Deteccao_Fraude_Cartao_Credito.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelo de detecção de fraudes em transações de cartão de crédito"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Solução desenvolvida por Danilo Morales Teixeira\n",
    "* 25/03/2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importando bibliotecas fundamentais de ciência de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Desativando avisos que não são importantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lendo arquivo de dados com a bibliteca Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados = pd.read_csv('creditcard.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exibindo cinco primeiras linhas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
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       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exibindo informações iniciais de cada variável"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "dados.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos das informações iniciais que parece não existir dados faltantes na amostra e que todas as variaveis numérica foram devidamente reconhecidas como float"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exibindo estatísticas iniciais de cada variável"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
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       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>3.919560e-15</td>\n",
       "      <td>5.688174e-16</td>\n",
       "      <td>-8.769071e-15</td>\n",
       "      <td>2.782312e-15</td>\n",
       "      <td>-1.552563e-15</td>\n",
       "      <td>2.010663e-15</td>\n",
       "      <td>-1.694249e-15</td>\n",
       "      <td>-1.927028e-16</td>\n",
       "      <td>-3.137024e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.537294e-16</td>\n",
       "      <td>7.959909e-16</td>\n",
       "      <td>5.367590e-16</td>\n",
       "      <td>4.458112e-15</td>\n",
       "      <td>1.453003e-15</td>\n",
       "      <td>1.699104e-15</td>\n",
       "      <td>-3.660161e-16</td>\n",
       "      <td>-1.206049e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  3.919560e-15  5.688174e-16 -8.769071e-15  2.782312e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -1.552563e-15  2.010663e-15 -1.694249e-15 -1.927028e-16 -3.137024e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.537294e-16  7.959909e-16  5.367590e-16  4.458112e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   1.453003e-15  1.699104e-15 -3.660161e-16 -1.206049e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observamos que existem quase 285 mil transações e que o valor médio é de 88 dolares"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verificando existência de elementos nulos e NaNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.isna().sum().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.isnull().sum().max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observamos que não existem elementos nulos e do tipo NaN. Não será necessário substituir este tipo de valores pela mediana por exemplo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determinando a quantidade de transações sem fraude e sem fraude no dado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transacoes sem fraude 284315\n",
      "Transacoes com fraude 492\n"
     ]
    }
   ],
   "source": [
    "print('Transacoes sem fraude', dados['Class'].value_counts()[0])\n",
    "print('Transacoes com fraude', dados['Class'].value_counts()[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transacoes sem fraude 99.83 % da amostra\n",
      "Transacoes com fraude 0.17 % da amostra\n"
     ]
    }
   ],
   "source": [
    "print('Transacoes sem fraude', round(dados['Class'].value_counts()[0]/len(dados) * 100,2), '% da amostra')\n",
    "print('Transacoes com fraude', round(dados['Class'].value_counts()[1]/len(dados) * 100,2), '% da amostra')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De todas as transações registradas, apenas 492 foram classificadas como fraude, o que corresponde a apenas 0.17% da amostra. Isto nos mostra que temos um dado extremamente desbalanceado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Função para converter a variável binária classe para string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fraude(dado):\n",
    "    if(dado == 0):\n",
    "        return \"Normal\"\n",
    "    else:\n",
    "        return \"Fraude\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gráfico com a contagem de cada classe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dados_tmp = dados.copy()\n",
    "dados_tmp['Class'] = dados_tmp['Class'].apply(fraude)\n",
    "sns.countplot(data=dados_tmp,x='Class')\n",
    "plt.xlabel('Classe')\n",
    "plt.ylabel('Contagem')\n",
    "del dados_tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verificando a distribuição dos valores e do tempo de cada transação para as situações normais e com fraude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 1, figsize=(10,6))\n",
    "sns.distplot(dados['Amount'], ax=ax[0], color='r')\n",
    "ax[0].set_title('Distribuição do valor da transações considerando ambas as classes', fontsize=14)\n",
    "ax[0].set_xlim([min(dados['Amount']), max(dados['Amount'])])\n",
    "ax[0].set_xlabel('Valor')\n",
    "\n",
    "sns.distplot(dados[dados['Class']==0]['Amount'], ax=ax[1], color='r')\n",
    "ax[1].set_title('Distribuição do valor da transações considerando transações sem fraude', fontsize=14)\n",
    "ax[1].set_xlim([min(dados['Amount']), max(dados['Amount'])])\n",
    "ax[1].set_xlabel('Valor')\n",
    "\n",
    "sns.distplot(dados[dados['Class']==1]['Amount'], ax=ax[2], color='r')\n",
    "ax[2].set_title('Distribuição do valor da transações considerando transações com fraude', fontsize=14)\n",
    "ax[2].set_xlim([min(dados['Amount']), max(dados['Amount'])])\n",
    "ax[2].set_xlabel('Valor')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observamos que a grande parte das transações são de baixo valor e as com fraude tiveram valores inferiores a 4000. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 1, figsize=(10,6))\n",
    "sns.distplot(dados['Time'], ax=ax[0])\n",
    "ax[0].set_title('Distribuição do tempo da transações considerando ambas as classes', fontsize=14)\n",
    "ax[0].set_xlim([min(dados['Time']), max(dados['Time'])])\n",
    "ax[0].set_xlabel('Tempo')\n",
    "\n",
    "sns.distplot(dados[dados['Class']==0]['Time'], ax=ax[1])\n",
    "ax[1].set_title('Distribuição do tempo da transações considerando transações sem fraude', fontsize=14)\n",
    "ax[1].set_xlim([min(dados['Time']), max(dados['Time'])])\n",
    "ax[1].set_xlabel('Tempo')\n",
    "\n",
    "sns.distplot(dados[dados['Class']==1]['Time'], ax=ax[2])\n",
    "ax[2].set_title('Distribuição do tempo da transações considerando transações com fraude', fontsize=14)\n",
    "ax[2].set_xlim([min(dados['Time']), max(dados['Time'])])\n",
    "ax[2].set_xlabel('Tempo')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observamos que as transações regulares apresentam uma distruição não simétrica com dois picos com médias aproximada de 5000 e 140000. Já as transações com fraude tem uma distribuição mais regular de tempo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As variáveis Amount e Time não estão normalizadas e apresentam uma grande variabilidade. Desta forma iremos normalizar ambas e vamos usar o RobustScaler que é menos suscetível a outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, RobustScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "robust_scaler = RobustScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados['Amount'] = robust_scaler.fit_transform(dados['Amount'].values.reshape(-1,1))\n",
    "dados['Time'] = robust_scaler.fit_transform(dados['Time'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.994983</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>1.783274</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.994983</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>-0.269825</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.994972</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>4.983721</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.994972</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>1.418291</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.994960</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0.670579</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time        V1        V2        V3        V4        V5        V6  \\\n",
       "0 -0.994983 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388   \n",
       "1 -0.994983  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
       "2 -0.994972 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499   \n",
       "3 -0.994972 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203   \n",
       "4 -0.994960 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921   \n",
       "\n",
       "         V7        V8        V9  ...       V21       V22       V23       V24  \\\n",
       "0  0.239599  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928   \n",
       "1 -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.791461  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.237609  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4  0.592941 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28    Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  1.783274      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724 -0.269825      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  4.983721      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  1.418291      0  \n",
       "4 -0.206010  0.502292  0.219422  0.215153  0.670579      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separando a amostra em variáveis de treino e teste. Neste caso usaremos a amostra toda para testar no final com o modelo utilizando apenas parte balanceada da amostra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold, StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dados.drop(['Class'],axis=1)\n",
    "Y = dados['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "strat_kfold = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Treino: [ 30473  30496  31002 ... 284804 284805 284806] Teste: [    0     1     2 ... 57017 57018 57019]\n",
      "Treino: [     0      1      2 ... 284804 284805 284806] Teste: [ 30473  30496  31002 ... 113964 113965 113966]\n",
      "Treino: [     0      1      2 ... 284804 284805 284806] Teste: [ 81609  82400  83053 ... 170946 170947 170948]\n",
      "Treino: [     0      1      2 ... 284804 284805 284806] Teste: [150654 150660 150661 ... 227866 227867 227868]\n",
      "Treino: [     0      1      2 ... 227866 227867 227868] Teste: [212516 212644 213092 ... 284804 284805 284806]\n"
     ]
    }
   ],
   "source": [
    "for indice_treino, indice_teste in strat_kfold.split(X, Y):\n",
    "    print(\"Treino:\", indice_treino, \"Teste:\", indice_teste)\n",
    "    X_treino, X_teste = X.iloc[indice_treino], X.iloc[indice_teste]\n",
    "    Y_treino, Y_teste = Y.iloc[indice_treino], Y.iloc[indice_teste]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_treino = X_treino.values\n",
    "X_teste = X_teste.values\n",
    "Y_treino = Y_treino.values\n",
    "Y_teste = Y_teste.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Distruição das classes: \n",
      "\n",
      "[0.99827076 0.00172924]\n",
      "[0.99827952 0.00172048]\n"
     ]
    }
   ],
   "source": [
    "label_treino_unico, label_contagem_treino = np.unique(Y_treino, return_counts=True)\n",
    "label_teste_unico, label_contagem_teste = np.unique(Y_teste, return_counts=True)\n",
    "print('-' * 100)\n",
    "\n",
    "print('Distruição das classes: \\n')\n",
    "print(label_contagem_treino/ len(Y_treino))\n",
    "print(label_contagem_teste/ len(Y_teste))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nesta etapa iremos utilizar o processo de subamostragem aleatória, onde dados serão removidos da amostra com a finalidade de gerar um balanceamento evitando assim o chamado overfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identificamos que existem 492 transações com fraude. Iremos selecionar aleatoriamente a mesma quantidade de transações sem fraude para balancear a amostra. Em seguida iremos misturar os dados para que os modelos possam capturar os padrões de cada tipo de transação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados = dados.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "tamanho_fraude = len(dados[dados['Class']==1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados_fraude = dados.loc[dados['Class'] == 1]\n",
    "dados_semfraude = dados.loc[dados['Class'] == 0][:tamanho_fraude]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados_novos = pd.concat([dados_fraude,dados_semfraude])\n",
    "dados_novos = dados_novos.sample(frac=1, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verificando dados iniciais da nova amostra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49505</th>\n",
       "      <td>-0.477003</td>\n",
       "      <td>-0.519455</td>\n",
       "      <td>0.714224</td>\n",
       "      <td>1.825262</td>\n",
       "      <td>2.179596</td>\n",
       "      <td>-0.353989</td>\n",
       "      <td>0.065773</td>\n",
       "      <td>0.872736</td>\n",
       "      <td>0.123405</td>\n",
       "      <td>-0.668567</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.168649</td>\n",
       "      <td>-0.683123</td>\n",
       "      <td>0.218256</td>\n",
       "      <td>0.302188</td>\n",
       "      <td>-0.062579</td>\n",
       "      <td>-0.218855</td>\n",
       "      <td>-0.013266</td>\n",
       "      <td>0.037355</td>\n",
       "      <td>1.301334</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263080</th>\n",
       "      <td>0.894031</td>\n",
       "      <td>2.132386</td>\n",
       "      <td>0.705608</td>\n",
       "      <td>-3.530759</td>\n",
       "      <td>0.514779</td>\n",
       "      <td>1.527175</td>\n",
       "      <td>-1.716268</td>\n",
       "      <td>1.132791</td>\n",
       "      <td>-0.574214</td>\n",
       "      <td>0.128904</td>\n",
       "      <td>...</td>\n",
       "      <td>0.163739</td>\n",
       "      <td>0.703910</td>\n",
       "      <td>-0.245076</td>\n",
       "      <td>0.460049</td>\n",
       "      <td>0.920281</td>\n",
       "      <td>-0.216586</td>\n",
       "      <td>-0.026219</td>\n",
       "      <td>-0.025001</td>\n",
       "      <td>-0.293440</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58832</th>\n",
       "      <td>-0.424406</td>\n",
       "      <td>-0.861373</td>\n",
       "      <td>0.772606</td>\n",
       "      <td>1.224435</td>\n",
       "      <td>-1.673920</td>\n",
       "      <td>0.311229</td>\n",
       "      <td>0.148230</td>\n",
       "      <td>0.338499</td>\n",
       "      <td>0.359594</td>\n",
       "      <td>0.130609</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.102619</td>\n",
       "      <td>-0.222311</td>\n",
       "      <td>-0.144030</td>\n",
       "      <td>-0.806968</td>\n",
       "      <td>-0.322518</td>\n",
       "      <td>0.791043</td>\n",
       "      <td>0.373845</td>\n",
       "      <td>0.207521</td>\n",
       "      <td>-0.262279</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280149</th>\n",
       "      <td>0.994596</td>\n",
       "      <td>-0.676143</td>\n",
       "      <td>1.126366</td>\n",
       "      <td>-2.213700</td>\n",
       "      <td>0.468308</td>\n",
       "      <td>-1.120541</td>\n",
       "      <td>-0.003346</td>\n",
       "      <td>-2.234739</td>\n",
       "      <td>1.210158</td>\n",
       "      <td>-0.652250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.751826</td>\n",
       "      <td>0.834108</td>\n",
       "      <td>0.190944</td>\n",
       "      <td>0.032070</td>\n",
       "      <td>-0.739695</td>\n",
       "      <td>0.471111</td>\n",
       "      <td>0.385107</td>\n",
       "      <td>0.194361</td>\n",
       "      <td>0.780968</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184379</th>\n",
       "      <td>0.487870</td>\n",
       "      <td>-1.141559</td>\n",
       "      <td>1.927650</td>\n",
       "      <td>-3.905356</td>\n",
       "      <td>-0.073943</td>\n",
       "      <td>-0.044858</td>\n",
       "      <td>-1.756999</td>\n",
       "      <td>-1.217416</td>\n",
       "      <td>0.364563</td>\n",
       "      <td>-2.770148</td>\n",
       "      <td>...</td>\n",
       "      <td>0.102081</td>\n",
       "      <td>-0.531496</td>\n",
       "      <td>-0.328741</td>\n",
       "      <td>0.393100</td>\n",
       "      <td>0.568435</td>\n",
       "      <td>0.786605</td>\n",
       "      <td>-0.146102</td>\n",
       "      <td>0.076211</td>\n",
       "      <td>0.041920</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time        V1        V2        V3        V4        V5        V6  \\\n",
       "49505  -0.477003 -0.519455  0.714224  1.825262  2.179596 -0.353989  0.065773   \n",
       "263080  0.894031  2.132386  0.705608 -3.530759  0.514779  1.527175 -1.716268   \n",
       "58832  -0.424406 -0.861373  0.772606  1.224435 -1.673920  0.311229  0.148230   \n",
       "280149  0.994596 -0.676143  1.126366 -2.213700  0.468308 -1.120541 -0.003346   \n",
       "184379  0.487870 -1.141559  1.927650 -3.905356 -0.073943 -0.044858 -1.756999   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
       "49505   0.872736  0.123405 -0.668567  ... -0.168649 -0.683123  0.218256   \n",
       "263080  1.132791 -0.574214  0.128904  ...  0.163739  0.703910 -0.245076   \n",
       "58832   0.338499  0.359594  0.130609  ... -0.102619 -0.222311 -0.144030   \n",
       "280149 -2.234739  1.210158 -0.652250  ...  0.751826  0.834108  0.190944   \n",
       "184379 -1.217416  0.364563 -2.770148  ...  0.102081 -0.531496 -0.328741   \n",
       "\n",
       "             V24       V25       V26       V27       V28    Amount  Class  \n",
       "49505   0.302188 -0.062579 -0.218855 -0.013266  0.037355  1.301334      0  \n",
       "263080  0.460049  0.920281 -0.216586 -0.026219 -0.025001 -0.293440      1  \n",
       "58832  -0.806968 -0.322518  0.791043  0.373845  0.207521 -0.262279      0  \n",
       "280149  0.032070 -0.739695  0.471111  0.385107  0.194361  0.780968      1  \n",
       "184379  0.393100  0.568435  0.786605 -0.146102  0.076211  0.041920      1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_novos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fazendo um novo gráfico de contagem para verificar o balanceamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dados_tmp = dados_novos.copy()\n",
    "dados_tmp['Class'] = dados_tmp['Class'].apply(fraude)\n",
    "sns.countplot(data=dados_tmp,x='Class')\n",
    "plt.xlabel('Classe')\n",
    "plt.ylabel('Contagem')\n",
    "del dados_tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calcularemos a matriz de correlação para identificar variáveis que sejam mais sensíveis as transações com fraude. Para que isto possa ser bem identificado devemos usar a amostra balanceada gerada. Iremos mostrar a matriz de correlação sem o balanceamento para mostrar que seria muito complicado identificar as principais variáveis neste caso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "correlacao_desbalanceada = dados.corr()\n",
    "correlacao_balanceada = dados_novos.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Matriz de correlação dados balanceados')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(2, 1, figsize=(24,20))\n",
    "\n",
    "sns.heatmap(correlacao_desbalanceada, cmap='rainbow', annot_kws={'size':20}, ax=ax1)\n",
    "ax1.set_title(\"Matriz de correlação dados não balanceados\", fontsize=14)\n",
    "\n",
    "sns.heatmap(correlacao_balanceada, cmap='rainbow', annot_kws={'size':20}, ax=ax2)\n",
    "ax2.set_title('Matriz de correlação dados balanceados', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos ver que para o caso com amostra não balanceada as variáveis parecem não possuir correlação. Já na amostra balanceada a correlação fica bem clara. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "V3, V10, V12 e V14 apresentam uma correlação negativa e quanto mais negativo o valor, mais indicará que temos uma transação com fraude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "V2, V4 e V11 apresentam uma correlação positiva e quando mais alto este valor, mais ele indicará que se trata de uma fraude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os gráficos de boxplot irão nos ajudara a entender melhor isso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1d8a3510>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aSgDKi4YSKtbx48e1e/duZTIZJZPJ0HEAAKh0T0taYmYXSxqQtErSp0IGSqVSIb89ppBOH5EkpS6hgREdC/h9AVB2NJRQsXbv3q2jR49q3bp1+upXvxo6DgAAFc3ds2Z2i6SHJCUk3evuO0NmWr16dchvjyl0dHRIkjo7OwMnAQCEREMpZrq6upROp0PHOGPHjx/X0aNHJUlbt27Vn//5n2vOnDmBU52ZVCrFhTMAICh33ySJ+8sAAMApscsbKtLu3buL1gAAAAAAoHSYUIqZapmAWbFixYT66NGjjF0DAAAAAFAmTCgBAAAAAABgRmgoAQAAAAAAYEZoKAEAAAAAAGBGaCgBAAAAmLbjx48rnU4rk8mEjgIACIhFuQEAAIAS6+rqUjqdDh1jVrzwwgsaHR3VDTfcoMWLF4eOc8ZSqVTVbFwDAOXEhBIAAACAaTl+/LhGR0clSb/+9a91/PjxwIkAAKEwoQQABVTTJ8nVZPxn0tHRETgJ8vHpPnBq1fI7ctddd02o3/72t+vuu+8OEwYAEBQNJQAoIJ1O6z93/kwN542EjoI8Zx0fG6wd3t0XOAnG7Xk9EToCgDJ67LHHJtSPPvpomCAAgOBoKKEizZs3T0NDQxNqYLY1nDeiO658LXQMINLWPTM/dAQAAAAEwBpKqEif//znJ9Rf+MIXAiUBAAAAACB+aCihIjU2NqqmZuxf35qaGn3oQx8KnAgAAAAAgPigoYSKlMlkZGaSxhpKmUwmcCIAAAAAAOKDhhIqUnd394nn7q6NGzcGTAMAAAAAQLzQUEJF6u3t1cjI2O5bIyMj6unpCZwIAAAAAID4oKGEivQ7v/M7E+ply5YFSgIAABAfCxcuLFoDAOKDhhIq0vj6SQAAACifv/iLv5hQ33bbbYGSAABCo6GEivT4448XrQEAADD7tmzZUrQGAMQHDSVUpKamJtXW1kqSamtr1dzcHDgRAABA9evt7Z1Qs44lAMQXDSVUpLa2NtXUjP3rm0gk1NraGjgRAABA9eNDPQDAOBpKqEjJZFItLS0yM7W0tCiZTIaOBAAAUPX4UA8AMI6GEipWW1ub3ve+93EhAwAAUCZ8qAcAGFcbOgBwupLJpNavXx86BgAAQKy0tbWpv7+fD/UAIOYiN6FkZneb2YCZbcs9PhY6EwAAAIAx4x/qMZ0EAPEW1Qmlf3D3r4QOAQAAAAAAgJNFbkIJAAAAAAAA0RbVCaVbzKxVUp+kv3L3X01+gZm1S2qXpIaGhjLHAwAAAADMtq6uLqXT6dAxMMn4z6SjoyNwEuRLpVJavXp1sO8fpKFkZr2S3lHg1J2Svinp85I898+vSrph8gvdfYOkDZLU2NjoJQsLAAAAACiLdDqt/9z5MzWcNxI6CvKcdXzs5qbh3X2Bk2DcntcToSOEaSi5e9N0Xmdm35b0gxLHAQAAAABERMN5I7rjytdCxwAibd0z80NHiN4tb2Z2obsfyJXXS9oRMg+AeBoYGNDRI4lI/IcaiLLdRxI6d2AgdAwAAACUWRQX5f6SmW03s2clXSXpttCBAAAAqpmZ3W1mA2a2Lff4WOhMAAAg2iI3oeTunwmdAQDq6+s1nD3AuDVwCuuema+59fWhY2B2/IO7fyV0CAAAUBmiOKEEAAAAAACACKOhBAAAAEm6xcyeNbN7zextU73IzNrNrM/M+g4ePFjOfAAAIEJoKAEAAMSAmfWa2Y4Cj5WSvinpXZKukHRA0leneh933+Duje7eWFdXV6b0AAAgaiK3hhIAAABmn7s3Ted1ZvZtST8ocRwAAFDhmFACAACIOTO7MK+8XtKOUFkAAEBlYEIJAAAAXzKzKyS5pH5JN4aNAwAAoo6GEgAAQMy5+2dCZwAAAJWFW94AAAAAAAAwIzSUULEymYxuvfVWZTKZ0FEAAAAAAIgVGkqoWN3d3dq+fbs2btwYOgoAAAAAALFCQwkVKZPJaPPmzXJ3bd68mSklAAAAAADKiIYSKlJ3d7dGR0clSSMjI0wpAQAAAABQRjSUUJF6e3uVzWYlSdlsVj09PYETAQAAAAAQHzSUUJGamppUW1srSaqtrVVzc3PgRAAAAAAAxEdt6ADA6Whra9PmzZslSYlEQq2trYETAQAAxEMmk9GaNWt01113KZlMho6DKjMwMKCjRxJa98z80FGASNt9JKFzBwaCZmBCCRUpmUyqpaVFZqaWlhYuZgAAAMqEnXYBABITSqhgbW1t6u/vZzoJAACgTCbvtNva2soHe5hV9fX1Gs4e0B1XvhY6ChBp656Zr7n19UEzTDmhZGYJM7vRzD5vZr896dzflj4aUFwymdT69eu5iAEAVD0zeyl0BkBip10AwJuK3fJ2j6SPSMpIWm9mf5937hMlTQUAABBTZnbEzF7LPY6Y2RFJ7xo/Hjof4o2ddgEA44o1lJa6+6fc/X9K+rCk88zs/zWzuZKsPPEAAABi518k/bukJe7+Fnd/i6Q9ueesUoug2GkXADCuWEPprPEn7p5193ZJ2yT9RNJ5pQ4GAAAQR+6+WlKnpH81s1vNrEaSB44FSBpbw7KmZuyvEOy0CwDxVqyh1GdmLfkH3P1zkv5Z0uJShgIAAIgzd98qqSlXPibp7IBxgBPYaRcAMG7KXd7c/dNTHP9HSf9YskQAAACQu49qbB3Lf5P0wdB5gHHstAsAkIpPKE3JzLhZGgAAoAzc/YC7b5K4BkM0sNMuAEA6zYaSpH+a1RQAAACYDq7BAABAJEx5y5uZPTjVKUl8HAGg6u15PaF1z7ChUpS8Ojj2OciCc0YDJ8G4Pa8ntCR0iCrDNRgAAKgEUzaUJC2T9GlJr086bpKWliwRAERAKpUKHQEFHEunJUlz38nPJyqWiN+XEuAaDAAARF6xhtKTkgbd/bHJJ8zsxdJFAoDwVq9eHToCCujo6JAkdXZ2Bk4ClBTXYAAAIPKKraG0S9KxQifcfXlp4gAAAMQe12AAACDyijWUXpL0FTPrN7MvmtkV5QoFAAAQY1yDAQCAyJuyoeTune7+W5I+IumwpH82s+fN7O/M7NKyJQQAAIgRrsEAAEAlKDahJEly993u/kV3/6CkT0m6XtLzJU8GAAAQY1yDAQCAKDtlQ8nM5pjZx83su5J+pLEx7D8oeTIAAIAY4xoMAABE2ZS7vJlZs6Q/lPS7kp6SdJ+kdnc/WqZsAAAAscM1GAAAqATFJpTukPRTSb/h7h939+/O1oWMmX3SzHaa2aiZNU46d7uZpc3sRTO7bja+HwAAQAUp2TUYAADAbJlyQsndryrh990h6ROS7sk/aGaXSVol6XJJF0nqNbNL3X2khFkAAAAio8TXYAAAALPilGsolYK7P+/uLxY4tVLSfe4+7O6/kJSWtLS86QAAAAAAAFDMlBNKgdRLejKv3pc7BgAAgDNkZp+UdLek35C01N378s7dLulPJI1IutXdHwoSEkDs7Xk9oXXPzA8dA3leHRybRVlwzmjgJBi35/WElgTOULKGkpn1SnpHgVN3uvt/TPVlBY75FO/fLqldkhoaGk4rIwAAQMyw7ACASEulUqEjoIBj6bQkae47+flExRKF/30pWUPJ3ZtO48v2SVqUVy+UtH+K998gaYMkNTY2Fmw6AQAA4E3u/rwkmZ30Gd6JZQf0/7d3vzGW1XcZwJ8vu3QTX2iT6Ub+UxrAhH9iOzapgapxWdZEIBibgIlsonGXUDfbmph0Q+SFCbwxMcJqU1atgLElRbJtE6ywg61AQmO3ulYoQhaqYW0Dy2BCMHZldn6+2Jl1ls4OnGVmzj1zP5/kJvd3zrmzz31xZ7957jlnku9V1fxtB55e3YTAuNuxY0ffEVjEzp07kyR33313z0kYJb3cQ2kJX01yU1VtqKoLcqx0+8eeMwEArHVnJ3l5wdptBwCAJfVyD6WqujHJ7iQbkzxSVQdaa9e21p6tqi8l+W6SmSSfdKo1AMC757YDAMBq6KVQaq3tTbL3JPvuTHLn6iYCAFgb3HYAAFgNo3bJGwAAq89tBwCAThRKAABjoqpurKpDST6WY7cdeDRJWmvPJpm/7cDfxW0HAIB30MslbwAArD63HQAAloszlAAAAADoRKEEAAAAQCcKJQAAAAA6USgBAAAA0IlCCQAAAIBOFEoAAAAAdKJQAgAAAKAThRIAAAAAnSiUAAAAAOhEoQQAAABAJwolAAAAADpRKAEAAADQiUIJAAAAgE4USgAAAAB0olACAAAAoBOFEgAAAACdKJQAAAAA6EShBAAAAEAnCiUAAAAAOlEoAQAAANCJQgkAAACAThRKAAAAAHSiUAIAAACgE4USAAAAAJ0olAAAAADoRKEEAAAAQCcKJQAAAAA6USgBAAAA0IlCCQAAAIBOFEoAAAAAdKJQAgAAAKAThRIAwJioqk9U1bNVNVtVkwu2f7Cq/qeqDsw9PtdnTgBg9PVSKBlmAAB68UySX03yxCL7XmytXTn3uHWVcwEAA7O+p393fpi5d5F9L7bWrlzlPAAAa15r7bkkqaq+owAAA9fLGUqttedaa8/38W8DALCoC6rqn6vqH6rq6pMdVFXbqmp/Ve0/fPjwauYDAEbIKN5D6V0NMwAA/KiqmqqqZxZ53LDEy36Q5LzW2s8k+d0kX6iqH1/swNbantbaZGttcuPGjSvxFgCAAVixS96qairJGYvsur219pWTvGx+mJmuqo8k+XJVXdpae2ORn78tybYkOe+885YrNgDAoLXWNp3Ca44kOTL3/NtV9WKSi5PsX+Z4AMAasWKF0koPM621PUn2JMnk5GR7b2kBAMZXVW1M8npr7WhVfSjJRUle6jkWADDCRuqSt6raWFXr5p4bZgAAllFV3VhVh5J8LMkjVfXo3K6PJ/lOVf1Lkr9Jcmtr7fW+cgIAo6+Xv/JWVTcm2Z1kY44NMwdaa9fm2DDzB1U1k+RoDDMAAMumtbY3yd5Ftj+c5OHVTwQADFUvhZJhBgAAAGC4RuqSNwAAAABGn0IJAAAAgE4USgAAAAB0olACAAAAoBOFEgAAAACdKJQAAAAA6EShBAAAAEAnCiUAAAAAOlEoAQAAANCJQgkAAACAThRKAAAAAHSiUAIAAACgE4USAAAAAJ2s7zsAACtr9+7dOXjwYN8xlsX8+9i5c2fPSZbHhRdemB07dvQdAwAAOlMoATAYGzZsyBtvvJG33norp59+et9xAABOai19qffCCy/kyJEjue222wY/g/lCb/kolADWuLX0H+Zdd92Vxx57LOeee2527drVdxwAgLEwOzub2dnZvPLKKznnnHP6jsOIUCgBMAjT09OZmppKkuzbty/btm3LxMREz6kAABa3Vr7Um56ezs0335wkefPNN3PHHXeYwUjiptwADMS9996b2dnZJMe+JduzZ0/PiQAA1r7777//+Ax29OjRPPDAAz0nYlQolAAYhMcff/yE9fzZSgAArJypqanMzMwkSWZmZrJv376eEzEqFEoAAADAoq6++uol14wvhRIAg3DmmWeesD7rrLN6SgIAMD5aa31HYEQplAAYhOnp6RPWr732Wk9JAADGx1NPPXXC+sknn+wpCaNGoQTAIFxzzTUnrDdv3txTEgCA8XHVVVedsHbJG/MUSgAMwvXXX3/C+rrrruspCQDA+KiqviMwohRKAAzCQw89tOQaAIDl9/ZL3FzyxjyFEgCDMDU1teQaAIDl55I3TkahBMAgzM7OLrkGAGD5ueSNk1EoATAIbx9mDDcAACvPJW+cjEIJgEHYtGnTCeu3/9U3AACW36ZNm7J+/fokyfr1681gHKdQAmAQtm/ffvyspKrKtm3bek4EALD2bd26Naeddqw6WLduXW655ZaeEzEqFEoADMLExMTxb8Q2b96ciYmJnhPB8FTVH1bVv1XVd6pqb1W9f8G+XVV1sKqer6pr+8wJwOiYmJjIli1bUlXZsmWLGYzjFEoADMb27dtzxRVXODsJTt2+JJe11q5I8kKSXUlSVZckuSnJpUm2JPlsVa3rLSUAI2Xr1q25/PLLnZ3ECdb3HQAA3q2JiYncc889fceAwWqtPbZg+c0kvzb3/IYkD7bWjiT5XlUdTPLRJE+vckQARpAZjMU4QwkAYDz9ZpKvzT0/O8nLC/Ydmtv2I6pqW1Xtr6r9hw8fXuGIAMCocoYSAMAaUlVTSc5YZNftrbWvzB1ze5KZJH89/7JFjm+L/fzW2p4ke5JkcnJy0WMAgLVPoQQAsIa01jYttb+qtib5lSS/1FqbL4QOJTl3wWHnJPn+yiQEANaC+v85Yriq6nCS/+g7B734QJLX+g4BrCqf+/F1fmttY98hhqyqtiT5oyQ/31o7vGD7pUm+kGP3TToryeNJLmqtHX2Hn2cGG19+F8P48bkfTyedv9ZEocT4qqr9rbXJvnMAq8fnHk7d3M22NySZntv0zdbarXP7bs+x+yrNJPlUa+1ri/8U8LsYxpHPPW/nkjcAgDHRWrtwiX13JrlzFeMAAAPmr7wBAAAA0IlCiaHb03cAYNX53AP0z+9iGD8+95zAPZQAAAAA6MQZSgAAAAB0olBisKpqS1U9X1UHq+ozfecBVlZVfb6qXq2qZ/rOAjCuzF8wXsxfLEWhxCBV1bokf5rkl5NckuTmqrqk31TACrsvyZa+QwCMK/MXjKX7Yv7iJBRKDNVHkxxsrb3UWvvfJA8muaHnTMAKaq09keT1vnMAjDHzF4wZ8xdLUSgxVGcneXnB+tDcNgAAVob5C4DjFEoMVS2yzZ8sBABYOeYvAI5TKDFUh5Kcu2B9TpLv95QFAGAcmL8AOE6hxFB9K8lFVXVBVb0vyU1JvtpzJgCAtcz8BcBxCiUGqbU2k+R3kjya5LkkX2qtPdtvKmAlVdUXkzyd5Keq6lBV/VbfmQDGifkLxo/5i6VUay57BgAAAODdc4YSAAAAAJ0olAAAAADoRKEEAAAAQCcKJQAAAAA6USgBAAAA0IlCCRgJVXVGVT1YVS9W1Xer6m+r6uKqeqbvbAAAa5UZDDhV6/sOAFBVlWRvkvtbazfNbbsyyU/2GgwAYA0zgwHvhTOUgFHwi0neaq19bn5DSYPrawAAAcFJREFUa+1Akpfn11X1wap6sqr+ae7xc3Pbz6yqJ6rqQFU9U1VXV9W6qrpvbv2vVfXp1X9LAAAjzwwGnDJnKAGj4LIk336HY15Nck1r7YdVdVGSLyaZTPLrSR5trd1ZVeuS/FiSK5Oc3Vq7LEmq6v0rFx0AYLDMYMApUygBQ3F6kj+ZOw37aJKL57Z/K8nnq+r0JF9urR2oqpeSfKiqdid5JMljvSQGABg+MxiwKJe8AaPg2SQfeYdjPp3klSQ/nWPfir0vSVprTyT5eJL/TPJXVXVLa+2/5o77RpJPJvnzlYkNADBoZjDglCmUgFHw90k2VNVvz2+oqp9Ncv6CY34iyQ9aa7NJfiPJurnjzk/yamvtz5L8RZIPV9UHkpzWWns4ye8n+fDqvA0AgEExgwGnzCVvQO9aa62qbkzyx1X1mSQ/TPLvST614LDPJnm4qj6R5OtJ/ntu+y8k+b2qeivJm0luSXJ2kr+sqvnSfNeKvwkAgIExgwHvRbXW+s4AAAAAwIC45A0AAACAThRKAAAAAHSiUAIAAACgE4USAAAAAJ0olAAAAADoRKEEAAAAQCcKJQAAAAA6USgBAAAA0Mn/AbMgqwd9+j/gAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(2,2, figsize=(20,8))\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V3\", data=dados_novos,ax=ax[0][0])\n",
    "sns.boxplot(x=\"Class\", y=\"V10\", data=dados_novos,ax=ax[0][1])\n",
    "sns.boxplot(x=\"Class\", y=\"V12\", data=dados_novos,ax=ax[1][0])\n",
    "sns.boxplot(x=\"Class\", y=\"V14\", data=dados_novos,ax=ax[1][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1da52a50>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1,3, figsize=(20,4))\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V2\", data=dados_novos,ax=ax[0])\n",
    "sns.boxplot(x=\"Class\", y=\"V4\", data=dados_novos,ax=ax[1])\n",
    "sns.boxplot(x=\"Class\", y=\"V11\", data=dados_novos,ax=ax[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observamos de todos os boxplots a existência de muitos outliers que podem prejudicar os modelos de classificação. Iremos analisar cada um deles com mais cuidado e faremos o processo de remoção destes outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J1Ci8RAtyzFuWCwu0lxvH/6YvOgdvREOwzECMK8ysAdAN73XEV4CHzOwk51xuX0gOF9Ohc7nFlPPYwVzO5XbsSM9gq3Nu2WHO/+nnambXAU8Bd+J9ydmD9+rhyUHF/Lm0GxH0+Ugxwf+efVf+mthn5NhPBHJdfEVERETkGCk3/mscyo2VG4tIMac5mEWkqC0HduPN5xUKpwCfOedGOufmACuARkdbiXPO75z72TnXH29Oth14c48damOoc+4z59w8vOQ/eI65X/GSt1PzqH4BcLL9eRjAKXjJ+Cq80QBZQC3n3LIc25qgGPc758Y45/6JN5qiLdAxjzZ/BU4LjGLJea9peK8B5pyL75RArMXBKcBE59xg59xvgQS8YY4yW/jziurgvWZ6yHq8/9ZOOHQg8DzaB5WZC2ST+7Nfm6PuFnjPVURERCRUlBv/r07lxnlTbiwixYpGMItIkXLO+c3se7wk6eMQVLkE6G5mJ+LNKXYX3utfq/NbgZmdGojnO2Az0AEvOTuUUC4BLjazsYH9Jwj6+9Q5N8/MxuAtPPJPYA7eq2s1nHMjgVfxRpK8bGaDgMaBOl4MvMa33cxeDpyPwHs9Lx4vUd7vnBtqZjfgJdoz8EY09MEbwbE8j9t6FW9hjg8Cq0Pvxku45zjnfsebj+1BM1sZiPdavOTymvw+twK2BOhhZmfizQt3NV58wYntD8BNZtYL77n0wkuiF4I3WaGZvQI8YmarAnXehTevH4EyW8zsNeA1M4vCe2WwAt5okN3OueEAZpaAl0RfW1A3LCIiImWPcmPlxvmk3FhEihWNYBaR4mAw0NPMfCGoqz/eb9q/AybiJcFHm5zvxFsdeyxeojUAeMg5d6ie2/AW3/gF+BL4Hm/V5GCXA58Cg/BGigwBygM451bhreR9El7C+hbeatCPBl1/L94q4A/iJYHfAOcDK4NivAkvwZ4XqK+Hc259bjcUaPM0vGT8J7xVtW/if6/wPY/3KuFLeHO3nQtcEHjtsTh4CW918U/wFlhJwHu2wT7Fu48X8X4eCcDQHGWeAD7Cm+NuCt5q6Z/lKHNXoJ5H8J79OLznuzKozKXAXOfcb8dzUyIiIiK5UG6s3PhIlBuLSLFif55HX0SkaJjZL8Ag59y7RR1LqJnZMLwVpycXdSxy/MwsDO+L2oPOuTFFHY+IiIiUPsqNpaRQbiwioBHMIlJ83Egp+zvJzCqaWT281/P+XtTxSMhUB/5PCbSIiIgUIOXGUlIoNxYRjWAWESkoZtYGmASkA5c75yYUcUgiIiIiIkVCubGISOmlDmYREREREREREREROSal6pUbERERERERERERESk86mAWERERERERERERkWMSXtQB5FS5cmVXp06dog5DRERERMq4WbNmbXXOJRV1HMqPRURERKQ4yCs/LnYdzHXq1GHmzJlFHYaIiIiIlHFmtrqoYwDlxyIiIiJSPOSVH2uKDBERERERERERERE5JupgFhEREREREREREZFjkq8OZjM7x8wWm9kyM7s/l/M3mdk8M5ttZpPNrFnQuQcC1y02s7NDGbyIiIiIiIiIiIiIFJ0jdjCbmQ94HTgXaAZcEdyBHDDSOdfSOdcGeA54IXBtM+ByoDlwDjAoUJ+IiIiIiIiIiIiIlHD5GcHcEVjmnFvhnMsERgE9ggs453YH7cYBLvC5BzDKOZfhnFsJLAvUJyIiIiIiIiIiIiIlXHg+ytQA1gbtrwM65SxkZrcAdwGRwBlB107NcW2NXK7tB/QDSElJyU/cIiIijJy2plDb69VJ/0aJiIhI6XU0uZXyIhEROSQ/I5gtl2PuLwece905Vx+4D3j4KK8d7JxLdc6lJiUl5SMkERERERERERERESlq+elgXgfUCtqvCWw4TPlRwAXHeK2IiIiIiIiIiIiIlBD56WCeATQ0s7pmFom3aN+Y4AJm1jBotzuwNPB5DHC5mUWZWV2gITD9+MMWERERERERERERkaJ2xDmYnXNZZnYr8A3gA4Y65+ab2ePATOfcGOBWM+sKHAR2AH0D1843sw+BBUAWcItzLruA7kVEREREREREREREClF+FvnDOTcWGJvj2L+DPv/zMNc+BTx1rAGKiEjo7dmzhw0bNrBhwwa2bt3K7t272b17N/v378c5h9/vx+fzERsbS1xcHOXLl6dKlSokJydTrVo1KlasiFlu0+yLiIiIiIiISFmSrw5mEREpmZxzrF+/nlmzZrFw4UIWL17MkiVL2L59+1/K+nw+YmJi8Pl8mBlZWVmkp6fj9/v/UjYhIYH69evToEEDWrVqRatWrahXrx5hYfmZeUlERERERERESgt1MIuIlDLbtm1j8uTJTJo0iRkzZrB582YAoqOjadiwIaeffjp169alevXqVK9enaSkJCpUqEBsbOxfRiU758jMzGTnzp1s3ryZtLQ01q9fz4oVK1i+fDljx45l9OjRAMTHx9OpUydOOukkTjnlFGrWrFno9y4iIiIiIiIihUsdzCIipcCmTZsYN24cX3/9NfPmzcM5R+XKlenYsSPt27cnNTWV+vXr4/P5jqpeMyMqKork5GSSk5Np2bLln877/X5WrVrFnDlzmDVrFlOmTOG7774DoEmTJnTr1o2zzz6b+vXrh+xeRURERERERKT4UAeziEgJtX//fr755hs++eQTZs6cCUCzZs249dZb6dy5M02bNi3wKSvCwsKoV68e9erV48ILL8Q5x6pVq/jxxx/57rvveO2113j11Vdp0aIFF1xwAd27dychIaFAYxIRERERERGRwqMOZhGREmblypW8//77jBkzhj179pCSksJtt93GueeeS926dQEYOW0Nc2asK9S4enVKwcyoW7cudevW5eqrr2bz5s18/fXXfP755zz55JM899xzdO/enSuvvJLmzZsXanwiIiIicnQOZvuZt34XNRJiSI6PLupwRESkmFIHs4hICeCcY8aMGQwfPpwJEyYQHh7O2WefzaWXXkqHDh3+MndycVGlShX69u1L3759WbhwIR9++CFjxozhs88+o127dlx33XV06dKFjCzHhl37Sdt1gJ37D7IraNuXkYXfOZwDh/csInxhJMRGsnzzXmIjfcRGhpMQG0HlclH4wornsxAREREpKTKyspm+cjuTl21lz4EsYiN93HBqPXUyi4hIrtTBLCJSjDnnmDJlCoMGDeLXX38lISGBm266iSuuuIKkpKSiDu9PRk5bc9jz6ZlRRHW4jJPrdGXR1PHMnfUtt9xyC1SoRmaDM8iu0Rrsz1N6+MyIDA/DDAxvTmgDsvyOAwezcTna8JlRuXwkyfHRVI2PpnalOFISY9XpLCKFxszOAV4GfMAQ59wzuZS5DHgU7/dmc5xzvQo1SBGRPGQczObn5duYsnwr6ZnZ1EuK428tqjF23kbe+Xkl/U6rT2JcZFGHKSIixYw6mEVEiqnp06fz4osvMnv2bKpWrcrDDz/MxRdfTHR08R454pxjR/pBNuzc72279rNh5wH2ZmT9UcZXMZXEv3ciasMc9s4dB7Pep/zaH2l7bi8atj2RuKgIYiJ8RPgsz9HZfuc4kJlN+sFs0jOz2b4vg7TdGWzadYA129OZu24XADERPhoml6NJ1fI0qlKe2Cj90yciBcPMfMDrQDdgHTDDzMY45xYElWkIPACc7JzbYWZViiZaEZE/y8r2M2LqalZu3Ufj5PKc3jiJlEpxACTHR/P2pBUM/Xkl/U6rR3x0RBFHKyIixYm+ZYuIFDNLlixh4MCB/PTTT1StWpX+/ftz0UUXERlZPEeLZPn9bNh5gNXb9rFq6z5Wb08nPTMbgDDzvpA0Si5PcnwUSeWjSCoXRcW4SMLMgKY4/2UsmDmJHz8dwU/Dn2PZpMZ063kDFRq3PGy7YWbERoX/0WGckhj7p/P7M7NZtmUvizftYfGm3cxdtwsDmlaL5+QGlalTKbbYTi0iIiVWR2CZc24FgJmNAnoAC4LK3AC87pzbAeCc21zoUYqI5OKl75eycus+Lm5Xk/a1K/7pXNUK0Vx9Uh3+b/JK3vl5JTecWq+IohQRkeJIHcwiIsXEjh07ePnll/nwww8pV64c//rXv+jdu3exG7HsnCNtTwZL0/awJG0Pq7elk+X3JquoFBdJ02rx1KwY88diMBG+sMPWZ2FhNO/YmabtT2Huz9/z4+cjGD7gbpp2OJWul11PQlLVY4ozJtJHyxoVaFmjAn7nWL9jP/M37GLGqh0s2LibGgkxnFS/Ei1rViA87PAxiojkUw1gbdD+OqBTjjKNAMzsZ7xpNB51zo3LWZGZ9QP6AaSkpBRIsCIih/y4ZAuvT1xG+9oV/9K5fEitxFj6nFib4VNWMWzKKnqfUJvoCF8hRyoiIsWROphFRIpYdnY2o0aN4tVXX2Xv3r307t2bf/zjHyQkJBR1aH84mO1nadpeFm7azdK0Pew+4E13kRwfRae6idSuFEftSrGUP47XJcN8PtqcdjbNO3Xml3EfM+WrD1ny21RO+fvlnNy9J77w46jbjFqJsdRKjOWMJsnMXruTn5dv5aNZ6xg3fxPdmibTrnbFwKhqEZFjlttfIjmniw8HGgJdgJrAJDNr4Zzb+aeLnBsMDAZITU3NWYeISMhs2nWAO0fPplGV8vy9VfXDlq2fVI5LU2vxwfQ1fDV3Ixe3r1lIUYqISHGmDmYRkSI0f/58+vfvz/z58znhhBN48MEHadiwYVGHBXidykvS9jBv/S4WbdpDZpaf6IgwGlQpT6Mq5WiYXJ4KMaGffy8iKprTevSmzaln892ot/nxs3eZP+1HzrvmDmo1bH7c9UeGh9GxbiId6lRk2ea9/LBoM5/+tp4Zq7Zzfusa1KgYE4K7EJEyah1QK2i/JrAhlzJTnXMHgZVmthivw3lG4YQoIvI/Wdl+bv/gNw4czOb1K9sxfeX2I17Tono8CbERfDVPHcwiIuJRB7OISBFIT0/n1VdfZcSIESQmJjJw4EDOPffcIp8T2DnHqm3pzFq9nd/X7yYz209spI/WNSvQonoF6iWVwxdWODHGJyZx8T8epNXJXfl6xKsMe/pfpJ5+Hmdceg1RMXHHXb+Z0TC5PA2qlGP22p18/fsmBk1cRoc6iZzVLFmLAYrIsZgBNDSzusB64HKgV44ynwNXAMPMrDLelBkrCjVKEZGAF75bwvRV23mpZxsaVCmXrw5mM6NljQpMWrqFXekHqRCrBf9ERMo6fXsWESlks2fP5v7772f16tX07NmTu+66i/j4+CKNac+Bg/y2ZiczV29n695MIsPDaFWzAq1qJlC3clyhdSrnpmHrjtR+ajATPhnG9O+/YPFvv3Bun1to3O6kkNRvZrRNqUjTavGMX5jGLyu28fuGXVzavhaNq5YPSRsiUjY457LM7FbgG7z5lYc65+ab2ePATOfcmMC5s8xsAZAN3OOc21Z0UYtIWTVr9XYGTVzOFR1rcUHbGkd1rdfBvJVvFmzistRaR75ARERKtXx1MJvZOcDLeInyEOfcMznO3wVcD2QBW4BrnXOrA+eygXmBomucc+eHKHYRkRIlMzOTN954g8GDB5OcnMywYcPo1Cnn2k+Fa/3O/UxeuoV563fhd1A7MZbO7arQskYFIsOLz8J3kdExnH3lzbQ48XS+fOclPnzlMZqf0IW/XXU70bHHP5oZIDrCR/dW1WlfO5GPZq1l+C+r6NIoiTObJhdpB7uIlCzOubHA2BzH/h302QF3BTYRkSLhnOOZrxeRVD6KR85rdtTX10iIISUxlq/mblQHs4iIHLmD2cx8wOtAN7w542aY2Rjn3IKgYr8Bqc65dDO7GXgO6Bk4t9851ybEcYuIlChLly7l/vvvZ8GCBVx44YU88MADlC9fNKNj/c6xZNMeJi3bysqt+4gMD+PEepXoUDeRKuWjiySm/KpRrwnX93+Nn78azU9fvMe6pQu54MZ7SWnUImRtVK0QzU2d6/PfORuYuGQLq7enc3mHWse1gKGIiIhIcTJx8RZmrNrBExe0IDby6F9sNjO6t6rG2z+tYMe+TCrGRRZAlCIiUlLkZ3haR2CZc26Fcy4TGAX0CC7gnJvgnEsP7E7FW9BERKTM8/v9DBs2jEsuuYSNGzfyyiuv8PTTT3fKVaMAACAASURBVBdJ57LfOeas3cnL45cyYupqtu/L5NwWVbn/nCZ0b1W92HcuH+ILD+e0Hldy9YMvEBYWxogB9zDx0+H4s7ND1kaEL4yL2tXkknY1Wbcjndd+WMaKrXtDVr+IiIhIUfH7Hc99s5iUxFh6Hsfo4+4tq5Hld3wzf1MIoxMRkZIoP7+qrAGsDdpfBxzune7rgK+D9qPNbCbe9BnPOOc+P+ooRURKoO3bt3Pvvffy888/c8YZZ/DYY49RuXLlQo/DOceCjbv5fmEaabszSI6P4rLUWrSsUaFET/1Qs0FTbnj8dca9N4hJY0ayYv5vXHjjfVSsUi1kbbSrXZHqCTGMnL6GoZNX0rJGBXq0Obo5CkVERESK0shpa/60P2fdThZu3M1lqbX4eNa6Y663efV46lSK5at5G7m8Y