Stock_Market_Predictor.ipynb

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      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import pandas_datareader as pdr\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "id": "9b19968a",
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      "source": [
        "#DATA COLLECTION #2017-06-08 #2022-06-07\n",
        "start = pd.to_datetime(['2018-08-08']).astype(int)[0]//10**9 # convert to unix timestamp.\n",
        "end = pd.to_datetime(['2023-08-09']).astype(int)[0]//10**9 # convert to unix timestamp.\n",
        "url = 'https://query1.finance.yahoo.com/v7/finance/download/' + 'AMZN' + '?period1=' + str(start) + '&period2=' + str(end) + '&interval=1d&events=history'\n",
        "df = pd.read_csv(url)"
      ]
    },
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            "text/plain": [
              "              Open         High          Low        Close    Adj Close  \\\n",
              "count  1258.000000  1258.000000  1258.000000  1258.000000  1258.000000   \n",
              "mean    123.723335   125.257783   122.056073   123.662120   123.662120   \n",
              "std      32.824927    33.123633    32.455461    32.749656    32.749656   \n",
              "min      67.300003    69.801498    65.349998    67.197998    67.197998   \n",
              "25%      93.533499    94.581877    92.474001    93.492498    93.492498   \n",
              "50%     116.659999   118.637501   114.645001   116.345001   116.345001   \n",
              "75%     158.346878   159.930752   156.224998   158.093746   158.093746   \n",
              "max     187.199997   188.654007   184.839493   186.570496   186.570496   \n",
              "\n",
              "             Volume  \n",
              "count  1.258000e+03  \n",
              "mean   8.208400e+07  \n",
              "std    3.820701e+07  \n",
              "min    1.762600e+07  \n",
              "25%    5.636905e+07  \n",
              "50%    7.073390e+07  \n",
              "75%    9.719900e+07  \n",
              "max    3.113460e+08  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1258.000000</td>\n",
              "      <td>1258.000000</td>\n",
              "      <td>1258.000000</td>\n",
              "      <td>1258.000000</td>\n",
              "      <td>1258.000000</td>\n",
              "      <td>1.258000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>123.723335</td>\n",
              "      <td>125.257783</td>\n",
              "      <td>122.056073</td>\n",
              "      <td>123.662120</td>\n",
              "      <td>123.662120</td>\n",
              "      <td>8.208400e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>32.824927</td>\n",
              "      <td>33.123633</td>\n",
              "      <td>32.455461</td>\n",
              "      <td>32.749656</td>\n",
              "      <td>32.749656</td>\n",
              "      <td>3.820701e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>67.300003</td>\n",
              "      <td>69.801498</td>\n",
              "      <td>65.349998</td>\n",
              "      <td>67.197998</td>\n",
              "      <td>67.197998</td>\n",
              "      <td>1.762600e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>93.533499</td>\n",
              "      <td>94.581877</td>\n",
              "      <td>92.474001</td>\n",
              "      <td>93.492498</td>\n",
              "      <td>93.492498</td>\n",
              "      <td>5.636905e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>116.659999</td>\n",
              "      <td>118.637501</td>\n",
              "      <td>114.645001</td>\n",
              "      <td>116.345001</td>\n",
              "      <td>116.345001</td>\n",
              "      <td>7.073390e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>158.346878</td>\n",
              "      <td>159.930752</td>\n",
              "      <td>156.224998</td>\n",
              "      <td>158.093746</td>\n",
              "      <td>158.093746</td>\n",
              "      <td>9.719900e+07</td>\n",
              "    </tr>\n",
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              "      <th>max</th>\n",
              "      <td>187.199997</td>\n",
              "      <td>188.654007</td>\n",
              "      <td>184.839493</td>\n",
              "      <td>186.570496</td>\n",
              "      <td>186.570496</td>\n",
              "      <td>3.113460e+08</td>\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "df.describe()"
      ]
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      "id": "c218ba9d",
      "metadata": {
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1258,)"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "dfc=df.reset_index()['Close']\n",
        "#When we reset the index, the old index is added as a column,\n",
        "#and a new sequential index is used\n",
        "dfc.shape"
      ]
    },
    {
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7b41365d4a60>]"
            ]
          },
          "metadata": {},
          "execution_count": 90
        },
        {
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          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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ijVUHI/OkRAEwQCFKIh3edXjShQLQQHUoYgYl2v019tY04cipVmw92tDp11q+qybg48GGeOq82ZNBRZl+2Q498iyejYfrcMOfVqpfy6lTg6IpkhX70jgDDPHodZIFgCtGleKaMX2x4MfnqrYXCwFopCbyNAlN7+paHEobf6JoYYBClETkT+ni7BFHgJk8ZlP3BSjixVrbUC5U+wyGHuRMR9AhHu9MmukjSkJ6PbNm6rE4TOOrQQkwxOMKbYhHrwZF1K8gA/NvGqvcH1SUpdyOVA2KuMjk2+uO4NKnl0fkeYmMMEAhSiLyEE+m3Qr52hooGBCHWrRdWSNN7roKADVNbZ16DqMMSa636VqwRm113u8xJ914yERk0ZTonBJWKQ5lFo/LLSlDZ+EO8Wj1zklVbucIw1OR6oWiDe6OnGqNyPMSGWGAQpREOrzDCDarWVlrJtCwh0O4uFVrmp5FmtgHpOKp5SHNuNEyCrbkC3agVvdut4T3Nx3z7J8WvP4EALSX/tpmMUCR1/TxBRd6mRA5MOlKBgVQN5bLFY5fzHx0BVvoU3djgEKUROSLlc1iVi6cgS5g4qfvqobOZTVCVafJ0ByuDVzwqqcrGRRxeKhXVvAeKIA66wMAW47U+z0mt7kHfK3uRXJWyxEgALAb1KCITPClc8QC35YgM5tCFalAhyhUDFCIkoi8um9mqlWpjWgLMMQjztyJdoCivdgH6qxqpM3gIprnHbIJlAUQpyife1pBSK+nPcbPdlYrt+XhnjzNcNFPLz1DFagoAUqYfVC0SrJ9QzxZqb4AJdjU61Axg0LdjY3aiJKIfEHNSUtR6hoCDaWIGZTm9uh9gt58pA7/Wn1Ita2msR0/fXsTyvPTcfeUQSE9j1GwVeS9eAfKoMh9SwYWZoQ0gwfwzfqRiXUZRgHK7IsH4fYLTsOZv/oYbQ63cuHvag1KTnoKPrx7EuxWM5qEn1WkMijaLsRE0cYMClESaRADFKscoATIoAgBSqtwcW9oc2DGs1/gyY93hvzalfVtuOSpZXjlq/1+j135wlfYW6OegfPepmP4z7ojeGrxLr/9jcjB1th+eSgShmky7fJwlvH36lskMHgwILtiVCkAYGAvT+O7o2KA0uw513kZ/vUsNqtZ6YnS7lTXoMhN9ERpttCOaXhpDk4vysLoslxcP7YvAPXPrSuYQaHuxgCFKAm0O114bslufLnnBAAgOzVFGeIJVFsgBihipuXznTX45ngDXvhsT8DiTtGzS3Zjd3UTfvX+NyHtLzY+C7Vxm3yM908djHMG5Cvb7dbg2SL5Qq63MJ+R2y8YiLd+OBH/ucPTh+Rkc4dyPmu9GZRcgxlBciAk7y+fx9Fluf776tSuBHPxkCIAERzi0Tl3gboQE3UVAxSiJPDKVwfw1OJdSlfRnPQU5QJplEGRJEk1xCNe3DOFws89mgX5jGhrTGS/W7RDd/spYfgk1E/v4vo3Yp2HHIz9fcVBNBkch9604GBSUyw4Z0A+8tJTlKZ2h062oKaxHav31wJQT/8V9cr0ZHiO17WhprFdmYHUK8uORfeej3fvPE/Ztzhb/zkCketWVh+oxXdeXoEdlQ1hP4dIbwgq0LAUUVexBoUoCeyqalTdz0u3Ba1B0fbPEIcKOpz6gUsgFoN1fV76fG/Qr213uEMKHNqV7q1mzLnkDGw8VIdZ5/ZX7fPUJ7vwyBXD/L62TcmghB6gyEwmk5JtuuTp5Ur2AtAfsgGAQcWZ+OZ4A/61+hD+32trle0pFjOGlGQDAD6dcyFONLV3LkARhoVW7a/FdX/8GtsenRb28wCe2iW9n1O7M7SfC1FnMINClAS0QwS9c1KRKvdBMRji0a7RIw4ViMNCoTYCU3el9Xy9XuHlJcOK/bYZHaPI7ZbQ4J2llGGzom9eOpb+dDJmndsf2Wm+z2LrDtbqfr2SfQmx3iOQpTt8s3nyDdbJGVSUCQBYIuwLqFcpPr0oExMGhjajSCtd8300d2Go51fvbcO2Y/4ZGBbOUjQxQCFKAmKzMADIsFuVIRCj/ht6GZTXVx3EjGe/UPUoCbTYoEisI5FnvzS2+U8lHliYAU0H+ZCyNCea2tHhdMNsAko0wyrZwrRbo+nLrTqL+4XjwjN6+W37/rn9lfV6tE4X2tGrtvfK7NTra2kDlK4Qp0+LQq0/IuoMBihESUCvQZhcM2EUYGgvPm0OF37+zlZ8c7wBT37im1kTagZFrP2QO642tPnXg6TbrCjOUgcYgWYayeSVjHvnpPlNE860+7ISaTb9kW05Q2S0MF8wz393DEZpClyHlOgHIQBwZt8c3e0DeukPCYXLqDi3MzLs+ueMGRSKJgYoRElAHOJ5/NqRAACr9yIuz+zR0l58jKarhppBEQsqT3kDFL0MitViQqWmKVxoGRTPcxZl+3eBNQv1L6kpZjz24TdYuqNKtc/ROs8U4c7UewCeLM0UofYECNwyvzQ3DQN06lMKM0LrYhtMQYZNNzDtjAyDoI5FshRNDFCIkoBNyCiU56cD8GVQPt9Zo9tWXjtzxmi6qivAasgiMSNzUglQ/DMoVp1i2lAClJYOb5dcnU/7/QrSldsbDtXhz1/sxy2vrsU/VhzAtGeWo7qhDfu9re4HdiGDoc00ZAdZ06cw0z/LkZUambkLJpMJpQYziMIl1vCImEGhaGKAQpQELMKyu3IbdKuwbf+JZr+v8R/i0b8YOQO0aFc9nzDzR+6yqjf12Gox+7WaD7TIn0zudKs3C6coKxX36HSjffjdbdhR2Yj/rD+CynpP1qZPblrQ1zKSpQlQjIZGZHoZDrPBbKfOKMtPD75TCAq9U6K1Q1Zs3kbRxACFKAmIQcSIPp4prFaz78/frdMITZu+Nxri0ftaPeLz1XuLZPWGh1IsJjzzndF4YNpg9PdmPgK1qJfJGRSjoGBsvzzDr7WaTcr3FyyoCET7tcEKVW0httTvrKG9syPyPPK5uXXSANw6aYCynRkUiiYGKERJQF7073sT+ymzSsTJJXohhl8NisEQT7AalIUbjmLj4To4haGgNk33VJHVbEZRdip+PPl09M5J8+4f/EIorzljFBQEWl+nrsWhfH+d6YMiE1cuBoLPCIpUjYiR04vUM4IOnvTPlIVCbGL38OXDlMCHs3gomhigECUBhzeIELMmYuJDr5W8HKDINR1GGZRAs3j21jTh3n9vxNUvfoUTjR3Kdnm4SO9rxaEn34rLoWRQAmdAAgUDf/x8r2+acRcCFG39SKot8FusTQhgSrJT8Y9bz+n0a+vRdrGdu2BLp56n1fvzkoM3+Vwyg0LRxACFqAfbU92EupYOJYOSYtGvb9AbpZEvPtlBijYD1aCcbPIFJeLMHDkY0Ptai5Dakfu3hDLEs2hrJQDjDEiowymhLsynRxscBcvGiMe08M7zcP4g/14qXSHXjsiO1bWq7i9YfwT/9+7WoAFgo7d3jBy82b3HzVk8FE1hByjLly/HFVdcgdLSUphMJixcuNBvn+3bt+PKK69ETk4OMjIycPbZZ+PQId9S6m1tbbjzzjtRUFCAzMxMXHfddaiqqvJ7HiLqvD3VTah4ahkueXo5HN5AQMxOiFkTMZHhckt48D+b8fpqz99ssJko2izIaysOYMl2z9+z0SfsFXs9CwHKw0PizBmxa6wvgxL4QuhySzjknYlktLBgqMMpqV0YdtFOxw2WjRGHvYxmynTF6UWZqpk8BZqAZc5bm/D3FQfxh0+MV6U+XNuCfd4i6v6FnpqgFKvn94gZFIqmsP8Sm5ubMWrUKLz44ou6j+/duxeTJk3CkCFD8Pnnn2Pz5s14+OGHkZrq+yO577778P777+Ptt9/GsmXLcOzYMVx77bWd/y6IyM8n33gyCjWN7cqFUDXEI+zrFD4Jf7D5GP699jA+3HwcQPCMgliDsqe6EY+8uw23/n0t3G7J8JP5/hPN2F3VqBzX4GLf7BBx6nGw9YJk4vDTxUP9W+UDxtkjkc1iVvrDdIZ2iCdQ3QugruvpSu2LkRSLGcseuEi5P7S3fuO4LUfrDZ+jutGT+SrLT0PfPE+AImd+GKBQNIUdsk+fPh3Tp083fPznP/85ZsyYgSeeeELZdtpppym36+vr8de//hVvvPEGLr74YgDAK6+8gqFDh2LlypWYMGFCuIdERDrE4RV5KCVFlUHx7Sum6uUur7JgQyNiHxQxuDje0BZwGur2ykbluMSgoEFoRa8EKEHW4hEv9KMMOrQaZVD65KYpTdq6OmQR7gwgsbuuUUv8rkqxmHH/1MH4/cc7DQOKQMN07Zr6E0CoQeEQD0VRRGtQ3G43PvzwQ5xxxhmYOnUqioqKMH78eNUw0Lp16+BwOFBRUaFsGzJkCMrLy7FixQrd521vb0dDQ4PqHxF5PPvpbsz7aDtONrVjq/BJWF7vBvD1qxA/0UtCDkUMJLR1q8GGRsQMijgUs6+mKWDmI8NmETI7vovzmHLfdGC7d4inPcgQj7gSsdGF3ijQ0lucsLOCZUy0mnT6wESDLxMl/JyFn5sjQKGz/LuRqgpQPLeZQaFoimiAUl1djaamJjz++OOYNm0aPvnkE1xzzTW49tprsWzZMgBAZWUlbDYbcnNzVV9bXFyMyspK3eedN28ecnJylH9lZWWRPGyihFXf6sDTn+7Cy8v2YexvPsXlz3+pBCliACK3lBczFaoMinCh0dZwbDxUh0evGm54DGINyvf+tkq5va+mOWAGxWw2+WpjzCZ8+eBF+NPNY1Ex1NcuXi7G/MuX+w1rSwBxGqzxW5o20BpTnosJA/Mx++LTDb8m2pp0OulGg3xexKEwMStltGAk4Ft5WlwuwcYiWeoGEc+gAMBVV12F++67D6NHj8ZDDz2Eyy+/HPPnz+/0886dOxf19fXKv8OHD0fqkIkS2gGdDrDLd9cAUAcgDa2eC6FqiEf4GjFA0Ra9NrY7cVqAFXblDEpjm0MJOIDgGZQ1+2uV17JazOibl45Lh5eoMiCHhBb88vegp9URvIeJNkC548LT8ObtE1GQ4Ws3f8t5A7Rf1mn2EIptywsi0+k1mDSdWh4xmxKon4m8n7gito1FstQNIhqgFBYWwmq1YtiwYartQ4cOVWbxlJSUoKOjA3V1dap9qqqqUFJSovu8drsd2dnZqn9EBBzUW0PHe0ERL0b13roOoz4o4ifhZk1Dtv+7YhiKdRbgk8lBxu7qJtX2v684iE1H6gy/7o+f78Xq/bXe49Iflmlq9x1LXWuH7j6AsBJxgILedJtV1cZennFjMplwTv985Kan4L5L/Nvhh+vlm8ciNz0Fr3z/7KD7/vaakbhqdCkW3nlel183EHl4RhwqE7MpeksOyAJmUBigUBRFNECx2Ww4++yzsXOnesrarl270K9fPwDA2LFjkZKSgiVLliiP79y5E4cOHcLEiRMjeThEPZ7eEIE8rCJegI7Xe4pAM1WzTIQaFO+F63BtC55bslv1fMNLcwJmUOQARa/T7LsbjwEAhgkt18XupvJKylaDGTb3VvgChlMt/isfy0LJoADArHP7KbfTha6vb9w2HivnTlHWKeqKqcNLsPGRS3Hu6YVB9y3NTcOzN4zB6LLcLr9uIPJ5UQ3xCLdbAmS65N8nuzB8ZpcDniDFy0RdEfYsnqamJuzZs0e5v3//fmzcuBH5+fkoLy/H/fffj+985zu44IILcNFFF2HRokV4//338fnnnwMAcnJycOutt2LOnDnIz89HdnY27rrrLkycOJEzeIjC1KFzgWhzuLBq30l8vrNG2dbgDWTy0n0XYDGDItcj/GPlQb/nS7GYYDKZ8PYdE/Gt+f6F7M4AAYrs7P55qGlqR4bNgtLcNOzRZFuMMigj+uRgWO9sfHO8QVlgUE9biAGKWOgp9iyxWswI0pU+odl1OvKKt+taHFi0tRKXDCuGRfOzaNcZ4pFXYa5saEd1Yxu+2HUCl4/qrdqHqKvCzqCsXbsWY8aMwZgxYwAAc+bMwZgxY/DII48AAK655hrMnz8fTzzxBEaOHIm//OUv+O9//4tJkyYpz/H000/j8ssvx3XXXYcLLrgAJSUlWLBgQYS+JaLkoVek2O504zt/Wqm7f166r95CDFBe+nwvjtW1Km3tRXLtxtn983HnRaf5PS5PMzZqhQ94MgVfPngRFt17Aep1Ao1AvUfyMjxBVV2AAEXJoATp2SLOsgm2kF9Pojddu7ld/fO645/r8J91/vV9cpZELEAu966SfKi2Bde99DV+8vYmPPvpbr+vJeqKsAOUyZMnQ5Ikv3+vvvqqss8tt9yC3bt3o7W1FRs3bsRVV12leo7U1FS8+OKLqK2tRXNzMxYsWGBYf0JExvRqAAK1hc8VMyiaJQK//8pq3QBFrD2462L/Gg0lg+J93YsG9/JbA+a80wtht1qQmmLBiSb/QCPFIIMC+DIdB0/619vIWjv8P+XrqRLa7ZcK9Sg9nTLE0+H7fVm576Tffst3nfDbpgzxCOe2T64nQDlW14rDtZ7hw4+36c/CJOosrsVDlMB0A5QAhYv5woyVCk3H1V1VTbo9T2wW34UpNcXit76Lyztzp11YbO/FmWep9inK8n2N3lCNxRx8evAzn+7GgvVHdPdpCzGDcuWoUmTarbjt/AF+Qxk9ma9I1he8Hj3V6ref3vnzBSi+n5FcyyQO6wWYBU7UKZFf/IGIuo1eMGI0tfeGs8uQLtRdXDKsGNee1QcL1h8N+Hzyuisybb3IX77cj2P1rcoU47QUi9/wibiej1mnkZpRkSygbrD2y/e24dqz+qKlw4lfvrcNaw+egtVsUpqtpQXogwIAA3tlYuMjl3SpnX0i0uuDos2gAfrDXnJQIw6P6RXduhmhUIQl118pUQ+jF1C0GBSr3jShn+q+yWTC5MFFqm2//uAbv6/TdmDVyzz8b0slFn/jWSAw1WZBqqpnhllVnDr/prF+X29UJAvod2d98bM9eGvtEeyracauqiYs3OCZLRTKejbJFpwAvvPidEvKukt6zWP1Migd3sBTzK7Jzyf2zAnQjJaoU5LvL5WoB9ErkjVqn67XOCw9hAu6dtgn2NBIqrfWRJatmbo7aVAhZk1UB0uBgga9Yad9NeoGdUo79iQqfA2H+PNoc8oBin9EYYL/z1Zu4ib+HFJt/j8TvYwMUVcwQCFKYHo1KMYBiv/FO5SZLNoAIVC2A/AMJ4gzPjLs/q9xWFP/EKjrqnr9IGDzkTp8tFVdkClPt47GisA9gXh+5boRvREZveHBDp11nGwWM7S/Bm72bKMIY4BClMD0ApRGg/Vd7Dr1Gb2y9DvEio3DUszhZVCKsuwBMyiAp1hV1L8gw/D5tDUwV77wld8+cp8XBij6TCaTEjTKQYje2kZ6AYqSQRHqhEwmk9+5DrRWElFnMEAhSmD6AYoDAws9F/wzin1dW/WyFAMK9QMDMQNi1gQkwQKUsvx0Vd3KwF7+r3H5mb0x/ybfTJ9+AdaksYvDP0GugakMUAylarq/6tWMBMqgaDNp2nPNGhSKNAYoRAlMr9V4u9MNuZRg2ojeyna9IR6rxYxRfXP8tg/r7b9NphegnN0/T7k9oDADZrMJN5ztWXX8ijNL/fa3WsyYNqI37rr4dPzwgoEoyzcOUPSKZI0wg2JM2wtFrwZFr9meXOek/Tn4ByiMUCiyOM2YKIEZLXcvp+VVC7wZ1Hn8+uoRfsMm/QvTsfDO85CT5j88o1eD8tS3R2PZrhrYLGYM9K7bM+/akXhg2hBV7xWtn1w62PAxmUU1tBB433Sdehfy0HaT1Ysn9GaA6RXJAv4zfphBoUhjgEKUwIxWk3U4PVcLMUAxGprRGxZJtVoMF7DTe57UFIvuNOZAwUmoxBV4021Wpd5Ej15ARR7y74JSg6IzXlbX4sD24w0YUpIFkzca1CuSBfyzVaxBoUjjEA9RAjMKUOShn7P756Moy45x/fJ09wOg6lki0yuolekFKNpeKZHU0uELSCRIfrNHRHoFueQhZzzkWTx6s262HK3H9Ge/wK8/2K5skxvwaX/GfgFKJA+WCAxQiBKaUVt7OVWfmWrFVw9djLd+ONHwOVJ1gpFAa9roBSjamTaR1CwMO5xs6gg4lMAMijE5EA3UB0X2t6/2K7eNhnjYc4aijUM8RAnqZFM7dlQ26j4mBy42izlokaldZ4gnUAbFqrNuTjQzKOJ6L84ghQ4MUIwp04w7jGfx6DEa4knVBCwc4qFIYwaFKEH9YuHWoPuEMgNGb/pxoMZpehmUaC68d8EZhSHvm5XKz1xG5CGeNmXmlyeg+O01I/HpnAsNv843i0f9Mw62MCNRVzFAIUpQW47WB90nlAu23Wr2mx0TqJ+IXjBiCja9pguuGtUHT35rlGrb5MG9/Pb7wXn9k3KdnVApQzwOdQbFajGhLD/Nb/9TzZ5Vpw1n8Wh+R4xmiRF1Fn+jiBJUoCyHLMMePEAxmUx+zxXoubs7lW82mzBtRIlq22m9MlX3b57QD/93xfDuPKyEk2rT74NigqfmSBtg3PfWRgBCozZL4N8RuZiWKFKYDyVKULYAhawAwprim5piQZswnTdQBsWo90o0aS+O4sX0b98fh4uHFHf3ISUcX5Gsug+K2Zv9yk614kRTh7L/5ztrAPgCD+1wobbDsNGMMqLOYoBClKCCpdT1Fukz4rl4OZT7gTIoYpHsszeMRt88/+GBSNPWP4gXy0Dr+JCPfA6dLjfeWHUIy3Z5AhD5x5mVmqIKUGQdBkM82oZ9DFAo0higECWolCCFqYGmCmtppxoHyqCIwcFVo/uE/BpdIQ9D+WYnmfDWDyeitrld6VxLgcm1Qy438LN3tijb5QxKpmY4cEhJFiRJMpzFo+050+FyQ5KkqNYjUXJhDQpRggp2HQhn6q82IMlPNx4eskWx50kg4if4FIsZ5wzIV601RIH5AhT9TIe2oLpvXppqWrf29+k7Z5fBajahJDtV2cY6FIokBihEPVSgXiZa2gBFW18gKs2J/pCOHrsmQKHwKAGKpsjZKIPS7nQrM3gA/yGeouxUrPrZFHxw9yRlWyzqk6jn4hAPUYIqEj65ji7LxcbDdarHrw5j+CU9jJ4Wd00ZhL01Tbh6TPcM78jE+ogUTmkNm1XJoOgHKNqgr93hVtWVaOuAAKAg0656PofTDdgjdsiU5BigECWoXG/X1IqhxX7DPSkWk9/ifYGIPS0+uGtSgD093Vpf+cE5oR9oFNh0LpYUmJwV065YrCTLNKe03elSMiImk3EzPovZBIvZBJdbYgaFIoofQ4gSlPzJdXRZDrI06fn5N40Nq7urOBxUXpAemQOMIhZihk/OoDS3qwMU+VRqz6hniMe3UGCgcy5nVziThyKJAQpRgpILGC1ms6rA8b8/mogpQ8PrCyLO+NFOH40Xo/rmKLd5IQyfPJTT6nCqtsuBhzYAaXe6DZu0acmPGy1eSdQZDFCIEpScQbGaTcgUApSx/fLDfi6xADWa6+p0xfybxyq3GaCETw48W/2GeEze/9X7tztcSpFssJofuWmgg0M8FEEMUIgSlC+DYkKf3K4Ny4izdixxOnzSW5g9xE/q4bN4sxytDvW5MxsM8XS4Qs+gyAEuA0eKJBbJEiUouZ+F1WLC9WP74uu9JzDxtIJOPZdYwxKvGRRRu9MVfCdSkQNPebFAmVKDoglMWzp8RbIpQXrfKDUozKBQBDFAIUpQLiGDYrOa8cJ3z+r0c/3gvAHYd6IZt04aENcFqMNLs7HtWAMuP5MN2sJlNMSj1KBo9m/pcCnBTNAaFGZQKAoYoBAlKLEGpatKclLx5++N6/LzRNs7Pz4PdS0dqh4wFBo5M9bq0K9B8YtQAPz0rU0AgjfGUwIUZlAogliDQpSg5BoUcxxnPCLNZjUzOOkk4wDF879JiFDkgONYfZvqvhE5w8IMCkUSAxSiBKVkUNi0jEIgByjaIEIOTMQ4V9tXJ9gQj5xhWbH3ZFcPk0jBAIUoQTldvj4oRMEYFT/rzeLRLhwYbIhn1f5aAMCrXx/o7OER+eE7G1GCimQNCvV8RgGKr1Gbb1umJkBhfxOKBQYoRAnK6Z1mnAjTgin2jAJZ3RoUTcakjdO6KQYYoBAlKGZQKBzmIBkUsdeJVTNsGE4CZU91U/gHF6aGNgcuf/4LvLB0d9Rfi2KHAQpRgurw1qAYXXiIRMEyKHddPAgl2am4Z8ogv6ycJEkhv07FU8s6fYyheu3rA9h6tAFPfrIr6q9FscMAhSgBOVxu7D/h+aRalpcWZG+i4DUoxdmpWDH3Ytx3yRl+M8PkbJ2R0WW5ETnGUJ1qcXTr61FsMEAhSkCHalvQ5nAjw2bBwMLMWB8OJQCjNZbEzXKwog1m3EEyKE9+a1TXDi5M6w6e6tbXo9hggEKUgOQW5JmpVg7xUEiMQgy9Rn/a4aBgIzzaacnRtvFwnXI7nOEnSiwMUIgSkMMlF8jyT5hC09zu1N2uF99qMyiuIEFAdxZqOzUVu9rOuNRzcC0eogQkv0kHa0FOJGsyCFBMOovw+M/iCRKgBGnkFil/+GQn3l57RLWtud2FdBsvZT0R392IEpC8KBunGFOozh/UK+R9/WfxBN4/pRuWW9hZ2Yjnl+5BZUObavuS7VVRf+1k8/WeE/jp25tQ3xrbYmQGKEQJSG5zH6wFOZEsP8OGKUOK/LZLOtUp2sA3aAalG4Ya2wyGct5cczjqr51svvuXVfjPuiP4wyc7Y3ocfHcjSkBy6/Hu+ORKPUd+hs1vm152RFt4/e2zywI+b3f8HhrVwQwuzor6ayerfTXNMX39sAOU5cuX44orrkBpaSlMJhMWLlxouO8dd9wBk8mEZ555RrW9trYWM2fORHZ2NnJzc3HrrbeiqSn63QeJegoHMyjUCXo1S3qXfTGDcs6AfNx18ekBn9dkMIU5kto69DMowaZAU+fF+tyG/e7W3NyMUaNG4cUXXwy43zvvvIOVK1eitLTU77GZM2di27ZtWLx4MT744AMsX74ct99+e7iHQpS05AyKtqEWUSC6AYrORUisQZkypCguAuEWgwCl3cmFDKMl1gFK2KXP06dPx/Tp0wPuc/ToUdx11134+OOPcdlll6ke2759OxYtWoQ1a9Zg3LhxAIDnn38eM2bMwJNPPqkb0BCRmrxQYDxcOChx2K0Wv23BMijxshhli0ENSgcDlKgJUnoUdRF/d3O73bj55ptx//33Y/jw4X6Pr1ixArm5uUpwAgAVFRUwm81YtWpVpA+HqEdyODnEQ+HTz6D472cRil478zsWjeZp2iGenLQUAL4ZbRQFPS1A+d3vfger1Yq7775b9/HKykoUFakrya1WK/Lz81FZWan7Ne3t7WhoaFD9I0pmDjenGVP47Lp9c3Rm8Vi6lkGJxshAS4e6j8v1Y/sCYAYl0sTzGeshnogGKOvWrcOzzz6LV199NaJFU/PmzUNOTo7yr6wscEU5UU+nTDNmozYKQ68su982/QyK7/27M0FwNC5s2iGeTLunQoEBSmSJgeCIPjkxPJIIByhffPEFqqurUV5eDqvVCqvVioMHD+InP/kJ+vfvDwAoKSlBdXW16uucTidqa2tRUlKi+7xz585FfX298u/wYc57p+SmTDNmBoXCUJrjv/J1sBqUznSJjUbtgjYQkdf/aecQT0TJQ2YmE/DLK/3LNLpTRPsD33zzzaioqFBtmzp1Km6++Wb84Ac/AABMnDgRdXV1WLduHcaOHQsAWLp0KdxuN8aPH6/7vHa7HXa7f+RPlKw4zZg6Qy+Dovc7JGZQUlM6UYMSheIFhyYQyfBmUNq5Fk9EeUePDVe/7k5hByhNTU3Ys2ePcn///v3YuHEj8vPzUV5ejoKCAtX+KSkpKCkpweDBgwEAQ4cOxbRp03Dbbbdh/vz5cDgcmD17Nm644QbO4CEKwu2W8N6mY9hX4+kb1F1roFDPcEZxJmZN7IeSnDS0dDhxqLYFo/r6p/HFDEpaiv/Mn2CiUbogB+UyZYiHGZSIkhvixcMq6WEHKGvXrsVFF12k3J8zZw4AYNasWXj11VdDeo7XX38ds2fPxpQpU2A2m3HdddfhueeeC/dQiJLOgg1H8dO3Nyn32UmWwmEymfCrq0YE3U+cxRNqgJKdakVDm6d+IRoBinaIJzOVNSjR4PIGggmZQZk8eXJYU8gOHDjgty0/Px9vvPFGuC9NlPRW7jupus8hHooGMYOSagstQHnjtgm4/PkvAUSnSFY7xJPFItmokDMo8dD/hu9uRAlE28eiM/UBRMFYOjHEc3pRpnI7mgFKXnoKfj5jKHLTPX1Qqhvb8f1XVqO2uSPir5mM5IUhGaAQUVhsmoyJXmdQoq6Sm6ABoQco4ohANLpnyDUod150Om67YCBsFt9xfb6zBk8tju3Kuz2FmxkUIuoM7ZuGfuMtoq4pFGb7pIU4xGOC73dTisKoS4dLvbyDNpt4qsUBANhX04QF64/AHes+7QlKzqCYE7EGhYhip7ld3U0ztRMzLIiCETMoqSFm6cTYOSpDPM7AAYpcN3PxH5Z57lvMuHIUZ4aGyzfEE+MDATMoRAmlUROgMINC0ZCXLgQottB+x8RP3NHIXfgyKJ7X0f7ua7OLmw/XReEoej5liIcZFCIKR2ObJkBhkSxFQXl+Or43sR/SbdaQ65xMUc6gfL6zBoAvc6LNoKSYNfcZvIfE6XLj0+3VOLt/Hgoy7b4hnjioQWGAQpRAmtocqvuhpt+JwmEymfBoCP1StF8ji3R8smxXjXJbHuKxmk0wmXyvZdH0BOIU/ND87av9+O3/dmBAYQY+++lkFskSUecwg0LxTL6mhdMrKxSLth4XXsPzIiaTSTWrTbuoIYc/g+twuvHb/+0AAOw/0QwAcMVRq3v+BIkSSJNfDQozKBQ/5CxKpCfQiNmQRiGLqFrU0GxWBUbsshzcA//Z5LctnoZ4GKAQJRBtBsXJqZQUR5QMSoTLZMVLpRiki69itZhU6/JoewaRv4Ubj/lti6ciWf4EiRKEyy35ZVB656TG6GiI/Mm9UCIdN7uEzMhVo/sot1s6fCsZW8wmtDmEAIXZxbAdrm3BQws2A4iPDAqLZIkShNzK22QCPrrnfByvb8MZxVkxPioiH1OUalDkgOfeikHIz7Dp7uNyS2hz+AKWOEgAJJzb/7EOh2tbAcRHHxQGKEQJ4mid542jOCsVQ0qyMaQkO8ZHRKQmF7BGehaP3BVWWwgrcrjcaBUyKi4OfwaVabeqsrLbjzcotznEQ0QhkSQJR095ApQ+eWkxPhoiffI1LdJ9UIwKN//2/XHKbadLQquDAUo4OlzGaxLEwxAPAxSiOLd0RxXG/eZT/OXLfQCAfgXpMT4iIn1Ry6BI6ueXXTykGD+55AwAgNPtZoASBrdbQofTF6Bk2tUDKvGQQeEQD1Gcu+XVtQCAk94alBGlObE8HCJD8iUt0hmUQDNLrN5iCYdLQpswxBONbrY9SbtTnT1Js1lUwz1s1EZEYRvYKyPWh0CkSymSjfDzysGG3od6ud+J06XOoHAKfmDiuQI8NTwiBihEFLbibE4tpvgk1y1EehaPb4VdnQyKd5vDLammGXOIJ7A2TYDS4WSAQkRdVJRlj/UhEOnyDfFE9nkDrQ9j8Q7xaDMoDFAC0wYo2gyKtt4nFliDQpRArGYT8tL1+0AQxVr0phl7/jfpXDTt3gCl3ckAJRRN7U68v+mYKivlcktwuNTnKx7OHgMUogSSnZYSF9P/iPREbZpxgCLZYm835cr6NhbJBrBww1F8tecE3BLw3/VHlO2FmTZUNbT77e9wGk9B7i4MUIgSSHYq/2QpfpmilEGRa1r0YvO+3r5Ah2tbVK3vWSSrdu+/N+puL8pK1Q9QAvRI6S6sQSFKIFmpKbE+BCJD5m5u1AYAfXI9AUpzhwtf7K5RtrsZoITEqOg+UBO37sIAhSiBZDGDQnFMXiywuxq1AUBqikUpHF978JSynRmU0OSkpWCOt9mdSDurJxYYoBDFMe2nwGxmUCiOmZU+KFFq1GZwxeqrs/zDySb/YQvyl5ueghSdE8shHiIKSJtmZQaF4plcgxLp5IUyxGMw9bV3rn+AsnDjsYj3Y+mJctNSMH1Eid92o1WjuxMDFKI4pu1VUMgeKBTHojWLxy0FDlBy0/Qzi9puqeQvzWZB/8IMjC7LVW0f2y8/NgckYIBCFMe062UUZjJAofgV7T4oRt1Nsw0CFHFtmWQWKJMkD+/8ePJpKMtPw9ThxbhsZG/cPeX07jo8Q8wXE8UxbQaFHVAonilr8UQtg6L/uN2q/1m7ud0FZEX0UBLS8fo21f1fXzUcD7+7DYAvQLl0eAkuHe4/1BNLzKAQxTFxbREAOKOY77YUv5QMSoSf1xVkiKepTT9T0swMCrYfb8C5jy9Vbbt5Yn/ldl56/BbeM4NCFMfanb4MyjPfGY3zTi+I4dEQBabUoES4StYdYLFAAEi361/KknmI571Nx/DuhqNYsqNatX3tLyoAAL+4bCg2HKrDJcOKY3F4IWGAQhTH5AzKgMIMXD2mT4yPhiiw6C0W6PnfKINy63kD8NyS3X7bkzmDcve/NvhtM5mAAu/snP93/sDuPqSwcYiHKI7JsxCMxtiJ4okcQNz455Wo1NQ9dEWgTrIAkJOegj98a5Ryf+JAT6YxmTMoeiRJf8HFeMV3PaI41tjmAGA8S4EonojXvqcW7/R7fP+JZsxdsBkHTzaH9bzBimQB4OoxfXDrpAGYf9NYZHiHfJrbk3OacTx0gY0EDvEQxbGGVs8nQHaQpUQgDsHoXSRn/W01DtW2YMXek/j8/otCfl53gNWMZRazCQ9fPgwAsGjrcQDJO8RT00O66DKDQhRHPttRjUm/W4qV+04CABqUDAo/S1D8E4cP9LJ+h2pbAAAHTrbAGUIrdZdbwrG6Vl8NSqAUikDOoGyvbAhp/56muiFyw2uxxACFKI784NU1OHKqFd/762oAwhAPMyiUAHZXNSq3g/3O/nf9kaDPd++/N+Lcx5diT3UTAOMiWS05QFmw/mhSZlGqG9UZlEu9M3WKEqwTNT+WEcUheQ0e+Y2ZNSiUCMQVhIOtG7X/REvQ53t/0zHVfaPFArXEBoc1je1KwJIstBmUkX1ycP/UweiVYAEKMyhEMXLkVAueWrzLcNXVz3ZU4+NtVQDiu5kSkex3141UbuutkCsKZYhHK9QZKA2tDuV2mzP5CmWrGtTvKXWtDgwqzkJueuwXAAwHAxSiGPnOyyvx3JLdmPPWJt3Hf/DqGuV27xz/1VqJ4s13zi7HNd5+Pa4gzVCcnWiWEqhIViQOBSXjEE+VJoMSySnf3YkBClGMHK1rBQCs8BbEBlKUnVipWUpecnDgCrIej9MdfgYl1BqUeyvOUG43JeFU4ypNDcp3x5fH6Ei6hgEKUYyF8qmwf0FGNxwJUddZvTNtgmVQtOtM+T/uH1iYQ7xilRek45z++QCM1+npybQ1KOedXhijI+kaBihEMWa0vgjgebP/4K5JyM9IrLFjSl7mEAOUuhZHwMf1hmYC/a1oZdgths/T09U2d8T6ECKCAQpRjMnvuXqNrcaU52JEn5xuPiKizguUQbEKAUZ9a+CLaLvO34M11BQKfFONk7HdfWMPyRoxQCGKMflToV4hW6JV3RNZAgQo4jTXYBkUvYC9MDP0v4e0FE8GpVVnqKgn+3rPiR7zPTNAIYox+Q398Cn/vhC57H9CCSZQkaxYD1LXGiRA0UxDTrGYkBPG30OqN0CRMzEnm9p7zNBHIN/9y6pYH0LEhB2gLF++HFdccQVKS0thMpmwcOFC5TGHw4EHH3wQI0eOREZGBkpLS/G9730Px46pm+3U1tZi5syZyM7ORm5uLm699VY0NTV1+ZshSkTyG/oRnQAlj7UnlGCsFv0MSpvDhUZhuKU+WIDi1AYo5rBW4k1N8Vze2h0uOFxuTH1mOc5+7FMcOBHeQoWJbkhJVqwPodPCDlCam5sxatQovPjii36PtbS0YP369Xj44Yexfv16LFiwADt37sSVV16p2m/mzJnYtm0bFi9ejA8++ADLly/H7bff3vnvgihBbDtWj6qGNvx3na/Nt8Vswoq9J/HBZs8CZwVCUJLLBm2UYJQMiiZAOanJXjhcbkgBpiK3axqsyUM2oZIzKG0OF5ranDjR1AGXW8KHW44r+yzaehzX/vGrsFdXThR/nHkW/nXbhFgfRqeF3f93+vTpmD59uu5jOTk5WLx4sWrbCy+8gHPOOQeHDh1CeXk5tm/fjkWLFmHNmjUYN24cAOD555/HjBkz8OSTT6K0tLQT3wZR/Dt0sgWXPfel3/a6Fgdu/PNK5f7UESV4Y9UhAIAt1N7eRHHCqEhW7picmmJGm8MNSfLsI2dctLRFsmEkT7yvIwcobtVw0TfHfAsI3vHP9QCAP3yyC8/dOCa8F0gAM0b2jvUhdEnU3/3q6+thMpmQm5sLAFixYgVyc3OV4AQAKioqYDabsWqV/thZe3s7GhoaVP+IEs03x+t1t2sL2iYMLFBup4b5qZEo1oymGctDOmKhrMNlnEHRK5INh93quby1OV2q56pp1F9aoqeQ10B6f/akGB9J10U1QGlra8ODDz6IG2+8EdnZ2QCAyspKFBUVqfazWq3Iz89HZWWl7vPMmzcPOTk5yr+ysrJoHjZRVIS6YFlZXhpe/cHZmDm+HN8a1zfKR0UUWUoGRTN80+5tzJZp9w1bOgJ0k+1ygCIM8TiEDIr8muKsubL8nrOUhJx5yg9jxlO8ilqA4nA48O1vfxuSJOGll17q0nPNnTsX9fX1yr/Dhw9H6CiJuk+oTab65qVj8uAiPHbNSNitzKBQYlGmGWuyI/KFM9Pu+512BAhC/PughDfGkypnUBxuVaZGDlbWHKhVtoXTXyWeSZKkBHYpBkNniSQqa1DLwcnBgwexdOlSJXsCACUlJaiurlbt73Q6UVtbi5KSEt3ns9vtsNu5FgkltkDpbFE4vR6I4o3RNOMOl2co0261wGo2wemWwhziCW9xQbFIVnwuh9PzPKdafEW7jk6srByPxAUY7ZbE/3AT8bBRDk52796NTz/9FAUFBarHJ06ciLq6Oqxbt07ZtnTpUrjdbowfPz7Sh0MUNwJ9WhSFM5WSKN4YFcnKQYLdalYKYwMFBto+KOFSAhSnukhWHuJpF9YC6upwUrwQz2eKNfHfR8LOoDQ1NWHPnj3K/f3792Pjxo3Iz89H7969cf3112P9+vX44IMP4HK5lLqS/Px82Gw2DB06FNOmTcNtt92G+fPnw+FwYPbs2bjhhhs4g4d6tK6+4RIlAqMiWTkIsFnNSLGYvUMv0atBkafo769pwtOLdynb5dcUpzEn+t+myy3hxj+tVAUlPWEGYNgBytq1a3HRRRcp9+fMmQMAmDVrFn75y1/ivffeAwCMHj1a9XWfffYZJk+eDAB4/fXXMXv2bEyZMgVmsxnXXXcdnnvuuU5+C0SJIZQ08lnludE/EKIoMsqgtAsBinzxDDTEo+2DEm4NSnFWKgCgoc2JL/ecULY7va8p1rgkegZlV1UjVgs1NSZTeAsrxquwA5TJkycHbK4T6DFZfn4+3njjjXBfmiih6S1+JirLT8NT3x7dPQdDFCVGGZT2cId4uhg0FGXr1y36MihCgNIDMigiW5hdd+NVVIpkichfoDfjvnlp+OKBi7vxaIiiw2iasXaIB4hugJKaYkF2qhUNmpV95edtF/oPJXoGRdtLqScM7wBcLJCo2wR6E8znmjvUQ1gMWt0rQzwWi98QT01jO1o71BfZ9ghkNdJt/p/B5ZkuPWmIp0kThIlrHiUyZlCIukmgT4u56QxQqGeQh3icAYpk5SEep8uNmsZ2nP3YpyjIsGHpTybjR6+vw5WjSlWzbDpLXjBQ5HC5seVIPd5cc1i1LZH1lIBEixkUom4SMIPCRQGph8iweab3tmgumr4+KL4hng6XG6v3e4o7TzZ34PXVB/H13pN4aMGWiNSF6C0V4XBJmPPWRs2xJXaAos2g9BQMUIi6SYc3nX3zhH6YOb5c9VifvJ7TapuSW553uLLW2witsc2zBo+cEVHXoEiqRQDF7rPNEcgKGK1l1eA9JlnCD/G0O4LvlIAYoBB1E18LajPuuniQ6rGpw/W7KBMlGrme6lRzBxasP4KRv/wELy/bq2QpPBkU3xCPOBvWLNzZf6K5y8eiN8QDeIIkUcIHKMygEFFX1DZ7VlHNS09BSU4q/vI934re/QszYnVYRBGV562nqm91YM5bmwAA8z7aoeokKw7xiP1Nqht8C/jJqx/LOjNrduvRBt3t2jWuOkJchiJesQaFiLqkssEToBTneBpI1QlvwFkhrnRMFO/kDq6aGllNkaxviMctTEeu8v6NAECDJkCpGFoc9rE0GVy4s1LVf28dfk3hEos2g/LgtCExOpLI4rsiUTep8i7vXpLtCVDEmQM9oakSEeAZwrSYTUE6yfqGeMRykEohg9Lovej+bMYQZNpTcPWYyC2FcvRUKwDg++f2x6tfH0j8IllvIPbrq4bj5on9Y3swEcQAhaibyEWD8hj9laNK8ebqQ7hoSFEsD4so4qw6AYpviMeiatQm7rWrqlG5LU9T7puXjhkje0f0+Kob5eFWm+rYEpUcoGSm9qxLes/6bojimEMoEgSADLsV786eFMtDIooKm8Xst7SD3HjNZvEN8XS4JDjdvuGVlg7/oZactM5Pwb9qdCne3XjM8PGcNM8lMNCaQIlA7pabae9Z7QpYg0LUTeRFylJ6SBtqIiNyIzaR3FreppnF0xagIVtaigXjB+R3+jjmXTsSfxaK0bUyvLVf0cygHK9vjXqGpsk7TpbZw2rZ+E5J1E3kcW69N2+inkQvCJd//9WrGbt1Vi32yc+wKdmWzki3WXHJMF9xrVz/JYt2gLL5SB0mzluKa1/6KirPL5OHeLTFv4muZ303RHHMKaS4iXoy3QBFZzXjDpcUMDhIs+k3WgvXe7PPw/JdNbBbLXjsf9v9nr/D5YYkSREvVl+w/igA4+nOkeBwuZXZT8ygEFHYXG5JmXbZlU+ERIlAL0uot5qxZ4jHOIOSEaEA5cy+uZh98SCk29XPly50mo1GHYokRb+25do/fq3c7mlFsnynJOoG4pTiFA7xUA+nl0FpFzIooQ7xRCqDIrtmTB/V/Qwh4xCNqcbdUXq75Wi9cpsZFCIKm7iyK4tkqaezmo0zKHarRcmwOFxSwFWL022RveCm26z4ySVnKPfFACgRpxq7NVO57dae9d7Ss74bojjlEN789N68iXoS7Vo3gLpIVuyD0taNGRRAPcRqs5iVv8doBCjRHuHRtrjvaQ0fGaAQdQOH2/PmZzIBFgYo1MPpBeFy4zabRROgBMqgGKxG3BXiEGuK5lgiTYryII92OYCehgEKUTeQC/BSzOYe9ymHSEs7jNm/IF25bU/x9UFxuKSANSiluWlRPTaL2aQEU07t4kEREO0MinZBxZ6GAQpRN5CnGLNAlpKBNkARh0/CyaBMG1ES8WMzm8UMikmph3G5E6MGpaHNgVe+2o+qhjZlvSIA+MF5/WN3UFHCAIWoG8gZFE4xpmSgnWbc7G1hbzZ5/gaUVvfOwNOM++ZFPoMiHpnVYobF7FtZOdKikUD5xTtb8av3v8GNf16pOnc/mzE0Cq8WW3y3JOoGDmZQKIloMyit3gDFbvXUlCirGbslvzV7RFmpkV9bRhxhtQpDPNrFDSMhGkM8n26vAgDsq2lWApRx/fJ65OzAnvcdEcUhrsNDycSiqbMSZ/AAgNXsG+LRFnqO65cHQF23EkkmIYdiNfuGeKJRgxKNHIpZOLfyDKjUKBQTxwO+WxKFafOROnx7/gqsO3gq5K/hOjyUTGqa2nW3ywFKivf/LUfrUd2o3ndAYQZWzL0YH91zQVSOTYydVEWy0ZjFE4WYRzx+uX6HAQoRYU91E6584SusPlCL7/9tdchf5yuS5Z8c9Xy3nT9Ad7vcQTbFGxTUtfjPQnFJEnrnpEWlBwqgrkExmUzKtP/oZFAiTzx+eYgnNaVnvq/0zO+KKAocLjcqnlqm3Nc2SQr8tb5pxkQ9Xe8c/eJWu/dCGihQj0YtiEg7y18+lkSpQRFnITGDQkQAgA2H6jr9tXKjNg7xUDIwyn7IGZRAfwdRD1Cgfm05gxLtRm2RWjhQVYPCDAoRAcD2451fMp1FspRMUq36AYpT6CZrxB3t7maa2Ki7ZvFE4um3HKlHbXOHcl8JUAzOd6LjuyVRiPbVNPlt0y7WZYTTjCmZGH2il4MPbT+gb4/rq9yOdgYlL92mui8fS1Q6yQq3I/F9XfHCl6r7vgwKAxSipHasvs1vW6CFzkQOFslSEkkVhnjunjLI94D3Gq0N1LOFfidRGGlRmTKkCDeeU4Z5144E4BvicUahUZsoGoGXXAcXrYLiWOO7JVGIWjo8bwY/Fzo2NreHGqCwkywlD3HIIS/dF3zIBZ7aQD3DblVuR6v/iXgM8649EzeeUw4Awlo80Z1m7Ori0JVeDcu+mmYAQP+CjC49d7yyBt+FiABfMDKgMAPpNgtaOlxKh8xg5GnGNg7xUBIQMyQZNt9lRm7gpi2SzbRb8a/bJuC9Tcdwd8UgdCdrNGfxCIM8ri5maPQ67m47Vg8AOL0os0vPHa8YoBCFSM6gpNstSoDS3BHaVGN5iMfKacaUBMQVu8Xhh0AZlImnFWDiaQXdc4ACazSHeCKYQdH7MORwSSjPT8egHhqg8N2SKERyBiXDZkW691NhS4gZFN8QDzMolBwuGVaMPrlpmDK0SNkmxyXafkAZ9tjVUESzUZsYlHQ1Q9NqsKjiyD45qt4oPQkzKEQhkjMoGd4MirgtGHl8O9D0SqKe5E83j4Vb8gUAgG+IJ8WqvqDGchaKPBzlikINilij1tUAxejDUF5G5BdUjBd8tyQKkbxkfLrNCrvVt1x8KJhBoWQjtpEXtwH+Q53WGGYALMrChZHPoDS1+1r5d2aIRyyM/dfqQ7r7aKdN9yQMUIhC4HC5lWAkw2ZVlo0PtFS89usBTjOm5HZm3xwA/pnEWM5ui2ajtiZhOYxwi2Qb2hyY/OTneOTdrQCAv365X3e/XAYoRMlNXtTMZPIM8chrirSzDwpRUB/cNQm3XzAQ908dDMA/k5gewz4e1ijWoDS2CQFKmBmUf606hIMnW/DaioMB9xtRmt2pY0sErEEhCsHJZs+S8HnpNlgtZuUTYKhDPPIMgVimsoliZUSfHIzok6PcT9PUnIwtz+vuQ1KkeIdr2wyKULuiSQhQQn2vkM37aIfq/pl9c7D5SL3ffmf3z+/cwSUAfpwjCsGJRs/6F4WZnnSqL4MS2ptOh5xBsfJPjkicdXLZyN4xnYXSOzsVAHCsrjXizy2ueH6iqT3kr9MuodHmcOkGJ6P69twZPAADFKKg3G4JN/11FQCgIMMOAJ3OoKT04DcTos6oa+0IvlMUleV7Otceqm2J6PO2OVyq94dKnaUyjNS3OlT373lzg+5+YgfenogBSogOnGjGR1uOR2zJbEocB042K7flplPhFsn+Y6VnHJk1KERq5fmxbdNekuPJoNQ0hp7hCMWP/rlOdf/9zcdC/lp5SFn28bYq5XZxtl25nW5jgEIAJj/5OX70+np8vrMm1odC3UzsP3DBoEIAgM0a+hCPU1j9TP60RpTs3r3zPNw8oR8e8BbOxorcgyXUDxuh+kxzrfjmWEPIX3uyST+r9LMZQ7DqZxUoyPAMNU8fUdL5A0wAPTv8ioINh07hoiFFwXekHkOsxL95Yn8AUPqghDKL51SLL117xajSyB4cUYIaVZaLUWW5sT4MpVGbI8rLKNe3OpRC3GCN6U42+wcoZhNw+wWnAQD+d8/52HykHhVDe/a1KOwMyvLly3HFFVegtLQUJpMJCxcuVD0uSRIeeeQR9O7dG2lpaaioqMDu3btV+9TW1mLmzJnIzs5Gbm4ubr31VjQ1NXXpG4km8ROwPYYdDxNJXUsHfrdoB27+6ypUNYQ+9hqP5F4Go8pylcZTtjAatdV632zy0lP8GlcRUWzJ9WRygOIUeh5FUrvTjTGPLsZFT37uVwSrpRegiF9SnJ2KS4YVq9Y86onCDlCam5sxatQovPjii7qPP/HEE3juuecwf/58rFq1ChkZGZg6dSra2nwXqZkzZ2Lbtm1YvHgxPvjgAyxfvhy3335757+LKBN/WbQXmK/3nsBP397kV9SU7G766yq89PlefLH7BB77cHusD6dL5G6Q2am+hGM4NSjyeHJ+Rs9tqESUqMQPG5IkYcZzX+CCJz6LaEZFjiNaHS4cr29Tze4RrTtYi+eX7I54PUyiCnuIZ/r06Zg+fbruY5Ik4ZlnnsEvfvELXHXVVQCA1157DcXFxVi4cCFuuOEGbN++HYsWLcKaNWswbtw4AMDzzz+PGTNm4Mknn0RpafylwPef8BVJNmt+sb77Z8/sjo+2HMe2R6d163HFs61HfeOte2viNzsWivv+vQkAcOSUbxqiUoPiCP4mJr/ZFGTag+xJRN0txeJrdd/udGNXlef96uDJZpxelBWR18hOTVF9iG1zuJCT5r+GznUvrfAekyeiufOi0/DiZ3sjcgyJKKJFsvv370dlZSUqKiqUbTk5ORg/fjxWrPCc+BUrViA3N1cJTgCgoqICZrMZq1at0n3e9vZ2NDQ0qP51p0+/8VVQNxhkSpo7XMyiGDh0sgWXPfcFfrFwS6wPpUvET1SZ3myKuNaGkR2VjQCA03vokuhEiUwOUDpcbtUHjg5nZGZsvvDdMX7BiN7Cfw8v3KrcltcF6snr7IQiogFKZWUlAKC4uFi1vbi4WHmssrISRUXqwh6r1Yr8/HxlH6158+YhJydH+VdWVhbJww7qo62+49pT04RXvtqPg96pp+IvHtNyHtrx1cZ2J7Yda8A/V+ovdhXv5J/x/cJsg1zvtrqW4AHKPm8GaXBxZD6NEVHk2ITmiU3C6uRtIS5joUd8D5wwsMAvQGnVCVDkVgSizB7e5ySYhJhmPHfuXNTX1yv/Dh8+3K2v39Dmuwh9teckfvX+N3jov55sgFvoi3KqJbYNh+KFmEnKSlX/gWmHyBJBhrf3Sf8CX7+G3HTPG04oWbNW76cy7bkgotgTFy4UW9N35b1KDG7SUiz+AYojtOdOi+EaRfEgogFKSYlnTnZVVZVqe1VVlfJYSUkJqqurVY87nU7U1tYq+2jZ7XZkZ2er/nUnvTUath6th9PlVk1BrdWpvE5Gtd5ALctuxdDe6p9VImaZHN5PQ+ICZ7lpntTrjspGfLj5OD7Zpp/9A4B27++PjW3uieJOivB3La4+/PySPZ1+zub2wAGK3hCPnnSbVWlpkIwi+p0PGDAAJSUlWLJkibKtoaEBq1atwsSJEwEAEydORF1dHdat83XZW7p0KdxuN8aPHx/Jw4kIh8utjAeKGtud+NcadSanjhkUAMApeVpthg1FWerC0Jow1qOIF/Iy7GIXWPEN58431uP2f6zDnupG3a+X1+HRLjFPRLFntZghT85sFLLlqw/Udvo55ax7VqoVZrMJ2UECFKMO5WkplqQOUMLOOTc1NWHPHl9kuX//fmzcuBH5+fkoLy/Hvffei9/85jcYNGgQBgwYgIcffhilpaW4+uqrAQBDhw7FtGnTcNttt2H+/PlwOByYPXs2brjhhricwRNohcsNh06p7lfWJ97FV4/T5cb8ZXsxfmBBp1bKrBUCFO0nh+qGxDtHcnGsOMW8NDfVb78DJ1pUVf97qhvxq/e/wYZDdQCYQSGKVykWM9qdblUGpTOWbK/Csfo2jCj1ZI6zUz3vf8FqUIzaFaTZLJ7eW22JNzQeCWEHKGvXrsVFF12k3J8zZw4AYNasWXj11VfxwAMPoLm5Gbfffjvq6uowadIkLFq0CKmpvjf0119/HbNnz8aUKVNgNptx3XXX4bnnnovAtxN5rQECFG0DsoO1zQZ7JpbF31ThyU92AQAOPH5Z2F8v1+IUZNjw/84fiH+vOQynNwtR05h4Tdt8C/35AgyrxYwZI0vwvy2+oR2X8ClIkiRUPLVc9TwMUIjik00OUIRAIK0TTTlv/ftaAMDc6UMAQMmcaAMUbe2aUYCSbmMGJSyTJ08OuGCeyWTCo48+ikcffdRwn/z8fLzxxhvhvnRMtHX4Pj27NLNTjtd5LrZDe2dj+/EGHDoZ2dUwu5skSbj172uxdIevRqiupQO5YU51O+FdRyI3PQUDCjOw+ucV+MMnO/H6qkMJOcTjdHt+B8QaFMB/yEb8u1i++4Tf8yTzGw1RPLNZzUC7ugalzemC2y3BHGL3Z/Hvf95HOwD4mjue1ku9IOJhzcrJ7QYfhNNtFvzwwtPw8MKtuHRYse4+PRnfMYOQMyi5aSl+n4D3eRu4DfT+8tUmeA1KTVO7KjgBgAOdCLrWeMduz/BOq83PsKE0Nw1AYg7xOHWKZAH/lYnFWqUl29WF4gBgsyR3RT5RvJL/lsVJD5IEw46vetp0mjZmeYd4LhpShAvO6KVsP6gNUIQMym+uHqHctlstuGl8OT64axKe/+6YkI+lp2CAEkSrsLjTBYN66e7TN89z8W1M8HHClnb/KL62ObyAYuPhOmXF54uFRRV7ebuoJloGxeWWIH8wsprVfy7agLVF6KHw2gr/ngb2FP65EcWjFKvnw4e2BqWh1QFJkkJqJyD+/cvkzzApFjNeu+UczL9pLADgpOZ9UF50NC89BTPHl2PGyBKcP6gQxdl2mEwmjOiToyyvkUz4jhnE/hOeJluZdisev24kpg33nwpdlpcOQF0BnogqdRb1O2Gw7LeRN1d7mrFdPbpUyaAAQC/vbJ5Em2Ysdo/1G+LRBChN3gDPaAiUs3iI4pP8t9mk+ZDZ0ObA44t2YNSvPsHKfScDPofe1OF0m7qKIsNu0d1Xzr7YrRaYTCb8ceZY/OPW8T1+McBg+I6pY+2BWvzli32QJAnLd3lqCSYP7oXCTDvm3zzWb3+510ebwx31JbujpandiRv+tNJv+7G6Vp291TznqQbN7U5sPFwHAJgxsrdqn0QNUMS6o5QgGRS5sZPRTAAWyRLFJ3mIp0VTC9LQ6sTLy/YBAH753raAz6EXoEwYqJ4FmeHtDFvT2K76ICPPFmWWVY2tLXVcP9+zblBZfjqOei/Qw/vkGO4/tLcvU9DY5kzIVWs3eqfCan20pRL3VpwR8GvfXHMYcxeo19kZXKJu6y73QznR1A6XW/JbFTpeOYW6kmBFsnLwZZQOZoBCFJ/kv01tWwmxi/iJIMPTzZohnj65afjWWPWyLBnejMrJ5g48+N/NeOL6UQB8rRnCnZDQ0/EdU0OMar/ecwKr93sKPktz/PteAMDYfnlIt1mR7m1JbLSYYLwzyiTurm7EPW9uwP1vbzL82icW7fDb1kvToC0/wwaTCXBL8dtxd/6yvfjFwi2qdTQcbmGIxxw4QDle7xkiM1qfhwEKUXyS/5b9ApRWMUAJ/L6lreG7Y/JpfjOA5CEeAHhr7RHldrX3w00vrniuwndMjQ+3HFdu/10odCwvSFduy1XWT1x3Jv59+wQAQEGmJ/JNtCJQmdGnfrcEvLvxGN5ed0TpECtqaHPoFgdrx16tFjMKMuJ3mOfFz/bg8Y924J8rD+Gmv/pW1ZYzKBazyW88WBtwHK/3ZNuMgtT0TvRVIKLoSzEKUMKY+KCtUUnV+UCSoXlf/GDzMXy5+4TynliUzQBFxCEegcstYfYbG/y2Tzq9EEVZvgzKTRP64ZoxfZTxRADonZOGw7WtIdVsxCPxU//AwgzsP9kMba1nY5sTeZrhq+3HGpRpuMH0yrLjRFM7qhvbMAzdu55SIC63hN9/vFO5//Xek1i0tRIr953E9WP7AvDPngD+Acq2Yw3Yf6LZrwbl8WtHYvzAAlhZJEsUl1KUIR51DaHehzIje6qbVPdTdT6QpNvV2+TrzY3nlANgBkWLAYpgb02T7vaHLx/mty1Dswy2PAQkp/kTTV2r5w/x+rF98eS3RqGhzYFnP92Nv365X9lHL8uyR+ecXTVaf8mCXll2bD8efxkUvaLWO/7pWStKXgJd2/ME0B+y+fbLK/DA1MGqbTd433yIKD4ZDfEsWH9EdT9Q4zbtBAm9j21GU4UPnvT01NKu2ZPs+JFOsOVIve72Pt4+J4H09jYiq0zQAOWkd3xVLvDNTk1BSba67kYvQNld5R+gPP3t0bqvIRfKxtswWKBl1eVZPHpFvXk6BW01je24/z+blftzLglcYExEsWfz9kGR+14Ve4dajmnez08FaMbZoQlQjNZxe+fH5/pt+3qvZ3go085hYBEDFMG1Z/XBFw9chN9ff6aybVy/PGTagyea5AzKq18fCLgUQLyS1xUSVx9Otan/WBo0fV4kScKrXx8A4GvKNqY81/AThlw4G0/dZDucbry8bG/Q/VIs/t9TxdBi/Pqq4boZNsBTeHzXxad3+RiJKLq0NShy52utZp1mlrIOzXo6RteBMeV5hs+hzcwnO54NgclkQll+Osry03HFqFK8seoQLglx/QNx1sr6Q6cwtl/4qwDHypFTLfhgs6c4uEjImmgXy9qnGc658431yu17Kwbh7imD/NacEMnjq9VxtGDgW2sPq4qh3/h/4/Hdv6zy2y/N5v/JxmY14+aJ/XGsrhW//uAbv8cHF2clfaMlokTgG+LxBBmFBrUgLQ7jbKs8xNOvIB12qxmXn6k/1A141tjR65vCAEWNGRQDqSkW3DJpAMry04PvDODMvrnK7daOxGrW9qfl+5TbfXJ9AUqqpmnQk5/swuJvPGvMbD5Sp1rJ98y+uRhdlqusPaFHXrNo+/HGiBx3JIjDVhk2CyaeVqC739AS46Jeo+nD2sUliSg+yUWy8hBPgUEvK72gQiavxfXLK4fjk/suDBhs/OcO/2EeACFl65MJA5QIKc1NQ1665+LsdCdGgPLVnhO4/Pkv8E9vIWivLDvGlPnSj3rLjW84dAoAcFBYRPDmCf1Cer3RZbkAoDvTJVbEtGxzhwsmkwnvz56EH14wEFcLxb7jB+oHLoB+AS1gvIQ6EcUXOYMif6gw+pveVdmIO99Yj81H6vwek99LQlnSYlip/gceBihqDFAiqF+BJ0Mgdh+NZzP/sgpbjzZA/qA//6azVPUjegGK/AlCLCx9cPqQkF4vN92mPGetQdMjp8uNdzYcwXubjuH6l77G1qP6hcuR0ub0fSIaUOj5+Y3sm4O5M4aif6FvuGrqcOOhPrtBBkXbrI6I4pM2C2oxm3QL4x9asAUfbj6OK1/4yu8xeYjHKLgJBQMUNQYoESQXUrY6XErTrnilLegqy0/D8FJ1O3+xSHZEH0/E/+rXB7DmQC2avYHKFaNKw/qjkmcJfbytUnfG09vrjuC+f2/C3f/agLUHT+Hy578M+bk7o13oe/DsDaNVj4k1bsXZ+p2EAeM3JLHYmojilzbrYTaZ8NlPJuO1W87BX743LqTnkGfxhNoxevwA/zrF3HROMxYxQIkg+UJ17783YuK8pVixN/Dql7F0qLZZuT2wMAMf3HW+X2OhVGHOvliD8cN/rEOLN4MS7rQ4OUB57H/bMWHeEnQ43ZAkSWkvvyrIiqGRJg/DzLnkDFUdEaDuaxDoU5HeJ62z++dhYK/MyBwkEUWVtgjeYvZ0D7/gjF6oGFaMiqFFQZ9D/tCnN+NPzys/OBtv3j5BNRGDGRQ1BigRlKIZx/zHygMxPBp/aw/UKgHA2951IEwm4MO7z0eOToMgSWg1dKa3fgTwrKUjZ1C0Le2D0XaivfKFL/Gt+Ssw47kv4HC5dYdFJv1uqV/GJ1zrDp7SbRDX7h3i0Rumucg7ddqoYC6QnDQu+kWUKDI0AYq2VUKazvucdhqx/IEmlBoUwPPeOWFggbKOGwDO+tNguBZB2sh5QKHxlNvu1trhUlZp/uCuSXjZO3NnypAi3Sm0AHBar0wUZtqRl56C7FT1r4rcdVf7hx2MNhDaUemb0bO3pkm3GdyRU61Yse8kLjyjV1ivJdtw6BSue+lrFGXZsfrnFarH5AyKXlvqs/vn478/Ohf9CkKbySUSZ0MRUXxL12QuLJpAQW8drXanW/W+Ic/iCbcGxcygxBAzKBGk/cW0muPn9IrZg1e+OqDcvuviQYZfk5piwfIHJuOje87HhIEFqmnHciYm3Hn7gRbMO17XhoZWz9DROf3V47MnQmiPL0kSlmyvwklNp9rPdtYA8KwY6tZM/W13GGdQAM9q1UY9EQIxavRERPEnXfNBS7v2lt6HuFbNlONwa1Bk3xrnWe9rtJClJo/4uYL2ANoApdWg1XEsiO3lD9d6pgjfcHYZRgX5o0i3WWG1mFGcnYpN/3cpJgz0BA7yKp/aTx7BGGVrAE/DOLlb7cwJ5RhSkqV7/EY+3V6NW/++FlOfWa5qMy2+1xzVLOYoZ1DsKZH9Uyjgol9ECUO7yrB2iEcbwADq93dJkoQalPDeS849rRCf3HcB3rhtfFhflwwYoESQVTPEs/14Q9y0va8RurceOeUJULRFocHYrRYUZKgvvOEO8ej9ocsa2pw47D22Xpl2/PdH5yoLD4bSHn/tgVoAwImmDry19rCyvUr4WnloqrHNgTlvbcQXu08AMF7EK1TaQtlwP0URUexoR1m0wy5671ti0zZxRfdQa1BEZxRnhV3Plwz4LhpB2l/ML3afwB8+2RUXQYo4xCMvgJWfEf6Utuw09R9R2EM8AQKUJz/ZicO1ngzH8D45yLBbMX6Ap0GaXj+Uf685hP4PfYib/rIKbreEnVW+epb5n+9VitaqG3zBmbwk+u8/3okF648q2zvzpiJa/sBFqvspBusREVH80RbhOzVDwXpFsmKWtkVYoydQlpjCwwAlgvRSey98tgfPLtkdg6NRq9ap4cjVWY03mGxNK3ttajQYa4BAQIzj5GJaufX8xsN1foHeg//dAgD4cs8JDPzZ//C5t9YE8ARhL362BwBQJWSP9tZ4ple/Jqy/I75OZ2Vpioi70qyJiLrXiD7qHlAOzcrEwTIoTR2eIW+bxczsaQTxTEaQ0UXpmU+NA5R2pwu3vLoGf/lin+E+kaA3xXZgJ2YZZWtm4aSH2Qelsc1/lo6WOK23MNNzu8PlVupeAKAuwLLnMvm8V+sM8Qwq8vQouXJUKfb9dkaXF+lK0RREa4f7iCh+leam4dM5Fyr3nSEEKGINitxZOyPM90MKjAFKBIXaoEe0YP1RLN1Rjd98uD0KR+Sjl0EpCtAd1Yi2/X24GRS9acRaC37sW0hLfP5bXl2j3F5z4JTu1+akpeAH5/VX7rc7XTghFNjuON6ANocLu71DPfdUDPIriOsMvxoUZlCIEsrpRb7Gig7NciXiLD+55UKD8F7WpAQorCOJJL6LRlCgtL4kSXh341Gs1HRKDSWjEAnaDMqL3z2rU8+jne0S7ieGa8/yTKk7u79vUcJBwhtDhs2irGkEqKvp1x30BSXyTCStn80Yggen+dYG+nrPSWWtoXSbBQ1tTnz7ZU8/mMJMOwYURKZXjTY4DTSURUTxTTvEc0ZxFkwmz4rsU4eXAAC2HWtQHm9WOmszQIkkns0IEgOUM/vmYPMRX2Hnyn21uOfNjQCA9Q9forR876762WpvHcZvrh6BK0eX+tWShEo72yXcDMpZ5XlYfv9FKM6xY3dVE9YcqEVNY7uS0XjbYBlyrdpm/yGeGSNLcN1ZfWG1mJGVakVjmxM/8GZdirLsKM9Px9qDp5Sfy+Vn9o5I9gTw7wDZmWwaEcUHbYAysFcmlv30IhRl2/H22sN4e90RZbgYEId4eEmNJJ7NCBJnuNx98SD8v9fWKvfFYYaqhjYlQHF3Q4Didks44V09uGJocaeDE0A9fTbDZunU4lbl3s6sI/rkYESfHCzdUQXAM9XPaBlyWWObA83tLpxs9n0/f/j2KL8OtXnpNjQKNSuFmXa/oOa+S84I+9hDxSJZosSltyK9/L4l1+G1dPjeX5q8s3gYoEQWz2YEib+c2mJSMSIXL5zubkih1LZ0wOWWYDIBBZldWyNGHIs9vSgzImtHXDS4CH/+3rigwQkAjPzlJ0hNMaNvnufN4sLBvXTXEXr6O6Nx3UtfK/c7XG4MLc3GvhO+RRL1vi5SGKAQJS5HgE+Och3eV3tOYu2BWozrn698AC3sxLpdZIzvohGkKqTS9AsRA5QGg0LRaPVLketP8tNtXb5wit+jdmpeZ5lMJlwyrBh9DNrDL7r3fNX9Nodb6WfSL19/nZwxZbn4zrgy5f41Y/rg11eNwIVneAKaF747JiLHboSzeIgSl3YWj0jscyKvb1bl7bXUmYkHZIwZlAgSF47S1maIVeGN7b4ARVwbxumWwqpd2FnZiOJse9B+JvIMHr2VgsMl1qCcMyA/wJ6RM6REP7NSmGnHpNMLdR8zm0343fVn4u6KQVi2swbfHuepTfn7LedE81AVnMVDlLi0NSgivSnH8ntsUQTeY8mH76IRVCJEz9rF54yHePT3CWbbsXpMfWY5Lnjis6D71kQyQBFm8fTqxvVm9AKLgb0ygha59slNw3fHl3f7rBpmUIgSz9ThxQCAW84bYLhPWor6w+dTn+zEh5uPAwAKGaBEFDMoETSqLBdzLjkDpblpfkMpRkM8EnwRisMpASEOYX62o9rzXEKwY0SewROZDIrv+8rpRIFsZ114Ri+MH5CPVftrlW3idOJ4wxoUosTz0syxONHUHnCoRptBeW7pHuV2uGuTUWB8F42wu6cMwvVj+yLFL4PiC0TaHL5gxSWkUOSF8gJpc7jgdLn91o7QI1eZ1yjpx66Pj6oClCgWmep55obRmObtQQAAZ5Xnduvrh0PbWZaI4p/ZbApaRxJoPTGuwxNZfBeNEm0tiRhQtDtdutu//8oaBHLwZDPOf+IzXPvS1+jQmQYnenrxLoz+1WJ8sq0Sn273TOONRAZFrOPtzFo+XdE7Jw0v3XQWHpg2GH+6eWxEZhBFC4d4iHqmzFTjgQeuSBxZDFCiRFsk6XT7AhExgyKu5yD2StHz/NI9qGlsx+Yj9aqVNPU8t3Q3Olxu3P6PdcoKwZ1ZvVgrX5hGF4t0pslkwo8nn45LhUxKPNK2vieiniHdZsWvrhxu8BgzKJHEACVKtJ/u1UM8nuDC7Zb8VtUNZO0BX/1FQ5AW+XozloeXdn1acEGmHW/ePgEf3DUprjMYsZaawjcqop7qxnPKdbdr1yqjrmE+qpuoh3g8t1cLAUcwNY3tOHDSV6OyYP1R5bbT5Q46S2VAYQbOKM4K+fUCmTCwICLP01M9MG1wrA+BiKLIZtV/v2Un2chiBqWbvPr1AeW2nEHRW2F4+a4a3a8/Xt9q+NwdmunJesM/N55T5reNIuu1W87B9yb2CzhFkYh6hsE6H/g4xBNZDFBioM2bQdHrHPu9v63GfqEdOwD8a/UhXPnCV4bP99iH21X3T+ospHfpsPiu2egJLjijFx69agSHd4iSwM8uG+q3Tdv/irqGZzOKnrtRv516uzfDIQ/7aEs51gi9PiRJwtwFWwK+zuurDqmmK5/Qycxo1wYiIqLOKxAmDJxRnInfXD2CdXkRxgAliob11m/RLmdQ5KEZ7erC4syeeoN1e7R2VDbgpHcW0M6qRr/HM+z8VE9EFCnFQr+Un146GDdN6BfDo+mZGKBEkdG6Om0d6gyKdmFBMUCp0cmG6LnsuS8x5allqGvpwAP/2ez3uLiGDhERdY2YQWkN0vaBOocBShQZtTvff6IZp5o7lAAlXbO2Q1snAhQAqGtxYPSjiztxpEREFA5xHbBItHAgfxEPUFwuFx5++GEMGDAAaWlpOO200/DrX/9aVRAqSRIeeeQR9O7dG2lpaaioqMDu3bsjfSgxp9dNNC3Fgg6XG1/sOaEEKH3z0lT7iNF4bYt/wWsonvnOaAwszOjU1xIRUXBfP3QxFt55Hk4vyoz1ofRIEQ9Qfve73+Gll17CCy+8gO3bt+N3v/sdnnjiCTz//PPKPk888QSee+45zJ8/H6tWrUJGRgamTp2Ktra2SB9OTGnXY/nlFcOUefJ3/2uDsoBgn7w0LPjxubjszN4AgHax02yHf+rw43svCPraV44qxVWj+wDwD4CIiKjrSnPTMLosN9aH0WNFPED5+uuvcdVVV+Gyyy5D//79cf311+PSSy/F6tWrAXiyJ8888wx+8Ytf4KqrrsKZZ56J1157DceOHcPChQsjfTgxpV0w8NqxfVXt7OVMic1ixlnleUpRrRiUyPtYzSakpVjwx5lnIUtYC2LGSP/pw7+//kyYzSbcdfHpmHftSMy/aWzkvikiIqJuEPEA5dxzz8WSJUuwa9cuAMCmTZvw5ZdfYvr06QCA/fv3o7KyEhUVFcrX5OTkYPz48VixYoXuc7a3t6OhoUH1LxFYNeuxaNsgH6v3ZIzkroTyHPo2YTFBOVi5cnQptv5qKmaM7K0KULQrFPcrSMe3xnmaspnNJtx4TjlG9OH4KBERJZaI9+V96KGH0NDQgCFDhsBiscDlcuGxxx7DzJkzAQCVlZUAgOLiYtXXFRcXK49pzZs3D7/61a8ifahRpy2STbGYcd7pBfhqz0kAwJFTnu6wcoAiL9Wtl0FJS7EoC9BlCCtm5qb7pihPG16CX189ItLfBhERUbeLeAblrbfewuuvv4433ngD69evx9///nc8+eST+Pvf/97p55w7dy7q6+uVf4cPH47gEUeP3oq2f/jWaOX28TpPgCIHMqneqcBtTv/VjsXsi1g9Lq6Lc8mwYvTKskfgyImIiGIr4hmU+++/Hw899BBuuOEGAMDIkSNx8OBBzJs3D7NmzUJJiadmoqqqCr1791a+rqqqCqNHj9Z9TrvdDru9Z1x4S3JScVZ5LtYfqlPW4rFrMihtQgZFvp2mWePhq4cuRkOrA6f1ykSf3DSYTMDkwb2641sgIiKKuogHKC0tLTBrZq9YLBa43Z6swIABA1BSUoIlS5YoAUlDQwNWrVqFH/3oR5E+nLikbZp2mneKWmqKTg2KN4OiXd+lT24a+uR6Zucs/emFus9LRESUqCIeoFxxxRV47LHHUF5ejuHDh2PDhg146qmncMsttwAATCYT7r33XvzmN7/BoEGDMGDAADz88MMoLS3F1VdfHenDiRviEg32FHUAd95phQB8QYi6BsUT2GkLbEUMTIiIqKeJeIDy/PPP4+GHH8aPf/xjVFdXo7S0FD/84Q/xyCOPKPs88MADaG5uxu233466ujpMmjQJixYtQmpqaoBnTmzijB5xxUuTydcSXw5Q9GbxcBlvIiJKJhEPULKysvDMM8/gmWeeMdzHZDLh0UcfxaOPPhrpl49bVmHYS8x42K1mZQVMOUtyuLYVR061oG9eutL2XluDQkRE1JNxLZ5uYpRBEWtLxNtzF2wBALR0OP0eIyIi6ukYoHQTcWqwGGyIwYpYZ3KotgXbjtVj/aE6v8eIiIh6OgYo3aS+1aHcFoMScbgnVSietZpN+Pk7W5X7HOIhIqJkwgAlyh6YNhgAcPfFpyvbxFk8RsM9JpNJqT8BmEEhIqLkEvEiWVL70YWn4YJBvTCkJEvZJmZN6gwyK9mpVjS3+wIU1qAQEVEyYQYlykwmE0b0yYHVop81qWlsV+173ume1vUFmerOuRziISKiZMIAJQZKcoz7vXzbuxJxS4cTTrdvTZ5UK39URESUPHjViwG5RT0ATDq9UPWYXGvS0uGCW/JtT7dxNI6IiJIHA5QYGF6ag+xUT8Dx1++PUz2W6d3e2OZEY5unB8r9UwdziIeIiJIKP5bHQJrNgi8evBhWs8lvHZ28dBsAT21KQ5ungFYe9iEiIkoWDFBiJCctRXe7HKDIfVNSLCYUZNi67biIiIjiAYd44kxuujpwcbgkVRdaIiKiZMAAJc6kpljYlI2IiJIeA5Q4lJXqG3m7Z8qgGB4JERFRbDBAiUM2oefJpcOLY3gkREREscEAJQ6JnWZTLPwRERFR8uHVLw6JU4+tLJAlIqIkxAAlDomrHTODQkREyYhXvzhkE4ISq4UZFCIiSj4MUOKQjTUoRESU5Hj1i0Ni3UmKmT8iIiJKPrz6xSGLEKBwiIeIiJIRA5Q4ZDYxQCEiouTGACUOiQEKh3iIiCgZ8eoXh8QhHi4USEREyYgBShxK5WKBRESU5BigxCFxsUAiIqJkxAAlDjFAISKiZMcAJQ4xQCEiomTHACUOndk3N9aHQEREFFP8qB6HJgwswNPfGYUBhZmxPhQiIqKYYIASp64Z0zfWh0BERBQzHOIhIiKiuMMAhYiIiOIOAxQiIiKKOwxQiIiIKO4wQCEiIqK4wwCFiIiI4g4DFCIiIoo7DFCIiIgo7jBAISIiorjDAIWIiIjiDgMUIiIiijsMUIiIiCjuMEAhIiKiuJOQqxlLkgQAaGhoiPGREBERUajk67Z8HQ8kIQOUxsZGAEBZWVmMj4SIiIjC1djYiJycnID7mKRQwpg443a7cezYMWRlZcFkMkX0uRsaGlBWVobDhw8jOzs7os/dE/D8GOO5CYznJzCeH2M8N4El0vmRJAmNjY0oLS2F2Ry4yiQhMyhmsxl9+/aN6mtkZ2fH/Q86lnh+jPHcBMbzExjPjzGem8AS5fwEy5zIWCRLREREcYcBChEREcUdBigadrsd//d//we73R7rQ4lLPD/GeG4C4/kJjOfHGM9NYD31/CRkkSwRERH1bMygEBERUdxhgEJERERxhwEKERERxR0GKERERBR3GKAIXnzxRfTv3x+pqakYP348Vq9eHetDirp58+bh7LPPRlZWFoqKinD11Vdj586dqn3a2tpw5513oqCgAJmZmbjuuutQVVWl2ufQoUO47LLLkJ6ejqKiItx///1wOp3d+a10i8cffxwmkwn33nuvsi3Zz8/Ro0dx0003oaCgAGlpaRg5ciTWrl2rPC5JEh555BH07t0baWlpqKiowO7du1XPUVtbi5kzZyI7Oxu5ubm49dZb0dTU1N3fSsS5XC48/PDDGDBgANLS0nDaaafh17/+tWodkmQ5P8uXL8cVV1yB0tJSmEwmLFy4UPV4pM7D5s2bcf755yM1NRVlZWV44oknov2tRUSg8+NwOPDggw9i5MiRyMjIQGlpKb73ve/h2LFjqufocedHIkmSJOnNN9+UbDab9Le//U3atm2bdNttt0m5ublSVVVVrA8tqqZOnSq98sor0tatW6WNGzdKM2bMkMrLy6WmpiZlnzvuuEMqKyuTlixZIq1du1aaMGGCdO655yqPO51OacSIEVJFRYW0YcMG6X//+59UWFgozZ07NxbfUtSsXr1a6t+/v3TmmWdK99xzj7I9mc9PbW2t1K9fP+n73/++tGrVKmnfvn3Sxx9/LO3Zs0fZ5/HHH5dycnKkhQsXSps2bZKuvPJKacCAAVJra6uyz7Rp06RRo0ZJK1eulL744gvp9NNPl2688cZYfEsR9dhjj0kFBQXSBx98IO3fv196++23pczMTOnZZ59V9kmW8/O///1P+vnPfy4tWLBAAiC98847qscjcR7q6+ul4uJiaebMmdLWrVulf/3rX1JaWpr08ssvd9e32WmBzk9dXZ1UUVEh/fvf/5Z27NghrVixQjrnnHOksWPHqp6jp50fBihe55xzjnTnnXcq910ul1RaWirNmzcvhkfV/aqrqyUA0rJlyyRJ8vxhpKSkSG+//bayz/bt2yUA0ooVKyRJ8vxhmc1mqbKyUtnnpZdekrKzs6X29vbu/QaipLGxURo0aJC0ePFi6cILL1QClGQ/Pw8++KA0adIkw8fdbrdUUlIi/f73v1e21dXVSXa7XfrXv/4lSZIkffPNNxIAac2aNco+H330kWQymaSjR49G7+C7wWWXXSbdcsstqm3XXnutNHPmTEmSkvf8aC/AkToPf/zjH6W8vDzV39WDDz4oDR48OMrfUWTpBXBaq1evlgBIBw8elCSpZ54fDvEA6OjowLp161BRUaFsM5vNqKiowIoVK2J4ZN2vvr4eAJCfnw8AWLduHRwOh+rcDBkyBOXl5cq5WbFiBUaOHIni4mJln6lTp6KhoQHbtm3rxqOPnjvvvBOXXXaZ6jwAPD/vvfcexo0bh29961soKirCmDFj8Oc//1l5fP/+/aisrFSdn5ycHIwfP151fnJzczFu3Dhln4qKCpjNZqxatar7vpkoOPfcc7FkyRLs2rULALBp0yZ8+eWXmD59OgCeH1mkzsOKFStwwQUXwGazKftMnToVO3fuxKlTp7rpu+ke9fX1MJlMyM3NBdAzz09CLhYYaSdOnIDL5VJdQACguLgYO3bsiNFRdT+32417770X5513HkaMGAEAqKyshM1mU/4IZMXFxaisrFT20Tt38mOJ7s0338T69euxZs0av8eS/fzs27cPL730EubMmYOf/exnWLNmDe6++27YbDbMmjVL+f70vn/x/BQVFaket1qtyM/PT/jz89BDD6GhoQFDhgyBxWKBy+XCY489hpkzZwJA0p8fWaTOQ2VlJQYMGOD3HPJjeXl5UTn+7tbW1oYHH3wQN954o7I4YE88PwxQSHHnnXdi69at+PLLL2N9KHHj8OHDuOeee7B48WKkpqbG+nDijtvtxrhx4/Db3/4WADBmzBhs3boV8+fPx6xZs2J8dLH31ltv4fXXX8cbb7yB4cOHY+PGjbj33ntRWlrK80Od4nA48O1vfxuSJOGll16K9eFEFYd4ABQWFsJisfjNvKiqqkJJSUmMjqp7zZ49Gx988AE+++wz9O3bV9leUlKCjo4O1NXVqfYXz01JSYnuuZMfS2Tr1q1DdXU1zjrrLFitVlitVixbtgzPPfccrFYriouLk/r89O7dG8OGDVNtGzp0KA4dOgTA9/0F+tsqKSlBdXW16nGn04na2tqEPz/3338/HnroIdxwww0YOXIkbr75Ztx3332YN28eAJ4fWaTOQ0/+WwN8wcnBgwexePFiJXsC9MzzwwAFgM1mw9ixY7FkyRJlm9vtxpIlSzBx4sQYHln0SZKE2bNn45133sHSpUv90n9jx45FSkqK6tzs3LkThw4dUs7NxIkTsWXLFtUfh/zHo714JZopU6Zgy5Yt2Lhxo/Jv3LhxmDlzpnI7mc/Peeed5zctfdeuXejXrx8AYMCAASgpKVGdn4aGBqxatUp1furq6rBu3Tpln6VLl8LtdmP8+PHd8F1ET0tLC8xm9dusxWKB2+0GwPMji9R5mDhxIpYvXw6Hw6Hss3jxYgwePDjuhi/CJQcnu3fvxqeffoqCggLV4z3y/MS6SjdevPnmm5LdbpdeffVV6ZtvvpFuv/12KTc3VzXzoif60Y9+JOXk5Eiff/65dPz4ceVfS0uLss8dd9whlZeXS0uXLpXWrl0rTZw4UZo4caLyuDyN9tJLL5U2btwoLVq0SOrVq1ePmEarR5zFI0nJfX5Wr14tWa1W6bHHHpN2794tvf7661J6err0z3/+U9nn8ccfl3Jzc6V3331X2rx5s3TVVVfpTh8dM2aMtGrVKunLL7+UBg0alHDTaPXMmjVL6tOnjzLNeMGCBVJhYaH0wAMPKPsky/lpbGyUNmzYIG3YsEECID311FPShg0blFkokTgPdXV1UnFxsXTzzTdLW7duld58800pPT09bqfRigKdn46ODunKK6+U+vbtK23cuFH1Xi3OyOlp54cBiuD555+XysvLJZvNJp1zzjnSypUrY31IUQdA998rr7yi7NPa2ir9+Mc/lvLy8qT09HTpmmuukY4fP656ngMHDkjTp0+X0tLSpMLCQuknP/mJ5HA4uvm76R7aACXZz8/7778vjRgxQrLb7dKQIUOkP/3pT6rH3W639PDDD0vFxcWS3W6XpkyZIu3cuVO1z8mTJ6Ubb7xRyszMlLKzs6Uf/OAHUmNjY3d+G1HR0NAg3XPPPVJ5ebmUmpoqDRw4UPr5z3+uuqgky/n57LPPdN9rZs2aJUlS5M7Dpk2bpEmTJkl2u13q06eP9Pjjj3fXt9glgc7P/v37Dd+rP/vsM+U5etr5MUmS0NKQiIiIKA6wBoWIiIjiDgMUIiIiijsMUIiIiCjuMEAhIiKiuMMAhYiIiOIOAxQiIiKKOwxQiIiIKO4wQCEiIqK4wwCFiIiI4g4DFCIiIoo7DFCIiIgo7jBAISIiorjz/wHnTJY8izhpJwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(dfc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "id": "39a6894e",
      "metadata": {
        "id": "39a6894e"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler=MinMaxScaler(feature_range=(0,1))\n",
        "dfc=scaler.fit_transform(np.array(dfc).reshape(-1,1)) #Fit to data, then transform it.\n",
        "#Reshaping to (,1) is needed for fit_transform function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "id": "2870ed81",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2870ed81",
        "outputId": "b655832d-cea4-4929-b4c2-ccc79a436a7f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.22725501],\n",
              "       [0.23228135],\n",
              "       [0.22716291],\n",
              "       ...,\n",
              "       [0.60627037],\n",
              "       [0.62846974],\n",
              "       [0.60936987]])"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "dfc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "id": "0af5de12",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0af5de12",
        "outputId": "d45b109d-f426-42f3-c55d-50a49e14c1b4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(817, 441)"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ],
      "source": [
        "#In case of time series data we should always do train-test split in sequential manner because here today's data is dependent on yesterday's data\n",
        "training_size=int(len(dfc)*0.65)\n",
        "test_size=len(dfc)-training_size\n",
        "train_data,test_data=dfc[0:training_size,:],dfc[training_size:len(dfc),:1]\n",
        "training_size,test_size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "id": "297bfae9",
      "metadata": {
        "id": "297bfae9"
      },
      "outputs": [],
      "source": [
        "#convert an array of values into a dataset matrix\n",
        "def datasetmatrix(dataset,time_step=1):\n",
        "    dataX,dataY=[],[]\n",
        "    for i in range(len(dataset)-time_step-1):\n",
        "        a=dataset[i:(i+time_step),0]  ##i=0 , 0,1,2....199 200\n",
        "        dataX.append(a)\n",
        "        dataY.append(dataset[i+time_step,0])\n",
        "    return np.array(dataX),np.array(dataY)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "id": "d0b0d887",
      "metadata": {
        "id": "d0b0d887"
      },
      "outputs": [],
      "source": [
        "#reshape into X=t,t+1,t+2..t+n , Y=t+n+1\n",
        "time_step=200\n",
        "X_train,y_train=datasetmatrix(train_data,time_step)\n",
        "X_test,y_test=datasetmatrix(test_data,time_step)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "id": "8d52ac90",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8d52ac90",
        "outputId": "f9bc0adf-1482-418a-91d1-8253fc3aebfc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((616, 200), (616,), (240, 200), (240,))"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ],
      "source": [
        "X_train.shape, y_train.shape,X_test.shape, y_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "id": "62fe915d",
      "metadata": {
        "id": "62fe915d"
      },
      "outputs": [],
      "source": [
        "# reshape input to be [samples, time steps, features] which is required for LSTM\n",
        "X_train=X_train.reshape(X_train.shape[0],X_train.shape[1],1)\n",
        "X_test=X_test.reshape(X_test.shape[0],X_test.shape[1],1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "id": "a54e8695",
      "metadata": {
        "id": "a54e8695"
      },
      "outputs": [],
      "source": [
        "##LSTM Model\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import LSTM\n",
        "from tensorflow.keras.layers import Dropout"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model=Sequential()\n",
        "model.add(LSTM(64, input_shape=(200, 1), return_sequences=True))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(LSTM(64, input_shape=(200, 1), return_sequences=False))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(32,kernel_initializer=\"uniform\",activation='relu'))\n",
        "model.add(Dense(1,kernel_initializer=\"uniform\",activation='linear'))\n",
        "model.compile(loss='mean_absolute_error',optimizer='adam')"
      ],
      "metadata": {
        "id": "VlwH_3IRWCET"
      },
      "id": "VlwH_3IRWCET",
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "id": "9f0579f0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9f0579f0",
        "outputId": "7538a157-b860-4780-eaf4-04f284edc496"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_8 (LSTM)               (None, 200, 64)           16896     \n",
            "                                                                 \n",
            " dropout_8 (Dropout)         (None, 200, 64)           0         \n",
            "                                                                 \n",
            " lstm_9 (LSTM)               (None, 64)                33024     \n",
            "                                                                 \n",
            " dropout_9 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 1)                 33        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 52,033\n",
            "Trainable params: 52,033\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "id": "c6bd0262",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c6bd0262",
        "outputId": "38e59ea6-5103-459a-9340-e610d6233b03"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "20/20 [==============================] - 10s 261ms/step - loss: 0.4468 - val_loss: 0.2562\n",
            "Epoch 2/100\n",
            "20/20 [==============================] - 5s 227ms/step - loss: 0.2527 - val_loss: 0.2297\n",
            "Epoch 3/100\n",
            "20/20 [==============================] - 5s 255ms/step - loss: 0.1281 - val_loss: 0.3854\n",
            "Epoch 4/100\n",
            "20/20 [==============================] - 4s 221ms/step - loss: 0.0647 - val_loss: 0.1073\n",
            "Epoch 5/100\n",
            "20/20 [==============================] - 5s 271ms/step - loss: 0.0592 - val_loss: 0.0850\n",
            "Epoch 6/100\n",
            "20/20 [==============================] - 4s 219ms/step - loss: 0.0544 - val_loss: 0.0747\n",
            "Epoch 7/100\n",
            "20/20 [==============================] - 4s 219ms/step - loss: 0.0565 - val_loss: 0.0585\n",
            "Epoch 8/100\n",
            "20/20 [==============================] - 5s 271ms/step - loss: 0.0585 - val_loss: 0.0868\n",
            "Epoch 9/100\n",
            "20/20 [==============================] - 4s 221ms/step - loss: 0.0654 - val_loss: 0.1204\n",
            "Epoch 10/100\n",
            "20/20 [==============================] - 4s 223ms/step - loss: 0.0621 - val_loss: 0.0831\n",
            "Epoch 11/100\n",
            "20/20 [==============================] - 6s 319ms/step - loss: 0.0531 - val_loss: 0.0635\n",
            "Epoch 12/100\n",
            "20/20 [==============================] - 4s 221ms/step - loss: 0.0544 - val_loss: 0.0801\n",
            "Epoch 13/100\n",
            "20/20 [==============================] - 5s 275ms/step - loss: 0.0564 - val_loss: 0.0489\n",
            "Epoch 14/100\n",
            "20/20 [==============================] - 5s 263ms/step - loss: 0.0531 - val_loss: 0.0585\n",
            "Epoch 15/100\n",
            "20/20 [==============================] - 5s 227ms/step - loss: 0.0540 - val_loss: 0.0616\n",
            "Epoch 16/100\n",
            "20/20 [==============================] - 5s 263ms/step - loss: 0.0539 - val_loss: 0.0456\n",
            "Epoch 17/100\n",
            "20/20 [==============================] - 4s 226ms/step - loss: 0.0517 - val_loss: 0.0474\n",
            "Epoch 18/100\n",
            "20/20 [==============================] - 5s 275ms/step - loss: 0.0511 - val_loss: 0.0447\n",
            "Epoch 19/100\n",
            "20/20 [==============================] - 4s 223ms/step - loss: 0.0501 - val_loss: 0.0420\n",
            "Epoch 20/100\n",
            "20/20 [==============================] - 4s 221ms/step - loss: 0.0570 - val_loss: 0.0464\n",
            "Epoch 21/100\n",
            "20/20 [==============================] - 5s 275ms/step - loss: 0.0478 - val_loss: 0.0423\n",
            "Epoch 22/100\n",
            "20/20 [==============================] - 4s 223ms/step - loss: 0.0526 - val_loss: 0.0436\n",
            "Epoch 23/100\n",
            "20/20 [==============================] - 5s 246ms/step - loss: 0.0469 - val_loss: 0.0425\n",
            "Epoch 24/100\n",
            "20/20 [==============================] - 5s 246ms/step - loss: 0.0457 - val_loss: 0.0415\n",
            "Epoch 25/100\n",
            "20/20 [==============================] - 4s 224ms/step - loss: 0.0428 - val_loss: 0.0417\n",
            "Epoch 26/100\n",
            "20/20 [==============================] - 5s 277ms/step - loss: 0.0474 - val_loss: 0.0380\n",
            "Epoch 27/100\n",
            "20/20 [==============================] - 5s 227ms/step - loss: 0.0449 - val_loss: 0.0431\n",
            "Epoch 28/100\n",
            "20/20 [==============================] - 4s 225ms/step - loss: 0.0443 - val_loss: 0.0419\n",
            "Epoch 29/100\n",
            "20/20 [==============================] - 5s 276ms/step - loss: 0.0452 - val_loss: 0.0374\n",
            "Epoch 30/100\n",
            "20/20 [==============================] - 4s 226ms/step - loss: 0.0433 - val_loss: 0.0416\n",
            "Epoch 31/100\n",
            "20/20 [==============================] - 5s 274ms/step - loss: 0.0447 - val_loss: 0.0333\n",
            "Epoch 32/100\n",
            "20/20 [==============================] - 4s 223ms/step - loss: 0.0450 - val_loss: 0.0350\n",
            "Epoch 33/100\n",
            "20/20 [==============================] - 5s 226ms/step - loss: 0.0420 - val_loss: 0.0381\n",
            "Epoch 34/100\n",
            "20/20 [==============================] - 6s 282ms/step - loss: 0.0440 - val_loss: 0.0394\n",
            "Epoch 35/100\n",
            "20/20 [==============================] - 5s 230ms/step - loss: 0.0425 - val_loss: 0.0341\n",
            "Epoch 36/100\n",
            "20/20 [==============================] - 5s 254ms/step - loss: 0.0420 - val_loss: 0.0392\n",
            "Epoch 37/100\n",
            "20/20 [==============================] - 5s 244ms/step - loss: 0.0440 - val_loss: 0.0359\n",
            "Epoch 38/100\n",
            "20/20 [==============================] - 5s 228ms/step - loss: 0.0417 - val_loss: 0.0393\n",
            "Epoch 39/100\n",
            "20/20 [==============================] - 6s 281ms/step - loss: 0.0407 - val_loss: 0.0387\n",
            "Epoch 40/100\n",
            "20/20 [==============================] - 5s 226ms/step - loss: 0.0436 - val_loss: 0.0329\n",
            "Epoch 41/100\n",
            "20/20 [==============================] - 5s 233ms/step - loss: 0.0415 - val_loss: 0.0354\n",
            "Epoch 42/100\n",
            "20/20 [==============================] - 5s 267ms/step - loss: 0.0415 - val_loss: 0.0367\n",
            "Epoch 43/100\n",
            "20/20 [==============================] - 5s 227ms/step - loss: 0.0390 - val_loss: 0.0413\n",
            "Epoch 44/100\n",
            "20/20 [==============================] - 6s 279ms/step - loss: 0.0402 - val_loss: 0.0354\n",
            "Epoch 45/100\n",
            "20/20 [==============================] - 5s 226ms/step - loss: 0.0391 - val_loss: 0.0312\n",
            "Epoch 46/100\n",
            "20/20 [==============================] - 5s 228ms/step - loss: 0.0400 - val_loss: 0.0330\n",
            "Epoch 47/100\n",
            "20/20 [==============================] - 5s 278ms/step - loss: 0.0371 - val_loss: 0.0343\n",
            "Epoch 48/100\n",
            "20/20 [==============================] - 5s 227ms/step - loss: 0.0387 - val_loss: 0.0334\n",
            "Epoch 49/100\n",
            "20/20 [==============================] - 5s 262ms/step - loss: 0.0413 - val_loss: 0.0351\n",
            "Epoch 50/100\n",
            "20/20 [==============================] - 5s 232ms/step - loss: 0.0396 - val_loss: 0.0318\n",
            "Epoch 51/100\n",
            "20/20 [==============================] - 5s 226ms/step - loss: 0.0399 - val_loss: 0.0311\n",
            "Epoch 52/100\n",
            "20/20 [==============================] - 6s 283ms/step - loss: 0.0371 - val_loss: 0.0306\n",
            "Epoch 53/100\n",
            "20/20 [==============================] - 5s 227ms/step - loss: 0.0372 - val_loss: 0.0348\n",
            "Epoch 54/100\n",
            "20/20 [==============================] - 5s 244ms/step - loss: 0.0376 - val_loss: 0.0319\n",
            "Epoch 55/100\n",
            "20/20 [==============================] - 6s 309ms/step - loss: 0.0369 - val_loss: 0.0308\n",
            "Epoch 56/100\n",
            "20/20 [==============================] - 5s 227ms/step - loss: 0.0368 - val_loss: 0.0376\n",
            "Epoch 57/100\n",
            "20/20 [==============================] - 6s 281ms/step - loss: 0.0373 - val_loss: 0.0318\n",
            "Epoch 58/100\n",
            "20/20 [==============================] - 5s 227ms/step - loss: 0.0385 - val_loss: 0.0316\n",
            "Epoch 59/100\n",
            "20/20 [==============================] - 5s 251ms/step - loss: 0.0374 - val_loss: 0.0317\n",
            "Epoch 60/100\n",
            "20/20 [==============================] - 5s 246ms/step - loss: 0.0372 - val_loss: 0.0342\n",
            "Epoch 61/100\n",
            "20/20 [==============================] - 4s 225ms/step - loss: 0.0379 - val_loss: 0.0314\n",
            "Epoch 62/100\n",
            "20/20 [==============================] - 6s 280ms/step - loss: 0.0358 - val_loss: 0.0298\n",
            "Epoch 63/100\n",
            "20/20 [==============================] - 5s 226ms/step - loss: 0.0376 - val_loss: 0.0297\n",
            "Epoch 64/100\n",
            "20/20 [==============================] - 5s 230ms/step - loss: 0.0355 - val_loss: 0.0341\n",
            "Epoch 65/100\n",
            "20/20 [==============================] - 5s 270ms/step - loss: 0.0360 - val_loss: 0.0320\n",
            "Epoch 66/100\n",
            "20/20 [==============================] - 5s 229ms/step - loss: 0.0386 - val_loss: 0.0333\n",
            "Epoch 67/100\n",
            "20/20 [==============================] - 5s 277ms/step - loss: 0.0366 - val_loss: 0.0318\n",
            "Epoch 68/100\n",
            "20/20 [==============================] - 5s 226ms/step - loss: 0.0403 - val_loss: 0.0311\n",
            "Epoch 69/100\n",
            "20/20 [==============================] - 5s 227ms/step - loss: 0.0359 - val_loss: 0.0314\n",
            "Epoch 70/100\n",
            "20/20 [==============================] - 6s 282ms/step - loss: 0.0371 - val_loss: 0.0315\n",
            "Epoch 71/100\n",
            "20/20 [==============================] - 5s 226ms/step - loss: 0.0337 - val_loss: 0.0288\n",
            "Epoch 72/100\n",
            "20/20 [==============================] - 5s 262ms/step - loss: 0.0381 - val_loss: 0.0324\n",
            "Epoch 73/100\n",
            "20/20 [==============================] - 5s 236ms/step - loss: 0.0349 - val_loss: 0.0315\n",
            "Epoch 74/100\n",
            "20/20 [==============================] - 4s 225ms/step - loss: 0.0337 - val_loss: 0.0344\n",
            "Epoch 75/100\n",
            "20/20 [==============================] - 6s 280ms/step - loss: 0.0326 - val_loss: 0.0283\n",
            "Epoch 76/100\n",
            "20/20 [==============================] - 4s 225ms/step - loss: 0.0345 - val_loss: 0.0285\n",
            "Epoch 77/100\n",
            "20/20 [==============================] - 5s 234ms/step - loss: 0.0338 - val_loss: 0.0333\n",
            "Epoch 78/100\n",
            "20/20 [==============================] - 5s 264ms/step - loss: 0.0331 - val_loss: 0.0315\n",
            "Epoch 79/100\n",
            "20/20 [==============================] - 4s 225ms/step - loss: 0.0345 - val_loss: 0.0280\n",
            "Epoch 80/100\n",
            "20/20 [==============================] - 6s 280ms/step - loss: 0.0339 - val_loss: 0.0318\n",
            "Epoch 81/100\n",
            "20/20 [==============================] - 5s 228ms/step - loss: 0.0331 - val_loss: 0.0283\n",
            "Epoch 82/100\n",
            "20/20 [==============================] - 5s 226ms/step - loss: 0.0347 - val_loss: 0.0286\n",
            "Epoch 83/100\n",
            "20/20 [==============================] - 6s 281ms/step - loss: 0.0319 - val_loss: 0.0278\n",
            "Epoch 84/100\n",
            "20/20 [==============================] - 5s 230ms/step - loss: 0.0326 - val_loss: 0.0288\n",
            "Epoch 85/100\n",
            "20/20 [==============================] - 5s 277ms/step - loss: 0.0331 - val_loss: 0.0278\n",
            "Epoch 86/100\n",
            "20/20 [==============================] - 5s 228ms/step - loss: 0.0333 - val_loss: 0.0274\n",
            "Epoch 87/100\n",
            "20/20 [==============================] - 5s 226ms/step - loss: 0.0330 - val_loss: 0.0323\n",
            "Epoch 88/100\n",
            "20/20 [==============================] - 6s 285ms/step - loss: 0.0338 - val_loss: 0.0278\n",
            "Epoch 89/100\n",
            "20/20 [==============================] - 5s 227ms/step - loss: 0.0326 - val_loss: 0.0295\n",
            "Epoch 90/100\n",
            "20/20 [==============================] - 5s 255ms/step - loss: 0.0308 - val_loss: 0.0276\n",
            "Epoch 91/100\n",
            "20/20 [==============================] - 5s 240ms/step - loss: 0.0314 - val_loss: 0.0265\n",
            "Epoch 92/100\n",
            "20/20 [==============================] - 4s 224ms/step - loss: 0.0357 - val_loss: 0.0295\n",
            "Epoch 93/100\n",
            "20/20 [==============================] - 5s 276ms/step - loss: 0.0311 - val_loss: 0.0292\n",
            "Epoch 94/100\n",
            "20/20 [==============================] - 4s 223ms/step - loss: 0.0321 - val_loss: 0.0272\n",
            "Epoch 95/100\n",
            "20/20 [==============================] - 4s 222ms/step - loss: 0.0314 - val_loss: 0.0265\n",
            "Epoch 96/100\n",
            "20/20 [==============================] - 6s 276ms/step - loss: 0.0305 - val_loss: 0.0274\n",
            "Epoch 97/100\n",
            "20/20 [==============================] - 4s 223ms/step - loss: 0.0310 - val_loss: 0.0284\n",
            "Epoch 98/100\n",
            "20/20 [==============================] - 5s 267ms/step - loss: 0.0353 - val_loss: 0.0253\n",
            "Epoch 99/100\n",
            "20/20 [==============================] - 5s 248ms/step - loss: 0.0352 - val_loss: 0.0306\n",
            "Epoch 100/100\n",
            "20/20 [==============================] - 5s 243ms/step - loss: 0.0304 - val_loss: 0.0278\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7b4135b7fa90>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ],
      "source": [
        "model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=100,batch_size=32,verbose=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "id": "eafb155c",
      "metadata": {
        "id": "eafb155c"
      },
      "outputs": [],
      "source": [
        "model.save(r\"AMZN.keras\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "eeda607d",
      "metadata": {
        "id": "eeda607d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb13190f-80cd-4c4c-c1aa-dc60d983e77e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20/20 [==============================] - 2s 56ms/step\n",
            "8/8 [==============================] - 1s 79ms/step\n"
          ]
        }
      ],
      "source": [
        "trp=model.predict(X_train)\n",
        "tep=model.predict(X_test)\n",
        "train_predict=np.array(trp).reshape(-1,)\n",
        "test_predict=np.array(tep).reshape(-1,)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Performance Metrics\n",
        "#Training Data\n",
        "bias=(sum(abs(y_train-train_predict)/y_train)/len(y_train))*100\n",
        "accuracy=(1-(sum(abs(y_train-train_predict)/y_train)/len(y_train)))*100\n",
        "print(\"Train_bias\",round(bias,2)),print(\"Train_acc\",round(accuracy,2))\n",
        "#Testing Data\n",
        "bias=(sum(abs(y_test-test_predict)/y_test)/len(y_test))*100\n",
        "accuracy=(1-(sum(abs(y_test-test_predict)/y_test)/len(y_test)))*100\n",
        "print(\"Test_bias\",round(bias,2)),print(\"Test_acc\",round(accuracy,2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pCFrTgMhB9tf",
        "outputId": "db818546-9a79-4a94-8e54-c7449be4f1e5"
      },
      "id": "pCFrTgMhB9tf",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train_bias inf\n",
            "Train_acc -inf\n",
            "Test_bias 4.31\n",
            "Test_acc 95.69\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-20-ccfa13810394>:3: RuntimeWarning: divide by zero encountered in divide\n",
            "  bias=(sum(abs(y_train-train_predict)/y_train)/len(y_train))*100\n",
            "<ipython-input-20-ccfa13810394>:4: RuntimeWarning: divide by zero encountered in divide\n",
            "  accuracy=(1-(sum(abs(y_train-train_predict)/y_train)/len(y_train)))*100\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "064f1060",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "064f1060",
        "outputId": "817b5f68-2381-4fb6-dcf3-352ca349f418"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plotting\n",
        "# shift train predictions for plotting\n",
        "train_predict=trp\n",
        "test_predict=tep\n",
        "train_predict=scaler.inverse_transform(train_predict)\n",
        "test_predict=scaler.inverse_transform(test_predict)\n",
        "back=200\n",
        "trainpredictplt=np.empty_like(dfc)\n",
        "trainpredictplt[:,:]=np.nan\n",
        "trainpredictplt[back:len(train_predict)+back,:]=train_predict\n",
        "# shift test predictions for plotting\n",
        "testpredictplt=np.empty_like(dfc)\n",
        "testpredictplt[:,:]=np.nan\n",
        "testpredictplt[len(train_predict)+(back*2)+1:len(dfc)-1,:]=test_predict\n",
        "# plot baseline and predictions\n",
        "plt.plot(scaler.inverse_transform(dfc))\n",
        "plt.plot(trainpredictplt)\n",
        "plt.plot(testpredictplt)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "448f3d5d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "448f3d5d",
        "outputId": "76a7d1bf-45b8-4d39-92ba-06a062a29fe0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "441"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "len(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "28ab3c23",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "28ab3c23",
        "outputId": "0107b0c6-f177-45ea-a390-0da3a1a6138c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 200)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "x_input=test_data[241:].reshape(1,-1)\n",
        "x_input.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "59744bdf",
      "metadata": {
        "id": "59744bdf"
      },
      "outputs": [],
      "source": [
        "temp_list=list(x_input)\n",
        "temp_list=temp_list[0].tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "adeab64c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "adeab64c",
        "outputId": "ee0b6841-44b1-4459-cb83-ae259a25f0cd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "200"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "len(temp_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "13a4d2d5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "13a4d2d5",
        "outputId": "e7d972ca-6569-4d4d-8a3a-b18df6037009"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.597279]\n",
            "201\n",
            "1 day input [0.50876632 0.50075202 0.52881462 0.53439197 0.5355225  0.54413977\n",
            " 0.54167767 0.54238116 0.51012296 0.51095203 0.49125546 0.46515247\n",
            " 0.45063127 0.41618736 0.44917412 0.46236379 0.44975194 0.45846969\n",
            " 0.4396273  0.43020611 0.42271941 0.39176762 0.39689277 0.43028147\n",
            " 0.42942729 0.42957803 0.42432729 0.45917314 0.45917314 0.45957512\n",
            " 0.42839723 0.42178986 0.40726862 0.40576125 0.41985536 0.39164204\n",
            " 0.37438238 0.36395627 0.36614198 0.34745032 0.34654588 0.31622222\n",
            " 0.31564439 0.28494384 0.27941675 0.24411866 0.25318813 0.27607536\n",
