{ "cells": [ { "metadata": {}, "cell_type": "markdown", "source": [ "# **Use case:** Heart failure clinical data\n", "This notebook illustrates the use of the KAAM model and its interpretability tools on a clinical dataset for heart failure prediction.\n", "The dataset contains clinical features of patients, and the goal is to predict the occurrence of heart failure events." ], "id": "2f95cb45751a4d36" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-10T19:36:38.367539Z", "start_time": "2025-12-10T19:36:38.360571Z" } }, "cell_type": "code", "source": [ "# Import libraries\n", "import os\n", "import shap\n", "import sympy\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import ipywidgets as widgets\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches\n", "\n", "from kan import ex_round\n", "from copy import deepcopy\n", "from scipy.stats import t\n", "from tueplots import bundles\n", "from tueplots.axes import color\n", "\n", "from src.data import load_data\n", "from src.utils import get_config, create_results_folder\n", "from models.models import Kan_model\n", "from representation import radar_factory\n", "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score, precision_score, recall_score, confusion_matrix, ConfusionMatrixDisplay\n", "\n", "np.random.seed(42)" ], "id": "63305bcf3672e4ff", "outputs": [], "execution_count": 36 }, { "metadata": {}, "cell_type": "markdown", "source": "### --> These are some auxiliary functions for the analysis and representation of the results ###", "id": "93f818579108bb9" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-10T19:36:40.512347Z", "start_time": "2025-12-10T19:36:40.488745Z" } }, "cell_type": "code", "source": [ "# Functions to get validation metrics with confidence intervals\n", "def get_metrics(y_true, y_pred, y_proba):\n", " y_true = np.squeeze(y_true)\n", " y_pred = np.squeeze(y_pred)\n", " y_proba = np.squeeze(y_proba)\n", "\n", " return {'accuracy': accuracy_score(y_true, y_pred),\n", " 'roc_auc': roc_auc_score(y_true, y_proba),\n", " 'f1': f1_score(y_true, y_pred, zero_division=0),\n", " 'precision': precision_score(y_true, y_pred, zero_division=0),\n", " 'recall': recall_score(y_true, y_pred, zero_division=0)}\n", "\n", "\n", "def get_bootstrap_metrics(y_true, y_pred, y_proba, n_bootstrap=1000, ci=95):\n", " y_true = np.array(y_true)\n", " y_pred = np.array(y_pred)\n", " y_proba = np.array(y_proba)\n", " original_classes = np.unique(y_true)\n", " metrics_list = []\n", "\n", " for _ in range(n_bootstrap):\n", " indices = np.random.choice(len(y_true), len(y_true), replace=True)\n", " y_true_sample = y_true[indices]\n", "\n", " # Check if all classes are present in the sample\n", " if not np.all(np.isin(original_classes, np.unique(y_true_sample))):\n", " continue # Skip this sample if any class is missing\n", "\n", " sample_metrics = get_metrics(y_true_sample, y_pred[indices], y_proba[indices])\n", " metrics_list.append(sample_metrics)\n", "\n", " # Group metrics by name\n", " metric_names = metrics_list[0].keys()\n", " all_metrics = {k: [] for k in metric_names}\n", " for m in metrics_list:\n", " for k in m:\n", " all_metrics[k].append(m[k])\n", "\n", " # Calculate mean and confidence intervals\n", " alpha = 1 - ci / 100\n", " t_val = t.ppf(1 - alpha / 2, df=n_bootstrap - 1)\n", " metrics_with_ci = []\n", "\n", " for k, values in all_metrics.items():\n", " values = np.array(values)\n", " mean = np.mean(values)\n", " std_err = np.std(values, ddof=1) / np.sqrt(n_bootstrap)\n", " ci_range = t_val * std_err\n", " metrics_with_ci.append(str(np.round(mean, 3)) + ' (' + str(np.round(mean - ci_range, 3)) + ',' + str(np.round(mean + ci_range, 3)) + ')')\n", "\n", " return metrics_with_ci\n", "\n", "\n", "# Representation functions\n", "def show_performance_results(y, res_y_pred, res_y_pred_proba, df, title, plot_title, folder):\n", " # Print metrics\n", " print(title)\n", " print(df)\n", "\n", " # Show confusion matrix\n", " # with plt.rc_context({**bundles.icml2024(column='half', nrows=1, ncols=1)}):\n", " conf_m = confusion_matrix(np.squeeze(y.to_numpy()), res_y_pred)\n", " display = ConfusionMatrixDisplay(confusion_matrix=conf_m,\n", " display_labels=['No Heart A/D', 'Heart A/D'])\n", " display.plot()\n", " plt.savefig(os.path.join(folder, plot_title.replace(' ', '_').replace('/', '_') + '_CM.pdf'))\n", " plt.show()\n", " plt.close()\n", "\n", " # Plot a bar diagram of the probability of each patient, where the color bar depends on the y_true value\n", " y_prob_pred = np.squeeze(np.array(res_y_pred_proba))\n", " y_prob_pred = np.where(y_prob_pred < 0.01, 0.01, y_prob_pred) # Umbralize the probabilities: the minimum probability is 0.01\n", " y_gt = np.squeeze(y.to_numpy())\n", "\n", " with plt.rc_context({**bundles.icml2024(column='half', nrows=1, ncols=1, usetex=True)}):\n", " fig, ax = plt.subplots()\n", " sort_idx = np.argsort(y_prob_pred)[::-1] # Sort the patients by probability\n", " color_vals = [['tab:red', 'tab:green'][int(y)] for y in y_gt[sort_idx]]\n", " ax.bar(range(len(y_prob_pred)), y_prob_pred[sort_idx], color=color_vals)\n", " ax.axhline(0.5, color='k', linestyle='--', label='Threshold')\n", " ax.set_yscale('log') # Plot in log scale the vertical axis for better visualization\n", " ax.tick_params(axis='both')\n", " ax.set_xlabel(\"Patient\")\n", " ax.set_ylabel(r\"LogProbability\")\n", " ax.set_title(plot_title)\n", " red_patch = mpatches.Patch(color='red', label='No Heart A/D')\n", " green_patch = mpatches.Patch(color='green', label='Heart A/D')\n", " plt.tight_layout(rect=[0, 0.1, 1, 1])\n", " ax.legend(handles=[red_patch, green_patch], loc='lower center', bbox_to_anchor=(0.5, -0.6),\n", " ncol=2) # Note: this can be uncommented, but may clutter the plot\n", " plt.rcParams.update(bundles.icml2024(usetex=False))\n", " plt.savefig(results_folder + plot_title.replace(' ', '_').replace('/', '_') + '.pdf', dpi=300)\n", " plt.show()\n", " plt.close()" ], "id": "9610856e4d7716da", "outputs": [], "execution_count": 37 }, { "metadata": {}, "cell_type": "markdown", "source": "### Let's start by loading the data and training/validating the KAAM model", "id": "93ed92cbcec48e3c" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-10T19:37:01.372350Z", "start_time": "2025-12-10T19:36:44.580729Z" } }, "cell_type": "code", "source": [ "print(\"########### KAAM model on Heart Failure Clinical Data ###########\")\n", "\n", "# Load data\n", "args = get_config('explainability')\n", "args['datasets'] = ['heart']\n", "create_results_folder(args['results_folder'], args)\n", "x_train, x_test, y_train, y_test = load_data(args['datasets'][0], args)\n", "results_folder = args['results_folder'] + os.sep + args['datasets'][0] + os.sep + 'use_case' + os.sep\n", "if not os.path.exists(results_folder):\n", " os.makedirs(results_folder)\n", "\n", "print(\"\\nData loaded:\")\n", "print(f\"Number of train / test patients: {len(x_train)} / {len(x_test)}\")\n", "print(f\"Number of features per patient: {len(x_train.columns)}\")\n", "print(f\"Proportion of patients with the positive outcome in the train set: {y_train.sum() / len(y_train):.2f}\")\n", "print(f\"Proportion of patients with the positive outcome in the test set: {y_test.sum() / len(y_test):.2f}\")\n", "\n", "### Train KAN model\n", "print(\"\\nTraining KAAM model...\")\n", "model = Kan_model(hidden_dim=0,\n", " grid=3,\n", " k=5,\n", " lamb=0.01,\n", " lamb_entropy=0.1,\n", " lr=0.001,\n", " weight=True,\n", " sparse_init=False,\n", " mult_kan=False,\n", " seed=0,\n", " batch_size=-1,\n", " steps=10000,\n", " early_stop=True)\n", "\n", "results = model.run_model(x_train.to_numpy(), x_test.to_numpy(), y_train.to_numpy(), y_test.to_numpy())\n", "\n", "### Validate model\n", "print(\"\\nValidating KAAM model...\")\n", "metrics_df = pd.DataFrame(get_bootstrap_metrics(np.squeeze(y_test.to_numpy()), results['y_pred'], results['y_pred_proba'][:, 1]),\n", " index=['Accuracy', 'AUC', 'F1', 'Precision', 'Recall'],\n", " columns=['Test'])\n", "show_performance_results(y_test,\n", " results['y_pred'],\n", " results['y_pred_proba'][:, 1],\n", " metrics_df,\n", " 'Test results using KAAM',\n", " 'Probability of Heart A/D using KAAM (no formula)',\n", " results_folder)" ], "id": "9215950792002dc", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "########### KAAM model on Heart Failure Clinical Data ###########\n", "\n", "Data loaded:\n", "Number of train / test patients: 800 / 200\n", "Number of features per patient: 21\n", "Proportion of patients with the positive outcome in the train set: 0.10\n", "Proportion of patients with the positive outcome in the test set: 0.10\n", "\n", "Training KAAM model...\n", "checkpoint directory created: ./model\n", "saving model version 0.0\n", "Early stopping at step 2073\n", "saving model version 0.1\n", "\n", "Validating KAAM model...\n", "Test results using KAAM\n", " Test\n", "Accuracy 0.824 (0.823,0.826)\n", "AUC 0.914 (0.912,0.915)\n", "F1 0.515 (0.511,0.52)\n", "Precision 0.363 (0.359,0.367)\n", "Recall 0.907 (0.903,0.911)\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2390733/3510064116.py:88: UserWarning: The figure layout has changed to tight\n", " plt.tight_layout(rect=[0, 0.1, 1, 1])\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 38 }, { "metadata": {}, "cell_type": "markdown", "source": "### Now that we have a trained KAN model, we can obtain an approximation of the formula learned by the model, and validate it. ###", "id": "e981ac1a4cb781a3" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-10T19:37:42.810676Z", "start_time": "2025-12-10T19:37:01.392530Z" } }, "cell_type": "code", "source": [ "### FORMULA APPROXIMATION ###\n", "## Obtain an approximation of the formula obtained by the KAN model\n", "n_digit = 2 # Number of digits to round the formula to\n", "model.model(model.dataset['train_input']) # The forward pass is required to adequately obtain the formula\n", "model.model.auto_symbolic()\n", "formula = model.model.symbolic_formula()[0]\n", "formula = [ex_round(f, n_digit) for f in formula]\n", "logit_formula = formula[1] - formula[0]\n", "\n", "# In the delta_formula, replace x_i by its name\n", "for i, col in enumerate(x_train.columns):\n", " logit_formula = logit_formula.subs(sympy.symbols(f'x_{i + 1}'), sympy.symbols(col))\n", "\n", "# Print the formula\n", "print(\"Logit formula:\")\n", "print(sympy.latex(logit_formula))\n", "\n", "## Validate the model using the approximated formula\n", "# We note that the metrics are similar (i.e., the formula does not introduce a significant decrease in metrics)\n", "y_pred = model.predict(x_test.to_numpy())\n", "y_pred_proba = model.predict_proba(x_test.to_numpy())[:, 1]\n", "metrics_df['Formula'] = get_bootstrap_metrics(np.squeeze(y_test.to_numpy()), y_pred, y_pred_proba)\n", "show_performance_results(y_test,\n", " y_pred,\n", " y_pred_proba,\n", " metrics_df,\n", " 'Test results using KAAM with formula',\n", " 'Probability of Heart A/D using KAAM (formula)',\n", " results_folder)" ], "id": "f4970c75ff48714f", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fixing (0,0,0) with x, r2=0.9999841451644897, c=1\n", "fixing (0,0,1) with x, r2=0.9999741911888123, c=1\n", "fixing (0,1,0) with x, r2=1.0000008344650269, c=1\n", "fixing (0,1,1) with x, r2=1.0000007152557373, c=1\n", "fixing (0,2,0) with 1/x^2, r2=1.000001311302185, c=2\n", "fixing (0,2,1) with exp, r2=0.9999995231628418, c=2\n", "fixing (0,3,0) with 0, r2=0.0, c=0\n", "fixing (0,3,1) with 0, r2=0.0, c=0\n", "fixing (0,4,0) with x, r2=1.0000008344650269, c=1\n", "fixing (0,4,1) with x, r2=0.9999170303344727, c=1\n", "fixing (0,5,0) with x, r2=1.0000011920928955, c=1\n", "fixing (0,5,1) with x, r2=1.000001072883606, c=1\n", "fixing (0,6,0) with exp, r2=1.0000009536743164, c=2\n", "fixing (0,6,1) with x^2, r2=1.0000007152557373, c=2\n", "fixing (0,7,0) with x, r2=0.9999540448188782, c=1\n", "fixing (0,7,1) with 1/x^2, r2=1.0000008344650269, c=2\n", "fixing (0,8,0) with 1/x, r2=1.0000008344650269, c=2\n", "fixing (0,8,1) with 1/x^2, r2=1.0000009536743164, c=2\n", "fixing (0,9,0) with exp, r2=1.0000009536743164, c=2\n", "fixing (0,9,1) with exp, r2=1.0000008344650269, c=2\n", "fixing (0,10,0) with exp, r2=1.000001072883606, c=2\n", "fixing (0,10,1) with exp, r2=1.0000009536743164, c=2\n", "fixing (0,11,0) with 1/x^2, r2=1.0000011920928955, c=2\n", "fixing (0,11,1) with exp, r2=1.0000011920928955, c=2\n", "fixing (0,12,0) with exp, r2=1.0000011920928955, c=2\n", "fixing (0,12,1) with exp, r2=1.0000009536743164, c=2\n", "fixing (0,13,0) with tanh, r2=0.9999246597290039, c=3\n", "fixing (0,13,1) with tanh, r2=0.9999142289161682, c=3\n", "fixing (0,14,0) with 0, r2=0.0, c=0\n", "fixing (0,14,1) with 0, r2=0.0, c=0\n", "fixing (0,15,0) with sin, r2=0.999126136302948, c=2\n", "fixing (0,15,1) with 0, r2=0.0, c=0\n", "fixing (0,16,0) with x, r2=1.0000007152557373, c=1\n", "fixing (0,16,1) with x, r2=1.0000005960464478, c=1\n", "fixing (0,17,0) with x, r2=1.0000008344650269, c=1\n", "fixing (0,17,1) with x, r2=1.0000009536743164, c=1\n", "fixing (0,18,0) with sin, r2=0.996749222278595, c=2\n", "fixing (0,18,1) with sin, r2=0.9972832202911377, c=2\n", "fixing (0,19,0) with 0, r2=0.0, c=0\n", "fixing (0,19,1) with 0, r2=0.0, c=0\n", "fixing (0,20,0) with 0, r2=0.0, c=0\n", "fixing (0,20,1) with 0, r2=0.0, c=0\n", "saving model version 0.2\n", "Logit formula:\n", "0.51 DiffWalk + 0.48 HighChol + 1.4 Sex + 0.21 Smoker + 0.82 Stroke + 1.14 \\sin{\\left(0.79 Age + 0.6 \\right)} - 1.08 \\sin{\\left(0.8 Age - 2.6 \\right)} + 0.13 \\sin{\\left(1.43 PhysHlth - 7.44 \\right)} + 1.17 \\tanh{\\left(0.63 GenHlth + 0.91 \\right)} + 0.74 \\tanh{\\left(0.72 GenHlth + 0.86 \\right)} - 4.27\n", "Test results using KAAM with formula\n", " Test Formula\n", "Accuracy 0.824 (0.823,0.826) 0.825 (0.823,0.827)\n", "AUC 0.914 (0.912,0.915) 0.911 (0.909,0.913)\n", "F1 0.515 (0.511,0.52) 0.517 (0.512,0.521)\n", "Precision 0.363 (0.359,0.367) 0.366 (0.361,0.37)\n", "Recall 0.907 (0.903,0.911) 0.902 (0.898,0.907)\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2390733/3510064116.py:88: UserWarning: The figure layout has changed to tight\n", " plt.tight_layout(rect=[0, 0.1, 1, 1])\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 39 }, { "metadata": {}, "cell_type": "markdown", "source": "### Now that we have the formula, we can obtain the delta matrix, which is key for the interpretability of the model ###", "id": "ddfbd7fa09d5f593" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-10T19:37:49.041891Z", "start_time": "2025-12-10T19:37:42.902166Z" } }, "cell_type": "code", "source": [ "##### DELTA MATRIX #####\n", "## Obtain the delta matrix, key for the interpretability of the model\n", "delta_train = np.zeros((x_train.shape[0], x_train.shape[1] + 1))\n", "for i in range(x_train.shape[0]): # For each patient\n", " for fs in logit_formula.args:\n", " formula_sum_term = deepcopy(fs)\n", " if isinstance(formula_sum_term, sympy.Float): # We have a constant!