tempreture_forecasting_using_LSTM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP56UuWAo3OOgFYBAbEKhXS",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Khaled-Mohammed-Abdelgaber/deep-learning-projects-/blob/main/tempreture_forecasting_using_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Drive mounting"
      ],
      "metadata": {
        "id": "1safthjomYOM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IigQ9iUrk33S",
        "outputId": "cd83ec80-4fc7-4fee-a7c8-8d717871b31b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "#first to mount the google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/forecaste_datasets/jena_climate_2009_2016.csv\")\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "ULFgJ3YJl3xG",
        "outputId": "0c5c0ed7-daa0-48b3-ef1d-90a4d450b777"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Date Time  p (mbar)  T (degC)  Tpot (K)  Tdew (degC)  \\\n",
              "0       01.01.2009 00:10:00    996.52     -8.02    265.40        -8.90   \n",
              "1       01.01.2009 00:20:00    996.57     -8.41    265.01        -9.28   \n",
              "2       01.01.2009 00:30:00    996.53     -8.51    264.91        -9.31   \n",
              "3       01.01.2009 00:40:00    996.51     -8.31    265.12        -9.07   \n",
              "4       01.01.2009 00:50:00    996.51     -8.27    265.15        -9.04   \n",
              "...                     ...       ...       ...       ...          ...   \n",
              "420446  31.12.2016 23:20:00   1000.07     -4.05    269.10        -8.13   \n",
              "420447  31.12.2016 23:30:00    999.93     -3.35    269.81        -8.06   \n",
              "420448  31.12.2016 23:40:00    999.82     -3.16    270.01        -8.21   \n",
              "420449  31.12.2016 23:50:00    999.81     -4.23    268.94        -8.53   \n",
              "420450  01.01.2017 00:00:00    999.82     -4.82    268.36        -8.42   \n",
              "\n",
              "        rh (%)  VPmax (mbar)  VPact (mbar)  VPdef (mbar)  sh (g/kg)  \\\n",
              "0        93.30          3.33          3.11          0.22       1.94   \n",
              "1        93.40          3.23          3.02          0.21       1.89   \n",
              "2        93.90          3.21          3.01          0.20       1.88   \n",
              "3        94.20          3.26          3.07          0.19       1.92   \n",
              "4        94.10          3.27          3.08          0.19       1.92   \n",
              "...        ...           ...           ...           ...        ...   \n",
              "420446   73.10          4.52          3.30          1.22       2.06   \n",
              "420447   69.71          4.77          3.32          1.44       2.07   \n",
              "420448   67.91          4.84          3.28          1.55       2.05   \n",
              "420449   71.80          4.46          3.20          1.26       1.99   \n",
              "420450   75.70          4.27          3.23          1.04       2.01   \n",
              "\n",
              "        H2OC (mmol/mol)  rho (g/m**3)  wv (m/s)  max. wv (m/s)  wd (deg)  \n",
              "0                  3.12       1307.75      1.03           1.75     152.3  \n",
              "1                  3.03       1309.80      0.72           1.50     136.1  \n",
              "2                  3.02       1310.24      0.19           0.63     171.6  \n",
              "3                  3.08       1309.19      0.34           0.50     198.0  \n",
              "4                  3.09       1309.00      0.32           0.63     214.3  \n",
              "...                 ...           ...       ...            ...       ...  \n",
              "420446             3.30       1292.98      0.67           1.52     240.0  \n",
              "420447             3.32       1289.44      1.14           1.92     234.3  \n",
              "420448             3.28       1288.39      1.08           2.00     215.2  \n",
              "420449             3.20       1293.56      1.49           2.16     225.8  \n",
              "420450             3.23       1296.38      1.23           1.96     184.9  \n",
              "\n",
              "[420451 rows x 15 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6cee7266-c7fb-443a-9f5a-5ce12c89e7e5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date Time</th>\n",
              "      <th>p (mbar)</th>\n",
              "      <th>T (degC)</th>\n",
              "      <th>Tpot (K)</th>\n",
              "      <th>Tdew (degC)</th>\n",
              "      <th>rh (%)</th>\n",
              "      <th>VPmax (mbar)</th>\n",
              "      <th>VPact (mbar)</th>\n",
              "      <th>VPdef (mbar)</th>\n",
              "      <th>sh (g/kg)</th>\n",
              "      <th>H2OC (mmol/mol)</th>\n",
              "      <th>rho (g/m**3)</th>\n",
              "      <th>wv (m/s)</th>\n",
              "      <th>max. wv (m/s)</th>\n",
              "      <th>wd (deg)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01.01.2009 00:10:00</td>\n",
              "      <td>996.52</td>\n",
              "      <td>-8.02</td>\n",
              "      <td>265.40</td>\n",
              "      <td>-8.90</td>\n",
              "      <td>93.30</td>\n",
              "      <td>3.33</td>\n",
              "      <td>3.11</td>\n",
              "      <td>0.22</td>\n",
              "      <td>1.94</td>\n",
              "      <td>3.12</td>\n",
              "      <td>1307.75</td>\n",
              "      <td>1.03</td>\n",
              "      <td>1.75</td>\n",
              "      <td>152.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01.01.2009 00:20:00</td>\n",
              "      <td>996.57</td>\n",
              "      <td>-8.41</td>\n",
              "      <td>265.01</td>\n",
              "      <td>-9.28</td>\n",
              "      <td>93.40</td>\n",
              "      <td>3.23</td>\n",
              "      <td>3.02</td>\n",
              "      <td>0.21</td>\n",
              "      <td>1.89</td>\n",
              "      <td>3.03</td>\n",
              "      <td>1309.80</td>\n",
              "      <td>0.72</td>\n",
              "      <td>1.50</td>\n",
              "      <td>136.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01.01.2009 00:30:00</td>\n",
              "      <td>996.53</td>\n",
              "      <td>-8.51</td>\n",
              "      <td>264.91</td>\n",
              "      <td>-9.31</td>\n",
              "      <td>93.90</td>\n",
              "      <td>3.21</td>\n",
              "      <td>3.01</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.88</td>\n",
              "      <td>3.02</td>\n",
              "      <td>1310.24</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.63</td>\n",
              "      <td>171.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01.01.2009 00:40:00</td>\n",
              "      <td>996.51</td>\n",
              "      <td>-8.31</td>\n",
              "      <td>265.12</td>\n",
              "      <td>-9.07</td>\n",
              "      <td>94.20</td>\n",
              "      <td>3.26</td>\n",
              "      <td>3.07</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1.92</td>\n",
              "      <td>3.08</td>\n",
              "      <td>1309.19</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.50</td>\n",
              "      <td>198.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01.01.2009 00:50:00</td>\n",
              "      <td>996.51</td>\n",
              "      <td>-8.27</td>\n",
              "      <td>265.15</td>\n",
              "      <td>-9.04</td>\n",
              "      <td>94.10</td>\n",
              "      <td>3.27</td>\n",
              "      <td>3.08</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1.92</td>\n",
              "      <td>3.09</td>\n",
              "      <td>1309.00</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.63</td>\n",
              "      <td>214.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420446</th>\n",
              "      <td>31.12.2016 23:20:00</td>\n",
              "      <td>1000.07</td>\n",
              "      <td>-4.05</td>\n",
              "      <td>269.10</td>\n",
              "      <td>-8.13</td>\n",
              "      <td>73.10</td>\n",
              "      <td>4.52</td>\n",
              "      <td>3.30</td>\n",
              "      <td>1.22</td>\n",
              "      <td>2.06</td>\n",
              "      <td>3.30</td>\n",
              "      <td>1292.98</td>\n",
              "      <td>0.67</td>\n",
              "      <td>1.52</td>\n",
              "      <td>240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420447</th>\n",
              "      <td>31.12.2016 23:30:00</td>\n",
              "      <td>999.93</td>\n",
              "      <td>-3.35</td>\n",
              "      <td>269.81</td>\n",
              "      <td>-8.06</td>\n",
              "      <td>69.71</td>\n",
              "      <td>4.77</td>\n",
              "      <td>3.32</td>\n",
              "      <td>1.44</td>\n",
              "      <td>2.07</td>\n",
              "      <td>3.32</td>\n",
              "      <td>1289.44</td>\n",
              "      <td>1.14</td>\n",
              "      <td>1.92</td>\n",
              "      <td>234.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420448</th>\n",
              "      <td>31.12.2016 23:40:00</td>\n",
              "      <td>999.82</td>\n",
              "      <td>-3.16</td>\n",
              "      <td>270.01</td>\n",
              "      <td>-8.21</td>\n",
              "      <td>67.91</td>\n",
              "      <td>4.84</td>\n",
              "      <td>3.28</td>\n",
              "      <td>1.55</td>\n",
              "      <td>2.05</td>\n",
              "      <td>3.28</td>\n",
              "      <td>1288.39</td>\n",
              "      <td>1.08</td>\n",
              "      <td>2.00</td>\n",
              "      <td>215.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420449</th>\n",
              "      <td>31.12.2016 23:50:00</td>\n",
              "      <td>999.81</td>\n",
              "      <td>-4.23</td>\n",
              "      <td>268.94</td>\n",
              "      <td>-8.53</td>\n",
              "      <td>71.80</td>\n",
              "      <td>4.46</td>\n",
              "      <td>3.20</td>\n",
              "      <td>1.26</td>\n",
              "      <td>1.99</td>\n",
              "      <td>3.20</td>\n",
              "      <td>1293.56</td>\n",
              "      <td>1.49</td>\n",
              "      <td>2.16</td>\n",
              "      <td>225.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420450</th>\n",
              "      <td>01.01.2017 00:00:00</td>\n",
              "      <td>999.82</td>\n",
              "      <td>-4.82</td>\n",
              "      <td>268.36</td>\n",
              "      <td>-8.42</td>\n",
              "      <td>75.70</td>\n",
              "      <td>4.27</td>\n",
              "      <td>3.23</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.01</td>\n",
              "      <td>3.23</td>\n",
              "      <td>1296.38</td>\n",
              "      <td>1.23</td>\n",
              "      <td>1.96</td>\n",
              "      <td>184.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>420451 rows × 15 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6cee7266-c7fb-443a-9f5a-5ce12c89e7e5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6cee7266-c7fb-443a-9f5a-5ce12c89e7e5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6cee7266-c7fb-443a-9f5a-5ce12c89e7e5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[5::6] #only take from 5 and increase by 6 and so on\n",
        "df #now df contain information about one clock not every 10 minuts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "L-cXL_XulPMf",
        "outputId": "d49146b4-23ef-475e-fe95-103d64794114"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Date Time  p (mbar)  T (degC)  Tpot (K)  Tdew (degC)  \\\n",
              "5       01.01.2009 01:00:00    996.50     -8.05    265.38        -8.78   \n",
              "11      01.01.2009 02:00:00    996.62     -8.88    264.54        -9.77   \n",
              "17      01.01.2009 03:00:00    996.84     -8.81    264.59        -9.66   \n",
              "23      01.01.2009 04:00:00    996.99     -9.05    264.34       -10.02   \n",
              "29      01.01.2009 05:00:00    997.46     -9.63    263.72       -10.65   \n",
              "...                     ...       ...       ...       ...          ...   \n",
              "420425  31.12.2016 19:50:00   1001.55     -1.65    271.39        -6.55   \n",
              "420431  31.12.2016 20:50:00   1001.33     -3.46    269.59        -7.13   \n",
              "420437  31.12.2016 21:50:00   1000.81     -2.48    270.62        -6.95   \n",
              "420443  31.12.2016 22:50:00   1000.32     -4.09    269.05        -7.23   \n",
              "420449  31.12.2016 23:50:00    999.81     -4.23    268.94        -8.53   \n",
              "\n",
              "        rh (%)  VPmax (mbar)  VPact (mbar)  VPdef (mbar)  sh (g/kg)  \\\n",
              "5        94.40          3.33          3.14          0.19       1.96   \n",
              "11       93.20          3.12          2.90          0.21       1.81   \n",
              "17       93.50          3.13          2.93          0.20       1.83   \n",
              "23       92.60          3.07          2.85          0.23       1.78   \n",
              "29       92.20          2.94          2.71          0.23       1.69   \n",
              "...        ...           ...           ...           ...        ...   \n",
              "420425   69.05          5.41          3.74          1.67       2.32   \n",
              "420431   75.60          4.73          3.57          1.15       2.22   \n",
              "420437   71.20          5.09          3.62          1.47       2.25   \n",
              "420443   78.60          4.51          3.54          0.96       2.21   \n",
              "420449   71.80          4.46          3.20          1.26       1.99   \n",
              "\n",
              "        H2OC (mmol/mol)  rho (g/m**3)  wv (m/s)  max. wv (m/s)  wd (deg)  \n",
              "5                  3.15       1307.86      0.21           0.63     192.7  \n",
              "11                 2.91       1312.25      0.25           0.63     190.3  \n",
              "17                 2.94       1312.18      0.18           0.63     167.2  \n",
              "23                 2.85       1313.61      0.10           0.38     240.0  \n",
              "29                 2.71       1317.19      0.40           0.88     157.0  \n",
              "...                 ...           ...       ...            ...       ...  \n",
              "420425             3.73       1283.24      0.73           1.20     212.0  \n",
              "420431             3.57       1291.65      0.81           1.16     201.4  \n",
              "420437             3.62       1286.24      0.24           0.52       9.4  \n",
              "420443             3.54       1293.37      1.25           1.60     199.2  \n",
              "420449             3.20       1293.56      1.49           2.16     225.8  \n",
              "\n",
              "[70075 rows x 15 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-039b4aef-fd76-46ec-a711-415f2e583fb2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date Time</th>\n",
              "      <th>p (mbar)</th>\n",
              "      <th>T (degC)</th>\n",
              "      <th>Tpot (K)</th>\n",
              "      <th>Tdew (degC)</th>\n",
              "      <th>rh (%)</th>\n",
              "      <th>VPmax (mbar)</th>\n",
              "      <th>VPact (mbar)</th>\n",
              "      <th>VPdef (mbar)</th>\n",
              "      <th>sh (g/kg)</th>\n",
              "      <th>H2OC (mmol/mol)</th>\n",
              "      <th>rho (g/m**3)</th>\n",
              "      <th>wv (m/s)</th>\n",
