LSTMprediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1cddc7ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import joblib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.layers import LSTM, Dense, Dropout\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn import linear_model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM\n",
    "import keras.backend as K\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.optimizers import Adam\n",
    "from keras.models import load_model\n",
    "from tensorflow.keras.utils import plot_model\n",
    "import yfinance as yf\n",
    "from datetime import datetime, timedelta\n",
    "from tensorflow.keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "546906ad",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "META\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "                  Open        High         Low       Close   Adj Close   \n",
      "Date                                                                     \n",
      "2013-07-22   25.990000   26.129999   25.719999   26.049999   26.049999  \\\n",
      "2013-07-23   26.100000   26.299999   25.969999   26.129999   26.129999   \n",
      "2013-07-24   26.320000   26.530001   26.049999   26.510000   26.510000   \n",
      "2013-07-25   33.540001   34.880001   32.750000   34.360001   34.360001   \n",
      "2013-07-26   33.770000   34.730000   33.560001   34.009998   34.009998   \n",
      "...                ...         ...         ...         ...         ...   \n",
      "2023-07-12  301.750000  309.450012  300.100006  309.339996  309.339996   \n",
      "2023-07-13  313.619995  316.239990  310.290009  313.410004  313.410004   \n",
      "2023-07-14  311.790009  314.880005  307.359985  308.869995  308.869995   \n",
      "2023-07-17  307.540009  311.709991  304.709991  310.619995  310.619995   \n",
      "2023-07-18  310.880005  314.200012  307.619995  312.049988  312.049988   \n",
      "\n",
      "               Volume  \n",
      "Date                   \n",
      "2013-07-22   27526300  \n",
      "2013-07-23   28221600  \n",
      "2013-07-24   82635600  \n",
      "2013-07-25  365457900  \n",
      "2013-07-26  136028900  \n",
      "...               ...  \n",
      "2023-07-12   36677100  \n",
      "2023-07-13   30281000  \n",
      "2023-07-14   22576000  \n",
      "2023-07-17   25323100  \n",
      "2023-07-18   20731900  \n",
      "\n",
      "[2515 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "stock = input()\n",
    "end_date = datetime.now()\n",
    "start_date = end_date - timedelta(days=10 * 365)\n",
    "df = yf.download(stock, start=start_date, end=end_date-timedelta(1))\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "23720993",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframe Shape: (2515, 6)\n",
      "Null Value Present: False\n"
     ]
    }
   ],
   "source": [
    "print(\"Dataframe Shape:\", df.shape)\n",
    "print(\"Null Value Present:\", df.isnull().values.any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b51e4445",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P39UQ9SMCHR7P2/ean45c0+hbULxcrQXFkEajwerVq1FQUICkpCQcPnwYKpUK/fv3F89p0aIF4uPjsXfvXgDA3r17kZiYiKioKPGcQYMGITc3V2yFMUepVCI3N1fyQ0RErkkQBOQrTVtQZg5ohtdGJDghIsDX2/kXZDlQa0pbUFxtDAoAnDhxAkFBQfD19cXUqVOxdu1aJCQkICMjAz4+PggLC5OcHxUVhYyMDABARkaGJDnRH9cfs2TBggUIDQ0Vf+Li4qwNm4iIylHeyr+2VliiESu5yqWAqxzGXMiBSuvCLSjNmzfH0aNHsX//fkybNg0TJkzA6dOn7RGbaPbs2cjJyRF/UlNT7fp8REQ1TTnr6tlcYYlGvO1fzmBZR/L2cv4FWQ5Ual0Lihxm8Vg9zdjHxwdNmjQBAHTs2BEHDx7Ehx9+iLFjx6KkpATZ2dmSVpTMzExER0cDAKKjo3HgwAHJ4+ln+ejPMcfX1xe+vs5bUZGIyN0JRi0ogiBAYafmjZLSbgQfTw/E1QrAucw8uzyPNbxY6h4AUKzWJY/lzbJylGqnjFqtFkqlEh07doS3tze2bSsb/X3u3DmkpKQgKSkJAJCUlIQTJ04gKytLPGfLli0ICQlBQoJz+h2JiMi0BUVjpklFpdFizq8n8OeJ69V6rpLSb+m+Xh749JEO6Nm0Nn58Mqlaj1ld9pza7Eq2ndFdn+WQoFjVgjJ79mwMGTIE8fHxyMvLw6pVq7Bjxw5s2rQJoaGhmDRpEmbNmoXw8HCEhITg6aefRlJSErp16wYAGDhwIBISEvDoo4/i3XffRUZGBubMmYPp06ezhYSIyImMW1DUWgFeRteoDScz8N2+FHy3LwVXFg6r0vPkK9WY8s0hAICPlwca1wnCt5O6VumxbImLBQI38pTYflaXoPjKoMvLqgQlKysL48ePx/Xr1xEaGoo2bdpg06ZNGDBgAABg8eLF8PDwwJgxY6BUKjFo0CB8+umn4v09PT2xfv16TJs2DUlJSQgMDMSECRPwxhtv2Pa3IiIiqxiPkTXXglKs0pjss9bH2y7gQlY+AF2CIierp3TDg5/pymLYs4tLru4UlpX78JPBrCarEpTly5eXe9zPzw9LlizBkiVLLJ5Tv359/Pnnn9Y8LRER2ZnxLB61mQTF8Ft1VS/gp6+XlYkwvCDKQdPIIGeH4FSGb4FAX+evhOP8FImIiJyuMmNQDKee6ge6Wstw1eBiVdUeg+zDsIUsNszfiZHoMEEhIiIIMG5BMU0eDMdp6Ae6WquopPrdRPZi2CLkwLIwsnDyWg4mfX1Q3L6/k/PrjTFBISIikwuymfzEJEFZtT8FL689Aa0VRVQ8OBhVlkYv3YOb+brWrZZ1Q1xvFg8REbkn0zEophmK4UwfpVqLl9eeAAD0bR6J/glRJueb42nQShET6leVUO3GMHWqYQ0okhaxM9flsZwMW1CIiKjCWTznMvKw7li6uG14QXuidNpwZRi2oEzt09jKKKkmYQsKERFVOItn0Ae7JNtVHSRr+O1cDt0IhgwnJelai2pmd9R9HWOdHQIAtqAQEREqVwfFkLKCGTg5hSoUlpiuWGxIbglKTZVtNN27cR15TLdmgkJERCYJSkWzdPKVlpOPvGIV2r6xGXct3F7uY3hzwKwsfLjtgmTbkStbl4cJChERmVyUCspJQADTBMWwxeV4Wg4A4E6hSnKOcTl9uc3oURh06cjjEu0YX/1zRbLdq2kd5wRihAkKERFBY5Q8lNdCAgDvbDwr2TbszskrLktMDKcgqzRGCUoNKyUvVwONZmAlxoY6KRIpJihERGQy5qSiBCW5dD0dvUKDAmy5RWX3VRlMV1aqpUXaIoNltkisZJCs88JwtFB/b2eHYBZn8RAREdQa4y6esmTiWnZRhfe/b9keaLXAp+M6INegBUWtEaBf1sVwXMt/BzRD27iw6gVNNmGYnL48tIUTI5FiCwoREZm0oOjHoOQr1RUOdgWA1NtFuJZdhBd/OY7cIoMERSst7gYA3p4KPN2vqS3CtinJNOMaNApFVfp/9OrwBEzpJZ/aNExQiIjIpHLszQIlACDtTqFVj1Ok