            " 0.27949211 0.24160634 0.25552459 0.24723393 0.25406744 0.2709251\n",
            " 0.26861376 0.2795926  0.28044679 0.27753251 0.30036949 0.29356109\n",
            " 0.28951626 0.30521825 0.33117051 0.33152223 0.33287886 0.37267402\n",
            " 0.41696618 0.38872774 0.40520854 0.42578444 0.44301895 0.44731499\n",
            " 0.45932387 0.46447413 0.47572931 0.49087862 0.46467512 0.4590224\n",
            " 0.49572738 0.50826387 0.47761355 0.4933658  0.46588102 0.47464903\n",
            " 0.47768895 0.46465001 0.49165744 0.48683379 0.47944757 0.44962632\n",
            " 0.46693619 0.45693718 0.44161205 0.4272667  0.40445484 0.40576125\n",
            " 0.40837405 0.43043221 0.4233726  0.43261795 0.42256867 0.4304071\n",
            " 0.46640863 0.4502544  0.4529426  0.44839531 0.45191254 0.44533027\n",
            " 0.45711306 0.46063029 0.49123035 0.45934902 0.45384703 0.43611007\n",
            " 0.43495439 0.4335726  0.43930069 0.42359869 0.43706473 0.43480365\n",
            " 0.43992877 0.43307015 0.42372432 0.37950753 0.38475827 0.37840211\n",
            " 0.37367893 0.35629369 0.37247303 0.38282377 0.37659323 0.3727745\n",
            " 0.3735282  0.37501046 0.39726961 0.40161591 0.39498339 0.39345087\n",
            " 0.4023445  0.39204398 0.38794892 0.38837601 0.40681643 0.41442875\n",
            " 0.42259378 0.44452631 0.43673815 0.42952777 0.43347213 0.45532929\n",
            " 0.47540273 0.48236183 0.49138108 0.50758553 0.51673037 0.52602594\n",
            " 0.53421612 0.56006787 0.58403537 0.59767728 0.61998664 0.61516304\n",
            " 0.61292704 0.62458423 0.65953059 0.62187088 0.63480931 0.61468569\n",
            " 0.57561913 0.59863198 0.61378121 0.61694676 0.62767433 0.67302173\n",
            " 0.67970451 0.66478133 0.6594803  0.6473709  0.64782318 0.65335023\n",
            " 0.66819805 0.67694094 0.69955181 0.70698825 0.70176267 0.63051327\n",
            " 0.62327778 0.64598916 0.6364926  0.63415616 0.61244973 0.6394069\n",
            " 0.64189407 0.62591576 0.60843    0.6215192  0.60780192 0.60174723\n",
            " 0.59735067 0.59727901]\n",
            "1 day output [[0.59293795]]\n",
            "2 day input [0.50075202 0.52881462 0.53439197 0.5355225  0.54413977 0.54167767\n",
            " 0.54238116 0.51012296 0.51095203 0.49125546 0.46515247 0.45063127\n",
            " 0.41618736 0.44917412 0.46236379 0.44975194 0.45846969 0.4396273\n",
            " 0.43020611 0.42271941 0.39176762 0.39689277 0.43028147 0.42942729\n",
            " 0.42957803 0.42432729 0.45917314 0.45917314 0.45957512 0.42839723\n",
            " 0.42178986 0.40726862 0.40576125 0.41985536 0.39164204 0.37438238\n",
            " 0.36395627 0.36614198 0.34745032 0.34654588 0.31622222 0.31564439\n",
            " 0.28494384 0.27941675 0.24411866 0.25318813 0.27607536 0.27949211\n",
            " 0.24160634 0.25552459 0.24723393 0.25406744 0.2709251  0.26861376\n",
            " 0.2795926  0.28044679 0.27753251 0.30036949 0.29356109 0.28951626\n",
            " 0.30521825 0.33117051 0.33152223 0.33287886 0.37267402 0.41696618\n",
            " 0.38872774 0.40520854 0.42578444 0.44301895 0.44731499 0.45932387\n",
            " 0.46447413 0.47572931 0.49087862 0.46467512 0.4590224  0.49572738\n",
            " 0.50826387 0.47761355 0.4933658  0.46588102 0.47464903 0.47768895\n",
            " 0.46465001 0.49165744 0.48683379 0.47944757 0.44962632 0.46693619\n",
            " 0.45693718 0.44161205 0.4272667  0.40445484 0.40576125 0.40837405\n",
            " 0.43043221 0.4233726  0.43261795 0.42256867 0.4304071  0.46640863\n",
            " 0.4502544  0.4529426  0.44839531 0.45191254 0.44533027 0.45711306\n",
            " 0.46063029 0.49123035 0.45934902 0.45384703 0.43611007 0.43495439\n",
            " 0.4335726  0.43930069 0.42359869 0.43706473 0.43480365 0.43992877\n",
            " 0.43307015 0.42372432 0.37950753 0.38475827 0.37840211 0.37367893\n",
            " 0.35629369 0.37247303 0.38282377 0.37659323 0.3727745  0.3735282\n",
            " 0.37501046 0.39726961 0.40161591 0.39498339 0.39345087 0.4023445\n",
            " 0.39204398 0.38794892 0.38837601 0.40681643 0.41442875 0.42259378\n",
            " 0.44452631 0.43673815 0.42952777 0.43347213 0.45532929 0.47540273\n",
            " 0.48236183 0.49138108 0.50758553 0.51673037 0.52602594 0.53421612\n",
            " 0.56006787 0.58403537 0.59767728 0.61998664 0.61516304 0.61292704\n",
            " 0.62458423 0.65953059 0.62187088 0.63480931 0.61468569 0.57561913\n",
            " 0.59863198 0.61378121 0.61694676 0.62767433 0.67302173 0.67970451\n",
            " 0.66478133 0.6594803  0.6473709  0.64782318 0.65335023 0.66819805\n",
            " 0.67694094 0.69955181 0.70698825 0.70176267 0.63051327 0.62327778\n",
            " 0.64598916 0.6364926  0.63415616 0.61244973 0.6394069  0.64189407\n",
            " 0.62591576 0.60843    0.6215192  0.60780192 0.60174723 0.59735067\n",
            " 0.59727901 0.59293795]\n",
            "2 day output [[0.58963096]]\n",
            "3 day input [0.52881462 0.53439197 0.5355225  0.54413977 0.54167767 0.54238116\n",
            " 0.51012296 0.51095203 0.49125546 0.46515247 0.45063127 0.41618736\n",
            " 0.44917412 0.46236379 0.44975194 0.45846969 0.4396273  0.43020611\n",
            " 0.42271941 0.39176762 0.39689277 0.43028147 0.42942729 0.42957803\n",
            " 0.42432729 0.45917314 0.45917314 0.45957512 0.42839723 0.42178986\n",
            " 0.40726862 0.40576125 0.41985536 0.39164204 0.37438238 0.36395627\n",
            " 0.36614198 0.34745032 0.34654588 0.31622222 0.31564439 0.28494384\n",
            " 0.27941675 0.24411866 0.25318813 0.27607536 0.27949211 0.24160634\n",
            " 0.25552459 0.24723393 0.25406744 0.2709251  0.26861376 0.2795926\n",
            " 0.28044679 0.27753251 0.30036949 0.29356109 0.28951626 0.30521825\n",
            " 0.33117051 0.33152223 0.33287886 0.37267402 0.41696618 0.38872774\n",
            " 0.40520854 0.42578444 0.44301895 0.44731499 0.45932387 0.46447413\n",
            " 0.47572931 0.49087862 0.46467512 0.4590224  0.49572738 0.50826387\n",
            " 0.47761355 0.4933658  0.46588102 0.47464903 0.47768895 0.46465001\n",
            " 0.49165744 0.48683379 0.47944757 0.44962632 0.46693619 0.45693718\n",
            " 0.44161205 0.4272667  0.40445484 0.40576125 0.40837405 0.43043221\n",
            " 0.4233726  0.43261795 0.42256867 0.4304071  0.46640863 0.4502544\n",
            " 0.4529426  0.44839531 0.45191254 0.44533027 0.45711306 0.46063029\n",
            " 0.49123035 0.45934902 0.45384703 0.43611007 0.43495439 0.4335726\n",
            " 0.43930069 0.42359869 0.43706473 0.43480365 0.43992877 0.43307015\n",
            " 0.42372432 0.37950753 0.38475827 0.37840211 0.37367893 0.35629369\n",
            " 0.37247303 0.38282377 0.37659323 0.3727745  0.3735282  0.37501046\n",
            " 0.39726961 0.40161591 0.39498339 0.39345087 0.4023445  0.39204398\n",
            " 0.38794892 0.38837601 0.40681643 0.41442875 0.42259378 0.44452631\n",
            " 0.43673815 0.42952777 0.43347213 0.45532929 0.47540273 0.48236183\n",
            " 0.49138108 0.50758553 0.51673037 0.52602594 0.53421612 0.56006787\n",
            " 0.58403537 0.59767728 0.61998664 0.61516304 0.61292704 0.62458423\n",
            " 0.65953059 0.62187088 0.63480931 0.61468569 0.57561913 0.59863198\n",
            " 0.61378121 0.61694676 0.62767433 0.67302173 0.67970451 0.66478133\n",
            " 0.6594803  0.6473709  0.64782318 0.65335023 0.66819805 0.67694094\n",
            " 0.69955181 0.70698825 0.70176267 0.63051327 0.62327778 0.64598916\n",
            " 0.6364926  0.63415616 0.61244973 0.6394069  0.64189407 0.62591576\n",
            " 0.60843    0.6215192  0.60780192 0.60174723 0.59735067 0.59727901\n",
            " 0.59293795 0.58963096]\n",
            "3 day output [[0.5869194]]\n",
            "4 day input [0.53439197 0.5355225  0.54413977 0.54167767 0.54238116 0.51012296\n",
            " 0.51095203 0.49125546 0.46515247 0.45063127 0.41618736 0.44917412\n",
            " 0.46236379 0.44975194 0.45846969 0.4396273  0.43020611 0.42271941\n",
            " 0.39176762 0.39689277 0.43028147 0.42942729 0.42957803 0.42432729\n",
            " 0.45917314 0.45917314 0.45957512 0.42839723 0.42178986 0.40726862\n",
            " 0.40576125 0.41985536 0.39164204 0.37438238 0.36395627 0.36614198\n",
            " 0.34745032 0.34654588 0.31622222 0.31564439 0.28494384 0.27941675\n",
            " 0.24411866 0.25318813 0.27607536 0.27949211 0.24160634 0.25552459\n",
            " 0.24723393 0.25406744 0.2709251  0.26861376 0.2795926  0.28044679\n",
            " 0.27753251 0.30036949 0.29356109 0.28951626 0.30521825 0.33117051\n",
            " 0.33152223 0.33287886 0.37267402 0.41696618 0.38872774 0.40520854\n",
            " 0.42578444 0.44301895 0.44731499 0.45932387 0.46447413 0.47572931\n",
            " 0.49087862 0.46467512 0.4590224  0.49572738 0.50826387 0.47761355\n",
            " 0.4933658  0.46588102 0.47464903 0.47768895 0.46465001 0.49165744\n",
            " 0.48683379 0.47944757 0.44962632 0.46693619 0.45693718 0.44161205\n",
            " 0.4272667  0.40445484 0.40576125 0.40837405 0.43043221 0.4233726\n",
            " 0.43261795 0.42256867 0.4304071  0.46640863 0.4502544  0.4529426\n",
            " 0.44839531 0.45191254 0.44533027 0.45711306 0.46063029 0.49123035\n",
            " 0.45934902 0.45384703 0.43611007 0.43495439 0.4335726  0.43930069\n",
            " 0.42359869 0.43706473 0.43480365 0.43992877 0.43307015 0.42372432\n",
            " 0.37950753 0.38475827 0.37840211 0.37367893 0.35629369 0.37247303\n",
            " 0.38282377 0.37659323 0.3727745  0.3735282  0.37501046 0.39726961\n",
            " 0.40161591 0.39498339 0.39345087 0.4023445  0.39204398 0.38794892\n",
            " 0.38837601 0.40681643 0.41442875 0.42259378 0.44452631 0.43673815\n",
            " 0.42952777 0.43347213 0.45532929 0.47540273 0.48236183 0.49138108\n",
            " 0.50758553 0.51673037 0.52602594 0.53421612 0.56006787 0.58403537\n",
            " 0.59767728 0.61998664 0.61516304 0.61292704 0.62458423 0.65953059\n",
            " 0.62187088 0.63480931 0.61468569 0.57561913 0.59863198 0.61378121\n",
            " 0.61694676 0.62767433 0.67302173 0.67970451 0.66478133 0.6594803\n",
            " 0.6473709  0.64782318 0.65335023 0.66819805 0.67694094 0.69955181\n",
            " 0.70698825 0.70176267 0.63051327 0.62327778 0.64598916 0.6364926\n",
            " 0.63415616 0.61244973 0.6394069  0.64189407 0.62591576 0.60843\n",
            " 0.6215192  0.60780192 0.60174723 0.59735067 0.59727901 0.59293795\n",
            " 0.58963096 0.58691943]\n",
            "4 day output [[0.58456093]]\n",
            "5 day input [0.5355225  0.54413977 0.54167767 0.54238116 0.51012296 0.51095203\n",
            " 0.49125546 0.46515247 0.45063127 0.41618736 0.44917412 0.46236379\n",
            " 0.44975194 0.45846969 0.4396273  0.43020611 0.42271941 0.39176762\n",
            " 0.39689277 0.43028147 0.42942729 0.42957803 0.42432729 0.45917314\n",
            " 0.45917314 0.45957512 0.42839723 0.42178986 0.40726862 0.40576125\n",
            " 0.41985536 0.39164204 0.37438238 0.36395627 0.36614198 0.34745032\n",
            " 0.34654588 0.31622222 0.31564439 0.28494384 0.27941675 0.24411866\n",
            " 0.25318813 0.27607536 0.27949211 0.24160634 0.25552459 0.24723393\n",
            " 0.25406744 0.2709251  0.26861376 0.2795926  0.28044679 0.27753251\n",
            " 0.30036949 0.29356109 0.28951626 0.30521825 0.33117051 0.33152223\n",
            " 0.33287886 0.37267402 0.41696618 0.38872774 0.40520854 0.42578444\n",
            " 0.44301895 0.44731499 0.45932387 0.46447413 0.47572931 0.49087862\n",
            " 0.46467512 0.4590224  0.49572738 0.50826387 0.47761355 0.4933658\n",
            " 0.46588102 0.47464903 0.47768895 0.46465001 0.49165744 0.48683379\n",
            " 0.47944757 0.44962632 0.46693619 0.45693718 0.44161205 0.4272667\n",
            " 0.40445484 0.40576125 0.40837405 0.43043221 0.4233726  0.43261795\n",
            " 0.42256867 0.4304071  0.46640863 0.4502544  0.4529426  0.44839531\n",
            " 0.45191254 0.44533027 0.45711306 0.46063029 0.49123035 0.45934902\n",
            " 0.45384703 0.43611007 0.43495439 0.4335726  0.43930069 0.42359869\n",
            " 0.43706473 0.43480365 0.43992877 0.43307015 0.42372432 0.37950753\n",
            " 0.38475827 0.37840211 0.37367893 0.35629369 0.37247303 0.38282377\n",
            " 0.37659323 0.3727745  0.3735282  0.37501046 0.39726961 0.40161591\n",
            " 0.39498339 0.39345087 0.4023445  0.39204398 0.38794892 0.38837601\n",
            " 0.40681643 0.41442875 0.42259378 0.44452631 0.43673815 0.42952777\n",
            " 0.43347213 0.45532929 0.47540273 0.48236183 0.49138108 0.50758553\n",
            " 0.51673037 0.52602594 0.53421612 0.56006787 0.58403537 0.59767728\n",
            " 0.61998664 0.61516304 0.61292704 0.62458423 0.65953059 0.62187088\n",
            " 0.63480931 0.61468569 0.57561913 0.59863198 0.61378121 0.61694676\n",
            " 0.62767433 0.67302173 0.67970451 0.66478133 0.6594803  0.6473709\n",
            " 0.64782318 0.65335023 0.66819805 0.67694094 0.69955181 0.70698825\n",
            " 0.70176267 0.63051327 0.62327778 0.64598916 0.6364926  0.63415616\n",
            " 0.61244973 0.6394069  0.64189407 0.62591576 0.60843    0.6215192\n",
            " 0.60780192 0.60174723 0.59735067 0.59727901 0.59293795 0.58963096\n",
            " 0.58691943 0.58456093]\n",
            "5 day output [[0.5824174]]\n",
            "6 day input [0.54413977 0.54167767 0.54238116 0.51012296 0.51095203 0.49125546\n",
            " 0.46515247 0.45063127 0.41618736 0.44917412 0.46236379 0.44975194\n",
            " 0.45846969 0.4396273  0.43020611 0.42271941 0.39176762 0.39689277\n",
            " 0.43028147 0.42942729 0.42957803 0.42432729 0.45917314 0.45917314\n",
            " 0.45957512 0.42839723 0.42178986 0.40726862 0.40576125 0.41985536\n",
            " 0.39164204 0.37438238 0.36395627 0.36614198 0.34745032 0.34654588\n",
            " 0.31622222 0.31564439 0.28494384 0.27941675 0.24411866 0.25318813\n",
            " 0.27607536 0.27949211 0.24160634 0.25552459 0.24723393 0.25406744\n",
            " 0.2709251  0.26861376 0.2795926  0.28044679 0.27753251 0.30036949\n",
            " 0.29356109 0.28951626 0.30521825 0.33117051 0.33152223 0.33287886\n",
            " 0.37267402 0.41696618 0.38872774 0.40520854 0.42578444 0.44301895\n",
            " 0.44731499 0.45932387 0.46447413 0.47572931 0.49087862 0.46467512\n",
            " 0.4590224  0.49572738 0.50826387 0.47761355 0.4933658  0.46588102\n",
            " 0.47464903 0.47768895 0.46465001 0.49165744 0.48683379 0.47944757\n",
            " 0.44962632 0.46693619 0.45693718 0.44161205 0.4272667  0.40445484\n",
            " 0.40576125 0.40837405 0.43043221 0.4233726  0.43261795 0.42256867\n",
            " 0.4304071  0.46640863 0.4502544  0.4529426  0.44839531 0.45191254\n",
            " 0.44533027 0.45711306 0.46063029 0.49123035 0.45934902 0.45384703\n",
            " 0.43611007 0.43495439 0.4335726  0.43930069 0.42359869 0.43706473\n",
            " 0.43480365 0.43992877 0.43307015 0.42372432 0.37950753 0.38475827\n",
            " 0.37840211 0.37367893 0.35629369 0.37247303 0.38282377 0.37659323\n",
            " 0.3727745  0.3735282  0.37501046 0.39726961 0.40161591 0.39498339\n",
            " 0.39345087 0.4023445  0.39204398 0.38794892 0.38837601 0.40681643\n",
            " 0.41442875 0.42259378 0.44452631 0.43673815 0.42952777 0.43347213\n",
            " 0.45532929 0.47540273 0.48236183 0.49138108 0.50758553 0.51673037\n",
            " 0.52602594 0.53421612 0.56006787 0.58403537 0.59767728 0.61998664\n",
            " 0.61516304 0.61292704 0.62458423 0.65953059 0.62187088 0.63480931\n",
            " 0.61468569 0.57561913 0.59863198 0.61378121 0.61694676 0.62767433\n",
            " 0.67302173 0.67970451 0.66478133 0.6594803  0.6473709  0.64782318\n",
            " 0.65335023 0.66819805 0.67694094 0.69955181 0.70698825 0.70176267\n",
            " 0.63051327 0.62327778 0.64598916 0.6364926  0.63415616 0.61244973\n",
            " 0.6394069  0.64189407 0.62591576 0.60843    0.6215192  0.60780192\n",
            " 0.60174723 0.59735067 0.59727901 0.59293795 0.58963096 0.58691943\n",
            " 0.58456093 0.58241743]\n",
            "6 day output [[0.5804085]]\n",
            "7 day input [0.54167767 0.54238116 0.51012296 0.51095203 0.49125546 0.46515247\n",
            " 0.45063127 0.41618736 0.44917412 0.46236379 0.44975194 0.45846969\n",
            " 0.4396273  0.43020611 0.42271941 0.39176762 0.39689277 0.43028147\n",
            " 0.42942729 0.42957803 0.42432729 0.45917314 0.45917314 0.45957512\n",
            " 0.42839723 0.42178986 0.40726862 0.40576125 0.41985536 0.39164204\n",
            " 0.37438238 0.36395627 0.36614198 0.34745032 0.34654588 0.31622222\n",
            " 0.31564439 0.28494384 0.27941675 0.24411866 0.25318813 0.27607536\n",
            " 0.27949211 0.24160634 0.25552459 0.24723393 0.25406744 0.2709251\n",
            " 0.26861376 0.2795926  0.28044679 0.27753251 0.30036949 0.29356109\n",
            " 0.28951626 0.30521825 0.33117051 0.33152223 0.33287886 0.37267402\n",
            " 0.41696618 0.38872774 0.40520854 0.42578444 0.44301895 0.44731499\n",
            " 0.45932387 0.46447413 0.47572931 0.49087862 0.46467512 0.4590224\n",
            " 0.49572738 0.50826387 0.47761355 0.4933658  0.46588102 0.47464903\n",
            " 0.47768895 0.46465001 0.49165744 0.48683379 0.47944757 0.44962632\n",
            " 0.46693619 0.45693718 0.44161205 0.4272667  0.40445484 0.40576125\n",
            " 0.40837405 0.43043221 0.4233726  0.43261795 0.42256867 0.4304071\n",
            " 0.46640863 0.4502544  0.4529426  0.44839531 0.45191254 0.44533027\n",
            " 0.45711306 0.46063029 0.49123035 0.45934902 0.45384703 0.43611007\n",
            " 0.43495439 0.4335726  0.43930069 0.42359869 0.43706473 0.43480365\n",
            " 0.43992877 0.43307015 0.42372432 0.37950753 0.38475827 0.37840211\n",
            " 0.37367893 0.35629369 0.37247303 0.38282377 0.37659323 0.3727745\n",
            " 0.3735282  0.37501046 0.39726961 0.40161591 0.39498339 0.39345087\n",
            " 0.4023445  0.39204398 0.38794892 0.38837601 0.40681643 0.41442875\n",
            " 0.42259378 0.44452631 0.43673815 0.42952777 0.43347213 0.45532929\n",
            " 0.47540273 0.48236183 0.49138108 0.50758553 0.51673037 0.52602594\n",
            " 0.53421612 0.56006787 0.58403537 0.59767728 0.61998664 0.61516304\n",
            " 0.61292704 0.62458423 0.65953059 0.62187088 0.63480931 0.61468569\n",
            " 0.57561913 0.59863198 0.61378121 0.61694676 0.62767433 0.67302173\n",
            " 0.67970451 0.66478133 0.6594803  0.6473709  0.64782318 0.65335023\n",
            " 0.66819805 0.67694094 0.69955181 0.70698825 0.70176267 0.63051327\n",
            " 0.62327778 0.64598916 0.6364926  0.63415616 0.61244973 0.6394069\n",
            " 0.64189407 0.62591576 0.60843    0.6215192  0.60780192 0.60174723\n",
            " 0.59735067 0.59727901 0.59293795 0.58963096 0.58691943 0.58456093\n",
            " 0.58241743 0.58040851]\n",
            "7 day output [[0.5784866]]\n",
            "8 day input [0.54238116 0.51012296 0.51095203 0.49125546 0.46515247 0.45063127\n",
            " 0.41618736 0.44917412 0.46236379 0.44975194 0.45846969 0.4396273\n",
            " 0.43020611 0.42271941 0.39176762 0.39689277 0.43028147 0.42942729\n",
            " 0.42957803 0.42432729 0.45917314 0.45917314 0.45957512 0.42839723\n",
            " 0.42178986 0.40726862 0.40576125 0.41985536 0.39164204 0.37438238\n",
            " 0.36395627 0.36614198 0.34745032 0.34654588 0.31622222 0.31564439\n",
            " 0.28494384 0.27941675 0.24411866 0.25318813 0.27607536 0.27949211\n",
            " 0.24160634 0.25552459 0.24723393 0.25406744 0.2709251  0.26861376\n",
            " 0.2795926  0.28044679 0.27753251 0.30036949 0.29356109 0.28951626\n",
            " 0.30521825 0.33117051 0.33152223 0.33287886 0.37267402 0.41696618\n",
            " 0.38872774 0.40520854 0.42578444 0.44301895 0.44731499 0.45932387\n",
            " 0.46447413 0.47572931 0.49087862 0.46467512 0.4590224  0.49572738\n",
            " 0.50826387 0.47761355 0.4933658  0.46588102 0.47464903 0.47768895\n",
            " 0.46465001 0.49165744 0.48683379 0.47944757 0.44962632 0.46693619\n",
            " 0.45693718 0.44161205 0.4272667  0.40445484 0.40576125 0.40837405\n",
            " 0.43043221 0.4233726  0.43261795 0.42256867 0.4304071  0.46640863\n",
            " 0.4502544  0.4529426  0.44839531 0.45191254 0.44533027 0.45711306\n",
            " 0.46063029 0.49123035 0.45934902 0.45384703 0.43611007 0.43495439\n",
            " 0.4335726  0.43930069 0.42359869 0.43706473 0.43480365 0.43992877\n",
            " 0.43307015 0.42372432 0.37950753 0.38475827 0.37840211 0.37367893\n",
            " 0.35629369 0.37247303 0.38282377 0.37659323 0.3727745  0.3735282\n",
            " 0.37501046 0.39726961 0.40161591 0.39498339 0.39345087 0.4023445\n",
            " 0.39204398 0.38794892 0.38837601 0.40681643 0.41442875 0.42259378\n",
            " 0.44452631 0.43673815 0.42952777 0.43347213 0.45532929 0.47540273\n",
            " 0.48236183 0.49138108 0.50758553 0.51673037 0.52602594 0.53421612\n",
            " 0.56006787 0.58403537 0.59767728 0.61998664 0.61516304 0.61292704\n",
            " 0.62458423 0.65953059 0.62187088 0.63480931 0.61468569 0.57561913\n",
            " 0.59863198 0.61378121 0.61694676 0.62767433 0.67302173 0.67970451\n",
            " 0.66478133 0.6594803  0.6473709  0.64782318 0.65335023 0.66819805\n",
            " 0.67694094 0.69955181 0.70698825 0.70176267 0.63051327 0.62327778\n",
            " 0.64598916 0.6364926  0.63415616 0.61244973 0.6394069  0.64189407\n",
            " 0.62591576 0.60843    0.6215192  0.60780192 0.60174723 0.59735067\n",
            " 0.59727901 0.59293795 0.58963096 0.58691943 0.58456093 0.58241743\n",
            " 0.58040851 0.57848662]\n",
            "8 day output [[0.5766234]]\n",
            "9 day input [0.51012296 0.51095203 0.49125546 0.46515247 0.45063127 0.41618736\n",
            " 0.44917412 0.46236379 0.44975194 0.45846969 0.4396273  0.43020611\n",
            " 0.42271941 0.39176762 0.39689277 0.43028147 0.42942729 0.42957803\n",
            " 0.42432729 0.45917314 0.45917314 0.45957512 0.42839723 0.42178986\n",
            " 0.40726862 0.40576125 0.41985536 0.39164204 0.37438238 0.36395627\n",
            " 0.36614198 0.34745032 0.34654588 0.31622222 0.31564439 0.28494384\n",
            " 0.27941675 0.24411866 0.25318813 0.27607536 0.27949211 0.24160634\n",
            " 0.25552459 0.24723393 0.25406744 0.2709251  0.26861376 0.2795926\n",
            " 0.28044679 0.27753251 0.30036949 0.29356109 0.28951626 0.30521825\n",
            " 0.33117051 0.33152223 0.33287886 0.37267402 0.41696618 0.38872774\n",
            " 0.40520854 0.42578444 0.44301895 0.44731499 0.45932387 0.46447413\n",
            " 0.47572931 0.49087862 0.46467512 0.4590224  0.49572738 0.50826387\n",
            " 0.47761355 0.4933658  0.46588102 0.47464903 0.47768895 0.46465001\n",
            " 0.49165744 0.48683379 0.47944757 0.44962632 0.46693619 0.45693718\n",
            " 0.44161205 0.4272667  0.40445484 0.40576125 0.40837405 0.43043221\n",
            " 0.4233726  0.43261795 0.42256867 0.4304071  0.46640863 0.4502544\n",
            " 0.4529426  0.44839531 0.45191254 0.44533027 0.45711306 0.46063029\n",
            " 0.49123035 0.45934902 0.45384703 0.43611007 0.43495439 0.4335726\n",
            " 0.43930069 0.42359869 0.43706473 0.43480365 0.43992877 0.43307015\n",
            " 0.42372432 0.37950753 0.38475827 0.37840211 0.37367893 0.35629369\n",
            " 0.37247303 0.38282377 0.37659323 0.3727745  0.3735282  0.37501046\n",
            " 0.39726961 0.40161591 0.39498339 0.39345087 0.4023445  0.39204398\n",
            " 0.38794892 0.38837601 0.40681643 0.41442875 0.42259378 0.44452631\n",
            " 0.43673815 0.42952777 0.43347213 0.45532929 0.47540273 0.48236183\n",
            " 0.49138108 0.50758553 0.51673037 0.52602594 0.53421612 0.56006787\n",
            " 0.58403537 0.59767728 0.61998664 0.61516304 0.61292704 0.62458423\n",
            " 0.65953059 0.62187088 0.63480931 0.61468569 0.57561913 0.59863198\n",
            " 0.61378121 0.61694676 0.62767433 0.67302173 0.67970451 0.66478133\n",
            " 0.6594803  0.6473709  0.64782318 0.65335023 0.66819805 0.67694094\n",
            " 0.69955181 0.70698825 0.70176267 0.63051327 0.62327778 0.64598916\n",
            " 0.6364926  0.63415616 0.61244973 0.6394069  0.64189407 0.62591576\n",
            " 0.60843    0.6215192  0.60780192 0.60174723 0.59735067 0.59727901\n",
            " 0.59293795 0.58963096 0.58691943 0.58456093 0.58241743 0.58040851\n",
            " 0.57848662 0.57662338]\n",
            "9 day output [[0.5748027]]\n",
            "10 day input [0.51095203 0.49125546 0.46515247 0.45063127 0.41618736 0.44917412\n",
            " 0.46236379 0.44975194 0.45846969 0.4396273  0.43020611 0.42271941\n",
            " 0.39176762 0.39689277 0.43028147 0.42942729 0.42957803 0.42432729\n",
            " 0.45917314 0.45917314 0.45957512 0.42839723 0.42178986 0.40726862\n",
            " 0.40576125 0.41985536 0.39164204 0.37438238 0.36395627 0.36614198\n",
            " 0.34745032 0.34654588 0.31622222 0.31564439 0.28494384 0.27941675\n",
            " 0.24411866 0.25318813 0.27607536 0.27949211 0.24160634 0.25552459\n",
            " 0.24723393 0.25406744 0.2709251  0.26861376 0.2795926  0.28044679\n",
            " 0.27753251 0.30036949 0.29356109 0.28951626 0.30521825 0.33117051\n",
            " 0.33152223 0.33287886 0.37267402 0.41696618 0.38872774 0.40520854\n",
            " 0.42578444 0.44301895 0.44731499 0.45932387 0.46447413 0.47572931\n",
            " 0.49087862 0.46467512 0.4590224  0.49572738 0.50826387 0.47761355\n",
            " 0.4933658  0.46588102 0.47464903 0.47768895 0.46465001 0.49165744\n",
            " 0.48683379 0.47944757 0.44962632 0.46693619 0.45693718 0.44161205\n",
            " 0.4272667  0.40445484 0.40576125 0.40837405 0.43043221 0.4233726\n",
            " 0.43261795 0.42256867 0.4304071  0.46640863 0.4502544  0.4529426\n",
            " 0.44839531 0.45191254 0.44533027 0.45711306 0.46063029 0.49123035\n",
            " 0.45934902 0.45384703 0.43611007 0.43495439 0.4335726  0.43930069\n",
            " 0.42359869 0.43706473 0.43480365 0.43992877 0.43307015 0.42372432\n",
            " 0.37950753 0.38475827 0.37840211 0.37367893 0.35629369 0.37247303\n",
            " 0.38282377 0.37659323 0.3727745  0.3735282  0.37501046 0.39726961\n",
            " 0.40161591 0.39498339 0.39345087 0.4023445  0.39204398 0.38794892\n",
            " 0.38837601 0.40681643 0.41442875 0.42259378 0.44452631 0.43673815\n",
            " 0.42952777 0.43347213 0.45532929 0.47540273 0.48236183 0.49138108\n",
            " 0.50758553 0.51673037 0.52602594 0.53421612 0.56006787 0.58403537\n",
            " 0.59767728 0.61998664 0.61516304 0.61292704 0.62458423 0.65953059\n",
            " 0.62187088 0.63480931 0.61468569 0.57561913 0.59863198 0.61378121\n",
            " 0.61694676 0.62767433 0.67302173 0.67970451 0.66478133 0.6594803\n",
            " 0.6473709  0.64782318 0.65335023 0.66819805 0.67694094 0.69955181\n",
            " 0.70698825 0.70176267 0.63051327 0.62327778 0.64598916 0.6364926\n",
            " 0.63415616 0.61244973 0.6394069  0.64189407 0.62591576 0.60843\n",
            " 0.6215192  0.60780192 0.60174723 0.59735067 0.59727901 0.59293795\n",
            " 0.58963096 0.58691943 0.58456093 0.58241743 0.58040851 0.57848662\n",
            " 0.57662338 0.5748027 ]\n",
            "10 day output [[0.57301587]]\n",
            "11 day input [0.49125546 0.46515247 0.45063127 0.41618736 0.44917412 0.46236379\n",
            " 0.44975194 0.45846969 0.4396273  0.43020611 0.42271941 0.39176762\n",
            " 0.39689277 0.43028147 0.42942729 0.42957803 0.42432729 0.45917314\n",
            " 0.45917314 0.45957512 0.42839723 0.42178986 0.40726862 0.40576125\n",
            " 0.41985536 0.39164204 0.37438238 0.36395627 0.36614198 0.34745032\n",
            " 0.34654588 0.31622222 0.31564439 0.28494384 0.27941675 0.24411866\n",
            " 0.25318813 0.27607536 0.27949211 0.24160634 0.25552459 0.24723393\n",
            " 0.25406744 0.2709251  0.26861376 0.2795926  0.28044679 0.27753251\n",
            " 0.30036949 0.29356109 0.28951626 0.30521825 0.33117051 0.33152223\n",
            " 0.33287886 0.37267402 0.41696618 0.38872774 0.40520854 0.42578444\n",
            " 0.44301895 0.44731499 0.45932387 0.46447413 0.47572931 0.49087862\n",
            " 0.46467512 0.4590224  0.49572738 0.50826387 0.47761355 0.4933658\n",
            " 0.46588102 0.47464903 0.47768895 0.46465001 0.49165744 0.48683379\n",
            " 0.47944757 0.44962632 0.46693619 0.45693718 0.44161205 0.4272667\n",
            " 0.40445484 0.40576125 0.40837405 0.43043221 0.4233726  0.43261795\n",
            " 0.42256867 0.4304071  0.46640863 0.4502544  0.4529426  0.44839531\n",
            " 0.45191254 0.44533027 0.45711306 0.46063029 0.49123035 0.45934902\n",
            " 0.45384703 0.43611007 0.43495439 0.4335726  0.43930069 0.42359869\n",
            " 0.43706473 0.43480365 0.43992877 0.43307015 0.42372432 0.37950753\n",
            " 0.38475827 0.37840211 0.37367893 0.35629369 0.37247303 0.38282377\n",
            " 0.37659323 0.3727745  0.3735282  0.37501046 0.39726961 0.40161591\n",
            " 0.39498339 0.39345087 0.4023445  0.39204398 0.38794892 0.38837601\n",
            " 0.40681643 0.41442875 0.42259378 0.44452631 0.43673815 0.42952777\n",
            " 0.43347213 0.45532929 0.47540273 0.48236183 0.49138108 0.50758553\n",
            " 0.51673037 0.52602594 0.53421612 0.56006787 0.58403537 0.59767728\n",
            " 0.61998664 0.61516304 0.61292704 0.62458423 0.65953059 0.62187088\n",
            " 0.63480931 0.61468569 0.57561913 0.59863198 0.61378121 0.61694676\n",
            " 0.62767433 0.67302173 0.67970451 0.66478133 0.6594803  0.6473709\n",
            " 0.64782318 0.65335023 0.66819805 0.67694094 0.69955181 0.70698825\n",
            " 0.70176267 0.63051327 0.62327778 0.64598916 0.6364926  0.63415616\n",
            " 0.61244973 0.6394069  0.64189407 0.62591576 0.60843    0.6215192\n",
            " 0.60780192 0.60174723 0.59735067 0.59727901 0.59293795 0.58963096\n",
            " 0.58691943 0.58456093 0.58241743 0.58040851 0.57848662 0.57662338\n",
            " 0.5748027  0.57301587]\n",
            "11 day output [[0.57125896]]\n",
            "12 day input [0.46515247 0.45063127 0.41618736 0.44917412 0.46236379 0.44975194\n",
            " 0.45846969 0.4396273  0.43020611 0.42271941 0.39176762 0.39689277\n",
            " 0.43028147 0.42942729 0.42957803 0.42432729 0.45917314 0.45917314\n",
            " 0.45957512 0.42839723 0.42178986 0.40726862 0.40576125 0.41985536\n",
            " 0.39164204 0.37438238 0.36395627 0.36614198 0.34745032 0.34654588\n",
            " 0.31622222 0.31564439 0.28494384 0.27941675 0.24411866 0.25318813\n",
            " 0.27607536 0.27949211 0.24160634 0.25552459 0.24723393 0.25406744\n",
            " 0.2709251  0.26861376 0.2795926  0.28044679 0.27753251 0.30036949\n",
            " 0.29356109 0.28951626 0.30521825 0.33117051 0.33152223 0.33287886\n",
            " 0.37267402 0.41696618 0.38872774 0.40520854 0.42578444 0.44301895\n",
            " 0.44731499 0.45932387 0.46447413 0.47572931 0.49087862 0.46467512\n",
            " 0.4590224  0.49572738 0.50826387 0.47761355 0.4933658  0.46588102\n",
            " 0.47464903 0.47768895 0.46465001 0.49165744 0.48683379 0.47944757\n",
            " 0.44962632 0.46693619 0.45693718 0.44161205 0.4272667  0.40445484\n",
            " 0.40576125 0.40837405 0.43043221 0.4233726  0.43261795 0.42256867\n",
            " 0.4304071  0.46640863 0.4502544  0.4529426  0.44839531 0.45191254\n",
            " 0.44533027 0.45711306 0.46063029 0.49123035 0.45934902 0.45384703\n",
            " 0.43611007 0.43495439 0.4335726  0.43930069 0.42359869 0.43706473\n",
            " 0.43480365 0.43992877 0.43307015 0.42372432 0.37950753 0.38475827\n",
            " 0.37840211 0.37367893 0.35629369 0.37247303 0.38282377 0.37659323\n",
            " 0.3727745  0.3735282  0.37501046 0.39726961 0.40161591 0.39498339\n",
            " 0.39345087 0.4023445  0.39204398 0.38794892 0.38837601 0.40681643\n",
            " 0.41442875 0.42259378 0.44452631 0.43673815 0.42952777 0.43347213\n",
            " 0.45532929 0.47540273 0.48236183 0.49138108 0.50758553 0.51673037\n",
            " 0.52602594 0.53421612 0.56006787 0.58403537 0.59767728 0.61998664\n",
            " 0.61516304 0.61292704 0.62458423 0.65953059 0.62187088 0.63480931\n",
            " 0.61468569 0.57561913 0.59863198 0.61378121 0.61694676 0.62767433\n",
            " 0.67302173 0.67970451 0.66478133 0.6594803  0.6473709  0.64782318\n",
            " 0.65335023 0.66819805 0.67694094 0.69955181 0.70698825 0.70176267\n",
            " 0.63051327 0.62327778 0.64598916 0.6364926  0.63415616 0.61244973\n",
            " 0.6394069  0.64189407 0.62591576 0.60843    0.6215192  0.60780192\n",
            " 0.60174723 0.59735067 0.59727901 0.59293795 0.58963096 0.58691943\n",
            " 0.58456093 0.58241743 0.58040851 0.57848662 0.57662338 0.5748027\n",
            " 0.57301587 0.57125896]\n",
            "12 day output [[0.5695311]]\n",
            "13 day input [0.45063127 0.41618736 0.44917412 0.46236379 0.44975194 0.45846969\n",
            " 0.4396273  0.43020611 0.42271941 0.39176762 0.39689277 0.43028147\n",
            " 0.42942729 0.42957803 0.42432729 0.45917314 0.45917314 0.45957512\n",
            " 0.42839723 0.42178986 0.40726862 0.40576125 0.41985536 0.39164204\n",