              "      <th>max. wv (m/s)</th>\n",
              "      <th>wd (deg)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>01.01.2009 01:00:00</td>\n",
              "      <td>996.50</td>\n",
              "      <td>-8.05</td>\n",
              "      <td>265.38</td>\n",
              "      <td>-8.78</td>\n",
              "      <td>94.40</td>\n",
              "      <td>3.33</td>\n",
              "      <td>3.14</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1.96</td>\n",
              "      <td>3.15</td>\n",
              "      <td>1307.86</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.63</td>\n",
              "      <td>192.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>01.01.2009 02:00:00</td>\n",
              "      <td>996.62</td>\n",
              "      <td>-8.88</td>\n",
              "      <td>264.54</td>\n",
              "      <td>-9.77</td>\n",
              "      <td>93.20</td>\n",
              "      <td>3.12</td>\n",
              "      <td>2.90</td>\n",
              "      <td>0.21</td>\n",
              "      <td>1.81</td>\n",
              "      <td>2.91</td>\n",
              "      <td>1312.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.63</td>\n",
              "      <td>190.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>01.01.2009 03:00:00</td>\n",
              "      <td>996.84</td>\n",
              "      <td>-8.81</td>\n",
              "      <td>264.59</td>\n",
              "      <td>-9.66</td>\n",
              "      <td>93.50</td>\n",
              "      <td>3.13</td>\n",
              "      <td>2.93</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.83</td>\n",
              "      <td>2.94</td>\n",
              "      <td>1312.18</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.63</td>\n",
              "      <td>167.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>01.01.2009 04:00:00</td>\n",
              "      <td>996.99</td>\n",
              "      <td>-9.05</td>\n",
              "      <td>264.34</td>\n",
              "      <td>-10.02</td>\n",
              "      <td>92.60</td>\n",
              "      <td>3.07</td>\n",
              "      <td>2.85</td>\n",
              "      <td>0.23</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.85</td>\n",
              "      <td>1313.61</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.38</td>\n",
              "      <td>240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>01.01.2009 05:00:00</td>\n",
              "      <td>997.46</td>\n",
              "      <td>-9.63</td>\n",
              "      <td>263.72</td>\n",
              "      <td>-10.65</td>\n",
              "      <td>92.20</td>\n",
              "      <td>2.94</td>\n",
              "      <td>2.71</td>\n",
              "      <td>0.23</td>\n",
              "      <td>1.69</td>\n",
              "      <td>2.71</td>\n",
              "      <td>1317.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.88</td>\n",
              "      <td>157.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420425</th>\n",
              "      <td>31.12.2016 19:50:00</td>\n",
              "      <td>1001.55</td>\n",
              "      <td>-1.65</td>\n",
              "      <td>271.39</td>\n",
              "      <td>-6.55</td>\n",
              "      <td>69.05</td>\n",
              "      <td>5.41</td>\n",
              "      <td>3.74</td>\n",
              "      <td>1.67</td>\n",
              "      <td>2.32</td>\n",
              "      <td>3.73</td>\n",
              "      <td>1283.24</td>\n",
              "      <td>0.73</td>\n",
              "      <td>1.20</td>\n",
              "      <td>212.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420431</th>\n",
              "      <td>31.12.2016 20:50:00</td>\n",
              "      <td>1001.33</td>\n",
              "      <td>-3.46</td>\n",
              "      <td>269.59</td>\n",
              "      <td>-7.13</td>\n",
              "      <td>75.60</td>\n",
              "      <td>4.73</td>\n",
              "      <td>3.57</td>\n",
              "      <td>1.15</td>\n",
              "      <td>2.22</td>\n",
              "      <td>3.57</td>\n",
              "      <td>1291.65</td>\n",
              "      <td>0.81</td>\n",
              "      <td>1.16</td>\n",
              "      <td>201.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420437</th>\n",
              "      <td>31.12.2016 21:50:00</td>\n",
              "      <td>1000.81</td>\n",
              "      <td>-2.48</td>\n",
              "      <td>270.62</td>\n",
              "      <td>-6.95</td>\n",
              "      <td>71.20</td>\n",
              "      <td>5.09</td>\n",
              "      <td>3.62</td>\n",
              "      <td>1.47</td>\n",
              "      <td>2.25</td>\n",
              "      <td>3.62</td>\n",
              "      <td>1286.24</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.52</td>\n",
              "      <td>9.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420443</th>\n",
              "      <td>31.12.2016 22:50:00</td>\n",
              "      <td>1000.32</td>\n",
              "      <td>-4.09</td>\n",
              "      <td>269.05</td>\n",
              "      <td>-7.23</td>\n",
              "      <td>78.60</td>\n",
              "      <td>4.51</td>\n",
              "      <td>3.54</td>\n",
              "      <td>0.96</td>\n",
              "      <td>2.21</td>\n",
              "      <td>3.54</td>\n",
              "      <td>1293.37</td>\n",
              "      <td>1.25</td>\n",
              "      <td>1.60</td>\n",
              "      <td>199.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420449</th>\n",
              "      <td>31.12.2016 23:50:00</td>\n",
              "      <td>999.81</td>\n",
              "      <td>-4.23</td>\n",
              "      <td>268.94</td>\n",
              "      <td>-8.53</td>\n",
              "      <td>71.80</td>\n",
              "      <td>4.46</td>\n",
              "      <td>3.20</td>\n",
              "      <td>1.26</td>\n",
              "      <td>1.99</td>\n",
              "      <td>3.20</td>\n",
              "      <td>1293.56</td>\n",
              "      <td>1.49</td>\n",
              "      <td>2.16</td>\n",
              "      <td>225.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>70075 rows × 15 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-039b4aef-fd76-46ec-a711-415f2e583fb2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-039b4aef-fd76-46ec-a711-415f2e583fb2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-039b4aef-fd76-46ec-a711-415f2e583fb2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.index = pd.to_datetime(df['Date Time'],format = '%d.%m.%Y %H:%M:%S')\n",
        "df[:25]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rk-vI2sblfEL",
        "outputId": "25bf2e36-0eea-401f-8df0-1d781fc6808a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Date Time  p (mbar)  T (degC)  Tpot (K)  \\\n",
              "Date Time                                                                \n",
              "2009-01-01 01:00:00  01.01.2009 01:00:00    996.50     -8.05    265.38   \n",
              "2009-01-01 02:00:00  01.01.2009 02:00:00    996.62     -8.88    264.54   \n",
              "2009-01-01 03:00:00  01.01.2009 03:00:00    996.84     -8.81    264.59   \n",
              "2009-01-01 04:00:00  01.01.2009 04:00:00    996.99     -9.05    264.34   \n",
              "2009-01-01 05:00:00  01.01.2009 05:00:00    997.46     -9.63    263.72   \n",
              "2009-01-01 06:00:00  01.01.2009 06:00:00    997.71     -9.67    263.66   \n",
              "2009-01-01 07:00:00  01.01.2009 07:00:00    998.33     -9.17    264.12   \n",
              "2009-01-01 08:00:00  01.01.2009 08:00:00    999.17     -8.10    265.12   \n",
              "2009-01-01 09:00:00  01.01.2009 09:00:00    999.69     -7.66    265.52   \n",
              "2009-01-01 10:00:00  01.01.2009 10:00:00   1000.27     -7.04    266.10   \n",
              "2009-01-01 11:00:00  01.01.2009 11:00:00   1000.87     -7.41    265.68   \n",
              "2009-01-01 12:00:00  01.01.2009 12:00:00   1000.30     -6.87    266.27   \n",
              "2009-01-01 13:00:00  01.01.2009 13:00:00   1000.03     -5.89    267.27   \n",
              "2009-01-01 14:00:00  01.01.2009 14:00:00    999.81     -5.94    267.24   \n",
              "2009-01-01 15:00:00  01.01.2009 15:00:00    999.88     -5.69    267.48   \n",
              "2009-01-01 16:00:00  01.01.2009 16:00:00    999.94     -5.40    267.76   \n",
              "2009-01-01 17:00:00  01.01.2009 17:00:00   1000.17     -5.37    267.78   \n",
              "2009-01-01 18:00:00  01.01.2009 18:00:00   1000.16     -5.25    267.90   \n",
              "2009-01-01 19:00:00  01.01.2009 19:00:00   1000.22     -5.11    268.03   \n",
              "2009-01-01 20:00:00  01.01.2009 20:00:00   1000.22     -4.90    268.24   \n",
              "2009-01-01 21:00:00  01.01.2009 21:00:00   1000.19     -4.80    268.35   \n",
              "2009-01-01 22:00:00  01.01.2009 22:00:00   1000.00     -4.50    268.66   \n",
              "2009-01-01 23:00:00  01.01.2009 23:00:00    999.77     -4.47    268.71   \n",
              "2009-01-02 00:00:00  02.01.2009 00:00:00    999.59     -4.54    268.65   \n",
              "2009-01-02 01:00:00  02.01.2009 01:00:00    999.34     -4.44    268.77   \n",
              "\n",
              "                     Tdew (degC)  rh (%)  VPmax (mbar)  VPact (mbar)  \\\n",
              "Date Time                                                              \n",
              "2009-01-01 01:00:00        -8.78    94.4          3.33          3.14   \n",
              "2009-01-01 02:00:00        -9.77    93.2          3.12          2.90   \n",
              "2009-01-01 03:00:00        -9.66    93.5          3.13          2.93   \n",
              "2009-01-01 04:00:00       -10.02    92.6          3.07          2.85   \n",
              "2009-01-01 05:00:00       -10.65    92.2          2.94          2.71   \n",
              "2009-01-01 06:00:00       -10.62    92.7          2.93          2.71   \n",
              "2009-01-01 07:00:00       -10.10    92.9          3.04          2.83   \n",
              "2009-01-01 08:00:00        -9.05    92.8          3.31          3.07   \n",
              "2009-01-01 09:00:00        -8.84    91.2          3.43          3.13   \n",
              "2009-01-01 10:00:00        -8.17    91.6          3.60          3.30   \n",
              "2009-01-01 11:00:00        -8.66    90.7          3.50          3.17   \n",
              "2009-01-01 12:00:00        -8.28    89.6          3.64          3.27   \n",
              "2009-01-01 13:00:00        -7.46    88.6          3.93          3.48   \n",
              "2009-01-01 14:00:00        -7.43    89.1          3.92          3.49   \n",
              "2009-01-01 15:00:00        -7.00    90.4          3.99          3.61   \n",
              "2009-01-01 16:00:00        -6.86    89.4          4.08          3.65   \n",
              "2009-01-01 17:00:00        -6.82    89.4          4.09          3.66   \n",
              "2009-01-01 18:00:00        -6.75    89.1          4.13          3.68   \n",
              "2009-01-01 19:00:00        -6.57    89.4          4.17          3.73   \n",
              "2009-01-01 20:00:00        -6.38    89.3          4.24          3.79   \n",
              "2009-01-01 21:00:00        -6.14    90.2          4.27          3.85   \n",
              "2009-01-01 22:00:00        -5.82    90.4          4.37          3.95   \n",
              "2009-01-01 23:00:00        -5.84    90.1          4.38          3.95   \n",
              "2009-01-02 00:00:00        -5.46    93.2          4.36          4.06   \n",
              "2009-01-02 01:00:00        -5.19    94.4          4.39          4.15   \n",
              "\n",
              "                     VPdef (mbar)  sh (g/kg)  H2OC (mmol/mol)  rho (g/m**3)  \\\n",
              "Date Time                                                                     \n",
              "2009-01-01 01:00:00          0.19       1.96             3.15       1307.86   \n",
              "2009-01-01 02:00:00          0.21       1.81             2.91       1312.25   \n",
              "2009-01-01 03:00:00          0.20       1.83             2.94       1312.18   \n",
              "2009-01-01 04:00:00          0.23       1.78             2.85       1313.61   \n",
              "2009-01-01 05:00:00          0.23       1.69             2.71       1317.19   \n",
              "2009-01-01 06:00:00          0.21       1.69             2.72       1317.71   \n",
              "2009-01-01 07:00:00          0.22       1.76             2.83       1315.98   \n",
              "2009-01-01 08:00:00          0.24       1.92             3.08       1311.65   \n",
              "2009-01-01 09:00:00          0.30       1.95             3.13       1310.14   \n",
              "2009-01-01 10:00:00          0.30       2.05             3.29       1307.76   \n",
              "2009-01-01 11:00:00          0.33       1.97             3.17       1310.43   \n",
              "2009-01-01 12:00:00          0.38       2.03             3.26       1306.98   \n",
              "2009-01-01 13:00:00          0.45       2.17             3.48       1301.73   \n",
              "2009-01-01 14:00:00          0.43       2.17             3.49       1301.67   \n",
              "2009-01-01 15:00:00          0.38       2.25             3.61       1300.51   \n",
              "2009-01-01 16:00:00          0.43       2.27             3.65       1299.17   \n",
              "2009-01-01 17:00:00          0.43       2.28             3.66       1299.27   \n",
              "2009-01-01 18:00:00          0.45       2.29             3.68       1298.68   \n",
              "2009-01-01 19:00:00          0.44       2.32             3.73       1298.07   \n",
              "2009-01-01 20:00:00          0.45       2.36             3.78       1297.05   \n",
              "2009-01-01 21:00:00          0.42       2.40             3.85       1296.45   \n",
              "2009-01-01 22:00:00          0.42       2.46             3.95       1294.72   \n",
              "2009-01-01 23:00:00          0.43       2.46             3.95       1294.29   \n",
              "2009-01-02 00:00:00          0.30       2.53             4.06       1294.33   \n",
              "2009-01-02 01:00:00          0.25       2.58             4.15       1293.47   \n",
              "\n",
              "                     wv (m/s)  max. wv (m/s)  wd (deg)  \n",
              "Date Time                                               \n",
              "2009-01-01 01:00:00      0.21           0.63     192.7  \n",
              "2009-01-01 02:00:00      0.25           0.63     190.3  \n",
              "2009-01-01 03:00:00      0.18           0.63     167.2  \n",
              "2009-01-01 04:00:00      0.10           0.38     240.0  \n",
              "2009-01-01 05:00:00      0.40           0.88     157.0  \n",
              "2009-01-01 06:00:00      0.05           0.50     146.0  \n",
              "2009-01-01 07:00:00      2.08           2.88     348.8  \n",
              "2009-01-01 08:00:00      0.72           1.25     213.9  \n",
              "2009-01-01 09:00:00      0.34           0.63     202.2  \n",
              "2009-01-01 10:00:00      1.45           3.00     292.6  \n",
              "2009-01-01 11:00:00      1.91           3.38     269.6  \n",
              "2009-01-01 12:00:00      1.84           2.63     184.4  \n",
              "2009-01-01 13:00:00      0.57           1.25     187.3  \n",
              "2009-01-01 14:00:00      1.25           2.00     144.0  \n",
              "2009-01-01 15:00:00      1.17           1.88     134.9  \n",
              "2009-01-01 16:00:00      1.40           2.13     145.5  \n",
              "2009-01-01 17:00:00      0.85           1.63     142.2  \n",