0iDHIEHJV6rxy+E0FCjVuJmve0yOPZEXjbYscZQTdvEQEZFJC8r/dl7C7CEtK6yHYuzqrULcNpi989HWC/jhUCrWH0/HP8m3AJS1pMiNpNR9DWhAEQQB/6bcQcptXRLqKbNZVWxBISIik8qxALB892UUq8pfffiRbvEm+wwLf/1wKBUA8Ne5G1WuPkv2ceJaDsYs3YuT13TVfQN85FU4jwkKERGZDJIFgPnrTyNfWX6C8lSfJniydyPJvrMZeTaNzVEUNazrKe2OdPBz7SB5zapigkJERLh8q8Ds/ooKtnl5Kkyqmt3IU5qc16hOYJVjI/swLs4ntwSFY1CIiGqwzNxiPP39ERy4fNvscXP1UBSKsjEaXh4ekhoolgT7ybPWRk1m3GoWEeTjpEjMYwsKEVEN9t7mcxaTEwDYcjrTZF+vpnUQ6OOJuqF+CPbzwrf7rlb4PIY1UBrWlmdrSk0bJKsyGhMUHsAEhYiIZKLYaFXiJpHSlWzNJSh+3h7YM7sfNs/sBW9PDzw/qHmFz3Pmeq54+4VKnE/2Zzww2stTXimBvKIhIiKHMi5z/sqwlpW+n77bZmrvxlj1RNdKP+f+clpsnKmmFWozN3NLTpigEBHVYMYJSqCPF96+N7Hc+3gbfdP29FCge5PalX7OHlacS/ajrGAKubMxQSEiqsFC/KVzJbw9FRVWFLU0PqOydTSaRgVVfJITKFCzphkfTc0Wb/9HhksPMEEhIqrBjC/KtYN84eNV/qXB0uKB306qXDePK5S6d/dBskq1BlvP6MYXPdm7EZ5lgkJERHJiPA4htpY/vDzMXxoCfDzhoQCesXAxqxfmb7Iv0EyripfM1nzRc4G8yWaUaq04QHrWgGbwkFmZe4B1UIiIajSNwSKBD3SKhUJhvovnlaEtMblXIxSVaOBvoSvH39t0f1x4gEllWU8XyATcvAEFGoMaKN4WElJnk2dURETkEIYtKPXCAgAA3ma6eCJDdFVGLSUnlo71aFIbwxLrSvbJbVG6mkj//65QQJatJwATFCKiGs1wtWI/b90lwcdolk7jOoEYmBBd4WMZt7w82bsRnhvUHIUl0mq0rpCgCG4+CEX//+4l4/8LdvEQEdVghi0o7eLCAJhetH6b0aPclhM948X2Zg/R1VQxLoXv5td+l6Au7dqTc7JoVQvKggUL0LlzZwQHByMyMhKjRo3CuXPnJOf06dMHCoVC8jN16lTJOSkpKRg2bBgCAgIQGRmJ559/Hmp1+QtSERGRbQmCgK/+uQwAiAv3R9dGEQBMu3gCfa3/LmuY5DQ2qk4b7CfP78bSQm3urawFRb4dKVa9S3bu3Inp06ejc+fOUKvVePnllzFw4ECcPn0agYFlaytMnjwZb7zxhrgdEBAg3tZoNBg2bBiio6OxZ88eXL9+HePHj4e3tzfefvttG/xKRERUGYeu3hFncgxLjBH3G3fxVIXhTJ0XB7WAj6cHBreORru4MNmVVK+J9C1ncm5BsSpB2bhxo2R7xYoViIyMxOHDh9GrVy9xf0BAAKKjzfdXbt68GadPn8bWrVsRFRWFdu3aYf78+XjxxRcxd+5c+PjIa7EiIiJ3Zdj1knqnULztW0EdlMownBkSGuCNufe0qvZj2ltNKtSmX7yxoqJ8zlStd2FOTg4AIDw8XLJ/5cqVqF27Nlq3bo3Zs2ejsLDsjb93714kJiYiKipK3Ddo0CDk5ubi1KlT1QmHiIisYJiIpN0u+5yuE+xb7cf2lPGFrzLcfZxMgVI3rKIq3XeOUuXItFotnn32Wdx1111o3bq1uP/hhx9G/fr1ERMTg+PHj+PFF1/EuXPnsGbNGgBARkaGJDkBIG5nZGSYfS6lUgmlUilu5+bmmj2PiIgqT2twFb6ZXyLeNl6fp0qPLfOF6MxxgfIsNlNQ2noW6OOGCcr06dNx8uRJ7N69W7J/ypQp4u3ExETUrVsX/fr1w8WLF9G4ceMqPdeCBQswb968qoZKRERmGLYSTOzeQLxtPBunKjQumKBIuHj4FfnhYAoA4PR1+X7hr1IXz4wZM7B+/Xr89ddfiI2NLffcrl11azMkJycDAKKjo5GZmSk5R79tadzK7NmzkZOTI/6kpqZWJWwiIjJg2IIyqUdDybGBCbqW7a2zelfpsY1L6LuCGtSAgj9PmO+xkBOrWlAEQcDTTz+NtWvXYseOHWjYsGGF9zl69CgAoG5dXSXBpKQkvPXWW8jKykJkZCQAYMuWLQgJCUFCQoLZx/D19YWvb/X7RImICEjPLsKW05n4YvclAECrmBCTaqL/e7QjilXaStU/McfVW1AEN25CMS6cJ1dWJSjTp0/HqlWrsG7dOgQHB4tjRkJDQ+Hv74+LFy9i1apVGDp0KCIiInD8+HHMnDkTvXr1Qps2bQAAAwcOREJCAh599FG8++67yMjIwJw5czB9+nQmIUREDtDz3b8kCYS51YUVCkWVkpOpvRtj2c6LeG2E+S+c5HxFBrO3dj1/txMjKZ9VCcrSpUsB6IqxGfrqq68wceJE+Pj4YOvWrfjggw9QUFCAuLg4jBkzBnPmzBHP9fT0xPr16zFt2jQkJSUhMDAQEyZMkNRNISIi+9BoBZPWDVuWwnhxcHOMT6qPGDMrG8udLcbeyJlWK+CFX45LphbHRwSUcw/nsrqLpzxxcXHYuXNnhY9Tv359/Pnnn9Y8NRERVdPT3x/BjnNZJvtteWFWKBQumZwYc8dpxv9cvImfD6eJ27aYrWVP8p1fRERENvX7sXSz+2VcTNSh3P1luHyzQLKtXxxSruQdHRER2YRKo7V4zNwYlJrODRtQkJlbLNn2867aAGhHYYJCROTmvtl7BU1f2WDxOBMUHXd/GXKKVJJtPy8mKERE5ESvrSt/GRF3vzBXhbqcFidXZbj2EgBk5hVbOFMemKAQEdVwbEHRUSgUqBvqBwC4ll3k5Ghsz3jg79hOcc4JpJI4SJaIyE29u/Es9l++XeF5HvyqKtIvnlesMt+CotZo4eXpmi+Y8UxcOZe5B9iCQkTktj7dcRGHr94xe6x2kI94O6/YNSqLOoJ+ZkuxWmNybM2/aWj1+ib8ddZ0qrYrMB74W6KWdzcWExQiohro3vb1xNvH03KcGIm8+JYOHD1h5jWZ9eMxKNVaPLbioKPDsgnj1QdeHS7var9MUIiI3FBFhTWHt4lxUCSuRd/i9P6W806OxPYM3xMj2sagdb1QJ0ZTMY5BISJyQxUt1hfsx49/axQoXb8bTP+OmDsiARPvqnixX2djCwoRkRtSacpPUOrV8kdM6YyVTx5u74iQXNpnuy6Jt328XPPSqW9BMV65Wq6YQhMRuSGVtvwBkF4eHtgzux9yi1UI8ZP3mixykJWnFG/7uWyCovvXNdITtqAQEbkldQUtKPov0UxOKsfXICmRe4l4S8QhKC5S94YJChGRGypv7R3AtisYuxPPSnR/+Mp8kT1LtKUZiqv8z7vmq0xEVIMUqzQm66hUpKIEhcwz7L4xnPVimM/p17DJyivGyWuuM0Vb/9u4SuVgJihERDKl1Qo4fPU22szdjLbzNls1k8Swi+eriZ3RPj7MDhG6H3+fsu4bw4lQhgmivpWly1vbMPzj3Tgj84qseuIYFNfIT5igEBHJVaOX/8SYpXtRUtoacvFGfqXvqy4dJBsW4I27W0Ri7VN3oUuDcLvE6U4ME5TCkrKE8HR6WRKSnJUPrUH2cuhKxcsJOJNKo8VLvxzH1jOZANjFQ0RE1VCsMi217m3FGjAlasHkPpoKircR4G8wAHb2mhPi7RKDLjO1VkCeC9VF2Xo6E6sPporbbEEhIqIqy8gpNtnn7Vn5K4u+BcXbYNBnPtfcqdDIdmVLAKw/fl28rTRaPDCn0LoxQc5knJi6ygBpJihEJCtarYAtpzORmWt6ga5JfjuWbrIvK0+Jub+dwpWbBRXeX1+ozXDl3Zv5SkunU6knezUyu9+4RetIatkijHJvl/I0SkhcIz1hgkJEMvPzv2mY/M0hDHh/p7NDcaq8YtNv6O9sPIcVe67goc/3VXh//Swew1aXWwUltgvQTXmZ6UZLzsoTX7uA0jEqz6w+Kh6Xe8/ZlVuFkm22oBARVcHmUxkAgNwa3h1hbrzJsdRsAMB1M90/xkrU+gSl7HFqBeiKsvlYMZalJprZvxkAoEeT2gCA/u/vEo/VCfZ1SkxVlZFTjHc2npXsc5FK90xQiEheCktMB4fWRHmlCVpVF/UrKu2SCDCYlfLlxM7o1igca57qXv0A3VhsLX8A5tesuWrUGgFUvHK0M33x9yWTfS7SgMIEhYjkpcjM7JWaSN9K8ky/pibHwgLKL0+fW6zCk98eBiCdNts+vhZWT0lC63qhNozU/egXAyxRV+69KN/0BPhi92WTfQoXGYXCBIWIZKWILSgAgOs5RQCAhrUD0SAiQHIs0Kf8VpVX1p4Ub4f5+9g+ODenX3dHqdaatI7MGtDMGSHZVL6LTJFmgkJEsnLtTpGzQ5AFfQtK3VB/k64GwwJi5hxPyxZvJ8SE2Dw2d6dvQTmSko1+70kHa0eaGYNSmfV75MSaisTOxASFiGTjXEaeSxXAspcrNwtwu3TWSEyYH27lS2ffVDROx7AVqmHtQNsH6OZ8vcq6xS7dLBAHlT7RoyECfE1br/y85Lm6sdqguNzn4zuJt11lnaaqjb4iIrKDNf+mibejQ/ycGIlz9Vm0Q7wd6u9tslCgUq2FRitY/OZumKAYjkGhyvHxkn5311e1v6tJbXSoX8sJEVWN4Xiunk1ri7ddJD9hCwoRycd3+66KtyOCaubYiVtGxdQs1awor5sn3+CYrxc/5q1l6TXz8lQg1N8bw9vUlexXa+U5TFafoCgU0t+pokHWcsF3LhHJhmGLgJeL9evbgiAISFqwXdzuUM4KxJYGEyvVGknhMA9XmVMqIxYTFA/dfuOWK41Wnk0Sn+/STTEWBF2iO61PYyTWC8X9nWKdHFnlsIuHiGQhX6mWFGeT53dSU1/vuYI1/6YhIsgXj9/VED0MmtKtla9USxalyzNTrM7XywNKtRYFFhKUTacyJdtBZsZMUPlC/c23MHiVVuU1Tvrk2IKy5K9kfP63dIrxi4Nb4MXBTgqoCqxqQVmwYAE6d+6M4OBgREZGYtSoUTh37pzknOLiYkyfPh0REREICgrCmDFjkJkp/YNJSUnBsGHDEBAQgMjISDz//PNQqzkwjqgmW7jhjGRbxrWvJF7/7RSOpeVg+9ksPLJ8f7Uey7gGTK9mdUzO0V88LXXx/Of7I+LtTvVroWVdzuKxlqVqsfpWPeMERSPDBOX/Np2r+CSZsypB2blzJ6ZPn459+/Zhy5YtUKlUGDhwIAoKyhaumjlzJn7//Xf89NNP2LlzJ9LT0zF69GjxuEajwbBhw1BSUoI9e/bg66+/xooVK/Daa6/Z7rciIpdxPacIr6w9gY0npV9kBBdoQ1HbaLTh5ZsF+PFQKt7ffF7c98Lg5vjvQGnNjVB/b7EybGXqxfw8rbvLTYGVA0vjfvTLBhi/pG/+cQYLN5w1cw+qDqva/jZu3CjZXrFiBSIjI3H48GH06tULOTk5WL58OVatWoW+ffsCAL766iu0bNkS+/btQ7du3bB582acPn0aW7duRVRUFNq1a4f58+fjxRdfxNy5c+HjUzMHxhHVVPcv24s0M7VPXKEFJSvPNqsDz/v9FHacuyFu+3h64Kk+TUzOG9amLo6kZAOAxS4esh99smcu6Vu28yJeGtLC0SG5tWoNks3JyQEAhIeHAwAOHz4MlUqF/v37i+e0aNEC8fHx2Lt3LwBg7969SExMRFRUlHjOoEGDkJubi1OnTpl9HqVSidzcXMkPEbkH4+RkTAfXGMAH2KaeRLFKI0lOAMC4Evnap7pjcs+GeHVYgkELivkunmGJuhkmzw9qXu3YSEq/MrS5NXrI9qqcoGi1Wjz77LO466670Lp1awBARkYGfHx8EBYWJjk3KioKGRkZ4jmGyYn+uP6YOQsWLEBoaKj4ExcXV9WwiUjm9FMgXaEFxVzBNP0qwpXVfeF2k33Gl7/28bXwyrAE+Pt4wt+7NEGxtGZR6Z05ONb2PD3Md/Ho3bd0D741mCovF6undHN2CFVS5QRl+vTpOHnyJFavXm3LeMyaPXs2cnJyxJ/U1FS7PycROUdgaQuBC+Qn2H/plsm+1Dumq92WR18x1lB5M4P1xzJyzHcvaTS6V47f8qunRxPT2Vj6QbKeFv6DDl29g1d/PWn2mDN1axTh7BCqpEoJyowZM7B+/Xr89ddfiI0ta46Njo5GSUkJsrOzJednZmYiOjpaPMd4Vo9+W3+OMV9fX4SEhEh+iMj1bThx3WSff+lCeHJZwr68GRpzfz9tsu/lNScq/diGv2PnBmUVSiMCzc8iAYC/L9wEALyz8azZLiZN6WPWxDoytvTxQ+3RyahqrH6QrKVBtHrbzmSWe5wqx6oERRAEzJgxA2vXrsX27dvRsGFDyfGOHTvC29sb27ZtE/edO3cOKSkpSEpKAgAkJSXhxIkTyMrKEs/ZsmULQkJCkJCQUJ3fhYhczIfbLpjsC/SVT2n2D7deQOOX/8TK/abN9oaJi2EysP/y7Uo9dr5Sjf+VFtICgG8ndRVvR4VYTlDaxIaKt/++cMPkuD4uS9/yqXJqBfpgau/Gkn3lDZI1NGPVkXKPO1J4oOtOPLEqQZk+fTq+++47rFq1CsHBwcjIyEBGRgaKinSD3EJDQzFp0iTMmjULf/31Fw4fPozHHnsMSUlJ6NZN1wc2cOBAJCQk4NFHH8WxY8ewadMmzJkzB9OnT4evr+U/SiKSH0EQqjxQ9L3N53A2I89kf4DYglKt0KpNqxWweKtu2u8ra02b7XMN1sf5+KH2GJigG0t3b/t6lXr82WtOSKam+nmXJWZR5axD1L1xWdeD4X30xASFLSjVZvz66gfJVvTaBvnJZ/zP/x7t6OwQqsyqBGXp0qXIyclBnz59ULduXfHnhx9+EM9ZvHgxhg8fjjFjxqBXr16Ijo7GmjVrxOOenp5Yv349PD09kZSUhEceeQTjx4/HG2+8Ybvfiogc4uW1J9Fh/hZk5RZbdT+VRouPtyeL237eZR9F+jLjzq6DsmzXRcm2cZeTvihbkK8XhiTWFfv5K5OwCYKA34+lm+x/sLNuAsCz/ZuZHNO7abBWDxMU+zJ8XwKAl9jFU/796oY6d6FLrUHrXiMXXs3aqjSvMn3Cfn5+WLJkCZYsWWLxnPr16+PPP/+05qmJSIa+P5ACAPh231X8d2Dlp7UWKqUzUO5tHys+Voi/PGbxvLtRWomzRKOFr5cuIbh0Ix+n0nXlDvKVuum+waXfmg3L098uKIFGK5hUJlUazfRJrKfrtlkwOhGvDGuJYD/Li7kZDqo9kZaDDvHScRJMUGzHOAGsaJCsnqMTlPTsIsz84Sge79EQg1pFS0rv69cPckWuGzkROZXhqrvWTq0tMKrh0T4uDHte6os9L/UVm9GdPUS2XVyYZLtYVfY7Go4zebRbfQAQk4qd52/g0o18aLQCOszfgs5vbcU7G8/i4BXdfb7bdxWtXt8keewHu+haThQKRbnJiS6OsuTu9d+ktaOUag32ls4sYoJSfcYtKGWVZMt/bR097uPVX09i/+XbePLbwwCMxkd5uu77gAkKEVlNoxXQ8c2t4rZxi0BFjNeR6dG0NmLC/BET5g9FaSEPZ8/i6W20Dk5GTlk31hd/lw1uVZeuZBtiMO6g73s7Jb/j0h0Xcf8yXbHKOb+eNJkZlG9mUUBLyhuf8sPBshIMTFCqT99ipqd/TSuawu3otXm2nc2SbKsNVld25fcBExQissrLa0+gw/wtkn0lVg6ULTDq4jFsEtd/OXV2C4rWKEE6dLWs1eTijQLj001aPpKz8k3OyS1WmexrXS8ED3SqfPHJ2UMtl1O/fLMsLs7iqT5fb/OXyIqu+TZaoqlStGaSIbWmbJ++1ccVyWeoMRHJXoFSjVX7U0z2qyrZgvLH8et4dd1JyTiKBzrFSupKyOWyqjb64H9l7UmMaBuDEKNEpFN93VIfwUYzN1bsuWLymOeNZi01qhOI9U/3tCquyGDLLShxtQLE23cKTQvAkXXMDUIGpBf9Lg3CceCKdGq5Ruu4DMU4kQbK3rsKBVtQiKiGMPyGbujEtRwUKNU4kZaDpTsu4tMdyShQmnZbrD6YIklOWkQH49372krO0Scrl24UoMFLf1g9Q8hWzH0zfWLFISjVZa0/T/ZqJE4rNk5Q1h01naVj/PoZ1j6xBcOIi63sdiNTIX7e4vTxFtHB4n7DcUBdGoZjcCtpkVGNA5v/jJ+qQKnGuxt109ddvVgfW1CIqNL2XjQt7Q4AZzPy8Ojy/fi3dKVdADiSko3Px3eSnHfDaPXfxpFBJo9l3DPR5e1tuLJwWNUCrobc0nEhwb5eyCtNtg5cuY2NJ8vWDHt+UHNxPEJFg1sBXSJnqF6Yf5Vi8/HyQIlai/4tIyX7DQcrqx3Zz+DGPhvfCSfSchAeVDbw9Vp22QKXCoVpa5sjW1CMG1D+OHEdPx1OAwCoHJkp2QFbUIhIYv+lW0i9XQitVsAn2y9IBoReumk6rkLPMDkBgC2nTct9GzeZv31vosk5cvnOp5/2nGfUEvTM6qMAAH9vT7EuBqBLGixJqKtbnuObvbZZSO6Zfk0BALUCpLNFDGuwqF384iQnibGhkmSyo0EJfJVGkAxKBaSDZG/lK/HE1wfx7d4rdonNsF5Q7SAfSQFBV8cWFCISnbyWg7Gf7QMAjOsaj5Wl4036tohEozpBiA6p2jd+PcP+ci8PBUL9TVsdjFtQWteT59pb1lQLPX0916bPrS9mZzw42bDVpGVdeb5u7uCBTnFidWGVRmuyqrVhgvLVP1ew9UwW/jp3A48mNbB5LIYtKCqNYHZMiqtiCwoRiQ4aDPZbaTAY9pu9V/HgZ3vx69FrlX4swz57Pf102ka1A3Fy3iAL95RmKCev5Zp0DTnSiLYxZvcH+VYuQXm6bxOz+43XebGGmKAYjTMpKW01iQrxxV1NXHMFW1dgOEhWrdGaJNqGXT76965GKyArrxgP/G8v5hrVr7EVlUZr0t3kypigEJHIUj2TFXuuYN+l2yaDPMtr3TA3vkLfXfLJwx0szpAwNzv2j+OmA07tacU/l8XbU3s3MnuOuQRl66xeJvv+O7C5SbLw1r2t8dIQy9OFK6Kvz2H8//XzYV0dlFHt61W44i7ZhkorYO49rdCjSW0Mb1MXgLQFxbBFY+mOizhw+TZW7LlidhB2VRg2mBSWaMxOb3dVTFCISHTGyq6I8rp8itXSZm9BEMT+8fJaH8xdVr29PCRr0NhTgVKNub+fFrcT6oZg1/N3Y+us3pLzzK0V1CQyGI90izfZP7yNtBUmopqVRv18dAnK9rNZYkG7TacycDNfN0NKpXafb9Fyp9ZoUS/MH9890RUDSmf8SBOUsnMNZ3aZq4lTFcZdOmv+LWvlnNyzoU2ew1mYoBCRyNI0YksMp1uaHpN+u796qxBKtRaeHgpEhVpeudzcN/9X1p5Epze3It1g9oS9/HiorBrrgIQoKBQKxEcEmCy6dvKa+WRu/sjW4u3mUabdXABQO6h6K7f7G7Q+7SmdWaUvcw4A5zJtO+aFLDMcjKxf98YwQTFMZA2n2FtbfdkSS6loh/gwvDy0pU2ew1mYoBARBEHA4ysO4niabhrs+w+0xZRe5rs2AODloS3QKiYEzw+yvECgcfIy88ejAIDYWv4mJcQNldcxsfWM6cwgW5tn0HqyyKBGi4eHAj6VqMqpUCjwy7QkDEiIwhcTdNOs28SGSs4xnAVSFYYJSlaeaZ0YNxonKXsqg2RE//aQtKBY6MpRqmyUoFj4z24bF+by3XxMUIgImblKbDdYz6N+RKDJty/DtWkm92yEP/7TE02jTOuY6ItDGSYogiDgSOk05Ku3CsuNpbzZMfZemfWOwTfcXs3qIDRAOvjRt5ypxIY61g/H5+M7IS5cV9m1VUxZgtIhvvoXDn8faRy3HNT9RaYMqyjr358qC108+lWrAdt18VjKRXMKXX+6MRMUohro+wMpaPDSH9h1/gYA4O8LNyTH9SsKx4eXlU5/Y2Qr7Hr+bpx/c4h4gTVOGNY/3UOsjmrYxWPN7J+YUMvjWuxVvv3A5dtYsOEM/jx5Xdz3YGfT9XG8DRKUx++yrn9f391jPB6lKgwHGAsCcOWWdV1zVH3jk3SrWP+ntCYNAASUjg0qMlgo0nCMiOEMm+s5tqmQbKm1bM2Ryv/NyRXroBC5MUEQ8Mzqo8jILcbjdzXE4NbROJ+Zh9lrTgAAxn95AIvub4vnfz4uuZ9+GuXLQ1tg6nf/AtB1K0QaraTrbbSUe+t6oTiboRv/YFgS/seDaeJtc8XZDPn7WO7+Samg9aUqNpy4jmkrdb+j4diQoYl1Tc41HENQ3qJ95qye0g2Hr95Bn+Z1Kj65AsYzoIzrcJD9vTGyNV4e2lLyfxFQOvhbvxjmL4fTsP54WdJrWGFWqbbR/5mFBKV/yyjbPL4TMUEhcmNZeUr8dkw3c+DA5duoF+aPIqOxIc/9dMzkfvrEw3BhvLAA05knhl0Vb47SDQ71Kx1fYtiC0qlBLey9pBvM2bsaF+jUO9VPUJRqDWb+cBTdGkVgfFIDMTkBysYQtIqpuMiZtavE1gr0Qf8E21w0mKDIg/H/Q5CvbjtfqYYgCPiv0d/W+cyyKcC2qvRrbjYZADxpYXq8K2EXD5EbMx6Idy27SNIKYIl+EGvHBrXQIjoYQ1pHWyzl/ki3ePRpXgcPd9FNr9V/aOcblIjXj90I9vOq1Poz+mnIo9vXw6l5g/DVY50B6BKu6tp8KhN/nsjAa+tMi2XFlMYWGVy9WTb2ZjgW5tej6SgySlCqOwiXqiaw9H2bU6RCh/lbyj23RKNFsUpT7ky4yjDs4qlj8L71t1BnyJWwBYXIjRWqTFcUtiQswBsPdo5HiVqL2Fq6C7Wvlyc2PNOz3EGdb46Sdtn4eZddPC/fLIBao8WizecBWK7Kauyfl/ridHouujYMh4dBSXylWoMrNwsw88ejmNa7MQYarSJbGRkGff8aoxkW+jot5a2rIwcBBt1gu87fEFfTbVg7EGM7x2Fi9wZOiqxmCzSo73OngkGqL/x8HB9uvYCcIhV+mppU5aUJDN/Bv0ztjl7/9xcA09W1XZG8/wqJqFrKa/o/NKe/ZHtMh1i8NKQFXhuRIElIrJ1xYtjsfTo9FwMW7xK3PSv5WKH+3khqHCGuFKxvMShWafHcT8dwJCUbUwzqfljDcKCtcXfXxRu6waYhlViZ2JkCfKQXn+s5uvowifVCMbV3Y4tVesm+Aqx83a9lFyFfqcaBy7crPtkCw2nGceH++HJiJ8wf2Qr1IwLLuZdrcP0Ui4gsysq1PFMg3GhMyX8HNrPJc0oHDUo/sD2qOLtW/5g38pTVXpfn0x0Xxds/HExFnWBfk8dsXS/U+G6y9vH2ZADSlhVyPC8rxyXpWaplUqn7GtxWKBTo28L1B8fqsQWFyI3pZ+AAwJMGhdf+O6CZ2DoBAAMToky+lVeHfqHAJaUXTr0LVVwnxF4tAnsv3jKb8MRUME5Grv37bDlxTdUZL+vORfnYgkLkpnKKyvrA28WFYfbQlpg1sBkuZOabzFLxsHHFSX2XzMUb0oSkqq0ffnYaE2KpMm3PprXLvZ9cWyrkGheVqRfmj2tGSzaoNVWvKqufxePiRWPNYgsKkZu6ZJAc/PBkNwC6Qa+t64WajCvxtnECoB9kWsdoNozxoNTKstQyYDhTqLLiwstvHfnwwXYWn++14QkAgMVj21n9vI7ABEX+apuZIaauxsrG+hYUN8xPmKAQuavs0haUVjEhFte+uad0Vs1UG9dM0D+f8arFmiq2R1tKGJ5dfcTqx9JW8GW1W6MIi8ce79EQZ+cPRq9m1S+2Zg/+NuymI/sIMTO7pqqJO2CQoLhhEwoTFCI3dTxVt/BfedMNPxjbDv++OkCyVowt6D8r/y1df0evqsWpPD0U+HRcB5P9W89kWT3AUH9+oIXWhrCA8mfwyHmcB1tQ5K+Bmdk1NuniqfIjyBcTFCI3tXirrvZIkK/lC66HhwLhgaYVYqtrz8VbZvc3jjRdXLCyhibWRRMz9x+4eJdVZcP1rThTejUW9/39wt0I8PFEi+jgcldaljsmKM73f/e1Kfd435aRJvts0sXjhhkKExQiN1RgMDbj3vb1HP78/Y0+hIP9vNCtUThetnL9GmMbnumJn6YmSfZdyMrH/kuVryOhvxYMSIjCW/e2xu8zeiAuPAB7Z/fDbzN6VCs+Z3Pl5MpdVFQ8sEeT2hjZTlqwsFpdPKX/KtywDYUJCpEb0icoHgpgaKL11Var6zGjlX7HdorD6ilJaBFdtWqZet6eHujcIBxzhrWU7LdmbIu29GLg6aHAuK71kRir694K9feWfQXZinhWtdAM2YzhGJP/9G0CQDer7aOH2uPYawPh7elh0mqpqsY8Y8GNR8lyRBWRmyhWaeDj6YGdF26gTumqvP7enk4ZPOdldKF8pn9TC2dWzaBW0XjzjzPitkpd+T58bekHujtey93wV3I5hn9vPl4eOD53ILw9PCSrdD/dtyn2XbqNKzcLUKTSSFY5tpYb5ydMUIjcwaUb+ej73k6T/f5OGpNgXFEz2Mal4+PCAyTbM384ilNvDK7UffWt6R5umKF4uHYDkNt4okdD/H48HWM7x5tdNiE80AcbnumJD7dewOKt56s1BkWPY1AA7Nq1CyNGjEBMTAwUCgV+/fVXyfGJEydCoVBIfgYPln5w3L59G+PGjUNISAjCwsIwadIk5OdXrcIkUU2UnJWPDSeuA9A18ZpLTgDYZQCstV4aUr1xJ5VRUM6aQ8b0XTy2Lk7naPFGSRoANK5T9UHIZDtzhidg3+x+JnWAjHl56t6DVZ3dBhi2oLj2+9kcqxOUgoICtG3bFkuWLLF4zuDBg3H9+nXx5/vvv5ccHzduHE6dOoUtW7Zg/fr12LVrF6ZMmWJ99EQ1VP/3d2Layn+x6/wNzPzhqMXz4sOds2CYymDa5GN3NXDIcz66fD/SjSp0mqPv4qnswoVy9eao1pLt1VO6ucUCce6iMl2r+jFD1WlB+TflDoDqDbSVK6u7eIYMGYIhQ4aUe46vry+io80PzDtz5gw2btyIgwcPolOnTgCAjz/+GEOHDsWiRYsQE1O55diJaqqb+WXl4sd/eaDcc9vFOWfRu9YxoYit5V9ukThb+/vCTQz/eDf+fXWAxXPS7hRCpXWP0uD6b9965RWYI3nSj9VSVaMOyrOlX1BKqvEYcmWXHssdO3YgMjISzZs3x7Rp03DrVllNhL179yIsLExMTgCgf//+8PDwwP79+80+nlKpRG5uruSHqKY6npZd6XPzqlAK3hb8fTyx6/m78b9HO1V8chV9Oq4DOsSHSfbdLiixeP5Ph1LR452/UFI6oNbVx6B4V3HlXJKPwNJKy4Ulzvk7lTubv8MHDx6Mb775Btu2bcM777yDnTt3YsiQIdBodH3EGRkZiIyU1kjw8vJCeHg4MjIyzD7mggULEBoaKv7ExcXZOmwil3Er3/JF2JgzVzq1dwIwNLEuPhjbvtLnP//zccm2t6drJyhFVoy7IXnSV3neeiZLHBtFZWyeoDz44IO45557kJiYiFGjRmH9+vU4ePAgduzYUeXHnD17NnJycsSf1NRU2wVM5GL0qxT3NlgPpk6wL4a1qQsPBfD95G7i/ul9mjg8Pkeq6to+AODj4i0Q6mpMTSV5MJzdtuvCDSdGIk92n2bcqFEj1K5dG8nJyejXrx+io6ORlZUlOUetVuP27dsWx634+vrC17f80dBENcXpdF0XZ8u6Idh5XvehNrZTHJ7p3xTzR7ZGeKAPDs3pj0AfL6dNM3YUrVGCEmE0a0kQBIuDFV29i6REXfa7fzHefl1pZD+GjYzp2cXVeixXbxE0x+5/oWlpabh16xbq1q0LAEhKSkJ2djYOHz4snrN9+3ZotVp07drV3uEQuTz9qP2ujcLFfV0bhUsqVNYO8nX75AQA6oX5S7ZvFZSIY0z+++Mx9Fm0A3nFKrN9/K6eoHSsX0u83T8hyomRUFUZ/h8aJ9uVVbu0KON3k9zv+ml1C0p+fj6Sk5PF7cuXL+Po0aMIDw9HeHg45s2bhzFjxiA6OhoXL17ECy+8gCZNmmDQoEEAgJYtW2Lw4MGYPHkyli1bBpVKhRkzZuDBBx/kDB6icgiCgPOZ+bhyqxAA0CGuFmJC/ZCeU4y2cWHODc5JzK0s/NiKA/huUlf88m8aAODBz/aZfX1c/RtnnWBfHHylP4J8WW/TVQX4eGFkuxisO5qOYpX1Y4oEQRBn9UUEuV8vg9Xv7EOHDuHuu+8Wt2fNmgUAmDBhApYuXYrjx4/j66+/RnZ2NmJiYjBw4EDMnz9f0kWzcuVKzJgxA/369YOHhwfGjBmDjz76yAa/DpH70WoFjFm2B0dSstG5Qdk3rtAAb2z7bx+UaLRmq1XWVP8k35LM5jmVnmvS0vLy0BZOWQLA1ioqBEbyt/lUJgDgzT/O4Imejay6795LZTNkfV18HSlzrE5Q+vTpU7Y4kRmbNm2q8DHCw8OxatUqa5+aqEbKylPiSEo2AODgFV33TuM6uoJc/j6e8If7d+WU5/6OsfjpcJpk3+RvDkm2fUtbWl4bnoDHe0gXMiRypiKDlpO/zmbh7haR5ZwtlZxVVoG9ql1EcuZ+KReRG3nrj9PotmCbyf5GLGkumjeyFRaMTkTXhmVjcv4tTej09Ks7B/rW7GSO5Gf+yFbi7cdWHLSqqye7UCXejqtluvSBq2OCQiRTWXnF+Pzvy2aPvTOmjYOjka8AHy881CUe/x3Y3OI5mbm6GRKBHK9BMtOgtnR5gt+Oplf6vtdzdO/rZ/o1dfnCg+YwQSGSoWvZRejylrTlZM9LfTE+qT4Wjk6UxSKAcmOpdSTU3xunSqdmB/owQSF5iQrxk2zfKSzB9FW6dbbKo9UK2HZGN34lLMA9x6Dxr5VIZopKNLhr4XZxe0yHWNzbvh5iwvzxxsjW5dyzZgv2Nf8hrS9sBwAdDKZ1EslBpNFA5wUbzgIA/jh+HVcWDrN4vy92X0JWnm4Gj7sm3u75WxG5KEEQ8M3eK5J9797XRlz1lCwL8pN+nNUJ9sWNPKVkX6i/e37TJNdV1ffkqv0p4m1vL/f8fGAXD5GdXMsusmrAW75Sjb7v7RS/QQHAmqe6MzmpJON6IC8NbuGkSIgqr6rT3ZtHB4u384vdc7FBJihEdrD34i30eGc7Xl5zolLnbzmdidavb8LlmwXivondG6BDPLskKsvHqA5EYmyokyIhss7LQ61PpjeV1k8BAHddZ5AJClE1nU7PxRWDxAIAXll7AoIArDlyDfnKir/dGNftaFwnEE/3de+F/uwtPtz9pl2Se5rSqzE+ebjyK3MDQIDBUhaj2tWzdUiywDEoRNWQmVuMoR/9DQB4cXALdGkYjqOp2bhkkLC8+utJLB7bzqrH3fbfPjaMsuYZkBDllpU1yX21jQ2r9LkqjRaFJbru4w3P9EQoZ/EQkTH9ysIA8M7Gs2bP2Xom0+x+vQKlGl4eCqhL22n3ze5nuwBrmBNzByLldiFaxZh27xx9bYATIiKqHGtq9Ow4VzYF2Z3XYnLf34zIAS4Zde2Y08ioEJPk/jfy0fe9neL21lm9EB3qZ/F8Kl+wn7fZ5KR9fBjCAlg7huQrwIrVx1NvF4q367rx5wXbQImqSKsVMH/96QrPyzaow2Hs8NU7ku0mkcEWzqTq8HSDhQHJvRl3SZb3xWbJX8nibS9P972MswWFyArH07LxT/ItFKk0qBMk/UbeJjYU/t6eWHR/W3yz9wpC/b2xaPN5FJVYnmp88UZZC8yC0Yl2i7um41Rtkjvj6caVmZijXzTUXTFBIaqkyzcLcM8n/5g9du7NwfD1KmuifWVYAs5n5mHR5vPIylPiVHqO2a6HZTsvAgD6t4zCQ13i7RM4SarJEsnVuTcH49cj1/DiLycgGK1OrNEKYqLdLi4M285mYXLPRs4I02Hct22IyMZOpeeY3f9Mv6aS5ETPy+Bb+7CPdpsczzFYiXRgQpQNIiRjDUubyfn6kivw9fJEk0jdSuWGtU2W776MxLmbsP/SLQBliwTWMSqT726YoBBVgkYrYMaqI2aPPdKtvtn93kZ9wx9tuwCVRitupxgMdHugc5wNoiRjvz/dA2/d2xqTerj3N01yJ7ovNoJBJ8/89adRWKLB2M/24VhqtvjZUT/CvWv9sIuHCMAPB1Ow+VQmvpjQyWzp6Z3nsyTb307qgs4NwuHnbXnkvXGC8v6W83h/y3k83DUeGo2AYW3qAgBaRHNgrL0E+XphXFfzCSSRHOk/fgQLg1BGLinrZo6txQSFyK2l3i7Ei7/oStI3nP0nejWrg2m9GyOpcYR4zq38EvH2ovvbomfTOhU+rqWBmfpFvrLydM20IVzAjohKeZRmKJYSFL2IQJ9yvyC5A3bxUI0mCAJ6vvuXZN+u8zfw0Of7MOD9svok35UmFe3jw3Bfx9hKPXbtIB90rG95LZ2/SosthTFBIaJSlZ1v5u7jTwAmKFTDHUszP/AVAC5k5QMA5v52CsdSswEAvZtV3HKip1Ao8MOUbhWeV9Xl1onI/ZR18QjYejoTs344ava8EoPxbO6KXTzklvRT9IzHk5SotWg2ZwMA4NCc/pKKjOaUqLVYseeKuN0syrrxIpUpohQexAqnRKSjKG1D0QrAE0aLiBq6dKPiKtauji0o5HZyClVIWrAdU787bHLsWFq2ePudDWehVOu+hfRuVgcvDdEteT6mQ1kXjj6Z0RvSOtomMf79wt3i7bsa17bJYxKR6xNbUCoo1VYTBtezBYXczt/JN5CRW4yMU8UoVmkkA8lu5inF2z8dTsNPh9MAAH7eHpjauzGGJdZFnWBf/PJvmsnjHp870OwMn4q8NjwBn+26hPHd62P1gVQsur8tYmv5o3ezOihSaSSDcYmoZtN/xGi05ScoX07s7IBonIsJCrmdjNIiRoCugqg+QTlw+TamrfzX7H30hdbiwnXT9vq1iMS2s2VTi/s0r4MQv6qNFXm8R0M83qMhAOCpPk3E/V8/3qVKj0dE7kvfxXPTYOagodeGJ+C+TrFV/jxyJeziIbeTnl2WoNwpLPsjf+B/ey3ex89b+qew9JGOksW7nu3fzIYREhGZV14j7YGX++HxHg1rRHICMEEhF2O8PoU5htVa7xSocDo9Fw9/vk/cZ26BrUKjBf18vDzw1WNlTajBfmxsJCL7Ky9B8fGqWZfsmvXbkkt77KsDaDj7Tyzecr7c89QGfbdKtQYzfziKPRdvifvWPHWXyX0MB8/qNYgoS2QCfNy7IBIRyYOinEoo5tb8cmdMUMglZOUVi4XNPtx2AYM/2IVNpzJQWKLGzXwlPt2RjIs3dHVLNNqyFpR/U7JxLjNP3G4XF4ZQf2989mhHyePPNNOFEx3ih8R6oWgaGYTIYD97/FpERBLGBaj1X45a1wuBfw37osR2a3IJao20a+dsRh6e/FY6jfiT7ck4/cZgSQvKR9suSM4pVum6cga2isaVhcNQotbi6q0CcQVRQx4eCqybfhe0gmCxbD0RkS0Zd/F0b1wbC0YnmoyTqwmYoJBL2Homs8Jz9ONIjJMZQ4NaSeuY+Hh5oGk5xdc8PBTwqHTxaSKi6pJ+3vh6edSIsvbmWJ2S7dq1CyNGjEBMTAwUCgV+/fVXyXFBEPDaa6+hbt268Pf3R//+/XHhgvRb7O3btzFu3DiEhIQgLCwMkyZNQn5+frV+EXJfhSVqvLbuVKXO1WoFs/UDQvy8MLp9PUzu1cjW4RER2YxxC4q3Z839gmR1glJQUIC2bdtiyZIlZo+/++67+Oijj7Bs2TLs378fgYGBGDRoEIqLy6Z+jhs3DqdOncKWLVuwfv167Nq1C1OmTKn6b0Fubd3R9Eqf+9afZ6DWmq5Rsf/l/nh/bDsE+bLRkIjkyzgdqWkzdwxZ/Wk9ZMgQDBkyxOwxQRDwwQcfYM6cORg5ciQA4JtvvkFUVBR+/fVXPPjggzhz5gw2btyIgwcPolOnTgCAjz/+GEOHDsWiRYsQExNTjV+H3NHBy7cl29v/2xuN6gSh1WsbUVCiQXigD24X6OqdLN99Gf1bRpo8Rk0bXEZErsnDqAmlJicoNv3NL1++jIyMDPTv31/cFxoaiq5du2LvXl2RrL179yIsLExMTgCgf//+8PDwwP79+20ZDrmJG/ll5en//E9PNKqjG9D69eNd0K9FJNZM645pfRoD0NU4URmNQRnWpq7jgiUiqgbTLp6am6DYtL07IyMDABAVFSXZHxUVJR7LyMhAZKT0G66XlxfCw8PFc4wplUoolWUXqdzcXFuGTU5wu6AEof7elZodczFLNz7p8/GdkBATIu7v1CAcyyeGAwC6NYrA0h0XcfFGgdiaovfh2Ha2C5yIyI6M66DUtNonhlwiNVuwYAFCQ0PFn7i4OGeHRNVwLiMPHd/cgud+OlbhuRcy85BeurZOedVcDceW3ClUiben9GoErxr8DYSIXEuRyrSqdU1l0988Olo3hTMzUzolNDMzUzwWHR2NrKwsyXG1Wo3bt2+L5xibPXs2cnJyxJ/U1FRbhk0O9vH2CxAEYO2Ra2g4+w+k3i60eO7/dl0Sb5c3wDXETPKyYHQiXh7asnrBEhE5UHSotChkUYnaSZE4n00TlIYNGyI6Ohrbtm0T9+Xm5mL//v1ISkoCACQlJSE7OxuHD5cV2dq+fTu0Wi26du1q9nF9fX0REhIi+SHXlW3QwiEIQM93/8L7m8+hQKn7Qzx45TYeX3EQ17KLoDWYMtysnHoljeuYFlq7brCqMRGRKwj1ly4EaDymriaxegxKfn4+kpOTxe3Lly/j6NGjCA8PR3x8PJ599lm8+eabaNq0KRo2bIhXX30VMTExGDVqFACgZcuWGDx4MCZPnoxly5ZBpVJhxowZePDBBzmDp4bIK1aZ7PtoezL8fbwwrU9j3L9MN6B6+8Lt4vF3xiSW29Tp4aFATKif2B0EACVq0+nGRESupH/LqIpPclNWt6AcOnQI7du3R/v27QEAs2bNQvv27fHaa68BAF544QU8/fTTmDJlCjp37oz8/Hxs3LgRfn5lzVYrV65EixYt0K9fPwwdOhQ9evTAZ599ZqNfieROaSFxyMwtxtVbBWaPxdYKqPBx//hPT8n2mA71rA+OiEhG6obV3HXAFEJl1q+XmdzcXISGhiInJ4fdPS6oz//9hSu3ChEX7o/U20Xi/jnDWuLijQJ8fyDF5D4X3x5aqRk/BUo1tp7JRMf6tSqV1BARyU2Dl/4Qb//76gCEB/o4MRrbsub6XXOHB5PTXLmlGxS7dFxHXFk4DA90igUAvPnHGbPJyY9PJlV6sb5AXy+MbFePyQkRuaznBzUXbxuPSalJWPebHEKrFXD1diGKSsqm0IX46f7w/Lwtz/NvER2MLg3D7R4fEZFcGE4OqMkrqTNBIYf4v83nsHTHRcm+mNK+Vd9yBr+qNBzoSkQ1S0gNbjUxxC4ecgjj5OTZ/k3FAmrGlRJfG54g3q7JU+yIqGYa2zkOQ1pH4//ua+PsUJyKCQpVWVZeMdKziyo+0QzDpMTPW/o2fOyuBvAqbdZsExta9QCJiFyQn7cnlj7SEfd3qtlV05mgUJVotAK6vLUN3RduR2GJGjfzlXjup2P4N+WO2fMb1g6UbDeLKiusZlghdtXkrlAoFFj/nx6Y2L0B5t7Tyj6/ABERyRrHoFCVbDldtrDjqv0pePOPMwCAdUev4cJbQyXnarUCLt/U1Te5r2Msejerg74tyhaMjA71F29HBPoCAFpEhzA5ISKqwZigUJUculLWUqJPTgDdmBFBEKBQKFCs0mDIh3+LyQkALBydaLJ4X12DtScCfGruyp1ERFSGCUoNc6egBPN+P4WHu9Y3mb575nouhnz4N5pEBmHLzF5QKEynt525nouX1pzAsdRsi88xfdW/+PNEBoJ9vZCnlC50ZW5l4Xq1ylpQjBfKIiKimokJSg2h1Qrw8FDgye8O48Dl2/j1aDquLBwmOefFX44DAJKz8vHHiesY3ka6NtLbf57BZwarCwNA7SAf3Mwvkez784Su+8c4OXl5aAuzsdUO8sVXj3VGkK8XvM0kMEREVPMwQXFjxSoNCks06DB/CwDg+8ndcODybYvnG7aYzFh1BD2a1EawnzduFShRO9DXJDkBgKWPdETnBuHYdiYTr6w9iYxc8ysID2kdjfFJDSw+993NIy0eIyKimocJipv6+8INPLr8gGTfQ5/vk2xrtQL2XLyFV9edRFGJxiS5aPfGFvH24rFtzT5PdIiuS6ZfyyhoBWDyN4dMzjny6gDUcqO1JIiIyP6YoLihf5JvmiQn5pzLzMN7W85JBrFaMvOHY+LthLohOH09FwAQazB+pH/LSDSICBDX2jn4Sn/UCfa1NnwiIiKuZuwOPtx6AYu3nse66Xdh1/kbeG/L+Wo93riu8Vi533TRPgCoHxGATc/2wuWbBWgRHWwykLawRI0z13MR6u+DJpFBZh+DiIhqJq5mXMMs3qpLSEYu+cdschLs54WJ3RtU+vFeHtrS4rHpfZrAz9sTLeuGmJ3lE+DjhY71w5mcEBFRtbCLx8mKVRrkFqkQGVK16bUlasuL6T03sBnOZ+ZjSq9G2HfpVrmPM7JdDFrFhCAqxA+Bvl5Y+URXjPtiPyZ2bwBfbw+s3JeC10ck1PjSy0RE5BhMUJxEEAQcSc3G6E/3AAD+