{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "PROJECT_COVID19_GLOBAL_WK4.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "LisER1atk_SK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from google.colab import files\n",
        "\n",
        "train_file = \"https://raw.githubusercontent.com/danielcaraway/COVID19/master/WK4_0413/train.csv\"\n",
        "train = pd.read_csv(train_file)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fv-GJ4Gonifn",
        "colab_type": "text"
      },
      "source": [
        "## JUST LOOKING AT CALIFORNIA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-7CrCyJlmDP8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "808c9b3c-55d7-4b54-a028-35aece471793"
      },
      "source": [
        "## Just looking at CA\n",
        "ca = train[train['Province_State'] == 'California']\n",
        "ca['Date'] = pd.to_datetime(ca['Date'])\n",
        "ca = ca[ca['ConfirmedCases'] > 0 ]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xVUAKtDMpXkU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2f34c77c-8537-49da-9c5a-f30391c3f321"
      },
      "source": [
        "ca"
      ],
      "execution_count": 25,
      "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>Id</th>\n",
              "      <th>Province_State</th>\n",
              "      <th>Country_Region</th>\n",
              "      <th>Date</th>\n",
              "      <th>ConfirmedCases</th>\n",
              "      <th>Fatalities</th>\n",
              "      <th>ds</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>19810</th>\n",
              "      <td>27523</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-10</td>\n",
              "      <td>144.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2020-03-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19811</th>\n",
              "      <td>27524</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-11</td>\n",
              "      <td>177.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2020-03-11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19812</th>\n",
              "      <td>27525</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>221.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2020-03-12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19813</th>\n",
              "      <td>27526</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-13</td>\n",
              "      <td>282.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2020-03-13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19814</th>\n",
              "      <td>27527</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>340.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2020-03-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19815</th>\n",
              "      <td>27528</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>426.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2020-03-15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19816</th>\n",
              "      <td>27529</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>557.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2020-03-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19817</th>\n",
              "      <td>27530</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>698.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2020-03-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19818</th>\n",
              "      <td>27531</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-18</td>\n",
              "      <td>751.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>2020-03-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19819</th>\n",
              "      <td>27532</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-19</td>\n",
              "      <td>952.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2020-03-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19820</th>\n",
              "      <td>27533</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-20</td>\n",
              "      <td>1177.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>2020-03-20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19821</th>\n",
              "      <td>27534</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-21</td>\n",
              "      <td>1364.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>2020-03-21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19822</th>\n",
              "      <td>27535</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-22</td>\n",
              "      <td>1646.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>2020-03-22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19823</th>\n",
              "      <td>27536</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>2108.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>2020-03-23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19824</th>\n",
              "      <td>27537</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-24</td>\n",
              "      <td>2538.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>2020-03-24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19825</th>\n",
              "      <td>27538</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-25</td>\n",