            " 0.37438238 0.36395627 0.36614198 0.34745032 0.34654588 0.31622222\n",
            " 0.31564439 0.28494384 0.27941675 0.24411866 0.25318813 0.27607536\n",
            " 0.27949211 0.24160634 0.25552459 0.24723393 0.25406744 0.2709251\n",
            " 0.26861376 0.2795926  0.28044679 0.27753251 0.30036949 0.29356109\n",
            " 0.28951626 0.30521825 0.33117051 0.33152223 0.33287886 0.37267402\n",
            " 0.41696618 0.38872774 0.40520854 0.42578444 0.44301895 0.44731499\n",
            " 0.45932387 0.46447413 0.47572931 0.49087862 0.46467512 0.4590224\n",
            " 0.49572738 0.50826387 0.47761355 0.4933658  0.46588102 0.47464903\n",
            " 0.47768895 0.46465001 0.49165744 0.48683379 0.47944757 0.44962632\n",
            " 0.46693619 0.45693718 0.44161205 0.4272667  0.40445484 0.40576125\n",
            " 0.40837405 0.43043221 0.4233726  0.43261795 0.42256867 0.4304071\n",
            " 0.46640863 0.4502544  0.4529426  0.44839531 0.45191254 0.44533027\n",
            " 0.45711306 0.46063029 0.49123035 0.45934902 0.45384703 0.43611007\n",
            " 0.43495439 0.4335726  0.43930069 0.42359869 0.43706473 0.43480365\n",
            " 0.43992877 0.43307015 0.42372432 0.37950753 0.38475827 0.37840211\n",
            " 0.37367893 0.35629369 0.37247303 0.38282377 0.37659323 0.3727745\n",
            " 0.3735282  0.37501046 0.39726961 0.40161591 0.39498339 0.39345087\n",
            " 0.4023445  0.39204398 0.38794892 0.38837601 0.40681643 0.41442875\n",
            " 0.42259378 0.44452631 0.43673815 0.42952777 0.43347213 0.45532929\n",
            " 0.47540273 0.48236183 0.49138108 0.50758553 0.51673037 0.52602594\n",
            " 0.53421612 0.56006787 0.58403537 0.59767728 0.61998664 0.61516304\n",
            " 0.61292704 0.62458423 0.65953059 0.62187088 0.63480931 0.61468569\n",
            " 0.57561913 0.59863198 0.61378121 0.61694676 0.62767433 0.67302173\n",
            " 0.67970451 0.66478133 0.6594803  0.6473709  0.64782318 0.65335023\n",
            " 0.66819805 0.67694094 0.69955181 0.70698825 0.70176267 0.63051327\n",
            " 0.62327778 0.64598916 0.6364926  0.63415616 0.61244973 0.6394069\n",
            " 0.64189407 0.62591576 0.60843    0.6215192  0.60780192 0.60174723\n",
            " 0.59735067 0.59727901 0.59293795 0.58963096 0.58691943 0.58456093\n",
            " 0.58241743 0.58040851 0.57848662 0.57662338 0.5748027  0.57301587\n",
            " 0.57125896 0.56953108]\n",
            "13 day output [[0.56783295]]\n",
            "14 day input [0.41618736 0.44917412 0.46236379 0.44975194 0.45846969 0.4396273\n",
            " 0.43020611 0.42271941 0.39176762 0.39689277 0.43028147 0.42942729\n",
            " 0.42957803 0.42432729 0.45917314 0.45917314 0.45957512 0.42839723\n",
            " 0.42178986 0.40726862 0.40576125 0.41985536 0.39164204 0.37438238\n",
            " 0.36395627 0.36614198 0.34745032 0.34654588 0.31622222 0.31564439\n",
            " 0.28494384 0.27941675 0.24411866 0.25318813 0.27607536 0.27949211\n",
            " 0.24160634 0.25552459 0.24723393 0.25406744 0.2709251  0.26861376\n",
            " 0.2795926  0.28044679 0.27753251 0.30036949 0.29356109 0.28951626\n",
            " 0.30521825 0.33117051 0.33152223 0.33287886 0.37267402 0.41696618\n",
            " 0.38872774 0.40520854 0.42578444 0.44301895 0.44731499 0.45932387\n",
            " 0.46447413 0.47572931 0.49087862 0.46467512 0.4590224  0.49572738\n",
            " 0.50826387 0.47761355 0.4933658  0.46588102 0.47464903 0.47768895\n",
            " 0.46465001 0.49165744 0.48683379 0.47944757 0.44962632 0.46693619\n",
            " 0.45693718 0.44161205 0.4272667  0.40445484 0.40576125 0.40837405\n",
            " 0.43043221 0.4233726  0.43261795 0.42256867 0.4304071  0.46640863\n",
            " 0.4502544  0.4529426  0.44839531 0.45191254 0.44533027 0.45711306\n",
            " 0.46063029 0.49123035 0.45934902 0.45384703 0.43611007 0.43495439\n",
            " 0.4335726  0.43930069 0.42359869 0.43706473 0.43480365 0.43992877\n",
            " 0.43307015 0.42372432 0.37950753 0.38475827 0.37840211 0.37367893\n",
            " 0.35629369 0.37247303 0.38282377 0.37659323 0.3727745  0.3735282\n",
            " 0.37501046 0.39726961 0.40161591 0.39498339 0.39345087 0.4023445\n",
            " 0.39204398 0.38794892 0.38837601 0.40681643 0.41442875 0.42259378\n",
            " 0.44452631 0.43673815 0.42952777 0.43347213 0.45532929 0.47540273\n",
            " 0.48236183 0.49138108 0.50758553 0.51673037 0.52602594 0.53421612\n",
            " 0.56006787 0.58403537 0.59767728 0.61998664 0.61516304 0.61292704\n",
            " 0.62458423 0.65953059 0.62187088 0.63480931 0.61468569 0.57561913\n",
            " 0.59863198 0.61378121 0.61694676 0.62767433 0.67302173 0.67970451\n",
            " 0.66478133 0.6594803  0.6473709  0.64782318 0.65335023 0.66819805\n",
            " 0.67694094 0.69955181 0.70698825 0.70176267 0.63051327 0.62327778\n",
            " 0.64598916 0.6364926  0.63415616 0.61244973 0.6394069  0.64189407\n",
            " 0.62591576 0.60843    0.6215192  0.60780192 0.60174723 0.59735067\n",
            " 0.59727901 0.59293795 0.58963096 0.58691943 0.58456093 0.58241743\n",
            " 0.58040851 0.57848662 0.57662338 0.5748027  0.57301587 0.57125896\n",
            " 0.56953108 0.56783295]\n",
            "14 day output [[0.5661659]]\n",
            "15 day input [0.44917412 0.46236379 0.44975194 0.45846969 0.4396273  0.43020611\n",
            " 0.42271941 0.39176762 0.39689277 0.43028147 0.42942729 0.42957803\n",
            " 0.42432729 0.45917314 0.45917314 0.45957512 0.42839723 0.42178986\n",
            " 0.40726862 0.40576125 0.41985536 0.39164204 0.37438238 0.36395627\n",
            " 0.36614198 0.34745032 0.34654588 0.31622222 0.31564439 0.28494384\n",
            " 0.27941675 0.24411866 0.25318813 0.27607536 0.27949211 0.24160634\n",
            " 0.25552459 0.24723393 0.25406744 0.2709251  0.26861376 0.2795926\n",
            " 0.28044679 0.27753251 0.30036949 0.29356109 0.28951626 0.30521825\n",
            " 0.33117051 0.33152223 0.33287886 0.37267402 0.41696618 0.38872774\n",
            " 0.40520854 0.42578444 0.44301895 0.44731499 0.45932387 0.46447413\n",
            " 0.47572931 0.49087862 0.46467512 0.4590224  0.49572738 0.50826387\n",
            " 0.47761355 0.4933658  0.46588102 0.47464903 0.47768895 0.46465001\n",
            " 0.49165744 0.48683379 0.47944757 0.44962632 0.46693619 0.45693718\n",
            " 0.44161205 0.4272667  0.40445484 0.40576125 0.40837405 0.43043221\n",
            " 0.4233726  0.43261795 0.42256867 0.4304071  0.46640863 0.4502544\n",
            " 0.4529426  0.44839531 0.45191254 0.44533027 0.45711306 0.46063029\n",
            " 0.49123035 0.45934902 0.45384703 0.43611007 0.43495439 0.4335726\n",
            " 0.43930069 0.42359869 0.43706473 0.43480365 0.43992877 0.43307015\n",
            " 0.42372432 0.37950753 0.38475827 0.37840211 0.37367893 0.35629369\n",
            " 0.37247303 0.38282377 0.37659323 0.3727745  0.3735282  0.37501046\n",
            " 0.39726961 0.40161591 0.39498339 0.39345087 0.4023445  0.39204398\n",
            " 0.38794892 0.38837601 0.40681643 0.41442875 0.42259378 0.44452631\n",
            " 0.43673815 0.42952777 0.43347213 0.45532929 0.47540273 0.48236183\n",
            " 0.49138108 0.50758553 0.51673037 0.52602594 0.53421612 0.56006787\n",
            " 0.58403537 0.59767728 0.61998664 0.61516304 0.61292704 0.62458423\n",
            " 0.65953059 0.62187088 0.63480931 0.61468569 0.57561913 0.59863198\n",
            " 0.61378121 0.61694676 0.62767433 0.67302173 0.67970451 0.66478133\n",
            " 0.6594803  0.6473709  0.64782318 0.65335023 0.66819805 0.67694094\n",
            " 0.69955181 0.70698825 0.70176267 0.63051327 0.62327778 0.64598916\n",
            " 0.6364926  0.63415616 0.61244973 0.6394069  0.64189407 0.62591576\n",
            " 0.60843    0.6215192  0.60780192 0.60174723 0.59735067 0.59727901\n",
            " 0.59293795 0.58963096 0.58691943 0.58456093 0.58241743 0.58040851\n",
            " 0.57848662 0.57662338 0.5748027  0.57301587 0.57125896 0.56953108\n",
            " 0.56783295 0.56616592]\n",
            "15 day output [[0.5645318]]\n",
            "16 day input [0.46236379 0.44975194 0.45846969 0.4396273  0.43020611 0.42271941\n",
            " 0.39176762 0.39689277 0.43028147 0.42942729 0.42957803 0.42432729\n",
            " 0.45917314 0.45917314 0.45957512 0.42839723 0.42178986 0.40726862\n",
            " 0.40576125 0.41985536 0.39164204 0.37438238 0.36395627 0.36614198\n",
            " 0.34745032 0.34654588 0.31622222 0.31564439 0.28494384 0.27941675\n",
            " 0.24411866 0.25318813 0.27607536 0.27949211 0.24160634 0.25552459\n",
            " 0.24723393 0.25406744 0.2709251  0.26861376 0.2795926  0.28044679\n",
            " 0.27753251 0.30036949 0.29356109 0.28951626 0.30521825 0.33117051\n",
            " 0.33152223 0.33287886 0.37267402 0.41696618 0.38872774 0.40520854\n",
            " 0.42578444 0.44301895 0.44731499 0.45932387 0.46447413 0.47572931\n",
            " 0.49087862 0.46467512 0.4590224  0.49572738 0.50826387 0.47761355\n",
            " 0.4933658  0.46588102 0.47464903 0.47768895 0.46465001 0.49165744\n",
            " 0.48683379 0.47944757 0.44962632 0.46693619 0.45693718 0.44161205\n",
            " 0.4272667  0.40445484 0.40576125 0.40837405 0.43043221 0.4233726\n",
            " 0.43261795 0.42256867 0.4304071  0.46640863 0.4502544  0.4529426\n",
            " 0.44839531 0.45191254 0.44533027 0.45711306 0.46063029 0.49123035\n",
            " 0.45934902 0.45384703 0.43611007 0.43495439 0.4335726  0.43930069\n",
            " 0.42359869 0.43706473 0.43480365 0.43992877 0.43307015 0.42372432\n",
            " 0.37950753 0.38475827 0.37840211 0.37367893 0.35629369 0.37247303\n",
            " 0.38282377 0.37659323 0.3727745  0.3735282  0.37501046 0.39726961\n",
            " 0.40161591 0.39498339 0.39345087 0.4023445  0.39204398 0.38794892\n",
            " 0.38837601 0.40681643 0.41442875 0.42259378 0.44452631 0.43673815\n",
            " 0.42952777 0.43347213 0.45532929 0.47540273 0.48236183 0.49138108\n",
            " 0.50758553 0.51673037 0.52602594 0.53421612 0.56006787 0.58403537\n",
            " 0.59767728 0.61998664 0.61516304 0.61292704 0.62458423 0.65953059\n",
            " 0.62187088 0.63480931 0.61468569 0.57561913 0.59863198 0.61378121\n",
            " 0.61694676 0.62767433 0.67302173 0.67970451 0.66478133 0.6594803\n",
            " 0.6473709  0.64782318 0.65335023 0.66819805 0.67694094 0.69955181\n",
            " 0.70698825 0.70176267 0.63051327 0.62327778 0.64598916 0.6364926\n",
            " 0.63415616 0.61244973 0.6394069  0.64189407 0.62591576 0.60843\n",
            " 0.6215192  0.60780192 0.60174723 0.59735067 0.59727901 0.59293795\n",
            " 0.58963096 0.58691943 0.58456093 0.58241743 0.58040851 0.57848662\n",
            " 0.57662338 0.5748027  0.57301587 0.57125896 0.56953108 0.56783295\n",
            " 0.56616592 0.5645318 ]\n",
            "16 day output [[0.56293213]]\n",
            "17 day input [0.44975194 0.45846969 0.4396273  0.43020611 0.42271941 0.39176762\n",
            " 0.39689277 0.43028147 0.42942729 0.42957803 0.42432729 0.45917314\n",
            " 0.45917314 0.45957512 0.42839723 0.42178986 0.40726862 0.40576125\n",
            " 0.41985536 0.39164204 0.37438238 0.36395627 0.36614198 0.34745032\n",
            " 0.34654588 0.31622222 0.31564439 0.28494384 0.27941675 0.24411866\n",
            " 0.25318813 0.27607536 0.27949211 0.24160634 0.25552459 0.24723393\n",
            " 0.25406744 0.2709251  0.26861376 0.2795926  0.28044679 0.27753251\n",
            " 0.30036949 0.29356109 0.28951626 0.30521825 0.33117051 0.33152223\n",
            " 0.33287886 0.37267402 0.41696618 0.38872774 0.40520854 0.42578444\n",
            " 0.44301895 0.44731499 0.45932387 0.46447413 0.47572931 0.49087862\n",
            " 0.46467512 0.4590224  0.49572738 0.50826387 0.47761355 0.4933658\n",
            " 0.46588102 0.47464903 0.47768895 0.46465001 0.49165744 0.48683379\n",
            " 0.47944757 0.44962632 0.46693619 0.45693718 0.44161205 0.4272667\n",
            " 0.40445484 0.40576125 0.40837405 0.43043221 0.4233726  0.43261795\n",
            " 0.42256867 0.4304071  0.46640863 0.4502544  0.4529426  0.44839531\n",
            " 0.45191254 0.44533027 0.45711306 0.46063029 0.49123035 0.45934902\n",
            " 0.45384703 0.43611007 0.43495439 0.4335726  0.43930069 0.42359869\n",
            " 0.43706473 0.43480365 0.43992877 0.43307015 0.42372432 0.37950753\n",
            " 0.38475827 0.37840211 0.37367893 0.35629369 0.37247303 0.38282377\n",
            " 0.37659323 0.3727745  0.3735282  0.37501046 0.39726961 0.40161591\n",
            " 0.39498339 0.39345087 0.4023445  0.39204398 0.38794892 0.38837601\n",
            " 0.40681643 0.41442875 0.42259378 0.44452631 0.43673815 0.42952777\n",
            " 0.43347213 0.45532929 0.47540273 0.48236183 0.49138108 0.50758553\n",
            " 0.51673037 0.52602594 0.53421612 0.56006787 0.58403537 0.59767728\n",
            " 0.61998664 0.61516304 0.61292704 0.62458423 0.65953059 0.62187088\n",
            " 0.63480931 0.61468569 0.57561913 0.59863198 0.61378121 0.61694676\n",
            " 0.62767433 0.67302173 0.67970451 0.66478133 0.6594803  0.6473709\n",
            " 0.64782318 0.65335023 0.66819805 0.67694094 0.69955181 0.70698825\n",
            " 0.70176267 0.63051327 0.62327778 0.64598916 0.6364926  0.63415616\n",
            " 0.61244973 0.6394069  0.64189407 0.62591576 0.60843    0.6215192\n",
            " 0.60780192 0.60174723 0.59735067 0.59727901 0.59293795 0.58963096\n",
            " 0.58691943 0.58456093 0.58241743 0.58040851 0.57848662 0.57662338\n",
            " 0.5748027  0.57301587 0.57125896 0.56953108 0.56783295 0.56616592\n",
            " 0.5645318  0.56293213]\n",
            "17 day output [[0.5613683]]\n",
            "18 day input [0.45846969 0.4396273  0.43020611 0.42271941 0.39176762 0.39689277\n",
            " 0.43028147 0.42942729 0.42957803 0.42432729 0.45917314 0.45917314\n",
            " 0.45957512 0.42839723 0.42178986 0.40726862 0.40576125 0.41985536\n",
            " 0.39164204 0.37438238 0.36395627 0.36614198 0.34745032 0.34654588\n",
            " 0.31622222 0.31564439 0.28494384 0.27941675 0.24411866 0.25318813\n",
            " 0.27607536 0.27949211 0.24160634 0.25552459 0.24723393 0.25406744\n",
            " 0.2709251  0.26861376 0.2795926  0.28044679 0.27753251 0.30036949\n",
            " 0.29356109 0.28951626 0.30521825 0.33117051 0.33152223 0.33287886\n",
            " 0.37267402 0.41696618 0.38872774 0.40520854 0.42578444 0.44301895\n",
            " 0.44731499 0.45932387 0.46447413 0.47572931 0.49087862 0.46467512\n",
            " 0.4590224  0.49572738 0.50826387 0.47761355 0.4933658  0.46588102\n",
            " 0.47464903 0.47768895 0.46465001 0.49165744 0.48683379 0.47944757\n",
            " 0.44962632 0.46693619 0.45693718 0.44161205 0.4272667  0.40445484\n",
            " 0.40576125 0.40837405 0.43043221 0.4233726  0.43261795 0.42256867\n",
            " 0.4304071  0.46640863 0.4502544  0.4529426  0.44839531 0.45191254\n",
            " 0.44533027 0.45711306 0.46063029 0.49123035 0.45934902 0.45384703\n",
            " 0.43611007 0.43495439 0.4335726  0.43930069 0.42359869 0.43706473\n",
            " 0.43480365 0.43992877 0.43307015 0.42372432 0.37950753 0.38475827\n",
            " 0.37840211 0.37367893 0.35629369 0.37247303 0.38282377 0.37659323\n",
            " 0.3727745  0.3735282  0.37501046 0.39726961 0.40161591 0.39498339\n",
            " 0.39345087 0.4023445  0.39204398 0.38794892 0.38837601 0.40681643\n",
            " 0.41442875 0.42259378 0.44452631 0.43673815 0.42952777 0.43347213\n",
            " 0.45532929 0.47540273 0.48236183 0.49138108 0.50758553 0.51673037\n",
            " 0.52602594 0.53421612 0.56006787 0.58403537 0.59767728 0.61998664\n",
            " 0.61516304 0.61292704 0.62458423 0.65953059 0.62187088 0.63480931\n",
            " 0.61468569 0.57561913 0.59863198 0.61378121 0.61694676 0.62767433\n",
            " 0.67302173 0.67970451 0.66478133 0.6594803  0.6473709  0.64782318\n",
            " 0.65335023 0.66819805 0.67694094 0.69955181 0.70698825 0.70176267\n",
            " 0.63051327 0.62327778 0.64598916 0.6364926  0.63415616 0.61244973\n",
            " 0.6394069  0.64189407 0.62591576 0.60843    0.6215192  0.60780192\n",
            " 0.60174723 0.59735067 0.59727901 0.59293795 0.58963096 0.58691943\n",
            " 0.58456093 0.58241743 0.58040851 0.57848662 0.57662338 0.5748027\n",
            " 0.57301587 0.57125896 0.56953108 0.56783295 0.56616592 0.5645318\n",
            " 0.56293213 0.56136829]\n",
            "18 day output [[0.5598413]]\n",
            "19 day input [0.4396273  0.43020611 0.42271941 0.39176762 0.39689277 0.43028147\n",
            " 0.42942729 0.42957803 0.42432729 0.45917314 0.45917314 0.45957512\n",
            " 0.42839723 0.42178986 0.40726862 0.40576125 0.41985536 0.39164204\n",
            " 0.37438238 0.36395627 0.36614198 0.34745032 0.34654588 0.31622222\n",
            " 0.31564439 0.28494384 0.27941675 0.24411866 0.25318813 0.27607536\n",
            " 0.27949211 0.24160634 0.25552459 0.24723393 0.25406744 0.2709251\n",
            " 0.26861376 0.2795926  0.28044679 0.27753251 0.30036949 0.29356109\n",
            " 0.28951626 0.30521825 0.33117051 0.33152223 0.33287886 0.37267402\n",
            " 0.41696618 0.38872774 0.40520854 0.42578444 0.44301895 0.44731499\n",
            " 0.45932387 0.46447413 0.47572931 0.49087862 0.46467512 0.4590224\n",
            " 0.49572738 0.50826387 0.47761355 0.4933658  0.46588102 0.47464903\n",
            " 0.47768895 0.46465001 0.49165744 0.48683379 0.47944757 0.44962632\n",
            " 0.46693619 0.45693718 0.44161205 0.4272667  0.40445484 0.40576125\n",
            " 0.40837405 0.43043221 0.4233726  0.43261795 0.42256867 0.4304071\n",
            " 0.46640863 0.4502544  0.4529426  0.44839531 0.45191254 0.44533027\n",
            " 0.45711306 0.46063029 0.49123035 0.45934902 0.45384703 0.43611007\n",
            " 0.43495439 0.4335726  0.43930069 0.42359869 0.43706473 0.43480365\n",
            " 0.43992877 0.43307015 0.42372432 0.37950753 0.38475827 0.37840211\n",
            " 0.37367893 0.35629369 0.37247303 0.38282377 0.37659323 0.3727745\n",
            " 0.3735282  0.37501046 0.39726961 0.40161591 0.39498339 0.39345087\n",
            " 0.4023445  0.39204398 0.38794892 0.38837601 0.40681643 0.41442875\n",
            " 0.42259378 0.44452631 0.43673815 0.42952777 0.43347213 0.45532929\n",
            " 0.47540273 0.48236183 0.49138108 0.50758553 0.51673037 0.52602594\n",
            " 0.53421612 0.56006787 0.58403537 0.59767728 0.61998664 0.61516304\n",
            " 0.61292704 0.62458423 0.65953059 0.62187088 0.63480931 0.61468569\n",
            " 0.57561913 0.59863198 0.61378121 0.61694676 0.62767433 0.67302173\n",
            " 0.67970451 0.66478133 0.6594803  0.6473709  0.64782318 0.65335023\n",
            " 0.66819805 0.67694094 0.69955181 0.70698825 0.70176267 0.63051327\n",
            " 0.62327778 0.64598916 0.6364926  0.63415616 0.61244973 0.6394069\n",
            " 0.64189407 0.62591576 0.60843    0.6215192  0.60780192 0.60174723\n",
            " 0.59735067 0.59727901 0.59293795 0.58963096 0.58691943 0.58456093\n",
            " 0.58241743 0.58040851 0.57848662 0.57662338 0.5748027  0.57301587\n",
            " 0.57125896 0.56953108 0.56783295 0.56616592 0.5645318  0.56293213\n",
            " 0.56136829 0.55984128]\n",
            "19 day output [[0.5583516]]\n",
            "20 day input [0.43020611 0.42271941 0.39176762 0.39689277 0.43028147 0.42942729\n",
            " 0.42957803 0.42432729 0.45917314 0.45917314 0.45957512 0.42839723\n",
            " 0.42178986 0.40726862 0.40576125 0.41985536 0.39164204 0.37438238\n",
            " 0.36395627 0.36614198 0.34745032 0.34654588 0.31622222 0.31564439\n",
            " 0.28494384 0.27941675 0.24411866 0.25318813 0.27607536 0.27949211\n",
            " 0.24160634 0.25552459 0.24723393 0.25406744 0.2709251  0.26861376\n",
            " 0.2795926  0.28044679 0.27753251 0.30036949 0.29356109 0.28951626\n",
            " 0.30521825 0.33117051 0.33152223 0.33287886 0.37267402 0.41696618\n",
            " 0.38872774 0.40520854 0.42578444 0.44301895 0.44731499 0.45932387\n",
            " 0.46447413 0.47572931 0.49087862 0.46467512 0.4590224  0.49572738\n",
            " 0.50826387 0.47761355 0.4933658  0.46588102 0.47464903 0.47768895\n",
            " 0.46465001 0.49165744 0.48683379 0.47944757 0.44962632 0.46693619\n",
            " 0.45693718 0.44161205 0.4272667  0.40445484 0.40576125 0.40837405\n",
            " 0.43043221 0.4233726  0.43261795 0.42256867 0.4304071  0.46640863\n",
            " 0.4502544  0.4529426  0.44839531 0.45191254 0.44533027 0.45711306\n",
            " 0.46063029 0.49123035 0.45934902 0.45384703 0.43611007 0.43495439\n",
            " 0.4335726  0.43930069 0.42359869 0.43706473 0.43480365 0.43992877\n",
            " 0.43307015 0.42372432 0.37950753 0.38475827 0.37840211 0.37367893\n",
            " 0.35629369 0.37247303 0.38282377 0.37659323 0.3727745  0.3735282\n",
            " 0.37501046 0.39726961 0.40161591 0.39498339 0.39345087 0.4023445\n",
            " 0.39204398 0.38794892 0.38837601 0.40681643 0.41442875 0.42259378\n",
            " 0.44452631 0.43673815 0.42952777 0.43347213 0.45532929 0.47540273\n",
            " 0.48236183 0.49138108 0.50758553 0.51673037 0.52602594 0.53421612\n",
            " 0.56006787 0.58403537 0.59767728 0.61998664 0.61516304 0.61292704\n",
            " 0.62458423 0.65953059 0.62187088 0.63480931 0.61468569 0.57561913\n",
            " 0.59863198 0.61378121 0.61694676 0.62767433 0.67302173 0.67970451\n",
            " 0.66478133 0.6594803  0.6473709  0.64782318 0.65335023 0.66819805\n",
            " 0.67694094 0.69955181 0.70698825 0.70176267 0.63051327 0.62327778\n",
            " 0.64598916 0.6364926  0.63415616 0.61244973 0.6394069  0.64189407\n",
            " 0.62591576 0.60843    0.6215192  0.60780192 0.60174723 0.59735067\n",
            " 0.59727901 0.59293795 0.58963096 0.58691943 0.58456093 0.58241743\n",
            " 0.58040851 0.57848662 0.57662338 0.5748027  0.57301587 0.57125896\n",
            " 0.56953108 0.56783295 0.56616592 0.5645318  0.56293213 0.56136829\n",
            " 0.55984128 0.55835158]\n",
            "20 day output [[0.5568994]]\n",
            "21 day input [0.42271941 0.39176762 0.39689277 0.43028147 0.42942729 0.42957803\n",
            " 0.42432729 0.45917314 0.45917314 0.45957512 0.42839723 0.42178986\n",
            " 0.40726862 0.40576125 0.41985536 0.39164204 0.37438238 0.36395627\n",
            " 0.36614198 0.34745032 0.34654588 0.31622222 0.31564439 0.28494384\n",
            " 0.27941675 0.24411866 0.25318813 0.27607536 0.27949211 0.24160634\n",
            " 0.25552459 0.24723393 0.25406744 0.2709251  0.26861376 0.2795926\n",
            " 0.28044679 0.27753251 0.30036949 0.29356109 0.28951626 0.30521825\n",
            " 0.33117051 0.33152223 0.33287886 0.37267402 0.41696618 0.38872774\n",
            " 0.40520854 0.42578444 0.44301895 0.44731499 0.45932387 0.46447413\n",
            " 0.47572931 0.49087862 0.46467512 0.4590224  0.49572738 0.50826387\n",
            " 0.47761355 0.4933658  0.46588102 0.47464903 0.47768895 0.46465001\n",
            " 0.49165744 0.48683379 0.47944757 0.44962632 0.46693619 0.45693718\n",
            " 0.44161205 0.4272667  0.40445484 0.40576125 0.40837405 0.43043221\n",
            " 0.4233726  0.43261795 0.42256867 0.4304071  0.46640863 0.4502544\n",
            " 0.4529426  0.44839531 0.45191254 0.44533027 0.45711306 0.46063029\n",
            " 0.49123035 0.45934902 0.45384703 0.43611007 0.43495439 0.4335726\n",
            " 0.43930069 0.42359869 0.43706473 0.43480365 0.43992877 0.43307015\n",
            " 0.42372432 0.37950753 0.38475827 0.37840211 0.37367893 0.35629369\n",
            " 0.37247303 0.38282377 0.37659323 0.3727745  0.3735282  0.37501046\n",
            " 0.39726961 0.40161591 0.39498339 0.39345087 0.4023445  0.39204398\n",
            " 0.38794892 0.38837601 0.40681643 0.41442875 0.42259378 0.44452631\n",
            " 0.43673815 0.42952777 0.43347213 0.45532929 0.47540273 0.48236183\n",
            " 0.49138108 0.50758553 0.51673037 0.52602594 0.53421612 0.56006787\n",
            " 0.58403537 0.59767728 0.61998664 0.61516304 0.61292704 0.62458423\n",
            " 0.65953059 0.62187088 0.63480931 0.61468569 0.57561913 0.59863198\n",
            " 0.61378121 0.61694676 0.62767433 0.67302173 0.67970451 0.66478133\n",
            " 0.6594803  0.6473709  0.64782318 0.65335023 0.66819805 0.67694094\n",
            " 0.69955181 0.70698825 0.70176267 0.63051327 0.62327778 0.64598916\n",
            " 0.6364926  0.63415616 0.61244973 0.6394069  0.64189407 0.62591576\n",
            " 0.60843    0.6215192  0.60780192 0.60174723 0.59735067 0.59727901\n",
            " 0.59293795 0.58963096 0.58691943 0.58456093 0.58241743 0.58040851\n",
            " 0.57848662 0.57662338 0.5748027  0.57301587 0.57125896 0.56953108\n",
            " 0.56783295 0.56616592 0.5645318  0.56293213 0.56136829 0.55984128\n",
            " 0.55835158 0.55689943]\n",
            "21 day output [[0.5554848]]\n",
            "22 day input [0.39176762 0.39689277 0.43028147 0.42942729 0.42957803 0.42432729\n",
            " 0.45917314 0.45917314 0.45957512 0.42839723 0.42178986 0.40726862\n",
            " 0.40576125 0.41985536 0.39164204 0.37438238 0.36395627 0.36614198\n",
            " 0.34745032 0.34654588 0.31622222 0.31564439 0.28494384 0.27941675\n",
            " 0.24411866 0.25318813 0.27607536 0.27949211 0.24160634 0.25552459\n",
            " 0.24723393 0.25406744 0.2709251  0.26861376 0.2795926  0.28044679\n",
            " 0.27753251 0.30036949 0.29356109 0.28951626 0.30521825 0.33117051\n",
            " 0.33152223 0.33287886 0.37267402 0.41696618 0.38872774 0.40520854\n",
            " 0.42578444 0.44301895 0.44731499 0.45932387 0.46447413 0.47572931\n",
            " 0.49087862 0.46467512 0.4590224  0.49572738 0.50826387 0.47761355\n",
            " 0.4933658  0.46588102 0.47464903 0.47768895 0.46465001 0.49165744\n",
            " 0.48683379 0.47944757 0.44962632 0.46693619 0.45693718 0.44161205\n",
            " 0.4272667  0.40445484 0.40576125 0.40837405 0.43043221 0.4233726\n",
            " 0.43261795 0.42256867 0.4304071  0.46640863 0.4502544  0.4529426\n",
            " 0.44839531 0.45191254 0.44533027 0.45711306 0.46063029 0.49123035\n",
            " 0.45934902 0.45384703 0.43611007 0.43495439 0.4335726  0.43930069\n",
            " 0.42359869 0.43706473 0.43480365 0.43992877 0.43307015 0.42372432\n",
            " 0.37950753 0.38475827 0.37840211 0.37367893 0.35629369 0.37247303\n",
            " 0.38282377 0.37659323 0.3727745  0.3735282  0.37501046 0.39726961\n",
            " 0.40161591 0.39498339 0.39345087 0.4023445  0.39204398 0.38794892\n",
            " 0.38837601 0.40681643 0.41442875 0.42259378 0.44452631 0.43673815\n",
            " 0.42952777 0.43347213 0.45532929 0.47540273 0.48236183 0.49138108\n",
            " 0.50758553 0.51673037 0.52602594 0.53421612 0.56006787 0.58403537\n",
            " 0.59767728 0.61998664 0.61516304 0.61292704 0.62458423 0.65953059\n",
            " 0.62187088 0.63480931 0.61468569 0.57561913 0.59863198 0.61378121\n",
            " 0.61694676 0.62767433 0.67302173 0.67970451 0.66478133 0.6594803\n",
            " 0.6473709  0.64782318 0.65335023 0.66819805 0.67694094 0.69955181\n",
            " 0.70698825 0.70176267 0.63051327 0.62327778 0.64598916 0.6364926\n",
            " 0.63415616 0.61244973 0.6394069  0.64189407 0.62591576 0.60843\n",
            " 0.6215192  0.60780192 0.60174723 0.59735067 0.59727901 0.59293795\n",
            " 0.58963096 0.58691943 0.58456093 0.58241743 0.58040851 0.57848662\n",
            " 0.57662338 0.5748027  0.57301587 0.57125896 0.56953108 0.56783295\n",
            " 0.56616592 0.5645318  0.56293213 0.56136829 0.55984128 0.55835158\n",
            " 0.55689943 0.55548477]\n",
            "22 day output [[0.5541073]]\n",
            "23 day input [0.39689277 0.43028147 0.42942729 0.42957803 0.42432729 0.45917314\n",
            " 0.45917314 0.45957512 0.42839723 0.42178986 0.40726862 0.40576125\n",
            " 0.41985536 0.39164204 0.37438238 0.36395627 0.36614198 0.34745032\n",
            " 0.34654588 0.31622222 0.31564439 0.28494384 0.27941675 0.24411866\n",
            " 0.25318813 0.27607536 0.27949211 0.24160634 0.25552459 0.24723393\n",
            " 0.25406744 0.2709251  0.26861376 0.2795926  0.28044679 0.27753251\n",
            " 0.30036949 0.29356109 0.28951626 0.30521825 0.33117051 0.33152223\n",
            " 0.33287886 0.37267402 0.41696618 0.38872774 0.40520854 0.42578444\n",
            " 0.44301895 0.44731499 0.45932387 0.46447413 0.47572931 0.49087862\n",
            " 0.46467512 0.4590224  0.49572738 0.50826387 0.47761355 0.4933658\n",
            " 0.46588102 0.47464903 0.47768895 0.46465001 0.49165744 0.48683379\n",
            " 0.47944757 0.44962632 0.46693619 0.45693718 0.44161205 0.4272667\n",
            " 0.40445484 0.40576125 0.40837405 0.43043221 0.4233726  0.43261795\n",
            " 0.42256867 0.4304071  0.46640863 0.4502544  0.4529426  0.44839531\n",
            " 0.45191254 0.44533027 0.45711306 0.46063029 0.49123035 0.45934902\n",
            " 0.45384703 0.43611007 0.43495439 0.4335726  0.43930069 0.42359869\n",
            " 0.43706473 0.43480365 0.43992877 0.43307015 0.42372432 0.37950753\n",
            " 0.38475827 0.37840211 0.37367893 0.35629369 0.37247303 0.38282377\n",
            " 0.37659323 0.3727745  0.3735282  0.37501046 0.39726961 0.40161591\n",
            " 0.39498339 0.39345087 0.4023445  0.39204398 0.38794892 0.38837601\n",
            " 0.40681643 0.41442875 0.42259378 0.44452631 0.43673815 0.42952777\n",
            " 0.43347213 0.45532929 0.47540273 0.48236183 0.49138108 0.50758553\n",
            " 0.51673037 0.52602594 0.53421612 0.56006787 0.58403537 0.59767728\n",
            " 0.61998664 0.61516304 0.61292704 0.62458423 0.65953059 0.62187088\n",
            " 0.63480931 0.61468569 0.57561913 0.59863198 0.61378121 0.61694676\n",
            " 0.62767433 0.67302173 0.67970451 0.66478133 0.6594803  0.6473709\n",
            " 0.64782318 0.65335023 0.66819805 0.67694094 0.69955181 0.70698825\n",
            " 0.70176267 0.63051327 0.62327778 0.64598916 0.6364926  0.63415616\n",
            " 0.61244973 0.6394069  0.64189407 0.62591576 0.60843    0.6215192\n",
            " 0.60780192 0.60174723 0.59735067 0.59727901 0.59293795 0.58963096\n",
            " 0.58691943 0.58456093 0.58241743 0.58040851 0.57848662 0.57662338\n",
            " 0.5748027  0.57301587 0.57125896 0.56953108 0.56783295 0.56616592\n",
            " 0.5645318  0.56293213 0.56136829 0.55984128 0.55835158 0.55689943\n",
            " 0.55548477 0.55410731]\n",
            "23 day output [[0.5527665]]\n",
            "24 day input [0.43028147 0.42942729 0.42957803 0.42432729 0.45917314 0.45917314\n",
            " 0.45957512 0.42839723 0.42178986 0.40726862 0.40576125 0.41985536\n",
            " 0.39164204 0.37438238 0.36395627 0.36614198 0.34745032 0.34654588\n",
            " 0.31622222 0.31564439 0.28494384 0.27941675 0.24411866 0.25318813\n",
            " 0.27607536 0.27949211 0.24160634 0.25552459 0.24723393 0.25406744\n",
            " 0.2709251  0.26861376 0.2795926  0.28044679 0.27753251 0.30036949\n",
            " 0.29356109 0.28951626 0.30521825 0.33117051 0.33152223 0.33287886\n",
            " 0.37267402 0.41696618 0.38872774 0.40520854 0.42578444 0.44301895\n",
            " 0.44731499 0.45932387 0.46447413 0.47572931 0.49087862 0.46467512\n",
            " 0.4590224  0.49572738 0.50826387 0.47761355 0.4933658  0.46588102\n",
            " 0.47464903 0.47768895 0.46465001 0.49165744 0.48683379 0.47944757\n",
            " 0.44962632 0.46693619 0.45693718 0.44161205 0.4272667  0.40445484\n",
            " 0.40576125 0.40837405 0.43043221 0.4233726  0.43261795 0.42256867\n",
            " 0.4304071  0.46640863 0.4502544  0.4529426  0.44839531 0.45191254\n",
            " 0.44533027 0.45711306 0.46063029 0.49123035 0.45934902 0.45384703\n",
            " 0.43611007 0.43495439 0.4335726  0.43930069 0.42359869 0.43706473\n",
            " 0.43480365 0.43992877 0.43307015 0.42372432 0.37950753 0.38475827\n",
            " 0.37840211 0.37367893 0.35629369 0.37247303 0.38282377 0.37659323\n",
            " 0.3727745  0.3735282  0.37501046 0.39726961 0.40161591 0.39498339\n",
            " 0.39345087 0.4023445  0.39204398 0.38794892 0.38837601 0.40681643\n",
            " 0.41442875 0.42259378 0.44452631 0.43673815 0.42952777 0.43347213\n",
            " 0.45532929 0.47540273 0.48236183 0.49138108 0.50758553 0.51673037\n",
            " 0.52602594 0.53421612 0.56006787 0.58403537 0.59767728 0.61998664\n",
            " 0.61516304 0.61292704 0.62458423 0.65953059 0.62187088 0.63480931\n",
            " 0.61468569 0.57561913 0.59863198 0.61378121 0.61694676 0.62767433\n",
            " 0.67302173 0.67970451 0.66478133 0.6594803  0.6473709  0.64782318\n",
            " 0.65335023 0.66819805 0.67694094 0.69955181 0.70698825 0.70176267\n",
            " 0.63051327 0.62327778 0.64598916 0.6364926  0.63415616 0.61244973\n",
            " 0.6394069  0.64189407 0.62591576 0.60843    0.6215192  0.60780192\n",
            " 0.60174723 0.59735067 0.59727901 0.59293795 0.58963096 0.58691943\n",
            " 0.58456093 0.58241743 0.58040851 0.57848662 0.57662338 0.5748027\n",
            " 0.57301587 0.57125896 0.56953108 0.56783295 0.56616592 0.5645318\n",
            " 0.56293213 0.56136829 0.55984128 0.55835158 0.55689943 0.55548477\n",
            " 0.55410731 0.5527665 ]\n",