8rxhikiIiVYfjqYc+t9cLkWNOsNpAKdgw6nOOc2mFk94Aczm+ecW57jun5AP4CUFP3DJCIl32+//cadd97Jjh07ePTRR7nsssswK9zOXOccSzfv5bsFaazfuZ/K5SLp2cHrWA4r5FgKSlRMHD1uuIf6LTswdvgrDP73P+h+9e20OOH0kLVRtUI0/+hSn3enruaO0bPZtf8gV51YJ2T1i4iIiBSWbL/juwVpVI2PplXNCsdV16FpMt78cQXb9mZQqVxUiKIUEZGSJj9TZKwDgt+bqQlsyFnIzLoCDwHnO+cyDh13zm0I/LkCmAi0zXmtc26wcy7VOZealJR0VDcgIlKcOOcYNmwYV111FZGRkYwcOZKePXsWeufy5j0HGP7LKoZNWUV6ZhaXtKvJP89sROuaCaWmczlYixO60O+JN0iuVZfP3nyGb95/g+ysrJDVHx3h4+qT6nBmk2T+/cV8Xvp+Cd46XSIiIiIlx8zV29m+L5OzmiWHJCc8r1V1sv2OcZomQ0SkTMtPB/MMoKGZ1TWzSOByYExwATNrC7yF17m8Oeh4RTOLCnyuDJwMBC8OKCJSauzevZvbb7+dZ599ls6dO/Pxxx/TvHnzwo3hwEGe/HIBr4xfyupt6fytRVXu7NaIdrUrlujpMPIjoXIyfe57jk5nXcj07z7n3WfvZc/ObSGrP8IXxpu923Fxu5q89P1SHh0zH79fncwiIiJSMmRm+ZmwaDO1E2NpXDU064E0qVqeeklxfDV3Y0jqExGRkumIU2Q457LM7FbgG8AHDHXOzTezx4GZzrkxwPNAOeCjwCi9Nc6584GmwFtm5sfrzH7GOacOZhEpdRYtWsTtt9/Oxo0bue++++jbt2+hjlp2zvHRrHU8N24R2/Zlklq7It2aVaVc1NGvCl6S+cLDOavXTVSv15gvh77IkP63cvE/HiSlccuQ1B/uC+P5S1pRMTaCIZNXsudAFv+5tDVhpbzzXkREREq+qSu2sftAFj07pIQsTzUzzmtZjdcmLGPLngySymuaDBGRsihfPQ/OubHA2BzH/h30uWse100BQvOtXkSkmPr222+5//77iY+PZ/jw4bRr165Q21+1dR8PfDqPX1ZsI7V2RYZd05G563YVagzFTYsTTiepRh0+fu1x3n3uPrr2vIGO3S4IyZepsDDjoe5NiY+J4IXvlhAb5eOJHi0KfRoUERERkfzKyvbz09ItNEouR93KcSGtu3ur6rzywzLG/b6RPlqnQkSkTCpbQ9tERELI7/czaNAgXn/9dVq3bs2rr75KXvPI51y9OxSy/Y7Jy7YyfmEa4T7jwrY1aF+7YpnvXD4kuVZdruv/Gl+8/RzfjnyTDSuX8Pdr7iQ8MvK46zYzbjujAfsys3jrxxVUiIngnrObhCBqERERkdBbuGkP6ZnZnFS/csjrbly1PA2rlOO/c9XBLCJSVqmDWUTkGKSnp/PAAw/w7bff0qNHDx577DGiogrvlcANO/fz6a/r2LDrAM2qxXN+6+rEx0QUWvslRXRsHJfd1p/JX45i4qfD2bllI5fe1p9yFSoed91mxv3nNGH3/ixen7Cc+OgIbuxcPwRRi4iIiITWzFXbqRATQYMq5Qqk/m7Nkhn80wrSM7OIjVQ3g4hIWZOfRf5ERCTIhg0b6N27N99//z333nsvAwYMKLTO5Wy/Y+LizQyauIw9B7Lo1TGF3ifUVufyYVhYGKee34tLbnmYTWtWMPTx20lbuzI0dZvx5AUtOK9VNQZ8vYgPpod+pLqIiIjI8diZnsmyzXtpl1KRsAKa0qtD3USy/I7Za3cWSP0iIlK8qYNZROQo/Prrr1x66aWsXbuWN954g2uuuabQ5t7dtjeDtyet4NsFaTSvXoF/dm1IixoVCqXt0qBph1O5+sGB+LOzGfbUnSydPS0k9frCjBcua0OXxkk8+Nk8raIuIiIixcqsNTtwQPvax/8GV17apVTEDGau2lFgbYiISPGld1dERPJp3Lhx3HfffVStWpVBgwZRv37hTIfgnGPmqh18NW8jYWFwWWotWtesoEXljkG1Og25tv8rjH6pP6NffpSul99Ap7MuPO5nGRkexhtXtqfP/03jzg9nUz0hmrYpBfclTkRERCQ//H7Hr6t3UD8pjsS441+HIljONUaSy0fz3zkbqFzur2/29eqUEtK2RUSkeNEIZhGRI3DOMXToUO68805atGjBqFGjCq1zeX9mNu9PW8Nns9dTMzGG289oSJtaCepcPg7xFSvT98GBNG53It998BZjh7+CPzv7uOuNifQx+KpUkuOjuGHELDbs3B+CaEVERESO3S8rtrEj/SCptRMLvK3alWJZvT2dbL8r8LZERKR4UQeziMhhZGdn8+STT/L8889zzjnnMHToUCpWLJyRqWu3p/PqhKUs2rSbc1tU5dqT65IQG9qRJ2VVZFQ0l9zyMCd178mvE8fy4SuPkZlx4LjrTYyLZGjfDmQczOa64TPZl5EVgmhFREREjs3oGWuJjgijWfX4Am+rTqU4MrP8bNp9/DmViIiULOpgFhHJQ3p6OrfffjsjR47k2muvZeDAgYWymJ9zjslLt/DWT8sBuPG0+pzaMKnAFmUpqywsjDMvvZa/XXUby+bO4N1n72Xf7uNfmKZhcnle7dWWxZt2c+fo2fg1ikdERESKwK70g4ybv4k2tRKI8BX8V//alWIBWL1tX4G3JSIixYs6mEVEcrF161auvvpqJk6cyMMPP8w999xDWFjB/5WZnpnFu1NXM/b3TTSpGs9tpzekVmJsgbdblrU/4zwuve0RNq9dybCn7mT75g3HXWeXxlV45LxmfLsgjee/XRyCKEVERESOzuez15OZ5S+U6TEAEmIjSYiJYNW29EJpT0REig91MIuI5LB27VquvPJKli5dyquvvsqVV15ZKO2u3raPV39YxtK0vZzXqhpXdkohJtJXKG2XdY3bnUSf+55l/769vPPEHWxYcfydwlefVIdenVJ4Y+JyPvttXQiiFBEREcm/0TPW0rx6PNUTYgqtzdqVYlm9bR/O6Q0uEZGyRB3MIiJBFi9eTK9evdi1axfDhg3jjDPOKPA2/c7x05ItvD1pBb4w48bO9TipfmUt5FfIajZoxjUPv0hkdAwjnrmHpXOmH1d9ZsZj5zenU91EHvh0Hgs37g5RpCIiIiKH9/v6XSzYuJueHWoVaru1K8Wx50AWO9IPFmq7IiJStNTBLCISMGvWLPr06YPP5+O9996jdevWBd7m3owsRvyyinHzN9GsWjy3nt6AmhU1JUZRqVS1Jtc89CKVqtVi9Mv9mTflh+OqL8IXxmu92lEhJoKb3pvFrv36siUiIiIF75Nf1xEZHkaP1jUKtd06leMAWKV5mEVEyhR1MIuIABMnTuT666+nUqVKvP/++zRo0KDA21y5dR+v/bCU5Vv2cX7r6lzRMYXoCE2JUdTKJSRy1f3PU7txSz4f/Cwzvv/iuOpLKh/FoCvbsX7Hfv71oRb9ExERkYLlnOO7BWmc2qAyFWIjCrXtKuWjiI4I00J/IiJljDqYRaTMGzNmDLfeeiv169fn/fffp0aNgh3p4XeOCYs3M2TSCiJ8YdzcuT4n1KukKTGKkaiYWK6480katT2Rce8N4qcv3j+uuQTb107koe5N+X7hZt74cXkIIxURERH5s0Wb9rBux366Nksu9LbDzKidGKeF/kREyhh1MItImTZixAjuu+8+OnTowPDhw0lMLNhVtvccOMiwKav4bkEaLWtW4JbTGxTqwiuSf+GRkVx66yO0OrkbP342gu8+eAu/33/M9V19Uh3Ob12dgd8uZvLSrSGMVEREROR/vl+QBsCZTaoUSft1KsWyZU8G+zKyiqR9EREpfPnqYDazc8xssZktM7P7czl/l5ktMLO5ZjbezGoHnetrZksDW99QBi8icqycc7z88ssMGDCAbt268eabbxIXF1egbS7fspfXfljGqq37uLBNDXqm1tKUGMVcmM/H+dfdRcduFzDt2894+OGHyco6ti9LZsYzF7ekQZVy3D7qNzbs3B/iaEVERETg+4VptKmVQJX46CJpv3YlL6derVHMIiJlxhE7mM3MB7wOnAs0A64ws2Y5iv0GpDrnWgEfA88Frk0E+gOdgI5AfzOrGLrwRUSOXnZ2No899hhvvvkml1xyCS+++CJRUVEF157fMX5hGkMnryQqwsfNXerToW6ipsQoISwsjLN63UTnC6/is88+44477iAjI+OY6oqNDOfN3u3JzPJz2we/cTD72EdEi4iIiOSUtvsAc9btolsRTI9xSI2KMfjCTPMwi4iUIfkZwdwRWOacW+GcywRGAT2CCzjnJjjnDv16cipQM/D5bOA759x259wO4DvgnNCELiJy9DIzM7n77rsZPXo0/fr14/HHH8fnK7hRxJt3H6D3kGmMX7SZNrUSuOX0+lSroCkxShoz47QeV/LQQw8xfvx4brzxRvbtO7YvTfWSyvH0RS2ZtXoHA79dEuJIRUREpCz7fqE3PUZRdjBH+MKomRDDKnUwi4iUGeH5KFMDWBu0vw5vRHJergO+Psy1f1k9y8z6Af0AUlJS8hGSiMjRy8jI4I477mDixIncc889XHvttQXa3qSlW7hz9Gz2ZmRxcbsatEupqFHLJVxYw9Po0e8gY4b8h/Mv7cUV/3qK2HLxx1RXhzqJvPnjcvZnZtO4avk8y/XqpH8XRUREJH++X5BGSmIsDauUK9I4aleK4+dlW8nM8hMZrqWfRERKu/z8TZ9bb4jLtaBZbyAVeP5ornXODXbOpTrnUpOSkvIRkojI0UlPT+fmm29m4sSJ9O/fv0A7lw9m+3n+m0VcNXQ6iXGR/PfWU2hfW1NilBatTjqTy27rT9ralQx/+l/s2bHtmOo5r1U1qsZH89GstezafzDEUYqIiEhZsy8ji5+Xb6Nr0+QizzvrVIol2znW7dQ8zCIiZUF+OpjXAbWC9msCG3IWMrOuwEPA+c65jKO5VkSkIO3du5d+/foxbdo0BgwYwOWXX15gbS1N28OFg37m9QnLubR9Tb645RQaJuc9OlVKpkZtT6DXv55i9/YtDB/wL3ZuTTvqOiJ8YVzRMYWsbMfoGWvI9uf6u1sRERGRfJm0dAuZWf4inR7jkEML/a3aqg5mEZGyID9TZMwAGppZXWA9cDnQK7iAmbUF3gLOcc5tDjr1DfB00MJ+ZwEPHHfUIiL5tHPnTvr168fChQsZOHAg55xTMNPA+/2Od6as4tlxiygXFc6bvdtxTotqBdKWFA91mrbmyrsH8MELDzNiwN30vvcZEpP/MgvUYSWVj6JHm+p8NGsdPyxKo1uzqgUUrYiIiJQmI6et+cuxj2etJSbCx7LNe1m5tWjnP46J9FGlfBRrtmseZhGRsuCII5idc1nArXidxQuBD51z883scTM7P1DseaAc8JGZzTazMYFrtwNP4HVSzwAeDxwTESlw27Zt4+qrr2bRokW88sorBda5vH7nfnr/3zSe+HIBpzaozLg7TlXnchlRs0FTet/3LJkZBxg+4G62bPjrl70jaZtSkfa1KzJx8RaWbd5bAFGKiIhIaZftdyzatIfGVcvjCyse07LVrhTLmu3p+J3e0hIRKe3yNdu+c26sc66Rc66+c+6pwLF/O+cOdSR3dc4lO+faBLbzg64d6pxrENjeKZjbEBH5s7S0NK666ipWr17NG2+8wemnnx7yNrL9jnd+XslZL/zInLU7efbilgzpm0qV8tEhb0uKr2q1G3DVA8/j/I4RA+5m05rlR13H31tVJ6l8FKNnrmX3Ac3HLCIiIkdnzfZ00jOzaVrt2BYfLggpiXEcOOhny56MIxcWEZESTcu5ikips379evr06UNaWhpvv/02J598csjbmL9hFxcN+pnH/ruA9nUSGXfHafTskFLkC6pI0ahSow59H/wP4RERvPvsvWxYsfioro8M9+ZjzszK5sMZazXSR0RERI7Koo278YUZjaqUK+pQ/lA7MRaANds0D7OISGmnDmYRKVVWrVpFnz592LVrF0OHDiU1NTWk9adnZjFg7ELOf+1n1u/cz8uXt2H4NR2oFUigpeyqVLUmVz0wkOjYcrz73P2sWfL7UV2fHB/N+a2rs2LrPiYs2nzkC0REREQA5xwLNu6mXuU4oiJ8RR3OHyqViyQ20sfq7epgFhEp7dTBLCKlxtKlS+nTpw8ZGRkMHz6cVq1ahaxuv9/x6a/rOHPgj7z10wouaVeT7+/qTI82NTRqWf5QMakqfR/4D+USEhn5nwdZueC3o7q+XUpF2tZK4IdFm1m+RfMxi5REZnaOmS02s2Vmdv9hyl1iZs7MQvubUBEpc7bszWDbvsxiNT0GgJlROzFWC/2JiJQB6mAWkVJh/vz5XHXVVYSFhTFixAiaNGkSsrqnrthGj9d/5q4P51C5XBQf3ngiz17SioTYyJC1IaVHfGISfR/4DxWrVOeDFx5h6Zzp+b7WzDi/TXXvv7MZa9mj+ZhFShQz8wGvA+cCzYArzKxZLuXKA7cD0wo3QhEpjZZs2gNAk6rliziSv0qpFMfWvZls35dZ1KGIiEgBUgeziJR4s2fP5pprriE2NpZ3332X+vXrh6TeZZv30G/ETC4fPJVtezN4sWdrvrjlZDrWTQxJ/VJ6latQkT73P0dSjdp8+MpjLJw5Od/XRoX7uKJjCvsPZvPRrHX4/ZqPWaQE6Qgsc86tcM5lAqOAHrmUewJ4DjhQmMGJSOm0JG0vVcpHFejgh+ysLJzff9TXpQSmkft19Y5QhyQiIsVIeFEHICJyPKZPn87NN99M5cqVeeedd6hevfpx17lgw25em7CUr3/fRGyEj3vObsx1p9QluhjNaSfFX2y5ePrc+ywfvPgwnwx6ih433EPLE8/I17VVK0Tz91bV+Wz2egZNXMatZzQs4GhFJERqAGuD9tcBnYILmFlboJZz7kszuzuvisysH9APICUlpQBCFZHSIDPLz8pt+zixXqWQ1Of3Z5O2ZgUrF8xmzZLf2bUtjb07t5O+ZxeYERkVQ1RMLInJ1alerzE16jamdpNWxJavkGt9NSvG4DNj5uoddG2WHJIYRUSk+FEHs4iUWJMnT+bWW2+lZs2aDB06lCpVqhxXfbPX7uS1H5by/cLNlI8K55YuDbj2lLokxmkqDDk20XHluPLuAYx66d98Pvg5sjIzadv5nHxdm1qnIsu37uWF75bQoU4inUL0xVFEClRuk/L/8RqCmYUBLwJXH6ki59xgYDBAamqqXmUQkVyt2LKXbL+jUfLxTY+xZf1qfp04lnlTxrN/nzflRqWqNUmsWpOa9ZtSLqESzvnJ3J/OgfS9bFm/mmnffIY/O4swn4/6LVJpefKZNGpzAhGRUX/UG+ELo3pCtEYwi4iUcupgFpESafz48dx55500aNCAIUOGkJh4bNNWHMz2M+73Tbz7y2qmr9pOQmwEd3VrRN+T6lAhJiLEUUtZFBkdwxV3PclHrz7Ol++8SNbBDDp0ze2N+T8zMy5sU4M9B7K4fdRvjL39VCqVizridSJSpNYBtYL2awIbgvbLAy2AiYEFYqsCY8zsfOfczEKLUkRKjcVpe4j0hVGnUuwxXb9ywWwmffE+qxfPJcwXTpP2J9Oo7QnUadqa8gmH/+V21sFM0tasYOGsyfz+ywSWzplGXHxFTup+Ge1P7/5HR3NKYiwzV+8gM8tPZLhm6RQRKY3UwSwiJc5XX33FfffdR/PmzRk8eDAVKuT+St7hpO0+wMhpa/hg+ho278kgJTGWh7s35fKOKZSL0l+NEloRkVFcdnt/Phn0NOPeG8TBzExO+tulR7wuKsLHa73acuGgKdz14RzeuboDYWG5DZAUkWJiBtDQzOoC64HLgV6HTjrndgGVD+2b2UTgbnUui8ixcM6xJG0P9ZLiCPcdXcft5vWrGD/6/1g2dzrxiUmcedl1tD7lLOLiE/JdR3hEJDXqN6FG/Saccck1rFo4hylfjea7D97il68/4tTze9G+S3dSKsXx8/JtLNi4mza18l+/iIiUHOpFEZES5ZNPPuGRRx4hNTWVN954g7i4uHxfe+BgNuMXbubTX9cxcckWsv2OLo2TePbEOnRulKSOOylQ4RGRXHLLw3w++DnGfziEg5kZnNbjSgKjGPPUvHoFHjmvGY98/jtv/bSCm7uEZhFLEQk951yWmd0KfAP4gKHOuflm9jgw0zk3pmgjFJHSZOveTHakH+TUhkn5viYrM5OJnw5n6jefEhkdw5mXXU/Hrj0Ijzy+KeHCwnzUa96Oes3bsXrRXCZ+OpyvR7zGvCnj6dLrNgBmrtquDmYRkVJKHcwiUmK89957PPXUU5x66qm8/PLLxMTEHPEav98xa80OPv11PV/O3cCeA1kkx0dx/al1uaJDCnUq57+DWuR4+cLDufCm+4iIjOSnz9/lYMYBzrzsuiN2MvfulMLU5dv4z7eL6VCnIql1jm1KGBEpeM65scDYHMf+nUfZLoURk4iUTkvSvLmSG+dz/uWNq5byxdvPs2X9atp1+RunX3INseXiQx5X7SatuOqB/zBvyni+HfkmHzz9TxJbns2sVVW4/tR6IW9PRESKnjqYReRPRk5bU6jt9eqUkq9yb7/9Ni+88AJdu3Zl4MCBRB5mlIVzjt/W7uSruRsZO28jG3cdICbCxzktqnJRuxqcVL8yPo1WliISFubj79feRURkNL98/RGZGfs5t/ctWFjer7aaGQMubsm89bu47QNvPuaKWnxSRESkTFuStoekclFHzAmcc0wd9wk/fDyUuPIJXHHXkzRo1aFAYzMzWp3clfotUxn33iAWTP+SSZuWse1vQ6hUSQsXi4iUNupgFpFizTnHK6+8wptvvsl5553HgAEDCA//619dzjnmrtvFV/M28tXcjazfuZ9IXxinNarMPWc35qzmVTW3shQbFhbGOX1uISI6ml/GfsTBjAP8/dq7CPP58rwmPjqC13u14+I3pnD3R3MY0jf1iCOfRUREpHTKzPKzcus+OtU9/FtNBzMO8OU7L/H71Ak0aX8y511zBzEFMGo5L3HxCVz8jwep06wtX414jQsvuphXXn6JNm3aFFoMIiJS8NTbIiLFlnOOZ599luHDh3PJJZfw6KOP4gvqgHPOsWDjbr6c63Uqr9meToTPOKVBZe7q1oiuzZKpEBPxl3oLe5S2SG7MjDMvvY6o6FgmfjqczIwDXHTT/fjC//rf7CEta1bgwb814dH/LmDIpJXccJpeMxURESmLVm7dS9b/s3ff0VGUbxvHv5Pee++FUBJ6AkgVpIgoVkBAioIiSlEBKSoqKCCiUiT8AKkioCi+KBYUBSR0QieUENIT0nvPZuf9A4hUCRCyIdyfc/Zks/PM5FqPh52955n70arU/4/2GLmZaWxYMI2U+PN0ee5F2j/RX2cXp18a1J8fY6Di9LcMGTKEmTNn8sQTT+gkixBCiOonBWYhRK2k1WqZNm0aGzZsYPDgwUyZMqXyhDgyNZ9fjiXzy/ELRGcUoq+n0M7fntFd6vFokAvWZjcv0AlRmyiKQscnB2JkbMKf65ewYcE0+oyeiqGR8U33GdrOh73RmczecoZgH1taetnWYGIhhBBC1AZnUwsw1Fduup5IWlIs6z57l7KSIvq/MY2A5m1qOOHVGrpYYurkTceHZ5H8+yLefvttUlJSGD781mtRCCGEqP1u3vDxCoqi9FQU5ayiKFGKoky+wfZOiqIcVhRFoyhKn2u2VSiKcvTSQ1bOFkLckkajYfLkyWzYsIFXX32VKVOmkJBVzIK/z9Fj7j/0mLuThdujcLYyYcYzjTnwTlfWDG9Dv1aeUlwW96U2jz7LEy+9SdSJcNZ//h6lxUU3HasoCp8+1wwXaxPGrDtCblF5DSYVQgghRG0QmZqPn4MFhvrXf6VPjDrF6pnjUVUtQ9/5QufFZQADfT1aeNlwIr2cZcuW8dhjj/H5558zY8YMtFqtruMJIYS4S7ecwawoij4QCnQHEoGDiqL8rKrqqSuGxQMvAhNucIhiVVWlwZIQokrKysqYMGECW7duZfTYsXi1e4pBy/ezOyoTgFY+tkx7MojHmrjgZGmi47RCVJ8WDz+GobEJm5Z+yto5Uxgw/mNMzW9826u1mSFfDmhB38V7mfDDMZYODpbZP0IIIcQDIiajkKzCMtrXc7huW9Txg3y/8COsbO0ZOGEWto4uOkh4Y8FetizcHkW5qsdnn32Gi4sLK1eupKioiI8++uiqVnhCCCHuL1VpkdEaiFJVNRpAUZRvgaeAygKzqqqxl7bJpUchxB0rKSlh7NixhIWFEfzkSyxL9yd7/RHcbUwZ170+fYI9cLMx1XVMIe6Zxg91wdDImI2LZrLmk4m88PYswOuGY1t42TL5sYZ8/OtpVu6OZVgH35oNK4QQQgid2HE2DYAG1/Rfjjp+kA0LpuHg5sXA8TOwsK5dbbSCfezQqhAel83D9R2ZOHEi5ubmLFy4EI1Gw6xZs6TILIQQ96mqtMhwBxKuwt3IFgAAIABJREFU+D3x0mtVZaIoSriiKPsURXn6RgMURRlxaUx4enr6bRxaCFFXFBYWMujFlwkL20V5877s02/MQ372rB7WmrCJXRjbNUCKy+KB0KBlO/q/OY3M1CRWzxxPSkrKTccO7+BLt0bOzPztNPujM2swpRBCCCF0ZcfZdBwsjLAzN6p87fzJQ5XF5cETP6l1xWWA1j52GOnrsevcv9/5R40axRtvvMHmzZuZNGkSGo1GhwmFEELcqarMYL7RPbfqbfwNL1VVkxVF8QO2KYpyQlXV81cdTFWXAksBQkJCbufYQoj73Np9cUTEpfLLommUpcdC64G0f7gHbf3tsTIxJCm7mG8PJtzyOELUJX6Ng3lh/AzWz32fQYMGsWLFCry8rp/JrCgKXzzfjKcX7mbUusP8PLqDXIgRQggh6rDCUg17ozNp5f1vATnm1BE2zP8QB1dPBk38BFMLKx0mvDlTI31a+dqyMzKDdx//9/WRI0eir6/PF198gaGhITNmzEBPr0rLRQkhhKglqvKvdiLgecXvHkByVf+AqqrJl35GAzuAFreRTwhRh8VkFLLo98Ns/GIy5RlxNH52DBNfeYFHg1ywMpHF+sSDzatBEwZPmk1BQQGDBw/m/PnzNxxnZWLI0iHBlJRree2bQ5SUV9RwUiGEEELUlJ2R6ZRptDRyu1hETjp/hu/mfYCdszuDJn6CWS0tLl/WMcCRs6n5pOaVXPX6K6+8wujRo9m0aROffPIJqirzzoQQ4n5SlQLzQSBAURRfRVGMgP7Az1U5uKIotoqiGF967gC054rezUKIB1NKbgmr98SybMtBsn79HMPiLPq/NZ1ne/fCxFD6rglxmZtvfb7++mu0Wi2DBw/m1Kkbf4TWc7Lki37NOJaYy3ubTsqXMiGEEKKO+vNUKrZmhnjbmZOeFMf6L97DwsaOFybMxMzSWtfxbqljwMWFCcPOZVy37fXXX2fIkCGsWbOG0NDQmo4mhBDiLtyywKyqqgYYDfwBnAY2qKoaoSjKdEVRngRQFKWVoiiJQF9giaIoEZd2bwSEK4pyDNgOfKKqqhSYhXhA5ZeU88OhBL7cdo646HNY7V2ECWUMnfIpAU1CdB1PiFqpfv36rFmzBhMTE4YMGcKBAwduOK5HkAtjuwbww6FEvt4bV8MphRBCCHGvlVdo+ft0Kl0bOZOflcbaz95B38CQFybMwsLGTtfxqqSRixUOFkaEnbt+7SVFUZg0aRLPPvssoaGhrFu3TgcJhRBC3Imq9GBGVdXfgN+uee39K54f5GLrjGv32wM0ucuMQoj7nFZVORCTxZ+nUiivUGlmlk3s1v9hZGzKCxNm4ujureuIQtRqPj4+rFu3jpdffpmXX36Zzz77jB49elw37s2uAZxKzuWjX07RwMWSh/zsdZBWCCGEEPfCgZgs8ko0tPcy4/Mpb1NWUszQdz7D1slV19GqTE9PoUM9B8LOZaDVqujpKdds12PatGlkZ2czY8YM3Nzc6Ny5s27CCiGEqDLpnC+EuKeSc4pZ/M95fj6WjLuNKU+75HJ+42eYW9ny4rtfSHFZiCpycXFhzZo1BAYG8tZbb7Fhw4brxujpKXzxfHO87M147ZtDxGYU6iCpEEIIIe6FPyNSMNFX+b9FM8hOu8Dzb3yIs6efrmPdto4BjmQWlnHqQt4NtxsYGDBnzhwaNGjA+PHjOX36dA0nFEIIcbukwCyEuCfKK7T8duICi3ZEkV1UTr8QD1rpxbBl6QwcXD158Z3PsXFw1nVMIe4rtra2rFixgvbt2/PBBx+wePHi6/otW5kYsmJoKwCGrT5IblG5LqIKIYQQohqpqsofESm4nP+Fgwf288RLb+LdsKmuY92R/+rDfJm5uTmLFy/GysqKkSNHkpKSUlPxhBBC3AEpMAshql1STjELt0exKyqDYG87xnWrT9mZnfy09FM8A4IYMvlTzK1sdB1TiPuSmZkZoaGh9O7dm/nz5zNz5ky0Wu1VY3wczFkyOITErGJGfnOIMo32JkcTQgghxP3gZFIeGYe2kHrsH1599VWadeiu60h3zMnKhIYuljfsw3zVOCcnFi9eTEFBAa+99hqFhXJnlhBC1FZSYBZCVButqrLjbBr/2xFFaXkFL7Xz4enmbuz/dS1/rF1Eg5btGDhuBsam5rqOKsR9zdDQkE8++YShQ4fyzTffMHHiRMrKyq4a09rXjtl9mrA3OpOpm05eN9NZCCGEEPePpes3YXDqNx7p1oOxY8fqOs5d61TfkfDYbIrKNP85rkGDBsybN49z584xbtw4NJr/Hi+EEEI3pMAshKgWWYVlfLUzmj9PpRLoZs3YrgH4O5jx+9dfEvbTWpp17EGfUe9hYGSk66hC1Al6enpMmjSJcePG8euvv/L6669fN7PnmRYejH2kHt+FJ7BkZ7SOkgohhBDibkRERPD36s8xd/Hl8zmz0dO7/7/GdwxwoKxCy/7orFuP7diRqVOnsnPnTmbOnCkXzYUQohYy0HUAIcT972RSLhsPJwLQN9iD5p42aMpK2fDlLM4d3UfbXn3p2nc4iqLc4khCiNuhKAqvvPIKtra2fPDBBwwbNozFixdja2tbOeat7vWJySzik9/P4GlrxuNN75+V5oUQQogHXUpKCiNeHUmFkTlD3/4IExMTXUeqFq187DA20GPnuXS6NHS65fjnn3+e+Ph4VqxYgbe3N0OHDq2BlEIIIapKCsxCiDum0WrZcjKFPecz8bA1ZUArL2zNjSjMy+G7eR+QFHOWnoNG0arbk7qOKkSd1qdPH2xtbRk3bhwDBw5k6dKleHp6AheL0HP6NOVCTjFvfXcUWzND2tVz0HFiIYQQQtxKUVERr7/+OgWFhZS1fZ1nHmqo60jVxsRQn9a+dv+50N+1xo8fT1xcHJ9++in16tWjffv29zChEEKI23H/31sjhNCJnKKLLTH2nM+krZ89Izr5YWtuRFZaMqtmvEVqQjR9R78vxWUhakjXrl1Zvnw52dnZ9O/fn+PHj1duMzHUZ/nQVvg6mDNizSFOJuXqMKkQQgghbkVVVd59913Onj2L66Ov0rBBAzztzHQdq1p1CnAkKq2A5JziKo3X09Nj9uzZ+Pv7M27cOGJjY+9tQCGEEFUmBWYhxG2LTM3ny21RpOWXMqC1F72buWGgp0dS9BlWfvQmxYUFDJ40m4bB7XQdVYgHSkhICOvXr8fMzIyhQ4fy999/V26zNjNk9bDWWJsa8uLKA8RmyErsQgghRG21bNkytmzZwohRYzireNAj0FnXkapdx/oX76jadRuzmM3NzQkNDUVRFEaPHk1BQcG9iieEEOI2SIFZCFFlqqryz9k0Vu+JxdrUkFGd69HE3RqAyCP7WPPJRIxMTHnpvbl41AvUcVohHky+vr6sX7+e+vXrM2bMGL755pvKbS7WJnw9vDVaFQav2E9aXokOkwohhBDiRnbu3MncuXPp1asXziGPoarQI6juFZgbOFviZGnMznPpt7Wfp6cn8+fPJzY2lgkTJlBRUXGPEgohhKgqKTALIaqkTKPl24MJ/HEqlcbu1ox82B8HS2NUVWXfHz/y3YIPcXD35qX35mHv4qHruEI80BwcHFi1ahWPPPIIM2bMYPbs2Wi1WgD8HS1Y+WIrMgvKGLLiALnF5TpOK4QQQojLYmJimDBhAg0bNuTjjz/mh0NJ+NibEehqpetod2Xd/vjrHusPJOBpa8bWU6ms2h1b+XpVtGnThilTpvDPP/8wf/78e5xeCCHErUiBWQhxS9lFZSzZeZ6TSbk8GuRC/1aeGBnoUaHR8NvqBWxdv4SGLdsxdPIcLKxtdR1XCAGYmpoyf/58Bg8ezKpVq3jzzTcpKioCoJmnDUsGB3M+vYChKw6QXyJFZiGEEELXCgoKGDNmDAYGBnz55ZfE5pQTHpfNC228URRF1/HuiZbetpRqtBxPzLntfQcOHEjfvn356quv+PXXX+9BOiGEEFVloOsAQojaLTqjgHX749GqKkPaetPA5eLsiZLCAn5Y9DExEUdo9/jzPPLciyh6cs1KiOpW1Zk8N+PTdRA9NGZs/fYrHn/2eZ5/40Os7BwB6N/Ki7X743h8wS5eaueDsaE+AAPbeN11biGEEEJUnVarZfLkycTGxrJ8+XLc3d1Z9H8nMDLQo09w3b070MfeDEdLYw7GZhHiY3db+yqKwnvvvUd0dDTvvvsuPj4+BAUF3aOkQggh/otUg4QQN6SqKnujM1mxKwZzIwNef7heZXE5Ky2ZFR+/SdyZE/QePo6ufYdJcVmIWqzNo8/y/JvTyEpNZvm0sSSdPwNAI1cr+rfyIjG7iNV7YynTaHUbVAghhHhALVq0iL///ptu/UdwHldW7orh+0OJNHaz4veTKde1l6grFEWhtY8dCdnFXMgtvu39jYyMmD9/PnZ2dowaNYr09Nvr5yyEEKJ6SEVICHEdTYWW/zuSxOZjydR3tuS1zhf7LQPER55kxfQ3KMrLYdDbs2je8VEdpxVCVEVAs9a8NHUuBkZGrJ41gZP7tgPQ2N2afiGexGUW8fVeKTILIYQQNe2vv/4iNDSUZh2606rbUwAcScihTKOlja+9jtPdey28bDDQUzgQk3VH+9vb2xMaGkpubi5jx46lrKysmhMKIYS4lSoVmBVF6akoyllFUaIURZl8g+2dFEU5rCiKRlGUPtdsG6ooyrlLj6HVFVwIcW/klZSzbFcM4XHZdGngyKCHvDG5dNv8oe2/smb2JEzNLXlp6ny8GzbVcVohxO1wcvdh+PsLcPdvyP8t/oTtG1ejarU09bChT7AHMRmFfLM/jpJyWY1dCCGEqAlRUVFMmjSJJk2a0GvIWBRFQVVV9sdk4mZtgoetqa4j3nNmRgY0cbfm6KWi+p1o1KgRs2bN4ujRo0ybNg1VVas5pRBCiP9yywKzoij6QCjwGBAIDFAUJfCaYfHAi8C6a/a1Az4A2gCtgQ8URZEVwISopY4m5LBoexQpuSUMaO1F90AX9BSFCk05v66az2+rF+Ab2Jzh7y/A3sVd13GFEHfAzNL64t0HnXqya/M6fgj9mNLiIlp42fJsSw/OpxXw0sqDFJRqdB1VCCGEqNNyc3MZNWoUZmZmLFiwAAMjIwDiMotIzSuljZ99nV3c71qtfOzueLG/y3r27MnIkSP58ccf+eabb6oxnRBCiFupygzm1kCUqqrRqqqWAd8CT105QFXVWFVVjwPXXm58FNiqqmqWqqrZwFagZzXkFkJUsx8OJdJvyV709RRGPuxPE3drAPJzMvn6k4kc3vEb7R5/nv5vTcfE3ELHaYUQd0PfwJAnXnqT7gNe5ezhvaz46A0yLiQQ7G1L3xBPDsRmMWjZfnKLynUdVQghhKiTKioqePvtt7lw4QLz58/HxcWlctv+mExMDPVo5mGjw4Q1y9veDCdLYw7E3lmbjMvGjBlD165dmT17Nnv27KmmdEIIIW6lKgVmdyDhit8TL71WFVXaV1GUEYqihCuKEi5N+YWoWZoKLdM2RzDh+2OEeNsyqnM9XKxNAEiKPsPyD8eQGn+eZ19/h659h6Gnp6/jxEKI6qAoCg89+iwvvD2LovxcVkwfy9nDe2juacP/XmjJqeQ8nl+6l/T8Ul1HFUIIIeqcuXPnEhYWxnvvvUfLli0rXy8o1XAyKY8WXrYYGTw4SyYpikJrXzsSs4uJSM694+Po6ekxe/ZsfH19GTduHHFxcdWYUgghxM1U5RPrRvfkVLWhUZX2VVV1qaqqIaqqhjg6Olbx0EKIu5VdWMaQFQdYuTuWYe19+XpYa8yMDQA4GvYHq2dOQN/AgBffm0tQ64d1nFYIcS/4Bjbn5Q8XYufiwYYF09i+cTVdGzqy/MUQ4jKLeH7J3jta1V0IIYQQN/bTTz+xfPly+vfvT79+/a7adig2iwpVpY2PnY7S6U4LT1sM9BTWH4i/q+OYm5uzaNEiAEaNGkVBQUF1xBNCCPEfqlJgTgQ8r/jdA0iu4vHvZl8hxD0UkZzLk6G7CI/L5rO+zXi/dyAG+nqUl5awefkXbF7+BV71gxj+4UJcvPx1HVcIcQ9Z2zvx4pTPadaxB7s2r+O1116jiaMhXw9vTXp+KX3+t5eoNPlyJkRVVGFx7HGKopxSFOW4oih/K4rirYucQgjdOHbsGO+//z6tW7fmnXfeuWqbVlU5EJuFr4M5TlYmOkqoO6ZG+jRxt2bTkWSKyu5uLQhPT0/mzZtHbGwsEydORKu9s8UDhRBCVE1VCswHgQBFUXwVRTEC+gM/V/H4fwA9FEWxvbS4X49LrwkhdGhDeALPLtpDuUZlw6tt6RPsAUBMTAwrPnqTo2F/0KH3QAZOmImZhZWO0wohaoKBkRG9h42j19Cx7Nu3j759+2JakMy6Vx6iVFNBn8V7CL/LvohC1HVVXBz7CBCiqmpT4Afg05pNKYTQldTUVMaMGYOTkxPz5s3D0NDwqu0HY7PILirnIT97HSXUvda+dhSUath4OOmuj/XQQw8xZcoUtm/fzoIFC6ohnRBCiJu5ZYFZVVUNMJqLheHTwAZVVSMURZmuKMqTAIqitFIUJRHoCyxRFCXi0r5ZwEdcLFIfBKZfek0IoQMl5RVM+uE4E384TrC3Lb+M7UBzz4uLh2zZsoW+ffuSn5PJgHEf0+W5odJvWYgHjKIoBHd5nK+//pqKigoGDhzIkW0/8cPIttiaGfHCsv1sOZmi65hC1GZVWRx7u6qqRZd+3cfFO/yEEHVcSUkJo0ePprCwkNDQUGxtba/afiG3mC0nU/BzNKex24M7wcPLzozWPnZ8+vsZErKKbr3DLQwcOJA+ffqwZMkSfvvtt2pIKIQQ4kaqtGqAqqq/qapaX1VVf1VVZ1x67X1VVX++9Pygqqoeqqqaq6pqr6pq0BX7rlBVtd6lx8p78zaEELcSn1nEc//bw3fhCYzuUo81w9vgYGFMWVkZH3/8MW+99RYBAQG8Mi2Uek1b6TquEEKHmjdvzsaNG+nQoQOzZs3isw8ns2JAII1crXht7SFW74nVdUQhaqvbXRx7OPD7jTbIIthC1B2qqjJ16lQiIiKYM2cO9evXv277u/93Eq2q8mwLDxTlRksZPRgUReGL55uBAm98ewRNxd21tlAUhalTp9KyZUveffddIiIiqimpEEKIKxnoOoAQ4t777cQFJm88DsDyoSF0beQMQGxsLG+//TYnT57kxRdfZNy4cXx/+IIuowohaoF1+y8urtN+yCQMXerz14blhB/tS+8Rkyl1tuSDnyP463Qqjwa5oFcNX4IHtvG662MIUUtUeXFsRVEGASHADVfRVVV1KbAUICQkpKoLbAshaqFly5bxyy+/8Oabb/LII49ct/2no8lsO5PG401csTM30kHC2sXD1owZzzRh7PojLNgWxbju9W+9038wMjJi/vz59OvXj9GjR/P999/j4OBQTWmFEEJAFWcwCyHuT0VlGiZvPM7raw/j62DOL2M60rWRM6qqsnHjRp577jkSEhJYsGABkyZNuq4PnBDiwaYoCm0efZaX3v0CRU+PdZ++jV/WAVr72BB2LoO1++Mp1VToOqYQtUmVFrhWFKUb8C7wpKqqpTWUTQihA9u2bWPu3Ln06tWLESNGXLc9o6CUaZsjaOFlQ1v/B7f38rWebOZGn2APFm47x4GYu++y6eDgwMKFC8nJyeGNN96grKysGlIKIYS4TArMQtRRJ5NyeeLLXXwXnsBrnf354bV2eNmbkZOTw5tvvsl7771H06ZN2bRpE927d9d1XCFELebm14BXPgylQYt2bPt+Ofl/LqSbtyFnLuSx5J9osovkS5oQl9xycWxFUVoAS7hYXE7TQUYhRA05ceIEEyZMICgoiI8//viGrS8+/DmCwtIKPn2uabXcFVSXfPhkEF52Zrz57RFyi8rv+niBgYHMmDGDw4cPM336dFRVbg4RQojqIgVmIeqYCq3KVzujeWbRbgpLNawd3oZJPRtiqK/Hvn37ePrpp9m+fTsTJkxg+fLluLi46DqyEOI+YGJuwXOj3qX3sHEkx5wl/Kt36GwST05RKYu2RxGXWajriELoXFUWxwbmABbA94qiHFUU5eebHE4IcR9LTEzktddew87OjkWLFmFqanrdmD8jUvjl+AXGPFKPAGdLHaSs3SyMDZjfvwVp+aVM+b/j1VIQ7tWrF6+++iobN25k7dq11ZBSCCEESA9mIeqUqLQCJm08zqG4bLoHOjP7uabYmRtRWlrKggULWLlyJT4+PoSGhhIUFHTrAwohxBUURaF5p0fxbtiUn76aw77vFuDfoj0X/J5g2a4Ynm7uRrC3na5jCqFTqqr+Bvx2zWvvX/G8W42HEkLUqJycHEaMGIFGo2HJkiU4OjpeN+ZkUi4TNx6nkasVIzv76yDl/aGZpw3jezRg9pYzjFhziE+fa4rtXfapHjt2LJGRkXzyySd4eHjQuXPn6gkrhBAPMJnBLEQdoKnQsmhHFL0WhBGVVsAX/ZqxdHAwduZGHD16lGeffZYVK1bQr18/Nm7cKMVlIcRdsXVyZciUOXTp8xKxx/eh99ccXIpi2Hg4iU1Hk+56xXchhBDiflVaWsro0aNJTExk4cKF+PtfXzw+kZjLC8v2Y25kwNLBwRjqy9fy/zLyYT+mPhHIjrNp9FoQdtc9mfX09JgzZw4NGjTgrbfe4ujRo9WUVAghHlxKbes7FBISooaHh+s6hhD3jdMX8pj4w3FOJOXyWGMXpj0VhJOlCSUlJSxYsIDVq1fj7OzM9OnT6dChwy2Pt25/fA2kFkLUFRfioti0ZDYZyfE4NO5Egmc3PJztGdjaCxuzqs0wGtjG6x6nFOLOKIpySFXVEF3nkPNjIe4PWq2WCRMm8Pvvv/P555/Tq1cv4Orz68TsIlbsjsHUUJ+XO/jd9WzcB0lSdjHfHownq7CMro2c6NzAiUEPed/x8TIyMnjhhRfIzc1l7dq1N7wYIIQQ4mo3Oz+WS6VC3Kdyi8r58OcInvhyFxdyi1n0Qkv+NygYJ0sTDh8+zDPPPMPKlSvp27cvP//8c5WKy0IIcbtcvevxyoehtO3Vl8xTu7AJ+4L0MwdZuD2KqLQCXccTQgghaswXX3zB77//zoQJEyqLy1dKyCpi+a5LxeWOUly+Xe62pozuUo9mnjb8dTqNpTujOZmUe8fHc3Bw4KuvvsLAwIBXXnmF1NTUakwrhBAPFikwC3GfqdCqrNsfT+fPtvP13lgGtvbir3EP06uJK4WFhcycOZNBgwZRXl7OihUr+PDDD7GwsNB1bCFEHWZgZES3fi8z/P0F2NjZo7dvFfr7VrHy72PsOJuGtpbdLSWEEEJUt7Vr17J8+XIGDBjAsGHDrtsen1nIit0xmBsb8EpHP2yreJePuJqxoT59gz3oG+xBZkEpvRfuYuIPx0jLK7mj43l5ebFkyRJyc3N55ZVXyM2984K1EEI8yGSRPyHuIwdispj+SwQnk/Jo7WvHh72DCHSzQlVV/vzzT2bNmkVKSgoDBgxg/PjxmJub6zqyEOIB4uoTwPD3F7Dvj43s3PQNZmnn+Dv1cc6nPUK/Vp5YmhjqOqIQQghR7X755RdmzJjBI488wjvvvIOiKFdtj8ssZOWeWCyNDXi5ox/WpvJ5eDcURaGFly2NXK1IySth5e4Yfj1+gde71GN4B19MDPVv63hBQUF8+eWXjBw5klGjRrFs2TJMTEzuUXohhKibZAazEPeBE4m5DF1xgH5L9pKRX8aCAS34bsRDBLpZER8fz6uvvsobb7yBjY0N69at4/3335fishBCJ/QNDGj/+POM+GgxHj7+GB39nqRNnzJ/4w7OpebrOp4QQghRrbZt28bkyZNp3bo1n3/+OQYGV8/hOhCTxcrdsViZXJy5LMXl6mNiqM87vRqx9a2HaVfPgTl/nKXnvJ3sjEy/7WO1a9eOWbNmcejQISZMmEBFRcU9SCyEEHWXLPInRC12LjWfL7ZG8vvJFGzMDHntYX+GtPXB1Eif0tJSli9fztKlS9HX12fs2LG88MIL153U3i5Z5E8IUV1UrZZju7aydcNySgrz0Pi0pfXjL9CzpT/6ev/O7pJF/kRtJYv8CSH+y969exk5ciQNGzZkxYoV103w2Hs+k2GrDmJhbMDwjr5YyZ0899S5tHx+PppMZmEZTT2s6dXE9ar/5lU531izZg0zZ87kqaeeYsaMGejr395saCGEqOtudn4sLTKEqKKaLLwm5xSz81w6J5NyMTMy4I2uAbzc0RdLE0NUVWX79u3Mnj2buLg4HnvsMSZNmoSzs3ON5RNCiKpQ9PRo3ulRGgS3Y/vGrzm0fTOHlh7jTPBTDOrfF2drM11HFEIIIe7IgQMHGDVqFN7e3ixZsuS64vKeqAyGrT6Ip60ZfYI9pE1UDQhwsmRs1wB2RqazIzKdsyn5dA90po2v/VUXtv/L4MGDycvLY+HChRgYGDB9+nT09OTGbyGEuBUpMAtRS6iqyvn0QnaeSycqrQBjAz1e6eTHq538sbu0wvSpU6f49NNP2b9/P76+vixbtoz27dvrOLkQQvw3U3NLeg0ZRXCXx9i4fAGZe9ex+EwYrZ4aRs+H2+o6nhBCCHFbDh48yMiRI3Fzc2PFihXY2Nhctf1wfDbDVh/Ey86Mda88xJ8RqTpK+uAx1NejayNnmnna8POxZH45foHw2Gweb+pa5WOMGjUKjUbD4sWLMTAw4IMPPriur7YQQoirVanArChKT2A+oA8sU1X1k2u2GwNfA8FAJvC8qqqxiqL4AKeBs5eG7lNVdWT1RBeibiiv0HIiMZc90Rkk55RgaWJAzyAXWvvaMayDLwCpqanMnz+fTZs2YW1tzXvvvUe/fv0wNJSZEEKI+4ezpx+vfTCXgzv/Yut3X3Fo1TQitjej/uwPaN2ska7jCSGEELd0ZXF51apVODg4XLU9IauIEV+H42RQfvnjAAAgAElEQVRpwrpXHsLBwlhHSR9sDhbGvNTOh5PJefx+8gLLd8WQnFPMO70a4Wl36zuoxo4di0ajYdmyZQC8//77MpNZCCH+wy0LzIqi6AOhQHcgETioKMrPqqqeumLYcCBbVdV6iqL0B2YDz1/adl5V1ebVnFuI+15mQSkHYrIIj8umuLwCRwtjnm3hTnNPGwz0L568FBYWsmrVKpYvX45Go+Gll17i1VdfxcrKSsfphRDiziiKQuuHu9OsTQd+/G4t58J+ZuiAPoR0fozPP5yIk5OTriMKIYQQN7R7927GjBmDm5sbK1euvK64nF9SzsurwynVaPl2RCspLuuYoig0cbemoYslYecy2HE2nb/PpPFiOx9ebOeDm43pf+47btw4FEXhq6++ori4mBkzZtz1ejdCCFFXVeVfx9ZAlKqq0QCKonwLPAVcWWB+Cvjw0vMfgIWK3EMixHXKK7ScScknPDaLc2kF6CkQ6GpFGz97/BzMK2+9Ki8t4eDfm1n41kays7Pp2bMn48aNw9PTU8fvQAghqoexiSkDhr5MQo8n+fnbVRzcsYUuXbfS/4XBvDV6JBYWFrqOKIQQQlTaunUr48ePx9/fn6+++uq64rKmQsvodUeISi9g9Uutqeckn2O1haG+Ho80dOKjp4OY/fsZloVFs3xXDD2DXHixvQ8h3rY3bIFxuchsZmbG/PnzKS0t5dNPP8XIyEgH70IIIWq3qhSY3YGEK35PBNrcbIyqqhpFUXIB+0vbfBVFOQLkAe+pqhp27R9QFGUEMALAy0tWkhd1i6qqJGYXczg+m2OJOZSUa7E2NaRrIydaedthZfpvmwtNWRmHdvzK7l++ozAvm/bt2zNmzBiaNWumw3cghBD3jqerE2HrvuTLzXtZHLqQdauX8+P33/HysJcYPHiQ3LEhhBBC5zZt2sS7775L06ZNWbx4MdbW1teNmf7LKf6JTGfWs03oEOBwg6MIXXO1NmVe/xaM79GANfvi+PZAPL+euEBjdyueD/HksSaulbPOr1zg3apFL7oPKOGP9Us4l5RBn1HvYWz6b5uNgW2khiGEEFUpMN9oJrJaxTEXAC9VVTMVRQkGNimKEqSqat5VA1V1KbAUICQk5NpjC3Ffysgv5VhSDscTckkvKMVAT6GxuzUtvWzxczRH74qr5JryMo7u/INdm9eTn5OJd8Om9Bn9HpMH99LhOxBCiJqhp6fwxlPt6PNwC97832aO/bmBhQu/ZMXKlbz04lAGDx58wy/zQgghxL2kqipLly5l3rx5tGvXji+//BIzs+v7967eE8vXe+N4paMvA1pLsbG287Qz451ejXizWwD/dySJr/fEMfWnCD74OYJ2/g70buZKcVkFpkb6lfs89OizmJia88uqeXz9ydsMeOsjLGzsdPguhBCidqlKgTkRuPK+fA8g+SZjEhVFMQCsgSxVVVWgFEBV1UOKopwH6gPhdxtciNoou6iME4m5HE/MITm3BAXwtjejQ4A7TdytMTHUv2p8SVEhh3f8yv4//4+CnCw8A4J4+tWJ+DSStuVCiAePu40pGyb35aee7Zm25k/yj/5OaGgoq1atZvDgQQwZMgRbW1tdxxRCCPEAKC8vZ9q0aWzcuJHevXvz8ccf37A1Qti5dKZtjqBbI2cmPyYL1tZmV85KvkxBYWg7H1LySjiemMPxxFx2RWVgoKfQwsuWjgEOlbOam3d6FHNrWzaGfszKj99kwPgZOLhKC0MhhICqFZgPAgGKovgCSUB/YOA1Y34GhgJ7gT7ANlVVVUVRHLlYaK5QFMUPCACiqy29EDqmqiopeSWcupDH6eQ8knNLAPCwNaVXE1eauFtjfUULjMvysjM48OcmDm3/lbKSInyDWvDUyxPwDWp5w/5fQgjxoFAUhadbuNOl4SDmbm3Nmi170D+/jcVLlrBq1SqefvpphgwZgq+vr66jCiGEqKPy8vIYN24cu3fvZuTIkYwdO/aG5+gxGYWMWnuY+s6WzO/fHH09OY+/X7lYmeAS6EL3Rs4k5RRzMDabI/HZhMdmEehmRacARzztzAho1pohk+ewfu5UVkx/g+defwekRYYQQty6wHypp/Jo4A9AH1ihqmqEoijTgXBVVX8GlgNrFEWJArK4WIQG6ARMVxRFA1QAI1VVzboXb0SImlKhVYnLKuR0ch6nLuSRXVSOwsVbrXoGuRDkZoX9TVaMTkuMZf8fP3J8z9+oWi2BrTvR9rE+uPoE1OybEEKIWs7a1JAPnwyiX4gnH/wcRPjx01il7OOHHzby3Xff0blzZ1588UVatWolF+aEEEJUm8jISMaMGUNycjIfffQRffr0ueG4Fbti+N+O82i0Kk80deOno9fe5CvuR4qi4GFrhoetGd0aObE3OpN90ZlEJOfRwNmSZ1q64+bXgOHvL+C7BR+y/oupeBrkMXToUDkfEUI80JSLXSxqj5CQEDU8XDpoiNqlqEzDzF9Pc+pCHmdS8ikqq8BAT8Hf0YJANysaulhiaXL9TGWACk05Zw7tJnzbL8SfPYGBkTHNO/bgoUefw9bJ9T//ri4WjLjRrWNCCHEv3erfOlVV2XQ0iU+3nOVCajoBBcfIO7mDvNwcAgMDGTx4MD179sTExKSGEosHhaIoh1RVDdF1Djk/FqJm/Pnnn0yZMgUzMzPmzZtHcHDwDcdVaFV6zQ/jXFo+wzr44udgUcNJRU0qLa9gf0wWf59JxVBfj2dbuBPoZk1ZSTE/fTWHM4d28+STT/LBBx/csEe3EELUJTc7P5YCsxA3oKoqZ1Ly2RmZzj+R6YTHZlNWocXEUI+GLlYEuloR4GyBsYH+TY+Rl5XO4R2/ceSfLRTkZmHj6EJwlydo3rEHZpayWJUQQlxW1YtpJeUVrNwdy6IdURQWFROiRJN7bCvxcbFYW1vz5JNP0q9fP+rVq3ePE4sHhRSYhXgwlJWV8dlnn7FmzRqaNWvG/PnzcXZ2vun4Wb+dZsnOaJ5q7kYbX/saTCp0KT2/lA3hCSTlFBPibcvjTV0x0lPIPfIbCxcuxM/Pj7lz5xIQIHenCiHqLikwC3ELWYVlhJ1LZ2dkBmHn0knLLwWggbMlDzdwpEKr4mNv/p+91So05Zw7doBju7Zy7th+VFUloGkrgh/pTb0mISh6ejX1doQQ4r5xu3drZBeWEbo9iq/3xoGq8rBdLgZx+9n9zzbKy8sJDg6mb9++PProozKrWdwVKTALUffFxMQwfvx4Tp8+zaBBg3j77bdvuJjfZesPxDPlxxO08bXjqebuNZhU1AYarZa/T6exMzIdO3MjBrT2YsKjDdi7dy8TJ06koKCAd999l+eee05aZggh6iQpMAtxjZLyCg7HZbM3OpOdkekcT8pFVcHGzJAO9RzoVN+RTgGOuFhfLE7crHWEqqpciD3H8V1bObl/B8UFeVhY29GkfVeCuzyBraNLTb4tIYS479xpO6DE7CIW7TjPD+GJVKgqvepb4p0fwV+/biIuLg5LS0t69OhB7969adWqFXpykU/cJikwC1F3abVaNmzYwJw5czAyMmLmzJl06dLlP/fZEJ7ApI3H6RTgSLdGzrKo3wMsOqOADQcTKCnXEvpCS7oHOpOens7EiRPZt28fXbp0Ydq0aTg6Ouo6qhBCVCspMIsHXlGZhkNx2eyPzmJfdCbHEnMor1DRU6CFly2dAhzpVN+Bph42NzxZvLbAnJWaxKmDYZzY8zcZyfHoGxjSoGU7mnXojl9QS/T0b94+QwghxL/utt/8hdxilu6MZt3+eMortPQMcqGNRRZn9m/jzz//pKioCGdnZx5//HF69+5NgwYNZFaRqBIpMAtRNyUkJDB16lT2799P27ZtmTVr1n+2xAD4PjyBiRuP06GeA18NCeHHw0k1lFbUVnkl5XyzL46knGKmPNaQVzr6oaoqa9asYe7cuZiamjJ16lQee+wxOe8QQtQZUmAWD5z8knLCLxWU98dkciIxF41WRV9PobG7NQ/52tHGz44QHzusbrJA35XW7Y8n40ICpw+GcTp8F6nx5wHwqBdIsw7dCWzVCRNzWeBDCCFuV3UtaJqeX8qyXdGs3x9PXomGIDcrBga7YJF1li2//cquXbvQaDT4+/vTvXt3unXrRmBgoHzpEzclBWYh6paysjK++eYbQkNDURSFSZMm0adPn1t+Dmw8lMiEH45VFpdNDPVlYWwBQJlGy4HYTH47kUL/Vp5Mf6oxRgZ6REdHM2XKFI4fP06HDh2YOnUqXl41v4C7EEJUNykwizqtQqsSlVbAkfhsjibkcCQ+h3Np+WhVMNRXaOphQxtfO9r42RPsbYuFsUGVjqvVaomIiOCff/7h+59+Iy0xBrhYVG7UqiONQjpgbe90L9+aEELUedVVYL6sqEzD/x1JYtXuWM6lFWBnbkTfYA+6+1sQeSiMP/74g/DwcLRaLW5ubnTt2pVu3brRsmVLDAyq9vkgHgxSYBai7tizZw8zZswgOjqazp078/777+Pq6nrL/X48nMj474/R3t+BZUMvFpfh5u3zxIOnfytP5v4VyZfbomjrZ8/iQcFYmxmi0WhYv3498+fPp7y8nBEjRjBs2DBMTU11HVkIIe6YFJhFnZKWX8LR+JzKYvLxxBwKyyoAsDY1pLmnDc09bWjlY0dLbxvMjKpeMMjLy2PPnj38888/hIWFkZmZiaIoeAQEEtiqEw2D22NlJ720hBCiulR3gfkyVVXZcz6T1Xti2XYmDY1WpYm7NX2CPejkbcqR/bv566+/2L17N2VlZdjY2NCuXTs6duxIu3btcHKSC4gPOikwC3H/i4yMZN68eWzfvh0vLy+mTJlC586db7mfpkLLvL/OEbrjYtFw+dBWmBr92wJPCszissvnMT8eTmTyxhN42Jqy/MVW+DqYA5CWlsasWbPYsmULTk5OjBkzhmeeeQZ9aakohLgPSYFZ3JdUVSU5t4SIpFwikvOISM7jVHIuybklABjoKTRytaKFl01lUdnXwfy2bncuKyvj2LFj7N+/n3379nH06FEqKiqwtramQ4cOdOrUiY4dO/J7ZP69eptCCPFAu1cF5itlFJTy09FkNh5K5NSFPAz1FTrUc6BnYxfaeVty6sgBtm/fzu7du8nIyACgQYMGdOjQgQ4dOtC8eXNMTEzueU5Ru0iBWYj7V3x8PKGhoWzevBkLCwtefvllhg4dirGx8S33Tcop5o31RwiPy6ZvsAfTn2p8VXEZpMAs/nXleczB2CxeXXMIraqyeFAwD/nZV247dOgQc+bM4dixY9SrV4/XX3+dHj16SKFZCHFfkQKzqPUqtCoxGYVEJOdyKjmPk5d+ZheVA6CngJ+jBUFuVjRxtyY9vxQ3G1MM9fVu6+9oystIiYsi9vQxYs8cI+HcKTRlpaAouHj5498kmHpNW+Ph30gW6hNCiDroQm4xJeUV/H4yhcTsYvQUaONrz6NBzjxc34GSjER27drFrl27OHLkCOXl5RgaGtKkSRNatWpFSEgILVq0wNzcXNdvRdxjUmAW4v5z+vRpli1bxpYtWzA0NGTw4MEMHz4cGxubKu2/5WQKE384hlaFGc805qnm7jccJwVmcdm1F8rjM4sYtvogcZmFzHimCf1CPCu3qarKH3/8wcKFCzl//jx+fn6MGDGCXr16YWh463WBhBBC16TALGoNVVVJySvhbEr+xUfqxZ9RaQWUarQAGOnr0cDFkiA3K4LcrAh0s6aRq+VVrS6qelKXl5VOYtRpEs+fJjHqNClxUVRoLhatnTx88WnUDJ9GzfBq0ARTc8vqf8NCCCFqnYFtvFBVlYjkPP6ISGHLyRTOpRUA4GVnRscABzoGONLUxYSzJ44QHh5OeHg4ERERVFRUoK+vT2BgIM2aNaNp06Y0adIEb29vWTCwjpECsxD3B41Gw/bt21m/fj179+7F3Nyc/v37M2TIkCq3O8osKOXVNYcIj8vG3caU/q08sbe49WxnIW6kuKyC9QfjiUoroEM9B3oEOTOkrU/l9oqKCrZu3cr//vc/IiMjcXJyon///vTr1w97e/ubH1gIIXRMCszinrpZsbe4rIKUvBJSLz0uPy8p11aOsTIxwNnKpPLhZmOCk6UJ+nq39yVdVVXyszO4EBdFStx5UuLOkRIXRV7WxVudDQyNcPUNwMOvEe71GuFVvzHmVlWbySCEEKJuuVFbjpiMQsLOpbMzMoO95zMoLKtAT4EGLla08rEl2NuWxk4mpMScuargXFxcDIC1tTWNGzemSZMmNGrUiIYNG+Lh4YGe3u3daSNqDykwC1G7xcXFsXnzZn744QdSU1NxcXGhf//+DBgwACsrqyodo0yj5eu9scz/+xyFpRo61HOgW6AzBvJvt7hLFVqVX09cYF90Ju42pnzzcpvKvsyXabVawsLCWLt2LWFhYRgaGtK1a1eefvpp2rdvL4sPCyFqHSkwi3tGVVW+CoshPb+UjIJS0vNLKwvKeSWaynEmhnqVRWSXywVlS2PMjG//Q7MoP5eM5HgyLiSQkRxPelIcKfHnKcrPvThAUbB38cDF2x8P/0Z4+DfC2csPfQO57UgIIcSt+z6XV2g5Ep/D7qgMDsVlczg+m6JLi8m6WpvQ2N2aJu7WBLqYY1GWRULUaU6cOMGJEyeIjIxEq714IdXc3Jz69evToEEDGjZsiL+/P76+vtjZ2cls5/uAFJiFqH1SUlL4+++/2bx5M8eOHUNRFNq2bcuAAQPo3LlzlQtyqqqy7UwaM349TXRGIZ0bONLc0wYnS+m3L6pXRHIuPx5OQlFg2pNB9An2uOE5QExMDOvXr+eXX34hOzsbR0dHevbsSY8ePWjRooX0ahZC1ApSYBZ3TVOhJT6riPPphZxPLyA6vaDyec6lPslwceE9J0vjq2Ylu1ibYGVicFtfpis05eRmppGdduGqYnLGhYR/C8mAgZExDq6euHj54+JdDxdvf5w9/TAyMa3W9y+EEOLBVaG92N4pLrOQ+KwiknOKySgoq9xuaWyAo6UxjpbG2JuAYUEaFdmJ5F6IIzUxmrSEGEqLiyrHW1lZ4ePjg6+vb+XDx8cHb2/vKi1AJWqGFJiF0L2KigoiIiLYvXs327Zt4+TJkwAEBATw5JNP8vjjj+Pq6lrl46mqyo7IdEK3RREel42fozlTnwikSwMn6ass7pnc4nJ2nE1jf0wWvZu5Mf3JIGzNjW44tqysjJ07d7Jp0ybCwsIoKyvDwcGBhx9+mI4dO9K2bdsqz9AXQojqJgVmUSWqqpKeX0p8VhGxmUVXFZLjMgspr/j3/xdHS2P8HMzxd7Igt6j84hdrC2OszQzRq0IhuUJTTl52JrkZKeSkp5Jz+WdmCrkZqeRlZ8IV/3+aWljh4OaJg6sXDm5eOLh64uDqibW9E4rcwiaEEKKGlZRXcCG3hKScYlJyS0jPLyEtv7RyPQEAYwO9S5+PRrRxVjAsSqcsJ4W8tGQuJMYTExNDWlpa5Xg9PT2cnZ1xc3PD1dX1qp+Xn8vigjVHCsxC1Lzy8nJOnz7N4cOHOXz4MAcOHCA39+LkkqZNm9KtWze6du2Kn59flY53uWisVVVOJeexIzKN5JwSrE0N6VTfkdY+drfdmk+IO/F8K08W/3OeL7ZGYmaoz8jO/gxr74up0c1nJhcWFvLPP/+wdetWdu/eTX5+Pvr6+jRq1IjmzZtXPtzc3OTOKCFEjZACs6iUX1JOQlYx8VlFJGYXkZBVRHxWEQnZxSRkFV31xdhAT8HHwRx/R3P8HC3wd7SofG5t+m+7icsnbqpWS0lxIUX5eRTl55CfnUlBbhb5OVkU5GRRkHvxZ35OJsUFeVflUhQ9rOwcsHZwxsbBBRsHZ2wcXbBxdMHB1VP6JQshhKj1VFUlv0RDWn5pZcE5/dIjv1Rz1VhTQ308bE1xNVew1GRjVJyJJieV0tx0cjPTyExLJTU1BY3m6v2sra1xcXHBwcHhPx/W1tbyZfMuSYFZiHsrJyeHyMhIzpw5w9mzZ4mMjOTcuXOUlpYC4O7uTps2bWjXrh1t27bFzs7uto6vqipfbI3kVHIexxJzySgoxd7ciIfrO9Lcy0b6LIsadbk9V2RqPp9uOcNfp9NwtjLmzW716RvsgYH+f///qNFoOHbsGGFhYRw+fJiTJ09WrgPh6OhIixYtCAoKwt/fHz8/Pzw9PaWHsxCi2t1VgVlRlJ7AfEAfWKaq6ifXbDcGvgaCgUzgeVVVYy9tmwIMByqAsaqq/vFff0tOoO9cSXlFZf/ji4vplZJWucBeKan5JaTllVJwzRdcS2MDPO3M8LQzxcvODDdLA+xNwN5Ii6VBBSVFhRQWFlJQUFD5yMnJIScnh9zcXLKzs4m7kE5xQR7FBfmoqva6bHr6+lhY22JhbYeFjR2WNvaXfjpcLCI7OGNl54i+fAAKIYSoox5v6kpidhGJ2cUkZRdf/Jlz6fec4qvaTV1mZayPvX4Jlmo+pmV56JfkUFGYRXlBNsX5ORTmZpOTnUV5Wdl1+xoYGGBtbX3Dh42NTeVzMzMzLCwsMDc3v+phYmLywBeo76TAfDfnzTcj58fifqXRaEhPTycpKYnExEQSExOJjksgNj6BpMSEysW4AfRMLDG0d8fQzgMTZz9MXethZGmLsYEeFsYGWJoYYGFsgIWJAX2DPbExM8Ta1BATw4uzP8s0WjIKLq4Jk5ZXysHYLLZEpBCXWYQCeNmb0cbXnibu1jJjWejEtes/HIjJ4pPfT3M4PgdvezN6N3WjR5AzTdyrdoG4vLycyMhIjh49ytGjRzly5AhJSUmV2w0NDfH29sbf3x8fHx9cXV1xdnbG1dUVFxcX/p+9+w6Tqjz7OP69Z2Yry7J0pIOAChZUBBQL9hpNNPaYGLtRY2KKib4mRpNoTDXRmBg1JhpbjBos0cQOKggqCtJEOggsLLC9zv3+cQ44DLuw7OwyW36f6zrszDnPec49Z5bde559Sn5+/nav88i0ZdTG41TWxKmqqaOipo66uJMRjZAVi5AZi5AVi5IRNc4fP6jpN2Y7EkcgVNXEKa+uJe4QjRgRC77GIhGyMyItGkNNXfDzZU1x8DOmuKKG4soaSiprKa6oobouTufsDHIyouRkRsnJiNK3IJvde+YxrFfYUa9XHnlNWH9KpLVpcgOzmUWBBcCxwApgOnCuu89JKPMNYF93v8LMzgG+5O5nm9lI4FFgLNAXeBkY4e51DV2vIybQ7k5l+MOyvLqO8upaSsqrKC6voLS8itKKKorLK9lQWsnG0go2lVVSXF5BcVkVpRWVlIRlampqMK+DeB3U1UBdNTGvpVM0Tm40TnYkTqbVEfNaMqglEq/B4rXUVFVSXl5OaWkpJSUl1NRs+wE3WUZGBl27dqWgoICCggJK4lnk5HUmt3MXcvLyycnrTKfOBVsak3Pz8jWNhYiIdGg7WliwtKqWlWGjc2FJ0EBSWJrwNfxDceJIIyCYTqq2klh1CZ2pIKeunMy6MjJqyojUVOBVZdRWlVFbUUpVWSmV5SVUV1bsMN5oNLqlsTk3N3fL45ycHDIzM8nOziYrK2ubLXF/dnb2VmUzMzOJxWJkZGRs9TUWi211LNJKcoadbWBOJW/eXr0dIT+uiztl1bWUVdVSWllLaVUtZVV1lFbVUFpVR2ll8AE+OyO6ZcvJiNIpK0p+dtDQmJ+dQV52TI2Hzai2tpaqqiqqqqqorKykqqqKsrIySkpKKCkp2fL5YXMHlMJ161hbuI5169ZTVLSespLiber07Hziud3w3O54fh+8S1/y+wwimpNPTmYUB+JxJ+7B90VVbR2lVUGjUn2yYkFjV+Li4gAZUeOQ3XvQNTeTvXbrTOdsLfYt6VVfHuDu/HfOGv761mKmL9lAXdzZrUs2x47szbgh3RnYLZeB3XLpktu479+SkhIWLVrEokWL+PTTT7d8XbFixZYFiDfLycmha4+eZHfKJ5aTB5mdqI3lUhnJosyz2FAdpcZiEMvCY1kQzcRjmRA+JhL8cScWMfoW5NAnP5te+cFaTMmPu+ZmEo0aUTMiEYiaUVFTx8byGjZVBNuG8mrWFFexelMFq4urWLOpkk8LS6moqaOiuo7ttVxFI8ZuXbK3unafcD2ozY+75GTQKStGViyyVcN6YnvM+rLqz0d4h6O+P1i2gaKy6i3XNyAvO0Z+dgadw6+ZsQhDenSivLqOypo6yqprWV5UztL15dQm/PDarUtio3MnBnXvRJ8w7p1ds0qaj7tTXh38rikJc5DNuUjwOMhBMqMRsjKiW/7A0ikrtiX/yM8JHmfF2v9inA3lx43588lYYKG7Lworegw4DZiTUOY04Obw8ZPAXRb8zzgNeMzdq4DFZrYwrO+dpr6QlvLavLW8Om8tcQ+SmcrSTcz891/weBx3x+N1uDvxeBz3eML+xOdxPO7g8XrKbn0O7kFP34SvhI3DVk8P4B2JAA0tCVRpBtnZxMMtOzubWHY2GdnZZOVkk921YEvPpby8PPLy8ujcufNWzxOPb/5wmfjDTwtiiIiIpCYvK8YefTqzR5/ODZZxd0qqaiksqWJdSRWbKmrYWFHDpvLgg9nmxxsrqtlYXsPG8hpKKmsoqw56HW0Rr4XqcqymAmqrsJrK4Gtt1Zav8XgVxXVVlNVVE6mpgnVV2OoSvK4G6mrw2hq8Ltxqa/B4g/0Hdkr3wXtxxGU/wQyMINf49rEj6NbAYkitTJPzZm9l89aVlZXxhz/8Yat9ySEmP5/zWTHF5dXEHeIeHI9vtQUpb108Tk1dnJo6D77WxqmNJ//hJDmixtyeoEzUjOyMKBnRCJlRIxaNEDHC7ynCx0bEbMs+Y/M/9V/Kk3c24u2q/y3dcT3bvvT6Akp6L+oNIB58HonX4XV1Wx7H43XB55J4HfG6rR/X1VZTV1tDXU01dTXVO/f/OiObeGYenpUHWZ3x7n3wvnlEc7qQ170XXXv0oUfvPlQCouwAACAASURBVPTo0omuuZl07ZRJflaM7MzoDtdvibtTUV1HSfih/4BBBcHPv/IaiitqqKypo3te1pY1YXp2zmJIz07kZ2foc4q0ambG8aP6cPyoPmwoq+bVeWt56ePVPDFjOX9/Z+mWcp2zYwzomkt+ToychD+yZcYiuAf/R5zwq0PcBxPvNxjrexS7T3B6V1SzsWg9xUXrKNsYbCWlGyip2IStK8Oqi7DqMqy6LMgR2H4bA0AkmoHFYlgkRnE0ykaLMsci1HmEuEUgEsUtGjRER6Kw+XHwwoHgB7OHXzc/j0SM3MwY2Rkxog4F0Qg9oxFi0SixLY3DhkP4WqGmro7sjChrq+tYEnbaq61L/MloWz3MiBjRiFEb96AB2LctFosYedkZxAwGZ8bIzfy8d3KkfNufWck/afoD/Rwqa4Pe3+XVdVQsqWN2dR3v1dYlxQexaITczBiZsaBXdixqZEQjRCNBNmThL6qtvyb8Dgvvq33+sF5NyTaalKA04UJNTYS2d1487tTFgzykzqGuLk51Qg5SGfbMb64kLBaxLb37M8P3MGKGhb3uI/b5V0t4PKpvPvk5jf9D6NixYznyyCObKerm0ZgG5n7A8oTnK4BxDZVx91oz2wR0D/dPTTq3X/IFzOwy4LLwaamZzW9U9LKr9QDW7bCUtAd6rzsOvdcdh97rNDt/112qbb/X8+bx1otPb7Xrp2kKBdjZ8bap5M1bvWftOD9u29+f6af7lzrdw9To/jVRQh7QpHs4uxljaeP0PZg63cPUNPv9+3dzVtby6s2PG9PAXN/fPZIb9xsq05hzcfd7gXsbEYukkZnNaA0L3UjL03vdcei97jj0Xncceq/TKpW8eesd7TQ/1vdnanT/Uqd7mBrdv9TpHqZG9y91uoep0f2rX2MmuFsBDEh43h9Y1VAZM4sBXYCiRp4rIiIiItIepJI3i4iIiIi0SY1pYJ4ODDezIWaWCZwDTEoqMwn4Wvj4y8Cr4Txyk4BzzCzLzIYAw4F3myd0EREREZFWJZW8WURERESkTdrhFBnh3HBXAy8BUeABd//YzG4BZrj7JOB+4KFwEb8igmSasNwTBAub1AJXuXvzrAAj6dDuhmlKg/Redxx6rzsOvdcdh97rNEklb+5A9P2ZGt2/1Okepkb3L3W6h6nR/Uud7mFqdP/qYeowISIiIiIiIiIiIiJN0ZgpMkREREREREREREREtqEGZhERERERERERERFpEjUwS5OY2XfNzM2sR7pjkZZhZr80s3lm9pGZPW1mBemOSZqXmZ1gZvPNbKGZ/SDd8UjzM7MBZvaamc01s4/N7Np0xyQty8yiZvaBmT2X7lhEGmJm14S/fz42szvSHU9bpXy8aZTjNp1yx6ZTTtY8lOekxswKzOzJ8GfgXDM7ON0xtTVm9u3w//BsM3vUzLLTHVNroQZm2WlmNgA4FliW7likRf0P2Nvd9wUWAD9MczzSjMwsCtwNnAiMBM41s5HpjUpaQC3wHXffCxgPXKX3ud27Fpib7iBEGmJmRwKnAfu6+yjgV2kOqU1SPp4S5bhNoNwxZcrJmofynNTcCbzo7nsC+6F7uVPMrB/wTWCMu+9NsKBzR1usuUFqYJam+C3wfUArRLZj7v5fd68Nn04F+qczHml2Y4GF7r7I3auBxwg+8Es74u6fufv74eMSgiSyX3qjkpZiZv2Bk4H70h2LyHZcCdzu7lUA7r42zfG0VcrHm0g5bpMpd0yBcrLUKc9JjZnlA4cD9wO4e7W7b0xvVG1SDMgxsxiQC6xKczythhqYZaeY2anASnf/MN2xyC51EfCfdAchzaofsDzh+QqU5LZrZjYY2B+Ylt5IpAX9jqDBKZ7uQES2YwRwmJlNM7M3zOygdAfU1igfb1bKcRtPuWMzUU7WZMpzUjMUKAT+Gk4zcp+ZdUp3UG2Ju68kGHm1DPgM2OTu/01vVK1HLN0BSOtjZi8Dfeo5dCNwA3Dcro1IWsr23mt3/3dY5kaCIV3/2JWxSYuzevapF1Q7ZWZ5wL+Ab7l7cbrjkeZnZqcAa939PTObmO54pGPbQS4ZA7oSDBE/CHjCzIa6u34HJVA+nhrluC1CuWMzUE7WNMpzmkUMOAC4xt2nmdmdwA+Am9IbVtthZl0JRm4MATYC/zSzr7j7w+mNrHVQA7Nsw92PqW+/me1D8B/pQzODYDjZ+2Y21t1X78IQpZk09F5vZmZfA04BjtYHv3ZnBTAg4Xl/NLynXTKzDIIPMv9w96fSHY+0mAnAqWZ2EpAN5JvZw+7+lTTHJR3Q9vILM7sSeCrMK941szjQg6BXlYSUj6dGOW6LUO6YIuVkKVGek7oVwAp339xz/kmCBmZpvGOAxe5eCGBmTwGHAGpgRlNkyE5w91nu3svdB7v7YIIfUAcomW2fzOwE4HrgVHcvT3c80uymA8PNbIiZZRIsTjApzTFJM7Og9eF+YK67/ybd8UjLcfcfunv/8PfzOcCr+tAlrdQzwFEAZjYCyATWpTWiNkT5eOqU4zaZcscUKCdLjfKc1IW/J5ab2R7hrqOBOWkMqS1aBow3s9zw//TRaKHELdSDWUQacheQBfwv7CEz1d2vSG9I0lzcvdbMrgZeIlj99gF3/zjNYUnzmwBcAMwys5nhvhvc/YU0xiQiHdsDwANmNhuoBr6mHqSyiynHbQLljilTTiatwTXAP8I/Ei0Cvp7meNqUcGqRJ4H3CaZY+gC4N71RtR6mfE5EREREREREREREmkJTZIiIiIiIiIiIiIhIk6iBWURERERERERERESaRA3MIiIiIiIiIiIiItIkamAWERERERERERERkSZRA7OIiIiIiIiIiIiINIkamEVERERERERERESkSdTALCIiIiIiIiIiIiJNogZmEREREREREREREWkSNTCLiIiIiIiIiIiISJOogVlEREREREREREREmkQNzCIiIiIiIiIiIiLSJGpgFhEREREREREREZEmUQOziHQYZva6md3VAvUONjM3szHh84nh8x4p1rvT9ZjZI2a2wsyONLP7zOyYVGJo4Bo9wrgmNnfdIiIiIrJrKDduHsqNRUTUwCwibZyZPRgmdG5mNWa21sxeM7OrzCwjqfjpwA8bWe/NZja7kWEsB3YDZu5E6I3xdljv+sYUNrNOwAjgXOA2YE9gSjPHlDIz6x2+V19p4PgdZrbczCJmtlv4wWCemdWZ2YP1lL/UzCabWZGZbQzf/0Nb/IWIiIiItDLKjT/XUXPjpHPPDb8XnmuR4EVEQmpgFpH24GWCZHMwcBzwLPATYHKYWALg7kXuXtKcFzazTHevc/fV7l7bnHW7e3VYrzeyfJm7j3H3ye4+3t0PdffK5oypObj7GuA54OLkY2YWAy4A/urucSALWAfcDkxroMqJwOPA0cA4YD7wkpkNb/bgRURERFo/5cZ06Nx487lDgV8Ck5s7ZhGRZGpgFpH2oCpMNle6+0x3/w1Bo+MBwPc3F0oeBmhmp5vZR2ZWEfZ+fSPsQXAh8GNgVEIPkAvDczzsAfKUmZUBP08eBphgvJnNNLNKM3vPzA5MuPaFZlaaWDh52F99wwDNbLyZvWpmZWa2ycxeMbO+4bETwp68G8LX85KZ7ZV0jX3M7OWE1/ygmXXZ3s01s4PC+CvN7AOCRtzkMiPN7HkzKwl7yjxqZn22U+19wBFh4pvoZKA38ACAuy9x92+6+4NAUX0Vufv57n6Xu3/g7vOBK4ES4ITtvS4RERGRdkq5MR03Nw6vnwE8CtwILNre6xERaQ5qYBaRdsndZwMvAmfUdzxM8B4D/gbsBRwOPBQefhz4NUFP2N3C7fGE038MvADsA9y9nTB+BVwPjCFI7J43s9ymvSIws/2A14CFwARgPPAEEAuLdAJ+B4wl+BCxCXjWzDLD83MJ7klpWOZLwCGECWsD1+wEPB/GPwb4Qfi6EsvsBrwJzA7rPQbIAyaZWUO/Z14EVgFfT9p/MfCKuy9pKKZGyASygQ0p1CEiIiLSbig37nC58c+AJe7+t508T0SkSWI7LiIi0mbNIUjo6tMXyACedPel4b4t88qFPShq3X11Pec+7u73JZQd3MA1bnX3l8IyXwdWAOcR9FBoiu8DH7r7ZQn75m5+4O7/SiwcXrOYILGdApxPkNxesHk4pJldBrxmZsPcfWE91zyfoMH26+5eCsw2s5/x+QcOCHoMf+ju1ydc+6sEvSrGAO8mV+rum+eMu9DMfuzu8fCDzYlAvfPP7YSfEnxQmJRiPSIiIiLtiXLjDpAbm9lxwNnA6J05T0QkFerBLCLtmQENzdH2IcH8dLPN7F9mdqWZ9WxkvTMaWe6dzQ/CBHQWMLKR59Znf+CVhg6a2e4WLPrxqZkVA2sIfs4PDIvsBXyUNNfe20B8O3FtPidxyOI7SWUOBA43s9LNG8HiLgC7b+f1PAD0I5gbEOBrBD1LntnOOdtlZtcClwOnu3txU+sRERERaYeUG7fz3DicPuRB4GvurtF8IrLLqAeziLRnI2lgzrGwl8BxBEPpjiMYfnabmR3h7h/uoN6yZogtTpDkJ0pe2TtZcvlkzwIrCRpYVwK1BD1VMhPOb+hDRUP7d3RNCBL154Hv1nNsTUMnufsiM3sNuIhgWOBFwMPuXtWIa24baNC4/FPgRHffpmeIiIiISAen3Lj958Z7E0xh8rLZllAjAGZWC4wK1ywREWlW6sEsIu2Sme1NsMjbkw2V8cA77v4T4CCCec/ODg9XA9EUwxifEE8ngoRv87C9QiDXzPITyu9oGNv7wFH1HTCz7gQ9Kn7u7i+7+1ygM1v/IXEOsJ+ZdU7YdwjB74K51G8OsI8lrDie+LoS4hoFLHX3hUnbjlYmvw84zcy+BIygiUMkzew6grnmTnb3KU2pQ0RERKS9Um7cYXLj6QRzYY9O2CYBk8PHi3eyPhGRRlEDs4i0B1lm1sfM+prZfmFj4+vAeyQturFZuOL0/4WrQA8ETgUGECSNAEuAQWZ2gJn1MLOsJsT1f2Z2rJmNIhjyVg08Eh6bRtDb4zYzG2ZmZwDf2EF9vwT2N7N7w9e5h5ldEsa/AVgHXBrWdwTwJ4KeGpv9I7zm38MVsw8H/gw81cAcc4Tx1gIPmNkoMzuWYDXqRHcDXYDHzWycmQ01s2PCODsnV5jkKYL5ku8H3g0XoNmKmY02s9FAPtAtfD4y4fj3gNsJenksCL8X+tgOVgAXERERaaeUG3fQ3Njdy9x9duIGbARKwufVO7i+iEiTqIFZRNqDY4DPgGUE87CdCvwEONzdGxqyt4lgtenngE8IVsa+1d0fDo//i2A17FcIelSc24S4fhDW+z4wHDhlczzuXkSwSMixBPPPXQbctL3K3H1m+Fr3BGYC84BzgBp3jxP0MNmXYEGWu8P6qhLOLweOJ0hG3wX+TTBn3EXbuWYpcEoY//t8vvp3YplVBPcyTjCc7+Pw+lWJ12+g/iqC5L4rDffQ+CDcDgO+ED5+IeH4VQRDKB8n+D7YvN25vWuLiIiItFPKjTt2biwissuZe0NTC4mISGtlZv0J5mSbmO5YRERERETSSbmxiEh6qQeziEgbY2bDCIbdHWRm3dIdj4iIiIhIuig3FhFJPzUwi4i0PTcQDMl7nWB+ORERERGRjkq5sYhImmmKDBERERERERERERFpEvVgFhEREREREREREZEmUQOziLQKZnazmT2Q7jh2FTMbbmarzOw/ZtbHzOalO6aOysweNLPndqL8QDNba2Y9WzIuERER6biUGys3ThflxiLSFGpgFpG0M7NewHXAT8PnvoPtwbQG3DxOAn4P/A94B+hIHyBW1/Oerk53XI3l7suAp4H/S3csIiIi0v4oN1ZurNxYRNoazcEsImlnZjcAR7n7MeHzPgmHTwH+AuyWsK/C3TfVU0+Gu9e0aLCSsjBhvhP4a8LuOncvbKB8prtXt2A8DwI93P2UnThnPPBfoK+7l7ZUbCIiItLxKDfuWJQbi0h7oB7MItIanAdM2vzE3Vdv3oCNyfvcfZOZ7Rn+df9MM3vDzCqBr5lZbzN73MxWmlm5mc02s/MTL2ZmU83st2b2SzMrCnsN/NzMLKHM2eG5FWa23sxeM7Pu4bE9zexZM1tjZqVmNsPMjku6RraZ3WFmy82syswWmtkVCcePNrPp4bHPwrIZCccjZnajmS0OY/jIzM5KOG5mdquZLQvrWGVm923vJpvZ3mb2vJkVm1mJmb1lZnuGx6JmdouZrQjrm2lmJyWcu/l+f9nMpoQxzTCzvcxstJlNM7MyM3vdzAY04j0vSXpPC8Pr5IXXucTMnjOzcuAGM8sxs7+Z2dLw2vPN7Nqk1/ekmT2WtO9XZjY14XmGmd1lZpvC9/V2wJLOiZrZj8xsSXitD83sy4ll3H0qUEnwIU9ERESkOSk3Vm6s3FhE2hQ1MItIWplZN2AkMKOJVdwO/BbYC3gByAGmAicDewP3AH8zs0OTzrsI2ASMA74DXA98MYxpEPAP4E9hvROBxOQsjyDpPxrYH3geeNbMhiaUeRQ4G7gmrONyoCSsf3B4zlRgP+BK4OvAzQnn/5Lgw8XlBPfn1+HrOCY8fh5wFXApMBw4DXivoZsUvqbJQAVwFHAgcC8QC4t8H/hmeC/2BV4C/m1meyVV9RPgVuAAgiTyUeB3wPeA8UBX4DcNxbETbgWeAEYR9ObIABYCpxPcj1uAn5vZ2TtZ703AucDXgEOBHsCXksr8BjgTuCS81u+Ah81sYlK5d4EjdvL6IiIiIg1SbqzcuAHKjUWkdXN3bdq0aUvbBowGHBjSwPEvBz+qttm/Z3jeVY24xjPAXQnPpwKvJZWZvLkMcAgQB/rsxOuYCXw3fLxPGNvEBsr+GviYcJqicN8VQDlBslgAVAMHJZ33J+Cp8PENwCwg2sj4fk2QhMYaOL4e+H7SvqnAfUn3+2vJ7w1wUtLrWLeDWFYDVUBpwnZdeCwvrPO2Rrymu4BnEp4/CTyWVOZXwNTwsRH0+rk24XgMWA48Fz7vDtQA+yfVcx/wRNK+e4H/7Mr/L9q0adOmTZu29r0pN96yT7mxKzfWpk1b29k2/3VORCRdcsKvlU08f6veHWYWA24kSPD6AZlAFvCfpPM+Snq+CugVPp5OkFTPN7P/Eiw28i93Xx9eI5+gR8WJBPPfxYBsYGB4/v4EidjkBmLeC3jb3RMnwZ9CcC+GAL0JkunXEkYmEu7bvKL2YwS9NBab2UvAi8Akb3ievf2BN9y9NvmABQvJdAPeSjo0heADRaLE+7Ym/DoraV93M4u6e10DsQDcBjyc8Hxd0vFteu2Y2beArxLc52yC93b2dq6RrC/QhWDhGADcvdbMZhDcWwh6qMSAyUn3PpOtXycEPV5yEBEREWk+yo0Dyo23ptxYRFo1NTCLSLptTp66Ap814fyypOc3EiSX3yLoCVFG0EMhK6lccrLpQBTA3WvM7EjgYOA4gmF6t5vZBHefS7AIx6EEQ+cWEiRTjxEkWpA0b1k9LLxefZzPpy86gaBHQ6LqMMZFZjYMOJZgOOLvgRvN7BB3r+8DyfZi2nysvpiS99XUc6y+fTu6B+vcfeF2jm/1vprZxcDPgG8TfMgpIRh6OCGhWLye62YkPN5RTPD5vT+GbRP7qqTn3YB6F18RERERaSLlxtvGodxYubGItHKag1lE0u1ToJhgPq/mcCjwtLs/4u4fAouAETtbibvH3f0td/8xwZxsGwjmHtt8jQfc/Wl3n0WQ/CfOMfc+QfJ2WAPVzwEm2NbdAA4lSMaXEPQGqAUGuPvCpG1ZQowV7j7J3a8l6E2xPzC2gWu+Dxwe9mJJfq1rCIYBJs/Fd2gYa2twKPC6u9/r7h+ECfjwpDKFbL2iOgTDTDdbSfC9Nn7zjvB+HJhQ5iOgjvrv/fKkuvcmuK8iIiIizUW58ed1KjdumHJjEWlV1INZRNLK3eNm9jJBkvRkM1S5ADjZzA4mmFPsOoLhX0sbW4GZHRbG8z9gLXAQQXK2OaFcAJxhZi+Ez28l4eepu88ys0kEC49cC3xIMHStn7s/AvyBoCfJnWb2R2CPsI7fhsP4iszszvB4BsHwvHyCRLnC3R8ws0sJEu3pBD0aLiDowfFpAy/rDwQLczwarg5dTJBwf+juswnmY7vBzBaH8V5EkFx+vbH3rYUtAE4zs6MJ5oW7kCC+xMT2VeAKMzuP4L6cR5BEz4VgskIz+z1wk5ktCeu8jmBeP8IyhWZ2F3CXmWURDBnsQtAbpNjd/wZgZgUESfRFLfWCRUREpONRbqzcuJGUG4tIq6IezCLSGtwLnG1m0Wao68cEf2n/H/A6QRK8s8n5RoLVsV8gSLRuA2509831XEOw+MY7wHPAywSrJic6B3gK+CNBT5H7gM4A7r6EYCXvQwgS1j8TrAZ9c8L53ydYBfwGgiTwJeBUYHFCjFcQJNizwvpOc/eV9b2g8JqHEyTjbxKsqn0Fnw/h+yXBUMLfEczddiLwxXDYY2vwO4LVxf9FsMBKAcG9TfQUwev4LcH7UQA8kFTmVuCfBHPcvU2wWvrTSWWuC+u5ieDev0hwfxcnlDkT+MjdP0jlRYmIiIjUQ7mxcuMdUW4sIq2KbT2PvohIepjZO8Af3f2hdMfS3MzsQYIVp6ekOxZJnZlFCD6o3eDuk9Idj4iIiLQ/yo2lrVBuLCKgHswi0npcTjv7mWRmXc1sKMHwvC+kOx5pNn2B+5VAi4iISAtSbixthXJjEVEPZhGRlmJmo4HJQDlwjru/luaQRERERETSQrmxiEj7pQZmEREREREREREREWmSdjXkRkRERERERERERER2nVi6A0jWo0cPHzx4cLrDEBEREZEO7r333lvn7j3THYfyYxERERFpDRrKj1tdA/PgwYOZMWNGusMQERERkQ7OzJamOwZQfiwiIiIirUND+bGmyBARERERERERERGRJlEDs4iIiIiIiIiIiIg0iRqYRURERERERERERKRJ1MAsIiIiIiIiIiIiIk2iBmYRERERERERERERaZJYugMQERGRju2RacuadN554wY2cyQiIiIi0lY1JadUPinSPNSDWURERERERERERESaRA3MIiIiIiIiIiIiItIkamAWERERERERERERkSZRA7OIiIiIiIiIiIiINIkamEVERERERERERESkSdTALCIiIiIiIiIiIiJNogZmEREREREREREREWmSlBqYzewEM5tvZgvN7Af1HL/QzArNbGa4XZLK9URERKR9qqqpw93THYaIiIiIiIjspFhTTzSzKHA3cCywAphuZpPcfU5S0cfd/eoUYhQREZFW7pFpyxpdtqyqluVF5azcWMGqjRWs3FhBcWUtnbNiDOyey8BuuQzqlkvfghxiUQ22EhERERERac2a3MAMjAUWuvsiADN7DDgNSG5gFhEREWFTRQ1vLChkxpIiauOOAT3yshjaM48eeVmsL61iaVE5H68qBiA7I8Jp+/VjvwEF6Q1cREREREREGpRKA3M/YHnC8xXAuHrKnWFmhwMLgG+7+/LkAmZ2GXAZwMCBA1MISURERFqbjeXVQcPy0g24OwcM7Mr+A7vStyCbrFh0m/IllTUsLyrnzU/W8fiM5SwsLOUL+/YlM6bezCIiIiIiIq1NKg3MVs++5MkTnwUedfcqM7sC+Btw1DYnud8L3AswZswYTcAoIiLSDlTXxvnfnNVMXVQEwIGDunLEiJ507ZS53fM6Z2cwsm8X9uiTzytz1/DGgkKWFZVz7kED6dMle1eELiIiIiIiIo2USgPzCmBAwvP+wKrEAu6+PuHpX4BfpHA9ERERaSMWFZby1AcrKSqrZsygrhy1Zy8KcrffsJwsGjGOG9WHoT3zeGLGcv74+kJOG92XAwd1a6GoRUREREREZGel0sA8HRhuZkOAlcA5wHmJBcxsN3f/LHx6KjA3heuJiIhIK1dVU8eLH69m2uIiunXK5JLDhjC0R15KdQ7rlcc1Rw3jiRnLeer9lXTOzmBE787NFLGIiIiIiIikoskNzO5ea2ZXAy8BUeABd//YzG4BZrj7JOCbZnYqUAsUARc2Q8wiIiLSCn1aWMq/3lvBpooaDh3Wg2P26t1s8yZ3zs7ggvGD+dMbn/LY9GVcNXFYs9QrIiIiIiIiqUmlBzPu/gLwQtK+HyU8/iHww1SuISIiIq1bdW2cF2evZvInhXTPy+Tyw4cysHunZr9OZizCV8YP4u7XFvLQ1KVcdOgQOmWllMqIiIiIiIhIirQcu4iIiDTZosJSzrjnbd78pJAxg7tx9ZHDW6RxebNunTI5d+xACkuq+N6TH+KutYFFRERERETSSQ3MIiIistPcncfeXcbJv5/C8g3lnD9uIF/av1+zTYmxPcN65XHC3n14YdZq/vj6py1+PREREREREWmYxpWKiIjITtlQVs0Pn5rFix+vZsKw7vzmrNG8MnftLo3h0GE9yIhG+NV/57N3vy4cMaLnLr2+iIiIiIiIBNSDWURERBrt7YXrOPHOybwybw03nrQXD100jt752bs8DjPjF2fsy/Beedzw1Cwqqut2eQwiIiIiIiKiBmYRERFphOraOLe9MJfz759Gp6woT39jApcePpRIxNIWU05mlFtP25uVGyu45/WFaYtDRERERESkI9MUGSIiIrJdC9eW8q3HP2D2ymLOGzeQm04eSU5mNN1hATBuaHdOG92XP725iDMO7M+gFlxgUERERETanrKqWpauL2NpUTl5WTEOGtyN7IzWkcuKtBdqYBYREZF6uTuPvrucW577mJyMKPdecCDHjeqT7rC2ccNJe/HynDXc8uwc7r/woHSHIyIiIiJptra4kikL17F0fTmFpVUARM2oc+e1+Ws5eGh3Dtm9R5qjFGk/1MAsIiIi29hQVs0PnvqIlz5ew6HDevDrs/ZLy1zLjdE7P5trjxnOz1+Yxytz13D0Xr3THZJ0YGZ2AnAnGAALngAAIABJREFUEAXuc/fb6ylzFnAz4MCH7n7eLg1SRESkHVtTXMlfJi+iLu4M6dGJAwZ1ZXD3XPoW5LCmuJI3FhTy2vxCpixcx+riSq45ahgFuZnpDlukTVMDs4iISDvyyLRlTT73vHEDAZjyyTq+88+ZFJVVc+NJe3HxoUPSOtdyY1x4yBAen76cW56bw4RhPTTsUdLCzKLA3cCxwApguplNcvc5CWWGAz8EJrj7BjPrlZ5oRURE2p/Ckirun7KYaMS44ojd6ZGXtdXx/l1zOX/cINYUV/LmgkIefHsJUz5Zx0OXjKVX59bZmUKkLdAifyIiIgJAVW0dP39hLl+5fxp5WTGeuSr9C/k1VmYsws2njmLp+nLum7wo3eFIxzUWWOjui9y9GngMOC2pzKXA3e6+AcDd1+7iGEVERNql9aVV3D9lEQ5cfOiQbRqXE/XOz+bMMQN46KKxLN9Qztl/nsqqjRW7LliRdkYNzCIiIsLakkpO/+Pb3PvmIr4yfiDPXXMYo/p2SXdYO+Ww4T05ce8+3PXaQlbqA4KkRz9gecLzFeG+RCOAEWb2lplNDafU2IaZXWZmM8xsRmFhYQuFKyIi0j4sLyrnvimLqY07Fx86pNG9kQ8Z1oOHLh7LupIqzvzTOyxbX97CkYq0T2pgFhER6cDcnWmL13P3awtZtbGCv3x1DD/94j7kZLbNKSZuPHkv4nH4wyufpDsU6Zjq6+7vSc9jwHBgInAucJ+ZFWxzkvu97j7G3cf07Nmz2QMVERFpL9aVVnHuX6ZSXRvnoglD6LOT64YcOKgbj1w6nrLqWs7889ssXFvaQpGKtF+ag1lERKSDqq6N86/3VzBr5SaG9crjkUvG0auBhNzd2bRpE6WlpZSVlVFRUUFubi4FBQV06dJ6ejr375rLeeMG8tDUpVw5cXcGde+U7pCkY1kBDEh43h9YVU+Zqe5eAyw2s/kEDc7Td02IIiIi7cvt/5nHmuJKLj1sKH0Lcnbq3MT1S746fjAPvLWY0+6awhVH7E73eqbY2LxmiYhsTQ3MIiIiHdD60ioenraUtcVVHD+yN4eN6LlV4/KGDRt4++23mT59OgsXLmThwoVs2rSpwfq6dO9Fr/5D6D1wKIP23JdBe+xDNJaxK17KNq6cuDuPvruMP7y6kF+duV9aYpAOazow3MyGACuBc4Dzkso8Q9Bz+UEz60EwZYYmDhcREWmC95Zu4Mn3VnDFEbvTv2tuSnX16ZLNpYcN5c9vfsrfpy7lyiN218LRIo2kBmYREZEOZt7qYp6YsRzDuHDCYIb36gxAUVERkyZN4oUXXmD27Nm4O/n5+QwfPpwTTjiBwYMH06VLFzp16kRWVhbl5eUUFxdTVFTEq+/OYvWyT1k4azpTnn2UzOxchu59AKPGTWSP/Q8mGtt1KUfv/Gy+Mn4Qf31rMVcdOYwhPdSLWXYNd681s6uBl4Ao8IC7f2xmtwAz3H1SeOw4M5sD1AHfc/f16YtaRESkbaqLOzdP+pje+Vlcc9Qw/j0zedDQzuvZOYvzxg7kgbcW8/j05Vxw8CAi1voXvBZJNzUwi4iIdBDuzqvz1vLKvLX07ZLN+eMG0bVTJss/+Zhp/32a22dOpaamhn322YerrrqKww47jFGjRhGN7rjnRpcDguGF1VWVLJkzkwUzp/LJh9OYN2MKeV26MfrwEzjwqJPJ79qj2V5P4pDGZL06ZxGNGN9+fCZnjRmwzXENb5SW4u4vAC8k7ftRwmMHrgs3ERERaaInZixn1spN3HnOaDplNV/z1tCeeXxhv778e+YqXvp4NSfuvVuz1S3SXqmBWUREpAOIu/P0Byt5b+kG9h9QwBf378fqRXN5/pmHWPzxB+Tk5XPeeedxxhlnMHz48CZfJzMrmxH7j2fE/uOJx+tY+NF03nv1eaY89yjv/OefjDn6C0w4+Ww65W+zplmz6pydwfih3ZnyyTomJk3/ISIiIiJt26byGn750nzGDu7Gqfv1bfb6xw3pzupNlUz+ZB2987M5YGDXZr+GSHuSUgOzmZ0A3EkwBPA+d7+9gXJfBv4JHOTuM1K5poiIiOyc2ro4T8xYzuxVxRy1Zy/271bLv37/Yz758F065Rdw7DmXceCRJ5ORlc30Ipi+nZ7BOyMSiTJi9HhGjB5P0dpVTHn2Ud797zO8//oLjD/+dCaccg4ZmdsuntJcDhvek2mLinh1/lrOOUg9lkVERETai9/8bz4by6u5+dRRWAtNYXHKvn0pLKni6Q9W0iMvi4HdUpvjWaQ9izT1RDOLAncDJwIjgXPNbGQ95ToD3wSmNfVaIiIi0jTVtXEemrqU2auKOWHP7mTM+y9/vvFyls6fxdFnXcI1v/wb4084g4yslu3h261XX069+Dtc8fN7Gb7fOCZPeoQ/33g5n85qub8752XFOHj37sxasYk1xZUtdh0RERER2XXmrCrmoalL+cr4QYzsm99i14lGjPPGDqRLTgb/mLaU0qraFruWSFvX5AZmYCyw0N0XuXs18BhwWj3lbgXuAPTJTkREZBeqqK7jgbcWs3BtKUf3rmTW327izX8/zJ4HTuAbt93PISed2eINy8l67DaAM75xAxdc/wssGuWRX9/IU3+6jYqykha53mHDepAZi/DKvLUtUr+IiIiI7Druzs3PfkxBbibXHTuixa+XmxXj/HEDKa+u46n3VxAspSAiyVJpYO4HLE94viLct4WZ7Q8McPfntleRmV1mZjPMbEZhYWEKIYmIiAhAVU0d97+1iJXrS9m/bDpT7/8xtdWVnP/dn3P6lT+kc9fuaY1v8F6jufzWezj8ixcwd/pk7r3pSpbOn9Xs18nNinHI7j2YvXITq9WLWURERKRNe/vT9by7uIhvHzOcgtzMXXLN3brkcMKoPsxbXcLDzTSVnEh7k8oczPVNcrPlTzlmFgF+C1y4o4rc/V7gXoAxY8boz0EiIiIpqIs7j05fxuo1a+k37wnmLpnHqPETOfGCq8np1Dnd4W0Ry8jkiC9+heH7HsTTf76dv9/+PQ495VyO+NJXiESizXadCcO689bCdby5oJCzxgxotnpFREREpOU8Uk9j7gNTFtM5K0bc6z/eUg7evTufrC3hp8/NYfyQbgzv3XpyapHWIJUezCuAxE9p/YFVCc87A3sDr5vZEmA8MMnMxqRwTREREdkOd2fShytZOHc2+VN+z6ZVi/niZddz+hU/bFWNy4n6Dt2DS3/yR/abcAxTnn2Ex377o2adMiM3M8a4Id34cPlG1pdWNVu9IiIiIrLrrNhQzsLCUiYM60FGNJXmrJ0XMeOMA/qTlxXjmkc/oLKmbpdeX6S1S+V/5HRguJkNMbNM4Bxg0uaD7r7J3Xu4+2B3HwxMBU5195ZbzUdERKSDe3NBIe+/9jzZb91Dbm4OF/3o9+xzyFHpDmuHMrNzOPWS73LS177J4jkzeeCWb1K4cmmz1T9heA+iEePNT9Y1W50iIiIisuu8saCQ7IwIY4d0S8v1O2dn8Msz92Xe6hLueHF+WmIQaa2a3MDs7rXA1cBLwFzgCXf/2MxuMbNTmytAERERaZyn31/OK0/cR+ZHTzF01AFc/OM/0Kv/4HSHtVMOPPJkvvqDO6iqqOCBW7/FotnvNUu9+dkZHDioK+8v28CmippmqVNEREREdo21JZXMWVXM+KHdyc5ovqnUdtZRe/bmwkMG88Bbi3l9vhaRFtkspTEF7v6Cu49w993d/Wfhvh+5+6R6yk5U72UREZGWMWX+Z/zg+98j49M3OPCoL3DOt3/SaqfE2JEBw0dxyc1/oKBnbx797U189NbLzVLv4cN74u5M+UQLCouIiIi0JZMXrCMWNQ7ZvUe6Q+EHJ+7JiN553Pj0bCqqNVWGCKS2yJ+IiIi0AstWr+OKyy4hsnYRR3z5Yg47+UzM6luLt+3I79aTr/3w1/zzDz/h33/5JSUb1nPIyWel9Lq6dspkv/4FvLukiKKyarp12jUrj4uIiIhI020sr2bm8o2MHdKNvKz0NmNtXljwiBG9+MvkRXzjH+9x7Mg+DZY/b9zAXRWaSFqpgVlERKSV2ZkVscuKN3LXLd+ldv0qjvrqdzn0qGNbMLJdKzu3E+de91Mm3f9rXn3yASrKSzj6zItTamQ+YkRPZi7fyF/fWsx3jtujGaMVERERkZbw1sJ1OM6hw9Pfe3mzIT06MXpAAW9+so4DBnale15WukMSSatdu+ymiIiINJuSjeu599brqCr6jH3Ouq5dNS5vFsvI5EuXXc+BR53COy/8k5f+cQ8ejze5vl752Yzsm8+Dby+hpFJzMYuIiIi0ZmVVtby7pIj9+hfQNbd1jT47Ye8+xCLGcx99hrunOxyRtFIDs4iISBtUXFTIX3/2HUo2rKPbCddw2glHpzukFmORCCdecDXjjz+d6S//m+cfvDOlRuaJI3pRUlnLQ1OXNmOUIiIiItLc3lm0npo65/ARPdMdyjbyszM4es9ezF9TwrzVJekORySt1MAsIiLSxpRsWM9Dv7ie4o0b4LDL+copRxFp43Mu74iZccw5l3HoF87lgzdfTKmRuV/XHI4Y0ZP7Jy/WwiwiIiIirVRNXZypi9azV5/O9M7PTnc49Tp49x706pzFcx+toqau6R0gRNo6NTCLiIi0IaWbNvDQHdezccN6KsdfwmlHT6CglQ0XbClmxpFnXMihXziPD958MZguo4nDEa86chjry6p5YsbyZo5SRERERJrDh8s3Ul5dx4RhrWfu5WTRiHHqfn3ZUF7DGwsK0x2OSNqogVlERKSNKC/ZxMN3XM+m9WupHHsR++43mv36F6Q7rF1u4ulf5eATvsz0Vybx8uN/aVIj89gh3ThocFf+/ManVNeqt4mIiIhIa+LuvP3pevrkZzOkR6d0h7NdQ3vmsW//Lry5oJCisup0hyOSFmpgFhERaQOqKsp55Df/x4a1n5F75BXk9hvBqfv1TXdYaWFmHH32JRx09KlMffFfTJ70jybVc9WRw1i1qZJnZq5s5ghFREREJBXvLFrP6uJKJgzrjjVyKriqinI2Fq6maM1K1q1axobC1dTV7ppFnU/cezfM4NV5a3fJ9URam1i6AxAREZHtq62u5onf/4TVSxdywDnXMaWsD2eO6kN2RjTdoaWNmXH8+VdSVVnBG08/ROeC7ux/xIk7VccRI3qyd7987nn9U844oD/RSPuex1pERESkrXhgyhI6ZUbZt4HRepXlZSyZ+yGL57zPmmWLKFqzkrLijdsWNCOvS1e69e7HgOGjGLTHPgwYPorM7JxmjbdLTgZjBndj2qL1HL1XL7p2kCnsRDZTA7OIiEgrFq+r46k/386SuTM56eLv8lJJfwZ2y2S/AR1vaoxkFolwyte/RVnxBp5/8Pfk5hewx/4HN/58M66aOIwr//E+/5n9Gafs2zF7hIuIiIi0JkvXl/HKvDVMHNGLjOjnA+9rqquYO2MKM998kWULZuPxOJnZOfQZNIzho8fTrXdf8vK7EonGsEiEmqpKiosKKS4qZO3KJbz9whO89dxjRGMZjBg9nn0nHMPu+4whGmueprHDh/fk3UVFvLmgkNNG92uWOkXaCjUwi4iItFLuzosP3838997i+POvZE3B3pStW8dXDx5EpJFDBdu7aCzGl6/6Px76xfd56p7buOD7t9N/2MhGn3/8qD7s3rMTd7/2KSfvs1ujh2CKiIiISMt48O0lxCLGuKHdACjZuJ53/vMkH075L5VlpXTr3ZdDTjqL3fcZQ//d9yQay2hUvdWVFaz4dC6fzJzG7KmvMXfGZDrlFzDuuNMZc/QXyMrJTSnuLjkZ7D+wgPeWbuDIPXuRn924uETaAzUwi4iItFJTX3yS9157nkNOOosh409k0isLOGBQV/p3TS35bW8ys3M459u38uDPvs1jv/0RF974G3r0HdiocyMR48qJw/juPz/ktflrOWrP3i0crYiIiIg0pKSyhn/OWMEp+/YlWl3Gy8/8k+mvTCIer2PPAw/lgIknMXjPfbHIzi8plpmdw9BRBzB01AEcc/alfDp7BjNeeZZXn3yAt//zBOOOO53xx5+e0vQZR4zoyXtLN/DWJ+s4cZ/dmlyPSFujRf5ERERaoTnvvsnLj9/HyLGHc9SXv87zs1aREY1w3Eg1gNanU34B533n50SiUR759Y2UbFjf6HNPG92XfgU53PXqQty9BaMUERERke3554wVlFZW06dwOnd9/+tMfekpRo49nCtvu48zvnEDQ0aOblLjcrJoLMaI0eM57zs/4+If/Z4Bw/fmjaf/zj03XMLc6ZObnBN2z8ti3/5dmLa4iPKq2pTjFGkr1MAsIiLSyiz/5GOeufcOBgwfxWmXfI/5a0tZsKaUo/fsRWcNtWtQ1167ce51P6WirIRHfnMjleVljTovIxrhiiOG8v6yjUxdVNTCUYqIiIhIferizn3PTqbbtHt48O7fMGDYXlz+sz9z2qXfo1uvllsro+/QPTjnWz/hwht/Q3anzjx590955Nc3srFwdZPqO2KPXlTXxXl7UeM7PIi0dWpgFhERaUWWLl3K43feTJfuvTjrmz+GWIznP/qMHnlZjN+9e7rDa/V2GzycM6++iXWrlvPkXbdQV9u4niNnjhlAj7ws/vj6whaOUERERESS1dXVcf1Pf03Rs78gWrmRX/3qV5z7nZ/Rs5HTnjWHAcNHcenNd3P8+VeyYuFc7v3Rlcx6+9WdrqdPfjYjd8vn7U/XUVJZ0wKRirQ+amAWERFpJTZs2MBll10GwLnX3Upu5y68v3Qj68uqOWmfPsSaYThgRzB07wM55evfYvGcmbz0yD2NGuKYnRHl0sOGMPmTdXy4fOMuiFJEREREANauXcsll1zC84/eT+ag/Xnxhec5+eST07L4ciQaZeyxX+TyW++hV/8hPHPvL3jqT7c1emTcZhP36EllTZyHpy5roUhFWhd9UhUREWkFqqqquPrqq1m9ejVnX3sz3Xr3ozYe5/X5axnQNYc9endOd4htyn6HHsvBJ53Je68+x4xXJjXqnPPHD6JLTgZ3v6ZezCIiIiK7wrRp0/jiF7/IBzNnUj36LK68/ma6d+ua7rAo6NmHr/7gl0w8/WvMefdN7r/lm6xb1fjG4v5dcxneK4/7pyyisqauBSMVaR3UwCwiIpJm7s5NN93E+++/zx13BHMvA3ywdCMbK2o4eq/eaenB0dYd/eWLGLH/wbz0jz/x6awZOyyflxXjwkMG8985a1iwpmQXRCgiIiLScT366KNccskldOvWjYMv/RmZww7m3LG7bkqMHYlEoxx26nlccP0dVJaVcP8t17Lgg6mNPv/wET1ZV1rNcx991oJRirQOKTUwm9kJZjbfzBaa2Q/qOX6Fmc0ys5lmNsXMRqZyPRERkfbor3/9K88++yzXXnstxx9/PAC18TivLVhL/645DO+Vl+YI2yaLRPjS5dfTq/9g/vXHn1HYiF4nFx4ymNzMKH9UL2YRERGRFlFTU8Mtt9zCLbfcwoQJE/jjfX/jlVXG6Qf0oyA3M93hbWPQHvtwyc130a13Xx7//c28/cITjZqCbWiPTgzt2YlHpi3dBVGKpFeTG5jt/9m77/Coiq+B49+76QnpJJSEhCSE3glVQRDEAiqioCIKgkIoggr+RECKioIIUgxSBAERBRQUXxsqKkV6r4GQTiC91y3z/kERJCGB9OR8nifPljsz9yxPws6ePXdG08yAIOBhoCnwbD4J5PVKqRZKqdbAh8D8u45UCCGEqIJ27drFvHnzeOihhxg5cuT1549EppCSpadnY6leLg5LaxuefnUm5hZWbPh4Glnpqbdt72xnyXMdvdh6LIbIxKwyilJUJYUVYNzQ7ilN05SmaQFlGZ8QQghRnrKzsxk7dixfffUVw4YNIygoiO9PJZFnMPHiPfXLO7wCObq6M3TKfJq278YfG1fy65efYjLdfukLTdMY1MGLw5EpnI5JK6NIhSgf5sXo2wEIUUqFAmia9jXwOHD6WgOl1I1/QXZA4V/xCCGEENVEREQEEyZMwN/fn1mzZl1PJF9be9nT2YaGtaR6ubgcXd0ZOG46a2e/waZP3mXwGx+wfl/B1cyuNazQNI2Jm47Rr43HLccHdaw4l26KiuWGAowHgGjggKZpW5VSp//Tzh4YB+wr+yiFEEKI8pGSksKoUaM4fvw4M2bM4OmnnybPYOKLvRF0a+hGA/eKveeIhaUV/QMn4eDsyt5fN5ORkkS/Ef/D3LLgquun2nny4a/BrN8fwXv9WpRhtEKUreIskeEBRN3wOPrqczfRNG2MpmkXuFLBPC6/gTRNG6Fp2kFN0w7Gx8cXIyQhhBCicsjIyGDMmDHodDqCgoKwtbW9fuxIZArJWXp6NnaX6uUS4tmgCY8Of53I4BP8tGbxbS9rdLC2oJ23M4cik0nN1pdhlKIKuF6AoZTKA64VYPzXu1yZG+eUZXBCCCFEeYmNjWXw4MGcOnWKjz/+mKeffhqAn05cIi49l2EVuHr5RppOxwPPjuSBZ0Zw5uBOvvr4bfJyC347d7K1pG/LOnx3JIbMXEMZRipE2SpOgjm/T7y3fFpTSgUppfyAN4Gp+Q2klFqulApQSgW4ubkVIyQhhBCi4jOZTLz55puEh4ezYMECPDz+/X5Wb7yxerliV3FUNi0638+9jz7L0Z2/cuD3rbdt283fDaUUu0MSyig6UUUUWoChaVoboJ5S6v9uN5AUYAghhKgqLl26xAsvvMDly5dZsWIFvXv3Bq5sdL1qdxi+bnZ0869cuaBODz3J4yP+R8TZ46yfN5nc7MwC2z7X0YuMXANbj8WUYYRClK3iJJijgXo3PPYEbvfX8jXQrxjnE0IIIaqEoKAgtm/fzqRJk+jYseNNxzYfjpbq5VLU/YkXaNimE9u+WkrY6aMFtnOxs6SVpxP7whKl2kTcidsWYGiapgM+BiYUNpAUYAghhKgKLl68yAsvvEBycjIrV668ae57ODKZ49GpvHiPDzpd5Zv3tuzSk/6j3uLihbN8OXcyOZkZ+bZr6+VM49r2fCmb/YkqrDgJ5gOAv6ZpPpqmWQLPADeVA2ma5n/Dwz7A+WKcTwghhKj0tm3bxpIlS+jfvz/PPffcTceMJkXQnxfwcJLq5dKi6XT0G/E/XGt78m3QeyTHXy6wbbeGbhiMil1SxSyKrrACDHugOfCXpmnhQCdgq2z0J4QQoiq6llxOS0tj1apVtGrV6qbjq3aH42BtzpNtb93zorJo2qEbT42ZyqWIENZ99BY5WbdWMmuaxnMdvTh5MY3j0SnlEKUQpe+uE8xKKQMwFvgVOANsVEqd0jTtHU3THrvabKymaac0TTsKvA4MKXbEQgghRCV17tw53nrrLVq1asX06dNvqVDeduoykUlZ3NfQTaqXS5GVjR1Pj5+BUoqNC2eQl5Odb7taDta08HRkz4VEMqSKWRTNbQswlFKpSqmaSqn6Sqn6wF7gMaXUwfIJVwghhCgdcXFxvPjii2RmZrJq1SqaN29+0/Ho5Cx+OXmZZzt4YWtpXk5RloxGbbsw4JW3iY0M5av5U/OdWz7exgMbCzO+3FvwRtNCVGbFqWBGKfWTUqqhUspPKTXr6nPTlFJbr94fr5RqppRqrZTqoZQ6VRJBCyGEEJVNcnIyY8aMwc7OjkWLFmGZz27TK3aG4uViS9O6DuUQYfXiUsuD/qMnE38xgu9WzEWZTPm2u7+xO3qjiV3nZQ1cUbgiFmAIIYQQVVpKSgovvfQSiYmJLF++nGbNmt3SZvmOUHQaDOlSv+wDLAUNW3eif+AkLoae5esF09D/Z+M/B2sLHm9dl63HYmQTaVElFSvBLIQQQojCGQwGXn/9dWJjY1m8eDHu7u63tDkUkczhyBSG3VMfnVQvlwm/5u3o9czLBB/azc4f1ufbxt3emlb1nNgTmkh6jnwYEIUrrADjP227S/WyEEKIqiQzM5PAwEDCw8MJCgqiZcuWt7SJS8/h6wNRPNnWk7pONuUQZelo0r4rj7/8BhHBJ9j0ybsYDTfPHZ/r6E223sh3Ry6WU4RClB5JMAshhBClbO7cuezdu5eZM2fesvbcNSt3heJgbc6AgHr5Hhelo2PvJ2h5Ty/+3vIFZw/9k2+b+xu5YzAqdp6XtZiFEEIIIQqSl5fHuHHjOHHiBPPnz6dTp075tvtsZxgGo4nA+/zKOMLS16Lz/fQdOp4LJw7yw8qPMd1wlVwLT0daeDiyfl8kSqnbjCJE5VO5F7oRQgghKrjvvvuOtWvX8vzzz/PEE0/k2yYq6coadCO6+WFnJW/NZUnTNPoMGU9CTCTfr/gQl1oLcPesf1ObmvZWtPFyYm9oIvf61yyfQIUQQgghKjCj0cgbb7zBP//8w/vvv0+vXr1uabN+XyRZuQZW7w6npacT/1xI5J8LieUQbelqc9/DZKQm89fmNbz0xgx6DXzp+jFfNzu+PxrD3F+D8XS2vaXvoI5eZRmqECVGKpiFEEKIUnL8+HGmT59Op06deOONNwpst2p3GDpNY2gVWYOusjG3tGTAuOlYWtuwceEMsjLSbmnTo5E7JqXYcU7WYhZCCCGEuJFSiunTp7Nt2zYmTZpUYFEFwO4LieQZTdzX0K0MIyx79z76LAH3P8qenzax79fN159v5emEuU7jUERyOUYnRMmTBLMQQghRCuLi4njllVdwc3Nj/vz5WFhY5NsuNVvPxgNRPNaqLrUdrcs4SnGNg3NNBoydRlpyApuXzMJkNN503LWGFW28nNkflkRsWk4BowghhBBCVD/z5s3j22+/ZdSoUQwZMqTAdjl6I3tCE2hW14FaDlV73qtpGg8OHkXjgHvZ9tUyTu79CwBrCzOaezhyLDoFvTH/TaaFqIwkwSyEEEKUsGvrz6WnpxMUFISzs3OBbb/aH0lmnpHhXX3KMEKRH88GTXhkyCuEnT7K7xtW3HI2DwCeAAAgAElEQVT8WhXzkj9DyiE6IYQQQoiKZ8OGDaxcuZJnnnmGV1555bZt94YmkqM30b3RrRteV0U6nRlPjHgTr0Yt+H7FXMJOHwGgrZczOXoTp2NuvWpOiMpKEsxCCCFECVJK8c4773Ds2DE++OADGjVqVGBbvdHE6t3hdPFzpVldxzKMUhSkddcH6fBAP/Zt28KxndtuOuZiZ0k7b2e+2h9FTEp2OUUohBBCCFEx7N69m3fffZdu3boxZcoUNE0rsG1WnoFdIQk0qmWPh5NNGUZZvswtLXl63Axq1qnHxkXvcCkiBF83O5xsLTgUKctkiKpDEsxCCCFECVq/fj3ffvstgYGBPPjgg7dt++PxS1xOy+ElqV6uUB54ZgQ+TVvz45pFXLxw9qZj3Ru5o1As+UuqmIUQQghRfZ0/f55XX32VBg0aMH/+fMzNb79R9fp9kWTlGenRqGqvvZwfa7saPDvhPWzsavDVvKmkJcbR1suZC3EZpGTllXd4QpQISTALIYQQJWT//v188MEH9OjRo9BLBJVSrNodhq+bHd0bVo/LBCsLnZkZ/UdPwcG5JhsXzyQ9+d/dzZ1tLXm6fT02HIgiOjmrHKMUQgghhCgfCQkJBAYGYm1tzaeffoqdnd1t22fmGli2IxTfmnZ4ud6+bVXl4FyTZyfMwqDP4+sF02jhZoUCDksVs6giJMEshBBClICLFy8yfvx46tevz4cffohOd/u32CNRKRyPTuXFLvXR6Qq+nFCUD9saDgwcP4Pc7Cw2Lp6JIe/f6pIxPRqgoREkazELIYQQoprJyclhzJgxJCUl8emnn1KnTp1C+3zyZwjx6bn0bla7DCKsuNzqejHglbdJvBTFH2vm4uNqzaGIZExKlXdoQhSbJJiFEEKIYsrKymLs2LEYjUYWL15MjRo1Cu2zenc49lbm9G/rWQYRirvh7lmffiP+R0xoMD+uWYS6Ovmv42jDsx3qselgNFFJUsUshBBCiOrBZDLx1ltvceLECebOnUvz5s0L7ROWkMlnO0N5sq0nXi62ZRBlxebTtA0PPz+WCycOYn3qB5Kz9IQnZJZ3WEIUmySYhRBCiGJQSjFlyhSCg4OZN28ePj6Fr6ccm5bDTycuMSCgHnZWt1+vTpSvxu3uoVu/5zm++zf2b9ty/fnRPRqg02ks3n6+HKMTQgghhCg7ixYt4pdffmHixIn06tWrSH3e/b/TWJmb8eZDBW98Xd207f4InR96ivB9v2IVvptDEbJMhqj85FOtEEIIUQwrVqzgl19+YcKECXTt2vX68+v3RRbY5/czsRhNCmdbi9u2ExVDt8cGERcVym9fr8DNsz509KKWgzXPdfRi7Z4IxvRogHc1XU9QCCGEENXD5s2bWbZsGQMHDuTFF18sUp/tZ2PZfjaOyY80xt3BupQjrFzuHziMpLgYgo98xylbFx5tVRdrC7PyDkuIuyYVzEIIIcRd+vPPP1mwYAF9+vRh+PDhRepjMJrYH5ZEw1r2uNawKuUIRUnQdDoef/kNanp48e2SWURERAAwqrsfFmYai/6QtZiFEEIIUXXt27eP6dOn06VLF6ZOnYqmFb5/SK7ByLv/dwZfNzuGdin8Cr/qRqczo9/IN3H18EG3fx3/HDpe3iEJUSxSwSyEEELchXPnzjFx4kSaNm3Ku+++W6SJNsCJi6lk5Bro4udayhGKkmRpbcPT42awcuYrDB42gmFvL8TKxpYAbxc2H47G28WWmvaFf2EwqKNXGUQrhBBCCFEywsLCGD9+PN7e3nz88cdYWFgUqd+qXeGEJWSy+sX2WJpLbWN+LK2sGfz6uyyaOoY9X8yhfeMgQOaKonKSv3IhhBDiDiUlJTF69Gjs7OwICgrCxsamyH33hCZSs4YVfu6FbwQoKhZn9zo8OWYqiZej+W75hyiTia7+NTE309geHFfe4QkhhBBClKjk5GQCAwMxMzNj6dKlODg4FKlfbFoOi7efp1eTWnRv5F7KUVZuDi41aT/oDYw5maybN43s7OzyDkmIuyIJZiGEEOIO5OXlMW7cOBISEvjkk0+oVatWkftGJWURnZxNZz9XdEWseBYVi0/T1vR+NpBzR/bw9/frsLe2oLOvK8eiUohNyynv8IQQQgghSkReXh6vvPIKly9fJigoCE9PzyL1U0ox/ftTGIyKt/s2KeUoq4b7OrbB1GEwCVEXmDRpEiaTqbxDEuKOFSvBrGnaQ5qmBWuaFqJp2qR8jr+uadppTdOOa5r2h6Zp3sU5nxBCCFGelFK88847HDp0iFmzZtGyZcs76v/PhQSszHW0redUShGKstC+12O07vogO7//kjMHdtLN3w1Lcx2/nY4t79CEEEIIIYpNKcXUqVM5dOgQs2fPpnXr1kXuu/qfcH45dZnXezeUTZCLyNrCjJbt78HY4lG2bdvGggULyjskIe7YXSeYNU0zA4KAh4GmwLOapjX9T7MjQIBSqiXwDfDh3Z5PCCGEKG9ffPEF3377LSNHjqRPnz531DctR8/Ji2m083bGSnaIrtQ0TePhF8bi2aAJ36+YS3pcJF39a3L6UhrRyVnlHZ4QQgghRLEsWbKEH374gVdffZWHH364yP0ORSQz68cz9GpSi5HdfEsxwqqno48reT5dadP9EVasWMG3335b3iEJcUeKU8HcAQhRSoUqpfKAr4HHb2yglPpTKXXtk9ZeoGjXVAghhBAVzK5du5gzZw49e/Zk3Lhxd9x/X2gSJqXo5Cub+1UF5haWPDX2bazt7Nm4cAZt3C2wtTRjm1QxCyGEEKIS+7//+z8++eQT+vXrx4gRI4rcLzEjl7HrD1PHyZp5A1sVeQNscYWHsw2eLrbE+fXhnnvuYcaMGezZs6e8wxKiyIqTYPYAom54HH31uYIMB37O74CmaSM0TTuoadrB+Pj4YoQkhBBClLwLFy7w+uuv4+/vz5w5c9Dp7uztU280sS8skUa17alZw6qUohRlzd7JlYGvTCM9JYn/WzGbbn7OhMRlcCE+o7xDE0IIIYS4Y4cPH2by5Mm0b9+emTNnFjlJbDQpXt1wlMTMPD59rh2ONhalHGnV1KG+CyEJ2Qx+dRr169dn/PjxXLhwobzDEqJIipNgzu9/GpVvQ00bDAQAc/M7rpRarpQKUEoFuLm5FSMkIYQQomTFx8czYsQIrKysCAoKws7uzteSOxqZQlaekXsb1CyFCEV5quvbiL4vvkr4mWOk7tmAvZUZv52ORal8p0RCCCGEEBVSZGQkY8eOxcPDg0WLFmFpaVnkvou3n2fn+QRmPtaM5h6OpRhl1dbS0wl7a3M2n0hk6dKlWFlZERgYSGJiYnmHJkShipNgjgbq3fDYE4j5byNN03oBU4DHlFK5xTifEEIIUaaysrIYNWoUycnJfPrpp3h43O5Cnfwppdh1IYG6jtb41JSNTqqilvf04p6+z3D075+pn7CXyKQsgmPTyzssIYQQQogiSU1NJTAwEJPJxNKlS3FyKvqG1NtOXWbhH+d5sq0nz7SvV3gHUSBLcx1PtvXk55OXsHKsSVBQEAkJCYwdO5acnJzyDk+I2ypOgvkA4K9pmo+maZbAM8DWGxtomtYGWMaV5HJcMc4lhBBClCmj0cjEiRM5c+YM8+bNo3nz5nc1zvm4DOLTc7mnQU1Zi64K6/HkUJp36kHI9g04xB3jt9OxmKSKWQghhBAVXF5eHuPGjSM6OppPPvkEb2/vIvf940wsY9YfpqWnE+/1ay5z3RIwuJMXeqNi08FoWrZsyZw5czh27BhvvfUWJpOpvMMTokB3nWBWShmAscCvwBlgo1LqlKZp72ia9tjVZnOBGsAmTdOOapq2tYDhhBBCiApDKcX777/Pn3/+yeTJk+nRo8ddj7U7JAF7a3NaeMrlglWZpmk8Ovx1vBu3xLB/PbEhJzlxMbW8wxJCCCGEKJBSiqlTp7J//37ef/99AgICitx3+9lYRq07TJM6Dqwd1gEbS7NSjLT6aOBuT0cfF9bvj8BkUvTu3ZsJEybwyy+/sGjRovIOT4gCFaeCGaXUT0qphkopP6XUrKvPTVNKbb16v5dSqpZSqvXVn8duP6IQQghR/lavXs369et58cUXee655+56nMtpOZyPy6Czryvmd7gxoKh8zC0sGfjKdFxreWB1YA2//nMUo0mqmIUQQghRMX3yySf88MMPjB8/nr59+xa535/BcQR+cZhGte35YlhH2dSvhA3u5E1UUjZ/Bl9ZCGDYsGEMHDiQZcuWsXnz5nKOToj8yaddIYQQ4ga//vorH374IQ8++CATJ04s1lj/hCRgYabRob5LCUUnKjpruxoMmjALa2trsv/8lJ0nZOdvIYQQQlQ8W7ZsYcmSJfTv35+RI0cWud9fwXGM/OIQDWvXYN3wjjjaSnK5pD3YrDYeTjYs3h6CUgpN05g6dSpdunRh+vTp7N27t7xDFOIWkmAWQgghrjp8+DD/+9//aN26NbNnz0ZXjKrjjFwDR6NSaOPljK2VeQlGKSo6R1d3Bk94FzN9Njs//4C0dNnwTwghhBAVx549e5g2bRqdO3dmxowZRV47+ddTlxnxxSEauElyuTRZmusY06MBR6NS+PtcPAAWFhYsWLCA+vXrM378eEJDQ8s5SiFuJp94hRBCCODs2bMEBgZSp04dlixZgrW1dbHG2xeaiMGkuMevZglFKCqTOvX96fniG/z22fusnjeD0VM+wNzCsrzDEkIIIUQ1d/78ecaPH4+Pjw8LFy7EwuLfJPH6fZEF9tsXlsjWozF4OtvQv40HP524XBbhVltPtfMk6M8QFv5xnvsauqFpGvb29ixdupRnnnmGkSNHsmHDBlxc5EpJUTFIBbMQQohqLzw8nJdeegk7OztWrlyJs7NzscbL0RvZG5ZEo1r2uNlblVCUorLpfE9X3Lu/QGr4STYGvY/JaCzvkIQQQghRjcXHxzNy5EisrKxYunQp9vb2hfZRSvH7mVi+PxpDw1r2DL/XV67OKwOW5jpG9/DjSGQKO84nXH/ew8ODoKAgEhISGDVqFFlZWeUYpRD/kgSzEEKIau3SpUsMGzYMk8nEypUr8fDwKPaYX+2PJDPXQFd/qV6u7p7q3x9Di8e5cHQPP6z6GJPJVN4hCSGEEKIaSktLY8SIEaSmprJ06VLq1q1baB+jSfHd0Ri2n42jnZczgzt5Y2kuaaSyMqBdPeo6WrPw93Mo9e/G0S1btmTevHmcPHmSV199Fb1eX45RCnGF/M8ghBCi2kpKSmL48OGkp6ezYsUKfH19iz1mjt7Ikr8u4FPTDl+3GiUQpajMatpb0abn4xga9+b47t/44IMPbvqAIKoeTdMe0jQtWNO0EE3TJuVz/HVN005rmnZc07Q/NE3zLo84hRBCVB85OTmMHj2aCxcusGjRIpo1a1ZoH73RxFf7IzkQnkT3Rm70b+uBma5oazWLknGlirkBhyNT2HlDFTPA/fffz8yZM9m5cydTpkyRIgZR7iTBLIQQolpKT0/n5ZdfJiYmhiVLlhRpol0U6/ZGEJ+eS68mtUpkPFH59WzsDk1649TqAdatW8eiRYvKOyRRSjRNMwOCgIeBpsCzmqY1/U+zI0CAUqol8A3wYdlGKYQQojoxGAxMmDCBw4cPM3v2bO65555C++QajKzZE87pS2n0bVmH3k1rF3kjQFGyBgR4UsfRmoV/nL+lSOGpp57itdde44cffuDDDz+UIgZRriTBLIQQotq5VsVx7tw5Fi5cSPv27Utk3Ow8I0v/DqWLnys+Ne1KZExR+dlbW3CvvzuXvHvT46FHWbp0KatWrSrvsETp6ACEKKVClVJ5wNfA4zc2UEr9qZS6tmDiXsCzjGMUQghRTSilmDZtGtu3b2fKlCk88sgjhfbJzjOyalcY4QmZDGjnSRfZsLpcWZmbMbq7H4ciktkVknDL8Zdffpnnn3+eNWvW8Nlnn5VDhEJcIQlmIYQQ1UpOTg6vvPIKhw4dYs6cOdx3330lNva6vREkZOTy2gMNS2xMUTV086+JvbUFMX6P8tBDDzF37lw2bNhQ3mGJkucBRN3wOPrqcwUZDvyc3wFN00ZomnZQ07SD8fHxJRiiEEKI6uKjjz5iy5YtjBkzhueee67Q9uk5elbsDCUmNYdnO3jRxqt4G1+LkjGwfT1qO1iz8Pdbq5g1TWPSpEn06dOH+fPns3nz5nKKUlR3kmAWQghRbWRnZzNq1Ch2797Nu+++W6QqjqLKyjOw9O8L3NugJu3ru5TYuKJqsLIwo3ez2hyNTqPLoHF069aNGTNm8NVXX5V3aKJk5Xf9cL7Xq2qaNhgIAObmd1wptVwpFaCUCnBzcyvBEIUQQlQHn332GatWrWLQoEGMGTOm0PYXU7JZsTOUxMxcXujsTbO6jmUQpSgKK3Mzxt7fgIMRyXx39OItx3U6He+//z733HPP9Yp1IcqaJJiFEEJUC5mZmYwcOZL9+/fz/vvv8+STT5bo+Gv3RJCYmcdrD/iX6Lii6mjj5UQrT0c++u0CH8ydT/fu3XnnnXdYs2ZNeYcmSk40UO+Gx55AzH8baZrWC5gCPKaUyi2j2IQQQlQT33zzDfPmzePhhx9mypQpha6fHJOSzTPL95CRa2DYPT74u9uXUaSiqJ7t4EU7b2dmbD1NXFrOLcctLS1ZuHAhTZs25bXXXmPXrl3lEKWoziTBLIQQosrLyMhgxIgRHD58mDlz5tCvX78SHT8z18DyHaF09a9JO2+pXhb502ka0x5tRmxaLp/9E8XChQt54IEHmD17NitWrCjv8ETJOAD4a5rmo2maJfAMsPXGBpqmtQGWcSW5HFcOMQohhKjCNm/ezLRp07j33nuZPXs2Ot3t0z6XU3N4dsVeUjL1DLvHB29X2UekIjLTaXz4VEty9EYmbzmZ74Z+dnZ2LF++HD8/P8aMGcPu3bvLIVJRXUmCWQghRJWWnp7OSy+9xPHjx/noo4/o27dviZ9jzZ5wkjLzZO1lUah23s480caDz3aGcTndwPz586+vmRcUFCS7f1dySikDMBb4FTgDbFRKndI07R1N0x672mwuUAPYpGnaUU3TthYwnBBCCHFHtmzZwtSpU+ncuTOLFy/G0tLytu3j0q4klxMz8lgzvAOezrZlFKm4G35uNZjYuxG/n4ll67FbLpACwMnJiZUrV+Lj48OYMWPYu3dvGUcpqivz8g5ACCGEKC2pqam89NJLBAcHM3/+fB544IESP0dcWg5L/7pA90ZutJWNUEQRTHq4Mb+eusx7P55m+QsBzJkzBwsLCz755BP0ej3jx48v9FJWUXEppX4CfvrPc9NuuN+rzIMSQghR5X333XdMmTKFzp07ExQUhLW1Nev3RRbYPj1Hz2c7w0jN0fNil/qcvZRehtGKuzXsXh9+OnmJ6VtP0dnPFXd761vaODs7s2rVKoYOHcqoUaNYtmwZHTp0KIdoRXUiFcxCCCGqpPj4eIYOHUpwcPD1pQhKw4wfTpFjMDGtb9NSGV9UPbUcrBnTowHbTsey63wCZmZmzJo1iwEDBrBs2TLmzJkjlcxCCCGEKLLvvvuOyZMn06lTp+vJ5dvJyDWwclcYKdl5DOlcX5bFqETMdBpzn2pFVp6RqQUslQHg4uLC559/joeHB4GBgRw4cKCMIxXVjSSYhRBCVDnh4eEMGjSIiIgIlixZQo8ePUrlPNtOXeanE5cZ39MfX7capXIOUTUNv9cHLxdb3vm/U+iNJnQ6HTNnzmTw4MGsWbOGSZMmodfryztMIYQQQlRwNyaXlyxZUmhyOTPXwMpdoSRnXUku+9SU5HJl08C9BhN7N2Tb6YKXygBwdXVl9erV1KlTh8DAQA4dOlSGUYrqRhLMQgghqpSTJ0/y3HPPkZmZyerVq7n33ntL5TxpOXre/v4kjWvbM6Kbb6mcQ1Rd1hZmTOvblHOxGXz61wUANE1j8uTJjBs3jq1btzJ69GgyMzPLOVIhhBBCVFR3WrmclWtg1e4wEjPyeL5TfSmQqMSG3+tLGy8npmw5ycmLqQW2q1mzJp9//jnu7u6MGDGCffv2lWGUojopVoJZ07SHNE0L1jQtRNO0Sfkc76Zp2mFN0wyapj1VnHMJIYQQhfnzzz8ZMmQI1tbWfPnll7Rs2bLUzvXhL2eJS89l9pMtsTCT72vFnevVtBaPtqrL4u3nCb58Zd1DTdMYNWoU7777Lnv27GHIkCHExcWVc6RCCCGEqGjWrVvHW2+9RceOHQkKCsLGxua27bPzjKzaHUZ8ei7Pd/KmgbsklyszM53G0sHtcLSxYOjnB4hMzCqwrbu7O2vWrKFu3bqMGDGC7du3l2GkorrQ7naNP03TzIBzwANANHAAeFYpdfqGNvUBB2AisFUp9U1h4wYEBKiDBw/eVUxCCCGqJ6UUa9euZc6cOTRt2pQlS5bg7u5eauc7GJ7EU0v3MOweH6Y9mv/ay7fbVEVUX4M6et30ODEjl94f78DD2YbNo7pgfsOXFX/99RcTJkzAwcGBJUuW0KRJk7IOt9rTNO2QUiqgvOOQ+bEQQohrlFIEBQURFBREz549mTdvHlZWVvm2vTYfvZZcvpyWw+CO3jSqbV+WIYtS1MHHhQFL/8HRxoJvRnWhZo38fxcAkpOTCQwM5NSpU7z//vs89thjZRipqCoKmh8Xp+SqAxCilApVSuUBXwOP39hAKRWulDoOmIpxHiGEEKJAer2ed999l9mzZ9OzZ0/Wrl1bqsnlXIORN789joeTDRN6Nyy184jqwbWGFTMfb8bx6FQ+2xV207Hu3buzbt06AAYPHizVJkIIIUQ1ZzQaee+99wgKCqJ///4sWLCgwOTyNVl5Blb/E8bl1Bye6+AlyeUqpoF7DVYObc/ltBxe/PwAGbmGAts6OzuzatUqAgICePPNN/n8889lY2lRYoqTYPYAom54HH31uTumadoITdMOapp2MD4+vhghCSGEqE4SExMZPnw4X331FcOGDWPhwoXY2tqW6jnnbTvHhfhMZj3RHDsr81I9l6ge+rSow4PNajH/t3OExGXcdKxJkyZs2LABX19fxo4dy9KlSzGZ5Ht7IYQQorrJzs5m/PjxrF+/nmHDhvHee+9hbn77uWhqtp7lO0KJSc3h2Q5eNK7jUEbRirLU1suZJc+15fSlNAK/OESeoeC5op2dHcuWLeOhhx7iww8/5IMPPsBoNJZhtKKqKk6CWcvnubv66kMptVwpFaCUCnBzcytGSEIIIaqLU6dOMWDAAI4fP86cOXN444030OlKdy3kz3eHsXxHKIM7edG9UelVSYvqRdM03u3XHBsLM/73zTGMppunU+7u7qxdu5Y+ffqwcOFCxo0bR0ZGRgGjCSGEEKKqSUhIYMiQIfz5559MmTKFN954A03LLyXzr5C4dJb+fYHUbD1Du9SnaV1JLldl9zeuxez+LdgVkkDgukNk5RVcyWxlZcW8efMYMmQIX3zxBa+99hrZ2dllGK2oiopTehUN1LvhsScQU7xwhBBCiNtTSrFp0yZmzZqFq6srX375Jc2aNSv1835/9CIzfzjNg81qMfOx5qV+PlG9uNtbM/3Rpry+8Rir/wln+L0+Nx23sbHhww8/pHnz5sydO5eBAweycOFC/P39yyliIYQQQpSFc+fOMXr0aBITE1m8eDH3339/oX0ORyYzbPUBjCbFy119qet0+w0AReX1331fHm9dl61HY3hg/g5e6OyNvbXFTcev7Qei0+mYNGkSderUYc6cOQwePJigoCBq165dZrGLqqU4pV4HAH9N03w0TbMEngG2lkxYQgghxK0yMjKYOHEi06dPJyAggG+++aZMkss7zsUzcdMxOvi4sPCZNpjpbl8xIsTdeKKNBz0buzPn57McjUq55bimaQwZMoRVq1aRlpbGwIED+eabb2TtPCGEEKKK2rZtG88++yx6vZ61a9cWKbn8x5lYBq3Yi6ONBYH3+UlyuZrp6OPK8528iUvPYenfF4hLy7lt+yFDhhAUFERERAQDBgzg2LFjZRSpqGruOsGslDIAY4FfgTPARqXUKU3T3tE07TEATdPaa5oWDQwAlmmadqokghZCCFH9nDx5kqeeeopffvmFV199lRUrVuDi4lLq5z0WlULgukP4udVgxQsBWFuYlfo5RfWkaRofDWiFu4MVo9YdIiEjN992HTp0YMuWLbRu3Zq3336bN954Q5bMEEIIIaoQk8nE4sWLGT9+PP7+/mzatIkWLVrcto/eaGL2z2cZvuYgDdxr8E1gF1zsLMsoYlGRNK7jwMtdfckzKpbuuEBYQuZt2/fo0YOvvvoKGxsbXnjhBTZt2iQFDOKOFWuxSqXUT0qphkopP6XUrKvPTVNKbb16/4BSylMpZaeUclVKlX6ZmRBCiCrFYDDw6aef8uyzz5KTk8Pq1asZOXJkqa+3DHA8OoUXVx/Axc6StcM64GhjUXgnIYrB2c6SpYPbkZSZx5gvD2Mw5r9Ji5ubG5999hnjx4/n559/5vHHH2f//v1lHK0QQgghSlpSUhIjRoxgyZIl9O/fn7Vr1+Lufvu9P6KSshi4bA9L/77Asx3qsWlkF9zsrcooYlEReTrbMuo+P+ytLFi1O4x9YYm3TRr7+/uzYcMGAgICmDZtGlOmTJF1mcUdKc4azEIIIUSpCgsLY/LkyRw9epRHHnmEadOm4ejoeEdj/HddsqIwmhR/n4vjr+B43Oyt+GJ4R9wdrO94HCHuRnMPRz7o34LXNx5j9s9nmdq3ab7tzMzMCAwMpFOnTrz55psMHTqUIUOG8Oqrr2JlJR8qhRBCiMrm4MGDTJgwgZSUFGbOnMmAAQMK3czvx+OXmLT5OCj4ZFAb+rasW0bRiorOxc6Skff5suFAFN8fjSEsIZPH23hQwyr/VKCzszPLly/n008/ZcmSJZw+fZqPP/4YHx+ffNsLcaPSL/8SQggh7pBer2fFihX069eP0CdqZ4QAACAASURBVNBQPvroI+bNm3fHyeW7kZiRy/IdF/j9TBx9Wtbhl/Hd8KlpV+rnFeJG/dt6MqSzN5/tCuP7oxdv27Z169Zs2bKFp59+mtWrV9OvXz+pZhZCCCEqEb1ez+LFixk6dCg2NjZs2LCBgQMH3ja5HJOSzfivjzBm/WH83Grw0/iuklwWt7C1NGdIl/r0blqLE9GpPLp4F6dj0gpsb2ZmxtixY1m2bBmxsbH079+fDRs2yJIZolBaRfslCQgIUAcPHizvMIQQQpSTEydOMH36dM6cOUPv3r2ZMmVKoZcF3k5RK5gNJhMHw5P5+eQlzHQaj7f2YM6TLUv9vEIUxGAysXJXGDEp2QTe58eE3o0K7fPPP/8wY8YMoqKieOqpp5g4cWKZfDFTVWmadkgpFVDeccj8WAghqq6wsDDefPNNTpw4wWOPPcbbb79NjRo1CmyflWdg6d+hLN9xAZOCwPv8eOX+BliY3Vo/KPNRcaOwhEy+P3qRlGw90/o25bmOXrf9EiMuLo7Jkyeze/du7r//ft555x1cXV3LMGJRERU0P5YKZiGEEBVCcnIy06ZN4+mnnyY+Pp6FCxeycOHCYiWXiyI1W89vp2OZ+0swW4/F4OViy/ieDWnl6VSq5xWiMOY6HYM6eGFjYcaqXWGci00vtE+XLl34/vvvGTZsGFu2bOHhhx9m06ZNmEz5r+UshBBCiPJhMBhYvXo1/fv3JzIykgULFjBnzpwCk8tGk2Lz4Wh6fPQXi/44T68mtdg+4T5ef6BhvsllIf7Lp6YdP43vSkcfF6Z+d5IXVu0nKimrwPbu7u4sX76cSZMmsWvXLvr27csPP/wg1cwiX1LBLIQQolzl5eWxadMmFi9eTEZGBs8//zxjxoy5beXGncivckNvNBEan8nBiCTOXEpDKWhYy55Ovi7417JHd/Wb/EEdvUr0vELcjfj0XD7bFYqlmY71L3eiUW37IvU7e/Ys7733HocOHaJ58+ZMnjyZNm3alHK0VYtUMAshhCgNZ8+e5e233+bkyZN0796dmTNnFlhUkWsw8t2Riyz7O5TQhExaeTrydt+mBNR3KfQ8Mh8V/zWooxcmk+LLfRHM/vksCvjfg414oXN9dLqCq5lDQkKYOnUqx44d47777mP69OnUqVOn7AIXFUZB82NJMAshhCgXSim2bdvG/PnziYyMpFOnTkyePBl/f/8SPc/6fZEopYhNz+V8bDrn4zIIT8jEYFLYWJgRUN+Zjj6uuNhZ3tJXEsyiokhIz+XL/REYjOqOksxKKX788Ufmzp1LXFwcvXr14rXXXsPX17eUI64aJMEshBCiJKWnpxMUFMS6detwcnJiypQpPPTQQ/kuU5CZa+Cr/ZF8tjOMy2k5NKvrwOjuDXi4ee3bJgJvJPNR8V83fr6JTs5i8paT7DgXT4C3Mx/0b4F/rYLnmEajkXXr1rFw4UIAAgMDGTp0KJaWt36OElWXJJiFEEJUCEopduzYQVBQECdOnKBBgwZMnDiRbt26FbpLdlHpjSZOxaRxMDyJzYcvEpGYSWaeEQB3eyv83WvgX8sen5p2t72kUBLMoiLp7OfKM8v3oDcq1r/ckca1HYrcNysrizVr1rBy5UpycnJ44oknGDlyJJ6enqUYceUnCWYhhBAlwWQysXnzZj7++GOSk5N58sknmTBhAk5Oty7JFpOSzdo9EXy1P5LUbD2dfF0Y3b0BXf1r3vFcWeaj4r/++/lGKcXmwxd55/9Ok56j58m2nozv5Y+ns22BY1y8eJHZs2fz+++/4+3tzZtvvkn37t1L7LOcqNgkwSyEEKJcXUssL1myhOPHj+Ph4cGoUaPo168fZmZmxRo7I9fAkchkDoQnczA8iSORKWTrrySUXewsqe9qS31XOxq418DJtujfsEuCWVQkgzp6EZaQybPL95JnNLHihXa08y788tgbJSUlsXTpUr7++muUUjz22GOMGDECb2/vUoq6cpMEsxBCiOJQSvH333+zYMECgoODadOmDVOmTKFZs2a3tD0alcLKXWH8dOISSikebFabl7v50tbLGZC5pShdGbkG/g6OY19YEkpBex8XejRyY+R9fgX22bVrF7NmzSI8PJyAgAAmTpxIq1atyjBqUR4kwSyEEBVQcSaKxUl+lqW8vDx+/PFHVq1aRUhICHXq1CEwMJB+/foV+XKq//47pefoCU/MIiIxk/DETC6l5KAADajjZI23qx31Xe3wdrHFwcbirmOXBLOoSK79PoYnZPLCqv3EpGTzv4ca8XJX3zuuGImNjWXlypVs3LgRvV7PAw88wNChQ2ndunVphF5pSYJZCCGqjjudmxVnHqiU4sCBAyxcuJDDhw/j5eXFuHHjeOSRR66/Z6/fF4nRpDh9KY3dIQlEJmVhZa6jfX0XOvu64pzP8m1ClLaUrDz+DI7jUEQyZjqNAQH1eKqdJ23qOeU739Tr9WzatIklS5aQmJhIz549GT16NE2bNi2H6EVZkASzEEJUQFU5wRwbG8vGjRvZuHEjCQkJNGzYkOHDh/Pwww9jYVH0pG92npEFv5/jYko2F5OziUjKIikzDwALM416zrZXE8q2eLnYYmVRvGroG0mCWVQkN/4+pmbr+d83x/j1VCy9mtRi3oBWONre+ZcpcXFxfPnll3z99dekpaXRunVrBg0axIMPPijr6SEJZiGEqErKIsGslOKvv/5i+fLlHD16FDc3N0aPHs2TTz550/w3NVvPpG+Psyc0kZQsPS52lnTxc6Wdl3OJzmWFuFsJGbn8FRzP6Uup5OhN+LrZ8VQ7T/q38aS2o/Ut7TMzM1m9ejVr1qwhPT2d7t27M3LkSCleqIIkwSyEEBVQSSWYlVLkGkyYlEKnaZjpNMw0rcgbgNyJ28Vs0OcRcvwAx//5g3NH9qCUwr9le9r3ehzf5u14rlP+l+ErpUjO0hOemEl4wpWfsMQszl1O53xcOqarb1X2VuZ4utheX/KirpMNZqXwGq+RBLOoSPJbM+/z3eF88PMZajlYEzSoLa3q3bqWY1FkZmby7bff8uWXXxIZGYmzszNPPPEEjz/+OA0bNiyJ8CslSTALIUTVUZoJ5qysLH744QfWrVtHSEgIdevW5aWXXqJ///5YWVkBV963j0WnsvFgFN8fuUhmnhGfmnbc4+dK4zoO6GT9WlEBPdqqDj+fuMw3h6LZH54EQJM6DnT2daWLnysdfF1wsP73y5O0tDS+/PJL1qxZQ2pqKq1bt2bIkCH06tULc3Pz8noZogRJglkIISqgwia6SinScgxcSs0mJUt/5Sc7j9QsPWiQlWckO89IVp7hehL2v8x1VxLNZtcSzzf8mN9yq8v/uJl2PXF9OTUHTdPQaVeWpABF7uUQMs7tIy3kIKbcLMxtHXBq0gXXlt2xcaqFQmEwKrxcbdEbFbl6I6nZepKz8kjK1JOSlYfhhheg08DT2RY/NztaeDqRnJmHh5NNsZa7uBuSYBYVSUG/j0cikxm7/giX03J4rqMXr/ZqiMtdXlZrMpnYs2cPGzZsYPv27RiNRvz9/enbty99+vTBw8OjOC+h0pEEsxBCVB2lkWA+f/483377LVu2bCEtLY0mTZrwwgsv0KdPn+sVywkZuXx35CIbD0ZxLjYDawsdfVrUpbajNR5ONnf1WoQoKzf+HYQnZPLjiUv8cyGBg+HJ5BpM6DRoVteRtl5OtPFypo2XE14utmRlZbF582a++OILoqKiqF27Nv3796d///7Vbj5Z1UiCWQghKqD/TnT1RhNhCZlEJWdxMfnKkhDpuYbrx800DUdbCxxtLLC3NsfKXIelmQ6Lq7c6TcOkFCZ1JTn97+2/943/ec5kuuG+UlcfX72fTxulFEajCUNCGProkxjCD6Eyk8DMEp1nSzTvdlCrIUozu36ea8lpB2sLrMx1WJjpcLSxwMXOEmc7S1zsLHC1s8Lb1Zb6Ne2o52yLpbmuwH8nIcTNsvIM/HY6lv1hSVhZ6Li/cS06+bpgrrvyd3Q3X5YkJSXxyy+/8OOPP3L48GEA2rZtyyOPPEKPHj2oW7duib6GikgSzEIIUXWs3xdJZq6BhIxc0nMMpOcayMjRk55zZa5tYabDwky7eqvjoea1qeNoTV0nG2o5WF+fmyYlJfHrr7/y3Xffcfz4cSwsLOjZsyeDBw+mbdu2pOUYOBiexL6wJPaFJnIyJg2jSdHGy4mBAfXo27IO9tYWMr8VlUJBc8gcvZEjkSnsuZDAvrAkTlxMJSvvyibrtpZm1HO2pZ6LLR5OluRFneTE3z9x4dSV+aRfs7Y073w/jdp2wcrGtsIv/ShuJglmIYSogNbviyQpM4/gy2kEx6YTGp+JwaTQgJr2Vng62eDhbENdRxtcalhSw8q83C6fy0xL4cLJQ4Qc30/oiUNkZ6aj6XT4NW9H807306htZyytb1+FcbeTB5mAC1E0sWk5/HTiEufjMnC1s+SBprVoVteR5zvnvzxNUUVHR/PTTz/xww8/EBISAoCfnx/dunWja9eutGvXrkqu2SwJZiGEqLxyDUZOx6RxNCqFI5Ep7ApJuL6PxzUaYGdljk4DvVGhN5puuqruerucFBxSQtBFHyMnJhiUCYda9fBt3xPnxp3IMbO9fnVeWEImSoGlmY7W9Zzo5OvCo63q4l/L/qYxZX4rKoOifn4zGE2ci83gaFQK3x6KJjI5i/j0XODK35mbvRW1zbMgfD+Xj+8kPSkOcwtLGrTqwJAn+9C1a1dcXV1L8ZWIkiIJZiGEqCByDUb2hyXx59l4th67SELGlYmuq50lDWvb06iWPd4lvFnd3chMSyE65DRR508TEXycmLBzoBR2Dk74tWhPg1bt8W3WFhs7+8IHu0oSzEKUjXOx6fx04hJx6bk42lgwopsvz7Svh2sNq2KNq5TiwoUL7Ny5kx07dnDo0CH0ej22trZ07NiRDh060Lp1a5o2bVolEs6SYBZCiMpBKUVEYhZHo1KuJJSjUjgTk0ae0QRAHUdrXOwsqedsS21Ha+ytzalhZX41uXxz8YZJKbJzcnHJuciOHTs5emAPcdHhAFg7uWPv3x48W5Nm5YZJcf3qQgfrK7cNa9nT0deFtl7OWN9mPi/zW1EZ3M3nt2u/29l5RqJTsohKyiIqKZuIpExy9CZQihqZ0djFHScn7Ah5GSlomkaLFi3o1q0b9913H02bNkWn0xVyJlEeJMEshBDlKDo5i7+C4/krOI7dIYlk641Ymuuo72pLw1r2NKxlT81iJn6Kw6DPI+FSFJfCzhMVcoqo86dJuhwNgM7MnLo+/vi1CKBByw7U8W6Adpdv9pJgFqLsmJQi+HI6ey4kEhKfgaW5jkdb1mVggCcB9V1KZIPMzMxM9u/fz44dO9i9ezdRUVEAWFpa0qxZM9q0aUPr1q1p3rw5tWvXRqtkGxhJglkIISqm5Mw8jl9M5UhkMkejUjgWlUJylh64cnl+Cw9HWns50aaeE63rOVPb0fq288nc7CwuhZ/j4oVgokJOE37mKPrcHHRm5ng1bI5fiwD8WgTg7lm/0r2XCVFRmJQiLj2XiMRMIhKzCE/MJCUzFy01BpuEYGyTzpF5ORSUwsnZhfYBAbRp0/p68cK1DTNF+ZIEsxBClKHsPCNHIpP561w8f56N43xcBgCezjb0aOROj8ZudPatyZYjF8s0LpPJSFpSAgkxkcRFhxEbGUpsdBiJl6IwGa+smWVTwwHPBk2p59+Uev7NqFPfHwvLknkzlwSzEOWjg48za/6J4NvD0WTlGXG1s6R3s1o82Kw2Xfxq3rTmeXHExcVx9OjR6z8nT55Er7/ygd/BwQF/f38aNmx4/cfPzw9HR8cSOXdpkASzEEKUr6w8A+EJWYTEZ3DmUhpnL6Vx5lI6l9NyANA0aOhuT+t6TrT2cqJ1PSf83Wtgbnbr+9q1+WRudiYJMVHERoVyMTSYi6FnSbgYiVJXqp1danvi27QNfi0CqN+kVaFLwAkh7l5KVh6eLrYcDE/iQHgywRGX0GLPoosLxjw5HDKTANCZm+Ph3YCmLVrSpmVz2jZvgp+fL7a2tuX7AqohSTALIUQp0RtNRCZlcTz6yvpuRyJTOHMpDYNJYWGm0cHHhR6N3OneyB0/N7ubqh5KOnGqTCayMtJIS04gPTmBlPhYkuNiSIqNITkuhpSEWIwG/fX2jq7uuHv64F7Ph1r1fKjl5YdrbU+pzBCiirn25U5mroG/z8Xzy8nLbD8bR0auAXsrcwLqOxNQ34UOPi608HC87SW9dyIvL49Tp05x5swZzp07x/nz5zl37hwZGRnX2zg6OlKvXj08PT3x8vK6fr927drUrl0ba2vrEonlbkiCWQghSl+ewcSSv0JIzMgjISP339vMPFKz/523mmkabvZW1HG0pvbVzfc8nWzyXVZOn5dLamIcaYlxJMddIj4mkoRLkSRcjCQ9JfF6O2u7Gnj4Nr7y49cYD99G2NRwKJPXLYS4VZ7BRFx6DpdTc7iUlsPFS7EkRZ4jJzYUXXIEuuQoNJPhamsNK8eaONeph4e3L15e3vh4eeLv401TPy/cHG3lc20pKJUEs6ZpDwELATPgM6XU7P8ctwLWAu2AROBppVT47caUCbQQ4kZKKXINJnL0RnL0JrL1RrLzjOQYjORcu9Wb0LhSwQAaOg00TUMDdDrQ0NA0MNNpmOk0zHW6q7dXH5tdu6/DTNMwKoXRZMJoAqNJkWswkpqtJy3HcOU2W8/l1BzCr17aczElG+PVzUDsLM1oVc+JNl5OtPVypqOvKzWszAt8fYUlmA36PHKzs8jNySI3K5PsjDSyMtLISk+98pORRlZaKukpiaQnJ5KekojJaLhpDEtrG5zd6+LsXgeXq7euderh7ln/jtZPFkJUXvldPZBrMLI7JIHfTsdxIDyJkKtXWlia6Wjm4UCjWvY0cK+Bfy17/N1rUMfRukQm6UopLl26RHBwMOHh4URGRhIVFUVUVBQxMTEYDDf/H+bk5MTXX3+Nt3fxNiq8G3eTYJb5sRCiOjMYTdfn7ll5RtJy9KRlG0jPuTKXTszIJTYtl9j0HOLScriclsPF5Gxu3FfP1tIMVztLatawoqa9Fa52lrjZW+FqZ44xN4fsjDQy01LITEslKz2FzLQUstJTSUuKJzUpntSEWLLSU2+Ky8LKmpp1vahZpx5udb2pWbcebh7eOLvXlQSUEJWA3mgiJUtPYno2zqRxNvgckWGhJMREkPn/7d1rbGT3Wcfx7zNzZs7M2J71bS/ZxGmSNm0JpSqILkWAqqZJKW+aN4gGIRQFRCiiiKImC20lVHjBpUUgpFaKGiVvEFKEqIAVAkJaLq9IlRQFQrq9bKvAbrL2Jlmv9+Lx3M7Di3M8vtTetWdmffwf/z7S2Tln5j8zj/exPT//z5wzr79KcvkC5t3eeMegUieamKE2OcP4oSmmpqeZnp5hanqamZlZjhye4cjUJIen6tTHYiYqpfT87OWIwhBOJTeqtsvH28963PgBi8AXgfuBc8DzZnbK3b+xbtgvA4vu/jYzexD4Y+Cj/T7nfuHuuEPXnSRbT9xJPJ2M8mw9cafTdVqdhGanS7OTvtiubrc2bKcvwltdNjtdmu30cqW99lgr7S7uULB08myp0e6tp5Nt6XrB0smzqGBExcLaZXH1+gI/escUcVTIliLl1fVSun2964uF1efc9NykE36hvGDvdGfLsMat3r5+3FZ38U3jt3ts3zD6+o+3+f7blbp5XOK+9unK3YR2x2l1E1rdbrrddVod3/C92mh3s8nh9LqVbIK42e7S6KTXN7OJ45V2lwtXWrS73d5zdLrOwMdZ7HhHmqdjvZtdJpgn4OkHEZB00zFJwljZOF6PectEmRO3lTk8FnH7dJXj9ZgkadBuX6bx3TM8840mzWaTVqtFs9nsLa1Wi1arxTdfvUin3aLdbKaTyI1lWqsTyo3lDe82/j5m1MbrVMfrTEzOcPs73sXE1Cz1qRkmpmaZmJphcvYYtYlDwfwcisjeiaMi977zKPe+8ygAF6+1+Pr/LvLCKxd58ewlvnJ6gaefP9sbX44KHK3HHKtXOFqvcKxeYWqsTL0SUc8+3Gi8EhFHBUrFdCkXC5Qi27hdNG655RaOHz/+fTV1Oh3Onz/PuXPnWFhYYH5+nvn5+WA+Vfwg5+NVvikbJ55lhXWZd3W91e32tnsZIcsJGy7b6U7lRrtLJ3G665b5pZXeDuRiL+9u3KFcLFjve+8n756lUipSLRWpltPLSrbEUWHDfdd2QKfbhYBy7c027Nwc0mMO+txb5vgb/D2wedyNqvXszRLtxGl3ElrZ36SdbkJrNcMnCe1OQqPdodlOWMn+5lx9I0ez3WWlk6SZvZXwnQtXaHe6tDodOp0O7XaHdrebrne6dDvpepKsZuf1OTpJc3TSwbptyoWEegnGS85UBHORc/naMmUSCkmTbrNBc2WZy41lXl++1svF7ebKtl9zXB1jfHKayZkjHLv9rRyaOdJbJg8foz412/dniIhI/krFAocnYg5PxMAkR4/fDh9Yu3250WR+fp6FhXnevHCeS28scOXNCywvvUFj4RWuvPIS59vb/w7xYgylCh7FUKpSKFcpxlVKlRqlckw5jimXy8RxTLVSoVKJqVUrVCtVxmsVapWYahxRi8vpUikxVilTi0uUoohyOSIulSiXIspRRKkUEZciyqWIKIp6H2hoZhQKhWw+b21Zf91+1fcEM3ACOOPu3wMws6eBB4D1AfoB4LPZ+l8DXzAz8/12Xg7giSee4PHHH9/ytkarS9IrecilX+e/YuM3jrPx28h6/65V5jssb3Vic+2a/7jOuKHZQdu3+lHZ7l427Ppk34kY7JfUzdQFzmbLbsVxTJy9QHUoUiyVKZVj4kqN+vRh4motXSq13nq5WiOujFGbqFMbr1OrT1KpjVEoDOcwdhGR6bEy999zlPvvOdq77uK1FmcuXOXbC1c4u7jMwlL6brOXX7vMV09foNHuXucRr291p3TBjIIZv/r+u/jkh97B3Nwcc3NzQ/iKcjEy+XhxcZH77rtv29tX2t3e0TtrhvglZP8dhm0IiJuz4vXy71a745/d0XPvqMLdDNw535z5h/KgQ39Ekc0KQHkX4y9ny6piVCIqlddycLVGdWyCydmjvUxcro4RV2vUxuuM1SepTRzqXUal3Ty7iIyaWjXmrjvfwl13bn/EW7vV5OrSIkuLi1y69CaXL11i+dpVGsvXaCxfo5nt0ConLVYa12guL9C+uEyj0+Jqu4VvOlI4L2bGww8/zGOPPZZ3KRsMMndzKxvnVs4BP7bdGHfvmNkSMAO8sX6QmT0CPJJtXjWzbw1Q127Mbq5FgqZ+jh71dLSon6NF/QzIL+xsWK49ffQP4NG8nnx7uz0vR2j5WD/HYVLfwqXehUl9C5P6Fib1bQdOnjzJyZMn83r6LfPxIBPMO3mz6Y7ekOruXwK+NEAtfTGzF/bDB7fIcKifo0c9HS3q52hRP0ePejoUQeVj9TxM6lu41LswqW9hUt/CpL6Fa5CTEJ0D1h+/eBvw2nZjzCwCDgEXB3hOEREREZH9SvlYRERERA6cQSaYnwfuNrM7zawMPAic2jTmFPBQtv6zwL/st/PLiYiIiIgMifKxiIiIiBw4fZ8iIztn3MeBZ4Ai8JS7v2xmvw+84O6ngCeBvzCzM6TvzHhwGEUP0Z6flkNuKvVz9Kino0X9HC3q5+hRTwcUYD5Wz8OkvoVLvQuT+hYm9S1M6lugTG+YEBEREREREREREZF+DHKKDBERERERERERERE5wDTBLCIiIiIiIiIiIiJ9OdATzGb2HjN7zsxeNLMXzOxE3jXJ4MzsN8zsW2b2spl9Lu96ZHBm9qiZuZnN5l2LDMbMPm9m3zSz/zazvzGzybxrkt0zsw9nv2fPmNnv5F2PDMbM5szsX83sdPba+Zt51yR7R3k4XMq84VK2DYvya1iUU8OkPBq+Az3BDHwO+D13fw/wu9m2BMzMPgA8ALzb3X8Q+JOcS5IBmdkccD/wf3nXIkPxLPAud3838G3gUznXI7tkZkXgi8DPAPcAP29m9+RblQyoA3zS3X8AeB/w6+rpgaI8HCBl3nAp2wZJ+TUQyqlBUx4N3EGfYHagnq0fAl7LsRYZjl8D/sjdmwDufiHnemRwfwacJP15lcC5+z+7eyfbfA64Lc96pC8ngDPu/j13bwFPk05ySKDc/by7/2e2fgU4Ddyab1Wyh5SHw6TMGy5l28AovwZFOTVQyqPhO+gTzJ8APm9mZ0n3+mtPZPjeDvyUmX3NzP7dzN6bd0HSPzP7CPCqu/9X3rXITfFLwD/mXYTs2q3A2XXb51D4Gxlmdgfww8DX8q1E9pDycJiUeQOkbDsSlF/3N+XUEaA8GqYo7wJuNjP7CnBsi5s+A3wQ+C13/7KZ/RzwJHDfXtYnu3eDnkbAFOkhFe8F/srM7nJ3vUNgn7pBPz8NfGhvK5JBXa+n7v532ZjPkB4G9Zd7WZsMhW1xnX7HjgAzGwe+DHzC3S/nXY8Mj/JwmJR5w6RsGybl15GhnBo45dFw2UHOIGa2BEy6u5uZAUvuXr/R/WT/MrN/Ij1c8N+y7e8C73P313MtTHbNzH4I+CqwnF11G+lhuyfcfT63wmRgZvYQ8DHgg+6+fKPxsr+Y2Y8Dn3X3n862PwXg7n+Ya2EyEDMrAX8PPOPuf5p3PbJ3lIfDpMwbHmXbsCm/hkE5NWzKo2E76KfIeA14f7Z+L/CdHGuR4fhb0l5iZm8HysAbuVYkfXH3l9z9iLvf4e53kB7e9CMK4GEzsw8Dvw18ROE8WM8Dd5vZnWZWBh4ETuVckwwgm1R8EjitMH8gKQ+HSZk3MMq24VJ+DYpyaqCUR8M38qfIuIFfAf7czCJgBXgk53pkcE8BT5nZ/wAt4CEdKiiyr3wBiIFn0wzBc+7+sXxLkt1w1s6PAwAAALJJREFU946ZfRx4BigCT7n7yzmXJYP5CeAXgZfM7MXsuk+7+z/kWJPsHeXhMCnziuwd5ddAKKcGTXk0cAf6FBkiIiIiIiIiIiIi0r+DfooMEREREREREREREemTJphFREREREREREREpC+aYBYRERERERERERGRvmiCWURERERERERERET6oglmEREREREREREREemLJphFREREREREREREpC+aYBYRERERERERERGRvvw/XTNQKpSmgYcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(2,2, figsize=(20, 6))\n",
