{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7277b9c2",
   "metadata": {},
   "source": [
    "# Genetic Matcher\n",
    "\n",
    "The GeneticMatcher can be used to optimize any function of the baseline covariates, both linear and non-linear. In this demo notebook, we show how to call the matcher in the PyBalance library, including an example of a non-linear balance function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5d3c98f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging \n",
    "logging.basicConfig(\n",
    "    format=\"%(levelname)-4s [%(filename)s:%(lineno)d] %(message)s\",\n",
    "    level='INFO',\n",
    ")\n",
    "\n",
    "from pybalance.sim import generate_toy_dataset\n",
    "from pybalance.utils import (\n",
    "    BetaBalance, \n",
    "    BetaSquaredBalance, \n",
    "    BetaXBalance,\n",
    "    BetaMaxBalance,\n",
    "    GammaBalance, \n",
    "    GammaSquaredBalance,\n",
    "    GammaXBalance,\n",
    "    GammaXTreeBalance,\n",
    "    MatchingData\n",
    ")\n",
    "from pybalance.genetic import GeneticMatcher, get_global_defaults\n",
    "from pybalance.visualization import (\n",
    "    plot_numeric_features, \n",
    "    plot_categoric_features, \n",
    "    plot_binary_features,\n",
    "    plot_per_feature_loss,\n",
    ")\n",
    "\n",
    "time_limit = 120"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0df76346",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <b>Headers Numeric: </b><br>\n",
       "        ['age', 'height', 'weight']<br><br>\n",
       "        <b>Headers Categoric: </b><br>\n",
       "        ['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3'] <br><br>\n",
       "        <b>Populations</b> <br>\n",
       "        ['pool', 'target'] <br>\n",
       "        <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>gender</th>\n",
       "      <th>haircolor</th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>binary_0</th>\n",
       "      <th>binary_1</th>\n",
       "      <th>binary_2</th>\n",
       "      <th>binary_3</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>62.511573</td>\n",
       "      <td>190.229250</td>\n",
       "      <td>105.165097</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.505065</td>\n",
       "      <td>161.121236</td>\n",
       "      <td>95.001474</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>pool</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50.071384</td>\n",
       "      <td>162.325356</td>\n",
       "      <td>84.290576</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44.423692</td>\n",
       "      <td>150.948096</td>\n",
       "      <td>82.031381</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.695052</td>\n",
       "      <td>132.952651</td>\n",
       "      <td>54.857540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>21.474205</td>\n",
       "      <td>168.602546</td>\n",
       "      <td>70.342128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>40.643320</td>\n",
       "      <td>188.188724</td>\n",
       "      <td>61.611744</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>target</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>29.472765</td>\n",
       "      <td>161.408162</td>\n",
       "      <td>57.214095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>41.291949</td>\n",
       "      <td>150.968833</td>\n",
       "      <td>91.270798</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>67.530294</td>\n",
       "      <td>155.124741</td>\n",
       "      <td>56.196505</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>target</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<pybalance.utils.matching_data.MatchingData at 0x1548a06b0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = generate_toy_dataset()\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab4cf5d7",
   "metadata": {},
   "source": [
    "## Optimize Beta (Mean Absolute SMD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9dc2b2f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [matcher.py:125] cpu\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'objective': 'beta',\n",
       " 'candidate_population_size': 1000,\n",
       " 'n_candidate_populations': 1024,\n",
       " 'n_keep_best': 256,\n",
       " 'n_voting_populations': 256,\n",
       " 'n_mutation': 256,\n",
       " 'n_generations': 5000,\n",
       " 'n_iter_no_change': 100,\n",
       " 'time_limit': 120,\n",
       " 'max_batch_size_gb': 2,\n",
       " 'seed': 1234,\n",
       " 'verbose': True,\n",
       " 'log_every': 1000,\n",
       " 'initialization': {'benchmarks': {'propensity': 'include'},\n",
       "  'sampling': {'propensity': 1.0, 'uniform': 1.0}}}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objective = beta = BetaBalance(m)\n",
    "matcher = matcher_beta = GeneticMatcher(\n",
    "    matching_data = m, \n",
    "    objective = objective,\n",
    "    log_every = 1000,\n",
    "    n_generations = 5000,\n",
    "    time_limit = time_limit\n",
    ")\n",
    "matcher.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9112f8bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [initialization.py:31] Optimizing balance with genetic algorithm ...\n",
      "INFO [initialization.py:32] Initial balance scores:\n",
      "INFO [initialization.py:37] \tbeta:\t0.233\n",
      "INFO [initialization.py:38] Initializing candidate populations ...\n",
      "INFO [initialization.py:86] Computing PROPENSITY 1-1 matching method ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 1/50, 0.000 min) ...\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: SGDClassifier\n",
      "INFO [matcher.py:142] \t* alpha: 1.5074398973827774\n",
      "INFO [matcher.py:142] \t* class_weight: None\n",
      "INFO [matcher.py:142] \t* early_stopping: True\n",
      "INFO [matcher.py:142] \t* fit_intercept: True\n",
      "INFO [matcher.py:142] \t* loss: log_loss\n",
      "INFO [matcher.py:142] \t* max_iter: 1500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:143] \tScore (beta): 0.0525\n",
      "INFO [matcher.py:144] \tSolution time: 0.001 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 2/50, 0.001 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.05835496346821344\n",
      "INFO [matcher.py:142] \t* fit_intercept: True\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta): 0.0291\n",
      "INFO [matcher.py:144] \tSolution time: 0.003 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 3/50, 0.003 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 4/50, 0.016 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 5/50, 0.017 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 6/50, 0.028 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 7/50, 0.029 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 8/50, 0.030 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 9/50, 0.031 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 10/50, 0.039 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 11/50, 0.040 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 12/50, 0.042 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 13/50, 0.043 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 14/50, 0.043 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 15/50, 0.045 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 16/50, 0.045 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 17/50, 0.046 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 18/50, 0.047 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 2.3905570899706423\n",
      "INFO [matcher.py:142] \t* fit_intercept: True\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta): 0.0289\n",
      "INFO [matcher.py:144] \tSolution time: 0.049 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 19/50, 0.049 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 20/50, 0.055 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 21/50, 0.056 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 22/50, 0.057 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 23/50, 0.058 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 24/50, 0.059 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 25/50, 0.060 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 26/50, 0.061 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.2699411413616818\n",
      "INFO [matcher.py:142] \t* fit_intercept: True\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta): 0.0288\n",
      "INFO [matcher.py:144] \tSolution time: 0.062 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 27/50, 0.062 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 28/50, 0.063 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 29/50, 0.073 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 30/50, 0.074 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 31/50, 0.076 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 32/50, 0.077 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 33/50, 0.078 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 34/50, 0.079 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 35/50, 0.080 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 36/50, 0.093 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 37/50, 0.096 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.5868985298319505\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta): 0.0249\n",
      "INFO [matcher.py:144] \tSolution time: 0.109 min\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 38/50, 0.109 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 39/50, 0.110 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 40/50, 0.111 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 41/50, 0.112 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 42/50, 0.114 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 43/50, 0.120 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 44/50, 0.133 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 45/50, 0.134 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 46/50, 0.135 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 47/50, 0.148 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 48/50, 0.161 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 49/50, 0.162 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 50/50, 0.163 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.5868985298319505\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta): 0.0249\n",
      "INFO [matcher.py:144] \tSolution time: 0.109 min\n",
      "INFO [initialization.py:66] \tbeta:\t0.025\n",
      "INFO [initialization.py:71] \tIncluded in initial population.\n",
      "\n",
      "INFO [initialization.py:132] Sampling 512 candidate populations according to PROPENSITY distribution ...\n",
      "\n",
      "INFO [initialization.py:132] Sampling 511 candidate populations according to UNIFORM distribution ...\n",
      "\n",
      "INFO [logger.py:34] Generation 0\n",
      "INFO [logger.py:35] \tremaining patients: 10000\n",
      "INFO [logger.py:36] \telapsed time: 0.18 min\n",
      "INFO [logger.py:45] \tbest beta: 0.02494 \tworst beta: 0.25118\n",
      "INFO [logger.py:34] Generation 1000\n",
      "INFO [logger.py:35] \tremaining patients: 7482\n",
      "INFO [logger.py:36] \telapsed time: 1.63 min\n",
      "INFO [logger.py:45] \tbest beta: 0.01146 \tworst beta: 0.24292\n",
      "INFO [matcher.py:211] Time limit exceeded. Stopping.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <b>Headers Numeric: </b><br>\n",
       "        ['age', 'height', 'weight']<br><br>\n",
       "        <b>Headers Categoric: </b><br>\n",
       "        ['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3'] <br><br>\n",
       "        <b>Populations</b> <br>\n",
       "        ['pool', 'target'] <br>\n",
       "        <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>gender</th>\n",
       "      <th>haircolor</th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>binary_0</th>\n",
       "      <th>binary_1</th>\n",
       "      <th>binary_2</th>\n",
       "      <th>binary_3</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55.261578</td>\n",
       "      <td>139.396134</td>\n",
       "      <td>94.438359</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63.113091</td>\n",
       "      <td>165.563337</td>\n",
       "      <td>67.433016</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58.232216</td>\n",
       "      <td>160.859857</td>\n",
       "      <td>71.915385</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58.996941</td>\n",
       "      <td>140.357415</td>\n",
       "      <td>115.606615</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>target</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.850195</td>\n",
       "      <td>189.983706</td>\n",
       "      <td>53.000581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5044</th>\n",
       "      <td>42.548928</td>\n",
       "      <td>129.729442</td>\n",
       "      <td>94.445375</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1144</th>\n",
       "      <td>29.400226</td>\n",
       "      <td>167.737236</td>\n",
       "      <td>76.118095</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5314</th>\n",
       "      <td>50.104985</td>\n",
       "      <td>163.663484</td>\n",
       "      <td>85.785445</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2174</th>\n",
       "      <td>54.372402</td>\n",
       "      <td>149.801277</td>\n",
       "      <td>92.946485</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8610</th>\n",
       "      <td>72.912497</td>\n",
       "      <td>185.908237</td>\n",
       "      <td>96.553338</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>pool</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<pybalance.utils.matching_data.MatchingData at 0x1506d3530>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matcher_beta.match()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "994754c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1800 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "match = matcher_beta.get_best_match()\n",
    "m_data = m.copy().get_population('pool')\n",
    "m_data.loc[:, 'population'] = m_data['population'] + ' (prematch)'\n",
    "match.append(m_data)\n",
    "fig = plot_per_feature_loss(match, beta, 'target', debin=False)\n",
    "fig = plot_numeric_features(match, hue_order=['pool (prematch)', 'pool', 'target', ])\n",
    "fig = plot_categoric_features(match,  hue_order=['pool (prematch)', 'pool', 'target'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "006f4021-ce9d-4f28-81c0-8ba424a948dd",
   "metadata": {},
   "source": [
    "## Optimize Beta^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b2458050-5934-422c-bb2e-de141f4a2975",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [matcher.py:125] cpu\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'objective': 'beta_squared',\n",
       " 'candidate_population_size': 1000,\n",
       " 'n_candidate_populations': 1024,\n",
       " 'n_keep_best': 256,\n",
       " 'n_voting_populations': 256,\n",
       " 'n_mutation': 256,\n",
       " 'n_generations': 5000,\n",
       " 'n_iter_no_change': 100,\n",
       " 'time_limit': 120,\n",
       " 'max_batch_size_gb': 2,\n",
       " 'seed': 1234,\n",
       " 'verbose': True,\n",
       " 'log_every': 1000,\n",
       " 'initialization': {'benchmarks': {'propensity': 'include'},\n",
       "  'sampling': {'propensity': 1.0, 'uniform': 1.0}}}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objective = beta2 = BetaSquaredBalance(m)\n",
    "matcher = matcher_beta2 = GeneticMatcher(\n",
    "    matching_data = m, \n",
    "    objective = objective,\n",
    "    log_every = 1000,\n",
    "    n_generations = 5000,\n",
    "    time_limit = time_limit\n",
    ")\n",
    "matcher.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "38b705c8-9aa0-4332-970a-efad5189276f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [initialization.py:31] Optimizing balance with genetic algorithm ...\n",
      "INFO [initialization.py:32] Initial balance scores:\n",
      "INFO [initialization.py:37] \tbeta_squared:\t0.263\n",
      "INFO [initialization.py:38] Initializing candidate populations ...\n",
      "INFO [initialization.py:86] Computing PROPENSITY 1-1 matching method ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 1/50, 0.000 min) ...\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: SGDClassifier\n",
      "INFO [matcher.py:142] \t* alpha: 1.5074398973827774\n",
      "INFO [matcher.py:142] \t* class_weight: None\n",
      "INFO [matcher.py:142] \t* early_stopping: True\n",
      "INFO [matcher.py:142] \t* fit_intercept: True\n",
      "INFO [matcher.py:142] \t* loss: log_loss\n",
      "INFO [matcher.py:142] \t* max_iter: 1500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:143] \tScore (beta_squared): 0.0603\n",
      "INFO [matcher.py:144] \tSolution time: 0.001 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 2/50, 0.001 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.05835496346821344\n",
      "INFO [matcher.py:142] \t* fit_intercept: True\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta_squared): 0.0374\n",
      "INFO [matcher.py:144] \tSolution time: 0.002 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 3/50, 0.002 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 16.16555309446666\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta_squared): 0.0357\n",
      "INFO [matcher.py:144] \tSolution time: 0.015 min\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 4/50, 0.015 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 5/50, 0.016 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 54.02072493419677\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta_squared): 0.0347\n",
