forecasting_with_SARIMA_model.ipynb

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      "name": "forecasting_with_SARIMA_model.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPgqP+TaY08MjhHLsTlHaAh",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/deepacefic/Covid19-Dashboard/blob/master/forecasting_with_SARIMA_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HAuxyvuWtnuQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "32255ed0-f2ee-468c-906f-41fc0a25e2a7"
      },
      "source": [
        "from sklearn import svm\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import math\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import mean_squared_error\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cUqg_GN1t_AY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "669c15b4-9e8a-4f17-830d-f0393c787f3a"
      },
      "source": [
        "# loading the datsets\n",
        "d = pd.read_csv(r'/content/covid_19_dataL.csv')\n",
        "d.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SNo</th>\n",
              "      <th>ObservationDate</th>\n",
              "      <th>Province/State</th>\n",
              "      <th>Country/Region</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>01/22/2020</td>\n",
              "      <td>Anhui</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</td>\n",
              "      <td>1.0</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>01/22/2020</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</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>01/22/2020</td>\n",
              "      <td>Chongqing</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</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>01/22/2020</td>\n",
              "      <td>Fujian</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</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>01/22/2020</td>\n",
              "      <td>Gansu</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   SNo ObservationDate Province/State  ... Confirmed Deaths  Recovered\n",
              "0    1      01/22/2020          Anhui  ...       1.0    0.0        0.0\n",
              "1    2      01/22/2020        Beijing  ...      14.0    0.0        0.0\n",
              "2    3      01/22/2020      Chongqing  ...       6.0    0.0        0.0\n",
              "3    4      01/22/2020         Fujian  ...       1.0    0.0        0.0\n",
              "4    5      01/22/2020          Gansu  ...       0.0    0.0        0.0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r8jKgcnVua7f",
        "colab_type": "code",
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          "height": 419
        },
        "outputId": "97ea16e7-f614-423e-f400-f73552a130f8"
      },
      "source": [
        "#Group by :It is like pivid functionality\n",
        "d2 = d.groupby([\"ObservationDate\",\"Country/Region\",\"Province/State\"])[['ObservationDate','Province/State','Country/Region','Confirmed','Deaths','Recovered']]\n",
        "d2.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ObservationDate</th>\n",
              "      <th>Province/State</th>\n",
              "      <th>Country/Region</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>01/22/2020</td>\n",
              "      <td>Anhui</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01/22/2020</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Mainland China</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>01/22/2020</td>\n",
              "      <td>Chongqing</td>\n",
              "      <td>Mainland China</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>01/22/2020</td>\n",
              "      <td>Fujian</td>\n",
              "      <td>Mainland China</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>01/22/2020</td>\n",
              "      <td>Gansu</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42987</th>\n",
              "      <td>06/15/2020</td>\n",
              "      <td>Zacatecas</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>550.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>324.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42988</th>\n",
              "      <td>06/15/2020</td>\n",
              "      <td>Zakarpattia Oblast</td>\n",
              "      <td>Ukraine</td>\n",
              "      <td>1680.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>686.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42989</th>\n",
              "      <td>06/15/2020</td>\n",
              "      <td>Zaporizhia Oblast</td>\n",
              "      <td>Ukraine</td>\n",
              "      <td>529.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>335.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42990</th>\n",
              "      <td>06/15/2020</td>\n",
              "      <td>Zhejiang</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1268.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1267.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42991</th>\n",
              "      <td>06/15/2020</td>\n",
              "      <td>Zhytomyr Oblast</td>\n",
              "      <td>Ukraine</td>\n",
              "      <td>1087.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>547.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>24475 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      ObservationDate      Province/State  ... Deaths  Recovered\n",
              "0          01/22/2020               Anhui  ...    0.0        0.0\n",
              "1          01/22/2020             Beijing  ...    0.0        0.0\n",
              "2          01/22/2020           Chongqing  ...    0.0        0.0\n",
              "3          01/22/2020              Fujian  ...    0.0        0.0\n",
              "4          01/22/2020               Gansu  ...    0.0        0.0\n",
              "...               ...                 ...  ...    ...        ...\n",
              "42987      06/15/2020           Zacatecas  ...   58.0      324.0\n",
              "42988      06/15/2020  Zakarpattia Oblast  ...   42.0      686.0\n",
