task 2  Stock Market Prediction And Forecasting Using Stacked LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7c544478",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b9b50699",
   "metadata": {},
   "outputs": [],
   "source": [
    "sp=pd.read_csv('hello.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fb1b9c3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-09-28</td>\n",
       "      <td>234.05</td>\n",
       "      <td>235.95</td>\n",
       "      <td>230.20</td>\n",
       "      <td>233.50</td>\n",
       "      <td>233.75</td>\n",
       "      <td>3069914</td>\n",
       "      <td>7162.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>234.55</td>\n",
       "      <td>236.80</td>\n",
       "      <td>231.10</td>\n",
       "      <td>233.80</td>\n",
       "      <td>233.25</td>\n",
       "      <td>5082859</td>\n",
       "      <td>11859.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>240.00</td>\n",
       "      <td>240.00</td>\n",
       "      <td>232.50</td>\n",
       "      <td>235.00</td>\n",
       "      <td>234.25</td>\n",
       "      <td>2240909</td>\n",
       "      <td>5248.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-09-25</td>\n",
       "      <td>233.30</td>\n",
       "      <td>236.75</td>\n",
       "      <td>232.00</td>\n",
       "      <td>236.25</td>\n",
       "      <td>236.10</td>\n",
       "      <td>2349368</td>\n",
       "      <td>5503.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>233.55</td>\n",
       "      <td>239.20</td>\n",
       "      <td>230.75</td>\n",
       "      <td>234.00</td>\n",
       "      <td>233.30</td>\n",
       "      <td>3423509</td>\n",
       "      <td>7999.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date    Open    High     Low    Last   Close  Total Trade Quantity  \\\n",
       "0  2018-09-28  234.05  235.95  230.20  233.50  233.75               3069914   \n",
       "1  2018-09-27  234.55  236.80  231.10  233.80  233.25               5082859   \n",
       "2  2018-09-26  240.00  240.00  232.50  235.00  234.25               2240909   \n",
       "3  2018-09-25  233.30  236.75  232.00  236.25  236.10               2349368   \n",
       "4  2018-09-24  233.55  239.20  230.75  234.00  233.30               3423509   \n",
       "\n",
       "   Turnover (Lacs)  \n",
       "0          7162.35  \n",
       "1         11859.95  \n",
       "2          5248.60  \n",
       "3          5503.90  \n",
       "4          7999.55  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0c23048a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>2010-07-27</td>\n",
       "      <td>117.6</td>\n",
       "      <td>119.50</td>\n",
       "      <td>112.00</td>\n",
       "      <td>118.80</td>\n",
       "      <td>118.65</td>\n",
       "      <td>586100</td>\n",
       "      <td>694.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>2010-07-26</td>\n",
       "      <td>120.1</td>\n",
       "      <td>121.00</td>\n",
       "      <td>117.10</td>\n",
       "      <td>117.10</td>\n",
       "      <td>117.60</td>\n",
       "      <td>658440</td>\n",
       "      <td>780.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>2010-07-23</td>\n",
       "      <td>121.8</td>\n",
       "      <td>121.95</td>\n",
       "      <td>120.25</td>\n",
       "      <td>120.35</td>\n",
       "      <td>120.65</td>\n",
       "      <td>281312</td>\n",
       "      <td>340.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>2010-07-22</td>\n",
       "      <td>120.3</td>\n",
       "      <td>122.00</td>\n",
       "      <td>120.25</td>\n",
       "      <td>120.75</td>\n",
       "      <td>120.90</td>\n",
       "      <td>293312</td>\n",
       "      <td>355.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>2010-07-21</td>\n",
       "      <td>122.1</td>\n",
       "      <td>123.00</td>\n",
       "      <td>121.05</td>\n",
       "      <td>121.10</td>\n",
       "      <td>121.55</td>\n",
       "      <td>658666</td>\n",
       "      <td>803.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date   Open    High     Low    Last   Close  Total Trade Quantity  \\\n",
       "2030  2010-07-27  117.6  119.50  112.00  118.80  118.65                586100   \n",
       "2031  2010-07-26  120.1  121.00  117.10  117.10  117.60                658440   \n",
       "2032  2010-07-23  121.8  121.95  120.25  120.35  120.65                281312   \n",
       "2033  2010-07-22  120.3  122.00  120.25  120.75  120.90                293312   \n",
       "2034  2010-07-21  122.1  123.00  121.05  121.10  121.55                658666   \n",
       "\n",
       "      Turnover (Lacs)  \n",
       "2030           694.98  \n",
       "2031           780.01  \n",
       "2032           340.31  \n",
       "2033           355.17  \n",
       "2034           803.56  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5f66621c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                     object\n",
       "Open                    float64\n",
       "High                    float64\n",
       "Low                     float64\n",
       "Last                    float64\n",
       "Close                   float64\n",
       "Total Trade Quantity      int64\n",
       "Turnover (Lacs)         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "eab1fd49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-09-28    1\n",
       "2013-04-10    1\n",
       "2013-03-20    1\n",
       "2013-03-21    1\n",
       "2013-03-22    1\n",
       "             ..\n",
       "2016-01-11    1\n",
       "2016-01-12    1\n",
       "2016-01-13    1\n",
       "2016-01-14    1\n",
       "2010-07-21    1\n",
       "Name: Date, Length: 2035, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp['Date'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "608912b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp['High'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dcf8cc9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GRmLTpk24du0ahg0bBm9vb8yaNQvNmzdHeno6Ll68iAYNGiA2NhbFixdHbGysdh0xMTGYMGGCxWkhrotyRgoyyiUhhJiwcuVKhIWFYfny5ahatSq6d++O2bNnY9GiRVCr1QgICEDdunURExMDID/w+Oeff5Ceno6kpCTcuHEDLVu2lHU/iGOTlDMyf/587Ny5E9euXYOPjw+aNGmCb775BlWqVDG4TExMDFq1aqU3/erVq6hatar0FBNCiJPwcffB6X6nZdu2NVy9ehURERFgmPys1KZNmyIjIwMPHjxAmTJl0LJlS8TExGDixIk4fvw45s6di6ioKPz111948eIFgoKC6H5PjJIUjMTGxmLUqFFo1KgRlEolpk+fjvbt2+PKlSvw8/Mzuuz169dRuHBh7ecSJUqYl2JiEZayQwixG4ZhrFJUIieWZXmBiGYaAO30li1bYt26dfjnn3+gUChQvXp1tGjRArGxsUhJSUGLFi3snm7iXCQFIwcPHuR93rBhA0qWLInz58+jefPmRpctWbIkihQpIjmBxIaozgghxITq1asjKiqKF5ScOHEC/v7+KFWqFABo640sWbIELVq0AMMwaNGiBebPn4+UlBSMGzdOzl0gTsCiOiOpqakAgMDAQJPz1qtXDyEhIWjTpg2io6ONzpudnY20tDTeH7EByiQhhHCkpqYiLi6O9zd8+HDcv38fY8aMwbVr1/D7779j5syZmDhxIhSKvEeIpt7Ili1btHVDmjdvjgsXLlB9ESKK2a1pWJbFxIkT8dZbb6FmzZoG5wsJCcEPP/yABg0aIDs7G5s3b0abNm0QExNjMDdl/vz5mD17trlJI8ZQd/CEEANiYmJQr1493rRBgwbhwIED+OSTT1CnTh0EBgZiyJAh+Pzzz3nztWrVChcuXNAGHkWLFkX16tXx6NEjVKtWzV67QJwUw5rZGH3UqFHYv38//vrrL5QuXVrSsl27dgXDMNizZ4/g99nZ2cjOztZ+TktLQ1hYGFJTU3n1Toh0r9LTsHJoPwDAsBXrUbh4SZlTRIjryMrKQnx8PMLDw+Ht7S13cgixC2PnfVpaGgICAkw+v80qphkzZgz27NmD6OhoyYEIADRu3Bg3b940+L2XlxcKFy7M+yOEEEKIa5JUTMOyLMaMGYNdu3YhJiYG4eHhZm304sWLCAkJMWtZYkVUYkMIIcQBSApGRo0ahZ9//hm///47/P39kZSUBCCv8pKPT16b9mnTpuHhw4fYtGkTAGDJkiUoV64catSogZycHGzZsgVRUVGIioqy8q4QMazZRTQhhBBiDZKCkVWrVgGAXs3oDRs2IDIyEgCQmJiIhIQE7Xc5OTmYPHkyHj58CB8fH9SoUQP79+9H586dLUs5sRw17SWEEOIAJBfTmLJx40be5ylTpmDKlCmSEkUIIYSQgoPGpinIqMSGEEKIA6BghBBCCCGyomCkIKM6I4QQQhwABSOEEEIIkRUFIwUZ1RkhhBDiACgYKWionxFCiIDIyEh0797d6uuNiYkBwzB48eKF1ddNXAcFI4QQQgiRFQUjhBBiIyzLQp2ZKcufNXtbXrx4MWrVqgU/Pz+EhYVh5MiRyMjI0H5/7949dO3aFUWLFoWfnx9q1KiBAwcO4O7du2jVqhWAvFF8GYbRdpBJCJekTs+Ia6Gu4QmxLfbVK1yv30CWbVe5cB6Mr69V1qVQKLB06VKUK1cO8fHxGDlyJKZMmYKVK1cCyBsqJCcnB8eOHYOfnx+uXLmCQoUKISwsDFFRUejZsyeuX7+OwoULa4cOIYSLgpEChgIQQohU48eP1/47PDwcc+bMwccff6wNRhISEtCzZ0/UqlULAFC+fHnt/IGBgQCAkiVLokiRInZLM3EuFIwQQoiNMD4+qHLhvGzbtpbo6Gh89dVXuHLlCtLS0qBUKpGVlYWXL1/Cz88PY8eOxccff4zDhw+jbdu26NmzJ2rXrm217RPXR3VGCCHERhiGgcLXV5Y/hrFOr4b37t1D586dUbNmTURFReH8+fNYsWIFACA3NxcAMHToUNy5cwcDBgzApUuX0LBhQyxbtswq2ycFAwUjBRoV2RBCjDt37hyUSiUWLVqExo0bo3Llynj06JHefGFhYRgxYgR27tyJSZMm4ccffwQAeHp6AgBUKpVd002cCxXTEEIIAQCkpqYiLi6ON61EiRJQKpVYtmwZunbtir///hurV6/mzTN+/Hh06tQJlStXRkpKCv78809Uq1YNAFC2bFkwDIN9+/ahc+fO8PHxQaFChey1S8RJUM4IIYQQAHkdlNWrV4/3t379eixevBjffPMNatasia1bt2L+/Pm85VQqFUaNGoVq1aqhY8eOqFKlirZya6lSpTB79mxMnToVQUFBGD16tBy7RhwcwzpB84q0tDQEBAQgNTUVhQsXljs5Tu3lixSs/mgAAGDosrUIKBksc4oIcR1ZWVmIj49HeHg4vL295U4OIXZh7LwX+/ymnJEChht7On4YSgghpCCgYIQQQgghsqJghBBCCCGyomCEEEIIIbKiYKSg4VYUoUojhBBCHAAFI4QQQgiRFQUjhBBCCJEVBSOEEEIIkRUFIwUMyxmPhqWxaQghEjAMg927d8udDOKCKBghhBACAEhKSsKYMWNQvnx5eHl5ISwsDF27dsUff/whd9KIi6OB8gghhODu3bto2rQpihQpgm+//Ra1a9dGbm4uDh06hFGjRuHatWtyJ5G4MMoZIYQQgpEjR4JhGJw5cwbvvfceKleujBo1amDixIk4deqU4DKXLl1C69at4ePjg2LFimH48OHIyMjQfh8TE4M33ngDfn5+KFKkCJo2bYp79+5pv9+7dy8aNGgAb29vlC9fHrNnz4ZSqbT5vhLHQzkjBQ23mgj1M0KITbEsC2WOWpZtu3sqwDCMqHmfP3+OgwcPYt68efDz89P7vkiRInrTMjMz0bFjRzRu3Bhnz55FcnIyhg4ditGjR2Pjxo1QKpXo3r07hg0bhm3btiEnJwdnzpzRpunQoUP44IMPsHTpUjRr1gy3b9/G8OHDAQAzZ840f8eJU6JghBBCbESZo8YP42Jl2fbw71vAw8tN1Ly3bt0Cy7KoWrWq6PVv3boVr169wqZNm7QBzPLly9G1a1d888038PDwQGpqKrp06YIKFSoAAKpVq6Zdft68eZg6dSoGDRoEAChfvjzmzJmDKVOmUDBSAFEwQgghBZxmNG+xOSkAcPXqVdSpU4eXk9K0aVOo1Wpcv34dzZs3R2RkJDp06IB27dqhbdu26N27N0JCQgAA58+fx9mzZzFv3jzt8iqVCllZWcjMzISvr6+V9o44AwpGChiWimYIsRt3TwWGf99Ctm2LValSJTAMg6tXr6J79+6ilmFZ1mDwopm+YcMGjB07FgcPHsT27dvx+eef48iRI2jcuDHUajVmz56NHj166C3v7e0tOu3ENVAwUoBRXEKIbTEMI7qoRE6BgYHo0KEDVqxYgbFjx+rVG3nx4oVevZHq1avjp59+wsuXL7Xz//3331AoFKhcubJ2vnr16qFevXqYNm0aIiIi8PPPP6Nx48aoX78+rl+/jooVK9p8/4jjo9Y0hBBCsHLlSqhUKrzxxhuIiorCzZs3cfXqVSxduhQRERF68/fv3x/e3t4YNGgQ/vvvP0RHR2PMmDEYMGAAgoKCEB8fj2nTpuHkyZO4d+8eDh8+jBs3bmjrjcyYMQObNm3CrFmzcPnyZVy9elWbe0IKHsoZIYQQgvDwcFy4cAHz5s3DpEmTkJiYiBIlSqBBgwZYtWqV3vy+vr44dOgQxo0bh0aNGsHX1xc9e/bE4sWLtd9fu3YNP/30E549e4aQkBCMHj0aH330EQCgQ4cO2LdvH7788kt8++238PDwQNWqVTF06FC77jdxDAzrBJUI0tLSEBAQgNTUVBQuXFju5Di1tKfJ+HHUYADAh9+tQWBoKZlTRIjryMrKQnx8PMLDw6neAykwjJ33Yp/fVExToDl8HEoIIaQAoGCEEEIIIbKiYIQQQgghsqJgpKChkhlCCCEOhoKRAswJ6i4TQggpACgYIYQQQoisKBghhBBCiKwoGClgqGiGEEKIo6FgpCCjuIQQQogDoGCEEEKI3ZUrVw5LliyROxmIjIwUPVKxs9u4caPegIeOQlIwMn/+fDRq1Aj+/v4oWbIkunfvjuvXr5tcLjY2Fg0aNIC3tzfKly+P1atXm51gQggh1sMwjNG/yMhIk8vv3r3bqmkqV66c0TS1bNnSqtuzhsuXL6N3794oUaIEvLy8UKlSJXzxxRfIzMyUJT1CwV6fPn1w48YN7edZs2ahbt269k2YAZIGyouNjcWoUaPQqFEjKJVKTJ8+He3bt8eVK1f0hpzWiI+PR+fOnTFs2DBs2bIFf//9N0aOHIkSJUqgZ8+eVtkJIgWVzRBC8iUmJmr/vX37dsyYMYP3kunj42P3NJ09exYqlQoAcOLECfTs2RPXr1/Xjm3i6enJmz83NxceHh52T6fGqVOn0LZtW7Rt2xb79+9HUFAQzpw5g0mTJuHPP/9EdHS0Xprl4OPjI8vxFIW1QHJyMguAjY2NNTjPlClT2KpVq/KmffTRR2zjxo1Fbyc1NZUFwKamppqdVpLnxeNEdmHvt9mFvd9mn96/J3dyCHEpr169Yq9cucK+evVK7qSYZcOGDWxAQABv2sqVK9ny5cuzHh4ebOXKldlNmzZpvytbtiyLvDccFgBbtmxZlmVZ9tatW+w777zDlixZkvXz82MbNmzIHjlyhLfesmXLst99953JNEVHR7MA2JSUFO00AOyqVavYd955h/X19WVnzJjBKpVKdvDgwWy5cuVYb29vtnLlyuySJUt461IqleyECRPYgIAANjAwkP3kk0/YgQMHst26ddPOo1ar2W+++YYNDw9nvb292dq1a7M7duwwmD61Ws1Wr16dbdiwIatSqXjfxcXFsQzDsF9//TXLsiwbHx/PAmAvXryonSclJYUFwEZHR2vTaGo/Bg0axHbr1o1dsGABGxwczAYGBrIjR45kc3JyWJZl2RYtWvCOi+ZRzz2+GzZs0Jtnw4YN7Icffsi+/fbbvO3l5uayQUFB7Lp16wR/A2Pnvdjnt6ScEV2pqakAgMDAQIPznDx5Eu3bt+dN69ChA9atW2cwms3OzkZ2drb2c1pamiXJJIQQWbAsCyXnXmZP7l5eYBjGonXs2rUL48aNw5IlS9C2bVvs27cPH374IUqXLo1WrVrh7NmzKFmyJDZs2ICOHTvCzc0NAJCRkYHOnTtj7ty58Pb2xk8//YSuXbvi+vXrKFOmjDV2DzNnzsT8+fPx3Xffwc3NDWq1GqVLl8avv/6K4sWL48SJExg+fDhCQkLQu3dvAMCiRYuwfv16rFu3DtWrV8eiRYuwa9cutG7dWrvezz//HDt37sSqVatQqVIlHDt2DB988AFKlCiBFi1a6KUjLi4OV65cwc8//wyFgl/zoU6dOmjbti22bduGTz/9VNR+idkPAIiOjkZISAiio6Nx69Yt9OnTB3Xr1sWwYcOwc+dO1KlTB8OHD8ewYcMEt9OnTx/8999/OHjwII4ePQoACAgIQOXKldG8eXMkJiYiJCQEAHDgwAFkZGTwtm9tZgcjLMti4sSJeOutt1CzZk2D8yUlJSEoKIg3LSgoCEqlEk+fPtXuLNf8+fMxe/Zsc5NGjKCWvYTYjzI7G0sHvSfLtsf+9Bs8dIZzl2rhwoWIjIzEyJEjAQATJ07EqVOnsHDhQrRq1QolSpQAABQpUgTBwcHa5erUqYM6depoP8+dOxe7du3Cnj17MHr0aIvSpNGvXz8MHjyYN4373AgPD8eJEyfw66+/ah+iS5YswbRp07RVBFavXo1Dhw5pl3n58iUWL16MP//8ExEREQCA8uXL46+//sKaNWsEgxFNHYxq1aoJprNatWr466+/RO+Xh4eHyf0AgKJFi2L58uVwc3ND1apV8fbbb+OPP/7AsGHDEBgYCDc3N/j7+/OOC5ePjw8KFSoEd3d33jxNmjRBlSpVsHnzZkyZMgUAsGHDBvTq1QuFChUSvR9Smd2aZvTo0fj333+xbds2k/PqRufs6yeioah92rRpSE1N1f7dv3/f3GQSQggx09WrV9G0aVPetKZNm+Lq1atGl3v58iWmTJmC6tWro0iRIihUqBCuXbuGhIQEq6WtYcOGetNWr16Nhg0bokSJEihUqBB+/PFH7TZTU1ORmJioDTIAwN3dnbeeK1euICsrC+3atUOhQoW0f5s2bcLt27fNSifLspLrixjbD40aNWpoc6IAICQkBMnJyWalUdfQoUOxYcMGAEBycjL279+vF/hZm1k5I2PGjMGePXtw7NgxlC5d2ui8wcHBSEpK4k1LTk6Gu7s7ihUrJriMl5cXvLy8zEkakYClbBJCbMrdywtjf/pNtm1bg9DLpKnin08++QSHDh3CwoULUbFiRfj4+OC9995DTk6OVdIEQK/RxK+//ooJEyZg0aJFiIiIgL+/PxYsWIDTp0+LXqdarQYA7N+/H6VKleJ9Z+iZVKlSJQB5gYxQy5Rr166hcuXKAKAtxuHee3Nzc83aD90qDgzDaNNvqYEDB2Lq1Kk4efIkTp48iXLlyqFZs2ZWWbchkoIRlmUxZswY7Nq1CzExMQgPDze5TEREBPbu3cubdvjwYTRs2FDW2s+EEGJrDMNYXFQiJ00Rw8CBA7XTTpw4wSuS8PDw0LZ80Th+/DgiIyPx7rvvAsirQ3L37l2bpvX48eNo0qSJtkgJAC83IyAgACEhITh16hSaN28OAFAqlTh//jzq168PAKhevTq8vLyQkJAgWCQjpF69eqhatSq+++479O3bl1dv5J9//sHRo0exfPlyANAWayUmJqJevXoA8uqcSNkPsTw9PfWOi9h5ihUrhu7du2PDhg04efIkPvzwQ8nbl0pSMDJq1Cj8/PPP+P333+Hv76/N8QgICNA2F5o2bRoePnyITZs2AQBGjBiB5cuXY+LEiRg2bBhOnjyJdevWiSreITZAuSGEEJE++eQT9O7dG/Xr10ebNm2wd+9e7Ny5U1vhEcjrz+KPP/5A06ZN4eXlhaJFi6JixYrYuXMnunbtCoZh8MUXX1jtrd2QihUrYtOmTTh06BDCw8OxefNmnD17lvfSPG7cOHz99deoVKkSqlWrhsWLF+PFixfa7/39/TF58mRMmDABarUab731FtLS0nDixAkUKlQIgwYN0tsuwzBYu3Yt2rdvj549e2LatGkIDg7G6dOnMWnSJHTo0AEfffQRgLx6Go0bN8bXX3+NcuXK4enTp/j8888l74cY5cqVw7Fjx9C3b194eXmhePHigvPEx8cjLi4OpUuXhr+/vzYHaOjQoejSpQtUKpXgflubpDojq1atQmpqKlq2bImQkBDt3/bt27XzJCYm8sq2wsPDceDAAcTExKBu3bqYM2cOli5dSn2MEEKIg+vevTu+//57LFiwADVq1MCaNWuwYcMGXqdjixYtwpEjRxAWFqZ92//uu+9QtGhRNGnSBF27dkWHDh20uQ+2MmLECPTo0QN9+vTBm2++iWfPnvFyFwBg0qRJGDhwICIjI7VFIJrcG405c+ZgxowZmD9/PqpVq4YOHTpg7969RoOBpk2b4tSpU3Bzc0OnTp1QtmxZ9O7dG926dcPevXt5dTvWr1+P3NxcNGzYEOPGjcPcuXMl74cYX375Je7evYsKFSpoc2R09ezZEx07dtRWRuZmErRt2xYhISHo0KEDQkNDJW9fKoZ1gooDaWlpCAgIQGpqqrbTG2KeF0mJWDcur6nXoAXLUbxMOXkTRIgLycrKQnx8PMLDw+HtxMUzxDJqtRpDhgzBoUOHEBsbq61X4kwyMzMRGhqK9evXo0ePHkbnNXbei31+09g0hBBCiBUpFAqsW7cOn376KY4fPy53ciRRq9V49OgRvvjiCwQEBOCdd96xy3Yt6vSMOB+WuoMnhBCbUygUGDdunNzJkCwhIQHh4eEoXbo0Nm7cCHd3+4QJFIwQQgghBEBepVY5am9QMU0BRnkkhBBCHAEFI4QQQgiRFQUjBY3jN54ihBBSwFAwQgghhBBZUTBSkFEuCSGEEAdAwQghhBBCZEXBSAFDmSGEEEIcDQUjhBBCCIDr168jODgY6enpcicFly5dQunSpfHy5Uu5k2IXFIwUYE4wLBEhxMYYhjH6FxkZKXcS7Wb69OkYNWoU/P39AQAxMTFgGIY3sq+91KpVC2+88Qa+++47u29bDhSMFDgUgBBC8iUmJmr/lixZgsKFC/Omff/995LWl5uba6OUWoeh9D148AB79uzBhx9+aOcUGfbhhx9i1apVUKlUcifF5igYIYQQG2FZFuoclSx/YnM+g4ODtX8BAQFgGEb7+eDBgyhbtixv/t27d4NhGO3nWbNmoW7duli/fj3Kly8PLy8vsCwLhmGwdu1avPvuu/D19UWlSpWwZ88e3rpiY2PxxhtvwMvLCyEhIZg6dSqUSiUAYM2aNShVqhTUajVvmXfeeQeDBg3Sft67dy8aNGgAb29vlC9fHrNnz9auA8jL+Vm9ejW6desGPz8/zJ07V/B3+PXXX1GnTh2ULl1a1O8GAGfPnkW7du1QvHhxBAQEoEWLFrhw4QJvnhcvXmD48OEICgqCt7c3atasiX379gEA7t27h65du6Jo0aLw8/NDjRo1cODAAe2yHTp0wLNnzxAbGys6Tc6KxqYhhBAbYXPVeDTjhCzbDv2yCRhPN7ts69atW/j1118RFRUFN7f8bc6ePRvffvstFixYgGXLlqF///64d+8eAgMD8fDhQ3Tu3BmRkZHYtGkTrl27hmHDhsHb2xuzZs1Cr169MHbsWERHR6NNmzYAgJSUFBw6dAh79+4FABw6dAgffPABli5dimbNmuH27dsYPnw4AGDmzJnadMycORPz58/Hd999x0sf17Fjx9CwYUNJ+52eno5BgwZh6dKlAIBFixahc+fOuHnzJvz9/aFWq9GpUyekp6djy5YtqFChAq5cuaJNw6hRo5CTk4Njx47Bz88PV65cQaFChbTr9/T0RJ06dXD8+HG0bt1aUtqcDQUjhBBCLJKTk4PNmzejRIkSvOmRkZF4//33AQBfffUVli1bhjNnzqBjx45YuXIlwsLCsHz5cjAMg6pVq+LRo0f49NNPMWPGDAQGBqJjx474+eeftcHIjh07EBgYqP08b948TJ06VZtTUr58ecyZMwdTpkzhBSP9+vXD4MGDje7D3bt30aBBA0n7rRsgrFmzBkWLFkVsbCy6dOmCo0eP4syZM7h69SoqV66sTaNGQkICevbsiVq1aul9p1GqVCncvXtXUrqcEQUjBQxVWiXEfhgPBUK/bCLbtu2lbNmyeoEIANSuXVv7bz8/P/j7+yM5ORkAcPXqVURERPCKfJo2bYqMjAw8ePAAZcqUQf/+/TF8+HCsXLkSXl5e2Lp1K3q91xNpTx6jcLESOH/+PM6ePYt58+Zp16FSqZCVlYXMzEz4+voCgKgcj1evXsHb21vSficnJ2PGjBn4888/8fjxY6hUKmRmZiIhIQEAEBcXh9KlS2sDEV1jx47Fxx9/jMOHD6Nt27bo2bMn7zcDAB8fH2RmZkpKlzOiOiNO7Pmjh/j3j0NQq12/chMhzohhGCg83WT54z7kzaVQKPReYIQqgPr5+Qku7+Hhofd7aOqAaOqVcGm2pZnetWtXqNVq7N+/H/fv38fx48fxdts2yH75EqnJj6FWqzF79mzExcVp/y5duoSbN2/yAgtD6eMqXrw4UlJSTM7HFRkZifPnz2PJkiU4ceIE4uLiUKxYMeTk5ADICySMGTp0KO7cuYMBAwbg0qVLaNiwIZYtW8ab5/nz54KBnquhnBEntmHCRwAAtUqFuu07y5waQoirKVGiBNLT0/Hy5UvtAz0uLs4q665evTqioqJ4QcmJEyfg7++PUqVKAch7mPfo0QNbt27FrVu3ULlyZdSpWRMAoFIpUb9+fVy/fh0VK1a0OD316tXDlStXJC1z/PhxrFy5Ep07591/79+/j6dPn2q/r127Nh48eIAbN24YzB0JCwvDiBEjMGLECEybNg0//vgjxowZo/3+v//+w3vvvWfGHjkXCkZcwMNrl80KRqjIhhBizJtvvglfX1989tlnGDNmDM6cOYONGzdaZd0jR47EkiVLMGbMGIwePRrXr1/HzJkzMXHiRCgU+Zn2/fv3R9euXXH58mV88MEHvHXMmDEDXbp0QVhYGHr16gWFQoF///0Xly5dMthqxpAOHTpg6NChUKlUepVcL126pO17RKNu3bqoWLEiNm/ejIYNGyItLQ2ffPIJLzekRYsWaN68OXr27InFixejYsWKuHbtGhiGQceOHTF+/Hh06tQJlStXRkpKCv78809Uq1ZNu/zdu3fx8OFDtG3bVtK+OCMqpnEBkoIKij8IISIFBgZiy5YtOHDgAGrVqoVt27Zh1qxZVll3qVKlcODAAZw5cwZ16tTBiBEjMGTIEHz++ee8+Vq3bo3AwEBcv34d/fr1433XoUMH7Nu3D0eOHEGjRo3QuHFjLF68WK85shidO3eGh4cHjh49qvdd8+bNUa9ePd4fAKxfvx4pKSmoV68eBgwYgLFjx6JkyZK8ZaOiotCoUSO8//77qF69OqZMmaLtN0SlUmHUqFGoVq0aOnbsiCpVqmDlypXaZbdt24b27dubtT/OhmGd4PU4LS0NAQEBSE1NReHCheVOjsNY1KcLAKByRDN0Hf+pqGWePbiPjZM+BgB88PX3CAqvYLP0EVLQZGVlIT4+HuHh4ZIrQxJxkm7fBAC4eXigRJlyVl33ypUr8fvvv+PQoUNWXa85srOzUalSJWzbtg1NmzaVOzlGGTvvxT6/qZjGBbBUgZUQQiw2fPhwpKSkID09Xa9Yxt7u3buH6dOnO3wgYi0UjLgAVm1m5pbjZ4oRQojduLu7Y/r06XInAwBQuXJlg5VeXRHVGXEB0kraKAAhhBDiWCgYcQEsqzY9EyGEEOKgKBhxBVTcQgqAu3Hn8cOoD5Hw3z9yJ8Uk3cHdCHFl1jjfqc6IC2AlnAhO0HiKEEFR8/PGGtkxZzombd8nc2qEeXp6QqFQ4NGjRyhRogQ8PT2t0hMqyZf7ulmsmlEgKytL5tQUbCzLIicnB0+ePIFCoYCnp6fZ66JgxMpYlsXLlOcoFFjMrtskhMhPoVAgPDwciYmJePTokdzJcUlpT/LGtlG4uSE1O0fm1BAA8PX1RZkyZXid1UlFwYiVHd/2E87+/htaDx6Beh262GWbFIwQ4jg8PT1RpkwZKJVKbedWxHo2LP0GAOBfvATemz5H5tQQNzc3uLu7W5wDSMGIlZ39/TcAQPSGH+wXjFD5NCEOhWEYeHh46A0URyyX+Txv7BdPTw/qWM6FUAVWFyCtO3iW80/KUSGEOCeqi+NaKBixFTteJ9S0lxBCiDOjYMQFmN0DKyGEEOIAKBhxAVTcQggpeKiYxpVQMGIj9izPlFJMwwtbKIghhBDiACgYcQVUTEMIKWioAqtLoWDEBVAFVkIIIc6MghGbsWcxDeWMEEIIcV4UjLgASa1puP2MgIIYQohzokIa10LBiAugYhpCCCHOjIIRG7Fn3SopxTT3LsXZLiGEEEKIGSgYcQFS4p7Yzetslg5CCCHEHBSMuACza35QlRFCCCEOgIIRW6E28IQQYjt0j3UpFIy4ALokCSEFDY3a61ooGCGEEEKIrCQHI8eOHUPXrl0RGhoKhmGwe/duo/PHxMSAYRi9v2vXrpmbZqfAOEF+BXWWRgghxBG4S13g5cuXqFOnDj788EP07NlT9HLXr19H4cKFtZ9LlCghddOEEEIIcUGSg5FOnTqhU6dOkjdUsmRJFClSRPJyhBBCCHFtdqszUq9ePYSEhKBNmzaIjo42Om92djbS0tJ4f06HKlcRQgghotg8GAkJCcEPP/yAqKgo7Ny5E1WqVEGbNm1w7Ngxg8vMnz8fAQEB2r+wsDBbJ7OAojojhBDnRK1pXIvkYhqpqlSpgipVqmg/R0RE4P79+1i4cCGaN28uuMy0adMwceJE7ee0tDQKSAghhBAXJUvT3saNG+PmzZsGv/fy8kLhwoV5f06HgnZCCCFEFFmCkYsXLyIkJESOTRNCCHEFVEzjUiQX02RkZODWrVvaz/Hx8YiLi0NgYCDKlCmDadOm4eHDh9i0aRMAYMmSJShXrhxq1KiBnJwcbNmyBVFRUYiKirLeXrig7MxMZKamoGhIKaus70nCXXj5+PKmUTcjhBBCHIHkYOTcuXNo1aqV9rOmbsegQYOwceNGJCYmIiEhQft9Tk4OJk+ejIcPH8LHxwc1atTA/v370blzZysk33FZ2unZDyMjkfMqEwMXLEeJMuUsWldGynNs+mS0ResghBBHQvkirkVyMNKyZUujPXdu3LiR93nKlCmYMmWK5IQVdDmvMgEAd+POWxyMPH/4wAopIoQQQmyDxqYhhBBCiKwoGLEVZ6hcRZVGCCHOyhnusUQ0CkYKBAo6CCGEOC4KRlxETtYrKHNz5U4GIYQQIhkFIzZizxzE3JxsLBvUC2s+GmC/jRJCiJyomMalUDDiAl4kJQIAsl5mSFqOpeIbQgghDoCCEUIIIYTIioIRm6EsREIIIUQMCkYIIYQQIisKRhydgUpaypwcy9dN/YwQQpyUpUNuEMdCwYiNMDau6f3XLz8JTjfWVT8hhLgMikVcCgUjJjyOv43MtFS5k6Hn5pmTwl8IBCMUoBBCCHFkkgfKK0ge37mFLdPGAwAmbd8nb2J0qNVqweksWHphIIQQ4lQoZ8SIhP/+MX9hG0cErIFgRFLXIZRhQghxUrYuCif2RcGIkzIUjFCRDCGEEGdDwYgRjvxgp2CEEEKIq6BgxEZs3ezMcDENBSOEkIKAimlcCQUjTspYBVaxaGwaQgghjoCCESdlsDiGckYIIYQ4GQpGbMXGNb0ltaYREZ+wLItnD+5DrVZZlC5CCLEHakzjWigYcVKsgaDB3KKXc3t3YuOkj3HkhxWWJIsQQgiRjIIRJ8WrM8J9RZBSTMOZ98SvWwEA/0UftjRphBBie5Q14lIoGLEVmYppzK0ywrIGin0IIYQQG6NgxAXwmxGbF41QvVdCCCFyoWDEFfBKacwNRihnhBDiPGzdlxOxLwpGXABvjAbzqoxQz62EEEJkQ8GIjdg3Zs/fmtk5HBSMEEIIkQkFIw5OalBDORyEWN/dfy/i4MrvkJ35Uu6kEOKS3OVOALE96vadEMtEzfsCAODp64vWkR/JnJqCK+P5M7mTQGyEckZsxY5t4HmbkpQzQkEKIVKkP30idxIKrNTkJKz5eFD+BKq/6lIoGHExVExDCHFFd/+5KHcSiA1RMOICKP4gro6CbMJQj6sujYIRG5HrwqGbNnFJDnNe0wPRYVBw4lIoGHEJvA5DxC/lMDd4QoyjSthEF3V65looGHFCukGEWpU/gi/dtIkrYtV0XhPiyigYcUL7vv/W8JdC92zKASFOj87hAo8yQlwaBSNO6MbJ4wa/ozFmiCuieJrooTojLoWCEVdD3YwQF0RBNiGujYIRG6HWNIRYEZ3XhLg0CkZcDt20iQui05rooFIa10LBiBHOmMvghEkmxCRHaSVGHW8RYhsUjNiKXDctKf2MOMgNnhBTqGkvIa6NghEXQwEGcU10XhP+Cx7D0OPLldDRdDVUTkNckDMWmRJCxKNgxEbkKlkWvGfTjZw4OQpGiC6qv+NaKBhxORJu2nSDJ86CzlWii4IRlyI5GDl27Bi6du2K0NBQMAyD3bt3m1wmNjYWDRo0gLe3N8qXL4/Vq1ebk1YiAr1BEldkr/P6yb14ZDx/ZngGev7JRjcnhGIR1yI5GHn58iXq1KmD5cuXi5o/Pj4enTt3RrNmzXDx4kV89tlnGDt2LKKioiQn1qk4QWsaQpyGHc7rF0mJ2DRlDNZ8PMjm2yLWQNGIK3GXukCnTp3QqVMn0fOvXr0aZcqUwZIlSwAA1apVw7lz57Bw4UL07NlT6uaJCZQzQlyRPc7rpDs3bb4NQogwm9cZOXnyJNq3b8+b1qFDB5w7dw65ubmCy2RnZyMtLY33R0SSctOmwIUQ4ix0MkIYBVV5dCU2P5pJSUkICgriTQsKCoJSqcTTp08Fl5k/fz4CAgK0f2FhYbZOpvXZqJgm51Wm0e8pvCCuiFXTQHmEuDK7hJa6FY80Wa6GmmZNmzYNqamp2r/79+/bPI2CHDDn4OpfMcZnEEgzd0qhooFWTQ8h9mCXzvwc8HonpKCQXGdEquDgYCQlJfGmJScnw93dHcWKFRNcxsvLC15eXrZOmnPQDeRMdIttqmzd2W+3zx7ex4UDv+PN7r1RuERJuZND7MXZT1xidVRM41psfjQjIiJw5MgR3rTDhw+jYcOG8PDwsPXmLWNBUQvjqDW9OcGKM97ft30xGf8ePYjfF86TOynEjuxSMZvaivIoc3Nx5vffkHz3jtxJAaB/T6Wj5VokByMZGRmIi4tDXFwcgLymu3FxcUhISACQV8QycOBA7fwjRozAvXv3MHHiRFy9ehXr16/HunXrMHnyZOvsgS05YLatqexq7k2bZVn8b/kinNixVfB7Z5T98iUAIPnubZlTQuzKHuetiG047EuGDZzftwvHf96IzZ+OlTspLufxnVs48sNyZKa+kDspDkNyMc25c+fQqlUr7eeJEycCAAYNGoSNGzciMTFRG5gAQHh4OA4cOIAJEyZgxYoVCA0NxdKlS6lZr61wbqiPb9/ElePRMiaGEOtw9iDaGSXHO3bAb+tiGpZlsfvbLwGGQfdPvrBq9/Nbpo0HALxMTUH3T76w2nqdmeRgpGXLlkZvDBs3btSb1qJFC1y4cEHqppybA7xAKXNz9KbRTd1yytxcPLp+FaFVqsHd0YsaXQadt3ZXwIutXqWl4s6FswCArIx0+PgXtvo2nj1IMD1TAUE1gJyNqQqq3O9NDZrnzIGJjDfKoz8ux445n+HP9atkS0NBY6ritlUU8IfvlePR2DH3c7zKSJc7KcLsfHzs8eJGg/3lo2DExbCs8f4YWN6/nTcYkbPs/nLsHwCAS38eli0NBQ017bW9/y1fhIRLcTj52895Exz8QUmtaVwLHU0bYRiZflru/VToXmIq58RZOPZ9klhbAQ8U7ElTSdzx2fEm4OCBmSugYMRG5BsnT3wxjalcFEIchcPUdSpADyVH21N7j9prn3PO0X5l+VAwYis2ulJMXx+mOj3jBiOWp4cQW8vNyUZOpvFhEEhBZL8HeUFq0i0Xm/fAWlDJVTHJZIDBq7/qvDkjVPGr4FjxYR+olEq5k1HwcK4xlmUd7ppztPSYxRX2wUooZ8RGbHehmIg2uNGIQBJYV2lNQ28qBQYFIvK7+48Dds1AtwCXQsGIzciUMwJTFVT5PbQKeXznlnUTZQP0QlEwOExdETt4+SIFf25cg2cP7DswaEriQ1yK5rcMY9VqpD/LH1U9I+WZXdMkBhWduBYKRmxEtixEk/2QmJ5X0zsgIbITOEcrvdlEhoTY3oFlC3Hxf3ux+dMxdt3u+vEf4fDqpbxp/1uxGI+uX9F+lq11oDEu8Ebi/HtgPQ54hrkIW10opt4UTdYZcb4KrEm3buCP9av4nTEZ+H1VylxsnT4RR9eusFPqiC0J9S+iULjJkBLbS7p9E4BjFEtd/SuG99kh62fYvjmNbddPeKgCq41Y++Jl1WpRF5+0gfScowLr1ul54x/lvHqlnWYoizY+7gKSbt1A0q0baDt0lF3SR2xI4HS21SOCHj2GOWIw4ngpIpagYMRGrHnxqlUq/PTJaPj4F0ZlU1nUUqJ5J4v8eWXpBn5fVq2yU2qIPThcnREHfCjbhQPst70DImfuodoZUTGNrVjxwnn28D6eP7yPh9cum5zX5M2bNV2B1SnIf28kdmG/c5ROKcMc8bexZ3fwNgtMHCDIcxQUjNiItU4x3bcBUwGEye/5HY2YnS55OFt6iaUET1Ebnbd0dhlR0MeBcbp7pfMp4GeYDUm8eFVKJZLv3hEMJrjhyKv0NOMrklSB1XkvMIPN+px3l4gQBztHC8J7rFBxiEPsN+UiuDQKRmxEahv4gyu/w+ZPx+Lc3p1G5zu961ej30urwOpYN3pCdDlLJWtXInhfcMBAwK7FNDa6VzpixWC5UDBiKxLPsWt/xwIAzuzeIbAuCSuzsBjHadBFXDDYsZhGfo67X4740LR5ihz3cLgkCkaMsOTBbW4nQSzL6m1X0o1AQj8krNq53jq5v4sD3huJDVCLBvsTvt/QBUdsi4IRGzH3Yam2MEAwXWUkf/3OfaOnm2OB4LK5II5LsN6aQv7rTTcFti6mcZ1xvJwDBSO2YmY0wqrVlp343DohpuqPqJ33AnPEbGNifUKXgjMG0S+SEvHsQYLcyTCbY44DY+s0Od955syo07PXWJYFWNZq0ba5D0vhTrvEr0vajdr1LjZnfFARYxzseJpxXbMsi3XjhgEARq3/Bd5+haydKqsSvHcV8FiEKrDaHuWMvPbbvC/w0yejoVblBwPcE0XyyWjmSaZWqy17oHIWNfk242D3eUnoGi4QXKLCNWcfMp473ui3YjjiQHn0IHctjneGySThUhyePUjA0/v3tNMsqsBq5tNSsFKppPqr4tPsdM0mpQRaxDUItqaxeyoswn25cLZK41qO8ODXS4Nt02SXQNgRflcHQcGIDoPRtsQT06IKXzqbkvIGwEqpM+JkN3VS8LhEsZsdsvttzSGemXqtDG2+Qc6mnfO4ORMKRmCrE816V4qk9LlyzgiXQ9wdic3J9BCw5j2B94IgU87I80cPcOvsKbOXd8RiGlvfA5w9/nC2AMoBzzAZiDhoUt/QLCnP1DuJpMQinJlN1xlxrpOVx0o5WMSxCd1Q7XKTteI2Hl67kr9amYKRDRNG4PeFc3H/8r/mrcARYn/da97W54Edmvba6mf93/JF2PTJaChzc220BeujYAQ6gYYFQQT3Qrdu5SrzckakdA3vDPiBFnEk/xw5gKj5M5GblWXdFdvzHLXRtvYt+ZqzCX7WvzInxybbNOTxnVtmLecQOSN2vl85cxHhlePReHr/Hu79e0HupIjmAGeY/Lj9bRh8yIk4L+9f+S//g0XBCH9jkt6mJLXsdd6LzSAqvrGLRzeu4uH1q9rPR9euxN2484g7vN+q25ErYLbmg4i7Jm7R6I4vP8OyyF7Iysiw2rZMMvP6kPOqOhm1DRcO/G7/0MDAi90/Rw4gdst665ybdL/SomAEgJgnuJgTz809v9sWa51jT+7F4+i6laLnf/bwPrZOn4g7F86anNeSaynrpR1voEIKyEWcnZmJf/84hMy0VLmTopWbk41tX3yCX2Z8opcTkpP1yg4psNFjiXtOGdiEWTmenAuN28Py/SuXoFapcOfCGd3ZHI9M11va02Sc+HUron/6Ue+lzJ6lNNzz4ejalTi3dycSb163bQIKGApGYL2TWuHmlv9BYramb0ARwfRcjv2DV+Zsyt/bNyPp1g3s+ma2yXnNrcB6YsdWrBjcVzu4nxwKSh8Dh1d/jyM/LMPub7+UOyla3ADE1sGHXXNG7LEtoV6P7Xgum3vdyFVMk5udrf23oxUr57zKlDsJLoWCEUDkTShvnpysV7h59iRyc7L15lC4cXNGpF30hYoW0/5bma2/bpsw8+I++ds2AJCUY8OyrMUPrid374jZkEXbcDQ3Tv8NAE70FmblB6sLtKYxlDMi9L05Tu/egYOrlmjTnPYkGUfXrcLzRw8F5jbz+MgU+3Pvo9kvX9p56yaa9haQFyJ7oWAEOjkEvKxa/RNw//ffYs/Cefhjrf6DWOFm+OdkWRand/2qfbjkZmfh3L5dSEl6BADw9PXRzpuZ9kLiHpjHnm8aB1csxrJBvfA4/rbdtkmcn1DdDfuctjZq2muD5vR/bfsJl2OO4uG1ywCA3d9+iX8O78f2WZ9abRuOkBMZs+lHnSm2PREcLSfG1VEwAvB79jRw0WnOS01djMuxf+jNwyjcOP/m/7SPrl/FX79swt7F8wEAJ3b8jNjN67B+/Ed6abBX8z97XmxXjkcDAM7t3WmV9TnCzZHos/phcYHnAW+kbBte25qWOU8S7gIAMlNf2Gxb9iPjdc4a/ADASr1A031Mi4IRWK/mvIITgOieYi9fPOd9fnD1dcub1wEB94YlmJVrA8aCEXMClbv/XsQDbosiAdYKIl6+SMHN0yeMzkNvNs5PttY0Ntqs3OekuZefXOk2ll7bp4kV/KcWxRFWRcEIgKuv39qNEnHiM5xiGqkBBbd5sd06RjK2TxIv9FfpaYia9wW2z54KteDIw69Z8U1gz+KvrLauguZy7B+4eGifDdZsj6wR5+r0jN93lu2u7X3ff4PU5CTjM5kdjZi3mMVkzDkwFexY48XK9mNsOU/ERMEI8ppq5RN/8DKeP+M9eN09vfLXopD209okK9eObzPcpqesUIsBO+E3x6OcESEsy+Lgyu/w5/rVSHv6xG7bfZJwF1FfzUDSrRviF5LpEFq1wyted/C226Hsly+xb8k3JuZynocTIPOAmDod1BHbomBEJKGb05qPB+H3BXO1nz04wYiXrx9/ZhNRNPdkN5qzYEXGgh7JN2ORs9vz1uLMPSjaFOdcs3bzRGNvi7/N/Rx3/7mArdMn4t8/Dolan1OPnyRAeFRu610VL5ISRaXh1y8/w16DgYt+ehzyWrJ1PyNC03hBiXMFdo6OghFdhs4vA5Ext3MxSy5YfjGNla4yUwGQsfRalAQjC9sz29UB75+OgHtDtXpFYCOr41aoPPLDMlGrE7rsnG1sGlu3puExcTwZBnj2IAH3L/+LGyePG5jLgS4ch6ngqV+3zyocZfccAAUj1mRBHQx71bjnbdNI0GPJDV/OYhouyloV5lS5DXIdQ6uW0nB+b1vvjoiHN7c+26Mb18SttyBeS7wg8vX/cocOoUDCqigY0WHwTVHEtcjya6pJ2q5Nhhk3mQbLbjD88lzhcRz0lqErWHb808K6x8PaZfwvTFXIdAY2zBnRDbhN/voMw1smWbDfH4G1OGBrGjn6GeFNs0YFVrofarmbnoWIJnDyqpS5SI6/Y7p1jdqxmvZadKE7zFuUo6TDsfA7+ZMvHaZkZWQgat4X+l/Y4fyyVR0Jo9ecOfulu4yYh5vDXJ+mOUxSBbpgkLVyrQuiYEQkMTcnoQvnfyu+w/UTx1CsdBkTy9rg7cnUjcloPyPSNsXPFTI2p60vYP2sVaLDpnVGrLc+k81UbcmKLSm4i1v7RUMvZ8RUnREwZu2PbJeSjBex4O/EyxmxxlYooNGgYhodvKBA8iWoP//1E8cA5FUaE71dOxXT2KxjJ0fJkaBoRJDFLQKs9LuaGszRUPN4ZW4u7l2KgzI31yrp0DKwX/cvX8pPk1nBFi8a0fvWkseR5BcXhjE8/IXxDUnbjpUYu5fYvE6YwLPA2eqhOVMxEAUjulhD/xaRM8K50UgOY9TWr8Ca8N8/xrdp7EZmyUVnbFnnuTZEyc586XQ3KEsfLKyRXAMpN787F84abcauUgoHGwmX4vDb3M8RvXGN6G1Jxd2t9GeW9cVi7PfiUubmaP/9KiNd3LrV0n7/+5f/lXxfk5WjpE9TTMP9vZ3gQe9M9yYKRvSYnz1r7mG/9nesTbqDP7Vzu/EZjBXTSO5nRNzvZvtIPX/99sihWf5hHxxYttDm27EmftNei1dm4fKGvzI1UvG/Rw9atm1dnB9DKTAqt7n4fQgZ6duH853Ykbv1znETB/TGqb/MCq7kyu2U81kaH3deb5q164zY4nboTAEIV4EPRlKTHxv8jv9GI2JlZp4E+5cuQEriI+1nU0U61mLNynT8ly1jy9qzn5H8dChzc/Ff9BGb9Dh67e9Yq6/TlqTcrE7+tg1rxw7l9RFiWVGm+LREb/zBonVLxkkLN8C0OIA2pz6Y2E3q5owIbp4/jz173bWclSv8SvD39s16m7J2axqboGDEOUV9NYP3mX/hGvq3MEua9nId/3mj2ctKYdUIWmQFVinXr1qlQkriQ6kJEfzn6V2/4tDq77FpymiJ63M9Um6oJ3ZsRerjJJz5/TfuCjj/tjg1lq7AJnhFnFZ86IgtghX71q0X3Ail1RrXuUyHyXHe8rXRiHaKVU4LGwQ0DlNnTyKzgpGVK1ciPDwc3t7eaNCgAY4fN9STHxATEwOGYfT+rl0T2dmOjek+7LbPnqYdituMJiVWSpUDsKTKiJUuhj2Lv8L68R9ZJR13/8nLcs1++dLidDk9M85TlVJpYFVmNC3lLu8gHeQZY83HhbX3V+9QCvz+esfInNY0LtD5nDUIVf6+fvIvnN//u7krtDxReuu0/irtQXIwsn37dowfPx7Tp0/HxYsX0axZM3Tq1AkJCcaLFq5fv47ExETtX6VKlcxOtC1lpafh2usWMGIrnkmZx5FYdWwa3sLWyRq5fe60+WnQ3ayr1ZwVIe7QflwVKELi1RkR+wbOq5xtvTpBTvcWZ+GbrOh7hMjt6OaMCB1P3Xm4KRA6PxyLsXptdkyFtgIr97fMm7ZvydeI2fQjniTctWOKDHO255CG5GBk8eLFGDJkCIYOHYpq1aphyZIlCAsLw6pVq4wuV7JkSQQHB2v/3NzczE60tTy8flVwuup1k0GprVqsVUxjP1asM2LFfhmsxup9AjiP1OTH+GP9KhxYukD/S3PejLnXAm9xSyuwOsi5YoxVi2kEWg8JrF9sUKefMyW0TcP1Sh5dvyJqO3K9bjvMveQ1Y/UIX6WlSV+hTeqdONZvJpakYCQnJwfnz59H+/btedPbt2+PEydOGF22Xr16CAkJQZs2bRAdHW103uzsbKSlpfH+bOHgisVGvxffkZd2Ad5HodrYeuuVkc2yyFkWrFqNzLRUva/smUPhID+z1eRmZ4meN8tI01Bzzj9uKxDu8vuXLrSsKbpOUtQqFfYu+QYXD+0TtfijG8IvFKakP3+Kq3/HGix+4uE8MDTn9ZXj0cg1o8WN1a99EU2rrdGJomzXkqNcxNoqI9yg3EHSpoNfJcx53sIkBSNPnz6FSqVCUFAQb3pQUBCSkoR7SwwJCcEPP/yAqKgo7Ny5E1WqVEGbNm1w7Ngxg9uZP38+AgICtH9hYWFSkimaqSxi6U178+dPffIYO+fPNLRiSeu1FU16X6XrB3uWdTPC4vdF87BqWH88uHaZ/6WNro3U5Mc4sWOrTgDkBDXfJVCrrNPk25zWMPwHWv4yj65fQcJ//2o/Sy6m0XlQXj95HDdOHsef61eLWn7bF5/oTbtz4Sxunj1pdLmNE0fiwNIFOL9/t8lt8EZgYllsn/kp/rd8Ef7+ZbPBZQyx9iCY+vco03VGHOPuk0+lVCL57h3B+63RtNr1PqoppjF87Zhzi7HJy5mDPF+kMqsCq+4Nh2VZgzehKlWqYNiwYahfvz4iIiKwcuVKvP3221i40HDfDNOmTUNqaqr27/79++Yk02JSb9rcE9Xom6mD3A406T22dYPedw+vX8aakZEmb+r56+LfZDX1PS4e2GNhKsX5ZdanOPnbNt5DzJy6EY5M0oOM+zav+zAy4+3OcDGNcI5NZlqqYJCrt16ddWVnZopKjyEqpRK7vpmNPQvnGe04LOdV3nbuGsi9NIRVq/H80QMAwK1zpySn76FO8GZwO+YcFxh4IFqlNY3t7ln/W7EYmz8di7N7ouy6XWMMX2uOXxTvKM8XqSQFI8WLF4ebm5teLkhycrJebokxjRs3xs2bNw1+7+XlhcKFC/P+bMLUySS5Dkj+PG4enkZnc4yHY156b53Vv6nu+no2Mp49xZ6F88Stycqdnmlu+GJlPHtqfAYnzBnJeP6M91lSdruRvi14b3ci71vGjqnu+lXKXKwa1h8rh/YTUQxivRsny7K8Hl2NvRBIolNMo+Hm7iF5VTdPn8COOZ+ZDNR0f9MXj5Pw4rF+7rOY1kyOUixsiGbIDKFgxPh5Z7v90u2cLr8Cq8SieyKapGDE09MTDRo0wJEjR