ml.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "import awkward as aw\n",
    "import uproot\n",
    "import hist\n",
    "import math\n",
    "import numpy as np\n",
    "from scipy.stats import kstest\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE, BorderlineSMOTE\n",
    "from IPython.display import Audio\n",
    "from matplotlib.offsetbox import AnchoredText\n",
    "import joblib\n",
    "from util import * \n",
    "\n",
    "import random\n",
    "import time\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import cloudpickle\n",
    "import pickle\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import ConfusionMatrixDisplay,  classification_report\n",
    "\n",
    "from sklearn.metrics import accuracy_score, mean_squared_error\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_validate, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import xgboost as xgb\n",
    "\n",
    "from scipy.stats import ks_2samp\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = ['frvz_ggf_508885', 'frvz_ggf_508886', 'frvz_ggf_508887', 'frvz_ggf_508888', 'frvz_ggf_508889', 'frvz_ggf_508890', 'frvz_ggf_508891',\n",
    "            'frvz_ggf_508892', 'frvz_ggf_508893', 'frvz_vbf_500757_02-00', 'frvz_vbf_500758_02-00',\n",
    "            'frvz_vbf_500759_02-00', 'frvz_vbf_500760_02-00', 'frvz_vbf_500761_02-00', 'frvz_vbf_500762_02-00', 'frvz_vbf_500763_02-00', 'frvz_vbf_500764_02-00']\n",
    "\n",
    "BDT_inputs = ['mjj', \"dphijj\", \"detajj\",\n",
    "              'MET',\n",
    "              'jet1_pt',\n",
    "              'jet1_eta',\n",
    "              'jet1_e',\n",
    "              'jet2_pt',\n",
    "              'jet2_eta',\n",
    "              'jet2_e',\n",
    "                \"njet30\", \n",
    "            #  \"centrality\"\n",
    "            'intLumi',\n",
    "            'scale1fb'\n",
    "             ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frvz_ggf_508885\n",
      "frvz_ggf_508886\n",
      "frvz_ggf_508887\n",
      "frvz_ggf_508888\n",
      "frvz_ggf_508889\n",
      "frvz_ggf_508890\n",
      "frvz_ggf_508891\n",
      "frvz_ggf_508892\n",
      "frvz_ggf_508893\n",
      "frvz_vbf_500757_02-00\n",
      "frvz_vbf_500758_02-00\n",
      "frvz_vbf_500759_02-00\n",
      "frvz_vbf_500760_02-00\n",
      "frvz_vbf_500761_02-00\n",
      "frvz_vbf_500762_02-00\n",
      "frvz_vbf_500763_02-00\n",
      "frvz_vbf_500764_02-00\n"
     ]
    }
   ],
   "source": [
    "df = read_data(datasets = datasets, variables=BDT_inputs, carpeta='C:/Users/jav90/Desktop/dpj/production_analysis/analisis_javier/Samples/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.copy()\n",
    "df1 = df1[df1['weights'] >= 0] #eliminar weights negativos y ceros\n",
    "df1['VBF label'] = df1['tipo'].apply(lambda x: 1 if x == 'vbf' else 0) #crear columna con 1 para vbf y 0 para ggf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4628576, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_var = ['mjj', \"dphijj\", \"detajj\", 'MET',\n",
    "              'jet1_pt',\n",
    "              'jet1_eta',\n",
    "              'jet1_e',\n",
    "              'jet2_pt',\n",
    "              'jet2_eta',\n",
    "              'jet2_e',\n",
    "              'njet30']\n",
    "\n",
    "\n",
    "\n",
    "X_ggf = df1[df1['tipo'] == 'ggf'].sample(n=2000000, random_state=228)\n",
    "X_ggf = X_ggf[X_ggf['njet30'] > 1]\n",
    "\n",
    "X_vbf = df1[df1['tipo'] == 'vbf']\n",
    "X_ggf_vbf = pd.concat([X_ggf, X_vbf], axis=0)\n",
    "X = X_ggf_vbf[ml_var]\n",
    "y = X_ggf_vbf['VBF label']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(736060, 16)\n",
      "(491044, 16)\n"
     ]
    }
   ],
   "source": [
    "print(X_ggf.shape)\n",
    "print(X_vbf.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>mjj</th>\n",
       "      <th>dphijj</th>\n",
       "      <th>detajj</th>\n",
       "      <th>MET</th>\n",
       "      <th>jet1_pt</th>\n",
       "      <th>jet1_eta</th>\n",
       "      <th>jet1_e</th>\n",
       "      <th>jet2_pt</th>\n",
       "      <th>jet2_eta</th>\n",
       "      <th>jet2_e</th>\n",
       "      <th>njet30</th>\n",
       "      <th>intLumi</th>\n",
       "      <th>scale1fb</th>\n",
       "      <th>weights</th>\n",
       "      <th>tipo</th>\n",
       "      <th>VBF label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1922897</th>\n",
       "      <td>4.135178e+04</td>\n",
       "      <td>-1.388952</td>\n",
       "      <td>0.015607</td>\n",
       "      <td>39954.585938</td>\n",
       "      <td>31994.083984</td>\n",
       "      <td>0.574745</td>\n",
       "      <td>3.799173e+04</td>\n",
       "      <td>30211.220703</td>\n",
       "      <td>0.590351</td>\n",
       "      <td>3.592268e+04</td>\n",
       "      <td>2</td>\n",
       "      <td>36.099998</td>\n",
       "      <td>0.041837</td>\n",
       "      <td>1.510301</td>\n",
       "      <td>ggf</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2076601</th>\n",
       "      <td>2.022060e+05</td>\n",
       "      <td>2.939579</td>\n",
       "      <td>1.369800</td>\n",
       "      <td>99959.726562</td>\n",
       "      <td>118938.937500</td>\n",
       "      <td>0.153307</td>\n",
       "      <td>1.219211e+05</td>\n",
       "      <td>54682.148438</td>\n",
       "      <td>-1.216493</td>\n",
       "      <td>1.008361e+05</td>\n",
       "      <td>6</td>\n",
       "      <td>44.299999</td>\n",
       "      <td>0.000452</td>\n",
       "      <td>0.020022</td>\n",
       "      <td>ggf</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4102287</th>\n",
       "      <td>6.640849e+05</td>\n",
       "      <td>2.925986</td>\n",
       "      <td>5.606311</td>\n",
       "      <td>16936.062500</td>\n",
       "      <td>48726.402344</td>\n",
       "      <td>2.052103</td>\n",
       "      <td>1.928805e+05</td>\n",
       "      <td>33005.652344</td>\n",
       "      <td>-3.554208</td>\n",
       "      <td>5.774382e+05</td>\n",
       "      <td>2</td>\n",
       "      <td>58.450001</td>\n",
       "      <td>0.031009</td>\n",
       "      <td>1.812492</td>\n",
       "      <td>ggf</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028220</th>\n",
       "      <td>6.245890e+04</td>\n",
       "      <td>0.889752</td>\n",
       "      <td>1.364045</td>\n",
       "      <td>69531.718750</td>\n",
       "      <td>36784.332031</td>\n",
       "      <td>2.508739</td>\n",
       "      <td>2.275718e+05</td>\n",
       "      <td>35335.957031</td>\n",
       "      <td>1.144695</td>\n",
       "      <td>6.129078e+04</td>\n",
       "      <td>2</td>\n",
       "      <td>36.099998</td>\n",
       "      <td>0.045243</td>\n",
       "      <td>1.633280</td>\n",
       "      <td>ggf</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920149</th>\n",
       "      <td>1.074340e+05</td>\n",
       "      <td>2.418122</td>\n",
       "      <td>1.041257</td>\n",
       "      <td>17352.517578</td>\n",
       "      <td>60429.839844</td>\n",
       "      <td>0.916951</td>\n",
       "      <td>8.790906e+04</td>\n",
       "      <td>39658.742188</td>\n",
       "      <td>-0.124306</td>\n",
       "      <td>4.094811e+04</td>\n",
       "      <td>2</td>\n",
       "      <td>36.099998</td>\n",
       "      <td>0.040848</td>\n",
       "      <td>1.474623</td>\n",
       "      <td>ggf</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4628571</th>\n",
       "      <td>1.367926e+06</td>\n",
       "      <td>0.648911</td>\n",
       "      <td>4.957650</td>\n",
       "      <td>216401.265625</td>\n",
       "      <td>116440.851562</td>\n",
       "      <td>-3.211884</td>\n",
       "      <td>1.447791e+06</td>\n",
       "      <td>114172.210938</td>\n",
       "      <td>1.745765</td>\n",
       "      <td>3.373078e+05</td>\n",
       "      <td>2</td>\n",
       "      <td>36.099998</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.009256</td>\n",
       "      <td>vbf</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4628572</th>\n",
       "      <td>1.540464e+06</td>\n",
       "      <td>-0.568785</td>\n",
       "      <td>5.033163</td>\n",
       "      <td>191440.421875</td>\n",
       "      <td>134930.218750</td>\n",
       "      <td>3.088001</td>\n",
       "      <td>1.482836e+06</td>\n",
       "      <td>115863.156250</td>\n",
       "      <td>-1.945162</td>\n",
       "      <td>4.136839e+05</td>\n",
       "      <td>3</td>\n",
       "      <td>36.099998</td>\n",
       "      <td>0.000305</td>\n",
       "      <td>0.011002</td>\n",
       "      <td>vbf</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4628573</th>\n",
       "      <td>1.694813e+06</td>\n",
       "      <td>-2.918931</td>\n",
       "      <td>5.825066</td>\n",
       "      <td>35132.878906</td>\n",
       "      <td>110081.000000</td>\n",
       "      <td>3.609008</td>\n",
       "      <td>2.034103e+06</td>\n",
       "      <td>76587.546875</td>\n",
       "      <td>-2.216058</td>\n",
       "      <td>3.554840e+05</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000271</td>\n",
       "      <td>0.000271</td>\n",
       "      <td>vbf</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4628574</th>\n",
       "      <td>7.387971e+05</td>\n",
       "      <td>0.789625</td>\n",
       "      <td>5.037199</td>\n",
       "      <td>97912.656250</td>\n",
       "      <td>105132.671875</td>\n",
       "      <td>-0.351721</td>\n",
       "      <td>1.122496e+05</td>\n",
       "      <td>33879.730469</td>\n",
       "      <td>4.685478</td>\n",
       "      <td>1.835801e+06</td>\n",
       "      <td>3</td>\n",
       "      <td>36.099998</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.010165</td>\n",
       "      <td>vbf</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4628575</th>\n",
       "      <td>1.889988e+06</td>\n",
       "      <td>-1.190403</td>\n",
       "      <td>5.106238</td>\n",
       "      <td>213900.062500</td>\n",
       "      <td>192589.812500</td>\n",
       "      <td>2.780378</td>\n",
       "      <td>1.558812e+06</td>\n",
       "      <td>112864.968750</td>\n",
       "      <td>-2.325860</td>\n",
       "      <td>5.831626e+05</td>\n",
       "      <td>2</td>\n",
       "      <td>36.099998</td>\n",
       "      <td>0.000330</td>\n",
       "      <td>0.011909</td>\n",
       "      <td>vbf</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1227104 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mjj    dphijj    detajj            MET        jet1_pt  \\\n",
       "1922897  4.135178e+04 -1.388952  0.015607   39954.585938   31994.083984   \n",
       "2076601  2.022060e+05  2.939579  1.369800   99959.726562  118938.937500   \n",
       "4102287  6.640849e+05  2.925986  5.606311   16936.062500   48726.402344   \n",
       "1028220  6.245890e+04  0.889752  1.364045   69531.718750   36784.332031   \n",
       "1920149  1.074340e+05  2.418122  1.041257   17352.517578   60429.839844   \n",
       "...               ...       ...       ...            ...            ...   \n",
       "4628571  1.367926e+06  0.648911  4.957650  216401.265625  116440.851562   \n",
       "4628572  1.540464e+06 -0.568785  5.033163  191440.421875  134930.218750   \n",
       "4628573  1.694813e+06 -2.918931  5.825066   35132.878906  110081.000000   \n",
       "4628574  7.387971e+05  0.789625  5.037199   97912.656250  105132.671875   \n",
       "4628575  1.889988e+06 -1.190403  5.106238  213900.062500  192589.812500   \n",
       "\n",
       "         jet1_eta        jet1_e        jet2_pt  jet2_eta        jet2_e  \\\n",
       "1922897  0.574745  3.799173e+04   30211.220703  0.590351  3.592268e+04   \n",
       "2076601  0.153307  1.219211e+05   54682.148438 -1.216493  1.008361e+05   \n",
       "4102287  2.052103  1.928805e+05   33005.652344 -3.554208  5.774382e+05   \n",
       "1028220  2.508739  2.275718e+05   35335.957031  1.144695  6.129078e+04   \n",
       "1920149  0.916951  8.790906e+04   39658.742188 -0.124306  4.094811e+04   \n",
       "...           ...           ...            ...       ...           ...   \n",
       "4628571 -3.211884  1.447791e+06  114172.210938  1.745765  3.373078e+05   \n",
       "4628572  3.088001  1.482836e+06  115863.156250 -1.945162  4.136839e+05   \n",
       "4628573  3.609008  2.034103e+06   76587.546875 -2.216058  3.554840e+05   \n",
