HeartDisease_Pred.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data analysis and scientific computing \n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "#data visualisation \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#worked on three classifiers to check the better accuracy model \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import data \n",
    "df = pd.read_csv('Heart.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#feature selection \n",
    "import seaborn as sns\n",
    "#correlations of features in the dataset \n",
    "corrmat = df.corr()\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize=(15,15))\n",
    "g=sns.heatmap(df[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f86c520d860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f86c522cda0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f86c51e9390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f86c519a940>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f86c514def0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f86c51884e0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f86c513aa90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f86c50f90b8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f86c50f90f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f86c505abe0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f86c50191d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f86c5048780>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f86c4ffad30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f86c4fb8320>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f86c4f698d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f86c4f1be80>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f86c4c99198>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='target',data=df,palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "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>age</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>thalach</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>target</th>\n",
       "      <th>sex_0</th>\n",
       "      <th>sex_1</th>\n",
       "      <th>cp_0</th>\n",
       "      <th>cp_1</th>\n",
       "      <th>...</th>\n",
       "      <th>slope_2</th>\n",
       "      <th>ca_0</th>\n",
       "      <th>ca_1</th>\n",
       "      <th>ca_2</th>\n",
       "      <th>ca_3</th>\n",
       "      <th>ca_4</th>\n",
       "      <th>thal_0</th>\n",
       "      <th>thal_1</th>\n",
       "      <th>thal_2</th>\n",
       "      <th>thal_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.952197</td>\n",
       "      <td>0.763956</td>\n",
       "      <td>-0.256334</td>\n",
       "      <td>0.015443</td>\n",
       "      <td>1.087338</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.915313</td>\n",
       "      <td>-0.092738</td>\n",
       "      <td>0.072199</td>\n",
       "      <td>1.633471</td>\n",
       "      <td>2.122573</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.474158</td>\n",
       "      <td>-0.092738</td>\n",
       "      <td>-0.816773</td>\n",
       "      <td>0.977514</td>\n",
       "      <td>0.310912</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.180175</td>\n",
       "      <td>-0.663867</td>\n",
       "      <td>-0.198357</td>\n",
       "      <td>1.239897</td>\n",
       "      <td>-0.206705</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.290464</td>\n",
       "      <td>-0.663867</td>\n",
       "      <td>2.082050</td>\n",
       "      <td>0.583939</td>\n",
       "      <td>-0.379244</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>0.290464</td>\n",
       "      <td>0.478391</td>\n",
       "      <td>-0.101730</td>\n",
       "      <td>-1.165281</td>\n",
       "      <td>-0.724323</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>-1.033002</td>\n",
       "      <td>-1.234996</td>\n",
       "      <td>0.342756</td>\n",
       "      <td>-0.771706</td>\n",
       "      <td>0.138373</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>1.503641</td>\n",
       "      <td>0.706843</td>\n",
       "      <td>-1.029353</td>\n",
       "      <td>-0.378132</td>\n",
       "      <td>2.036303</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>0.290464</td>\n",
       "      <td>-0.092738</td>\n",
       "      <td>-2.227533</td>\n",
       "      <td>-1.515125</td>\n",
       "      <td>0.138373</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>0.290464</td>\n",
       "      <td>-0.092738</td>\n",
       "      <td>-0.198357</td>\n",
       "      <td>1.064975</td>\n",
       "      <td>-0.896862</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age  trestbps      chol   thalach   oldpeak  target  sex_0  sex_1  \\\n",
       "0    0.952197  0.763956 -0.256334  0.015443  1.087338       1      0      1   \n",
       "1   -1.915313 -0.092738  0.072199  1.633471  2.122573       1      0      1   \n",
       "2   -1.474158 -0.092738 -0.816773  0.977514  0.310912       1      1      0   \n",
       "3    0.180175 -0.663867 -0.198357  1.239897 -0.206705       1      0      1   \n",
       "4    0.290464 -0.663867  2.082050  0.583939 -0.379244       1      1      0   \n",
       "..        ...       ...       ...       ...       ...     ...    ...    ...   \n",
       "298  0.290464  0.478391 -0.101730 -1.165281 -0.724323       0      1      0   \n",
       "299 -1.033002 -1.234996  0.342756 -0.771706  0.138373       0      0      1   \n",
       "300  1.503641  0.706843 -1.029353 -0.378132  2.036303       0      0      1   \n",
       "301  0.290464 -0.092738 -2.227533 -1.515125  0.138373       0      0      1   \n",
       "302  0.290464 -0.092738 -0.198357  1.064975 -0.896862       0      1      0   \n",
       "\n",
       "     cp_0  cp_1  ...  slope_2  ca_0  ca_1  ca_2  ca_3  ca_4  thal_0  thal_1  \\\n",
       "0       0     0  ...        0     1     0     0     0     0       0       1   \n",
       "1       0     0  ...        0     1     0     0     0     0       0       0   \n",
       "2       0     1  ...        1     1     0     0     0     0       0       0   \n",
       "3       0     1  ...        1     1     0     0     0     0       0       0   \n",
       "4       1     0  ...        1     1     0     0     0     0       0       0   \n",
       "..    ...   ...  ...      ...   ...   ...   ...   ...   ...     ...     ...   \n",
       "298     1     0  ...        0     1     0     0     0     0       0       0   \n",
       "299     0     0  ...        0     1     0     0     0     0       0       0   \n",
       "300     1     0  ...        0     0     0     1     0     0       0       0   \n",
       "301     1     0  ...        0     0     1     0     0     0       0       0   \n",
       "302     0     1  ...        0     0     1     0     0     0       0       0   \n",
       "\n",
       "     thal_2  thal_3  \n",
       "0         0       0  \n",
       "1         1       0  \n",
       "2         1       0  \n",
       "3         1       0  \n",
       "4         1       0  \n",
       "..      ...     ...  \n",
       "298       0       1  \n",
       "299       0       1  \n",
       "300       0       1  \n",
       "301       0       1  \n",
       "302       1       0  \n",
       "\n",
       "[303 rows x 31 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "dataset = pd.get_dummies(df, columns = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal'])\n",
    "standardScaler = StandardScaler()\n",
    "columns_to_scale = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']\n",
    "dataset[columns_to_scale] = standardScaler.fit_transform(dataset[columns_to_scale])\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "165"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_suffered = dataset[dataset['target'] == 1]\n",
    "patient_suffered['target'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#total males and females \n",
    "df['target'].value_counts().plot(kind='bar')\n",
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    93\n",
       "0    72\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#after heart disease is detected \n",
    "dataset_final = df[df['target'] == 1]\n",
    "dataset_final['sex'].value_counts().plot(kind='bar')\n",
    "dataset_final['sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter',x='target',y='thalach',color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f86c155eeb8>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(\"target\", y=\"thalach\", data=df,\n",
    "                   color=\"#bb3f3f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8b3ME47G63W6/P7dwFazXZ1CL/3znf7CLP7GxsUpNTTU5UeDsdntY5emJSM3eXe6ahASNHDnS6zoJHR0+H6u/9ftiGw6HQwMHDfK5jL/5gSxjxjYcDsdFy/c2RzAeq8PhUEJCwmXzPO/t9rpj2qme7gwbNkyNjY2SpMbGRsXHxwdz9wAABbn4MzIyVFFRIUmqqKjQ1KlTg7l7AIBMLP7i4mI98MADOnTokCZPnqy///3vmjt3rnbs2KGsrCx9+OGHmjt3rlm7BwB4Ydo5/pdeeqnb6evWrTNrlwCAAAT1VA8AIPQofgCwGIofACyG4gcAi6H4AcBiKH4AsBiKHwAshuIHAIuh+AHAYih+ALAYih8ALIbiBwCLofgBwGIofgCwGIofACyG4gcAi6H4AcBiTPsELly+ylavVkt9vdf5ccnJmjlvnunb8Gf3rl0qXbrU6/zaXbuk0aN7tQ+EJ38/+754fkUyih891lJfr7k+CrO0ri4o2/DnbEuLz308Vl3d630gPPn72ffF8yuScaoHACyG4gcAi6H4AcBiKH4AsBiKHwAshuIHAIvhck70OX/XUEtcQw+EEsWPPufvGmqJa+iBUOJUDwBYDMUPABYTklM9GRkZGjx4sKKiohQdHa0tW7aEIgYAWFLIzvGvW7dO8fHxodo9AFgWp3oAwGJC9o5/zpw5stlsuv/++3X//ff7XNblcslutwcpmX/t7e1hlacn+iK70+mUIybG6/y206flcDh8bsPfMk6ns0vO7nL3NkcgOf9bXS3no496nX9w3z7lZGZ6ne92u/skRyi24Xa7L1q+tzmC8VgDOeb+fq6SNCAxUXfNnOlzmb4WrG4JSfFv2LBBSUlJampqUmFhoVJSUjRp0iSvy8fGxio1NTWICX2z2+1hlacn+iJ7TUKCRo4c6XX+wEGDfM4PZJmEjo4uObvL3dscgeQcJKnkppu8zn+sttbnNhwOR5/kCMU2HA7HRcv3NkcwHmsgx9zfz1U6N3RzsF/nfd0t3v4TCcmpnqSkJEnSsGHDlJmZqT179oQiBgBYUtCL//Tp02ppafF8vWPHDo0ZMybYMQDAsoJ+qqepqUnzvvvIs87OTk2fPl2TJ08OdgwAsKygF//VV1+tt99+O9i7BQB8h8s5AcBiKH4AsBhG50QXZatXq6W+3ucywRhS+cKhnZ1Op2oSEoKeA7gcUfzooqW+PiyGVL5waGdHTMxF12UztDNwaTjVAwAWQ/EDgMVQ/ABgMRQ/AFgMxQ8AFkPxA4DFcDlnBPF3jX1ccrJmfjcOEgB4Q/FHEH/X2JfW1QUxDYBIxakeALAYih8ALIbiBwCLofgBwGIofgCwGK7quYxcOJRxdz759FP95KabvM5nqGPgHH+vJ/uBA0r183nh4XqJNcV/GblwKOPu7Ni+3ecyDHUMnOPv9fRYdbXmTpvmcxvheok1p3oAwGIofgCwGIofACyG4gcAi6H4AcBiKH4AsJjL/nJOf0MZS8G51tZfjkCuCeYaeyCy9PReAKfTqZqEhC7LmNFPl33x+xvKWArOtbb+cgRyTTDX2AORpaf3AjhiYjRy5Mguy5jRT5zqAQCLofgBwGIofgCwmJAU/wcffKA77rhDmZmZKi0tDUUEALCsoBd/Z2enli1bprVr12rbtm3aunWrDh48GOwYAGBZQS/+PXv2aPTo0br66qsVExOj7OxsVVVVBTsGAFiWzTAMI5g7/Oc//6mamho9++yzkqSKigrt2bNHTz/9tNd1du/erdjY2GBFBIDLgsvl0sSJEy+aHhHX8XcXHABwaYJ+qicpKUnHjx/3fN/Q0KCkpKRgxwAAywp68d9www06fPiwjhw5oo6ODm3btk0ZGRnBjgEAlhX0Uz39+vXT008/rV/+8pfq7OzUvffeqzF+xqgBAPSdoP9xFwAQWty5CwAWQ/EDgMVQ/H787W9/U3Z2tqZPn67i4mK5XC4dOXJEBQUFyszM1MKFC9XR0RHqmJKkJUuWKD09XdOnT/dM+/bbb1VYWKisrCwVFhaqublZkmQYhpYvX67MzEzl5ORo3759oYotqfvsL7zwgu68807l5ORo3rx5OnnypGfemjVrlJmZqTvuuEM1NTWhiCyp+9zf+8tf/qLrrrtO33zzjaTIOOaS9MYbb+jOO+9Udna2Vq5c6ZkezsfcbrdrxowZys3NVX5+vvbs2SMpvI65w+HQL37xC/385z9Xdna21q1bJylEr1EDXh0/fty4/fbbjba2NsMwDKOoqMgoLy83ioqKjK1btxqGYRi/+93vjPXr14cypsfOnTuN2tpaIzs72zPthRdeMNasWWMYhmGsWbPGWLlypWEYhlFdXW3MmTPHOHv2rPH5558b9913X0gyf6+77DU1NYbb7TYMwzBWrlzpyX7gwAEjJyfHcLlcxldffWVMnTrVOHPmTNjkNgzDqK+vNx555BHjZz/7mdHU1GQYRmQc848++siYNWuW4XK5DMMwDKfTaRhG+B/zwsJCo7q62jCMc8f54Ycf9nwdLse8oaHBqK2tNQzDME6dOmVkZWUZBw4cCMlrlHf8fnR2dqq9vV1nzpxRe3u7hg8fro8//lh33HGHJOmee+4JmyEnJk2apKFDh3aZVlVVpby8PElSXl6eKisru0y32WyaOHGiTp48qcbGxqBn/l532W+77Tb163fuwrOJEyd67v+oqqpSdna2YmJidPXVV2v06NGed3jB1l1uSVqxYoWefPJJ2Ww2z7RIOOYbNmzQ3LlzFRMTI0kaNmyYpPA/5jabTa2trZKkU6dOKTExUVJ4HfPExESNGzdOkhQXF6eUlBQ1NDSE5DVK8fuQlJSkRx55RLfffrtuu+02xcXFady4cfrBD37gKaQRI0aooaEhxEm9a2pq8rwIhg8frqamJknnbpwbMWKEZ7lwfxzl5eWaPHmypIuzJyUlhVX2yspKJSYm6vrrr+8yPRKO+eHDh7Vr1y4VFBTo4Ycf9pR7uB/zkpISrVy5UlOmTNELL7yg4uJiSeF7zI8ePSq73a4JEyaE5DVK8fvQ3NysqqoqVVVVqaamRm1tbSE9t9lbNputyzvQSPHaa68pOjpad999d6ij+NXW1qY1a9ZowYIFoY5ySTo7O9Xc3KxNmzZp0aJFWrhwoYwIuOJ7w4YNWrJkid5//30tWbJES318zm2otba2qqioSCUlJYqLi+syL1ivUYrfhw8//FBXXXWV4uPj1b9/f2VlZemzzz7TyZMndebMGUnS8ePHw3rIiWHDhnl+PWxsbFR8fLyki4fOCNfHsWXLFlVXV+vFF1/0vCDCediPr776SkePHlVubq4yMjJ0/Phx5efn6+uvv46IY56UlKTMzEzZbDalpaUpKipKJ06cCOtjLklvvvmmsrKyJEl33XWX5zeVcDvmbrdbRUVFysnJ8eQNxWuU4vchOTlZ//vf/9TW1ibDMPTRRx/phz/8oX7yk5/oX//6l6RzT7hwHnIiIyNDFRUVks6NhDp16tQu0w3D0O7duzVkyBDPr5vh4oMPPtDatWv12muvaeDAgZ7pGRkZ2rZtmzo6OnTkyBEdPnxYaWlpIUz6/6677jp99NFHeu+99/Tee+9pxIgR2rJli4YPHx4Rx3zatGn65JNPJEmHDh2S2+3WlVdeGdbHXDp3/nznzp2SpI8//ljXXHONpPB6nhuGoaVLlyolJUWFhYWe6aF4jXLnrh9/+tOftH37dvXr10+pqal69tln1dDQoCeeeELNzc1KTU3Viy++6PljWCgVFxdr586dOnHihIYNG6b58+dr2rRpWrhwoRwOh5KTk7Vq1SpdccUVMgxDy5YtU01NjQYOHKjnnntON9xwQ1hlLy0tVUdHh6644gpJ0oQJE7Rs2TJJ507/lJeXKzo6WiUlJZoyZUrY5C4oKPDMz8jI0ObNmxUfHx8Rxzw3N1clJSXav3+/+vfvr0WLFik9PV1SeB/za6+9Vs8995zOnDmj2NhYPfPMMxo/fnxYHfNdu3bpoYce0tixYxUVFeV5LGlpaUF/jVL8AGAxnOoBAIuh+AHAYih+ALAYih8ALIbiBwCLiYgPWwf60okTJzR79mxJktPpVFRUlOLj43Xs2DElJiZq+/btAW/rlVde0aBBgzRnzpweZTh69Kgee+wxbd26tUfrAX2B4oflXHnllXrrrbckdS3u78sYuNxR/MB5Ojs79dRTT+nzzz9XUlKSXn31VQ0YMECbNm3Sxo0b5Xa7NXr0aK1cubLL3cSSvC7jdDr1zDPP6MiRI5Kk3//+90pMTPS6L8BsnOMHzlNXV6eHHnpI27Zt05AhQzxDc2RmZqq8vFxvv/22UlJStHnz5ovW9bbM8uXLNWnSJL399tt68803NWbMGJ/7AszGO37gPFdddZVSU1MlSePGjdOxY8ckSQcOHNCqVat06tQptba26rbbbrtoXW/LfPzxx55PsoqOjtaQIUPU3NzsdV+A2Sh+4Dznj7kUHR0tl8slSVq8eLFeffVVXX/99dqyZYtnQLDzBbJMIPsCzMapHiAAra2tGj58uNxut/7xj3/0aJn09HSVlZVJOvc3hFOnTgUlM+ANxQ8EYMGCBSooKNCDDz6olJSUHi2zdOlSffLJJ8rJyVF+fr4OHjwYrNhAtxidEwAshnf8AGAxFD8AWAzFDwAWQ/EDgMVQ/ABgMRQ/AFgMxQ8AFvN/u/iXhtw66jAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matplotlib histogram\n",
    "# plt.hist(df['thalach'], color = 'blue', edgecolor = 'black',\n",
    "#          bins = int(180/5))\n",
    "\n",
    "# seaborn histogram\n",
    "sns.distplot(df['thalach'], hist=True, kde=False, \n",
    "             bins=int(180/5), color = 'red',\n",
    "             hist_kws={'edgecolor':'black'})\n",
    "\n",
    "plt.xlabel('Thalach')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_age = df['age'].max()\n",
    "min_age = df['age'].min()\n",
    "mean_age = df['age'].mean()\n",
    "(77, 29, 54.366336633663366)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77, 29, 54.366336633663366)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_age,min_age, mean_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['age'], hist=True, kde=False, \n",
    "             bins=int(180/5), color = 'red',\n",
    "             hist_kws={'edgecolor':'black'})\n",
    "\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plotter\n",
    "\n",
    "pieLabels= 'Type0','Type1','Type2','Type3'\n",
    "res = df['cp'].value_counts()\n",
    "figureObject, axesObject = plotter.subplots()\n",
    "axesObject.pie(res,\n",
    "        labels=pieLabels,\n",
    "        autopct='%1.2f',\n",
    "        startangle=90)\n",
    "axesObject.axis('equal')\n",
    "plotter.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>index</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>160</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>161</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>342</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>166</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>162</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>163</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>164</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  \\\n",
       "0        0   63    1   3       145   233    1        0      150      0   \n",
       "1        1   37    1   2       130   250    0        1      187      0   \n",
       "2        2   41    0   1       130   204    0        0      172      0   \n",
       "3        3   56    1   1       120   236    0        1      178      0   \n",
       "4        4   57    0   0       120   354    0        1      163      1   \n",
       "..     ...  ...  ...  ..       ...   ...  ...      ...      ...    ...   \n",
       "160    160   56    1   1       120   240    0        1      169      0   \n",
       "161    161   55    0   1       132   342    0        1      166      0   \n",
       "162    162   41    1   1       120   157    0        1      182      0   \n",
       "163    163   38    1   2       138   175    0        1      173      0   \n",