      "INFO [matcher.py:144] \tSolution time: 0.027 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 6/50, 0.027 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 7/50, 0.028 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 8/50, 0.029 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 9/50, 0.030 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 10/50, 0.038 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 11/50, 0.039 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 12/50, 0.041 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 13/50, 0.041 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 14/50, 0.042 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 15/50, 0.043 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 16/50, 0.044 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 17/50, 0.045 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 18/50, 0.046 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 19/50, 0.048 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 20/50, 0.053 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 21/50, 0.054 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 22/50, 0.055 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 23/50, 0.056 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 24/50, 0.057 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 25/50, 0.059 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 26/50, 0.059 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 27/50, 0.061 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 28/50, 0.062 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 29/50, 0.072 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 30/50, 0.073 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 31/50, 0.074 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 32/50, 0.075 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 33/50, 0.076 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 34/50, 0.077 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 35/50, 0.078 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 36/50, 0.091 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 37/50, 0.094 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.5868985298319505\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta_squared): 0.0311\n",
      "INFO [matcher.py:144] \tSolution time: 0.107 min\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 38/50, 0.107 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 39/50, 0.108 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 40/50, 0.109 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 41/50, 0.110 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 42/50, 0.111 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 43/50, 0.118 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 44/50, 0.130 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 45/50, 0.131 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 46/50, 0.132 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 47/50, 0.145 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 48/50, 0.158 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 49/50, 0.159 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 50/50, 0.160 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.5868985298319505\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta_squared): 0.0311\n",
      "INFO [matcher.py:144] \tSolution time: 0.107 min\n",
      "INFO [initialization.py:66] \tbeta_squared:\t0.031\n",
      "INFO [initialization.py:71] \tIncluded in initial population.\n",
      "\n",
      "INFO [initialization.py:132] Sampling 512 candidate populations according to PROPENSITY distribution ...\n",
      "\n",
      "INFO [initialization.py:132] Sampling 511 candidate populations according to UNIFORM distribution ...\n",
      "\n",
      "INFO [logger.py:34] Generation 0\n",
      "INFO [logger.py:35] \tremaining patients: 10000\n",
      "INFO [logger.py:36] \telapsed time: 0.18 min\n",
      "INFO [logger.py:45] \tbest beta_squared: 0.03106 \tworst beta_squared: 0.28163\n",
      "INFO [matcher.py:203] No improvement in last 100 iterations. Stopping.\n"
     ]
    }
   ],
   "source": [
    "match = matcher.match()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "60c826d7-ce64-4004-ad74-9956b77973d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CNTzfeeedruFbMeTnn392MVB27tzpRn8p3iqWK+Yp8Rw/fryL6+FucyR6jIa7t23bNlP7p23+85//uBiu84HzzjvPypUr5x6jfVAcV+xcuHChderUyd59913r2rWre6weo+u6XdtoHxSnX3jhBStZsqRLjnW+4sV69WKLhpuXKVPGXf73339dZ8UHH3zgGgzUSH/iiSda06ZN3XD3W265xW2nhgDdr9dW4q1OBTUonHvuuanq7ej96lzmiiuucB0g2n+dUwQ3+CvWr1mzxr0ndTTkBnrSAcCn1AKspNqb86QW4mHDhrkCZepZVi+yktJnnnnG3a9ArMA0fPhwl8CKt403N+vyyy93wSV4myNRS3hwb3W4+ycFChRw25QoUcJdVy+1eruV5HonAueff75LrDXM7oEHHgg8v5Jhr0X/tNNOcwFbreBq1RclyWr9Vs+5Thwk7TD2Bg0auHljOqnIly+fG6Km/WzUqFGgdV29DzpOSsg1X13Ukq/jFeycc84JzMnXvmlun1ravUYJUe+JgvqePXsCLfoAAKRHPcmjR492sdpLQBWjlDgqZinuHDp0yDVoe6O7lDSqUVmxSklpONuEE+u1XfXq1TO1f9422gc1fivWikbIqbH73nvvDTyXnl/nCV6SLjqH0Mg2nS+IYuvatWtdsVY1wOvcRo0A2kaN5aKRg8FD8pVo63m+//5715CvZF7nLUq2g4vlaZ+UoE+YMCFwm85ZgmkUghL1ggULuuuLFi2ySZMmpUrSvZES6o2P2SRdJ1dq/VArjFo89McsXbp0utvrA6eWG30INbxSB0lDJ9S7AgCxRMO3gouSnHnmmW4ItgJp48aNXUv2gAEDDkskvSFg+r480jbhUMIZPOwunP3zvpOVPHsJunz33Xe2d+9eN8ddwVr7qB8NP0v73a8GB0/x4sVdq3nwbXptnYyoF99L0tUI8OKLL7rWecWIpKQk96MW71ANDaKgrgYHtd4HU6NAsODeBalVq5Z77WBeYk6Snpp6OnSSp54QjajQZ1IneRmZP3+++5yuXr3anTTp86WTKwCINcEju9S4rClpXhKs32qoDp5+paRUcVTJbLVq1cLa5kgUt5SYhqo9k9H+iR7jXfZ69jUizTsP8WK98j0ltsE0fN5L0EW5Xd26dVPth24LjrfJycku0f7mm29cQ7saCfT8Ov9Qkh6KGt41fe6ee+7JMNa3bt06kKCLRh2qMn6wwoULu/3TMcstUU3S9UfT8EUNJ1DgVbKuIQ8aHhHqhFDuu+8+NyxRB1gnSJpLqKGbKtSjlh0AiBVpA4d3XcmnEl0ln5qLFUzXlRBJONuEQ0PQlVxlZv88wQm6aL6ZgrGS+bQt+8HD6SX4ZEO0fajb1Hsgeq8aJaBgriFyej61on/77be2e/fudN+f5pwp4BYrVizdbdLbH++1PWokUOBWUon/HV81rmjUg4Zh6qRKJ0Gqgq+REaEopmtKgno41IuiE69LLrnEzQEMZ7lAAMgrFBPT5j2Kp14s1W8lxsG8uK5YrgQ8nG2ORDFTDdaKl8Hx8Ej7Jxo9F5xoKyFW0qyRcprOFszrac9qrJd+/fq5BnaNZFPMVwxXx++RYr14w+SzE+tVT0cNA2nPW2ImSVeg1XAHb86fenc0xEJFB3QCF4qKCOg+b7iDhnD8+OOPLrknSQcQS9S7HEyJiqiBUgFTPclqkQ4eyvXXX38F5poHbxM8tCx4m3CoIVRFYBSkgoNrRvuXHr2uCrDpu16jp3KSWsi1T+qBVcAWDatPK23jgPZp37597rGZKagXik4SdLzSnoTEM8X4TZs22dy5c92Jn+YdqoihEm4NYQxFQwv1+dDnzqPeEp0DkKQDiCWKifpO9Oqb6LpqqnixVDEqbe+z4rh3X7jbHIlGxyk+Ko4Fj1o70v6FokKwGlmmHulw1mbPjEOHDrk6NZqDrtEDohge3GgQKtZrnxSD1OmQXm97uHSMFOcV73NL1M4i1MOj3g21sHg01FEt516l3VDUSqS5AWqdEfWSKHAfadgcAOQ1SnpVwdQLihq2psDiDdtW5VbNR/eWGFGSqeHBSoI84WxzJCrYotfX0LXM7F8o6unWUH0Vcgme862RVephzQ6dEHgnD6IW9VDLo6jlW8VxgoexK7ao8VexyXusCvFllhoFzj777Gy9j1ijmK4TqeCeGc3p//LLL11PRCj6e2iIpkZeiGK+Yj+xHkAsUt0UL7dRzNZwbi+WKF5rmq/ipDdqTJXKVaBNU8HC3eZINAJMMVrfzZnZv1CUnGs6smrIqMCqR9/rKmaXHfny5XM998EdBZrn7sXv9GK9evp1nLRPanTwqMBtVmK9VnHJaIp2nu1J18FRcE5bnEAnd6rqmx6dXGqIg5bh0fAGVe3VIvX6IKRHrSv68XgnhvqA6Qf+4v1N+Nsgpz5PCixqefWGK3m/d6xdYfbfmJPry7J4r5t2yFR6tM/HHnusC4xaokRJtr4zFYS951BPpOZJaQkRFS5RD7JGJKkSadptlPRojpe+M9Nu4wXe9PZNQ+ZUTV1Ls3gjlsLZP28OWvDzqmVbxV80HF2t8pq7rvniGgqtueTB24Y6XhndpnnpKuyi+W26rBMDb3+DH6fieapAqyHVOpFQYTu1yGtkluKKjqeWmdGSMkfan+D3p0YL9RZPnjw55LHUbdpen8m0c/5i+ftu+fLlqZbc8WK94rJO2EI16mg1AM1p1OdaibmeQ/Mdg3vW0yLWZw0xF7HwGQkV6yMd77MS67WdEk51OCr2KKFWg6TioYZl6341lN9+++1upRYNHVd8UszUyCLvdcLZJu3vUDRaWXmVVkbxhnkfaf+Cny84JioZVs6laU0aGaUkWg3gwbE11HlCOLc9/PDDro6ZEn6N1NK+qN5J8DZqDFYxOxWU1f16jF5b70WxRecwOv/Q3H2d42T02mmP35tvvnnYOUJOx/uEFO+VI0xr+OkPpqHqwUMqVPVXJ0tph2x4tEafhrrpg6hhiVOnTnWt9KoAmF51Pc1jVyXjtFRwgOq7QGxTy6kClRIBb56RClEd36aN7U1Kith+FElMtJ9nzcqwlzmYenWVwKhhUkPW1KOoxNOrmh5M96u1WEPP0lubNaNtlFwqIfKKr4Wigi3qJVdvtxpXw9k/JclKnNLORwt+XT2vkvW0+6S5ZqrcqrXQPUqq1UAbXKxOjbqKJeXLlz/svWqIn4a3qVid4kxwT64C87Jly9z+6eRGFA7ViKFjocaD4Pn0ofZHy8JoGy15I9dff737rKUtSuPRXD999rRv6vEPpuIzarjQcUw71z+v0+dFc9GD47CW9dPIOX0GQk0xUMNS9+7dXYOSKv/rb6p6NDrh8k6m0iLWA/ErVKyPRrzPbKz/9NNPXeejlgJTYq39VT6jJczSUtE1fReq91YxKlTF9oy20dx0NSR7MS8UxUFNj9M+qZp7OPunJFmxUw0EoSgp1j6pp16JfvDcdT1OrxlcAFyFXwsUKJAqr1MnhKbw6fEexVLFECXgXrV5xZvgY6+YqkYFxXU1Fnv1YtRZrB/F7+A10kPtj963jqu3eoymY7388suuCn16VfNzIt5HLUnXH1sHUfPRtDyOR4Fc4/xVPC4tvSl9KB5//HF3MuTRh00nSkruw21d98ryx9rJUCxQC5MaXbRWcXB1RSAr1HKrL0olp8EJpL6cvSHgkaAkMr0EOhQVQ9H3ZHrfa5mlr3oFKX1XhrMUSyhKnBTg9D5yev9igYYIauh8eo2/+ixqKL7iT9rGFsUlfUZiMUnXyY7WwNUygB7VkVFBOP0PhjoZ9aoUaxkcj2K/pi+oOF+o2ECszxpiLmLhM5JerI90vM9srNfIMg3BTrvkZzTpOOp4qeE93P3LiXOMvOLXX391f+eMGmJyIt5Hbbi7qgwqMKtlJDhJ10lger05qqSnN5221V2tIGoZSo8KCHlFhILpi8avXzbg74OcoSHYChiawxRczEuBPKOiJ9Gmffb2Oyd4Q7Ky85zBQ5Zzev9iQXBxvlB0rHTMQsWeWI5FGlKoWB9Msd47DwhFIyzUg5421uskUD+hqucT67OHcyLk5c9IerHe7/He21c/xVKNbvOKxIW7fzlxjpFXHBdGsbiciPdRO4racRU00nABtYqLWumUbAcXNFLLu9drrmGGGsag+Qfeh0EtPZoDSWV3ALFE33sqhOJXft8/+IdiupZb049o2GDa4oW6T+uge+cDGlaoXnSvWq9OwDW3Uok6y9sBiBUafaXear/y+/7FsqguwfbAAw+41nXNMdQcAw0f0FA2Vcvz6H7NMQieR67CQArUahXTY9TzrucCgFiRXo0Nv/D7/sE/NHRdtWQ0VFbxeuHChS4JV1HD4J5zzX1URWLxqhJr5JyKDql+gBr3vdUEACAWqG5KRnPEo83v+xfLopqkq0KgisdoDrpa1rUsT3BRHrnppptStbarcJGKBGiNO7W4aziGV7QHAAD4j1YB6N+/v0vQVYE3uCiPtGvXzvWcewUA9VsF/1REVvNJdV2Fgfw61BYAgJhJ0j3pVf5N7z4Vk1HLOgAAyBs0XS3tsqseVdfVcPe0tGygfgAAiCexPbMfAAAAAIA8hCQdAAAAAACfIEkHAAAAAMAnSNIBIM5p+av3338/2rsBAAByyerVq2306NEc3zzCF4XjACDSVDF68+bNEXs9VaeuWbOm+ZGqas+ZM8fOP//8aO8KAAB5Nt77OdYvXrzYbr31VrvllluivSsIA0k6gLgM2A0b1Lc9SXsj9ppFE4vYwkWLfRu8AQCINZGO98R65BSSdABxRy3qCtjjr21uDasUz/XXW7hul/V6cZ573XCT9G+++cZ27txpxx9/vH3//fe2detW69at22FLWO3du9c+//xzW7t2rdWpU8e6dOli+fPnP2ybzz77zJYtW+bWmj7ttNMO2wYAgFgTyXiflVj/999/2xdffGFXXnmli/srV660Vq1a2XHHHXfYtt9995398ccfVrZsWXc+UKpUqSxtg7yBJB1A3FLAblnLnwFs8uTJLvlOTk629u3b27p16+zmm2+2KVOmWIcOHdw2uu3kk0+2ggULWps2bWzkyJFuvenp06dbsWLFDtumRYsW9swzzxy2DQAAscyv8f733393Q9Bfeuklq1q1qpUsWdLF+vvuu88GDx7stklJSXHT0VQ/pnv37rZo0SK78cYbXRxv2rRp2NsgbyFJBwCfUgu7etFPOukkd33AgAF2/fXX22+//WYJCQk2ZMgQK126tGs5L1y4sOttb9asmT3++OM2bNgw9xhvm2+//db27dtnBw8etObNm6faBgAARMf+/fvt3HPPDcRkFXK99NJL3c9RRx1lb731lk2dOtX+/PNPq1WrViAhv+6661z8l3C2Qd5CdXcA8Cm1fnsJutxwww1uGJuGrcvHH39s/fr1cwm6lClTxvr06eNu94SzDQAAiB41wnuUXJcrV86NphPF67PPPtsl36JG+ptuuikwFS7cbZC3kKQDgE9Vq1Yt1XVvPvqaNWts9+7dLvCmnaNeo0YNt8yKhLMNAACIHk1Hq1ix4mHxX7FeFK9DxXHvvnC3Qd5Ckg4APrVp06ZU1zdu3Oh+V6pUyc0nL1GiRMhtNOdcwtkGAABEj2rPbNu27bA4rVgvitfpnQ94sTycbZC3kKQDgE/9+uuvrviLZ/z48W5+2tFHH+2un3rqqe42zT3z5rW9/fbb7nZPONsAAIDomTBhQuCy5pCr97tjx47ueufOne2TTz5Jlci//vrrbkqc1wMfzjbIWygcBwA+pVZ0VWm97LLLbP369TZu3DiXYBco8N+v7kcffdTatm3rlllp166d/ec//3HLrd19992B5/C20fO0bt3avvzyy8O2AQAA0Rvu/thjj9mCBQvc6LexY8da//79rXHjxu7+a6+91t544w078cQT7eKLL7aFCxe6Oehaus0TzjbIW0jSAcQtrWnq59fRGukjRoxwy67JzJkzXaLtqVevngvqaoHXOul9+/Z1Cb2CfNptVPl1+fLldvnll1vv3r1TbaMEXz30AADEokjE+6y+RqFChWz27Nn27rvvunXSX3zxRevRo0eqJF696++8844rHtuyZUt7+OGHrU6dOpnaRmu3a3k35A0k6QDiTvny5a1oYhHr9eK8iL2mXk+vm1lqSfda00PRMLZbbrklw+fQNgrMO3bscGuw5suXeqbTmWeemen9AgDA7yId77Ma6/WY4ArvoRJ5NbBn5EjbqNF+9OjRmd43RAdJOoC4o9bkhYsW2+bNmyP2mgrAel0AABCb8Z5Yj5xCkg4gbgO3n5PmTp06HVbtFQAAxE68VyHY66+/Ptq7AR8iSQcAHwqejwYAAGKPqq+rwCuQFkuwAQAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgES7ABiEurVq2yzZs3R+z1ypcvn6l1Wjdt2mT79u2z6tWrm5+sXLnSSpQoYWXLlo32rgAA4Kt4n9lYv3v3bvvnn3+sfv365ic6B9m7d6/VqFEj2rsSt0jSAcRlwG7QsIEl7UmK2GsmFk20RQsXhR28tW7qH3/8YZ9//rn5bf32Cy64wAYPHhztXQEAwFfxPrOx/rvvvrMzzzzTDhw4YH7yxBNP2Jw5c+zLL7+M9q7ELZJ0AHFHLeoK2B3u6GClapTK9dfbvnq7zRg5w71uOIH733//dT9qYV+0aJG7TT3qycnJtmHDBne9ZMmSVrVq1cMeq/sV7KtVq+Zeb/v27Xb00UcH7l+9erVr6S9WrJi7nJiY6K4H27lzp3t9vWb+/PkDt6u1Xy3ramH39qtevXqWLx8zpwAA8R3vMxvrFU8VV8WLqWXKlHE/y5Ytc9cLFy7serMLFEidsu3atcvWrl3rYrAur1u3zo466igrVKiQu1/7kJKSYhUqVHDxXHFd9wfbv3+/e47KlStbkSJFDjsH2bNnT2C/dE6hUXSIHJJ0AHFLAbt83dQJqh+899579tFHH7nh7ueee6677dlnn7U1a9bYQw895K5v27bNDh48aE8//bRdcsklgcc+8MAD9uuvv7okfN68eW4InVrqly5d6p5rxYoV7r7jjz/e1q9fb927d7cHH3zQPXbHjh12zTXX2KeffuqGs2/dutX1mA8dOtTdP3z4cPv777/tzTffdNvI7NmzCdwAAF/zY7xXPFVcVSz3Yn2vXr3s4osvDlxXIq/G9+uvv94ee+wxS0hIcLerh7tnz542YMAAe+WVV1yi/cknn7iEXs/x4YcfugS9VKlS1rx5cxf7f/rpJ/dYJe8jRoywxx9/3MVvxfozzjjDXnzxRbe9zj90HqLX9vZjzJgx1rVr16gdq3hEkg4APqNEefHixSGHu/fp0ydw+YMPPnDBuG3btqla7X/88UcXtKdOnZrqOdXzrqRaSfp9993nTg6UpAc/t1rr1SKvwP3XX39Z+/bt7ZhjjnEnDS+99JJrAGC4OwAA2dO4cWMXVzXc3eux9gRfX7JkiXXs2NFatmzpEnNPUlKS6zHX6LaCBQsGGupnzZrlHlO7dm37+uuvXXJ93HHHBR6nRv/XX3/d5s6da3Xq1HEN9Oedd54NHDjQ7U/fvn3dOQjD3aOLMYoAkMdo2JoCcMOGDa1SpUqupzxYo0aN7MorrwxcVy/6jBkzbNiwYW54u6h3PHiYu4bWqfVcrfJqtdfzS7du3ez999+P2HsDAADmRtOpB1w97UrSp02bdthheeSRRwIJuqiB/uabb3YJunTq1MnOPvvsVI958sknrXfv3nbo0CEX6zWq7qKLLiLW+ww96QCQR6hnXcn377//7pJzzSFTr7eGwQdTy3jaIXWiuWseBfW6desGri9YsMD91pC6tFq0aJHj7wUAABxOc8wV670h6xrZpt7y4N5w0TlAlSpVAtdVt0a1ZtRQn7bH3jtP0BB2Ncq//PLLNmHChFTb6bXUO+815iO6SNIBII/QEDQlzOoV94q8aM65WsODBRd7Ey/gqlU+mIKxx2uJ/+qrr0IWpAMAALlP88U13UxF5SpWrBhoQNcQ9IxivaarKZYrEU8v1usxKvaqWjTB0+fgPwx3BwAfUhKuVvFgCxcudMPWvARdw+C8CrAZ0bB4BW7NVfdoHlvwnLdWrVq5KrKTJ08+7PHB+xFqvwAAQOYppqqhPbixXbFew9u9BF33ffPNN0d8LhWVa9q0qWvIDxY8JU7nAiocS6z3P3rSAcCHGjRoYGPHjnXBVUPQtBzaiSee6Kq7ai65qrur8rqqtB6JHn/11VfboEGD3AlBuXLlXOE4LdXmVYrVbZqzPmTIENfjrnlsmpuu+egaOnf77bcH9ktVZc866yz3XCzBBgBA1mjameLwG2+8YSeccIJbfk2xfvTo0dalSxd3/ZlnnnG96MFD29Nz11132aWXXuoKvioZnzhxov3yyy+uId4zcuRI99wqKKvedDUCfPvtt25K3aRJkwKxXqvHKOFXYwFLsEUeSTqAuKU1Tf36OirioiXUlByr8qqqsSqIKwD379/fBe4bb7zRpkyZ4hJsj5ZhCWXUqFFu2Pv999/vknxVa9c67MFroypBV2B+9dVXbfz48a5ivCq5q8CMR4m8Ggc09F5D6FiCDQDgd5GI91l5DSW/qqiuuPvoo4/aZZddZnfeeadbo1wrsCiBP+WUU1zsXb58eeBxmqeu6W5pqUq7nk+N/ErQ27Vr584VtDKLR6u2/Pzzz/bEE0/YTTfdZKVLl3YN85qn7lHsV/V3Ne5v376dJdiiICElnG6YGKMTXq0DqA9dyZIlo707SENDaT/77DM7/fTTU1WsBLJCc7MU2FTp1EtIV61aZQ0aNrCkPf+bp5XbEosm2qKFi1ItlRbp/ysl5frO03w0ff9pPVUtw6Kgjuh8Fj3EpZzHMQ0PMRex8BlJ7/s10vE+2rFeleDTzlVXkq9pb2rszy3qjdd3rneOEe/25kC8pycdQNxR8FQQ1bzsSFHvdbSCtrz99tuuZ/788893LfQPPfSQG8IWvE46AACxJNLxPtqxXiu+aAm2fv36ud529ab/8MMPbvg88haSdABxSUE0moE00jS8XUXmNFxeLe2an/bee+8d1sILAEAsiad4r/o1mmc+ZswYt/65hsRrWlqzZs2ivWvIJJJ0AIgDWppFrev33HMPQ9EAAIhR55xzjvtB3sakAQAAAAAAfIIkHQAAAAAAnyBJBxAX4nAhC/gMn0EA4HsWsS8lB845SdIBxDRvKZL9+/dHe1cQ57zPYNrlcQAA2eMtDafVS4Bo8z6H2VmykMJxAGK+YFrRokVt06ZN7ssyXtfv1BqmShK1dme8HoNoH399BvVZ1GcSAJBz1PhZunRp27hxo7uu79qEhAQOcYRwjvG/HnQl6Poc6vOYnUZ5zhQAxDQF6SpVqtjy5ctt5cqVFs+BIykpyRITEzlxiRI1jmgZIE4cASDnVa5c2f32EnVEDucYqSlB9z6PWUWSDiDmFSpUyOrWrRvXQ96Tk5NtxowZ1qFDh2wNv0L2PoeMYgCA3G2Ur1ixoot5iBzOMf5H51g5Ma2NJB1AXFByVKRIEYtXChgHDhxwx4AkHQAQy/GO2h+RP+acY+QsJiYCAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE9EvXDc1q1bbfLkybZhwwZr2rSpnX322WEtT/PLL7/Y119/7dZB7NGjh1WqVCki+wsAADK/hu6HH35oCxYscNWXL7jgAitVqtQRH7dmzRr75JNPbOfOnda1a1c79thjOfQAgJgX1Z50rVmsxPyNN96wLVu22I033mjnnHOOC+YZrcM3YMAAO/XUU926x/rp0qWL/fXXXxHddwAAcGQHDx607t272+23327//vuvvfzyyy7ZVgKekQkTJli9evXsq6++sh07dlj//v3d+QIAALEuqj3pd9xxh9WoUcP1iBcoUMBuuOEGa9CggU2aNMkuueSSkI8ZO3asjRs3zvWkN2rUyN1211132d69eyO89wAA4EiUWH/33XeuMb169eq2f/9+a926tQ0dOtTF81C0bd++fW306NGuYV6GDx9OgzwAIC5ErSdda+lpCFvv3r1dgi5HH320dejQwd5///10HzdmzBjr2bNnIEGXMmXKuOFzAADAXxTTO3fu7BJ0KVSokPXq1cs++OADNzoulBdeeMEqVKjges89+fLlcw35AADEuqj1pK9atcqSkpLsmGOOSXV73bp1bebMmSEfozlpCxcutDvvvNM+//xz15tetWpVN4+9XLly6b7Wvn373I9Hw+YkOTnZ/cBfvL8JfxuA/6t4Ecvfd4sXL7YzzzzzsFivWLx+/fqQjew///yztW/f3sX8jz/+2IoXL26dOnWyxo0bp/s6xPqsIeaCzwiyi++RnI/3UUvSd+/e7X6XLFky1e0qJOPdl9b27dvd76efftqKFStmJ510ko0fP97Nc/vyyy+tZcuWIR/38MMPu2FyaU2dOtUVnoM/TZs2Ldq7AMQc/q/8ac+ePRarFNNDxXrvvlC2bdvmYr4KzCnBV7I+cOBAe/zxx93UuFCI9dnDdwP4jCC7+B7JuXgftSRdreLBiXdwYPbuS+8xSqy//fbbwO0qHDd48GCXdIcyZMgQu+222wLX1XqvufCqFJv2xAH+aGHSP7n+rgULFoz27gAxgf8rf/NGeMUixe5Qsd67L73H/PHHH7Zs2TKrWLGiu61Zs2auUf6qq66yxMTEwx5DrM8avhvAZwTZxfdIzsf7qCXpNWvWdMm2isOcdtppgdt1Pb05Z6VLl7bKlSvbCSeckOr2E0880d588810X6tw4cLuJy0lgCSB/sXfB+D/Kl7EcixSTE+7AouuezE9lIYNG7reBi9B92K9is5pulz9+vUPewyxPnuIueAzguzieyTn4n3UCsflz5/fLbemqq/e2HzNW1MFWA1v82hdVQ1v91x88cX2/fffpyo2o8dkNE8NAABER48ePWz69Om2YsUKd12rsahhXbd7/vzzT7v77rsDPQyK9X///betW7cuVaxX436tWrWi8C4AAIiTJdgeffRRVxhGc8tbtWrlEvJzzz03VeD+z3/+Yz/99JNbQ12GDRtmHTt2dC3qbdu2tVmzZrm10rWMGwAA8JfLLrvMJk+e7OK9Cr0qpitRf/DBBwPbaM75iBEjXDV3TUPr1q2bW8nl+OOPt/PPP982bNjgVoTRMqyhRsYBAGDxnqSfcsopduWVV7pkOtS8sHBpXvjvv//ulmFRAH711Vfd0PeEhITANuedd54L0sHLranqq6q9rly50m6++WY744wzXCE5AACQM66//no76qijrE+fPukOSw935NxHH31kU6ZMcT3mamRXbA+O202aNLEHHnggUFBOXnrpJZsxY4bNnj3bjj32WHvkkUfoRQcAxIUsJelaOkXBWxVWL7nkElfEpXXr1lnagRIlSrgTgPQoAU9LregXXnhhll4PAAAcmRJjVUwfOnSode/e3TXOq9J6gQKZP3VQ4/vpp5/uftKbt67h7ml16NDB/QAAEE+yNCddw820tqnmimseeZs2baxp06b25JNP2qZNm3J+LwEAQERde+21rrr6559/7hrUNfy8evXqNmjQIDc8HQAA5I4sF47TMPfevXu7ueBLly61s846yy2DVq1aNVf47YcffsjZPQUAABGlHvBTTz3V3nrrLVfETXVhJkyYYI0aNXLD1nX7gQMH+KsAAJCDsl3dXT3pL774oo0bN85VXVXLe758+axTp05233335cxeAgCAqNmyZYu9/vrrbiTdxo0bA/ViVNRVSfzBgwf56wAAEM0kfdeuXa7IW7t27dxapjNnzrSRI0e6VvZnnnnGJk2a5JZJe+KJJ3JqPwEAQAQdOnTIDXW/6KKLrGrVqi6+X3rppfbPP//Y+++/b0899ZQbDq8VVubMmcPfBgCAaBaOU5XX4sWL2+WXX26vvfaaKySXllrYVRUWAADkPVr67IsvvnAruei3lj9Nq3Tp0m5VlpSUlKjsIwAAsShLSboqsN5+++1WsGDBw+7T+qcnnHCCu/zHH39kfw8BAEDEqZK7estDNbircJxq0GhNcy2VBgAAojzcfciQISETdFEhGQAAkLe9/fbbtmTJkpD3aS76zz//HPF9AgAgHmS7cFyw7du3u2HwAAAgdinea1k2AAAQ5eHuV1xxRcjLXoGZ33//3a2ZDgAA8qbHHnvM/vzzT1uwYIG7PH78+FT3r1+/3vWwN2jQIGr7CABALMtUT7rWQvXWQ/Uuez/58+e3c88997BgDgAA8g4tp6a4rmJw3mXvRw3yjRs3tunTp1upUqWivasAAMSkTPWkewl4+fLlbfTo0bm1TwAAIEoGDx7sfj///PNuDfR69erxtwAAwO/V3UnQAQCIbQMGDIj2LgAAEJfCTtJ79eoV6E33LqeHIe8AAOQ9jzzyiFs+Vau4fPLJJxkupaptNPQdAABEKUkvUKBAyMsAACA2qL6MYnxCQkLgcnq0DQAAyHlhZ9vjxo0LeRkAAMSGQYMGBS43atQoqvsCAEC8ytF10gEAAAAAQATnpIeDOekAAOTdOenhYE46AAA+mpMOAABiz5HmoQdjTjoAAD6akw4AAGJ7TjoAAIgO5qQDAAAAAOATrJOOsKxatco2b94ckaN16NAh93v+/PmWL1/k2pH27dtnhQsXtlhWvnx5q1mzpvldJD9v0ZJX/haIL6yTDgBA9LFOOsJKmBo2qG97kvZG5GglJibaxIkTrUOHDpaUlGSRki/B7FCKxbTEoom2aOEiXyeH+rw1aNDQkpL2WCxLTCxqixYt9PXfAvGHddIBAIg+1knHEalHUwn6+GubW8MqxXP9iB3KV8jWmNmMwSdavkP7I/IXmrtsk13z5mJr0aeFVW9dPSKvGWnbV2+3GSNnuL+nnxND7Z8S9DZXDrOSVWpZLNqxboXNenW47/8WiD+skw4AQPQVyO6w5H/++cddrl69ekSHJiPylKC3rFUq118n2Qq6JP3YmiWtoCVbJOzes9v9Ll65uJWvWz4ir4mMKUEvW7M+hwnwgU2bNtm2bdtco1KsTwsCACDaspRV79+/3+6++24rXbq0HXXUUe5Hl++9915LTo5MUgUAAHLXhAkTrE6dOlaxYkWrV6+eFS1a1Hr27Glr167l0AMA4Kck/dZbb3VLso0aNcrmzZvnfnT51Vdftdtuuy3n9xIAAETU+++/b3379rXevXvbrFmzbMGCBfbBBx/YypUrrWvXrnbw4EH+IgAA+GW4+/jx423q1KnWpk2bwG3HHnusNWnSxLp162ZPP/10Tu4jAACIsLfeesuNkBs6dGjgtoYNG1rnzp2tdu3a9uuvv1qrVq34uwAA4IeedFXf1rC3tOrXr+/uAwAAeVt6sV5D3lWHhngPAICPetI1zO2JJ56wBx54wBISEtxtKSkp7jbdBwA4soULF0bsMKnQp8yfPz9iRT737dsX80XGYnm9e8Xz5557zs444wyXmHumTZtm27dvD5nAAwCACCbp/fv3T1U4bsSIEfbuu++6oW5K0H/55Rf766+/7JJLLrF4o3WdtZRSrIpkIgHEg+R9/11asFevXhF7TfV6Tpw40Tp06GBJSUkRec18CWaHUiymJRZNtEULF8VMov7UU0+l+s6fPXu2e28nn3yylShRwpYsWWI//vijtWvXzv7++29r0KBBVPcXAIC4TtLTJqE9evQI9JRI06ZN3U+8VXdXgt6gQUO3rnNsS7B12/eaWe4vwQbEugMHDrjfD5xfz05vWjEir3koXyG3tOGMwSdavkP/bSTITXOXbbJr3lxsLfq0sOqtq1ss2r56u80YOSOm1rtXD3lwvFedGc+uXbusSpUqgfgfb/EeAADfJemTJ0/O3T3Jo3QyowS9zZXD3LrOsWjT8kU2b8Kjtm3PfxMLADmjdvmi1rJWZBq+kq2gS9KPrVnSClruJ1e79+x2v4tXLm7l65bP9ddDzlChOAAAkAfnpONwStDL1qwfk4cmaXesjxIAAAAAgDyepO/du9d+/vlnN9zbG7rpueKKK3Ji3wAAQJTNnTvXli5danv2pG6wPe2009zwdwAA4IMk/ffff7ezzjrL/v33X9u5c6eVK1fOtmzZ4u6rWrUqSToAAHmcCgx2797dZs6c6VZyUYX3bdu2uWKxivvHHHMMSToAALkgS+vw3HrrrXbRRRe5AjPevGy1srdt29auv/76nN5HAAAQhUrvWrpPMV7V3CdNmuSS9AEDBlizZs3cbQAAwCdJupZbu/POO13Lun60JNvRRx9tr7zyio0dOzbn9xIAAESUYr0a3rX0Wr58+VysL1mypD3zzDOuYV7LsQEAAJ8k6WpJ11A3qVChgq1du9Zdrlatmm3YsCFn9xAAAETc1q1bQ8Z6Jeyai068BwDAp9XdNcR92LBhduONN9q4ceOsYcOGObNnAADAFxTr1YPeqFEjW7x4sc2fP9/NSQcAAD5J0jXU3TNy5Eg7//zzrXXr1la9enV75513cnL/AABAFFx66aVWq1Ytd/nKK6+06dOnW4cOHaxQoUL2+OOPW+XKlfm7AADglyT9kUceCVyuW7euq/a+a9cuK168eE7uGwAAiJKrrroqcDkxMdHef/99t/xqwYIFLX/+/PxdAADw43B3VX39559/3GX1ogMAgNizadMmV4+mZs2aJOgAAPixcJwqvN59991WunRpO+qoo9yPLt97772WnJyc83sJAAAibsKECVanTh2rWLGi1atXz62V3rNnz0AROQAA4KN10lUkbtSoUTZv3jz3o8uvvvqq3XbbbTm/lwAAIKI0vL1v377Wu3dvmzVrli1YsMA++OADW7lypXXt2tUOHjzIXwQAAL8Mdx8/frxNnTrV2rRpE7jt2GOPtSZNmli3bt3s6aefzsl9BAAAEfbWW2+5EXJDhw4N3KYVXDp37my1a9e2X3/91Vq1asXfBQAAP/Skq4CMhr2lVb9+fXcfAADI29KL9Rryrjo0xHsAAHyUpGuY2xNPPGEpKSmB23RZt+k+AACQtymeP/fcc7Znz55Ut0+bNs22b98eMoEHAAARHO7ev3//VIXjRowYYe+++64b6qYE/ZdffrG//vrLLrnkkhzYLQAAEGlPPfWULVy4MHB99uzZrqL7ySefbCVKlLAlS5bYjz/+aO3atbO///7bGjRowB8JAIBoJembN29Odb1Hjx7u9759+9zvpk2buh+quwMAkDephzw43qvOjGfXrl1WpUqVQPwn3gMAEOUkffLkybm0CwAAwA9UKA4AAOTBOekAAAAAAMBHSfrvv/9uV1xxhVuG7fjjj3eX//jjj5zdOwAAEDUa+n7nnXdax44drXnz5nbeeefZRx99xF8EAAC/Jen/+c9/rEWLFrZixQrr1KmTnXrqqe6yArjuyyyttfr555/b6tWrM/W4jRs32ocffugaDAAAQM75559/rEmTJvbBBx+4+H7OOedY4cKF7aKLLrLbb78908+3cuVKF+vnz5+fqccdOnTIPvnkE5s+fXqmXxMAgJiekx5s6NChNnLkSLvttttS3T5q1Ch335lnnhnW8+zcudNtq0qyWmNdFeLvuOMOu++++4742IMHD9qFF15oM2fOtOuuu85Gjx6dlbcCAABCePLJJ61169auMTx//vypKr6r2rvidaVKlcI6dkOGDHGV47UizIIFC1xDv3rkteb6kWg1mQceeMDq1KljixYt4m8FAIh5WepJV1J91VVXHXa7bstMAL377rttzZo1tnjxYvvuu+9cL/zw4cPtm2++OeJj77//fndy0KhRo0zvPwAAOHKs11S24ARdlLg3btzYLbsajilTpriGffWEz5gxwyXpOldQ4n0k33//vb322mt25ZVX8ucCAMSNLPWkV6hQwQ1X69ChQ6rb582b5+4Lh9ZWHz9+vA0cONDKlCnjbjvllFPsuOOOszfffNPNf0uPgvzrr7/uhslruD0AAMhZXqz3llzz7Nixw5YtWxZ2vFdM17rqJ554ortesWJFu/zyy13y/fDDD6f7uK1bt1rv3r3ddhomDyDzVq1addgyypmlKSei74N8+bJfc1rLN2vqjF+UL1/eatasGe3dALKfpKvH/OKLL3ZD21U0TmbNmmUPPvig9evXL+y5bv/++68de+yxqW7XvLeM5qtt2bLFevXqZePGjQsk9+F8GXjruXsnGN4ar9ld51VfXImJiVYwn1mBhP9+icWaQvkT3Hu0AoUt2Qrm+ut5rxGJ1/Kk5C/k3mOh/IWsQEqW/i18r2BCQfce9Zn18/rG/E/Fxv8V/1OZ48f/yb59+9ppp53m4ucZZ5xhJUuWdL3njz76qJui1qBBg7CeRzFdtWvSxvq1a9e6mF6uXLmQj1Pvuc411GgfTpKem7E+lnnHhmMUe3Su3brVcbYnaW+2nkfnDq+++qr7PkhKSsr2fuVLMDuUYr6RWDTR5syeY9WrV4/2ruRZfI+EL9zv2oQUdWlnkuaDK0g/9thjtm3bNndb6dKlbdCgQa4KbNqhcaGoEnzTpk3txx9/DLSuix7//vvv25IlS0I+7uyzz3YnBho65wV6BfCM5qRrjruG0ac1YcKEsObDAQCQm/bs2WM9e/a07du3u2TYLzRvXLHdi8kFChSwyy67zMX/cHvSdeKrxv3gOPz111+70XNLly61o48++rDHPPPMM64H/aeffrKCBQva4MGD3dz4jKbUEesBALES77PUZagq7HfddZcrBKOW8ISEBKtSpYr7Ha5ChQoFdjTYrl270h0CM2nSJDfUXT3pCtaiN/j333+766o8G2oftJ/BRe7Uul6jRg3r2rVrtk+GvGH/nQY+Z2Vq1LVYtG7xbzbzudvtpb7N7KLWVXL99dTTN63MZdZl61tW0CLT+zFz8Trr9tRv1vaWtlanQx2LRVv+3mJTBk1x/0NpR7D4Cf9TsfF/xf9U5ni9vn6yYcMG13Om2KoGeQ0/V+xUop4ZivehYr2EivdauUVT4ZR0f/rpp+42NRLoMYr1atgPVbAuN2O91yupnn8/0SiE7Pb+qVdn2rRp1qVLF9cggtjhxdMZg0+0Y2uW9EX88FtsyCvnRn7H90jOx/ssJelq9VZvuhLiatWqZeUp3NwP9binXXZN12vXrh3yMcWLF3e95uoB92jIvIrQaPi7etlDJek6CQh1IqBglN2ApLk5GvqTfMjsQEr25+n40f6DKf8d3nRgX8SSZtFrRer1Eg7ud+9x/8H9diDhgMWi5JRk9x71mfXziRj/U7Hxf8X/VOb48X+yT58+rhe9c+fObrScfrJCVdlDxXrFZTXwh5ry0q1bN9eLrh9RnFejvGK9ktJQSXpuxnrN623cuIklJaVubIi2xMSitmjRwhyZT5sTxwn+jKf5Du3Pke/9nIgffosNeeXcKK/ge+TIwv2cZSlJr1q1qguwaqHOqiJFiriib++9956rHitqpVf1Vw2jCy5Gp0Rcw+JOP/109xMsnOHuAAAga7E+u7p37+5q1qgnXI3tMnnyZNfD7U2P06i8n3/+2SXnlStXDoyW83jD3dPeHikqvKUEvc2Vw6xklVrmBzvWrbBZrw53+0bRKwBZRXFBfxYXzFKSrpb166+/3l588UUXTLNKVV3bt29v11xzjRu+NnbsWNdLH7zUiualqSVdc9gBAEBk3HTTTW7+uRrDta55Vqmg7Msvv+ySdcV3LbOqhFw1aTy6fOGFF7pGAT8Xb1KCXrZm/WjvBgDkWILesEH9HCkuOHHiRDe9IlaLCy5auCiiiXqWkvSHHnrIzVXTMDVVWPfml3vWr18f1vO0atXK5syZ45LzL774ws466yx3UqBedo9ODIoVK5buc6g3vkmTJll5GwAAIIOGdBV2a9mypYvDXi+4591333UN7UeixykJf/rpp12Vdp07KPYHV4fX1DnNfXcriYSgddm9eewAgJyhkThK0Mdf29waVkn9HZ8Zh/IVsjVaJnvwiW56RXbMXbbJrnlzsbXo08Kqt45+o+321dttxsgZER+1lKUk/fHHH8+xHVDgHTNmTLr3q8c+I08++WSO7QsAAPhfD7hqvaSnbt3wi6WqQf/ee+9N936NpstoKLvWS9cPACDnKUFvWatUlh+vagVK0lWgMLt1C3bv2e1+F69c3MrXLW/xKtNJuoaoqcqqVm7Tuqlt2rTJnT0DAABRsWbNGvvll19cz8Hxxx9vF1xwAX8JAAD8mKSrlVuBWkPXVEVdw95V/OXcc8/NvT0EAAARo6XGtBTR7t27XUV3FXPVVLSnnnqKvwIAABGQqTXDRowY4Sqsqgq7Kq6rgJxuAwAAseHZZ591xeI2bdrkasxoHvkLL7zguzXCAQCIVZlK0hctWuQSc/Wi60cJu24DAACxQXH9uuuusxIlSrjrp512mjVr1swVkQMAAD5L0lVZtVSp/xUV0GWqrQIAEDt27tyZKtaLrut2AADgw8Jx48aNO+JtV1xxRfb2CgAARM3UqVPd3HTPunXrDrtNPexaTg0AAEQ5Se/bt+8RbyNJBwAg71KxuLQWLFiQ6vq0adNI0gEAiHaSrmXXAABA7Pryyy+jvQsAAMS1TM1JBwAAAAAAuYckHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnygQ7R0AAAAA8rpVq1bZ5s2bzS8WLlwY7V0AkEUk6QAAAEA2E/QGDRpaUtIenx3HBFu3fa+ZlYr2jgDIBJJ0AAAAIBvUg64Evc2Vw6xklVq+OJabli+yeRMetW17DkR7VwBkEkk6AAAAkAOUoJetWd8XxzJpt9969QGEiyQdAAAAAOKsdgF1C/yLJB0AAAAA4rJ2AXUL/IgkHQAAAADirHYBdQv8iyQdAAAAAOKsdgF1C/wrX7R3AAAAAAAA/BdJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD7hiyR97969tn79ejt06FDYj9mxY4ft2bMnV/cLAADkjIMHD7pYv2/fvrAfs3//fvv333/5EwAA4kpUk3Ql5TfffLOVLl3a6tevb1WrVrXJkydn+JjXX3/dmjZtajVr1rQKFSrYcccdZz/99FPE9hkAAGTOhAkTrHLlyi7WK+YPHDjQUlJS0t1ecb179+5WtmxZO+aYY6xKlSr20ksvcdgBAHGhQDRf/LHHHrO33nrL5syZY40bN7bnn3/eLr30UmvYsKG7HqoV/uuvv7a3337b3a8WdiX5Z5xxhi1ZssQFcwAA4B9z5861Pn362Msvv2xXXHGFu96pUyfX2H7TTTeFfMxnn31mt956q3388cdWsGBBe+edd6xnz55WvXp1l7zjfxYuXJitw+GNYpw/f77ly5e9vhuNkihcuLCv/jzly5d3nzUAyEuimqQ/99xzdvXVV1uTJk3c9euuu86efPJJ11o+evTow7bPnz+/jRs3LnC9UKFCds8999gLL7xgP//8s3Xr1i2i+w8AADI2duxYF+eVoEvLli2td+/e7hwgvST9/vvvT3X94osvthEjRtgXX3xBkv7/kvftd7979eqVrY9gYmKiTZw40Tp06GBJSUnZeq58CWaH0h8gERWJRRNt0cJFJOoA8pSoJekbN260VatWWdu2bVPdftJJJ9ns2bPDfh71oIuGygMAAH9RTE8b69u1a2fPPvusqy9TsmTJIz6Hksc1a9YQ64McOHDA/X7g/Hp2etOKWf77HMpXyNaY2YzBJ1q+Q/9N/LNi7rJNds2bi61FnxZWvXV184Ptq7fbjJEzbPPmzSTpAPKUqCXpmzZtcr/LlSt32LCkH3/8Mazn2L17t914443WuXNna9asWYbDr4IL1eikQJKTk91PdoeJqRW6YD6zAgnhF77LSwrlT3Dv0QoUtmQrmOuv571GJF7Lk5K/kHuPhfIXsgIpUR1gkmsKJhR071Gf2ex+7nMT/1Ox8X/F/1Tm+Pl/MififahYL0qewknSb7/9dktISLDLL7883W3iLdZ7sbl25TLWtNZ/j2dW6DtBSXqjmuWsoGX9OO3al+z2p2y1slb5mMoWb3HPz5+R7J6/5WT88FtsiPS5kd8+J3xGIv8ZCfc5ElIyqtySy3OoGjVqZN98842dfPLJgds1x3zq1KlHnGOlQHz22WfbypUrbcaMGVaxYvqtyPfdd58NHz48ZCGbokWLZvOdAACQPVqtRHOut2/fHlbSmpccddRR7r09/PDDgdumTJlip59+uq1YscLdn5FHHnnEDX/XPPWOHTumux2xHgAQK/E+ak1YKv4iWo4lmK5Xq1Ytw8eqYNz5559vy5cvd0l+Rgm6DBkyxG677bZUres1atSwrl27ZvtkSIVWNI+r08DnrEyNuhaL1i3+zWY+d7u91LeZXdS6Sq6/nlpqp5W5zLpsfStbrfqZMXPxOuv21G/W9pa2VqdDHYtFW/7eYlMGTXGNWscee6z5Ff9TsfF/xf9U5ni9vrFI8T5UrFedGVV8z8jjjz/uGtlVQC6jBD0eY31Oxeac+m7w4/98JOMen5G8+TmJ9LmR3z4nfvseiYfPyI4w433UkvQSJUq44jEqAqOCMF73//Tp011vumfLli2u19ybc+4l6JqLrgQ9nLnoqjQaqtqoKsbqJztUCVVz5ZIPmR1I8cWy8zlu/8GU/xaTObAvYkmz6LUi9XoJB/e797j/4H47kPDfeX6xJjkl2b1HfWaz+7nPTfxPxcb/Ff9TmePn/8nsUnL9xhtvuKGCXvXwzz//3Nq0aROIzepZUK0aJdVK3mXUqFGuOOxHH31kXbp0OeLrxFusz+nYnN3vBj/+z0cy7vEZyZufk0ifG/ntc+K375F4+IwUDPM5ovrpuPfee+3NN990xWO0JIsqvyo4DxgwILDNnXfe6VrBvSXYLrzwQleERlXelbBrqJx+du3aFcV3AgAAQrnhhhtcEn7NNdfYr7/+6lZxee+999w5gEdD2WvXrm3r1q1z15955hkbNGiQjRkzxurVqxeI9V49GwAAYllUk/RzzjnHrXmu9U+1Prp6zDWUwCsoI7rsDX/X8AANE9HkfY3lV+u89/Ppp59G8Z0AAIBQqlSp4mL71q1b7ZJLLnE94++//76ddtppgW2KFSvm5qYXKPDfAX6qTaNedS27Fhzrhw0bxkEGAMS8qJdV7NGjh/vJqGCMp0yZMq4lHQAA5B2NGzd2iXl6unfvniq+aw46AADxKvqTIQAAAAAAgEOSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+EfUk/Y033rDjjjvOqlevbt27d7fffvstVx4DAACiY+7cuda1a1cXt1u3bm0TJ07MlccAABALopqkT5o0ya6++mq78cYbbfr06S4Qd+rUyTZs2JCjjwEAANGxevVqF6fr169vX331lV1zzTXWp08f+/jjj3P0MQAAxIqoJukjRoywvn372hVXXOEC8QsvvGAFChSw5557LkcfAwAAouOZZ56xMmXK2JgxY6xevXp27bXX2kUXXeTieU4+BgCAWBG1JH379u1umHrnzp0Dt+XPn99OOeUU++6773LsMQAAIHpmzJhhp556qiUkJARu0zD2OXPm2N69e3PsMQAAxIoC0XrhtWvXut+VKlVKdbuuz5s3L8ceI/v27XM/wcm+/Pvvv5acnJyNd2G2Y8cOK1KkiO1a85dZcpLFoqT1y9x7XLx5v33z185cf71D+QrZnsZ77Luluyzfof0WCX+s3e/e4561e2zz75stFu1ct9O9R31mt2zZYn7F/1Rs/F/xP5U5O3f+97s1JSXFYo1it4aup43bhw4dclPVjjrqqBx5TLzF+pyKzTn13eDH//lIxj0+I3nzcxLpcyO/fU789j0SD5+RneHG+5Qo+fPPP7VnKd9//32q22+//faUevXq5dhjZNiwYe5x/HAM+AzwGeAzwGfAz5+B1atXp8SaGjVqpNx9992pbps2bZp7v8uWLcuxxxDro//55YdjwGeAzwCfAcuReB+1nvQKFSq435s2bUp1u6579+XEY2TIkCF22223Ba6rJV4t6+XKlUs1lA7+oJaqGjVquMJBJUuWjPbuADGB/yt/U4u6WterVq1qsUbxOVTclvLly+fYY4j1WcN3A/iMILv4Hsn5eB/VJL127dr2/fff27nnnptqHtr555+fY4+RwoULu59gpUuXzpH3gdyjBJ0kHeD/Kl6UKlXKYlGbNm1cnA727bffWqNGjaxEiRI59hhiffYQc8FnBNnF90jOxfuoVne/6aab7OWXX7ZZs2bZgQMHbOTIkW4eWr9+/QLb3Hrrrda+fftMPQYAAPjDgAEDbPHixa5i+8GDB12h1/Hjx7ulVD2ffPKJ6yFft25d2I8BACBWRa0nXW6++WbbuHGjq9auYi/VqlWz999/3y234tFwgK1bt2bqMQAAwB+aNm1qkyZNcvFbU89UgGfgwIHWv3//wDaK5yrIo4Q83McAABCrEjQxPdo7oTniu3fvDjmEbdeuXa7HPO3w9Iweg7xNJ2sPP/ywm1+YdpoCAP6vkHep4b1YsWKWL1/qgXz79+93cxrLli172H3pPQY5g5gLPiPge8R/fJGkAwAAAACAKM9JBwAAAAAA/0OSDgAAAACAT5CkAwAAAADgEyTp8A2VR3j00UetcePGVrNmTbv00kvtn3/+ifZuAXnaTz/9ZJdffrlVqlTJVccGANm+fbtb6q527dpWt25dGzRokO3du5eDgwAVc3z22WetefPmqZZIBDzLli2z66+/3po0aWL169e3Xr162dKlSzlAOYAkHb7xwAMPuCT9sccesylTpril97p06eKq/gLIvB9//NFuueUW69ixo2v40moZACAXXniha8SbPHmyjRs3zv3u168fBwcB+jz8+eefdvXVV6daIhHwqBNACfrbb79tH374oWvoa9++vVsuG9lDdXf4ZgmYChUq2PDhw+3WW291t23YsMGqVq1q48ePd73qADI/OiUhIcFdbteunQukL7zwAocRiHOzZ8+2448/3n7++Wdr3bq1u+2DDz6wHj162IoVK1yjHuDFkG+++cY6depkq1evturVq3NgEPI8Q/bs2WMlS5a01157zXr37s2RygZ60uEL8+bNc2vhdu7cOXCbhuc2a9bMvvvuu6juG5BXBQdOAPDMmDHDSpUqFUjQpWvXru6E+/vvv+dAgRiCLJ1nJCUlue+RIkWKcASziSQdvrB27dpAYh5M15kDBQBAzsbcihUrprqtWLFi7oeYCyCrBg8e7OoXqNEP2VMgm48HcoRa3SR//vypbi9QoIAdOnSIowwAQA7G3LTxlpgLIDueeOIJe/PNN11dKY3UQfbQkw5f0Hx02bRpU6rbdd27DwAA5EzMTRtvVaRVFd+JuQAy6+mnn7ahQ4fa+++/7+oXIPtI0uELWt6jcOHCqebCaemP+fPnW5s2baK6bwAAxBLFVVXrXrRoUap56t59ABAuLdOnJRzfe+89O/300zlwOYQkHb5QokQJu+KKK+zhhx92ay6q8IT+4TVc5uKLL4727gEAEDNOPvlkV5j19ttvd73n6lVXL9ipp55qDRs2jPbuAcgjtGKMvkeUoJ9xxhnR3p2YQpIO33jyySfd2oo6QVByPmvWLPvss8/cUg4Asra0oQq46Ef/T6+//rq7fOKJJ3I4gTim+egfffSR7d69230naLlTDXOfMGFCtHcNPqKOE30+zjnnHHddDTu6Pnny5GjvGnxCyyardpTWS/fON/QzcuTIaO9ansc66fCd5ORkNzdOVWYBZM/mzZsPu00FGUuXLs2hBeBGruXLl89NOQOCac1r/YQa/cjnBaJpM17x52BFixZ1P8g6knQAAAAAAHyC4e4AAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDiDXaO3MvXv3coQBAMimQ4cO2b59+3zzPOE4cOCAJScnR+S1gFhCkg4g1yxevNgSExNt/fr1HGUAALLh/ffft0qVKmX7GE6dOtWKFSsWkb/FDTfcYJdeemlEXguIJSTpAAAAQJzInz+/FSlSJNq7ASADJOlAHA09P9IQdG+bUEPjgh+T3nbh3p+Z19JQuXCeDwCAeBIqNnoxXj+Kn6GceuqptmXLlnSHv2t4etrn1jYZOdL9mXmMXj94iPz+/fsz/dxAXkeSDsS4Dz74wGrVqmWFCxe2xo0b25NPPnnYEPTff//dOnfu7FrWS5UqZWeddZatWLEicP+MGTPcY1599VWrV6+e207P9eOPP6Z6rf/85z9Wp06dwGt99tlnh+1PuK/10ksvWbVq1ax48eI2c+bMXDs+AADkJU8//XQg1h533HH222+/pYqxpUuXdj9Fixa1o48+2p5//vkMh7t7w+hHjx7tfpcoUcKWLFni7tNjjznmGCtUqJDVr1/fbes5ePCgDR061CpUqGAFChSwunXr2ttvv53hvofzmL59+9pFF11kvXv3ducArVu3zvYxA/IaknQghi1fvtwuvvhiu/7662379u325ptvuiQ92Nq1a61Tp052+umn27///murV6+26tWru+Q5bbEXBdKvvvrKPVe7du2sZ8+egdb2VatW2QUXXGADBgywbdu22euvv26jRo3K8mtpX9UIoN6Atm3b5toxAgAgr1D8/fbbb+2nn36yrVu3uiT8iiuuCNzfrFmzQE/6nj17bOzYsTZ48GCXmB/peb/88kubP3++e6wa5JW067GPPvqo7dq1yzXEf/HFF4HHPPHEE/byyy+7zgC91p133mmXXXaZzZo1K93XCfcxH374oTVv3tw2btzo9gmIOykAYtYdd9yR0rx581S3vfDCC8qqU9atW+euDx06NKVjx44pycnJKfv370/Zt29fyo4dO1IKFSqUMnPmTLfN119/7R6zaNGiwPMsXLjQ3bZ69Wp3ffDgwYe91nPPPZfl1/rll19y+egAAJB3vPvuuy4+btq0KXDbd999l5KQkJCyZ8+ew7Y/cOBASlJSUkrfvn1T+vfvH7h9ypQpKfnz5z/seVeuXBm47dChQykVK1ZMefDBB9PdnwoVKqQ88cQTqW47/fTTUy688MLA9X79+qX06NEjU4+57LLLUlq0aHHE4wHEMnrSgRivrt6yZctUt6W9PnfuXPvuu+/ckDINcStZsqQbhpaQkOB6x4NpeJ1HQ+lEveaycOHCw55bw/Cy+loNGzbM1nsHACDWKG6WL18+VSzWiDb1hItGpQ0cONCNUtMQdd3/xhtvuJFrGdHQ+Jo1a6Ya+aZe7JNOOink9prTvmnTpsOGordp08adD2T3MZwDIN4ViPYOAMg9+fLlC6v4i4apH2kemSiZjtRrFSxY8IjbAAAQTzKKw/Lwww/bxx9/bJ988ok1adLExVJNefv7778zFXO910mvuJtifqj7dV3V47P7GM4BEO/oSQdimFqiZ8+eneq2tNfV2635bbt3787Wa6lQ3M8//5zqtrRzzHLqtQAAwOEUh88++2xr0aJFINHV/PXMqlq1qlWpUsXF7FDKlCnj7k/73Kolo/OBnHoMEK9I0oEYpiJuS5cutXvvvdc2bNhg33zzjY0YMSLVNjfccIP7rQJzqgq7efNmVxyuW7dugeFz4b7WsmXLXNVWVY6fPn16rr0WAAA4nJJdFXhbsGCBG7Kuoe+aapYVOndQobfXXnvNDX2fM2eOXXrppYH7hwwZYiNHjnQrueh+FYtVTL/jjjvSfc6sPAaIRwx3B2KY5qRp2JuC9DPPPON61lWp9ZZbbnFLt4iWW9ESZ3fffbd17drVLY+iiqp6jJZI84aoafvgYXa6rNu84WtqddeJwW233eaWbNFrPfTQQy4x9x6X1dcCACDeaUi4li8NljZmKr4qOe/YsaO7TUueXnXVVS4hTu95Qj2v9O/f381V16owt956qzVo0CBV47viuyrB33zzza4jQEu06Zzj2GOPDWyj3nzNjc/MY7R9ekPmgXiRoOpx0d4JAJGjFnElxSrgAgAAAMBfGO4OxLibbrrJzfdSUq71TdXKfuWVV0Z7twAAAACEQE86EOO+//57l5j/8ccfVrlyZTef7M4777QCBZjtAgAAAPgNSToAAAAAAD7BcHcAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCcKWBw6dOiQrV271kqUKGEJCQnR3h0AQJxLSUmxnTt3WtWqVS1fPtrPcwKxHgCQV+N9XCbpStBr1KgR7d0AACCV1atXW/Xq1TkqOYBYDwDIq/E+LpN09aB7B6dkyZKWFyQnJ9vUqVOta9euVrBgQYtnHAuOA58H/i9i7Ttix44drvHYi0+Iz1gfb/Li/yrgZ/xPxU68j8sk3RvirqCdVwK3/umKFi3q9jfeAxnHguPA54H/i1j9jmAKVnzH+niTl/9XAT/ifyp24j0T3wAAAAAA8AmSdAAAAAAAfIIkHQAAAAAAn4jLOekA4nM5pv3791ssz0MrUKCA7d271w4ePGjxzK/HolChQiyvBgC5TN/7igPxyK/xL54ULFjQ8ufPn+3nIUkHEPOUnC9fvtwl6rG87mblypVdJet4Lz7m12Oh9VBr167tknUAQM5/969fv962bdsWt4fWr/Ev3pQuXdr9HbLzNyBJBxDzAWvdunWuVVNLXihRikVqgNi1a5cVL148Zt9jXj4W2iet263PYs2aNTl5AoAc5iXoFStWdKsGxGOS6sf4F2/nnHv27LGNGze661WqVMnyc5GkA4hpBw4ccF+YVatWdUE71ofzFylSJO4Ds1+PRYUKFVyirs8ky00BQM7R0G4vQS9XrlzcHlq/xr94kpiY6H4rUdfnMatD3/nrAYhp3pwshhgj2rzPIPMEASBneXPQY7kxHnmH9znMTm0EknQAcSEeh73BX/gMAgDfs4h9CTlwzkmSDgDI0Pbt22306NG2Y8cOXzwPAADIeePGjbPff//dN88Tz5iTDgDI0KZNm+zWW2+1c88910qWLBnW0dq6dau9/vrrdvXVV7sCNll9HsAvVq1aZZs3b7ZYVr58eVfYEEB8evDBB23gwIHWtGnTsB/z6quvWps2baxx48bZeh6kRpIOAMhxGzZscAn5BRdcEEjStSTJzTffTIKOPJmgN2jQ0JKS9lgsS0wsaosWLSRRBxC2+++/3+6+++5USXrfvn2tWbNmHMVsIEkHAJ9Rj/Nbb71l11xzjf3888+2ZMkSq1+/vp188smHbfvrr7/arFmzXJGSE088MVUCHM7zaC3V9957z2655ZbAbVq+5eWXX7bLL7/cypQpc9hrqhDKs88+6y4XKFDAatWqZaecckqgUMrevXvtjTfecJdfeeUVK1WqlNWpU8e9rrbVY0K9ByXzXbt2ddVQ076Ha6+91ubMmWN//fWXey69HhAp6kFXgt7mymFWskqtmDzwO9atsFmvDnfvld50IDJeeukla9++vYurv/zyi4uDZ555pqvOHmzNmjX25Zdf2r59+6xt27bWpEmTkM+j+3/44Qc3Kubss89O9Txjxoyxc845x4466qjAbePHj3fJdYsWLULu37vvvuteW3OstZzYSSedZNWqVQvcP3HiRDeFTfumcwdVlL/pppvcNiVKlMjSe9AyZj///LM7Ft27d4/bYoAk6QDgMwpk6oWeMGGCW8pDJ8x33HGHXXLJJfbCCy8EtlNirWFmCsRaH/a6666zDz74wLp06RL28yxevNhtE5ykaxkb3datW7eQSboC6IoVK9xlnVhoWPuNN95o3333nVWvXt1VL9dre40AGvperFixkMPd076H/v3720cffWSnnnpqqvcwadIkF/D1/HoPF198sT3//PO5+ncA0lKCXrZmfQ4MgBxxzz33uNisuNuuXTsXRx944AH324uTH374oV166aXWqVMn1+ituDlo0CAbPnx4yOc5/vjjXcP3iBEjUj3PbbfdZvXq1UuVpD/yyCNuWlp6Sfq6detcvFfcnz59uttWib1itne/lhXdsmWL285bbiztcPdw34Ma8pOSktx7+PHHH91tc+fOPazRIh6QpAOAT6l1+7XXXnOX1YusoKXebfWYf//9965VfObMmW4umNZGVZKuJHfRokWp1uHO6HmyupSYCsAFO//8811QVvKvhHzIkCGuN/2+++5zibUsXbo01WPSvgfRcHj1mqd9D6effrobTieXXXaZde7c2Q2x09rjAADkVWrI1ogy9Rzv3LnTmjdvbk888YRLYHfv3u3i+l133eUSVpkyZYrrbe/Ro0eqIeV6HvXG63xAPd8tW7YMPE9WqVc82Isvvuhu85J0Jf6K42o4VwIfSmbeg5JxnRsUKFDA9cyr4UGj/RT34w3V3QHApwYMGBC43KpVK5fIfvzxx+66fuu6l9x62y9btsz++OOPsJ8nq9RyPm3aNBewvYRdJxmZEeo9qHU91Hvo2bNn4HLr1q1dq/7y5cuz9R4AAIi23r17B2q3aMRYnz59AjFaPeKq8aLRah4NAT/mmGPs008/Dft5skM92Wp0f+qpp2zlypXuRw0C4crMe7jooosCU+KKFy9uDRs2tL///tviET3pAOBTwfO+RD3SwcPIvR7qtNvrvuChaxk9T1Zs3LjRDctTIFWCraF0Wl7t33//zdTzhHoPNWrUCPkegufaez3s+/fvz/J7AAAgL8R6JasqvJo2Vuq+cJ8nK9QYrgZyzSPX6DXNc1evuCjeh5oOF0pm3kPalV8KFiwYt7GennQA8CnN4U6bHFeqVMldrly58mH3e9d1X7jP47VYax65xwvC6VHvuQKuers1jF6t6x06dHABPTNCvQftW6j3AABAPMZ6Dfves2fPYdtkJtZ78T441h8p3mvovOrBzJs3zxWIe/rpp61fv37uvszE+8y8B/wPSToA+JQKvnk0BFxFVLyq5mrVVgVX3e5REFVl9LQVUzN6Hq/n+s8//wxs89lnn2W4X+o1L1u2rKviKqrUqtcO1RqeNigHC/UeVIQu1HsAACAWKRH2kmdNJXv77bcDMVrTuxRPVawtePi5Gsm9AqvhPI8X74NjvS5recmMYr0S++Ae83Hjxh22nfYvo1ifmfeA/2G4OwD41OTJk908LlViVWBU1XbN45IzzjjDzjrrLNeDrfVIVWFVc8a0XdrlSjJ6nqOPPtpOO+00u/DCC918NlVnVdGWjKhAjHrPr7jiCvd4FXVRsRtVkPeodVz3qaiMThI096xRo0apnifte1B1dyXpeh/xuuQKACC+aH53x44dXWyeOnWq6xFX8VVRg/jIkSPdfO758+e7yuhaqkzxV8uhpX0exVstYzZjxoxUzyPXX3+9K9ymoeoqLKfVYNIOLw+m4rIaMq+lUVW8VcuizZ49+7DtNP3tueeec8uvqvBb2mJzmXkP+B960gHAp77++mu3triWOVN11rQFYJR8q6qq7lcFVG2v5dUy+zy6rqVQFGC1nYK7qqwrsIrmkem6F8xVfE6t4FqvXI9RVXe14CvR9qiX/dtvv3UnHpoTp0aCtM8T6j1oeF3we1D1dj0muAFALfu6Le38OwAA8hotuaalRdULrnXMNbw8eBi4hphr1Jm3moka27V0aajn0bJneh5VX0/7PCrMqlited6Kn1999ZUNHjw4Vf0XxXGv2roay5WUqwK7etXVsP7TTz+5+Bs8v3zUqFHuHELLsKmoXNrnCfc9aGUXFYoLdsEFF7g11eNRQkpmJxHGgB07drhWHH3gMmpB8hOdwGoIqlqygpclikccC45DZj4PSiJVBbx27dp5Zp1NBVYFTbWCq1BLOLTkir7b9J3mDUPPyvPEglDHwg8y+izmxbjkdzl5TNUoddxxx1mXoa/F7Drp/65abNNG9HUNZVq6KRKI58gpeTHWi5Loxx9/3Hr16pUjz6NCb36Mf/Fmbw7Ee/56AAAAAAD4BEk6APhMqCHe0XweAACQ80IN8Y7m88A/KBwHAD6juWKjR4/2zfMAAICcd//99+fo82i6F2IDPekAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgDEuU2bNtnq1auPuN26devs33//tXj0999/2/bt2zPcZvHixXbgwIGI7RMAAOHas2ePLVq06IjbKdb9888/cXlg161b534yovOlHTt25Pq+sAQbgLi0atUq27x5c8Rer3z58lazZk3zoyeeeMLmzJljX375ZbrbbNu2zVq2bGlfffWVlS1b1vKyZcuWufdQunTpsB9z8skn24MPPmhXXHFFutvccccdbrvbbrsth/YUAJCX4r2fY/2PP/5oXbp0sZSUlAy3u/TSS+3MM8+06667zvKyDRs22MGDB61q1aphP2bIkCGusX38+PHpbvPhhx/alClT7LPPPrPcRJIOIC4DdoMGDS0paU/EXjMxsagtWrTQt8H7SB5++GE75ZRTrGHDhpbXde3a1QYOHGj9+/fP0ecdNmyYO0ZXX321lSxZMkefGwDg/3if12P9tGnT7LfffrOPPvrI8rphw4a5xpnJkyfn6PP269fPRowYYdOnT7dTTz3VcgtJOoC4oy9tBew2Vw6zklVq5frr7Vi3wma9Oty9briBWy3Aas2tVq2a7dq1y/VkV69ePeS2un/9+vVWvHjxdJNDbbNx40bXolykSJFMD5F76aWX7L333svU/gVvo/euIXRHH3104H5d15Ax3Z8vX77DhperR6JUqVLueQoVKmRlypRx96llXEPxKlasaImJiYedkGl/du/ebcccc0zgMZ6VK1dacnKye05v2F+DBg0C9+/cudO9lxo1aqR7PHQc9bolSpRIdbtGGhx11FH2+uuv24033hjWsQUAxEa8z0qsV7xau3at1atXz8UmXVZMLFDg8BRN9yv2Ka6lNxJM2yjOKYZlZdTb6NGj7fLLL7eCBQuGvX/B2+iyhosrFipuy759+9xtVapUscKFC6d6PW9oue5T/PXOCTw6t9G+lCtXzoJt2bLFTdUTnSfo8cH0N1As13N6sV5/k6JFi7rL+/fvD7wX772mpcfq/KdSpUqpbtf7uuyyy2zMmDG5mqQzJx1A3FLALluzfq7/ZOXE4IEHHrCLLrrIOnfubHXq1HEJZ5s2bVxQ8SjIXHvttS54dejQwW137733phrKFrxNu3btXNC+5557jjjcLZiGuCvItm/fPlP7522jnutGjRoFhooreJ599tkuOJ500klun5566qlUr6lh4wMGDLBatWpZq1atXEJ+/fXXuyFmuk3vRScpTz/9