              "      <td>2998.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>2020-03-25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19826</th>\n",
              "      <td>27539</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-26</td>\n",
              "      <td>3899.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>2020-03-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19827</th>\n",
              "      <td>27540</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-27</td>\n",
              "      <td>4657.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>2020-03-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19828</th>\n",
              "      <td>27541</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-28</td>\n",
              "      <td>5095.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>2020-03-28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19829</th>\n",
              "      <td>27542</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-29</td>\n",
              "      <td>5852.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>2020-03-29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19830</th>\n",
              "      <td>27543</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-30</td>\n",
              "      <td>7138.0</td>\n",
              "      <td>146.0</td>\n",
              "      <td>2020-03-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19831</th>\n",
              "      <td>27544</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-31</td>\n",
              "      <td>8210.0</td>\n",
              "      <td>173.0</td>\n",
              "      <td>2020-03-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19832</th>\n",
              "      <td>27545</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>9399.0</td>\n",
              "      <td>199.0</td>\n",
              "      <td>2020-04-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19833</th>\n",
              "      <td>27546</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-02</td>\n",
              "      <td>10773.0</td>\n",
              "      <td>238.0</td>\n",
              "      <td>2020-04-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19834</th>\n",
              "      <td>27547</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-03</td>\n",
              "      <td>12004.0</td>\n",
              "      <td>265.0</td>\n",
              "      <td>2020-04-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19835</th>\n",
              "      <td>27548</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-04</td>\n",
              "      <td>12837.0</td>\n",
              "      <td>289.0</td>\n",
              "      <td>2020-04-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19836</th>\n",
              "      <td>27549</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-05</td>\n",
              "      <td>15034.0</td>\n",
              "      <td>348.0</td>\n",
              "      <td>2020-04-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19837</th>\n",
              "      <td>27550</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-06</td>\n",
              "      <td>16019.0</td>\n",
              "      <td>380.0</td>\n",
              "      <td>2020-04-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19838</th>\n",
              "      <td>27551</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-07</td>\n",
              "      <td>17351.0</td>\n",
              "      <td>432.0</td>\n",
              "      <td>2020-04-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19839</th>\n",
              "      <td>27552</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-08</td>\n",
              "      <td>18897.0</td>\n",
              "      <td>495.0</td>\n",
              "      <td>2020-04-08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19840</th>\n",
              "      <td>27553</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-09</td>\n",
              "      <td>19710.0</td>\n",
              "      <td>544.0</td>\n",
              "      <td>2020-04-09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19841</th>\n",
              "      <td>27554</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-10</td>\n",
              "      <td>21081.0</td>\n",
              "      <td>583.0</td>\n",
              "      <td>2020-04-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19842</th>\n",
              "      <td>27555</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-11</td>\n",
              "      <td>21706.0</td>\n",
              "      <td>605.0</td>\n",
              "      <td>2020-04-11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19843</th>\n",
              "      <td>27556</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-12</td>\n",
              "      <td>22795.0</td>\n",
              "      <td>641.0</td>\n",
              "      <td>2020-04-12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Id Province_State  ... Fatalities         ds\n",
              "19810  27523     California  ...        2.0 2020-03-10\n",
