successively used by the plt_importances_bars,plt_global_importance_bar and plt_feat_bar_plot + functions. + + Parameters: + model: An instance of the Isolation Forest model + X: Input Dataset + n_runs: Number of runs to perform in order to compute the Global Feature Importance Scores. + name: Dataset's name + pwd_imp_score: Directory where the Importance Scores results will be saved as pkl files, by default the current working directory + pwd_plt_data: Directory where the plot data results will be saved as pkl files, by default the current working directory + + Returns: + imps: 2-dimensional array containing the local Feature Importance values for the samples of the input dataset X. The array is also locally saved in a pkl file for the sake of reproducibility. + plt_data: Dictionary containig the average Importance Scores values, the feature order and the standard deviations on the Importance Scores. The dictionary is also locally saved in a pkl file for the sake of reproducibility. + path_fi: Path of the pkl file containing the Importance Scores + path_plt_data: Path of the pkl file containing the plt data + """ + + name='GFI_'+name + fi=np.zeros(shape=(n_runs,X.shape[1])) + for i in range(n_runs): + model.fit(X) + fi[i,:],_=diffi_ib(model,X) + + # Save the Importance Scores in a pkl file + path_fi = pwd_imp_score + '\\imp_score_' + name + '.pkl' + with open(path_fi, 'wb') as fl: + pickle.dump(fi,fl) + + + fi[fi==np.inf]=np.nan + mean_imp=np.nanmean(fi,axis=0) + std_imp=np.nanstd(fi,axis=0) + mean_imp_val=np.sort(mean_imp) + feat_order=mean_imp.argsort() + + plt_data={'Importances': mean_imp_val, + 'feat_order': feat_order, + 'std': std_imp[mean_imp.argsort()]} + + # Save the plt_data dictionary in a pkl file + path_plt_data = pwd_plt_data + '\\plt_data_' + name + '.pkl' + with open(path_plt_data, 'wb') as fl: + pickle.dump(plt_data,fl) + + + return fi,plt_data,path_fi,path_plt_data + +def plt_importances_bars(imps_path: str, name: str, pwd: str =os.getcwd(),f: int = 6,save: bool =True): + """ + Obtain the Global Importance Bar Plot given the Importance Scores values computed in the compute_imps function. + + Parameters: + imps_path: Path of the pkl file containing the 2-dimensional array of the LFI/GFI Scores for the input dataset.Obtained from the compute_imps function. + name: Dataset's name + pwd: Directory where the results will be saved as pkl files. By default the value of pwd is set to the current working directory. + f: Number of vertical bars to include in the Bar Plot. By default f is set to 6. + save: Boolean variable used to decide weather to save the Bar Plot locally as a PDF or not. + + Returns: + Obtain the Bar Plot which is then saved locally as a PDF. + """ + + #Load the imps array from the pkl file contained in imps_path -> the imps_path is returned from the + #compute_local_importances or compute_global_importances functions so we have it for free + with open(imps_path, 'rb') as file: + importances = pickle.load(file) + + number_colours = 20 + color = plt.cm.get_cmap('tab20',number_colours).colors + patterns = [None, "/" , "\\" , "|" , "-" , "+" , "x", "o", "O", ".", "*" ] + importances_matrix = np.array([np.array(pd.Series(x).sort_values(ascending = False).index).T for x in importances]) + dim=importances.shape[1] + dim=int(dim) + bars = [[(list(importances_matrix[:,j]).count(i)/len(importances_matrix))*100 for i in range(dim)] for j in range(dim)] + bars = pd.DataFrame(bars) + #display(bars) + + tick_names=[] + for i in range(1,f+1): + if i==1: + tick_names.append(r'${}'.format(i) + r'^{st}$') + elif i==2: + tick_names.append(r'${}'.format(i) + r'^{nd}$') + elif i==3: + tick_names.append(r'${}'.format(i) + r'^{rd}$') + else: + tick_names.append(r'${}'.format(i) + r'^{th}$') + + barWidth = 0.85 + r = range(dim) + ncols=1 + if importances.shape[1]>15: + ncols=2 + + + fig, ax = plt.subplots() + + for i in range(dim): + ax.bar(r[:f], bars.T.iloc[i, :f].values, bottom=bars.T.iloc[:i, :f].sum().values, color=color[i % number_colours], edgecolor='white', width=barWidth, label=str(i), hatch=patterns[i // number_colours]) + + ax.set_xlabel("Rank", fontsize=20) + ax.set_xticks(range(f), tick_names[:f]) + ax.set_ylabel("Percentage count", fontsize=20) + ax.set_yticks(range(10, 101, 10), [str(x) + "%" for x in range(10, 101, 10)]) + ax.legend(bbox_to_anchor=(1.05, 0.95), loc="upper left",ncol=ncols) + + if save: + plt.savefig(pwd + '//{}_bar_plot.pdf'.format(name), bbox_inches='tight') + + return fig, ax, bars + + +def plt_feat_bar_plot(plt_data_path: str,name: str,pwd: str =os.getcwd(),is_local: bool =True,save: bool =True): + """ + Obtain the Global Feature Importance Score Plot exploiting the information obtained from compute_imps function. + + Parameters + ---------- + plt_data_path: Dictionary generated from the compute_imps function with the necessary information to create the Score Plot. + name: Dataset's name + pwd: Directory where the plot will be saved as pkl files. By default the value of pwd is set to the current working directory. + is_local: Boolean variable used to specify weather we are plotting the Global or Local Feature Importance in order to set the file name. + If is_local is True the result will be the LFI Score Plot (based