       "164    164   38    1   2       138   175    0        1      173      0   \n",
       "\n",
       "     oldpeak  slope  ca  thal  target  \n",
       "0        2.3      0   0     1       1  \n",
       "1        3.5      0   0     2       1  \n",
       "2        1.4      2   0     2       1  \n",
       "3        0.8      2   0     2       1  \n",
       "4        0.6      2   0     2       1  \n",
       "..       ...    ...  ..   ...     ...  \n",
       "160      0.0      0   0     2       1  \n",
       "161      1.2      2   0     2       1  \n",
       "162      0.0      2   0     2       1  \n",
       "163      0.0      2   4     2       1  \n",
       "164      0.0      2   4     2       1  \n",
       "\n",
       "[165 rows x 15 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_disease = df[df['target'] == 0]\n",
    "non_disease\n",
    "disease_present = df[df['target'] == 1]\n",
    "disease_present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "195"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_disease['thalach'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>index</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>321</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>58</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>118</td>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>65</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>72</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>115</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>215</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>124</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>199</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>125</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>154</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>138</td>\n",
       "      <td>220</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>152</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>157</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>163</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>164</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  \\\n",
       "1        1   37    1   2       130   250    0        1      187      0   \n",
       "44      44   39    1   2       140   321    0        0      182      0   \n",
       "58      58   34    1   3       118   182    0        0      174      0   \n",
       "65      65   35    0   0       138   183    0        1      182      0   \n",
       "72      72   29    1   1       130   204    0        0      202      0   \n",
       "115    115   37    0   2       120   215    0        1      170      0   \n",
       "124    124   39    0   2        94   199    0        1      179      0   \n",
       "125    125   34    0   1       118   210    0        1      192      0   \n",
       "154    154   39    0   2       138   220    0        1      152      0   \n",
       "157    157   35    1   1       122   192    0        1      174      0   \n",
       "163    163   38    1   2       138   175    0        1      173      0   \n",
       "164    164   38    1   2       138   175    0        1      173      0   \n",
       "\n",
       "     oldpeak  slope  ca  thal  target  \n",
       "1        3.5      0   0     2       1  \n",
       "44       0.0      2   0     2       1  \n",
       "58       0.0      2   0     2       1  \n",
       "65       1.4      2   0     2       1  \n",
       "72       0.0      2   0     2       1  \n",
       "115      0.0      2   0     2       1  \n",
       "124      0.0      2   0     2       1  \n",
       "125      0.7      2   0     2       1  \n",
       "154      0.0      1   0     2       1  \n",
       "157      0.0      2   0     2       1  \n",
       "163      0.0      2   4     2       1  \n",
       "164      0.0      2   4     2       1  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#young age people\n",
    "young_age = disease_present[(disease_present.age>=29)&(disease_present.age<40)]\n",
    "middle_age=disease_present[(disease_present.age>=40)&(disease_present.age<55)]\n",
    "old_age=disease_present[(disease_present.age>55)]\n",
    "young_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index       12\n",
       "age         12\n",
       "sex         12\n",
       "cp          12\n",
       "trestbps    12\n",
       "chol        12\n",
       "fbs         12\n",
       "restecg     12\n",
       "thalach     12\n",
       "exang       12\n",
       "oldpeak     12\n",
       "slope       12\n",
       "ca          12\n",
       "thal        12\n",
       "target      12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disease_present[(disease_present.age>=29)&(disease_present.age<40)].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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hhhvw53/+5zj11FMBAB/60Iewfv16AMDZZ5+N1772tbjzzjtx4oknYmRkBP/6r/86XdNlGIZhGIZhGIZ5zqlWq9i2bdukQjCmCmi1Wh1oPBEXkY0lZvny5ZwDyDAMwzAMwzDMXkGRPoC700TTXgSGYRiGYRiGYRiG6Y/rugP1+Xs2prUIDMMwDMMwDMMwDPPcwQKQKUwQBLjllluwbNmy6Z7KlARBgFtvvRVbtmyZ7qkwDMMwDMPMCGq1Gn76059i5cqV0z0VZgiwAGQKs2zZMvzbv/0bPvzhD0/3VKbkoYcewlVXXYWvfOUr0z0VhmEYhmGYGcEdd9yBL3zhC7jm6mumeyrMEGAByBTG9/3pnsKzEgQBALADyDAMwzAMs4e0220AQLPZnOaZMMOABSBTmCiKpnsKz8peVuSWYRiGYRhm6HQ6HQDoajnA7D2wAGQKYy4OZYYFIMMwDMMwzGDMhE1+pjgsAJnCzISLw0wQqQzDMAzDMGWC1097NywAS8zExARqtdp0T2NKZoIAZAeQYRiGYZi9Hd/3sW3bNrLxzBov7vA6am+EBWBJCYIAp59+Ot72treVdhcmP6+yCq2yzothGIZhGIaKj370ozj77LPx+OOPk4xn1ngzYbOfGRwWgCUlX2EzDMNpnMnU5C8KZb1AGAHIQpBhGIZhmL2Vhx56CACwY8cOkvFYAO7dsAAsKfkPXFkdwJkwR65ixTAMwzDM3o5Z51Ctx8war6zrO8YOFoAlJf+BK+uHLz+vsu4QsfPHMAzDMMxsgVoARp1yru8YO1gAlpSZIADzoq+sYarmtWMhyDAMwzDM3g61ACzr+o6xgwVgSZkJAjB/USi7A8ghoAzDMAzD7K2Y9Q7Vesys8cq6vmPsYAFYUmZCeOVMKAJTVvHMMAzDMAxDDecAMnsCC8CSMhMKrLAAZBiGYRiGmX6GVQSmrOs7xg4WgCVlJoSAzgQByLl/DMMwDMPMFqjWjCYENIw4B3BvhAVgSWEBSENZXzum/NTrdfzhD38g66k0DLZu3Yq7774bzWZzuqfCMAzDTCNmw3sYIaC8mb73wQKwpMyECpv5eZV1jiwAmaJ873vfw6c+9Sl87Wtfm+6pTMm1116LT3ziE7jpppumeyoMwzBMCaAuAtP7mNk7YAFYUmZCDuBMqgLKu1fMoDQaja7/y0i9XgdQ7jkyDMMwzx3UDiDAAnBvhAVgSZlpIaBlvTiYOXIbCGZQZkICfDgD5sgwDMM8d1DdD2aCEcEUhwVgSZkJ+XUzITyAnT+mKDNBAEYR92liGIZhMjgElNkTWACWlNnYBzCOY1x00YV462mn4Z577rEeD+BdK6Y4M6EJ7kyYI8MwDDN8TKRTmQXg5z//eVx04UUkYzF2sAAsKTPBeqe+OIRhiMceexz1RgMrVqywHg8o72vHlB9z7pRZXKVlunl3lmEYZlZDXgU0pI9Eu+222/DY449xdFYJYAFYUmZjCOgwdptYADJFMedgEAbTPJOpMTmALAAZhmEYoNwOoIEF4PTDArCkzKQCK72Pi5J/nkFAs+hmAcgUZSa4a2ZufJ4zDMPMXvKCiuqeNUwByPes6YcFYEmZCSGgQRDAEdljW4bRVsK8drzbtHcTxzE+8pF/xvnvOR+bNm0iGTMidtdqtRr+4f/8Az5w4Qfg+z7JmBGxSF21ahXefd65+My/fIZkPIZhmL2B//mf/8E73v4O3HnnndM9lb4MY80YhiGkkADoNuUNvCabflgAlpSZ4gBWVPbYlvwFht4B5IvN3kwURbj//gew9pm1ePrpp0nGpHYAN27ciNWrVuOJx5/Arl27SMakDgFduXIlNqxbj9/c+RuS8RiGYfYG7r//fmzZugUPP/zwdE+lL8NIGwqDEBVZIR3TwAJw+mEBWFJmQg5gEATwpEgfU4xnoHJIjADslPQ1ZGgYys3PVNgkElfDmKNJ0h9G3yeGYZiZSBzH+OEPf4gf/OAHZEKj7G2BhmEaBGGQCkBqI6Ksr+NsQk33BJj+zAQBGIYhKjo6gNwBpC4CU9YwWoaGYeQqhEnxlzCiz6egE2xJoRoix7ys0QYMwzB7ys6dO/G1r30NAPCGN7wB++67r/WYZc8Jz8+LsgroiKxOGp/ZO2AHsKQMY7FITRgE8JItBOocQGoBGLEA3KsZxoaJOafDgN4BpBOpw3MAOUSHYZiZyDA2k020RVmF0FDugWEAL3EAqXMAeVN++mEBWFJmQg5gEAaoKh0CSjHHYeYAxrPoYrNu3Tq8973/Bx/+8IdLe+5QM4xzxzh/QUkrqsVxnIY2U4VMD2MXmbGn2Wzioosvxv/7hS9M91QYpvQMSwwB5V2PDWeDMUSVcwD3WlgAlpQZEQKacwCpBSBZlcTktZtNi9mnnnoKTz21CsuWLSMrNlJ2huMAhqTjUQvATqeT3kRnQtlvpjibNm3CY8uX42c//el0T4VhSs8woy3Kel0cxnOOoijNAWQHcO+DBWBJmREhoGGISlIEhkKw5cegdgCjTjlfw2EwGxfxw3jO1C0WqG/QXc+ZKE9xJkQezEZ4scQwe85wCm7RbghSQ71mjOMYYRSiIj0A9AKwrK/jbIIFYEmZCYv4IAjgSkCAtghM1R1CFdBZtIAaRjhk2RnG58WEflLlAFLnpeTHoPq8DCN3hrGHF0vM3k6j0SDtX2egCwGlLbhFzbA2GKtDKgLDIaDTDwvAkjITQkCDIIDrAFIKkouDWcRWFeD7bevxgHwbiNkjAGfC5gE1Q8kBTHI+Op0OycIk/15Qt00ZRghoWRc6s5FhbGAtX74cN954IxqNBvnYzN5No9HA5s2bycb77ne/i9NOOw2f/vSnScYbSghoQJ8DuHnzZtTrdZKxqO/76Ya8qnZ9TcVs2pQvK9wGoqTMhEV8GIaQDqAcQbqgrXoCAXEOYFlF9DBgB5DKsese0/M8q/GGdYPufWzDTLjuzEaGcf36zGc+gy1btmDOnDk44YQTyMdn9l4uvPAiPPPM0/jOd76DxYsXW4+3fv16AMC6deutxwKGuzlGda3dunUrzjnnHOy//wH49rf/P+vxqF3PYTuALACnH3YAS8pMWIhpASjIBKBxAEc4BNQKFoCEOYA6xbWUjl06hqB7nzkEtJwMQwBO1GoA6K61zOzhmWeeBgCMj4+TjBcQu2vDqQJKmxJgXruNGzeQjEftepr3hIvA7L2wACwpMyIENAyhHEA6NBecdluHfY64ggWgBbNRAA7jOQdBCMfVj6lz9kgFpecMpVXFbDl3ZgLDuAckexss9Pdytm3bhv/n0kvx3//93+RjU1dIHkYkA/WYVONRf6aHFWEyokbIxswzm9ZkZYUFYEkJwxCOoOuxR02n00EUdaAcQDm0C1pKB3A2toGYCQKw0+lg69atZPlH1M85iiLEcQzp0o1JPcd0DNdJK9SRjYnynjvDYHx8HDt37pzuaUxJfrFIVTzBjFLWDUaGhqeeegp/evBBfPOb3yQfm67lTtD1vy3DKAITpnOkudZSr0nMc3aETJvW25Cm5CQhoNSRArNpTVZWWACWlCiK4EoJR9AUWKHGzIlSABoHsOrRO4DRLLrYzIRF/Ne//nW8/e1vx9vffnb6vtswLHHlzAgBSBOC3TUmynvuUPPII4/gjDPOwJlnnok//vGP0z2dvuQXsWQLJ+Iekkw5GabApzoX0xDQkl7HoihCJ+4k49FuTlNhxqvICmnESkV6EKC7xxhYAE4/LABLShAEkI6AdJxS3qDNxcCEgFLlAEpHwJOA7wckO92z0QEcRj9FajZt2gQAaDSaqCW5SDYMTQB69GOSj+c5Q2lVUdZzhxpzLgIgrWxIyTBSAszVtYz3F4aOYQpAagfQL2kxq2H2KKbCzMsjEoDZGk9BSUUyZv45c+TB9MMCsKREUQTlOFBOOR3A7OIgSB1AVwm4EujEMcnznu1VQMta4CE/rzI6gGZ+coYIwDiOSXtx9j7em8mffxTn4jAYRkhbv7H3dqIowtNPPz2r7gfDfK5U54653pY1kmEY18VhOYCe9MgFoOu4JGPmN/W5D+D0wwKwpJgKm9JxSnmzMhcH6QBKxHQCUGoBCNCIl6wIzOy52Pi+jyR9lGxBu2PHDqxevZrs5ke96E4dOzWcEFCKc9H3fQglusa3IS8Azfi2+L6vP9SYPQIw/zxnggCkWnR3kvvKbBKA1113HS644AJ897vfJRtzx44dJFEMw2ImhIC22/raFUVhqXuuKukhCMspANOqnYrYARQKypFkobSG2RSVVVZYAJaUMAyhBF2LBWryIaDKAUnfPt/34UrAlXqR3Gq1rMfMh4DOlh0n3/fheXRCo9Vq4Z3vfCf+4R/+AV/60pesxwOAdjt7bymES3o+EuXDpRscxA6g4+r3hfI5C49OsAVBAHh6B6as7vFNN92E973//bj6mmtIxstfZ6ie84033ohPfepT+NWvfkUy3jAaW5sxqe4vjz/+OM6/4IKhFBuhwhT6oSr4EwQBzjrrLLzrXe8iGW8YzIwQUNoQy2E5gBV3ZCiFaijIHMAKQsIiMK6joByX5HlzCGi5YAFYUlIHUMyMHECfIDFaO4AYigPY+3hvxvd9VLzssS31ej0dZ/v27dbjAfq9lip7bIuZn/JKngOoAKFo+2aCUAC2/XYqAMu48QQAv/nNb7DiySdx6623koxnXkfhKjIH8Lof/xh/+MMf8LOf/YxkPOoQ0CiK0ush1f3lsccew9qnn8Ztv/wlyXjDgDolwJw7ZXYAh7l+oHsdadMWhiUAq+4chGFIWp+AinzfPopiOtkaz4VyFMn7wg5guWABWFK0AHQgS5oDmFUB1TmAFBecVqsFV8ZwVfa1LTOhnyI1vu/DJQxdHEaOVLPVhKerS5O8z5ljF9OFQgKQHp1j5/s+hBRkAjALAaVz7Hw/ACrlFoDmHOxEEclnutVqQUgJx3NJzkUAaCfvRZvIUaQOaRtGSGkZ71O9RMRhrzPhnjLM94U6B7D3cVGoPy9mTlVvDtmY1K1dzHP2ZAVhRJwDKOiLwLAAnH5YAJaUIAjSENAy3lhTx0WCLEzV99twnTh1ACnExmy84LTbbSgVw3FoXsP8GM1m03o8QC+6K9XJ4xclFWxud3hpUYYVAgoZQ0ja54wKnQPoBz6Ep3dgKOZYq9Vw2WWX4eIPfhC//e1vrccDgFZuXhSCrd1uQ7gKUHQOoAmJpxqPeiNrGEUtzH2KMtT+K1/5Ci6//HKy607abmAWCUDqOebfXzoB2IaXiCvKDTyAtgpoxdNN0Sk+19SuvhmvSpwDqENAFYeA7oWwACwpYRgm7lr5Q0CpqoA2m014UsBLcgBJBOAsDDlot1uQElDKIbmZmkW24wCtNs1CrN1qkzqAvu9DKgFH0i4gJGEorS6wogUgaQhohbgIDGEI6KpVq3Dvvfdi+aOP4ne/+531eADQIs4fbbVaEEoiVpLMAfSTaxeVAKR27IZRKdiMSZlp/ZOf/AR33XUXnnnmGZLxqENAZ8I9hdrJH0ZUje/7qFZGAdAVBRNCQEma3LV8CGj+axuoP9NdDiBpFVAXSrAA3BthAVhSwjCEFIAS5SwCkzqAjnYBSUINWy24CqQO4GwMAW2321ASkJJGXJkxKlWg1bQfL45jtNv0AtCR9AKQMgew3W4jljFAVKk0zV0jzAH02z5QUV3j25B/b6nEVavVAhy6QlGtVgtwFZkAjOMsDHkYIaDUApA6BLQT0YiivNNEde02c5ytDiBlqGHv46JESSh3paoFIFUOoJIupKTJXesNAaXOh6N0ACuKNgTUdRRc4ZbyOTN2TKsA/OhHP4q//uu/xsknn9z3+3fffTeOOuoonHrqqTj11FPJKhDOBMIggHIcSKecH5Ts4iDgEjmAOgcwE4A0OYDD659VVlqtFqSOaCNdxFerdLuzUdRBRUfTkIR3aQEIODIpZGJJGgJa0V+ThNL6bUACIAoB7W0DQTOmr3d1HEEyXvoZpnbXql73+BY0m03ESgGuQoPgXMwXiSALKSUWbMMMAQ2JyuQPM09xGHmPZXUD8+8vVQEhyvHMZ6RC7ABK6eq2DZQ5gO7crq9tGKYDGEWRtdg3z9FNisAEvv3ryEVgyoWazj/+tre9Df/FhRkAACAASURBVOeccw7++Z//ecqfWbp0Kb761a8+h7MqB0EYouoIxLFTSgcw64tjcgDtL2DtdhveKOAp+jYQvY/3Zny/jbFR7QBSLuIrVWDndlpHESAUgMoIwHI2gm+324AC4oCuUI2QSQw2iPIU/QBQDoSStA7gHI8sj6vdagML5gKNNqEAlIBSaDQa9vMznzkhSNrjAPRtIIYhAM041G5d72OKMYfRzDuKIjhO+YKqeguiKGW37KN+X8znpVqlE4C+70MpD46gWT+lc6yUWwBKIeEmzWvDMIRrqsEVIF8ERjkKzcD+WjsbazKUmWm9Wh199NGYP3/+dE6htERhACkcLa5K2I9rUg4gwQWs1W7DU4LWAZyFF5x229fCXMZkC2QAqI4ArVbb+nU04ylX9+0jdQCJXM9UAFa6v7Ybsw0hASgaARgEgW4sL2kqlZpQLKEcCEWzcDLvrZhTIXPXoiiCGNFvDMX5XW80oGPPFcm5mC5gqxX4bfvPCzC8xtZU4+XHoR6PckzzOpZZpFJDvXlA/ZxTd60yBoCuKrR0FJSkCV0059+INxwBSFXYSToS0lHp17bjAboRvDuENhCzZUO+zJRvu6qHBx98EG95y1twwQUX4Mknn5zu6Txn+H6QFYEhiOempksASi2ubD7QnU4H7bZuBO+RtoGYfSGg7bYPpQApY/gE4ZDGFRkZMePbjWnGUy7gkgrAOMkBpHMAHRcQgqqdhq9jLlR3IZPi47V1E05FIwDT35cOhHRIduLT93aOh3qjTjgenQBsJAJQEAlAMycxoi1u6r5mlItugD5MNSLqk0YtevPj+ETjzQRHg7oi5vAcQC0AqTbwlPIgiYrAmDkaAUhdBZSqqIyS2q3rHb8IQRBAOQpCCLjcCH6vZFpDQJ+NQw89FHfccQfmzp2LO++8E+9///uftflvu93G8uXLn6MZDo9mswE1ogB00KjVS/ecTFU20wcQAB566CFUKpVC45kLqpcrArN27Vrr550vWvL4449jx44dVuOVHePiaAcQ2Lljp/VraN7raiIA//SnP1k596tWrQKgWzZIF9i4caP1HLdv3w4hY8ikwMqjjz4KIUTh8dauXQtAh5QKJbBhwwbrOTabDYhEAI5PjFuPt2XLFu3+JQ7g6tWrrcZMm1krB7EU2Lp1q/Ucn376aUAIiDkV1DfYj7d9+3YAgJhTRQxgxYoVWLBggdWYExMTwPx9AU+h2WhYz9GcO0gE4LJlyzA2NmY15pYtW9LHq1atwujoqNV4K1eu1A8qFYzXaiT3l61bt6aPH374YetQQ/NeA8CaNWuwcOFCq/EAYNeuXQD0e07xnNesWZM+fvTRR63fl2GQP3eWL1+OffbZh2w8imt3dn/RnxHb6xigzx0pFBxHYvv27WRzHElcypUrV6JarVqNuWHDhvTxE088gfHxcavxtmzZAikyAfjoo49a3ac3btyYjqUcF82JpvXruH79+vTxM888U7p17Wyj1AIwfzF97Wtfi8svvxzbt2/f7Y2gUqngFa94xXMxvaEikh6AMRyIWJTuOT366KMAtMhwk4p8Bx98MObNm1doPCPMPCngCAFXCYyOjlo/b0dKXUinAxx00EF40YteZDVe2anXtcsilf4XI7Z+DX/7299CKQHX07v6z3/+8/HCF77Qeo7KBaSK4bqu9RyVkmkRGAA45JBDCm9GAMB9990HABASkEpgbGzMeo5R1NFX3GSOtuONjIwArkgdwEWLFlmNuXnzZv0gsfUprqV33HEHnGRXx/d9vPzlL7cS5qtXr9YPkhDQffbZx3qOQRBAeC7guoiiCIcccgg8zys8ntnlFiNapL7whS/EAQccYDXH/L1w//33t37OExMT+oHnQcD+XASS8zHh4IMPxty5c63GS4U0gP32249kjirJiZJSkoyXdzQOPvhgLFq0yHpMavLnzp/92Z/h+c9/vtV4c+bMSR8vWLCA5PMHZA4gxWfa87T7Jx1Fcn954IEHAGQOIMX5eM8996SPX/SiF+GQQw6xGm90dBTKkaloe/GLX4z999+/8HhjY2Nwpf68uI5CHNuvJfI5iRTXMebZ2Z3ILnUI6JYtW9JQkmXLlqHT6VjvXs0UwiCEcpykEXz5rPLeHMD8sSKYsCkT/llRgqwIjJnfbAg5MK+ZSqqAUjRFbzQacD0Bc+22DZPLh4AqN86cJwta7TakygSgbYiO7/sQDiAc2tYSQgJCZX3ibMfLO4C2c0xfM6kdQIowp0ajocWVp9CJIus5pjmFc6vp+DZEUaTfi2SOFGOmn49EEFHlNPV7XJSshUiFvA9gfnyq8coaAjoTcpqG2UKEqvo3AIxU53d9bYPv+1BSi0CKlIB2uw0hRNoIvqxFYJTjQgp9HbM9H4MgSAvKuI6LgKC670z4vMwmptUB/NCHPoR77rkHO3bswGte8xpceOGF6Qfh7LPPxi233ILvf//7kFKiWq3i6quvtto9nkkEoRaAMYAwpMnRoKQ3BzB/rAi9AtAlEoCdJByyHZYzR8P3fXzzm99EvV7HvHnzcP7550NKWXi8XgHYatEs4pVCKgCNg2czHmAEINAgyA3z2204ni4CA9CIISfZORAEhWWiKNL90ZQgKwKj+woCQtE0gk9/X+m8QgoBWK/XEXtZYm+9XrdyZlNxlghAqs0InQPopmPahJVmhW9o5ggk11YpgSgiFYCoePAJKp92jYnyFqoxwo+6qAzlmKtWrcItt9yCc8891zrMEKB/HanzUdOq0JW5EILmvq8LoY3AcRTabYL7i+/DVR7cpCw0aRsf0Alz6WQhoLbvte/7uRBQmkbwLADLxbQKwKuvvnq33z/nnHNwzjnnPEezKRd6N0cghkBI0GKBGt/3IR0drknqACbaxyNqYq5Lc2ePy8aKFSvw4x//GK4rEAQxTjzxRLz4xS8uPF4qAJMcQKpFvOvGUEQC0Py+6wHKA+rj9jfodrsNOUeHvZqvbQiCIBWTguB1NL9vcgD9No2jGEsB4WRf25A+x8TWb1EJQFfqMFDoPEObXC4j2ISn4HiutVtnzkXtUrpdx4qSFarRbgFFa4kwDCE8F3EzIi28IbwKfKK86Pz5Qr1AJnMp/eEJQKr7y3/8x3/gvvvuw3HHHYfDDjvMejxqYZ5/zpStYpRbgetWyRxAT86H40jUKHq4tttwVQVuUhaa2gGkcsyVIyGTMBiKIjCuyARgGIXodDpWrU5YAJaLUoeAzmbCMIRMqoBStFigJggCqCT0TCU5gDY3F7NoMj0APRmT7Jx3Op3UoSzjBcfk4ix5RffXRZkcAupbO5+1Wg3KjVMHkGrRbVzFOlHvNSkzB5BCsJlwUiHtHbtMXAFQOqzb9nxstduAFBBCQLgO2RxFKgDtF2ITtRpQUfof6NxjeC4EoQAEoQBMRWqSK0XmACYfQFoHsELWZij/maNsxQLQh4CGQxCAVG0gTDEQqogVageQWvSae5arqmQCsN1uQykXSnlkIaCu9OAq7QBSh3XT9QGkcwBNFVAAaSio7ZgzoWrubIIFYAmJ4xhhFGU5gCVtA2GcP0XgQKRhIMkC3pMxWgQLp6jTSedXxguOEXzzkjx920pgaY+9RAAC9mKoVpuA6wKmLgaFA+h6AsIRUB7QbDSt35t8I3iAKAcwJwApHUAzrr1ga6UFYISyb9vQ5QBKh8SlnKhN6PDPRFzZ5num556rAE+RudFGUHYdK0gqAOdqB7CMAjA9HyuVtHKw9Zi+DxMmQNXMu99jG6h7FQ4jBNRAtWEZBIHuZYNy5mamAtCtQKkKaQ6gki78gGYzwpUepKMgIEr5OoZhCCXo2kD4vg9XZDmA5pgN7ACWCxaAJSTLr0scwICmrxIlWgAaB1Afs7k4ZA6g/rqiBJpNe2dIt0TQ8yxjo15TlnxBUjzVVgBmN9NMANreULVgA2kIqOvp98T19IaHzSI5jmP4vg7ZpAoB1TmA+rEOAbV7DbscwOR1tH1f2r6vG8EDWrARh4CShA/XahAVF6KShYBajZcTbB1XkY0nKh5QoRGpzWYTQkqgSlOoBkiurUNxAOl6FfrtNkSVcLyhOIB6zDAISO6p1C5OHqoNS9/3oTztRpcxNzOLWvHguhWavrDtNqR0oaRH0gu31WpBKU/3xHM98hBnqs+0clRaBIbSATT/swDcu2ABWELMjUQ5DqQop3jRO2L6sUtQBCYVgIlY8xTNwimKshDQMjqA4+PjEILOAcyHgLqJgLG9odbrdXge4DgCrutYL5B1SKl+TCEqg2QxJxVdCKjv+4CjF4iOgnU+XJcDSDnHtFCNfdGWLgHoSuvxOp0Omo2mFlZE4qpWq8HxXAhHAJ5CjdABpHQpdVsJmqqiQFK8xKVZ2AHm/HbSwjdURYmMoKRYIOfHoG5WH8cxeThkeQVgMBQBSNVkvdlswnUrEMKBUkQhoEkjeKVcsnNbOToExiVyKYMgSIvKkJyLQUheBCatAkokKlkAlgsWgCUkLwBVknBbNgHYHQJqnwM4KQRU0cTZd3IhoGW84OzcuRMjVQeuApQSqSNYlK4cQIK2DWEYot324Sbhn16FZhGvXL24cSvZsaKYxaEjdaHE/DGbMYXSAlBIWO8id+cA6oe2C5PAtIEAECv7sKReBzDwfSuXpF6v69+vKKCqT0bbHNe0rQQAVFydY2hBOp8KbQ6g8FwIKSGktB4PMIJNQiiaanza4c7CBKhCNgWhAKQOAdWVeCPA03MsoxuWh9IBlBUdjkzpXClvhMwBdF39nihVsd4w0eLehIB6JCHOughMIgClRxYCWlF056IuHEgXAhr4AVyTAyhpog/y70MZN+RnGywAS0h3CGiJHcDk7HEJcpqMSHFzIaC2ArDT6SCO41IXgdECEBBCYE5VYOfOnVbj5V9H81ravI5mgWzy/1zXfhFfq01AmfE8c8xeAFI6gO12K80ndAiqdqYOoJs5gLbnt3YAEwFI0KswE4ASQjnWLolxs0XVhZAOhCutz52JiQnEyS6R8FzU63QhpcJx4HguiQMYJ86aU/FIBGAQBKmgpHIAhSkVDEIBWKXrk5avVEopKIVHF6Y6DAFoNl3IBGBAGwKablC7VZLxGo1GKgBdt4pm0+66aObkKi0AAYL7QaudVgB1JU0IaBiGqLi0mxHKUZBUAjAIoBIHUA3BASzbmnY2Mq1tIJj+pCEWQiBOHEDq3UVbgiCAFPpGRdEGotlswlW6rQSQtIFIKlgWLTtsLjYy+XWKC87jjz+ORx55BABw0EEHYcmSJVbj7dixHVWvA8BBtRJjh2VJ9n5FYGwcQLMYzgRgh2QR7yU5j0YA2oyZOoC5HEDr/Lp2OxVqQiYFLixI5yORXnVtFhGdTgdhEKY5gDFBq4p0jokDaI555s0fkDScOXH/nKpnHeJcq9UQ5xzARk27jEX7w05MTCQhpUkobcWzPr+1ADShDC6ZAMScEQgp6cSVyj4wtmOGYYhOFEEmOYBUhTwAwKmOkOYUikoVca28AtBAtWHp+wHUPDoBaF431x0hqbCZdwBdt4qJCbuUBXMdVNKD42QbHHOSqryF5thuY05ys1LKIzu/q0pvmFAJcykkXRVQP4CStFVAWQCWCxaAJcR8MFzpAOh0HSsL/aqA2oaAVlQm9LzcInlkZKTQmGYHlTIH8Oqr/w2rVq0GAIyNzsV1P/6J1Xjbtm3DPnP14znVGNu2bbEaTyerCwgBuEkIo40ATB3AJFTTqwATE3aL+IlaDfs/Tz+mcADNzZjUAfTbcJLn7ChYl8rPO4CIuo8VIV28yuxDaNu2od1ua6fOEYgJmst3hVcCiCuutbgan5jIVYpydZ5hs1l4cTcxMaELwCTEBA7gRK2W7pjERALQDwL9XhM5gO12G5AKgigEND2/q3ShhukYlSqtA1ihcylnQg5g4PuYW9GfjzKGgDYaDSiVdwDtBKB5X/MC0Do6otWCO2IcQBpHOggCVFw6ARgEAVTVhUqes7UADAO4yWYblajkENBywSGgJSQLAc1yAEvnAPo+aQ5gs9lM8/8AHQJqjhfFXGwoQ0C3bt2KQw8SOOZQgYla3epGEMdxGgIK6L7R27dtt5pfo9GA5ybVWQlyAM1iOM0B9IBxCwEYRRFazVY6nskBpHIA6UJA/S4H0Pftqgb2ywG02UVOFzRpFVCaIjDCfFgIQgPTENCRRAxVFHZa5rhO1CZSQSkICst0OYqgEam6CEzynF3XulANkPStkxKQDpkAjKVMwwRsHQ3z+0YAUjkkQinEyqXNKSSsfDpMAUjmABKHgKYhlt6I3piwRBeByQRgq9W0utZmefC6DyBAs8GRzwFstQgEoB+QO4A6B9BNv7ZBt4HorgLKRWD2LlgAlpCsCEyZcwDbqbCiaAPRaDTSjX0g2+SnEICS6DUMggATEzXMmyswb44ec/v24oJtYmICQRBi7ogea86IwPhEzTqXMq2wSSA0zCLeyxeBmagVvkGngjIRfsrVLapIisAoXanUcewXoEHgp2LSUfYbHP2qgFIKQKGEdZiqFoDdtr7NHNOCRkkIKEZc6xxX3VYiORkr9oVldE5hJgBR8bTLaEG9Xk8LysDztGi1JEgFII0D2Gq1EOfixMkqyOpywWTNvIVyAeWiSTQeAIghuGEAXa9CA4VDEscxAj+Ak1TZpAx7dd0R68gIAGg0ugVgFEVW53jqAKpKWmWTIiLEVYkDqCpotWh6e7qJS0kWAurItA0ETQ5gdwgot4HYu2ABWELMB1cKUV4HMAjgmj6AiRC0ueA0m024TiYqKAUgVQjo1q1bAQBjI8BYEnG2ZUvxkE0z3txkrKRvtJWobLVa6fM1aUg2VdWMAKzkQkCDICx8Q+0NKRVCwKs4Vov4fAgoAEjXvoed7wdZERiCIkfpgtgFSRXQzFEU6f8+RQ6g2y0AbV7HXbt2aXWffJhF1cP4eHEHMAxDtJqtTFAmQtDm3Nk1Pp6OY8a0GS+KIjQbjXRMUfFQrxE5gI6D2KHJAWyZHEBqB1C5cIgcu1arBaEUhFSkIaWUYarD6FVooFggR1GEOO7AUS4k0fsSBAEcqeAomjYQugiMfk+85H+be1Y+B9A4gDafGSNIPZkLASVwAHWzehfKoansS54DGAZZG4gh5AByCOj0wwKwhKQ7bNIhCa8cBn5XCKj+3yoHsNlERWUCkDQENJkflQAcnQOMJQ7gMASgOV4EnU+hn6eUev1t8xqaPoX5EFBzvAhmce3m1tyuZ7eInyQAFW2BFYq+fe12GxAAHJrx0nymXA4gRRXQOO0rSCMAnZFKVqCl6qJRbxTeKMpyChNxRdBaolardQlAUfGs3GizcBWpZW6fAxhFETqdjg6HJAoBTcWVoikCk21wuBAujdDQDqAHuC5JyF2+CEz+axuCINAuJcopANMKydIjE2y+70NKF5JIuPSGgJpjRTHP2c0JQOtrN5CFgCrPui8skFXZVET9FNPxkh1Lmw15c83pbQRPJQAd4bADWAJYAJaQrAqoAzkD+gA6QkA6tjmADXgqq+Q3DAfQ9oKzadMmAMC8OSJ1ADdv3lx4PPO7o8lYo0kxGBtR2Ww20tBPIXTjdtsQUK/ipIt4zzJnLxWAleyY8mKaHMBc2waK8Mr8ePnjRefouEK/jomBZTPHSQ6gpOkDGCd9BY0TaB0COpILr0xyAW03D0zunxFuRceL43hSERhUPYRBUPh5p+IxdQArCHzf6r3JCv7oHEDbUF8AWlARhoBmFWR1A1KKENBWq4VYSgjlWhc4AoYXAup4dEVl8lAskNNrmXLhKLr+dTJxem0FQRzHutJwKgD1a0khABVRG4i8owjoENA2USN4JRVcAgEYxzHCSOcAOsJeAKYmRBoCSisAXemyACwBLABLSD4H0C2pAxgEIXJFO6Ecu53pZrPZlQNYIRSAxiShEoBjcwBXCcwdcbBx48bC423ZsgVCAHOSIjCjBGGljUYjDf0EdBioTTjNxMREGv4JZKGgRRfdaU5hbky3EltVFk13uRNd4Fg6gKlTkCsCk/87RWi1WhBJcR44AARVFdAsBDSwLFTTarUyuzzZNbFZyO/cuRNxNROAphjMroKFYNJNgqrX9X/RzYNWq4UoDLPxgFQM2m5wpKLSsy9Uk15XpQM4kshda6ViDSAMAXU9QCk6AZjkANqGNwPDqQLq+75+zkKU0gHMNrM8ONKlE4COgpQugsDeOY7jTuYAevYOYNqmQlVIHEBzLnsq6wNI8jqGAaSjQ0Ap2rAAgHQUhBDWYaVZIcLuKqBUOYCuo0pnasxGWACWkO4Q0HI6gGEYpM4aoNeLVgKw1eqqAmrcQJuFRG8bCNsb6oYNGzA6x4GbzG3enBgbNmwoPN6mTZswNteBk4h8zxWoVpxUaBYhXwQG0Os7WxfH9bLQWSPcbBfxvSGgRccDuovAAIAjY5IdX8ocQB3OloSUCgHHtava2c8BBOw/g71x3TZz3L5zR48DqB8XLQSTbjoYd01JCCUJHMXuHMCuvzUgabhnWqhG/08jAOmKwOjzUekxQesAxkpZl/JPx0wEYLvVstrcAPI5gMRFYKTOUyy1AFQuHKIG5r7vw5EunMTFsUmtMOeJ69HlAKZVQKVL6gBmRWA8hFFoX2TF10VWKEJAM9NA3wSlpcDKBGDiAAraHEB2AMsBC8ASki8CQ1XBkhrtAGYhm8qxC0HTfQBzIaDJopsmBFR0fV2U9evXYf7cbBEyf1QfK8qmTZswd073zXN0DqwFYN4BVCq2upnu2rUT+T7gtg5gPwGoewva5wBmgi0m2UFOHUCCENBWqwXI7NwRShBVAe0WbFZzbE8WgFabBzt3pa4fkDmAtgJQ5FxFp1qxduvQEwLa9T3LMUXygbERgPkQUKEkSel93/cBpSAcB0Lau4r5IjBUIaDNVgtCuRDK1dUsLZ93+hwJi8AYAUiVXwcgDbenuOdnDqALQegAOtKFlPZhgebeZEI/XQIBmFUB9VLRRhMCmhREkfZjRlGETtyBki6kY3/u9Ao2RSQAh9UGwnNYAJYBFoAlJNvNKWcfQB1vHqWhlYBeLxado66yFcLNOYplDAFdt3YtFoxmXy8YBbZs2Vr4prpx44Y0l9AwOreDDRvWFxpP51O00xxAAFDSVgDuMm2zAGQOoI0A9Cq62bjBrehS4EXfn3a7DalEunDSIaD0OYC2i4jYzQlASwdwUh/A5H/rhY7b3dul6Lnt+76uhpkTgCASgPmQzbjiWocjo5rFIwtLB9AIvbQIDEGl0vS66kjAkdal98MwRCeK0vw/4doLtvQ67bqA66JB4AA2m610PMA+THUYOYC+7+v3RdqH8RmM00kbAuqShYBqB1DBkfYVNs29yesJASWrAirt2xeY8bycA5g/XoRUpDouXMf+fclMA5mMa3c+pnUohlQERjmKBWAJYAFYQrIPn4Ak3A2kItttyo7ZFIExi4d8CKh0AEfQhoDahKrU63Xs2LkL+4xlwmXhmEAcx1i3bnAXMAgCbN++A2Oj3cfnzdXFYYqEOwVBgE6nY9ZLAHQOYNGeRbpIRq3LAXQc3bahaMimLirTfcy2GbwRgAapYFU0It+zDyBqA9HudgChaMJU8zmAAMFCh8gBNCIv7wCiops+7tixo9CY4+Pj+qKQs7jjimsdjtwvBNTeAax0jW0jADNHWgLKPgQ0Cx92k3Ht2yxkIaDasaPo26cdaTetsmkbVpoJwCoAuw0Yg+/7iIlDQM1VgrIKqKM8OITVK6kcwDQENG0DQVcFlMoBNOd2vg+g7ZhZmg9NNdXJIaDS6vzpLQIjCQrLAPqc1r0KuQpoGWABWEKyXZLMASzTh6WfALRxAM3FPl8FVAgBz3VI20DYvIZr164FACwcy44ZMfjMM88MPJ4RefPmiq7j80YF2m2/kEtiXqsuB9AtvpvaarUQBEGXAwjota2N66K8bnFr6yq22+3UpQPoq4CaIjBWbU5arbT/HwDEKiZtBG+EoFW1yXY7tcuFEBBKWgvAvAMohICcU7FyAJ1qrq0EAFRd3cuv4Hh6jHwIaKX7ewNSq9UgHCfbdUqEII0DqIvA2C4Ws3DN5IQkEoAmnBSuS1IlsZ2EgBqhat/b04TSKjgukRsWBBDSBQgFYKejr4/UIaBU/Rnbbd0GwiFw17IQUH2TUa69A5i6a0mTdceyd+akKqDJ/xTXb90H0IXftjsX+4WAUhaBEULAJQhVjaIIjnDgCMF9AEsAC8ASkn34RCn7AOarlBqkKH7DygRg93FPCdIQUJsLzpo1awAAi+bnHMB5urXb008/PfB469frMM95Y93HjSNovj8I6W4qURXQ3ibwBs+LCy/id+3aBbfSXwDaVHOUefdYweqGmvXY019ThIDqvmu5A5aFanqrgNr27YvjWL9muV0dYdFbMHX58g4ggHjEs3IARbV7PFH1sKtgc/neojKAKSyjrBzALpFKUAW0qw0EqQOYCUCKKqDCNcV57ENK9fnYBlwvHZdkjspNNjdo8hTbvq/fF4JKjgZzHyV3AKWLtqXQABIH0HEhpX1hkKwIjBZ+prqorQOokvcZSPr2EbTcyVcBzR8vQl6w0VYBlcn/djmA5nfd3M6qJNh8iqIIUjhwhJ1DydDAArCElL0ITBaimh1TTvEk/bTMsuw+XpF2N/206WgSTmrzGq5evRrSEZg/NzvmKoH5ozIVh4NgqofO6wkBnU8hAPNVQC1aIpjQukkCsBJj166CLs7ErilDQK0cwNy54yhY9UrLKufprwVVCGheAFqGqU4qAmNZBTQVvW5eABYvDmJEnpjT/WbHIy62FxSAu8bH0am43QerHuoTtUKbOxMTExCu0qGVOawLy+QFpePA8TwaB1BKwHEQhaFVRcxeARhLIgfQXHhc3bbBZsPN933EcazHJMwBdMwcidywwKd3AM19itQBTEJAaVxPkwNoLwCzHMCR9JjnVa2LeBm30ghuPAAAIABJREFUDtDOnXUBL2TCjzQHMMlTtC3s1JsDKIWkqQKa27Wk6FfIIaDlggVgCelXBKaMApAqBDTrs9MdDulaCkCzAHGEzl2zWZCsWrUKi+aLtGWDYeG8DlY9tXLg8datWwdXibQHoGEsEZhF2kv0CwF1XR2yU+S5pwKwTwho0byr2kStqwk8YO8AttttCJk9P+MAFl0k9zqAFAJQl93PvhaKptdVegW3zAHMcgpzH2rXKfz5m8oBxIiH7Tu2Fxpz165d3eGaAFD1EMdx1n5hAIxb14tNYZmJiQl0Kr0uZYWsDYRI2jZYhyMDmQBUyjo3rNlsZjtPrn3IZvrZyIWAkuQASpP3SBUC6gNSISYsAhMmC2P6IjBE/ev8ADKXA0girtzsJuO6I9ZFYIxIAwBlKfbzOYUAbQ6gdgDp2kCkRVsEbRsIPaadqAQyB1CCHcAywAKwhGQf5iwEtEwCMBOo2THlAKG1AOw+7skOSQioEFoE2gnAlVg0b7KgeN58YO269QPfBNevX495o6I7nwmAlAJjo06hwjJ9cwCVDqcqcrMyIq/XsfOSHMBBBVYURWg0ml0tIAAKB7DVnQNombM3jCqgOrwyd0ABbd9uB1koJzt/pF0V0EwUZG5YLIu3dtm5c6d219xud02MeNi1c2chcb5rfLIANCGhRc6d8fFxxJ476bhVZdGJCXRVTQIQey5JERjTB7DrmMV4JgdQEIWApkVlXHvBlm8rYYrAUDiAZiwK1xMAAt+HSIrAUIRXAkA0FAdQ5wDaVpAFdN4jtQPYLQDtHMB2u03qAKaN5ZP2DxS9BfNVQJW0f18mFYGhcgDzAtBxSQSgIxw4jlOqNe1shQVgCQnDEI4QcISAFOVrA5EXqAbpJLuhBZgqBFQ7gPYC0DiARS84u3btwvbtO7HvAjHpe/su0M7ioHmA69atxbzR/oJ03tyOlQDsDQEFii2epsoBrFR1H8hBxzSL4F5BqXRxyMKL5GZPDqARbkUXjJP6ABIUgdGCLftaKKDdsttBFl07MHYhoFlYYDZmLIs7gDt37oQzZ7K7hjkewiAceIc/jmPUJmpdPQABWDVuHx8fR9zj1pkxbQrLiJ4PTFzxtDAsiHlPhZSpQ2tzLk7KASQQQ81WC3GaU2gv2NLfdb20aShJGwiVhYBS5AAGQahdRakK3/96GUYIqExyAAOCOQaBcQBpcgBdtwIhsuuOqwhCQHMOoJR2DmCr1dIFrJIbC0UIaCqwkiIwZA6gyHIAbcbsHQ8AFEEOYKfTSUNAOxEXgZluWACWkDAMIZPQT6OxymSX9w0BFdnO5aBMFQLqKYGWxY0gHwIqLBzAp556CgCw74LJ39tvHz3nFStW7PF4URRh48aNmD/W//vzx7RAHBTzOnaFgFr0U9y1axeEwCTHrmgz+LQJfI8uEEK3ligcAtpqdeUASkvHzpzfaRVQIeBYuGFRFOmbXf78VvYujsiPZ1kFdFJOIQCo4qXyd+zYgU6vWEPxZvCNRkP3rusTAgoUE4C7JsYnjwcAleI5e7VarbuxPAB4XmFHEciHgDqpA0gZAgqKENBGI9dWwl4AmuuVcF0SRxFIenHKTPRSCMAwaQSvm6yXLwcwbX3h6Gb1nU7HetzA95OQUvsQUC0Au3MMXM8+BNSIU8DeATQhpWlRGYJG8F0hoAT5o5OKwAiJKCy+ZsyKwOReR8uwUiBfBZRzAMsAC8ASEoZhGvophIAqmV3eLwRUOsI6B7AnWgyeZTn/rAiMsAoBNeLOiL08C0YBzxVYuXLP8wA3b96MMIwwf2zyeAAwf0ygXm8MvGjsJwCVhQAcHx9HpepMClM1Dt6geYBTCUBzrLAA9Ce3gQDsxVBu8xNC2ufX9TqANmE/vu9nPQABawHYzwGEcgoLg23btwNzJgtAJK7goJVAzWdBTMqvc7u+PwgTExOTxtNjeqhNTBQKcW41m5PnWPEwUaMqAmMvALPz0QhAad22odlTBAagCQHN5wBah6m229nukHLRsgzZjOMYYRhASEXqAJr7FsU9PwgCCOF0hWza5gEaB5AiBFQLwJGuY66qoNm0K5ClnO4QUJvwXN/307w/ICsGQ9FoXTuACkFI5QCaNhB2bl0mULOboG1YKZAIQDgkYzH2sAAsIXkHEABkSQVg1/rTKX7DmjoH0G6XrTcEtOiO04oVKzBvroORymTBJoTAvguAJ598Yo/HM+GdUzmAC8a6f25PmSoHECi2eNq1a9ek8E/A3gH0+pgurhcXFoB+2zfGCIDMuSt67qQCMHd1dGTxDY5MXOUOSoEwjAqfk7oB9WQBaDtH0b2rUzhPcefOnd1N4BOKOoDpuUHkAMZxjPpEH7cOACqeFnMDfmbSQjS9Y1YqqNfqhYsSdQnAIYSACmVfwKTZbGbhlQRVO7tCQKUCIEhDQAVBFdDQVGOVCpD24XGGKKINAZXJc5YEoYtG9DpSkTWCd3t2BF13xC4ENOlTaFDS0y1FCtJut1PRB+iiMoDd+Z13AKXj6igRixoFvQ6gY+nW9Q0BFfZOZdYGgvsAlgEWgCUk7wAC2l0rkwDs3wbCfvE52QEsHoIGZI6fELpfX9HF9uOPP4Z9F0y9eNtvAbBy5co9Hv/ZBKA5bprP7ympA5h7HW1zAF1v8kW6aOP23TmAyosxXrCfm+/7fR1AmxBQ4QiI3GfQkQQtFvLnt6VLGQQBqQOYFRrp/lAXeQ2jKEJtYmJyBVAgPVbUAURvGwhPJ5AWccujKJo8HjKXcdANifTnvV4H0EUUDp4za8g3gqeoAppVfM1CQAO/eNVcAGi12qnwowgBzTYkdD83x7XP2csXgYFUVkWYgO7cTCFdBEQhoCaVgqoKqJMIP0Hg2EVRhDiOtaPo0AhApbpDQJVbsSsC09MGQkq7iq+6rUROUDouBIqnBJgxAUA6LhRBKG3a8zhxAKVjV2VzWAKw0+nAcXQfQM4BnH5YAJYQs0tiUE654qX7FoERQFgw5lxX7dJFb/K4Emj7QeGFSbcDWCwEtF6vY/36Ddh/Yf9wTQBYvFCg3fbxzDPP7NGY69atg+dObgFhGJurReugDmCr1YKU3a0q7BzAnZMKtgAEOYD9HECLENAgCLv7ABJUAXV6NiMoQkDREwIKFJ9jEASI81dvoj6AvSGgRZ5zWiG2XxGYJGSzqAM4qRG8EHCqg+fspeNN4QACgzdvNz8/aUyv2HiGbgfQXgBm77Xq+t9OVLZyIaWUDqCpLGrXzFvP0c89Z/s2ENmmiS4CU7QKdh4jsAA6B9BJ3g+KkM00P9qhDAHtyQF0q1bv9STBRiIAc709hYCyPH+yEFDdBgKwe7/75QBSjKdyOYDSoQsBdUTxiCyGDhaAJUSHgObFVbkcwP45gFnoyqC02+1JBWAALQDjuHiD+S4BKIrtqGb5f1P/zOJEHD7xxJ6Fga5bt65vCwiDlALzCrSCaLVacN0eEW3pAPZbHxsBV1QAqn5jusUWyGEY6l1FwhDQIAjg9JyPwsIBTH+vKwRU/2cTphrnRSpRCGhvXHcRV8OIu74hoNKBU/XoQkCR5NgNKADTc63fCZ60hijsAPYJAQVQqFchkFxvnSQXl6AvbLvdTnrjJBfwxIEoei5GUaRdq542EBQCUOTCSm0FoO/n+wAqBJY5gJkDqCAI8riA7veVTAAmTp0RbBTFS6RUaREYOwHY6sqvA3QV0CgKi19v/d4QUBdtyyIweSFkxrQRgHmBpQic1MlVQKkEYN4BtBeAnU5H9wEUEp0OC8DphgVgCYmiqKfFQjkFYPdaUVg5gK7sLwDN94uQDwEtKgAff/xxANitA7hPUgjmySef3KMx1659ZsoWEIYirSC0k9p9TBYUgHEco1ar9XUAHadY1c5arQbX63YoDW4FaDSaA7u0WZ+r3PwIBKDouTIKghDnrqIytoVqekJAhdAhq/Yi1d4BTMVdnyqggBaGgwrAdLOhT9++TkUNvBmRCsA+4xUNAc3GnFwEpuv7A6LPRyPW7PsA6lBIlW5AmbBS+x6SpsWC6j5OMiaFAPQzlzJZwNuEvWbOrM4B7ETFc3oN1AIwCIJU+JE6gFKRtYFQ7uQQUPO9IvhB0OUASmnXZqHXUQQSUUkhpB2Vjm0bmqvHy1UBtTgX0/nlblrSkQgDewdQCOMAcgjodMMCsISEYQgpugVgmezyLNwgOyYFEBacoxaAk48bUVj0xp9VAS3eBuLJJ5/E/NH+BWAMjiOw3wLgsccee9bxwjDE5s2bp8z/M5hWEIMsUNrtdlcBGCCLeBp0wdhutxEEYV8BCGiTY9AFcr1eh+v1fx1dT4vOQW/6vU3bgUz0Fr2hhmHYna8HAE5xJzoLucsdlD3fG5Ag6KkCCgDKsZ9jz4c6ShzWQdidAwgAnarCzl2DO4CO50LIPresyuCN1qcM1wRSB2/QUvRTh4C6Xd8flCAIUpFG4QB2CSEgvUhYV7l1u0NAbUUqILIPM0GvQl05N3MA49iuJUL6u45M50kRIjdpfAt8309z/xyC98XMyZFu6izazLPVakFNcgAr6feKMDkE1LPqf9gbAgroSqAUjp0uAmP/OlI7gFlO4TCqgAo4QiJiB3DaYQFYQiYJwJKFgOZDKw0yybErIrJardYUAlD/X/SG1e0AxoVE9GOPLcd+uykAY1i8j+4X+Gzv06ZNmxBFHcwfnVpQAsC8MYFmszVQqwWdA9g9V+MIFm7a3n8NX6hqZ61W6xv+qcfLfmYQ+glA2xxAvWvefUxIewHYzwG0GlP2hqkWdwD75QCKghUn03O2X489ABjxsH3AIjC1Wg2iT8EWAEDFHbjRehYC2mfMSjHBlgrGHldRJB8imxDQVAAm/1sLwHzZXEtXsdetE1ICjmPtAAo3cylj5VoVBtG9OKOcA2hfeKMrBJQgHBLofl8DqjYQydyMYCMRgI6CcOzPxXZ7cgiocQBtiibJLgdQWfVonMoBpCkCo2hDQFMHUFkXgZFCdqWpSKGsegsCJgSUG8GXBRaAJUQXgSmvAMxKGOddSv1/kXnqvj2TRRZVCGjRRvDj4+PYtGlzmuO3OxYv0ovvNWvW7PbnNmzYAACY92wO4Gj3z+8JugFu9+to3M9Bb6Zm8duvYIs+Hg8cdlev16Dc/u+BKigAe5u2A5nQsmoE3xsCKlF4FzkLFcsdtJ1jGHZVKQW0ACx6nUgXM727Ohh8wZieF1XV9/ui6mJ81+Ahm3GfcE1AO26DnjepGOvbk6SYAKzVavrD1mvDV+wFoHH+ioryPHrzYHKpYPs+l9nzdixDNtvtdlq90szRtpcbgFT4CQI3rDsElF4AFk2pyNNu+3CkqQJq7zRlRWC0OHAsmphHUYQgCKB6dgVtHEBdM8BPW1QAOgQ0jjsWLXeCSTmA0rHPARQQcISTOoAkIaA5B9BaAPbsgipHWue56kbw+nlzDuD0wwKwhGgBmH0tS9Yzpa8DKLq/NwhTCUBjRtgUJwCyIjCDvoamqMv+C5/9Zxfvs2eFYNavXw8AmDe6+/HmFegF2Go1u9Z1gKlYNnjJ6md3AIGJicEX8WoKE8cIzUEXycNyAEWPkBZOkndXgGzxmTtIMMdJIaAWOYC+7wNCdIdYFiwsMz4+DqfqZXlrvVQ91Gu1gT6PE7Ua4t5GoQbPRbPRGGi8TABOHlM4Ao7nDnwuNhoNOBVvcnGnRFAWFYD6vU5eS4IQUD1edjLatpbIqtxmH26h7EI2dc++7L0RykWrbVcZUs9xCA6gI0ncMIA+BDQI/MwBJMg1y4eAAtpds910Uj19gUxIaJH3JnPC8tUr7d5r3+8WlGZMWwEopXa4JcG5MzkH0LEKsdRCrfv6bZtXCOg1WJoDWKI17WyFBWAJMbskhrKVzM0uNtkxU9ijkAB8lhzAojesrj6AIh5YAJqiLvvt8+wO4IJRoOI5z1oIZuPGjVBSYO7I7scbm5v9/J6iHcDJx6UcvJ+icT+mEoCeV8AhqU8dAmrWjoMukrMwrOyYrQAMwxAQkwWgbYEVkVtDCIo59hbTsQgB1aK353ZgEQIqpigAAwCouuh0OgO91xMTE/3DNQGgogbOH63X6zrEcAqRKjxv4BzARqORhnt2jaUkhJQDj2fIO4BUOYAxYQhob2N589haAMru8VoW46Vh2A6dAOyXA0jpABatqp3HD4JcFVC6UEMnF1Za9FzM+tZ2f2aUhQOYCsDcuWNbZCXoKSpjxrR9HU3opxGoNmu8bLNbpv/bjqd6HEDbMQEgCnWLMwF2AMsAC8ASEvW2gXBomsJSkYUbZMfsHMB2VzipweSv2eYAmhDQQee2YsUKLBhzUJ2icEkeIQT2WxA/qwDcsGEDxnbTAsKgpMDoXKdACGi/sQZ/DZ89BFQveAcpUtNoNKZ0AG0FoNO1GdH9vUHpVwRGyKxB86Bk4ZW5g5YCMArDSQ5gbFEsancCsEgIaKcyhVsHpOJwkBzSWr3Wt2InAAhvcIet0WjAmWI8AICnBj4X6/V61nelzxwpQkCpBGB31SQaAdhbWIbUAZQufIKqi2ZMipy9rhBQIgcw/X3lIbCsuAgAYRCmYo2iEXyWAyjT/22d494iMCYktMj5k/XX684BBIq/N0EQpCItHdNxC7XIMYRhmL6Gtg4lkBeATjKmRKfTseqh3PuclWVYKZA0ghdOEgLKDuB0wwKwhPQ6gGXLAcxuAtkxsxYtnAPYT7hYLuRt20A8+eQT2Hf+nl9A912gC8Hs7u9s3LgBo3P27MI3OqczkAM41esoCwhAs1idUgC6OkdlT8eN4xjNRstEw03COIODFnromwPoCAhhs+Pr928DUTD/If1MEIaA9nUAbV3KSQJQZH9rAMYnxqd264D0e4PkkDbqDYjdhIACgwvAqQQlAHRcVcgB7PTm/yUIt3gRkyiKED8HDqB9BdnuXiy2VUDjHgeQougGhuAAUoaApvcs6ZJs+gZhmCsCY/c+5383LwCL59YlIaA9YSHSIj8zm18uB9CyyqZ263pzAJVVkZ4gCDIHMNlttHXsTE4hkDmBNs+5NwTUEQ6BAMw1gmcBOO2wACwhvUVgHCHQKbsD6HR/bxD0Dtvk49JSAE7KARxgbvV6HRs3btqj8E/DfvvoELy1a9dO+TMbN25MwzufjbE5WjDuKTr5ffJxKwE4Vc7egI5dEAQ6rGQqAZjcrwddJGeLsO7jjkVBlKn6ANrcTIGeKqDWAjDqFpQAYss5it6cwoIhoOPjE1P2AAQwcJXNOI7Rajb75usBSI8PItgajQbiKcQaAMBVqA8oAOu7EZVxAUFpCIIgO8ETAWizEPPzbSUAawGYOjU5wRZbtm3wfb87pNSi2AiQ+wymOYD2gq0rBJRAXOXHFMojKZMfBkEa9kpRBMacd0bwCot2A+b8kKo3vLK4A5jlAHYXgQFsrrXBpBxAJRVCy6ItmQNIkwOYL9oik+tFUZctiiKonjAYihzAKOrkisCwAJxuWACWkJmSA5g3IByLEFC9Gzb5uG0IaH6eQmCgHafVq1cD0K7enrLvfP0iPPXUU32/32w2Ua83MDZ3z0Tl6Fxg27bte/yaBkHYVwA6Tjzwa9hsNqFU/6btQCYA91SwmZ+TU+gCWVAA9gsBBbQALHzDj8IpBGDx8Eo9qfwEe743IJ0omuQA2ghAXfl0clGZInOs12sQuwsBTb63pwKw1WrpUKYpwivN8UEEVrPZRNwv8Tg35qDnYrPZzHrh9RAXGM8QhiFi895QFYHJfWCEpXjJCqx0VxYtWjQJ0NUr84JSKGXVaD37DGbCpet4ATJnPxOA9gvkRGBJ17rkPtAdaugQthvIj2ldBKYnB1AmX1sVgcmdOzZuWBRFunVBTz6cdOw3JGTP+2IrAJ3cHIWw2yjSa9A+AtByU4JDQMsFC8AS0i8EtGwCUIuqvEjV/xf5UE/lAKqCC1BDbwjoIEnHq1atAgA8b/6eO4AL5+l11VQCcMuWLQCA0Tl7Nt7oHF39dcce9kzr178OKCYAG40GXHfq5z6oA5gm/E+xhhdCQLmisADszdlzbN2wPgKw6GdwdyGgRcaMokgLoj6CzcqlnCIEdJA5djodNBu7ceuAgR3AqfrrGUxo6CDnTqPZnPpkBABVQAC2WtidxW0TAtqbA2hzPwjCsLsNhLQTlfl+eNmYliGgvt8znl3IZpav191PkcRVdGRaTIiqCqhQLskCOYyyEFAKBzDfBxDQz902BFSq3iIwxCGgFs+7X1VR/bVdCKiO8jIVO2lCQPMhm9JSAOp+ff1DQIvmFZpxTRVQ8zUzfbAALCGdTtRVBMYRolQVk/QuTvfi08YB1DH2k8WGTW9BILnYQIsLMWAI6Jo1a+C5Dsb2UKwButrmPmPOlL0AjQCcu8cCsPv3dofufxT2D6Ut0MNudwVbgGydO7ADuLs1dwEBmLq8ffr2FRdD0eQ+gE7xIjC6qmj3homwcADTz1jve20peuNJz3nwDZhms6kXCLsTgAOGbJrNAzGVY1fAAWw0G1M7igBEAceu1WxOPaZSWnQWIMwLQFG82nI6Xo8DaBu+mOUAdoeVWufsEYap5sWaHo8uHBKOQ+YAZptFiqQKaLcDaB9qmG6qEuQAmvdS9oSFmK+LnD+97RDMHAFbATi5DUTR+wHQ7bBJgnPHOGsGM7ZNCKjoIwBtxgSATi4E1HYsxh4WgCVkJoSATgq5s2gDEUbRbnMArQSg2TgfsA/gmjWrsXBe/KzVOntZOBZjzZrVfb+3bds2AHjWFhCGOSPdv7c7+hXmMTjO4DfTVqu1e7Gmsp/7/9l71xhLrvJs9Fmr9q13777NjKfnZo/xeMY2ODciMEQRPiE4HAOOQy4/UCLxB0XhRxIlREL8ISgiRFEUJPLj6CgSIl9yAslHTo4TH2JiYueAPu7YBsvG+MrYnlvP9Mz0vfetap0ftat2XVbVrlrvW7urmXoke7qrer97VdWqtdaznveSBeN4j+S/sWr54z7GtbjCxykxe7Ztx+xBAo5jtvOpja8jxMyOF54aBZCiUia4gOZpo0fCEhO2AG52JyFyq8fJ5MoK/10G9Lo9aDMmBWzmfWdicWsBCEIZA9u2feInhACkpKs4ocKZPApg0KYgZIf0bWqKe5qSypC7ZsAeDwHkcwENJoHhiPu3h/aY9BJVoeBnPXIgCH3RJ4ARckVxiYy6qAZ/phHASEkEaZEVQO8eUhO2ePasgBuMZHABjSqAFpFUAoCjRi6gqAhgGVARwBLC3SUZ/24R0rsXgShBBcYdKe8LrZTCcJhOACkuoNJfOOWLAXz99ddxYC7/dx6YB1ZWLmvbfO3aNQBjYjcJ3t95n0vDOPYhfs4tI5Jvctnd3YVlJd+vvElbMhFAKz8BHCclCB8XgrBxYNuI8n4hYRz/4LqU6gmgaVyKayNO2ExVg+FwCBW9aANi4PeHNHVNCMhG9qQovs0kwlbP7wLa6/XCZQuiqNXQ7/UyuzvZtu0m3EhpY8+wkHnIBRTu/aMsnGLuvkwuoDEFkOReOQi5gFKL1cdjAM0SHIXbOLpfQrLEZgLhGECOxbHjjAmgEAKSkLQl2D7p27SM25mkAAohYBkm/dERQEuY19nT2QNcQkhRaINxhVLSyVDUBZRKAB3H8Una2Cbd+8D1yhqXwaoI4N6iIoAlhO1Es4C6pLAscIlV+JipC6iuqLwHSm1BwPM3H7cv645qt9vF1avXsDSXT/0DgKWOuzjT1e+7fv06GnWBei2b3ZlReaS1tbWJf5uUDMU7lremVLe7mxoi5a3NshK2LARQWoqNAEpKzJ4uCczoHTSJf0hSFAHarjSi3YioekZdQL2XOo/NcbxeiroGVyHMqtj5f5dErkaDR56+M+j3JyqAQHbFSZsIJdRGC/0eIZlVkJxLWjr2UF3BkT3/uGn7pAx7S0hJcpEbxuIUaQpbyF0TYFHsghkxBUNsJhBUAGukOm6eLaXUWAEctZVKNICAAijMFcBoUfkgLKtOcwEVgSQwhGcdLbAetGkT1mRu2QY+BTAalkN1sdS5gFITywCAcpSfBAYAqX9XoKMigCWE5yftoWw1U4LEyoP3e94Bx1euNJxIEhbJQFipFMg+2Hi19xY6+b9zoeN+38rKSuzc+vo6Wq3spFJKgZmWzEQA/RiXhPs4zFnDLqmovAdjAphiU1r5k9WMFyRRY5R+48TIlafgmUyo7gI5Yk8IwKA2pW8PiCmAQghmlTL/ju9Etc5D3cqsAHp9IkldE0JA1LPXiXPjZQfpnbGWT3HSxsFF7BmXs3GcUAcXRALousYHOiRxYTeIlpUAAIumNMVJKk0BjBIXDsUuRCoZEnkAgfFF0t3toiUb3J9pfWdsc0wAyQqgjBNA09jCaPtcW+b9e6x4RtUwmgIYVOxYYutGyVXG7aPZdBXAqJcXnbQ5jrsmE6gUwDKgIoAlRHQ3R4Dm8sONtCQweduZlMTDtSlornwBouq6gGabADzyNt/OrwDOj2r86Qq4r6+vo9XIN3i2mu7nJiHtPppksJxIAEfnsqo4uoLtUUiCC2gsCYxQpMkvptgR1GitAgiXVJq00f9MtHsSPAVsTVkJEwLoP7+0EgsAlCUzE7Zxnblkm6JmZe47PonQ1Z7xkFNVnEQAhWXBHg6NNxBC5JzoAhpVFN3NCPMwA3fzIC7Bk8kVY5xiKF4vYI+66AYQcgHlVACp7YuRXtCydobaF4gBNFXCktwrvWMU9/igO6QgKGze9cbjFGlKajBpCxcB1LmAkghgEUlgRq6lHO6kFeioCGAJ4SgV9vghTvjcSHMBNSaACVyLcu1KKd+uyJEExsu6mScDqIfOjLsWWF1djZ3b3NxAs56PADbqCpubmxP/LjUJjMhfw67f76eSNW+dm3VHPhMBlMAgp1LpTyAaRZrDdThozzuXF+6CO37ctLTEWCXQxACauvxEVCa3gZHvywCPMCXGwo2gLJl78yCNsAlL5u6LqYQyZ6EmbgikAAAgAElEQVTwiTYJcXaObYd9nInzQcwe3IU8yV0s8mILKY2TmCilRnUuw8QF4CCAY+UqdJxiU0j/flLn6agCSFFbtAqgoKvHrk3vPpoTyiIIoNe+UBIYwrPRkWjXpnD7KeGd4VcA4y6gvDGA9HfGXdsK/35WLqB7i4oAlhBxf25AlYwAci2QxwRQzwApsVzxGMBsbbt27RoEgNlW/u8UQmC2ZWkTt2xubqLR0HwoBc2GwlYGAuhPfExEI6k2o2/TFQ0yqzhpMYoepDWKzcqB8YIk2kBl3m80LqCUrJ2O4+hHWkPVJYn0Imem27DNYZwAUhTAtM4DADWZX7FL7TwGBHCCvdDfTsA4y2SCTULSEaVU6FkLYlIwtz/GB3BSfF3MnjTejIiSDABkhW38zngTAsNiNrgRw5TUIqQqMrUvpAASC3DHYwAFlCJ4HQCxzQMAkLLGRtgEQe3VKYru7zSX36DLJkdClKhi59k0JVhRQgnwEFU1IoCee2lFAPcWFQEsIeIE8CdXAUwjLu5x82t3A5nHWUCdjIPN9evXMdOSfmmLvGi3lJYA7u7sJNWyTkSjNqpZNgGJsXBA7hIYgBszmKbWuRnlshceH9fhSv4bk1jFxOsmkCGl4i6gnn3TJDA6BdAtLZG/jX4bNLswxu+KjqQaLEzGStiEqcWS6GesTTmRXI3O5SVrMbfFIHLWQByXI+EngK46G7QrjBdOSqkRoYzGj9IUQF0WJlMFMKSsjRsIgObSFrLJsOj2noEQkrzgjtlMG3wzYnzN4fhRrmv2/jUtjzOe++PvjDSMVUwivcFzHG2k2HQ/p3ybHGpYVLETxDILOhdQwUDalFKhLKAVAdxbVASwhHBT5Y4hSpYERkVcVAHzBfJ4wNafz+O6qbNt4gK6sbGBVtPoKwEArYbChiZub7fbTcuOr0WtPiowPQFJ2TABs3s4HMZrPUYhcyRaSYtR9GCiVCb1HyEou5+azxFcQJVS2pHWtI1FKICKyQU0E1kDACnR7+dU11I6j8qhiiWW0Yi0D8h+7bEskwn2TBa1sWcjzQlgjAgFbFLUjKQJgaK6hGwyuLQBGD9zJjXDf0mYCSBnjKJg2jwI2xxfN2njINY++MeM3pXRtXHFw+nvIUeMXWBzmiEhiqescbVPOSrRBZTsqooqCUxZUBHAEsKNXQu7gKJEOyVRggqMO1LeQXs8YOvPS8JCPkhUXSUxW9s2NzfRyhmrF0Sr4cb7RdvS7w/yE0ALmQpI+zuzmnMmBDApcUkQ0mDRnUT0vXN5EwokJUShxABGXe48ewAxa2cUhoRtrABGThgqioCnMkXbl3+SHpO1Ceq5lT1jaVbClnczIt0FNJ/7q5a0RNqXx14Q8f5oTtbS1GNTcBPAMQkKk97QOZM2YrzY5lAAneA7w+wCyqEK6T5LzQKqUwDJz0TzHpoq0lrVk0DOY4Q3YpNCsLy+uF9cQDna6a1tKwWwHNhTAvixj30Mb3/72/G+971Pe14phU9+8pO477778MADD+DZZ5+dcgv3BlGFTUBkdl+cBoLJVXwYrh+KVgCDWUBVRleV3d3d3EQtiHrNVfuCsG3bvW+6ehcpsGS2Gn7jiTl+zkRpcl1U0v9G5CAc2Qmg2QZCdLKCgHFsik7hNu3fnj09M6fuSscbSdksSXo4eWxmIlej83bGxESZCGCOezmRrAXOZe2PiYl5IvaMnnf02RD6om6B7P1Ocq9kJIDjz4T8YIztARriy7AADasuzAogIwEMEqz4mGFmMwjHcJxNcwEVhv1RNzZ6RIvLpTT4O21zOuwCSoHrWsmYBVTpykBwvDMusZQMZLICHYRlLh2//uu/jt/5nd/BRz/6Ue35r33tazh79iweffRR/OAHP8AnPvEJfPGLX5xyK6ePoW3jtbVt/J/few5nDi4AMM82VQQcxwGUwv/zTB/PX3Fw500SP33ULGtZ8O+VUnjkGRsvXXZw+rDE/363lat+XxRupi335zxEstfbTS1jdmFV4dxlhROHBY4dik+odQvoRQjguOZR3N6lVYULK8CxZeBIxJ5lZXOz9K5NAfjeUwoXLwJHjwI//7PueSPX3IBb79NPAisXgeWjwE+/2Z1gBbLf06BqpZTCj74HXLkI3HQMuPPnR/YMSJtnVymFV74LXD8PLB0HoMz6jW8PwMVvOdg6B3ROAPU597ypoqGg0P26gn0OsE4AzV8QoLpjKSg4X18Fzu0AJ9oYXbSZrVFsmP2Ns1DnNiBuXoD42WP+uayIuqcqpWB/80Woc1chbj4I622n4ZUdGGasozX+fvd+2d96FurcKsSJm2C97Y2+vaztjCqors2noc6tQNy8DOuen85NYGJt/PaTUOcuQpw4CuueN5MIh2PbUNfXMHz8qxBHlqEM+3bw+xUU7B8+A+fSJcgjR0k2XXvA8Ic/gHPpIuSRY6DQoOBYNvjhk3AunYdod0Ltz92+0ecGzz8FZ/US5PKJ0HeZ2+R1AR2Dbs+7trULz2P9wvOYP3oGSvHEPb7y3P+H61dewc7mFczPmpHK4DvzzW/9I86dewY3n/gp3HPPBwCYKYv+ZxTw/R99BedXXsDSwlGj9kXx6BOfx8sXn8Gpo3ejM7MYuYZ8sB0HW9tX8Plv/x84efB2ADzuvl89+xhevv4iZmozJJvuZrnAP734P/Hs1R/iTQffhBOz+eeDeDtV5QJaIuwpAXzLW96Cc+fOJZ5/7LHH8Gu/9msQQuBnf/ZnsbGxgcuXL+Pw4cOpdr/yla/gP//zP8nte/e734377ruPzWbQXhocx8EzV64DAL7x+mXcdWgh00s3rTYqpbDVd/CN8y3cf//9eOSRR7DeHfjn8iDouvjIMza++3rdt6kwAFXV6A8cPH12lAglo53hcIhGwqbchVWFL327jvt+5X586dFH8N57BjESKCViMZtJSuelVYX/+mYTv/Ir9+PRRx/Bu97eC5FAT72L+vjrrhUAXnkFuHJ1/FyArnGsmfdtTz8JvPbjsM2f+fl8ymLw+n/0PeDCKwF7qou73oKRipO//wgBvPJdYPXFsU1ldWHPme8gb50H1PrYnnWw639fXiil4Gwr1H/UwvtG9nroGqsu/gL5+U20Ljm4//734pFHHkG340ApsyFdOQ5wdQfN8zv+NXeHTuj7csFT0L75IprPr/g2ewqo/cIZt3NlvJVB1cr+1rNoPn9hbA8KtbffDZXDLTKqgtnfehrNF14LtVHefARA/v4NAdjffhKNF37s2+sDEAeWwn+XA4N+H2pzEworwEsvQ7RaZCVMXb6M4atn3Ta99CJEvU4aZ1W/j8H/+m8AgP3SjyBP3ubapixqL1+A88qPYt9l2kYA6H/rUffAS08btytqt//C91A7fprUvhhoQh2AcVuuvPhNAMDlF7+JeqvDoir+4Bv/l39MOAdItr717c/jhRceC8z7ynjO8j7z/Nlv4Zvf/1ftuTzw+u9TL30VP77ytN/GIwtvMLYJAJsbG1jdWMWljXN46rVvkGx57dzub+ELz/yP0HHK+3Jh+wKe337Bv+Yzs/Q+7nqPyVwKKve6dr/zA07sKQGchJWVFRw5csT//ciRI1hZWUklgL1eDxcuXMDOzuTMiZNw4cIFPPfcc/7PVJtBe2mIvhSrOz3Yjj3xs9Nq48bGBnoD4Fd/7X58+MMfhlIKX/3yvwEAXnvtNczNzWX+Pq/mHgTw3EUH998/tvm//uvfAAhcv349032LYn19Hd2BwvfPOji6KGAPh5nsdHe7mJ3Vnzt3WeG+X7kfv/d7HwagcO7Ff48TQOEmUQl+l/dcovP6hRXgV4L2Xvo3HDkUtOWulJ999llYKXXLXnvtNQDA6lWE7uHjjz2EW252+5TJPQSAC+fCNv/rsYfwMz/vrt/X1tYy2fWfM4CViL2vPP4Q7nqLe29se3I/D2J1dRUQwNVXwzb//f99CN3ubu5r9khEfw341feM7T385YcAAC+++GLoWrJgc3MTqg/c/+DY3r/990OQyH7/gvA3za70cP/9v+rbfOg//h22FEbPuT8YANt93P++B8f2Hn8EgDsOZ7V5+fLl0O/q7JXQc3novx8FfuEMAGAw6GeyG6ypqc6uROx9BXj73QCA7e3tTPYuXrwYbuOrFyI2/wsYEcCzZ8+mvnceXn311dFPAs6r5yL2HoN10CWAL7/8MjY2NpINaRDNHKqGQ6yvrxs9Z6/0hlpbC9u0bVy7ds3I5sbGBlQko6taczcwn3/+eTRy1r7Z3t52bVyLv2d55xcPKysr2uNXrlwxHhdXV1cB5aD/o++gdtzt05cuXTK2B7jzbxAvvPACZpMmowm4fv167Jg97GNjY8O4jdH3GwC2t7eM7LnvtcCrrz4Rel/++7+/hXq9ic3Nzdx2z58/DwB4/eIPtefy2jt79iwA4NzVF8Nz1iOPAzB/Pptb8dJOlL64tbWF3UF87ffjH//YyF6328VGfwPvefC94+fyH+4Gz0svvZR7DPPg5WHw1kEvvfQS1iJjURTc69r9zg84UWoCaIJms4kPfvCD+OAHP8hq96677mK3mRW2cjMn3XXXXal/N602djodCAF3t04pfPnLX8a85ZLWEydOTGxnEPPz8wDcAaFuqZDNpaZCz5ZYWFjIZdPD7OxsOEZfTL6HANBstRLViROHBb706CMAFL7y6Jfx3nviW7UKgGXJ0HdtbW3554I4tgw8OrL36KNfxrveHrE1+sAb3/hGyJS4Km+Ralnh5zIzM/4bk3sIALVa2GZrZFMIZH42Tz75pP+zjLSxPrKnRteRp53f/Ka7uy0jbRQAWq2Z3Nc8TmISsTfiAKdOnZrogRBFp9MBIu+L6ADCEVhcXMzdxnq9rm0jpBtPYvKca7UaIETYXtvtb4cPH85s86mnnoo01grb7IzroNRq9Ux2n3jiiUBDI/ZmR+RCuO97FnszwZfCbUjE5rgA6K233prJZoikxeyNv+/UqVM4fvz4RHupUArz8/NGz3nXyyisScCztLRkZHN+fj42sKmR/TvuuAOtVr6Cqv7iUtPGW265xaiNP/xhnBAAwKFDh4zHxW984xuxY0eOHDG2ByDmEXXmzBl/jswLHVmj9B0A+P73vx8/mHFejeI73/kOAKBWa4bel9nZZQACc3Nzue16BHpoxzMMHzt2LLc9TwGsyXp4zpLuO3369GksLCzksgkEkhEFQOmL7XYbSrNoOXnypJHNRqMBKcJj7axwie5tt92Gm2++2aid8BxAR94Xt91228TxkHtdW8Q6eS/5wSSkkcpSZwFdXl7GpUuX/N8vXbqE5eXlPWzRXkEYB1oXBSncXaKHHnoI3W4XzRo9ZqFVFyGbrXo4S1ZeBNuSJwbQsizYCZdx7JDAe+8Z4PKL/651/wQAx0GMrHm/R2/PkUMC73p7D+de+reY+2fwGtLIXxC1Wvi5jOsOmvsU1ethmx7/gMr+bIJ/V29E7HkCQQ57IdsAahGbroeJuQuRtML2JHWrTEba14B2ws6FugzZhJXDr1IHK/z+oZG/Flksc1yjFrnu8Y0UuetsKohGPWJv1BknuEhr2+iFAupseq7puftjQhvLk8NrquCLiSsG1KQoFehoNNrhOavR3usmxdCoNcNzVs2dtMrSv5PWNhQXUEta4edi0a/ZGSWB4agpWIGOUhPAd77znXjooYeglML3v/99zM3N5d59329IeiHK9J6kvbRleqGDg2IeAthoNJAWTnTskMBb3yi15A8AbBsxtydPodPdniOHBN78JhEjfwDgKFdNnDaEEBP7nFLZiek47XO2v+MBwdYU1oW6XeD9Dr8/TIrlzEHYfBfMFJPCoC+mGvTIYcY2+t+daDLfRs5+Qto9MrneNHs3DGEr0TxaLJKuk//6i1iblKs/aryRiDGz6dZNDKMigCXCnrqA/vEf/zG+853v4Pr163jHO96B3//93/ddsD7wgQ/g3nvvxVe/+lXcd999mJmZwac+9am9bO5UkPji/YS+KFmJgQmCmSxFjgQj7XYb19bNG9QfAjMzYbcnz21vaAcyx2WAjkzmhUJ6xnsdZIZi0yqHYpdlEa8UIPeA7Fbgw/g5T3h/HIWalW36GSt2aTZNCGW6PcCAVCbZzEkoi0Jx5Ep/3SY208i0KYEu/r7zTGDRfrQfNgxM72zqMzH0BjH+vhKA0r5k4aBc60ZnFM5U1QEsB/aUAH76059OPS+EwJ/+6Z9OqTXlQNILUbY6gFzwB4KU7zKdBIMF6/MUlG+3Z3FxaD7x9geujSCklKjXa7Azpr73MLSBem3ya+q7mOpOqvy1hqS0MKkkg6NGsWMZkIXoK6WvCTUJ5Xkz9BBCaBtZpskvTY3MszDxydWkzRZHwcrYdzLZdFTuvpjaGZ18C/CJpNKLJcrYxkkwXSwWQQC196ggwst93RSCpbPJRdj87NiE+5f02bIQofG9ir8zCk6pyG+sheUZugGkrBsLKLNAVRWDZSDKNAfeiCjPG1YBQMoL8RP6okyajFSGv0n8bDAGUHMsCfPz8+j2zSfJ3T6wsLAYO95sNpGhpnsIgyHQiiat0CC1RARcRS8PajULk+YOx1GZMiS69mqjzyT/jXIAK60AowZJ5Mo/V3Lwt9F0gZx2LrtNn+BM6jy2k5kMjclVsk1h0BfTCaUT/u6sbUy67tG4k9VeUSjindAv1M0VrDRiwE0A2QiWihcgN8FY/aTbS7r3ZSFWaZuCXsFwU5tFMzTPuvHzYX4NlVLFBxQQv2C8qTGeb6o6gHuLcowEFXwkSvlTbocp8u7oeJNR0losj5thFMG6YHkGnIWFBex2HePdqd2+1GYGm5mZwSCenCwVgwHQbk8mgKkxSAaXYVkZCKCdXXXxYyAnEUDGBXIBHm1GSOy/Bbg5GYOJSPsZSu3JBLDZaGay6fexNJs5COC4L6Z1xnyEbaJKmZNQhsDYR6IJcHwQPC2klIl9x8Rm2j0yHR+KIENhAmhsRm9T2fHvYAGtoZztSVUAVfb3OQhuhXua4/N+cQHlUACnEmRfYSIqAlhhTzHJNdDJkdwhimgWUCAbATx48CCGtkK3P/FPtd+5tePg4MGDsXOdTgf9nASwPwBmZyfXvUq7R0rlXzjV67V0tU4pOE5gwT8BWRRAxwHqtWz2PEgp9TvIKGbCJ8U0MSF9kWNmk6uNfrzqcDIBbGTsO34fm6DY5e2LqZ1xRDaz2vT+TmlKFwTtcbmAmiLtOZMIoGYRL6U0el/S4oXLRADDn+VReH1XfodOAHXXphKOU2yaIu1eKWXmAjo9Akh30c3+XZktsLSjaFRJYMqDigCWDPslmJcLSeURPOTJNBn/7NgtYhz2M/k+euRtazf/d/YHwGCotARwbm4evZyksj+QmepApcUA6spSTEK9XteV4QrZ9P4uqz1AW9orZDOrPQ/pGQh5d5BNkaYAkhaMRauUI/t52thsjlS9DApg1vpwY1UxpfPY2QlgJpXSyUcAJ6qUhBhAXe+hx+vxuVdq+4dSECRCCW2ZFBpJzX48n03FFqIxzqLrRL6DYCsIwnyaDFpf1M3JjkMjgLrHQUtIpH++xkmJNPesCAWwTPA2390kMO6x/dDun2RUBLDCnmKyCygtCYyfBXR0zE5bRI7glRrZ2M4/OG1su//q6lW6sYX5rqXbB+bmJiuAabvmJiS60Wimr7eH3t9ly1DqE8C0NbdBxtPE6zJc6KSRtdTvS0FaG7lVyrzJfjykJd/J00aPAKph+nsmbGdMFifA7xMphE0N7dx9MdXeqPNnJYD+dyfV4xrZK0MSmKTPmm5GWJalXSCb2hNCQFqWdiwzvX9JbaFswATvo5cwi0qufAIz2injTlKDHNlyddC1x9RcugJom22WpMQAkghlwnnj56O5Z0VsPpaJYIVzMlQKYBlQEcAKucHpZuFNAklZTm1CXJijiQHMMuAcOXIEALC+nf8710ek0bMRxOLiIrq97LaUUtjdVVhaWpr4t2n3yKSWYLM5gQDa47/LAm+BnKYAuiUvstnzwJ0yvrAkGQmxmfxuTrnNAUgvv5Hnnviq3mACARzkJ4BppFIN7cz2arWae00TFMXgd0+CTxST2mjbqNXrbOoLKXmJrj8SNtq0C3WVPT5YB3c8i780pjYLVQAVaDGeAYxjSYfh7zBA4XGPRKRlznUcm3ezbcK5JIyfZ7iNqgAX0LIk50kEU2bfYAxgRQD3FiXvcRV+0jEmgPrzDkUBVOEyEEC2AWdxcRHtmRbWNvMPTte33H+PHTumtbvbdTLXI+wPANtR2oQyUXgLIy3PUICVseaah1ZrJhMBzLpA9v4uzaZji9wK4DSLUGc5n+czpu7NqSqlqWugSE7kkWdRO+NlrJ1AADGw0W63M9n0iV0CuVJKQdnZFUAhBGr1ejJZC3xXbgUwqcyLbed2b/bBXG7ASonZM0GtVmNVAN3P6m2WSQHUuYBSF/G+W6RdFAE0n0+TbZqNOeM5S08ATZ512rWxelsQPEJM25IG7tChIsrF+G0R+TbkKxSHigBWyA3OwSFLFlDTSVoFjOZJAiOEwPETJ3B9M/93Xt9QWFiYR6fTiZ07cOAAAGC3m83WTjf8uTSkKWGOgYraarVgD5Of5XC0zp3JUKICyKYAOrbIrOJ48DM6ppzLC25SmfgZx2wxNrVEHgaL2rECmF7vxBkMMvcd32YSYRsdzxpTCIz64wQCKITITQATVcocCmUUsd6oiG58Ce+FKblKVgANCW9KW1jbSLAHRMYXxawA2sPE55TbVhDEGEBdvzPtiv6915AA2x6SCCBX/Gjy/MJf1qX0CiARujqAFfYWP9k9rkLpkZYd0psXTCdpJ1BzwBtustadueWWk7i2lX+QurYJ3HLLLdpzXmKY7YzJZXZ2wp9LQ9pC1U2uklcBbGGYgQBmXXR7f5ckkAAuOcyziAf4YwABQMjkkgicae1NFcAiCGCai3Aem56qp/rJD1o5DlQOBXCiW+mIbObpO81Wy4/L08K20Wg2MxMtKSXqaaRyODQmgLq1EkkBTIivY3UBBVDLOebEPqshBqYqarEuoIolaUvw88oestkKQsGsvEKaTdPNCD9zrlYBpBFArgyySUlgVPQ8A/a6RmgQ7jPVX7MpwmUgwscq7A0qAriPUJaXRQgRDyEJnMsDb9BLS8hnSgBVMAlMzsKjJ0+exOa2g14/+z1XSuHqhsDJk7dqz+clgF4W0iwE0N+pTFAA897DmZmZVLI2HIz/LguyEEB7mD2m0EMhKeOZ3V8SYwAN41sTa7nBfDHmutwlncvextnZWfeHfgq5Gp3LTQCHCZ1nkF8BbDWb6SrlYIhGzr7YaDYT25gnRjEK7oyBbnZcPpdNLSlTCnWCulav11kJYNEKIFcSmHGNSjoBLEIB5CQ8vguo5jkPh0OjZ52W5ZYzBtCLmaUkY8pybK8gUurCciQ68ubYsqxpb1RUBHAfoSwDBKeLnJQSUkrYCbXcTGx6cAzLQADArbfeCgBYXc/+fVu7QK/v+J+N4qabbnL/biejvdHfHTp0aOLfjndT43BdQPMTwLSi9XldQL3FedIa3j2nciuAieoaKPEZUpcjAzC0mdjGHMXLgyhC9awlLBaBfMSg0Wi46k1awcuROuiTxQnw+piaoABmJZSuzVaqC6ga2mi18hG2ViuZAGIwRDvju5IFlLnAsvQbEuwEsJ4vnjcIt0YkXwxgoQQwoABSs7z6NoeD3GN2FEkxgJQ2FqEAxtQ1pQAoIwLIHRKQVF6JMr9M+q4yQLfJ790F0+c9LgMx/n9FAPcW5elxFfYNuIlozbJSXUBNd31VwKjX5qwK4G233QYAWF3PPkBdWXP/9tSpU9rzCwsLqNdr2MpYXmJ7R2FurpOJFKUpYY6Tv7xCu93GYJB8rzwFMK+Kk6QAKqUwHKjMhNJDYh1JAhmSkm+DA9C73FE2N4pxAdW7BZrYnJ3tQPVSmH7P7Ty6OFkd/D6R4FaqDFxA2zPtiQpg3r7YarX8tsQwHObe3PDBnQSmVgMnudKPzwqNhnkMYKPR0CqAeccxD0nXRktUE1CHmBTAsQvooDCXQG4XUFMku4Cq0Pk8SMssanLdyfaUUZ1ZD7o1VJkIYLGoCsGXBTdKj6vACO4MUbWahaGOAPrnzRYmthOPAcw64Nx0003ozLZx+Xr2AerydfffJAVQSonDN92EzYzlJTa3gOXleDkJHYQQqNUs7RretpF7N77dbsNxAFsnzQK+OphXxbEThCE7Z0yhh7RJnZQyvugYQEJ8a/I7plLJaxqSinkD+dvY6cz6JE8HjxxmJYCNRsNNiJFErgb5FEXA7Y8iNQnM0CWJOTA7005UAOVwmEuhDEKXBIayWKwx19hLUgAbpllPATTqegLIS1LNNxeByDvDpAD6bpFDHgLITTQ4FUA/cZJWATR7NtNKAqOU0ntNEFCmGMAiNhn9GEARP1Zhb1ARwAq5keYfbkYAa7B1aUCJSWCU4xi7gAohcPvp07iylv16Lq8pHDt2NHUheuToMWxuZ7O5tSO19QST4MbNxI87TvZshh68axj09ee941kXtfV63SX6Cbwgb0yhh7S+YRwDqJvgDNwhPbhp8vXnaLvS8XOmu9K1BFUo9H0ZMT+/AGRQAOfm5jLZE0Jgpj2T7Faa06UUGBHANB9nA5fNdrud3EYDRdEHc7xQ0rM2fV+0qpwyV+sAoNmMfzatiP0kJI0THARQKeUXbufKAqrsPo8ipLlf3C6gpvAVwBgBzFeDMwjuDcHkOEU1UtLNUHYFUGg2Ek3zPPif9++h+XtcgRfl6XEV9g20SWDU+FxeuAQwftz7DtNJOpQFNGcSGAC4/fbTWF1XenKqwZU1gdOnz6T+zZEjR7C1M/keKaWwsa1w9OjRTN8NuPdRHwOYv76eTwCT1rMDj9RlnwRbM62JBDCvSjJOJR4+TikfUlwx4QAIhLKIGECtC6hh3OP83BwEI0G6bKsAACAASURBVAEE3H6RmFl0RLry9J12O90FVA7yK3YzMzOQSUlgcpS9iEI3WpAUwISNInYFkEAAdS6gdNKb/XgW6MpAcGYBpaqJgD6BEI/ba+A7iAogVHhOpiiAaS6grIXgFWCVyAU06RkYJ6lBPAsoNQZwnAU0v0dWhWJQEcCSYT/sjHAvkOv1ut4FlBgD6NiBLKDesRwE8PTp0xjaClczJILZ7Smsbzk4ffp06t8dPXoUu10H/UH6wLe967pf5iGAjUY9MQto3nvoueclKoCDkatfDrTbbXYCmBb0b+wCyhgPB/DvSqfGuVAIYALy9p35+XmINBfQbn4C2JntJKprykABbLfbcNIS1RgQwNnZ2URSqfqDXO1LBdEFtJ6gAJqOs3qiVwQBJFxzkS6gSvkFTrlcQDHso1YrxgWUjfQS4buAJtQfNcmaO54PeGMAo/YUFMvz0X0XJ0zXk1LGE6FRbfqfD1DAigDuLSoCWCE32Algo65XAKlJYAKDS14XUAA4c8ZV81auTf7MyihWcBIB9Fw6N7bS7Xnn87iAuoum+HGbQAD7KS6geRe0s+3ZiQQwr81Ed0hlvtDRkigFyJRaeWnQF8oefRezAmi6iNC6qRrGAM7Pz8PpJnQcAOgO0Gg2csV7djrJBBC9AaSU+ZLAtNtQgyFUgrqvevkJm2tT00YFKDt73cModAsuymKxXtPX2DN9X/QuoPwE0DS+FShGARx/lo8ABp8rTwxg/FhZsoAmxgCC7gKqm+aN3fd1BtWN5wLqgRoDiKoMRGlQnh5XAQC/lF8EdG2h+Ic36o3UJDAUF9BoDGAeBfDo0aNot2d8cpeGlWvuv5MI4LFjxwBkIICb4b/PgkajkZgEhtsFtN93F/p50Ol0EhXFYT/8vVmRtJhRILiAWvFkOgrmLj9pCy6SAqg9R0mTzxUDOA81sKESkqyobh+dHOofAHRmZyGSagv2B5hpt3ONPX4/03RwBQWn3zcigE5P08FHLm6mBJA7C2jSeMobA0gjgM1mM64AUtxeC4wBdJOAOuFjhgi2s0YsAwHwbx4UQwD1LqAm/SdEyiOgKYBhKNCSwEzLBZRmL+72Svkub+0lKwWwNKgI4D5BeehfunuAySDWaDQx1GSb9MYG04VEcHffhABKKXHmzBmsXJ9891euuQlgJmU29Fw61zfT7a1vuW5ehw8fztzeRqOp3fk0IYCee16/pz8/GAjMzeUjgLOzHdgDff/Im1TGwzhIP3JCmS/ukkoimCoQaQogzQVUd46gUibEAJq4gAIAugm7B90BFhcWc9mcm5uDSHIB7Q0yZxT14JM7navqaNzI2xfb7bbrbx3tjCN7pi6g7DGACQqg6fuiddXjUAAjHVISFrnFZgEdK4BcSWA4bAHldgH1+k1SEhiTsinBxDxJ5/JACIGaVdOolMqNpWUE/d7ykSkppWaIUP45CqpC8OVBRQBLhsTdlbIpgIwxUvVGkgJoHgwO6MtA5CGAAHDHHXdidU1pCWoQl9cE7rjjzon2ZmdnsbAwj/XNdHvrm8Dy8uFck3USARwO8y/GfAKY6AIqjBbd3C6gaX3D2AU0IWmLpLhXJoCU6CAKZa4acGZTXVhYcH/YTeg8uwMsLeYjgLOzs3p1DQAoBFBHKpUZYfP/3okuaM0IpQdutSBpLGCNAVTKKIbLg04BLNM1A5EEVAW4gLIkgWFWADmTwIwJIJ8CWERZIK13hPJiac3A/Vy4Pcd075pKOZcFwULwXnKivOuxCryoCOA+QZlcQLWDA8E9oNFoYOho3ErJCuA4CYwn3uTdcTp9+jRsR2F1LflvtrsKG9uOHzM4CcePn8D6xBhAgRMnbs7R0pQYQDv/YqzRaKDRqCcqgP1eviQeQLoL6IDqAsoU85H2OZK6FkURMYBQtEWOJs7FP5cDiyNypxIUQNkd5HYfnpubg+oPtckd0B9gPmdf9PqZ0hHAEYEzJpUxBdAJn2dA6RVA0MpA6GMAy5UEJuhuqJjrAALFKYBlcQGVUqJerycqgCYbCL5HCGOZE6uWoAAyEPQgypQF1PXyKigLqJD+uqxSAPcWFQGskBtpLqAmg+wkAmi6k6yUMq4D6MFPBJMSB+gliZkU/+fhxIkT2NhKfvWUUljfBI4fP56jpfoYQEoinU6no1UAFYB+38lNAGdnZzHo63f8hn2g1Wrm7j/jCT8O0wWolVAInpRgJQrCcxFCJBarZ00CM0LeNnoEMEkBVN3++G8ywidjmqQtsj802owAAKTE7OUlbJ5NFd3VVmaE0gO3AphUL5Q3Cyi1DmB8zCeT3hzHs0CXBZTTBbSMWUC5E5W4Xit6BZBCADmTHLnlleKqfr1hvnnATcwBTgfQ9Ods2s5xGQgBOaIeFQHcW1QEsGRI3MmZcjvSkLYDZKwAavI7UJPAhFxAR82y7YREEglYXl5GpzObmgl05bp73VkJ4PHjx7G942CQUApipwsMhsqMAOpFHKPF2NzcnJ4AOuPzedDpdKCUPkPboA/M5iwrAQT6hsYmtwuoaRKYItxU3Yykcbck08lZX8vN/SdvGz0XUKUhgEopOP1hbgLo9zUNATSJAfT/fioKIL8LKGWxmJTwx7QvJi3UyS6gEVC8YJIWtFwKIJiSwJQ9BpBTAQTc5xwngAxZQBn7tz5rLk0BnFYWUOM6gMyJ/oCgAji2UbmA7i0qArgPULY9Ep0CSPEPbzabqXUAzRXAOAHMu+MkhMCZM2dweS150Lt8XeH48WOZCz17xC7JDdRLEJOXALpxM+FjlHs4P7+gdQH11uB53fh8tzvNsx70zRSSNBdQ8x3feD1FBcAy3JHnTgIDJCeq4VUpzVxAO52O2w6dAjiq9+LHCWaETwBjrliuomjcF3UKIJUARknqqMObKoA60BVAPhfQIhRA3Wc5kk9EwVcIvqxlIIqPAaSg1WppXECVfy4v0hRAytioayO3C2gRMYCmsCwrsT4jNQZQQvoxgBX2FhUBLBn2QxIY7eJz9LtZFtAGBrZC3Qpfoxe3R8kC6ruAjv412XE6ffoMrq4nJ4K5siZw+nS2+D8gQAATMoGaEsCkMhDeubyYn5/HoK+J9xzdwrwLWm8RH63nbNVcAjjXybeIB9JdQElZQKMgqGvcSWCAcrdRCIG5hXlXAazJ6EkAwNLSUi6bYwUw7l6pHIeWBTRK7EeDhbkCGGljAQpgmeLhkvoctwJYBAGkLJyDLqBcZSCC11hUEpgyKYCu14peAeSOATTeEKzXtYXgKerxjZgEJlgH0CQrewV+VASwZEh+kafckBSkKYCmMYADG7jraLg7HuwI/7wJnEASGMqA4yWCuboeP7fbcxPA3H777ZntebX9kgmggmVZuUpAAJ47TfiY97vJbqpLAOPHvVtoqrosHAwfXz4BDAcyt0spUIwLaFI8XBncK0NtKXEbFxcWgd0+xMmbQsfFIfcZGyuAUXVt1Bnz9h3LstCamQH6fYiT4VqbYt7tp6YxgLo2NlstVvWkiCQwJJc2TXu4YwCpKoeI7DxR6goCkfvFlARGCOFnGy6jCyhnFlAgWQGs1+tGfdz7jM7Rh+ICqlPDKARQB7oLaPyii0gCY9ovg3UARRUDWArwatgVCoIqlWSeNlAZK4BDB/e8oQaBGs5edXDrQYnr2w5evCLMBxylYDEQwFOnTgEArqwpLB8IP4fLo+QweQjgzMwMlpYWsb65Bl1058YWcOTIcu7rTiOApjGA3Z5COyKEeDZNMjkCwIHDQGcBuHbZ/fmWM8Arz9JcQHXTCHcMIC2NeMQckQBKywKGUaO8bTR1AQWAA0tLePX8Ncg3nQAEoC6uQRxdBEabR2wK4IhsmWwedDodXO0NYL3xtlEbVyGOHoK6to7WzEzu96/VakFKGR9jlMIswf2zmCQwfC6ggFujLxpdTVEAC3EBlQLBRlIJZTQJjJ+ciQjLsuDY9g3iAqqPAWw2829YAu71WpY+xtX02dR0mUqh2BW7smUB1YSYj88ZYBwDKKoYwJKgIoD7Aar8ZSCynEtCo9GAo9y13D23WbjnNndgffgHCg3CosRxHGA0RpuWgQDc4u3NZgOX1+IJI66MykPcdtttuWyeOHECVy5pJEUA61sSt5/JVwIC0C+aaDGA83BsFZsITBVAj+ANB8DJOwRO3jE+1+8pIwKYpgCaLmiTCI9lWGMvrR3cxeq5XUClZRm900tLS5AvD9wF2ZtuBt7k9mf7qbMAwJcFVJkTwLm5OVzt7bptfOMp4I3uRs/w8e8Y9UUhBGbabWw7ESrkKHQYS0AA5XIBBdz2RBNslSkLKBCfQ9kI4CgJDFVRDNodgJ9seSiTC2hSEhjK5kGtZmnnedN2NjQbJoqoAE6LAJoiSQEMkre88BVAIatC8CVB5QK6X1Ae/pfqH24yOHiD/SCybhraCo2m+SJCVwbCZMdJSolbb30DVjV8bXVdYWlpMbdL27Fjx7G5pSt9obCxqXw30TzQTZpUBRDQhF057j3J6yLn2Yu6lTqOwnCgjBbxxSSBibuAKgXUCigDYd5GC7otWm4XUNNU9IuLi3B2NP7Du33U6/XcLsmWZWGm3U50Ac27GQHArR2oywLa6xv1RWDkNqp5YfLWKQyCe7GYvMHBq2hwK4DURa6MfD76e174GScVoBSPYgcAcpRtmENNLHsZiCQXUErf0SmAlL5Tq2vKQBBjAHWgEkAdl2KNAVS0NobLQFQKYBlQEcB9AAWUygU0yUUu8dwEJBHAgQM0CbvIwcGFkgQGcBW+q+siNlmtrovc6h/gxgFu7zoYDMP2dkclINgIYMq5SfAW1bE1twJmZ9u5