Stock Price Prediction Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get stock data from either quantopian or quandl as a CSV file.\n",
    "import quandl\n",
    "quandl.ApiConfig.api_key = \"axLRx8zPHQ7B8idwcNkW\"\n",
    "df = quandl.get('NSE/TATAGLOBAL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": 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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Close</th>\n",
       "      <th>Total Trade Quantity</th>\n",
       "      <th>Turnover (Lacs)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-07-21</th>\n",
       "      <td>122.10</td>\n",
       "      <td>123.00</td>\n",
       "      <td>121.05</td>\n",
       "      <td>121.10</td>\n",
       "      <td>121.55</td>\n",
       "      <td>658666.0</td>\n",
       "      <td>803.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-22</th>\n",
       "      <td>120.30</td>\n",
       "      <td>122.00</td>\n",
       "      <td>120.25</td>\n",
       "      <td>120.75</td>\n",
       "      <td>120.90</td>\n",
       "      <td>293312.0</td>\n",
       "      <td>355.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-23</th>\n",
       "      <td>121.80</td>\n",
       "      <td>121.95</td>\n",
       "      <td>120.25</td>\n",
       "      <td>120.35</td>\n",
       "      <td>120.65</td>\n",
       "      <td>281312.0</td>\n",
       "      <td>340.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-26</th>\n",
       "      <td>120.10</td>\n",
       "      <td>121.00</td>\n",
       "      <td>117.10</td>\n",
       "      <td>117.10</td>\n",
       "      <td>117.60</td>\n",
       "      <td>658440.0</td>\n",
       "      <td>780.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-27</th>\n",
       "      <td>117.60</td>\n",
       "      <td>119.50</td>\n",
       "      <td>112.00</td>\n",
       "      <td>118.80</td>\n",
       "      <td>118.65</td>\n",
       "      <td>586100.0</td>\n",
       "      <td>694.98</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>222.00</td>\n",
       "      <td>223.65</td>\n",
       "      <td>218.85</td>\n",
       "      <td>219.55</td>\n",
       "      <td>219.40</td>\n",
       "      <td>965858.0</td>\n",
       "      <td>2131.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>219.95</td>\n",
       "      <td>221.05</td>\n",
       "      <td>218.05</td>\n",
       "      <td>218.95</td>\n",
       "      <td>219.10</td>\n",
       "      <td>716275.0</td>\n",
       "      <td>1571.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>219.00</td>\n",
       "      <td>220.35</td>\n",
       "      <td>213.60</td>\n",
       "      <td>214.20</td>\n",
       "      <td>214.60</td>\n",
       "      <td>1081778.0</td>\n",
       "      <td>2340.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>214.95</td>\n",
       "      <td>215.35</td>\n",
       "      <td>209.90</td>\n",
       "      <td>210.55</td>\n",
       "      <td>210.05</td>\n",
       "      <td>2096553.0</td>\n",
       "      <td>4433.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>210.60</td>\n",
       "      <td>214.90</td>\n",
       "      <td>210.00</td>\n",
       "      <td>213.50</td>\n",
       "      <td>213.80</td>\n",
       "      <td>1213181.0</td>\n",
       "      <td>2583.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2100 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low    Last   Close  Total Trade Quantity  \\\n",
       "Date                                                                       \n",
       "2010-07-21  122.10  123.00  121.05  121.10  121.55              658666.0   \n",
       "2010-07-22  120.30  122.00  120.25  120.75  120.90              293312.0   \n",
       "2010-07-23  121.80  121.95  120.25  120.35  120.65              281312.0   \n",
       "2010-07-26  120.10  121.00  117.10  117.10  117.60              658440.0   \n",
       "2010-07-27  117.60  119.50  112.00  118.80  118.65              586100.0   \n",
       "...            ...     ...     ...     ...     ...                   ...   \n",
       "2018-12-31  222.00  223.65  218.85  219.55  219.40              965858.0   \n",
       "2019-01-01  219.95  221.05  218.05  218.95  219.10              716275.0   \n",
       "2019-01-02  219.00  220.35  213.60  214.20  214.60             1081778.0   \n",
       "2019-01-03  214.95  215.35  209.90  210.55  210.05             2096553.0   \n",
       "2019-01-04  210.60  214.90  210.00  213.50  213.80             1213181.0   \n",
       "\n",
       "            Turnover (Lacs)  \n",
       "Date                         \n",
       "2010-07-21           803.56  \n",
       "2010-07-22           355.17  \n",
       "2010-07-23           340.31  \n",
       "2010-07-26           780.01  \n",
       "2010-07-27           694.98  \n",
       "...                     ...  \n",
       "2018-12-31          2131.09  \n",
       "2019-01-01          1571.63  \n",
       "2019-01-02          2340.31  \n",
       "2019-01-03          4433.50  \n",
       "2019-01-04          2583.05  \n",
       "\n",
       "[2100 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries and packages\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2100, 7)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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6LQlpYibyh01lamyPUWN36r5Pypt2AbQ6AgsAAAAAiGPN6QQRKLAw6kWx9Ksa9XrpoAoXHtTSr2oMf+67SvelJAVpxwUWHk+vBruf+rWxqN7t9arv6pp2AbQ6AgsAAAAAQJME2iWkvN49TXA4nbr9v6Uqr2/YBeT/NpUZ/tw3le69Jbpl/rhkI8Fk0vHv6vzhus1V29TEA62OwAIAAAAAYtyivVUhvV5TKyy+rbS7Lff4ttIuu8M7MPjGo8KiW4bZ7XUotzatIbCIeAQWAAAAABDjXv/CeAlGcwXqeeEZWKw76L38wnrckNI6h361qkSvf+k+z24eTTGD3dp08RfVGrL4kEa9eUT7yqw//Kz7GIO8BBGGwAIAAAAAYtzq70PbryFg002PJSGbPPpHSFL9D4nB+4fqNOyNw3rzm1qvMd2bUWFRZXXo9x+U6qsKuz46Uq9Zmxuaa6Z6JhaIeAQWAAAAABDjspJC+7De1CUhRw22ObU6nFr6VY0u/U+xDlZ7nzebpC7p7oGFZ2GH0aqONd/Xqdr244llXzcEISmBJo2IQ2ABAAAAADEuyxLaR7/A25r+GEAcqLJr21Gr15h6h3TnhlKf1yhINyvRI6Hw3trUO7HwtdIjmcAi6iQGHgIAAAAAiGZVLelOacAzSPBUZ5du/2+pntvtu9lnvd2pI7W+5+XZcLPhfd1f2wzSCaNo5lC1XUaZzcFquzqneb8PIgMVFgAAAAAQw7YftepYXWg7TAbIKyTJb1ghNSwJ8cez4aYUXIWF0dyuW3NUFoMTP19ZrBqj1AMRgcACAAAAAGKYv2UXzWU2tXx5RZ3d//mu6d6VD2aPJ1ijHhYJBnP70KDppyTtLLVpYYi3fEXoEFgAAAAAQAwzelhPOu5J8Je905p8zawkk1oaWQSqsCgwWKrhudGH3WBFia/qD1/v9t9DxmEG2h6BBQAAAADEEbNJmjO8nXpmmnV2pyRNG5LZ5GtkWhJ0agdLi+ZRHyCw6JTm/bjq2TvD5vS+htEyEUkyGCpJstDCImLRdBMAAAAAYpTd4OF9QDuLftk7rVmVFccbVZiizcXeu38Ey9+SEJOkIR2SvI57VlgY9RKtb2J/0aRgGnKgTfqEclAAACAASURBVFBhAQAAAAAxqsagycPM07JCcu1zOie36Of97Vzy1DntDHfvMLekwsLHohDiishFYAEAAAAAMarOILA4v0vLgoZGmUb7hDZBiY8tTf8yLEdXnmBc/WEOooeF1UcO4msFitF3hMhAYAEAAAAAMcpzy86CtASZQrDDhyQlt7D3Q7GPwGJ8z1SfP5Po8QRrVGHhqzdGvY8lKLUEFhGLwAIAAAAAYpTnw3iKZ4lCC1ha2PvhSI1xYJGV5Pu6iR5hi9GqEl8rTYyWx0gEFpGMwAIAAAAAYlStR1VBKAOL5BZeq8hzcj9I8FMBYvaqsPAe42u71KN1xkmGj2kgArBLCAAAAADEKK8KC89tNloguYV//jaqsBjc3v9WqZ4VFo4floT8YVOZ5u6oVL+cRJ2V37QeHfSwiFwEFgAAAAAQozx7WISywiKppRUWNd6lDbNOz/b7M149LBzSrmNW/f2zSknSjmM27Thma9I8WBISuVgSAgAAAAAxKpw9LJJa2sPCo+nmuB6p+lmB/+oIzwIRm9Opp3dVtmgetUbrShARCCwAAAAAIEaFc0lIC3c1VZHHkhB/zTYbmRMCN91sKiosIheBBQAAAADEKM/qgdQQVlgEsz2qSdLsn/hf5tEoPYgwxXNIKLIGelhELgILAAAAAIhRnlt5hrLCIhhOSdefmKH1l3XUNX3S/I5ND6Jkw+y1rWnLwwZf252i7RFYAAAAAECM8qywCGUPi6YYlGvR5T1T/Y7JCKbCwmBbU2cL8wYqLCIXgQUAAAAAxCjPh/FQBxZP/jTHtUzjzpMz/Y5NCxBIpFuC6GHh2XQzBBUWtXbJ2dLUA2HBtqYAAAAAEKM8lzuEsoeFJF3dJ10/K0iR1eFUj8xE/Xlbhc+xAQOLoCos3MeEqjiizi6l8HQccbglAAAAABCjPHfASDaH/j26pAd30UCBRDA9LLy2NQ3BLiFSw/fU2v09EBhLQgAAAAAgRtV49rBow4fyNM8GFB4yglgS4l1hEZoSC/pYRCYCCwAAAACIUXV299ehXhLSFKFYEuLdw6IlM/qRZyUKIgOBBQAAAADEKM8eFsltGFgEaqoZ1JIQjwoLm9OppmYWL5+fqz7Z7t0RCCwiE4EFAAAAAMQoz21NU9twSYglwf97B7WtqccQu8P3co5Oqd6Pu3POytFFhaleu6UQWEQmAgsAAAAAiFGeD+Kh3tbU08pLOri9PrtTUtA/G9S2ph5PsDan97KXRuN7pnodG5RrkSSlePQJpYdFZCKwAAAAAIAY5RlYhLvCYmh+sm4/OUOS1DE1QX88PTvonw2uh4XHkhCHU298VWN8PYMlJjnJDcc8l8bU2IKdJVoT25oCAAAAQIzy3tY0/EtCZp6WrTtOzpQlwaSkJrxfMGGK50Yj249afY41WmLS2PjTs/koFRaRiQoLAAAAAIhRntuattYuIemWBMOw4nKDZRqNEkzB9LBwH/Pvb2p9js1N8X7cbQwsPIMbelhEJgILAAAAAIhR9R4P4k2peAiHP/0k+CUiRjwrLPzpYBBYNC47SUkksIgGBBYAAAAAEKOsHs/hSW38BNjBs9tlEzUlb8kw6GFhTqDCIpoQWAAAAABAjPKqsAiwtWhbuaRbSlDjEpswf3/bpNLDIjoQWAAAAABAjLI53F83ZUlFuPTN9t77YcYpWUH9bFM2OTkp16Lc5B8/8E+P22KVCovoEAG/rgAAAACAcKh3RF6Fxf1n/NjHIifJpK+u7qyTci1B/WxTKiySzCbNGZ6jHplmDcq16IEzf3xfz5UptTYCi0jEtqYAAAAAEKOsHoGFQVuHVjeqMEVvXdJB20qsuqRbinKSg59UU/OWMd1TNaa7984kKV5LQpp2XbQOAgsAAAAAiFFWjyUhljbeJaTRT/KT9ZP85Cb/XFOWhPjjGViwJCQyRUC+BgAAAAAIB68KC1NkBBbN1ZQlIf7QwyI6EFgAAAAAQAxyOp2q96ywiPInwGArLIblJ/k9n5JIYBENWBICAAAAADHI8xk8wSSZI6DpZksEmn+yWcq0JOjuU/3vOuLdw4LAIhIRWAAAAABADPLeIaSNJhJC/iosJvZJ05zhOUoIYtmL55KQGnYJiUgx8CsLAAAAAPDk1XAzyqsrJP89LNItpqDCCokKi2hBYAEAAAAAMch7S9NYCCx8n8vwd9IDu4REhzYLLB577DGNHDlShYWFOuGEEzRhwgTt3LnTbcyNN96onJwct/+df/75bmPq6uo0ZcoU9erVSwUFBbrqqqt04MCB1vwoAAAAABBx6uzur6O94aYk+duVNcMSfCCTYnZ/7fldITK02a/s+++/r8mTJ2vlypVatmyZEhMTNW7cOB07dsxt3IgRI7R7927X/1577TW389OnT9fy5cv17LPPasWKFaqoqNCECRNkt/MbBwAAACB+fVlhc3udl2r2MTJ6mP0s+UhvQmDh1cOCCouI1GZNN5csWeL2+qmnnlK3bt20ceNGXXzxxa7jycnJys/PN7xGWVmZXnjhBT3xxBMaOXKk6zqDBg3S2rVrdd5554XvAwAAAABABPui3D2w6Jcd/Xsu+Fv1kR7snqeSUhPpYRENIqYoqLKyUg6HQzk5OW7HN2zYoN69e+u0007T73//ex05csR1bsuWLbJarTr33HNdx7p27ap+/frpww8/bLW5AwAAAECkqbC6P4R3SImYx79mS/RbYRH85/OssKCHRWSKmIht2rRpGjRokM4880zXsfPPP19jxoxR9+7d9c033+j+++/X2LFjtXbtWiUnJ6uoqEhms1nt27d3u1ZeXp6Kiop8vtfevXvD9jlgjO88unC/ogv3K7pwv6IH9yq6cL+iC/erdXx3OFFSkut1XUWp9u494vsHDETavfq+IkFSiuG5sqLvtddzaxQfiuslKc31urLOFnGftTmi8TP06dPH57mICCxmzJihjRs36q233pLZ/OO6qp///Oeufw8cOFBDhgzRoEGDtHLlSo0dO9bn9ZxOp0x+kjd/XwhCb+/evXznUYT7FV24X9GF+xU9uFfRhfsVXbhfrSetvExSpet1l7xc9emTFfTPR+K9qimpl7Yahy79undVn/zkoK6TV+eQPjroel3rTIi4z9pUkXi/WqrNa4KmT5+u119/XcuWLVOPHj38ju3cubMKCgr0xRdfSJI6duwou92ukpISt3HFxcXKy8sL15QBAAAAIOJV2dyXOXj2bYhGiX62Zm3KkhDPbU2rbU4dqmbjhkjTpoHF1KlTtXjxYi1btkx9+/YNOL6kpEQHDx50NeEcMmSILBaL1qxZ4xpz4MAB7d69W0OHDg3bvAEAAAAg0tV4BBbp/jpWRgl/mUtTtjVNNtgw5eldlSqvd2jJF9XaWlLfjNkh1NpsScidd96pV155RQsXLlROTo4OHz4sSUpPT1dGRoYqKyv18MMPa+zYscrPz9c333yjWbNmKS8vT5deeqkkKTs7WxMnTtTMmTOVl5endu3a6e6779bAgQM1YsSItvpoAAAAANDmquOtwqIJn8+ohcBj2yr12LaGJTRmk/Tiee11YaFxvwy0jjYLLJ555hlJ0mWXXeZ2fOrUqZo+fbrMZrN27typl19+WWVlZcrPz9fZZ5+t+fPnKzMz0zX+wQcflNls1qRJk1RbW6tzzjlH8+bNc+uFAQAAAADxJhYDCz95hTKasCQkELtTuuWDY9pzVeeQXRNN12aBRWlpqd/zqampWrJkScDrpKSkaPbs2Zo9e3aopgYAAAAAUc/qcA8sjJZBRBtfmUuCSUoJ8ecrqvlxx5HSOofWHazToFyLemVFxN4VcYFvGgAAAABikM1jh89EPzspRgtfS0IyEk1+d4psidI6h87812EV1TiUajbp3xd30Kl5SYF/EC0W/V1XAAAAAABe7O4FFjJHf14hX31D05vQcLOp/rS13FVtUWN36rUvqsP2XnBHYAEAAAAAMcjmdE8szP4aQEQJX1UixbUOw+Oh8OSOKrfXc3dW+RiJUCOwAAAAAIAY5IjBCguzjydYa5jyCqdH6CNJ3TJioBlIlCCwAAAAAIAYZPNILGJgkxCfFRYXdk0Oy/vtL7d5HetN081WQ2ABAAAAADHIq4dFLCwJ8fEEO6l/epOvdXKuJeCYfQaBhefuKwgfAgsAAAAAiEHeu4S0zTxCyegz3HJShi4qTG3yte4cnBlwTKXVO5yo8UyCEDYEFgAAAAAQY5xOp3aXWd2OxUCBheHWpVnN3CEkLzXw43C1zSCwMDiG8CCwAAAAAIAYc+t/S1Vndz+WGAuJhYGuGc3rKeGrH0aj3lmJxhUWBBathsACAAAAAGLI1xU2LdhT7XU8FnYJkaQ//yTb9e+sJJPGdk9p1nUsAZ6G95Xb9O53tV7HjaouEB60NwUAAACAGLK1xGp4PFBFQbSY3D9dmUkJ2ltq0y97pyk9UPLgQzAVJ6u/r/M6VlZPYNFaCCwAAAAAIIYk+yilMMdIfb3JZNKEE9JafB1fO44EUmN3qs7u9Pk9I3Ri5FcWAAAAACBJyWbj4zxfu7O0oOKktM4ReBBajMACAAAAAGKIzwqLGFkSEipJPoKdYPhadoPQYkkIAAAAAMQQX5UUzV0CEatSWlBycuW7JcpLSdDEvmmaOiRL5fUO/XN3ldqnmHVt37SY3ZGltRFYAAAAAEAMsfvoCZnIM7SblvagOFLr0GPbKmU2mfTm1zXaVWqTJN2+oVS/7J2m3w5I1+D2SaGYatwiYwMAAACAGOIrsEhgSYibUDXNnL21whVWNHppX7XGvlWsatuPvS7e/rZWszaX6ZMj9SF533hAYAEAAAAAMcThq8KCpz83SWH+PsrqnVr6Va0kaeW3tbry3RI9tq1SF604oi/LbQF+GhKBBQAAAADElHs+LjM8TtNNd6ZW+D6+q2wIJn65qsR1rN4hrf6+NuzvHQsILAAAAAAgRlRZHfqk2HgHCyosWt+XFXZJ3lUvm4pYFhIMfmUBAAAAIEYcqnYYHs9NTqDpZhBuG5QR0uu9uK9aNoM1Ol3SW7CnahwhsAAAAACAGFHto+PmlMGZrbIEItqd2iFJc89uF9JrfmhQTeGrMSrcEVgAAAAAQIyotXk/CZ+eZ9GNA0NbORCrumea9cveadpzVSe3ipSTcy3Nvuba7+u8jlUb3Cd4I7AAAAAAgBhRZfNeEtIhheUHvtx3Rpbr3z/pmKTB7ZMkSR1TzVpxSQdd1zdNfxmWo1tbsFRknUFgUUNgEZTEtp4AAAAAACA0yuu9H4SdTh6OfbnlpEwNbGfRkVqHxnZPdTt3ZsdkndkxWZK0cG9VUNcrzDBr6pBM/e79Utexj454LwmpYU1IUAgsAAAAACBGVFi9KywMej7iOOd2SQk4xmipjZHkBFNQy0eqrNyUYLAkBAAAAABiRIXBgzCPxi13UWHgUEOSks1SvxyLArU3La033s0F7poUWHz33Xe6+eabNWDAAOXl5WndunWSpOLiYt1888365JNPwjJJAAAAAEBgFQYPwlRYtFzXjETdfnLgPhbJZpOSzSZ1TPX/qH2khsAiGEEHFl999ZVGjhyp5cuXq3///rLb7a5zHTp00KeffqoFCxaEZZIAAAAAgMCMKix4NA6Nmadlq/i6Am29It/nmGRzQ21FpzT/jU6Lau1+z6NB0D0s7r//fiUkJOi///2vUlNT1bt3b7fzo0aN0ltvvRXyCQIAAAAAgvN1pc3rGBUWoZOYYFKS2feCj8bAonOaWVtLrD7Hldc7VWd3usbDWNAVFmvXrtXkyZPVtWtXmUzeX2phYaG+//77kE4OAAAAABCcbyptWvpVrddxdgkJrUQ/GUPyD0/YndMCP2pvPlKvF/ZUaXep72Aj3gVdYVFRUaFOnTr5PF9fXy+bzTvNAwAAAACE32NbKwyPsyQktPwVRSQdV2ERyCX/KZYkpZildy7tqEFB7C4Sb4KusOjSpYt27drl8/zHH3+snj17hmRSAAAAAIDgOZxO/XNPtY9zrTyZGJdgsOKgUcoPgUW75OD3t6i1S//3UVmL5xWLgv4Wx4wZo0WLFmnnzp2uY41LQ5YuXao33nhD48ePD/0MAQAAAAB+fVPpu4kjK0JCK9HPU3RjhYUloWm9KdYdrGvJlGJW0IHFHXfcoYKCAp1//vm64YYbZDKZ9Ne//lUXXHCBJk2apJNOOkm/+93vwjlXAAAAAIABf9tkkleEltlPhUXyD0EFvTRDI+jAIisrS2+//bYmTpyoTz/9VE6nU2vWrNHevXs1efJkLV++XCkpKeGcKwAAAADAQLGfbTIdlFiElP8eFg3/t6kVFjAWdNNNqSG0eOSRR/TII4+ouLhYTqdTHTp0MNw1BAAAAADQOoprfVdYWOm6GVL+loSkuJaENO/a1TaHJq89pg8O12lM91T9ZViO321UY10zv0apQ4cOysvLI6wAAAAAgDZW4iewuLR7aivOJPb5a7rZGC4kNrPC4tldVfrPt7Uqr3dq0d5qrTrgvU1tPAk6sPjHP/6hyy67zOf58ePHa/78+SGZFAAAAAAgeCV1xoFFTpJJl/Vg6X6oJfrIIxp7WPg6H8gfPi53e/3aFzXNu1CMCDqwePHFF3XCCSf4PN+7d28tXLgwJJMCAAAAAATPc0nIvadn6aXzcrVhfL76ZFvaaFaxKzvJ+FE6uQU9LIx6jfh4m7gR9Mffv3+/BgwY4PN8//79tX///pBMCgAAAAAQvBKPppsnZCXq4m6p6pxmbqMZxTZfeURyC3pY7CuzeR3rntmktpMxJ+hPb7PZVFvre/1MbW2t6urYOxYAAAAAWptnhUWHlDj/03yYpfpY85Hcgh4W6w56P0/bg2iYerjarjRLbPaWDPq3+IQTTtDatWt9nl+zZo169uwZijkBAAAAAJrAs+kmgUV4pfrYuePHwKLp13z3O+8CAavD/5a0f99eoQGvHlL3RQf1VlHsVdME/TVeccUVWr16te6//37V19e7jlutVj344INavXq1rrjiirBMEgAAAADgm2dg0T4l9h5eI8mk/umGxxubbjanh8XK77wrLKwGfS0aldc7dP+n5bI7JYdTenh/kqpibA/boJeE3HTTTXrnnXf06KOP6rnnnlPfvn1lMpm0e/duHTt2TMOGDdPvfve7cM4VAAAAAODB7nCq0ub+YJudFJtLBCLFr/qkadqHZV7Hk37IiZq7S4gnf/nDJ8X1qjuudUmV3aQlX9ZoYl/jMCUaBV1hYbFY9K9//Uv33HOPCgoKtG3bNm3dulVdunTRrFmztHTpUiUlJYVzrgAAAAAADx5ZhSwJUoKJwCKcMiwJ+vjyjl7Hf2y6GZrv/x+7qjTtw1K9Y7BcZNzKEq9j/9xdFZL3jRRNajlqsVh066236tZbbw3XfAAAAAAATWDz6HOQSFjRKnKTvf/+35IeFr7M21mleTurNH9EO43vmSZJ2nXMajh2c7FVJbX2mFkSRCcWAAAAAIhinhUWoXxYhm9JBo03W9LDIpD7Npe7/v3ZUePAQpKO1sVOHwufFRYffPCBJGn48OFurwNpHA8AAAAACD+7R4WFjw0sEGLJBl+0q4eFj9BoSHuLtpT4Dhv8+aLCfty/bT7H1XgmWFHMZ2Bx6aWXymQy6dChQ0pKSnK99sXpdMpkMuno0aNhmSgAAAAAxBOn06kvyu3KSTb5LfH3rrAgsWgNRo01G5fj+KqwuPKENG0p8W7WGazG5+4vyuM8sHj88cdlMplksVjcXgMAAAAAwu+364/plf01ykg0aeF5uRpRkGI4zuaxAsDCkpBWYfR83HjI1y4h7VMStGZMni789xHVN2PlRlGNQ/lpZn1Zbvc5psYeB4HFNddc4/c1AAAAACA8dpda9cr+GklSpc2puz8q0wfjfAQWTs8lIfyhua35qrBIMZt0Sockbf1FJ+0rs2n51zV6elfwO3t8XWlTfpo5bpaEBJW9VVZWasyYMVqwYEG45wMAAAAAcW/N93Vur3cc8/2Aavf4Sz1NN1tP/5wfawASTFKvzIbXvu5BXkrDic5pZp3dOdlwpxF/vqqwq7zeoeJa3+UZcRdYZGRk6NNPPw33XAAAAAAAalrjTM8KC7Y1bT0PD81WbnKCks3SX4blKCXRfw+L/FT3XiQHqnwv7TDyVYVNJX7CCkmqjoclIZ4GDRqkPXv2hHMuAAAAAAA1/LXeU2PDRU+ePSyosGg9IwpStO+XnSRJCcfdG189LDqmud8cX4HFlMGZGt0tRe8drNPMj3/czvRIjSNgj4raeKuwkKRp06ZpwYIFeu+998I5HwAAAACIe3UGz7FVPh5EPQ+zrWnrSjCZ3MIKSTIbJE7JZinDI8nokGL8SH5WfpKGdEhSdpL7+X98XqW7P/K/y0i5NXYCi6ArLF599VV17dpV48aN00knnaTevXsrNTXVbYzJZNLjjz8e8kkCAAAAQDw5Vudd9l9U41CGwRYgdofHkhC2NY1IuckJXhUy15+YoVe/qPEam5/WsHQkxaBUw7O/iadZm8v1/wZlxMQun0EHFi+++KLr39u3b9f27du9xhBYAAAAAEDL3Lj+mF7aV+11/NtKu3plJcrucOoPH5dp2Ve1Ois/Sdf0SXMb52s5AtpWO4MGm6fnWQzHdkptGJvSzHKZWAgrpCYEFseOHQvnPAAAAAAg7u0rsxqGFZJ0sLphnch7B+v05I6GrTBf/aJGnrUYVFhEJqMdQUwmk07IMmt/ufsaoMZwI9ns9SNxJageFg6HQ0VFRaqr8196AgAAAABovqd3Vfk8V//D0o8/ba1wO77YY0kBPSwik68tTGsNdqxtrJBIjfObGTCw+Mtf/qKePXuqf//+Kiws1A033KDqauPEDwAAAADQfHV+doBo3A2k1KC/xfF8bamJtuUrsKi2+76fyQQWvr388suaNWuWrFarBg8erOzsbC1evFh33XVXa80PAAAAAOJGvZ8swuZwavWBWu0qNfiT/HHY1jQy5frYEcSowqJRUpyHT35/lZ9//nl16dJFmzZt0po1a7Rjxw5ddNFFeu2111RV5btUCQAAAADQdFaH7wqLTUfqdcU7Ja04G4SSrwqLGj9VNf7ONTo5171xZ8fU2Ems/H6SHTt26LrrrlOXLl0kSUlJSbrzzjtVX1+vvXv3tsoEAQAAACBeVFl9P6Cu/LZWfvIMl8+P+a/AQNvolGbcQfN3AzPcXt9wYrrr390z/HfdHJRr0Yvn5apfdsN+GmaT9Pfh7Vo408jhN7CorKxUt27d3I41vq6oqDD6EQAAAABAM+0utfo8V+4nzDheQuz8gT2q/aJXquvfWUkmXVyYYjju+hPT1f6H6osOKQm6bVCm61zXDN8bez59TjutHpOnrhmJWj0mT7NPrNP6yzrqQh/vE438bmvqdDqV4PHb3vja4fDf6AUAAAAAELx6u1NfVNgDDwwgicAiItx7erZqbE4V1Th015BMpVuMb0z3zERtHN9R249aNbi9Re1T3KsqfnNiup4y2D1mZEGyq8FquiVBI9rb1aedxWtcNPMbWEjSp59+quTkZNfryspKSdLGjRtVVlbmNX7s2LEhnB4AAAAAxIfPjvqurmiKeG/UGCkK0s1aeF77oMbmpZp1bhfj5R/5PpaSJMbBfQ4YWMybN0/z5s3zOv7www+79oaVGqoxTCaTjh49GtoZAgAAAEAc+LCoPiTXMcfBg2w86ewzsGjlibQBv4HFE0880VrzAAAAAIC4Nv0j7wp2f7qkmXWguuVLSBDZCtKMk4lEU+wHU34Di6uvvrq15gEAAAAAcavW5t1Qs192onaX+d7xIzvZpAPV4ZwVIkGOj+1Q46HCIg4+IgAAAABEthq7d2ARaLcHp49NQwblxlbjxXiX7aOLamLsF1gQWAAAAABAW7M5vNOHgnTj3gWNLvIRaEwdkml4HNHJKLAwm+TWUzJWEVgAAAAAQBuzOtxfd0pN8PsX9Ct7peribsaBRY/MgHsrIIpkWrx/EeKlryq/yQAAAADQxqweFRaJCSaf21Ze0StVT53TTpJkSfAOOxBbjH4P4uWeU2EBAAAAAG3Ms4WFJaGh7N/IkPYWmUwmmUwm3XdGttu5M/OSwjRDtKX0eGhYYYDAAgAAAADaWFMqLNKP2x5icv90Dcv/MaS4pk9aeCaINpXsK72Kcc0KLOrq6vT999+rvr6+2W/82GOPaeTIkSosLNQJJ5ygCRMmaOfOnW5jnE6nHnroIfXv31+dOnXS6NGjtWvXLq+5TJkyRb169VJBQYGuuuoqHThwoNnzAgAAAIDW5lnibzH53gUi9bgTlgSTXr2gvV44N1dvXdJB1/VLD+Ms0VZ8bBQS85r0sbds2aIxY8aoa9euOumkk7RhwwZJ0pEjRzR27FitXbs26Gu9//77mjx5slauXKlly5YpMTFR48aN07Fjx1xj5syZoyeeeEKPPPKIVq9erby8PI0fP14VFRWuMdOnT9fy5cv17LPPasWKFaqoqNCECRNkt9ub8tEAAAAAoM147hLSUGFhPNbz4TXTkqAx3VP1k/zkMM0Obc1ChYV/27Zt0yWXXKIvv/xSV111ldu5vLw81dbW6sUXXwz6jZcsWaJf/epXGjBggAYOHKinnnpKxcXF2rhxo6SG6oq5c+fqtttu02WXXaYBAwZo7ty5qqys1OLFiyVJZWVleuGFFzRr1iyNHDlSQ4YM0VNPPaUdO3Y0KTwBAAAAgLZk8+hhkZggtUs2flwzx8sWEXChwiKABx98UJ06ddLGjRt1zz33yOl0/y/qnHPO0SeffNLsiVRWVsrhcCgnJ0eS9PXXX+vw4cM699xzXWNSU1N11lln6cMPP5TUUPFhtVrdxnTt2lX9+vVzjQEAAACASOfZw8KSYFKXdLPhWOKK+JMcpyFV0NuabtiwQbfffrsyMjIMe1cUFhbq0KFDzZ7ItGnTNGjQIJ155pmSpMOHIZKKvAAAIABJREFUD0tqqN44Xl5eng4ePChJKioqktlsVvv27b3GFBUV+XyvvXv3NnueaB6+8+jC/You3K/owv2KHtyr6ML9ii7cL29flSZISnG9ttbWqPZgqSTvJpqHDx3UXmvrLIHnXkUGhzVFnvUGRvcmGu9Xnz59fJ4LOrCoq6tTVlaWz/Pl5eVNm9VxZsyYoY0bN+qtt96S2eyeIppM7kmS0+n0OuYp0Bh/XwhCb+/evXznUYT7FV24X9GF+xU9uFfRhfsVXbhf3iqtDi14p0TSj38YzspI06D+3aQN3hsKdC3orD7dUsM+L+5V5MjcXSRVWd2Oed6bWLxfQS8J6dmzp7Zs2eLz/Pr169WvX78mT2D69Ol6/fXXtWzZMvXo0cN1PD8/X5K8KiWKi4tdVRcdO3aU3W5XSUmJzzEAAAAAEMke+rRCGw67V7H72iFEkk5un+T7JGJSro9+JrEu6E99xRVX6JVXXnFrZtlYxfD3v/9d7777riZMmNCkN586daoWL16sZcuWqW/fvm7nunfvrvz8fK1Zs8Z1rLa2Vhs2bNDQoUMlSUOGDJHFYnEbc+DAAe3evds1BgAAAAAiVVGNXQv2VHkdT/TTs8BXbwvErumnuK92uPd036sfYknQS0JuueUWrVmzRpdffrn69u0rk8mkGTNmqKSkRIcPH9bIkSP161//Oug3vvPOO/XKK69o4cKFysnJcfWsSE9PV0ZGhkwmk2688UY9+uij6tOnj3r37q0///nPSk9P1xVXXCFJys7O1sSJEzVz5kzl5eWpXbt2uvvuuzVw4ECNGDGiad8EAAAAALQSp9Opof8q0p4ym+F5S3z+QR0+DG5v0R9OzdKL+6p0Sock/U+/9