on the LFI scores of the input samples), otherwise the result is the GFI + Score Plot (based on the GFI scores obtained in the different n_runs execution of the model). + save: Boolean variable used to decide weather to save the Score Plot locally as a PDF or not. + + Returns: + Obtain the Score Plot which is also locally saved as a PDF. + """ + #Load the plt_data dictionary from the pkl file contained in plt_data_path -> the plt_data_path is returned from the + #compute_local_importances or compute_global_importances functions so we have it for free + with open(plt_data_path, 'rb') as f: + plt_data = pickle.load(f) + + name_file='Score_plot_'+name + + patterns = [None, "/" , "\\" , "|" , "-" , "+" , "x", "o", "O", ".", "*" ] + imp_vals=plt_data['Importances'] + feat_imp=pd.DataFrame({'Global Importance': np.round(imp_vals,3), + 'Feature': plt_data['feat_order'], + 'std': plt_data['std'] + }) + + if len(feat_imp)>15: + feat_imp=feat_imp.iloc[-15:].reset_index(drop=True) + + dim=feat_imp.shape[0] + + number_colours = 20 + + plt.style.use('default') + plt.rcParams['axes.facecolor'] = '#F2F2F2' + plt.rcParams['axes.axisbelow'] = True + color = plt.cm.get_cmap('tab20',number_colours).colors + ax1=feat_imp.plot(y='Global Importance',x='Feature',kind="barh",color=color[feat_imp['Feature']%number_colours],xerr='std', + capsize=5, alpha=1,legend=False, + hatch=[patterns[i//number_colours] for i in feat_imp['Feature']]) + xlim=np.min(imp_vals)-0.2*np.min(imp_vals) + + ax1.grid(alpha=0.7) + ax2 = ax1.twinx() + # Add labels on the right side of the bars + values=[] + for i, v in enumerate(feat_imp['Global Importance']): + values.append(str(v) + ' +- ' + str(np.round(feat_imp['std'][i],2))) + + ax2.set_ylim(ax1.get_ylim()) + ax2.set_yticks(range(dim)) + ax2.set_yticklabels(values) + ax2.grid(alpha=0) + plt.axvline(x=0, color=".5") + ax1.set_xlabel('Importance Score',fontsize=20) + ax1.set_ylabel('Features',fontsize=20) + plt.xlim(xlim) + plt.subplots_adjust(left=0.3) + if save: + plt.savefig(pwd+'//{}.pdf'.format(name_file),bbox_inches='tight') + + return ax1,ax2 + + +def plot_importance_map(name: str,model, X_train: pd.DataFrame,y_train: np.array ,resolution: int, + pwd: str =os.getcwd(),save: bool =True,m: bool =None,factor: int =3,feats_plot: tuple[int,int] =(0,1),ax=None): + """ + Produce the Local Feature Importance Scoremap. + + Parameters: + name: Dataset's name + model: Instance of the Isolation Forest model. + X_train: Training Set + y_train: Dataset training labels + resolution: Scoremap resolution + pwd: Directory where the plot will be saved. By default the value of pwd is set to the current working directory. + save: Boolean variable used to decide weather to save the Score Plot locally as a PDF or not. + m: Boolean variable regulating the plt.pcolor advanced settings. By defualt the value of m is set to None + factor: Integer factor used to define the minimum and maximum value of the points used to create the scoremap. By default the value of f is set to 3. + feats_plot: This tuple contains the indexes of the pair features to compare in the Scoremap. By default the value of feats_plot + is set to (0,1) + plt: Plt object used to create the plot. + + Returns: + Obtain the Scoremap which is also locally saved as a PDF. + """ + mins = X_train.min(axis=0)[list(feats_plot)] + maxs = X_train.max(axis=0)[list(feats_plot)] + mean = X_train.mean(axis = 0) + mins = list(mins-(maxs-mins)*factor/10) + maxs = list(maxs+(maxs-mins)*factor/10) + xx, yy = np.meshgrid(np.linspace(mins[0], maxs[0], resolution), np.linspace(mins[1], maxs[1], resolution)) + mean = np.repeat(np.expand_dims(mean,0),len(xx)**2,axis = 0) + mean[:,feats_plot[0]]=xx.reshape(len(xx)**2) + mean[:,feats_plot[1]]=yy.reshape(len(yy)**2) + + importance_matrix = np.zeros_like(mean) + model.max_samples = len(X_train) + for i in range(importance_matrix.shape[0]): + importance_matrix[i] = local_diffi(model, mean[i])[0] + + sign = np.sign(importance_matrix[:,feats_plot[0]]-importance_matrix[:,feats_plot[1]]) + Score = sign*((sign>0)*importance_matrix[:,feats_plot[0]]+(sign<0)*importance_matrix[:,feats_plot[1]]) + x = X_train[:,feats_plot[0]].squeeze() + y = X_train[:,feats_plot[1]].squeeze() + + Score = Score.reshape(xx.shape) + + # Create a new pyplot object if plt is not provided + if ax is None: + fig, ax = plt.subplots() + + if m is not None: + cp = ax.pcolor(xx, yy, Score, cmap=cm.RdBu, vmin=-m, vmax=m, shading='nearest') + else: + cp = ax.pcolor(xx, yy, Score, cmap=cm.RdBu, shading='nearest', norm=colors.CenteredNorm()) + + ax.contour(xx, yy, (importance_matrix[:, feats_plot[0]] + importance_matrix[:, feats_plot[1]]).reshape(xx.shape), levels=7, cmap=cm.Greys, alpha=0.7) + + try: + ax.scatter(x[y_train == 0], y[y_train == 0], s=40, c="tab:blue", marker="o", edgecolors="k", label="inliers") + ax.scatter(x[y_train == 1], y[y_train == 1], s=60, c="tab:orange", marker="*", edgecolors="k", label="outliers") + except IndexError: + print('Handling the IndexError Exception...') + ax.scatter(x[(y_train == 0)[:, 0]], y[(y_train == 0)[:, 0]], s=40, c="tab:blue", marker="o", edgecolors="k", label="inliers") + ax.scatter(x[(y_train == 1)[:, 0]], y[(y_train == 1)[:, 