dKrH3X333Xb++edbz549XUBu3bq1myvuufPOO91JwfPPP2/nnnuu+xEFe13WcTrhhBPcCYbed7Dvv//e6tata8cdd5zb7qqrrjrsWJ122mmpGjMAAPER77MS6zXNrHnz5nbLLbdYhQoVrEWLFi52ff3116m2e+GFF9z9J554oksazznnHJeEpt1G9ynmKmkNtU1GlGBPnTrVxbHM7J+3ze233+4a68866yzXUHDo0CE3fFyP0zmKYv2VV17pEl+P7r/kkkvc+YMazHUu0bZtW5s7d64df/zxrvFb5woXXnihez7Pu+++G4jhxx57rHu/wb3/b731ln3xxRc2c+bMwHbz5893jfx33XWX2xcdS51baMpfMJ0j6Nymfv36rmNB+6HG+WA6Rp9//nmq95LTSNIBwKc0f0yBQMFBSaRab4PniCmw/Oc//7Hff//dta5/8MEHNmrUKHvnnXdCbqMEWsFUtwVvcySzZs2yxo0bH9ayf6T987ZRANY23333nbvtggsucAFSt6nne8aMGW5YmgJq2qRYJwIq0qLg+9xzz7kThR9++MHd9uabb7ph615rurzxxhu2YMECmz17trtdCXXfvn0D97/99tvuBOO+++5zreteC7sCuFdAb82aNe61g5N70f6pkUDb/Prrr27OWto5aTqh+OmnnzLVCAIAiF9JSUm2fPlyN8JLPcTqpdW8cI0IE8UzNVK/+uqrrmdZcVOjzRT/PN42L7/8sotdK1asOGybI/nll1/c6Dcl3JnZP28bNcArjiquqkH7oYcecjHzzz//dPusH8VnxftgireKyYq9eh09vm3btm6anc5b/vrrL/c8OsfxaLqaF8N1LvH4449b7969A8n0zTffbBdffLFrsPC2U1J+//33u2OkRngl43pN7Xuwb775xm666Sb32tpm79699sgjjxwW69UJono+MZ2k64+sE0y1boRLLRc6sFTSBRCr1CKtlmnR0GrNgVKyqkAp6g1WINIQM1FLdK9evdztnrTbKPApkAVvcyQKUhp6ntn9E/Wgq+Xco+T222+/dcm8AuCSJUvc8HvN5X7//fdTPb+CcO3atd3l008/3Q0x11wwbxjheeed52KATgCCKUHWiYKCvXrs1ZKuHoL06ORGjQk6Jt6wNg0VVE9+2v1RK7+o0UI9Cno/wdRjoFEH8VoFPyP6WynWZ6bnQY8JdRIFALFESaCGgickJLhEUt95Xs+wYpNipEaJieKUEt1x48a5BDLcbcKJ9Rr6HWraXEb7F7yNN3RccVgj5DSCTjFRsV4Jfo8ePQ6L9Ro51717d3e5cuXKboTdKaecEhhK7o2KSxtvvWlzem41yGsou+J9etQTr9F3gwcPdr3josdoBF4w9frr3ME7t9HltK+t0XSapnekSvB5NklXUq5WH/WoNGvWzP1h1MtxpKq8HTt2dAdHB1gfJD0HyTqAWKMibcFztZs0aeJ+q3VcQVcJT9OmTVM9Rt+lClgSzjbhUA+5Wowzs38eDYUPpoRaj+nTp4+rHqthcQqAal1P2/uctiKrgmnwbToZ0E9wAq754Boap4Cu51VruAQPw09LLex6Hm//0xM8T05UA0Bz1oJ5x8mbi4f/jXDQSaOGJSrma0RE8NDFtDT3UdM0dIKkWK85mDphC2epQADISzRKTUOrPWqQ1jDrpUuXuuv6HSqOK49SXhTuNkeiuKXt0343H2n/RI3twfPC1VivnnXNcQ+O9a+88kpgXnh6sb5YsWIhbwuO9er1V0O5XlO95RoNt3XrVteomx7tk7ZRL3h2Y73yTh2n3Iz1US0cN3LkSJs0aZLNmzfPney9+OKLrhdIJ0rpnSwpaOsDpOEMCt6aX6AWFz3+hhtuiPh7AIDckrb12+uBVID0EtS0PYy67gXAcLYJh+aAp50fd6T98+TPnz/VNtofJeMKsAq6OUlD6TRPXI29mk+mRlwN+1NvfkYJoXoHFHCVGKYtapNZOkFQI3LaonLxTH9rTTlQA4pivCoHq6dC8/5vvfXWkI9RD4zOD9R7oXmKmleppYM0ukHTNwAgViivSRt/FE+9WKrfoeK4eLE8nG3CifWKleodDk5Uj7R/6cV6UXE1r1c6J/Xq1ctNt9N0PC9R1j4fKdZ7+55dXmOAN9ov5nrSNTRDS9V4S/ooAVevi5L19Gj4ooZAeCdAapVXa45uB4BYogZMVToNnielYKu5XgqIajVPmzxrSRC1Lks424RDQ9GU7KoFOtz9S4/mhGm4XKjiajoJyA7NW1MvvYbBB7/XtNTiH/xaKhSnfdKct2BZGaGlE4ZOnTpl+nGxbOzYsa5HRydVostHmnKhmK6eG6/yvooEKvYT6wHEGjVcqzaLRyOGNCLNmxuueK34GjzaTLFNo5K86V/hbHMkyqlUKV11VTKzf6GosVrf36GWP8turD948KCL9+o99xL0hQsXHjZiLm2s17FQXElbSyarsd4bCR5zPekacqA/sk7YgqlXPKNJ+Jr/+Oijj7rhb2qFVxVCFVFQsg8AsUSt4KqOrvlS+p7T9596HjX0SjQvTAmpGjf13akeZCXkmmPtCd5G1dk1F0wFU4K3ORJ93yqwqWhL8PzyI+1fKDpZUCEbLVGmuWSaI68WaRWyU3Kbne9yBUsFbFWLPeOMM1zQVrX7tHTioIRcjQ/aXtc1LP6aa65xc+Z0oqIArCH4zz77bNivr0D/ySefuOXq8D/6rKWN9arO/8wzz7hGnlDzH/U50GdCx19/J50QqlCf6h4AQCxRI7FqnmgEkTohFcM0x1qjh+S2225zHZgaKaYOTcW24cOHu8Js3pQzbxt9d2p1E40sS7vNkahhX0Xh1IiuuePh7l96VKRWibSmK6mIm0bfqeFAPfUqgpdV+fPnd+clem/qGFANmkGDBh3Wm+81EqjmjNdYoVHcqhSvfdIQfOWiijNpV3M5Ep1LqeE53GObp5J0zVOQtOveqTiRKvemR63vas1RYQQ9Vj07qujn9caHooIF+vF4PT9qXclua06kePuZV/Y3N3EsOA6Z+TzodrUCawiUNwzK+71j7QqzCBTh1tqp3utmNBQrmPZZhVQ0bFtVTzXcV4mtlhDznqNbt25uGRIFGBWHUbEzBUBNF0pvGw3NUjAK3kbFzvTYjPZNibV6Pr1l1MLZP81B9o59MFVsVQ+/lkhRlVUNsVMFeCX83rYq0KaTgeDHqodeDQDBt6nxQEFatyl+KElW8ZpPP/3UFcvTe77jjjvc0DvvcQrsQ4cOdfFEsUHJuOKIArr2Se9TjQdq4Mhof3TMdOy823Rio33QcQl1LHWbjoc+k2lPJmL5u13xPlSs9+4LlaSrV0jL5OjEU4/Vdt5nJBqxXn87N80kn1mBhPD+h/MavTe9R73XSH0eiefIyc9S2lgf6XiflViv7RTDtHyaipopuVYCrDjlPYe+L1UB/cEHH3TFU5VwPvnkk24aUaht1Fiu62m30f+3YmZG+6Z6IVr2VL3SqhUWzv5p6lqo59X5h3I2JfcqFqt90jmDknxvW71G2nnwmo9e9P/jenChWi2X5t02ceJE10HgLTGrYnC6TSMBvG0U4xXf9Z4UDzQfXvPjtVKL9knnC9pvzZvPaH+U0Ktj2LtNjQxafk1TudI7ljkR7xNSorROjFqBNE9QvT4qBOdRFWL1juv+UNTTolYLtWDoD6G5bWpl18mUThBD0QmkWpPSmjBhQqbmZQLIe1TwRF+6Sqi8YVFqOT2+TRvbG8GK0UUSE+3nWbPcfoRDrcIKkkoa/UChQkmSgr+Gh/tt//xAJwTqjdd861BUVE6fPY06SDu8TnPk1Puh0QWhkta8TCc3Wq4neAkbjWRQxX4tE6T709K26oXRCZ4a4dUgr3mNOjZqgAmFWA/Er1CxPhrxPrOxXt9nSrxV5NUv1Git+dvKq/y4f9GmefaK5xktb5cT8T5qPelqERHtfDBd9+5LSy0Xmm+hlg8l6N7wRg3J0Iliekm6WmzUGu9Ra4r+eVQNMK+cDKnVZdq0aW5oiVeMIV5xLDgOmfk8aHiVvijVA6v5Sd7yWQsXLAiM6IkEtSCHOy9MdJKhk45wv6OURKv6qHp7NTQtNwTP2c7s/kVSJI5FKGmXown1WVRPhpJ477PoCZ7bH2sUb0PFevUu6KQ6FPWIaHikN0pOPRlqxNcwRSXsuh7JWK8itfq7dRr4nJWpkX7Nhbxs6+ol9vXj17mGEU35iATiOXJKqFgfjXif2VjvFV/LqViaE/FPHZ+5tX+x4O40S7blVryPWpKuD4+GS2jIgXpnvFaHL7/8MlWQ1TwDvVEFWu8Dl7Z4kdaj9ZL2UNQaFKpir1f5OC/Ji/ucWzgWHIdwPg8atqTvDY3ACZ47pCHW+vErLSvi7Xc4vCFXmXlMJPcvkiJ9LMKlfdE+hfqsxvL3ukbLadqB/i7e30OFezRP3YvNu3fvdg3xmo6h5F0xPVSsV8NQeqsC5Gas136rBkPyIbMDKf75TOUkvTe9R73XSH8eiefIrvRivd/jvb7rNOQ6p2JVTse/nN6/eJEvB+J9VJdgGzZsmJtbruqACtajRo1yvTMqThDcMq4qg3/88YdrHVMruuYSKmFXISQNjVfvBUMuAcRbS200+X3/4B9aHlUV3jU3Upc1Ik5FCINHZuiy4rt6wjSaTttq6oCKA2ma25IlS9xwdhU1Yg16ALHi1FNPdXOb/crv+xfLopqkq6qeChppbL8qEqqQkIZZBReYUZGA4GEjao3XBH/9qAqv5rJpfvo555wTpXcBAADSoyHtiu1qmFfhQY3CUON6cGVgNcJrOVX1lIuSdA2vVAVgFRfUEFLVQ0hvWhsAALEkqkm6qDS/ftKj6oHBNL5fvev6AQAA/qe55ZMmTUr3flUBXrp0aarbVG8meBkgAADiBRMMAAAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnol44DgCiYdWqVbZ58+aIvZ6qUwevVOEnjz76qP3+++82fvz4aO8KAAB5Nt77Odb/8MMPbhnLFStWRHtXEAaSdABxGbAbNqhve5L2Ruw1iyYWsYWLFvsyeG/dutXWr18f7d0AACBPx3s/x/qkpCRbuXJltHcDYSJJBxB31KKugD3+2ubWsErxXH+9het2Wa8X57nX9WPgBgAgFkUy3hPrkZNI0gHELQXslrVKmR8NGzbM1qxZYw0aNLCPPvrI9XZrzeh77rnHChT431f3e++9Z08//bStXbvWNQDcdddddsopp6R6ruBt6tSpY4MHD7aOHTtG4V0BABB5fo33U6dOtVtvvdUee+wxGzNmjOvpbtWqlT3++ONWqVKlwHa//fabDR8+3P744w8rW7as9enTxwYMGJDqubTNfffd56avadh9qG2Qd1A4DgB8aMuWLfbaa6/ZjBkz7Mknn7SHH37YXnrpJZekez7++GPr2bOnXXDBBTZp0iQ77rjjrFu3bvbrr7+G3Gby5MnWpk0b69q1a6ptAABA5O3Zs8cWLlxot9xyi91+++02btw4N0T/rLPOspSUFLeNpqO1b9/eJd5vv/223XDDDa6xXecGnuBtXnnlFbvuuusO2wZ5Cz3pAOBTxYsXt7feestKlCjhru/atcuuuuoq11uu29Ri3q9fPxewDx06ZLVq1XIt6A888IC9//777jHB20izZs1szpw5qbYBAADRoWT8hRdeCIyCmzhxoovn6mU/7bTTbNSoUVa9enW3TUJCgrVo0cI2btxo999/v910002WP3/+wDbPP/+87dy509q1a+eG+gdvg7yFnnQA8KnmzZsHEnTp0KGDK/yyZMkSO3jwoBv2lnbYuoL8/Pnz3eVwtgEAANGjxPukk04KXK9ataodffTRgTg9b948O/nkk912wXF827Ztrtc93G2Qt5CkA4BPFSpUKNX1IkWKuN979+61AwcOuJ/ChQun2kbXdb+Esw0AAIiefPnyWcGCBQ+L916c1u9Qcdy7L9xtkLeQpAOAT2memoaxBxeFUSv5Mccc44KvCsXptmBqea9bt667HM42AAAgejTqTfE+eJ7633//HYjT9erVCxnHNYS9du3aYW+DvIUkHQB8StXdH3roITdfTdXdNRf9vPPOs4oVK7r7Nc9c1WD//PNPd/2HH36wCRMm2I033hh4jrTbfPvtt26ee/A2AAAgegYNGmS7d+92CfuQIUOsWLFids4557j7VATum2++cUXjvHMDVXq/5pprAiPswtkGeQuF4wDELa1p6ufXUeGXmTNnWrly5VwhmBNOOMGeffbZwP2qBqu5ZlqupWjRorZ//34X3LVUW0bbKNkP3gYAgFgWiXif1ddQbG7atKkr/JacnOyWWHvnnXfc7dKyZUt7+eWXXQE4LammZF4N9iNHjgw8h7eNYr4SdvXGp90GeQtJOoC4oyVKiiYWsV4vzovYa+r19LqZoeT8ww8/tH379rkkPe3jtV66esm1vuqmTZvc8HY9Jr1ttKxbhQoVDpv7duedd7rkHQCAWBLpeJ+VWK9pbI8++qhbalVV2zVaTvPUg/Xt29cuv/xy27Bhg5UsWdL1tKelbXr37m1Lly61atWqpSo86zX8L1++PIvvDJFGkg4g7mie9sJFi93yJJGioK3XzQol32kLwqS9X9Vgd+zYccRtQilTpkyW9gsAAD+LdLzPTqxXYl65cuUM769SpUpYzxEqidewdy3thryBJB1AXFIQzWogBQAAeQPxHnkRSToA+ND999/vlk8DAACxqWvXrrZgwYJo7wZ8iCQdAHxIhWMAAEDsUnE4RvUhFJZgAwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJlmADEJdWrVplmzdvjtjrlS9fPlPLrLz33nu2du1au/HGG81PHn74YWvZsqWddtpp0d4VALlg4cKFETuuhw4dcr/nz59v+fJFpt9o3759VrhwYYtlmY03sS6S8T6zx15rpD/77LPux08+/PBDW7lypd18883R3pW4RZIOIC4DdoOGDSxpT1LEXjOxaKItWrgo7OA9c+ZM++OPP3yXpKvxICEhgSQdiDHJ+/a737169YrYayYmJtrEiROtQ4cOlpQUme/jfAlmh1IspmU23sSySMf7zB577d/YsWN9l6T/9NNPNmfOHJL0KCJJBxB31KKugN3hjg5WqkapXH+97au324yRM9zrhhO4v/jiC/vyyy/t33//tf79+7vbBgwYYFu2bLFJkya56yVLlrRmzZrZpZdeavnz5w889p133rHt27fbCSecYJMnT7YDBw7YI4884u7Tc06ZMsW19J911lk2ffp0q127tp199tmBxy9fvtzefvtt27hxo9WrV8969+5txYsXd/c9/fTT7oRCLewrVqxwtz355JPuRBtA3qbvCnng/Hp2etOKEXnNQ/kK2RozmzH4RMt36L+NBLlp7rJNds2bi61FnxZWvXV1i0WZjTexLpLxPrPHXvFUcVUjSrxY36lTJ2vfvr3df//97rpGfRx99NGu8axs2bKBx2r0yWuvvWZ33XWXvfrqq7Z69WobOnSoVa5c2ZYuXeritLRr1841rGv7O+64I/D4nTt32vjx423x4sVWtWpVu+CCC6xOnTqBcwWdh2zatCmwX/369bMWLVrk8BFDRkjSAcQtBezydcub31SqVMkqVKhgycnJ1rx5c3dbqVKl3HBQ7/q2bdts5MiRNm7cOBdQPd9//73r7S5XrpxddtllVr36f09En3/+edcifv3117vnVZK+a9cuF3i9JP3zzz+3nj172iWXXGLHHHOMffbZZ+411Jqu59OJghLyKlWqBPYjuIEAQN5Xu3xRa1kr9xsvJdkKuiT92JolraAl5/rr7d6z2/0uXrm4L7/7EV/xXvFUcVVJtBdTq1Wr5m73ru/du9c1qI8YMcJ+++03d37gNair9/3TTz+18847z5o2beoep2T85JNPtq5du7rG+mHDhrmpc3peL0n/559/3OgVNfSrQWDZsmVuGpsS+44dO7rzD72OXjv4HASRRZIOAD6joHjssce6BNhrxfYoEHuuu+4611r/9ddfu6DsUfKtQO0F8z179tg999xjTzzxRGD4/Lnnnutew7N//37r27eva9VXci+33Xabde7c2SXqjz76qJ1++ukueLdp0+aw/QIAAOFTPFVcfe655w6LqcHXb7nlFpd0jxkzxiXrwaNfHnvsMRfPPRr9pvMBNdarYV+P1ai4YIrt2kY98Z4aNWrY7bffbr/88os7N9B5iJ6fWB89JOkAkIf8+OOPrlV9/fr1dvDgQStQoIAr9BScpB933HGBBF00t11D5S+++OLAbWpBb9iwYeD67Nmz3XN+9dVXbj58SkqK+9GQ+7lz50bwHQIAEN9UtO3dd991vd7q0dYUtLRFHdUD371791S3fffdd/b4448Hrmu4/DnnnGM///yzu664rt53DYO/4YYbArFer/P777+7xFznFYg+/goAkEeoBX3UqFGupbxu3bpWpEgRNyR9x44dqbYrU6ZMquuaVyYash4s+LpX+Vat5wULFkyVzAcn/AAAIPco0e7SpYtdeOGFLgaXKFHCDUlPG+uLFi2aaqUCrVygbTKK9RpZpx8Ns2/SpEngdr2OevXhHyTpAOBDaiFPS0PRn3rqqVTVl++9994jPpfmoolayo866qjA7bruUeEYadu2reuJz8x+AQCAzAsVU1XtXbVhVHPG8+233wYa3NOjhF0J+Zo1qvRgIWN9sWLF3PxynQtkNJSdWB99kVmUEgCQKeoN37p1a6rbNCRNLeAeVXQ9UtAWtZaramvwEi/qgfcqtIsS8wYNGrikX63xHg2B11IsGe0XAADIPMVUTV1TtXWPqr0Hx3pVYP/oo4/Cej4Vgn3zzTddnRkvhmt+ejAViFWDf3Ayr7iv84Lg/SLWRxc96QDilpZL8evraJ7Z8OHDrUePHq64jJZgGzRokCsCo3njqu6u+WOqtH4kml+mBF0VYFWpXcF33rx5VqtWLVdYRvRbgVwBXnPV1aO+YcMGNy/uhRdeCDyX7r/77rvdfRpuzxJsAAC/i0S8z8prqEiberW7devmCsNqCTYVeFXRVg15V7xWzNeKK+G477773HxzVWvX9DXVsVEjvRfrRcVg1UivBvxTTjnFNQqo2KzOM7wh7zoHUcHZ888/3ypWrMgSbFFAkg4g7mid8MSiiW4900jR6+l1w6X1SBctWuSKuGmOmYanDRw40AVuJdgK3Aqu06ZNC6xtKioOl5SUdNjz6QTgr7/+cpXgtR9Kwk888cRU+9SoUSNXmGbGjBlu/VZVjtd2weugq9CMqrsvWLDAvQ5LsAEA/CrS8T6zsV5D1LW0mpZS1cg4TU9TbFa81hB3DTvXyixaRi145JwScPWGp6VlV5WY60ePffjhh10hOTW4e4oXL+56zX/99Vf32qVLl3bLtQXXn1GDgXrwdQ6yfft2lmCLApJ0AHFHyeeihYsCxdIiQUFbr5sZtWvXdj/BFJi9dUtFrdyilnBRC3pwi7nnzz//dK31ffr0cddVIV6NAFoTNZiKxp166qkZ7lfr1q3dDwAAfhbpeJ+VWF+yZMlALPcoWdew9OAl0oJpJNxVV1112HNpiLqGuF9wwQXuXEBV4SdNmmR33nlnyM4A/aRHr6EfRAdJOoC4pCCa2UCal2m+m4KxhsBpHXUl6RoWp4quAADEqniK9xrdduWVV7okX8m/Yv3xxx/vhrIjbyFJB4A4oKFsGrampV1UpGbMmDFuCRYAABAblJhrGpzmmKsXXdPkGPmWN5GkA0Cc0DA8FY8DAACxSfPcVb8m1NQ35B389QAAAAAA8AmSdAAAAAAAfIIkHUBcSElJifYuIM7xGQSA3OWtdALk9c8hc9IBxDQtKaa1QrW+aIUKFdzlWA0I+/fvt71798b9PDQ/Hgsl6PoM6vOnzyQAIOcUKlTIfd9rPXHFel2P1Xif1+JfPElJSXHHX/Fex1+fw6wiSQcQ88uRVK9e3f755x9bsWKFxXJgSEpKssTExLg8MckLx0L7os+iPpMAgJyjhKh27dq2bt06l6jHK7/Gv3hTtGhRt+xfdhpKSNIBxLzixYtb3bp1LTk52WKV3tuMGTOsQ4cOcd9T69djoX0hQQeA3KFeSyVGBw4ccEuNxiO/xr94kj9/fitQoEC2G0lI0gHEzZdmLCdIem86MSlSpEjcB2aOBQDEJ29KUbwmqMS/2MFkBQAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8IupJ+osvvmiNGze28uXLW6dOneyXX3454mNWrlxpffr0cevN1qtXzx5//HE7dOhQRPYXAABkzk8//eSWBFKsb9q0qY0bN+6Ij9m7d6/de++91qBBA6tWrZpdffXV9u+//3LoAQAxL6pJ+ltvvWU33XST3XPPPS45V7J+6qmn2tq1a9N9zD///GNt2rRxSw19/fXX7mfLli32448/RnTfAQDAka1YscK6dOniYvfcuXNt0KBBds0119h7772X7mPU8H722Wfb+++/b6+++qr99ttv1rFjR3cZAIBYF9V10h9++GHr27evXXLJJe76mDFjXNB+/vnn7YEHHgj5mKFDh1qZMmXszTffDKx5rOcBAAD+88wzz1iFChXssccec9c1Eu7LL7+0Rx55xHr06BHyMe+8845Nnz7dFi1aZHXr1nW39erVK6L7DQBA3PWkb9u2zf7880/Xcx7YmXz57JRTTrHvv/8+3Zb1Dz/80Hr27BlI0AEAgH8ppiu2B1PPunrVk5KSQj5GPegnnHBCIEEHACCeRK0n3RvSXrFixVS363p689I3bdpkO3bssJIlS1q3bt3cdlWrVrXLL7/cbrnlFpfkh7Jv3z7349FzSHJysvvJC7z9zCv7m5s4FhwHPg/8X8Tad0Re2tesxPu0SbpivRreN2zYYLVq1TrsMUuXLrXmzZu7KXGTJk2y4sWLW+fOne3BBx9089ojHeu1r4mJiVYwn1mBhNisgVMof4J7j1agsCVbwYi8pvc6kXq9lPyF3HsslL+QFUiJ6mDSXFMwoaB7j/rMxvL3CmIn/sWb5DD/NlH/hkqbWOt6SkpKyG0PHjzofmsOu4rOtGvXzmbNmuV61hWYhwwZEvJxGg4/fPjww26fOnWqFS1a1PKSadOmRXsXfINjwXHg88D/Rax8R+zZs8diWahYLxnFe01rGzx4sOtxVzJ/5ZVX2gUXXGDffPNNVGL9xIkT///SZotJFavaTe3/+x4/i/BLTytzWWRe6ASziSf8/+X/tefElupmfSb2sTVr1rgfxKe8FP/izZ4w431CSnoRMpdt3rzZzVHTkLbzzjsvcLvmqi1fvty+++67wx6zf/9+K1asmAvUY8eODdx+8803uwJyKiwTbut6jRo13D6oVz6vtLroH05DBAsWjEyLs19xLDgOfB74v4i17wjFJfUQb9++Pc/EpXC1atXKjjvuuFRxe8KECW6Oud63esnT6tq1q/3111+u6JxnypQpdvrpp7vEQ6PoIhnr58+f76rTdxr4nJWpEZtD8Nct/s1mPne7vdS3mV3UukpEXlM96ErQu2x9ywpa7vf8zVy8zro99Zu1vaWt1elQx2LRlr+32JRBU2zGjBl27LHHRnt3EGF5Mf7Fmx1hxvuo9aRr54455hj3JRKcpH/77bd20UUXhXxMoUKFXKBP+6HTda+XPZTChQu7n7T0uLz2Ac6L+5xbOBYcBz4P/F/EyndEXtnPrDjxxBNdobhgaljXii6hEnRp27atLVu2LOQxSi/e52asV8+/5s8nHzI7kBL11Wtzxf6DKf+tEXBgX0QS5mB6vUi8ZsLB/e497j+43w4kHLBYlJyS7N6jPrOx/L2C2Il/8aZgmH+XqEYazSN/5ZVXXGKu9VBV0X3jxo3Wv3//wDY33HCDHX/88YHrAwcOdEu3zZw50w2Tmz17thv6fuGFF0bpXQAAgPQMGDDAJdyPP/64i/Xq5VEc1zmAR0VhlbB79WquvfZadz7w9NNPu56hdevWuXMEFZNT7zgAALEsS0l6esPKM+v66693Sbd60jWMXUH7448/tqOPPjqwjQJ68Nh9zUd76KGH3G+1RJxzzjl23XXX2d13350j+wQAAMytwKLiU9nVqFEj++CDD+zFF190sf7SSy+1e++916666qrANgcOHLDdu3cHXk/D2b/44gt77bXX3HzyBg0aWOXKlTNcWx0AgFiRpeHumuOiYeeaG66ibaVLl87yDihQ60ct5aG6/5999tnDThLUKq8fBfUCBaJe+w4AgJijnm6tU64VVPr27ZuqAT2zNJdcP+nFejXW79y50yXxwcPkVTSOWA8AiDdZ6klfsGCBderUye6//36rUqWKXXbZZTZ9+vR0q7RmZ3y+5pe5JUFCIEEHACB3qLjbbbfd5ka4ab3yjh07uorr6a1tnp1Ynz9/fjfcPSEh4bD7iPUAgHiTpSS9YcOG9thjj9k///xjb7/9tu3atcutW16nTh23/MnKlStzfk8BAEDEaAWWW2+91U1x03Kniv1at1zDzvv16+duAwAAOS9bhePUuq054e+8846NHDnSFXy57777XLKuQm6szwgAQN7XunVre+6559xa4erx1vxyFXHT7aGWTAUAAFFK0ufMmeOKtmnIu5J0tbgvWbLEBWz1rp977rnZeXoAABBlq1evtgcffNAtm3r++efbKaec4lZlUcX29u3b22mnnWYbNmyI9m4CABAzslR1bfTo0fbqq6+6uekKzrp81llnBeaNKZCrAmuJEiVyen8BAEAETJkyxZ566im3ZFqzZs3c/HTVoAkuFjtq1Cj76aef3PlApUqV+LsAABCtJF1BW5VeVd29evXqIbfRkiljx47N7v4BAIAoePnll61WrVpu7nmrVq3S3e6ee+6x+vXrR3TfAACIZVka7n788ce7ZdNCJeiXXHJJ4PLVV1+dvb0DAABR0aJFC7vhhhtCJugjRoywP/74w13u3r27W9ccAABEMUmfNGlSyNu1BFt69wEAgLzjm2++sfXr14e876uvvrKNGzdGfJ8AAIgHmRruvm3btpCX5dChQ/bDDz+4InIAACBv2r17tyUnJ9uBAwfc5bTxXom75qAT7wEA8EGSXqZMmZCXPfny5bNHH300Z/YMAABEnJZWnT59urusKu6hnHHGGW7ddAAAEOUkffbs2e631kX1LnsKFixoNWrUsLJly+bsHgIAgIjReug7duyw66+/3i6//HJXhya4Mb5ChQou3gMAAB8k6V7xmOXLl7uKrwAAILbUq1fP/f7444+tVKlSVqRIkWjvEgAAcSXsJH3z5s3ud/ny5a148eKB66FoGwAAkLeoB33//v0uOU9MTLRdu3a5n1C0jUbRAQCAKCXpGt7mVXD3LqdH2wAAgLzl/PPPd/PRp02bZo888khgbnoo2qZz584R3T8AAOJB2En6r7/+GvIyAACIDWPHjrWdO3faMcccE7icHm0DAACimKQ3b9485GUAABAbjj766MBlTW0DAAB5YE56OJiTDgBA3p2THg7mpAMA4JM56eFgTjoAAHl3Tno4mJMOAICP5qQDAIDYc6R56MGYkw4AgI/mpAMAgNiekw4AAPLQOulHmp/OnHQAAPL2OulJSUkZzk9nTjoAALmDddIBAIDDOukAAEQf66QDAACHddIBAIg+1kkHAAAO66QDAJCHkvS0du/ebRMmTLCFCxe6640aNbKePXta0aJFc3L/AABAlGhJ1U8++cRmzZpl27Zts9q1a9vFF19sNWrU4G8CAEAuyZeVB82ZM8fq1KljQ4YMcUuz6Wfw4MGuBX7u3Lk5v5cAACCilJSfcMIJdtFFF9nXX39tS5cuteeff97q1q1rL730En8NAAD81JPev39/O+ecc2zMmDFWpEgRd9vevXvtpptusn79+tns2bNzej8BAEAEPfLII5acnGwrVqywypUrB25//fXX3XnAueeeaxUqVOBvAgCAH3rS//zzT3vooYcCCbrosm7TfQAAIG9TPL/jjjtSJehy+eWXW/369W3JkiVR2zcAAGJZlpJ0DWtfv379YbfrNg2DBwAAeVt6sf7AgQO2efNm4j0AANEe7q7h7J7bbrvNFY557LHHrHXr1q6wjOapDxw40AYNGpRb+woAAHKRhrcfPHjQXb766qutS5curiBs9+7drUSJEq73fMSIEdapUyerWLEifwsAAKKZpCcmJh522xlnnHHYbVdeeaX17ds3+3sGAAAiSsn49OnTU92mWjOhaNh7586dI7RnAADEj7CT9O+++y539wQAAETV6NGjXVX3cDRt2jTX9wcAgHgUdpLerl273N0TAAAQVU2aNOEvAABAXiwcBwAAAAAAfLJOukycONHeffddW7Vqlav0GmzevHk5sW8AACCKVBR2zJgxtnTpUtuzZ0+q+8aOHWtt2rSJ2r4BABCrstSTPmrUKLvlllusUaNG9ssvv9gFF1xgNWvWtPnz51vbtm1zfi8BAEBE/frrr9a+fXsrVqyYW3KtVatW7uevv/6yChUqWJUqVfiLAADglyT9hRdecL3oDz74oLt+991328cff+ySd/WsAwCAvO2ll15yy6o+//zzriH+kksusZdfftm+//57++2331yiDgAAfJKkL1++3E444QR3uUiRIrZz5053WUuvffvttzm7hwAAIOKCY72WYfVifcuWLV3SrkQdAAD4JEnXHPRChQq5ywrUGvIua9assQIFsjzNHQAA+ERycnLIWK9zgA0bNhDvAQDIJdnOqC+//HK7+OKLrUuXLvbNN9/YeeedlzN7BgAAfKF3797WsWNHNx992bJlLnlnnXQAAHyUpK9bty5weciQIVapUiWbOXOmKyZ344035uT+AQCAKFDtGRWNEw17nzJlik2ePNlq1aplN998c6CXHQAA+CBJr1y5cuByQkKCXXXVVe4HAADEhjJlyqS63qlTJ/cDAAB8Otx99+7dNmHCBFu4cKG7ruXYevbsaUWLFs3J/QMAAFGSkpJin3zyic2aNcu2bdtmtWvXdlPcatSowd8EAAA/FY6bM2eO1alTxw111zqq+hk8eLAdffTRNnfu3JzfSwAAEFFKyjXM/aKLLrKvv/7ali5d6pZjq1u3rlueDQAA+KgnvX///nbOOefYmDFj3BJssnfvXrvpppusX79+Nnv27JzeTwAAEEGPPPKIq/C+YsWKVNPcXn/9dXcecO6557JWOgAAfulJ//PPP+2hhx4KJOiiy7pN9wEAgLxN8fyOO+5IlaB7q7rUr1/flixZErV9AwAglmUpSdew9vXr1x92u27TMHgAAJC3pRfrtU765s2bifcAAER7uLuGs3tuu+02Vzjmscces9atW7vCMpqnPnDgQBs0aFBu7SsAAMhFGt5+8OBBd/nqq6+2Ll26uIKw3bt3txIlSrje8xEjRrgq7xUrVuRvAQBANJP0xMTEw24744wzDrvtyiuvtL59+2Z/zwAAQEQpGZ8+fXqq21RrJhQNe+/cuXOE9gwAgPgRdpL+3Xff5e6eAACAqBo9erSr6h6Opk2b5vr+AAAQj8JO0tu1a5e7ewIAAKKqSZMm/AUAAMiLS7B5tmzZYn/99Zebk65Kr+XKlcu5PQMAAFG3Z88eF+vVw167dm076qijor1LAADEtCxVd09KSnJz1CpVqmRt27a1k046yV3WbboPAADkfc8884xVq1bNWrRo4YrF1apVy/1eunRptHcNAICYlaUkXRXcv/rqK5s8ebKtW7fOLdGiyyo2k9nq7rNmzXLFZ7p16+Yeu2nTprAf++6771rHjh3dSQQAAMg5H374oVsnffjw4bZ8+XL7999/bebMmVa4cGE7++yz7dChQ2E/l84TtDKMYr2Ky/7yyy9hP1avfeqpp1qfPn2y+E4AAIiDJP2dd96xSZMm2bnnnmuVK1d2vei6rKRZt4fr+++/t/bt21uFChXsmmuucUFbvfK7d+8+4mPViq+AryF4tOgDAJCzFOuVoN90002uB71MmTJ2wgkn2Mcff2xbt261P/74I6zn2b59u5144om2YMECu/baa91Sbor1P//8c1hLwl1yySW2du3asLYHACBuk/SdO3eGnJOm23bs2BH28wwdOtTOOecce/zxx61Hjx4u8Ktn/qWXXsrwcfv373dB+5FHHmGdVgAAckF6sb5QoUKugT7ceP/cc8/Zrl27XM/8+eefb2PGjHFD5u+9994jPvauu+6yevXquXMFAADiRZaS9JYtW9rIkSNdwTiPLitpPu6448IuRKOedA2Z8xQvXtytuTp16tQMHzt48GAXtC+77LKs7D4AAAgj1ms6WdrRbf/5z3/cKLbGjRuHdQwV07t27WpFihQJ3KbRd19//bUdOHAg3cd98cUX9t5779mzzz7L3woAEFeyVN1dPd+aV6bg2bp1a3fb7NmzbcOGDfb555+H9Rz//POPm8+mgjTBdF2BOz2fffaZvf/++zZ//vyw93ffvn3ux+O1/msYnX7yAm8/88r+5iaOBceBzwP/F7H2HeHHfb3llltcvZmaNWvaySefbCVLlnTJuWrJPPbYY274ezhWrlwZOFcIjvUaFafRczVq1Ag5h11z1zWFTq8b7Viv85XExEQrmM+sQEL4c/HzkkL5E9x7tAKFLdkKRuQ1vdeJ1Oul5C/k3mOh/IWsQEq2FjjyrYIJBd171GfWj98ryF15Mf7Fm+Qw/zZZ+oZSRfclS5bY888/b3/++aclJCS4Xu0BAwa4+enhUHAWFxCCFC1aNHBfWpqTduWVV7qgXapUqbD39+GHH3bz6kK17uv18pJp06ZFexd8g2PBceDzwP9FrHxHaHSZ35QtW9bmzZtnL7/8skvMNQ+9TZs2rnddvezhUkwPFeu9+9JSctG7d2+76qqrrF27dr6J9RMnTvz/S5stJlWsaje1/+97/CzCLz2tTIRGRp5gNvGE/7/8v/ac2FLdrM/EPrZmzRr3g/iUl+JfvNkTZrxPSAkes56J1vXRo0dbduiLo3r16vbJJ5/YmWeeGbhdQfn3338PWSBGQ96GDBmS6uRgzpw5Vrp0aTvmmGNcdfn8+fOH1bqulvvNmzeH3ULvh1YX/cN16dLFChaMTIuzX3EsOA58Hvi/iLXvCMWl8uXLuyJrfolLaohXVXVNL8uOpk2bup744JVYVGj2oosusi1btrjGgGB///23i+kqLlegwH/7EpYtW+Zi9vHHH++ScRWii2Ss1+i9Dh06WKeBz1mZGnUtFq1b/JvNfO52e6lvM7uodZWIvKZ60JWgd9n6lhW03O/5m7l4nXV76jdre0tbq9OhjsWiLX9vsSmDptiMGTPs2GOPjfbuIMLyYvyLNzvCjPdZ6kkfO3asm5Ou4jFZpaFuFStWtLlz56ZK0jVsPlTw9eawpZ0Dd/XVV1vz5s3thhtusHz5Qk+x13Ix+klLH9689gHOi/ucWzgWHAc+D/xfxMp3hB/3U9PLVDguu0m61lhXrA+mWK9h9GkTdKlateph095efPFFV8fmvvvuS3d/cjPW6/wiKSnJkg+ZHUjJUjkf39t/MMW9RzuwLyIJczC9XiReM+Hgfvce9x/cbwcS0q+HkJclpyS796jPrB+/VxAZeSn+xZuCYf5dshRpNNxN66Rn1xVXXOGG0Wkuu6hXXb3out2jeW+al+Yl9loXPfhHxebUI6/LGnYPAACyL6divWL4Tz/95Ea7eTVp3njjjUBsl2+//dbF8U2bNrmh8WljvRJ6DVnX5XLlymV7nwAA8LMs9aQrSGoJtP79+1ujRo0O61HXfeFQi/jChQvdsLbatWu7ee6jRo1K1ZO+ePFi1+IOAAAip1WrVnbxxRe7xLl9+/auUTyYllELpw6NtnvooYfcqDn1gi9dutS6d+/upq959BpK1IOHqwMAEK+ylKRrXpnmiakXPJRwk3S1lmttdM01U296/fr1Dxv6NmjQoAzXYtU+aE46AADIOS+99JIblvfpp5+6n7S00kq4xWK1dOo111zjqsNXqVLFatWqlep+zVnXEPcKFSqEfPy1117r1lgHACAeZClJVxGWnFSnTh33E4oS9yO19AMAgJylZVZVWzanppJpmHp6NWeUnGuUXlbOEwAAiDWZmpOuHm0ti6Keay2BpssZ9XIDAIC855FHHnEF3DTiTcugLViwINq7BABA3MhUkj506FD78ssv7eabb3bLsKnE/9133517ewcAACLqo48+smHDhtl5553nasdonrjmpmv9cgAA4LPh7po//sEHH9gJJ5zgrp922mnWs2dPGzNmTG7tHwAAiCDF+nvuuSfQCH/jjTe6OeSqH6NCrwAAwEc96WvWrLHWrVsHrh9//PFuKRUAABAb0sb6YsWKWePGjYn3AAD4MUk/ePCg5c+fP3BdFd51GwAAiA0HDhxIFeu9eK/bAQCAD6u7N2/e/Ii3zZs3L3t7BQAAoqZ///6p1kX/+++/D7tt7Nix1qZNmyjtIQAAsStTSboKxqWV0ZIpAAAgb+nRo4ctXrz4iLG+TJkyEdwrAADiR6aS9NGjR+fengAAgKgbMGBAtHcBAIC4lqk56QAAAAAAIPeQpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ESBaO8AAAAAAMSiVatW2ebNmyPyWocOHXK/58+fb/nyRaYvdt++fVa4cGGLZeXLl7eaNWtG9DVJ0gEgjwToaAQJAACQ9fjfsEF925O0NyKHMDEx0SZOnGgdOnSwpKSkiLxmvgSzQykW0xKLJtqihYsieg5Gkg4AuRygGzRoaElJe7L9XImJRW3RooUk6gAA5AFqoFeCPv7a5tawSvFcf71D+QrZGjObMfhEy3dof66/3txlm+yaNxdbiz4trHrr6haLtq/ebjNGznB/S5J0ADEhJ3qQ83rvsd6/EvQ2Vw6zklVqZfl5dqxbYbNeHR7xIAEAALJHCXrLWqVy/TAmW0GXpB9bs6QVtORcf73de3a738UrF7fydcvn+uvFE3rSAfi6BzlWeo+VoJetWT/auwEAAACfI0kH4NseZHqPAQAAEG9I0gHkKnqQAQAAgPCxTjoAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE8UiPYOAGmtWrXKNm/enO6BOXTokPs9f/58y5cvdDtT+fLlrWbNmhxcAAAAAHkKSTp8l6A3bFDf9iTtTXebxMREmzhxonXo0MGSkpJCblM0sYgtXLSYRB0AAABAnkKSDl9RD7oS9PHXNreGVYqH3OZQvkK2xsxmDD7R8h3af9j9C9ftsl4vznPPRW86AAAAgLyEJB2+pAS9Za1SIe9LtoIuST+2ZkkraMkR3zcAAAAAyC0k6UAMz80X5ucDAAAAeQdJOhDDc/OF+fkAAABA3kGSDsTo3Hxhfn7sWbhwYcjbGVkBAAAQG0jSEXfJTGZEe6g4c/PhSd7334aYXr16hTwojKwAAACIDVFP0jdt2mRvv/22bdiwwZo2bWoXXHCB5c+fP8PHfPvtt/bjjz9agQIFrF27dnbiiSdGbH/hf/v278swmcmMxKKJtmjhIqrEI+oOHDjgfj9wfj07vWnFw+5nZAX8/vmdNGmSLViwwKpUqWKXXnqplS1bNsPH/PXXXzZ16lT7999/rXHjxnbuuece8fwAAIBYENUk/e+//7aTTjrJBd/jjz/ehgwZYq+++qp99tlnIQOxhnOecMIJVqxYMZeY79mzx0477TS74oorbMyYMVF5D/BvMtOiTwur3rp6lp9n++rtNmPkDJZyg6/ULl805MoHrHoAP38nd+nSxf755x+78MILXbL+0EMP2cyZM9NtAB08eLB9/PHHdsopp7hk/q677nKPUSN98eKhpwABABAropqk33nnnXbMMcfYtGnTXHXqfv36Wb169VzP+mWXXXbY9gkJCfbcc89Zq1atArcpST/99NPt2muvtSZNmkT4HcDPilcubuXrlvddVfZIDdUHAD8YN26c/fzzz7Z06VLXi66kXQ3zQ4cOtTfffDPkY5TMKyn3Vq649dZbrU6dOvbCCy/YwIEDI/wOAACIkyRdQfrTTz+1UaNGBYJwrVq17OSTT7YPP/ww3SQ9OEGXFi1auN9qoSdJR7QpQW/QoKElJe3JgWdLsHXbVd099HrxkUz6oz03H8jp5Qn5TEeOYnrnzp1dgi6aqqYYP3z4cEtJSXGxPa3jjjsu1fX/Y+8+wJuq2/+P3y0UKLKX7ClLEARBAQVBnDgQFVBAFAQUxL1wAa5H9HFPXDgehigqKoIiiKAIiIOhDNmg7D1aZvu/Pl//J7+0tCUtbXOavF/X1atNcnJykia5z/0d97dkyZJWrVo1F+sBAIh0YUvS16xZY/v377datWqluF6XNQQuVB988IEVKlToqOQ92IEDB9yPZ/fu3e73oUOH3E9e4B1nXjnerNLJtQpgaX6thu+mxbs+vduT8xVw+yiQr4DlT876WzwuJu7fY0lKCvl137x5s47AWvd7xIqemPWkdtuaZbbg4+dt58F8WX4dJPFIsnsOffv2teOdm//L3F+scuXKmf5fxsWa5Y/5N2nKLN33WP8Dv382suN1kAL5Ytx+LH/BNP/nobwf9LnK7Hvab5SkNW922jGXJ9TUKY20Sm95Qi1NOPeXXzP1ns5JefX/EYqlS5fapZdeelSs37Nnj23cuDGQvGdk3rx5tnDhQnv44YfDEuuz63PsZ8f6jskJoXxvZafsOj/ws6ycuyC857XZic+U/z9Toe4jJlnN2GGgYNuoUSOXkGueefAQ+E8++cQNizuWGTNmuHlu//3vf+3WW29Nd7uhQ4e6FvvURo8ebYULFz6OZwEAwPFTjZVu3brZrl27rFixYhH1klaqVMk1FCoWe6ZNm+bmmy9btsxNe8uIRk2oDo2mw02YMCHNnnch1gMAIiXeh60Z0Sv8snPnzhTX63LRokWPef85c+a4lvm77rorwwRdVJDuzjvvTNG6XqVKFTv//PPzzMmQWl00d1+NEnFxudPiHA4antqmTRu39nfjqsXSbSX8tmR3O2/HKIuzo1ujZi3dYBe+uMBa3d7KarapmeVj2bZim026Z5JrDGrcuHGmjr/d3a9ZySq1s/zYG5YusFmv3WVv9WpkXZpXyNLrkF2vRVZeh+x6LXasW2bTnhmQ4WP7/bORW++JUN4PP6/YYuc9+5tlh6yMrvDLd8T8tbutzbBZmX5P5ySv1zcSKd6nFevlWPFeld312S5fvrwrOJdegp7TsT67Psd+FkrcyW6hfG9lp+w6P/CzrMZshC9mZSc+U/7/TIUa78OWpGt+q6q0axjchRdeGLh+yZIlVr9+/QzvqwI0GsZ40003ucIyx1KwYEH3k5pO6P14Up+RvHjMmaH5oxqeGpt08JgBW7entU3MkYNuHwePHLTDMf9Wes+KQ8mH/j2W2NiQX3Pv+A8lmR1OTnsubCgOHkn+d5ju4QNZfh2y67XIyuuQXa+F7hvqY/v1s5Hb74mM3g9JBxLcPrJr5YMdO3ZYjRo1LK99R+i+WXlP5yS/HEdOOPnkk12sD6ZYr3nmJ554Yrr30/tLCbrOFSZNmuR+hyvWZ9fn2M8yE3eyW0bfW9kpu84P/CyrMRvhj1nZic+Ufz9Toe4jbEm6lli74oor7P3333fJtgLrH3/8YT/++KONGzcusN3HH3/sigSpx1zmzp3rWsV1n2HDhoXr8AEgzwrXygeITqrU3qtXLzeNTUPbNdRP9WR0ffAUOJ0PaM558eLFXU+7EnTNA/z6669Zdg0AEFXCWjXjqaeestatW9sZZ5xhTZs2dXPNrrnmGuvUqVNgm2+++cZmz57tkvR9+/a5HnQl9KoOH7wMS5cuXdySLgAA5NZSh1SJP7arr77aPvvsMxfvtWSqRsPJY489FthGPe3PPvus3X777S5J79mzp/3+++9HzWVXLRvdBgBAJAtrkq6KrgsWLHDJ+aZNm1zgbdu2bYptlHyfddZZ7m8NM3jggQfS3JerSAoAwDEcOPhvBfAePXpky9z8JYuXsERhBhS7NSruu+++sz///NMuuugiu/jii1PEbSXfKgJbokSJQOzXPM7UNEQeAIBIF/b1J1RdXcE4PRra7lFAD+49BwAgszQSS7Jrbr6qj6vOCjKmau76SYsqtwfH9+xoQAEAIK8Ke5IOAEA4MDcfAAD4UWSWKAUAAAAAIA8iSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ/KH+wAA4FgWL16c7m1JSUnu9/z58y02Nv12xzJlyljVqlV5sQEAAOBrJOkAfOvQgYPud48ePdLdJj4+3saMGWNt2rSxxMTEdLcrHF/IFi9ZSqIOAAAAXyNJB+Bbhw8fdr8fu6KOdTilXJrbJMUWsH/MbMaglhab9G9Sn9riDXutx5vzbOvWrSTpAAAA8DWSdAC+V6NMYWtavXiatx2yOJekN65azOLsUK4fGwAAyJq1a9e6BvRonK4HZIQkHUDUyI5gydz28J6MccIDAJETE+rVq2+JiQkW2WJsw679ZpZ2ZwOQFpJ0ZBtOwOFXBw4eOObc9lDFF463JYuXMGw+rCdjnPAAQF6nRlvFhDN6D7FiFapbJNqyaonNG/2U7Uz4d/oeECqSdGQLTsCRF+a2N+nZxCo3r5zl/exat8tmPD2Due1hPBnjhAcAIotiQqmqdS0SJe6L9FECyCkk6cgWnIAjLyhSvoiVqV0m3IcR1Y73ZIwTHgAAEOlI0iNMdhXgyOq8W07AAQAAACDrSNIjbch5/XqWmJD+WtGhYt4tAAAAAOQ+kvRIG3KekGht7m1jxatkvYIk824BAAAAIDxI0iOQEnTm3QIAAABA3hMb7gMAAAAAAAD/IkkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAn8gf7gM4cuSI/fTTT7Zp0yY75ZRTrG7dujlyHwAAED6LFi1yPxUqVLCWLVtabGxsjtwHAIC8LqzRbseOHS7odu/e3d555x1r1qyZ3X333dl+HwAAED633HKLtWjRwkaMGGFdu3a11q1b2+7du7P9PgAARIKwJukPPvigC7h//vmnTZo0yb755ht77rnn7Ntvv83W+wAAgPD44osvbPjw4TZ9+nSbOHGiLVy40NavX2+PPPJItt4HAIBIEbYkPTk52caMGWM33HCDFS1a1F3XqlUrO/30023UqFHZdh8AABA+is9t2rSxJk2auMslS5a06667LsO4nZX7AAAQKcI2J33dunW2c+dOa9iwYYrrNcf8t99+y7b7yIEDB9yPZ9euXe739u3b7dChQ8f5TMw2b97s5scfD82zS0pKSvd23ZaQkGA//PBDunPyli1bZoUKFbI9q/aY7c/6sezZsMftRyMWtm3bFtJ9tK3us/efv8wOJWb5sRM3rnT7Wbr1oH3/1540t0mKLWAJDRLsh+V7LTbp4FG3/7H+oNtHwvoE27pwa9S+Dtn1WmTldciu18JPr4Pf3xO8DqG/FuF8P6S7rz17Ao3RkUa94Oedd95RcVtxc8uWLVa2bNlsuU9Oxvrs+hz7WSjft9ktlO+t7JRdn30/y87vpZzG5yr78Zny/2cq5HifHCYLFizQkSXPmjUrxfX33ntvcq1atbLtPjJkyBB3P354DXgP8B7gPcB7wM/vgXXr1iVHmooVK7o4HOy7775zz3fZsmXZdh9iffjfv/zwGvAe4D3Ae8CyJd6HrSc9Pj7e/d67d+9RrQvebdlxH7n//vvtzjvvTNErrZb10qVLW0xMjOUFar2pUqWKG01QrFgxi2a8FrwOvB/4XETad4Ra1BXLKlasaJFG8TmtuO3dll33iYRYH23y4mcV8DM+U5ET78OWpOtLOS4uztasWZPiel2uWbNmtt1HChYs6H6ClShRwvIiBTECGa8F7wk+G3xHRN73ZfHixS0SKT6nFbeVbGtptey6TyTF+miT1z6rgN/xmcr78T5sheMUSNu3b28fffRR4DrNM/vuu+/s4osvDlw3e/ZsmzBhQqbuAwAA/EHxefLkya6mjNeLMHbsWLvwwgsDNVbWrl1rI0eOdLVXQr0PAACRKkZj3sP14AsWLLAzzzzTLrnkErf2udZCVU/5zJkzrUCBAm6bPn36uET9jz/+CPk+kTp8Ra0uKoQT7a3NvBa8Drwf+FzwHZF3JCYmunitWN2zZ0+3rNqUKVNs1qxZVr9+fbfNuHHjrHPnzm7Yc+XKlUO6D/I+4jnAZwppC2tzdKNGjWzevHlWo0YNmz9/vl1//fUuEAcn2wrSl156aabuE4k0imDIkCFHDeWLRrwWvA68H/hc8B2Rd2iI+o8//mjXXHONi9/16tVzv4OT7WrVqln37t3thBNOCPk+yPuI5wCfKfiwJx0AAAAAAPwfJnYBAAAAAOATJOkAAAAAAPgESToAAAAAAD4RtnXSEbpDhw656vVbt261Jk2aWK1ataL25Vu5cqWr7tu0adOoLSB05MgR+/33310VZK0l3LhxY4tWGzZssF9//dUVHzrttNOsVKlSFs20jNWMGTOsYcOGduqpp1o0WbVqlfueTK1r166uQjgA/1A5pLlz59qaNWusdu3aUfd9BeSE/fv329dff20xMTHWsWNHXuQ8jiTd5zZu3GjnnnuuW45GgeyHH36wQYMG2cMPP2zRZPHixXbnnXfaX3/9ZevXr7fHHnssKpP0b775xm6//XZXAblKlSr2008/udfhiy++iKql+XSC17t3b7eyg1Z8UAOWKj8/++yzduONN1o0OnDggF1++eX2559/2h133BF1J71K0Pv27WtXXnlliuuvuOIKknTAZ4mEvqvU2NysWTPX8H7RRRfZ//73P4uNZYAnkBX33nuvjRo1yq12lS9fPpL0CECS7nNKTNVL+Msvv1ihQoVs0qRJ1qFDB7vgggvs9NNPt2ixZ88eu+WWW+zCCy+0ihUrWrRScvrVV1+5HnTZsWOHG10xePBge+GFFyyaXged1I0YMcK1GMtzzz1nAwcOtB49egSWcYq2AK0T3oSEBItWxYsXt5EjR4b7MABkQN/ValTVT4UKFWzp0qVudNz7779vvXr14rUDsqBy5cq2YMECe/nll4mDEYIk3cfUe/7pp5+6D5wSdFFiUrduXRs9enRUJenR9FwzokaKYCVLlrQ2bdq4L+Zoot6WLl26pLhO00CSkpJcL020JelffvmlG+L222+/uWH/0erw4cOuEUtOOeUUq1q1argPCUAq6u3TNBQl6KJzGp3b6HqSdCBrbr31Vl66CMO4Ih9btmyZG8Kq+aXBdPK5cOHCsB0X/EPvD81B1nsiGmk+uoZIDhs2zO666y57+umnrXTp0hZN/vnnH+vXr587wY22xom0GjZffPFFN+1B04M0skKjLgD4p8bOkiVLOK8BgGOgJ93Hdu3aFegtDabiWCqSBGgKwL59+9xQ52ikWgWaAqKCgupdj7aiiioi2K1bN/c+0FD3aKbaBHofnHjiie7ynDlz3CgTNWBFa50CwG/27t3rRjyldV7jnfMAAOhJ97X4+PhAUEs9P9u7DdHrgQcesLFjx9qECROsUqVKFo00/1xTP2bPnm333Xefde7c2VasWGHRQnM4VShOc9E0F1s/u3fvdtfpdYm2JN1L0OWMM85w9Tv0+QDgD97UPc5rACBjDHf3Ma84mJYoCabL3m2ITqru/8orr7heZCUjMNejrDnJ6kGNFuXLl3d1CiZPnuzmpOtHIys00kYrAUQ7Df9X5X8A/qAOBs1F57wGADJGku5jGv7VokUL++ijjwLXrV692vUaXnzxxWE9NoSPKrlr3q0S9FatWkXlv2LLli1ubmMwrbkr0VQsTD3FXg+696MT4EsuucT1skfb3PxgO3futKlTp9KIBfiMzl9UFFfTdUQrUmgZUc5rAOD/MCc9DyxV0q5dO7cmtIZzDh8+3M2zvOqqqyyaaK6aKliLqnerirUSEg3zbdu2rUULVfrXGvEDBgxwvaVebQItPXXppZdatFDhIVUyveyyy9x68cuXL7e33nrLrrnmGjvrrLPCfXgIg9tvv92th96yZUv3HaH3Q4kSJez+++/n/wH4rKG5efPmLmapqrs6IjTq5Y477gj3oQF5lkbUbd682a32o+kk3nKkV1xxhRUuXDjch4csiEmm9G2eKI717rvv2rZt29zySn369LECBQpYNPn7779t0KBBR12vNcJV1TtavPnmm66ae2paO16VzaPJ2rVr3bxrzUHXsO9zzz3Xzj77bIt2KiKo7wktcRRNFMrGjx9v06dPt5iYGGvcuLF1797dJe4A/GXDhg0unul7XCsxqLhj6mJyAEKnDpylS5em2bnDZytvIkkHAAAAAMAnmJMOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAJBHPP3007Zq1aoMt3nhhRds6dKluXZMALIXSTqAHNe1a1cbMmQIrzQAAMfpgQceOGYCPnToUFu4cGGeea23bt1q7733nr344os2d+7ccB8OEHYk6QBy3K5du2zfvn280gAA5II77rjD6tWrlyde6/nz51vdunXt/ffft3nz5ln79u3twQcfDPdhAWEVk5ycnBzeQwCQUxTwhg8fbn///bc1btzYnnrqKWvQoIG7LSkpyYYNG2ajR4+2nTt3WqNGjeyJJ56wJk2aBO7funVr69Gjh914442B63r37m2lSpWyZ555xl3u2LGj26eS8KlTp7r9du/e3bX0x8TEWP/+/e2dd96x/PnzW+HChd191Ep+++23u/vpsb/++mv3uJdccok9++yztmDBAouN/b82xD59+tjhw4ddKzsAANFM8XTChAlWtGhRl+CWLl3aOnXqZAUKFEgx3P2iiy5yya83RL5z5862efNm++2336xkyZJ2+eWXW6FChQL3eeutt2zbtm0u/lasWNHatWtnlSpVSvHY3n7WrVvn9tOsWTN3jqHzgvPPPz/Ftu+++67VrFnTzj777AyfT8uWLa1ChQr26aefustfffWVXXrppfb777+7cxcgGtGTDkQoJeC33nqr9evXz2bMmGEDBgxw1wUPhXvppZdcwJ02bZpVr17d2rZta5s2bQpss2PHDktMTEyx3927d9vevXtT9JIr+a9WrZo7adD+Hn/8cfv444/d7f/9739dst+rVy9bsmSJ+9G2up+Op2rVqvbdd9+5YK4ThhUrVti3334b2L+S+FGjRrnbAACA2f3332+33HKLG9KuRvGzzjrL9u/fn+5wd22j5PrOO++0RYsW2SOPPGKtWrVyDeCePXv2uJiroefjxo2zk08+2aZMmZLi5dZ+rrzySrvnnntcoq7H/Oeff1yDfHC/3/r1661v374prkuLEvzZs2e7xnjPxRdf7BoHdAxAtMof7gMAkP2UWCtRVvKs5Fhq1KjhWtVFQVU91q+88op16NDBXae/FYw1H+w///lPph5PLd4K/KJkX/tU4t2lSxcrUqSIxcXFWXx8vJUpUybF/TSkbdCgQSmuu+KKK2zEiBF2wQUXuMvq6S9WrJjrZQcAAGYFCxa0H374wcVXJdYa2v7666+7Ye7pUa/2Rx995P5Wj7kS4cmTJwfOA7w47lGju5Jx9WgHK1++vOvt1mg5OfXUU13yPn36dNfY743kU4P8sXrRFy9e7H57Pf6eOnXqBG4DohE96UAE+vPPP93w83PPPTfF9V5AXb58uSUkJLiWd4+Gt+lyVgrNqLU9WNmyZd0JwLEosKem1vTPP//ctm/f7i4rYe/Zs6cb3gcAAMyuv/56l6BLiRIlXIFWJc4ZUU+6R0PklUQHV4lXr/f333/vkn018q9evdqdTxw5ciTFfq655prA+YSoAV5T3xSvPRodp06C4O3S4o3MK168eIrr9ZyCR+0B0YYkHYhA3vA1L4CHersuHzp0KMN9pzV0LXj+eEbbpdUTkJpa4atUqeJ60P/44w/79ddf3Tx4AADwL83hDqY55BpinhHNYU8v5h88eNDOOeccl/wr7qqhXY35ul3D4FM3xKfVwP7JJ5+4KXHq4dfUNe3rWE444QT3W/cLpilx3m1ANKJrCohAGvamnuc5c+a44W2p1apVy/Lly+eGsGl4ukdFYDRHzaOW7dSBUy3raQXojOhYVFAuFGp1v+GGG1yLvIJ8ixYtrH79+pl6PAAAIpkKwAVTPRkNQ88qTXf75Zdf3PxyTTET1ZnRsPVQnHfeeVauXDn78MMPbdasWa6IXOXKlY95P2+Yu0b4BZ+vKP5fffXVWX4+QF5HTzoQgTRMTAVbNN9bibp6tVeuXGkPPfRQoDVdvdO6rKFu6ll/+eWX3dInN998c2A/Z5xxho0dO9YVkdFwNw2BUyKfWZr3prlloSbqan3XsPs333yTXnQAAFIZOXJkIKZqepuKtSpRzioNLdfoNtWPEZ03vP322yHfXw3sOq947bXX3LGEOgJOQ+419S24MUDFbNUhoIr1QLSiJx2IUFp+RUueqQDbgQMHXIu2isJ5nnvuOVfxXb3UCsZKpBVYVazFo3VKNeRcw+hUAE6F51TsLbNUgVYF4dQ4oBMALcGWEfUGqFCcqrzTkg4AQEobNmxwS6SdeeaZrsdbMVqxNquU4Gs5NtWyUbE3rQoTPF89FErMVTVeHQWaox4qJfZ6fCXlKnL7wQcfuPOT008/PQvPBIgMrJMORDi1tKvae3pzu9SLrttTz1ULpiRfc9c091xz0/Tb25/mjek2bw10r1Vfj5t6n5rfph+tp6r9pL5fMFWb1dA51kYHAMBSNKCroOqyZcvcOumKqT169EgRc5Usa6m0hg0bBu5z3XXXpWiIV2N+8+bNXaIvGjU3ZswY91sFYTWabvjw4W45N2899bT2E0z3U8Id3CkQirVr1wbmtGvd9NRrrgPRhiQdgO9oXpxODtTj3rRp03AfDgAAOAaNvGvUqJGbrtagQQNeL+A4MNwdgK+oFV4FZDSfngQdAAB/0zrtr776qisa16VLlxQJ+oIFC2zixIlp3k8F6jSsHcDRSNIB+MqPP/7ohtKntTwbAADwFxWWVeG5m266ya2Nnnq6nJL4tISyVCsQrRjuDgAAAACAT7AEGwAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgE/ktCiUlJdn69eutaNGiFhMTE+7DAQBEueTkZNuzZ49VrFjRYmNpP88OxHoAQF6N91GZpCtBr1KlSrgPAwCAFNatW2eVK1fmVckGxHoAQF6N91GZpKsH3XtxihUrFu7DQSqHDh2yyZMn2/nnn29xcXG8PkA24HPlb7t373aNx158wvEj1vsf30sAn6loszvEeB+VSbo3xF0JOkm6P4N24cKF3f+GJB3gcxVNmIKV/a8lsd6/iPcAn6loFXOMKddMfAMAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfCIq56QDiD5Hjhxx8x+jlZ57/vz5bf/+/e61QO5TjY18+fLx0gNADi5vdfjw4aiNc8T68FOc1/nW8daYIUkHEPH27t1rf//9twve0UrPvXz58m5VC4qThYdedy23UqRIkTAdAQBEroMHD9qGDRssISHBohWx3h9UALtChQpWoECBLO+DJB1ARFNruhJ0fWGWLVs2ahPUpKQk11ihBDE2lplO4Thx2rJli3sv1q5dmx51AMjmGLdq1Sr33VqxYkWXHEVjvCfWhz/Wq7FI8V7vR8X7rJ5zkaQDiPihX/rSVIIeHx9v0UqBW4GjUKFCJOlhovfg6tWr3XuSYe8AkH0U3xTntP60GuWjFbE+/HSuqelta9asCZx3ZQXdKQCiQjS2qMNfeA8CQM5ipBgi5X1Ikg4AAAAAgE+QpAMAMqQhW9WrV3fzqf2wHwAAkP3atm1ro0aN8s1+ohlJOgAgQ5pDrQRby9qEasWKFS4h37hx43HtBwAA5A41ou/ZsydT9znrrLNs7Nixx70fpEThOABAtksrIVfSrmqnWoYMAADkfWkl5NOnT7dixYqF7ZgiAT3pAOAzixYtcgnt119/bR07drSTTz7ZOnXqZMuWLUuxnZLg6667zurVq2fNmjWz//znPymS4uD9XH755daiRQu74oorUuxn5syZbptg6v3WdeoNT4vWoNXt+jnppJPs3HPPtTFjxgRu37Fjh7Vv39793bJlS7fdDTfcYP/8848bAhfcux7qc5g6dao79oYNG9pll13mrgcAIC9r3ry5vfXWW9anTx879dRTXa/0hAkTUmyzf/9+e/jhh61JkybufKBv375uPfi09qPbWrdubW3atDlqP4rXSp6DXXDBBTZixIh0j69z584uBteoUcNatWplQ4YMscTExMDtisdK0gcNGhQ4J5Du3bvbF198kenn8M4771j//v3ddnoe48aNs2hFkg4APqMlO5S89urVy66//nr73//+Z/nz53eJrxcc9fvss8+23bt328iRI23o0KH22muv2V133ZXmfpQIDx8+PM39aJtgSpJ1nXrD01te5Pvvv3c/kyZNcvsfMGCAffLJJ+724sWL2wcffOD+/vjjj912Tz755FG965l5DjfffLML6moM0OOr8YJh8wCAvGzdunUuvmk9bcXNiy++2DVI//zzz4Ft+vXr52LfU089Ze+++64bkabG8eAYGLyf119/3Tp06HDUfrQEaHCCLWo8VwxOz8svv+xi+HfffWePP/64S/xvvfXWwO1vvPGGlS9f3u655x63nRrU0+pdD/U53Hbbbda4cWN3TqDOia5du9qff/5pUSk5Cu3atStZT12/4T8HDx5MHj9+vPsNHK/ExMTkRYsWud95xe+//+6+o955553AdQkJCcllypRJHj58uLv86quvJpcuXTp53759gW0+/fTT5Pz58ydv3LjxqP0cOXIkeceOHcl79+5NsZ9vv/3WbRNs3bp17rrFixe7y8uWLXOXV61ale4xDx06NLlDhw6By7qv7qN9eVLvJzPPQcfp0f113dKlS5Mj5b1IXMp+vKb+R7xHNMd6OfHEE5M7d+6c4rorrrgi+fLLLw/EzZiYmOTp06cHbt+6dWty4cKFk0eNGnXUfrxYr9/B+5F8+fIlT5o0KcVjNWjQIPn5558PXK5Vq1by66+/nu7xzp49OzkuLi758OHDgeuqVauW/NZbb6XYLng/mXkOd911V4r91K1bN/nFF19MzmuyI97Tkw4APqWhXh71Hms4+Pz5893lefPm2emnn26FCxcObHPOOee4VunUQ8Ez2k9Wff75567FX8PWNMTtpZdecq30mZGZ53DKKacE/larvWzZsuW4ngMAAOEWHKNFQ9W9GL1gwQLLly+fGwbvKV26tDVq1OioOJ7RfrJKvdg9e/a0pk2buiHvV155pRsVt379+pD3kZnnEBzrvXgfrbGeJB0AfKpAgQIpLhcqVMjN6xL9LliwYIrbvcveNqHsJyu++eYbu+aaa+ySSy5xw9emTZtmAwcOtAMHDmRqP5l5DgrwqSUnqzEaAIDIjfVxcXEWGxt7VKzM6Vi/fft2V0dGBeA0hH7KlCmBqWyZifeZeQ7E+v9Dkg4APvXHH3+kSEgXLlzo5ptJnTp13OXgRNVrkfa2CWU/RYsWdb/37t0b2Gbt2rXHTNLPO+88V9xFc8fUup66Vd0LtBkl0pl5DgAARKLgGO31PAfHes0jDy74qp7sxYsXZxjrU+/Hi/fBsT4pKcnNSU/PL7/84uaVa176GWecYbVq1XKFYVNTvD9WrA/1OeD/kKQDgE8NHjzYtm7d6oLfc88954KpCsBJ7969XWXUZ555xt2+c+dOV7jlwgsvDFRXTWs/zz//fIr9qKr6CSecEKjuqv2oAmtGKlWq5Iaqb9682V1W9XivdT14iFpMTMxRFemDZeY5AAAQid5//32bM2eO+3v27Nnu8k033eQua3qaVma54447bN++fS6xVkxXkqsK6qHux9uXCtHqvtqPKrUr7qanYsWKrsfcqwivYm8PPvhgmttlFOsz8xzwf0jSAcCnVJ21bt26VqJECVcdXcFVwVD0+6OPPrJXX33VVVMvV66cGzqW1lIq2k/9+vWtWrVqNmzYsBT70X1VnVWJeZkyZVzSnnpeW2rqQddc9CpVqlipUqXcUPcuXbqk2EYt9kq49dhVq1Z1S7CllpnnAABAJOrWrZtLVjVPW/PIlVhfffXVgdtHjx5tu3btcvFWsVLLkn366adWsmTJo/Zz7bXXWs2aNd0w9dT7USP9kiVL3H4Ub9XQrt7x9GjJU626omXatL2WRVNMT02x/s0333QxPb0G9lCfA/5PjKrHWZTRUgN6g+jNonkW8Be1rE2cONF9EWgOC3A8NN9Jrb8akq35WXmBeqkVDFUsRYnzpk2bXPDW8mlp0brjKr6W+vsseD8KjMuXL3dF3lLPWxO1bGv+mR7vyJEjbikU9ZjrM6hCblpOpXLlyimOQd+lamUvW7asGxKny7pPMN2uEwEl3zqGtPaT0XPQEmwaSq9E35vPprClZdkqVKhw1Jz2vPpeJC5lP15T/yPeI5pjvTfqTKPJevTo4YaSKz5rdFta1OutmKiEOb39KFHXlDWdM3jT2VLbtm2ba/zXMHXF1yJFigRir0ba6e/g++oxdR89ruJvWnFc5xA6V9G26hBIaz/Heg4679B5QvDz37hxo/t/6njzkuyI92mf8QEAfOPEE0/M8Hav2vmxKCiml+grAVaCLgrcSuY9uk/wZU9wcFEgTuuEQEm0etw9ae0no+egE5bU99Ew+vT2AwBAXnSsXuVQE1Vtl16iL0rgPd6oOk/qhnYvDqtR3JNW/NU5RPA2ae3HO7b0BJ8rZPb8JhIx3B0AAAAAAJ+gJx0AfEbzvTVMSsO+/LAfAACQ/ebOnZstMTq79gP/IEkHAJ9Ja4j38e5H88UAAIB/pDXE+3j2Q6yPHAx3BwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBIMqNHz/eXnzxxWNu98wzz9ivv/5q0ej++++3WbNmZbjNgw8+aCtWrMi1YwIAIFRLliyxm2666ZjbTZgwwUaOHBmVL+z7779v7777bobb6LXRa5TTWIINQFRau3atbd26Ndcer0yZMla1alXzo9mzZ9svv/xit912W7rb/PDDD/byyy/bLbfcYnnd4MGD7bzzzrPWrVuHfJ///e9/VrduXWvZsmW625QoUcJuv/12+/LLL7PpSAEAeSne+znW//333/bGG2/Y8OHD091m165d1rdvX5s0aZLldaNHj7a9e/dav379Qr7PtGnT7PDhw9arV690tzn55JPt4osvtqVLl1qxYsUsp5CkA4jKgF2vXn1LTEzItceMjy9sS5Ys9m3wPpZHHnnE+vfvbwULFrRICNwVK1bMVJIeCp0IqAHg999/tyZNmmTrvgEA/o/3eT3Wv/7669agQQM79dRTLa+bMWOGa5zJTJIeiqZNm1rt2rVdg8c999xjOYUkHUDU0Ze2AvYZvYdYsQrVc/zxdm9YbXNGPOIeN9TAPXbsWNuxY4edccYZNnHiRPd3p06d7Mwzz0yx3T///ON6edevX281a9a06667zkqWLHnUNh988IGtXr3a6tWrZ9dff/1R22REQ7inTp2aYghYKMfnbdOiRQsbN26ca50eNmyYu00t0B999JHt3LnT6tevbz169LBChQqlGF6ulupNmza5Xn7d1rt3b6tUqZJ9+OGHNnfuXJdo33jjjSlasp977jn766+/LCYmxsqXL2/t27e3s846K3D7008/bVu2bHGJ+rx589x1Xq+C7qfj3LZtm3seV1xxxVGvhXrJNey9cOHCdu2111q1atUCtxUvXtw6dOhgb731lr322mshv74AgLwf77MS6+fPn+9i66BBg1xsW7NmjTVr1sy6devm4phn//79Ltb/8ccfVqpUKbv66qvd6K5g2kbDtdVQrPh3zTXXHLXNsbz55pv28MMPZ+r4vG0eeOABGzFihK1bt85N/1KM1jmAhofrPKJKlSrWtWtXq1y5cmD/Ot6kpCR3nN99953ryb/yyivdecOPP/7ohpUXKFDAunfvnuK5qKf/888/D8ReJc6dO3e22Nh/Z3J/8cUXbgSgXhNviL9GuukcSOcdo0aNsmXLllmdOnXcOZFiejC9hjq3UU+8zkWCzyNEr+3zzz+fo0k6c9IBRC0F7FJV6+b4T1ZODBRchg4daldddZUlJydbYmKitW3b1iW+nsWLF1vDhg1t5syZLugpwWzcuLFt3rw5zW0UMD/55JOjtjmWb7/91gVX/WTm+LxtFFwVADVETHScGjauExkl3UrWFWAVDD06GVGgViDV8EEdQ/Pmza1jx45uDr33fM8///wUx3rSSSe5HoBGjRpZQkKCXXbZZW6Yvket3xoNoOei7bzeAh33KaecYosWLXInN0ri77777hT7Vi/522+/baVLl7Y5c+a4Y049hFK98998803Iry0AIDLifVZi/apVq+zVV191Dd5Kbr1pU2qA9hw8eNBatWplL730kotPy5cvdzFOjeept3nllVesXLlyaW5zLLqPjie4sT2U4/O20f22b9/uYml8fLxLgvW3km01WmhOvI5JDe/Bw8vVAKCpdErG1Yiu/fTu3dvuvPNO1yCh41LDgDojPCeeeGIghitJ17mGOgo8FSpUcLFat3nbFS1a1HUQ6FxEDQ7aRudI5557borXQY0Fffr0sXz58tm+ffvsnHPOsa+//vqoWK/np+eeU+hJBwCfUgKoRFeJpSigqNVWPbxxcXF27733umTXmwN9xx13uED0+OOPu2Au3jZqVd69e7cLhkoug7c5FgUx9dJn9vhEibda2RVQZc+ePS74KflW67R33KeffrpLptWD7lGg/vTTT93fGiFQtmxZ12uu+4oaCKpXr+56FtQQIUrKg2kfPXv2tIEDB7pWfwVxHaMCrNe67s3Be/TRR+2+++5LMUwy2GmnnWafffaZ+1snKepFV0NBcCGeWrVq2cqVK+3AgQMRMTUAAJCzNMrsoYcecnFI1Pis2KXEVUmuhqArHim2eCPHlHDqdjUsi7eNElrRdvoJ3iaUWC+p4/2xjs/b5r///a9dfvnlgfup11y92+px9ihxVpwNbjxQ77eGpp9wwgnuss4Zpk6d6hJqb4SderzVoK/YKzqP0Y9HjQZqvNcoOzXo60fJuM5TgmO0Em4ds3rivV731LFeHQ/Tp0+3IkWKuMtqeFAD/YUXXpgi1ote2xo1alhEJukbNmxwwyA0pFEvmoZPeCd36dEQBL24GkKhlhkN+dDJGwBEEm/eU/DwKg1D07AxDdn6/vvvXWD26LtT34cKZJ5QtjkWJdZesMrM8XmJrZegiwKxkmINYdP3uIKh1xP/22+/pdi/irt51JuuIfrBLd5KkvV81LrvJemHDh1yifPChQtdjNCx6/EUazSSIC06Jm2Xuupt6uGKwb32amHXc9djB/NeJzWIEJcsRS+PYr1OaNSYo6kC6u3JiM4LNMJBr7F6U9Soo14YAIg0Xbp0CfythnXFNyWKyo3U23zJJZekmNql71DFdn1PKsYGb6P4k9Y2x6I4qMbl/PnzZ+r4RI3gF110UWAbjWRTj7T2p0ZyL9aroSF1rNcIAC9BF8XWuLi4FFPgNEoudbzV4+scRyMDjxw54h5LDQ1K0