LaeUqsIOrBISkrSG2+8oaeeekqvvfaaUlJStH//fvXq1Us33XSTbrzxRiUkBP9f1TPPPCNJuuyyy9yOT506VdOnT5ck3XrrraqpqdGUKVNUWlqq0047TUuWLFFmZqZr/IMPPiiz2axJkyaptrZW55xzjubNm+fVCwMAAAAAIsUDn1b4DCukhl1CJOm8LsladaDOdXxYPstB4pHJZNIdgzN1x+DMwINjSNCBhSQlJibq5ptv1s0339ziNy4tLQ04xmQyafr06a4Aw0hKSopmz56t2bNnt3hOAAAAANAa/ry1wu/59B+aWDw8NFtnLilS46anU4fE1wMr4luTAgtf6urqlJycHIpLAQAAAEBM21dmDTgm3dIQWPTJtuitSzrozW9q9dNOyRpRkBLgJ4HYEfQajnfeeUcPPfSQ27FnnnlGhYWFKigo0K9//WtZrYH/wwMAAACAePby/pqAYxJNPzbdHJqfrPvOyNaFhYQViC9BBxZ/+9vftHfvXtfr3bt3a9q0aerUqZNGjhypJUuW6B//+EdYJgkAAAAAscDmcAZcDiJJZfWOVpgNENmCDiz27NmjU045xfV6yZIlSk1N1apVq7R48WJdfvnleumll8IySQAAAACIBSu/rQ1qXCmBBRB8YFFaWqrc3FzX63Xr1unss89WVlbD/q8//elP9fXXX4d+hgAAAAAQI/aX+94Z5Hjnd2H5BxB0YNG+fXt9++23kqSKigp98sknGjZsmOu81WqVw0EKCAAAAAC+JJtNAcd0TkvQ5b1S/z979x0dR3X+Dfw7W7S76l2y3ItwA2xcccWADTYxmN5LTELAJKQR8ntJIYSeEBICBAjdtFBMszFgwNjGNsbGDXdZuFu9a3e1fef9Y7XSzp07s7NNWknP5xzOwds00ra5z31KFxwNIclN85SQyZMn4+WXX8bo0aPxxRdfwOv1Yt68eR3XHz58GEVFRQk5SEIIIYQQQgjpDcwaAhYbFhUiw6h5b5mQXktzwOLuu+/GhRdeiB//+McAgGuuuQajRo0CAIiiiI8//hizZs1KyEESQgghhBBCSG+QoiFgkWfWd8GREJL8NAcsRo0ahS1btuDbb79FZmYmZsyY0XFdS0sLbr/9dsycOTMhB0kIIYQQQgghvYEufLyCENJOc8ACAHJycrBgwQLZ5dnZ2ViyZEncDooQQgghhBBCeiOPX1S9fkxOREs0Qnq1iN8NR44cwcqVKzsmggwePBg/+tGPMHTo0LgfHCGEEEIIIYT0Jl6VOQUCgIenZHfZsRCS7CIKWDzwwAN4/PHH4fP5JJf/5S9/wW9/+1v88Y9/jOvBEUIIIYQQQkhv4vbxMyxuOiUVVw1PxfRiUxcfESHJS3PA4rXXXsNjjz2GqVOn4o477sCYMWMAAPv378eTTz6Jxx57DIMHD8b111+fsIMlhBBCCCGEkJ7MzZSEDErXY+WCfAxMp1IQQlia3xUvvPACJk2ahI8//hgGQ+fdhg4divPOOw8LFizA888/TwELQgghhBBCCFHgYUpCLh1qoWAFIQo0D/c9ePAgLr30UkmwIshgMODSSy/FwYMH43pwhBBCCCGEENKbsBkWRhobQogizQELo9EIu92ueL3NZoPRaIzLQRFCCCGEEEJIb8RmWBg1r8gI6Xs0vz0mTJiAV155BbW1tbLr6urqsHTpUkyaNCmuB0cIIYQQQgghvYmHabqZQhkWhCjSXCx11113YdGiRZgyZQpuuOEGjBw5EgBw4MABvPHGG7DZbHjuuecSdqCEEEIIIYQQ0tPJSkL0FLAgRInmgMWMGTPw2muv4a677sJTTz0luW7AgAF45plnMH369LgfICGEEEIIIYT0FmxJSAqVhBCiKKJ2tAsWLMD555+PnTt34tixYxBFEUOHDsW4ceOg09E7jRBCCCGEEELUePxUEkKIVhHPz9HpdJgwYQImTJiQiOMhhBBCCCGEkF7LzWRYGGjflxBF9PYghBBCCCGEkC5CGRaEaKeYYTFu3LiIH0wQBOzcuTOmAyKEEEIIIYSQ3srNTgmhppuEKFIMWAwYMACCQG8eQgghhBBCCIkXtummkXLeCVGkGLBYuXJlVx4HIYQQQgghhPR6bEmIkUpCCFFE8TxCCCGEEEII6SJs000aa0qIMtW3h8/nw7333ouXXnpJ9UFefPFF3HfffRBFUfV2hBBCCCGEENKXUYYFIdqpBizefvttPPHEE2FHmE6cOBGPP/44li1bFteDI4SQ3s7jF/HaQTtePWiXncAQQgghpPehppuEaKcasPjwww8xZ84cjB8/XvVBxo8fj3PPPZcCFoQQEqGfr2/CHRub8cuNzViyvqm7D4cQQgghCcY23TRQvIIQRaoBi507d2LOnDmaHmjWrFk00pQQQiLgF0W8c9jR8e9lhx3wU2kdIYQQ0quxGZWUYUGIMtWARVNTE/Lz8zU9UF5eHpqaaHeQEEK0avPKgxMuXzccCCGEEELiRhRFbK9zo6zZw73ewZSEmClgQYgi1YBFeno6GhoaND1QY2Mj0tLS4nJQhBDSF9g9vIAFZVgQQgghPdmdm1pwzsd1mPpBLZ7fb5Nd7/RSwIIQrVQDFqNGjcKaNWs0PdDatWsxatSouBwUIYT0BbwMC3bXhRBCCCE9R6Xdh5fK7B3/fnqvPGDBftdbqIkFIYpUAxYXXngh1q5di5UrV6o+yCeffII1a9bgoosuiuvBEUJIb2bjloRQwIIQQgjpqZYfc0j+fcQqr/V0UkkIIZqpBiwWL16MYcOGYfHixbj//vtx7NgxyfXHjh3DAw88gMWLF2PEiBFYvHhxQg+WEEJ6EzvbJhzykxhCuttRqxcPbm/Fu4faIIY0hXX5RLxebsdHRx3w0UheQgiB0yvi/21ukV3e7Or8vveLoqxflVmf6CMjpOcyqF1psVjwzjvv4KqrrsI///lP/Otf/0J6ejoyMzNhtVphtVohiiJKS0vx9ttvw2w2d9VxE0JIj2fnZFiwda2EdKc2rx9zltei2R14XbZ6/PjJqHQAwI1fNWDVSRcA4Bdj0/HAlKxuO05CCEkGK487uJe/Vm7HHadmAOBlVwCCQBkWhChRzbAAgGHDhmH9+vV45JFHcOaZZ8JgMKCmpgZ6vR7Tpk3DI488gnXr1mHo0KFdcbyEENJr2KjpJklyb5S3dQQrgEAjOQCod/o6ghUA8NRem2QHkRBC+qLdjfypIH/+rrXj/6nhJiGRUc2wCDKbzbj11ltx6623Jvp4CCGkz+A13XQyaaJevwinT0SaQaAdGNLlDjZ7uZdXt8mDE+UtXkwuTEn0IRFCSNIy6MJ/TzuY73lquEmIurAZFoQQQhIjXA+LCrsPsz6qxYDXq3DdV43wUp8A0sW8Iv8118BG1kATbgghpMXNzzQbk9O5R0wZFoREhgIWhBDSTXg9LNq8nSc7z+y1YX/7Dvcnx5149zC/NpaQRPEqVHnUOORXUDkTIaSvcyj0odrX5MX1qxvQ6vbLR5pSwIIQVRSwIISQbsLrYWENuewpZnb7kvVNCT8mQkIp9YBdV+WSXaZ0ok4IIX0Fr9Qz6OPjTrz1Q5u86SaVhBCiigIWhBDSTeyc7evW9nRSUSEVX+lyQuLN5RPxvx/aZJf7/CJWnXBybx9vDq+IsmYP3JS9QQjpAdQCFgDwwPZWWXCXSkIIUaep6SYhhJD4U8uw4KXcA4DHD6TQvHbSBXhBCQDY1ehBvVP++ox3D4tjVi/OW1mHGocfE/ONWD4/H2lG2mchhCSvcJlmrR4RF35WL7mMmm4Soo6++QkhpJvwmnNZ2xtx7m/ij0YLt3tD4u9Aswdbal19LrvlcKvShBB5w00gfhkWR61evHe4Dbetb+oI3G2r92B1hbwMhRBCkkk0pXGUYUGIOsqwIISQbrK/Sb4gbHUHTnb2K4yTtHtFZJsSelgkxNIyO379TTNEAFcOt+C52bndfUhdhhd/SNEBLxywc2/Pdr6Pxt5GD85bWcdtSPtlhRMXDbHE/DMIISRReKWe4VCGBSHqKMOCEEK6weoKJw62yIMSVo8fXr+Ih7a3cu/X5Ir8ZIhE75+7rAgund855MAJGz+Q1Bv5ORklbj8UMx3YRnLReOdQGzdYAQAD0qgWihCS3KLJgqQMC0LUUcCCEEK6wdPMBJAgq1vEiwfssCmc9PxH4X4k/hqdPhyzScsfttfzS3V6o0jjDw1xCKb9e4/y61sv0Ek9ISS5WZneVH+dlBn2PjTWlBB1FLAghJBucEihP4DV48f/bW5RvB9vagNJjN2N8ufoYDMFLJQcUChjikS/VOXTEpe/b/UQIYT0PK1Mb6q5/c1h70NjTQlRRwELQgjpYqIootLOb1zY4ta+KNtR78bGRh28tJBLiP2c4MQ+Tt+R3irSHqPrKl3Y2xhbQKfArFz2QaNNCSHJzOUTERqvMAjAmBwDxuSotwykkhBC1FHAghBCulirR3pSE4rX1yKUXggs3F4ps+PsFXX49T4zrvqyoc9NsOgKvH4h9U5+oKk38kX4mhIBLD3Ib8iplc2jXFYSrykkhBCSCGx2RWaKDoIg4OP5+ar3o6abhKijgAUhhHSxWHaKfWKgnOSlkEkNqytc+LbWHY9DI+2OWr1YXyVvLqnUELI3iiZxh+35ESmHyntDKchHCCHJoJXJkMxMCQQics16XDpUecIRZVgQoo4CFoQQ0sVUNpE1+fSEE7uY1PtVJ5yxPSjp8OERBya9V4NvauRBILun7wQstMTVbhuTJvl3LOVJoiiiqo0yLAghPVMr8+WeYexcZtlVvvgpYEGIOgpYEEJIF3PH2HNiaZk87b7GQdvP8eD1i7hzUzOUEin6VIYFwv+u/ZlRo7FkD31VyR+XGvTBEUfUj00IIYkmLwnpDESoVROqtO4hhIACFoQQ0uU8EQYsBqZLz2Z4afdfVVCGRawq7T7M/qhWdTynWo+F3san4Vftnyp9bcYSz/nlhmbV67UEizx+EV9VOLG/qWumubh9IvWPIYQAkDfNzgzJsLjplFTF+1EPC0LUUcCCEEK6WCRr3t+dnoFdlxeFvV2Nw49/7rLGcFRk6UE79oUZzdm3MizCK2YCFpEG40JVtIXvf6FWciKKIhZ9Vo9LP2/AjI9q8cER+Qhgvyji/m0tyH65ApeuqkedI7qeG35RxE/WNqLw1UrMXl6HCs7Un/1NHsz4sAYj/leFlw/E1oyUEJL8rB7lDIsLBllwaq6Rez8qCSFEHQUsCCEkgURRhINZ5EaSNj+nvwmCIGBCPv9EJ9R921ojPj7S6W87wwd8PP6+M14zXOxBJ8h3BqNNQGFTqZWoNeXcUe/p6DviF4GHdsifz3cPO/DYLhuAQAnKzzc0RXG0wNpKF95rL1HZ3ejhBiQe3tGKvU1e1Dv9+L/NzWhWydwhhPR88qabncsss0HA6oUFOH+gWXY/EwUsCFFFAQtCCEmQVrcfF3xaj5LXKnHVlw0dC91INunT2xeEeSZtH9ctNEoh4fpKloUvTMTCLwIGHRuwiO5vs44zkYWHDf6FWl8tfYzyFq+sXGP5UWkfjM9Pavu5LDY4+A9OdtPyY51lWm4/8PlJKtsipDdjm25mGaXf2ya9gBlFKbL7GXUUsCBEDQUsCCEkQZYetGNTjRsiAlM8nt0X2NmNZIc+uz1QkWvW9nF9IsaxkiS8vtLHQstvyZyPR51h8YDG7CC1gEUb57qTTKkG7/7OKAJQtiimxYS7hyiK2FTjwpvlds0ZJ4SQ5KE01jQUr/yD/RwlhEjRW4QQQhLgsxMO/Pk76SLsnq2Bf2vdhR6VbcDg9oabeRoDFsdt6j0YSOwiybDY2+jBb75pwhO7rTH1d+gObFztymEWyb9vH5sGoxCfDIsmjQv0yjYfLlkVyFr6+YYm+EMyKI5Y5a/9fU2dl31b4+JOItH6swHg3UNtOH9lHX5ojfx9ptab0+bx4/IvGrDgk3rcvqEZc5bX9rjXCyF9nXxKiPx728xpsEkZFoSoo4AFIYTEmc8v4s5vWhSv17ILPbe/CSvm50NoXxDma5x7RhkW0Wnzal+02jXurts9fiz4tA4vl7Xhnq2tKFhaieXV+h4zVYJdL88uMeHW0Wkw6oAz8o24fUw6DMxZxFGrD6e/W42p79fgu1q35p+lNcvh6b02rKl0oc0r4o3yNnx2orPM4ggniPB6eaC3hCiKuOVrfr+KRqe25/64zYtb1zdhcwS/l1bP77djdUVnMOWw1YevNZbJEEKSA1sSkmGUByIslGFBSMToLUIIIXFW0ebjTjwwCIGd1P+2l4aoeXBKFgosnUGKfI0ZFpWcaQW9zZ5GD76tcUl212PxyXEH+r9WpXj9zGJpzXGlhmkWALDssEOWInz/DybJwjSZ+Zi/r14Q8Lczs1F7YwnWXFiIAekGpHBOvo/bfChr8eK29Y2agzNaS0lWHJP2gfh7SKPUw63y52XFMSfu+rYZS9Y3KQbzjnIyM3jWVbrCNiIVRRFbal34vkEe1LhtfRNu/KoBVZzXT3mL/BgOcS4jhCQvq0rTzSDKsCAkchSwIISQOOPt9AKBZpsDXq/CFxoWrGnMSc2cEhO0NBL/9x4bGp29N2gxYVk1Zn5Ui/mf1OPnG5rj8pgPbGtV7S+QzZx0bq/TtsN+WOF1sL6H7JyzsYbg608IKQNR2xk81OrDdxr/Vu4oyx+CwYZWtx8NClM4nt9vx1uHHNzrAGBPk0fTz2rSMOXjrm9bcN7Kepy1vI57/fJjTvxlqzz7ijeulW0iSghJDg6viM01Llk/KjbDIpMyLAiJC3qLEEJInNVrTDFXw5aADEo3YPn8fE33/dfu8BkcPdFXFU4ctnYGY/73Q1tcpqKUh+lHMJXp6r6vWdvOt9IIzp4yyYU9fF7AzCCoR9HePcwPFHj9InY1uNHo9MEvirKfpZWz/Y6xvOf2NmoLWNjClK00OH14gTPelPUOJ3jCyzBZccwZtywiQkh81DkCZW/nf1KPCe/VSD7P1caaBlGGBSGRo4AFIYTEWTzGXvJOamYUm/DUzOyw9/2qoneOT3xijzwQo2XXW43HL4YtR5hZbJL8WylzguWKdhWeIG6fGFGwhN30551Th9sZ/PCIQzYe1esXccEn9Zi9vA4T3qvBtjptAQMepw+4eW0jJrxXE/Vj7NEYsGgKExQJbfAZKaUGm3tjeExCSPw9sceGuvbPgpN2H14t6wxSamm6yc+woIAFIWooYEEIIXHGG6+oJpsZffbuvDzF2143IhWPnpmFS4da8NCULLw2Xr5bu7fJK1sk9gY76uPf7FBtTCYQWJAPyzRILjtq9XJT+LU+drNb7NKdc6dXxH3bWlD4aiUGv1GFX2/kN58URRFfnHTir1tbsKXWJethoeNkU/B6WISqc/pxgumrsrrChS3tpSLNbhEP7ZBO0zHrgWtGpEInABPzjZg/0Kz6M94/olzuocURq091VK3N48f2Oje+b1APbCw9GD67QonSy7BGY78UQkjkfH4Rf93agtkf1eL+bS2avjefZALnT+7t/Herh82w0DbWNEVbT21C+ixD+JsQQgiJRKQBi8Uj02DUC1hb4cJ5A804t79J8baCIOCW0em4ZXTg3wcPiphUYMRWZpe60eWXNO3sqY7bvHhkhxWCALS45X9XX4zVFU6VLAi9ADw5IwdZKTrkm3UdZQcef2BnbUiG+leoUk+FD486sOZNJ5aenYs5JeqL8ViJoogLPq3D9vrO18crB9vw09HpODXXKLntmkoXrviiAQDw7D47CizSPQ1+hoWAFB2glrhR5/BjSEbnv98oly7s1zCjRk16Ac/MysEzs3IgiiK8IjB/ZR221UefiaFGRKCkJJ2TLnLc5sXZy+sUn8tQyxTKX7RQCoBVOyhgQUiirDjm7Cih3NXowfj8FFw42BLmXlK1Dn/H+zf0u18nAOmcTEkL57JwpXWE9HWUYUEIIXHWpnHsZdBVI1LxhzMy8fnCAvxuXAZ3J1uJIAAr5hfILq9x9Iw+CeH8eE0j3vyhDW+Ut3GvZ7MAIsULLhl1wHOzc7D9siJcPSIVADCcybLQUhZSp/IctLhF3Lu1VfH6eClr8UqCFUFv/SD/ey4/2rngdvhEHGemaiglU6RyTsBDHbdJ/1amMFkZoenRgiDAqBOw+sJC1NxYgm2XFuGSIZEtKLSwKrxnH9zeqilYEamfrpNOUFFK8KjqA1N/COkuf9wibYAbOnWIRxRF8D69hv+vCjbmMyTNIEgaFAfxMiyo6SYh6ugtQgghcWbzal/g/Ht6NkZlG8PfUIXFIGAGM3qzrhfszNo9fu5iO1Ss7ULePSRfuK+5sBBXDk/F4JAMCrYs5JCGgEVjmIXu9w2ehJfuNCscg5cT6PmqUn0qhVKZNS8zIdSWWnegsWb77xqujIRT9g0gEOgYnmXAlcPjH7Co5pReiCLwtsJ0kVSDgNn9lDOhwll22IEN1Z0lTko9LCooYEFIwrDjx8ua1b9vah1+7kSpFreIt5jvkhSFD0xehkW4z0RC+joKWBBCSJy0uP247etGPLtPWy371MIU3DQyLS4/u4gp/+gNGRbsjhVPLH0tnV4RD+6Q76ixpRIAMCRD+vet0tBbIFyDSxFAZYJ7FCgdAu/v1j9NvYRIr5D5Ey7D4r/77RjyRhVOfbcaayqcYTOQwmUYjc4JH+C7KsKgxq1fy/t6HGpTPg6jDhiUHlvJ1T++73ztKcU42f4fhJDEUWt++dkJB0a+Xa14/f/bLM3WUMrM4mVYhPkIJaTPo4AFIYTEybP7bHiLsyM7Noff68AQx0/gArP0wXpDhoWmgEUMGQo/Xtsou2xMNv+5KmDGzKqVewCBngRKZQahylsSNwXC4xcVd+jrOcffGGYKhtIm4EENv0OrR0RVmx83rWnExhr1TI6TYRbp/VLVAwXXjkgN21+E1ejyS0o0AGB7i/LP+dEgS9hATTjNIdEkXsYLAJy09fz3MemZPH4Rr5fb8dYPbZqaDPc0bZwood0rovjVCvxhS7Mk6+rxXVZc/aX8+yIaZj3QP+QzbHimnls6QgjpRAELQgiJk4c5u/UAMLWQnzoez0ZbRcwi7qNjsU1OSAbWcPNGEX2Ghcsn4rMT8vGv5ytMpMhnA0JhFvdax4fubYpvI8myZg8WflqHU9+pRsHSSixZz58IwssQCderIR6T91o9ImpjzP4x6QVkGvkH89zsHDw5IxvpCtersTH1RbVu5cf457Tofkao0HsrvdRP2H2yQAohXeFn65rwiw3NuG19E/KXVmJnAqY0dafDrfxgoNMHPL3Xjonv1WBvowe7Gty4d1v8+g0JgoBHp2Uh36xDgVmHh6eEH1VOSF9HAQtCCEmw0/P4KezxbLTFZlhsrfPgzfLoxywmg+Uagi7RNt1UCij86rQM7uVswCJcNsK2Om2BiH1N8c2w+O2mZmyodofNUmADFn5R7JiCokSpVCOLM7ov0fLM8jfPZUMtuHJ4KvQ6Aa4oEhNamIBNo0rAwmwQwvbuCDWYUz4SGmxT2sFu84poSkDTT0JYoiiivMWDqjYfnF4RHxyVfv6et7IOB8P0eEhmO+vdWHa4Da3tn/0/XaeeMWH3ini5zK75szwSFwyy4Idr+qH8mn44L8zYZkIIBSwIISThiiz8j1p9PLasO36GfEH0JmcSRE/y4ZHwAYsI+ptK8AIWc0pMyDbxnyu27tgdJkX64+PSYx+YrseUghSk66X32x/nDIuN1dp2QY/ZfJJF8q6G8MehtD7/04RMTT9TKzb4xsN7ni4Y1Hnir5QpE/TW3FzZZaFjc/2iiBW1/LKS348PBLVylLqDcrw8J1dWpx4aiFBLJmKntRCSCL/d1IzJ79di9NvVKH6tUna92w98WaFezpWMvql2IeflCsxZUYefrmvC7OW12NvowYHm8MHiDdUuNGnMliOEJA4FLAghJMGUmhnGmFEuUcgJimzQuHhNVlo6p2stCTlu80p2B0MXp0FsFkUoPXOV2nQSURSxiik3+ff0bHy+sADLJkoDGQea4zcpJNLSgSu/aOj4/9cVxsaGUuoLUcgJlsXiv7Nzwt7muFW+iP9/9wtJAAAgAElEQVTRoM5Gm6fmGDoabxZZdHhyRnZH4PCq4RacP8CMqYXSyTrBnhI+v4ib1/JLaeb1N+H2MekAgByF4BbvVTuhIAUHri6WXBZa8qQ0JQQI39ODkFhVtfnwcln4z4AN1T0rYFHr8OHq1Q2SyR5HrT7M+KhW0/11gvKkJUJI14msKxUhhJCIKY0tNcQxw4I3sSCCDeCklKahqaGWkpBXD9rx62+a4ReBO05Nx/2Ts7gZFjeUpio+Bts9Xi3I4PFLp7ToBWBmcaCPSV5KIDASLL9w+oCvq1w4u3/sacGRbgR+VemCzeNHulEHW5h+IWkGAcUKmUKmOMYrBqTpcY6GvwWv34Y55PUiCAKenZWDByZnIc0oINWgw+XDUmH1+DsCLGxZyTfVLswoNuHtQ2348Kg8u+erhQWYUNAZ5FAKWJyeZ8T3nIyVDCZFpS0k6qUWeGumHV6SYFozvUozk3/Z4PCK2NngxvBMAx7daUUrJzitVa3DH3VJ1s1xmgBGCOnmDIuNGzfi6quvxujRo5GdnY033nhDcv2SJUuQnZ0t+W/u3LmS27hcLtx1110YNmwYSkpKcPXVV6OioqIrfw1CCFH0+jm5SNEL3OBBPHtY5Jr1uGWU9ARJROS77rFqcPrwfYM7Ll3lw/VUALRlWPzjeyuCh/PUHhtqHT5ZvwIAmN2P3xwVkI+dU1vfO5mDSjUIkmyRMcxYzpfL4tNrxKGW9qEg2Ak/XDZLrlmn2MmeN6ZvVLYB/5kZeTO5cQr9Xlh3np4u+feto+WLA0EQUGDRI7V9HI/FIEiyQc4pkT7fb/zQBr8oYlcjf/FmZl4EBQoBnAcmZ0n+/bP2Y0vRSSetePyBHeC/bm1RzaKwxrDgIkSLTI3RbUcsc6S7gMcv4kef1mHBJ/UY/XY1nj8Q22er0ytqbqDMWjyKAhaExEu3BizsdjvGjBmDRx55BBYLf2b6nDlzUFZW1vHfu+++K7n+7rvvxooVK/Diiy/ik08+gdVqxVVXXQWfj1IoCSHdb3j7jlQap/5DQ8VDRB6dJl0gevzSXdxE21nvxsT3anDW8jr86NP6mIMWjRp2tsL1sHB4RUkPABHA3Ztb0MwsAm8oTVUdLcdmwyiNoQQCE0hCpTD39TP3jVfGsT2agEV7Jki4XUilqRxAYGoHq8iix3WlaSi7qhgvnhW+xCPojPyU8DcCcF1pWkezz+wUAb85nd8sVc3lw1Il2SFHrT4ctfrw7D7+IocNzAxX2G2eXpSCu8ZlIN0gYFKBEXecGgiuCIIgyxqa/mEt/rXbpnqcWqblEBILrRN6owmKdqXNtW5srw8EHOMRW3H6RE3jtXkUErAIIVHo1rfTeeedh3vuuQeLFi2CTqeQamoyoaioqOO/nJzOE5+Wlha89tpruO+++3D22Wdj/Pjx+O9//4u9e/di7dq1XfRbEEKIsmDdfL5ZnjdfHeN4Rx42bb8rJww8vKO1IxCwudaN1UyDNlEU8fguK2Z8WIO7NjWr1u27fCKszIkiL8ATriSEl9r/3hEH7vmuRXKZUnq/0s9WC5SwGRbsQnfJGGl2wDfVLtQ6Yg+yO6LoQBrMsAi3i5ilsgNr4pQ2TW4vnShK1eOyYfxSm4GcMqbxGjMshmUa8O0lRXjz3Fxsu6wIxamR16Vkm3Q4JUv68ypUMh3YwIxJLyCP87rR6wT8cUImTt5Qgi8XFmJgemdgI5VZGWrJInr1YM9unkuSn9bFfbJnWJyIskHto2dm4ZlZOfj+8iLJ+GavKP9svHq4BbP7mXDJEP5Ga5CWHkyEEG2SPv63adMmjBgxAhMnTsQvf/lL1NXVdVy3c+dOeDwenHPOOR2XDRgwACNHjsTmzZu743AJIUQiuBAextmNXVsZ/wZm7PSE18rbsEchxT3eVp2U/j7vHpYutN474sC921qxt8mL5w/Y8d99yjvLbHZFgVmHDYsKZbcLt0Y/1MLvBG9jdgrVFuQAp4dFSKDE7ROxrtKFk7bAz2IzLNgeD2wTVqtHxIRlNfgmxoZ2UWVYtAcswmWzqAYsOCfms/pJMyVOzZUHIl7gNNfUWhICAP1S9bhgkAV5nGCgVuyvdbhVeXIAr7doUWpkp1FswIJl1AE3nSIN8Jy0+9DopKxRkjhas+G6MmMvGtH0bdp6aSFuGZ2Oa0akYnCGQRZgZvvl3D42Hcvn5+OZWTlQa0PFfmcQQqKX1N1z5s6diwsvvBCDBw/G8ePH8cADD+Ciiy7C2rVrYTKZUFtbC71ej7y8PMn9CgoKUFur3AG4vLw80YdOGPQ371no+YpcYI0qXWickubHDz/8AADI9xsBSBdjM3N8cflbhz6G2W8C0Lmy+ttOK/6xsxV/H+3G7LzELXqWVRkASBepTpsV5eWdkyj+s0t6bEv3NeN8UzX38Z4/Ln28dJ0XhvqjODcvBasbOr+6KqqqUO5R/r1O1sv/7jzO5nqUl/OPBQAa3EDo8+vyBJ67MpuA3+83odKlg0kn4smxLmQYRACdu2+CzyN5jhorj0uuBwIBlD9trMF/T48+aPFtvR5AZ1+G8Zk+/G6YGzfsNEOEAB1EnJ3nk/z99lc2oNxUjWqbGWp7GDqXDeXljdzrKtsE2e+T23oC5SGVFZfl6bG/MQU+CNALIl4f70Ru63HMzU/Bl/WB45me40PzycNojvg3j57PLX1N7jpRB6XXy8mjh9HABC0yROn9AfXPT6Nf/e/8/GlO1LtdCH0eAWDFrqOYniMPKvlEYF2DHiadiMnZ/h7faDdW9N0VnWMtOgAamt222hU/B6IR7+frZK30MzCc+QVeiLVHUR6yZGjzSr/HjzITiRoqjqO8MRC4GWw244iD/6arO34YbUm9yooMvbd6lp74fJWWlipel9Rvpcsuu6zj/8eOHYvx48fjtNNOw6pVq3DRRRcp3k8URdVaZLU/CIm/8vJy+pv3IPR8Rcfm8QMbqySXPTKjAKXtEw8m++14o0K6FLtrSiFKS2KbDsE+X8XHGoBW6UhNHwTcd8iMY2eWxPSzlGysduFvG+pllxfkZKK0tHMXvXVXNYDOk79yuw7p/YehH5PO7/GLeG5DpeSyfhlmlJYORFZFI9DQWeZRUFSMUoWSAwBIqW0CED6lfmhJIUpLlZuk5Tl9wJbOgIZf0KH/0OGY87/qjswGl1/Al205uG10OrCjMxsww2JCaenAwO9cXo7TTxkKbJMHR7a36mN6773Q0AygM0owbUAmFp6Rjc9KXFhT6cKcEhOOWn1Yvb5zZOdRXxpGjMhH86ZKziN2GpCXhdJSfhPNoX4R+fuqO8obFg0x47RR/SW3+U0pcNFpXjh8IkZnG6Bv3318baiIl8rs8PtF/HhUmmySRqJlHqoHWjuDRC+eUA5ujTllhKyXye8sTlwRMh722hGpKC3tz961w+iTjSizy8uUgMCkn4snDEeL24/f7Zd+luQVlaB0sDwF/davG/H2ocDjzSxOwfL5+dCpnP/0Vs0uP+75+jgys3Lw69PTuSV4RFlVlQvYLf8MZwkpFpSWDorLz0zEuUaOaAcOykOe90/KxAm7D8/tl/anycjIQGlprvTGG9Qb959aOhQF7elWZ1Q04sgR+fv5htJUjB+t/DnQ09B5Yc/SG5+vpA5YsPr164eSkhIcPnwYAFBYWAifz4eGhgbk5+d33K6+vh7Tp0/vrsMkhPRBbN+CzBRBMp5xRJb843YsJ00+VsFGhKyWBE4a+MvWFu7llW2dO8JP7LbiUKs8E2JPo0cWsDjJqUMOltawGfU+EdhQ7cIxqxcLB1tkpQtaU5h5ZQ2h2IVqs1vE03vtsjKMY1Yf1lZJsyTYY8hM0KJ8A1NSEhylOrXIhKlFgf8vTpWWPGyqceOYzQdXmOQbtSkCBp2Ad+fl4dHvrcgx6fDnCZnc2w3nvAcsBgE/H5vOuXXXiCQjgTeG+Nz+Jpzb34TVFS7kmXQdE0GUnJZr5PZVAYArhwcCb1kpOggINIgNCu3n4vaJ+Ou2Vvxnr7SkakO1G7saPBivsXFpb3Lr+iasOmkETtqwrd6NTy8o6O5D6lHUxjSHiqbsrCsplQjecVqgKS8bsGD7JGmRHvL5PSbHiPc5AYsnZkQ+IYkQoqxHBSwaGhpQVVWFoqIiAMD48eNhNBqxZs0aXHHFFQCAiooKlJWVYerUqd15qISQPobtnp7OrKxHcHpYJGLhaumGRl9b6/g9MladcOLJ3VZcV5qK+7a1cm9zoNmDeQOkWSblnL4TwfNpds24tMyOb2rcAIAndtvwzcWFHbv3O+vdWHaYvzhkhWuQZuA8VQ9sl/9Om2vd2Fzrllx2kPl9UvQCjDr+aFSPX4yq9rnV7cfeps6fIwCYXixPjR6SYcCYHAP2td/WLwJXf9kgux0r3HTcM/JT8Oa5eeo3SkK8IEQkdIKAZfPycKjViwKLPmwvFF4vj6DrSzszhRaPTMNLIeNuW0Ma//1yYxPeOsR/XdcmoJFvT7DqRGdW2aYaN6wef5dn6/RkWuMQyT4lhNfIef7Azu8Xkx6S4Kza+5FHJwChyTtjc+Tf61cMs6hmeRNCItetn+Y2mw27du3Crl274Pf7cfLkSezatQsnTpyAzWbDn/70J2zZsgXHjh3D+vXrcfXVV6OgoAALFy4EAGRlZeGGG27APffcg7Vr1+L777/HrbfeirFjx2LOnDnd+asRQvqYcJMhiiw6lIbsMI/JMSSkizh7HDxWjx9HrV6I4VahGoR7jD9vbcX921sVT4jLmuXBifs4gYDg307PLDCDwQoAKGvxIm9pJS7/vB476t1Y8En4FOcg3qSLUIY4n4AqTaq0RzlCj50GU5KqV5x8csEgaWnBAeY5mJgvP4nPM/fOxZ/WDIv7J/GzRoDAuNIRWcawwQogkGHBMzhdjyEZnZ8PmUymVDBgUdbsUQxWAMnfFDEReJOQon0f9UXvH26TlDWpSfaABe/1/8jUrI7/f2JGZ4miXggEBiORbhQkwQheluTvx0c+YpkQoq5bz0B27NiB2bNnY/bs2XA4HHj44Ycxe/ZsPPTQQ9Dr9di3bx+uvfZaTJo0CUuWLMGIESPw+eefIyOj88PgoYcewsKFC7F48WLMnz8faWlpeOutt6DXU/0iIaTrsCdyZibDQhAEPD0zB5MKjJhSkIKnZsgnJMSD0kI4cJ2IPY0eTHyvBuOX1eCKLxrgjzFowU7b4Hm5TLmHRFmzPDuDN9XkkqGBRbaWBeaXFS6cvaIuohF84UtCND+UDC8AoCTaBaeb2VlkJ5OEWjhIvW9KvkWP+ydLF+hzB2hvZNeTqGVYDE/147YxaXh6ZjZ+cWp8ylb6pepQwAn+nFUi/ftmMy/04BSXHfXqE3+21btVr+9tttS6MPTNKtnlNrUPQtKhyeXHkg1NitdfNlQa3Gzzde3fdV+TB5esqseiz+qxq0H9te0XA6VSoX55arokEHjlMAuenpmNW0an4bMLCmQTmwDgtjHKQYwM5otgYJoeA0Ieo3+qnptNSQiJTbe+q2bNmoXmZuV+4O+//37YxzCbzXj00Ufx6KOPxvPQCCEkImxmA680Y3JhCr5cKB/NGU+8lNigb6pd+PCooyNt/MsKFz474ZTtuEeiNcbeGGXNXkmj5Jo2fjOFM9rr8kdlx7/vB6C+wAcCqf86obM0JRJ3n6G8O89q8/rBTp3gOdDswU/XNaGmzYe7z8jElEJp3wK1AMy4PCMKLTrF8oECsw5LxqTD6hGxs96Nq4enojQrMX/37qa2Y/zzIW7cPDW+teiCIODPEzPxy43Scx/2+Spi+rrUtD9XFXb1ZiNP7rHhr5My+0zjzWf22rmXR9OboC96o9zO7V9zbn8TnpudA4tBwHshPRq6OsPi5xuaOoJ0t61vwsZFhYrlFhuq5QENtipIEARcW5qGa1X6Ef5xQiae3cd/XVk4GxH/nJaNOzY2wS8Cj07LonIQQhKgd+Z4EkJIF3MwlQ1shkVXUdtYvPLLBlm2w/P7+SdmWllj3Mls9YhoDgl6fFenvos2sSD6hoLDMpQDAeEyLIDoghUAkMspzbhmBH+ySTDDYke9G3/+rgUfHnFwy27u3NSMPY0e1Dn9+L/NzahzSFcdauVGgiBgaIbyfkWBWQeDTsAfzsjEO/PycanKFJaejpfRMixDjztPT8dMzhjReLjxFPkO7qB06WuzX6r0NVPV5oPbJ4YNWADAZyecYW/TG+ysd+MDhQamsQZS+4rgZB9WVooOeWa9rLTR6UPMWXla+UVRklG0r8mr2vRz2WF5Jl+TK/JjzTDq8PCULO51tQ75+++8gWaUXd0P5df0iyn4TwhRRgELQgiJgyNWacQiv5tq/q8rVV5c8nbSajgnYJEIbQYYrdD07e1hUtqHR5luO7e/Ce+fn694vZaARbTSjfLHvmtcBrefQZtXxEmbF+etrMOTe2z48dpGfHRUugBtdPqwMWQ30eMH9jZJSwXC9eRgJ7OEyrf0nZJKO2eswPbLi/HniVlI5Ebpr0/rLDEx6YGrh0vft8XM87Opxo3i1yoljTiVaG0025PtanBj7sd1itdvrnUpXkc6KQV2gvF2nSDIsgW7KsuC93MaOf1KgMBY21cPygMW0ZYGsT1kgmb1652lcYQkOwpYEEJIHOxl+i6MzemeFPr5A82YVqQ9C8EdW7xClnp9Cmd0ZTgbqt0dO1e8kaYPhPRTyDHpkK1wMqlmfF4KSlQW6eEW+LFI50wrGJZpwPpFhZjD9C5o84p465BDkinzhy3S8gHeeFg2NT4lTMyhVOV5ylNo1tkbHWqVN33tCr89PQO3jErD2SUmvHp2HgqYIFExJ2ikNcOnSqGsSqs2rx8Pbm/FLzY0YV+Tes+M7vLxcafqZIuHdli77mB6sBaFgHNoc2Mj81LsqvYgvAbSh0ImLm2sduGdQ21o8/qxroofoIp2DKvShJlfndZ9I5gJ6cuoMwwhhMTBHubEPtJxafFi0An4eH4+8pZWarx9bD+PTSkutOhwsCWyx1iyvglpBgHvzsuTLbbyTDpZCv3wTAO2hWk+yEo3CqplErH+HQDg0DXFuPXrJnxZIT15LrQoP3gGk32x9KBdllFR2Sb9Gz+2S74Yq2D+buEyRqarBLXGR9AktKe7bUw6Hg5Z3F4ypGtSujNTdHh0mnJ/jKwUAWZ9IAVfzcZFhZjxUa3kslgmhYiiiJLXOptYvnfYgaPX9UtoBlI0ajUEZfrSaNPjNi/+sLkFHr+IP0/M0vz9o9SYOPTpTtEJADpvp9YnKZ54r+Mn99hwdn8zXj1ol/WB4Yk2G4SXYfHA5ExMKaQMC0K6Q9/4JCeEkAQSRTFpMiyAwO6YUg2u7LYxrkOOMaUwajv3auxeEc/vt6OaaQT50fx8ZDITE4ZFURbCPkaoNIOAIg1lELeOVu4ef1Y/E/LMetm0ByDMJArmd2GDFUF/2NKMGR/W4KY1DZp6FKSEyRhRKvs4f6A5YY1Nk9GiIZaOoFGGUcCDGt83iSYIgqwshGdUtgFfLSyQXBZLwGJzrbQky+ETsboiuXpiWD1+vMJJ/2ftbkjO7JB48/pFXP55Az4+7sSqky4sWa889cPrFyU9cfYrZNCEtmBiYz5xqALUpIHTX6OhvSRES7ACiP69wAt0DUynPV5Cugu9+wghJEYVdh9aQ0ojMlMEWRO9rnbNiFSsq3KFXdzqYyzUP2qV7nSOjGGx+8FRhyzjgG0+CABDowhYBBtfTitKwaYa6aLs3kmZmpqk/nVSFprcfrxzSN4jIHjc15em4fFdto4T65+pBDkAYIzGwNbT7SUfe5u0lTCwPVVYvB4rD07JwuKRvbfBJs+obCPWLyrEtjo3ZvUzoTCJ+ncUW/Sy9xdLrxNQwGTw2CPM2a91+NDq9mN4pgEvHJD3yKhTmCbTHZpdfgzhjDHl2dngwfTi3rsjvuxwGx7daUWVwyfpRbG70QObxy8pRfOLIm5e24TPTjhQZNHjhbNyYff6cUTh9RU6ZcbIBD+7KsPid9/KgxK7Gz1oVuhjwTN/oPoIZyW8DAteLyJCSNeggAUhhMTIxuziFFv03T7aLNukw1tz85D9coXq7WIthTjKLIy1zKAflW3AgWb+gjq0J4ZRx5+wwavvDyej/QT0vklZuHhVPexeEaOzDVhzYaHmiS5mg4DnZudiRpEdv/pGejKd3X6cOSYdXj83F8/staF/mh53jctQfUytAYtIKf19g3h/1yVj0vrMOMxQQzIMGKIyNaW76MO8N