fuFuxIUHWP042UUlJvteHtoCbWPD0KVhuNjKYbgq8KL72yI+PADhgT54d+NZPNw1Hn2MZtHc1aQ2Ds3pj4hAHygUCsweYrlVhYiIyNY4BsVJPt2RjHc3nhO3E+uF4vene1j9OOcz8zBw8S4AQO9mdTB7aAu0iDb/mtzKV+LM9Tx0bxwBDw/T7hkiIiJ7sub6zRYUJyhWaSTJCQCcuJYDpVojWeW3MnadvwFAt+De1493KffciCBf9GjKWTVERCR/TFCc4Hhajtn9O87dwKBW0RXe/05BCdrP34LwQB/cLtB18XA6LxERuRPO4nGC6av+Nbv/k+3JFd5XEATM+F53f31yAgDPDWxum+CIiIhkgC0oDiYIAm7kKQEAfVtEIirED98f0NUcuZCVV+59D165jfuX7TV7zHDBPSIiIlfHBMXBfvn3mnj7gwfbIcTPG0MTo/Ho8gMoVmlRVKKBv4/pOJSLN/Ix7TvzLS8d69dCu7gwe4VMRETkcOzicaAStRbP/VRWMj7IR5cfNqpTVtTs231XcDYjF8/9dAwXb+QDALJyi9HvvZ24ma80+7iWVgkmIiJyVUxQHOjzv8tWA24bGypO9a0XVtY98/afZzH4g7/x8+E0DPnwb+QUqbDlTKbkcQJ8PHFq3iBxu35EoJ0jJyIicix28TjQl7svi7eXPdqxwvNL1Fq0nbcZMaFlVWbbxYXhuye6ItDXC5+O6wClWoPaQZzBQ0RE7oUJigP1axmJHw+lIbFeKOqGVn5Qa3pOMQDg68e7oHezOuL+oYl1bR4jERGRHDBBsRNBELD2yDXUDfXH8z8fQ9qdIvHYPW1jTM6f2rsxlu28WO5jto8Ps3WYREREssQExU4OXb2DWT8eM3vMz9t06M9LQ1pUmKAE+/K/i4iIagYOkrWTz3ZdsnjMUjn7Lg3CAQCNapsOej34Sn9x4T8iIiJ3xwSliopVGvx1LguFJWqTYyqNFltOZ5q5l45SrTG7/5VhLTHj7ib4fko3jE+qDwB4qEscjr42gKXsiYioRrF5gjJ37lwoFArJT4sWZXU6iouLMX36dERERCAoKAhjxoxBZqbli7lcfbv3Kh776iAe/GyfybHDV++Y7BvZrmzcyb7Lt80+Ztu4MDw3qDmiQvwwd0QrXF4wFAtGt0FYgI/tAiciInIBdmlBadWqFa5fvy7+7N69Wzw2c+ZM/P777/jpp5+wc+dOpKenY/To0fYIw66OpOqSkONpOUjPLpIc++1YumR7XNd4vD6ilbg9vlv9Ch/fw0PBLh0iIqqx7DLq0svLC9HRpqvy5uTkYPny5Vi1ahX69u0LAPjqq6/QsmVL7Nu3D926dbNHODaj0QqYvvJfbDyVIdn/8fYLWDC6jbidlaur+Dp/VGuM7RQHb09dsrHr+buRkVuMLg3DHRo3ERGRq7FLC8qFCxcQExODRo0aYdy4cUhJ0S2Gd/jwYahUKvTv3188t0WLFoiPj8feveYXwQMApVKJ3NxcyY8znMvIM0lOAOD7A6l4Z+NZcXtraeXXIF9P+Hh5iC0h8REBTE6IiIgqweYJSteuXbFixQps3LgRS5cuxeXLl9GzZ0/k5eUhIyMDPj4+CAsLk9wnKioKGRmmF369BQsWIDQ0VPyJi4uzddiVcuVWgcVjS3dcxIOf7cWu8zfEfUqV1hFhERERuR2bd/EMGTJEvN2mTRt07doV9evXx48//gh//8pXTzU0e/ZszJo1S9zOzc11SpJiuNCfOfsu3ca+SwfE7TEdY+0dEhERkVuy+zTjsLAwNGvWDMnJyYiOjkZJSQmys7Ml52RmZpods6Ln6+uLkJAQyY+jqTRaFJZIpwe3rheCY68PNHv+yHYx8PbkLG4iIqKqsPsVND8/HxcvXkTdunXRsWNHeHt7Y9u2beLxc+fOISUlBUlJSfYOpVpeXnNCvP3ufW3Qsm4I3ru/HUL9vfFk70Ym5791b6IjwyMiInIrNu/iee655zBixAjUr18f6enpeP311+Hp6YmHHnoIoaGhmDRpEmbNmoXw8HCEhITg6aefRlJSkqxn8Hy5+zJ+OpwGAAj08cQDneLwQKeyLqZZA5rB19MDH21PBgCseKwzgliWnoiIqMpsfhVNS0vDQw89hFu3bqFOnTro0aMH9u3bhzp1dKvwLl68GB4eHhgzZgyUSiUGDRqETz/91NZh2NRH2y+ItwtKTKvA+np5YtbA5hiSWBen0nMlKw4TERGR9RSCIAjODsJaubm5CA0NRU5Ojl3HowiCgJfXnsD3B1LFfcMS62LJuA52e04iIiJ3Zc31m/0Q5dhx/oYkOXlzVGsMS6zrxIiIiIhqBiYoZqw7eg3PrD6KNrGh4r4PH2yHke3qOTEqIiKimoPzYI0UKNV4ZvVRALp1dvSGt4mxcA8iIiKyNSYoRl5ee8Jk34rHOsPTgwv3EREROQoTlArc0zYGfZpHOjsMIiKiGoVjUIysO5oOAJjYvQG6N45A9ya1nRwRERFRzcMExUCxqqzGSf+WUejRlMkJERGRM7CLx4BKU7b6cKcGtZwYCRERUc3GBMWAWlNWs44L/RERETkPr8IGVFpdC4pCAc7aISIiciImKAb0LSjeHnxZiIiInIlXYgP6BMXLk60nREREzsQExcCmUxkAOP6EiIjI2XglNhAZ4gsA6NO8jpMjISIiqtlYB8XAiDYx8FAoMKR1tLNDISIiqtGYoBjw8FBgRFsuCkhERORs7OIhIiIi2WGCQkRERLLDBIWIiIhkhwkKERERyQ4TFCIiIpIdJihEREQkO0xQiIiISHaYoBAREZHsMEEhIiIi2WGCQkRERLLDBIWIiIhkxyXX4hEEAQCQm5vr5EiIiIiosvTXbf11vDwumaDk5eUBAOLi4pwcCREREVkrLy8PoaGh5Z6jECqTxsiMVqtFeno6goODoVAonB1OuXJzcxEXF4fU1FSEhIQ4O5xKYcyOwZgdwxVjBlwzbsbsGK4cc0pKChQKBWJiYuDhUf4oE5dsQfHw8EBsbKyzw7BKSEiIy7yR9BizYzBmx3DFmAHXjJsxO4YrxhwaGlrpmDlIloiIiGSHCQoRERHJDhMUO/P19cXrr78OX19fZ4dSaYzZMRizY7hizIBrxs2YHaOmxOySg2SJiIjIvbEFhYiIiGSHCQoRERHJDhMUIiIikh0mKERERCQ7TFCIyKVxnD+Re2KCUg0XL17ExYsXAQBqtdrJ0VTOyZMn8csvv0Cj0Tg7lEq7cOECFi1ahHPnzjk7lEpLTk5Gr1698O233wJwjYtoRkYG0tPTUVRUBEC3pITc6dfl0nOF1xmA+Bq7Ild5jQHX+Vw2lJ+f7+wQrHb16lWkpaUBgE2vLUxQqmj79u1o2rQp7rvvPgCAl5e8Vw0oKSnBpEmT0KZNGxw5cqTCNRDkQKPRYPr06UhMTMSZM2dw48YNZ4dUoZKSEowfPx4tWrTA7t27cerUKQCQ9ZpRKpUKTz75JJKSkjBixAgMGTIExcXFsn6PqFQqTJ06FUOHDsV9992Hb775BoC8X2dAF/e0adMwevRojB8/Hvv27ZP9BV+lUmHRokVYu3YtAPm/xoDu7/CFF17AlClTMGvWLFy6dMnZIVWopKQETz/9NEaNGoXRo0fjhx9+kP17AwDWrVuHhg0bYsaMGQAAT09Pmz22fD+BZO7cuXPo1asXbty4gc8//xyAfLP1jz/+GBERETh79iyOHDmCN9980yU+ZN5//30cO3YMO3fuxPLly9GjRw8A8v0Gt3DhQtSqVQtXr15FcnIyRowYgYyMDAC2/VZhS9euXUOvXr1w4cIFrFq1Cs888wxSU1Px0ksvOTs0iy5duoTOnTvj7NmzeOGFFxAaGoqFCxdi6tSpzg6tXBkZGejatSuOHz+OESNG4Pjx45g6dSr+7//+D4A8W6w2bNiAtm3b4oUXXsAvv/yC9PR0APL9GwSAn376CQ0bNsShQ4cQGxuLH374AVOnTsWePXucHZpF3377LRo0aICTJ09iwoQJyMvLw4cffohNmzY5O7QKHThwAF27dkVqaip++eUXADb8vBPIKlqtVhAEQXjxxReFyZMnC6+99poQGxsrKJVKyXG5yMnJEcLDw4W+ffuK+86cOSMkJycLubm5TozMMq1WK+Tn5wtJSUnC559/LgiCIOzZs0f43//+J/z9999CXl6ekyM09cUXXwht2rQRfvzxR3HfvHnzhMaNGzsxqop9//33Qtu2bYXr16+L+8aPHy/MmTPHiVGV75NPPhH69OkjFBQUCIKge78sXbpUUCgUwi+//CJoNBonR2jezz//LLRq1UpIS0sTBEEQsrOzhblz5wp+fn7CyZMnBUGQ1+dHfn6+MHnyZOE///mPsGDBAqFTp07Cp59+6uywynXkyBFhyJAhwoIFC8R9qampQsOGDYWVK1c6MTLLzp07J9x3333C4sWLxX1Xr14VoqKihC1btjgvsAro/86mT58uPP3008KkSZOEnj17CiUlJYIg2Oa9zBYUK+lbHm7cuIFhw4bh/vvvh7e3N15//XUAQGFhoTPDEwml33BCQkKwaNEiHD16FFu2bMEDDzyA4cOHY/Dgwejfvz+++uorJ0dqSqFQID09HZcuXcLgwYPx3//+F2PGjMHXX3+NMWPG4N5770Vubq6zwwRQ9q333nvvxdGjR3H//feLxwIDA+Hv7y+OU5Kj7OxsXLhwAdHR0QCA69ev4/jx4wgPD8fu3budHJ15ycnJUKvVCAgIgCAIUCgU4vv97bffxq1bt5wcoZT+PXLjxg3cuXMH9erVA6Bb1fXJJ59Ejx498OSTTwKQV/dJQEAAJk6ciKeeegovvfQS4uPjsWHDBhw/fhyAPFt8SkpKkJCQgPHjxwPQdU/FxsaiVq1aOHPmjJOjM69OnTp4/vnnMXHiRHHfrVu30LZtWwQFBUGpVDovuHJ4eHhAEAQkJyfjkUcewb333otbt25h6dKlAHSvfbWfo9qP4MYOHDgAQPqHqP8gzM7ORkFBAZo1a4bZs2dj6dKlGDduHGbPnu3UD0h9zIJBE+zEiRPRpEkTDBo0CCEhIfjyyy/x4YcfIjExEXPmzMH27dudFS4A869zbGwsIiIiMGfOHFy9ehXbtm3Db7/9hm3btuHw4cN48803ndrMbPw6h4eHixcX/b6uXbvi9OnT8PPzk+x3FnOvc1JSEkJDQ9G1a1fcd999iI+PR