       "4628574 -0.351721  1.122496e+05   33879.730469  4.685478  1.835801e+06   \n",
       "4628575  2.780378  1.558812e+06  112864.968750 -2.325860  5.831626e+05   \n",
       "\n",
       "         njet30    intLumi  scale1fb   weights tipo  VBF label  \n",
       "1922897       2  36.099998  0.041837  1.510301  ggf          0  \n",
       "2076601       6  44.299999  0.000452  0.020022  ggf          0  \n",
       "4102287       2  58.450001  0.031009  1.812492  ggf          0  \n",
       "1028220       2  36.099998  0.045243  1.633280  ggf          0  \n",
       "1920149       2  36.099998  0.040848  1.474623  ggf          0  \n",
       "...         ...        ...       ...       ...  ...        ...  \n",
       "4628571       2  36.099998  0.000256  0.009256  vbf          1  \n",
       "4628572       3  36.099998  0.000305  0.011002  vbf          1  \n",
       "4628573       2   1.000000  0.000271  0.000271  vbf          1  \n",
       "4628574       3  36.099998  0.000282  0.010165  vbf          1  \n",
       "4628575       2  36.099998  0.000330  0.011909  vbf          1  \n",
       "\n",
       "[1227104 rows x 16 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_ggf_vbf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val_test, y_train, y_val_test = train_test_split(X, y, test_size=0.3, random_state=228)\n",
    "X_test, X_val, y_test, y_val = train_test_split(X_val_test, y_val_test, test_size=0.5, random_state=228)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = { \"n_estimators\": 800,\n",
    "            \"max_depth\": 12,\n",
    "            \"learning_rate\": 0.5,\n",
    "            \"gamma\": 0.6,\n",
    "            \"min_child_weight\": 1,\n",
    "            \"reg_lambda\": 100,\n",
    "          #  \"scale_pos_weight\": scale_pos_weight,\n",
    "            \"eval_metric\": [\"logloss\", \"auc\"],\n",
    "            \"early_stopping_rounds\": 5,\n",
    "            \"objective\":\"binary:logistic\",\n",
    "            \"verbosity\": 1\n",
    "            }\n",
    "\n",
    "model = xgb.XGBClassifier(**params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3556267    0\n",
       "4600529    1\n",
       "2212861    0\n",
       "260032     0\n",
       "4516133    1\n",
       "          ..\n",
       "2602793    0\n",
       "3371629    0\n",
       "343341     0\n",
       "1198883    0\n",
       "4427099    1\n",
       "Name: VBF label, Length: 858972, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:312: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:314: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:345: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:336: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:440: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(data):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:312: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:314: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:345: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:336: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:440: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(data):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-logloss:0.39586\tvalidation_0-auc:0.97091\n",
      "[1]\tvalidation_0-logloss:0.28695\tvalidation_0-auc:0.97432\n",
      "[2]\tvalidation_0-logloss:0.23480\tvalidation_0-auc:0.97619\n",
      "[3]\tvalidation_0-logloss:0.20760\tvalidation_0-auc:0.97745\n",
      "[4]\tvalidation_0-logloss:0.19327\tvalidation_0-auc:0.97802\n",
      "[5]\tvalidation_0-logloss:0.18537\tvalidation_0-auc:0.97853\n",
      "[6]\tvalidation_0-logloss:0.18113\tvalidation_0-auc:0.97882\n",
      "[7]\tvalidation_0-logloss:0.17864\tvalidation_0-auc:0.97904\n",
      "[8]\tvalidation_0-logloss:0.17742\tvalidation_0-auc:0.97909\n",
      "[9]\tvalidation_0-logloss:0.17661\tvalidation_0-auc:0.97919\n",
      "[10]\tvalidation_0-logloss:0.17623\tvalidation_0-auc:0.97921\n",
      "[11]\tvalidation_0-logloss:0.17602\tvalidation_0-auc:0.97923\n",
      "[12]\tvalidation_0-logloss:0.17584\tvalidation_0-auc:0.97924\n",
      "[13]\tvalidation_0-logloss:0.17578\tvalidation_0-auc:0.97926\n",
      "[14]\tvalidation_0-logloss:0.17571\tvalidation_0-auc:0.97927\n",
      "[15]\tvalidation_0-logloss:0.17568\tvalidation_0-auc:0.97928\n",
      "[16]\tvalidation_0-logloss:0.17564\tvalidation_0-auc:0.97929\n",
      "[17]\tvalidation_0-logloss:0.17563\tvalidation_0-auc:0.97929\n",
      "[18]\tvalidation_0-logloss:0.17553\tvalidation_0-auc:0.97931\n",
      "[19]\tvalidation_0-logloss:0.17553\tvalidation_0-auc:0.97932\n",
      "[20]\tvalidation_0-logloss:0.17556\tvalidation_0-auc:0.97931\n",
      "[21]\tvalidation_0-logloss:0.17554\tvalidation_0-auc:0.97931\n",
      "[22]\tvalidation_0-logloss:0.17552\tvalidation_0-auc:0.97931\n",
      "[23]\tvalidation_0-logloss:0.17555\tvalidation_0-auc:0.97931\n",
      "[24]\tvalidation_0-logloss:0.17551\tvalidation_0-auc:0.97933\n",
      "[25]\tvalidation_0-logloss:0.17554\tvalidation_0-auc:0.97932\n",
      "[26]\tvalidation_0-logloss:0.17554\tvalidation_0-auc:0.97932\n",
      "[27]\tvalidation_0-logloss:0.17554\tvalidation_0-auc:0.97931\n",
      "[28]\tvalidation_0-logloss:0.17552\tvalidation_0-auc:0.97932\n",
      "[29]\tvalidation_0-logloss:0.17554\tvalidation_0-auc:0.97932\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:312: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:314: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:345: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:336: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n"
     ]
    }
   ],
   "source": [
    "model.fit(X_train, y_train, eval_set=[(X_val, y_val)], verbose=True)\n",
    "test_predict = model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:312: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:314: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:345: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:336: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9339313072484869"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy 0.9339313072484869\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n"
     ]
    }
   ],
   "source": [
    "test_accuracy = accuracy_score(y_test, test_predict)\n",
    "print(\"Test accuracy\", test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, test_predict)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False)\n",
    "plt.xlabel('Predicción')\n",
    "plt.ylabel('Valor Real')\n",
    "plt.title('Matriz de Confusión')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2437d9d54f0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggf_as_ggf = cm[0,0]\n",
    "ggf_as_vbf = cm[0,1]\n",
    "vbf_as_ggf = cm[1,0]\n",
    "vbf_as_vbf = cm[1,1]\n",
    "bars1 = np.array([ggf_as_ggf, ggf_as_vbf]) / (ggf_as_ggf+ggf_as_vbf)#+df_not_used[df_not_used['tipo'] == 'ggf'].shape[0])\n",
    "bars2 = np.array([vbf_as_ggf, vbf_as_vbf]) / (vbf_as_ggf+vbf_as_vbf)#+df_not_used[df_not_used['tipo'] == 'vbf'].shape[0])\n",
    "pos1 = [0,3]\n",
    "pos2 = [1,4]\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(pos1, bars1, width=.8, color='sandybrown', edgecolor='white', label='True ggF')\n",
    "plt.bar(pos2, bars2, width=.8, color=\"navy\", edgecolor='white', label='True VBF')\n",
    "plt.xticks([.5,3.5] ,['Classified as ggF','Classified as VBF'])\n",
    "\n",
    "for i in range(2):\n",
    "     plt.text(pos1[i], bars1[i] + 0.01, str(round(bars1[i],3)), ha='center')\n",
    "     plt.text(pos2[i], bars2[i] + 0.01, str(round(bars2[i],3)), ha='center')\n",
    "plt.title(\"Classify with XGbBoost\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.95      0.95    110401\n",
      "           1       0.93      0.91      0.92     73665\n",
      "\n",
      "    accuracy                           0.93    184066\n",
      "   macro avg       0.93      0.93      0.93    184066\n",
      "weighted avg       0.93      0.93      0.93    184066\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, test_predict))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = { \"n_estimators\": 800,\n",
    "            \"max_depth\": 12,\n",
    "            \"learning_rate\": 0.5,\n",
    "            \"gamma\": 0.6,\n",
    "            \"min_child_weight\": 1,\n",
    "            \"reg_lambda\": 100,\n",
    "          #  \"scale_pos_weight\": scale_pos_weight,\n",
    "            \"eval_metric\": [\"logloss\", \"auc\"],\n",
    "            \"early_stopping_rounds\": 5,\n",
    "            \"objective\":\"binary:logistic\",\n",
    "            \"verbosity\": 1\n",
    "            }\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Klomogorov-Smirnov Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:312: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:314: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:345: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:336: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:312: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:314: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:345: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\xgboost\\data.py:336: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n",
      "C:\\Users\\jav90\\AppData\\Local\\Temp\\ipykernel_5272\\1231902512.py:10: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(probs_train, label=\"Train\", shade=True)\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\jav90\\AppData\\Local\\Temp\\ipykernel_5272\\1231902512.py:11: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(probs_test, label=\"Test\", shade=True)\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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FIhERERERGdQUikREREREZFBTKBIRERERkUFNoUhERERERAa1vOo+JyIiIiLSHyUSCWKxWK7LyCt2ux2r1ZqVcykUiYiIiIj0EtM0qayspLGxMdel5KXi4mKGDRuGYRg9Ok+/CEWvvPIKjzzyCGvWrGH06NFceOGFHHzwwbkuS0RERESkR9KBqKKiAo/H0+M375JimibBYJDq6moAhg8f3qPz5TwU/fnPf+bnP/8511xzDXvttRfz589n9uzZDBs2jGnTpuW6PBERERGRbkkkEplANGTIkFyXk3fcbjcA1dXVVFRU9GgqXU4bLZimyX333ccpp5zCiSeeyOjRoznvvPPYY489WLBgQS5LExERERHpkfQaIo/Hk+NK8lf6e9vT9Vo5HSlasWIF69at44c//GGr44899liOKhIRERERyS5Nmes92fre5jwUAQSDQc4880wWLVrEqFGjOO+88/je976Xy9JERERERHpNdUMQf0u0z69b6HVQUaKRq83lNBQFAgEArrrqKi688EIuv/xy3njjDc4//3yeeOIJdt999y6fM73oSvqPUCjU6t8ycOle5gfdx/yhe5k/dC/zw+b3MRKJkEwmSSQSJBKJzPNqGkJceOe7RGKJds/Tm5x2K/Mu35fyEnenX3PNNdfwyiuvbPE5ixYt6nItp556KiNHjuSWW27p8mvTEokEyWSSUChEMpls9Zhpmp0eSTJM0zS7XUUPvf7661x66aXccMMNnHDCCZnjZ599NgCPPPJIl873xRdfEI32feIWEREREWmPzWZjm222wel0Zo6tWO/n6ocXcOTeYykrcvVZLbVNYV7550puPXc3th1R2OnXNTc3E4lEMn8+8MADufzyyznwwAMzx8rKyrpcT1NTExaLhYKCgi6/Ni0SibBmzRri8Xi7jzscDqZMmbLV8+R0pGjo0KEATJgwodXx8ePH8+6773brnHa7nfHjx/e0NMmiUCjEypUrGTt2bKZLiAxMupf5Qfcxf+he5g/dy/yw+X2MRCKsX78ep9OJy/Vt+HE4UgFjeFkBI8q9fVaf3d6y8fqt69ma9p5bUlLCqFGjelRPV2rYEpvNxujRo1sFT4ClS5d2/hxZqaSbdtxxR7xeL//5z3+YMWNG5viSJUsYPXp0t85pGIY6fPRTbrdb9yZP6F7mB93H/KF7mT90L/ND+j5aLBYsFgtWq7VVu2iLNdUA2mKxYLV0v410V1kslsz1e9K+On2u9DlefvllHnroIfbZZx/+9Kc/MXPmTB588EHeeustfvOb3/DNN9+QSCTYfvvtufTSS9lrr70AOPnkkxk5ciS33XZb5hznnXceDz30EBs2bGDChAn8/Oc/Z/r06R3WYbVasVgsuN3uNiGrK00YctqS2+Vy8dOf/pQHHniA1157jdWrV/PQQw/xwQcfcPrpp+eyNBERERER6aTVq1dTXV3NK6+8wqWXXsqXX37JRRddxKGHHsqrr77Kiy++SGlpKVdeeWWHy102bNjA73//e+644w7+9Kc/4Xa7mTNnDn2x2ifnm7eef/75uN1u7rnnHqqqqhg3bhxz585l5syZuS5NREREREQ66fzzz2ebbbYBYPHixVx33XWt+gaccsopnHXWWdTV1TF8+PA2r4/FYvziF79g8uTJAJx++ulccMEF1NTUUFFR0au15zwUQeoL1siQiIiIiMjANXbs2Mx/T548maKiIh555BGWL1/OqlWr+OqrrwBadeLb3Lhx4zL/nW7A0NONWTsjp9PnREREREQkP2y6pmfBggX84Ac/4IsvvmDSpElceOGF3HHHHVs9h8PhaHNsUEyfk8Fp6ZpG5v7hc646eQYjyn25LkdEREREsujxxx9n5syZzJ07N3Ps6aefBvom5HSVRoqkz9U1hfjl4x+yfF0T//p8Xa7LEREREZEsGz58OF9//TULFy5k7dq1vPTSS9x3330A/XJfUY0USZ+KxBLc/MQCkkmTbUcUsmBRJcceMDHXZYmIiIj0uZqGYN5e7+KLL6a2tpZzzz0XSO1Desstt3DFFVfwxRdftFo71B8oFEmfMU2T+37/GSs3+Dn7yClU1rXw8j+W0hSIUORzbv0EIiIiInmg0OvAabfy4tvf9Pm1nXYrhd6263a64uuvv27156OPPpqjjz661bGSkpJWU+fSDjzwwMx/p6fTdXSOmTNntrlWb1Eokj7zzifr+Nfn6zj+wImMLPdR6HVgAp98VcX3ZnRvs14RERGRgaaixMODV30Pf0vfTyMr9DqoKNEGwZtTKJI+8+XyesYML2TKuDIACjwOthnqY8H/FIpERERkcKko8Sic9CNqtCB9ZnVVgKGb/c8/YXQJn35dTTyRzFFVIiIiIjLYKRRJn0gkTTbUBhk6pHUomjSmlFAkzqIVdTmqTEREREQGO4Ui6RN1zXESSZOhpa1D0YgyL4VeBx8vqspRZSIiIiIy2CkUSZ+obowBtJk+ZxgGE0aXsGBRZS7KEhERERFRKJK+Ud0Uw+e243Xb2zw2aUwJ62taWF8byEFlIiIiIjLYKRRJn6hujFFe4mr3sXGjirFZDRZqCp2IiIiI5IBCkfSJ6sYYFcXudh9z2q2Mqijg61UNfVyViIiIiIj2KZI+EI0laAgkKC9pPxQBDClysaGupQ+rEhEREcmdeFMNiWBzn1/X6inAVlTe59ft7xSKpNetq2nBBCo6mD4HUFroYslqjRSJiIhI/os31bDm4Ysx49E+v7Zhc7DNufd3KRjNmTOHP/3pT1t8ztdff93tmj755BNM02TGjBndPkdPKRRJr1tTnWqgUN7B9DlIhaLmYIyWUKzdZgwiIiIi+SIRbMaMRyme9SNshWV9dt24v5bGD14iEWzuUij6+c9/zmWXXZb585577sk111zDIYcckpW6TjjhBG699VaFIslva6pa8LksOB3WDp9TWpgaRaqsa2HcqOI+qkxEREQkd2yFZdiHjMh1GVtVUFBAQUFBm2Pl5fkzDU+NFqTXralqpti75fydCUX1wb4oSURERESy5B//+AdHH300O++8MwcccAD33nsv0ei3UwPfe+89jj76aKZOncruu+/OnDlzaGpqAmDixIkAXH311cyZMycn9YNCkfSB1dUBin0djxIBeFw2XA4rlbVqtiAiIiIyUPzzn//kkksu4ZhjjuG1117jhhtu4K9//StXXHEFAPX19Vx44YX86Ec/4vXXX2fevHl8/PHH3H777QC8//77AFxzzTX8/Oc/z9nXoelz0quC4Rh1TREmj/Rt8XmGYVBa6NJIkYiIiMgA8vDDD3PMMcdw3HHHATB69Gh+8YtfcOqpp7J27Vqam5uJRqOMGDGCkSNHMnLkSB5++GESiQRAZgpee1P0+pJCkfSq1VWpVpPFvq3/qJUUuthQG+jtkkREREQkSxYtWsR///tf/vjHP2aOmaYJwLJly9hnn3047LDDOPfccykvL2fWrFnsu+++HHDAAbkquV0KRdKrVm1oxjCgyLPl6XOgttwiIiIiA00ymeSnP/0pRx11VJvH0qNAd911FxdccAH//Oc/+b//+z+uuOIKpk+fzu9+97u+LrdDWlMkvWp1pZ8hhS5sVmOrzy0tdFHTGCKeSPZBZSIiIiLSU9tvvz0rVqxgzJgxmX8qKyu5/fbbaWlp4T//+Q+33HIL2223HaeddhqPPPIIt9xyCx9++CF1dXW5Lj9DoUh61coNfsqLO960dVOlhS6SSZPaxlAvVyUiIiIi2XDWWWfxxhtvMG/ePFasWMG///1vrr76apqbmykvL8fn8/Hcc89xxx13sGrVKpYsWcLrr7/O2LFjKSkpAcDj8bBs2TIaGnI3Y0jT56RXra0OsMO2xUB8q88dUpQKTxtqWxg2xNu7hYmIiIjkWNxfO+Cvd9BBB3HPPffwm9/8hocffpji4mK+973vcfnllwMwbtw45s6dy7x583juueewWCx897vf5dFHH8ViSY3PnHHGGfz2t79l2bJlPPzww1mvsTMUiqTXJJImjYEIhV4HnQlFRV4HFsNQBzoRERHJa1ZPAYbNQeMHL/X5tQ2bA6unZ13evv7661Z/Pvjggzn44IM7fP5+++3Hfvvt1+HjF110ERdddFGPauophSLpNf5AhGTSpMBjB3Prz7daLRQXOLVXkYiIiOQ1W1E525x7P4lgc59f2+opwFZU3ufX7e8UiqTX1PnDABR47MQ6mXNKC51U1isUiYiISH6zFZUrnPQjarQgvaZhk1DUWam9ihSKRERERKTvKBRJr6n3hzEAr6vzoWhIoYvKumBm0y8RERERkd6mUCS9pr4pjM/jwGLZ+h5FaSWFLkKROP6WaC9WJiIiIiLyLYUi6TX1zenOc503pDDVlruyTlPoREREJD9oBkzvydb3VqFIek19UwhfF9YTQWoDV4DKOrXlFhERkYHNbk+9DwoG9b6mt6S/t+nvdXep+5z0mrqmcCbkdJbLacPrsmmkSERERAY8q9VKcXEx1dXVAHg8Hgyj88sKpGOmaRIMBqmurqa4uBir1dqj8ykUSa+p94cZM7ywy68r2dhsQURERGSgGzZsGEAmGEl2FRcXZ77HPaFQJL0ikTRpDEQo8HRtTRGkptBt0EiRiIiI5AHDMBg+fDgVFRXEYrFcl5NX7HZ7j0eI0hSKpFc0BSKYJl1utACpUPTFstpeqEpEREQkN6xWa9bewEv2qdGC9Ir6ptTGrd0JRcUFTur9YRKJZLbLEhERERFpQ6FIekV9cyoUFXSx+1zqNQ5ME5q0V5GIiIiI9AGFIukV9U1hDAO87q6PFKWDVL0/nO2yRERERETaUCiSXlHvD1PgcWC1dL3tZLo5Q2NzJNtliYiIiIi0oVAkvSIdirrD69ZIkYiIiIj0HYUi6RX1TeFurScCsFkteN12GpoVikRERESk9ykUSa+o84cp6EbnubQCj51Gv6bPiYiIiEjvUyiSXtHgD1PYzelzAD63I9PBTkRERESkNykUSdYlEkkaA5EejxQ1aE2RiIiIiPQBhSLJusZABNOkRyNFBR4H9Zo+JyIiIiJ9QKFIsq5hY5jpyUiRz2OnsTmCaZrZKktEREREpF0KRZJ16Vba3W3JnX5tJJYgFIlnqywRERERkXYpFEnW1fnDGAb43N1ryQ3fBqoGbeAqIiIiIr1MoUiyLt15zmIxun0O38Y9jtRsQURERER6m0KRZF29P4yvB1Pn4NsmDQ1qtiAiIiIivUyhSLKurilMgbf7U+cAnA4rdpuFBu1VJCIiIiK9TKFIsq6+hxu3AhiGsbEtt0KRiIiIiPQuhSLJuvqmcI86z6X5PHY1WhARERGRXqdQJFmVSCRpaolQ2IM9itIK3A41WhARERGRXqdQJFnVGIhgmj3buDXN57Fr+pyIiIiI9DqFIsmqbGzcmlbgcdCo6XMiIiIi0ssUiiSrmgJRoGcbt6YVeOz4g1ESiWSPzyUiIiIi0hGFIskqf0tqZMfjykYocmCaqSl5IiIiIiK9RaFIssrfEsNht2C39fxHK70uSR3oRERERKQ3KRRJVvlbInizMEoE307BUwc6EREREelNtlwXUFVVxd57793m+K233srRRx+dg4qkJ5qDsaxMnYNUKDLQSJGIiIiI9K6ch6KvvvoKp9PJW2+9hWEYmeMFBQU5rEq6q7kliseVnR8rq9WC123XSJGIiIiI9Kqch6IlS5YwduxYKioqcl2KZEFTSwS3M3s/Vj6PXSNFIiIiItKrcr6m6Ouvv2bcuHG5LkOyxN8SxZuFdtxpBR4HDc0aKRIRERGR3tMvRopKSko48cQTWbFiBWPGjOG8885rd51RZ5imSTAYzHKV0ln+QIRth/sIR74NMpFIpNW/u8LttFDTENQ97SdCoVCrf8vApPuYP3Qv84fuZX7QfexfTNNstTxnS3IaiuLxOMuXL2f8+PHMmTMHn8/H/PnzOfvss3niiSfYfffdu3zOWCzG4sWLe6Fa2RrTNGkORgm3+Fm1Ktbm8crKyi6fMxkLUVMf1T3tZ1auXJnrEiQLdB/zh+5l/tC9zA+6j/2Hw+Ho1PNyGopsNhsfffQRVqsVl8sFwE477cQ333zDY4891q1QZLfbGT9+fLZLlU4IReIkkusYOaKCMWNKM8cjkQiVlZUMGzYMp9PZpXNuCFSxZN16Jk2a1OmkL70nFAqxcuVKxo4di9vtznU50k26j/lD9zJ/6F7mB93H/mXp0qWdfm7Op895vd42x7bffnvef//9bp3PMAw8Hk9Py5JuaA6nprgV+Ty4nK42jzudznaPb0lpoYdoPIlhdWSt1bf0nNvt1v9neUD3MX/oXuYP3cv8oPvYP3TlA/WcNlr45ptv2HXXXfnoo49aHf/yyy812jMA+VtSa4a8WWrJDeDzpIY869WWW0RERER6SU5D0bhx49huu+345S9/ycKFC1m2bBm33norn3/+Oeedd14uS5NuaG5JrSPK5ohO4cZQpLbcIiIiItJbcjp9zmKx8PDDD3PXXXdxySWX4Pf72WGHHXjiiSeYMGFCLkuTbkiPFGVr81YA38b23o1+hSIRERER6R05X1NUVlbGrbfemusyJAv8wSh2mwWH3Zq1czodVqwWg6YWhSIRERER6R0537xV8oe/JZrV9USQWiDnc9vxt0Szel4RERERkTSFIsma5pYo7l7oEOdx22kKaKRIRERERHqHQpFkjb8lmtX1RGkel40mjRSJiIiISC9RKJKs8bdE8TizH4q8Lo0UiYiIiEjvUSiSrGkORntlg1Wv244/oJEiEREREekdCkWSNb01fc7rsqv7nIiIiIj0GoUiyZrmYBRvbzRacNloDsZIJs2sn1tERERERKFIsiIcjRONJXtnpMhtJ5k0CYZjWT+3iIiIiIhCkWRFc0sqsPTWmiJAHehEREREpFcoFElW+Deu+emtNUWAOtCJiIiISK9QKJKsaA6mRnF6Y02Rd2PQalIHOhERERHpBQpFkhX+jVPbemfzVjvGJtcQEREREckmhSLJiuaWKFargcNuzfq5LRYDj8uWmaInIiIiIpJNCkWSFf6WVDtuwzB65fwet13T50RERESkVygUSVb4g72zcWuaNnAVERERkd6iUCRZ4W+J9ko77jSPy4ZfI0UiIiIi0gsUiiQr/C1RPM5eHilSS24RERER6QUKRZIVqZGiXgxFbrs2bxURERGRXqFQJFnR3MvT57wuu7rPiYiIiEivUCiSrGju7UYLbhvRWJJwNN5r1xARERGRwUmhSHosFk8QjiZ6udFC6txqtiAiIiIi2aZQJD3m37jWx9vLa4oAteUWERERkaxTKJIeS4eiXl1TtDEU+dVsQURERESyTKFIeqw5mA5FvdmSO3XuJk2fExEREZEsUyiSHuuLkSK7zYrDblEHOhERERHJOoUi6bHmligWA1wOa69ex+e2a6RIRERERLJOoUh6zN8SxeO2YxhGp19jW/F/2L55FxKxTr8mtVeRQpGIiIiIZFfvLQKRQcMfjOLtwtQ5S/0qHAuewsDEvmg+sUkHEh+3N9icW3yd22WnKaDpcyIiIiKSXRopkh5rbonidnYyXycTOD5+GtNXRni300iWbIPjPy/jnn8tRqhpiy/1umwKRSIiIiKSdQpF0mOBYKzT64ls37yDpXEt0Ynfx/SWEpv0AyIzT8OIhrAtf3+Lr/VqTZGIiIiI9AKFIumxQKhzI0WWljocX/yFxKhdMAuHZ46b7mISQydgW/4vSCY7fL3XbccfVCgSERERkexSKJIeaw7Gth6KTBPvf/+AaXMS23ZWm4fjI6ZiCTZgrfxfh6fwuuy0hGLEEx0HJxERERGRrlIokh5rCW09FBU0r8JRtZjY9vuBzdHmcbNgKMmCodiWvtfhOdIbuDarA52IiIiIZJFCkfRYoBOhqLhpGQlXEcny8e0/wTCIj5iCdcOXGC317T7F6051uGtSKBIRERGRLFIokh6JxhLE4smthqJC/0rixdts8TmJiklgtXfYcMGzse23v0Ud6EREREQkexSKpEcCodTmq1sKRZaWWpzRJuLFo7d8MpuDxNDJGxsuJNo8nBkpUgc6EREREckihSLpkcDGbnBbCkX26q8wMYgXjdrq+eIjd8YS9mNd/582j7kcVqwWA7/2KhIRERGRLFIokh5JjxS5thCKHFWLCbnK2m2wsDnTV06ycDi2FR+2ecwwjFRbbq0pEhEREZEsUiiSHtnq9LlkAnvtNwQ9Qzt9zkTpGKw134DZtvW2x2VTowURERERySqFIumRQHDLochSuwwjHqGlC6EoWTQKIxbE8Fe2eczrstOk6XMiIiIikkUKRdIjgVAUu82C3db+j5K1ahFJm5uIs6TT50wWDcc0jNRo0WY8Lpumz4mIiIhIVikUSY+0BLe8R5F1w/+IF48Cw+j8Sa12TN9QrDVL2zzkcWlNkYiIiIhkl0KR9MgWN26NBLA0rNl6K+52JIpHYqltO1LkVSgSERERkSxTKJIeCYRiHXaes1YuwsDc6qat7UkWjcQSbMBoqW913OOy0RyMYppmt+oVEREREdmcQpH0SCAYw+2wtvuYtXIRSV85ptPX5fMmi0YAYKltPYXO47ITiyeJRNtu7ioiIiIi0h0KRdIjzcFoh9PnrNVfkSjp+tQ5ABwekp7SNs0WPK7UtTSFTkRERESyRaFIeqTD6XORFizBBsyCzrfi3lyyaGSbUOR12wGFIhERERHJHoUi6ZFAByNFlqa1ACR95d0+d7J4JBb/Boi0ZI5lRoqCCkUiIiIikh0KRdIjLeH2u89ZGtdiWqyY7s7vT7S5ZNFIAKybrCvyuDRSJCIiIiLZpVAk3RaLJ4jGkh2HIm8ZWLr/I2a6CjGdvlbNFhw2CzarQbNCkYiIiIhkiUKRdFsgGANoPxQ1rCHpK+vZBQyDxGbrigzDwOvWXkUiIiIikj0KRdJtgVAHoSiZwOLf0KP1RJlTFY3EUr8a4t+GII/Ljr8l0uNzi4iIiIiAQpH0QEcjRUZzFUYyjunNRigagWEmsDSszhzzOG00b7y2iIiIiEhPKRRJtwVCqdEbt7P15q2Wxp53nkszvUMwDUvmnABul42mgEaKRERERCQ7FIqk2zqaPmdpXEPSVQh2V88vYrFieodgaVqXOeR1aU2RiIiIiGSPQpF0WyAYw2Y1sNvajhSZ3h42WdhE0juk1UiRx2WjWfsUiYiIiEiWKBRJtwVCMdxOe5vjloa1WZk6l2Z6y1IjRaYJpBstKBSJiIiISHYoFEm3BULRNuuJCPuxRPxZDUVJXxlGPIIRrAPA67IRiycJR+NZu4aIiIiIDF4KRdJtgWCsnfVEqWluZjZD0cYudpbG1Loijys1OqXRIhERERHJBoUi6baWUAxXO6HItNox3cXZu5DTh2lzZZoteFypayoUiYiIiEg2KBRJtzUHo21GiqzpJguGkb0LGQZJb1lmpMi7caSoWaFIRERERLJAoUi6rf3pc2uyup4ozfQNwdKUmpqnkSIRERERySaFIum2QCiG27FJKErEMPyVvRKKkt5yjOZqSMRw2K1YrYZCkYiIiIhkRb8KRStWrGDatGm8/PLLuS5FOqEl1HqkyOKvxDCTWW2ykGb6yjDMZOoahoHPZddeRSIiIiKSFf0mFMViMS6//HKCwWCuS5FOiMWTRGKJ1qFoY+e5ZBY3bk1Leoe0uobbZdNIkYiIiIhkRb8JRXPnzsXn8+W6DOmkQCgVSDbdp8horiTpLASbI/sXtDlJuoowmr5ty61GCyIiIiKSDf0iFH388ce88MIL3HbbbbkuRTopEIwBtGrJbQnUYLqLeu2apq+sVVvuppZIr11LRERERAYP29af0rv8fj9XXnkl1157LcOHD+/x+UzT1BS8PlDX0AyA1ZIkHAkD4GzaQNxdQjTaegQnFou1+nd3Ga4SHDVfE46EcdoNahvDutd9LBQKtfq3DEy6j/lD9zJ/6F7mB93H/sU0TYxObhOT81B04403Mm3aNH74wx9m5XyxWIzFixdn5VzSsSXrUv+z11ZvIOS3gmkyNVCN315GQ1VVu6+pr6/v0TV9MRsjIs2sXfYVsXCShqao7nWOrFy5MtclSBboPuYP3cv8oXuZH3Qf+w+Ho3PLOnIail555RUWLlzIq6++mrVz2u12xo8fn7XzSftqoxuAOrYfNxa7zYIRbsL6eRxP+SgcQ4a2em4sFqO+vp7S0lLsdnu3r2kpsEPVh4wttLJ2aAlfrV3P5MmTe/iVSFeEQiFWrlzJ2LFjcbvduS5Hukn3MX/oXuYP3cv8oPvYvyxdurTTz81pKHrppZeoq6tj3333bXX8hhtu4PXXX+e3v/1tl89pGAYejydLFUpHYgkDq9XA53FjGAaWptUAWAvLsXSQyO12e6fTertsFZgWK65gDUW+oUTjSSw2By5Hzgc8Bx23263/z/KA7mP+0L3MH7qX+UH3sX/o7NQ5yHEouvPOOwmHw62OHXjggVx88cUcfvjhOapKOiMQiuFx2jI/bJZADSZgunqv0QIWC6ZnCJamdXiHTgOguSWmUCQiIiIiPZLTd5NDhw5t9/iQIUM6fEz6h0Cw9catRqAa01UI1t79kUp6h2BpXItnTOo6/pYI5SUanhYRERGR7usXLbll4AmEoq3bcTdXY7qLe/26pqcUS3MVno3Xbg5qryIRERER6Zl+N+/o66+/znUJ0gmBYAz3JtPWjOYqTO+QXr9u0lOCEQvhs6T2KPJrA1cRERER6SGNFEm3BILRb6fPmebGjVtLev26pid1DWe4BqvFUCgSERERkR5TKJJuaQ7FMtPnjHATRiJK0lPc69c13cWYgLW5Gq/bTrNCkYiIiIj0kEKRdEtLKIbbaQXAaK4G6JM1RVjtmK4ijOYqPC6bRopEREREpMcUiqRbAqFvu89ZmqsxMTDdvdiOexOmuzjVbMFlVygSERERkR5TKJIuiyeSRKKJTCgyAlWpdtyWvunbYXqKMx3oFIpEREREpKcUiqTLWkIxgNYjRX0xdW6jpKcUI1CD12mhqSXSZ9cVERERkfykUCRdlg5F6UYLlkA1Zh80WUgz3SUYyThDbCGag7E+u66IiIiI5CeFIumyQHqkyGEDM4kRqCHZhyNF6bbcZUaTus+JiIiISI8pFEmXbTp9zgg1YSRifbJHUZrpKsC0WCk2G4jEEkRiiT67toiIiIjkH4Ui6bJAZvqc9dt23H04fQ7DgukuoTBeD4A/oNEiEREREek+hSLpspZQDMMAh92KJVCVasft6pt23GmmpxhPtA4Av5otiIiIiEgPKBRJl6X3KLIYBkagJrU/kcXapzUk3SU4QzUAasstIiIiIj2iUCRd1tJq49aqPm3HnWZ6SrGFG7ETVygSERERkR5RKJIuawnFcDk23aOob6fOwbcd6IbZmxWKRERERKRHFIqky1LT56xgmhvbcfdd57m05MZQtI2rRaFIRERERHpEoUi6LBCM4nTYINKMkYxjugr7vgi7G9PuZpgjQJMaLYiIiEgWRWMJFi6u4um/LiYQ1Ievg4Et1wXIwBMIxSjyOrC0pLq/5WL6HKSaLVS0+PlCI0UiIiKSBetrAzz52iI+/bqaSPTbfRBPPnhyDquSvqCRIumydPc5o6UWANNZkJM6TE8xQ2jUPkUiIiKSFU/NX8z/ltexz7RR/OzYaewxZTivf7CCcCSe69KklykUSZcFQzFcThuWljpMmxPsrpzUYbpLKE420BQI5+T6IiIikj+ag1E++t8G9pw6gn13HcXQUg+zdh5BSzjG2x+vznV50ssUiqRLTNMkEE6FIqOlvs83bd1U0lOCw4yQCPpzVoOIiIjkh39+to5kEqZuX545VlLoYqdxZbzyz2UkkmYOq5PeplAkXRKJJUgkTNyO1PQ505WbqXPwbVtud6QW09QvKhEREem+tz5ezYTRxRR4HK2O7zV1BJV1QT76ckOOKpO+oFAkXdISigHgdlpT0+dy0Xluo/SmsaX4CYY111dERES6Z3Wln6VrGtl1YkWbx0ZVFLDtiEL+9O7SHFQmfUWhSLoksDEUuRxWjGAdyRyGIqx2YnYvZVZt4CoiIiLd987CNXhcNiaNLW338T13HsFXqxr4amV9H1cmfUWhSLokPVLktUQwErGcjhQBJJ1FlFma8WuvIhEREemGRCLJOwvXsPP4cmzW9t8aTxxbSlmxm7/+e2XfFid9RqFIuiQTiuJNADlttACpPZI0UiQiIiLd9dmSGhqaI0yf1HbqXJrFMNh+m2IWrajrw8qkLykUSZekp8+5Y40AOR8pMrwlG0eKFIpERESk697+eDVDSz2MKPNu8XnbVPiorAvqPUeeUiiSLmkJxbBZDezhekyrA2zOnNZjeIrxWqIEmppyWoeIiIgMPMmkyadfV7PTuCEYhrHF524zNNVxd8nqhr4oTfqYQpF0SUsohju9R5G7CLbyC6S3JTd2oIvVV+a0DhERERl41tUECIbjjBm29ZkvpYUuvC6bQlGeUiiSLglkQlEdpjN3exSlpdtym/7q3BYiIiIiA0464Iws9231uYZhMKqigK9XKRTlI1tnn/jxxx936cTf+c53ulyM9H8toRguhw1LSy3JgqG5LgfsLiI4sAZrcl2JiIiIDDBfr26gosSN29m5t8Sjhvr48MtKTNPc6nQ7GVg6HYpOPvnkDm++aZoArR5fvHhxD0uT/igQiuFyWFIjReUTcl0OACGrD2dY3WBERESka75e1cCois7PfNmmooC3Q2tYX9vSqdElGTg6HYqeeuqpzH+vX7+e6667jh/96EccfPDBlJeX09jYyDvvvMPvf/97fvnLX/ZKsZJ7gWCMInscoyVK0pX76XMAEZsPb0RD2SIiItJ5kViCVRv87DRr206/Jt1s4etVDQpFeabToWi33XbL/PfJJ5/MaaedxmWXXdbqObvuuisul4snnniCQw45JHtVSr8RCEUZ6WsBUnsE9QcxRyFF4eW5LkNEREQGkOVrm0gkzUzQ6Qy300Z5sZslqxv43oxterE66WvdarTw3//+l913373dx6ZNm8aSJUt6VJT0X4FQjBICQO73KEpLOosoMoLEIuFclyIiIiIDxNerG7DbLAwr9XTpdaMqfHy9qr6XqpJc6VYoGjZsGP/617/afexvf/sbo0eP7lFR0n8FQzGK8G/co8iV63KAb9ty+yvX5bYQERERGTCWrG5gRLkPq7Vrb4e3GVrAivV+IrFEL1UmudDp6XObOv3007nxxhuprq5mv/32o6SkhNraWv72t7/x7rvvcvfdd2e7TukHkkmTYCROgelPjRL1k64rFm8JAIGqdQwZMy7H1YiIiMhA8NWqeiaOLuny67YZWkAiabJ8bROTty3thcokF7oVio477jji8TgPPfQQ8+fPzxwfPnw4d955JwcffHDWCpT+IxiJY5rgifv7zdQ5ALu3kJhpIVq7IdeliIiIyADQ2ByhpiHE/jO6PrtpWKkHu83C16sbFIrySLdCEcBJJ53ESSedxLJly/D7/ZSUlDB27Ngslib9TUsoBoA71oDpG5bjar7lctpoSnrwNFbmuhQREREZANKbtm4ztOsd5KxWCyPKfZlzSH7odihKGzeu9XSlYDDIwoUL2XvvvXt6aulnAsEoYOKINJJ0Tcx1ORlOu5XKpBt3U1WuSxEZMGy2Hv/6l35C91Kk675e3YDPY6fY5+zW67ep8PGVmi3klW79Jl23bh033ngjCxYsIBqNtvscbd6af1rCMTxGFGsiQrwfTZ8zLAYthhdrS22uSxEZEBwOByNHjsThcOS6FOkh3UuR7lmyqoFRFT6Mbq6PHlHm4/3/rKclFMPrtme5OsmFbnWfu/XWW/n000/5yU9+wuTJk9l1110544wzmDhxIoZhMG/evGzXKf1ASyhGqaV/teNOC1p9OCINmEl1ghHZGtM0icfjmKaZ61Kkh3QvRboumTRZsqaBbSq6vwl9eYkbgHU1gWyVJTnWrVD08ccfc+mll3Lttddy9NFH43Q6ueKKK3jppZf4zne+w9tvv53tOqUfCARjlFo2btzaz0JR2FaAhSRxf12uSxEZEJLJZK5LkCzRvRTpmvW1AYLheJc2bd3ckKLUtiTrFYryRrdCUUtLCxMnptaUbLfddixatAgAq9XKCSecwIcffpi9CqXfaAnHqLAHMS02sLtzXU4rcUcqpMUb1GxBREREOrZqQzMAw4d4u30Ol8NGgcfBupqWbJUlOdatUFRRUUFtbWr9xpgxY2hqaqKmpgaA4uJi6ur0aX0+CoRilNmDmK6ifrNHUVrSWUgSg5hCkUgb/W1qVX+rR0QGl9VVzfjc9h6vBSordmmkKI90KxTts88+3HvvvXz22WeMHDmSYcOG8fjjjxMIBHjppZcYOnRotuuUfqAlGKPE2oLp7Hr7yt7mdNppMd0KRTLgzZkzh4kTJ3b4z6xZszp9rsrKSs4++2zWrVvX47rWrl3LxIkTefnllzPHAoEAM2fO5Ljjjuuw6c6m/H4/V155JQsXLswcO/nkkzn55JN7XN/WdHSdQCDAMcccw0477cRbb72VOb5kyRIuvfRSZs2axU477cSee+7JJZdcwldffdWt69fW1nLZZZcxc+ZMpk+fzuzZs6murt7q61atWsW5557LjBkzmDlzJjfccAOBQOs3YaFQiJtuuolZs2Yxbdo0zjrrLJYvX97hOd95553MbI/Nff7555x88snssssu7LHHHlx11VX6oFPyzpqq5syaoJ4YUuRmrUJR3uhW97mLL76YL7/8kvvuu48nn3ySSy+9lDlz5vDkk08CcP3112ezRuknAuEYxUYLpqsi16W04XLYaEy6GapQJHmgvLy8w4Y1dnvnP9n8v//7P957771sldXGk08+icfj4YEHHuhU97PFixfz5z//mR/96EeZYzfccEOv1bc1gUCAn/70p3z11Vc88MAD7LPPPgB88803HHvsseyyyy5ce+21DBkyhMrKSp555hmOOeYYnnrqKXbZZZdOXycej3PWWWcRCAS48cYbicfj3HXXXZx55pm8/PLLHd5Tv9/PqaeeSllZGbfddhv19fXccccdrF27lsceeyzzvJtuuomvvvqKK664Ap/Px7x58zjllFOYP38+RUVFrc750Ucfcdlll7V7vS+//JJTTjmFPfbYg3nz5lFdXc3dd9/NBRdcwO9///tOf70i/d3qKj9DS7o/dS6tvNjNl8tqMU2z213spP/oVigqKSnhD3/4Q+ZTrsMPP5wRI0bw+eefs/POO7PbbrtltUjpHwLBGIU0YzrHbf3JfczlsNGYcBOr35DrUkR6zOFwdOlNd64cddRRnHjiiZSUlHT7HOPHj89iRZ2XDkSLFy/moYceajUC98QTT1BSUsKjjz7aag+g73//+xx00EE8+OCDPPLII52+1t/+9jcWLVrE/PnzM1/v5MmTOeyww/jrX//K4Ycf3u7rnn/+eRobG3n55ZcpLS0FYOjQoZx99tl88sknTJ8+nc8//5z/+7//46GHHuJ73/seADNmzGD//ffnueee47zzzst8vY8++iiPPvooBQUFBIPBNte744472GGHHXjwwQexWFITSXw+HzfffDNr1qxhm2226fTXLNJfJRJJ1te0MGVcWY/PVVbkIhxNUO8PM6Sof621lq7r1vS5tIqKb0cMZsyYwU9/+lMFojwWCgbxmCFMV/e7tfQWp8NKU9JDrLFa6xVk0Fi9ejXnnnsuM2fOZOrUqRx77LGZkaGXX36Zq6++GoD999+fOXPmABAOh7nnnns46aSTmDZtGrvuuiunn356m73l3nzzTQ4//HB23nlnjjrqqHanjc2ZM4eLL7448+f6+nouu+wyZs2axZQpUzjiiCN45ZVXgNQIxSmnnALAKaeckpnKtvm0tmg0yr333sv+++/PzjvvzGGHHcaf/vSnVtd9/fXXOfroo5k2bRqzZs3i+uuvp6mpqdPft5aWFs466yy+/vprHnnkkTZTEmtrU5/8bt7VzePxcM0113DwwQdnjr388stMnDiRjz76qMPrvf/++2y77batAuD48eMZN27cFkfy3n//faZPn54JRAB77rknXq+Xf/7znwB88MEHuFwu9thjj8xzSktL+c53vtPq3H/84x958cUXuf766znppJPaXKuhoYEFCxZw/PHHZwIRwIEHHsh7772nQCR5o6o+SCyepKLE0+NzDSlOBaH1araQFzo9UpT+y7Wzbr311i4XI/2bEUq96TCd/S8UuR1W/Ek3xMIkg36s3qKtv0ikH4vH4+0et1qtGIZBMpnknHPOoaKigttvvx2bzcZTTz3Feeedx1//+lf23XdfzjvvPB566CHmzZuXWUNy5ZVX8vHHH3PmmWeyww47sG7dOu677z4uu+wy5s+fj2EYvPPOO1x88cX88Ic/5IorrmDx4sVcccUVW635iiuuoK6ujl/84hf4fD7+/Oc/c9VVVzFs2DB22mknrr/+en75y19y/fXXM3PmzHbPcfnll/Pee+9x3nnnMXXqVN577z3mzJmD3W7nsMMO48EHH+T+++/nhBNO4NJLL2XNmjXcd999fP7557z44ou4XK4t1hgMBjn77LNZtGgRjz32GDNmzGjznH333Zf33nuP4447jh/96Ed897vfZbvttsMwDA466KA2z33hhRe2OOK1bNkyxo4d2+b46NGjWbFixRZfd8ghh7Q6ZrVaGTVqVOZ1y5cvZ8SIEVit1jbnfvXVVzN//t73vsexxx6L2+1m7ty5ba719ddfk0wmKS0t5bLLLuOdd94B4IADDuDaa6+lsLB/bcMg0l1rqlKd5yqysKaotNCFYaT2KpoyvucjT5JbnQ5Fm38KVl1dTTweZ8SIEZSXl9PY2MiaNWtwOBxMmjQp64VK7tnDDWCjX44UuZw2mpKpX3CxxiqFIhnQ1q1bx4477tjuY1deeSVnnnkmdXV1LF++nPPPPz+zFmbnnXdm3rx5RKNRSktLGT16NJCaqjVq1Cii0SgtLS1cc801TJ8+nfLycvbYYw8CgQC33XYbtbW1lJeX88ADD7Dzzjtzxx13ALDXXnsBcNddd22x7gULFnDBBRfw/e9/H4DddtuN4uJiHA4HPp8vExzGjx/fbohYsmQJb7zxBtdccw2nnnoqALvvvjvr1q3jo48+Yq+99uKhhx7imGOOabV2dcKECZx44om89NJLnHjiiR3Wlw5En3zySebP7TnhhBOoqanhscce45e//CWQmja+5557csopp7DzzjtnnltaWtpqJKc9zc3NjBkzps1xr9dLS0vHnzA3Nzfj9bZd9+D1ejPNFgKBAB5P20+8Nz93+mehI/X19QBcc8017L333jz44IOsXLmSu+++mzVr1vDcc89pzYTkhdVVzbicVgo8W18HuTU2q4XSQpc2cM0TnQ5F6U+NAF599VXuvPNO5s6d2+ovh6VLl3L++ee3mlog+cMV86dCUT8cKUpPnwOIN1bByAk5rkik+8rLy3nooYfafWz48OEAlJWVMX78eK677jref/999txzT/bee+8tjuo7HA4ee+wxYrEYixYtYsWKFaxZs4Z//OMfQGrqWjgc5n//+x8/+9nPWr324IMP3moomjlzJnPnzmXRokXstdde7LPPPlx11VWd/rrTYeXAAw9sdTw9svHee+8RjUY57LDDWj0+Y8YMRo4cyYIFC7YYir788kuKi4t59tlnueaaa5gzZw5//vOfKS8vb/Pcn/3sZ5x22mn861//4t///jcfffQRr776Kq+99hrXXHNNZipgZ2xpSu+WgkZnXreljVu7EmJisRgAO+64IzfffDOQCqSFhYXMnj2bDz74gD333LPT5xPpr9ZUNVNR4slayB9S5FZb7jzRrTVF99xzD7Nnz24ViCD16d8ll1zCb3/726wUJ/1HLJ7EZzYTt7jA2rO+/r3BYbOQsNhJ2NzEGqpyXY5IjzgcDqZMmdLuP2VlqSkahmHw+OOPc+SRR/L+++9z+eWXM2vWLC655JItrq/517/+xQ9/+EOOOeYYLrroIv7yl79kOseZpklTUxOmabZpnrDpGtKO3HPPPZx22ml8+eWXXHvtteyzzz6ceeaZnW4J3tjYCMCQIUPafTz9daW/B5sqKyujubl5i+cvKirid7/7HdOnT+f222+nqamJq666qsPwUVRUxGGHHcbNN9/MW2+9xZ/+9CfGjRvHHXfcQUNDQ6e+Jkg1K2hvRCgQCFBQ0PGHTJ15XUdNE1paWrZ47s2lR6T222+/VsfTo4TpTdpFBrrVVc2UF2evKUJ5sYu11QpF+aBboaihoaHD+cU2m63DKQkycLWEYpRYgsTsPW9h2SsMA7fTSsTmUyiSQWPo0KHceOONvP/++7zyyiuceeaZvPnmm9x7773tPn/16tVccMEFTJo0iWeeeYYPP/yQ5557rtUb4eLiYiwWS2aD7rR0YNmSgoICrrjiCt555x3++te/Mnv2bD799FN+8YtfdOrrSf+9kp7KlbZs2TI++eSTTHvpzWsDqKmp2WoXvIkTJ2amd0+dOpVzzjmHDz74oNUHeVVVVey555784Q9/aPP6HXbYgUsvvZRoNMqaNWs69TUBbLvttqxevbrN8dWrVzNuXMfdPNt7XSKRYO3atZnXjR07lsrKyjYjRqtWrdriuTeXXvO0+X5T6bVtW1urJTIQJJMma6sDWWmykDakyE1VfZB4ouNRWxkYuhWKdtllFx566KE2n0ZWV1czd+7cDhfQysDVEo5RYgmQdPS/jVvT3E4bQYuXeKP2KpL899lnn7HHHnvw3//+F8MwmDx5MpdeeikTJkxg/fr1AK26iEFq+lgkEuHMM89k5MiRmekj//rXv4DUSJHT6WTatGm8+eabrUZQNp1C3Z5169axzz778Le//Q2A7bbbjrPOOos99tgjU8/mzQA2N3369Havdeedd3LzzTczdepUHA4Hr732WqvHFy5cyPr169l11123eP7NnX/++UyZMoX77ruP//73v0BqxMlms/Hcc88RiUTavGb58uU4nc521wh1ZM8992TZsmUsXbo0c2zp0qUsW7Zsi5vxzpo1i48//rhVSHz//fcJBoOZ1+2xxx4Eg0E++OCDzHPq6+tZuHBhlzb6HTduHCNHjmT+/Pmt7vvbb78N0G5DCpGBprYxRCSayGooKit2k0iaVNdrQGCg69Y+RVdddRUnn3wy++23H9OmTaO4uJi6ujo+++wzioqKOpwLLwNXIBilxNKC6eq/bVldDhsB06ORIhnwotEon3/+eYePT5w4kR122AGXy8WVV17JRRddRFlZGf/3f//H4sWLM+td0iMvf//739l7773Zcccdsdls3HPPPRx55JG43W7+8pe/8O677wLfNh6YPXs2p556KhdeeCHHHnssK1as4OGHH95izSNHjmTYsGHcdNNNBAIBRo8ezZdffsl7773HOeecA5CZzvXuu+9SVFTUpinPpEmTOOigg7jjjjsIh8NMnjyZf/7zn/zjH/9g3rx5FBcXc/bZZ/PAAw9gt9vZb7/9WLt2Lffddx/jx4/nqKOO6tL32Wazcccdd3DUUUcxe/ZsXnnlFXw+HzfeeCMXXHABP/rRjzjxxBMZN24coVCIDz74gGeffZaf/exnmVGr+vp6Vq9ezfjx4/H52v/Q6JBDDuHhhx/mrLPOymycetdddzFhwoRWa3AXLVqEw+HINKE44YQTeOaZZzj99NO58MILaWxs5I477mDvvffOBMAZM2awyy67cNVVV3HllVdSXFzM3LlzKSgo4Pjjj+/098IwDK688kouueQSLr30Uo455hiWLl3KPffcww9+8AN22GGHLn1vRfqjNdXZ6zyXVrZxKt66mgAjyvvvB8eydd0KRZMmTeK1117jySef5NNPP2Xt2rWUlJRwxhlncOqpp1JcXJzlMiXXWkJxii1BEq7+25bV7bDREHaTCNVjxmMYtv639kmkM2pqajj22GM7fPyVV15h8uTJPP7449x1113cfPPN+P1+xo4dyy9/+UuOPvpoINX4YI899uCuu+7i3//+N4888gh33XUXc+fO5ec//znFxcXssssuPP3005x88sksXLiQiRMnMmPGDB599FHuvvtuLrzwQkaNGsUtt9zCueeeu8W6582bx9133819991HQ0MDw4cP58ILL+Tss88GYPvtt+ewww7j2Wef5V//+lebER9IbSA6b948fve739HQ0MC4ceO4//77Mx3t0gHwmWee4YUXXqC4uJiDDjqISy65pN0ubFuz7bbbcuWVV/KLX/yCG264gbvuuot9992XF198kccee4yHH36Y+vp6HA4HO+ywA/fcc0+rRhDvvvsuV199NU899VSHsyQcDgdPPPEEN998M9dddx12u51Zs2Zx9dVXt9oc9sILL2TkyJE8/fTTQKqz3VNPPcUtt9zC5Zdfjtfr5aCDDuLKK69sdf5f/vKXPP7449x+++0kk0l23XVX7r333kxw66yDDjqIhx56iAceeIBzzjmHoqIijjvuOC699NIunUekv1pT1YzDZqGowJm1cxZ6HdhtFtbVtPCdrJ1VcsEw82inyy+++AKAKVOm5LiS/POvBUsZ+ferCE48CGNE1z4xjEajVFVVMXTo0MyC7t7w0ZcbiNes4vv8H6POvR/HkJG9dq3BKhgMsnjxYiZPntytN6DSP8RiMWpqaigvL8du14cHA5nuZf7Q79fed/8Ln7FoRT0X/HhqVs8798XPmTqhnAt+PFX3sZ/pSjbo9EjRK6+8wj777ENJSUlmh/ItOfLIIzt7ahkAIk01AFjdhfTXpYRup42lYQe4IN5QpVAkIiIiGWuqminP4tS5tCHFLtapA92A1+lQNGfOHF588UVKSkqYM2fOFp9rGIZCUZ6JN6W6PZnufjx9zmmnIWoHj5VYg5otiIiISIppmqypambW1Ox/YFpW5Oa/S9t2xZSBpdOh6O23385scJfuRiODhxmoJ2mC2Y+7z7kcVkwMDE9xagNXEREREaChOUJLOJ7VJgtpZcVu6v1hQpF41s8tfafToWjkyJHt/ndaPB4nEAioyUKeMoL1hAx3mxa//YnblfpxTroKiSkUiYiIyEZrqtKd57K/zqesKBW0NtS2MKxEa/sGqm69w43H48ybN49XX30VgI8++ohZs2ax++67c+qpp25xN3UZmOzhRsJG9j9dySaXMxWKYvYCTZ8TERGRjDVVzVitBiWF2d+IuKw4dU6tKxrYuhWK7r//fh566CH8fj8AN910E8XFxVx99dWsXr2au+66q9Pnqqur44orruC73/0u06ZN4+yzz2bZsmXdKUt6kTPWSMTav7uouB2pUBS2+og3VpNHjRVF8tKcOXP43ve+l9Mafve733HAAQew8847c9RRR/Hee+9t9TX19fVce+217LXXXsyYMYPTTjuNRYsWtXrOqlWrmDhxYpt/DjvssFbPe/HFFzn00EPZZZddOPjgg3n22Wfb/O5auHAhJ5xwArvuuiv77rtvZi8oEem8NVXNlBW5sVqMrJ/b47LjclipatAGrgNZt/Ypmj9/PrNnz+bEE09k2bJlfPPNN9x2220ceeSRFBcXc/vtt/PLX/6yU+e64IILSCaTPPLII3i9Xu677z5OO+003nzzTdzu/j0yMZh44n4anSNyXcYWWa0GDruFFsNLYSxCoqUJm68412WJSD/1xBNPcMcdd3DBBRew00478dJLL3Heeefx1FNPMWPGjHZfY5omF110EcuWLePyyy+noqKC3/72t5x00kn8+c9/ZpttUhtcL168GIAnn3yy1d9lLte3n1L/4Q9/4LrrruPkk09m//33Z+HChfzqV78iEolwxhlnAPDNN99w+umnM336dO69916qqqq48847Wbt27VY31BWRb62vbWFIUfZHidKKC5xUKxQNaN0KRdXV1Uydmurx/u6772KxWNh7770BGDZsGM3NzZ06T1NTEyNHjuScc85hwoQJAJx//vkcccQRfPPNN+y8887dKU+yzDRNfGaAWoc316VsldthoynpZDgQb6xSKBKRdoXDYR588EFOP/10LrjgAgD23ntvjjvuOB544AGeeOKJdl+3cuVKFi5cyE033cSPf/xjAHbddVe++93v8uc//5kLL7wQSIWiYcOGsfvuu3dYw0svvcT06dO59tprAdh9991ZsWIFzzzzTCYUvfrqqxiGwQMPPIDXm/odnEgkuOGGG1i3bh0VFRXZ+YaI5Ln1NQEmjSnttfMX+5zU1CsUDWTdmj5XUVHB2rVrAXjnnXeYPHkypaWpH7TPPvuMYcOGdeo8RUVF3HXXXZlAVF9fz5NPPsmwYcMYP358d0qTXpAM+rEZSZL9uPNcmstpoyGe+iRI64pEtuzAAw/knnvu4ZZbbuE73/kOM2fO5Morr6SxsXGLr/vBD37AxRdf3Ob4EUccwXnnnQek3rg/8sgjHHbYYey8887ssssuHHfccXz44YcdnnfixInMnTu31bG5c+cyceLEVscWLlzISSedxNSpU9ltt9246qqrqK+vb3OuLW0f8Z///Ae/388BBxyQOWYYBgcccAAfffQR4XC43ddFIhEAfL5vfx96PB6cTmer79tXX33F5MmTO7x++lybngeguLi41XkikQg2m63VaFO6odHW7pOIpMTiSWoaQ708UuSiqiHUa+eX3tetUHTYYYdx6623cuaZZ/LJJ5/wox/9CICbb76ZuXPn8sMf/rDL57zuuuvYfffdmT9/PjfffLN2Ae5HIo3VAJjOghxXsnUup5WmsInF5VVbbpFOeO655/j000+59dZbueyyy3jvvfc455xztrgm7/DDD+e9995rta5l2bJlfPXVVxxxxBEA3HnnnTz44IMce+yx/Pa3v+VXv/oVjY2N/OxnPyMU6v4bh48//pjTTjsNl8vFvffeyzXXXMOCBQs45ZRTWgWZF154gfPPP7/D86TXro4dO7bV8TFjxpBIJFi9enW7r5s0aRLf/e53efDBB1myZAmNjY3cdttthMNhDjnkkMzzFi9eTEtLC8cddxxTpkxh1qxZ3HnnncRiscxzTjnlFN5//33+/Oc/09zczL/+9S/+9Kc/Zb6HQObv11tvvZWGhga++eYbHnjgASZMmMCkSZM6/40TGcSq6lswTRhS1HvLMkoKnNQ0BLWeeQDr1vS5Sy65BI/Hw8cff8xll13GCSecAMAXX3zBGWeckfmksCtOPfVUjj32WJ599lkuuOACnnvuOXbccccun8c0TYJBDV9mU8P6NQAk7B6i0WiXX59+E7Dpm4He4rRZ8AejGIXFhGrX4dTPQlal38z25E2tgMPhyOlfnOn/F03TxDAMHnnkEQoKUh96FBYW8rOf/Yx3332XPffcs93XH3LIIcydO5c33niDww8/HIC//OUvFBYWstdeexGLxaiqquLiiy/muOOOy7zOarVy6aWX8r///Y+pU6eSTCYxTbPV74ZEItHmz5vWfOeddzJ27FjmzZuH1WoFYMcdd+SII47gxRdf5Pjjj88c2/R1m0t3SXU6na2e43Q6gdQoTEevvfbaazn33HMzHwAahsFNN93ElClTiMViNDQ0UFVVRTweZ/bs2Vx88cV89NFHPPbYY6xfv55f//rXQGrE7cMPP+TKK6/MnHvWrFlcccUVmWtvu+22zJ49m5tvvpmnnnoKgBEjRvC73/2OZDLZp79fO2IYRrf+bpDW9Pu196xclxpJ9rkNwpH2R4F7yuuyEI4mqG1ILSHRfewf0n/PdUa3QpFhGJxzzjmcc845rY7//ve/787pADLT5W6++Wb+85//8Mwzz3Drrbd2+TyxWCyzwFWyI/7NYkpMC02hCLaq7o++bD69pTfEY2H8gSjhAjvh9SvZoJ+FXrFy5cpclzBg2Ww2Ro4cSTweJ5lM5rSWZDLJ7rvvTjgczoyy7LTTTlitVv75z38yYcKENjVaLBacTic77bQTf/7znzNrZl599VX22muvTNi4/PLLgVSjgDVr1rB27Vr+/e9/A6l1qTU1NYTDYRKJBDU1NZnzB4PBNn8GMs//73//y7HHHtvqOS6Xi9GjR/Puu+/y/e9/v1Nfe3rta21tbSZcwbdhye/3t7pG2qpVq7jooosYNmwYN954Iz6fj3/84x9cf/31RKNR9t13XyKRCHfccQejRo3KTCcfO3YssViMxx57jJ/85CeMGTOGq666ii+++IJzzjmHSZMmsWLFCp588kkuvPBCfvWrX2EYBs899xyPPvooRx55ZOb7+/TTT3P66adz3333Zaau52oqncViwWazsW7dOuJxbVyZDfr9mn3/+aoZqwXqa9bTUJv97nMAwebUBxNfLF7B8FKH7mM/4nA4OvW8boUiSP2F8uGHHxIMtj9UeOSRR271HPX19fz73//mBz/4ATZbqhSLxcL48eOprq7uVl12u13rkbJs9fIPaU66GDa0gtJCZ5dfH4vFqK+vp7S0FLu9dzc1awg3sqq6hoKKEUTXLGKbrczpl64JhUKsXLmSsWPHqjtkD/SHkaLGxkYsFgtjxoyhvLy81eMlJSXEYjFWrlyZWfCf9vjjj7Pbbrtx9NFHc/PNN2O321m7di3r1q3jlltuyZzryy+/5KabbuLLL7/E7XYzbtw4hg8fDqTWk5aXl+NyubBara2u7/F42vwZoLy8nKqqKpLJJM8//zzPP/98m6/L6/W2+Vo6kg4rbreboqKizPF0QBo1alS753rggQcwTZMnnngis7bnoIMOIh6PM3fuXH784x9jGAajRo1q89qDDz6Yxx57jOrqaqxWKwsWLODGG2/MNGyA1PS8888/n0WLFrHnnnvyzDPPcOihh3LTTTdlnrP//vtz8MEH85e//IVLLrmExsZGiouLe/33a0cMw9CU9yzQ79fe8+9lXzGkKNpmumw2DQnFeH3hf3F4S4GA7mM/sXTp0k4/t1uh6F//+hcXX3xxh0ODhmF0KhTV1tYye/Zsfvvb37LXXnsBqb+sFy1a1O29K/TLuRcEmwiYLnxuZ6fTdnvsdnuPXt8ZPo+TRNLE4htCsqURl92Kxd71ICdb5na79f9ZHjAMg6amplZvphOJBI2NjZSVlTF16lT++Mc/tnrNtttui91uz6wtfffdd1m+fDkjR45k5syZGIZBIBDg3HPPZeLEicyfP5/tttsOi8XCe++9x9///ndsNht2ux2LxYJhGG3ezG/653RjA7vdTnFxMYZhcNppp3HooYe2+Xrcbneng0H6w7P169dTVlaWOb5u3Trsdjvbbbddu+eqrKxku+22axOYdtttN9544w38fj+BQIAPP/yQQw45hMLCwsxz0iMpFRUVmQ/+dtttt1bX+e53vwvAihUrmDp1KqFQiBkzZrR6zrBhw9h2221Zvnx55rjdbs9ZKAIyH2xKz+n3a/ZVN4QpK3bjcvZeowWnw4ndZqEpmGSIU/exv+js1DnoZii666672G677bj66qsZOnQoFku3+jUwYcIE9t57b2666SZuuukmioqK+M1vfoPf7+e0007r1jmlFzTXEki6KLRbt/7cHEtv4Bqxp9ZHxJtqcJS1/cRWRFL++c9/Eo1GMx9YvP3228TjcXbffXd8Ph9Tpkxp93WFhYXst99+vP322yxZsoTDDz8885fP8uXLaWxs5JRTTmk1cv/Pf/4ToMNpgz6fj6rNpuh++umnrR7fYYcdWL58eau6wuEwF198Mfvss0+nZwpMmzYNj8fDG2+8kdn+wTRN/v73v7Pbbrt1+AHOtttuy0svvZQZndm0zoKCAoqLi1mxYgU33HADFouFY445JvOc119/HZ/Px4477pjp4Lpw4ULGjRvX5uvdZpttGDJkCMXFxXzyySeZtbuQmmWxcuXKzNYYIrJl62tbmLBNca9ewzAMin1OahvDbFfSq5eSXtKtULRs2TIefPDBDje364q7776bu+66i0svvZTm5mZmzJjBs88+y4gR/Xuj0MHECDUQMMuw27sXfvuSy5n6kQ4aPpyk2nIrFIl0bMOGDZx33nmccsopbNiwgbvvvpu99tqLmTNnbvW1hx9+OBdffDGJRKJVx7Rtt90Wn8/Hww8/jM1mw2az8cYbb2RGnTqaZbDvvvsyf/58pk6dypgxY3j55ZdZtWpVq+fMnj2bs88+m8suu4zDDz+cRCLB448/zn/+859W3eY+//xzSktLGT16dLvXcrvdnHHGGTzwwAPY7XamTZvGSy+9xP/+979MQwNIjQxVVlayww474HA4OP3003n11Vc57bTTOOeccygoKODNN99k/vz5XH311dhsNqZPn87uu++e6Uo3fvx43n33XZ5++mnmzJlDYWEhO+ywAz/4wQ+47bbbaGpqYurUqSxdupS5c+ey4447csABB2C1Wrnooov41a9+hdfr5eCDD6ahoYHf/OY3WK3WNlMbRaStWDxJdUOQ3acM79TzDX8l1uolJIbvgOkt2/oLNlHkc1LTGAZ6d1aM9I5uhaIRI0a0asXaEwUFBdx4443ceOONWTmfZJeZiGOL+glZtunSEGSuuJ2p0ayWpBOn1UZcexWJbNGhhx5KYWFhpqvoUUcdxaWXXtqp1+6zzz4UFBSwzTbbsO2222aOFxQU8OCDD3L77bfzs5/9DK/Xy+TJk3nmmWc466yzWLhwYbtTpK+++mri8Ti//vWvsdlsHHLIIVx22WWZzU0B9txzTx577DHmzZvHxRdfjN1uZ8cdd+SJJ55gl112yTzv2GOP5aijjuK2227rsP4LLrgAq9XKiy++yOOPP8748eN58MEHmT59euY5f/jDH5g3bx5vv/02o0aNYuTIkTz//PPcfffdXHfddSSTScaPH8/cuXM58MADgdTa2Hnz5jFv3jyefPJJampqGD16NL/61a/4yU9+kjn3nXfeyUMPPcTvf/977r//fkaMGMHRRx/NBRdckJmOdtJJJ1FQUMATTzzByy+/TElJCTNmzGDevHlss802Oe06JzIQpNpkw5DCrU+ds1QtxvX+wxjxVOOZROlY4ttMJz5ub7Bv/fXFBU5qGoMoFA1MhtmN1b4vv/wyjz/+OA8//HC7i0lz5YsvvgDocLqHdF2sqZo1887jrcRuzPp+++15tyYajVJVVcXQoUN7fU2RaZo8/uoiDt9rW7Zb8RKeCd+h7MAze/Wag0kwGGTx4sVMnjxZc6UHsFgsRk1NDSeeeCIzZ87cYnCQ/i19L8vLy3O6pkh6Tr9fe8fCxVX84rcfcuXJMyj2dbzG2Lbi3zg+fopkyWiikw7E2rgWa/USLPUrSIyYSmTWOR2+Nu0fn6zh319s4PKjhuo+9hNdyQbdGil69dVXqaqq4oADDqC0tBSXq3V6NgyDt956qzunln4m4U+10Y7ZvDmupHMMw8DttBIIxbH6SohppEhERGTQWl8bwGY1KPR2/KGs/X/zcXz5F+LDdyI2YX+wWEkMnURi6CSsVV/hWPQ68XWfkxi5yxavVVzgJBCKEY3ndrsF6Z5uhaJhw4Zl2plKfov7awGI2X05rqTz3E4bLaFYKhTVrMl1OSIiIpIjG2pbGFLkxtLBEgBL7XIcX/6F2NjvEh+7O2z2vETFRBJVX+FY+Byh8ong6LjNdnokqrElkb0vQPpMt0JRdzZVlYEp7q8lhh2rY+C0tXY5rARCUWwVpQSXfoJpJjGM/t8kQqSvvfnmm5pyJSJ5bX1NC6VFHa8Hsv/vVZLeIcTHfrdNIALAMIhN+B7OBU/h+O/LRGec2OG5SgpS12lq0UbGA1GP3ikuW7aMp556ijvvvJOqqioWLlyYtQYM0j/Em+sI4sIxANpxp7mcNgLBGFZfKSTiJJrrc12SiIiI5MD62kCHTRYstcuwVS7aGIg6fktsugqJbTcL+7J/Yqn5psPnFXgdWAyNFA1U3QpFyWSSa6+9lsMOO4xbbrmFxx57jNraWh588EGOPPJIKiu1jiNfxP11tJgunI6BE4rcjtT0OVtBaqMArSsSEREZfOKJJNUNIYZ0MFLk+PJVkt4yEuUTtnquxMhdSBSOwPnx09DBXmtWS2rtUpNC0YDUrVD04IMP8uqrr3LTTTfxwQcfkG5gd8UVV5BMJrnnnnuyWqTkTrypBn/CiXOAjRS1hFNrisBQKBIRERmEqhuCJJMmQ4rargOy1CzFWrWYWEfT5jZnGMTH742luQpLzdcdPq3I56BR0+cGpG6FopdeeomLL76YH/3oR6129J48eTIXX3wxH3zwQbbqkxyL++toig+sUOR22giG45hYsXoLiTdU5bokkX7HYtE6u3yheynSvg21LQDtjhQ5vvwLSV85yfLtO32+ZOFwku4SbCs/7PA5RV4HjQGNFA1E3fpNWltby+TJk9t9bOjQofj9/h4VJf2DmYiRDDYRMJ0Dak2R25nqH5IaLSrVSJHIZgzDwGazDYgNmWXLdC9FOra+pgWr1aDI27pZlKXmG6zVX3d+lCjNMEgMm4xtzacQC7f7FI0UDVzdCkVjxozhvffea/exBQsWMGbMmB4VJf1DfGODguakC8eAWlOUqjWQbsutUCTSSjQaZd26dUSj0VyXIj2keynSsQ11LQwpdGGxtA4+9iXvkPSWkSwb3+VzJoZOxkhEsa37vN3HiwucBMJJ4tqraMDpVkvuU089leuvv55YLMZ+++2HYRisWrWKjz76iMcff5w5c+Zku07JgfQeRQHTNeCmzwEEgjEKCkqIrP0qxxWJ9D/xuD7JzBe6lyLtW18TaNuOOxbGuuEL4mNmdm2UaCPTXUSieBS2Ff+X6lq3maKNm8TW+sMUFg6cPR6lm6HoJz/5CfX19Tz00EM899xzAMyePRu73c5Pf/pTjj/++KwWKbmR8KdGigLJgRWKXOnpc6Eo1oJSkpEgiVAzVndBjisTERGRvrK+poVxo4paHbNu+AIjESNRMbHb500M2wH7V3/HCNZjekpbPVbk2xiKGsNsN6rbl5Ac6FYoAjjrrLP44Q9/yIIFC7DZbBQUFDB16tRWjRdkYIv7a0lancSwDahQZLNZsNkMAqE4trLUL6tYQ5VCkYiIyCCRSCSpbgjynR2GtjpuW/0xycLhmO6iDl7ZiXOXb499yTvYVi0gNvmgVo+lR4pqGkPdPr/kRpdD0Wuvvcbvf/97/vOf/2SG7F0uF7vuuivHH3883//+97NepORG3F9L3O7DAOz2gdXdyO200xKKYS2oACDeUAkjuj53WERERAae2qYwiaRJ6aYbt8ZCWDf8j/i2s3p2cpuTRPl4bCv+TWzSD1pNw7PZLLidFmob22/EIP1Xp0NRIpHgsssu429/+xtDhw7l0EMPpaysDNM0qaysZMGCBVx00UUcccQR3Hbbbb1Zs/SRuL+WqM2Dw24dcJ2NUhu4RrE43BhOj5otiIiIDCKV7bTjtq77D0YyTqJi65u1bk1i6A7Y/vsyloZVJEvHtnrM57JQo1A04HQ6FD333HO8+eab/PznP+ekk05q8yY5kUjw+9//nltuuYUZM2bw4x//OOvFSt+K++uIWNw4B1DnuTSX00ogFAPA5ishpr2KREREBo0NdS0YBhT5vm3HbVu9kETRCExXz6fTJ0tGYzq82FZ9THSzUOR1WjR9bgDq9JyoV155heOOO46TTz653VEDq9XKiSeeyDHHHMOf/vSnrBYpuRH31xLEPaDWE6W5HVaag6lQZPWVEG/YkOOKREREpK9U1rVQUuDEZt34VjfagrVyUVZGiQCwWEiUjsVatajNQx6XlfqmSHauI32m06FoxYoV7L333lt93l577cWSJUt6VJTkXjIeJRlqJpB0DaiNW9M8LjvNwdS+HbYCbeAqIiIymFTWBSnZZD2Rbe3nYCZIlGcpFJEaLbI0rccINbU67nVaqPeHMU0za9eS3tfpUBQKhSgq2nqnjpKSElpaWnpUlORewl8HQFPCiXOANVkAcLtsBEIxkkkTa0EpiUADyZg+tRERERkM1tcGWjVZsK5ZSLJ4FDizt3dQonQ0AJaq1vshepwWovFkZhq/DAydfrdrmiZW69ZHDCwWi5JxHog3p0JRY9wxIEeKvC47yaRJMBzH6isBIN5Uk+OqREREpLeZpkllXfDbUBRpwVr1VY/2JmqXw0vSV461anGrw15X6u11rdYVDSgDbwhA+kTcXwtAfdQ+IBsteDZu4NrcEsW2MRRpCp2IiEj+aw7GCEXimVBkrf4Kw0ySGLJd1q+VKBmdCkWbDAh4nKm313VN6kA3kHRpn6Ibb7wRn2/Lw46BQKBHBUn/EPfXYTg9tDQxMBstuO0A+INRhpUVgdWW2qtIRERE8lpl3cZ23OlQVLmIpKcUstB1bnPJktHY13yC0VyFWTgMALfTgsXQSNFA0+mRou985zt4vV5M09ziP16vlxkzZvRmzdIHEv46rJ5CIrHEgAxFHse3I0WGYcHmK1VbbhERkUFgw8Y9ikoLXWCaqVBUMrpXrpUsHoVpWFpNobMYBgUeh0LRANPpkaKnn366N+uQfibmrwVXAabJgFxTZLEauJ22TAc6q69Y0+dEREQGgcr6FrwuGy6nDaO5Gkuwnti4vXrnYlY7yaIRWCsXEd9+v8zhAq+d2iaFooFEa4qkXQl/LUlHaqqkYwCuKQLwuGz4WzaGooJSYtqrSEREJO9V1gYpLdo4da5qMaZhIVm8Ta9dL1kyGmv115BMZI4VeOwaKRpgFIqkXXF/HXF7KhQNxOlzQKuRIltBKfHGasxNfmGJiIhI/tlQ10JJQXo90f9IFg4Hm6PXrpcoGY0Rj2CpX5k5Vuh1UNuoRgsDiUKRtJGMRUiGA8RsHmDghqJNR4psBUMgmch01RMREZH8VFnXklpPlExgrfqaZOmYXr2eWTAM0+Zsta6o0OugtimkbWoGEIUiaSO+cePWsDGwQ5HXZW81fQ4gVq8pdCIiIvkqGktQ7w8zpMiFpX4lRjxMopeaLGRYUtPzrJWbhCKPnUg0QTAc791rS9YoFEkbiY2jKUHDjQHY7QPzx8TtshEIxUgmTazeIrBYFYpERETyWFV9ENOEkkJXaj2RzYlZMKzXr5soHY2lbjnEI0BqpAjUlnsgGZjvdqVXxZtTI0WBpBOH3YphGDmuqHu8LjvJpEkwHMewWFNtuRWKRERE8tamexRZKxelGixYev/tbrJoJIaZxNawGtgkFKkD3YChUCRtxP11WFxegjFwDtDOcwAe58a9itJtuQtKFIpERETy2Ia6FmxWgwJHAkvdChK9vJ4ozfQOwbQ6sDWsBMDnsWOAmi0MIApF0kbcX4vVU0QoEh+QexSlud12gG+bLRQOIVa3LpcliYiISC+qrAtSWujCXvMNhpnstU1b2zAsJAuHYd/Ygc5qMSjwOqjTSNGAoVAkbcT9tVg8hYQicZwDdD0RgNuxcaQo02xhCPGmGsyEFj2KiIjko8ralsx6oqSrCNNd3GfXThYOS40Ubew4V+RzaE3RADJw3/FKr4k31WD1FhEMD+yRIqvV2GyvoiFgJok3Vee4MhEREekNGza247ZULyFZPAr6cF10snAElkgAR9QPQKHHqVA0gCgUSRtxfx1WTyGhcAyn3ZbrcnrE49xkr6JCteUWERHJV8mkSVV9kAovWJrWkywe2bfXL0x1ufMGU+8zCn0OahSKBgyFImklGW7BjIaweotpCcdxDeBGC5Bqy50eKbJ4CsFqUygSERHJQw3NYWLxJNsYVRiYJIv6NhTh8JBwFeFtSb3PKPI6qGtSo4WBQqFIWklv3Gr1blxTNMBDkcf17UiRYViwFQxRKBIREclDG2pT7bjLo2sxHd4+XU+UligY+m0o8jkJReIEw7E+r0O6TqFIWon7awBIOgqIxZO4HAN9+pw9E4oArAXaq0hERCQfVda1YAA+/4rUKFEO9llMFAzDHaqGRJwinxNAo0UDhEKRtBL314FhIWK4gIG9TxGAx20jEIqRTKY6wdgKSonVr89xVSIiIpJtG+qClPqsWBtWkSgakZMaEr6hWMwktqY1FKU3cNW6ogFBoUhaiTfVYPUUEIwmAQb8miKP004yaRKKpNpw2wqGEPfXYiY0lC0iIpJPNtS2MNHnx0jG+7zJQlrCW0bSsGKrX0nBxlCkvYoGBoUiaSXeXLdx49ZUaBjwI0Wu1PS/9BQ6a+EQME1iDVW5LEtERESybH1tgO3t1ZhWB6a3PDdFWKyEnSXY6ldis1oo8NipadT0uYFAoUhaiTelNm4NhhJAHowUuewAm+xVpLbcIiIi+aiyLshIcwPJwuFgyd1b3LCrNLWJK1DodWqkaIBQKJJW4v7Uxq0tkRiGwYDevBXA7UyNFDVvHCmyuAswbA6FIhERkTzSHIwSDEUZElmbs6lzaWHXEKyhRoxQI4Veh9YUDRAKRZJhmknizfVYvUWEwnGcditGDjq3ZJPVauBy2vAH0225Daxqyy0iIpJXNtS2MMzaiC0R7vv9iTYTdqVmpVjqVlDkc2oD1wFCoUgyEi1NkIhj9RQRDMcGfDvuNI/Tij/wbVtuW0GJOtCJiIjkkcq6FsbZqjANC8nCYTmtJW7zkHT4sNQtp8inDVwHCoUiyfh249YigpE4jgG+nijN47ITCH3bbU4jRSIiIvllQ20LE5y1mAVDwWrPdTkkCiqw1q+iyOugJRQjvLELrvRfCkWSkd641eopJBhKTZ/LB26njaZAJPNnW+EQEs11JGORLbxKREREBooNdS1sZ6vK2f5Em0t4y7E0rKYw3Zbbr9Gi/k6hSDIS/joMqx3D6UlNn8uTUOR12WkOfjtSlO5AF1dbbhERkbzQXL2BAlpyvp4oLeEtx4iFKLUEAG3gOhAoFElG3F+LxVuEYRi0RGIDfo+iNI/LRnMwimmawMa9ilBbbhERkXzh3NgCO9lfRop8qX2SiqOVgDZwHQgUiiQj3lSL1VMIQCgcH/B7FKW5XXaSSZNgODWf1+L0YthdarYgIiKSB8LROOXx9UTsheDw5LocAEyHF9Phxe5fi9dlU7OFAUChSDLSexQlkyahSDxvRop8rlQXvXQHOsMwsBWVEa1bl8uyREREJAuq6oKMtdUQ8w7NdSmtJH0VWOpXU+h1avrcAKBQJBnpkaJINIFpkjctub3u1CLHhsC3n9LYCocQq12Tq5JEREQkS9ZXNTLS2oBR0j+mzqUlCyqwNKxJbeCqkaJ+T6FIADATMRItTVi9RbREUk0J8mWkyO2yYrEYNDZvsldRYTnRuvWZdUYiIiIyMPlXL8FmJLGW9o8mC2nJggosET9D3VGNFA0ACkUCQLy5HjCxeooIhVJrb/JlpMgwDArcdhqbNxkpKirHjARJBBpzV5iIiIj0WKJqKXGsmBubG/QXpq8CgJHWOjVaGAAUigRIdZ6DbzduhfwZKQLweuw0Nm+yV1FRGQCxurW5KklERESywNmwkkZLCVj61/sW01WIaXMx1KylKRAlFk/muiTZAoUiASDurwPA4i0iGE5Nn8uX7nMAPredhk1CkdWX+uUZrVUoEhERGciGxNYTdPavUSIADINkQQUlsdS+iPXawLVfUygSINVkwXB6sNgcBMNxbFYDqzV/fjx8bker6XOGxZpqtqAOdCIiIgNWuKGGQlqI+YblupR2JX3leIOp9xqaQte/5c+7XumRhL8Wq6cIgGA4llejRAA+j52WcLzV0LWtsEwjRSIiIgNYzTdfAmDpZ00W0syCodjDjXiMMHWNGinqzxSKBICYvwarpwCAYDiOM0+aLKT53HYAmgKbrCsqLCOmUCQiIjJgNa9cTFPSjbeoONeltCtZkGq2sK2ziVqNFPVrCkUCbNyjyJsaKQpF4jjt+TVSVODZuFeRf9NmC+UkAg0kwy25KktERER6IFm1lKpEEd6NH372N6a7BNNqZzt3I3Xaq6hfUygSoPX0uZZwLO9CkcdlxwAaNxspAojWrc9RVSIiItJdZiKGq3ktjdYhWCxGrstpn2GQ9FWwja1eI0X9nEKRkIyGSEaCmZGiYCiWV+24AaxWA7fL1qrZglVtuUVERAasSOVKLGaCoKsfdp7bhOkrZzg12sC1n1MoEuJN3+5RBBCMJPJm49ZN+TwOGgPRzJ8tNgdWb7GaLYiIiAxAkXVfE8eCuXHdTn+VLBhKYaKR5iZ/rkuRLch5KGpsbOT6669n7733Ztddd+X4449n4cKFuS5rUIk3VQNg9RYDEIzk30gRbNyraLM9AmxFarYgIiIyEIXWLqE6UUhhgTvXpWxR0pcayfK0bCCRNHNcjXQk56Fo9uzZfPbZZ9x999289NJLTJ48mTPPPJPly5fnurRBI95UA4YFi7uAeCJJLJbM05Eie6s1RZBuy629ikRERAaa0JqvqIoXUeR15rqULTK9pZhYGGapb9UFV/qXnIaiVatW8cEHH3DjjTcyY8YMtt12W6677joqKip49dVXc1naoBJrqsHqLcawWAiGYgB5O1Lkb4mS3ORTGmthGfHGKsxELIeViYiISFfEA42YgToqE0UU+Ry5LmfLLDZi7hJGWBu0rqgfy2koKikp4ZFHHmHKlCmZY4ZhYBgGfr/mXfaVeFNNq/VEQN5t3gpQ4HaQTJr4W75dV2QrKgczSax+Qw4rExERka6IrP8GgBqzuN+2496U6S1jpK1Bbbn7sZzOkSosLGSfffZpdeyNN95g1apVXHPNNd06p2maBIPBbJQ3aETrNmC4CgiHIzT6U3v2WIwk0Wh0K6/snFgs1urfueLc+EFSTX0A18bfn0lXIQCBdctxectyVNnAEQqFWv1bBibdx/yhe5k/dC+7JrBqEVGLG8NTmPP3F5vq6D2PwzeEEdalLKpuIBgsykVpg5JpmhhG59q196uFI59++ilXX301Bx54IPvuu2+3zhGLxVi8eHF2C8tzRQ1VxMq81K1axerq1CcYTY11hALZHUisr6/P6vm6Kp5IArB05TrMsCt10DTx2pxsWPIfwomCHFY3sKxcuTLXJUgW6D7mD93L/KF72Tm+pf+hxizEaU1SVVWV63La2Pw9jydqY5SRYP3Sr1hcrODblxyOzk2v7Deh6K233uLyyy9n11135c477+z2eex2O+PHj89iZfnNjEep+lsLJSPG4BwzhtpQDQbNjBoxrNPJemtisRj19fWUlpZit+d2iNu5qAW7q5DRY4ZnjjWvLMdjjVE8eXIOKxsYQqEQK1euZOzYsbjd/bvbj3RM9zF/6F7mD93LzjOTSarfrqIqMZbyikKGDu0/Mz06es9jRAtg/T8pMQNM1vuNPrN06dJOP7dfhKJnnnmGm2++mYMOOohf//rXnU507TEMA4/Hk8Xq8lu0rhEAV3EZTpeTaAIcDitOZ/Y7udjt9h7d22zweRwEwglcrm+/vnBxBYmGDfq56QK3263vVx7Qfcwfupf5Q/dy66I1qzFjYVaFfWxX5M75e4v2tHnP43AQNlw4Wyp1f/tQVz7gz3lL7ueee45f/epXnHjiidx999398gc7n2X2KPIVAxAMx/KyHXeaz9POXkWFZcTq1mOayRxVJSIiIp0VXvcNYFAVL6Swn7fj3lTQVkxBuDLXZUgHcvrud8WKFdxyyy0ccMABnHPOOdTW1mYec7lcFBRojUdvS+1RZGD1pBoOBCNxnPb86zyX5nM7qK5v3YjDXjwUMx4l3liNvWRYjioTERGRzoisW0LcM4SY30aRd+B8mB5xDaEssqJLi/+l7+Q0FL3xxhvEYjH+/ve/8/e//73VY0cddRS33XZbjiobPOJNNVg9hRiWVBAKheN5uUdRms9tZ0lLpNUvJFvxUACiVasUikRERPq5yLolBBxDcNgtuJ0D5z1LwltGSfP/8Dc0UFRamutyZDM5DUXnnnsu5557bi5LGPTiGzduTQuGY7jyeqTITiyWJBhO4HWnfvwtbh8Wl5do9Sq8k2bmuEIRERHpSDIaIlq7hrrCmRR7nTCARlwshUOhEupWLqWodLdclyObyfmaIsmtWGN1ZuNWgJZ8HynypDrBNAa+XVdkGAa24qFEqlbkqiwRERHphMiGZWCarI74KPQNnPVEAPbiMuKmhZb1y3NdirRDoWiQizdVtxkpyutQ5E7NPW5sjrQ6bi8eSrR6VS5KEhERkU6KrPsGw+5kpd9OoW/grCcC8Lic1Cd9xGv0fqM/UigaxMxEjESgIROKTNMkFE3kdShyO63YrAZNm4UiW8lQ4o1VJKPaUE1ERKS/Cq9bgrVkBIFwYkA1WQCwWA0aKcDauDbXpUg7FIoGsbi/Dkwz0447HE1gJs28bsmNYeDzOKjbrC23Pd1soWZNLqoSERGRrTBNk8i6JUQ9FQAUDbDpcwAtthLcoSrMZCLXpchmFIoGsXhTDUBmpKglFAPAnc+hCCjyOahtaD0iZCsuB8MgWrUyN0WJiIjIFiWa60i0NOK3pTq3DbSRIoCwsxSrmSBWvyHXpchmFIoGsW9DUWqPokBwYyhy5Xko8jqpaWodigyrHVthudYViYiI9FPhdUsAqEoW43HZsA/AbrlxTxmA3m/0QwpFg1isqQaLuwDDmurIFkiPFA2gnv/dUeRz0hSIEIsnWx23FVcQ0S8pERGRfimy9musvlIqA8aAHCUCcHp9NCdd6njbDykUDWKb71HUEopiGOAYgJ+8dEWRz4lpQn3TZuuKSlId6EzTzFFlIiIi0pHw2q+xl42ktjE04Npxp/ncdmoTPoLrFYr6G4WiQSy+2R5FgWAMt9OGMYA2QuuOoo0tPGsbN19XNAwzEiThr81FWSIiItIBMx4jUrUCe9ko6prCA3akyOe2U5csIFa9MtelyGYUigaxeFN1pvMcpKbPuZ35vZ4IwO2w4rRbqdksFNlLUh3oImq2ICIi0q9EqlZAIk7MO5RILDEgO88BeDeOFBFsJBFqznU5sgmFokHKTCaIN9e3HikaJKEIw6DI56R2s2YLFk8hhsOtxY8iIiL9TGTdErDaqE2kmkOVFAzMUOR22qgzU1+D3m/0LwpFg1Qi0ADJBFZvSeZYIBjN7z2KNlHkdVCzWVtuwzCwFw/VLykREZF+Jrz2axxDRlLTFMFmNSjwDMzpc4bFIO4sJGlY9X6jn1EoGqRijdUA7YwU5XeThbQin4O6zUaKAGwlFdqrSEREpJ8Jr/sa+5CRVDUEKfI5MSwDd/2zx+2kxVpEtEqhqD9RKBqkMnsUDcI1RZDqQBcMx2kJxVsdtxcPI9awgWQskqPKREREZFPx5noS/jrs5dtQXR+keIBOnUvzuOw0UKC23P2MQtEgFW+qweLyYrGlhp/jiSSRaALXIApF0E4HupKhYJrEatfmoiwRERHZTHjd1wAbR4pClAzQJgtpPredqpiPWM0azGQi1+XIRgpFg1Rqj6Jvp861pDduHSxrijpsy10BGJrnKyIi0k9E1i3B6i0mhJtwJE5JoSvXJfWIz21nXdiNmYgRa6jMdTmykULRIBVrqNysycLGUOQaHKHIarVQ4LG3CUUWmwNrYamGtEVERPqJ1Kato6huCAIM+OlzXredmrgPQOuY+xGFokEq1lCJtWCTULRxpGiwTJ8DKPS2bcsNYC8dQWT90hxUJCIiIpsyEzGiG5bjKBtFVX0Ii2Xgdp5L87rthE0HprNAM1P6EYWiQchMxEg012HztTNSNEimz0FqCt3mbbkBHENGEq1cgZmIt/MqERER6SuRqlWYiRj28m2oaQhS7HNgGcCd5yA1fQ4g5i5VKOpHFIoGoVhjDZgm1oLSzLGWUBSH3YLVOrB/0XRFkc9JvT9EMmm2Om4fMjL1yVTN6hxVJiIiIgCRdV+DxYq9ZBhV9UGKCwb2eiIAl8OKzWoQtBVrun4/olA0CMUbqwCw+r4NRYFQDI/TnquScqLI6yCeMGlsbt1+2146DAyLptCJiIjkWHjdEuxDRmBYbVQ3BCkZ4OuJADAMvG4HjUYRCX8diXBLrisSFIoGpVhDJVisWD2FmWOBUAzXINm4Na043ZZ7s3VFhs2BrXioQpGIiEiOhdd8haNsFIFgjGA4nvm7e6DzumxUx70AmkLXTygUDULxhkqsvhIMy7e3PxCM4RpE64kAvB47VovRpgMdgH3ICCLrv8lBVSIiIgIQ99eR8NfiKB+T6Tw30Ntxp3nddtYHXWCxqgNdP6FQNAjFGipbNVmA1Joi9yDqPAdgGMYWmi2MIFq7hmQ0nIPKREREJLz2KwDsFdtQXR/EYjEoHOCd59J8bjuNwRi24gqNFPUTCkWD0ObtuCE9fW5whSLYQlvuslFgmkS1AFJERCQnwmsWYy0sw+ryUVUfpMjnwJInDaG8bjuBYBRbYQXR6pW5LkdQKBp0TNMk3liNdZORomTSpCUcxz3I1hRBal1Rekh+U7aicgyrnbCm0ImIiOREePViHOXbAFDdEMyb9UQAXpcd04S4t4xo9WrMZCLXJQ16CkWDTCLQgBmPYtuk81wokiCZNAfVHkVpQ4pcNLfEMvs0pRkWK7bS4Wq2ICIikgPJcAvRmlU4ykcDUJ0n7bjTvBv3Kgo5SjDjUWINVTmuSBSKBplYQyVAmz2KgEG3pghSoQhgQ23bdpgONVsQERHJifC6JWCaOCpG0xKK0RKOU5oP7bg3Sm/g2mQUAepA1x8oFA0y8Y2haNNGC4FQapTE7Rp8oajA68Bht7CuJtDmMfuQkcQbq0kEm3NQmYiIyOAVXvsVFpcXa8EQqutT09zzaaTI4bBit1tojFiweAq1hrkfUCgaZGKNVVg8hRi2bzdqzYSiQTh9zjAMhhS6WF/bTigqGwlAZIOm0ImIiPSl8JrF2MtHYxgG1Q1BDItBoTc/Os+l+dx2GgNR7MVDiSgU5ZxC0SATb6hqpx13DIvFwGEfnD8OQ4rcrG9npMjqK8VwuLWuSEREpA+ZiTiRdd9kmiysr22h1OfEmied59K8LjtNgQi2kmHaq6gfGJzvggexWMOGVp3nAALBKB6nFYz8+mXTWWVFbur9EcKReKvjhmFgHzJCHehERET6UKRyBWY8iqNiDADrawKUFuXP1Lk0n9tOY3MEe+kwEs31JIL+XJc0qCkUDTKxhqq2oSgUw+W0d/CK/Jf+Rbu+3WYLI4msX4ppmn1dloiIyKAUXrsYw2rHXjKMRMKkqj7IkCJ3rsvKutT0uQj2kmEAGi3KMYWiQSQZCZIMNWPbpPMcpKbPuQbheqK04gInNqvRbgc6e9koksEm4k3VOahMRERk8AmvXoy9bBSG1UZNY4h4wqQsH0eKPA6C4TgxRyGGzUFEoSinFIoGkfbacQM0B2ODcuPWNIvFoLSw/XVFqfnMBuHVi/q+MBERkUHGNE3Ca7/KrCfasLERUmlh/oWigo2NI/wtcWzFQ9WBLscUigaR9MZgmzdaCISig3KPok2VFrpYV9N2pMji9GArGUpo1f9yUJWIiMjgEm/YQDLox16R2rR1XU2AQq8DhyP/Prwt2LhXUYM/jL1EHehyTaFoEIk3VGLYXRhOT6vjwVB80Iei8mIXNY1BorFEm8ccFWMIr/oyB1WJiIgMLqHVi8AwcJRt7DxX05KXo0SQ2h/SajGob051oIvVrScZj+a6rEFLoWgQiTVUYisoxdiky1wsniQSSwzKPYo2VVrkwjShqi7Y5jHn0G2JN9UQb6rJQWUiIiKDR3jV/7CXDsficGGaJhvqWvJyPRGkutwWeBw0Noexlw6HZIJYzdpclzVoKRQNIrHGKqy+4lbHAsGNG7e68m9YuitKC90YFqPdTVwdG4fwQ6s1hU5ERKS3mKZJaOUXOIZuC0CDP0IkmsjLznNpPo+dhuYItuIKwNAUuhxSKBpE4g2Vbdpxt4RSw7SuQT59zmo1KC1wttuW2+LyYisZRljrikRERHpNrH4DiUBDJhSlu8LmdShy26lvCmGxObAVlqktdw4pFA0SZiJG3F/bph13ILRxpMgxePcpSistdLGuuu1IEaRGi9RsQUREpPeEV30JhiUzQ2N9bQCPy4bHnb8f3BZ4HDQEUh9Q20rUgS6XFIoGiXhTDZgmVt9moWjj9DlXHnZ16aqyYjdV9UESibYbtTqGbku8sYq4vy4HlYmIiOS/0MovsA8ZicXuBFKbqudrk4U0n8dOOBInHIljLxlGpGqlNozPEYWiQSJWtwEAW+GQVsebQ1FcThsWq9HeywaVsiI3iaRJdX3bZguOijGA1hWJiIj0BtM0Ca36H46hYzPH1tcG8nrqHKRGioDUuqKSYZjRkDaMzxGFokEiWrcWw+bA4iloddwfiOLL42HprhhS5MJqMVixwd/mMavLi624QuuKREREekGsdi3JYBPOYan1RIFgjOaWGEPytPNc2rehKIy9ZBgA0cqVOaxo8FIoGiRiteuwFZZhGK1veVMggsep9UQANpuF8hI3y9c1tvu4o2IMIe1XJCIiknWhVV+CxYqjPLU/0bdNFvI7FLmdVmxWgwZ/BIvbh8XlUwe6HFEoGiSidWuxbjZ1DjaGIpdCUdrwIT5WrPeTTLa3rmgs8YZK4s31OahMREQkf4VWfomjbBSGLTVysq4mgN1uodDryHFlvcww8HkcNDSHMQxDzRZySKFokIjVrcNWVN7meFOLps9takSZl1AkTlW764rGAhDWuiIREZGsMc0k4dVftlpPtKE2wJBCV6sN5/OVz+2gwR8BwF46nP9v777j46rOBO7/7tw7vUga9WJbcu+2wDa2CcUE2BRSls1mU/BueBPSIWGXTbLv7oYUNslnIYGQLPUlDUyL6QmYEprpbrg32ZasYnVppNH0e+/7x1iDBxVL2NZIo+f7+egj6dyj8TM6knyfOec8J9p8OMMRTU6SFE0CeqgHIxxE8xWktccTBqFIArczy1+FGYUivwvVonC4MTDgmur0oOUUEq6VJXRCCCHE6RJrPYoRDqbOJwKob83+Igv9vC4rXb0RAKz+MvTeTvS+gfch4sySpGgSiHc0AqDlpCdFPX3JuvjZXP9/tFRVocjv4tAgSRGAvXQGoZotUi5TCCGEOE0idbtA1bAVVgDJ+5Pu3iglfleGIxsbXpeVrp4opmli9ZcCED12KMNRTT6SFE0CsfZGUJQBB7cGgsmpWo/sKUpTWuDmSFNg0H1F9vI56MEuWe8rhBBCnCbhul3YCqagqMn7kfrmXiC5emMy8LpsROM6kaiO6slDsTllCV0GSFI0CcQ7GlE9/tQfm349x5Mil1OSohOV5buJxnSOdfQNuGYrmopidRA6uDkDkQkhhBDZxTT0AecT1TX34HVZcU+S+xOPK/k8+4stWP2lRI/VZDiqyUeSokkg1tEw4NBWSBZZsFtVrJr8GJyoMM+Fpg6+r0hRNexlM+k7sCkDkQkhhBDZJdp4ADMawl42K9VW19xLYd7kmCUC8PSfVdRfbCG/TJbPZYDcDU8C8faGAUUWIHlwq1v2Ew2gqgrFfvegSRGAvXw2sebDJHq7xjgyIYQQIruEarZicbix5if30sQTBk1tQYonydI5AKdNRdOUAcUWEsHuzAY2yUhSlOWMRIxEd9ugSVGgT84oGkppvovaph4MfeC+Ikf5LFAUQjVbMhCZEEIIkT1Ch7ZiK5mROly+qa0P3TAnVVKEouB12ejqfa8sN0CsWWaLxpIkRVku0XkMMAdUngPoDkYnzXrd0Sor9BCN6zS1Bwdcs9hd2AqnEjooS+iEEEKIDyoR7CLWUou9bGaq7WhzD5qq4Pc6MhjZ2EueVZScKUoVWzgmxRbGkiRFWS7WX457qOVzMlM0qIIcJ5qmDF2au3wW4SM7MOLRMY5MCCGEyA7hw+8CSlpSVNfcQ2GeC4ua/Ye2nsjrstF5fE9RqtiCzBSNKUmKsly8vRGLw43F4U5rT+gmfZG4zBQNwaIqlOV72FfbOeh1e/kczEQsebaCEEIIIUYtdGgb1vwy1OP3KKZpUtfcS/EkKrLQz+uy0h2MpM5BlGILY0+SoiwX72gcdJaoty+GaYLbIYUWhlJVlsPR5t5U6fITaTmFqF4/fVKaWwghhBg109AJH96eNkvU1ROlLxyfNOcTncjjtBKLG4QiCUCKLWSCJEVZLtbegDpIOe6evuSNvttpG+uQJoxpJV4Ui8KuwwNnixRFwV42i9DBzalXdYQQQggxMtGmGoxIMK0U99HmHgCK/c5MhZUxPnd/We7+CnRSbGGsSVKUxUzTGHKmKBCMATJTNBybTaW8wMPOmrZBrzsq5qL3dsr0thBCCDFKoUPbUGxOrPnlqba65l5yvXbstsl3b9KfFLV3hwEptpAJkhRlMb23EzMRQ/MVDrgW6Iti1SzYrPIjMJzp5UMvobMVT8Pi9BLc9UoGIhNCCCEmrtChrdhLZ6BY3rsPOdrcQ1He5JslArBaVVwOjbZAMimSYgtjT+6Is1isvQFg0HLcgf5y3Mrkqu4yWsMuobOoOCsXEdy1EVNPZCA6IYQQYuLR+wLEjh1OWzoXiSVo7gxR7HcP85XZLcdto6M7kvrcml9GtKkmgxFNLuMqKbrzzjtZu3ZtpsPIGvGORrCoqO7cAdeS5bgn3/T0aKWW0B0afAmdc/oSjHAvoUPbxjgyIYQQYmIKHX4XMLGXzUi11Tb1YJpQkj/5iiz0y/HYaesKpT63+svQg11SbGGMjJukaN26ddxyyy2ZDiOrxNuT+4lOnJru1x2M4ZIzikakqtzH0WODL6Gz5pWg5ZUQ3ClL6IQQQoiR6Nv/Ntb8clSnN9V2sL4br9tGjnvyFoDK8dhp70kvyw0Qk73LYyLjSVFLSwtf//rXuemmm6isrMx0OFkl1l6PNkjlOUhWn/PIGUUjUlniG3IJHYCzajF9Bzehh4NjHJkQQggxsRixCOFD23BMnZfWfuBoF+UF7km9rD/HYyceN+jtSxbDUt25WBxuIo0HMhzZ5JDxpGj37t1YrVaefPJJlixZkulwsoZpmsRaatHySgZcM3ST3nBcZopG6GRV6JyVi8HQ6dv7xhhHJoQQQkws4cPvYiZiOKbOT7V19UTpCEQoL/IO85XZr3+WrD2Q3FekKArW/AoijfszGdakkfGk6KKLLuI3v/kNU6ZMyXQoWUXvaceIhrAOkhQFwzFMw0wWWhAjMqMih7rm3rS1vv1Ulxd76Qx6d7485nEJIYQQE0lw35toeSVo3vdWstQ0dKEoJGeKJjGPy4aiKKmy3AC2wgqijQcxDT2DkU0OWbfT3jRNQqGBN66TTeRo8lUFw+UnEknfC9PWmVzmZdMgFoud8Vji8Xja+4movNCJw67y2ruNfHTV1AHXtYoF9L3zBD1NR9ByizMQ4dgIh8Np78XEJOOYPWQss8dkGEszESd0cDP2mcvT7k321XZQmOMAdGKxiX3zf6r3PF6XRktHMPX9MX0lmPEIPfUHsRYOvP8QwzNNE2WESzKzLimKx+Ps3bs302FknKNmM3bNTkNrJyhdaddqW5PTsn29ncTDYzdZ2Nk5+J6ciaLcr7Ftfxszi3Ts2vu+b7oDj2ql/tXHicw6PzMBjqHa2tpMhyBOAxnH7CFjmT2yeSy11hq8sQgdFh9GXR0AhmlS0xBgaqGNlpaWDEd4+nzQex67alLf3MXROiPZoCfwoHB022vEplSfxggnD5ttZMU7si4pslqtzJw5M9NhZFxXzXPo/lLyBylecSzYgmoJMqW8BDjzGxrj8TidnZ34/X6s1om7ZM+Xm+BIyxHaw05WLRy4LLGvawFq8y4qP/5lFDXrfrWA5CuYtbW1VFZW4nROzgP2soGMY/aQscwek2EsA/WvEfXmUz5ncerV+4bWIPFEO7MriyjMnfjP+1TveQo6W2npDDN12rRUW8/hYgrMIDnz5g3zlWIwNTUjP+cp6+7cFEXB5Zq8Ne77tbcfxV46A4fDPuBaKGrgcVqx2QZeO5OsVuuIs/XxyGazMaMsh0172rigeioWNT2hVBecS/uRdzGObMW7+MLMBDlGnE6n/J5lARnH7CFjmT2ydSxNQ6f18Fac06txOh2p9rqWVmxWC6UFPiyW7Kk890HvefJ8TvYfDWC12lCP32dEi6YQaz6UlT8XZ9pIl87BOCi0IE4/Ixoi0d06aOU5gEAwikuKLHwgC2bk0x2Msrd24LS4NbcIe/ksAm89kTpjQAghhBAQqduNEQ7iPKHqHEBNfRdlBZ6sSohORY7HjmGYdPVGUm3WgikkOpvQw70ZjCz7SVKUhWKtyXW6g1Weg2RS5JZy3B9IQa6TknwXr+9oGvS6e965xNqOEj6yY4wjE0IIIcavvn1voXry0PylqbZINMHR1iAVRZ4MRja+5HiSq3jSK9AlKzRH5byiM2pcJUW/+MUvuPfeezMdxoQXbT4CFhXNVzDo9c6eCF6XJEUf1IKqfGqP9dDYOvCwVltxJVZ/GYG3nshAZEIIIcT4Yxo6ffvfxjFlbtpypkONAUzDpLxQkqJ+boeGpiq0d783U6R68pKHuDZIUnQmjaukSJwesZZatNyiQTf7R2M6vaE4PvfY7ifKJtNKfeR67Gx4q3bAMjlFUXDPW034yHaiLbWZCVAIIYQYR8JHdqD3deOoXJTWvre2k1yPHa974u43Pt0URSHHbacjEE5rsxZMkUNczzBJirJQtOUI1iHOyunsSb7ykOORP0AflMWisGJ+MYcaAhw42j3gumPafFR3DoG3nxz74IQQQohxpnf7i2i5xVj9Zam2hG6y+3AHVWW+DEY2PvncNtq608+rshXIIa5nmiRFWcY0dGJtR7H6B99P1HF8OtYnr8qckqklXkoL3Dz9+hEM/X2zRRYV15yVBHe/RqKnI0MRCiGEEJmnh4P0HXgH5/QlaUvnauq7iMZ0qspyMhjd+JTjsaftKQKwFk7BjEeItdVnKKrsJ0lRlol3NIGeQMsdPClqD4SxW1UcNnWMI8syisLKBSW0d4fZtLd5wGXXzLOwWB10vvJABoITQgghxoe+Pa+BYeCsWpzWvvNQB7leO36fLOd/vxyPnZ6+GLHYe7NC1vwyUCxSbOEMkqQoy8SO72MZqvJcRyCcnCUaRd12Mbj8XCczp+Ty/DtHiUQTadcsNgeexRcS3PFysvCFEEIIMQn1bn8Je9lMVKc31ZbQDfYc7kjOEsn9yAD9Wxw6et4rtmDRbFjzimVf0RkkSVGWibYcSVYpsQ9+KnR7dxif7Cc6bZbNKyYW13lpS8OAa65ZZ6PlFNDxwh/k3CIhhBCTTqytnuixGpwzqtPaDx7tIhrXmS5L5wbVnxS9f1+RtWAKkfp9mQhpUpCkKMskK88NXmQBksvnpPLc6eN2Wlk6q5DXtjdSd6wn7ZpiUfFWX0Kkbhehmi0ZilAIIYTIjN6dL2Oxu3CUz05r31HTTp7PTp4snRuU3abhsKkD9hXZiqeR6Gom0TvwAHlx6iQpyiKmaRJtPoI1b/CkKBJL0BdOkCNFFk6rxbMKKcpz8dDfDhCJpS+js5fPxlYync4X/oipJ4Z4BCGEECK7mIZOcMfLOCoXpR0REk8Y7K3tlFmik8j12mntDKW12YorAQjX7cpARNlPkqIsoge7McI9WPNKB73eEThejluSotPKYlG48KwK+kJx/rIxff+Qoij4zv474p3H6Nn2fIYiFEIIIcZW+PC76H3duKYvTWs/cLSLWNyQqnMn4fc5aWrvS2tTHR603GIitZIUnQmSFGWRWPNhALSTlOPO8ch09enmddtYtaiUrftb2XUovQy3Na8E54xqOl++n0RPe4YiFEIIIcZOYMsGtLwSNH/6C7U7D7WT73OQ65V7keHk+5IHuMYTRlq7rbiScO2ODEWV3SQpyiKRhn1YHB5Ud+6g1zsCYRw2FZuU4z4jZk/JpbLUx2Mv16Rm5fr5zroURdVofeq3mKYxxCMIIYQQE1+so5FwzVbcc1emnU0UiiTYc7iD6eUyS3Qy/hwHpgktHemzRfbiKhKBNuKB1gxFlr0kKcoikYb9WAunpP0BOlF7ICJFFs4kReG8pWXYrCp//OtuwpH39hBZ7E5yV32aSO1OejY/k8EghRBCiDMr8M5fsDg9OCsXpbVv2deCacLsqXkZimzi8PscKAoce19SZCueBiiyhO4MkKQoS5h6gmjTQWyFU4bs094dlv1EZ5jdpnHpOVPpDcVZt2EfCf29Utz20hm45pxD59/uJdY+sIS3EEIIMdHpoV6CO17GNWt5WoEFwzB5e9cxqspycDq0YR5BAKiqhRyPnWPt6cUWLHYXVn8J4brdGYose0lSlCViLbWYiRi2wqlD9ukIyBlFYyHHY+eS5VOpbe7hiVdq0s4o8lVfjOrOofWJX0s1OiGEEFmnZ9tzmKaBa/bytPaahm46e6LMq/JnKLKJx+9zcKw9OKC9f1+RnIF4eklSlCUiDftA1bD6B688F4kmCEUS+GSmaEyUFLg5f2k5W/a18vw7R1N/uBTNRs65lxNrqaXj+d9nOEohhBDi9DH1OD2bnsZZtQTV4U679tbOY+TnOCjOG/xweTFQvs9Bc0doQPJjK65C7+0k0dWcociykyRFWSLSsB+bvyxtqvpE7YHkAWCSFI2dmVNyOWdBCS9vaeD5t99LjGz55eSs+Dg9WzYQ2PR0hqMUQgghTo/gntfR+7pxz12Z1t4ZiLC/rov5lX4YYt+zGMjvcxCN63T1RNPabUXTQLHIeUWnmSRFWcA0TSL1e7EOs58oVY5bCi2MqUUzC5KJ0dYGNrxZl0qMXLOW4Z63io7nf0eoZkuGoxRCCCFOjWmaBN56CnvZLKy5RWnX3tnTjM2qMqMiNzPBTVD5OQ5gYLEFi82BNb9MkqLTTJKiLKD3tKMHu4bdT9QeiOC0a1KOOwMWzSxg1cJSNr7byF9fP4JhJBMjb/Wl2Mvn0PLYr4i11mU4SiGEEOKDCx3cTKy1Fvf81Wnt8YTB5r0tzJqSi6bJbedouBwaDrtG8/sOcYXj+4qO7JR9RaeR/HRmgUjDPgCshRVD9ukIhGXpXAYtmJHPuYtLeXPnMdZt2EcspqNYLOSeezmqO49jD9xAvLMp02EKIYQQo2YaOp0v3YetZDr2kulp1zbvbSEcTTBfCiyMnqKQ77MPmCmC5HlFRihAvKMxA4FlJ0mKskCkfh+qLx/V4RmyT3u3JEWZNq8qn0tWTONgQxd3Pb6TnmAUi9WOf80XUVSVpj/9t5TqFkIIMeEEd20k3t6Ad+mH09pjcZ2XttQzsyIXn0eW738Qfp+DpkFmiqxFU8GiEj6yIwNRZSdJirJApGEftoKh9xMBdAQickbRODC1xMsnPjSdQDDKbY/s4GhzL6rLi//iL6FY7TTd+9/EWo9mOkwhhBBiRMxEnM5XHsAxZT62gvQVK2/tPEZfJMFZc4qG+GpxMvk+J929USKx9GM8LJoNW3EloYObMxRZ9pGkaIIzomFirXXDHtoaiiQIRxPyKs04kZ/j5FPnz8Bh07jr8Z289m4TFoeb/Iv/BYvdTdN9/02kqSbTYQohhBAn1bPtOfTeDjxLL0prj8QSvLKtkTlT8vDKi7IfmP94sYXm9x3iCuAon024bhdGdOA1MXqSFE1w0aaDYJpYhymy0NqZ/GXJlaRo3HA5rXz83CoWVPl5+o0jrNuwjyh28i/+Z1R3Lsf+9F8Ed23MdJhCCCHEkIxomK7X1uOcXo01pzDt2uvvNhGL61TPKRziq8VI5HjsqBaF5sH2FVXMAUMndHh7BiLLPpIUTXCRhn0oNidaTsGQfRrbgmiqQq5XkqLxRFUVzllYyiUrpnKoIcAtD23jUEuM/Eu+hGPqfFqfuIWOF+/FNPRMhyqEEEIM0P3WExiRPryLL0hr7wsneG17E/Mq/bid1gxFlx3U4/dvgxVb0Dx5aHnFhA5uykBk2UeSogkuUr8PW0EFijL0UDa2BvH7nFgscmDaeDSt1Mffr5mJz2Xj93/ZzeMb67Av+wTesy4l8NYTND/8c/S+QKbDFEIIIVJi7Q10v/kY7nmrUd25adde3lKPbhosmSWzRKeD3+fg2CDFFgDs5bMJ1WyRF1BPA0mKJjAjESNSvxdbceWw/RraghTkOsYmKPGBeJxWPrpqGucuLmPbgVZufWg7R51zyVvzRaINB6i/61pCB+WQVyGEEJlnmgZtf70d1Z2Dd1H6LFFDay9v7GyienYRToeWoQizS0GOk+aOPhK6MeCao3wORjhItPFABiLLLpIUTWCRut2YiRj28llD94kl6AiEKch1jmFk4gNRFOZV+fmHC2fhddm475l9PLwtgfWCL2PNK6L54Z/R9sydGLFIpiMVQggxifVufZ5owz5yzvkkivbe8jhdN3n0pRr8OQ4WzRh6Wb8YneJ8FwndpKElOOCataAci8ND3wFZQneqJCmawEKHtqK6c9Fyhi512dwewjSTrzKIicHrtvGRlVP58LIp1Lf28OvHD7DNdR7usz5GcPtL1N/5Hfr2vy2nWAshhBhziZ4OOl78E86ZZ2N/30qVje820tIZ5rwl5bJk/zTK9zmwWi3UHusZcE1RLNjLZxGSpOiUSVI0gYUObsFeNhNFGfoPT0OrFFmYkBSFqvIcPrNmFvMq/by8rZH/26TQPO+zqJ48Wtb/L80P/Q/xruZMRyqEEGKSME2T9mfvRlGt+M66JO1aW1eIFzcfZdGMfFmdcpopFoXiPBdHmgbfX+yomEO8s4l4Z9MYR5ZdJCmaoGIdTSS6W7CXzx62X1ObFFmYyKxWlRULSvjMRbMozHXx8Btt3Ncyn945HyfafJj6O79Dxwt/QA/1ZjpUIYQQWa532/OEDmzCt/xjWGzvJT6GbvLoyzW4HFY5qPUMKfG7ONrci2EMXCViK50BqkafHOR6SiQpmqDCh7aCRcVWUjVsPymykB28bhsXLZvCJ8+bjqZZ+MOmOI/GzidSvoyeLc9y9P++Qdfrj8h+IyGEEGdEpKmG9ufuwTV7Oc6p89OuPft2HUebg5y/tBxNk1vLM6GkwEM0rg9amtui2bCXVBE6IEnRqZCf3Amqr2YLtuJKLNrQp0RLkYXsU+R38dHVVXx8dRVxVO7e5eNJPkyffzZdrz7E0d9+PZkcRQYv3SmEEEKMlh7qoeWRG7HmleA7+yNp13YcbGfju42smF9MSYE7QxFmv8JcJ6pFoa5p4L4iAEfFXCL1e0j0do1xZNlDkqIJyIiFiRzdg2OYqnMAx9r6pMhCliotdHPZuVV8bHUlcdXJPQeKecJYQ8A1ha5XH6buN1+j86V18sdRCCHEKTENndbHb8GMhsk777Mo6ntltps7Qjzy0kFmVOSwaEZ+BqPMfqqqUJTn5MggxRYAHFMXgEUluHvjGEeWPaSA/AQUrt0FegJ72fD7iRrb+qTIQjZTFMoKPZQVemjrCrGjpp0/1lrItZVwaVEr5jt/ofutJ3DPW03OistwlM3MdMRCCCEmmK5XHiBcuwP/RWtR3Tmp9lAkwX3P7MHnsnLeknIYpuiTOD2K/W4O1HdhmuaAIlsWuxNH+RyCO14id+UnMxThxCZJ0QQUqtmK6s1H8w3/qowUWZg8CvNcfHj5VIJ9MXYf6eCxozaURCEXFHUx69AO+nZvxF42C2/1xXjmn5u2QVYIIYQYTPebj9P9xmN4z7oUe+mMVHskmuAPf9lNKJLgU+fPkH1EY6Qk38W7B9to6wpT5HcNuO6cvpSul9cRbT6C/SR7zsVAkhRNMKZpEqrZMuyBrf3q23opzhv4SyOyl8dt45yFpZw1t5jDDd1sqfPyfHchCzzdLOs+RvSvd9Dx3O/xLDwP7+ILsZfPGbakuxBCiMmpZ8uzdL54L56F5+OZf26qPRJL8Ie/7KGtK8zHVlfidQ+9t1mcXsV+F4oCdc09gyZF9rIZWBwegjtflqToA5CkaIKJt9Wj93bgKPvYsP0isQSdgQgLqmSN72Rk1SzMqfQzZ1oebd1h9tV1cX9TPvZEFav87czY9Sa9255Hyy3Gs+gCvAvPw+ovy3TYQgghxoHena/QvuFuXHPOwbPkolR7LK7zp7/u5VhnHx9bWUm+FHIaU1arSn6Ok9qmHpbPLxlwXbGoOCsX0rvrVfwXrU3b/yVOTr5bE0xw7+soVju24mnD9pMiCwIARaEwz0VhnovVi0qpPdbDnvpCnm+roELrYrmtndLXH6N748PYiitxzzsXz7xVWP2lmY5cCCFEBvS8+zfan74D54yl+JZ9JLWaoC+c4L4Ne2hsC/LRVVUUDjJTIc68Er+LI0NUoIPkErq+fW8RPrwd16yzxzCyiU+SognENA16d7yMY9pCFNU6bN/GtqAUWRBpVNXCjIpcZlTkEo4kONIU4PXGAB3dM6iytrOks5PiVx+i6+V1WAun4p6zAvfsFdhKpssSOyGEyHKmadL1yoN0v74e18yz8a24DEVJ7hVq6wrxx7/uIRTV+eiqKoolIcqYknw3uw530NUbJW+QezwtrwQtr5jeHS9JUjRKkhRNIJHaXeg97bhWfeqkfWsauinMc0mRBTEop0Nj/vR85k/Ppy8cp+5YD68f66E90MNUtZ0FHZ2Uv/EU3a+tR/Xk4Zp5Nq4ZZ2EWSwU7IYTINmYiTutf/o++3RvxVl+Ce/65qRfDDjV0s27DPhx2jU+dN132EGVYSX5yX1FNfdfgS+gUBWfVEnq3v4geDqI6PRmIcmKSpGgC6dn+IpqvAGvBlGH7xeI6hxsDnD23eIwiExOZ22lNJUiRaIKGliC7Wnp4rq2HfKOTWXoH03duwv3uC2BR8eSUEexZgWX2MuylM1AsaqafghBCiA8o3t1K6+O3ED1WQ+6H/hFn5UIADN3kpS31vLilgbICNx9eNgWbTf7eZ5rDriVni2o6Bk2KAJyVi+nd9jx9e14bcNiuGJokRROEEekjtO8tPIsuOOlSpsONARK6yZRi7xhFJ7KFw64xc2ouM6fmYugmrV0hGlqDPNkWJNbbwTStnemJTpQ3nyT4xiNgc+GaNh/HtIU4py3EVjRVkiQhhJgggrs30vb0nVisdvIvuRJbYfJF145AhIefP0BDWy9LZxVRPadQVp6MI1VlPt7a1UwoksDlGHgrr7q8OCrm0f32U3irL5H/l0dIkqIJIrj3DUwjgXP6kpP23V/XhddtI9cjU9zig7OoCiUFbkoK3CyjmGhsGvXNAbbWd/BKxMAZbqMi0knlwVoKD25DRQerE8fUubimzscxZR620hlYNPk5FEKI8UQPB+l47h6Cu17FUbmInBUfx2JzYugmb+1u5rm3a7HbND7xoemDln4WmVVZksObO46x90gnZ88rGrSPe8G5dGy4m74D7+CZu2qMI5yYJCmaIHq3v4i9dAaqyzdsP9M02V/XxZQir5wuLU4ru01jaokXuxKiuLgY3ZhJc0cfezpDtHX0Yu1rodTSRcWBekoO7cRKAlNRUQun4amcj6N8NvbyWWi+QincIIQQGWCaBr3vvkjnS/dhJmLkrL4cZ9ViFEXhSGOAJzceprUzxJxpeZyzoASrVWYYxiOXU6M438Wuw+1DJkW2ggpsxVV0v/EY7jkr5f/dEZCkaAKItTcQbTxA7nn/eNK+rV1huoNRVi4cfJ2pEKeL06FRVZ5DVXkOUEoiMZO2rjANXSHe7QqiB1rJTXRQ2thNacuL+JS/AKDbPFhLZ+GbOhtH2cxksu/OyeyTEUKILBdp2E/7c/cQO3YIZ9USvNWXoLq8tHSGeHHTUXYe6qAoz8Unz59OoRz8Pu5Vlubwzp5mItEEDvvgt/Pu+efS9dJ9RI7uxjlt4RhHOPFIUjQBBHe+jGJz4qiYc9K+B+o60VSF0gKpNiLGlqZZKC10U1roBgrBrKQvkqC9O8yO7gi93V2ovc3kRTspDh2lqHYXDiUOQMyWg1JQiXfKTHKmzsJeMh3V65dXtoQQ4hRFGvbTtfEhwoe3o+WVkn/pl7EVTaW1M8SLr+9n58F23C4r51eXM2tKrvzdnSAqy3y8tesYe2s7qZ4z+GyRvWwmWl4J3W88JknRCEhSNM4Z8Si921/CWXnys4kA9tZ2UV7oQVXlj5rIMEXB7bTidlqZVuoDioA5hCMJOnsibO8OEQl0ogZbcAU7KAg1kGjYQ+Tt44mSxUnUW4ZWMA1vRRX5lbNwFk/FYpWzt4QQYjimaRA+soPuN58gUrsDLbeI3A/9I/Yp8zjU2Msbf93Dgbou3E4rqxeXMXtqntw3TDAep5WiPBe7D3cMmRQpioJn/rl0v/4I0ZZa7MWVYxvkBCNJ0TjXu+159FAP7nmrT9o3HE1Q19LLalk6J8Yxp0Oj3OGhvMhDMlGai2GY9IZi1AYihLo7oLcNa6QDT0cXeZ1v4ah5kRYFDBT61BwirmKUvAqcJVPJm1JFUeV0bA5npp+aEEJklB7upXfHS/RseZZEVzNaXim5532WeMFMthzs4J03ttPeHSY/x8GHlpYzsyJXkqEJrKrMx5Z9LURiCRy2wW/pHdMWoL77N7rffIziT187xhFOLJIUjWNGLELX64/gnL4Uzes/af+D9d2YhsmU4uGLMQgx3lgsCjkeOzkeO5TnANOTF0yTYCTB4UCQWHcL9LZjDXfi6unAF6jFfTRK7B1oAAKmh15rPglPEZbcUpyF5eSWT6WwooI8n1OWhAghspKRiBE6uIXgrlcJHdoKpolj6gKc1R/lcJ+bd3e1sb9+CwowrcTLygUllOS7pBhTFqgq9fH27mYO1HWzeFbBoH0Ui4p7/mp6Nm8gtvpybEXTxjjKiUOSonGsZ+uzGOEgnkXnj6j//rou/D47HtfJl9kJMSEoCh6nFY8zD0ry0i4Zhklbb5BIdytGTzuWUCfWaABP9z68XZtRa03YBO2mhQOGh