              "19811  27524     California  ...        3.0 2020-03-11\n",
              "19812  27525     California  ...        4.0 2020-03-12\n",
              "19813  27526     California  ...        4.0 2020-03-13\n",
              "19814  27527     California  ...        5.0 2020-03-14\n",
              "19815  27528     California  ...        6.0 2020-03-15\n",
              "19816  27529     California  ...        7.0 2020-03-16\n",
              "19817  27530     California  ...       12.0 2020-03-17\n",
              "19818  27531     California  ...       13.0 2020-03-18\n",
              "19819  27532     California  ...       18.0 2020-03-19\n",
              "19820  27533     California  ...       23.0 2020-03-20\n",
              "19821  27534     California  ...       24.0 2020-03-21\n",
              "19822  27535     California  ...       30.0 2020-03-22\n",
              "19823  27536     California  ...       39.0 2020-03-23\n",
              "19824  27537     California  ...       50.0 2020-03-24\n",
              "19825  27538     California  ...       65.0 2020-03-25\n",
              "19826  27539     California  ...       81.0 2020-03-26\n",
              "19827  27540     California  ...       94.0 2020-03-27\n",
              "19828  27541     California  ...      110.0 2020-03-28\n",
              "19829  27542     California  ...      124.0 2020-03-29\n",
              "19830  27543     California  ...      146.0 2020-03-30\n",
              "19831  27544     California  ...      173.0 2020-03-31\n",
              "19832  27545     California  ...      199.0 2020-04-01\n",
              "19833  27546     California  ...      238.0 2020-04-02\n",
              "19834  27547     California  ...      265.0 2020-04-03\n",
              "19835  27548     California  ...      289.0 2020-04-04\n",
              "19836  27549     California  ...      348.0 2020-04-05\n",
              "19837  27550     California  ...      380.0 2020-04-06\n",
              "19838  27551     California  ...      432.0 2020-04-07\n",
              "19839  27552     California  ...      495.0 2020-04-08\n",
              "19840  27553     California  ...      544.0 2020-04-09\n",
              "19841  27554     California  ...      583.0 2020-04-10\n",
              "19842  27555     California  ...      605.0 2020-04-11\n",
              "19843  27556     California  ...      641.0 2020-04-12\n",
              "\n",
              "[34 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfGjVDAKmMxX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "52bc36be-9802-4c4b-acaf-b02a4bade292"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "ca.plot(x=\"Date\", y=[\"ConfirmedCases\", \"Fatalities\"])\n",
        "plt.title('California')\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3cvR-v9rnadJ",
        "colab_type": "text"
      },
      "source": [
        "## CALIFORNIA + PROPHET\n",
        "\n",
        "++ Heading back to WEEK ONE\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K341-zYqnkqu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "69acda2c-1282-483f-9281-d504885b402d"
      },
      "source": [
        "from fbprophet import Prophet \n",
        "df = pd.read_csv('https://raw.githubusercontent.com/danielcaraway/data/master/ca_train.csv')\n",
        "df.head()"
      ],
      "execution_count": 12,
      "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>Id</th>\n",
              "      <th>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>Date</th>\n",
              "      <th>ConfirmedCases</th>\n",
              "      <th>Fatalities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>36.1162</td>\n",
              "      <td>-119.6816</td>\n",
              "      <td>2020-01-22</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>California</td>\n",
              "      <td>US</td>\n",
              "      <td>36.1162</td>\n",
              "      <td>-119.6816</td>\n",
              "      <td>2020-01-23</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>California</td>\n",
              "      <td>US</td>\n",
              "      <td>36.1162</td>\n",
              "      <td>-119.6816</td>\n",
              "      <td>2020-01-24</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>California</td>\n",
              "      <td>US</td>\n",
              "      <td>36.1162</td>\n",
              "      <td>-119.6816</td>\n",
              "      <td>2020-01-25</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>California</td>\n",
              "      <td>US</td>\n",
              "      <td>36.1162</td>\n",
              "      <td>-119.6816</td>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id Province/State Country/Region  ...        Date  ConfirmedCases Fatalities\n",
              "0   1     California             US  ...  2020-01-22             0.0        0.0\n",
              "1   2     California             US  ...  2020-01-23             0.0        0.0\n",
              "2   3     California             US  ...  2020-01-24             0.0        0.0\n",
              "3   4     California             US  ...  2020-01-25             0.0        0.0\n",