            "24 day output [[0.55146164]]\n",
            "25 day input [0.42942729 0.42957803 0.42432729 0.45917314 0.45917314 0.45957512\n",
            " 0.42839723 0.42178986 0.40726862 0.40576125 0.41985536 0.39164204\n",
            " 0.37438238 0.36395627 0.36614198 0.34745032 0.34654588 0.31622222\n",
            " 0.31564439 0.28494384 0.27941675 0.24411866 0.25318813 0.27607536\n",
            " 0.27949211 0.24160634 0.25552459 0.24723393 0.25406744 0.2709251\n",
            " 0.26861376 0.2795926  0.28044679 0.27753251 0.30036949 0.29356109\n",
            " 0.28951626 0.30521825 0.33117051 0.33152223 0.33287886 0.37267402\n",
            " 0.41696618 0.38872774 0.40520854 0.42578444 0.44301895 0.44731499\n",
            " 0.45932387 0.46447413 0.47572931 0.49087862 0.46467512 0.4590224\n",
            " 0.49572738 0.50826387 0.47761355 0.4933658  0.46588102 0.47464903\n",
            " 0.47768895 0.46465001 0.49165744 0.48683379 0.47944757 0.44962632\n",
            " 0.46693619 0.45693718 0.44161205 0.4272667  0.40445484 0.40576125\n",
            " 0.40837405 0.43043221 0.4233726  0.43261795 0.42256867 0.4304071\n",
            " 0.46640863 0.4502544  0.4529426  0.44839531 0.45191254 0.44533027\n",
            " 0.45711306 0.46063029 0.49123035 0.45934902 0.45384703 0.43611007\n",
            " 0.43495439 0.4335726  0.43930069 0.42359869 0.43706473 0.43480365\n",
            " 0.43992877 0.43307015 0.42372432 0.37950753 0.38475827 0.37840211\n",
            " 0.37367893 0.35629369 0.37247303 0.38282377 0.37659323 0.3727745\n",
            " 0.3735282  0.37501046 0.39726961 0.40161591 0.39498339 0.39345087\n",
            " 0.4023445  0.39204398 0.38794892 0.38837601 0.40681643 0.41442875\n",
            " 0.42259378 0.44452631 0.43673815 0.42952777 0.43347213 0.45532929\n",
            " 0.47540273 0.48236183 0.49138108 0.50758553 0.51673037 0.52602594\n",
            " 0.53421612 0.56006787 0.58403537 0.59767728 0.61998664 0.61516304\n",
            " 0.61292704 0.62458423 0.65953059 0.62187088 0.63480931 0.61468569\n",
            " 0.57561913 0.59863198 0.61378121 0.61694676 0.62767433 0.67302173\n",
            " 0.67970451 0.66478133 0.6594803  0.6473709  0.64782318 0.65335023\n",
            " 0.66819805 0.67694094 0.69955181 0.70698825 0.70176267 0.63051327\n",
            " 0.62327778 0.64598916 0.6364926  0.63415616 0.61244973 0.6394069\n",
            " 0.64189407 0.62591576 0.60843    0.6215192  0.60780192 0.60174723\n",
            " 0.59735067 0.59727901 0.59293795 0.58963096 0.58691943 0.58456093\n",
            " 0.58241743 0.58040851 0.57848662 0.57662338 0.5748027  0.57301587\n",
            " 0.57125896 0.56953108 0.56783295 0.56616592 0.5645318  0.56293213\n",
            " 0.56136829 0.55984128 0.55835158 0.55689943 0.55548477 0.55410731\n",
            " 0.5527665  0.55146164]\n",
            "25 day output [[0.550192]]\n",
            "26 day input [0.42957803 0.42432729 0.45917314 0.45917314 0.45957512 0.42839723\n",
            " 0.42178986 0.40726862 0.40576125 0.41985536 0.39164204 0.37438238\n",
            " 0.36395627 0.36614198 0.34745032 0.34654588 0.31622222 0.31564439\n",
            " 0.28494384 0.27941675 0.24411866 0.25318813 0.27607536 0.27949211\n",
            " 0.24160634 0.25552459 0.24723393 0.25406744 0.2709251  0.26861376\n",
            " 0.2795926  0.28044679 0.27753251 0.30036949 0.29356109 0.28951626\n",
            " 0.30521825 0.33117051 0.33152223 0.33287886 0.37267402 0.41696618\n",
            " 0.38872774 0.40520854 0.42578444 0.44301895 0.44731499 0.45932387\n",
            " 0.46447413 0.47572931 0.49087862 0.46467512 0.4590224  0.49572738\n",
            " 0.50826387 0.47761355 0.4933658  0.46588102 0.47464903 0.47768895\n",
            " 0.46465001 0.49165744 0.48683379 0.47944757 0.44962632 0.46693619\n",
            " 0.45693718 0.44161205 0.4272667  0.40445484 0.40576125 0.40837405\n",
            " 0.43043221 0.4233726  0.43261795 0.42256867 0.4304071  0.46640863\n",
            " 0.4502544  0.4529426  0.44839531 0.45191254 0.44533027 0.45711306\n",
            " 0.46063029 0.49123035 0.45934902 0.45384703 0.43611007 0.43495439\n",
            " 0.4335726  0.43930069 0.42359869 0.43706473 0.43480365 0.43992877\n",
            " 0.43307015 0.42372432 0.37950753 0.38475827 0.37840211 0.37367893\n",
            " 0.35629369 0.37247303 0.38282377 0.37659323 0.3727745  0.3735282\n",
            " 0.37501046 0.39726961 0.40161591 0.39498339 0.39345087 0.4023445\n",
            " 0.39204398 0.38794892 0.38837601 0.40681643 0.41442875 0.42259378\n",
            " 0.44452631 0.43673815 0.42952777 0.43347213 0.45532929 0.47540273\n",
            " 0.48236183 0.49138108 0.50758553 0.51673037 0.52602594 0.53421612\n",
            " 0.56006787 0.58403537 0.59767728 0.61998664 0.61516304 0.61292704\n",
            " 0.62458423 0.65953059 0.62187088 0.63480931 0.61468569 0.57561913\n",
            " 0.59863198 0.61378121 0.61694676 0.62767433 0.67302173 0.67970451\n",
            " 0.66478133 0.6594803  0.6473709  0.64782318 0.65335023 0.66819805\n",
            " 0.67694094 0.69955181 0.70698825 0.70176267 0.63051327 0.62327778\n",
            " 0.64598916 0.6364926  0.63415616 0.61244973 0.6394069  0.64189407\n",
            " 0.62591576 0.60843    0.6215192  0.60780192 0.60174723 0.59735067\n",
            " 0.59727901 0.59293795 0.58963096 0.58691943 0.58456093 0.58241743\n",
            " 0.58040851 0.57848662 0.57662338 0.5748027  0.57301587 0.57125896\n",
            " 0.56953108 0.56783295 0.56616592 0.5645318  0.56293213 0.56136829\n",
            " 0.55984128 0.55835158 0.55689943 0.55548477 0.55410731 0.5527665\n",
            " 0.55146164 0.550192  ]\n",
            "26 day output [[0.54895663]]\n",
            "27 day input [0.42432729 0.45917314 0.45917314 0.45957512 0.42839723 0.42178986\n",
            " 0.40726862 0.40576125 0.41985536 0.39164204 0.37438238 0.36395627\n",
            " 0.36614198 0.34745032 0.34654588 0.31622222 0.31564439 0.28494384\n",
            " 0.27941675 0.24411866 0.25318813 0.27607536 0.27949211 0.24160634\n",
            " 0.25552459 0.24723393 0.25406744 0.2709251  0.26861376 0.2795926\n",
            " 0.28044679 0.27753251 0.30036949 0.29356109 0.28951626 0.30521825\n",
            " 0.33117051 0.33152223 0.33287886 0.37267402 0.41696618 0.38872774\n",
            " 0.40520854 0.42578444 0.44301895 0.44731499 0.45932387 0.46447413\n",
            " 0.47572931 0.49087862 0.46467512 0.4590224  0.49572738 0.50826387\n",
            " 0.47761355 0.4933658  0.46588102 0.47464903 0.47768895 0.46465001\n",
            " 0.49165744 0.48683379 0.47944757 0.44962632 0.46693619 0.45693718\n",
            " 0.44161205 0.4272667  0.40445484 0.40576125 0.40837405 0.43043221\n",
            " 0.4233726  0.43261795 0.42256867 0.4304071  0.46640863 0.4502544\n",
            " 0.4529426  0.44839531 0.45191254 0.44533027 0.45711306 0.46063029\n",
            " 0.49123035 0.45934902 0.45384703 0.43611007 0.43495439 0.4335726\n",
            " 0.43930069 0.42359869 0.43706473 0.43480365 0.43992877 0.43307015\n",
            " 0.42372432 0.37950753 0.38475827 0.37840211 0.37367893 0.35629369\n",
            " 0.37247303 0.38282377 0.37659323 0.3727745  0.3735282  0.37501046\n",
            " 0.39726961 0.40161591 0.39498339 0.39345087 0.4023445  0.39204398\n",
            " 0.38794892 0.38837601 0.40681643 0.41442875 0.42259378 0.44452631\n",
            " 0.43673815 0.42952777 0.43347213 0.45532929 0.47540273 0.48236183\n",
            " 0.49138108 0.50758553 0.51673037 0.52602594 0.53421612 0.56006787\n",
            " 0.58403537 0.59767728 0.61998664 0.61516304 0.61292704 0.62458423\n",
            " 0.65953059 0.62187088 0.63480931 0.61468569 0.57561913 0.59863198\n",
            " 0.61378121 0.61694676 0.62767433 0.67302173 0.67970451 0.66478133\n",
            " 0.6594803  0.6473709  0.64782318 0.65335023 0.66819805 0.67694094\n",
            " 0.69955181 0.70698825 0.70176267 0.63051327 0.62327778 0.64598916\n",
            " 0.6364926  0.63415616 0.61244973 0.6394069  0.64189407 0.62591576\n",
            " 0.60843    0.6215192  0.60780192 0.60174723 0.59735067 0.59727901\n",
            " 0.59293795 0.58963096 0.58691943 0.58456093 0.58241743 0.58040851\n",
            " 0.57848662 0.57662338 0.5748027  0.57301587 0.57125896 0.56953108\n",
            " 0.56783295 0.56616592 0.5645318  0.56293213 0.56136829 0.55984128\n",
            " 0.55835158 0.55689943 0.55548477 0.55410731 0.5527665  0.55146164\n",
            " 0.550192   0.54895663]\n",
            "27 day output [[0.54775494]]\n",
            "28 day input [0.45917314 0.45917314 0.45957512 0.42839723 0.42178986 0.40726862\n",
            " 0.40576125 0.41985536 0.39164204 0.37438238 0.36395627 0.36614198\n",
            " 0.34745032 0.34654588 0.31622222 0.31564439 0.28494384 0.27941675\n",
            " 0.24411866 0.25318813 0.27607536 0.27949211 0.24160634 0.25552459\n",
            " 0.24723393 0.25406744 0.2709251  0.26861376 0.2795926  0.28044679\n",
            " 0.27753251 0.30036949 0.29356109 0.28951626 0.30521825 0.33117051\n",
            " 0.33152223 0.33287886 0.37267402 0.41696618 0.38872774 0.40520854\n",
            " 0.42578444 0.44301895 0.44731499 0.45932387 0.46447413 0.47572931\n",
            " 0.49087862 0.46467512 0.4590224  0.49572738 0.50826387 0.47761355\n",
            " 0.4933658  0.46588102 0.47464903 0.47768895 0.46465001 0.49165744\n",
            " 0.48683379 0.47944757 0.44962632 0.46693619 0.45693718 0.44161205\n",
            " 0.4272667  0.40445484 0.40576125 0.40837405 0.43043221 0.4233726\n",
            " 0.43261795 0.42256867 0.4304071  0.46640863 0.4502544  0.4529426\n",
            " 0.44839531 0.45191254 0.44533027 0.45711306 0.46063029 0.49123035\n",
            " 0.45934902 0.45384703 0.43611007 0.43495439 0.4335726  0.43930069\n",
            " 0.42359869 0.43706473 0.43480365 0.43992877 0.43307015 0.42372432\n",
            " 0.37950753 0.38475827 0.37840211 0.37367893 0.35629369 0.37247303\n",
            " 0.38282377 0.37659323 0.3727745  0.3735282  0.37501046 0.39726961\n",
            " 0.40161591 0.39498339 0.39345087 0.4023445  0.39204398 0.38794892\n",
            " 0.38837601 0.40681643 0.41442875 0.42259378 0.44452631 0.43673815\n",
            " 0.42952777 0.43347213 0.45532929 0.47540273 0.48236183 0.49138108\n",
            " 0.50758553 0.51673037 0.52602594 0.53421612 0.56006787 0.58403537\n",
            " 0.59767728 0.61998664 0.61516304 0.61292704 0.62458423 0.65953059\n",
            " 0.62187088 0.63480931 0.61468569 0.57561913 0.59863198 0.61378121\n",
            " 0.61694676 0.62767433 0.67302173 0.67970451 0.66478133 0.6594803\n",
            " 0.6473709  0.64782318 0.65335023 0.66819805 0.67694094 0.69955181\n",
            " 0.70698825 0.70176267 0.63051327 0.62327778 0.64598916 0.6364926\n",
            " 0.63415616 0.61244973 0.6394069  0.64189407 0.62591576 0.60843\n",
            " 0.6215192  0.60780192 0.60174723 0.59735067 0.59727901 0.59293795\n",
            " 0.58963096 0.58691943 0.58456093 0.58241743 0.58040851 0.57848662\n",
            " 0.57662338 0.5748027  0.57301587 0.57125896 0.56953108 0.56783295\n",
            " 0.56616592 0.5645318  0.56293213 0.56136829 0.55984128 0.55835158\n",
            " 0.55689943 0.55548477 0.55410731 0.5527665  0.55146164 0.550192\n",
            " 0.54895663 0.54775494]\n",
            "28 day output [[0.5465857]]\n",
            "29 day input [0.45917314 0.45957512 0.42839723 0.42178986 0.40726862 0.40576125\n",
            " 0.41985536 0.39164204 0.37438238 0.36395627 0.36614198 0.34745032\n",
            " 0.34654588 0.31622222 0.31564439 0.28494384 0.27941675 0.24411866\n",
            " 0.25318813 0.27607536 0.27949211 0.24160634 0.25552459 0.24723393\n",
            " 0.25406744 0.2709251  0.26861376 0.2795926  0.28044679 0.27753251\n",
            " 0.30036949 0.29356109 0.28951626 0.30521825 0.33117051 0.33152223\n",
            " 0.33287886 0.37267402 0.41696618 0.38872774 0.40520854 0.42578444\n",
            " 0.44301895 0.44731499 0.45932387 0.46447413 0.47572931 0.49087862\n",
            " 0.46467512 0.4590224  0.49572738 0.50826387 0.47761355 0.4933658\n",
            " 0.46588102 0.47464903 0.47768895 0.46465001 0.49165744 0.48683379\n",
            " 0.47944757 0.44962632 0.46693619 0.45693718 0.44161205 0.4272667\n",
            " 0.40445484 0.40576125 0.40837405 0.43043221 0.4233726  0.43261795\n",
            " 0.42256867 0.4304071  0.46640863 0.4502544  0.4529426  0.44839531\n",
            " 0.45191254 0.44533027 0.45711306 0.46063029 0.49123035 0.45934902\n",
            " 0.45384703 0.43611007 0.43495439 0.4335726  0.43930069 0.42359869\n",
            " 0.43706473 0.43480365 0.43992877 0.43307015 0.42372432 0.37950753\n",
            " 0.38475827 0.37840211 0.37367893 0.35629369 0.37247303 0.38282377\n",
            " 0.37659323 0.3727745  0.3735282  0.37501046 0.39726961 0.40161591\n",
            " 0.39498339 0.39345087 0.4023445  0.39204398 0.38794892 0.38837601\n",
            " 0.40681643 0.41442875 0.42259378 0.44452631 0.43673815 0.42952777\n",
            " 0.43347213 0.45532929 0.47540273 0.48236183 0.49138108 0.50758553\n",
            " 0.51673037 0.52602594 0.53421612 0.56006787 0.58403537 0.59767728\n",
            " 0.61998664 0.61516304 0.61292704 0.62458423 0.65953059 0.62187088\n",
            " 0.63480931 0.61468569 0.57561913 0.59863198 0.61378121 0.61694676\n",
            " 0.62767433 0.67302173 0.67970451 0.66478133 0.6594803  0.6473709\n",
            " 0.64782318 0.65335023 0.66819805 0.67694094 0.69955181 0.70698825\n",
            " 0.70176267 0.63051327 0.62327778 0.64598916 0.6364926  0.63415616\n",
            " 0.61244973 0.6394069  0.64189407 0.62591576 0.60843    0.6215192\n",
            " 0.60780192 0.60174723 0.59735067 0.59727901 0.59293795 0.58963096\n",
            " 0.58691943 0.58456093 0.58241743 0.58040851 0.57848662 0.57662338\n",
            " 0.5748027  0.57301587 0.57125896 0.56953108 0.56783295 0.56616592\n",
            " 0.5645318  0.56293213 0.56136829 0.55984128 0.55835158 0.55689943\n",
            " 0.55548477 0.55410731 0.5527665  0.55146164 0.550192   0.54895663\n",
            " 0.54775494 0.54658568]\n",
            "29 day output [[0.5454483]]\n",
            "30 day input [0.45957512 0.42839723 0.42178986 0.40726862 0.40576125 0.41985536\n",
            " 0.39164204 0.37438238 0.36395627 0.36614198 0.34745032 0.34654588\n",
            " 0.31622222 0.31564439 0.28494384 0.27941675 0.24411866 0.25318813\n",
            " 0.27607536 0.27949211 0.24160634 0.25552459 0.24723393 0.25406744\n",
            " 0.2709251  0.26861376 0.2795926  0.28044679 0.27753251 0.30036949\n",
            " 0.29356109 0.28951626 0.30521825 0.33117051 0.33152223 0.33287886\n",
            " 0.37267402 0.41696618 0.38872774 0.40520854 0.42578444 0.44301895\n",
            " 0.44731499 0.45932387 0.46447413 0.47572931 0.49087862 0.46467512\n",
            " 0.4590224  0.49572738 0.50826387 0.47761355 0.4933658  0.46588102\n",
            " 0.47464903 0.47768895 0.46465001 0.49165744 0.48683379 0.47944757\n",
            " 0.44962632 0.46693619 0.45693718 0.44161205 0.4272667  0.40445484\n",
            " 0.40576125 0.40837405 0.43043221 0.4233726  0.43261795 0.42256867\n",
            " 0.4304071  0.46640863 0.4502544  0.4529426  0.44839531 0.45191254\n",
            " 0.44533027 0.45711306 0.46063029 0.49123035 0.45934902 0.45384703\n",
            " 0.43611007 0.43495439 0.4335726  0.43930069 0.42359869 0.43706473\n",
            " 0.43480365 0.43992877 0.43307015 0.42372432 0.37950753 0.38475827\n",
            " 0.37840211 0.37367893 0.35629369 0.37247303 0.38282377 0.37659323\n",
            " 0.3727745  0.3735282  0.37501046 0.39726961 0.40161591 0.39498339\n",
            " 0.39345087 0.4023445  0.39204398 0.38794892 0.38837601 0.40681643\n",
            " 0.41442875 0.42259378 0.44452631 0.43673815 0.42952777 0.43347213\n",
            " 0.45532929 0.47540273 0.48236183 0.49138108 0.50758553 0.51673037\n",
            " 0.52602594 0.53421612 0.56006787 0.58403537 0.59767728 0.61998664\n",
            " 0.61516304 0.61292704 0.62458423 0.65953059 0.62187088 0.63480931\n",
            " 0.61468569 0.57561913 0.59863198 0.61378121 0.61694676 0.62767433\n",
            " 0.67302173 0.67970451 0.66478133 0.6594803  0.6473709  0.64782318\n",
            " 0.65335023 0.66819805 0.67694094 0.69955181 0.70698825 0.70176267\n",
            " 0.63051327 0.62327778 0.64598916 0.6364926  0.63415616 0.61244973\n",
            " 0.6394069  0.64189407 0.62591576 0.60843    0.6215192  0.60780192\n",
            " 0.60174723 0.59735067 0.59727901 0.59293795 0.58963096 0.58691943\n",
            " 0.58456093 0.58241743 0.58040851 0.57848662 0.57662338 0.5748027\n",
            " 0.57301587 0.57125896 0.56953108 0.56783295 0.56616592 0.5645318\n",
            " 0.56293213 0.56136829 0.55984128 0.55835158 0.55689943 0.55548477\n",
            " 0.55410731 0.5527665  0.55146164 0.550192   0.54895663 0.54775494\n",
            " 0.54658568 0.5454483 ]\n",
            "30 day output [[0.5443418]]\n",
            "31 day input [0.42839723 0.42178986 0.40726862 0.40576125 0.41985536 0.39164204\n",
            " 0.37438238 0.36395627 0.36614198 0.34745032 0.34654588 0.31622222\n",
            " 0.31564439 0.28494384 0.27941675 0.24411866 0.25318813 0.27607536\n",
            " 0.27949211 0.24160634 0.25552459 0.24723393 0.25406744 0.2709251\n",
            " 0.26861376 0.2795926  0.28044679 0.27753251 0.30036949 0.29356109\n",
            " 0.28951626 0.30521825 0.33117051 0.33152223 0.33287886 0.37267402\n",
            " 0.41696618 0.38872774 0.40520854 0.42578444 0.44301895 0.44731499\n",
            " 0.45932387 0.46447413 0.47572931 0.49087862 0.46467512 0.4590224\n",
            " 0.49572738 0.50826387 0.47761355 0.4933658  0.46588102 0.47464903\n",
            " 0.47768895 0.46465001 0.49165744 0.48683379 0.47944757 0.44962632\n",
            " 0.46693619 0.45693718 0.44161205 0.4272667  0.40445484 0.40576125\n",
            " 0.40837405 0.43043221 0.4233726  0.43261795 0.42256867 0.4304071\n",
            " 0.46640863 0.4502544  0.4529426  0.44839531 0.45191254 0.44533027\n",
            " 0.45711306 0.46063029 0.49123035 0.45934902 0.45384703 0.43611007\n",
            " 0.43495439 0.4335726  0.43930069 0.42359869 0.43706473 0.43480365\n",
            " 0.43992877 0.43307015 0.42372432 0.37950753 0.38475827 0.37840211\n",
            " 0.37367893 0.35629369 0.37247303 0.38282377 0.37659323 0.3727745\n",
            " 0.3735282  0.37501046 0.39726961 0.40161591 0.39498339 0.39345087\n",
            " 0.4023445  0.39204398 0.38794892 0.38837601 0.40681643 0.41442875\n",
            " 0.42259378 0.44452631 0.43673815 0.42952777 0.43347213 0.45532929\n",
            " 0.47540273 0.48236183 0.49138108 0.50758553 0.51673037 0.52602594\n",
            " 0.53421612 0.56006787 0.58403537 0.59767728 0.61998664 0.61516304\n",
            " 0.61292704 0.62458423 0.65953059 0.62187088 0.63480931 0.61468569\n",
            " 0.57561913 0.59863198 0.61378121 0.61694676 0.62767433 0.67302173\n",
            " 0.67970451 0.66478133 0.6594803  0.6473709  0.64782318 0.65335023\n",
            " 0.66819805 0.67694094 0.69955181 0.70698825 0.70176267 0.63051327\n",
            " 0.62327778 0.64598916 0.6364926  0.63415616 0.61244973 0.6394069\n",
            " 0.64189407 0.62591576 0.60843    0.6215192  0.60780192 0.60174723\n",
            " 0.59735067 0.59727901 0.59293795 0.58963096 0.58691943 0.58456093\n",
            " 0.58241743 0.58040851 0.57848662 0.57662338 0.5748027  0.57301587\n",
            " 0.57125896 0.56953108 0.56783295 0.56616592 0.5645318  0.56293213\n",
            " 0.56136829 0.55984128 0.55835158 0.55689943 0.55548477 0.55410731\n",
            " 0.5527665  0.55146164 0.550192   0.54895663 0.54775494 0.54658568\n",
            " 0.5454483  0.5443418 ]\n",
            "31 day output [[0.5432653]]\n",
            "32 day input [0.42178986 0.40726862 0.40576125 0.41985536 0.39164204 0.37438238\n",
            " 0.36395627 0.36614198 0.34745032 0.34654588 0.31622222 0.31564439\n",
            " 0.28494384 0.27941675 0.24411866 0.25318813 0.27607536 0.27949211\n",
            " 0.24160634 0.25552459 0.24723393 0.25406744 0.2709251  0.26861376\n",
            " 0.2795926  0.28044679 0.27753251 0.30036949 0.29356109 0.28951626\n",
            " 0.30521825 0.33117051 0.33152223 0.33287886 0.37267402 0.41696618\n",
            " 0.38872774 0.40520854 0.42578444 0.44301895 0.44731499 0.45932387\n",
            " 0.46447413 0.47572931 0.49087862 0.46467512 0.4590224  0.49572738\n",
            " 0.50826387 0.47761355 0.4933658  0.46588102 0.47464903 0.47768895\n",
            " 0.46465001 0.49165744 0.48683379 0.47944757 0.44962632 0.46693619\n",
            " 0.45693718 0.44161205 0.4272667  0.40445484 0.40576125 0.40837405\n",
            " 0.43043221 0.4233726  0.43261795 0.42256867 0.4304071  0.46640863\n",
            " 0.4502544  0.4529426  0.44839531 0.45191254 0.44533027 0.45711306\n",
            " 0.46063029 0.49123035 0.45934902 0.45384703 0.43611007 0.43495439\n",
            " 0.4335726  0.43930069 0.42359869 0.43706473 0.43480365 0.43992877\n",
            " 0.43307015 0.42372432 0.37950753 0.38475827 0.37840211 0.37367893\n",
            " 0.35629369 0.37247303 0.38282377 0.37659323 0.3727745  0.3735282\n",
            " 0.37501046 0.39726961 0.40161591 0.39498339 0.39345087 0.4023445\n",
            " 0.39204398 0.38794892 0.38837601 0.40681643 0.41442875 0.42259378\n",
            " 0.44452631 0.43673815 0.42952777 0.43347213 0.45532929 0.47540273\n",
            " 0.48236183 0.49138108 0.50758553 0.51673037 0.52602594 0.53421612\n",
            " 0.56006787 0.58403537 0.59767728 0.61998664 0.61516304 0.61292704\n",
            " 0.62458423 0.65953059 0.62187088 0.63480931 0.61468569 0.57561913\n",
            " 0.59863198 0.61378121 0.61694676 0.62767433 0.67302173 0.67970451\n",
            " 0.66478133 0.6594803  0.6473709  0.64782318 0.65335023 0.66819805\n",
            " 0.67694094 0.69955181 0.70698825 0.70176267 0.63051327 0.62327778\n",
            " 0.64598916 0.6364926  0.63415616 0.61244973 0.6394069  0.64189407\n",
            " 0.62591576 0.60843    0.6215192  0.60780192 0.60174723 0.59735067\n",
            " 0.59727901 0.59293795 0.58963096 0.58691943 0.58456093 0.58241743\n",
            " 0.58040851 0.57848662 0.57662338 0.5748027  0.57301587 0.57125896\n",
            " 0.56953108 0.56783295 0.56616592 0.5645318  0.56293213 0.56136829\n",
            " 0.55984128 0.55835158 0.55689943 0.55548477 0.55410731 0.5527665\n",
            " 0.55146164 0.550192   0.54895663 0.54775494 0.54658568 0.5454483\n",
            " 0.5443418  0.54326528]\n",
            "32 day output [[0.5422179]]\n",
            "33 day input [0.40726862 0.40576125 0.41985536 0.39164204 0.37438238 0.36395627\n",
            " 0.36614198 0.34745032 0.34654588 0.31622222 0.31564439 0.28494384\n",
            " 0.27941675 0.24411866 0.25318813 0.27607536 0.27949211 0.24160634\n",
            " 0.25552459 0.24723393 0.25406744 0.2709251  0.26861376 0.2795926\n",
            " 0.28044679 0.27753251 0.30036949 0.29356109 0.28951626 0.30521825\n",
            " 0.33117051 0.33152223 0.33287886 0.37267402 0.41696618 0.38872774\n",
            " 0.40520854 0.42578444 0.44301895 0.44731499 0.45932387 0.46447413\n",
            " 0.47572931 0.49087862 0.46467512 0.4590224  0.49572738 0.50826387\n",
            " 0.47761355 0.4933658  0.46588102 0.47464903 0.47768895 0.46465001\n",
            " 0.49165744 0.48683379 0.47944757 0.44962632 0.46693619 0.45693718\n",
            " 0.44161205 0.4272667  0.40445484 0.40576125 0.40837405 0.43043221\n",
            " 0.4233726  0.43261795 0.42256867 0.4304071  0.46640863 0.4502544\n",
            " 0.4529426  0.44839531 0.45191254 0.44533027 0.45711306 0.46063029\n",
            " 0.49123035 0.45934902 0.45384703 0.43611007 0.43495439 0.4335726\n",
            " 0.43930069 0.42359869 0.43706473 0.43480365 0.43992877 0.43307015\n",
            " 0.42372432 0.37950753 0.38475827 0.37840211 0.37367893 0.35629369\n",
            " 0.37247303 0.38282377 0.37659323 0.3727745  0.3735282  0.37501046\n",
            " 0.39726961 0.40161591 0.39498339 0.39345087 0.4023445  0.39204398\n",
            " 0.38794892 0.38837601 0.40681643 0.41442875 0.42259378 0.44452631\n",
            " 0.43673815 0.42952777 0.43347213 0.45532929 0.47540273 0.48236183\n",
            " 0.49138108 0.50758553 0.51673037 0.52602594 0.53421612 0.56006787\n",
            " 0.58403537 0.59767728 0.61998664 0.61516304 0.61292704 0.62458423\n",
            " 0.65953059 0.62187088 0.63480931 0.61468569 0.57561913 0.59863198\n",
            " 0.61378121 0.61694676 0.62767433 0.67302173 0.67970451 0.66478133\n",
            " 0.6594803  0.6473709  0.64782318 0.65335023 0.66819805 0.67694094\n",
            " 0.69955181 0.70698825 0.70176267 0.63051327 0.62327778 0.64598916\n",
            " 0.6364926  0.63415616 0.61244973 0.6394069  0.64189407 0.62591576\n",
            " 0.60843    0.6215192  0.60780192 0.60174723 0.59735067 0.59727901\n",
            " 0.59293795 0.58963096 0.58691943 0.58456093 0.58241743 0.58040851\n",
            " 0.57848662 0.57662338 0.5748027  0.57301587 0.57125896 0.56953108\n",
            " 0.56783295 0.56616592 0.5645318  0.56293213 0.56136829 0.55984128\n",
            " 0.55835158 0.55689943 0.55548477 0.55410731 0.5527665  0.55146164\n",
            " 0.550192   0.54895663 0.54775494 0.54658568 0.5454483  0.5443418\n",
            " 0.54326528 0.54221791]\n",
            "33 day output [[0.5411989]]\n",
            "34 day input [0.40576125 0.41985536 0.39164204 0.37438238 0.36395627 0.36614198\n",
            " 0.34745032 0.34654588 0.31622222 0.31564439 0.28494384 0.27941675\n",
            " 0.24411866 0.25318813 0.27607536 0.27949211 0.24160634 0.25552459\n",
            " 0.24723393 0.25406744 0.2709251  0.26861376 0.2795926  0.28044679\n",
            " 0.27753251 0.30036949 0.29356109 0.28951626 0.30521825 0.33117051\n",
            " 0.33152223 0.33287886 0.37267402 0.41696618 0.38872774 0.40520854\n",
            " 0.42578444 0.44301895 0.44731499 0.45932387 0.46447413 0.47572931\n",
            " 0.49087862 0.46467512 0.4590224  0.49572738 0.50826387 0.47761355\n",
            " 0.4933658  0.46588102 0.47464903 0.47768895 0.46465001 0.49165744\n",
            " 0.48683379 0.47944757 0.44962632 0.46693619 0.45693718 0.44161205\n",
            " 0.4272667  0.40445484 0.40576125 0.40837405 0.43043221 0.4233726\n",
            " 0.43261795 0.42256867 0.4304071  0.46640863 0.4502544  0.4529426\n",
            " 0.44839531 0.45191254 0.44533027 0.45711306 0.46063029 0.49123035\n",
            " 0.45934902 0.45384703 0.43611007 0.43495439 0.4335726  0.43930069\n",
            " 0.42359869 0.43706473 0.43480365 0.43992877 0.43307015 0.42372432\n",
            " 0.37950753 0.38475827 0.37840211 0.37367893 0.35629369 0.37247303\n",
            " 0.38282377 0.37659323 0.3727745  0.3735282  0.37501046 0.39726961\n",
            " 0.40161591 0.39498339 0.39345087 0.4023445  0.39204398 0.38794892\n",
            " 0.38837601 0.40681643 0.41442875 0.42259378 0.44452631 0.43673815\n",
            " 0.42952777 0.43347213 0.45532929 0.47540273 0.48236183 0.49138108\n",
            " 0.50758553 0.51673037 0.52602594 0.53421612 0.56006787 0.58403537\n",
            " 0.59767728 0.61998664 0.61516304 0.61292704 0.62458423 0.65953059\n",
            " 0.62187088 0.63480931 0.61468569 0.57561913 0.59863198 0.61378121\n",
            " 0.61694676 0.62767433 0.67302173 0.67970451 0.66478133 0.6594803\n",
            " 0.6473709  0.64782318 0.65335023 0.66819805 0.67694094 0.69955181\n",
            " 0.70698825 0.70176267 0.63051327 0.62327778 0.64598916 0.6364926\n",
            " 0.63415616 0.61244973 0.6394069  0.64189407 0.62591576 0.60843\n",
            " 0.6215192  0.60780192 0.60174723 0.59735067 0.59727901 0.59293795\n",
            " 0.58963096 0.58691943 0.58456093 0.58241743 0.58040851 0.57848662\n",
            " 0.57662338 0.5748027  0.57301587 0.57125896 0.56953108 0.56783295\n",
            " 0.56616592 0.5645318  0.56293213 0.56136829 0.55984128 0.55835158\n",
            " 0.55689943 0.55548477 0.55410731 0.5527665  0.55146164 0.550192\n",
            " 0.54895663 0.54775494 0.54658568 0.5454483  0.5443418  0.54326528\n",
            " 0.54221791 0.54119891]\n",
            "34 day output [[0.5402075]]\n",
            "35 day input [0.41985536 0.39164204 0.37438238 0.36395627 0.36614198 0.34745032\n",
            " 0.34654588 0.31622222 0.31564439 0.28494384 0.27941675 0.24411866\n",
            " 0.25318813 0.27607536 0.27949211 0.24160634 0.25552459 0.24723393\n",
            " 0.25406744 0.2709251  0.26861376 0.2795926  0.28044679 0.27753251\n",
            " 0.30036949 0.29356109 0.28951626 0.30521825 0.33117051 0.33152223\n",
            " 0.33287886 0.37267402 0.41696618 0.38872774 0.40520854 0.42578444\n",
            " 0.44301895 0.44731499 0.45932387 0.46447413 0.47572931 0.49087862\n",
            " 0.46467512 0.4590224  0.49572738 0.50826387 0.47761355 0.4933658\n",
            " 0.46588102 0.47464903 0.47768895 0.46465001 0.49165744 0.48683379\n",
            " 0.47944757 0.44962632 0.46693619 0.45693718 0.44161205 0.4272667\n",
            " 0.40445484 0.40576125 0.40837405 0.43043221 0.4233726  0.43261795\n",
            " 0.42256867 0.4304071  0.46640863 0.4502544  0.4529426  0.44839531\n",
            " 0.45191254 0.44533027 0.45711306 0.46063029 0.49123035 0.45934902\n",
            " 0.45384703 0.43611007 0.43495439 0.4335726  0.43930069 0.42359869\n",
            " 0.43706473 0.43480365 0.43992877 0.43307015 0.42372432 0.37950753\n",
            " 0.38475827 0.37840211 0.37367893 0.35629369 0.37247303 0.38282377\n",
            " 0.37659323 0.3727745  0.3735282  0.37501046 0.39726961 0.40161591\n",
            " 0.39498339 0.39345087 0.4023445  0.39204398 0.38794892 0.38837601\n",
            " 0.40681643 0.41442875 0.42259378 0.44452631 0.43673815 0.42952777\n",
            " 0.43347213 0.45532929 0.47540273 0.48236183 0.49138108 0.50758553\n",
            " 0.51673037 0.52602594 0.53421612 0.56006787 0.58403537 0.59767728\n",
            " 0.61998664 0.61516304 0.61292704 0.62458423 0.65953059 0.62187088\n",
            " 0.63480931 0.61468569 0.57561913 0.59863198 0.61378121 0.61694676\n",
            " 0.62767433 0.67302173 0.67970451 0.66478133 0.6594803  0.6473709\n",
            " 0.64782318 0.65335023 0.66819805 0.67694094 0.69955181 0.70698825\n",
            " 0.70176267 0.63051327 0.62327778 0.64598916 0.6364926  0.63415616\n",
            " 0.61244973 0.6394069  0.64189407 0.62591576 0.60843    0.6215192\n",
            " 0.60780192 0.60174723 0.59735067 0.59727901 0.59293795 0.58963096\n",
            " 0.58691943 0.58456093 0.58241743 0.58040851 0.57848662 0.57662338\n",
            " 0.5748027  0.57301587 0.57125896 0.56953108 0.56783295 0.56616592\n",
            " 0.5645318  0.56293213 0.56136829 0.55984128 0.55835158 0.55689943\n",
            " 0.55548477 0.55410731 0.5527665  0.55146164 0.550192   0.54895663\n",
            " 0.54775494 0.54658568 0.5454483  0.5443418  0.54326528 0.54221791\n",
            " 0.54119891 0.54020751]\n",
            "35 day output [[0.5392429]]\n",
            "36 day input [0.39164204 0.37438238 0.36395627 0.36614198 0.34745032 0.34654588\n",
            " 0.31622222 0.31564439 0.28494384 0.27941675 0.24411866 0.25318813\n",
            " 0.27607536 0.27949211 0.24160634 0.25552459 0.24723393 0.25406744\n",
            " 0.2709251  0.26861376 0.2795926  0.28044679 0.27753251 0.30036949\n",
            " 0.29356109 0.28951626 0.30521825 0.33117051 0.33152223 0.33287886\n",
            " 0.37267402 0.41696618 0.38872774 0.40520854 0.42578444 0.44301895\n",
            " 0.44731499 0.45932387 0.46447413 0.47572931 0.49087862 0.46467512\n",
            " 0.4590224  0.49572738 0.50826387 0.47761355 0.4933658  0.46588102\n",
            " 0.47464903 0.47768895 0.46465001 0.49165744 0.48683379 0.47944757\n",
            " 0.44962632 0.46693619 0.45693718 0.44161205 0.4272667  0.40445484\n",
            " 0.40576125 0.40837405 0.43043221 0.4233726  0.43261795 0.42256867\n",
            " 0.4304071  0.46640863 0.4502544  0.4529426  0.44839531 0.45191254\n",
            " 0.44533027 0.45711306 0.46063029 0.49123035 0.45934902 0.45384703\n",
            " 0.43611007 0.43495439 0.4335726  0.43930069 0.42359869 0.43706473\n",
            " 0.43480365 0.43992877 0.43307015 0.42372432 0.37950753 0.38475827\n",
            " 0.37840211 0.37367893 0.35629369 0.37247303 0.38282377 0.37659323\n",
            " 0.3727745  0.3735282  0.37501046 0.39726961 0.40161591 0.39498339\n",
            " 0.39345087 0.4023445  0.39204398 0.38794892 0.38837601 0.40681643\n",
            " 0.41442875 0.42259378 0.44452631 0.43673815 0.42952777 0.43347213\n",
            " 0.45532929 0.47540273 0.48236183 0.49138108 0.50758553 0.51673037\n",
            " 0.52602594 0.53421612 0.56006787 0.58403537 0.59767728 0.61998664\n",
            " 0.61516304 0.61292704 0.62458423 0.65953059 0.62187088 0.63480931\n",
            " 0.61468569 0.57561913 0.59863198 0.61378121 0.61694676 0.62767433\n",
            " 0.67302173 0.67970451 0.66478133 0.6594803  0.6473709  0.64782318\n",
            " 0.65335023 0.66819805 0.67694094 0.69955181 0.70698825 0.70176267\n",
            " 0.63051327 0.62327778 0.64598916 0.6364926  0.63415616 0.61244973\n",
            " 0.6394069  0.64189407 0.62591576 0.60843    0.6215192  0.60780192\n",
            " 0.60174723 0.59735067 0.59727901 0.59293795 0.58963096 0.58691943\n",
            " 0.58456093 0.58241743 0.58040851 0.57848662 0.57662338 0.5748027\n",
            " 0.57301587 0.57125896 0.56953108 0.56783295 0.56616592 0.5645318\n",