    "\n",
    "distribuicao_v3 = dados_novos['V3'].loc[dados_novos['Class'] == 0].values\n",
    "sns.distplot(distribuicao_v3,ax=ax[0][0], fit=norm)\n",
    "ax[0][0].set_title('Distribuição de V3 \\n (Transações com Fraude)', fontsize=14)\n",
    "\n",
    "distribuicao_v10 = dados_novos['V10'].loc[dados_novos['Class'] == 0].values\n",
    "sns.distplot(distribuicao_v10,ax=ax[0][1], fit=norm)\n",
    "ax[0][1].set_title('Distribuição de V10 \\n (Transações com Fraude)', fontsize=14)\n",
    "\n",
    "distribuicao_v12 = dados_novos['V12'].loc[dados_novos['Class'] == 0].values\n",
    "sns.distplot(distribuicao_v12,ax=ax[1][0], fit=norm)\n",
    "ax[1][0].set_title('Distribuição de V12 \\n (Transações com Fraude)', fontsize=14)\n",
    "\n",
    "distribuicao_v14 = dados_novos['V14'].loc[dados_novos['Class'] == 0].values\n",
    "sns.distplot(distribuicao_v14,ax=ax[1][1], fit=norm)\n",
    "ax[1][1].set_title('Distribuição de V14 \\n (Transações com Fraude)', fontsize=14)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(2,2, figsize=(20, 6))\n",
    "\n",
    "distribuicao_v3 = dados_novos['V3'].loc[dados_novos['Class'] == 1].values\n",
    "sns.distplot(distribuicao_v3,ax=ax[0][0], fit=norm)\n",
    "ax[0][0].set_title('Distribuição de V3 \\n (Transações com Fraude)', fontsize=14)\n",
    "\n",
    "distribuicao_v10 = dados_novos['V10'].loc[dados_novos['Class'] == 1].values\n",
    "sns.distplot(distribuicao_v10,ax=ax[0][1], fit=norm)\n",
    "ax[0][1].set_title('Distribuição de V10 \\n (Transações com Fraude)', fontsize=14)\n",
    "\n",
    "distribuicao_v12 = dados_novos['V12'].loc[dados_novos['Class'] == 1].values\n",
    "sns.distplot(distribuicao_v12,ax=ax[1][0], fit=norm)\n",
    "ax[1][0].set_title('Distribuição de V12 \\n (Transações com Fraude)', fontsize=14)\n",
    "\n",
    "distribuicao_v14 = dados_novos['V14'].loc[dados_novos['Class'] == 1].values\n",
    "sns.distplot(distribuicao_v14,ax=ax[1][1], fit=norm)\n",
    "ax[1][1].set_title('Distribuição de V14 \\n (Transações com Fraude)', fontsize=14)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparando as amostras com fraude e sem fraude, identificamos que no caso de fraude a variável V3 tem uma distribuição diferentes do caso sem fraude, onde neste caso os valores para fraude são mais negativos. A variável V10 fica mais centrada em torno de zero para o caso sem fraude e mais negativa para o caso com fraude. As variáveis V12 e V14 tem comportamento semelhante a V10. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Faremos o processo de remoção de outliers nas variáveis que apresentaram maior correlação, onde espera-se que isto aumente a eficiência do modelo em identificar transações com fraude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Será calculado o intervalo interquartil (IQR) para identificar os outliers, onde o mesmo é calculado da diferença entre o terceiro e o primeiro quartil. O que estiver acima do IQR será removido"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "v3_fraude = dados_novos['V3'].loc[dados_novos['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v3_fraude, 25), np.percentile(v3_fraude, 75)\n",
    "\n",
    "v3_iqr = q75 - q25\n",
    "\n",
    "v3_cut_off = v3_iqr * 1.5\n",
    "v3_inferior, v3_superior = q25 - v3_cut_off, q75 + v3_cut_off\n",
    "\n",
    "outliers = [x for x in v3_fraude if x < v3_inferior or x > v3_superior]\n",
    "\n",
    "dados_novos = dados_novos.drop(dados_novos[(dados_novos['V3'] > v3_superior) | (dados_novos['V3'] < v3_inferior)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "v10_fraude = dados_novos['V10'].loc[dados_novos['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v10_fraude, 25), np.percentile(v10_fraude, 75)\n",
    "\n",
    "v10_iqr = q75 - q25\n",
    "\n",
    "v10_cut_off = v10_iqr * 1.5\n",
    "v10_inferior, v10_superior = q25 - v10_cut_off, q75 + v10_cut_off\n",
    "\n",
    "outliers = [x for x in v10_fraude if x < v10_inferior or x > v10_superior]\n",
    "\n",
    "dados_novos = dados_novos.drop(dados_novos[(dados_novos['V10'] > v10_superior) | (dados_novos['V10'] < v10_inferior)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "v12_fraude = dados_novos['V12'].loc[dados_novos['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v12_fraude, 25), np.percentile(v12_fraude, 75)\n",
    "\n",
    "v12_iqr = q75 - q25\n",
    "\n",
    "v12_cut_off = v12_iqr * 1.5\n",
    "v12_inferior, v12_superior = q25 - v12_cut_off, q75 + v12_cut_off\n",
    "\n",
    "outliers = [x for x in v12_fraude if x < v12_inferior or x > v12_superior]\n",
    "\n",
    "dados_novos = dados_novos.drop(dados_novos[(dados_novos['V12'] > v12_superior) | (dados_novos['V12'] < v12_inferior)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "v14_fraude = dados_novos['V14'].loc[dados_novos['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v14_fraude, 25), np.percentile(v14_fraude, 75)\n",
    "\n",
    "v14_iqr = q75 - q25\n",
    "\n",
    "v14_cut_off = v14_iqr * 1.5\n",
    "v14_inferior, v14_superior = q25 - v14_cut_off, q75 + v14_cut_off\n",
    "\n",
    "outliers = [x for x in v14_fraude if x < v14_inferior or x > v14_superior]\n",
    "\n",
    "dados_novos = dados_novos.drop(dados_novos[(dados_novos['V14'] > v14_superior) | (dados_novos['V14'] < v14_inferior)].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Criando um novo boxplot para verificar se os outliers foram devidamente removidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(2,2, figsize=(20,8))\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V3\", data=dados_novos,ax=ax[0][0])\n",
    "ax[0][0].set_title(\"V3 com redução de outliers\", fontsize=14)\n",
    "sns.boxplot(x=\"Class\", y=\"V10\", data=dados_novos,ax=ax[0][1])\n",
    "ax[0][1].set_title(\"V10 com redução de outliers\", fontsize=14)\n",
    "sns.boxplot(x=\"Class\", y=\"V12\", data=dados_novos,ax=ax[1][0])\n",
    "ax[1][0].set_title(\"V12 com redução de outliers\", fontsize=14)\n",
    "sns.boxplot(x=\"Class\", y=\"V14\", data=dados_novos,ax=ax[1][1])\n",
    "ax[1][1].set_title(\"V14 com redução de outliers\", fontsize=14)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observamos que ainda existem outliers porém numa quantidade bem menor e com valores de máximo e mínimo mais condizentes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizaremos três modelos de aglomeração para tentar isolar as transações com fraude e sem fraude baseados em seus padrões. Os modelos usados serão TSNE que funciona bem mesmo nos casos de amsotra pequena e também usaremos PCA e TruncatedSVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "import matplotlib.patches as mpatches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dados_novos.drop('Class', axis=1).values\n",
    "Y = dados_novos['Class'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_reduzido_tsne = TSNE(n_components=2, random_state=42).fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_reduzido_pca = PCA(n_components=2, random_state=42).fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_reduzido_svd = TruncatedSVD(n_components=2, algorithm='randomized', random_state=42).fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "azul = mpatches.Patch(color='#0A0AFF', label='Sem fraude')\n",
    "vermelho = mpatches.Patch(color='#AF0000', label='Fraude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1dc5eb90>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Jjo4mKiqKiIiI0rpCQkIICwurtM+goCAiIyOJjo4mPDycuLi40jIzZ84kOzubNWvWlJ7rBwcHExcXVy5+ay1hYWFERUXhcDhKl0dGRpa+DwoKYsqUKbWe64eHh9O/f/+6NmFL1uAvPkVE6mPH99tY3XcUZdOvMbn7+PKooXR6YyYDzh/qt9iakvJpNVNSthY//vgjDoeDHj16ALB27VqOOeYYjj/+ePbu3cvy5csZOnQoRUVFbNy4kV69enk9huzsbNq3b09ISAhLly5ly5YtgHvU0549e8jKyiIqKooFCxZw5plnEhcXR2xsLJ9//jkjRoxg9uzZpXWNHTuW5557jtNPP52QkBA2btxI586dy31oRSSwffyPtwh54dlKyx1YElZ+zIKBP3H+hneIiNP/1y2BtXaH5/ceY8xcYDCw2xjT0XOx2BHw8gxRIiJ1s3vSNThwZ7ZKLmFKXn91/BmcV/id32KrC53rt3oN/uLTX3ejBPKo+eZGbekdase6yV79HTx2c5XrtmWs5lBagdfb0l93o5TYtGkTxhi6d+8OwIoVK+jYsSOdOnVi9+7dfPzxxwwZMoSioiJ++uknevbsWe7OkiNHjuB0Okv3UXZdWRXvRiksLCy9I2b37t3ExcWRn5/PokWL2LJlC7m5uaUxZGRkEBUVxfz58xk1ahRBQUFER0ezaNEihg4dyowZM0rvWhk5ciTPPvssgwYNIiQkhE2bNtGpU6dyfWx970RRUrYWubm53HTTTRw8eJDg4GC6d+/OtGnTCA0NZc6cOdx8881kZ2fjdDq59dZbm+RE7bLLLmP8+PGkpKTQr18/TjjhBMD9Tfx9993HkCFDSE5OLl0OMGPGDCZPnkxERES5LP4111xDRkYGAwYMwFpLu3btmDdvntdjFpGm8f3ibzjywP0E1VAmfl8GHz/wCuc+fZ3P4pKmYYyJBBzW2hzP6zHAQ8C7wO+ARz2/5/svShFprfbv2l+aiC07pqTkdbCtfTSLv+lcv3VrzBef/robJVBHzTdHakvvUDvWzYLR11H1+Ev3l5kpB9JZtWqVV9vSX3ejlLDWcuONN1bqYxMTE3nnnXcq9bGDBw8ud2dJREQEwcHBpfsou66sinejhIaGliZXr776asaPH89pp51W2sdGRUWRkJDA/fffz6hRo0hOTqZXr16ld7rMmjWrXB9bcjfKjTfeyK5duxg5cmS5PrZsPPW9E8XUNsGtL6WkpNhVq1aVW7ZhwwZ69uxZ+j452btPjGvfHppy7vuKt0Y1FxXbvaH0B1ptoOOvfPxHDuXx4+K1hEaF03N03zrNuVfipd6TSPzh61rLZfUYyBXfv1rfcJuENz8Dxpj0Mg/iaPGMMccCcz1vg4FXrbWPGGMSgTeBo4GtwERr7f6a6qqqj5Vftfa/VU1BbepdgdieX7zwEQdvqHrUD7gvMs8p+rHGOvx1rt9cz9G9papz/VbYx1b84nMx7i8+zwCyrLWPGmPuBhKstXfWVJcv+9hA/FvQXKktvUPtWDcLQo6vNikL0GX+axyIOOT1pGxzzqc1hr/6+fr2r81upGyg/oOLiNTG5XIx77pnsa/OIrzwMADrIxMpGjyCPjdOpM+5g2rdPu7H9LrtLIC+cJOGs9ZuBvpWsTwL90WjiIjf9L1kOGk3UONFZn3pXF98KAmY65k7sOSLzw+NMSuBN40xV+P54tOPMYpIK2CBbqceR1N/uaM+NvA0u6SsiEhzteDWaYTN/G+5ZVGHs2DpfLYunc8PbeLo/8FMeoyoepT6voy9OGxxnfYVOWJIo+MVERGpSVRUFMUmiGBbXG5O2RJHQlvvSFQJfPriU0R8KS8kioii3HLLSvpOpwkmKirKL3GJf9X9nlkREWkwZ6GTvFmzaiwTmXeQ9aMvrvaJkh/f/EydRyP1mTSmnhGKiIjU3ylbP8dVYZkFnCaICw9ryhQRERGAiUfScRoHFe9ndAGj9q/wR0gSAJSUFRHxovzcfNbOXcF3H63B5fr1MnXXD9uJOlL9lJ8lydZQZz7zr3u2yjKOL9LqFENeaBTHDOpW15BFREQaLKFDAuOLfiT77InkB4WTF9KGwt/fwoTC7/0dmoiISECZULiB/Mk3kBfchvzgcLLHX8L4oh81SrYV0/QFIiJeMu+P/8U5czptCt23payL7UDM838CIDIxGosBz39rcviz5cBtlZYHFRXWKQ573kRC24TVI3IREZHGuWzew8DD/g5DREQkoF0w9RaYeou/w5AAoZGyIiJesOCO6YRM+3dpQhYgJnsXzq3bWPfeSuI7J7C/x4C6TT8QVfUcfHnde9W4mQUOnj6B82fdUffARURERERERMTnlJStg6CgIPr161f6k5GR4fV9ZGRkcNJJJ3m9XhFpesXOYnJfnFHt+rUPTAVgxIwHOdwmvsa6LDDo8aq/Oe339z/idASXlisrLySCdi8+x2UfPU5QcFCdYxcREWntdK4vIiLSNNTH1qzZTV+wsEMHCnbv9lp9YUlJnLVrV41l2rRpw9q1a6td73Q6CQ5udk0pIl6Q+e0WsndkEZ27t9oyoZs3AHDskB5Er32XpX99EbNgLhH5h8qVs8D+gadzzqg+VdbTZ/wg8qb9h01/eZT4vRmA+8nWzjHnMPGNewkO1d8hERFp3nSuLyIi0jTUxwaeZjdS1psfoMbUN3PmTCZOnMj48eMZM2YMubm5nHHGGQwYMIDevXszf/58ALZs2VIuY//kk0/ywAMPAJCenk7fvn0ZOnQoU6ZMKS1TXFzMHXfcwaBBg+jTpw9Tp05t+AGKSJN45bTbeC/kBL4ZMIaMcy7BUnn0aonisIjS1+2Obc+Fr93DxJyVcPvdHIpJIj8kguyYDjju+AtXfPVcjfsd8rvTuDRzIX3TF9Fr+Qecn72CS+Y+oISsiIi0CM3tXL/i6Byd64uISKBqTX3syJEjm0Ufq6v4OsjLy6Nfv34AJCcnM3fuXACWL1/OunXrSEhIwOl0MnfuXGJiYti3bx8nn3wy5557bo31XnXVVTz77LOMHDmSO+74dQ7I6dOnExsby8qVKykoKGD48OGMGTOG5OTkpjtIEamzl1JvJeGLheXmh61qrtiSJG3Y2DFV1nP2Y1fBY1cBcGhPNhs+WM3Wtb9wdL+a/193OBx06XNM/QMXERGRSnSuLyIi0jT81cd++umnhIaGBnwfq6RsHVQ33Hr06NEkJCQAYK3lnnvuYdmyZTgcDrZv387uGr41yM7O5uDBg4wcORKASZMmsXDhQgAWLVrEunXrmDNnTmnZTZs2BeyHSKQ1cblcxH/xYZ0e2GWA4uBQ2g08mm1rMziqX9dKZfJz83mzzyUkbPseA+wFlkck0OftqfSsZhoDERER8R6d64uIiDQNf/Wxb775Jg6HI+D7WCVlGyEyMrL09ezZs9m7dy/p6emEhITQtWtX8vPzCQ4OxuVylZbLz88H3B86Y6pO61hrefbZZxk7dmzTHoCI1NvGZRtwVDtRgWde2KNOBCAkez8RzkJy7/oz32BY2utkxr//L+I7J5SWf7vbeBL3by1XR9SR/Ww8+xI6/PwZ8V0SEBEREd/Tub6IiEjTaOo+dtiwYURHRzftQXhBs5tTNlBlZ2fTvn17QkJCWLp0KVu2bAGgffv27Nmzh6ysLAoKCliwYAEAcXFxxMbG8vnnnwPuD2GJsWPH8txzz1FUVATAxo0bOXz4sI+PSESqUpe5Wy9c9xohh/YTc6jspOeWxO+W817qdaVL1r27ktj9W8uleEu6lmCXkwUX3+eVmEVERKRxqjvXT0pK0rm+iIhII7TmPlYjZb3ksssuY/z48aSkpNCvXz9OOOEEAEJCQrjvvvsYMmQIycnJpcsBZsyYweTJk4mIiCj3Tfk111xDRkYGAwYMwFpLu3btmDdvns+PSUQq6z7seL43DoKsq8r1eaFRpD0+h5js8k+hNLhH0SZmrGP1nOUMuGAo3/73beJq2Ffw9+tKXx/anU1hXiEJRyficOj7NBEREV/Sub6IiEjTaIo+9pRTTsEYE/B9rLG2+ttwfS0lJcWuWrWq3LINGzbQs2fP0vcLO3Tw6hPjwpKSOGvXrtoLNlBOTk6zGDJdUcV2b6i0tDRSU1MbH1Az1trboCUe/xuX/h+Rb82sNK+sBeKn/JuMd5YSt8T9h9/52M0E3/VMuXIuDAd6DMC0bUfC8g+r3c/B+M70e/mfpN/0fyT+8o17WeLRdL7rJlJvq3ni80Dizc+AMSbdWpvilcpamar6WPlVS/xb5W9qU+9qqe3pr3P95nqO7i1Vneurj204X/axLfVvgT+oLb1D7eg93m7L5p5Pawx/9fP17V+b3UjZQP0HF5HW46JX/8I70ZGYWS8QUlwIQH5oJElPPszwa89k+yfpNW7vwJK4KZ3CTe7Zaat7aJgdMJgfzruKRGde6RQHcVlbyb3zDpZaF6fdfp7XjklERCQQ6FxfRESkaaiPDTzNLikrIhIIzp96M0y9ucp1KbdM5Lu3X6p10u5Q3FMgVJuYXbWCcGceVFhvgF1/fxLXredqKgMRERERERGRZkhX8yIiXnbs0OM42D65zuWrGykbk72z2m1icvey5yfv3XoiIiIiIiIiIr7T6KSsMSbcGPO1MeYbY8x3xpgHPcuTjTErjDGbjDFvGGNCG7qPQJr3tjVQe4s0zu5NO4nbu6XR9VSXrC2RsfyHRu9DRETE33Tu6VtqbxGR1kN/832nIW3tjZGyBcDp1tq+QD/gTGPMycBjwFPW2h7AAeDqhlQeHh5OVlaWPkg+Yq0lKyuL8PBwf4ci0mxtWrwWh3XVqWx1f9ksUOyoeYaZrO8y6hWXiIhIoNG5vm/pXF9EpPVQH+s7De1fGz2nrHX/6+Z63oZ4fixwOnCpZ/ks4AHgufrW36VLFzIzM9m7d29jQ/WL/Pz8ZnfSEx4eTpcuXfwdhkizFdk+juw6ljVUPads1vGDCNmxldic6qcoiOiU2MAIRUREAoO/zvWb4zm6t+hcX0SkdWju+bTG8Ec/35D+1SsP+jLGBAHpQHdgCvAzcNBa6/QUyQQ6V7PttcC1AElJSaSlpXkjpICRm5tLVFSUv8Ooty1bGn/rNbiPv6X9m9ZXa2+DVnn8baHgydsJKnZC5/Y4H6v6gWBlWePAAtbhICgxkWO6tGX/TztwHjpQ7TYF3UObRdu2ys+AiIjUSUhICMnJdZ+H3VvS0tLo37+/z/crIiLiK/7qYwNBc+nnvZKUtdYWA/2MMXHAXKBnVcWq2XYaMA0gJSXFpqameiOkgJGWlkZLO6b6aO3HD2qD1nr8q3YGsW3yjTgeuZbgu56ptbwFTlz2HscOPa502a6OO/ii/zjCi/JKR9OWHVVbaBzs6X8qE+Y/RmyHuCY4Cu9orZ8BERERERERkep4Y07ZUtbag0AacDIQZ4wpSfp2AXZ4c18iIoHm0O5s3rv9BV4792/sWPE9x82bSXFMPFlH9yI3Ir7GbQ3w+YW3l1vW4fhOHPfWdA60PaY0EVuSmAVwWBeJq9OYf8rvcbnqNoetiIiIiIiIiPhfo5Oyxph2nhGyGGPaAKOADcBS4AJPsd8B8xu7LxGRQJX++mcsOvZ0HM8+SczCNwma8i9++M1kwhOiueLnd/jt3s85FNOhxjoSd21i3g3/Kbes99kDuXT7hxRd++sUCBUTtIkZ61jz1nLvHpCIiIiIiIiINBlvjJTtCCw1xqwDVgKLrbULgLuA240xPwGJwHQv7EtEJOAc3p/L5mtup01hbrnl4UVHKNySyaG9hwgODSb+pmtrrcs5awb5ufnlljkcDo5kVv3Ar5IEbcZHKxoUu4iIiIiIiIj4XqPnlLXWrgMqzZ5rrd0MDG5s/SIige6zp+cRUXCoynXGulhw4gQYMgwTGkJMLXW1KcwlffYycnfsJa5bZ4ZckQpA8er0GrcLjY1uQOQiIiIiIiIi4g9eedCXiEhrlrN5OxE1rI89uAM+mlP10w6rcODGmzDAPmDudeE4fn8Dcbt+KveQr7KKjYOhN02ob9giIiIiIiIi4idefdCXiEhrUnA4nzcv/T+Y91adyleVUK2pnAVCnfkEPfcvHDVsbzHk7D5Yx9pFRERERERExN+UlBURaddEIkUAACAASURBVACXy8UbQ64h8q2ZRBTkeKXOiiNpTYXf1Qm2xXx+0xNeiUFEREREREREmp6SsiIiDbD8hcUk/riyzlMSlGWreH04LKbOI2mrEr1eD/oSERERERERaS6UlBURaYCMt5cAdZ+SoCxT4XXW0ScR3sjRtg5b3KjtRURERERERMR39KAvEREf29/peEy37piQYI69eCwH7n2SoAaNuf1Vdvd+XopORERERERERJqakrIiIg1w9HmpHFo6H0v9R8t2v+9mhl49CoA9P+9i/57NDaqnZJsiRwh9/3FrPbcWEREREREREX/R9AUiIg0w7NqxZHXrX69EqgUOJnQhY8FnbF6xyStxZHXtzTGvvkDfCYO8Up+IiIiIiIiIND0lZUVEGiAoOIgLvp5B7oTLKAwKq9M2Bojbn0nMgtdZf8q5fHj/q7Tv1oED7Y+t9yjZ3PBYhv+ynCs2zaH/b4fWO34RERERERER8R8lZUVEGigipg0XzbmPQz361nvbIOui6B8PsfSp+XR7+M8Um6BKZVym6j/RLuOg//sziO+SUO/9ioiIiIiIiIj/KSkrItJIITu2Nmg7B5Yjd97J3rWb6PzSVLKO7YfTEUxBcBv2Dx5F/DNPc7hNXLltCoPCiH7sMY47tZc3QhcRERERERERP9CDvkREGmHXjzuIObSrQQ/qKvXcM3T6w8cM/PGNSquOXDyCtMfeIvu7n2nTJYlT7ryItl3bNSZkEREREREREfEzJWVFRBphz8btDU/GegTZYlY88w7n/ffGSusi4iIZ939XNnIPIiIiIiIiIhJINH2BiEgjHDWgG05HMLaGMjWtK5G97CsKDud7KywRERERERERCWBKyoqINEJ85wRyho7GUH3y9cDQMbhqqaftjyt5o9/lSsyKiIiIiIiItAJKyoqINNL58x4hq9fQStMYFAaF4YpPZPjjN9f6x9YCiRnf8uHdM5ooShEREREREREJFErKiog0UkRcJFesnUm3D+fguunPuG6+g6Pnzea0X5bhCm3DA3em8cbAm1jTeQSuav7sliR0Dy34yHeBi4iIiLRixpggY8waY8wCz/tkY8wKY8wmY8wbxphQf8coIiItlx70JSLiJSec0ZsTzugNwKE92Vx7xUecdkEEm2OPA2Bzu96kdz2DS1c8ToQzr8o6TH7Vy6V1M8YEAauA7dbac4wxycDrQAKwGphkrS30Z4wiIiLN0C3ABiDG8/4x4Clr7evGmOeBq4Hn/BWciIi0bBopKyLSBJ65eyGZIUmVlu+N7syy435T/YY9ezVhVNKMlVw0lii5aOwBHMB90SgiIiJ1ZIzpApwNvOh5b4DTgTmeIrOA8/wTnYiItAYaKSsi0gS+3N4Gqrnh7btOQxj7/atVzkE77JHrmzw2aV7KXDQ+Atxe5qLxUk+RWcADaCSPiIhIfTwN3AlEe94nAgettU7P+0ygc3UbG2OuBa4FSEpKIi0trekiLSM3N9dn+2rp1JbeoXb0HrWl9zSXtlRSVkSkCeQ5wqtdVxjcBmscGOsqt/xIbHu6Dune1KFJ89Pgi0Z/XTA2R83lxK05UZt6l9rTu9SerZsx5hxgj7U23RiTWrK4iqK2ujqstdOAaQApKSk2NTW1uqJelZaWhq/21dKpLb1D7eg9akvvaS5tqaSsiEgT6JidwdbEE6pc1yE7A0eFhKwF4vZv49v3VtF3wmAfRCjNQWMvGv11wdgcNZcTt+ZEbepdak/vUnu2esOBc40x44Bw3HPKPg3EGWOCPV98dgF2+DFGERFp4TSnrIiIl2Vl7mP0969x3prnic3Louu+78H+mjMbtnlhpW1Ksmxbl33joyilmSi5aMzA/WCv0ylz0egpo4tGERGRerDW/sVa28Va2xW4GPjEWnsZsBS4wFPsd8B8P4UoIiKtgEbKiog00vqFa1jzjxmw5RdseBsitv9M+8Jc2h/eibNoBJeseobVR53KZ93Gk7ppHsfvXlNtXc4pz/BFj6MYfv2ZPon965eX8sMTM4n4eT2hRUcw1lIUHM6Rk0/j4sVPEBysbsKfrLV/Af4C4Bkp+2dr7WXGmLdwXzS+ji4aRUREvOUu4HVjzMPAGmC6n+MREZEWTFfbIiKNsOTRORy5728kVJiOoCwLDNi2jL7bPsdgcVH9bQqhxYXsue0ussalkHh026YIudQH97yE64l/kFjhzvcwZx5hn3/AG53Xc9nuxU0agzSYLhpFRES8wFqbBqR5Xm8GNI+UiIj4hKYvEBFpoMP7c8m5/4Eq54ctq2RqgiBcOLA4qihTVqgzn88ee8OLkVaWm5VD/r/+iakmEvcct1v58N6XmzQOqTtrbZq19hzP683W2sHW2u7W2onW2gJ/xyciIiIiIiJ1p6SsiEgDLfzzNEJcRZWWV/UUpvqWOfLL9gbFVFdf/+9jworzq11fEt/el5o2OSwiIiIiIiLSGikpKyLSQEc+W95kdUd2P7rJ6gbYl/59ncqZospJZxERERERERFpHCVlRUQayAY13Z/Qgx8vw+Wqfp7axsrP3Fmnco7BmlZNRERERERExNuUlBURaaiQ0AZvWtOcsgBtN6XzytAbmiwxa+o0yQJ0GDmwSfYvIiIiIiIi0popKSsi0gBfTP2Itj98XWOZ4hoSn3VJiSauXspnzyyoZ2R1kzCsf53KFR/R86NEREREREREvE1JWRGRBvj56Zm1FzJ1G41aky0vz290HVU57Z6LyI7tWGMZF4ahN5zVJPsXERERERERac2UlBURaYCwHRm1ljkSHtv4HeXkNL6OKkTERTIy7SWyOvcsnUqh4pQKB/qMICohpkn2LyIiIiIiItKaKSkr9XZwxwEWPfQa79/5P37+8kd/hyPiF0VRcTWuP9C2K+FXTKq1ntrmlg3p1bMeUdVP55OO5oqMeYTe9yB5IRGly4uNg/2DRnFF+otNtm8RERERERGR1izY3wFI8/L+nf+j8JmnCS12zzP5w1Pw5cDTuXDJU4RFhvs5OhHfiT7/XHj+aSyV54fdn9Sd33zzOiHhISz43+wa/9Dmh0RyuOsJtN2UXqmu/JAITv371V6PvaIxf7sY/nYxmd9uYf+WvZwwqg+h4Q1/iJmIiIiIiIiI1EwjZaXOvn7lU+xTj5cmZME9yi8h/RPevujv/gtMxA/O/ufvyeozolJC9kC7ZMZ9NYuoxGjCIsMx4TV/WVFw6mguSZ/BwdMnUOz4NX17MOEous1+ns4nHd0E0VetS+9j6HNOihKyIiIiIiIiIk1MI2Wlzn54cgaJFW62LklIhS9ZwJGD9xARF+n7wET8IDg0mMtXvsDyFxbzy2sfYouKaDdqGBPvPL901HhhXgGO3Oxq6ygMCmPc9LsIbRPGZR89TtbWO9m45Buik+I568x+OBz63kxERERERESkJWp0UtYYcxTwEtABcAHTrLX/NsYkAG8AXYEM4EJr7YHG7k/8J3j7FgD2RHXmmy4jyAmPJ/HwTvpt+4zY/P3s/D6TbsOO93OUIr7jcDgYft1Yhl83tsr1+7dmYWz1s8YWhkYQ3zmh9H3i0W0ZetUZXo9TRERERERERAKLN0bKOoE/WWtXG2OigXRjzGLgSmCJtfZRY8zdwN3AXV7Yn/iJM64ta6K78+GJl4L5dQTf111Hc/6a5xjetX258h9O/ZTdQfsZ9+i7xJLHmYMiuPz+cQQFB/k6dBG/SDg6EWsqTnDwq4LEjj6MRkREREREREQCRaPvjbXW7rTWrva8zgE2AJ2BCcAsT7FZwHmN3Zf4V8jYsXzU85JyCVkAZ1Ao7/WZTGhkWOmyKbe9zcMLIJ9QDoXEsi2kAy+sjeGuS17H5XL5OnQRn3G5XLx/5/94ucs45rcbgfX8mS07XrbkdcerL/Z5fCIiIiIiIiLif16dU9YY0xXoD6wAkqy1O8GduDXGtK9mm2uBawGSkpJIS0vzZkh+l5ub22KOyTE4kcv7Hal2/ctvLSHYFhNsXAT3CmNSrxwS44qZNCGnTKlYFs7/kMj4iKYPOEC0pM9AQ7S249+/MZOgDnnE3H62e0GnRJyP3VypnCs2gchuLe9vXlVa22dAREREREREpDZeS8oaY6KAt4FbrbWHTA237JZlrZ0GTANISUmxqamp3gopIKSlpdFSjum+y1/lk+yE2guWMWlCDi/Pjy63bHTCDu6fNc6boQW0lvQZaIjWdPzffbSGw3+8DsuvD8FzPnYzwXc9435tgsgeOJIRT9/OsUN6+C1OX2tNnwERERERERGRuvBKUtYYE4I7ITvbWvuOZ/FuY0xHzyjZjsAeb+xL/GPF3NWs3dsGQqtYaS3UMQkP4Cyu/sFHIs3ZdzM+IJJfE7IVBdti4lalsf61fix/YDqOsDBOvOoc+k4Y5MswRURERERERMTPGp2UNe4hsdOBDdbaf5VZ9S7wO+BRz+/5jd2X+Mf6T77n7hf2URSaUHUCtq4JWc+2gwYneT9IkQDgKnLWWiYIF0z5F3Ge95nvv876keO5ZNHjOByNnuZbRERERERERJoBb2QAhgOTgNONMWs9P+NwJ2NHG2M2AaM976UZmj5lDUVBnod41WNEbCXGcIxzJ2ded6p3AhMJMMnnuj/btY0Fr/jQr7hP32PR/bObKiwRERERERERCTCNTspaaz+31hprbR9rbT/PzwfW2ixr7RnW2h6e3/u9EbD43o+Ho6teYW3537U4xrmLKTPOIjQsxEuRiQSWQZNGktV9QLXTF5QwVbze+8rbTRSViIiIiIiIiAQa3SsrtQq2xdWvrON8sj1cO3jxrQnEdYj1YmQigcXhcHDBiv+RPea3uEz9/ryGZGc1UVQiIiIiIiIiEmiUlJVa9W+fV/UKYwhz5tG+cC+RRTkY66Jd4T4u7r6Ps9psJHn/BsKcRxjz4xucGbcV66whuSvSQkTEtOHS9//BSZ+/R3ZMxzpvV5jUpQmjEhEREREREZFAoqSs1Gjnxl2s3tOm2vUFIRHsCW3H4ZBoTovfydyPfkvvY8PoPe8pLv76KeKP7GXgL0uIevN/vNXvMgoO5/swehH/SR7cnaMeuRuofY5ZgGNvuqJpAxIRERERERGRgKGkrNToH3ct4UBoQu0FreWTg5357NWv2HHfowRVmPLAAm23b+CdPz7fNIGKBKDMhV8A1DjHrNMRTNHvb2LEDWf5JigRERERERER8btgfwcggWvnxh2sKepcc0aphGde2XmvfcfpuXsrr/b8zlq0DLjVazGKBLSgoBpXFwaFccoPS2jbtR0A+zOzWPj7Jyne+CO0T+KEP15MyqWn4HDo+zMRERERERGRlkRX+i2cy+Xi65eXsuBPL/LF8x/iLHTWedtZTyyr00O8ysorrDUgMtdn1qtOkeaq3eCTalx/OL5jaUL2/Tv/xxfJI4j7+B0St35H4qpP2HvVtbwy7AaKNR+ziIiIiIiISIuipGwLlrHqZ17tfCZ7J1+PeeYJDt50C291OJ3vPlpTp+2/217/ffY5KY68kMjqY0rsyb7MA/WvWKQZOnH8EKD6OWWDhw0DYN17K7FPPYYDV6UyielL+fCeWU0VooiIiIiIiIj4gZKyAc5Z6GThX1/mpeMu5JXOZ/FK6m1s+vT7WrcrdhbzxdnXEr9vS7nlMTm7+W7i9Rzen1trHUGmLo8nAqy7XFzhQS69axQbjh9VfrXn98E2iazvNIRug7rWrV6RZq5Tr6MojojGUDkxWxAUxikPXsWRQ3msu/quGmcJ2f/a200YpYiIiIiIiIj4mpKyAazYWcyrJ1+H6/GHSfzlG+L3bCb+iw/4bsyFLJ+xpMZtv3pxMXH7q54mIDLvIJ8+PqfW/Q/r1abadSHOAoKLPXMVGEN31w7+/ffexLSP5Tf/vJKFJ17OgTZtAXAZBxuSBvLK4DsYcVQ+0YnRte67OcrK3MecK5/kpZ6X8FK/K1n00Gv1mi5CWqa4Hl3I6j+SspMz50S35+gZU+h4YhfmnHw18QdqHpYelr2viaMUEREREREREV/Sg74CWNo/55H47edYyj9rK8RVRMafHmTwpFSCgqt+kNDu1T8SVkPdB9dvqnX/l/91DEsvfo9twR3KLTcuJ/dc0IaUM3uxaWUG7Y5JILn/yNL1J51+IufeuI+n5vTh/GiYPerfuEwwI+N3cedzE2vdb3O0ZfVmlp8+iejD+yhJZRd9t5z3H3mY4sdv4ZWH3mXgX66k5+g+fo1TfM8R5OCKr6exdc0v/PTxWqI6JnDmxSMICg7iq5mfkLgpvdL/4xXlJx3lq3BFRERERERExAeUlA1gW16eTxJVJ2ticnbz7Xur6PebIVVuG310B2p65labzkm17j8iNoKpL5/FtHs/5ItfgsgzoXQPP8Tvfn8ig87tD8DgzglVbjvm96eSenkBixZ+zJ/HBTNgdE86HX96rftsrj694j4SD1cezRjsckJxEfGfvc+mzxeSef+DjP7rhX6IUPzt6P7JHN0/udyyX+Z9Sgw1J2QBOlz+2yaLS0RERERERER8T0nZAFVwOJ/DWTXP+7rxzSXVJmWH33wu7z/6JBEFhyqtK3KEMPzOuiUGY9rG8OfnL+TPdSpdXmibMKISIkk9P7UBWzcfWZn7SPhxVa2jHYOsi9yHH+HQNWOJSYr1VXgSwBzVjHSvaNfsuXD/JU0cjYiIiIiIiIj4iuaUDVDv/fdTtsYfV2OZ4Lmvc2h3dpXrImLacMxzj1MQXH5eWKcjhJiH/077bh2q3E7qL2dXNgZb62hHgFBnPl9Oea/JY5Lm4YRJZwGVHwJWlgUSf/mGrWt+8UlMIiIiIiIiItL0lJQNUBt/OMDao0ZQEBRebZmw4gK+/M+71a4fPOk0Tt2wmIIrb+DA8HEcufhqBq1eyOl3/KYpQm61OvU+mvyQ6h+KVtGhn6t+AJu0Pn0nDGL/yWNrTOiXrNu7cYcvQhIRERERERERH9D0BQEqPi6UvD3RbOg4kH6ZX1RbLm/PgRrradu1Hee/cIu3w2uQvNx8VsxdTVFhMUPG9yGmfcu4hf/da5+mTVFe3Td4bx57N0+m3bHtmy4oaTYuXfoU7//pBQpffIE2hZWnLLGAywRx9KAePovJ5XKx5q3l7Fj+PeHtYhhx83jaREf4bP8iIiIiIiIiLZ1Gygao864bRpCriIzEE6tcX3K7c5dT+/kuqEZ46f73OHvip9z7ZhEPznMx4coVPHvLHFwul79Da5SszH2EvD6rXttE5h3g49unNFFE0twEBQdx7r+v58R5/6PYlP+TXDJPcfag03yWxN+yejOvdhrLrssn45jyJIUP3MfHCQN45Yw/4Sx0+iQGERERERERkZZOSdkA1fG4DvzxFCeb2vUhKyIJ+DURW5KoyQ+J4JcFn7Pn513+CrNGK99dQ+bm/Zw79h2mrY6hMPjXqRiKgsJ4Y3M7XnnwAz9G2HhrX0kj2FVU7+1cyz9vgmikJs5CJ1mZ+yh2Fvs7lCqdOLovkX9/mPyQX0ekGiDrxJM5/73HfBKDs9DJ52OvJj5ra7nlDizxyxbw+piGPPJPRERERERERCpSUjaAXXj3mUz7WzIZoy9nS8LxpHU/jw1JA0rnmAwvOkLknFl82nc8Pyz51q+xVvTq3z/g9mkHOewKZX9oYrXl3viquFmPlrWumh7RVNOG3o1DqnfkUB4vj7iR96P68FXycBa2OZHX44az7t2VTbK/9De+4KXek3grejCvtk3lwOZd5Gbl1GnbM+76LWdmfkb43x/B3non3T6cwxXfzCIyIapJYq3oi6kfEnew+rlr475YyNq5K3wSi4iIiIiIiEhLpqRsgDtuWA+ueWQ87/b7PQeikui5e3WlfF5EwSFWXvWXRu9r7+Y9fDXzE35Y8m2jEqUHtu/nheXBWFP7xys7NI7cfXVLWAWi/pefhtMRUu/tzMDBTRCNVORyuZjT7zISViwm2LpHyBog+vA+tv72ct69dVqD6y7MK6Awr6Dcsk+ffpftk64h8YevicjPJjZ7J46DWcztfyn5ufl1qjcyIYoz7r6Ac564mhPO6N3g+Bpib/oPNa43wI9X3tqsv0gRERERERERCQRKyjYDj9y5hNzQGE7a8RVApSe1WyBh5yY2L9/YoPrzc/N5JfU2lp+QStbv/8DPZ17Aq0edzYbF6xpU38L/fUVRUFidyhpbTHhs832A0M+ffkt+WGS9tnE6gjn2wlFNFJGUterVz0jc9l2V6wxgpvyLHz6pfZT5od3ZfP3yUr6Z/zXfzF/JSz0v4aOYviyK6ctLPSay5u3lFDuL2fng4wTZygnLhJ0bWfy3lxt7OE0usnNSrWWijuznnav+6YNoRERERJqOMSbcGPO1MeYbY8x3xpgHPcuTjTErjDGbjDFvGGNC/R2riIi0TErKBrjDB3JZ5+wEuKcrqEpJkjZ7R1aD9vHWmXcQ/8UHODwjCS0Qv2cz68+/mn0Ze+tdX25OYZ3LJhVlERpW/5GmgWDdeyvZec2NROUdLDd62QJHQqPZ3/5YikxQuW0sEOxysv2Pf25wEl3q7pd5n9a43oFlw7jLmPfHKVWO/nS5XLx5yT9Ycswp7J18PZkXTGLbBZNI/Gk1BgtYEjPWseXS37P4odeIzq3+/5cDH35SZf2BZPitE3BV+tqnMvPm7EqjhEVERESamQLgdGttX6AfcKYx5mTgMeApa20P4ABwtR9jFBGRFkxJ2QC3f/sBrMOd2NsVc0y15QqDwug24sR61799/Vbivv649OFhlPkdkX+ItAdm1bvOgacmu1/YWiZOtS7+clP3etcfKNb+bQrBLvfT6MumsQwQUZhD3J5fCLHlHypVdj7gL+541idxtmYmJLjWMqHFBYRMe6bK0Z/vXP0UkXNmEVr8awLSeFLwhl//PUNcReyZ+UbNO3L++kC4JY+9zSudx/FB2Im8HTGAV0bfwYHt+6vddOm/5vFa/AgWhBzPu6E9efmos/npy5qnGmiImHYxHGzXtdZy4c481rz5pdf3LyIiIuIr1i3X8zbE82OB04E5nuWzgPP8EJ6IiLQCSsoGuNefXV76Ov2Y0yisZlqA/DETiEmKrXf9mxavxWGrHxt3ZO36etc58Jy+nMh2MGVqrZCgDXHmc9dYy8Bz+te7/kAR8VPVbVNypI5anuYVsn61lyOSik6afHadyzrenM2hvYdK3xfmFcBbr9V5+/idm8gLi652fcSwIQC8d/sL5N97D/F7fsZgCS86THzau7yfcglHDh6utN0H98zi8F13EZO7FwMEWRcJu37i+9Tz+ekz7ydm2151GVD7s+iKDtdtjlwRERGRQGWMCTLGrAX2AIuBn4GD1lqnp0gm0Nlf8YmISMtW+zAy8ZvCvAIWbYlyf2cLHIxox+spt3D2t7NIPLIbgCJHCLmpZ3PhW/c1aB+R7eOonAb61aF8Q2FeAaFt6jZHbIknZ5zDQ394D4ipcn28K4c+pwyqV52BxhkcBgWVH1JW+83fbiUjoKXpnDi6L6v6nkriN8tqLRvmzOP791dx8pWnA7BtTQZtqvj3rY7LEUTIVVfD809XWpcT3Z7Rj1zFkUN5FEx9njZVbB+/L4N5Pc6h899uZ+TN4wEodhaT/9RTVPV/X5AtZvklt9M984M6x1gXo++/hFcXfEzi919VW6YwKIyTzjvZq/uVqhljwoFlQBjuPnuOtfZ+Y0wy8DqQAKwGJllr6z53jIiIiGCtLQb6GWPigLlAz6qKVbWtMeZa4FqApKQk0tLSmirMcnJzc322r5ZObekdakfvUVt6T3NpSyVlA9jOH3eRF1L+IVLb47sz7ZSH6HBoK+GFudz7WConjKz/tAUl+k8cxpyb2hOds6fc8pLpDL5IHMY3F77NlDkT6zX3a0zbGJ586zI+fO9DgouLcAaV33ZPaDvuunsFr35wFA5H8xyw7Ro+Eha93eDt7dARXoxGqnP511N59ay/EPvJ/FpHL4dGhpe+jvaMPC87tUdNsnsO4opn/8DCmCgOTJ1ObPZOik0QzjZRnLJsNold2vL1K5/SpjC32jpiD+4g909/Zt6P2zhvyg1s/uJHwpx51ZaP2/NLHSKrn+DQYC5dOZ2P7nuZwqf/RWhxYaU2KDrvIuI6xXt931Klkvnuco0xIcDnxpiFwO2457t73RjzPO757p7zZ6AiIiLNlbX2oDEmDTgZiDPGBHtGy3YBdlSzzTRgGkBKSopNTU31SaxpaWn4al8tndrSO9SO3qO29J7m0pbNMxvWSsR3isPhclaem9UYdsUeA/EJjUrIgjsBc+wzD1eaFsEAXx8zim3xPdhAZ+Y8vrhB9R86kFcpIQuAtWSGJPH1vDUNqtffjhzKI/bEbhwJq3okcG0ORbdn1DM3ezkqqYrD4eDyjx7j1G1fkTUgFah6uENuZCJ9Jgwufd++Wweyju1bKSFb1bb5IRGc/PQdAJz1yCQu3vMJwzKWM2b/ahJ7HkPnk44GwJg6jqOe/hwHtu/n+7drHuFrrKtJHhYWHBrM2Y9exZA1C8nqcwouzwPrciMSKJz8Ry549S9e36dUTfPdiYiINA1jTDvPCFmMMW2AUcAGYClwgafY74D5/olQRERaOo2UDWAx7WMZEL6LVYVdyq+wFozhzJOrn7+yImehk5ceeJ8F6UUcMWEkmsNMvqgLZ1w5gsGXjyQt/998/Pg8OhzawpHQaNZ1HkZGYs/SeWGXfn2QSxtwDIXOakYmeurN+GEPze0m6PQ3vuDna24jMj+bCM+yYuMgyLpqHVXpwnCg7wjOfONh2nfr4INopURshzguXvYfXu9zGYmbvym3rtg46PDA3QSHlv+TOGLmw6waewWReQdKlxkgJyKRoOJCDHDkpBSG/fMWug//9W43h8NBfOcEnIVOjhw4zPLpH3Pi2Sn0njCYBWHRtU6LEFJcyPKp72On/qfGcrlRbZt0pHmnnl24Iv1FcrNyOLQnm6QeHQkK1rQbvmaMCQLSZ7HpIgAAIABJREFUge7AFOox352/bq1sjprLLU7NidrUu9Se3qX2FKAjMMvTzzr+n727jpOq6uM4/rlT203sLgsS0o20IqWkSCkliIpii4o+dhcWKhaKgXRLg4BII93dsSy1XZP3Pn9ssLMTOwuLCP7er9fzOHPPueeemZ1dX37n3N8BpmmaNl9RlL3AFEVR3ge2AT9fy0kKIYS4cUko+y/3yid38uSTKzlrLHPpoKLQ1O80/V/r59MYlkwzD/SewyljNJhyjmUQylszHfy9egqv/dyPoDLhrKjey/MYjssLfowG7ysD46pEXda410rKmWSOP/Q0QVbnSrx6TcWmN2F0OJd0tBgCICyCrM59CKlekdb/u5fQ0pe3ulZcOZOfkb5bx7HguR/Imr8IQ3YG1orVqPv6IzTs3cKlf+UW1QjdOY+/Xv+F7E1bwM+fMr06cc+rfZwC3I0TVzJu8BsYUhOxlS1H01EvsWvMPPRzpmN4/2GSXhrFcr0fth59CXriCfji4yLnmrh2O5GqzWO7BliiYhjf5lkavzmEmu3qXtZ74ovgqBCCo3z/EkiUrCupd3etbq28Hl0vtzhdT+Q9LVnyfpYseT+Fpmk7AZddhzVNOwo0dT1DCCGEKFkSyv7Lla1ShvEzujH7q+Vs3ZGMyaBwZ+eK3H5ff59XyP346vycQNaNRedjuGvJLmq3ro7/9ysxGwLd9gsyOJyeO+wO9izaii3LSs2ODQgMD3J7XkTpIPSqDYfOtYRBtPUCLe/1HAT/G60aMRV/q/ut0YwOK8ltuqEePYrObIZatdDpDQSkpxGyaDpp62NZaVfp+vmQ67aO7o3AL8ifXj8OA4b51L9UxdLUf/gu/t69j7B9m7B8tIVJMxbS6JPnqdOlERM7/o/w5XPI/3oh5QxHOt5D3m9E3lJGk8OCaeY4zPc/RqlPPyX+/ZGEpSa4rK7WAE3RofMzFT23E7vgxC6OdFzEppsbcd+uCbKS9QZ2OfXuhBBCCCGEEEL8O0kydB0ICPan/2td+HTafXwwaQBtBrUsVqi3ZL/3kGbm+J0EhgVyd9VCYWOBWra7tDgmvrsAgNXfLWRamTac6jWQswMfYlHsbcwc8oXb2pamABPP3wlGh8XpeIQ1mQ/fqn/dhZPph457bQ+uWpH7j8zijhU/oR49Stifv6NTHShohKXEo/vmM6b2fPsfmasoGYdW7mVXl4FE7VmPQbVjUO1EHdjEoV4PsOjNiYQvdy0z5m19uDJpLBd3HqbWz5+QFFPVpa8CpLXtRpW+HQAPyx/dnBN1eCtT7n7Dx1clrhdS704IIYQQQgghbkzXVyImLku2zt9re2pmTuzz1Je9qKrGX2ootCnRhLUOtsxcT/KzLxCafj7/uL8tC/9xo/mh63tux+/+7B1M+7YBg2sl07VsAs+2MjNtZieqtax6ma/o2gmsEOu1PXXHfiZEd2BzrXZEndzjEqhpQPCi6cTvPnnV5ihK1vrhX+Bvy3I5bnJYyPzss2KP52c34z/+B+LvGYQWGk5i8w75G+1lBoSTPeBh+i34iOaD25LlH+Yx4HV3POCvBcWej/jXiwH+UhRlJ7AJWKpp2nzgJeB5RVEOA1FIvTshhBBCCCGEuK5I+YL/gHJKCkdwX5YAoM7NwUDO5kQWVe8xqk83hbHljdGU0xxu28PWLGbBt7fT9cm2Lm2lK5XhkY+v/83Bb3upH2t++wlToZW/kBO4ltq41OmYu1WQiqayfdwyyn3y0FWbpyg5QXs3e2xzF9b6SgOiDmwipX0PuiRuJP1COhFxkU7lB+rM+YmDnftjUO2eByrAZDejqup1twJdeCb17oQQQgghhBDixiT/5f4f8NB9FZ1KERTkZ8ui7wuXQlSD4v1m6X36Ch7bwsxJTJlx7LLmeD1IO5eK0d9IqZEjsBpcVx9739LMmab6clO6uJaOrDvAtP4foi+0eVtBxfmZezo3cOVibDYHp7cd5c8Pp7Ftxvr8UiCb/ve1z4EsgENnlEBWCCGEEEIIIYS4DshK2f+A1ve14LHD8xmz0d9pw60Qayqfv16N0DJh+cda1jJx9IDnsXaVa0G7AzPQac71YzVAQyHevxwpZ1MJjw5zP8B1aNuM9ex68ROiTu8FICm6KnEfvEPqkTNkn0rAsXEDEYknijVmyo79V2OqooTMfGgkfuN/JMiniq5XxmQ3M7tGDyISc0panAH2RpQj+oUnidqxymUjMG9SasnCSSGEEEIIIYQQ4nogoex/xMC37qJ3hpkF36/k9Ik0mrauSMt7W7v0G/RaByYN3ICqc//RMBuDsBj88bdlOQVFCnCwTH1sehOmAKPbc69Huxdu5cTAoUQ5rPnxXOTZQ2S+9DI2vxAcYaXxT08s1pgaELpyPmf2DSO2ZlyJz1lcmU0TV+E//odixbEqCroCZxQOUr0FqxoQkXjSqU94cjyZr7+O3st5hdl1Bvqs+c7l+IG/drF11AzsSSmENaxFm9f6c2LjIY4sWI8xJIhmj99FqYqlfbyKEEIIIYQQQgghSoKEsv8hAcH+3PNiR699giKCibCnkmiKctuuU+1cDC1P+UTn5bSp/pEsq9mXmkoCgWGe69deT6zZFrYNfZ3I3NvXnUNojUBLGoHn04o9rgLoc+vKxn70QInMVZScfV9PIorilSZID4zEVrUWhlPHUexWwtLOObXbdSaMqtUpeM17nPe88PX0hVajF0l1oNmc6z3PeuQrTGO/JzQvMF63mOXff41RtZH31cnaLz9H9+Qwuo18pHjXE0IIIYQQQgghxGWTUFa48FM817BUdQYa//QOE16bTcULezGoNo5H1WB73O0oOhg2vFaR4yfHJzHrm9Wcis8iuow/vZ++jdI3lfxKvZO7TjH28zXsPavHpFNpVdef+17rSGBoQJHnpp5NYW6TgUSeO1Jk3+LcXi6uAxfOF/uUsKxE2LE6/3nhz4RRtZJtCiLAmpl/TAHSg0sTknHB69i+fr4MaBxauZf63ZsAsGPORvzGfuey4teo2pzGNKg2+PozNt1SnSb33e7DlYQQQgghhBBCCHGlSiSUVRTlF+Au4LymaXVyj0UCU4GKwHGgj6ZpySVxPXF16Yq4cVun0/H6vKcZ8+4yNp024NB0NI1MYcjw5lRpXNnjedkZZsa9u4gpuwKxGcKBcEiGaY9v5ZXegdw5pFWJvYbdf+3j+Y+PkmWMJW9J4NG9sLrfHEZP6V5kMDt/4PtEnj3k07WKG8hqgKroaDK4QzHPFL5a8PJvpI75Bb/sNCwBIYQ9NJjOHz/otAnW8c1HWPfOr9gPHkKJiKTqY/fS/IF26MpXgJN7XMYsTvjurl+ANRPjm++Quv84jmwz5Tu3RN13HL7+vNhjeZpbUKng/GO7vppChIfz3R3bN/I3CWWFEEIIIYQQQoh/SEmtlB0LfAOMK3DsZeBPTdNGKIrycu7zl0roeuIqqhRu53Sm+zajw8LNjSsSWiaMV37qV/RgGiz4ZjlTF57lBGVw6CNdPnVWvT8jZmTSuFMSEeUir/wFAJ99tj0nkHWai8ZRfQy/vbOIxz/v5fFcu9VO4JolJTIPdxQgq1s/YmrEFtlXFN+4pkOJ2raSvK3m/G1Z8OUnTPhzHfdv/RmA9b/+ydnHnyPMYck/L/GR5cwZakCtUh9V0TltZldSq6EDokLpMPHl/OfJ8Ums+vYrDKrn1el517Yresx+wQSbU13mowDJpStSuUX1/GPqxYvFmpvf4b3F6i+EEEIIIYQQQojLpyu6S9E0TVsFJBU63B34Lffxb0CPkriWuPruG9oQfeGQSMtZPdumdCKhZcLcnOVq7qg/OXQ4lY/+0HNUXw6H3vMGYBZDAL9/u+ay51zQ2cPnOKyLzZ9zPiUnxlq11+b1/D9eG4epQFhXkmx6Ezz3En2mv3FVxv+v2z5nI5HbVrpti9q1hq0z1mG12Dj57Otuf8YGzU7U4S3YdEaser/84wqQWKEOGQHhAMXaBCyvv6rouLldA6fjEeUiMTz7gtfx8sJXS++BdDuzkvSgUi4BsU1npPbXbzutBDZWqeRxLu4EZSVxdIP31eEOu4PV3y1k8t1vMLXP++xbutNrfyGEEEIIIYQQQrh3NWvKltU0LQFA07QERVHKuOukKMpQYChA2bJlWbFixVWc0j8vIyPj+ntNOhj+YDbnkh2oij73oEawzkZspWifXk9mchYJgQ6iwmFQ93SfLhtodJTIe2XJsjKou9Vju0EzebyOzWzDHpMNHz9TrGtqig7F08ZM5cpgzx0voFoV/IL9WbVqVbHGv579k78DifHHMXj52R07f5CEeYn4vzUQz2tTQQ/YA4JxhIWg2uwYgwKoUCoEa5aF9ONnMJizAFxW1LpVrgyOj5/BERTKgbMHOXD2oFNzUOdKpNR6DyXRe21ZQ1Aof0yfTcCbA1zmrgAJSiKpBd7nqKG3kt3K7Z9dj3bs2cLJ7Hi3bardQfK+4xhsZgLbRANwcNdq9p3YReTN5byOe13+HRRCCCGEEEIIIa6ia77Rl6ZpPwI/AjRu3Fhr06bNtZ1QCVuxYgX/9GvavWIfP43aSrJZR40YhWFfdCMwNKjY41izLSz9ZR0XEtJp1aOO13qxhT1290R2a3EM6p7O+DkhPp3zRLNM2gxuU+x5Fma32rn77oWkGd2v6G0VdJohj/R02zZj8KcETPrJ92spBlIb3Eq9V4ZweNCTBFhcA2j7x89gfmc8NSd8Q727Gvs89o3in/wdGPdQT6JOeb4NP7VCbcKG9Ie3RhU5lt0YSErVekTs2wiaypmwGCIfH0Lv9x4k9WwKmYnplK4azdSb7iLi4nHP43z8DGm/H6D3go+91jL+85NZmF97xevcgxKOYbJluW2/WKs59+/4zenYnB7PeS2NUFhK6270XPaZ07GstGyWvPwztvG/EWhOcyqdoJHzL5HMZ/9H10+HeBz3WvwdFEIIIYQQQggh/s1KpHyBB+cURYkByP1n8bc0F8X25sBJPPb5eTbb4jiij2XB+Rg691/P6skbijVOcnwSrw2ewScLHfy0I4zH3tjPx0OnYrV4v/U/z3FraLGuF2xLo8fTbYp1jicGk4E+jQvd4J1bysDkMDP46SYez83avqtY17rt8Eru3/gjDXo2o8qE79gb0wSzIQCLzsTxyOosq9Ybc3gZ+qb+Tb1u/71A9p/m36yp1/aAls2ockdDNJQiSxCYbNlE7f07fwV0WGoCjhHvM3/4T4RFhxNbuzxGk5Fb53xLaliMx3HsAcHct/JLspIymNLrHcZVu5dxLR5n89Q1qOqlVbaVWtXxOh9j0vmc+rie2uOPuxxLL1WhiFfpLKi68xcv5gwzMxoMwDjmawLNaYBrLVsA26gvOX/kbLGuJYQQQgghhBBC/JddzVB2LjA49/FgYM5VvNZ/gsPuYM6Xy3iyx0Qe7DqZjx6ewundp/Pbl/6ymuWprrcRO3RG3hyf6nOgarXYeOLhpazPjsOhy6kDm20IYl5CNG/cP82nMQI1z+UDCgu3JjPi2YoEhgX6fE5RHni/Gw/WSSHYlrtyVVEoZzvHhw+Xosat1Tyep9l8X1WoAcFRl3a7H/PrIebUf4Qv7viKkR2+YXLT4Wyq3JEUNaDYobi4PF2+eSqnbm8BeeGrVe9Ht++HUanpzSTVb4WC99qwutzWwvVbs8aMwZp9qR5tpaY3c0/8UpKjXANQDTBkZzCx/Qusr9qKkHmTiDq2k6jNyzk3cAjj692fP1ZY+UhUL2GxPSwKh6Lz2G4Lcd0kr9RDA7y8QleZU6eRePLSBmGLX/qZqFN7iwywjaqVpU+MBODsoTNsnrKaM/vcl0EQQgghhBBCCCFECYWyiqJMBtYD1RVFOa0oyhBgBHCnoiiHgDtzn4tiOrrlGH/P2sKF4xd4se8UPv3TyA5HHId0sSw4F8MDL+5i68IdAIyd7jkEsen9mPzBIp+uuej7lZwyRLs2aBprM2M5svFokWO0urmIOpvkrI59snkms+Z2pUGnuj7NrTiGfNSdub/fyejny/DbW+WZurgPzXvd4vUcv1o1fB4/MzASvyB/AOL3xbPT4WZzsVwzZxz2feLisgVHhVB17gSy/JxXamf6h1NryWT8g3N+Xvcs+5LE8rU9jqMqnv80BmWncPCvPU7HLhxKICLxpEt4mRfoRqxZgLs4tdSBTcx+6HMAouJKkVyrmUsInHdWVL+epNS7zWN7qX7dXcbv8M59ZN77gM8bk4WnJvDHk1/mP0+bv8TpdXhjWLeCCTEd2FyrLecGPczWeu2YWLodCRLOCiGEEEIIIYQQLkqkpqymaf09NLUvifH/i45sPMq7b2/giD4WAN3PO1F1cTmhn3IpIjEbAhkx6gBTOtUlRfVcrxLgwOFL9U63TF3Lnre/IeTYHhwGI+bGrWg/+n/E1Ihl8+bzgJuNexQFUFi3cC9VmnqvL/voh13Z1n8uEOzS5mfPpkulNB77oAtBEa7tJckU4EedtjV97n/L0/dwcM5Ep7qZnoQ+9UT+49P7z4GXIO9ilt5jmyhZtTvUp3bGJjZPWUP8ml3c1L4RDXo2c+qzb/FWIk7tdfszTomsgGoyEXnWc5BuDDA6PU/OXV3q6TPj9bM0fzbwKgB3THqfFa0HEZaa4HRuYqM23Pf+IBIf6cTSWwcRnnjSqT2p6R0MeG+Q07B7/tjGlle/JeDA9vxVwb6Eq7o1K/Ifm1K8bz5WUKA5DZ051Wle4SnxrGvcjagFI30eRwghhBBCCCGE+C+45ht9CVdpF9MY9sZOUkyx+SGsquSGeoprrdQzxrIcWHOQSF02qYR7HLfyTTmbfa3/eRkXHn+GKM2R0+CwELB2EStbbqH95pmYDN6jm9VbUlnffQKVY4z0e+pW4urEufQJDAtkzPQeLJj7B7U5TYZdT5kAB92630SbQR3Q6a5m5YzLV7V1LbZ060/IvMke+6goZN49gH4fXArBKtaLQ1F3oyk6158REB3suCrzFZ417ncbjfvd5rZtz0c/EeVh/agxOw1jz4Hw8zdu21PDYqna2nmVbcVmVTmk98PksLg9x5sgc05pEZOfkdja5el+cD7L35tM8ppNKH5+VB7Ylc5Dc35nylSJpvfheaz4eDoX/tqIzt+PqoO70vm+1k6/U1umruXU4MeIclwqI+JLIAugtxc8p+gV73l0aG6DX39rJiknpKS4EEIIIYQQQghRkISy/0LTR/5Fiiki50lewOcm6Ct4POVcOo89VJmXfsty21fvsNH/f3egqipHXv+UCM01JAxJP8+y4d9y56C7+ePXDJdVuQBoGnuVOFBhZzzMeekgz7XbS6/hHVzG8wvyJzImjB/mdUdVVdZM2cim1SfZt/13Ot3XkCqNva+2vVb6zHiTFZ/X5/RPUzAkncdWJpbARvXRbDaM4aE0G9aT2JrOQXTZKmW4xT+BzVbX1cyg0ec+31friqvPdPKIx7ag7BRqDO7A39NnEJ7mvHmVAx03ffCyy5cKwVEhmDt0x7Roms8rUvNkBERg8ru08jYoMphuXzwCPOK2v3+wP53eGwQMctsOsPeFj4h0+F7XuaDsqpdKidj9giAr2WPfvNeqoqBD8/i61ZSUy5qLEEIIIYQQQghxo5JQ9l9o78EM8LLitTCjw0LNVg0Ijw6j6x9TWHA22ikU1KkOXutlIigimDN7ThFx8TgaOaUPdpRvxcmIahhUKzXObiV63Tqa//4OraZPZHWG6wrYwiGtptMzcjk07XyauFpu+pO78nfwAg7pYoGyAEx++yShtl3cVdvBA291KtFNvq6UTqej3Ys94cWeTsetFhuJxy4QEee6oRLA29/exXNDFua+zhwGh5VSgSpNezS6qnMWxWMLCoUM11vzNXICxnW9hxFZIJDNCx/1qGSeT2bll3M5Of0PNIuViNub0u71/rT+5DGWHj9JxL6N6HNXmGYERuJXqrTXuRju7Zv/ODMpg9Vfzib9+Bki69xMq6e7YgrwK9ZrO3vgDJFnDxU7HAawKQYavf9k/nN7VBlIPu2xvwI4FB0pZSoTdc5zuQe9w8bSD6Zx52t9ijkjIYQQQgghhBDixiSh7L9QkL8C3u6CLrQSs+HJlTisOaHfKz/1o8/mY3z70ToSs3XcXBYef+dOSlUoRXpiOvH7zmBXDGT4hzGx6QukBUTlj3Mg+hbqn1qF/+DPuCVAR7iawbzM6p5X6ebR6Zk6ah3DR7sPXD58aj6HdOWc560opJnCmXQI1vWby49TuxMY6r0m7rVitdiYPfgTdPNmEWDNwKY3kdG8Pd2mvE1Y9KXwPDw6jJ/n9WXDzC1sXXeS4GATXR5uzp5je7yMLq6FkO53wY9fuQSXCmA3+hN54ZhT/4J9Mj/4EJvDQn40v2MVi0Z/j8luplTuCvT04FIEPfQAPT98gHXr15Ly6DD0P4xy2ezrYqO2DPzhGQA2TVzFiUefJ8CSTiBgBmaN+IJGs0ZTrY3nTckKs1tsLnP2RXJkBWp8+SZ1ulz6AiHwloZweKvHczRAr6n4J58tMgRO+vRL7C/2wmCSf+0IIYQQQgghhBDyX8f/Qp17VmP52Ey35QN0qgNVl1Nf1mi30Pjkcm4/OJsV75Si98/PAVClcSVGzqyUf05yfBIv3DuRTVllceiM+LX7lEBrhlMgq2gqnXZPoEH8GsjNEOsA5grtWFqzj9dNrADWHlXY2nkKmaqJisHZDHqwDrfcVR/VrvJ3WmnQ4z7c1TSOG2IY9+4iHvusV/HfrH/AtI4vErF2Uf5zo8NKxNpFzG12lP6HZjmFTDqdjhb3NqHFvU0uDeCc74l/gS6fDWHK6r+J2rfB6XhGQATB2Z5v1wcwOSwuAWSALdMpbg3JuIh59PdcfLgzAD2+eYLTQ7uy+s1fsO3dD2VKU21Ib1p1aYTeoCflTDInHxlGgC3L6Voh6efZ0vsJKif86XOYGVunPKvCYwlLOeO2PTGuJlF9exBcrhSZCYn4R4RSrVMj4ure5NK39uCOHJv6s8fANe9YkDUDB7r8FcLuBGcmsm/pDup2vcWn1yGEEEIIIYQQQtzIJJT9F2rRuzEd5k1mSWKs03G9w0avbd8TZk7EYgikdHo8fg4zAJnb3a/GtFpsPPnwUk4a4iA3V7UYg7AYg5xC36bHl9Igfo1T+KIBjU8u52JwDNsqtPY65/N+ZfIfX7TC1u8v8vKpFegr2LDr/T2fmHv9NXusPOb1CtfGsY2HCV+7yCWU0oDIMwdY/c182j7f4xrNTlwuU4Af9239lZVfzOX0jMVgsRJ+ezO0hAswe4LH8/I+B4UDSk8bXG1o2AX1i+HssWyjdseG9J/9Dgte+pX0Mb+QOHQpK1FIrnYLfnVrE1wokM0TmnaWtT8spvXTd/n02nQ6HdH/e4asV19Byd18i9z5ZfuF0Pb3LynfoKJPY9W6sz6bqzch6sCmIlfCegtk82iq7xuHCSGEEEIIIYQQNzIJZa+CFePXMXHacU7aQunT28bOWTN45MO7CAj2Ek4W8vovfWn602rmLzpNmkVPlJZGw/UTico867a/PsJ9DdrFP6zipCHa/UUKrFxtdHJFzqGCzfltfxUZyhZe1avqDHwzJ5Xhw4MwOKzY9Savp1s1vffxS9ixjYfZMXYxmkOlzsA7qdrK/UZc69//jTA3x/Ne6ZnF60BC2euS3qB3qR087/kxXs/xFEp6Om5yWLBnZ3Do7kGYx/1A/Lpd6L/7gpD88zSiDm7GemSn1+sm7Snecut2L/ZkpVHPqU++JeLCcVQUkmo0oeXoV30OZPP0+Os7Znf7HxFbVqAUKr9QHBmBkdTo0OCyzxdCCCGEEEIIIW4kEsqWsGkjFjNqjR8o5cAIdiWdacci2d7vd0bPvMdpl3VvdDodnYa2ptPQnOd2q50ZZce59MtbvVb7yXvdjrNl03kg1m1bHkVTCc9O9NgekeW6IZLrIK6xVJoxjOyMTFqGpbAqo5zbcgx5apa2F32NEqCqKlO6vU7oklkYcwOmg798y4ZbO9N/2efoDc7hsG7Vn17HU4zyK3S9SjuXyqrPZpBx5BQhVW+i9f/uoeXzPVn17ShMqtXtOSqKS11YXxhVG3tf+BD/lPMU3NIu77fB5HB/vTzpi/9kXKXl6CpVovFrD1Kjfd0ir9n62bvh2btJO5eKIcBUZM3mfUt3cuD3Vej9/Gj8aFdiauT83QgtHcr9f49m3M29iTqxu8jrehL27NM+//0TQgghhBBCCCFudN4LhYpiyc4w89MKh2v9VU3joBLL/K//uuyxDSYDN3/3EVaD82pbBcjoNYgGPZu5Pc9kLHq7H03Rkeof6bE9NaBUoRN8D6U0VeWVb7pRk3jXQDZ3HH97Fg8838LnMS+X1WxlfMMHCVsyEwoEaxoQsXYRsx8d5XJOcKbnsBqg6sDOJTxL8U/YPHk1Syq3Q/nyE0LmTYaRI1h8U1sW3vceRtXm0l8DkspWQRn2Amqxt9DKLXdx9jCB5jSPfbyNGxW/j6jTe4lYvYCDnfvy18jZPl87tGyY10DWkmlmXONHONrlXoxjvkb3zWdsqteeGQ9+7voiLoMGKNHRdHpnwOUNIIQQQgghhBBC3IAklC1BG37fSpYx2LUhN4xctc6HFadeNO7fiqab5pHRYyCJ1ZuQ1KIj0eN/ou/U1z2e07FnjZwHboJUnepAp+asUN2aW56gYK+8x7qOnelV4Tx3Rp6hT+ULkLvDfFGMDguBoQGERIXww5x+fPZgMPV0p/G359bOVBQqOxL4/Lk4KjWs6NOYlyv1XAqzS99Gqb1/51y6QFveY9usGU7n2K12r/GbBjTp36okpyn+ARmJ6Rx/+FkCrBlOxwMs6USuW+xyi37eavSGYz7grs8exu+Nt8g2Xfo9tyt67DrvK6Z9iXFTS1fEYnANTwv/5uo1B4lvvEdWWrYPoxZt1oAPidqxyuk6ek0lYMKPTIi+k7VuqskmAAAgAElEQVSjF5NyJhnsrmG1L5QC/y+EEEIIIYQQQogccu91CbJZvYeVdh/2uLFabMz/5i/Wb7iAToFbW0bT5fHW+Tuvl6tTgb7T33B7rsPuYNeyvZizrNRrX5PAsEAad2tA26mT+Cu1nEv/TmXPs/BCDGgaGyp2oFRGAnXP/J3frgBJLToyYOILHN9+gsPbTvHdjETwtAt8ofIEneOS0RvLAjnlGJr3uoXmvW7BbrVzYvtJAsMDialWRK3aEjLntoeJMqd63awoJOMiVost/xZrnUGHQ9Gh19z/4Ow6uRX73273om1sfflLgg/uQNXpyW7QgsAaVVwCWW/yPi87v5lOnc4N6fBmf7Ke7cG2SSuxZpmp06Ml1iwzq9/8BZYvIcTD6uqU8FgUFMJS4p2O530mQ3p3p81r/Vn1yXQyT5zBsnc/pQ5vdft5DbBmsGHMYtoO7+mm1XeWTDOmpfOcXmdBEYknSXl6GH+8HEVUEavGvVHtvn2RI4QQQgghhBBC/FdIKFuCmnSth2nm31j17jf0uqV2iNvjedIupvHk/Qs5po8B4gBYuwxmL53ON5N6eL0Feflvaxk16RwXTVEA+I9ZSc8a2Tz+eQ/eGdePup8sYcGqZFIcfsT4ZXNvr4qsXZG7Nk5R0BQ98+s9xKaKd1D5wm5AoW6H2nR5vjNP9JrCHi1nPuSO71ZuIOtny6KiPpmIyCDsVtdasQaTgSpNK3t9L66Eqqpsm7aWg1OXoSgKlXu3I+L4rpwpejkvMyDcqealTqcjud6tlNqx2m3/9KZt3V57+sdLWLYuEYMO+vWpDK55uPgHbP99A8f7P0xUgXqtARuXYt2y6rLGc6RcKj0QGBrArY91cmrvN+ttTm1/gA233UOAJb3QqnOF2FeG4R8RwvnHnsGgXvq9UICk2Or0/uABAkMD6DbyEQDGNXvU63yyL6Ze1usoKPH4BfxtWUX281bGw9sXHXlMIYFF9BBCCCGEEEIIIf5bJJQtQeHRYdxdJZ0Zx11D2QhrEjq9nj/HrqX1gGb5K18L+uqFBRzTxzqvOM2tR/vd/+bxwug+AKyfvolfxx3iqD0CP9VKzdAMNmaVRS0QmJoNgUw+HIjp1bk8MqIHfV7uRJ+Xna+3ZNFxlzmcC63AudAKAOgTTjN+6EayjXFeN+nKmydolLIlcdFUigMEcmAP3H9zGlM/WkTfV4pXe9WSaebo5mPsm7wMc/w5QqtV5PaX+xBaOtTredZsC1NaPk7U7rXk9bwwb7JvdTq63O1yqMOEd1nZog8hGc6lJ1LDYugy8S2nY2kX0ug/8C9STeFAHKiwa4qVIXcnc9ttdgwG+XX7J+184WOnQDaPyWG5rPHCbqnjsU1VVY6uP4Alw0ydOb+w8dlPiTiwGZ2m4jCYCB/5Ga2fvguA3WXHs/Wt0ZgO78XhF4Dpzjvp9dUTLl+6aFnew9LYZjUu63UUFF6+FDa9CWMRG415k/dXwVM4m+UXQumIoMseXwghhBBCCCGEuBFJSlTCnvmqF2FvzGfWZpVkUwSgEWxLJ9kUyZhtwDY7/lOW0DY2laFvtKd0pTJATqiz+mwoGHEOP3Mfrzhm4gVg8Y8r+XCOA1VXDgxgJpC/zeEeqwP/vl3Hg1a72xC4VvVQ1m5zc1JuALsmKy5nPgXm4ZGiEGDL5KLJeVMwDYVv1vhRZ8V+arcpOkTKSs3i06fncPTABbrv+Bk/hxk/gMWwZPT3xH3zCc0fbM/G2VuZNG4fJ7P8CNXb6NgyjD4vdWTOI18StXutU0Dky0q+xNot6ffrCy7HY2rEcueu2Sx/6SfMa9cBCgFtb6frR0MIiw536vvE/QtJNcW4hOpWxcjbg6fx/kTZ6OifkpmUQdTJPR7bVRR0bnauKvxZyXue5RdKx9f6ux1r4/i/OPTCB4QnnQIgyz+M8Iceot3q0diybWw/sJ3Wbdrk96/TpRF1uvxY9IvI9l4z1pp+5TVlA0MDyGhxJxFrFvj0e+JO0CefcGLUWEqd3uu2PeKNl90eF0IIIYQQQggh/ssklC1hOp2OBz+4m8GqSsqZZOYsXkmGMcopqDMbg1h0IYhFT++lnn4pI37uRmBooPtNwnJl6INQVZXRMxOdVsQWJc0YxsmdJ9n793FWrUrArkLTRpH0HNaWToOb8cuWnTgK10YtECgWGcbm0VSyjcFuz9F0eqb9vI13ighlVVXluUG/c8QawRM7R+DnMDu1B1gzSBz6BCPebs6CuoPRlJzQ+Dxw+G/Y1G8KTZbNyXkJBV9OEVO/WLEeg7b+jE53KdnOTMpg4dPfYFuyBIMlE3PF6tT59GUa3dvS7RjZ6Vkc10fnXtA1VF+XFFnELERJcji81zB1KHqy/UMIyk7OP2ZXDBg0u1M4qQBmYyANFo0jtGyYyzh7l+7gzMNPE65e2gQr0JwK333B8u++RFN02L58kT2WMGp3bFis16D4+XltTz6WUKzxPLl72tvMbn6CqJO7i31utimIlA++pFTqGRwo6AsE3RqQ3fchur50DytWrCiRuQohhBBCCCGEEDcKn+7qFsWn0+k4uu0UVkyew01FYacax0P3/YHVbKOM7YJrn7zxNJVRw2bm14z1iaaBpvLh238z4g8967Lj2GiJ45v1gQzuPZfJX650DWQLzc+nawD19fFezzmXWvQuZxt+38oeLY5aZzbiZze77WPXGfizxr1oit6lbaMljkS/Um7OKjDdQo8TY6tz357JToGsOcPMrEb3ETjtV8JS4gnKTiFq3wZO3zeElaPmuR339J4Er++XTe89YBMly5Zt895Bp+PuMyswvfUu2QMexu+d92mxfzlJTe/AoeR8V2VX9FyseyudTq+maquabofZ/Mb3GFX319KhodccGMxZHOzxAPuW7izWa9CV816MOGnL/mKN50lQRBDB7W4n0881dM5jNl0qP1DwdyjAmkl46hmA/EDWpjOS3LobNVfO5d4JL5XIHIUQQgghhBBCiBuNrJS9ig5si4dYigw3z5rKMHnEEnq1CGD0Zvd9rAZ/t7VqnRQOfxUFvcPOfn05l7bTxrIsOZgKJh9fjCeKQm3lNI8+3ZAnvrroMYAuG1Z0/r9++TGgDKHmZI99TkRWJ9vkecO0PbHNqZR0wGN7SutuoGloqkrD/w3irs6NXPose2sCkfH7XW7n1msqCW99jOOJLugNzqFwXO1Y0E55/FlfSc1OUXwB4UEuKzcLMvuH4h/sz52v93U6Pmjtt6SeTeH8oQTK1ihXZA1j40H3t+wXZrKb2fTiSGpuH+tTf4CwWlXgL8/tjqwrL18AMKXrK4Qvn4O7rw1SIsoR+ciDNO/fmj+HfEDY9jUYVDspURUISTqNXnP9ssWo2ghaswRNG1Ii8xNCCCGEEEIIIW5EslL2MuxZsZ9Fo1eyf+1Br/3KxHoPdApavzOTAW905p6bzqP3FOBp7gOmfG4CQYfe6LEt3eC5XIIvAmyZPNPKzPez+1PvjtpUVc+4vY6iOrj3oQZFjmc05nwck4LKeOxj13tPkVMDvK8k1gcFMvDPzxn01xfUcRPIAiQt/BNwX/YgJOMCexZtdTkeEBJABfu5nCcFf065j5uGXfQ6L1Gy/IP9sBs8r052GDx/jsKiw6naqqZTILvnj22Mb/kEE8u2Z1ylnswf/hNWiw2HX4DHcQoL2e/6ufGmWvecUhmefuvLtm6c/1hVVTZPXs2U3u8wtc/77Jy3yadrHNt4mLDlc12ukfe86udv0PmDQZSrU4H7N/zAbQdXYH3kaRwRpd0GsnlMDgvrBr1K2rlUn+YhhBBCCCGEEEL810goWwwnd53i/i5TePTzc3ywAB4ekcBDXSeTcPCs2/6tBzVHr3mvbZnHoSnodDqe/eZeBtXPdN/J1/quUHSAS06t1yuRbQxi4p+ZfPHkDJb+tJp3Pr6NGNt5pz4KGk/dZqFOW/e3fxfUqX8D0FT2lb2FTA+rYculHEWnOjy+votBZQHXICvveVzH5kXOA7vde7PZ/e3q347tSLAtzWW1slGz8f6Evm7PEVfHxWPnPZbAAAhPPcOMBz/3aay1oxdz+O6BRG76k/Ck00Sd3osy6lOmNH0Yvzva+z6pYu6iVb1tXRLr3Oqy8RhASlQFWg/vAYAl08yEBg9y7v6HCZk7ieDfx3Oq10DG3/oUDrv3vz+7Ji5DQXOZWt7zIzMuLdXd/vsG/qrdGdOYr4k6vKXI+Ued2suym1qx8JWxRfYVQgghhBBCCCH+aySU9ZHVYuPZFzdwVB9zKRDUNA7qYnnumVVuww+Tn5GY0kYC7B5C1gIa3Xyptuv5C5Yrn7APAW64NZkoa+LlX0PTSDRF8fvpsrwzR+WlF9YxatStvH4X9Cx/jgfrpFC5QgB9X+ns03BVm1Wha/Q5bMYApt3yNOl+4S59gi2pNDq5wuPry/KP4K9qvVyCLAVIKledlkM7FjmPwJZNPbZlm4Kp4WHDpojocBYu7MojDdK4WY2nhhbPa12hctUIDAapFPJP8g8OQEPxuMpUAwIm/FjkilK71c6pV97DoDoH9RoQtfdvQirHkhRb3ac5pddy/VzF7z7JjAc+Y2Lnl1nw0q9kJmU4td+z8luSW3XFnlv7WUMhsWZz7lg9DlNAzkrg3weOIGrf3y71kiM3LmXesNFe56QU9cVM7u+ZNdvCgQefI8CS7r1/IX4OC+pnH5GVVLzzhBBCCCGEEEKIG52Esj5a/MMqzhtL5zzJCwRz/3naWJYV49e7PS8oPJBJo5vQLeYsRof7sFWn2qnfvHz+86xs31bXXqnuDWH01y1pGXAag5uSCWWsF3ixvY1yNvcrgQsHo6eM0bzzwl90erQ1w7/rw5CPumMweQ4jk04n8vb9k+nYaT5tuixlcJfJNG5RjudbWwgKNjGx5f/YclNbl2Ct/f5ptDw8H0V1/z79Xakj8Y07Yc3dXMuhGEhq0p5u635zqQXrTvsPh5AenPOzLnxtwwNDCAz1fMu6Tqdj8HvdGLtgAD/NH0Dnx1oXeT1R8kLLhpF0c0OPi1Pzju8cNS3/2ObJqxlX736mhrVgYvSdzHxoJFunriYkw7X0RN755+f9Re9dU7ENHUZihTokl7oJu+L6mTcbAmnx5XCnY4vfnMDmRp0JmDiG8GW/w8gRzK90B/v/3JXfJzA0gIHLR9LmxBoqzp9Cs/0ruH/nb5StGpMzbmY2gQumOc2p4OPMGbM8vUUANBh8J6qb8DrvebX+HdgxZxNTq99DcOblfYGjANlnpXyHEEIIIYQQQghRkCzf89G+3ReBaI/te7adpf2D7ttK31Sal37sy3CrnZ7d5pFsinRqV3UG3v/tIlNaJRIZF0VQgA7SSnDyhRgcVrpWSGbIh/eg0+n4ZNp9OOwObBYrm+fv4sSBC8RVieS2vj2Z8+WfXFBcV6y6pWns1mI5uesUFeqW99o1PTGdoQ8v56wxFnIXCR8hlnd+tzO8ncLno+9k5terSYhuzfY6damwagaR6WdQgKzASM6FVvBcfkFRSKjWnPsWfMDZ/fGUrhJNaFnPO8sXtGPJLmaO20Vyk0FUP7iCCmd2oNccpIVGE/bIg/QY8YBv74W4JswZZk5tO0Z4uSiaf/86uzsP8FrGQE1MAuDPj2eS9cbrROXVSc1KgvE/cGBxJSI9ng2Ys/EP9iOmaU0yDxxFtVixdepI6o49BO/bklNqAwV/exZ77ujDppAoDB06Uq1vW6wjPsBQqC5rcFYyW/sNo9q5Zeh0l74zC4sOJyzadYX2H6+MxeClREpAmvcwtHyDiqzp2pfQBVOcjitAYqX6JA3/gIhzR7y/Bz7QWUpg9b8QQgghhBBCCHEDkVDWR2GhJkjw0h7ueVOhPPvXHsoJZDXNZZVpljGYqV+s5PHPe3Fru4osmGxx269YCp3fKvg0LZpH06JHA0rfVJqstGwykzOIKh+F3qBHbwjgtr5NuS23/+IfVjJyhREMPi6oVhRA4eTeBJdQ9uiWY/wxaRsWq0qzNhXZtvYkZ41uNvRSdIz+w8I3SzdjMeRFQbH4NavCK/cE0vLeJgSGBjC14wyvUzEYFIIig6nS0rdbywF+fnUOv+4MBaUc+MG2ujXwq57JsA4KfZ/r6BSSiX8Xh93BzEEfo587A39rJhoKSVUbEfPaa1jfeSO/hEVhAbWqYc22kPjBJwQVCkg1IPLCMew6g0v5gjy6C2eZHNOB8KRT5H918TeYS91EVvN2RKxZiD133alRtRKWmgDTx3Js1mRMHjbKCkuJZ/Ok1TQdWPQq69RFy/G2tV12iPeN7wBu7nsH8YtmYlQv1UrWgPBjO9F7LABRTFfwZ0wIIYQQQgghhLgRSSjro+5DWzD5uT04dK5vmdFh4e5Hby1yjL0bTgABHoPWwyeyAbitX1PqTJnMbuKuaM551wmxpVE/PI0gfz3HjyRTevNxPnhuCVst0ag6A5HWJHo3MzD43W5Op4+fnQCGGN+vp2mARsXasU6Hh98zkQ3mcqDkBEQzxmYSYPPPXyFbmLtNviyGAD6ekcHM7jYggJaVHMyO9zyPAU/5sKFXAUc2HmXszlCi005SMXE/Dp2eg2UakhpYim+WZnDHIxavZQvEtTXpjuFErl1U4IhG1KEtJH5+EmvVW4g6tMUlmDUbA7n9rfvZMWsDQdkpLmPm9c0IKU14qvtvZCJS3H8IIy6eQFtzwmOkafJQyiRP0qFL46qqyrIPp5MwcTaKxYKpUQNue3MwcfVuQrF6H8fQpp3XdnNmNscfeY5A1XnzOgVKLpAFtICgEhtLCCGEEKIkKIpSHhhHzu2QKvCjpmlfKYoSCUwFKgLHgT6apiVfq3kKIYS4ccnSPx/FVIvm0RYWlEKr23SqnafbQ0S5om/wjYp2DRsLCg/J+XHodDre/rw9Fe0JKF5uTfZF/6oX8dcsrMmMY/HFGKYdK82Lv2ay2RaHquTc/p9kimTMtlC+H36p/qTD7uCEvqzngTU3gY2iUE9/hrg6OWFydoaZo4eS2WCJcw6iNY1sY/FDmmxDEPO/XwPAk591J8jmfvOgevp4KjWsWKyxZ43ZQI/tP/Dg+g9pe3AWd+yfzuOrXuP2Q3PIMgSx5Oe1xZ6vuDJ2q51Dq/dxbONhVNX9qlKAvUt3EOEUyF4KVEMyLhDQpBGJNZo6BbJpIWWoPO47ylaN4cTSjV7noeo9fHvgRV4A7GmBqLcNyABiGlUF4PTOE0wt1RrbO29S6vBWok7tIWTORLY07syKL+ZiqF/f4xxsOiPdfnre5bjdamfBS78yrnJPFkU1I7CYm3e5Y9cZsSvuy4nYdEbCq8S6bRNCCCGEuIbswHBN02oCzYEnFUWpBbwM/KlpWlXgz9znQgghRImTULYYBrzehe+HR9M+/Ax1lNPcGXmGH18tT6/hHXw6v/WAZoRbPXzJqqn0HJQTsKSdT+Xpp1dz3BCDViDoUDSVEKvrij5vFuwxcMFU2vlgoY3K8szcG8CFExeZ++wPTKg70OtqPpPDgr7QLd0Gh5WbyxmwWnJW3X334lxsiptA6wpKMpw9mwlAQLA/E75vSlU1Pj8oNzis3BERz9cz+xV7XG3NSmqc21YoLNO49cgCapzbQtKFrMues/DdmX2nmfXIV/xW735+j2zBwXY92HtrVybHdmTDb3/l9zt3KIE/3p7EkvemsOmpD73eHZ+9fRf37xpPteWzMb31LlFjvqfX6WWY07OYO+wHmD7Z65wik04W+3UU9Qn3tgGZQ9ETv34PqqqyovNjhKafd+ln0Bwkv/waobUrewx4LT36Exga6HTMYXcwqdlQGDmCqFN7i1yx6yuDaoMnhpHlH+p03GwMpMKEH9H5sMGeEEIIIcQ/SdO0BE3TtuY+Tgf2AeWA7sBvud1+A3pcmxkKIYS40Un5gmKq07YmddrWvKxzDSYDrw2N442fLmA2OIclA2skU6ddWwB+eXcJCW7qrWqKDiVvhaqP9WbTjL5tcAU5O8SPazecGif/phRQy68W28vf7rbvbaUSCQ0yMDv+0mpau97ErFPRHO03jVEz+7P8qB933+Lz5X1SsfKl11O6Uhl+XTCAzOQMkhNSKFOlLCa/4q9qtGZbqHL8b8D9DvaNTqygTtO2VzDra+vkrlN8/d4qtqZHYVcMVNWfZ8gD1WjRu/G1npqT+cN/Qv16JH6ag4IVmjUgPPEkZ4c+xc7Isez7bREBc6egz11FXlTVVCUxZ7Orqq1qUrVVTY6uP8iMSl0ITzqNHiiqKIWnerRXi15zoH3+EdOOnCLi/FGP/UyqFe2zER7nZj1zzuXYipFziNq99qq8JsuYMZhLxWKpdBvGMlGE3FyBTi/0IigymBUrVpTw1YQQQgghSo6iKBWBhsAGoKymaQmQE9wqiuJmIwwhhBDiykko+w9r0bsxE+qfY8pXqzkWb8Ggg2ybxpLd/qzqPJXbaxtZf1hzX29V00jzi8h57EMga7SbsRn8izW/0NSz+Y9vPzSHk5HVSAqKdupTxnaBgU82Yei7x6HwAjhNY7s9jpUT/ibdEAIUb4Vpu7DTbLkQTKop3KUt3JpC18c6uhwPiggmKCK4WNcpKPl0Ev72bI/tEdnnaXx3g8se/1o6cyCBx17cRpoxLv+3fT/lePmXVN41r6f1fS2u7QRz7VqwBUZ95raOad4n3aDa2fnYG0ScP+bUq6jfhLDzxzh3KIGyVWOwWmxs6PYI4QU+50W5VntU6RfNKbKPt7kFbVnlcix+6kKiijjvcgVZ0wk6cwDOHCCldTe6fPj6VbiKEEIIIUTJUhQlGJgJPKtpWpri4119iqIMBYYClC1b9h/7EjojI0O+8C4h8l6WDHkfS468lyXnenkvJZS9BqJvLsuzX9/D+umbePXXZGx6v/wQdsIBMOo83FJcjNv+Q6xplDOlsp/yvp+TnUR06on850HWdAav/4ht5W/nSOm6GMOCaVbdxKBX7mTJ2L+x690Evrlz/Gvpcco4jBS9DtFZcKCBj1+qyqsfHyDJdKlOb6Q1iQ9froFfUPFCZl9ExEViMQbgZ3MfzFojo9Hprs9KH2M+XEGa0bWep0NnYPSEk/+aUHbHp+OJ8GFjqdALOZ/P4oSKes3B+i9n0ePbJ1n77QKnLx6uxNVeQXultV6NNjOqqjp/di3mK5xV0TQgfOU8dszpS/3uTa769YQQQgghLpeiKEZyAtmJmqblbbBxTlGUmNxVsjGAay0pQNO0H4EfARo3bqy1adPmn5gyK1as4J+61o1O3suSIe9jyZH3suRcL++lhLIlRFVV1k7dxJIFRzBbNRrUCafns553PldVlZE/H8dWuEyBpuWEtB4Y7dnYDO6DToPdQsNTK4lOO0Xli7vZUqc7lPUtlFVUB+0OTEdXKBjzt2fT/NgftDj2B20TtxMYmnNtu93zxksADgf0aBFIZjF3cC8bHUiddrWYcWtVlo9dx6mjyZSvHMEdD3bDYLo6H1dTgB/mNl3wWzrTKWjLexwzpPg1av8ttp81gclNg6ZxyhDNhWPnKV3p2t+RpSac8amfXvP+ufPEmphTi/ni9oMEFtHXF0lRFYhILH6tWV/lffYy/UIIusxwNqVcdZcvEwKbNIQDm658gl7k/f7s/XW+hLIlQHaGFkIIIa4OJWdJ7M/APk3TRhZomgsMBkbk/rPo25eEEEKIyyChbC6H3cGYl+cyb5eOTH0gAY5sOlWzMuzre4o8V1VVXhswmdWZceTUhof1m+Cnvqt5fEAmWWnZ+WFmnsMbjubUjS1cGzb3saLa0XQGl3aHzojOYXO7I/ytRxfS8uilXehv2T2XDZHNsBldQ1yj3UyoNQWLzo/YlGM0PrGcKMtFt69PAVKiKji9htt61OXbtQfRFJ3z/HPn26JFNF2ebMP0CXPQqXZUXe5HTXWg1xw49K5Job89i55P5dSwNfkZ6fRoa7fzuRp6THmDGbeeImr/xvxjCpDWtR99X+/7j83Dm8ykDBYN/4HspX+is1nQaten5UePU7lZVY/naEWs5VQvL+Mscbq48nB0R5H9bHoTRoe12OMH3ZSzWji4fAzFecnuVsNmm4LpfXAuR1fvZX/fRwm4whWt7uRdM8iSjkPR59fP9ZUGVH75cZfj7d9/kCWzZhCclXTVV/qqlpLZREzk7wy9VVGUEGCLoihLgQfI2Rl6hKIoL5OzM/RL13CeQgghxPXmVmAQsEtRlO25x14lJ4ydpijKEOAkcO81mp8QQogb3PV5T/YVsGSaWTJmFTM+/YNj244DOaHqsHumMOFAJKmmcOx6E+mmMKYfL83AzlNQi0iuZn66NCeQ1ZxXhdoMfiRbjAztO4fM5Ayntuz03FvlPZQk0PJCzELtqs4AikKYNSX/mNFhoeWRBbQ4utipb6Atg75bv8bk5rZ8m8Gf8okHeXrFS/TePpqbkg/mBzXulH5yiNPzuFpxdCh9znX+ikJlRwJNutRizhPfYrRk8USp7Qyte5GnWmQx/YtafPhAOAH2TKfT/OzZvD4gjNAyvm9MVpICQwMYuOM3Yib9SlafB8keOJSaq+bRf/Y7/4rSBZlJGcyq34+ACT8See4I4UmniVi9gG1t++TUY/Wgki3efYOiEJN6nNKVSl2lGRfPzYO6ohYREaaFliWzdefLGv+mNvUBaPV8D8ymIJ/PKzwjh6JHGTCIRc9+z5aXv7wqgWxBGjnlFy5WapD/vCg2nYmA9z7g1sc6ubRFlIuk6dLxJFZpmP/a3P11y7tOWnBprAVW7hdn3XtEg8vbEFE4k52hhRBCiKtD07Q1mqYpmqbV0zStQe7/FmqalqhpWntN06rm/jPpWs9VCCHEjek/tVJ28Q8r+eL3dDKNIYA/rDxGM/+1dOteme2OOLfnHDfEMOm9hQx86y6P4y5clQS6WI8B63FDDD+8vojnv730JWuNW6sSNHJZ7lyKR9UZaF3RTNuuoWSlm8k4eAaWur+rpnR6PJT/h7YAACAASURBVKq7UFHT2F7+duqdXku51GP5hxXAqvfD5MhZ5WbTmbANGMy9b+Tcwm/NtrBy0kZSEjMpW9afm86d4SzhWAyB+NmzaVU6mdsbBbOuTkdMdjP2as8QMulHzMZAyv8wkphq0cRUi2b67anMGrWS+DOZxMYE0uupVkSUi3Sd51WQlZrF0l/XkZpspmGrStS9ozYAOp2ORve2pNG9Lf+ReRTHouE/EHn2kMtxf1sW2598l7pd3f/8q8dvYH+F8qT7Rzgd16l22h6YwZG1Tana6tqEZw67gxVfzObsp98RlhzvUjqjMFvlGvSe/CbzqqwjJOOCz9fJNgVT9fZaAARHhRA76hPOPjUck933+qrZpmCym7RCTThDqbHfA1DS1Y3drVzNe25MOk9S2SpEnjvidYwsv1A6HF1GmJcvNyo1vZlK+6dw7lACaWeSSTx5lgtDnkRXoDSEAlgM/rTfMhO/QD+WvTEW/7HfO82vqJW2qd//SPJT3f6x3+v/gsvZGfpabUJyPbpeNgO4nsh7WrLk/SxZ8n4KIYQQ4lr7z4Sye1bs56O5dhxOIajCBkscxybEg6m0x3PnrMtmoJex0+xG93U7C1h+2MhTFhsmv5yyA35B/vSuZ2fcvgKdCpcy8OJcop0mdzcEYOWoi+Stw7XrDBwqU580/0giM89hNfhhd1ejNvc6+6NvcQplgfxAFsD4f/buOs6K6n3g+GdubXewRXcj3V3SpTQGCIoogorxxVbEVhTwJyJKl4CkNNKdi3Qt291xa35/bO+dezcoXc/79fLF3jkzZ87MjXWf+5znmPUYV68g+ImuRISn8dXaRFK1rmQv4OWQ/wqSzQyomcLTbz/OrmpdsC8S8LI3pBPy/GvU67MPZy8X3P3ceHbWgBJd6/20Zd5evt2YTmDCDepFnGDPggzWuQcybvFLVG1e46GPp6Qydu5WXDJNBjzDLhN5JRy/2pYLejllJjP26GccqDmAKxWaYlRpqRx/mfbXNxGUeJOMxFTLTskOmB5afYKIOwnUaBhAs36NbY4vNiSWH2bu5Gi0M3pJRzV1HE+PqU7tVlWJDUtk1oRVVK/uSv8XO+Po6kBWWiarmj+D1/XTuJf0JiQl4uTpTMdDKzjarD92xgzFwGDRbeqxTxdaIK7N+O7EdNnJgVnLSb8RgjnsLl63zts8tc6Qgd7FGc9D5x7YtH9bfWozU6kx5wMinpmMxmwo1GaU1KS6eKPt2ZtBi15DZ1/Mh1GOCjX9qVDTn5rU40bNDRwa+z+cwm4gSyrS6zdnwMbPcPfLfnbSDh6zCEIXdw9c02LZ/fZChv32eonGI9hW1pWhH9UiJP9G/5bFAP5NxD29v8T9vL/E/RQEQRAE4VH7zwRll/90GpMqsPDGnD9qY7S2M7kysKzfWlCQUxZRBpu7kKjzoO/Anbzc157+L2UvADZh9gA0Mzez9jQka91KHJAFCPDJH1Oz0Z3Z8YYDsU5+/P7YZNLs87PkHLOSbPZjVFleW9Ggk4M+lb9e+IzVDZ7DpHVV7khSseaWL1nj5nOt3mjC3KthZ8zkCTsTjiodWrMeB30qB77ewOOfjC3xdd5Plw5c4fPNRh6/tIaG4UfyG2IucKLTIbI2LqJOt4aPZGzFURmU63PmPk+ZKZYlKgA0jRvjum01/S78Sr8LvxZ6bjN0ztTo1MDimAu7LvLuF5eJ0XkDjnA4kSpzV/L5t50IqO1vsX9iZBITJ+4jWhtA7lvlMoG8uSQN9ZLzjBqsYWuUB0TB4n07+Pyt2vz9y0a8rp8u1T1QV68GQEC9itTfuoTjz76DZ2j2txqZWkcy2nTFfOM6XmGXAUhzcEc3dhwDv7esrepTzZchP78CQGpcCusfG4lnhGUmci4ZsNu9BXiwdVityXLx4PbzM3DKCciakUh3cIfH+/H4nJdwrXBvZT+qt65N9WtrrbbbRdwpU7/ph4+VdUhCAfeyMrQgCIIgCIIgCILwz/SfCcreSVBZvVpZUts8toqDcjZhrlFj63J6YVL2olc2ZGid+Gy7jEq1l74vdkGlUvHsrAGM0xu5cz6E8OuxvLVMObhWkMZkYPiL+VPsD687w446w7nh24gMnXOhfdPt3PJr3SoEfavEX877OTdgp5R5mIw9Jqn4l8tGXWvwz78PKXYpbGw+lZEnvkEjG0m9E15sHw/Kip9OUTMmgobhR5CBDK0zJyp347pvIyTZzPFXVjDreC10DgqZxY+YXL8xHAhVbEtx8qZikyoApCWkMv+trRy4pSFDsiNIqkUTnwZUjwm2CLZLw0ej0aqIvB6FZ0VPdHZaUuJSmPHlTVJ0BWrNyjK3Nf68+sp+lm15wqLG7uJZO4nWKmSaq9SYUAP5WdNJOndmfnqRYae24ViK6zeqtLR+55m8x7W7NKT2rQ2Enr9DclQiVVrVyluILvziXdLiU6ncvFqJnktnLxeGBq/h97pD8Iy+qbhPYsW6eIcEl2LE948ZcI+9U+i5UyHjnJGAY8v69xyQLQm9szukxZX+QPV/5lfMAyNWhhYEQRAEQRAEQSifHvgKRpIk9ZYk6YokSddzVoh+JNzsrKxenhOwdDYkW2k389xk21O3Ww1uytSOerSmEqw2Lkl8uymj0OJh4Vci2L36PCcP3EZdTB92xgyG1Upi29KTrPtqB3Fh8Xz1ezLBQW0tArIFz6kUkPVPvEXN6PwV761lAEpAo/Cj9Li0srirA4XAdKhnTS4EtgHAs+GjKxEQGi9RPzw7cy/aJYh5nT7hcI2+RLtWJMqtMker9KLv4F3cORfyyMZoTdtPXyBLkx10LFp51WXSBNQaNVlpmUwatZkNYRWI03mRrnXmqqYSa5pO4UyN7nnPb7q9K6ljJnHe4MfjQ/YybNpl+g7ayeznVrHm6z2kFM2Gznnt3NX4cXj1CYuxHb9mLNW1xOi8idOVvM5oup0rHl99RvW2tS3aghpVpl6PxnkBWYCA+hWp2aFuqYLrjq4OtF75tdVFwDxCLmIq5kuXByXFzd/qezP025/zfk6OSiIpMpHwS6Gsf/57Vo/4hEM//onJaOWzrxTchmaXGinNQl8A7r273PO5hbyVobtKknQ2578+ZAdje0iSdA3okfNYEARBEARBEARB+Jd4oFEGSZLUwFzgcaAeMFKSpHoP8pxFndsZzOznVmHKjYHKRcIKkkRNczjLFrTFVx9TqF1rzOK1biYa9yx+SvuwGb3YvKwtHV1C0RazgFCG1olTm7ODod+/8juj37zG4kse/B5SAZNS/VcAs5k+PmFUkBNZecOb3y568PU+O56YcLxEi4X5G6Isrl1nzOCOZ23SdC5EuQRxx6OW1eNloHnIXvySyjaN+UqFx0hx9qHDy9YXTHvQPB3NOBjSkIH1TSZi0FhWac3QOvPqG/+8KdfVWtWk5tqFxAfWyQvQpTh5w/Q36ftZdgbp6i92cVvjb/E8y5KKQ5V6UffQJmruXEf/yP3sjPVjd2JgXqA3Q+PE5kg/Np60HcC7ci7SYltpA3UA8R4Vi90nrmU3HD/7jP6RB+g4pW8ZzlI6NTvUpdGfy4it3NDimlTIqAsshPUgKN3HuMoNcUy1nqHqEXmDEysOsLjqYA4EteRgxVacbtQN3cIfcPp9MYkvTWV51QHE3i75wmhK+nwxgbjGHUtVuiHevxbdPxx3T+cVxMrQgiAIgiAIgiAI5dWDnlvaErguy/JNAEmSVgIDgb8f8Hkxm83Mfm41W6P9Ab/8hiIZo+76BN6Z1Rqvil6s2z6Ma8dvcnTrRbz9XOg8piMOziVfY93Jw5lZy0cTcv4uo/6nPA06V/jNOHYs2M+qG97FhsY99XG8Mb4iCxdLhGj9Cy0IplcILCqJkVwtrv2Odz3ueOfHyDuFbKNSwlXFwEvuttqRp4h0q2z1PDpDBm1ubqNe5Al0xkzkHlMITIjGpNbSdP3/KWYvxofGEXs3gYoNg0p1v0tr0LCa7DxTGa1JT4KTn9X9IrW+BO+9RIMudR/YWMqiYd9mNOz7BxGXw8lMTqdikypodPlv4UOnkwAXxazoFJ0bWwa9RZ3JTxIRlsxlAi0XlpNlYnVeNsfg42eZjV3Dy8Qd26WLLVRu3wCubrPanuTmz6i9cwpd38NQs0Ndjjg4WLwHHkYdWcX3XWAghqgQtCa94jEGtY6wp57HSzbmBXWL9uMZeZ2tA15l3PnFZR6bRqdhzPH/48jCXdxa/ieyXg9qFe5Hthf6+JKBdAd35Mf7M3je1EIZzIIgCIIgCIIgCIIg5HvQEY9A4G6Bx6FAqwd8TgC2zt2XHZAtGngCvPRxVHfJoEEtF4ZN74qrd/507Zotq1GzZbV7OnelRhXx1J8k3sYU7eqNA/n+m9NAkM2+apnDaNXIgT3brnNVClC8HsD69hzGYoK3nmlRtLq0sdjgU9GV3wtSmYyMPvElfsn5T7nRmMHo4z9wsfNT1Opcv9D+YZfCmPXWPs4bA5AlFfbGa/SsmMz0H4Y+kGBc++EtOfLHOZL/ii1239Cr0f+4oGwu/zoBittNsu1nT2fIIOuD99jbeypQz/L1kvNYks2K9ZGdDCn0GN8dyP7S49LOcxxeeZS9CbVKlXMfYIjiqfnPsi4rCbtlC1AVyRFNdvKh15m1Dz0gm0sXeuuRnFeJy9HdpLTtgePBrYrtWfYuuKRlv56tPfsy4HnpOGHBIQQ2qFTmsahUKto915N2z/XM2xa87Qxnv1yKOSICdcUgWrxl+T4XBEEQBEEQBEEQBMHSg456KMUJCkVgJEmaCEwEqFChAvv27bsvJ45zSGTsQJ2VVi3VK7mi0Wk4HVz8CvCGTAMxESlkGLMjTw4aMz7+LmjttVaPmfS8HaFxKXi5mxg7MKVQmx16YiWZlv2daCqlWOkhV3bA2Le2lrEUt2/ZuWRmIfd7keKqg1Z19GGsRnkcTvpkXLoPLtxHoC/y7CnUVmsKPbdmk5lbN5Np1M+NRqQV7IXVyzYSULXkNUdLo8XEmsQ/7snYrGRs5T9K/tr79lpMTU29b33Z0nO4I80zlZ8bSTbh26UfJmTq27lQ2c76a8lJpSfNrKXg/ZFkMwFeao4cPUxWWiYZd8JQG/U4N3NmtL21xelkJFnG013Oew9oZAMVg9zZv38/3k83wzCiIcl3opCyMpFVKtSurlQI9OLCjWC4UdY7cW90Hz6N0VCC+tAlFeiL8bOXy3SoRPYngLG/5fEmtRZ7kxFjCQtInLt6nmuxtjP4S80Bgt7pn/cwnBjCFV7rD+s9IAiCIAiCIAiCIAj/Fg86KBsKFCweGQSEF9xBluWfgJ8AmjdvLnfu3Pm+nPiJ2WuIsFFr9f9erUD9znUstpvNZg6vOcnurTcwmWRq1HBhxRGZlCLTul0Myfw4uxaVm1ifyr/+653EkMGSP/LHUdEYydffdcK/lh/LvlrOJQLLcHWlVEwWLcCwU79RM+a8zX3C3KqxpOVryFZWVB9zfD4e8dcLbTN+9jKaN+agAfx3rKV2l+z6vL+9u4lFZzwUx6qSzaz4siaBdR/cvXlhwFIuyMp1Taubwpm0deR9O9e+ffu4X69rW5Kjkxg3drdiCYKefy8nMGQfAAZnf5a0/0CxfIEkm3laOonu0F9c8mtOir0HroZkHKsGsc5QGbM5jecOvo+jIRUZ2NfwWS4GVrE6pnbXN+I6uRFhc07g3bgG45dPeWQZsCW1ccMV1HPnIGMZtlfaVpzc98D9IAMJPtVw6teb7p+MZWflztgb0oo9BiSanN1JQP3ia/k+CA/rPSAIgiAIgiAIgiAI/xYPejnxE0BNSZKqSpKkA0YAGx/wOQHwd1CuwQigMemp2MByCrhRb+S1J1fw5tIMdsYHsCcpkJ9OuZKic7PYN0XrypyPD9ocw+DpPahR1ZlXOmQytk48n46yZ9mWJ/CvlV3PdHCfnDEUXXzsfismIAuQ7GA7M/V2QGNWNX/ZakC2q1sYTg5qm32YjPkLJZ27mJz3s86YQd2IEzQKPYhnWhRmlZoTf14qdsz34qslQ6hmCre49776GD7/vtsDPfeD4urrxvzv29LaPhRNTg1Sr9Rw+p9bSLOcgCyAT2oELW7vVCxf0P76Jvy2r8A3NZxO1zfSN/g3Ol5ZT4NdPyMZjTQJPYCjITV7d0Bnsr2o3aEaA0h28CS9Yk0m/wsCsgB9Ph9PXKMOFsHXRM+KJPcdQZq9GzLk/VcWSseVpC8JcEiKZtCPL+Hq40r6Y21LdEx8vVaPLCArCIIgCIIgCIIgCIKlBxohkWXZKEnSFGA7oAZ+kWX54oM8Z64nR9Tk9JI0UKiNaVRpWfPVXsZ/OrDQ9t/e28LxrKDCGYQ2skzPpHkXOw6VWsWwGb0U2/q80JlLF1azPsSHB7mUkMakx6i2Vsoh25mKHWka8heSQmgoPrA2uhGjyLroaPV4P197dK1bwe9XFNuT7D3wKxAUstMCeqgffoxeF5dhVyC4d9G/JWEurYDuti/sHji6ObJ460iC91xk9S/nMJmhe+/KdBozBJXqQX9X8eD41/LjyzWjWdxpGi7HdqM1ZSm+srpdXoNfUginK3Ui2cELj7RomofsoXbUmUL75R5rb8ygacg+vNMiCrXXjTzJmUqdbb5PZCSOOjXhi8lreevnEfd+kQ9B0Ki+hCIjRUVi9vTGu1dHjDsP4r1lpcW+pc2ezXzqBQLaNeTa98tRh93G5OoJ+ky8wq8We6wMOOhTObxgB+0n9abz3Nc40vEEThmJVo+J969J301flWKEgiAIgiAIgiAIgiA8aA88bU2W5a2A8io1D1D74S2ZELyRn8+4KmYELrrgRpsDl6nXIb+EwfYzWaCl8P62Fs9S3fvte3Xekww6eZM5s45wLtM3P3gqmxUDynlkM76GOKJ1Pjb799THUc0pg5MG2wuKxbgEsa3+GHr9vQy1nJ/RmuDiR+cNc1A72LFk+t+YFa5ZMpvo90wLLh1wI3brFtwz4rKHWGCfv2oNxvDjIZ76MLv+ZI/Hq3Dj56v0O/9LoYWeZKB+xHGMa09ybngrGg9sYXPc96pB1/o06Gp9YaLQ4FDO7b+Gp48zLQc3Ra2xnQ18Pxj1RiKvRvLHwmPExmeh06po+JgvHYc3L7QonS2PL3qLna3OoUsMU2yXgAYRx2gQcazE4wpMvEmSY+EvIirFX6XJ3f2crdix2ON3hnnwYmxyia/hUYi6FsHOTk/jEXOb3CIQ5qgbxKpVeF88rBiALfi4uABtYrdBjPzpZVQqFW2eyc/ITo1PZcOT76M5eRhJlnHISEItmyyOz+07ZMshmNSbik2qoDq8hr1Tv8Xl4HY0Zsuq0NqWLfGqVPwXSIIgCIIgCIIgCIIgPDz//LnE9yCgkjuctTIpWJL45vNTLCgQlE3CeiaoklpS1L0ML0/15tX4bl01Lh+8yt71wXh4OpCZYeDnc27WMxAlFQaKDxBW0GXSs0cgJ7dSbG3ZcxU7cMOnAfUiTuBgSCPStRJPvdKGik2qAPBUo+MsCna3OG5UnUQqNazIzhWnWdtqBp2vrKNO1Ck0ZiMGlZY/mjzPVb+mVLiZHyDsPLYN59/9oVBAFvKDThrZzLkPfyx1UDYhIpFtCw+TnJhFw5ZBtBnWDIC4O7FodBo8Aku2gFh6cgbvjl/HsYwAZEkFpOP580bemliRNkObl2pMJT3f969tZG+IE6lal5znKj+QtuVP+GrzEUY1yuC5zwblbTcZTZzYeJbkuDQadamNX40KADh5u+Az610uLfmT6kfWl6kWqsUYdc4EB7SiUVh+cFICel9cSpXYS5yq3IW7HjWtvsb0GnsuH7xOy0FN73EkD872wa/jFXO70DYVMt7BhwHb99CMREJQHbxClUtvJHTqz5g/P7PYvvuz30l+7wM8TPmLixVXykDtYJf3c2CDSvj3aEvm/i2Kz7PDplXE3Hwen2q+xfQqCIIgCIIgCNnCgkM48O4v6C9eAhdXKj81kA4v9vlXzyoUBEH4pynXQdnYyFTAyWr7DaM3ZrM57xeLnyqFG1hfHCyv/qgkgSxjlmHPb4fo+lS7exrnrTO3+XDmEa6pAgAv1GYjbVzi6eGRys4EK4tdyTIJOk90xgz0GgerfdtpZHo914GN21YSjI1s2ZyAbaq9B8er9gTARZ/E0pW3WLDsDrV8Zca90pbaTaJYu+oaURkafByMDBtWnY6jhgDg7etEir2WTY3Hs9n8NGrZyEhnPVf9su+ph5s273QqlQrf9Eib90Vz+5rN9qLWfbWDH3bL6NVOgBNcSafCL+uQZfIyiqubwpk6pR5N+zS22df7E9ZxNDOIguGxeJ0nM3+O5bfaoQQ1sJ15XBomo4mpo9dnL/qWe4sUApsGjT2//W1P6otriI7XE5akIsLsRoY2+zWu2hBMB7fdVKvsxIozWjK1TuD2OMN8blAz5vw9BWaz1PZsajQek0rDyUpdaR6yJ69NAqrFXSS2ZnMMhhgidQrBv5zXl3uFf26WbFhwCJ5XTpb5PqmQ8Qq9RKbWAa0hCzX5GefxLbszfMunFsec++ME6TP/h87KlxNF5Y6tSt/CnznhW/bjaeU4jdnI2eV76TFzeKmuRxAEQRAEQfhvOrv+GDdHTcLFmJG3LXX6fpb/vptRe74SgVlBEIT7pFx/mtZrYXthG73GnvjQ+LzHQe4K+Wk5gVg3fWJ2oCw3WCZJXFUF8u5aI188v7rMY0xLSGXqm2eyA7I55zJJag6mBZGQKuOkT1E+MGcc7bzikMyW05xz++rWwRe1Rs13K4YyskYsvvpYHAxp1DCH8/bjMjuWtaSXd4RFEFBtMpCic+NvArml9md7XADPvnUJOwct364bzYptw5mzbjQdR7XOO6bnhPY4GbLHK6vUGNX52XyS2USLDpXyHt8+eQOTzt7mvTG6liyrFSB47yW+3aNBry7QpywTpfXJDsjKMsgyN9QBvPZ9JJf2X7ba14XdFzmSHqCYWZylcWDRlwdKPK6S2PPb4eyAbAkXfPs9xJcDqUHcVAfkBWQBzJKav1ICWRTsnh2QzbGx8XguVWhKwZBdlEsQF1o+wclKXTgX2I40nY0vIwC9xg61bARJYmfd4axsNpWL/i2541WH5L4jaHlsI7P3TmdUj5xF8YpeiyRR0RhJrTY1SnSNj0LkxbtIyPecUWxvyCCpTQ/kV2ZgemEa9rWqM/bQXHR2Wot9z8362SJb3JbcsV1991vM5vygr6S2/VGueghlNwRBEARBEIR/P7PZzMVJb2NXICAL2ckBHoe2cujHPx/NwARBEMqhcp0p26R3Q7RzdmAoEBwsSGU24OTpDMDuRQf5K0UhKzUnKOevSyXTaEdW0axUWeaPsAr03HGBxj0bFjummDsx/Pj+Lk6E22OU1LiTQbzOr9C5cjNxTxqCCCKSNBvZu+diHJk5TM3H643I6gJPpyTRSAplwMvZCyvZOdnz4jdDebHAsaF/hzJx+B/c1vgXuh4nQzJpOsvSCVkaBz6fd51V/Rorfjvq4GzP22O9+GBZskVwVFapeXNZJtV/WkSnO1upEHKe4kKuvmOGFrNHvtULz2BWFXn+rNQG1mvsefHT2/h9eo5uj9kz9r0+eQGzPb8d4sNV6cg2FkbbEVOBxt/tZsDUblb3KY1Df4UCATZLSxRitaSFtbIBDmx47Hnc0mPxTg0nxc6NaNdK2fvnPAkqs5GuV9bS4s4exT5cspJ47sB7rGgxjXhnf2751OeWdz2QJOZM8iCoUWUABr7Sjf1HVnJSXziT2NGQypuvWq/d+08Q2KQq4ZIKSTbfc2BWG3yGfvvnALBn125WDHgH9b6d6PRppPhVo9L0CXR6uT/qOzdL3bcMeIZd5uTyA7Qc0wmAikO6kXpom2KWr0Gto+3Yrvd2QYIgCIIgCMJ/QvCWU7gnhFpsz/1/zFuLNtBhcp+HOyhBEIRyqlxnygL0Doy32tbMPgoH5+zg4Yp1IdY7kWUuE2QZkIW8QNjmlReLHUt8aBwTJ+5ne2wA8TpPkrVuhGj9lHfO6TdUZ6U9t0+dJ0cOhBUOyOZ1ITP/tQ1M6Lecif2X89s7m8hIzQQgIzWTF6cdLxyQzTlvms6t0BjyyDIRWl8u7be+Snyn0W1Y+k0jhlaKxl8fXagfyWyiw7nlVAg5n5cbaC1HMLZhe3q8N8rWpRcSbn3xeUV6jQMhGj8WXXDnyQEbeWf0cpZ/spVZK5PzF1uzQlap+XIH3DhZ+oBaQUa9kQ3f7OJclO3zWShp8LaIJEdvbvg2Itqtcl7gP5dZpWFX3RHc9KqXt63oc+OalciA878ULuMB3DgfkbePWqPmq1UjmNHdSBNNKLXkcNx1Bn79rimNezQo07gfFr/aASQ0bIeE5bXn5s+WOKc1J4s1MzWTxEu3cN22GqeMBLQmPZ5hl0l99TW2vvUrJhe3Uo8zb7Gv3SfztrWf3Ie4ms0Ug8nmsRNKXEtZEP6t4kJi2TVrDbtnryUuNPZRD0cQBEEQ/rVSo4r5wyrVykxOQRAEodTKfVD2pa8GUtMcbrHdWx/H67Pys8ci9NZrz5YkCJaWYS52n58+2EWM7v6ugu6kT2ZXfIBlgyxzzlyRlTe8uSwF8jeBLDjrynNPbCAlLoVNP+wjTudleZwtOfchOS7V5m4Btf2Z8GFv4lWFM3yrxf2Nf3J28Dv3jha8s6kOHsRVboDLN18z9uQC1KWYcu3tbKWhBCUBYnXe7E0OZN5RJzI1JVvszazSsPrH4yUeX1FZaZlMHrqaL/doS/6aKEl5gxKWQAAUX9enKnfJby7aNeCffAfflMLfnPtXLrz4m1qjZsDUbvywfjS/bB5JhUoeBNQuEvz/hxqw6QviKjUodO0GlRbpldeJ96tR4gxaY5PsBep2fbAMtSFLcZ/073/Aa0jfMo/V3jv/vqs1aoaf+IX0EeNJdvVDr7Ej3r8m9h99wpAFU8t8DkH4pzObzawZ+zkH0GFMQQAAIABJREFUa3Ym672ZZL7zPw5U78zap7981EMTBEEQhH+lml0bY5Q0VpMR7BrUs9IiCIIglFa5LF9w51wI82Yd4GySG2ZUuMga1Co9Jk12RqJkNqHCxOaFxxgxoyuu3q64SpkklaRzK1PH69ctPuPtSIgWSpkUWdw4tBhLPpVdlrmt8eeLqZtyfslaWUTMBp0xk/qdHit2v0sHruVkFud/k+qfdNvmMaqBQxi3ZEapxwQwdFRdDi5ItmwoY1ZpSdyNVqjlW0K/vLuVv3PryJZ0jCXZ7x6vN96pgvWuc/51zkoimux6zb76WNo+OfiezvlP4h7gwZhraziz+hC3d57Ezt2FDlMH4VXJm9XhMbD6erF9GCUNXLnMkvYvIUeE4/aicukAh6wUvOpV4Wrzbnid3G3RnuARhHtCqGIg2KDS0uGlgYW22TnZ88SSGUDZ3kOC8G/058ylOK5cWGib1mxAu2wBf1byo/eHYx7RyARBEATh38mnmi8pHXrjsX9zXmms3H8zdU50+PCZRztAQRCEcqTcZcreORfCpDfOcSg9iDStCxlaJ6J1vnkBWciefh6t82XxJQ+eGr2TqBvR9GyWU5qguEzDgtO+c/710scxeGoXq4foswzMeHJZ6TNTixlHVVMEWVIporw5Abs9SYEci3Ut02l7V0zE1bv4Y108LTNOi8tCtfMs3Ziy0jK5uO8yocGhtBjwGE/VT7BY9EySi89gLiv5HuKfey/mjNNK8LyCPoo29ndprr1LTXMYzbWhjG+chIMhrewnLQHXjHjSdcppxzLZ0/hjnbOzXl0MSXz4Zr1yt/qqSqWi2YgODF04jX5fTcCrUk4ms8l2ED73k0MjG/GIvYXnsR14hgTbPEYCxhz6Afe5c4it2YwkN3/iKtZDM/N9zJWqWc3MTevcJ39cgvAflvDLEqttsT9bbxMEQRAEwbonN88isVN/TFJ2DpcEJHpVosby/yOgbpDtgwVBEIQSK3eZsvM/PUiqtuQZoDE6b758ayefLh3O+ZGrOKEvwS+ZIgtyxWtcef2p9UyZ3oy6HetY7P7d1PUczgiymRXppk+gmkMaCVlq7qh8kVXFT92/LfngTHrx4y1KlknXWl88rOB+uePVGTPp6BNPw6b+HN9wmuYDmtgMxtVuXwv/WReA/Dq8l/ya0fXKWtSyZXDLqNLSduqQEg3fbDYz/9X1/HHJnnStMxBFNdNB3pjRhO7D7dm06CTJqUbqN/CidouKLJ57muAERxK17vc1c7ZpXRslL4qRivLicwBIEnPndsKvhmXWauOt5/j4u+tE55Y8kGU8DAn469LxdZXYl+QPUhmCpDnPdZvaGlp8v4oz7YegM+VPu8/9dvx20GM098+kRrUUBr/cBRevEryOyomKvVoT//tii8W0ch8XzCLIZevVlqlzouHg1qhUKtpN7EW7ib3y2hLC4jnwyYeKC3cBmELulvk6BKG8MJvNuMVZfy+4xor3iSAIgiCUhc7BjtG7viT29htc23se1wBP6vZQXuxZEARBKLtyF5Q9m+gC2tIdcyLVF7Ms89WakWyZ9xef/UnJA1uShCxpOWcK4qVPbzFPo6ZW25r57TLsvO2UPSZrAUHZzAv9Xek3ZRAAXR/fjr4EQVlZpcHOaKDUpdZLMV2+oRTK6FE1WLcmlN1x/uzaIgEp+Mxfz1svVKXloKaKh6pUKl5/vjpnY/Lrj6bZu/Nn/dH0CV6CVKBKkRkJ7asz8KnmW6Jh/fDKOlbf8sl/nmWZm2p/ps2+wa/fNeXlOcMK7T+7Qx32/HaId9caS3bdJaA2Gxk6pUOZj6+kSyFYVi554aZPxKeKchZk0z6NWdu7ISf+OEtsRBIN2lajcpPOee19e/1Bks5d8VibJInePhG88PN4VCoVEXO/5u60/+GUkV3oXwLiardg7O65uFYo/eJU5UGrp7qw9OP6eIVctJjKleLsjUtqbIlrzgJon5uEo6vC4oFA7PVI1DayvNXxMaU4kyCUTyqVinQHt7zPqaIyHP6bn1WCIAiCcL94V/HB+5luj3oYgiAI5Va5+6pLLlVYJJtRrSMzJQOVSkX/KV3o7hlR/EEKMjWO/Ph14cWfjHpjTjanMpXZyP/6qeg3Jb/upKvZ9kJaBWWgxUMfb9lQmgWfrJFl+vTwZ9P66xzXByEXCFTH6Lx5+/+iCP071OrhLQc1pUqgPd09wqlkjKS2HEb7IY8RtOY34tv2Jq5qYxLa9aHqxhX0mTWuRENKS0hl47UiGao5QeZ0rTOLv9yveNzR/fc3Y+rJWol4BimXozCbzez8+QDThi5jQv/lRN5JsLhPo0fWsFpaYWBj2eYiZyqVilaDm9J3chcqN6lcqK2mk/XXTqHSDgVeH41Uofw43ZeZv4zI+/a7zfjuDIraj8s3X6OaMZMaO9YyLnjpfzYgC9n3feChX4hv3jVvKpdJpSG+ZQ/0DZS/nFCS7FoBuw8+ZsC3k6zu49+gIga19dIkJn8xbUwQAEzdegMUWowk7+eefR72cARBEARBEARBEEqs3GXK1ndK4ri+dFOqvfVxOHvmB04HjGzArnkJZTp/cGqRlei1arTGTAwae8X9g8yxPP788ELbOlQ3sb6EMUSjSkNVbTKexkxiTE6YJBV1HJOIy1BxWxNQpmvII0ks3JxInMYfJMvSC5kaR36evZ/3F4+y2oXOUcf7i0dabG8yqFWZhnTxr6s2a9P+HaYcjE5JK9uiXJLZiKsxBZOkJlNlj585gWFd3Rg2Y6ji/mazmffHrWRPUiCQHThrYUjh6VcvMGtSdF5mcYeRrZgaup0Fuw2k5ZSS0Jqy6Fc5kQmzhyn2XRLjJjTgzNxYTCrLt/aQKnHcidRzJrMCJpUWf0M0o3t5MGjaaMW+dA52dJzSt8xjKS/MZjM3j13H3smeoEaVGHtkPglh8URdDsOvbhDuAR78+e5STEd3lKg/vZcf3d9+wuY+zl4upLXvhftfmxRLGNR59amyXYwglDMDF81gbfOLeN25kLdNAuKqNGLYwtce3cAEQRAEQRAEQRCKUe6Css+90pxzs26RpVGeFqzkyfb2herj1O9YC6fvduUFy0pDonBQ0GQ0oZMNGCgSlM2p4fl4K8u6pBM/epxTY7YSovEr9nxZGkeCZUdQg7spgR8+aUi1ZlXZOGc3n+9EuY5t7jYbNW5zxRazONm+WB9C/w4lqN7DydxzdHMAGwUb7NXK2adpWYqbiyWrNCTpPKhlDufL/2trNTs21+5Fh7IDskXubabGkdnzb7G2QC3eYa/3ov/kTA7/fhpDlpEWfZrjEehZtoHmaNqnMTMjDvD96ljiddl92RvTGVwngy59anF69mIeC7kDXj40mDaGZsPbYdQb2f/dJsI27gUJggZ2ocNL/dHoyt3HQ6mtHjUb3bqlaE0GAI5pHXGcPp3HPx5b6LnqPGMY6+YvxC0x3God2FzOIVdY0vFlVA721HmmP81GKJfBGLzuI9Z2jMPr4uG8bUaVFtXkqbQca31hQUH4L3F0d2LU3ys58P0mQjfkfIYN6kqvKf3EZ5ggCIIgCIIgCP9o5e4vlrodavPd6yZmfH6dZK3tqdb2xnSG1c9k1DuDC223c7LnycdMLLK9cLqixq5JhR5H3E0mTasQyJMkasthjH53BEa9kR0LD3L2VBSODmr6jnmMn1cNYMXsHRw+n0aGSU0UrmQVzRAtEvhL1HnwyXtHWLi5Kv2mdOHvC2vYHKkQ2JUk3PSJJGldsR0+Kp5RrWPeR/uZtcJ6tuz9VK9TbfxmXyRS51O4IededG6lXE9VX7ZE2by+r6oCeGvyn/zfRuWs0lzbtoUAQYrB7midN2e2XqBZv8Z52+yc7Okyru09DM5Sj/Ed6PqUiXM7gslK09OoezOOzN/G9b4j8MgtmXAbIsfsYe3W50g/dBSvOxfIDTGmH9nO8h+XM+zUYqs1T/8L1oz9HMc1i/LeITJgb0jH9NnH7PV0ocv0QXn72jvb03nfYnYNm4HX9dM2+7U3ZWJ/ZDsAkXv+YOmPfRi15yuLhRMcXR0Yd3YRf+88x9UNh9C5ONJ+cj+8KinXGxaE/yqNTkOXVwfDq4OL31kQBEEQhDwxt6JZPecgoZEZVPC2Y9gLrR9aso0gCIJQDoOyAA261qPd0vNsi7EelG3nFMq78wfi5KFc7/WZT/qzrO9O9FbKDihlmToZUnn+/fwAW0JYPOmy1mpGqiRB3J1YXnphH6HaCkB2AHXtRyEMq3qGV74fxvicfWPuxLD4s72cu20mwuxKhtZJMQP2ihRI2KUwAusG8uaC4Qw8cIVtK88THq1HbwQvFxWt2gfQ49l+3D59h+0rz3L8SibXVYGKl+liSMJe1hNTNAhawOkEV6ttslnGbDbft5U6VSoV08ZXZuavCRjUdvkNkkQ9whj2uvK0cH8PFcEKpXdLJCer+CJBXD18rfBCbkWkZtkOcifGWM/y1WcZ2Db/L86cjsHeTkXf4Q1o2L1+mYas1qhp2ic7+JsclUTyRx9jp1DD1n75AhygUHanDHjdPs+m8V8yfM07ZTp/eaBZs7TQVxZSgX+jPvoCCgRlAQLqV2TcpRWEBYewt/t4POJCCrUrZdDKgMehrez8sAW93lf+YqNej8bU69FYsU0QBEEQBEEQyuKvZUf4cGkSWZqcBKJU2PDaZV57/Cr9Xupq+2BBEAThvih3C33lGjq+aeGFjYo4nBrApYPXrLZnpeutB2QBtUlPQ1UodsYM7I3ptLYPZd7HdanWrGrePlG3YwHJaomARL2OD6bvzAnIFiSx9rYPe347lLfFp7IPr857ksVbR+CJlcWccs4TG5pfD7duh9pMn/sEX64ZzZz1o3lv8Uh6T+yEWqOmestqTP56CLO/64arISfDt8gCYaPbqJkxsYrV+wBgUngZrf1iO0/0XsPVm2l077uTt0YuI+aO8orxN47fZO60dXw7ZQ1H153CbLa+6jxAuydbMP9/VejkEkaAIZrqpnAmNE7mh9VD0dlpFY8ZOak5KrPRZr825dzbyydDFJv3/HaIif2Xc8NopfyALCOZjdTvoBzQjbkVzZiB6/lit5ZdCQFsjvTjhe9imTV+ZbH3oziH527Czpip2CYV+bfQzzu23tN5/80ir4ShM2Vhbbk8p9Q4q8cGNqhEjwOLSXLzL7RdAov+cu911LJ1ZR2qIAiCIAiCIJRKWkIqnyyJtyj5Z1Rr+WqbkZhb0Y9oZIIgCP8t5TJTFqBOu1oMrnyWdXeLBjyzyZKK/80NZ32nOopTtO0cdbjrE0nUKU+HN2ns+NvoR3OnSN76uhfeClOKA+v4w6UrVsfoZZfFOUMAUCSTNufn9Rtu0/WpdhbHVXEzEJau3KfOmEm1ppWtnlOJX40K/PBJQ77+6DDnDP7IkhoffSyjuzkzbEb2Qk+Vfl5ltcZtXYfCi6L98vZGfrngBlp7IAW9xp4DqUFcnfgXvy7vhYtXdq1es9nMF8+vYVO4L0jZ39CuXZRKg19X8PXSITanztdpV4tP2tVSbIu5E8Pi2Xs5c9uMWpJpU8+O0W/14NUed/hue2bhYLtsBql0303sX36Uak2C8qb2LHl/M/93ygUItP6OkiR0OXVJlXw0fSfh2iJThWSZrdH+NJq3j35TunLj5E0WfXOEM7HOpKkdAZnqqlieGVOd9iOsL5yWGZeEcqjaNoeMpOJ3KqfsnBysBmSBYmsxV6jpz4Arm9j17hJM9g4kVmuC982zVouFaBLLmsYtCIIgCIIgCKWz9adDpGuVZ4wa1Hasm3eISV+IskCCIAgPWrnNlAWoUdv2okxpWmfWf7tHsW3/sqNoycmslJXDMyZJzbGsIF6cuAd9huVKUqu/3I2tmq1RmfbIkspqgCc2UznCN+a5JqiLZn3mjLF7QEJe0BPAqDeSnmQlgltAtWZV+WHDaLYvb83G+Q34fdtghs3oldf+/NjKqHIzj2U573xaUxbjJ+dPrU5PzmDFaeVxR+l8WPnF7rzHG77ZxaYIPwrdI1kmWA7iiykbih2zkpALd3lm4mHWh1bgtsafG+oAll7xYvzobXQa0YJ1i1ozuVUao2rGUUcOK1VAVmfM5POdGt5ekcXIGdeYMngZdy6EsOhYyUKeWRoH5r6/22J7bEgsZ/T+lq+znNfFxu2R7F9+lAnvXmdfchBJOneMah1GtR1XpEDeXJLG1vn7rJ63ctemgGWWZnGSvSuW8ojywyPIkww7V6vv3iSvSsX24ezlwqC5k/GqV5UnTvxqc/FBY1CVsg1UEARBEARBEEopOjLNZntsXBlXSf4XS4tPZcWg91jt2opN2jos9e/Jn+8sedTDEgShnCvXQdmgWtbroOY6dT7RYtuaz7Yzc1UWMbqc7FepcNAwT872MG0FNv6wr1AfkdciWXTBSk3bnD5i7Wwv2ONjb2TTnD18OmElb49czhfPr2LDN7uo3qIqb/bX4GJIzttXJZvo4hbGa/OGZvcdEstbI5fRc+Bueo45weBev7P8o/zp6Mmxycwav5I+vTfSse8uBvZax6zxK5HNZjyDvCxqwHYc1ZoPR9hTyRiZfd2SRE1zOF+84EvjHg3y9ju99Xx2vVsrTlzMDxD/sTtnCrhClvD+GA+y0pSn3Nvy9Xv7FbObw7UVmD9zG+5+boya2YfJXw/hlslKqQEFKrMxO8M257mTkThrDGL6a8dtlrko6kSsi8W2yBsxyCq11eB8gkHLF79FFq6hW2hwav5vQzwmo3K5jsaDWxFXpVGh6fO5/9oqjOD93FM2Wss/n49mKgayTZKKpks/L1Vfjq4OZPUcAFgGx81I1JvxTNkGKQj/MTF3Ytgyby+7Fx0kI7X0vyMEQRAEQYDqdW0nL1Wrbn3NkPJIn2Xg9xbjcN2yEqeMRFTIeMTewTT7Y9Y+89WjHp4gCOVYuQ7KNunVAG+99dqPAFp14ccZqZks2Gu0zKDMDcZaCZydPl34PN+9s9t6FmYxU59zz3faEMhnO9VsifJnf2ogf4T58eUeLYOG70WjUfHH+p589ISWN3uZWPVNfT5aOgqdnZb0pHRemLiXA6lBeQHDGJ038447MW/6OrLSMpk8ditbo/1J1rphVmmJ03mxNdqfvqOPsOrTbYpD6jy2Lcu3DWfzgiZsXfQYi7aMpHn/JoX2Uattv6RUUn5IKs7kaHW/LI0DsSG2n7ui9BlZnMlSLlcBcPhu4aCmuQRZsq76JNo7hWJW5WT/5j53OYt/RdlYAE2JUbLMIq5UPwCNSW/1GGe1gQSdh81+43ReXDpwVbFNpVJR/cPp3K3cDDkn91MCkp28bX4AVG7fwEZr+dd52kC8fppHkps/JkmFSVIT71uNWttXU69b6RfeGrryf8S37kXBzPAsjQOaGf+j+cgO93HkglD+mM1mPp2wkmGTz/HpNhXvrTMxcNhu1n6x/VEPTRAEQRD+dbo/3Q5fg/J6H66GJAZO6QxkLxh8++QNMsv5F6EHvvkDr5CLigkZmhW/khAmSo0JgvBglNuaspAdjPrkrTo8/2VUdpmAguTsOq5duhWu43ls/Wnl+jrFBFJ12sLtESmqsoe85QI1ZmXZ4tzpWmc+WZdJjWYRdBnX1uLw1V/sJkLrq9j175cccfpsB7c1/op9G9U6fjikodKaE7R5ooViH+5+VjKAgaZ9GuK8cBepWsuMUIC2j+Uf66VOJxHlmr12xgy8K9n+BreorHQ9JpVW8boAsiRdoce1NTEEy0EW++WRZZJ1bsikKLfnnsNszsseLk5NneUvdFdfN9p7xLAvOdBy7LKZpjV0XLtZbNeKmbIhF+7y9oxD2c933Uk4V02gWsoNxkxpSdLUmTb7u/z7Pur2aFT8icuxNs90o80z3e5LXzoHO8YemMOd0ze5tP4QWmdHOk3oibOX8ntFEIR8819dz5Yo/0K/V9O1znz3lxm/SsdpP7zloxucIAiCIPzLaHQavvykGW/872Shvxu99XF8/EYdMpLSWdtzGu5nDqCWTZzSOqLvOYChK95G52Bl9t6/WPimvXiiXHhQa9Kzf/YqMqPiMETH4dq4Np3fHolrBet/EwuCIJRUuc6UBajfuR4Tm6dlL+hUkCTRRBNKzwmFM9QMeuUp4Fbl1nLtW73Q5iB3WxPDlTnrk6lmDFeczl+UUa1jxdxjim0ngpMVt0N2Zt7GY5k2+5YlFcuXK2ddFsfOyZ5nO2ss7zdQyRjJE693z3s8sFtO0LVgSYicnzv6JGDnlF8WIC0hlW9fWsuw3mvo1+sPpg5Zxumt5wr1v/qrnPrA1u6ZpKFj390M7LWOedPXMX5CXZsZqrlOJyoHjnPVJqJEAVmV2cCz4+sptr09fzCPaUIL9aM1ZTGpeRpDnm+DZDZZrW0M4KZPpG7H2nmP0xJSuXnqFtNeO5YfgAdS7dw579OcL5ZEozbYrhVlNpTyvSAUy2w2c3X7KRIuXAeVhKOH9VIfgiBkM+qNbLqkU2zL/n117SGPSBAEQRD+/ao1q8qqrUP5dJQ9E5um8MFQDb9vGUitNtXZ1noMXqf3oZKz/x6wN6TjumUlq3q89ohH/WiofvwW5/VL8Di0FfW8b/izRg/+3nmu+AMFQRCKUe6DsgBj3+/Hx8PtqC+F4qZPJMgQxVP1E/h65ZMWtVNb9G2E1lSKwuaSRDvHUNo+0bzQ5qmzeiIpBCZtSdW5clMTUOL9Q2OMitvVxcQHo6xk0RbqO73kdVKLevLN3rw3WEMNcziSbMZdn0jfChH8uORxHJzz+x00rTsD/CMpVGVTkmgghfL6D4PyNmWkZjJp1GbW3vYhUutLos6dU4Ygps2NZfevhwBIiUth+Rnb39rqNfaYVRridF4sv+bF0l8v8flEL5z1VoLYOQHSDK2zYpAZsgOnn83vydBK0TgZcjJqFYKnPvoYPhnjQosBjyn24+jqwPfrR/PjNB+ebZjElDbprFvQnLHv9yOwbiDtXCLzSiYoeaqLHTo7LQkRicx4chl9Rx9h3Psh+eUVCpZdILucRZKn9SxhGWg+ZZDVdqH0Tqw8yAanx9C/OxPXLSvJfPst1js15fymE/f1PMnRSSzt8TpL/XuyuMog9n5VtkXzBOGfIvJ6JKla67XtQjLFlxuCIAiCUBYqlYoOI1sx7oN+dHu6HWqNmr++/gOPmFtA4cxRGfA4tpNbx68/krE+SAH9uwDKCyPLWAZNnDKTODtqGmZz6ROxBEEQCirX5QsK6jy2LZ3HWk71L8rdz42B1ZNZe9uyVqivIYZJQ3xYvzmU8CxH3NRZ9G7twoi3R1gEd30q+/BCq3RSin60W5laX+L2ArxcsvdLjk1mx6IjpCXrada1Bm1beHL6iI2+StC/q7r4DFJbeozvQI/xsG/fPjZv76y4j0qlYsZPwxl6/CZ/rjiLwWCidddqtBw0stD9XDErp9xCESaVhh9WRNJlnJnjG8/ZXN1eySlDEEPS9Mx5rw4TP76F0dpCWoC/IZYIXeFgtmQ2MbmTjHclb6bNfYIXswzE3IxGo9Owc+lJwsJScXPTUSXAifXb+5VoTIG1/ZB3XuPq1UTSfjjAkCkd8Aj05IOFQ/h40jr+SvDFLGnz9vfVx/BsP0/6vdSbrPQsXnz2T0I0QaC2cZIcsfXbERh2QbEtPrAOlR+rWqIxC8VLjU8lfNxE7GQTMvn/g2tvzODmsGeoHX+yUGZ4Wf31/RaSp7+KR4HPnfQ332DxvCWMu/H7PfcvCI+CWwU3VGYjZkl5QUQX6b+3QnRRkVfC0adnEdS4ssX/jwiCIAhCaUTtPorSahbZv4FlLq07SNWWNR7uoB6wTtMHsXzBCrxCgi3arP3l7JYYxulVh8TaEIIg3JP/TFC2NF7+bgiuMzez/pSZBJ0HGpOe5k7RvP5tDypU96XXxJL1M+qdvuzcvpOubmGEJkCQB0ya2YnV3x9m800X5SBiCQOyyDJDxzRk7efbmb9XzunLngVno/HSp6PS6PIXpyqNnEBu73YPr0ZO9ZbVeLFlNavtB8+lgUp5PDE6b/7edxmz2frUflsWLrvF4q2teabZJRactR6UDXLW8+poZ35feZnYNBUBbjJPPtOIxj0aYDKa2L3oEGdPRuLspKHfMy0Y817fvGP37dtXorHsX36UD5ckkKlxB9whDpZPOM67Y9yIuJ1IcIwdZp0WjUlPA100b87qRFC9Ttw6c5tXhi7jdKYfZoXgtTUVGlVDX3EKmoVzURUI4iV4V6Hv/oUl7kco3sZxs3HLmf5V9B2uNRv4Y9J3PLn0jXs6x+k1h0mZPt3im3wZ8AoJZs1TX/LEb//NKWfCv5uLlwtN7SI5aSiS3Z/z+6pbYzuMeiMa3X/vf2nO/H6E4Gmz8IzILjl0yNUP72mT6TFz+CMemSAIgvBvpbK3nSiguQ+JBP80Gp2GYacWs/HZL1Dt+hP7zGSSvSti1trhFX7F6nEJN8If4igFQSiP/nt/wZSASqXi2VkDeNpsJjE8AScPpzJnsWnttHy4dFShbdPmPsGEuBQOrDnFrC0lDMIW4WpIxmz24Lv9WmRNgbRIWSZO512mPgGQJNo6hPLkGyPK3sd9ZpRt3yN9poHWg5pgt/ZQqbNlI83ZU2JHvNmT5UN3kVZ0gbKcP/o7d6hA6yHNaD2kWaHmmDsxvPz8Hu5q/IAKAKx48xpj659i4ucln/6fHJvMR0viydQUnoabpXHgneVpmFWOoHMEsusJnzUF8fL0o3z2YRNefvcSKdqgUhUj0Zj0DHi+LT6VBxI6eQDHv9+AISmFoK7N6T2hB2pNCVJthRIzBitnJOfKOHH6nvo3m81cnfg6SkuG5b57zBvWASIoK/w7vfl5dya/eJDoAr/fXLIS6XF1Dd47zvPn12+SEFiLam+9SLtJvR7hSB+eK3svcHv0JDxNWXkZ+K7Jkeg/eJddKhXd337iUQ9REARB+Beq80w/Inb+Xmh2Vy692o4F9ncxAAAgAElEQVT2z/V+FMN64BzdnRix7n3g/bxta0bPhtWWQdncexPUqu5DGp0gCOWVCMraoFKp8AzyeiB9u3i50LJPA9hysVQlC3KNbqdmxa/ByFKRzKHcuqOl7A/AXx9FZTcDSRnwxsiV9O1flU5jWpdqKuS1YzfYsvQMGRlGHmvhj33t4o8pTqNKKm6GKbdpTHrqtK+