Corona_EDA_viz.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Corona.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/EXYNOS-999/corona_prophet/blob/master/Corona_EDA_viz.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dfp7rHLzuVKm",
        "colab_type": "text"
      },
      "source": [
        "# Mathematical Simulation of nCOV Transmission Model\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "We are also investigating the use of a  deterministic SIR metapopulation transmission model of infection within and between major Chinese cities to confirmed cases of 2019-nCoV in Chinese cities and cases.\n",
        "\n",
        "reported in other countries; to study th epidemiological and population dynamics occurring\n",
        "over the same time scale. (edited) \n",
        "\n",
        "We define and study an open stochastic SIR (Susceptible – Infected –\n",
        "Removed) model on a graph in order to describe the spread of an epidemic on a\n",
        "cattle trade network with epidemiological and demographic dynamics occurring over\n",
        "the same time scale."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g53qK3rd2f2G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlgA8DC23fHg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data=pd.read_csv(\"/content/2019_nCoV_data.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQj8MW0K3nXd",
        "colab_type": "code",
        "outputId": "b1626bc9-c4fa-472b-f155-5d49bc8d9888",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        }
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 434 entries, 0 to 433\n",
            "Data columns (total 7 columns):\n",
            "Sno               434 non-null int64\n",
            "Province/State    349 non-null object\n",
            "Country           434 non-null object\n",
            "Last Update       434 non-null object\n",
            "Confirmed         434 non-null float64\n",
            "Deaths            434 non-null float64\n",
            "Recovered         434 non-null float64\n",
            "dtypes: float64(3), int64(1), object(3)\n",
            "memory usage: 23.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yIv5Ggbq3zoH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data['Last Update'] = pd.to_datetime(data['Last Update'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sALmuR6x4S3v",
        "colab_type": "code",
        "outputId": "cc9036cc-af24-4c94-c9f4-f067c86c1406",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        }
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 434 entries, 0 to 433\n",
            "Data columns (total 7 columns):\n",
            "Sno               434 non-null int64\n",
            "Province/State    349 non-null object\n",
            "Country           434 non-null object\n",
            "Last Update       434 non-null datetime64[ns]\n",
            "Confirmed         434 non-null float64\n",
            "Deaths            434 non-null float64\n",
            "Recovered         434 non-null float64\n",
            "dtypes: datetime64[ns](1), float64(3), int64(1), object(2)\n",
            "memory usage: 23.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Uapn9px4gUH",
        "colab_type": "code",
        "outputId": "24c1d3f0-3a44-4d95-8432-fcfa499b0c4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "type(date)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 546
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "069qIP-B4uQn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "usa=data[data['Country']=='US']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVMlbZaI6IP3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mchina=data[data['Country']=='Mainland China']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dm5WvRoK7353",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mchina=data[data['Country']=='China']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ts7yIJ_d8v8e",
        "colab_type": "code",
        "outputId": "169c764b-0f50-47fc-a42c-acbe94e3cd86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "mchina.sort_values(['Last Update'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sno</th>\n",
              "      <th>Province/State</th>\n",
              "      <th>Country</th>\n",
              "      <th>Last Update</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Anhui</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>33</td>\n",
              "      <td>Xinjiang</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>31</td>\n",
              "      <td>Tibet</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>30</td>\n",
              "      <td>Tianjin</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>29</td>\n",
              "      <td>Taiwan</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28</td>\n",
              "      <td>Sichuan</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>Shanxi</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>26</td>\n",
              "      <td>Shanghai</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>25</td>\n",
              "      <td>Shandong</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24</td>\n",
              "      <td>Shaanxi</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>23</td>\n",
              "      <td>Qinghai</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22</td>\n",
              "      <td>Ningxia</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21</td>\n",
              "      <td>Macau</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>Liaoning</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>Jilin</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>Jiangxi</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>Jiangsu</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>Inner Mongolia</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Chongqing</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Fujian</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Gansu</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Guangdong</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>Guangxi</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>34</td>\n",
              "      <td>Yunnan</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>Guizhou</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>Hebei</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>Heilongjiang</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>Henan</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>444.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>Hunan</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>Hainan</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>35</td>\n",
              "      <td>Zhejiang</td>\n",
              "      <td>China</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Sno  Province/State Country  ... Confirmed  Deaths  Recovered\n",
              "0     1           Anhui   China  ...       1.0     0.0        0.0\n",
              "32   33        Xinjiang   China  ...       0.0     0.0        0.0\n",
              "30   31           Tibet   China  ...       0.0     0.0        0.0\n",
              "29   30         Tianjin   China  ...       4.0     0.0        0.0\n",
              "28   29          Taiwan   China  ...       1.0     0.0        0.0\n",
              "27   28         Sichuan   China  ...       5.0     0.0        0.0\n",
              "26   27          Shanxi   China  ...       1.0     0.0        0.0\n",
              "25   26        Shanghai   China  ...       9.0     0.0        0.0\n",
              "24   25        Shandong   China  ...       2.0     0.0        0.0\n",
              "23   24         Shaanxi   China  ...       0.0     0.0        0.0\n",
              "22   23         Qinghai   China  ...       0.0     0.0        0.0\n",
              "21   22         Ningxia   China  ...       1.0     0.0        0.0\n",
              "20   21           Macau   China  ...       1.0     0.0        0.0\n",
              "19   20        Liaoning   China  ...       2.0     0.0        0.0\n",
              "18   19           Jilin   China  ...       0.0     0.0        0.0\n",
              "17   18         Jiangxi   China  ...       2.0     0.0        0.0\n",
              "16   17         Jiangsu   China  ...       1.0     0.0        0.0\n",
              "15   16  Inner Mongolia   China  ...       0.0     0.0        0.0\n",
              "1     2         Beijing   China  ...      14.0     0.0        0.0\n",
              "2     3       Chongqing   China  ...       6.0     0.0        0.0\n",
              "3     4          Fujian   China  ...       1.0     0.0        0.0\n",
              "4     5           Gansu   China  ...       0.0     0.0        0.0\n",
              "5     6       Guangdong   China  ...      26.0     0.0        0.0\n",
              "6     7         Guangxi   China  ...       2.0     0.0        0.0\n",
              "33   34          Yunnan   China  ...       1.0     0.0        0.0\n",
              "7     8         Guizhou   China  ...       1.0     0.0        0.0\n",
              "9    10           Hebei   China  ...       1.0     0.0        0.0\n",
              "10   11    Heilongjiang   China  ...       0.0     0.0        0.0\n",
              "11   12           Henan   China  ...       5.0     0.0        0.0\n",
              "12   13       Hong Kong   China  ...       0.0     0.0        0.0\n",
              "13   14           Hubei   China  ...     444.0     0.0        0.0\n",
              "14   15           Hunan   China  ...       4.0     0.0        0.0\n",
              "8     9          Hainan   China  ...       4.0     0.0        0.0\n",
              "34   35        Zhejiang   China  ...      10.0     0.0        0.0\n",
              "\n",
              "[34 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfkfieKK81eD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hk=data[data['Country']=='Hong Kong']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bTSFBAt08-vm",
        "colab_type": "code",
        "outputId": "eaae4a4b-3a8d-4b96-c4e8-ad2c01929af0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "hk"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sno</th>\n",
              "      <th>Province/State</th>\n",
              "      <th>Country</th>\n",
              "      <th>Last Update</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>51</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>2020-01-23 12:00:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>109</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>2020-01-24 12:00:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>151</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>2020-01-25 12:00:00</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>194</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>2020-01-26 23:00:00</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>242</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>2020-01-27 20:30:00</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>297</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>2020-01-28 23:00:00</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>348</th>\n",