2hoKP744w8MHToUb7zxhk0+aKrKXMzBwcHw8/PDn3/+Kb7m//zzD+bNm4eTJ09i48aNJvdxtJ9//hlbt27F9evXxbE8np6eiI6Oxt9//y2eFx0djZdeegkHDx7Eli1bADjvfWIYM6BLlrp3747mzZsDAKZOnYq0tDSsXbsWgiDIYoySPmZ911OXLl2waNEixMTEAAC8vb2Rk5ODgoIC3HXXXc4MVWT8OteqVQtdunRBWFgYAGDGjBno0qULsrKyMGLECIwePVrynnEG45gBXTeOQqGAp6cnlEolunXrhnvvvRfLly/HI488gvfff7/6yVW122Dc0Nq1a4WYmBghIiJCuHz5siAIgqTZuLi4WGjatKmQmZkpCIKuKd/Pz0/w9fUVDh8+7JRmWnMxq9Vq8fjBgweFl156Sbh586a47/Lly8KoUaOEoUOHOjpcQRDKj/n27dvCpEmThODgYGH06NGCRqMR/w+++OILITQ0VCgsLJRFzJa6FJKTk4X4+Hjhq6++clyAZpiLWaVSiccvX74sbNiwQUhISBC++eYbcf93330nBAYGCqmpqY4O2WzM+m7U06dPC6NGjRJCQ0OFsWPHCkFBQUKXLl2Ea9euCWPHjhVGjBjh8Hj1vvnmGyEyMlLo0qWLUKdOHeGuu+4SfvnlF0EQBOHff/8VEhIShIULF4q/iyAIQkZGhnDPPfcIjz76qGxiXrt2rSAIuve24efZU089JfTu3VvYunWrIAjO65IqL2atViv5m7xy5YrQtGlTITk52Smx6lX0Ous9+OCDwsaNG4X8/Hzhn3/+Ee6//34hKSlJdjELgu5zOjo6Wnw/z5w5U/Dz8xP8/f2FQ4cOVfv5nZ8Cy8zKlSvx9ttvo1evXmjZsiUWLlwIAOK3Ba1WC0EQ0KFDB6xatQrt27fHJ598grFjxyIgIAA5OTlQKBQOHTBrKWbD0dQdO3bEK6+8goiICHFfgwYNEBQUBEEQUFBQ4LB4KxNzrVq10K9fP/j4+ECj0YjNiQDQunVr+Pj4OLzJtqL3hjE/Pz/4+vo6dUqppZgNZ501aNAAd+7cgaenJx555BGx5aFHjx4oKSkRm/SdHbOPjw8EQUDLli3x0UcfYfHixahduza+++477N+/HzExMSgpKUF8fLxD4wV0A+Q//PBDLFiwAG+//Tb+/vtv/Prrr2jcuDG++OILFBUVoX379ujRowfWrFkjGbAZFRUFb29vh7dIlBfzZ599BqVSCQ8PDygUCvE98fTTT6O4uBjr1q1DQUEBBEHA+fPnZRWzQqGQfF7s2LEDAMRWFQC4ffu2rGL28PAQrxmrVq3CoEGDEBgYKLZgFRcXi61EcokZ0E2X7927N9asWYM2bdrg22+/Rf/+/VG/fn3xPVOdAbNMUErpX8QmTZqgX79+eOedd3DPPfdgx44d4htcf6HMz8/HunXrMHv2bPTo0QOnT5/GokWLMGDAADz88MMAHDPtuLIxA7rm2qCgIMn9i4qKkJ6ejtatWyMwMNDu8VY25pKSEgDAPffcg0cffRS//fYbtm7dKiYvu3fvRrt27dCuXTvZxGz8RygIAurVq4eoqCjs27cPgGO7HKyNWShtss/KyhIvlH/88Qc6dOiALl26yC7muLg4PPbYY/jkk08wcuRIALpZMikpKWjSpIlD4jVUUFCAGzduYMKECXjsscfg4+OD7t27IyEhAbm5ueJ7et68eVCpVPjss89w7do18f5FRUUIDw+XVcyGX7L0F/wWLVrg3nvvxaFDh/Dmm2+ic+fOGDdunMNmqVkTs77779dff8WwYcPg7++Po0ePYuDAgZg/f77DutIqG7OXl5c4pkpPo9Hg4sWL6NSpkyTBcnbM+m5fjUaDH3/8EePHjxdnA77zzjto0KABZs2aBaCa046r3Qbj4s6fP2/STKlv/j558qRwzz33SLpA9COUf//9d+HgwYOS+23atEmYP3++oNVq7dr0aW3MxudmZ2cLKSkpwuOPPy60bNlSOHz4sN1irWrM+q6eS5cuCePHjxcCAwOF0aNHCw899JAQHh4u/O9//xMEwb5NzNV9nbVarfDMM88I3bt3F/Lz8+0WpyFrY9Y3LW/ZskXo3bu30Lp1a2HZsmXCY489JoSHh0tmFsglZuNzr1y5IqSlpQnjxo0T2rdvL1y9etXuMQuCadxHjhwR37f613XlypVCu3btJF06P/30k9CzZ0+hfv36wnvvvSc8+uijQmRkpPD333/LNmbD4wcPHhS8vb0FhUIhTJkyxeQ8OcWcn58v9O3bV/j++++FadOmCZ6ensK4cePEz3E5xiwIglBYWCikpaUJTzzxhNC8eXPhr7/+EgTBsZ93lY159erVwv79+yWPtWzZMuH//u//qn0trLEJyg8//CA0aNBAaN68udClSxdh+fLl4jHDF/TLL78UEhIShC+//FIQBGnfvfH59u6PrWrMhv2bGzZsEKZNmyZEREQIffr0ES5cuCDLmI1f52XLlgnPP/+88Nhjjwlnz56VZczmxqJMnTpVmDZtmt0/xG3xOv/zzz/CiBEjhEGDBgkjR450ide5sLBQmDNnjhAeHi707NnTIeMMjOP+4osvJMcN43v44YeFiRMnCoIgSN4DaWlpwpQpU8QxYI5+rSsbs/HfoX4698CBA4WLFy/KPuajR48KCoVCUCgUQrdu3YTTp0/LMmbD8YK//PKL8J///EeIiopyymd0ZWM2l+Tp/24Nf5/qqJEJyubNm4UGDRoIS5YsETZu3CjMmjVL8Pb2Fj777DNx4KX+TZ6WliZMmjRJ6Ny5s1h/w97Ztz1i1n84XrlyRViyZIk4yE3OMbvi66yPWf8H6ojfoboxFxcXi4+l0WiE7Oxs2cds+LoePXpU2Llzp91jrijuoqIiQRAE8VtjUVGR0KZNG+Hbb7+1+Hj6+7hKzMeOHRN++OEHl4l5165dQp8+fRxST8RWMZ86dUpYtGiR0z+jrYnZVgmJsRqVoOizu3nz5gkdO3aUfMg99dRTQqdOnYQ1a9aY3G/9+vVCp06dhNdff104duyYMHz4cCElJcWlYh42bJjLxeyKrzNjdr+Yqxr3tWvXhAYNGgjnz58XBEHXhD5z5kzG7ICYn332WcbsgJjt/d6oUYNk9YOPTp8+jcaNG8Pb21sc7PPmm2/Cz88P69atMylPfvfdd6NLly5444030LFjR6hUKkRGRrpUzGq12uVidsXXmTG7X8xViRsAtm7diri4ONStWxfPPPMMEhIScPXqVahUKocM0KzJMaekpEClUjlkYLqtY3al19nu7w27pj9OtnnzZuHpp58WFi9eLBnE89lnnwnBwcEmzfCfffaZ0KxZM2HHjh3iufn5+cLixYsFT09PoU+fPsLx48cZM2NmzG4cc3XiNhzMeP/99wu1atUSIiIihFatWpkMqmfMjJkxl88tE5T09HRh+PDhQmRkpDBu3DghMTFRCA0NFf9jzp07J9SrV0949dVXBUGQDl6Ljo6WzFw4deqU0LVrV0kBK8bMmBmz+8Vsy7gLCgqE4cOHC7GxscLq1asZM2NmzFXgdglKQUGBMGHCBGHs2LHCpUuXxP1dunQRRx/n5uYKb775puDv7y/2Y+v75Hr37i088cQTjJkxM+YaFLM94rZFJU3GzJhrUszG3G4MSkBAAHx9fTFx4kQ0bNhQLIIzdOhQnDlzBoIgIDg4GA8//DA6dOiABx54AFevXoVCoUBKSgqysrIwatQoxsyYGXMNitkecXfs2JExM2bGXB0OT4kcwHBEsn4O98MPPyxMnjxZcl5aWprQpEkToUGDBsJ9990nxMTECH379hUyMjIcGq8gMGZHYcyO4YoxC4Jrxs2YHYMxO55CEJy8xKqD9OjRA5MnT8aECRPEkd0eHh5ITk7G4cOHsX//frRt2xYTJkxwcqRlGLNjMGbHcMWYAdeMmzE7BmO2M6emRw5y8eJFISoqStKHZu/KntXFmB2DMTuGK8YsCK4ZN2N2DMZsf243BsWQUNo4tHv3bgQFBYl9aPPmzcMzzzyDrKwsZ4ZnFmN2DMbsGK4YM+CacTNmx2DMjmP/JXedSF+M5sCBAxgzZgy2bNmCKVOmoLCwEN9++61DCz1VFmN2DMbsGK4YM+CacTNmx2DMDuSklhuHKSoqEpo0aSIoFArB19dXWLhwobNDqhBjdgzG7BiuGLMguGbcjNkxGLNj1IhBsgMGDEDTpk3x/vvvw8/Pz9nhVApjdgzG7BiuGDPgmnEzZsdgzPZXIxIUjUYDT09PZ4dhFcbsGIzZMVwxZsA142bMjsGY7a9GJChERETkWtx6Fg8RERG5JiYoREREJDtMUIiIiEh2mKAQERGR7DBBISIiItlhgkJERESywwSFiIiIZIcJChHZxcSJE6FQKKBQKODt7Y2oqCgMGDAAX375pbjMe2WsWLECYWFh9guUiGSJCQoR2c3gwYNx/fp1XLlyBRs2bMDdd9+NZ555BsOHD4darXZ2eEQkY0xQiMhufH19ER0djXr16qFDhw54+eWXsW7dOmzYsAErVqwAALz//vtITExEYGAg4uLi8NRTTyE/Px8AsGPHDjz22GPIyckRW2Pmzp0LAFAqlXjuuedQr149BAYGomvXrtixY4dzflEisjkmKETkUH379kXbtm2xZs0aAICHhwc++ugjnDp1Cl9//TW2b9+OF154AQDQvXt3fPDBBwgJCcH169dx/fp1PPfccwCAGTNmYO/evVi9ejWOHz+O+++/H4MHD8aFCxec9rsRke1wLR4isouJEyciOzsbv/76q8mxBx98EMePH8fp06dNjv3888+YOnUqbt68CUA3BuXZZ59Fdna2eE5KSgoaNWqElJQUxMTEiPv79++PLl264O2337b570NEjuXl7ACIqOYRBAEKhQIAsHXrVixYsABnz55Fbm4u1Go1iouLUVhYiICAALP3P3HiBDQaDZo1aybZr1QqERERYff4icj+mKAQkcOdOXMGDRs2xJUrVzB8+HBMmzYNb731FsLDw7F7925MmjQJJSUlFhOU/Px8eHp64vDhwybLxwcFBTniVyAiO2OCQkQOtX37dpw4cQIzZ87E4cOHodVq8d5778HDQzck7scff5Sc7+PjA41GI9nXvn17aDQaZGVloWfPng6LnYgchwkKEdmNUqlERkYGNBoNMjMzsXHjRixYsADDhw/H+PHjcfLkSahUKnz88ccYMWIE/vnnHyxbtkzyGA0aNEB+fj62bduGtm3bIiAgAM2aNcO4ceMwfvx4vPfee2jfvj1u3LiBbdu2oU2bNhg2bJiTfmMishXO4iEiu9m4cSPq1q2LBg0aYPDgwfjrr7/w0UcfYd26dfD09ETbtm3x/vvv45133kHr1q2xcuVKLFiwQPIY3bt3x9SpUzF27FjUqVMH7777LgDgq6++wvjx4/Hf//4XzZs3x6hRo3Dw4EHEx8c741clIhvjLB4iIiKSHbagEBERkewwQSEiIiLZYYJCREREssMEhYiIiGSHCQoRERHJDhMUIiIikh0mKERERCQ7TFCIiIhIdpigEBERkewwQSEiIiLZYYJCREREssMEhYiIiGTn/wGYA4YZ2Ti6TQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Adj Close'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "24995698",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_var = pd.DataFrame(df['Adj Close'])\n",