3jTjxw5giZNmohez8WLFxESEiJl0zZhOhaR2rRX3MpZlnWI6JVlgUc3rpl/0+be+KxcefeXmZ9avA5nL6X59ctpvM9mZ/HrHQ7W2JfCqzBQZ0R3Fce2bsDP0ydrP6eb6GDLWvWWsjIysHbMEMT89KN2mlppuKt5jQdXLyM7M6+5t6GUcANo7kPK3dgLhwnc4sTc7Gxcjv2DPwMnMcrcXKwbOxTrxg7N73ZAO59u5VSBYET3NzanArMd7ldiX1TcPb1snBLD936rD2hqk/qr8j9bzCF51N6JEydiwIABaNiwISIiIvDDDz8gISEBI0aMAJBXxPLw4UNs2rQJALBkyRKUK1cONWrUQE5ODrZs2YKoqChERQlkyTkaC5r2Gj9RHeRkYdUGK9lKXpXY34oRF/++Eqj8KpUjBHyW4PbKC+RV7jSHXjGNWtp5nbeMcJ2RvE/8z8l3b2v/nZHyDEWCDb94WOsY/XPkANKeJPO6iDc27o12+6wa2774BJGLVhqZi5szkr9Od0/zgxGu2M1rkfPqlU668n+XnMz8vnGyM1/ytiuqNY01Hpr2eMCJzNVx9/Cwanf9QvRaPWlyRli17qR8jvLG46S3PcnBSJ8+ffDs2TN8+eWXSExMRM2aNXHgwAGULVsWAJCYmMjrcyQnJweTJ0/Gw4cP4ePjgxo1amD//v3o3Lmz9fbCbIaO2usTT+JR5Y+8ayRnxEE6erJmNif/IjVWTGO1TZrmpG8Ihpg/ZpHu7yA9q5mfM6L7nbFz3USarXSMhAIPtZiWMuAMvyAiLdxj4OYhvZhGi7Mp3UBEdwb+WEK69fV064yIqMDqoNeFYM6IQFrdPD0BG/ddqHvestrpRnIVzfldqQdWLcnBCACMHDkSI0eOFPxu48aNvM9TpkzBlClTzNmMzZm6KCUHDSIrvDrKyWLdYIT3Kf+fMr4tCPWWqJGbnQV3Ty+navom2EfFa3fjzsO/REkUK6Xf8sx48MD/Mu1JMvyLl9D7XXiBkLEKsSa2rf+9dc5BoWtVak6S4bRwXzKsFIyYugcYLjMyMZ9QMQ3/+JiVw2YiuQ+u/odLfx5GiwFD4Fs4QPr6Ib6YRmGHPqoMB/7ScxXtzlHTZULBHptGwo0yKyPd5HDnf6zP7/jNaCDjKCeLjXJGjP+u8jz8uTe6548eYOnA95xutF1DD5HHd24hav5MbJz4sfCCek07hQ/Qv0cP4sfRg3F07QqBVYgbbVbKckJpM5/lwYghvD5WOPcAhcJ2t0/RxWcixqbRXZfJ30Uog8LEzXL7rKm4cuxPxGxaa3zdRrcrrphG87vbcoBKQ13o61ZgtTQgYUQWW0th5B3MoRXsYMQUzlHdMGGEYJ8GALD503FY0v9dnUWN3IQdJhZhLcq44FV8NaMegl1x9vPCgbxxJJxttF1D/Yw8jr9ldDndc/E+p2kp91gd/yWvnhe33oV2PiPdwRstipEQ8D97cB+P7xhuZSd2PRpqlbhiGpM4++flV8gqqzSZKyuyDpbuS4+7u0Bmt4ScrLwZjH9tTKpAix+xBG9FAvvOcIJA6QNpimPonOYVR4Pl/5Yib6bZnDpARYKCzUugUQ54/xXBrGIaV2G60zP+CZl064bgfNwKe/nLGi+mcYSmvdYIGl48TkKRoGDeb2mNpr1WYaCfEUepsyOVwZw5U7uj8/2RH5dzvmOF/627CiPFNMbqskipPLlxkoGcHTEE0q4Z1kH8KoT3n7t/gaGmO2BkWRand/2KoPIVEV63gaGZTK2E808jRWQ6B9fdS7+lieScERPpMYZRWHB9C+Q0Cd2jubkJOVnieyWWQq/OiLYCq871YsatJCcrv46Ql6+fWekzRnLP4Q6iYOeMSHg7kczo26JjNO21RhJSk/PehLgP+Hv/XrB8xVbAO368TsCs2wumvRh6iJh8y7bCYGNqtRqpyUnYu+QbJN3h58QYzxmx4TVmYj1ZLzOkrkR4ssSKw7fPncbf2zcb7oEZYuJH4XoqxlpGAYCHYDAisc6IuHqkwotaUOwg+IImuF3DFXqtxWDOiE5Qbs75a05rNmkbMP71sZ83Yt24YdKvDxsr0DkjJm+UFrxBG++sx+zVWpU1H8rcdR1da6SZpJ0zhJ7ev4fku3d408xvlSIvw1nH4t+yjX5nImdkz+L5SI6/jRsnj+uswvDvGTV/JsrXb4TuU2YY2Lz69f9adlEI/TZCYyMZXYeBuzg/babTmfbksYiNScgZ4dVZ0X1j538W6oND9z5mrCK0pSwJDoRyVQS7iOfmeNqo3o6YewTL+a8kNn4AmHrRPfv7bwDyimPf6PaeTdMiRcEORkyw5GEtpcdK2bAsMlKeW7QKzc1H7MPE3sVTP00epT/RUaJBidQqJWK3rEfJsuGo1qxV/hcmcx+Mfcd56BnLQWFZpDwSLp83lXNw58JZw/1CsJr1W3ZNCKX8Vbp1emCVPnq3mHnEHzOjb9I6n929vE1uy+QYRyZyI4zhBhSsWi0xWNBpwaVSCW+Xl+Fpn5yR/B5YdXNGzFi3xODWjA2Ims1WgZy5HCs1dmakZsPrGSzIGTFRqc9R6oxc+uOQtVZmnfVY0d7v5gtOt/ZgZfYSH3ce5/buxIHli3jTTRf55X9vbLwWY6sx9ptZktMkeJM3Zz0Cy6uU0uqMGNp/7v49vmO8srDoTZnY35xXmcjOzIRKqcTD61fyl9Pr2p//OfHGNf1tSS2mkSgz9UX+h9fFNAn//Yvlg/vq9yxrBDeuSE1+jGWRvRG98Qe9+VheMY1tHmFiciF1K7CKvafzKyebmUCR6zfGnOEMbKlg54yY6FfAkhuksRPiwLIFSLx13ex1W4uU3jdNRdGis9kZBsrcXOS8yjS7PwKxHl67IjjdIVv7iPDyRYrgdFPFiZr9vXnmBPYs+krwO1NYVm2wtYCY68RgLoVQk0krUUsMRoR+C2VuLq/V2N1/xNSHMr0vpn6zrZ9NAAA0eqcnb8wWvSIXnTSnP9Pvfl93GZM90wrWGRHep+ePHmDDhBHaz5pmtweWLUDOq0wcXPkdarRoY3x7r6U/e4Ydcz5D/c7dcef8GShzspF0W6B1Fa+YxkY5I7o5ddoKrDpdGJhTZ8RBcsbdhFpeyahg54wYrLCWN92yNz7Dy1rSBbt/sRJmL6sRWrkaAPEPopVD++X3UmmA1FYTq4b1R9qTZNHLWNPVv2IkzR+7ZT32L10gfxAjolOu/Fn1s/ZjNq0zsXrD+6dWqw22XBTzpv3jqA+NbtPYw7lQYDGT6xcOJKQ27dVfx9rRg/H39s2Gl1CrkXz3Du8BLyY4E3tv0R88TjewELMe3WIaE8dL6DQwcG5c+/sY77Om2ESoVY8papUSCf/9i93ffmm0bhm/0zPbBCOGfle9IjNziml4fZXY+J5i5OexrNM+6yvQwYghmhPRkoePzZqPWuHaq9KkWd4/RO5f1ssM/LF+tfEEic8Y0fZFcOfCWXELGWEo98Oazu3diWt/x+Lp/Xs235YxBs9HkZUhFW4Cl7vI8uu8B6zwyWfZOEKmA39RZdsCwbDUnBFljv78hnKjNI79vBGbPx2LWE6gJ+ZSMDfXVb8yqoiKlrrLmAhGNC3kTPn71604+dvPvGmaY+VTyF/UOgwxVhdE4cZ9o7fNfdbw78q/XsxpFWnznBEjTXu5gSgFIw7EcM6IFYIRud+ijdBWOpWwjKkOpAy+bTGMTu130105pyQ9MjmPxq1zp0TPaw5u2sWOdWIrhgJc07GIJhgxP1uWVasNPiDOvK6db9Z6NRVYjTxUxXT/LfQbSO1nJDdLaIwYIxgG5/buBABc+N8e44nRYXYwoltnRMSD7dTOX3ifTeWmPLj6n/52hdYbtU1/4utzRGFxfQQjwQgnOLXFffb8/t24efoEb9qOOdORkfJcJ2dE55oU+6Jo4wqsxjrM414T7g5WZ6RAByOGZKamIOdVpvG+Qkxx0GAkrEbt/LJ/CftnKgdCmZtj8DteJS/ujcTAhbhvyTei02XrcSocpXw3j/hiGiFuAr+V2Mp0arXaJjni+U17jQQjInJGsgQq5qokBo+52RJHgjX0MiPi2je3CFh33df+Mt2LsG5Pwzbt9MxKFUoT/vvH8Da457GV77NJt28iZtNanNixlTc9O/MlojesgX4HdPnb1y9SE2bfF1WdYIRzTVBrGkdi4KQ48/tvWBbZGyoLupO2ZYsNob4ExOg5fQ7enTJDe8Ow5kWhzDESjHB+C97btYHtpz97Knq7lrzti8E7jg46qJ7YfkYUproKNx6N2KYFmJjWNCIecFeO/ak3TWprmtxsfm+epn5XscWM5es30ptmSTHNoxtXse/7b3H334v8HBmRrN2ahstUU9u7cefxr4gWfMa6eXfjXPOWdk2g6+ULw+vLSHku0Jom//OdC2dFnXPcZbIzM81Mqbj1657C3LpNFIw4EFOPYqnZvLx12/CN2pxOi4qXKYdytevBw9ubkzFivZuSsWCEe/MTNbiYhCBJsB6EFakNBVIyMNR89e/tW/Tn5Y6hoQlGBIrIxJZ526qjuIfXr+LioX02Wb/0nBGdYMRKuRce3j7685h57bGsGtu++ATXTxxD1LwvzFqHWde96L4r8q4RQ5dK1PyZOPLDMjy5Fy89DZptcHJGfpv7ud3GmFKrlHqVTx/duMqfR1Qdnvx5bJJ2Iy8YjtytQYEORkxdYJYEI1JvhKKx5uVo8B+kef+2xhuSZrUPr102OA8/d4Fb3mvx5nHrzCnb/dZwrItX6Lhf/N9e4Q7FOLOq1So8unFN+CHEq+tmpAKrjbKWD61agj/Xr8ad82cMzpPDGVhMCs15kfUyw3j/Kpr5da53swMknd+K20+Ixsmd281cteXHwVQFVmPSniYbvW/k1wkzHrhnPH9mdhp0X0BiNxtvJSaFsZ8373ziVw7d9fVs/vJighErXktZGfpduvOGEtAd1NJIfRK5FexgxARjb/tyKVOztlktdbhluZq3F2tl1z57eN9ohM+9qfNzRiy/GJLv3sb5/bstXo8hjh6MPHv4wOS8Fw7swbYvJgt22GVoUK305/yiMlZtuJ8RazDWdNxUixZDWLUaKmUuVgzui5VD3hcsyuHSvR6s0W16RspzZAgUOz4SCFDEsMb5KLkzOOSdGvcuxeHHUYMRNX8mcg30qCs699CCc0m3aDZHasVjM6lVKp0KrAItuMTcU60UBJzevQMrhvTFpejDePkiBce2bsir/G9k9cYGvJRbgQ5Gmn8g3PeBhqELzlaEsnN1tYr8yKwiIIW7ftv8e/9e1JvPnI5wTPVKyb1AxdyspF4it8+dlriEeA5VTCN48zBdifKi0XoFwlm6P3wcySuPt3UwYou3NLVKhacJ+c2x/7diMQAgZtNa4fk5OWyhlatZpZgmNVnEODUSCOWyGCN0bSb896/0DbMs/jl8IG/5S3E4tmW94Gyalw1T14olZ5JupfWcV9YLRowlW61S6hR/Cs1jOhix1gvOX9t+AgAcXr0U/1uxGGf3ROHnzyfrvGAYLqZxrMr5BTwYCSgRZPT7pwl37ZOQ18TUf/Dy9eV9Dq5QSdS6PTmBjrFeC401vTU0yqOpG4/BSqAOEJmbeut2qAqsIrJYtdPN6RlSJ7D5Y90qznrVNh3AwBZ1Rh5eu6xXpg/AYE4at8K6u6eHdYpprHyOZ0rMJdoybbzVts2t8Kjb2ZmGu6enyJWZfzZVf6ul2ctaQqVU6lx3+sdWVDGNDZrzalo7ZqWnQbcoibdt3R5kHUiBDkbkftPVZU7LkN6zvhY1HzfXxVCriFqt2xtdx4rBfXF69w7+RIYxWSubV1dBzM3ZjkHKv0cPGv3e2Iip9mbsjUfjxqm/8r4Weafh31v5y3BbNaUmP8ar9DRxCTWDrUaS/XPDGtHzcnNG8vqQMDNnhFtmb+W3T7nK+VmW5V/nBu6dxlr68dJuwb3X278wChUNNHt5Y4w2KFPq5ozozyymcrBNOsTk/pwC9cBYlsWNU38hJTG/DyfNvuRkvXKI+iMFOhiR/U1Xh6iWJjrENrf09OHmjAhvp/1HY03mn2qyBrmMpZuBzgOdd6M2vi17MDia7Gu8br7lTrBAzohumvYt+QZrxwzBPVFjqOiuk/+VPS8PW40DJYVKxT3WavPTxIsZrXvO2CIgLlW1usHvSpQpp/23mPuTNmdEcHwb/bSbsz8Mw1ihUzXpVEr91jS6WFMjIsM654RucRDDaxign8brJ45h73dfI+qrGbyvUhIfYtmgXpL6drKVAh2MuELOiNgnhm7xji4f/8J5qzMjM95UR0fiulbmTpZ2sVrSmsZUJWVHqvAl+MYtkKTU5Mc4xWmtYSzZtsgyNoclxTSmegcW42TUNl4RSG5WtuQxjDR4LRasHDzYIiA29KZet0MX+HHGBeLW1cgykEum6WJc6D4i9FuY272AmAHylLm5Vu2+IC8A4LZUMTSPcWJyy16+SMEf61fjScJdJN+9gwv/28Pbl5NR/F51+Y8B/ReM6yePC6UEca/rAWlyVOXkWMP22Zmthp82l7un9GhfbDxVomw4Zxn9hbTjFJgToJm4MfAuUBs8+x7fERjZUyQpwYgt3kpfpadh2xeTUaVJczTt/YGJtLC8f+f1iyHmBzUWjRgpA7djsC6m99MS5crjyd07etMv/G+vxds/8Su/x83Hd25acF7l/47GeiY2h9hePsVSuLkZPK8LFQ3Ei8eJAIC4Q/sMttwSJHDu8CqDW9C9gEqpNPkimZuTjTUfDUTh4iUwcMFyydsQ3K5OPyPZAs1qz+6JQqsPP4K7zrgv969cwqHV36NJr/4oLGKw08M/LMOd82cQd2ifdpq7pydqt+kIADit1zQ8//fgXtInft2C6yeOCdaNY9VqePoYf0m1J8d6GtuZrYafNpe7h8jKXzzG9yHivfdR+c2mqNGibf4SAtmtZg+axLIms2/VauFyVoOVMO34tm66mEYt+G9ruXDgd6QkPsIpnTcdIdzf6+G1K1g2qDcux/5hWQKMFCkk3bph2boluCH45pYn4r1+8A0ogu6ffI6Ra3/W+95Qyw65cH9G3X4oHA2jUBh8U1e4u2vvLkm3b4obu8dIzg2/MnheXYU7F85JSG0eVW6OyWAk+c5tZGe+xBMrNkLQrTMiVB/p3z8Oascr4vpt7udIfZyEM7t3iLq/CXUKl3w3f5ruMTP0ezx/9AA3z5xA4s3ret+xALx8TLfgtJcCnTNiy86yzOHu7S15GVMX5Rvde+tF6ULjuWi6WJY6WJharcbub+cYnScrndvhlOFWINp9sWPJgakAgzUQSFmLStKbYf72f5v3ueiljL19am6MiTevy15B15Amvfoh4r33wTCM3fqUsIxjFH2JocrNNXgNmNPM39g1ojssxJ5FXwl2L2CKMjfX5BAB3BdN7r0lNysLHgL32dvnT6NY6bJG16lWqXRepoR/t6Tb+kG85hp89iBBfxRl7r3vNTeBInujt3peBVaR559azWvYoFapbD7WlzEFOmfEEYIRP06tcFMXf/3O3fQnmsjcEWouLBiMmHHjAUz3pJh8Lx6/zJyi/SzUeiM78yXWjhmCo6+bkmab2eOmtbx8kaLtY8aR2uXbpD8VlgWrVuPnzydZf91WpLlZWzI+jl+RolatQ2CQTHWLPLykv8wAhosfFQo3yUV1moe10GL8oIcxKxAB8nIzTaWKWwSvqVN0YsfPWDroPb1B+O7Gncfub+dg3dihJrfNDSSqGWxibKKrA92RlwV+f8FxpIysl3tdiG5JB/7LrK2GfBCrQAcjZWrWljsJ6Dw6/yHAHTskqHxF3nxtBn+MlgOG5C0zZjIAoNPoSSZvzkL1YoSCEXNrp+e8Mj7QU8oj3XJm/Qpg/0UfRdqTZPxzeL9ZabCmjOfPsPqjAdgwfgQA/gX67L7hXkKBvDe2B1f+Ex3kpj9/Kpila09H163CZRM9kzoUC4pW3Tw8LBriQYzMtFTZXnL8ihY1azlDuRlqtVpyJX9uHQeN+Ljz+OmT0XjMKfazpDqSQuFmsjsB3kNWmReAnvwtr4jvKKf/nMy0VNy/+l/+giYDyfzvzR1oTvf31g0Cnt6/h9ws/jhJedsz8qNx0yIyGGZ1RuK2VfN6sQp0MKJQuAnnNgjwK2LehW6Kl69ffno4QULnMZ/w5iseVlZ78ld7qyXGb92F6s1ambyqhW4mQgOm1e3wtqR0a0jpxwGAcHM4zptCZlqqWemwlgevx9hJf/YE7OtcA42ja1fg6f17hhbFnxtWY/vsqaLrMPw253PZi0aeP7yPQ6uW2G17789ZYNHyluSMqFUqqHJtFyikPU3GqmH9rV7JVKwXSYlmLWfoHFSZUfk259UrrBr+AV48TtJO2zl/Jp4m3MXOb/Lrz+i2BpGiatMWpoMkzve6wWHW63GKnj96iFXD+uMMp+8kU7mf3N/KUPGnqaTpBmzcsYKe3r+HnyaPQvqzJ/rrNXLuc1s3ScuY4+SoUM6IvMQeAN1mt0LDgnv6+OKtvgMlbZ/bSRA3GPEvXpw3n+5F4vY6J8Oc5smMQM5IjRZtJK/HHMY62QKA/d9/a5d05CeBnwbfwgHaf2dnvtQ7PxIuxRlc16XXQ6MbGtb9VXoaLkUf1uYmPdfLNXJ9hYoWMz0TR/P+/CEbLKl0rszNNWtcFrGun5S/eaQ5DN0DzX0xyEx9gbQnyUa3c/+yGV3Sv+bu6SnpJUy36bdm4EWh8bRMPQ+49wvDxRrG06Zb3KpWq/Hg6n/IysjAgyv/GVgKErKTxOeM8H8nCkZkJbacjDu2S9WmLdCo23t687QdNgoBJY13Ma+L25yXG8HrtlAR0/RRV512nQWn6wYwpapWt2OfK8Zb0+iW5+qq2rSFVVOjVil5RU3cgDAnM1Pv4SW2wqlQ7fVd336Jw6uX4siPK8xMrfPzLVIUjd7pKXp+/XnNP0+z0tMMDmlgDVcsbdlkgXbDR5u9rKHcgJCKlR2uY0gNU/crbt0g3ZwRzW1HaB1XjkcbXS8vGBFZHGeqXt21v2OxfdZUbPp0DDyN9Acl9lAY6qpfFwuWdznZpT6VEQU+GIHISomackfgdWQu0FFQQIkgo90hC3Hj1NUoXDy//TnDKNB75nzt57w+JYQNXLAc789ZqDe9YdceJrdfpUlzbR0Ue5BaSUq3ybHosS9Eunn6BJZF9saxnzcC4L8dqFRKZL/kV6YVewO6fvIYYjb9iIyU58hMfQEASLxxLe+7E4absXKZW6nYkbl7eOjlduh66/1BAICOIyfofWdp0Pz8wX2j3xcNKSU4XUyze032vxxqt+kouYt034Ai6PDxeIOdnlV6o4k1kmYbpoIRFbc4RanznTJvAEGBVZiqJM4rpjHw8NY9