            " 0.56293213 0.56136829 0.55984128 0.55835158 0.55689943 0.55548477\n",
            " 0.55410731 0.5527665  0.55146164 0.550192   0.54895663 0.54775494\n",
            " 0.54658568 0.5454483  0.5443418  0.54326528 0.54221791 0.54119891\n",
            " 0.54020751 0.53924292]\n",
            "36 day output [[0.5383043]]\n",
            "37 day input [0.37438238 0.36395627 0.36614198 0.34745032 0.34654588 0.31622222\n",
            " 0.31564439 0.28494384 0.27941675 0.24411866 0.25318813 0.27607536\n",
            " 0.27949211 0.24160634 0.25552459 0.24723393 0.25406744 0.2709251\n",
            " 0.26861376 0.2795926  0.28044679 0.27753251 0.30036949 0.29356109\n",
            " 0.28951626 0.30521825 0.33117051 0.33152223 0.33287886 0.37267402\n",
            " 0.41696618 0.38872774 0.40520854 0.42578444 0.44301895 0.44731499\n",
            " 0.45932387 0.46447413 0.47572931 0.49087862 0.46467512 0.4590224\n",
            " 0.49572738 0.50826387 0.47761355 0.4933658  0.46588102 0.47464903\n",
            " 0.47768895 0.46465001 0.49165744 0.48683379 0.47944757 0.44962632\n",
            " 0.46693619 0.45693718 0.44161205 0.4272667  0.40445484 0.40576125\n",
            " 0.40837405 0.43043221 0.4233726  0.43261795 0.42256867 0.4304071\n",
            " 0.46640863 0.4502544  0.4529426  0.44839531 0.45191254 0.44533027\n",
            " 0.45711306 0.46063029 0.49123035 0.45934902 0.45384703 0.43611007\n",
            " 0.43495439 0.4335726  0.43930069 0.42359869 0.43706473 0.43480365\n",
            " 0.43992877 0.43307015 0.42372432 0.37950753 0.38475827 0.37840211\n",
            " 0.37367893 0.35629369 0.37247303 0.38282377 0.37659323 0.3727745\n",
            " 0.3735282  0.37501046 0.39726961 0.40161591 0.39498339 0.39345087\n",
            " 0.4023445  0.39204398 0.38794892 0.38837601 0.40681643 0.41442875\n",
            " 0.42259378 0.44452631 0.43673815 0.42952777 0.43347213 0.45532929\n",
            " 0.47540273 0.48236183 0.49138108 0.50758553 0.51673037 0.52602594\n",
            " 0.53421612 0.56006787 0.58403537 0.59767728 0.61998664 0.61516304\n",
            " 0.61292704 0.62458423 0.65953059 0.62187088 0.63480931 0.61468569\n",
            " 0.57561913 0.59863198 0.61378121 0.61694676 0.62767433 0.67302173\n",
            " 0.67970451 0.66478133 0.6594803  0.6473709  0.64782318 0.65335023\n",
            " 0.66819805 0.67694094 0.69955181 0.70698825 0.70176267 0.63051327\n",
            " 0.62327778 0.64598916 0.6364926  0.63415616 0.61244973 0.6394069\n",
            " 0.64189407 0.62591576 0.60843    0.6215192  0.60780192 0.60174723\n",
            " 0.59735067 0.59727901 0.59293795 0.58963096 0.58691943 0.58456093\n",
            " 0.58241743 0.58040851 0.57848662 0.57662338 0.5748027  0.57301587\n",
            " 0.57125896 0.56953108 0.56783295 0.56616592 0.5645318  0.56293213\n",
            " 0.56136829 0.55984128 0.55835158 0.55689943 0.55548477 0.55410731\n",
            " 0.5527665  0.55146164 0.550192   0.54895663 0.54775494 0.54658568\n",
            " 0.5454483  0.5443418  0.54326528 0.54221791 0.54119891 0.54020751\n",
            " 0.53924292 0.53830433]\n",
            "37 day output [[0.53739107]]\n",
            "38 day input [0.36395627 0.36614198 0.34745032 0.34654588 0.31622222 0.31564439\n",
            " 0.28494384 0.27941675 0.24411866 0.25318813 0.27607536 0.27949211\n",
            " 0.24160634 0.25552459 0.24723393 0.25406744 0.2709251  0.26861376\n",
            " 0.2795926  0.28044679 0.27753251 0.30036949 0.29356109 0.28951626\n",
            " 0.30521825 0.33117051 0.33152223 0.33287886 0.37267402 0.41696618\n",
            " 0.38872774 0.40520854 0.42578444 0.44301895 0.44731499 0.45932387\n",
            " 0.46447413 0.47572931 0.49087862 0.46467512 0.4590224  0.49572738\n",
            " 0.50826387 0.47761355 0.4933658  0.46588102 0.47464903 0.47768895\n",
            " 0.46465001 0.49165744 0.48683379 0.47944757 0.44962632 0.46693619\n",
            " 0.45693718 0.44161205 0.4272667  0.40445484 0.40576125 0.40837405\n",
            " 0.43043221 0.4233726  0.43261795 0.42256867 0.4304071  0.46640863\n",
            " 0.4502544  0.4529426  0.44839531 0.45191254 0.44533027 0.45711306\n",
            " 0.46063029 0.49123035 0.45934902 0.45384703 0.43611007 0.43495439\n",
            " 0.4335726  0.43930069 0.42359869 0.43706473 0.43480365 0.43992877\n",
            " 0.43307015 0.42372432 0.37950753 0.38475827 0.37840211 0.37367893\n",
            " 0.35629369 0.37247303 0.38282377 0.37659323 0.3727745  0.3735282\n",
            " 0.37501046 0.39726961 0.40161591 0.39498339 0.39345087 0.4023445\n",
            " 0.39204398 0.38794892 0.38837601 0.40681643 0.41442875 0.42259378\n",
            " 0.44452631 0.43673815 0.42952777 0.43347213 0.45532929 0.47540273\n",
            " 0.48236183 0.49138108 0.50758553 0.51673037 0.52602594 0.53421612\n",
            " 0.56006787 0.58403537 0.59767728 0.61998664 0.61516304 0.61292704\n",
            " 0.62458423 0.65953059 0.62187088 0.63480931 0.61468569 0.57561913\n",
            " 0.59863198 0.61378121 0.61694676 0.62767433 0.67302173 0.67970451\n",
            " 0.66478133 0.6594803  0.6473709  0.64782318 0.65335023 0.66819805\n",
            " 0.67694094 0.69955181 0.70698825 0.70176267 0.63051327 0.62327778\n",
            " 0.64598916 0.6364926  0.63415616 0.61244973 0.6394069  0.64189407\n",
            " 0.62591576 0.60843    0.6215192  0.60780192 0.60174723 0.59735067\n",
            " 0.59727901 0.59293795 0.58963096 0.58691943 0.58456093 0.58241743\n",
            " 0.58040851 0.57848662 0.57662338 0.5748027  0.57301587 0.57125896\n",
            " 0.56953108 0.56783295 0.56616592 0.5645318  0.56293213 0.56136829\n",
            " 0.55984128 0.55835158 0.55689943 0.55548477 0.55410731 0.5527665\n",
            " 0.55146164 0.550192   0.54895663 0.54775494 0.54658568 0.5454483\n",
            " 0.5443418  0.54326528 0.54221791 0.54119891 0.54020751 0.53924292\n",
            " 0.53830433 0.53739107]\n",
            "38 day output [[0.5365025]]\n",
            "39 day input [0.36614198 0.34745032 0.34654588 0.31622222 0.31564439 0.28494384\n",
            " 0.27941675 0.24411866 0.25318813 0.27607536 0.27949211 0.24160634\n",
            " 0.25552459 0.24723393 0.25406744 0.2709251  0.26861376 0.2795926\n",
            " 0.28044679 0.27753251 0.30036949 0.29356109 0.28951626 0.30521825\n",
            " 0.33117051 0.33152223 0.33287886 0.37267402 0.41696618 0.38872774\n",
            " 0.40520854 0.42578444 0.44301895 0.44731499 0.45932387 0.46447413\n",
            " 0.47572931 0.49087862 0.46467512 0.4590224  0.49572738 0.50826387\n",
            " 0.47761355 0.4933658  0.46588102 0.47464903 0.47768895 0.46465001\n",
            " 0.49165744 0.48683379 0.47944757 0.44962632 0.46693619 0.45693718\n",
            " 0.44161205 0.4272667  0.40445484 0.40576125 0.40837405 0.43043221\n",
            " 0.4233726  0.43261795 0.42256867 0.4304071  0.46640863 0.4502544\n",
            " 0.4529426  0.44839531 0.45191254 0.44533027 0.45711306 0.46063029\n",
            " 0.49123035 0.45934902 0.45384703 0.43611007 0.43495439 0.4335726\n",
            " 0.43930069 0.42359869 0.43706473 0.43480365 0.43992877 0.43307015\n",
            " 0.42372432 0.37950753 0.38475827 0.37840211 0.37367893 0.35629369\n",
            " 0.37247303 0.38282377 0.37659323 0.3727745  0.3735282  0.37501046\n",
            " 0.39726961 0.40161591 0.39498339 0.39345087 0.4023445  0.39204398\n",
            " 0.38794892 0.38837601 0.40681643 0.41442875 0.42259378 0.44452631\n",
            " 0.43673815 0.42952777 0.43347213 0.45532929 0.47540273 0.48236183\n",
            " 0.49138108 0.50758553 0.51673037 0.52602594 0.53421612 0.56006787\n",
            " 0.58403537 0.59767728 0.61998664 0.61516304 0.61292704 0.62458423\n",
            " 0.65953059 0.62187088 0.63480931 0.61468569 0.57561913 0.59863198\n",
            " 0.61378121 0.61694676 0.62767433 0.67302173 0.67970451 0.66478133\n",
            " 0.6594803  0.6473709  0.64782318 0.65335023 0.66819805 0.67694094\n",
            " 0.69955181 0.70698825 0.70176267 0.63051327 0.62327778 0.64598916\n",
            " 0.6364926  0.63415616 0.61244973 0.6394069  0.64189407 0.62591576\n",
            " 0.60843    0.6215192  0.60780192 0.60174723 0.59735067 0.59727901\n",
            " 0.59293795 0.58963096 0.58691943 0.58456093 0.58241743 0.58040851\n",
            " 0.57848662 0.57662338 0.5748027  0.57301587 0.57125896 0.56953108\n",
            " 0.56783295 0.56616592 0.5645318  0.56293213 0.56136829 0.55984128\n",
            " 0.55835158 0.55689943 0.55548477 0.55410731 0.5527665  0.55146164\n",
            " 0.550192   0.54895663 0.54775494 0.54658568 0.5454483  0.5443418\n",
            " 0.54326528 0.54221791 0.54119891 0.54020751 0.53924292 0.53830433\n",
            " 0.53739107 0.53650248]\n",
            "39 day output [[0.5356379]]\n",
            "40 day input [0.34745032 0.34654588 0.31622222 0.31564439 0.28494384 0.27941675\n",
            " 0.24411866 0.25318813 0.27607536 0.27949211 0.24160634 0.25552459\n",
            " 0.24723393 0.25406744 0.2709251  0.26861376 0.2795926  0.28044679\n",
            " 0.27753251 0.30036949 0.29356109 0.28951626 0.30521825 0.33117051\n",
            " 0.33152223 0.33287886 0.37267402 0.41696618 0.38872774 0.40520854\n",
            " 0.42578444 0.44301895 0.44731499 0.45932387 0.46447413 0.47572931\n",
            " 0.49087862 0.46467512 0.4590224  0.49572738 0.50826387 0.47761355\n",
            " 0.4933658  0.46588102 0.47464903 0.47768895 0.46465001 0.49165744\n",
            " 0.48683379 0.47944757 0.44962632 0.46693619 0.45693718 0.44161205\n",
            " 0.4272667  0.40445484 0.40576125 0.40837405 0.43043221 0.4233726\n",
            " 0.43261795 0.42256867 0.4304071  0.46640863 0.4502544  0.4529426\n",
            " 0.44839531 0.45191254 0.44533027 0.45711306 0.46063029 0.49123035\n",
            " 0.45934902 0.45384703 0.43611007 0.43495439 0.4335726  0.43930069\n",
            " 0.42359869 0.43706473 0.43480365 0.43992877 0.43307015 0.42372432\n",
            " 0.37950753 0.38475827 0.37840211 0.37367893 0.35629369 0.37247303\n",
            " 0.38282377 0.37659323 0.3727745  0.3735282  0.37501046 0.39726961\n",
            " 0.40161591 0.39498339 0.39345087 0.4023445  0.39204398 0.38794892\n",
            " 0.38837601 0.40681643 0.41442875 0.42259378 0.44452631 0.43673815\n",
            " 0.42952777 0.43347213 0.45532929 0.47540273 0.48236183 0.49138108\n",
            " 0.50758553 0.51673037 0.52602594 0.53421612 0.56006787 0.58403537\n",
            " 0.59767728 0.61998664 0.61516304 0.61292704 0.62458423 0.65953059\n",
            " 0.62187088 0.63480931 0.61468569 0.57561913 0.59863198 0.61378121\n",
            " 0.61694676 0.62767433 0.67302173 0.67970451 0.66478133 0.6594803\n",
            " 0.6473709  0.64782318 0.65335023 0.66819805 0.67694094 0.69955181\n",
            " 0.70698825 0.70176267 0.63051327 0.62327778 0.64598916 0.6364926\n",
            " 0.63415616 0.61244973 0.6394069  0.64189407 0.62591576 0.60843\n",
            " 0.6215192  0.60780192 0.60174723 0.59735067 0.59727901 0.59293795\n",
            " 0.58963096 0.58691943 0.58456093 0.58241743 0.58040851 0.57848662\n",
            " 0.57662338 0.5748027  0.57301587 0.57125896 0.56953108 0.56783295\n",
            " 0.56616592 0.5645318  0.56293213 0.56136829 0.55984128 0.55835158\n",
            " 0.55689943 0.55548477 0.55410731 0.5527665  0.55146164 0.550192\n",
            " 0.54895663 0.54775494 0.54658568 0.5454483  0.5443418  0.54326528\n",
            " 0.54221791 0.54119891 0.54020751 0.53924292 0.53830433 0.53739107\n",
            " 0.53650248 0.53563792]\n",
            "40 day output [[0.5347966]]\n",
            "41 day input [0.34654588 0.31622222 0.31564439 0.28494384 0.27941675 0.24411866\n",
            " 0.25318813 0.27607536 0.27949211 0.24160634 0.25552459 0.24723393\n",
            " 0.25406744 0.2709251  0.26861376 0.2795926  0.28044679 0.27753251\n",
            " 0.30036949 0.29356109 0.28951626 0.30521825 0.33117051 0.33152223\n",
            " 0.33287886 0.37267402 0.41696618 0.38872774 0.40520854 0.42578444\n",
            " 0.44301895 0.44731499 0.45932387 0.46447413 0.47572931 0.49087862\n",
            " 0.46467512 0.4590224  0.49572738 0.50826387 0.47761355 0.4933658\n",
            " 0.46588102 0.47464903 0.47768895 0.46465001 0.49165744 0.48683379\n",
            " 0.47944757 0.44962632 0.46693619 0.45693718 0.44161205 0.4272667\n",
            " 0.40445484 0.40576125 0.40837405 0.43043221 0.4233726  0.43261795\n",
            " 0.42256867 0.4304071  0.46640863 0.4502544  0.4529426  0.44839531\n",
            " 0.45191254 0.44533027 0.45711306 0.46063029 0.49123035 0.45934902\n",
            " 0.45384703 0.43611007 0.43495439 0.4335726  0.43930069 0.42359869\n",
            " 0.43706473 0.43480365 0.43992877 0.43307015 0.42372432 0.37950753\n",
            " 0.38475827 0.37840211 0.37367893 0.35629369 0.37247303 0.38282377\n",
            " 0.37659323 0.3727745  0.3735282  0.37501046 0.39726961 0.40161591\n",
            " 0.39498339 0.39345087 0.4023445  0.39204398 0.38794892 0.38837601\n",
            " 0.40681643 0.41442875 0.42259378 0.44452631 0.43673815 0.42952777\n",
            " 0.43347213 0.45532929 0.47540273 0.48236183 0.49138108 0.50758553\n",
            " 0.51673037 0.52602594 0.53421612 0.56006787 0.58403537 0.59767728\n",
            " 0.61998664 0.61516304 0.61292704 0.62458423 0.65953059 0.62187088\n",
            " 0.63480931 0.61468569 0.57561913 0.59863198 0.61378121 0.61694676\n",
            " 0.62767433 0.67302173 0.67970451 0.66478133 0.6594803  0.6473709\n",
            " 0.64782318 0.65335023 0.66819805 0.67694094 0.69955181 0.70698825\n",
            " 0.70176267 0.63051327 0.62327778 0.64598916 0.6364926  0.63415616\n",
            " 0.61244973 0.6394069  0.64189407 0.62591576 0.60843    0.6215192\n",
            " 0.60780192 0.60174723 0.59735067 0.59727901 0.59293795 0.58963096\n",
            " 0.58691943 0.58456093 0.58241743 0.58040851 0.57848662 0.57662338\n",
            " 0.5748027  0.57301587 0.57125896 0.56953108 0.56783295 0.56616592\n",
            " 0.5645318  0.56293213 0.56136829 0.55984128 0.55835158 0.55689943\n",
            " 0.55548477 0.55410731 0.5527665  0.55146164 0.550192   0.54895663\n",
            " 0.54775494 0.54658568 0.5454483  0.5443418  0.54326528 0.54221791\n",
            " 0.54119891 0.54020751 0.53924292 0.53830433 0.53739107 0.53650248\n",
            " 0.53563792 0.5347966 ]\n",
            "41 day output [[0.5339779]]\n",
            "42 day input [0.31622222 0.31564439 0.28494384 0.27941675 0.24411866 0.25318813\n",
            " 0.27607536 0.27949211 0.24160634 0.25552459 0.24723393 0.25406744\n",
            " 0.2709251  0.26861376 0.2795926  0.28044679 0.27753251 0.30036949\n",
            " 0.29356109 0.28951626 0.30521825 0.33117051 0.33152223 0.33287886\n",
            " 0.37267402 0.41696618 0.38872774 0.40520854 0.42578444 0.44301895\n",
            " 0.44731499 0.45932387 0.46447413 0.47572931 0.49087862 0.46467512\n",
            " 0.4590224  0.49572738 0.50826387 0.47761355 0.4933658  0.46588102\n",
            " 0.47464903 0.47768895 0.46465001 0.49165744 0.48683379 0.47944757\n",
            " 0.44962632 0.46693619 0.45693718 0.44161205 0.4272667  0.40445484\n",
            " 0.40576125 0.40837405 0.43043221 0.4233726  0.43261795 0.42256867\n",
            " 0.4304071  0.46640863 0.4502544  0.4529426  0.44839531 0.45191254\n",
            " 0.44533027 0.45711306 0.46063029 0.49123035 0.45934902 0.45384703\n",
            " 0.43611007 0.43495439 0.4335726  0.43930069 0.42359869 0.43706473\n",
            " 0.43480365 0.43992877 0.43307015 0.42372432 0.37950753 0.38475827\n",
            " 0.37840211 0.37367893 0.35629369 0.37247303 0.38282377 0.37659323\n",
            " 0.3727745  0.3735282  0.37501046 0.39726961 0.40161591 0.39498339\n",
            " 0.39345087 0.4023445  0.39204398 0.38794892 0.38837601 0.40681643\n",
            " 0.41442875 0.42259378 0.44452631 0.43673815 0.42952777 0.43347213\n",
            " 0.45532929 0.47540273 0.48236183 0.49138108 0.50758553 0.51673037\n",
            " 0.52602594 0.53421612 0.56006787 0.58403537 0.59767728 0.61998664\n",
            " 0.61516304 0.61292704 0.62458423 0.65953059 0.62187088 0.63480931\n",
            " 0.61468569 0.57561913 0.59863198 0.61378121 0.61694676 0.62767433\n",
            " 0.67302173 0.67970451 0.66478133 0.6594803  0.6473709  0.64782318\n",
            " 0.65335023 0.66819805 0.67694094 0.69955181 0.70698825 0.70176267\n",
            " 0.63051327 0.62327778 0.64598916 0.6364926  0.63415616 0.61244973\n",
            " 0.6394069  0.64189407 0.62591576 0.60843    0.6215192  0.60780192\n",
            " 0.60174723 0.59735067 0.59727901 0.59293795 0.58963096 0.58691943\n",
            " 0.58456093 0.58241743 0.58040851 0.57848662 0.57662338 0.5748027\n",
            " 0.57301587 0.57125896 0.56953108 0.56783295 0.56616592 0.5645318\n",
            " 0.56293213 0.56136829 0.55984128 0.55835158 0.55689943 0.55548477\n",
            " 0.55410731 0.5527665  0.55146164 0.550192   0.54895663 0.54775494\n",
            " 0.54658568 0.5454483  0.5443418  0.54326528 0.54221791 0.54119891\n",
            " 0.54020751 0.53924292 0.53830433 0.53739107 0.53650248 0.53563792\n",
            " 0.5347966  0.53397793]\n",
            "42 day output [[0.5331813]]\n",
            "43 day input [0.31564439 0.28494384 0.27941675 0.24411866 0.25318813 0.27607536\n",
            " 0.27949211 0.24160634 0.25552459 0.24723393 0.25406744 0.2709251\n",
            " 0.26861376 0.2795926  0.28044679 0.27753251 0.30036949 0.29356109\n",
            " 0.28951626 0.30521825 0.33117051 0.33152223 0.33287886 0.37267402\n",
            " 0.41696618 0.38872774 0.40520854 0.42578444 0.44301895 0.44731499\n",
            " 0.45932387 0.46447413 0.47572931 0.49087862 0.46467512 0.4590224\n",
            " 0.49572738 0.50826387 0.47761355 0.4933658  0.46588102 0.47464903\n",
            " 0.47768895 0.46465001 0.49165744 0.48683379 0.47944757 0.44962632\n",
            " 0.46693619 0.45693718 0.44161205 0.4272667  0.40445484 0.40576125\n",
            " 0.40837405 0.43043221 0.4233726  0.43261795 0.42256867 0.4304071\n",
            " 0.46640863 0.4502544  0.4529426  0.44839531 0.45191254 0.44533027\n",
            " 0.45711306 0.46063029 0.49123035 0.45934902 0.45384703 0.43611007\n",
            " 0.43495439 0.4335726  0.43930069 0.42359869 0.43706473 0.43480365\n",
            " 0.43992877 0.43307015 0.42372432 0.37950753 0.38475827 0.37840211\n",
            " 0.37367893 0.35629369 0.37247303 0.38282377 0.37659323 0.3727745\n",
            " 0.3735282  0.37501046 0.39726961 0.40161591 0.39498339 0.39345087\n",
            " 0.4023445  0.39204398 0.38794892 0.38837601 0.40681643 0.41442875\n",
            " 0.42259378 0.44452631 0.43673815 0.42952777 0.43347213 0.45532929\n",
            " 0.47540273 0.48236183 0.49138108 0.50758553 0.51673037 0.52602594\n",
            " 0.53421612 0.56006787 0.58403537 0.59767728 0.61998664 0.61516304\n",
            " 0.61292704 0.62458423 0.65953059 0.62187088 0.63480931 0.61468569\n",
            " 0.57561913 0.59863198 0.61378121 0.61694676 0.62767433 0.67302173\n",
            " 0.67970451 0.66478133 0.6594803  0.6473709  0.64782318 0.65335023\n",
            " 0.66819805 0.67694094 0.69955181 0.70698825 0.70176267 0.63051327\n",
            " 0.62327778 0.64598916 0.6364926  0.63415616 0.61244973 0.6394069\n",
            " 0.64189407 0.62591576 0.60843    0.6215192  0.60780192 0.60174723\n",
            " 0.59735067 0.59727901 0.59293795 0.58963096 0.58691943 0.58456093\n",
            " 0.58241743 0.58040851 0.57848662 0.57662338 0.5748027  0.57301587\n",
            " 0.57125896 0.56953108 0.56783295 0.56616592 0.5645318  0.56293213\n",
            " 0.56136829 0.55984128 0.55835158 0.55689943 0.55548477 0.55410731\n",
            " 0.5527665  0.55146164 0.550192   0.54895663 0.54775494 0.54658568\n",
            " 0.5454483  0.5443418  0.54326528 0.54221791 0.54119891 0.54020751\n",
            " 0.53924292 0.53830433 0.53739107 0.53650248 0.53563792 0.5347966\n",
            " 0.53397793 0.53318131]\n",
            "43 day output [[0.53240615]]\n",
            "44 day input [0.28494384 0.27941675 0.24411866 0.25318813 0.27607536 0.27949211\n",
            " 0.24160634 0.25552459 0.24723393 0.25406744 0.2709251  0.26861376\n",
            " 0.2795926  0.28044679 0.27753251 0.30036949 0.29356109 0.28951626\n",
            " 0.30521825 0.33117051 0.33152223 0.33287886 0.37267402 0.41696618\n",
            " 0.38872774 0.40520854 0.42578444 0.44301895 0.44731499 0.45932387\n",
            " 0.46447413 0.47572931 0.49087862 0.46467512 0.4590224  0.49572738\n",
            " 0.50826387 0.47761355 0.4933658  0.46588102 0.47464903 0.47768895\n",
            " 0.46465001 0.49165744 0.48683379 0.47944757 0.44962632 0.46693619\n",
            " 0.45693718 0.44161205 0.4272667  0.40445484 0.40576125 0.40837405\n",
            " 0.43043221 0.4233726  0.43261795 0.42256867 0.4304071  0.46640863\n",
            " 0.4502544  0.4529426  0.44839531 0.45191254 0.44533027 0.45711306\n",
            " 0.46063029 0.49123035 0.45934902 0.45384703 0.43611007 0.43495439\n",
            " 0.4335726  0.43930069 0.42359869 0.43706473 0.43480365 0.43992877\n",
            " 0.43307015 0.42372432 0.37950753 0.38475827 0.37840211 0.37367893\n",
            " 0.35629369 0.37247303 0.38282377 0.37659323 0.3727745  0.3735282\n",
            " 0.37501046 0.39726961 0.40161591 0.39498339 0.39345087 0.4023445\n",
            " 0.39204398 0.38794892 0.38837601 0.40681643 0.41442875 0.42259378\n",
            " 0.44452631 0.43673815 0.42952777 0.43347213 0.45532929 0.47540273\n",
            " 0.48236183 0.49138108 0.50758553 0.51673037 0.52602594 0.53421612\n",
            " 0.56006787 0.58403537 0.59767728 0.61998664 0.61516304 0.61292704\n",
            " 0.62458423 0.65953059 0.62187088 0.63480931 0.61468569 0.57561913\n",
            " 0.59863198 0.61378121 0.61694676 0.62767433 0.67302173 0.67970451\n",
            " 0.66478133 0.6594803  0.6473709  0.64782318 0.65335023 0.66819805\n",
            " 0.67694094 0.69955181 0.70698825 0.70176267 0.63051327 0.62327778\n",
            " 0.64598916 0.6364926  0.63415616 0.61244973 0.6394069  0.64189407\n",
            " 0.62591576 0.60843    0.6215192  0.60780192 0.60174723 0.59735067\n",
            " 0.59727901 0.59293795 0.58963096 0.58691943 0.58456093 0.58241743\n",
            " 0.58040851 0.57848662 0.57662338 0.5748027  0.57301587 0.57125896\n",
            " 0.56953108 0.56783295 0.56616592 0.5645318  0.56293213 0.56136829\n",
            " 0.55984128 0.55835158 0.55689943 0.55548477 0.55410731 0.5527665\n",
            " 0.55146164 0.550192   0.54895663 0.54775494 0.54658568 0.5454483\n",
            " 0.5443418  0.54326528 0.54221791 0.54119891 0.54020751 0.53924292\n",
            " 0.53830433 0.53739107 0.53650248 0.53563792 0.5347966  0.53397793\n",
            " 0.53318131 0.53240615]\n",
            "44 day output [[0.53165185]]\n",
            "45 day input [0.27941675 0.24411866 0.25318813 0.27607536 0.27949211 0.24160634\n",
            " 0.25552459 0.24723393 0.25406744 0.2709251  0.26861376 0.2795926\n",
            " 0.28044679 0.27753251 0.30036949 0.29356109 0.28951626 0.30521825\n",
            " 0.33117051 0.33152223 0.33287886 0.37267402 0.41696618 0.38872774\n",
            " 0.40520854 0.42578444 0.44301895 0.44731499 0.45932387 0.46447413\n",
            " 0.47572931 0.49087862 0.46467512 0.4590224  0.49572738 0.50826387\n",
            " 0.47761355 0.4933658  0.46588102 0.47464903 0.47768895 0.46465001\n",
            " 0.49165744 0.48683379 0.47944757 0.44962632 0.46693619 0.45693718\n",
            " 0.44161205 0.4272667  0.40445484 0.40576125 0.40837405 0.43043221\n",
            " 0.4233726  0.43261795 0.42256867 0.4304071  0.46640863 0.4502544\n",
            " 0.4529426  0.44839531 0.45191254 0.44533027 0.45711306 0.46063029\n",
            " 0.49123035 0.45934902 0.45384703 0.43611007 0.43495439 0.4335726\n",
            " 0.43930069 0.42359869 0.43706473 0.43480365 0.43992877 0.43307015\n",
            " 0.42372432 0.37950753 0.38475827 0.37840211 0.37367893 0.35629369\n",
            " 0.37247303 0.38282377 0.37659323 0.3727745  0.3735282  0.37501046\n",
            " 0.39726961 0.40161591 0.39498339 0.39345087 0.4023445  0.39204398\n",
            " 0.38794892 0.38837601 0.40681643 0.41442875 0.42259378 0.44452631\n",
            " 0.43673815 0.42952777 0.43347213 0.45532929 0.47540273 0.48236183\n",
            " 0.49138108 0.50758553 0.51673037 0.52602594 0.53421612 0.56006787\n",
            " 0.58403537 0.59767728 0.61998664 0.61516304 0.61292704 0.62458423\n",
            " 0.65953059 0.62187088 0.63480931 0.61468569 0.57561913 0.59863198\n",
            " 0.61378121 0.61694676 0.62767433 0.67302173 0.67970451 0.66478133\n",
            " 0.6594803  0.6473709  0.64782318 0.65335023 0.66819805 0.67694094\n",
            " 0.69955181 0.70698825 0.70176267 0.63051327 0.62327778 0.64598916\n",
            " 0.6364926  0.63415616 0.61244973 0.6394069  0.64189407 0.62591576\n",
            " 0.60843    0.6215192  0.60780192 0.60174723 0.59735067 0.59727901\n",
            " 0.59293795 0.58963096 0.58691943 0.58456093 0.58241743 0.58040851\n",
            " 0.57848662 0.57662338 0.5748027  0.57301587 0.57125896 0.56953108\n",
            " 0.56783295 0.56616592 0.5645318  0.56293213 0.56136829 0.55984128\n",
            " 0.55835158 0.55689943 0.55548477 0.55410731 0.5527665  0.55146164\n",
            " 0.550192   0.54895663 0.54775494 0.54658568 0.5454483  0.5443418\n",
            " 0.54326528 0.54221791 0.54119891 0.54020751 0.53924292 0.53830433\n",
            " 0.53739107 0.53650248 0.53563792 0.5347966  0.53397793 0.53318131\n",
            " 0.53240615 0.53165185]\n",
            "45 day output [[0.53091776]]\n",
            "46 day input [0.24411866 0.25318813 0.27607536 0.27949211 0.24160634 0.25552459\n",
            " 0.24723393 0.25406744 0.2709251  0.26861376 0.2795926  0.28044679\n",
            " 0.27753251 0.30036949 0.29356109 0.28951626 0.30521825 0.33117051\n",
            " 0.33152223 0.33287886 0.37267402 0.41696618 0.38872774 0.40520854\n",
            " 0.42578444 0.44301895 0.44731499 0.45932387 0.46447413 0.47572931\n",
            " 0.49087862 0.46467512 0.4590224  0.49572738 0.50826387 0.47761355\n",
            " 0.4933658  0.46588102 0.47464903 0.47768895 0.46465001 0.49165744\n",
            " 0.48683379 0.47944757 0.44962632 0.46693619 0.45693718 0.44161205\n",
            " 0.4272667  0.40445484 0.40576125 0.40837405 0.43043221 0.4233726\n",
            " 0.43261795 0.42256867 0.4304071  0.46640863 0.4502544  0.4529426\n",
            " 0.44839531 0.45191254 0.44533027 0.45711306 0.46063029 0.49123035\n",
            " 0.45934902 0.45384703 0.43611007 0.43495439 0.4335726  0.43930069\n",
            " 0.42359869 0.43706473 0.43480365 0.43992877 0.43307015 0.42372432\n",
            " 0.37950753 0.38475827 0.37840211 0.37367893 0.35629369 0.37247303\n",
            " 0.38282377 0.37659323 0.3727745  0.3735282  0.37501046 0.39726961\n",
            " 0.40161591 0.39498339 0.39345087 0.4023445  0.39204398 0.38794892\n",
            " 0.38837601 0.40681643 0.41442875 0.42259378 0.44452631 0.43673815\n",
            " 0.42952777 0.43347213 0.45532929 0.47540273 0.48236183 0.49138108\n",
            " 0.50758553 0.51673037 0.52602594 0.53421612 0.56006787 0.58403537\n",
            " 0.59767728 0.61998664 0.61516304 0.61292704 0.62458423 0.65953059\n",
            " 0.62187088 0.63480931 0.61468569 0.57561913 0.59863198 0.61378121\n",
            " 0.61694676 0.62767433 0.67302173 0.67970451 0.66478133 0.6594803\n",
            " 0.6473709  0.64782318 0.65335023 0.66819805 0.67694094 0.69955181\n",
            " 0.70698825 0.70176267 0.63051327 0.62327778 0.64598916 0.6364926\n",
            " 0.63415616 0.61244973 0.6394069  0.64189407 0.62591576 0.60843\n",
            " 0.6215192  0.60780192 0.60174723 0.59735067 0.59727901 0.59293795\n",
            " 0.58963096 0.58691943 0.58456093 0.58241743 0.58040851 0.57848662\n",
            " 0.57662338 0.5748027  0.57301587 0.57125896 0.56953108 0.56783295\n",
            " 0.56616592 0.5645318  0.56293213 0.56136829 0.55984128 0.55835158\n",
            " 0.55689943 0.55548477 0.55410731 0.5527665  0.55146164 0.550192\n",
            " 0.54895663 0.54775494 0.54658568 0.5454483  0.5443418  0.54326528\n",
            " 0.54221791 0.54119891 0.54020751 0.53924292 0.53830433 0.53739107\n",
            " 0.53650248 0.53563792 0.5347966  0.53397793 0.53318131 0.53240615\n",
            " 0.53165185 0.53091776]\n",
            "46 day output [[0.53020346]]\n",
            "47 day input [0.25318813 0.27607536 0.27949211 0.24160634 0.25552459 0.24723393\n",
            " 0.25406744 0.2709251  0.26861376 0.2795926  0.28044679 0.27753251\n",
            " 0.30036949 0.29356109 0.28951626 0.30521825 0.33117051 0.33152223\n",
            " 0.33287886 0.37267402 0.41696618 0.38872774 0.40520854 0.42578444\n",
            " 0.44301895 0.44731499 0.45932387 0.46447413 0.47572931 0.49087862\n",
            " 0.46467512 0.4590224  0.49572738 0.50826387 0.47761355 0.4933658\n",
            " 0.46588102 0.47464903 0.47768895 0.46465001 0.49165744 0.48683379\n",
            " 0.47944757 0.44962632 0.46693619 0.45693718 0.44161205 0.4272667\n",
            " 0.40445484 0.40576125 0.40837405 0.43043221 0.4233726  0.43261795\n",
            " 0.42256867 0.4304071  0.46640863 0.4502544  0.4529426  0.44839531\n",
            " 0.45191254 0.44533027 0.45711306 0.46063029 0.49123035 0.45934902\n",
            " 0.45384703 0.43611007 0.43495439 0.4335726  0.43930069 0.42359869\n",
            " 0.43706473 0.43480365 0.43992877 0.43307015 0.42372432 0.37950753\n",
            " 0.38475827 0.37840211 0.37367893 0.35629369 0.37247303 0.38282377\n",
            " 0.37659323 0.3727745  0.3735282  0.37501046 0.39726961 0.40161591\n",
            " 0.39498339 0.39345087 0.4023445  0.39204398 0.38794892 0.38837601\n",
            " 0.40681643 0.41442875 0.42259378 0.44452631 0.43673815 0.42952777\n",
            " 0.43347213 0.45532929 0.47540273 0.48236183 0.49138108 0.50758553\n",
            " 0.51673037 0.52602594 0.53421612 0.56006787 0.58403537 0.59767728\n",
            " 0.61998664 0.61516304 0.61292704 0.62458423 0.65953059 0.62187088\n",
            " 0.63480931 0.61468569 0.57561913 0.59863198 0.61378121 0.61694676\n",
            " 0.62767433 0.67302173 0.67970451 0.66478133 0.6594803  0.6473709\n",
            " 0.64782318 0.65335023 0.66819805 0.67694094 0.69955181 0.70698825\n",
            " 0.70176267 0.63051327 0.62327778 0.64598916 0.6364926  0.63415616\n",
            " 0.61244973 0.6394069  0.64189407 0.62591576 0.60843    0.6215192\n",
            " 0.60780192 0.60174723 0.59735067 0.59727901 0.59293795 0.58963096\n",
            " 0.58691943 0.58456093 0.58241743 0.58040851 0.57848662 0.57662338\n",
            " 0.5748027  0.57301587 0.57125896 0.56953108 0.56783295 0.56616592\n",
            " 0.5645318  0.56293213 0.56136829 0.55984128 0.55835158 0.55689943\n",
            " 0.55548477 0.55410731 0.5527665  0.55146164 0.550192   0.54895663\n",
            " 0.54775494 0.54658568 0.5454483  0.5443418  0.54326528 0.54221791\n",
            " 0.54119891 0.54020751 0.53924292 0.53830433 0.53739107 0.53650248\n",
            " 0.53563792 0.5347966  0.53397793 0.53318131 0.53240615 0.53165185\n",
            " 0.53091776 0.53020346]\n",
            "47 day output [[0.52950835]]\n",
            "48 day input [0.27607536 0.27949211 0.24160634 0.25552459 0.24723393 0.25406744\n",
            " 0.2709251  0.26861376 0.2795926  0.28044679 0.27753251 0.30036949\n",
            " 0.29356109 0.28951626 0.30521825 0.33117051 0.33152223 0.33287886\n",
            " 0.37267402 0.41696618 0.38872774 0.40520854 0.42578444 0.44301895\n",
            " 0.44731499 0.45932387 0.46447413 0.47572931 0.49087862 0.46467512\n",
            " 0.4590224  0.49572738 0.50826387 0.47761355 0.4933658  0.46588102\n",
            " 0.47464903 0.47768895 0.46465001 0.49165744 0.48683379 0.47944757\n",
            " 0.44962632 0.46693619 0.45693718 0.44161205 0.4272667  0.40445484\n",
            " 0.40576125 0.40837405 0.43043221 0.4233726  0.43261795 0.42256867\n",
            " 0.4304071  0.46640863 0.4502544  0.4529426  0.44839531 0.45191254\n",
            " 0.44533027 0.45711306 0.46063029 0.49123035 0.45934902 0.45384703\n",
            " 0.43611007 0.43495439 0.4335726  0.43930069 0.42359869 0.43706473\n",
            " 0.43480365 0.43992877 0.43307015 0.42372432 0.37950753 0.38475827\n",
            " 0.37840211 0.37367893 0.35629369 0.37247303 0.38282377 0.37659323\n",
            " 0.3727745  0.3735282  0.37501046 0.39726961 0.40161591 0.39498339\n",
            " 0.39345087 0.4023445  0.39204398 0.38794892 0.38837601 0.40681643\n",
            " 0.41442875 0.42259378 0.44452631 0.43673815 0.42952777 0.43347213\n",
            " 0.45532929 0.47540273 0.48236183 0.49138108 0.50758553 0.51673037\n",
            " 0.52602594 0.53421612 0.56006787 0.58403537 0.59767728 0.61998664\n",
            " 0.61516304 0.61292704 0.62458423 0.65953059 0.62187088 0.63480931\n",
            " 0.61468569 0.57561913 0.59863198 0.61378121 0.61694676 0.62767433\n",
            " 0.67302173 0.67970451 0.66478133 0.6594803  0.6473709  0.64782318\n",
            " 0.65335023 0.66819805 0.67694094 0.69955181 0.70698825 0.70176267\n",
            " 0.63051327 0.62327778 0.64598916 0.6364926  0.63415616 0.61244973\n",
            " 0.6394069  0.64189407 0.62591576 0.60843    0.6215192  0.60780192\n",