              "      <td>349</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>2020-01-29 21:00:00</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404</th>\n",
              "      <td>405</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>2020-01-30 21:30:00</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Sno Province/State    Country  ... Confirmed  Deaths  Recovered\n",
              "50    51      Hong Kong  Hong Kong  ...       2.0     0.0        0.0\n",
              "108  109      Hong Kong  Hong Kong  ...       2.0     0.0        0.0\n",
              "150  151      Hong Kong  Hong Kong  ...       5.0     0.0        0.0\n",
              "193  194      Hong Kong  Hong Kong  ...       8.0     0.0        0.0\n",
              "241  242      Hong Kong  Hong Kong  ...       8.0     0.0        0.0\n",
              "296  297      Hong Kong  Hong Kong  ...       8.0     0.0        0.0\n",
              "348  349      Hong Kong  Hong Kong  ...      10.0     0.0        0.0\n",
              "404  405      Hong Kong  Hong Kong  ...      12.0     0.0        0.0\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jUlmQAwa9w2O",
        "colab_type": "code",
        "outputId": "e5e271de-2c61-427e-85ae-424bb282aa63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "grouped_country=data.groupby(\"Country\")[['Last Update','Confirmed', 'Deaths', 'Recovered']]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-541-ba52f192f5cb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrouped_country\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Country\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Last Update'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Confirmed'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Deaths'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Recovered'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, **kwargs)\u001b[0m\n\u001b[1;32m   7892\u001b[0m             \u001b[0msqueeze\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7893\u001b[0m             \u001b[0mobserved\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobserved\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7894\u001b[0;31m             \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7895\u001b[0m         )\n\u001b[1;32m   7896\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(obj, by, **kwds)\u001b[0m\n\u001b[1;32m   2520\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"invalid type: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2521\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2522\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, **kwargs)\u001b[0m\n\u001b[1;32m    389\u001b[0m                 \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m                 \u001b[0mobserved\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobserved\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m                 \u001b[0mmutated\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmutated\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    392\u001b[0m             )\n\u001b[1;32m    393\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36m_get_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, mutated, validate)\u001b[0m\n\u001b[1;32m    619\u001b[0m                 \u001b[0min_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    620\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 621\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    622\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGrouper\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m             \u001b[0;31m# Add key to exclusions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Country'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3FoErvck3s4B",
        "colab_type": "code",
        "outputId": "4cf1a1dc-310a-4463-99de-220df7225644",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        }
      },
      "source": [
        "grouped_country['Country'].reset_index()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-439-a4fe163c9dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrouped_country\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Country'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    247\u001b[0m             raise IndexError(\n\u001b[1;32m    248\u001b[0m                 \"Column(s) {selection} already selected\".format(\n\u001b[0;32m--> 249\u001b[0;31m                     \u001b[0mselection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_selection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    250\u001b[0m                 )\n\u001b[1;32m    251\u001b[0m             )\n",
            "\u001b[0;31mIndexError\u001b[0m: Column(s) ['Last Update', 'Country', 'Confirmed', 'Deaths', 'Recovered'] already selected"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R1YNmdjZAtUI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "china=data[(data['Country']=='China') |( data['Country']=='Mainland China')|( data['Country']=='Hong Kong')]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HD_PjM8yBXJm",
        "colab_type": "code",
        "outputId": "dc970f37-0c00-4ef7-8b23-7a4670334637",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "china['Country'].replace('Mainland China','Chinese Sub',inplace=True)\n",
        "china['Country'].replace('Hong Kong','Chinese Sub',inplace=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py:6786: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._update_inplace(new_data)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1EuKfLA4CCoR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Chinese=china.groupby('Last Update')['Confirmed','Deaths','Recovered'].sum().reset_index()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fW8zUomUCTjA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Chinese.to_csv('sample_data/coronokimaakiankh.csv',date_format='%Y-%m-%d %H:%M:%S')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7a4ST5lJC9_4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x=Chinese[['Recovered','Deaths']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2QSkCxSERJ3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y=Chinese[['Confirmed']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qtHsS2TVEZSV",
        "colab_type": "code",
        "outputId": "02e579ad-1df1-4601-a4a8-4ec75e2d3f01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "sns.regplot(np.arange(0,10,1),Chinese['Confirmed'],data=Chinese)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f414e54e780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 220
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAD4CAYAAAApWAtMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IMA3qpxAEvlX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import statsmodels.api as sm"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EQjzlJIpHQ2a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.svm import SVR"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NzFND07JLJsO",
        "colab_type": "code",
        "outputId": "cb3d8d79-86a7-4f08-c15c-cf8142bf742a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "model=SVR(kernel='linear',C=0.5)\n",
        "model.fit(x.values,y.values)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=0.5, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
              "    kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 378
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HEOeCQ-i0fTm",
        "colab_type": "code",
        "outputId": "bdf192b4-2c48-40db-fe69-20b6f71919e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.svm import SVC\n",
        "svc=SVC()\n",
        "pf=PolynomialFeatures(degree=3,include_bias=True)\n",
        "x_poly=pf.fit_transform(x.values)\n",
        "svc.fit(x_poly, y.values)\n",
        "y_poly_pred = svc.predict(x_poly)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yxsEn6hw0EGZ",
        "colab_type": "code",
        "outputId": "77ef4e79-cc14-4244-c212-868c3d34f43a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "res"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 499.9666 ,  670.77313,  784.50525,  670.77313,  980.88434,\n",
              "       2319.6394 , 3888.8464 , 5365.3    , 6962.0835 , 7006.502  ],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 423
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EiTOXh0cusnl",
        "colab_type": "text"
      },
      "source": [
        "Slight errors in prediction yet to be improved"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-0X09N8s-Od",
        "colab_type": "code",
        "outputId": "dde7f173-eafc-486a-a78d-b8e64bdaedce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "plt.suptitle(\"Predicted in red ,Actual in blue \")\n",
        "lines=plt.plot(np.arange(0,10,1),res,c=\"red\")\n",
        "sns.regplot(np.arange(0,10,1),Chinese['Confirmed'],data=Chinese)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f41410d60b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 425
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAEVCAYAAAAl9QikAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G-0eWMFLLrJR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JQpizrdgHrbv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "res = mod.fit(maxiter=1000, disp=False)\n",
        "print(res.summary())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hywL3MC5JLnn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FTurKyHUu2JA",
        "colab_type": "text"
      },
      "source": [
        "No correlation between variables ,thus more feature engineering is required"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pJLl4a-UZ9Oy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sns.heatmap(data.corr())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gUe6VTySoAuP",
        "colab_type": "text"
      },
      "source": [
        "# Features\n",
        "- Who Reports\n",
        "- Seafood wholesale market\n",
        "- Population Density\n",
        "- Stock Market Price\n",
        "- Geographical Conditions\n",
        "- Visits to Outbreak zone\n",
        "- Wild Animals in China\n",
        "- Base Reproduction Rate\n",
        "- Incubation Period \n",
        "- Medical Personal Deployed\n",
        "- Realtime Social Media Sentiment\n",
        "- Analyis of flights\n",
        "- Analysis of wuhan metro data\n",
        "- Highway Traffic using Google Sattelite API\n",
        "- Attendance in Schools\n",
        "- Medical/Army Personal Deployement\n",
        "# Note: Subject to change\n",
        "# *Investigating the possibility of Transfer Learning for prediction.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MEzkN-lAPIQp",
        "colab_type": "code",
        "outputId": "2e0cb7eb-985a-48ce-c8d5-15af658f6501",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "!pip install prophet"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting prophet\n",
            "  Downloading https://files.pythonhosted.org/packages/1b/1c/61c969e76119a8257220869a6b3b57cd2ad4fbe0fd69b9b6845bfcf135aa/prophet-0.1.1.tar.gz\n",
            "Requirement already satisfied: pytz>=2014.9 in /usr/local/lib/python3.6/dist-packages (from prophet) (2018.9)\n",
            "Requirement already satisfied: pandas>=0.15.1 in /usr/local/lib/python3.6/dist-packages (from prophet) (0.25.3)\n",