J5d22/3MIJJZ1Pu91C6gcLODmoC7DiAwLQVQGauei4uLUIMhVOSlVrt9zC8uGC1MOnOdxAQrpgqg7GkIoEFheQ+dTkdbBoKzCDxAXNAmvBdUBTCKsmcB5VYA2QigJSP2zTENpYmqAAJxBdAkZt1DzYorgJQ26orVQ5XLBTTP92SBNgsoFGQ0g1sOeGsvV0WUoWMV9gYVAdwnKLsLKCUJjEdOorUABzZtF9lxFEsSGAC49dZbsdtzsBsgL46jcG1D4eTJW3PbO3r0KABgI5IIxisN4Z3PgzQXUNMsoIBmze2YLbiFEJidbccUQO93EwJoWZb7bjC6gCaRK+MkAgW4gNZ0xIyQmjxRAayb2fMV8WgpiN0+lhbyuX96mOvM6XcjYNZ3Op0OdIUuZX9grADOdTpaN1UKAZxWFlDSgnYKBJCeBIaXAIaegXLYFvCenTIqgNxuqa4LaFQBdNBqURRA3kQ1urqZVBdQHcrkAqq7hyrheFaMXUArBbAsqAhghdxIGwRMYwABoB8pszAYwjgYXCkVcgGVRAJ4881uDNPVjXEbN3ZcN9WTJ0/mtucTwO3w8U0CAdSSZUIWUN8FVFPWLK/Lq4dOp5NIAE3c+IDkBQ1FAYzxP+4SC2QX0OlkKq0bts8vBt8NP2zRHeDAgQNmNufntS6gtXrdaKOo0+nA0dUBNCgs72F2dlajAOYvUzEJ7C6gzO6VwH5QAGn2/PYoVwHkIoBWwS6gZVMAo3My1QXUrePK6AKqqwNYQBmIMhFAKaUmPk+R2ujFCLsxgJUCWAZUBHCfoPQKIKEOoEdOYgqgYz4RRAcW7/ZFExVkxS233AIAuLYxPnZtRAa9c3ngE8BIMXjv9+Xl5dw2i4oBjCqASrlF4k0wNzeXSABNVRI36F93nDEjJrc9DgKoscl7zQqWoT0/yctunACabh7Mzc3FydVIXTMZH+fm5qCGNlRk4HF6PWPC5rqAarKAltwFlFUNG4GyQJ6OAkgyF1EAaYvjILx2FuESCJQvBjCurtFcQC1NfDQlhKauqQMI8KuhdALIWwYiCmoMYLAQfEUAy4GKAJYQune2TAQwbeAzmVy8yT6eBEagaTgRRAcWatapQ4cOodls4Prm+PPXR+6bnjqYB3Nzc5iZaWFzO9yezW2FgweWjAgbdxbQpCQwpi6grs15DPvhvuzFAJra5I5p0rqAcidYGcFcpeRN5JFEKE3v4dgFdBxjp5SCs9snqce6MhCdOQJZA4CACugRQloSmPgYw+0CWjYCqPs8RQHU9bsyu4AqpfzkLVR4dssYA1hEFtAo6ApgXLGjpFDQ2gNtg0OHItZ43DGAPC6g0m+X6YZ8BR5UBLCUKA/Z00HvH+4OFhQFsB9TAIXxRDCuOYPQv6Y7TlJKHD9+3Cd9AHB9U6Ez2zZyXRRCYHl5GVtRF9BtgeUj+d0/gfQkMKaEstlsaGsLmhPAOQwH4f49JMQAAqMsbYwKYHISGF61zq1ZZUjYplQGom6Y+EbrAjp0oIZ27hIQHubmdDGADubnzFyH/f4WzAQ6+tm0L7bbbW2NvXa7bWQvCdwuoNQZR7fQpCyQdZ8tWxKYcHsctgU8VzkJYHrJRkyhn5doZSCKiQGMg9sFlNLGQuoARsAXAyhhCSt0rMLeoDwjQYVEKJRLAUybQEwGiLECGIkBJCSBie4sUQkg4JZuWN8eP4f1LeDY8ePG9paXj2BzJ3wvt3aFkfsnkOwCalmW8cDd6XTiWe0JBFAbAzgYnzNBzdLHw1EImyb8gZ1cWYYlFsY2+dySkgrBm97Ddrvtuo8GFEDPHdQ01lPbPxzlJl4xgO/iHMwEOlIDSTGAOY6bgjIfcC+QAX4X0P2gAPqfV24RWzYX0Kh9ZnBnr6QgSQGkqMe1Wly9oscAxtcN3C6gVALIWVHP0mRS5Y0BrBTAMqAigPsEZSKAWgWQEAOYWAaCQACTYgApBPDo0aNY31L+wLi+I3DsGIUAhhVAx1HY2naMCWBSIo9Gw3whNjfXiYU0uccJBLAXIfp9t+2mk35SpkpOxY6y+8kdr+fbLDrukeACKoTA3PxcSAH0fjYlgFoSpRSdrAUygSoOBTDtu5iwH1xAKQvkIkhqsQqg4lMAR3aKmvPLlGxEN787Dl0BZE8Cozn+k+4CGoUCTwygJSRkpQCWAhUB3CcoD/1Ln9h5y0DQk8BwZQEFXMJmOwrbXZesbRDIGgDcdNNN6PUd9Afu9LLTdUOcbrrpJiN7SQoghQDOzy/oQppIi27bVrAD9Q+HfaA9O2PaRNSsAlxAp+BeaVpkPa0t3G1sEBY58/PzQDegro1+JiWBicJRpM0IAEBIAXR/NiVsMzP6fpx0PAv2Ywwgi72iFLsi7KlyEsCyZ5vUz+/UQvD8LqC6GMCyKYCcNqWU8RhApfTlhzIiqABaoyQwlQK4t6gIYAmhfWVLpAAm+YdLIYwGnHEZiPDxgW3uClKEAnjkyBEAbumG7RFZoxJAANjeQehfVgIIoNEwn0w7nTktuSIlycDY7RNwFcDOrHma/CLKQMRAUOy074RKTuSSBdy1CpMUQMoiZ2lhUUsAuRVA0/g6z54KxQAW4wLKHQNIwTTUNY75ipuwFQ6m5hV9meWPAaQlEHLHxujgyFsHcHy8HOAmgIkKoMXjAloRwHKgPCNBhX2DpEWh6cQyVgDHg6xSCoMhQxkIpiQwwJiYbe4obI7I2uHDh43tHTp0CACw5RHA3fDxvNC5pCiCGx/gLoR1LqCkNPkYJ34BRgSwY0YogdH1TSEJDGfsjCIUWU9rC68CaB4DCLibBLI/HFsrggCC3hd1LqCmCmBS+nqKAqgDdwwglXUUQdaiFsqmAMbsMzFAw0TVWkwj2QjFXlEEMB4DaGyOfYOxOPB1HP0mPy0GcDh05wJLWn7G3IoA7i3K1oMrJKBMu59JMYCmu0O6GMDhiHRQFcCoCyhlwBkTQEAId7A1JWsAcPDgQQDAzoj4eQTQO54X2nulzOMoAXchrFuQUBWSYCIYeyBIhbKTYjRYYwCJahg0ZZoo7jTcheCLiFN0XUDHBNBzryxLgpVGowGrVosogDQCmKT0lZ0AlpFc7R8FkDP9xjiDtWnJokko031MmpsocxZ3qYr9QAC5n6l+HqFtCHprL6tSAEuDSgEsI6IvsyqVB2jiLpBlWAfJzVIpQwTQ+7lMWUBnZ2fRaNSx3VXYIpI1ADhw4ACAMfHb2VGwLGkcI5WkANJcQDtaAmi6QPY+F1QAhwNBSpJRq+kVQFMyxK1wA5oJWhUTA2jaxsRMpQTSOzc3B6fbHy9kewPMtNvGNovIsNlut8NlIHoDSMsyHneSiF6ZXEC1C2SizULIWsljAAOWACAeM2WIooifhxvCBZQxCyh3WaBpgtUFlLgJGs4CKiEgfFWwwt5g4kjwxBNPZDpWoVhwuZdwoIgMUc1Gg5UAxgvB64/ngRACB5aWsL0LbHfdoqimsXCAu1icmWlhp+tOVttdN0GG6X1Mei6UyZR70e0thIeBNfdwQFsgc7tDau8/UQ3Tvb8WwR73ooQ77hHw3IcdX85XvSE6HSJZy3E8q00VcFPFYICZmRnjhVMSAaS8g9woQgGM2SuJjamCibd5ZsqYIVFHTimENSk8gRK2IKVkJdFFbI5xYxp1ALnKQNRGGUCllJUCuMeY+DQ/+clPZjpW4cZB0mKRMjg0Go1QDCAXAeQqBO9h6cAB7PTcjJ2LC/Pk3dSlpUXsdt2fd7tjVdAUulizIgig6XPRJ4ExT+QB8O/EFuHyo1MA62VPAkOwB2iybPYGpFjPpPg6St+ZnZ0NKYCqPyTZ07VRSMlOsCgL3CIUoBvRBTT0DISAowuWNoDj2HH7hijbPYuiOAUwjP3gAlq08psHRSSBCcYAAkBNWhUB3GMk9uCnnnoKTz31FK5du4bPfe5z/vGtra3qoe0FSjSQJ2UBpbi0NZpNDJwt/3ePDHJlAeVQAAFgcXEJq5cELKmwuLREsgUAS0sHcf3KJQBAtydw9GZzl1IAkFLAtsMTCWU3VbcYloQFraeQ2KM1t3IUWQHUxcNRJvykBTJ3+nSKAui2Jb5gYHUBVYrsAgoA6A2Bjvvv/LI5ARRCQEgBFalLQomvm52dBa6sjw8MBqS+qNsYkSUauwH+GCnt50t2zUUgNJcIwaYAOrYTt18ScJOUohRAThSRFEwHyr2dRgygIsYA+gRwpABawqpcQPcYiW/KYDDAzs4ObNvG9va2/1+n08Hf/M3fTLONNzzKsy/kYj+4gHqbFJxJYADXRXO3J7Dbd8kgFUtLS+j23fvW7QksLi6S7OkUQG4CaBF2AaMuoN74T3UBjSX9LiDmgzMLKBQtCQx3rcIirtl7pp6LpRjY5ILoUhNnTCKA7TZE0AW0P8As1R058qyLUNy4FcAyuoCWPQbQfwbC/R8XYfPslHGjnZuUFuYCug9jAKn3lnOdWISXl+8CGlAAKwK4t0jswW9961vx1re+Fe9///tx/Phx7O7usmcyq5AVqlQbqkUkyWg0mxhsj3/3CCBZAWR2AZ2fn0e356BmSeN09nF77mJit2ueIt+DEBJAeOHArwDSkpc0GnUMRz6gHhEkxwDGFEBjc9MhgKAtIKYS9wgmF9CRi6XoD8kE0JIS0WVxkmtoFszMzEAMxxZZSKoQcAIEjUoAueOuinALLMQmu0VejOcSAQjJRo7skQsoBwHkVuymFQNIcQGVUrKyoSJiAKf1TvPWAaTFAA5Gc35NuvfTkrWKAO4xJj7Ny5cv4z3veQ/uv/9+AMCPfvQjfOITnyi6XTc0dK9s6ZPAEBNGNJvNUAwgVxkID1wK4NzcHIa2wtaOQypd4GFhYQHdnoPh0HV7Nc0A6oFbAdRt+lAUQMBdsPsK4CD5e7JCPxGXSwHUNYcymXIrgPpi9bQJ3yf1/VFMEwMB1LWH0ndmZmagAgGpYjgkb3SKSBvLpgBOJS6sTDuWI0TvGZUcBecSIRkJ4JCPAOpAue5pKYD0eGs+BljEhrcORcQAchJAgPZcgmUgAFcBHASTAVSYOib24E996lP47Gc/67um3Xnnnfje975XeMMqlBeJZSAIC+Rms4WhMx6sBiMyWDYXUL+Mga1YCODc3BwcB35heboCyKs06RbDFAXQs+kRP3u0AUhRcbhd2gqJAYwyQOKGSSGLEs0to7Qx6AKqlILTp5Mr3fVRaobFs4AytDHqangDEsDy0T9+jL1MBCAlbJtHzfDsFKWOUEic7rOUvlgE0eAuVl/Eekd3z8oU85lUCJ5yzVEFsCYqBXCvkWlmOnr0aPhDJaojU2H6KCIGsNFo+KofAAy5XEBH4HIBDSoYVDUDGCfKWN90f6eSSimLVwCp739rpuUTPy4FMDqf7gcXUIq9IhYlOpJKsec/08HQlfSVYlcAhRCkxV2r1YIaDsfFtwdD0maEro3UJDDci8WyZ4bcLwgtXgVfSnvPblHqCIWwcauSSX2RMmcJIYqPh0s5booiFF9eBZDuAurVAASqGMAyYOLTPHr0KJ588kkIITAYDPDZz34Wp06dmkbbKpQURZWBGNjjwcojg6YTQbwMhIAQfAogwFPc2bO3NYp/pBJAIcLPgOqaq1sMUwlge6btE0DvXwoB1LenXDu+uiQwZBfQHMczQbNYoLTR7zsD2yWBoCm9uvaQNyO89ox2nBwGBXAa5Qu41YIyksKyJT+Lwl+8CgEwpbS3bRvOyA4HAeSONeNWAIH4nAUUsznGbY/yPcUogMW6vSrinDUYDPwEMIBbD7ByAd1bTHyan/jEJ/CP//iPWFlZwTve8Q4899xz+PjHP87y5V/72tfw7ne/G/fddx/+9m//Nnb+X//1X/G2t70NDz74IB588EF88YtfZPnefYkSTdDJZSDMiUaj0YCtUQCpLqBBSEHP1BZcwHIkRfII3+a2O3hTFRJuF1AdAacvumdiBJDixqev+2RsrpAJn9teMW6qvPYsy0K90RgRQPd95CaAQtLGxTFJHULZDuA4pL4I6FVKCsruLjYtUIlGUTGAAgJCWrAZ1Izggrjf75Pt6UAhqrrP0glg/Bg9wyZfFtBpuYBSngv3Bk6SCyi1DETNGq8naqJWEcA9xsSneeDAAfz1X/81+xfbto0/XLHnpwAAIABJREFU+7M/w+c+9zksLy/jN3/zN/HOd74Tt99+e+jv3vOe97ARzgo8SIw/IgyI9Xodg8CaZjCq9UVVAIOQkr5wCpI+DgLoqYhboxjAshFAz8UuOGFxqC6O7daxY4sBjM2n/ElguOsAlo0AciuAANBsNbEztP0dHX73SkYF0OJRKaPPuoiQibITQBYtouAMllTi4i9ehQCsGpRSsG2bRAyCpI+DAHJvHkzLTZE7BpCCaSmAZSr7UcQcOBgMUBMBBbBKArPnmPiWffKTn4wd63Q6uPvuu/Gud73L+IuffvppnDx5EjfffDMA4L3vfS8ee+yxGAGsUD4kxwASCsE3GhgOA1lAR2OhKQH0BtPdHrDVVXjiZZ7MasHFIXWhCIwJ3/au+zvVrZQ71gxwB/3gfSOTgmYTnjmbqPQmtadsSWC47RVCUnXHiIupVrOF7YENxaQAxtwr2VxAh8Aouy1VASys3lwA3ItF9gyEDPaiForIkkiBR9Cc7Q3Yq+cAuItcynjb6/X8n7u9Yghg+RRA3o0nIQSr//C05gNyPFzB10wtAzEcDlEP1L6tyRr6/YoA7iUmPs1er4fnnnsOJ0+exMmTJ/H8889jZWUF//Iv/4I///M/N/7ilZUVHDlyxP99eXkZKysrsb979NFH8cADD+AP/uAPcPHiRePv209QcBcm73//+92U+SXb7dUOAsRYMzcJTJwAUpPAbHaBnR7wyJMOlKLvnAcXh9SFIjBegO7shn83Bfduqs4mdUHbbDbheASQSQHkXBoW4l7JHAM4LQWQunnQaDYA2/GDernJFTXBije+KHusUpaNAOpAGceKIFL7wSa3Avj444+7Pwy6GJ59xv2RqGh0u93Az7skW0mgEEAdSaE/J95xx/NYCa2hCOSqiM023T2jtFEIAUc5oWumoIgY836/j5oIE8BKAdxbTFwZPv/88/jCF77gP/gPfOAD+O3f/m18/vOfxwMPPFBo437pl34J73vf+9BoNPBP//RP+OhHP4q///u/T/2MR1j3M5RSuP/++/HhD38YSik88vC/wx4OS3Nd165dix0Tgnbv19fXY3UALUvi+eefN7J39uzZ2DFHKayurpLu49bWlv/z+fPnybuA3oS/23Pv4SuvvEKyqZvcqdcczyyqSPa2t7d9tdcjgi+//LLx5HL9+vXY7ieljefOndMev3TpkrFN3XPZ3Nw0tnflyhXt8VdffdV8gadZlFy5coU27ii4xGrUpgsXLpCKPEevjdoXL1265P4wtAHLtU295ig5GxLHbt3C8MKFC8Y2dRuptm2T2hh1V3Qchzxfqch93NnZYb2P1Ofy7W9/O3bs2WefJdVyff311wEAotXB1tY2+R7qFtgvvPACrl69amTv5Zdf1n4HrZ3xcefll182JjFra2tQCK+h/uM//sO4jd4z0bVxdXXVyKbuufz4xz82vub19fXYNT/00EN4+eWXsb29ndte0hxImbOuXr0aSgJTlzVsb9P7eAVzTCSA6+vr2NnZ8dPV7+7uYm1tDZZlkSby5eXl8eQLVxFcXl4O/c3S0pL/82/91m/hr/7qrybabTabuOuuu4zbVQYIAI888giUUvjyl7+MmhSwarXSXJdu0JupC3Q6HeM2PvHEE3AUYDsKlhQYOq77p6m969evxw8qt/A65T7u7Oz4P585cwa33nqrsS1gvKAdDoFms4E3velNJHs6te/o0aOka+505kKk/8477yTZO3LkCBwvCYzt7qTefffdxva+/vWvx45JKY3bGNyFD+Lmm282tql7LouLi8b2kjZGbr/9drzhDW8wsimkACLc8ciRI6RnvbCwgIvrl3wF8I477sBtt91mbC8659SI46JP1oY2VM29+De84Q0km9FnTRnHAL3ScOjQIWObukWmZVmkNsZc9YVgn69arRbrfaSMETp7AHDLLbfg2LFjxjY9kmrNzGPQXSPfQ92mWjD0Ji82Nze138F9H++8807jGPuFhQUIiNAaqtFoGLcxSaU6c+YMDhw4YGRTd82UeXp+fj52zQBw6tQp3HLLLbnt6eJPLWnhwIEDxm2cmZlBXY7HxrqsAw5Ks679SUUawZ5IAD/0oQ/hwQcfxD333AOlFL773e/i937v97Czs4O3v/3txo36qZ/6KZw9exavv/46lpeX8aUvfSmWbOby5cs4fPgwANfd4kYpPyGEQLfbxUMPPQQAWGia18QpAt6k0hmtxd57Zx3ffG1Icg/wFna244biDG2FBqEWkM5FiqMMRHChQ6lV5MGyLNRqFoZDG03ChoqHImIADx48gLX1a6jVgH4P+Lmf+zmSvXq9DttWAAQcG2g0aPdRX/jX3F4hRdaZ7RUTl8KfBKZRr7sv9SjFL/Wd4Xav9NvD6KYaRRExgBR3sSJS+cc+zxEDyOyyyW1Pdx+piVt2d123Tzm7iN7aRSilSP2Hu+/oyFARCYmoGTaFDK+hFhcXje0l3X/u50J1h5RShq6ZAu/+LzUXYTs23n/q1/DFl/9vsgtoXQTWT5UL6J4jlQAqpfCLv/iLuPfee/H0008DAP7oj/7IV+o++tGPmn9xrYaPf/zj+NCHPgTbtvEbv/EbOH36ND7zmc/g7rvvxi//8i/jH/7hH/D444/DsiwsLCzgL/7iL4y/rwIfvEVhqy6w2BL4xTfU8I3XHNLg4O2ae6UgbIcWu5ZE9KiTVbBNHAQQcBecw+GOGy9VAKiL+Hq9AUsCnQ5wrUe/7nq9PorHVHBsoFanpvzWwXxyLmLC57aX9EwpNosoNFOv1yFcad//nYLiCODYTZW7jVRMgwBSwU2uirAZ/bxTAAEMJnExgeeuZ80uoa8Uut0uKds0d98pIgZQv4HHS64o2C8EkBPe/DJbm0Wn3sE7T/xv+OeXvkhaS/R7/ZgCWFSpkwrZkLryEkLgd3/3d/Hwww+TMn4m4d5778W9994bOvaHf/iH/s8f+chH8JGPfIT9eyvQ4BO9wBjmEJNaeIsurwC85wJqCh0BFAnH8yA46FOVNQ/edTYadOWBO6MaMCJsGD9u6gLZU3sdG3CIzxnYH4V/dUlgKEhafHDfCxaC5Th+PFfZyJXvUsqoUkZxIyqARahr3DajMYZUewAfAZSdJf93bgJIIRq6zxaRAIj+nvONE9MigGUiQ/pC8LRN/n6/j5kgAbRqGAzKc803IiauFt74xjf66l+FPUSJUmB7i0wVWMU6ikaIvEVXUAGsE1wDtUSPwQU0CHqxWhdjAliMqy+VFNRqNQQZIPW6vc8rxyWB3AtuKoqo28b9PUUogLo1E0ffEY5yBwjwvTMe2BRAxy0CHzpWEnCn8teSK2Nro88XVWMvAKpyOQ2SmhQ/nBW+Ajh3KPS7KXRtpBBAHUkpggByZ9iktLEIAsj9XLifgW6NZzMQwIYVdAGtoz8YlK68y42EibPxD37wAzz88MM4duxYaCfq4YcfLrRhFcoL3SDggDZoewtDtxSEcAkgIfZRGwOYcNwU3ApgrUZfeBahAPqELfK7Kbzr9dbcdeJ1c8cAJk3sXM+bA9NSAKmo1WqAPXYBLVtJkvFmhANRkJsqFbEFkhCkxaKWPJZMAdQRDerYHXX5JNsrgABubW1BSAvWrBuzpku6kgfcCmARz0W380RV1zjfwP2jAPJdtXaNpxyaC2i/7yZ+GaEua1BKufUBS7bpdqNg4mz82c9+dhrtqLCP4O8OBcYwxeUCOlqbDB2gxuwCmnbcBFyLbW8RWtQgSF2Q+nX2Rs+bSoS86/UIYIOZFIyOGtsrRF1jRjFt5L8+y7LcwWE0WFD7TlEEEPZYAeQm+tQ26mLVKC6gRRTz5iaAOldKchujLqAFKIBeEhdTbGxswJrpQDZnAdAJoONMgwBSYwB1x8oTA5iEMimASTC9F1EFUCkFh6oA9npozIYVQGBEDCsCuCeYuII9fvw4jh8/jlarBSGE/1+F4qB7ZRVrqWsadItPagygTwpGl2k7iqSITcMFlOs94FQAdaASVXcRz2wPrguocgCrEALIjzIlgSliV1pLo4nXbFmW+1I7PASQG357Ai6g3G6qVHArgEUk8tAtaCljrY4Acit2RSiAVAK4ubkJ2ZyFaPEQQKV4M5XqXUD5kwpR4D6XeO1aUxQxvygNaabGj2q/h0oARx/31p+Uub8XVQAtd5wt4rorZMPEme6xxx7DX/7lX+Ly5cs4cOAALly4gFOnTuFLX/rSNNpXAUAx+fnMMd4dGoMrCYwXA+gomiKW5ALKSQC5FrOeHY6FJ3dGNSBA2CK/myKoACoF1KxyLbj3gwI4rUylVEgpQwpgKV1UgUJJKrdyVYQLKHsZCIySPhgmMNG5UnK7gCqlSGUWdO0J1ok1wfr6OtDsQLY6498J0KlzlAV3EcScG0qpsi2ZYnAipFlAlDQJjNt/vPZSxu/BIEwAGwEFsMLeYOLT/MxnPoN//ud/xq233orHH38cf/d3f4ef+ZmfmUbbKgRQpjhZIURsIKAmgfE+O04CI0pZBqIIWCMCVJQ6wuECCsBngGwKoHIVwFqNdt3TSvtdJvKyH9oIjNyHlWLrO9wuoOOMxspXMqjvIXd/5I7jmpYCSFnYcRNApZQ26yc3kaYqgNfX1iFmOhD1FoRVIxPAIhRAISJzP9EFlBvcGWSLcCmN3jMhBImYJ7WR7gLqwlZuXzcdG5VS6EWSwDQsNwNzRQD3DhNn41qthqWlJTiOA8dx8La3vQ3PPPPMNNpWYYQybmZJKUKklMsF1PZcQBV/HUBuBZBLbfEG1bISwKIW3crxNg7KpQDuB+wXBVAI4SuAZQwf8Mcsxymtm+p+cAHVfZ6yoOUmgEn3i5K0JTaXSEnO2rm2vgbZmoMQAtbMHNbW1kj2uEtVdLvd2DtchAsopT/atg1RylXTGNF7JoQkEyHOK+ZWAL33ryHjMYCVC+jeYeLKa35+Htvb23jLW96CP/mTP8GBAwfQbren0bYKQZRJAoQ3EIwnQEVUAKOF4B0lSPb2UwygN6iWbXHsQQgRcvflUhQ9BbCMqpUO0yjwnRWFxKXojhHHHdcF1DVexv4tpQyRVKCELqAxArg/FEDKwk6npFEIYBLR63a7mJ+fN7IZa4+gEcDhcIitjQ102m575Mw8rl+/bmwPKMYF1FUAg3O/guOYZ4jUdT1u19yylRqItlEKSdqMKKwMxMis5z5t+oy9PlcPKYCVC+heYyIBvPPOOzEzM4OPfexjePjhh7G5uUn2c6+QH2VKAgMAVswFVJEW8t6iy5uwbIemACYtFhxGAsgFb6Ir4wIZ4FcAo5MLlQDqJz/+96WszycIWhtv0HvGHKfIuRjTb1jR4oWKKOa9nwmgKYbRZyMkaW3kqX1yxiWAYmYeV69dM7anAm7NQZBjADXvdL/fR6vVMrTKW7dPlwSmCJi2Uff+CSEKIYBUF1D4WUBpCqA3XjU0MYCVArh3mLjC/va3vw0pJaSUeP/73w8AeOCBBwpvWIUASriGklIiOLdwu4BSYwqn4QLKtdArOwEEEJqjWQmgiseT5oW+8K+5vWm5V5atOLGO/7H0cc9GSfu3EGIUp6j83zlBuYdaoicE+gQFcFoEkLKg1RFAytidFJtHIWyxzUQpsbW1ZWzv2ojsyVENQNlexNVL54ztaVViYqyZ6wIaH6+73S6BAPLCcZzYUFNEDKCpTd39l5ClIkLRGEAHtPho79pCheCtigDuNRIJ4Oc//3l84QtfwGuvvRYifNvb23jzm988lcbdsGBe0BYBKSXASAB9BbDAGED3uHn9rChuFALI3a6QC2gJXQOn1Z6yuYDqwNLHGdvKXW8OcPuj7ZRTASxiEb8fCKDOlZKiACYRPUrSltgcIyQ2CQTw6tWrAACrvQAAkLML2Fhfw3A4NJoLdfdfEF0N0wggJxzHvOYctwLI7V6Z+Fx2ee8hwKEAuuByAfUSvwBAc/RzRQD3DomjygMPPIB3vOMd+PSnP42PfOQj/vHZ2VksLi5OpXEVxiibD7ub3W/8u3J4XED9GECHpgBqy0AwxwBWoEMRNw5cG9N5N1iLE5eL8yaibONOEfCfq4r8bghOkqpXAN2aWqZIih+0bZu46A6DQgp0hK0IBdCUANq2HXuuQlrY2jKPAVxdXQUAyNklAIA1uwSlFK5du4bDhw/ntjdNAkjLYKk7RksCw2kvCaY29c9FYLeEMYBcSWB8AliVgSgVEgng3Nwc5ubm8OlPf3qa7amAhEicki3EpJQIDrNcZSAcPwkM3QVUIH4vbcYyEPtlccydhIIlMQhQ2IKbihvVBbTo3qxLw0+2yfDs/SQw4HEB5Xxf9Atr/hhA77jJmOslAYmCoq5tb28DUo4nBNAIYJICaOoCqnX1lBLbBAVwdXUVENKPAbRGRPDKlStsBBBEArizG88CClDLX8TfD4raaw9tTabS8riA6u6/FBJdwj102xJ/LlxlIJxRGQi6Ahh3AeVWjytkR7nS71VIRNmohpQyUgieKQmMFwPoKLoCGBkP3RhAPhfQMtYU1IHHjY/PXnhyppcG0McAlj+hSfnayLsQ8z8vBJviWeg9Y9qQiKKQGEBmF9C046b2yAQwojSVngAKiV6vq82ymgVXrlxBvbPoJiUCIDsuAfSUwbxIVJoIzyVJAaTY1L0flHHHTc7DRwCT2sKrAEr2e5h2fBLGm7SeAlicC2ilAO4dKgK4T1A2tUlXCJ6DAI7TDvMngeEuA8FFAL1ny/GMiyBDjuOEptOy9cWytScrStfuApLAKKVGazExykpYsmsuAJzXqFUABU0B7Pf7iZkcTZC0cCXHAEbaSPHe4HYB3dzcjB+UVvK5DFhZuQwxe8D/3eq4P1++fNnIXhEKYHd31yeoE7+LAJICOPL+CaKYYu5mbdT1OSkkugXEwrEpgODPAlrVAdx7VARwH6CM4UJ+fS/Qd4eAQAxggABS7EVJC+DeR07VrowEMM0+1+f5rpvFTIBklBfTUim5wbJ5IIQ/05TNHbkIRN8PyvuSpABS6gAmfdaUACapaJSaeJubm64LaACOJu4uK7jbqCN5HjEyJYCXVlYgO2MCKJttWI0ZVgIopMTOjhnpVUqh1+vGlNmk78pjNwoqAeRMPMXt6ZPkAtrv90gb1EUWgveekemmvHfNwTqANWnBEuXKfnqjoSKA+wRlW+gEXUA56rl5n+VyAXUngYTjTLhRCKB/nZ6nHFv2UxYz7IV/p5Vhk9J/iokBLEY9hoD/sLkXU2UbF4EpxAAKYDgYGN9LbgVQS66IroYbW1sxAgiYEw0t0SPU7dMrgG57TUpB2LaNq6tXYM0fCh235g5iZWXFqI26+y+ENE42Mhj1OW4XUB0o87Q9HEIwuoAmfZZTAfTuqWn/LqwQ/Oh3LwmM6fzijS3NgAsoADRqjYoA7iEqAlhKlG9RE0WQ7DmMBNAbyKgKoM4NxHUBLZ8C6NkpKqaQS3Xx7ieVRHNPVmUkARMhShhDqrmN1Gc9jgEULPb2w7MungC699KUsCURQFNVUbv4JxZF39rc0ipNpuqan1QmCCmNFUB9DKC5C+jq6qpb+qBzMGxy7iAuXLxk1MYkomGabMSzx10GohgFcPJ3ZEVSW0zbmKQAJp3LAu4YQCG82PzReozoAqrLAgq4MYEVAdw7VASwhOBOi1wEggMBhwIYSwJDTCqTVAjecfgUQC410WsTByHgnkz9zzMmgQl/nh4XpnP3LdsmStne32nBtm1ACjYFsAgX0KI3JIpIApN4LqvNohVAKUgEcGdbrwCaFlrXJZWBEMYEcGNjI36Q4AJ66ZJL8qz5m0LHrbmbsHLpklEfSiLmpmqd9zylhgBSnrXu0ihz69C2p6IAcroje/eUpqTyZQEFvOzIYTucWUABlxBWSWD2DhUB3Cco2wIy6ALKqwBilCyCXgdQ56zAQoaYbEXtFPWMqe2MTsZsi/hCXUB5bBeJstWk1LmAsmUBlcUogGwEMKBSkjckojGzBSmApmqBu+CKv3ymO/Hc7pX9fh+DwUCbbIREABkVwI2NjRiJFqMkMFpyOAEXL14EgLgL6Pwh9HpdrK2t5bapVQClxGDQN3oP/f4Wey40d1/dZh2JAA4GsWdDeaW5FcA0F1DTd4Y7UykQXePxJIGpRxTAulWvFMA9REUAKxghmARGBY9R7GFEABnsJZWBcBzi4i4w0Jqm+06yWZQCyKawje4nN3HhyzQZOLYPFMAbwQV0PyqA5DZGPk+pf1iEAtjr9bSbL6YuoEkxgFvUBCuMLqBbWgVQYtOwcLubpCayQSklAGFMAIW0/MyfHjxC6BHEPOCONUtSAIWkEUDdO1wmBbAIAhi9h5K4qVPE5rElJfwkMKArgA2rHoshbFYK4J6iIoD7BKVTAC2LVQH0BgZHjXfrKAktiioDESR9XETIs1OUIsSlAHpPg2sR7+UG4VYo3S8hmWRHEQlWuFFEa/yMfEwKYBQs95BZAZxGIXiAQAATXEBNd+L1LqAS29QEK5r5xJgAbiUpgGaK4sbGhlYJs1ozRm28cOECanMHfRXRQ23+sH8+L3Z2dmLxehSlKSkGUAirVATQ1tQBpIxuRRDA6D2Uo/hR7mQ6NBdQXgWwEUkAA7iKYFUIfu9QEcB9ADcUt1yLxaJiABWTvaJcQIMTE5cCuC8IoIj8ToBPAIV2HWpsL3KU2R6RGEQ/SiS+xbSRX6UcDodQUkCU2AXU8RRkpiy3MUWxoBhAU8LmKoAFE0BCfF0RBFAXAyiExPa2GUnd2Nj0k76EbLZmjRTA8+cvQMwdih13FUBhRAB3dTX7CLFm3kI9TgAF+yKeMrfa9jC2eVymQvA6BVAQYwCVUtq5lOoCOrZDI4DdbjdUAsJDQ9bRr1xA9wy1vW5AhQwQgLJLRgADA5jXMopiJ/zddx4X0OQkMHwEkC8JjOcCSrdXhAuof51MLqDhshKKxd44X5mLkolrhcTXTQMsLqABBZC6aVJIHUClEGSA1IQRsTYSnnNaDKApYesmEEBTRdEv2h68bimxW4ACaB4DuAVh1cNvoZTY3d4cLZ7zzV3rGxsQgRgpD6rZzk0AlVI4f/48arfdEzsnrDrq8weMCWCM9EpzouErgJJPAUwaA0kuoEMbtQjXKJsLaFwBFP45ExThTRLOAqoCx/Kj3++jIetQSuHx17+OF6+/gjNLt6Ema9jp8aqeFbKjIoD7AtHl7d5DSN4kMEEXUM8ehVDqYgA5ykAEF7DcCiCHPW53GsBtV0AgYVUAORLBaJ91yd4XXXPKRgBjfUfwbB64CqD0f6eg2CQwdJu66yNnAY2SKxE4Z2KTWQH0E6wEr10I9Lpd2LadO5lXYgyglEbqmm3b6HW7EJ1m+ISQUMrB7u4u2u12LpuuC6hGAWy2XXUwB9bX17G7u4O5hcPa82LuMM6dP5/LJjBNF1BhXFw+ac6juYAOgYY+I6bJmmI6MYAcWUB5YUkLavR4qC6gvV4PdVnD469/Hf/jh18EAHzj4hM4OX8Cw3rJ5uobCJUL6D5B2VxArWASmNG/lKydQghIIUZZQN1jFEKplNLxP/KCscgYwKIIAZsL6OiGUolqUAHkUGWnlQWUpAxNSQHk3gnmcAGFLLcC6CcRYkhUoyNl5Cyg0UUrUQFMygJKKgMRzYhJIBoeAYzGw0FIIwXQb4MmBhDIryrato3dne1EAriek6SeH5E7K4EAWgs34fx5EwWwq3V7dc8RFMCYqmiuACbNoabjhFJq5BES798UmzqYjhM7O8W4gOY5ngUymARmZMd0jecpgE9dfiZ0fKO3WSWB2UNUBLBkSH6Rp9yQCQi6gTi+okOTc4QUrDGAJucmoZAYwJEdLntR0EnvIDSdcrmAeplFiyCAFBSSnIU5BnA6EOQ2DkpOAH2XzUBAKuWadZk0yVlAY+SKgQByK4AatQ4gEEAh/T4TtGmiAPoET5MFFEgoY5GC7e1tt89o5ifRmsXWVj4F0COAtQQCWFtYxvbWZu5r39HFABKei+u6GA/cFsI8Cyi3Aujb0/Rv07EnqS3GheA1LqBCCNRkjRhLyVwHcLQeA8aF4CkuoHVZR98Oj48KqiKAe4iKAJYMiS9syRigFcgCyhGzB7iDSzAGkD0L6AiUBV5wEjFNmx5F2V1APQWQPQuo4MkCqlcAy/W+lD0GUHu/uLLmSuEvPMtGAMdqNA8BTFrMmNpMUwBNF04D5kLwW9vbcbI2sm9KAK1WK35CytzqWqgNTGTII2IxhRKuAtjd3c01N5w/fx4QEpYmCQwAWAvL47/Lgd0UF1ATorG7u4tavRX3rCEUl08aD0zHCe9zOgXQdCwrxgU03r5GvWVcB7AQBTCY54FaCL7bi9UABNzn1O9XSWD2ChUBLBl0L2z5IgDD5IwjZg9wB5wis4ByEJgiXECHjFlAi6g3N95VjfxuiGBSGZcAMiiKDLGEHqaSBRS0583dxiIKCQOjTRIpS6sATosAGsfrMWcBtW17nJgnYtM4CczOTqICaJIJdHNzE2jOxE9IaZQFNEkB9MhQXhfQtCQ1otUO/00GXLhwAfX5QxCWPiWDpwzmJoDdrlZpAsyzgNbrzdhxIaSxcsVNrqIJy4Ioiwtot9eNuYACQLPWKlUdQDdz8ygJjKIrgA1NFlApBPpMG+kV8qMigBWMwF0GAsAo6cTYHoVQ6oqDe7+XVQG0C3IBJccAOnYoCQzXIt5bc1MT82gVwJJtmcTaw0B8OcHt5uRhaPMqgNH2cBJAUSABNB0r0hRAE5t+++IyjnkMoJcFNGIPILiANuJEA0Jic9M8BjDuDvn/s/dusZJlZ5ngt/Y1LufkpbLyXhdjV2W53Nhgu7HLPXbTGNtgw9CI7lbDk6EFngEhARI8IfHgeQKBRPdDC1lIgDQvI1rCppsxULZrsMduD9M2jOnGpnDZLtcl65ZVmZV5TsTee13mYe219iX2jhN7rT/y7HTFL1XlOXEi/lj7vr6MxGvMAAAgAElEQVT1ff/3u42xMqnpZgCBYaDyqaefBjvRLf8EylYQjA0GgMtlhwuoZxuIsAcAFkXutKD1WmMAlVJYdgBzAEiihLydhm8NYKXy8mMAtQR0dYGDIagWpXZx22MHAO+QGJukrXFzIAKA1gTGb2gA1t/sffbl2BvBb4MBFJw3TGDIagDLnBQM4NhNQDsZwC30AXQN6pV4E7zgGvyFdwID2HrNIfpAGXXTdsCNAawAIB0DuFgseuWVLgzgqzdvQqWrElAWBFguDgfff+wYiGoAbS1eFwOYDmMAdQuIZ3vr/wDoVhD7Zwa1guCcgxdFRx9AhjBOnSWgYdQBAAN3WSk1ADTnRtfi8RhqAPM8h1KqkwFMwolfH8Ce110jYHWjPwoA2CEB9ZSz78IvdgDwTomRAcBtSEDBKE1gRK8qkGqCR14DONJG8Jw3x0UJABnzc0ms56uHDwO4jcUW6hpAagloXw0gCQMYVgzg2NpANHpS2l6knm0bBrx+VPQxgCwInHJSA0AhRHdbCQ+p4c2bN8E6AKArqDyqBnBoPsPuddcAaunqpgDw+vXryJaLXgdQm/fEOTz19NMbj7GvaTsARHHqzABGUQcw96gr7LsfeJvAELqAUi6O9TmpAkASugHzbQWlBLTIC8QdEmfD1FLNpXYxLHYAcGTRO7G7zeM4KoJ6G4j6ax7RJdtwDSVX99hYawCNBHK0DKDgdYKEtg0EAyQFoGyfOmO7YEbeB7B7LP4uoIILIAjKyYT/g77r/CZp8rzlGkBnCWied9YzsSiiBYBwA4BWXtnDrrlIQG/dugWWdADAMufQOsB+BpABLBg8RivvJGAADau3jgE0f7/67NWNx1gBwNWTJ3AEgItFnwSUNb5zSFC3gagYwM2/66igbFZv9lHQMfVOohTLBW0NIJUEVHpKQAtedEtAPeTsu/CPHQDchVNsQwLKAjoXULNi1RVUElCKm5bpWwRQAcptAMDq80HI6BnAkbWB6AvSPoBM11aOJbZVA1jwQoO/LTGAgN91aLcv2F4bCIC+Zx+LIr8awHZO5jZGCyRWWja41ddJKXVN4RoGcKhpy+HhoWbrOkBvkKaDGcCbN28iSCboPC4DGUDbA3BNDaD5+8HBrY1bQdjj0sE0hZEb06QZwO4aQPP3obGtGsCuY+N676EEV+uAeRK6AfMqCJ3Q0HYB9WsEn+c5og4AGOwkoMcaOwA4stiKA+EWon4DowBsgGYAqSSgqosVIjaBoWjbUH8o0TCAzd8ZgZW/EJWcNgjoGEBrAkPhAtoR1NcMtQtoF0vtG2OTgAougHB7LqCA3/ndYADLMfrko2YA+1o2IAydJk12HB2STZcxWoDXNhsBEMTxYAC4WCx0PVPaYQLjCAAPDg4QdOUDwOJ08Bh1vg6XUsAyl5uCyqtXr+q6vP0za98XnjwLABvXAa6TgAYeALCzBrD8DldX2iGvb5qvay7im7MdLvdGs4+6agDjKNGMv0Ns2wXULGC6zPGUUiUD2N0GAtgxgMcVOwA4shgb0OuLOjiTVAwgA1mto7gNbSAoAKB1LSNwwwS21QZC2J0XMEYOAKn6CvZ9zxiiuwZwPAzgtkxghOkDuCUTGN+cY5eAFkXRAwADLwZwlQxjWPowgB1jZEkymNEwwGmdBHQoY7dYLMA6pIsAwOLhY9QupT0AMAgRJJONQerVq1cR75/pbQFhIjxx1r5/k9gKAMwyRFGy8vo2agC920AQ1gBSPl/WHZcknCAbqQuo9GAAzX7fSUDHFzsAuAun2I4EtOwDaH73YRTX3PioJKCUDGAY6gfKNhYA/CWW1UM6CIhdQAN/Joy6D+BWor2JBOY3lLEtFlUIUfYBpJGAdu0zCgDIagDQZ5v7JjJ+ALDjD4EbA9hfAwinflyVBLTj3h+5A8DONhA+JjDxKnABABUnw2sADw6g4g6AWkaQTjce47NXr4LtrWf/ANgm8c8999xGeS0b15bmAgiixAns51k3A+jbl7IrvGsAOy4a1+cgpSrL7KOuuY0PA7iN0PO5JgPoAwDDLtMk7CSgxxk7ALgLp2hIQIlcQA0BSNUIvi/GCACj8t5IzYb5MmxKKQgh7XyRGgAy+DNh1DKi2xWSgPGlCkqjAxO2vjWo5JXe18zIGUBqAKi3bfW+qkI3F9BqHB0S0Hz4GC3r03Xvj+PBrFDFAPZLDV0YQNXBXOkxDq+7Ojg46GYoazk3BZXPPfe87vN3RARximi6vzEAXMc0hbGb2Uie5wjXMICUAHAbjeBHLwENE+R55jQ/2UbpECvbcgHVwpvLHM/cc2LW7wI69mf1d2rsAOAunKLuAurrEGXCu41ELdbd9sYkAW0DwG3cCH22t/1ZRggAjQuoTz88oCpQ7/2ewfnoH6bUEtDbUt/ouXhgrw9CALgtBrAOAH3O776xuABAKaV2yO26LwaB03avrwEcDijXSUBVFA1m1+z7OxlAN2OZxWLZywAiSnA4EADeunXQCVBNqHgzCWie57j+ysuW3Tsqgv0zwxnADiQUODBNnHMIIXoAoDsDuLU+gCOVgFoGsGN8Saj37VjYsPp8zscExuz3qIsB3ElAjzV2AHAXTtHFAFIAwDoD6AMI1W1gACnAWl0C2s7vFMQ2+dUDVQela6dpBO8vAaVvgkseXSYwIxreNuol7fVB6AK6XQbQfMU4JKB2uzpObhWGngCw/Re32t61DGAUD5YaWgawB7AFyXDXzsVyAfQwgCyOBzOAh4vDtQwgi1McbABSX3zxRQBAuHfXRt/L9s7guRde2Oi9ttaz45kclEzTkDD51jGArn0kh7y+cb6O89H1HkcpjzfXXxczG5cA0NUxmDrqElCfRf5NJKA7AHg8sQOAd1CMaULbVQPoLQE1jeA9HKdMKNUNCszfXKP+YBojA9i1ZT6T+7akJiBwFaU3gbk9LqDUsa5Vye2ObRjpNCZjW3QB9cmpqpvXViWgXmCt607m2Ai+1yafuY1xPQCMBktALbvXAwBdTFsWyyVYvOpACACIksET7uViAfSYygAAks0koC+UYC7YEACGe6fx0osvbnRfW8cAhlGMPBt27lgAGHbsRw8rf2rzqXWfo24D4TLGyoRp9bhE5b4dCxhqzvH8JaBRlwS0TLeTgB5P7ADgHRSUEknf6GoDQS0B9QKAHVCIEazwm0kSRTsEYJUB9L8RtmoAQQd4AS0BpZrEmzl3H4DbNLblYEkZ7fORETCfnd9D2QYCRAAwCEjAFbDlNhAEY6SUgK5jABEEKByuwyrnqkeyyxgt0OhyAY1iLJfDwJUBdyyhA4BZlq1lALMBYxRCgBcF2BoTGBZPcHh49BhfeuklAJszgOH8LhR5vlGPwQpodDSrD+PBcl+TL+hwK2UeAJCaAbSLi2v+NjRuGwAs2ySMhQFkQX2O5y4BtXOcDgbQHCmKudQuhscOAO7CKbraQFABVAriRkm1FWdIq2cnaIgOVA+RMGj+7hor+475AcCVGkACxq7OKlLUAErVfaxHxQB2SUDXVqq6hes12L2vGM3iAaUJTEeQ1LgybBUAumz3uqbW3jWA7XBk9rMsa4DnRkTRYKmhZQx7AJtykGzmWQYW9TGAMfI83/g8twC1r6aw/FuWHc18Xrt2DQAQzk5u9N3B/FTjc+uikhp21QDGkFIOOt4mXycD6CHj2x4DOE4JqAWAHVPvsTGA+twpJaAeDKA5z7qMb3YmMMcbOwC4C6fYShuImuuU+X1sYW5UcUQDAO3NMWj+Thk+OavGurD/estUawwgqmeMe77b1AieOrbBALrGNtpA2HMnYLbNwjYYQGoJqLdCoOO+5QUAO26DzBEA9tZIMaZ7Ng6MPM8RRFH3WlsYDZdXLpdgUdRZuwYAKhruLJrnORB199ljYQyl5Mb70rpr9rmKln/bhFW8du0awmTa26OwHeEAAJjneaO1ST2CkmkawthVDGBXM2/NDNIBQPf7xDZcQClNwYqiAOu0gKkYsrGwYWEY2vmYjwmMZQC7AGC5I8ayza+12AHAXThFlwSUrAaQwgRmS30AK8bOvyF6Ix9RGwhvNNWKVQZQkU3iGWOaUfSUgN4JjeC7wlf6ShnbcD5tyCuhgaBvW4muoJOAtl5ziL57gssY1zW1RhA45Vx3z9ItX4bl1PLKHnAVhYNBQbaOrQMGG8twziGF6G+0Xo59U6Bq37cGACJKIAQ/8vlw/fp1BLMTG30vAATTE/ZzR0VRFJ1gDahknEOOjXlv0NHMG9DMIFUNoM8iYyUB7XClJZbHuwLAMIw6L+nIAZhvM+rzLx8TmAoA9pvAjP1Z/Z0aOwC4C6dotIEgYgANG0QFKPvCl9Uwfa0pblomR7AtCahnzpUHMYlpSzVIBu3i6RN9DdWpGUDq89FnePRjod+HK2wT8wOA26j1bG6fPwMoeto2UDOACBi4oGUAgeH7siiKfnAVRuBFMWh/GgawL1gYDZKAWpDTM0Y2EAxVDGA/SDV/OwpUvvLKK8Bkf6PvBYBgqt+7CQDknHfW6wHVNg85J61SpSdnEIRO13YfA0gN1vq/yz2nyxg554h69mFYgmuX/dj3PPB5TmiVl95Gs1hJLQFtv2cXtzd2AHAXTrEVCShYg78aqwQ0MMwVAQCsbo76922shNGwONU/FG0g7KHVLjVe+UbVT2FAbKMG0DX6+gD6hD1PgooBpOxJaYLaBIbSNOmo1zfL1akBhRDD9+VR4xg6zjzPgU5zB1hZwxCmKc9zsB7mCkBZV7g5Q2K/uw9UDhyj/e4jWMrGe3vi+o1XEUz2NvpeAGDJFCwIcePGjSPfyznvBebMQWpYMYDdx9pVAkqt3thGDaAeIw2jyDm3QK8dUTCcmT0qfAGgCbPIGoY91/qa2NUAjjd2AHAXTjF2Cei2woAXinYIQPUQoTKB6ZKAejUwN3LN8nfGlDezplqmLX0M3qB8A14fTYxofNtlAMsXmJ+pzFYZQFOP6plPSklWf9RefGmEYz1lf42U20SMc17p19vhADTyPO/PB+i6QgcAyPpA6sBJt823BqRuyirevHlzGABkDOFkvpELKOe8H6w5HJeqVr0/p98iRzNc7xPrrpmxMIBdUkjA7bhsM5pzPHcTGKty6mIAt7jwvYujYwcARxbboPK3EfXVISoGsNNJbmQhhChr1/xr4YBazQIZAFwN0kk3kQS0hglGBYSA23QNbulUH9N9omGwUv67DQaQRgJKs9/0hJaGfVj7Gcd6yl6GxHEixjmv9OutYOWq1lCmSa0BgCyMwIvN89nv7mFdDNjcdIyW1TsCpAJHA8Bbt26CpbONvtcES2cbA8A+0OvCAFaGTj05WUD27GIeC0XtBUuqoMonpewF0cYkxWU/1h07qaLeCN6nD2AFADtaxexqAI81dgBwZNE7+bzN4zgqDFsH0LWBCEoX0G3XAPqEUsqqxSgZQCMBJa9b8/x8ezwUElCdqLa6OC78twuiaJvAjBoAMvu/rbCULveKteNgQa/77ZE51yzUDd12IUR/vmA4ANSAcg24Guh+WgHAHpA6EAwdVVOoc+q/rZOACiFQ5DmCZLrR99rc8WSjGki9UNm3zcNrzexCZW/O0Im5up09XMcAMnQJSfc+DJg7A7iNuVJjjufRB7A6d/rHOIZj81qMHQAcWfReJCMDQ3UTGNI2ELV8YwSAUkoEzJSu+SOXRksEjO9GuLKNjGa72coP9OE6ztvFwm/DBOZ2mcM4fdaTPd661Jdg13VunyOjsa6eyfU6lFL2bKabBFQIAdV333dgNDjn/fkAIAzB+eY1UlUvyvVj3HS7j2LC6t+1Lufh4aHOs6ahfGfEExyUn10XUsreVhrmHjHkuBwtAQ2czkfqa5qa0aeOdQDQgGuXba+DtfbrrtFUebm7gNrProEboy/X+A6NHQC8Q2JsYKhRIOxhEVwPxlhDxeAtKd1CmBpARgSE2i6g27gRUk7iKcxvVseznZu/6zUztmutK+6EMa5IQD0by2+jVyF1PkpTi/UMoBubehQDODT6JK8APBjANeMbKDWsANt6lnIoAFw7xhIkrctpHELX9RPsChalG7Ws0LWo64GGCwPYvxDt1pakL7bhAjqG0POHPgaQXg7pDwD1/pQefQDXHRNGoLrYhXsc6wz7s5/9LH7oh34I73//+/Gxj31s5e95nuOXf/mX8f73vx//5t/8Gzz99NPHMMrbG10XbLcH1fFGfcXpTmMA/c1qtsAAemfqD/p+inQ367Gd18CdUYd7J4yxKa+kWzTZTtDstz4TGC92hPCQHjW5HDr5lFJW+vV2OLiq6jYa68BVACnExjnXsqi1MW663ZWJznqQ2nhvRxh56Nqeh12pw81cUHWddd82D2eaqudU/31nbOqVdozh3rPuuLgA8+qz9M8DVpbkAJUE1CXfJvWDYzg2r8U4NgAohMBHP/pR/P7v/z7+7M/+DP/lv/wXfP3rX2+854//+I9x4sQJPProo/jpn/5p/PZv//Yxjfb2xR0lAS2DDAAGdDWA6z7rk7f+kCN94G2pBpAsRijVvN0xJnDVF2Ma4+1i5sZ0/lCO8ajPkEqcHU8bfQ/s+7CD1FDKtc+6ofJFVT2c+hI233dEVEzYOpB6dE5bS9hhTpM/9wRuffn/RP7cE6sfDGNk+dESWO20vF4C6gQAe7d7PNLzvvBaCCUag/YQ6BNh0893fLa53vLBRwJqx+L8yV1sK/ormbccX/nKV3D//ffj3nvvBQD8yI/8CD796U/jgQcesO/5zGc+g1/8xV8EAPzQD/0QPvrRj669gL4Tov9Cvs0DOSKaPWJWX3MJ0weQggFkjEEIYDKZ4IMf/CA++clPohBLr/HVxwk2LrOW2xlja7BO/fC7Lexa//xso7hdDOCd4Hw6JgC4lSh44z62zHPv46LyvJnT0XretHRRWdbIl+WZEwOo85Wfy/tzbhp1oKjyZSvfEkNBan1b2vnyvGwSv0E9ZZ80NX/uCSw+9R/xwx94P/78L/8j8L5fQHLhDdUbGIOURzNE5rjwfNEYI88XzfdsGPX3Fq2cRZmT8joc0xyP8l5r9tGyaO7DZXG0sc/RY1zd/94MYE0Cuq6R+6ax4M3tXnCaOdl3Wjz66KP4i7/4C68cRl25Lo4NAD7//PO4cOGC/f38+fP4yle+svKeixcvAgCiKML+/j5eeeUV3HXXXb15syzDV7/61e0M+pgiZAwMbFTbde3aNQDAI/eFFrA98cQTuH79unNObQFe3caeeuopnDhxwilXXhSQAH70gx/Ez//8z0MphUc/+XHvcV6/fl3fFJXCrVu3vI/Jk08+2fj9G9/4hlcj2JVnMNP9plzH+dxzzwEAzp0H7robePIbwGKx9Npuc+6YUEp55cvyrBNMPf74407nzwsvvND5+re//W3E8TDJVm8Emu1w3e5nnnmm8/V//Md/xGw2zFoeAK5evbr6ItNmFa5jfOqppxq/KwW88sorzvlu3brV+frTTz/tnPNb3/rWymtPPfUU9vf3nfJpi/7WpIsxp+229wYp8MHafezjj30GgNt1c+3aNV1SwItmzj/Xk43HH38cp06d2jjf4WIBjTR4I98nHvu/7HueeOKJjVoXAMByuQSCVP/SGuMnHvsr+76vfvWrGzWlrs5B1pHvc/ZQPfnkkxtd288++6zOxhjA80a+P33s8+VXsSNz2nKW1gQ9f/Yf8MMfeD9+/n/9XwAAjz79Dw0AyIIAeZ4fedxv3rwJKEDwrDHGP3/si/Y9Xed+X9j7DQN4K+enHvsioihxes503nfAcP36dadr2hyfqKNP47PPPuuU89q1a+iCV1evXh2cz1wHRWsffv5TX7Tnosv97PDwEKxD0Pftb3/bqXm7GeskmuA9l96Nq4dXwZjbHLT+HMhE85r5r3/5fwPQz9wxzW+PO5599llrFOWT46h9emwAcFuRpikefvjh4x6GV7T74JydT3Dz1cWotutLX/oSAOCf3hPir5/SK5JXrlzBuXPnnHOmkwlQVCDm/vvvd97mNEmwPwE++clP6gffn/85Lp0CvvE88MADDzQWH4bEqVOnSgYQ2Nvb8z4mbbD3+te/vsGCD400TXB4WK0mxhHDiRMnnMdpwMTZ88C99zM8+Q2F2Wzqtd2f//zn7c8K+nz3yZemEyRToL2IeuXKFZw+fXpwvj7Q+LrXvY7sGmT7QHQQOed7+eWXO19/6KGHsLe3eXNpE52gMY0wn8+dxxhFzccLY8Dp06ed8924caPz9XvuuYfguFT323vvvdc53/7+PlgSQ9UYNRYETttt7dPPnm3cx4IrDwClEmZozs997nMAgOD+1zVysjNnoJ55BleuXMGZM2c2zjebToFlgeD+17fG+Cb7ngceeACXL1/eKN8knQBFKTe7/8FWzrfY9z388MMbTWzr74nuf6iRL7ryNvu3TZ81dR+C6P5/0sr3SOO963ImSbf5S3LpIc38Afjzv3wU0/f9wsp74ig+cqz7+/sAu44zr3trY4xnHvoB+54h97MXX3xR/6CAS697WyPnfQ+9Fy88/d+xv78/+Hx84olVmWsYRs73CQMo7734Jjz9/Ncaf7t06ZJTzi984QumgNk73/7+Pp7DS3j4UnMfvuO+99pbkMv9bG9vD9N4hgVvgobv+q7vcr6XnTp1CmmU4t2X/hn+j3/8YwRB4JTLgF4F4K3n3tzY7vecfwceffKzOH/+/Kjmt8cdDz/8MD784Q+T5FoHAo8NAJ4/f96yC4Bm+86fP7/ynqtXr+LChQvgnOPmzZtOE7o7LQLG8Kazp7Cfxrhy5iSeffUA37w5LqrcPFilopOAmj6AJnwdrM6eAE7vZfivj30c33sfw5n9AN94Xo7SXZQq0jTFfLbA3h5w7izwtX8gktNsS2WnAF9tYMAY0j3g0huBG88DSgIvftP9fDSfO/WgHl+QAi//D//9GN4HsAkQXmQQLyqwhb9FN3vbKeAWh+IK+MaBt+yVvfEsIBTYxX3If3iRXPZKEez1l4CQge3NIP/mH2mSAnZ83pLkNEHwtjdDPfcC2IVzkH/9t255ynEEb7yC/MQJfOKznwW78iDCd/5TiC/+tfs4lUL4znehAMMnPvs5BFfeiGB/H6KHVT5yjEohfud7ynyfR3DlnyB+57shvv61oxOsJIQ9Dsk7fwA5GD7x2S8guPI9SN75L8C//Pm1H+8PheSRDyBnDH/62S8ifOjtSN75fohnvumWTSlMHvlRLBnwp5/9a0QPPYLJO3/U/PHIz9t7k2y+N7nwBuB9v4BHn/4HTNvyTwBKSrA+051amOPy+nf9W4ABj37uyzjzxh/A6x/5t3jl21858vOd+cp4yyM/BYDhsc99Gfc99IN4yyM/iU/9p1/3um7e8X0/iZdfeQoXLzyE//e//bH3feyh1z2C+fQUnnn+H3DXyYv4/N/8J6+cDMCH3vEz+PYLj+Pukxfx2P/nls9INX/0LT8FBob/57Ev4533/SB+5C0/iRduPus0PkCfT/NkDz/8wI/i6y8/jmk0w189+SnnfCanISKkcp831WtOf/KhHwMD8OVP/zXee+Gf4cfe8AE8+uRnvca5C/c4NgD45je/Gd/61rfw1FNP4fz58/izP/sz/M7v/E7jPe9973vxJ3/yJ3jrW9+Kv/iLv8AjjzwyKm34tiIIA9x3cg//9rtfDwD437/y9dGBFjOees2evwlMoE1gKCZiZa+G931PdYr/zTfcnayqtKUunhgQUaULggAnTgD/0yN6G7/2OCMx02k1g3AfIMw+rP/ulc4+oC89zHDpYeDp/6Hw4jf9awD3LjGcfojh5lMKL/8P5XV+B0GA8KxE+ojOsXxsjUvfgDGyN+yBnZtA/s0rUN848H5Is8snEVy5GwCgHn9pa0ZMPvnYxbsQvvn1UC9eh/ybf/QzJugYo+82MzCEb7oCvOkKAED99d86jdFuL4Dwke8D8H3VH5XbOHX/Vs0eRo+8C8C7AADi7/974zsH5YPOlzzynuYfHYwj9Hu5HUv6yA8AqFirTRwFV/PBbnP6yAeABlE3bIz2e8t800f+51a+aozrGErzN6VW6/mSC29YAX42pEC4rgdhY5x6jG9410+aw7zx+LrzAUpJMMbwPe/6KQA/VXuHnyHRlSvvxnyuS3v+25f+2ClXO+/3vvF9+N43vg+vvPqcNwAEgO+78j5835X34eWbzzsDQAOqGGP40e9p78Pm9w0dI2PAP7//vfjn978Xf/f83+KvnvyU9zPL1AAquD//qjmE3u6ffOO/BPAvAQCHnrWPu/CLY0MVURThN37jN/CzP/uz+NCHPoQPfvCDePDBB/Hv//2/x6c//WkAwL/+1/8a169fx/vf/378wR/8AX71V3/1uIZ7WyNgAWRtFVGO0PimeiBUi5i+YwxKYGC23Ovm1WITUctLYY2sPMe3ElTswxYmtADs+JTjxLOdk9QwoFw4sOGZ2i5u1LYZINiPqv2ac7raxBaNf13HWJ3LtUF6HqPVc8cPRNcn352ve+f0PycNwGqGW971+8ptX/buK8dFvO7tNTmHb3cYBMA6Q5byWbjpOOsAsDPksP5mFjSpdWM8OqetDRTDzHeU5L3y0Xqsvceq4Quh9Ul8Z0pHhqi616rO153z1cbp62DZ3pcmtytQ6+0V6tFrLwgC+/l6Ll8VlcnjwwB2HRMT5jXXOsVd+MWx1gB+//d/P77/+7+/8dov/dIv2Z/TNMV/+A//4XYP69hDX3j1G1jVJHQsYS7qugTU9yK24Ipiwt1xsyKxMq6xVxSgvL4fKSIIWGNu4gvYVhhARXOc64CSQjrcFa7jbAMXCnC+IttSQLDBSn5vvjYY8hzjyjbrV72OdRdg852Q6HwmLc31bHNSAf3WREcp5bQf7Ti6bg6O4Lwu6+rKtw0AOGTbwzA8Elz1NnXvywctnVw3xsEAcB1ILf+2brsNiFP86J5+jeAFkuRos/VOZx8AACAASURBVBp9XLrHqByAhp3E92y3UtLrHG8CNul9H6uPU3mCoToTpvO533fauerhclzqeesA0HyHLwNocvos3tUloO0w89yxERyvlRiXrnAXAFYvBgWFIBzXoaqzJBSMHVAyOVAkktJOBpBggqcfrDTApT4WMhlt2w7Tc5wrLA78b9asfmx0Qq98QYsB9D3OK/uL4Ngw1gHMN6jlWZevPjbXCXxvPgDMU3mw8llqBpBg8tBkiIjytW880m27qwl1N2BznnB3TOJdJ8lhGIL1AbYNgFA7oig6Ely1zYWOGh+AflBZvr5pzgoArmnFUP5t3XZPJhP99QMBoOI55hu4/IZhuBasHTW+rnz1z3bl9GIAG4DN/b5TzUtWwZAvqKzAkDugDIIAokP2W8/vzuw3QbRrLhN1sCpBAMo7ACDFOHfhHru9PsJgjDUWfaXqmNgfc9RrAKlWcYIgJAOULAhWFs4pQJY5NhRSSJOvPjbfCILmueM7zi45pP9xrva/ltIS5GuBK8D/gd+WgPo+TBvzeE9Wv5rYovGv6xirfPWTx2+bV3ISnYtt1pOsBpDqOHfceJzr9YBuQCTdJtzVRL775jgUVIZh2C9fcACAcRyDrQWAwg0A9uXcAKy1xwcAag0ANH9bN04DAGU+0NytWNrPrgsNALvHaF4fcv4cxaQq6cYAdgI2x3O7ma8GhgbKfI8aow8DGIZhL4iWAxcj2mNslw0BdBJQpZSWZzuENQzE6nb7gN5d+Mdur48wwo6LeWwXSF26SMVeBQErAaX+3efmFQbhCqiikG4a9kqqzetQjsoHVPMTX3ll0NpuX6ayDQBBsN31FUEtb/Y9b4ImR+IpSa5WpZv5vIFGfZCSWgJKzwCSMXayGiPFuUjFejZzKpJ8WsLYkoAOZK0auQCoHgmoF6vYnoRKNwC4jrEzE+8hvTN1vjXsmhDDGUUAkN21dmogSLXbsgEDuK5WLwxDpJMpVD7MBEPli43avERR1NswfhOA2g47ie/Zj1IOOy4mOgGbh+FINS9ZlYBSgUof0BKGIUTPcREOwLw+xq4aQJ/5RLsGcIj0up0H0H1v2yEIxrkL9xgXqtgFACAIuwDguDTS26kBDBqA0rfmqkOJBcBvnGEYQpbAheKmVW+nAVCA6KYcUnqOs4sBpBijyaUU4Gkqugp6ZfN7XMdn822LAfSQdVfsUJVPO8F5yl4JGcB2TiXdZIsmVsCL9AdsDSBNsGrelt75sgU6STcD6AMqV0CbA1sHlICoDwyVrw8BgEmSrDVGUYIjSdON85l9pETPGMvv2nSMlgEURf+beLFRzr29PcjsYKPvNSGzg40BYB8DKB0AoN2PvaCSOzNXAFB3Q5UeDGCXu6ovGGqDSh9AGcdxLwA04HrI9VIfo+oAgGMwganmODsGcGyx2+sjDNbhAjq2C2Q7DKBpA0Gxsh9uRQKqJ3g0QAhYZQB95ZVhuFoPR8kAKlCwlLWcCs7SEhNahlb97nucu8CVTz6gBHvbkICakH41hd0AkJix85jYNfLVGMXG6z45t8UAOgKrxme6GECpEHoBwPbqmAcA7AVXw4FGkiQAX+OMyQXSDVwwG/mAfpBaAsBNnDUb7+P9AFBtmHN/fx9qAABUSkJkh7rJ+xGhGcAe1rMcnwsA7MspxDBprokucODKJvblc6l5rEdbAuoDWqIogujZh9wBmNfHWJdYKtDUAFoA6MHKmv0uOgCgqYfcMYDHE+NCFbsAsCpp05P4cV0gdVmEuawpasOoagC11r75GgXLZrT2VADQAmlP1qrKRysBrVZUTUJ/CWgdGFAxiu1tHmIV3zc+C3oJjk0YNEGqdlN1N2HuAkM09XrVa8yTPW6zTRRtIBo1duW/LhMmE9QAMIoiQNQOtMcYK/niKnhxrbmqcq4ygEEYDr6HrweAHGEYDtqfmgFcJwEtNgZrdnxAL6hUAxnAtGQf1ToAWBq7pEcwladPnYRc3NzoewFALQ8ApXDy5Mkj3xvHMWTPNsuB21x/b78E1I0BbNcW6rIAt3O7nk/KJqCs/805Z4sBdL3+eA97bIChKwCsy2gl0eIYBQNoFw86zG8MKPS5h+/CPXYAcIShdeJ3DgNIKl9EJd30li+0XiNjABUgFM1Ny+rjRZXfJ9rA11eqWj1QYf+lZAApFjc061KdK1SsJ5XBCmDGWHvBcz92jZEEALbcp3zGaGVjSunJiVTe14xme8uTkaB+pLHdjnVw9ViR3nkwgL1grXwtcZCK9YLKgeYqJpIk6QVDigvEA8AaoM1R1oOrAtPpdON8cRzruu0+yWYJko4Caybs+9aM0fztKKB66tQpYLk5AJTLW9Xnjog4jiFF0e286CA1NO8VPfJcwYcBcxPV80Wfj771eu18gL/MsA0qfQClkYB2ySG53Oy86YogaLqL+rKeJmcdAIaOz+kdAzjeGBeq2AUAA4SaAHBsF8jKJN6j/shEZbDS/A6XWMcAUkwYhaAB5TYfkQlMGIaN+aIkqruq1wCSAkCpZas+sQJ6JS3rqQjOmyAIVtpAUBuikMory5/JQCXBijQABHWJ5ZYYQG8A2GAAhxuh1HMBaDKKNq8bYKsakK8ygO4AsEeyKTZrWl6PNE0hi7wTuAAAigKTATWAjDENQvsYwA3BmolN2jconiNNJ0c+D++66y6Iw1f7t7UV4vC6/dxRsc6tVIrNahS78skOIC2lgFLS6Ryv2DVhcwHu13Q7n87pzqwBdZUOL3P71QAC6JSBcofjYqLtLkoBrHTOsn7bQwJq9ntX7aMxhtkxgMcTOwA4wjBGIybGDADNwjmVHJKqplBLIpoPYAqZpTkOBac5Jm0A6LsfwzBaKUEiAUM10oUCpJpcVAxg/VgrCYQRgfENYQ1gFLUkoBKIKYCLlUMSMYAt+tjnwdzFrtEwgOUYBc0qNwAyCWgcx4BSlRGMGG6EYmKdBBRCOp0/faYoaqC7pok0TaGk7G4PwPnGzJqJ6XSqj0MP08QEH8QAAkCSpv2sIi+QpOnGi5f2u9f17ytypNOjWzWcOXMGkucbO4HKgxsAgNOnTx/5XgNoZcc4Jc8RhtGg423yiS4A6AlcgAr4kQHA2jUjPHNWEsYmA+i6YAKgUwbKPU1gulxAKQyypJKlC6jbAr/ZT1x1gF6Pbd6Ff+wA4AgjCIKGBFSo8TaCpwSAYRii9AVpfIdrri4TGN9x2puZ8JezAXVGsfm7T77mHN5vnFXxv/5dEkhf686nFMekbQLjayyz4l5JxRyvSEAJwFVdAkolhTQpqVw7CQFgo8aOIGej1QKBq+iKbNNjhbuXrSvzDgVXjZxtUCmGyzWBihHrkkSqElwNCQuwih7AVuSYbdAIvR5pmvYCtqFjTJIEjAVQRdb7HlVkmKSbAUAAEAevbPTd5n133333RuMEACm6AGCBOBk24bbMVcdxFgNZ1HrY50sJ+H3r9arnVZ0BpK0B9HEVNfuo6Dgu5jVXCeg22kCYXD41gObcKTpYz50E9HhjXKhiFwC6GMARm8DAjI+mKbpSVSNzfwDYYgAVEFGxV0Q1gNQAMIoiy4Yppbxr9upAH6CpAayAAc1+XJW9+o3RmI3UWU/zPe5jjFYYQBJpbs0QhQL0qpYElKIGEFJ61cK1cyoCcGWiwXwSGBKsgDYPBtAYqHS1MGBCOOWsxrcKAF1qCisA2MHY8QLTDZqW18MAwF6AVeQbNUJv5JxMofoAJS8GAWnGGCbTKVS+HgDO50eD1LNnzwIA5IYAUN56BdPZfCMAbPZRJ2ATOZJkGDA3+0h0AGkhNjO96Yo2YPNphdDMV52PJrdrzkoCqvMY+aYrYw4ARQcDWHjsR+0bQcsAVs9+pWsAPUE571AyGFDoAnp34R87ADjCCKMmeyU9WZxtRF26SCVRNRLQbdYA+jKp9eNAKQHl5b2Rgg2zHhkE+5Expvdlbc5NygB6SheBcjytGkAfCSigzxN7/hDIczVwqb0gGR1wKf+lOM6UDGAFAOkYQO06WbrxedTXmWg4lRIwgG2AZeoBnSe0PS6bTLjVXK0DgC75bE1cF8AqOGYD5ZoW3OTdjJ3MM8zn84E5p/0MYJFjNh3GKE6nU6hi2ft3VSwx3wCknTt3DgAgbr680feKWy9b0HhUGBDRJwEdCqIrCWgHAOTuzFUl2aRlAOs1dj6ArZ7T1il6LBRVALCfAXQFgI26R6IaQJNLKuk8dzL3Fd5V97iTgB5r7ADgCCMMI4gaehGeE7FtRN3Ig7IGUIHOrbPD54BMAtr+2TXaDCCFRM4wnwYI+t5co6gClZJA+lqvAaQClHUGUCk4O5bZnEHVt49CAhpFTQaQeTKfdRbV/OvSF66RM2oCQAg3YxATFlQKaUGb9/kdRys1gDQAUJGwlFXfuSYD6LrCHfe1RRDCKWflYrkKAF0mnuskoEwMc+wEYMFdF8OmBIcSYrAEdDadAkVPzV6RYbpBvV4j32wGlfcDQFZsBlLPnDmjyz1uXdvoe+Wta7h44fxG761qAFf3o+T54GPNGEOapt0MYPnaUFAJ1NtLNNk1Xwaw7lYqHNpe1KNa7G7WKbrMJY4CgFE0rDbThO77uCUJKKSXC+g6AFjsAOCxxg4AjjBW6fzxAUDqOi6Ts84A+q5erfQ6JpCAUgPAqqZQ/07pAmr+9ZfdNXP63qzrMlpFBAAbDpsEOYNaTgoJaBRGQH3OLRlNfV3NEdP/3IlAyQACJSitXdS+504cxTVwRSgBldLKX33yVY3Cy4PtCQCTOF4Fa2Vel5zmM6plsqIEH9Rg3YQBY6oLYBW0ABBZ1njPpjGdTsF6AWA+mKXc399bCwCRLzYaYxiGuOvM3RA3jwaASinIm9dw/vxmALCSgHYAtiIbLM0FtJkONQBsM3bSoUl9PSqXzVUXUH9ZaROkkjOAPHNahAHKOaMS1rVTeDKp9c9KJSHhzgCGYYgwDDtrAPlOAnqssQOAI4w2eBEjBIDV6pCiZQCJXEA7ASDBhJYaAJpt5MKveXl9TKoFAElYRZuTbh8a1V0UeYKCOF4FgLEvqKzVAG6BAYQnkF5hAAnac0RRBQBVeSGSmLZIOgYwSWqSSIIawK46RQoAaOv2uF/9UZKmnQygcmixUB9fOyfjboDSArwuyWaROwNAdDGAuRsAnM1mQE9NISvywfn29/bA1jh3qny5cc5LFy9A3nzpyPep7BAiX+DChQsb5bUAsEOqKooMk4kD25tOwDv2Iy9ZRp8aQOMuav71B4AVI809c66AVA8XUHNc8g5mNhcZ0oG1mSYqTwZ9/95GDaBPriSOUciOukfp7iC7C//YAcARRpcJzNgAYKXdp3UBFYqmJYJmwpoIUCj/2rBtMYBaCul/jBtgTTW/wz1nDCk1I6QUhaS0tKKXdAygFLXrRZSMm2fOSl5J5A5ZNyWiYgANYPM0gQFaElAq1844MoXCJPniqKoBpJCABkGg7c6pJaDGFKUEWq4r+2marrB1AKAcWiw0xtEybWFCODE4FQPY4QKaD3fsrBjAVeBiXtvb2xs8xj5TGcUzN5DawwAqpSCWi43HePHiRchXXzzyfaIEiRcvXtwor9km2bHdSgzfZkCDF9EBXAwodMlZuZU22TVXRqhLAkrFAJqx+TCAFgCKDgDIM0wc9iGwKlMVSrd18enN3HYB9bkvxnGCvNP4ZicBPc7YAcARRhiG4PUaQE+Dh23EtvoAKlX5efgyLl01gGNjAKlNZerbTcUAxnEMKap5N5UEVEoAyg8I2fERS0DDoDIRUlKbwvg8TOM4BquTLp61lCttGyQRW2fBlX9TdJNTCWXNUHylPkmS2LFBSERx7HVcANNcXpI0gm8zbMpTAjpJkxUJqFIKqnBjAPsAoKuktGrb0GQAlZSQDhJQC5yyDoCVuQNAmWWdDddVNtxUZm9vDyo77P5jkQFKDgKA/PDVTqBWD37jBfv+TcLsd9GRVxRuAHA6nYJ3MIrmNR8JqKSWgIpVBtAfAIrGvz4AMOOr+zHjmdM+BGoAUFVj9K6Db5jA+M1BkyTplIDmJQPoukC2C78YF6rYBYBuCejYXEDbAJDWBdSfcWkbgwBl/Zqn3LB+HChWreqyTyoAWBm2VK9R5KSUlAJ0JjBRFEEKZSd4UvoDjTBq1gCS1I62mtWTSkAJ9qOur2sygN41e8a1k0wCmoDVGECyRRhBIwFdMVnhfgzgJJ2sSkDLcfowgKoNAAe2QzBRMXYtCWgJCIcygFEUIZ1MoDoAoPIAgEqKlebySinIfDl4jHt7exDZAkrJlb+pbDFojJcuXQIAiCNYQPGqKwBc3Y+yWDoBwNmsBwCWYMYlZxuwCU+wZlqn1CWgPoxd/XOyxQC6jNHso7wDAOZ8qR1rHaIO1sy/vvOJBgMIv3xpknS2vjCs4A4AHk/sAOAII4paLqAjBIB1ExhKBlBWC/HekjvRKQGlAS/tn33C7EsKCWidDTP/+oIhk9MASioJqJaV+rVDqOdDjbHzrSusG8toBpAAADZMYAjdK8t/fRrLAy2HTYIWCwCQxEkDAFIASlZz2HRpXt4O2/5C+EtA7UTG1gDy5usDYzKZgK2ANW7/5jy+Vk7FuVM++5lWzZ4xhRnKrgHA3v7+Wgno/v7+oHy9stIiB5QaXgO4v68fUl11iiUzuOkYKwD4wtr3iRsv4uSp0xuDrHUAkBdLp2M9nU63JgE1kk1fCSig5Ya84QJaIAwj70bmFBJQs486GUDhBszrY6lLQL2N0ChrANPUsn31KGRuTWJ2cftjBwBHGNoF9M5oA2FcQKl64pnm5fXvcAnNWjUBIAXbVJ/AUgFAA/x8QQugxyeEcQLTr/lLLBMIQdlWogYACRrBWyvxcs5NBSrrAJBCploHgEoQmMoADQAYk9bX0UhA4zhuGKyQMYrQPfZiT7MfwABfaZ1UfSSlKwwb95OApmkK1nYBLdwnyVFUToRbbRsUd5OUhmHY3Rg9dweA+3v7wLJDArp0YwDt+9sg1bGm8MSJE/rzHTJQ85p5z1Fx+fJlAAC/fhQAfB733HN54zHGcazbSbUAoFISfIBJTT0mk0k3A1ga4rj2r2OM2f6CpnG9z30iSZKGBFQ4OtyaqBqZ+wPAJEkQBAGyjv2YO9SjmuiUgBI4igOlBBR+EtA0TTtrAHNRaKfjXRxL7ADgCEMzgJW8RBDU91BH3cqfyqW03QbCt0ZKg9M6kPY3B9kGA1gBQKIawFIOSQXYkiTRRo7EDKAqWUUqBlDWAJv3GMOaayeBQU+7EbwSymuMVjpc5mRUixuEPfYALf0Bp2MAdQ1gJQGlsA+Pwkg3lZfSWyHQloAqTwYwTdPVej0PBpAxpp1Fa6BSKeXMAAKlzLMlAXV17ASAkyf2rZSykTNbIEnTwcfcMoAtWanrGA27J5cHHWPUAHBTUDmfz7F/4iTEjfUAUN58Efdc3hwAAmXNXov1FB5s3XQ6RVGsHhdeLJGmqdMcgDFWMnalBJTAFTKJ41YNYO6lFKhkqtz+a3ucDgzGGKaTaacENOO0DCCZBBTSW1KaTlLkctUpOBeFlrnv4lhiBwBHGG35ovB0YNpGmJuDMH0ASWsAm9/hEpVso3pNSCCiYCDKoJOARo1/fcKMTyk6xs5KQLfAAFLVAAJVvz4lGMkY620gfAvq6wygUsobAALamMZeLAQsfBLH1qzFMIA0pi1Sg0CifPUWC6626fWwrSqkf01hVw1gHMfO97LJZLJSr6c8AKAZo6ozgJ755vM5LQO4vw/WKa9cYG+g/BOowNiKBLQEmUPHuI4BlMthDCAA3HPP5bUSUJkvwQ9uWLZw05jOZhAtwGYYQbcawNkKoASAolhiOh1WR1kPbTJWgivuXxMWxzF4rc8eF4XXvbbLBTSKImelwHQ26zGBWQyuR10ZY40BpHDXBowLqN8if5qmnW0gclnoBaldHEvsAOAIo+0CykfPAMLbgt7klFKRAMB6jaIJSSA3pDaB0TkNA0gHAGtlV0QMICOvAaQygWlLQKlAZQUo/fsKajfM8hciMx1buwbNAFLINa1TKVXbhjgGq7VYoDgXlW2yLpGmBAxgzaiGjgHk9l8f9mEymay2WPCcJE8mkyarWOZ3BYD7e3ur8krHpu1ACZ66GMDlAicHACsTfQDQtabQvF8tOySgJSs4CABevgx54/nev4vyb0MB4N58DtFmAHM30AuUAJDntg+eCZ4vMHUELkATsPmawAC63qxdA+gjAe2qAfS5L86mUyxbwFwphczBkMhE1QaiHKMS3gvK1SK/ZgB9JaCZ6GIA850BzDHGDgCOMFYYQIKVaeqoNOe6N1zgyZCYnJWrqL/tPtBkACnZpvbPfjljsnx1MEQF2PQKLbP70pfFaZrAENWFAY2aPX+QGrdcQP3dK5VtX6D/8W6nEbVlqkRgDSCXbCrCfBUAFCTyobhkAJWU/nWUZVuKeg2gK7ACKgaw0cKgrAF0lYtNJhML+vQY/RjA/f19sFYbCAMIh9bXmXxiuVht25AtvQBgG1QaSag7A9ghAV0eIE0ng+6Rly9fBj+43tsKwrSAGAoA5/MZeN4EqaZezwVomM+06wCLYoG5BwBMksQyf6YWkLIGkIvC65nVZgC55F5u4rP5fIUBzEUGBUXMAFLVAEpvE5jJZIKsgwHMRI7JdCcBPa7YAcARhgaAzRrAsUpAjXmeb4N1oMkA+kruqpt29ZpQNNLArp/9cupt9W1RAXQzgCR9AAldQC1IlYD07IfXyGcYQEEzxoYJDMEYVVmbqSiZVGEYQKL9WOajAmzWtZO7ty6oR5IkgNJ9BRlRDaA1qhHSe3tXauw4Rzpx32YLyuqMnSdgm06nDVmpkYP6SEBXagA9GMCTJ0/q49ECRCxb6L+5jA9dNYDDWjaY2N/f1yC/iwHMDrE/EKQaYNdXB2gYQOMYumnM53PIgpYBBIAibwJpni8wn7sDwDRNbe2fqQX0ua6TJGlJQHPEift1be4JvNaqwuc+MZ/PkPHmPjSMoDcAtDWAfiwl0ASAvmVI2gRmlQHMRO61QLYLv9gBwBHGahsI/4kJdaz2AfQHQ2EYQkEDlyDwa+7cCQApWw6AEgCWNYAjZQCTJNGN4KnbQAgag6NtAEAtr9TnIA2jaFw7QSYBjWoSUKpG8LbFAlEfwCRJAE4rAQWgT0YCwGbHJGhMYICmcYvi3IulrBqtV6vnylOyOZtOweoMoGc+3Ri9VdOUZ4jj2Gkibxm2lhOoWi4HyzUBfTyiKFo1lnHsKxiGIWbzPajFrZW/qeXBYJbyKADIb7yA02fODD4+s44aQAoGsGizioW7dBEAkiQG53QS0DStTGUAgHM/mWG7VQUX3Gt8s9kMS97chxkVAKwzgJ5lC5UEVHibwEwmE2QdLURyuZOAHmfsAOAII4oicGN1rtQoGcBt9AE0Obnw72l2R0lAQ3oJKGUNYBzH9T7Z3jfsOmCjlIBWANDfYCWKIihVAUCKBuYAtPyTt15zjKgmAVUEbqparmkkoHSN4FXNBMbbVdT22ZNgXJJMHowEFFKSWJKnaWplmuACU08JqM6zygD6SEBZvSm6p6R0Pp9DZllDsqnyDHMH+SdQr7GrwItSCjJbDKqtM8EYw6wDpKpsgXQycbpuTpw40WkCg+UBTp0axlIaZo/31AHKGy/g3oHyT0AfF95i6ygYwLYRDC8WTvlMpGlqgR/nOZIk9Sr/SJKk1Qi+8LpPhGGIgAXgsnIqjWP3e/dsNsOyxcwuuftxATqMaogZQKn82kBMJhPkooCsW2FjxwAed+wA4AjDSECVUpYJHBsD2AaAVG0gAK2eCkO/U7OLAeSUTA4IAWC5UkeRr86uGUBE0QhecEVuAmPmoFQMoHm2UAFA1CSgvjWAdowCdBLQetsGIia1cgGlrAHUbF0Yht4LRfZc5pqSJmMASy07Rb66cyfzaK8AVKCsYQTjCdim02kFUFFJQF3z2cbotTpAlS2dAaCVedYZu2wJKOUEAM0YVb5aA+jiKgoAp06dhOxgAJEdDJapzmYznDx1upcBlK++MLj+DygBYNauATy0f3PJB6wygEV+6AUA6zV7vvJKQAPKtgTUe7EtjpsMoIekVAPA5j40v3ubwGzJBdS3sby5BxatXoCZLJzvO7vwjx0AHGEYKaRU2gHUvDamsBJQCSi49cTpy1lIf0axWwJK5LzY8bNPGBMYismnlasQ9u3TD2hFyigCFalBKQFVUpsSkbaBIGQpIUBmAqPHSAsADVNH2QZCcQ0AfVuwNMYjJBQXJAxgFEVggqYNBABMpxN78THhbwIDoFMC6rrt0+kUqm7a4ikBtTV2dSfQPMMJR3BVSUCrSbKRb7oCwBP7+8CyDQAXuum8Q5w6eRKsqw3E4sBpjLoVxIur+bJD8MVNJwA4m80gBYfkNTBUgmCXSXcXAFRKIc/c2xcA5vliGsH7g7UkSaykFNC1exSqlToD6KMUmM/nyIplgw3zrQG0z1RjVEPIAGoJqL8JDAAsRVMGmolsxwAeY+wA4AjDyviUtGYwY2YABbEEVBBY2ldAqO6mqsgcLNs/U+SkAJRV01paCaiUFWPnuw+DQDu8msVAqrowStaz2bbB3zyoIQGlGmNYmcAoAjMd7VRKywDWJaAUhi1NBpDOBIYpzQBSSEAn6aQhAaVgAOuMHQrdO8urt2BHDaDrSnzlsllN7lie6/YQDlEBwJoEdEkAADv6AJ7Y9xhjCwAqwSHzpZNRzT2XL0PeXGUADSgcagADVICtLgMV+SEmk6nTs8YCwFpdoeAZlJIEDKAGbL6OnUDJANYBIAWojGNbV8hFgcSj/YwBeVltPxoG0HU/VnMnYwLjr2aofB4UpKcJjLkHtltBZMUOAB5n7ADgCMNM5AqhRssArraBoKwB9N9eywDWWhYJ4V9LWf88NQCkyGfBkKQDQ+bzBRFgY4whikLTzoyUAaR0KrULtNKf4d6GBNRKF6FdQueAcAAAIABJREFUO6kkoKoEQ2RjBKAKP+MEE2YlXwkBWXAyBhBCEDKAU7C6C6jHGLsBIPeSTRkXUFUuLvpKQG2fvazJALpOZvf29kqXzRpjV/7sYgJjc7YAIMvdTGUALVMVi1vNukeHJvAmLl68CH7rOlTL+dS0gLh48eLgnFXNXrUfeb7AzPG4WBOYmjS38DCVMaEZu5Jd8zRssfmIJaC6Dt70AfR1AdX7v94LcOEJANsMoJCcbK4jlIDw7ANo7i11BlBIgUL6SeR34Rc7ADjCqBydJPhIGUBzM1CKpsF6PWch/QqOgX4JKAV4MUEFyk2ebTCAjNGBFyoACGjHU06UbxsAMAzDrUtAKValIbUMC9K/7tF+3tD6FGM0n8/96ma68kH5M/qAqfdUYAT7ENAg1ZisqKKgrwHkNDmtBttTUmoZwJoEVGWZM7gKgkDXD9aAhpGAurBrgAaOckUCunTqUwiUJjCCAzWmyTSBdxmjAXj85kuN10X5uwsANGBC1CSbIltgzxFkTCYTBEHQkIAWHjWFJrQJTOUC6gsA0zRF0WIASSSghqWUfotZFgDWWkEYNtB1EcbW/psaQOW/4NYwgZFEDGDtuBg2cAcAjy92AHCEUYGXygRmvI3g9X/UJjBUrJABgMZNlRJI09UA0jGADRdQoS22fcMygHnzd5+Io8hKSqnklYrY+MYwdeQuoIQ1gEwosrYS9fo6KtfOOmBLPJzzVvJl/v3CTJg2EEzSmMCkZR9ApRRk4bfC3VkDmBdejEvlLFrY3EmaOt/PKgZQM2zasXPpBQr290+QSkD39vYg86VlPQFdX+fDAOpxVc3gfQDghQsXAGClDlC8+iL2T5x0Ot5dElCeH2Jvz+24MMYwnc4afQALD1dRE3UGkIscqYe80uSTUkBKfQ0W3N9pUrOKJbvmKVM1x7JuBLMsFs7SXKC691cMoL9BViUB1Qygz3zHANusxgAaNnBnAnN8sQOAI4yKzpdWAjpWAEjZBqKSvvr3xDM3aN4saSIFgKOWgAoaxrOes8j1caYAvlEckUlAG7JX2XzNNaIogjTySoL+kV0SUAqQyiSsDJRqjBCKtBE8AKiMRgJqwX5OCwBVSZlTXIPWBVRKQCmvyaed+PM6A8gx85SAAtU+9GUpV2oAiwJQypldA4CTJ080+vap5QJBEDgDXwv0Shmo4gUU584A0ADRuhOoAYAuILUCgC0G8NWXcPHiBacxWgBYq1UUno6d8/m8xQD6S0AbDCBBvZ5h+7jIrbsohZLBNquX3CtfJQGtA8BDzD32YZcJDJUElJerqtQmMKYv4I4BPL7YAcARRrWao0YvAZWEEtDqhgOExBLQbQBAKgbQ7EtKCWjpko/YsxlsPWdR0OQDNAPIiRhAKwHl+j+KnFEUQRIarNjxcJAygJCwck06ACgBqRBGkVc/rkZOono9m4OQAYxqfQCpGEBVcCux9G1CHQSBBWsAwAo/6/QVUFkUmEz88xkXUJW7NVivx8kTJ8DqffvKthKuk9CKpVyU//qNsWIAq0m89GAAT506hTSdWMmnCXXrGi5ecAOAZttE3QTGs2ffbDYjl4AaBlApReLYaT5f8BxFCSy9JaBJjIJXJjD0NYB+x2W1ETydBNS4n/o8XywDuJOAjip2AHCEUV3M4zWB0bVlgXaHJGjbALQawROxQsYEho8YAFK6gLZNYCidFzUApNl/URRbF1AyBpBYAgpVtpUgYFLrMlUqBtDUrhkGkM6pVAGcBgxVkk1O4rC5LQmoEoLESAeoMYCFPwBkjCGdTBqN4BnnNBLQogKAs5k7AAzDEJPprGoDUTKBfhLQfd37rwyVLbC/7yb/BFZlqihBkT8AXGUAXVhFxhjOnjsHcfNalU9J8JvXLDs4NCoJaAXYeO4HNPb25i0JKA0ABLS0UhAYtlgGkOfWDZSUAfQco9lXizYD6CjNBVYloFz6t4GoPBn0dtO4gK5KQH3uZbvwix0AHGFUUsjxtoEAgDAIIZU2gqFvBE/DZlgJKBHrUg8K0AtsqQbQSkCJaqRAzAAmMVkfwDAMwRjTijtCCSig8ymCmtSt1QBKbKcGUEgS05Y6A0jaBiLzMy6ph21VQdRY3kx2VEazwj2ZTBo1gBQuoEBlLKO4fzPm+d7cmsAYIOgqrwRKk5Vl0wTm5El3AGjG0mYAfSWgjV6FywNMplPnc+jihfNQt162v8uDG1BS4Ny5c075zDFt1ABm/hJQXqxKQH1NYAAN2CgAoC3/EDl4KTOkcBY1LqBcEJnA1PZjxmkYQG5BKl0fwKLcbhoX0DoDuJOAHnfsAOAIo4sBHFsNIACEYQChtFENJQNYEEhAKzMUvf/GzACaPBT7sM58agko3SSeUzKAYcUA+j7wGWOI40hLQCnlldAAUFI1WQe0BJRS+ioUvQSUS0DQGCbZHIq2HpWyBtAea+4/aQJqk83Fsvm7Y0xns5VG8BQA0ORkBfeqPwKAvfle1QaCiAGURa5rMwGwbOncWB6oMYCmBjDzYwDn8zkYC6CypgnMiRNuLqUAcP78eYhbFQMoSjB4/vx5p3xhGGIymUKUNYCS55CC+wPAVlsJbQ7jfj5Wi5Y5OKeTgHKe2959VI3gtUw197qXpaXhUqMPoCcArOY7+nqhYACr+RgdA7jkFau/LNnZnQnM8cUOAI4wmiYw42UAg5IBJK8BJDCBqTNhwHZqAH3ro0yY7aZkAKV1AaVhSABtAkPBKOqckTVsodpuyt6H1rxEApIADG1LAqpqElBKExjKJutdP7tGmwGkyGkBIJEE1PYqXNIAwNl0qmsKUTps5rnXpKktAWXcvxfX/v7eCgPoLQEFqlYQmXvPvq58vgxgEASY7+81XUCzQy+W8uzZsxDLA9sL0ADAs2fPOueczWbgJdDgJfj1N4FpSkAnk6nX4mVl2lKQmsAUIrOtG0gYQFFAKu0s6jNGxhhmszkWrUbwPsclDEMELIBQXLdtIGgETwkAgyDAJJ00GsHvJKDHHzsAOMKoAGDFAI4TAAbWeZGCDTMPEU6Q73ZIQKnCbDcFA6hdOgPT15p0Ek9ZA1gHkiQAMIlJ20DYvkocAMECR7MPoAJj/s6iuoF5xQDSgVQ5WgBo5L7UEtCun13DjmlJIwGdTacVAygEoBSpBBSeLqAAsDefg5l8ud5uHxOYSrK5tP/6AEAzubb5CNwrT+yfaEhAkR3gpGObCqACeuLgFQCALP+9++67nXPO53NrAmP6Afps82w2Q54d6t6j0ADQdwJvrhfBC3DPFgv1fJznth8gSXN5yW0rCN8xzmazlTYQvvsxiiJwKaxUlZoB9J2fTCeTnQvoyGIHAEcYlZ67YgDHZgIDaAmo7hlNKwFt/+wSdh9aE5jxAmnDJFId4ziOyxpARirjU4qGUQSaDyeqMVI2grdMKpFRTbsNRETksKlKx06AbtFE1wAqkr591OCKMYY4SaC2wABS5bMTmoym/mg2m4G1mraT9BbkhDWA89UaQAoGUGVLKCkgc/fG8oCR3UVWAgpPBhDQrSpkVgeAh859CoEK6ImD6+W/ryBJUy8gvbdXSTY5Qb3efD6HUhKylFYWuV+/R6BuMraAlILYBCZrfIdrxHGsaxQlzX1nPp9bCaiQHAXPvfejBoCFbdswJgkoAEymUyz5qgnMDgAeX+wA4AjjTmEAw7CUgEpFagLT/tklNMMSWuknHzEDaIIOAEYlM8vIWRyKfO2cVO6nlC6glgEsASCdwyYADhKDlW0xgMYFNB0hA2jzbKEGEKB14lVLGgA4nU7BiiYA9GEL4jhGEIY2l/KUlAIlMCiZP+Q54iTxmoBa0JMvLZD2AUKMMcz35lUNYL5Emk68xnhifx8sq7eBcG8sDwBnzpzReUoAKA9u4K67zngtFGkGUI+RCgAClflLkS+cG8ubMNdLVo6TTgJKVwOoGcDC5vMd497eHEuu96FpB0HBAAopIJS+V9C1geCN311jOp02GMAlzxCFEdmcYhfDYwcARxh3Sg1gGIYQZQkSSXPw2sOYpCl6HK/0ARzzzYYOACZWAkpq5AG685CadUliDQAVsQkMVV/BMAwRhEEJ2OiYKyW3AQA1qzhGCShQguctAcBtMIC+Y5xOp5VjZwkEfVbNGWN6QlxwKCmhhPBehZ/P55BZBqUUVJ55T2brbRso+goCwGw+t8yfyjNM535j3N/ft+0klJSQ2YIGAB7esP+evfuM1xjn8zlkobdZEAAN81nT/oEXdBLQvDTUIXMB5TkKIhfQOI7BRSUB9b1PzGazFQDoywBqoxpuAduYagABfR/LWgzgjv073tgBwBFGUwI6ZhfQ0NbYUUtAKfJFcWyZP0oTmHe+8x143evu987TDqq2EnEJfKn6AG6NxSmDpgYw2aoElAKcx1EExTVIJQXmnN4FlAl6AEh1D0vihKz3IbCFhactMID1nn2AP1ugexUWVgZKAQChpO5XmGfaudQjGgCQgAEEgP29vZoL6BJ7c898+/u2+bupKfQBgNPpFEmaQhxqBlAtbuCuu+7yGuNsNrN9AAVB3WMFAEtZqWcDc6C6hrMSAJK5gIrcmsBQSEClFChKF0sKAJiXuTJOwwDGURMAjqkPIABMZ00GMNsBwGOP8aGKXdQAoBo9A1gIukb19RxUzpC2DQShBPSjH/3fbBH8GCNJ4q20gWj/7BPUxzpN0lFLQAHNXGUyB4iAuXWvLGjuEXUGkEniNhAgXDyoyWepGUDSNhAZjQHFdDqFLLQFvWme6SvZnE6nuFFUzep989nJa5FDFQX2PEGBBRV5pmWg8AeAe/M5cOOlMu8Seyf8xri/vw+ZZ1BCQJVSUB8AyBjDqdOn8erhqwAAcXgDp0+f9hrjbDaDKPcfrQS0kpVSMYAZEQNYrwEMgrDxmmuYMRm2jsIExjSCp2IAoziCUHQMYBAECFiAXNCYwEwmE/38K2PJc21wtYtjix0DOMK4kySghgEcWw0goIEQ34IElDFGxtZtI5ISDIkRS0CpmaEkSaAkgxTGCZXGRIgSAEZxbBvBkzJXJQCkbgMxRoMVAA1zmlEzgEQS0Mq0hZOYwNjP88JKS72dSksQoPIMKHJvABjHMZI0hcozElMZ83lWtlhAkXmPseotuPDuK2jizOnTkIubUDyHzJckAJAXGZSUFgj6gH1znHkpK83zhffiQVUDqAGg7zXdZADp+gACsMYtvte0NoFpMoC++1GbwHAyF1AACMIAXNHk0zWAzUbwk9kOAB5njHcW+xqOO8kEZlsAkKQpelkLB9wZJjBUYSSggkjGRz1BBrbD9irBIKVeCaXIBxBLQOPYSkBJ5ZUFdR9ACXD6c4eqxjUhXpDYFgBElpO0+6j69nFbA+jN2Jm6QiIJqB1PnoMVOUlvr9lspsEfIQA0ElCWZ975KpnqoQWAPgwgAJw6dQpY3oRc3AQAnDzp3lgeqI6LKJYQxQKhp+mGyVfkCygpwQv/ek8znrxkFX3BWhiGCIKAXAIKVEY1vved6XQKLgpwyclMYPRzn44BBIAwCOjaQEynzUbwOwnosccOAI4w6jWAQo25DUQlAR1bGwhAm6FUNYDjBdLUkSRp2QieRsa3DRaH2gW03gg+JpjEm20WRCYwQDkJKRnAlKCdRpsBpKp7hFBQY2YAy8kcY4zcfIoiX10CGieJd7sPC64Ia/Ymk4luLVFKSilaVQBlb8GiIAOAyDML2nwB22w2s6ysIgAutlVFvrQ1gL4M4KlTpyCXtyCXt+zvPlExdgvwfEkm9eXFwrKAFMAFoJOA6hwpOC/AeYE49r8GKwkoDQA0+ywrFsg4zX6M4giF5NYFlIQBDEJSAJjxOgNIs1C0C/fYAcARhrm5iJIBpJC0bSPCMLIMIM3NpjodSSSgaWLHx4lqw+6EiKIIwrBhRMclDAObmyLM8aWaxGsGkN5gRRKxa4BmpJUAIGj6M1JLQLVTaWgbwY+RXQOqY0PRS9Hkaef2CWrJtAF7Glz5u4CazzNRSUppawD920oAGkxpCaieNFK0qpBFBqUkFEH/uooBpJOAnjhxAmJxE3JJzADmmgGkOs5FvkRByFwBQE4kAQXKtg0lA0jZzsZIQMkAIF8QM4DC9gEkYQDDkMxUZjKZNExqliIjuU/swj12AHCEEQQBGGMoyhpACkZjGxGEAYoSWI2VAbR9AAldQMceumlt9TNFmJs/1SS+ykfV+qJqBE8JXKgawQNl7ZoA2EgloDaHkFBCktXLNnIThAWAW2hJQrUYYYJiH9pJUsFtzZ4vY6drADkUpwGUdQZQ5UQS0OkUKHIgz5BOJiQMBAAgzyCLggRQAqUDKIHDJqABoJIC4ubLAPwlpWY8WgK6JJFrBkEAXiwtA0hVA5jnNPV1gFZYcFGAC83C+wY1ADT7bFksLQPoew1GUQSuCnCiZvVAqfIibAMBaOZP/7uTgB537ADgCIMxhjiKbBuIMbaAAJoM4DhNYBIIqSeglG0gxh5xHFvXeDoAGJb/0jKAZHVhtTYQFA986j6AQF0CSsMA2n1HxAAC5XYSSkBXchPmGWs9KmPV8aWYzDZMYDhHnCTe181kMtH1hEQS0Kr34ZKkryCgwQszjCIBoDQTUHlws/G7z/gAALnuVRgEgfd2GwYxf/rvAfgDwEYNYL7EzNN0gzGG6XSmAWBOI10MwxBhGNoaQJJ7bZqA8xyc56SLbUuiZvUWDPElsmKBSeq/wNHuA0hxL9suAMx3APCYYwcARxpRFFkJaExgarGN0DcH/fM4GcCaC6jQIIZCMkYd9957LwDg3LlzJPniODZKMcK2DdthACl7H0qhdO/DkTKAGlwxMhdQk0MRAsAojmBofWoASAX2t8VGU+Y0ixCkjeW5btuQTvzrR9M0LfsA0jCAZnKnDg8av/vEbDbTDGBBYxdvWcrDm43fXaPOAKp8iels7v18OX/+PABg+Y0vYTKZ0jKAnEZyN51OwYuFlYBS5Iyi2AJAmhrApGQAi1FKQM0+y/kSOdFxieMYQgnrAkpjAhOStoEAgCXPoJTCsthJQI87jgVZXL9+Hb/yK7+CZ555BpcvX8bv/u7vdmrdH374YVy5cgUAcPHiRfze7/3e7R7qsUUUheBSQUhpJ99jizAMwQWd/IzaBVRr4vUDmUsaYLCN+NCHPoTv/d7vxaVLl0jyaQaQ1vTGHI9t1ABShAWAEogn42QA4zgGEwwgas9R1QDSHes4joGcbgJRD2oJKLW8GSAEqeWiHSUDqBm7ApPUf9U8TVMt1ySSlJrPqyUdKJhOp7ZVBYWktAKpNAzgdDoFY6ysU1xiNvcf41vf+lb8/u//PrIsw1133UXmICuKDKpYYja76D3GyXQKXmTgZUsNKvCyWNCAK0Cfj4ssR6BCJAQLJturAdQS0OnU/9yJItMHUNjffUPXABYk+SwAFBlyWUBB7RjAY45jQRYf+9jH8K53vQsf+chH8LGPfQwf+9jH8Gu/9msr75tMJvjEJz5xDCM8/oiiGFxKFFKS1UlRB3UbCGoTGNMOAdAmMFQ1Q9TBGMPly5fJ8sVxDNOnnmqSbIAaOetCCQBLF9B6nzjXaDOAZLVhks6opm0CQyKbjmOgnNiNngEkY7dplQdAte+oJaCK08grLeArWyxQWO/HcQyx0CwOxRin0ylknoEVGWan7/LOR80ABkGAdDKFzJdQxRLzmZ+pDKDvs/fdd593HhOVBDSD4DQ1V7PpBDcWlQsoRc44jnHr1k37s28kSYybIkOoIkxHyACafZbxJXK+xGTqvw9tH8DSBZRqTlaUq6BUDGAucisD3TGAxxvHIgH99Kc/jR//8R8HAPz4j/84PvWpTx3HMEYd+mI2EtBxOlfqNhD659FKQEuGUkialgh3QmxD0maCaoJs8xik6hnWObegmXRviwHcCgDkCmFII2+O4wQq344EdKwGQttxKtXnIIXqoFkDWJBMuC2ruMwaNYs+kU6mUIQAcDKZQAkBltO4ilYM4K3G7745VaGb1c9H2NTaHAdZLCEKGgA4nU4higycqH0BQG+cpOv/SwloOl4AmPMMGc8wJQCA7RpAahMYKgCYCb3N9dd2cTxxLAzgtWvXbL3T2bNnce3atc73ZVmGn/iJn0AURfjIRz6C973vfUfmzrIMX/3qV0nHeyyhFISSEFJCQoxymw4ODmCm71evXvUeIzfWldDniG++g4MDCwC5ABgwyv1IHa+++qr9+fnnnyfZZiE0KKA4LgDw0ksvlXklSb5XXnlF58uB5XLpnbMopXGqZAC/+c1vNvarSxweHupG8Fzh1Vdf9R7j008/rX8oJIIwINmPUgqgBIAvvvgi6fXyzDPPkOQzx0Fwmvvi9evX7c9PPvmkPfY+oaRmZfM8p7svcg5wASX9r5mXX9Yuk8iWiOIYX/va17zyAdCtYkoZH8V9xxxntTgg2Y/PPfeczQfoZ5bvJDmKQqDQLKWU8eieL+a+LXgGUWQ4PDz0HqMQwuYDgG9/+9t44YUXvHKq2kLgE0884X1csiwD5wXCQCLP/Y/L888/r/OWAPDrX/+614JbXrY2yfkSuVgikan3GG/evAkuKgD4rW99q3duvWlwwaHKWd6TTz6JrOyj6RJXr14FoM1fDANINZ/YhVtsDQD+9E//tJ3k1eOXf/mXG78zxnovpMceewznz5/HU089hQ9/+MO4cuXKkfKINE3x8MMPuw98JDGbz8CLQ3CpMN/fG+U21ZvU3n///d5jNA8rALhw4YJ3vnPnzlkJqJC6aH+M+5E6vvjFL9qfv+u7votkmw0zcvnyZZJ8TzzxBAC9wkiR7/HHHwcASM5w+vRpsnPRNIJ/4xvfiLvvvtsr5913360ZQKlIzm+7Ul5IxDHNfW9/bx94SU+U77vvPtLrhepcNIuH+3s090WzeAAADz74IIkEb29vD89D3yN9x6iU7gWrigKMc5w5c8Y75zPPPKNzZxnSdEKyH/f29nDjeQ0EHnzwQe+c5h4hFwc4d+6cd74zZ84AqADgww8/7H2sT506hVcWGcAznDv3+lE+X6I41n0AeY5Lly55j/Huu+/Gk0+/ZGsA3/KWt3grQ+bzOV58Uf/83d/93d5s05kzZ/DMt18EIEmeB2auk/MlojDCm970Jq98SikwxrQEVGQ4c8b/uJw9exayZgLzpje9ybsvZZ0lf/DBB3HPPfc45zp9+jQAIOMVAHzDG94wymvmOynWAeytAcA//MM/7P3bmTNn8MILL+DcuXN44YUXcNdd3fp+44h177334h3veAf+/u//nlQfP+aIohjFUkKoMbeBoJVsbqMG0DKAEognrz0JKJVk0wSVaye1CYzZZl7QSH2NpFJxOpOjOI51PkXoKgpoCeiE5h6RJMlrzgV0GzWACaELKGMMSZoi5xxMCDKJHAAgWyIhkMgBQJpOtGsn6CSgXT/75jMAkCLnfDaDevU6WDFeS/tJOkFRNpan2o+CZ+A8QxzHRC2b9HUShhHJMyZJEnDJESiafqa2WX2xJGsVkyYpcp4hJ+qHF8cxClmZwIytNZepM64zgGO9Zl4rcSw1gO9973vx8Y9/HADw8Y9/HD/4gz+48p4bN25Ymvzll1/Gl7/8ZTzwwAO3dZzHGdrSV+lG8AQ3sG0EtWtnHQxQuYAqBUipIIj6w90JUX9A0U3iaU1gzLmjtlADSDbGKLQmMFQ1gDKnc+ys9wGMKM1LSlOZsQJA6h6S1PcxAIiJQWqSplrHzjmpCYxaZkgTf5dEAJjUgKSvqQywRQC41ACQaoyMF5AjtrRP0pQUAKZpal1AE6JzJ4poe3smSQLBCwhR0AJATgMAASCdTJCLDDmROY9uHVaZwFBsN+W90dYAyhz5DgCOIo4FAH7kIx/B5z//eXzgAx/AF77wBXzkIx8BAPzd3/0dfv3Xfx2Aln/8q3/1r/BjP/Zj+PCHP4yf+7mfe00BwDCKIEbfCJ62gXI9qFYBAc3+cVkZM3ynR33iTnVcDDinmnSb40vZBqLrZ58IwxCK0OWWGpjbfFLfLyhiO4sHOqjA1TaNjeiNaogmi0mi9cicmAHMM8u++EZ9MkcxxjpAo5rEM8ZsqwoyUFlkUEQGK9uIySRFsdDGN1SglxO6igKwvY4pW8VwkYMLTnuvBd19cTKZoOA5Ck7DHkdRBKUUcpGDMUZuzOd7bCwDWJOAUpyPu3CPY0EWp0+fxh/90R+tvP7mN78Zb37zmwEAb3vb2/Cf//N/vt1DG03EcYxMqlFLQOs3GKrJnQky233ohXMhGckk4k6IbUxoDVM3dgaw/bNPRGEIU/JOKtkkyteclND22Gvnp4htMHYUsY37mNmPZAAwTaFKBpBi0lRJQDNMiCZh9fsrxRjrOSjyMcaQJCmyTLtXUrXoUPkSSsrRAsA0SfHyLVoAKKVAkS8IASBtb08NAAuEStI5OHf87BOTNC3bQNBJQAHdWoLKFToI6e6NQRAgTRLkOwZwNHEsDOAujg7d1BNlG4hx1q5tQzrVlds1rCxQ6v/Guh+p405qA0FdA9j+2SfCiLY2jHqMjXwhPQNItR+NaQvVw54aAG5jwcTkpASA4BySGgCCbhWemrHb39+3P/uaWZiIS5lqnCQk957JZAJRsmtjncxOJin4kg4Amhz58ibZuUO9YJIkCYTg4LwgZwCp1BbpJMUi13JkytZFmcgQR/Q9UinmeGmSNmoAdwzg8cY4qaVd6D6ASoHfIQzgGM1G6hLQ1xIDuE0ASG0CQxXbkOgYUBWEAclkcWsSUNAd522sdP/Wb/0WnnnmGS8HuXps85weq1HNJE2BWzcBSWNqUc9BdZzrOSnG+LrXvQ6/+Zu/ieVyibe//e3e+YBqXFTPAmqWchuRpil4Rlf3aKV82S2cPX3COx9QXSdU1585pyURAxgEAcIwhBCCpLcnoPfjjfwV+7NvmH2Yi2wrBlkUOdN0ohvByx0AHEOME1nsomzqeefUAG5zVd7EFTS2AAAgAElEQVQ1zH4TQoPAse5H6rgTGEAz6aaSgG7D+dQwgFQGK9THZZu1a5Q5L126hEuXLpHkAujZY2rzKWBLDGBpikYNAKnAkMnDGCO5BhljeOtb3+qdpx7G8IbKvOROAYBdP/vmK7IDpOlZ73xAdZ1QSkBNUJ3fYRhBCEHHAKYpFjkde2wZQL5ESKQIIWcAJylyUaAQ2l1trNfMayV2EtCRRhiG4FK7gI4VuGyzBpDKBRR4bUtAqVdUx8oAbqN2jXpVepsSUCrJzzYXD6jCTJZOnKBhH+pBdX4b+SKVdDFJEiCjWzXfxgTZuGBORuqGCVT7LiVrfTF+AEgN9i0DuLxFts3UCybbuI9VrD7N8yBNUxyUAJBWAppbF2LfoFZHpGmCXBTIRI4oisjnAbsYFuN8wu+irAFUEHcIAzjuGkAFIcZbS0kd22CG/t2/+3f4zGc+Q+bEu83zheyBX66ihkQPfGqQeqcwgNTxnve8B1EUWcMwyqCakPzMz/wM3v3ud5M1OU6SBCrP7M++sQ2p70/8xE/g3Llzo+7Vm5aOp1Sg9+1vfzu+9KUvIYqi0Ta0ppb7mnxSFGQAkFoyvY0FDqMEoVpsS5LENm2nlIBmPEM0HSsDOEF2I0cuCrL2M7twj3E+4f//9u49uqYrjwP49+bmKQiaSihtxWNiDTMxQlpVRJIKiUZSj7YSOqjSooOa1ai3UkOKDqXjNaa0VImlxkqnmOmgJmFqqBp0WF4d9Uxw807u3fNH3CsU4567T+8+Od/PWtZKbpKf3z3n7HP3PvtFN/d0cRimB1DFOYCuVUBd20CYowGoRw9gbGwsYmNjpcQC9O0BlDYH0Fkp0WE4jawNwn18fOBwOHTppVS1vAQGBkq9FquT9WAiODgYUVFRUmIBNyuxFVWVRVVXNQwNDUWfPn2kxNJLfHw8/Pz88MQTT0iJFxERgTlz5kiJpRfZC/7oMX9UzwagtBydC9VIWnFZds+sa69CiXMA5fcABqDQXoJyh7yHB6Sdmi0LqloExmGcHkAV5wC6egDtMFUPoBF6cWTvA6jHteic66FHD6DM1VQdEh8S6fHwwEhk90zLIrtHQ48KshGkpKQgJSXF22n8pPTqAZQVD6g+BFTN0RbArR5AqfN6b5JxHJ3HsMxehkDfOv/nt92L6WORsxBaQEAA8h0VqLCXswGoADU/7ehmD6CA3SA9gLIrTjJuNtV7AO0Kb6chW/Vzofq1EyRpvpAuK2Lq2AMoe6sKVVd9MxpZDyRkk91bYISeXpJD9sMDPbYQkd0DqM+2QHJzlH0cnee5QoceQFn1u4CAAFQ4KlDuqDDNquwqM98nvEFU9QCqPQRUzx5AmdtA3Bw5ZZqKjhF6cSIiIhAfH48uXbpIiafPpP+q60XWqm+6VEpunl9VF6oxiiFDhqCkpMTbadyT7B47i8UCq68v7JWVpjrPZiT72tFjBVk9G4CytwVSdZ5i9R5AVRuAfn5+KHdUoNxeAX9JCzGRdrzzK8rX1xcVdjsAdSvxevYAytwGosxkDUA9G+ayBAcH47e//a20eEZrXEnbTsMAORrB888/7+0U7kuXOU1WK+yVlaa5L5pVcHAwgKpGv6rDh43wIMtX8mgL2cfx1j6A5dKmLbiGgErsASy3V6DCUYGAAM+3viDPsAGoKCMMxTLKENDyiqq95lQ9jrLpuTqrqnQZXumc8yFxvp6T7HkpMucUumKbpLwYgS5z9m7eY3mea7akpCSEhobi4Ycfljq3/s6vPSF7b089t4GQda+VPZdSj/csuwfQ39//5hDQSoRwCKjX8c6vKCMMxVJ9CKjriVjl7d/XdNXPhapzmmTT41qU/YGv60I1OuRolocHRqDHsDshzPVgzKyCg4PRvXt3afHM2gCU3Usp+zjq+RBUagPQXolKRyUXgVEA7/yKMsJQLKNsA+FsAJpl0rGq14ueqpcXWR9WzjhKL0yg4wbKpA49Kt0tW7bEkW+/xSOPPCIlHplDSEgIYmNjUVBQgHbt2kmJKfu+o88iMHLvtb/85S8RFRWFkJAQNGjQwON4ukwxuPkZKLPXs9JRiTJHuWnqYyrjp72ijDAUyygbwZu5B9As9H3iq+4WC3rOnSF16HF9z3/3Xal7SJI5WK1WZGZmSo3ZqVMnnDt3DnFxcVLi6XGvlT0iJCIiAnPnzpUSC9CnzqjHEFAAKK4o4dxjBfDTXlFGGAKq5xxAmUNAy0w8B9AsdBleqeP8OmmVEslDdDjsU0169ABaLBZT3itIPU2aNMH48eOlxTPCEFDZjDAH0HnvKqksZQNQAfy0V5QRVnI0yiIwZtsGwoyVeD2GI8v+wDdCjqrea8zOCA8EiVRhhPlwsuk5BFR2A7DCzn0AVaDmlUyGmwOoYg+gj48PLBaLawioWRqAZryxVn9goOoTXz0rJRwCWrMFBt5aMt2M5ZvIHXouCqbqPVLXzxcfNVc+Jc+oeSWTIeYAqt4AtFgs8PW1oryyaj9FVY+jbA0bNsTEiROlTCw3IiP0rkmL6cMeQDOIjo7G2LFjUbduXde+bkR0d0aYby2bHp8vsheB0WU7G9LMHDViAzJaD6Ds7QZkxbNarSivrOoCNEsDEAC6devm7RS8RtbDCNlDfvRYNEl2jqoObzK7wMBA9OzZ09tpEBmCnnPCVb1H6jnv0eIjf3sONgC9T80rmQw3B1A2mfsBVdhvfU01n6pPPzkHkIhIf9V7yevUqSMlprNOouo9Uo8HjM73LKs+xh5AtbBGrCizNwClTTr29UV5yc2vecMxBSM0rmR/QLMHkIioSlBQEDZu3AghhPQh06rWx/TcBkLW+C42ANXCBqCijLDqmxEagFZfq2sRGFVv3CSXrA8WvVZAkxnTSeZGvQAQEBAgJR4RkTfUrVtXajzn/VvVhoueDUAhJdrtx07Veq2Z8Awoyuw9gNKGHPj6uYaAqnrjJjnmzJmDS5cuoWnTplLiye4BrFu3LjIzMxEQEKBsA7B169YYM2YMHn/8cSnxiIhqgoEDB6JFixaIjY31dip3peccc5lTcu72NXkHz4CijNAA1DMvmT2Arq8VPY4kx69+9Sup8WT3AALQrfIgK0dfX18kJydLiUVEVFM0btwYqamp3k7jnvSYYy77QSWHgKqFEz4UZYQGoBGGgPpa+cSJtNGjAagXI+RIRET6qN5LJ6vOKHt1dyNsb2YmrDUoyqwNwIYNGwLQZ2NrVY8jqUn1Zb+rM0KORESkP9k9gELSLEBuBK8WNsEVpUd3vmyynw4BwJQpU3DkyBGEh4dLiefry0nHpI2RGlVGypWIiOSzWCwQQkhfFVpWXa9JkyYYOXIkSktLpU/ZIPexRqwoPSb0yqZHXq1atUKrVq2kxbNy0jFpZKQhoKo+JCIiop+WqnMALRaL0vMozUb9mo1JGaEH0AgV4+rDDFQ9jqQmPXq49WKEskhERPoRomqopuw5gM64VLOw1qAos/YAysZJx6SVkR4YGKEsEhGR/lTdBoLUwhqxotgDKIez0WexWAyRL6mjVatWaBjWEFFRUd5O5Z5iY2Nx8dJFNGvWzNupEBGRAlRdBZTUwgagooywCqgRbg7OY+frq+YxJHVFRUVh7Zq13k7jvpKSkpCUlOTtNIiISBGyF4GhmolnV1FGaAAa4ebgPHZWA+RKREREpIXzobz0bSA4B7BGYq1YUdUbV6o2tFTNqzrnEFAr5/8RERFRDSerbuZsUBphtBe5T/0avEkZoQFohJsCewCJiIjILFTdBoLUwrOrKCM0AFXNqzrnjdAIuRIRERF5QvYcQA4BrZlYK1aUERqARugBdB47DgElIiKimo5DQOlBqNmyIO4DKIlrCKiiC+kQERERycJVQOlB8Owqygj7ABrhqdCtBiAvdSIiIqrZZPcAUs3EWrGijDAEVNW8qru1CIyajWgiIiIiT/n7+wPgNhD0YDgxSlFBQUFITk6G1WplD6AHXA1AzgEkIiKiGmrkyJHYv38/WrZsKTWuEep65D7WihVlsVgwZswYb6dxX4bqAVS0EU1ERETkqV69eqFXr17S4hmhjkfa8eySZkZ4KuRaBZQ3MiIiIqIH4qzjcQhozcRaMWlmhAags+fPwgYgERER0QNxPkA3Ql2P3MdaMWlmhJsCb2BERERERLewAUiaGWF8+K25f2wAEhERET0II9TxSDsuAkOa1alTB8G1aqHz0097O5V7atKkCQCgefPmXs6EiIiIyBjCwsJgtVoRFxfn7VRIBxZRw2Z3Hj16FK1bt/Z2GqYhhFB+eKURciQiIiJSCetPxna/NhH7d8kjRrgxGCFHIiIiIpWw/lRzsQFIRERERERkEmwAEhERERERmYRXGoA5OTlISkpCZGQkDh8+fM/f27VrF3r06IGEhAQsW7bsJ8yQiIiIiIio5vFKA7BVq1ZYtGgROnTocM/fsdvtmDFjBlasWIFt27bhz3/+M06cOPETZklERERERFSzeGUbiAdZkv+bb77BY489hqZNmwIAkpKSsHPnTrRo0ULv9IiIiIiIiGokZecAXrx4EeHh4a7vw8LCcPHiRS9mREREREREZGy69QC+9NJLuHLlyo9e/81vfoP4+Hi9/luUlZXh6NGjusUnIiIiIiIyKt0agKtXr/bo78PCwnDhwgXX9xcvXkRYWNj//buAgABuBE9ERERERKZ1vw4xZYeAtm3bFqdPn8a5c+dQXl6Obdu2oXv37t5Oi4iIiIiIyLC80gDcvn07unTpgn/961945ZVXMHToUABVvXwvv/wyAMDX1xdTpkzBsGHD0KtXL/Ts2RMtW7b0RrpEREREREQ1gkUIIbydhExHjx7lEFAiIiIiIjKt+7WJlB0CSkRERERERHKxAUhERERERGQSbAASERERERGZhG7bQHgL9wEkIiIiIiIzKysru+fPatwiMERERERERHR3HAJKRERERERkEmwAEhERERERmQQbgERERERERCbBBiAREREREZFJsAFIRERERERkEmwAEhERERERmQQbgERERERERCbBBiAREZFkV69e9XYK/1dBQYG3UyACwPJC6tDjWlTx2jFFA7CoqAjvvfcekpKS0L59ezzxxBPo378/srOzpf4/PXr00Py3x44dc31dUVGBJUuWYMSIEZg/fz5KSko0xRw1ahS2bNmCoqIizXlVd+7cOWRmZmLBggUoKirCpEmTkJycjDFjxuD77793O57D4cDGjRsxfPhwPPvss0hNTcXYsWORl5enOcfKykqsX78eQ4cORe/evdG7d28MGzYM69atQ0VFhea4dzN58mS3/8Zut2P9+vVYuHAhvv7669t+tmTJEk15lJSUYPny5VixYgXKysqQnZ2NESNGYO7cudLOvSfXNiD/+l67di3y8/MBAGfOnMHAgQMRHR2Nfv364fjx427Hk11WAGOUF5vNhqysLCQmJqJjx46IiYlBz549kZWVhRs3bmiOey/Dhg1z+28KCwvx7rvvYsKECdi6dettP5s2bZrb8S5fvoypU6di+vTpKCgowKJFi9C7d2+8/vrruHTpktvxAODatWu3/SsoKEC/fv1w/fp1XLt2ze14u3btcn1ts9kwceJE9O7dG+PHj8eVK1c05ZiVleUqM4cPH0ZcXBz69++P2NhY7Nu3z+14qampWLJkCc6ePaspnzsdPnwYGRkZeOONN/DDDz/g17/+Ndq3b4/nnnsO//73vzXFlP3Zz/LC8qJKeXHmJbPMmLG8yL4WAWNcOwBgEUIIqREVNHLkSCQkJKBTp07IyclBcXExkpKSsHTpUoSFhWHcuHFux2zXrh0sFgsAwHkIS0tLERgYCIvFggMHDrgVLzU1FZs3bwYAzJkzB9euXUNaWhp27NiBa9euYe7cuW7n+PTTT6Ndu3bIzc3Fk08+ieTkZHTt2hX+/v5uxwKAgQMHIikpCYWFhfjss8+QlpaGnj17Ys+ePdi6dSs+/PBDt+JlZmaicePGePLJJ/GXv/wFtWvXRnR0NJYvX464uDhkZGS4neO4ceNQp04dpKamIjw8HABw4cIFbN68GdevX8fChQvdinevG4AQAikpKbd98DyIt956C6WlpWjbti0+++wzdOjQAZmZmQBuvwbc8frrr6NRo0YoLS3FqVOn0Lx5c/Tq1Qs7d+7ElStXMG/ePLfiyb62AfnXd1JSErZt2wYAGD58OPr164eEhATk5eVhwYIFWL9+vVvxZJcVwBjlZejQoYiJiUFqaioefvhhAFUVvs2bNyM3NxerVq1yO+aRI0fu+roQAiNGjMCePXvcijd69Gg89thjiIqKwsaNG+Hn54d3330X/v7+msrM0KFD0a1bN5SUlGDr1q2uB0U7duzA3r17sXTpUrfiAUBkZCQaN25822sXL15EWFgYLBYLdu7c6Va86u/rrbfeQmhoKPr374/t27dj3759mh4W9e7d29UgyMjIwIQJE/CLX/wCp06dwvjx492u5HXv3h09evRATk4OQkNDkZycjJ49eyIsLMzt3ACgb9++GD16NGw2G+bNm4fMzEwkJibiH//4BxYuXIhPPvnE7ZiyP/tZXlheVCkvgPwyY8byIvtaBIxx7QAAhAn07t37tu/T0tKEEELY7XbRo0cPTTFnzpwpJkyYIC5fvux6LTY2VnOOKSkprq+fffZZUV5eLoQQwuFwiOTkZI9i2mw2sXnzZjFs2DARExMj3nzzTbF7926Pcuzates9f/ag7nxf/fr1E0IIUVZWJhITE92OJ4QQzzzzjKaf3UtkZKTo3r27iI2Ndf1zfv/zn//c7XjV33NFRYWYNGmSeO2110RZWZmmYyhE1fUiRNW10qlTJ+FwOFzfa7l2ZF/bQsi/vqufS2d5dtIST3ZZqR5TCPOUFyGqykxGRoZIT0//0b+2bdu6Hc95fTstWbJEDBgwQOTn54s+ffq4He9+5+XO/+tBrVy5UgwZMkQcO3bM9ZonZab6+7ozJ605JiYmioqKCiHErWvHSUuZqZ7j/v37xdSpU0WnTp1Eenq6WL9+vdvxZJcXIeR/9rO8dL3v//WgWF48Ly9CyC8zZiwvsq9FIYxx7QghhK9nzUdjqFWrFv75z38iOjoaO3fuRL169QAAPj4+rh4Od02aNAnffvstxo0bh/j4eKSnp7t6TbSw2Wz44osvIIRAeXk5/Pz8AAAWi0VzXOff1a5dG3369EGfPn1QUFCAzz//HMuWLUPnzp3diufj44NTp07BZrOhpKQEhw8fRtu2bXHmzBnY7Xa38/Pz88PZs2fx6KOP4siRI6737O/vr/k9h4SEICcnBz169ICPT9UIZ4fDgc8//xx169Z1O17Tpk2xevXqHz0hAoCuXbu6Ha/6MFRfX1/MnDkTixcvxqBBg1BcXOx2vOosFgu6dOniOnZarx3Z1zZQdX1v374dDodDyvWdmJiIN998E6+99hoSEhKwevVqJCQkIDc3967n6v+RXVYAY5SXRx55BMuXL0dqaipCQ0MBAFeuXEF2djYaNWqkKWbz5s0xY8YMPP744z/6mZYyU15eDofD4SrPI0eORFhYGNLT0zWVGYfD4fo6JSXlnj9zx5AhQ9CrVy/Mnj0bjRo1wujRoz0qM1evXsUf//hHCCFQWFgIIYQrntYcX3zxRQwfPhwvv/wynn76abz99tt45plnkJubi8jISM25AkB0dDSio6MxefJkfPXVV8jJycGAAQPcihEQEIA9e/bAZrPBYrFgx44diI+Px759+1zn3l2yP/tZXlhetJaX6tebjPICyC8zZiwvsq9FwBjXDgCYogE4bdo0TJo0CWfOnEGLFi0wa9YsAEB+fj4GDhyoOW6bNm2wevVqrF27Funp6SgrK9Mcq2PHjvjb3/4GAIiKisKVK1cQGhqKy5cvo379+ppi1qpV60ev1a9fHy+88AJeeOEFt+NNmDABI0aMgI+PD95//30sW7YMx48fh81mw8yZMzXFGzRoEPz9/VFZWYkFCxYAqDov3bp1czseAMyfPx9ZWVmYPn06QkJCIISAzWZDTEwM5s+f73a8wYMH48aNG3dtVGgZb96mTRvs2rULXbp0cb02atQohIWFaZqf4YxZVFSE4OBgvPPOO67Xz549i+DgYM0xZV3bANChQwf89a9/BSDn+h47diw2bdqEcePG4ezZsygvL8eGDRsQHx+PrKwst+PJLiuAfuXFz88PdrvddT17Ul4WLFiAZcuWIT09HVevXoXFYsFDDz2E7t27uz1c2mnUqFH3rHRpmTcbGxuL3NxcdOrUyfVaWloaQkND8fbbb7sdLy4uzlVexo4d63r9zJkzaNasmdvxnMLDw/H73/8eO3fuxJAhQ1BaWqo5Vv/+/V3zUVNTU1FQUIAGDRrg8uXLaN26taaYGRkZaNWqFdatW4fTp0/DbrfjzJkziI+Px8iRI92Od7cKmNVqRZcuXW67vz2oadOmISsrCxaLBStWrMC6deuQmZmJhg0bYsaMGW7Hc8as/tk/e/ZsANo/+1leWF60lpe7HStPygsATJ8+HfPmzZNWZsxYXgC51yJQde20bNkS69evV/baAWCOIaBCCHHixAnx1VdficLCwtte//vf/6455qFDh8ShQ4eEEFXdsosWLRJffvml5ngHDx50xfvPf/4jVq1a5VG8O3OUEbN6jt99951YsWKFR/EOHDgg/T075efni/z8fDF+/Hgp8ZwmTJigdLzqMZ3DQT1x8eJF0bFjR4/j3En2+37jjTekxtPjvAwfPlzY7XbNf+9wOMTVq1dd38vOcf/+/WLlypWah73eK+aqVaukxVQ9njPm+++/r3yOMs+1HudFtWvx4MGD4saNG0IIIYqLi8XChQvF8OHDxdy5c12va4lps9mEEEKUlJSIhQsXildeeUVzzOo5yopXPb/33nvPo3h3xiwuLha/+93vxODBg6W8Z+d5kZ2jp+da9nl2xrzzXHuaY/Xj6Om5/tOf/iTOnz/v9t/9lDHvjFdSUiKOHz8uNaan9DiOQghhikVgPvzwQ3z88ceIiIjAsWPHMHHiRMTHxwPQvvDG4sWLsWvXLlRWVuKpp57CN998g44dO2Lv3r3o3Lmz26182fHuFvPQoUOIiYlRJkc93vOIESN+9FpeXh5iYmIAAB988EGNiscc1YxnlBz79u2LjRs3AgA+/fRTfPTRR4iPj8eePXvQvXt3DB8+3KOYGzZswEcffYSEhATNMVWPd7eYH3/8sUfH8c7zsnbtWqk5yjjXep8XT4/hnTFlHMekpCRs2bIFvr6+mDx5MoKCglxDu44dO4bFixe7neOdMQMDA9GjRw/NMVWPd7eYnh5H5qhmju3bt0dQUBAeffRRJCcnIzExEQ0aNHArxv1iJiUloWfPnh7FlB3vzpgy3rceOQIwRw9gcnKyq+fv3LlzIjU1VaxevVoIoX1yeXJysqisrBTFxcWiXbt2tz3Z0TLJU3Y8I+Sox3vu06ePGD9+vMjNzRV5eXkiNzdXPPXUUyIvL0/k5eW5HS8lJUXpeHrElH0MjZCjEc6LXjk6paWluXoXi4qKPF58SlZM1eMxRzXj6RGz+mJLdy6o4sliIzJjqh5Pj5jMUc0cU1JShN1uF7t37xaZmZkiJiZGDBkyRGRnZ7vqe96OadYchRDCFPsAOhwO11yoJk2aYM2aNdi1axfeeecdzYvAWK1WWK1WV6u8du3aAIDAwEBNk29lxzNCjnq8502bNqFNmzb44IMPUKdOHcTExCAgIAAdO3ZEx44d3Y6XnZ2tdDw9Yso+hkbI0QjnRY8cHQ4Hrl+/joKCAgghXE8Va9WqBavVqkRM1eMxRzXj6RGzZcuW2LRpE4Cq5eMPHz4MADh16hR8fbUtqSA7purxmKN5crRYLPDx8UHnzp0xe/Zs7N69Gy+++CJ2797tGoXn7ZhmzREwySIwDz30EI4ePeqaDBwcHIw//OEPmDhxIr777jtNMf38/FBSUoKgoKDb9vSw2WyaGi+y4xkhRz3es4+PD1566SUkJiZi9uzZCA0N1bTiolHiMUc14xklx8LCQqSlpblWzbt06RIaNmyIoqIizQ/HZMdUPR5zVDOeHjFnzZqFWbNmYenSpahfvz6ef/55hIeHo1GjRq7F5bwdU/V4zNE8Od5Zxvz8/BAXF4e4uDiUlJS4HU+PmGbNETDJRvAXLlyA1Wp1bURZ3ddff4327du7HbO8vPyum0Tn5+fj8uXL+NnPfubVeEbIUY/3fKcvv/wSBw4ccHsDU6PG0yOmGXM043uurqSkBFeuXEHTpk2Vjal6PD1imjFHFd9zYWEhvv/+e1RWViI8PNy1xL0nZMdUPR5zrPk5njp1yqOVYn+KmGbNETBJA5CIiIiIiIgAU8wBJCIiIiIiIjYAiYiIiIiITIMNQCIiIiIiIpNgA5CIiIiIiMgkTLENBBERkUyvvvoqLly4gLKyMgwaNAgDBgzAp59+ihUrVqBOnTqIjIyEv78/pkyZgvz8fEydOhXnz58HAEycOFHT6tNEREQysAFIRETkptmzZ6NevXooLS1F37590a1bNyxduhTZ2dkIDg7G4MGDERkZCaBqf63BgwcjOjoa58+fx9ChQ5GTk+Pld0BERGbFBiAREZGb1qxZg+3btwMAfvjhB2zZsgUdOnRAvXr1AACJiYk4ffo0AGDv3r04ceKE628LCwtRVFSE4ODgnzxvIiIiNgCJiIjckJeXh7179+KTTz5BUFAQMjIyEBERgZMnT9719x0OBzZs2ICAgICfOFMiIqIf4yIwREREbrDZbAgJCUFQUBBOnjyJgwcPori4GPv378f169dRWVmJL774wvX7nTt3xpo1a1zfHz161BtpExERAQAsQgjh7SSIiIiMory8HK+++ir++9//olmzZrDZbBg1ahROnz6NlStXIiQkBBEREQgPD8fYsWORn5+PGTNm4OTJk7Db7YiOjsaMGTO8/TaIiMik2KVHe5IAAACISURBVAAkIiKSwDmvr7KyEqNGjcJzzz2HhIQEb6dFRER0G84BJCIikmDx4sXYu3cvysrK0LlzZ8THx3s7JSIioh9hDyAREREREZFJcBEYIiIiIiIik2ADkIiIiIiIyCTYACQiIiIiIjIJNgCJiIiIiIhMgg1AIiIiIiIik2ADkIiIiIiIyCT+B6eD/rfmidCSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "sns.violinplot(x=df.age,y=df.target)\n",
    "plt.xticks(rotation=90)\n",
    "plt.legend()\n",
    "plt.title(\"Age & Target System\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['red','green','blue']\n",
    "explode = [0,0,0.1]\n",
    "plt.figure(figsize = (5,5))\n",
    "#plt.pie([target_0_agerang_0,target_1_agerang_0], explode=explode, labels=['Target 0 Age Range 0','Target 1 Age Range 0'], colors=colors, autopct='%1.1f%%')\n",
    "plt.pie([len(young_age),len(middle_age),len(old_age)],labels=['young age','middle age','old age'],explode=explode,colors=colors, autopct='%1.2f%%')\n",
    "plt.title('Age States',color = 'blue',fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f86c499f128>]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.barplot(x=df.age.value_counts()[:41].index,y=df.age.value_counts()[:41].values)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Counter')\n",
    "plt.show()\n",
    "plt.plot(max_age, '--bo')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f86c1384278>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['age'],y=df['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "dataset = pd.get_dummies(df, columns = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal'])\n",
    "standardScaler = StandardScaler()\n",
    "columns_to_scale = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']\n",
    "dataset[columns_to_scale] = standardScaler.fit_transform(dataset[columns_to_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dataset['target']\n",
    "X = dataset.drop(['target'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "knn_scores = []\n",
    "for k in range(1,21):\n",
    "    knn_classifier = KNeighborsClassifier(n_neighbors = k)\n",
    "    score=cross_val_score(knn_classifier,X,y,cv=10)\n",
    "    knn_scores.append(score.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot([k for k in range(1, 21)], knn_scores, color = 'red')\n",
    "for i in range(1,21):\n",
    "    plt.text(i, knn_scores[i-1], (i, knn_scores[i-1]))\n",
    "plt.xticks([i for i in range(1, 21)])\n",
    "plt.xlabel('Number of Neighbors (K)')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('K Neighbors Classifier scores for different K values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_classifier = KNeighborsClassifier(n_neighbors = 10)\n",
    "score=cross_val_score(knn_classifier,X,y,cv=10)\n",
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "randomforest_classifier= RandomForestClassifier(n_estimators=50)\n",
    "score=cross_val_score(randomforest_classifier,X,y,cv=10)\n",
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=2)\n",
    "from sklearn import tree\n",
    "\n",
    "model = tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = model.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy_score(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "pd.DataFrame(\n",
    "    confusion_matrix(y_test, y_predict),\n",
    "    columns=['Predicted Not Survival', 'Predicted Survival'],\n",
    "    index=['True Not Survival', 'True Survival']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import neighbors, datasets, preprocessing\n",
    "\n",
    "x_normalized = preprocessing.scale(X)\n",
    "\n",
    "xTrain, xTest, yTrain, yTest = train_test_split(x_normalized, y, test_size=0.21, random_state=29)\n",
    "\n",
    "knn = KNeighborsClassifier(6)\n",
    "knn.fit(xTrain, yTrain)\n",
    "predictions = knn.predict(xTest)\n",
    "\n",
    "print('accuracy is :',accuracy_score(yTest, predictions)*100)\n",
    "\n",
    "plt.scatter(yTest, predictions)\n",
    "plt.xlabel('ytest')\n",
    "plt.ylabel('predictions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}