            " 0.60174723 0.59735067 0.59727901 0.59293795 0.58963096 0.58691943\n",
            " 0.58456093 0.58241743 0.58040851 0.57848662 0.57662338 0.5748027\n",
            " 0.57301587 0.57125896 0.56953108 0.56783295 0.56616592 0.5645318\n",
            " 0.56293213 0.56136829 0.55984128 0.55835158 0.55689943 0.55548477\n",
            " 0.55410731 0.5527665  0.55146164 0.550192   0.54895663 0.54775494\n",
            " 0.54658568 0.5454483  0.5443418  0.54326528 0.54221791 0.54119891\n",
            " 0.54020751 0.53924292 0.53830433 0.53739107 0.53650248 0.53563792\n",
            " 0.5347966  0.53397793 0.53318131 0.53240615 0.53165185 0.53091776\n",
            " 0.53020346 0.52950835]\n",
            "48 day output [[0.52883196]]\n",
            "49 day input [0.27949211 0.24160634 0.25552459 0.24723393 0.25406744 0.2709251\n",
            " 0.26861376 0.2795926  0.28044679 0.27753251 0.30036949 0.29356109\n",
            " 0.28951626 0.30521825 0.33117051 0.33152223 0.33287886 0.37267402\n",
            " 0.41696618 0.38872774 0.40520854 0.42578444 0.44301895 0.44731499\n",
            " 0.45932387 0.46447413 0.47572931 0.49087862 0.46467512 0.4590224\n",
            " 0.49572738 0.50826387 0.47761355 0.4933658  0.46588102 0.47464903\n",
            " 0.47768895 0.46465001 0.49165744 0.48683379 0.47944757 0.44962632\n",
            " 0.46693619 0.45693718 0.44161205 0.4272667  0.40445484 0.40576125\n",
            " 0.40837405 0.43043221 0.4233726  0.43261795 0.42256867 0.4304071\n",
            " 0.46640863 0.4502544  0.4529426  0.44839531 0.45191254 0.44533027\n",
            " 0.45711306 0.46063029 0.49123035 0.45934902 0.45384703 0.43611007\n",
            " 0.43495439 0.4335726  0.43930069 0.42359869 0.43706473 0.43480365\n",
            " 0.43992877 0.43307015 0.42372432 0.37950753 0.38475827 0.37840211\n",
            " 0.37367893 0.35629369 0.37247303 0.38282377 0.37659323 0.3727745\n",
            " 0.3735282  0.37501046 0.39726961 0.40161591 0.39498339 0.39345087\n",
            " 0.4023445  0.39204398 0.38794892 0.38837601 0.40681643 0.41442875\n",
            " 0.42259378 0.44452631 0.43673815 0.42952777 0.43347213 0.45532929\n",
            " 0.47540273 0.48236183 0.49138108 0.50758553 0.51673037 0.52602594\n",
            " 0.53421612 0.56006787 0.58403537 0.59767728 0.61998664 0.61516304\n",
            " 0.61292704 0.62458423 0.65953059 0.62187088 0.63480931 0.61468569\n",
            " 0.57561913 0.59863198 0.61378121 0.61694676 0.62767433 0.67302173\n",
            " 0.67970451 0.66478133 0.6594803  0.6473709  0.64782318 0.65335023\n",
            " 0.66819805 0.67694094 0.69955181 0.70698825 0.70176267 0.63051327\n",
            " 0.62327778 0.64598916 0.6364926  0.63415616 0.61244973 0.6394069\n",
            " 0.64189407 0.62591576 0.60843    0.6215192  0.60780192 0.60174723\n",
            " 0.59735067 0.59727901 0.59293795 0.58963096 0.58691943 0.58456093\n",
            " 0.58241743 0.58040851 0.57848662 0.57662338 0.5748027  0.57301587\n",
            " 0.57125896 0.56953108 0.56783295 0.56616592 0.5645318  0.56293213\n",
            " 0.56136829 0.55984128 0.55835158 0.55689943 0.55548477 0.55410731\n",
            " 0.5527665  0.55146164 0.550192   0.54895663 0.54775494 0.54658568\n",
            " 0.5454483  0.5443418  0.54326528 0.54221791 0.54119891 0.54020751\n",
            " 0.53924292 0.53830433 0.53739107 0.53650248 0.53563792 0.5347966\n",
            " 0.53397793 0.53318131 0.53240615 0.53165185 0.53091776 0.53020346\n",
            " 0.52950835 0.52883196]\n",
            "49 day output [[0.5281737]]\n",
            "[[0.5972790122032166], [0.5929379463195801], [0.5896309614181519], [0.5869194269180298], [0.5845609307289124], [0.5824174284934998], [0.58040851354599], [0.5784866213798523], [0.5766233801841736], [0.5748026967048645], [0.5730158686637878], [0.5712589621543884], [0.5695310831069946], [0.5678329467773438], [0.5661659240722656], [0.5645318031311035], [0.5629321336746216], [0.5613682866096497], [0.5598412752151489], [0.5583515763282776], [0.5568994283676147], [0.5554847717285156], [0.5541073083877563], [0.5527665019035339], [0.5514616370201111], [0.5501919984817505], [0.5489566326141357], [0.5477549433708191], [0.5465856790542603], [0.5454483032226562], [0.5443418025970459], [0.5432652831077576], [0.5422179102897644], [0.5411989092826843], [0.5402075052261353], [0.5392429232597351], [0.538304328918457], [0.5373910665512085], [0.536502480506897], [0.5356379151344299], [0.5347965955734253], [0.5339779257774353], [0.5331813097000122], [0.5324061512947083], [0.5316518545150757], [0.530917763710022], [0.5302034616470337], [0.5295083522796631], [0.528831958770752], [0.5281736850738525]]\n"
          ]
        }
      ],
      "source": [
        "# Next 50 days prediction\n",
        "from numpy import array\n",
        "lst_output=[]\n",
        "n_steps=200\n",
        "i=0\n",
        "while(i<50):\n",
        "\n",
        "    if(len(temp_list)>200):\n",
        "        x_input=np.array(temp_list[1:])\n",
        "        print(\"{} day input {}\".format(i,x_input))\n",
        "        x_input=x_input.reshape(1,-1)\n",
        "        x_input=x_input.reshape((1,n_steps,1))\n",
        "        yhat=model.predict(x_input,verbose=0)\n",
        "        print(\"{} day output {}\".format(i,yhat))\n",
        "        temp_list.extend(yhat[0].tolist())\n",
        "        temp_list=temp_list[1:]\n",
        "        lst_output.extend(yhat.tolist())\n",
        "        i=i+1\n",
        "    else:\n",
        "        x_input=x_input.reshape((-1,n_steps,1))\n",
        "        yhat=model.predict(x_input,verbose=0)\n",
        "        print(yhat[0])\n",
        "        temp_list.extend(yhat[0].tolist())\n",
        "        print(len(temp_list))\n",
        "        lst_output.extend(yhat.tolist())\n",
        "        i=i+1\n",
        "\n",
        "\n",
        "print(lst_output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "f8d32804",
      "metadata": {
        "id": "f8d32804"
      },
      "outputs": [],
      "source": [
        "days_200=np.arange(1,201)\n",
        "days_pred=np.arange(201,251)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "1e3a0475",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1e3a0475",
        "outputId": "c1bcfd32-73de-45aa-ffd8-caacebeb8b3e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1258"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "len(dfc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "73f9ef84",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "73f9ef84",
        "outputId": "f8f5b6c0-9fec-4190-87df-f33b4671638c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7b4148c73f10>]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(days_200,scaler.inverse_transform(dfc[1058:]))\n",
        "plt.plot(days_pred,scaler.inverse_transform(lst_output))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "7ff62fad",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "7ff62fad",
        "outputId": "b248e6ab-ddaa-4dbc-9d2d-69b23612efd2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7b4148cc2500>]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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WHCqt73NtlcOl9fjzf3IBAM/cMBWh/vb35gyGv9IL+tYOFt4Skcuzq4fFaDQiJycHaWlpXTeQSJCWloasrKw+r/nss8+QmpqKVatWQa1WY9q0aXjxxRdhMvUcM8/Ly0NERARiY2Nxxx13oKSkZAgfhzzJ+dOYXaGGpdo2pXno+/kovaS4coqlx+TrPoaFDK3tePjdQ+gwi7guMRy3Jo8d8s+6EH/bWixc7ZaIXJtdgaW6uhomkwlqtbrHcbVaDa2272mahYWF+OCDD2AymfDVV1/h6aefxp///Gc8//zztnNSUlLwxhtvYMeOHfj73/+Os2fPYuHChTAYDH3es62tDXq9vseLPI91OCiss3fB0NaBJif3BAx1SvP5rplmGRb6+ri21wydtZ+eQEltMyIDvfHiL6dDEBxbbNudbWozh4SIyMWN+Cwhs9mMsLAw/POf/0RSUhKWLl2Kp556Cps3b7adc8011+DWW29FYmIi0tPT8dVXX6G+vh7vvfden/dcv349VCqV7RUVFTXSH4OcwDpDaKLaz/bF6uzCW0cFlssnh8JHLkVZfQuOnGsAYBkC27avBB8fKoNEAP5y20yovL2G3eaB2HpYOCRERC7OrkH4kJAQSKVS6HS6Hsd1Ol2/9Sfh4eHw8vKCVNq1pPiUKVOg1WphNBohl/fuWg8MDMSkSZOQn9/3nitPPvkkMjIybL/W6/UMLR6ovHMNlnCVN7QNrWis6oCuoRUTQv2c1qaaYazB0p3SS4or4sPwxdEK3P+vHHSYxR71OquvnITk6KBh/YzB8ONqt0TkJuzqYZHL5UhKSkJmZqbtmNlsRmZmJlJTU/u8Zv78+cjPz4fZbLYdO3PmDMLDw/sMKwDQ2NiIgoIChIeH9/m+QqFAQEBAjxd5nvLOmpWIQG9oVEoAntPDAgBLZlpmymn1rbb7ymUS3HRJJB68Im7Y9x+MwQ4JVRnaWOdCRE5l9zSHjIwMLF++HMnJyZgzZw42btyIpqYm26yhZcuWITIyEuvXrwcAPPDAA/jb3/6G1atX46GHHkJeXh5efPFFPPzww7Z7PvbYY7j++usxfvx4lJeXY926dZBKpbj99tsd9DHJHVV09rBEqJQoC/C2HHNy4W2NA4purdKmhOH1u2ej3WRGRKA3wlVKBPnKR7Rm5XwBttVu+w8jlYZWLHplJ8YH++KLhxY4fAE7IqLBsDuwLF26FFVVVVi7di20Wi1mzpyJHTt22ApxS0pKIJF0ddxERUXhm2++waOPPorExERERkZi9erVePzxx23nnDt3DrfffjtqamoQGhqKBQsWYO/evQgNDXXARyR3ZQ0n4YHe0KiaATh/LZYqB/awCIKARfGOX1/FHtYeloGGhA4W16PJaMLJCj12nalyepuJaHQa0kISDz74IB588ME+39u5c2evY6mpqdi7d2+/99u2bdtQmkEezrqPUGSgEpqAziEhp/ewWJflH34PiysYTNFtrrZrtt6bWUUMLETkFNxLiFxSY1uH7W/94SpvqDsDi7N7WKzrsAxlWX5XZC26HaiGJVfXtWzArjNVKKpuGvF2ERGdj4GFXJK1fiVAKYOvQuYSRbfGDjMaWiy1Ho4YEnIFXUNC/dewnK6w9LAE+cohisDbe4uH/XNziutwy9/34L/eOzLsexHR6MDAQi7JOhwUEWgptrUOCVUZ2tBhMvd73UiqbbL0rkglwoivj3KxdBXd9t3D0tpuQlGNpUdlTfpkAMB7B0rRYhza7s4Nze34w8fHcMvmPThQXIcPD57rsSM3EVF/GFjIJVV0m9IMAMF+CsgkAsxiV+HrxWadehzsK/eYmTJ+yoGnNefpGmEWgTE+Xrg1OQrjgnygb+3Ap4fL7P5ZXxwtx5UbduHf2SUQRcBHblmbKbuwdugfgIhGDQYWckkVtkXjLD0rUolgW6LfWYW3tsDiIcNBwIVnCZ3WWupXJmv8IZUIuGvueADAm1nFvbYUGMihkjo8+O9DqG5sQ2yoL95dORd3dt5rb2HNcD4CEY0SDCzkksrP62EBALXKuYW31Q5a5daV+FuLbo0dMJt7BxDrDKF4jWVxxluTx0LpJcGpCj0OFNcN+ufkdJ6bGhuMr1cvROqEYMyNtazky8BCRIPBwEIuqfy8HhYATp/aXOPANVhchXVasygCze2961JydZbAMlnjDwAI9JFjyQzLCr1vZQ2++Lawc2ZR0vgxUMgsQ0HJ0UGQCEBRTTPrWIjoghhYyCWdX8MCwDa1Wat3bg2LJ/WwKGQSyDrrcfqaKXRa2zOwAMBdqZahnC+PluO5L05CP4gl+89WWQJLTIiv7ViA0gvTIlUAWMdCRBfGwEIuRxRFWw9LhKorsGj6GRKqazLiq2MVfQ5pOFLXkJDn9LAIgmDrZTm/8La2yYgqgyWkTVJ3BZZpkSr8OmUczCLw2u6zWPTKTmzfXzLg8z/b2cMSE+rb4/jc2GAAHBYiogtjYCGXU9fcjrYOy9RltaorHFiHh84fPnjqk2P43TsH8dEh+2eu2MMTi26BrplC+vMCi7XgNirI21aca/XiL6fjrd/MwYRQX9Q0GfH4h8dw09/3oKmP6dFNbR229XNiQ84PLKxjIaLBYWAhl2PtXQn1V9jqHQB0W+22a0io2diBzFOVAID9Z0d2WMETi24BwE/RWXh7XtiwFtxOVve9G/qlk0Kx45FL8cfrpsBPIcPh0np8dqS813nWdVzG+Hgh0Kfns2MdCxENFgMLuZzybrs0d9e96NY6pfanvGpbb8yxsoYRbZcnFt0C6HdIqGuGkH+va6y8pBLcuzAWt8+JAgCcqtD3Oqeo2rJxZcx5vSsA61iIaPAYWMjl2HZp7la/AnTVsLS0m2zDF9+e1NneP6MzoLWPmS6OYDaLqGnyvBoWAPDvZ3n+vgpu+5MQYemF6SuwnK1uBADEhPj1eS3rWIhoMBhYyOWUdw4NhAf27GFRekltS+Lr9K0wmUV8f7rS9n6HWeyxs7AjNbS0w9RZVBrk61lDQv62GpauwGI2iziju3APi9WUcEtgOV1h6LWgnHVKc2xo7x4WgHUsRDQ4DCzkcsrrLT0skYHevd6zFt5qG1qRU1yH2iYjVN5emB9n+Vv6SA0LWQtuVd5ekMs864+Ntefjw5wyWyg7V9eCZqMJcqkE0X0M5ZxvQqgf5FIJDG0dOFfXsxbFNkOon/uwjoWIBsOz/s9LHqFrWf7egUXdrY7l25NaAMAV8WGYGRUIADhRPlKBxTMLbgHg7nnRUHl7IVdnwAc5pQC6ZghNCPODl/TC/5vwkkoQF2YJPifKew4LXSiwsI6FiAaDgYVcjq2G5bwhIaBb4a2+1Va/clWCGtMiLF94I93D4mlTmgFA5eOFh66IAwD8+T9n0GzsGFTB7fmsw0Ld61jqmoyob7YMNUUH999TwzoWIroQBhZyKSazaFuzI6KvHpbOIaHd+dUoqmmGXCrBpZNCbX9Dz9Ua0Nbh+MLbPQXVAHqvI+Ip7kodj6ggb1Qa2vB/P53Fad3gC26tpoRbzu0eWKz1KxEqJbzl0j6vA1jHQkQXxsBCLqXSYCmmlUkEhPr37s2w9rDs61xzZV5cMPwUMowd441AHy+0m0Sc0TY6tE2t7SZ8caQCAHDDzAiH3ttVKGRSrEmPBwBs3lWAg52bFdoTWBKsPSzarsDS3wq352MdCxFdCAMLuRRrwa06QAlp5x433WlUPUPMVQlqAJYl5qdHjsyw0DcntDC0dSAy0BtzY4Idem9X8ovEcMyMCkSz0WQblhvKkFBpbYttinTXlOaBA0uA0ss2NfpA0eB3gSai0YOBhVyKtWg2Kqj3cBAAaAJ6Hk+borb9+7QRCiwfHrQs+X/zJZGQ9BGiPIUgCHjquim2XwcoZbYercEY4yu3zeKyruHSVXDb9xos3SWNGwMAyClmYCGi3hhYyKVYh166B5HuNN1Wv50RFWibNQTA1sNy3IGBRdvQit15VQCAmy4Z67D7uqrZ0UFIn2p59vGaAAiCfQHt/MLbws5dmgdT+5MUbaljYWAhor4wsJDLqGhowb4iS23KdYnhfZ4zxqdrHZSrE3qGGutMoVytAcbO5fqH6+NDZTCLwOzoMYNaj8QTrLt+KhZP1eDBzplD9uheeGs2i7Z9hAbz7JLGW3pYTlbo0WzsvYlih8mMr45V9FqRl4hGBwYWchlfHrX0rsyJDupzDRbAMmwxc2wglF4SXDu9Z6iJCvKGytsLRpPZtkrrcIiiiA8PngMA3DwKelesIgK9sfmuJFw6KdTua609LCcrDNDqW9HaboZMImDsmL7/e3YXGeiNcJUSJrOIw6X1vd7f+vNZ/O6dg3jxq1N2t4uI3B8DC42ov32fh4z3DqPFeOGpxp937vR7/Yy+e1estixPxncZl/Uq5BQEAdMiLV+YjhgWOnKuAfmVjVDIJLi2nx4f6skaWHK1euRXWgpuxwX5DGrxOQC4pLOX5WAfw0JfHrMsFPifEzqYzWKv94nIszGw0IhpMZqw4dsz+OhgGR774EivPWa6K6lpxpFzDZAIwOJpA4cDlbcXxo7x6fM9Rxbefphj6V1ZPE2DAKXXsO83GkQH+0LpJUFruxk/5Fr2ebrQDKHukjsDy4HzAkulvhVHOntdapqMODrCO3MTkethYKERk1dpgPUvwl8ercBfv8/v99zPj1p6V+ZNCOlz/ZXBclThbVuHCZ919viMpuGg4ZJKBMRrLL0sX3f2iNgXWCyFtweL63r0omR22+QSQI9NL4lodGBgoRFzusJSR2LdYXnDt2fw9bGKPs8d7HDQhVgDyymtAe2moRfefnNCh4aWdmgClJgfFzKsNo021mEh64rFF1o0rrv4cH94e0mhb+1AflXXAoDfdW7DYN2v6AcGFqJRh4GFRox1xdNbksbiN/NjAAAZ7x3p1fuRpzPgtNYAmURA+lTNsH7muCAf+CtlMHaYkacb2oq3Hx08h9+/fwQAcNMlkX0uYEf9SwjvudicPT0sXlKJbSNL6wJyzcYO7M63bI3wp+unArAM+VUaWh3QWiJyFwwsNGKsPSzxGn/84dp4XDopFC3tJqx86wAOlXTVKHzeOTvo0kmhCPQZ3m7IgiDYpjfbOyzUbjJj3afHkfHeEbR1mLFocihWLbJ/au9oZ+1hsYodxKJx3VmnN1vXY9mdV422DjOigrwxPy4YiWMt/3135lY5oLVE5C4YWGhEiKKI0509LPGaAMikEvz19lmIDfVFRUMrfvn/9uChdw+htLYZXxx1zHCQ1fTOL7Tj5YMPLJWGVvx6y168mVUMAHj4yol4bfls+CpkDmnTaBLfLbB4e0mhDrCvJikp2hpYLGvyfHfKMhyUNkUNQRBw+eQwABwWIhptGFhoRFQa2lDX3A6JAExUW/6GrfL2wvb7UnFr0lgIgqVu5Yo/70RhVRMUMkm/q9vayzoEUVY3+E30Ht1+GPuL6uCvkOH/liUj46pJHr0M/0jyU8gwLsgyiysmxNfu1XIvibIElqKaZlQaWpF5yhJMrur8/XFFvCWw/JRXPaw6JSJyLwwsNCKse8nEhPhC6SW1HQ/1V+CVW2fgi4cWYN6EYLSbLDNBFk0Og7+Dpg5b/0avG2SNQ0NLO7IKagAA792firQExwSn0cy64q09BbdWKh8vTOoMua/tPouaJiP8lTLMjrHMIEqMVCHYV47Gtg7s71wZmYg8HwMLjYjTnXvJxJ9Xz2A1NUKFd+5Nwf8tS8avksfiyWvjHfazrfsLaRvaBnX+3sIamEXLfjfn11/Q0CyYaFkld07n/kD2staxvPFzEQBLoLUuPieRCLhssuX+rGMhGj0YWGhEWHtYpmj8+z1HEASkJajx8i0zMD7Ycfv0WANLTVPboIYM9nTOQOH0Zce5M2UcflqzCMtSxw/p+qTO9VjaOveEOr/XyzosxPVYiEYPBhYaEdbdeq2LiF1MQT5yyCQCRBGobrxwL8tuW2AJHummjRqCICAqyMfu+hUraw8LAMgkAi47b1+jhRNDIZUIyK9sRGlt87DaSkTugYGFHM7YYUZB56JfkwfoYRkpEomAsM7VcnX6gQOLtqEVBVVNEAQgNZY9LK4iOtgHwb6WKe4psUG2xQetVN5etlBj3QKAiDwbAws5XGF1I9pNIvwUskHt0jsSwjqHhXT6gQtvf+7sXUmMVEHlw/2CXIUgCJjXOUR3TT97S1mHhXYc1160dhGR8zCwkMPlarsWjBvqkMBw2WYKXSiwFFgCyzzWr7icddcn4G+/noVfzxnX5/uLp2oglQjYU1DT75YPROQ5GFjI4U5ZV7gNv/jDQVaaQfSwiKJo62FZwMDickL8FPhFYkS/6+FEh/ji/stiAQBPf3ocdU3Gi9k8IrrIGFjI4bqvcOssXUNC/dewFFQ1Qadvg1wm6VHkSe7j4SsnYmKYH6objXjm8xPObg4RjSAGFnI46x5CU5zYw6IeRA+LtXdldvSYHovbkftQyKR45dYZkAjAJ4fLbbs6E5HnGVJg2bRpE6Kjo6FUKpGSkoJ9+/YNeH59fT1WrVqF8PBwKBQKTJo0CV999dWw7kmuqa7JCG1nSJikdmZgsdSwVA7Qw2INLPMmcDjInc2MCsTKhZahoT98fAwNze1ObhERjQS7A8v27duRkZGBdevW4eDBg5gxYwbS09NRWdn31EKj0YirrroKRUVF+OCDD5Cbm4stW7YgMjJyyPck12VdMG7sGG+HLbU/FLbVbvvpYekwmZFVaFmOn/Ur7u/RqyYhNsQXlYY2PPvFSWc3h4hGgN2BZcOGDVi5ciVWrFiBhIQEbN68GT4+Pti6dWuf52/duhW1tbX45JNPMH/+fERHR+Oyyy7DjBkzhnxPcl2uUL8CAGp/S2BpaGlHa7up1/vHyhpgaO1AgFKGaZGqi908cjCllxSv3JoIQQA+PHgOW34sdHaTiMjB7AosRqMROTk5SEtL67qBRIK0tDRkZWX1ec1nn32G1NRUrFq1Cmq1GtOmTcOLL74Ik8k05Hu2tbVBr9f3eJFrsE5pdmb9CgAEeMug9LL89u5rWGhP52aHqROCIeWuzB4haXwQnlhs2ZPqha9O4cOcc05uERE5kl2Bpbq6GiaTCWp1z3091Go1tNq+F28qLCzEBx98AJPJhK+++gpPP/00/vznP+P5558f8j3Xr18PlUple0VFRdnzMWgEnbKtweLcHhZBELoKb/vYtXl3HvcP8kT3XRqLexfEAADWfHgUP3CvISKPMeKzhMxmM8LCwvDPf/4TSUlJWLp0KZ566ils3rx5yPd88skn0dDQYHuVlpY6sMU0VCaziDNa56/BYmUdFjp/plBbhwk5JXUAWHDraQRBwB+unYJfzoqEySzid+8cxMHO/9ZE5N5k9pwcEhICqVQKna7n1EGdTgeNRtPnNeHh4fDy8oJU2jVtdMqUKdBqtTAajUO6p0KhgEKhsKfpdBGcrW5ES7sJCpkE0Q7cfXmowgL63k+osKoJxg4z/JUyTAh1fjvJsSQSAS/fkojaJiN2nanCrzZnwV8pg1QigZdUQJCvHH+8LgGpE7jZJZE7sauHRS6XIykpCZmZmbZjZrMZmZmZSE1N7fOa+fPnIz8/H2az2XbszJkzCA8Ph1wuH9I9yTXtOmMZZkkaP8Yl6kL6W4vljM7SCzRZ7bytA2hkeUkl+Pudl2BOdBA6zCLqmttR3diGioZWnCjX49439+PouXpnN5OI7GBXDwsAZGRkYPny5UhOTsacOXOwceNGNDU1YcWKFQCAZcuWITIyEuvXrwcAPPDAA/jb3/6G1atX46GHHkJeXh5efPFFPPzww4O+J7kHa72AdVM6Z+tveX7r1Gtn7CRNF4+PXIbtv52LktpmGDvM6DCL6DCJeGnHKfycX4O7X9+P936birgwP2c3lYgGwe7AsnTpUlRVVWHt2rXQarWYOXMmduzYYSuaLSkpgUTS1XETFRWFb775Bo8++igSExMRGRmJ1atX4/HHHx/0Pcn1NbZ1IPusZebNIhcJLGH9bIB4hoFl1BAEAePPG578x13J+PWWvTh6rgHLXsvGBw/MQ0Sgc3YVJ6LBE0RRFJ3diOHS6/VQqVRoaGhAQIBzZ6eMVjuOa3H/2zkYH+yDnY9d7hJDLXsLa3DbP/ciNsQX3z92ue34/Je+R1l9C7bfNxcpsaxjGI1qm4y4ZfMeFFY1IS7MD+/9NhVBvnJnN4to1LHn+5t7CZFDWIeDFk0Oc4mwAvRdw2JobUdZfQsA9rCMZkG+cvzrnhSEq5TIr2zErZv3oKi6ydnNIqIBMLDQsImiiB9yXat+BQDC/C1DQk1GEwytlv1lzugaAVj2Ggr04d+oR7PIQG/865450AQoUVDVhCWbfsaezv2liMj1MLDQsJ0o16PS0AYfuRQpsUHObo6Nr0IGf4WlTMs6tdk6Q8iZGzOS64gL88dnD87HzKhANLS0466t+/CvrCJnN4uI+sDAQsP2fedw0Py4EChk0gucfXGpVZZhocrOYaFc20q8DCxkERagxLb75toWm3v60xN44sOjaGrrcHbTiKgbBhYatu9dbDpzd2rrTCFDz8DCHhbqTuklxYZfzcDji+MhCMC2/aW45i8/IbtzR28icj4GFhqWmsY2HOlcgGvRZBcMLLbl+XsOCbHgls4nCAIeuHwC3rknBZGB3iipbcZtW/biuS9O9rnjNxFdXAwsNCw7c6sgikBCeAA0ncMvriSsc6aQtqEVVYY21DQZIQjAxDAGFurbvLgQ7HhkIZYmR0EUgdd2n8W1f/kJP56pcnbTiEY1BhYalu9dcHZQd9YhoUpDq613ZXyQD7zlrlVrQ67FX+mF/74lEVvvTkaYvwKF1U1YtnUf7nvrAEprm53dPKJRiYGFhqzdZLb9rXNRfKiTW9O3rrVY2mz1KxwOosG6Il6NbzMuw2/mx0AqEfCfkzqkbdiFDd+eYVEu0UXGwEJDdrC4DobWDozx8cLMqDHObk6fui8eZwssLLglO6i8vbD2+gR8vXohUmOD0dZhxquZebjslZ34V1YR2k3mC9+EiIaNgYWG7GBJPQBg3oQQl9iduS+2ISF9G07bCm65fQPZb5LaH/9emYJNv74E44J8UN3Yhqc/PYG0Dbvw2ZFymM1uv8sJkUtjYKEhy6t0/SGW0M7Vbo0mM06UNQAAJmu4Oy8NjSAIuC4xHN9lXIZnl0xFiJ8cxTXNePjdQ7j21Z+w43gFgwvRCGFgoSHL61zmfpLadQOAQia1bWrXYRYhl0oQfd7uvUT2ksskWJYajV2/X4SMqybBXyHDaa0B9799ENf9dTe+OaGFB+wrS+RSGFhoSMxmEfmVlsAy0cVrQqx7CgHAhDA/yKT8bU+O4auQ4eErJ+KnxxfhoSvi4KeQ4VSFHr/9Vw6ufXU3vjhaDhN7XIgcgv/npiEpq29BS7sJcqkE44N8nN2cAVkLbwEuyU8jI9BHjv+6ejJ2P74IDy6Kg69cilMVejz470O46n934f0DpSzOJRomBhYaEuuaJrGhvi7fY6HpFli4JD+NpEAfOR5Ln4yfn7gCj6RNhMrbC4VVTfj9B0dx+Ss78frPZ9Fs5HRooqFw7W8acllndO4xHAR0zRQCWHBLF0egjxyPpE3Cz09cgSeviUeInwJl9S145vOTmP/S99j43RnUNRmd3Uwit8LAQkNinSE0Mcz1A0BYtx4WTmmmi8lPIcNvL5uA3Y8vwvM3TsO4IB/UNbdj43d5mPfS91j36XGU1HDlXKLBYGChIXGHGUJW1hoWf4UMES643xF5PqWXFHfOHY/v/+sy/PX2WZgaEYCWdhPezCrG5f/zA1a9cxCHS+ud3UwilyZzdgPI/bjTDCEAmBkViGBfOa6eqoYguOYCdzQ6yKQSXD8jAr9IDMeeghr848dC/HimCl8eq8CXxyowO3oM7l0Yi7QpapddjJHIWRhYyG7uNEMIsCwet++pNH4BkMsQBAHz40IwPy4Epyr02PJjIT47Uo79RXXYX5SD6GAf/GZBDG5JGgsfOf83TQRwSIiGwJ1mCFkxrJCrmhIegA1LZ2L341fggcsnIEApQ1FNM9Z+egJzX8zE+q9Poby+xdnNJHI69/i2IZfiTjOEiNyFRqXE44vjkfXklXjmhqkYH+wDfWsH/rGrEAtf/gEPvXsIh0rqnN1MIqdhXyPZzZ1mCBG5G1+FDMvnRVuKdE9X4rXdhdhbWIvPj5Tj8yPlmBEViN/Mj8Y108Ihl/HvnDR6MLCQ3dxphhCRu5JKBFyVoMZVCWqcKG/A1t1F+PxIOY6U1mP1tsN4wf8U7pw7HrfPGWfb5JPIkwmiB+zQpdfroVKp0NDQgIAArrMxksxmEVPXfYOWdhMy/+syTAhlaCG6WKoMbXh3Xwn+tbcYVYY2AIBcKsF1ieFYljoes8aNcXILiexjz/c3e1jILu42Q4jIk4T6K/DwlRNx/2UT8NWxCryZVYRDJfX4+FAZPj5UhhljVbgrNRq/SAyH0kvq7OYSORQDC9nFHWcIEXkauUyCG2dF4sZZkTh6rh5v7CnCF0cqcORcA468fwQvfHkSv5odhTtTxiOKf7EgD8FvHLILZwgRuZbEsYHY8KuZ2PPkFfh9+mREBnqjrrkd/9hViEtf+QErXt+HzFM6mMxuP/pPoxx7WMgu1hlCkzhDiMilhPgpsGpRHO6/bAIyT+nwr73F+CmvGj/kVuGH3CpEBnrj9jlR+NXsKIT5c4sKcj8MLGSXPFsPCwMLkSuSSgRcPVWDq6dqUFjViH9nl+D9nHMoq2/B//znDDZ+l4e0KWr8OmUcFsSFQMJFFclNcJYQDRpnCBG5p9Z2E748WoF3sotxsKTedjwqyBu3zR6HW5LG2jYJJbqY7Pn+ZmChQSutbcbCl3+AXCrByWfTWXRL5IZOa/V4N7sEHx0qg6G1A4ClV2bR5DDcNjsKl08O5Z9tumg4rZlGBGcIEbm/eE0AnlkyDU9cMwVfHqvA9v0l2F9Uh+9O6fDdKR3C/BW4OWksbk0ai1j2opILYWChQeMMISLP4S2X4paksbglaSzyKw3Yvr8UHx4sQ6WhDX/fWYC/7yxA8vgx+FVyFK5NDIefgl8X5Fz8HUiDll/ZGVg4Q4jIo8SF+eOp6xLw+/R4fH9ah/cOnMPO3EocKK7DgeI6rPvsBK6ZpsHNSWORGhvMQl1yCgYWGrSCKktgiWNgIfJIcpkEi6eFY/G0cOj0rfjw4Dl8kHMOhVVN+OhQGT46VIbIQG8smRmBmy6JRFwYe1vp4mHRLQ2KKIpIfOY/MLR2YMcjCxGv4XMmGg1EUcSh0np8mHMOnx8ph76zUBcApkeq8MtZkbh+RgQ3YKQh4SwhcrgqQxtmv/AdBAE49exi7lNCNAq1tpuQeaoSHx86h525VejoXD1XKhEwPy4ES2ZEIH2ahvUuNGicJUQOZx0OGjvGm2GFaJRSeklxXWI4rksMR01jG744WoGPDpXhSGk9fjxThR/PVOGpT47hyilqXJ8Ygcsnh/L/F+QwDCw0KIVVTQDAxeKICAAQ7KfA8nnRWD4vGmerm/DZ4XJ8ergMhdVN+PJoBb48WgF/hQxXT9Xg+hnhmB8XAi8uh0DDwMBCg2LtYWFgIaLzxYT4YnXaRDx8ZRyOlTXg8yPl+OJoBSoaLIW7Hx48h0AfL6QnaHBdYjjmTQjmWk5ktyH9jtm0aROio6OhVCqRkpKCffv29XvuG2+8AUEQeryUyp5LQN999929zlm8ePFQmkYjxBpYYkN9ndwSInJVgiAgcWwgnrouAT8/fgXevz8Vd80djxA/Oeqb27H9QCmWbd2H2S98h8c/OIqduZUwdpid3WxyE3b3sGzfvh0ZGRnYvHkzUlJSsHHjRqSnpyM3NxdhYWF9XhMQEIDc3FzbrwWh9xz+xYsX4/XXX7f9WqFgxbkr4ZAQEdlDIhEwOzoIs6OD8KcbpiL7bA2+PFqBHce1qGkyYvuBUmw/UAp/pQxXTVEjfZoGl04MhbecNS/UN7sDy4YNG7By5UqsWLECALB582Z8+eWX2Lp1K5544ok+rxEEARqNZsD7KhSKC55DztHabkJpXTMA9rAQkf2kEgHzJoRg3oQQPHPDVGSfrcXXxyvwzQkdqgxttjVevL2kuGxSKNKnqXFFvBoqby9nN51ciF2BxWg0IicnB08++aTtmEQiQVpaGrKysvq9rrGxEePHj4fZbMYll1yCF198EVOnTu1xzs6dOxEWFoYxY8bgiiuuwPPPP4/g4OA+79fW1oa2tjbbr/V6vT0fg+xUXNMMUQT8lTKE+rHni4iGTiaVYH5cCObHheCZG6Yhp7gOXx+vwH9O6FBW34IdJ7TYcUILmURASmwQrpqixlVTNYgM9HZ208nJ7Aos1dXVMJlMUKvVPY6r1WqcPn26z2smT56MrVu3IjExEQ0NDfif//kfzJs3DydOnMDYsWMBWIaDbrrpJsTExKCgoAB/+MMfcM011yArKwtSae/uwfXr1+OZZ56xp+k0DN0LbvsaziMiGgqpRMCcmCDMiQnC2l8k4ES5HjuOa/HNCS3yKhvxc34Nfs6vwZ8+P4mE8ACkTQnDlVPUmB6p4vYAo5BdC8eVl5cjMjISe/bsQWpqqu34mjVrsGvXLmRnZ1/wHu3t7ZgyZQpuv/12PPfcc32eU1hYiAkTJuC7777DlVde2ev9vnpYoqKiuHDcCPlrZh7+/O0Z3HRJJDb8aqazm0NEo0BRdRO+PanDtyd1OFBcC3O3b6owfwWuiA/DovgwLIgLgS8XqnNbI7ZwXEhICKRSKXQ6XY/jOp1u0PUnXl5emDVrFvLz8/s9JzY2FiEhIcjPz+8zsCgUChblXkSF1Sy4JaKLKzrEFysvjcXKS2NR09iGH3KrkHlKhx/PVKHS0IZt+0uxbX8p5FIJUmKDsGhyGC6fHIqYEF/2BHsouwKLXC5HUlISMjMzceONNwIAzGYzMjMz8eCDDw7qHiaTCceOHcO1117b7znnzp1DTU0NwsPD7WkejZCuISEW3BLRxRfsp8AtSWNxS9JYtHWYkF1Yi+9PV+L705UoqW3GT3nV+CmvGs9+AYwL8sHlk0Nx+eRQzI0Nho+cvS+ewu7/khkZGVi+fDmSk5MxZ84cbNy4EU1NTbZZQ8uWLUNkZCTWr18PAHj22Wcxd+5cxMXFob6+Hq+88gqKi4tx7733ArAU5D7zzDO4+eabodFoUFBQgDVr1iAuLg7p6ekO/Kg0FKIockozEbkMhUyKSyeF4tJJoVh3fQIKqprww+lK/JBbif1FtSipbcZbWcV4K6sYcqkEs2PGYOHEUFw6MRRTwv3Z++LG7A4sS5cuRVVVFdauXQutVouZM2dix44dtkLckpISSCRd69HV1dVh5cqV0Gq1GDNmDJKSkrBnzx4kJCQAAKRSKY4ePYo333wT9fX1iIiIwNVXX43nnnuOwz4uoNLQhsa2DkgEYFywj7ObQ0RkIwgC4sL8EBfmh5WXxqKxrQNZBTXYmVuJnblVKKtvsRXuvvT1aYT4KbAgLhgLJoZi4cQQqAOUF/4h5DK4WzMNaE9BNX69JRvRwT7Y+ftFzm4OEdGgiKKIwuom/HSmCj/mVSOroAYt7aYe50wM88P8uBDMmxCMuROCEaDkui8XG3drJocp4HAQEbkhQRAwIdQPE0L9cPf8GLR1mHCwuB6786uwO68aR8sakFfZiLzKRryxpwgSAZg+NhDzJgQjNTYYydFjWP/iYvhfgwZUUMk9hIjI/SlkUqROCEbqhGD8Ph2obzYiq6AGPxdUY09+DQqrm3CktB5HSuvx950F8JIKmDE2EKkTgpESE4xLxgcywDgZnz4NiFOaicgTBfrIcc30cFwz3TIbtby+BXsKapBVUIO9hTUoq2/BgeI6HCiuw1+RDy+pZWPHlJggzI4JQvL4MfDnENJFxcBCA+rqYWFgISLPFRHobZs6LYoiSmtbkFVYjezCWuwtrEF5QytyiuuQU1wH7CyARAASIgJsGzwmR49BmD+LeEcSAwv1q8VoQnlDCwCuwUJEo4cgCBgX7INxweOwdPY4iKKIc3UtyCqswf6ztdhXVIvimmYcL9PjeJker/9cBACIDvZB0nhLeEkaPwZxoX7cQsCBGFioX2ermyCKQKCPF4J85c5uDhGRUwiCgKggH0QF+eBXyVEAAG1DK7LP1uBAUR32F9UiV2dAUU0zimqa8eHBcwCAAKUMl4wfg0vGWV4zolQcRhoGBhbqV2F153AQl7omIupBo1JiycxILJkZCQBoaGnHwc4howPFtThcWg99awd25lZhZ24VAEAQgMlqf8waF4hZUWMwc1wge2HswMBC/SqoZMEtEdFgqLy9sKhzQ0YAaDeZcapCj5ziOhwsqcfB4jqU1bfgtNaA01oD3t1XCgDwU8iQOFaFGVGBmDE2EDOjAqFRsRamLwws1C/rHkIsuCUiso+XVILEsYFIHBuIFfMtxyr1rThYUodDpfU4XFKPo+ca0NjWgT0FNdhTUGO7Nsxf0XmtqvMVyGF5MLBQP0xmEUfO1QNgwS0RkSOEBSixeFo4Fk+zTKXuMJlxRteII+cs678cOdeAXK0elYY2fHdKh+9O6WzXRgZ6Y1pkAKZHqjAtUoWpESqE+o+u7WsYWKhPXx+vQHFNMwKUMqROCHZ2c4iIPI5MKkFCRAASIgJw+5xxAIBmYwdOlutx9FwDjp6rx9GyBhRWNaGsvgVl9S345kRXiFEHKDAtQoWpEQFI6Pzn2DHeHltzyMBCvZjNIv72fT4AYMX8GFa1ExFdJD5yGZKjg5AcHWQ7Zmhtx4lyPY6da8CxsgYcL2vA2Zom6PRt0OkrkXm60nZugFKGhIgATAm3vBLCAzBR7QeFTOqMj+NQDCzUy7endDitNcBPIcNv5sc4uzlERKOav9ILc2ODMTe2q7e7sa0Dpyv0OF7WgOPlepws1yOv0gB9awf2FtZib2Gt7VypRMCEUF/EawIQH+6PeI0/4jUBCFcp3ao3hoGFehBFEX/9Pg8AsCx1PFQ+7F0hInI1forePTHGDjPyKxtxorwBpyoMOFlh+WdDSzvO6BpxRteIz4503cNfKcNktT8mayyvSWrLy1ULfBlYqIedZ6pwvEwPby8p7lnA3hUiInchl3XVxFiJooiKhlac1upxqsKAXK0Bp7V6FFQ1wdDaYdsvqbsQPzkmqf0xMcwPE7v909lBhoGFbERRxF8zLb0rd84dh2C/0VWBTkTkaQRBQESgNyICvXFFvNp2vK3DhMKqJuRqDcjVWYLMGZ0B5+paUN1oRHVjz6nWABDsK8euNYvgp3BOdGBgIZs9BTU4WFIPhUyClZfGOrs5REQ0QhQyqa0wt7umtg7kVzbijM6A/MpG5FU2Iq/SgNLaFgiC4LSwAjCwUDevdvau3D5nHHcdJSIahXwVMsuqu1GBPY43GzugbWh1TqM6SZz608llHD1Xj+yztfCSCvjtZexdISKiLj5ymdNXPWdgIQDAG53bo/8iMQLhKm/nNoaIiOg8DCyESkMrPj9aDgC4e160cxtDRETUBwYWwrvZpWg3iZg1rve4JRERkStgYBnljB1mvJ1dDIC9K0RE5LoYWEa5r49XoMrQhjB/Ba7p3EGUiIjI1TCwjHKvdxbb3jl3POQy/nYgIiLXxG+oUexQSR0Ol9ZDLpXYtjYnIiJyRQwso9ibe4oAAL+YEY5Qfy7DT0RErouBZZSq1Lfiy2MVAIAV87jJIRERuTYGllHq7b3FaDeJSBo/BtPHqpzdHCIiogExsIxCre0mvJNdAgD4zXz2rhARketjYBmFPjtSjpomIyJUSqRPVV/4AiIiIidjYBllRFHE1t1nAQDL5kVDJuVvASIicn38thplsgprcFprgLeXFLfNjnJ2c4iIiAaFgWWU2bq7CABwc1IkAn3kzm0MERHRIDGwjCLFNU3IPK0DANzNqcxERORGGFhGkTf2FEEUgcsnhyIuzM/ZzSEiIho0BpZRwtDajvcPnAPAqcxEROR+GFhGiU8Pl6OxrQNxYX5YODHE2c0hIiKyCwPLKPFzfjUA4MaZERAEwcmtISIisg8DyyggiiKyz9YCAObGBju5NURERPZjYBkF8iobUdtkhNJLgsSxgc5uDhERkd0YWEaB7MIaAMAl48ZALuN/ciIicj9D+vbatGkToqOjoVQqkZKSgn379vV77htvvAFBEHq8lEplj3NEUcTatWsRHh4Ob29vpKWlIS8vbyhNoz7s7RwOSonhcBAREbknuwPL9u3bkZGRgXXr1uHgwYOYMWMG0tPTUVlZ2e81AQEBqKiosL2Ki4t7vP/yyy/j1VdfxebNm5GdnQ1fX1+kp6ejtbXV/k9EPYiiiOzCzsASG+Tk1hAREQ2N3YFlw4YNWLlyJVasWIGEhARs3rwZPj4+2Lp1a7/XCIIAjUZje6nVXTsEi6KIjRs34o9//COWLFmCxMREvPXWWygvL8cnn3wypA9FXQqrm1Dd2Aa5TIKZUYHObg4REdGQ2BVYjEYjcnJykJaW1nUDiQRpaWnIysrq97rGxkaMHz8eUVFRWLJkCU6cOGF77+zZs9BqtT3uqVKpkJKS0u8929raoNfre7yob9belVlRgVB6SZ3cGiIioqGxK7BUV1fDZDL16CEBALVaDa1W2+c1kydPxtatW/Hpp5/i7bffhtlsxrx583DunGXVVet19txz/fr1UKlUtldUFHcd7k/2WUvBbQqnMxMRkRsb8SkjqampWLZsGWbOnInLLrsMH330EUJDQ/GPf/xjyPd88skn0dDQYHuVlpY6sMWeo3v9ytwY1q8QEZH7siuwhISEQCqVQqfT9Tiu0+mg0WgGdQ8vLy/MmjUL+fn5AGC7zp57KhQKBAQE9HhRbyW1zdDqW+ElFTBr3BhnN4eIiGjI7AoscrkcSUlJyMzMtB0zm83IzMxEamrqoO5hMplw7NgxhIeHAwBiYmKg0Wh63FOv1yM7O3vQ96S+7e1cf2XG2EB4y1m/QkRE7ktm7wUZGRlYvnw5kpOTMWfOHGzcuBFNTU1YsWIFAGDZsmWIjIzE+vXrAQDPPvss5s6di7i4ONTX1+OVV15BcXEx7r33XgCWGUSPPPIInn/+eUycOBExMTF4+umnERERgRtvvNFxn3QU4nRmIiLyFHYHlqVLl6Kqqgpr166FVqvFzJkzsWPHDlvRbElJCSSSro6buro6rFy5ElqtFmPGjEFSUhL27NmDhIQE2zlr1qxBU1MT7rvvPtTX12PBggXYsWNHrwXmyD7cP4iIiDyFIIqi6OxGDJder4dKpUJDQwPrWTqV1jZj4cs/QCYRcPRPV8NHbnc2JSIiGlH2fH9zYxkPZe1dmT5WxbBCRERuj4HFQ+3OqwLA/YOIiMgzMLB4oCpDG746Zll07+qp6gucTURE5PoYWDzQ23uLYTSZMTMqEJdw/RUiIvIADCweprXdhLf3WnbDvndhjJNbQ0RE5BgMLB7m08NlqGkyIjLQG4unDm71YSIiIlfHwOJBRFHEa7vPAgDunhcNmZT/eYmIyDPwG82D/JRXjTO6RvjKpVg6hztYExGR52Bg8SD/19m78qvZUQhQejm5NURERI7DwOIh8nQG/HimCoIArJjHYlsiIvIsDCweYuvPlt6VqxPUGBfs4+TWEBERORYDiwcorGrE+wfOAQDuXRjr5NYQERE5HgOLB3jhy1PoMItYNDkUs6ODnN0cIiIih2NgcXM/nqlC5ulKyCQC/viLBGc3h4iIaEQwsLixDpMZz31xEgCwLDUaE0L9nNwiIiKikcHA4sbeyS5BXmUjxvh4YfWVE53dHCIiohHDwOKm6puN+N/vzgAAMq6eDJUP110hIiLPxcDipjZ+l4f65nZMVvvj9tlc1ZaIiDwbA4sbqmsy2nZkXnt9AvcMIiIij8dvOjd05Fw9OswiYkJ8MT8uxNnNISIiGnEMLG7oeFkDAGB6pMrJLSEiIro4GFjc0DEGFiIiGmUYWNzQ8TI9AGAaAwsREY0SDCxuprbJiLL6FgDA1MgAJ7eGiIjo4mBgcTPW4aCYEF8EKLn2ChERjQ4MLG7GWnDL4SAiIhpNGFjczLFz1oJbDgcREdHowcDiZqxDQtMi2MNCRESjBwOLG6nrUXDLwEJERKMHA4sbOV5u6V0ZH+wDlTcLbomIaPRgYHEjx1hwS0REoxQDixvhkvxERDRaMbC4ES7JT0REoxUDi5uobzaitNZScMsZQkRENNowsLgJ6/5B44J8oPJhwS0REY0uDCxugsNBREQ0mjGwuAkuyU9ERKMZA4ubYA8LERGNZgwsbqChuR0ltc0AgGncQ4iIiEYhBhY3YF3hNirIG4E+cie3hoiI6OJjYHEDpyosM4SmhnM4iIiIRqchBZZNmzYhOjoaSqUSKSkp2Ldv36Cu27ZtGwRBwI033tjj+N133w1BEHq8Fi9ePJSmeaSTnYFlSjiHg4iIaHSyO7Bs374dGRkZWLduHQ4ePIgZM2YgPT0dlZWVA15XVFSExx57DAsXLuzz/cWLF6OiosL2evfdd+1tmsc6VWEAAEwJ93dyS4iIiJzD7sCyYcMGrFy5EitWrEBCQgI2b94MHx8fbN26td9rTCYT7rjjDjzzzDOIjY3t8xyFQgGNRmN7jRkzxt6meSRjhxn5ldbAwh4WIiIanewKLEajETk5OUhLS+u6gUSCtLQ0ZGVl9Xvds88+i7CwMNxzzz39nrNz506EhYVh8uTJeOCBB1BTU9PvuW1tbdDr9T1enqqgqhHtJhH+ChnGjvF2dnOIiIicwq7AUl1dDZPJBLVa3eO4Wq2GVqvt85rdu3fjtddew5YtW/q97+LFi/HWW28hMzMT//3f/41du3bhmmuugclk6vP89evXQ6VS2V5RUVH2fAy3clprCWPx4f4QBMHJrSEiInIO2Uje3GAw4K677sKWLVsQEhLS73m33Xab7d+nT5+OxMRETJgwATt37sSVV17Z6/wnn3wSGRkZtl/r9XqPDS1d9SscDiIiotHLrsASEhICqVQKnU7X47hOp4NGo+l1fkFBAYqKinD99dfbjpnNZssPlsmQm5uLCRMm9LouNjYWISEhyM/P7zOwKBQKKBQKe5rutk5xhhAREZF9Q0JyuRxJSUnIzMy0HTObzcjMzERqamqv8+Pj43Hs2DEcPnzY9rrhhhuwaNEiHD58uN9ekXPnzqGmpgbh4eF2fhzPw8BCREQ0hCGhjIwMLF++HMnJyZgzZw42btyIpqYmrFixAgCwbNkyREZGYv369VAqlZg2bVqP6wMDAwHAdryxsRHPPPMMbr75Zmg0GhQUFGDNmjWIi4tDenr6MD+ee6s0tKK60QhBACap/ZzdHCIiIqexO7AsXboUVVVVWLt2LbRaLWbOnIkdO3bYCnFLSkogkQy+40YqleLo0aN48803UV9fj4iICFx99dV47rnnPGLYx2wWse6zE4gJ8cVvFsTYde3pzvqVmGBf+MhHtNyIiIjIpQmiKIrObsRw6fV6qFQqNDQ0ICDAtYZODhTV4pbNWRAE4OvVCxGvGXz7/rGrAOu/Po3rpodj0x2XjGAriYiILj57vr+5l9AIs9agiCLwv9+eGdK1XOGWiIhGOwaWEXayc1gHAL45ocPxsoZBX8spzURERBYMLCPMuvBbiJ+lHmfDIHtZ2jpMKKhqBADEM7AQEdEox8AygsxmEblaSy/JSzdNh1Qi4PvTlcgprrvgtfmVjegwiwhQyhChUo50U4mIiFwaA8sIKq5tRrPRBIVMgssnh+LmSyIBDK6WpftwEJfkJyKi0Y6BZQSd7iyanazxh0wqwUNXTISXVMDu/GrsLex/c0eAC8YRERF1x8AygqyhI15jmeUTFeSDpbMtq/tu+M8ZDDSjnDOEiIiIujCwjKCTfczyeXDRRMhlEuwrqsUPuZV9XieKIntYiIiIumFgGUHWGULdQ4dGpcSKedEAgPVfnUaHydzrOp2+DXXN7ZAIwCQ1e1iIiIgYWEaIvrUd5+paAABTzlvd9neL4hDo44W8yka8d+Bcr2tPdQad2FA/KL2kI99YIiIiF8fAMkKs+wBFqJRQ+Xj1eE/l7YXVV04EYFmXpbGtw/aeKIrYcUwLgMNBREREVgwsI8Q6HNTfom93pIxHdLAPqhvb8M9dBQAsYeX5L09h+4FSAMAvEsMvTmOJiIhcHAPLCLnQLB+5TIInrokHAPzzp0JUNLTgmc9P4rXdZwEAL/xyGtKnai5OY4mIiFyczNkN8FSD2QcofaoGyePH4EBxHX65aQ+0+lYAwPqbpuP2OeMuSjuJiIjcAXtYRoCp25L88Zr+A4sgCPjDdVMAAFp9KwQBePnmRIYVIiKi8zCwjIDimia0tJug9JIgJsR3wHMvGTcGd80dD28vKV65ZQZ+1bmwHBEREXXhkNAION3ZuzJZ7Q+p5ML7AD27ZCr++IspUMg4hZmIiKgv7GEZAV1L8g9uWrIgCAwrREREA2BgGQHcB4iIiMixGFhGwGBmCBEREdHgMbA4WENLO8rqLUvyD3ZIiIiIiAbGwOJgpzuHgyIDvXstyU9ERERDw8DiYN+c0AEApkawd4WIiMhRGFgcqLqxDf/eVwwAuGPueCe3hoiIyHMwsDjQlp8K0dpuxoyoQFw6McTZzSEiIvIYDCwOUttkxL+yLL0rD18RB0G48IJxRERENDgMLA6ydfdZNBtNmBoRgCviw5zdHCIiIo/CwOIADS3teHNPEQDgIfauEBERORwDiwO88XMRDG0dmKz2x9UJGmc3h4iIyOMwsAyTobUdr+0uBAA8eEUcJIPY7JCIiIjsw8AyTG9lFUPf2oHYUF9cOz3c2c0hIiLySAwsw9DQ3I5/7CoAYKldkbJ3hYiIaEQwsAzDP38qgL61A5PUfrhhRqSzm0NEROSxGFiGqNLQiq27iwAAj109mb0rREREI4iBZYj+3w8FaGk3YUZUIK5KUDu7OURERB6NgWUIztU1451sy6q2a9Inc90VIiKiEcbAMgQbv8tDu0nE/LhgzI/jnkFEREQjjYHFTvmVBnx08BwAS+0KERERjTwGFju9mpkPswhcnaDGrHFjnN0cIiKiUYGBxQ5ms4iduZUAgPsvn+Dk1hAREY0eDCx2KKxugr61A0ovCaZHqpzdHCIiolGDgcUOh0vrAQDTI1XwkvLRERERXSxD+tbdtGkToqOjoVQqkZKSgn379g3qum3btkEQBNx44409jouiiLVr1yI8PBze3t5IS0tDXl7eUJo2og6V1AEAa1eIiIguMrsDy/bt25GRkYF169bh4MGDmDFjBtLT01FZWTngdUVFRXjsscewcOHCXu+9/PLLePXVV7F582ZkZ2fD19cX6enpaG1ttbd5I8rawzIzKtCp7SAiIhpt7A4sGzZswMqVK7FixQokJCRg8+bN8PHxwdatW/u9xmQy4Y477sAzzzyD2NjYHu+JooiNGzfij3/8I5YsWYLExES89dZbKC8vxyeffGL3BxopLUYTTmsNAIBZ4wKd2xgiIqJRxq7AYjQakZOTg7S0tK4bSCRIS0tDVlZWv9c9++yzCAsLwz333NPrvbNnz0Kr1fa4p0qlQkpKSr/3bGtrg16v7/EaacfKGmAyi1AHKBCu8h7xn0dERERd7Aos1dXVMJlMUKt77p2jVquh1Wr7vGb37t147bXXsGXLlj7ft15nzz3Xr18PlUple0VFRdnzMYbEWr/C4SAiIqKLb0SnuhgMBtx1113YsmULQkIct4T9k08+iYaGBturtLTUYffuj7V+hQW3REREF5/MnpNDQkIglUqh0+l6HNfpdNBoNL3OLygoQFFREa6//nrbMbPZbPnBMhlyc3Nt1+l0OoSHh/e458yZM/tsh0KhgEKhsKfpw3aopB4Ae1iIiIicwa4eFrlcjqSkJGRmZtqOmc1mZGZmIjU1tdf58fHxOHbsGA4fPmx73XDDDVi0aBEOHz6MqKgoxMTEQKPR9LinXq9HdnZ2n/d0hoqGFmj1rZAIQOJYLhhHRER0sdnVwwIAGRkZWL58OZKTkzFnzhxs3LgRTU1NWLFiBQBg2bJliIyMxPr166FUKjFt2rQe1wcGBgJAj+OPPPIInn/+eUycOBExMTF4+umnERER0Wu9Fmc53Nm7MlkTAB+53Y+MiIiIhsnub9+lS5eiqqoKa9euhVarxcyZM7Fjxw5b0WxJSQkkEvtKY9asWYOmpibcd999qK+vx4IFC7Bjxw4olUp7mzciuupXAp3aDiIiotFKEEVRdHYjhkuv10OlUqGhoQEBAQEOv/+vNmdhX1EtXr4lEb9KHvkZSURERKOBPd/f3BDnAjpMZhwtqwcAXMIeFiIiIqdgYLmA01oDWtvN8FfKEBvi5+zmEBERjUoMLBfQff8giURwbmOIiIhGKQaWC+D6K0RERM7HwHIBh0u5JD8REZGzMbAMoKG5HQVVTQAYWIiIiJyJq6ANQJAA665PQHFNM4L9Lu5WAERERNSFgWUAAUovrJgf4+xmEBERjXocEiIiIiKXx8BCRERELo+BhYiIiFweAwsRERG5PAYWIiIicnkMLEREROTyGFiIiIjI5TGwEBERkctjYCEiIiKXx8BCRERELo+BhYiIiFweAwsRERG5PAYWIiIicnkesVuzKIoAAL1e7+SWEBER0WBZv7et3+MD8YjAYjAYAABRUVFObgkRERHZy2AwQKVSDXiOIA4m1rg4s9mM8vJy+Pv7QxAEh95br9cjKioKpaWlCAgIcOi9RyM+T8fjM3UsPk/H4vN0LE97nqIowmAwICIiAhLJwFUqHtHDIpFIMHbs2BH9GQEBAR7xm8NV8Hk6Hp+pY/F5Ohafp2N50vO8UM+KFYtuiYiIyOUxsBAREZHLY2C5AIVCgXXr1kGhUDi7KR6Bz9Px+Ewdi8/Tsfg8HWs0P0+PKLolIiIiz8YeFiIiInJ5DCxERETk8hhYiIiIyOUxsBAREZHLY2C5gE2bNiE6OhpKpRIpKSnYt2+fs5vkFtavX4/Zs2fD398fYWFhuPHGG5Gbm9vjnNbWVqxatQrBwcHw8/PDzTffDJ1O56QWu5eXXnoJgiDgkUcesR3j87RPWVkZ7rzzTgQHB8Pb2xvTp0/HgQMHbO+Looi1a9ciPDwc3t7eSEtLQ15enhNb7LpMJhOefvppxMTEwNvbGxMmTMBzzz3XY38YPs+B/fjjj7j++usREREBQRDwySef9Hh/MM+vtrYWd9xxBwICAhAYGIh77rkHjY2NF/FTjDCR+rVt2zZRLpeLW7duFU+cOCGuXLlSDAwMFHU6nbOb5vLS09PF119/XTx+/Lh4+PBh8dprrxXHjRsnNjY22s65//77xaioKDEzM1M8cOCAOHfuXHHevHlObLV72LdvnxgdHS0mJiaKq1evth3n8xy82tpacfz48eLdd98tZmdni4WFheI333wj5ufn28556aWXRJVKJX7yySfikSNHxBtuuEGMiYkRW1panNhy1/TCCy+IwcHB4hdffCGePXtWfP/990U/Pz/xL3/5i+0cPs+BffXVV+JTTz0lfvTRRyIA8eOPP+7x/mCe3+LFi8UZM2aIe/fuFX/66ScxLi5OvP322y/yJxk5DCwDmDNnjrhq1Srbr00mkxgRESGuX7/eia1yT5WVlSIAcdeuXaIoimJ9fb3o5eUlvv/++7ZzTp06JQIQs7KynNVMl2cwGMSJEyeK3377rXjZZZfZAgufp30ef/xxccGCBf2+bzabRY1GI77yyiu2Y/X19aJCoRDffffdi9FEt3LdddeJv/nNb3ocu+mmm8Q77rhDFEU+T3udH1gG8/xOnjwpAhD3799vO+frr78WBUEQy8rKLlrbRxKHhPphNBqRk5ODtLQ02zGJRIK0tDRkZWU5sWXuqaGhAQAQFBQEAMjJyUF7e3uP5xsfH49x48bx+Q5g1apVuO6663o8N4DP016fffYZkpOTceuttyIsLAyzZs3Cli1bbO+fPXsWWq22x/NUqVRISUnh8+zDvHnzkJmZiTNnzgAAjhw5gt27d+Oaa64BwOc5XIN5fllZWQgMDERycrLtnLS0NEgkEmRnZ1/0No8Ej9j8cCRUV1fDZDJBrVb3OK5Wq3H69Gkntco9mc1mPPLII5g/fz6mTZsGANBqtZDL5QgMDOxxrlqthlardUIrXd+2bdtw8OBB7N+/v9d7fJ72KSwsxN///ndkZGTgD3/4A/bv34+HH34Ycrkcy5cvtz2zvv7883n29sQTT0Cv1yM+Ph5SqRQmkwkvvPAC7rjjDgDg8xymwTw/rVaLsLCwHu/LZDIEBQV5zDNmYKERt2rVKhw/fhy7d+92dlPcVmlpKVavXo1vv/0WSqXS2c1xe2azGcnJyXjxxRcBALNmzcLx48exefNmLF++3Mmtcz/vvfce3nnnHfz73//G1KlTcfjwYTzyyCOIiIjg8ySH4ZBQP0JCQiCVSnvNstDpdNBoNE5qlft58MEH8cUXX+CHH37A2LFjbcc1Gg2MRiPq6+t7nM/n27ecnBxUVlbikksugUwmg0wmw65du/Dqq69CJpNBrVbzedohPDwcCQkJPY5NmTIFJSUlAGB7ZvzzPzi///3v8cQTT+C2227D9OnTcdddd+HRRx/F+vXrAfB5Dtdgnp9Go0FlZWWP9zs6OlBbW+sxz5iBpR9yuRxJSUnIzMy0HTObzcjMzERqaqoTW+YeRFHEgw8+iI8//hjff/89YmJieryflJQELy+vHs83NzcXJSUlfL59uPLKK3Hs2DEcPnzY9kpOTsYdd9xh+3c+z8GbP39+r2n2Z86cwfjx4wEAMTEx0Gg0PZ6nXq9HdnY2n2cfmpubIZH0/DqRSqUwm80A+DyHazDPLzU1FfX19cjJybGd8/3338NsNiMlJeWit3lEOLvq15Vt27ZNVCgU4htvvCGePHlSvO+++8TAwEBRq9U6u2ku74EHHhBVKpW4c+dOsaKiwvZqbm62nXP//feL48aNE7///nvxwIEDYmpqqpiamurEVruX7rOERJHP0x779u0TZTKZ+MILL4h5eXniO++8I/r4+Ihvv/227ZyXXnpJDAwMFD/99FPx6NGj4pIlSzgNtx/Lly8XIyMjbdOaP/roIzEkJERcs2aN7Rw+z4EZDAbx0KFD4qFDh0QA4oYNG8RDhw6JxcXFoigO7vktXrxYnDVrlpidnS3u3r1bnDhxIqc1jyZ//etfxXHjxolyuVycM2eOuHfvXmc3yS0A6PP1+uuv285paWkRf/e734ljxowRfXx8xF/+8pdiRUWF8xrtZs4PLHye9vn888/FadOmiQqFQoyPjxf/+c9/9njfbDaLTz/9tKhWq0WFQiFeeeWVYm5urpNa69r0er24evVqcdy4caJSqRRjY2PFp556Smxra7Odw+c5sB9++KHP/2cuX75cFMXBPb+amhrx9ttvF/38/MSAgABxxYoVosFgcMKnGRmCKHZbipCIiIjIBbGGhYiIiFweAwsRERG5PAYWIiIicnkMLEREROTyGFiIiIjI5TGwEBERkctjYCEiIiKXx8BCRERELo+BhYiIiFweAwsRERG5PAYWIiIicnkMLEREROTy/j9yKzh65llJVwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df2=dfc.tolist()\n",
        "df2.extend(lst_output)\n",
        "plt.plot(df2[1200:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "0d5e97d3",
      "metadata": {
        "id": "0d5e97d3"
      },
      "outputs": [],
      "source": [
        "df2=scaler.inverse_transform(df2).tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "3d1e6fa6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "3d1e6fa6",