            "Requirement already satisfied: six>=1.8.0 in /usr/local/lib/python3.6/dist-packages (from prophet) (1.12.0)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.15.1->prophet) (2.6.1)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.15.1->prophet) (1.17.5)\n",
            "Building wheels for collected packages: prophet\n",
            "  Building wheel for prophet (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for prophet: filename=prophet-0.1.1-cp36-none-any.whl size=12168 sha256=f4a4ed8d375e2c67e05662c6049b433d46ff378f5610d0f8e9056993daa12c01\n",
            "  Stored in directory: /root/.cache/pip/wheels/77/3e/f3/1c536bf1f871f818686e7fbf31cab18d52787a72dea8640756\n",
            "Successfully built prophet\n",
            "Installing collected packages: prophet\n",
            "Successfully installed prophet-0.1.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6kjcR7P8UOFM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from fbprophet import Prophet\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aut9XklKkv8I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Chinese.columns=['ds','y','Deaths','Recovered']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T3-tUovOmntr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "chinese=Chinese.drop(columns=['Recovered','Deaths'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NtnfmNZUnAKR",
        "colab_type": "code",
        "outputId": "d92dcb79-f03b-41e6-92d3-c39f5d4998d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "m = Prophet()\n",
        "m.fit(chinese)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:fbprophet:n_changepoints greater than number of observations.Using 7.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<fbprophet.forecaster.Prophet at 0x7f414a4e0278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 340
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JxldK0gtnQQb",
        "colab_type": "code",
        "outputId": "b8a87a3c-8521-41c0-ab4f-0a15c1b1271d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "future = m.make_future_dataframe(periods=7,include_history=True)\n",
        "future.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2020-02-02 21:30:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2020-02-03 21:30:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2020-02-04 21:30:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2020-02-05 21:30:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2020-02-06 21:30:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    ds\n",
              "12 2020-02-02 21:30:00\n",
              "13 2020-02-03 21:30:00\n",
              "14 2020-02-04 21:30:00\n",
              "15 2020-02-05 21:30:00\n",
              "16 2020-02-06 21:30:00"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 362
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QBJEW6V9nd9S",
        "colab_type": "code",
        "outputId": "bdd45c86-55aa-4859-cf66-357afc28063f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "forecast = m.predict(future)\n",
        "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>yhat</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2020-02-02 21:30:00</td>\n",
              "      <td>11388.767903</td>\n",
              "      <td>10358.962954</td>\n",
              "      <td>12466.034543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2020-02-03 21:30:00</td>\n",
              "      <td>12299.577751</td>\n",
              "      <td>11217.323964</td>\n",
              "      <td>13261.040391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2020-02-04 21:30:00</td>\n",
              "      <td>13210.387601</td>\n",
              "      <td>12194.150419</td>\n",
              "      <td>14266.787489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2020-02-05 21:30:00</td>\n",
              "      <td>14121.197450</td>\n",
              "      <td>13137.950937</td>\n",
              "      <td>15041.554091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2020-02-06 21:30:00</td>\n",
              "      <td>15032.007300</td>\n",
              "      <td>13956.748674</td>\n",
              "      <td>16023.241639</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    ds          yhat    yhat_lower    yhat_upper\n",
              "12 2020-02-02 21:30:00  11388.767903  10358.962954  12466.034543\n",
              "13 2020-02-03 21:30:00  12299.577751  11217.323964  13261.040391\n",
              "14 2020-02-04 21:30:00  13210.387601  12194.150419  14266.787489\n",
              "15 2020-02-05 21:30:00  14121.197450  13137.950937  15041.554091\n",
              "16 2020-02-06 21:30:00  15032.007300  13956.748674  16023.241639"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 363
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HaOKXhhHnhSK",
        "colab_type": "code",
        "outputId": "289e8f36-105e-4052-d12a-6a80a514963c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        }
      },
      "source": [
        "m.plot_components(forecast)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAn4AAAGoCAYAAADYX+jPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1PiJiIiImMT/AN0ZSCUC5JkxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 648x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 364
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAn4AAAGoCAYAAADYX+jPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1PiJiIiImMT/AN0ZSCUC5JkxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 648x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2DTEiZFnogI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "res=m.predict(future)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FTl6hKUPoUkJ",
        "colab_type": "code",
        "outputId": "3e13c94a-741c-4688-8dbc-297d44570997",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "res"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>trend</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "      <th>trend_lower</th>\n",
              "      <th>trend_upper</th>\n",
              "      <th>additive_terms</th>\n",
              "      <th>additive_terms_lower</th>\n",
              "      <th>additive_terms_upper</th>\n",
              "      <th>daily</th>\n",
              "      <th>daily_lower</th>\n",
              "      <th>daily_upper</th>\n",
              "      <th>multiplicative_terms</th>\n",
              "      <th>multiplicative_terms_lower</th>\n",
              "      <th>multiplicative_terms_upper</th>\n",
              "      <th>yhat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>14016.811098</td>\n",
              "      <td>-1578.695342</td>\n",
              "      <td>376.259980</td>\n",
              "      <td>14016.811098</td>\n",
              "      <td>14016.811098</td>\n",
              "      <td>-14649.227535</td>\n",
              "      <td>-14649.227535</td>\n",
              "      <td>-14649.227535</td>\n",
              "      <td>-14649.227535</td>\n",
              "      <td>-14649.227535</td>\n",
              "      <td>-14649.227535</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-632.416436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-23 12:00:00</td>\n",
              "      <td>14927.620666</td>\n",
              "      <td>-759.016567</td>\n",
              "      <td>1233.277838</td>\n",
              "      <td>14927.620666</td>\n",
              "      <td>14927.620666</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>278.393133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-24 12:00:00</td>\n",
              "      <td>15838.430235</td>\n",
              "      <td>212.288027</td>\n",
              "      <td>2282.089339</td>\n",
              "      <td>15838.430235</td>\n",
              "      <td>15838.430235</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1189.202701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-25 12:00:00</td>\n",
              "      <td>16749.239804</td>\n",
              "      <td>1091.746470</td>\n",
              "      <td>3052.901176</td>\n",
              "      <td>16749.239804</td>\n",
              "      <td>16749.239804</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>-14649.227534</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2100.012271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-25 22:00:00</td>\n",
              "      <td>17128.743909</td>\n",
              "      <td>698.413147</td>\n",
              "      <td>2703.725442</td>\n",
              "      <td>17128.743909</td>\n",
              "      <td>17128.743909</td>\n",
              "      <td>-15440.539965</td>\n",
              "      <td>-15440.539965</td>\n",
              "      <td>-15440.539965</td>\n",
              "      <td>-15440.539965</td>\n",
              "      <td>-15440.539965</td>\n",
              "      <td>-15440.539965</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1688.203943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-01-26 23:00:00</td>\n",
              "      <td>18077.504169</td>\n",
              "      <td>2501.485119</td>\n",
              "      <td>4446.280129</td>\n",
              "      <td>18077.504169</td>\n",
              "      <td>18077.504169</td>\n",
              "      <td>-14661.589542</td>\n",
              "      <td>-14661.589542</td>\n",
              "      <td>-14661.589542</td>\n",
              "      <td>-14661.589542</td>\n",
              "      <td>-14661.589542</td>\n",
              "      <td>-14661.589542</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3415.914627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2020-01-27 20:30:00</td>\n",
              "      <td>18893.437993</td>\n",
              "      <td>3213.056281</td>\n",
              "      <td>5215.110020</td>\n",
              "      <td>18893.437993</td>\n",
              "      <td>18893.437993</td>\n",
              "      <td>-14685.302830</td>\n",
              "      <td>-14685.302830</td>\n",
              "      <td>-14685.302830</td>\n",
              "      <td>-14685.302830</td>\n",
              "      <td>-14685.302830</td>\n",
              "      <td>-14685.302830</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4208.135163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2020-01-28 23:00:00</td>\n",
              "      <td>19899.123869</td>\n",
              "      <td>4235.275071</td>\n",
              "      <td>6220.373795</td>\n",
              "      <td>19899.123869</td>\n",
              "      <td>19899.123869</td>\n",
              "      <td>-14661.589542</td>\n",
              "      <td>-14661.589542</td>\n",
              "      <td>-14661.589542</td>\n",
              "      <td>-14661.589542</td>\n",
              "      <td>-14661.589542</td>\n",
              "      <td>-14661.589542</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5237.534327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2020-01-29 21:00:00</td>\n",
              "      <td>20734.032898</td>\n",
              "      <td>7390.092176</td>\n",
              "      <td>9459.262255</td>\n",
              "      <td>20734.032898</td>\n",
              "      <td>20734.032898</td>\n",
              "      <td>-12294.903747</td>\n",
              "      <td>-12294.903747</td>\n",