Bo6sTIxpn8tvF0gVlZWQOrDhGg441mdRDxzd7zchSgevNqXHbb4pykPr9qTu4qyn8HMgqNYsveXBu0DICvdQ0GVh81vGmeQfI0DgrtplVWsXt0Vofpr0TTIpOaYKRdSqzkZe7yvhUzi7REdSoMkELppaqD6F0ZDd3sPIaBpC8Sv85c2n3Bc7OWY3KyZHKPVvikhprc3+tPq3U5xCEfwq/GhVYsrIXa77ay+m/k9EZMmh+bCEuGfF5+3iGXSF+ylT2pX9O52kDHuFoH47jb83DM6cGftHf+AmzPmOXSqLj1P7lcpVsQRAE4cFp+kRbls7tg8ehrRZt2pem4uZnexHk8qT9O+M4um4FOmNm3rbcgGxclUb07dHokY1NEITyQRQee4S8K3lT3RReqgCqZDYxOCiKkTMfJyzFSky9DAFZd30C8SoXjmYEcZEgjmYG8c4aAx89vQqArLRMDq0+wcGVx0hPzlDs45sX1/DMRyGsve3Dlih/Pt4Mt64lEB8aV+rxFPTUG53RGXPOWWShqwZhR1j39V4Anps9iBdapVpdlEtJhtaFt5ZnMvi5U1w4H8fHI+xopArFPWdBuLF14pmzaphi5mjYpTDOGQOsLr51zhTE1mh/7kab+OCpFTYLwUdGli1glljCgKxkNtFIFUof3wgWvluFIa/1LNP5hLJpNutlxYUDIPt/7DrOmV7ivrLSs1gS+Dg3eg/DdetqnNf8Suxzk4s9LtOxdMF7QfincfJw5umP+zNn3Wiaae4WCsjmUiETPuub/8TCG9or5622OWalkPXO/1gX2J1rf/39EEclCIIglAej9nyF+s2ZxFWsR7KbH3F1W+G1YD79vhj/qIf2UPnXCSDwp29Jc8gPREtAXMV6PL597qMbmCAI5YbIlH3Epr5Un9fnhJdo2r3KbKS1YwRjXuuZncVrZyTEVIaTFsmkddEnosJMlsbRYr+dCQGon17O3kgPsrTZ7fa/7mV0MyPPfJKfibTz5wP8HuJL0TUr9ZKWD17+k+/WjS7DQLOpNCoGnF3A+aD2XKvQBBkJ97Ro2t7cQuOwI+xTmxjzfj8u7rvMsgMm0BXzXYNCJrFRrWNnfADSrjvM+yN/rCajiVWzt7PjSBIpRi2VnLMY80wDmvVrTOjlKJBK9r3GzvgAGn+3h0HTuiu2V67qBndL1FWZjGuQzHOflf05EO5Ng77NONO2N56H/yy0XQaSew2jSvOSL5awqvEYvKJvFnqnSeS/85S+kpEBz5dKWAxbEP4FTMeOWm1zSwwn5PQtqjSv/hBH9PCZNbpi93FJiebE0MlUDd/1n6y5KwiCYIskSb8A/YBoWZYb5GzzBFYBVYDbwJOyLCc8qjE+KiqVit4fjYWPxj7qoTxyLcd2ofGQvzi6YAcpYTFUbN+QvgNbPuphCYJQTohM2Ues6eONmDuzGq3t7mYHC61kXQKYVRoOZ1Zk4uSDxIbEMrBfpewGG8coKhiQlGVSdO7E6xSmT+fs92dcYF5AFiBT68TC826s+2pH3rb1m0Is+85xJsufmDsxpRtjAcHbzlEzNpghZ39k+q6pvLznVZ4/MJPGYUcAqBhxAZPRxMxPL5Kss1IqoOA9spFJvCfam4Sw7Owrs9nM6yNWMu+YE9dVAUTpfDihD2La/P9n7z4Do6i6AAy/sy29NxJC772DSBcQUYoiHUEUsGHBhvWzYENEsBdQVJDekSK9g4AgvRNaeu/JtpnvRwrZbElCEkLgPn/Ynbkzc2cSsjtnzj03gTXfbKV6kxAk2Wz3+kuKTN3Yo3hlJdD35Fz2zt+HQW+02fahZ7vhZUguxtUoPrXZSCMieP9hFRM+L359W6F8jN75NW7Tp5MUUIsMF28Sg+ri++N3jFz7SbH3kZ6Yju+VnOy4wr/Fko1lkBOQTezQmwc+GGljrSBUVrb/juc/nFDdXL32ykTVtXux2nmlRHFgzpby7YwgCELl9DtQuEDqm8BWRVHqAVtz3wt3OSc3Z7pNGkC/L8bRQgRkBUEoQyIoexto2Kk+U34dVOyyAwk6P+Z8so3e47vwYGDJyh9YKcW28zel5L+O19uuewogq9REno+56eO4eORkA0mAzqzHzZBmcTvubNaza8EB4hxNcFYoEG2PSa3j1O4LAGyds5eD+lCr9rJKzffrs/Ct6k0rp6gbdXwLUJuNDD38LUOO/ICLMZ2W4Xvofmw+ixsOIT0hzfoc3Z2Z+npdfAzWw3FviqLw4QhXZv81kt7jupTNPoVS6/5Sfx6L/JuhqQcYHb6Oe8f3LtH2Zzf+ZycUlUNGwlSgBnGmkwfeP3zDmD3f3WSPBeH2pO3c2eZyCUj2q061ljVvaX8qwgPfvkiyX/VitU04faV8OyMIglAJKYqyCyj85Xsg8Efu6z+AOzKz4fyOU8y/fzJzGwxjXtcXObryQEV3SRAE4a4kxrLdJly9XKlljuKyOrhY7fdfUWHQGzkZIYH9eGjJlHCCsBhdICaDCY1OQxVXI5G2k0BRyyaqNw656W416dOKvzUuOJts17KlfgPCwxLB5tzzNtg7x9zz9w7M2c+WzdeAUJvtM7Qe7FnyLx9+25eXJmwirNDP7Z7LG6kdf8piiLkC+IafYc3jU21mRzbr1YTlXerz3pgl7MkMtVpfUIAhjjhdgN31gYZ4uo8e7HAfQuUT1Lga0bmvbf0WGzVO9Li4nYs7TuIZ4kO9bk1KNFGfIFQWvWY8w6atm/FMjbZYbpbU1Pzwtbvi9947xId+R5ey8ZUfUdatxj3T/uhan4bFC94KgiAIBCmKEgWgKEqUJEmB9hpKkvQU8BRAUFAQO3bsuCUdTE9PL9Wx0qKTkCMjcX+gBlADgKuX/+XK3Gt4V7d/f3EnKu21FHKI61h2xLUsO5XlWoqgbDkzZBqYOmExUQkmgnzUDHmqHfU62K5zN+Gxmrw7PwNZpSkyQJqg8eHrF1dwTWsniCubQWU9OZVDeRmfxQzMqsxGVJqcG9+hQ+tyZH62ZYPc7NF73KPxqepbsr4UYtI4QYGgbN6sl3qNMw/9NInLJ6PgcLbd7YtFkgg2xtK4e05mabadIHOedeuu0POJzvy+dih7Fh3k6D/hHLxg5IomhOYRe3N2WXD3uf/qdvyNLH9kM2igc9Ly8qe9+eeFY5jU1vUCJdnMmGapNG1bg9f/yLTbN1e1yXHnhUqpWoua7HP1xT3TMqkj7/9DevOO+FT1pd2orhXSP0G4VfxC/bnvn0VsfmY6Tgd2oTHpSavVhCbvP0eb4XfP6ACvKt4MXfAWcWHj2NO4Fzqz3qpNqkcgfcaLyR0FQRDKmqIos4BZAG3btlW6d+9+S467Y8cObvZYKdHJbOvbDZ3J+r5JllTU3Lf2jq/JXlBprqVwg7iOZUdcy7JTWa7lnZ9KUs4izkTwzsgFPPDAX/R+YD0vPDKfY5tPArD2221cjtSzNroKh42hrI8NZtyUK6yYvsnmvrqOvIePRrpS02R7SHxBskrDuusOAp0qNS5G62HyDikKKuXGjNVeBsc17avK8fmBxc7DO/BksxTUcoFIpiThjIF3fxhgZw/Fs/l/f+CenVNvteBkRjISRpUWr1Bf7h3ajmBjbKmO42zK5M3n6+efU/OG7g7bH8wOZcFH61CpVHQdeQ8vfjOYZtVytnXTp9rdzkWfzistP2Bys7f5bvxvmAyWAdSgOoG8fL9keS0BnSmbKcOcmTD1YZp0rY/Wxs13nroBJawzLFQaTRZ9j0myfOAiAelufgzZ9lXFdEoQKkBQvWAe2/olQ9IP8Uj2ccacWXhXBWQLCqgdSOBX09BrnC2Wp7v60nLx92KSL0EQhOKLkSQpGCD339LdYNxm9n37l82ALIBKkTn09fJb3CNBEIS7mwjKlkLkuSienvQvO9Oqkq71JEvrxn+mUCbNjGT73L3MWG+k8CBjWaXhm20ScZdtf753G9WRPzcM56+fmwOOA2u2MikLeqmfK6MbJlLVEI2/IYFOLuH46O3XLK0tR7NoRiPeH6jiq6e8WL2uH4EG+xN0yYrleTz56QCWfNOc8S1SGVkvgRnjPKhR1xcPv2KWFbAjdcvO/NcFr6YKBXdDGlMGzUalUjH14zYEGOKLvV9JNtNECqeeHEEXt3Am3q+merMbZRaGvnofvo5qvEoS8/aaLRZdjckJsMZ62C8/oAC9zyym29nl1Pzjc2Y2fhqjwTIAO3BSL+ZNbcAj1WLo5BrOsNpxLPi2JT3G3AuAZ6AX9wUl5O7Q8vdEa9Yz+vkORZ2+UEk17duae8/vJKFND9Lc/UnxCiZj8Fgejd+Fi5tz0TsQBOGO1OmZB7jv0g7kF14jfeAo1G+/T//r22jUu3lFd00QBKEyWQM8nvv6cWB1BfalzGUnOJ5Y2JhYthMPC4IgCI6J1IlSmPXJDpJ11rVSjWonvp4fjUHnD1iPgTepdTz/7A66NdYw/JVu+Ib65a/bv/QQyxafJy5TjUbxzRm2D2hN2QSmRWDQOBHnXrXIEgOSbKbr0DZ4BnrxdIHl2+fu470lehSpUDxekRn9aFVCGgQT0uBGSYSpH7TklQ9OkqzzKdReIUJXhTde2MqctSPyFwfVCWTsx/3z35dJDY8ikj7DTL6c2nGWJt0bsvSv6myes5dP1hZRhkGRmdA2g1ZdWvLx9FPszghi9zb4astxOnvF8N7sQXj4eTBpRADvLTfb3U2axhOzyYxak5O56OkiQQYcqHU/g47+XKyTa3x1D38M/IjxG6ZYrKnerBqv/lAt/338tXjO779ItWahuLg788ZPg5EnLGNbQgBmKaewsJ8hgdefqEqd9rWLcWyhsgqoGcCYf36q6G4IgnCb8Q7xof+MCRXdDUEQhEpBkqSFQHfAX5KkcOB9YCqwRJKkccA1YEjF9bDsVe/ekuhfb5S+KiygY4tb3SVBEIS7msiULYUjMXay0hSFeJ2/w20jtEEsuODHiPH7ObntNACz31jF63MzOaAPJUwdnBOQVWQ6XVzLC9tfZ8yBzxm/dwoT9nxA1aSLDssb1CMaz0Avi2WyLFOvbXWeapuBT4EMUF9DIpN7y/QeZz3ss37HunStYWOIS255hfNSCCe3n3F4rqXl3q2T3XVpTl7EeoTy3YzDnN17Ho1OQ99nuuFktlNfVlFAURhxcAbmb2bwyheXidAG5V9Ls6RmZ1pVPhiXM3SnfvuaDvsmoSCpbnyl6T+oHgDnglqzpeFQDKob2cxy7lcfW3Vm3fZsRpZlbAk/Gc6zA+fzyHMnePLTCPoP2c4XzyxBJUm8P3cEy35owYeD1MwY58GKdQPpPFxkyQrCnUKSpDmSJMVKknSywDJfSZI2S5J0IfdfH0f7qIxkWWbjBwuYV60fy91aM79Kb1a/8CNmk/2HZIIgCIJQEoqijFAUJVhRFK2iKKGKovyqKEqCoig9FUWpl/uvg2FzlU+roZ1IqNYYiRt5L3n/pniH0PXlgRXUM0EQhLtTqYKykiQNkSTplCRJsiRJbQute0uSpIuSJJ2TJKlP6bp5e5JtPl8smQytB1OmneTKsavMO+lptb5j2Ea6XlyDU4H6of4ZUQz/9yt8Mm2XQNCas/loejeLZVt/38uwB5cz/I1L/HzYAyfJxLgWyXzzjBcr1w1gwEs97fYxPM7OTXBuJurFoxFFnWap9Pp4LAnuVWyu21F/ELJKwwkllPFTo/ho7EJkWaa9V4LtnUkSdeOOUzPpPJcCmpOl88hfnv+vorA3LZjIc1GENg7Fw5Bit281zDEWE3Z1GtqOAcHRIEkcqtmL73pMI8k1gJUtJmBUO9ndj2d2Itnp1jVi0xLSmPjqQU7IoSi5v2/ZGldWRwTxyfilAATUCKDnE51p/3Dr/IxdQRDuGL8DDxRa9iawVVGUesDW3Pd3lKUjPsX0yYf4Rl/A2ZCBd8I1ND99xYJuL1V01wRBEASh0lKpVDy0azYJjTpAgYSRhOpN6Lr1d5xEKSxBEIRbqrSZsieBQcCuggslSWoMDAeakHMz+YMkSXdctKi+R4btFZKEnyGeKnkTTznIaEVRiNQGMXfGPmSV5SVSm410uLzR5mY6s4F2V7faXOdjTuPAulP57/csPsiHS/VEaQPzM0WjtYH8etSLF39KYehDK1k2zfZxAPw8Hf+aBNd0MOFYGfAM8qLmN59wpFo39OqcLwqRXjVZ1uo5TlbteKOhorAxIYSpE5ZwMMV2n3zTo3ng1HwAorxq2j6gJCGr1Bzbfh6Ad5+sgiRbB6Y1ZgMfftTeYtm6H7ZzKlzG2ZSBizEDX9JxcdXy9Hv3kepi/zpl6NxxdrcO2i79cjsJOr/8fhU8160JgURfjLG7T0EQKj9FUXYBhbN0BgJ/5L7+A3j4lnaqnEWcvIbryvlWlWsUwPfgZo6tPlgR3RIEQRCEO4JfqD9jjs+l+cEN+Pz4HQ12rGLMpRWENq9R0V27IxxdeYC5rZ5kkU8n/gy+n+VPziA73c4ozhIwm8xEnrpOUsQdlbwtCHe9UtWUVRTlDIBkXbtzILBIURQ9cFmSpItAe2B/aY53u3HVSWCd3AiAgorpn7dl+8Ejjmub5q5LSrceuu6TGYeLKdPupiHJl20uj9UFMGMnXLuylBe+GsTUOZHIeTVhC/Yl93WMLoCvdkNG2l88/lF/q/09OrYlW76Mtq5DC1QxxtJuYPnPdt11dGc2b7zGjOSRSCg2+5J3PhtiAlFsZIy66NN4fP+nOOeWNnA22gmq5/IOcAOg07D2zA45y2ef/cd1xRcJhfraBN75ohuhTW5M6PXz6yuZd9YX1DfqDGfhhsGYQJt+LVlTrS0BZ9ZY1HDKe300tAtdw+KoUjfIog/HzqYBlmUo8s5VljQcXH+SAS8GWa8XBOFOFqQoShSAoihRkiQF2msoSdJTwFMAQUFBZVPnu5ylhMfD1Oftrg+Lv0TSDvufjTcrPT29UlyfykRc07IlrmfZEtdTuJOYTWZO7ziLIdtE0x4NipXxWr1VLaq3qnULenf32PntWlJenYyfkpvQkx4P835mya59DD8xH52L/ZGTjqybPIe02XPwSI9DQSKxTks6zn6fel0alWHvBUGoCOU10VdV4J8C78Nzl91RziVqQWtjhaKQqPNFo1ZTq54P7sZU0rXWpQnySIqZe9r6cmif5XK91iVnd9guxJ6tdXXYvxWXfcl4aonNychsmX9IxdD0bFzcLT/Em/ZoxPhtF/nliBtKgWxeT2MKH73dzGL4fnn6cO5wGn68gTX7MnLqwNqh2EnKznLy4LpvferFHQegSdRBjlXrkpM9XChw7mtIpP3AzvnvG3ZpyB9dGto9ZmJ4AgtPuUPhQysKmYqOXfP/gSYtOJ4SR/PIG88mJOBCQHP21u1H8yVHGP52X4vNnTTYmisun5vHzX2wC4Jwd1AUZRYwC6Bt27ZK9+7dK7ZDxbDmxZ9Q//iN3fWpfYfSfc0Iu+tv1o4dO6gM16cyEde0bInrWbbE9RTuFJt/3c33S+KIzx1d5/bdFoa1lnny0wEV3LO7i8lgIuq9z/BQLEdYKoDf1RNsmbKQBz8b63Afsizz74LdXF61E0mrofGYvlze9C+q76bjUWCPfpf+49gDo/jvkeFovdwIbteIdo91E6XsBKESKjIoK0nSFsBWQc93FEVZbW8zG8tsjuGvjFk8efo+qsEopdldf/r6aQxmA0MfNTlsp1WMBNXzYZx/IgapYJRXQ2a/19GZbafjerr4MVprf78AkuLGaAfHLmzblm24eVsHe2v09OB/HQ0kxadgNis4OanxCfIgRo4mZke03f2VdRZC1a5uPNvFjQsXU5BtZcsWwbNPf0yG7jn7Ap52jiBTZxkwlxSZEH8ndu/ZXez9JsWkMmKQClup037eZiLToml0vytpPQcTY+6PkykLSVHQa11wUTszkmx07rLVteoy1Is6ybZ/firFjKqG523/f+Zuz0S5288fxDUoBzGSJAXnZskGA7YLjFdSTYb34OyPM60eSOa9r/lI9wrplyAIgiDcbv796ygfrTAg55U7I2fOkjknwPnj9Yx898EK7N3d5dSGI3ikx1stz/suE//XFnAQlM1Oz2bJPePxO3cI99xlV5f9gVmts6g5mbc/F0MGLP4VgIRZsOTlABr/PoMWAy3L6wmCcHsrMiirKEqvm9hvOFCtwPtQINLO/itdFk+ePXMX8Xec7TqhQYY4lm54kF27djFveSwRWsd1Vyd1zabf4E68N2kzJ8whIKmQFJm2Wdnce+B3XPWWgbnrVVuwsNEEzBpdmZ0PwFdPedG2e8sy2195ZSHsn7+IdTHBllmuNjJeCxty+Hfqxp3If+8DXGszmNiQxqTpVdQOkhj1wj3UalWzRP1Z9OkG5u23nbk8emAaYZvTychWOCr7geJuVR9WpZhZNKMxIQ2CLbaVZZnXhizkoCHUcqeKgs6cTagUw+A+AQ4naju9+Rgnf12LbDBS46HOdHjivluW3QwiE+VuP38Q16AcrAEeB6bm/mvvAWmlVOfeBuy79wF89/1tsVwCEuq2pu/jPSqmY4IgCIJwm/l9zilkVajNdYt3ZzNclm/p9/67mTHTTl3D/AYGh6tXjvoUv3OHrErdac2Ot8vjmR7HhZHPEnpmI37V/Yu1jSAIFa+8/kKvAYZLkuQkSVItoB5wx83MMe7N7ngaU6yWS4rM+EcC8j8A05Wih5iv2JZMQK1Aflw9ikXT6vHFWDcWf9mQ2k2r8EvH/7G7bn8u+zXiXGBLtjUbwaM7vmP+F43o7Bqes4PCk4nlTuhVEl6GZFr2aVqibSrKK18/TCtNuFUQtoohFp3JdiF1D0MKMf51iXWvil7tTKxnKOljJvLWvo/4esUo5qwbwbtzhpc4IAvQeUCznMnAbP0cgANpARyVc59TFA4cSxL9qsZbBWQhZ4bULxYP57n2GdQxR+JkysrfxqBxIUwdzLQtGr5/eYXVtrIsM7/PZC4/OBS35XPx+Gshic9M5M+Gw8lITC/xOQqCcOtJkrSQnHrsDSRJCpckaRw5wdjekiRdAHrnvr+jjNj8JZkjxpHh4g1Ats6N1L5DGXxgjri5FARBEIRcYVkedtcl6PxIuGqduSmUj0Z9WjksL+ja0X4Gq9lkRrtlHWA5SqhgcLY4XAzp7PhwXjFbC4JwOyhVTVlJkh4BvgUCgHWSJB1VFKWPoiinJElaApwGTMBERVGsp6+v5ILrV+HHz1vy9ZTdHMkKxKTSUFuOYeywatz3eKf8dp6qbKxDt5ZiCkzmFNo4lNDGoWz9fS+LwwLASWFP3dwJuHKzQT99dTMzl4/ik/nDmfDwEs5LherGShIq2YQsFe9HLCkyTz/ojkZXXmWGy5aTmzPfrhzFok/X89MeLSa1DiSJaF3OfDcakx6T5kYwXCWbqOuaxgvLXyK4nnXws7j0GdlcPnoN7yAvi0m5QpuG0st/N5sTrX8OEjImte3AvNakZ2w7PaM/GGL3mGqNmpH/e5Dqiw7w5vxsmxnBS857MvRqHAE1AvKXbfzffLy3rbZ62up3+Rirhk9h1KZpJTl1QRAqgKIo9oqn2k+PvwNodBqGzJ2MLL9GekI6bj5uok6aIAiCIBTijIFUWysUBUmRcbVRlk4oH26+7jBiDMz9KX9Z3n1Ymrs/vT8ea3fb9Pg0XAz2k2YcjwW1lHnqfAlaC4JQ0UqVbqIoykpFUUIVRXFSFCVIUZQ+BdZ9oihKHUVRGiiKsqH0Xb091WhRnRnLR7FtbU92rOjC3PXDLQKyAHV8TDkvHGSuesoZVstWrLqS86JgAE6SQFE4nB1M/LV41Bo1380fSB+/SJxNObNRexhT6OUT4ThRVlHwMSThYsqgERF8OsrV4RD425EsyyzemZUTkC1IUTBpnFDJphttVRr+M4UydNIZpj+7GFmWS3ysb15cRv9HtzJ+ahSDXz7LuH4LuHDgUn6bd38dyoi6CfnZ0zpTNp3cwlEc/DczqnV0H9S8WJlfWzeE5bywUaLBrNKydf6/AMRciGLVc9+R+t0POc0LtMt77bp7I/oM2xnFgiAItwuVSoVngKcIyAqCIAiCDV3q2LnhkyRaaKNw83G3vV4oF4/MfgnzxFdIc8spH6BIKhKadKTT9vn4hdovKeDu70Gms/2JweUShGXVAX5FNxIE4bZROdIiKwGVSoVKdyOwJssyy7/YTLpvJruTg0CNw3qnvRpbL4vL1oLWejmShCypiTgXjX91f1y9XPnf7yN4NT2Lqc+sYEdiEFuSvHKOaUdjKZJZG0cW/wRvAyaDiYgzkXj6u+MV7M2BlYeJ09n4cMu9zrLK+tdbUalZFV4F19dW8dyMQcU+9pfPLWN1RNCNn4eicE6qyovvn2Luz54E1AhArVEzceYgnjGZSY1Jwc3fg3N7L7AvLMz+jiWJ+d/u5+1faxTZB7PZ8cAVk1Fm3Zu/Y54xDa1itvmrk0dnyiY5MomgUmQNC4IglERWejYLP93E7mMZGGSJJiHw+GtdqNqoakV3TcgVeyma6NPXCW5Sg4DagRXdHUEQBKEIT33cl8Mj13JFY/md3suQzOufd66gXt29VCoVA756GvP08cRejMHNzx3PAPvB1jxqjRrloYdh+VyrUY4SkD1iHNkXwtCdPop7ZqLVZKgUaN/mlWFldTqCINwCIihbTvKCeKMHGjGrHYXHoJoxigmfPmK1PMDJQKSthE5FQa2YqdbI8sP37THL+ddYzXYwNi9tVpLQmA08/VyTYp5JxZNlmV/eXMOq4ypStV6555JbM9/AQ1MAACAASURBVFd1c9lTq047MS4jGyc35/xlUeej2bboMGazQpeHm+bXlk2OTmH9NW/L65ob+E3TejF/+k4mfTs4f5Vao8anas7EbqENqoCjoCwQFlu8rN0O9wazbQO2JzRTFA5vPU/P/T85isXny9a54SMKwAuCcItkpmbx7PBVXFKHgMoLVHA1Fna+fJSv3k6jUdeGFd3Fu1pCeDzrBr6Bz4l9qBSZK5KK5BZdGLD6c7xDfEq0r9SYFLa+9wdp+//F7dmebNm3lO6vPVJpyiMJgiBUJm4+7kx+tRkzZhwnXPZCjUxT92Qmf9OboFpBRe9AKBdqjZrghiFFNyzg4T8ms/hqOL7/bstfpiCR3n84Q39/NX9k5ZKRn+G29Her7RXA9NSLNOjRrDRdFwThFhPfkMvBtRPXWXPdHyQbkz4VCqapZRNf/3QfOhcbNUftZdZKEg2kaHxDbwxNOLHlVE5A1p7cfdWTI3lmQgPa9GtRrHO5HXz/8oqc2rp5sW1JAqQCjxBtBCmLkKlxY8bwHwm6+C+6lASS3ALZWaUn0d41AZh9+DI9fPbx/m/DOLb1NEY7NWEBTl6xPyOmT1VftIrRYV+8nYtXur3P+C4sWbs0J6hRmCThGXnR7hPTwk9bDff3R+fk+GGBIAhCWfnjww05f7sK/b3O0Lrzxef/MUcEZSuM2WRmfecn8Ys4lz+RiEqR8Tu6kzWdnuSxS8vtltiRZZmY89EoikyVBiFEnQ5nV9dReKbF4geYsjqgf/9TFvy+lO5Lv0DnrCWwXhUxWZsgCEIZWfPNVqZvBFkdkp9AckDvwawp2/jfb/bK0gu3I52TltH7f+Ts1hOcXrwNlVZD6/EPUr1VLYt2Qxe8xZFHunHyy3mow87njKJt1JR2/xtHw54iICsIlY0IypaDrYv+Q1F5Wa+wETjsVy2BwJrWQwRTY1M4ZQi0W4LAqdBPbvf6c4Dj+jEj68bz3Mzy+3BODE/gvy1ncfN0pt2AlmVSAzAtIY1V590d/6bm1tktdmA2t+06WuNUowG9zyym2ZV/qHH1EKtaPs35oFYoSGxLrsqZh1bQsY4C2H/S7Kx2HFStUdMTtdlGxnRuP/o/XLtY3dboNHw79yE+fOYvDmRXtTpfryzbs6sWrimb0LIbwxe8XaxjCoIglIVdp405f8dtZPmfV4UQdzmWgFpiuHxF+Oe3rfhGnANufF5I5E4MGX6aQ/N20uHxHlbb7f9tK5femYZP3BUAkvxroDg545sWa9Eub4LJ423vz5nsxM0Pz2ef4sHPxpbTGQmCINwdYi7FMH2jhFx45KCisDE+hJ7LDnHv4HYV0znhpjXs2azI4GrrIffSesi9t6hHgiCUJ5GqUA5MpuJPIrXvspqo89FWy68eD7eewKqAmCzLdRpN0T9KH3+XYverJMwmMx8/uYhBTx3h/eUmXvstnYcfWsO2P/aWet/Ht55Frymi3zeRKZtHr3VjbbOxXPOpj1qR6XtyHmqzMX9/UbpAVlwPQmO2kQ2bWxLivi4BDo+h1qr57tXg/InY8kkSDwVF0W1Ux2L319PfkxZNvGyeb7Kr436kPjScWuuXMObQLNuZ2YIgCOVEL9t5spZXCibRerJL4daI2HrI5vK8T5lrW6zXH168l7inX8An7gq5BYXwib+KT4Fs28L7yeORkYAy/TPWTPq5lD0XBEG4u3382mbrgCzkf7a+/0ucxaTEgiAIwu1HBGXLQZcBubN2KUUPS4/T+fP5m1utlnv6uzvc3ltrOSS+17AWoMh2t9Ga9fQd16nI/tyMLycu5++4YIsgcpLOlymLs8hOzy7Vvp1d7Qem85U0IFu4vaTiYM1eALga06mZeNZyvaJgUuuQZLPVfpqrwhnwwn1FHrJZzyYsmd2eUfUTaKsNp7tnBNOfcOetX4aXrO+Azsl2BvLRal0xS7b/S6d6BjFi1Yc07l15ylYIgnDnqOdt/7PA05hC9WYOyu8I5Urj4Xhmbq2nm9WyU+9/i1rJ+UzMLShk9bqwwsv1v/6CPqN03xEEQRDuZueyHNf8ztK6Mfm9o5gMplvUI0EQBKGkRFC2HDTsVJ9OruHFCxYqCkf0VchISrdYvHHBfw63f6in5TDP2m1q8WCVWNvbKAoTuyt4V7FRUqGUMlMy2XjN9oySJrWWhNhM5n2wli+fXcLKGZtLfAPW8oGm+BoSS9/RIgLksR6h+a+1Jr3lytxrqqjUeBmSCTDEUcUQS1ttOL26BmAs5hcd31A/nv1yEF+tGMXH80dyz6A2JTuHXL0ea2czczfRLYg1LcZjUFkGsmUkWiz85qaOJQiCUBbGTmyD1pz7t7XQ3+PBrRGTQFWg9i88jGzngZ5ZUtPhBeuJSD2vnCr1cV2zU7mw83Sp9yMIgnC3kotxKx+n82fTr3tuQW8EQRCEmyGCsuXkk3nDGFwjFlVuJgmKnZIGkoSs0rB17j/I8o02+09lOdx/raZVrJa9OWsIz3fMJNAQj9psRGfKprY5km+f92Pw5D43fS72yLLMbx/+7bC8QLqs5efDHqwMD+LL7ToeHfQ3Z3adtdu+MLVGzQtD/VHLpXzCK0lI9n4GgJshBYWcAGakdy3bjRSFFJ03yWpPonWB/GsMZcYOJx4ZvIU9iw6Urn8l4F/dn1HNbA/1PVulLd93n8r6JqPJ1rhgUGnx+PILGt/f8pb1TxAEobBGXRvy8Vgfgo03Hh66GdN4vHESYz/uV8G9u7uFNq+B6Ylnba6TJ0y0OXu0VY30m6RzFaV0BEEQblY9J8eJK2rZiFt2MhdOxtyiHgmCIAglJYKy5USj0/DcFwPwT4/ilc0vUD/mmMP207ZoGdd/McnRKQCYFcdZtiajdYBRpVIx/O2+rNj4KDvX92Lbhj7MXT+CVg+UzyyMHzy+iIUXHE8uVnjAYrLOh7c+OV2iYTS9x3Vh5vMBtNeF42NIwtWYjsZsQJsbdHaUBdvDM4LHGiQye3IV2jlHWjfI3bZ5+D4k4HhoJ1Jd7JxTbiDBqLa8iUzXevD+H0nEXbac3MSQpSfs8GXMxkJlD8rAhM8fxs+QYHNdts6dY9W6kDJoLD0u76b7i/3L/PiCIAglIcsy1y/Go0JBYzbgbUiiX4NsHp/yECqV+CpS0R75+UUC5vxEQvMuJFapR0LLbgT+PpuHv59os31mm85295XkVx2jg5r4eVK8q1K3a6Ob7rMgCMLd7oknGtlMXNGY9fQ6s4iXtr7CizsmEzrnMxb0f1eUjBEEQbgNifGC5ejYigOoFDMas54OVzZxIagFip0hgigKF1QhTJm4lhnLR9Gylpqwa7abuhtTadr9nvLreDH8s+Iw25Kr2p9ky8HkW/E6P7bP20/vcV2KfbzWfZvTum9zm+s+fXIh62ODrY7XyTWcD+eNyL/hfyvUh+ee3UmUtkDpB0miceQBGkUdIrzN/Vz0bl/sPhWk17iw5Js9TJw5CLPJzNcvreDvS65kat0ZPTCTVY/M581Pu1O1UdWb2r8tzQOy2Z5ie52LKYPhPz6Nq2f5TO4mCIJQEu+OWsiu9FDQugKQrNaxOAwujljCzGUjRGD2NtB+dA/aj+5RrLY9f57MrnsP45Eeb7E83dWHbn/PxiPQi79f/AZ7n0AmSU2tqW+Ln7sgCEIptH+4Ne8l72XGwnhSdN45CxWFR//7idrxN8rMOJuycP57KYu7RjHm8K8V1FtBEATBFvFtuByZsm/U/QxNvsTAo7Nx1afabixJoCj8m12FuKtxjHmjB96GJJtNR90joXOp2CF/f87NLUFgr+5tEfV0r1+yfW434+05I/jqGW/qyxH4GhKpY47k3f7w2ULLG/2AWoHMXdaf5zpkcI9zOF3cw3m1cwYvftyL3td30++3N0lUrCc0Ka5rkTklJz4Zv4QV1wLJ1OZNniLxnymUcS8f4Y///UX0xbIZQjTmhQ7ozLafeHdUh2Eo5SRrgiAIZeHvWTvZlWbjgZSicNgYys4//7n1nRJKJaRRKD0OLiP1wWEk+4aS7BNKSp/BdD2wgmota+Id4sPwZe+jrVWDhAbtyda6YlRpydK6Et+8M7VXzqPjuF4VfRqCIAiVXr021clQ33gEVi3pPLXjT1F4HKEC+B3fw8kN/93S/gmCIAiOiUzZctSkXzti5p/Pf98o5jD1Y4/yS6f3SHQPtt5AkpAlDddPR9G6b3N+mN6GGe/v4j99EGaVliBDHCN6ejB4csXW3zu05j+OGUNKFdKvVsfxbKEl1bZfS+b0y6mbemrHWeIjkoi7HE9QHcsJ0VzcnRn57oOMtLGPd5/+iwRdqI01xRPoqyX6Ygxb4gJBss4UTtd6Mvso/HrkFH2Dt/PWL8Nv+lgA9TrU4Z1Hw/luUQxxzkEA6IxZdLiyifaX1rG99ix4fDyP/PxiqY4jCIJQGrOWx4EuwHpF7t/ITxckUb9dRJmOJBDKX1C9YEasnuKwjauPO2NOzrtFPRIEQbj7LPpuPyZ1UP77mgk5iTOF02Py3p9fvpOmfVvdms4JgiAIRRJB2XLkGeSF4mtZn1SlmAlKC7cdlFUUJEWmWsOcSbyqN6vGVytGoc/IRp+hxzPQ61Z0u0i//nIaRXXzwUt/QwI9Rg8owx7lOLXjLB9PPcZ1bc71Uy04QUf3aN6fPQhndye2ztnL1i3XMZoU2rT04ZFJ9+Hi7gxARlI6R/XBNoOpxaGWTQx+ugMH159EVjn+byWr1KyLCWZPn9V89npdmvdqUuzjXNh5muNzNyIbjWSdvoDnif2Mk40kuAWj1zgTkB6BzpyToa0z62HO92ypEUyvt4eU+JwEQRBKKys9m1itv+M2Wnc+nLyTWX/ZelwmCIIgCII912KMFu/NRdyHqCt4tKUgCIJgSQRly5lPzSBSnplE9rx5uGckIEtq6puvc4Z21o0liVa6KAJq3Wex2MnNGSc351vU46JdNPre9G+OtyGJz95pjEZXtr96ieEJvDr1AunaKvn1bGVJzd7MUN55YgVmGY6YQoGcTKwDB2Dl4L/44efuBNQIICU2DVmlLvpAioyzKYts7Y0yB2rZyAvdzdRoWYPz/14DrCdhsyVF583EmTF8bTDT+kHb9XLzmE1mFvaZjM+uteT9JrgWWO+XEWX1RDxP1I9/gAjKCoJQARRZyS/PY/eBl6JwmmAizohsWUEQBEEoilFv5JsXlxERoydJr7a4LzsX1JpuF1ajYJktqwCKpKLt0w/d4t4KgiAIjoig7C0w8NtnMc98ivjLsXgEeDLA2w23V1aw8Jw3SoFAYE1TFB/8+EAF9rR4NIr1LJ9FqWOOYGBPfx565oFyCTAvmrGTdG1uVnLejX9uIOCQvqrNoECUNpBPX9lE7VAndp2TkdT+Fj+PwnwMiUx4yJPuIzuz8pudXL6Sjp+vjkFP35MfSOg0tC3Oy3eQrXG1u5+CFJWayd+Es7BJMGFHrrPozzNEZGjx1RkZ0LcqDzzdFZVKxV8v/YTPrrUWX7AKvnaU2+sRd7VYfREEQShrrp4u1JMjuaAKsd0gd4SIolITfSleBGUFQRAEwYHNv+7mspLFksu5ZYE0WNzjJLgH80/N+7nnyiaL7STgUusH6d+0+q3tsCAIguCQCMreImqNmqB6N0oWPDdjEA8dvcraPw6TmWWidYcQuo8eglpTjGzNCtbOP43tKZ7F30CR+ejzLlRvVq3c+nTuSpbtFQUzs2xkaR3Sh3AoTAVa+/uWFJmJHbMY8saA/J/P41P622yrVktISvEyZfNka90Y/vRhDBonFCkUtBCpwMn1cPTwUt7+ZRiZS5bjhWXwtbhFFrJdSvCzEgRBKGNNqqu5cN3yoZjWlE3nS2tpHr4PV2M6cW7BXK0+jFYPNrOYoFEQBEEQhBypsSlMXZbB0EcLfU4WSj7Z3uBRYjyr0+r6TjyzEklyC+Bw9R7EedXgBVkWn7M2JFyLZ8trP2HYtw8AXad76fXFM/hVd1yCSRAEobREULYC1WhZg4kta1R0N+ySZRl9pgEnV53Fh/fzH/bi+HN7SNBZ1su1Nzx1ZP2kcg3IArg7S2Asup0VSeVwWK27MZVnH3Bh4KQH7e4iMzWL+KvxBNQKYNMve8jSupe4G3qti/VCRWF9TBV6rzmCW2pMifeZl0mr6mO/74IgCOVpzddbWRUeZPEUSZLNDD38LdWTLuQvC8iIgllfsTw5hSHz36yAngqCIAjC7W3tz3vRa9yANOuVhRJRToe053RIe6tmsWFxVKkbZLX8bhZ1NpKd9w7DIy32xsIVl9i0eRM99i+iSgM7o30EQRDKgAjKClYMeiM/vLqKTRedSNV64W5MpXftLF74ciA6FyeC6gTy6+yuzPl4K4euqVAUiebBRkZPupeN84+w8bhMlqQjSJVOtQBvHpwxqNz73LNPdXYuM9/cxnYCsvc6X+PjJUPR2SmIn5aQxucv/sXeJH+MaiecTKeoKiWBuow+uHP79dpPiYz2qUFwwiWHzQvXjpKAhFotGDzrlbLpjyAIQgnN25AI2kCLZfVjj1I96YLV3ywA3bI/SfhsvMhMEQRBEIRCoiLTAbci29klm/HwK8X2d6gtE6biWzAgm8szLYYtT09j+KbpZKdl4erjJrKMBUEocyIoK1h5a9QSDuhD84f0p2s9WXndk+ujlvHVilEA+Ff3Z/KsYYSfDueP6Xs4FaXig7f30bm5C/OXPoirZ07m544dO6z2H34ynF+m7eZ4jA4VCq2rmZnwTg8CagVatS2uzFQD4KD0Q+FsWEeTzuTal1WNl4Yv453PuhHaONRinSzLvPT4Ws5LVfMPq9e4EIaNjNdSMqu1rG7yJE/vehdQbNaUJfd1Qr02SD4+gETooJ70eaF/mU+qJgiCUBwZSelEaQOt/t7Wjj8F2C7BopGNHF24k55vPHqLeikIgiAIlYN/oAuEF69tlZQr3HN5IyHJYeg1rlwIbE5QejiHfk+n60v9RHCxALfDu+yu89i3ibWerdCaDaR4BeP15BgemvbkLeydcLuT5ZzSheL/lHCzRLRGsHBs04mcgKyNoOW/xlB2zt9Pt1EdATiz+xyTPrlIhjY4/zfp0inYNXw1Py8cgMloIvpqEgP6rCRV7Y6LnE0zz1SOpXqSoQ0BXc4262Pgn2f28vO3HQmuX+Wm+m3Qm3AYlC1q9m8725yQQ3n+5YPMX+SNm8+NsgQ75//DeSnkpoK9NyPJNYBTLQfQ9OjqG90DUj2CMLm4o/j6Ue2JR+k7aYD4QBAE4bagddailo2YVQ6KdttSDn9DBUEQBKGyG/hsF2aPP1pku9pxJxl85HvUSt4owiQC0yMAyJj8OgvW7GDk1uniniGXxmSwv04xgznnOnqlRMHMz1ken8yjc8RIxLvd3p/+JmzqD3hHnMeocSKjbTd6zX6T4Iai3IVQMuIvsWBh17pzOS/s3BTPnBuV//qLz4+QUbh+qqJwWR3M5xNXMeixPaQYtSTqfDGpdaRpPdmXFUqG1nriqUSdLz9M2XbT/e7wUBNUsjknKGpPEZN+2ROv82PRtK3572VZ5qf54bb3k/teZ7Iz8VgpZDduxcmBkzjRpC9XOgwgZOlcRiTuYnTEesacmEePVx4WX64EQbgtmE1m9i49TBVzotW6C4HNgZxs/8IMaifaPNajnHsnCIIgCJWPIduIt8H6c9Xi/keRuf/0ggIB2UJNAZ8969j/65by6WQllBzaoETtVQv/IDUmJf99alwqf708m3ldX2ThgP9xevOxsu6icJvZOm0FSS9MwjfiHCoUnEzZ+P6zkR33DiPhWnxFd0+oZEQER7BQVFAvXufHiS2niL8WfyNTtKC8WT+TgsjWlqxm0aF4jxK1L6hqo6rc5xdTdLDVUdDWgT+Ou/P5U4sx6I0s+exvIrSOC+SbpJIloYcao5Bkk8M2mxOq8Je+MWurPcJCn358+tM1oi+WfAIwQRCE8nT12DVG9FvB/5Yabf6tvBTQnDD/JjbLFxiGjsE7xKf8OykIgiAIlci+ZYcYPPE4yU5+1isL3P8EpkXgk2U/KJTXMuz31XbbVGYGvZHtM1ax/MkZbP54MZmpWRj0Rk5t/I+zW09gNlkHq+u9MxG5BMfQmbI5seofAC7tO8eGeg+g+m46vvs34rlhCZcfHMrS0dPK6IyE243ZZCZ26kwkG+kFnmmxbH1jVgX0SqjMRFBWsNBzcNMi2xzfd4WMpMycN3aCoNJNBD9LGsgs7H9zhtJGW0ShpZscFiurNPwVVYUPxi7llz1FB35ldcmG68ZJ3vQOsC4wb8nyuFHaQD58VTzlFgTh9iHLMpPf+IdIrf0a4QoSy1o9x666A0hy8ceo0hLhVYsVLZ8mzqfWLeytIAiCINzeDFl6/vxoHW/8loasKnCvpMhozHrUZgM+GTE469NQmY2oi0jyyN88Lb2celxxzu84xYrg+8h84w2c5/2M4cP32BDUkdV+HbnSbziXHhjMksDu7Ji5xmI7jbMOo8ZyXhDZ5qPjG1S5c3bsG/Yq7hkJFusUwHXRrxxetLv0JyXcdsL2n8fTxsRwefR7993C3gh3AlFTVrDQsFN9Qo3/Ea61X9vVJ8CN0CZV8Tb8R7LO22abBjFHCAsoOsBbUD2djeE4JaDWqJk271EGD9pAks73xoqyqvOqKOxOC0bROH6W4WpMJ1NXsqxfvcaFf6OyaON6ncPGUNt1agufg6JwwhxC+Olwq4nIBEEQKsK+pf8WOZIAScKs1rK3bj/21u1nsSrjXDTPl2P/BEEQBKGySAxP4JnxW4nUBuWnUjkZM/HMTuTlre/gbMrOn/hXBi4EtOBCYHNkJFQ2iwTd4Nq62U33y6A3snXKIuJWbwSDAZf2bejx8ZP41wy46X2WlkFv5PCgZ62CZc6mLCgQp/ZMiyXtjTc46O9B+9E9MBvNXJvwck67AvKuX+GJlQGydO60GtKJs1tP4Bt9webkywCnv1tCm+FdyuL0hNuIVGRao5gbQSgZkSkrWHnn9aY5gUAb2a7uxjR6jrkHtUZNn5pplitz22vMejpdWkeziP2OD1Rg/yqziXHjm5S6705uznz5QTOCjQU+kCUJX1v1lwqoYYrksQYJOXVp7ZEklCL+CqvNRqqo0xy2sSdR50uTeu58/6IvwYYYJEXOP669/iCpiDxfVIatIAjCrXHsn+tFtvHOjKNZ+D4aRR3EyZhpsc4kvpYUW1xYLOsmz2HVxB84s/l4RXdHEARBKGOfvvx3TkA2l0o2Mezfr3E1pOFsygZuhH9UQIO4Y9wb9jfZhbI+8+64srRuxLkFk+HsRfcPHnd4bLPJzKaPFjG36WPMqzmQBX3f4sq/l9BnZLOo+WPI0z7G79wh/C4fw3XxHLa26M/lgxfL6MxLbv+PG/BMK15ZN5Uic/aTnwFIj07EudB3kYIK34UpSLhNmoSzuzMJYVE22+S3TRC1Re9EtTs2INXLfgKbc9fOt7A3wp1AZMoKVpr1bMKItStYeNGyZpHGbKCOUwqvjFpOVX8NddSpPHDqT3bX6U+GsxdIEkGp17j/9AL8M6IJSr0GdLA+gI3MT1mt4eyRCNr2b1nq/tfvWJfF62tzYPlhrl2Mp0aDQFr2vYexj6wmXBtklTlb2xzJ7+uGoVKp8PxoLT8ccCvOIzCb2rnF0LyxF7OO3Fzfd53IZt2xiyToisg0A1AUVIpMrRYiS1YQhNtDZqb9YZOSbKbP6fm0DN+bX4fLoNKxteEQjlbvBkCzoOINu7zbrZr4PdIvP6CRTWiBsFlfc6h5Z4bu/B5nd+eK7p4gCIJQSmkJaRzKDALpxn1Lo6hDVE25jKNPSt+sOKI8quOadg292hknczaypOJ8UCu2NhhCurM3I6pFOMxqNZvMzL/nGfyO7SL/bjDiLP/tWM+ern3wCztqlR3qnpnE7rHvUev0glKe+c1JOBmGUwnau189C4A52+AwIBJfrw262Ah0WWlkhNShzmvj6fR0HwBqdmzIESQkFJuBWU1tUZLpTqRSqQh591VSJ7+BSrGsRpziFUzfzydUUM+EykoEZW8zaQlp/D5lI/vOmTEqahoFGHji5Y7UbnNr/6hPnDmI9mv+Y+XCMyRmKBjMEudVIRyTcwKAx2KgRnwSI6/vonn4XhLdgtCYjXhnxeV/KGVp3bGarsVBKYF5+xWGZGTj5Fb6G0qVSkXHIe3oWGDZtz/34KOXN3FUH4wsqdGa9XTyieftHx/Jn+Bs/b500NgvPaAxGzCpdXbXffDzAHRuziwcsIE0nVeJ+31FE1y8hrnXsZ1zFAG17ivxcQRBEMpDm3tDWb3M9u1i50traRW+x2JApU420Pf0fJLcAklyDWTiRz1vTUcrsb2zN6Gd9Y3FMgXwO76HJT1eZPDmmbh6l2yiTUEQBOH2khSZjFllOUdFzcRzxdrWQ59Mhs6D77t9hs6sx6jWYVLfCFkuvlaFRy/FElTHdv33HdNX4ndsl1XgVWfKRrstZ4Iw6wxS8L1whITwePxC/YvVz5txfscpoo6HUaVZTRr0uFGCwateNbJLsB+jLiebWOXkeB6QGhOG0/3lATbXVW1ana3NO+N3fLfVtTKqtNzz3hMl6FH5SYpIxGQ0kx6bgl+tQDwDPCu6S5Vet0kD+MfbnfOf/IDntbOYtE5kd+hOn9lv4FPVt+gdCEIBIih7G0lLSOOpUeu5rq0CuZ8P0Smw/92zfPlyOi3uv/naPzej3YBWtBvQiv3L/+X139KtgqnXfOuT7OyHd3YCAemR+cvzPpROVO1ISOGdOqjtmqH14MDq/+g6sqPdNqURUCOAb1aMIv5aPDFh8VRrGoKn/40PpZhLsTlBUXuBY0Xh8TZZLDqkJ0PrYdVudMtM3H1zArpju2v5dh82S0CgyNaZuHn7Km79W0mioRLB+z/0K7qtIAjCLdJ5eHtcF/5NptbyC79KNtHm6nbA9jC/jmF/46ZP5eAPqTw4dWz5d7QSu/T13QfqZgAAIABJREFUXArPvZ13Tf2O7mRDSGfkwSN49PfX8h84CoIgCJVLlbqBaEynMWluJKvIxRzJ52ZI5XD1HpjVOrJsJJOYVVrW/rqfcZ8OtLl9xKK1+GH789reXUrOcoX02NRyCcpGnglnS7+X8Lt2EoCLwIFqjem55muqNq1O5+cfYtWn03HPLN4cJcYOOUPM3av4kqZxRmeyDummufvT+7m+Dvfz8PovWdVzIn7nDuUvy3TyJHjaFGp3qFfMsysfO7/5i4iPZuCVHGlRezixUQcGbPhKBA9L6Z6x93HPWJEcJZSe+LZ+G/ntw405AdlCsjWuzPjqZAX0KMe0WVdtBgoVlZo1Lcah11hmtkpAZI8hxHpWK/GxViwLu9luFpt/dX+adG9oEZAFMOoNOS/sBEW9jMk88dEAvn2vAQ2JIK9Ck7chiadap9FrWCsmPfon9/dZy8+7QGvSW+1LUmT6BERb167Na1eMgGxH53BmTvDkl7Uj8QwseTauIAhCechMzWLyyCVkaayzNN30qbiY7Ndsq5lwhsD0CBIPitqoRdFERzhc72zMxHXhr6x44stb1CNBEAShrGmctEiFkjsuBLYo1rZGlZbTwe0ctklNMdhfmZFRrONYbebiTdWm1W9qW0fMJjPb7huH37WT+aNtFMDv+ml29BqHyWDCyc2ZhnO/JtPZ8v7O7nRnJ4+TGpeKRqchcObnVvezGS7eNJ3/LboiMmk9g7wYc/JP6vy9DOm1t3Cb9gX9o3fTuYhgbnnbMXMNaa++hldyTuJUwdrD/mcOsL71UAx6Y4X1TxCEG0Sm7G1k73k5P0PWgqJwSR1C3OVYAmrZHmZSXuIuxxKnLZyTc0OET11mdZ5Cy/DdBKSFk+HkxZWgZny3bBxLhm0v8fFOZPliMpjQ6G79r2ZIwxACDAeI09l+utvKPyegUP/eevzyVz2SIhLJSMokqH4Qq2ZuY/Rb55HV1cBGdQOdKYsGugRGjaxH52Ej6LPqCH/OPcPVDBfcVEbCJT9kteMPfQC12UCbVj4079moVOcqCIJQlmRZZtzQNVzXhtoY06jgkZWIklt3zZa8TTTeYkhdUUw+/pASWWQ7adlCMr99HldPlyLbCoIgCLeX7XP3YdRa/v2+GNCMiwHNqOlguyiPaixo/xoGreO//U1b25+oyKlFM7hS8oek2pGPlcs93L5ZG/GJvwLc+L6Q9693wjX2/rCebpMG0GJge+pc2cbOactIPXsZtxohJK/5G7+IM1b79Im7wpoWQ3F981HSZ67HfcyTGLVasiJi8GpUh36TH7VbCig1LpUTq/5BpVHTakgnnN2dadizGQ17lmxUqyzLmPRGdC4lqYZbvP1GfPY13g7aeCdeZ+93a+nx6iPEXIgi/EgYQY1CCW1ew2b7pIhEdk1dTMqB/1BdDcM1OQY1MkatCxl1mxL4wZAyPQdBuJuIoOxtxKCoba/IzZ7MTC1JpZyyER+eVGT2ZrqzN3vq9s9/31ITjqunC4Ob6rEdZbZPr3ElMzmjQjJAVSoVTzzkzbRNueUF8p5OSxIuxgzGvXavRXufqr5cPRHBS6/+Q4wuAOz8+AAMGhecNdCqTxMizkQQ2iiYr5a1zB9aOuKBxVxX2/9ylMes1vHdfh2Ldq7n68/bUKOl7Q9OQRCEW8WQpefjCctyArIFuBjScc9OxjcjCp3ZwPnA5jSIPWZzHzl/bSVav3jzX+pPrj/Ctd3H8agWSIdxvYvMbqlo+oxsjq08gFlvpNnDHXD38yAlOhkAryr2b6VCnhiK/v3jVvXrCnM2ZBC25wxNH2xtsfzoygOcmDEfJToKVbVqtH7rcRr3Ll72lSAIglB+stKzmf78SrbE+ufUky18DyapWNp6ImPdkznQYjytru0EJA7Wup96MUfRmbNY3WJCkWXSqhmjue/xwXb70fXj8ezdsBoXQ3qx+55QszmP/TCxBGdbfNF7j+PqaP3+4zApp+6ru58HD32eU8s1KSKRfT/OtPt56RN3GZPJgF/4aZhzmoSG7Rl5+DeHgeXl42aiWvB7frmDtS944P7ySzzw0ehin09GYjqrH5+KbvsGXPTppHpWwW3UCPp99VSZlB2KuxSDd1J4ke2iP5nB6rfeQa2YkYBoYFvtlvRePZPghjeKEB78cyfXn5qEszHTqnySRp+Gy6n9GMLasWnKQu5/b0Sp+y8IdxsRlL2NNPTNJs7OZ5+PIZHQJp1vbYeAGi2q42QKQ6+x81FYuAaqIjN8eE79nKe/eIRfZq8q0fE8jSm4+9ufaKu8DXixJyrVNuaujSVSG4ikyDQikpffbk2tVjUt2sZfi2fyV9fI1NmfvbSgQ4ZQ+o/YgyF3eEyIcTdPDQ/m3kFtebibV04N2oIc1JmN1/nx5ss7mb3sEdz9Ku56CYJwd8tKz2bisJWcV90IyLpnJ3P/6QXUjz1mlRmbrXHB2ZRltR8JyBo5jvrdmxR5zMgz4cScuk5Ii5oE1Qsm4Vo863o+g9+VEwCkAivf8qXOT9NoO6JLqc6vvGyaspCUL2bimp0CwOaJOvQuXnikxwGQWLUhjae+Rpvh1v13C/ElziMIz7QYu/vPuwF1L/SAc9XEH9DO+pr8KnJXjnNpzwauvfU/HvhwZBmcmVCZRJ4J57/fNiGbzDR/rCc1Wteu6C4Jwl3LbDIzcfgKzktVHSZ6IKkwq7ScCW7PmSrtCEm6iFd2Itd96hDuVRtJkVEKBGWdjZlka3Pv4ySJxkTw0fe9UGvsHyS4YQiNVvzC6UefwlWfWmTfTZKG+5ZPL7c65k4BVlNHW673t10bNSs1Z5RjMWbryCmHcPYg617/hRaje5N8PZ5aHRtYPCRd98ZvOM/9yWI7F30apqmfsDc0kE5P9ynyOCaDieXtHs+vjQvgmRoNP87kz7/+BidncHOj2qj+dJs04KauqcaleA+lPdNirZb5hR1le5fHGHxtAzoXJ8L2nyVy3ESc5aJLHaR99jnpE/uJe1NBKCERlL2NPPFiBw5OuYhe42IViBvRxcnhh+f/2TvP8CiqLgC/sz2bng0QQui9hl5FQKRIr4JUFRVRAUWxYUfBgvoBdkWU3nvvvXfpvSQhkN6TLTPfj03fkg1JCGXe5/EhO7edmV3n3nvuKUWF3suNTuUSWBWmt6OAzbnZ1ppTGNFK5IkB7TKv+boLWXWdWdymlz9T3VjsiUm6vfEU3d6AmNuxaPUa9N72FdILf9xFspPQDjZIklUhm/7cwtSl+GyJBZYewmBMpp4uitPmUoi5Mq06Sjp2y6M8c2oPpuPK74s9kLyMjMzjyb+freOiIsuaQmVJY9Dh7zEk3bEJVCABOnMKSWp33E3WeHXRPkFcrNCSC961SA7Xs7nHXJ7rX5G2Q1uSm4ird1nfazx+5w4iIBEiKIip9wTExmC48V8OSxiP5Ghuvjiaso3XU6pq6SK593tl/8ytGCd+jj7bE9JYjGgSIzKv+IWeJ2T4KJTaGdTv3Syz3voJs7F8+xVeuZ5ubisgAYgOqEqFxpUzr906cR3ln9Nt5FFIIqnfTCbmlc5y0o/HBFEUWTJoMrplc1BKIgrgv+nfs6ddDwaum1ws600ZmQcNQRA6A1Oxqkj/kiTp66Icb9Nfe6wK2fwgCNz2qUTH/Qs4F9CYFw5M4nRgc7ZV7YlF7Q6CQN/aydRr7k9ceByhG/bidng3W5r9gdnbD2WFipTp8gStR3fPtA69c+k2phQjNTsEc7RKbfRn9jsVwajU4jv5S4du764iiiKbPpvH3bnLUcVHYy5TgZrjX6DJ4Cdp+WYf9vw6HbUdxaBZoabluD52+wyoHkh8HoeYGWTMocpffuTMLz8CcF2pIfmp7vRb+il3r94h6dffsBfQQEDiypS/XFLK7v5pTWZs3OzztgQYQrLCLCSf2sOc+esYvP/XfL+Tt777p105XcU7NpTdU1dT+ekGHG8/0CWFLFjXePv/2ECHD+RQBjIy+UFWyj5AVGtRhW9fT+LHn89xXWXdRPoYYxj0hIZBH3UpNrnGTe9DykuL2RpVElGw/mT0xkTGdHejVouKHNxwDje9hqeGtrRJnmUo7Y2nMY4ETV7hCCTaeoXx2vcDiugu8o9vaWeReODSTVtrL6fYS+aV/neUxkCUCM+UDOPkbSVh6lIu9WUSlOwdOJ5K1/JnkSwjIyNTGOw4bc6MUqM2pVIr/BCGJOvmJ/dxkoB106E3JWWqFPc3GcopZcXMOrGSDx8tTOO16+sY9HHWvGc2mtnY+nkMd69kthUkEcPJXYCtUhKsB4W7vphN/9nvFsKdFh6Xvv4DQx6xdQFUoolTH02jfu+5gNXdMfnHH9DloZAFSFXrafD7FzmuHZy6FDdJtDuu2mJk37SVmS6fMo82mz6bh/vSWTa/JJ/tK1k5qgx9/hxbTJLJyDwYCIKgBH4GOgAhwGFBEFZJknS2qMbcvTMUyKdSFmvi5UStD+0uLUcAGt/aSZ3bB7lmqIVZoUa34ypXfjPhZ4wlMHvDuNtw8wzJu9Yw/38zqPTpWK5OnI5f2AWrPN4BSOWq2B8z/d+UgSN4+uuXCnygJ4oic9u9hd++DVnu8bFh3Hl+P+vOfEiXScPQT5hA6pdfoMw2j4mCAu1771Oysv0QcIdn78TkbYBsStm8Qv8I2eqoLUa8Ny9lcYVDeEaH4O44bRjuYVdcutfQ1bvwtSODPZkMJ3ayeeICOn8+2KW+wXoA67b43zzvM6/y8G2HCF2yEYMpf/vdO9sOgayUlZHJF7JS9gGjUbdg5nQLJuzCbVISUihfr1WxJL3Kjkqj4tNZz/Hqlbsc2XgGvYeWVv2fzgxKXqlRRceNBUhUeTgsVlpMjGiSyhM9alOpUTuH9e4VURQ5OHMbl2csQ4qPR1enJk9+/gIB1QPzbpwH3noFOElcei9sulMCi1rtsmWxb9JdfCMjubLvApVbVi9cYWRkZGTyIEGyZjYURAsmtY6yMc43JRlvNQk4W66VVSGb+30nKPh7n0DPmETcfa3zx97fN+B790qOPrJvnBy9LVPOX76Huypa9KGuy+R1JSvRypFZ29CZkm3qZNx7ot4Po08JlHXr0vrLlylbv0KOeqaYeJylfUmNjHVZLpmHm4i/5+GHrXU1gHHxIsTfRxe715KMTDHTFLgsSdJVAEEQFgA9gSJTykpOFH7OqBBxhqqR/+W4pjOnUvPOMQBSVHrczLZzR3b8wi8RPWo0fulSCIBXXDj8F44FBUqyFKEZ5Qke/lRo37hQPCyOLdyL374Ndrw+JFJ//IH4t3rR8ZPnuNQmmMNfz8YcEoKyTCBN3h/mMOzR/F6f4rV2gU0MVFdCGeS2YPWJvpXnt2N0c81lX1Dm790aM/03yIdS9ugfa1C78FvK6zkIahXe/+WOrZc3YmhovtvIyDzuyErZB5TA6vfmbhl6NoQ9v21C76enwzu90HnoCk2mUpVL0vW1kvlup5QsmB0ER1JJZoZ93q2gotlFFEXmd/0Qny3Ls+LnXTjM3tVLqTLvd4J7NilQ/z2fq8XWX2PzVqDmA4sy3eQsr/4EgSp3T+KbEglA1JUwWSkrIyNzX0lJTCVJaXWQkxTWd7xJqXGpbbhnWU5WaGP9YOd9l6rSs3PBEbqMagvA7e1HsLfdyevNqyrp75I8hUVqYipHZm0nLSGZmj2aEVgzyKaOUeeJW5pryVNERdbcaU51fgporFmfoQd+dVhesmUwqWsX2FzP2ASXbdfQpkzm0cQ9KsxhmWdSJKmJaei9nGdul5F5xCkD3Mr2OQRolruSIAivAK8AlCpVih07dtzzgM17e1EhPsGlugYfC0N7Wut6p+gxPzvGYV01YHZRBvu+FPbbuwF3os+xbEY4fpULZuwSnRiK8hv796AEdm7dhmd6bNfA97JCBIQRQZidZ54cnYj+yZKYn3T8XDIpUxKzg7Hzg87X4NL37z3yScxdnBg05UIP+ftdNSuBuUrB70fvXwLal3H5t5PxHN21+gL9fyADiYmJ8jMsJB6WZykrZR8RzGYzUxu/ReVz2/FIj/uy6rspGAe+wJAZRZMJ01Xqau9y3Gy7MQUIdo8qsnH3z9iCz5blNqeuOmMSp195n7rdNxfIEqRhl2D6rVvMkhu5FNWFqKR1RFD0Jbr+9w8SVted8s1rFOl4MjIyMrlZ//suzEptjnfeuYDGNLy102m7BK03CxuPJUXj3KokLSUrhpnKO/9JIyQEGo67f1mAt323nKjPJ2UmRTnysYKEJ7vSf+1kNNqsWOGqjp1g8T95ug4CJAW3yPy7du+WHPpEiSI9S3JuDO1s9AU5aD22O4u//yXT4ie7pXF0UE2eGfRgJkWTKXxSPfzQxtq3ZkrReqDz0N5niWRkHjjsvWZtzA8lSfoD+AOgcePGUtu2be95QLPRTLteO5CUeSdpGtozgdkrPEAQeGPbF6iMcXbriQgo7tEC1xUkrMoEzzl/02hAq3vuZ/YnY/Dbv9FhuThmPG2/78WWSYsJ/3km3nevkqr1xPRkB7rNfA+vUllh8kRRZE7twRguH3NpbPM3Y1C9N+2eZQeIKlebfkdno/fJO5KrKIrMqTcMw4XDLvUtAW1NF1yW5eTKw4S89IlLawxnY+a3bcZzNA16ibavy6GQCsKOHTsoyLtEJouH5VkWyDdJEITvBEE4LwjCKUEQlguC4JOt7ANBEC4LgnBBEIS8o17LIIoilw5e4cyO85iNLp9LAfBjqw+pcWZTjgDo7sYEvGdNZ8O3xRtvdPT4prilJ3XJjt6UyOvvtrDTonC4MmMZYH9S8YkO4fTao5zfe5Gt/+zlxokb9zTGmz/154cRnjzpEUo9RQi+adEFUsgKogVBtNgWpCcHa3FlLUMPfMOQQ9/hZkpCAOIat3vgEtnIyMg8+oTcsM3IfNOvGieCnrC5nn1LuL7WEFK0Xo7flZKEQjTTvFvdzEtNx/RBzMcWQQIsr46lRvu6edYtDE6tPkzihA9zZKlWSCI+O1ezdOCXOep2+2UMUeVq53k3ZoWKuu8+n/k5oHogiU/3snGrBIg1lKPtB85jsmu0atrt+IeoSsHZ+hCIqtmMLjv/eiDc1eMj4tn1yzrWT5jFwTk7uHrwEhaznTlRpkDoevcEcv5/mfG3qX3XB+K3ICNTzIQAZbN9DgIcm5gXAiqNig97qG0SKTtEENCnxeNutJ2LM7AoVETq88hTUQAy5pJzvy8tUD8l2jZ1Wh4/az6zWr5O2qcf4Xv3Cgok9GnxeG9eyrbyTzD7yTGsfutPZpXrzlptLZcVsq7i6BtJ1nqh/PBTBp6e75JC1mK2EHc7lt67fiP5uRHEewVgVGicqs1NCtc8kDII7tmEqFrN71kh6wiJrOeQ26I643qSmy/tJmYpZGNCo9nw6TzWjJ/BxZ1n+G/dUZa//jOzWo7i3xoDmd3yNbZ+vQRjSlohS1s4nF5/nH8bvMhij0YscW/IrEp9OLnyUHGLJfMIUlBL2c3AB5IkmQVB+Ab4AHhPEIRawECgNhAIbBEEoZokSfLK2gE7Zu9j+pww7mhKAOA9+QJDnlTz3IS8E3zFR8ZT9swOu2UKJK78NBfe7VWY4uaLai2r8svnCn6fcoD/ErwRgGDvOEa+29J5PNqCEmf/1BggTufH3KnnuelWznphyVXqKffw5c+d8QvKHX3IOU17NaRpL6vb59TRi1l8/V4FznIBtkEQaMtZgm9uz7Hpj6rTir5rvr33AWVkZGTyye2L4fz40VYOJJay+hXmSl64vvZQDlboSNW7J6gefpRoj9IcLdeWNhdXcqVEba6UCs6srjclkqzOFXdcEOjgf5fS1dpnXirXoCJHnh+F9p9fXJIxum4rhk0fVZDbzBcnv/obv1xJtDKeinbjSpJj383csOl93Bl4ej7bJi3i7uqtSGYzmtDreCVG5LBgVYlmzr7yAZGXXsOjpB8Nn2vNgFUTWfaiDyxfjD41HougIq5ha56Z94VL7uaBNYMYdmER1w5d5s7Zm5SpX8km9mxxseyl/6Gc9Qeq9KViZPp/R3Q+eL01ms5fDClW+R4levz0OvOOn8FwIsuqXQCiKjeg3+wPik8wGZkHh8NAVUEQKgKhWPe1g4p60GdebcPclfO4rso74Zd/QihDD37r1BJWLZoyQ50VJWKcY8WwK7R9tx8bvv0GtcV+mB6f2BA4HGK3TG0xWq1s92+0iR9bWORWcGbM1aYnn+apD/oRdvoWokWkQpPKdg+1LGYLy1/6EXHZYtxTYklT6UgJboFH3z4o3bTELFqOX+R1u2MnGsowq+ZzoNcTNLAbbd/umefB2bN7f2fZs1+g274OrTkFk0Kdw3DLEWZBicqBN07GtehSlan8xThCtx5BsWIROqPVSMiscaPxxlkYylnDRq1+608sv05DbTEiAJf+Z22vgazv6QqkHt7K4h9+o/3uWYWS86Ww2PvnJiJffxP/bOort1tnCOk3lMtDXqHvzLeLUTqZR40CKWUlSdqU7eMBoF/63z2BBZIkpQHXBEG4jDVg+v6CjPeocmzdST5dkIwlXSELEKfx4ecDoJ2yiT7vdHTa/siyI3imOVZA+jhwUbufVG1WmSmLK9/XMcVyFcHOSalZULKg8ZtEuwVkc7sVOCUGMW7kJv5ePeCerEQib0ay6rJH4QQFSZfLzxhNkC6ZXt3K0vHlUaQmvsDBvzeTEhFLlc6NqdKqZiEMJiMjI+MaR9ec5O1f7mBWBuEgVDgIAtEeARz06MzhCk/jlxiOqFSzqOHriKqcFh/PNjBx7lIIR1ICsChUuJsS6Fotldd/eNam287fv8L2f37J060uSedNu78/ued7vBcU1x0n79KaUrh1/CrV22VZ7WrctHSeOBQmDuXI/N3cGfZSjvvK+Nc7NpS0CRNIA5aP9SPo28/oP+tdjGlvEXk5HO/Svrj7OU6mCRB/Jw6TyYQhKCu+bsWmVajY1H5W7TMbj3N6xlrE1DTKdm5Ji1c6olQ5+rILhyXPf4/b3D/slrmnxmKZPJFNahUdPx5YpHI8Lqg0KoYc/I3ji/ZyYe5GsIiU792WZ0Y8LVvJysgA6QZHbwAbsc52f0uSdOZ+jD1r7UBG9ZzLGamsfY8SSUJnSuL5fV+ilixIQIRHICUT7RvyKtPrFGVgNff69pNtuUrc7RiHCtkHkcw5evMyNniuyEyEdkTnjdSlB73/GZ+ZEBtgQdcP8Nm2MvOz1pyK9uh2OLodAB+sMflzPwMR8Iu4BhHXAEg5tYc5i9YzeP+vKFVK0pJSOTJnJ6kxCVTr3DjzkFXnoWPQukmkJX1C1PUIvMv4saJ6D3yibRXbSW4+tNq/hJSYRC60c2zElejmR5vDS7MUpy8+TWrim9w8cgWv0r6cv32RSi2qAbB/5lYUP01x2SXbJ+YWG3u9zfBz811sUbRYzBZuvjcRLwf2hLo5f7A2wJ+uk4ffZ8lkHlUKM6bsi8DC9L/LYFXSZhCSfk3GDn//eRqLwn7M1Tlb4uk1TnS6SPav4M91wXGcudTcVkiPAQeXH2ORuRGVartxx7MsWnMKtcMOUvv2QS6Waki0R4C1YsZiRxBAkrisCOTwyhM0653/hCdLpu0iTVVIZ7TpcgVqk/llZVbGTZ2HjjZjuhfOGDIyMjL5QBRFJk6/jFnjevIsUaFCLRppfHU9hqRwdlXuxtVS9UEQ8DVGM2D80xyft4tav/2LFBeP0lOPTluFLV8aaT2uNzoPLRe3nyEtMYXyTaoiZdqR2hkLgdjmHWkz7a37bv1p8fSBqJs2162SCviWK2FTlsHNrUfRkveG2SM5msjRb3G20jxqdahPYO2yTuufXn+cY2MmYbh+CoAY/wqU/XA0bUbbT66ZPTlmxqohbv0i5n5Xi14H/skRs68wMaaZ0MybkWe9yGm/I054FoVCgdloRqFS5EuBePdKOKEnruFfLZBSVQJybJgfRxQKBY0GtqbRQDmWsIyMPSRJWgesu9/jKhQKfl76HL26ryZW42dbQRBIVbvzb/sf8U6JJEnjhVGlZcjBKZSOtx+OzdH8Eu/mg1dKrEOlbe744/awIND206F53ZZTTCkPj0I2N8pszvzuqXGwbDZrViyi5LRveWJkZ26duI7XtlVOn6GA1eI3d53cM5wEGE7sZE7DF8GYhueN82jNKQCc+FhgR7MOPLvxO7Tu1kTfWndd5lrhifV/srvrKHyzWeTGewUQvGAaZWqXJSk6kTOCCqVktiun0dvPxpJV56GjWtvamI1mYg/sYZ5/W9wTIrAoVOR3hjVcPsbmrxbTYUL/fLYsfM5tOolXwl2ndaQpk9jiqefpD4tfXpmHnzyVsoIgbAEC7BRNkCRpZXqdCVgTM87NaGanvt1dVGFmrXwQcSXjW3AXD+oKjrJt6ti+dTtKtRMrFQ2kfD8enSnZbrGnh2+xPdfiynh3OTKWns9pgQZk2SaVJcbchZJIDFU5zm56KzmGlB35d8PxrA1Da9j2mz1Dan7RSA9/BsuHJethUfG43z/Iz+BR4fi6/4jMh0IWAIuFilHnqHnnKEpJ5NkTv3LZvw5R+lJ4pkazpdQENFiyFhnhwKWjmNfBuu+mYNbo8UyMAOCSxgOjdwDecbftDhXbshNDd0695/vLDzGh0ZxddwSdlzvBvZtRclBvLJNO2WyoBCCqWiNKVra3jLKiNbiu7FRJFi53Gcjxp3pS7fmuSGYLNTo2tFGYXt57jot9XsBgTslcfPlGXid+3DvsBLuK2c1fLLBJjikBhpCzrBrwKUN2/M9lOfPDnmlrULoQ3co7NozlHo1QWkyoRRMmpYaEZk/xzL8f4V8hp9I7NTGVVS99j7hhHW4psZhUWrTGJBRY/ZAFrHF4A8aNov27fYrkvmRkZGTuFZVGxex/2jHupbVcEsrYtZg1qnREeGYZ9SRq83dwZlJqMNdtTFRoCIbQ83brCECKxoNUDx987VhZSoDm088zXdbbvOj0AAAgAElEQVTvlcA6ZdnlFYB3fHiB+nlQ0IppRL/xFtcaVGHfZzPwySPZWm4vmbzq+Z87YKdMwu/gJlaV2o9q8DC6/PBKpnIWoHzDSpQNXc+Rebu5e/IykiihjU3g/NKdeAcZKFMziLgGT2A4tiNHvxlrAu8+jo2C5rd7E6++NTPXZyoxf7lxMu7N+NlHzFqynn7bp7sUo7eoSEtIybOOAMRM/g7jWz0e+0NemYKTp1JWkqSnnZULgjAc6Aa0l6TMyOQuB0cvzKyVDyKuZHz73+RlRGp8bAskCUES2Ti/RZ6x4rZdFLn18ef4J+WczK6WrMuLZ/7B3ad4rGWLI+PdqS2n+WeVfXcfBE88jXEkOMn6PaZVCm0Ht833uD8sXsyym7b9Du2ZwOyV+c8cDtBKH8ILL/e+p7YPCg9L1sOi4nG/f5CfQVEhCEJnYCpW18q/JEn6uijHi76T/8OqEsm3aXMpy2VPAKpGnqYqp0lVaFHjWBnnZkwCY1K2z4m4GRPtZpM2C0rcKpXDbDSj0hSmE1BOLGYLi5+diG7tksz4bJfc/Qn88kNuNO2A36HNOerHe5biqfmTnPbZfHR3dk/9wWXXTSUSPttWcHebNYlnqFKLqdcA+s37gMSIBHZ9u5i4OQvxSbeeyWl1I3Fr0nSwo5S9M2sxBmyVygAe+7eQHJuUY5N0cuUhTn03C+n2bYTAMtR7ZwjBPZ0na7FH7MWbLlvUZD98VluM+O3bwKbGJ+l2bhVeJbwAMBlNLK7RB787VzLrqrIlT824J5+om6RO+IB1sYl0mTQs33LLyMjIFCW+pX2YuXYwseEx9HjxKKJS7bT+Ld8qVI045XL/aosRv0NbMj9Hla+DskoV/JvVQ+3lTuKtcMq2DqZB3xaIosjsusMwXDyCgIQEmJRa/Kd+Q+uRz9zrLWaiUCgo+fYbpH36kcM6RR2CobBRIrJrxOf4nb8/iaEyno0+LQH+/pk1s2ciDH6BtMgoLLHxKNz1WOLikdLSUNy8hm9U1tx7fMbPbDOU58mNv7G34xV8om/l6Deq7hMM+u4lu+P+t/Yofoc2Y+6bM6SeSaEhTaXD3ZiAkIdSOgMJMJzey9KeH963Q3Z71OgQzA2VDq051Wk9fWocxxfto9nwdvdJMplHlQLtXNI3hO8BbSRJym6muQqYJwjCD1gTfVUF5FR1dji36zxGR1+DIFBPddul5B1PvfIUp6uUYsV7c3C/fQ1RqUJdP5iX543GLdsp2ePAmQM3ATuna+mnzCZBhSCJSIKt26PSYqTzSy3vady+rzRj5YeXsCgKRyGgEM0MGlGvUPqSkZF5tBAEQQn8DHTAehB6WBCEVZIknS2qMWs/UQWWX7Af484B8TqDzUYq47NOvLdsuwokzAgo04MZgNWCVDXnD+adOsuQw38WWVzMZc9PwXN1zphnnkmRxI4bT4OVc0kI68XlWauRklPwadWI7h8PwsPg+FAuJjSaTaOno1TrUaUnw8gvGksamqWzmNchGt2BHbgZE7FzzAtYn71v5HWiQiJzxJgF0MQ6TgajFk1E34zMVMrObj8O311ryXSsvXmaW/02cmvMeLp9b3/j5giPcqXIO/2IY7zjbrP143/p/dto4u/EsSq4H352Qkk4IvGnnzF++hwarXOFh4yMjExx4BPgy/D6Scz8z9Gb3crJoNY0vrENr7RYu+UiAiuDXyJF40HVuyepf2sPatF6GCgBhhunKTXxTRo/ZxvSRKFQMPzMHCxmC2FnQvAs6YlPaTuhFQrA0x/2Z6tKScSkKXgmRSEBFkFJXKM2EB2F4erJQh3vfuB56aTLcVULG50pGf752aVDTwHwi7rB6cbPoNJ6E9mgDYIEgk5Hhec60354O5YO+RrTrl0ozEbM7l5IWh2Cry+SyUz21USixoutNfpzPqARokKFZ2oMza9uoNHN7S5ZAkuA14Etdtcp9wt3Pw/MvQagXfJvnnXNqQ9v6A2ZB4eCao9+ArTAZsG6STsgSdKrkiSdEQRhEXAWa1iD1yXJBd+0x4zIm5G8NekSiRrvbAmnsvA0xTPu0xYu91fnqdrUOTy5sMV86CgZ5AUHHf/cdJIRN2MqMVrb+K8WhZqL+6/SuHv9fI9bPrgcY9udZ+p2CYuiYJs7d1MCo7vpCe5YN+/KMjIyjyNNgcuSJF0FEARhAdYkm0WmlA2sXhqFdBZRcH3poDUn2yzCC8PSRWXH6kICDKf2sHvaGtq82aMQRslJWlIqwvJFdsuUkoXjE/9k6L5faDHCqYNRJrFhMaxr0B+fGPsuofl9Tt671jrNwp0dtdp2jjKWCAQHMdTS1G6UqFzKai3V9BX8T+62qSMAlunfE/5Kl3xlUI67fAu9y7Xtk7B1FzCalc9+ki+FLIB7SiwXtpyibtdGBZRCRkZGpmjo83pr5r58GKPSgaGNJJGqcWdLjWfpc9J+0kQFEv5J4ewp3Z0bhpqcDmzOoEM/oLWkZs43535aaFcpm4FSpaRscPkC3o1j2r/bB/GdXsSERKN112Yeal47cpmzLbq63E/GTFjclrUPU/IysD4vj7Q4PI7vJF7vR6sd80i4G8vaEi3wMGVz6c8II3UtZ3ujUsvcpu9Yc7ekO1An6HzZXOs5UtV6nriy1iUZVKKZkKNX7Splwy+EcWblAdR6LY2HPUX4uRBun7hKQN0KVG5Z/R7v3JY+s99lxcrFuDkIDwnW+63Ts1mhjSnz+FIgpawkSfZT91rLvgK+Kkj/jzrzf9hJojr9ZZNdIStJCEh883YlKjeuVDzCPWTEhsdxYf8V/Mv40KRbPZSLDzlUjFbzTORQmoMEKYLA9z9fYP49KGUB+rzTkaZdQlj++wF2XJC4oy6Z7z4qWsL4Y0lv3DweLwtnGRmZfFEGuJXtcwhgszIs7LjtI/vFkWjRuFzfPS0Fc5cxBRozv0TqYu/pPvOKfZyWmIp64os4ipTmrlSzY8cOzGkmUmKTUCgV6P08ERT2t4UxV2/j8X4fh/3dC2LeVTBr3Dh27jicy3ndb+IgzCFP2m/k5cv+wwdICI/BZ1ADzIMaOOz/wME9+Ny2zn2uxJNWNvLBHFyw34i7Wse2rdvw7Fkdc89q+W4farlL1EMQ91qOz124yM9T5mHBt4wfb3fT8N0aI2Zltjk4I3Jg+j5SlYcNlHdKVObf4d4VOFCpM20urcjqLsqxx8T9QqFQ2MSordi4CnsN5fB1cuhmVGhI03liqtuI5l+NQu/vyZZBH+N17jAaqTBnWtdx9aD0QcQrOZqdXV5FmxCNuynvGKsA/wU2t02mnc6Bip1pfGMbOrPzvjIV6oLAslemkhISjvluBELITTxib6NJ9ypKBba9laV4jwX+U6iIL1EedHpQKhH8S1D91b40Heo4vMDNE9e5se8c5VvWpFy2JLEqlQr1iFfgN8fx9M19B+Ed4NyCXUbGFYou8JpMnpy/7iBOiSAgIRB6JZJ6He6vTA8bxjQTX49cwva7BkxKLZBABfMhnvC1sDMhyKa+mymJJk39OOQkmMZtRcHccYJqBTF6aj9SRi5ild1Iys6pXUYhK2RlZGTywqWEmoUdt/1O2bv0HXvWpRAGhoRQhh/4BqUlza6wKSo33PJYnN8LsdUa0/fM