        "outputId": "6952cd55-24ff-4251-fae8-770a60c18e03"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7b4148b9d6c0>]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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FJMcqyh9uLgcA7D1txMZ5E1p1bqJQCXoWDxERBeffZSel24YkLcb2yZICFsBLBsUmKPqlJGhUKPlJH6hVKnRO0+PJ/+5Bs6V1Yzzyx9Gq1dLji07XNrfqvEShxK48RERhZkhMkG7rNBq8dvNwDOrqHEIRpxnLA5INB84qMi1atRrJOi0emtQPI7o7ilpbWTMifxyNRgWdS4AiX7SQKFIYoBARhVlaojNZfVFeKrJT9Xh4cn9pm9iUzbVIdvOR827HAM4hodYGKJVGZ4ZEq1YhwWX9HdeMClEk8K+QiCjMxCGV0T0zpWxJ784p0n6VY0pxerJO8XvizB+NWiUdA8gClJbWDfG8seaQdFujVkHnEqA0croxRQHWoBARhZk4pCIfOunWKRmLfzUa6cnO4Z+rBuTgxzPOtcbE1vdalyGXREeGw9xin+mjDnJIRp4x0ardh3gAYMvRaozqmRnUeYlCiRkUIqIwk8+Ykbu0TzYG5qdL9+dc2VexXxzCcR2CSZTN3jG1IotidcwOKvlJb6hU7kM8ALBw3SG3bUTtiQEKEVGYiRkU+TCNJzqtGht/f6V0XxxqcQ1s5AFKa+pQqhvsCwR2zUgG4B4AAcCRNqzzQxQKDFCIiMJMrEHRBDASk5+RJNWnNEkZFOUvygOWXadqg76ekxeaAACd0+yN3jwN8YjHEEUKAxQiojCzeahB8SXDUSzb5MigaNXe36pLZTN9AlHXbMH+yjoAwNAC+/CSzkPk5LruD1F7Y4BCRBRm4uxhf0M8InFWjTjEo/UQQIzqYS9gdS2g9ae2yQJBABIT1MhJSwTgeYhHnG1EFCkMUIiIwsw5xBNYMJGgVQYongIIMfthtgZXJNviaP4mz8p4Or+noIioPTFAISIKs2CHeMQhl0azvQ+KpyyJGFRYWoIbimnxcC2e4iZPKysTtScGKEREYSauQhxovxIx+LjQaF8BWeshwyEFKEFmUMQZRfKgR5zVI8f4hCKNAQoRUZiJQUGg5SLirJoNB+wrF7vO4pEfE2yAIh4vH8Jp8tA51soiWYowBihERGFmC7YGxSVj4mloSAxagq1BcWZQnI9xy+hC9MhKxn0T+mJmcXcAnMVDkcdW90REYSbGEIEO8bjWnCR4mGbsHOJpew1Kdqoe6x7+CQCgytiMf5QeV6x4TBQJzKAQEYWZVIMS4BCPa1bE04waZ5FssLN43Id45MQgivEJRRoDFCKiMAt2Fk+qXpnc9lQkqwthkaycWjYMxZk8FEkMUIiIwswqZVACC1AKM5MV9xM81aBoW1eD4hzi8fz2L6+TYaEsRRIDFCKiMBMTEYFmUG4dXai473OIp5UZFE8zgwBAJftU2F9Rh/c3Hgv6MYhCgUWyRERhZrMFl0FJ1mkx/qLO0jRj331QgstyiMGGt2BJnkH56RvfArDP6LljbM+gHoeorZhBISIKs2CHeABnjQnguV6kPWpQRDtPBr9iMlFbMUAhIgozZ5Fs4L+j0zoDBU+rGYsZFHOws3g89EGR87Q50OnRRKHEAIWIKMxsbcygeKoXSUu0j9Dvq6iDsdkS8HlbbL6nGXtqJhfsislEocAAhYgozIJt1AYou8l6CiZyDHrp9pCnv8Y9/ywL6LziasbealA8BVHMoFAkMEAhIgqzYFvd23/HeTtRq3Hbn5WiV9xfvqcCX+w4jd9+ugMNphav5/XU6l7OUzASzHUThQoDFCKiMAt2NWMA+M/3J6Xb+gQPjdq07tvu++gH/LvsJBZtPOb1vBY/RbKAe8fbQKdHB6v8fCNe+Xo/LnhYTRmwzx5af+AsqozNYXl8im6cZkxEFGbBrmYMAFkpOpx3fHDrPWRQfPmh/AJOVDeiwKXhGwBYxWnGXmpQAHtAYpNNXw6mdsYfq03AHe9tAQBUGptxoLIeO07W4v1fjnI7tvTwecx6dwsSE9TY+dRkj0EZxS7+3yYiCrPWDPG8+oth0m19kB/Mq36swjV/+VZad0euJaAMinKft4La1thytBrfHDyHbw6ew4HKegDAeke/F1fHqxsBAM0WGyqZRYk7DFCIiMJMyqAEkUIZ2i1Dut2aIZaaRgsaLVa37SbHtGSdjznPrgFKKDMoR87VB3xsTaNzdlK9j7oaik0MUIiIwkwseA3mgz5R53x79rYkzgyXlviuPPVIqWm0Dxt1StF5/T3XgCiY/i3+1DU7Aw354wguT3LTkfNYubdCus8AJf4E9We3cOFCDBkyBAaDAQaDAcXFxfjqq6+k/c3NzSgpKUFWVhZSU1Mxffp0VFZWKs5RXl6OadOmITk5GTk5OXj44YfR0sI/PCKKXa1q1CY7WIDnCOVXl/XyeQ5PXWarG+xZiU7J3gMU1zgqlLN46mQ9W+TXcKqmSbpd22TBzW9vwvflNdI2BijxJ6gApVu3bnjxxRdRVlaGbdu24corr8R1112HPXv2AAAefPBBLF26FJ9++inWr1+P06dP44YbbpB+32q1Ytq0aTCbzdi4cSPef/99LFq0CE8++WRonxURURSx+FlB2BOVLCiwecmg+AscPGVQLjgyKJkpCV5/L1yzdgDA2OQMNEyyIag1+6qk257qTXxNnabYFNQsnmuuuUZx//nnn8fChQuxadMmdOvWDe+88w4WL16MK6+8EgDw3nvvYcCAAdi0aRPGjBmDr7/+Gnv37sWqVauQm5uLYcOG4bnnnsMjjzyCp59+Gjqd94ieiKijqm2yZw3Sk7wHBb7YvIzx+EtseApQqh0zgzJ8ZFBch6JavEVIrSDPoNTJgo7y843SbWOTe2dcBijxp9Uji1arFR9//DEaGhpQXFyMsrIyWCwWTJw4UTqmf//+KCwsRGlpKQCgtLQUgwcPRm5urnTM5MmTYTQapSyMJyaTCUajUfFDRNRRiH0+fGUtfPFWg+Iv02H2MMTTaLZ/0KfqvX8/dQ1QrCEMUIzNngMNebv+8x76otR5+T2KXUEHKLt27UJqair0ej3uueceLFmyBEVFRaioqIBOp0NGRobi+NzcXFRU2AudKioqFMGJuF/c5838+fORnp4u/RQUFAR72UREESNmLXzVffhS3DvL43a/AYqHDEqzxb4t0UPzN+d5lfdDmUGpqPU8XVg+9FPtIUAxBbkoInV8QTdq69evH7Zv347a2lr8+9//xqxZs7B+/fpwXJtk3rx5mDt3rnTfaDQySCGiDsNZ9xFcgLLl0Qk4caERFxd28rjf36wgzwGKve7DV/O3cGVQBEHA3jOeM+C7TtXiQoMZnVJ0aDR7nx5N8SPoDIpOp0OfPn0wYsQIzJ8/H0OHDsVrr72GvLw8mM1m1NTUKI6vrKxEXl4eACAvL89tVo94XzzGE71eL80cEn+IiDoCq02QPnANicEN8eQYEjGie6bX/f4yKBare2AhBiiJCYEHKOIKyG311W7vmfJTNU24+e1NsNkE6RrlTC3u2yi2tXl2u81mg8lkwogRI5CQkIDVq1dL+/bv34/y8nIUFxcDAIqLi7Fr1y5UVTmrtVeuXAmDwYCioqK2XgoRUdQRaz4AIEkXXMt6f1xn8Qzplq64b7a6f6g3t/gf4nGdbBSqDIq/NXX2V9Zhz2kjmjxlUCzMoMSboAKUefPmYcOGDTh27Bh27dqFefPmYd26dZgxYwbS09Mxe/ZszJ07F2vXrkVZWRnuvPNOFBcXY8yYMQCASZMmoaioCLfffjt27NiBFStW4PHHH0dJSQn0er2fRyeijqLZYpUab3178Bwue2kN1sqmkcYTMXuiVgXfst4feSAxtk8WHprUT7Hf3GKDIAhSy3ubTZCGfXxlUFyHhlo8ZGJaw1NGx9X5BhOamEEJiLdsU6wI6l9LVVUVZs6ciX79+mHChAnYunUrVqxYgauuugoA8Oqrr+KnP/0ppk+fjvHjxyMvLw+fffaZ9PsajQbLli2DRqNBcXExbrvtNsycORPPPvtsaJ8VEUXM0XMN6P/Ecjzyn50AgNve2YwT1U14ddWBCF9ZZIgBSrJOq+htEgryIR4VVG7t681WAbPf34bLX16HZotVUcfhK0CpNJoU90OVQfE0q8hVg8nqOUBhBsXNrPe2YOyLa1Db6D4tOxYEVST7zjvv+NyfmJiIBQsWYMGCBV6P6d69O7788stgHpaIOpC3NxwGAPxr20m8dONQafvZOhNarDZoQ9k3vQMQh3hCPbwDKGtFVCq4rfZrslilBmjfH7+AAV2c9XuJQWRzQjWLx1PRrqsGUwuaWSTrl7nFhm8OngMAfLX7DG4e5XvZg44ovt4piChiztQ2Y9izKxUNueLBgco6AEBKGAIU1yJZ1yGkg1XOhfmSdBo0O4ZJtGqVz0Dx7vHKFvqeWua3RiAZlDpTiyKD8mvHtXCIR0m+NMDZOpOPIzsuBihEFFLemooB9vVU/hxHQz1Wm4AHP9kBILS9RESuRbKuGZS3NxyRbguQ90DxHSw9evUAxf3Tsg/DtrAEmEERA5SXbxwiZX2aOcSjIP9/4mlILBYwQCGisHFdoRYAPvvhVMx+43MlX+Du5IXQfMjLqV0yKK41KHImi002xdj/W/8Xc8biFyPt/aYOVdV7/H8ZLG+ZmEem9JduN5ha0Ghy1u2IWaGdJ2uw/sDZNl9DrJD/bVlD8P8mGjFAIaKQkr9Xepu1cdvfN7fT1USWfIrx7HE9w/pYKpUKWo33ItxKY7P0oearSZtoSLcMPDrNnklpMFsDmoHjj7chnnuv6I25V10EAKhptEiN7TKSE6B3BFPG5hbMencLDlXVtfk6YoF8KrY1RLOsok3QnWSJiALV7KVuYH9lfHzINJicz9912CQcfAUeD3yyXbodSAYFsNeqiEIxk8fcYj/HT4d0wbKdZxT78jOSANhrKy44ZqVkJCegxqVk6fj5RvTJSWvztXR0DbLgNxzDh9GAGRQiCpuF6w5H+hIiSsygdM1I8tv1ta1UADqn6fHSjUNwVVGuz2P91aCI5Nccim6yYgala6ckadv8GwYDALo5tp240IiaRufaRa6Fvyk+FjmMJ42y4DeUizlGEwYoRBRSApxvlvEeoDRItRShn8HjzU0jCzD94q4+jwk0QAl1BkUsks2SrUkkrjOUa0gEYM+QiBmBTsk6t2vVhjnQ6yjk6xXFag0KQ1EiighTizWgWoiOTMygJLfDt375hB69nwAk0CEeZQYldDUoGUnOAKVzmr2LeFqi8jVK0KiQpNO4ZVBCUQvTUQmCgJLF36PZYkOfnFRpO2tQiIgCEOiXOVOLLeYDlAbHt9xw9EBxJZ9ynOjndfW3X6RSqaBRq2C1CaHJoDgCFJ1Wja/uvwyN5hZphWfXACXJEWS5/o2EauHCjuhsnQlf7rIvuBjq4DEaMUAhoogIpKtoR9fgmDWTrAv/W22q7APeX4Yk0CEeAFKAEooPQXGas06rVnS1BeyBiE6rlv4uxFoTvctzCdW6QB3R4bMN0u1j55y3rTEatLEGhYhCytPHh0H24Sl+8YunACVFH/4MSpoiQPH9eK4f+r6INR+hGEYwNtlfj/SkBI/702RDYeLSAK5DPIF0o41VZ2qdvXTkXYJjNWZjgEJEIeVpiEeejhZT9/EQoMgXCgy3tETnh76/VZODzaAAoRlaqWmyz87xGqDIgiyxsNj1WuM5g+Lt3wwzKEREARA85lCcxALOePgmLPaqaI8alGAyKIHWoACyDEoIhnhqm+z9TbwFKDVNzlV5kxPsz8e1O24816B468Qbq0EbAxQiCjv526f4gRMXGRRxmnEYZ/GIw2cTBzh7n/gNUIIY4tGo7ce2dSprs8Uqradj8BKgGGRZIHFdIdd2/vHwd+NK7Atj9hKI2GJ0mjEDFCIKLZf3ygW3Xqy4L37wmOLgg6Y9Mijf/O5KrJo7HhflOrur+gtAkoIY4hEzKG39ll7XbH8tVCplrYmc/G9FnrG5uDBDuh2rM1a8+e/2Uxj27Eq8uvKA9wxKjL4mDFCIKKTkb5Wv3zIc04Z0UewXA5S6ZgtiXXtkUNKTE9xav/ubvt2aGpS2DvEcdCxvoNOo3bIiosHd0qXb8nWF/vXrYlzWNxsA0BIHQ4Nyjy3ZDQB4bfVBr6tBs5MsEVEA5G+Wnr4pi9/I73hvq9dvhLHiXL191eYML0Ma4aJRq+Cr4WowQzxioNCWb+nbT9TgVscCkTo/BbwieRCl1ajRKdneL8XbMEeskq8ibfHy/4A1KEREAZAXMYrfvuVD5PsqnAsFih/gsUgQBBxwZA3kXT/bS4LG+9u7v06zcqHIoKzYUyHdDvTD1K3FvRgoxXhQ64u3gD5WW90zQCGikJK3Iu+X53vVWbEuIRadvNAEY3MLtGoVenVOaffH97VmTTBDPNoQTDOWT3tusnhe4dqVOKQjSnAU68ZqvYU38mfrOsTT1bEC9Jaj1YoeKbGCAQoRhZT4Dfeuy3pKC8B5E8t1KN+XXwAADOyaHpGW/g1m74FAYoDDLIBsFk8bAoNghiDWPHQ5XrpxCG68uJtie4LWHijF4ywekTyD0j0rGbeN6S7dv+XtTag0NkfissKGAQoRhZT4Dbco39nKXD6OPucnfaTbYmfRWHSqxv6NtncEsieuXDM4wWRQzC32QGfPaWOrHz+YrEevzqm4aWSBWyGt2IhOnHIbL+SjN2L9zdyrLsL6h3+C/l2cGcpj5xtx6YtrFC3wOzoGKEQUUuK3PK3a89vLQ5Mukm4/8d/d7XJNkXCuzv5B2jlVH9HrEBfm+9nwrtK2YAIUcf2XF7/a1+prCEWn08LMZADA8erGNp+roxKzk2J9keswntUmYNvxC+1+XeHCAIWIQkpM5yfIponKvz+rVCrkp9uHfk5eiK1xc2OzBS1WG05UN+Ld744CALIjHKAkqFXQazXIMTivI5hZPKFgCcEsEzFAORFnAYq8M7NYVC7+25KvYC2KpaZtDFCIKKTEqZDyDMq9V/QGAFwzNB8A8PzPBgNQBjEd3dk6E4Y+8zVufnsTnvpij7Q9O00XwatydmKVN2cLJoMSCqHo0yGubix2o41Ha/efBeCcqq3xUAhtbIqduq7wr2BFRHFFTEPLG23dM743xvbOxoAu9rqUUT0zAdi/Wdc1WxQL3XVUq36shCAA245fQM9sZ81H787tP8VYTvwQS5Z1sw1mLZ5QCMXMm1CuCdSReEqISEM8HqaSG2NoZhwzKEQUUs4hHufbi1qtwtCCDOmbX4pei85p9iGHA5X17ifpgOSL2h2VFSrKW9BHgtZjBiXwt/7+jqnivqYt+xOK3iXOhnHxlUHxFI6J/7a6pLvPkoulmXEMUIgopCw2sUjW9wfaIMcsn71nWj87JJp4Gvu/e3yvdh9OcaV21CnIv20H06jtkSn9AUDKfrVGKLIe2jjtg+JpWnVWin3YMM/DNP5lO88oZs11ZAxQiCikxAyKp/SzXCfHm2yDKTZS0p5S6/Om9o/AlSiJQzzyeLE1re5du5iWHb+Af5YeC+jDMJRDPLHa1t2Tv64/7HG7mH1Uq1WYOihPse9snQkr9lSG/draAwMUIgop51RI3xmUhBA0AIsmtS7FiZ/eUwyVh1kW7U3MoMivRecneJQTMxeuAcr0hRvxxH/3YJ2jcNMX+bDMc9cNDPixFdfhJVCKZfO9TO3OSXPOyHpzxsVu+1f/yACFiMiNp1k8nsTaB444e2J4YQbeuu1iXNIjM8JXZOfMoDgDlGACJzHQPHy2AQvWHnLbf+y8/8ZgYtbj2esG4vbiHgE/tpw2xgLa1krRaZAlm7ru6f9lFMTFIcEAhYhCKtAMSqyl7MUAZcrAPEwZ1CXCV+MkBij6INrbe/p9AHh5xX63/f6G8gBnUNGW2UPyVZVjpcaiNYp7Z7lNL+7vsuaVCrERoQT1Fzt//nxccsklSEtLQ05ODq6//nrs36/8g73iiiugUqkUP/fcc4/imPLyckybNg3JycnIycnBww8/jJaW2BiHJop3gdagiPsDrU+oN7Vgwp/W4dmle9t2gWFwocGMakcLdkNSdEyZFgPAS3p0AgBMGpiL4YUZmD2uZ1DncV0V+VRNE5pk6/wEMrtHzKp56tsRKPnjxEMWRf4c5Qsndk5zL4x9+/aRKMhMku4LHuf+dDxB9UFZv349SkpKcMkll6ClpQWPPvooJk2ahL179yIlxTnv/6677sKzzz4r3U9OTpZuW61WTJs2DXl5edi4cSPOnDmDmTNnIiEhAS+88EIInhIRRVKgs3ikb8QBDvEs+eEUDp9twOGzR/HkNUVtu8gQ2nqsGjP+vlmabZEeJQHK8gfGY+mO05h9mT0g0Ws1WPJ/Y4M+j9YlE/b7/+wMOkARW927niu463AGSi02ARFYf7FdyVd9njwwD98cPAfA899XYVYyVj54Ofo/sRwAcKY2NhYNDCpAWb58ueL+okWLkJOTg7KyMowfP17anpycjLy8PNdfBwB8/fXX2Lt3L1atWoXc3FwMGzYMzz33HB555BE8/fTT0Oki23WRiNrGUx8UTxKCnTYaZWn9zUfO44Uvf8SOk7WK7dESoPTJScWDV13k/0A/XGuJTtU04chZZ92Jr//PFxrM6JSic2bV/NQl+b4OZ3ATD1ONGx2z29QqIC3R+VHt7e8rMUGD/9x7KaYv3Ii9p40QBCEqirTbok01KLW19n+YmZnKYrAPP/wQ2dnZGDRoEObNm4fGRufaCaWlpRg8eDByc3OlbZMnT4bRaMSePXtARB1Xs8UqfXgk631/xRXT/YE23vIX8LQni9WGX7y9yS04ASK/9k6oudYSqV0+9LwN2yxYewjDn1uJT7edkP4mQjXEE4rGb9GuwZGlStFpFfVDGcneA+CB+QZo1SqcbzCjPAbWLGr1v3ibzYYHHngAY8eOxaBBg6Ttt956Kz744AOsXbsW8+bNwz//+U/cdttt0v6KigpFcAJAul9RUeHxsUwmE4xGo+KHiKJPnaMXiEoFpOp8J2gTNMEVycpT/JGuQXCdUizqnpWMi3Ij29o+1FyDCtcYw9v/CbGg9vef7ZIClLZ0o5Vfx7KdZ1p9no5C7A+UpNNIHZgBZTbFVWKCBn0dnYujsVYrWK1ei6ekpAS7d+/Gt99+q9h+9913S7cHDx6MLl26YMKECTh8+DB69+7dqseaP38+nnnmmdZeKhG1E7HNdqpOKy1S540YcOw6VRtQOlr+TX7Wu1swrCADv53cr41X3DreFmTrlZ3S4dPqrlwzV64ZFH/ZjKQEjVSDomlDDYr8dX388924bUz3Vp+rIxCD4IzkBOg0zmykuGiiN5OKcvHjGSN2nKyBzSb4/XcYzVqVQZkzZw6WLVuGtWvXolu3bj6PHT16NADg0CH7/Pm8vDxUViqbyIj3vdWtzJs3D7W1tdLPiRMnWnPZRBRmYgbF17c8kfhtes9pIxZ66ZgpJ/8G/e2hc/jL2kMR60LrbUG2rBgb3gH8Zz381YMk6TRodAxXBNMgLt5VN9hnhWUk6xQZlGQ/yxSU/KQPknUanKs3Y19FXVivMdyC+msRBAFz5szBkiVLsGbNGvTs6X+62vbt2wEAXbrY+wIUFxdj165dqKqqko5ZuXIlDAYDioo8V+br9XoYDAbFDxFFH2eA4r9QVP7B95c17g3AXHka1rnqlfVBXF1omFtsHhuWAYEFZh2N63Rx1/8P8iG65bvP4GdvfodDVcoFII85Fk+M9MrOHUmNY9p6p+QERfbQXwZFp1WjuFcWAGDpztPhu8B2EFSAUlJSgg8++ACLFy9GWloaKioqUFFRgaamJgDA4cOH8dxzz6GsrAzHjh3DF198gZkzZ2L8+PEYMmQIAGDSpEkoKirC7bffjh07dmDFihV4/PHHUVJSAr0+9r59EMWTC4431YAyKPLVjgMYFvG0aNrp2uZ2b9r18dZyrNzruZV4rBXIAu5Fsq4ZE6usyPnRJbvxQ3kNrv2Lc+j/bJ0JNgHITNEhz8Pqu+RZdYN9iCczRZlBSdL5n1990yUFAICPt5SjWTZduaMJKkBZuHAhamtrccUVV6BLly7SzyeffAIA0Ol0WLVqFSZNmoT+/fvjoYcewvTp07F06VLpHBqNBsuWLYNGo0FxcTFuu+02zJw5U9E3hYg6pl2n7LNaxEI9X+QZlEDKNrwNJZg8BC7htPpHZ/Z3TK9M/OfeS6X7nWMwQHHt/ur6gSf//yIOSzSa3T8UY/G1CSejo57LkJSgmMWTHECAMnFALrpmJOFCowX/LjsZtmsMt6Dykf6+qRQUFGD9ev8p1+7du+PLL78M5qGJqAM4ctae2h+Y738YNtgMirdiTJPFhkQ/4/KhJH4ITyrKxeu3DEdiggZj+2Rh54laTBqY6+e3Ox7XIkvXup9nlu7FZX2z0SfHd1CamcIeV8EQg7zkBOXHdLKf2XGAvV7rrst64umle/Ha6oP42fCufoeGohErlogoZMQalEDavcuHDgKZaGDxMh25Kcwp7P+UncSzS/dKtRfi7Iq7x/eSAqP37xyFLY9NREZybH4IL/7VaOm2p++pt/xts99zZKa2/bW5uDADgHI131glZqqSdGpFliqQDAoA3Dq6Owozk3G2zoS/f3M0LNcYbgxQiChkGsz2ACXVT5M2QNlV9EKjBWdqm3we762hW7jH2B/6dAfe/e4olu6wFxyKqXd5R0+tRh1QbUBHdWmfbHTPsi9Zku0hODhbZ/J7jvwQ1J+8dKO9lrHJbI35BQMbzWIfFC16Zaciz5CI/nlpATcs1GnV+N0U+zT8v244jEpjx2t/zwCFiEKmweTsfumPawOwe/5Z5vN4bxmU5pbwBChVxmY8/YWzu3XZ8Quw2QSpB0q0tLRvL/dd2ReA+xBPoG4cUdDmayjITIZaBdSZWnC23n9Q1JE1WewBeXKCvVHbht/9BP+777KgzjFtcBcML8xAo9mKZ5d1vMZtDFCIKGTqHR9egYx367TKAMVT23g5i5calJpGz03T2kIQBIyevxqLNh5TbN94+DzEbHu0rFrcXhIchZpiDY6rvae9d/jumpGEfnn+C6f90Ws1yM+wr9p7IgZaufvSZHZ2kgXsGZFglwpQqVR4/vrB0KhV+N/OM1i3v8r/L0URBihEFDLit+vUAAKU9CTPNQlvrD6IT7aWu20X+22M6pGJl28cgj459p4aN7+9CXtO+w5ugvXa6oNutRbGZgtue8dZa9GehbnRQGyy5m021V3/2Ob1d6/o1zlk1yH+bTWZY3s9niapBqVtf2dF+QbceWkPAMCT/93ToaYdM0AhopCw2QRp5kFqAH1QPM3q2HO6Fn9aeQCP/GeX2z6LowZlSLd0/HxkARITnG9f4rovobL7lHs24L/bO3bTq7Ya2aOTz/2narzXEPUKYYM2sSeI2dpxPmhbQ/y3lBSCQPiBqy5Cl/RElFc34tWVB9p8vvbCAIWIQuK8I/WvUgVWn+EpQJEPE/zHpX+DmEERpyen6Z2PEerFA+tNoR826uiyU/U+V9L1Jc8QugZtYibHU+O+WNIsTjMOQfF1ql6LZ6+zL+r79jdH8N2hc20+Z3tggEJEbWazCdIsnM6p+oBmGhg8ZFke/vdO6fZDn+5Q7BNT02LTqi4Zzg+90AcovgtBp1/sew2yWCVfSyc/PRH3XtE7oPV1WhvYeLwGx///9m7Q194aLaHLoADAVUW5uGVUIQQBmPuv7bjgpZYomjBAIaI2eWn5Poz4w0qUHb8AAOjiKGL0R6VSIS3A5lFWmyAVw4o1CF1lj2ML8ZTTekc/F61ahTG9MhX7Jg/MxQs3DArp43UU8pbr3Tol45Ep/f0Wv143LB+je2b6PKY11xDrGZQmc2hqUOSe+OkA9O6cgkqjCfd9/IPflagjjQEKEbXJm+sO40KjBc//70cAQHYQHUPFvha+VBmbcdn/W4P/7ToDwDlDKFc2bOClRYpX1Q1mHD/f4HW/mEFZdt84/Pry3op9Y3plQa+NrwJZkTxASXDMwpJvc9UjKxmv3TzcbcHBthCzc+Yo/3BtC6tNkDJEocqgAPYutG/ccjGSEjT45uA5PBflU48ZoBBRSIizO4L5xuf6weVpkcHtJ2pwutbZZCrF0QSuk6xrqzXIDMrEV9bj8pfX4UxtE8rPN2KVy+J/9bLZSK4fEIH0eIlV8uEcMVDwNcTz9LUDQ38NXjIo3x0653UKdEcjn2kTSGv7YBTlG/DqL4YBAN4vPY53vo3eLrMMUIgopIIp6tO6rpTroRmbayt7MUCQF+J6m/rqic0mSB9k3x48h/Evr8Wv/rFN6hHRYGpBs6NJVnpSgtvzSQ6gS26sUmRQHIFJgksGpYusY2w4Wv/rPRTJbjpyHjP+vhkXP7fSZ2aso5AvtiifrRYqUwbl4ZEp/QEAzy3bi3+UHgv5Y4QCAxQiCqlgvvHpXT7cPK2r0+SyMq4YIKTIAoVgupuuP3BWui0vyt14+DwA4OQFe7FvelIC0hIT3Hq6MIPiuK31nEHJl9UGhaPbrqcMinxWyoo9FSF/zPbWJJtirApkqe9WuOfyXvj15b0A2PujvPdd9GVSGKAQUUgFM8TTMzvF7zGNLgGK2PJeXgdS0xh4av/ORVs9bjc5gqOTF+wdSrt1sn/QunaM7RTHq/LKMyhiYOLaEVj+jb9LCNbf8XYN8hoUcYkFAFL2qyMTA/VQTDH2RqVS4fdT+uPu8fYg5ZmlexWLYkYDBihEFFLJQRT1BdIfwzWrIq5o2zc3VXoDP1dvxoK1hwK/SA/EDzZxtpDYp8W1Luai3NA1HetoxCwT4FyN2teU8nB023Xtg2KzCVi++4y03xSmtZnak7hQYLi7FatUKsyb2h8PT7YvKvjud0dx56KtOBcl6xwxQCGiVvP0bSuYDIpKpcI/fjnK6/73vjuKrceqpfu9slOQlmjPaCRo1Nj+5CSkOB7v5RX721QkKX6wNbp8e3WdsRPqosWOSgxMNC5DEHdd1guJCWrcPqZ7WB5XzKB87ShsLq9uVBRRm2Igg3K+3v533Ckl/Os9qVQqlPykDxbcejH0WjU2HDiLKX/+RjEUGin8l0ZErebp22qwfRsu7Z3ldd8zS5XTIP8+a6Tivk6rRv8uBqkHSyC9MS4uzMD35TXolZ2CI+ecBZXitE5nB0/3t8fHpw3we/54IQYorj1o+uSkYvuTk9zqi0JFDFCPnmvAz9/aiK3HLij2x0IDN7HpYX56YD2FQmHakC7ok5OK33z0PQ5U1mPWu1tw25hC/OH6we12Da6YQSGiVvP0bbV7pv+6EjmtRh1QN9LuWcke13TpnpnsvJ4A0vviMvZX9s9RbK8wNmPrsWppSEmeXheHoq7op/ydeCYGIK5JtASNGolhLO68cYSzi69rcAKgQy2G582pGntGKD/Apoeh0i8vDV/MGYc7Lu0BlQro0o4BkifMoBBRqzW7BATDCjIwto/3jIg3iQlqqejxqqJcrHTpSwIAN40s8Pi7v5vSH5/9cApAYBkUcRl71zf/H8pr8PO3SqX78v4nqx66HOfqTOgRQFFvLHvhZ4Px6BL7Qo5iBsU1KNSqwxOYiAxJvj+2YiqDkhH6ImN/EhM0ePragfjZ8K4oyje0++PLMYNCRK3mOmOiKN/Qqm/O8mEhb4WBd13Wy+P2vPRE5Br0AAL7cBJnBfn7diqfQZGq18Z9cAIAo2Vt/8VaENd1i0LZNdYTf118Y6FI9nSNGKBELoMxtCAjoDW1wokZFCIKWrPFirLjF9xmuGS2sjGXPFuR4qGGJSlB47Oluqepp944AxTf305DuQZKrJAPxYkBnLhukShBE94Mijd6rRqmFltMZFBOO4Z4Ij3EEmnMoBBR0J74fDdm/H0z/uBYf0fU2h4h8qxJdqoevxrX02W/77cq8Vu1vxkcgiBIUzhz0vwEKGGe4tkRyQtfxf9ndSbXACUyHytiU7jy6saIPH6oWG0CKoz2AKVrBDMo0YABChEF7dOykwCALUerFdszWzktUp6tUKtVePynRYomX/6CBV2AC8iZWmxSUae/lvWuHWTJpVGb4/ZVA3IVx7RHgPLHnw9125aRbP/bO3K2AcZmS1ge90KDGT+UuxfmhlJVXTOsNgFatQqd0/RhfaxoxwCFiEKmV3brmpjJAxCxr4a8x0qin+EWbwvIuZK3zU9O0EgFvXOvusjt2MkD8/xcdfyRBx/i/6cHPbx24SafySO6KDdNui3WcITaVa+ux8/e3IiNh8/5P7iVxGvPNSRCE+aC42jHrwhEFDIDurSu6l8RoDg+A6vqnN0sE/0URgYaoIhN2HRaNbQaNd67YxTqmi3olKzD2D7ZSNZpsHZ/FW4dVYj05PA3yepo5BkU8cMzMUGDW0YV4KMtJyJ1WQDsayTlGvSoNJqCWpspGOccDdTW7z+LS3tnh+Uxqhvs2Z/s1PhdUkHEAIWIQmLV3PE+C1l9cR3iceW/BsXzlFdXjY4PLrHAU6dVIyvVnkYf0b0TgNYHWfFAPoVY/u3+t5P6AVDhppHumY32cq7ehKwUe4BS1xyeAEUUzm7CtU32AMV1Dah4xCEeImozlQrok5Pm/0AvPA3xyKX4qQfRu2RQGs0tmPjKevz20x2K48QZPMGsF0RO8ink8mAlK1WP+TcMxvDCTpG4LADALaMKkeqYVSZfPDBU5A3gUvzUL7WF0RGghGMl6I6GAQoRBUUQ3Nff8bApKIoMiuNDsJ+spsBfwareEXCIXWA3H63Goap6/LvspCLdLwYonELcdv6CxvY2sShX+jupN4W+SPaCbMXss3XhW0xPLPBlBoUBChEFqSUMy7HLMyjiEM9bt4+Qtvn7MOzsGKYRPzhMsm+7n31/Urr98L/tGRWdn5oW8u63ky7CNUPzMa5PeGow2iJFClBCn0H5dJvz7+ivG45gzuLv8c1B7wvqfbj5OP6384zX/d4Ym+wBtSGRAUp0hcBEFPU8FaJeNyy/TedMVAzx2P/bSVak6m/huRxHJ1mxf4T8G+4PJ2pw2xgB6w+cxckL9hkS6X7apZN3c67sG+lL8CrVMfQS6iLZnSdr8MrKA4pty3aewbKdZ3DsxWlux5+obsRjS3YDAKYNcd/vS3WD/W+3E4u0mUEhouB46tT54g1D2nRO+ZCLWHwpH9bxNzsn19F0rdJDgGJssuC/20/jjve2StuevW5Qm66XosvyBy4DACQl2P9mmkK8YOC+M3Ve91k89N6RDwd52u+LGER37RTfTdoABihEFCRPwUJbazo8DfHI13QRMyPedHI0iBPT42frnQFKbZMFL6/YL92f0D9H0TODOrbnrh+E/nn2mVdJOvvfjLzfTSiYfAQZpy6491yR12QF23r/xAV7J9yCTsl+jox9QQUo8+fPxyWXXIK0tDTk5OTg+uuvx/79+xXHNDc3o6SkBFlZWUhNTcX06dNRWalcmbS8vBzTpk1DcnIycnJy8PDDD6OlJbzTwogoNAJZMThYigDFw0yRgfnpfn7f/s1ZbGN/Uvahca7ejFOyxl156e2/QiyF3vIHLsNT1xThlkucq1yLf0fNIc6g+PqbP3a+wW2bvErLFMS1NFusqDTag+tuzKAEF6CsX78eJSUl2LRpE1auXAmLxYJJkyahocH5P+jBBx/E0qVL8emnn2L9+vU4ffo0brjhBmm/1WrFtGnTYDabsXHjRrz//vtYtGgRnnzyydA9KyIKG9deI4vuvKTN55R3ipVPM17x4Hg8dNVFKPlJb5+/L077bDRbUd1gxjcHnZ0+j55TfoDkGRigxIL+eQbcObanItOW6DKbK1R89dc5U+ue3ZMHNMFkUMRAOlmnQWYr17WKJUFVii1fvlxxf9GiRcjJyUFZWRnGjx+P2tpavPPOO1i8eDGuvPJKAMB7772HAQMGYNOmTRgzZgy+/vpr7N27F6tWrUJubi6GDRuG5557Do888giefvpp6HT8n0IUzVzfcK/ol9Pmc3oa4gGA3p1T8ZsJ/osyxcZrjWYrDlR6rxcAgC5xvgBbLBOHGkM9xNPoY1aQp8eSZ3CCCVDEzF+3TkmKnjPxqk01KLW1tQCAzMxMAEBZWRksFgsmTpwoHdO/f38UFhaitLQUAFBaWorBgwcjN9e5wNTkyZNhNBqxZ88ej49jMplgNBoVP0QUGfIF+Zb9ZlxIzpmsc291H9zvO4ojzVZpmMebwV19DxdRx5UUpgzKuXrvfU+aPWRX5EFJMMNNYpM2Zk/sWh2g2Gw2PPDAAxg7diwGDbJXxFdUVECn0yEjI0NxbG5uLioqKqRj5MGJuF/c58n8+fORnp4u/RQUFHg8jojC7+hZ+5DJyO6dMChEH/aJXmpQApXiCFDMVpvUKrzIQ8v6q4py0TendQsaUvQLVw2KrwBl3X5nL5S6ZguOnWtodQZFnB6dEsZW+h1Jq1+FkpIS7N69G99++20or8ejefPmYe7cudJ9o9HIIIUoQvactmcwhxZkhOycysUCgw9Q5LOItpfXAACyZIut3XBxV7xy07BWXx91DGItU6gzKL46x245Wo1z9SZkp+ox8ZX1qDSa8OvxvaT9wRTJ1osBSpR16Y2UVmVQ5syZg2XLlmHt2rXo1s25OFReXh7MZjNqamoUx1dWViIvL086xnVWj3hfPMaVXq+HwWBQ/BBR+6s0NuP78gsAgM5p+pCd11Or+2DIFyl8v/Q4ACAj2RmgaON82fp4IWYedp8y4saFG0NSiyIIgmIWmCdi7Yg4A2eZrINsMBkUcSmGcK7105EEFaAIgoA5c+ZgyZIlWLNmDXr27KnYP2LECCQkJGD16tXStv3796O8vBzFxcUAgOLiYuzatQtVVVXSMStXroTBYEBRUVFbngsRhdnoF1Zj+4kaAKH9ludtmnFbbD1aHfJzUnTLkHVf3Xb8grRY5LKdpzHu/63BjL9v8riWlC9VdSacqzcrtpU9PhH5sunq8sZsgDLjwiGe1gsqQCkpKcEHH3yAxYsXIy0tDRUVFaioqEBTk6N9dHo6Zs+ejblz52Lt2rUoKyvDnXfeieLiYowZMwYAMGnSJBQVFeH222/Hjh07sGLFCjz++OMoKSmBXh+6b2REFFqu7cNTQ/gtTx6gBPsB4o28uZuaGZS4kOHSHv5/u+yZjPe+O4aTF5rw3aHz0jBKoFynqf/68l7IStWjuLdzLaJKl6nG8kLyilrf2Re5BkeBdzKHeAAEGaAsXLgQtbW1uOKKK9ClSxfp55NPPpGOefXVV/HTn/4U06dPx/jx45GXl4fPPvtM2q/RaLBs2TJoNBoUFxfjtttuw8yZM/Hss8+G7lkRUchVunRzTQ7ht7xEnfOtyBxka3Bvnrl2oHSb8Ul8SPeyArBVtsBlsJ1dzzuyJxcXZmDZb8bh4Un9AChb2Fe7ZFDk9p4JfNZpg2M6cyiD/44sqHeYQL7ZJCYmYsGCBViwYIHXY7p3744vv/wymIcmoghzbTcvn3nTVjrZ3OJgP0C8uWVUIZ76wt66gEM88UHvYZXqFqtNEUwEO8PnvGPxvpy0RMWsNfk5axstXj8fxbqUQIhZylAG/x0Z1+IhooBUNyi/JbaEKNMBQNGUKlSt9HVatRT4jO/bOSTnpI6nwWRVBL3BBsBi/Yl8VhgAjL/I+TdV22Txmvmra7YE/FjiEE8qh3gAMEAhogCJ/UVEXdLD05G1tRmUpXPcm8Zt+N1P8P4vR2HCgLZ3u6WOqc5kUQS9wWZQDlfVAwDyXToQ3zSyAMW9sgAANY0Wr4F1XXNgNS/3ffQDvjt0HoCycWE8Y4BCRAGRByi/vrwXivLDM92/tRmUwd3ScZ9LW/y89ERcflFntg2PY/WmFsVaOsEGwOKstRHdOym2a9Qq3DK6EABQ02RWnHdC/xyM6mnvsH62zoSzdSZ8d+ic12GgBlMLvthxWrrPDIodAxQiCogYoMwe1xPzpg4I+fnFD4CfDunS6nPMKu6Ozml6zCruHqrLog7O2NSiXLzPElyAIk4hzveQMcxwFOXKMygJGhXeueMSzL9hMADgfIMZlzy/CjP+vhkfbi73+BgNLsszcBaPHV8FIgqIuE6It5kSbfWvXxejvrkF6cmtP39Wqh6b503gtGKSVBiblWvj+FiZ2FWL1SY1T0tNdP+4FKc1G5ucAYpYqJvm4fi/f3MEt41xD55dFyPkLB47ZlCIKCDiWLqnN95Q0KhVbQpORAxOSO5wVb0UZADBZVAaZIGDp2EXMVi/0GjBbz76AYBztmt2intfL09BDuAhg8JZPAAYoBBRgMTiwqQQTi8mCrfXVh9U3DcFkUExOmbg6LVqxXIKoowk+8yeJosVu07VAgAaHMGQWq3CqrmXK4731nq/0WV7TgiXkejIGKAQUUCaHd88kzjDgDqwQGfVAM7F+9ISPWf20hK18FV/3cdl5ewztc0eC2XlXZpvHV0IrYYfzQADFCIKkLhCrKdmWEQdxfHzDf4PcvA3rKlWq5AaxHBMo9mKOg+t9sUMSt+cVEUH5HjHAIWIAiKmp5lBoY7sb98cRdnxCwEdKzZZ81V3lexS0PqTfsqmgDNdZpTVNLg3bhML0Lt1SkICsycSvhJEFBBx9kOih7F4omjWPSsZf5s5Uro/feFGv79jsdrw+Oe7AfjuSyIvaO2fl4Y/3TRMsf+Zawdi62MTpYJai829SPfkBfuCgq7N4OId32mIKCDNzKBQB5CfnghAWWiaoFG7rXTsz0dbynHGsUqxzwyK7N/D76f2R2aKsiW+SqVC5zS9lBmxeGiJf+JCIwCgIDM5qGuMdQxQiCggzY4+D6FcJJAo1BbfNQYzi7vjn7NHS9tUcDZVC1T5+Ubpdqre++/KA5QUH5kWncZeTWtpcS+SPVFtf6xCBigKnGxNRAGRalAYoFAU65GdgmevG6TIVAhA0D12NLJ+Or4zKFrZbe//NhIcQ6OeFhU84RjiKejEAEWOGRQi8ksQBGcNCgMU6gBci02D7YAcaIByts4k3e6c6r1/ibchnmaLVTpHQSZrUOQYoBCRX6YWG8T2DYkJfNugjkev1UirDwci0ABlaEE6AHsAlGNI9Hqca4BiarHi1ZUHcM0b30qPF65lJDoqDvEQkV/y9uDMoFBHIzZH+/PNwzD6hdVQq+zbfK1yLd/nqwbl1+N7IztVj9v9LFAp1aA4ApSnv9iDj7ackD2Glqtuu2CAQkR+iU3atGoV+zRQhyOWpYrBtU0ALFYBOq33gEAry6DofUyt75Gdgocm9fN7DeK/G7OjSFYenADhW+OqI+M7DRH51cR1eKgDE4tX5cOT/lY1lg/xaDVtz2z4mmYM+O61Eq8YoBCRX+JCgXoGKNQBXVzYCQCg06iltXOavSzcJ5KvmROKrKE4i0cMUDq7LAjI/kLuGKAQxaC6ZgvmfbYLpYfPh+R87288BgA4V2/yfSBRFHnm2oG4pEcnPHSVfQhGpXKunVPvYU0cObPVGaD4mj4cKNcaFDHoF9U0urfAj3cMUIhi0F/WHMJHW8pxy982heR8H2894f8goigz69Ie+PSeSxU9UMRaD3+rGsuHYsb1yW7ztVgcAc/izeWoNDZLCwReVZQLAJgxurDNjxFrOOhFFIPE1tlEpJSWmADUNsPY7DtjYXF0Tr7n8t7QhmCIZ/2BswCAHSdrMfqF1dL2l28cApWKU4w9YYBCFIM404bIM0NSYBkUseOrLgQFsr6kJSYoCnLJie9iRDFIF8IARZ7q/sP1g0J2XqJISEu0Zyrq/GVQHH/3oQr2X7t5mNs2vVbN4MQHBihEMUjno29DsOTFfDeO6Bay8xJFgliDYmzyk0Fx9CsJ1b+l64Z1xeSBuYptWgYnPjFAIYpB8m998umSrWFucWZQQpmZIYqEbMd6Of5mpJlDnEEB3BcDnHf1gJCdOxbx3YYoBsk7X5paPDeGCpTzjVoFNb/xUQeX4+g/8tcNR3weJxbJJoQwGykOLwHAP2ePwm1jfLfHj3cMUIhikLzzZYOffg/+iBkUZk8oFsiDhJM+ZrtZwlAkKx8uGlqQEbLzxiq+4xDFIHk37UY/HTP9kQKUEH6TJIqU0b0ypdvVDWavx0mzeEL4dy/AOdwqNowj7/iOQxSDWmQRSoPZdwbF1GLF7lO1XmtVTAxQKIb07pwKg6NQtt7HVGMxMA9lDYr8nxiHS/0L+pXfsGEDrrnmGuTn50OlUuHzzz9X7L/jjjugUqkUP1OmTFEcU11djRkzZsBgMCAjIwOzZ89GfX19m54IETnJpwb7G+L5/X924advfOu2uqooHMWCRJHUOycVAFDn499GqKcZA0Bmii5k54oHQb/yDQ0NGDp0KBYsWOD1mClTpuDMmTPSz0cffaTYP2PGDOzZswcrV67EsmXLsGHDBtx9993BXz0ReSRfR6TB5HuIZ8kPpwAAr60+4PlczKBQjBFXDvaVQRFb04fy7/6Gi7ti2pAuePGGwSE7ZywLehBs6tSpmDp1qs9j9Ho98vLyPO778ccfsXz5cmzduhUjR44EALzxxhu4+uqr8cc//hH5+fnBXhIRuZAP8TSaWyAIAgTBd1rZ7GW2D4tkKdY41+Px3qzNWSQbur97vVaDBbdeHLLzxbqwvOOsW7cOOTk56NevH+69916cP+9cUbW0tBQZGRlScAIAEydOhFqtxubNmz2ez2QywWg0Kn6IyDtx3Q8AqDdZMfdfO9Dr0S8x8g+rsOHAWew+VYu/rDkIU4szu+JtOrIYoOiZQaEYYXDM5Nl8tNrrMeGoQaHghLyMeMqUKbjhhhvQs2dPHD58GI8++iimTp2K0tJSaDQaVFRUICcnR3kRWi0yMzNRUVHh8Zzz58/HM888E+pLJYpJzRYrquqcTagazS3SMM65ehPu+aBMmtkjXwTNa4AShtkMRJE0skcmPt56Avsr6rweI+//Q5ER8gDl5ptvlm4PHjwYQ4YMQe/evbFu3TpMmDChVeecN28e5s6dK903Go0oKCho87USxaILjcqpkyaLMvCQF9B+uk1ZGGuzCW7DQKxBoVhzUa69SFa+jIMrCwPziAv7K9+rVy9kZ2fj0KFDAIC8vDxUVVUpjmlpaUF1dbXXuhW9Xg+DwaD4ISLPahqV4+pNLm/CYqtvADh8tkG6bbUJONdgz7wIgoCnv9iDd789Khvi0YTrkonalfi33Oyjy7JUJMshnogJ+yt/8uRJnD9/Hl26dAEAFBcXo6amBmVlZdIxa9asgc1mw+jRo8N9OUQxzzWDUtukDFh8jamfqG4CAOw5bcSijcfw7LK9qHdMxUxM4Bs1xQbxb9nkI4PCGpTIC3qIp76+XsqGAMDRo0exfft2ZGZmIjMzE8888wymT5+OvLw8HD58GL/73e/Qp08fTJ48GQAwYMAATJkyBXfddRfeeustWCwWzJkzBzfffDNn8BCFQK1LBsU1o2LzsXhgeXUDRnTvpEh9Hzpr71GUyAwKxYjEBN8ZlFV7K6XAPFnHv/tICTo03LZtG4YPH47hw4cDAObOnYvhw4fjySefhEajwc6dO3HttdfioosuwuzZszFixAh888030OudaeUPP/wQ/fv3x4QJE3D11Vdj3LhxePvtt0P3rIjimGvGpMYlo1LvoTlVr+wUAMCyHWdgswmKc+w7Y581l8g3aooRYrBttQmKmizRf74/CQDo1ikJOYbEdr02cgo6g3LFFVf4XL59xYoVfs+RmZmJxYsXB/vQRBSABpe1d2rcAhb33g9X9MvBkXNHsXpfFXo9+qVi3P3HM/aZDsygUKzQy4Yrmy1Wt2EccZbbfVf2bdfrIiUOrhHFmEaXDMnx895XbAXs4/Eje3RSbDPLvlWKRbasQaFYIe/p42l6vfg3n6Lngn6RxHccohgjZlDE/g3n6k2+DsfFhZ0wtk82ctL0Po9LSmAGhWKDSqWSghRPU43FbUk6fkRGEl99ohjT6Fi92HVacKfkBI/HazVqpCclYMtjE3HXZT29njeRAQrFEDFAOXbOPcPYZBazhvybjyQGKEQx5Pj5Bvyj9DgA97V1UhM9p6sfmdJPun1xYSePxwAc4qHYkqyz/3soPXLObZ84xMOsYWRxgI0ohjy37Efptut04lR9AoAm6f6Pz06BSqX8lqjxsZggx+MplkwsysEHm8rRZPZQg2IWh3gYoEQSvxIRxZAKozMAuW+CcgZCql75Zpuk07ilsPvnKbs073tuCp66pgg3DO+KSQM9d3om6og6p9qnDze3uNegiBmU5AQG5ZHEV58ohohpawC4dXQhFm08huoGs9s+rZdMSWFWsuJ+YoIGd471XpdC1FGJU41di2QFQeDMtSjBV58ohoip6T/9fCiyU/WKfibyqZW+umPK1+ohilWJjn8PrtOMV+6thDg66q1ui9oHAxSiGNLg6IHStVMSAGVNiXxVVl/1JOIsIKJYpncMb8rX42m2WHH3P53rxLFINrIYoBDFkAZHcJHqCEAUAYosm+Kr+K/R7H0BNaJYIS0YKMuguC4DoVJ5Lxqn8GOAQhRDGkzKDphaLxmUVB8ZlFnF3QEAUwexKJZil9gnSF6D0mhicB5NOMBGFCMEQZAyKCmOGTvehni6OYaAPJl39QCM7ZONsX2yw3SlRJGXKBXJOjMojRYOb0YTBihEMeJEdZNU3Jeicx/ikS+IdkW/HK/nSUzQcEoxxTwxg2KSTTNukGVQfjGyoN2viZQYoBDFiPdLj0m3xeI+1wzK5yVj8f3xC/j5iG7tfXlEUUXsASQPSsRZcHqtGn/42aCIXBc5sQaFKEaI/U4AQO0ITOQBSnKCBsMKMvDLcT1Z/EdxryDTPsx5qqYJi747iuoGMxZtPAYAyDHoFRlHigxmUIhihBigvDR9iLTN2GSRbk8dzGEbIlFOWiKyUnQ432DG00v34ttD57Hqx0oA9uFSijyGiEQx4ly9CQDQ2eBstHbsvHOl1j45ae1+TUTRLD3JucK3GJwAwOxx7J4cDRigEMWIZq7AShQU+cw2kUatwu9kK3xT5DBAIYoR4nRJ1wUAicgzvYcAxWoTpBk+FFkMUIhihNgR09ObLhG58xSIDC3IaP8LIY/4TkYUI0zSCqz89kcUCK3GfTbbU9cUReBKyBMGKEQxghkUouBYbYLbtuwUruYdLfhORhQDrDYBZitrUIiC4SlASUtk941owQCFKAaYZSuyesqgyBu2EZGdhQFKVGOAQhQD5OuJeApQtAxQiNyYLO6rF2vZQTZq8P8EUQwQpxhr1SqPb7A6vukSubnQaPZ/EEUM37WIOpB/bT2BK/+4DkfO1iu215vsy8S71p+IjaiGFWa0y/URdSTn65UBSr9cdluOJgxQiDqQ3/1nJ46ca8BjS3Yrtv94xggA6N05RbF96ZxxmFXcHX+6aWi7XSNRRzFtSBfF/Q/vGh2hKyFPGKAQdUBnapWLmR2srAMAFOWnK7b3y0vDM9cNQk5aYrtdG1FH8ex1g3DfhL7S/exUTjGOJixXJuqAjp1vhMVqk5aEP+tYKDDPwECEKFDpSQmYe9VFuHpwHgyJCf5/gdoVMyhEHVSlsVm6fbbOPpbeOY3fAImC1T/PgPyMpEhfBrkIOkDZsGEDrrnmGuTn50OlUuHzzz9X7BcEAU8++SS6dOmCpKQkTJw4EQcPHlQcU11djRkzZsBgMCAjIwOzZ89Gfb2y6I+InCpqm/GP0mOKbVV1Jum2mEFhgEJEsSLoAKWhoQFDhw7FggULPO5/6aWX8Prrr+Ott97C5s2bkZKSgsmTJ6O52fltb8aMGdizZw9WrlyJZcuWYcOGDbj77rtb/yyIYtz1C77Dk//do9h2w5sb0WS293Goa7IAsKesiYhiQdA1KFOnTsXUqVM97hMEAX/+85/x+OOP47rrrgMA/OMf/0Bubi4+//xz3Hzzzfjxxx+xfPlybN26FSNHjgQAvPHGG7j66qvxxz/+Efn5+W14OkSxqUI2nCO3/UQNintnoVlaKJCjtkQUG0L6bnb06FFUVFRg4sSJ0rb09HSMHj0apaWlAIDS0lJkZGRIwQkATJw4EWq1Gps3b/Z4XpPJBKPRqPghIuCVlfux4cBZnK61BzBch4eIYkVIA5SKigoAQG5urmJ7bm6utK+iogI5OTmK/VqtFpmZmdIxrubPn4/09HTpp6CgIJSXTdRhbT12ATPf3SLdT9QyQCGi2NAh8sHz5s1DbW2t9HPixIlIXxJRuxJrSz781WiM65Pt9Tg9h3iIKEaE9N0sLy8PAFBZWanYXllZKe3Ly8tDVVWVYn9LSwuqq6ulY1zp9XoYDAbFD1E8aXLUmPTITkGSznuWhBkUIooVIQ1Qevbsiby8PKxevVraZjQasXnzZhQXFwMAiouLUVNTg7KyMumYNWvWwGazYfRothkmcmW1CTC32BcDTErQIEHjfWViZlCIKFYEPYunvr4ehw4dku4fPXoU27dvR2ZmJgoLC/HAAw/gD3/4A/r27YuePXviiSeeQH5+Pq6//noAwIABAzBlyhTcddddeOutt2CxWDBnzhzcfPPNnMFD5EGTbEn4pAQNcmXdYvVaNUyO4EW8T0QUC4IOULZt24af/OQn0v25c+cCAGbNmoVFixbhd7/7HRoaGnD33XejpqYG48aNw/Lly5GY6HxT/fDDDzFnzhxMmDABarUa06dPx+uvvx6Cp0MUe77adUa6nZigxv0T+uJgZT1uuLgrrh2aj6eX7sEHm8oBACqV9+wKEVFHohIEQYj0RQTLaDQiPT0dtbW1rEehmGa1Cej96JfS/WMvTnM75q31h/HiV/u87iciihbBfH4zH0wUxY6fb5Bup+k9JzynDbYvGd+7c0q7XBMRUXvgasZEUazR7Kw/+dc9xR6PKchMRum8K5HG1ViJKIYwQCGKYmIL+x5ZyRjQxXs6tEs6V2IlotjCIR6iKNZssc/QYQt7Ioo3DFCIoliTtAggAxQiii8MUIiimDjEk8QAhYjiDAMUoigmZlB8tbcnIopFDFCIolizNMTDf6pEFF/4rkcUxZpZg0JEcYoBClEUE/ugsAaFiOINAxSiKFbbZAEAGJLYhI2I4gsDFKIoVttoD1AyGKAQUZxhgEIUxcQMSjoDFCKKMwxQiKJYjSNAyUhmgEJE8YUBClEUO19vAgCkJ+kifCVERO2LAQpRlLJYbTh5oQkA0CM7OcJXQ0TUvhigEEWp8upGtNgEJOs0yDMkRvpyiIjaFQMUoihVZbQP73RJT4RKpYrw1RARtS8GKERRoslsRYOpRbp/vsEeoGSl6iN1SUREEcMAhSgKCIKAyX/egDEvrJba25+vNwMAslNZIEtE8Ucb6QsgIsDY3ILy6kYAwNBnvoapxSbty0phBoWI4g8zKERRQOwYC0ARnABAlwwWyBJR/GGAQhQFLjSave7rzBoUIopDDFCIosDfvjmiuD9tcBfpdmEme6AQUfxhDQqRD4IgoMUmIEET3lh+2c4zivsv/Gww9AlqDO2WgdG9ssL62ERE0YgBCpEPv1y0Fd8cPIfvfn8lcsPYLK1zmh5n6+zTih+7egDSkxPwyk3DwvZ4RETRjkM8RDJWm4CVeytxrt4Em03A2v1n0WITMOFP68PyeHtO12LQUyuk4GTV3Mtx1/heYXksIqKOhAEKkcwHm47jrn9sww1vbsQ5R6M0AFCHqZHr2xuOoF7WnC3HwIJYIiKAAQqRwrKdpwHY18GprHUGKHWmFrRYbd5+rdWSEjSK+4bEhJA/BhFRR8QAhUjGahOk2xXGZum2IADVDd6nAreGIAiokfU/eenGISE9PxFRR8YiWSIZqzM+UQQoAFBpNCGnDYWyJ6ob8WnZSdwyqgD/LD2ON9cdlva9ddvFmDKoi4/fJiKKLyHPoDz99NNQqVSKn/79+0v7m5ubUVJSgqysLKSmpmL69OmorKwM9WUQtYpNlkF56at9in37K+uCPl+VsRmP/HsnVu2txBtrDuL11QdRPH+NIjgBgAFdDK27YCKiGBWWDMrAgQOxatUq54NonQ/z4IMP4n//+x8+/fRTpKenY86cObjhhhvw3XffheNSiIJiE5wBSp2seBUAdpyowY0jugV1vlv+tgmHzzbgk20nvB7TJyeVzdiIiFyEJUDRarXIy8tz215bW4t33nkHixcvxpVXXgkAeO+99zBgwABs2rQJY8aMCcflEAVMXoMievnGIXj43zvx3eFzQZ/v8NkGj9uHdEvHkv8bi+oGM3RaNVSqME0TIiLqoMJSJHvw4EHk5+ejV69emDFjBsrLywEAZWVlsFgsmDhxonRs//79UVhYiNLSUq/nM5lMMBqNih+icJBnUABgbJ8sXNy9EwDgyNkGPLpkF/ZX+B/qeXvDYdzwpntWsDAzGUdeuBpfzBkHjVqFzml6pCdx5g4RkauQByijR4/GokWLsHz5cixcuBBHjx7FZZddhrq6OlRUVECn0yEjI0PxO7m5uaioqPB6zvnz5yM9PV36KSgoCPVlEwFwz6D86efDFFN/F28ux5++3u/zHEfPNeCFL/fh+/Iat33/u28c1OFqqkJEFENCPsQzdepU6faQIUMwevRodO/eHf/617+QlJTUqnPOmzcPc+fOle4bjUYGKRQW8vDk85KxyEtPRLPFqjjmu0Pn0GK1QetlfZ7/bj/l9fxp7HNCRBSQsPdBycjIwEUXXYRDhw4hLy8PZrMZNTU1imMqKys91qyI9Ho9DAaD4ocoHJrM9mDk/V+OwrCCDABAoksztQazFbtPex9mXLuvKmzXR0QUL8IeoNTX1+Pw4cPo0qULRowYgYSEBKxevVrav3//fpSXl6O4uDjcl0Lkl9h2vqCT72zfiepGr/tO1yr7p9x1WU/otGrccWmPNl8fEVG8CPkQz29/+1tcc8016N69O06fPo2nnnoKGo0Gt9xyC9LT0zF79mzMnTsXmZmZMBgM+M1vfoPi4mLO4KGIEwQBDY4AJVXv+5+GsdnicbvNJkgdZ/vlpqFfXhoem1aEB6+6yK2tPREReRfyAOXkyZO45ZZbcP78eXTu3Bnjxo3Dpk2b0LlzZwDAq6++CrVajenTp8NkMmHy5Ml48803Q30ZREE7W2+CWCOb4hKgvHfHJZj32S40t1hR02jBk//dg1tHFbpND65pskiFtsvuG4cER51Kso5Nm4mIghHyd82PP/7Y5/7ExEQsWLAACxYsCPVDE7XJ6h/ttSO9O6cgWafMdvykfw42PToBd7y3Bev2n4XVJqDs+AWM7JGpOO5cvX2BwYzkBCk4ISKi4PEdlMjh1IUmAMClvbO9Nk6rNDpXOD5UVe+2/1ydfX92qj4MV0hEFD8YoBA5iIsD5qV7XxDwppHOVvfnPaxufNaRQclK0YX46oiI4gsDFIp7zRYrnlm6B/8uOwkAyEnznv24fUx3FDkW9jtf7x6gnHNsy/ZxDiIi8o8BCsW9N9cdxnvfHZPuj3C0tvdEq1HjZ8O7AgAq65rd9u87Y++Pku8jC0NERP4xQKG41Wyx4rElu/D66oPSttvHdEevzqk+f2+AI4OyYf9Z1DZaMHvRVnyytRxVdc1Y8oO9i6xr8SwREQWHcx8pbi3eXI4PN5dL9/9zbzFGdPcfWIzqaT+mztSCf246htX7qrBa1j02IzkBE/rnhP6CiYjiCDMoFJcEQcD6A2cV24Z2ywjod3VatdR0bdepWrf9UwfleV2nh4iIAsN3UYpL73x7VBGg3Hdln6CCCkOSPfm4+5T7mjxiES0REbUeh3goLv2/5fuk25+XjJUWBgxUWmICKo0mnKppcttXlM8AhYiorZhBobjzQ/kFWKz2dvTTL+6GIV3Tgz6HIVEZ208emCvdHpgf/PmIiEiJGRSKG6dqmvCnr/fjs+/tM21uHNENf/z50FadK9OlEdvd43vj6LkGXNo7G4lcFJCIqM0YoFBcaDJbcd1fvpPWygGA/7uid6vPd1nfzlj1o3PmzsB8A75+8PI2XSMRETlxiIfiwtx/bVcEJzNGF/rtd+JLz+wU6fbDk/sxa0JEFGLMoMg0mFqw6sdKdM1IatdGW1abgL+sOYTEBDV+fXnrv9WTZy+v2IevdlcAAP56+wj0zUlVBBitIV8McEyvrDadi4iI3DFAkfnL2kNYuO4wAODrB8fjoty0kJ270tiM6gYztGoV9pw2YsqgPFQ3mPH0F3vw9d5K6biB+ekY1zfb7fcFQfC6wm68s9kEHDnXgD+vOoCTF5rwp5uGorcjO3Kgsg4L1tr/n17aOwuTB+aF5DGz05w1KK71KERE1HYMUGR+OqSLFKDMfGcL/jtnLHINrV9TpbrBjGeW7sHSHadhE1x2fuL5dxauP4TRvTKx61QtOiXr8Na6w/hk2wkAwIT+OXjnjktafT2xqKbRjHs/+B6lR85L2yb8aT3enHEx9lfU4e/fHJG2vz1zZMgeNyvFmUHpwnV3iIhCTiUIgutHZ9QzGo1IT09HbW0tDIbQ9ZwQBAF//Hq/9I0bAH59eS+M7J6Jq4pyffymuwZTC6780zpUGk0+j1Op7Ou/XDcsH9MXlvo9766nJyEtMQHLd5/B2Xozrh2SD0OSFiqVCsZmC5buOI2JA3LbFFhFuz+u2I+Pt5bjp0PysXTHaZxvcF9VWK4gMwmv3zwcwwu9LwLYGufqTbDZBOTE8GtNRBRKwXx+M0DxYPWPlZj9/jbFtltGFeLxaQOQotei3tSCH8ovYM2+KmkV3I/uGoPi3s5ahM++P4m5/9oBABh/UWdcPSgPf/vmCC7r2xnpSQl4bfVBjOqRiT/dNBQFmckAgMeW7FKsDSPqnKbH2Tp7oPPAxL74YFO5ouATAG4Y3hWfORaqu35YPv588/DQvBhRxmYT0OvRLxXbEhPU+OvtIzG+bzbmf7UPb284ggSNColaDYZ374QFtw5HWmJChK6YiIhEDFDaqMVqw5j5q3Gu3vc3c1d7npmMFL0WjeYWFD25AoA9AzNv6gDFcYIg4FBVPXp1ToVG7awrudBgxvDnViqOnVSUi7dnjsSNCzdi2/ELAV/LZX2z8c3Bc7hpZDe8eMMQqFTAwvWH8dLy/Zg9ricenzagQ9a0HD5bjwl/Wg/AXqiak6bHo1cPUNTtWKw2aNWqDvn8iIhiGQOUEDh8th7VDWaM7N4JH205gUeX7PL7O9Mv7obmFiv+t/OMtG3xr0bj0j7uRa/efLXrDP7f8n347eR++O7QOfzfFX1QkJmMjYfPYeY7W9BiE9ApOQEv3TgUuQY9jp9vxNZj1fhH6fGgn6NWrUKXjERM6J+LJ35apAiW2qq20YJNR8/jin6dodeGZgruuXoTlu04jaeX7kVRFwO+vP+ykJyXiIjaBwOUMLj/4x/w3+2nAdiHUAZ1TcetowulTIknyToN9j47JWTXsOd0LVRQeVzr5UKDGUk6DbYdu4Bf/WMrmi22oM59y6hCGJK0WL//LB6a1C/omhvAPvzy0or9WLrjtLRGzUNXXYS7xvfy2yek/Hwjdp+uhU6jhlajwuieWThV0widRoNkvQaPL9mN5XsqpOOnDsrDwttGBH2NREQUOQxQwqDFasMf/vcjemanYNalPaTtH20px+Gqevxz03GYWpxBQZ4hEc9cNzBk01qDYXVMGVIB+OyHU3j326NobrHi0akDkJ+RhDX7KqFRq1Fe3YiPtrjXvIj+evsIj9cvCAKOn2+ERq1Ct05J2Hj4PF5decDnEFTXjCScqmlCURcDnrluIPp0TkWnFB3O15uwYk8lnlu2F00Wa8DP8fFpA/Cry3oFfDwREUUeA5QI2HmyBluOVmNQ1/QO17jrnn+WKbITcvLiX4vVhjfWHMLrqw/6PN+1Q/ORlaqTCoiDoVbBfUo2gMLMZDzx0yL0yEpGvakFQ7plhHRIioiIwo8BCgWl3tSCr/dUQK/VYNLAXPxr2wk8tmS3tH/uVRc5pkFv9Fk4PP6izpg5pjsmFuXC1GLFk5/vQb2pBYYkLWoaLVI3V0/65abhbzNHojArGS98+SM+3XYCr/5iGE5UNyJBo8YvLilg0SsRUQfHAIXarPx8I8a/vNbjvgSNCn/8+VD0zzOg0dyCwsxkZMlav3sjCALW7q9CYoIGW45Wo9LYjCv65WBUj0x0cunGys65RESxhwEKhcSpmibc888y/HjGiBbHuMuiOy/BFf1yInxlRETUEQXz+c1W9+RV14wkLP3NOADAyr2VUAEMToiIqF0wQKGAtGbaMRERUWupI30BRERERK4YoBAREVHUYYBCREREUSeiAcqCBQvQo0cPJCYmYvTo0diyZUskL4eIiIiiRMQClE8++QRz587FU089he+//x5Dhw7F5MmTUVVVFalLIiIioigRsQDllVdewV133YU777wTRUVFeOutt5CcnIx33303UpdEREREUSIiAYrZbEZZWRkmTpzovBC1GhMnTkRpaWkkLomIiIiiSET6oJw7dw5WqxW5ucreGrm5udi3b5/b8SaTCSaTSbpvNBrDfo1EREQUOR1iFs/8+fORnp4u/RQUFET6koiIiCiMIhKgZGdnQ6PRoLKyUrG9srISeXl5bsfPmzcPtbW10s+JEyfa61KJiIgoAiISoOh0OowYMQKrV6+WttlsNqxevRrFxcVux+v1ehgMBsUPERERxa6IrcUzd+5czJo1CyNHjsSoUaPw5z//GQ0NDbjzzjsjdUlEREQUJSIWoPziF7/A2bNn8eSTT6KiogLDhg3D8uXL3QpniYiIKP6oBEEQIn0RwaqtrUVGRgZOnDjB4R4iIqIOwmg0oqCgADU1NUhPT/d5bMQyKG1RV1cHAJzNQ0RE1AHV1dX5DVA6ZAbFZrPh9OnTSEtLg0qlCum5xeiO2Rknvibu+Jq442vijq+JO74m7uLpNREEAXV1dcjPz4da7XueTofMoKjVanTr1i2sj8HZQu74mrjja+KOr4k7vibu+Jq4i5fXxF/mRNQhGrURERFRfGGAQkRERFGHAYoLvV6Pp556Cnq9PtKXEjX4mrjja+KOr4k7vibu+Jq442viWYcskiUiIqLYxgwKERERRR0GKERERBR1GKAQERFR1GGAQkRERFGHAYrMggUL0KNHDyQmJmL06NHYsmVLpC8pbObPn49LLrkEaWlpyMnJwfXXX4/9+/crjmlubkZJSQmysrKQmpqK6dOno7KyUnFMeXk5pk2bhuTkZOTk5ODhhx9GS0tLez6VsHjxxRehUqnwwAMPSNvi8fU4deoUbrvtNmRlZSEpKQmDBw/Gtm3bpP2CIODJJ59Ely5dkJSUhIkTJ+LgwYOKc1RXV2PGjBkwGAzIyMjA7NmzUV9f395PJSSsViueeOIJ9OzZE0lJSejduzeee+45yOcaxMNrsmHDBlxzzTXIz8+HSqXC559/rtgfqtdg586duOyyy5CYmIiCggK89NJL4X5qrebrNbFYLHjkkUcwePBgpKSkID8/HzNnzsTp06cV54i116TNBBIEQRA+/vhjQafTCe+++66wZ88e4a677hIyMjKEysrKSF9aWEyePFl47733hN27dwvbt28Xrr76aqGwsFCor6+XjrnnnnuEgoICYfXq1cK2bduEMWPGCJdeeqm0v6WlRRg0aJAwceJE4YcffhC+/PJLITs7W5g3b14knlLIbNmyRejRo4cwZMgQ4f7775e2x9vrUV1dLXTv3l244447hM2bNwtHjhwRVqxYIRw6dEg65sUXXxTS09OFzz//XNixY4dw7bXXCj179hSampqkY6ZMmSIMHTpU2LRpk/DNN98Iffr0EW655ZZIPKU2e/7554WsrCxh2bJlwtGjR4VPP/1USE1NFV577TXpmHh4Tb788kvhscceEz777DMBgLBkyRLF/lC8BrW1tUJubq4wY8YMYffu3cJHH30kJCUlCX/961/b62kGxddrUlNTI0ycOFH45JNPhH379gmlpaXCqFGjhBEjRijOEWuvSVsxQHEYNWqUUFJSIt23Wq1Cfn6+MH/+/AheVfupqqoSAAjr168XBMH+DyohIUH49NNPpWN+/PFHAYBQWloqCIL9H6RarRYqKiqkYxYuXCgYDAbBZDK17xMIkbq6OqFv377CypUrhcsvv1wKUOLx9XjkkUeEcePGed1vs9mEvLw84eWXX5a21dTUCHq9Xvjoo48EQRCEvXv3CgCErVu3Ssd89dVXgkqlEk6dOhW+iw+TadOmCb/85S8V22644QZhxowZgiDE52vi+mEcqtfgzTffFDp16qT4t/PII48I/fr1C/MzajtPQZurLVu2CACE48ePC4IQ+69Ja3CIB4DZbEZZWRkmTpwobVOr1Zg4cSJKS0sjeGXtp7a2FgCQmZkJACgrK4PFYlG8Jv3790dhYaH0mpSWlmLw4MHIzc2Vjpk8eTKMRiP27NnTjlcfOiUlJZg2bZrieQPx+Xp88cUXGDlyJH7+858jJycHw4cPx9/+9jdp/9GjR1FRUaF4TdLT0zF69GjFa5KRkYGRI0dKx0ycOBFqtRqbN29uvycTIpdeeilWr16NAwcOAAB27NiBb7/9FlOnTgUQn6+Jq1C9BqWlpRg/fjx0Op10zOTJk7F//35cuHChnZ5N+NTW1kKlUiEjIwMAXxNPOuRigaF27tw5WK1WxQcLAOTm5mLfvn0Ruqr2Y7PZ8MADD2Ds2LEYNGgQAKCiogI6nU76xyPKzc1FRUWFdIyn10zc19F8/PHH+P7777F161a3ffH4ehw5cgQLFy7E3Llz8eijj2Lr1q247777oNPpMGvWLOk5eXrO8tckJydHsV+r1SIzM7NDvia///3vYTQa0b9/f2g0GlitVjz//POYMWMGAMTla+IqVK9BRUUFevbs6XYOcV+nTp3Ccv3tobm5GY888ghuueUWaXHAeH9NPGGAQigpKcHu3bvx7bffRvpSIubEiRO4//77sXLlSiQmJkb6cqKCzWbDyJEj8cILLwAAhg8fjt27d+Ott97CrFmzInx1kfGvf/0LH374IRYvXoyBAwdi+/bteOCBB5Cfnx+3rwkFx2Kx4KabboIgCFi4cGGkLyeqcYgHQHZ2NjQajduMjMrKSuTl5UXoqtrHnDlzsGzZMqxduxbdunWTtufl5cFsNqOmpkZxvPw1ycvL8/iaifs6krKyMlRVVeHiiy+GVquFVqvF+vXr8frrr0Or1SI3NzeuXg8A6NKlC4qKihTbBgwYgPLycgDO5+Tr301eXh6qqqoU+1taWlBdXd0hX5OHH34Yv//973HzzTdj8ODBuP322/Hggw9i/vz5AOLzNXEVqtcg1v49Ac7g5Pjx41i5cqWUPQHi9zXxhQEKAJ1OhxEjRmD16tXSNpvNhtWrV6O4uDiCVxY+giBgzpw5WLJkCdasWeOWNhwxYgQSEhIUr8n+/ftRXl4uvSbFxcXYtWuX4h+V+I/O9YMt2k2YMAG7du3C9u3bpZ+RI0dixowZ0u14ej0AYOzYsW5Tzw8cOIDu3bsDAHr27Im8vDzFa2I0GrF582bFa1JTU4OysjLpmDVr1sBms2H06NHt8CxCq7GxEWq18m1To9HAZrMBiM/XxFWoXoPi4mJs2LABFotFOmblypXo169fhxzKEIOTgwcPYtWqVcjKylLsj8fXxK9IV+lGi48//ljQ6/XCokWLhL179wp33323kJGRoZiREUvuvfdeIT09XVi3bp1w5swZ6aexsVE65p577hEKCwuFNWvWCNu2bROKi4uF4uJiab84rXbSpEnC9u3bheXLlwudO3fusNNqXcln8QhC/L0eW7ZsEbRarfD8888LBw8eFD788EMhOTlZ+OCDD6RjXnzxRSEjI0P473//K+zcuVO47rrrPE4nHT58uLB582bh22+/Ffr27duhptTKzZo1S+jatas0zfizzz4TsrOzhd/97nfSMfHwmtTV1Qk//PCD8MMPPwgAhFdeeUX44YcfpBkpoXgNampqhNzcXOH2228Xdu/eLXz88cdCcnJy1E6p9fWamM1m4dprrxW6desmbN++XfGeK5+RE2uvSVsxQJF54403hMLCQkGn0wmjRo0SNm3aFOlLChsAHn/ee+896Zimpibh//7v/4ROnToJycnJws9+9jPhzJkzivMcO3ZMmDp1qpCUlCRkZ2cLDz30kGCxWNr52YSHa4ASj6/H0qVLhUGDBgl6vV7o37+/8Pbbbyv222w24YknnhByc3MFvV4vTJgwQdi/f7/imPPnzwu33HKLkJqaKhgMBuHOO+8U6urq2vNphIzRaBTuv/9+obCwUEhMTBR69eolPPbYY4oPmXh4TdauXevx/WPWrFmCIITuNdixY4cwbtw4Qa/XC127dhVefPHF9nqKQfP1mhw9etTre+7atWulc8Taa9JWKkGQtUAkIiIiigKsQSEiIqKowwCFiIiIog4DFCIiIoo6DFCIiIgo6jBAISIioqjDAIWIiIiiDgMUIiIiijoMUIiIiCjqMEAhIiKiqMMAhYiIiKIOAxQiIiKKOgxQiIiIKOr8f9dMb+ps9QXdAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(df2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f5c17203",
      "metadata": {
        "id": "f5c17203"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.2"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}