              "      <td>-12294.903747</td>\n",
              "      <td>-12294.903747</td>\n",
              "      <td>-12294.903747</td>\n",
              "      <td>-12294.903747</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8439.129150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2020-01-30 21:30:00</td>\n",
              "      <td>21663.817952</td>\n",
              "      <td>7640.869616</td>\n",
              "      <td>9610.126680</td>\n",
              "      <td>21663.817952</td>\n",
              "      <td>21663.817952</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8656.338354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2020-01-31 21:30:00</td>\n",
              "      <td>22574.627802</td>\n",
              "      <td>8642.806186</td>\n",
              "      <td>10559.735026</td>\n",
              "      <td>22574.627767</td>\n",
              "      <td>22574.627824</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9567.148204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2020-02-01 21:30:00</td>\n",
              "      <td>23485.437651</td>\n",
              "      <td>9484.886400</td>\n",
              "      <td>11448.369020</td>\n",
              "      <td>23485.437536</td>\n",
              "      <td>23485.437749</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10477.958053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2020-02-02 21:30:00</td>\n",
              "      <td>24396.247500</td>\n",
              "      <td>10441.804014</td>\n",
              "      <td>12329.000628</td>\n",
              "      <td>24396.247274</td>\n",
              "      <td>24396.247700</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11388.767903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2020-02-03 21:30:00</td>\n",
              "      <td>25307.057350</td>\n",
              "      <td>11286.669090</td>\n",
              "      <td>13319.370558</td>\n",
              "      <td>25307.057002</td>\n",
              "      <td>25307.057688</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12299.577751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2020-02-04 21:30:00</td>\n",
              "      <td>26217.867199</td>\n",
              "      <td>12162.105321</td>\n",
              "      <td>14200.767196</td>\n",
              "      <td>26217.866706</td>\n",
              "      <td>26217.867684</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13210.387601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2020-02-05 21:30:00</td>\n",
              "      <td>27128.677048</td>\n",
              "      <td>13140.471040</td>\n",
              "      <td>15140.364810</td>\n",
              "      <td>27128.676396</td>\n",
              "      <td>27128.677677</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14121.197450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2020-02-06 21:30:00</td>\n",
              "      <td>28039.486898</td>\n",
              "      <td>14025.924885</td>\n",
              "      <td>16085.273264</td>\n",
              "      <td>28039.486078</td>\n",
              "      <td>28039.487700</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>-13007.479598</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15032.007300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    ds         trend  ...  multiplicative_terms_upper          yhat\n",
              "0  2020-01-22 12:00:00  14016.811098  ...                         0.0   -632.416436\n",
              "1  2020-01-23 12:00:00  14927.620666  ...                         0.0    278.393133\n",
              "2  2020-01-24 12:00:00  15838.430235  ...                         0.0   1189.202701\n",
              "3  2020-01-25 12:00:00  16749.239804  ...                         0.0   2100.012271\n",
              "4  2020-01-25 22:00:00  17128.743909  ...                         0.0   1688.203943\n",
              "5  2020-01-26 23:00:00  18077.504169  ...                         0.0   3415.914627\n",
              "6  2020-01-27 20:30:00  18893.437993  ...                         0.0   4208.135163\n",
              "7  2020-01-28 23:00:00  19899.123869  ...                         0.0   5237.534327\n",
              "8  2020-01-29 21:00:00  20734.032898  ...                         0.0   8439.129150\n",
              "9  2020-01-30 21:30:00  21663.817952  ...                         0.0   8656.338354\n",
              "10 2020-01-31 21:30:00  22574.627802  ...                         0.0   9567.148204\n",
              "11 2020-02-01 21:30:00  23485.437651  ...                         0.0  10477.958053\n",
              "12 2020-02-02 21:30:00  24396.247500  ...                         0.0  11388.767903\n",
              "13 2020-02-03 21:30:00  25307.057350  ...                         0.0  12299.577751\n",
              "14 2020-02-04 21:30:00  26217.867199  ...                         0.0  13210.387601\n",
              "15 2020-02-05 21:30:00  27128.677048  ...                         0.0  14121.197450\n",
              "16 2020-02-06 21:30:00  28039.486898  ...                         0.0  15032.007300\n",
              "\n",
              "[17 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 366
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S6CCx5k-o-uq",
        "colab_type": "code",
        "outputId": "a23fbeac-9f8e-4478-f821-0ed67f376ab5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "res.iloc[10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ds                            2020-01-31 21:30:00\n",
              "trend                                     22574.6\n",
              "yhat_lower                                8641.42\n",
              "yhat_upper                                10557.7\n",
              "trend_lower                               22574.6\n",
              "trend_upper                               22574.6\n",
              "additive_terms                           -13007.5\n",
              "additive_terms_lower                     -13007.5\n",
              "additive_terms_upper                     -13007.5\n",
              "daily                                    -13007.5\n",
              "daily_lower                              -13007.5\n",
              "daily_upper                              -13007.5\n",
              "multiplicative_terms                            0\n",
              "multiplicative_terms_lower                      0\n",
              "multiplicative_terms_upper                      0\n",
              "yhat                                      9567.15\n",
              "Name: 10, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 331
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Sb_fVVWpE8Q",
        "colab_type": "code",
        "outputId": "a7f20384-a471-413f-8467-f2a890c3b9e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        }
      },
      "source": [
        "m.plot(res)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAGoCAYAAABbtxOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsQH82n9KpuAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 337
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAGoCAYAAABbtxOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzde3iV5Z3v//c6r5yP5MAKp5iIgIIc\nRKwWsZai7QxOp9ZjRx2dYcZOtx3bPdP+9m7dm+uamTrOdHp1O+30R7fbwd0CddtWup1K+dUWW1uR\nKgcVlAZIhKyck7WSrPN6Dr8/AikU1JWQsPKEz+u6vIAnz8pzP7eL8MmT7/29XbZt24iIiIiICADu\nfA9ARERERGQqUUAWERERETmNArKIiIiIyGkUkEVERERETqOALCIiIiJyGgVkEREREZHTTHpAvv/+\n+6mpqeHyyy8/62Nf/epXcblc9PX1AWDbNg899BBNTU0sXryYvXv3jp67efNmmpubaW5uZvPmzaPH\nX3vtNa644gqampp46KGHUNc6ERERETkfkx6Q77vvPnbs2HHW8RMnTrBz505mz549euz555+npaWF\nlpYWNm3axIMPPgjAwMAAGzdu5JVXXmHPnj1s3LiRSCQCwIMPPsi3v/3t0ded61oiIiIiIrnyTvYF\nVq9eTVtb21nHH374YR577DFuueWW0WPbt2/nnnvuweVysWrVKqLRKJ2dnezatYu1a9dSWVkJwNq1\na9mxYwdr1qxhaGiIVatWAXDPPffw7LPPcvPNN7/vuKqrq5k7d+4Zx7LZLD6fb/w3K+Omuc8PzXv+\naO7zQ/OeP5r7/NC8v7e2trbRSobTTXpAPpft27cTCoVYsmTJGcfD4TCzZs0a/XNDQwPhcPg9jzc0\nNJx1PBdz587l1VdfPeNYR0cHM2fOHM8tyXnS3OeH5j1/NPf5oXnPH819fmje39uKFSvOefyCB+RE\nIsE//MM/sHPnzgt9aTZt2sSmTZsA6OrqoqOj44yP9/b2XvAxyQjNfX5o3vNHc58fmvf80dznh+Z9\nfC54QD569Citra2jT4/b29tZtmwZe/bsIRQKceLEidFz29vbCYVChEIhdu3adcbxNWvWEAqFaG9v\nP+v8d7NhwwY2bNgAjHzHcK7vqPRdVv5o7vND854/mvv80Lznj+Y+PzTvY3fB27xdccUV9PT00NbW\nRltbGw0NDezdu5e6ujrWr1/PU089hW3b7N69m7KyMurr61m3bh07d+4kEokQiUTYuXMn69ato76+\nntLSUnbv3o1t2zz11FNn1DSLiIiIiIzVpAfkO++8k2uuuYbDhw/T0NDAE0888a7nfvSjH6WxsZGm\npib+/M//nG9+85sAVFZW8uUvf5mrrrqKq666ikceeWR0wd43v/lN/uzP/oympiYuueSSnBboiYiI\niIi8m0kvsdi6det7fvz0Dhcul4tvfOMb5zzv/vvv5/777z/r+IoVK3jzzTfPa4wiIiIiIqdoJz0R\nERERkdMoIIuIiIiInEYBWURERETkNArIIiIiIiKnUUAWERERETmNArKIiIiIyGkUkEVERERETqOA\nLCIiIiJyGgVkEREREZHTKCCLiIiIiJxGAVlERERE8sa0bPrjGbKmle+hjPLmewAiIiIicvFJZk06\nB1O0DiRIZE2um1dJRaE/38MCFJBFRERE5AKxbZtoMkvbQIKu4TQel4uyoJesZed7aGdQQBYRERGR\nSZUxLHqG0xztj5PImgS9bmYU+XG5XPke2jkpIIuIiIjIpBhKZWkfTHIiksLGpjTgoyYw9ePn1B+h\niIiIiDiGYVr0xTMcG0gwmMzi97ipLPThnqJPi89FAVlEREREzlsiYxAeTNEWSWKYFsV+LzXFgXwP\na1wUkEVERERkXCzLJnJy0V1PLI3H7aIs6MPrds7T4nNRQBYRERGRMUkbJl1DaY4NJEhlTQp9nim9\n6G6sFJBFRERE5H3Zts1gyuBENEl4MIkLF6UBL6UOWHQ3VtPvjkRERERkwmRNi75YmqP9CYbTBn6P\nm6pCv6MW3Y2VArKIiIiInCWWHll0904kgWVDid/j2EV3Y6WALCIiIiIAmJbNQCJD60CC/ngGn8dF\nedCHx+GL7sZKAVlERETkIpfMmnQOpmgdSJAxLYouoqfF56KALCIiInIRsm2b6MkWbV3DaTwuF6VB\nL+UeX76HlncKyCIiIiIXkYxh0TOc5mh/nETWJOh1T6sWbRNBAVlERETkIjCUytI+mOREJIWNTWnA\nR800bNE2ETQrIiIiItOUZdt0DaU4NpBgMJnF73FTWeib1i3aJoICsoiIiMg0k8iMtGh780SUYJmX\nYr/3ol50N1YKyCIiIiLTgGXZRE4uuuuJpfG4XRT5vFQpGI+ZArKIiIiIg5mWTfdwisO9cVJZk0Kf\nZ3TRXTSR79E5kwKyiIiIiAMZpkXnUIqWvgRpw6Is6KVUi+4mhGZRRERExEEyhkXHUIqWvjimZVEW\n8FEWdG6ki6UNfnG0n6tnl+d7KKOcO5siIiIiF5G0YdIeTXK0P4FtQ3nQi9fBm3q0Dyb53r4OfnSo\nm3jG5BOL61he6M/3sAAFZBEREZEpLZk1ORFN0tqfwAWUFfjwup3Zps22bfZ3DLFlb5gXj/Xjcrn4\nyKUzuPmyGhqrivI9vFEKyCIiIiJTUCJj0BZJ8M5AEo/bRUWBD49Dg3HWtPhpSx9b9oZ5qydGWdDL\nvStmcduSemYUB+iNZ/I9xDMoIIuIiIhMIbG0QdtAguPRJH6Pi+oiv2M39ogms/zwjS6ePtBBbzzD\n3IoC/p8PNfGxBTUEfZ58D+9duSf7Avfffz81NTVcfvnlo8f+5m/+hssuu4zFixfz8Y9/nGg0Ovqx\nr3zlKzQ1NTF//nx+8pOfjB7fsWMH8+fPp6mpiUcffXT0eGtrK1dffTVNTU3cfvvtZDJT6zsQERER\nkVwMpbLsD0f55bF+uofT1BT5qShwZjhuG0jwDy+08LEn9vCNX7fRWFXI129ZxNP3LOcTi+undDiG\nCxCQ77vvPnbs2HHGsbVr1/Lmm2/y+uuvc+mll/KVr3wFgEOHDrFt2zYOHjzIjh07+PSnP41pmpim\nyV/91V/x/PPPc+jQIbZu3cqhQ4cA+MIXvsDDDz/MkSNHqKio4IknnpjsWxIRERGZMNFklldPRHnp\n2AADiSwzivyUF/hwOSwY27bN7nciPPTsm9z61Gs8d6ibm+bPYNunlvGNP76Ca+dVOibsT3qJxerV\nq2lrazvj2Ec+8pHR369atYpnnnkGgO3bt3PHHXcQCASYN28eTU1N7NmzB4CmpiYaGxsBuOOOO9i+\nfTsLFizgZz/7GVu2bAHg3nvv5b//9//Ogw8+ONm3JSIiIjJutj2y692Rvjh9sQyFPg+1Jc7c8S5t\nWDz/dg9b94U52p+gqtDHX6yazScW11M5RbpSjFXea5D/1//6X9x+++0AhMNhVq1aNfqxhoYGwuEw\nALNmzTrj+CuvvEJ/fz/l5eV4vd6zzj+XTZs2sWnTJgC6urro6Og44+O9vb0Tc1MyZpr7/NC854/m\nPj807/mjuR9h2zaDKYPj0SSxlEHQ66bA78FMQzQ28deLRQcm/pOeFEka/Oi3Uf7vbweJpk0ay/38\nzTW13DC3BL/HDYlIzjv5xZMGPf4kyeDUaFuX14D893//93i9Xu6+++4Lcr0NGzawYcMGAFasWMHM\nmTPPOudcx+TC0Nznh+Y9fzT3+aF5z5+Lee4ty6Y3lqalL85wxqC4vJgZ/gtTh1teXTuhn6+lN86W\nfWF2HO7BMG2ua6zk7qUhljeUjbssJBvPUFNXTsUUeeKct4D87//+7zz33HO88MILo5MZCoU4ceLE\n6Dnt7e2EQiGAcx6vqqoiGo1iGAZer/eM80VERETyzbRseoZT/LY3TiJrUhLwUlPsvFIKy7b5VesA\nW/aF+c2JQYJeN390eR13XhlidkVBvoc34fISkHfs2MFjjz3Giy++SGFh4ejx9evXc9ddd/G5z32O\njo4OWlpaWLlyJbZt09LSQmtrK6FQiG3btrFlyxZcLhc33HADzzzzDHfccQebN2/mlltuycctiYiI\niIwyTIuuoZEnxinDpDTopSbgvGCczJo8d6ibrfs7OB5JUlvs5z9dN5ePX15H6RQph5gMkx6Q77zz\nTnbt2