R0095K+dyMuhSX/6xES/H+LOfU3dGJNY8Jv2UzGNvMQ+HHVbeAi9TYRWrmrwYTl6w68Yumyd3nQ3Dw/0+mIearXpgBYDhqBpnwFoOXAYFG5uCKteSzufUIUmjRJlyiG0clW96QYrJHFOwA4fjUXh4iW1n9/o3svgdsQoVrqM0e+lnvC6v6etbpBnf/8NAP/GoVaqEKtT/+OvXzaJWt/5/b/j/P7fsWbEQKwa/gFvBFyxFd/krt0ulze798LYzVGCRYfcG32Fho0lrzvbRIXrwFJhetM6jpyAHp+Z7i+Ee4zl0OOzLw1+5ycQqHz8wxbUbNnW8ENVoXC4Xqo154Ruuk7+tg0piQ+1n7n1MFRK/f2zxgCCrKFcUk0PBSyLPYu+wuE1y4yuh1s0Y2yw1KTbN/OaNVtJ+rMnDjX2VoEPRsQOWqTbakS3iCG0cjUAAg8ahkG74WPg5esreENQuLmjTM06aD98DLx8/dC4Rx80eFu/Uq2xnBFDvPwKmZxHt/6IOePjcD17YLzFidTWI9zKtl3GfypYV8eauBfknkXzTO6PhlAZOdf9K5e0/xZT7yEj5bnsNwdbKhoSKji9Tvu8itQehnIYOQ+h8Lr1RW9Pk2uQY6LZePEw/WDar0hRq+fI2ULxMOF+MgKCguFTyN/gcnI3pZei7dBRAPRbCZ7YsRWbpozVfk5PyS8aURuoJ2SqMnRAySAUDS3Nm8btRC0+7rzR5TUdjklpwmys3sbDa5dxYJllFcC5Tv62jfdSTcU0MhN7w9fNMdBt06+5WelGy+M270TtNh3y5tEpcqjatIXJMWNqtGgD34AiqCph2OwSZcPRY9psXmVMQ9x0ghF7DgYnZlvc4K50tZq2TA4Afg15KRVMfxw92PgMnH0VkzNyeM1S0dt2Rm+PnSI4vXXkcKPLMQyDvl8uQO8ZXxksUhHi8Tr7+66JAQS9C/kjICiYv6y3j2ALNEdjKBejQeduRkfkzs40llvkODkjAUHB2vtsziv9AIrbm/KBpfkPbYP1vEzk+gSWCtNrCn5+3y6xyRVdpMtbxsQ4SzdPn5C8TqPbo5wRxyGmI6t+8xahaZ/8cUNYlkVwxcq8eTQXiW5/HboBiMa7n87E22M/EfyOq+PICfho1U9G32x0lapaHeF1G4iaV/fBGFAy2MCcNiAmGOHcMMxt1y8Wy7JmNWeUylDuE3eMlYevmxg7At23Q0NaDBiC2m07YsA3S002hS/OGQ2WS0wPkKWqVENYjdpwkzB8gufrniZNvc16eHmjSFAIb1pIxcpWO/cCQ0ujxev+gqzlDYHK9BqV3miCuh26oF7Hrugz+xs06PKu/kxGrsPb505ZI4lW0XXCNO2/UxIfiV7OUFDw9/bNxpdTqfTuB8EVKpncnibHxZwXO3PG6rEEt2RA7mLhAh+MeIsoygipWIX3ZpSblQWGYXgPfE0Ft9LVa8Ddy3Al1oHfLkOHEeMQXq+h6DRK7aLX1DVgrIc+ezXxBcRF4rzBtV4/EN77fK5N0pPx/Bn2fve1TdbNHajK0IMteuMaZGVk4NbZU8h55Tjdntdo3tr0TAyDhl3eRbtho1GyXHmUqVVX+5VQ8YFQUVWgyKBHw1DRSZUmzfWmeRi5Jrk8vb31cj0Vbm4WFV+6ubuj52dfYuxPv2HgguVo0LkbBnxjnZyvMjXraCv8AoAnZ6yRyhHN0GX8p2AYBoxCgdJVawi+1HSdMFVvmpgRxK01AJ0Y1d5qiaDwCmYt+yJJfODClReM8HNGfAOKiF7enJwGg/VQOEy1OJTi6l/5rYfkzhlxver6EjV+7308f/RAm33r5u6OAd8sxcZJIw0u4+NfWG+amyZnROGGcZuicDfuPAJL61eGK1E2nDeCrhx0i2a4FG5uqNq0hWAbfGv7+9ctqG4i+ClVpRpunc17O9PkkpTlPOisiVsBztpiN+dXXmaMZPmvGNLXZmkwV+22HU1W3G2h2x8INwtcIDtcqGdgqc1uDWVp123XGddP8Js36rbK6jv7W94wBRru3t68sUu6jP9Uk2BJaeNqMXAoytXh128pWa682esDgA+/W42k2zdR6Y0I3m89bPkGpCQ+REBQsGAxrVALrcqN34J/sRLajrYa9+iDas1amUxDCQO5W7YgNpdBpVTqjbZ8YPkiUfujq2R4Bb3BIl+JaTFlwbkipuuAHXOmm71+Xdz9s3eujK4CnzPiU8gfPT/7UltO3GX8VIMtQvrM/gbVm7dGk179AAAlw/O7bNd9SytXtwGvlYpdmbhwS1WrgbK166Fep66C33PfZCs2kt5iQYpDq74TnP7+nIXoPGYyqjZtqZ3GfTv1FzEMNwBJNwZrXuTGZKWnIWbTj6LmbTlwmI1Tk6/9iLGC0z1FjOzpo/Pg4+YEFS6hfx0I1W8IqyFteAa/AOGiIC8/P71pD6/yi71KhgsHA6Wr1eRVVq/YKAKA9NxJWysaHIrqzVrp5eJ4FyqEkEpVDNYXMzQ6d/XXuV/Fw8qiaZ8BJnOpxHQbYI6mvT+Al68fuozXz60RY+PEj7HtC9PF36ZUe6slmvTqpxcEaZrnG/X63DanQqicAYHcOSMFPhjRGPD19+g3bxEqNHwTQH7rmDI162jnKV21BjqNmqjNqmvcM/8tVmrPkrZlPBhRKNzw3vQ5aB35keD39d/uhuYfDEbk4lXoNlnc6LCV3miCXl/MM/h9myHCOU0J//2b1+afo1XkcIRWropqb7XkBSDc4o1eM/jb6jJ+ql5Tz3J16qPn1Fmi0i+FoRs6IO7BDeQ1/RXDFq04CpfQ75hv5NqfUbKccDa4m4hxi3wK83MLuS0IGnTujtptOuLdT2caXL5io8ZoOdD0QGVcQkEOAL0KqACg1Cn7d/f00uubZ9T6X+BTyJ+X86nJSSgaLNz6RwxDrTa6ffIF73OnURPFr9PMYiNDAV/Ee33RdeI0Xt9GhpSpWRvN+g16vVw/wVwuc4XXa4hR639BlYi3zFr+xWPhLvFN1Q/R1XLQMHh6+1g0OKY5PZq+fCFf83CqM+IgvHz9EFKxivaNrdvk6Wg5cCjeHidc6x/Iq5zaafQkVGzUGI1s9KZgDrHNlQ3x8PRCo649UEygzwVD3pn0GS9w02UsS5dbJOTp44P6nd7J/5JTt4B70ysaHMoLlCo3bsq7cTTrF4men32JsJq1UapqDbG7IUqz9yMNflc8rJxVt+Xh5YUSAln6H34nbUyg9+csRJ12ndCkV38MW67f342Pf2F4Cgyt3mMav38NQw8z3UqfNVq01f7bw9sL7YaPNtgs+813+6Db5M8ld9qla/jKjRi6bJ22sqoxDMMgtHJVTPj5d7zRvRd6fTFPW38s4r1+KFu7Hi9YYBQKvVyqfvMWiXp4G1Kx4ZvaysEVGr6pzZ2wpeJhZTHgm6UYsYb/cHZz90DlN5sKFkHrKla6rLYOXZNe/TB2cxS8BZZ7893ektPH7dukSHD+OaWbQ6F5aRTr1M7tkub3LpR3LphzL7197hR2L5iDzNQU0zPrOPHrVsnLWAvljDgo34AiaPB2d5PNY6s3a4Vukz/nlTPLTewQ0mJpuriX2scHt3za0FusLt/CRXifDeWMAHk3pLfHfoLIxavAMAzvYtL01eLm7oG+s7+RlG5jipcph6yM/EGpdIsnxOaMiOVdyB89dQKCZv0iERgqvlkrkFd00XboKES8977BeYR6f9Rs5813+8C/WAnUadeJ933FRo1R6Y0mes1s33w3vym8qWaxUveFq/rrugCtIj+Cf7HigsMx6FY61PRVAeQVvzR7fxAvkC5cvATemz4HFXUeeNxKt0OXrUVIxSqCFeBLlCmH1h9ych2NFBX2+nwumvTuj/bDxxicp1XkcPgGFEHkopXoOW02hq3YYHBeMUqWKy964M82gz/Wm6YbGLh7eAh2sCjlZUaDe70P/IbTWZjONjuNmoS2Qw3X67NE26GjtOesOQ9oVW4ubp87jWNbjB8nWw2+KqT7lC9MzkN1RojDG7hgOQYvWYPwuvktgHQfkBrch3FAyWA0/2Aw3uo7EP7FigvOr0s3u5/bz4FuiwaGYVC1aQvtTY9bpKFbtNBj6izB8YSk6j3jK15FS/3u6qUNB2CKdyF/3k2rTM06ek053+o7EF3GT0W5OvXRykDRm5uI1hFCLU40rSre6jsAw1ash49/fvDl7uGJbpM/xzuTPtOrA8JtUWaoSCFy0Sq0HjwCVZo0M5k2QzqMHI/B3/+A+gbqPwHAe9Pn8D6bG/xw90PTBF6oYuXABctRr2N+eoxVW/IvVhwRPd832kqjfqd3MGLNZhQrXeZ1XTSR9aWsQDf4zKO/z5XfbKo3zZxsf+4y3Bc83S16+fryct+sidtK0lAxjbEiaQ1DRUYa3HpVthZSSb+Hbl3U6RmxPit3XObp7YOiIaWg4OR0lKvbAJGLV6FoSChvDJFeX3yl/bcyNweNuvaQlF2r+3bOawJqojIqN1jS+65eQzTvFyk6HboqN34Ljd7pmVecwUljvQ5dePN5eHmhdtuOZm9Hl2b0Zk2/HDU5nVf5v34oVWzUGFUi3kLPz75ErVbtBNejO1iYtpUIgPIN3shLu7cPigSFGGyarpv7FGSkzwUPT86DxMD5WKx0GOp16CKqToohCoWbyfocJcqG8wIJQ32cmCJUN8LY262mSEvKEAbvfjoTRYJC9CqmytUtO6NQ4N2pM01WKG01aDivU8Lweg1R6Y0INJN4zUkZENRW/Q6JycU1PoaM/Pw5AWvJ8Aqizp/k+DtGhx2xNQpGXJCtOlHVbRZYrFQYBi/5gdc3SXCFStqigNYfjuDNb+itnUu3wqaC8wAwdUHVbNkWrQYNQ/95i01uR4qqTVug64Sp2gHeGnXtiUpvNsE7kz5Dw678TqS8/f3Rbtho3sPeXAyjgH9gXjDy/pffot/cRajK6UPjw8WrMGz5el7rLw9vb8F+WHRznLhvsjVfv2EyDIMPv1uNj3/YwlsfFzc7vtMo/YHsNLgBjTk9UVobNwdLTL0IIULnn1+Rouj52ZeCrU96fT4XYzdHSeqbonz9Rhiy9EeEVqlmVhptoXy9RiYrlHp4e/Na5/WYOguePr6CQ1u8N32uwd5zDdYbEripWTp0hRCxo4I7WusqXa049Zt6z/hKVKvC6I1rcOP037ZMllEUjLgk20QjYss4m/Tqj9EbfkWF12/cGvU7deXlnAjRGx1Vwg2HUShQv3M3vd5xparZqj2GLF2LBl3ehZefH5r07s/73rtQIbwz8TNUeqMJFAo33g30ze55uUBVIswrenhn4mfaf3/8Y35Q4Onji5BKVXgPRA8vb8G3ON1+WPrM/kav7hP3rZKbFa1wc4Ontw96ffEVek6brVcnQs2Z11hvvdzAVbfjKDlUfyuvbkmIwICSYhl6Ey9Xp742B4A7/hSjUBgeY8cFlapSHQA/EHVz98DHP2zBwG/z63+UKFtOr/fpHtNmo8e02YJ1fgAIBiO2yBnRvWcJ6Tz2E8kd9FlCanGUdyF/hNdvhKpNW6DjyAnw8vUTrJwuxNPE8CS2RMGIC9Fk7YrtCl6qsrXroX7nbgb7o+AyNOZOibLljC4XWIp/kdsqe1rh5oZ6nboKvuG81XcAigQFo+WAIRi59meTxQC9ZsxHrTYdMGTpWl7A9ua7fSSnq3yDRhi8ZA0Gf/+D2W/wQF6ZdkBQMN6bPhelDbQminjvfZSpWVuwZUKZmrVRTuA8MjQYmy7ucdMUNdmTbpFKy0FD0Wn0JKPNi00JEGgSrVGh4ZvoM+trDLJjr6RyMZTz6lekKD5a9RNGrOZ3kOcbUIQXNLt7eiKofEX04DS7L1Wlmln3LaE+ZaRo8cFgXs6Vbh0w3Ur4HUdOQLWmLeDu6YmRa3/W9kMjRrvho7X/lnKelKpaHX1nfyt6fr8iReHu4YG3x36izbV2c/fA0GVrtfMUChTuikJMSzRbKfA9sLqSIUt/xNOEeyhTy3ATW0swDINWgyzrhMvHvzAGfLMUmz/lBzRlataGf7GSaPROT950a/ffUr7BG7hz/gwadnkXb/UdiMY9+uL4zxvxX/QRtBn8MYoEBfMCCjEDpAWGlhJsDfFW3wFo2ucDqFVKbP50HAJLlUblN5ti/1LhkTfbDP4Ybu4ekgaAM6RMzToYunSt0Xma9Opv9HshhYuXxIBvlmqbPhrzwdff41Vaqn3HO3qNUTBgOfXxPLy8tS1vzFWmVh20+GCwcPf2DGPVgRztOWClZEbSZugh5+Xrh9YffgSGUWjrW3Arqxrruwcw3EKw++QvsH224foszfpFwt3DA9E/8TsZfGfSZwirURvefoVQtWkLrPk4r9+UMjXr8ubr+dmX+PvXLWgzZKRe9wQ+/oXRbfJ0LOrDrzcmpMHb3VCxUQSO/JAXhBQTGdQDeS32dHvznvjLXlz83x7cuXiO16dPmVp1DQ42GVAyGL4BRZCZ+gINu/TQdrzIMApt7qiXjHVhzApGVq5ciQULFiAxMRE1atTAkiVL0KyZ4Wzp2NhYTJw4EZcvX0ZoaCimTJmCESNGGJyfmMevSFG7NhczV8ly5dFiwBC8Sk9DhQZvIj7uHBr36CNYkbFY6TC0Gz7G4j4oNLqMnYKHN64irHotMAoFfAsHoP1HY9Fy4DCTIyibg2EYuLl7YNDCFdrcAm4w8t70ufhtXl5/KY5UT8AYsV2ZmzuWiDUwCgVg5aaKDMPYrOfRgoDbwgjgN5sVMxaOEIVA9/ZcdTu8DU9vH71ghFupuFBgMUzY9jtUSqVesVpYjdomcyVKhldAcvxtg997eHkj4r3+8PL1Re8ZX8HDy9tgjm+VJs15QxkEV6iE8vUaQqXML+oMr9cQDMOgfuduqN+5Gy8Y6mVi3K5hy9dDmZsDLx9fbTBSvkEj3D53Oi+tVu6aQArJZ8D27dsxfvx4rFy5Ek2bNsWaNWvQqVMnXLlyBWXK6HejHh8fj86dO2PYsGHYsmUL/v77b4wcORIlSpRAz549BbZACoKGnNFDQ02U49du08Fq2/Xw9ka52vV40xiGsUkgorsNIWVr18VHq35CSuJDi8crIfms2Sso0WWdXBtuMGKqONbQEB1CAy7mTVeg1+dztcUOPabNxuEfliHj2VPB+RUKNyg8zauU+u6UGTh/4HekP3uqNyZS9ykzePVQuJ0Glq/fCHcunOXN//bYT/DyxXM8uPIfytSso21CzDAMChUNRMaLFL2GARpihshw9/TUNhLoPGYyEv77BxE938edC2fh5u4hugsGW5AcjCxevBhDhgzB0KF5XTcvWbIEhw4dwqpVqzB/vn5vhKtXr0aZMmWwZMkSAEC1atVw7tw5LFy40GWDEVbNGrxIpKwDahaMu+GbKsuygBpg3KRtS/1KCbAsFL7mN6kk1lMosJjB7G1iHls1+7SXEmXDkXApTu5k2JSYfkj6zV2EW2dP6vWto8Htav/dqTPxIikR9Tp2hUqphDun6Ce8bgMMX7EB/xw+gKDyFYVWZbZCgcXQ4oPB+C/mqDYYCQwtre2M0ZD2H41F0u0bCK/XEMd//gmFigaCYRh0HT8VV/+KRfXm+cWKjEKBAd8shVqt1ssl7jP7G5zYvoXf0Z4I1d5qiWpvtQSQN/BiXkVX+XJGGFZC4WROTg58fX2xY8cOvPtu/pvtuHHjEBcXh9hY/ZFemzdvjnr16uH777/XTtu1axd69+6NzMxMeAiUFWZnZyOb0948LS0NYWFhSE1NRWG9TrHMd3TyKhRRlgSDvCakLMsiR52rPYEYMPB3L4RsdQ5y1DlgGAYKMHBn3KFklXBj3MACyFXnauf3VHigmFcgUnPTkKXKAou88k4FGLCckk+WZcEAcFO4gwGgYlXwcfOBUq0CCzUCPArDQ+GB5zkpUEABFauCG+MGhmHgwbjD153/Jq9m1UjLTUe2Ogeq14XlDABPhRcAFu4KdyigQC6bi0DPolCzaiRmPX6dNgUUDAN3xgMs1MhW55gss3Zj3KBkldqbAQMGef/P+awzXc2qtWkT00ssdx1uCrfX5ZoM3Bg35KhztOthXv8fd70Mkz9NO53V3661e6sVI1eVCjWbBTfGD+5uwnUvDI1nordPRuj+Jtzpmmma308s7bxM/nq4v7+aVWu/yz96jN40JavMuwYY7lzM6xaI+WvM+z7vk4LJu75eqbKQo73m+P9Vvj6/1GwOlKpUuLv5Q8EYbknA/Y0YMPBQ5F0Dqtdl6IK/seAky8+jvH1UQA01wLJQqTMBxgMKRv8eqflNwWrOdWjvNYbPHUD9+jdXMAooXm+PgSLvulflaNel/WUYBizLQsWqkKN8BhYqeLgVgYIxPWaSsd+EAQOWVSFH9RSAAl7uht/qDe0PCxZqNhc5qmdgWRZe7kEAI+VsNs3Y2rj7p5lPpX4JhnGHG+Ot/1vqXAdqlkUOyz2PLU+X9v4nco26xyir0iu0HjNYYmqMS0tLQ0BAgMnnt6SckadPn0KlUiEoiF+rPCgoCElJSYLLJCUlCc6vVCrx9OlThISE6C0zf/58zJ4t3MOnNQXkBiPYx3S2lL856/YojAAPywOnQE9xdUAUjAJFPI13Xa87fykf/d+e2IP9mgW6okLupivPEluwvGK1PuHiF2nKWWEdBAAuP/hHtm2bVWtIN+uJ+4Yjdn6h6RrTpk3DxIn5I1hqckas7Yn3PWS8TAFev3UpoMh7Y8tLJVgAPgpv5LJKKNW5r3M2WCiYvLJFj9c5JAwYqMGCZfO+L+Tmh1fqLKhZdV6vla9zQXiRNKMAWFb7JufGuCGHzYEb3LRvLV4KL2SzOXBn3PFKlfk6VYA74w4fNx9kKNNR2L0wGCjAMHidK6IEN/fF180HKlb1+q0pL9cjL3fH43WK2NdpV2unMa9ziYzJO3YsWG1uQ/67tua/eevIn+7G5OXO8A87A8FXTehW2mehhhrs6yUUcHudDnDSACgY5vVnNW+t/Ddo7uB7Yt4grJl7Ymh7+tvQ3X/dubj7ZHBrjP56NL8hd9+FD7dAml7/r+J1joWaVWuPP6PtKUBzpeStmHs9aT55aN/28+fNP1+EPwNAUY+iyHx9LfCuqNc74Ma466Vb8yk/54W7v/m/B4u8a1oFlTZnBNplhXDOI7Pex1m9f6mhhhvcwEL9er0KvXmhzdli9f5tajuaXBA1m3c15fUbw8JT4cXJMcv//fPui2465xF3W6zOll+flSKucVPnnIG7gnadelczk39FmH/Fik0ntLl4mvtg3tL8e57QNaDZhjvjATdGISKthufgpo2bHvH454xXdeuVPEglKRgpXrw43Nzc9HJBkpOT9XI/NIKDgwXnd3d3R7FiBpqBeXnBy0CX1NbU+ZvxNt8GIYQQQoyTVMvL09