141l7jDj+ktxJZXjKughNzScgqLCsjPcaCqlgw9SSGEGB090kf40Db6DmwiVLMFMxbGml+OfcEa6i0V7Dga5uBTTSR0k8JcJ6sWljCjPAf7ELMJYmLyuG0U5jrZur9lyKQIwDXzbEIHNtG24W7K1v5UXiQcgvx2jFNGLEz3G4/hmnkWmifvpP1jcZ19tZ3MnnryvkJkA4tFwZPjxZPjBWakXYsZBoneLiI9Hei9ndDXhS/Wgy16BFdkJ9Y2HQ4k+7abGgcND0GLl5gtB9PlR/Pm48grwFNQTF5xMQWFfnI8djm8UAiREaahEz12iPCRHYSP7CDSsA8MHS2vlFjZEo6Y5exqNmh6rQ+TRor9Ls6eW0xlqQ+vW86Ky2YLqvJ5eVsDx9r7KC1wD9pHUTV8yz5K59/+RHDXq3gXXTDGUU4MkhSNUz2bn8GIhvAsPG9E/bftbyUSSzB3miRFQmCxoOXk48nJH3ApYZok4iH0YDex3m70YBdquIe8WBBrvBF74ACOQCy5Ju+4ZlPjgOGkz+ImZvVhOnxY3LlYvXk4cvLx+PPJKSzEX1iIx22XV+GEEKfEiIWJNtUQadhPpGEfkfp9mLEwaHbivnLaClawuzePmgadRJ2J3RaivNDD+dUVVBR5cDrk9m6ymF6ew+b9Lby6rYF/umToKsX20hk4pi2k44U/4p61DItj8ARqMpPfmnHIiIbofvMJXDPPRnXnnry/YfLa9iZ5RUiIkVAUsLlR/W6c/vIBl00grCdQor3EggFifT0YoV6MSC+uWB/eeDfWvmPYgxHsLfHU1+lAiwlHTDshxUVMdaPbPZh2HxaXD5s3F4cvF1eOH29+PrkFBTh9XiwWORxRiMlMDweJtdXRd/QAroPv0vbOH9C7joFpYmoOQo5CWq2z2B/zUdPpwOy04LBrFPudLJvnobTAhd/rQJGZ7EnJoiosmlHAW7uauWRFBH+OY8i+vrMupe2p39L56kMUXPr/jGGUE4MkReNQ18Y/Y8QjI54lOlDXRUcgwrmLy85wZEJMEqqG6crD6spjqB16BsnkSQ8HiQYDJEJB9HAQM9oHsRBqIowt2oEt3IitK4qDGP33LFGgBdBNhQh2ohYXcc2FYfOAw4PF6cXq9mH35uD05eLJzcObl4fDl4PF4UaRREqICcU0TfS+APHOJuIdjcQ7Gom1NxBrqUMPdgJgKBYilhyOKbk0xBdSF/HSabjRNAv5OU4KCpxcMNNJsd+F12WVQgkiZc7UPLYdaOO17U188vzpQ/ZT3Tl4Fl1Az+an8S65SEp0v48kReNMuHYngbefwnvWJaiukVWRe/XdRor8Lor8cgK1EGNK1VA9ubg8uSftGjF0oqEQsWAviUgQPRzCiIYgFkKJh7HoEbS+TqzBY9jMGA4lhk3RAQgdf+sXxUbcYkfXnBhWF9hcKA4PmtON1enB7vHgcHtxer3Y3R5UuwvF7sRic2KxOzFN84x8O4SYzIxIH4meDhKBNuKBVhKBVuJdrUTaG9EDLSiJGAAmCiGLhy7DTWssh9ZEOR2Ghx482G0qBbku/IUuqr12/D4HPo9NluSKYWmahQVVfjbvbeaiZVOGLbjlnruS8OHttD5+M+Vf+gUWuxxn0U+SonFEDwdpffJWbMWVuOetGtHXNLYGqT3Ww4eXTTnD0QkhToViUXF4vDg83hH1j+kGgXCMaKiXRDiEHgljREMY8TDEIyjxCJZEDDUaRTODaEYjNiUBSgKLEgfFIDLEY5uAB426FxyYmh00B2h2LHYHFpsD1eZAc7jQHA5sDifW4+2K1YFitWOx2tPep39sQ1Gkkp/IDqZpYkZD6KEAiWA3el8gWcSlu51IoINETwdGsBMl3I2qR1Nfp2MhaDrp0e10Gy4CRiUBw0Wv4sZw5uCyO/G5bfhcNqrcNqo9NuxWaG1tpbi4GJtNlsKL0Zlf5Wd7TRtv7Gzi0nOGLrutqBq553+Wjg130/rUbyn+h+sk6T5OkqJxwjRN2p+5EyMaJv/iL434puK17Y143TYqS+VsIiGyiapa8HgceDxDrw8/kWmaxOMGfbEEnTGdaCxGIhohEY1gxCIYsShGIgqJGGYihhmLoOk6lkgCixHHShirEsSq6GjoaIqOFR2rcvwNHYsyshkmQ9EwVCumasNUbaDZULT0BMpitaPaHKh2B5rdjmZ3otntWFKJly3ZX+tPuPofw4ZFs4GqyX/kYsRM08CMRTCiIWJ9vUR6e4n29RIJ9hDv6yUR6kU/vneQSBBLLIga78OaCKGipz2WbioETQd9hp2QaaPPcBA0pxLT3MQ1N4bdi+Lw4HLYcTs13E4blQ4Nj8uK3aoOuewtFouNxbdCZCm7TWPeND9v7jzGygUl+Dz2IftacwrJXf33dL3yIN1vPEreuf8whpGOX5IUjRPB3Rvp2/sGuR/6R1R3zoi+pjMQYeehDlbML5ENlkJMcoqiYLOp2Gz9+42GXk4bi8VoaWl57xVp0yShm8TiBnFdJ54wCMd1EgmDWMIgnjBIJHQSegIjHk8mVYk4JOKYRgJFj2ExEmAmUIw4FkNHiSWwGAm044mVRgRN6UslXJpiYO3/GON44pVgpH/KTBR0RUsmYBYrhkXDtFgx1eQbqhVU2/H3ViyqFaxWFNWKRbOiaBqW1MdWLKqGmmpXsWgaFlXDYtGOf6ymvSmW4x9bLFhUCyjJN0VRjn+sHL/5VY63vfc5ioKSHLT+0eN4w3ttx98rg117b9DH5VJIXTcwTCP5XjcwEjqGqaPHdXTDQE/omIaOrifQEzpGIoGeeosf/zye+tiIxzAS8eNvMczUz2AMElHQ45CIJn8OEzEsRhRVj6GaMaxGDCsx7MSHjNcwVWKmlahpJWJaiWEjptiIqz4SFge66sS0OjFtLnC40exOnHYrDpuG06bid2g4bKok6SLjlswq5FBjgPUv1vCly+YPe4yEY8o8PIsuoOvlB7AXV+KaefYYRjo+SVI0DkQa9tH+9J04KhfjrFw4oq8xDJM/v3gAl11jztTcMxugECK7KQqapqBpFk73fwuGbpIwDHTdRNcNEoZJQjeI6iZ9J7Qn3+vouo6pxzH140mXHgcjgaLHwdBRjETqzWImsJg6SiL5vv9NNSNY6MOCgdr/pgzysWIABiomKOb75gMmjjygeUPy4/4UyeS9m6ETPx7s8/RrJ2tIUk640P+xAiOeTRz4eMmfvJH89CVMCwlUEqjox0c1oagYaOiKSlzRiFhcGGoOhuW9GUszNWPpAGtyVlJ1ONGsNmxWCzZNxa1Z5EVGMWE57BrnLy1nw1t1vLWrmdWLS4ft71l8IfHOY7Q8fguln/9vHOWzxyjS8UmSogyLNOzj2P0/xZpXQs45l434617Z2sDR5l4+vroKq1UqUQkhxieLqmBTVYYs43eGmaaJYYBhGBimia4nz6pKfp58gckwTUzdwDCSMximoYOhg2Ek95Qc/xgMMA1MwwTz+OeGiWIaybTANDAxUUwTTI7P4pjQ/z4Z0Ptmd8zjl02U/o8xjz8e6V93/CsUSD2GaRjEYjFsNusJMxXvJSknfCNO+ERBUfpTI+WECahkW+pxlBMe4/jsVv8Ml9I/e9XfX+mfATv+vv9zheTMGUoy2eifTbOoKIoFxWJJLhdXLVgUNdmuWrBYVBRVA1VFVbXUzJ2iaqkZM/X4mxDiPRXFXhZMz2fDm0eYUZFD8TBFuBTFQu65/0DXS+s4dt/1FH/me7hmVI9htOOLJEUZdGJClLfmC1isQ6//PNHR5l5e2FTP0lmFlAxxerEQQojkDbuqgqpm5+3zgKWQQohJb8X8Yhrbgjz8/H6+8ZmlaOrQs58WmwP/h9fStfHPND/8c4o+cfWIj4TJNlIiKENCh7cnEyJ/CXkXfXHECVEkluDBF/ZTmOekek7RGY5SCCGEEEJMJKpqYc1ZFbR0hbl/wz7iCWPY/opmI++Cz+GsXETrE7fQtfHPmHpijKIdPyQpGmNGPEr7s/fQ/MBPsBZUkLfmi8lKSiMQjiZYt2EffeE4a86qGHYDnRBCCCGEmJzyc51csnwqNQ1d/O6pXYQjwyc5ikUlZ9Wnk8UXNj5E4+++T7T58BhFOz5kPCkyDINbb72V8847j6VLl3LVVVdRX1+f6bDOiEjDfhru/ld6tj2Hb9lH8V808oSovTvMbeu309AS5JLlU/G6ZZmEEEIIIYQY3JQSLx9bXUVzR4g7H99JTzA6bH9FUfAuuYj8j1yFEY/Q+Lvv0/Hivejh4BhFnFkZT4puu+027r//fn7605/y4IMPYhgGX/nKV7KmXr9pmoSP7ODYgzfQ9Mf/F0XVKPzYN3DPXTnis4gOHu3mtvXb0Q2TT50/nbJCzxmOWgghhBBCTHRFfhef+NB0QuE4tzz0Lq+920hCH345nS2/nIKPfhXP4gsJvPMXjv7mq7Q/ew/xruYxijozMlpoIRaL8bvf/Y7rrruOCy+8EICbb76Z8847j+eee47LLht5NbbxKNJ4gPan7yTWWouWV0LO6r/HWbkIxTK6Db8PPr8fv8/BxcunnnAGiRBCCCGEEMPL9dr51Pkz2LKvhWferOWNXcf4uxXTWDAjH00d/AV6xaLiXXQBrplnEzrwDsGdL9Oz5Rmc05finrUc16xlaL78sX0iZ1hGk6J9+/bR19fHqlWrUm0+n4/58+ezadOmCZ8U9W5/iURPO/4P/zO2kukf+GC3WFynsswnCZEQQgghhBg1p0PjQ0vLWTC9gHf2NPPQCwewv6IypzKPhdMLqCrz4XYOPDtBdXrwLrkIz4IPETq8nUjdbtqf/f9gw13YiipxTJmLraQKe8l0bAVTULQMnb9wGihmBo/Dfu6557j66qvZvn07Docj1f6d73yHSCTCnXfeOarH27p1K6ZpYrWOjwExwr2YiTgWp/eUHqe7N4rdpmLVMr7a8QMxj58JYrFY5MTvCU7GMjvIOGYPGcvsIWOZHSbKOBrHD9JO6Ca6kUwFLAqoqoJNU7ENdwamaSSr0+nx985162dRk+eRqSoWuwcsmb13jcfjKIrCWWedddK+GZ0pCofDAAPOVrDb7QQCgVE/Xv8P33j5IVQUBdNIYIS6T+lxvBYgcfxtAlIYB5vXxGkhY5kdZByzh4xl9pCxzA4TZRz7Dz+2Q3rABhADI/4BH9jQAR1Tj4PVgaKO7MiZM0XpP0x6BDKaFPXPDsVisbSZomg0itPpHPXjVVdP3lN4hRBCCCGEEB9MRpPZ0tJSAFpbW9PaW1tbKS4uzkRIQgghhBBCiEkmo0nR3Llz8Xg8vP3226m2np4e9uzZw/LlyzMYmRBCCCGEEGKyyOjyOZvNxhVXXMFNN92E3++nvLycG2+8kZKSEi699NJMhiaEEEIIIYSYJDKaFAFcc801JBIJ/uu//otIJMLy5cu55557xk0FOSGEEEIIIUR2y2hJbiGEEEIIIYTItIlQNVAIIYQQQgghzhhJioQQQgghhBCTmiRFQgghhBBCiElNkiIhhBBCCCHEpCZJkRBCCCGEEGJSk6RICCGEEEIIMalJUiSEEEIIIYSY1CQpEqfMMAxuvfVWzjvvPJYuXcpVV11FfX39kP27urr4t3/7N5YvX86KFSv48Y9/TDgcHsOIxVBGO5YHDx7kq1/9Kueccw6rVq3immuuoampaQwjFoMZ7Tie6Mknn2TOnDk0NDSc4SjFSIx2LOPxOL/85S9T/a+44gr27t07hhGLoYx2LDs6Ovi3f/s3Vq5cyTnnnMO1115LS0vLGEYsRuLOO+9k7dq1w/aR+56JQZIiccpuu+027r//fn7605/y4IMPYhgGX/nKV4jFYoP2v+aaa6irq+MPf/gDv/71r3nllVf40Y9+NLZBi0GNZiy7urq48sorcTgc3Hvvvdx99910dnbyla98hWg0moHoRb/R/k72a2xs5Cc/+ckYRSlGYrRj+aMf/YhHH32Un/3sZzzyyCP4/X6uuuoqent7xzhy8X6jHcvvfve7NDU18fvf/57f//73NDU18a1vfWuMoxbDWbduHbfccstJ+8l9zwRhCnEKotGoWV1dba5bty7VFggEzMWLF5tPPfXUgP5bt241Z8+ebdbU1KTaNm7caM6ZM8dsbm4ek5jF4EY7lg8//LBZXV1thsPhVFtTU5M5e/Zs84033hiTmMVAox3Hfrqum5///OfNf/7nfzZnz55t1tfXj0W4YhijHcujR4+ac+bMMV966aW0/mvWrJHfyQwb7VgGAgFz9uzZ5t/+9rdU2wsvvGDOnj3b7OrqGouQxTCam5vNr33ta+bSpUvNj3zkI+YVV1wxZF+575k4ZKZInJJ9+/bR19fHqlWrUm0+n4/58+ezadOmAf03b95MYWEhM2bMSLWtWLECRVHYsmXLmMQsBjfasVy1ahW33XYbDocj1WaxJP+k9PT0nPmAxaBGO4797rjjDuLxOF/72tfGIkwxAqMdy9dffx2v18v555+f1v/FF19Mewwx9kY7lg6HA7fbzeOPP04wGCQYDPLEE09QVVWFz+cby9DFIHbv3o3VauXJJ59kyZIlw/aV+56JQ8t0AGJia25uBqC0tDStvaioKHXtRC0tLQP62mw2cnNzOXbs2JkLVJzUaMeyoqKCioqKtLa77roLh8PB8uXLz1ygYlijHUeAHTt28Lvf/Y7169fLnoVxZLRjeeTIEaZMmcJzzz3HXXfdRUtLC/Pnz+cHP/hB2g2ZGHujHUubzcYvfvELfvjDH7Js2TIURaGoqIj77rsv9eKTyJyLLrqIiy66aER95b5n4pDfLHFK+jcK2my2tHa73T7ovpJwODyg73D9xdgZ7Vi+37333st9993Hddddh9/vPyMxipMb7TiGQiGuu+46rrvuOiorK8ciRDFCox3LYDBIXV0dt912G//6r//K7bffjqZpfOELX6Cjo2NMYhaDG+1YmqbJ3r17qa6uZt26dfzxj3+krKyMb37zmwSDwTGJWZwect8zcUhSJE5J/9Kp928UjUajOJ3OQfsPtqk0Go3icrnOTJBiREY7lv1M0+SWW27hhhtu4Bvf+MZJq/CIM2u043jDDTdQVVXF5z73uTGJT4zcaMdS0zSCwSA333wzH/rQh1i8eDE333wzAI899tiZD1gMabRj+cwzz3Dfffdx4403cvbZZ7NixQruuOMOGhsbWb9+/ZjELE4Pue+ZOCQpEqekf0q4tbU1rb21tZXi4uIB/UtKSgb0jcVidHd3U1RUdOYCFSc12rGEZPnff//3f+eOO+7gP/7jP/jud797psMUJzHacXzkkUd44403qK6uprq6mquuugqAyy67jDvuuOPMByyG9EH+vmqalrZUzuFwMGXKFCmxnmGjHcvNmzdTVVWFx+NJteXk5FBVVUVdXd2ZDVacVnLfM3FIUiROydy5c/F4PLz99tuptp6eHvbs2TPovpLly5fT3Nyc9kf9nXfeAeDss88+8wGLIY12LAG+973vsWHDBn75y1/ypS99aYwiFcMZ7Tg+99xz/OUvf+Hxxx/n8ccf54YbbgCS+8Nk9iizPsjf10Qiwc6dO1NtkUiE+vp6pk2bNiYxi8GNdixLSkqoq6tLW14VCoVoaGiQZa4TjNz3TBxSaEGcEpvNxhVXXMFNN92E3++nvLycG2+8kZKSEi699FJ0XaezsxOv14vD4WDJkiWcddZZXHvttfzoRz8iFArxwx/+kE9/+tNDzkaIsTHasXz00Ud5+umn+d73vseKFStoa2tLPVZ/HzH2RjuO779Z7t/0XVZWRm5ubgaegeg32rFctmwZq1ev5vvf/z4/+clPyM3N5dZbb0VVVT71qU9l+ulMaqMdy09/+tPcc889fPe73+U73/kOALfccgt2u53LL788w89GDEfueyawTNcEFxNfIpEw//d//9dcuXKluXTpUvOqq65KnXFSX19vzp4923zkkUdS/dvb282rr77aXLp0qXnOOeeY119/vRmJRDIVvjjBaMbyyiuvNGfPnj3o24njLcbeaH8nT/TWW2/JOUXjyGjHsre317z++uvNc845x1yyZIl55ZVXmgcPHsxU+OIEox3Lmpoa82tf+5q5YsUKc+XKlea3v/1t+b0ch77//e+nnVMk9z0Tl2KappnpxEwIIYQQQgghMkX2FAkhhBBCCCEmNUmKhBBCCCGEEJOaJEVCCCGEEEKISU2SIiGEEEIIIcSkJkmREEIIIYQQYlKTpEgIIYQQQggxqUlSJIQQQgghhJjUJCkSQghxUmvXrmXOnDlpbwsXLuTCCy/kxz/+MYFA4JT/jYsuuogf/OAHp/w4jz76KHPmzKGhoWHIPg0NDcyZM4dHH30UgN/85jfMmTMndX3t2rWsXbt22H/n/V9zKk7XcxdCCPHBaJkOQAghxMQwf/58rr/++tTn8Xic3bt386tf/Yq9e/fywAMPoChKBiMcuaKiIh566CGmTp066PUTn6cQQojsJ0mREEKIEfF4PCxdujStbfny5fT19XHrrbeyffv2AdfHK5vNNmysM2fOHLtghBBCZJwsnxNCCHFKFi5cCEBTUxNr167luuuu45prrmHp0qVceeWVAPT29vLzn/+ciy++mEWLFnHZZZexfv36AY8Vj8e54YYbWL58OcuWLeP73/8+nZ2daX3+/Oc/c/nll7N06VIWL17Mpz71KZ555pkBj7V161Y+/elPs3DhQi677DKefvrp1LX3L597v/cvn4tGo/z85z/n3HPPpbq6mv/4j/8gGo0O+LqRxLZv3z6uvPJKqqurWbNmDU8++eRQ31ohhBBjRGaKhBBCnJIjR44AMGXKFACeeeYZPvnJT3L77bdjGAaRSIQvfOELdHR0cM0111BeXs4LL7zAf/7nf9Le3s7Xv/711GM988wzLFmyhF/84hd0dnZy0003UVNTw8MPP4yqqqxbt44bbriBq6++mrPPPptAIMDdd9/NddddR3V1NSUlJanH+uEPf8g3vvEN5s2bx2OPPca1116LzWbj4osvHvVz/Pd//3c2btzItddey7Rp03jooYd46qmn0vqMJLaWlhauuOIKKisrufHGGwkGg9x00010dHR8kG+9EEKI00SSIiGEECNimiaJRCL1eSAQ4J133uH222+nuro6NWNktVr58Y9/jM1mA+D+++/nwIEDPPjgg1RXVwNw3nnnkUgkuO222/jc5z5Hbm4uAHl5edxzzz24XK7U59/61rd49dVXWbNmDfX19Xz5y1/mm9/8ZiqO8vJyLr/8crZs2cLHP/7xVPvVV1/Nl7/8ZQDOP/98amtrue2220adFB08eJBnn32WH/3oR3z+859Pxf+JT3yCmpqaVL+RxPaHP/wBXde566678Pv9AFRVVfHZz352VDEJIYQ4vSQpEkIIMSKbNm1iwYIFaW0Wi4XVq1fzk5/8JFVkYfr06amECOCdd96hvLw8lRD1++QnP8n69evZvn07F1xwAQAXXHBBKiGCZFU2TdPYtGkTa9asSVVo6+np4fDhw9TV1fH2228DEIvF0h7/Yx/7WNrnF198Mb/5zW/o6+sb1fPevHlzKpYTn/ff/d3fpSVFI4lty5YtLF26NJUQASxZsoSysrJRxSSEEOL0kqRICCHEiCxYsIAf//jHACiKgt1up7S0FI/Hk9bP7XanfR4IBCgsLBzweAUFBUAyiej3/n4Wi4W8vLxUn6NHj/LDH/6QN998E6vVyvTp05k7dy6QnMka7PH75efnY5omwWBwxM+5P35Izlqd6P2xjiS2QCBARUXFgH9jsO+PEEKIsSNJkRBCiBFxu90sWrRo1F+Xk5NDXV3dgPa2tjYgPdno7u5O66PrOl1dXeTn52MYBl/96lexWq2sX7+eefPmoWkaNTU1PPHEEwMePxAIpCVG7e3tqKpKTk4O7e3tI46/P7729va0GZ0TYx1pbHl5eYP+2+9/3kIIIcaWVJ8TQghxRi1fvpzGxka2bduW1v7kk09itVpZvHhxqu31119P27f07LPPkkgkOOecc+jq6uLIkSN85jOfYdGiRWha8nW9V199FUgmJid6+eWXUx8bhsGGDRtYsmQJDodjVPGvXLkSgA0bNqS1v/TSS6mPRxrbypUr2bZtGy0tLamvrampob6+flQxCSGEOL1kpkgIIcQZdfnll3P//ffzrW99i2uuuYaKigpefPFFHnnkEb797W/j8/lSfdva2rj66qtZu3YttbW1/OpXv+Lcc89l1apVKIpCeXk569ato6SkBJ/Px8aNG/nTn/4EQDgcTvt3b7nlFnRdp7S0lAceeIAjR47w+9//ftTxT5s2jX/6p3/i5ptvJpFIMG/ePJ544gn279+f6pOfnz+i2P7lX/6F9evX8+Uvf5mrr74aXde5+eabsVqto45LCCHE6SMzRUIIIc4op9PJvffey5o1a/j1r3/NN77xDbZs2cL//M//cPXVV6f1/cIXvkB+fj7f+ta3+PWvf80nPvEJfvvb36aKONx2220UFxfzgx/8gO9+97ts376d22+/nenTp6cKIvT7+c9/zp/+9Ce++c1v0tLSwt13382KFSs+0HO4/vrrueqqq7jvvvv49re/TSQSSSslPtLY8vLyeOCBB6ioqOAHP/gBP/vZz/jiF7+Y2nskhBAiMxTz/TtThRBCCCGEEGISkZkiIYQQQgghxKQmSZEQQgghhBBiUpOkSAghhBBCCDGpSVIkhBBCCCGEmNQkKRJCCCGEEEJMapIUCSGEEEIIISY1SYqEEEIIIYQQk5okRUIIIYQQQohJTZIiIYQQQgghxKQmSZEQQgghhBBiUpOkSAghhBBCCDGpSVIkhBBCCCGEmNT+f4xjNjsdaW0AAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs_train = model.predict_proba(X_train)[:, 1] \n",
    "probs_test = model.predict_proba(X_test)[:, 1]    \n",
    "ks_test, p_value = ks_2samp(probs_train, probs_test)\n",
    "\n",
    "\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "sns.kdeplot(probs_train, label=\"Train\", shade=True)\n",
    "sns.kdeplot(probs_test, label=\"Test\", shade=True)\n",
    "\n",
    "plt.title(\"Klomogorov-Smirnov\")\n",
    "plt.xlabel(\"Probabilidad\")\n",
    "plt.ylabel(\"Densidad\")\n",
    "plt.legend()\n",
    "plt.text(0.5, 0.5, f\"Estadístico KS: {ks_test:.4f}\\np-value: {p_value:.4f}\", \n",
    "         horizontalalignment='center', verticalalignment='center', \n",
    "         transform=plt.gca().transAxes, fontsize=12, bbox=dict(facecolor='white', alpha=0.5))\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Matriz de correlación, variables de entrenamiento')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "sns.heatmap(X_train.corr().abs(), cmap='Blues', annot=True)\n",
    "plt.title(\"Matriz de correlación, variables de entrenamiento\", fontsize=16, fontdict={\"weight\": \"bold\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scree plot')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import factor_analyzer as fact\n",
    "\n",
    "fact.calculate_bartlett_sphericity(X_train)\n",
    "fact.calculate_kmo(X_train)\n",
    "# Generamos una nueva instancia en el objeto factorize e implementamos el análisis\n",
    "factorize = fact.FactorAnalyzer(n_factors=10, rotation= None)\n",
    "factorize.fit(X_train)\n",
    "# extraemos los eigenvalues\n",