\n", " delta_train[i , -1] = float(formula_sum_term)\n", " else: # Since it is a KAAM, it depends on a single variable\n", " assert len(formula_sum_term.free_symbols) == 1\n", " variable_in_the_expresion = list(formula_sum_term.free_symbols)[0]\n", " variable_index = x_train.columns.get_loc(str(variable_in_the_expresion))\n", " delta_train[i, variable_index] += float(formula_sum_term.subs(variable_in_the_expresion, x_train.iloc[i, variable_index]))\n", "\n", "\n", "delta_test = np.zeros((x_test.shape[0], x_test.shape[1] + 1))\n", "for i in range(x_test.shape[0]): # For each patient\n", " for fs in logit_formula.args:\n", " formula_sum_term = deepcopy(fs)\n", " if isinstance(formula_sum_term, sympy.Float): # We have a constant!\n", " delta_test[i , -1] = float(formula_sum_term)\n", " else: # Since it is a KAAM, it depends on a single variable\n", " assert len(formula_sum_term.free_symbols) == 1\n", " variable_in_the_expresion = list(formula_sum_term.free_symbols)[0]\n", " variable_index = x_test.columns.get_loc(str(variable_in_the_expresion))\n", " delta_test[i, variable_index] += float(formula_sum_term.subs(variable_in_the_expresion, x_test.iloc[i, variable_index]))\n", "\n", "## Revalidate with the delta matrix\n", "y_delta_pred_proba = 1. / (1 + np.exp(-delta_test.sum(axis=1)))\n", "y_delta_pred = (y_delta_pred_proba > 0.5).astype(int)\n", "metrics_df['Delta'] = get_bootstrap_metrics(np.squeeze(y_test.to_numpy()), y_delta_pred, y_delta_pred_proba)\n", "show_performance_results(y_test,\n", " y_delta_pred,\n", " y_delta_pred_proba,\n", " metrics_df,\n", " 'Test results using KAAM\\'s delta matrix',\n", " 'Probability of Heart A/D using KAAM (delta matrix)',\n", " results_folder)\n", "# Note that small differences may happen due to the fact that we limit the number of decimals in the formula constants!" ], "id": "f24f25478e513189", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test results using KAAM's delta matrix\n", " Test Formula Delta\n", "Accuracy 0.824 (0.823,0.826) 0.825 (0.823,0.827) 0.851 (0.849,0.852)\n", "AUC 0.914 (0.912,0.915) 0.911 (0.909,0.913) 0.914 (0.912,0.916)\n", "F1 0.515 (0.511,0.52) 0.517 (0.512,0.521) 0.558 (0.553,0.562)\n", "Precision 0.363 (0.359,0.367) 0.366 (0.361,0.37) 0.407 (0.402,0.411)\n", "Recall 0.907 (0.903,0.911) 0.902 (0.898,0.907) 0.906 (0.901,0.91)\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2390733/3510064116.py:88: UserWarning: The figure layout has changed to tight\n", " plt.tight_layout(rect=[0, 0.1, 1, 1])\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 40 }, { "metadata": {}, "cell_type": "markdown", "source": [ "### INTERPRETABILITY TOOLS ###\n", "We compare the interpretability tools provided by the KAAM model with SHAP values for a black-box model." ], "id": "2d7594d42e36dab5" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-10T19:38:04.919203Z", "start_time": "2025-12-10T19:37:49.133099Z" } }, "cell_type": "code", "source": [ "### INTERPRETABILITY ###\n", "# Global interpretability: use the KAN model to obtain the global importance of each feature by using the var of the delta matrix\n", "global_importance_train = np.var(delta_train, axis=0)\n", "global_importance_test = np.var(delta_test, axis=0)\n", "\n", "# Sort variables by importance\n", "variable_names = x_train.columns.tolist() + ['Constant']\n", "sorted_idx_train = np.argsort(global_importance_train)[::-1]\n", "\n", "# Keep only variables with non-zero importance\n", "num_of_zero_var = (global_importance_train < 1e-6).sum()\n", "sorted_idx_train = sorted_idx_train[:-num_of_zero_var]\n", "\n", "# Plot the global importance\n", "x = np.arange(len(sorted_idx_train))\n", "width = 0.4\n", "with plt.rc_context({**bundles.icml2024(column='half', nrows=1, ncols=1, usetex=True)}):\n", " plt.bar(x - width / 2, global_importance_train[sorted_idx_train], label='Training', width=width, color='tab:blue')\n", " plt.bar(x + width / 2, global_importance_test[sorted_idx_train], label='Testing', width=width, color='tab:orange')\n", " plt.title('Global Feature Importance (KAAM)')\n", " plt.xticks(range(len(sorted_idx_train)), np.array(variable_names)[sorted_idx_train], rotation=90)\n", " plt.xlabel('Feature')\n", " plt.ylabel('Importance')\n", " plt.tight_layout(rect=[0, 0.05, 1, 1])\n", " plt.legend(loc='lower center', bbox_to_anchor=(0.5, -0.85), ncol=2) # Note: this can be uncommented, but may clutter the plot\n", " plt.rcParams.update(bundles.icml2024(usetex=False))\n", " plt.savefig(results_folder + 'global_feat_imp_kaam.pdf', dpi=300)\n", " plt.show()\n", " plt.close()\n", "\n", "## Compare with SHAP values for a black-box model\n", "# Train the SHAP explainer\n", "explainer = shap.Explainer(model.predict, x_train)\n", "shap_values = explainer(x_test)\n", "\n", "# Plot the SHAP values and save the plots\n", "shap.summary_plot(shap_values, x_test)\n", "shap.plots.bar(shap_values)" ], "id": "d2582b6133019f82", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2390733/1008799201.py:24: UserWarning: The figure layout has changed to tight\n", " plt.tight_layout(rect=[0, 0.05, 1, 1])\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "PermutationExplainer explainer: 201it [00:14, 4.44it/s] \n", "/tmp/ipykernel_2390733/1008799201.py:37: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n", " shap.summary_plot(shap_values, x_test)\n" ] }, { "data": { "text/plain": [ "
" ], "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 41 }, { "metadata": {}, "cell_type": "markdown", "source": [ "## USE CASE: analyze individual patients with the interpretability tools provided by KAAM ##\n", "\n", "Imagine that the clinician receives a **patient with low risk** (PATIENT A), as assessed by the model: our tools allow checking with a higher security that it is indeed a low-risk patient, as there are several patients in the historical records with a similar profile.\n", "\n", "However, this dataset is highly unbalanced: what if the model predicts as **high-risk a patient that is actually low-risk** (PATIENT B)? We can also check this with our tools. Namely, note that the closest patients in the dataset are mostly low-risk patients, which may help the clinicians to decide on the patient\n" ], "id": "9b5b3eb6a6187a5c" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-10T19:38:05.091724Z", "start_time": "2025-12-10T19:38:05.026660Z" } }, "cell_type": "code", "source": [ "def get_closest_patients(tr_x, tr_y, tr_d, patient_d, index_feats, patient_inf, n_closest):\n", " dists = np.linalg.norm(tr_d - patient_d.values, axis=1)\n", " idx_closest = np.argsort(dists)[:n_closest].tolist()\n", " pred_prob = (1 / (1 + np.exp(-tr_d[idx_closest].sum(axis=1))))\n", " real_label = tr_y.iloc[idx_closest].values\n", " closest_data = tr_x.iloc[idx_closest].values[:, index_feats]\n", " closest_data = np.concatenate((closest_data, pred_prob[:, None], real_label[:, None]), axis=1)\n", " closest_data = np.vstack((patient_inf[None, :], closest_data)) # Add the current patient as the first row\n", " new_df = pd.DataFrame(closest_data, columns=np.array(tr_x.columns.tolist())[index_feats].tolist() + ['pred_prob', 'real_label'])\n", " new_df = new_df.map(lambda x: round(x, 3) if isinstance(x, float) else x) # Limit all new_df values to having 3 decimal numbers at most\n", " new_df.index = ['Test'] + ['Train patient ' + str(j) for j in idx_closest] # Add the index to the new_df\n", " return new_df, idx_closest\n", "\n", "def get_radar_plot(tr_d, patient_d, patient_pred_prob, n_feats, idx_vars, cols_vars, y, n_closest, closest_idx, patient_title, c):\n", " theta = radar_factory(n_feats, frame='polygon')\n", " cohort_avg_prob = 1 / (1 + np.exp(-tr_d.mean(axis=0).sum())) * np.ones(len(cols_vars))\n", " title = (f\"Test Patient \\n Predicted: {patient_pred_prob:.3f} | True: {y} | Average: {cohort_avg_prob[0]:.3f}\")\n", "\n", " with plt.rc_context({**bundles.icml2024(column='half', ncols=1, nrows=1, usetex=True)}):\n", " fig, ax = plt.subplots(subplot_kw=dict(projection='radar'))\n", " ax.set_title(title)\n", " # Plot the average of all train patients\n", " _ = ax.plot(theta, cohort_avg_prob, label='Average', color=c[0], linewidth=0.5)\n", " ax.fill(theta, cohort_avg_prob, alpha=0.1, color=c[0])\n", " # Prepare for individual patient plotting\n", " avg_delta = tr_d.mean(axis=0)[None, :]\n", " avg_matrix = np.repeat(avg_delta, tr_d.shape[1], axis=0)\n", " # Plot the closest patients\n", " for j in range(n_closest):\n", " np.fill_diagonal(avg_matrix, tr_d[closest_idx[j]])\n", " pat_proba = 1 / (1 + np.exp(-avg_matrix.sum(axis=1)))\n", " _ = ax.plot(theta, pat_proba[idx_vars], label=f'Closest patient', color='#2ca02c', alpha=0.9,\n", " linewidth=1.25)\n", " ax.fill(theta, pat_proba[idx_vars], alpha=0.1, color=c[1])\n", " # Plot the current patient\n", " np.fill_diagonal(avg_matrix, patient_d.values)\n", " pat_proba = 1 / (1 + np.exp(-avg_matrix.sum(axis=1)))\n", " _ = ax.plot(theta, pat_proba[idx_vars], label='Test patient', color=c[2], alpha=0.7, linewidth=1.15,\n", " linestyle='dashed')\n", " ax.fill(theta, pat_proba[idx_vars], alpha=0.1, color=c[2])\n", " ax.set_varlabels(cols_vars, fontsize=6)\n", " plt.legend(loc='lower center',\n", " bbox_to_anchor=(0.5, -0.45),\n", " ncol=3) # Note: this can be uncommented, but may clutter the plot\n", " plt.savefig(results_folder + f'{patient_title}_radar_patient_pred_{y}_feat_{n_feats}_closest_{n_closest}.pdf', dpi=300)\n", " plt.show()\n", " plt.close()\n", "\n", "\n", "def get_curves(tr_d, patient_d, cols, n_feats, cols_vars, n_closest, closest_idx, patient_x, patient_title, c):\n", " tr_d_df = pd.DataFrame(tr_d, columns=cols)\n", " with plt.rc_context({**bundles.icml2024(column='half', ncols=1, nrows=1, usetex=True)}):\n", " fig, axs = plt.subplots(n_feats + 1, 1, figsize=(6, 1.6 * (n_feats + 1)))\n", " x_vals = np.arange(tr_d.sum(axis=1).min(), tr_d.sum(axis=1).max(), 0.01)\n", " theor_proba = 1 / (1 + np.exp(-x_vals))\n", " patient_pred_prob = (1 / (1 + np.exp(-tr_d[closest_idx].sum(axis=1)))) # Average probability (i.e., \"average patient\")\n", " axs[0].plot(x_vals, theor_proba, c[0], alpha=0.2, linewidth=0.8)\n", " for idj, feat_name in enumerate(cols_vars):\n", " if idj < n_feats: # Only plot the first n_feats features\n", " j = idj + 1 # The first plot is already used for the theoretical curve\n", " idxs = np.unique(x_train[feat_name].values, return_index=True)[\n", " 1] # Keep only unique values of x_test[feat_name]\n", " axs[j].plot(x_train[feat_name].values[idxs], tr_d_df[feat_name].values[idxs], color=c[0], linewidth=0.8)\n", " axs[j].scatter(x_train[feat_name].values[idxs], tr_d_df[feat_name].values[idxs], color=c[0], alpha=0.1)\n", " for jj in range(n_closest):\n", " axs[j].scatter(x_train.iloc[closest_idx[jj]][feat_name], tr_d_df.iloc[closest_idx[jj]][feat_name],\n", " color=c[1], alpha=0.8, s=30, marker='o')\n", " axs[j].scatter(patient_x[feat_name], patient_d[feat_name], color=c[2], s=70, marker='x')\n", " axs[j].set_ylabel(feat_name)\n", " axs[0].scatter(patient_d.values.sum(), patient_pred_prob, color=c[2], marker='x', s=70)\n", " # axs[0].set_xlabel('Logit')\n", " axs[0].set_ylabel('Probability')\n", " axs[0].set_title(f'Patient results')\n", " # Add legend\n", " red_patch = mpatches.Patch(color=c[2], label='Test patient')\n", " green_patch = mpatches.Patch(color=c[1], label='Closest