              "2009-01-01 18:00:00      0.55           1.00     183.7  \n",
              "2009-01-01 19:00:00      0.41           0.75     176.7  \n",
              "2009-01-01 20:00:00      0.68           1.13     195.2  \n",
              "2009-01-01 21:00:00      0.44           0.75     206.3  \n",
              "2009-01-01 22:00:00      0.33           0.63     175.7  \n",
              "2009-01-01 23:00:00      0.65           1.00     219.3  \n",
              "2009-01-02 00:00:00      0.41           0.88     155.0  \n",
              "2009-01-02 01:00:00      0.28           1.00     148.1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c7acf372-48ff-483f-b909-265add7d8b5e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date Time</th>\n",
              "      <th>p (mbar)</th>\n",
              "      <th>T (degC)</th>\n",
              "      <th>Tpot (K)</th>\n",
              "      <th>Tdew (degC)</th>\n",
              "      <th>rh (%)</th>\n",
              "      <th>VPmax (mbar)</th>\n",
              "      <th>VPact (mbar)</th>\n",
              "      <th>VPdef (mbar)</th>\n",
              "      <th>sh (g/kg)</th>\n",
              "      <th>H2OC (mmol/mol)</th>\n",
              "      <th>rho (g/m**3)</th>\n",
              "      <th>wv (m/s)</th>\n",
              "      <th>max. wv (m/s)</th>\n",
              "      <th>wd (deg)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date Time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2009-01-01 01:00:00</th>\n",
              "      <td>01.01.2009 01:00:00</td>\n",
              "      <td>996.50</td>\n",
              "      <td>-8.05</td>\n",
              "      <td>265.38</td>\n",
              "      <td>-8.78</td>\n",
              "      <td>94.4</td>\n",
              "      <td>3.33</td>\n",
              "      <td>3.14</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1.96</td>\n",
              "      <td>3.15</td>\n",
              "      <td>1307.86</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.63</td>\n",
              "      <td>192.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 02:00:00</th>\n",
              "      <td>01.01.2009 02:00:00</td>\n",
              "      <td>996.62</td>\n",
              "      <td>-8.88</td>\n",
              "      <td>264.54</td>\n",
              "      <td>-9.77</td>\n",
              "      <td>93.2</td>\n",
              "      <td>3.12</td>\n",
              "      <td>2.90</td>\n",
              "      <td>0.21</td>\n",
              "      <td>1.81</td>\n",
              "      <td>2.91</td>\n",
              "      <td>1312.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.63</td>\n",
              "      <td>190.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 03:00:00</th>\n",
              "      <td>01.01.2009 03:00:00</td>\n",
              "      <td>996.84</td>\n",
              "      <td>-8.81</td>\n",
              "      <td>264.59</td>\n",
              "      <td>-9.66</td>\n",
              "      <td>93.5</td>\n",
              "      <td>3.13</td>\n",
              "      <td>2.93</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.83</td>\n",
              "      <td>2.94</td>\n",
              "      <td>1312.18</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.63</td>\n",
              "      <td>167.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 04:00:00</th>\n",
              "      <td>01.01.2009 04:00:00</td>\n",
              "      <td>996.99</td>\n",
              "      <td>-9.05</td>\n",
              "      <td>264.34</td>\n",
              "      <td>-10.02</td>\n",
              "      <td>92.6</td>\n",
              "      <td>3.07</td>\n",
              "      <td>2.85</td>\n",
              "      <td>0.23</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.85</td>\n",
              "      <td>1313.61</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.38</td>\n",
              "      <td>240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 05:00:00</th>\n",
              "      <td>01.01.2009 05:00:00</td>\n",
              "      <td>997.46</td>\n",
              "      <td>-9.63</td>\n",
              "      <td>263.72</td>\n",
              "      <td>-10.65</td>\n",
              "      <td>92.2</td>\n",
              "      <td>2.94</td>\n",
              "      <td>2.71</td>\n",
              "      <td>0.23</td>\n",
              "      <td>1.69</td>\n",
              "      <td>2.71</td>\n",
              "      <td>1317.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.88</td>\n",
              "      <td>157.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 06:00:00</th>\n",
              "      <td>01.01.2009 06:00:00</td>\n",
              "      <td>997.71</td>\n",
              "      <td>-9.67</td>\n",
              "      <td>263.66</td>\n",
              "      <td>-10.62</td>\n",
              "      <td>92.7</td>\n",
              "      <td>2.93</td>\n",
              "      <td>2.71</td>\n",
              "      <td>0.21</td>\n",
              "      <td>1.69</td>\n",
              "      <td>2.72</td>\n",
              "      <td>1317.71</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.50</td>\n",
              "      <td>146.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 07:00:00</th>\n",
              "      <td>01.01.2009 07:00:00</td>\n",
              "      <td>998.33</td>\n",
              "      <td>-9.17</td>\n",
              "      <td>264.12</td>\n",
              "      <td>-10.10</td>\n",
              "      <td>92.9</td>\n",
              "      <td>3.04</td>\n",
              "      <td>2.83</td>\n",
              "      <td>0.22</td>\n",
              "      <td>1.76</td>\n",
              "      <td>2.83</td>\n",
              "      <td>1315.98</td>\n",
              "      <td>2.08</td>\n",
              "      <td>2.88</td>\n",
              "      <td>348.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 08:00:00</th>\n",
              "      <td>01.01.2009 08:00:00</td>\n",
              "      <td>999.17</td>\n",
              "      <td>-8.10</td>\n",
              "      <td>265.12</td>\n",
              "      <td>-9.05</td>\n",
              "      <td>92.8</td>\n",
              "      <td>3.31</td>\n",
              "      <td>3.07</td>\n",
              "      <td>0.24</td>\n",
              "      <td>1.92</td>\n",
              "      <td>3.08</td>\n",
              "      <td>1311.65</td>\n",
              "      <td>0.72</td>\n",
              "      <td>1.25</td>\n",
              "      <td>213.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 09:00:00</th>\n",
              "      <td>01.01.2009 09:00:00</td>\n",
              "      <td>999.69</td>\n",
              "      <td>-7.66</td>\n",
              "      <td>265.52</td>\n",
              "      <td>-8.84</td>\n",
              "      <td>91.2</td>\n",
              "      <td>3.43</td>\n",
              "      <td>3.13</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.95</td>\n",
              "      <td>3.13</td>\n",
              "      <td>1310.14</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.63</td>\n",
              "      <td>202.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 10:00:00</th>\n",
              "      <td>01.01.2009 10:00:00</td>\n",
              "      <td>1000.27</td>\n",
              "      <td>-7.04</td>\n",
              "      <td>266.10</td>\n",
              "      <td>-8.17</td>\n",
              "      <td>91.6</td>\n",
              "      <td>3.60</td>\n",
              "      <td>3.30</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.05</td>\n",
              "      <td>3.29</td>\n",
              "      <td>1307.76</td>\n",
              "      <td>1.45</td>\n",
              "      <td>3.00</td>\n",
              "      <td>292.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 11:00:00</th>\n",
              "      <td>01.01.2009 11:00:00</td>\n",
              "      <td>1000.87</td>\n",
              "      <td>-7.41</td>\n",
              "      <td>265.68</td>\n",
              "      <td>-8.66</td>\n",
              "      <td>90.7</td>\n",
              "      <td>3.50</td>\n",
              "      <td>3.17</td>\n",
              "      <td>0.33</td>\n",
              "      <td>1.97</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1310.43</td>\n",
              "      <td>1.91</td>\n",
              "      <td>3.38</td>\n",
              "      <td>269.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 12:00:00</th>\n",
              "      <td>01.01.2009 12:00:00</td>\n",
              "      <td>1000.30</td>\n",
              "      <td>-6.87</td>\n",
              "      <td>266.27</td>\n",
              "      <td>-8.28</td>\n",
              "      <td>89.6</td>\n",
              "      <td>3.64</td>\n",
              "      <td>3.27</td>\n",
              "      <td>0.38</td>\n",
              "      <td>2.03</td>\n",
              "      <td>3.26</td>\n",
              "      <td>1306.98</td>\n",
              "      <td>1.84</td>\n",
              "      <td>2.63</td>\n",
              "      <td>184.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 13:00:00</th>\n",
              "      <td>01.01.2009 13:00:00</td>\n",
              "      <td>1000.03</td>\n",
              "      <td>-5.89</td>\n",
              "      <td>267.27</td>\n",
              "      <td>-7.46</td>\n",
              "      <td>88.6</td>\n",
              "      <td>3.93</td>\n",
              "      <td>3.48</td>\n",
              "      <td>0.45</td>\n",
              "      <td>2.17</td>\n",
              "      <td>3.48</td>\n",
              "      <td>1301.73</td>\n",
              "      <td>0.57</td>\n",
              "      <td>1.25</td>\n",
              "      <td>187.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 14:00:00</th>\n",
              "      <td>01.01.2009 14:00:00</td>\n",
              "      <td>999.81</td>\n",
              "      <td>-5.94</td>\n",
              "      <td>267.24</td>\n",
              "      <td>-7.43</td>\n",
              "      <td>89.1</td>\n",
              "      <td>3.92</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.43</td>\n",
              "      <td>2.17</td>\n",
              "      <td>3.49</td>\n",
              "      <td>1301.67</td>\n",
              "      <td>1.25</td>\n",
              "      <td>2.00</td>\n",
              "      <td>144.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 15:00:00</th>\n",
              "      <td>01.01.2009 15:00:00</td>\n",
              "      <td>999.88</td>\n",
              "      <td>-5.69</td>\n",
              "      <td>267.48</td>\n",
              "      <td>-7.00</td>\n",
              "      <td>90.4</td>\n",
              "      <td>3.99</td>\n",
              "      <td>3.61</td>\n",
              "      <td>0.38</td>\n",
              "      <td>2.25</td>\n",
              "      <td>3.61</td>\n",
              "      <td>1300.51</td>\n",
              "      <td>1.17</td>\n",
              "      <td>1.88</td>\n",
              "      <td>134.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 16:00:00</th>\n",
              "      <td>01.01.2009 16:00:00</td>\n",
              "      <td>999.94</td>\n",
              "      <td>-5.40</td>\n",
              "      <td>267.76</td>\n",
              "      <td>-6.86</td>\n",
              "      <td>89.4</td>\n",
              "      <td>4.08</td>\n",
              "      <td>3.65</td>\n",
              "      <td>0.43</td>\n",
              "      <td>2.27</td>\n",
              "      <td>3.65</td>\n",
              "      <td>1299.17</td>\n",
              "      <td>1.40</td>\n",
              "      <td>2.13</td>\n",
              "      <td>145.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 17:00:00</th>\n",
              "      <td>01.01.2009 17:00:00</td>\n",
              "      <td>1000.17</td>\n",
              "      <td>-5.37</td>\n",
              "      <td>267.78</td>\n",
              "      <td>-6.82</td>\n",
              "      <td>89.4</td>\n",
              "      <td>4.09</td>\n",
              "      <td>3.66</td>\n",
              "      <td>0.43</td>\n",
              "      <td>2.28</td>\n",
              "      <td>3.66</td>\n",
              "      <td>1299.27</td>\n",
              "      <td>0.85</td>\n",
              "      <td>1.63</td>\n",
              "      <td>142.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 18:00:00</th>\n",
              "      <td>01.01.2009 18:00:00</td>\n",
              "      <td>1000.16</td>\n",
              "      <td>-5.25</td>\n",
              "      <td>267.90</td>\n",
              "      <td>-6.75</td>\n",
              "      <td>89.1</td>\n",
              "      <td>4.13</td>\n",
              "      <td>3.68</td>\n",
              "      <td>0.45</td>\n",
              "      <td>2.29</td>\n",
              "      <td>3.68</td>\n",
              "      <td>1298.68</td>\n",
              "      <td>0.55</td>\n",
              "      <td>1.00</td>\n",
              "      <td>183.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 19:00:00</th>\n",
              "      <td>01.01.2009 19:00:00</td>\n",
              "      <td>1000.22</td>\n",
              "      <td>-5.11</td>\n",
              "      <td>268.03</td>\n",
              "      <td>-6.57</td>\n",
              "      <td>89.4</td>\n",
              "      <td>4.17</td>\n",
              "      <td>3.73</td>\n",
              "      <td>0.44</td>\n",
              "      <td>2.32</td>\n",
              "      <td>3.73</td>\n",
              "      <td>1298.07</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.75</td>\n",
              "      <td>176.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 20:00:00</th>\n",
              "      <td>01.01.2009 20:00:00</td>\n",
              "      <td>1000.22</td>\n",
              "      <td>-4.90</td>\n",
              "      <td>268.24</td>\n",
              "      <td>-6.38</td>\n",
              "      <td>89.3</td>\n",
              "      <td>4.24</td>\n",
              "      <td>3.79</td>\n",
              "      <td>0.45</td>\n",
              "      <td>2.36</td>\n",
              "      <td>3.78</td>\n",
              "      <td>1297.05</td>\n",
              "      <td>0.68</td>\n",
              "      <td>1.13</td>\n",
              "      <td>195.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 21:00:00</th>\n",
              "      <td>01.01.2009 21:00:00</td>\n",
              "      <td>1000.19</td>\n",
              "      <td>-4.80</td>\n",
              "      <td>268.35</td>\n",
              "      <td>-6.14</td>\n",
              "      <td>90.2</td>\n",
              "      <td>4.27</td>\n",
              "      <td>3.85</td>\n",
              "      <td>0.42</td>\n",
              "      <td>2.40</td>\n",
              "      <td>3.85</td>\n",
              "      <td>1296.45</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.75</td>\n",
              "      <td>206.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 22:00:00</th>\n",
              "      <td>01.01.2009 22:00:00</td>\n",
              "      <td>1000.00</td>\n",
              "      <td>-4.50</td>\n",
              "      <td>268.66</td>\n",
              "      <td>-5.82</td>\n",
              "      <td>90.4</td>\n",
              "      <td>4.37</td>\n",
              "      <td>3.95</td>\n",
              "      <td>0.42</td>\n",
              "      <td>2.46</td>\n",
              "      <td>3.95</td>\n",
              "      <td>1294.72</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.63</td>\n",
              "      <td>175.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 23:00:00</th>\n",
              "      <td>01.01.2009 23:00:00</td>\n",
              "      <td>999.77</td>\n",
              "      <td>-4.47</td>\n",
              "      <td>268.71</td>\n",
              "      <td>-5.84</td>\n",