4PvtTTm9RtJ3f7nJ5y4aU0jHD4R15WmoppTauHuogWqFm8fCp9ZEbSzQb2Bb0/W5PLjjg3NiiUdqytcKDDrMK0oBd83eDBnRWeD0mM2H/70XQsGpCl/jq6r6gx0s9laXRGwONTixdY6efaHX4TmgFW/VB1uGqkeMFbCy7BQ64FECEms5PuGJoSQHoYdNBCPfghdhd09i9QOppfEEJXRoUBgOkJ2ig73bmvVdGy8wI85ivPG4FMyuTAFWy8rwjGrF2fkp0RVm89L1b9gUOdO3rQiE6YVadvZjaZJqRaTC9QDISa9gIuHWPBh+1jIK4dZ+mSwIpmd298smQbDWnZeHgAglfnAiSQN/pcbmzoWvW+Ut6GI02+F1wi3u3wfQZlHWZigXU+2p9GjGKwAgJvWNAIAfnVqOr6qlDek3FbnxuZa2cUdhoUE8GQlIV3wcvj8ZGxlSHNKTHjxrBzkRNlAmM30A4CB3Zw1SUhfRgELQgiJkZfZcTIk0cLv3P4mrK5QHvEnRrlZ5vWLuPyLBslJs4Dwdb63jUmX1ckrUVp4WTRmRISaWNDZZLJfql51rGc404tTkGEUOrJBrhhmwYIoU4/VGoHG4henqmd2AMBzs3NwdokJghAoISLJ5aZTUnGfQmBvfJ6xIyvEpAd0QucUEY8/sAseLhjp8YuynjE1nPKPgy3Js/BvCNeFNERTBKUDPY3W3+3fe2zcy8PFtG4Z3TkNoztKQppibJRx5TAL8qKJbLdLM+owNsfQUYI3qcDInfZECOka9O4jhJAYsRtdyZRh8ZvT1Reu0aZ7v3qwDWuZnbv+aXqY9ILqWNUiiy7qXa8gc4SL/Hn9TapNNyOVYdRh1Y8K8LPRafj71Cw8Oyun20qA7p2YKfn3nBITnp+dg0Uapl2k6AXcNDINN56SFnOmDYm/PLMeJ67vh1fm5MquG5ndGRgUBEFeFuIJ/74u1xiIONgSv+aVTq+IWodP0vgxEmkRLBopYBG980ICsOz32eEuGAXcpuH1qyYen/cvzsnF/IFmXDDIjKdn5sT8eISQ6FGGBSGExCiZMyxmFptQeUM/DH+zmptCHG3A4reb5A3RitsbZN47MRO3fN2E45xUckEQNAcshmfyd8gizbD4y6T4T34Yk2PE389UHkvZFUZkGvCr09Jxep4RR60+nNvfhMFJ2IuBRC/DqMPFQy3AWunlbNAuzSBI+r+0eUVkh6lKYicbKTli9cHlE2MebXqg2YPLP2/ASbsP4/KMeGpmDk6LcPyzi/kMG5FpwJcLC/Dn71rwWrm0v4XVI2rKNOmJEhmw+Mkoad+H7UzZ383rmnDJUEtCg7Rq5S5aqPXn0GpUthFvzc2L+XEIIbFLon1AQgjpmdj02nDN8rpaqkGH/87m7xBFOFAAgPIovLT2rbipRSbsuqIY9zC7/0HZnA7sPI8qBAR4Y0L7p+oVG36WcCaNJLuNiwrD3qbAooMgCDinvxk3j0qjYEUfwq7nUpkgXoPKglYURfhFEXs0Biz8InAoDrvqz+2z46Q9EMT8vsGDWR/V4g9btI2nDGIDFg9NyUK2SYffjcuAAPkiN5KJEj1JYwJ/Ly0jub+uSmxD01hG8wLo9ildhJD46nlncYQQkmTYppsJaksQk9AU8lBa08JDfXRUPtYTkGc+KGVv55v10JKxe05/fl8IXjb53quKsfWyIu7OWqwlKIk2o1haQnPeABPG5hoRbmM40tIY0nuwC3f2vXf7+ibu/U7YvJj5US1yX6lU7G/AczAODSxfKpOPTH1hvx3WCKKmTub3DmZ9DM4w4JlT5b16IpmY0pMkMmCRxxl5zHr1oPy5jKc29ks1AgPS9En/mU8IiQy9owkhJEZ+ZgXNywDobiOzjTirHz9HnDfKUI1SDTO7y3vlsFTJojuYamwxCFgcZtycWkqv2hIkuIMbqrtHzIbzwOQsBI9QAPDHCYHMlBcUsmKCKGDRd7EL9yNW6et+d6MHnxyXBxYf3mHtaCQYibI49rEI5fYDDU7+4rTZ5ceP1zRizNtV+OOWFoiiKJtQEdpXcWK2H6OZwKyWXh49USJLQtI5EzJYSll28eKIIdA0b4Ap6T/zCSGRoYAFIYREwOkVseKYA2srnR2N49hzqyiGWHSJd+fl4TnOInhfhCefVQoBDraXR1GqHo9Pz8aITAPm9TfhzpAGoI9MzcKXCwsU/1a5KjtkSqUfPDmmJH0yQpyRn4KPF+TjrnEZ+OSCfIzLC2RcXDosFc/MysGPT0nFxwvyZffrrbvHJDz2PcAGMADg2tWNcIa8Rh7Y1oo3f2iT3U6LQwmcFNLKmQjh84u4/qsGfHjUgco2P/6z14a1lS7FDIsgNmgaa2lBsookwyLfrMOLZ+XgzxP4JXostoErT6I/e9jHf3BKFiYXGDEmx8D9Dgs1VyEzjxDSc1HAghBCNHL5RJy9ohY3fNWIi1c14N6tgZGD7EJdn4QZFkBgKsSVw+XjKysjzLBgRyEG8QIZN56Shq2XFeHd8/JREpI1IQgCJhWkYEohf6KIWkpvSZq+YwynAOCJGcrNL3PiOB0kkWYUm/DHCZmYViTNgrlmRCoen5GDmcXy7JiaCJ830nP9Z2bna1wvAD8bky65/sph/Kkw39UFeg00u/x4fLc16p/fEuOYSTVWThbEqpNObKiW9klYXeGSlcKED1j0zh4WvL8Zz21j0lB2VURjEcwAACAASURBVDEuG5aKwRna+jqw4zt5n6+JDgS5med5Ur4RXywsxDcXF+HK4am4f3KmYrB7dkmYbrOEkB6nZ5zJEUJIEvjshBP7Q2q5n91vg8Mr9pgMi6BfnSpd7FRyyijU8HZEA48T+eKAXWAEhatBfnpmNr5cWIDNlxTixlOUy0syekjAIhrVDgpY9BXXjEjFEzOycfPINHyyIB/9UqWLz5sUSqxO2LzYVufGzI9qZZ9TkYjHjvpghUaIvKyPrXXypo5P7bWFz7BgFtu9NQuJXdDz3DMxE49Mze4IoPdXKLMbl9fZZLPQosMopqzmhtJAgCBUov+ubFuTFOZ5vuPUDPxwTT+cvL4f8kK+Ky4abEZGBKNvCSE9A72rCSEkRKvbj7u+bcbFq+qx4pi0Bnx3g7R0wuUDvqlxwcecXLFz65NNCXPiGmnAwqawu8c2j9QiV6HBm1pJCNCZoXFKtrSjfZFFej+lDI6eiO18H0lpDOnZdIKAG09Jwz+nZ2NqkXwHeVqRCXM4O8v7m7049+M6bm+XoFyTDjU3luDYdf1w5TALbihNxfNM2n08FqhsdkTQG+XygEW9Ql+LY8yo5Aym3wJbztCTe1g0On34/ISTm7mm9LcEAp+By+fn47chJXgAvy/Qb09Px7+nZ+O0XCOGZ+rxxIxsWQ8mQRBwO5PR0+YVZb2b4okdt83rC5Vt0iHdqMM78/Jw/kAzritN7fZR04SQxKCznT7upM0Ljx8YSie+hAAA/rXLiuf3Bzqgr610YU6JCe/Ny4NeJ+CwVV7H/VWFC2fkSxfN+iRv+MXuzgZPiEVRxNdVLrR6RMwfaIYx5CSxwelDmkGHFD1/8SIAuI05qdViZJYRgLw5YLS9Jx6ZmoXFawMTElJ0wO+Yk/ae7MkZ2Vi0qqHj33eO6z2/G4nduFwj1lZKJ2U8qWESyMziFJj0Akx6Ac+dlQtA3tdGKUgZzsZqF25b34QTNvWgqNsnSnbRKzQGUTOZDCq2YWQiS1kSqbrNh1kf1aKuPXBz3gAT/jY1u+NcjV3QB21YVIixOQZu00n2cx8ArhiWitE5RqwPM0ZZrxNg1gPOkKfF4RWRpqFBZzRkGRYq8euJBSl4e25eQo6DEJIcaJXahy0ts+NX3wRmoF821IIX5+R28xER0v3+tVt6gr+20oVXDtrxk1HpqOOk4H901IHROdKP0mQvCWFTg3c3enDNlw349ISz47L5A814a24eRFHEreub8M4hB/qn6vHSHHnDsztOTcfc/iZMKIg8m4FNPw7KjnIs3SVDUyFAwPZ6N64Ynooizkl6TzW7nwn/ODMLn51w4uz+ZswfSM3lSKfROcbwN+LgZSGxmQrlLV6c+UEN3p6bh8EZ2k8d797cEjZYAQDNbj8KLYH36uYaF1ZXyEeU8rAlIcUKwdieZvlRR0ewAgA+P+nCuqoalF/dDyuOOVDVJg/E3DwyDafmKr8GDDoBUwpSsKW93KYkVYfSLO3PZapBB2dIOmGbV0RadC+5sNiSl5Qk7QtFCOkaFLDoo3bWuzuCFQDw3hEHri8NnAQT0lcpjYq7c1MLfjIqnZumfNLuwwdHpBkCydp0M0hWEtLmR2WbU3LZZyecOG7zotbhxzuHAr9fRZsP929vldyuyKLD/ZOzoj4WpUVWdgy9Jy4easHFQ/lNCHsyQRDw09Hp+OnoyDNZSO+3aIgFt61vivh+Z3JKTHg75weavbh9QxNWLijQ9Lhun4hdjdomEDW7AgGLvY0enP9Jvab78PRnAhZqpTDJjF8GAkz/sJb7O+29slixR0Wox6Zn465NzXD4RNw3KSuiEdypBgGNIXEku1eEtldC5DxMBgm1pSCkb6OPgD7IL4qYs6JOdvkNXzV2w9EQkjy+q5U3egva0+iR7HiFYncDkz3DotCsg17DMe5q8OD1g3bJZWznfi0j8NQoNeKL9XEJ6WssBkHz6MqgHJOA8XnyoKFSM9yN1W40axypub9Z+7jk7fWB275zSPvYVbZ/BRAY4Rmqxd0ze1jwxtQCygGYPI0ZaaflGvHZjwqw7qJCnBXhNA223CaRk0LCNd0khPQtFLDog45Z+V94NoUvnyaXX3EqACG9yXeczvRBb/3QhgaFgAUr2Ztu6nUCii3hd+N2N3oQrhl9rCUXStkoZgpYEBIxtp+OGgHAg5P5u+wWvQCld+CeJm2BiK+rlMs6rmDGsD6+2wqbx49/a+i5EXTvJHlwhv3cUGtOmcwibXKa0gWVb/KRsV3XdJNKQgjp25L8tJokglLkHgg03Qv1791WDHuzCqPersbyo/LGeIT0Joda5U01g1ZXOKH19MyQ5E03AcDmDR982V7nDhuwGKiQIRGJv/IWHrSjRkjEtO5E/2lCJn64phjXlvLHoQqCoPh5p3Wq0INM+VjQb05Lx82jpD/3QLMXd2xo5t7+jlPlJVALB5nxk1Hyy9meFmrnO8ks0mCArgu+c9iAhZ1Ng4gj9qGTfROAEJJY9BHQB6l9EYZeZ/f48ZetrRDbL//NN/yTCUJ6OqvHj99804T3jygH5fY3KwczWD1hM0hLycW2eg98YUbXnabS5E2ri4fI+01QwIKQyJk0fviMyzMizxxdsFGp10+oSrtPMlEi1LWlqZhWZMIsZgzyB5xNkamFKdweOf+ZJW/+C8g/N3pqhkW0U1kSSTYyNpEZFtR0kxASggIWfZBawOKRndaOqPlxprN3g8a6VUJ6mv/us+PlMu210+FYekA5wxgNEwUaXX7sb1IO1PRP1eMGhR3aSPAa/PWEvyEhyUZraUAsTW2bNZSIvsL0vglaMiYNpVmBzx7emE3W36YGghWhvTluPCUVWQrHz2ZY9NSARSKzF6KVyqQ5xLMkpMHpw12bmvGzdY0oa/aAfWhquklI30ZTQvogtS+ZJ/fYcLDZg7fn5YOXYegXxS5JPSSkK71/hB+sGJVtwIEIMiuCtHRr7243j0rDlxpGB6p1+X9hTk7U40dDWTjZFOzCgxASntb3TbYp+vdXuAwLURTx3mH5Z+rTM7MlJSjXjEjFO4fVS02Dny93jsvA7H4mOHyiLDMjFJs04uqZQ0JkG0RrLyzgNksHgAkR9C2JRWoCm27+3+YWLGt/LfBeEwKddxLSp1HMsg9qC1O7vuqkCw6vKEvJC9y3Z+5WEKJmHyeLoF+qDkMzoovpDkpP/liw0g5lJKZxxiFGg5dNoTW1nRDSSev7JieGQGNrmMkbuxo9ONQqjxSwY9O1BHZDM0EmF6Zgdj+T6uKVLR3oqT0satqk52lFqXqc1U/+eZudEvlkmGjJe1jE72+7LEzgihDSt1HAog/SEnTo91olXjogT+mM5xcUIcnAr9CjYXJBCvLMyh+RwzKUT7bj0Ygy0WKtCV48MjVOR9I1DeMI6Qu0Nt3UErC8ZgT/PR4uCHCEE6z49WnpshKQNA15/pkpkX029IYeFm6fKMmwEAAUmHWyXj+zilNw+Np+skBQoqQnaEqIx9/zniNCSNeigEUvpvQloLWZ0ysH5SmdydgIipBY8F7TOSYB/zc+E/kKAYslY9KQpbJD2RMCFkprhfMHaMua+GOCd/XyLfT1REikTBo+esblGWHUELD81WnpGMIJzIYLWDQy5QyXDLHg3knyxplaGv9GGszsiT0sXD4R/9plxW+/acb+Jg9qHdKAT4FFB4NOwDUjUjGxvfyjJFWHh6Zmd2mwl82w2N+sbbytmuo2H6a+XxPz4xBCejc6I+yF9jR6kP1yBQqWViL/lQpZvSl7MjGbk2aoRMsoREJ6klZOA7lNFxdhbK5RsSTkquGpmJjPr6MekqFHRg/oEKa0EztLw+fBgDQ98qOcMKDk0TOzEDyi28akxaVkhZC+Rilz6n/n5mJGcaCk4j8z+RM2WKOyjdhxWRH+d26u5HJnmJ31BmY8CC/oAfCb7caKzbBw+ETZuPZk8+hOK/66rRUvldlxwad1sr5JhZbA389sEPDlwgLsuaIIe68sjsuEpkikMt9ryw47sKvBHdNj/r/NLThsVW80cvvY2Bs7E0J6Njoj7IVmflTb8f9eEfjDlhbJ9WzAYv5AM56ama3psSnDgvQ2rcxr+pQsA4rbU5cvG2bhnmybDQJuG8M/iXpoinwnMRkp1bqXaOjcH0v9u5JbRqfj20sKse6iAjwyVdvnESFEKtUgIIMJBJyaa8SCQRasXFCA5fPzcWoEC11BEJDOLFTDZViw/QhyFT4vwmV5LFH4jFVj0kuzTDz+5O+99Y9d1o7/b3KJuPyLBsn1xSHZZoIgYEC6oVuaUPIyYv692xb147n8wIecUbahxuYYcPuY9Kh/BiGkd6CARR/wvx/a4Aj5wmYzLnJNOlynUKvKoh4WpLdZcUx6whR6sp9u1OHRM+WLZ7NewIgs+Un/hYPNuGCQRXZ5MjIqxCXU+nYEnT8wMTXTI7ONGJenPAGAEKLOoBNw+9jOBd75A81Yf1FBTI8ZSV+IZpcfZS3SDIEiDUFQ1pAMPR6MIvgrCALymADJqhNO/HRdI4a+WYkzllVjXWX46UjJJJq/XyIYOF8N7x2Jvlnm6nr13+vNc3OxflEhBvSAJtaEkMSigEUvo9S34pPjnV8qvICFIAhYNi8v7OPbqSSEhOH0iqh1+JI+DTeIfT+w5+KFnF4KwRP41Qs7FwICgDtPz4j78SWKUup4uOyJrBQBPx9LO16EJKu7z8jEposLsfXSQrw9Ny/m3XizQV5mweMXRaw8Ll/ADsuMfMG57dKiqPszsJ9hN69rwrLDDjS5RByx+vDbTU1RPW4i2D3hz6mKk6SfTyGnDDA7wqaooaqc6ve9YJCFGjITQgAAFLbsZRwKqY/vHXFgYLoeXj9Q65B+QQa/3OcOCL9raqUMC6Jif5MHV3zRgJN2Hy4abMbSs3OTfn46my7MpkvzeikEAxYTC1KwfH4+1lQ4cU5/M8Yr9LVIRkotIoarLC5uG5OGxSPTElISQgiJn9E58etvwK5T2QyL72rdmLeyTvH+o7IjP9XUxzDFKM+sByAfVR10qNUHh1fkjlPuahV29f4NAFBkSY4Mi1n9TNAJQOi+WLNbhNcvwhDh89Xq9uP9auXXBfWtIISEorPOXkYpYPHJcSfOW1mPCz6tx+5GaWfn0PrScBMCXudMDiEk6Ik9NpxsPwFbfsyJdVXJn3rLvmfYppPZKgELINC09i+TsjQ1q0wmRk7TzSdmZCuOGkwzCHhkajZGZndtozdCSPdiJ284Q2IBoijipjUNUHLpUIusB0ao60rl5ahnl8T2WcqWhPA0c5otd4cXOOPjWYNURmh3JYtBwNZLi2SXr1UpsfGLInY3enDS5pVkXV7/VSNq3fznaWC6Hr8fl9gpVISQnoUyLHoZpVRNNbkhNeupvCLFEFvq3PihxcOt3yfkfz9IA1qvlLVhTom2fgd+UcTyo060ef24fFiq4hSLeGMzLCzMz83kpLxqGR2Y7Ni6dAC4gbN4CMrV0NuCENL7sJ+JDp+IF/bbsL7ahWlFJlS2KS/+H5mq3ofi5pFpeKNc+r3x10mxLVa1fFZtqHLhiuHaenclitcv4rn94QMWM4qTJxjOa0L9/za3YGt7hq7V40dNmw9DMgz4vsGDm9c24pgtsImRZ9LhpTk5AAR8rbCZsfTsXMwbYAp7LkoI6VsoYNHLKGVYKEnRBWrSgwo01Ep+XeWmgAXRpC2Cnid/2NKCZ/cFTt4+Pu7Em+eG76kSqz2NHqw87pRcxs6a1wkCbhmdhufbTyx/Njot6ctctDDqBCwemYqXywKLhV+MTVf9vXgd4gkhvR+bYdHo8uN33wamj3101Mm7S4fCMOUMEwtScPDqYqw4FugxcU6JCafH2HhXaSpJqFu+bsK+Jg/+Mqn7pjqx2a5KkmlMNq+nRIMrEJA42OzBRZ/Vo9rB/95vcPlx56YWTC5Ufn4XDekZTasJIV2LAha9iNMr4m5mhGk45/Y3S76Arh2RiqUH7XCplFXaNDSJIr2b2yfi4R2t+LbWjUuGWHCLwiI+knFywWAFEChhqnf6kM9p8hUv31S7sGhVvexyXl3z36dmYX77ZIxzYkxXTib/nJaNCwZZYBCAOSG/18NTsmSfJUoNfQkhvVu0vR7m9df2WVlo0eMno+LXyFdrj51n99nx+/GZ3dbLgh0xz/PPack/4jn41fDUXptisCLoUKsXaUb+3/vLhbFNsyGE9F7JE7YlMXtsl1W1lpBnCdPtf3x+CnZcVowfnxJIldQLwPBM6aLRluQzzUnivXO4Df/abcOmGjd+v7kF2+v5O0WRBCxYbHPYeLtnawt4sTc2wwIIjMo7t78Z5/Y394rsiiBBEDBvgBlnM7/XkrHpsu7v04t6T6CGEKKdQQCi6YH55Myc+B+MBukKC2KWwyeiwRm+6WWof++2YvTbVbji83q0MH0wfH4RT+2x4pZ1jfjypHrmCcDPiH17