              "4   5     California             US  ...  2020-01-26             0.0        0.0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7eVKoyoam_ZR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "4dae211c-c38b-46f1-b4e9-84b083f20789"
      },
      "source": [
        "sm = df[['Date', 'ConfirmedCases']]\n",
        "sm.columns = ['ds','y']\n",
        "m = Prophet()\n",
        "m.fit(sm)\n",
        "future = m.make_future_dataframe(periods=365)\n",
        "future.tail()\n",
        "forecast = m.predict(future)\n",
        "\n",
        "fig1 = m.plot(forecast)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FM19rahNn1n9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "outputId": "76a10fe7-e151-4067-9d5c-a9e71dd88bf8"
      },
      "source": [
        "df = df[df['ConfirmedCases'] > 0]\n",
        "sm = df[['Date', 'ConfirmedCases']]\n",
        "sm.columns = ['ds','y']\n",
        "m = Prophet()\n",
        "m.fit(sm)\n",
        "future = m.make_future_dataframe(periods=30)\n",
        "forecast = m.predict(future)\n",
        "fig1 = m.plot(forecast)"
      ],
      "execution_count": 19,
      "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:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:n_changepoints greater than number of observations. Using 6.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7LyF9b7moLOT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "0be7e867-ccda-416e-cb7a-e3460af40fc6"
      },
      "source": [
        "forecast.head()"
      ],
      "execution_count": 28,
      "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>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>2020-03-10</td>\n",
              "      <td>79.223423</td>\n",
              "      <td>25.618567</td>\n",
              "      <td>138.026576</td>\n",
              "      <td>79.223423</td>\n",
              "      <td>79.223423</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>79.223423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-11</td>\n",
              "      <td>159.323325</td>\n",
              "      <td>102.371433</td>\n",
              "      <td>213.228246</td>\n",
              "      <td>159.323325</td>\n",
              "      <td>159.323325</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>159.323325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>239.423227</td>\n",
              "      <td>185.210339</td>\n",
              "      <td>296.219647</td>\n",
              "      <td>239.423227</td>\n",
              "      <td>239.423227</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>239.423227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-03-13</td>\n",
              "      <td>319.523130</td>\n",
              "      <td>262.933860</td>\n",
              "      <td>377.954821</td>\n",
              "      <td>319.523130</td>\n",
              "      <td>319.523130</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>319.523130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>399.637257</td>\n",
              "      <td>345.261322</td>\n",
              "      <td>455.485276</td>\n",
              "      <td>399.637257</td>\n",
              "      <td>399.637257</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>399.637257</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          ds       trend  ...  multiplicative_terms_upper        yhat\n",
              "0 2020-03-10   79.223423  ...                         0.0   79.223423\n",
              "1 2020-03-11  159.323325  ...                         0.0  159.323325\n",
              "2 2020-03-12  239.423227  ...                         0.0  239.423227\n",
              "3 2020-03-13  319.523130  ...                         0.0  319.523130\n",
              "4 2020-03-14  399.637257  ...                         0.0  399.637257\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1mLojw8kodYz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ca['ds'] = ca['Date']\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n276UIH1pAhk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "c48fe0a6-cde8-4953-d68f-57ab9e59e3a8"
      },
      "source": [
        "m1 = forecast.copy()\n",
        "m2 = ca.copy()\n",
        "df_merged = pd.merge(m1, m2, how='left', on='ds', suffixes=('_v1', '_v2'))\n",
        "df_merged.head()"
      ],
      "execution_count": 26,
      "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>multiplicative_terms</th>\n",
              "      <th>multiplicative_terms_lower</th>\n",
              "      <th>multiplicative_terms_upper</th>\n",
              "      <th>yhat</th>\n",
              "      <th>Id</th>\n",
              "      <th>Province_State</th>\n",
              "      <th>Country_Region</th>\n",
              "      <th>Date</th>\n",
              "      <th>ConfirmedCases</th>\n",
              "      <th>Fatalities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-03-10</td>\n",
              "      <td>79.223423</td>\n",
              "      <td>25.618567</td>\n",
              "      <td>138.026576</td>\n",
              "      <td>79.223423</td>\n",
              "      <td>79.223423</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>79.223423</td>\n",
              "      <td>27523.0</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-10</td>\n",