              "      <td>90.1</td>\n",
              "      <td>4.38</td>\n",
              "      <td>3.95</td>\n",
              "      <td>0.43</td>\n",
              "      <td>2.46</td>\n",
              "      <td>3.95</td>\n",
              "      <td>1294.29</td>\n",
              "      <td>0.65</td>\n",
              "      <td>1.00</td>\n",
              "      <td>219.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-02 00:00:00</th>\n",
              "      <td>02.01.2009 00:00:00</td>\n",
              "      <td>999.59</td>\n",
              "      <td>-4.54</td>\n",
              "      <td>268.65</td>\n",
              "      <td>-5.46</td>\n",
              "      <td>93.2</td>\n",
              "      <td>4.36</td>\n",
              "      <td>4.06</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.53</td>\n",
              "      <td>4.06</td>\n",
              "      <td>1294.33</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.88</td>\n",
              "      <td>155.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-02 01:00:00</th>\n",
              "      <td>02.01.2009 01:00:00</td>\n",
              "      <td>999.34</td>\n",
              "      <td>-4.44</td>\n",
              "      <td>268.77</td>\n",
              "      <td>-5.19</td>\n",
              "      <td>94.4</td>\n",
              "      <td>4.39</td>\n",
              "      <td>4.15</td>\n",
              "      <td>0.25</td>\n",
              "      <td>2.58</td>\n",
              "      <td>4.15</td>\n",
              "      <td>1293.47</td>\n",
              "      <td>0.28</td>\n",
              "      <td>1.00</td>\n",
              "      <td>148.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7acf372-48ff-483f-b909-265add7d8b5e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c7acf372-48ff-483f-b909-265add7d8b5e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c7acf372-48ff-483f-b909-265add7d8b5e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp = df['T (degC)']\n",
        "temp.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "LSypu7p2lfH0",
        "outputId": "07bd13cd-79f4-4dde-9316-3979c8d11fcf"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6f7ceee410>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#[[[1],[2],[3],[4]]]                                  [5]\n",
        "#[[[2],[3],[4],[5]]]                                  [6]\n",
        "#[[[3],[4],[5],[6]]]                                  [7]\n",
        "# past values depend on which model will forecast      output\n",
        "#the previouse shape since the model need 3d tensor\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "def df_to_X_y(df,window_size):\n",
        "  df_as_np = df.to_numpy()\n",
        "  #scaler = StandardScaler()\n",
        "  #df_as_np = scaler.fit_transform((df_as_np.reshape(1,-1).T))\n",
        "  X = []\n",
        "  y = []\n",
        "  for i in range(len(df_as_np) - window_size):\n",
        "    row = [[a] for a in df_as_np[i:i+window_size]]\n",
        "    X.append(row)\n",
        "    y.append(df_as_np[i+window_size])\n",
        "  return np.array(X) , np.array(y)"
      ],
      "metadata": {
        "id": "EVygYTIolfLU"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "WIDOW_SIZE = 5\n",
        "X,y = df_to_X_y(temp,WIDOW_SIZE)\n",
        "X = X.reshape(X.shape[0],X.shape[1],X.shape[2])"
      ],
      "metadata": {
        "id": "Khc23yPNoPQL"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape , y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kgrVeBFUsRem",
        "outputId": "05f67484-a94c-4411-c7d9-a23329efa53c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((70070, 5, 1), (70070,))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.1)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train,y_train,test_size = 0.111)"
      ],
      "metadata": {
        "id": "PJhGLmNZsRiA"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape , X_test.shape ,X_val.shape #80% train , 10% test , 10% validation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JKVag80EoPTo",
        "outputId": "b82952e3-eb68-4a12-ffa6-f0838ee61fbf"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((56063, 5, 1), (7007, 5, 1), (7000, 5, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape , y_test.shape ,y_val.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "asxGr6SKoAvs",
        "outputId": "3a68e108-22ea-43c6-8dac-8a504d97d9a7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((56063,), (7007,), (7000,))"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import *\n",
        "from keras.callbacks import ModelCheckpoint #to save checkpoints\n",
        "from tensorflow.keras.losses import MeanSquaredError\n",
        "from tensorflow.keras.metrics import RootMeanSquaredError\n",
        "from tensorflow.keras.optimizers import Adam\n"
      ],
      "metadata": {
        "id": "rT42G-tmmJ8J"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model1\n"
      ],
      "metadata": {
        "id": "x_7QhvMEnbHe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model1 = Sequential()\n",
        "model1.add(InputLayer((5,1)))\n",
        "model1.add(LSTM(64))\n",
        "model1.add(Dense(8,'relu'))\n",
        "model1.add(Dense(1,'linear'))\n",
        "model1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_AuNqqTSmVXm",
        "outputId": "8f0a5030-02b4-4a35-9e5b-4ce841fec4df"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 64)                16896     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 8)                 520       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 9         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 17,425\n",
            "Trainable params: 17,425\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cp = ModelCheckpoint('/content/drive/MyDrive/Colab Notebooks/forecaste_datasets/model1/',save_best_only= True) #make check point\n",
        "model1.compile(loss = MeanSquaredError() , optimizer = Adam(learning_rate=0.0001),metrics = [MeanSquaredError()])"
      ],
      "metadata": {
        "id": "g0qtnb7UmVaw"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model1.fit(X_train,y_train , validation_data = (X_val , y_val),epochs = 10 ,callbacks= [cp])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O9kB0YGQoGxu",
        "outputId": "3516329c-2bf9-49de-e440-5b81a4cc04d4"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1748/1752 [============================>.] - ETA: 0s - loss: 37.3853 - mean_squared_error: 37.3853"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f3fa22af050> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1752/1752 [==============================] - 18s 8ms/step - loss: 37.3102 - mean_squared_error: 37.3097 - val_loss: 3.9302 - val_mean_squared_error: 3.9292\n",
            "Epoch 2/10\n",
            "1747/1752 [============================>.] - ETA: 0s - loss: 2.1080 - mean_squared_error: 2.1080"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f3fa22af050> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1752/1752 [==============================] - 17s 10ms/step - loss: 2.1043 - mean_squared_error: 2.1043 - val_loss: 1.2124 - val_mean_squared_error: 1.2120\n",
            "Epoch 3/10\n",
            "1747/1752 [============================>.] - ETA: 0s - loss: 0.9592 - mean_squared_error: 0.9592"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f3fa22af050> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1752/1752 [==============================] - 11s 6ms/step - loss: 0.9587 - mean_squared_error: 0.9587 - val_loss: 0.8154 - val_mean_squared_error: 0.8152\n",
            "Epoch 4/10\n",
            "1746/1752 [============================>.] - ETA: 0s - loss: 0.7415 - mean_squared_error: 0.7415"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f3fa22af050> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1752/1752 [==============================] - 12s 7ms/step - loss: 0.7414 - mean_squared_error: 0.7414 - val_loss: 0.7154 - val_mean_squared_error: 0.7154\n",
            "Epoch 5/10\n",
            "1744/1752 [============================>.] - ETA: 0s - loss: 0.6784 - mean_squared_error: 0.6784"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f3fa22af050> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1752/1752 [==============================] - 12s 7ms/step - loss: 0.6781 - mean_squared_error: 0.6781 - val_loss: 0.6809 - val_mean_squared_error: 0.6809\n",
            "Epoch 6/10\n",
            "1741/1752 [============================>.] - ETA: 0s - loss: 0.6558 - mean_squared_error: 0.6558"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f3fa22af050> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1752/1752 [==============================] - 11s 6ms/step - loss: 0.6557 - mean_squared_error: 0.6557 - val_loss: 0.6618 - val_mean_squared_error: 0.6618\n",
            "Epoch 7/10\n",
            "1743/1752 [============================>.] - ETA: 0s - loss: 0.6462 - mean_squared_error: 0.6462"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f3fa22af050> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1752/1752 [==============================] - 12s 7ms/step - loss: 0.6467 - mean_squared_error: 0.6467 - val_loss: 0.6567 - val_mean_squared_error: 0.6566\n",
            "Epoch 8/10\n",
            "1751/1752 [============================>.] - ETA: 0s - loss: 0.6411 - mean_squared_error: 0.6411"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f3fa22af050> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1752/1752 [==============================] - 11s 6ms/step - loss: 0.6412 - mean_squared_error: 0.6412 - val_loss: 0.6490 - val_mean_squared_error: 0.6490\n",
            "Epoch 9/10\n",
            "1752/1752 [==============================] - 7s 4ms/step - loss: 0.6364 - mean_squared_error: 0.6364 - val_loss: 0.6678 - val_mean_squared_error: 0.6678\n",
            "Epoch 10/10\n",
            "1742/1752 [============================>.] - ETA: 0s - loss: 0.6329 - mean_squared_error: 0.6329"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f3fa22af050> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1752/1752 [==============================] - 12s 7ms/step - loss: 0.6335 - mean_squared_error: 0.6335 - val_loss: 0.6448 - val_mean_squared_error: 0.6448\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "acc = history.history['mean_squared_error']\n",
        "val_acc = history.history['val_mean_squared_error']\n",
        "loss = history.history['mean_squared_error']\n",
        "val_loss = history.history['val_mean_squared_error']\n",
        "epochs = range(1, len(acc) + 1)\n",
        "plt.plot(epochs, acc, 'bo', label='Training mse')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation mse')\n",
        "plt.title('Training and validation MSE')\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'bo', label='Training mse')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation mse')\n",
        "plt.title('Training and validation MSE')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "wlwEfCtGoG0U",
        "outputId": "751eacf9-6a80-4625-d02b-b56505c548b5"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEICAYAAABGaK+TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXxU9b3/8deHRXZEIFIkStCyVAskEIJgteBeoYJeVCgq/GhF1FahvbWlrUJt6dVK1UutXHEBlyj607qj1VK86HWpYSkC4nUDRFECSgAxyvK5f5wzYRIyySSZzOTA+/l4zGPO+Z7tMyfwnjPfOXOOuTsiIhI9jTJdgIiI1I4CXEQkohTgIiIRpQAXEYkoBbiISEQpwEVEIkoBLgCY2bNmNi7V82aSma01s1PrYb1uZt8Mh//LzK5JZt5abGesmT1f2zrlwKcAjzAz2xH32GtmX8aNj63Jutz9e+5+T6rnPdC5+yR3/11d12NmOWHYN4lbd6G7n17XdVeyrSHhth6r0N43bH8xrm2EmS03s21mttnM/mFm3cJp081sV4V/h1tTXa8k1qT6WaShcvfWsWEzWwv8yN3/XnE+M2vi7rvTWZs0eMXAIDPr4O5bwrZxwP/GZgg/OdwLnAv8A2gNnA7siVvPQ+5+YXpKlop0BH4ACo+wNpjZL8zsE2CumR1mZk+bWbGZfR4OZ8ct86KZ/SgcHm9mL5vZzHDeD8zse7Wct5uZLTaz7Wb2dzP7i5ndn6DuZGr8nZn9T7i+582sY9z0i8xsnZltMbNfV7F/BprZJ2bWOK7tHDNbEQ4XmNmrZrbVzDaa2a1mdkiCdc0zs9/Hjf88XOZjM5tQYd5hZrYsPJr90Mymx01eHD5vDY9kB8X2bdzyg83sDTMrCZ8HJ7tvKvE18DgwOly+MXABUBg3Ty7wgbsv9MB2d3/U3ddXsV5JIwX4gesbQHugKzCR4G89Nxw/CvgSuLWK5QcCbwMdgT8Cd5mZ1WLeB4B/Ah2A6cBFVWwzmRp/APw/4HDgEODfAczsWGB2uP4jwu1lUwl3fx34Aji5wnofCIf3AFPC1zMIOAW4vIq6CWs4M6znNKA7ULH//QvgYqAdMAy4zMxGhtNOCp/buXtrd3+1wrrbA88As8LXdhPwjJl1qPAa9ts3Vbg3rAfgDGAl8HHc9KVALzO72cyGmlnriiuQzFKAH7j2AtPc/St3/9Ldt4RHTzvdfTswA/huFcuvc/c73H0PcA/QGehUk3nN7ChgAHCtu3/t7i8DTybaYJI1znX3/3X3L4GHCY4SAUYBT7v7Ynf/Crgm3AeJPAiMATCzNsBZYRvuvsTdX3P33e6+Fri9kjoqc35Y30p3/4LgDSv+9b3o7m+6+153XxFuL5n1QhD477j7fWFdDwJrgO/HzZNo31TK3V8B2ptZT4Igv7fC9PeBIUCXcH2bw08c8UF+fvhJJfZYlOTrkRRQgB+4it29NDZiZi3N7Pawi2EbwUf2dvHdCBV8Ehtw953hYKIjsETzHgF8FtcG8GGigpOs8ZO44Z1xNR0Rv+4wQLeQ2APAuWbWjKCPd6m7rwvr6BF233wS1vEHgqPx6pSrAVhX4fUNNLNFYRdRCTApyfXG1r2uQts6gnCNSbRvqnIf8GNgKPBYxYnhG9n57p4FnEjwSSG+e+phd28X9xiaxDYlRRTgB66Kl5n8GdATGOjubdn3kT1Rt0gqbCQ4wmsZ13ZkFfPXpcaN8esOt9kh0czuvpogAL9H+e4TCLpi1gDdwzp+VZsaCLqB4j1A8AnkSHc/FPivuPVWd1nQjwm6luIdBXyURF1VuY+ge2hBhTfa/bj7G8BfgW/XcZuSIgrwg0cbgj7lrWF/6rT63mB4RFsETDezQ8xsEOU/8qeyxkeA4Wb2nfALx+uo/t/3A8BVBG8U/79CHduAHWbWC7gsyRoeBsab2bHhG0jF+tsQfCIpNbMCgjeOmGKCLp+jE6x7AdDDzH5gZk3M7ALgWODpJGurlLt/QNCNs9+XvuG+vMTMDg/HewFnA6/VZZuSOgrwg8ctQAtgM8F/wOfStN2xBF8EbgF+DzwEfJVg3lrX6O6rgCsIQnkj8DmwoZrFYn3Q/3D3zXHt/04QrtuBO8Kak6nh2fA1/AN4N3yOdzlwnZltB64lCPzYsjsJ+vz/J+xLPr7CurcAwwk+pWwBrgaGV6i7Vtz9ZXf/uJJJWwkC+00z20Hw93iM4IvqmAus/HngO2KBL/XPdEMHSSczewhY4+71/glA5ECnI3CpV2Y2wMyOMbNG4Wl2IwjOPxaROtIvMaW+fYPgi68OBF0al7n7ssyWJHJgUBeKiEhEqQtFRCSi0tqF0rFjR8/JyUnnJkVEIm/JkiWbwx9TlZPWAM/JyaGoqCidmxQRiTwzq/grXEBdKCIikaUAFxGJKAW4iEhE6TxwkQPUrl272LBhA6WlpdXPLA1C8+bNyc7OpmnTpknNrwAXOUBt2LCBNm3akJOTQ+J7cUhD4e5s2bKFDRs20K1bt6SWafBdKIWFkJMDjRoFz4WF1S0hIgClpaV06NBB4R0RZkaHDh1q9ImpQR+BFxbCxImwM7xK8bp1wTjA2Brdc13k4KTwjpaa/r0a9BH4r3+9L7xjdu4M2kVEDnYNOsDXJ7j3daJ2EWk4tmzZQm5uLrm5uXzjG9+gS5cuZeNff/11lcsWFRVx5ZVXVruNwYMHp6rcSGrQAX5UxRtSVdMuIrWX6u+bOnTowPLly1m+fDmTJk1iypQpZeOHHHIIu3fvTrhsfn4+s2bNqnYbr7zySt2KjLgGHeAzZkDLluXbWrYM2kUkdWLfN61bB+77vm9K9UkD48ePZ9KkSQwcOJCrr76af/7znwwaNIi8vDwGDx7M22+/DcCLL77I8OHDAZg+fToTJkxgyJAhHH300eWCvXXr1mXzDxkyhFGjRtGrVy/Gjh1L7EqrCxYsoFevXvTv358rr7yybL3x5s2bx8iRIznttNPIycnh1ltv5aabbiIvL4/jjz+ezz77DIBZs2Zx7LHH0qdPH0aPHg3AF198wYQJEygoKCAvL48nnngitTutKu6etkf//v29pu6/371rV3ez4Pn++2u8CpGD0urVq5Oet2tX9yC6yz+6dk1NLdOmTfMbb7zRx40b58OGDfPdu3e7u3tJSYnv2rXL3d1feOEFP/fcc93dfdGiRT5s2LCyZQcNGuSlpaVeXFzs7du396+//trd3Vu1alU2f9u2bf3DDz/0PXv2+PHHH+8vvfSSf/nll56dne3vv/++u7uPHj26bL3x5s6d68ccc4xv27bNN23a5G3btvXZs2e7u/vkyZP95ptvdnf3zp07e2lpqbu7f/755+7uPnXqVL/vvvvK2rp37+47duyo9b6q7O8GFHklmdqgz0KB4GwTnXEiUr/S+X3TeeedR+PGjQEoKSlh3LhxvPPOO5gZu3btqnSZYcOG0axZM5o1a8bhhx/Op59+SnZ2drl5CgoKytpyc3NZu3YtrVu35uijjy47r3rMmDHMmTOn0m0MHTqUNm3a0KZNGw499FC+//3g/tu9e/dmxYoVAPTp04exY8cycuRIRo4cCcDzzz/Pk08+ycyZM4Hg9M3169fzrW99qy67KSkNugtFRNIjnd83tWrVqmz4mmuuYejQoaxcuZKnnnoq4TnQzZo1Kxtu3Lhxpf3nycxTlfjlGzVqVDbeqFGjsnU988wzXHHFFSxdupQBAwawe/du3J1HH320rH8/XeENCnARIXPfN5WUlNClSxcg6IdOtZ49e/L++++zdu1aAB566KFar2vv3r18+OGHDB06lBtuuIGSkhJ27NjBGWecwZ///OeyPvdly9J3x0AFuIgwdizMmQNdu4JZ8DxnTv13X1599dVMnTqVvLy8Gh8xJ6NFixbcdtttnHnmmfTv37+se6Q29uzZw4UXXkjv3r3Jy8vjyiuvpF27dlxzzTXs2rWLPn36cNxxx3HNNdek+FUkltZ7Yubn57tu6CCSHm+99VbaPso3ZDt27KB169a4O1dccQXdu3dnypQpmS4rocr+bma2xN3zK86rI3AROaDdcccd5Obmctxxx1FSUsKll16a6ZJSptqzUMysObAYaBbO/4i7TzOzecB3gZJw1vHuvry+ChURqY0pU6Y06CPuukjmNMKvgJPdfYeZNQVeNrNnw2k/d/dH6q88ERFJpNoAD08i3xGONg0f6es4FxGRSiXVB25mjc1sObAJeMHdXw8nzTCzFWZ2s5k1S7DsRDMrMrOi4uLiFJUtIiJJBbi773H3XCAbKDCzbwNTgV7AAKA98IsEy85x93x3z8/KykpR2SIiUqOzUNx9K7AIONPdN4Y/0/8KmAsU1EeBIhJNQ4cO5W9/+1u5tltuuYXLLrss4TJDhgwhdqrxWWedxdatW/ebZ/r06WU/W0/k8ccfZ/Xq1WXj1157LX//+99rUn4kVBvgZpZlZu3C4RbAacAaM+scthkwElhZn4WKSLSMGTOG+fPnl2ubP38+Y8aMSWr5BQsW0K5du1ptu2KAX3fddZx66qm1WldDlswReGdgkZmtAN4g6AN/Gig0szeBN4GOwO/rr0wRiZpRo0bxzDPPlN28Ye3atXz88ceceOKJXHbZZeTn53Pccccxbdq0SpfPyclh8+bNAMyYMYMePXrwne98p+ySsxCc4z1gwAD69u3Lv/3bv7Fz505eeeUVnnzySX7+85+Tm5vLe++9x/jx43nkkeCEuYULF5KXl0fv3r2ZMGECX331Vdn2pk2bRr9+/ejduzdr1qzZr6aGdtnZZM5CWQHkVdJ+cp23LiJpMXkyLE/xrzRyc+GWWxJPb9++PQUFBTz77LOMGDGC+fPnc/7552NmzJgxg/bt27Nnzx5OOeUUVqxYQZ8+fSpdz5IlS5g/fz7Lly9n9+7d9OvXj/79+wNw7rnncskllwDwm9/8hrvuuouf/OQnnH322QwfPpxRo0aVW1dpaSnjx49n4cKF9OjRg4svvpjZs2czefJkADp27MjSpUu57bbbmDlzJnfeeed+9axcuZJly5ZRWlrKN7/5TW644QaWLVvGlClTuPfee5k8eTLXX389H3zwAc2aNSvrBpoxYwYnn3wyd999N1u3bqWgoIBTTz213MW9akq/xBSRehPfjRLfffLwww/Tr18/8vLyWLVqVbnujopeeuklzjnnHFq2bEnbtm05++yzy6atXLmSE088kd69e1NYWMiqVauqrOftt9+mW7du9OjRA4Bx48axePHisunnnnsuAP379y+7AFZFscvOZmVl7XfZ2dgyscvO3n///TRpEhwnP//881x//fXk5uYyZMiQssvO1kWDvx64iNRdVUfK9WnEiBFMmTKFpUuXsnPnTvr3788HH3zAzJkzeeONNzjssMMYP358wsvIVmf8+PE8/vjj9O3bl3nz5vHiiy/Wqd7YJWSruhxtspedXbx4MU899RQzZszgzTffLLvsbM+ePetUYzwdgYtIvWndujVDhw5lwoQJZUff27Zto1WrVhx66KF8+umnPPvss1Wu46STTuLxxx/nyy+/ZPv27Tz11FNl07Zv307nzp3ZtWsXhXH3f2vTpg3bt2/fb109e/Zk7dq1vPvuuwDcd999fPe7303FSy2TzsvO6ghcROrVmDFjOOecc8q6Uvr27UteXh69evXiyCOP5IQTTqhy+X79+nHBBRfQt29fDj/8cAYMGFA27Xe/+x0DBw4kKyuLgQMHloX26NGjueSSS5g1a1bZl5cAzZs3Z+7cuZx33nns3r2bAQMGMGnSpJS+3thlZ0tKSnD3cpednTx5Mn369GHv3r1069aNp59+uk7b0uVkRQ5QupxsNOlysiIiBwEFuIhIRCnARQ5g6ewilbqr6d9LAS5ygGrevDlbtmxRiEeEu7NlyxaaN2+e9DI6C0XkAJWdnc2GDRvQZZyjo3nz5mRnZyc9vwJc5ADVtGlTunXrlukypB6pC0VEJKIU4CIiEaUAFxGJKAW4iEhEKcBFRCJKAS4iElHJ3BOzuZn908z+ZWarzOy3YXs3M3vdzN41s4fM7JD6L1dERGKSOQL/CjjZ3fsCucCZZnY8cANws7t/E/gc+GH9lSkiIhVVG+Ae2BGONg0fDpwMxC60ew/BnelFRCRNkuoDN7PGZrYc2AS8ALwHbHX32D2HNgBd6qdEERGpTFIB7u573D0XyAYKgF7JbsDMJppZkZkV6ZoMIiKpU6OzUNx9K7AIGAS0M7PYtVSygY8SLDPH3fPdPT8rK6tOxYqIyD7JnIWSZWbtwuEWwGnAWwRBPiqcbRzwRH0VKSIi+0vmaoSdgXvMrDFB4D/s7k+b2Wpgvpn9HlgG3FWPdYqISAXVBri7rwDyKml/n6A/XEREMkC/xBQRiSgFuIhIRCnARUQiSgEuIhJRCnARkYhSgIuIRJQCXEQkohTgIiIRpQAXEYkoBbiISEQpwEVEIkoBLiISUQpwEZGIUoCLiESUAlxEJKIU4CIiEaUAFxGJKAW4iEhEJXNT4yPNbJGZrTazVWZ2Vdg+3cw+MrPl4eOs+i9XRERikrmp8W7gZ+6+1MzaAEvM7IVw2s3uPrP+yhMRkUSSuanxRmBjOLzdzN4CutR3YSIiUrUa9YGbWQ7BHepfD5t+bGYrzOxuMzssxbWJiEgVkg5wM2sNPApMdvdtwGzgGCCX4Aj9TwmWm2hmRWZWVFxcnIKSRUQEkgxwM2tKEN6F7v5XAHf/1N33uPte4A6goLJl3X2Ou+e7e35WVlaq6hYROeglcxaKAXcBb7n7TXHtneNmOwdYmfryREQkkWTOQjkBuAh408yWh22/AsaYWS7gwFrg0nqpUEREKpXMWSgvA1bJpAWpL0dERJKlX2KKiESUAlxEJKIU4CIiEaUAFxGJKAW4iEhEKcBFRCJKAS4iElEKcBGRiFKAi4hElAJcRCSiFOAiIhGlABcRiSgFuIhIRCnARUQiSgEuIhJRCnARkYhSgIuIRJQCXEQkopK5qfGRZrbIzFab2Sozuypsb29mL5jZO+HzYfVfroiIxCRzBL4b+Jm7HwscD1xhZscCvwQWunt3YGE4LiIiaVJtgLv7RndfGg5vB94CugAjgHvC2e4BRtZXkSIisr8a9YGbWQ6QB7wOdHL3jeGkT4BOKa1MRESqlHSAm1lr4FFgsrtvi5/m7g54guUmmlmRmRUVFxfXqVgREdknqQA3s6YE4V3o7n8Nmz81s87h9M7ApsqWdfc57p7v7vlZWVmpqFlEREjuLBQD7gLecveb4iY9CYwLh8cBT6S+PBERSaRJEvOcAFwEvGlmy8O2XwHXAw+b2Q+BdcD59VOiiIhUptoAd/eXAUsw+ZTUliMiIsnSLzFFRCJKAS4iElEKcBGRiFKAi4hElAJcRCSiFOAiIhGlABcRiSgFuIhIRCnARUQiSgEuIhJRCnARkYhSgIuIRJQCXEQkohTgIiIRpQAXEYkoBbiISEQpwEVEIkoBLiISUcnc1PhuM9tkZivj2qab2Udmtjx8nFW/ZYqISEXJHIHPA86spP1md88NHwtSW5aIiFSn2gB398XAZ2moRUREaqAufeA/NrMVYRfLYYlmMrOJZlZkZkXFxcV12JyIiMSrbYDPBo4BcoGNwJ8Szejuc9w9393zs7Kyark5ERGpqFYB7u6fuvsed98L3AEUpLYsERGpTq0C3Mw6x42eA6xMNK+IiNSPJtXNYGYPAkOAjma2AZgGDDGzXMCBtcCl9VijiIhUotoAd/cxlTTfVQ+1iIhIDeiXmCIiEaUAFxGJKAW4iEhEKcBFRCJKAS4iElEKcBGRiFKAi4hElAJcRCSiFOAiIhGlABcRiSgFuIhIRCnARUQiSgEuIhJRCnARkYhSgIuIRJQCXEQkohTgIiIRpQAXEYmoagPczO42s01mtjKurb2ZvWBm74TPh9VvmSIiUlEyR+DzgDMrtP0SWOju3YGF4biIiKRRtQHu7ouBzyo0jwDuCYfvAUamuC4REalGbfvAO7n7xnD4E6BTohnNbKKZFZlZUXFxcS03JyIiFdX5S0x3d8CrmD7H3fPdPT8rK6uumxMRkVBtA/xTM+sMED5vSl1JIiKSjNoG+JPAuHB4HPBEasoREZFkJXMa4YPAq0BPM9tgZj8ErgdOM7N3gFPDcRERSaMm1c3g7mMSTDolxbWIiEgN6JeYIiIRpQAXEYkoBbiISEQpwEVEIkoBLiISUQpwEZGIUoCLiESUAlxEJKIU4CIiEaUAFxGJKAW4iEhEKcBFRCJKAS4iElEKcBGRiFKAi4hElAJcRCSiFOAiIhGlABcRiahqb6lWFTNbC2wH9gC73T0/FUWJiEj16hTgoaHuvjkF6xERkRpQF4qISETVNcAdeN7MlpjZxMpmMLOJZlZkZkXFxcV13JyIiMTUNcC/4+79gO8BV5jZSRVncPc57p7v7vlZWVl13JyIiMTUKcDd/aPweRPwGFCQiqJERKR6tQ5wM2tlZm1iw8DpwMpUFSYiIlWry1konYDHzCy2ngfc/bmUVCUiItWqdYC7+/tA3xTWktAjj8DSpXD55ZCdnY4tiog0fJE4jXDpUrjhBujWDUaPhldfBfdMVyUiklmRCPA//AHeew+uugqeew4GD4aBA6GwEL7+OtPViYhkRiQCHCAnB2bOhA0b4C9/gW3b4MILoWtXuO462LQp0xWKiKRXZAI8pnXroC989ergaDwvD6ZNgyOPhPHjYdmyTFcoIpIekQvwmEaN4IwzYMECWLMGLrkk+LKzXz846SR49FHYvTvTVYqI1J/IBni8nj3h1luD7pU//Qk+/BBGjYJjjoEbb4TPPst0hSIiqXdABHhMu3bw05/Cu+/CY4/B0UfD1VcH3SuTJgXdLiIiB4oDKsBjGjeGkSNh0SL4179gzBiYNw+OOw5OPx2eeQb27q3ZOgsLgy9SGzUKngsL66FwEZEaOCADPF6fPnDnnUH3yowZsGoVDB8edLvMmgXbt1e/jsJCmDgR1q0Lzj9fty4YV4iLSCYd8AEe07Ej/OpXsHYtPPhgMH7VVdClC0yeHJxnnsivfw07d5Zv27kzaBcRyZSDJsBjmjbd92vO11+H738/OK+8e3c4+2xYuHD/X3muX1/5uhK1i4ikw0EX4PEKCoJukHXr4De/gddeg1NPhd69Yc6cfUfdRx1V+fKJ2kVE0uGgDvCYI44Ifs25fj3MnRscpV96aXD2yi9/GZzZ0rJl+WVatgz61EVEMkUBHqd58+DXnEuXwn//NwwZEpxH/tOfwre/DZ06BfN17RocoY8dm/4adTaMiMSYp/Gyfvn5+V5UVJS27aXC2rVBH/mdd8LWrXDYYdC+fXDOebt2wXhsuKq2du2gRQsILp9eO7GzYeK/UG3ZMjNvJoWFwZe469cHXUkzZmTmDU3kYGBmS9w9f78J7p62R//+/T2qduxwv/1298svd//BD9zPOst98GD3b33LvXNn9xYt3IOvPxM/DjnE/fDD3Xv0cC8ocD/9dPcLLnC/9FL3X/zC/T/+w332bPf5892fe879tdfc16xx/+QT99JS965dK19v167p3Rf33+/esmX5Glq2DNrTXUfXru5mwXO6t686VEe66gCKvJJM1RF4Cn31FZSUBEfqn38ePMc/qmr7/HPYtav22z75ZGjSJPlH06a1n/+yy6C4eP8aOnWC+fODTxmNGpV/TjRc27bHHoNf/AJKS/dtv0WL4IqV5523b97YJ56ajNdkmQceaBifihrKp7NM1RF/OBGrY9Ik+PLLffO0aAG33x5cxbQun4RrIlX7I9ERuAK8gXAPwqiqoP/jH4PL6FbUrBkMGBBcvKuqx65dlbfv2ZP+13swiL3pxKtuPJl5KhsvLa38Jidm5b+Ar2pdiYZrMm3r1sp/5dyoEbRtu3/QVvw8WbEtmfG6qvjmnYpHbL2bN1e+P7p2Dbpnk6+x8gCvyz0xMbMzgf8EGgN3uvv1dVnfwcwsOEJo0QI6d658npyc+jm6cQ9CPNnQP+MM2Lhx//V06hT8SCr2n2vv3vLPiYZr0/ajHyV+PX/+c+0DoabL/Pa3ieuYOnX//VzVeDLzJBq/8cbKa3APjkSrW1ei4ZpO+8tfKq9j7164+OJgOFWffqpqu/bayuuA4PLTif72qXjEr/f22yuvIWW/IamsXyWZB0FovwccDRwC/As4tqplotwH3lA0hH69htAH3lC+D1AdqiMddZCgD7wuAT4I+Fvc+FRgalXLKMAPHJl+I2kIbyKqQ3Wkq476CPBRBN0msfGLgFsrmW8iUAQUHXXUUXXaGSLxMv0mojpUR7rqSBTgtf4S08xGAWe6+4/C8YuAge7+40TL6EtMEZGaS/QlZl1+ifkRcGTceHbYJiIiaVCXAH8D6G5m3czsEGA08GRqyhIRkerU+jRCd99tZj8G/kZwRsrd7r4qZZWJiEiV6nQeuLsvABakqBYREakBXY1QRCSi0vpTejMrBtalbYP1oyOwOdNFNCDaH/toX5Sn/VFeXfZHV3fPqtiY1gA/EJhZUWWn8xystD/20b4oT/ujvPrYH+pCERGJKAW4iEhEKcBrbk6mC2hgtD/20b4oT/ujvJTvD/WBi4hElI7ARUQiSgEuIhJRCvAkmdmRZrbIzFab2SozuyrTNWWamTU2s2Vm9nSma8k0M2tnZo+Y2Roze8vMBmW6pkwxsynh/5GVZvagmTXPdE3pZGZ3m9kmM1sZ19bezF4ws3fC58NSsS0FePJ2Az9z92OB44ErzOzYDNeUaVcBb2W6iAbiP4Hn3L0X0JeDdL+YWRfgSiDf3b9NcJ2k0ZmtKu3mAWdWaPslsNDduwMLw/E6U4Anyd03uvvScHg7wX/QLpmtKnPMLBsYBtyZ6VoyzcwOBU4C7gJw96/dfWtmq8qoJkALM2sCtAQ+znA9aeXui4HPKjSPAO4Jh+8BRqZiWwrwWjCzHCAPeD2zlWTULcDVQCX33D7odAOKgblhl9KdZtYq00Vlgrt/BMwE1gMbgRJ3fz6zVTUIndw9divwT4BOqVipAryGzKw18Cgw2d23ZbqeTDCz4cAmd1+S6VoaiCZAP2C2u+cBX5Cij8hRE/btjiB4U8On9VAAAAEiSURBVDsCaGVmF2a2qoYlvEVaSs7fVoDXgJk1JQjvQnf/a6bryaATgLPNbC0wHzjZzO7PbEkZtQHY4O6xT2SPEAT6wehU4AN3L3b3XcBfgcEZrqkh+NTMOgOEz5tSsVIFeJLMzAj6ON9y95syXU8muftUd8929xyCL6j+4e4H7VGWu38CfGhmPcOmU4DVGSwpk9YDx5tZy/D/zCkcpF/oVvAkMC4cHgc8kYqVKsCTdwJwEcHR5vLwcVami5IG4ydAoZmtAHKBP2S4nowIP4U8AiwF3iTImIPqJ/Vm9iDwKtDTzDaY2Q+B64HTzOwdgk8p16dkW/opvYhINOkIXEQkohTgIiIRpQAXEYkoBbiISEQpwEVEIkoBLiISUQpwEZGI+j93NU6gFSu+LwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1.save(\"/content/drive/MyDrive/Colab Notebooks/forecaste_datasets/model1.h5\")"
      ],
      "metadata": {
        "id": "nlRfVu7BoG_1"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss , mse = model1.evaluate(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wFY6XYcKmJ_N",
        "outputId": "d5e56108-de14-4978-e4c6-713664ad6d6e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "219/219 [==============================] - 1s 2ms/step - loss: 0.6520 - mean_squared_error: 0.6519\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import load_model\n",
        "model1 = load_model('/content/drive/MyDrive/Colab Notebooks/forecaste_datasets/model1.h5',compile = False)"
      ],
      "metadata": {
        "id": "u2BVxAu004dK"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jBfiDfvm04pP",
        "outputId": "ebf172f6-7865-47f3-90be-92c45201e7f9"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 64)                16896     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 8)                 520       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 9         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 17,425\n",
            "Trainable params: 17,425\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_prediction = model1.predict(X_train).flatten()\n",
        "train_results = pd.DataFrame(data = {\"Train prediction\":train_prediction , 'Actuals':y_train})\n",
        "train_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "TGPDUkCE04s1",
        "outputId": "42b29f68-15ea-4357-bd63-1c2763bf67b4"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Train prediction  Actuals\n",
              "0             15.025857    15.48\n",
              "1              7.815012     7.88\n",
              "2             15.164196    15.43\n",
              "3              8.931535     8.82\n",
              "4             19.704830    19.67\n",
              "...                 ...      ...\n",
              "56058         16.217760    15.62\n",
              "56059         13.957929    14.24\n",
              "56060          5.890140     5.92\n",
              "56061          0.958104     0.41\n",
              "56062         20.246914    20.00\n",
              "\n",
              "[56063 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dcc526e7-195c-4de1-8339-c4177b2b99b7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Train prediction</th>\n",
              "      <th>Actuals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15.025857</td>\n",
              "      <td>15.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.815012</td>\n",
              "      <td>7.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15.164196</td>\n",
              "      <td>15.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.931535</td>\n",
              "      <td>8.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19.704830</td>\n",
              "      <td>19.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56058</th>\n",
              "      <td>16.217760</td>\n",
              "      <td>15.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56059</th>\n",
              "      <td>13.957929</td>\n",
              "      <td>14.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56060</th>\n",
              "      <td>5.890140</td>\n",
              "      <td>5.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56061</th>\n",
              "      <td>0.958104</td>\n",
              "      <td>0.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56062</th>\n",
              "      <td>20.246914</td>\n",
              "      <td>20.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>56063 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dcc526e7-195c-4de1-8339-c4177b2b99b7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dcc526e7-195c-4de1-8339-c4177b2b99b7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dcc526e7-195c-4de1-8339-c4177b2b99b7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(train_results['Train prediction'][:100])\n",
        "plt.plot(train_results['Actuals'][:100])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "cCmZ34U19O4-",
        "outputId": "fc252fda-3cd6-4b84-a002-d976f3999bce"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6f701a35d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_prediction = model1.predict(X_val).flatten()\n",
        "val_results = pd.DataFrame(data = {\"Validation prediction\":val_prediction , 'Actuals':y_val})\n",
        "val_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "UVVtz0uI9q1y",
        "outputId": "38b47221-c897-4c5a-f70a-477601aad387"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Validation prediction  Actuals\n",
              "0                 12.562805    12.98\n",
              "1                 13.592465    13.68\n",
              "2                  6.719965     6.84\n",
              "3                 -0.670557     0.75\n",
              "4                 22.043058    22.46\n",
              "...                     ...      ...\n",
              "6995              22.528526    22.96\n",
              "6996               3.932530     4.21\n",
              "6997              12.236050    13.08\n",
              "6998               7.566131     7.84\n",
              "6999               2.133734     3.06\n",
              "\n",
              "[7000 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-922f643d-f195-4b01-bfdc-8bdf20588d74\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Validation prediction</th>\n",
              "      <th>Actuals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12.562805</td>\n",
              "      <td>12.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.592465</td>\n",
              "      <td>13.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.719965</td>\n",
              "      <td>6.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.670557</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>22.043058</td>\n",
              "      <td>22.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6995</th>\n",
              "      <td>22.528526</td>\n",
              "      <td>22.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6996</th>\n",
              "      <td>3.932530</td>\n",
              "      <td>4.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6997</th>\n",
              "      <td>12.236050</td>\n",
              "      <td>13.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6998</th>\n",
              "      <td>7.566131</td>\n",
              "      <td>7.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6999</th>\n",
              "      <td>2.133734</td>\n",
              "      <td>3.06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7000 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-922f643d-f195-4b01-bfdc-8bdf20588d74')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-922f643d-f195-4b01-bfdc-8bdf20588d74 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-922f643d-f195-4b01-bfdc-8bdf20588d74');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(val_results['Validation prediction'][:100])\n",
        "plt.plot(val_results['Actuals'][:100])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "XSKvlj_Z-me8",
        "outputId": "4f267ac4-c8c1-4428-b28a-ae4b3220f5cc"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6f70100410>]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_prediction = model1.predict(X_test).flatten()\n",
        "test_results = pd.DataFrame(data = {\"Test prediction\":test_prediction , 'Actuals':y_test})\n",
        "test_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "x3eaPysm_IB9",
        "outputId": "ceb8f631-0135-4821-d982-a2eeeb6c9d22"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Test prediction  Actuals\n",
              "0           17.243498    16.97\n",
              "1           26.458067    25.36\n",
              "2           17.801254    17.84\n",
              "3           17.126484    17.77\n",
              "4           11.608786    11.90\n",
              "...               ...      ...\n",
              "7002        12.856270    13.07\n",
              "7003        13.375313    14.55\n",
              "7004        13.020781    11.40\n",
              "7005        16.737185    16.30\n",
              "7006        -0.033109    -0.11\n",
              "\n",
              "[7007 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-922818dd-b9af-45ce-9be6-459745d55f03\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Test prediction</th>\n",
              "      <th>Actuals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.243498</td>\n",
              "      <td>16.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26.458067</td>\n",
              "      <td>25.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.801254</td>\n",
              "      <td>17.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>17.126484</td>\n",
              "      <td>17.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11.608786</td>\n",
              "      <td>11.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7002</th>\n",
              "      <td>12.856270</td>\n",
              "      <td>13.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7003</th>\n",
              "      <td>13.375313</td>\n",
              "      <td>14.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7004</th>\n",
              "      <td>13.020781</td>\n",
              "      <td>11.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7005</th>\n",