kVfXx8NDQ1s3LiRr3zlK6TTadauXQuMLNT71re+xaJFi7jttttYuHAhXq+Xb3zjG3g8Iz9+\n+Nd//VfWrVuHaZrcf//9LFq0CIB//Md/5I477uBLX/oSS5cu5YEHHpjsWxIRERE5p6xp0TGYoqUv\nTtayKA/4KA3mfcnXmHUPp3n6QAc/fKOLobTBotoS/v7m+dzYVI3XM+lN0PLOZdu2ne9B5MOKFSt4\n9dVXzzjW0dFxUddH5ZPmPj807/mjuc8PzXv+TPe5zxgW7dEkR/oTWLZFedCHbwoEyWhf95hqkN/s\nGmbL3jAvtPRiAzc0VXPX0hCL60smte1cbzzD1bMvfA3yufIgTIEuFiIiIiJOlcqanIgmae1PYAPl\nQS9ej7NKDwzLZtfRPrbs7eD1ziGK/B7uWBri9iUzmVkWzPfw8kIBWURERGSMklmTdwaStEXiuHFR\nXuDD43bGJhinxNIGzx7s4nv7OugcThMqDfL56xtZv6iWIv/FHREv7rsXERERGYN42qBtIMHxaBKv\n20Vlgd9xwbh9MMn39nXwo0PdxDMmy0KlfO76RlY3VjnuXiaLArKIiIjI+xhOGbQOxAkPpvC5XVQX\n+R2zbTKMbFCyv2OILXvDvHisH5fLxUcuncFdS2eyoLYk38ObchSQRURERN7FYDLL0b44XbE0AY+b\nGUX+SV2sNtGyps3zb/ewZW+Yt3pilAW93LtiFrctqWeGA/sxXygKyCIiIiKnsW2baDLL0b4EPfE0\nBV43NQ4LxtFklh++0cW2fSfoT5rMrSjg//lQEx9bUEPQd2F28HMyBWQRERERRoLxQCJLS2+MSDJL\noc9DrcOesrYNJNiyL8x/vNVD2rBYXl/IIx+ZxzVzKxxVEpJvCsgiIiJyUUtlTbqH0xwbSJDKmhT5\nPY7aDtq2bV45HmXLvjC/bovg97i4+bIa7lwaopoY5dWV+R6i4yggi4iIyEXHsmwiySzHIwm6htO4\nXS5KA15KA86JRmnD4vm3e9i6L8zR/gRVhT7+YtVsPrG4nsqTG25E+2J5HqUzOeddICIiInKeEhmD\n7uEMbQMJUoZJgdfjuIV3/fEMz7zeyTOvdxJJZmmuLuK/rb2UdfNn4Pfmf/e+sbJtG9OaWhs7KyCL\niIjItGZaNpFEhnciSXpiGTwuKA16KQ06Kwa19MbZsi/MjsM9GKbNdY2V3L00xPKGMkcF/FNMy2Yw\nlcWwIFQWoHgKPb2fOiMRERERmUDxtEHXcIq2gSQZ06bQ52ZGkc9RYdKybX7VOsCWfWF+c2KQoNfN\nH11ex51XhphdUZDv4Y1LxrAYTGdx4WJuZSGzyoMUTrGd+6bWaERERETOg2nZ9McztEUS9MczeNwu\nygJevB5nlR4ksybPHepm6/4OjkeS1Bb7+U/XzeXjl9dRGvTle3jjksiYxDIGQa+HRbUl1JYEp2xJ\niAKyiIiIOF4sbdA5lKItksQwLcd1ojilezjN0wc6+OEbXQylDRbVlvD3N8/nxqZqx4V8GKkvHkob\npA2LsgIfK2aVU1Xoxz3Ft7RWQBYRERFHMkyLvvjIgrtIMovX7aI06MM7xcPXubzZNcyWvWFeaOnF\nBm5oquaupSEW15c4qiTklNPri+tL/cytLKIs6HXMvSggi4iIiKMMpbJ0DKY5Hk1iWjbFDn1abFg2\nu472sWVvB693DlHk93DH0hC3L5nJzLJgvoc3Lk6oL86F80YsIiIiF52sadEXG9nMYyhl4HO7KA/6\n8DjwaXEsbfDswS6e3t9Bx1CaUGmQz1/fyB8urJ1SnRzGwkn1xblw5v8FERERmfZs22YoZRAeSnIi\nksLCpsTvdeTTYoD2wSTf29fBjw51E8+YLA2V8vDqRlY3Vjky6Nu2zXDaIHWyvnh5QznVRVO/vjgX\nCsgiIiIypWQMi57hNG2RBMMZA7/bTUWBM58W27bN/o4htuwN8+KxflwuFx+5dKS+eEFtSb6HNy6n\n1xfXlfiZV+Ws+uJcKCCLiIhI3tm2zWDKoD2aJDyYwsamJOClpsiZT4uzpsVPW/rYsjfMWz0xyoJe\n7l0xi08uqXfsE/DpUl+ci+l5VyIiIuIIacOkZzjNsf4EiYyJ3+umstCH26FPIwdTWX7wehdPH+ig\nN55hTkUBX/xQE3+woIagz5Pv4Y3LdKsvzoUCsoiIiFxQtm0TSWY5EUnSOZzChWvkaXGJc2NJ20CC\nrfvCPPdWD2nDYuXscv7rh5v5wNwKR4b96VxfnAvnvhNFRETEUVJZk+7hkU4UqaxJ0OumutDv2NpV\n27bZcyLKlr1hftUWwe9xcdNlNdy1NERTdVG+hzcuF0N9cS4UkEVERGTSWNbI0+LDPcOkBj24XS5K\nA15KHdrODCBtWDz/dg9b94U52p+gstDHX6yazScW11NZ6M/38MblYqovzsXFe+ciIiIyaRIZg66h\nNO9EkqQMEyNtUl/p3KfFAP3xDM+83skzr3cSSWZpri7iv629lHXzZzi2JvdirC/OhQKyiIiITIhT\ntcWt/Ql6YhncLigLeikNeommPI4Nxy29cbbsC7PjcA9Z0+aD8yq5a1mIFQ1ljryni72+OBcKyCIi\nInJebNtmIJGlpTdGJJGl0O9hRpHPkeHxFMu2+VXrAFv2hfnNiUGCXjd/tKiOO5bOZE5FYb6HNy6q\nL86dArKIiIiMy6lgfLg3xmAyS5HfQ02JM3v8npLMmjx3qJut+zs4HklSU+znM9fO5eNX1FEW9OV7\neOOi+uKx0+yIiIjImNi2TX88w+GeGIMpg+KAx7GbX5zSPZzm6QMd/PCNLobSBgtri/m7m+bz4eZq\nvB5n1uQmMiaRRBZ3saX64jFSQBYREZGc2LZN38lgPJw2KPZ7qXX4E+ODXcNs2Rfmpy192LbNmkuq\nuGtZiCX1pY4sPTi9vrg06GVBbQkLGqtUXzxGCsgiIiLynizLpjeWpqUvznDaGNnUw8FPjE3LZtfR\nfrbsC3OgY4giv4fbl8zk9itnEioL5nt44/K7+mKbupLAaH1xZ2da4XgcFJBFRETknE4F48O9ceIZ\ng1KHB+NY2mD7wW6+tz9Mx1CaUGmQz61uZP2iWood2pf59+uLG8qCFDn0XqYSzaCIiIicwbJsemJp\nDvfESGRNxwfj8GCKbfvD/OhgN/GMyZUzS/nr1Y1c31iFx6FPV+MZg3jGJOhT/+LJoIAsIiIiwMiP\n6XuGU7zdGyeVNSkNeqkJODMY27bN/o4htuwN8+KxflwuF2ubq7lrWYiFtSX5Ht642LbNYMogY1pU\nFPhYMKuEqkL1L54MCsgiIiIXOdOy6R5Ocfi0YFzq0GBsmBb/X0sfW/eFOdQdoyzo5d4Vs/jkknrH\nPgU3LJtoMotl2zSUFzC7vICyAme2nHMKBWQREZGLlGnZdA2l+G1vfGRXtaCXUofWrw6msvzg9S6e\nPtBBbzzDnIoCvvihJv5gQQ1BnyffwxuXlGEylDbwuNxcUl1IqKyAAofei9M482+BiIiIjJthWnQN\npfltX5y0YVF+cjtoJ2obSLB1X5jn3uohbVisnF3Of/1wMx+YW4HbgW3aYGQxYSJrUuT3sqS+lJri\ngGN7MTvVpM/2/fffT01NDZdffvnosYGBAdauXUtzczNr164lEokAI7U1Dz30EE1NTSxevJi9e/eO\nvmbz5s00NzfT3NzM5s2bR4+/9tprXHHFFTQ1NfHQQw9h2/Zk35KIiIgjGabFiUiCF4/280b3EAVe\nNzXFfsct7rJtm1eOR/jss29y61Ov8X8PdbNu/gy2fWoZ3/zjK7huXqXjwrFl20QSGXpiaYr8XlbO\nruCDjZXMLCtQOM6DSZ/x++67jx07dpxx7NFHH+XGG2+kpaWFG2+8kUcffRSA559/npaWFlpaWti0\naRMPPvggMBKoN27cyCuvvMKePXvYuHHjaKh+8MEH+fa3vz36ut+/loiIyMXOMC2OnwzGB7uHR7aE\nLgo4LhinDYvtB7u487t7+asfvMlbPTE2rJrNcw+s5JG1l9JUXZTvIY5Z1rToi2foT2SYWR7kunlV\nrJhdTlWR35EblUwXk/7zlNWrV9PW1nbGse3bt7Nr1y4A7r33XtasWcM//uM/sn37du655x5cLher\nVq0iGo3S2dnJrl27WLt2LZWVlQCsXbuWHTt2sGbNGoaGhli1ahUA99xzD88++yw333zzZN+WiIjI\nlJc1LToGU/y2L45pWZQHffg8zlvc1R/P8MzrnXz/jU4GElmaqgt5ZG0z6+bXEHBYyD8lmTUZThv4\nPG7m1xRTXxog4FV98VSRl4Kj7u5u6uvrAairq6O7uxuAcDjMrFmzRs9raGggHA6/5/GGhoazjr+b\nTZs2sWnTJgC6urro6Og44+O9vb3nf3MyLpr7/NC854/mPj8ulnk3Tm7w8U4kiW3blAS8+Nwu4sn8\njSkWHRjza45F0nz/7Qg/ax0ma9lcHSriE9fUsrSuAJfLRTLaSx5vaexsiGcNMoZNkd9DQ3kBFYU+\n3Kks/anJueTF8p6faHmvyHe5XBfsRwgbNmxgw4YNAKxYsYKZM2eedc65jsmFobnPD817/mju82M6\nz3vGsGiPJjnan8DCR6i+fErVr5ZX177vOZZt8+u2CFv2htlzIkrQ6+aWy+u448qZzK0svACjnHin\nbwM9qybA3MpCygt8Fyz/TOf3/GTJS0Cura2ls7OT+vp6Ojs7qampASAUCnHixInR89rb2wmFQoRC\nodGSjFPH16xZQygUor29/azzRURELia/C8ZxLBvKC3x4HbZ5RDJr8h+Hutm6v4N3Iklqiv185tq5\nfPyKOsqCzisLAW0D7WR5+bZy/fr1o50oNm/ezC233DJ6/KmnnsK2bXbv3k1ZWRn19fWsW7eOnTt3\nEolEiEQi7Ny5k3Xr1lFfX09paSm7d+/Gtm2eeuqp0c8lIiIy3aUNkyN9MXYd6aOlL05p0Ed1kd9R\n4bh7OM3jL7Xysf+5h0d/fpQiv4e/u2k+P/rTq7jvqlmODMfxjEFPLE3KtFhUW8INTdXMrylWOHaQ\nSf8/deedd7Jr1y76+vpoaGhg48aNfPGLX+S2227jiSeeYM6cOTz99NMAfPSjH+XHP/4xTU1NFBYW\n8uSTTwJQWVnJl7/8Za666ioAHnnkkdEFe9/85je57777SCaT3HzzzVqgJyIi017aMDkeSXKsP4EL\nKHPgE+ODXcNs2Rfmpy192LbNmkuquGtZiCX1pY7s3qBtoKcXl32RNg5esWIFr7766hnHOjo6VKeT\nJ5r7/NC854/mPj+cPu+prMnxaJLWk8G4vMCHxyEBLNrXTUllDbuO9rNlX5gDHUMU+T3csqiO26+c\nSagsmO8hjstU3wba6e/5yXauPAhTYJGeiIiI/M7LL7/Mrl27WLNmDddccw0wUp97PJKkdSCO2+Wi\nwkHBGEZ2hnvmrQg/anmHjqE0odIgn1vdyPpFtRQ7tOxA20BPb858V4qIiExDL7/8MjfeeCOZTAa/\n38+Pf7KT2kuX8M5AArfbRWWB31HBODyYYtv+MD862E08Y3LlzFL+enUj1zdWOeo+TqdtoC8OCsgi\nIiJ5Zts2pmXz0xd+RiaTwTRNMpkM//6D57n7Ly+lqsjvmK2TbdvmQMcQ390X5sWj/bhcLtY2V/OH\njQVcPX9Ovoc3LpZtM5jMkrVsZhQFuLy+lMrCC9emTS48BWQREZEJZNs2hnXyP9PGsKzRP2cMi5Rh\nkjYsMqZF2rBIZy2ylo2NTfElV+L1+bABr8/HB1evprLQn+9byolhWvy0pY8t+8Ic6o5RGvByz/IG\nPrlkJrUlAaJ93fke4phlTYvBlIGNzeyKAmaVFVISVHS6GOj/soiIyLuwTgXdkyHXtH4XftOnwm72\nZNA9GXgNywIbOPV00QZcI79x48LjduF1j/zqcbkoDnhHyw1mXHct/7Z1O6+9/BLLr7mOxctX5unO\nczeYyvLDN7p4+kAHPbEMsysK+OINl/CxhbWOrck9tQ20X9tAX7QUkEVE5KJgWTZZ0yaRMX7vCa9N\nKmueEXLTJ5/ymrZ9ZtgFwMYFuF2/C7ketwuvy0XgtLA7XouXr3REMG4bSLBtfwfPHeomZVisnFXO\nf7mxmQ/MrXBMOcjvG04ZJLMmpUEvS0NlzCgOOLZWWs6PArKIiEw77dEkvfHMyRIGk7RpYVk28UiU\norgPTnU4dQE2I0H3ZNj1ul34PW4KfB7HBr3JYts2e05E2bI3zK/aIvg8Lm6aX8NdS0M0zyjK9/DG\n5fT+xTOKAywJlV7QbaBlalJAFhGRaWU4ZfBG5xCFPg9ej4uA102hfyTs+lJeyoucUdM7laQNix2H\ne9i6L8yRvgSVhT42rJrNJ66op8qh82laNoOpLIY10r94boXqi+V39E4QEZFpw7Zt3u4ZJuh1O7a/\n7lTSH8/wzOudfP+NTgYSWZqqC3lkbTPr5tcQ8DqztZlhWkRTBgBzKgqZXRGk0K/3ipxJ7wgREZk2\nemNpemMZaksC+R6Kox3pi/PdvWF2HO4ha9pcN6+Su5bO5KpZ5Y4tPcgYFoPpLB63m+bqIkLlQS28\nk3elgCwiItOCadkc7I5RVqB/2sbDsm1+3RZhy94we05ECXjd3LKojjuunMncysJ8D2/cUtmRHe+C\nXg+X15ZSV6qNPeT96auIiIhMCyeiSdKGSWlAT4/HIpk1+Y9D3Wzd38E7kSQ1xX4+c+1cPn5FHWVB\nX76HN26xtEE8o44UMj4KyCIi4njJrMnhnhgVBc5cMJYPPbE0T+/v4AdvdDGUNlhYW8zf3TSfDzdX\nO/YJq23bDKcNUoZFZaFPO97JuCkgi4iI47X0xvC4wasnhO/rYNcwW/aF+WlLH7Ztc/0lVdy9NMSS\nmaWODZKnbwVdVxKgsaqIsgLnPv2W/FNAFhERR4sms7RHU9QU6+nxuzEtm11H+9myL8yBjiGK/B5u\nW1LPHVeGCJUF8z28cTMtm6IUWdUAACAASURBVEgyi23bzCovYE5lobqXyITQu0hERBzLsmwOdQ9T\n5Pc49unnZIqlDbYf7OZ7+8N0DKWZWRrg4dWN3LKo1tFBMmtaDKYMXC5orCqkobzAsdtay9Tk3L8d\nIiJy0eseTjGYzFJTrIV5pwsPpti2P8yPDnYTz5hcObOUv17dyPWNVY5eqJY2RoKxz+Pisppi6kuD\n+B3aj1mmNgVkERFxpKxpcag7RrmDOy1MJNu2OdAxxHf3hXnxaD8ul4u1zdXctSzEwtqSfA/vvCQy\nJsNpg0K/hyUzS6gpVqs2mVwKyCIi4kitAwkMy77onyAapsVPW/rYsi/Moe4YpQEv9yxv4JNLZjp+\nw5RY2iCRMSkt8HLV7HKqCv24HfwEXJxDAVlERBwnljY40hdnRtHFuzBvMJXlh2908fSBDnpiGWZX\nFPDFGy7hYwtrHV2Pa9s2gymDjGkxoyjA4pmllBeoVZtcWArIIiLiOId7YgS9btwXYWhqG0iwbX8H\nzx3qJmVYrJxVzn+5sZkPzK1w9HxYtk00mcWwIFQWYG5lIaUqn5E8UUAWERFH6Yul6RpOUVfi3PZk\nY2XbNntORNmyN8yv2iL4PC5uml/DXUtDNM8oyvfwzothjfQwtoE5FYXMrghS6Fc8kfzSO1BERBzD\ntGwOdg1fNAvz0obFjsM9bN0X5khfgooCH39+9WxuXVxPlcPLSzKGxWA6i9vlpqm6iFB5kIDXuaUh\nMr0oIIuIiGO0R5MkDWva1x73xzM883on33+jk4FElqbqQr68tpmb5tcQcPiixFTWZChtEPR6WFRb\nQl1pEJ86UsgUo4AsIiKOkMqaHO6JURF09j9dyaxJbyxDTyxNXzxDTyxDbzxNXyxDTzxDXyxDVyyN\nadlcN6+Su5bO5KpZ5Y5fpJbKmvTE0hT5vSwNlTGjOODonswyvTn7q4yIiFw0jvbFcbmYsv1vDdOi\nL5GlN5amN34yAMcy9MYz9J4MwT2xDPGMedZrC3xuZhQHmFHk54r6EtaVzeCjl9Uwt7IwD3cycWzb\nZjhtkDIsvB43y2eVU1Xkd3zYl+lPAVlERKa8wWSW49FkXkorTnVX6B0Nu+nTQu/v/hxJjCw0O53H\n7WJGkZ8ZRX7mVRaycnbFyJ+L/Sd/HQnF022rbMu2GTrZqq22OMDS6iISEZNq7XgoDqGALCIiU5pt\n27zVPUyhb+JDZCxt0BfPjD7xPT30nip/6ItnMKzfj75QWeijushPTXGAhbUlZ4XeGcV+ygt8jm69\nNlaGZTOYymJaNrPLC5hTWUhxYCRqJCJ5HpzIGCggi4jIlNY9nCaSzFIzhqePGcMaCbjx35U59MQy\ndPQPEjW66T1Z65vInl3uUOT3UFPsZ0ZRgGUNZdQU+ak++eeakyG4qsivhWWn+V1HChfzKgtpKC9w\n9GYlIgrIIiIyZWVNi0PdMcrG0NZt2/4wX9117KxyB7/HRWWBl9rSAi6tLuLauRWjobd6tOwhQKFf\nwS5XyazJsDpSyDSkgCwiIlPWO5EEWdOiLMfOFV1DKR5/qY1lDWV8dEENM06WQFQX+SkLehns76G8\nunaSRz292bZNLG2SzJqUFnhZdrIjhVsdKWQaUUAWEZEpKZExONIbp7Iw94V5X/tlKwAbP3IpdaUX\nz057F4Jlj+x4l7VsaooDLAmVUl7gm1aLC0VOUUAWEZEp6XBPDJ/HnXOv3D3HI7zQ0sdfXjNH4XgC\nGabFYMrAwmZ2RQGzy3+38E5kutI7XEREppyBRIaOoRR1JbkFXcO0+OddxwiVBfmT5Q2TPLqLQ9qw\nGEobeNwubQUtFx0FZBERmVIsy+Zg1/CYFuZ970AnxwYSfPUPFzp+K+Z8i2cM4hmTQp+HxfUl1BQH\npuzmLCKTRQFZRESmlPBgkuG0QW2Obd364xk27X6HD8ypYHVj5SSPbnqybZuhtEHasKgo8LFwVgmV\nhX4tvJOLVl6/Jfza177GokWLuPzyy7nzzjtJpVK0trZy9dVX09TUxO23304mkwEgnU5z++2309TU\nxNVXX01bW9vo5/nKV75CU1MT8+fP5yc/+Ume7kZERM5X2jB5uydGVUHuT4//9VdtpA2Lz69p1IKx\nMTItm4FEht54lqpCPx+YW8mquZVUqyuFXOTyFpDD4TD/43/8D1599VXefPNNTNNk27ZtfOELX+Dh\nhx/myJEjVFRU8MQTTwDwxBNPUFFRwZEjR3j44Yf5whe+AMChQ4fYtm0bBw8eZMeOHXz605/GNM9u\n/C4iIlPf0f44NuT8I/3XO4b4v4e6uXtZiDkVhZM7uGkka45spBJNZpldUcj1l1SyJFRG2Ri+MRGZ\nzvL6BNkwDJLJJIZhkEgkqK+v52c/+xm33norAPfeey/PPvssANu3b+fee+8F4NZbb+WFF17Atm22\nb9/OHXfcQSAQYN68eTQ1NbFnz5683ZOIiIzPUCrLOwNJKnIMaaZl80+7jjKjyM8DK2dP8uimh1TW\npCeWJpG1uKymmDVN1cyvKabQr4pLkdPlLSCHQiH+83/+z8yePZv6+nrKyspYvnw55eXleL0jf1Eb\nGhoIh8PAyBPnWbNmAeD1eikrK6O/v/+M47//GhERcQbbtnmre5gCrwd3jmUSPzrYxVs9MT77wXna\n/e59xNIGPbE0NrA0VMb1l1Qxp7IQvxY0ipxT3r5ljEQibN++ndbWVsrLy/nkJz/Jjh07JvWamzZt\nYtOmTQB0dXXR0dFxxsd7e3sn9fry7jT3+aF5zx/N/ZkG4hlO9MSoKPQRjb3/+UNpk8dfamVxTQFX\nV1lE+7pzuk4sOnCeI3UO2x7pSJE1bSoKfMwrD1IS8GLF0nTnMMcTTe/5/NC8j0/eAvJPf/pT5s2b\nx4wZMwD44z/+Y371q18RjUYxDAOv10t7ezuhUAgYeeJ84sQJGhoaMAyDwcFBqqqqRo+fcvprft+G\nDRvYsGEDACtWrGDmzJlnnXOuY3JhaO7zQ/OeP5r7EYZpcfjYAPUzSwjm2Gf3//35EWIZiy+uvYyK\nGcVjut5032rasGyiySy2bdNUX8DsikJKctyqe7LpPZ8fmvexy9vPVmbPns3u3btJJBLYts0LL7zA\nwoULueGGG3jmmWcA2Lx5M7fccgsA69evZ/PmzQA888wzfOhDH8LlcrF+/Xq2bdtGOp2mtbWVlpYW\nVq5cma/bEhGRMToeSZI2zZzD8W97Y3z/9U4+sbieS8cYjqeztGHRG08zmDK4pLqQNU3VLKovnTLh\nWMRJ8va35uqrr+bWW29l2bJleL1eli5dyoYNG/jYxz7GHXfcwZe+9CWWLl3KAw88AMADDzzAn/zJ\nn9DU1ERlZSXbtm0DYNGiRdx2220sXLgQr9fLN77xDTwe1aKJiDhBMmvy274YlQX+nM637ZGFeaVB\nL395zZxJHp0zJDImsYxB0Ofh8tpSaksD+LSxh8h5cdm2bed7EPmwYsUKXn311TOOdXR06McQeaK5\nzw/Ne/5o7kcc6BikdzhNRWFuAXnH2z18acdh/uuNTXz8ivoxXy/a1z0tSixs22Y4bZAyLMoKfDRX\nF1E1xTf20Hs+PzTv7+1ceRC0k56IiORJJJEhPJiipii3cJzImHz9l60sqClm/aK6dz3v9df28NrL\nL7H8mutYvHx6ldyZls1QOkvWtKkrCTCvqoiyoFcbpIhMMAVkERG54CzL5lDXMMV+T87h7ok9x+mN\nZ3jsDxbgeZcnpa+/tocH7/wjstkMPp+ff9v67LQIyYZpEU0ZAMyuKGB2eQFFAf0TLjJZ9LdLREQu\nuM6hFENpg5riQE7ntw0k+O7eMH+4sJYr6kvf9bzXXn6JbDaDZZoYZHjt5ZccHZBThslQysTncTG/\nppj60gCBHBczisj4KSCLiMgFlTEs3uqJ5bxjnm3b/MuLxwh43Xzm2rnvee7ya67D5/NjkMHr87P8\nmusmYMSTy7JtTOvkf6f9PmvZFPm9XDmzhJqS4Ls+NReRiaeALCIiF9Sx/gSWZefcaeEXxwb49TsR\nPre6kar3qVdevHwl/7b12Qteg2zbNqY9UiM8GnhP/mrZYGPjwnXGr6d43W78Hhd+r4cCj5uA10XA\n66aiwE9loU/1xSJ5oIAsIiIXzHDKoHUgTnWOC/NShsm/vHiMxspCbluSW9eKxctXjjsYW7aNZdkY\np8Ktxe+C7mi