              "      <td>144.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-11</td>\n",
              "      <td>159.323325</td>\n",
              "      <td>102.371433</td>\n",
              "      <td>213.228246</td>\n",
              "      <td>159.323325</td>\n",
              "      <td>159.323325</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>159.323325</td>\n",
              "      <td>27524.0</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-11</td>\n",
              "      <td>177.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>239.423227</td>\n",
              "      <td>185.210339</td>\n",
              "      <td>296.219647</td>\n",
              "      <td>239.423227</td>\n",
              "      <td>239.423227</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>239.423227</td>\n",
              "      <td>27525.0</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>221.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-03-13</td>\n",
              "      <td>319.523130</td>\n",
              "      <td>262.933860</td>\n",
              "      <td>377.954821</td>\n",
              "      <td>319.523130</td>\n",
              "      <td>319.523130</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>319.523130</td>\n",
              "      <td>27526.0</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-13</td>\n",
              "      <td>282.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>399.637257</td>\n",
              "      <td>345.261322</td>\n",
              "      <td>455.485276</td>\n",
              "      <td>399.637257</td>\n",
              "      <td>399.637257</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>399.637257</td>\n",
              "      <td>27527.0</td>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>340.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          ds       trend  yhat_lower  ...       Date  ConfirmedCases  Fatalities\n",
              "0 2020-03-10   79.223423   25.618567  ... 2020-03-10           144.0         2.0\n",
              "1 2020-03-11  159.323325  102.371433  ... 2020-03-11           177.0         3.0\n",
              "2 2020-03-12  239.423227  185.210339  ... 2020-03-12           221.0         4.0\n",
              "3 2020-03-13  319.523130  262.933860  ... 2020-03-13           282.0         4.0\n",
              "4 2020-03-14  399.637257  345.261322  ... 2020-03-14           340.0         5.0\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zwDD9eMCpBKl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sm_merge = df_merged[['ds','yhat_lower', 'yhat_upper', 'yhat', 'ConfirmedCases']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "glbRmwB2pmsy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "3fde6c67-1419-4493-d7cf-003b69381b00"
      },
      "source": [
        "sm_merge.plot(x=\"ds\", y=[\"yhat\", \"yhat_lower\", \"yhat_upper\", \"ConfirmedCases\"])\n",
        "plt.title('California: Prophet vs Actual')\n",
        "plt.show()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4I5dddlmTz2dw1FFH8fLLL3PwwQfXninMnz+fo48+GlWlR48eLF68mMWLF/Pjjz8yY8aM2nVjrT7VhABVZePtt+PZkkOnSY/gTkgIdEhBqzHVRB7gelXtDgwErhaR7sDNwP9UtRvwP+c5wMlAN+d2OfBv8CcP4C7gSGAAcFdNAnGWuazOeift/UcLjJaez2DAgAF8/fXXbN26Fa/XyxtvvMFxxx0HwDHHHMPDDz/MscceS9++ffnqq6+IjIykXbt2HHzwweTk5DB//nwAqqurbaY1E3Lyp71O8cwvaH/99UT37h3ocILaLquJVHUjsNF5XCwiPwGdgNOBIc5irwCzgPFO+avqHxv7WxFJFJEMZ9mZqpoHICIzgZNEZBaQoKrfOuWvAmcA/22aj9iyWno+g4yMDB544AGGDh1a24B8+umnA/5ksH79eo499ljcbjddunThkEMOqY3znXfe4dprr6WwsBCPx8PYsWPp0SMwM8QZ09QqVqxgy4MPEnfccSRfNGrXK7RxuzWfgYh0BWYDPYHfVTXRKRcgX1UTReRj4AFVneu89j/8SWIIEKWq9zrldwDl+JPIA6r6f075McB4Vd1p53ybz6BtCIa/qWl9vCWl/Pq3v+GrqGC/D94nLClp1yu1EXs9n4GIxAHvAmNVtajua85ZQLPPkiMil4tIpohk5uTkNPfb7Tabz8CYwFNVNt19N1Xr19PpkYctETRSo3oTiUg4/kQwTVXfc4o3i0iGqm50qoG2OOXZQN0h/zo7Zdlsq1aqKZ/llHeuZ/k/UNXngOfAf2bQmNhbUnPMZ3DmmWeybt267coefPDBnbZHGNOWFUx/i6L//Ie0MdcS0+8PB8CmAbtMBk4V0IvAT6o6qc5LHwGjgAec+w/rlI8WkTfxNxYXOgnjc+D+Oo3GJwC3qGqeiBSJyEDgO+BC4Ikm+Gwh4f333w90CMa0Cur1kjP5cXKfe47YwYNJufzyQIfUqjTmzOBo4ALgRxFZ7JTdij8JvCUilwC/AWc7r30KnAKsAcqAvwM4O/17gAXOchNrGpOBq4ApQDT+huNW2XhsjAkMb2Eh2TfeSOnsOSSedRbpd9yO7KQDh/mjxvQmmgs01O//T/Usr8DVDWzrJeClesoz8TdKG2PMbqlcvZr1V4+meuNGOtx9N0nnnL3rlcwfhO4VyMaYkFf0+Qw23HILrtgY9n3lFWIO7xvokFotSwbGmFZHvV5ynniC3GeeJfqww+j0+OOEp7cPdFitmg1U18yaaz4DY9oqb1ER66+6itxnniXxrOHsM/VVSwRNwJJBEArGZOD1egMdgjFUrlvHr2edTek38+kw4S46TJyIKyIi0GGFhJCtJnrw+wdZmbeySbd5SPIhjB8wvsHXW3I+g65du5KZmUlqaiqZmZnccMMNzJo1iwkTJvDLL7+wZs0atm7dyk033cRll13GrFmzuPPOO4mPj2fNmjUMHTqUp59+GpfLxYwZM7jrrruorKzkgAMO4OWXXyYuLo6uXbtyzjnnMHPmTG666SbOPffcJv0+jdkdWlVF9tjr8BYVse8rU4g5/PBdr2Qazc4MmlAg5jOoz9KlS/nyyy+ZP38+EydOrD3L+P7773niiSdYsWIFv/zyC++99x5bt27l3nvv5YsvvuCHH36gX79+TJq07XKSlJQUfvjhB0sEJuC2Pv88latWkXHfvZYImkHInhns7Ai+uQRiPoP6nH766URHRxMdHc3QoUP5/vvvSUxMZMCAAey///4AjBgxgrlz5xIVFcWKFSs4+uijAf+kNoMGDardls1rYIJBxaqf2frMsyScdhrxxx8f6HBCUsgmg0Cpmc9g06ZNjZrPIDMzky5dujBhwoTdms8gLCwMn88H8If1dpwOouZ5feWqyrBhw3jjjTfqfR+b18AEmno8bLztNtzx8aTfdmugwwlZVk3UxFpqPoOuXbuycOFCAN59993tXvvwww+pqKggNzeXWbNm1Q6P/f3337Nu3Tp8Ph/Tp09n8ODBDBw4kHnz5rFmzRoASktL+fnnn3f/gxvTTPKmTKFi2TI63HmHDTrXjCwZNLGa+QzOPvvsRs9ncOKJJ9Y7n0GfPn0oLy+vd/277rqLMWPG0K9fvz+8T+/evRk6dCgDBw7kjjvuoGPHjgD079+f0aNHc+ihh7Lffvtx5plnkpaWxpQpUxgxYgS9e/dm0KBBrFzZtA