bh7mlASaW/5okBk3nNK9ZStaBFtTvKqhx5kI4PsG6fnCmBwDXj8nF5MKek7TakJI16IMi17k5bLwtZCs03PlpR0laXo8PiMHj88InGw8sduKe7a2dlxvp0khfd4vNjRL/n3P1hasXCDfHdFaouTj7N4n+nW2tY4fZOH1duiLls/Px4Wf1aPFLcKiF3rEiTMhJP4EQYBZL2gOQI/PM+J/c/NQnNo9zX7YnhtqWiP4njnQ7MFf2s+Fqtpc+OOWFjw1MwdtXj8e+96Kx3bZOm677LADOy4vwpCMztNsURTx0VEn9jR5cNVwC5ycnmPnDTDh/IFm+PxiTJNSulIsyXen5xrx9aLC+B0MIaRXooBFL1LvjHxHWstOBNvhW8vccNK3bKx2y3abAMCu4QS3xe3Hozutssu1pMtGS6nhl04A+qf1go6acXB6Xgq2XlqEnQ0enJJlwOAM+rogpK/SGrD49WnpuLcb+0IAkZWw8L63WP/Za8MTu62oYbL+Xi9vw0NTsvDsPpskWAEEMgnu3tyC/80N9GISRRF//96Kh3cEvuue2WvDHyZIm4teMyK1I9OtpwQrgEDW7VcV4TNKeC4eSj0rCCHh0RloEhNFEeUtXqyrcuH7Bg+enJGtmo5u0QuSKSED0vRodvlVSzi0zM5m6w21LEJJ3zP4jSrZZW1eETaPH8dtPgzLMMDMOZH86dpGfFEhDyDURZiqq5XXL+Jn6xq5152RZ0QmZ4xpX1Vg0WPeAArgENLXaWknVH9TiaZzikSLpLQgXMDiqNWLP21pgdJZz5zltThs5X9XfXrCiWf22nD+QDMmvFcjuc7uFbGUyYqNJDMk2Vz6ufJoWzULBmqbIkYI6dvozDxJ+UURE9+rwZQPanHXty14vbwNX1e5Ve/DNpL+9IJ8vH9+7JMWspha9ncPO1DdlpjFJEl+obPUw6l3+nHmB7WY/mEtzllRKzs59PpFbrACACrsiXmNHWj2KjYGO6sXNdQkhJB4CVcql2YQkiJYAQDFqXrcPjZNctnfpmYhxyQ/vla3+vfZG+VtisEKAIrBiqBXyuyyYEXQwRav5N89ZVz2T0alhb9Ru71XFqtef0oW7ZsSQsKjgEWS0gkCRjAf5GsqlVPufH5RVotZkqrHlEITHpsWW3rm2Bx5n4tpH9agioIWfY7PL+LmtU0R3edke+BhX7MXb/8gbcrVqrK7daTVi4+OOrC20hlRkITV7PLjss/rkf1yBW77ulG1r8ZZ/ShgQQghrHABi1HZybXwfGhKNrZdWoQnZmRj/aJC3DomHasXynslhMuwaIii1DZUGROUUNNd00oiNVvj9+S+K4vRP02PX52qPAGmJ5W+EEK6DwUsktjCwdLaPrWZ3WywItModHwRXDk8Fedz0u7OH6DtS2cAp6a/ySVixVGHpvuT3mNzrRsfxPC8P7k3UOfr9Yv41y4rLvtCOY30rUMO3LSmERevasCj38t7XGj1+2+bsbo9i+OtQw787ttmxdtOKaSABSGEsExhFtPXl2rfde8qw7MMuPGUNJzW3lx8WKYBvz5NunhuCZNhsaVOPbM1nkw9pCRES9Xk36ZmoaT93HHBICr7IITEhgIWSWxcnjSzYXWFC9f///buPLypMv0b+PdkaZombdJ9oYsttOxQBKFslU3cYAqKsqgow4AiLjODC+A68kJxpCrjDxhRRxRRUHABcVxgUMAB19Iqaxl2KC2Upvua5P2jNG3WJm2SnrTfz3VxXfTkJDknT5Zz7nM/972zCBvyKqyuOJ8tN4/iN5+HHyiXYNO4UOhmdcELg4IglzQEIRYOMC/4ZI8gCLjdRmEkT7edJPHZ+L+W25Y5orgaRHv7aAX+9kspsu20Q7W0LLv1AYsPT5gHWCxbqjW6Mc7fZ65wERF5k6MMi7uTA3Bvd9/oIqSxONt2lGHxU2EtfndwocjdAi3n9YqUM4GV5sev4XYKoPQLZJYuETnHN74dO6kUjfVUjM/PVGP+Xh3eONxUrKmgUo/0rZfM1tMqbA/tI30DUTAzBr/fGYUBYc4XplpsI7jhTHVt6lhC7LyvnOV39bjlmZ9KXL6voRXTQlyZSvJk/0CXH5+IqDNwFLB4pI8aEgcFwcXEMmDhaFri/kLb9ZXseev6YJy7O7pV2yWXADf5SAFKvxYCFgJgymoBgGsCpTbrh9wX571gEBH5NgYsRMzR1d7MA6Wm/797rMLqdstCmc215sCiq0aGVSO0ZssYsOh8ogPaVhWs8cpMaxqAFLeizen6POczQuQ+ko5LRORtjgIWKh/JDACsW7mX19kOalfXG62mi6hkAqZ2td2GUyIAtycFQC2X2K3xEO5v/3XaNTECSUHiqgNiT4pGBkelJyKVErP3hFQiYEyMdTAmUcmOc0TkHN/5lemkugbZPkEsrmn6ol9qI10+2AOtGYMtrq5famMxKvI9ba2PZXl1yxWuTkH66mw1Hvnefr0KS+xmSkRkm6OuTSofmkpnual6G+fMbx+pQLcP8rHConbSogGBeD09BLb29u9DWi5urvGTICbA+ofmr/3U6BNinVErVhFKKcY66KhlqwvX2C7W6yulDFgQkXN4iC5yT7VQZ8JeIU7Lgp3uEKE0D578fKkWeoP1D86psnr8z4XK2OQ76uzEDJICncu8aLy4ZeugrSV/+a/OpSke/+/X0pZXakbOauVERDY56pahkvvOd6dlVwq9xW9KeZ0Bz/xUgnIb3aQaa4Odv8d82sfSwRr8qWdTMc9aW1EQAI/0VaNfqPVU3Pm97XfREKtZLrQ2BYBxsdYZFjbquRMR2cSAhchNtlHsspHRaMTefOs5lqtGaDGtm/sLYPUPlZtdYS+rM2L7mWrsya/BUV0d6g1GPPp9MVI3F2DQxwV4Nbf1hRJJnGwFqABgxVCtzeWWKq4eBLamGvr+wlr8UOh8xXZHXXVs8aGsZiIir6qx890P+Faw1/Knp94iDpNXUm8zWAEAQVd/JAJkEhTMjMF7Y0Kwb1KEVcDBsmA6AIyM8sNtiUosTA00dV4bGumHs3dHI9ROUUoxuynOH0MinK+DFqGU4pZm3UJGRPnBB3ebiNqJb0yY68QEQcDiAYE2uySU1xtRUGWdpnmXh9qLySUCgv0kKGpWS2Dmris21zUCePm3MjzsoP82+R47030xpos/ugRIcb7ScXGK/xbU4mKl3hS4sPS3QUF47mf7mRE7ztUgLdIzrUd9pWgcEZG31djJGvA1lgELy2LOxxxkhwY2qw2mkAp2M1kf7qPGu8cqUXX1NftLXzWeHRgEQRCQGuaHA1MiYYRvBXosSQQB748NQdcPLjp9n9UjgvHG4XLUG4F5vdS4dMb5KZtE1LkxYOED7u+lxgfHK3GyzPxkcO2hCuy6YJ5hsdyJeZRt4edCRLy01sjCnB1MvYOrbElBLQcsAGDwJwUotShmdu7uaKivXr364HgljuhsHzQa4fxBs0om2A2MWArzlyBSyRQLIiJbqjtMwMJySoj57d9dsN8ZpKuTRTFj1TJ8MC4Ebx2pQHeNHI+nBkJo9rwyHw5UNBfqL8XPt0Vg0MeFTq2vVUjweGrTNOdLDtYlImqOR+g+QOMnwY+3RVotX/JrKXKKzNPeEwM9G4Ny9YpAUSs6O5B42athAThfKd4yWBGhlJiCFUBjizzb93UQL7FiWQ3enmCFgP8boe0wB5FERO42p6ftzM0ZHph+6klSi5+p5jFtg9GIneerbd6vl1aGBLXzV2xGxfhj/ZhQPD0wqFVTIH1FN40cExPM61O8lObZC2dE1PkwYOEj5BIBYxxUZW7k6avErv7wXqw0YNMFGZb+Wop8J66+k7hZFihrztkAgaUeWvP5vjOSVfhpciTuSbY+EC61NyfFBmdSmEMVEvxvejRuinN/kVoioo7iwV5qRDQ7vhge5Yd5vVTI9HBWp7tZdQlpFgX//UodCiw6XKSGypEW4YfVI4PNsiSoybujQ7B4QCB6aGWYmRLgc0EsIhI/TgnxIWFOBCOiAjxbxcjVwoR//PYKLlX7ASjDttNV+D4jwqpKN/kOhxkWrWxt11Nr/TXUVSPDkus0WJ9Xabb8TJnz3WdqLOJjaRF+2N+saKdSKuDlYVrWriAiakGsWoZ9kyLw+5U69AmR+2ShSMC6VlHzuLblFNtb4v3x/thQb2yWTxMEAU+kBuGJVMdd7YiIWosZFj4kzL/l4Qp3Yp220Lk4xeNSs1ZoR3T1+G+B810eSHxs1bCY1rUhO8FWMUxnYlM9g233n9cqJHh/bIjZsm/O19jchp8KazHs0wJo3z6PGTuLcKqs3qqq/aYbQrF0sAY3xflj1Qgtcu+IRMY1zKwgInJGqL8U18f4+2ywArAuutk8YHHaok7Y8CjPFHgmIiLXMGDhQyKcOEjwdPbChcq21aTIdbHVJImLZQs4AHh+UENK8JQkJa4JbHqP/mO4FiemRyMp0PH7toeNDItGjX3vm/vqbMMcY6PRiOIaAyrrDZi2uyeaMAAAIABJREFUowiHihuyL744U41Z314xq3chFRpqwczvrcbGcaG4K1mFcKXvHnQTEZHrZBY/Kc2nOZZb/MCFKHiITEQkBpwS4kPsXYn2JRc7UR2LrJwyvH20Ar1D5Fg1QoswH74q1ajeooZF1lCNaRqSXCLg24kR2Ha6Ct00Mgy9mnGRrJXjRJn9cXc0jUljI2BxWFePcbFG3PFNEXbn267onn3ZPDDm34GLnhERkXMcdQmptKiRFNDKaY5EROReDB/7kHFdFJgQ72/39tbWEHA3G+eYJidLna9B4MsOFddhya+lOFehx1dnq/H6oYr23iS3sKxhYdk1RquQ4J4UlSlYAQApGsdxUaWDYEKQjUKeATIBn56qshussMWVdrxERNQxWf7cNE+qsGyD3dpC0kRE5F4MWPgQqUTAe2NDkX9PDFaP0FrdvjA10OvbFORn/oN+QxcF7utuu/0Z0HnanL6SW2b290s5ZXbW9C2W9SOcSVxIbiFgEeDgoNDWlBClVMD9u4tbfuJmmGFBRESW02abTwmxLLrJDAsiInHglBAfpJQJNqeHXBfh5/VtuSXOHzEqKf7v93JEB0ixdLDGrBODpRIPByzK6wzIvlyHHlpZu9YoKHOh/aYvqbaY2WGZYWFLrMrxOAQ4CCZo/axv+/JsVYvPacnVdrxERNTxWP4UlNUZsSKnDL/bqK8llqxVIqLOjgELH9U1yHrovHFStmRQEJ75udT09xOpQUgKkuGx/oFQSgUIgoB6B+fqJbWeO5HX1RiQvrUQZ8r1CFVI8OWtYUjWeLfuR73BiH/8Xo4vrxaG7GgOFZsf1HVpIRgBAP1DHY+Bo0KxgiBgZJQf9lxsCoJ9dc75qSCNmGFBRESWPwVny/X4f7+W2lzXVoYfERF5H7+NfZStH1JvnJP9qaca9/dUYVikH/45MhhJVwMnATIJhKvFrHoFy5Ggtn0iW1LruQyLtYfLcaa8IQWgqMaAV3LLPfZc9qw5WI4XfrF98OPr9AYjTljUIOkb0nJAKNRfikf6qFv9vCOi295azpntJCKijk0mOH+gZO84hoiIvIsBCx82vVuA6f/RARL09kIXEaVMwItpWnxxSzimNXt+S7clKm0uL683okbvmSyLt46YF7Z8/3ilR57Hkc0nXZ+u4CvyK/Vm2TOhConTV6BeuE6DHyZHoF8rAgdqedu/pu5Otl9XhYiIOgepkz8nM1MCTBdhiIiofXFKiA9bNlgDjZ+AK9UG/KVfoMPUem8rdlCr4kKFHho/AXKpgEA3nIw2qvJQIMQVOUXW82Ab5VfqEe2ghafYXbBoSRvr4tWn7lo5dk0MR5f3LphqYdydbD/o1Sjcv23vka5BUqRHe7++CxERiYuzh0kt1V4iIiLvYcDChwUrJFg+xLpbiBgMjvDDumO2Mxzu312MHy811SRIj1bg72ka9NC2LUOk2lHxDBHouekijkyNQpSPBi2qLDrStqblm1QiYN+kSPzj9zKEKCT4a7+WO9tEKtsWsBgT488rZURE5PSUENY9IiISD04JIY+4NUFps8MDALNgBQDszq/Boh9K2vycHiyP4bSWrt7843ffbW/avP0b4FyHEFsSg2R4ZVgwnhmogcqJDJuINnZ7iQ/0zQARERG5l7NxCHaWIiISDwYsyCM0fhLszojAssEa9AvUt7j+r5ftt0JtLXsBE3f57kI1/n6g1NQOzWg0wtBCksfWU77bPaTeIiDkrY5vbc2wiFczkYyIiJyvYcEMCyIi8eCRPHlMvFqGB3urUXGlELlljq9y17Yc03BIbyNS0HhlvlZvxAfHK1FYpcedXQOQENj2t/27xyrwyPc6AEBWbhl+vi0SJ8vauBMiV2fxGsu8VDMlWNG2gEUc5yITEREAqbNTQrwVkSciohYxw4I8Ltq/5doStS2lJrSg3Eb9isYpDFm5ZXj0vzoszS7Dnd8UoaqNtS5Ol9WbghUAUKMHXj9Ugb0Xa1q8r1HcZTYcsnzZvHU8Z6/+xGc3hjl1f04JISIiwPnaS8ywICISDwYsyOO6KFo+S9cbbWdJOKu8zvq+/yvV4/VD5ViR01Q34mhJPT460bZ2p28crrBatvdiDf7v9/IW71vvwxGLeoN7ali4y/UxCiwbrDFb9sfuKtyR1NRSd1ikH8L8GbAgIqKG3604JzpcOdmxm4iIvIBTQsjjQv2cO0mvMRgR0MqT4Io62xU3n7RRzPPnS7WYmaJq1fMAwIbj1gGLAw7amTZnK7DiK6wyLNrxgC46oOHJZ/dQ4Ztz1dh1oQaxKin+3E+NUIUEqWF+qNEbMbtH68eZiIg6nrk9VXjmp1KH6/gxw4KISDQYsCCP87dzYhvkJ6C0tuksuFYPBDjxjvz1Ui32XKzBmC7+6BvS0Aq1woVpHtX6tgUNVDIJimsc16uQCsCpu6IR916+2XJXtlNsLDMsvHk8F6KQ4EpNU1Cq59UWuAqpgI/Hh+J8hR6RAVJT1sf83mrvbRwREfmMB3qpHQYs5BKgf2jb2qwTEZH7MOmNPM5WCYJewTL4WWRTNNaxuFKtx4J9Osz+9goOF5tnLnx7oRo3fnEJz/1citFbC3FE13C7K5kLRdVt63/axYkijomBMgTaadlp8NFpIZZdQrw5JeSlNPOpH4sGBJn+LwgCYtWydp+iQkRE4ieXCBgfqzBbdmeSErfE+6OHVoZ/DA/mVEIiIhFp14DF999/j2nTpqFnz57QarXYsGGD2e1GoxGZmZno0aMHoqKicOutt+Lw4cNm69TU1ODxxx9HUlISYmJiMG3aNJw/f96bu0Gt8Je+gVBYnGDWXM18eHx/Cd46UoEtJ6twxzdFphP8qnojJn1VhMbZH/VGYPvphjah5yuc79CRX6GHwWjEydL6VhXgtOyWYUtjwc/VI7RWt50p981uIpb1N7w5JSTjGiWeGxiEcV0UWDlMi0HhvPpFRESt89d+gabC0SqZgCXXafD+2FDsnxyJ6d0C2nfjiIjITLsGLCoqKtCrVy8sX74cSqXS6vaVK1di1apVePHFF/Gf//wH4eHhmDx5MsrKmoooLlq0CNu2bcNbb72FL774AmVlZZg6dSr0et88Keyo5vRsqiUQ5Cfglnh/yC0uYDS2Nt1yssq07FyFHrlX60PsulBt9bgFVQ13eveYdV0Je/JK63HT9ssYsKUAQz4pwKmyeqfvCziXodE49WNGsgpRSvOP2cErztW7EBvLDAtvtTVtfK6/9AvE5vFhuLe7ym7nECIiopakRSqwY0I4soZqsG9yBCIDmFFBRCRW7RqwGD9+PJ599llkZGRAIjHfFKPRiDVr1uDPf/4zMjIy0KtXL6xZswbl5eXYvHkzAKCkpATr16/HCy+8gNGjRyM1NRWvv/46Dh48iG+//bYd9ojsefraIMzpocLNcf7YNC4UKrnEKsPCXmvTqquZF+dsZCbUG4Bz5fX4b0Gt2fIZDq6Q1BmAHy81rH+mXI/1doIdtXojHt+nwzUbLuD2ry/jSnXD8xfXOBGwaDZFZUKCeTDuULFvBiwsM0vYpp6IiHxVapgfZvdQI17Ncm5ERGIm2hoWp0+fRkFBAcaMGWNaplQqMWzYMPzwww8AgAMHDqCurs5sndjYWHTv3t20DomDxk+Cl4Zq8cG4UAyNbJg7almFe+Nxx+1GbRXL/PhkJWZ/V2y2rG+IHHN7Ot8dIivXdjvSVQfL8caRCuhqjdh5vgavH65Ard6IUifqZTQvrtk72Hz6wqFi1zI6xMLy5fdmhgUREREREXU+og0rFxQUAADCw8PNloeHhyM/v6HzQmFhIaRSKUJDQ63WKSwstPvYeXl5bt5aaomt11xerwDQlIb52u9lmBJYCMA8O+Lc2XPIKzXgfKEMgJ/ZbbpaI34oNM+uUBurEXDlNAZqFPilpOHxb4+qw5aL9useHD2WB8vz7xUHlACaFr5xsBSj/Aqsts+exn0OrJAA8Dctzy6oQF5ekVOP0V4at/1yLXC+WoJeagMuXjJ//ct0xcjLs/85I+/hd5pv4Xj5Do6Vb+F4+RaOl+/gWPkWXxyv5ORku7eJNmDRyHKuutFobHH+ekvrOHpByP3y8vJsvub3GSvw6/c6098GCNCHJQC4ZLZebFwskiMVUJWWAGdsZ0M0F61VIyUlHl92M2Ln+WpEKaVIDfPDxX9fwvcXa23e54IqDqO7NAUVDlyuRYXefDuu1AnIV3YBUAxLUUoJLlY1TRW5NyUAycldAACRtQYgt6m96dlqCeISu8FfpHMqGsfr10u1uPOryyirM6JviBw3xvoDaKofEx4aguTkIPsPRF5h7/NF4sTx8h0cK9/C8fItHC/fwbHyLR1xvEQ7JSQyMhIArDIlLl++bMq6iIiIgF6vR1FRkd11SLxmplgXpDxWYj1donEqgrMdPYLkDYEAuUTATXFKpIY1ZAUkBtqPz6051BQIqTcYMeUb2xkQi38sMft7ULgcxffF4Ls/RCDMv2FfNH4CHuqjbtoePwni1U2ZJHojcLxU/NNCXvu9HGVXp7/8dqUOm0+aT9mx07WViIiIiIjILUR7ypGQkIDIyEjs2rXLtKy6uhr79u3DkCFDAACpqamQy+Vm65w/fx5Hjx41rUPidluSeUHKIzrrgpT1V4s92qphYUuQn+23dYzKfhXwr8/VYM53V2A0GnGgqA6X7XQCaZ5FAQARSikEQUBkgBQ/TI7AxnEh2DcpEska8+knsRbP7Uzhzvb2yakqs79PlZkXPfVjDQsiIiIiIvKgdp0SUl5ejhMnTgAADAYDzp07h9zcXAQHByMuLg7z5s1DVlYWkpOT0a1bN6xYsQIqlQpTpkwBAGg0Gtxzzz149tlnER4ejuDgYDz11FPo3bs3Ro0a1Y57Rs5KDjI/sT9ooyDl+mOVuD5aAV2NcwELe+3JurTQtuyjE1W4t7sKduIdNkUpmx4z1F+Km+Ks2/MCQKDFg5bViT9g0ZLBEX4tr0RERERERNRK7RqwyM7OxsSJE01/Z2ZmIjMzE9OnT8eaNWvw6KOPoqqqCo8//jh0Oh0GDhyIjz/+GIGBgab7LFu2DFKpFLNmzUJ1dTXS09Pxz3/+E1Ipe2r7gq4a87fgV2errdbZcrIKkxKVVlf87ekfaru4ZoATNSNeP1SO+3upW1yvUVKQc++zxmkqjcqc6DTSnixbmFq6PlqBoZEMWBARERERkee0a8Bi5MiR0Ol0dm8XBAGLFi3CokWL7K7j7++Pl156CS+99JInNpE8LFnj3Fvw05PWwYpwfwke6aPGMz+Xmi3vG2I7YNEr2H6XkEZ5JfWodCGYEKtybvsDLQo+lNaKM8Ni0/8qsfQXf0hzChyuN61bQIvFb4mIiIiIiNpCtDUsqHOIUkqg8Wv5xHfHeevMiyNTo3B/LzWCFU337x8qh8bOnI5ewTKkRytMf0/raj1944iuHhcq9VbL7Ql0YtsBINBBhkW9wYhPTlbi05NV0LeQ2eBJBZV6PPJ9Mc5USXCyzPFr0M9OUIiIiIiIiMhdRN/WlDo2QRAwNFKBL21MBWmupNb8RD5CKYFUIkAK4JWhwVj0ow4qmQR/H6Jx+Fxbxofim3PViFBKMSjcD/d2r8HNX1w2W+/l3DI7j2BN5WRrUpVFwKIxi6O63ojbv7lsard6T3IAXhsR7PTzu9MHxytR40SsRioAKVp+dRARERERkWfxrIPa3eP9A1sMWFgKVTRlUUxKVGJSou1il5bkEgG3xDetOzRSgYFhcvxyuak7yZly5zMs1E729rTsqFFRb8Brv5fhmZ/Mp7N8cLwSWUO18JN6f7qFs/sdoZRAzg4hRERERETkYQxYULsbGO6H9GgFdufXOH0fe9M+WsPZoIPt+zp34m4ZgFhzqMLmevXGhjoaR3V1iAyQYniUwuZ6VvczGCEV0Ka6ElInX4Ywfxa0JSIiIiIiz2PAgkShi8q1k2BnAwXOsKwvYUkmNAQSbHF2Sogr8ZXhnxWa/v9SmgZzetrvWmIwGnH/7mJ8dKIK/ULk+PCGUES10L7Vnnon62eE+bP0DREREREReR7PPEgUXA1AKJ0MFLT1uQUA07sF2N4GqQCtwskpIa2c4vH+8UqHt+88X4OPTjR0UMm9UofVB8ttrlddb8Sqg+V4NbcMV6ptT/2ocLI7SqyLwSUiIiIiIqLWYMCCREHtYgBC6cYaD4EO0h+WD9FgfJy/zdtuTfB3upaDZQ0LZ2U3q61hyz8PmQco/vG77YDFY/t1eOrHEjz/Symm7iiCwWgdnCi3l0ZiITGIiVlEREREROR5DFiQKLiaMeHvxgwLe1NC+obIcX8vNSKVtj8mGdc4V+gTABRuTEowGo2ovhpcKKgyWN1+3ccFyC2qNVv2+ekq0/9/ulSHkHUXUFJrft9Si7/n9FTh/bEhGBBm3sJ0pJN1NYiIiIiIiNqCl0pJFFzNmPB3Z4aFnaKbjVNFbBWZjFJKcGu87cwLW9zVVaOy3oA7vynC3ou1GBnlh/MV9Vbr5JXU46G9OuzOiADQEODQ1VpnTyzPLsWf+wYiv1KP46X12HvRPMhxR5ISgyMUGB3jjyW/luDHwlpMSQrAdRF+btkXIiIiIiIiRxiwIFFwNWPCnVNC7NWwCLi6TSE26lSsHhkMiQsdORQOtjdUIUFRjXWmhC3rj1WaAgt7LAIMzeVeqUNlvQEBMgnslKzAmkMVdruV+EuBXsENmRVKmYBlg7VObR8REREREZG7cEoIiYKrU0LclbEA2M+waAxYaPwEdGtWtyHMX4K0SNeyDBx1CXnq2iAUzozBHUm2p5jUNeve8eQPJU4/Z35FQxCkst65YEhzj/QNbFO7VyIiIiIiorbiGQmJgqsZE0dLHBejdIW9DIvGIIogCHh5mBY9tTL0DpZh07hQBMhc++g4CrD0DJbBTyrgjetD8M2t4Va3j/i0EOcr7KRJOHBE1/AaVThZTLO5v/QNdPk+RERERERE7sQpISQKrtakGOzGOgp2MyyabVN6tAL7Jke2+jnsTQnpEyJHWrN9GRQut1rnaEk91h2tQFmda5kSL+eW4YZYf1S2ImDhzraxRERERERErcEMCxKFABdOkBVS4NZ45zt0tMRel5AAO8tbI8JGp5H0aAW23xwGoVktDEEQbHYUeSmnDP+0U2+i0Z/7qs3+/uVyHV4/XI7KOtcDFkRERERERO2NAQsShcQg82SfXsHWyT8TE/zxZGogvrolHElB7ksOCvSzMyXEjYU9Y1XWUYhXhmqhsVHcosb12R8AgPu6qzDUorbG56erXZ4SIhcY4CAiIiIiovbHgAWJwjWBMjzYWwUA6BIgxeoRwbizWRFKlUzAa8ODsWhAEFLD3NtW015xyViV+4IigiBgdg+V6e/B4X5ICrKRStFKapmAeLUULwzSmC2/UKlHhYtFN6fHWLdKJSIiIiIi8jbWsCDRWDZYi6evDYJCIkAqEfDswCDoag24UGnAE/0DobXRXtQd7E0JuSXe363Ps3yIBn2C5SivM2Bmd5XZVJC2SgqSQSII6GGRmVJQqUeFxZSQkVF+dluizuoegHuDL7ttu4iIiIiIiFqLAQsSlebdN2LVMnx4Q5jHn1Nlo37Gm9cHIzLAfRkQQEOnkFnNsizsGR2jwK4LNS49dnRAw+sWKJdAJRNM00BqDQ1ZFs3FqmW4I0mKj05UmS2f3UOFrKFa5OUxYEFERERERO2PU0Ko0xMEASOimqaZXBsmx5SkgHbbnheu06BPiHW3EEeimgVXogLMP9YnS80DFiqZgOtjFFaPkaB2b4CGiIiIiIioLRiwIALwenoIZnQLwJ1dlfjXqJB23Za+IXLszYjAnowIp+8zMLwp4BKpNA88/OuoeXcRlUzAqGjrgEWKlglXREREREQkHjxDIQLQRSXF6pHB7b0ZZvqGyNE/VI6cojqr2zR+AkpqG6Z9TElS4q5uTRkhUS1MZQmQC4hVyzAwTI5fLjc8dohCgpFR1kEMIiIiIiKi9sKABZGIDY/yswpYdAuS4b2xIfj0ZBX6hcpxU5w/JM0KeEYqHSdO9bs63eQfw4PxxA86lNcZ8bdBQVDZ6ZZCRERERETUHhiwIBKxFI11LYtuGhl6aOVYOMB2nYuWMizGxzZ0P+kdIsf2m8PbvpFEREREREQewEuqRCIWZ6MQZorGcZwxQW3/9hVpGkgl7munSkRERERE5CkMWBCJmGUBTcC8wKYtN8X5270tLZJ1KoiIiIiIyDcwYEEkYpYtSgEgLcJxwMJfJuDUjGibt/VkJxAiIiIiIvIRDFgQiVioQoLkZlNABof7IbKFGhUAoFVI8NSAQLNls3uoOB2EiIiIiIh8BgMWRCImCALeHhWCG2MVuDXeH6tHap2+733dVbguvKEwZ8Y1/nhxiMZTm0lEREREROR2zA8nErk+IXJsuiHM5fuFK6X4ZkIEDEajWdtTIiIiIiIiX8AMC6IOjsEKIiIiIiLyRQxYEBEREREREZHoMGBBRERERERERKLDgAURERERERERiQ4DFkREREREREQkOgxYEBEREREREZHoMGBBRERERERERKLDgAURERERERERiQ4DFkREREREREQkOgxYEBEREREREZHoMGBBRERERERERKLDgAURERERERERiQ4DFkREREREREQkOgxYEBEREREREZHoMGBBRERERERERKLDgAURERERERERiQ4DFkREREREREQkOoJOpzO290YQERERERERETXHDAsiIiIiIiIiEh0GLIiIiIiIiIhIdBiwICIiIiIiIiLRYcCCiIiIiIiIiESHAQsiIiIiIiIiEh0GLKhFL7/8MkaPHo24uDh07doVU6dOxaFDh8zWMRqNyMzMRI8ePRAVFYVbb70Vhw8fNltn3bp1mDBhAuLj46HVanH69Gmr51qxYgVuvPFGxMTEQKvVenS/OiJvjdXp06fx0EMPoX///oiKikL//v3xt7/9DVVVVR7fx47EW+NlMBgwbdo09OnTB5GRkejevTvmzp2LCxcueHwfOxJvfhc2qq6uxvDhw6HVapGdne2R/eqIvDlWffv2hVarNfv3/PPPe3L3Ohxvf7Z27tyJG264AdHR0YiPj8cf/vAHj+1bR+St8dqzZ4/VZ6vx36effurx/ewovPn5On78OGbMmIGkpCTExsZi3Lhx2LFjh0f3ryPx5lgdOHAAkyZNQnx8PBITE/Hoo4+ivLzco/vXWgxYUIv27t2L2bNn46uvvsLWrVshk8kwadIkFBcXm9ZZuXIlVq1ahRdffBH/+c9/EB4ejsmTJ6OsrMy0TmVlJcaMGYOFCxfafa6amhpMmDAB8+bN8+g+dVTeGqu8vDzo9Xq8/PLL2L9/P/7+979j48aNDseWrHnzs5Weno63334bP/30E959912cOnUKd999t0f3r6Px5ng1euaZZ9ClSxeP7E9H5u2xeuKJJ3D06FHTv8cee8xj+9YReXO8Pv/8c/zxj3/E1KlTsXv3bnzzzTf8LnSRt8ZryJAhZp+ro0eP4q9//SvUajXGjRvn8f3sKLz5+Zo6dSpqamrw2WefYffu3UhLS8OMGTNw8uRJj+5jR+GtscrPz8ekSZNwzTXXYOfOndiyZQuOHDmCBx980OP72BqCTqcztvdGkG8pLy9HfHw8NmzYgJtvvhlGoxE9evTAnDlzTAdpVVVVSE5OxpIlSzBr1iyz+2dnZ2P06NHIyclBQkKCzef47LPPcO+990Kn03l8fzoyb4xVozfffBNLly7lj1IbeHO8vvjiC8yYMQMXL16Ev7+/x/apI/P0eG3fvh1LlizBO++8gyFDhmDXrl0YMGCAV/ato/HkWPXt2xdz587Fww8/7LX96eg8NV56vR79+/fH448/jnvvvder+9SRefO3a9CgQRg+fDhWrlzpsf3p6Dw1XkVFRejatSu2bt2K9PR0AEB9fT0iIiLw9ttvIyMjw3s72UF4aqzWrVuHF154AXl5eZBKpQCAgwcPYvjw4fj111+RlJTkvZ10AjMsyGXl5eUwGAymKRunT59GQUEBxowZY1pHqVRi2LBh+OGHH9prMwneHauysjJO42kjb41XcXExPvroIwwaNIjBijbw5HidP38eCxYswNq1azlGbuDpz9Zrr72GxMREjBgxAitWrEBtba3btr0z8tR4HThwAOfOnYOfnx/S09ORkpKCyZMnIycnx+370Jl467drz549OH78OO677762bnKn5qnxCgkJQffu3bFp0yaUl5dDr9dj3bp1UKvVGDJkiNv3ozPw1FjV1NRALpebghWNjwMA+/btc9PWuw8DFuSyhQsXom/fvhg8eDAAoKCgAAAQHh5utl54eDgKCwu9vn3UxFtjdfbsWbz22muYPXt26zeWPD5ezz33HGJiYpCYmIhz585h06ZNbd/oTsxT46XX6zFnzhzMnz8f/fr1c98Gd2Ke/Gzdf//9ePPNN7Ft2zbMnTsXq1evxoIFC9yz4Z2Up8br1KlTAIClS5diwYIF+PDDDxETE4MJEyYgPz/fPRvfCXnrWOOdd95Bnz59mGnWRp4aL0EQ8Mknn+Dw4cOIi4tDREQEli9fjs2bNyMqKsp9O9CJeGqs0tPTUVRUhFdeeQW1tbXQ6XSm2kuNzyEmDFiQSxYvXoz9+/dj/fr1ZlE5oOGLqjmj0Wi1jLzHW2NVWFiI22+/HaNHj8b8+fNbvb2dnTfG65FHHsHu3bvxySefQCqVYu7cuTAaOSuwNTw5XllZWZDL5XjooYfcsq2dnac/Ww899BDS09PRp08fzJw5E1lZWVi/fj2uXLnS5m3vjDw5XgaDAQDw2GOPISMjA6mpqVi5ciU0Gg0DuK3krWON4uJibNu2jdkVbeTJ8TIajViwYAFCQkLw73//Gzt37kRGRgZmzpzJIt+t4Mmx6tmzJ9asWYM1a9YgOjoaKSkpSEhIQEREhNVziQEDFuS0RYsWYcuWLdi6dSsj7WYuAAAJlklEQVSuueYa0/LIyEgAsIrsXb582SoCSN7hrbEqKCjAxIkT0bNnT7z++usMULWSt8YrNDQU3bp1w+jRo/HWW29h586dokz9EztPj9d3332HPXv2ICwsDKGhobj22msBAOPGjcOcOXPavgOdSHv8bg0cOBAAcOLEiTY9Tmfk6fFqfJzu3bublslkMiQlJeHcuXNt2PLOyZufr/fffx8SiQR33HFHq7e3s/P0eO3evRtffvkl3nzzTaSlpSE1NRVZWVkICAjAhg0b3LIPnYU3Plt33HEHjh07hsOHD+PEiRNYuHAhLl++3GIdmfbAgAU55cknn8TmzZuxdetWpKSkmN2WkJCAyMhI7Nq1y7Ssuroa+/bt45y1duCtsbp48SImTJiAlJQUvPXWW5DJZG7Z/s6mvT5bjVcaOdfeNd4Yr1WrVmHv3r3Ys2cP9uzZg48++ggA8MYbb7Bdpgva67P122+/AWg6sCTneGO8UlNToVAokJeXZ1pmMBhw8uRJxMXFtX0nOhFvf77Wr1+PSZMmQaPRtGm7OytvjFdlZSUAQCIxP72USCSmYw5qmbc/WxEREVCr1fj444/h7++PUaNGtWXzPYJnGNSixx57DJs2bcJ7770HrVZrmtukUqmgVqshCALmzZuHrKwsJCcno1u3blixYgVUKhWmTJliepyCggIUFBTg+PHjAICjR4+ipKQEcXFxCA4OBtBQC6G4uBhnzpwBAOTm5gIAkpKSoFarvbnbPslbY5Wfn48JEyYgKioKmZmZKCoqMt03LCxMlOlkYuSt8frxxx+Rk5ODtLQ0aDQanDx5EsuWLUN8fDzS0tLaZd99kbfGq/nVlMbHB4DExES2OHWSNz9bP/30E0aOHImgoCBkZ2dj8eLFuPnmm3kC7AJvjVdQUBBmzZqF5cuXo0uXLoiPj8fatWtRUlKCO++8s1323Rd587gQaCgCeOTIEbz66qve3dEOwlvjNXjwYAQHB2P+/Pl44oknoFQq8c477+DUqVO48cYb22XffY03P1tr167F4MGDoVarsWvXLjz77LN47rnnRFlAn21NqUX23rhPPvkkFi1aBKBh7tTy5cuxbt066HQ6DBw4ECtWrECvXr1M62dmZuLFF1+0epxVq1bhrrvuAgDMmzcPH3zwgdU627Ztw8iRI92xOx2at8Zqw4YNdutVONOWjBp4a7xyc3OxePFiHDx4EBUVFYiKisK4ceOwYMECngC7wJvfhc2dPn0a/fv3Z1tTF3hrrA4cOIDHHnsMx44dQ21tLeLi4nDbbbfh0UcfRUBAgGd2rgPy5merrq4OS5YswcaNG1FVVYV+/fph6dKlSE1N9cCedUze/i584IEHkJ2dzc5zreTN8crOzsaSJUuQnZ2N+vp6pKSk4IknnmDAwkneHKv7778fX3/9NSoqKpCcnIyHH34Y06ZN88BetR0DFkREREREREQkOqxhQURERERERESiw4AFEREREREREYkOAxZEREREREREJDoMWBARERERERGR6DBgQURERERERESiw4AFEREREREREYkOAxZEREREREREJDoMWBAREZFX7NmzB1qt1vQvJCQECQkJGDp0KB544AHs2LEDRqOx1Y+fm5uLzMxMnD592o1bTURERO1F1t4bQERERJ3LlClTcMMNN8BoNKK8vBx5eXnYvn07Nm7ciFGjRmHdunXQarUuP+5vv/2GF198ESNGjEBCQoIHtpyIiIi8iQELIiIi8qr+/ftj6tSpZsuWLVuGZ599FqtWrcKf/vQnbN68uZ22joiIiMSCU0KIiIio3UmlUixduhRDhw7Fjh07sG/fPgBAfn4+nnrqKVPWRGRkJIYMGYJXX30Ver3edP/MzEzMnz8fADBx4kTTtJN58+aZ1qmpqUFWVhbS0tIQGRmJ+Ph4TJ06FTk5Od7dWSIiInIKMyyIiIhINO6++27s27cPX3/9NYYOHYqDBw9i27ZtmDBhAhITE1FXV4cdO3bg+eefx6lTp/Dqq68CaAhSFBQUYN26dViwYAFSUlIAAImJiQCAuro63H777fjxxx8xdepUzJkzB6WlpXjnnXdw00034YsvvsCAAQPabb+JiIjIGgMWREREJBq9e/cGABw/fhwAMHz4cOTk5EAQBNM6Dz74IObOnYt3330XCxcuRFRUFPr06YPrrrsO69atw6hRozBy5Eizx127di327t2LLVu2YOzYsabls2fPxrBhw/D0009j+/btXthDIiIichanhBAREZFoBAUFAQDKysoAAEql0hSsqK2tRXFxMYqKijB27FgYDAZkZ2c79bgffvghUlJSkJqaiqKiItO/uro6jBo1Cvv370dVVZVndoqIiIhahRkWREREJBqlpaUAgMDAQABAfX09XnnlFWzcuBEnTpywanuq0+mcetxjx46hqqoKXbt2tbtOUVERYmNjW7nlRERE5G4MWBAREZFoHDx4EACQnJwMAFi8eDHWrl2L2267DQsWLEB4eDjkcjlycnLw3HPPwWAwOPW4RqMRvXr1wrJly+yuExYW1vYdICIiIrdhwIKIiIhE47333gMAjB8/HgCwadMmDBs2DP/617/M1jtx4oTVfZvXubCUlJSEoqIipKenQyLhjFgiIiJfwF9sIiIiand6vR5PP/009u3bh/HjxyMtLQ1AQ7tTy2kgFRUVWL16tdVjqFQqAEBxcbHVbdOnT0dBQQFWrVpl8/kLCwvbugtERETkZsywICIiIq/KycnBpk2bAADl5eXIy8vD9u3bcfbsWYwZMwZvvPGGad2MjAy8/fbbmDVrFkaNGoXCwkK89957CAkJsXrca6+9FhKJBFlZWdDpdFCpVEhISMCgQYPwwAMPYNeuXXjmmWewe/dupKenIzAwEOfOncN3330HhUKBzz//3GuvAREREbVM0Ol0xpZXIyIiImqbPXv2YOLEiaa/JRIJ1Go1YmJikJqaiilTpmDcuHFm96msrERmZiY++eQTXLp0CV26dME999yDa6+9FhkZGVi1ahXuuusu0/rvv/8+Vq5ciRMnTqCurg7Tp0/HmjVrADQU8HzzzTexadMmHD16FAAQFRWFgQMHYvr06RgzZowXXgUiIiJyFgMWRERERERERCQ6rGFBRERERERERKLDgAURERERERERiQ4DFkREREREREQkOgxYEBEREREREZHoMGBBRERERERERKLDgAURERERERERiQ4DFkREREREREQkOgxYEBEREREREZHoMGBBRERERERERKLDgAURERERERERic7/B2EgqxColtuLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the imported data\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.title('Close Price History')\n",