3Lwr5iKv2Me3Tt3gVO+xDsvjgmqSVPY03gc2o5Ss6tFYpYaE2s1o8cM4PAN8ODFnKwgCJetWInLMFDRpifmW0x6uWtamqdyovmwmddraV6oue+l/qGb9kSPxVlTwkwzYOR2tu45ZNQZiuHLc6RgxbbrTa8sUwLV40ou7vYM27d6SYWYgjniDSi3Kc+a9Uflum+BRgm4RO1GqnCRUfUCQ43MXLvLzlHmY6PpaO4KfDGHRr/tRSyq7XpbReuuBmKM5IUafMyni2dJNcihlVZUfXGOgalM+IvzFUTaJISUgqd9wBsz/0KbN8JP/YDFbmFN3CP6Xj90nSR8d/CKv56v+DUMNh2UmlZbb3hWoGHXOYR2w/m5FBK73HYYW0Wn4hdy/cbVoxpAtnjxXIeLQZmZ9XAN1XBRIEsbqdWgw4SVQKDg7dAyeidaDiP+AkyiIMwTh1q0rfjXL0/T1HuyOiEazfH5mHgGAFLU7qhdG0Hd6/tccMjL2kJWyRUj45TvEhMezcPJ62vRvSECVUjnKtSpwkuMEL7+COvQ9+nw5YjHb4spY09wASBLXVaW5Hi+hEo1ICFjSA+NXtNzmvfeCsZjMcCjaYZ/2Ys3eC72eb8jqr27lXTEbStHEgNdkNwgZGZk8cTmhZmFSqnJJqgubuIADbwMASaJO6D46n52XYxGbHQsK9lbuytMXlhS6jPqGwYXeJ8COMT/i7MhOGxmGR0jOzYbGYsRwajcXn96dI0FZGJB3tHjXyUshm6LxIKV+c5p//QZVW9d0WK/PX28SPv5ZjvyxDnNqGtV7taZrh6zY5oq4mDxl8W3guH97mNU6cKKUNStUTjM5mxUqEleuYee+I3gr1A5/c/aQgJIfvv1QKGRlZGRkguoEMe7n/uzYsYMh1aOYczGnRelt7wqEeVcgMO66TVuTQs2pMq1yXEtVZc1EIgLNP3mhSOQuDJoOacMp73858f5UPK6cREAgIaAi7Zb9j7INKzpsp1QpGX5uPstH/YTmr+n3RdYHJXzC/UbpZK62lrsWzdK6ViocK2MJMISez7pwbAe3+u5AAnJH/FcgWq2x//2VZODw+wpM7XvQ9vwWzq44hGi2UKlNXco2rCivG2QKFVkpWwSIosiPbyxh5Q0Dg3srmL1Pz097TtOtzA7G/9YfhUJByOkQzid4gAMvUF9jDM16tbJfKANYld47YkphjUSePu1l+zfDvcfLGMvkcZUI7tQms636f5vSLWttKSNaT8wsZgtrftrOsk0RRFr0uAlGmlcQGf5eO0pUzDssQbUWVRhW5zQirr20teYU3u6ho2KDCi7Vl5GReaw5DFQVBKEiEAoMBAbdj4E/ndSGwe9dQHKUCVoQKB1/E5UT5ZgSkQsBjVBZzLS9vCJHWUEyLIsIdJoy8h5bOyb+Thze+zY5reORGue0PLsb4/3cqCXpvOl2a3tmkq68CKge6DBZl1i+IjixnDErVHT5ali+5DOWrwpnIhyWW4aOgNkzHCpmVaLZas2TTS57v6Hs1zL+Tuo7jG7je+dLXhkZGZkHgVe/78uhbvO4KJTJuigILK8/kmePTqdEYtY5barKjRXBr5Coy+lqHRSbZVUY07gdlZpVLXK5C0K97k2o133OPbXtMX0UCxcuxNtB7HRn2JtTohq0RUpMRBN+C0kQECxmvJKse8jHTRmbQfU7xzkb2MyuFbd7Whxlsv3e7heOvgtXviMFIj5bV7D5DQWD12bl7YkJiWb9yCmYw+/i06YZXb59AZVKVqvJ3Dvyr6cIWPDVBpbfKkX2dIuSQsXq2wEoXl/M+F8H8MUHu4jXlLHbXimaeHtISVQa+etxxolt5xEVeT+jeI0PG5eeJbhTVtKsIcHJzDxtRykrSbzQL5C0pFRGD1zKWYJAGQhKiANWhMLmUYf434dVqPmkYxeNDF7+uhfLF6zK7Dt37GAEgU7+YVSp4k3XkU/g5e+VZ58yMjIykiSZBUF4A9iI1Vfgb0mSztyPscvVLUsz910cSLUNEQNQPuocjW9ud2rjkKbUkaTxslkVmxRqhFGjsfz+C1qz/RA/qUodOottmQTw5jt4lfJ27Ubywa0TV21cJh8GMmxNlvb4AIW7HkGppMrAjjQe1BqFwrpIuXrwEoe/m4/x9l30NSrT5uMhxIVFs+/dn1CfPYGoVEHTFvg0qInC0xMR7GaTloDYei1ZOvArSrUKptUb3VySseobg4gZtc/upjdZ60X3/71G6Itd2TPiM/wuH0OBVdGsS42ze+SZux8LkORZEq9sG3EBiH/mWbzKlWZuQAc8o0NJ0XmS5u2PMiUJbXIcKtGMIImIKEg0lKHES0Po+NmgzOcmIyMjU9x8+X07xozdQ3i2HBYJWh9u+FbLVMqGeZVnftO3MapyhkZTiBZaXF1vbaM30HXhxPsneDGgVCmp/uvX3Bw2Ek0+PCoga05J1PthrNuYOm8Oomu/nMm4F3s0yVefsR36YI6Jxf/Itny1u584mu8dUe3OcarcPcnlkjk9lgRJpEfZiKwYyA8JGd+716ZlJMd/gt7LjSXPT0E39y+8M1a5p3az6tefqLVuLjXa1Sk2WWUKh4ird9k9aR7Jl2+gLVuaVu8/R2BtJ96BhYSs9StkIm5E8Oc+pcMnu/pmCXoeuMRZqTRge4oEUFGKoO3Q54pW0EcAb1894DgjYnbWhhh4ev0pGj5jdcMcMbkX0vsrmHfSLXOR4m5KYERbFR1GdOaXccusClk7JKk9+err44xPNbFk1n9EJkiUMSh57tWmVG5qG4vJaEzfyOf+rtM/+/tpeW5CF5fuQ0ZGRiYDSZLWAeuKY+xypbUcuGa/rMGtXYDjoLcCcDLoCSxKNXc9yiAhIKQvbtWiiaiNO+h8eiO7vpxLyqXrqAP88axSHqVGReX2DanaphYL+nyG25rFqCQzEmBU6fD+7BPav9e3KG4X/0oBXHNwTw8yAukWvPs3Zl6L2LiEOZMbM/DYP6waORXd3D/xyCg8tJkDs35DFBT4SdnShm1airQJfO2MIWFNsKIWzfif2AUnIHn1fBZ/9wsl/3k3Txmbv9ieuTM74Hdoc47rFkGB3ycfoPPQUblldSqfm09ybBJpSWnsnDQf5R/THN5zbPveuAUFoNLraDyqGwHVA9k/Ywu31u9D6aYj+OXuHP91Ofz4DRl2Yx4psXikxNrt0y/iGpbJE1lw5CyD1k3K856Km2uHLnNoygJMd6NQB5SgYreW1OnWxGVraRkZmYeDwOqlmbeyFyN6L8Hj9jX8k8Kpefsw3qlZYdoC42/Q8ew8NtQekpUkTJLoe+xnSsfdIKpea56a+bFNcq1HkUYDWlGq5lp2jP8F9x3r0IjGfLX3SI4mpWoFGuRSyAJICtfd2RPdfOm//AvunAvlZBOrUrYg64tktTs6U3KeicUkIEXjSVKthnif3IdaMjkcN0nrjXuacw+g3CiQ6HP8N46Va8PpwBYkazwoFX+Tgc/XpdNrIzj4ZCUufvgtfnev5qvf4kYBbPlsNkGt6qKb+2eOZyYBWnMqp7sNo0aSHLf4Yebgv9sJffVNtObUzFjGhxfNwmvSRNq9XbReVbJSthC5dvw6b71/FJPG8aQmKpQsn3EMhACHdVJFOUaJKzTp2QDfv1YTo8k7MZdFqeaDqTdZ2KA8PgFWK6qXvu7FoPgUjq07hVKpoFG35mjcrP8Lbj9rAQeeuQDXlYG8Pi0K0l2G/ouAzZ9f5d2uN+j6muMMj/ZQOMjMLSMjI/Og0ndUC5a9fQ6znRAGXimOY3YLwHW/Guyo1hskiSulgrlcog5VI/4DrItbv8vH0Xnp6TfzbYf9DFz2GfAZqSlpqNTKIncbK1W1NFHVmuB/8XCRjnO/MFw8wlqvBuhE2w2ZAJmJyvLCggIlIlo7G1ufqJskXLjG1hNLaDSsPQDbP59NwvHTKL28qPFybxoNaIVCoWDQzqls+XIhd+atQBkfg6VCZepPGEG97jktj/Q+7uh93DElJDmbolF5e9DnrzdzXGv1ckd4uSMA4RfCcFs+z6V7zEACvDcv5fDcTtTu3hS9V2FGBS481rz9F9L07/HI9h1GLP2XDSodln6D6fvvO7K1r4zMI4RGq+bzic3Z8Mw8ysdctCmXgLphB9herXemUramEMrwo3+h83JD95glFw6qV54hG78hOfYTFrd8GZ+8m+RAM3cGkZ8Ow79CzqRpxsYtcd+9Ns/2EgK+74xFo1VTtn4FtldvguGCa2sLR6F59KakdM+YrENuewiA3phAWlgI7W/txWQycWHTCaJOXyP+9CVMEZEgWlBotKjLBsLq+S7JlV0WpWShyY1tNL6xjehSlRlwYRlad+tvrNnwdjQb3o6YkCjWtBiGX/jlfPVfnMTsO0bsivUYcl0XsN63mzGJdZ/OpsvnQ4tBOpmCkhybxK3Xx+OWy0tPJZqIm/AJEb1bUaJS3uEr7xV5VVYIiKLItyMXMezja0Q6UchmcCpEQunEbSLIM38uFY8rKo2Kd4YFoLakuVQ/Se3J0mk7clzTe7nxxMBmtOjfJFMhC5CICwuUXJavFoWaH1enkhCVM2GJp096X7ldNiQJJJGn+xdNUhoZGRmZoqJMzTK884wClcVWGRfjbn/RImFV4i2vP9KagDH9HXqxVMPMOgIgIGFMcu29rnPT3rc4Xh2Xfku8Z6m8Kz4kaOwoZPOLEsfKWwlQWkykfjyB7RVbs61KOzR//4zh+E58dq4mfMiLzGlvVbwrVUo6fTaIYRcXMTh8M8MO/GajkM1O+Y7NMsfIPSZA2Q7OE2aemLM13+EoMp7V3edfYbN/Y2Y1G8mdS7fz1UdRc2nnWaRpU1DYUaprzanoF8xg6bDvikEyGRmZoqRSo4r4dbUeftm+FwV2V+5KkjZd/ShJDOhdDp9A38dOIZudqOsRoMv/4ZpSsnDod1vl61PT3yLRPbfKzhprPUXtjlmhIjqoJj5Tf6TjJ1kesc+s+oHoANdi+dqbs4Uc/0oka3Onr7LF9+4V1o+ejiHIn5YvPk33H17m6Z/fQhl5lxJnD2A4sROv1fOR7Iwo5fo3O+Lrb6P9/EuEdz6g1p41DAtZl6mQzTF+kIFnzy8l7YXXiPUri0XIaZCWovXELDxYtoOawAC0UeF2yzKekjjpSxY8++X9E0qm0NgzbRVuDpLOqi1G9nyzoEjHf7B+7Q8pK37YwqqwUriaJfCGqnTWh1xxRhWihUEv1rXTSsYebQa34I8qV5jz0yFO3VUTofa3GxIig6vXk1zq1yAkkkD+47umqvRs+Gsf/d/rlHlN7+VGK30Ie5NzhUMQBDoablO5af4sa2VkZGQeBLqNfoomXe4y4rV9xGqyHNuPlmtH7bBDOaw1Mqw7TpdpTqompwu1JZfLX6xfEH7lbDc2xU3pGoH0vrmJbZMWErFiI4LZjHendoh//YbGQfzbx5nsM7FGNIGdw2jfXWuYWfkGCj8/hLvhSF7e+HV4kmavdyfi8m28g/y5uO4It+auRIiJRlG9Bp3/HE/jQa2Z82kdDDdPZ/62Mv6NLlODZ15s71S21OgEp5a2eaGSzBiO7WBvvc6U/+dnGg14ogC9FR5Hvp+PVx5rUeXSeST/9IYcykBG5hGjzy+jWHDgIIbLWS7UZkHFosZjuGHIlgdDEPhmUTwly5+hXvvaxSBp8XPt0GUOtx+Ef2ocZhrm3SAX5mTbOT+wdlla71/EtjFTUR/eiyCKpNVpSMspY6ncsrrDvkpUKsngG6uY2/Yt/PZvcDpuisYDjTEJpYP3vAAozCZSBr2EZcUyPJKjHSZPtWzbCnyU+XljtzcxhF/K1Z9k014AknVe6FPjM6+ZBRWUKEmPcf2dyp8drbuOPn+MhT/GYjFbODBjC5Gnr+JTrSwdXulEXGgM+6ctJy06Dn2AP0gSKZGxgETi8dNor11AbUwhVe+DWL02ykvn8IkNdXn8DEyCEpVkcXpILQGdp41hbbODkOzcG8xj+Wx2Ta/Pk6Ndi6sv82CQePMO9lPAW0kJy3+CwPwgK2ULgRXbokFZ2qkyMJPcyZ6y/a2yGAnW3UXrFlgEUj66VG1Wmc+bVUYURV7stoDLSvsJ1AAMvq5tw8QC2A/FRKXYXJs0dyB/T1jN+pNmYhRe+ItxdGuqY+hnA+55HBkZGZniplTlkjzf3o3/7SZzfgvzqcS6OkPpeHY+6nRFnABc8a/D5poDbfqoEHU+x+eAca89sO7VOg8dXSYNh0nDM6/NuxGGZv0ip+3MCKhybaIyXP8fd0re/A9upn8IA84f4tD0KTlm4UwfpLAL7K+ykpQBL9Jz7wxW9nofn2O7UEoWREFBbHBruq/4Os/fT/zugzYuiPeC1pxK6NCXsZh+o+mQNoXQY8Ew3cl706A1p3J591mnlsgyMjIPHxo3LQNPzGLLZ3MJmbMKS5qRfZWeyamQTSdF7cFrP96l1R8n+GxGX9weM4vZ3a9/iyE1f/FSIevwr2afJ+2WB9YMYsjm/HsjKBQK2kx9k5PNNtn1dJAQiOvYhw7TxrC7bheUJsdGRjpLKh4NqlPny9UcqNTK4Y5WZczar17ZdwHD1RN2FbgCEOtXFgQBi5sHvr270HPy85xdd4yr6w6g9tTT4vXunL11nntFqVLSamSnHNdKVCpJj/+NzFc/F3ec4fhPSzHHJ2KJisJwao/DuhYUxLfpysB1XyMoBFa8MhXN7N9t3MglIKbVM2i9tSibNkNac9WplkAAbn05FWSlbLFzas1RYiLvsPa9mTQY9rTThF0lGlYj/l/HffnUqVIEEmYhK2ULgQjRHbspgHOTWyGbjtacTJpKj1mp4agpiJHf36HTv8eZMGPAA7sxfRD5ftRipwpZhWim78tN8+xHFEVCFCXyrGdD+vdbt4mtDEqVkpe/6cXL+e9VRkZG5oGm97inWbnsd66VyLK4ORX0BDf9qvHcoe/xTo3heNkn2Vh7iE3bEgkh1Lp9CIBUtR6vCR8UWbKuoqLv/AksrHUSQ9gFu+Vx3qVptXkmJ2duJHb9FgSzGU3TxrT+/Hl2fjgD9zULXI7h+rjgbMOjANwX/s2qXbvpd2ohKfEp3D5zi4CaQZkx/i7tPsvhKQswnziBpFSia9IIvwY18Ao0EHn6Kr7nDubYfFoEJRdL1uemXzVUooka4UcpE+cgk10ulJLIxXcnPxBKWbfqleHYDofljiymZGRkHg00WjVdJj/PqtJl+XZzXtt8gb3JQbzy7HJ+/K0D/o9Boi+w7vO8/tvvuBwFphGjMM+di3tqzuSPAhDVqB1V29QqdLnKNajIkeGvov3nlxzXJQSk0W8z+AfrLjK50VI63yYAACAASURBVBPoDmy010UmxqRUfMv4EecTiHdsmN06qZVqZv4dctRqIetofrCUCGDY6Tk5rtXv3Yz6vbNCBRVEKVtYVGtbm2ptrWvRG8eucqp5V7tK7jSVjuYn1hFYPWvP3vfvccR++QIrO7yB15VTKEXrYa9aNOG3dz3b/NZn+tDmNZfqEiIL76Zk8s3JlYc4P2wsHsnRKL4ZAz9M4+gP37C9fU8GrpmEUmWruGvxUkcWfhaET0yITVmSmy/d3nPdCvxekJWyhYBBSHLu6p6hjHVgSZum0udU2EoSGyMDqTllM/3e7WS3jUxOjClprAkxOI6SLEmMaplG+frlXerPWZD0jP4QBJt/K1pu06zvs/kTXkZGRuYhRqlS0vH8Au6EBnGmdDPSVG4Exl6lUuQZLOlJRWLc/CkfeZYQ3yrWa5JI3bADPHV+CSrRTGSVBvTdPwOPh9ClWuuuY8DZRWz88B9iV29CFReNqFZjCQiixDNt6PXBAPRebpRv8Crwao62zy37FIv5I5b6tsD9Hqx2ANKUWjSWtMdO2eZ3+xJL6jzLoKsrM7OG3zx+jR09R2O4fQnv7JVvncW4DDK2SdmXCilqPQsav0m4d4XMa4cqdqTBzR10Ojsvz+cqAb4R1wg5dYOgeq6tMYqK1hOGcHj+3w4zcGfcS+naxSunjIxM0fL08y35ad02ktUejiul7zuvKQPp++pxXmttZsAHz9wnCYsXpWh2WGZRqujz2xhOdG7KuRfH45lwFwHr4V18q04MXPc1AKmJqdw6ehXPAB8CqheOl2ufP8dy8Il6XJg+zxrSJ7AMdd4eRsP+LTPrtP9tPPua7sXNmGjTXgJEQUHtPi1RKBQYXn8Z81ef296joCT481GZnwPqlOcyjpWNitKl7Vx9sCnfsBInX3sT6ecfc+zrzQo1hm+/yqGQzcAn0JfhZ+YSevom+5v1xs2YmPlMcodwAMfPy6TRF96NyOSLy3vPcb3/C3hIOf8fVyDhs3UFK0YG0HfGWzbtVBoVrdb9wZ7uo/CNvJF5Pd4rgOAF04o85JOslC0EurX25qf92ChWEQQ0kokXg+P4+6S38/AGdkIarN4RTb93i07uR4lTW85iUTgOTaAWjTw3oYtLfSkUCmqr7nBKDHJcKbtCNv1zLUL56tcOsnWzjIzMY4elSnVqntxFzfCjnA1owvbqfThQ2bq506fFk6zxAEGRmfCwT+BtqtcOwNTiRSr0aEnXTg2KU/wCo3XX0WPqqzD11bwr50KpUuL/yQekfPh+vtuaFCqaHF5DUmQcJ3uMwD01LnPr8TjMRIY7l9k9fTXt3u5NcnwKe59+HkO8/UQcjthSY0AOhSwAksTxcm0JirlMnXRLbkdkrN5MqcWfpDWgeiDR5Wvjf+O0wzpxHiUxVHg8LOJkZB5X9F5ujOvtwZerRevcmwcWpYaf9iqpsfk0wR3q3AcJiw+FQkFMhboYrp20Wx5fuR7nNp/i6nOv4mXOcvFXShY0R/YSevoWR6YtQ1i2MFMxGlUxmFb/THQaO9ZVmg1vR7PhjvONBNYuS5M9SzjUuh/6tMQcikEBSHiqB4E1rXvYTp8NYl2qkcRffsM9JQaAOJ8yVJj8AcE9s7xHq7ery6EyNfALtbV2FRFo+K6tp9PDQI//jeTMM0058f18LOF3UFeqQIuPhlGhcWWn7XZ9+Cde6d9tXqEKspPxXah6P1weX48KZqOZw91fwUtyfOhiXrwQ8c+xdvU1FRpXplzoBo7M3cndE1fwrVaWTi+0R6UpepWprJQtBJ59vxNnhs1ne1w2JZ4gUFsIITDQn2+WASo7/0s7CGeQwU38iLkdi29pn8IX+hEjLeXeNkMWs4XNf+/h4J4wlCqBdp0r06JfI14b24Cx390kTeUkK2f6d9dEfYtR45tTrUXxuy7KyMjIFAeNvxjFlV57ueZfh5X1cwZqSdZm8yQRBKqIYYz6oc9jF8POGW3f7snsmYvxv3Q0X+1iy9ehbN1yANRIyFIeJsYns6zjOAxHtz/yLushK7bD273Z+c0SvPKpkE1TajlbuonDeP8ng1rnqZQF62/8wobD7H7+E4S4GFCpUCQloE2OQyGJCJKEUjQjSCISAsluPqQGVQadBiQJQVCAQoFbjSo8+fHQAlld1flqHGFDXsyhlM+eDK3Mp+Plw2MZmceAziPbEBOxjp8P6F3KeyIJShbNPPXIK2UBan8xmtBhI1FKFiDrHWlWqAmeNJbDb32LwWybI8Q9NY69PV7HN/J65jUJMFw7ydHOw/E+tTozjE5RUr5BRdxOrmXDgI/xObUXpWQhReOB2LMfA/4Zn6Nul6+fx/j5YC7vOotap6Fyq+p254C2K6ayu8MLOcIdWAQlyjffoVaH4CK/p6KidqcG1M7nwb/5pH2FfV4IQFSZGgybOe6e2ssUjM2fz8crwXlsfY+UGOJux+Jbxs9uuUKhoOnQdjD0/iZil5WyBeTcrvN8/+0xzgu5rColEZCIupuEUeU4nYRCtCAq7AekNSl1jB2xgX/WPCsvoPOgXvuaCP8eRnLwLEuKsSTFJLJoyjaOnU0g2SgQb1ZzR+WHqFAB1g3QhtnJNF00n2/nDWCaQsHvU4/yX6o/JqXW4YLmVIo/Feo7DhwtIyMj86hTp0tDFrYczmmceBhIEj3L3GHMD73RussK2QwsZgsnlh6g/CsDuPGTEf8b/7nc1nDtJPF34vAqlcNZHw8vPU/P/Ijj9ba73FfmplRQkqbRI6o0aJPj0UjFbwHqlHTr67srN5Jf+88UjUf6GsA+iTpvh2XZcUuLxzLxMxeTh0l4pkTjeclOBueTuziy8G/MCg3Jnv54vzycZ74ahkKhIDYshuhLoSzp2hCNMQWTWkdixdo0+HpsjqRdjQa0IuLspyR/+w1aszVDuJB+r35ffESbN3u4JKWMjMzDz3MfdUH59QZ+34lzQ5N0bsc/ykd4WTQa2Bpz2s9c/PgHvLC+I6PL1KDm5Heo2bkBN/s7PiD1yaaQhaxDT/eUGLZ/MpP+s7LcXO9cus3lHf/hFWig9jMNCnU/X7JyAMOO/En8nThiw6IJqBGIxs1+/niNVu1UsSqKImeW7kZUazEpVJhVWpLK16Dt3K8o36Biocn8sCBp816jZqyZIsvVQR0bhahzw/+lIQz7fHCRyydjn4hl6/Jch6Uptbj7e94XefKDrJQtADf/u8XYSVdIVttJLiUoOENZGlmcxIgTBETBSYYwSeKqsjTbZ+2n/fOtCi7wI4ynwZNmbmEcSLOjHJUkBnX2Y+jAjdzV+APeVr9OjZ2OJIlDaUHM/GQNL3/di2ltazDr0zX8cczxyzlN5UbI2TAqNXr8Ji0ZGRmZDPzqViLieknHFQSBNbcMNF1xnDaDW9w/wR5gji7cy8XXPsy08PQTFFbLlHTrnbxQILH7h2V0/eYFm7KA6oHElKiAb8R1u21zW9AKQKrGnbprZ2cmyphVuS+Gm45d4Z2RR2R2p+1cUQtk1CvT8ylunbiO74X8WRkDeKTGoTUlkaa2HyvML9m5xUUGGVaohaHOELCGXPKOC4Mpk1nx4w8ke/rjnhCJctJItEZr1m2lKRndxcPc6jOEKwoNif5BSP6lcG8cTNvPh6Ib24NDMzeTHB5DhQ4Nqd2xfiFIJyMj87Dx7Pud6fpaMu8OXc5JZ6HZAH+dY7ffR42MMAFbN2+l0Y0D+AT6AtY8JYIkOXynO3vPpxw+DljjzS7p9j5e+zahlCzEAqf8ylLnj8kE92zipIf841XK2+ZgNr8sGfw17kv+xTf9s9poxu3SUXa+8AVDjs147IzD/Lp3hGknnc7rAmBSqBl8YSEqlaxSexAQUm2t23OT3KoDGq3jkJfFxeP1f1ghM3PKHucB1AGpII843TLz2IHQe+/jMWLiP/0IVuTMmKcUTbzUMIGde+6kK2TzIP2ZbzqRZRmkVOaxzZJEfAN9ndeRkZGRecTpP6pFptWiIyxKNZNmR5Ecn/fC6VEn5NQNbjz/Wg6Xe0ESXVbIZpAcal9xqFAoqDxxPBY7h79GpdZmo5Hg7kez/cszFbIAJQf3Bu5NwZrRf0TdVkT7lXOpTaLWm+RBL5PiQEmau/+ocnV4cmx39n05CyW2GZbzQiWZaXRzh/WDnd9uc+dh52zkKQq0ljR8Y0PRWNIc1tGIRvzuXsVwdj+6Wb+xtWYXQk/doN3bven63YuyQlZG5jHH3UvPzysHM7G/Gk+TA4MhSaTKjnnM6/4RZuPjo5xVqpWZClkAjZuW6PJ17u2dnm5hubjLu/juXY8i23zuE32Ly4NGcvt8mKPWxcL/27vz+Kiq84/jn2cyWYEQdgJhVWRVVoNQFxAQUJEqoCBiq1bkp9aqrVtp1dalti5VW21dq7ghVRHcUFywUkUKigoisggCsoctJCHLnN8fM8GETIAks2Qy3/frxYvcc+/MPPfkzpzMc8+yecUPpLz8bIVyBzRZ9jH/e+bDyAcVZafdPokdHXoe8hpwQMqNNykhW4t4jz32kPt3NmrDWc/fHKFoqkZJ2RpYtiUyb8K01EP0ppUDUuun8NCsiTx2XQsu7bWHq36Sz6uP9+Pc3wxlcX6LKj3Xbn5cNXHweX0wV/mXvaN8mzTvr4jEvaxuWWQWbzvscfsSG/D24/MjEFHt9tEtTx4YXl6qtMdlVZKgTftUvrDIgEuG0nraI+w4ug9FCUnkJ9UnZ+AIjl/8OvXvvYec44ew49gTKZr8K05f+TZZx7Ur9/jTbj2fnAHDa5Rw9G7+gRGfPMeuxofuoQVQMvg0zn36N4zN+4zOH86i6LKryE2teNOzxBLYNfQcxi6ehjfJy/4164I82+GV4CF77Vx6rf8PZWs9sbiAyX32MuW5X5LTLPZGwdQr2M2nk24oV/bmb5/imdYjea7FUJ4dfgO5OXuiFJ3URN6efN64/kmmZU/mmZ9cyYd/ex2fr+o3JCQ+Db5wIE88cDyZReVv5nl8JYxY9hytd6+h4Zx/M2PMrdEJsJbofvuvKA5MbVOVNjlz3Eg2r/iBhgvmBu1lmVK4j49u/VcII625z556J+jN4NLYV784N7IB1QJJqcmc9/k0Sv7vGnKyurKzSVt2NmlLXlJ9ChLTyMnsRKvpTzFCUxXUKiffeSkFSf6b+ge/Z7d36se569+qca/ycFFqvwa8FuY/gpzDcIy8oE94X6eO6XpyF7qe3OXA9rbvtlLiqVo39ea298DPrTpnclbWR8zaWDGxm1RcwG13n1T9YEVE6pC778rmwqmrK50rvdS2zfsiFFHtVbR8RdDyYAnQyobQ7U5vwbArzjjk6/QdfxJ9x1dsp7K6t4WrRh3ysR6Ph4nz7ueFViPI2FG9xGfDbWvxlZRw9orZzDz/DjLmvlzpscXrvj/w89EDu/DNy/NJzN9Z7vwd/lWwXX4+aRn+P769LVvAEUzFW/o8PoydrTvzk+l3U69RfXx/eJpuy15gW2pLWvQ+mvG3jCajpf8P94kb3uSZLuNpWslK3bVxITUHNN68kpUfLafDgE5Mb3M6jbav48CyFu+v593MN+j6zgw6n9ItipFKVWxdvZm5Ay8gI2f9gXnzchfO5dknX2H8gkdq5ZBMqX1adc7khdlnc++w28nZuo+Uojy6bv4fDfb/2IM2be4sdv3w63I9SOOJv838J8um3k+T75f624zmHWi8dU2lj9ndMJPzbhjDgsfnknCIzjwFX30dhoirz/kOnXJ2vqqN3qkrkuulcNaDU+DBKdEORY5Q6x5t6fzSE3w++fc03rwSAGce8s67mEnTavcip7U3shgwoPMR5rQPM5yz0mPMaF+ymW8WrourYSSh1rhNExoW7qrSY8YMKz9N9DV/P4dubKjwe+pXfxutu1R/lWQRkbqkfa923HVRAxKLKx9qDdC5Z9VGL9RJGYcfYVHa4uxu0IK9aRW/HKfm7Wb+g6+HOLDyPB4PjS46v1w8ZeUnHXqqAQ+OLd9sIC2jHp0mDDvMi5VP5u+c8SpQce5bgPoL3qMg19/TuN/1E/FVkhotG7MBO3oM5Iz9X3Ph2lkcdcIxtOzcinOfv4nLv3iUWxb8kSn/uPBAQhb85z/pm+kUXfpL8pIrTllVOq/cwa8VTaU1sXPdVqafMZVG29dViC3RV8Tnoy+NdGhSA3Mm3ExGzvpyZQ5osvS/zLnuiegEJTHJm+Ql84elnLxqNtnr3i2XkAVILClk5QdHvuhkXdR3/ElcuPplhuz8gtN2LeHcb2eyLzV4u+2A3tMfJMGbQP3M4Ku6l7IG6WGItvp6TjwVn3kqtBGl2+3OGhThiESqr8fI3kxa/zrd/vsGbV99nvTjOjPu2RtqdUIWlJStkZ///jSyirYc5ijfgXlKD8mMpoXb6OnZQGKZ4YzfeVtx55vGhaNfYufGICv1ymEleBMY0aVqSe3+Z/Qot/38bXP4+uBVxZ3j44I2PHL9rJqGKCJSZwwcezwTt/yblMLc8jsCN7VaFWzipAn9oxBZ7XL0L8YAh07klU5nkLF3Cw3ydlY4Pqm4gNypU/nyjUXMvvoRpp0whWcHX8vCZz4IaazDb5tETvawCmnPXU3a8pPFsw8MFwumBA+tjm0PQM9zBpCXUvELaek5NR91arnypD07Kn3exJJCdm/y18kxg7rjufaGCvPnFiYk4QJRFyUkseuUUYz98OEq/3Hu8Xj46cNXMnrbx7Sd+Rz5F0xmR69TyMkeRv377qXbuzPY0abbgfqpDYPJS/DQ8cRupMz3Dz09+HfngPr7trN20eqIxyZVl7tjLxlL5lf4vCj9ve6e+VqkQ5JYVy/4uiil11j95pqaDSClfgrJ9fz/2j74JwoTkiscU3TxFXQdehwAx47qx+6Mih12Suu14yVnhzPcKss6rh17h51doY0wYEfHXgyYfFo0whKpkQ7ZR3PsGX3xJMRGulPTF9RAgyYNePTZETx+y9t8uCqB3Qn1A18IjNSSPIa0zSXJVfzgrsxuTwPqFxZS5E2psO97byZ3XP0W9/xbc5dUR86uwiod7ysqP1TjtU/zIbFB+QR74Oe3lnm4vMYRiojUDZtX/ECLpfO5IG0lM3tfxo76gS8nZrTeuYohbikez/joBlkLnHDxEKa/MoaGhxjODxWTaQdvJ7gSVo67lJSivANDmrfNf4NnnzyD89+7JyS9AxK8CUz86EEWPfchK59+A1/BfpoNzmbMDeNIqZ9CkztuZd9115V7zIFevn0H0bR9M8A/HDD919dQdMcfsTLpJf+iXd0Z/9tzyz3H/hZtqP9d8BvS+ckNaNKu2YHtM/58ERsmnson97xI4bYcGvXuxvAbx5G/ax/bVm6i1XHtSG9Wsx5KSanJHHtmP449s1+Ffcesmcn3n3/Hzu+30+6ETuSs3cbujTmYGblbdrDqwWdovHIxpWnjcE97sKv/UJq2b1Zpr/XS1/527mLa96vCimYSFXs27zrkIoCJuVUbESbSesIoCn73SYXPIsN/w63zkB6VPDJ+nfDzU9nY73Xm/3Ea+79dhadZM3pdM4Eep/841aDH46HrP+9kzaTLSSnKO1BuwK7Boxl5ydAoRH5o41+7ndlXtiJ/+ouk791KfnIDioaewdhpN5Lg1do2IuGmpGwNpTdN59qHxnFtkH15e/J5Zsa7/t5BR9BbtsibwlpaV7p/YV5L9mzfQ3rT2jXsIRZ8vKUBHOFUW/WLdtOqW/k7nNs9lU8KvTOpEYX7izSXl4gIsOGz1Xicj2b7NnHp/D+wvtHR7ElpRJN9W2i5Zx05bbtHO8RawePxcP6bd/Lp08P49rFXSFj+JQ33bK7Wc5X94gf+hF+j+W/wwV8GMuTGsSGI1h9v9qTBZE8aXGHfoKvPYvbaTbiHH8AbSBwZUJyUyuhZd5U7dvit5/NJm+as/MtjpG1cRWFKAxKHj+Ccv13Bwn+9z5rHX4KdOSQcdRQZpw+Bh8rP5XpgXtgzz8abVP7P2Kzj2jFu2vXlytLSU2nStmmNz/9ItO3dgba9/QuDZbQo38vsxCn+BWCWzfwYb1oyfS8YxMd/f529d/2lwu/vcPK8aaQU51U63K0ED12vHAdAYWIq3v17KzkSNr3yLtx0bqX7pXZo3imTvJSGpBXsDrq/MKtjhCOSWDfo1z/luZlzabK4/MiKgsQ0ejxyR60f7hstrXu05bwZvzvkMb3HDKBFl9l8dMu/2L9sOaSnc9TF5zDy0mG1sl5LR4Pw8JUU5u/Hm5xYK+MUqauUlA2TTd9uZsfGHELZD8Ln8bJt7XYlZauhIKFi7+PKpLqiCg1RRsletgUZrgLQoGiPErIiIgHNOrdmC6XJM0fbnSsPOqB5NMKqtfr/bDD9fzaYlR9+zTdDz6HizG6HF6ynE8DGp1+GECVlD+es+y9j6y9H8+nfX6NwTy7tT8tmb4uioCvdDrhkKAMO6i30wlm/J/2tGQd6+7Lha4o/8pIzcATpC97D6ysK7DB2/mQE46fdENbzCYeWnVvRsszv47SbJ/BZ13asv2BymfPzy0+qR6sH/kzutl1sevwF6m1aS4LHy95RE2gzYiA5V/4KKllMJgEfmyddxvKmL5LXLIu0DcsrjSlpzTchOTcJL2+SFxtzLjz3WIX3u8Pocv3F0QpNYlSCN4GJ8x9i3r2vsvH52ZCbS2KP7px02yVkHdcu2uHFvFbd23DeSzdHO4wqS0o98lG+IhIaSsqG2KLXlnD/w9+w1psJwKTRR9ZL9kgk+IrIPCYzJM8Vb9r4trPWcwR15xzbkpqybsk62vX68Q+S4ccaz66gfK/nwM9DO+SHJ2gRkRjUrk9HPuzYkyZrgvdw7HLlhKjEVdt1OqUbK66+Dt8D9+A5xMrNwVT2V4ZnT2SHNDc/qiWj/vrj4lHz5s07osd9/vInpL81o0KyyesrJuWzj8le8jZfPP8+JfuL6D72ZDpkHx3SuKOpz7iBmOcxvpr6ABlrvsSZsbtbfwY8eB2dTurqP+gmf6/XefPmMejqy5jW4wKaHOYa8eBj0SW/I7lvXzhEUrYkUV/AY8XZj1/DjJzdpM15Ba/zr5VQkFSPer++NmgPdpHDSfAmMOSGMXDDmGiHIiISt5SUDaHlH63g+oe3UOjNLJO8O+ir0hFOZRDsMX1SNpOWnhqyeOPJxNGZ3PHGERwY+N3szdlXrvgXd57FqokvsqAgq9yxPRM28Mv7ItMLSUQkVgx95V4+GPxzMnZuKFNq5F9wKdkXnBK1uGq7M+++hO/GncL/7nuRovffI2PnxnL7D05a+jB8ngS8vuCLWbr2sTFX6NePzCSD4MnltII9rHxvCSNumxTpsCKm95gB9B4zAJ/Pn2g93LDR1HUrjuh5G2xew4n3Pc6C154PutCXAenj9TdMrEjwJjBh9m1sXX0FX738MYlpyQy+YBBpGZUvtCciIiK1m5KyIfTk3xZR6A0k7Q5KvCb4iijxJFav16wZDQp3cucL+sO5ukZOOYVdO97kqY987EtscMhj04py6dS//Mrg3iQv9/x7IoteW8L7r63AAScP60j/MRM0546IyEFadW/DuHVv8uF9s9g6/zMSM9Lp+8sxHDWwc7RDq/U6ZB9Nh+lT2fH9ZczJnkDGju8P7DNgV6Ms6N2XpGZNOP7qsXx88xM0nPtyhYRtiSXQ+3eXRDr8avHtrXzOU4D87cHn0axrjvTvieKkNCjYc0THNm3fnD1DzyH93VcqLubTuA0T77+s6oFKxBXsy2fH9ztIqZdC86NaMuT6c6IdkoiIiISAkrIhtGJ3GiQF31fiSaR10WY2Jras1nPfMqU1qfWPfF5UqWjC1NM5Z18Bn81ZyuZ1O/n73BL2e1Mr9F7+abf9JNcLXtf9RvWi36hekQpZRCRmJaUmM2zquYAWEaqOJm2bcvY3r/LeH59n5/sfQ4KHlqOHMu6GseXmMW/18i28OGIfGR+/jQXmo81LTqfpH35Lj5G9oxV+lTTM7gmL3q9Q7j8bo9PI4yMdUq3mGTwEZj132OP2ZPp7Sp//1p+Y87tu7HzoEVLzdlGUmELRScM477Xbwh2q1NCcm59l14MP02DfjgNlO1p1pvfDt3DsGX2jGJmIiIiEgpKyIZRISfAdgaTfFk+j6j2xc3Q/Wb2LQiG5XgoDxvQDoE2nJdzz0LdsSGwB+HvIju5awJR7zo5miCIiIgCkZdRj1H2XApdWekxSajKTPnyADV+uY/lrn5LSqD5DLjyVlBi6kTvod+fz5rRnaJC7/UCP39L/d/Q6mY79O0U3wFpm5D+uZtaCBTTesrrSY0rMQ/+n/3Rge8Ttk+D2ujsFRF2zecUPvHPmVTRZ+xUHj+9q8sMKVo65iJS5L/4477CIiIjEJCVlQ+iEDj5mbQyyw4xj3A9s9tVjT0LVF1RIK95HgybpNQ9Qyuk3qhfTR/Vi9cI15O7axzED+6s3soiIxKSs49rF7IrZ6c3SyZ7zFPMnXE+T9V8DUGJe9gwYytjX7opydLVPerN0xiz9N+9MfZo973xAYs5WUvfl4C0pBGBPRiuOm3YvnU/pFuVIpTq+//w7PjllPE3yK1+oL7lkPwtufIhO//17BCMTERGRUFNSNoQuvXU4C3/+DpsSm5crTy3ex7XX9WTmM1/w9vaGVXtS58hLrM+yed/QfVCXEEYrpY7K7hjtEEREROJax/6d6LhmJms+XcnO7zbT7oQuNG3fLNph1VppGfX46UOXA5dHOxQJsXmT/3TIhGypxK+XRCAaERERCSclZUMoo2VDnph2Gk/84R0+We0odgk0SDAeub03Hft2IKtba5b9/O0Dw+UPcP5Z04IuAhYoe++VpUrKiohIzHrnsf/w3MyNrHeNSfUVcGKbfK6463TSm2okiPyoY/9OoOkKJE6VFJfQ8Mv/HtGxPm/i4Q8SCbHVC9fw0F8+4at9jXAYPVJzuOyafnQ9SVPtiYhUh5aND7H05g255qFxzJhzXFh7VQAADdRJREFULq+8PYZWHRrTsW8HwJ+0/fk5LTDfQXPPmgVPyIqIiNQBL9zxJn+c7Vid0IpCbwq7kzJ4Y0smUya9RX5uQbTDExGpFUqKivH6igOL3B2a56RTwh6PSFkrP13N/928nIX7s8j31qPAm8aioiyuvPM7ln6wPNrhiYjEJCVlI8jn8/HEK1txnoTKD3Iu6PYpZ+ruo4iIxJ783AKe+sgXdN/33pa8fO97EY5IRKR2SkpNJiezE4frqrEroxXD/3ZVRGISKfWPvywgL7F+hfL93lQevv+zKEQkIhL7lJSNoPVfbfDPN3tw4jUgtWhfxR6zZpyQsoGepx0bgQhFRERCa/HrX7Av8eD1w3/08WeHnztRRCRetL/xclyZtKwr839BYhp7Rp7L6UteplHrxlGJT+LXl3mVX3NLi1vi8wW/ASsiIpXTnLIRdKChqmSqgobsY1SHPN5ekcjupAwaFu5ieOciptx9bgSjFBERCR2f79ADcQ+zW0Qkrpx0+el8kL+fTX96gIa7N2HAriZt6XD79Qz8xbBohydxzB2yD7fhfE5dvkREqkhJ2Qhq17MtzQoXsy2padD9vVuVcNWD47kKKC4sxpukX4+IiMS2fmccR8ozH1LgTQu6P7tH5b1oRUTi0eBfn43vmtGsW7SGhMQEsnq2w+NRtiuUzGwccCvQFch2zi0qs+8m4BKgBLjKOfd2VIKsZbql7ODz4qyg+7p4NpPgPcQUfSIiEpRa9wjyeDz8YnRjzFUc2pFetJuLbxx0YFsJWRERqQvSGqYxoVfhjwVlpvBpWbSV8647NQpRiYjUbh6Phw7ZR9O2dwclZMNjKXAO8J+yhWbWDRgPdAdGAA+bmbKNwGVX9SG5OL9CeWLJfi67rHsUIhIRiX01auHN7DYz+9LMlpjZO2bWKlBuZvagma0K7O8TmnBj3xmXD+bWMYm0L94EgLekkOOTNvCPP/ci85iWUY5OREQk9C7502iuPqmAzKKtYEZycT6nNNjIPx8dTL1GFRcNERERCSfn3HLn3Iogu0YD051z+51z3wGrgOzIRlc79RjclfuuzaKHbcB8JZjz0YWN3HN5C/qe2TPa4YmIxKSadse82zn3ewAzuwq4GZgCjAQ6Bf71B/4R+F+AIRedyJCLoHB/EQkJHg31EBGROm/s9cMZez0U5u/Hm5yonl8iIlIbtQYWlNneECgToOewHvxzWA+KC4sBje4UEampGn2KOuf2lNmsx48LhI4GpjnnHLDAzDLMLNM5t6kmr1fXJCUnRjsEERGRiEpKTY52CCIiEgfM7F0g2FDEqc65WZU9LEhZ0CUpzWwyMBmgRYsWzJs3rzphVllubm7EXquuU12GhuoxdFSXoRMrdVnjW1tmdgdwIbAbGBwobg2sL3NY6R3GCknZaDVmkRIrF0K4xPv5g+pA5x/f5w+qAxEREYk859zQajxsA9CmzHYW8EMlz/8o8ChAv3793KBBg6rxclU3b948IvVadZ3qMjRUj6GjugydWKnLwyZlD3eH0Tk3FZgaWKXySuAWqnCHMVqNWaTEyoUQLvF+/qA60PnH9/mD6kBERERixmzgeTO7D2iFfzq+hdENSURE6qrDJmWrcIfxeeAN/EnZI77DKCIiIiIiIhIpZnY28DegGfCGmS1xzg13zi0zsxnA10AxcIVzriSasYqISN1Vo1U2zKxTmc2zgG8CP88GLjS/E4Ddmk9WREREREREos05N9M5l+WcS3bOtXDODS+z7w7n3FHOuc7OubeiGaeIiNRtNZ1T9i4z6wz4gHXAlED5m8DpwCogD7iohq8jIiIiIiIiIiIiUifUKCnrnBtTSbkDrqjJc4uIiIiIiIiIiIjURTWavkBEREREREREREREqkZJWREREREREREREZEIUlJWRESkFjKzcWa2zMx8ZtbvoH03mdkqM1thZsMrew4RERERERGpnWq60JeIiIiEx1LgHOCRsoVm1g0YD3QHWgHvmtkxzrmSyIcoIiIiIiIi1aGesiIiIrWQc265c25FkF2jgenOuf3Oue+AVUB2ZKMTERERERGRmlBPWRERkdjSGlhQZntDoKwCM5sMTAZo0aIF8+bNC3twsSo3N1f1E2Kq09BSfYaW6lNERESirVYlZRcvXrzdzNZFO44Qawpsj3YQURTv5w+qA51/fJ8/hLYO2oXoeWoFM3sXaBlk11Tn3KzKHhakzAU70Dn3KPBo4LW2DR48uK61saGk92roqU5DS/UZWqrPiupUGxtJEf4eq2s3dFSXoaF6DB3VZejUprqstH2tVUlZ51yzaMcQama2yDnX7/BH1k3xfv6gOtD5x/f5g+rgUJxzQ6vxsA1AmzLbWcAPR/Bada6NDSVdp6GnOg0t1WdoqT4llCLZxuraDR3VZWioHkNHdRk6sVKXmlNWREQktswGxptZspl1ADoBC6Mck4iIiIiIiFSBkrIiIiK1kJmdbWYbgAHAG2b2NoBzbhkwA/gamANc4ZwriV6kIiIiIiIiUlW1avqCOurRaAcQZfF+/qA60PmL6qAanHMzgZmV7LsDuCOyEdV5uk5DT3UaWqrP0FJ9SqzStRs6qsvQUD2GjuoydGKiLs25oGuDiIiIiIiIiIiIiEgYaPoCERERERERERERkQhSUjbMzOw3ZubMrGlg28zsQTNbZWZfmlmfaMcYDmZ2t5l9EzjHmWaWUWbfTYHzX2Fmw6MZZziZ2YjAOa4ysxujHU+4mVkbM/vAzJab2TIz+1WgvLGZzTWzlYH/G0U71nAzswQz+9zMXg9sdzCzTwN18KKZJUU7xnAxswwzeynw/l9uZgPi8RqQ2BJvn9fhYGZPmtlWM1tapkzv/WpSmxpaZpZiZgvN7ItAff4hUB437bPEPrVV1ac2KnTUPoWO2qbQitXv4ErKhpGZtQGGAd+XKR6Jf6XsTsBk4B9RCC0S5gI9nHPHAd8CNwGYWTdgPNAdGAE8bGYJUYsyTALn9BD+33c3YELg3OuyYuDXzrmuwAnAFYFzvhF4zznXCXgvsF3X/QpYXmb7z8BfA3WwE7gkKlFFxgPAHOdcF6An/nqIx2tAYkScfl6Hw1P42/Wy9N6vPrWpobUfONU51xPoBYwwsxOIr/ZZYpjaqhp7CrVRoaL2KXTUNoVWTH4HV1I2vP4KXA+Unbh3NDDN+S0AMswsMyrRhZFz7h3nXHFgcwGQFfh5NDDdObffOfcdsArIjkaMYZYNrHLOrXHOFQLT8Z97neWc2+Sc+yzw8178H4it8Z/304HDngZ+Gp0II8PMsoAzgMcD2wacCrwUOKTO1oGZpQMnA08AOOcKnXO7iLNrQGJO3H1eh4Nz7j9AzkHFeu9Xk9rU0Ar83Z0b2EwM/HPESfssdYLaqhpQGxU6ap9CR21T6MTyd3AlZcPEzM4CNjrnvjhoV2tgfZntDYGyuuxi4K3Az/Fy/vFynkGZWXugN/Ap0MI5twn8jTjQPHqRRcT9+G/G+ALbTYBdZW5S1OVroSOwDfhXYOjI42ZWj/i7BiS2xPXndZjpvR8Ccd6mhkxgWOMSYCv+EV2riZ/2WWKf2qrQ0+dpDal9qjm1TSETs9/BvdEOIJaZ2btAyyC7pgK/BU4L9rAgZS5IWa13qPN3zs0KHDMV/xCH50ofFuT4mDz/w4iX86zAzOoDLwNXO+f2+G9SxQczOxPY6pxbbGaDSouDHFpXrwUv0Af4pXPuUzN7AA1dktovnt6jEmPiuU0NNedcCdDL/OsczAS6BjssslGJHDG1VVKrqH0KDbVNNRfr38GVlK0B59zQYOVmdizQAfgi8OGUBXxmZtn4M/RtyhyeBfwQ5lDDorLzL2VmPwPOBIY450rfAHXm/A8jXs6zHDNLxN84P+eceyVQvMXMMp1zmwJTdWyNXoRh9xPgLDM7HUgB0vHftcswM2/gTl1dvhY2ABucc58Gtl/Cn5SNp2tAYk9cfl5HiN77NaA2NTycc7vMbB7+uRDjpX2W2Ke2KvT0eVpNap9CT21TjcT0d3BNXxAGzrmvnHPNnXPtnXPt8TeifZxzm4HZwIXmdwKwu7Sbf11iZiOAG4CznHN5ZXbNBsabWbKZdcC/4NnCaMQYZv8DOgVW/EvCv7jZ7CjHFFaBeVueAJY75+4rs2s28LPAzz8DZkU6tkhxzt3knMsKvO/HA+875yYCHwBjA4fV2ToIfMatN7POgaIhwNfE0TUgMSnuPq8jSO/9alKbGlpm1izQCwkzSwWG4p8HMS7aZ6kT1FaFnj5Pq0HtU+iobQqNWP8Obj92YJRwMbO1QD/n3PbAh9jf8a/+mAdc5JxbFM34wsHMVgHJwI5A0QLn3JTAvqn455ktxj/c4a3gzxLbAndq7gcSgCedc3dEOaSwMrMTgY+Ar/hxLpff4p9jaAbQFvgeGOecO3ii/TonMHTiN865M82sI/4FGRoDnwMXOOf2RzO+cDGzXvgnWE8C1gAX4b8BGHfXgMSOePu8DgczewEYBDQFtgC3AK+i9361qE0NLTM7Dv8iHwkE2iTn3B/jqX2W2Ke2qvrURoWO2qfQUdsUerH4HVxJWREREREREREREZEI0vQFIiIiIiIiIiIiIhGkpKyIiIiIiIiIiIhIBCkpKyIiIiIiIiIiIhJBSsqKiIiIiIiIiIiIRJCSsiIiIiIiIiIiIiIRpKSsiIiIiIiIiIiISAQpKSsiIiIiIiIiIiISQUrKioiIiIiIiIiIiETQ/wPjgBOR9Mig/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1728x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(24,6))\n",
    "ax1.scatter(X_reduzido_tsne[:,0], X_reduzido_tsne[:,1], c=(Y == 0), cmap='coolwarm', label='Sem fraude', linewidths=2)\n",
    "ax1.scatter(X_reduzido_tsne[:,0], X_reduzido_tsne[:,1], c=(Y == 1), cmap='coolwarm', label='Fraude', linewidths=2)\n",
    "ax1.set_title('t-SNE', fontsize=14)\n",
    "ax1.grid(True)\n",
    "ax1.legend(handles=[azul, vermelho])\n",
    "\n",
    "ax2.scatter(X_reduzido_pca[:,0], X_reduzido_pca[:,1], c=(Y == 0), cmap='coolwarm', label='Sem fraude', linewidths=2)\n",
    "ax2.scatter(X_reduzido_pca[:,0], X_reduzido_pca[:,1], c=(Y == 1), cmap='coolwarm', label='Fraude', linewidths=2)\n",
    "ax2.set_title('PCA', fontsize=14)\n",
    "ax2.grid(True)\n",
    "ax2.legend(handles=[azul, vermelho])\n",
    "\n",
    "ax3.scatter(X_reduzido_svd[:,0], X_reduzido_svd[:,1], c=(Y == 0), cmap='coolwarm', label='Sem fraude', linewidths=2)\n",
    "ax3.scatter(X_reduzido_svd[:,0], X_reduzido_svd[:,1], c=(Y == 1), cmap='coolwarm', label='Fraude', linewidths=2)\n",
    "ax3.set_title('SVD', fontsize=14)\n",
    "ax3.grid(True)\n",
    "ax3.legend(handles=[azul, vermelho])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observa-se das aglomerações que existe alguma separação entre os tipos das transações"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Criarems agora a amostra de treino e teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_treino, X_teste, Y_treino, Y_teste = train_test_split(X, Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testaremos os modelos de machine learning regressão logística, Support Vector Classifier, Arvore de decisão e floresta aleatória"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelos = {\n",
    "    \"Regressao Logistica\": LogisticRegression(),\n",
    "    \"KNN\": KNeighborsClassifier(),\n",
    "    \"SVC\": SVC(),\n",
    "    \"Decision Tree Classifier\": DecisionTreeClassifier(),\n",
    "    \"RDC\" : RandomForestClassifier()\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizaremos validação cruzada para treinarmos o modelo com amostras diferentes e aumentar a acurácia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modelo:  LogisticRegression possui acuracia 93.0 %\n",
      "Modelo:  KNeighborsClassifier possui acuracia 93.0 %\n",
      "Modelo:  SVC possui acuracia 93.0 %\n",
      "Modelo:  DecisionTreeClassifier possui acuracia 89.0 %\n",
      "Modelo:  RandomForestClassifier possui acuracia 93.0 %\n"
     ]
    }
   ],
   "source": [
    "for nome, modelo in modelos.items():\n",
    "    modelo.fit(X_treino, Y_treino)\n",
    "    score_treino = cross_val_score(modelo, X_treino, Y_treino, cv=5)\n",
    "    print(\"Modelo: \", modelo.__class__.__name__, \"possui acuracia\", round(score_treino.mean(), 2) * 100, \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observamos que regressão logística, KNN, SVC e RFC tiveram acurácias iguais. Iremos refinar cada um destas modelos com o objetivo de melhorar as acurácias. Isto será feito com o GridSearchCV que varrerá todos os parâmetros de entrada de cada modelo encontrando a melhor combinação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score Regressao Logistica Validacao Cruzada:  93.99%\n"
     ]
    }
   ],
   "source": [
    "log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n",
    "grid_log_reg.fit(X_treino, Y_treino)\n",
    "log_reg = grid_log_reg.best_estimator_\n",
    "log_reg_score = cross_val_score(log_reg, X_treino, Y_treino, cv=5)\n",
    "print('Score Regressao Logistica Validacao Cruzada: ', round(log_reg_score.mean() * 100, 2).astype(str) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score KNN Validacao Cruzada:  92.71%\n"
     ]
    }
   ],
   "source": [
    "knears_params = {\"n_neighbors\": list(range(2,5,1)), 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']}\n",
    "grid_knears = GridSearchCV(KNeighborsClassifier(), knears_params)\n",
    "grid_knears.fit(X_treino, Y_treino)\n",
    "knears_neighbors = grid_knears.best_estimator_\n",
    "knears_score = cross_val_score(knears_neighbors, X_treino, Y_treino, cv=5)\n",
    "print('Score KNN Validacao Cruzada: ', round(knears_score.mean() * 100, 2).astype(str) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score SVC Validacao Cruzada:  92.85%\n"
     ]
    }
   ],
   "source": [
    "svc_params = {'C': [0.5, 0.7, 0.9, 1], 'kernel': ['rbf', 'poly', 'sigmoid', 'linear']}\n",
    "grid_svc = GridSearchCV(SVC(), svc_params)\n",
    "grid_svc.fit(X_treino, Y_treino)\n",
    "svc = grid_svc.best_estimator_\n",
    "svc_score = cross_val_score(svc, X_treino, Y_treino, cv=5)\n",
    "print('Score SVC Validacao Cruzada: ', round(svc_score.mean() * 100, 2).astype(str) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score DTC Validacao Cruzada:  92.71%\n"
     ]
    }
   ],
   "source": [
    "tree_params = {\"criterion\": [\"gini\", \"entropy\"], \"max_depth\": list(range(2,10,1)), \n",
    "              \"min_samples_leaf\": list(range(3,10,1))}\n",
    "grid_tree = GridSearchCV(DecisionTreeClassifier(), tree_params,n_jobs=-1)\n",
    "grid_tree.fit(X_treino, Y_treino)\n",
    "tree_clf = grid_tree.best_estimator_\n",
    "tree_score = cross_val_score(tree_clf, X_treino, Y_treino, cv=5)\n",
    "print('Score DTC Validacao Cruzada: ', round(tree_score.mean() * 100, 2).astype(str) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score RFC Validacao Cruzada:  93.57%\n"
     ]
    }
   ],
   "source": [
    "tree_params = {\"criterion\": [\"gini\", \"entropy\"], \"max_depth\": list(range(2,10,1)), \n",
    "              \"min_samples_leaf\": list(range(5,10,1))}\n",
    "grid_tree = GridSearchCV(RandomForestClassifier(), tree_params,n_jobs=-1)\n",
    "grid_tree.fit(X_treino, Y_treino)\n",
    "tree_clf = grid_tree.best_estimator_\n",
    "rfc_score = cross_val_score(tree_clf, X_treino, Y_treino, cv=5)\n",
    "print('Score RFC Validacao Cruzada: ', round(rfc_score.mean() * 100, 2).astype(str) + '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observamos uma melhora na regressão logística indicando ser o melhor modelo para este problema, seguido pela floresta aleatória"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Criando subamostras para validação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_subamostra = dados.drop('Class', axis=1)\n",
    "Y_subamostra = dados['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Treino: [ 52019  52562  52728 ... 284804 284805 284806] Teste: [    0     1     2 ... 56963 56964 56965]\n",
      "Treino: [     0      1      2 ... 284804 284805 284806] Teste: [ 52019  52562  52728 ... 113925 113926 113927]\n",
      "Treino: [     0      1      2 ... 284804 284805 284806] Teste: [112646 112654 113254 ... 170887 170888 170889]\n",
      "Treino: [     0      1      2 ... 284804 284805 284806] Teste: [167149 167679 168285 ... 227852 227853 227854]\n",
      "Treino: [     0      1      2 ... 227852 227853 227854] Teste: [223199 224141 226397 ... 284804 284805 284806]\n"
     ]
    }
   ],
   "source": [
    "for indice_treino, indice_teste in strat_kfold.split(X_subamostra, Y_subamostra):\n",
    "    print(\"Treino:\", indice_treino, \"Teste:\", indice_teste)\n",
    "    X_treino_subamostra, X_teste_subamostra = X_subamostra.iloc[indice_treino], X_subamostra.iloc[indice_teste]\n",
    "    Y_treino_subamostra, Y_teste_subamostra = Y_subamostra.iloc[indice_treino], Y_subamostra.iloc[indice_teste]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_treino_subamostra = X_treino_subamostra.values\n",
    "X_teste_subamostra = X_teste_subamostra.values\n",
    "Y_treino_subamostra = Y_treino_subamostra.values\n",
    "Y_teste_subamostra = Y_teste_subamostra.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Balanceando amostras para ficarem com a mesma quantidade cada classe. Usaremos NearMiss para selecionar a quantidade de transações sem fraude na mesma quantidade das com fraude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from imblearn.under_sampling import NearMiss\n",
    "from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline\n",
    "from imblearn.metrics import classification_report_imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_nearmiss, Y_nearmiss = NearMiss().fit_sample(X_subamostra.values, Y_subamostra.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "contagem_nearmiss = Counter(Y_nearmiss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contagem NearMiss: Counter({0: 492, 1: 492})\n"
     ]
    }
   ],
   "source": [
    "print('Contagem NearMiss: {}'.format(contagem_nearmiss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calcularemos as métricas dos problemas de classificação para determinar o melhor modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "acuracia_subamostra = []\n",
    "precisao_subamostra = []\n",
    "recall_subamostra = []\n",
    "f1_subamostra = []\n",
    "auc_subamostra = []\n",
    "\n",
    "\n",
    "for indice_treino, indice_teste in strat_kfold.split(X_treino_subamostra, Y_treino_subamostra):\n",
    "    pipeline_subamostra = imbalanced_make_pipeline(NearMiss(sampling_strategy='majority'), log_reg)\n",
    "    \n",
    "    modelo_subamostra = pipeline_subamostra.fit(X_treino_subamostra[indice_treino], Y_treino_subamostra[indice_treino])\n",
    "    \n",
    "    previsao_subamostra = modelo_subamostra.predict(X_treino_subamostra[indice_teste])\n",
    "    \n",
    "    acuracia_subamostra.append(pipeline_subamostra.score(X_treino_subamostra[indice_teste], Y_treino_subamostra[indice_teste]))\n",
    "    \n",
    "    precisao_subamostra.append(precision_score(Y_treino_subamostra[indice_teste],previsao_subamostra))\n",
    "    \n",
    "    recall_subamostra.append(recall_score(Y_treino_subamostra[indice_teste],previsao_subamostra))\n",
    "    \n",
    "    f1_subamostra.append(f1_score(Y_treino_subamostra[indice_teste],previsao_subamostra))\n",
    "    \n",
    "    auc_subamostra.append(roc_auc_score(Y_treino_subamostra[indice_teste],previsao_subamostra))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import learning_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analisando curva de aprendizado para determinar melhor modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learning_curve(estimator1, estimator2, estimator3, estimator4, X, Y, ylim=None, cv=None,\n",
    "                        tamanho_treino=np.linspace(.1, 1.0, 5)):\n",
    "    \n",
    "    f, ax = plt.subplots(2,2, figsize=(20,14), sharey=True)\n",
    "    \n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "        \n",
    "    tamanho_treino, score_treino, score_teste = learning_curve(estimator1, X, Y, cv=cv, train_sizes=tamanho_treino,n_jobs=2)\n",
    "    media_score_treino = np.mean(score_treino, axis=1)\n",
    "    std_score_treino = np.std(score_treino, axis=1)\n",
    "    media_score_teste = np.mean(score_teste, axis=1)\n",
    "    std_score_teste = np.std(score_teste, axis=1)\n",
    "    \n",
    "    ax[0][0].fill_between(tamanho_treino, media_score_treino - std_score_treino,\n",
    "                     media_score_treino + std_score_treino, alpha=0.1,\n",
    "                     color=\"#ff9124\")\n",
    "    \n",
    "    ax[0][0].fill_between(tamanho_treino, media_score_teste - std_score_teste,\n",
    "                     media_score_teste + std_score_teste, alpha=0.1, color=\"#2492ff\")\n",
    "    \n",
    "    ax[0][0].plot(tamanho_treino, media_score_treino, 'o-', color=\"#ff9124\", label=\"Score treino\")\n",
    "    ax[0][0].plot(tamanho_treino, media_score_teste, 'o-', color=\"#2492ff\", label=\"Score Validação Cruzada\")\n",
    "    ax[0][0].set_title(\"Curva Regressão logistica\", fontsize=14)\n",
    "    ax[0][0].set_xlabel('Tamanho treino (m)')\n",
    "    ax[0][0].set_ylabel('Score')\n",
    "    ax[0][0].grid(True)\n",
    "    ax[0][0].legend(loc=\"best\")\n",
    "    \n",
    "    \n",
    "    tamanho_treino, score_treino, score_teste = learning_curve(estimator2, X, Y, cv=cv, train_sizes=tamanho_treino,n_jobs=2)\n",
    "    media_score_treino = np.mean(score_treino, axis=1)\n",
    "    std_score_treino = np.std(score_treino, axis=1)\n",
    "    media_score_teste = np.mean(score_teste, axis=1)\n",
    "    std_score_teste = np.std(score_teste, axis=1)\n",
    "    \n",
    "    ax[0][1].fill_between(tamanho_treino, media_score_treino - std_score_treino,\n",
    "                     media_score_treino + std_score_treino, alpha=0.1,\n",
    "                     color=\"#ff9124\")\n",
    "    \n",
    "    ax[0][1].fill_between(tamanho_treino, media_score_teste - std_score_teste,\n",
    "                     media_score_teste + std_score_teste, alpha=0.1, color=\"#2492ff\")\n",
    "    \n",
    "    ax[0][1].plot(tamanho_treino, media_score_treino, 'o-', color=\"#ff9124\", label=\"Score treino\")\n",
    "    ax[0][1].plot(tamanho_treino, media_score_teste, 'o-', color=\"#2492ff\", label=\"Score Validação Cruzada\")\n",
    "    ax[0][1].set_title(\"Curva KNN\", fontsize=14)\n",
    "    ax[0][1].set_xlabel('Tamanho treino (m)')\n",
    "    ax[0][1].set_ylabel('Score')\n",
