9-Heart Model_Github.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      "age         303 non-null int64\n",
      "sex         303 non-null int64\n",
      "cp          303 non-null int64\n",
      "trestbps    303 non-null int64\n",
      "chol        303 non-null int64\n",
      "fbs         303 non-null int64\n",
      "restecg     303 non-null int64\n",
      "thalach     303 non-null int64\n",
      "exang       303 non-null int64\n",
      "oldpeak     303 non-null float64\n",
      "slope       303 non-null int64\n",
      "ca          303 non-null int64\n",
      "thal        303 non-null int64\n",
      "target      303 non-null int64\n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.2 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</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>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 6,
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C9g5Lf08ne+LdOskX0+v/tYnpH07yqdbaM6rqLUn2a6394grb3Cf9XPjd1to/DtOum+RrSV67dI6Ptco141daax8Zpj0g/Ry95tJ9cYXtfC7Jq1prLx9en51VjvOq+v+G5W84vN5uW0yUc2tr7Z4T0z6V5P+21v5iJ6u9Q9V7hnwryb1baydOTH9dkmu11n63qm6V/kXTK5P8aZLHtNb+fpVtLq/zYennyG1aa18apj0+yRGttZsMr7+R5EWttRcNr7cm+XKSz7TWdlugaajvd5Mc1lp7yzBtz/Qg2kuT/Gf6cXH91tr3hvfr+q21X53Yxj8kuf/SebCj631r7RtredZYoaxPSfLQ1tqdJ/bz4PRA4PuSHL3UXutlB89Pb0hyvdbag3fUrq21F06ccw9rrb1tWObH0j+cH9Zae3tVvSnJxa21x07s5xfTv4y59vbOy1my2v11O8tf6X3ejKZ1fZ2G5XXdzjKvSPLjrbWlnqBvSA8877c0NG7iufEurbWThmm3Sb+fPrG19tI13rOPSn/2HfXDLPOuqvZN8v2PnLZfrr3P/PaH+eGFi7nPgecm/dnvgmmXZ3eRg2mGDA/+z01ytyQ3zLYeZvulf9N+6rKL/KeWbeKgJL9cVT9YYfO3ztDFcxP4r/RvjE+rquOSfDDJP6Yfrz+Z5PVV9dqJ5fdI8v2lF62171bVI9O/XTkh/Rv9eXFAkr3T22d7fvSho7V2RfUhaAesd8F20UnLXh+U5C515aFfW5Nco6quld7T4vAkX66qf03/Nu29a8jddMXkvoagy/fS22XpPDp36cPG4MT0c/C2rbWPDg8cx1XVh5J8OMnbW2tf34m6jrGW9/3Uif8vlevGSb4wrP/Py5Y/PsnhVbW1tbawuwo6Iz6xFFwanJjkycOH4A8lOSfbjqF/TfKu1tpFUyjnSibPiYPSvzX+dtWVOiBcM/2anvTeRK+rqt9LPy7fsRQU2I47pfdmOGPZNvdODxwlvTfHO7az/q2S7JmJ+09r7ftV9f9W2efutL3j/NzhQ/OR6d823yz93nDN9Hvoiqrql9N7p9wuvRfOHunXm2sPvV5Xa4uVyrRUrhuvtOBucLsk10gfPjk5fa8kn0mS1tqXq+rPk/zvJG9bHlxaQ52T5KJlx9GP6jQEFH88Vz4GFqrq5Oz+nvG3Tj/ejp/Y1+VDEO+A9ADTpAPSP+BNOjH9w+Kk7V7vq2oha3jWqN6j9vD03lDXGeYv/4CwX/p5+ZettZesXtXdYsXnp9bad5ctt6N2nTT5TPGd4VxfWuagJLepqodPLF/pbXnL9A/ks27V++sa3+d5sVuvr7Oqes/OR6WnVbhm+vVzeeLt05blXbpt+rPkp5cmtNbOrKrJc2st92yYewJMs+W96b2NHp3+bfCW9F9E2iv9hr28u9nyLs9bhm08bYVtb9QH4dGGB9VDkxyc5L7p38A+P70re9Lb55PLVlv+Aflew7SbpQ89nJeHgYt3cb1Z7aq4fNjilvQHmHeusOwlrbXzqucEODS9m/Yrkzylqn5ppR5Ny6zUBqu1S5v8t7X2iOr5nu6fPgzgeVV1aGvtEzvY7+6wlvd9sv5LZV/6sLeW68fVQmvtwuFb/kPSry/PSXJUVd2ltTYLv7g2eU5sSb92H7LCct9LktbaUUOvjQcm+dUkz66qh7XWln/IntzmQvqD8PLr5tKXE6sdb0vHzbSOp9WO8xcmuV/6cJ4z0+vxj+n30KuonkPt/UlenT7s/DvpvYVfn/7hO9n5c2+pXOv1levSdh+Y5KvL5l068f+le+D+VbXHUhB+jXVOVq7T8vd4I46B1Y63la7fu1qGyev9Uhtv91mjqu6WPuT4yPQvs76fPuR0eW67/05/nntYVb1+vb+h3t7zU/Whg5N2tl2vsqvh3y3pgcxjV1jm3DUVevq2e47vxPs8L3bb9XVWVR8K95L09/DE9CGQT0kf4jxp+fPp9q4tk9N3eM9m5yyksjDHj6vzWrf57XO2yVTPkXNAkue11j7SWjs9yWQS0S8k+fmq2nti2vLuuZ9Oz8dwdmvtzGV/myr/UOuOb60dmT7W+7L0PA9fTXKrFep31tK6VXVweh6MB6UHll42hSqsly+m39Tvs8oyd1v6T/UhcgelHz/TdFmunLNkez6d3mNo+ft7ZhtyVrTWLm6tvae19mfpN/G7pw8jXW0/e2TifBmCVNfLldtlv6q62cTru6fnKvlRz7/W2mdaa0cPQyQ+m56nYSOs5X1fzefTP0ROOjjJGXPYeymZOAcmXn9xqa6ttStaax9urT01PW/I/ul5aGbNp9NzOVyxwvnwraWFWmtntNZe0voQyHem58VJVj4fPjNMu/EK2/zGsMyp2f6x9qX0DyG/sDRh6Mr+U9tZflet9Zox6Z5J3tBae1dr7bQk38i25LUruXP6teHJrbVPtD6M/GbLllmtLabh8+mBpP1WeP/OS5Kqemj68PBD0nviPGti/bXUeVWtte+n56eZPAa2pt+rd7cz04+FH12/hqFcd87KvWM+n5XP/+W2e71vrZ2fHT9r3CPJOa2157fWTmqtfTG9N8RyF6f3+LgkvQfsPjuq8FjbeX76zWWL7Uy7Tj5TXD/JT2fbvfPT6cORV7pnb5Zf3Vrt/rrW93mz2Yjr66xYXtd7JjmhtfbK4ZnuzKytd9EX0q+dP7rODUPkJn8cYC337F1pe9hU9GCaHd9NH57wmKr6enqX08mhXW9J78Xzmur5RPZL/xYh2fYtwyvSv3F7a1W9MD1Pw23Sv2159Gb5IDl803af9K7d30z/VuFG6Q89RyU5tnrekQ+kd2u+c3rOhRcPD29vTs859IEhj8JJVfW+1tqOhjnMvNbaJVV1TJIXVNVl6d3bb5QeWFzq3v3HVfXF9PZ6Ynqg8m9X2t4GOjvJXavnC/pBth/cfk6S91XVeenDUhbTAwAHttb+snpukK3p3ypflJ6A9uL0IU9L+7nXkHPj0okb+uVJXlZVfzb8/+Xpw6gmh5lekuSNw9CSfdO/kX176/k4bpmezPE96d9G3zb9IftNu9geO2WN7/tq/jrJf1bVs9ITSt49yZ8kefyqa21eP1lVL07/Zv1O6d/iPzlJqurX0od5fSz9uvuA9ONxo4Z47YwPp3/D+u6qelp6GW+WXuZ3p+fZemH6t8hnJbl5eiLefxrWPzvJdarqPulDZy5qrZ1RPSfXm6r/kuBn0odk3zt9SMD70xPenlZVr0zv6XJZkl9OH373rap6Y5IXVtV30q/Rz04/V3dnT8mzs7ZrxqQzkzykqt47lOW5O1jvS+nPQX86rLOUWH3Sqm2x1srsLkMPvBel/1rmliQfT79eHZzeTh9J8qr0/EEfH66Z/1JVHxh6W66lzmvxsiRPH3KLfCH9HLt+dnNv2dbaD6vqVdl2vJ2b/gXStdJ7Xd1+2SrHJjmhqp6afo7cN1cdHpescr0f5h+VVZ410o+1/arqYenD9B6YqwZxJuvwwGE7H6iq+7fWVkplMNoOnp9+flmZVmvXSUdU1bfTg4rPT3+2XPq1wGOSfKJ6DpvXpvf6OCDJoa21P12POu5uO7i/rvl93mTOzvpfX2fF2blyXc9M8vvVf1TmrPTnyLsM/9+u1lMrfDj9c9jj0p8l/zr9GXTpurfqPbv13E1nJ7ll9R+Q+EqSC9tEviaYB5vhwnC1MPTOeFh6b5PPpnfffMrE/AvSe+TcIX2c8PPTP4wn/UEprSdsvUf6B/Djhu38TXqX3nX5xZJ1ckF69/73p/ceeV76t60faD0B46PSf9HjtPREkodl243hb9IfcJ6RJK21z6UPGXx1Vf3ExlVhXT03/ab2nPSHxrflyvk+/iK9zv+V/k3Nb0zjg9AyL0ofWvD59CEDK47Zb60dl/5t76HpD3OfSPKkbAsgfS89iHp8tvUseFBrbSl3zBHp36h9adjPkovSH4Tfkn7zvzj9fJt0Znrvj/enP5x/NtsCMBcl+Zn0D+5npP+C3MvTAxgbZUfv+3a11j6d/ospD0uv13PSE/a+YV1KOn1vSs958Kn0wPvL0t+zpB9DD0nyf9Pb8Y+S/I/hWjFTWmst/cH0Y+lB4jPSh2vsn/5hbyH9J9jfNMx7e/qH2COH9U9ID4q8Lf18eOqw6UcM6/x1+gPwe9I/iJ43rHdG+gfz26e34YlJfiM9/0TSz8kT0xMXfzj9fDw9w71oN1nTNWOZJ6YHDU9IHy5+XCbyZSzXWjslvS5PSz8vHp7+q5aTy+yoLabhWenn8NPT2/249OeDs5O8Ib2cL0+S1tqHhv//XVVdZy11XqNj0n+t7E3pbfKDoRzrkdT5L9KvvW9Ofz9vk+R+7ap5hTIE0R6VHvA6Jf29e94K21ztep8dPWu0/uMUL0lv21PSA3zP3V4FhoDSr6YPp3n/kM9mPWz3+WmFZdfarn+R/mx1cvqvXv36Uu+k1tqp6T8D/1NJ/iM9YP3cbKK0DIMV7687+z5vIut+fZ0hy+v6r+nn/tvSv6y8QXq6hbX4/fR778fSc729Nn2I3dLnsB3ds5N+zv1relL1/07yP0bUDWaSX5HbxIakiv8nPfP8rubmYU7Utl8Tu+PwIYI1Kr/qAbts+LD81fQPsst7P3A1MPSmOj29F9CzdrT8NLner01t+2Wx67fZyE8HM6W2/VL3j359j91jGHr//Q+edou5/xW5+x54TuJX5JiWqvr99J8B/mr6t6nHpD/MCS4BsCGq6o7pPfo+leS66T0Hk6v+UiFzakgWft/0nj17pw+5vWV6L1GAuVNV907/lbjT0nvzvSC95+jHplisubbQtmShzW+AaWFO+/kIMG0uN0nvvnuT9O7H70jyzFXXAIDd78/Tc5Fdlj505p4zMBSXjbOYPmTsRenDvj6b/i3+ZvhZeoBdsWeS/5Wex/HC9OGCD287/hVjuFoxRA4AAACYuqUhch849ZZzP0TuV3/+rGTOhsjN7zsGAAAAwIYwRA4AAACYGYupLM5xf5jFzOdIsvl9xwAAAADYEAJMm1BV7V1VR1XV3tMuy7Rpi047dNphG23RaYdttEWnHbbRFp126LTDNtqi0w7baAtYG0m+N6GlxGeZs4Rgu0JbdNqh0w7baItOO2yjLTrtsI226LRDpx220RaddthGW2ycpbb+l1NvlWvvs3XaxVk3P7xwIQ/8+S8nc3ZM6cEEAAAAwCiSfAMAAAAzYyGVhdS0i7Fu5rVuAkw7qaoqyc2SXDjFYuyz9G8vztWatui0Q6cdttEWnXbYRlt02mEbbdFph047bKMtOu2wzay0xT5JvtbkuWFGCTDtvJsl+cq0CzGYlXLMAm3RaYdOO2yjLTrtsI226LTDNtqi0w6ddthGW3TaYZtZaIubJ/nqtAsBKxFg2nkXJsk5n94/+17n6p3C6t5HPnLaRZgZC3tOuwSz4TsHXTHtIsyELRfPb0LCnbXHBVfv6+SS6525OO0izIzrf+bb0y7CTDjnITeadhFmxmXXc34kyY9/QoeEJPn6L2uHJdc8z0e1JLnhqZdPuwgz4YorLsl//tvRyXRH0sCqXLV20b7X2ZJ997l6f3Dautc1pl2E2SHAlCTZck0BpiTZ0gSYlmy97Op9nVyydS8foJfssdUvPCfJ1r3dQ5dsuYbzI0n22FNgJUm2XFM7LNm6t49qSbLHnp6rYLNw1QIAAABmxkLbkoU2v19ULsxpGq35fccAAAAA2BACTAAAAACMIsAEAAAAwChyMAEAAAAzYzGVxdS0i7Fu5rVuejABAAAAMIoAEwAAAACjCDABAAAAMIoAEwAAAACjSPINAAAAzIzFbMnCHPeHWUybdhHWxfy+YwAAAABsCAEmAAAAAEYRYAIAAABgFDmYAAAAgJmx0LZkoc1vf5iFJgcTAAAAAFyFABMAAAAAowgwAQAAADCKABMAAAAAo0jyDQAAAMyMxWzJ4hz3h1mMJN8AAAAAcBUCTAAAAACMIsAEAAAAwChyMAEAAAAzY6FVFlpNuxjrZl7rpgcTAAAAAKMIMAEAAADMuKp6fFWdVVWXVNXJVXXPHSx/eFX9v6q6uKrOq6qXVNU11qt8AkwAAAAAM6yqHprkpUmen+SOSf4jyQeqar/tLP/wJH+V5Nn6QogWAAAgAElEQVRJDkjyyCQPTXL0epVRgAkAAABgtj0pyetba69rrZ3eWjs8yXlJHred5e+e5PjW2ltaa2e31j6Y5K1J7rxeBZTkGwAAAJgZC9mShTnuD7OQtvTffaqulPD70tbapcuXr6q9khyU3iNp0geTHLyd3Xw8yf+sql9orX2qqm6V5AFJ3jim7KvZVO9YVd2/qj5eVd+rqm9X1fuq6tYT8w+uqlOG8YgnVdWDq6pV1R0mlrldVb2/qn5QVedX1Zur6obTqREAAABwNfWVJN+f+Hv6dpa7YZKtSc5fNv38JDdZaYXW2j8keVaSj1fV5Um+lOTfWmvLg1S7zaYKMCW5dpIXJ7lLkvskWUzyrqraUlX7JHlvktOS3Cm9IY+ZXLmqbprko0lOSe8Wdv8kP57k7dvbYVXtXVX7Lv0l2We31woAAAC4url5kutO/O0oP1Jb9rpWmNZnVB2S5JlJHp8eI3lIkl+rqmeNKO+qNtUQudbaP02+rqpHJvlmktsl+cX0hn10a+2SJJ+vqp9I8tqJVR6X5NOttWdMbOMPk5xXVT/dWjtjhd0+PcmRu7cmAAAAwNXcha21C9aw3LeSLOSqvZVunKv2alry3CRvbq29bnh9WlVdO8lrqur5rbXFXSrxKjZVgGkYDvfcJHdL7yK21ANrvyS3TXLqEFxa8qllmzgoyS9X1Q9W2Pytk6wUYDo6vdfUkn3Su7EBAAAAu9li25LFttkGXK3dYlux09F2tdYuq6qTkxya5F0Tsw5N8s/bWe1a6aO+Ji2k93qqqy4+3qYKMKUPgTsvyaOTfC09wPTZJHtl5a5hyxtty7CNp62w7a+vtMMhwdaPkmwtS8AFAAAAsN5enOTNVXVSkhOTPCa9s82rk6Sq3pTkq621pTxO703ypKr6TJJPJrlNeoed97TWFtajgJsmwFRVN0hyQJLHttb+Y5j2ixOLfCHJw6tq74ms68t/fu/TSX4rydmttSvWu8wAAAAAY7XW3jbERY5IctP0zjYPaK2dMyyyX67cY+l56Z1wnpfkJ5L8d3rQ6ZnrVcbN1Ofsu0m+neQxVXWbqrp3rjx07S3p9XlNVR1QVfdL8ufDvKWeTa9I8mNJ3lpVv1BVt6qq+1bV31bV1g2qBwAAAMBOaa29srW2f2tt79baQa21j03MO6S1dtjE6ytaa89urd2mtXbN1tp+rbU/bq19b73Kt2kCTEMCqoel51H6bJKXJHnKxPwLkjwoyR3SfyXu+UmeM8y+ZFjma0nukf7zfscN2/mb9J8D3O0JrgAAAACuDjbNELkkaa19OP0X4ybVxPwTktz+RzOqHp7k8iTnTizzxfSf5wMAAABmzEK2ZGHz9IfZaQtXSR89HzZVgGlHqur3k3w5yVfTA03HJHl7a+3iqRYMAAAAYI7NVYApyU3Sh8XdJP1X4d6RdUxgBQAAAMCcBZhaay9I8oJplwMAAADg6mSuAkwAAADA5raYZKHVDpfbrOb1F8bmN2sWAAAAABtCgAkAAACAUQSYAAAAABhFgAkAAACAUST5BgAAAGbGYrZkcY77w8xr3eazVgAAAABsGAEmAAAAAEYRYAIAAABgFAEmAAAAAEaR5BsAAACYGQttSxba/PaHmde6zWetAAAAANgwAkwAAAAAjCLABAAAAMAocjABAAAAM2MxlcXUtIuxbua1bnowAQAAADCKABMAAAAAowgwAQAAADCKABMAAAAAo0jyDQAAAMyMhbYlC21++8PMa93ms1YAAAAAbBgBJgAAAABGEWACAAAAYBQ5mHbRvY98ZLbudY1pF2OqPvlXr5p2EWbGQUc9btpFmAk3OMklJUmu/Y2FaRdhZnznAN9jJMkV16ppF2FmnH/IjaZdhJmwsHebdhFmxt7fdZ1Ikotu7JhIkmt8w/VyySU3Wpx2EWbCnhddMe0izIS64urVDgvZkoU57g8zr3Wbz1oBAAAAsGEEmAAAAAAYRYAJAAAAgFEEmAAAAAAYRUZeAAAAYGYstspim9+k//NaNz2YAAAAABhFgAkAAACAUQSYAAAAABhFDiYAAABgZixmSxbmuD/M4pzWbT5rBQAAAMCGEWACAAAAYBQBJgAAAABGEWACAAAAYBRJvgEAAICZsdi2ZLHNb3+Yea3bfNYKAAAAgA0jwAQAAADAKAJMAAAAAIwiBxMAAAAwMxZSWUhNuxjrZl7rpgcTAAAAAKMIMAEAAAAwigATAAAAAKMIMAEAAAAwiiTfAAAAwMxYbFuy2Oa3P8y81m0+awUAAADAhhFgAgAAAGAUASYAAAAARpGDCQAAAJgZC0kWUtMuxrpZmHYB1okeTAAAAACMIsAEAAAAwCgCTAAAAACMIsAEAAAAwCibMsBUVb9dVadV1cVV9e2q+nBVXXuY94iqOr2qLqmqL1TV4yfW+/2q+kFV/dTEtJdV1RlL6wMAAADTs9i2zP3fPNp0vyJXVTdN8tYkT03yriT7JLlnn1WPTvLsJH+S5DNJ7pjktVX1w9baG1trb6qqX0vy91V1cJJfSfLYJPdorf1wO/vbO8neE5P2WaeqAQAAAGxKmy7AlOSm6eV+Z2vtnGHaaUlSVc9K8uTW2juH6WdV1e3Sg0hvHKY9NsmpSY5N8pAkz26t/ecq+3t6kiN3bxUAAAAA5sdmDDD9V5KPJDmtqo5L8sEk/5hel59M8vqqeu3E8nsk+f7Si9bad6vqkUmOS3JCkr/awf6OTvLiidf7JPnK2EoAAAAAzItNF2BqrS1U1aFJDk5y3yR/muT5SR40LPLoJJ9cttrCstf3GqbdLMm1k1ywyv4uTXLp0uuqGlN8AAAAYBULbUsW5jRPUZK5rdumrFXrjm+tHZmeZ+myJPdI8tUkt2qtnbns76yldYfcS09ND0hdkORlU6gCAAAAwNzYdD2YququSe6TPjTum0numuRGSU5PclSSY6vqgiQfSE/Ofeck12+tvbiq9kny5iQva619oKrOTXJSVb2vtfaOja8NAAAAwOa36QJM6b2O7pXk8CT7JjknPbH3B5Kkqi5K8pQkL0jyw/QE4C8d1v2bYdozkqS19rmqelqSV1fVCa21r25kRQAAAADmwaYLMLXWTk9y/1XmvyXJW7Yz7w9XmHZs+i/KAQAAALALNl2ACQAAAJhfLZXFzO8PbLU5rdumTPINAAAAwOwQYAIAAABgFAEmAAAAAEYRYAIAAABgFEm+AQAAgJmx0LZkoc1vf5h5rdt81goAAACADSPABAAAAMAoAkwAAAAAjCIHEwAAADAzFltlsdW0i7Fu5rVuejABAAAAMIoAEwAAAACjCDABAAAAMIoAEwAAAACjSPINAAAAzIyFbMnCHPeHmde6zWetAAAAANgwAkwAAAAAjCLABAAAAMAocjABAAAAM2OxVRZbTbsY62Ze66YHEwAAAACjCDABAAAAMIoAEwAAAACjCDABAAAAMIok3wAAAMDMWMyWLM5xf5h5rdt81goAAACADSPABAAAAMAohsjtooU9k+w57VJM10FHPW7aRZgZJx/1qmkXYSYc+NLHT7sIM6EWtk67CDNjYe9pl2A2XHq9mnYRZsYPfvryaRdhJhzwou9Ouwgz4/u3v+G0izATvv2zvvdNkhudsjDtIsyM5nEiSfLNJ14y7SLMhIWLLk0+Pu1SwOoEmAAAAICZsdAqC21+v6Cb17r5qgQAAACAUQSYAAAAABhFgAkAAACAUQSYAAAAABhFkm8AAABgZiy2yuKcJsJOMrd104MJAAAAgFEEmAAAAAAYRYAJAAAAgFHkYAIAAABmRmtbstjmtz9Mm9O6zWetAAAAANgwAkwAAAAAjCLABAAAAMAoAkwAAAAAjCLJNwAAADAzFlJZSE27GOtmXuumBxMAAAAAowgwAQAAADCKABMAAAAAo8jBBAAAAMyMxZYstvnMU5T0+s0jPZgAAAAAGEWACQAAAIBRBJgAAAAAGEWACQAAAIBRJPkGAAAAZsZi25LFNr/9Yea1bvNZKwAAAAA2jAATAAAAAKMIMAEAAAAwigATAAAAAKNI8g0AAADMjMVUFlPTLsa6mde66cEEAAAAwChzGWCqqi1V9bSqOrOqLq2qc6vqmVW1f1W1qnpYVZ1QVZdU1eeq6pBplxkAAABgs5rXIXJHJ3l0kicm+XiSmyb5mYn5L0xyeJLPJ3lSkvdU1S1ba99evqGq2jvJ3hOT9lmvQgMAAABsRnMXYKqqfZI8IcmftNbeOEz+UpKPV9X+w+uXt9b+aVj+cUnun+SRSV6wwiafnuTI9SwzAAAA0C20ykKbzzxFSea2bvM4RO6A9B5HH1llmROX/tNauyLJScN6Kzk6yXUn/m6+e4oJAAAAMB/mrgdTkot3cb224sTWLk1y6dLrqvmMNAIAAADsqnnswfTF9CDTfVZZ5m5L/6mqPZIclOQL61wuAAAAgLk0dz2YWmuXVNUxSV5QVZclOT7JjZL8bLYNm/vjqvpiktPTE4FfP8nfTqO8AAAAAJvd3AWYBs9NckWS5yS5WZKvJ3n1xPy/SPK0JHdMTwD+G621b210IQEAAIArW2xbstjmccBVN691m8sAU2ttMcnzh78fmfgVudNba3cLAAAAAKPNZ9gMAAAAgA0jwAQAAADAKFerAFNr7ezWWrXWTpl2WQAAAICrWkxlsc3xX2qX2qWqHl9VZ1XVJVV1clXdcwfLX6+qXlFVXx/WOb2qHrBLO1+DuczBBAAAADAvquqhSV6a5PFJjk/y2CQfqKrbtdbOXWH5vZJ8KMk3k/x2kq8k+ckkF65XGQWYAAAAAGbbk5K8vrX2uuH14VV1vySPS/L0FZb/wyQ/luTg1trlw7Rz1rOAV6shcgAAAAAzYp+q2nfib++VFhp6Ix2U5IPLZn0wycHb2favJzkxySuq6vyq+mxVPaOqtu620i8jwAQAAACw8b6S5PsTfyv1REqSGybZmuT8ZdPPT3KT7axzq/ShcVuTPCDJ85I8OckzxxV5+wyRAwAAAGZGy64nwt4M2ra63TxXzol06Q5XvbJaYdqSLen5lx7TWltIcnJV3SzJU5I8Z6cKvEYCTAAAAAAb78LW2gVrWO5bSRZy1d5KN85VezUt+XqSy4fg0pLTk9ykqvZqrV2206XdAUPkAAAAAGbUEAw6Ocmhy2YdmuSE7ax2fJLbVNVk3Oenk3x9PYJLiQATAAAAwKx7cZJHVdUfVtUBVfWSJPsleXWSVNWbquroieVfleQGSf6mqn66qh6Y5BlJXrFeBTREDgAAAJgZi62y2OY3B9Ou1K219raqukGSI5LcNMlnkzygtXbOsMh+SRYnlj+vqu6b5CVJTk3y1SR/k+SYcaXfPgEmAAAAgBnXWntlklduZ94hK0w7Mcnd1rlYP2KIHAAAAACjCDABAAAAMIoAEwAAAACjyMEEAAAAzIzFtiWLbX77w8xr3eazVgAAAABsGAEmAAAAAEYRYAIAAABgFDmYAAAAgJmx2CqLraZdjHUzr3XTgwkAAACAUQSYAAAAABhFgAkAAACAUQSYAAAAABhFkm8AAABgZiymspj5TISdZG7rJsC0i75z0BXZcs0rpl2MqbrBSQ6fJQe+9PHTLsJMOO3wV067CDPhZ17reFiycI027SLMhH3P0g5L9rrAvSNJznnIjaddhJlx6Q0Wp12EmXDDz2iHJPnWgVunXYSZsfWyaZdgNuz71utOuwgz4YrLL5l2EWCHDJEDAAAAYBQBJgAAAABG0U8dAAAAmBmLrbLY5jNPUZK5rZseTAAAAACMIsAEAAAAwCgCTAAAAACMIsAEAAAAwCiSfAMAAAAzQ5LvzUkPJgAAAABGEWACAAAAYBQBJgAAAABGkYMJAAAAmBlyMG1OejABAAAAMIoAEwAAAACjCDABAAAAMIoAEwAAAACjSPINAAAAzAxJvjcnPZgAAAAAGEWACQAAAIBRBJgAAAAAGEWACQAAAIBRJPkGAAAAZkZLspj5TISd9PrNIz2YAAAAABhFgAkAAACAUQSYAAAAABhFDiYAAABgZiy2ymKb3xxM81o3PZgAAAAAGEWACQAAAIBRNk2AqaoOq6rvTbscAAAAAFzZTgWYqurfq+ql61WYif2cXVWHr/d+AAAAABhvtyb5rqpKsrW1dsXu3C4AAABw9SDJ9+a05h5MVfWGJL+U5AlV1Ya/w4Z/71dVJyW5NMk9h+UfVFUnV9UlVfXlqjqyqvaY2N5RVXVuVV1aVV+rqmOH6f+e5BZJXrK0n2XleHBVnTFs90NV9ZPLtnlKVT22qs6rqouq6h1Vdb2JZQ6pqk9V1Q+r6ntVdXxV3WJXGg8AAACAnRsi94QkJyZ5bZKbDn/nDfNekOTpSQ5IcmpV3S/J3yU5Nsntkjw2yWFJnpkkVfXbSZ44TP+pJA9OctqwrYck+UqSIyb2s+Rawzb+IMk9kuyb5B+WlfM2SX4nyYOS3D/JHZK8YtjvHkneneSjSX4+yd2TvCZJy3ZU1d5Vte/SX5J9dtBOAAAAAFcrax4i11r7flVdluSi1to3kqSqfmaYfURr7UNLy1bVM5P8VWvtjcOkL1fVs9IDUc9Osl+SbyT5cGvt8iTnJvnUsJ/vVNVCkguX9jNhzyR/0lr75LCfP0hyelX9QmvtU8My10jyB621rwzL/GmSf6mqJye5LMl1k7yvtfalYfnTd1D1pyc5ci1tBAAAAHB1tLt+Re6kZa8PSnJEVf1g6S9Dz6equlaSdyS5Znrg6bVV9ZuTw+dWccXkvlprX0jyvfSeU0vOXQouDU5Mr+dtW2vfSfKGJMdV1Xur6glVNdlDaiVHpwellv5uvoZyAgAAALtgKQfTPP/No90VYPrhCts9Mn142tLfgenD4S5prZ2X5LZJ/jjJxUlemeRjVbXnGva10nC27Q5xm5jXkqS19oj0oXEnJHlokjOq6m7bXbm1S1trFyz9JblwDWUEAAAAuNrY2QDTZUm2rmG5T6f3GDpzhb/FJGmtXdxae09r7c+SHJIe9DlwB/vZI8mdl15U1W2TXC/JFyaW2a+qbjbx+u5JFpOcsTShtfaZ1trRrbWDk3w2ye+uoU4AAAAArGDNOZgGZye5a1Xtn+QH2X6A6jlJ3ldV56UPh1tMT6p9YGvtL6vqsPQA0ieTXJTk99J7Mp0zsZ97VdU/JLm0tfatYfrlSV5WVX82/P/lST4xkX8pSS5J8saq+vP0JODHJnl7a+0bVXXLJI9J8p4kX0vvRfXTSd60k+0AAAAAwGBnezC9KMlCks8n+e/0ZN1X0Vo7LsmvJTk0yX8m+USSJ2VbAOl7SR6d5Pgkpya5T5IHtda+Pcw/Isn+Sb407GfJRUmOSfKW9NxKFyd52LLdn5nknUnen+SD6T2UHj+x/s8k+af0Hk2vSQ9S/e+1NgAAAAAAV7ZTPZhaa2ekDzmb9IbtLHtckuO2M+/dSd69yn4+keT2y6a9YWJf79xBOV+V5FUrTD8/yW+uti4AAAAwPfOcCDvJ3NZtdyX5BgAAAOBqSoAJAAAAgFHmKsDUWjuqtXaHaZcDAAD4/9m782jLzrJe1L83laRCkwACAbwYAugRVCAIEUOjEQxwwGuDjgMcr5cwvEGTK13AYOQA8UBGFBByEEPPQVA8iCKKwgnNVbqAtAGEQAgkELpIm4akKqTqvX+suWGx2dXO2rVWzf08Y3yj9przm3N93xqVquLHO98FwEayp98iBwAAALBuuis90T5FSSa7t0lVMAEAAACw/wmYAAAAABhFwAQAAADAKAImAAAAAEbR5BsAAABYGttT2Z5pNsJOMtm9qWACAAAAYBQBEwAAAACjCJgAAAAAGEUPJgAAAGBpbO/K9p5mn6Ikk92bCiYAAAAARhEwAQAAADCKgAkAAACAUQRMAAAAAIyiyTcAAACwNLorPdFG2EkmuzcVTAAAAACMImACAAAAYBQBEwAAAACj6MEEAAAALI3tXdk+0T5FSSa7NxVMAAAAAIwiYAIAAABgFAETAAAAAKMImAAAAAAYRZPvvXTQNZtyUG9a9DIW6gZf2bboJSyN2raxfy+suMNLTln0EpbCJ086Z9FLWBp3eabfE0my6dpe9BKWxpabTLOpJXvvsK/6/zuTZMtNF72C5XDQdxa9guWx/ZBFr2A5XHc9f28kybZNG+tz6K70RBthJ5ns3vyNDgAAAMAoAiYAAAAARhEwAQAAADCKHkwAAADA0uiubJ9on6JEDyYAAAAAWJOACQAAAIBRBEwAAAAAjCJgAgAAAGAUTb4BAACApdFJuhe9ivUz1a2pYAIAAABgFAETAAAAAKMImAAAAAAYRcAEAAAAwCiafAMAAABLY3sqlVr0MtbN9onuTQUTAAAAAKMImAAAAAAYRcAEAAAAwCh6MAEAAABLo7vSPc0+RUkmuzcVTAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEWTbwAAAGBpbO9KTbQRdjLb3xSpYAIAAABgFAETAAAAAKMImAAAAAAYRQ8mAAAAYGl0z8ZUTXVvB2wFU1UdXVVdVceMvM8lVfW4fbUuAAAAgI3mgA2YAAAAAFgOAiYAAAAARln6gKmqDqqqJ1XVRVW1tao+X1VPnptyu6r6l6q6uqo+UlXHrbr+16vq48O1l1TVE/bzFgAAAAAm7UBo8n1WkpOSPD7Ju5LcKskd5s6fmeSJST49/PzXVfWj3X1dVd0tyd8kOSPJa5LcM8k5VfX17n7F7rx5VW1Osnnu0OGjdgMAAADsUHeluxa9jHUz1b0tdcBUVYcneWyS3+vuvxgOfybJu6rq6OH1s7v7n4f5T0vy8SQ/muSTSU5N8rbufvow98Kq+okkv5/kFbu5jNOTPG3cTgAAAACma9kfkbtjZtVDb9vJnI/O/fzl4dcj565/96r5707yY1W1aTfXcFaSG82NW+/mdQAAAAAbwlJXMCW5ZjfmfGfu5x5+XQnOau5Y5o7ttu7emmTrdy+uaZayAQAAAOytZQ+YPp1ZyHS/JC/di+s/keTeq47dM8mF3b1t5NoAAACAfUwPpgPTUgdM3b2lqv4kyTOr6trMHm+7eZKfzM4fm1vxp0neX1VPyazJ93FJfi/JKeu0ZAAAAIANZ6kDpsHTk1yX5L8n+eHM+iy9cHcu7O4PVdV/Ga59ynDtU3f3G+QAAAAA2LWlD5i6e3uSM4exWq2a+601jv1dkr/byf2PHr9KAAAAgI1r2b9FDgAAAIAlt/QVTAAAAMDGsb0rNdFG2Mlsf1OkggkAAACAUQRMAAAAAIwiYAIAAABgFD2YAAAAgKXRPRtTNdW9qWACAAAAYBQBEwAAAACjCJgAAAAAGEXABAAAAMAomnwDAAAAS2PW5LsWvYx1o8k3AAAAAKxBwAQAAADAKAImAAAAAEbRgwkAAABYGt018R5M09ybCiYAAAAARhEwAQAAACy5qjqlqi6uqi1V9cGqus9uXvewquqqev16rk/ABAAAALDEquqhSc5OcmaSuyZ5Z5I3VdVRu7juNkmePcxfVwImAAAAgOV2apKXdfdLu/uC7n5ckkuTnLyjC6pqU5K/SvK0JJ9d7wUKmAAAAICl0RtgDA6vqiPmxua1Po+qOjTJ3ZK8edWpNye5504+yqcm+Wp3v2wnc/YZARMAAADA/veFJJfPjdN3MO9mSTYluWzV8cuS3HKtC6rqXkl+O8lJ+2Slu+Hg/fVGAAAAAHzXrZNcOfd66y7m96rXtcaxVNXhSf4yyUnd/bVRK9wDAiYAAACA/e/K7r5iN+Z9Lcm2/GC10pH5waqmJLl9kqOTvKGqVo4dlCRVdV2SH+/uz+zNgnfGI3IAAAAAS6q7r03ywSQnrDp1QpLz1rjkk0nulOSYufGPSf5l+PnS9VinCiYAAABgaXRXumvXEw9Qe7m35yR5VVV9IMl7kjwqyVFJXpgkVfXKJF/s7tO7e0uSf5+/uKq+NXvv/r7j+5KACQAAAGCJdfdrquqmmX0z3K0yC5Ae1N2fG6YclWT7otaXCJj22sFXHJRN127sJwy/cceNvf9529b8MsmNZ9thP9BfbkO6yzNPWfQSlsZHTjtn0UtYCsc++eRFL4Els2lXLTw3kNq26BUsh4Ov8Xdoklxz5KJXsDxu8MVFr2A5fOf6061i2RPbDvY5kHT3OUnW/Ad2dx+/i2tPXIclfR8JAQAAAACjqGACAAAAlkcPY6omujcVTAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEWTbwAAAGB5dKW7Fr2K9TPRvalgAgAAAGAUARMAAAAAowiYAAAAABhFDyYAAABgaXTPxlRNdW8qmAAAAAAYRcAEAAAAwCgCJgAAAABGETABAAAAMIom3wAAAMDS6K5016KXsW6mujcVTAAAAACMImACAAAAYBQBEwAAAACj6MEEAAAALI+u2Ziqie5NBRMAAAAAowiYAAAAABhFwAQAAADAKAImAAAAAEbR5BsAAABYGt2zMVVT3ZsKJgAAAABGETABAAAAMIqACQAAAIBRDviAqWZeXFXfqKquqm9V1dmLXhcAAACwF3oDjAk64AOmJA9McmKSX0pyqyT/vtDVAAAAAGwwU/gWudsn+XJ3n5ckVXXdgtcDAAAAsKEc0BVMVfWKJH+W5Kjh8bhLhlMHV9Xzh8flvl5Vz6iqmrvulKr6dFVtqarLqupvF7B8AAAAgEk40CuYHpvkM0keleTYJNuSvDbJI5K8LMk9ktw9yYuTfC7JS6rq7kmel+S3kpyX5IeS3GdHb1BVm5Nsnjt0+D7fBQAAAMAB7IAOmLr78qq6Msm27v5KkgyFSpcmeXx3d5JPVdWdkjw+yUuSHJXk20n+qbuvzCx4+vBO3ub0JE9bv10AAAAAK7or3bXriQeoqe7tgH5EbifeO4RLK96T5MeqalOSt2QWKn22ql5VVb9ZVdffyb3OSnKjuXHr9Vo0AAAAwIFoqgHTDg1VSz+d5OFJvlHmVZQAACAASURBVJzkvyf5SFXdeAfzt3b3FSsjyZX7b7UAAAAAy2+qAdPPrvH60929LUm6+7rufmt3n5bkzkmOTnLf/btEAAAAgGk4oHsw7cSPVNVzkrwos2qlRyd5QpJU1S8luV2SdyT5ZpIHZRa0fWoxSwUAAAC+T+96CstlqgHTK5NcL8n7MvtmuT/L7JvkkuRbSR6S5IwkhyX5dJKHd/fH9/8yAQAAAA58B3zA1N1nJzl77vXxc6dPXmP+u5Icv/o4AAAAAHtnqj2YAAAAANhPBEwAAAAAjHLAPyIHAAAATEd3pbsWvYx1M9W9qWACAAAAYBQBEwAAAACjCJgAAAAAGEUPJgAAAGB59DCmaqJ7U8EEAAAAwCgCJgAAAABGETABAAAAMIqACQAAAIBRNPkGAAAAlkgNY6qmuTcVTAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEWTbwAAAGB59DCmaqJ7U8EEAAAAwCgCJgAAAABGETABAAAAMIoeTAAAAMDy0IPpgKSCCQAAAIBRBEwAAAAAjCJgAgAAAGAUARMAAAAAo2jyvZdufNH2bDp0+6KXsVDXXb8WvYSlsfXGPoskOeLiiXar20ObrvU5rDj2yScveglL4f1nvmDRS1gad3ruKYtewlLY/A1/TnyX/7szSXL1LfxbIkkO92+J79p2Pb8nkuTqW/g9kSTbt2ywz6FrNqZqonvzVzoAAAAAowiYAAAAABhFwAQAAADAKHowAQAAAEujezamaqp7U8EEAAAAwCgCJgAAAABGETABAAAAMIqACQAAAIBRNPkGAAAAlkcPY6omujcVTAAAAACMImACAAAAYBQBEwAAAACj6MEEAAAALI+u2Ziqie5NBRMAAAAAowiYAAAAABhFwAQAAADAKAImAAAAAEbR5BsAAABYGtWzMVVT3ZsKJgAAAABGETABAAAAMIqACQAAAIBR9GACAAAAlkcPY6omujcVTAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEWTbwAAAGB5dM3GVE10byqYAAAAABhFwAQAAADAKEsZMFXViVX1rUWvAwAAAIBdW5ceTFV1aHdfux73BgAAACashzFVE93bPqlgqqp/rarnV9VzquprSd5SVTeqqhdX1X9U1RVV9f9V1V3mrrlLVf1LVV05nP9gVd29qo5P8j+T3KiqehhnDNccWlXPrKovVtW3q+rfhvnza7lXVb29qq6uqm9W1blVdZPh3OFV9VfDtV+uqscPaz97X3wOAAAAABvRvnxE7hFJrktyryS/m+Sfk9wyyYOS3C3Jh5K8rap+aJj/V0m+kOTY4fwfJ/lOkvOSPC7JFUluNYxnD9f8z+H+D0ty5ySvTfK/q+rHkqSqjknytiQfT3JcknsneUOSTcP1zxmu/+UkJyS5T5Kf3tmmqmpzVR2xMpIcvucfDQAAAMB07ctH5C7q7tOSpKrum+ROSY7s7q3D+SdW1a8m+Y0kL05yVJJndfcnh/OfXrlRVV2epLv7K3PHbp/k4Ulu3d1fGg4/u6oemOSRSf4wyWlJPtDdp8yt6+PD9YdnFoL91+5+23DskUm+lJ07PcnTdv9jAAAAANhY9mXA9IG5n++W5IZJvl5V83Oul+T2w8/PSfLSqvqtJG9N8tru/sxO7v/TSSrJhavuuTnJ14efj8msqmktt0tySJL3rRzo7sur6lM7ec8kOWtY64rDM6u8AgAAACD7NmD69tzPByX5cpLj15j3rSTp7jOq6tVJHpzkPyf5o6p6WHf//Q7uf1CSbZmFV9tWnbtq+PWanaxvJZVa3U6rVk+cN1RgrVRhZVW4BQAAAOxLmnwfkPZlD6Z5H8qs/9J13X3RqvG1lUndfWF3P7e775/kdZk96pYk1+Z7fZNWfHg4duQa91x5lO6jSe63gzV9JrMeTz+zcmDoqfRjI/cKAAAAsKGtV8D01iTvSfL6qnpAVR1dVfesqmcM3xR3veFb546vqttU1b0ya/Z9wXD9JUluWFX3q6qbVdX1u/vCzBqDv7KqHlJVt62qY6vqSVX1oOG6s5IcW1XnVNWdq+oOVXVyVd2su69M8hdJnlVVv1BVP5nk5Um2Z7L5IQAAAMD6W5eAqbs7s2+Pe0dmIc6FSf5XkqOTXJbZI243TfLK4dzfJHlThmba3X1ekhcmeU2Sr2bWvDuZVTi9MsmfJvlUkn9Mco8klw7XXZjk/knuklmvpfck+ZXMvt0uSU4djv1TZiHYuzMLtbbs448AAAAAYMPYJz2Yuvv4NY5dmeQxw1jLw3dxz5OTnLzq2HcyC6F2+K1u3f32JPfawbkrk/zmyuuqusFwrxfvbC0AAAAA7Ni+bPK99KrqrknukFl1042SPHU49Q8LWxQAAADwPZp8H5A2VMA0eGKSH8+skfgHk9xnvvE4AAAAAHtmQwVM3f3hJHdb9DoAAAAApmS9vkUOAAAAgA1iQ1UwAQAAAEuuazamaqJ7U8EEAAAAwCgCJgAAAABGETABAAAAMIqACQAAAIBRNPkGAAAAlkb1bEzVVPemggkAAACAUQRMAAAAAIwiYAIAAABgFD2YAAAAgOXRw5iqie5NBRMAAAAAowiYAAAAABhFwAQAAADAKAImAAAAAEYRMAEAAAAwioAJAAAAgFEETAAAAACMImACAAAAYJSDF70AAAAAgBWVpHrRq1g/tegFrBMVTAAAAACMImACAAAAYBQBEwAAAACj6MG0l27y4a/n4E2bF72Mhbrs+JsveglL46r/9J1FL2EpHHqFP1KSZMtNpvpUNXvrTs89ZdFLWBofe/w5i17CUnjQCQ9d9BKWxvZD/d2RJBf/waZFL2Ep3PDDG/vf1/Ouvpn/NpLk0PtdvuglLIVtV29d9BJgl/ypBQAAACyPrtmYqonuzSNyAAAAAIwiYAIAAABgFAETAAAAAKMImAAAAIDl0Rtg7IWqOqWqLq6qLVX1waq6z07mnlRV76yqbw7jrVX1M3v3zrtHwAQAAACwxKrqoUnOTnJmkrsmeWeSN1XVUTu45Pgkf53kF5Icl+TzSd5cVf/Heq1RwAQAAACw3E5N8rLufml3X9Ddj0tyaZKT15rc3b/Z3ed09/nd/ckkJ2WWAd1vvRYoYAIAAADY/w6vqiPmxua1JlXVoUnuluTNq069Ock9d/O9rp/kkCTf2OvV7oKACQAAAGD/+0KSy+fG6TuYd7Mkm5Jctur4ZUluuZvv9cdJvpjkrXu+zN1z8HrdGAAAAGCPjWiEfUD43t5uneTKuTNbd/vKmVrj2A+oqtOSPDzJ8d29ZfcWuecETAAAAAD735XdfcVuzPtakm35wWqlI/ODVU3fp6qemOQPk/xid390r1a5mzwiBwAAALCkuvvaJB9McsKqUyckOW9H11XV7yd5SpIHdvcH1m+FMyqYAAAAAJbbc5K8qqo+kOQ9SR6V5KgkL0ySqnplki929+nD69OSPD3Jf01ySVWtVD9d1d1XrccCBUwAAADA0qiejanam71192uq6qZJnprkVkn+PcmDuvtzw5Sjkmyfu+SUJIcm+dtVt/qjJGfs+Qp2TcAEAAAAsOS6+5wk5+zg3PGrXh+9H5b0ffRgAgAAAGAUARMAAAAAowiYAAAAABhFDyYAAABgefQwpmqie1PBBAAAAMAoAiYAAAAARhEwAQAAADCKHkwAAADA8tCD6YCkggkAAACAUQRMAAAAAIwiYAIAAABgFAETAAAAAKOsS8BUVcdXVVfVjUfe55Kqetw+XNe/VtXZ++p+AAAAwL5VPf0xRfskYBLcAAAAAGxcHpEDAAAAYJTRAVNVvSLJzyd57PBYXCc5ejh9t6r6QFVdXVXnVdWPz113+6r6h6q6rKquqqr3V9Uv7uK9Tq2qj1XVt6vq0qo6p6puuGrOvarq7cN7frOqzq2qm8zvuaqeWVXfqKqvVNUZYz8DAAAAgI1sX1QwPTbJe5K8JMmthnHpcO7MJE9Icvck1yV5+dx1N0zyxiS/mOSuSc5N8oaqOmon77U9yWOS/FSSRyS5b5JnrpysqmOSvC3Jx5Mcl+TeSd6QZNPcPR6R5NtJ7pHktCRPraoTdvSGVbW5qo5YGUkO38n6AAAAADacg8feoLsvr6prk1zd3V9Jkqq6w3D6yd399uHYHyf556o6rLu3dPdHknxk7lb/rap+LckvJ3n+Dt5rvs/TxVX1lCQvSHLKcOy0JB/o7lPm5n181W0+2t1/NPz86ar6vST3S/KWHWzx9CRP28E5AAAAYF/qmo2pmuje1rsH00fnfv7y8OuRSVJVNxgeVftEVX2rqq5KcockO6xgqqpfqKq3VNUXq+rKJK9MctOqusEwZaWCaXfXtLKuI3cy/6wkN5obt97F/QEAAAA2lPUOmL4z9/PKF/GtvOezkvx6kicnuU9m4dDHkhy61o2q6jaZPVL378N1d0vy/w6nDxl+vWYP17Syrh1+Dt29tbuvWBlJrtyN9wAAAADYMPZVwHRtvr/P0e64T5JXdPffd/fHknwl32sOvpa7Z/ZI3xO6+73dfWGSH14156OZPe4GAAAAwH6yrwKmS5Lco6qOrqqb7eZ9L0rykKo6pqrukuTVu7juM5kFTI+uqttV1W8l+d1Vc85Kcuzw7XJ3rqo7VNXJw5oAAACAZdcbYEzQvgqYnp1kW5JPJPlqdtJHac7jk3wzyXmZfdPbuUk+tKPJ3X1+klOTPCmzx+R+M7MG3PNzLkxy/yR3SfK+zL7d7lcy+wY7AAAAANbB6G+RS74b7By36vArVs05P0nNvb4kyX1XXfPnq645etXr5yZ57qprXrVqztuT3GsH6zx+jWO/utZcAAAAAHbPejf5BgAAAGDiBEwAAAAAjLJPHpEDAAAA2BeqZ2Oqpro3FUwAAAAAjCJgAgAAAGAUARMAAAAAo+jBBAAAACyPHsZUTXRvKpgAAAAAGEXABAAAAMAoAiYAAAAARhEwAQAAADCKJt8AAADA8uikJtoIO4km3wAAAACwFgETAAAAAKMImAAAAAAYRQ8mAAAAYHl0JtunKMlk96aCCQAAAIBRBEwAAAAAjCJgAgAAAGAUARMAAAAAo2jyDQAAACwPTb4PSCqYAAAAABhFwAQAAADAKAImAAAAAEbRgwkAAABYGtWzMVVT3ZsKJgAAAABGUcG0lz73kJtn0+bDFr2Mhdq2eaKx616447O/ueglLIXPPeTIRS+BJbNp66JXsBw2f8OflysedMJDF72EpfDGt7xm0UtYGrf9p5MWvYSlcLsXbV/0EpbCl37ukEUvYWlce4TfE0lyh8ddueglLIXrtm/Npxa9CNgFFUwAAAAAjCJgAgAAAGAUARMAAAAAowiYAAAAABhFwAQAAADAKAImAAAAAEY5eNELAAAAAPiuHsZUTXRvKpgAAAAAGEXABAAAAMAoAiYAAAAARhEwAQAAADCKJt8AAADA0qiejama6t5UMAEAAAAwioAJAAAAgFEETAAAAACMogcTAAAAsFwm2qdoylQwAQAAADCKgAkAAACAUQRMAAAAAIwiYAIAAABgFE2+AQAAgOXRmXaT74nuTQUTAAAAAKMImAAAAAAYRcAEAAAAwCgCJgAAAABG0eQbAAAAWBrVszFVU92bCiYAAAAARhEwAQAAADCKgAkAAACAUfRgAgAAAJZHD2OqJro3FUwAAAAAjLLfAqaaOa2qPltV11TVR6rqN4ZzT62qL1XVTefm/2NVvaOqDhpen1pVH6uqb1fVpVV1TlXdcG7+iVX1rap6QFVdUFVXVdX/rqpbzc05uKqeN8z7elX9SVX9RVW9fn99DgAAAABTsz8rmJ6R5JFJTk7yk0mem+Qvq+rnk5yZ5JIkL02SqvrdJD+X5Le6e/tw/fYkj0nyU0kekeS+SZ656j2un+SJSX5ruP6oJM+eO/+kJL85rONeSY5I8qs7W3RVba6qI1ZGksP3dOMAAAAAU7ZfejBV1Q2SnJrkvt39nuHwZ6vq3kl+p7vfXlX/V5Lzq+qPkzw6yaO6+3Mr9+jus+dueXFVPSXJC5KcMnf8kCS/292fGd73+UmeOnf+0UnO6u6/H87/XpIH7WL5pyd52p7tGAAAAGDj2F9Nvn8iyWFJ3lJV88cPTfLhJOnuz1bVE5O8KMlruvuv5idW1S8k+cPhXkdktvbDquoG3f3tYdrVK+HS4MtJjhyuv1GSWyR538rJ7t5WVR/Mziu5zkrynLnXhyf5wu5sGgAAANgz1bMxVVPd2/4KmFYCnAcn+eKqc1vnfv65JNuSHF1VB3f3dUlSVbdJ8sYkL0zylCTfSHLvJC/LrGppxXdW3buT1BrH5q0+//2Tu7fOr3FVQAYAAACw4e2vHkyfyCykOaq7L1o1Lk2SqnpokockOT7Jj2QWJK24e2Zh2BO6+73dfWGSH96TBXT35UkuS/IzK8eqalOSu+79tgAAAADYLxVM3X1lVT07yXOHb4V7V2aPud0zyVVJ3pZZP6Undfe7qurEJP9cVW/q7vcm+cyw1kdX1Rsya9D9u3uxlD9LcnpVXZTkk5n1ZLpJfrCqCQAAAIDdtL8ekUtmFUn/kVnT7Nsl+VaSD2XW4+gVmfVGen6SdPdbhgbdf1lVx3T3+VV1ambfAndWkncM93nlHq7hT5LccrhuW5IXJzl3+BkAAABYtM60y0Amurf9FjB1dyd53jBWe/sa80/N7JvnVl4/N8lzV0171dz5V2QWVM3f4/WZ67E09HR69DAyVFNdkORv9mQvAAAAAHzP/qxgWrihWfj9Mwu0Nif5vSS3TfLqRa4LAAAA4EC2v5p8L4vtSU5M8v4k705ypyS/2N0XLHJRAAAAAAeyDVXBNHxj3b0WvQ4AAACAKdlQARMAAACw5DT5PiBttEfkAAAAANjHBEwAAAAAjCJgAgAAAGAUPZgAAACApVE9G1M11b2pYAIAAABgFAETAAAAAKMImAAAAAAYRcAEAAAAwCiafAMAAADLo4cxVRPdmwomAAAAAEYRMAEAAAAwioAJAAAAgFH0YAIAAACWhx5MByQVTAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEWTbwAAAGBpVM/GVE11byqYAAAAABhFwAQAAADAKAImAAAAAEbRgwkAAABYHj2MqZro3gRMe+naG2/PQYdtX/QyFmrzNxXArbj8Ljdb9BKWwtabbuz/JlYc9lX/bayobYtewZLwW+K7th/qnx5Jctt/OmnRS1gaF//SSxa9hKXwgEcds+glLIVrf/lnF72E5eGfVTNVi17BcvA5cADwT14AAAAARhEwAQAAADCKgAkAAACAUTRCAAAAAJZG9WxM1VT3poIJAAAAgFEETAAAAACMImACAAAAYBQBEwAAAACjaPINAAAALI8exlRNdG8qmAAAAAAYRcAEAAAAwCgCJgAAAABG0YMJAAAAWB56MB2QVDABAAAAMIqACQAAAIBRBEwAAAAAjCJgAgAAAGAUTb4BAACApVHDmKqp7k0FEwAAAMCSq6pTquriqtpSVR+sqvvsYv6vV9Unqmrr8Ouvref6BEwAAAAAS6yqHprk7CRnJrlrkncmeVNVHbWD+ccleU2SVyW5y/Dr31TVPdZrjQImAAAAgOV2apKXdfdLu/uC7n5ckkuTnLyD+Y9L8pbuPqu7P9ndZyV523B8XQiYAAAAgOXRG2DMHF5VR8yNzWt9HFV1aJK7JXnzqlNvTnLPHXyKx60x/9ydzB9NwAQAAACw/30hyeVz4/QdzLtZkk1JLlt1/LIkt9zBNbfcw/mj+RY5AAAAgP3v1kmunHu9dRfze9XrWuPYmPmjCJgAAAAA9r8ru/uK3Zj3tSTb8oPVR0fmB6uUVnxlD+eP5hE5AAAAgCXV3dcm+WCSE1adOiHJeTu47D1rzL//TuaPpoIJAAAAWBrVszFVe7m35yR5VVV9ILPw6FFJjkrywiSpqlcm+WJ3r/Rx+h9J3lFVT0ryD0l+JckvJrn3qMXvxLpUMFXV8VXVVXXjncw5saq+tR7vvzNVdUZVnb+/3xcAAABgb3T3a5I8LslTk5yf5OeSPKi7PzdMOSrJrebmn5fkYUkemeSjSU5M8tDu/rf1WqMKJgAAAIAl193nJDlnB+eOX+PY3yb523Ve1nfpwQQAAADAKHsdMFXV5qp6XlX9R1Vtqap3VdWxO5l/YlV9vqqurqq/T3LTVefPqKrzq+p3qurSYd5rVz9mV1WPrKoLhvf8ZFWdsur8n1TVhcP1n62qp1fVITtZ122r6qKqekFVCdwAAABgkXoDjAkaE6g8M8mvJ3lEkp9OclGSc6vqh1ZPrKp7JHl5ZqVcxyT5lyT/bY17/miS/5Lk/0zywGHun8/d56QkZyZ5cpI7JvnDJE+vqkfM3ePKzJ4t/Ikkj01yUpLHr7WBqvqpJO9O8truPrm7t68xZ3NVHbEykhy+g88DAAAAYEPaq4Cpqm6Q5OQkv9/db+ruT2QW5FyT5LfXuOSxSc7t7j/u7gu7+3lJzl1j3mFJHtHd53f3O5I8OsnDquqWw/mnJHlCd7+uuy/u7tcleW6S31m5QXc/o7vP6+5LuvsNSf40s9Bq9R6OS/L2JM+Z67K+ltOTXD43vrCTuQAAAAAbzt5WMN0+ySGZVf8kSbr7O0nel1ll0Wp3zOxr9Oatfp0kn+/uL6yac1CSH6+qmyf5kSQvq6qrVkZmlVC3X7mgqn5jeFzvK8P5p2fWTX3eUUnemuQZ3f3sXez1rCQ3mhu33sV8AAAAgA1lb79FroZfVz85WGscm5+/p3ru15Uw7KQkq79Wb1uSVNXPJvlfSZ6WWYXU5Zl9Ld8TVs3/apIvZVYd9bLuvmKHC+jemmTryuuqvd0KAAAAwDTtbQXTRUmuTXLvlQNDI+27J7lgjfmfSPKzq46tfp0kR1XVD8+9Pi7J9iQXdvdlSb6Y5HbdfdGqcfEw/15JPtfdZ3b3B7r700lus8b7XJPkl5JsyaxvlL5KAAAAsCwW3YRbg+89tlcVTN397ap6QZJnVdU3knw+yWlJrp/kZUnusuqS5yU5r6pOS/L6JPfPrIn3aluS/EVVPTHJEcN1f9PdXxnOn5HkeVV1RZI3JdmcWah1k+5+TmbB11FV9bAk70/y4CS/tpM9PHi4z5uq6oHdfdWefxoAAAAAG9uYb5H7gyR/l+RVST6U2TfAPaC7v7l6Yne/N8n/k1nT7vMzC5iescY9L0ryuiRvTPLmJP+e5JS5+7x0uM+JST6WWZPuE5NcPJz/h8yafj9/eJ97ZtaDaU1DoPSfM3uE741D83IAAAAA9sDe9mBKd29J8phhrD73r1nVd6m7X57k5aum/uka174gyQt28r6vTvLqnZw/LbNqqnlnz50/I7NKqJXXV2X2aB0AAAAAe2GvAyYAAACAfa16NqZqqnsb84gcAAAAACxPwNTdZ3T3MYteBwAAAAB7ZmkCJgAAAAAOTAImAAAAAEbR5BsAAABYHj2MqZro3lQwAQAAADCKgAkAAACAUQRMAAAAAIyiBxMAAACwNKpnY6qmujcVTAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEWTbwAAAGB59DCmaqJ7U8EEAAAAwCgCJgAAAABGETABAAAAMIoeTAAAAMDSqJ6NqZrq3lQwAQAAADCKgAkAAACAUQRMAAAAAIwiYAIAAABgFE2+AQAAgOXRw5iqie5NBRMAAAAAowiYAAAAABjFI3J76Rbv7Rx8yETr2nbT1Udu7P3P+/pPymqT5GYf3r7oJSyFLTdd9AqWx8HX+HMiSa6+RS16CUvj4j/YtOglLIXbvciflyse8KhjFr2EpXDul85f9BKWwrFPPm7RS1gaB1236BUsh08+3T+skmT71VuSRy16FbBz/lcxAAAAAKOoYAIAAACWhybfByQVTAAAAACMImACAAAAYBQBEwAAAACj6MEEAAAALI3q2Ziqqe5NBRMAAAAAowiYAAAAABhFwAQAAADAKAImAAAAAEbR5BsAAABYHj2MqZro3lQwAQAAADCKgAkAAACAUQRMAAAAAIyiBxMAAACwNKo71RNtVJRMdm8qmAAAAAAYRcAEAAAAwCgCJgAAAABGETABAAAAMIom3wAAAMDy6GFM1UT3poIJAAAAgFEETAAAAACMImACAAAAYBQ9mAAAAIClUT0bUzXVvalgAgAAAGAUARMAAAAAowiYAAAAABhFwAQAAADAKEvb5LuqXpHkxt39q4teCwAAALCf9DCmaqJ7U8EEAAAAwCgCJgAAAABGWXjAVFW/UVUfq6prqurrVfXWqrrBGvM2V9Xzquo/qmpLVb2rqo6dO398VXVVPbiqPjLM+bequtOq+9yzqt4xvN+lwz1/4P0AAAAA2D0LDZiq6lZJ/jrJy5PcMcnxSV6XpNaY/swkv57kEUl+OslFSc6tqh9aNe9ZSZ6Y5Ngk/5HkH6vqkOH97pTk3OE97pzkoUnuneT5O1nj5qo6YmUkOXyvNgsAAADsUvX0xxQtuoLpVpk1Gn9dd1/S3R/r7nO6+6r5SUOF0clJfr+739Tdn0hyUpJrkvz2qnv+UXe/pbs/llkYdYskvzac+/0kr+7us7v70919XpLHJPm/q+qwHazx9CSXz40vjN00AAAAwJQsOmD6SJK3JflYVb22qk6qqpusMe/2SQ5J8u6VA939nSTvy6zyad575uZ8I8mn5ubcLcmJVXXVysisoumgJLfdwRrPSnKjuXHrPdsiAAAAwLQdvMg37+5tVXVCknsmuX+SRyc5s6rusWrqyiNzqwvJao1ja77V8OtBSV6U5HlrzPn8Dta4NcnW775hrfX0HgAAAMDGtegKpvTMu7v7aUnumuTafO+RthUXDcfvvXJg6Kt09yQXrJr7s3NzbpLkPyX55HDoQ0l+srsvWmNcu083BgAAALBBLLSCaahUul+SN2fWkPseSW6eWWh055V53f3tqnpBkmdV4EQmTwAAGdhJREFU1TcyqzY6Lcn1k7xs1W2fWlVfT3JZkjOTfC3J64dzf5LkvVX150lekuTbmT0+d0J3P3pdNgkAAADsvs7uPat0oJro3hYaMCW5IsnPJXlckiOSfC7JE7r7TVX10FVz/yCziqtXZfZNbh9I8oDu/uYa8/5Hkh/LrMfTL69UJ3X3R6vq5zMLnt6Z2SN2n0nymnXYGwAAAMCGsOgeTBckeeAOzp246vWWzL7x7TG7uO27uvundvKe78+s3xMAAAAA+8DCezABAAAAcGBb9CNyAAAAAN9VPRtTNdW9TSZg6u5/zaynEgAAAAD7kUfkAAAAABhFwAQAAADAKAImAAAAAEaZTA8mAAAAYAJ6GFM10b2pYAIAAABgFAETAAAAAKMImAAAAAAYRcAEAAAAwCiafAMAAABLpSbaCHvKVDABAAAAMIqACQAAAIBRBEwAAAAAjKIHEwAAALA8umdjqia6NxVMAAAAAIwiYAIAAABgFAETAAAAAKMImAAAAAAYRZNvAAAAYGlUz8ZUTXVvKpgAAAAAGEXABAAAAMAoAiYAAAAARtGDCQAAAFgePYypmujeVDABAAAAMIoKpr305V/oHHS9icaOu+mwr9Sil7A0bn7+tv+/vbsPuvQu7wL+vUhCyssmIOHNZtognQRGKTAQEuLUIuCU0ZFplWGq1A6OYO1MNSCIk0GFSoEWa8u02Jek7USjFK21rRSFDlhETMqLWKGTQkiblVIS0ghsNg3ZZHMu/7jPsg9P9/23z577uffzmfnN7nOfc59z/X6Z7E6+uc51Nl3CLNz1tHM2XcIsPOSBTVcwH1993KYrmIc9t53df19s9cj/ff6mS5iFL/yF8zZdwmzc/+IrN13CLFz++uduuoRZ+Nibf3rTJczGpdd//6ZLmIXL3nLPpkuYhYMPHsjnNl0EHIcOJgAAAACGCJgAAAAAGOIjcgAAAMBs1GpaS7XUvelgAgAAAGCIgAkAAACAIQImAAAAAIaYwQQAAADMR6/XUi10bzqYAAAAABgiYAIAAABgiIAJAAAAgCECJgAAAACGGPINAAAAzEb1tJZqqXvTwQQAAADAEAETAAAAAEMETAAAAAAMMYMJAAAAmI/uaS3VQvemgwkAAACAIQImAAAAAIYImAAAAAAYImACAAAAYIgh3wAAAMBsVE9rqZa6Nx1MAAAAAAwRMAEAAAAwRMAEAAAAwBAzmAAAAID56PVaqoXuTQcTAAAAAEMETAAAAAAMETABAAAAMETABAAAAMAQQ74BAACA2aie1lItdW+L7GCqqodU1T+uqlur6kBVfa6qXr9+7Eeq6paqureqfr+q3lRV5226ZgAAAIDdaqkdTG9N8sokr07y4SRPTPKU9WP7k7w8yReSPC3JdetrbzvSC1XV+UnO33Jpz45UDAAAALBLLa6Dqar2JLk6yeu6+1939+9194e7++eSpLt/qLtv7O693f3uJP8yyUuP8ZLXJNm3ZX1+h7cAAAAAcNKq6tFVdUNV7VuvG6rqUcd4/p+qqp+sqs+sP+n1uar6iaq68GTfe4kdTE/N1HH0gSM9WFUvSfKqJN+S5JGZzuDuY7zeW5P82Jaf90TIBAAAADuje1pLtbN7e2eSi5O8aP3ztUluSPJXj/L8P71er01yc5JvTvIz62svOZk3XmLA9NWjPVBVVyZ5V5I3JHlfpo6k707ymqPd090HkhzY8hqnrVAAAACA06GqnpopWLqyuz+yvvbKJDdV1WXd/Znt93T37yT561su/d56hvW/rapzu/vgib7/4j4il+SzmUKmFxzhsT+f5P9295u7++Pd/dlM6RwAAADAmbSnqi7Yss4//i3H9Nwk+w6FS0nS3b+VqbnmqpN4nQuT3H0y4VKywA6m7r6vqn4kyduq6v4k/zPJY5P82SS3JvmmqvruJB9L8leSfNfGigUAAADOVtvH7/xgkjcOvN4Tktx5hOt3rh87rqp6TJJ/muRnT/bNFxcwrb0pycEk/zzT5wZvT/Iz3f3zVfXjSd6RaU7Te9bPfeOG6gQAAADOThdn+lb7Qw4c6UlV9cZMo36O5fL1r0ca8FRHub79fS7IlJPcnCnsOimLDJi6e5Xkzeu1/bHXJXndtstvPxN1AQAAAMdWPa2l2rK3/d19rC8dO+QdmeZJH8veJN+a5PFHeOyxSb54zJqq9iR5b5J7knxXdz9wAnV9nUUGTAAAAABL0N13JbnreM+rqpuSXFhVz+nuj66vXZFpptKNx7jvgkxfhHYgyYu7+75TqXOJQ74BAAAAzird/buZupCuq6orq+rKJNcl+fVD3yBXVd9YVZ+uquesf96T5DeSPCLJ30lyQVU9Yb3OOZn318EEAAAAsAwvS/ITmUKjJPnPSX5gy+PnJbksycPXPz8ryRXr39+67bWelOmjdydEwAQAAACwAN39pSTfc4zH92Ya+n3o5w9u/XmEgAkAAACYj84JfOfZLrbQvZnBBAAAAMAQARMAAAAAQwRMAAAAAAwxgwkAAACYjeppLdVS96aDCQAAAIAhAiYAAAAAhgiYAAAAABgiYAIAAABgiCHfAAAAwHyselpLtdC96WACAAAAYIiACQAAAIAhAiYAAAAAhpjBBAAAAMxHr9dSLXRvOpgAAAAAGCJgAgAAAGCIgAkAAACAIQImAAAAAIYY8g0AAADMRiWphQ7CTqb9LZEOJgAAAACGCJgAAAAAGCJgAgAAAGCIGUyn6GF/cG7OOf/sPr77HrvadAmz0edsuoJ5OOf+TVcwD6vzNl3BfDziDzddwTw8+LClftL+5N170dn9d+ch91/g79CvcRRJkocc3HQF83Dp9d+/6RJm45aX//SmS5iFF/3SyzZdwiysHlzwQKIj6Z7WUi10bzqYAAAAABgiYAIAAABgiIAJAAAAgCECJgAAAACGmLQJAAAAzEb1tJZqqXvTwQQAAADAEAETAAAAAEMETAAAAAAMMYMJAAAAmI9er6Va6N50MAEAAAAwRMAEAAAAwBABEwAAAABDBEwAAAAADDHkGwAAAJiN6k71QidhJ4vdmw4mAAAAAIYImAAAAAAYImACAAAAYIgZTAAAAMB8rNZrqRa6Nx1MAAAAAAwRMAEAAAAwRMAEAAAAwBABEwAAAABDDPkGAAAAZqO6U92bLmPHLHVvOpgAAAAAGCJgAgAAAGCIgAkAAACAIWYwAQAAAPPR67VUC92bDiYAAAAAhuzagKmqnldVXVWPGnydvVX1qtNVFwAAAMDZZtcETFX1wap6+6brAAAAAODr7ZqACQAAAIB52hUBU1Vdn+Tbk1y9/lhcJ7lk/fCzqurjVXVvVd1YVZdtue/JVfVrVfXFqrqnqj5WVS888zsAAAAATkj38tcC7YqAKcnVSW5Kcl2SJ67XH6wfe3OS1yR5dpKDSX5hy32PTPJfkrwwyTOTvC/Ju6vqm070javq/Kq64NBKsmdwLwAAAACLsisCpu7el+T+JPd29x3dfUeSB9cPv767/3t335zkh5NcVVXfsL7v/3T3z3b3p7r7s939T5L8fpIXn8TbX5Nk35b1+dO0LQAAAIBF2BUB03F8csvvb1//+rgkqapHVNXbqurmqvpKVd2T5ClJTriDKclbk1y4ZV18GmoGAAAAWIxzN13AafDAlt8f+iDjoeDsXyT5jiSvTXJrkq8m+Y9JHnqiL97dB5IcOPRzVY3UCgAAALA4uylguj/JOSd5z7club67fyVJquqROTwcHAAAAJiZ6mkt1VL3tps+Irc3yRVVdUlVXZQTq/3WJH+tqp5RVU9P8s4TvA8AAACAE7SbwpYfzTTY++Ykf5QTm6P06iRfTnJjkndn+ha5T+xUgQAAAABno13zEbnuviXJc7ddvn7bc347SW35eW+S52+7519tu+eS01UjAAAAwNlo1wRMAAAAwFmge1pLtdC97aaPyAEAAAAwQwImAAAAAIYImAAAAAAYImACAAAAYIgh3wAAAMBs1GpaS7XUvelgAgAAAGCIgAkAAACAIQImAAAAAIaYwQQAAADMR/e0lmqhe9PBBAAAAMAQARMAAAAAQwRMAAAAAAwRMAEAAAAwxJBvAAAAYD56vZZqoXvTwQQAAADAEAETAAAAAEMETAAAAAAMMYMJAAAAmI3qTvVCBxUli92bDiYAAAAAhgiYAAAAABgiYAIAAABgiIAJAAAAgCGGfAMAAADz0T2tpVro3nQwAQAAADBEB9MpuuiTD+Tc887ZdBkbdd69Bzddwmzc+er7Nl3CLFzwixduuoRZOPiw2nQJs/HAw51Fktz7+GX+X6pT8dAX7Nt0CbPwlFft33QJ81H+nEiST7/pMZsuYRYue8s9my5hNl70Sy/bdAmz8N73/LtNlzALd+9f5dGXbroKODYdTAAAAAAM0cEEAAAAzEcnWW26iB200OZ2HUwAAAAADBEwAQAAADBEwAQAAADAEAETAAAAAEMM+QYAAABmo7pTvdBJ2Mli96aDCQAAAIAhAiYAAAAAhgiYAAAAABhiBhMAAAAwH51koXOKkkz7WyAdTAAAAAAMETABAAAAMETABAAAAMAQARMAAAAAQwz5BgAAAOaje+FDvpe5Nx1MAAAAAAwRMAEAAAAwRMAEAAAAwBABEwAAAABDDPkGAAAA5mOVpDZdxA5abbqAnaGDCQAAAIAhAiYAAAAAhgiYAAAAABhiBhMAAAAwG9Wd6t50GTtmqXvTwQQAAADAEAETAAAAAEMETAAAAAAMETABAAAAMOSMBUxV9cGqevuZer/jqao3VtVvb7oOAAAAYIvu5a8F2lUdTFX10E3XAAAAAMDXOyMBU1Vdn+Tbk1xdVb1eT66qn6+q26rqq1X1maq6evt9VfWrVXVNVX0hyS3r60+sqves77utqv5mVe2tqldtuffCqrq2qu6sqrur6r9V1dPXj708yRuSPH1LPS8/E2cBAAAAsDTnnqH3uTrJpUl+J8k/W1/7cpLPJ3lpkruSXJXk2qq6vbv/w5Z7X5Dk7iR/KUmtr/2bJBcleV6SB5L8WJLHHbqhqirJe5J8KclfTrIvyfcl+UBVXZrk3yf5c0lelOSF69v2Hanwqjo/yflbLu05qZ0DAAAALNwZCZi6e19V3Z/k3u6+Y8tDb9jy+9uq6qpMgdPWgOmPk7yiu+9Pkqp6SqZQ6PLu/vj62iuSfHbLPX8xydOSPK67D6yvvbaqvjPJS7r72qq6J8nBbfUcyTXb6gQAAAB2yoLnFCVZ7N7OVAfTEVXV30vyiiTfnORhSR6aZPvg7U8dCpfWLktyMMknDl3o7lur6stbnvOsJI9M8v+mZqaveViSJ59kmW/N1CF1yJ5MnVcAAAAAZIMBU1W9NMmPJ3lNkpuS7E/yj5Jcse2pf7z91qO95JbfPyTJ7Zk+QrfdV06mznUH1KEuqGwLrAAAAADOemcyYLo/yTlbfv62JDd2908dulBVJ9Jd9OlMdT8zyf9a3/ctSR615TmfSPKETB+B23uC9QAAAABwCs7It8it7U1yRVVdUlUXJbk1ybOr6juq6tKqelOSy4/3It396STvzzQQ/DlV9cwk1yb5apJDH2R8f6auqF9dv/4lVXVVVf1QVT17Sz1PqqpnVNVF62HeAAAAAJykMxkw/WiSB5PcnOSPkrw3yX/K9I1uH0nymCQ/ddS7v973Jvlikg8l+ZUk12X6iN19SdLdnenb4z6U5BeS3JLkXUkuWd+XJL+8ruE31/X8jYG9AQAAAKfDoSHfS14LdMY+ItfdtyR57rbLf3u9trpmyz0vP8pr3Z4pQEqSVNXFSR6XqSvq0HP2J/kH63Wk1ziQ5CUnvAEAAAAAjmij3yJ3qqrq+Zm+Je5TSZ6Y5G2ZPvL2oQ2WBQAAAHBW2pUBU5LzkrwlyZ/J9NG4G5O8rLsf2GhVAAAAAGehXRkwdff7krxv03UAAAAAp9kqSW26iB202nQBO+NMDvkGAAAAYIEETAAAAAAMETABAAAAMETABAAAAMCQXTnkGwAAAFim6k51b7qMHbPUvelgAgAAAFiAqnp0Vd1QVfvW64aqetQJ3ltV9V+rqqvqO0/2vQVMAAAAAMvwziTPSPKi9XpGkhtO8N5XJTnl9iofkQMAAADY5arqqZlCpSu7+yPra69MclNVXdbdnznGvU9P8g+TXJ7k9lN5fwETAAAAMB/d01qqw3vbU1VbHznQ3QcGXvm5SfYdCpemt+rfqqp9Sa5KcsSAqaoenuQXk/xAd9+xraYT5iNyAAAAAGfe55Ps27KuGXy9JyS58wjX71w/djQ/nuTG7v61kTfXwQQAAABw5l2cZP+Wn4/YvVRVb0zyhuO81uXrX4/U+lVHuZ6qenGS5yd55nFe/7gETAAAAABn3v7uvvsEnveOJO86znP2JvnWJI8/wmOPTfLFo9z3/CRPTvKVbR+N++Wq+h/d/bwTqC+JgAkAAABgtrr7riR3He95VXVTkgur6jnd/dH1tSuSXJjkxqPc9sNJfm7btU8leXWSd59MnQImAAAAYD5WndSCh3yvdmZv3f27VfXeJNdV1fetL1+b5NcPfYNcVX1jkg8k+d7u/mh335Hkjq2vs+5k+lx333Yy72/INwAAAMAyvCxTB9JvrNcnk/ytLY+fl+SyJA8/3W+sgwkAAABgAbr7S0m+5xiP78009PtYr3HMx49GBxMAAAAAQ3QwAQAAAPPRPa2lWujedDABAAAAMETABAAAAMAQARMAAAAAQwRMAAAAAAwx5PsUHTx436ZL2Lg6eHDTJczGg/ce2HQJs3DwAf9eJMmD55zSt3ou0oPnOoskWd23zEGOp8Kfl5ODK+fwNeXPiSRZ3evv0CQ5+KB/Nw5ZPejvjiS5e/9q0yXMwt33nG3nsPAh31nm3gRMJ29PknzsN9+66TqYkw9vugAAdpPPbLoA5ufvbrqAefjcpgtgdh596aYrmJ09Se7edBFwJAKmk/eFJBcn2b/BGvYk+fwM6pgDZzFxDhPncJizmDiHw5zFxDkc5iwmzmHiHA5zFhPncNhczmJPpv8ehVkSMJ2k7u4kf7jJGupwK/n+7j6r02tnMXEOE+dwmLOYOIfDnMXEORzmLCbOYeIcDnMWE+dw2IzO4qz+58D8CZgAAACA+eiFz2Ba6N58ixwAAAAAQwRMu9OBJD+4/vVs5ywmzmHiHA5zFhPncJizmDiHw5zFxDlMnMNhzmLiHA5zFnACqhfamgUAAADsHlV1QZJ9L3zS38+5Dzl/0+XsmIOrA3n/bT+ZJBcuacaZDiYAAAAAhhjyDQAAAMzHqpMs+NNWq2XuTQcTAAAAAEMETAAAAAAMETABAAAAMETABAAAAMAQQ74BAACA+ejVtJZqoXvTwQQAAADAEAETAAAAAEMETAAAAAAMMYMJAAAAmI/uaS3VQvemgwkAAACAIQImAAAAAIYImAAAAAAYImACAAAAYIgh3wAAAMB8rDrJMgdhJ1nvb3l0MAEAAAAwRMAEAAAAwBABEwAAAABDzGACAAAA5qN7Wku10L3pYAIAAABgiIAJAAAAgCECJgAAAACGCJgAAAAAGGLINwAAADAfncUOwk4y7W+BdDABAAAAMETABAAAAMAQARMAAAAAQ8xgAgAAAOaje+EzmJa5Nx1MAAAAAAwRMAEAAAAwRMAEAAAAwBABEwAAAABDDPkGAAAA5mO1SrLadBU7Z7XMvelgAgAAAGCIgAkAAACAIQImAAAAAIaYwQQAAADMR/e0lmqhe9PBBAAAAMAQARMAAAAAQwRMAAAAAAwRMAEAAAAwxJBvAAAAYD4M+d6VdDABAAAAMETABAAAAMAQARMAAAAAQ8xgAgAAAOZj1UmWOacoyXp/y6ODCQAAAIAhAiYAAAAAhgiYAAAAABgiYAIAAABgiCHfAAAAwGx0r9K92nQZO2ape9PBBAAAAMAQARMAAAAAQwRMAAAAAAwRMAEAAAAwxJBvAAAAYD66k1Vvuoqd08vcmw4mAAAAAIYImAAAAAAYImACAAAAYIgZTAAAAMB8dCdZ5pyiJGYwAQAAAMCRCJgAAAAAGCJgAgAAAGCIgAkAAACAIYZ8AwAAAPOxWiW12nQVO6eXuTcdTAAAAAAMETABAAAAMETABAAAAMAQM5gAAACA+ehO0puuYuf0MvemgwkAAACAIQImAAAAAIYImAAAAAAYImACAAAAYIgh3wAAAMBs9GqVrtWmy9gx3cvcmw4mAAAAAIYImAAAAAAYImACAAAAYIgZTAAAAMB8dCfpTVexc3qZe9PBBAAAAMAQARMAAAAAQwRMAAAAAAwRMAEAAAAwxJBvAAAAYD5WndQyB2EnMeQbAAAAAI5EwAQAAADAEAETAAAAAEPMYAIAAADmozvJatNV7BwzmAAAAADgTxIwAQAAADBEwAQAAADAEAETAAAAAEMM+QYAAABmo1edrmUOwk6SNuQbAAAAAP4kARMAAAAAQwRMAAAAAAwxgwkAAACYj14lWW26ip3Ty9ybDiYAAAAAhgiYAAAAABgiYAIAAABgiIAJAAAAgCGGfAMAAACz0atOV2+6jB3Tvcy96WACAAAAYIiACQAAAIAhAiYAAAAAhpjBBAAAAMxHr5KsNl3Fzull7k0HEwAAAABDBEwAAAAADBEwAQAAADDEDCYAAABgNg7mgaQ3XcXOOZgHNl3CjqjuBf9TAwAAAHaFqvqGJLclecKmazkD7kjypO6+b9OFnC4CJgAAAGAW1iHTQzddxxlw/5LCpUTABAAAAMAgQ74BAAAAGCJgAgAAAGCIgAkAAACAIQImAAAAAIYImAAAAAAYImACAAAAYIiACQAAAIAhAiYAAAAAhgiYAAAAABgiYAIAAABgyP8HozmgtNjcEr8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 10,15\n",
    "rcParams[\"figure.dpi\"]= 100\n",
    "plt.matshow(dataset.corr())\n",
    "plt.yticks(np.arange(dataset.shape[1]), dataset.columns)\n",