    "features = ['Open']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0b3bc687",
   "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",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-07-22</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-23</th>\n",
       "      <td>0.000309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-24</th>\n",
       "      <td>0.000928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-25</th>\n",
       "      <td>0.021226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-26</th>\n",
       "      <td>0.021873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open\n",
       "Date                \n",
       "2013-07-22  0.000000\n",
       "2013-07-23  0.000309\n",
       "2013-07-24  0.000928\n",
       "2013-07-25  0.021226\n",
       "2013-07-26  0.021873"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "feature_transform = scaler.fit_transform(df[features])\n",
    "feature_transform = pd.DataFrame(data=feature_transform, columns=features, index=df.index)\n",
    "feature_transform.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7ab68a4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = feature_transform.values\n",
    "y_train = output_var.values.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a5237390",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX =np.array(X_train)\n",
    "X_train = trainX.reshape(X_train.shape[0], 1, X_train.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a61ff5b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm = Sequential()\n",
    "lstm.add(LSTM(32, input_shape=(1, trainX.shape[1]), return_sequences=False))\n",
    "lstm.add(Dense(1))\n",
    "lstm.compile(loss='mean_squared_error', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3bc22a8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "315/315 [==============================] - 8s 5ms/step - loss: 32669.1348\n",
      "Epoch 2/100\n",
      "315/315 [==============================] - 2s 5ms/step - loss: 29893.4590\n",
      "Epoch 3/100\n",
      "315/315 [==============================] - 2s 5ms/step - loss: 26816.1719\n",
      "Epoch 4/100\n",
      "315/315 [==============================] - 2s 5ms/step - loss: 24036.5781\n",
      "Epoch 5/100\n",
      "315/315 [==============================] - 2s 6ms/step - loss: 21660.6484\n",
      "Epoch 6/100\n",
      "315/315 [==============================] - 2s 5ms/step - loss: 19637.8320\n",
      "Epoch 7/100\n",
      "315/315 [==============================] - 2s 5ms/step - loss: 17901.0586\n",
      "Epoch 8/100\n",
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      "Epoch 93/100\n",
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      "Epoch 94/100\n",
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      "Epoch 95/100\n",
      "315/315 [==============================] - 4s 12ms/step - loss: 142.8938\n",
      "Epoch 96/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "315/315 [==============================] - 2s 6ms/step - loss: 135.9810\n",
      "Epoch 97/100\n",
      "315/315 [==============================] - 3s 8ms/step - loss: 129.5004\n",
      "Epoch 98/100\n",
      "315/315 [==============================] - 2s 7ms/step - loss: 123.4231\n",
      "Epoch 99/100\n",
      "315/315 [==============================] - 3s 8ms/step - loss: 117.7227\n",
      "Epoch 100/100\n",
      "315/315 [==============================] - 3s 10ms/step - loss: 112.3741\n"
     ]
    }
   ],
   "source": [
    "history=lstm.fit(X_train, y_train, epochs=100, batch_size=8, verbose=1, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "22512f47",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sahit\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\engine\\training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['META_scaler.joblib']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lstm.save(f'{stock}_model.h5')\n",
    "joblib.dump(scaler, f'{stock}_scaler.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "514d18cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "Expected Close Price: 18.879616\n"
     ]
    }
   ],
   "source": [
    "openp = float(input())\n",
    "scaledopenp = scaler.fit_transform([[openp]])\n",
    "scaledopenp = scaledopenp.reshape(1, 1, scaledopenp.shape[1])\n",
    "expectedclosep = lstm.predict(scaledopenp)\n",
    "expectedclosep = scaler.inverse_transform(expectedclosep)\n",
    "print(\"Expected Close Price:\", expectedclosep[0, 0])"
   ]
  }
 ],
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