    "factor_screeplot = factorize.get_eigenvalues()[0]\n",
    "# graficamos los eigenvalues contra la cantidad de dimensiones estimables\n",
    "plt.plot(range(len(factor_screeplot)), factor_screeplot, 'o-', color='tomato')\n",
    "plt.xlabel('Número de Factor')\n",
    "plt.ylabel('Valores Propios')\n",
    "# visualizamos el criterio de corte\n",
    "plt.axhline(1)\n",
    "plt.title(\"Scree plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mjj</th>\n",
       "      <th>dphijj</th>\n",
       "      <th>detajj</th>\n",
       "      <th>MET</th>\n",
       "      <th>jet1_pt</th>\n",
       "      <th>jet1_eta</th>\n",
       "      <th>jet1_e</th>\n",
       "      <th>jet2_pt</th>\n",
       "      <th>jet2_eta</th>\n",
       "      <th>jet2_e</th>\n",
       "      <th>njet30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3556267</th>\n",
       "      <td>2.858828e+05</td>\n",
       "      <td>2.486339</td>\n",
       "      <td>4.152604</td>\n",
       "      <td>32395.636719</td>\n",
       "      <td>35407.410156</td>\n",
       "      <td>-1.557427</td>\n",
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       "      <td>35230.921875</td>\n",
       "      <td>2.595177</td>\n",
       "      <td>2.374987e+05</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4600529</th>\n",
       "      <td>1.665840e+06</td>\n",
       "      <td>-1.116634</td>\n",
       "      <td>4.210449</td>\n",
       "      <td>233870.609375</td>\n",
       "      <td>329462.281250</td>\n",
       "      <td>-1.682948</td>\n",
       "      <td>917478.375000</td>\n",
       "      <td>126548.531250</td>\n",
       "      <td>2.527501</td>\n",
       "      <td>7.974178e+05</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>2212861</th>\n",
       "      <td>5.425114e+04</td>\n",
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       "      <td>32920.222656</td>\n",
       "      <td>-0.295575</td>\n",
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       "      <td>30504.076172</td>\n",
       "      <td>1.219843</td>\n",
       "      <td>5.642772e+04</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>260032</th>\n",
       "      <td>7.590763e+04</td>\n",
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       "      <td>73568.648438</td>\n",
       "      <td>0.639353</td>\n",
       "      <td>90137.742188</td>\n",
       "      <td>51953.625000</td>\n",
       "      <td>0.944471</td>\n",
       "      <td>7.711336e+04</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>4516133</th>\n",
       "      <td>1.470252e+06</td>\n",
       "      <td>1.397715</td>\n",
       "      <td>5.533992</td>\n",
       "      <td>171162.078125</td>\n",
       "      <td>148240.375000</td>\n",
       "      <td>-1.454019</td>\n",
       "      <td>334865.156250</td>\n",
       "      <td>57647.234375</td>\n",
       "      <td>4.079974</td>\n",
       "      <td>1.705229e+06</td>\n",
       "      <td>5</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2602793</th>\n",
       "      <td>1.808710e+05</td>\n",
       "      <td>2.712765</td>\n",
       "      <td>2.621271</td>\n",
       "      <td>63139.863281</td>\n",
       "      <td>62579.144531</td>\n",
       "      <td>-3.434310</td>\n",
       "      <td>971308.187500</td>\n",
       "      <td>32253.638672</td>\n",
       "      <td>-0.813039</td>\n",
       "      <td>4.406517e+04</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3371629</th>\n",
       "      <td>7.766109e+04</td>\n",
       "      <td>-2.864583</td>\n",
       "      <td>1.073243</td>\n",
       "      <td>83472.851562</td>\n",
       "      <td>34098.093750</td>\n",
       "      <td>-0.065554</td>\n",
       "      <td>34649.226562</td>\n",
       "      <td>33064.425781</td>\n",
       "      <td>-1.138797</td>\n",
       "      <td>5.742496e+04</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>343341</th>\n",
       "      <td>2.587692e+05</td>\n",
       "      <td>2.560996</td>\n",
       "      <td>2.731060</td>\n",
       "      <td>37221.273438</td>\n",
       "      <td>73745.789062</td>\n",
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       "      <td>52858.539062</td>\n",
       "      <td>-0.920955</td>\n",
       "      <td>7.716077e+04</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1198883</th>\n",
       "      <td>2.116611e+05</td>\n",
       "      <td>-2.543470</td>\n",
       "      <td>3.448723</td>\n",
       "      <td>14797.860352</td>\n",
       "      <td>41817.003906</td>\n",
       "      <td>0.215262</td>\n",
       "      <td>43553.968750</td>\n",
       "      <td>31587.707031</td>\n",
       "      <td>3.663985</td>\n",
       "      <td>6.166338e+05</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4427099</th>\n",
       "      <td>1.878782e+06</td>\n",
       "      <td>-2.846742</td>\n",
       "      <td>4.817425</td>\n",
       "      <td>12915.691406</td>\n",
       "      <td>170787.375000</td>\n",
       "      <td>2.272604</td>\n",
       "      <td>837663.312500</td>\n",
       "      <td>164566.281250</td>\n",
       "      <td>-2.544821</td>\n",
       "      <td>1.054840e+06</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>858972 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mjj    dphijj    detajj            MET        jet1_pt  \\\n",
       "3556267  2.858828e+05  2.486339  4.152604   32395.636719   35407.410156   \n",
       "4600529  1.665840e+06 -1.116634  4.210449  233870.609375  329462.281250   \n",
       "2212861  5.425114e+04 -0.025529  1.515418   95086.046875   32920.222656   \n",
       "260032   7.590763e+04  1.216819  0.305119   81117.273438   73568.648438   \n",
       "4516133  1.470252e+06  1.397715  5.533992  171162.078125  148240.375000   \n",
       "...               ...       ...       ...            ...            ...   \n",
       "2602793  1.808710e+05  2.712765  2.621271   63139.863281   62579.144531   \n",
       "3371629  7.766109e+04 -2.864583  1.073243   83472.851562   34098.093750   \n",
       "343341   2.587692e+05  2.560996  2.731060   37221.273438   73745.789062   \n",
       "1198883  2.116611e+05 -2.543470  3.448723   14797.860352   41817.003906   \n",
       "4427099  1.878782e+06 -2.846742  4.817425   12915.691406  170787.375000   \n",
       "\n",
       "         jet1_eta         jet1_e        jet2_pt  jet2_eta        jet2_e  \\\n",
       "3556267 -1.557427   88213.867188   35230.921875  2.595177  2.374987e+05   \n",
       "4600529 -1.682948  917478.375000  126548.531250  2.527501  7.974178e+05   \n",
       "2212861 -0.295575   34976.656250   30504.076172  1.219843  5.642772e+04   \n",
       "260032   0.639353   90137.742188   51953.625000  0.944471  7.711336e+04   \n",
       "4516133 -1.454019  334865.156250   57647.234375  4.079974  1.705229e+06   \n",
       "...           ...            ...            ...       ...           ...   \n",
       "2602793 -3.434310  971308.187500   32253.638672 -0.813039  4.406517e+04   \n",
       "3371629 -0.065554   34649.226562   33064.425781 -1.138797  5.742496e+04   \n",
       "343341   1.810105  231688.796875   52858.539062 -0.920955  7.716077e+04   \n",
       "1198883  0.215262   43553.968750   31587.707031  3.663985  6.166338e+05   \n",
       "4427099  2.272604  837663.312500  164566.281250 -2.544821  1.054840e+06   \n",
       "\n",
       "         njet30  \n",
       "3556267       3  \n",
       "4600529       4  \n",
       "2212861       2  \n",
       "260032        7  \n",
       "4516133       5  \n",
       "...         ...  \n",
       "2602793       3  \n",
       "3371629       3  \n",
       "343341        2  \n",
       "1198883       3  \n",
       "4427099       3  \n",
       "\n",
       "[858972 rows x 11 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_factor_scores = factorize.transform(X_train)\n",
    "extract_factor_scores = pd.DataFrame(extract_factor_scores, columns=['Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5', 'Factor 6', 'Factor 7', 'Factor 8', 'Factor 9', 'Factor 10'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 9 (\t) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "\n",
    "graph1 = sns.histplot(extract_factor_scores['Factor 1'], kde=True, ax=ax, label='mjj', linewidth=0)\n",
    "graph2 = sns.histplot(extract_factor_scores['Factor 2'], kde=True, ax=ax, label='dphijj', linewidth=0)\n",
    "graph3 = sns.histplot(extract_factor_scores['Factor 3'], kde=True, ax=ax, label='detajj\t', linewidth=0)\n",
    "graph4 = sns.histplot(extract_factor_scores['Factor 4'], kde=True, ax=ax, label='MET', linewidth=0)\n",
    "graph5 = sns.histplot(extract_factor_scores['Factor 5'], kde=True, ax=ax, label='jet1_pt', linewidth=0)\n",
    "graph6 = sns.histplot(extract_factor_scores['Factor 6'], kde=True, ax=ax, label='jet1_eta', linewidth=0)\n",
    "graph7 = sns.histplot(extract_factor_scores['Factor 7'], kde=True, ax=ax, label='jet1_e', linewidth=0)\n",
    "graph8 = sns.histplot(extract_factor_scores['Factor 8'], kde=True, ax=ax, label='jet2_pt', linewidth=0)\n",
    "graph9 = sns.histplot(extract_factor_scores['Factor 9'], kde=True, ax=ax, label='jet2_eta', linewidth=0)\n",
    "graph10 = sns.histplot(extract_factor_scores['Factor 10'], kde=True, ax=ax, label='jet2_e', linewidth=0)\n",
    "\n",
    "ax.set_title('Distribución de Factores')\n",
    "ax.set_xlabel('Puntaje Factor')\n",
    "plt.xlim(-5, 5)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "c:\\Users\\jav90\\envs\\dpj_env\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n"
     ]
    }
   ],
   "source": [
    "# params_grid = { \"n_estimators\": 800,\n",
    "#             \"max_depth\": 12,\n",
    "#             \"learning_rate\": 0.5,\n",
    "#             \"gamma\": [0.1, 0.6, 2, 6],#, 100, 1000],\n",
    "#             \"min_child_weight\": [1, 20, 50],\n",
    "#             \"reg_lambda\": [10, 50, 100, 150],\n",
    "#             #\"eval_metric\": [\"auc\", \"logloss\"],\n",
    "#             # \"scale_pos_weight\":[scale_pos_weight/4, scale_pos_weight/2, scale_pos_weight],\n",
    "#             }\n",
    "\n",
    "# xbgClass_grid_cv = GridSearchCV(xgb.XGBClassifier(),\n",
    "#                                  {#'min_samples_split': np.linspace(0.1, 1.0, 10),\n",
    "#                                  'criterion': ['accuaracy'],\n",
    "#                                 'max_depth':[12], \n",
    "#                                 'n_estimators':[800],\n",
    "#                                 'learning_rate':[0.5],\n",
    "#                                 \"gamma\": [0.1, 0.6, 2, 6],#, 100, 1000],\n",
    "#                                 \"min_child_weight\": [1, 20, 50],\n",
    "#                                 \"reg_lambda\": [10, 50, 100]},\n",
    "#                                 cv=5,\n",
    "#                                 n_jobs=-1).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9575682569194663"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# xbgClass_grid_cv.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gridsearchCV_xgbCLass_11_oct.pkl']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(xbgClass_grid_cv, 'gridsearchCV_xgbCLass_11_oct.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnpicklingError",
     "evalue": "invalid load key, '\\x0d'.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnpicklingError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\jav90\\Desktop\\dpj\\production_analysis\\analisis_javier\\ml.ipynb Celda 22\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/jav90/Desktop/dpj/production_analysis/analisis_javier/ml.ipynb#X34sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m pkl_model \u001b[39m=\u001b[39m cloudpickle\u001b[39m.\u001b[39;49mload(\u001b[39mopen\u001b[39;49m(\u001b[39m'\u001b[39;49m\u001b[39mgridsearchCV_xgbCLass_11_oct.pkl\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m'\u001b[39;49m))\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/jav90/Desktop/dpj/production_analysis/analisis_javier/ml.ipynb#X34sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m pkl_model\u001b[39m.\u001b[39mbest_score_\n",
      "\u001b[1;31mUnpicklingError\u001b[0m: invalid load key, '\\x0d'."
     ]
    }
   ],
   "source": [
    "pkl_model = cloudpickle.load(open('gridsearchCV_xgbCLass_11_oct.pkl', 'rb'))\n",
    "pkl_model.best_score_"
   ]
  }
 ],
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