              "42989      06/15/2020   Zaporizhia Oblast  ...   16.0      335.0\n",
              "42990      06/15/2020            Zhejiang  ...    1.0     1267.0\n",
              "42991      06/15/2020     Zhytomyr Oblast  ...   19.0      547.0\n",
              "\n",
              "[24475 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wayYpW3f24eE",
        "colab_type": "text"
      },
      "source": [
        "we will forecast India's growing cases with SARIMA models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zT-d1z9iufKO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "5ad63df2-4587-40f5-f446-1368087893f6"
      },
      "source": [
        "d.rename(columns={'Country/Region':'Country'},inplace=True)\n",
        "d.query('Country==\"India\"').groupby(\"ObservationDate\")[[\"Confirmed\",\"Deaths\",\"Recovered\"]].sum().reset_index()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ObservationDate</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>01/30/2020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01/31/2020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>02/01/2020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>02/02/2020</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>02/03/2020</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>06/11/2020</td>\n",
              "      <td>297535.0</td>\n",
              "      <td>8498.0</td>\n",
              "      <td>147195.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>06/12/2020</td>\n",
              "      <td>297535.0</td>\n",
              "      <td>8498.0</td>\n",
              "      <td>147195.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>06/13/2020</td>\n",
              "      <td>308993.0</td>\n",
              "      <td>8884.0</td>\n",
              "      <td>154330.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>06/14/2020</td>\n",
              "      <td>320922.0</td>\n",
              "      <td>9195.0</td>\n",
              "      <td>162379.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>06/15/2020</td>\n",
              "      <td>343091.0</td>\n",
              "      <td>9900.0</td>\n",
              "      <td>180013.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>138 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    ObservationDate  Confirmed  Deaths  Recovered\n",
              "0        01/30/2020        1.0     0.0        0.0\n",
              "1        01/31/2020        1.0     0.0        0.0\n",
              "2        02/01/2020        1.0     0.0        0.0\n",
              "3        02/02/2020        2.0     0.0        0.0\n",
              "4        02/03/2020        3.0     0.0        0.0\n",
              "..              ...        ...     ...        ...\n",
              "133      06/11/2020   297535.0  8498.0   147195.0\n",
              "134      06/12/2020   297535.0  8498.0   147195.0\n",
              "135      06/13/2020   308993.0  8884.0   154330.0\n",
              "136      06/14/2020   320922.0  9195.0   162379.0\n",
              "137      06/15/2020   343091.0  9900.0   180013.0\n",
              "\n",
              "[138 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "42HvNijAuf_B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "3a0a7930-478b-434f-cbe6-48388a2884c4"
      },
      "source": [
        "from fbprophet import Prophet\n",
        "daily_cases = d.loc[(d[\"Country\"]==\"India\") , [\"ObservationDate\",\"Confirmed\"]]\n",
        "daily_cases['ObservationDate'] = pd.to_datetime(daily_cases['ObservationDate'],dayfirst=True)\n",
        "print(daily_cases.shape)\n",
        "daily_cases.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(348, 2)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ObservationDate</th>\n",
              "      <th>Confirmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>430</th>\n",
              "      <td>2020-01-30</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>491</th>\n",
              "      <td>2020-01-31</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>547</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>607</th>\n",
              "      <td>2020-02-02</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>672</th>\n",
              "      <td>2020-03-02</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    ObservationDate  Confirmed\n",
              "430      2020-01-30        1.0\n",
              "491      2020-01-31        1.0\n",
              "547      2020-01-02        1.0\n",
              "607      2020-02-02        2.0\n",
              "672      2020-03-02        3.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-DCZB91ukIU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "daily_cases.columns=['ds', 'y']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WnRsEZndunN7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "4b54b90a-d0c2-465c-9e0b-4bf01f34f326"
      },
      "source": [
        "y = daily_cases.set_index(['ds']).sort_values(by=\"ds\")\n",
        "y.head()"
      ],
      "execution_count": null,
      "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>y</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ds</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-02</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-03</th>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-04</th>\n",
              "      <td>1998.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-05</th>\n",
              "      <td>37257.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-06</th>\n",
              "      <td>198370.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   y\n",
              "ds                  \n",
              "2020-01-02       1.0\n",
              "2020-01-03       3.0\n",
              "2020-01-04    1998.0\n",
              "2020-01-05   37257.0\n",
              "2020-01-06  198370.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bRiyJ_UDuqqT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "61686ff6-1cf6-4c3a-f9a8-376c2624c7da"