MTDRo0wJEjR3jTjxw5giZNhMdfiIiI0Jv/8OHDaNiwoWB9EUIIIYQULJKrnE+cOBFr167F+vXrcfXqVUyYMAEJCQnafkOmTZuGgQMHaucfMWIE7t27h4kTJ+Lq1atYv3491q1bh8mTJ1tvLwghhBDitCTXGenTpw+ePXuGL7/8EomJiahZsyYOHDiAsmXzepRLTExEQkKCdv7w8HAcOHAAEyZMwIoVKxAaGoqlS5e6bLNeQgghhEgjqWmvXMQ2DSKEEEKI4xD7/HbunoEIIYQQ4vQoGCGEEEKIrCgYIYQQQoisKBghhBBCiKwoGCGEEEKIrCgYIYQQQoisKBghhBBCiKwoGCGEEEKIrCgYIYQQQoisJHcHLwdNJ7FpaWkyp4QQQgghYmme26Y6e3eKYCQ9PR0AEBYWJnNKCCGEECJVeno6AgICDH7vFGPTqNVqPHr0CP7+/mAYxmrrTUtLQ1hYGO7fv++SY9648v658r4Brr1/rrxvgGvvH+2b85Jr/1iWRXp6OkJDQ6FQGK4Z4hQ5IwqFAqVLl7bZ+gsXLuySJ5+GK++fK+8b4Nr758r7Brj2/tG+OS859s9YjogGVWAlhBBCiKwoGCGEEEKIrAp0MOLl5YWZM2fCy8tL7qTYhCvvnyvvG+Da++fK+wa49v7RvjkvR98/p6jASgghhBDXVaBzRgghhBAiPwpGCCGEECIrCkYIIYQQIisKRgghhBAiqwIdjKxcuRLh4eHw9vZGgwYNcPz4cbmTZNL8+fPRqFEj+Pv7o2TJkujevTuuX7/OmycyMhIMw/D+GjduzJsnOzsbY8aMQfHixeHn54d33nkHDx48sOeu6Jk1a5ZeuoODg7XfsyyLWbNmITQ0FD4+PmjZsiUuX77MW4cj7hcAlCtXTm/fGIbBqFGjADjfMTt27Bi6du2K0NBQMAyD3bt387631rFKSUnBgAEDEBAQgICAAAwYMAAvXryQbd9yc3Px6aefolatWvDz80NoaCgGDhyIR48e8dbRsmVLvePZt29f2ffN1P4B1jsXHe3YARC8BhmGwYIFC7TzOOqxE3Pvd+brrsAGI9u3b8f48eMxffp0XLx4Ec2aNUOnTp2QkJAgd9KMio2NxahRo3Dq1CkcOXIESqUS7du3x8uXL3nzdezYEYmJidq/AwcO8L4fP348du3ahV9++QV//fUXMjIy0KVLF6hUKnvujp4aNWrw0n3p0iXtd99++y0WL16M5cuX4+zZswgODka7du20YxcBjrtfZ8+e5e3XkSNHAAC9evXSzuNMx+zly5eoU6cOli9fLvi9tY5Vv379EBcXh4MHD+LgwYOIi4vDgAEDZNu3zMxMXLhwAV988QUuXLiAnTt34saNG3jnnXf05h02bBjveK5Zs4b3vRz7Bpg+doB1zkVHO3YAePuUmJiI9evXg2EY9OzZkzefIx47Mfd+Z77uwBZQb7zxBjtixAjetKpVq7JTp06VKUXmSU5OZgGwsbGx2mmDBg1iu3XrZnCZFy9esB4eHuwvv/yinfbw4UNWoVCwBw8etGVyjZo5cyZbp04dwe/UajUbHBzMfv3119ppWVlZbEBAALt69WqWZR13v4SMGzeOrVChAqtWq1mWdd5jxrIsC4DdtWuX9rO1jtWVK1dYAOypU6e085w8eZIFwF67ds3Ge5VHd9+EnDlzhgXA3rt3TzutRYsW7Lhx4wwu4wj7xrLC+2eNc9ER9k/MsevWrRvbunVr3jRnOXa6935nv+4KZM5ITk4Ozp8/j/bt2/Omt2/fHidOnJApVeZJTU0FAAQGBvKmx8TEoGTJkqhcuTKGDRuG5ORk7Xfnz59Hbm4ub/9DQ0NRs2ZN2ff/5s2bCA0NRXh4OPr27Ys7d+4AAOLj45GUlMRLs5eXF1q0aKFNsyPvF1dOTg62bNmCwYMH8wZ+dNZjpstax+rkyZMICAjAm2++qZ2ncePGCAgIcKh9Tk1NBcMwKFKkCG/61q1bUbx4cdSoUQOTJ0/mvZ06+r5Zei46+v4BwOPHj7F//34MGTJE7ztnOHa6935nv+6cYqA8a3v69ClUKhWCgoJ404OCgpCUlCRTqqRjWRYTJ07EW2+9hZo1a2qnd+rUCb169ULZsmURHx+PL774Aq1bt8b58+fh5eWFpKQkeHp6omjRorz1yb3/b775JjZt2oTKlSvj8ePHmDt3Lpo0aYLLly9r0yV0zO7duwcADrtfunbv3o0XL14gMjJSO81Zj5kQax2rpKQklCxZUm/9JUuWdJh9zsrKwtSpU9GvXz/e4GP9+/dHeHg4goOD8d9//2HatGn4559/tMVzjrxv1jgXHXn/NH766Sf4+/ujR48evOnOcOyE7v3Oft0VyGBEg/tWCuQdYN1pjmz06NH4999/8ddff/Gm9+nTR/vvmjVromHDhihbtiz279+vd+Fxyb3/nTp10v67Vq1aiIiIQIUKFfDTTz9pK9CZc8zk3i9d69atQ6dOnRAaGqqd5qzHzBhrHCuh+R1ln3Nzc9G3b1+o1WqsXLmS992wYcO0/65ZsyYqVaqEhg0b4sKFC6hfvz4Ax903a52Ljrp/GuvXr0f//v3h7e3Nm+4Mx87QvR9w3uuuQBbTFC9eHG5ubnpRXnJysl5U6ajGjBmDPXv2IDo6GqVLlzY6b0hICMqWLYubN28CAIKDg5GTk4OUlBTefI62/35+fqhVqxZu3rypbVVj7Jg5w37du3cPR48exdChQ43O56zHDIDVjlVwcDAeP36st/4nT57Ivs+5ubno3bs34uPjceTIEZNDstevXx8eHh684+mo+6bLnHPR0ffv+PHjuH79usnrEHC8Y2fo3u/s112BDEY8PT3RoEEDbbabxpEjR9CkSROZUiUOy7IYPXo0du7ciT///BPh4eEml3n27Bnu37+PkJAQAECDBg3g4eHB2//ExET8999/DrX/2dnZuHr1KkJCQrTZptw05+TkIDY2VptmZ9ivDRs2oGTJknj77beNzuesxwyA1Y5VREQEUlNTcebMGe08p0+fRmpqqqz7rAlEbt68iaNHj6JYsWIml7l8+TJyc3O1x9NR902IOeeio+/funXr0KBBA9SpU8fkvI5y7Ezd+53+urNZ1VgH98svv7AeHh7sunXr2CtXrrDjx49n/fz82Lt378qdNKM+/vhjNiAggI2JiWETExO1f5mZmSzLsmx6ejo7adIk9sSJE2x8fDwbHR3NRkREsKVKlWLT0tK06xkxYgRbunRp9ujRo+yFCxfY1q1bs3Xq1GGVSqVcu8ZOmjSJjYmJYe/cucOeOnWK7dKlC+vv7689Jl9//TUbEBDA7ty5k7106RL7/vvvsyEhIQ6/XxoqlYotU6YM++mnn/KmO+MxS09PZy9evMhevHiRBcAuXryYvXjxorZFibWOVceOHdnatWuzJ0+eZE+ePMnWqlWL7dKli2z7lpuby77zzjts6dKl2bi4ON41mJ2dzbIsy966dYudPXs2e/bsWTY+Pp7dv38/W7VqVbZevXqy75up/bPmuehox04jNTWV9fX1ZVetWqW3vCMfO1P3fpZ17uuuwAYjLMuyK1asYMuWLct6enqy9evX5zWPdVQABP82bNjAsizLZmZmsu3bt2dLlCjBenh4sGXKlGEHDRrEJiQk8Nbz6tUrdvTo0WxgYCDr4+PDdunSRW8ee+vTpw8bEhLCenh4sKGhoWyPHj3Yy5cva79Xq9XszJkz2eDgYNbLy4tt3rw5e+nSJd46HHG/NA4dOsQCYK9fv86b7ozHLDo6WvA8HDRoEMuy1jtWz549Y/v378/6+/uz/v7+bP/+/dmUlBTZ9i0+Pt7gNRgdHc2yLMsmJCSwzZs3ZwMDA1lPT0+2QoUK7NixY9lnz57Jvm+m9s+a56KjHTuNNWvWsD4+PuyLFy/0lnfkY2fq3s+yzn3dMa93khBCCCFEFgWyzgghhBBCHAcFI4QQQgiRFQUjhBBCCJEVBSOEEEIIkRUFI4QQQgiRFQUjhBBCCJEVBSOEEEIIkRUFI4QQQgiRFQUjhBBCCJEVBSOEEEIIkRUFI4QQQgiRFQUjhBBCCJHV/wGR95r9Y4zPPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "sp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "152975cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16280"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da=sp.filter(['Close'])\n",
    "das= da.values\n",
    "tr_data_len=math.ceil(len(sp)*8)\n",
    "tr_data_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f4abe14b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[233.75],\n",
       "       [233.25],\n",
       "       [234.25],\n",
       "       ...,\n",
       "       [120.65],\n",
       "       [120.9 ],\n",
       "       [121.55]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "das"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c9e0fd0f",
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-09-28</td>\n",
       "      <td>234.05</td>\n",
       "      <td>235.95</td>\n",
       "      <td>230.20</td>\n",
       "      <td>233.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>234.55</td>\n",
       "      <td>236.80</td>\n",
       "      <td>231.10</td>\n",
       "      <td>233.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>240.00</td>\n",
       "      <td>240.00</td>\n",
       "      <td>232.50</td>\n",
       "      <td>235.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-09-25</td>\n",
       "      <td>233.30</td>\n",
       "      <td>236.75</td>\n",
       "      <td>232.00</td>\n",
       "      <td>236.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>233.55</td>\n",
       "      <td>239.20</td>\n",
       "      <td>230.75</td>\n",
       "      <td>234.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>2010-07-27</td>\n",
       "      <td>117.60</td>\n",
       "      <td>119.50</td>\n",
       "      <td>112.00</td>\n",
       "      <td>118.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>2010-07-26</td>\n",
       "      <td>120.10</td>\n",
       "      <td>121.00</td>\n",
       "      <td>117.10</td>\n",
       "      <td>117.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>2010-07-23</td>\n",
       "      <td>121.80</td>\n",
       "      <td>121.95</td>\n",
       "      <td>120.25</td>\n",
       "      <td>120.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>2010-07-22</td>\n",
       "      <td>120.30</td>\n",
       "      <td>122.00</td>\n",
       "      <td>120.25</td>\n",
       "      <td>120.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>2010-07-21</td>\n",
       "      <td>122.10</td>\n",
       "      <td>123.00</td>\n",
       "      <td>121.05</td>\n",
       "      <td>121.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2035 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date    Open    High     Low    Last\n",
       "0     2018-09-28  234.05  235.95  230.20  233.50\n",
       "1     2018-09-27  234.55  236.80  231.10  233.80\n",
       "2     2018-09-26  240.00  240.00  232.50  235.00\n",
       "3     2018-09-25  233.30  236.75  232.00  236.25\n",
       "4     2018-09-24  233.55  239.20  230.75  234.00\n",
       "...          ...     ...     ...     ...     ...\n",
       "2030  2010-07-27  117.60  119.50  112.00  118.80\n",
       "2031  2010-07-26  120.10  121.00  117.10  117.10\n",
       "2032  2010-07-23  121.80  121.95  120.25  120.35\n",
       "2033  2010-07-22  120.30  122.00  120.25  120.75\n",
       "2034  2010-07-21  122.10  123.00  121.05  121.10\n",
       "\n",
       "[2035 rows x 5 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp= sp.iloc[:, 0:5]\n",
    "sp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e459fcd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[234.05],\n",
       "       [234.55],\n",
       "       [240.  ],\n",
       "       ...,\n",
       "       [121.8 ],\n",
       "       [120.3 ],\n",
       "       [122.1 ]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr_set = sp.iloc[:,1:2].values\n",
    "tr_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5cb93922",
   "metadata": {},
   "outputs": [],
   "source": [
    "#scalling of data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "928e17b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range = (0, 1))\n",
    "\n",
    "data_tr_scaled = scaler.fit_transform(tr_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8553cb44",
   "metadata": {},
   "outputs": [],
   "source": [
    "ft_set = []\n",
    "lals = []\n",
    "for i in range(60, 586):\n",
    "    ft_set.append(data_tr_scaled[i-60:i, 0])\n",
    "    lals.append(data_tr_scaled[i, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9282bb75",
   "metadata": {},
   "outputs": [],
   "source": [
    "ft_set, lals = np.array(ft_set), np.array(lals)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a8fa155a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(526, 60, 1)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fts_set = np.reshape(ft_set, (ft_set.shape[0], ft_set.shape[1], 1))\n",
    "fts_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "159374a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#building lstm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b3cb42ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pip in d:\\downloads\\anaconda files\\lib\\site-packages (22.2.2)\n",