NOiXnHdR+c/XoKeVqzX8QSf82hKnjo6gmmkgM4DvNc64oa7a/6f9+ZTi9ATTzzhToL0QqdH8/w0T0FzLBXg1YOkf6bX+gMAkUItzsGUqIoCkgKgeqlTJ4K6rIAsoWwTCiVImsOVmePzpJ77rlZtnQBoyL0Sa8UAJV4K4qmrqSoIBlNQTes6L2YoQT/77LPtySefdMPmtH9viH1GgVSt5AruGhaXkdSPrQCdOl7pddLJSmbm/EdDgt6uXTu3hJ9OIDUdQP/3jIYJLliwwJ2UTZ482cV61VNQg4/mOwJAJFFCmjr+KJ56sVS/04rj4sXyULYJJdZrFJgazTNzfKIpbcGjx9RIrvioeeTBsV497U899dRxxXrRqDv10iu2q5ddowj1+BnFesVnzX8/VoNFKLFeDRq6LvV5UMT0pGu4g/5xX331lTupueGGG9zwAQ1l1PDEtGgt3wEDBgSGTtx1110ukKvwgFeQCAAiQepWY29IloZ0KSAqoKYepqWiLl6rcCjbhEIJtYrCqBU8uJBNRseXHvVma1icesm94WLZRWu4//zzz67wm3dCobn4qQU/B9G8eJ2YaL5ber3toVJPsU5G0uqJiFZ672j4onpQlHDrxEY9MSospLn/aXnnnXdc3QA1xHv/L/3WFA3NVQSASKEG5o0bN7r55qJYq/jqxVJ9F6YVx8WL5aFsE0qsF3V8Bg8lP9bxpUVJvBrL1ZEaPH89OyQnJ7s58CqI6w2vV/Kduo5M6ljvHZNiUepCcJnlTSHIySr4YetJ1z9cQx2DqwPqn63CQ161vrToDaKhix6dWKlyn4bPAUAk+fPPP91Io+BGSgVRb/6T5n5rCRB9B4qSUwWt4OIpoWxzLEqo1TqtIeSZOb60tGnTxp1MKEFTsu7RyYWS7OOhIO0NnRclg88+++xR26mXW638Hs1PV6KuOem6v7cvzbfPLA29U4V3/B/FdI2S84a3q1dCUyM0fNB7vdOK9RoJonOF4F4QYj2ASKSCbx5N89H3nTd8XHFcDZZKLr34pAZL5Uwq5BbqNsei71clnYpjmTm+tKhXW8VfNU9dxeA8+l7XCKnjlZSU5PYVfHwazp5RrNcx6dxHq8AE97gHn8eESq+Ritkda9rW8QhbU79OyJRgpz6ZU7GCtHo+glvkNW9QJ42aD6FeE1UOVu96evQ4+vF4/xgF/+ATAPiD9z/hf4Psej8pEdAXun7E+717/WqztHOEbF+WxXtc77GPRcesFmgNAdcwXw1XU2+kRh55c7uUVGqOtoaQKVhobrV6p1VYxXscbxsFXiXQWj4s9TbBiWlaFIR0AvDee++5IcuhHp/3E7xfDZvTnHEFb63Fqrlj6sHWj4YyB2+b1uuV0XXalxJuVaFVY4CWWPOG3wXf79JLLw0s1aZWdc3Z00mHnqNeH43w0n3V+h9c5Tatxw5+fnoO+h+oUSSt19JrRNB7UolqsEj+vlMF3NQF/RTrdUKVXq0AFRNUz43+l3p/qc6BRoZoebv0EOvzHuI9cjLW53a8z0qs13aKQ5oGNHv2bDcXWp2YKu6qxmPdriKpKlqqGKd4ru9UxVzdx3scbxt9Z2ral6YIpd4m9e+09O/f300t9gq0hXJ86e1XjQSK9Zp2psYCdRaocV893l59mbTOE0K57u6773brn6thQo0Aek3UUx68jRoR1FCvYfEaVXjbbbe5zgTFI80z13nC33//7aYF6DXL6LFTPz/VWAk+j8qJeB+2JN3r6dBc9GCarxbcMpKa/rmq5KuTLJ1oaiiHVzRAb5a0aH7iI488ctT1aslJvS4e/ENLLgHHS8OO1SunudmaGytK3ArFx9ucd4/+XsgperxjzZcKpmNVEqzWYS33pRFEGgKsuVTePjRvSt9/KoSihEYjk5RUBjdApt5GVdJTb6NGzyZNmmR4bAqICniaO65gF8rxqYiNvs9T71fzx7QEixpklaSpwVUnH8GvjwrQadh48H0fe+wxF1iDr9O652rs9a7TEnNqFddzVYE8NUyoSIyK0njbqCKtqsmrCq4SO12vBgMVs9HrpLlmmkqlRDKj41GVep0UeNcp1uh/oNcnrddSr5linxL54FEEklHcy+v0nNOK9Rk9b70v9L9Qz47eL6ITQw3DTG+IJbE+7yLeIydifTjifWZjvb4f1XitBm7FUg0j1wokiknB+xg+fLiLUfoO1FKkSiqD41rqbdSLnHobfXcqac3o2NTTrG20sooS6VCOT9OO9f2ber/6n2gklUbJqVK7YqNib3CM9JZxDb6vRlrFxcWluE5rmQcXxVOCrMRfw86956pl0oLPERSzf/rpJ/eaKK6L/jfaTtcrh9R5jM4/MjoeDY3Xa+ddp/xR7zEl++m9ltkR72OS0xtrlsPUwqMTKwXd4JL2ahXRyVvqyn+ikyl9ALWkkLdMjw5fc9u8ueyhtq5ruKUKGAVXSoQ/KHFQwFbicKxK/8CxqOVWQUVLdQVXCtVontRrXOckJXOZmQeuRFItvN6SX8dL35UKUkqWUs/TCtXEiRNd4qrK7dl9fJFAIw0UtHUCkt57UbFP8Sf4vejFJb1H1NgRaXFJ7xfF+RdeeCFwnU4A1cOinp60hgvqRFEF/ZSoB69Tr5NQNcAEV+b1EOvzHuI9cjrW53a8z2ysHz9+vKvDlV1VwrMj1qtwpzo/laRn9/FFgilTpri4ldFqI9kR78PWk643sIZMqLUnOEnXZa8ab2p6w2gOhIa+efQGVA+Q1sVLj1pN0lqvVgkgSaB/8f9BdtC8ZH1P6KQ++MRegVw/fqVj9o47O3hDso5nn+oZz6njiwTHKmim10qvWVrfbZEci9TjknoFFl1WVdz05vNpKsIFF1yQ4jrFfg1rVPLurSQQjFifdxHvkVOx3u/x3jtWP8X64GJo2X18keD8oOVYczLeh+0V18FrjoB6Hryh75oDqOEHXbt2DWynyq//+c9/3N8azq4hDRqmENxyrqGc3jp9ABAJNFRbc8P8yu/HB/9QTNcUhCVLlrjLGo6q4oXBsV7xX1MpvKX+FNO1xm7w3D3Ffo2mSytBB4C8SAmx5kn7ld+PL5KFdY0YLZmm+QRqHdebQAFYcw4039yjIK1CBQ888IBL7FU0RttoOLyGXWqsv67XXEUAiBTHuzxItB8f/EMN8qrkrqJ+6oHQlDb1equooUf1AbSkzqBBg1wlYp0fnHPOOa6In2ooqCiQhmCmN60NAPIi9fBrCWq/8vvxRbKwJuka5vb777+7ioGal3brrbdaixYtUmyjyu3t27cPXNYcNlXjU8EhLSWkIj1K9FmTFgAA/9GQP1XC1Ug5FX9VUSAl66oY7NG0tZdfftmNlvNODFVoSCPlNJ9UhZLUMKQh8gAARLqwJumi1vTUS7MEa9eu3VHXqVS+t3g9AADwv1atWrmftKj4q4a7p563pwKiAABEG6oAAAAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAFFu0aJFrqgXAACITFu2bLHx48eH+zCQVwrHAUA4qGL01q1bc+3xtLZz1apVzY+0ZrWWxZoyZUq4DwUAgDwb7/0c6+fPn2+dOnWy5OTkcB8KQkCSDiAqA3b9enUtIXF/rj1m4fhCtnjJUt8GbwAAIk1ux3tiPbILSTqAqKMWdQXskf1OtfoViuT44y3esNd6vDnPPW6oSfqCBQssMTHRTj31VPvjjz9sx44dbvmqwoULH7XtvHnzbP369VazZk2rV69emvvTNsuXL7eGDRvaySeffNzPCQAAv8vNeJ+VWL9hwwb79ddf7eKLL7alS5famjVrrFGjRlahQoWjttVtmp5WqlQpa9asmeXLly/NbTQyrnLlynb66aenuQ3yBpJ0AFFLAbtp9eLmR2+++abNmDHDDh486IbPbd682fbu3WvffPONnXLKKW6bXbt22WWXXWaLFy92ybcC/fnnn2+jR492a0yn3kYJvIa7pd4GAIBI5td4P2fOHLvmmmusbdu2tm7dOitatKj9/vvv7hygZ8+ege3uuOMOd52S81WrVrntJk6caNWqVTtqGzXue/tKvQ3yDgrHAYBPLVy40P7zn//Yjz/+aEuWLLEzzzzT+vfvH7h96NChLnlX6/t3333nEnD9fv3114/aRkn6F1984YJ/6m0AAEB47N+/32rUqOFGzc2aNcuee+45u/nmm12hN/nqq6/s1VdftR9++MGmT59uf/31l2u8v+WWWwL78LbR7fpb5wypt0HeQpIOAD5Vp04du+KKK9zfsbGxNmjQIJs5c6abYyfqDR84cKCVLFnSXa5evbpdd911NmrUqMA+QtkGAACEz/333x/4+8Ybb7SCBQval19+GYjjGg7ftGlTd7lQoUJ23333udv37NkT8jbIW0jSAcCnNMc8WK1atdzv1atXW0JCgush967z1K5d290uoWwDAADCJ3/+/FalSpXAZc0jV4O6F6c1vD2tOC6Z2QZ5C0k6APiU5pMH27lzp/tdunRpi4+Pd0XkvOuCt9HtEso2AAAgfA4fPmz79u1LN07rd1px3Lst1G2Qt5CkA4BPqRCcqrZ7Pv/8cytbtqyddNJJFhMTYy1atHDXebT26fjx410VeAllGwAAEF7e0HbR3PSVK1day5Yt3WXF66+//toVkvV89tlnrrfdqwIfyjbIW6juDgA+pZ7wCy64wG677TbbuHGjPfHEE66gjOaqyVNPPWWtW7e2fv36ud8KyFpm7eOPPw7sw9tGc9xOO+00Vx0+9TYAACA8NLz97rvvtr///ttVZB82bJh16tTJLaEmt956q73zzjtuZZbrr7/eFYJ9/vnnbezYsa4xPnibCy+80Lp06eKGuL/wwgsptkHeQpIOIGppTVM/P46WZBkwYICryq5hayNHjrQrr7wycLuWYtF6qG+99ZZrhVehOQXl4PVZvW20LIsSdG3z4osvptimQYMGgcQfAIBIkxvxPquPoSJvWnVFSbbitSq7B1dlP+GEE+znn3921dsnTZrk1kmfNm2aW/El9TavvPKKTZkyxcqVK3fUNrquY8eOx/kskVtI0gFEHS1LUji+kPV4c16uPaYeT4+bWWo51096lGArMc+ItlGr++7du61YsWKuUnywa6+9NtPHBQCA3+V2vM9qrFcDuka+pUeJ+cMPP5zhPrTNQw89lG6sb9SokZvuhryBJB1A1FEv8uIlS23r1q259pgK2sG91wAAILLiPbEe2YUkHUDUBm4/J82NGze2SpUqhfswAADI0/wc71XU7dJLLw33YcCHSNIBwIf69u0b7kMAAAA56IwzzrAxY8bwGuMoLMEGAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMswQYgKq1du9a2bt2aa49XpkyZTK3TevjwYUtOTra4uDjzk4MHD1q+fPncD4DIktvfi0lJSe73/PnzLTY2d/qNDhw4YAULFrRIltl4E+ly832d2dden4FDhw757j3p13OQaEKSDiAqA3a9+vUsMSEx1x4zvnC8LVm8JOTgPWjQIPvjjz/s66+/Nj9p1aqVXXXVVe74AETW92L9enUtIXF/rj1mfHy8WyO6TZs2lpiYO9/HsTFmSckW0TIbbyJZbsf7zL72kydPtksuucQlxX7y0EMP2S+//GJTpkwJ96FELZL0PNjyHA60yiKS6POqgN3m3jZWvErxHH+8Xet22YynZ7jHDSVwHzlyxP2ohX3//n9PmAsUKOBatdXi7l1Oq+cpdeu3er61rUe3ebSvmJgYy5//6FCgx069f22v++sxvOMqVKhQJl4JAH6l7ycl6CP7nWr1KxTJlcdMii1g/5jZjEEtLTbpYI4/3m8rt1jf/y21Jj2bWOXmlS0SZTbeRLrcjPeZfe2DY7oXUxWPNVJNIz68y+nF6OAeeP2dejvtXzHeO6cIPhfIKNandw6SW6Nd8C+S9OxooatX3xITEyySxccXtiVLFvOFj4iigF2mdhnzm+eff95efvllF2BLlCjhrvv4449tzZo1dvfdd7vLCrwNGjSwZ5991s4+++zAfW+//XZbunSp1ahRw/VQlS5d2lavXm1btmyxnj172nfffeca3a644go3xLRt27b2+OOPB4K8Ws/fe+892759u9WsWdMefvhh69Gjh7v9yiuvtN9//90WLFgQuM+mTZusePGcb+gAkDuUoDetnjuf6UMW55L0xlWLWZz9m6zkpH0J+9zvIuWL+PK7H9EV7xVPFVeVEHuxvn///ta3b19r2rSpu6xEuXLlynbnnXfawIEDA/f94osvXGx+8skn7YknnrCdO3fab7/9ZnXq1HFxW3Fc5wka/abzAT3W7NmzA/cfOXKkPfbYY7ZixQorV66cXXvttfboo4+6pF/nHzoPCT4H0flEp06dcv01imYk6dnRQpeYYGf0HmLFKlS3SLR7w2qbM+IRWmWBXKJEfOPGjWkOd/eCtIaGDh8+3Dp27GjLli2zsmXLBrbR8LTBgwfbhg0brEiRf3vE+vXr5z7Df/31l5UqVcruuecemzlzpkvSPTo50O0//PCD1apVy77//nsXlE888UQ777zz3ElBs2bNGO4OAMBxUiKuuKrh7l6Ptce7rAR++vTprmFdDecdOnQIbLNv3z6bOHGiS8ArVKjgrlOCPWrUKPvyyy+tRYsWLrm+4YYb7PTTTw/c78MPP3TnGfp91lln2apVq6xLly7utqeeeso19uschOHu4UWSnk2UoJeqWje7dgcAGdKwNiXVr776qusd79q1a+A2BfKhQ4e6VnRvxI9OBCZNmmTVqlVzQ9ZeeOEFGzt2bOA+69evt3fffdfmzZvn7q/W+9atW1v37t3t/fffd0k6AADIXeoNVxL92WefpUjSRecAXoIur7zyit14442uzoNifa9eveyjjz6yHTt2BLbRSDjVlVGCrt5ynRc88MADdsstt7gkHf7A5AIAyCPWrVvnArWGsGsuuIahrVy50l0frF69eoEEXdQ7Lo0bNw5cV7hwYbedR8m5EvPmzZu73veiRYtasWLFXOL+zz8akAoAAHKa6r7ce++9bpi75oIr1o8YMeKoWK/b1KgefD8NX2/UqFGK7byh816dmj///NPtPzjWa7j7rl27cq2AI46NJB0A8ojrrrvOzRv/9ddf3W8Nh6tfv/5RVWFTL5niFXvxljvypL4sf//9t9tv8M+0adNy5PkAAICUnn76afvkk0/s888/D8Thm2+++ZixXo3z+gkuEJs61nu3ffDBB0fFeiXoWnEB/kCSDgA+pOCruWjBfv75Zze3rHr16i7xVjE4tZofS926dV3g1rw1z549e2zJkiWBy02aNHH71Py2zB4XAADIPMVUJc7BibVi/aWXXmqnnXZaIBEPLvqWHlWFV+E4FZALNnfu3MDfKgzXsGFDYn0eQJIOAD6kaqxKojW8TS3caglXNXcNP1dBF93WrVu3wDItGalUqZJ17tzZzUFTZXbt86abbrK9e/cGttGcNrXU33XXXa6SvIrMaUicqr++9NJLKY5rzpw5rpJs6kI3AAAgc7Fe8V0j1hRT1VuuWP/VV1+5GKx6MYrdStxDcdttt9mbb75pEyZMsM2bN9uLL75oU6dOTbGN4vro0aNdVXidD2jlGPWsqxMg+Li0Uoxq2njnIMhdFI4DELW0pqlfH0cJ+OTJk11L+u7du13irCVVBgwY4OaSlyxZ0i2/oh7x4LVR1eqe1lqor7/+uismc84557gl2K666ipX7dVbY1VUTE6BWcuw9OnTxy25qO169+4d2ObBBx90leK1nYbGsQQbAMDvciPeZ+UxvEKv119/vRsdpwZ0JdGaetauXTs3Ck5xW8uy6brgXnPVpklNsVuJtc4V1Duv4nCK/YsXLw5sc/nll7skXsu3PfPMM27Oux5LS7d5rr76are6jOrUaK46S7DlPpJ0AFFHSWp84Xib8fSMXHtMPZ4eN1Qq6DJu3Lijrk89P1zBPJjWNk2LgvBbb73lCsRoWLtaxtXaftJJJwW20fV33HGH+0mPWvi1dBsAAH6X2/E+s7FehgwZ4n6CaVWVjGg4vEa8pUXLq6mx3atHo6Vag2O9XHjhhe4nPSouq6rwCB+SdABRRz3ESxYvSTfA5QQFbT1uuCjYqtdbPeMJCQnuhEAt8RdffHHYjgkAgEiK9+GO9Rq6/uyzz9rAgQOtePHirgdcvebff/992I4JWUOSDiAqKYiGM5DmNq2tqh7y5557zhV+a9asmf3444+uZx0AgEgVTfFea55r6TY1yKt+jQrHan5769atw31oyCSSdACIAho+r9Z1b7g7AACIPJrXrnXQifV5G2dqAAAAAAD4BEk6AAAAAAA+QZIOICpoKRKA9yAARC5iPSLlfUiSDiCiqYK5HDx4MNyHgijnvQe99yQAIHvExcW531q9BAg3733ovS+zgsJxACJa/vz53XqfW7ZscV+W0VpIJSkpySWJWh89Wl+DcL/+eg/qvaj3JAAg+6jxs0SJErZ582Z3Wd+1MTExUfcSE+vD34OuBF3vQ70fj6dRnjMFABFNQbpChQq2atUqt35oNAeOxMREi4+Pj8oTFz9Q44iWAeL1B4DsV758effbS9SjEbHeH5Sge+/HrCJJBxDxChQoYLVr147qIe+HDh2yGTNmWJs2bY5r+BWO733IKAYAyNlG+XLlyrmYF42I9eGnc6zsmNZGkg4gKig5KlSokEUrBYzDhw+714AkHQAQyfEuWmt/EOsjBxMTAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnwl44ToWMfvjhB9u0aZOdcsop1qBBg5Dup/VmZ86c6dZBVLXiaC4IBQCA3y1YsMAWLVrkqi+fddZZIRV22r9/v/3444+2Z88ed5+yZcvmyrECABC1Penbt2+3M844w3r37m2jRo2yli1b2u23337M+z377LNWvXp1e/31191Pq1atbN26dblyzAAAIHP69+9vrVu3ttGjR1vPnj3tzDPPtF27dmV4Hy0ZWLNmTbv33nvto48+cg3yX331FS89ACDihbUn/YEHHrDExERbuHChFSlSxObMmeMS9YsuusguuOCCNO/z6aef2n333WeTJ0+2c845x123bNmyqF0PEQAAPxs/fry9/fbb9ssvv1jjxo1t586d1rRpUxs6dKg9//zzad5n/fr1dtlll7nk/sknn3TX7du3z+bNm5fLRw8AQBT1pCcnJ9uHH37oetGVoIt61fWjlvb0DBs2zK644opAgi61a9d2re0AAMBfFNPPPvtsl6BLiRIl7Lrrrssw1g8fPtzy589vjzzySOC6E044wfXAAwAQ6cLWk67h6RrqlnoOesOGDe23335L8z7qdf/111+tb9++tnTpUteiXrFiRWvRooXFxcWl+1gHDhxwP57du3e73+p9P94e+KSkJIuPj7e4WLP8MUkWifTc9Bz1XHNjxIL3GIyOAPhcRYtI/r77448/7Lzzzjsq1m/evNnVl0lrnrlq1Six11z0adOmucb85s2bW+nSpcMS66OBdz6TFFvADln651TZyXuc3Hq85HwF3HMskK+A5U8Oe1mmHBEXE5er52zwF86h/S/Uz2XYvqG8uWhqUQ9WqlSpdOepbdu2zX3pfPnll/b0009bkyZNXEKv1vavv/7azVNPi4bKBbfGezRkXoXnjteYMWP+/19bLSKVK2n9xoyxf/75x/3klm+//TbXHguIFnyu/CkhIcEilWJ6WrHeuy2tJF3Ju05kTj/9dNcDr+KySvbff/99u/zyy8MS66OBzmcU5XMv0v/r25Ldc+eBWpiNafH///6/9pzIUtms55ieuX7OBn8h1uf9eB+2JF2tfN4cs2BqNfduS82r4L5mzRoXrAsWLOiCuArH3XnnnW6+elruv/9+d3tw63qVKlXs/PPPt2LFih3X85g/f74rZtPu7tesZJXaFol2rFtm054Z4Ir4eMMVc5L+p/pyUc9LRiMkAPC5ihRer28kUkxPK9Z7t6UX7+fOnesqwtetW9ddN2jQILv++utdAp9WbMjJWB8NvPOZGYNaWuOqufN6qQddCfp5O0ZZnOV8r++spRvswhcXWKvbW1nNNpE5TXLbim026Z5JuXbOBn/hHDpy4n3YkvSqVau6ILt69eoU1+tyrVq10rxPmTJlrGTJkq6onBJ00T4uvPBCGzlyZLqPpW297YPpvsebBMbGxrph+IeSzA4nR+ay83pueo56rrmZNGfH/wcAn6u8IJK/6xTT04r16t3WcmxpUa2ZgwcPBhJ0ufTSS+2pp56ylStXprg+N2J9NPDOZ2KTDuZKwhxMj5cbjxlz5KB7jgePHLTDMYctEh1KPhSWczb4C997/hXq5zJsWWWBAgVcT+nYsWMD12k423fffWeXXHJJ4LqffvrJPv/888BlVXtVL3owXU5vqDsAAAgfxXQNOd+xY0egcKxiv1ZyUSLhjZB77733AsMAO3bsaGvXrk3R46BYr7XVK1euHKZnAgBA7ghr1Qy1iKtSa5cuXVzxt3fffddOPfVUV/XVM2LECJs9e7YL2PLoo4+6bbt37+7uq2XbvvnmG5syZUoYnwkAAEiLir0qAW/fvr1de+21Nn36dFu0aJGL7x4Nbe/Vq5ede+65rodd5wW6XffRuupqxH/ppZfcOYCqvAMAEMmy1JOuZdN+/PHH435wVXfVHKh69eq5au033nijff/99ymGASgRDy4So2Hyququ+3pz1ZYsWeLmpQMAgOzx2GOP2UcffZSiYnpWaH65qrVrPrnitRrjg+eai0bDqYHeS8DVwz5x4kS3TroSehWN/eqrr+yBBx447ucFAEBE9qSrWqSWRjnppJNcy7cCa3rzyo5FgVkt4+nR/lMrV66cKxADAAByhoalq1Fe87w1eu2GG27IciEq9Y7feuut6d7erFkz19seTCu36PH1AwBANMlST7qGl6voS48ePeytt95y1VNV0OWzzz5jTUYAACLA4MGDbePGjW7J019//dX1gJ922mn26quvBuaXAwCA7JflwnFKzB9++GFbvny5Wy5La6B27drVFXS5++67XREYAACQdxUpUsT1oM+cOdMNVdecca1FXrFiRbvmmmvc9QAAIHsdd3V3VV7VfHL9aM5Y8+bN3dqMGgr/5ptvZs9RAgCAsDl8+HAg1qsXvX79+q4Se9u2bd1QeAAAEOY56ZqnNm3aNFd59dNPP7Xy5cu7OWMa7l6pUiW3zfjx413re79+/bLxcAEAQG5R77li/QcffGD79+93vedacUXD3kVrlmvFld9//92aNGnCPwYAgHAl6TVr1rT169e7qutaw1zD32JiYlJsoyXTNGcdAADkzaXT3n77bbfKyrBhw9yyaCoAl/p84PTTT7ddu3aF7TgBAIg0WUrSVc194MCBVqZMmaNuU5EZ9awrad+7d292HCMAAMhl6i2/5ZZbrFGjRkfdpiHvWi6tQIECNmHCBP43AACEe066isaklaBLVpdiAwAA/jFu3DjbvHlzmrd17tzZ1Z8BAAA+LBwXLDEx0eLj47NzlwAAwGcU71MPfQcAAGEY7j506NA0/xZVdtc6qmkNiwMAAHmDisSpIJx+9PePP/541LS2efPmWZ06dcJ2jAAARLJMJelTpkxJ82+Ji4uz6tWr27PPPpt9RwcAAHKVKrXPnTvXtm/f7v5Wsu6JjY21smXL2tixY9Od9gYAAHIxSfda01XVXUusAQCAyPL888+730OGDLErr7ySEXIAAOSF6u4k6AAARDYViQUAAD5O0h966CH3+/HHHw/8nR5tAwAA8pb33nvPli9fbr169bIffvjB/Z0ebVOrVq1cPT4AAKJByEl6cOGY1EVkAABA3rdw4UJXBLZTp06Bv9OjbQAAQBiT9O+//z7NvwEAQGQILv562mmnhfVYAACIVtm6TjoAAAAAAMjFOemhYE46AAB5d056KJiTDgCAj+akAwCOz9q1a23r1q259jImJSW53/Pnz3drXeeGAwcOWMGCBS2Saa3wqlWrWqQ41jz0YMxJBwDAR3PSAQDHl6DXr1fXEhL359rLGB8fb2PGjLE2bdpYYmJirjxmbIxZUrJFtPjC8bZk8ZKISdSD56QDAIA8tE46ACDr1IOuBH1kv1OtfoUiufJSJsUWsH/MbMaglhabdDDHH++3lVus7/+WWpOeTaxy88oWiXat22Uznp7h/p+RkqQDAIDwY510AAgTJehNqxfPlcc6ZHEuSW9ctZjF2aEcf7x9Cfvc7yLli1iZ2mVy/PGQPVgnHQCA8GOddAAA4LBOOgAA4cc66QAAwGGddAAAImBO+pEjR9zvfPnyZcfxAAAAHzp48KAVKFAg3IcBIILk9kon4RBpq4DAx0l6cnKyvfPOO/b888/bsmXL3HV16tSxO++803r37p3dxwgAAMLghx9+sCFDhtjPP/9sCQkJVrFiRevZs6fdf//9VrRoUf4nAI4rQa9Xr74lJiZE9KsYH1/YlixZTKKOnE/Shw4dai+88ILdcsst1rx5c3fd3Llz7Y477rC///7bBg8enJXdAgAAn9DSq+edd55169bNbrrpJpeUq2H+5ZdftpkzZ9r06dPDfYgA8jD1oCtBP6P3ECtWobpFot0bVtucEY+wCghyJ0l/7bXXbOzYsXbhhRcGruvYsaOdddZZroWdJB0AgLztjTfesNtuu82eeeaZwHUXXXSRS9pr165tCxYssEaNGlm0iPRhuYsXLw73ISBKKUEvVbVuuA8DyPtJelJSkrVq1eqo61u2bBmYow4AAPKu9GK95leedNJJ7vZoES3Dcs1ibMOu/WaWO0tDAgCyMUlXj/moUaOsf//+Ka7Xda1bt87KLgEAgI8o1o8ePdo6depkMTExKZZp27Bhg9WrV8+iRTQMy92yaonNG/2U7Uw4HO5DAYCoF3KSPmzYsMDfqlB4880322effebmpKuQ3C+//GJTpkyxgQMHRv2LCgBAXvThhx/a6tWr3d8aGff111/bKaecYhdccEFgTrpif9u2bW3Tpk1WrVo1iyaRPCw3cV+kjxIAgAhM0seNG5fictOmTW379u32zTffpLjup59+yt4jBAAAuVbNfc6cOYHLXm95cJG4k08+2TZv3hyVSToAAL5K0tVTDgAAIterr74a7kMAACDqZWlOOqJTblV+9YoRzZ8/32JjYy23HDhwwAoWLGiRTAWfNF0FAAAAQIQl6ZqHvm7dOlfx9PDhlEVGNFcNkePQgYPud48ePXLl8eLj423MmDHWpk0bS0xMtNwSG2OWlGwRLb5wvC1ZvIREHUDItm3bZitWrLCEhJRzlhs3bmwlS5bklQQAwA9J+po1a6xr164p5q2lTuARObxGmMeuqGMdTimX44+XFFvA/jGzGYNaWmzSvw0EOe23lVus7/+WWpOeTaxy88oWiXat22Uznp7hqhTTmw4glO/+Pn362AcffJBmXP/222/t3HPP5YUEAMAPSfrtt99uderUsQkTJljZsmVtz5499vPPP9uAAQPcbYhMNcoUtqbVc37t1EMW55L0xlWLWZwdstywL2Gf+12kfBErU7tMrjwmAPjZG2+8Yb/99pub6qT4ftddd7kGvgcffNAKFChg7du3D/chAgAQkWKzWv1VS7JpfqsUKlTIzjnnHBs5cqQ999xz2X2MAAAglynW33333Va3bl1XH0Q/DRs2tI8++sjdpilvAADAJ0m65qdVrFjR/V2qVCm3FIvUr1/fDYUHAAB5W3qxXgU2a9SoQbwHACCHHHfpbK2N/uKLL7p5ri+//LJVr149e44MAACETfA8dMX6ESNG2Pr1623atGk2b9481kgHAMBPc9JVNM7zn//8xzp06GBPP/20FS5c2EYejM0AAF3ZSURBVFXlBgAAeVu7du2sfPny7u9+/frZxx9/bJUqVXKX77vvPgpQAgDgpyT9ww8/DPzdvHlz+/vvv23ZsmUuYBcrViw7jw8AAISBCsR5tNSaCsRqKbYTTjghMAweAAD4aJ10z5EjRyx//vyumAwAAIjcJdlq164d7sMAACDixWZ1ntrbb79tDRo0sPj4ePejJF3z1QAAQGRQFXet3lKkSBG3kkvlypXtgQcecEuvAgAAH/WkDx061F544QW75ZZb3HB3mTt3rt1xxx1u6PvgwYOz+zgBAEAu+v777+28886zbt262U033WRFixZ1U9tUJHbmzJk2ffp0/h8AAPglSX/ttdds7NixduGFFwau69ixo5111lnWs2dPknQAAPK4N954w2677TZ75plnAtdddNFFLmnXsPcFCxZYo0aNwnqMAABEoiwNd09KSrJWrVoddX3Lli3dHHUAAJC3pRfry5QpYyeddJK7HQAA+CRJV4/5qFGjjrpe17Vu3To7jgsAAISRYv3o0aNTrJcuCxcutA0bNli9evXCdmwAAESykIe7Dxs2LPC3llq7+eab7bPPPnNz0hXAf/nlF5syZYoNHDgwp44VAADkIC2xunr1ave3RsZ9/fXXdsopp9gFF1wQmJOu2N+2bVvbtGmTVatWjf8HAADhStLHjRuX4nLTpk1t+/bt9s0336S47qeffsreIwQAALlWzX3OnDmBy15veXCRuJNPPtk2b95Mkg4AQLiTdPWUAwCAyPXqq6+G+xAAAIh6WZqTDgAAAAAAfLIEm2gu2uuvv26LFy92c9I1/K1///524oknZu8RAgCAsDh48KC98847bgj8zp07rUaNGm6p1SZNmvAfAQDATz3ps2bNcmuk/u9//7OYmBjLly+f+1vX6bbM2rZtmy1ZssT279+fqfvt27fP5s2bZ//880+mHxMAAKRPdWdOPfVUe+CBB1yCXrx4cRfjmzVrZs8//3ymX7rExEQX67XfzFJFed0XAIBokKUk/a677rK+ffu6Kq+qBDtmzBj3t667++67Q97PoUOH7LrrrrOKFSvaeeedZ+XKlbMRI0aEfH/dV8Xq/vvf/2blaQAAgHS88MILVrJkSVuzZo2NHz/e3n//fZs9e7Z9+umn9tBDD9mOHTtCfu2GDx9uZcuWtfPPP98qVKhgffr0cdXjQ/HWW29Z48aN7fLLL+d/BQCICllK0n/77Td78MEHLTb2/+6uv9Xa/uuvv4a8nyeeeMImT55sS5cutXXr1rkgrkT/999/P+Z9NdRePfANGzbMylMAAADHiPW33XabFStWLMX1HTt2tDp16tiff/4Z0uun3vcBAwbYyJEjbe3atfbHH3+4ZdyeeeaZY9530aJF9uijj1r37t35XwEAokaWknStlaqkOjUFX90WKrWOqzW9evXq7nK3bt2sbt269vbbbx9z2Ntjjz1mH3zwQYqGAgAAkD3Si/Wap666NKHGe8X60047LdATrqlxmtf+5ptvHnN4fNeuXd3Q+kqVKmXxWQAAkPdkKcPt0qWLC5wTJkxwa6Xq58svv3TX6ScUGzZssPXr19vpp5+e4voWLVq41vv0JCQk2NVXX+2CdpUqVbJy+AAAIIRYP2TIEDdyTY3wmpf+888/W6dOnaxEiRJ2yimnhPQaaoRd6ljfsmVLW7lypdtneu644w5r3ry5XXXVVfyvAABRJUvV3TVETUPg1CruzSlT8bjevXuHPD9cQ9WldOnSKa7X5a1bt6Z7v1tvvdUVrQm1MUAOHDjgfjy7d+8OzInXz/FISkqy+Ph4i4s1yx+TZJGoQL4Y9xwtf0E7ZHE5/njeY+TGY3mS8xVwz7FAvgKWPznLix74WlxMnHuOes8e7/seli3fG0mxBXLtfZ7bnys+U5njx8+kkvEnn3zSTWXTcHVP27Zt7Ysvvgh5JJvifVqx3rtNCX9q48aNsylTprjisKEi1uetWC98L0V3rI+Gc2g9t9z8f3iP4ff/fTQ7FOL/JiZZ66dlkZJpzSdXhXfNTytTpkzI99X96tWrZ9999521a9cuRRKuwKx5aKlNmjTJrrnmGndy4M2RU7KupP2ee+5xVWjTMnToUHvkkUeOun706NFWuHDhkI8ZAICcoFFimvK1a9euo+aA++HYFLPV612zZk2rVq1apu6vZdvUK//UU08Frps4caJdfPHFrihd1apVU2yvgnR6HDUQaHSdvPTSSzZt2jQ3l123pfUaEesBAJES77OUpKv1XC1Cx2Pv3r3uwFRIRgfq0bC2PXv22DfffHPUfcaOHeuCdjCdOBQpUsTNV9OQOvXoh9K6rqHyamQ43pOh+fPnW5s2bazd3a9ZySq1LRJtWLrAZr12l73Vq5F1aV4hV1rWvy3Z3c7bMcriLHdaAmct3WAXvrjAWt3eymq2qWmRaNuKbTbpnkk2Y8YMVykZ4eN9b8wY1NIaV82dhCy3P1d8pjJHcUkN3X5K0lWJ/d5777Vzzz33uPaj97pqz6iOjEe1Z/r37++WUi1QoECK7TUP/tJLLz1qipxeGzXuv/zyy9a6deujHodYn7divfC9FN2xPhrOoXesW2bTnhmQa/8P9dJ+++23btWsuLjcG5GK7I/3WRrXq2VUNA9dS6ZllRJrzVFTa7qXpGud9KlTp9r999+fIlirxUEF5dKa867ecw2901Ix6SlYsKD7SU1v3uN9A6vBQsVtDiWZHU6OzCJ2B48ku+dohw/kWtIseqzceryYIwfdczx45KAdjjlskehQ8iH3HPWe5Ys7vLzvjdikg7n6mcrNzxWfqczx42fSi/XH65xzzrE33njDDh8+bPnz/3vaoZo2SrS9BF0nK6tWrbIGDRq4RvTUw9wHDRrkloHLaPg7sT5vxnrheyk6Y300nEPruYXj/5EdOQ5yRqj/lyx9Ivr162cPP/xwit7prNCyKuod11JsGvau4XCqFnvjjTcGttEw9SuvvPK4HgcAAGSOVl9RkdaNGzce10s3cOBANy1O09U0ZF3F6L766is3PN2jnp8mTZq4qvEAAES7LPWka264hpYrwdbcsNRD1WbPnh3yUDr1pGvomuaZq1Lsjz/+aMWLFw9so7lqGVV/VQ87S7MAAJC9NP9b9WE0B13LpmkEXDDFblVfPxYN65s5c6ZbOlU94hUqVLDJkye7Ya4eFY/TUNDU5xOeihUrWv369bPhWQEAEKFJuqq6e+udHi/NmdBPegYPHpzh/dVQAAAAstcZZ5yR4bQ2DYfPTPG4ESNGpHu75r1nNJRdRWX1AwBANMh0kq5KrOq5Vr259u3bZ7rKKwAA8DcVd9W8uVKlSrne8lB6zAEAQBiSdFUm7NChg6vGKieccIIbrh48ZA0AAORd27dvd4VdV6xYEbhu2LBhdt9994X1uAAAiBaZKhynYnGdO3d2AVw/Kuim6wAAQGR49dVX3bIwf/31l6tKPHz4cFfEVcvGAAAAnyXpCxYssP/+979WsmRJ96O/Fy5cmHNHBwAAcpVivdZHV7G4QoUKuRVXTjrpJFu+fDn/CQAA/Jakq8q6qrR6VFBmx44dOXFcAAAgDBTXg2O9EO8BAPBx4bjvv//+mNe1bdv2+I4KAACEzfz58y1//vwpEndVX8+XL1/gOi2ZplF1AAAgzEl6u3btjnmdKr8DAIC86e677z7qut9++y3F5W+//dYtnQYAAMKYpK9atSqbHx4AAPjJqFGjXMG4YylfvnyuHA8AANEmU0l69erVc+5IAABA2J144onhPgQAAKJapgrHAQAAAACAnEOSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPpE/3AcAAKmtXbvWtm7dGrEvzOLFi8N9CAAAAPApknQAvkvQ69Wrb4mJCRbZYmzDrv1mVjzcBwIAAAAfIUkH4CvqQVeCfkbvIVasQnWLRFtWLbF5o5+ynQmHw30oAAAgQkbQJSUlud/z58+32NjcmdV84MABK1iwoEWyMmXKWNWqVXP1MUnSAfiSEvRSVetaJErcF+mjBAAAwKEDB92L0KNHj1x5MeLj423MmDHWpk0bS0xMzJXHjI0xS0q2iBZfON6WLF6Sq4k6SToAAAAAZLPDh/8dMffYFXWswynlcvz1TYotYP+Y2YxBLS026d8Ggpz028ot1vd/S61JzyZWuXlli0S71u2yGU/PcCM9SdIBAAAAIALUKFPYmlbP+Ro0hyzOJemNqxazODuU44+3L2Gf+12kfBErU7tMjj9eNGEJNgAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfCLsSfrLL79stWrVsiJFiljLli1t1qxZGW7/448/WqdOnezEE0+0SpUqWdeuXW3lypW5drwAACBzZsyYYaeffrqL9bVr17bhw4dnuP3atWvtlltusZNOOslKlSplrVu3tm+//ZaXHQAQFcKapL/77rt233332bPPPmsrVqywVq1a2fnnn2/r1q1Lc/sjR47Y/fffb9dff70tXLjQZs6caQkJCda+fXvbu3dvrh8/AADImOL7RRdd5OK7GtWfeOIJu+222+zDDz9M9z6PPPKI1a9f377++mtbvny5XXjhhdahQwebPXs2LzcAIOKFNUn/73//a71797bLL7/c9Yw/88wzVqxYMXv99dfT3D5fvnz2ww8/WMeOHa1cuXJWvXp1e+WVV2z16tU2Z86cXD9+AACQsVdffdUqVKhgjz/+uIvdXbp0sW7dutnTTz+d7n3eeecdGzBgQKAn/cEHH3R/f/LJJ7zcAICIF7YkfceOHbZ48WJr165d4LqYmBh3+aeffgp5P9u2bXO/ixYtmiPHCQAAsk6j3oJjvWgE3Lx589xouFBoJN2uXbuI9QCAqJA/XA+8YcMG91ut6sHKli1rv/zyS8hB+6677rLGjRvbaaedlu52Bw4ccD+e3bt3u9+HDh1yP8cjKSnJ4uPjLS7WLH9MkkWiAvli3HO0/AXtkMXl+ON5j5Ebj+VJzlfAPccC+QpY/uSwfSxyVFxMnHuOes8e7/s+J/GZiozPFZ+pzPHzZzI74v255557VKxPTk62zZs3u1FxxzJs2DDbuXOn64FPD7E+b8V64XspemO9EO+zH58p/3+mQt1HTLKiZBgsWrTIGjRo4IrJqCCMR0n3hAkTbOnSpRneX4fdt29f++KLL1wxuTp16qS77dChQ938ttRGjx5thQsXPs5nAgDA8VGPshJQ9RZr2lckqVq1ql177bVuLrpHReC8Oeo1atTI8P6au677v//++xkm6cR6AECkxPuwdRmWL1/e/d6yZUuK69Wqrvnpx0rQNVft888/t2nTpmWYoIuKzd15550petKrVKniThCO92Ro/vz51qZNG2t392tWskpti0Qbli6wWa/dZW/1amRdmlfIlVbAb0t2t/N2jLI4y51W4FlLN9iFLy6wVre3spptalok2rZim026Z5JrGNPoE7/iMxUZnys+U5njjfCKRIrpacV6TXFLPZoutXHjxtl1111nb775ZoYJuhDr81asF76XojfWC/E++/GZ8v9nKtR4H7YkXYVglFxPnz7drrjiikDy/f3331v37t0zvO/AgQNd4J46dao1bNjwmI9VsGBB95NaXFyc+zkesbGxlpiYaIeSzA4nh31Fuxxx8Eiye452+ECuJc2ix8qtx4s5ctA9x4NHDtrhmMMWiQ4lH3LPUe/Z433f5yQ+U5HxueIzlTl+/kweL63coirtwb777jtr1KiRnXDCCeneT0XidD7w2muvWa9evY75OMT6vBnrhe+l6Iv1QrzPOXym/PuZCnUfYc0qNbRdFVy/+eYb1+X/0EMP2fbt2+2mm24KbHPjjTfaqaeeGrh866232kcffRQI8AAAwL9uvvlmW7Nmjavurh6EL7/80kaNGpVihNunn35q+fPnt3/++cddHj9+vOs512ovN9xwQxiPHgCA3BfWJL1fv342ePBg10JeunRp++qrr2zixIkpisioONzhw4cDldxffvll97tJkyYuoHs/I0aMCOMzAQAAadGoOdWaUQO7RtH179/fFYLr2bNnYBsV5FG898rkaP76wYMH3XlCcKzv2rUrLzIAIOKFvYz1vffe637So3loXtBWIp9eRTytoQ4AAPxH1d0XLFiQ7u1XXnmli+9KxGX27NmB2B9Mww0BAIh0YU/SjyV1QPYCOAAAiAwqIhcc32l4BwBEM5qkAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnwp6kT5s2za688ko766yzrH///vbPP//kyH0AAEB4rF271vr16+fidufOne2HH37IkfsAABAJwpqkT5061c4//3w75ZRT7OGHH3YB+cwzz7Tdu3dn630AAEB4bN++3Vq2bGlbt261wYMHW+3ata19+/b2448/Zut9AACIFPnD+eBKstU6PnToUHe5TZs2VqFCBXvjjTfsnnvuybb7AACA8HjllVfsyJEjNnbsWIuLi3MN7X/88YcNGTLENbxn130AAIgUYetJ37dvn82ePdsuvvjiwHXx8fGupTy9AJyV+wAAgPBRfD7vvPNcsu259NJLbcaMGXbo0KFsuw8AAJEibD3pf//9tyUnJ1vFihVTXK/L6SXcWbmPHDhwwP14du3aFRhOd7zBXsPsCxUqZHv/+cvsUKJFosSNK91zXLr1oH3/154cf7yk2AKW0CDBfli+12KTDlpu+GP9QfccE9Yn2NaFWy0S7dmwxz1HvWe3bdtmfsVnKjI+V3ymMmfPnn+/WxXjIo2mpbVo0eKouH348GHbsGGDVa1aNVvuQ6zPW7Fe+F6K3lgvxPvsx2fK/5+pkON9cpgsXLhQR5Y8c+bMFNffc889ySeddFK23UeGDBni7scPrwHvAd4DvAd4D/j5PbBu3brkSFOpUqXkhx9+OMV1U6dOdc93+fLl2XYfYn3437/88BrwHuA9wHvAsiXeh60nvXTp0u536hYJFYnxbsuO+8j9999vd955Z+ByUlKS60XXfWJiYo7reSD7qaWqSpUqtm7dOitWrBgvMcDnKuKpRV2t66lHikUCxdq04rZ3W3bdh1if9xDvAT5T0SY5xHgftiRdxd70M3fuXDfPzDNnzhw7++yzs+0+UrBgQfcTrESJEtnyPJBzlKCTpAN8rqJF8eLFLRI1bdrUxe1gits1atRINxZn5T7E+ryLeA/wmYomxUOI92Fdgu2GG26wt99+2/WYiqq4Ll682F3vefzxx+3qq6/O1H0AAIA/9OnTx3755Rf78ssv3eWVK1faBx984K73fPfdd9asWTPbvHlzyPcBACBShX0JthUrVrj1T9Xlr+A8fPhwO+200wLbrF692i27kpn7AAAAfzjzzDPtxRdfdA3u5cuXd0Vgr7nmGrv33nsD22gK2q+//moHDx4M+T4AAESqGE1MD/dBbNq0ySXbtWrVssKFC6e4bc2aNbZ3715r0KBByPdB3qYKvU8++aSbX5h6mgIAPlfImxTLV61aZSeeeKKVK1cuxW07duxwDfCNGzdOsexaRvdB3ke8B/hMwcdJOgAAAAAACPOcdAAAAAAA8H9I0gEAAAAA8AmSdAAAAAAAfIIkHb6iJfUuvvhia9OmjSsct3v37nAfEpCnqSr2kCFD7IwzznAFGQEg3FTJv1u3bq6K//XXX29Lly4N9yEBedq+ffvsrbfesrZt21qnTp3CfTjIBiTp8A0tpadgrST9vvvus8mTJ9tFF11kSUlJ4T40IE/67bff7KyzznJ/JyQkuNUyACCcFi5caK1bt7YyZcrY0KFD7ciRI9aqVStbt24d/xggixo2bGhz5syxChUquM8Y8j6qu8MXFKT1xTJw4EAbPHiwu27lypVuib0vvvjCLr300nAfIpDnJCYmWoECBSxfvnwuWVcQV2MYAIRLly5dbOPGjTZjxgx3WQ3xdevWtQ4dOtiLL77IPwbIgj179ljRokVdw9fIkSNt+fLlvI55HD3p8IUFCxbYli1bXC+6p2bNmnbyySfb1KlTw3psQF4VHx/vEnQA8AvF9OBYHxsb6xJ0Yj2QdUrQEVlI0uELa9eudb8rVqyY4npd9m4DAAB5e97s9u3bifUAcAwk6fCFQ4cOud8FCxY8qifQuw0AAORdxHoACA1JOnyhdOnS7ve2bdtSXL9169bAbQAAIG8PyY2LiyPWA8AxkKTDFxo3buzmzs6dOzdw3f79+12FyqZNm4b12AAAwPFTnFe8D471oqrUxHoA+D8k6fCFUqVKuXUd//vf/7oKlfL000+731dffXWYjw4AAGSHPn362Lhx4wLLRE2fPt2+++47dz0A4F/5//9vIOy0NNSVV17plmIrWbKkHThwwD766CMrV65cuA8NyJMOHjzo1h+WpUuX2l9//WW//PKLVa9e3Z0kA0Bu69evny1atMiaNWtmlStXtn/++ccefvhhu+yyy/hnAFmkRq558+bZ+vXrXXFGfb5k4sSJnEfnUayTDt9RNXf1ptepU8fNXQOQNcnJyfbrr78edb0KMjZo0ICXFUDYKJFQgl61alUrXrw4/wngOKgh3huJGkzTSziXzptI0gEAAAAA8AnmpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIORIiffvrJypcvb0eOHEl3m/nz57ttdu/enavHBgAAskflypVt2rRpGW5Tt25dmzBhAi85kEeRpAMR4uDBg7Zp0yZLTk5Od5tDhw65bZKSkiwvWLdunT388MNWq1Yta926dbgPBwCAsNu4caMdOHAgw20U6/fv3295waxZs+yaa65xDQvNmjWzBx980Pbs2RPuwwLCiiQdiCKnnnqqbdiwwYoXL255wTnnnGNxcXHu95YtW8J9OAAA5Al//fWXXXrppeZ3y5Yts7vvvtsuv/xy+/LLL+3ZZ5+1zz77zK644opwHxoQViTpQISZPHmytW/f3mrUqGEdO3a0lStXBm77888/XaLutVB7Q+S//fZbO//88+2kk06yiy++2JYuXRq4j5J6baOfKlWq2Nlnn21jx45N8Zjefr755hs788wz3VC82bNnW6NGjeyDDz5Ise3y5cutQoUKtmjRomM+Fx3H4MGD7cQTT8yGVwYAgMiwatUq69y5s9WpU8datmzp4m8wjT5TbPcoLr/33nvWrVs312PdokULlxSnvo9iecWKFV2P9iOPPHJUj7328+6777okunr16vbMM89Y//79rW/fvim206i+0047zYYPH57h89BIuZkzZ1rXrl3dc9E5xnPPPWdTpkyxv//++zheISBvI0kHIszAgQPtgQcesC+++MJiYmLsggsucMPc0xru7g2RHzRokEuGv/rqK9dzrcDvUYI8b9489/Pjjz/ajTfeaH369ElxQuDtR63h//nPf2zu3LkuOOux33jjjRTHN2LECCtXrpydfPLJx3wusbF8RQEAkNpdd93lYqwS7Ysuusj1mi9evDjd4e4aIu/1WE+cONEuu+wyF+vXrFkT2EY92Ir1v/zyi0u+x40bZw899FCKx9V+7rjjDrvyyivthx9+sJtuusntc+TIkbZjx47Adpozrzo46izIbJz3GgYKFCjAPx7RKxlARJg2bZomoyePHz8+cN3OnTuTixYtmjx69Gh3ee7cuW6bHTt2pLjPr7/+GrjP/Pnz3XUbN25M97HuvPPO5Guuueaox54xY0aK7ZYsWZIcExPjfsvhw4eTK1WqlPzCCy9k6rk9+OCDyXXr1s3UfQAAiET58uVLHjhwYIrr2rVrl9y7d+/A5eLFiyd//PHHKe7z1FNPpbhPuXLlkkeOHJnu40ycODG5bNmyRz324MGDU1x35MiR5GrVqiW/8sorgeu6d++efMkll2T6uSUmJiafeuqpyeeee26m7wtEErqpgAij4eYezT1v2LChLViwIMP7NGjQIPC3erll69atges0RE5D0DSEXkPh3nzzTVu9evVR+2nSpEmKyxpSd9ZZZ7nec1Hvu+aW9+jR4zieIQAA0S041otibWZivZQtWzZFrNewc/WKK3ZrWtq1117rYnZCQkKGsV694b179w7E+l27dtmnn37qrssMrU6j84Nt27a58w4gmpGkAxFGw9WDabiYhqNnJF++fEdd51WJHz16tBvapiFtSrI1FE5/p7XPQoUKHXWdhsZrXvrhw4ddANcQu9KlS2fhmQEAgJyI9Wp4V22apk2b2ieffGK//vqrm3suqfebVqxXQq7zAw1xHzNmjBUtWtQuueSSTCXoPXv2dPVsNFS+UqVK/KMR1UjSgQijAOlRYFWBNhVjySoVb1FireVRtB/1pK9YsSLk+2vOW2JiopuvprlzN9xwQ5aPBQAApIz1ogT5eGK9CsCWKFHC1afRCDwVj9MyqKFSQTnNkVdjvH7UC5+6ISE9qpNz3XXX2fTp0+377793xeSAaEeSDkQYFYHT8DQVidMa4+rBvvrqq7O8P1V0V8u29qlAOmrUKBs/fnzI94+Pj7fu3bu7gnYaSq+WegAAkHWvvfaa6+0WFYpVXB4wYMBxxfrNmzfbnDlz3OXff//dHn/88UztQxXe33nnHVc8NtSh7jqv6NWrl0vO9aNVZgCY5edFACKLKryqcvrevXtdUqzqrMezLvqdd97pgraGnqlVXHPV1KsevExbKIFbJxS33357piq2azk4nYTouahKrXrxZeHChW4uHQAA0UiJrZYt0zKpiqvDhg2zdu3aZXl/Wn5NFeNVf0ZD54sUKeJivZZDy8z5h4a5qyc+lBVcRJXkNSVOj6d59cE0r71Vq1aZfi5AJIhR9bhwHwSA46eh7du3bw8ksircUqxYMbcMm0e96ioSo2XVdH3q+3it2mpNL1OmjOXP/3/teBqyrjljCqT79u1zSbM3tzyt/aQOwqeffrotW7YsU8PYtM+05tip8YHl2QAA0UjLq5UsWdIl02rELliw4FFDyxXH1UCv21Lfx6MCbRrtVrhw4RTnCXv27HHbpj5nSG8/HsVrDXtXD3y/fv1Cei4a9afjSEupUqVYhg1RiyQdQI5S0t+lSxe37qnmpAMAgMjz6quv2pAhQ2zt2rUpEn8AmcdwdwA5RkPYbr31VtejP3Xq1BS3qUJ8enPbNYTvxRdf5D8DAIDPqTJ88+bN3VJtqggfnKBrhRhNm0uLitP99ttvuXikQN5BTzqAHKMh8hqKp6HzwcPuveH4uj0tGn53PPPoAQBA7tBUOA2L1/D01MPuFecV79NbEo76MkDaSNIBAAAAAPAJlmADAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPCJ/BaFkpKSbP369Va0aFGLiYkJ9+EAAKJccnKy7dmzxypWrGixsbSfAwAQzaIySVeCXqVKlXAfBgAAKaxbt84qV67MqwIAQBSLyiRdPejeyVCxYsXCfThI5dChQzZ58mQ7//zzLS4ujtcHyAZ8rvxt9+7drvHYi08AACB6RWWS7g1xV4JOku7PZKJw4cLuf0OSDvC5iiZMwQIAAEx8AwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8IirnpIe6TNvBgwfDfRhROyc9f/78tn//fjty5IhFI83Fz5cvX7gPAwAAAEAuI0lPg5LzVatWuUQd4VkvuHz58q76fjQXUSpRooR7HaL5NQAAAACiDUl6Ggnihg0bXC+mlsOJjWVGQG5T48jevXutSJEiUfn66z2YkJBgmzdvdpcrVKgQ7kMCAAAAkEtI0lM5fPiwS5AqVqzolgFD+KYaFCpUKCqTdImPj3e/laiXK1eOoe8AAABAlIjODCgD3hzoAgUKhPtQEOW8RiLN0QcAAAAQHUjS08E8YIQb70EAAAAg+pCkI9tpiPYXX3zhm/0AAAAAQF5Bko5s99tvv9kVV1yRqfts2rTJvvzyy+PeDwAAAADkZSTp8IW5c+da586dU1x34oknWseOHcN2TAAAAACQ20jSI8D69evtq6++ckt3LVmyxL755hvbuHFjmtuuXr3aJk2aZD///HOgSF5m9qP7T5kyJcV1W7dutfHjx7v7pWXXrl3udv3osZctW5bi9h07dtjs2bNdVXdvuz///NMqVapk1113XZaeg/z111/uWP/5558MXz8AAAAA8AuWYIsAP/30k0tmzzrrLJeknnDCCTZ//nx7++23rXv37oHtbr31VnvnnXesefPmtmLFCitRooRNnDjRrQcf6n6+/vpre+aZZ2z58uWB/f7xxx/WqVMnV4U8f/6j31Lbt2+39957z/29f/9+12uux/jkk0/c9lu2bLGpU6e6hFvbKdk/99xzXXKv4e5aFi+zz6Fdu3buOWitdSXzeg49evTIwf8CAAAAABw/etIjhNZ2r1Onji1cuND1Sj/11FM2YMAAlyDL559/bm+88YbNnDnTvv/+e9fLXLx4cbvtttsytZ+sqFGjRqCHXEn+ypUrXU+9km3R4z344IMWFxfntvnss8/s2muvPWo/mXkO55xzjpvTPmPGDBs6dKjdc889WT5+AAAAAMgtJOkR5IEHHgj8rcQ6NjbWJkyY4C6PHj3aLrvsMjv11FPd5fj4eLv33ntdUqykNtT9ZJV6x9XjrmHq06ZNs1q1atmsWbMytY9Qn0O+fPls4MCBgcvqldewffXMAwAAAICfMdw9QhQqVMgqVKgQuKxh5NWqVXPzt2XVqlWudzlY7dq1XfK8Zs0aq1+/fkj7yQrNCb/wwgvd3PWTTz7ZihYt6ualpzeHPT2hPgcNcS9QoEBgGz0nSUxMdD3vAAAAAOBXJOkRQnO99eMlpLJz504rXbq0+1u/dTmYd9nbJpT9qFc9dXJ94MCBDI/tsccec5XaNb9d95devXrZhg0bMvUcQ30OAAAAAJBXMdw9ggQPSVdCvHbtWmvRooW73KpVKzfUXMXdPJr7rWHn5cqVC3k/FStWdMm1eqU9mvedERVwa9iwYSBB19D0b7/9NsU2KlKnY1OF9/Rk5jkAAAAAQF5ET3qE0LD022+/3Q37VsL75JNPunXHTzvtNHe7bhsxYoRdcMEFrvq55oe/+OKLNm7cuEztR1XTVVH9mmuusS5duti8efPsgw8+yPDYLr74Yle4rWrVqu6+w4cPt927d6fYpkGDBq5w3ODBg61p06au5z21UJ8DAAAAAORV9KRHCCXUWsZs8+bNrvK5Kp4HJ8+aB66lz9q2beuWLNu7d69Nnz7dFWLLzH50u5Y5q1mzpqvUXr58edfz3rFjx0BPuRJsXfbceOON9vrrr7uEXo95yy23uORaPeMe9YTruPS4ejxVZk+9n1Ceg9ZWV6NAsGLFirn9BA/hBwAAAAA/iknObPWuCKBeXBUQU7VvJXDBNB9bBcq0bFheSerUk9ynT5+j5muHaz/HS0Pe9T/S/8ZL/KNRXnwvwr80TUSNWx06dHCjVpB34hIAAIgu0ZsBAQAAAADgMyTpESCtId7h3A8AAAAAIGsoHBcBWrZs6X78sh8AAAAAQNbQkw4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkI9MOHTpkhw8fPuZ2Bw8etKSkpKh8hffv32/Jycnp3q7bDhw4kKvHBAAAAMD/WIItRGvXrrWtW7dabilTpoxVrVrV/Khz585WuXJle+WVV9Ld5p9//rEmTZrY/PnzrUKFCpbXE+4CBQpYbGxobVp79+61okWL2qxZs6xFixbpNmDUrVvXPvzww3S3AQAAABB9SNJDTNDr1atviYkJllvi4wvbkiWLfZuoH8sDDzxg1113XZ5P0DViID4+3qZNm2Zt27bNtv0WLFjQBg0aZHfccYdL5gEAAABASNJDoB50Jehn9B5ixSpUz/F3zu4Nq23OiEfc44aapGsIekxMjOXPH9q/VMPQj9UzHMo2admwYYPrIV64cGGmjs/bxntMXVYym3qYuLZJq7db2+q2tLZJ77loyLm2123qLU/rdq/nW4+R1nbpHdOxbu/WrZvdeeedNnv2bHrTAQAAADjMSc8EJeilqtbN8Z+sNARcdNFF1qdPH+vYsaOVKFHCihQpYrfddpsdOXIksI2SzFtuucXdHhcXZ6eccopNmjQpxX5C2eZYPv30U6tevbrVqVMnU8fnbdOpUyerWLGiXXLJJe76zZs3u4RW9znhhBNcQhvc+6zh5ertHjJkiBtir78rVapkY8eOta+++srq1avnEmv9Tt1rrSHn3vGceOKJNmDAALc/T61atdxvHYu2a968eeCYevToYcWLF3f3vfzyy90Q/2A//PCDNWvWzAoXLuymB/zvf/9LcXuxYsWsdevW7jgBAAAAQEjSI8j7779v7du3dz3ZU6dOdcnfs88+m2IIupLW77//3vbs2WPdu3d3SfPy5csztc2xzJw500477bRMH5988MEHLuFdtWqVffPNN264+QUXXOCSc123Y8cO69mzp1144YVHJcUfffSRS4R3795t11xzjRtu//DDD9v48eMtISHBzjnnHHddsNWrV7uGCfWUK6meO3euDR48OHD733//7X5PnjzZbac59urh1/PQ4//yyy+2bds26927t3355Zcp9v3WW2/ZO++8445Hr6saILz9eZT0z5gxI+TXFgAAAEBkI0mPIOphvvXWW11P8hlnnGH33XefPf/88+62xMREe/XVV+3xxx+3U0891fXuak60espfeumlkLcJhZLXcuXKZer4PEq+lUh7w9wnTJjgEumXX37Z9WRruLmSXfVwf/bZZynuq4S8YcOGrtdcPeIaqv7QQw8FetJvuukmW7ZsWZoFADUkXVML7rrrrqP2m9oXX3xhS5cutdGjR1vt2rWtUKFCdtlll7n9B1PPfuPGjd2IBB2PjkFJfTD13qdO3AEAAABEr7An6f/973/d0GQlMhqqPH369Ay3V4J1/fXXuyRQSY+SNe0DZk2bNk3xMqg3e+PGjbZr1y5buXKl6y32hmt7lCwvXrzY/R3KNqFKa/mxjI7PU79+/RTb/Pbbb+52JeiqmK4fDRNftGjRUcltzZo1A39rG6lRo8ZR1+3cuTNFz70SeyXaul299OvWrcvwuc2bN8891rGK4gUfj2hofPBjizcfHgAAAAAkrNnBm2++aY888ogbEqzku0OHDu5HPafp6du3r0vcNKR637599sILL7jeUiVb0S51YuytUa4k0EsEU69brsv58uULbHesbUJRpUoVN6Q9M8fnUWNNWsmuhpqn/hk2bFiK7dIqzpZRQbc5c+ZYv379bOjQoW4YvRooPvnkkxTz5NOifYay/ntGj+1RI4VeLwAAAAAIe5Ku+cg33HCDG+KsXkYNsy5VqpQNHz48w17MK664wg0zVkJ36aWXuuHMuj7aKelMfVkJoHqflehqmLkqiQdTIbUGDRq4v0PZJhRnnXWWm9udmeNLj3rb1cO/ZMkSy246xpNOOsmuuuoqN7RfUheWU+OEGhGCE3cdk+bHa2m+4/Xzzz9bmzZtjns/AAAAACJD2JL07du3219//WVnn312ip5HrUX9008/pXs/VdT++OOP7c8//3RVuFVJXD3vnTt3tminEQaPPvqobdq0yRVde/rpp90ca9Ecb63J/eCDD7pCZerB1d+ao60q66FuEwo1oqgnPXgJtmMdX3pUVV3D7fX/VWE3FWlTcq0RFSpudzzU8KD3oOa9a7+aY/7iiy+m2EbvyWrVqrnH1vtNve06Js0179q1q5tjruejkRwa1ZEZGvr+448/uiJ3AAAAABDWddKVAErZsmVTXK+55updTI+KcanSuOYRi+alv/HGG9ayZct076MCYt5616Jq26Iq3foJpssalq3hzN6QZu/37vWrzY6eap0j66R7jxvKsGqPElcl1JpTrqro/fv3t5tvvjmwD712onnXGt7dqFEj+/rrr93yYJnZRiMYtN55esdWpkwZu/baa92ICBV8C/X4vP16w+K99cX1+BqSroJyOiYtm6bicWeeeaa7r7bzCs15+wrlOjUQPfbYYzZw4ECXMCvx1tQJTcEIfm4qbqfq7Br5odEGv//+u2tk0HWqfK9tVYFetRHSOx7RvPfgofKqdq9ieqrFkNZr6e1L78nMTDcA0uJ916X+zoM/8H8BAACemOS0KnzlAhX+Uk+memy1VrRHPblalzu94c1XX311oLK2EqZvv/3W9WiOGDHC/U6LEjwlXqlpH94wZ4+SxPLly7th2GoAEBUSO/2MM2x/YqLllkLx8fbz/x8OHgotW6aK7HqufrBlyxY3KkJLl6kwoN+OL9w0p75Vq1b23nvvuYaQtKjXXu89NWipUQNA5NIykd26dXOFMr0ilwAAIDqFrSfdq4y9efPmo5I7Jclp0W1aW1vFvbwq4Bp6fOWVV7oe2/SS9Pvvv9/uvPPOFD3pSn7PP//8o06GlDwpMSpSpIjr+RQ1JixetCjNpbtyinqjtSRYqNS4oN5bv5zc6TiCq6Rn5vjUbqQ12jVXPZTia3mRXodjrT2v96JqBGjOuvdejAaq2t+sWXNLTEywSBYfX9h++WWuG6WSWz21atQ877zz0izQiPDyRngBAACELUkvWbKkS7SnTZvmkmwvOdNlDWtOPeRXw329E8u0qoRndNKp5NAbfhxM90l9PxUIU2IYXBFdqlev7n78Sr3++vHrcl6ZOT5v6Lf3f4hWeu56DdJ6n0YyTWnYvn2bndF7iBWr4N/P3PFOaZkz4hH3XIOXCcwN0fZ+yiv4nwAAgLAn6XLPPfe4Ocnq2dGc8qeeesr1JmiuskdLZKna+B9//OHWym7fvr0bMq310TXcXcOpVUhO84Wj2cSJE83P/H588B8l6KWq1g33YQAAAADRk6T36tXLDWvWPHRVyD7llFNc0h08D1s96Boq7RkzZowr7qU5zqrIrcrbqhKuZB8AAAAAgLwsrEm63Hrrre4nParcHkzV4FNfBwAAAABAJIjeCb8AAAAAAPgMSToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6cg0Vdd/8cUXeeUAAAAAINKqu+cVa9euta1bt+ba45UpU8aqVq1qfqQ16ytXrhzuwwAAAACAiEOSHmKCXr9eXUtI3G+5pXB8IVu8ZKlvE3UAAAAAQPYjSQ+BetCVoI/sd6rVr1DEctriDXutx5vz3OOGmqTfddddVqdOHUtKSrJp06bZkSNH7IYbbrAOHTqk2G7ChAk2atQo2759uzVq1Mjdr3z58pneBgAAAACQ/UjSM0EJetPqxc2P5s+fb8OHD7f27dtb79693eXLL7/cPv30U7vkkkvcNu+9957ddNNNNnToUKtbt67b/vTTT7c///zTihYtGvI2AAAAAICcQeG4CFK2bFmXlCs5HzJkiN1888328MMPu9vUsz5o0CCXfOt3p06d7Msvv3TXv/TSSyFvAwAAAADIOSTpEUS96Pnz/9/giIsuusj1qB84cMDWrFljmzZtSjH8vUCBAnb++efbzz//7C6Hsg0AAAAAIOeQpEeQ1MPRdTk5Odl2795tu3btSnObYsWKBW4LZRsAAAAAQM4hSY8gf/31V4rLS5cutSJFirhh8DVr1kxzmyVLlgRuC2UbAAAAAEDOIUmPIJMnT7aZM2e6v3fu3GnPPfec9ezZ010uXry4m2P+xBNPWEJCgrvuu+++sylTplivXr1C3gYAAAAAkHOo7p7JpdH8/Djt2rWz7t27W8mSJW316tVWu3Zte/TRRwO3v/zyy66onJZ1q1y5susx1+2tW7fO1DYAAAAAgJxBkh6CMmXKWOH4Qm7t8tyix9PjZsZpp51mkyZNcon14cOH7ZRTTrGYmJjA7ZUqVbK5c+e64es7duywevXquYQ+WCjbPP7441awYMHjfIYAAAAAgNRI0kOgXuXFS5ba1q1bLbcoQdfjZpaqu5988skZbqPE+1gy2qZhw4aZPi4AAAAAwLGRpIdICXNWkmYAAAAAAEJFkh4hnn322aOWTgMAAAAA5C0k6RGicePG4T4EAAAAAMBxYgk2AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ9gCbYQrV271rZu3Wq5pUyZMla1atWQtx83bpyVLFnS2rdvb37y3HPP2aWXXmq1a9cO96EAAAAAgO+RpIeYoNerX88SExItt8QXjrcli5eEnKi/9957dtJJJ/kuSR88eLDVrFmTJB0AAAAAQkCSHgL1oCtBb3NvGytepbjltF3rdtmMp2e4xw0lSf/qq69sxYoVtnPnThs2bJi7rl+/fjZ37lz7/fff3eXSpUvbGWecYY0aNUpx3w8//NAqVKhg5cuXt6lTp7re+GuuucbdNmfOHJs9e7ZVqVLFzj33XBs5cqSdffbZ1qBBg8D9N27caJMmTbKEhAS379atWwdue/311+3QoUP26aef2pIlS6xIkSI2cODAbHudAAAAACDSkKRnghL0MrXLmN8oQVYyfODAAZeoy5EjRywxMTFweenSpXb33XfboEGD7P777w/c9+2333aJtu5/3nnnBZL4p556yvWCd+3a1SXqus+GDRvstddeCyTp48ePt+uvv97OP/98K1eunD377LPWrFkzGzt2rMXExNju3bstOTnZ9u3b545DxwQAAAAASB9JegTo3Lmzvf/++264u9eTLpdffrn78fTv39/1dPfp08fKli0buF499kriixf/d5SAkvGhQ4e6IfRer7p6xQcMGBC4z7Zt2+zaa691c+EvuOACd52S8nr16rnrdEz33XefPfbYY2674OMAAAAAAKSNJD3C/fHHH/bzzz+7RDwpKcn1Zi9evDhFkn7xxRcHEnSZNm2axcXFuV50T+/evVMMVZ84caLrfV+4cKEtWLDAXadecw2XnzVrlkvSAQAAAACZQ5IewR544AE3PP2iiy6yihUrusQ7NjbWtm/fnmK74ITd60k/8cQT3baeggULWqlSpQKX169fb4UKFTqq4r0quZ922mk59pwAAAAAIJKRpEcozQPXvPIpU6ZYu3bt3HV79uxx1x2LEvQtW7akuE695jt27EixRJzmwj/yyCMugQcAAAAAHL//6ypFnqbh6krMPfpbw9uDh7GrSFwo2rRp4xLwL7/8MkUV+ODCb+qdV0+7isUF01z1NWvWpHtcAAAAAID00ZMeIbQ0miq3qxdcS51pCbYOHTq4ueHdunWzlStX2vfff++GvB+Lln279957XdE4zUU/fPiwW2bthBNOCAyB1/D5d955x92uOehNmjSx1atXu7/HjBlj1apVCxyXitmtWrXKSpQowRJsAAAAAJABkvRMrl/u18fp27evS5y1Lrq33JmWSFMP+PLly11V95dfftlefPFFq1OnTuB+SsQrVap01P4ef/xxdx8l3VonfciQIS7xVqLt6d69u+t1/+KLL9zweFV5f+WVV6xYsWKBbZTI6xiUpO/alTuvHwAAAADkVSTpIdD86/jC8Tbj6RmWW/R4etxQaV3ySy65xP0E0/JnwTSHPNgNN9yQ5v6UVCvp9pZX++STT1zir3XQgymBv/nmm9N/HvHx1qtXr5CfBwAAAABEM5L0EGj495LFS46qZJ6TlKDrccPl66+/ttGjR7ueclVyV2/4o48+auXKlQvbMQEAAABApCNJD5ES5nAmzbmtf//+bp759OnT3Tz3W2+91V0GAAAAAOQcknSkq0WLFu4HAAAAAJA7WIINAAAAAACfIEkHAAAAAMAnSNLTkZycnLv/CSCVpKQkXhMAAAAgyjAnPZW4uDi3nJnW/S5btqz7G7mfnB48eND2799vsbGxUdlApOev96Cef4ECBcJ9SAAAAAByCUl6Kvny5bPKlSvb33//batXr86t/wNSJamJiYlujfVobiQpXLiwW1EgGhsqAAAAgGhFkp6GIkWKWO3ate3QoUO5/x+Be91nzJjh1mjXyIZobSzKnz9/VDdSAAAAANGIJD2DJEk/yH163Q8fPmyFChWK2iQdAAAAQHRiHC0AAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPhH2JH3KlCnWsWNHa9GihfXt29fWrVt3zPvs3r3bHn30UWvbtq116NDBxo8fnyvHCgAAAABAxCbp3377rV100UXWvHlze+KJJ2zTpk125pln2q5du9K9z86dO61ly5buvvfdd5/7GTVqlM2aNStXjx0AAAAAgIhaJ33w4MHWtWtXe+ihh9xlJegVKlSwN954w+6999407zN06FDXk/7zzz/bCSec4K47++yz7eDBg7l67AAAAAAARExP+t69e23OnDluuLqnUKFC1r59e5s6dWq69xs9erRde+21gQTdU6BAgRw9XgAAAAAAIrYn/Z9//rHk5GTXcx6sYsWKbp56WrZu3WpbtmyxWrVq2cCBA+3XX39121933XV22WWXpftYBw4ccD8e9cTLoUOH3A/8xfuf8L+JTklJSRYfH29xsWb5Y5IsEum56TnquebW+5zPlb/xfQcAAMKepHsnJAULFkxxvS6nd7LiJdp333233X///datWzfXG3/VVVe5IfK9evVK835PPvmkPfLII0ddP3nyZCtcuHA2PBvkBNUdQHQaM2bM//9rq0WkciWt35gxrrFSP7mJz5U/JSQkhPsQAACAT8Qkqzs7DDZs2OB6wb/44gu79NJLA9f37t3bFi1aZLNnzz7qPomJiW6Yu5LzkSNHBq7v06ePzZs3z3755ZeQe9KrVKnieuaLFSuW7c8Nx0eNNEokzjvvPIuLi+PljDLz58+3Nm3aWLu7X7OSVWpbJNqxbplNe2aAzZgxwxo3bpwrj8nnyt8Ul8qUKeMKpxKXAACIbmHrSdcwdyXpKgAXnKQrOW/Xrl2a99Hw0AYNGli5cuVSXK/L3hD2tKh3PnWPvSgBJAn0L/4/0Sk2NtY1yB1KMjucHPZVInOEnpueo55rbn8H8bnyJ2IRAADwhPUMWOuiv/3227Z69epAUbglS5bYDTfcENhG66FrOLunf//+9tFHH9nff/8d6JHX0NgLLrggDM8AAAAAAIAw96RrmLiG5R2vBx980FauXGl169a1E0880a2B/tZbb1nTpk0D26xdu9Yl7sFJ+ooVK6x+/fquN17J+uWXX27Dhg077uMBAAAAACDPJemVKlVyQ9Q1f1w92Pny5cvy8L4PPvjAnnvuOVe1vXr16m5Ie7AhQ4akKKgTExNjzz77rLt+/fr1bm556uXYAAAAAACImuHu48ePd8lyp06drFq1aq5HfPny5Vk+CPXKq2c8dYIuSsLV056aCuvUq1ePBB0AAAAAEN1J+kUXXWQff/yx68m+55577KuvvrLatWvb2Wefbe+//z5LyQAAAAAAkNuF40qXLm233XabW/7spZdecpXZr7/+ejdXXMn7nj17jmf3AAAAAABEleNagm3jxo1uTvmIESNchXYNf9ea5Zs3b7ann37aJe9a7xoAAAAAAORQkq456UrMJ02a5OaL33jjjdazZ0/Xsx48JD71euYAAAAAACCbk/QePXpYly5dbMaMGdayZcs0tylZsqTdd999Wdk9AAAAAABRKUtz0vv27et60tNK0AcNGhT4+/HHHz++owMAAAAAIIpkKUl/4YUX0r3tqaeeOp7jAQAAAAAgah1XdffU5s+fb2XLls3OXQIAAAAAEDUyNSe9RIkSaf4tSUlJbsm1O+64I/uODgAAAACAKJKpJP3tt992vzt37hz42xMXF2fVq1e3xo0bZ+8RAgAAAAAQJTKVpF911VXu97Rp06xt27Y5dUwAAAAAAESlLM1JJ0EHAAAAACCMPelFihRxv/fu3Rv4Oz3aBgAAAAAA5FCSPnLkyDT/BgAAAAAAuZykX3755Wn+DQAAAAAAfLhOOgAAAAAAyMU56aFgTjoAAAAAALk0Jx0AAAAAAPhkTjoAAAAAAMh+zEkHAAAAAMAnWCcdAAAAAACfYJ10AAAAAAB8gnXSEZK1a9fa1q1bc+XVSkpKcr/nz59vsbG5NyPjwIEDVrBgQYtkZcqUsapVq4b7MAAAAAAcb5KeWnJysk2ZMsUWL17sLp988snWvn17i4mJyeou4eMEvX69upaQuD9XHi8+Pt7GjBljbdq0scTERMstsTFmSckW0eILx9uSxUtI1AEAAIBIStJXrFhhnTp1cgl65cqV3XV///23S9THjx9vNWrUyO7jRBipB10J+sh+p1r9CkVy/PGSYgvYP2Y2Y1BLi006aLnht5VbrO//llqTnk2scvN/39ORZte6XTbj6Rnu/0lvOgAAABBBSXqfPn2sWrVqNnnyZCtfvry7bsOGDdavXz9329SpU7P7OOEDStCbVi+e449zyOJckt64ajGLs0OWG/Yl7HO/i5QvYmVql8mVxwQAAACAbEnSZ82aZStXrgwk6FKhQgV74403rFatWlnZJQBEHW+6UKTWeqDOAwAAQC4l6VWqVLGDBw+meUKm2wAA6Tt04N/vzx49euTayxSOWg/UeQAAAMilJH3AgAHWs2dPGz58uJuHLosWLbKbbrrJ3QYASN/hw4fd78euqGMdTimXKy9Vbtd6oM4DAABADifpwUPbZdOmTdagQQMrXLiwq/Tu9cz89ddfdvvtt2fxcAAgetQoUzhX6jyEo9YDdR4AAAByOEl/5plnsvgQAAAAAAAgW5P03Jw7CQAAAABANMqdEr8AAAAAACBnCsfJTz/9ZOPGjbO1a9cGiiB5xo8fn9XdAgAAAAAQtbLUkz5y5Eg777zzbPPmzfbJJ59YmTJlbNWqVfb5559boUKFsv8oAQAAAACIAllK0ocNG+bW21WyLm+//bbNmzfP7r77bouJicnuYwQAAAAAICpkabj78uXLXU+6xMXFWUJCgluK7b777rM6depk9zECAAAAABAVstSTfuDAAYuPj3d/V6pUyRYtWuT+3rt3rx06lPPr7wIAAAAAEImyXDjOc9VVV7nl2fR7woQJgR52AAAAAACQC0n6rFmzAn8/8cQTrlicrjvrrLPskUceycouAQAAAACIellK0lu0aBH4u0CBAvbYY49F/QsJAAAAAEDYhrsnJyfblClTbPHixe7yySefbO3bt6e6OwAAAAAAuZmkr1ixwjp16uQS9MqVK7vr/v77b5eojx8/3mrUqJHV4wEAAAAAIGplqbp7nz59rFq1arZu3TpbtWqV+1m7dq1VrVrV3QYAAAAAAHKxcNzKlSutfPnygesqVKhgb7zxhtWqVSsruwQAAAAAIOplqSe9SpUqdvDgwTTXT9dtAAAAAAAgl5L0AQMGWM+ePW3RokWB6/T3dddd524DAAAAAAA5ONw9eGi7bNq0yRo0aGCFCxd2ld4TExPd9X/99ZfdfvvtWTgUAAAAAACiW8hJ+jPPPJOzRwIAAAAAQJQLOUnv0aNHzh4JAAAAAABRLktz0oNt2LDB1q9fnz1HAwAAAABAFMtSkn7kyBF78sknrWTJklaxYkWrVKmS+1vX6TYAAAAAAJBL66Q/8sgjNnz4cBsyZIi1aNHCYmJi3Nrp//nPf1wBuUcffTTkfa1evdreffddV4julFNOsRtuuMEKFSoU0n2///57e++99+z888+3bt26ZeWpAAAAAACQt3vS33nnHfvoo49cFXcl6WeccYb7W9eNGDEi5P388ccf1rhxY7d8W+3atV3i37ZtWzt06NAx77t582a7/vrrbcL/a+/Og6Qqzz0AvzPMDJsB0UTEABoogoq4cYWIVSAE4yVxAYOXQqUqWqaiJiZGFNQ/FJOoMRqXMtE/TMVAxGgCkYBZyg1QNoGAKMYoECOICuIGyjLgzK3v5A6XkcVhYKbPNM9T1dX0mT59vjPTR/vX77c89ljMmzevPqcBAAAATT+kv/POO3HCCSfssD1tSz+rqzFjxkTv3r3jD3/4Q4waNSqefPLJeP7552P8+PG73S8t+TZy5Mj4wQ9+EB07dqzPKQAAAEBxhPSjjjoqxo0bt8P21G39yCOPrNNrVFZWxhNPPBHDhw/ftq19+/YxcODAmDp16m73/dnPfhZVVVXWYwcAAKCo1GtM+k9+8pMYOnRoTJkyJauEJ88991zMmDEjJk+eXKfXWLFiRdat/Ygjjqi1PT1+9tlnd7lfOs6dd94ZCxcuzMbC18XmzZuzW41169Zl9+n4delav79LX4i0bNkyqkorYkuUN/jxao7RGMeqUd2sIjvHimYVUVZdr8si98pLyv/zd6yqyvX7vub9Vl4aUVZSFcWoollJdo5R1rzR3ueNfV25pvZMnq9JAKBxlVSnvuP18OKLL8bPf/7zeOmll7KwfPTRR2dd1tPkb3Xd/9hjj43Zs2fHySefXKsL/KRJk2LZsmU77PPhhx9mXepvv/32OOecc7Jtxx9/fDaO/a677trlscaOHZtNdvdpDz30ULRq1aqOZwwADWPDhg3ZBKjp/3Nt2rTxawaA/Vi9SoYpEKeJ4tLM6vVV8yHkgw8+qLX9vffei7Zt2+50nxSq089TBT/dairyjz/+eDaJXJq0rrR0xx781157bVx55ZW1KumdOnXKZoX3YeizLV68OPr16xfPXHNyHNe54T88pkrfE+3Oj9PenxDl0TjVpTmvvBX/ffcL0feKvtGlX5coRu8ufzf+evVf45lnnskmbMz7+23AVfdGu07dohi99coLMefeUXH/hcfG/5zUoVGO2djXlWtqz9T08AIAqFdIHz16dHzve9+LsrL6dwtOITmF8VSJHzx4cK0Z34855pid7jNgwIAdKuZPP/10Nnlcqqbvqvt78+bNs9unlZeXZzd2L33xkZbWK62qbLTQnKRjNdbxSj6pzM6x8pPK2FqyNYrRluot//k7lpbm+n1f837bUhWxtbpe02bkXuUn1dk5xtbNjXpNNeZ15ZraM3m+JgGAxlWvlJ26tM+fP79WN/X6fBBPk8al6vcll1wSBxxwQDbePN1S9/TtJ6Nbvnx5Ng4+TUr36YnpUmhPXe1TJR0AAAD2u5B+wQUXZAH7uuuuywJyRUVFrZ+ntdPr4pZbbolBgwZloT/dpk+fHpdffnmcfvrp254za9asmDt3bhbSAQAAoJjVK6TXjO++9NJLd/rzus5Fd9BBB8W8efOyMbKrV6+Om2++eYeu7hdddFGceeaZu3yNH/3oR3HooYfuUfsBAACgaEL6+vXr910DysqytdF3pW/fvrvd/6yzztpnbQEAAIAmE9LTOq6pcp1mVk/V8rPPPjuuv/56E94AAABAY4f0H//4x3HHHXfEsGHDssdpnfQ0AdzO1iAHAAAA9swerW80YcKEmDRpUowbNy67TZw4MdsGAAAANHJIX7lyZa3x42lm9hUrVuyDZgAAAAClezomffvl1tK/0zYAAACgALO7Dxky5DO3TZ48ee9aBQAAAPuhPaqkp9nc67INAAAAaOBKugo5AAAA5KSSDgAAADQcIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICfKCt2AzZs3x+OPPx6rV6+Onj17Rp8+fT5zn9deey3mzp0bZWVl2fM7d+7cKG0FAACAoq2kr1mzJk444YQYM2ZMPPXUUzF48OC4+OKLd/n86urqOPfcc+NrX/taTJ06NSZMmBDdu3eP22+/vVHbDQAAAEVXSb/mmmuivLw8q4q3bNkynn/++ejVq1ecffbZceaZZ+40pA8fPjx+//vfR0lJSbbtoYceigsuuCCGDh0aXbt2LcBZAAAAQBMP6VVVVTFx4sS48cYbs4CeHH/88dG3b9945JFHdhrSS0tLY9iwYbW2DRo0KAvvr7zySsFC+ooVK2Lt2rVRrF5++eVCNwEAAGC/ULCQvnLlyli/fn0cddRRtbanxwsWLKjz60yZMiWaNWsWxx133G7HvadbjXXr1mX3W7ZsyW5744033oj/+q+TYuPGDVHMWrZsFW9+VBU9o7zBj7Xl/45Rc98YqptVZF8WVTSriLLqgk/V0CDKS8qzc0xfkO3t+74hpfaldpaXRpSVVEUxqmhW8p8vJ8uaN9r7vLGvK9fUnsnzNQkANK6S6lSGLoAlS5ZkE8XNmTMnvvKVr2zbnsanT5o0KZYtW/aZr/HSSy9llffLLrssbrnlll0+b+zYsVnF/tNSV/lWrVrtxVkAwN7bsGFDnHfeefHhhx9GmzZt/EoBYD9WsJJhTRf3VE3fXqpy1yU4pxCfJpA766yz4qabbtrtc6+99tq48sorax2jU6dO2f57+2Fo8eLF0a9fvxhw1b3RrlO3KEZvvfJCzLl3VNx/4bHxPyd1aPDjpUrfE+3Oj9PenxDl0TjVpTmvvBX/ffcL0feKvtGlX5coRu8ufzf+evVf45lnntltz5NCc00Vx3XlmtozNT28AAAKFtLTsmkVFRXZcmrb+9e//hXduu0+7C5fvjxOPfXU7Pab3/wmG6u+O82bN89un5YmrUu3vZGOvXHjxthSFbG1ujiXna/8pDo7x9i6udFCc5KO1VjHK/mkMjvHyk8qY2vJ1ihGW6q3ZOeY3rN7+75vSK6p4riuXFN7Js/XJADQuAqWKtMHkrTkWupyXtPjPo3vnj59elYdrzFt2rT43e9+VyvEp3Dev3//GD9+fDYeHQAAAIpBQWfIuvXWW7Mx5WeccUb06dMnHnzwwezx+eefv+05aS30tETbiBEjsirggAEDorKyMnr37h333XfftucNHDgwjj766AKdCQAAADTxkN69e/dsArnf/va3sXr16rjuuuuygF5WVlYrfHfp0mXbrM81S7MtXbq01mul9dUBAACgKSv4WlMdOnSI0aNH7/LnabbbGq1bt45f/OIXjdQyAAAAaFzFOdMZAAAANEFCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATuQipK9cuTIWLFgQ69ata9B9AAAAIM8KGtI3bdoU3/zmN6N79+4xcuTIOPTQQ+Oee+7Z5/sAAABAU1BWyIPfeOONMW/evFi+fHl06NAhJk+eHEOHDo3evXtHnz599tk+AAAA0BQUtJL+wAMPxMUXX5yF7WTIkCFxzDHHZNv35T4AAADQFBSskv7mm2/G6tWro1evXrW2p4r4okWL9tk+yebNm7NbjQ8//DC7f++992LLli17dR5pTHyLFi3io1WvRmzZGMVo49v/ys7xlbWVMf3V9Q1+vKrSitjQY0M8u+yjKK2qjMaw5M3K7Bw3vLkh1r64NorR+rfWZ+eY3rPvvvtu5JVrqjiuK9fUnlm//j//ba2urm6QvwcA0HSUVBfoE8GSJUuiZ8+eMXv27Dj55JO3bR89enQ8+uijsXTp0n2yTzJ27NismzwA5FmaFLVjx46FbgYAsD9W0svLy7dNBLe9jRs3RkVFxT7bJ7n22mvjyiuv3Pa4qqoqq6IffPDBUVJSslfnQcNUUjt16pR9WG3Tpo1fMbiuil76vjxV0w877LBCNwUA2F9DegphpaWlsWrVqlrb0+POnTvvs32S5s2bZ7ftHXjggXvVfhpeCuhCOriu9hdt27YtdBMAgP154rhWrVpF3759Y8qUKdu2ffzxx/Hkk0/Gaaedtm3bsmXLto03r+s+AAAA0BQVbEx6MmPGjCxcjxo1KhtjntY7f+211+L555+PAw44IHtOmsl97ty52Xj0uu5D0+/unipKaYI/lXRwXQEA7E8KugRb//79Y9q0afH666/H3XffHT169IiZM2fWCtvdunWLE088cY/2oWlLQxNuuOGGHYYoAK4rAIBiV9BKOgAAAJCTSjoAAADw/4R0AAAAyAkhHQAAAHJCSCd3li5dGgsXLoxNmzYVuilQFDZv3hxz5syJ5cuXF7opAAB8BiGd3FizZk22rF7v3r1j+PDhcdhhh8WUKVMK3Sxosj744IO45ppromvXrtnSlbfddluhmwQAwGcQ0smN73znO7F169ZYtWpVVk0fPXp0jBgxIt5+++1CNw2apHQtHXjggVnPlOOPP77QzQEAoA6EdHJh7dq1WdX8qquuilatWmXbrrjiimjWrFk8/PDDhW4eNEk9evTIKumHHHJIoZsCAEAdCenkwgsvvBBVVVXRq1evbdtatGgRPXv2jEWLFhW0bQAAAI1FSCcX3nvvvez+4IMPrrU9Pa75GQAAQLET0smF8vLy7P7TM7pv3LgxKioqCtQqAACAxiWkkwuHH374tomutpced+7cuUCtAgAAaFxCOrmQxp63b9++1pJrr776arz88svZ0lEAAAD7g7JCNwCSNIv7zTffHJdeemm0a9cuq57feOON0b9//xg8eLBfEtRDdXV1zJo1K/v3unXrsuUMZ86cma2gcOKJJ/qdAgDkUEl1+hQHOTF58uQYP358rF+/Pk455ZS4+uqro3Xr1oVuFjRJlZWVMXDgwB22p+ElEyZMKEibAADYPSEdAAAAcsKYdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSoUisWbMmHn744aiurt7lc95///3sOVu2bGnUtgEAAHVTUr27T/RAkzF9+vQYMGBAFsDLysp2+pwFCxbESSedlIX1Aw88MJqClStXxuLFi6Nt27bRq1evaNWqVaGbBAAADWbnn+SBonTQQQfF8OHDo6KiIvJu/fr1cdFFF8XChQujR48e8frrr8eqVati/Pjx8fWvf73QzQMAgAahuzsUmdQ55sUXX4w//elPsWzZslo/a9euXQwZMiTKy8t36CL/j3/8Ix577LF49dVXa+2zcePG7DnpNmnSpFi0aNEOXeprXqeqqirmz58fEydOzLY98sgj8eabb9Z67tatW7Ptb7311m7PIx03hfTly5fHlClTsmp6+oLh29/+9l7+hgAAIL9U0qHInH322fHGG2/EF77whZg1a1bcdNNNMWrUqOxnKfCOGDFiW3f3FMzT44ceeigL02mfadOm1donheXJkydn/968eXPWZb5z587xt7/9LT73uc9l22te58EHH8yq3V/+8pejZ8+ecc8998Rzzz0Xd9xxx7b2/fnPf45vfetbO4T3TzvkkENi8ODBtbZ17949Pvroo+xLgpKSkn3+uwMAgEIT0qHItG/fPgvCKcSmivZ5550XQ4cOjS5duuxynzTWO1Wrk3HjxsWll14al19+edYtPnWRT1XyGpWVldGvX78seN9www21XufII4/MqvE1UtX76quvjltvvXVb9f7Xv/511p5U1a+LmTNnxooVK7IvGH71q1/FL3/5SwEdAICipbs7FJmrrrpqW4gdNmxYdOrUKR599NHd7pNCeY1TTz01q56nYLy91M09daH/4x//GB07dox58+bt8Drf//73az0+99xzs1A/derU7PHq1avjL3/5S9aNva5SJT4dM31R8PnPfz66du1a530BAKCpUUmHInPEEUfUevylL30pm3Rtd1K1vEbz5s2z+02bNmX377zzTpx22mnZ/XHHHRdt2rSJpUuX7nTyuQ4dOtR6nGZiT93gU/X8nHPOySZ9++IXvxhf/epX63w+Nd3uk+uuuy6+8Y1vxL///e+sHQAAUGxU0qHIpPHmn36cKtD1deedd2bBPQX9VAVPFe0U2ne2euPOxomnLu9p/Hoag/7AAw9k49HrO5585MiR2fksWbKkXvsDAEDeCelQZGomeUvSOO40K/opp5xS79d7++23sy7mNWuvp3XYa8av18WJJ54Yxx57bHz3u9+Nf/7zn3HhhRfW+bif/iLg73//e3afutsDAEAx0t0disxPf/rTbPmzNDv6XXfdFYMGDdqj7uU7my0+jS3v1q1b1p09dVlPXd/3pLt5qqZfdtllWVsOP/zwOu3z9NNPx7333htnnHFGNhleWlbu/vvvz8bcp9nlAQCgGKmkQ5FIoTytIz5nzpxsJvVUdU4Twm1fWU9jz9NzasaT1+xTWvr//ylo2bJlti0t0VYT0tOEcWnSt7T82iWXXJKNMT/99NN3OPb2r7O9tDZ7sicTxqVZ6e+77774+OOP49lnn43WrVtny8Pddttt9fjtAABA01BSvbOBpQD7UAr1Y8aMiZUrV0aLFi38bgEAYBd0dwcaTBoTP2PGjLj++uuzWdq3D+ip4r+rWefTjPR9+vTxlwEAYL8jpAMN5rXXXounnnoqfvjDH2a37c2fPz9mz5690/369+8vpAMAsF/S3R0AAABywsRxAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAEPnwv5Xo9OmaJkgfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x1800 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "match = matcher_beta2.get_best_match()\n",
    "m_data = m.copy().get_population('pool')\n",
    "m_data.loc[:, 'population'] = m_data['population'] + ' (prematch)'\n",
    "match.append(m_data)\n",
    "fig = plot_per_feature_loss(match, beta, 'target', debin=False)\n",
    "fig = plot_numeric_features(match, hue_order=['pool (prematch)', 'pool', 'target', ])\n",
    "fig = plot_categoric_features(match,  hue_order=['pool (prematch)', 'pool', 'target'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c6c9831",
   "metadata": {},
   "source": [
    "## Optimize Gamma (Area Between CDFs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2159ab59",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [preprocess.py:340] Discretized age with bins [18.05, 27.54, 37.04, 46.53, 56.02, 65.51, 75.0].\n",
      "INFO [preprocess.py:340] Discretized height with bins [125.01, 136.68, 148.34, 160.01, 171.67, 183.34, 195.0].\n",
      "INFO [preprocess.py:340] Discretized weight with bins [50.0, 61.67, 73.33, 85.0, 96.66, 108.33, 120.0].\n",
      "INFO [matcher.py:125] cpu\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'objective': 'gamma',\n",
       " 'candidate_population_size': 1000,\n",
       " 'n_candidate_populations': 1024,\n",
       " 'n_keep_best': 256,\n",
       " 'n_voting_populations': 256,\n",
       " 'n_mutation': 256,\n",
       " 'n_generations': 5000,\n",
       " 'n_iter_no_change': 100,\n",
       " 'time_limit': 120,\n",
       " 'max_batch_size_gb': 2,\n",
       " 'seed': 1234,\n",
       " 'verbose': True,\n",
       " 'log_every': 1000,\n",
       " 'initialization': {'benchmarks': {'propensity': 'include'},\n",
       "  'sampling': {'propensity': 1.0, 'uniform': 1.0}}}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objective = gamma = GammaBalance(m, feature_weights={'age':2})\n",
    "matcher = matcher_gamma = GeneticMatcher(\n",
    "    matching_data = m, \n",
    "    objective = objective,\n",
    "    log_every = 1000,\n",
    "    n_generations = 5000,\n",
    "    time_limit = time_limit\n",
    ")\n",
    "matcher.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "32393c2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [initialization.py:31] Optimizing balance with genetic algorithm ...\n",
      "INFO [initialization.py:32] Initial balance scores:\n",
      "INFO [initialization.py:37] \tgamma:\t0.217\n",
      "INFO [initialization.py:38] Initializing candidate populations ...\n",
      "INFO [initialization.py:86] Computing PROPENSITY 1-1 matching method ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 1/50, 0.000 min) ...\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: SGDClassifier\n",
      "INFO [matcher.py:142] \t* alpha: 1.5074398973827774\n",
      "INFO [matcher.py:142] \t* class_weight: None\n",
      "INFO [matcher.py:142] \t* early_stopping: True\n",
      "INFO [matcher.py:142] \t* fit_intercept: True\n",
      "INFO [matcher.py:142] \t* loss: log_loss\n",
      "INFO [matcher.py:142] \t* max_iter: 1500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:143] \tScore (gamma): 0.1083\n",
      "INFO [matcher.py:144] \tSolution time: 0.002 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 2/50, 0.002 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.05835496346821344\n",
      "INFO [matcher.py:142] \t* fit_intercept: True\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (gamma): 0.0391\n",
      "INFO [matcher.py:144] \tSolution time: 0.003 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 3/50, 0.003 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 16.16555309446666\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (gamma): 0.0376\n",
      "INFO [matcher.py:144] \tSolution time: 0.023 min\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 4/50, 0.024 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 5/50, 0.025 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 54.02072493419677\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (gamma): 0.0337\n",
      "INFO [matcher.py:144] \tSolution time: 0.040 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 6/50, 0.040 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 7/50, 0.042 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 8/50, 0.043 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 9/50, 0.045 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 13.179630432958701\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (gamma): 0.0269\n",
      "INFO [matcher.py:144] \tSolution time: 0.060 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 10/50, 0.060 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 11/50, 0.062 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 12/50, 0.063 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 13/50, 0.065 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 14/50, 0.066 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 15/50, 0.067 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 16/50, 0.068 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 17/50, 0.069 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 18/50, 0.071 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 19/50, 0.074 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 20/50, 0.089 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 21/50, 0.090 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 22/50, 0.092 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 23/50, 0.093 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 24/50, 0.094 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 25/50, 0.096 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 26/50, 0.098 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 27/50, 0.100 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 28/50, 0.102 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 29/50, 0.121 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 30/50, 0.122 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 31/50, 0.125 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 32/50, 0.126 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 33/50, 0.127 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 34/50, 0.129 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 35/50, 0.130 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 36/50, 0.150 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 37/50, 0.157 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 38/50, 0.177 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 39/50, 0.178 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 40/50, 0.180 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 41/50, 0.181 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 42/50, 0.183 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 43/50, 0.198 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 44/50, 0.218 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 45/50, 0.220 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 46/50, 0.221 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 47/50, 0.240 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 48/50, 0.260 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 49/50, 0.262 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 50/50, 0.263 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 13.179630432958701\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (gamma): 0.0269\n",
      "INFO [matcher.py:144] \tSolution time: 0.060 min\n",
      "INFO [initialization.py:66] \tgamma:\t0.027\n",
      "INFO [initialization.py:71] \tIncluded in initial population.\n",
      "\n",
      "INFO [initialization.py:132] Sampling 512 candidate populations according to PROPENSITY distribution ...\n",
      "\n",
      "INFO [initialization.py:132] Sampling 511 candidate populations according to UNIFORM distribution ...\n",
      "\n",
      "INFO [logger.py:34] Generation 0\n",
      "INFO [logger.py:35] \tremaining patients: 10000\n",
      "INFO [logger.py:36] \telapsed time: 0.28 min\n",
      "INFO [logger.py:45] \tbest gamma: 0.02688 \tworst gamma: 0.23595\n",
      "INFO [logger.py:34] Generation 1000\n",
      "INFO [logger.py:35] \tremaining patients: 7355\n",
      "INFO [logger.py:36] \telapsed time: 1.80 min\n",
      "INFO [logger.py:45] \tbest gamma: 0.01164 \tworst gamma: 0.22939\n",
      "INFO [matcher.py:211] Time limit exceeded. Stopping.\n"
     ]
    }
   ],
   "source": [
    "match = matcher.match()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "862b23fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1350x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1800 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "match = matcher.get_best_match()\n",
    "m_data = m.copy().get_population('pool')\n",
    "m_data.loc[:, 'population'] = m_data['population'] + ' (prematch)'\n",
    "match.append(m_data)\n",
    "fig = plot_per_feature_loss(match, gamma, 'target', debin=False)\n",
    "fig = plot_numeric_features(match, hue_order=['pool (prematch)', 'pool', 'target', ])\n",
    "fig = plot_categoric_features(match,  hue_order=['pool (prematch)', 'pool', 'target'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pybal2",
   "language": "python",
   "name": "pybal2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}