              "      <td>16.737185</td>\n",
              "      <td>16.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7006</th>\n",
              "      <td>-0.033109</td>\n",
              "      <td>-0.11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7007 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-922818dd-b9af-45ce-9be6-459745d55f03')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-922818dd-b9af-45ce-9be6-459745d55f03 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-922818dd-b9af-45ce-9be6-459745d55f03');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test_results['Test prediction'][:100])\n",
        "plt.plot(test_results['Actuals'][:100])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "Ln4jV0ds_XmM",
        "outputId": "70cd67b9-d707-416e-88fc-627224fcadee"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6f70062b90>]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD4CAYAAAAJmJb0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9eZBl2V3f+Tl3f1sulZm1drda3S0JgYQkUMiAwAaEGAIwxjbDgGcwMQHDH8ZgwIHMOEwMERMxwzjMjg1IwFgjPBpjpBgwaAAJgyWBttbWanVL3ZJ671qyqjLzrXc/88c557779puZL6u6s943oqIy33Zv3vvu937Pb/n+hJSSFVZYYYUVTies270DK6ywwgornBxWJL/CCiuscIqxIvkVVlhhhVOMFcmvsMIKK5xirEh+hRVWWOEUw7ndO1DG9va2vPfee2/3bqywwgorvKjw8Y9//LqUcmfacy8okr/33nt58MEHb/durLDCCiu8qCCEeGrWc6twzQorrLDCKcaK5FdYYYUVTjFWJL/CCiuscIqxIvkVVlhhhVOMFcmvsMIKK5xirEh+hRVWWOEUY0XyK6ywwgqnGCuSX2GF243P/AEM9m/3XqxwSrEi+RVWuJ3oXoN3/RB89t23e09WOKVYkfwKK9xOxD0AZNy/zTuywmnFiuRXWOE2otdXJP/Y8zdv856scFpx6kheSsmnntlnNdZwhRcDOl1F8obsVzgcZJ7xyXf+L/Tbq5vkLJw6kv/E03t897/9az71zCqRtcILH2kS6h+i27sjL1I8+cjHeN3nf5lH3/+u270rL1icOpK/cqAulqvt8DbvyQorLEYaqe+pyFYkfxQM2tcByJPBbd6TFy5OHckfDBIA9vvJbd6TFVZYjKxQ8vHt3ZEXKaKOCtPkyeomOQunl+QHK5Jf4YWPLNZKPl+R1FGQ9hTJy1W4ayZOHcm3Q0XuByuSX+FFgEyHGUS2UvJHQd7XubcVyc/EsUleCHG3EOIvhRCPCCE+K4T4Z/rxnxNCPCeE+JT+9+3H393FiDo3+AX33xF2Vtn2FV74MGGGFckfEYM9YKXk52EZ4/9S4J9LKT8hhGgBHxdCvFc/90tSyn+zhG1Uxs7ep/iH9ge5vPdp4Otu5aZXWOHQMCRvrUj+SBDRgfphlbieiWMreSnlZSnlJ/TPHeBR4NJxP/eosEKl4K3Bjdu1CyusUBm5Trxa+STJ//R/+jR/+vCVW71LLyo4muTFSsnPxFJj8kKIe4HXAR/RD/1TIcRDQojfFUJsznjPjwghHhRCPLi7u3vsfbAjFaNzw1W4ZoXqePxqh+f3b30Zngkz2FNI/o8+/Tz/9bHjXxOnGW7SVj+sVkIzsTSSF0I0gXcBPyGlbAO/AdwPvBa4DPzCtPdJKd8qpXy9lPL1Ozs7x94PL1Z3dj9eNUOtUB0//v98in/9p5+75dudRfJSSuIsp70qIJgLP+0AYK3CNTOxFJIXQrgogv8PUsp3A0gpr0opMyllDrwNeMMytrUIQaJIvp7u3YrNrXBK0AkTOmF6y7crdbjGlqNknmQSKU9ZldhH3wZ//rNL/ch6rkheTFkJraCwjOoaAfwO8KiU8hdLj18ovezvAw8fd1uLIKWknqmTvpYfEKf5SW9yhVOCOM2Jbsf3RYcZnDGSitIMOFmSf35/wL/4g4du3XXyhffBx98OS/SVauRdYJW4nodlKPk3Aj8AfPNYueS/FkJ8RgjxEPBNwE8uYVtz0Y1S1lEkvyk6p0sFrXCiiLO8INZbCh2uccaUvLnhnOR3+OOf+iTf8Omf5plrt6hIIYshOoDOkpLJUtKSmuRXSn4mjl1CKaX8ICCmPPWe4372YXEwSNgQ6qSfocPBIGan5d/q3VjhBYz/+d2f4e++5gJfd//2yOOvTR9iLTzHrS67NZ414yQf3wKSb13+G77R/jBf3H8KLh4/H7YQxrph91FYuzD/tRUQ9g4IhDpO0xLXKyicqo7Xg0HCpiH5lZJfYQx5LnnnR5/m/Y9dn3juX4nf5R/23nnL98k0QTlMV/LtMCHPT8Y2O+8b35dbdJ2YkMru55fycd394XlckfxsnCqSbw9S1lG+3Juiy0F35Uy3whBxpohzPAad55KACD+/9d8XK1OJV09Oj8lLCZ3oZBLCVqgq0NLs1pD85T1d7njt0aV8Xu9gGGay85Wgm4XTRfK9Hi0xIAzU0rN/sKoxXmGIKFHkPh57j7McnwT3NqhBUxXijiv5ZHgjOqkySluTfJ4e4vMf+zPoXD3S9owZm1ySkg/biuT70seRKyU/C6eK5M1JzzbvByBqr0h+hSGiTJH7uJKP0hyPBFfe+lprS6toj3Sk6sSsOuDk4vKuLjfOq9ocRx34v78XfvPr4ckPHnp7tlQrEnnt0aVU2EQ9db3ftDZXJD8Hp4rk446K0TlnXw5A1lmR/ApDGHVcJlBQpO+RToRMxvHeR67yK+97fKn7ZJcthktlgLdCyQepCp9UVvJ66Hjev4l8+3fBX//qocjaxM2t6AC6R1sNlJF2VS9M2z4zkbheYYhTRfJpVyWSvHOK5PPeyr9mhSFMMnNcycdphkeCx3ySf89nLvOODz+51H0aSRiW/FfKIaWTUvL1zJB8NRXc6any5J+L/3s+5H0tvPdnkR/77crbc0h5Kj+rfllCXD7rKZLve1u4K5KfiVNF8qZaQGy/AgCrvyL5FYYw5D7e9BTHMbaQ+MSk2ezGoEGc0YuWW0tfThim8XBkZXkfT4rkm7pbtKqSj/qqcu38xXv4aX6Ka3KDLz7015W358iEh+W96pclxOVluE8mBVlwZiKnscIQp4rkhfaWZkvF5O1oRfIrDGHU8biSTzS5BsSEc7o/B0nGIMmWWtJYjiUn8bC6J05z/pXzDv6O9ekTIfkwyVhHkbas2C0aD/oAvPal5/mrt3wTu2KLfP+5ytt0ZMoVuUXXWlO18seECPfp0EA6wUrJz8GpInkr1CTf2KEnmvjRyr9mhSFmhWuyEslHyWylPtDPDea8pow87PD8n/w85LNfb5dJPhqSfJRm/A/2+/hO60MnQvJ77Q4NocJDecUSyiRUNwXbb+DaFgfONo2oet7LISHG4Unr7qUoeTtu0xVNcHz8lZKfiVNF8k68T4oNfoues0EtWTlRrjCEIfkom63k55F8qJ/rxdXq1h/9wLu5+LH/nWc/97GZr3HyhIH01H5EpXBNkhKIhAvWzRMh+U6pkUhWDNckkVLyTlAHoOefZS2tSPJS4pES4/LZ5OJSKmzc+IC+3QTbwxMp+ZxQ252MU0XyfnJA314DIQi9DRrZiuRXGCJOc77T+hAb4fMjj6daQdtCEsWzyygHcTby/yKEfRXz7nY7M1/jEtNBkWZSismbfbpk7Z0Iyff3S+RcsZEoNSTvNQAIa+eVd0zcX/xmvVpIcHgkvYgI96F77XA7PQY/7TCw18BR1iVx6fitMMSpIvlaekDorAEQe2dYy9sn1hK+wgng0f8M73nLiX18lKT8ivvrvDn605HHs2RIDknYm/n+vib3qsnXTBN1NocEXZkUJJ+W9iPXA77Pc4OD/vJrwMP2UMnnWbWVSaZJ3qup/c2a59UTnctV3gxAs17jMXmXeuyYcfl61iF2hyQfRSuSn4ZTQ/LGZjhy1wHIalucEe3b4hG+whHxhffBp0/OPyaJQmwhccYGTJSrWpJwNiGHRUy+4ncqUZ9lyHEaXFIGoqb2IxruV67fUyMkGxxU294hEHeHRQmyYkw+04lhL1BKXqxdBCC6+WyFN6ttbLSafCHX00GPGZdv5B1Sbw3hBMAwnLTCKE4NyYdJzgZdMn9DPVA/wyYd9vuriTHLRphkfM9v/A2ffHrJie00KojxJJDpzx7vjjTDtGE0+TkOk3CtquSlIfl4xmdKSUDMwGrq/RvebGTpPX5/+XNeTU+J3nCl95gbj6dj8t6mIuvu7tML32smYG20mty0Nhg4a8erlZeSpuyR++tYjspppKtwzVScGpI/GCSsiy5ZoEbJWo1tfJHSPlhV2BwZB8/C294E7dEY9tM3+zz41B6ffmbJOY80hDytTDqHhQmfjA/oyJIhoWbx9HCNlLIg+X7FmLzQn5vPIh/9d8Z2Q297MlwD0IiO3x06DtkfkrysGJM3++TX1P7WtlTYpYqSz/SN1HZ97t6s85z7Etg9+rjFJOzgigwZbGC5SsnH4cqQcBpOFclv0kXUFMk7LW1Str/8C+SOwZf+Cp57EJ59cOTh6x11wfYrlhJWRa+nCTY5mYvVKGo3H13djSj5GUQRpXlRDNKvWF1j/o581upEh41ip6FfV1LypWOwFl9FLnGaEqga8+F+HO7vCepqfzc3t2jLGmmFWnmTVLYcj5dsNXgsv6SU/BH/ru6eyimI2gZCk3w5p7HCEMsY/3e3EOIvhRCPCCE+K4T4Z/rxM0KI9wohHtf/bx5/d2ej3e1SFxFWYwsAf121T0cHx8vg39Ewy+mxxNpuV5FTWFHRVsXzNzTxnBDJ54bkx+wLZIkcZiVJw9INraqSt1K1PTkjXGMqaFKnpfavTFLp8D3nuElvycfa0o1EQPWVU9InlC6B6wKw1fS4Ks8guosTr0XlkOPz0u0GHw8vQbg/sUqsip42I3Sam9iuSrxmFcM1X/jFb+Uzv/cvjrTdKkiznN//2DNkL5Cij2Uo+RT451LKLwe+BvhRIcSXAz8D/IWU8mXAX+jfTwwDbSvsNM8AUN84B0CyMik7MtpPfRqA3eefHHl8Vyv5qk1BVWGZhOgJxeUNmXsyGbEvyEpKPp9ByIMkY4c9vkw8XVnJW5km+XQ6+ZgO19xTMfm85F1TvtGdZ/m18l5yQMfW+auqXuxpSIiHa6tBcNtNnytyE7e3OGeQ6mNsuR73btX5dKyTr1c/e+h9BxhokvcaZ7A0yVeNyTfbXyK5shxP+2n4yBM3ecu7HuKjT9xc/OJbgGOTvJTyspTyE/rnDvAocAn4e8Db9cveDnz3cbc1D5E+6X5LjXVrbCqSl93JKUArVIO9+wgA4VjM9Wa7w2+6v0TrYDm+4MX2dBgljWaXMR4HRUyZZNSJMq1A8nHGTzjv5m3uL1ROvNqG3GeEEUwMOfeNkh/uh9DvTe06F8QNDvqHI/lulPLMzdk3y0CXG8fSPoSSDwnxEUKRfODa3LC2qIWLV8up7j+wbZ+XbDf4vLxHPXH14WrbHkPUUQTqt7awdbimqpK3SbFOcMiIWeld67wwwkdLjckLIe4FXgd8BDgnpTTruCvAuRnv+REhxINCiAd3d4+uuhNN5rV1RfKujskzWPnXHAm9GzQSdexEd1SpWTe+yLfZH+Ou9ieWuknjyBgOTobkjZL3RTxibSBLJC9nhIoGScYZ0eaMaFdewdi5vsjT6Z9ZhDACVfZbVvJCvydaewkXxOGV/K/9l8f5737rQzOfr6Wq3DgTNiKvtjKxswGx8EYe63hnaSbX51o3wFBlC9fjpVsNOtTp1S5OKPk/ffgK3/GrH5hrFAeQ9BTJ19a2cHxVglpekc2DQ3ai4wLNd8useG83lkbyQogm8C7gJ6SU7fJzUmWNpgaopJRvlVK+Xkr5+p2dow8TTs1JX9ef4TWIcLEHL4wl04sN6RV18R3IOsFgNHltdZSyF0sOq5iql/iESN6oY59kJsnnM1R3mGQ0CGmIiH5Y7eJ19Wg/MStco5t3rEAp+fKKwqwC0o37OH8Ekn92b8DVTjQ1YSulpCk7ZP4GKY6qaKoAKw2JhT/y2CA4i02+sHs1S9S5td2AuzZrOJbgcnD/BMl/+L/8IT+++3Mc9Oar4FzbDDfXt3GMkq+YeFVK/uRI3hjhXe++MAaZLIXkhRAuiuD/g5Ty3frhq0KIC/r5C8DJZkB1SZhdP2N2irZYx41e3CQvpeQf/+5H+fPPLr9Weh6uPP5xAD6Qv5pGPLrCCrqqmsKaoVCPClO/Hs/pOj0W0mG4ZsRueEYsvIxBnNMQenxdONumoAxHK3mRTb8pmGofJ1Bd2rKkRIv8xPYDtMSAfvtwpcB7vZgsl1NXHQPtQJkHm2QcRsmHJNYoyScN0/U6P4FqQim26+PYFnefqfM4L4HrjxXH/8pByGt3/1/+G/tB+u35YdZ8sEcuBa2NM9je4cI1jsxOdMiIERDXu6dEyQsVoPsd4FEp5S+Wnvoj4Af1zz8I/OFxtzV3P4wDpSF5oOesE8Qv7jr5MMl5/2O7fOzJW3uz6jz9GfZkk+drr6CW99XoN41WpKJwyyZ5t1Dy3aV+bgFNJuMkXybhWUnSgVbyAHnYnvqacXia5K1ZSt6Ej2oNHRsvKXkz4Pvsy9Q2Dyp0lZZws6eO5bSO7/1OnzUxQNQ3laFfVZLPI5IxJS9bF9QP7fkVNpkeTGIal16yVedT8SWQWdH5+seffo43WipGP+jO78GwwgPa1Ak8F1eT/KxzNw6XFOskST47ZSQPvBH4AeCbhRCf0v++Hfh54M1CiMeBb9G/nxiccJ8EB9x68djAffGblLXDhB+3301rf0o1QBpVr3E+JLwbj/KU/ZKi4YWOWknkueRMosI3TrZkktdK/qQSr0W4RoyGa0TJT13Mick3hXpORBVJXs+MtWcqeU3kQZ0YdzRck4fkCPwdNRuBdnXfdoCg+wx/2/o0nXCSzNragdJubJIJpzLhOXlIagejj22o70e24CZkwmCOp24SX3XPJu+7qfJnJmTz0Cc/xI5Qxzbqzj/GVnSgbIYBT8fk8woxeZnnuCLDOcHE66lT8lLKD0ophZTyK6WUr9X/3iOlvCGlfJOU8mVSym+RUp6oFHWTAzWMQGf+ASLvDM2s2gX5QkWn0+Gn3D/glTfeO/nkO/4BvPdnl75Nmeeci56gu/5yrHVV6iZ1PfNeP+ai0LN0l03yun59ntfLcWBlJiYfj1TXiCwmwSFHzIyfh3FGXSt5EVcL1xQkn0//TEPyvh8Q4Y7cbJw8IhEBliZRp1fBBExDSsn3Ru/m19xfm6rk+5rk3cYWGU7lcI2bR6TWKMnXN88RS5vo5vybkFHyjlbd/+Mb76VTu4cYD3n1YZ660WPn2jBRHPfnizM3OaBnqVyG6xslvzgGnmlb5ZMM15hV4vXOKYrJvxAQJPvKZriENNhkQx4svVvwVqLX1rmGeEoI48YXYH+xb8hh8dQTj9FkQHDpVXgbyoSqf0Mptd1uxCVN8t4yST5Xw7Th5EhelMI1I4NDsogEhwgPkS0O19hJtXCSr29a44ZoBkZ52l5AgjsyyNvJIxX/bp5Xiv4QJN+NUnbYY1306Uzxbgr1wPtgfZtM2FhVSV5G5PZouGa7VeMam8R780lelmwNAFqBy4+9+cv4XH6Jm1/6JH/80GXeaD2MFDYASX++OPPTDqFuIjNKngrhmlTfCE5ykpQh+Ru96YnvW41TQ/K1kgOlQV7bVkmr/ovXnS7s6M6+dJJYksEBNw+Wv1J56hE15OLCy7+a5tm7AehfVyR/Y7/DWaFUljdDoR4JJSLMTyhcY+LcgUiISwlJK4+JhUeMN0x4jiGKQnyhyNBJKih5KQm0knfyGeGaIoRRIxHuyLbdPCSzfHA89sUG9UP41+z1EraEcq6cFtuO9HeqvrZNJhyErEbynozInNrIY1sNj6tyc2HnqikPNfFzgO97wz08590H1x7hTz75JF/rfI747q8DIBvM/14HaYfYkHyg9qlcJTULSaxJ/gQnSRkBkWTyxObzHganhuRbeZvEGyV5Y3HQ2XvxWhuYpg8vGyO+PMPNBuzuL5/ku8+oTteLL3sd22eUP0mklVp/96nidZ5cIsmXVFh+QrYG5bK58oAJK4tJhUss/GED0xjKpOMki29CMg2xhFJx466XBiZO7WolL0r75+ZREf/ed3ZoxdV7SG72Y7ZRJG/qycsw5cbNzbMqJl9RyfsyRo7F5Ldbqut1UThJFuGa4UrAtS3ue9XfYkvucc/1D1CTIfLL/i4AeTjfXrmRd4rr3XY8cilGq6RmwJRy3gqShxdGXP5UkHyUZqzRJQ1G7XHspqqZ7+2doEnZ9S/A41Pi5UtComOT/jjJ62qXk2jqcG98npvOWURtg3OtgGtys4jJxzcUyUdWnUCGyxvKUm5IqjJp6AhwSiuPckjIzjXJW97M+HkeDVdSfrpYyZfLQF05/UI3ytPxA1LhYulwjZQST8ZkOjTS9s6xWXXMHnCzF7GlE5hJb0psu68qzvzWFjl25cSrT4QcU/LbDZ8rcgt/cHWu2Vjxt7qjN4mXf+XfAuCHnfcghYX35d+hXh/OCYlJSYsumRF1QhDjjIS7ZuFWhGvibLhK3H0BxOVPBckf9GM26cIYyXvapKz+8O/BZ/5AuSnmS54D+aFfh3f98HI/s4Rcq64gHyU+U8Y3bpt7XFw5CLk7UUlXgLNrWqn19Y3y4BkA9hv3USMarTc/BkY8109IyZfDJmV7YUXyHonwC2uFceQl0vHz/sJYa9RfTPImJu/5dUXy+lwmmSQgItOquR+cYyevbs9xcHBQDOnOelNKiAd75AgI1smFg5AVOnizFJcMOUbSazWHXXEGNxvAnKojo+R9fzSmL869CoDXW48hLr4Oa/0SmRRzPyuPerhkRacwMBHumvlnaCXvrZT8iwudThdfJIj6KMlv3P0VXJUbXPj8O+BdPwS//SZ4+F1L3XbY21duehUy+0eBHCglVpejJG9irbPivUfFp5+6xv3iefxLrwaUP8mevUVNd706nWfJEXSb91IXUXXb3QWIS8paLLn+3qB8QyybWdl5TGp5pJY/M0lKqaKmSZ8wmX9zi/VNoS3r+DPCNSZE5Wolb1ZlUZoRiJhck3xUP0+TPnJBCMMgKtlr51OmStnRPj3RAMsmtxzsCjF5484pxpS8EIKep8TUvFp5mcUk0sZzndEnGttgxgje940gBD1Rx5qT9+geaKuS0vUejyWuZ8EoeVtI8ooDzMmzQ13fUZqzFqi/c0XyS0JPO1DaOgZvcOmue/ijN/0lrwp/m9/4sv9LPXjzS0vd9lOX9TK6fzIeOSJSF2mDQdEuDdDvaJKfRSBHRPvZz+GJjNY9ry4e6/lnaSY3IM+p95/npnWG3F+jRrQ0J8qyj/uym6wMnJKiLndH2jImE64i+Rmqm1J1U0sM6C24uZmGrgPRKqpsxiHTiFwKPE/dYIYknxMQk+uxdmlTVTiNG8XNQtIudUeHk+EaN94vyg9z4WBVIPnCT8irTTwXN7Qt1bxa/iwmxsG1p1DOuS9X/9/3jQAMRB17xvAWgP6BrvMvkXxaUcmnyZDY4zlTwEbwN78Kv/n11V6LUvJn1wJsS7wg/GtOBcmHmuTd1tbEc//T376P7/v6r+D/+JTDwN2sNnT4EBA6CRd3Tia5a2mSbzGgW6p5jrpqGb7s2GKuPeTrl4Ykn9TP4ZBC/wZr8RX23fPgNagRjfisHwflC25Wh+hx4ZaUfDm56+QJueWRWf7EQJFinzTpSCxa9Bks8Hc3s2K71ppS8tPCO2lMhIvv2mTCxdbnMk5zasRITfLoWaqD689U+jtlyUfGmhL28JN2MfA+F9WUfKRvWtYUks8buut13rWVql4Ez5lCOXe9AYIN9T8wsOpTq8kM+tpx1m0Ou9sTvJHE9SyUTeDiioO/o6ufJz9EqXKc5gSuxVbDWyn5ZSHXvjX+2vbU5//lt7+S73rNRZ6I1rh55ampr5mGX37r2/jt//juua8xDUEH15d78zBwY3WR+iKhW4rzRj1F/u6Slby9/6T64cxLi8dys5zuXGYrvUa3dgHLa+CJjH64HEI2AzRyKYqmpWXCJDOL30tK3pExmeWT2X7RwDQOS9/Mo2CbJouVfKITrwO7hSXk1PI+kUXEOHi2RW55o0pexKBDI/aGGbNXjeTpDeP3djxJ8vW8Q6zLjXPLwaoQk4+0krfcSZK31tVNaF64RuQxMS6OJSaf/Iafgh/9KJgxfnZjspqsvC/GZrhM8sLFrhCuyUohmqQiyT/x7BXyQ4Rr4izHsy22m/4LwqTsVJD815xXX5yL5y9Mfd6yBP/bP3g1V+Qm6UH1STTfeeXf8ron3jr3NV6mFdsJVfB4yfAiHXSH8dWkr372ZoQCjop671na9gZoj3MAR1/Eyd4znJXXiRsXsXw1VSjuL8dnJtWJ1zb1kSqYZSHOFHFmutmm7DbpyoTc9sjtYORGUIad6hVb/Twt0V/oKZ/pWv9Qk2k0baxgFhHjIoQgs72iCzNKMxXi0STvb6rjn1UYswfgDhTJRyLATUZJXkpJM++Q6IH3VZV8qm9a5ryXsbHW4oZcQ978wuwP0F3FQkwheceH1tCJPLYbk9VkJSS6Oqi2NiT5zHIrOUuaxCsMh7YsgojbOGSVizaiJMdzLLZb/krJLw2Xvgq++zcRGy+Z+ZKm76gEYoUBBwa+DAsSnwUvV1+UcP9kwjVBNkxAhZ1hfDUtSD458pzMcUgpORM/Rzu4a3QftlRD1N4XPoonMvK1u4uLPRpUa/FfBEPy+7KJU1HJSyn5xNPVDOiiNFedrrqBpmxm5aDCNbkdzLxpOprk08Z5mgwWhmuM/07sqbhxNMV0TaQRMcqwK7fcIcknOQFJoWzXmk125boarF4BfnyDvtWgY2/gjZV79mPlQCkDRfLSclQobgFiHX5yvPrEc9tNn/fnr4bP/9nMBKXIEmVrXAGp2yTIZ5N8pstCGyWST4VXqZw4L6n9qpOkXH3uqyR2AaIsx3NstpteMQ/5duJ0kPzGPfDa74dgbe7LBv5ZGule5Uk4jkxwF6jKQDcEndSYwXrWIdOnKSrVPGe6hNJCVp/sswA3ezF3cZWodc/I42vbF8mlIH/6IwDYm/fgBMocKl2SY2Sql84HNCt30n7ksWd532+9hYefWVxeqIgzJnGNre+okpe2h3RmK3k366vz0NiplHjNdbVQrst6DUmWYeUxiVDzUnPLK7YdZzk1IoQOjazXXPZkc2oSdRoayU367hlip0WQjZ6fvV7IOr1i4L20XOwK4ZpE/z2OP0nyW02P/5x9rXKC/dJfTX2/KP2ti5C5zYlqsjJyXXHW3Bjm4DLLK3Iacz87OTzJ+zo/kFVotgIVk/dsix0drrnd1gang+QrIl01xYMAACAASURBVGmcU6S4YMCBgUuKu6CrM5B6jmdvjGguPwR/+KPHrstvyB4Htvoyj/h5lKx/qVBVUAXPXT/gAjcQpXg8wNmNFjdYY+PmQwAE2/fiBkrJJ0vyfjeJ0J7dmllXPg7xxPt5i/v79L/w1wtfG2dKyRuSLx8zj5jc9pFOoKpapjR4eVmfyKpj1ddpVVDyuUnUajJNpvjxCN1pCyBtv+jCjKMIR+QIrZrXag5daoho8aopyyXr+T6hv0XitqjloyTf3r+BJSRWQ6lgKWzsCkrehJ/cYLqS/0D+laTe2swSZStPyEQ1JZ97LepyMJscwwM6skYzGNbsZ1Y1JS9LMXkzknARTI9KHFUk+STl5enn2Wn5xFlOe4pJ3K3EHUXyYq1CFUAJrkzw56nKLClMtazxMYOf+2P45O9VVl/TECYZa/ToBGq/01LNc/mCzysqkkW48fwXsYUkOHv/yOPn1wOuyM1iCd06/1Lcmgp7ZNFylLwpaQztNfyKJJ/p45EcLM6HRImqPc98FSM3bpNZLtU51Eo+EAlxOkngft4nses4tTV8kTAYzA/jSR1+MtYa05S88mdX4RpF8uq7ZG4IppKlFbh0ZQ1rmkndGA4GCVu0SYNtUm+NpuyRlBw3B7r80GtqFWy5Kt68ACYx7s5Q8gkOly9+K3zuT6Y2s1lZXPytiyD9Fk0REsXTlbmyGa5jlZK4ueVWKkLIS6vetOK4wLpU3/uk4nX2iuQR3vLMP+GVfTV453bH5e8okvc2lG1usl8t+eqR4DPnBJVqeb3xCVTGsOkY5YCdfkhLDIibar/L/ilWqTknXpINQP/K4wBsXHrZyONbDY9rUinSfdlg68wZvJoK1+RLUvImfBJ7awTzjnn5PTpklXUqkHya4xOTmlZ4vfSO0xyPBGn7oMMj0RQC9/OBJnn1/mxKk9HIvulz4mrFnE4L15SUPLanSD7Pi3i+IXnbEgzsRiVjtJu9iG1xgGxsk/trrIn+SOmtqTE3lWjSciqFa8zKxJz3Mnaaqov189vfqprGpth8WLK6krd00r/XnX6MnaRNT4zuR255leyDyyRfaZJUllLT38ekovKvp2q/791T1sm3Oy5/R5F8Y1slFLu7i2tepZR4JNTmqMq0NAaunowlAM1q4Rgkbzr75Joi+fJEIqdkd5sMltM8lN18AoD6uVGSd2yLA1f5AD3PDk3fwTckP6dp5TAw1S6pt6GUZYU8g/GTEb3F4bdYJ16lr8I1Rskrkk/B8UDXpY+PH0yznLockDoNvIZKWKb9BYSbDgilS62hcxdTbsS2VJ22ANLR7f5ZTK5Vs+0NK1liu4k7p+LEYK/TZ1N0sZpnwV9njf6Ip3xsHCj1wHtpV1PyuV6Z+LVJJb/ZUH/DX6evIA62eO6D7+CZm6N/rwrXVFPyVk2do1kjD72kw8BujTyW236lnpFyuCavEGPPS9d4VeVvrDG2rqkw4u0uo1zWjNffFUJcE0I8XHrs54QQz41Ni7qt2Ny5SCJtBgsGHADESaxCF0QjtrRlhPpCb8sazWxMdZia4YrJmmkwTR9y/W69weEXzi01i8RL8l93208T4UHz3MRzg0CR/HX7LEKIIvGaL2kVUVS7aD8SWWFIuAlZOYMKidc4wRdpUVViRv5FSYwrMnCCQjmPx8/DVM13Td0Glk7u59H8MJxIBgzwCeqa5Kd0Vzp5PCQ+W/+fRYWFgO0Pa9JTp0FQgeS7N1W3q7t+DmobtMSATkkExF31nWpt6oH3FatrDMlPU/KubbHd9Pg/P/wc7+x+FVvP/SW//J5PjLzGzhMyq5qSt/UxDmeMAPTTDpEzuh/S9nEq+NGU692rDP7udYYr9KwiyZvqsODm59hh/9SEa/498G1THv+l8rSoJW3ryLiwUecaG6QVwjWxrmt2RM5gWo0zEOoRZZfFWdZlB1kexWcGGx+D5E3Th71+kQxrZCKRn/UIpVrqV60SWIRW/1luuBfAmvxaJHXVENX2dWOUGbO4NJKPiKSLE5jSzMWEZrqN/WixpYSpiTZK3rTAJ5p8LccvGn2SMT/7QZzRYIB0G0UFl1xQOirSASEeno5hZ9OUfJ6QWeocCq3k0zgsbpzlSpbca6kkfz5fdUf7iuRrG+dx6uqG2S+X3mqSdxujMflFbqJSx9lrU0ge4Nf/0Vfxb/7b1/BV3/7DBCLhZXsfGP1bZUJesbrGbejegv70cE0t6xalsMX+2R7eIcM1VcYFjhy7qoPCS13TX28/fDpIXkr5fuDWTpo+As6vB1yVm1jdxYnXcjdcOKPhx9SI73kXsYTk4KYuo0wGMNBLzWOQfNzVnX2tbfrURiYS+Xmf66zrfT1+uEZKyXbyPN36XdNfoAc292u6u9HUS1fwVq+ENCTCxdUrhFnHvAyTiGwki796JiZueWqe6pDk9Ug/xy+UfDx2Uw/1VKjcbQybxKZ0ko7sWzogEn5RjTItOe7IhNzSYRpN8kkcFqp5hOTNdhdU2MRtlZ+on7mAo71d4pIatXrXVCmoGXhvuzgiJ8kWhGySAbG08b3pRP01923xPV99F6/+mm9lz9rkK3ofmfhbM6tauMbTN6e4N53k67JL5o2WS0vbq+YsOULyi0l7UDp2VcI1WS6HXdNug2/2Hrnt/jUnHZP/p0KIh3Q4Z3Pxy08WrcDlhjiDN1gcwy1n0sMZo8gSTfKDhiLGPWNtUJ6Sc4yYfNobdvYNrPpIHL4u++wJdTFMi/ceFtc7EXdzlXR9ekOZ2HqATAo6669QD7hKcc8afH1opJHycampz61C8qZBaS1b3BBlrIxtr0YivCJumiRlkq/rXRlz/EwyGiJEek0wK4EFZGtlIbEIilLTfEr4yZUxuSY+o+STsF+oZrfUXSoMqS3YrtQeSv76ebymbsQq2Q374TUOrE2wbL2jKoSSJvPjxiINCfFHKlqmwrLYd3aoZaP7acsEaVVT8r7Oe0xNbucZLfrkumO3gOPjkSxekZRIvsokqah0o8kWHCPQvjWmoe6Bb+ZreYjrnZPxYqqKkyT53wDuB14LXAZ+YdqLhBA/IoR4UAjx4O7uyTQUldHzdmjGi0m+XENb9gYvIzHJN91p27mh3f/KJZrHUPKZbt9urG8R2Y2C1MhzGgzo2uoirlQlsABXLj9DQ0TYW/dNfb527gG+Nvp1Di68UT1gOyQ42OlywjVCk7whxbhCk5VJRG7KA/Jsfj+CuRHaXo2k5PhoYuXCDbC1kh+fMTuIM5qESsVrkrcXVLrYWUgsfDyt5OUU1eig7BTM9kGJC0PyTikmb9Wrkbzo6/xEYwe/pSt7SiTfjK/T9UoeT7YJ+S0i+QFRxcRpagcTTYRq1VLt/bWWTm5PGQFoKszkeOOj7WMLSbyAiEdIvoIyj0dIvsLrte9QLhx44M1sy5sEB19c+L6TxImRvJTyqpQyk1LmwNuAN8x43VullK+XUr5+Z2fnpHanQFQ/Rz3vjZQ/TkNauiiTGVNqMv14cPZeAAYHmuTLRk3HcVTUnX1Ba2vEzyPXF3qoW+aXEZM/eO4xABrnH5j6/Nk1n2tsstMaDn0IRYC1LJLPIhK8IiY/ra58HOZ4BCKhPaMSwyAzFSt+TbfAqwvWHDvL9XEMyY95moRRiC8SLG8Yk3cWDPN2spDYCopqFDllxaM8c9TxtErhGlNnLtxhuMbRFSdxf37C1w2vK291v0VNu7JmOrYtpWQju0EYnC1eLwzJLzDgEmlIhD/3NQapNY3kU/KKSr7e0t244STJ93TFmVWbVPIA0aLvTX44JZ+WjndeQclHWUZAQmr5cP83AfBA56ML33eSODGSF0KU3cL+PvDwrNfeSsjCUfHK3NeVwzXjJXUGmSbbjYuq5DDSlsdF0hXoz1gFVIGI9olwEV6dxBn6efR0Mij21UVcJba4COGu8tnfvOvlU5+/f6eJJeC+nWHiLbaCwoXzuLCyiFh4RbNNlU5av+Qr1L4+v2LKdNQ6Xp3M8ooBIplesVmOX8TAx5OksV6xiVoLHJ8EF3cRyechqRXg+zU1f3TKOfKIkVrJW+4w8Vq8tuT46OoQRr8z/2bmRzdp25sgBL4O15jBM+0wZYc9ssaQ5E24JltA8nYWkljVSD5z6vhjneIuCdKuRvK1xuxVi6k4s+ujJG856jgucpYcUfJVXCtLq4kqtgYmXJPbPmzcww3/bl4Tf/K2Whssq4TyncCHgFcIIZ4VQvwQ8K+FEJ8RQjwEfBPwk8vY1nFh64aoRW6UZXWczqikMCPh1s6rEEfe1SRfUvLHSYo6UVtN8EGZNtV0e7W50PO6WnYvo+NV7j0JQOPs9HDN3WfqfPhfvomvu3/oFxJbtaWSfCq8okQvixaTfCAH3EARWffG/GS6OUZuUFPDQUy4RhOq7QU4RZJ09G9K9IXuaBUf2fW5VrgAbh6S2gGeYxPhTq7opOm0VcTpaJLP4gjM0JQSyfs6GVk2qZuGRnKTnquTqmY8nu663t1vsyU6RRIdwCqU/PykpZ2FJKIayedOMNG17Oqu4ioQtkOPYKqNw2CKlzyAcIbhrrkoVcCJKvbBpWlcVeyGjU20mep1ZftreIN4hG7/ZOYWV8Gyqmu+X0p5QUrpSinvklL+jpTyB6SUr5ZSfqWU8ruklCdjuH5I1LdUkrRzbb43dznOPZ6IM8jjHrG0qTc36FJHmOlQ7ec4wDTBHJ2AnaRNz9L16G6TOmq26ECTvKUHlS9DyQedp7hubY0QyzjOtoIRq9jUqlU2E1sEO49ILK9IvM465mXU5YDrnrppR/vzv15yTMmbFnhD/rYXDOPnYyRvbvKmNyC0m3OtcAE8GZHZPkIIIrzJkYZGReowg+XpKVBJOJyM5Qy9WUycOpo2mLuEtWyPyNcE6DXJsApP+f1dtdpxNy4O36BJflFSUZ2faiQvnRoB0VC9SolLWqxaqqBPfaqNg0ki+40xkjc5jQWiSuQxmRTE0q7m+VS2D6lwncW6s9pM9Wpf/AYaIqLzxY8seOfJ4Y7qeAVo7ajGot6CKTvlJMssVSmSHgN86r5Nx17H1dYGyf5zPJGrJfFxkqJ+2mFg67psv0WTAf04Ky50Z001LcklTFJaD59jz7u4+IUlpHYNN1+OkjfDtKsq+TRVteudhnLMTNrzrQ1yfYwcPyC3/GJsYvG4GxShovH4eaab0FwdF0/sBrU5VrgAfh6R29omQXiIsaV+sQ0dZrA1SWVxiMgmwzUmTp3MqPQCVeq5yQFpoBOrlkVf1AuS719XVsX1M5eK9whHk/wClepmoYozV4B069SIiE0y3NzQDkHyA6uGPWU6VNI1FWejxXoj4a55yFJSHBLcSiRvJV1SqWiySninCNeYgS8X1KDy8Oocr/0Txh1H8me3t+lJn3jBAIbyXTufoSpF3KNPgGdbDJxNglh9AbOD53laKgLOjlFiWEs7RLrpQ/gtGiKiO4iKjH9tU+UXplVuHAZSSs6ll+k371n84hJSpzYRez0q7Dwis7yiQ3RRJ21v0McVGfn6PeRSILsLKrNMqaRbJ7f9wtbXNMSMKPmxm6bUas7VseLEbVGT/blxVp+IzNgk4E9MuzI+KKZ00tEkn8chVhqR4AzLHIHW2ga5FMUcgWnY70Vs0UY2hgUMfauJpyuBoj0VojRCB4bhmiyZH65xZERmB3NfU8CtUyMmNINVjkDykdUY6eo2yMxg+7XRUZ92QfILrrc8IcUmFi6iAmnbcYc99AyCKiWU2ejoxrUdFTmo6pd1ErjjSP7Cep2rchM5Z1QZDBNyMLvF3kr6DEQNIQSxv0k924c8xx3s8ozc0Z9zdJKv5x1SbY0rdDy419kn1WquubGjVMYxlfzuzX3OiT3yOUNXpiF3AvwZM1EPCydXI/hqmuTlApIf6Ni0Xd9kXzSx+gtIvhQCye1hTbUheccLCj+ecRdFqXMvpkknc5u06BOlM8o2c7Vkl1rNJcKdJPloNCRjLAzyNMTOBsRj8e/1uk+XYMS/aBz7N3dxRYbVGiZWQ7tZDA7J9Xe+uT1seBOaeLMFMXkvDyuTvHDrWEIy0MnzoirlMCQ/YwSg7O+RSUFrfVzJD1dCc/ctS0iFTUI1knfTLm1Lj0qsquRFXJzXnc0NDmS9OPa3A3ccya/VHHbFGdz+ouV9ibxmEI6d9giFVmC1LTZkm/DgCrZMuSy3CKVbqXV6FlqyS2bqsnWoYNDdL5pENje3iHArlYLNw+6zyn3S275/wStHIR21LE8W1KhXgSNjMtsj8ANSaS2Mfw70asYK1jiwNnHDBdYG5kaoSd4nJs7yYhXk+jVc1yWR9kRvg9SxYdONm3nD0Nn0bWkLBUdXClk+zlhoIC7ZKUBJyScRdhYRjZH8Ws2lSw05ZTC3QX9PEYlbIvnYaVHTg0Os7hVSLERJ6VsVwzWeHCYTF8H44Ee6oS3WQsesWqogcRoE06ayRW061GkFozcME+5a6BEvUxJcdeOt4D/vZz36jnEurZJ4zVQzlA61bdY9dtnE6p3MeNAqqOYYdIoghKDjbnNfND9GNrJkn6Hk7WzAwFInUzR3OEObm1ee5DwQ184RJe7U+ugqSNKUZqmzz62ZVu/9whlv68wZQtzCUfEwePbqdT74579P3Ouwtv8IXwG0LkyvkZ8F6dapiUiNlKsdTy+o7k/VUdnHRywwKIu0eZVTa9F1Nqkt8K8pjpEbgOPji4Q4y4ubueMFpSTp6PEUJgGorQWk16Il+nSilDONSXUq4z4CislOifCLunwDE1YQOszg+Jrk0wgnnyxXdG1rZjLSYLCnyoKDzWH1TOK2qOsGKS/cZd/aZLvkTWTCNYsqR7xSnHkRzGjIUDe0JXFEAAinupJP3Sa13qSSV17yDTbGOm/N8VtkOiayhAybtKKS97MeN2sXIRktv5yFouNVK3nLEuxbZ9ganHyj5yzccSQPMAjOsd79kJqNOm2wMKNKfhbhuNmA1NIk3NrBExmdZz/LeWDrwr1ET3tHVtnd9j6bQiJ000dh2tQ9wIo69KRP3fdo404k9argS//fr/B9T/5q8Xskalx44DWH+gzp1akT0Uky1mvVaqBnwZMJuVZ6ofAnq1HGYGrX3foabW+LM/1H5r6+OEZOgHR8fBKiJC/Oj6urW1SSdHTblvHn8XQ4J1ijyYBrM0YAJmFPTW7VzVWp7eOPTWgy4w4tTQae3r5MIpw8Ip1SrjiwGvjx7I5Xk3xunBmSfOatsyF7SClpRLt03G1K/a6Fks8XhGsCGRVx5kWwTa+DJnmTDD2MklfVZFOcO+NJL3kYTVzPg5ApmXDIhIOVLybtmuyT+evkPVGpez1Kc3yRFDd4gK63xT3x5xa+96Rwx4VrALLGOWVmNJjTWKLjiCkW1oxacC9T04IAgnW1BN7/krJYvesl9xHJo4dSevtKfVnaZCrQzTDJ4AARd+iLusoF4FUeMFyG3d8lxIUf+wT85Gfxf+YLuI3D2QsJt0FdRPSj48+YdYnV4A4grkTyepB5Y5042GYjX2D9m0XkCBUX1iQfZ3lx4Rq3yAivMC8zsJOuMvXSF64VtJQ7aW864RoHTdOxmlo+7piSN/XcpnTS00pUZhFuHk2tZIns6clIA+Nb09oakrz011ijr1Zb6XVCf7SrvCD5Od8hmWf4Ihkp6ZwH2x8dDWmMvaxDKPnca9GQfeTY+EzlJT9J8o5XTclbeUqKTVpxXGBT9pH+GglOJeVvlLwxuwMIg7NsZDeUqLwNuCNJ3tZjALM5yRBDzl2a2DMIx89DUh13bepKF3f3YRJp88oH7lfdqkdMivbbiuQd3dkXNHXSr3+AnXTpC62WhDtBSlUgojZd0YSt+2H9LvCnW8jOQ7EsP0ZXLwBS4pMUjUGx8LEXHDfjaxI01pH1beUSOafs0s5CdUMUApxAxeTTIcmbErxE+NhjSVI77RMSFKs+Ecx3STQ2GJbpoLWCibm1RnEaBVqQfBrhyoh0Svw7cRp46ey/UfR3yRA4zaFWl8E6TRGy1+1zRu6R1EdnBdg6GZqnsz3lC6sAd3JgyDQYY7XUkHypq7gqhN/CFnLCqM7PhhVno9tUpLrIj0bkRskvjslHYV/d3II1YhxkBeUfGZIvKflCVB5jFOhxcEeSvHdGVRe0r82ZEKUv/r7dxJ6h5H05IDMkv6VI/iXJl7jOBg+cX1ceIkdU8rGuB/a0/4hphsmjDm7SIbS1WhIe1hEqXOy4zcA6PLGXITxjJjbfdnch9DEy4YDYCmYecwOTl6g11hFNlWhsX59dpmZlw3mqODU8kRHHybBW2jE3GA973Hcl7Re5F/V2TfIzyhmTgRnfpx0oSyWbBmXPHADPtYmk+r64MiKbouRTp4k/pz7f7l+nLdZGSi8tLRKee+5ZtkQHOTYQZlgnP5vA4r5ZmVSLybs1MyhFvc/c0Ez+oQqG1WSjq+1a1iVxp5C8N8xpzP1cmZILh8xyF44L7Go/JCs4hJJPMmpExQ0ewGopbogXNOydFO5IkjclZJ3dOQ1R+oSGdgs3m6IqpSQwHuOAo7tPN0WXjrdDzVXt7OIIKhuGXvKBdhI09qsy7OBmPeISydtH2IaftKcqosPAdIDGFQZ8zEVR+aJrna3JROU4CpJvbeKsK+LqzPGvcUodm1bJ8VFkMSlWQYyJ5U8cTy/rEVnDi9bUy6eD6cos1uRmvHCk7Q89xjWyogNXEadnW0Q4kMb4M2rSM69FXc5OSLvhLl1ntBPUeLzsP62so5z10YY324RQ5iQVQ63ITdXMIri6a9msrBLTi3CIcI2pJhufDtWQvaKseGSbZiW0MFyjZs0qk7r5pG0Ghti1dVLhIiokXtNUTZQrh2vcDRU5aO8+u/D9J4E7kuQ3z6umn+jGHCWfRcTSJrFrONNa99MIhxxplrCN4RI5bZzHsQQRRwulwNAetmj60Ek/EbUJsh6JHn+WCq9SKdg4/BmK6DAYxl4X2wLPQ1FpopV8NsWqdgK6ysSvt6htKKXU359dpmbnyhsHyi3wfUQaqzCO2RfLH5nsAyr3EpeUvFs3fufTVzAmTGESkLkT4DF6jspNWKDm6MZaFHhy2BY/An+NOuHU6VBSShrxdaLa2ZHHzb6mV1XiL9i6NPK87S6OyRvbZ9uvFpP3TKmpLj3OStYRVWFWS2F5mHeWUCckN5485W0a5bzgerNkSi7sSoO/B1rJu/V1UhxEhXBNXswtGN4Q6/qYdxd02Z8U7kiSP39mg8vyDGhTrqnIYhJc3bo/STimdhozbNlrFLXNzuZdCCFIhHdkJZ9rldjYMGPabPoEWEmXIO+TuZrkLW+ClKqgnnfJvcmL5TAoluULRuEtQlLydAfjR77gYo06DPARtkNjS6lTM/puGqYp+TQaIHLdXaqRisnj6eUDYnt40fpNHTqbNtSCYZjC1NVLJ8CXyUjizSQI3RLxmZCAz/SadCtQN+VpPuu73Ygd9oYuq8W+6pXgnrKSbm2PTv4yMfl55YHG9tmuqOSDhmlo081QujzzMErejC4sDzzJjO1vsDHxei/QMfkFpaCWrq7J7cUkb+xDvMYGqXAQFcRUXppbYLChO4xNx/Gtxh1J8pt1l+fEOdz2bCUvsphEuOQzWvdjTWyilLDsO+rLt7ajVgrpEUMpAIT7pNKi0Rx+oZUPSZe67JNrZZ+VBmBUxSDOaNGbHLxwSJgBH+NDNg4LM27PkG9uLzY+E0mXPupCWtPVJHlntpJ382GcuyD5ZIClz7NBZvsTSdIg75PYwylNgbbwLQ9WL8PYYBhvfJwalpAjlVYmQeiW1HEiVDVWjajoli3D0uq2O8U7/8lrHXbYx9scDceYcN9m/wkA1s/ePfK87WqSnxOTNzNv7dKkqnnw9c3IdC0b87PDKHlTaFD26unP8pIHbJPUXRCTt3VMPrc83AXjAs1UqKC5QSrcSiWXeeFJNDx/21tbdGVA3p5vb35SuCNJXghBp3YXrcHsGK7QSj536hO2qTAc4m2VvviJdv/bungvAJl1tFAKgBUe0BENRKlxJbLqOEmHJgOk1yq24crDbWOvF7FGf+rFchj4Na0sK9gCz0OsSdEoeenU8FlwsaY9BjpOfmatRVvWkb3ZDSeqo1YRgSGbLA4R2hjNILMC3LFzVpMDUmd4no1RGTO6T42a8wzJF92Yw2RyXpD8kAwS3b0cECOnJDkdPR2qezA50/bK5WexhRyxLAA1OhLgnuwZUiz8tbHqGh2umVc5kg5GcwyLUCQdNeGZ7mXnEInXaSMA+3reqlH5oxu1lLPkgqosQ/KygpI3A0NqrU1dV1/hOjPn2B3e0LaaPrtyA9G9PV2vdyTJA2TrL2Ervz6zI9XKlcKTTo0a0cTsSDPE2w6Gce2ts0pRusazvuR2eFg48YEqcSxv027QSG5gCVlUH6hl5+G2sX+wjyPyicELh4VfH12WHxVmBJ8hX+nWCKbcWMvw0mEy1LEtbop1nDldhZ6MCyVvlyZA2Xk8rLpBx8/HlbwMycvlg2bO64zGJEPyxjbZ5Bqi0khD01HtlUg+FR5WFhLMqEk3xDfoTir5vatPAbBxdtRkrqFJfkccsCc2wRq95B1nsZI3Nye3Islje8o+QTcRFuEat3q4pqZXsFlptTTLS94gqWA6ZukJVVUGf5ucS2Nts7KSLzrlS0retgR7drXZ0ieBO5bk/R01HOPGc9PtDaw8JhUueMqfJUxHk11R33iMDxWerStszFCG3HIPHUoxcJPJEsfEaXAmU0Rmbi655eMuUCTj6BzoNvcZF0tVGFtgeUwlnxQkry8Mt0ZATDZnKLOqMBqSTtveJJhjbeDKuBi1Zypa8nig3C9L4ZrcVjX0BlJKGgzI3dK5sB0G+DPnvMpkQC4FgR79J/T2osEwrFV02paVvHDxTLOTO0nypiFuJBmp0buhVqWmB8TA8ltkUtX3t52tifc5WsnPq64x4Tg3qBauQQgi/GLIe55OhqYWHeD/KgAAIABJREFUodEy1WTD1VKkK878WSRfwarAlilSOEjLW3jdGDO4RmtT19XP7iUokE4qeYCOu0Ujvj3WBncsyZsxd1eeeHTq8yZWK9w6gUjoh6NfnkQrebcc19aTmlhTcdHM9ieW/lXhpx3CsRLHxGlyFqXiTJNUbvuHDtf026PlmUeF8Ko5Ri6CUYq2DlEIp4YrMvqD2bXyfjYaJ+85Z6jHk2EMUETtlwy27ILkQ+w8UTdzjdzxRyphojimJmJyb5TgeqKOPWsEYDJggEfgqoSuaYxJyrmLNCKVFp5XDhV5hZmYmBKTNx7q8ZTBIamxsm2NJl6xrGK6WG+s2xWGIZR54ZpsPPxUAZHwi05xo+TdKTeuWQhqNSLpjkyHSvTfbUJQ44jxKpB8hrQcpOOryVxzulBF1CbGwfJqSrBVEVMmMjDWOBYGZ1lLp38/TxrLGv/3u0KIa0KIh0uPnRFCvFcI8bj+/3A98yeMSy99JQDty49Pfd6WiVJ4uqJgvKvTVJR49ZLCe+0/gjf/r0X7uzwkAUf7l0G3cdeyDrEzmhjNvSaeUCsKV8clpbbNPQxCTfKNGRdLZZhqi+SY4RpdYmcUtphxzMsI8gGpOySdKNiilU2vW08yiS+GHt9O0R2pwjWZNSRaaQe4ZMWYOJN7KXxrNEKrMVTdExvsM8Cn5qnae1MznYSjJB/j4jvDSzATLnXtcTOtJr1RDA4ZVfJSSqyeDgWMNTsBBcknY+WVAI5uhpLZbJVq7LL9+mFIPsAxqtaYwB1CyQsh6Ina0BwOyHWMvLE2uSIBdEhlAcmTklsO2B6WkHM9e0TcoYcugxXVSF6U3E7LyOpnqRFOnVt70liWkv/3wLeNPfYzwF9IKV8G/IX+/QWDzZ1L9AlIrz8x9Xkrj5W/hWda90dPThqaOu0SEZ9/Fbzxx4tflW95XMmz4uqTj2D90lfw2C9/B3l/j0beJfHGSX6o7IvmKMdX8zOn1E7PQqRjuvX16RdLZTgBOQLrGINRYFhbbEIXJnE33tJeRp3+SAglre2wRneqHWyc5fgkSF2BYbaTJ5FKyJZI3sRSzQyBsD+P5GdMDEsHhHgFgReGXSUlL7KIGAen5KaYWR5NabplJ5W88VAfL9280YvZzG4QupvFpKkyjNdLPlZeCaU4+bx4c3J4Ja+6lhXhyULJV0+8gq4mK4XEZLhPIm1azelVYYrk5+dyHJkiLXfY4Txn3oOTdIvkvlLyi8M1VuF2Onr+hF5hpbdheMiyZry+Hxhfi/w94O3657cD372MbS0NQnDdvYDfnV5G6eSJunvrL3Y5aQaQR5rkG7PLEI3hVhUDsecf/GNckXHfwYe58ctv1F7yY1UEJZI3JWaYeupD2CeYemOnfszFlRCEBFjp8cI1plnGKGxDiuFgOslLKanLQVFGChTTkNIpZZRRojy+jZIvfE7SEEcmoyRfVMKofYo1yVvBaOgsshszLQasNCTEL+bhmvBQWpo/qkjeG5mZm1kudWG8dCaVfFBfI5eCfEwNPnm9x1mxT9qYVPGgurYB7PULE8+ZoSHMUfJm5m3tEEo+EX7RRGhI3juEkgcYiDpOOSQWHnBAg+YMx9PUqqLkM6TlIoxP0hz/eSfpElq6e7eCDQIwHA4zpuQd0/V6/dZ3vZ5kTP5caXj3FWDqN1AI8SNCiAeFEA/u7t7axES/fhdnoucmKmeAQuEVXZ1jDT+5VvK1+pxac6c6AXvPfIBn5Q7/6dW/hYx6uCKDsc4+UYr/1/TSHaOODmGEZhqtxj//KIiEv9BnZhFMi78bGJLXnjgzOmnDMFIVKKWbnqMHZXRuTPqDGNMoU+ViHAtJQ1yZkI+QfE1vW924TJ22CMbzI7PnvFrpgLhUsWPCUNmIko9JxKjTd3k/rGmVLJalQhjjJH+jz1mxh7U2qdQBYt3Z7G9cnHxSCFJpze3mlGlIKq1DKfGkPP9Xd4+7jj3/TWNQrpvDY2xFB3RpYFvT7cGrWBU4pEjLQegVTBLNvm68rEfiHJbk9bU+puTr2i+rO8d646RwSxKvUg3DnBqzkFK+VUr5einl63d2JhNDJ4rNe7mLazx7c1KJ2vriNz4cE/4scY9QutRrc8rC7GoNGuQZ97Q/wRebX833f8/38qdv/I/8fvp3OLj0jaMfVxuSfMMMMnYma7AXItTLff94zVAAkVXDOaaSL09ngmEVxyxPnJ72FCk3onnav6Z3c5Lk4zjGEXmh0o0FMGmEIxNkaSydVZC8dlDUZXSOP17p1KKWzx4mE4uhkiuaxpIxkmdUkZZXFI4/Ga4BFcKwxurzn7qhlLy/eWnqexLt9dLcuXvq8yn2XCVvJQMi4+BZEZkd4JnQSRaT4ODZh6Ob2BldLTlxh741ezWRWR72gjJHR44q+fLqahxB1iV2TBWbh1MhXFMInjElb2a9hjdPF8lfFUJcAND/354i0TlonH+Amoh54qnJuLwrE3LbK+KQyXiZYNKjR0Ddna1OREWV3X36E7Rkl8GlrwfgH3/r1/DqH/093vSN3zzyujLJB3qIyNCHpTrJ23GbSART47eHhTITO+YgcU3yvh6kXVjVzuikHXTN6L+hum7q8FW3O6n+jSovKlac4XnxGJZWwrDc0SRJU12n7Yyt2DK3SYNZJD862cmsUPLSORpxxdTIS+9xZlgIRFYDeyzh+9T1DjtiH3uGks/1zXx9566pz6fCQcxTqWlIOGWIyTxk5a7lLCbGxZqhwGdu1mmOjAD00/ZUL/lim8LFWajkM7Cc4tpM5gwZqclS3sd2cahC8tOV/NbWDqF059qbnxROkuT/CPhB/fMPAn94gts6ErbufgUA15/5/MRzTqHk9Z08HCV5kfQYEODMUycVwzXXPvVeADZf9abisVdeWCMYu4GYipoQD/TYNuPRfRiSd+I24ZyL5TBI7BruMcM1pmbc1+rV02Q/q5M21O3m5Ztera7eE0+5MSRjHbXl8+LOUPLmBmPcLt3aeBK8QV2GU5Pqbh4SWyUlr29aeSlBXfRhlFDeD3tGkjN2RkMYAHu7l3HIi/6McQR3fSWX7QuF/cM4UmzEnBpwtTI5nCDInRo+2mI4S0iPMIQudZvUZJnkO0TO7NVnlcZAhxQsF1uTfDrDtVJKSUP2i2IHaXs4FarY7DxSA2bs0XO73Qq4JjcQ3enWBn/2az/G3/z57y/8/KNgWSWU7wQ+BLxCCPGsEOKHgJ8H3iyEeBz4Fv37Cwr1c2qmae/KZEOUi7r4fWPCNdbVaSV9BmK+v7bxSJELphxZT/5XHssv8eUvf9nc1/lavZuSOCgp+QVjz0Y+J+sUcdrjIrUW+8wsgkwjIuni67pyU5aahtOVsvFxd0vq2sTZpw1ON4RdVKxYDhkWVhbhko6Sq0mS6tpwk2A3N3sD4daxhCwGcpehJjsNSd7ctMrDye0xOwUYJflZ3aWJ08TPht9FKSXhng4BTCmfBHjNd/4TLvzs57Ds6avOHHtudY2VhSSHVPK5UyPQhCvyUX+gyp/hNqnL4fGt5fOdUzPLnxs3z7IMW0iw3UIcZTMSr70opckAzGrRdnErhGvcPCSxJhPMnmNx0zqDO6Ur+/r167z5+jtwn//4ws8/CpYy41VK+f0znnrTjMdfGFi/mxyB2H9y4ilXJkjLJahrJT+mKu20PxJ3nQZD8nE4YOYlksacP/gkf157My8P5l8IRVt7ydvcTBZKwmpqehBnNPPpntxHQerU8OUxmzzSkAiXpi45DLR7Yz6jycrUiXslkh9OBpq84WTjHbVC6IEuoeoxKDk+2vpzzA1GFlVU45VOugKo1ylIvNiXPCQrmXH5WpWXLTTsPB65EQDDMBLgBtNJPnObNHrDCo29fkIzvg4eM5X8IqTCmdvNaWcR8SFJHrdOQESWS+3ZfwSq8Vv4IkEmIcINaMgemTdHyS+wD07iCBvAdoaTwGaIo3a3w0WRFcUO0nJVqfICuHlEak8/Vh13m3PRpN3wFz/zN2wLycYDb1j4+UfBHdvxCoAb0PV2aPafJcnGZkmSqEYjnXiVY4lNN+sTWQuUvInvzgmlZM98jEBGdC+8ceHu1tYUyUel5JOZEZpW7Drd68esiR5yCUlXgNypE0xx6TwMRBop730dszWeOLPG+ZmyxjLxuqXxeeMwx6Zce57ogd2+SEdyE8ZO13R5EnWVRUF9VEGaqU+D/qRJmScjMnu4LT8IyKWAspKXCZk1dlMvkcOsmvRcDw4xFWFP6qQrAK3pSn4RMmyYS/Lh1Jmzc+HWqImYME6w8oRUHJ7kTUVTv7sPyQCfhHy8rLiERX40ZtassN0Rk7pp6GunT9uY+Nke9oLmKVCW1rOO1cDfYS29PvF450sfBeCeV33d3M8+Ku5skgfi1j1c+v/be/Ooyc76vvPzu2tt79K7WupudUtqoY1F0IAEGARmzGJs4cTOwMSxxyGW7YMThpNxxjg5OZkz5iSOnXE853hIsIlJMh6c4MGG4BhswBBjMEgQIVothBZaTUstdffb/W613e2ZP+7zVN3a3q2q3vet6vs9p4/0VtV776333vu9v+e3fL+8wDMLmSVwkqQjz47Xsrjrnup04zqhvTbJmwtprVTKwunPESth7vb71j3Wkpa4DTOKiEYKINpguuZKNWCWGhSHb58ETfJ9BNw2A9MzbtB6sA4Ysmpb/7UF1lxvI5F8OzoOxWv3YGfI1XS1mClPCVepUqDodZKUUR9t9hnY8lWzw/Sj4Do0cdu6JoCTBL0Wf5l0zSCSl8IMFeqsBikpn71c5ZCWuhiUrlkPsTjIGqmINDrdXI+7WenUa6u6/rD5In9jLk1fRl/4561p135a8gbKXluPxkgeY7k47uD0HkBN2w6aWozoQCBY5z5zVTCQ5KPSQcqqBl0BmX/xES5ZB/Dnt7YSWw/XPMnb+05wTC7y+PPtmzUMQyxR6c1vOjK6CMdLaoTO2qp8LQJeI5JXT3+R0+oELzl5fP1jNaYRTrtoaneR0npYrIXMSq0doQwJ5ZYo9RFw2wwkbnTmbHWL4yBNHEPyhZn2g8ovmrbIPiRvpG6zBh3itnLb4mRJXhdJ9d/TCqrU8DvkB6BdGA26B7biEJeIJKM9Y1mSFsszDyBHBZ39+enO030rwfX6E4UU5ihLk6XV9PjOLtQ4ZF1FlfZ1pHs2g3iddI2bNHpTS+vAtKk2a6tbjuRvvfcdfCR5B3Pf/ijhF38daGvq94NyCmtqxIea5MV2W2nOZEDh1UyFe8Y7wDbpncFNFEopvYob8LfSD+GsrnwjjDnaeJwrc3cM3O6wuOZJvnL4JNfJVZ56rl0QaUXetgeWRROvJZtq4CUNYnttkjdaHeEgAg6q7Lv6CA87L+HIng2YJDs+kXjs29e2GnS8zZH8lWqTWaoD5Vo3Da3SWQ+2TvKpGFyG8Gw39V0d0Hqq9DBQ1lDFc5xUT7zPdLEh7KweeiQehbhX8dHk180qwo6q1Cj2tP+5g6wP9e91m34EeO1pSNrdW10bBdLuKbH635qOjixXdDrh7OUqx9xlpI9kwUYR42CtFcmrZkeb6UZgVjpBfVX7qm4+kj+6twRv/mf8efwK/P/+EQCc8hpT2jpdM2hVGUcZktcP0UHG36a47+vWXDMZvNbwVBgrCgNcvQBcPYy2nLEBPPP0OY7L81g3vHzgdofFNU/y7r5UcthafKb1mjmRppe2IT5WV4dMQTVI1onk3T7j7B04+2UcIhYPvaZjvH0tOIfv4vDJu9s/6wdJvI6BscHK8iK2KPzKaPTixC2tqxi5Hqy42dNO2KT3b97aZ7BKU7mt4hmAa0s6sNPnwZDoB3QHyVs+ZS0GZmUiYOMVmgR1SBKuWz3D89Ir7NWyPmx0z09oku+SJWiK19Y1wcxhdBKnGdBprEGIpo22vrxII4z5b09c4qi70qs+uQkkYq8ZyaeesxsIQjIw0hRBY1XXH7bW4/E/v+4Wfu/Qr/BYkurku2uRvOPjSUwQ9f8ucaijfNtdsxsLINRtukbX3kzIRmE7iHj0Ww/yuV97F80gfa0ZxRSk97waFPamJL9yqS2lcv7MVwE4dNs9g7/XkLjmSZ49xwEo1dodCyaSN8v4QArYWcJJktRQ2B08fQftjo94QG65/u1PsqyKzNx238aP94G/gNe+r70Pz5DSxki+vpJqrg8rM2wgrYht6+p6dp9iVVN8ZADJW+Eqta72VdEdM/08dY3VnvEBhdQbt6SHmbIPCy+b9jn3VfYGz/Hn7n0923RN6qwrkjfCZtI1DBOI32Hq7hJ0tExmjyMY3IuFp+sQtdWrfOrh51ishRy2F4ck+bUjeZ/mwOh0EJyWHEgVe4uRPKSGG//H/3gPP5f8Iz4U/QgcfvEaH9Z58wFBVayjdst2W966/Qr10HakMkqtJpKPMg+F5dOf4c31P2XhuXSYMoiS1GBowN+qfPgkVeVjn/3L1mvBubRtcvam8XTWQE7yLZKfqa9B8hlFvfQDur1uHWNjU8TrS8BJjDz+Gb6UvJRTN2+tYAbtrpJBucVuNFe6ugaGhL2GLPDZy1WuVNcXZ+vXMx5ahYGaOFaw2ndGYZAzkGpp47QfyrHlpwVoOknedx2ayk0j8od/n7pV4q/93s4n08sfd3UAmcheuiL5UDzsjEKi19Wfn/6ObrldgxCNZlFzdZGPfuUstx0s49UvDUfy1jokr4K+JiZrwdZtsGFzVUuEbL5P3uDmAxV+8odew69F7+bQnsE5ebMiCxr974VYd8aI47ZWwIPSNcYwxLQtW7rw2pGT1zWj6mLaMRPEqUZS0se6EeDw/r18JnkV+85+GsI6Sinmrp5mwT0MpRGlT/sgJ/nyfpp4zDTbxRDT8mhIPrIKOEmbcMyATLf8bDfM0r9vm9b5BykECzzov4YX37D1Tpe2ouLGVCiN8cIoxMkgczN3mVo3wpgf+7//il//bO80cTecJCDuIrxQ/M4HawZuVKVh9T5gB0byfaz2YstLh11ozzMA+I5NAxe3eQUe/WO+aL+O2dnedtNCsT/JNzXJdwuMRZbfHnlPEj2E1UmcZgqzuUZPekk7Jp05+yxnLizzwKlZRMUwRE4+EXsgyUeRUfDcoPWfRkuvp1EbmuQBfvYHbuJLv3QfNx0YfM+tNxgYtwqvPp4OTgbeN0YEztcKnjpdk02LmiHHxoom+ShJ+/oHRPKH54o8e+P9+HGVFx78BE9dqnJb8hTV/WusTkaAnORFqFsl7IzIVqSf1uamSxX12ifX+Lta67jX+wUTZfdGpMGj/4VA2ZTueuuG8/H992G6fzYWySd13W43MpI3y/LOtMWfn3mBq7WQF5bXPy5H9bYThnYBZxDJx7UO6z+DSFysfrLOkamxtEk+sQtpBxXtgTJIJxMbeJy49AUIq/zO6mt42129rW1+WY+7d3UAmb9Dt+lHZLWldzFk36Ud1Lre1iB5kyN+4txzzBYc3nZCXztDpWsGa6U3mvXUU3iTkbynH/5xUMVRUW+ReZMQEW7ct/b9ZoKyQY0OkY7kbddpyx4PIHlprHTIh5htx5l0jWnGCLTBeKDVTtda9bzrJ/42F9jHc1/6KN9+4imOWpcoHX/lmt9rWOQkDzStUoceiNGzMBd2qqjXJpy6dguy/LUj+YLvEyq7t7ijFOHpT/HV5E7e+JJbhjp2k17YaCTfMpwYEcm7hf4FyI9/I01/Xa2tn65xVbNT0x2IrQLOAAMIr8v6zyAcIDVr9TFXzpKOnUnX2FZawPXjKlcKx3iYk7z9xb0kXyr1tz40wmZOVwCQNXVvmT13FejMw2atwSMzgVmROn/r1FGKZkx+i9OuYCL5/t1RJg3Xz6lqLbQH2mqt6fFxw1pHrM/YEFq2lyH5AS2XwXJLSx7oK4NgXKCialrnMpLWrFGkPjhXYuGmd/LixkOc/ouPA7Dv5Pjy8ZCTPNClfU07vWJu/tgp4WemOs3EpVNYm+R9x9JDMF0R6aXvUK6e46+cV3Pq+HC5uIH7GACraUh+NDl5r9Vl0o7kLyzV+csnLuERsriBnHzaadJll+ZkpGq7UEiqHQNhBpF4HcXN9huNtCXTbnd4JJmOGqsr8mrqwucfxq/nNTcf4MBML+k6rkegnJ7WWiOqZnela1K/3/TYmiZn7HaTvJ6QXmu6VE8qz0qNn7r3OBjBqy1Ou0I6sm/Tn+S/ePossDlXKGgPtCVBDYewp/4wDrTu1wHpmiSTk7dti6Zy2quqLrhhp4hfS9AsE0yZ7i9VSyP5lm/BgJy8wR1v/TkcSXhP+Pvp8Vx/95qfHxY5yQOhXcLLFPlMEdNEVsop4Kv2yW1qK8B1Sd61CXB6ouzwzKfT/7nt7QMNEDYKz05JXjYYyTuBzjUWRiNr4Gnhrmxu+hPffJb96ipfKf8SP1X9vXW34areTpPELuINkEsoqjqx2/u3jywPu48KoRV1TtQCHXnTbu32QDxiLD6ycg8/+tI+RhsadfGRsLvwmpK+20WKiV3A09eQSSdYXcNLZkJ6zelSr0yCcPchh2P7SrCiSX6L066Qdtf0S9d87Ovn+OifpSP3d9xyfFPbNDIQKqjp+sP2RfKDpr9NJG+bIiruQJIvR4vUvXYAZnLySaaFstUY0EjrXGHQxJFk3dSWdfBF1A++jOvlCoulG0e2qh64v7FufUIQOyUKGbU7k3czT2/llinSJIhSfRvjEtUtP9uNgqsHqbqi7Nojn+Th5GZee/fwBRfLkpTkB1ysWdSDOFXys4o9UqhbhekyqVfTv4lSik8++CQfm/kt9scXORp/n3gdyQMv479qoNwiPk1UHynfkqr3bV+NxOurJ24lvSSfnQ7NTsICnLVv5NPxPVyx9/GWuwbnuhsUes5trAt23emaxC60TN3NHEY3yZvj6JE7yEIE8Wf4oZv1SmHlAhT3bnnaFUBZvST/B18/xwc+8W3uv14rfl5356a22VLRDNN0DdsQyZsuqcEkn0bylr72A3H61nCSRDEbLxL6WZLXq4SMh7CpGdma5I1GkvTx5+1G8dRPAjB703jz8ZCTPACRU6bYh+RbN79bpEDQmuo0+WevvLZcr4myO4o7S88yd+XbfNF6FffeNKSRtkaItyGSv1pLdWtCbzQywwAlrQT5V2ee4d986Ske/N4V3rvyW9wSfpe6t5c9ssJyfQ1RJ5VOCdIVvYpbokjAarOTfKIooiyNDus/g9geQPJRo6ctUWXy4W4Xyf+r4vt4X/he3nDrQeYG+IkCNKXQUbCHjP58qTM6S2wfH52Tv/xE+l2KneffMVOY6/SkS2EOa/V5ePj/hSc/P1TRFdJ0jZNJ13z/So0P/NG3ecOtB/iZk42UoPec2NQ2xfEIlY0VplaW25GuMdPfg2ZGTLukicrTSL73elmsh+yVFZJSZrLcBHxZaQpN8m6gSV7zgr2R+sVdfxMK81g33bf+Z4dETvKYSL3RitSTqEvrxCtRokEtSMnKRGt+cW2yFBFC3I48cXT+m+k+j78BzxnNnz/ozkWffwie+kLP54wCZeyNbnloOox+avab1P/sgzz7H/4e99tfIXjDP+HygVezl+W1i6/6JuuO5G0/FT5b6npAVFe06mOh92+fSH+pWTtp9ua5MwJi3SSfnhfhR182OFUD6fyE093L31whVtLKSRsop5A+zJTC/84fs6jKLB7qLLgZklpXDMyfgTOfhD/+BRALXvf+tT+/DpTlYGdkdJ9brKMUPPD6m3AWHof9L+qoZ2wUdSlgm/TgtpC8TtcMmGJVse6ucdIHdyT9jb+vrNTYywpSzpK8TtdkI3ndjOGH6TVppEWsjZB8aS/8w8fh7p9c/7NDYiR68pMO5ZYp06AWRHiO15qQNDed5ZWwRVFr1IFS28S7W2O8DwLxcDIEfO65C9wEvPz24bpqsojE7Ri0Of+Jf0K5eo49HzjT8bmr1TAdABplDtCrwLHXcPL5b/N+92EAHtrzdk7d978iF/4++2SFJ2pryL82azjQk8e0/TJFAs5VA47sad80teoic4DVpx4S234rJdKxraTXai9L8k6hc3ntuzZF1+bNt/fKGWQR9iP5YJUqxR7VylbHRWOR4tOf5T/Fr+JQqfNBYDo+lLMOyb/y78HFM/DivwVHX7Up79V+UJaDk+muqekVa8mz4eJjcGxrI/dN8XE1AcomtW+2ArslVTAgktckb9JkEf1bbhevXMQShTPbPv8tGYQMyZsaSzFKv2PSR9J6TWyyLXWrGDvJi8hZYAWIgUgpdWrc+9w0vDIlGlwOYuZLbT0LM01qtMOD6iqwD9XSGF+/4yASDzdzIVWX0sGJo4eHW2KvtY94+QJz0XNp73zmQrpSC7hJqljF0e0by4K/+6cIQBxx9sIL3HnwOhDBrhxgVmosrVaB/pojQbOeknwXsTnFWVyJWV1dAdqdQA3t72r3KRyrAaYR/TS+O6QM/M59/+BtB3nTiw5S6ibqLoR2kULUqScvzRVWKPZYNxriTh79JHZU5XPW6/jtm/d3fMYtlKgpn6a3TsfVK9+z9vubRZd/qSH5CnVY+j4c/JktbTYQHzfU3Vwj8BNeD+46JK90Tt5x147ka1fTYrafIfm2DEKG5HVgVU7S1YoRwttQumYbsV2R/BuVUr1q+bsEUpjBk5havQbzxVY3jJF7tXVKoqEHXZSWny2v4+QEKQFno+ykvkiihJn5/Wv81uYQWV6HmfZsfBULxdXzZ9hzoq1ut1gLmKWKUxpN+2QPbIfjR25o/ejNHgCgtngJ6G8iHTTqlOiU+wXwZtK/T33pEnC09XpTT+w6fYreie3j0XvTpnro/SUEALyuyOsf/ODaNowGsV3AC7r86YNVqqrAdYXOW8u01SXf/I8sqHn23vkmil7ng8BzPX4k+FXeet0reeuGjmBEEKcjJ1/VWvWzK0+lLxy4fUubbUqBQpQSoGxDuqYt8bFeukbr0Fhu37mKxmJ6Tst72sFQKyer+M5HAAAgAElEQVSfIXnjYTujdNOBkc/wNyfmNm7kOXnaQ03Nqo7K4s5IvjXVqdM0EtaoUaDg9PfMzCK2ugZ06ousUmSuNLrla2RlCo5xyJxKv8elp093fO5KNWBWaqOTGV4Hhbm0rS9YemHgZ4Ju/1UNfzYtSja1oJpBqGcUsv6uBonj93UGSmUTugu7mhCUIFvsNIr7tHlawSqrFKl0m4zoSNB57iE+Hb+ad778WM/2fNfiKXXD9keCdkryRqLXNBiUl9MCMQdv29JmQ6tAKdYkP0T3z0YxW0nv49U+OkrQJnlHk3wsHlYfb9twOb1eK/vaJO/6muQzK2bjYVuQkKhRbRV8nQH+vDuF7SB5BfyZiHxDRB7oflNEHhCRh0TkoUuXek1utwNGf6Vh2gD109os0dyu0X0rXKWB36Mx3g9plJ2ZkmsusUx5ZEVXMAbG6TFHKxexSG/W2nOdOfnzC1VmpI41InGy9VCaTyP5cHXwIs5MJ3YPJBVn9+vf7U/yfqlPXcH2Uh/OpMvKUTWJ7e50Tbq/Ju6Wc9qJ22t9aEerNKTUc21kB2S+XLiPe2/u7azyHZv9FZ8je7eXJJTl4kpMGKfkbiL5wtXvprWE+eNb2m5kp76sAJY7/kjeq+wjQQiuPtf3/RbJGx0acVv3TcfnqikPuZkBs1ZxXpN8HCcUaLJAeh2uLF5uRfLOAH/encJ2kPzrlFIvB94GvFdEXp99Uyn1YaXUKaXUqQMHDmzD4fTCLP3Duonk0xPp6RPrFTqnOvfWzvKCtXZRziC2/I62Pqe5TM1ae4hqs4gzkfzy5fYFbi080fG5J599Hptk7MMXBlJOz2eyBslH3SbbGgVN8nG1m+TTHG+h0vugahUsu9pJ3T7aOGZ/oWx9XiBxSh1DcpCKpzX76OqYouD3kwPcfPd9fYfgbEv4q19+Iz/+8v6prbFBr2TiOCX3ehAjAvbl78CBF6V1ly0gsotUDMlvQySPW+Cycx3lle/1f9+kawzJWx52n0jeqi2QIB3KkK7+HRMANpoNHEm4YqfXaXXxYiZdc42RvFLqWf3fi8AfAeMVatgCjP6KGXIiCtKRdX1xtwwimjVornBd4ynOOBvLUyZdHR9utEzNHl2fOqQPErOP1YWU5K+qCrOrT7c+U21GXL6k88fbRPKU0mhV6gsDP2IiebsrkhdzgxlBNY1YPzDm9/cpHjsmb9oZXXt93Hpa/rsMMRTmFikSkGRM4L2oStBHV8ekYP5Lci8/tgaJ+469oRXiSKENPUJtflFtxpQ9B7n0HTi4dVu62C7gSKJ3Mf5IHmClfJxD4TmiOOl5T2lCd702yfcr1LuNBVZkBqx2OlZsh0hZrQCwXk0DvlUvDfbqS5da/gfryRpsN8ZK8iJSFpEZ8//ADwGn1/6t7YdXTiP5WGtIp8bS7Zu/0NIOrxE+8yAWCUv7N6Y3kdhex5LQj1YI7NFG8sppk3zj6gUATrt3cV10vpW6OHNhmYrWT982ki+mRO3Urwz8SNyy5uu6MYq6G0dPE7ZQWyBQdkuNMYuW/n+XQJWvAlSP4qN2LhoiksctY4mikdHt8ZNahwevQbDvVh5ObuLBPT/M7YdHIykxKpiahJHirYcRB916Ok27xXw80Olzu03tgvG+k5zgAucWeg3WaaVrzNBZ/3RNIbhK1em9vkKcll9BU6duG6U02GiuLCCmq8e9tiL5Q8CXReRbwNeBP1FKfWbM+9w0zFBTbAqrcbNjGW/eT5pVzj78BRIl3H3vD21o28oudDjIF+NVQne0N3naVZLuI1pOSX7x4KspEHD1uScBeOT8ErPogtR2kbztsGrN4AeDST4K+qdrcIs08XGbnSRv1xdYktm+Hqiio/Ww0WW6Tq/GtzFA7zYr2QxEL8tNVIdSFFSduI/kgsxczzuDX+XVp8Y/xr5ZGJKPonYkf6fzbPrmFjtrgA57zO3IyQMUrnsRRQk4/8yTvW/qdJStB7uU5eH0KdSXo6s0+rSxhuKCXg0EmivUTDowF65eAeNktt6cwzZjrCSvlHpaKfVS/e9OpdQHx7m/rcKY9SZaZEvigDDTXWoUBZOwSuPpr/C0dZTX3HXzhrat7M6Oj7KqEvsjjuRsLx2ZVwq18gLLqsjem1Myef7pRwA4/ewSN5b1cWwXyQM1Z55itDjwfdOR4PYZIKnaM3hB5++6zaus2P2P3/S+Z00j2qYXndtv+e8OEcmb+QkjWEdYwyYh6SO5cPexef7ua0/w7lf2dtXsNFokryP5WhBz0tIkf3DrJJ/9m9vbkZMH9h2/C4Cl75/pfTMJCZTdChASy+8IwEDr1iRLHbo1Bp2RvJYwmEtbhpPalbZR+7WUrpkUGCVFpR2frCToLMjp5dfK0iI31s9QPfiKjatHOqbjI4Y4pEQDNWKSbemwxCFW7RILzHHTHWl/fPXZ9GJ/5Pwit+/RYl/bSPJNbw+VeGng+8Zpp3vqFKDhzPYMGxXDq9T7LKUh4wyUSdc0g2ZqDtItBuYPT/Its2pjmNLlJpRFyXP4pz9yB3Ol8asxbhbd6ZpaEHGLOpfqA80NUQTOkF23CNy4UD6cPpSii9/tfTMJiTLBmzLdWBks1kP2yXKHbo1BhIPoSD7UJF/acyiVLK5dTQ3psUYm/jcq5CQPiLHxC9ok37GM1yRfuvQws1LnyEvu2/jGzdItatLQDjJSXMNxfgtodZVEDbz6ZRatPVx33fVcUTNw6busNEKevlzl5hk98DIiLfmNIPT3Mq+WaYT99cpNJO/16UhounMU406SL8dLNL3+fz9D8lGG5AMt/dsdXbkjSNeY1ttmi+T19bOOBPVuQ4vko3Ykfzw5l+bjh5FM8NppK9vdnkieykGqUsZf7E3XSBwRSbuYqmyvJ5K/slJlj6x26NYYROK2SN74BvilCstUkMZiKoS3hgn7TiEneWiRuBXodq8k7IzwHI8Im9dYjwKw7/bX92xiEAzxqKjRkjSwRjxxaiQBVNSgFC6w4uxDRLjgHaO88jSPPreMUnCsrItMo04XrYGkuI+9sjJQpKzlv9onkg+9eWaSldaQDsBcskRU6D/MZffRE2/oVEr3gJGr99ftSLUZmEloMyRnpnHtbVwpjQJWi+RTAqsFEUfCZ+DA1ouu0Glmvl2RPCJcLd7I3vq5HplqSQLi7JC/4+ERQeZzywvpIJQz26vPH4uDrdM1LSXaQpkVawYnWMROetVOdwNykgewLGoUEG0BaCdBzzK+KT77ZIWmv29Tsqst38lmnepS2krolkcbyZtURBTUmY2u0PDT1sXVyk0cDs/x7fNpuuSG2uNQPrAlRcGtQsr72MMKV1f7k7zRGfH6jIInhXnmZZUVLTfcDJrMSRVV6i/RbKZKs85AxsVLuvTdTU5+GIPpliuW6bSopn9npzTaFtlxQ5xOkveaC8wmi0Pl46Fd3Ia2RMh2oDF3MzfyLBdXOmcYJImIyEbyPpaols48QFXr1hTmeudgYnFaE7KxFiPzihVq1gxesIQTN3qF8HYBcpLXaEgRW1u52UlA3HWymqLH0o/fu6klrInkm80adT2i749YVqCVi64uUlZVwkK61FT7b2WeFR567Anum30e76nPpgqG2wi7cgBXYlaW+vfKG50gv9+UYHEP81RZ1quApctplGX1WUpDO5KPM8bpgSmQdZG8P4JIvkXyeuneWE0jebc4WZF8d7pmb5B2aLF3Y80Fg5A1ure3qbsGwD10K4flCt979vmO1yWJOiL5Vsttxvi7oSU4sro1BpF4WMrIjRuSL1PXtSMnaRKsZfiyQ8hJXqNpFXFjE8mHxF0RXlH3ytvHXr2p7Vo6Fxw06gQr6WBPYWa0JG96kOMr5wBIymkUUr4hHWS5+sxp3ud+Avw5ePXPj3Tf68GbMyJlA/RrogZN5eC7vasLKe3Fl5BlrSG/ciXdhjvTfzLa6RPJh8bIoct0XWyXUNlruzCtg7b1YXrDN3Uk729Agno3wbKNVroeFgp1AXlI+Yvsg9X1t6+tcO5o6mJ1+ZnODhtJIuJMTl6MDWC2G2s5lTTI6tYYZCN5pVO7hWKFwJ2jFC9rSevd1T4JOcm3EFhFnFhH8irsifCKemBqs9raLQf5Rp1Q52wrc6NxhDJorRYup+Pcls4nHroptRf8Yeur3F39Mtzz80PfuJtFUYuUNZcu9n1fonTwrN+Up1tJ/061pfTGMw8Kf3YAyev0QJyRmjVSFE6xt3c9wB3KscgMySkdyRtdnUJlwkhep2uSKCBOFJ6+D/p1CW0GWQtEb5uGoQDmjqRppvqF73S8LiokkmxO3qRS29dLstqrW9N6z3JbkbzS0X+hVCH256io1VTtNI/kdy+CjJm3owKS7mW8WwTbh8Mv3dR2W241QZ1Yj+hX5kdL8naL5M8C4M0dBuDAkVuoK4+/Y3+OyK3APb8w0v1uBOU96aoiWm2Lz333hRW+8YwekIobvf6rGt5M+ndqLKepnrp+UJT39jetdoyyZEZq1hTI3D4dLyvWDMkQRehiy6w63UdUTyP54syIay5jRpvkQ+phzIwYkh+uQG/kQADcwvaRvOy7mRgL+0qndpOVRCSZdI2VqWW1XqsvEGO1J64ziMVte+Hq1K7jl0kKeyjRoJhUibbBHGWzyJ2hNCKnhN/UhVEV9pL83NH0ot/kUEdLI6VZR9UXaSqHmcpou1vMtKi6+gwAxT0pyYtlc8E9wk3R0wSnHsAZcevmRuBp4wWVESn71Y//JavPPs4P3n2S1zYXB0oLFOe0EuVK+rvxSvqgmNnb3/TEdMxkTSMS3dboFXtJXr3749zeZ1m+UZhIHk3ycWOFSFnMlCerhdLoyiRxQK0ZMYMmvSEjea+QjeS3kfwcn6vuYWaqZzteFjUgXZOJ5N3GAivWLPN9JqoTy20JmklUp4mLb9mtB8K+ZIGL1s6ILK6FnOQ1YqdMSZ0HwFEhqnug4Z0fAtUrerQejIVg2KxjNZZYlTL77NEuoEwk766mU4qV/W1v0ur8bdQWLlD6gb8/0n1uGHqoxIiUhXHCz1785/yA/wjolOn3pP/ATUnn86NqGvUnWpFydhDJ6zZJlTFOTwLTz9z7YD1868b0hwZBbIeGclvqg6qxwipFZtYw/96NMC2USRRSC7KR/HAkb1ZPTeXgb8B7YZRYnTnBkcvnWG6EzGpzH0tFxJKN5M0qu329FIIrVO15+iU1E8vFNdPrYZ0GPj5glTTJq0UurOfPuwPISV4jcUsUlZYKJSTpztVuMafYyhMHdexgmVWpMNpkDTi6qFWuP8cVVWHfTDuCuvOnfyslx9L2GIX0wCvRwMdppKmqpy4u8zJ5ggvXvYmFE+/gU3/9GMtzt/Iv+vxqQadr4lpK8lZ9gRVKzAzo1PB0SkBlumtMvtzvE8mPAg3xsaKUFEUbhlznT9ZtZbfSNRHVIKJCncgp4VjDEbOvVzoRzvaPCO0/yYmFr/Gdiyu87Fh67VtJRJIledeka9qRfCVapFHuf68oy2ula6y4TiDp77v6OrVE9fgW7AZM1tU4RiRuhRINwjjBIUIN0VqXhen/joIGbrhEfcQKlNBO11TiJR5XRzhaaR+7NXMQa2Zj2vfjwqo91xIpO/vd09wmdWq3/zB3veE9HHtDSBD1XyEZuWHRtQyncYUlmWNQfOm5PokSyETySudOC2NKoTSlgKWFqaxghRpFnBGv1MYNW3ueqjigHsRUqBO7M0OTg0lnhbL9NFO6/nYK3w157pkneJnuiLPVAJLX6b0kUcyqJYJC/5VlqlqpNemjBk1N8saqEugRwtsNmKyrcZzwypRoUmtGeCqEEXlSOi1xswZ+tEpzxFry0GlScEXm1zWg3m5UnXmKYdpZVP3egwDsP5naCswWXPZXBkQ/bpEGHo6WG/aDq9ScwZ0rnmsT4HSQvAQ1QmW3DGBGjSzJ22GVhrW7ZGY3AruVk4+o6nRNP5G1zaKgC9NDafZvEXuPpe3DjUyHjaWijtZoU6g3LbdL9ZB9LJEU+89hpKqVWskyrhNa6e8XZ9tr82SXKVBCTvJteBVcianXa3hEbdGvYTdrhm6CBsV4hdAbvaRA1jh4xdmhtMwaaHrzVOKUqL2Lj9DEwz60sWnKqh4ZByhFi9TdwcVj37FSO7+MM5RENer4yDAaLGsgsAo4pisrWp1okk8j+bTwqobMxwPYtk1DuR2iYNsFR+vgl1faxjndkbytp3BNN9bC8gpzUkMq/Uke28XV6RonbrRIvjzXLrYm9u5SoISc5FswolK1pctatXA0kbzvm46PeiozPGaSr3mjzvgPj6iwjzm1TBgnXFd9jEulkxtW6qvac/hh2n8+kywR+oNJ3rEtmnhIJpK3ohqNMQ6ohFYBV5O8F9cInd5+/N2OdromotqMmZE6UhjNddoQfyiLxS2jvJ8IC7fRnrS2VIyysiRvJqTTSN7o1vTrkYdU0MxE8mlPfPr7s3N7U9co6JG03g3ISV7D0hOR1UXdzz0i/Wszrq/CBhVVJfFHPyiTFfcKCgOikB1EUtjLHlb41rkr3M73aBx8yYZ/t+nOUoqXSOKEebVCUlz7IZbV/AawonordzoORHYRN0lJwk+qRBNI8o6xTYwDamGakx8ZyVMg3gmSF6FGCStsO0TZdEbyZoYl0UFB7aoetpvvT/JYLi4RSilc1SDW+ffZkscS+rxv49DXRpGTvIZTSJendT3WLCNK1xiNFGoLuBIjY1Ao9FyPUKWdEElpZ4us/SDl/VSkwXce/ioVaVA+/ooN/27ozlFOVlheXsSXsOUbOwiBeG3zBsCJ6i3doXEgsot4Kt1fMakRu5MlTgbg6EieOKLWjKhIHXtEJB+I3zlluo2oWyWcDMk7KkT1ycm3zLm1bk1lT3+SF8fDl4gwSvCSRiv/blvCMmmQKNciyYvIW0XkcRF5UkR+edz72yoc3WIXLqeDNzKqSN61aSoXu5Y+PExP7Sjhu1ZacARkwFJzJ2EbrZmnPg/AgVs3Lg0R+/PMqBUWF1LRLGeAbo1BiIck7UjeSeoE1viW0IldxE8akCQUaaC8CYzkXRPJ6z55ajgjElkLrEKP2N92oWmVcaNMJN+VrjGtxyYn39atOdx/gzrFGIZNfJodHrZVSz/cd5krFIzfyNsGfht4G3AH8G4R2br9+xjh6Ys6rmqSH9GEnojQxMVrpNt1K2MgeVNwBNw+Otg7DdNidsvyX9PEx9lg0RUgKe5hnlUuPZ8OerkDdGsMQnFbmt8AbqZANg4kbhGfBoRVLBRqBF0p241WJJ+E1JtNKtIYWbqmas/SsHemGN20y3hadBDQrdFtknc9E8mnJK+qqWzGIAE8bGMU38BXnSRfd9K/l7ULSX7c66hXAU8qpZ4GEJE/AO6nNeu4e+AZHRIzeDPCMexAXCphWgDyRiwzDODZFle0/ou/d0AUsoMo6hznK+S7PFe6nRs3oWcvxb34ErH8fCq+Vuqj851FZHm4Sbvw6iZ1wjEWoxOnRFE1CevLuICMoCtlu2GGoYgj4oZWoBxVuuatv8FOZYUjp4wftAuvNnFHusZ4ChiSl/oCETbOAOc0I4NQbzbYQ9ARtTecOQhBrkGSvwH4fubn80CHVq+IPAA8AHDs2M6ZHPvl9OY0gzfWCI2HAzzmk/ThURyxzDCkq4UAN9VNGVQ02kFUtDa3KzHNAy/e1O86+qGYXE49OysDJA0MInGxMyTvJw3icba1eWWKEnBl8Qp7Abu4fa5bo4KYmZAkJGkM9qndCl758lMj2c5WELkVKqpNP47qjOTNhLSZq4hXLrJqzfXVrYE2ydeqq1wvATjtFUrkz0EdrD6G9DuNHS+8KqU+rJQ6pZQ6deDAzon7FEppusZtapIfYSQfics8aW6wNDee7pdQPBaYZd/s7rvIyplCVvHGzd30ru5ZLi6nkfzcOoJiseXjZHLyvmp0LKtHDRO5LV1O00mTSPJYNomS1L+0oT11J3BF0o3Eq1BStZYNoEMMmUjeiKapKODJiysUG5cGuo5BW8ituryoN9C+rmI/jf7tXViTGTfJPwsczfx8RL+261DQypCeHryxRlglz1qCzYxYS94gwOOymhs8PbqDsEp7UvlW4NBtmzNd8fU04d76M4TKplBZWw8/tjwc1Sb5Ak1iZ3w5YWMrWFt4DgCvNFla8gYRNiqOkMBE8hP4sOqC8ipUqNPUshkOcYf1peOkTRESN/nSN8/wWus03i0/MHB7phmjoR3erIxvcKJTPFYfG8udxrhJ/kHgpIicEBEPeBfwqTHvc0sQt0SihGKUaoKP0l0+yujglGbHI/f7jHWUbyU3s6e0+zwmsSyWZYYGPt4miq7QXvkcSZ5lSWbXtV6MLa8lB4tSFFQTNcY8qaUjt+Zi2v0zaa5QBpHYSBJhTRHJ489SlibVehOVJLjSGckDaVdaHOB86//Bl4jyawd7LphIPtQ2j1nnK1VJa2HWDsh5r4exkrxSKgJ+Efgs8Bjwn5VSj45zn1uGCDUpUE7S5ao9Qq2TWA/jrFJs+WmOGr9efj+/5v4CnrPjGbj+KB+guveOTZuIl+fTQuus1Fmx13e1SiwPV0fyKqxjiUK541tC20abaDn1Ey1OmCuUQYSDJCF2MNrC607CFMGrq4uEoX7wW53XXygu9doqb679Cef3vAoO3Dpwe6ZOF9bSlK6dieSvHP0f+PHmP4U9x0f4DUaDsU8pKKX+K/Bfx72fUaAhBWaTFZD2oMQoENkehFCVMuOyk/Adm32VXRjFa+z5m78JW8hXZv1w1xInM4htv6X5HTRWU4lbb3zpGlvLYVi1tP1u0lyhDGJsSKJ2X/kU5OQtXR9prC4SzVXS/rMu4cEAlzuWv8wBWeKFe//l2tvTEtdxrTeSv+OGPfzrmZdyeG73DUPtLrnCHUZDSlxPmlt1RhnJa9/H2hgUKA0KrsXMbtYxP/H6Lf2aeCUaeBQICPz1O5OU3Y7km1VN8mOM5I0xhl9PB2nKMxMayUsayTuR7iufApJ3dX2ksbpEGKbF/+6VdCwO17HAC3KAQ69455rbs03HXSNN6boZ56uXHp3na7/y5lEd+kixi1lh+9G0ilhJWol3vdHl5I2VYHMMWvIG73/zrTj2eJQWdxrLMkNBLRCtIU5moGwfT0fyzXqaerD88ZN8OVwgUDaV8u7rrtgIYhxERRTiVZQI4k2WhWE/uNoNrFldars/dZG8kUF+4thPcGidVKJRrbSamuT7mMPvRuQkn0FoF9Eic62R51HAyBaH7vjynK+/dfd5S44KNXsWogVUaf32U2X72CQQRwT1NPVgj5HkPX2jz8ZXqFJkjzuZt1QiNhJH+HGVpl+mMCZp5u2EryP5sLZErHPy3ZF8JB7NxOHA6x9Yd3smkreaad0u62G7m7FLq3Q7g8BunzR3hDl5pavysTeZS/mdRmtkvLx++2nLByBqELZIfnw5eWOMMa9WqMnkackbxNgQh8xIndCZ/CgeaLXbxvXllvtTN8k/7L+Cj7l/g1tvOr7u9kzHnRdpkh+TpeSoMZlhx5iQ7ad2RxrJa93qMcgMXwsI3DlogLOObg3QlnqNA0I9vekUxncz+prkLVETaRhiEItDEodUiIncySCv9WCK4EljmTjSkbzTSfJ77/8g19n2hkxlTArXj9LrqpCnayYPWZLParQPDb3MkxEp+11riLw0Iiuuo1sD7RH9JKgTN9IiojtGkjc+pgDNCSd5FYdUaBBPC8nrdlbVWCbWcsLdkfybbtu4DIhR6yzE6QrRPOB3O/J0TQZJpgvDHaF2DTqSt4vr93nn6EWspwlLA3S+O6A1vsOgTtzUJD/GZbWXeYAEE2gYYpCIA0nEjNRJxuBethOwTK9/c7WVk7eGmFNxdCRfTtLryh5ja+4okZN8BkYLvKlcLHt0fxpjJOCUJ7OHeqfhz6YF1z3711fYNH/roNkgDtKb0R9n7tSyqGsF0MiedJIPqVCfivZJACw79fcNVkhiE8lvfZbEpGsqVEmQkbnHjRt5uiYL3TYW4DDS02cKNmPQkr8WcOdbfpalw4eY239k3c8aYbmoWUc1a0BbRnpcaFCgSDDRuexYHBzqzEoNNQXTrgY1KWGHq8RhSvKWM0wkr+3+qNLApzQhHUh5JJ+B6Q0etfGwraPLwszuM9meBDjz1zP3+p/f0GeNaUMU1FE6kh93gcx4yE6iK5RBYjm4jNbfdTegYZVwoipJZEh+65G8pyN5T+Kx+gaPGjnJZ2DrvlczIDEqnLw+TTdcf93aMrk5hodpcwubdQhr1JRP0R+vkXRgPGQnWNRLiUOBgKIEkymXPAANbQGYxMPn5LMeE0FO8pMJU6gZdSQ/c8u9cOIN2PtuGul2c/TCSETHYQMJa9TwKTj2WPcZaHtBKUxuLltZNvOSdo2Myt91NyB0yvhxtdVCabtD6DtlHhA5yU8ojJl3NGKS5+Dt8NOfGqtQVo4URj00DhpYYY0GPpY13txpZLqnJpjkE3HYS9r/7U6oJn4/hE6ZQlIjMSQ/RLoGEULSgGGcvsGjRk7yGbj6Jo12yF0+x/DoIPmoti2509BK6wCTHAEry8WXlAi98vS0+sZuhaKqoaLhC6+QqlZC+8E+CchJPgNPCxqNPJLPsW1wdOE1CevYcZ2mjP9mjLW9oBHEmkRkvU+nKSefuBVKqt7KyTvucPd2pBsS45zkJxO+vknjnOQnFk7BkHwTJ67T3IZltZmU9ic4As6S/CQXkLuh/Bkq1AmaqQqlNUSfPLQDwLGaw48YYyN5EflnIvKsiDys/719XPsaFfyyJnkrT9dMKozZSxI2cOJGK5UyTigdyZvrZxKhsrZ4U0Ty4s/gSkxQ17aeQ0qIm0g+cSYnkh/3MNRvKqV+Y8z7GBmKLZLPI/lJhWvs+MIGXlIntNefkh0Wyk33WZqZ3Ei+wxZvWiZeaXfMSe0KAM4whVdScxVU+8E+CcjTNRkUi2ViJeoG7M8AAAqISURBVCQ5yU8sPK0eqqImXtIg2oab0UjOVmbXd67areiI5KdoGMrUF6x6SvL2kIVXk8pVzuR0yo2b5H9RRB4RkX8nIn1n+kXkARF5SEQeunTp0pgPZ21YtkWVIok1OT2wOTrhey6hsiFq4KvGtuRO73jFDxDsv5PCJMtW6Eg+wm6JvE0DTMeTE4wmXdOq17nXSCQvIp8TkdN9/t0PfAi4GXgZcAH4V/22oZT6sFLqlFLq1IEDO+9udFXmabiT2wp3rcNzLJq4EAX4qkGyDZG8e8c78H7xK7COfdyuhh70qVtlmBBNlo3A0ylYP0zNt90hu2tMKlcmaOZlqKtSKbUh51oR+R3g08Psa7vw8Bt+lxty+YGJhe/Y1HGQsIpHRDJBy+odhY7km3aZ6cnIt3v+i1EayTtDKkeaSF68yYnkxxZ6iMhhpdQF/eOPAafHta9R4v43vnanDyHHEPAci0U8PL08VxO0rN5R6Eg+mGC55H4oaQvAcpxa9rnecIVXU6+zJkiMbpzry38pIi8DFHAW+Lkx7itHDgBsSwhw8fTynAm6GXcSoskrmBJ/V4Oi7niaVSvECLY1nI5RoiP5cfoGjxpjI3ml1N8Z17Zz5FgLIS6VMI3cxJ2cm3FHoesJk6yJ3w9FHcnPUiPEYVipOhPJOxNE8nkLZY6pQyBuq9AmeSS/IZhJ0Nidpow8iFskxMYSlXYODQnTauoUJue6ykk+x9QhEo9SnCoqij85N+OOQkfyiTddkTwi1NFGMjI8ySe6duHlJJ8jx84hFBdfNQCwc5LfEMRMgk6RpIFBTdLUSjSC7LTSkidOYXIehjnJ55g6RBl54Um6GXcSLcekKST5hpWSfDyKdI3+O/l5JJ8jx84hqz00SbnTnYRo8rKK05WTh7T3H1Kz8qGhaxeFMZvDjxI5yeeYOkQZWQpvCklrHLB0Tt4qTLDI2gCETkryo0jX7JlJt+WN2Rx+lMhJPsfUISsVbcTDcqyN4wdTcj9y+OAOH8noEere/1FE8icOpfpE1gTJGuQkn2PqkOQkv2lUSilpFSfY+GQQEnd0JI9XAcudKIGyCVZUypGjPxI7TdeEyqZYmJybcUdx/LVw36/A0Vfv9JGMHMobIcmf+hm48TUtGYhJQE7yOaYOShfH6vgUvHyxuiG4Rbjvf9vpoxgLlDZBSUZB8sU9cOye4bezjcjvgBxTB6Uj+Ro+RXf4trkckw3RJigjIfkJRE7yOaYOJl1TUz6FnOSvedgtkr82r4Wc5HNMHURrhjfEx7XzS/xah6UtAK9VW8/8DsgxdTDpmoZMj41djq3DLaVOb3m6JkeOaYGO5IOc5HMAvrYAVFZO8jlyTAVEG1EHVt4+mQN83fufR/JbgIj8hIg8KiKJiJzqeu8DIvKkiDwuIm8Z7jBz5Ng4xE1JPrTySD5H2zhEXaM5+WEfbaeBvwH82+yLInIH8C7gTuB64HMicqtSKh5yfzlyrAtTeI3sPJLPAaWZVIogT9dsAUqpx5RSj/d5637gD5RSTaXU94AngVcNs68cOTYKS0fycU7yOYBCnpMfC24Avp/5+bx+LUeOscPyNMk7OcnnALEdahQpFidHVGyUWPfRJiKfA67r89Y/Vkp9ctgDEJEHgAcAjh07NuzmcuTAdtN0TZybeOfQ8H7iw9x58PadPowdwbokr5R68xa2+yxwNPPzEf1av+1/GPgwwKlTp9QW9pUjRwdsHckrJyf5HCmcO390pw9hxzCudM2ngHeJiC8iJ4CTwNfHtK8cOTpga61v5U6OsUOOHOPCsC2UPyYi54F7gT8Rkc8CKKUeBf4zcAb4DPDevLMmx3YhmT/BB8P/iWcOvGGnDyVHjh3HsN01f6SUOqKU8pVSh5RSb8m890Gl1M1KqRcppf50+EPNkWNj8Fyb34nfAcW9O30oOXLsOPKJ1xxTB0+LkhW9a1N1MEeOLHKSzzF18F1N8rnMcI4cOcnnmD74TkruBTe/vHPkyO+CHFOHY3tLvPeNN/Om2w7t9KHkyLHjuDbnfHNMNWxL+KW33LbTh5Ejx65AHsnnyJEjxxQjJ/kcOXLkmGLkJJ8jR44cU4yc5HPkyJFjipGTfI4cOXJMMXKSz5EjR44pRk7yOXLkyDHFyEk+R44cOaYYotTu8ekQkUvAM1v89f3A5REezqTgWvze1+J3hmvze1+L3xk2/71vVEod6PfGriL5YSAiDymlTu30cWw3rsXvfS1+Z7g2v/e1+J1htN87T9fkyJEjxxQjJ/kcOXLkmGJME8l/eKcPYIdwLX7va/E7w7X5va/F7wwj/N5Tk5PPkSNHjhy9mKZIPkeOHDlydCEn+Rw5cuSYYkwFyYvIW0XkcRF5UkR+eaePZxwQkaMi8hcickZEHhWR9+nX94rIn4vIE/q/e3b6WMcBEbFF5L+LyKf1zydE5Gv6nP8nEfF2+hhHCRGZF5E/FJHviMhjInLvtXCuReT9+vo+LSIfE5HCNJ5rEfl3InJRRE5nXut7fiXF/6W//yMi8vLN7GviSV5EbOC3gbcBdwDvFpE7dvaoxoII+IdKqTuAe4D36u/5y8DnlVIngc/rn6cR7wMey/z8a8BvKqVuAa4C79mRoxoffgv4jFLqNuClpN99qs+1iNwA/APglFLqLsAG3sV0nuuPAm/tem3Q+X0bcFL/ewD40GZ2NPEkD7wKeFIp9bRSKgD+ALh/h49p5FBKXVBKfVP//wrpTX8D6Xf99/pj/x54584c4fggIkeAHwZ+V/8swJuAP9QfmarvLSJzwOuBjwAopQKl1CLXwLkmtSQtiogDlIALTOG5Vkr9N+BK18uDzu/9wH9QKf4amBeRwxvd1zSQ/A3A9zM/n9evTS1E5DhwN/A14JBS6oJ+63lgGt2r/zXwj4BE/7wPWFRKRfrnaTvnJ4BLwO/pFNXvikiZKT/XSqlngd8AzpGS+xLwDab7XGcx6PwOxXHTQPLXFESkAvx/wP+ilFrOvqfSftip6okVkXcAF5VS39jpY9lGOMDLgQ8ppe4GqnSlZqb0XO8hjVpPANcDZXpTGtcERnl+p4HknwWOZn4+ol+bOoiIS0rwv6+U+oR++QWzdNP/vbhTxzcmvBb4URE5S5qKexNpvnpeL+lh+s75eeC8Uupr+uc/JCX9aT/Xbwa+p5S6pJQKgU+Qnv9pPtdZDDq/Q3HcNJD8g8BJXYH3SAs1n9rhYxo5dB76I8BjSqn/M/PWp4Cf1v//08Ant/vYxgml1AeUUkeUUsdJz+0XlFJ/G/gL4Mf1x6bqeyulnge+LyIv0i/9IHCGKT/XpGmae0SkpK93872n9lx3YdD5/RTwU7rL5h5gKZPWWR9KqYn/B7wd+C7wFPCPd/p4xvQdX0e6fHsEeFj/eztpfvrzwBPA54C9O32sY/wb3Ad8Wv//TcDXgSeBjwP+Th/fiL/ry4CH9Pn+Y2DPtXCugf8d+A5wGviPgD+N5xr4GGndISRdub1n0PkFhLSD8Cng26TdRxveVy5rkCNHjhxTjGlI1+TIkSNHjgHIST5Hjhw5phg5yefIkSPHFCMn+Rw5cuSYYuQknyNHjhxTjJzkc+TIkWOKkZN8jhw5ckwx/n9MgFEDpksiZAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uThJKJre_h6i"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}