4BRf8LuLa4HKD3+3GfzLc+j1uAj43frebgNeNz+PG43bhdbvO+lUBWGTqUUAWEZEL\nwrZtDvcME/S6cecYCr/zWpjwUIp/+8QVeMfQ29eybQzztJKFk8F3MGlgxDNnhFzXybHhAo/Lhd/j\nxu91U+D3EPC58bndBL0jx84dct0qfxCZZhSQRUTkguiLZ+iJZagtyW1hXudQiid/c4IPN1dz1azy\nnK9j2TbdsTTlQd/Ik9zAySe6XjdDxKifWXbOJ7kel2tK9xEWkQtHAVlERCadadm82TVM2Ri2Pf7a\nL44B8NcfnDema0WSWRqrCllYe3a3i45MMOeALiIXL+1FKSIik+5ENEnaMAn6cmtRtud4hJ8d6edP\nr5pFXWkw5+sYpoULuKSqaJwjFRFRQBYRkUmWzJoc7olRUZDbwjzDtPinXUcJlQX5k+UNY7rWQNLg\nsppi9QoWkfOigCwiIpPqaF8cjxu8Odb3bjvQQetAks9f30jAm/s/U8msSZHfw8yygvEOVUQEUEAW\nEZFJFE1mOR5JUh7MbVOQvniGb+8+zgfmVvDBeZVjutZgKsuiuhJ1lBCR86aALCIik8KybA51D1Pk\n9+Tc6/dfX2olbVh8/vrGMfUHHkxlqS0Jvu9GIiIiuVBAFhGRSdE9nGIwmaU4kFvnitc7hnjurR4+\ntSzEnIrCnK9j2TZpw+aymuLxDlVE5AwKyCIiMuGypsWh7ljOpRWmZfPYrqPUFPu5f+XsMV0rkhhp\n65ZrEBcReT8KyCIiMuHaBhIYlo0/x0V22w928XZPjIeum0ehP/cOFFnTwu12Ma8y9yfOIiLvRwFZ\nREQmVDxtcKQvTmVhbk+PB1NZvvGrNpaFSlk3f8aYrhVJZllQU5xzEBcRyYW+ooiIyIQ63BMj4HXj\nznGR3bd+/Q7DaYO/WdM0poV5iYxJScBL/Rg2EhERyYUCsoiITJj+eIau4RRlOdYe/7Y3xvff6OTW\nxfU0z8h99zvbthlOGyysK8Gttm4iMsEUkEVEZEKYls3BrmFKcwzHtm3z2M+PUhr08pfXzBnTtQZT\nBjPLAlQWqq2biEw8BWQREZkQ7dEk8YxJgS+3RXY7Dveyv2OIz1w7L+dQDSNBPGtaXDpDbd1EZHIo\nIIuIyHlLZU0O98SoLMit1Vo8Y/D1X7aysLaY9Ytqx3StSDJL04wiCv1q6yYik0MBWUREztux/jgu\nF3g9uf2z8sQrJ+iLZ/jbNZfkvJgPIGNYeN2uMW0kIiIyVgrIIiJyXgaTWdoiSSoKciuTaBtIsGVf\nmD9cWMvl9aVjulYkmWVhbTG+HIO4iMh46CuMiIiMm23bvNU9TJHPk1OLNtu2+eqLxwh43Xzm2rlj\nulYsbVBe6KNObd1EZJIpIIuIyLh1D6eJJLM5b/P84rEBXn4nwl+smkNVUe4dKGzbJp4xWVhbMqZe\nySIi46GALCIi45I1LQ51x3LueZwyTP7lxaM0VhVy25L6MV0rmsoyq6KA8hzLOEREzocCsoiIjMvx\nSIKsaRHIcZvn//1qOx1Daf52zSU5L+YDMCwbw7Jpqs59IxERkfOhgCwiImOWyBi09MZzXpjXOZTi\n33/Tzoebq1kxq3xM14okM1xaXZxzf2URkfOlgCwiImP2294YPo8bT47bPH/tF8fABX/9wXljuk7K\nMAl4PMyuKBjPMEVExkUBWURExmQgkaFjMJ1zPfArxyP87Eg/96+cNeYOFINJg4W1xWMqyRAROV/6\niiMiIjmzLJuDXcOU5Ni1wjAt/nnXUUJlQT61rGFM1xpOGVQV+agpCYxnqCIi46aALCIiOesYTDGc\nNij051YPvO1AB60DST5/fWPOi/lgpK1bwjBZoLZuIpIHCsgiIpKTtGHyVk+MqhxLK/riGb69+zjX\nzq3gg/Mqx3StgWSWuRUFlObYQk5EZCIpIIuISE6O9scBcq4HfvylVjKmxeevv2RMT4EN0wIbLlFb\nNxHJk9yKyERE5KJkWTYDiQxH+uL0JzLUFOdWD3ygY4j/eKuH+1Y0jLkDRSRlsKCmmIBXbd1EJD/y\n+gQ5Go1y6623ctlll7FgwQJefvllBgYGWLt2Lc3Nzaxdu5ZIJAKM1KM99NBDNDU1sXjxYvbu3Tv6\neTZv3kxzczPNzc1s3rw5X7cjIjJtZE2L9kiSF4/185sTUTKmRV1JEHcOT4JNy+axnx+hptjP/Stn\nj+m6qaxJgddNQ7nauolI/uQ1IH/2s5/lpptu4u233+bAgQMsWLCARx99lBtvvJGWlhZuvPFGHn30\nUQCef/55WlpaaGlpYdOmTTz44IMADAwMsHHjRl555RX27NnDxo0bR0O1iIiMTTJr0tIb4+dH+nmz\ne4iAx01NcYAif+4/cHz2zS4O98b57Afn5byY75TBlMGiupKc+yuLiEyGvAXkwcFBfvGLX/DAAw8A\n4Pf7KS8vZ/v27dx7770A3HvvvTz77LMAbN++nXvuuQeXy8WqVauIRqN0dnbyk5/8hLVr11JZWUlF\nRQVr165lx44d+botERFHGkpleaNziF1H+mgdSFAW9DKjKDDaeeL11/bw5L/+C6+/tuc9P080meWb\nv25jWaiMj1w6Y0xjGExlqSkJUFXkH/d9iIhMhLzVILe2tjJjxgz+9E//lAMHDrB8+XK+/vWv093d\nTX19PQB1dXV0d3cDEA6HmTVr1ujrGxoaCIfD73pcRETe2+n1xQPJLAGPmxlF/rMW1L3+2h4evPOP\nyGYz+Hx+/m3rsyxevvKcn/NbL7/DcNrgb9aMbWGeZdukDYvLaorV1k1E8i5vAdkwDPbu3cvjjz/O\n1VdfzWc/+9nRcopTXC7XhH6h3LRpE5s2bQKgq6uLjo6OMz7e29s7YdeSsdHc54fmPX/yOfemZdMf\nz3AimiRtWBT43AR9I6UQg/Gzz3/phR1kM2ksyyJLhpde2MHsOXPOOu/IQIofvNHJ+kvLmeGKEe2L\n5TymwaRBXWmQoX6ToXHf2fvTez5/NPf5oXkfn7wF5IaGBhoaGrj66qsBuPXWW3n00Uepra2ls7OT\n+vp6Ojs7qampASAUCnHixInR17e3txMKhQiFQuzateuM42vWrDnnNTds2MCGDRsAWLFiBTNnzjzr\nnHMdkwtDc58fmvf8udBzn8yahAeTHOtPYtleqmrKctq847obb+K7//NbGNkMXp+f6268ifLq2jPO\nsW2bb/3sdUqDXh664bIx9S/OmhZWocmKS6rwj2EzkfHSez5/NPf5oXkfu/f9SvT4449PyqK3uro6\nZs2axeHDhwF44YUXWLhwIevXrx/tRLF582ZuueUWANavX89TTz2Fbdvs3r2bsrIy6uvrWbduHTt3\n7iQSiRCJRNi5cyfr1q2b8PGKiDjV6fXFR/vOri9+P4uXr+Tftj7LX37+v7xrecWOw73s7xjiM9fO\nG/PmHpFklgU1xRckHIuI5OJ9nyB3d3dz1VVXsWzZMu6//37WrVs3YWUPjz/+OHfffTeZTIbGxkae\nfPJJLMvitttu44knnmDOnDk8/fTTAHz0ox/lxz/+MU1NTRQWFvLkk08CUFlZyZe//GWuuuoqAB55\n5BEqK8e2Y5OIyHRzqr742ECC/ngG/7vUF+dq8fKV71p3HM8YfP2XrSysLWb9otpznvNuEhmTkoCX\nmWXBcY1LRGQyuGzbtt/vJNu22blzJ08++SSvvvoqt912Gw888ACXXHLJhRjjpFixYgWvvvrqGcc6\nOjr0Y4g80dznh+Y9fyZr7g3ToieWpqU3TiJrUujzUByY3Gq6r/+ylf/9Wjv/fvsSLq8vHdNru4fT\nrJpbQWXhhelcofd8/mju80Pz/t7OlQchxzZvLpeLuro66urq8Hq9RCIRbr31Vv72b/92wgcqIiJj\nl8qaHO2L8fOj/RzoHMJ3sn/xZIfjtoEEW/eF+cOFtWMOx9FklvrSwAULxyIiuXrfr5xf//rXeeqp\np6iurubP/uzP+Kd/+id8Ph+WZdHc3Mxjjz12IcYpIiLnMJwyOB5JcDyaxONyUVbgw3uBNtmwbZt/\n3nWUoNfNZ66dO6bXmpZNxrS4dEbx5AxOROQ8vG9AHhgY4Ac/+AFzfq+lj9vt5rnnnpu0gYmIyLnZ\nts1AIsvR/jh9sQwBr5vqIn9O20BPpBeP9rP7eJTPX9845s09osksTdVFFE3yE24RkfF4369MGzdu\nfNePLViwYEIHIyIi7+5UffGRvgTxjEGhz0NtSSAvY0kZJv/yi2M0VhXyycX1Y3ptxrDwuF3MrSyc\npNGJiJwffesuIjLFpbImHYMpjg4kMC2bkoCHmuL8BONTnnq1nY6hNN/6xBV4PWNrzxZNZblyZim+\nMb5ORORCUUAWEZmizqovDnrHHEYnQ8dgis2/aWdtczUrZpWP6bWxtEFZgY/aErV1E5GpSwFZRGQK\nOVVf3NqfoDeexu/JT33xe/naL4/hcsFnVzeO6XW2bRPPmnwgVIb7Ai0kFBEZDwVkEZEpwDAtemNp\nWk6rL853GcW57H4nws+P9PPgB+ZQN8b658FUloayIOUFY9tpT0TkQlNAFhHJo7RhEo6mODaQIGva\nlAanZjAGyJoW/7zrKA1lQT61rGFMrzUsm6xl06y2biLiAArIIiJ5kMiYHOoa4ng0iZupU1/8Xr63\nv4O2SJKvrV9IwDu2sUaSGS6tLqbA55mk0YmITBwFZBGRC8S2bSLJLMf6ErSFBymvDlBVOLXqi99N\nXzzDpt3HuXZuBR9srBrTa9OGRcDjYXZFwSSNTkRkYikgi4hMMtOy6RlOcbQ/wXB6pL64otBHuUO2\nWD7SF+erLx4la1l8/vpLxvz6aCrL8lDZlH9CLiJyigKyiMgkSRsmnUNpjvTFR+qLT+tfHB3O8+De\nh23bvHI8yndea2f38ShBr5vPrW4c81PgWNqgqtBHTZ42NBERGQ8FZBGRCTaYzNIeTdI+mAKg3AH1\nxadkTYufHO7lu3vDtPTFqSr08ekPzOETi+spC46t+8Sptm5LG8pwOaCMRETkFAVkEZEJYJgWffEM\nR/vjDKUM/B43lYU+R9QXw8imJN9/o5Pv7e+gN56hsaqQR9Y2c9P8GvxjXJB3SiSZZU55AaVjDNYi\nIvmmgCwich7iaYOOoRStA8kpsw30WHQMpti6P8z2N7tJZE1Wzirny2ubuWZOxXk99TVMC9uGS6qL\nJnC0IiIXhgKyiMgYWZbNQCJD20CS3ngar9tFWdCH10G7wx3sGuY7e9t5oaUPl8vFRy6dwaeWhZhf\nMzF9igdSWRbWlBBUWzcRcSAFZBGRHKUNk66hNMf6E6QMc8rudvduLNvmpdYBvvNaO3vDQxT5Pdy9\nrIE7rpxJ7QQuoktlTQq9HhrK1dZNRJxJAVlE5D3Yts1gyuBENEl7NInb5aI04KU06JwvnynD5Mdv\n9fDdvWHeiSSpKwnw8OpGbllUS3Fg4u9jMGWwcnY5Hgc9URcROZ1zvsKLiFxAWdOiNzbytHg4bRDw\nuqkucsamHqdEEhn+z+ud/J8DnUSSWS6rKebvbprPh5urJ62rxlAqS02xn6oiZ/R4FhE5FwVkEZHT\nxNIG4cEU70QSmLZNqd/rqDIKgLaBBFv2hfmPQz2kTYsPzqvk7mUhlk9yuzXLtkkZFitmlaitm4g4\nmgKyiFz0zJOL7loHEvTHM/g8LsqDPkeVCNi2zf6OIf73a+388tgAPo+Ljy6o5e5lIeZVFl6QMUQS\nWeZVFlHioPITEZFz0VcxEbloJbMmnYMpWgcSZEyLIr+zFt0BGJbNz4/08Z3XwhzsHqYs6OWBq2fx\nycUzL2iZQ9a0cLlcNFZdmDAuIjKZFJBF5KJi2zbRZJbjkSQdQyk8LhelQS/lHmdtZpHImPzoYBdb\n9oXpGEozqzzIF2+4hD9YWJuX1moDySyL60rHvamIiMhUooAsIheFjGHRM5zm2ECCeMYg6HUzo8jv\nuFrZ3lia7+3v4PtvdDGcNlgys5SHVzeyurEqbyUhyaxJScDLzLJgXq4vIjLRFJBFZFobThmcGExw\nIpLCxqYk4LxFdwBH+uJ857V2dhzuxbJtbmiq5lPLQlxRX5rvoTGUyrJqbiVuB9Vsi4i8FwVkEZl2\nTMumP57haH+caDKLz+2iosBZi+5gpBzkleNRvvNaO7uPRwl63f9/e3ceH1V97oH/M/uWmUz2ZRIg\nIWELhH3VWhARpRYr8EOUWu+tlmrtdWlr9VVta3uraG/bX21te3/cYuVehWi1FxQUtVp61aIIBBFQ\nCRIgmcm+TJJZz/L9/RGSBlkMITNnZvi8/9IzZ875nifhzJPvPOf5YnllAW6YWoii9MRYhKMzJCHf\naUWmnW3diCh1MEEmopQRjMpo6Aqjtj0ESVGRloQt2oDeB95e/aQFz+z1oqY1gCy7Cd+aNxLLKwuQ\nbk2cWmlFFYgq6rAtT01ElCiYIBNRUhNCoCMkobYtiOaeKPQ6wG01wphkD90BveUgL3zYgGf3+dAS\niKI0y44fLSrHVWNzE/Lht46QhLJsBxwxWI2PiEhLvKsRUVKKyiqausM40hZEWFJgMxqQ4zAlzEN3\n+/fswp6db2P63EtROX3WOff1+cPYVO3FloNNCEoKZhW78cNF5Zg7MiNhruezorIKo16HUXHqsUxE\nFE9MkIkoqQSjMmrbgqj3hwEATosRrgSbwdy/Zxduv+ErkKQoTCYz/rBp8xmT5ION3Xh6Tz3eONIK\nnU6HK8fk4KvTPElRstAZljC5wAVTjJasJiLSUmJ9qhARnUNLTwTV9X7odUCm3QR9gs6u7tn5NiQp\nClVRICOKPTvf7k+QI+0hpwAAIABJREFUVSHw1tF2bHivDvubQ3CYDVg9rQirphQiz5kc9dKBqAyX\nxYh8F9u6EVFqYoJMRAlPCIHatiA+au5Bhs0ESwLW4w40fe6lMJnMkBGF0WTG9LmXIiwrePmjZjy9\n14sTHSHkOoy457JSXFuRh7QEmwE/FyEEeqIK5o7MYFs3IkpZyXNXJqKLkqSoONDQhYbuCHIc5qRo\n1VY5fRb+sGkz9ux8G+XTL8G7Uh6+u/59dIQkjMtNw8+uGosZmSqyc/O1Hup584clFKVbkcG2bkSU\nwpggE1HC6onIqPb6EYoqyEuydm2ukolo6MzAH3c3I6KcwBdKMrF6mgfTi9Kh0+nQ2dqk9RDPm6IK\nSKpAeU7i10gTEV0IJshElJD66o0tRj2yHMkxW6kKgZ3HOvDsPh/+cbwDZoMOS8bnYfU0D0pSoNtD\neyiKMdlpsJkMWg+FiCimmCATUUIRQuBoWwAfNweSot4Y6J3p3nqoCc990IATnSFk2U345pwRWDap\nIGmS+3OJyCr8YRlOixEjMhJjBT8ioljS/JNHURRMnToV11xzDQCgtrYWs2fPRllZGa6//npEo1EA\nQCQSwfXXX4+ysjLMnj0bx44d6z/G2rVrUVZWhrFjx+LVV1/V4jKIaBhIiop9Xj8+aQkgx2FO+OT4\neEcQ/7HjUyz54y784u9HkW414mdXjcXWW2bhG3NGJn1yHJIUNPdEEFFUTC50Ys7IDBjZ1o2ILgKa\nzyA//vjjGD9+PLq6ugAA9913H+655x6sWrUKt912G9avX4/bb78d69evR0ZGBo4cOYKqqircd999\nePbZZ3Ho0CFUVVXh4MGD8Pl8uOKKK3D48GEYDPwKkCiZ9NUbhyU1oeuNP1tGYdTrsGhMDq6fUoiJ\n+U6thzcseiIygpICl8WIaZ505KRZ2LGCiC4qmk4F1NfXY9u2bbj11lsB9H61+uabb2LFihUAgJtv\nvhmbN28GAGzZsgU333wzAGDFihV44403IITAli1bsGrVKlgsFpSUlKCsrAy7du3S5oKIaEiau8N4\np7YdqiqQaU/MJaJ7IjKqqr1YsWEP7tpyEJ+09OCbc0Zg6y2z8O9XjU365FgIAX9YQlNPBDazAbNH\nZmBeSSbyXFYmx0R00dF0Bvnuu+/Gz3/+c3R3dwMA2tra4Ha7YTT2DquoqAherxcA4PV6UVxcDAAw\nGo1IT09HW1sbvF4v5syZ03/Mge/5rHXr1mHdunUAgMbGRvh8vlNeb2lpGd4LpEFj7LWhddyFEPD6\nwzjWEUK6xQjZoENnt6ZDOk2dP4rNhzvx2qd+hGSB8dlW/OCSfHxhhBMmgw4IdaAzdP7H7elsH/7B\nDoEQvcm/rApkOUwoS7chzagg3BlCQ6fWoxt+Wv/OX8wYe20w7kOjWYK8detW5ObmYvr06dixY0dc\nzrlmzRqsWbMGADBjxgwUFhaets+ZtlF8MPba0Cruff2N23RGjCrKTKj+xqoQ+MexDjw3oIziyrE5\nuH5yISqGcabYnZ03bMc6X4oq0BmWoKoC5QU2jMy0J9WCJReC9xrtMPbaYNzPn2Z3w3feeQcvvvgi\nXn75ZYTDYXR1deGuu+5CZ2cnZFmG0WhEfX09PB4PAMDj8aCurg5FRUWQZRl+vx9ZWVn92/sMfA8R\nJaaeiIy99X5EZBW5CVRv/NluFNkOM745ZwSum1SA7CR/4K6PrKjoDMvQASjJsqPIbWPbNiKiz9Cs\nBnnt2rWor6/HsWPHUFVVhcsvvxzPPPMMFixYgOeffx4AsGHDBlx77bUAgKVLl2LDhg0AgOeffx6X\nX345dDodli5diqqqKkQiEdTW1qKmpgazZs3S6rKI6HP01RsLkTj1xsfag/j5346c1o3ipa/PxDfm\njEyJ5Dgqq2gORNAVVTA2Nw3zy7JRnsOexkREZ5Jw36c99thjWLVqFR588EFMnToVt9xyCwDglltu\nwU033YSysjJkZmaiqqoKAFBRUYGVK1diwoQJMBqN+N3vfscOFkQJSFV7+xt/0hJAps0Es8Yt3OJV\nRqG1sKSgKyLDajRgUp4L+S4LW7UREX0OnRBCaD0ILcyYMQO7d+8+ZZvP52OdjkYYe23EK+5RWcXB\nxi40dkeQ7TBDr9Ou3rivjOLZD3yo6wwj22HGisoCXDcxP659iztbm2JagxyIyghEFTgtRpRlO5DL\nVm0AeK/REmOvDcb93M6UDwIJOINMRKklUeqNj7UH8dwHPmw91IygpKCywIlvzhmJheXZMKXIjKoQ\nAt0RGSFJRabdhIoRLmTaTdBp+AcJEVEyYoJMRDHT1BVGtc8Pm9GgSb1xXxnFs/t82Hm8AybDyUU9\nUqyMoreHsYyI0rvIytQiB9y2xKjvJiJKRkyQiWjYqarAp20BHNao3rgnIuOlQ014bkAZxW1zR8a9\njCLWFLV3cQ9ZFShy2zAqww6nlbd1IqILxTspEQ2rqKziw4YuNPdEkJsW33rjY+1BPPuBD9sGlFHc\nNnckLi9LnTIKAJBVgc6QBAAYlWlHsdsKu5m3cyKi4cI7KhENm+6wjL31nYgq8as3PlMZxZVjcnD9\nlEJMyEudMgqg948Pf0SCXqdHebYDHrcVFiO79hARDTcmyEQ0LE6tN459GcPFUkYBAGFZQVe4t1Vb\nRZ4T+S5rSs2IExElGibIRHRB4l1vfLGUUQBAMKqgJyrDYTZiSqELuU5rQi3JTUSUqpggE9GQ9dUb\nN8Wh3lgVAuvfO4F1756AMYXLKIDeUpWgJMNtN2NmsRuZdjN7GBMRxRETZCIakoH1xnkxrjcORGX8\n+NXD2PFpG64el4t7LiuJSxlHPPW1aosqKnIcFkz2uOC2sYcxEZEWmCAT0Xlr6gpjn88PaxzqjU90\nhPC9lw7heEcQ3/1iKVZNKUyppFEVva3aJAXwpFswKtMOl5U9jImItMQEmYgGTVUFjrQFcKQlgEy7\nKeY1v2/XtuPBVz6GQa/DE8smYWaxO6bniydFBdoCUagQGJlhx8gMG1u1ERElCN6NiWhQBtYb58S4\n3lgIgafer8fv/3EM5TkO/OKaCShMt8bsfPEkKSo6wxKCURmTR9rhSbfBamKrNiKiRMIEmYg+Vzzr\njYNRBT95/TDeqGnF4rE5+OEV5SmRQEZkFf6wDItRj/G5TujSZIzITtN6WEREdAZMkInonBq7wtjn\n9cNujn29cX1nCN996RBq24O4+wslWD3Nk/T1xiFJQXdEgd2kx5RCZ3+rNl+4U+uhERHRWTBBJkpw\nQghNksS+euOalh5k2c0xrzd+93gHfvDyxwCA33xlIuaMzIjp+WJJFQLdERlhSUW61YgZxenIYqs2\nIqKkwQSZKAEpqkBLTwRH24MISwrKsx1xXT0tIiv4sKEbzT0R5KZZYl5v/D97vHjinVqUZtnxiy9P\nQFG6LWbni6WQ1Luwhw46FLgsKHbb2KqNiCgJMUEmSiAhSUGDP4yj7UFIigqnxQib0YCDTd34qLkH\nJZl2FLltsMWwJrcrLGFvvR+yGvt647Ck4Kev1+C1wy1YWJ6NHy8aA7s5ueqNZUWFPyJDUQXcNhMm\nF7iQ5bDEfEVBIiKKHSbIRBrrWyDieHsQvq4wDDod0q1GGA3/7IWbY7RAVgWOtgXxaWsAxW4bRmTY\n4bQO7z/hgfXGGbbY1hv7/GF8b+sh1LQE8O1LRuHmGUVJM9PaV0IRkVVYDHqMzrIj32mFw8JbKhFR\nKuDdnEgjsqKipSeCT9uC6A7LsJj0yHGYz5okGvU6ZDvMUIVAY3cEJzpDyHFYUJptR8YFfo0f73rj\n9+s6cf+2j6CoAr++tgKXlGTG9HzDJSQp6I7K0AkdPG4rPOlWuK0m1hYTEaUYJshEcRaMyvD6w6ht\nD0FRBVwWA3Kdgy9l0Ot0cNt6Z5d7IjLeO94Bp8WIsmwHctIsMJxnshaRFXzo60JLQIpLvfGmah8e\nf+soRmb01huPyEjsemNJUdE1oIRiCksoiIhSHhNkojgQQqAjJOF4exCN3VEY9EC61QTjBc48plmM\nSLMYEZIUVHt7l34uy7YP+oG+QFTGJ8c6oKgqctNiW1IRlhU8/NcjeOXjZiwYnYWHFo+BI0FXjvts\nCUVZlgN5TgtLKIiILhK82xPFkKSoaO7uLaMIRGXYjAbkOIa/q4HNZIDNZEBUVgf9QF+DP4T93i7k\n5NvhinG9cWNXGN/b+hE+bu7BbXNH4uuzioc8U71/zy7s2fk2ps+9FJXTZw3rOINRBT2SDD10KExn\nCQUR0cWKCTJRDAQiMur9YRzvCEIVAk6LEbkx7ggBAGajHjlGCxRVoLY9iCOtARS5bRiZYYPL2luW\noaoCNa0BHGntQZrVGPNZ3D31nbh/28eIKip+tXQCLivNGvKx9u/Zhdtv+AokKQqTyYw/bNp8wUmy\npPSucKeKkyUU2S5kp1ni1lKPiIgSDxNkomGiqr1lFLVtQbQEIjDqdXBbTeddEzwcDHodsuxmCCHQ\n3B1BfWcIWQ4zSjLtON4e7K837orEtt74uQ8a8Kv/O4qidCt++eUJGJVpv6Bj7tn5NiQpClVRICOK\nPTvfHlKCPLCEwmo0oDybJRRERPRP/DQgukBRWUVTdxhH2noX9bCbDHGZLR4M3Wce6NtT54fJgJjX\nG0dkFY++eQQvHWrCF0oy8e9XjUXaMCSf0+deCpPJDBlRGE1mTJ976Xm9/7MlFEXpVqSzhIKIiD6D\nCTLREHWHZdT7QzjREYKAgMtigiuBZyB7H+iL/XmauiP4/taPcLCpG9+YPQLfmDNi2DpjVE6fhT9s\n2nxeNcgDSygyWEJBRESDkLif5kQJSFUF2oNRHG0Poi0QhUmvQ4ZNmzKKRLTP58d9Wz9CSFLxi2vG\nY35Z9rCfo3L6rM9NjFlCQUREF4KfFkSDEJEVNHZFcLQtiLCswm7SJ0wZRaJ4YX8D/mPHpyhwWfCH\n5ZNQmuWI+xiCUQUBSYEOgOdkFwqWUBAR0fligkx0Dl1hCXUdIdT5Q4DQwW0zwjXMyzsnu6is4j92\nfIr/PdCIeaMy8PBV44Z9Cexz+WwJxeRsJ0soiIjogvCTnugzFFWgLRDFp20BdIYkmA16ZNnNMV1h\nLlm1BqL4/tZD2N/QjX+dWYzb5o6MS7mJKgS6wjKiCksoiIho+PHThOiksKTA5w+jtj2IqKIizRyf\n3sXJ6sOGLty79SP0RGQ8umQcrhiTE5fztgWiEGAJBRERxQ4TZLqoiZMzkSc6Q/B2hqDX6ZBuNcJo\nMGk9tIS2+UAjHvvbEeQ6LHhi1RSUZce+3lhRBVqDUXjSrRif64TZyBIKIiKKDSbIdFGSFRWtJ8so\nusIyzAY9sh3mYV8COtVIiopf/f0o/ry/AbNHuPHIknFIt8b+j4morKIjLGFsjgOlWQ7+nIiIKKaY\nINNFJSQp8PlDONoegqSocMVpCehU0BaI4r5tH2Gfrws3TS/CHZeMgjEOpQ09ERkhWcGMonTkOq0x\nPx8RERETZEp5Qgh0hiQcaw+isTsCvU4HN8sozsvBxm7cu/UQ/GEZD189FovH5sblvO3BKMwGPS4Z\nlRXXzhhERHRx4ycOpSwhBFoDURxu7kFXWIbVpEcOyyjO29ZDTXjkjRpk2c14cuVkjM1Ni/k5VSHQ\nGpCQ4zChsjCd9cZERBRXTJAp5aiqQEtPBJ+0BBCIynBajMh1sozifMmKil+/VYuqfT7MLE7H2iXj\n4bbFftZdVlS0BqMYnZWGMTkOdqggIqK402xapq6uDgsWLMCECRNQUVGBxx9/HADQ3t6ORYsWoby8\nHIsWLUJHRweA3tnAO++8E2VlZaisrMTevXv7j7VhwwaUl5ejvLwcGzZs0OR6SHuKKtDgD+Gto23Y\n6/XDoANy0yywmQxaD+28+fxh/L//dxRL/vgefvDyxzjSGojr+TuCUdzxvwdQtc+HG6d68NvrJsUl\nOQ5JCtpCEqYWpmNcXhqTYyIi0oRmM8hGoxG//OUvMW3aNHR3d2P69OlYtGgRnnrqKSxcuBD3338/\nHn30UTz66KN47LHH8Morr6CmpgY1NTV47733cPvtt+O9995De3s7fvKTn2D37t3Q6XSYPn06li5d\nioyMDK0ujeJMUQWausP4pCWAsKTAZTUi15J8M8ZCCOxv6MbGai/+dqQVOgCzRmTg7dp2vHa4BZeV\nZuLrM4sxscAV03F83NyD7710CB1BCT9ZPAZfGp8X0/P16QxJ0Ol0uGRUJtLjkIwTERGdjWYJckFB\nAQoKCgAATqcT48ePh9frxZYtW7Bjxw4AwM0334z58+fjsccew5YtW/C1r30NOp0Oc+bMQWdnJxoa\nGrBjxw4sWrQImZmZAIBFixZh+/btuOGGG7S6NIoTWVHR2BXB4dYAIrKKdKsRriRcSU1WVPy1phWb\nqn042NQNp8WIr04rwsophch3WuAPS3h2nw9V1T78y9EPMKvYjX+dVYwZRenDXk/9ysfN+NnrNXDb\nTfjjykqMz3MO6/HPRIje/sZumwlTCtNhTcIZfyIiSi0JkU0cO3YM1dXVmD17NpqamvoT5/z8fDQ1\nNQEAvF4viouL+99TVFQEr9d71u1nsm7dOqxbtw4A0NjYCJ/Pd8rrLS0tw3pdNHjnE3v5ZI1xXWcI\nsirgNBthNugQCgOhGI5xuHVHFGw74seWTzrREpThcZrwbzNzcWWpCzaTHoh0ojPSu+/KMiuuGTkK\nW2s68fyhDtz+wocYn23F6omZmO0Zel/gns52AL2z8H+sbsWfP+pAZa4NP7ysABmGIDpbg8N1uWek\nqEBnWEKhy4Iipx3tLeGYni+R8H6jDcZdO4y9Nhj3odE8Qe7p6cHy5cvx61//Gi7XqV8d63S6YZ0h\nW7NmDdasWQMAmDFjBgoLC0/b50zbKD4+L/ZRWYWvK4ya1gAUGJGf74bJkHzdDY53BFFV7cNLh5oQ\nllXMLE7HD67w4JKSTOjP8fvuBrCmoAA3z1Px0sFGbNhdjwd3+FCe7cC/zizGwvJsGIZSs+vIxA9f\n+Ri7TnRi5eQCfOeyUhjjENewrMAfljG7xIkRGbaLsrsI7zfaYNy1w9hrg3E/f5omyJIkYfny5Vi9\nejWWLVsGAMjLy0NDQwMKCgrQ0NCA3Nzefqsejwd1dXX9762vr4fH44HH4+kvyejbPn/+/HheBsVY\nRFbg7QzjSFsAQiApexgLIbC73o+Ne714u7YdRoMOV43NxQ1TCzEm5/zaplmMeqyYXIivTMzH9k9a\n8NT7dfjBKx9jxE4bbp5ZhCXjcgf9h8OnHRH89MV9aA5E8KNF5VhakT+Uyztv3WEZkqpizsgMZNrN\ncTknERHRYGk2/SaEwC233ILx48fjO9/5Tv/2pUuX9nei2LBhA6699tr+7f/93/8NIQTeffddpKen\no6CgAIsXL8Zrr72Gjo4OdHR04LXXXsPixYs1uSYaXmFJQU1LD/5+pA01rQGkW03IdpjjMrs5XKKy\nipcONmH1M9W4/YUPcaCxG7fOHoGXvj4LP75yzHknxwMZDXpcMyEPz31tOh770njYTHr8++s1+MpT\nu1G1z4uwrJzz/a8fbsFd208gqqj4rxWVcUuO2wJRmAw6XFKSyeSYiIgSkmYzyO+88w7+53/+B5Mm\nTcKUKVMAAI888gjuv/9+rFy5EuvXr8fIkSPx3HPPAQCWLFmCl19+GWVlZbDb7fjTn/4EAMjMzMQP\nf/hDzJw5EwDwox/9qP+BPUpOIUnBiY4QatsD0EMHt800tNIBDbUHo3hhfwOe39+AtqCE0iw7Hryi\nHFePy4VlmBe90Ot0WFiejcvLsrDzeAee3FWHX+w4iid31eHGqR6sqCxA2oCHFxVV4Pf/OIYNu+tR\nkWPFL78yGdmO2Ceqitr7MF6hy4KKfFdSlscQEdHFQSeEEFoPQgszZszA7t27T9nm8/lYp6MRn88H\nd3YujnUEcaI9BL1eB7c1+RLjI60BbKr24pWPmxFVBOaNysCNUz2YPcId1xrbaq8fT+6qw87jHXBa\njLh+SgFWTfFArwMeeOUT7DzegeWT8nHrRCdy8mI/cxyVVbSHJIzLdaA0a+gPFaYS3m+0wbhrh7HX\nBuN+bmfKB4EEeEiPKBCRcbQ1gG5/G0wGHbIc5nM+rJZoVCHwj2Md2FTtxXsnOmEx9pY+3DDVg5JM\nuyZjmupJx2+vS8dHTd148v06/PG9Ojyz1wuXxYi2oIQHFpbhukkF6GxtivlYAlEZQUnBzOJ05Dqt\nMT8fERHRhWKCTJrpDss41hFEfWcIkUAUHk9yJcZhScHWj5pRVe3FsY4Qsh1mfGveSCybVBCXVecG\nY3yeE/9xzQQcbQvgqd31ONTYjf9vxXhMLoztYiN9OkJRmPR6XDIqC04rbzdERJQc+IlFcdcVlnC0\nLYCGrgjMBj1yHGb4w8akSY6beyL48wcN+MuHDfCHZYzLTcNPF4/FojHZCVtXW5rlwE8Xj43b+VQh\n0BKIItdhRmVhOszDXHdNREQUS0yQKW78IQmftgbQ1BOBxdibGCdTLepHTb3LQL92uBWqKjB/dBZu\nnObBlEJXUl1HrMmKitZgFKOz0jAmxwF9ktWRExERMUGmmOsIRnGkNYCWQBQ2ox65aRathzRoiirw\nf0fbsLHai2pvF+wmA1ZWFuD6qYUoSrdpPbyEE5IUdEVkTClMh8fN+BARUXJigkwxIYRAR0hCTUsP\n2gIS7CYD8pIoMQ5EZbx4sAlV+3zw+sMocFpw92Ul+EpF/ikt0+ifOkMSAGDeqMyEqcEmIiIaCn7S\n07ASQqA9KOGTlh74gxLsZgPynMmTGPv8YTz7gQ+bDzQiEFVQWeDCv106CvNHZ8PIUoEzEqK3v3G6\n1YSpnnRYTQath0RERHRBmCDTsBBCoDUQxeHmHvjDMtIsBuQmSWIshMAHDV3YuNeLHZ+2QQdgYXkO\nbpzmwcR8p9bDS2iy2vtzH5lhw/g8Z9L1rSYiIjoTJsh0QVRVoKUngsOtAfREZDgtxqSZMZYVFX+t\nacXGai8ONfXAaTHiq9OKsHJKIfKT5Bq0FJYV+MMyJuY7MSLDxgcViYgoZTBBpiFRVIHm7jAOtwQQ\nlBS4LMakefiuKyzhLx824s8f+NDUE8UItw3fXzAa14zPg93M8oDB6A7LkFQVc0ZmINMe+2WqiYiI\n4okJMp0XRRVo6g7jk5YAwpICl9WIXEtyJMbHO4KoqvbhpUNNCMsqZhan4/7Ly3BJSWbS9GBOBG2B\nKBxmA2aOyISDDywSEVEK4qcbDYqsqGjs6i2liMgq0q1GuJIgORJCYHe9Hxv3evFWbTtMBh2uGpuL\nG6YWYkxOmtbDi6v9e3Zhz863MX3upaicPuu839+3+EeB04KJBa6EXRSFiIjoQiV+hkOaCkkKfP4Q\nattDkFQVbosJ6UmwZHBUVvHqJy3YWO1FTWsAGTYTvjF7BJZXFiDbcfGVBOzfswu33/AVSFIUJpMZ\nf9i0+byS5Kisoj0kYVyuA6VZDtYbExFRSkv8TIc04Q9JONEZgrczBL1Oh3SrEUZD4ve2bQ9G8cL+\nBjy/vwFtQQmlWXY8eEU5rh6XC8tFvNzxnp1vQ5KiUBUFMqLYs/PtQSfIgaiMkKRgRlE68lzWGI+U\niIhIe0yQqZ+iCrQFojjSFoA/JMFs0CM7SZaDPtIawKZqL175uBlRRWDeqAzcONWD2SPcSTH+WJs+\n91KYTGbIiMJoMmP63EsH9b6OUBRGvR7zRmXBmQTfHBAREQ0HfuIRIrKChq4IPm0NIKqoSDMnR0cK\nVQj841gHNlZ7setEJyxGPa6ZkIcbpnpQkmnXengXTBUC/pAEVeDkDP7QZ8Arp8/CHzZtHnQNshAC\nzYEoch1mTCp0wWJkdw8iIrp4MEG+iHWFJdR1hFDnD0GH3jIKdxKUUYQlBVs/akZVtRfHOkLIdpjx\nrXkjsWxSQUoscSwpKvxhGQAwIsMGm8mAI60BSIoMl9UA6xCT1crpswZVViErKlqDUYzOSsOYHAf0\nXPyDiIguMkyQLzKqKtAejOJoexBtgSjMBj2y7OakaHPW3BPBnz9owF8+bIA/LGNcbhp+ungsFo3J\nPmtHhQvt3BBPwaiCnqgMq9GAcblpKHBZYT5ZN13stqG5O4ya1iCaeyKwmwxIi0EXkZCkoCsiY0ph\nOjxu27Afn4iIKBkwQb5IRGUVTd1hHGkLIiwpcJgNSVFGAQAfNXVjY7UXrx1uhaoKzB+dhRuneTCl\n0HXO+uIL7dwQD0II+MMyoooKt82EGcXu3j9YPjNra9DrUJBuQ77LivaghE/bAmjuicBs0CPdahyW\nOuvOkAQAmDcqMyVm4omIiIaKCXKK64nIqOsMoa4jBBUC6RZTUvQvVlSB/zvaho3VXlR7u2A3GbCy\nsgDXTy1EUfrgZjYvpHNDrMknyyhUIeBx2zDCbUP6GZLSnTt3YseOHZg/fz7mzp0LnU6HLIcZWQ4z\nusISjneEUN8ZgkGvQ7rVBOMQyiGEEGgLSnBZjZjiSYfNxHpjIiK6uCV+pkTnTQiB9qCE2rYgWgIR\nGPU6uG0mGJKgljQQlfHiwSZU7fPB6w+jwGnB3ZeV4CsV+eddUjDUzg2xFJYUdEUUGA06lGU7UJhu\nhfUsCenOnTuxcOFCRKNRmM1mvPHGG5g7d27/6y6rCZMKTCjLdqC+M4Ta9iBU0ftHkHmQLe0UFWgO\n9C63PS437YIeBCQiIkoVTJBTiKSoaOqK4NO2AIKSArspecooGnsk/OnQUWw+0IhAVEFlgQv/duko\nzB+dPaRZUeD8OzfEihACPREFIUmB02rElEInctIsn5uM7tixA9FoFIqiIBqNYseOHackyH1sJgPK\nc9IwKtOOxq4wjrQG0RmWkGY2wm4++2xwWFbgD0uYVeLEiAwb2+ERERGdxAQ5BQSjMur9YRw7OYPo\nspiQmwRlFEII7G/orS/+25FW6AAsLM/BjdM8mJjvHJZzDLZzQywoqoA/LEFWBfKdFkz2uOC2mQad\niM6fPx9ms7nFzrSrAAAZxElEQVR/Bnn+/Pnn3N9k0KM4ww5Pug2tgShqWnvQ1B2GzWSA03JqnXJ3\nWIakqpiY78TIFGiJR0RENJwSP4uiMxJCoDMk4Vh7EI3dJ8sorLEpoxjuThCyouKNI63YuNeHg03d\ncFqM+H/GZ+CmueXIdybHjPe5RGQVXREJep0OIzPsKEq3wjGEP1jmzp2LN95445Qa5MHQ63XIdVqQ\nk2ZGZ6i31Kap55+lNh1BCXazATNHZMLf1nze4yIiIkp1TJCTjKyoaOmJ4NO2ILrDMmwmA3JiuNrd\ncHaC6ApL+MuHjfjzBz409fTWvX5/wWhcMz4P0a5WuJM8Oe6JyAhKCqwmAybmuZDnspy1/dxgzZ07\nd9CJ8WfpdDpk2M3IsJsRiMg40RnC8fYg8pwWTCxwwWTQw39BoyMiIkpNTJDj5LPdCM5XSFLg84dw\ntD0EWRFwWQzIjUNCORydII53BFFV7cNLh5oQllXMKErHfZeX4dKSzP7+y9FYDD4O+la7k1SBLIcZ\nFflOZJ6hTZvWHBYjxuc5UZbtgFGvY70xERHROTBBjoPP60ZwNn09co+3B+HrCkOv6/2KfKgPrQ3F\nUDtBCCGwu96PjXu9eLu2HUaDDovH5OCGqR6MzU2L8ahjr2+1OwGBEW4bit12OK2J/8/pQme0iYiI\nLgaJ/4meAgbbjaCPooqTZRQBdIVlWIz6mJZRnMv5doKIyipe/aQFm6q9ONwaQIbNhFtnj8DyygJk\nO8xxGnXshCQF3REZZoMeY3PTUOCywDLEpZ+JiIgoMTFBjoPBdiMISwoauyM40hqApKhwWowJ0aZt\nMJ0gOoJRPL+/Ac/vb0BbUEJplh0PXlGOq8flwjLInryJSgiBroiMiKwi3WbCNE86stMsSdFXmoiI\niM4fE+Q4+LxuBF1hCXUdIdT5Q9BDh3SrEUZDciz1e6Q1gE3VXrzycTOiisC8URm4caoHs0e4k77O\nVVZ7O4WoQqDQZcXITPuwLetMREREiYsJcpx8thuBqgq0BaP4tDWAjpAEs0GPLLu5/6G1RKYKgZ3H\nOrCx2ov3TnTCYtTjmgl5uGGqByUp0FM3LCvojijQ63QYnd3bV5jLLxMREV08mCDHWVRW0dAVxtG2\nIMKyAoc5eVa7C0sKtn3UjE3VXhzrCCHbYca35o3EskkFcNuSY8b7XPratDnMRlQWOJE7iNXuiIiI\nKPUwQY6jT5p7cKw9CABwWYxwJUHXAwBo6YnguQ8a8JcPG+APyxiXm4afLh6LRWOyk74rwsDV7nLT\nLJhU6ELGeax2R0RERKknOTK0FKCqArXtgaQpowCAj5t78MxeL14/3AJFFfji6CzcONWDqR5X0ieQ\nUVmFPyJBh97V7ordQ1vtjoiIiFIPM4I4S/TkWFEF3jraho3VXuz1dsFuMmBFZQFWTSlEkdum9fAu\nWCAqIxBVYDUaUJHnRJ7TCnOSd9kgIiKi4cUEmQD0Jo4vHmxC1T4fvP4w8p0W3P2FEnxlYj7Sknxm\nVRUCXWEZUUVFhs2E8cXO3pl8tmkjIiKiM0juzGeA7du346677oKiKLj11ltx//33az2kpODzh/Hs\nBz5sPtCIQFRBZYET/3bJKMwvy47rin2xIKu9y0ArQqDYbcOIDBtc1uR/mJCIiIhiKyUSZEVRcMcd\nd+D1119HUVERZs6ciaVLl2LChAlaDy0hCSGwv6EbG6u9+NuRVugALCzPwY1TCzGxwKX18C5Y5GR9\nsVGv72/TZmWbNiIiIhqklEiQd+3ahbKyMpSWlgIAVq1ahS1btjBB/gxZUfHGkVZs3OvDwaZuOC1G\nfHVaEVZOKUS+MzlazZ1LX5s2u8mAyQUutmkjIiKiIUmJBNnr9aK4uLj//4uKivDee+9pOKLE0hWW\n8L8HGvHcPh+aeqIodlvx/QWjcc34PNjNyT2zKoSA/2R9cZbDjIkFvW3aWF9MREREQ5USCfJgrVu3\nDuvWrQMANDY2wufznfJ6S0tLzM4thECgvQOmUPxqYOu7ovjfjzvw6tEuhGWBKXk2fHtGIWZ7HNDr\ndIh2tSIat9GcW09n+3ntr6hAd0SGEAK5TgtGuCxwGBVE/CE0+mM0yBQUy995OjfGXhuMu3YYe20w\n7kOTEgmyx+NBXV1d///X19fD4/Gctt+aNWuwZs0aAMCMGTNQWFh42j5n2jYcVFXA0W2E2xHbUgYh\nBPbU+/FMtRdvH22HQa/DVWNzcMNUD8bmpsX03BfKnZ33uftEZBVdEQl6nR6TR9q4DPQwiNXvPH0+\nxl4bjLt2GHttMO7nLyUS5JkzZ6Kmpga1tbXweDyoqqrCxo0btR5WXEVlFa8dbsHGai8OtwTgthlx\ny+xirKgsRLbDrPXwLlhf/2KbyYCJeS7kuSxJv4ofERERJaaUSJCNRiOeeOIJLF68GIqi4Otf/zoq\nKiq0HlZcdASjeOHDRvz5Ax/aghJKs+x48IpyXDUuB1Zjcs+sDqwvzrCZMKHYiUz2LyYiIqIYS4kE\nGQCWLFmCJUuWaD2MuPm0LYBN1T68/FEToorAvJEZuHGaB7NHuJN+GWhZFfCHJSiqQJHbhhFuG9Jt\n7F9MRERE8ZEyCfLFQBUC7x7vwMa9Xrx7ohMWgx5fGp+HG6d5UJJp13p4Fyx6sn+xXqdDSaYdRW7W\nFxMREVH8MUFOAmFJwbaPmlG1z4va9hCyHWZ8a95ILJtUAHcKzKwGowo6ghL0TpX1xURERKQ5JsgJ\nrKUnguc+aMBfPmyAPyxjXG4afrp4LBaNyU76BFIIga6IjIiswm0zYXy+E+NLslhfTERERJpjgpyA\nPm7uwTN7vXj9cAsUVeCLo7Nw41QPpnpcSV9frKgCnWEJigoUuiwYlWlHus0Eny/M5JiIiIgSAhPk\nBKGoAm8dbcPGai/2ertgNxmworIAq6YUosht03p4F2xgffGoDDuK3FbYzfz1IyIiosTDDEVjgaiM\nlw42oWqfD/X+MPKdFtz9hRJcW5EPpzX5fzzBqIKeqAyr0YCKPCfyXdakLw8hIiKi1Jb8GViSaugK\n49l9Pmw+0IieqILKAie+fckozC/LhjHJSw2EEOiOyAjLKtJtJswodiOL/YuJiIgoSTBBjrP9vi5s\nrPbib0daAQCXl2dj9VQPJha4NB7ZhVNO9i+WVYF8pwUlWQ6kW41JXzdNREREFxcmyHEghMCz+3z4\nyeuf4JPmANLMBtw4rQjXTy5Avsuq9fAumKSo8IdlAMCoTDuK0q1wWPirRURERMmJWUwc6HQ6/OEf\nx9AdlnHv/NH48oQ82M3JvwBGSFLQHemtLx6Xm4YClxVmI+uLiYiIKLkxQY6TZ2+ajmpfJ/LSknvG\nuK++OCQpSLeaMM2Tjuw0CwysLyYiIqIUwQQ5TvKcFuiTuBZXUQW6IhIkpbe+eIonHW6bifXFRERE\nlHKYINM5DawvHplhR7Gb9cVERESU2pjp0BkN7F/M+mIiIiK6mDBBpn5CCHRFZETYv5iIiIguYkyQ\nCbIq0BmSoAqBQpcVozLtSLeZtB4WERERkSaYIF/EwrKCrogMg06P0dl2eNJtsJmSv/0cERER0YVg\ngnwR6onICEoK7CYDJhe4kJtmgdHA+mIiIiIigAnyRUMVAv6QBEkVyHFYMLHAhUw727QRERERfRYT\n5BQ3sE1bcYYVxel2OK38sRMRERGdDTOlFNW3DLTZoMfY3DQUuCywGFlfTERERPR5mCCnkL5loMOy\nCpfVyGWgiYiIiIaACXIKUFSBzrAERQXynWaUZDmQbjWyvpiIiIhoCJggJ7GIrKIrIkGv06Ek0w5P\nuhV2M3+kRERERBeC2VQS6mvTZjUZMDHPhTyXBSa2aSMiIiIaFkyQk4QqBLrCMqKKiiyHGRX5TmRy\nGWgiIiKiYccEOcHJJ9u0qRAoTrdhRAbbtBERERHFEjOtBBWWFPhPtmkry3agMN0KK5eBJiIiIoo5\nJsgJRAiBnoiCkKTAebJNWw7btBERERHFFRPkBKCoAv6wBFkVyE2zYLLHBbeNy0ATERERaYEJsoai\nsgp/RIIOOozKtKMo3QqHhT8SIiIiIi0xG9NAICojEO1t01aR50Se0wqzkW3aiIiIiBIBE+Q4EgJo\n7okgw2bC+GInstimjYiIiCjhMEGOE71ehwl5TmTYTXBZTVoPh4iIiIjOgglyHI3MtGs9BCIiIiL6\nHCx8JSIiIiIagAkyEREREdEAmiTI9957L8aNG4fKykpcd9116Ozs7H9t7dq1KCsrw9ixY/Hqq6/2\nb9++fTvGjh2LsrIyPProo/3ba2trMXv2bJSVleH6669HNBqN67UQERERUWrRJEFetGgRDhw4gP37\n92PMmDFYu3YtAODQoUOoqqrCwYMHsX37dnzrW9+CoihQFAV33HEHXnnlFRw6dAibNm3CoUOHAAD3\n3Xcf7rnnHhw5cgQZGRlYv369FpdERERERClCkwT5yiuvhNHY+3zgnDlzUF9fDwDYsmULVq1aBYvF\ngpKSEpSVlWHXrl3YtWsXysrKUFpaCrPZjFWrVmHLli0QQuDNN9/EihUrAAA333wzNm/erMUlERER\nEVGK0LwG+cknn8TVV18NAPB6vSguLu5/raioCF6v96zb29ra4Ha7+5Ptvu1EREREREMVszZvV1xx\nBRobG0/b/vDDD+Paa6/t/2+j0YjVq1fHahinWLduHdatWwcAaGxshM/nO+X1lpaWuIyDTsfYa4Nx\n1w5jrw3GXTuMvTYY96GJWYL817/+9ZyvP/XUU9i6dSveeOMN6HS9q8l5PB7U1dX171NfXw+PxwMA\nZ9yelZWFzs5OyLIMo9F4yv5nsmbNGqxZswYAMGPGDBQWFp62z5m2UXww9tpg3LXD2GuDcdcOY68N\nxv38aVJisX37dvz85z/Hiy++CLv9n4tnLF26FFVVVYhEIqitrUVNTQ1mzZqFmTNnoqamBrW1tYhG\no6iqqsLSpUuh0+mwYMECPP/88wCADRs29M9OExERERENhSYr6X37299GJBLBokWLAPQ+qPef//mf\nqKiowMqVKzFhwgQYjUb87ne/g8FgAAA88cQTWLx4MRRFwde//nVUVFQAAB577DGsWrUKDz74IKZO\nnYpbbrlFi0siIiIiohShSYJ85MiRs772wAMP4IEHHjht+5IlS7BkyZLTtpeWlmLXrl3DOj4iIiIi\nunhp3sWCiIiIiCiRMEEmIiIiIhqACTIRERER0QBMkImIiIiIBtAJIYTWg9BCdnY2Ro0adcq2lpYW\n5OTkaDOgixxjrw3GXTuMvTYYd+0w9tpg3M/t2LFjaG1tPW37RZsgn8mMGTOwe/durYdxUWLstcG4\na4ex1wbjrh3GXhuM+9CwxIKIiIiIaAAmyEREREREAxgeeuihh7QeRCKZPn261kO4aDH22mDctcPY\na4Nx1w5jrw3G/fyxBpmIiIiIaACWWBARERERDZDQCXJdXR0WLFiACRMmoKKiAo8//jgAoL29HYsW\nLUJ5eTkWLVqEjo4OAMAzzzyDyspKTJo0CfPmzcMHH3zQf6zt27dj7NixKCsrw6OPPnrWc27YsAHl\n5eUoLy/Hhg0b+rc/8MADKC4uRlpa2lnfGwwG8aUvfQnjxo1DRUUF7r///v7XfvWrX2HChAmorKzE\nwoULcfz48SHHJR5SKfZ9XnjhBeh0uoR+mjfV4v7cc8/1X8uNN944pJjESyrF/sSJE1iwYAGmTp2K\nyspKvPzyy0OOS6wlW9wB4KqrrsLkyZNRUVGB2267DYqinHPMiShR4j6Ye3efPXv2YNKkSSgrK8Od\nd96Jvi/A//znP6OiogJ6vT6h7+99Uin2P/zhD1FZWYkpU6bgyiuvhM/nu+D4JAyRwHw+n9izZ48Q\nQoiuri5RXl4uDh48KO69916xdu1aIYQQa9euFd///veFEEK88847or29XQghxMsvvyxmzZolhBBC\nlmVRWloqPv30UxGJRERlZaU4ePDgaedra2sTJSUloq2tTbS3t4uSkpL+4+3cuVP4fD7hcDjOOt5A\nICDefPNNIYQQkUhEXHrppeLll18WQgjx5ptvikAgIIQQ4ve//71YuXLlBccnllIp9n3X8IUvfEHM\nnj1bvP/++xcanphJpbgfPnxYTJkypf94TU1NFxyfWEql2H/jG98Qv//974UQQhw8eFCMHDnyQsMT\nM8kWdyGE8Pv9QgghVFUVy5YtE5s2bRJCiLOOORElStw/79490MyZM8XOnTuFqqriqquu6t/v0KFD\n4uOPPxZf/OIXE/r+3ieVYt/3b0EIIR5//HHxzW9+czhClBASOkH+rKVLl4rXXntNjBkzRvh8PiFE\n7y/amDFjTtu3vb1dFBYWCiGE+Mc//iGuvPLK/tceeeQR8cgjj5z2no0bN4o1a9b0//+aNWvExo0b\nT9nn826cA915551i3bp1p23fu3evmDdv3qCPkwiSPfZ33XWX2Lp1a9LcQPskc9zvvfde8V//9V+D\nfm+iSebYr1mzRjz66KP945k7d+6gj6O1ZIp7NBoV11xzjaiqqhJCiEGNOVElQtyFOPvnps/nE2PH\njj3r8YQQSXd/75MKse87/2233XauS00qCV1iMdCxY8dQXV2N2bNno6mpCQUFBQCA/Px8NDU1nbb/\n+vXrcfXVVwMAvF4viouL+18rKiqC1+s97T2D3W8wOjs78dJLL2HhwoXnHFsySPbY7927F3V1dfjS\nl740pONpJdnjfvjwYRw+fBiXXHIJ5syZg+3btw/puFpI9tg/9NBDePrpp1FUVIQlS5bgt7/97ZCO\nG2/JFPfFixcjNzcXTqcTK1asAIBBjTkRJUrcz/W56fV6UVRU9LnnSTapEPu+0qRnnnkGP/3pTwd7\n6QnPqPUABqOnpwfLly/Hr3/9a7hcrlNe0+l00Ol0p2z729/+hvXr1+Ptt9+O5zD7ybKMG264AXfe\neSdKS0tPee3pp5/G7t278fe//12TsZ2vZI+9qqr4zne+g6eeekqT8QxVsse9b1tNTQ127NiB+vp6\nXHbZZfjwww/hdrs1GeNgpULsN23ahH/5l3/Bd7/7XezcuRM33XQTDhw4AL0+cedEki3ur776KsLh\nMFavXo0333wTixYtOuX1M405ESVK3M/1uZmqUiX2Dz/8MB5++GGsXbsWTzzxBH7yk58M6/i0krh3\ny5MkScLy5cuxevVqLFu2DACQl5eHhoYGAEBDQwNyc3P799+/fz9uvfVWbNmyBVlZWQAAj8eDurq6\n/n3q6+vh8Xjw3nvvYcqUKZgyZQpefPHFs+53Noqi9L//Rz/6Uf/2NWvWoLy8HHffffcp+//1r3/F\nww8/jBdffBEWi+UCohIfqRD77u5uHDhwAPPnz8eoUaPw7rvvYunSpQn9IEcqxB3onWVYunQpTCYT\nSkpKMGbMGNTU1FxgdGIrVWK/fv16rFy5EgAwd+5chMNhtLa2XkhoYioZ4w4AVqsV1157LbZs2fK5\nY05EiRT3z/4efzbuHo8H9fX1Z31/sknF2K9evRovvPDCcIQnMWhd43EuqqqKm266Sdx1112nbP/e\n9753SiH7vffeK4QQ4vjx42L06NHinXfeOWV/SZJESUmJOHr0aH8h+4EDB047X1tbmxg1apRob28X\n7e3tYtSoUaKtre2UfT6vNu2BBx4Qy5YtE4qinLJ97969orS0VBw+fHhwF6+xVIr9QIleo5ZKcX/l\nlVfE1772NSGEEC0tLaKoqEi0trYOIgraSKXYX3XVVeJPf/qTEKL3AaaCggKhqurnB0EDyRb37u7u\n/jpRSZLEypUrxW9/+9tzjjkRJVLcB3PvFuL0B8W2bdt2yuuJfn/vk0qxH5jT/OY3vxHLly8/z2gk\nroROkN966y0BQEyaNElMnjxZTJ48WWzbtk20traKyy+/XJSVlYmFCxf2/6BvueUW4Xa7+/edPn16\n/7G2bdsmysvLRWlpqfjZz3521nOuX79ejB49WowePVo8+eST/dvvvfde4fF4hE6nEx6PR/z4xz8+\n7b11dXUCgBg3blz/GPoeUlq4cKHIzc3t3/7lL395mKIUG6kU+4ES/QaaSnFXVVXcc889Yvz48WLi\nxIn9T/onqlSK/cGDB8W8efNEZWWlmDx5snj11VeHKUrDL9ni3tjYKGbMmCEmTZokKioqxLe//W0h\nSZIQQpx1zIkoUeI+2Hu3EEK8//77oqKiQpSWloo77rij/4++v/zlL8Lj8Qiz2Sxyc3NPeXAtEaVS\n7JctWyYqKirEpEmTxDXXXCPq6+uHK0ya40p6REREREQDJHwNMhERERFRPDFBJiIiIiIagAkyERER\nEdEATJCJiIiIiAZggkxERERENAATZCKii8xDDz2EX/ziF1oPg4goYTFBJiIiIiIagAkyEdFF4OGH\nH8aYMWNw6aWX4pNPPgEA/OY3v8GECRNQWVmJVatWaTxCIqLEYdR6AEREFFt79uxBVVUV9u3bB1mW\nMW3aNEyfPh2PPvooamtrYbFY0NnZqfUwiYgSBmeQiYhS3FtvvYXrrrsOdrsdLpcLS5cuBQBUVlZi\n9erVePrpp2E0cr6EiKgPE2QioovUtm3bcMcdd2Dv3r2YOXMmZFnWekhERAmBCTIRUYq77LLLsHnz\nZoRCIXR3d+Oll16Cqqqoq6vDggUL8Nhjj8Hv96Onp0froRIRJQR+p0ZElOKmTZuG66+/HpMnT0Zu\nbi5mzpwJnU6Hr371q/D7/RBC4M4774Tb7dZ6qERECUEnhBBaD4KIiIiIKFGwxIKIiIiIaAAmyERE\nREREAzBBJiIiIiIagAkyEREREdEATJCJiIiIiAZggkxERERENAATZCIiIiKiAZggExEREREN8P8D\nsQH82n9KpuAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ibx9Jo7rph-k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qjOL8yWR60jR",
        "colab_type": "text"
      },
      "source": [
        "# CLEANED NEW PART"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MA1Yt1fo62oj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data=pd.read_csv(\"/content/china.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CRvt6V457Fta",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data.columns=['DROPPER','Provinces','Country','Last Update'\t,'Confirmed'\t,'Deaths'\t,'Recovered']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wGhchIxS7Ggj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data=data.drop(columns=['DROPPER','Provinces','Country','Deaths','Recovered'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rxKLA2rY7UZm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data.columns=['ds','y']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sVwA8zm67tyR",
        "colab_type": "code",
        "outputId": "f489c0d9-9d0b-4d75-d432-11e7f0dae309",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        }
      },
      "source": [
        "from fbprophet import Prophet\n",
        "m = Prophet()\n",
        "m.fit(naan)\n",
        "future = m.make_future_dataframe(periods=2,freq='d')\n",
        "future.tail()\n",
        "forecast = m.predict(future)\n",
        "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>yhat</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>401</th>\n",
              "      <td>2020-01-30 21:30:00</td>\n",
              "      <td>284.669331</td>\n",
              "      <td>-247.332107</td>\n",
              "      <td>817.933405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>402</th>\n",
              "      <td>2020-01-30 21:30:00</td>\n",
              "      <td>284.669331</td>\n",
              "      <td>-262.057132</td>\n",
              "      <td>828.740476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>403</th>\n",
              "      <td>2020-01-30 21:30:00</td>\n",
              "      <td>284.669331</td>\n",
              "      <td>-279.703899</td>\n",
              "      <td>834.699035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404</th>\n",
              "      <td>2020-01-31 21:30:00</td>\n",
              "      <td>291.022828</td>\n",
              "      <td>-236.213101</td>\n",
              "      <td>840.514340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405</th>\n",
              "      <td>2020-02-01 21:30:00</td>\n",
              "      <td>297.376325</td>\n",
              "      <td>-251.902110</td>\n",
              "      <td>797.270394</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     ds        yhat  yhat_lower  yhat_upper\n",
              "401 2020-01-30 21:30:00  284.669331 -247.332107  817.933405\n",
              "402 2020-01-30 21:30:00  284.669331 -262.057132  828.740476\n",
              "403 2020-01-30 21:30:00  284.669331 -279.703899  834.699035\n",
              "404 2020-01-31 21:30:00  291.022828 -236.213101  840.514340\n",
              "405 2020-02-01 21:30:00  297.376325 -251.902110  797.270394"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 528
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-DXE2_I-uut",
        "colab_type": "code",
        "outputId": "8567c6c4-24c9-427b-f009-e201e1d695af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        }
      },
      "source": [
        "m.plot_components(forecast)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAGoCAYAAADW2lTlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 648x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 529
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAGoCAYAAADW2lTlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 648x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nO-ci7pF8a8v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "res=m.predict(future)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aBunjw34_dvf",
        "colab_type": "code",
        "outputId": "2a835222-0329-4140-f4de-cfb9904a6b48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "source": [
        "res.groupby('ds')['yhat'].sum().reset_index()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>yhat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>473.242707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-23 12:00:00</td>\n",
              "      <td>670.160790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-24 12:00:00</td>\n",
              "      <td>854.300973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-25 12:00:00</td>\n",
              "      <td>925.411913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-25 22:00:00</td>\n",
              "      <td>545.427501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-01-26 23:00:00</td>\n",
              "      <td>3312.286877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2020-01-27 20:30:00</td>\n",
              "      <td>3636.721631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2020-01-28 23:00:00</td>\n",
              "      <td>6043.230856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2020-01-29 21:00:00</td>\n",
              "      <td>10489.585840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2020-01-30 21:30:00</td>\n",
              "      <td>8255.410605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2020-01-31 21:30:00</td>\n",
              "      <td>291.022828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2020-02-01 21:30:00</td>\n",
              "      <td>297.376325</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    ds          yhat\n",
              "0  2020-01-22 12:00:00    473.242707\n",
              "1  2020-01-23 12:00:00    670.160790\n",
              "2  2020-01-24 12:00:00    854.300973\n",
              "3  2020-01-25 12:00:00    925.411913\n",
              "4  2020-01-25 22:00:00    545.427501\n",
              "5  2020-01-26 23:00:00   3312.286877\n",
              "6  2020-01-27 20:30:00   3636.721631\n",
              "7  2020-01-28 23:00:00   6043.230856\n",
              "8  2020-01-29 21:00:00  10489.585840\n",
              "9  2020-01-30 21:30:00   8255.410605\n",
              "10 2020-01-31 21:30:00    291.022828\n",
              "11 2020-02-01 21:30:00    297.376325"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 534
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-HcvKhH-6S8",
        "colab_type": "code",
        "outputId": "d8db3414-b168-4bbe-b19c-81181e8bdf08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        }
      },
      "source": [
        "m.plot(res)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAGoCAYAAABbtxOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 532
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAGoCAYAAABbtxOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}