    "    ax[0][1].grid(True)\n",
    "    ax[0][1].legend(loc=\"best\")\n",
    "    \n",
    "    \n",
    "    tamanho_treino, score_treino, score_teste = learning_curve(estimator3, X, Y, cv=cv, train_sizes=tamanho_treino,n_jobs=2)\n",
    "    media_score_treino = np.mean(score_treino, axis=1)\n",
    "    std_score_treino = np.std(score_treino, axis=1)\n",
    "    media_score_teste = np.mean(score_teste, axis=1)\n",
    "    std_score_teste = np.std(score_teste, axis=1)\n",
    "    \n",
    "    ax[1][0].fill_between(tamanho_treino, media_score_treino - std_score_treino,\n",
    "                     media_score_treino + std_score_treino, alpha=0.1,\n",
    "                     color=\"#ff9124\")\n",
    "    \n",
    "    ax[1][0].fill_between(tamanho_treino, media_score_teste - std_score_teste,\n",
    "                     media_score_teste + std_score_teste, alpha=0.1, color=\"#2492ff\")\n",
    "    \n",
    "    ax[1][0].plot(tamanho_treino, media_score_treino, 'o-', color=\"#ff9124\", label=\"Score treino\")\n",
    "    ax[1][0].plot(tamanho_treino, media_score_teste, 'o-', color=\"#2492ff\", label=\"Score Validação Cruzada\")\n",
    "    ax[1][0].set_title(\"Curva SVC\", fontsize=14)\n",
    "    ax[1][0].set_xlabel('Tamanho treino (m)')\n",
    "    ax[1][0].set_ylabel('Score')\n",
    "    ax[1][0].grid(True)\n",
    "    ax[1][0].legend(loc=\"best\")\n",
    "    \n",
    "    \n",
    "    tamanho_treino, score_treino, score_teste = learning_curve(estimator4, X, Y, cv=cv, train_sizes=tamanho_treino,n_jobs=2)\n",
    "    media_score_treino = np.mean(score_treino, axis=1)\n",
    "    std_score_treino = np.std(score_treino, axis=1)\n",
    "    media_score_teste = np.mean(score_teste, axis=1)\n",
    "    std_score_teste = np.std(score_teste, axis=1)\n",
    "    \n",
    "    ax[1][1].fill_between(tamanho_treino, media_score_treino - std_score_treino,\n",
    "                     media_score_treino + std_score_treino, alpha=0.1,\n",
    "                     color=\"#ff9124\")\n",
    "    \n",
    "    ax[1][1].fill_between(tamanho_treino, media_score_teste - std_score_teste,\n",
    "                     media_score_teste + std_score_teste, alpha=0.1, color=\"#2492ff\")\n",
    "    \n",
    "    ax[1][1].plot(tamanho_treino, media_score_treino, 'o-', color=\"#ff9124\", label=\"Score treino\")\n",
    "    ax[1][1].plot(tamanho_treino, media_score_teste, 'o-', color=\"#2492ff\", label=\"Score Validação Cruzada\")\n",
    "    ax[1][1].set_title(\"Curva DTC\", fontsize=14)\n",
    "    ax[1][1].set_xlabel('Tamanho treino (m)')\n",
    "    ax[1][1].set_ylabel('Score')\n",
    "    ax[1][1].grid(True)\n",
    "    ax[1][1].legend(loc=\"best\")\n",
    "    \n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Users/danilo/opt/anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=42)\n",
    "plot_learning_curve(log_reg, knears_neighbors, svc, tree_clf, X_treino, Y_treino, (0.87, 1.01), cv=cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Das curvas de aprendizado observamos que regressão logística e SVC são os melhores modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.model_selection import cross_val_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "previsao_reg_log = cross_val_predict(log_reg, X_treino, Y_treino, cv=5,method=\"decision_function\")\n",
    "previsao_knn = cross_val_predict(knears_neighbors, X_treino, Y_treino, cv=5)\n",
    "previsao_svc = cross_val_predict(svc, X_treino, Y_treino, cv=5,method=\"decision_function\")\n",
    "previsao_dtc = cross_val_predict(tree_clf, X_treino, Y_treino, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculando o score pela curva ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression:  0.9735874178247483\n",
      "KNears Neighbors:  0.921577764833153\n",
      "Support Vector Classifier:  0.9726803694765749\n",
      "Decision Tree Classifier:  0.9313597403678123\n"
     ]
    }
   ],
   "source": [
    "print('Logistic Regression: ', roc_auc_score(Y_treino, previsao_reg_log))\n",
    "print('KNears Neighbors: ', roc_auc_score(Y_treino, previsao_knn))\n",
    "print('Support Vector Classifier: ', roc_auc_score(Y_treino, previsao_svc))\n",
    "print('Decision Tree Classifier: ', roc_auc_score(Y_treino, previsao_dtc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verificamos novamente que regressão logística é o melhor modelo seguido de perto pelo modelo SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_fpr, log_tpr, log_thresold = roc_curve(Y_treino, previsao_reg_log)\n",
    "knear_fpr, knear_tpr, knear_threshold = roc_curve(Y_treino, previsao_knn)\n",
    "svc_fpr, svc_tpr, svc_threshold = roc_curve(Y_treino, previsao_svc)\n",
    "tree_fpr, tree_tpr, tree_threshold = roc_curve(Y_treino, previsao_dtc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph_roc_curve_multiple(log_fpr, log_tpr, knear_fpr, knear_tpr, svc_fpr, svc_tpr, tree_fpr, tree_tpr):\n",
    "    plt.figure(figsize=(16,8))\n",
    "    plt.title('Curva ROC \\n Top 4 Classificadores', fontsize=18)\n",
    "    plt.plot(log_fpr, log_tpr, label='Score Regressão Logistica: {:.4f}'.format(roc_auc_score(Y_treino, previsao_reg_log)))\n",
    "    plt.plot(knear_fpr, knear_tpr, label='Score KNN: {:.4f}'.format(roc_auc_score(Y_treino, previsao_knn)))\n",
    "    plt.plot(svc_fpr, svc_tpr, label='Score SVC: {:.4f}'.format(roc_auc_score(Y_treino, previsao_svc)))\n",
    "    plt.plot(tree_fpr, tree_tpr, label='Score DTE: {:.4f}'.format(roc_auc_score(Y_treino, previsao_dtc)))\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([-0.01, 1, 0, 1])\n",
    "    plt.xlabel('Taxa de Falso Positivo', fontsize=16)\n",
    "    plt.ylabel('Taxa Verdadeiro Positivo', fontsize=16)\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_roc_curve_multiple(log_fpr, log_tpr, knear_fpr, knear_tpr, svc_fpr, svc_tpr, tree_fpr, tree_tpr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observamos pela curva que novamente regressão logística é o melhor modelo, com acurácia quase máxima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision_log, recall_log, threshold_log = precision_recall_curve(Y_treino, previsao_reg_log)\n",
    "precision_svc, recall_svc, threshold_svc = precision_recall_curve(Y_treino, previsao_svc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import recall_score, precision_score, f1_score, accuracy_score\n",
    "Y_pred_log = log_reg.predict(X_treino)\n",
    "Y_pred_svc = svc.predict(X_treino)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regressão Logistica:\n",
      "Overfitting: \n",
      "\n",
      "Recall Score: 0.91\n",
      "Precision Score: 0.67\n",
      "F1 Score: 0.77\n",
      "Accuracy Score: 0.77\n"
     ]
    }
   ],
   "source": [
    "print('Regressão Logistica:')\n",
    "print('Overfitting: \\n')\n",
    "print('Recall Score: {:.2f}'.format(recall_score(Y_treino, Y_pred_log)))\n",
    "print('Precision Score: {:.2f}'.format(precision_score(Y_treino, Y_pred_log)))\n",
    "print('F1 Score: {:.2f}'.format(f1_score(Y_treino, Y_pred_log)))\n",
    "print('Accuracy Score: {:.2f}'.format(accuracy_score(Y_treino, Y_pred_log)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC\n",
      "Recall Score: 0.87\n",
      "Precision Score: 0.95\n",
      "F1 Score: 0.91\n",
      "Accuracy Score: 0.92\n"
     ]
    }
   ],
   "source": [
    "print('SVC')\n",
    "print('Recall Score: {:.2f}'.format(recall_score(Y_treino, Y_pred_svc)))\n",
    "print('Precision Score: {:.2f}'.format(precision_score(Y_treino, Y_pred_svc)))\n",
    "print('F1 Score: {:.2f}'.format(f1_score(Y_treino, Y_pred_svc)))\n",
    "print('Accuracy Score: {:.2f}'.format(accuracy_score(Y_treino, Y_pred_svc)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pelo recall vemos que regressão logistica é melhor, porém, pelas outras métricas SVC se mostrou melhor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizaremos agora a técnica SMOTE que cria pontos sintéticos com a finalidade de balancear a amostra. SMOTE usa a distância entre os vizinhos mais próximos da classe minoritária e entre estas distâncias cria pontos sintéticos. Costuma ter uma acurácia melhor comparada a subamostra porém levando mais tempo para treinar o modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista_acuracia = []\n",
    "lista_precisao = []\n",
    "lista_recall = []\n",
    "lista_f1 = []\n",
    "lista_auc = []\n",
    "\n",
    "log_reg_sm = LogisticRegression()\n",
    "log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "rand_log_reg = RandomizedSearchCV(log_reg_sm, log_reg_params, n_iter=4)\n",
    "\n",
    "\n",
    "for treino, teste in strat_kfold.split(X_treino, Y_treino):\n",
    "    pipeline = imbalanced_make_pipeline(SMOTE(sampling_strategy='minority'), rand_log_reg)\n",
    "    modelo = pipeline.fit(X_treino[treino], Y_treino[treino])\n",
    "    best_est = rand_log_reg.best_estimator_\n",
    "    previsao = best_est.predict(X_treino[teste])\n",
    "    \n",
    "    lista_acuracia.append(pipeline.score(X_treino[teste], Y_treino[teste]))\n",
    "    lista_precisao.append(precision_score(Y_treino[teste], previsao))\n",
    "    lista_recall.append(recall_score(Y_treino[teste], previsao))\n",
    "    lista_f1.append(f1_score(Y_treino[teste], previsao))\n",
    "    lista_auc.append(roc_auc_score(Y_treino[teste], previsao))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuracia: 0.9299075025693732\n",
      "Precisao: 0.9356075031722172\n",
      "Recall: 0.9016393442622951\n",
      "F1: 0.9179514259324731\n"
     ]
    }
   ],
   "source": [
    "print(\"Acuracia: {}\".format(np.mean(lista_acuracia)))\n",
    "print(\"Precisao: {}\".format(np.mean(lista_precisao)))\n",
    "print(\"Recall: {}\".format(np.mean(lista_recall)))\n",
    "print(\"F1: {}\".format(np.mean(lista_f1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estes novos resultados mostram claramente que o modelo de regressão logística ficou bem melhor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista_acuracia = []\n",
    "lista_precisao = []\n",
    "lista_recall = []\n",
    "lista_f1 = []\n",
    "lista_auc = []\n",
    "\n",
    "svc_sm = SVC()\n",
    "svc_params = {'C': [0.5, 0.7, 0.9, 1], 'kernel': ['rbf', 'poly', 'sigmoid', 'linear']}\n",
    "rand_svc = RandomizedSearchCV(svc_sm, svc_params, n_iter=4)\n",
    "\n",
    "\n",
    "for treino, teste in strat_kfold.split(X_treino, Y_treino):\n",
    "    pipeline_svc = imbalanced_make_pipeline(SMOTE(sampling_strategy='minority'), rand_svc)\n",
    "    modelo_svc = pipeline_svc.fit(X_treino[treino], Y_treino[treino])\n",
    "    best_est_svc = rand_svc.best_estimator_\n",
    "    previsao_svc = best_est_svc.predict(X_treino[teste])\n",
    "    \n",
    "    lista_acuracia.append(pipeline_svc.score(X_treino[teste], Y_treino[teste]))\n",
    "    lista_precisao.append(precision_score(Y_treino[teste], previsao_svc))\n",
    "    lista_recall.append(recall_score(Y_treino[teste], previsao_svc))\n",
    "    lista_f1.append(f1_score(Y_treino[teste], previsao_svc))\n",
    "    lista_auc.append(roc_auc_score(Y_treino[teste], previsao_svc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuracia: 0.9198869475847893\n",
      "Precisao: 0.9240766866706587\n",
      "Recall: 0.8918032786885245\n",
      "F1: 0.9067685818129083\n"
     ]
    }
   ],
   "source": [
    "print(\"Acuracia: {}\".format(np.mean(lista_acuracia)))\n",
    "print(\"Precisao: {}\".format(np.mean(lista_precisao)))\n",
    "print(\"Recall: {}\".format(np.mean(lista_recall)))\n",
    "print(\"F1: {}\".format(np.mean(lista_f1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observamos que o modelo SVC também melhorou e ficou com acurácia abaixo da regressão logística"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "  Sem Fraude       0.89      0.97      0.93        92\n",
      "  Com Fraude       0.96      0.87      0.91        83\n",
      "\n",
      "    accuracy                           0.92       175\n",
      "   macro avg       0.93      0.92      0.92       175\n",
      "weighted avg       0.92      0.92      0.92       175\n",
      "\n"
     ]
    }
   ],
   "source": [
    "labels = ['Sem Fraude', 'Com Fraude']\n",
    "previsao_smote = best_est.predict(X_teste)\n",
    "print(classification_report(Y_teste, previsao_smote, target_names=labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import average_precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_score = best_est.decision_function(X_teste)\n",
    "precisao_media = average_precision_score(Y_teste, Y_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score Medio precision-recall: 0.98\n"
     ]
    }
   ],
   "source": [
    "print('Score Medio precision-recall: {0:0.2f}'.format(precisao_media))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Curva Precisao-Recall Superamostra: \\n Score Medio Precision-Recall Score =0.98')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision, recall, _ = precision_recall_curve(Y_teste, Y_score)\n",
    "\n",
    "plt.step(recall, precision, color='r', alpha=0.2,where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.2,color='#F59B00')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precisao')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('Curva Precisao-Recall Superamostra: \\n Score Medio Precision-Recall Score ={0:0.2f}'.format(precisao_media), fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE(sampling_strategy='minority', random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xsmote_treino, Ysmote_treino = sm.fit_sample(X_treino, Y_treino)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg_sm = grid_log_reg.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg_sm.fit(Xsmote_treino, Ysmote_treino)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred_log_reg = log_reg_sm.predict(X_teste)\n",
    "Y_pred_knear = knears_neighbors.predict(X_teste)\n",
    "Y_pred_svc = svc.predict(X_teste)\n",
    "Y_pred_tree = tree_clf.predict(X_teste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_log_reg = confusion_matrix(Y_teste, Y_pred_log_reg)\n",
    "cm_kneighbors = confusion_matrix(Y_teste, Y_pred_knear)\n",
    "cm_svc = confusion_matrix(Y_teste, Y_pred_svc)\n",
    "cm_tree = confusion_matrix(Y_teste, Y_pred_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, ''), Text(0, 1.5, '')]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x864 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2,figsize=(22,12))\n",
    "\n",
    "\n",
    "sns.heatmap(cm_log_reg, ax=ax[0][0], annot=True, cmap=plt.cm.copper)\n",
    "ax[0, 0].set_title(\"Regressão Logistica \\n Matriz de Confusão\", fontsize=14)\n",
    "ax[0, 0].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
    "ax[0, 0].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
    "\n",
    "sns.heatmap(cm_kneighbors, ax=ax[0][1], annot=True, cmap=plt.cm.copper)\n",
    "ax[0][1].set_title(\"KNN \\n Matriz de Confusão\", fontsize=14)\n",
    "ax[0][1].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
    "ax[0][1].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
    "\n",
    "sns.heatmap(cm_svc, ax=ax[1][0], annot=True, cmap=plt.cm.copper)\n",
    "ax[1][0].set_title(\"SVC \\n Matriz de Confusão\", fontsize=14)\n",
    "ax[1][0].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
    "ax[1][0].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
    "\n",
    "sns.heatmap(cm_tree, ax=ax[1][1], annot=True, cmap=plt.cm.copper)\n",
    "ax[1][1].set_title(\"DTC \\n Matriz de Confusão\", fontsize=14)\n",
    "ax[1][1].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
    "ax[1][1].set_yticklabels(['', ''], fontsize=14, rotation=360)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regresao Logistica:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.97      0.93        92\n",
      "           1       0.96      0.87      0.91        83\n",
      "\n",
      "    accuracy                           0.92       175\n",
      "   macro avg       0.93      0.92      0.92       175\n",
      "weighted avg       0.92      0.92      0.92       175\n",
      "\n",
      "KNN:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.99      0.94        92\n",
      "           1       0.99      0.87      0.92        83\n",
      "\n",
      "    accuracy                           0.93       175\n",
      "   macro avg       0.94      0.93      0.93       175\n",
      "weighted avg       0.94      0.93      0.93       175\n",
      "\n",
      "SVC:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.98      0.92        92\n",
      "           1       0.97      0.83      0.90        83\n",
      "\n",
      "    accuracy                           0.91       175\n",
      "   macro avg       0.92      0.90      0.91       175\n",
      "weighted avg       0.92      0.91      0.91       175\n",
      "\n",
      "DTC:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.99      0.92        92\n",
      "           1       0.99      0.82      0.89        83\n",
      "\n",
      "    accuracy                           0.91       175\n",
      "   macro avg       0.92      0.90      0.91       175\n",
      "weighted avg       0.92      0.91      0.91       175\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Regresao Logistica:')\n",
    "print(classification_report(Y_teste, Y_pred_log_reg))\n",
    "\n",
    "print('KNN:')\n",
    "print(classification_report(Y_teste, Y_pred_knear))\n",
    "\n",
    "print('SVC:')\n",
    "print(classification_report(Y_teste, Y_pred_svc))\n",
    "\n",
    "print('DTC:')\n",
    "print(classification_report(Y_teste, Y_pred_tree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = log_reg.predict(X_teste)\n",
    "score_subamostra = accuracy_score(Y_teste, Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred_sm = best_est.predict(X_teste)\n",
    "score_superamostra = accuracy_score(Y_teste, Y_pred_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'Tecnica': ['Subamostra aleatoria', 'Superamostra (SMOTE)'], 'Score': [score_subamostra, score_superamostra]}\n",
    "final_df = pd.DataFrame(data=d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tecnica</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Subamostra aleatoria</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Superamostra (SMOTE)</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Tecnica  Score\n",
       "0  Subamostra aleatoria   0.92\n",
       "1  Superamostra (SMOTE)   0.92"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por fim, observamos que ambos os tratamentos deram resultados semelhantes e foram capazes de identificar transações com fraude com grande sucesso"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Criaremos agora um modelo de aprendizado profundo para tentar melhor as acurácias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Activation\n",
    "from keras.layers.core import Dense,Dropout\n",
    "from keras.optimizers import Adam\n",
    "from keras.metrics import categorical_crossentropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_inputs = X_treino.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelo = Sequential()\n",
    "modelo.add(Dense(n_inputs,input_shape=(n_inputs, ), activation='relu'))\n",
    "modelo.add(Dense(60,activation='relu'))\n",
    "modelo.add(Dropout(0.2))\n",
    "modelo.add(Dense(60,activation='relu'))\n",
    "modelo.add(Dense(30,activation='relu'))\n",
    "modelo.add(Dense(2, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_6 (Dense)              (None, 30)                930       \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 60)                1860      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 60)                0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 60)                3660      \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 30)                1830      \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 2)                 62        \n",
      "=================================================================\n",
      "Total params: 8,342\n",
      "Trainable params: 8,342\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "modelo.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelo.compile(Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/danilo/opt/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Train on 559 samples, validate on 140 samples\n",
      "Epoch 1/50\n",
      " - 0s - loss: 0.5287 - accuracy: 0.7191 - val_loss: 0.3991 - val_accuracy: 0.8786\n",
      "Epoch 2/50\n",
      " - 0s - loss: 0.3371 - accuracy: 0.8873 - val_loss: 0.2518 - val_accuracy: 0.9071\n",
      "Epoch 3/50\n",
      " - 0s - loss: 0.2550 - accuracy: 0.9213 - val_loss: 0.2008 - val_accuracy: 0.9357\n",
      "Epoch 4/50\n",
      " - 0s - loss: 0.1822 - accuracy: 0.9410 - val_loss: 0.1799 - val_accuracy: 0.9429\n",
      "Epoch 5/50\n",
      " - 0s - loss: 0.1695 - accuracy: 0.9481 - val_loss: 0.1784 - val_accuracy: 0.9286\n",
      "Epoch 6/50\n",
      " - 0s - loss: 0.1697 - accuracy: 0.9392 - val_loss: 0.1975 - val_accuracy: 0.9071\n",
      "Epoch 7/50\n",
      " - 0s - loss: 0.1684 - accuracy: 0.9374 - val_loss: 0.1832 - val_accuracy: 0.9143\n",
      "Epoch 8/50\n",
      " - 0s - loss: 0.1414 - accuracy: 0.9481 - val_loss: 0.1839 - val_accuracy: 0.9143\n",
      "Epoch 9/50\n",
      " - 0s - loss: 0.1413 - accuracy: 0.9571 - val_loss: 0.1787 - val_accuracy: 0.9214\n",
      "Epoch 10/50\n",
      " - 0s - loss: 0.1158 - accuracy: 0.9642 - val_loss: 0.1936 - val_accuracy: 0.9143\n",
      "Epoch 11/50\n",
      " - 0s - loss: 0.1129 - accuracy: 0.9517 - val_loss: 0.2067 - val_accuracy: 0.9143\n",
      "Epoch 12/50\n",
      " - 0s - loss: 0.1044 - accuracy: 0.9624 - val_loss: 0.1948 - val_accuracy: 0.9143\n",
      "Epoch 13/50\n",
      " - 0s - loss: 0.1100 - accuracy: 0.9606 - val_loss: 0.2048 - val_accuracy: 0.9071\n",
      "Epoch 14/50\n",
      " - 0s - loss: 0.1021 - accuracy: 0.9642 - val_loss: 0.2089 - val_accuracy: 0.9143\n",
      "Epoch 15/50\n",
      " - 0s - loss: 0.0868 - accuracy: 0.9642 - val_loss: 0.2018 - val_accuracy: 0.9071\n",
      "Epoch 16/50\n",
      " - 0s - loss: 0.0919 - accuracy: 0.9785 - val_loss: 0.2025 - val_accuracy: 0.9143\n",
      "Epoch 17/50\n",
      " - 0s - loss: 0.0773 - accuracy: 0.9785 - val_loss: 0.2213 - val_accuracy: 0.9071\n",
      "Epoch 18/50\n",
      " - 0s - loss: 0.0816 - accuracy: 0.9696 - val_loss: 0.2302 - val_accuracy: 0.9071\n",
      "Epoch 19/50\n",
      " - 0s - loss: 0.0681 - accuracy: 0.9821 - val_loss: 0.2172 - val_accuracy: 0.9143\n",
      "Epoch 20/50\n",
      " - 0s - loss: 0.0657 - accuracy: 0.9767 - val_loss: 0.2650 - val_accuracy: 0.9071\n",
      "Epoch 21/50\n",
      " - 0s - loss: 0.0605 - accuracy: 0.9767 - val_loss: 0.2596 - val_accuracy: 0.9071\n",
      "Epoch 22/50\n",
      " - 0s - loss: 0.0659 - accuracy: 0.9803 - val_loss: 0.2636 - val_accuracy: 0.9071\n",
      "Epoch 23/50\n",
      " - 0s - loss: 0.0551 - accuracy: 0.9767 - val_loss: 0.2896 - val_accuracy: 0.9071\n",
      "Epoch 24/50\n",
      " - 0s - loss: 0.0495 - accuracy: 0.9803 - val_loss: 0.2863 - val_accuracy: 0.9071\n",
      "Epoch 25/50\n",
      " - 0s - loss: 0.0532 - accuracy: 0.9803 - val_loss: 0.3549 - val_accuracy: 0.9000\n",
      "Epoch 26/50\n",
      " - 0s - loss: 0.0795 - accuracy: 0.9696 - val_loss: 0.2855 - val_accuracy: 0.9286\n",
      "Epoch 27/50\n",
      " - 0s - loss: 0.0628 - accuracy: 0.9750 - val_loss: 0.3657 - val_accuracy: 0.8929\n",
      "Epoch 28/50\n",
      " - 0s - loss: 0.0624 - accuracy: 0.9839 - val_loss: 0.2857 - val_accuracy: 0.9143\n",
      "Epoch 29/50\n",
      " - 0s - loss: 0.0446 - accuracy: 0.9893 - val_loss: 0.3251 - val_accuracy: 0.9143\n",
      "Epoch 30/50\n",
      " - 0s - loss: 0.0289 - accuracy: 0.9911 - val_loss: 0.3665 - val_accuracy: 0.9143\n",
      "Epoch 31/50\n",
      " - 0s - loss: 0.0322 - accuracy: 0.9893 - val_loss: 0.3649 - val_accuracy: 0.9143\n",
      "Epoch 32/50\n",
      " - 0s - loss: 0.0288 - accuracy: 0.9928 - val_loss: 0.3758 - val_accuracy: 0.9143\n",
      "Epoch 33/50\n",
      " - 0s - loss: 0.0339 - accuracy: 0.9875 - val_loss: 0.4028 - val_accuracy: 0.9071\n",
      "Epoch 34/50\n",
      " - 0s - loss: 0.0208 - accuracy: 0.9928 - val_loss: 0.3899 - val_accuracy: 0.9071\n",
      "Epoch 35/50\n",
      " - 0s - loss: 0.0270 - accuracy: 0.9911 - val_loss: 0.4019 - val_accuracy: 0.9214\n",
      "Epoch 36/50\n",
      " - 0s - loss: 0.0227 - accuracy: 0.9928 - val_loss: 0.4461 - val_accuracy: 0.9000\n",
      "Epoch 37/50\n",
      " - 0s - loss: 0.0250 - accuracy: 0.9911 - val_loss: 0.4141 - val_accuracy: 0.9143\n",
      "Epoch 38/50\n",
      " - 0s - loss: 0.0244 - accuracy: 0.9928 - val_loss: 0.4331 - val_accuracy: 0.9000\n",
      "Epoch 39/50\n",
      " - 0s - loss: 0.0265 - accuracy: 0.9911 - val_loss: 0.4445 - val_accuracy: 0.9071\n",
      "Epoch 40/50\n",
      " - 0s - loss: 0.0209 - accuracy: 0.9964 - val_loss: 0.4297 - val_accuracy: 0.9143\n",
      "Epoch 41/50\n",
      " - 0s - loss: 0.0201 - accuracy: 0.9946 - val_loss: 0.4560 - val_accuracy: 0.9071\n",
      "Epoch 42/50\n",
      " - 0s - loss: 0.0179 - accuracy: 0.9946 - val_loss: 0.4523 - val_accuracy: 0.9071\n",
      "Epoch 43/50\n",
      " - 0s - loss: 0.0151 - accuracy: 0.9964 - val_loss: 0.4880 - val_accuracy: 0.9000\n",
      "Epoch 44/50\n",
      " - 0s - loss: 0.0148 - accuracy: 0.9964 - val_loss: 0.5065 - val_accuracy: 0.9000\n",
      "Epoch 45/50\n",
      " - 0s - loss: 0.0092 - accuracy: 0.9982 - val_loss: 0.5010 - val_accuracy: 0.9071\n",
      "Epoch 46/50\n",
      " - 0s - loss: 0.0215 - accuracy: 0.9946 - val_loss: 0.5384 - val_accuracy: 0.9000\n",
      "Epoch 47/50\n",
      " - 0s - loss: 0.0320 - accuracy: 0.9911 - val_loss: 0.4277 - val_accuracy: 0.9214\n",
      "Epoch 48/50\n",
      " - 0s - loss: 0.0246 - accuracy: 0.9928 - val_loss: 0.4404 - val_accuracy: 0.8929\n",
      "Epoch 49/50\n",
      " - 0s - loss: 0.0184 - accuracy: 0.9964 - val_loss: 0.4834 - val_accuracy: 0.8857\n",
      "Epoch 50/50\n",
      " - 0s - loss: 0.0144 - accuracy: 0.9964 - val_loss: 0.4897 - val_accuracy: 0.8857\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x1a23521410>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelo.fit(X_treino, Y_treino, validation_split=0.2, batch_size=25, epochs=50, shuffle=True, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "previsao_subamostra = modelo.predict(X_teste, batch_size=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "699/699 [==============================] - 0s 33us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.10256046908261396, 0.9756795167922974]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelo.evaluate(X_treino,Y_treino)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "175/175 [==============================] - 0s 44us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.9372428406987872, 0.9257143139839172]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelo.evaluate(X_teste,Y_teste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "previsao_fraude_subamostra = modelo.predict_classes(X_teste, batch_size=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Matriz de confusão',\n",
    "                          cmap=plt.cm.Blues):\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Matriz de confusão normalizada\")\n",
    "    else:\n",
    "        print('Matriz de confusão')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, fontsize=14)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('Classe Verdadeira')\n",
    "    plt.xlabel('Classe Prevista')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_subamostra = confusion_matrix(Y_teste, previsao_fraude_subamostra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matriz de confusão\n",
      "[[89  3]\n",
      " [10 73]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['Sem Fraude', 'Com Fraude']\n",
    "\n",
    "plot_confusion_matrix(cm_subamostra, labels, title=\"Subamostra aleatoria \\n Matriz de confusão\", cmap=plt.cm.Reds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_inputs = Xsmote_treino.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelo_SMOTE = Sequential()\n",
    "modelo_SMOTE.add(Dense(n_inputs, input_shape=(n_inputs, ), activation='relu'))\n",
    "modelo_SMOTE.add(Dense(2*n_inputs, activation='relu'))\n",
    "modelo_SMOTE.add(Dropout(0.2))\n",
    "modelo_SMOTE.add(Dense(2*n_inputs, activation='relu'))\n",
    "modelo_SMOTE.add(Dense(n_inputs, activation='relu'))\n",
    "modelo_SMOTE.add(Dense(2, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelo_SMOTE.compile(Adam(lr=0.0001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 630 samples, validate on 158 samples\n",
      "Epoch 1/50\n",
      " - 0s - loss: 0.7183 - accuracy: 0.4492 - val_loss: 0.5393 - val_accuracy: 0.7532\n",
      "Epoch 2/50\n",
      " - 0s - loss: 0.6663 - accuracy: 0.4937 - val_loss: 0.4921 - val_accuracy: 0.7532\n",
      "Epoch 3/50\n",
      " - 0s - loss: 0.6356 - accuracy: 0.5111 - val_loss: 0.4624 - val_accuracy: 0.7722\n",
      "Epoch 4/50\n",
      " - 0s - loss: 0.6090 - accuracy: 0.5635 - val_loss: 0.4423 - val_accuracy: 0.7848\n",
      "Epoch 5/50\n",
      " - 0s - loss: 0.5796 - accuracy: 0.6127 - val_loss: 0.4270 - val_accuracy: 0.7975\n",
      "Epoch 6/50\n",
      " - 0s - loss: 0.5536 - accuracy: 0.6540 - val_loss: 0.4138 - val_accuracy: 0.8165\n",
      "Epoch 7/50\n",
      " - 0s - loss: 0.5411 - accuracy: 0.6952 - val_loss: 0.4013 - val_accuracy: 0.8101\n",
      "Epoch 8/50\n",
      " - 0s - loss: 0.5150 - accuracy: 0.7397 - val_loss: 0.3884 - val_accuracy: 0.8165\n",
      "Epoch 9/50\n",
      " - 0s - loss: 0.4993 - accuracy: 0.7714 - val_loss: 0.3766 - val_accuracy: 0.8291\n",
      "Epoch 10/50\n",
      " - 0s - loss: 0.4797 - accuracy: 0.8111 - val_loss: 0.3661 - val_accuracy: 0.8291\n",
      "Epoch 11/50\n",
      " - 0s - loss: 0.4582 - accuracy: 0.8508 - val_loss: 0.3557 - val_accuracy: 0.8291\n",
      "Epoch 12/50\n",
      " - 0s - loss: 0.4471 - accuracy: 0.8524 - val_loss: 0.3442 - val_accuracy: 0.8418\n",
      "Epoch 13/50\n",
      " - 0s - loss: 0.4239 - accuracy: 0.8746 - val_loss: 0.3353 - val_accuracy: 0.8418\n",
      "Epoch 14/50\n",
      " - 0s - loss: 0.4143 - accuracy: 0.8825 - val_loss: 0.3273 - val_accuracy: 0.8418\n",
      "Epoch 15/50\n",
      " - 0s - loss: 0.3919 - accuracy: 0.9206 - val_loss: 0.3182 - val_accuracy: 0.8354\n",
      "Epoch 16/50\n",
      " - 0s - loss: 0.3780 - accuracy: 0.9016 - val_loss: 0.3113 - val_accuracy: 0.8481\n",
      "Epoch 17/50\n",
      " - 0s - loss: 0.3656 - accuracy: 0.9048 - val_loss: 0.3027 - val_accuracy: 0.8481\n",
      "Epoch 18/50\n",
      " - 0s - loss: 0.3480 - accuracy: 0.9175 - val_loss: 0.2953 - val_accuracy: 0.8671\n",
      "Epoch 19/50\n",
      " - 0s - loss: 0.3324 - accuracy: 0.9206 - val_loss: 0.2899 - val_accuracy: 0.8734\n",
      "Epoch 20/50\n",
      " - 0s - loss: 0.3389 - accuracy: 0.9111 - val_loss: 0.2845 - val_accuracy: 0.8797\n",
      "Epoch 21/50\n",
      " - 0s - loss: 0.3061 - accuracy: 0.9254 - val_loss: 0.2771 - val_accuracy: 0.8734\n",
      "Epoch 22/50\n",
      " - 0s - loss: 0.3073 - accuracy: 0.9190 - val_loss: 0.2730 - val_accuracy: 0.8797\n",
      "Epoch 23/50\n",
      " - 0s - loss: 0.2948 - accuracy: 0.9254 - val_loss: 0.2669 - val_accuracy: 0.8797\n",
      "Epoch 24/50\n",
      " - 0s - loss: 0.2934 - accuracy: 0.9175 - val_loss: 0.2636 - val_accuracy: 0.8797\n",
      "Epoch 25/50\n",
      " - 0s - loss: 0.2705 - accuracy: 0.9286 - val_loss: 0.2616 - val_accuracy: 0.8797\n",
      "Epoch 26/50\n",
      " - 0s - loss: 0.2672 - accuracy: 0.9254 - val_loss: 0.2592 - val_accuracy: 0.8861\n",
      "Epoch 27/50\n",
      " - 0s - loss: 0.2584 - accuracy: 0.9254 - val_loss: 0.2544 - val_accuracy: 0.8797\n",
      "Epoch 28/50\n",
      " - 0s - loss: 0.2646 - accuracy: 0.9286 - val_loss: 0.2499 - val_accuracy: 0.8797\n",
      "Epoch 29/50\n",
      " - 0s - loss: 0.2470 - accuracy: 0.9286 - val_loss: 0.2487 - val_accuracy: 0.8797\n",
      "Epoch 30/50\n",
      " - 0s - loss: 0.2509 - accuracy: 0.9302 - val_loss: 0.2466 - val_accuracy: 0.8861\n",
      "Epoch 31/50\n",
      " - 0s - loss: 0.2472 - accuracy: 0.9317 - val_loss: 0.2457 - val_accuracy: 0.8861\n",
      "Epoch 32/50\n",
      " - 0s - loss: 0.2359 - accuracy: 0.9317 - val_loss: 0.2418 - val_accuracy: 0.8861\n",
      "Epoch 33/50\n",
      " - 0s - loss: 0.2188 - accuracy: 0.9381 - val_loss: 0.2403 - val_accuracy: 0.8861\n",
      "Epoch 34/50\n",
      " - 0s - loss: 0.2285 - accuracy: 0.9333 - val_loss: 0.2378 - val_accuracy: 0.8861\n",
      "Epoch 35/50\n",
      " - 0s - loss: 0.2283 - accuracy: 0.9302 - val_loss: 0.2351 - val_accuracy: 0.8861\n",
      "Epoch 36/50\n",
      " - 0s - loss: 0.2125 - accuracy: 0.9397 - val_loss: 0.2343 - val_accuracy: 0.8861\n",
      "Epoch 37/50\n",
      " - 0s - loss: 0.2081 - accuracy: 0.9365 - val_loss: 0.2322 - val_accuracy: 0.8861\n",
      "Epoch 38/50\n",
      " - 0s - loss: 0.2076 - accuracy: 0.9413 - val_loss: 0.2312 - val_accuracy: 0.8861\n",
      "Epoch 39/50\n",
      " - 0s - loss: 0.2047 - accuracy: 0.9413 - val_loss: 0.2302 - val_accuracy: 0.8861\n",
      "Epoch 40/50\n",
      " - 0s - loss: 0.2020 - accuracy: 0.9365 - val_loss: 0.2290 - val_accuracy: 0.8861\n",
      "Epoch 41/50\n",
      " - 0s - loss: 0.2051 - accuracy: 0.9397 - val_loss: 0.2267 - val_accuracy: 0.8861\n",
      "Epoch 42/50\n",
      " - 0s - loss: 0.1956 - accuracy: 0.9444 - val_loss: 0.2258 - val_accuracy: 0.8861\n",
      "Epoch 43/50\n",
      " - 0s - loss: 0.1952 - accuracy: 0.9460 - val_loss: 0.2254 - val_accuracy: 0.8797\n",
      "Epoch 44/50\n",
      " - 0s - loss: 0.1997 - accuracy: 0.9444 - val_loss: 0.2240 - val_accuracy: 0.8734\n",
      "Epoch 45/50\n",
      " - 0s - loss: 0.1870 - accuracy: 0.9444 - val_loss: 0.2247 - val_accuracy: 0.8861\n",
      "Epoch 46/50\n",
      " - 0s - loss: 0.1833 - accuracy: 0.9413 - val_loss: 0.2248 - val_accuracy: 0.8861\n",
      "Epoch 47/50\n",
      " - 0s - loss: 0.1954 - accuracy: 0.9349 - val_loss: 0.2255 - val_accuracy: 0.8861\n",
      "Epoch 48/50\n",
      " - 0s - loss: 0.1741 - accuracy: 0.9460 - val_loss: 0.2234 - val_accuracy: 0.8797\n",
      "Epoch 49/50\n",
      " - 0s - loss: 0.1855 - accuracy: 0.9397 - val_loss: 0.2216 - val_accuracy: 0.8797\n",
      "Epoch 50/50\n",
      " - 0s - loss: 0.1732 - accuracy: 0.9365 - val_loss: 0.2210 - val_accuracy: 0.8734\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x1a1afc2c90>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelo_SMOTE.fit(Xsmote_treino, Ysmote_treino, validation_split=0.2, batch_size=50, epochs=50, shuffle=True, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "previsao_superamostra = modelo_SMOTE.predict(X_teste, batch_size=50, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "previsao_fraude_superamostra = modelo_SMOTE.predict_classes(X_teste, batch_size=50, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matriz de confusão\n",
      "[[90  2]\n",
      " [12 71]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_smote_superamostra = confusion_matrix(Y_teste, previsao_fraude_superamostra)\n",
    "labels = ['Sem Fraude', 'Com Fraude']\n",
    "plot_confusion_matrix(cm_smote_superamostra, labels, title=\"SMOTE Superamostra \\n Matriz de confusão\", cmap=plt.cm.Oranges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "699/699 [==============================] - 0s 32us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.10256046908261396, 0.9756795167922974]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelo.evaluate(X_treino,Y_treino)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparando as duas redes neurais observamos que a com SMOTE apresentou melhor acurácia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se comparamos SMOTE com a rede neural, observamos que ambas tiveram um ótimo desempenho, porém, a rede neural classificou melhor as duas classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}