      "Collecting pip\n",
      "  Downloading pip-23.0.1-py3-none-any.whl (2.1 MB)\n",
      "     ---------------------------------------- 2.1/2.1 MB 9.3 MB/s eta 0:00:00\n",
      "Installing collected packages: pip\n",
      "  Attempting uninstall: pip\n",
      "    Found existing installation: pip 22.2.2\n",
      "    Uninstalling pip-22.2.2:\n",
      "      Successfully uninstalled pip-22.2.2\n",
      "Successfully installed pip-23.0.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install --upgrade pip\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "109ed388",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in d:\\downloads\\anaconda files\\lib\\site-packages (2.11.0)\n",
      "Requirement already satisfied: tensorflow-intel==2.11.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow) (2.11.0)\n",
      "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (0.4.0)\n",
      "Requirement already satisfied: packaging in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (21.3)\n",
      "Requirement already satisfied: flatbuffers>=2.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (23.3.3)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (2.2.0)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (1.51.3)\n",
      "Requirement already satisfied: tensorflow-estimator<2.12,>=2.11.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (2.11.0)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (1.6.3)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (1.4.0)\n",
      "Requirement already satisfied: protobuf<3.20,>=3.9.2 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (3.19.6)\n",
      "Requirement already satisfied: keras<2.12,>=2.11.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (2.11.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (4.3.0)\n",
      "Requirement already satisfied: numpy>=1.20 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (1.21.5)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (3.3.0)\n",
      "Requirement already satisfied: libclang>=13.0.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (15.0.6.1)\n",
      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (0.31.0)\n",
      "Requirement already satisfied: wrapt>=1.11.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (1.14.1)\n",
      "Requirement already satisfied: h5py>=2.9.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (3.7.0)\n",
      "Requirement already satisfied: setuptools in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (63.4.1)\n",
      "Requirement already satisfied: six>=1.12.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (1.16.0)\n",
      "Requirement already satisfied: tensorboard<2.12,>=2.11 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (2.11.2)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (0.2.0)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.11.0->tensorflow) (0.37.1)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (0.4.6)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (0.6.1)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (1.8.1)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (2.0.3)\n",
      "Requirement already satisfied: markdown>=2.6.8 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (3.3.4)\n",
      "Requirement already satisfied: google-auth<3,>=1.6.3 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (2.16.2)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (2.28.1)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in d:\\downloads\\anaconda files\\lib\\site-packages (from packaging->tensorflow-intel==2.11.0->tensorflow) (3.0.9)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in d:\\downloads\\anaconda files\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (4.9)\n",
      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (5.3.0)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in d:\\downloads\\anaconda files\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (0.2.8)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (1.3.1)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in d:\\downloads\\anaconda files\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (1.26.11)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in d:\\downloads\\anaconda files\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in d:\\downloads\\anaconda files\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (2022.9.14)\n",
      "Requirement already satisfied: idna<4,>=2.5 in d:\\downloads\\anaconda files\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (3.3)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in d:\\downloads\\anaconda files\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in d:\\downloads\\anaconda files\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (3.2.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "64bc75cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.python.keras.models import Sequential\n",
    "from tensorflow.python.keras.layers import Dense\n",
    "from tensorflow.python.keras.layers import LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4016da97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 2/50\n",
      "27/27 [==============================] - 0s 877us/step - loss: 0.0118\n",
      "Epoch 3/50\n",
      "27/27 [==============================] - 0s 982us/step - loss: 0.0118\n",
      "Epoch 4/50\n",
      "27/27 [==============================] - 0s 777us/step - loss: 0.0118\n",
      "Epoch 5/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 6/50\n",
      "27/27 [==============================] - 0s 659us/step - loss: 0.0118\n",
      "Epoch 7/50\n",
      "27/27 [==============================] - 0s 620us/step - loss: 0.0118\n",
      "Epoch 8/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 9/50\n",
      "27/27 [==============================] - 0s 588us/step - loss: 0.0118\n",
      "Epoch 10/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 11/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 12/50\n",
      "27/27 [==============================] - 0s 952us/step - loss: 0.0118\n",
      "Epoch 13/50\n",
      "27/27 [==============================] - 0s 609us/step - loss: 0.0118\n",
      "Epoch 14/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 15/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 16/50\n",
      "27/27 [==============================] - 0s 614us/step - loss: 0.0118\n",
      "Epoch 17/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 18/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 19/50\n",
      "27/27 [==============================] - 0s 624us/step - loss: 0.0118\n",
      "Epoch 20/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 21/50\n",
      "27/27 [==============================] - 0s 681us/step - loss: 0.0118\n",
      "Epoch 22/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 23/50\n",
      "27/27 [==============================] - 0s 669us/step - loss: 0.0118\n",
      "Epoch 24/50\n",
      "27/27 [==============================] - 0s 833us/step - loss: 0.0118\n",
      "Epoch 25/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 26/50\n",
      "27/27 [==============================] - 0s 900us/step - loss: 0.0118\n",
      "Epoch 27/50\n",
      "27/27 [==============================] - 0s 931us/step - loss: 0.0118\n",
      "Epoch 28/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 29/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 30/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 31/50\n",
      "27/27 [==============================] - 0s 638us/step - loss: 0.0118\n",
      "Epoch 32/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 33/50\n",
      "27/27 [==============================] - 0s 663us/step - loss: 0.0118\n",
      "Epoch 34/50\n",
      "27/27 [==============================] - 0s 767us/step - loss: 0.0118\n",
      "Epoch 35/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 36/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 37/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 38/50\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0118\n",
      "Epoch 39/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 40/50\n",
      "27/27 [==============================] - 0s 926us/step - loss: 0.0118\n",
      "Epoch 41/50\n",
      "27/27 [==============================] - 0s 816us/step - loss: 0.0118\n",
      "Epoch 42/50\n",
      "27/27 [==============================] - 0s 797us/step - loss: 0.0118\n",
      "Epoch 43/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 44/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 45/50\n",
      "27/27 [==============================] - 0s 1ms/step - loss: 0.0118\n",
      "Epoch 46/50\n",
      "27/27 [==============================] - 0s 964us/step - loss: 0.0118\n",
      "Epoch 47/50\n",
      "27/27 [==============================] - 0s 505us/step - loss: 0.0118\n",
      "Epoch 48/50\n",
      "27/27 [==============================] - 0s 737us/step - loss: 0.0118\n",
      "Epoch 49/50\n",
      "27/27 [==============================] - 0s 606us/step - loss: 0.0118\n",
      "Epoch 50/50\n",
      "27/27 [==============================] - 0s 699us/step - loss: 0.0118\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1c8eac85400>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