    "plt.xticks(np.arange(dataset.shape[1]), dataset.columns)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000237B6C536A0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000237B8959588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000237B8060A20>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000237B8088F60>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000237B80B8518>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000237B80DEA90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000237B8112048>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000237B81385F8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000237B8138630>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000237B818E080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000237B81B55F8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000237B81DDB70>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000237B8210128>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000237B82376A0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000237B825FC18>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000237B828F198>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1500 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['target'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Count of each Target Class')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 8,6\n",
    "plt.bar(dataset['target'].unique(), dataset['target'].value_counts(), color = ['red', 'green'])\n",
    "plt.xticks([0, 1])\n",
    "plt.xlabel('Target Classes')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Count of each Target Class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Machine Learning\n",
    "\n",
    "I'll now import train_test_split to split our dataset into training and testing datasets. Then, I'll import all Machine Learning models I'll be using to train and test the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dataset['target']\n",
    "X = dataset.drop(['target'], axis = 1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 40 candidates, totalling 400 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:   18.2s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best paramter combination is \n",
      "{'criterion': 'gini', 'max_depth': 4}\n",
      "The best AUC score was \n",
      "0.850937113850337\n",
      "('age', array([0.08345966]))\n",
      "('sex', array([0.07069618]))\n",
      "('cp', array([0.37691689]))\n",
      "('trestbps', array([0.]))\n",
      "('chol', array([0.04217286]))\n",
      "('fbs', array([0.]))\n",
      "('restecg', array([0.02320693]))\n",
      "('thalach', array([0.08716439]))\n",
      "('exang', array([0.]))\n",
      "('oldpeak', array([0.03929215]))\n",
      "('slope', array([0.]))\n",
      "('ca', array([0.1913626]))\n",
      "('thal', array([0.08572834]))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 400 out of 400 | elapsed:   19.8s finished\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "Hyper_paramters={'criterion':['gini', 'entropy'],'max_depth':[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]}\n",
    "Tree_Gridsearch_paramters=GridSearchCV(DecisionTreeClassifier(),Hyper_paramters,scoring='roc_auc',n_jobs=-1,cv=10,verbose=1)\n",
    "Tree_crossvalidation=Tree_Gridsearch_paramters.fit(X_train,y_train)\n",
    "#Tree_crossvalidation=Tree_Gridsearch_paramters.fit(X_train,Ytrain.values.ravel())\n",
    "\n",
    "print (\"The best paramter combination is \")\n",
    "print(Tree_crossvalidation.best_params_)  #gets best estimator\n",
    "Final_Model=Tree_crossvalidation.best_estimator_ #gets best estimator\n",
    "print(\"The best AUC score was \")\n",
    "print(Tree_crossvalidation.best_score_)  #gets best score for best estimator # -0.83828599184062269\n",
    "importances=Tree_crossvalidation.best_estimator_.feature_importances_\n",
    "importances\n",
    "#Tree_crossvalidation.cv_results_  #gets summary of results\n",
    "#Print all importance\n",
    "Names=list(X_train.columns.values)\n",
    "for f in range(X_train.shape[1]):\n",
    "    print((Names[f],  importances[[f]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Method 1 to show the tree\n",
    "from IPython.display import Image  \n",
    "from sklearn import tree\n",
    "#!pip install pydotplus\n",
    "import pydotplus\n",
    "\n",
    "# Create DOT data\n",
    "dot_data = tree.export_graphviz(Final_Model, out_file=None, \n",
    "                                feature_names=Names)  \n",
    "                                #class_names=iris.target_names)\n",
    "# Draw graph\n",
    "graph = pydotplus.graph_from_dot_data(dot_data)  \n",
    "\n",
    "# Show graph\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[21  6]\n",
      " [ 6 28]]\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "   Heart disease       0.78      0.78      0.78        27\n",
      "No Heart Disears       0.82      0.82      0.82        34\n",
      "\n",
      "       micro avg       0.80      0.80      0.80        61\n",
      "       macro avg       0.80      0.80      0.80        61\n",
      "    weighted avg       0.80      0.80      0.80        61\n",
      "\n",
      "Accuracy :  0.8032786885245902\n",
      "Sensitivity :  0.7777777777777778\n",
      "Specificity :  0.8235294117647058\n"
     ]
    }
   ],
   "source": [
    "#Prediction Using the Model\n",
    "y_pred = Final_Model.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "cm1 = confusion_matrix(y_test, y_pred)\n",
    "print(cm1)\n",
    "print(classification_report(y_test, y_pred, target_names=[\"Heart disease\", \"No Heart Disears\"]))\n",
    "\n",
    "#Calculate sensitivity and specificity\n",
    "\n",
    "total1=sum(sum(cm1))\n",
    "accuracy1=(cm1[0,0]+cm1[1,1])/total1\n",
    "print ('Accuracy : ', accuracy1)\n",
    "\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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