    "plt.plot(df['Close'])\n",
    "plt.xlabel('Date', fontsize=18)\n",
    "plt.ylabel('Close Price', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1680"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Considering only Closing Price column \n",
    "data = df.filter(['Close'])\n",
    "dataset = data.values\n",
    "training_data_len = math.ceil(len(dataset)*.8)\n",
    "training_data_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.16584967],\n",
       "       [0.16319444],\n",
       "       [0.1621732 ],\n",
       "       ...,\n",
       "       [0.54595588],\n",
       "       [0.52736928],\n",
       "       [0.54268791]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scaling data\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "scaled_data = scaler.fit_transform(dataset)\n",
    "\n",
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([0.16584967, 0.16319444, 0.1621732 , 0.14971405, 0.15400327,\n",
      "       0.15236928, 0.14562908, 0.14338235, 0.14848856, 0.15257353,\n",
      "       0.14522059, 0.13521242, 0.12724673, 0.13031046, 0.12806373,\n",
      "       0.13459967, 0.12908497, 0.13562092, 0.12377451, 0.1190768 ,\n",
      "       0.11397059, 0.13112745, 0.12479575, 0.14603758, 0.14501634,\n",
      "       0.14726307, 0.15196078, 0.1503268 , 0.14971405, 0.16156046,\n",
      "       0.17177288, 0.1693219 , 0.16830065, 0.17238562, 0.19097222,\n",
      "       0.18913399, 0.19138072, 0.18382353, 0.17544935, 0.17279412,\n",
      "       0.16830065, 0.17034314, 0.17810458, 0.16973039, 0.16278595,\n",
      "       0.17401961, 0.18035131, 0.17422386, 0.17116013, 0.16564542,\n",
      "       0.16033497, 0.17381536, 0.17851307, 0.18811275, 0.19750817,\n",
      "       0.21425654, 0.21098856, 0.21772876, 0.20445261, 0.21568627])]\n",
      "[0.21282679738562094]\n",
      "\n",
      "[array([0.16584967, 0.16319444, 0.1621732 , 0.14971405, 0.15400327,\n",
      "       0.15236928, 0.14562908, 0.14338235, 0.14848856, 0.15257353,\n",
      "       0.14522059, 0.13521242, 0.12724673, 0.13031046, 0.12806373,\n",
      "       0.13459967, 0.12908497, 0.13562092, 0.12377451, 0.1190768 ,\n",
      "       0.11397059, 0.13112745, 0.12479575, 0.14603758, 0.14501634,\n",
      "       0.14726307, 0.15196078, 0.1503268 , 0.14971405, 0.16156046,\n",
      "       0.17177288, 0.1693219 , 0.16830065, 0.17238562, 0.19097222,\n",
      "       0.18913399, 0.19138072, 0.18382353, 0.17544935, 0.17279412,\n",
      "       0.16830065, 0.17034314, 0.17810458, 0.16973039, 0.16278595,\n",
      "       0.17401961, 0.18035131, 0.17422386, 0.17116013, 0.16564542,\n",
      "       0.16033497, 0.17381536, 0.17851307, 0.18811275, 0.19750817,\n",
      "       0.21425654, 0.21098856, 0.21772876, 0.20445261, 0.21568627]), array([0.16319444, 0.1621732 , 0.14971405, 0.15400327, 0.15236928,\n",
      "       0.14562908, 0.14338235, 0.14848856, 0.15257353, 0.14522059,\n",
      "       0.13521242, 0.12724673, 0.13031046, 0.12806373, 0.13459967,\n",
      "       0.12908497, 0.13562092, 0.12377451, 0.1190768 , 0.11397059,\n",
      "       0.13112745, 0.12479575, 0.14603758, 0.14501634, 0.14726307,\n",
      "       0.15196078, 0.1503268 , 0.14971405, 0.16156046, 0.17177288,\n",
      "       0.1693219 , 0.16830065, 0.17238562, 0.19097222, 0.18913399,\n",
      "       0.19138072, 0.18382353, 0.17544935, 0.17279412, 0.16830065,\n",
      "       0.17034314, 0.17810458, 0.16973039, 0.16278595, 0.17401961,\n",
      "       0.18035131, 0.17422386, 0.17116013, 0.16564542, 0.16033497,\n",
      "       0.17381536, 0.17851307, 0.18811275, 0.19750817, 0.21425654,\n",
      "       0.21098856, 0.21772876, 0.20445261, 0.21568627, 0.2128268 ])]\n",
      "[0.21282679738562094, 0.19975490196078427]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Preparing the Training data\n",
    "train_data = scaled_data[0:training_data_len, :]\n",
    "\n",
    "x_train = []\n",
    "y_train = []\n",
    "\n",
    "for i in range(60, len(train_data)):\n",
    "    x_train.append(train_data[i-60 : i, 0])\n",
    "    y_train.append(train_data[i, 0])\n",
    "    if i<= 61:\n",
    "        print(x_train)\n",
    "        print(y_train)\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, y_train = np.array(x_train), np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1620, 60, 1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preparing the LSTM model\n",
    "model = Sequential()\n",
    "model.add(LSTM(50, return_sequences = True, input_shape = (x_train.shape[1], 1)))\n",
    "model.add(LSTM(50, return_sequences = False))\n",
    "model.add(Dense(25))\n",
    "model.add(Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compiling the model\n",
    "model.compile(optimizer = 'adam', loss = 'mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1620/1620 [==============================] - 187s 116ms/step - loss: 8.3394e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f7fd402cb50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, batch_size = 1, epochs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preparing the test data from index 1620 to the last\n",
    "test_data = scaled_data[training_data_len-60: , :]\n",
    "x_test = []\n",
    "y_test = dataset[training_data_len:, :]\n",
    "for i in range(60, len(test_data)):\n",
    "    x_test.append(test_data[i-60: i, 0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = np.array(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting the model\n",
    "predictions = model.predict(x_test)\n",
    "predictions = scaler.inverse_transform(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.679063560849145"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Root Mean Square Error of the model\n",
    "rmse = np.sqrt(np.mean(predictions - y_test)**2)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-19-308770e25339>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  valid['Predictions'] = predictions\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train = data[:training_data_len]\n",
    "valid = data[training_data_len:]\n",
    "valid['Predictions'] = predictions\n",
    "\n",
    "#Visualize the data\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.title('Model')\n",
    "plt.xlabel('Date', fontsize = 18)\n",
    "plt.ylabel('Close Price in Rupees', fontsize = 18)\n",
    "plt.plot(train['Close'])\n",
    "plt.plot(valid[['Close', 'Predictions']])\n",
    "plt.legend(['Train', 'Val', 'Predictions'], loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Close</th>\n",
       "      <th>Predictions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-27</th>\n",
       "      <td>152.10</td>\n",
       "      <td>152.967590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-28</th>\n",
       "      <td>153.10</td>\n",
       "      <td>153.342773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-02</th>\n",
       "      <td>157.70</td>\n",
       "      <td>153.678497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-03</th>\n",
       "      <td>156.20</td>\n",
       "      <td>154.909363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-04</th>\n",
       "      <td>155.35</td>\n",
       "      <td>155.967575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>219.40</td>\n",
       "      <td>218.957214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>219.10</td>\n",
       "      <td>219.525177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>214.60</td>\n",
       "      <td>219.477905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>210.05</td>\n",
       "      <td>218.060806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>213.80</td>\n",
       "      <td>215.281830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>420 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Close  Predictions\n",
       "Date                           \n",
       "2017-04-27  152.10   152.967590\n",
       "2017-04-28  153.10   153.342773\n",
       "2017-05-02  157.70   153.678497\n",
       "2017-05-03  156.20   154.909363\n",
       "2017-05-04  155.35   155.967575\n",
       "...            ...          ...\n",
       "2018-12-31  219.40   218.957214\n",
       "2019-01-01  219.10   219.525177\n",
       "2019-01-02  214.60   219.477905\n",
       "2019-01-03  210.05   218.060806\n",
       "2019-01-04  213.80   215.281830\n",
       "\n",
       "[420 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the valid and predicted prices\n",
    "valid"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}