    "model.fit(ft_set, lals, epochs = 50, batch_size = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1f10a397",
   "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>Open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>234.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>234.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>240.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>233.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>233.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>117.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>120.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>121.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>120.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>122.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2035 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Open\n",
       "0     234.05\n",
       "1     234.55\n",
       "2     240.00\n",
       "3     233.30\n",
       "4     233.55\n",
       "...      ...\n",
       "2030  117.60\n",
       "2031  120.10\n",
       "2032  121.80\n",
       "2033  120.30\n",
       "2034  122.10\n",
       "\n",
       "[2035 rows x 1 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_testing_complete = pd.read_csv('hello.csv')\n",
    "dt_testing_processed = dt_testing_complete.iloc[:, 1:2]\n",
    "dt_testing_processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b37780a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prediction of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cdcb079a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2095,)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_total = pd.concat((sp['Open'], sp['Open']), axis=0)\n",
    "tet_inputs = dt_total[len(dt_total) - len(sp) - 60:].values\n",
    "tet_inputs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "97b88447",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29, 60, 1)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tet_inputs = tet_inputs.reshape(-1,1)\n",
    "tet_inputs = scaler.transform(tet_inputs)\n",
    "tet_features = []\n",
    "for i in range(60, 89):\n",
    "    tet_features.append(tet_inputs[i-60:i, 0])\n",
    "tet_features = np.array(tet_features)\n",
    "tet_features = np.reshape(tet_features, (tet_features.shape[0], tet_features.shape[1], 1))\n",
    "tet_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e85036db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-0.3280373 ],\n",
       "        [-0.32799456],\n",
       "        [-0.32799456],\n",
       "        ...,\n",
       "        [-0.32820338],\n",
       "        [-0.32822806],\n",
       "        [-0.32819846]],\n",
       "\n",
       "       [[-0.32799456],\n",
       "        [-0.32799456],\n",
       "        [-0.32799044],\n",
       "        ...,\n",
       "        [-0.32822806],\n",
       "        [-0.32819846],\n",
       "        [-0.3263575 ]],\n",
       "\n",
       "       [[-0.32799456],\n",
       "        [-0.32799044],\n",
       "        [-0.32806692],\n",
       "        ...,\n",
       "        [-0.32819846],\n",
       "        [-0.3263575 ],\n",
       "        [-0.3263493 ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[-0.32817954],\n",
       "        [-0.32819188],\n",
       "        [-0.32816392],\n",
       "        ...,\n",
       "        [-0.32635012],\n",
       "        [-0.32625476],\n",
       "        [-0.32614622]],\n",
       "\n",
       "       [[-0.32819188],\n",
       "        [-0.32816392],\n",
       "        [-0.32820585],\n",
       "        ...,\n",
       "        [-0.32625476],\n",
       "        [-0.32614622],\n",
       "        [-0.3261939 ]],\n",
       "\n",
       "       [[-0.32816392],\n",
       "        [-0.32820585],\n",
       "        [-0.32828233],\n",
       "        ...,\n",
       "        [-0.32614622],\n",
       "        [-0.3261939 ],\n",
       "        [-0.32624653]]], dtype=float32)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = model.predict(tet_features)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "60940398",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1256, 5)\n",
      "(1256, 5)\n"
     ]
    }
   ],
   "source": [
    "x_tr = sp[0:1256]\n",
    "y_tr = sp[1:1257]\n",
    "print(x_tr.shape)\n",
    "print(y_tr.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0d62dca4",
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-09-28</td>\n",
       "      <td>234.05</td>\n",
       "      <td>235.95</td>\n",
       "      <td>230.20</td>\n",
       "      <td>233.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>234.55</td>\n",
       "      <td>236.80</td>\n",
       "      <td>231.10</td>\n",
       "      <td>233.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>240.00</td>\n",
       "      <td>240.00</td>\n",
       "      <td>232.50</td>\n",
       "      <td>235.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-09-25</td>\n",
       "      <td>233.30</td>\n",
       "      <td>236.75</td>\n",
       "      <td>232.00</td>\n",
       "      <td>236.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>233.55</td>\n",
       "      <td>239.20</td>\n",
       "      <td>230.75</td>\n",
       "      <td>234.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>2013-09-04</td>\n",
       "      <td>142.00</td>\n",
       "      <td>145.35</td>\n",
       "      <td>140.65</td>\n",
       "      <td>143.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>2013-09-03</td>\n",
       "      <td>144.10</td>\n",
       "      <td>145.20</td>\n",
       "      <td>140.70</td>\n",
       "      <td>141.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>2013-09-02</td>\n",
       "      <td>139.40</td>\n",
       "      <td>144.40</td>\n",
       "      <td>139.35</td>\n",
       "      <td>144.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>2013-08-30</td>\n",
       "      <td>138.10</td>\n",
       "      <td>140.65</td>\n",
       "      <td>136.70</td>\n",
       "      <td>139.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>137.00</td>\n",
       "      <td>140.40</td>\n",
       "      <td>137.00</td>\n",
       "      <td>137.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1256 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date    Open    High     Low    Last\n",
       "0     2018-09-28  234.05  235.95  230.20  233.50\n",
       "1     2018-09-27  234.55  236.80  231.10  233.80\n",
       "2     2018-09-26  240.00  240.00  232.50  235.00\n",
       "3     2018-09-25  233.30  236.75  232.00  236.25\n",
       "4     2018-09-24  233.55  239.20  230.75  234.00\n",
       "...          ...     ...     ...     ...     ...\n",
       "1251  2013-09-04  142.00  145.35  140.65  143.60\n",
       "1252  2013-09-03  144.10  145.20  140.70  141.80\n",
       "1253  2013-09-02  139.40  144.40  139.35  144.00\n",
       "1254  2013-08-30  138.10  140.65  136.70  139.20\n",
       "1255  2013-08-29  137.00  140.40  137.00  137.10\n",
       "\n",
       "[1256 rows x 5 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "1eff182d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.62434536, -0.61175641, -0.52817175],\n",
       "       [-1.07296862,  0.86540763, -2.3015387 ],\n",
       "       [ 1.74481176, -0.7612069 ,  0.3190391 ]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "np.random.randn(3, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "cba45d3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#DRAW A SINGLE NUMBER FROM THE NORMAL DISTRIBUTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8b20ac2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7506296245225899"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8f0bcbda",
   "metadata": {},
   "outputs": [],
   "source": [
    "#DRAW 5 NUMBERS FROM THE NORMAL DISTRIBUTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ce06d941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.4621079370449745"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7fff5719",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.87262077473541"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "np.random.normal(size = 1000, scale = 100).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "53fb5904",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plotting the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "742c31c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,6))\n",
    "plt.title(\"stock market price prediction using lstm\")\n",
    "plt.plot(dt_testing_complete['Close'])\n",
    "plt.xlabel('Date ',fontsize=18)\n",
    "plt.ylabel('Total Trade Quantity is ',fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "efb01d38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c8eafe9790>]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp[\"Date\"]=pd.to_datetime(sp.Date)\n",
    "sp.index=sp['Date']\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(sp[\"Open\"],label='ClosePriceHist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "73ff5d41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(sp['Date'])\n",
    "plt.xlabel('Turnover in (Lacs)',fontsize=12)\n",
    "plt.ylabel('Total Trade Quantity',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5c02a4e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}