0]], s=60, c="tab:orange", marker="*", edgecolors="k", label="outliers") + + ax.legend() + if save: + plt.savefig(pwd + '\\Local_Importance_Scoremap_{}.pdf'.format(name), bbox_inches='tight') + else: + fig,ax=None,None + + return fig, ax + +def plot_complete_scoremap(name:str,dim:int,model,X: pd.DataFrame, y: np.array, pwd:str =os.getcwd()): + """ + Produce the Complete Local Feature Importance Scoremap: a Scoremap for each pair of features in the input dataset. + + Parameters: + name: Dataset's name + dim: Number of input features in the dataset + model: Instance of the Isolation Forest model. + X: Input dataset + y: Dataset labels + pwd: Directory where the plot will be saved. By default the value of pwd is set to the current working directory. + + Returns: + Obtain the Complete Scoremap which is also locally saved as a PDF. + """ + + fig, ax = plt.subplots(dim, dim, figsize=(50, 50)) + for i in range(dim): + for j in range(i+1,dim): + features = [i,j] + # One of the successive two lines can be commented so that we obtain only one "half" of the + #matrix of plots to reduce a little bit the execution time. + _,_=plot_importance_map(name,model, X, y, 50, pwd, feats_plot = (features[0],features[1]), ax=ax[i,j],save=False) + _,_=plot_importance_map(name,model, X, y, 50, pwd, feats_plot = (features[1],features[0]), ax=ax[j,i],save=False) + #fig.suptitle("comparison between DIFFI and ExIFFI "+name+" dataset",fontsize=20) + + plt.savefig(pwd+'//Local_Importance_Scoremap_{}_complete.pdf'.format(name),bbox_inches='tight') + return fig,ax + + +def print_score_map(model,X: pd.DataFrame,resolution: int ,name: str ,pwd: str =os.getcwd(),save: bool =True): + """ + Produce the Anomaly Score Scoremap. + + Parameters: + model: Instance of the Isolation Forest model. + X: Input dataset + resolution: Scoremap resolution + name: Dataset's name + pwd: Directory where the plot will be saved. By default the value of pwd is set to the current working directory. + save: Boolean variable used to decide weather to save the Score Plot locally as a PDF or not. + + Returns: + Returns the Anomaly Score Scoremap + """ + mins = X.min(axis=0) + maxs = X.max(axis=0) + mins = list(mins-(maxs-mins)*3/10) + maxs = list(maxs+(maxs-mins)*3/10) + xx, yy = np.meshgrid(np.linspace(mins[0], maxs[0], resolution), np.linspace(mins[1], maxs[1], resolution)) + + #S1 = model.Anomaly_Score(X_in=np.c_[xx.ravel(), yy.ravel()]) + S1=model.decision_function(np.c_[xx.ravel(), yy.ravel()]) + S1 = S1.reshape(xx.shape) + x= X.T[0] + y= X.T[1] + + plt.figure(figsize=(12,12)) + levels = np.linspace(np.min(S1),np.max(S1),10) + CS = plt.contourf(xx, yy, S1, levels, cmap=plt.cm.YlOrRd) + plt.scatter(x,y,s=15,c='None',edgecolor='k') + plt.axis("equal") + if save: + plt.savefig(pwd+'\\Anomaly_Scoremap_{}.pdf' + .format(name),bbox_inches='tight') + plt.show() + return \ No newline at end of file diff --git a/plt_data/plt_data_GFI_glass.pkl b/plt_data/plt_data_GFI_glass.pkl new file mode 100644 index 0000000..8feb0c9 Binary files /dev/null and b/plt_data/plt_data_GFI_glass.pkl differ diff --git a/plt_data/plt_data_LFI_Xaxis.pkl b/plt_data/plt_data_LFI_Xaxis.pkl new file mode 100644 index 0000000..8e0bc81 Binary files /dev/null and b/plt_data/plt_data_LFI_Xaxis.pkl differ diff --git a/plt_data/plt_data_LFI_glass.pkl b/plt_data/plt_data_LFI_glass.pkl new file mode 100644 index 0000000..5f4db7c Binary files /dev/null and b/plt_data/plt_data_LFI_glass.pkl differ diff --git a/plt_data/plt_data_LFI_ionosphere.pkl b/plt_data/plt_data_LFI_ionosphere.pkl new file mode 100644 index 0000000..6eb11e1 Binary files /dev/null and b/plt_data/plt_data_LFI_ionosphere.pkl differ diff --git a/plt_data/plt_data_LFI_lympho.pkl b/plt_data/plt_data_LFI_lympho.pkl new file mode 100644 index 0000000..506811f Binary files /dev/null and b/plt_data/plt_data_LFI_lympho.pkl differ diff --git a/pyod_mod_functions.py b/pyod_mod_functions.py new file mode 100644 index 0000000..9bb76ba --- /dev/null +++ b/pyod_mod_functions.py @@ -0,0 +1,221 @@ + +from sklearn.ensemble._iforest import _average_path_length +from sklearn.utils.validation import _num_samples +from sklearn.utils import gen_batches, get_chunk_n_rows +import numpy as np +import time +from math import ceil + +# The functions below have been adapted from the sklearn source code + +def decision_function_single_tree_pyod(iforest, tree_idx, X): + #return _score_samples_pyod(iforest, tree_idx, X) - iforest.offset_ + return _score_samples_pyod(iforest, tree_idx, X) + 0.5 + + +def _score_samples_pyod(iforest, tree_idx, X): + n_feat= X.shape[1] + if n_feat != X.shape[1]: + raise ValueError("Number of features of the model must " + "match the input. Model n_features is {0} and " + "input n_features is {1}." + "".format(n_feat, X.shape[1])) + return -_compute_chunked_score_samples_pyod(iforest, tree_idx, X) + + +def _compute_chunked_score_samples_pyod(iforest, tree_idx, X): + n_samples = _num_samples(X) + max_feat=int(iforest.max_features*X.shape[1]) + if max_feat == X.shape[1]: + subsample_features = False + else: + subsample_features = True + chunk_n_rows = get_chunk_n_rows(row_bytes=16 * max_feat, + max_n_rows=n_samples) + slices = gen_batches(n_samples, chunk_n_rows) + scores = np.zeros(n_samples, order="f") + for sl in slices: + scores[sl] = _compute_score_samples_single_tree_pyod(iforest, tree_idx, X[sl], subsample_features) + return scores + + +def _compute_score_samples_single_tree_pyod(iforest, tree_idx, X, subsample_features): + n_samples = X.shape[0] + depths = np.zeros(n_samples, order="f") + tree = iforest.estimators_[tree_idx] + #features = iforest.estimators_features_[tree_idx] + features=np.arange(X.shape[1]) + X_subset = X[:, features] if subsample_features else X + leaves_index = tree.apply(X_subset) + node_indicator = tree.decision_path(X_subset) + n_samples_leaf = tree.tree_.n_node_samples[leaves_index] + depths += (np.ravel(node_indicator.sum(axis=1)) + _average_path_length(n_samples_leaf) - 1.0) + scores = 2 ** (-depths / (1 * _average_path_length([iforest.max_samples_]))) + return scores + +def diffi_ib_pyod(iforest, X, adjust_iic=True): # "ib" stands for "in-bag" + # start time + start = time.time() + # initialization + num_feat = X.shape[1] + estimators = iforest.estimators_ + cfi_outliers_ib = np.zeros(num_feat).astype('float') + cfi_inliers_ib = np.zeros(num_feat).astype('float') + counter_outliers_ib = np.zeros(num_feat).astype('int') + counter_inliers_ib = np.zeros(num_feat).astype('int') + in_bag_samples = iforest.estimators_samples_ + # for every iTree in the iForest + for k, estimator in enumerate(estimators): + # get in-bag samples indices + in_bag_sample = list(in_bag_samples[k]) + # get in-bag samples (predicted inliers and predicted outliers) + X_ib = X[in_bag_sample,:] + as_ib = decision_function_single_tree_pyod(iforest, k, X_ib) + X_outliers_ib = X_ib[np.where(as_ib < 0)] + X_inliers_ib = X_ib[np.where(as_ib > 0)] + if X_inliers_ib.shape[0] == 0 or X_outliers_ib.shape[0] == 0: + continue + # compute relevant quantities + n_nodes = estimator.tree_.node_count + children_left = estimator.tree_.children_left + children_right = estimator.tree_.children_right + feature = estimator.tree_.feature + node_depth = np.zeros(shape=n_nodes, dtype=np.int64) + is_leaves = np.zeros(shape=n_nodes, dtype=bool) + # compute node depths + stack = [(0, -1)] + while len(stack) > 0: + node_id, parent_depth = stack.pop() + node_depth[node_id] = parent_depth + 1 + # if we have a test node + if (children_left[node_id] != children_right[node_id]): + stack.append((children_left[node_id], parent_depth + 1)) + stack.append((children_right[node_id], parent_depth + 1)) + else: + is_leaves[node_id] = True + # OUTLIERS + # compute IICs for outliers + lambda_outliers_ib = _get_iic_pyod(estimator, X_outliers_ib, is_leaves, adjust_iic) + # update cfi and counter for outliers + node_indicator_all_points_outliers_ib = estimator.decision_path(X_outliers_ib) + node_indicator_all_points_array_outliers_ib = node_indicator_all_points_outliers_ib.toarray() + # for every point judged as abnormal + for i in range(len(X_outliers_ib)): + path = list(np.where(node_indicator_all_points_array_outliers_ib[i] == 1)[0]) + depth = node_depth[path[-1]] + for node in path: + current_feature = feature[node] + if lambda_outliers_ib[node] == -1: + continue + else: + cfi_outliers_ib[current_feature] += (1 / depth) * lambda_outliers_ib[node] + counter_outliers_ib[current_feature] += 1 + # INLIERS + # compute IICs for inliers + lambda_inliers_ib = _get_iic_pyod(estimator, X_inliers_ib, is_leaves, adjust_iic) + # update cfi and counter for inliers + node_indicator_all_points_inliers_ib = estimator.decision_path(X_inliers_ib) + node_indicator_all_points_array_inliers_ib = node_indicator_all_points_inliers_ib.toarray() + # for every point judged as normal + for i in range(len(X_inliers_ib)): + path = list(np.where(node_indicator_all_points_array_inliers_ib[i] == 1)[0]) + depth = node_depth[path[-1]] + for node in path: + current_feature = feature[node] + if lambda_inliers_ib[node] == -1: + continue + else: + cfi_inliers_ib[current_feature] += (1 / depth) * lambda_inliers_ib[node] + counter_inliers_ib[current_feature] += 1 + # compute FI + fi_outliers_ib = np.where(counter_outliers_ib > 0, cfi_outliers_ib / counter_outliers_ib, 0) + fi_inliers_ib = np.where(counter_inliers_ib > 0, cfi_inliers_ib / counter_inliers_ib, 0) + fi_ib = fi_outliers_ib / fi_inliers_ib + end = time.time() + exec_time = end - start + return fi_ib, exec_time + + +def local_diffi_pyod(iforest, x): + # start time + start = time.time() + # initialization + estimators = iforest.estimators_ + cfi = np.zeros(len(x)).astype('float') + counter = np.zeros(len(x)).astype('int') + max_depth = int(np.ceil(np.log2(iforest.max_samples))) + # for every iTree in the iForest + for estimator in estimators: + n_nodes = estimator.tree_.node_count + children_left = estimator.tree_.children_left + children_right = estimator.tree_.children_right + feature = estimator.tree_.feature + node_depth = np.zeros(shape=n_nodes, dtype=np.int64) + is_leaves = np.zeros(shape=n_nodes, dtype=bool) + # compute node depths + stack = [(0, -1)] + while len(stack) > 0: + node_id, parent_depth = stack.pop() + node_depth[node_id] = parent_depth + 1 + # if test node + if (children_left[node_id] != children_right[node_id]): + stack.append((children_left[node_id], parent_depth + 1)) + stack.append((children_right[node_id], parent_depth + 1)) + else: + is_leaves[node_id] = True + # update cumulative