patient')\n", " blue_patch = mpatches.Patch(color=c[0], label='Train patients')\n", " plt.tight_layout(rect=[0, 0.05, 1, 1])\n", " axs[-1].legend(handles=[red_patch, green_patch, blue_patch], loc='lower center',\n", " bbox_to_anchor=(0.5, -0.6), ncol=3)\n", " plt.rcParams.update(bundles.icml2024(usetex=False))\n", " plt.savefig(results_folder + f'{patient_title}_curves_patient_feat_{n_feats}_closest_{n_closest}.pdf', dpi=300)\n", " plt.show()\n", " plt.close()\n", "\n", "\n", "def analyze_patient(patient_idx, training_covariates, training_label, training_delta, patient_title, max_feats=10, n_closest=5, max_curves=10, c=['b', 'g', 'r']):\n", " # Specific patient information\n", " cols = training_covariates.columns.tolist() + ['Constant']\n", " patient_delta = pd.DataFrame(delta_test[patient_idx].reshape([1, len(cols)]), columns=cols)\n", " x = x_test.iloc[patient_idx]\n", " y = int(y_test.iloc[patient_idx])\n", " patient_predicted_prob = 1 / (1 + np.exp(-patient_delta.values.sum()))\n", "\n", " # Find the closest patients in the training set\n", " variances = np.var(training_delta, axis=0)\n", " idx_vars = np.argsort(variances)[::-1]\n", " num_of_zero_var = (variances < 1e-6).sum()\n", " idx_vars = idx_vars[:-num_of_zero_var]\n", " if training_delta.shape[1] > max_feats:\n", " idx_vars = idx_vars[:max_feats]\n", " patient_info = np.concatenate((x[idx_vars].values, [patient_predicted_prob, y]))\n", " closest_patients_df, closest_patients_idx = get_closest_patients(training_covariates, training_label, training_delta, patient_delta, idx_vars, patient_info, n_closest)\n", " print(\"\\nClosest patients in the training set:\")\n", " print(closest_patients_df)\n", "\n", " # Plot the radar chart (show only high-variance features)\n", " idx_vars = idx_vars[::-1]\n", " cols_vars = [cols[i] for i in idx_vars.tolist()] # Change the order of idx_vars and cols_vars to have the importance in clockwise order in the plot\n", " n_feats = min(max_feats, len(cols_vars)) # Number of features to show in the radar plot\n", " if n_feats >= 3: # We need at least 3 features to plot a proper radar plot\n", " get_radar_plot(training_delta, patient_delta, patient_predicted_prob, n_feats, idx_vars, cols_vars, y, n_closest, closest_patients_idx, patient_title, c)\n", "\n", " # Curves plot: show only the ones that do matter!!\n", " idx_vars = idx_vars[::-1] # Revert again the order to have the right plot order\n", " cols_vars = [cols[i] for i in idx_vars.tolist()]\n", " n_feats = min(len(cols_vars), max_curves) # Number of features to show in the curves plot\n", " if n_feats > 0: # There is something to show\n", " get_curves(training_delta, patient_delta, cols, n_feats, cols_vars, n_closest, closest_patients_idx, x, patient_title, c)\n", "\n", "\n", "\n", "def get_comparison_radar_plot(tr_d, n_feats, cols_vars, idx_vars, patient_a_d, y_a, patient_a_pred_prob, patient_b_d, y_b, patient_b_pred_prob):\n", " theta = radar_factory(n_feats, frame='polygon')\n", " cohort_avg_prob = 1 / (1 + np.exp(-tr_d.mean(axis=0).sum())) * np.ones(len(cols_vars))\n", " title = (f\"Test Patient A Predicted: {patient_a_pred_prob:.3f} | True: {y_a} | Average: {cohort_avg_prob[0]:.3f}\\n\"\n", " f\"Test Patient B Predicted: {patient_b_pred_prob:.3f} | True: {y_b} | Average: {cohort_avg_prob[0]:.3f}\")\n", "\n", " with plt.rc_context({**bundles.icml2024(column='half', ncols=1, nrows=1, usetex=True)}):\n", " fig, ax = plt.subplots(subplot_kw=dict(projection='radar'))\n", " ax.set_title(title)\n", " # Plot the average of all train patients\n", " _ = ax.plot(theta, cohort_avg_prob, label='Average', color='tab:blue', linewidth=0.5)\n", " ax.fill(theta, cohort_avg_prob, alpha=0.1, color='tab:blue')\n", " # Prepare for individual patient plotting\n", " avg_delta = tr_d.mean(axis=0)[None, :]\n", " avg_matrix = np.repeat(avg_delta, tr_d.shape[1], axis=0)\n", " # Plot patient A\n", " np.fill_diagonal(avg_matrix, patient_a_d.values)\n", " pat_proba = 1 / (1 + np.exp(-avg_matrix.sum(axis=1)))\n", " _ = ax.plot(theta, pat_proba[idx_vars],\n", " label='Patient A',\n", " color='tab:pink', alpha=0.7, linewidth=1.15, linestyle='dashed')\n", " ax.fill(theta, pat_proba[idx_vars], alpha=0.1, color='tab:pink')\n", " # Plot patient B\n", " np.fill_diagonal(avg_matrix, patient_b_d.values)\n", " pat_proba = 1 / (1 + np.exp(-avg_matrix.sum(axis=1)))\n", " _ = ax.plot(theta, pat_proba[idx_vars],\n", " label='Patient B',\n", " color='tab:purple', alpha=0.7, linewidth=1.15, linestyle='dashed')\n", " ax.fill(theta, pat_proba[idx_vars], alpha=0.1, color='tab:purple')\n", " ax.set_varlabels(cols_vars, fontsize=6)\n", " plt.legend(loc='lower center',\n", " bbox_to_anchor=(0.5, -0.45),\n", " ncol=3) # Note: this can be uncommented, but may clutter the plot\n", " plt.savefig(results_folder + f'comparison_radar_patients.pdf', dpi=300)\n", " plt.show()\n", " plt.close()\n", "\n", "def get_comparison_curves(tr_d, cols, n_feats, cols_vars, patient_a, patient_b, patient_pred_prob_a, patient_pred_prob_b, patient_a_d, patient_b_d):\n", " tr_d_df = pd.DataFrame(tr_d, columns=cols)\n", " with plt.rc_context({**bundles.icml2024(column='half', ncols=1, nrows=1, usetex=True)}):\n", " fig, axs = plt.subplots(n_feats + 1, 1, figsize=(4, 1.4 * (n_feats + 1)))\n", " x_vals = np.arange(tr_d.sum(axis=1).min(), tr_d.sum(axis=1).max(), 0.01)\n", " theor_proba = 1 / (1 + np.exp(-x_vals))\n", " axs[0].plot(x_vals, theor_proba, 'tab:blue', alpha=0.2, linewidth=0.8)\n", " for idj, feat_name in enumerate(cols_vars):\n", " if idj < n_feats: # Only plot the first n_feats features\n", " j = idj + 1 # The first plot is already used for the theoretical curve\n", " idxs = np.unique(x_train[feat_name].values, return_index=True)[1] # Keep only unique values of x_test[feat_name]\n", " axs[j].plot(x_train[feat_name].values[idxs], tr_d_df[feat_name].values[idxs], color='tab:blue', linewidth=0.8)\n", " axs[j].scatter(x_train[feat_name].values[idxs], tr_d_df[feat_name].values[idxs], color='tab:blue', alpha=0.1)\n", "\n", " axs[j].scatter(patient_a[feat_name], patient_a_d[feat_name], color='tab:pink', marker='x', s=45)\n", " axs[j].scatter(patient_b[feat_name], patient_b_d[feat_name], color='tab:purple', marker='o', s=20)\n", "\n", " axs[j].set_ylabel('Contribution', fontsize=10)\n", " axs[j].set_xlabel(feat_name, fontsize=10)\n", "\n", " axs[0].scatter(patient_a_d.values.sum(), patient_pred_prob_a, color='tab:pink', marker='x', s=45)\n", " axs[0].scatter(patient_b_d.values.sum(), patient_pred_prob_b, color='tab:purple', marker='o', s=20)\n", " axs[0].set_ylabel('Probability', fontsize=10)\n", " axs[0].set_xlabel('Logit', fontsize=10)\n", " axs[0].set_title(f'Patients Results', fontsize=12)\n", " # Add legend\n", " pink_patch = mpatches.Patch(color='tab:pink', label='Patient A')\n", " purple_patch = mpatches.Patch(color='tab:purple', label='Patient B')\n", " blue_patch = mpatches.Patch(color='tab:blue', label='Train patients')\n", " plt.tight_layout(rect=[0, 0.05, 1, 1])\n", " axs[-1].legend(handles=[blue_patch, pink_patch, purple_patch], loc='lower center',\n", " bbox_to_anchor=(0.5, -0.9), ncol=3, fontsize=10)\n", " plt.rcParams.update(bundles.icml2024(usetex=False))\n", " plt.savefig(results_folder + f'comparison_curves_patients.pdf', dpi=300)\n", " plt.show()\n", " plt.close()\n", "\n", "\n", "def compare_patients(patient_a_idx, patient_b_idx, tr_x, tr_y, tr_d, max_feats=10, max_curves=10):\n", " # Specific patient information --> PATIENT A\n", " cols = tr_x.columns.tolist() + ['Constant']\n", " patient_delta_a = pd.DataFrame(delta_test[patient_a_idx].reshape([1, len(cols)]), columns=cols)\n", " x_a = x_test.iloc[patient_a_idx]\n", " y_a = int(y_test.iloc[patient_a_idx])\n", " patient_predicted_prob_a = 1 / (1 + np.exp(-patient_delta_a.values.sum()))\n", "\n", " # Specific patient information --> PATIENT B\n", " patient_delta_b = pd.DataFrame(delta_test[patient_b_idx].reshape([1, len(cols)]), columns=cols)\n", " x_b = x_test.iloc[patient_b_idx]\n", " y_b = int(y_test.iloc[patient_b_idx])\n", " patient_predicted_prob_b = 1 / (1 + np.exp(-patient_delta_b.values.sum()))\n", "\n", " # Find the closest patients in the training set\n", " variances = np.var(tr_d, axis=0)\n", " idx_vars = np.argsort(variances)[::-1]\n", " num_of_zero_var = (variances < 1e-6).sum()\n", " idx_vars = idx_vars[:-num_of_zero_var]\n", " if tr_d.shape[1] > max_feats:\n", " idx_vars = idx_vars[:max_feats]\n", "\n", " # Plot the radar chart (show only high-variance features)\n", " idx_vars = idx_vars[::-1]\n", " cols_vars = [cols[i] for i in\n", " idx_vars.tolist()] # Change the order of idx_vars and cols_vars to have the importance in clockwise order in the plot\n", " n_feats = min(max_feats, len(cols_vars)) # Number of features to show in the radar plot\n", " if n_feats >= 3: # We need at least 3 features to plot a proper radar plot\n", " get_comparison_radar_plot(tr_d, n_feats, cols_vars, idx_vars, patient_delta_a, y_a, patient_predicted_prob_a, patient_delta_b, y_b, patient_predicted_prob_b)\n", "\n", " # Curves plot: show only the ones that do matter!!\n", " idx_vars = idx_vars[::-1] # Revert again the order to have the right plot order\n", " cols_vars = [cols[i] for i in idx_vars.tolist()]\n", " n_feats = min(len(cols_vars), max_curves) # Number of features to show in the curves plot\n", " if n_feats > 0: # There is something to show\n", " get_comparison_curves(tr_d, cols, n_feats, cols_vars, x_a, x_b, patient_predicted_prob_a, patient_predicted_prob_b, patient_delta_a, patient_delta_b)\n", "\n" ], "id": "74b23a6127c9c88b", "outputs": [], "execution_count": 42 }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-10T19:38:06.928096Z", "start_time": "2025-12-10T19:38:05.109299Z" } }, "cell_type": "code", "source": [ "# PATIENT A\n", "# Find a true negative patient\n", "true_negatives = np.where((y_test == 0) & (y_delta_pred == 0))[0]\n", "patient_a_idx = true_negatives[0]\n", "print(\"\\nAnalyzing PATIENT A (true negative). Index in test set:\", patient_a_idx)\n", "analyze_patient(patient_a_idx, x_train, y_train, delta_train, 'patient_A', n_closest=1, max_feats=args['max_atribs_radar'], max_curves=args['max_plot_curves'])" ], "id": "ec8a550975af601", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Analyzing PATIENT A (true negative). Index in test set: 0\n", "\n", "Closest patients in the training set:\n", " Age Sex GenHlth HighChol DiffWalk Stroke Smoker \\\n", "Test -0.011 0.0 0.457 1.0 0.0 0.0 0.0 \n", "Train patient 33 -0.011 0.0 0.457 1.0 0.0 0.0 0.0 \n", "\n", " PhysHlth pred_prob real_label \n", "Test -0.487 0.242 0.0 \n", "Train patient 33 -0.028 0.243 0.0 \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2390733/3679153897.py:104: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", " patient_info = np.concatenate((x[idx_vars].values, [patient_predicted_prob, y]))\n" ] }, { "data": { "text/plain": [ "
" ], "image/png": 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p6urijTfeICcnh88//5yUlBReeukllEolZrMZlUrFnDlz8PX1vWhldHNzY9q0aUybNo3m5mbKy8uZNWsWmzdvpra2Fp1Ox1VXXYVGoyE9PZ2lS5fi5uZ20co7mRF7xvPA2bNn0Wq1bN26FYVCwZo1a1i4cCEmkwmFQkFMTAzh4eHj9lCPpWccKr+zZ89SVlZGV1cXcrmc/fv3s23bNjw8PFi/fj2PPfYYU6ZMGYfSi9gRxThO2Gw29u/fT05ODnv27GHp0qUsX76ciIgImpubmTZtGtHR0fj7+4/7tcdbjHYEQaCxsZGysjJqa2tRqVScOnWKnTt38vHHH5OcnEx6ejrz58+fMMsjlzKiGMdIU1MTL774Ips3b6a5uZnVq1dz++23Y7FYkMlkjgkZDw+P81aG8yXG3nR0dFBRUUF5eTlSqZTu7m7effddXnrpJWJiYkhPT2flypX4+Picl+tfDohiHCXHjh0jJyeHnTt3Eh8fz89//nOuvvpqjEYjwcHBxMTEMGXKlFH3GHZPDb1eP6hxut0Ot7i4mD/96U/nXYx2bDYbtbW1lJWVYTAY8Pf359ixY7z44ouUlZWxatUqNmzYwMyZM89rOSYjk8o29ULxzDPPcPPNNyOVSikoKOC//uu/iI6OJigoiNtuu40bbriB4ODgUQtxJD9HnU7nMLT28wvkgV/sYO/JJrostjHXbSSkUilhYWHcdNNN3HLLLQ572GeeeYZt27ZRX1/P7Nmzyc/PP+9lmWyIPaOLdHd3M2PGDFavXs3s2bPx9/dHrVYTFhaGVDo+77bs7Gzi4uLQaDQUFRVRWFhIVlaW47ggQEMD5G6r5ft9GcT7fMRH1wZyOlTF9C4fkkqb8AgJwTN0Gr7havwjZxIUPoNgRSgebuM/XLZarVRVVVFaWkpLSwuHDh3i6NGjfPTRR+N+rcmMuLThIvv27cNsNrNkyRI6OzsBmDp16rgJ0c5gfo7d3XD8OPxtSwX+9X9kSf073NzWgc0k48hNKmYq/JF/e46wD08ilZxEKhWQSKBdAntmKHnlZxH4e6i463MTARJf3KeG4RMaiXLaDAKiZhIcHIPCQ+lyjy6TyQgODubUqVN4e3tz//33k5uby4kTJ7juuuvGqUUmP2LP6CLLli3juuuu48Ybb+TWW2/lq6++oqurixtvvHHcvtfsLlWpqanodDo6OiR0dz/Ii7squUb1B+7+4W0U7Z00+3hiuW0ZesUMYq+YSbBvID4eXnS2d3Ds0zqOHjHgY6vguvBTdM/o4vhMLxqtzTy89RiBjT0vEiRgl96f0q/EpvQlrtadhFNtyKaE4BkagW94DP6RVxIUeRWBihDcpH2XZDo6Ovjkk08ICAggJiaGTz75hF27duHp6cnmzZvHpU0uB0QxukBZWRkzZ85k7969KBQK5s2bR3d3N8XFxeMuyKysbKZOnc8LL0iReATguzSfat93uKGqi3s+MfKy0cL3tmBMre2sfzyVGbOuxE0iRSKRoJT7ovD0QYIbtedk7Ho3kJIvFdyy0MSKpEZCgi20meow1pbTWltBZ10VXU31HFgYSqO1mdjPS0k8cBZ+fDLsHeX7N03hwwUhBMoDWbGnGpkqELcpIXRExKO+cj5z585FIpHw4YcfYjQaWblyJdXV1cN6mYj8hChGF/jNb37D6dOnWbt2LQkJCY5F7/EUZGcnvP8+aLUglX3B7f6/46qTR8i/PYTGmdNZKF9IpDQGX08flHJfvNzlCIJAeVMVUaowOqydmMytdFq78PH0Qunpi6fMA3OnhEMf+/FuYSAqPyurHmrghoR23Ib4UOnobKO5vgJTTTntdZV0NdTyfbgn3wcLNLcb2JBzHM9OGwJQPXsGyduOOIa3FRUVnD59mqeeeopHHnmEJ554YtTtcTkhitFJzGYzERER/P3vf2fKlCnceuutfb6txiJIQYDaWnjhBXj3XZgz9zDzLP9FzPES3K02GsOCMCbejyrmJvy8fFF4+uLeS0U2m43ypiqi/cMd365dVgumzlZaOttwl8pQyn3x9fRGEKT8UOrJ67uDKK+Qs2xpEw8sa0KlEnDlU7Gjq4v9e15g5k4dbatWcMcvn+vTFvv27aOsrIycnBy+/vpr0SjACcQJHCfR6XQEBgYSGhpKRETEgIfLzc2NefPmUVxczOHDh50SpNUKR47A5s1QXQ03/uxLFq35K3fnvo1bt0BdVAjti5YRcMViZngr8fHwdvqh9pC5EyTzJ8Dbj9bOdkzmVgztRnw9fYhR+7Lxl500t0jYt9+fx55UM11tZvXKBmZe0clIVnpdlm5OnW3Eu/RbpBK4YtmqAW0RFRWFUqmktraWDz/8kMWLFztV7ssZsWd0kgULFnDfffdx9dVXc8cddwwpNGd6yOZm2LkTXnsNwqcJaG7eTZn5ZQ76V2GzdbPqExve19xO2BWLUcp7hpnDMVjP2B9BEOi0dmEyt9LW1Y7c3ROlpy/eHl7YBAnHT3jz+jtBtLTKWP6zRu5MbMbbe2A+XZZuvq2ow1su49nSPzGrQcYfMw8MeEm0tbVx4MABDh48SFVVFXl5ecPWQUQUo1McP36chQsX8t5776FQKJgzZ86w59sF2dnZyYIFC3B3d6e7G779FrZs6VmeSLxdYO4VBXTt+QuhJ0upV3nwxuMaFnovYpbvTBSePk4vlzgjxj7ls3XT0tmGydyKACg9fVDIfXGTuFFX78ZbewM5clTJ/HmtPJLSwLRwK1LpT0JUeHnS7FnFptJckmY8yL/c8OtBr3P48GFaW1tZtmwZ5eXlhIaGOlWfyxVRjE6QmpqKzWbjZz/7GYsWLcLPz2/ENHZBtrd3UVe3gO3bZbi7Q0qKjQD3l2jb9Syh31dgk8DZq2PxWLKCyJj5yGWeLn9fuSpGO4Ig0G4xYzK30mEx4+PhhVLui1zmSZdFwsdHFLyzLwhPD4EVSecImqbHz8eTmFB/dr/7Fz7t/I5/XfMCc0IGjwxQW1vLl19+yV//+lcWLFjA7373O5fqdbkhinEEjEYj4eHhvPbaa6hUKhYtWuR0Wqu1m0ceOYebm4X1G6ZS5fEBb5zZzp07PiS6qoPK667A97ZVREbGI5OO3p1qtGLsjaXbisncM+HjJpX2LI94+ABSyirceH6nH+poMxv/pQ0BgeO/eAApcPtePW5DTMkKgkBhYSE1NTU89dRTlJeXI5OJ0xRDIbbMCOzYsYPZs2c7AkO5gsnkRkVFKL/NfJsf/vFbXrhdSbeblFN33ELwlNu5NvTqCTPL6O4mI9BHhb+3krbODkydrRjamvHx8MJd2cmT6xv43X/Ow7L+B6qrjqGqb8Vw581DChF+CiMil8uRyWS888473H///RewVpcWoqH4MAiCQE5ODg8//DAWi4WwsDCX0r/yCixdKqHj8E6mf1fDrXVh/C7kNzw0+1dMC7tmwgixN1KJFIXch3C/qQQrAmnr6sCGQJhKxexrWjn4oYLaT95DIoHwxJHjmkZFRdHc3MyaNWvIycm5ADW4dBHFOAwHDx6ksbGRa6+9lqioKJc8861WKCiA25aUE3DwAPUhflw/aw1YZNhsY/Ou0L29iyNHP0P39q5Bj79/sIiUx38+pmtYbd0Y2ox4e3ghCAJd3RYeuLuBV14PRPLll7T7enLNopG33fP09CQsLIzFixfz0Ucf8f3334+pXJMZUYzDkJOTw6pVq2hqanJ5iPrRRxATAzV7fotHp5W2JcsIV4UilUipaakftSCPHP0MgPkJN6BUKHn/YNGAc25fosFPOfIk01BYbd3UNNfh6e5JsG8ACk9vTOZWwsOseHqcJPBsEx1zr8FD5ulUfjExMbS1tfHAAw+ItqrDIIpxCKqqqnj77bdZunQpwcHBeA+26DYEgtCzhHH/zypQHdxP/VQ/Zl6/EqlEwlRF0JgE+c333zEtLBwApULBye++cTmP4egtxCk+/g5b19bOdgRsRGg+QntHJKr7Vzqdp7+/Pz4+PiQlJfHiiy/S1tY2rmWeLIhiHIKtW7eSmJiI1WolJibGpbSVlT3+hvXn/oqb1UrrLXfi+WMvMh6CNJ2n7d4GEyKAh8wDT5kHLZ1tNIWU8EV4EDOuc35naPtEjr+/P9HR0ezcufO8lP9SRxTjIFgsFrRaLSkpKbi5uREcHOxS+hdegKUPGNip+IK/r5vL9HkP9TkulUgIGaUgZ105E1OLCegR5dUzZ7lUtqEYSoh2lHJfzjWVo/ruOyLMM9nzpmvhJadNm0Z7ezurVq3iueeeQ1xRG4goxkF46623kMvlREdHEx0d7dKsp9kM770H5sjn6ew2ExewGB+PgUGaJKPsIecn3ICppYUjRz+jsroKzS23cq6lge3btzse8J5jlYN+Tw7GSEIE8PHwwvTdIR5+t5pV0mBefrnH2dlZZDIZkZGRJCQkoNfr+eyzz5xPfJkgLvoPwq233sott9zC3Llzuf322/H0dG6iAuD11+HgoRru+SKOo3OnsuieZ/H3GnoyxSYInGtpwCbYCFVMcXrRvqvbgsncSmtnG7Yfb6FU0rNYr/T0QTbM+l9vnBGinZPPPUbo6bME7fiCP/x/Efz613D99U5dBsARkuO9996jpaWFHTt2OJ/4MkDsGfvx7bff8umnn7J48WLCw8NdEqLN1jNEnS//Lb6tnYQEx+MnVwybxpVvSEEQaOtsp8ZUR6WxFpvNRohiClGqnvXPKT7+dFm7qDDWUGtqoL3LPOxw0BUhdnS1EFxaSW3UVLy9Avn5z3u8TVx5lSsUCgICAli2bBn5+fk0NDQ4n/gyQBRjPzZv3kxSUhKtra0uL2d8/z24yWoJ+XQvjUG+RMYnI5WM3MQjCdJq66apvZkKYw2N7Ubk7nKi/MMIVgQid//JltXLXU6IcgoRqlA8ZDLqWhupNNZi7Gihe5A8nRUiQOWJPXhabHRdez319WeYMwf0eqivd6mJiImJQSaTsWDBAl544QXXEk9yRDH2orW1le3bt/Pggw/i4+PjcvTvLVvgrmn/gVd7F/WLbiPAy/n0/QXZ3d1Nh8XMuZYGKpqqMVu7CPLxJ0IVir+XckAcmt64u8kI8FYR5R+Gv7eS9q52KpqqqW810GntclmIAJYTHyFIIGrJGurrzyCV2li2DFwdaYaEhGCz2Vi5ciVbtmyh25UPz0mOKMZevPrqq8TGxuLn50dMTIxLEzcmE3zxRR3Tv3iXpkAfQuY+gIfMtfAbdkFKkFBhrOFcSwMyqYwIVQihyin4eHi5VCaJRIKvpw9hflMJ8+uZEa5uruNsUzUeMg+nhdhl62LPTAufJF5N7PQee9SmpmqWLYM334SuLhfqKJUSFRXFzJkzMZvN7Nu3z/nEkxxRjD8iCALPPfccjz76KB0dHYSHh7uU/rXXIPz21/jkWl9ql9xOgPfo9tSQSiR4yjyQSCTIpDL8vZS4u409yJWnzAN/b78fg1ZJkf94DWf4pvUbSkPckSxNRiKREBwcTV1dOX5+MGsW7N/vWlmioqJoampi9erVor1qL0Qx/sjhw4c5c+YMCQkJREZGuuTqY7XCyzoT56J2c2RhDEHXLMPHw2tU5RAEgdbONoJ8AnCTuo3JdK5PGX8cmso95Ph7KWnpbHN6ra/69H4U7Tbmh2gACAqKpK2tifZ2EytW9ATPcqWIXl5ehISEoNFoKCwspKysbDRVmnSIYvyRnJwcHnnkkVHZoX7+OcwMexpFg4Eb5Qvw91KN2iOjrasDiUSCj4d81IYB/en/jaiQ+2CxWem0jjy+tNm6ufbNQzzx5jnUyp79M2QyDwIDp1FXV8aMGdDeDuXlrpUpOjqazs5Oli1bRm5u7ihqNfkQxQjU1dWh0+m4++67CQwMRKEYfjmiN4IAmzc38sDp51n7zjlmuV2N0nP0OzGZzK0o5b5IJJJRGwb0ZrDJGqlEisLTB5O5dcT0VWeO4GfsoGvO9X1mhoODo2lsPIsgWHjwQdi61bVyBQUF4eHhQXJyMv/85z8xm82uVm3SIYoReOGFF1i4cCFSqdRlO9Rz5yCo8Q8o2s003XwbfnI/pxfc+9NltdBp7ULRS8xjsWUdbtZUKfelraudbtvws5mm4+8DEHhzSp/ffXz88fJS0NhYiUYDhw719JDOIpFIiImJITQ0lKCgoAGb+1yOXPZi7O7uZsuWLaxcuRJBEJg6dapL6Z//ZxMaow6Tygufa+/GTz76LcFN5lZ8PL0GLFuMRpAjLV94uLnjKfOkpXNoDwpBEPD65ita/byIuXbZgOPBwTHU1ZXh6SmwcCHsGty9ckgiIiJoaWkRHY9/5LIX4549e7BarcyYMYPo6GiXYsh0dUHdR3/Ar70Dw2INcndv5E76+PXHJtho6WpD6Tm4mF0RpLPriEq5b0+EuCEmcurOnSTonInW62YjG2T3qoCAcLq6zLS2NpKS0rPm6Mqyobu7O9OmTePGG2/kyy+/5Pjx484nnoRc9mLMyclhzZo1GI1GoqKiXEq7+71WFpjfoEUlRzH3Pse33mho7WzHXSobNkaqM4J0ZUHfx8MLQYAOy+Dfa8fNX/H+gin4LFk+eHmkbgQFRVJXV05oKPj6wldfjVDRfsTExNDc3MzKlSsve8fjCSFGo9FIZmYm2dnZJCc77yen1WrH9K1RWlrKgQMHuO222wgNDUUulzud1maD/zugY8uKEMoeWg7I8B3lxI0gCH0mboZjOEG6alnT4zjsQ/MQEznFktN8tCCMKxJSBj0OPRM5TU01WK1mVq503V7Vz88PPz8/7r33Xl555ZVBt8JzhtE+QxOJCSHGoqIiAgMDycjIIC0tDYCSkhJKSkqGTRcQEDCm627ZsoX77ruP9vZ2l5czTp5upznmdTy9fYiJSsTX0xu3UYZJtJuo+Xo6F01ggCCFHkHWtjS4ZOIGoJD70mExY+m29vnd0FJFV/1ZrlbOQe429JqpXO6LUhlEff0Zrr8evvkGmpqcurSDmJgYvLy8mDNnDtu3b3ct8Y8M9gxdakwIFyqj0Uh8fDxpaWlkZGQAkJmZSWBgIACNjY3ExsaSkJDA0aNHMRqNxMXFOd6iBoMBtVrtEGdeXh4bN25EpVINec2Ojg6mTZvGc889x5QpU1i0aJFLQ8z/XvdbgloK6Lx/BdMVNxKiCBoxDP9Q1LcacJO6EeDtWtwaGwJ1LY2YrT17LXq7y5niE+DyULmu1YCs3/W/O5TDtDfexPCvv2XObf86bHqj8Rxnz57kmmtuYft2NxQKeOIJ5x+r7u5uDh48iF6v529/+xvfffedy3UY7Bmyv8zz8vK48cYbee6550hMTCQpKQm1Wu1S/heCCRE3VaVSUVpaSnJyMsnJyRQUFBAbG4tarUav16NWq0lKSnIcA4iPj2fjxo3k5uaSm5uLWq0mLS2N5ORkEhMTR7zml19+SVtbGzKZjNbWVvbu3etSmX0qPmN2k4kyIQwBgZoWF90XBsHUOfK633C0W8ycMVaPy/XN1XoEwGC1UlKyx6n0x4+/R2ionFdemUVMzPCjmsHw9vbm1KlTVFZWEhER4VLawZ6h3Nxcx/OQkJDA3r17UavVE1KIMEHEqNPpSEpKoqCggPj4+D7H+g9F7eK0/x4XF0dmZiYFBQUYDAYSEhJQqVQjfntcf/31hIeH09DQQHR0NEuWLHFpJvXpQ+XYDnyNrHgPUk06QT7+eLs7/83Zm8Z2IzbBxhQf14bdVls3tS0NuCGh02bBXSojVDnFKbet3tS01OPtLu/je6mffQuSz07gW/41cXcMvpeGnYaGs9TXlzNz5kKeecaNtDSBu+66y+nrWywW9u/fT2VlJYsWLXJZiDD4M9T/eYiPjyc3N5ekpJHjvV4MJsQwNTs7m8bGRubNm4fBYCA1NZWioiIKCgqQy+XI5XKysrIoKSmhqKjI8XYrKiqisbHRkU9iYiJZWVkkJiY6hirD8fTTT7N7926eeuopZs2a5ZJxeG2thX3LryOis56O//c3PLwDCVFOGVX9u7otVBlrifQPG9Y1qje9J2sCvf0401SNl8wTG4JrEQOsXVQ11xHpH9rn2labherfP4gbEq7aXor7IEsb0DP59M03HzBlSjS+vtE8+igcOAAuzIVRWlpKZWUla9eu5Y9//CMPPfTQyIn6MdQzZH8e7M9MXl6e49tyojEhxHixaGxsZNq0abz++uv4+flx0003OZ1WEOBXj/6DB7/6T87efD3et20gQhXaZxNTV6gx1eHlLkflNfKW2/1nTe07F0eqwqhvM7gUwqOhtQkbNoJ9Awcc+/rtPxFZeIi2jP/mmsXrB03f2mrg1KnDzJ59B7t3y6iuhj/+ceT62hEEgf3793Pu3Dl+9atfcfbsWTw8RvftfakzIWZTLxaBgYGkpKTwzjvvYDAYMJlMTqeVSOChJ9fzTXA4JzmDRdpJyxi++ZSewy/A2xlu+cJVSx2bYKOlsw3lEFZDITet5MP4AIosnw+ZR11dGYGBEYCMXbtg7dphLzmA+vp6rFYr+fn5rFu37rIVIlzmYgRIT0/nlVdeQaVSUe6i60FCgowXQ57mYHwARzo+xmR23i2pP94eXgj0TMIMhTPriK4IsrWzHQ832ZBWQ0GBar67dR5HpKepax84MWSxdGIwVBMcHMOpU6BSQWTkiFXtQ1lZGe7u7uzdu5fU1FTXEk8yLnsxzps3jyuuuILPP/+cs2fPYrVaR070I25u8PgSDdKWKMqrD2Nur6OtywVr6V5IJBKUw3hSuLKg74wgBUGg+UdDg+FYoLwJRbOZ459qBxxraKjA1zcAuVzBK6/A+vU9IwZn6ejooK6ujvfff5+77rqLSFeVPMm47MUokUhIT09n+/bteHt7U1lZ6VL65Ulu+O+7k19uL6Xr47whrVmcYagF+NHErBlJkGZrF922bnxGMDRIUMTzZH4lU17dQbftp3IJgkB9fTnBwTGYTHD6NLiwdSUA5eXl+Pv78+KLL5Kenu5a4knIZS9GgIceeoizZ8/S2NhIWVmZS0NNhQKmz1hBzdSphH1eQlPzWaecdgdDJnXrCRbcS9CjEaKd4QRpMrei8PQecRnEw80T05w5+J8z8sOxn0wPm5vrsNlsqFQhvPkmPPgguPK5Z7PZOHPmDCdPnkSpVKLRaJxPPEkRxUjPYvOaNWt4/fXXaW9vx2AwuJR+/XoZ++T/gofFhvWTAqecdodCKfelpbMNm2AbkxDtDLaVgNXWTVtX+4hDVDsBC1MQgMb3nnf8VldXxpQpUdhsUvbsgUceca1cNTU1yGQyXnnlFTZs2DDqHZcnE2IL/Mj69evZtWsXPj4+Lk/kTJ8OtdZ1nAubSkTxcc4Z9KMOkyGXeeImlWIyt41ZiHb6RwwwdbTi5S53OtBVSOh11EUFE3TsS0wtNXR2tmEy1TNlShTFxXDlleCqmXBZWRmdnZ0UFxezevVq1ys1CRHF+CNXXHEFixcv5uDBg1RXV9PZ2el0WqkU1q2TcXjqv+BusdF5OH9Yp93hkEgk+Hr4YGg3InNzH7MQHWWUSHrWEgUwmk0oPJzf4g6ge/6tuFu6+X7Ps9TVlaNSheDu7sXOnbBhg2sTNyaTCaPRyNtvv82KFSvGbPA/WRDF2Iv09HS2bduGSqXizJkzLqVduhQ+/m4dRXfP5uXZ7VS0V41qmcNq66bF3IpMKsNsMVNjqu/ZG3EMthk2wYbJ3EqNqY4umwWZ1A2jucWl3jvq+hTeWxTCLv9T1Nef+TFcIxiNMGeOa+UpKyvDz8+PV199VZy46YUoxl4sW7YMQRAoLS2lvLzcJQF4esKyu2UYgjfS6Snh446PHN4UztI7nGKEKoRI/1C8PbwwtDdT0VSNob0Za7fzSy9d3RYa2pqoaKp2+EtG+YcxTRXqchhILw8FrQtvodStnhqhBoUiiLw8ePTRniUeZ7FYLJw9e5bPPvuMq666ioSEBOcTT3JEMfZCJpORlpbGzp07EQSBc+fOuZR+zRr48s3buKY1kGvf2EtVnfP71w82WeMmdUPlpSBCFcIU30C6rJYRN7XpvTlOVa/NccL9pqKU+yKVSEcd5Gqh6mam1nXQ9s1+urokfPhhzyyqK1RWVuLr6ysuZwyCKMZ+rF27lkOHDiGRSFwOrhsaCmEhbsxru5PrTpkwHXoJ6wjR12Dk5QuJRIK3h5wQZRCRqlA8ZO7UtzZytt+mNsYOk2NzHC93OZGDbI5jZzSCjHSP4uf7jVzz9gHeL2xi4ULwcSG4gSAIlJWVUV9fT01NDSkpQ0cQuBwRxdiPkJAQ7r//fvbs2UNDQ4NL+89LJD2TGfuPr6MhaiqRx05QWfftsGlcXb6QuckI8PYj0j+MAG8/2rs6qGyuAXoiBtg3x1GNsDkOuC7Ils42WuNvxKu9ixPvbsZV67XGxkbMZjM6nY7HHnsML6/RRV2frIhiHIT09HR27NiBv7+/y8scN94IlZUyuu/uWXc07H9xyG/Psawj9mxq402YXzChyp5NbaYqglzeHMdZQXbbbLR2thO5YCWdMilzW3W46qNbXl6Ot7c3b775JuvXD+4FcjkjinEQFi1aRFhYGMePH+fMmTMubVsmk8FDD8GXlanUR4UQeewE1XWnBpz3kxCd3w1qKDzGuDGOM4Js7WzDQ+ZOoCKUr6Zcw5WGM5SfPuz0NcxmMzU1NRw4cIBbb72V2NjYMZV5MiKKcRDs9qovvfQScrmcqqoql9KvWAGFhW54JP0Cs4eUz8vf6nO8b4/oesya88Fwguwdva61TcIh82qkCHz72v85nf+ZM2dQqVRs27ZNnLgZAlGMQ/Dwww/z7bffYjKZXJ7IUalg7lxoIpWXnlzC+1MqqerocUEaDxO388VQgjRbO+kWbPh6eFN4yI9Zc2/m7Xtj0cY20Nk98vKNzWajvLyc06dPI5VKXQrJcTkhinEIlEoljzzyCG+88QYtLS00uRh/cP16yMt342exjyNg4+CZ1ye0EO0MJshmcytKTx9sgoR9BwJ56EETUxfcgVHSwaHykTdnPHfuHBKJhJ07d7J+/XrcXFmYvIwQxTgMGzZsID8/H4VC4fJEzqxZPeH/I7o1rPi8m6XadzhVUTKhhWintyCrTXW0d3WgkPvyzXdywkO7CAq0cVvIItRnWjmz428j5ldWVobNZuOjjz7i8ccfvwA1uDQRxTgM1157LfPmzePjjz+mqqqKLhf2y3Zzg8ceg7zXpKhvfBR3iw2p7lm6zUYuiaBDgoCXuxxLtxWpRIIUKa+/E8SqFfVIJDDVawr3fOfGVe8WU1nx9ZDZtLa20tjYyLvvvsuDDz5IcHDwBazEpYUoxhGw26sqlUrOnj3rUtp774UjR7qxKmegv2U+oWfqMOf8mm/KDtPYZhzgRDwR6LJ20dBq4ExTNW2d7QT4qPBw8+CHWgPn6j2Yc+1PYUEUt96JtFvgq9eeHTK/8vJyVCoVL730kjhxMwKiGEfggQceoKWlhYqKCpcdjwWhg/nzvyc763o6rtpN7YrV+De04v/8n6msKeGssYYaUz1tXR1jMgQfKz1bl7dT3VxHVXMdNiDUbwrhqqm448uxIzP4n6w4bl/6A4Lkp1nWmTfcS6tKjqSoCEv3wFGD1WqloqKCkpISoqKiWLBgwQWs1aWHKMYR8PDwYN26deTn52O1Wqmvdy5yeEdHB5988gmPPtrJiy/6oS+V8r9vPMO+eb+nPNSLF4Q3OCetRC7zoKG1ibPGGowdphE3Lx1PrN3WPkbo3h5eRPqHEuQdQH2tD/+XG8KT/286phYPtv61kpsXNvJdRT3W7h5Buru50z4/HmVDCycOvjog/6qqKuRyOdu3byc9PX1CfydPBC7ruKnOUlFRwfTp09mzZw8KhYIbbrhh2PPtQgwICGDu3LmOh7CtrWdDUe27x2hd8J94eTWR1jybOdc/TofFjKmzFbOlEx8Pb5RyXzxlHk49wDabjfKmKqL9w0f0mBcEgQ5LJyZzK+2WDrzdvVDKffFy98TaLeFIsS9vvBuERCJhZVIDSxa14Cm3X0fgdGUDlm4bMyOnIHOTUlX1LeZf/4Lam+fy4F/397nOBx98QFNTE6tWraK6uhpf39FvJHs5IIrRSe6//35mzJjBokWL0Gg0Q9pV2oUYGBjInDlzBhVTdze895mez7Y8xK3HSzl2zWLif76RAD83LDYLJnMrLZ1tuEtlKOW++I4Qq8YZMXbbumnpbP8xNqsNhdwXpacPblIZjU1S3nkvgI8Oq4ib3cYjDzWgjrYwWFaDCfJve5/is8BW8h58gyCvIACampr49NNPee2111CpVPz97393opUvb0QxOklhYSEPP/wwOp2OoKAgrrrqqgHnOCPE3tSfK+fTJ+4h4HQVh4Nmc9z/eZKWdRCr7kIi6bEFNZlbsdisKDx9UMp9BzV9G06MndYums2ttHW24ylzRyn3xcfDG5sg4etv5ejenkJDowcP3tvIsqXNKHxHfhz6C/LQuY/RVhbw2DVrWD2nJ4pxSUkJLS0t3HHHHZSUlDBr1qwR873cEcXoJDabjZkzZ/KrX/2K6Ohobr/99j4PvqtCtNPeYaLw3+4m6LOTVIaHkCfk0dgUwd23N3LLwmbkngKd1i5M5lbautrxlHn+KKifDML7i9Em2Gjr7MBkbqWr24Kv50/D3tY2CfsOqHj/YACR0zpZtaKB66424+quBL0FGR2u5G+vP8l11fDYlmNYLVb27dvHF198wYEDBzh48KBrmV+miBM4TiKVStmwYQOvvPIKMpmMmpoax7HRChHA20vJ3c8doPHe24ioruWm2EfI/P0RztV78sT/m85z/wzB0OBDkE8gkf5heHvIMbQbB/X8t3RbaWwzUtFUg9FswlfuTZR/GIHeAVRV+vCXv4fxy42xCIIb2v8r49lNlcyd7boQe9pDwoxpQbi7SSmvMrG4KZBZxWf4+rO3qaioEO1QR4HYM7pAU1MT4eHh5Ofno1KpWLhw4ZiE2BtBEHj31f/kGdsBPNw8+PfYtczwuorCQ0p26gJxl9m4f1kD8+LakLkJdFjMNJtb6bCY8XaXO7YF8PHomZCRyzzpskj46LCCd94LwsvLxsPLG1g4vw33cdzOwt5D1lSdIOKvm2hccj1ed/6a2tpaMjMzOXPmDO7uY/MquVwQxegijz/+OG5ubtx7771cf/31nDhxYsxC7M17P7zL/x7JZvnbZ4i+cyXX3pSEzQanSz146bUgTpz0ZvFNRpbdYcDfz4bVZsVkbqHZ3Mo0vxDc3dypq3fjzT2BHDnqx43Xt/BISgPTwq2DTsiMBzabwKmz9TT+/Rf41zfT/Ju/88+tL5GQkMDvf//783PRSYgoRhc5duwYixcv5p133qGtrY3Q0FDi4uLGdQ3tiy/20vCvj+PZasaQch/X3/ekI39Ti4S396h4Y3cAIcGdJN3bwJVXtHPWWEXDmRns2h1Ma5uM5T9r5M7EZrxdi8g4aho7jRx6+X+Y/f5Rvrp7Gb/8Sx56vd6lPS8vd0QxjoIbbriBa6+9lvvvvx+JREJERATR0dEoFIqREztJ+Q9H+fYXy1HWGqlJnM+Nj/0eN+lPH3fdVig54cX2nUHU1LrT3tXFNVfYWL2ykZlXdLoUsW00GFvqOXviA1q+Ospnge18HGnD09zN7587xZ89p6KIuAadTjdyRiIORDGOgrfeeotHHnmE2NhYVq1aRUJCAk1NTQQGBhIdHU1ISMi4hKtvqCvn8C/uIeBUFefmTifuV88g7xd8WBCgrgG+r6piwTWhyGTnZyzaZDHxQ+lnWA7tR/ZDKf5VTUhtIACfzZ3CV0tuoPN4J5+/9RkmUxtvvfWWaP7mIqIYR0lrays7d+7kueeeo7y8nIcffpi7774bNzc3BEEgOjqaqKgo5K7spz0IPUsfyyjvqKTk7jlkXvUkAZ6qPudYu20cO1VF/BXhyNzGR4zG1gbOfnmIlq+O8t6VAt/5tDGtxswTO8tpUXhiiArHOn0WXVOv4ZMj35KXl0d8fDzp6ek88MADeHoOvuejyNCIYhwjgiBw+PBhcnJy0Ol03HTTTaxYsYIrr7wSo9FISEgIMTExBAYGjvq70tpt4ZlPNvHu2X2EWbzJiF5DaNjMXsfHLkajxUTZyY8wH/kYtx9+QFXdhFt3T8S7d+6Koe3G+YR1hxDe6EVU7DxOnPiKl19+ma+//pqHH36YDRs2MHv27FFdW6QHUYzjSF1dHS+88AJbtmzBYrGwZs0abrvtNjo6OpDL5URHRxMRETGqqX5BENjx5T+x/fEPhDdY8P5lJtOvvhkYnRib2xqo+OIQpm+Okx/vRo3VwMJjBpZ9UEerUk7nzOl4XpeAx4zrMdv8kCBFKpWyd+9eduzYQXBwMOnp6TzyyCP4+fm5XB+RgYhiPA90d3ezd+9ecnJy2L9/P/feey9JSUmEhITQ0tJCREQEMTExKJVKl/P+9K2/YfnvP2KTCFjTUrn2piSnxNhsaeHM8QO0Hv8Mt9M/4F9tcPR8uY/NInhmAvO8rmSOahaq4JmcraikuroaPz8/ysrKeO211zhw4AD33Xcf6enpLF68WPTCGGdEMZ5nSktLyc3N5Z///CdTp05l9erVzJ8/H5PJhJ+fHzExMYSFhbk04fPV4Tc595v0H5c+7iVu2ROUnK7uI8bmdgMVXxyipvo79sR2UW1pYOXuKq471UKbUk7HlbF4x80ncuEy1DMXgCChsrKS8vJy2tra8PHx4YMPPmDbtm1YLBbS0tJYu3YtYWFh56upLntEMV4gOjo6KCgoICcnh6+//poVK1Zw3333IZfLsVqtREZGEh0djbeTC4NnSo/yzZM9Sx9frF2GKvR2ppi+pfXrkp6er6oRt27o9JTyv7+cw1WBV7NAiOTasARirlqA7EeD85aWFsrLy6moqMDb2xuDwcDrr7/Orl27WLhwIenp6dxzzz2iFc0FQBTjReDYsWNs3ryZV199lbi4OH7+859zzTXX0NTUxNSpU4mJiWHKlCkjDgMb6sp59R9r0M0wY7XB754vxa/N+mPPp8Zn7nwiFi5DfdVNDvFBj2F5bW0tZWVlGAwGVCoVR48eZfv27ZSXl7N69WrWr1/PzJkzh7m6yHgjivEi0tTUxPbt28nJycFoNLJq1SruuOMOrFYrMpmM6OhoIiMj8fAY2pi03dLOXw//hW8qT7PcbTozr75lgPjsdHR0cObMGc6cOYNUKsVqtbJ7925efvll1Go1TzzxBCtWrMDHld1sRMYNUYwTAJvNxoEDB8jJyWH37t3ccccdpKSkEBkZSXNzM+Hh4cTExODv7z9oeovFwp49e7jrrrsGDCcFQaChoYHy8nJqa2vx9/fnu+++49VXX+XTTz9l+fLlpKenc8MNN4gTMheZSeFCpdPpiI2NRavVkpmZ6TDD0uv1lJSUADiO2f++4YYbSE5Oxmg0Ehsb60iTmZlJUVHRgGsUFRVRVFSEVqsddzMvqVSKRqNh165dlJaWMnfuXP793/+dxx57jC+//JKWlhY++eQTPvjgA6f3/rBYLOj1eg4cOMDRo0dpaWnh8OHDLF++nD//+c8sXbqUs2fPsmPHDubPnz/uQtRqtY7/EhMT0ev1I6ax30ej0djnvtjbvnfe9vs6mRiFJ9vEIy4uDrVaTeqPe5QlJyejUqnQaDQAGI1GSktLycrKcvz92WefERsbi0qlIjMz0/GwxMbGOtL1JiAggKKiItSubr3kIhEREfzhD3/gP/7jP3jzzTfJycnhz3/+M8nJyTzwwANYrVZOnjzpmPDpH1emubmZsrIyKisrUSgU1NfXU1BQwO7du0lMTOT555/njjvuOK9RvXU6naO9gUHbczA0Gg1qtRqVSgWAWq1Go9Gg1+spKioiICDAca7BYBj3cl9sJoUY+5OSkkJBQQFGoxGDwUBAQAB6vd7RU9r/jouLc7xhc3NzycjIcNxw+5u4pKSEjIwMxwNiR6vVOh6W84GHhwfLly9n+fLlnDx5ks2bN/Poo49yxRVX8Oijj6JUKjl48CCBgYFERkYCcPjwYceSyTfffMO2bduor69n7dq1fPfdd8TExJyXsvYnLy+PtLS0Ab/b2zovL4+NGzei1WppbGzEaDSSlpaGWq1Gr9f3GdkAjrbPy8sjMDAQlUpFYWEhBQUFpKWlERcXd0Hqdb6ZFMPU/vR+sxqNRkfPqVar+/ydkpJCbm4uAQEBxMXFkZ2d7bixCQkJBAQEUFhYOCD/3NxcNBrNeRNif66++mr+8Y9/UFVVxdq1a9m6dSv33HMPhYWFmEwmTp48CfRMCL3xxhssXbqUN998k6eeeorKykr+53/+54IJcTCysrIoKSkhNzcXg8FAYmIi0HN/5s2bR1paGnl5eY7fkpKSSEpKGjAKiY2NddyfxMREMjMzHekmA5NSjAUFBSQnJ494XlJSElqtFo1G43gg7A9Afn4+wIAeEXqGxfbvzwuJQqFg/fr1nDhxgnfeeYeGhgbuu+8+nn76aX7729+ycuVK3Nzc+Pjjj/n00095+OGHx2yoPhoSExP7vMRUKhUqlQqDwUBCQsKAl5i9jfsPPSfjUHQ4JsUw1T701Ol0GAwG4uPj0Wg0jm+XkpISxxCp99/Q89a2f1/2HloZjUbHW7eoqAij0UhxcbHjuFqtJjMz0/FddCGRSCQsXLiQhQsXcu7cObZu3YpCoWDVqlWDvjwuNKmpqWRnZ6PVah3DfrVaTVpaGsnJySQmJpKRkdFnUqf3J4TRaHT8Zv9cKC4uJi0tjYKCAuRyeZ9zJgvi0oaIyARhUg5TRUQuRUQxiohMEEQxiohMEEQxiohMEEQxiohMEEQxiohMEEQxiohMEEQxiohMEEQxiohMEEQxiohMEEQxiohMEEQxiohMEEQxiohMEEQxThCGiv/SO45PbwaLA2N3W9LpdCQmJmI0GkeMF9M/vozIxWNS+DNOBoaK/zKcf2Jv51udTkdjYyMZGRmO9IOd1x97bJ8LFbVAZGhEMU4gBov/YneYtjvsFhYWOqIM9I4DY48rY6d3XJje5wEcPXq0TzgSkYmBKMYJhD3+C/TE2bH/ZhdpXFxcn54yMTERtVpNbm6uI/jWYPQ+T6/XU1BQAEB8fLzjb5GLj/jNOEEYKf5LXFycQzgJCQkD0vePOzMc9l63d+hDkYuP2DNOEIaL/1JaWtrnXIPBQGlpaZ9zs7KyyMzMJDMzk3nz5jni+vQ/b+PGjeh0OtRqtSNqW+/YPiIXDzEGziWCTqdDo9FgMBgoKSlxDGdFJg9iz3iJoNfr2bRp05ARz0UufcSeUURkgiBO4IiITBBEMYqITBAc34yCINDZ2XkxyyIictni6en5kxirq6sxmUwXszwiIpctSqWyR4xWqxWTyURgYCBKpfJil0tE5LLCZDLR2Nj4kxihZ5eji7FrkYjI5YwgCDQ2NvadwLmQe7rrdLpJuRX0aBjM9eliuTYN5bJ1KZOcnIxWq8Xf3x+tVuvUdoGjYbRtZ9fdRVv0Ly4uJi8v77I3VB7K9Wmsrk32h8LZXX3P9/kXk6ysLIf5X2pq6rBtOpp6jVfbXRQx6vV6UlJSyMzMdDjANjY2OuwrU1JSgIHbTcfGxjoeVvv23kAf1yK78bM97Vj2K/zlwV9S314/tsoCU7yn8Nclfx302FCuT/Zyl5SU9HF5gh6hHj161GEwbs/j6NGjjmOlpaUEBgY60iQnJzs8QOxeHPb8MzIyHFt06/V6x6amvfPuv+W3/fxxEeO//dvA33x94Y9/7Pn7nXfgwIGB56xYAddfD21t4OMzZPb93cSMRiMlJSVOtRtcuLYbVIwbNkBV1ZB1c4rwcNi8efBjdkNltVqNVqslIyODxMRE9Ho9aWlpZGVlOTbVhJ4H0+5eZDQa0ev1FBYWkpGRMcC1KDc3t0/aic5wrk8AmzZt6uPylJaWRmFhIRs3biQzM7NPXe3tsnHjRvLz8/s8hImJiQQEBJCVlUViYiIFBQV92tH+ogsICECv1w9oR/v9UqvV5OXl9XkxXmr0r9tw7QYXru0GFeNQIhovVCqVY9/22NhYMjIyiIuLQ6fTkZGR4Xi7qFQqjEZjH1ef/Px8xzHo6UmysrKIj48nISGB3NzcPmnHwlC92Xhid30abuik1+sdN3v58uUUFRWxbt06gD517X1ssJeRvc1UKtWAduxP/3vQP49x5dlnhz9+zz09/w3FML3iYPSv20jtBhem7S64BU52dnYfcalUKrRaLWlpaY63hX276ezsbFQqFcXFxQ43n/7be/fGYDAMSDvRsQ+1MzMz0el0jjrZXZvsLk86nY6srCzy8/NRqVQkJiYOqGvvY2q1moKCgj4PQ0lJCVqtlo0bNw5oR/v5J06coLS0dEDeer2+z1bfg+U/keldflfbDS5M20kEQRDMZjNlZWXExMRccksbomuRc2i1WkdcHRHXON9tZ9ffJe9CJboWOUdAQMAl04tNNC5U213yPaOIyKWOXX+i14aIyAShjxhFP2MRkQuPXXcyAJms59OxpaXlgprEiYiI9OgOeoXdqKqqEl2oREQuEkqlkv8foKDb6SObDZ4AAAAASUVORK5CYII=" }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2390733/3679153897.py:80: UserWarning: The figure layout has changed to tight\n", " plt.tight_layout(rect=[0, 0.05, 1, 1])\n" ] }, { "data": { "text/plain": [ "
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du3fvnvU1AADzZ9tmLaAXXzRzgC65xKwKDTjZlCBUU1Oj2267Le3Y4cOHtX79+kW9UE9PjxobG9N6iM7W1tamXbt2pa7H43HV1dUt6nUBAKYC7OhRMx9ozRrJ7WZXeECaZY7Q/v375Xa75fV6dfnll+vxxx+f9Qm9Xq8sy5KU3oMUiUTU2tqqUCik1tbWaR9bWVmp6urqtAsAYOEsy1SBPf+82RT1TW8yCyISggBjxgUVA4GABgYGVFNTI0m65557tHHjxhmfsK2tTd3d3XK73ero6JBlWQqFQmppaZFkJkt3dHRkp/UAgGnFYqYHaHTUrAO0ejWLIQLTmTEIbdu2LRWC5srlcqm5uTnt2OT5Rp2dnfN6PgDA3E0OQK97HatBA7OZMQgdPHhQAwMDcrlc6u3tldvt1o4dO/LVNgDAHMViZhJ0ImF6gAhAwNzM+Gty5513yrZtPfjgg2poaNA//uM/5qtdAIA5sCzpl7+UDh+WamulN7/Z9AQRgoC5mbFHqK2tTe3t7flqCwBgDmxbGh42iyGeOcMQGLAYMwahs2WjjB4AsDC2LQ0NmQAkmQC0ejUVYMBizBiE+vr6dPnll8vlcsm2bQ0MDGhoaChfbQMAyGyF8eqr0ssvmw1Q16wxw2AEIGDxZgxCTU1NaRVfwWAw5w0CABhnzkivvGJC0LJlrAQN5MKUEeWBgQE1NDRo1apVisViqq+vT1127txZiDYCwNJh24u+36lT0nPPmc1Qjx+X6uvNjvCEICD7pgSh1tZW+f1+dXZ26t57753TatIAAEnhsPS2t0mDgzPfb3DQ3C8cTjs8MiI9+6xZCVqSXv966fLLJRbZB3JnytDYli1bUj0/GzZsUCgUSq0mHY/H2fYCAKZj25LfL4VC0nXXSQ89JE23V+LgoLk9EpH8ftmPPqbocJleftksgnjBBdKll0rnnpvn9gMONaVHKBKJ6Kc//akef/xxlZWVqa+vL3U90x5hAOB4ZWXSvn2Sx2NCznXXTe0ZmhSCTtdfqaP/8O968mdleuklE4De/GYzEZoQBORPmW2nD1SXl5ertrZW9jTj17FYTGNjY3lrXDweV01NjWKxGD1RAJaGyT0+Hs9Ez9Cvj49EXtErdQ2K7fmGqq+4SBdeyNAXkA+ZMsWUobHe3l5t3bp12ifZv39/7loIAKWgrs6En2QYuu46jf3L1zX02/9Lrz63XGfWXqvV9+9V3Vsv0rJlhW4sgCk9QsWEHiEAS9bgoI5f+34dOzyiqNw6Tyd1waUrVfuDf1f5pdPMHQKQU3PuEQIALNyZM2b152PxOo3e/v+T+1Mf1pV6Wit0Uvq3H0mEIKCoEIQAYJFsW4rHTQCyLOn886WLxo6o9s8bVa5JE6Z/+7czV5MBKIisb9FnWZZ6enrU1dWlSCSSOh6JROT3+7Vt2zZZlpXtlwWAvDt5UnrhBbPw4XPPSZWV0lVXSVecN6hVH71W5QP9ZsL0j340czUZgILJehBqb2+X1+tVc3NzWrm9ZVnq7OyU3+9Xd3d3tl8WAPLi9Gmz6elTT0m//KVZ++fSS03p+9q1UuUr01SNveMd5ithCCg6WR8aC4fD8ng8kpTWI+T1elPfJ28/WyKRUCKRSF2Px+PZbh4AzNuZM9LwsBSNmi0vqquliy82W16UT/7vZKbSeWnaajKGyYDCy3qP0Gyi0WjaRq6Ttbe3q6amJnWp4x8IAAUyOmo2O/3Vr6QnnjDzf9xu6S1vkS67zHyfFoJsW9q+ffoQlJQMQ8meoe3b5743GYCcyHoQ8nq9qTlAZ/f8BINBNTc3Z3xsW1ubYrFY6jJI1zGAPDp92uz2/vTTZt5PNGp6fd70JrPv1wUXSOdk6kcvK5M6O6WGhpl7epJhqKHB3L+sLDdvBsCcZH0dIcuy1N3dLbfbLa/XK7fbrVAopEgkokAgII/How0bNqilpWXW52IdIQC5dvKkFIuZaq8TJ0zFV22tCUAL2urCtucWbuZ6PwBZkSlTsKAiAEcZHzel7rGY+XrmjJnzU1Njwk/GHh8ASxoLKgJwrFOnJsLPyIi0bJkJPpdeKlVV0TEDOBlBCEDJGR01gSceN19HR82QV02NmaKzfHmhWwigWBCEACx5Z86YsvZk8Dl5Ulqxwgx5XXqpCUHlea+RBbAUEIQALDmnT0uvvTZxOXnS9PJUVZn1faqqmOsDYG74pwJAUbNtE3SOH58IPqOjpsfn/POlNWvMV4IPgIXgnw4AReXUKVPGfvy4uZw8aSYzJ4PP6tXSypUMdQHIDoIQgIKwbRN6Tp40wSd5GR83oWflSunCC833TG4GkCsEIQA5d+bMROA5eXLiUlYmnXeeCTu1tdK6deY65ewA8oUgBCBrTp82vTzJnp7k92fOmLV7VqwwQaemxnxfWVnoFgNwOoIQgHkZHZUSifRLMvDYtgk3y5eby+rVE99XVBS65QAwFUEIQJqxsenDzunT5uv4uNmDq7JyIvTU1pqvlZUMawFYWghCgIOMj5tAc/q0CTvJ7ydfHxszFVmVlWY4q7LSLEyYDD7LllGxBaB0EISAJc62zRyc0dGJy+Trk78fGzM9NsuWTVzOPdfM15l8nTV5ADgF/9wBRSQZaiZfxsZmvn7mjHnsOedMhJhzz50IOMnvzznHBB1CDgBM4J9EIEvGx81lbGzhl/Fx81wVFeZyzjnpl2SQmXxJhhzm5gDA/BGEUNJs24SLuX5NBpnk9/M5llRWNhFkpruce+5EFVVFhZlvkww9ya+EGgDIj6wHIcuyFAwGFY1G5fP55PF4ZjyO4mDbE18nfz+X2zIdy8clU5hJfn+2sjITPDJ9TV6SAaW8fKJCavKx6e6XvE6IAYClI+tBqL29XX6/Xx6PR01NTQoEAjMenyyRSCiRSKSux+PxbDcvzfHj0tDQxB/wpPlcX8xjc/la8wkt85H8Iz/5a6Zji70kA8pc7nN2mMl0GwAAk2U9CIXD4VRvTyQSmfX4ZO3t7dq9e3e2m5RRcgjj7P/BZ/N6IZ5rpq/zve3sYwAAlJKimiPU1tamXbt2pa7H43HV1dXl7PVWrDAXAADgTFkPQl6vV5ZlyeVypc0DynR8ssrKSlWy+RAAAMiTMtue7yyRmVmWpe7ubrndbnm9XrndboVCITU0NKQdn8tk6Xg8rpqaGsViMVVXV2ezmQAAwEEyZYqsB6FsisVicrlcGhwcJAgBAIAFS063sSxLNTU1qeNFNUfobCMjI5KU03lCAADAOUZGRtKCUFH3CI2Pj+vo0aOqqqpSWYmVLSWTKb1dxYdzU9w4P8WLc1O8ODeSbdsaGRnRmjVrVD5pPZWi7hEqLy/XunXrCt2MnKqurnbsD2Wx49wUN85P8eLcFC+nn5vJPUFJLDEHAAAciyAEAAAciyBUIJWVlbr99ttZN6kIcW6KG+eneHFuihfnJrOiniwNAACQS/QIAQAAxyIIAQAAxyIIAQAAxyIIFYlwOKxIJFLoZmAafr+/0E3AWSKRiPx+v7Zt2ybLsgrdHMjsM9nT06Ouri7+LSsy/L7MjCBUBJJ/aOeyES3yKxwOKxqNFroZOItlWers7JTf71d3d3ehmwNJ7e3t8nq9am5uVmtra6Gbg0n4fZkZQajAurq6FI1G+WNbpKLRqNxud6GbgbN4vd7U9/wHojiEw+HUuaBHqLjw+zKzot5io9T09PSkXfd4POrt7VVbW5ui0aj8fr86OzsL1Dpnm+7cRCIRNTY2KhAIFKhVkKY/N8l/2KPRqBobGwvRLGDJ4fdlegShPJruB9Dtdsvlcsnr9aqjo6MArYI0/bkJBoNqbW1VKBRSa2sr56dAMv3DHQwG1dzcnOfWIBOv1yvLsuRyueh1KEL8vmTGgooFFolE1NPTI5/Pp2g0Kp/PV+gm4Sz01BWfrq4uBQIBeTwebdiwQS0tLYVukuNZlqXu7m653W55vV7CUBHh92VmBCEAAOBYTJYGAACORRACAACORRACAACORRACAACORRACAACORRACAACORRACAACORRACAACORRACAACORRACAACORRACAACORRACAACORRACAACOdU6hGzCT8fFxHT16VFVVVSorKyt0cwAAwBJl27ZGRka0Zs0alZdP9AMVdRA6evSo6urqCt0MAABQIgYHB7Vu3brU9aIOQlVVVZJMo6urqwvcGgAAlqZEQhofl8rLpcrKQremMOLxuOrq6lLZIqmog1ByOKy6upogBAAoWsUaNE6dkqJRaWREGhuTKiqkqirJ7ZaWLy906wrj7Kk2RR2EAAAoZsUcNE6dko4cMV9XrpTOPVcaHZWGh6WTJ6W1awvfxqRCBkmCEAAAC1DsQSMaNW2rrZ04tmyZuQwPm9vXrClc+6TiCJIEIQAAFiCbQcO2Ta/IqVMmRJ08mf792ddn+/74cfP6o6MTz1tWJlVXS+efb4LbihUmrLndUk2NuS3T13NykBaKJUgShAAAmKdTp6QXXpBefVV69FHzBz0enwgdJ06YyznnTA0404WXU6dMGEqqqJDOO89cli+f+/e1teb7c84xYcjlmrjNtk3Py2uvmbYeO2ZCx5EjUixmjp39dWzMtGfFipmD0ly+rlxphr6SiqXHiiAEAChqhZg/YtvSK69Izz0nHT5svp79/ciI+QO/dq20bp35g798ubm4XOayZo0Z6plPqDnvvMX3wCQSpq2VlSZYnO30aXOf9eszf6a2bcLcdAHp7K9Hjki/+MX0t7/2mnm+ZI9U8rJs2URQqqoyl5YW056VK83nm0jk/pznLQhFIhF1dHQoEokoEAjI5XLl66UBAEtQLuePjI1JR49ODTfJ688/b17/wgulSy81geHSS6X3vc98TfZUrFq18KCRS5WV5rMaHp6+fcePm56YmdpWVmYCycqV0sUXL7wtY2PmHE4OR6++Kj37rAm4IyMTl2QAPPdcE8LGxxf+unOVtyBkWZY6OzvV09Oj7u5uNTc35+ulAQBLzGLnj5w+LQ0OZg46L7xg/kCvXWuCTTLsNDRMfH/JJaZ3JpOjRxcXNHLN7Taf1fBw+md4/Lj57Nzu/LSjomKihyxpth6r0VHzuPI8bASWtyDk9XpT33s8nmnvk0gklEgkUtfj8XjO2wUAKD6zzR85csSEnemGrA4fll580fQu1NVNBJ36eum66yaur1s3/R/huSqWoJHJ8uUm6CV71U6cMOGitrbw5f3Z6LHKlrzPEYpGo2psbJz2tvb2du3evTvPLQIAFJNEwvzhXr5ceuYZ6YknpJ//3AxXHTlienOGhkxvTTLUXHqp9OY3Sx/60MT1iy82f/hzpZiDxuQ2rllTnAs+FkuQLLPtyfPUcysYDMrn82W8fboeobq6OsViMVaWBoASNj5u5oyEQqYK68c/NpNvx8akq64yl2Qvztq1ZoLt5s2mmqkYFGPQWAryuY5QPB5XTU3NlEyRtyDU1dWlQCAgj8ejDRs2qKWlZdbHZGo0AGDpsm0pEjGhJxSS+vrMJZGQ3vpWadMmMz9n0yYTgM6uoCr0RGRkXz6CZMGD0EIQhABgabNtM29ncugJhczwx1veYiYnJy9XXWWGR6SJiciT5wglJY8XelVkLC2ZMgXrCAGAw2Xrf+O2bebvJENPMvjEYmb+zubNUmOjdOed0pveNPNrFcv8EZQ+ghAAONRi52ccPZoeekIh83xXXWVCz0c+In3pS6bnZ77zPZbCRGSUBoIQADjQfNfpeemliWGt5OWVV6Q3vMEMa73//dKf/qmZ45OtCczFXPGE0kEQAgAHmmmdnv5+6Sc/MevxJEPP0aPSlVea0OPzSa2t0saNZgPPXCP8IJcIQgDgMMl1elauNBVYfX3SwYPSk09KP/2p6Sm69FLp7W+Xrr1W2rXLVHBVVRW65UD2EYQAwEFs2wSeQMD09DzyiAlEb3+75PVKv/u7ZiKzbZuVmGfaYgIoBQQhAChxR49KweDEJRYzk5mvvVb6whek17/ebLCZlFynJx/7PAGFRhACgBLz2mvSww9Lvb3m8otfmOCzbZv0b/8mXXON2aIi0zo9xbBhKJAvBCEAWOLGxswwVzL4/OQnZrPRbduk3bul66+fuu4O6/QABkEIALLNttPHmhZ7v2ke1t8/EXy+/33zNFu3Sp/4hPTVr0oez8zPwTo9gEEQAoBsCoclv1/at890y2QyOCht3y51dppZyrMYGpL27zfBJxg0837e+U5Tyt7WZp5ivjuts04PQBACgOyxbROCQiHpuuukhx6S6uqmBo3BQXN7JGLu/9hjU3qGTp2SfvQjE3p6e6VDh0w1l88n/eM/Su9+txnSygbCD5yMIAQA2VJWZnqCfh1yxq59j+5pv1Vfs3r10okjumjFWt3i2qYdbXtUcXjAjF/t2yeVlWl83JS1J4e7fvhDM0y1bZv02c+aYa+LLir0GwRKD7vPA0C2DQ5q7Nr3qOLwgAbcFbrJv1pDq1dq1bHjur/zmOqjYxpbX68X7/tv9T61Rr29ZtjrxAmTobZtMz0/b3jDgqYQAZhGpkzBKhEAkG11dbqn/VYNuCtUHx3Tt/YO6z1HztX/r/OMnox+QL9T+Xdae/qg1r9jjfbulS6/XOrpMROX//M/pT/6I+mNbyQEAfnA0BgAZFkiIX3N6tWe5tXq2LNWjxz7oCL/50a9VW/XJRUDil/131q15W/1xJ/v1oUXFrq1gLMRhAAgi8bHzSTnp+79LR3/2Q36wGsX6Df0XX1KX9W/6bf0J/9zhX6wdlSjqlRV1e5CNxdwPIIQACzS6dNmLZ9vflP61rfM9WVXrtGq6/9U//X9b+nyoXjqvl/+2jJ9ZGetzrvEwxYWQBHI669hV1eXurq68vmSAJATr71m5vV84hPShRdKO3aY1Zn/9V+ll1+WvvRHP1XwwL/p8qG4Blcv0y27rtTg6mWqO3Za+zqP6RbXNsrWgSKQ1yDU0NAgy7Ly+ZIAkDVDQ9K//Iv04Q9LF1wg/emfSpdeasrdn39e+ru/k264QTrnxUHtaNuj+uiYDrsr9JGdtfrB2lF9ZGetDrsrtD46ph1te8x6QgAKqqg6ZhOJhOLxeNoFAAppcHAi4Fx0kfSVr0hXX20WkP7FL6Q77pC2bJlU4fXrxRIrDg9obH29HvjKHTrvkjfrHFXqvEverAe+cofG1tebdYSuu44wBBRYUc0Ram9v1+7dTB4EUFi/+IWZ7/PNb0qPP25Wcf7oR6X/9/9m3jVDtm22zYhEJI9HFQ89JH9dnX430ZK+svQ7Pz6xsvT27dOuLA0gP4qqR6itrU2xWCx1GeR/SgDywLalgwelL3zBLGK4aZP0yCPSZz4jvfSSdOCA9Id/OEsIkkyY6eyUGhpS22tIJvycd96krSzq6sztDQ3m/oQgoGDy2iMUCoXU39+f8fbKykpVMnsQQB6cOSM9/LDp9fn3f5ficelDH5K+9CXpfe+Tzj9/gU/s9c6th6eujp4goAjkNQg1Nzfn8+UAOMB8dk4/edJMbP7mN80KzuecI/3Gb0j33CNdf720bFmWGjXXcEMIAgquqOYIAcBcnTpltqQYGZHGxqSKCqmqSnK7peXLJ+5nWdJ3vmPCz3/9l/S615n5Pt/6lpn0XFFRsLcAoAgQhAAsOadOSUeOmK8rV5r1e0ZHpeFh0+tTUSE98IAJPwcOmH27PvpR6X//b+nNb6YjBsAEghCAJScaNSGotnbi2NGjpsfnP/9TevJJ09vz0Y9K//APksdTuLYCKG4EIQBLSiJhhsNWrpQGBqT/+A8Tfp59VnrXu6SmJrPuz5Yts88ZAgCCEIAl5fnnpX/6JzP09ctfmuV4fv/3JZ9Pqq42pfCxmJlADQCzIQgBKHovv2z29br3XrO+z9vfLn3yk9IHPyi5XOn3HR01c4TY0BTAXBCEABSlaNRMdr73XrP24DXXSB//uHT//WYNoOHhqSFIko4fN3OHGBYDMBcEIQBFY2TEzPm5914z9LVxo/Sbvyl99avSunUT9zt1ylSHDQ+nV40dP25K593ugr0FAEsMQQhAQZ08KX33uyb8fPvb0uWXm/Dzf/+vtGHD9I9Zvlxau3ZiHaETJ8xwWG3t1HWEAGAmBCEAeXf6tFnh+d57zfYWF11khr3+7M+kq66a23MsXy6tWTO/laUB4GwzTifct2+fJOnQoUN5aQyA0jU2Ju3fL+3caYLPpz8tXXyx9IMfSL/6lfTnfz73EDTZlA1NAWAeMgahm2++Wa2trZKkTZs2qa2tLW+NAlAaxselH/9Y+qM/MkNZn/iECS3f/rZ0+LB0111mj1JWegZQKBmD0JYtW3TnnXemrgeDwbw0CMDSZttSOCy1tEj19WZH91OnpH/7N7Mtxt/+rfSOd1DeDqA4ZJwj5HK5NDAwoHg8rjvuuEO2beezXQCKxFzn4Pz852bOz733Si++aHZ1/4d/kLZty+Ku7gCQZRn/T7Zz507Ztq0dO3bIsizt378/n+0CUGCnTpn9uw4fNltZHD5srp86NXGf/n7pjjukt7xF2rxZ+sUvpPZ26ZVXpK9/3Sx4SAgCUMzK7AxdPfF4XNXV1fluz5Q21NTUKBaLFbwtgJNk2t39+HGzds+PfmQWNnz8cem97zXl7h/+sFRVVeiWA8D0MmWKjENjXV1dcrlcamho0MaNG7Vv3z5FIhG5XC7t2LEjL40GUBhn7+5+7JiZ4Pwf/yH19ZlVnv1+s7s7ixcCWMoyBqEHH3xQHo9H/f392rhxo+6991594QtfUE1Njfbt26ft27fP64Usy1IwGFQ0GpXP55PH41l04wFkX3J397Ex6b77pG99y/QAeb3SRz5idnavrpbWr6dkHcDSlzEINTU1aefOnTp8+LD279+vWCwmj8ej6upq3XXXXfMOQu3t7fL7/fJ4PGpqalIgEFh04wFk18mT0r59ZkuL//5vs8rzb/yGdPfdpvxdYnd3AKUlYxCyLEs33nijBgYG1NLSov7+foVCIUUikQX15oTD4dTjIpHItPdJJBJKJBKp6/F4fN6vA2B+RkfNKs/f+IZZ5fnii6Ubb5Ta2qQ3vGH6+7O7O4BSkTEI3XbbbWpsbJTb7VZNTY1uvvlmRSIRhUIh3XXXXTlpTHt7u3bv3p2T5wYwYXxc+uEPTfjp6TGLHP7mb5pVnjdtMuXvw8PTP5bd3QGUkhn/T1dfX6+amhpJ0v79+7Vp0ybt2bNnQRVcXq9XlmVJUsYepba2NsVisdRlcHBw3q8DYHq2LYVC0h//sXTJJVJjo+nV+eY3peeeM8NfyVWekxuXDg+bfcFs23wdHmZ3dwClJWP5fNKBAwd05513av/+/RobG1vwC1mWpe7ubrndbnm93jkNr1E+DyzeL35hen6+8Q3p5ZdNpdfHPy5t3WrK4jM5dWpid/exMTMcVlXF7u4AlqZ5lc/H43F1dXVpz549Gh4e1s6dO7Vt27ZFNcDlcqm5uXlRzwFgbp57zqzw/I1vSE8/bRY2vPNO6QMfMMNgc8Hu7gCcIC0IHTp0SHfeead6enrU2Niozs5OhcNh3XbbbYVqH4A5evllKRAw4eexxySfT9q1y1R9LaZDlfADoJSlBaFIJKLh4WHt2bNHO3fulGTCEYDiZFlmjs83viF9//tmocNPftJUf11wQaFbBwDFLy0I3XTTTbrppps0MDCgu+++W5IUjUYlFceWGwCkEyfMKs/f+Ib0ve9JV11l5vz80z9JdXWFbh0ALC2zTpa+//77dfDgQe3fv18HDx7MV7skMVkazjCXOTijo9KDD5rw861vmcUNP/5xc7niivy2FwCWonnvNZaU7CXau3dvThsIOM1sVVnj49LDD0+s9bNypVnr5+GHpY0bTZk7AGBxZg1CSck5QwAWL9Pu7tGomej8/e+b8DM6KjU1mV6gd7yD1ZwBINvmHIQAZM/Zu7s/84yZ4Pzv/252en//+6V//mez1s85/JYCQM7wTyyQZ8nd3V96Sfra18zE50jEhJ4vflF697vN/davJwQBQK7xzyyQJ7YtPfWUmfPT3S0NDkrXXy/deqvZ5LSqauJ+7O4OAPlBEAJyyLalJ54w8316esyKz+9/v/T7vy9t2zb9nl3s7g4A+UMQArLMtqVweCL8vPii9KEPSV/6kglBK1dKR4+yuzsAFAOCEJAFtm2qvZLhZ2hI+vCHzY7u733v1P293G7p5EkThiZXjR0/zu7uAJBPBCFggcbHpZ/8xASf++83E6A/8hHp7/7ODHvN1KOzfLlZFDG5jtCJE2Y4rLaW3d0BIJ8IQsA8jI1J//3fE+EnkZA++lGpq0u64QZp2bK5Pxe7uwNA4RGEgFmcOSP94Acm/OzbZ4bBtm83pe/veY8Z1loMwg8AFA5BCJjG6Kh04IAJP9/8pgk7N90k3XefWeenoqLQLQQAZANBCCVtPsNOiYQUDJrw861vSStWSI2NZrVntrcAgNJEEEJJmm1D06STJ82u7j090n/8h5ms3Ngoffe70tveRvgBgFKX1yDU1dUlSWpubs7ny8JhMm1oOjxsgk9t7cSmpt/+tnThhWZj0/37pc2b2dUdAJwkr0GooaFBwWAw4+2JREKJRCJ1PR6P56NZKDFnb2gqmSD08MNmmOuHP5Tq6kz4+eEPpbe+lfADAE5VVENj7e3t2r17d6GbgSUsuaHpypVmU9OHHpJ6e83XSy+V3vc+6Q/+wKzzw1o9AICcBKGenp606x6PR16vd9bHtbW1adeuXanr8XhcdXV1WW8fStOZM6aHp7tb+vGPpV/+0vT2bN0qff7z0uWXT2xoatuFbi0AoBjkJAg1NjYu6HGVlZWqZFEVzMOLL0r/9V/S975nen7OPddUeDU3mwUOz96qgg1NAQCT5XVoLBQKqb+/P58viRJz5oz06KOmqut735N++lNpyxbpAx+QbrvNTHZ+6SUzMXryHKEkNjQFAExWZtvFO0gQj8dVU1OjWCym6urqQjcHBfLyy6bX57vfNaXu55xjNjL9wAekG2+UVq9Ov3+mqrHkhqZr1zI/CACcJlOmKKrJ0oBk1v159FHT4/Pd70qPPy41NEjvf7+0a5f5fqaVndnQFAAwVwQhFIWXX5YeeGCi16e83PT6fPaz5usFF8zv+djQFAAwFwQhFMTYmPTYYxO9PocOmfk973+/9L/+l5n3k439vAg/AICZEISwKPPpcXnlFdPr873vma+S6e35n//TfL3wwty3FwCAyQhCWJC57OU1NiYdPGiCz/e+J/X1SZs2mUnOf/iHZi8vdnEHABQSQQjzNtNeXi+8ID31lNnF/YEHTG/RjTea1Zzf+17pda8rdOsBAJhAEMK8Td7La3zcVHUdOGA2Mv3pT6WrrpI+/GGzm/vb3mbK3QEAKEb8icK8vPqq6el58kkTgPr6zIal114r3XKL9M53StXV0vr1TFQGABQ/ghAysm0pEjH7diUvP/uZdNFFpqrrhhuklhbpjW+c6PVJ7uU1Pl7YtgMAMBcEIaScPGl6eCYHH8syZe3veIf0p38qeb1mPlBlpbRs2dTnYC8vAMBSQhBysBdekH7yk4nQc+iQqfp6xzukd73L9PZ4vVNXYj561EyMni4IsZcXAGApIQg5xOiomdMzOfgcOSK95S0m+PzRH5mv69ebOT8zcbtN79Hw8PR7eZ294zsAAMWKIFTkFrpFxKuvToSen/zErOdTWSldc40JPM3NZp5PVdX828ReXgCAUkEQKlJzWbAwaWxM+vnP0+f29PdLr3+9CT633CLt2SNdeWX25u6wlxcAoBQQhIrQTAsWnjwpnX++Wa8nGXoeecSEkbe/3QSfv/kb6eqr8zNERfgBACxlBKEiNHnBwmQJe1+fFAqZjUqffdbM5XnHO6Tf+A2po0N685tZuBAAgPniT2eROHXKBJynnjJhZ3BQOnxYeuYZMwfnLW+RGhqkP/5js3Lzli30xgAAsFh5C0KRSEQdHR2KRCIKBAJyuVz5eumiMTZmAs6vfmUuTz898f1zz0krVkiXXWbm3lxxhfTud0sbNkhveMNE6GHBQgAAsidvQciyLHV2dqqnp0fd3d1qbm7O10tnlIuJvrYtDQ2lh5xk6Hn2WenMGcnjMROXr7hC2r7dfL3iChOATp82PUEsWAgAQO7lLQh5vd7U9x6PZ9r7JBIJJRKJ1PV4PJ6TtsynIiuT48dNsJmud2d4eKJX54orzFyeT33KfF9fbyY/Z1JZadrCgoUAAOReToJQT09P2nWPx5MKQtFoVI2NjdM+rr29Xbt3785Fk1Jmq8hau3YiDJ05Y4aspuvdeeEFs7losmfniiukD3zAfL388oWtz5PEgoUAAORHmW3bdr5eLBgMyufzZbx9uh6huro6xWIxVVdXZ6UNye0hamvNdds2iw9GImZH9ZdfNkHpV78ya/FIZt5OMuxMDj4XXjj7KswLlY1eKwAAYMTjcdXU1EzJFHkLQl1dXQoEAvJ4PNqwYYNaWlpmfUymRi9UIpE+/+a735V27TJhY80aM2x16aVmf603vtGEnUsvLWxZOgsWAgCweAUPQguR7SB08qQ0MCDV1JienGPHpFdeMQHovPMmKrKS1wEAQGnIlCkctY5QebkZYhodNT1Cq1ebSxIVWQAAOIuj/uQnK7KOH5/+9uPHze0MQQEA4AyOCkLSxGTj4WGzZo9tm6/Dw1RkAQDgNEU9NJacvpTt9YSqqkxp/NDQREXW+eeb46dPmwsAACgdySxx9tToog5CIyMjkqS6uroCtwQAAJSCkZER1dTUpK4XddXY+Pi4jh49qqqqKpXlasGeIpNcO2lwcDBrayctBbxv57xvJ75nifftpPftxPcsFf/7tm1bIyMjWrNmjconVUUVdY9QeXm51q1bV+hmFER1dXVR/iDlGu/bOZz4niXet5M48T1Lxf2+J/cEJTlusjQAAEASQQgAADgWQajIVFZW6vbbb1elwxYz4n0753078T1LvG8nvW8nvmdp6b7vop4sDQAAkEv0CAEAAMciCAEAAMciCAEAAMcq6nWEnCYSiaijo0ORSESBQEAul0uSZFmWgsGgotGofD6fPB5PYRuaA11dXZKk5ubmtON+v1/RaFR+v18+n68QTcuZ6d5zqZ/rmd5fqZ3rTO+1lM+xk87v2Zz4+yyVyL/dNopGX1+fbdu2HQgE7M7OztTxlpYWu7+/37Zt225sbCxI23Ktr6/P7ujomHKsubnZHh4eLkyjcmy691zq5zrT+yvFc53pvZbyOXbS+T2bE3+fbbs0/u1maKyIeL3e1PeT/+cQDodT1yORSN7bVSgej0ebN29WfX29Y953qZ/rTO+vFM91pvdayufYSed3Lkr5XM9kqZ1vhsYKpKenJ+26x+NJBaFoNKrGxsZCNCvnZnrfZ3O5XGpubpbH41FPT49aWlry0cSsm897LiXTve9MSuVcY3qcX2dZauebIFQgmYJOMBicMtbq9XplWZZcLteSH2NeSMDzeDxL4n8VmcznPZf6uZ7t/S31cz1ZpvdaSuf4bE46v3NRyud6LpbK+WZBxSLS1dWlQCAgj8ejDRs2qLm5WaFQSA0NDeru7pbb7ZbX6y3JX6iuri719fWps7NTlmUpFAqlfoHcbndJ9pBN955L/VxblpX2/txud8me60zvtZTPsZPO79mc+Psslca/3QQhAADgWEyWBgAAjkUQAgAAjkUQAgAAjkUQAgAAjkUQAgAAjkUQAgAAjkUQAgAAjkUQAgAAjkUQAgAAjkUQAgAAjkUQAgAAjkUQAgAAjkUQAgAAjnVOoRswk/HxcR09elRVVVUqKysrdHMAAMASZdu2RkZGtGbNGpWXT/QDFXUQOnr0qOrq6grdDAAAUCIGBwe1bt261PWiDkJVVVWSTKOrq6sL3BoAAJBNp05Jw8PSa69JY2NSRYV0/vlSba20fHl2Xysej6uuri6VLZKKOgglh8Oqq6sJQgAAlJBTp6RXX5XOnJFWrZLOPVcaHZWOH5dGRqTq6uyHIUlTptowWRoAAORdNGrCUG2ttGyZVFZmvtbWmuPRaH7aQRACAAB5lUiYXp+VK81125aee27i9pUrze2JRO7bUtRDYwAAoPREo9LDD0tPPy0dOiSFw6YX6Kc/NXOEzj1XOnFCGh/PfVsIQgAAIGfGxqSnnpIeeWTi8stfSuvWSV6vdP310h//sfTGN5qhMcnMFaqokMrzMG5FEAIAAFnz8svSo49OhJ6DB83xt71NuvpqqaNDevvbzSTp4WEzJ+hsx4+b45WVuW8vQQgAACzI6dPS44+n9/YcPiy94Q0m9Hz849Lf/I3p7amoSH/sqVPSyZMmDK1cmV41tny55Hbn5z0QhAAAwKxsWxocTA894bAJMVdfbS6f+pS0ZYvkcs3+fMuXS2vXmvlCIyNmTlBFhekJcrtzUzo/HYIQAACY4vhxqa8vPfi88or01rea0PPpT5uvl11mSt8XYvlyac0aUx02Pm7mBOVjOGwyghAAAA5n29Izz6SHnieekC68ULrmGnP57GelzZulFSuy//r5Dj+TEYQAAHAYy5Iee2wi9Dz6qOkB2rzZ9PK0tZmv69YtvLdnqSAIAQBQwjKVr9fXm7Dz/vdLu3ebIa9k+bqTEIQAACghZ5evP/aY6dU5u3z9wgsL3dLiQBACAGCJWkz5OgyCEAAAS0C2y9dhEIQAAChC+ShfB0EIAICCK3T5upMRhAAAyDPK14sHQQgAgByifL24EYQAAMgiyteXFoIQAAALRPn60kcQAgBgDihfL00EIQAApkH5ujMQhAAAjkf5unMRhAAAjkP5OpIIQgCAkkb5OmZCEAIAlBTK1zEfBCEAwJJF+ToWiyAEAFgSKF9HLhCEAABFifJ15ENOglBXV5ckqbm5OXXMsiwFg0FFo1H5fD55PJ5cvDQAYAmifB2FkpMg1NDQoGAwmHasvb1dfr9fHo9HTU1NCgQCuXhpAMAScHb5+iOPSCdOUL6O/Mvb0Fg4HE71AkUikWnvk0gklEgkUtfj8Xhe2gYAyB3K11HMimqOUHt7u3bv3l3oZgAAFoHydSwleQtCXq9XlmXJ5XJlnB/U1tamXbt2pa7H43HV1dXlq4kAgHmifB1LXU6CUCgUUn9/vyQzSToUCqmtrU3d3d1yu93q6OiY9nGVlZWqrKzMRZMAAItE+TpKUZlt23ahG5FJPB5XTU2NYrGYqqurC90cAHCU2crXkxfK17EUZMoURTVHCABQGJSvw6kIQgDgQJSvAwZBCABKHOXrQGYEIQAoMZSvA3NHEAKAJYzydWBxCEIAsERQvg5kH0EIAIoUu68DuUcQAoAiQPk6UBgEIQAoAMrXgeJAEAKAHKN8HSheBCEAyDLK14GlgyAEAItA+TqwtM0YhA4cOKAbbrghdf1jH/uY7rvvvpw3CgCKEeXrQOmZMQjt2bMn9X1zc7M2b96c8wYBQLGgfB0ofTMGIb/fr5aWFg0MDGj//v3auHFjnpoFAPlF+TrgTGW2bduZbnS73brnnnu0fft27d27V5FIRO3t7XlrXDweV01NjWKxmKqrq/P2ugBK32zl68kL5etAaciUKWbsEdq/f782bdokSdq5c6f279+f21YCQA5Qvg4gkxmD0KZNm7Rv3z5t375djz/+uLZu3ZqvdgHAglG+DmCuZhwau/nmm3Xo0CE988wzkqS2tjaGxgAUlUTClK9PDj6Ty9eTF8rXAWdb0NDYli1b9LGPfSx1PRgM5jUIAcBkk8vXf/KTifL188+nfB3AwswYhFwulwYGBhSPx3XHHXdohs4jAMi62crXf//3KV8HsDgzDo1J0t13362DBw9q1apVuvPOO1VTU5OvtjE0BjiIbUu/+pUJO8lhrsnl68keH8rXASzEvIbG4vG4JKm6ulq33Xab7r//ftXW1uY1BAEobcPD6eXrjz7K7usA8m9KEFq1apWam5vV1tYmycwTikQi8ng88vl8zBECMG+zla9/4APSn/855esA8m9KEGpqakqFnba2Ng0PDysSiaimpkaf//zn895AAEvPyy+n9/RQvg6gWE0JQrW1tZLMYoodHR0KBoOpIbHVq1fnt3UAil6yfH1yb89zz7H7OoClYUoQsm1bl112mQYGBtTS0pLaff7QoUO677779LnPfS7vjQRQHOZSvv57v0f5OoClY9qqsYGBAbnd7lRP0MDAgILBoDweT15Xl6ZqDCis48elUCi9t+fVVyfK15MXytcBFLtMmWLW8vlCIggB+TO5fD15efJJytcBlIYFrSwNoHTNVr7+hS9Qvg6g9BGEAAc4c2Zq+frTT1O+DgBZD0KWZSkYDCoajcrn88nj8aRu8/v9ikaj8vv98vl82X5pAL82uXz9kUekgwfTy9fvuovydQCQchCE2tvb5ff75fF41NTUpEAgIEkKh8OSpL1798pFOQmQNbOVr//Wb0l/+7eUrwPAdLIehMLhcKoXKBKJpI57PB5t3rxZ9fX16uvrS+spSkokEkokEqnrya0+ABi2LT3/fHrooXwdABYub3OEXC6Xmpub5fF41NPTo5aWlin3aW9v1+7du/PVJKDozVa+zu7rALA4WQ9CXq9XlmXJ5XJN2+vj8XjSeooma2tr065du1LX4/G46urqst1EoCjNVr7+jndIu3ZRvg4A2ZT1dYQsy1J3d7fcbre8Xq/cbrdCoVAq/LjdbjU2Ns7puVhHCKVstvL15IXydQBYPBZUBApotvL15IXydQDIDRZUBPKI8nUAWBoIQsAiUb4OAEsXQQiYB8rXAaC0EISAGVC+DgCljSAE/Brl6wDgPAQhOBa7rwMACEJwBHZfBwBMhyCEkkT5OgBgLghCWPIoXwcALBRBCEsK5esAgGwiCKGoUb4OAMglghCKxvi49MwzlK8DAPKHIISCoXwdAFBoBCHkBeXrAIBiRBBCTlC+DgBYCghCWDTK1wEASxVBCPNC+ToAoJQQhDAjytcBAKWMIIQUytcBAE5DEHIwytcBAE5HEHIIytcBAJiKIFSiKF8HAGB2BKESQPk6AAALQxBaYjKVr1dVmR4eytcBAJg7glCRm618/TOfMV83bGBCMwAA80UQKiKUrwMAkF8EoQLKVL7e0GCGuShfBwAgt5wVhGx7bolirvebh0zl6x6PCTsf/KD0pS9Jb3kL5esAAOSLc4JQOCz5/dK+fVJdXeb7DQ5K27dLnZ2S17vgl6N8HQCA4ldm27Zd6EZkEo/HVVNTo1gspurq6oU/kW2bBBIKmS6Yhx6S6uqUSJh5OeXlUmWlTAi67jopEjHjU489NqeeodnK15MXytcBACiMTJnCGT1CZWWmJ+jXIWfs2vfonvZb9TWrVy+dOKKLVqzVLa5t2tG2RxWHB0xY2rdv2hBE+ToAAKXDGT1CSYODGrv2Pao4PKABd4Vu8q/W0OqVWnXsuO7vPKb66JjG1ter4uEfpIbPZitfv+YaytcBACh2eesRsixLwWBQ0WhUPp9PHo9nxuN5VVene9pv1Y2f+YLqo2P61t5hffEWl778tWHVRccUqT1HX//DL2j9gTrK1wEAcICs9wi1trbK7/fL4/GoqalJgUBgxuOTJRIJJRKJ1PV4PK66urqs9QglEtINX92mk88/qW/tHVbNsUr9RNfoEV2th859h35Y8TaNn1mpq7csS/X0UL4OAMDSl7ceoXA4nOrtiUQisx6frL29Xbt37852k1LGx6WXThzRmdUr9cVbXHr//9mkP9WXdLUe0fF3PabVb/1Hrbh4QPv/8Kc677ycNQMAABSJopos3dbWpl27dqWuJ3uEsqW8XLpoxVqdfP5Jfflrw1qnp/Vx3StJGnxymT5yda3Oq36zysuz9pIAAKCIZf1PvtfrlWVZkpQ2DyjT8ckqKytVXV2ddsmmykrpFtc23d95THXHTuuF1ct0y64rNbh6meqOnda+zmO6xbXNlNIDAICSl/U5QpZlqbu7W263W16vV263W6FQSA0NDWnH5zJZOpdVY4fdFdo+qWpsX+cxrZ+magwAACx9mTKFc8rnJy2WOLa+fvZ1hH696CIAAFj6lmQQisVicrlcGhwcXPzK0tdfLx06JK1fL33nO9K6dVNXln7hBbPp1+HD0qZN0ve/T7kYAAAlIDnv2LIs1dTUpI4XdRB64YUXsjpZGgAAONvg4KDWrVuXul7UQWh8fFxHjx5VVVWVynLQM5NMh4vuccKCcQ4Kj3NQeJyDwuMcFFY+Pn/btjUyMqI1a9aofFJ5eFGVz5+tvLw8LbXlSi4q1DA/nIPC4xwUHueg8DgHhZXrz3/ykFgSK+YAAADHIggBAADHcnQQqqys1O23365KVlAsGM5B4XEOCo9zUHicg8Iq5Odf1JOlAQAAcsnRPUIAAMDZCEIAAMCxCEIAAMCxinodoWyzLEvBYFDRaFQ+ny+18Wum48i+TJ91JBJRR0eHIpGIAoGAXC5XYRtawmb7eff7/ers7CxQ60rfbJ9/OByWy+Xi36Ec4m9Bcejq6pIkNTc3p44V5BzYDtLS0mL39/fbtm3bjY2Nsx5H9mX6rPv6+mzbtu1AIGB3dnYWpG1OMdPPe19fH78DOTbT59/c3Jz6XUDuZDoHnZ2ddm9vr93f38+/Q3nQ19dnd3R0pB0rxN9jR/UIhcPhtB6I2Y4j+zJ91l6vN/U9/wvLrZl+3qPRqNxudyGa5RiZPv+uri5Fo1FFo9FCNc0xMp2Dm2++WVu3bpXP51NHR0ehmudohfh7zBwhFJVkdyjyr6enh8++gHp7e9XW1ibJDE+iMPx+v8LhsILBYKGbgjxxVBDyer2yLEtSeq9DpuPIvpk+62AwmDZWjNzIdA4ikYhaW1sVCoXU2tpaoNaVvkyfv9vtlsvlks/no2c6xzKdg66uLvl8PvX29ioQCBSodc5WiL/HjlpQ0bIsdXd3y+12y+v1yu12KxQKqaGhIe04YSh3Mp2D5CRpj8ejDRs2qKWlpdBNLVmZzkGyN4jJ0rmV6fP3eDypXjl6RnNrpnMQDAbl8XhStyF3urq61NfXp87OTlmWVbC/x44KQgAAAJM5amgMAABgMoIQAABwLIIQAABwLIIQAABwLIIQAABwLIIQAABwLIIQAABwLIIQAABwLIIQAABwLIIQAABwLIIQAABwLIIQAABwLIIQAABwrHMK3YCZjI+P6+jRo6qqqlJZWVmhmwMAAJYo27Y1MjKiNWvWqLx8oh+oqIPQ0aNHVVdXV+hmAACAEjE4OKh169alrhd1EKqqqpJkGl1dXV3g1gAAgGw6dUoaHpZee00aG5MqKqTzz5dqa6Xly7P7WvF4XHV1dalskVTUQSg5HFZdXU0QAgCghJw6Jb36qnTmjLRqlXTuudLoqHT8uDQyIlVXZz8MSZoy1YbJ0gAAIO+iUROGamulZcuksjLztbbWHI9G89OOou4RAgAgkZDGx6XycqmystCtwWzGx805O3Vq6iV5fGREev55c98zZ8zxREL6vd8zw2MrV5r7JBK5P+cEIQBAUUr2CoyMTMwfqaqS3O7cDJmUAts2w0uTQ8dMgWQht8/22NHR9DZVVJjzNflSWWl6gM47z3xfWWmO33KLuf+550onTpiglGsEIQBA0Tl1SjpyxHxduXJi/sjwsHTypLR2bXGGobGx9KCQy8CR6XbbTm9TMnicHUbODiZnH1uxYiJ0zvT42W47Z5qkkUhIhw+b25ctm3r76KgJROV5mMBDEAIAFJ3J80eSli0zl+Fhc/uaNemPmdwbks8ekMm3n90bcs458wsNk68nQ8hcHpvp9uTcm2JTWWl694aHpw9Cx4+bc5+PoVCCEACgKJw4Ib34ovTcc9KTT5qwMzQkvfyy+f7kyfTQMV3vy3S9IfPpwUjevnKlqWRayGNn6w2B4Xabczo8nN7rd/z4RBDMB04RACBnbNvM8XnxxfTL0aNTj8ViJjhcdJHpDVizRrrwQmn9emnzZjNUk5xPMjpqjtfUZA4jxdobAmP5cjPEmZwHduKEGQ6rrc3vPDCCEABg3mxbsqypgWa6gHP8uAknF19sws3FF5vLVVdJPl/6sVWrTMiZaf7I6dOm92f9eqrIlrrly835L2RlIEEIAJAyPm6Go6YLNWdfTyTMkMbZAWfz5qnHXK65984U0/wR5EchzyVBCAAcYGxMeuWVzL02yWMvvWTWdampmQgxyUDzzndOPXbWbgVZUyzzR1D6CEIAsISNjprwkqnXJnn9lVdMb8+qVRNhJhlo3vCG9OsXXWTm4xRSscwfQekjCAFYWmx7bmMsc71fkTp1ava5N0ePSseOmbd54YVTA86mTenHLrpoaQ0nFcP8EZQ+ghCApSMclvx+ad8+qa4u8/0GB6Xt26XOTsnrzV/75uC11+ZWQTU8bHpALrooPcxceql0zTXpx173utIu0yb8IJfKbPvsVReKRzweV01NjWKxGLvPA05n29Lb3iaFQpLHIz30kFRXN7W3YHBQuu46KRKRGhqkxx7Lec+QbUvx+NwqqEZGzATgiy5Kn0x89uTiiy+WVq82YQjA4mXKFCX8fwgAJaWszPQE/TrkjF37Ht3Tfqu+ZvXqpRNHdNGKtbrFtU072vao4vCACUv79i0qBNm2maMy0+Ti5OXkSbNv0tmh5q1vld73vvSQ43Yv6VE7oKTQIwRgaRkc1Ni171HF4QENuCt0k3+1hlav1Kpjx3V/5zHVR8c0tr5eFQ//IOPw2fi49Oqrc6ugOn3aVEZl6rWZfKy6moADFCt6hACUhro63dN+q278zBdUHx3Tt/YO64u3uPTlrw3r4qj0iKtO/7Hjz3T1oTq9+N3pA87LL5ty8traqYHm2munBpyVKwv9pgHkCj1CAJaUREK64avbNPL08/qDf3qrHh+5To/oah3RWr2qC2SrXOecP6w31Nemgsx0vTgXX0wJNuAk9AgBWNISCenHP5a+9z2p776/UmLwKrW4I/pdfUdf0B26RM+rY+cKHbzsJZ17Trke/fTPdd55hW41gGJHEAJQlMbHzQ7kvb1SMCg9/LDZpuH666W667+n5VW/re/e+0vV6XTqMf/3m8v0kZ21Ou+SN6u8vHBtB7B05OSfiq6uLnV1dU057vf71dTUpGAwmIuXBbDEPf+89M//LP3Wb5ny8ne9S/rBD6T3v186eFA6ckT613+VPve+Mv3Hvz2lumOnNbh6mW7ZdaUGVy9T3bHT2td5TLe4trH2DIA5yUmPUENDw5SwEw6HJUl79+6Vy+XKxcsCWGIsS/r+902PTzAo9fdLb3+72ZH893/ffH/uuWc9aHDQlMhHx3TYXaHtO2s1tHpUH9lZq32dx7Q+OqYdbXukd3585kUXAUB5HBrzeDzavHmz6uvr1dfXJ4/HM+U+iURCiUQidT0ej+ereQDyIJGQHnnEhJ7eXrM24hVXmOBz991miaAZ6yJ+vVhixeEBja2v1wPtt+o8q1fnnDii8y7x6IGvTFpH6LrrUosuAkAmeQtCLpdLzc3N8ng86unpUUtLy5T7tLe3a/fu3flqEoAcs20zzyfZ4/ODH5ig4/NJn/60tHWrtG7dPJ5s+3azYrTHo4qHHpK/rk6/m2hJX1n6nR+fWFl6+/a8rCwNYOnK+3RCj8eTcWisra1NsVgsdRkcHMxv4wAs2gsvSF/9qvSJT5gS9Xe8Q9q/X7rxRunRR81aPl//uvQ7vzOPECSZMNPZabbNmNTTU1lpVnROzQmqqzO3NzSY+xOCAMwgJz1CoVBI/f39kiTLshQKhRSJRCRJbrdbzc3N0z6usrJSlcxwBJaUWMzkjuRw17PPmi3BfD6zP+rVV5u9tbLC651bD09dHT1BAOaEBRUBzMvp0xPzfIJBkzcuu0zats2En+uuk2pqCt1KAEjHgooAFsS2paeemujx+cEPzJYTPp+0c6d0333MRwawdBGEAExx5MhEj08wKMXj0nveY8JPe7v05jcz6gSgNBCEACgeNz09yVWcn35a2rLFBJ977zXzfJi+B6AUEYQABxodNRVcyeGuRx+VNmwwwefLXzbbWLDuKQAnIAgBDmDb0i9+MdHj89BDpuTc55P+x/+QvvEN6ZJLCt1KAMg/ghBQoo4eNev3JMOPZUnXXmvCz1/8hZnnw8akAJyOIASUiJERM88nOdz1y1+aNQV9PrNR6TvewTwfADgbQQhYokZHzY7syR6fRx6R1q836/l86Utmnk9tbaFbCQDFjSAELBG2bXp5kj0+Dz1keni2bjXbVXz96yYIAQDmbl5BKB6Ps8IzkEcvvZS+ns/Q0MQ8n927pbe+lXk+ALAYMwahAwcOaM+ePYrFYrJtWwMDA3rmmWfy1TbAcV57TXr44Ynhrp//3GyvtW2b9LWvmXk+y5cXupUAUDpmDEItLS3q6OiQ2+2WJAWDwbw0CnCKM2fMPJ9kj89PfmLK2H0+6fbbpRtukH796wcAyIEZg9DHPvYxbd26NXV9w4YNOW8QUMpsW/rVryZ6fL7/fencc808n09+UvqXf5Hq6wvdSgBwjim7z+/fv1933XWXJGloaEhlZWVyu92ybVt9fX0aGhrKW+PYfR6l4OWXzXo+yV6fV16R3v1u0+uzbZu0cSPzfAAg1+a8+3xDQ4MaGxvV0NAw5UkYGgNmd/y4meeTDD4/+5m0aZMJPv/8z9I732lWdQYAFN6UIFRTU6OdO3dKMpOlb7jhhtRtfX19+WsZsEScOSP19U2Utf/4x9Lataa350/+xKzns3p1oVsJAJjOtHOEYrGYdu7cqYGBgdRE6WTV2I4dO/LaQKDY2Lb0zDMTPT4HDpihra1bpY9/3PT6eDyFbiUAYC6mzBFKGhgYUDAYTBsi27RpU94aJjFHCMXjlVdM4ElOcn75Zeld7zLDXT6fGfqqqCh0KwEAmWTKFBmD0HTOHirLNYIQFiuRkMbHTY/NfPbZOnFC+uEPJ4a7nnzSLF6YnOD8zndKK1bkrt0AgOya82TpvXv3yu/3q/asTYps21YsFtPY2FjuWwss0qlTUjRqNiIdGzO9NVVVZk2e6RYkHBubmOcTDEo/+pF08cUm9LS1mfV8Lrgg/+8DAJBbU4KQz+fT8PCwampqptz50KFDc3rSrq4uSVJzc3PqmGVZCgaDikaj8vl88jCJAjly6pR05Ij5unKlWadndFQaHpZOnjQTmSsrpf7+iR6fAwfMY2+4Qbr5ZqmrS9qwQSorK+x7AQDk1pQgNDAwoPoMK7oNDw/P6UkbGhqmlNq3t7fL7/fL4/GoqalJgUBgyuMSiYQSiUTqejwen9PrAZNFoyYETe7UXLbM9A5985vSoUOmx+fFF80Ql88nff7zZisL5vkAgLNM2yO0efPmKXe0bVuHDh1a8NBYOBxO9QJFIpFp79Pe3q7du3cv6PkBycwJGhkxPUGSqe667z4z3+fnP5euvFK65hrp7//e9P4wzwcAnG1KEGpsbNTHPvYxSdJ9992X+j55PZfa2tq0a9eu1PV4PK66urqcviZKy/i4me/zy19Kf/d3ZkXnD35Q+v3fN70/q1ZJsZjZxoJFDQEA006WTs4PikQiuummm1K3+Xy+Bb+Q1+uVZVlyuVwZ5wdVVlaqcj6lPcBZfvxjafduKRSSPvEJ6b//W1qzZuL206fN8BdbWgAApGmCUNmk2aFlZ80UPft6JqFQSP39/ZLMJOlQKKS2tjZ1d3fL7Xaro6NjMW0G0ti29MAD0h13SE88If3O70hf/rKZ7Hy248fN3CHyNgBAmmYdoY997GO69dZbZdu2Ojs75ff7U7d1dnbmfHhsMtYRwkzGx83k5zvukJ5/XvrsZ6XPfMaEnOmqxo4fN6Xza9dOX0IPAChdc15Qsby8XBs2bNB06ywODAzkdR0hghCmMzoqfeMb0p13SvG4dNtt0s6d6ROf57uOEACgtM15QcXe3l5t3bp12ifZv39/7loIzOLUKelf/kXq6DDB5vOfl377t6cf5lq+3MwNWujK0gAAZ5gShDKFIEnasmVLThsDTOe116TOTumv/spUfd1xh9TUJJ0z7ZbB6Qg/AICZzPin5MCBA9qzZ4/KyspS6wg988wz+WobHC4aNev9/M3fSJddJu3ZI33oQ1R8AQCyZ8Yg1NLSoo6ODrndbkWjUYXD4Xy1Cw720kvSX/+19A//IG3ZInV3m8UP2e4CAJBtMwYhv9+fNlTG0Bhy6bnnpLvvlv75n6WtW6UHHzSrQAMAkCszDjIEAgFdfvnl2rJlixoaGjLuQQYsxtNPS5/6lNn+4tgx6Sc/kf7zPwlBAIDcm7VHyOv1yu12S9KUjVSBxXj8cTPx+T/+w6wC/cQT0hVXFLpVAAAnmbFH6KabbtKhQ4dUU1OjgYGBtO02gIX68Y/N/l/veId00UXSr34l/dM/EYIAAPk3YxC6+eab1draKknauHGj2tra8tIolB7blnp7peuuk973Puktb5EGBqS//VvpkksK3ToAgFPNGIS2bNmiO++8M3WdoTHM1/i49O//Lr397dLHPy75fGZSdHu79LrXFbp1AACnm3GOkMvl0sDAgOLxuO64445pt90ApnPmjHTffSbwDA9Ln/uc2Qbj/PML3TIAACbM2CO0c+dO2batHTt2yLIsttjArBIJqavLVID96Z9Kf/RHUiRiNkQlBAEAis2UTVcne/zxx+VyubR+/fo8NmkCm64uHcePmwD0l38puVxSW5v0m785t20wAADItUyZYkqPUENDg9773vfq8OHDGhoaksfjUUVFhSoqKpgsjSksS/qLv5AuvVT61381W2I8+aT0yU8SggAAxW/aIPTAAw+keoGGh4c1NjamsbEx5ggh5ZVXpC98wQSgYFD6t3+TDh6UPvpR9gIDACwdU/7Pftlll6W+P3sn+tWrV+e+RShqg4Nm+Ouee0wp/He/K73znYVuFQAACzPl/+5DQ0MZ7zzTbShtzzwj7dghXX65dPSo9N//LX3nO4QgAMDSNiUIDQ8P65vf/OaUO+7bt0/Dw8N5aRSKxxNPmPV/3vxmUxL/+ONSICBt2lTolgEAsHjTVo01NDQoFovJ6/VKksLhsCTpmWeeyWvjqBornEceMfuA9fZKv/d70m23SQUqHgQAYNHmXDUmSaFQSO3t7aqvr1dtba1aWlrmHIIsy1JPT4+6uroUiUTSbvP7/WpqamKF6iJl29KBA9LWrdK2bdLrX2+2wfjKVwhBAIDSlLHAubGxUY2NjfN+wvb2dvn9fnk8HjU1NSkQCEia6FXau3evXC7XtI9NJBJKJBKp6/F4fN6vj/mzbenb35a+/GWzAer//J9m+MvtLnTLAADIrawXOofDYXk8HklK6xHyeDzavHmz6uvrp/QUJbW3t6umpiZ1qaury3bzMMnYmHTvvdJb3yo1N0uNjWYfsNtvJwQBAJwhbyu+uFwuNTc3KxAIqKenZ9r7tLW1KRaLpS6Dg4P5ap6jnD4t/dM/maGvz39e+vSnzRDY5z4nVVUVunUAAORP1tf+9Xq9sixLLpcr1TM0mcfjydgjVFlZqcrKymw3Cb924oRZ/+fuu82+X3/6p6Yi7NxzC90yAAAKY8a9xhbCsix1d3fL7XbL6/XK7XYrFAqlwo/b7Z7z3COqxrIjFpP+4R+kv/5rad066YtfZAVoAICzZMoUWQ9C2UQQWpxjx6T/+3/N/l9vfrMJQO99r1RWVuiWAQCQX/Mqn8fSduSI9NnPmn3AQiHpP/9T+uEPpfe9jxAEAMBkBKES0t9vqr82bJCef156+GHpv/5Leve7C90yAACKE0GoBPzsZ9InPylddZV06pQUDkv33y9t3lzolgEAUNwIQkvYwYNm0vOWLabs/ec/l772NemNbyx0ywAAWBoIQkuMbUsPPSTdeKN0/fVmGKy/X/rHf5SmWa0AAADMgCC0RNi29J3vSO96l+kFuuYaswr0X/6ltGZNoVsHAMDSlPUFFZFdY2Nmvs8dd0gvvij98R9L3/uexGoCAAAsHkGoSI2OSv/6r9Kdd0onT0otLdLv/Z503nmFbhkAAKWDIFRkTp6U/vmfpbvukpYvN3uBfeIT0rJlhW4ZAAClhyBUJOJxac8e6f/8H+mii8x+YDfdJFVUFLplAACULoJQgQ0NSX/7t+byhjeYXeE/8AFWgAYAIB+oGiuQo0elz33ObIPxk59I3/ym9KMfSR/8ICEIAIB8IQjl2cCA9OlPm/V/nn1WOnBAevBB6brrCEAAAOQbQShPfv5z6ZZbzPBXPG5Whf73f5fe9rZCtwwAAOciCOVYX5+Z9Oz1miqwp54yZfFvelOhWwYAAAhCOfLDH0rve5907bXSJZeYYbCuLjMkBgAAigNBKItsW/qv/5Le/W7p//v/pIYG6fBh6a//Wlq3rtCtAwAAZ6N8PgvGx03V1x13SIOD0mc/K33721JNTaFbBgAAZkIQWoTRUekb35Da26XXXpNuu03asUNasaLQLQMAAHNBEFqAU6ekr37VbINxzjlmG4zf/m22wQAAYKnJehCyLEvBYFDRaFQ+n08ej2fG40vJa6+ZbTD+6q+kCy4wPUGNjSYMAQCApSfrk6Xb29vl9XrV3Nys1tbWWY9PlkgkFI/H0y7FIBqVdu82q0AHAlJnp/T449Jv/iYhCACApSzrQSgcDqd6eyKRyKzHJ2tvb1dNTU3qUldXl+3mpUkkzG7vicT0t7/0ktTSYgLQww9L3d3SI49IH/6wVE69HQAAS15R/Tlva2tTLBZLXQYHB3PyOqdOmb2+Dh82W14cPmyunzplbn/uOekzn5Hq66Vf/lLq7ZX275e2bmUbDAAASknWB3a8Xq8sy5LL5UqbB5Tp+GSVlZWqrKzMdpPSnDolHTlivq5cKZ17rqn+Gh6WfvYz6etfN8NfH/2o9Oij0lvektPmAACAAiqzbdvO5hNalqXu7m653W55vV653W6FQiE1NDSkHZ/LZOl4PK6amhrFYjFVV1dnpX1Hj5rQU1s7cexnP5P+9m9Nz8/27dKf/7l0+eVZeTkAAFAEMmWKrAehbMp2EEokzDBYZaUpdf/FL8wiiD/+sfRbvyX93u9Jq1dL69eb+wAAgNKQKVM4quZpfFwaGzPDYZIZHnvjG80WGKtXmy0yYjFzPwAAUPocFYTKy6WKCjMnaNkyadMmc0kaHTW3UxEGAIAzOOpPfmWlVFUlHT8+/e3Hj5vbGRYDAMAZHBWEJMntlpYvNxOmT582w2GnT5vry5eb2wEAgDMU9dBYch53tleYrqqSzpyRhobMnKGKCun8883x06fNBQAAlI5klji7Rqyog9DIyIgk5XyFaQAA4AwjIyOqqalJXS/q8vnx8XEdPXpUVVVVKpvjks7xeFx1dXUaHBzM2tpDS43TPwOnv3+Jz0DiM5D4DJz+/iU+A2niM3j++edVVlamNWvWqHxSVVRR9wiVl5dr3bp1C3psdXW1Y096ktM/A6e/f4nPQOIzkPgMnP7+JT4DSaqpqZn2M3DcZGkAAIAkghAAAHCskgtClZWVuv3223O+eWsxc/pn4PT3L/EZSHwGEp+B09+/xGcgzf4ZFPVkaQAAgFwquR4hAACAuSIIAQAAxyIIAQAAxyq5INTV1aWurq4px/1+v5qamhQMBgvQqvyZ7v1blqWenh51dXUpEokUqGX5MdN7LeWfgUzvm3NvlPK5n8zpv/8SfwMikYj8fr+2bdsmy7JSx53yc5Dp/Usz/AzYJaavr8/u6OiYcqy5udkeHh4uTKPyaLr339LSYvf399u2bduNjY2FaFbeZHqvpf4zkOl9c+5L/9xP5vTff9vmb0BfX59t27YdCATszs7O1HGn/Bxkev8z/QyUXI/QdDwejzZv3qz6+vqSTsKZhMNheTweSSr595/pvZb6z0Cm9825L/1zPxsn/Qxk4qSfAa/Xm/o+ed4l5/wcZHr/M/0MFPUWGzPp6elJu+7xeNI+gMlcLpeam5vl8XjU09OjlpaWfDQxp+bz/kvVdJ9BJqX4M4C54dzDiT8D0WhUjY2NhW5GwZz9/mf6GViyQWghJ9jj8ZRMEp7P+/d6vbIsSy6Xa8awsNRM9xnM9l5L6Wdgskzvu1TP/XSceu5n46Sfgdk45WcgGAyqubk57ZiTfg6me/9J0/0MlNyCil1dXerr61NnZ6csy1IoFEq9abfbXfIJebr339DQoO7ubrndbnm93pL+JbAsK+29ut1uR/wMZHrfnPvSP/eTOf33X+JvQFdXlwKBgDwejzZs2KDm5mZH/Rxkev8z/QyUXBACAACYK0dMlgYAAJgOQQgAADgWQQgAADgWQQgAADgWQQgAADgWQQgAADgWQQgAADgWQQgAADgWQQgAADgWQQgAADgWQQgAADgWQQgAADgWQQgAADhW1oOQZVnq6elRV1dXatv7JL/fr6amJgWDwWy/LAAAwLydk+0nbG9vl9/vl8fjUVNTkwKBgCQpHA5Lkvbu3SuXyzXtYxOJhBKJROr6+Pi4otGoVq1apbKysmw3FQAAOIRt2xoZGdGaNWtUXj7RD5T1IBQOh+XxeCQprUfI4/Fo8+bNqq+vV19fX+o+k7W3t2v37t3ZbhIAAIAkaXBwUOvWrUtdL7Nt287mC2zbtk29vb2SpM2bN6uvry/t9mAwqHA4rJaWlimPPbtHKBaL6ZJLLtHg4KCqq6uz2UwAAFAIL7wgffCD0uHDGrvkUv2/239P34h9X6+cPKoLz1ujj9dcr9/Z/c+qeP45af166TvfkSYFl4WKx+Oqq6uTZVmqqalJHc96j5DX65VlWXK5XNP2+ng8nilzh5IqKytVWVk55Xh1dTVBCACAUvDGN0oPP6yxa9+jisMD+shtf66v+VdrfPVKjb76C33kL36o2uiYxtbXq+LhH0h1dVl9+bOn2mS9R8iyLHV3d8vtdsvr9crtdisUCqXCj9vtVmNj45yeKx6Pq6amRrFYjCAEAEAJ6bz3Lt34mS+oPjqmwdXL9MVb6vXlrw2o7thpHXZX6IGv3CH/b04dPVqoTJki60EomwhCAACUnkRCuuGr2xR/6hX94f+7Us+ONCgsr76n9+ul1eX6yM5anXfJm3XgU72aZqBoQTJliqwPjQEAAJzt1CkpHJYeeUT60Y+kRw98TWOxC/X5Vc+oaeQH+oT+VWOq0BdvWa+h1aM658QRjY/nvl0EIQAAkFW2LQ0MmNCTvDz+uORySddcIzU0SE9f8leqKP+mvv21F1Sn06nHfvlrA7/uEfKoPA/LPhOEAADAooyMSAcPpgcfy5I2bZKuvlr67GfN1/XrpeRc5QvvvVA3fuY51U0zR2hf5zE98JVtWRsWmwlzhAAAwJyNj0u//GV66HnqKVPhfvXVE5dNm6TlyzM8yeBgqmrssLtC2/2rNbR6pVYdO659nce0PgdVY8wRAgAA8zY0JD366EToefRRaXRU2rLFBJ7du6W3v11as2aOTzg4KF13nSoOD2hsfb0eaL9V51m9OufEEZ13iUcPfGWbdrTtUcXhAem666SHHsp6Cf1k9AgBAABJJuA8+WR6b88zz0hXXJHe2/OmN0nnnruAF7Bt6W1vk0IhyeNJhZxEwvQ0lZfLDIf9OiwpEjETih57bGJMbYHoEQIAAGmOHk0PPaGQtGyZ6eG5+mrp4x83uWXVqiy9YFmZ1Nkp+f3Svn2pnp4pc4Hq6kxI2r7d3D+H+43SIwQAgANMLl9PXo4cMb07k3t7rrxSua/Wsu25hZu53m8O6BECAMAhZitfv/pq6dOfNqNOVVUFaOBcw00Oe4KSCEIAACxxCylfh0EQAgBgCZmtfP3666W2tlnK15FCEAIAoIhlvXwdaQhCAAAUidnK1z/6UamjYxHl65iCIAQAQIHkvXwdUxCEAADIg9nK1z/1KbNkTl7K15GS9SBkWZaCwaCi0ah8Pp88Hk/a7X6/X52dndl+WQAAikbRl68jJetBqL29XX6/Xx6PR01NTQoEAqnbwuGwotFoxscmEgklEonU9Xg8nu3mAQCQdZSvL11ZD0LhcDjVCxSJRNJui0ajcrvdGR/b3t6u3bt3Z7tJAABkDeXrpSVvc4R6enrU2NiY1kN0tra2Nu3atSt1PR6Pqy6HO84CADAbytdLW9aDkNfrlWVZcrlcafODIpGIWltbFQqF1Nraqo6OjimPraysVOWUndcAAMgPytedJ+ubrlqWpe7ubrndbnm9XrndboVCIfl8PknzmyzNpqsAgFyarXz96qspXy8VmTIFu88DAByhqHZfR96x+zwAwDEoX8dcEYQAAEse5etYKIIQAGBJoXwd2UQQAgAUNcrXkUsEIQBA0aB8HflGEAIAFAy7r6PQCEIAgLxg93UUI4IQACDrKF/HUkEQAgAsGuXrWKrmHIQOHDigG264IZdtAQAsAZSvo5SkBaG9e/fK7/ertrZWyZ03ysrKZNu2YrGYxsbGCtJIAEDhUL6OUpYWhHw+n4aHh1VTU5M6NjAwoPr6eh06dCjvjQMA5Bfl63CatCBUX1+f+v7AgQNqbGxUWVmZ3G63AoFA3hsHAMgtytfhdBnnCHV3d2tgYCDVO3TPPfdo48aN+WoXACDLKF8HpsoYhLZt25Y2RAYAWDooXwfmJmMQOnjwoAYGBuRyudTb2yu3260dO3bM+oSWZSkYDCoajcrn88nj8UiSIpGIgsGgAoGAent7s/cOAACUrwMLVGYny8Omcffdd+vgwYPasmWLbrvttjk9YWtrq/x+vzwej5qamlJziyzLksvl0l133aXGxsZUQJpJPB5XTU2NYrGYqqur5/iWAKC0zVa+nrxQvg5MyJQpZlxHaHL4icfjcwoj4XA4rRcoyeVyKRwO6+DBg2ppaZn2sYlEQolEIu01AcDpKF8HcidjEDpw4IA6OztlWZYkE2qeeeaZRb2Y1+vVli1b1NXVpebm5im3t7e3a/fu3Yt6DQBYyihfB/IrYxBqaWlRR0eH3G63JCkYDM7pCb1eb2oYbLrhr+bmZnV1dU372La2Nu3atSt1PR6Pq66ubk6vCwBLEeXrQGFlnCN09913L2hozLIsdXd3y+12y+v1yu12KxQKybIsRaNRud1uNTY2zqlxzBECUEpmK19PXihfB7IvU6ZIC0L79+9XR0eHysrKNDQ0lFpM0bZt9fX1aWhoqCgaDQDFbi7l61dfTfk6kC9zmizd0NCgpqYmNTQ0THmCuQ6NAYATUb4OLE1pQWjnzp0qKyvTli1b0laR3r9/v/x+f77bBgBFid3XgdKRFoTcbrf27Nkz5U5bt27VPffcM6cFFQGg1FC+DpSutCC0YcOGQrUDAIoC5euAs6QFoWg0mvGOfX199AgBKDmUrwPOlhaEhoeH9f3vf1/XX3992p327duX94oxAMg2dl8HcLa08vlYLKbNmzertrY2tRhiOBxWNBrVwMBA3kvYKZ8HsFCUrwOYbE7rCCXt3btXfX19ikaj89pwNdsIQgDmarby9eSF8nXAmeYVhDI9AT1CAIoBu68DmK957z4/edNV27Y1MDCw6E1XAWAhKF8HkCtZ33QVABaD8nUA+ZQxCH3sYx/T1q1bU9dZYwhALlC+DqCQpmy6etddd0kSm64CyDp2XwdQKHPedLWxsZFNVwEs2lzK1z/9acrXARRWxqqxQlSJTdcGeoSApYHydQDFbN5VY11dXXK5XGpoaNDGjRu1b98+RSIRuVyuGbfasCxLwWBQ0WhUPp8vtTBjJBJRR0eHIpGIAoGAXC5X9t4dgLxi93UApSJjEHrwwQfl8XjU39+vjRs36t5779UXvvAF1dTUaN++fdq+ffu0j2tvb5ff75fH41FTU5MCgYAkE5A6OzvV09Oj7u5uNTc35+YdAcg6ytcBlKqMQaipqUk7d+7U4cOHtX//fsViMXk8HlVXV+uuu+7KGITC4XBaL1CS1+tNfZ+8/WyJREKJRCJ1PR6Pz+/dAFg0ytcBOEnGIGRZlm688UYNDAyopaVF/f39CoVCikQiGYPMXESjUTU2Nk57W3t7u3bv3r3g5wYwf5SvA3CyGbfYGBgYkNvtTk0uikQi6uzs1F133ZVx8nJra6va2trkcrnShsYkU3nm8/kyNma6HqG6ujomSwNZQvk6AKda9F5jjz/+uDZu3Djr/SzLUnd3t9xut7xer9xud6onKRAIyOPxaMOGDWppaVlwowHMjt3XAWDCnIJQQ0ODQqFQ6vtDhw7J5XLJtm3FYjGNjY0VRaMBTEX5OgBkNqfy+WQIkqSOjo60LTYOHTqUh2YCmAvK1wEgO9KC0IEDB3TDDTdIUloIkqTh4eH8tQpAGsrXASA30obGysvLtXnz5il3sm1bhw4dYmgMyIPZyteTF8rXAWDu5jQ01tjYqI997GOSpPvuuy/1ffI6gOyjfB0ACictCO3du1c1NTWSzGKIN910U+q2mcreAczNbOXrn/qU1NlJ+ToA5EtaECqbVEpSdlZZydnXAcyM3dcBoPilBaGdO3fq1ltvlW3bOnjwoA4cOJC6rbOzk+ExYAazla9/9rOUrwNAsZkyWXrDhg2abo3FgYEBJksDvzZb+XryQvk6ABSHOU2W7u3tnVI2n7R///7cthAoYpSvA0BpmvMWG4VAjxAKgfJ1ACg9c+oRApyI8nUAcC6CEByF8nUAwGQEIZQsytcBALMhCKFkUL4OAJgvghCWpEzl62vXmrBz3XXsvg4AmF3Wg5BlWQoGg4pGo/L5fPJ4PKnburq6JEnNzc3ZflmUOMrXAQC5kPUg1N7eLr/fL4/Ho6amJgUCgdRtDQ0NCgaD2X5JlJjZytc/+lGpo4PydQDA4mU9CIXD4VQvUCQSmddjE4mEEolE6no8Hs9q21CcKF8HABRKUc0Ram9v1+7duwvdDOQQ5esAgGKS9SDk9XplWZZcLlfa/KC5aGtr065du1LX4/G46urqst1E5Anl6wCAYpf1INTW1qbu7m653W51dHTIsiyFQiH5fD6FQiH19/dnfGxlZaUqKyuz3STkCeXrAIClhr3GsCCzla9ffbXp9aF8HQBQDNhrDItC+ToAoBQRhDAF5esAAKcgCIHydQCAYxGEHGa68vUXXpgoX//d35X27JFe/3rK1wEApY8gVMJmK19/+9ulW28183woXwcAOBFBqIRQvg4AwPwQhJYodl8HAGDxCEJLBOXrAABkH0GoCFG+DgBAfhCEigDl6wAAFAZBKM8oXwcAoHgQhHKI8nUAAIobQSiLKF8HAGBpcXQQSiRMGXp5uVRZOb/HUr4OAMDS58ggdOqUFI2aHpyxMamiwgxNud2ZQwvl6wAAlJ6sByHLshQMBhWNRuXz+eTxeGY8nm+nTklHjpivK1ea8vPRUWl4WDp50vToVFRQvg4AgBNkPQi1t7fL7/fL4/GoqalJgUBgxuP5Fo2aEFRbm34sHJZ+/GPp5z+XnniC8nUAAJwg60EoHA6nensikcisxydLJBJKJBKp6/F4PKttSyTMcNjKleZ6MGjm8bz4oilXf+tbpQ9/2JSvv+UtlK8DAFDqimqOUHt7u3bv3p2z5x8fN3OCksNZb3iD9Nd/LW3cKJ1/vil3j8Wk+npCEAAATpD1P/der1eWZUlS2jygTMcna2trUywWS10GBwez2rbycjP/Z3TUXF+7VnrXu0wIkszxigpCEAAATlFm27adzSe0LEvd3d1yu93yer1yu90KhUJqaGhIOz6XydLxeFw1NTWKxWKqrq7OSvuOHjUToyfPEUpKHqfyCwCA0pIpU2Q9CGVTLoJQpqqx48dN6fzataz7AwBAqcmUKYpqjtDZkhkt25Omq6qkM2fM2kDJdYTOP98cP33aXAAAQOlIZomz+3+KOgiNjIxIkurq6grcEgAAUApGRkZUU1OTul7UQ2Pj4+M6evSoqqqqVJaDzbni8bjq6uo0ODiYtaE3zA/noPA4B4XHOSg8zkFh5ePzt21bIyMjWrNmjconVUUVdY9QeXm51q1bl/PXqa6u5ge/wDgHhcc5KDzOQeFxDgor15//5J6gJArFAQCAYxGEAACAYzk6CFVWVur2229XZWVloZviWJyDwuMcFB7noPA4B4VVyM+/qCdLAwAA5JKje4QAAICzEYQAAIBjEYQAAIBjFfU6QtlmWZaCwaCi0ah8Pl9q49dMx5F9mT7rSCSijo4ORSIRBQIBuVyuwja0hM328+73+9XZ2Vmg1pW+2T7/cDgsl8vFv0M5xN+C4tDV1SVJam5uTh0ryDmwHaSlpcXu7++3bdu2GxsbZz2O7Mv0Wff19dm2bduBQMDu7OwsSNucYqaf976+Pn4Hcmymz7+5uTn1u4DcyXQOOjs77d7eXru/v59/h/Kgr6/P7ujoSDtWiL/HjuoRCofDaT0Qsx1H9mX6rL1eb+p7/heWWzP9vEejUbnd7kI0yzEyff5dXV2KRqOKRqOFappjZDoHN998s7Zu3Sqfz6eOjo5CNc/RCvH3mDlCKCrJ7lDkX09PD599AfX29qqtrU2SGZ5EYfj9foXDYQWDwUI3BXniqCDk9XplWZak9F6HTMeRfTN91sFgMG2sGLmR6RxEIhG1trYqFAqptbW1QK0rfZk+f7fbLZfLJZ/PR890jmU6B11dXfL5fOrt7VUgEChQ65ytEH+PHbWgomVZ6u7ultvtltfrldvtVigUUkNDQ9pxwlDuZDoHyUnSHo9HGzZsUEtLS6GbWrIynYNkbxCTpXMr0+fv8XhSvXL0jObWTOcgGAzK4/GkbkPudHV1qa+vT52dnbIsq2B/jx0VhAAAACZz1NAYAADAZAQhAADgWAQhAADgWAQhAADgWAQhAADgWAQhAADgWAQhAADgWAQhAADgWAQhAADgWAQhAADgWAQhAADgWAQhAADgWAQhAADgWOcUugEzGR8f19GjR1VVVaWysrJCNwcAACxRtm1rZGREa9asUXn5RD9QUQeho0ePqq6urtDNAAAAJWJwcFDr1q1LXS/qIFRVVSXJNLq6urrArQEAALmQSEjj41J5uVRZmZvXiMfjqqurS2WLpKIOQsnhsOrqaoIQAAAl5tQpKRqVRkaksTGpokKqqpLcbmn58ty85tlTbYo6CAEAgNJ06pR05Ij5unKldO650uioNDwsnTwprV2buzA0GVVjAAAg76JRE4Jqa6Vly6Tjx83X2tqJnqJ8yHqPkGVZCgaDikaj8vl88ng8qdv8fr+i0aj8fr98Pl+2XxoAACwBx49LBw9KTz0l/fSnUjgsDQxIP/+5dP75podoZMTMHcrVnKGkrAeh9vZ2+f1+eTweNTU1KRAISJLC4bAkae/evXK5XNl+WQAAUKReekl69FHpkUfM5eBBybYlr9dc/uRPzNfzzzf3P/dc6cQJM4E617IehMLhcKoXKBKJpI57PB5t3rxZ9fX16uvrS+spSkokEkokEqnr8Xg8280DAAA5lEhIjz8+EXoeeUR67jnpjW+Urr5a+sQnpL/6KzP/Z8UKMxx2ttFRM3G6PA8TePI2Wdrlcqm5uVkej0c9PT1qaWmZcp/29nbt3r07X00CAACLYNvS88+nh55w2FR+XX21ufyP/yFt2SLV1KQ/9uhRMzF6uiB0/LiZK5TrYTEpB0HI6/XKsiy5XK5pe308Hk9aT9FkbW1t2rVrV+p6suYfAAAU3vHjUiiUHnxefVXauNGEns98xnzdsEGabUMIt9tUhw0Pp1eNHT9ueovc7ry8JZXZtm1n8wkty1J3d7fcbre8Xq/cbrdCoVAq/LjdbjU2Ns7pueLxuGpqahSLxVhHCACAPBofl555Jj30PPmk9LrXSddcM9Hj4/WaIa6FyOc6QpkyRdaDUDYRhAAAyI/hYemxxyZCz6OPmgnLDQ0Toefqq6VJu1NkTb5Wlp4uU7CgIgAADnPmjCldn9zb8/TTksdjws4HPyh96UvSW94y/RyebMvHXKBMCEIAAJS46crXy8ult73NBJ+775be/nbpggsK3dL8IwgBAFBC5lK+/nd/J73hDWZOjtMRhAAAWKIWU74OgyAEAMASMV35+rFj0lvfOv/ydRgEIQAAitBs5evvfKf0x3+8uPJ1EIQAACgKs5Wvf/GLuStfdzKCEAAAeVZs5etORhACACDHKF8vXgQhAACyiPL1pYUgBADAAlG+vvQRhAAAmCPK10sPQQgAgGlMV77+xBPSRRdRvl5KCEIAAIjydafKehCyLEvBYFDRaFQ+n08ejyftdr/fr87Ozmy/LAAAc0b5OpKyHoTa29vl9/vl8XjU1NSkQCCQui0cDisajWZ8bCKRUCKRSF2Px+PZbh4AwIEoX0cmWQ9C4XA41QsUiUTSbotGo3K73Rkf297ert27d2e7SQAAB6F8HfORtzlCPT09amxsTOshOltbW5t27dqVuh6Px1VXV5eP5gEAliDK17FYWQ9CXq9XlmXJ5XKlzQ+KRCJqbW1VKBRSa2urOjo6pjy2srJSlZWV2W4SAKBEUL6ObCuzbdvO5hNalqXu7m653W55vV653W6FQiH5fD5J85ssHY/HVVNTo1gspurq6mw2EwBQ5GYrX0/2+FC+jrnIlCmyHoSyiSAEAM4xXfn6yZPS5s0ToYfydSxUpkzBOkIAgLyjfB3FgiAEAMg5ytdRrAhCAICsonwdSwlBCACwYJSvY6kjCAEA5ozydZQaghAAYFrsvg4nmFcQOnz4sNavX5+jpgAACmm28nV2X0cpmjEI3XPPPdqxY0fqut/v1wMPPJDzRgEAcovydcCYMQhNXhG6sbFRW7ZsyUujAADZRfk6ML1ZV5a+//779fnPf16BQEAulyuvQ2OsLA0A8zdb+XryQvk6nGTOK0vv3btXHR0dKvv1dH/bthWNRtXY2Kjh4WENDQ3lr9UAgBlRvg4szpQg1NDQoEAgkLZzvGQC0f79+/PWMADAVJSvA9k1JQht2rQp451ra2tz2hgAwATK14Hcm3ZozO/3Twk9tm0rFotpbGwsb40DACehfB3IvylByOfzaXh4WDXTDCYfOnRo1ie0LEvBYFDRaFQ+ny81xBaJRBQMBhUIBNTb25uFpgPA0kX5OlAcpgSh+vr6ae8Yj8e1f//+GYfOJKm9vV1+v18ej0dNTU0KBAKSJLfbrebmZlmWpUgkMmUOkiQlEgklEom01wSAUkD5OlCcyme68dZbb9Vll12myy+/XOvXr59TT044HE7rBUpyuVwKh8M6ePDgtCFIMiGqpqYmdamrq5vPewGAopBImNDzN38jffzjUn29tGaNGdp69VWz+/ojj5ihsGBQ+ou/kD70IUIQUAgzBqGmpiY9++yzevDBBxWNRrVt27ZFvZjX69WWLVvU1dU17e1tbW2KxWKpy+Dg4KJeDwByzbbNGj333Sd99rNmEnN1tRna6u2VrrpK2rvXhJ6f/Uy65x5pxw7pTW9iDR+gGEwZGovH46mFhsLhsMrKymRZlu6//351dnbqc5/73IxP6PV6ZVmWXC7XtD0/zc3NGYNQZWWlKisrF/I+ACAvKF8HSsuUlaXf+973qqOjQxs3blQsFlM0GlV9fb327t0rj8ejrVu3zviElmWpu7tbbrdbXq9XbrdboVBIlmUpGo3K7XarsbFxTo1jZWkAhTRb+XpywULK14HilylTTAlCycCT7A3y+XzauHFjvtsriSAEIL9mK19PXihfB5aeOQehsx04cEAdHR2pKrAbbrgh541NIggByJUzZ8ycncmVXJPL15MXyteB0jDnvcaSc4QOHz6szs5OdXZ2SjJl9W63O38tBoAsSpav/+QnE+XrFRWUrwNONyUINTU1KRKJKBKJqLGxUT09PXntBQKAxZpt9/VPflL6+79n93UA0wSh/v5+dXR0aNOmTbr//vvV29urWCymj370o4VoHwDMKFm+npzTw+7rAOZjyhyhgYGBtNWl77//frW2tmpgYEAdHR2zls9nE3OEAJzttdemlq8PDU2UrycvlK8DmGzOc4QGBgY0MDCgPXv26P7775fH41FjY6P8fn/G7TcAIBdmK19/17ukz32O8nUACzftpqsbNmzQTTfdpGeffZbwAyBvhocnhrcefZTd1wHk3pQg9L//9//Wn/3ZnxWgKQCcJFm+fvbu6xs2sPs6gPyZEoS+/e1v63d/93e1fv16HT58WOvXry9AswCUmtnK1//yLylfB5B/U4KQ3+9PrRcUDAa1Y8cOSWaSUTAY1Pbt2/PbQgBLDuXrAJaKKUHobW97m3bs2KFwOKxoNKqOjg5J0tDQkGKxmMbGxvLeSADFi/J1AEvZlCC0Y8cOBQIBrV+/Xl//+tf127/926nbDh06lNfGASg+s5Wvs/s6gKVkShBqbm5ODY0lEonU8Xg8roGBAW3atCl/rQNQUJSvAyh10y6o2NramhoaW7VqlWzbVjQandPQmGVZCgaDikaj8vl88ng8kqRIJKKOjg5FIhEFAgG5XK5ZG8eCikB+zVa+zu7rAJaqBe0+v3//fm3dujV1/dChQ7P2CLW2tsrv96d2qw8EApKkcDgsr9ernp4eRaNRNTc3L7jRABZvtvJ1dl8HUErmvLL0ZJNDkCSVzWHAPxwOp/UCJXm93tT3ydvPlkgkpgzHAcgOytcBYKopQWjLli06ePCgJKmhoUHhcFi1tbWybTsrVWPRaFSNjY3T3tbe3q7du3cv6vkBmPL1Q4fSe3uef57ydQA425QglAxBktTR0TFlaGw2Xq9XlmXJ5XJN6fkJBoMzDom1tbVp165dqevxeFx1dXWzvibgZJPL15OXQ4fSy9d37KB8HQCmk3GO0IEDBxQOhyVJmzdv1vXXXz+nJ7QsS93d3XK73fJ6vXK73QqFQqlJ0h6PRxs2bFBLS8usz8UcIWAqdl8HgPmb12TpG2+8UaFQSA0NDZKkUCikLVu26IEHHshfi0UQAsbHpV/9Kj30PPnkRPl6MvRQvg4AM5vzZOm7775bHo9HDz744JTjbW1tam9vz31rAYeaXL6eLGE/dWqifP1P/oTydQDIpilBaGhoSHv27Jlyx9tuu02f//zn89IowAlmK1//0Iekv/gLytcBIJemBKHVq1dnvPNll12W08YApeyll9JDD+XrAFB401aNHThwYNo79/b2pnajB5AZ5esAsDRMmSxdXl6uDRs2aLpisoGBgbzuPs9kaSwFcylfv/pqytcBoJDmPFm6t7d3yorSSfv3789dC4ElYrby9T/4A8rXAWCpmHGvsUKjRwiFRvk6AJSGBe01BjgN5esA4CwEITgW5esAAIIQHIPydQDA2QhCKEmUrwMA5oIghCWP3dcBAAtFEMKSQ/k6ACBbCEIoarOVr7/rXdLnPkf5OgBgYbIehCzLUjAYVDQalc/nk8fjSd3W1dUlSWpubs72y6JEUL4OAMinrAeh9vZ2+f1+eTweNTU1KRAIpG5raGhQMBjM9ktiiaJ8HQBQaFkPQuFwONULFIlE5vXYRCKhRCKRuh6Px7PaNhQW5esAgGJTVHOE2tvbtXv37kI3A1lA+ToAYCnIehDyer2yLEsulyttftBctLW1adeuXanr8XhcdXV12W4isozydQDAUpX1INTW1qbu7m653W51dHTIsiyFQiH5fD6FQiH19/dnfGxlZaUqKyuz3SRkGeXrAIBSwe7zmBG7rwMASgG7z2NOolHpsccoXwcAOANByMEoXwcAOB1ByEEoXwcAIB1BqERRvg4AwOwIQiWA8nUAABaGILQEUb4OAEB2EISKXKby9YsvNmGH3dcBAFg4glCRoXwdAID8IQgVEOXrAAAUlrOCkG3PbdLMXO83T5SvAwBQXJwThMJhye+X9u2TZtrIdXBQ2r5d6uw0E28WaC7l61/5ivT611O+DgBAoTgjCNm2CUGhkHTdddJDD0l1dUokzGTk8nKpslImBF13nRSJmPs/9ticeoYoXwcAYGlyRhAqKzM9Qb8OOWPXvkf3tN+qr1m9eunEEV20Yq1ucW3TjrY9qjg8IHk85v4ZQlCm8vWNG83QFuXrAAAsDc7afX5wUGPXvkcVhwc04K7QTf7VGlq9UquOHdf9ncdUHx3T2Pp6VTz8g9Tw2Uzl62fvvn7eeYtvIgAAyL687T5vWZaCwaCi0ah8Pp88Hs+Mx/Oqrk73tN+qGz/zBdVHx/StvcP64i0ufflrw6qLjumwu0L7vvi/9Man6vTIP1G+DgBAqct6j1Bra6v8fr88Ho+ampoUCARmPD5ZIpFQIpFIXY/H46qrq8taj1AiId3w1W06+fyT+tbeYV18bEw/05v0iK7W/sp36D9XXqPE8AZt8JTrmmvMMNfVV1O+DgDAUpe3HqFwOJzq7YlEIrMen6y9vV27d+/OdpNSxsell04c0ZnVK/XFW1zy/Z+36Q/093q7HtXLG59R9Zvu1IpLfqknb/sRw1wAADhAeaEbMFlbW5tisVjqMjg4mNXnLy+XLlqxVquOHdeXvzag39S9GlatenWjvtP/Wa278Dtae+EKlRfVpwIAAHIl63/yvV6vLMuSpLR5QJmOT1ZZWanq6uq0SzZVVkq3uLbp/s5jqjt2Wi+vLtOndl2uwdXLVHfstPZ1HtMtrm2mlB4AAJS8rM8RsixL3d3dcrvd8nq9crvdCoVCamhoSDs+l8nSuawaO+yu0PZJVWP7Oo9p/TRVYwAAYOnLlCmcUz4/abHEsfX1s68j9OtFFwEAwNK3JINQLBaTy+XS4ODg4oKQbUvXX2+We16/XvrOd6R166auLP3CC9IHPygdPixt2iR9//usiAgAQAlIVqJblqWaSds8FHUQeuGFF1RHrwwAAMiSwcFBrZu0GGBRB6Hx8XEdPXpUVVVVKstBz0wyHS66xwkLxjkoPM5B4XEOCo9zUFj5+Pxt29bIyIjWrFmj8knl4UW911h5eXlaasuVXFSoYX44B4XHOSg8zkHhcQ4KK9eff800O5+zYg4AAHAsghAAAHAsRwehyspK3X777apkBcWC4RwUHueg8DgHhcc5KKxCfv5FPVkaAAAglxzdIwQAAJyNIAQAAByLIAQAAByrqNcRyjbLshQMBhWNRuXz+VIbv2Y6juzL9FlHIhF1dHQoEokoEAjI5XIVtqElbLafd7/fr87OzgK1rvTN9vmHw2G5XC7+Hcoh/hYUh66uLklSc3Nz6lhBzoHtIC0tLXZ/f79t27bd2Ng463FkX6bPuq+vz7Zt2w4EAnZnZ2dB2uYUM/289/X18TuQYzN9/s3NzanfBeROpnPQ2dlp9/b22v39/fw7lAd9fX12R0dH2rFC/D12VI9QOBxO64GY7TiyL9Nn7fV6U9/zv7DcmunnPRqNyu12F6JZjpHp8+/q6lI0GlU0Gi1U0xwj0zm4+eabtXXrVvl8PnV0dBSqeY5WiL/HzBFCUUl2hyL/enp6+OwLqLe3V21tbZLM8CQKw+/3KxwOKxgMFropyBNHBSGv1yvLsiSl9zpkOo7sm+mzDgaDaWPFyI1M5yASiai1tVWhUEitra0Fal3py/T5u91uuVwu+Xw+eqZzLNM56Orqks/nU29vrwKBQIFa52yF+HvsqAUVLctSd3e33G63vF6v3G63QqGQGhoa0o4ThnIn0zlITpL2eDzasGGDWlpaCt3UkpXpHCR7g5gsnVuZPn+Px5PqlaNnNLdmOgfBYFAejyd1G3Knq6tLfX196uzslGVZBft77KggBAAAMJmjhsYAAAAmIwgBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHIggBAADHOqfQDQByzbZtJRKJQjcDwCKce+65qqioKHQzUIIIQih5R48eVTweL3QzACxCWVmZ1q1bp/PPP7/QTUGJIQihpJ05c0bxeFyrVq1SdXV1oZsDYAFs29arr76qF154QZdffjk9Q8gqghBK2pkzZyRJVVVVWr58eYFbA2ChLrjgAh0+fFijo6MEIWQVQQiOUFZWZr55/nnp2LHFPdnq1dIllyy+UUvM87HndezEwj+71StW65Ia531u87HYH89S/tFM/Q4DWUYQgnM8/7x05ZXSqVOLe57ly6Wnn57yF6epqUnbtm1Ta2urOjo61Nvbq0AgsLjXmkYkEpFlWfJ6vVl/7kyejz2vK//+Sp06s/DPbvk5y/X0HzydMQzdddddcrlccrvd6uzsVCAQUCgUkiT5fL4Fv+5CFOQzzsKPZ4YfTUnz//nM1mdQiM8SmA+CEJzj2LHFhyDJPMexY1P+2nR0dMjj8aijo0PNzc0z/vEOh8OSNK8/DvN9zEJeI5NjJ44tKgRJ0qkzp3TsxLFpg1BPT4+GhobU0tIiSfJ4PJIkt9utYDC44CCU688sq59xFn48M/xoSprfz6c0cQ4WqpCfJTAfBCEgS87+w2FZlsLhsO677z61tbUpFArJ7XYrFAqpv79fq1atSvtHv6mpSR6PR5FIRNu2bUs9XzgcVktLi+677z6tWrVKkUhE0WhUDQ0NkpR6/q6uLg0NDcmyLPn9/tT9l8IfluR7SEq22eVySTKfQSgUSutZSH6WZ38OM33OTv6Mz/759Hg8uuuuuzQ0NKQNGzZM+Sx6enoUjUZlWVbae871ZxmNRlPnr7m5OY+fEJyKBRWBHOns7FQ0GtW2bdskmSGC++67TzfffLM2bNgw5Y/ntm3btGXLFgUCAQUCATU0NMjtdqu3t1eSUo/xeDyyLGvK83s8Hm3ZsiX1R2W61yhWlmUpGo1mvL29vV3Nzc1qaWlRa2tr2mc5n8/ZyZ/xdFwul7Zs2aLm5uYpn0XyMzj7PSfl6rOcfP6AfCAIATmS/F9wcgji5ptv1pYtW7Rz586Mj0n2gLhcLnV3d6cdm+35z36OpWTbtm2pP6CZRCIRSaYnaPJnOd/P2amf8XTcbnfq+9k+i+mO5+KznMvvCZBNBCE4x+rVZjbpYi1fbp5rGpFIRJFIROFwWH6/X01NTalJwN3d3XK5XKlhhEAgIMuy0h4fDofV1dWltrY2WZaV+h94MBhMPeaJJ55Qf3//lOef/NqRSCTjayzE6hWrtfycxX12y89ZrtUrpv/cknODWltb1dPTo2AwKMl8HgcPHlRbW5t6enrU0/P/b+/ubR2EwSgMH0uhoohEDxJsywpUDHA38QxAxQLQ0HGL6EY3QooSBWPjvM8E5vBZMv7jR3VdP2T5bs7BZrxDeT4pTUmP9SlJ1lpZayVpk0Xf9+q6bvPM/7nIsm3b+/sDjmDWdV19NwJwZVkWDcOgsixv9wgFfHy+aRpVVXX4CalXxXB8PviMT3R8/ugsN30Z2AmbpfFdiiLYi1ayLNtlZsGV4lp4H8h8KviMwy3PjdCzBF7FjBCixlckEAf6MlxhjxC+AuN94Nzow3CFpTFE7XK5lfg8z1zRD5zU309XjTFKksR3cxAZlsYQvXEcNU2T72YA+IAxRnmeK01T301BZH4Bh9ncOc6ZIjAAAAAASUVORK5CYII=" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 43 }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-10T19:38:08.651038Z", "start_time": "2025-12-10T19:38:07.032364Z" } }, "cell_type": "code", "source": [ "# PATIENT B\n", "# Find a false positive patient\n", "false_positives = np.where((y_test == 0) & (y_delta_pred == 1))[0]\n", "patient_b_idx = false_positives[0]\n", "analyze_patient(patient_b_idx, x_train, y_train, delta_train, 'patient_B', n_closest=1, max_feats=args['max_atribs_radar'], max_curves=args['max_plot_curves'], c=['b', 'g', 'r'])" ], "id": "43f0b57a32b6e713", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Closest patients in the training set:\n", " Age Sex GenHlth HighChol DiffWalk Stroke Smoker \\\n", "Test 0.972 1.0 0.457 1.0 0.0 0.0 1.0 \n", "Train patient 253 0.972 1.0 0.457 1.0 0.0 0.0 1.0 \n", "\n", " PhysHlth pred_prob real_label \n", "Test -0.487 0.807 0.0 \n", "Train patient 253 -0.487 0.807 0.0 \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2390733/3679153897.py:104: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", " patient_info = np.concatenate((x[idx_vars].values, [patient_predicted_prob, y]))\n" ] }, { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2390733/3679153897.py:80: UserWarning: The figure layout has changed to tight\n", " plt.tight_layout(rect=[0, 0.05, 1, 1])\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 44 }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-10T19:38:10.495411Z", "start_time": "2025-12-10T19:38:08.766267Z" } }, "cell_type": "code", "source": [ "# Let's compare both patients\n", "compare_patients(patient_a_idx, patient_b_idx, x_train, y_train, delta_train, max_feats=args['max_atribs_radar'], max_curves=args['max_plot_curves'])" ], "id": "fa08a733c5ca9ed7", "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2390733/3679153897.py:199: UserWarning: The figure layout has changed to tight\n", " plt.tight_layout(rect=[0, 0.05, 1, 1])\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 45 }, { "metadata": {}, "cell_type": "markdown", "source": [ "# **INTERACTIVE INTERFACE**\n", "We can also do this in an interactive way, so that a clinician can do an interactive study of a patient. Note that we use the standardizes values, for an actual clinical application, these values would not be standardized, but the actual values of the patient" ], "id": "20f4080c8e862c33" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-10T19:38:10.722889Z", "start_time": "2025-12-10T19:38:10.597395Z" } }, "cell_type": "code", "source": [ "def interactive_pat(DiffWalk, HighChol, Sex, Smoker, Stroke, Diabetes, PhysHlth, GenHlth, Age):\n", " x_patient = pd.DataFrame(np.zeros((1, len(variable_names)), dtype=np.float32), columns=variable_names)\n", " x_patient['DiffWalk'] = DiffWalk\n", " x_patient['HighChol'] = HighChol\n", " x_patient['Sex'] = Sex\n", " x_patient['Smoker'] = Smoker\n", " x_patient['Stroke'] = Stroke\n", " x_patient['Diabetes'] = Diabetes\n", " x_patient['PhysHlth'] = PhysHlth\n", " x_patient['GenHlth'] = GenHlth\n", " x_patient['Age'] = Age\n", " # We provide here a formula example: different seeds and/or runs may provide slightly different formulae\n", " delta_patient = pd.DataFrame(np.zeros((1, len(variable_names)), dtype=np.float32), columns=variable_names)\n", " delta_patient['DiffWalk'] = 0.52 * DiffWalk\n", " delta_patient['HighChol'] = 0.49 * HighChol\n", " delta_patient['Sex'] = 1.4 * Sex\n", " delta_patient['Smoker'] = 0.21 * Smoker\n", " delta_patient['Stroke'] = 0.82 * Stroke\n", " delta_patient['Diabetes'] = 0.09 * (0.51 - Diabetes)**2\n", " delta_patient['PhysHlth'] = 0.13 * np.sin(1.43 * PhysHlth - 7.45)\n", " delta_patient['Age'] = - 1.15 * np.cos(0.79 * Age + 2.18) - 1.09 * np.cos(0.8 * Age + 8.41 )\n", " delta_patient['GenHlth'] = 1.18 * np.tanh(0.63 * GenHlth + 0.92 ) + 0.76 * np.tanh(0.7 * GenHlth + 0.87 )\n", " delta_patient['Constant'] = - 4.32\n", "\n", " analyze_patient(delta_patient, x_patient.iloc[0], -1)\n", "\n", "Age = widgets.FloatSlider(value=0.0, min=-4, max=4.0, step=0.1, description='Age', disabled=False, continuous_update=False, orientation='horizontal', readout=True,readout_format='.1f')\n", "Sex = widgets.Dropdown(options=[0, 1], value=0, description='Sex')\n", "GenHlth = widgets.Dropdown(options=[-1.41, -0.48 , 0.46, 1.39, 2.33], value=0.46, description='GenHlth')\n", "HighChol = widgets.Dropdown(options=[0, 1], value=0, description='HighChol')\n", "DiffWalk = widgets.Dropdown(options=[0, 1], value=0, description='DiffWalk')\n", "Smoker = widgets.Dropdown(options=[0, 1], value=0, description='Smoker')\n", "Stroke = widgets.Dropdown(options=[0, 1], value=0, description='Stroke')\n", "PhysHlth = widgets.FloatSlider(value=0.0, min=0.0, max=1.0, step=0.1, description='PhysHlth', disabled=False, continuous_update=False, orientation='horizontal', readout=True,readout_format='.1f')\n", "Diabetes = widgets.Dropdown(options=[0, 1, 2], value=0, description='Diabetes')\n", "\n", "f = lambda Age, Sex, GenHlth, HighChol, DiffWalk, Smoker, Stroke, PhysHlth, Diabetes: interactive_pat(DiffWalk, HighChol, Sex, Smoker, Stroke, Diabetes, PhysHlth, GenHlth, Age)\n", "\n", "widgets.interactive(f, Age=Age, Sex=Sex, GenHlth=GenHlth, HighChol=HighChol, DiffWalk=DiffWalk, Smoker=Smoker, Stroke=Stroke, PhysHlth=PhysHlth, Diabetes=Diabetes)" ], "id": "ba7ca6c393ff2265", "outputs": [ { "data": { "text/plain": [ "interactive(children=(FloatSlider(value=0.0, continuous_update=False, description='Age', max=4.0, min=-4.0, re…" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "9e8f75b32f7c435db726eec2d04db9f6" } }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 46 } ], "metadata": { "kernelspec": { "name": "python3", "language": "python", "display_name": "Python 3 (ipykernel)" } }, "nbformat": 4, "nbformat_minor": 5 }