3vxuypyrXryHn8CeKHDSPeBmdsVrs1n0EwsfkM6jdhwgTi4uK44YYbtiufNWsWDz/8MB9//HGLx7Q3guFvagJDvV5+O/8CKteu5YCP/0OYM8+32Tt7PZ+B2TWbz8CYppM/7XXKFy2iw623WCJoAXZmEOTa+nwGofg3NbtWtX49a/9yOjED+tPlmWf+0PnB7LmGzgysN1GQs/kMTFujqmy8/Q7E7Sbj7rstEbQQqyYyxgSVgrfepuy772h/042Ed+gQ6HDaDEsGxpigUb1hA1seeoiYQQNJPOusQIfTplgyMMYEBVVl410TUJ+PjHvuseqhFmbJoIlt2rSJc889lwMOOIAjjjiCU045ZY8u4pozZw49evSgT58+ZGdnM3z48GaI9o/i4uJqH//888+ccsopdOvWjcMPP5yzzz6bzZs3t0gcpu0peOttSufMof24cUQ4w7eYlmMNyE1IVTnzzDMZNWoUb775JgBLlixh8+bNHHTQQbu1rWnTpnHLLbdw/vnnA2x3hXINj8dDWFjz/AkrKio49dRTmTRpUu14SbNmzSInJ4f09PRmeU/TdlWsWMHm++4j9qijSBp5XqDDaZPszKAJffXVV4SHh3PFFVfUlh122GEMHjyYG2+8kZ49e9KrVy+mT58O+HeuQ4YMYfjw4RxyyCGMHDkSVeWFF17grbfe4o477mDkyJH8+uuv9OzpnyJ6ypQp/OUvf+H444/nT3/6E1OmTOGMM85g2LBhdO3alSeffJJJkybRt29fBg4cSF5eHgC//PILJ510EkcccQTHHHNM7VXG69atY9CgQfTq1Yvbb7+9Nu7XX3+dQYMGbTdw3pAhQ+jZsye//vorxxxzDIcffjiHH34433zzDQAbN27k2GOPpU+fPvTs2ZM5c+YAMGPGDAYNGsThhx/OWWedRUlJCQA333wz3bt3p3fv3n+4SM60Hd7iYrLGXoc7KYmOD/8LcdluKRBC9sxg0/33U/lT0w6rEHnoIXS4teGBsmrmK9jRe++9x+LFi1myZAlbt26lf//+HHvssQAsWrSI5cuX07FjR44++mjmzZvHpZdeyty5cznttNMYPnw4v/7663bb++GHH1i6dCnJyclMmTKFZcuWsWjRIioqKjjwwAN58MEHWbRoEddddx2vvvoqY8eO5fLLL+eZZ56hW7dufPfdd1x11VV8+eWXjBkzhiuvvJILL7yQp556apefBaB9+/bMnDmTqKgoVq9ezYgRI8jMzOT111/nxBNP5LbbbsPr9VJWVrbdENmxsbE8+OCDTJo0iauvvpr333+flStXIiIUFBTswV/EtHaqysZbb6U6O5t9p75KWHJyoENqs0I2GQSTuXPnMmLECNxuN+np6Rx33HEsWLCAhISEBoe33plhw4aRXOefZujQocTHxxMfH0+7du1qj+Z79erF0qVLKSkp4ZtvvuGsOr0zKisrAZg3b17tQHcXXHAB48fveujv6upqRo8ezeLFi3G73bVtIv379+fiiy+murqaM844gz59+vD111/XO0R2u3btiIqK4pJLLuG0007b7alBTWjIe+UVimd+Qfvx422OggAL2WSwsyP45tKjR4966/Z3pr7hrXdlx2Gl627D5XLVPne5XHg8Hnw+H4mJiSxevLje7dXXa6NHjx58/fXX9S7/6KOPkp6ezpIlS/D5fERFRQFw7LHHMnv2bD755BMuuugixo0bR1JSUoNDZH///ff873//45133uHJJ5/kyy+/3OVnN6Gj7IdFbHn4EeKH/R/JF40KdDhtnlXONaHjjz+eyspKnnvuudqypUuXkpiYyPTp0/F6veTk5DB79mwGDBjQYnElJCSw33778fbbbwP+U/MlS5YAcPTRR9c2dk+bNq12nfPOO49vvvmGTz75pLZs9uzZLFu2jMLCQjIyMnC5XEydOrV2fuTffvuN9PR0LrvsMi699FJ++OGHBofILikpobCwkFNOOYVHH320Nh7TNnjy88keN47wjAwy7rvPupEGAUsGTUhEeP/99/niiy844IAD6NGjB7fccgvnnXcevXv35rDDDuP444/noYceokMLX1k5bdo0XnzxRQ477DB69OjBhx9+CMDkyZN56qmn6NWrF9nZ2bXLR0dH8/HHH/PEE0/QrVs3unfvztNPP01aWhpXXXUVr7zyCocddhgrV66sPVOZNWsWhx12GH379mX69OmMGTOmwSGyi4uLOe200+jduzeDBw/ebqpNE9rU52PDjTfhzcuj8+THcCckBDokgw1UZ4Kc/U1DT87TT7P18SfocPfdJJ1zdqDDaXNsCGtjTMCVzp/P1ieeJOEvfybxbBtuIphYMjDGtIjqzVvIvuFGIg7Yn4wJE6ydIMiEXG8iVbUfWYhorVWY5o+0qors68fhKy9n31dfwRUTE+iQzA5C6swgKiqK3Nxc24mEAFUlNze3ttuqab18lZVkXXMt5ZkLyZg4kcgDDgh0SKYeIXVm0LlzZ7KyssjJyQl0KKYJREVF1V6QZ1onX3k5WVePpnT+fDrcfTftTjs10CGZBoRUMggPD2e//fYLdBjGGMBXWsr6K66kbOFCMu6/n8Qzzwh0SGYnQioZGGOCg7e4mPWX/4PypUvp+NBDdkbQClgyMMY0KW9BAb9fehkVK1fSadIkEk48IdAhmUawZGCMaTKevDx+v+RSqtasofPjjxN//NBAh2QaaZe9iUTkJRHZIiLL6pRNEJFsEVns3E6p89otIrJGRFaJyIl1yk9yytaIyM11yvcTke+c8ukiEtGUH9AY0zI8OTn8PmoUVWvX0vnppy0RtDKN6Vo6BTipnvJHVbWPc/sUQES6A+cCPZx1nhYRt4i4gaeAk4HuwAhnWYAHnW0dCOQDl+zNBzLGtLzqzZv57cJRVGVl0+XZZ4k7ZufDsJvgs8tkoKqzgbxGbu904E1VrVTVdcAaYIBzW6Oqa1W1CngTOF38V4cdD9SM+/wKYF0OjGlFtKqK3y/6O54tW9jnheeJHXhkoEMye2BvLjobLSJLnWqkJKesE7C+zjJZTllD5SlAgap6diivl4hcLiKZIpJp1xIYExwKP/2UqnXr6Pivh4hpYHY8E/z2NBn8GzgA6ANsBB5psoh2QlWfU9V+qtovLS2tJd7SGLMTqkreiy8SedBBxA21NoLWbI+SgapuVlWvqvqA5/FXAwFkA13qLNrZKWuoPBdIFJGwHcqNMa1AyddfU7l6DSmXXmJjgrVye5QMRCSjztMzgZqeRh8B54pIpIjsB3QDvgcWAN2cnkMR+BuZP1L/IEJfAcOd9UcBH+5JTMaYlpf3wouEZWSQcPLJgQ7F7KVdXmcgIm8AQ4BUEckC7gKGiEgfQIFfgX8AqOpyEXkLWAF4gKtV1etsZzTwOeAGXlLV5c5bjAfeFJF7gUXAi0326YwxzaZ88WLKMjNJv+VmJDw80OGYvRRSM50ZY1pO1jXXUvr993T78n+4nKlPTfCzmc6MMU2mcu06ir/4gqTzRlgiCBGWDIwxuy3v5ZeR8HCSzz8/0KGYJmLJwBizWzw5ORR+8AHt/nomYSkpgQ7HNBFLBsaY3ZI39TXU6yXl738PdCimCVkyMMY0mrekhPw33iD+hBOI2HffQIdjmpAlA2NMoxW89Ta+4mJSLrk40KGYJmbJwBjTKFpVRd4rrxBz5JFE9+oV6HBME7NkYIxplMKPP8GzeTMpl14a6FBMM7BkYIzZJfX5yH3pRSIPPpjYwUcHOhzTDCwZGGN2qeTrr6la84sNSBfCLBkYY3Yp98UXCeuYQcJJ9U16aEKBJQNjzE6VLVpEeeZCUi76uw1IF8IsGRhjGuQtKWHLw4/gbteOxOF/C3Q4phlZMjDG1Kvq99/5bcQIyhcvpv0tN+OKiQl0SKYZ7XI+A2NM21P67bdkjxkLwFlebx0AACAASURBVD4vvkDswIEBjsg0NzszMMbUUlXypk3j90suxZ2WSte337JE0EbYmYExBvBfYbzpvvspmD6duKFD6fivh3DHxQU6LNNCLBkYY/Dk5ZF97RjKMjNJufxy0sZci7jdgQ7LtCBLBsa0cRWrVpF15VV4cnPp+PDDtDvt1ECHZALAkoExbVjJ7Nlkjb0Od3w8+772GtG9egY6JBMglgyMaaMqVq0ia+x1RHTdly7PPEN4+/aBDskEkPUmMqYN8uTmknXlVbjj4iwRGMDODIxpc7SqiqwxY/Dk5rLva69ZIjCAJQNj2hRVZdM991CeuZCODz9sbQSmllUTGdOG5E99jYK33yHlH/+wXkNmO5YMjGkjSubNY/MDDxD3pz+RNubaQIdjgowlA2PagMp168i+bhyRBx5Ip4ceRFz2r2+2Z78IY0Kct6iIrKuuRtxuOj/9NK7Y2ECHZIKQNSAbE8LU6yX7+huoWr+efV9+iYjOnQIdkglSdmZgTAjb8q+HKZ0zhw533kFM//6BDscEMUsGxoSowo8+Im/KFJLOP5+ks88OdDgmyFkyMCYEVa5dx8YJdxPTrx/pN48PdDimFbBkYEyI8VVWkj1uHK6ICDo+8jASZk2DZtfsV2JMiNny4ENUrlxJ52f+TXh6eqDDMa3ELs8MROQlEdkiIsvqlCWLyEwRWe3cJznlIiKPi8gaEVkqIofXWWeUs/xqERlVp/wIEfnRWedxEZGm/pDGtBVFM2aQ//rrJF90EfFDhgQ6HNOKNKaaaApw0g5lNwP/U9VuwP+c5wAnA92c2+XAv8GfPIC7gCOBAcBdNQnEWeayOuvt+F7GmEaoyspm4+13ENWrF+3HXRfocEwrs8tkoKqzgbwdik8HXnEevwKcUaf8VfX7FkgUkQzgRGCmquapaj4wEzjJeS1BVb9VVQVerbMtY0wjaXU1G66/Hnw+Ok16BImICHRIppXZ0zaDdFXd6DzeBNRUTHYC1tdZLssp21l5Vj3lxpjdkPP445QvWUKnRycR0aVLoMMxrdBe9yZyjui1CWLZJRG5XEQyRSQzJyenJd7SmKBXMmcOuc+/QOLZZ5Nw8smBDse0UnuaDDY7VTw491uc8myg7mFJZ6dsZ+Wd6ymvl6o+p6r9VLVfWlraHoZuTOio3ryFDeNvJrJbN9JvvSXQ4ZhWbE+TwUdATY+gUcCHdcovdHoVDQQKneqkz4ETRCTJaTg+Afjcea1IRAY6vYgurLMtY8xOqNfLhptuwldWRqdHJ+GKigp0SKYV22WbgYi8AQwBUkUkC3+voAeAt0TkEuA3oOZa90+BU4A1QBnwdwBVzRORe4AFznITVbWmUfoq/D2WooH/OjdjzC5sffZZyr77joz77iXywAMDHY5p5cRf5d/69OvXTzMzMwMdhjEBUTJvHusvu5yEU06h478ewi7PMY0lIgtVtd+O5TYchTGtTOXq1WSPGUvkgQfSYcIESwSmSVgyMKYV8Wzdyvp/XIFER9HlmX/jjrOJakzTsLGJjGklfBUVrL/6ajx5eew7dSrhHTsGOiQTQiwZGNMKqM/HhptvoWLpj3R6fDLRvXoGOiQTYqyayJhWIGfy4xR/9hntb7iehGHDAh2OCUGWDIwJcgXvvU/us8+SeNZZJF98caDDMSHKkoExQaz0u+/ZeNddxB41iA533mE9h0yzsWRgTJCqXLuOrGuvJWLffej02GNIeHigQzIhzJKBMUHIk5/P+iuuQNxuujzzDO6EhECHZEKc9SYyJsj4KirIGn0Nnk2b2OeVKUR07rzrlYzZS5YMjAkiWlVF1pgxlP/wA50mPUJM376BDsm0EZYMjAkS6vWSfdN4Sr+eTYeJd9vcBKZFWZuBMUFAfT423nGn/1qC8eNJOvvsXa9kTBOyZGBMgKkqm//5AIXvvUfq6NGk/P2iQIdk2iBLBsYEWM7jj5M/dSrJF11E6tVXBToc00ZZMjAmgHJfeIHcfz9D4lln0X78TXZRmQkYSwbGBEje66+z5eFHSDj1VDpMuMsSgQkoSwbGBEDhhx+yeeI9xB1/PB0f+Cfidgc6JNPGWTIwpoUVzZzJhltvI2bQQDo9OsmGmTBBwZKBMS2oYsUKNtxwI9G9etHlySdxRUYGOiRjAEsGxrQYb0EBWddcizspic5PPYkr1qasNMHDrkA2pgWo10v2DTfi2bKFfae9RlhKSqBDMmY7dmZgTAvIeeIJSufOJf3OO4ju3TvQ4RjzB5YMjGlmxV98Qe4z/pnKks46K9DhGFMvSwbGNKPKtevYMP5monr3Jv2O2wMdjjENsmRgTDPxlpSSdc01SGQknSc/hisiItAhGdMga0A2phmoKhtvvZWqX39lnxdfJDwjI9AhGbNTdmZgTDPIe/FFimfMoP0NNxA78MhAh2PMLlkyMKaJlX7zDVsmPUrCKSeTfNGoQIdjTKNYMjCmCVVnZ5M97noiDziAjHvvtcHnTKthycCYJlL2wyJ+Pf8C1OOh8xOP44qJCXRIxjSaJQNj9pL6fGx9/nl+u+ACJCyMfaZMIaJr10CHZcxusd5ExuwFT14eG8bfTOmcOcSffBIZEyfijo8PdFjG7DZLBsbsobIFC8i+/ga8BQV0mHAXieecY20EptWyZGDMblKvl9znniPniSeJ6NKFLs8+Q9ShhwY6LGP2yl61GYjIryLyo4gsFpFMpyxZRGaKyGrnPskpFxF5XETWiMhSETm8znZGOcuvFhHri2eClmfrVtZfdhk5kx8n4ZRT6Pruu5YITEhoijODoaq6tc7zm4H/qeoDInKz83w8cDLQzbkdCfwbOFJEkoG7gH6AAgtF5CNVzW+C2IxpEr7KSoo++ZQtj07CV1RMh3smkjh8uFULmZDRHNVEpwNDnMevALPwJ4PTgVdVVYFvRSRRRDKcZWeqah6AiMwETgLeaIbYjNkt1Zu3kP/mGxRMfwtvXh6RhxxCxxdeJOrggwIdmjFNam+TgQIzRESBZ1X1OSBdVTc6r28C0p3HnYD1ddbNcsoaKv8DEbkcuBxgn3322cvQjWlY+ZIl5L06laLPPwevl7ghQ0i+8AJiBg60swETkvY2GQxW1WwRaQ/MFJGVdV9UVXUSRZNwks1zAP369Wuy7RoDoFVVFH0+g7ypU6lYuhRXXBzJI88jaeRIIuzgw4S4vUoGqprt3G8RkfeBAcBmEclQ1Y1ONdAWZ/FsoEud1Ts7Zdlsq1aqKZ+1N3EZs7uKv/ySTXdNwJOTQ0TXrqTfcTvtTj8Dd5zNU2zahj3uTSQisSISX/MYOAFYBnwE1PQIGgV86Dz+CLjQ6VU0ECh0qpM+B04QkSSn59EJTpkxzU6rqtj8wINkXXU17rRUujz3LPt/+gnJI0daIjBtyt6cGaQD7zv1p2HA66r6mYgsAN4SkUuA34CzneU/BU4B1gBlwN8BVDVPRO4BFjjLTaxpTDamOVVlZZM9bhwVS5eSNHIk7cffZBPQmDZL/J17Wp9+/fppZmZmoMMwrVTxF1+w4dbbQJWMe+8l4cQTAh2SMS1CRBaqar8dy+0KZNOm+Kqq2PKvh8mfOpWonj3p9OgkIrp02fWKxoQ4Swamzahav57ssddRsXw5SRdeQPsbbrBqIWMclgxMm1D0+Qw23nYbuFx0fvIJ4v/v/wIdkjFBxZKBCWmqSu6zz5Lz2GSievem06RJRHSu95pGY9o0SwYmZKnHw6Z77qVg+nQS/vJnOt57L2LVQsbUy5KBCUm+8nKyr7+Bki+/JOWyy0gbd50NI2HMTlgyMCHHk59P1hVXUr50Kel33E7yyJGBDsmYoGfJwISUqvXrWX/pZVRv2kSnxyeTMGxYoEMyplWwZGBCRvmy5az/xz/A42Gfl18m5vC+gQ7JmKDi9XkbfM2SgQkJJbNnkzX2OsKSkujy/HNE7r9/oEMyplmpKqXVpeRX5pNfkU9BZQH5Ff7Hm0vz2FKay9Zy//OiqkJKPYVU+oob3J4lA9OqaXU1+W+9xeb7/0nkwQexz7PPEpaWFuiwjNktPvVRXFVMUWURBZUFFFYV+u8rC8kt8+/cc8ryyK8ooKAyn5LqQsq8hfjw1Ls9l0KM10WCFxJ9Pjp5PaT5qkjzVnNtAzFYMjCtkrekhIK33yHv1VfxbNxI7NFH02nyZBtp1ASFKm8VBZUFtTv0vIo88iryyC3LY1NpLlvKcsktz6WwsoDi6gLKvcUovga3F+UV4r2Q6FO6Ojv2VK+HJJ+XJK+PZK+XRJ+PJK+PRK+XcHccnohEvNHJSEwK7sQUwhPSCI9L4VpurPc9LBmYVqV60ybypk6lYPpb+EpKiOnfnw533kHcccchrj0ekd2YBnl9XgqrCsmvyK/dqedV5JFbnsvm0jy2luWRV15AYVUBJdVFlHmKqdaKBrcX5XUR7xWSvD7293pI91WR5qsi0eujnc+/M2/nq3nsI9wVjy8yCW9UEsQk426XSnh8KpHxqbhiUyAmGaKT/fcxKRCdBO7wnXwiSwamFatYuZK8l1+m8JNPwecj4aQTSf7734nu1SvQoZlWRFUpqioiryLPf+ReUVB7BL+1PJ8tpXnkluWTX1lAUVUhxdUFVHiLUOoZ3Vkh2ucizisk+JQOXi8pvmrSvFUk+zwk+ny0q3PEnuzzkuAVPBFJVEeloDGpSLs0whLaE5mQRlhcmn9nHpvqv49JgahEcLfMbtqSgQla6vNROm8eeS9PofSbb5CYGJLOG0HyhaNsSAkDQLW3unZnXlMlU1BZQG55PhtLcpxG1FwKKvMpripw6tnr71HjUiHG6yLOC+18247a071VJPu8pDjVMTXVMnHqxhOeSHVUEhqVhMQkERabTER8CuGxKUhMkv+IPToJ4tpDbBpEJRIRpGewlgxM0PEWF1P4/vvkT3udqt9+IywtjbRx40g652zc7doFOjzTTGp6x9RUw+RX5JNfme9Ux/hvW8vy/Dv26kLKPEU7rY6J8IlTHaPs4/XQ3ltNuq+SZK+PJK+X5DpH7oleHxESQVVEEt6oZDQmGVd8CuEJ6UQktMcVlwYxqf4demyq/xaZQGQIXdVuycAEjcrVq8l7/XUKP/wILSsjuk8fOo4eTcKJJ9iYQq1MTXVMTXfH2mqZygJyy/zVMf5ujwUUVxVS4imiwlvc4FF7uE+I8wqJXqW910t3XzXtfVUk+rzb1bXX7NijJQpXeDu8kUlodDKuhGTC41KIiE8lLC51+zr2mltEDDuraQ91lgxMQKnHQ/FXX5E/7XXKvv0WiYgg4dRTSRo5kuiePQIdnnFUeiu368teUFngP4Iv39btMa9821F7ubeo4eoYnxDr81fHJPp8dPP6d+wpvmoSvT6SfM6Ru1Mdk6AgYUlURybhc3rHhMWnEZGQRnh8GlJTxx6b6j96j0mGsMgW/oZaP0sGJiC8xcUUvPU2edNew7NhI2EZGaSNG0fi8L8Rlpwc6PBCmqpSXF1MQUVB7RF7zdF7TXVMblke+ZX5FFUVUOopbLg6RiHG5/J3e/T62M/nIc1bTbrTOybJ663duSfVVMfgpioyGU9UKhqTiqtdGuHx6US0S8cV5+zQY1O3HblHJkAIVccEK0sGpkVVb9pE3qtTKZg+HV9pKTEDBpB+yy3EDx2KhNnPcU+Ue8opqCggvzK/9j6/Ip+csrzaeva8igIKq/wXK/n7tNd/1B7mE+K8LhK8SpJz1J7urSTVt61Pe5LXR6LzOFKi8USm4IlKhugUXAlJhMclExmXQlhsMkQn+htQoxL9j2NTISqRMNu5Bx377zMtomLVKvJeesnfNVSVhBNruob2DHRoQWXHi5VqhhfILc+rrY7JLc+nsKYR1VuERyvr3ZY4XR9rjtoP8HpJ81XR3lfl36k79e01j5O8PtzuOKoj/Y2oxKTgjk8jIj6N8PhUpKZHTKzTkBqTCuFRLfwNmeZiycA0G1WlbP58cl98idJ589pc11Cf+iiq9Pdpz63Ira2KyS3PZUtZHlvL8p0de4HTO2bnFytFev192tv5lA5eDz19/qP2ZGdHnuhUx9T0a48iEm9EIp5o56g9PpXwhFQi4tOci5Vqbsnb6trd4Vhte9tkycA0KfX5qFjxE6Vz51L02WdUrlyJOy2VtOuuI+ncc1pt19Aqb1Vtw2lhZSGFVYX++8pC8ioKySnNJ7fcfxGT/0pU/9gxDQ0xEOl1E+tcrJTi9XGQr5r2XudKVKfLY00de5LPS4IXfBHt8EQm44tOQWJTaxtRt12slLJ975jw6Bb+lkxrZsnA7DXP1q2UzptHydx5lM6bhzcvD4Co7t3JuO9eEv78Z1xB1jW03FNeWwWTV5FXW8++tTyXzSW5/t4xFXnOjr2ASl9Zg9ty+4Ron9RerLSfz0O6t4r2vuraHjHJNb1jnKoZnzuOqsgkfNFJEJ2COy6F8JrqmLpH6zU9ZKITweVuwW/ItDWWDMxu8xYWUrFiBaXffEPJ3HlU/vQTAO7kZGKPPpq4wUcTe9RRLTZ6aE3vmO127s7jTaW5zlWoeRRU5FNU7e8d01A9e81VqAleSPL66Oz10N5XRQdfpdN46q+GSXD6tif4fLhcsdvVs7tikwmPTyMyIQ1XTN0xY+qMHRMWQXClR9PWWTIwDVKfj+r166n4aSUVq1ZSuXIVFatW4tmw0b9AWBgxffuSdt11xA4+mqhDD22SweJq+rTX3ir91TM5ZXlsKtla2zumqLY6pqjB3jHhPiHW2bmneL0c7NvWOya5TrfHZKeHTIy6qIpIpjrSfxWqJKQSnpBGREJ7wuKd6piYOmPHODt2a0Y1rZ0lAwP4j66rf/+d8iVLKF+8hIoVK6j8+Wd8ZU71iNtNxH5diel7OJEjDibqkEOJ7tt3l0NGl3vKa8eLqe0lU1FIXoX/KtScMn9PmYJK/869zNvwUXtNn3b/lag+9vN6SPNV0945ak+u7fpYs3P34XbF1lbHaEwqYXFp/qF849P81TF1BwWLSYGodtbt0bRJlgzaKF9pKeU/LqN88WJ/AliypLau3xUTQ1T37rT761+JOuRgIg8+hMhuByKRkf4+7ZUFbKksIL9wMXmb/b1jNpVudY7ac8mrzKOoKt+pjqlqMIYInxDrFdp5lfY+Lz28/hEfU3ze2n7sNX3ak70+oojAG+EfyldjknHFpRAen0pEfCqu2NQdGlC3XYlq1THG7JolgzbAV1VF5c+rqVi+nIrlyyn/8UcqV60Cn7+ni+7Tkar+h1DYrQNb9k/itxRhS3kBuWUbya/8iaIVhZQsKXKGGKh/ZiW3M3ZMOy+k+Hwc4lyslOLz0M7ZsdeMz57o8xLnc6ERSXgik/DFpOCKrRliIJWwuPY7jBuTbL1jjGlmlgxCRIWnwj+sQOFmin9aRtVPK2HVL0SsziJmfS5ur3/HXxbl4teMcFYOCmNlJw+rOwql0VuALf4N5YBrC0Q7fdoTfTXVMVWk+qq3Gxgsxbvt6D08LAGP04gqMSm441KJSEghPL79DtUxTi+ZyHgbYsCYIGLJIAh5fV7/BBzleeRv/o3SdWuo/P03PBs3UVlYiKe4GF9ZGa6yCsIqqgiv9BBd6SOqChLKIN7p2l4cBWs7CL/3Eza2DyMvFTTeS4qW0sFbxTFeH38p9ZJctG2c9iSflxgNpzoyEU9UChqTgis+rbYR1VUzfG/dhlTr9mhMq2fJoJn51Ld9A2rFtqEGtpbnk1OcS8XGjbg35hC9JZ92W0tIzaskPV/pUACxlVC3ibbaDRURUBUOngjQcEXCfbhifEiYUh7lJTzZQ1xiFftFV9NLw/BFJuGJTEajk5HYFMLiUolMSHUuVkr+47R54TGE21G7MW2KJYPdVNPtsbCysLbLY35F/nYTXW8byreAyupC4suU1EJoX6i0L9h2f0SBkloEYXUuUvW5lPJ4xZPgpfoAH2VxHmLjPLSLqaZdtA+NTvTXs9cO5Zu6beyYWGec9roNqeExVh1jjNmlNp0MdpzoevuhfPPYUppLbnkehZUFlFQV4C0rIqKikphKiK6EmEoluhLiy6FdmZJeKhxaCkmlSkKZEluuRFX8cUcskT7C4zxEJPhwdwlHEmNwpyYQ3jGdqE6diEzq4J9ZKbbuLRWi2tmO3RjTLIImGYjIScBkwA28oKoP7M76qlrb7bHmll+eR0FRDvn5m8nPz6G4MJey4nyqy4rwlRfjqqwgtkKJcXbsMRUQUwnxldChAuIqILZSiaqCyCpF2PmO2BXpwxXtxhUbgSs9mrDEeCKS2hGemkp4RkfC99mXiK4H4krp5K+SiYiznbsxJigERTIQETfwFDAMyAIWiMhHqrqioXUKf1nFR38+EqmqwlXtwV3tJcKjhHsgwgPRHmhXDY29HlZFkQjFFe4jPNyHRLqQuHBIi8QVG4M7Lp7wxHaEt0vGnZiCKzkNV3IH3KkZuNP3wd2+MxLelifNM8a0ZkGRDIABwBpVXQsgIm8CpwMNJgPxeIgsLga34grzERbpI8ythLkUt8uH26WEu31Eu31Eul1oVAwaEwdx7XDHJxKRlEpEchphKf4duiR2cAYIc4YYcAfLV2OMMc0vWPZ4nYD1dZ5nAUfuuJCIXA5cDtCrYxRHnrsvEptCeFwqEfEp23rHRCdDTNK2UR8jdj5kgjHGtHXBkgwaRVWfA54D6NevnyZe8d8AR2SMMaFh74eYbBrZQJc6zzs7ZcYYY1pAsCSDBUA3EdlPRCKAc4GPAhyTMca0GUFRTaSqHhEZDXyOv2vpS6q6PMBhGWNMmxEUyQBAVT8FPg10HMYY0xYFSzWRMcaYALJkYIwxxpKBMcYYSwbGGGMAUdVAx7BHRKQYWLWLxdoBhXvxerAtE0yxNGaZYIqlMcsEUyyNXSYV2BoksbTFv0EwxdLYZQ5W1fg/lKpqq7wBmY1Y5rm9eT3YlgmmWCzeoFlmp/8HQRhvSP0NgimWvf3NhHo10X/28vVgWyaYYmnMMsEUS2OWCaZYGrtMS72P/Q2CP5bGLlOv1lxNlKmq/QIdhzGBZP8HZnc19JtpzWcGzwU6AGOCgP0fmN1V72+m1Z4ZGGOMaTqt+cyg1RKRl0Rki4gs26H8GhFZKSLLReShQMXXXESki4h8JSIrnM84xim/R0SWishiEZkhIh0DHWtTEZEoEfleRJY4n/lup3w/EflORNaIyHRngMaQIiInicgq5zPe7JSJiNwnIj+LyE8icm2g42xK9f1vt5bft50ZBICIHAuUAK+qak+nbChwG3CqqlaKSHtV3RLIOJuaiGQAGar6g4jEAwuBM4AsVS1ylrkW6K6qVwQw1CYjIgLEqmqJiIQDc4ExwDjgPVV9U0SeAZao6r8DGWtTcqay/Zk6U9kCI/BPWjUUuEhVfaH2O2/gfzuhNfy+7cwgAFR1NpC3Q/GVwAOqWuksEzL/IDVUdaOq/uA8LgZ+AjrV/KM4YoGQOUJRvxLnabhzU+B44B2n/BX8STGU1E5lq6pVQM1UtlcCE1XVB6H3O6/vf7u1/L4tGQSPg4BjnKqDr0Wkf6ADak4i0hXoC3znPL9PRNYDI4E7AxdZ0xMRt4gsBrYAM4FfgAJV9TiLZOGf+jWU1DeVbSfgAOAcEckUkf+KSLeARNfCWsPv25JB8AgDkoGBwI3AW04VQ8gRkTjgXWBszVGTqt6mql2AacDoQMbX1FTVq6p98M/gNwA4JMAhBVIkUOF0bXweeCnA8bSI1vD7tmQQPLLw1yGrqn4P+PAPNRBSnHrzd4FpqvpePYtMA/7WslG1DFUtAL4CBgGJIlIzn0goTvPa0FS2WUDN3/19oHcLxxVoQfv7tmQQPD7A37CGiBwERLDzMWdaHedM50XgJ1WdVKe8blXB6cDKlo6tuYhImogkOo+j8Teo/oQ/KQx3FhsFfBiYCJtNQ1PZ1v7OgePwNzKHtNby+7beRAEgIm8AQ/Af+W8G7gKm4j9l7gNUATeo6peBirE5iMhgYA7wI/4zH4BbgUuAg52y34ArVDUkjpRFpDf+BmI3/oOvt1R1oojsj79RNRlYBJxf03kgVIjIKcBjbJvK9j4nMU4D9sHf6+YKVV0SwDCbVAP/26fQCn7flgyMMcZYNZExxhhLBsYYY7BkYIwxBksGxhhjsGRgjDEGSwbGGGOwZGCMMQZLBsYYY7BkYIwxBksGxhhjsGRgjDEGSwbGGGOwZGCMMQZLBsYYY7BkYIwxBksGxhhjsGRgTNATkTNEREXkkEDHYkKXJQNjgt8IYK5z32gi4m6ecEwosmRgTBATkThgMP55os91yoaIyGwR+UREVonIMyLicl4rEZFHRGQJMChwkZvWxpKBMcHtdOAzVf0ZyBWRI5zyAcA1QHfgAOCvTnks8J2qHqaqc1s8WtNqWTIwJriNAN50Hr/Jtqqi71V1rap6gTfwnz0AeIF3WzZEEwrCAh2AMaZ+IpIMHA/0EhEF3IACnzj3ddU8r3AShDG7xc4MjAlew4GpqrqvqnZV1S7AOuAYYICI7Oe0FZyDv4HZmD1mycCY4DUCeH+Hsned8gXAk8BP+BPEjssZs1tEdcezTWNMMBORIcANqnpaoGMxocPODIwxxtiZgTHGGDszMCaoiEgXEflKRFaIyHIRGeOUJ4vITBFZ7dwnOeUjRWSpiPwoIt+IyGF1tnWSc1HaGhG5OVCfybQOdmZgTBARkQwgQ1V/EJF4YCFwBnARkKeqDzg79iRVHS8iRwE/qWq+iJwMTFDVI52hKH4GhgFZ+BucR6jqikB8LhP87MzAmCCiqhtV9QfncTH+3kKd8F+J/Iqz2Cv4EwSq+o2q5jvl3wKdnccDgDXOhWlV+C9YO71lPoVpjSwZGBOkRKQr0Bf4DkhX1Y3OS5uA9HpWuQT4r/O4E7C+zmtZTpkx9bIrkI0JQs4Ade8CY1W1SERqX1NVda5Irrv8UPzJ4P/bu2PVqKIoCsP/KhQEIT6ACViInYhaxhdQnTnMkQAAAcJJREFUREGwUvIGVoKijY2tiGAXrQyoRcS8gKB2A4Kxs7OwUFDEQhCC2+Kc4BTCRMd4J/B/1bDnMuwpLmvOPcM+i0h/wZWBNGOS7KIFwUpVrfbyh76fsLmv8HHs+sPAMnCmqj718ntgfuxj9/ea9FuGgTRD0pYA92ibwrfG3loDlvrrJeBpv34BWAUu9smmm0bAwT6yYjdt/PXadvevnct/E0kzJMki8AJ4A/zo5Wu0fYPHwALwDjhfVZ+TLAPneg1go6qO9886CdymDbi7X1U3/9sX0Y5jGEiSfEwkSTIMJEkYBpIkDANJEoaBJAnDQPqnktxIcnnoPqQ/ZRhIkgwDaVpJrid5m+QlcKjXLvUzCdaTPBy4RWkiB9VJU0hyjDbq4QjtfnpFO4PgKnCgqr4n2Tdgi9KWuDKQpnMCeFJV36rqK7/m/6wDK0kuABuDdSdtkWEgbY9TwF3gKDBK4ipcM80wkKbzHDibZE8/pvI07b6ar6pnwBVgDtg7YI/SRP5akabQzyp+BLymnTEwAgp4kGQOCHCnqr4M2KY0kVNLJUk+JpIkGQaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSgJ8a4LQKINXucQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jpt8ZweBqxW1",
        "colab_type": "text"
      },
      "source": [
        "## The ACTUAL Confirmed is so great, yhat + lower and upper all look like the same line. Woof."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Inmhduqbp11a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}