importance and counter + x = x.reshape(1,-1) + node_indicator = estimator.decision_path(x) + node_indicator_array = node_indicator.toarray() + path = list(np.where(node_indicator_array == 1)[1]) + leaf_depth = node_depth[path[-1]] + for node in path: + if not is_leaves[node]: + current_feature = feature[node] + cfi[current_feature] += (1 / leaf_depth) - (1 / max_depth) + counter[current_feature] += 1 + # compute FI + fi = np.zeros(len(cfi)) + for i in range(len(cfi)): + if counter[i] != 0: + fi[i] = cfi[i] / counter[i] + end = time.time() + exec_time = end - start + return fi, exec_time + + +def _get_iic_pyod(estimator, predictions, is_leaves, adjust_iic): + desired_min = 0.5 + desired_max = 1.0 + epsilon = 0.0 + n_nodes = estimator.tree_.node_count + lambda_ = np.zeros(n_nodes) + children_left = estimator.tree_.children_left + children_right = estimator.tree_.children_right + # compute samples in each node + node_indicator_all_samples = estimator.decision_path(predictions).toarray() + num_samples_in_node = np.sum(node_indicator_all_samples, axis=0) + # ASSIGN INDUCED IMBALANCE COEFFICIENTS (IIC) + for node in range(n_nodes): + # compute relevant quantities for current node + num_samples_in_current_node = num_samples_in_node[node] + num_samples_in_left_children = num_samples_in_node[children_left[node]] + num_samples_in_right_children = num_samples_in_node[children_right[node]] + # if there is only 1 feasible split or node is leaf -> no IIC is assigned + if num_samples_in_current_node == 0 or num_samples_in_current_node == 1 or is_leaves[node]: + lambda_[node] = -1 + # if useless split -> assign epsilon + elif num_samples_in_left_children == 0 or num_samples_in_right_children == 0: + lambda_[node] = epsilon + else: + if num_samples_in_current_node%2==0: # even + current_min = 0.5 + else: # odd + current_min = ceil(num_samples_in_current_node/2)/num_samples_in_current_node + current_max = (num_samples_in_current_node-1)/num_samples_in_current_node + tmp = np.max([num_samples_in_left_children, num_samples_in_right_children]) / num_samples_in_current_node + if adjust_iic and current_min!=current_max: + lambda_[node] = ((tmp-current_min)/(current_max-current_min))*(desired_max-desired_min)+desired_min + else: + lambda_[node] = tmp + return lambda_ diff --git a/sklearn_mod_functions.py b/sklearn_mod_functions.py index 4bb7c1e..b7b055b 100644 --- a/sklearn_mod_functions.py +++ b/sklearn_mod_functions.py @@ -44,11 +44,12 @@ def decision_function_single_tree(iforest, tree_idx, X): def _score_samples(iforest, tree_idx, X): - if iforest.n_features_ != X.shape[1]: + n_feat= iforest.n_features_in_ + if n_feat != X.shape[1]: raise ValueError("Number of features of the model must " "match the input. Model n_features is {0} and " "input n_features is {1}." - "".format(iforest.n_features_, X.shape[1])) + "".format(n_feat, X.shape[1])) return -_compute_chunked_score_samples(iforest, tree_idx, X) @@ -79,4 +80,3 @@ def _compute_score_samples_single_tree(iforest, tree_idx, X, subsample_features) depths += (np.ravel(node_indicator.sum(axis=1)) + _average_path_length(n_samples_leaf) - 1.0) scores = 2 ** (-depths / (1 * _average_path_length([iforest.max_samples_]))) return scores - diff --git a/tests/test_interpretability_module.py b/tests/test_interpretability_module.py new file mode 100644 index 0000000..48f2c23 --- /dev/null +++ b/tests/test_interpretability_module.py @@ -0,0 +1,77 @@ +import numpy as np +from sklearn.ensemble import IsolationForest +import os +import sys +parent_dir = os.path.abspath(os.path.join(os.path.dirname('DIFFI'), '..')) +sys.path.append(parent_dir) +from interpretability_module import diffi_ib, _get_iic, local_diffi +from utils import local_diffi_batch + +def test_diffi_ib(): + # create a random dataset + np.random.seed(0) + X = np.random.randn(100, 10) + # create an isolation forest model + iforest = IsolationForest(n_estimators=10, max_samples=64, random_state=0) + iforest.fit(X) + # run the diffi_ib function + fi_ib, exec_time = diffi_ib(iforest, X) + #Check that all the elements of fi_ib are finite + assert np.all(np.isfinite(fi_ib)) == True + # check that the output has the correct shape + assert fi_ib.shape[0] == X.shape[1] + # check that the execuiton time is positive + assert exec_time > 0 + +def test_get_iic(): + # create a random dataset + np.random.seed(0) + X = np.random.randn(100, 10) + # create an isolation forest model + iforest = IsolationForest(n_estimators=10, max_samples=64, random_state=0) + iforest.fit(X) + estimator=iforest.estimators_[np.random.randint(0,iforest.n_estimators)] + is_leaves=np.random.choice([True, False], size=X.shape[0]) + adjust_iic=np.random.choice([True, False], size=1) + lambda_outliers_ib = _get_iic(estimator, X, is_leaves, adjust_iic=adjust_iic) + + assert type(lambda_outliers_ib) == np.ndarray + assert lambda_outliers_ib.shape[0] == estimator.tree_.node_count + assert np.all(lambda_outliers_ib >= -1) == True + + +def test_local_diffi(): + # create a random dataset + np.random.seed(0) + X = np.random.randn(100,10) + # create an isolation forest model + iforest = IsolationForest(n_estimators=10, max_samples=64, random_state=0) + iforest.fit(X) + #Select a single sample from X at random + x=X[np.random.randint(0,X.shape[0]),:] + fi_ib, exec_time = local_diffi(iforest, x) + + assert np.all(np.isfinite(fi_ib)) == True + assert fi_ib.shape[0] == x.shape[0] + assert exec_time >= 0 + +def test_local_diffi_batch(): + np.random.seed(0) + X = np.random.randn(100,10) + iforest = IsolationForest(n_estimators=10, max_samples=64, random_state=0) + iforest.fit(X) + + fi_ib,ord_idx,exec_time=local_diffi_batch(iforest, X) + + assert np.all(np.isfinite(fi_ib)) == True + assert fi_ib.shape[0] == X.shape[0] + assert ord_idx.shape == X.shape + # Every element in ord_idx must be between 0 and X.shape[0]-1 + assert np.all(ord_idx >= X.shape[1]) == False + assert type(exec_time)==list + assert np.all(np.array(exec_time)>=0) == True + + + + + diff --git a/tests/test_plot.py b/tests/test_plot.py new file mode 100644 index 0000000..ebde350 --- /dev/null +++ b/tests/test_plot.py @@ -0,0 +1,297 @@ +import os +import matplotlib.pyplot as plt +from matplotlib import colors +from matplotlib.pyplot import cm +from matplotlib.pyplot import * +from utils import * +from tqdm import tqdm +import pickle +from interpretability_module import * +from plot import * + +def test_compute_local_importances(): + + #Create a path to save the pkl files created by compute_local_importances + test_imp_score_path=os.path.join(os.getcwd(),'tests','test_imp_score_local') + test_plt_data_path=os.path.join(os.getcwd(),'tests','test_plt_data_local') + name='test_local' + + #If the folder do not exist create them: + if not os.path.exists(test_imp_score_path): + os.makedirs(test_imp_score_path) + if not os.path.exists(test_plt_data_path): + os.makedirs(test_plt_data_path) + + np.random.seed(0) + X = np.random.randn(100, 10) + # create an isolation forest model + iforest = IsolationForest(n_estimators=10, max_samples=64, random_state=0) + iforest.fit(X) + + fi,plt_data,path_fi,path_plt_data=compute_local_importances(iforest,X,name,pwd_imp_score=test_imp_score_path,pwd_plt_data=test_plt_data_path) + + """ + Tests on the pkl files + """ + #Check that the returned path are strings + assert type(path_fi) == str + assert type(path_plt_data) == str + #Check that the pkl files have been created + assert os.path.exists(path_fi) == True + assert os.path.exists(path_plt_data) == True + #Check that the pkl files are not empty + assert os.path.getsize(path_fi) > 0 + assert os.path.getsize(path_plt_data) > 0 + #Check that the pkl files can be loaded + assert pickle.load(open(path_fi,'rb')) is not None + assert pickle.load(open(path_plt_data,'rb')) is not None + + """ + Tests on fi and plt_data + """ + #Check that all the elements of fi are finite + assert np.all(np.isfinite(fi)) == True + # check that the output has the correct shape + assert fi.shape[0] == X.shape[0] + #Extract the keys of plt_data + plt_data_keys=list(plt_data.keys()) + imp,feat_ord,std=plt_data[plt_data_keys[0]],plt_data[plt_data_keys[1]],plt_data[plt_data_keys[2]] + #Check that all the elements of imp are finite + assert np.all(np.isfinite(imp)) == True + #Check that the size of imp is correct + assert imp.shape[0] == X.shape[1] + #Check that the size of feat_ord is correct + assert feat_ord.shape[0] == X.shape[1] + #Values in feat_ord cannot be greater than X.shape[1] + assert np.all(feat_ord>=X.shape[1]) == False + #Check that the size of std is correct + assert std.shape[0] == X.shape[1] + #Check that all the elements of std are positive (standard deviation cannot be negative) + assert np.all(std>=0) == True + + +def test_compute_global_importances(): + + #Create a path to save the pkl files created by compute_local_importances + test_imp_score_path=os.path.join(os.getcwd(),'tests','test_imp_score_global') + test_plt_data_path=os.path.join(os.getcwd(),'tests','test_plt_data_global') + name='test_global' + + #If the folder do not exist create them: + if not os.path.exists(test_imp_score_path): + os.makedirs(test_imp_score_path) + if not os.path.exists(test_plt_data_path): + os.makedirs(test_plt_data_path) + + np.random.seed(0) + X = np.random.randn(100, 10) + # create an isolation forest model + iforest = IsolationForest(n_estimators=10, max_samples=64, random_state=0) + iforest.fit(X) + nruns=np.random.randint(1,10) + + fi,plt_data,path_fi,path_plt_data=compute_global_importances(iforest,X,nruns,name,pwd_imp_score=test_imp_score_path,pwd_plt_data=test_plt_data_path) + + """ + Tests on the pkl files + """ + + #Check that the returned path are strings + assert type(path_fi) == str + assert type(path_plt_data) == str + #Check that the pkl files have been created + assert os.path.exists(path_fi) == True + assert os.path.exists(path_plt_data) == True + #Check that the pkl files are not empty + assert os.path.getsize(path_fi) > 0 + assert os.path.getsize(path_plt_data) > 0 + #Check that the pkl files can be loaded + assert pickle.load(open(path_fi,'rb')) is not None + assert pickle.load(open(path_plt_data,'rb')) is not None + + """ + Tests on fi and plt_data + """ + #Check that nruns is positive + assert nruns >= 0 + #Check that all the elements of fi are finite + assert np.all(np.isfinite(fi)) == True + # check that the output has the correct shape + assert fi.shape[1] == X.shape[1] + #Extract the keys of plt_data + plt_data_keys=list(plt_data.keys()) + imp,feat_ord,std=plt_data[plt_data_keys[0]],plt_data[plt_data_keys[1]],plt_data[plt_data_keys[2]] + #Check that all the elements of imp are finite + assert np.all(np.isfinite(imp)) == True + #Check that the size of imp is correct + assert imp.shape[0] == X.shape[1] + #Check that the size of feat_ord is correct + assert feat_ord.shape[0] == X.shape[1] + #Values in feat_ord cannot be greater than X.shape[1] + assert np.all(feat_ord>=X.shape[1]) == False + #Check that the size of std is correct + assert std.shape[0] == X.shape[1] + #Check that all the elements of std are positive (standard deviation cannot be negative) + assert np.all(std>=0) == True + +def test_plot_importances_bars(): + + # We need a feature importance 2d array with the importance values. + # We can extract it from the pkl files created by the test_compure_global_importances + # and test_compute_local_importances functions + + #We create the plot with plot_importances_bars and we will then compare it with the + #expected result contained in GFI_glass_synt.pdf + imps_path=os.path.join(os.getcwd(),'imp_scores','imp_score_GFI_glass.pkl') + + imps=pickle.load(open(imps_path,'rb')) + + #Create a path to save the plot image + plot_path=os.path.join(os.getcwd(),'tests','test_plots') + + #If the folder do not exist create it: + if not os.path.exists(plot_path): + os.makedirs(plot_path) + + #Create a name for the plot + name='test_Glass' + f=6 + fig,ax,bars=plt_importances_bars(imps_path,name,pwd=plot_path,f=f) + + """ + Tests on ax + """ + #Check that the returned ax is not None + assert ax is not None + assert fig is not None + #Check that the returned ax is an axis object + #assert type(ax) == matplotlib.axes._subplots.AxesSubplot + #Check that the x label is correct + assert ax.get_xlabel() == 'Rank' + #Check that the y label is correct + assert ax.get_ylabel() == 'Percentage count' + #Check that the xtick and y tick labels are correct + x_tick_labels = [tick.get_text() for tick in ax.get_xticklabels()] + y_tick_labels = [tick.get_text() for tick in ax.get_yticklabels()] + assert x_tick_labels == ['$1^{st}$', '$2^{nd}$', '$3^{rd}$', '$4^{th}$', '$5^{th}$', '$6^{th}$'] + assert y_tick_labels == ['10%', '20%', '30%', '40%', '50%', '60%', '70%', '80%', '90%', '100%'] + + #See if the plot correctly changes if I pass from f=6 (default value) to f=9 + f1=9 + fig1,ax1,bars1=plt_importances_bars(imps_path,name='test_Glass_9',pwd=plot_path,f=f1) + + #Check that the xtick and y tick labels are correct + x_tick_labels1 = [tick.get_text() for tick in ax1.get_xticklabels()] + assert x_tick_labels1 == ['$1^{st}$', '$2^{nd}$', '$3^{rd}$', '$4^{th}$', '$5^{th}$', '$6^{th}$','$7^{th}$','$8^{th}$','$9^{th}$'] + + """ + Tests on bars + + The main test o perform on bars is that the sum of the percentages values on each column should be 100. + """ + assert type(bars) == pd.DataFrame + assert bars.shape == (imps.shape[1],imps.shape[1]) + assert np.all(bars.sum()==100) == True + #Same on bars1 + assert type(bars1) == pd.DataFrame + assert bars1.shape == (imps.shape[1],imps.shape[1]) + assert np.all(bars1.sum()==100) == True + +def test_plt_feat_bar_plot(): + + # We need the plt_data array: let's consider the global case with plt_data_GFI_glass.pkl and + # the local case with plt_data_LFI_glass.pkl + + plt_data_global_path=os.path.join(os.getcwd(),'plt_data','plt_data_GFI_glass.pkl') + plt_data_local_path=os.path.join(os.getcwd(),'plt_data','plt_data_LFI_glass.pkl') + + name_global='test_GFI_Glass' + name_local='test_LFI_Glass' + + plot_path=os.path.join(os.getcwd(),'tests','test_plots') + + ax1,ax2=plt_feat_bar_plot(plt_data_global_path,name_global,pwd=plot_path,is_local=False) + ax3,ax4=plt_feat_bar_plot(plt_data_local_path,name_local,pwd=plot_path,is_local=True) + + y_tick_labels_local = [tick.get_text() for tick in ax3.get_yticklabels()] + y_tick_labels2_local = [tick.get_text() for tick in ax4.get_yticklabels()] + y_tick_labels_global = [tick.get_text() for tick in ax1.get_yticklabels()] + y_tick_labels2_global = [tick.get_text() for tick in ax2.get_yticklabels()] + + """ + Tests on ax1,ax2,ax3,ax4 + """ + #Check that the returned ax is not None + assert ax1 is not None + assert ax2 is not None + assert ax3 is not None + assert ax4 is not None + #Check that the x label is correct + assert ax1.get_xlabel() == 'Importance Score' + #Check that the y label is correct + assert ax1.get_ylabel() == 'Features' + #Check that the x label is correct + assert ax3.get_xlabel() == 'Importance Score' + #Check that the y label is correct + assert ax3.get_ylabel() == 'Features' + #Check that the xtick and ytick labels are correct + assert np.all(np.array(y_tick_labels_local).astype('float')>=len(y_tick_labels2_local)-1) == False + assert np.all(np.array(y_tick_labels_global).astype('float')>=len(y_tick_labels2_global)-1) == False + + +def test_plot_importance_map(): + + # Let's perform the test on the Glass dataset + with open(os.path.join(os.getcwd(), 'data', 'local', 'glass.pkl'), 'rb') as f: + data = pickle.load(f) + # training data (inliers and outliers) + X_tr = np.concatenate((data['X_in'], data['X_out_5'], data['X_out_6'])) + y_tr = np.concatenate((data['y_in'], data['y_out_5'], data['y_out_6'])) + X_tr, y_tr = shuffle(X_tr, y_tr, random_state=0) + # test outliers + X_te = data['X_out_7'] + y_te = data['y_out_7'] + y_te=np.ones(shape=X_te.shape[0]) + X=np.r_[X_tr,X_te] + y=np.r_[y_tr,y_te] + name='Glass' + # create an isolation forest model + iforest = IsolationForest(n_estimators=10, max_samples=64, random_state=0) + iforest.fit(X_tr) + plot_path=os.path.join(os.getcwd(),'tests','test_plots') + + fig,ax=plot_importance_map(name,iforest,X,y,30,pwd=plot_path) + + """ + Tests on ax + """ + + #Check that the returned ax is not None + assert ax is not None + assert fig is not None + +def test_plot_complete_scoremap(): + + # Here we'll use a random dataset with just 3 features otherwise it takes too much time to + #create the plots + np.random.seed(0) + X = np.random.randn(100, 3) + #Assign at random the anomalous/not anomaoous labels + #Create a random array of 0 and 1 of shape=(100,) + y=np.random.randint(0,2,size=100) + name='test_complete' + # create an isolation forest model + iforest = IsolationForest(n_estimators=10, max_samples=64, random_state=0) + iforest.fit(X) + plot_path=os.path.join(os.getcwd(),'tests','test_plots') + + fig,ax=plot_complete_scoremap(name,X.shape[1],iforest,X,y,pwd=plot_path) + + """ + Tests on ax + """ + + #Check that the returned ax is not None + assert ax is not None + assert fig is not None + diff --git a/tests/test_sklearn_mod_functions.py b/tests/test_sklearn_mod_functions.py new file mode 100644 index 0000000..c5c9316 --- /dev/null +++ b/tests/test_sklearn_mod_functions.py @@ -0,0 +1,109 @@ +from sklearn.ensemble._iforest import _average_path_length +from sklearn.utils.validation import _num_samples +from sklearn.utils import gen_batches, get_chunk_n_rows +from sklearn.ensemble import IsolationForest +import numpy as np +from sklearn_mod_functions import * +from sklearn.utils._testing import ( + assert_allclose, + assert_array_almost_equal, + assert_array_equal, + ignore_warnings, +) + +from sklearn_mod_functions import _score_samples +from sklearn_mod_functions import _compute_chunked_score_samples +from sklearn_mod_functions import _compute_score_samples_single_tree + +def test_decision_function_single_tree(): + + X_train = np.array([[1, 1], [1, 2], [2, 1]]) + clf1 = IsolationForest(contamination=0.1).fit(X_train) + clf2 = IsolationForest().fit(X_train) + X=np.array([[2.0, 2.0]]) + tree_idx=np.random.randint(0,len(clf1.estimators_)) + + assert_array_equal( + decision_function_single_tree(clf1,tree_idx,X), + _score_samples(clf1,tree_idx,X) - clf1.offset_, + ) + assert_array_equal( + decision_function_single_tree(clf2,tree_idx,X), + _score_samples(clf2,tree_idx,X) - clf2.offset_, + ) + + #The decision function values could not be equal because clf1 and clf2 have + #two different contamination values. + + assert_array_almost_equal( + decision_function_single_tree(clf1,tree_idx,X), decision_function_single_tree(clf2,tree_idx,X), + decimal=1 + ) + + #Check weather the two decision function values are different + + assert not np.array_equal(decision_function_single_tree(clf1,tree_idx,X), decision_function_single_tree(clf2,tree_idx,X)) + +def test_score_samples(): + + X_train = np.array([[1, 1], [1, 2], [2, 1]]) + clf1 = IsolationForest(contamination=0.1).fit(X_train) + clf2 = IsolationForest().fit(X_train) + X=np.array([[2.0, 2.0]]) + tree_idx=np.random.randint(0,len(clf1.estimators_)) + assert_array_equal( + _score_samples(clf1,tree_idx,X), + decision_function_single_tree(clf1,tree_idx,X) + clf1.offset_, + ) + assert_array_equal( + _score_samples(clf2,tree_idx,X), + decision_function_single_tree(clf2,tree_idx,X) + clf2.offset_, + ) + assert_array_equal( + _score_samples(clf1,tree_idx,X), _score_samples(clf2,tree_idx,X) + ) + +def test_compute_chunked_score_samples(): + + X_train = np.array([[1, 1], [1, 2], [2, 1]]) + clf1 = IsolationForest(contamination=0.1).fit(X_train) + clf2 = IsolationForest().fit(X_train) + X=np.array([[2.0, 2.0]]) + tree_idx=np.random.randint(0,len(clf1.estimators_)) + + assert not np.array_equal( + _compute_chunked_score_samples(clf1,tree_idx,X), + decision_function_single_tree(clf1,tree_idx,X) + clf1.offset_, + ) + assert not np.array_equal( + _compute_chunked_score_samples(clf2,tree_idx,X), + decision_function_single_tree(clf2,tree_idx,X) + clf2.offset_, + ) + + assert_array_equal( + _compute_chunked_score_samples(clf1,tree_idx,X), _compute_chunked_score_samples(clf2,tree_idx,X) + ) + +def test_compute_score_samples_single_tree(): + + X_train = np.array([[1, 1], [1, 2], [2, 1]]) + clf1 = IsolationForest(contamination=0.1).fit(X_train) + clf2 = IsolationForest().fit(X_train) + X=np.array([[2.0, 2.0]]) + tree_idx=np.random.randint(0,len(clf1.estimators_)) + subsample_features=np.random.choice([True, False], size=1) + + assert not np.array_equal( + _compute_score_samples_single_tree(clf1,tree_idx,X,subsample_features), + decision_function_single_tree(clf1,tree_idx,X) + clf1.offset_, + ) + assert not np.array_equal( + _compute_score_samples_single_tree(clf2,tree_idx,X,subsample_features), + decision_function_single_tree(clf2,tree_idx,X) + clf2.offset_, + ) + + assert_array_equal( + _compute_score_samples_single_tree(clf1,tree_idx,X,subsample_features), _compute_score_samples_single_tree(clf2,tree_idx,X,subsample_features) + ) + +