      },
      "source": [
        "y.plot(figsize=(20,5))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yNU2XcAjAkUFw6d51LfA4HZzYR0RlhcElIiIiiykJJVqnB68UcCXlw8jwCNdVMnPJQoquL9lcutLjwpaOIPYc68fErJLHldmT8bleKlvM4HI68OmNIfzi1DCbqudA1WXaJXEGv8+Nj97QiOeO9sXL6oiI7I7BJSKiMnd5KoKx6Uihl2FrV2cuMbiUD0o8c8mH/rFZ9j6xiKZLuFNkdDza1YZZRWeDYxMY5xJnmsGORzrD0HSJnxzqtXJZtqZqelpliAtt7QhhaHIOb50ZsWBVRETFh8ElIqIy98c7juCPnzpS6GXYGoNL+adq0VKWUMCHGUXDxIxa6CXZUrQsbunLyfUhP25sqcGT+1ielSsjCzLdYMeaphp0tAWw80APA6xZUvWlSz+TufumJlR5nNh9hEFVIioPDC4REZW5kakI3mMfIEsZN4R1lW50D07yJi8PjHKtVr8PANDPvkuWUDQ9ZYmWEAKPdrXhWO84jvexwXEu5ieXpR/seGRTGKcGJ3Gsl8c+G6om4V6i9DMZr9uJe29uwZ5j/ZhTNQtWRkRUXBhcIiIqc4qqY2B8ln0hLGRkLt3UWoupiMYeQHmgahIuh0CL3wsAPOYWUXWZVhbNQ7eG4HE58BSzl3KiZNDQ2/DALUFUuBzYeaDHqmXZWraZSwDwYEcQE7MqXn3vksmrIiIqPgwuERGVOVXXoWiSDV8tZNwQ3tRaC4ClcfmgajpcDoHWWHBpgMElS0SPc+rLyUClB/fd3IJnDvViVmEWR7a0DBp6G/w+N+69uQW7Dvfx2GdB1fSUfcWS+fCaBiyr8mDXYfa8IiL7Y3CJiKjMGVk1vWPTBV6JfRkTntYawaVBliFaTdWjpSyNNRVwCGYuWSU6SSu9G+9Hu9owMavixeMDFq/KvuYbemcW7NjWGcb4jIKXTw5ZsSxb03QJZxYNvQHA7XTg/vUt+PnJQUzNse8bEdkbg0tERGXO6AfUc5k9aayixjKXmmor0FDtwelBZi5ZTdWi5VpGgKl/jJ9vK0SPc3qXk7evrsfyZZV44m2WxmXLyILMtAfQHWsa0Or3YscBHvtMKbrMqAxxoa0dIcwqOl46MWjiqoiIig+DS0REZU5Rjcwl3nxbRdGMJrwOrGmqxmk2ULecos+Xa7X4fRiYYOaSFRQ9dUNvg8MhsL2rDW+cGcH5kSmLV2ZPRllcpplLTofAwxtDePW9YZaIZkjT9YwaqC/UubwOQb+XpXFEZHsMLhFRUZmOqLg4yvKsfFJjmUt9DC5ZZj7bQKC9qQanh65wYpzFohOeojeErbVelsVZQNclpMysufRnNobhEMBT+5lBkw0li55Lhm2dbdAl8MwhBjkyoWjZN/QGokHVBzuC2Hv6EkanIiaujIiouDC4RERF5fG9Z/HA378GnZPL8sbIqullWZxljACey+lAe3M1JmdVDLGBuqVUXY+Xa7UGvMzWsIAS/1ynf+Pd4vfiYzc0Ycf+nni5KKVP0zOfFmdY1VCFTSvqsOPARQa3M6DF+rflYsuGIFRdYs+xfpNWRURUfBhcIqKiMjodwfiMgktTvPHOF+MGj2Vx1pkvixNY01QNAOy7ZDFFm2803er34sqcislZpcCrshc14XOdie1dbRianMMvTw1bsSxbU7Js6G14ZFMYZ4ancOjimJnLsjVVzy1zCYgOc1jTVI3dR/pMWhURUfFhcImIiopxs9I3xiyDfEnMXOLTbGsYn2u304H2phoAYN8li6maHs82aPH7AIDZSyYzpiCm29Db8LEbm9BQXYEnWRqXscRzSTbuX98Kr9uBHft7zFyWrUXPJbkFl4QQ2LIhiLfPjrIEnYhsi8ElIioqRvkQS7TyR9F1VHqcmIpomJjhqGQrqAnlQw3VHgQq3Tg9xMwlK6m6jJdrtfq9AMC+SyZTE3qJZcLtdGBbZxivvDuEITZaz4hRFpdtJk2N143717XiuSN9mFU0M5dmW2ZkLgHR0jgAeO4os5eIyJ4YXCKiohKJTy5jU+980GINeZcvqwQA9PC4W8LIDnM7HBBCoL2pGt0si7OUoulwG9Piao3gEoPWZlKznFwGREvjNF1i50Fm0GQifi7JIZNm26YwJudUvHh8wKxl2VpiFmQuVjZUYUPYj12HGVwiInticImIisr85DI+zc4HY4rZyvoqAMwYs4qR4WFk0qxpqsF7Q5MsQ7SQmjDhqbmWmUtWiE9BzLK59G2rluGpfWwunQkty1LERLevqke4zoedBxjYS4dmUuYSAGzpCOF43wS6mblKRDbE4BIRFRXjZqWHQY68MI73ioZo5hKbeltjfnx49Gu3vakaY9MKRjiW2jJKQlmcx+VAQ3UFey6ZbD7Qkd2N96NdbTg3Mo23zo6auSxbi0/oyyHY4XAIfGZjGK91X+I5Pw3R4QDm3DI9cEsrhAAbexORLTG4RJSFmYiGf3/7Ap+2WiDeXJoXvHlhNIdtrvHC63aw0ahFFvamaW/mxDirafrVpSzBgJeZSybLdXLZJ9e1osbrwpP72Ng7XdlO6FtoW2cYUgLPsCwxJU2XOR9vQ3OtFx9cXY9nj/TxGpKIbIfBJaIs/Oq9Ifzpj4/hSM94oZdiO0YmDYMc+WE8BXe7HAgGfAzqWSR+QxjPXOLEOKup2tU3hC21XmYumcwoY862H43P48TWjiD2HOvH+Ixi5tJsK5c+V4nallXi9tXLsPNAD4McKSi6nnV23mK2bAji7KUpHOvlNSQR2QuDS0RZmFWM0i02PzabcRM+PqPgyhwnl1ltvtG0QCjgY88liywsZWmurUCN18XMJQspC5rwtvq9bOhtMjOyaB7tWo45Vcfuw71mLcvWVE2HyyEgRO7Bjm2d0bLE/ecvm7Ay+zIzcwmIZuy5nQK72dibiGyGwSWiLERi2TUXR3mjYjbj2AJsLp0P8+VajmhwiZlLllDUq3suGRPjmLlkHTWh5xIAtPh9mJhVMcWgtWnUBb3EsrEu5MfNwVo8wdK4tCz8XOfi/vUtqPI4sWM/j/1SosMBzLtl8le68ZHrm/Ds0b543zIiIjtIeaYUQrQJIX4hhDghhDguhPj92PZlQoiXhBCnY/9bF9suhBD/UwjRLYQ4KoTYmPBen4+9/rQQ4vMJ2zuFEMdiv/M/RexxTLJ9EBWa8bSWmUvmUzUdVR4nAKB3jMfXaoo235A3FPDh0pUIZhWtwKuyH1XXIcTVpSztTTWcGGQhdUET3lY/J8aZzQhO51qi9WhXG473TeAdlgmltPBznYtKjwv3r2/F80f7MR1h0DUZVdfj/fLMsrUjiMGJObzNZvZEZCPpfDupAP5YSrkWwO0AviqEWAvg6wBellK2A3g59s8A8EkA7bE/XwHwbSAaKALw5wA+AOA2AH+eECz6NoAvJ/zefbHtyfZBVFCcaGYdRZNYUV8FAOgd402g1RJ7poTqfADY78oKiiavGdfe3lyNS1ciGOXEOEtEy+ISM5eiwSX2XTJPYnA6F1s6QqhwOdjYOw2qyf1/HtnUhqmIhheODZj2nnaj6TLnAOpCH7+pGZUeJ3YfYTkoEdlHyuCSlLJfSnkw9vdJACcBhABsBfCj2Mt+BOCh2N+3AvgnGfUmgIAQohXAvQBeklKOSikvA3gJwH2xn9VKKd+U0Y6C/7TgvRbbB1FBzQeXmFljNkXTEQx44XE6WBaXB4nlWqFANLjE0jjzqdq1N4RrmqIT45i9ZI2F5UNBf/Tzzb5L5tFMKIsDAL/PjfvXt+Inh3sxE2Hm5FJU3bzMJQDoWlmHFfWV2HmAU+OSUTSZ82d8IZ/HiU+sbcaeYwOIqHrqXyAiKgEZnSmFECsB3ArgLQDNUsr+2I8GADTH/h4CkPjoqSe2bantPYtsxxL7WLiurwgh9gsh9g8PD2fyr2Qbvzw1xAuyPFLiZXEznLJiMlWXqHA50RrwMsiRB/FG006BoBFcYlDPdOoiDWHbmzkxzkqKpl91E95UWwGAmUtmWtioPhfbu9owOavihXf6U7+4jBkNvc0ihMC2jWG8cWYEF0f5wGwxVmQuAcCWjiDGZxS8+l553rsQkf2kHVwSQlQDeBrAH0gpJxJ/Fss4svQOe6l9SCm/I6XcJKXc1NjYaOUyitLw5Bx+9wf78K2fv1fopZQNI3NpTtVx6QpLWsykxDI8QgEfy7PyQI1Pi3Ogxe+FQzBzyQoLJ5cBQNDvRZXHyYlxFlE1eVVZnNftRH2VB/0TDC6ZZX5aXO5ZHR9YtQwr6ytZGpeCqpnX0NvwcGcYQgBPH2T20mKURTJPzbC5vRF1lW7sPsKpcURkD2ldDQgh3IgGlv5VSvnj2ObBWEkbYv87FNveC6At4dfDsW1LbQ8vsn2pfVACI2Pp39+6gMlZpcCrKQ9KwkQzlsaZS42lnwcDPmbQ5IHxWXY5BdxOB1pqmTFmhcVuCIUQWNNUzbI4i6i6fs2Epxa/l5lLJlITzh+5EkLgs11teOvsKM4M87+JZBbLgsxVKODDHdc1YOeBHuicXnYNzYJjDkTLST+5vhUvnRhkQ3UisoV0psUJAI8DOCml/JuEH+0GYEx8+zyAXQnbfyc2Ne52AOOx0rYXAXxCCFEXa+T9CQAvxn42IYS4Pbav31nwXovtgxIYaemTcyqf+OWJURYHsKm32SKxJryhgA+Dk7PsRWAxI7hkZNWE6hjUs4KiX5u5BABrmmpYFmcBKWWsT8rVN4Stfi8zIk2kxnsumXPjvW1jGE6HwFP7mUGTTLSht7n9fwBgW2cYPZdn8Banl11FSml6n6tEWzcEMaNoeOnEoCXvT0SUT+mcKe8A8NsA7hJCHI79uR/AfwdwjxDiNICPx/4ZAPYAOAOgG8B3AfwHAJBSjgL4LwD2xf78ZWwbYq/5Xux33gfwQmx7sn1QgvmbQ4EfvH4u/iSRrKMk9Dy4yMwlU6mx8qFQnQ9Ssj+K1eJlcbGbw2DAx8wlC6hJGsK2N1djcGIO4zPMOjWTkXyx8Iawxe/FAMviTGNMm1yYIZatplovPnZDE54+2HNVhjDNUzVrsmjuvbkFNRUu7DjAh5SJjKb1VhxzAOhauQytfi92H2ZpHBGVvnSmxb0mpRRSyluklB2xP3uklCNSyrullO1Syo8bgaLYlLivSimvk1Kul1LuT3iv70sp18T+/CBh+34p5brY73wt1l8JyfZBVzOmPT18axi9YzN44R2Ok7Waoumo9rqwrMrDzCWTKVr0CSEnl+WHGm/IG8tcCvgwMD4bv6Amc6j64k142+MT45i9ZCYlSblWq9+HsWmFAzBMomjm33g/2tWG4ck5/OJddkJYzMIpiGbxeZx4YEMrXjg2gCtzLNEyGNl5VmSLAYDDIfDghiB+9d4wLk+xhycRlTZrzpSUV5HYRfR961uwqqEK39t7hhPMLGaMpQ3X+RhcMpmSUBYHMLhktciCzKVQnQ+qLjE0yewOMymaXPTmpL0pNjGOTb2vMjGr5PQ9lqxcq9XvBQBmL5lkPvPRvMvJj97QiKaaCpb5J7FwCqKZtnW2YUbRsOcoJ/YZVIszlwBgy4YgVF3y4TARlTwGl2zAeEJb4XLgix9ehSM949h37nKBV2VviqbDEw8usSzOTMZUrdZA9CaQ/X+spS7suWQE9XjcTWUETRcK1/ngdTtwmk294072T6Dzv7yEX78/kvV7xBtNL1IWBwD94/x8m0HTzWvobXA5HdjWGcYvTg2xLHoRVjWXBoCNywNY3VjF0rgExrnEaWFw6eZgLVY3VmH3kd7ULyYiKmIMLtmAEVzyOB3YtjGMuko3vvPqmQKvyt6MsbThukr0Xp5hpphJNF1Cl9EblQqXE401FWy+a7H4KPHYzWG4jhljVkjWJ8XhiE6MY3Bp3g9fPwdFkzn9t69oyTKXop9vBi3MYUVZHAB8dlMbdAk8fZCNvRdabPKkWYQQ2NYZxr5zl3Hu0pQl+yg1ZjetX4wQAls2BPHW2VGem4iopDG4ZAOJ0558Hid+6/YVePndQY7ytZCaUBY3p+oYvjJX6CXZwjWTy9hc2nLGtEnjmAdjmUss9zRXNCC9+Fdue1MNugfZcwkAxqYj+Mnh6NP7WSX7vkjxXmILjnlLrZG5xBs4MyQ7zrla2VCFD66ux5P7LkJn/7erqEkmT3+8cjQAACAASURBVJrl4VvDcAhg5wEG9oD5ht5mNa1PZsuGIKQEnjvKxt5EVLoYXLKBiHp1z4Pf/uAKuB0OPP7a2UIuy9YisdKttrpKALwRN8vCJ4ShOgaXrKaoVweXKj0u1FW6edxNpuoy6ZPvNU3V6BufxeQsJ8Y9ue8i5mKfyZlcgktJMmp8HicClW6WxZnEyn4027vacGF0Gm+ezb480o5UXVpaotXi92JzeyOePtjDwQ5IPhzAbKsbq7E+5McuTo0johLG4JINxMviXNEvvqYaLx66NYidB3owyskTloj2XBLxEiIGl8yxsP9POJa5xLJD68xPwpm/cA7V+ViOaDJ1iSa8xsS494fLuwxF0yX++c3z6FpZBwCYiWQ/in5hFmSillovS09MYkVDb8N961pQ63WxsfcCxkRVK23rDKN/fBa/fv+SpfspBVoeGnobtnYEcax3nJUHRFSyGFyygcUuor+0eTXmVB3/8ub5Qi3L1tTY5KdQLLh0cZRNvc0Q0a4usQgGfIioOi5dYZDUKvHeNAk3K6GAjw29TRadMLn4zUl7szExrrxL437x7hB6Ls/gC3esgsflyC1zaZGgqSEY8LEsziTGAwEr7ru9bic+fWsIL7wzgLFpfgcYNF23PNBxz9pm1HpdLI1DQl8xC0sRDQ/cEoQQwO4jzF4iotLE4JINLPbk8PrmGnzk+kb80xvncupbQYuLxCY/VXpcqK/yMHPJJMZn2WOUxQXYXNpqi6X8B5kxZjpVT5651Fbng8flQHeZN/X+0Rvn0Or34hNrm+FzO3P67op/rhe5CW/xM3PJLEqs3FMIa4Id27uWI6Lq+MkhTtEyWNnQ2+B1O7G1I4SfvjOA8ZnyLtfNZ+ZSi9+LD6xaht1H+vj9S0QlicElG4gkqQf/8ubVuHQlgl2HeVFmNkWbb6gZrvOh5zIzl8ygLBgfbmSGMYvGOuoiN+GhgA/TEQ1j0+V9U2EmVZNwuxb/ynU5HVjdUFXWE+O6hyax9/Ql/MZty+FyOuBzOzETMaPn0rXHvLXWi5GpCB+8mEDTrS3RWhusxfqQH0/su8ib7RjF4obehm2dYcypOp4/2m/5vorZUoFqK2zZEMKZ4Skc75vIy/6IiMzE4JINxHsuLbjYuGNNPW5sqcH39p7lRZnJrg4uVTL4YZJ4iZbr6sll7P9jncUyD4xeYswYM4+i63AvcXPS3lyD00PlWxb3vb1nUeFy4Dc+sBxAtPG2VWVxLf7oxLihCU75zFV0CqK1N93bu9rw7sAkjvWOW7qfUqFp1jb0NtwS9uP65mrsOFDePa+0Jc4lVvjkuha4nYKlcURUkhhcsoFkjUuFEPjy5tU4PXQFv3xvuBBLsy01oX9KuM6HnrEZjks2QfyzHLtw9vvcqKlwMchhocUaTYcC0SmIPO7mSVXK0t5UjZ7LM5iOqHlcVXEYnpzDjw/1YltnGPXVFQCiZTm5TYtL3tC71R8LWnNiXM5UTVqe0bGlIwiv24En2NgbwPwDAasJIbCtM4xDF8bKumRX1a/OqLZaXZUHd7Y34tkjfbyuJKKSw+CSDShLTGt5cEMQzbUV+N7eM/lelq1FFpTFRZtO8yl4rtRFGmeG6nzsaWUhZZGgRzAQzexgRp55lNgQgGTam6ohJfD+UPlNjPvnN85B0XQ89uFV8W0+tyOnsjV1iT4pRuYS+y7lTtWX/lybodbrxv3rW/Hs4b6yDL4uZHUpYqKHbg3B6RBl3dh7vsQ2P5lLQDSg2j8+i33nRvO2TyIiMzC4ZAMR1XhCe+0Xn8flwOc/tBKvd4/geB9Tys2ysCwOAC7yRjxnEe3az3Iw4GNZnIUUTb+mpHZZlQdet4OZSyZSU5bFVQNA2ZXGzUQ0/POb5/Hxm5qxurE6vt3nya3nkrJg8mSi1lhwiRPjcqdqS3+uzfJo13JMzqnYc2zA8n0VO0XT81IWBwBNNV589PpGPHOoJ14eVm7mS2zzd8t0T2yowS6WxhFRiWFwyQaU2OSyZNNafvO2Faj0OPH43rN5Xpl9JZbFtS2LlliwqXfuFitlCcUml5E1FivXEkIgxKCeqRRVX/LmZEV9FdxOUXZNvXce7MHlaQVfuXP1Vdt9OZfFGRm9134vVlW4UOt1YYBlcTlTdQlnHkq0ulbWYXVDFZ7cd8HyfRW7xOuPfHhkUxiDE3N49XR5tlcwgkv5CugBQKXHhXvWNmPPsf74A2QiolLA4JINJGbRLMZf6cZnN7Vh95E+lgGYJPGYG/1pWLqVO+Mizr2gLG58RsGVOZZDWEHRr+25BAChukoG9Uyk6Ev3XHI7HVjVUIXTg+UTXNJ0icf3nsGGtgA2rai76mc591xK0Sel1e9j5pIJFE2HOw8lWkIIfLarDfvOXcb7w+Xz38hiNF3CmaeyOAC468Zm1FW6y7Y0TtPzOy3OsGVDEGPTCl7rLs+gHhGVJgaXbEDRZMqxtF+8YxV0KfHDX5/Lz6JsLqLOB5d8Hicaqj3MXDJBJF7KMn8RF4pNjGP/H2somoTHtUhwKeDjMTeRmsZNeHtTDbrLqCzu5ycHcW5kGl/ZvPqazFuv24nZnMrikmcuAdG+SwMTDC7lSksRNDXTwxtDcDkEnirzxt6Kruc1c8njcmBrRwgvHR/E2HQkb/stFoqW32lxhjuvb4Tf58buwyyNI6LSweCSDURSZC4BwPL6Stx7cwv+7a3zmGIGSM7UBdNaQnWVzFwygVHKktgDKBgLLrFEyxrRaXHXXjSHAl6MTEVy6ntDUbouocvUNydrmqpxYXQ6p0bWpeS7r55BuM6He29uvuZnOZfF6cl7LgHRvkvMXMqdouWvuXRTjRd339SEpw/2xHtqlRtdl5Ayf5PLDNs6w4hoOp4twx5AWnw4QH6PucflwP3rW/CzE4P8HiaiksHgkg2oWnpPsb60eTUmZlU8tb+8n/qZYWEpYpgTzUyhLJK5FK6L9bRicMkSyaaYhWLHnaVxuVP0a3uJLaa9uRq6BM4M239i3MELl7H//GU89uFVi37+fJ7cgktKiglPLX4vLl2ZYz+THKm6nteMju1dbbh0JYKXTw7lbZ/FRNGv/Y7Mh3UhP25qrcWOMiyNW+y6JF+2bAhhOqLh5ycH875vIqJsMLhkA+mUxQFA54o6dK6ow/dfP1u2Uz/MIKW85oY8XBctIdJ5XHOiLNLQu7G6Am6nYImWRZQkwWmjlxgzxnKX7ijr9qYaAOUxMe57e8+g1uvCZze1Lfpzr9uJWUWHlNmdU9UUpSytfi+kBAZZGpcTVZN57UVzZ3sjWmq9ZdvYO91ziRW2dYZxtGccpwbsf35KNJ+5lP9jftuqZWiurcAulsYRUYlgcMkGImlmLgHAlzevwsXRGbx4nON8s2U0nfZclV1TiYimY/jKXKGWZQvxPikJ6ecOh0Crn5PLrKLqi5fVMnPJPPOTy5b+yl3ZUAmnQ6Db5hPjzo9M4afvDOA3b1+BqgrXoq/xuZ0AgLksM4tSNfRu8Uc/3+y7lJto5lL+LiVdTgce2RTGr94bRn8ZTvsrxOQyw0MdQbgcAjsPlFf2+3ygOv+3TE6HwIO3BPGr94YwPq3kff9ERJlicMkGFDV1zyXDPWtbsKK+Et/de8biVdnXYtk18dItNvXOiWocW9fVF86hgI9BDosoSTIPmmsq4HQwY8wM82VxS98QVricWFFfafuJcd9/7SycDoHf/dDKpK/xuaPn12x7jagpGnoH/V4AYN+lHKmazGtzaQB4pLMNugR27i+/Ei11keuPfKmvrsBdNzbhmUN9ZdXzSi1g5hIAbOkIQtEkXninvyD7JyLKBINLNqBo+qLTnhbjdAh88Y5VOHRhDAfOj1q8MntS1GufYrXVRUuI2HcpN/HeBguyDUJ1nFxmFTXJQACX04GWWi+DeibI5Ml3e1O1rcviLk9F8NT+HmztCKG51pv0dT5PNHMp275LqRp6t8SCSwNlmP1iJkWXcOa50fHy+krcsaYeT+6/WHal6PESrQL0/wGARza14dKVOfzq1HBB9l8I81mQhTnm60N+rGqowu4ybKZORKWHwSUbSLfnkuGRTWH4fW5899WzFq7KvowsBM9iTacZAMmJssi0OCA6MW5wcrasnpbmS7R/2OIXzcwYM8d80DT1zUl7Uw3OjUzbttH0d/aewayq4St3rl7ydV53bsGlVA29a7xuVFe4mLmUI03X4S7ATff2ruXouTyDX78/kvd9F5JS4Cyaj97QiIZqD3aUUWncfJ+rwtwyCSHw4IYg3jgzwh5xRFT0GFyygUx6LgFApceF3/zAcrx4YgDnR+w/lchsi5XFed1ONFRX4OIoy+JykWwqSzjgg5TAAG8ETbdw8mEiZoyZwyirSOchQHtzNTRd4uwl+52bL12Zw49+fQ4P3hLE9c01S77W6LmUe1lc8mPe4veif4znlFyoSwSnrfSJtc0IVLrxZJlNv1WTZPfmi9vpwEMdIbx8cggjZdJjUi3QhL5EWzYEISXw3FGWxhFRcWNwyQaWujlM5vMfWgmXQ+D7rzF7KVOLlcUB0ewlZi7lRk2S8h9iZphlVD15z5RQwIeBidn4DQ1lJ5NR1muaqgHYc2Lc//er9zGraPj9j7enfK1RFjebQ1mcEEs3Pm71e9HPTICcKJpekECH1+3EQx0hvPjOAC5PRfK+/0JJ9h2ZT9s2haHqsmwmmBWyibphTVM11oVqsftwb8HWQESUDgaXbEDR9GvKiFJprvViy4YQntrfg7Hp8rkwM0Oy5rzR4BIzl3IRzwpb2HMpwMllVlGXuDkMBnzQdInByfJ4Qm2VZL3EFnNdYzUcArZr6j00MYt/euM8Hro1hOsaq1O+3mdCWdzC88hCLbVe9lzKkaoXJnMJALZ3tSGi6XjmUPnccBe6RAsAbmypxfqQHzsPlEdDdS3NaZ9W27IhiCM94zhnw6xWIrIPBpdsQFEz67lk+NLmVZhRNPzrWxcsWJV9GTeKCwN64bpK9I7NlF2DUTMpmg6nQ8Cx4Amh0Xy3j8El0y3Vs83IGONxz02qyWWJvG4nli+rRPeQvYJL3/7V+1B1if94V+qsJSCh51LWZXF6yqBHa8CHock59nLLgarJggU6bmqtxYa2AJ7cdxFSlsf3bjGUaAHAts4wTvRP4HjfeEHXkQ9Gn6sCJi4BAB7cEIQQYGNvIipqDC7ZgKKnvohezE2ttdjc3oAf/fqcbZvHWmGpsjhFkxhilkfWko219rqdaKypYP8fCyhL9GyLZ4zxuOck1eSyhdY01diqLK5/fAb/+tYFfGZjCCsbqtL6ndynxcmUTY9b/V5ICQzznJ01Vc+s56PZtm9qw6nBSRzpsX+QA0jMXCpspGPLhiA8TkdZZC9pug6XQ0CIwh7zVr8PXSuXYdfh3rIJphJR6WFwyQayKYszfGnzagxNzvFJSAaWKosDwNK4HEQ0PWkpCyeXWWOpshaWI5rDmFyW7lSt9uZqnL00ZZuMmn/8xfvQdYnfSzNrCZgvi8u251I6vQiNjEhOjMueqsmC9qJ5cEMrfG4nntxXHhnYmQaqrVJX5cHH1zZh1+E+2z+cLFTT+sVs7Qji/eEpnOifKPRSiIgWxeCSDWRbFgcAd7Y34IbmGnxv7xk+CUmToiYviwPYdDoXqibhdiUPLrE8y3yKmrznks/jxLIqDz/TOYpnG6R5nm5vqoaiSZwfKf1Ade/YDJ7YdwGf7WpD27LKtH/PjGlxqYIerbHgEqdQZi+bgSJmqvG68cAtrdh9uA9Tc2rB1pEvxZK5BACPdLZhdCqCV94dKvRSLBXNgiyO26X717XC5RB8IExERas4zpaUE0XT4XZld6EhhMBjm1fh3YFJvNZ9yeSV2VM8C8F1bVkcwMylXEQnDyXJoqmLZi4xCGouRdfhSRLQA5gxZoZk2Y7JtDfVAAC6bVAa9/++0g0Bga99bE1GvzdfFpddVoSipw56tNZGz9n9bOqdNS2N8kOrbe9qw1REw/PH7D+mPT4trgiCS5vbG9BUU4GdBy4WeimWSqd/W77UVXlw5/WNePZwH/t7ElFRShlcEkJ8XwgxJIR4J2HbXwgheoUQh2N/7k/42Z8KIbqFEKeEEPcmbL8vtq1bCPH1hO2rhBBvxbY/KYTwxLZXxP65O/bzlWb9S9tNJMcnh1s7gmisqcB39541cVX2ZdwoLry4M/oCMcsje0s2lw74MKfquHSF0w3NFG3Im/zCmRljuVMznDZ0XVO0L1GpT4y7MDKNHfsv4nO3tSEYK7FMV0Us4JltzyUtjSlmtT4XfG4ny+JyoOiy4CVanSvqcF1jFZ7cZ+8gB5AwebLAx9xYw6c3hvCLU8O27luWTv+2fNqyIYi+8VkcuHC50EshIrpGOt9OPwRw3yLbvyWl7Ij92QMAQoi1AB4FcHPsd/5RCOEUQjgB/AOATwJYC+BzsdcCwP+IvdcaAJcBPBbb/hiAy7Ht34q9jhaRS88lAKhwOfH5D67Aq+8N49RA6T8pt5pRFrfYjWK4zoeLzFzKWjrNpRnoMI+UMtZzaYnMpTofei8zYywXqpbZhKdKjwvhOh9Ol/jEuL9/5TQcDoH/kGHWEhDNqvW5nVn3XEoVNDX20er3siwuB+oS2ab5IoTAo13LceD8ZVtk+y1FK6LMJQB4pDMMTZf4yaHeQi/FMoWciLiYe9Y2w+t2YNdh+x5zIipdKc+WUspXAYym+X5bATwhpZyTUp4F0A3gttifbinlGSllBMATALaK6OiFuwDsjP3+jwA8lPBeP4r9fSeAu0WhRzUUqaWyPdL1mx9YAa/bge/tPWPSquzLKItbrJQoXFfJzKUcqEuUsgTZXNp06TSaDgZ8mFE0XJ5W8rUs21HiN4Tpn6fbm6pLOrh0amASPz7Ui9/6wAo013qzeg+fx5l1z6V0ewG1Brwsi8uSrkvoMv2gqZU+vTEEl0PYPntJifdvK/wxB6KTLTvaAth5oMe2DyBUvbBN6xeqqnDh4zc1Y8+xAdsMfSAi+8glIvE1IcTRWNlcXWxbCEDiN3tPbFuy7fUAxqSU6oLtV71X7OfjsddfQwjxFSHEfiHE/uHh4Rz+lUqPpktoeu7BpboqDx7pbMOuw30YmuRT3KWoScrigGjmUt/YTPzpImUmoibPognFelr1MnhnGuOznKyJOpAwMY7HPWtG5lImI9vbm2vw/vCVkjyXaLrEnzx9FH6fG1+7K/OsJYPP7cy6LG6pKYiJWmp9zFzKktH/p5ANvQ0N1RW4Z20znj7Ya+vpZVoRHXPDI5vCODU4iWO944VeiiWiD72KJ7gEAFs7QhidirBXKhEVnWy/nb4N4DoAHQD6Afy1aSvKgpTyO1LKTVLKTY2NjYVcSt4ZTy2ybeid6LEPr4Ki6/inX5/P+b3sLJKiLE7RJAN0WVJ1HZ4kF3F+nxs1FS5mLplISWPykNGonsc9e9n0SVnTVI2IquPCaOmV2f7Lm+dx+OIYvvHAWiyr8mT9Pl63I+vgUnQ4QBqZS34vBifnSjKIV2hLPWgphO1d0ellPz85WOilWMY45sWUSfPALUFUuBzYeaCn0EuxRLFlLgHAndc3oNbrwrOHOTWOiIpLVsElKeWglFKTUuoAvoto2RsA9AJoS3hpOLYt2fYRAAEhhGvB9qveK/Zzf+z1lCAeXDKhHnxlQxXuuakZ//LWeUxH7D/SN1upyuIAsDQuS6q2dP+fICeXmWo+oyaNzCUe96ylU364UHtTNQDg9GBp9ZDpG5vBX/30Xdx5fSO2dgRzei+fx4nZLMviVE2mlW3Q4vdC06WtGxJbxfhcF8uN9+b2RgT9XluXxs2fS4onc8nvc+Pem1uw63Bf1j3Sipma49AcK1S4nLh/fStePD6QdekwEZEVsjpbCiFaE/7x0wCMSXK7ATwam/S2CkA7gLcB7APQHpsM50G06fduGS3Q/gWAbbHf/zyAXQnv9fnY37cBeEXataA7B/NTiMy5uPvynasxNq3gaZs+gTJDqrI4AOhhU++sRFI0hzWaS5M5lDSmmAUq3aj0OHncc6BmmbkEoKT6Lkkp8Y1d70CXwDcfWodc2yTmVhaXfuYSAPZdykKxlWg5HQLbNrXh1dPDtg2Ga0bmUpGVaW3rDGN8RsHLJ4cKvRTTaUWYuQREp8ZNRTS8/K59M/WIqPSkvCIQQvw7gDcA3CCE6BFCPAbgr4QQx4QQRwF8DMAfAoCU8jiApwCcAPBTAF+NZTipAL4G4EUAJwE8FXstAPwJgD8SQnQj2lPp8dj2xwHUx7b/EYCvm/JvbDPzZXHmXNxtWlGHDW0BPP7aWZYJJBEvi1vkmBtZHj2j9rywtZqq6YtmhBlCzFwylZLGFDMhRCxjjAHTbBm9aTJpwlvjdSPo96K7hIJLL58cws9PDuEP72lH27LKnN/Pm0NwSdHS7LkUCy6x71LmMp2CmA+PdIYBADv22zN7KZssyHy4Y00DWv1e7Dhgv+Ou6qknTxbCB1bXo6mmArtZGkdERcSV6gVSys8tsvnxRbYZr/8mgG8usn0PgD2LbD+D+bK6xO2zAB5Jtb5yF0mjrCUTQgh8efMqfO3fDuHnJwdx780tpryvncTL4hY55l63E001FSyLy5KSYnx4MODD+IyCK3MqqitSnr4ohfmGvEtfOIcCPvSN8eY7W9mWsqxprsHpEhmtPqdq+K/Pn8B1jVX4wh2rTHlPn9uZdbnaUpMnEwX90QcC/QwuZWx+CmLx3Hi3LavEh9c0YMf+HvzeXe1FmXGSCy0eqC6ObDGD0yHw8MYQvv3L9zE4MZv1hMhilKpcv1CcDoEHbgniX948j/EZBX6fu9BLIiLKaVocFYGlAh3Zuu/mFoQCPnxv7xnT3tNO4k9rk1y0hut8uMiyuKykGh9uTIzrY/aSKeKZSymCHqE6ZozlItsMj/amanQPXYFeAlmkP3j9HM6NTOMbD95s2sMOn8eZdQ8XVUuvlCVQ6UaFy4GBCQaXMqWmef7It+1dbegdm8HrNpykZZyzizFotq2zDboEfnywN/WLS4iq60V5vAFga0cQEU3Hi+8MFHopREQAGFwqeYrJmUtA9InYFz+8CvvOXcbhi2Omva9dKJoOIZJf3IXrKpm5lKWUwSWjuTSPrynSPX+EAj6MTkXY6D9L2WZ4tDdVY1bRiz6wNzQ5i79/+TTuvrEJH7nevImtufRcip5LUh9vIQRa/V5mLmUhm3LPfLhnbTPqKt22bOydbrZpIaxqqMKmFXXYeeAi7NQiNd3hAIVwS9iPFfWV2HXEXgE9IipdDC6VuHj/H5O/+LZ3taHG68J3mb10jYgm4XY6kjarDdf50Dc2w55VWVD1pS/i4g3Ti/xmu1SkOxDACOoxYyw7aizQkWmD6/Zmo6l3cZfG/d8/PYWIpuM/PbDW1Pf1up1ZT0LSdJl2Rk2L34sBNvTOmJrGQIBCqHA58fDGMH52YgCjU5FCL8dUxnVFsWbSPLIpjPeHp3DIRg8mVV3CWWTZeQYhBLZuCOKN90cwxOxLIioCxXm2pLSZ3dDbUF3hwm/cthwvHOvHxVGWeCVSNH3JZprhukqousQgv+gzpqj6kr0NGqsr4HYKBjlMEp98mCpzKT4Fkcc9G2oGgY5EaxprAACnB4u3qffxvnHsONCDL9yxCqsaqkx972hZnJ7V76bb0BsAWv3sKZaNYi7R2t7VBkWT+PFBe02+jV/zFWmw4/71rfC6Hdhpo4nDqr70NV+hbekIQpfAc0f7C70UIiIGl0qdFT2XDL97x0o4hMAPXj9n+nuXMlXTlwzmhXkjnjVFl0s+BXc4BFr9PpbFmSSipjd5aD5ziTfg2VA0PavSIX+lG001FThdxBPj/vpn76HW68JXP7rG9Pf2uZ2IaHq8t08mojeE6WcuDU7MlkRvq2KiFXGJ1vXNNbh1eQBP7rNXiZamSzhE9LuwGNV43bh/XSuePdKXdb+0YpNu/7ZCWdNUg7Wttdh9hFPjiKjwGFwqcVb0XDK0+n144JZWPLnvAsZnFNPfv1QZZXHJzAeXmPGVqXT6pIQCbC5tFiNzKVXmY3OtFy6HQO8YP9PZUFOcM5bS3lxdtMGlA+dH8cq7Q/hfPnId/JXmTyryuZ0AgFk1i+BSRplLXqi6xKWp7CbTlat45mORZtE82tWG00NXcPCCfUq0ohNVi/N4G7Z1hjE5q+LF4/ZoMq2meOhVDLZ0BHH44hjOj0wVeilEVOaK+2xJKUWynEKUri9tXo2piIYn3r5gyfuXolRlccEAM5eylc5NeDDgY1mcSYyeKakaTTsdAi1+LzPGsqRoetbj2tubatA9OFl02RdSSvzVT0+hodqDL9yx0pJ9eD3R4FI2fZdSDQdI1OqPnrMH2NQ7I0bmdLE19DZ86pYgKj1OPGWjxt5qllmQ+XT76nqEAj7blMZpenFnLgHAgxuCAIBnmb1ERAXG4FKJUy0siwOAdSE/Pri6Hj94/Vy8eXi5S1UW53U70VxbwcylLKRTPhSq82FwYjaetUfZyyTzMciMsawpOWQurWmqxlREQ1+RBT5e676Et86O4msfW4NKj8uSfcQzl7Ior4n2uUo/cwkAJ8ZlaD44XZyXktUVLjx4SxDPHu3DlTl7TLpUSyDQ4XAIfKYzjNe6L9niOyPbsuZ8CgV86FpZh12H+4ruQQQRlZfivCKgtFlZFmf48p2rMDAxi+eP8YkIkN6NYriuEhdHS/+iKt+iWWEpjm3AB10yy8AMSgbTnsIBNj3Olqpnf3PS3hSbGDdYPBPjpJT4f148hVDAh899YLll+zGCSzPZBJc0mbJRvaElFlziOSUz8wMBivfGe/ttbZiOaHjOJhkdqp5+Rl4hbdsYhpTAMzZoqK5lEKgupC0dIZweuoJ3B4rnu4KIyk/xf0PRkuaDS9Z98X30+iZc11iF7756lk9EEC1FTHWhEa7zoYf9aTKi6RK6TB3oMMoO7fBEtNAyU/IuOQAAIABJREFUuTkM1fkwMDGbVXPlcqdq2d+ctDdHJ8Z1F1HfpeeP9eNIzzh+/+52VLiclu3H54meC7Iqi9NT928zLKv0wON0oG+c55RMGJlLxTq5DABubQugvakaT+63R2lcqQQ6ltdX4gOrlmHngZ6Sv25U9fQD1YV0/7oWOB0Cuw7bI5BKRKWp+M+WtCSjVM3KJ1kOh8CXNq/Gif4JvPH+iGX7KRWqpsOTogFyuM6H/jHeiGdCSbN/WCjWMJ39f3KnZHBzGAr4oOkSAxPM7shUJv1/FlpW5UF9lQenB4sjuDSraPjvL7yLG1tq8JnOsKX78maZuaTpElKmX67lcAg0+yuYuZQhIzhdzGVaQghs72rDoQtjOGWDjA4lh0B1vj2yqQ3nRqax//zlQi8lJ2oOPfPyqb66ApvbG/DskT5OviSigmFwqcQZN4epgh25+vStIdRXefDdvWcs3U8pSLcsTtUlBic5fShdqp5e/zCjPwozl3KXbkAPSMgYY1AvY7lOG4pOjCuOG+Mf/vocei7P4D99aq3lQYVsy+Iy+VwbWmt97LmUofmy2uK+8X54Yxhup8CTNmjsHW3oXRqX7p9c14JKjxM7SjxrrBT6XBm2bAiid2wGBy+UdkCPiEpXaXxDUVL56LkERJ8g//YHV+AXp4bRXSQ3OYWSblkcAPSMsjQuXYqa3g2h1+1EY00FgxwmUDM4fxgZYywdylyuDWHbm2pweuhKwctLLl2Zwz+80o27b2zCh9sbLN+fLzYtbjbDsjgjUJ1JtkFrwMvMpQxpxnEu8mDHsioPPnFzC5451IM5NfMSy2KSSaP6QquqcOFT61vx/NF+TEdKt6F6OlNsi8Unbm5BhcuB3TbpMUZEpac0zpaUVD56Lhl++/YVqHA58L29Zy3fVzFLryyuEgDQwwBI2hQ9s8llDHLkLpPMgxAzl7KmajKnvjTtzdWYnFUxVOBMyL/9+XuYVjT86f035WV/2WYuaVrmQY8WfzS4VOgAXimJZ4iVQLBj+6Y2XJ5W8NKJwUIvJSfRRvXFf7wN2zrDmIpoeOHYQKGXkjWthDKXqitc+PhNzXj+aD/bMhBRQTC4VOIiecpcAqL13A9vDOPHh3oxXMblXumUxQUD0dItBpfSl0mgIxzwMchhgvmG3qnPH163Ew3VHpYjZiGXaXEAsCY+Ma5wfZfeG5zEv711Ab/1geXx9Vgt67I4PfOHLq21XkQ0HaNTkYz2Vc7iGWIlEOz48JoGhAK+ki+NU3U97V5ixeC2Vcuwor4SOw+U7tQ4RdfhLpHgEgBs6QhiZCqC19kjlYgKoHS+oWhRipr+KHEzPPbhVYioOv75zfN52V8xijbnXfpCo8LlRHNtBXousywuXZmWaPWOzTDLIEeZ9kwJBnwMmGZB0XKbNtTeFJ0YV8i+S/9tz0lUVbjw+x+/Pm/79MbK4jKdFmdMMcvkJrzFH83MY9+l9M2XHxb/paTDIfDZTW14rfsSLpZwuXp0clnpBDqEENi2MYw3zoyU5HHXY8MBnCXwGTd89IZG1Hhd2HW4t9BLIaIyVDpnS1qUoulwiPxNa1nTVI27b2zCv7x5HrMZPk22CyXNhpptdZW8Ec/AfBPeNMri/F7MqTpGmGWQk3hZbZoXzqGAj5lLWVBzfPLdUO1BoNKN00OFyVz61XvD+OWpYfzHu9qxrMqTt/0amUuZftdk1dA7NiiAwaX0qXksyzfDtk3R6YY7SjiLRi2haXGGhzvDEAJ4+mDpHXdFz/xcUmgVLic+ua4FPzs+WLbX6URUOAwulThFz37Edba+tHk1RqciJXmhYAZFkyknmgHRpt4XmbmUtvjkw3T6/8R6WrE0LjeqJuEQ0af66QgFfOhjxljGFDW3bAMhBNqbqtFdgLI4VdPxzedPYPmySvzOh1bkdd9upwNOh8i4LM7IqMmoLC4WXBpgL7e0qVn0tiqkUMCHO9sbsWP/xXgz8lJTamVxQPS433FdA3Ye6IFeYsddy2I4QDHYsiGEK3MqXnl3qNBLIaIyU1rfUHQNRU0v0GGm21cvw7pQLR7fe7bkLhTMkE5ZHBBt6t0/PsumimnKpJQl3lyaWTQ5UfTMxlqH6nyYVdiXJlOZHufFrGmqwXtDk3kP7D21vwfvDV7Bn37yRlS4nHndNxDNXpqJZHYOVeONptM/5vXVFXA5BDOXMhDP6iihG+9Hu9rQPz6LvaeHC72UrJRaQ2/Dts4wei7P4K2zo4VeSkaUEgugGj54XT0aqiuw+zCnxhFRfpXW2ZKuoWg63Ckml5lNCIEvb16NM5emyvKpSLr9U8J1Pmi6xMAEb1bSEW9On8bnmZPLzJFpcJpBvexEp8XldkPY3lSNsWkFl67kL7A3Mavgb146ha6VdbhvXUve9pvI63ZiNsPx8Zn2EgOipeXNtdGJcZSe+FS+Egou3X1TM+qrPCXb2FvRc+vfVij33tyCmgoXdhworeNeqplLTofAA7e04pVTQ5iYVQq9HCIqI6X3DUVXSTeLxmz3r29Fq9+L7+49k/d9F5qi6WmWxUVLt9h3KT3x/h1pXMTV+lyornAxyJGjTKeYBRnUy4qaZp+2pbQ3xybG5bGp99++dBojUxH8nw+shRCFubnyeRyYzbSht5555hIQLY1j5lL6lNiNd6mMaQcAj8uBhzeG8NKJQVy6UnpTbzVdL7lABwD4PE48sKEVLxwbwJU5tdDLSZtagj2XDFs7goioOl58Z6DQSyGiMsLgUomLaPnvuQREe2F84Y6VeOvsKI71jOd9/4WUfllc9EacwaX0ZJJ+LoRgc2kTKJrM6Abc+EzzuGdG0WXODwGMiXHdeWrq/e7ABH70xjl87rbluCUcyMs+F+NzOzPuuTR/LsnsmLf4vcw0zYAa+y4sVOAxW9u72qDqEs8cLL1pWqXY0NuwrbMNM4qGPUf7C72UtKklmJ1n6GgLYPmySuw+wtI4IsofBpdKXLrNpa3w6G3LUV3hKrvsJTXNsrjWgBdCAD1s6p0Wo39HujfhoTofM2hylGnmo9/nRpXHyeBShlQTHgI011agpsKF03lo6i2lxDd+chy1Xhf+90/cYPn+lpJNcCmbnktANHOJDevTp+qypLKWDGuaatC5og5P7LtQcv9fq7osyANFM2xcHsDqxirsLKFpfVo8O6/0jrkQAls2BPF69yUMT5Zelh4RlabSO1vSVRS1MJlLAFDrdWN7VxueP9ZfNjebUsq0s8UqXE4013iZuZQmRTWCS+l9noMBL/o42SknmQY9hBAM6mVBzTBDbDFCCLQ3V+elLO4nh3vx9rlR/B/33Yi6Ko/l+1uK1+3ETMZlcdlmLvkwp+oYm2aPknREe4mV5mXk9q42vD88hQPnLxd6KRlRNb0kA3pA9By2rTOMt8+N4tylqUIvJy2KltlDr2KzpSMIXQLPH2X2EhHlR2leFVBctKF34b70vnDHSgDAD18/W7A15JNx0+JJ80KjbZmPmUtpmh8fnt5pKRSoxNi0gqkS6t9QbKLNYTM7fwRZjpgxRTenN157U43lZXHj0wr+2553sSHsx/ZNbZbuKx0+jxOzmWYu6Zk39AaimUsA2HcpTZn2bCsmn1rfiuoKF54oscbeSolOizM8fGsYDgE8fbA0spe0Euwrluj65hrc2FLD0jgiyhsGl0pcRNNzfiKei3BdJT65rgVPvH0Rk2UwkULNcCxtuK4SF0d5I54O4wlhuhfOIfb/yZmq6RlnHoQCPvTxmGfErPHh7c3VuHQlgtEp6ybG/eVzJzA6FcF/fWg9HEVwQ5XPsriWWHBpYCLzz/eZ4StY+fXny+phgqLJkiwXAoCqChce3NCK54/2l9S1i6aXbs8lIPrf2Ob2Rjx9oCceuClm8f5tJfo5B6LZSwcvjOHiaPmcm4iocEr3bEkA0p9cZqUvb16NyTm1ZEf7ZiKiZVa6Fa7zYWBiNn6zQ8kZF3Hpfp5DgeiNIINL2VM0mXHmY6jOh8vTCqYjzBhLh5QSqp57WRwArGmKToyzKnvplXcH8fTBHvyvH7kO68N+S/aRqXw29A76owHrbDKXntofzcQopwwBzaSMvELZ3rUcM4qGZ4+UUINpPffJk4W2rTOMvvFZvPH+SKGXkpIRACvlgN6DtwQBlNe5iYgKp7S/oSja86CAZXEAsKEtgNtWLsMPXj9n+yCKkV2TbllcuM4HTZcss0hDxplLgUoAYP+fHChZZD6GArGMMR73tGRborWY9uboxDgr+i6NTyv4+tPHcENzDX7v7jWmv3+2vB4nZiKZfa+oemYPAQyNNRVwOgQGeL5Oi1kZeYWyIezHjS01eHLfhUIvJW2qLuEu4UAHANyzthm1Xhd2HCj+B5LGoJFS/py3LatE54o67D7M4BIRWY/BpRKnmDCFyAxf2rwKvWMz2PPOQKGXYqlsyuIAsKn3/8/ee8e3cd/3/6/PYXMBkDgBUDJlyZKsQVqkbMe2HNnyjGPJjiXZTX4ZrUd/TfLtcNPUHWma0Sbf1k3bpG1+je2sNomHbEfyiveQPLWHNSxZk6K4B0CCAA64z++PuwMhiQQPwB3WvZ+Phx4iQRD4+Hw63L3u9Xq9NRDP0BVWX+2AzcLIuZQD8QTPWPQIKHHETtrumpgQTXM/TvvcTlTaLYZMjPvWsx9iYCyGB9e1wmG16P762eKyZdG5lOX4cIvAUF/toJsBGhGl0i30BuSC6TuXN2N35wgOnAkWejmaiJdwFFHFabNgdZsPv9vXjZHx4o4kTjiXSnubr2nz4VBPCAe7S2M/JwiidCntoyWBWKI4xtJet7ABLbWVeHjz0ZIb7ZsJYhaxOACm6uHIFjXKovViRRAYGt1OctDkQFzK3LnkI+dSRohZCh2TwRjD3Hr9J8ZtOdyPp3acxpdXFk8cTkWNxWXyuZLpcTqVRrcTZ2gKpSZKeXKZym1tftgtQsnE+uUbiqW9zQFgXXszonEJz+0p7khipo7qYuVTS5pgERi5lwiCMJzCqxJEThRD5xIgX+jffVUL9nSO4INjg4VejmHEMhxL2+R2gTFyLmkheUGYQcyTyqVzI5bgsFkzO37UVzthFRhtd41k6sibjrn11bo6l6LxBP5u4z7MnlmBr1xTPHE4FZfdgoTEkyKdFtQoYjYXhE1up2bn0sBoFPf8YiuGDCxYL2biEi/5/h9vpR03Lm7E0ztPZ+yQKwQJiZe8oAcASwNuzKuvKvpoXDl0LgFAbZUDV86txabdXWV9A5ggiMIz7VkBY+ynjLFexti+lMdmMMZeZowdVv72Ko8zxtgPGWNHGGN7GGPLUn7ni8rzDzPGvpjyeDtjbK/yOz9kjLF070GcTTHdxbpjWQDeChse2nys0EsxDDVuofVC0W4V0FjjJHFJA/Es7Od+TwXF4nJAnhaXeXSoyeOk7a6RXISOyZjXUIXeUBQjYX3iJA9vPoaj/WP41upFcNqKJw6noq4pk1LvbKfFAUBjjQvdIxFNF2AHu0N45UAv9pdIpEpv4kV0/pELdy1vxsi4iJf29xR6KWlJDgcocUEPkF2Y6zoC2Hly2LABBXowcfwu/W2+utWHzqFx7Dg5XOilEARRxmg5Wv4cwE3nPPYAgFc55/MAvKp8DwA3A5in/LkPwI8BWSgC8E0AlwG4FMA3U8SiHwO4N+X3bprmPYgUxHhxdC4B8h3mz18+G68e7MHRvuI9WciFbOIWAa+LYnEaiMUzc4UB8uSynmAk+f+FyIxsC3n9HhfF4jSSPGbo1NkxT50Y15d7NK5zKIwfvXYYNy5qwMr59Tm/nhG4FHEpE1dJMmKbxb7t8zgRjiUQjGifhjgeK37HixHIUxBLX1z6xJyZaJ7hKvpi7+RwgDLY5gBw2yV+WASGDds7C72UKcm2v60YuXFRA+xWAc/Q1DiCIAxk2rNdzvlbAM7NOa0B8Avl618AuC3l8V9ymfcAeBhjTQBuBPAy53yQcz4E4GUANyk/q+Gcv8fl24S/POe1JnsPIoVsYi1G8vlPXACbRcAjW8rTvZRpLA4Amr0V5FzSgNz/w6CYFzXh9zghcdB0pywRE9mNtfZ5XORc0kjS7ajTVM959crEOB2icd9+Zj8YGP7u1kU5v5ZRuOzy/pmJgKNOi8smPtTodgKY+piy+XAfLnjgObxzpD/5WCauqnIim2mTxYggMKxvb8bbRwZwcqB4bwSpES1LGbjFADlivfKiOjy9szP531ZsJMpgWpxKtdOGVQvq8eyerrKf7EwQROHI9qyggXOutvB1A2hQvvYDSA1QdyqPpXu8c5LH070HkYKYRazFSOqqHbi9zY8N2zsxWIY9FJnG4gDZuXRmZJzcNdMgZuGi8XvkaXwkdGSHKGXX2RbwkGNMK6rQoddFeMDrgtMm4HCOUZLHt57CS/t78Mer5sGvlLQXI64sYnFiFsdplSZFXJqq1Hur0im49fhQ8jGziksJKTvnYzGytiMAgaGoO4D0dkEWA2vbA+gJRvHW4b5CL2VS9BzIUAysafOhfzSGd48OFHopBEGUKTl/QimOI0NvOUz3Hoyx+xhj2xhj2/r6ivMDyijkzqXiOtG4Z0ULonEJ//veiUIvRXeyi8VVkLtGA9nsy34vTS7LhXgiu1iL3+uifVojuUS0JkMQ1Ilx2YtL+7uC+MbGfbhy7kzcd/UcXdZlFNl0LuVSwtvolo8pmezbpVAEbQTyDYHiOv/Ilia3C5+8qA5PbOssWldH0rlUJkIHAKxa2ABvha1oo3ET27w89vOV8+tR7bBiI02NIxQ453ifxEbDCUZEfNg1Uuhl5IVsj5Y9SqQNyt+9yuOnATSnPC+gPJbu8cAkj6d7j/PgnP+Ec97BOe+oq6vL8j+pNBETUlHF4gBgXkM1Vs6vwy/fPV52J93ZxOICigByinqX0pKNuKS6DMi5lB3ZXhySY0w7E50d+h2n59VX40hPdp1LwYiIL/9qOzwVNvz7XZcU/YVqsnMpk1hcIvtYXH21A4wBXRmIS2HTdi5JZePoAIA7l89CdzBS9C6acihRV7FbBaxp8+PlD3swHC4+t7uYHA5QHtvcabPgxsWNeHFfd9mdnxPZ8V9vfIw7f/Ie3vm4f/onE1lz7YNv4JYfbin0MvJCtme7mwCoE9++CGBjyuNfUKbGXQ5gRIm2vQjgBsaYVynyvgHAi8rPgoyxy5UpcV8457Umew9CgXN5PHOxOZcA4N4Vc9A/GsPGXaenf3IJkV0sTr4Qp96l9MQTPOOTZqfNgtoqB7pI5MgKMSHBnmXpMUCOMS2IBnR2zK2vQtdIBKFIZhPjOOf4+hN7cGpoHP/52WWorXLotiajcNmziMVJ8rEkk/42FZtFQF2VA91TxOImw7SF3lk6H4uVVQvrUVtlx2NbizMal4zYFuE5Xy6sbQ8glpCKsmg6ofO0z2JgdasPoWgcbxya8p49YSKO948BADoH6XzOSPpHi088N4ppP6EYY78B8C6A+YyxTsbY3QC+D+B6xthhANcp3wPA8wCOAjgC4CEAXwYAzvkggO8A2Kr8+bbyGJTnPKz8zscAXlAen+o9CAV1ckg2F4dGc8WFM7GwqQYPbz6maaRzqZBNLK7R7YTASFyajliW5bB+L5VLZ0s8h0JvACTqaSAbQXo61IlxH/eNZfR7j2w5ht992I2/unkBOi6Yodt6jCSbzqV4jkXTTW4nzlAsblriUnHe3MoWm0XAHcsCePVAL3pDxRf5VY8lxe42zJTFfjcWNtXgiSKMxomS/s7TQnPFhTNRW2XHpiIU8wiCKH20TIv7Pc55E+fcxjkPcM4f4ZwPcM5Xcc7ncc6vU4UiZUrcVzjnF3LOl3DOt6W8zk8553OVPz9LeXwb53yx8jtfVfqVMNV7EBNkI3TkC8YY7l3RgsO9o3jjo+K0mGeDmEUszm4V0FjjRCfF4tIST3DYs4h4+j1OctBkiZhlIa/qGCNRb3riBsQq5jWoE+O0R+O2HR/E9184iBsXNeDuq1p0W4vRJDuXMnAHZTMcIJUmtyujziWzFnrL4nR5CR3rlzcjLnE8taP4XNfqDcVyisWprG0PYE/nCA51Zxf3NYpEmcXiANn5dsuSJrxyoDdj9ytBEMR0FJ8qQWhGjOt/R1xPPr3Uh4YaBx7efLTQS9GNWDw7QS/grSDn0jRk29/h98jOpXJyyOWLeELKevIQOca0ofa06Rllafa6YLcKOKKx1Lt/NIqv/HoH/F4X/nlda1ZxsUKhxuIycQfFpdwGXTS6nZmJSyaNxYkJXnYumgvrqnDpBTPw+NZTRfeZkpDULrHiPOfLhdvafLAKDBuKbFpfvAxjcQCwus2PWFzCSx/2FHopBEGUGeX3CWUikuXSRVborWK3CvjSFS14+8hA2TTkT9w5zFRccpG7Zhpi8ewiFn6PC9G4hIEx8+SZ9SAhcUg8e3E64KF9WgtxA0p4rRYBc2or8ZEG51JC4viTR3diOCzivz63DDVOm27ryAfZxeJy6wJqcjsRisY139U3q3MpIfGsxeliZv3yZhztH8PW40OFXspZJAu9y0zQA4CZVQ5cu6AeT+/sSjrEi4F4GcbiAGDZLA8CXhc2Flk0TpI4LnjgOWw5TOXSBFGqlNfR0mSoH8DF2Lmk8tlLZ6HCbsEjm48Veim6kE0sDgACMypwZmS8qE6aig3ZbZCFc0kpTCehIzOSU3CyPH74PE5yjGkgWcKr88XJvIZqHNbgXPr3Vz7C20cG8J01i7HI59Z1DflgIhan/diZ66CLRmUKpVb3knk7l8ovFgcAn1rSiGqHFY9uPVnopZxFcvJkkbrVc2VdRzP6R6N481DxVCmUY6E3IFdXrG714e0j/egfjRZ6OUnUz7RvP/thgVdCEES2lOcnlEko5s4lFXeFDes7mrFpd1dGMYNiJRmLy9AtFvC6IHHgzHDpbwOjEBPZRVnUyWVULp0ZufZ3kGNMG0aND59XX4XOoXGEY/Epn/P6oV788LUjWNcewPrlzbq+f76wCAx2q5CZcylH0aPJLRfWay31Dps4FlfM5x/ZUmG3YnWbD8/vPYNgEXXSTAjV5SV0qKycX4faKjs2FFGxt1iGnUsqa9r8SEgcz+89U+ilEARRRpTfWYGJEA3o8jCCu69qgcQ5fv7O8UIvJWeSF+QZuhACXvlihUq9pybbEt6AR3EukbiUEWI8N0cNOca0oV4Q6n0RnpwY1zv5xLjTw+P4s8d2YUFjNb5z22Jd3zvfuGyWzDqXcuwCasrQuWTWWFw8IZVd55LKXctnISJK2LSreGJD5dr/o2KzCLitzY9XD/ZgsEhuWiQk+VhSSj11WpnfWI35DdVFtY8TBFH6FLcqQaQlphR6F3MsDgCaZ1TgpsWN+PX7JzAWnfoueykgxrOLxTUrF+JU6j012TqXalxWVDmstG0zRJRy62zze2TBlES99IgJYy4I5zXI4tLh3vN7l8ZjCXz5VzuQSHD8+P9pT0bLShWXzZLhtLjsi+oBoKFGFpe0OpfMWugdz3LaZCmw2F+DhU01eGxr8RRMJ2NxZdb/k8rajgDEBMfGXcUxra8cS+tTWd3mw7YTQ3TjkyAI3SjfTygTUAqxOJV7VsxBMBLH49uK50QtG8SEBMaQ8clGo9sJgZFzKR3xLCMWjLFk/w+hnXiO5bBJcYlEvbRMFHrre5yePbMSNgs7r3cpIiZw3/9sw97OYTy4vhUttZW6vm8hcNktGcbichM97FYBtVUOdAe17dvm7Vwqz0JvQP5cuWt5M/aeHimagSTJWFyZCnoAsKCxBkv8bjyxrTiicQlJKssCdZXVrT4AwDO7KRpHEIQ+lOdZgUkoJXFp2Swv2md78dO3jyULEksRUTmZztQibbMIaHK7yF2TBjEhZd1r4Pe4qHMpQ3Ith1UdYyTqpceonhSbRUBLbSUO90yIS2JCwld/vQObD/fj/96xFDcuatT1PQuF05aNuJTb52KT26nduWRCcYlzjkQZO5cA4LY2P+xWAY8XiXtpwrlUvtscANa2B7D/TLAoRL1ydy41z6jAslmeonGKEQRR+hS/KkFMiWjQHXGjuHdFC04NjuPFD7sLvZSsEePZTTQDAL+XxKV0iAkp+4iW10UiR4bEspx8qMIYg99D2306RAMnPM2rr8YRJRYXT0j400d34ZUDvfjObYuxrqM0C7wnw2UTMuxcyt1t0Oh2au9cMmEsbmJEe/leeLsrbLh5cSOe3nm6KNxpE9u8NM75smV1qw92i1AUxd4JHYTqYmd1qw8Hu0P4qOf8iDVBEESmlPcRs8xRnUt2a2mc3F1/cSNmz6zAQ5uPFnopWZOLABLwuigWlwYxwbO+IPR5XBgOiyXf6ZVP9Cia9ntdFIubhniOIl465tZX4eRgGOOxBL7+5B48t/cM/vaWhfj85bN1f69C4rJn1rkUz3I4QCpNbqdmN6QZnUu5Oh9LhTuXNyMYieN3+wp/UyyeKP9YHAB4K+247uJ6bNzVlZzQWyjiUvaO6lLhlqU+CAxU7E0QhC6U91lBmVNKsThA7in6gytbsPPkMLafGCz0crJClHjWdw2bvRU4E4wU/GSpWIlnWegNTPT/UDROO3pELMi5ND1Gug3mNVRB4sC9v9yGp3acxp9ffxHuWTFH9/cpNK4MY3GilP2xRKXR7UQwEtckWIsJnrzwNwuiQXHPYuPylpmYPbOiKIq9k9Nqy1xcAoB17c0YHIvhtYO9BV1HPMHLfh+vq3bgyrm12LS7C5yXbm0FQRDFQWmoEsSklJq4BADrOgJwu2x46K1jhV5KVohxKevpfAGvC5wDZ0boYnwyxBzs5wGvLC51ktChmZgOxw+fx4WRcRGj5BibkliWEya1MK++GgCw5Ug/vrzyQnz12rm6v0cxkHHnkg4XhE1ueWJcd1BbNC4SN5d7ySz9P4LAsL6jGe8eHcDx/rGCrkV1m1rKPBYHACvm1aK+2oEN2wsr6unR31YKrG714eR+paODAAAgAElEQVRgGLtODRd6KQRBlDjlf8QsY2Il1rkEABV2Kz532Sy8uL8bJwYKe6KWDbnF4ioAgHqXpkBMZC/c+T3ytqWIlnb0mGLm95JjbDrikgSLwDIeAqCFltpKNNY4ce+KFvzFjfMNeY9iwGWzIJJBLE5MSDoUesv7tvbeJXM5lyYml5XO+Ue2rG0PQGAo+LRb0SSCHiDvV7cv8+P1Q33oC0ULto64VP7OJQC4cXEj7FYBm3ZTNI4giNwo/7OCMkY08I64kXzxigtgFRh+uqX03EtiDicaSXcN9S5NityTkt0hqa7aAavAKKKVAXr0d6hxRBL1psbIWIXdKuCdB67F39xycdkKS4DSuZThtLhcPxdV55LWiXHFUPicTybE6fLd71Qaapy4dkE9NmzvLGj8UZ20W+6dSyrr2gNISBy/3Vm4SWYJSTLF9q5x2nDt/Ho8u+dMSU90Jgii8JC4VMKUYiwOkE/UVrf68fi2TgyHY4VeTkbI0+Ky295NbicsAiPn0hTEcuhcsggMTR7tBbyELJQCuV0cUhxxesQEN/QYLZjgrnqmnUvxhJRzx1VDjRKL0xhjNlup90QsrrTOP7JlfUczekNRvHGor2BrSN4QMMk2n1tfjbZmDzZs7yxYF5CY4KaIIQLA6jYf+kJRvHd0oNBLIYiyYHAshm3HS7NjOBfMccQsU0pVXAKAe1a0YFxM4Ffvnyz0UjJCTEiwZxmLs1oENNY4SVyaArnQO8dyadq2mlGdj7lcqNRVOWCzMNruaYhLue3XhNy5FBElSBrvqIs6uMWcNgtmVNrRpdG5FI6Zq3dsIhZnjn37mgX1qKt24NECFnuLJnKLqaxtD+BQTwh7T48U5P0TOrggS4VrF9SjymHFxl2Fc4oRRDnxs7eP4XMPv2+6ovzSUyWIJGrnkr0ExaWFTTVYMa8WP3/nOKIlVISaa/4+4HVRLG4SEhKHxHMvl6ZYnHbUi8NctrkgMDS5XeQYS4OYQ9yTkHHZLQCAqMZJm3GdoiyNNU7tnUui2TqXzOVcslkErG0P4PVDvejVWPKuN2pcyWICt6LKra0+2K0CNmzvLMj7iwnJNNvbabPghkUNeGFfd0mdlxNEsTIwFkM0LpnO2WyOs4IyJelcspbmB989K+agLxTFpl2lUyAYyyEWB8il3uRcOh9Rh/6fgMeFnmAk+VpEevS6C+4nUS8t8YQEm0kuTozCZZPFJa0naLn0t6XS5HZq71zKoHC8HNDjmF1qrO9oRkLi2LCjQEKHDjcESg23y4YbFzVi466ugvSaJSQOm0kEVABY0+ZHKBIvaPyTMJ5wLI7/fvNj6tcymNFI/Ky/zYJ5jphlSLyEY3EAcPW8WsxvqMYjW46VjGUwl1gcIDuXuoOR5HhyQiYplOZwEuf3uiBx7dOdzI5e0578XoojpsMso6yNRBWXtF5cxiWui6DX6HZS59IUmKnQW6WlthKXtczA41tPFeScJZEwn3MJkIu9R8ZFvHqgN+/vHU9wU23vKy+ciZmV9pK66Utkzlsf9eF7LxwsWNzULIQiIgBgNEriElEixEp8LC1jDHevaMHB7hA2H+4v9HI0kWssrnlGBTin0e3noseFik+dXEbbVhNiXJ+LQ5/HhZ4QCaZTISbMMW3ISJz2TJ1Lki6Cns/jwlBY1CRqmU5cSka0zHUaedelzTg+EMb7x/Jf0ipKpX3Oly1Xzq1Fk9uJJ7bnv+9Kr4htqWC1CLhlaRNeOdBjugtiMzEyLoseQeVvwhjUf0Nm+7dkrrOCMkNMSLBbhJIeQb2mzYe6agce2ny00EvRRO6xOGW6Fjk9zmIiYpGDc0kRl0i404ZeEYuAxwXOgZ4C9ZAUO/GEuWIVRuBU3KLjGqNnosR161wCtLkhzRaLSzqnTSZ03Ly4CdVOKx4rQLG3PAWRlfQ5XzZYBIbPLPPjrY/68v45k+sNxVJkdasP0biElz7szsv7cc7xLy8dwqlB6iPNFyElphUyWVwr34QoFkeUGmK89KcQOawWfPETs7H5cD8OdgcLvZxpERMSbDnG4gBQqfc5qHdkcymnTzqXSLjTRFwn56OfBNO0mO3OtxGohd6aY3EJSRdBr8kti0taepfCJnUumS3y6bRZcFubH8/vPYORcH7v+ickc0W0UrljWQASB57akd9JZnIszlz7+LJZXvg9LmzanZ9oXHcwgh+9dgQv7DuTl/cjgKAqekTJuWQkSXGJnEtEqZCr0FEsfO6y2XDaBDy8+VihlzItuXZ5NNY4YREYXYifQ1yHclinzYLaKgfF4jSih1sMmHCM0XafnBhNi8uZTAq9JWXypC7OpaS4NP2+bbZiVDMWeqvcubwZ0biEjbvzK3SICV6yHZu5MqeuCh2zvdiwPb99V3Gp9G/iZoogMNza6sPmw/0YGI0a/n7B8fhZfxPGo8bhyLlkLNS5RJQcsQQvizHA3ko71rU3Y+Ou0wUb8asVMcdYnNUioMntJOfSOYg6ldP7PU4SOTSiTovLxS0GAE0e+QKcHGOTQ9Picsepiksaomd6TtRqzMC5ZDYSJu3/AYDFfjcW+2vyHo1LSJJpnUsAsK4jgI/7xrDz1HDe3jNuUrfYmjYfEhLH8/uMj8YFlQtw9W/CeFRRKUjikmFwzqlziSg95M6l8vjQu/uqFsQljl+8e7zQS0lLLMFzdosFvC5yLp2DqNPkIb/XReKSRvRwiwFytLWu2kFdV1MQT+jT/2NmXBkUeusV9wSACrsVbpeNJlBOgpjczuY8jbyzoxkfdgWxL4/TlkSJm85Fk8qnljTBaROwYXtn3t5TTOR2Q7FUWdBYjXn1Vdi0y3h3nuqiGaFy6byRdNSQuGQY4VgCqqGZxCWiZCiXWBwAXFBbiesXNuB/3zuJcKx4/xHGJSlnp0fAW0Hi0jno51xyoWt4vCBjoksNPScP+T0k6k2FKJnz4kRP1Ficls6lpLik0zZvcjvJuTQJ8aRDzJxix+o2PxxWAY9uPZm395QLvc17LKl22vCpxU14ZneX5v61XAlF4qh2WvPyXsUEYwxr2nzYenzI8M/2pHOJxKW8MVHoTdvcKFIjh2YT8cz7KVUGlNsdlXuvnoORcTGvd6UyRYxLOV+MB7zy6PZo3FwFsOkQdbog9HlciIgSBsZieiyrrBF1nDxEjrGpiZu4J0UvXFnE4vSKazW5negO0r59LmoszoyRIQBwu2y4ZUkTNu7q0jzFMFfMGtFKZW17AKFIHC/mYZKZJHEEx0W4XTbD36sYubXVBwB4xuBi72TnkskuwAuJKuhR55JxpAp3Y+RcIkqFWLy8Llo6ZnvR2uzBI1uOFW05qqhDLK7ZWwHOga5huhuuMuFcyjEWp5RLU0RreuI6itMBxbkkFem/20KiinhE9kzE4qRpnzvhXNJnmze6XRSLm4SJKHP5nINkyvrlzQhF4nmbcjUeSyT/LZiVy+fMhN/jystNyNFYHBKHacWl2TMr0dbswaZdRotLFIvLN0nnEk2LM4xQiqAUInGJKBXkiFb5XLQwxnDvihacGAjj5f09hV7OeXDOdYm4BJKj26nUWyWu04WKX9m2VC49PaKOXUA+jwuxuIT+MeMny5Qacam8bgIUAoci6GvpXEoK1TrFh5rcTvSPxshpeg56dbaVMpe1zMAFMyvwaJ6KvUfGRXhMKnSoCALDHe0BbDnSb/hNpJGwfOFdY+JtvrrVh/1ngjjSGzLsPSgWl3+oc8l4KBZHlCTlFosDgJsWNcLvceHhzUcLvZTzSEgcnCPnyU+BGRUAQL1LKejZuQSAIloaiOvYBTThGCOHx7nEE5KpL8D1gDEGl82iqWclOcVMN+eSPDGuZ2Rq4TTXHr5SZKKzzXz/7SqMMdy5fBY+ODaIo32jhr/fcNi8Ea1U1i4LgHPgqR3GupdUJ42Zt/mnlzZBYDDUvaTG4si5lB845ymdS+YSPfKJKihVO6wYK+IuYSMw71lBGSCWWSwOkDt3/uCqFmw7MYSdJ4cKvZyzSMYAcozFNVQ7YBUYOZdSUMWlXONDbpcNlXYLCXcakI8f+lyAk2NsasQEN/UFuF647BZN3TZq0bSehd4AcGZk6n3baTPf/1/1zrcZy45TuaPdD4vA8Pg242NaI+Mi3BXmFTpUZs2swGUtM7Bhe6ehwzuCJC6hvsaJT1w4Ext3dxm2rVXnUjQukUM0D4yLCcSVmwPUc2Uc6mdkk8dJziWidIiV0bS4VO5c3oxqpxUPbz5W6KWchSjp466xWgQ0eZwkgKSgftDlum0ZY/B7XdS5pAFR0m/yUFJcGibB9Fxkhxg5l3LFZbNojMUpxxIdC70BoDs4tSvPjD04I2ERDqsAp818/+2p1Fc7ce2CemzY3pm8SWIUcizObuh7lArrOppxfCCMbSeMuwlJziWZNa1+nBgIY0/niCGvH0wpPlZdTIRxqG6lKocVo9S5ZBjqdm50u6hzKRMYY8cZY3sZY7sYY9uUx2Ywxl5mjB1W/vYqjzPG2A8ZY0cYY3sYY8tSXueLyvMPM8a+mPJ4u/L6R5TfpTP0FMSEpNsJdDFR5bDis5fOwgv7zuDUYPFcrIpx/UYvBzwVJC6loFehNyBHtCgWNz3yFDN9jh81ThuqnVZyLk1CXMduKzPjtAmaxKVYXJ+bACqNblk4PZOm1NtlQoFlKByDt4KEDgC4a3kz+kejeP1gr2HvISYkjEbjphc6VG5e3IgKuwUbDHSMkbgkc+PiRtgtAjYaFI1LjWalCk2EMaiOGp/HiYgoJW/uEvoSisbBmJxWoWlxmXMN57yNc96hfP8AgFc55/MAvKp8DwA3A5in/LkPwI8BWYwC8E0AlwG4FMA3VUFKec69Kb93kw7rLRvKsXNJ5UtXXgCBMfz07eJxL+k5HSfgdVEsLgU9t62PxCVNiAlJt+gQoIp61Ll0LrGEfg4xM+OyWxDREIsbVi4IPTrFh6ocVlQ7rWknxrns5ouGDYVF3bZxqfPJi+rQUOPAYwYWewd13q9LnUqHFbcsacKze7oQNqjPhMQlGbfLhpXz6/Dsni5DJjkHx0XUKPFaKvU2HjUK51O6MkMk6BlCKCKiym5FtdNGsTgdWAPgF8rXvwBwW8rjv+Qy7wHwMMaaANwI4GXO+SDnfAjAywBuUn5Wwzl/j8tB31+mvBYB+YK8HGNxANDkduHTS5vw+NZTRVPyp1fpNAAEvBXoCUYpX66g57b1e10YDoumu1OQKWJC3842coxNjp4OMTOjNRY3NBYDAHgr9XPVNLmdaTuXXCbsXBohcSmJ1SJgbXsArx/qTStC5oLeomk5sLY9gLFYAi/s7Tbk9UfGRVgFhgoTxl7PZXWbD72hKN4/OqD7awcjcTQrg26K5Xy/nFEFvAlxic6VjWA0EkeV04oqpxVjGm6MlRO5nhFxAC8xxrYzxu5THmvgnJ9Rvu4G0KB87QeQelunU3ks3eOdkzx+Hoyx+xhj2xhj2/r6+nL57ykpYvHy7vK4Z8UcjMUS+M0HJwu9FAD6RrcCVIB8FnqOtZ6YXEbbNh16dwH5vS6cJjfeWXDOEUtIcFjp4iRXnBrFpUFFXJqhY2Sr0e1KH4sz4cUnxeLOZn1HMyQOPGnQBLPhsHxBWGNyF00ql7bMwKwZFdiw3ZhtPjIuosZlAzVyAKsWNKDSbsGm3fpG4zjnCI6LyXNiisUZjyom+UlcMpRQJI5qpxXVDvM5m3MVl67inC+DHHn7CmPs6tQfKo4jw8OcnPOfcM47OOcddXV1Rr9d0SAmpLIegbzY78Yn5szEz98+nuzRKCR6RrfUuzTUuyQT03Hbqh+YnSQupSWe4DlP50vF73EhGImTxTqF4HgcCYmT20AHXDZt0+KGwzEITN+L8KYaJ3UuncPwuAgPiUtJZs+sxBUXzsRjW09BMig6BAAeEpeSMMawtj2Ad48OGNLPOTIumj4Sp+KyW3DDokY8v/eMro57dXJZwEvOpXwRSsbi5GEVISr1NoRQVES104ZKEpcyg3N+Wvm7F8DTkDuTepRIG5S/1YbD0wCaU349oDyW7vHAJI8TCuXcuaRy79Ut6A5G8NxeY4oEM0HfWJwigJC4BGDCuaRLobeXnEtaiOncueRLOsaod0llMCy7aGZW0UV4rrjsFkS0OJfCMbhdNlh0FE4b3U70j0anvMlhtolpnHMMh2Mkmp7DncubcXIwjPcMiA4Nj8vHEhL0zuaO9gAYM8YxFozEySmWwuo2H4KRON76qF+311SnwyWdSzQtznCShd7KsAqz9QHli9FIHFUOORZnNrK+smCMVTLGqtWvAdwAYB+ATQDUiW9fBLBR+XoTgC8oU+MuBzCixOdeBHADY8yrFHnfAOBF5WdBxtjlypS4L6S8FgG1y6O8xaWVF9XjwrpKPPTWMchGuMIh6hjdaqhxwiowKvVW0FO4q6+Wty1FDtMT1PmurCrqnR6mfVplcCwKAJhR6SjwSkof7Z1Loq59S4B8h5dzoDc0uXBqNufSWCwBMcHhJXHpLG5c1Ai3y4bHtulf7K3G4si5dDZ+jwtXXDgTG7Z36u4YI+fS2Vw1txbeChs27tLvPr8ag6urdsBhFSgWlweCEREWgaG+RnEukbhkCBSLy44GAFsYY7sBfADgOc757wB8H8D1jLHDAK5TvgeA5wEcBXAEwEMAvgwAnPNBAN8BsFX5823lMSjPeVj5nY8BvJDDesuOWEKCzVreWXBBYLhnxRzsPxPEux/rfzcwE9RYnB5RRIvA4PO4yLmkoG5bPWJaFoGh0e2kculp6B+NoVZHR03AQz1i5zIwqjiXdBY7zIhTYyxuKBzTtW8JkDuXAExZ1my2wt9hxZHncdF+nYrTZsHtl/jxwr7u5DbSCzUuRE6a81nX3ozOoXG8f2xw+idngN43YEodm0XALUub8MqBHt0GpqhxzxqnDTUuG8Xi8oAqeqgT+uIGxHgJIBSVtzPF4jKAc36Uc96q/FnEOf8H5fEBzvkqzvk8zvl1qlCkTIn7Cuf8Qs75Es75tpTX+innfK7y52cpj2/jnC9WfuervNDWlSKj3DuXVG6/xI+ZlXY8tPloQdehp7sGkG3A5FySERMSrALTrTjT73GRyJEGSeIYHItipo6OmtoqB+wWgbquUhg0YHKZWZFjcdN37w2OxXTf3k1u+Q7vVL1LTtOJSzS5bCrWdzQjFpfw2536tjgMh0VUO626xj3LhRsXNaLaYcUT2/V1jMnOJfNdGKZjdasfEVHCy/t7dHk91alU47LB7bIlxSbCOFRxyYxxrXwSisidS1UkLhGlQkLikLh+Qkcx47RZ8PlPzMbrh/pwpDdUsHXoGYsDVHGJLsQB+QRDzzuyfq+LOpfSMDwuQuL6dgEJAkOTx0miXgoDY+Rc0guXzYJYQkr2s02FPMVMX9GjMSkuTb5vmy0WNxQm0XQqLvbVYGnAjUe3ntI1yj8yLpKYNwUuuwWfbm3CC3u7MaqTo4ZzTrG4SeiY7YXP7dRtapzasVSjOGlGqHPJcEIREdUOGxxWC+zW8r+GLARiQkJElFDtsKLahCIe7VUlit5CR7Hz+ctnw2EV8PDmYwVbg56xOAAIeCvQG4pqKqktd/pCUd0jWt3BSPLfCXE2A6NyF9DMKn27gPweEvVSGRqLocJuMV3hsxGoAk4kzeRQzrkhnUvVDisq7ZYpnUtmE5eo/yc9dy5vxsHuEPaeHtHtNUnoSM/a9gDGxQSe33NGl9cbiyWQkDht83MQBIZbW31466M+DI3lHv08z7lEnUuGExyPJwWPGhMKH/lALUmvolgcUUrElItmM8TiAPki+I72AJ7aeRp9oWhB1qBmzF06RSACNNUsSf9oDHXV+gkdPo8LEp+6I8Xs9CtdQLU6X4T7PS7qukphcCyGGeTu0AU1epaudykcSyCWkHTvXGJM7nGb6nii12dCqZDsXKLJZZNya6sPTpuAR7fqF9MaDseo4yoNy2Z5Mae2Ehu26zM1Tu3+IXHpfFa3+RCXOJ7fl7uQp8bgqp1W1LhsU07kJPQjNSlgxshWPlBL0qudNlQ6zHV+AJC4VLKIcX37f0qBu69qQSwu4X/eO1GQ91cnBdXrJIIEvBUAgFMUI0JfKIo6HV00fhLu0jIwZpBzyetCb2jqke1mY4DEJd1IOpfSOD2THVcGiB5Nbhc5lxSGwnThnY4apw23LPFh064uhGP6xHyGx0W4KRY3JYwx3NEewAfHB3G8fyzn1xuhfXxKLm6qwYV1ldi4K/doXDASh9MmwGG1oMZJ2zofqJ1LgCx+EPoTisrHjyqH1ZTxQ3P915YRaq7cTFNqLqyrwnUL6/G/750oSJSsJxhFhd2im9LfPEMWQMxe6s05l8UlHZ1LfnVyGYlLk5KcYqZjFBGQtzvnU3fTmA1yLumHKuCMpzn2G9kF1ETOpSTDYRFVDqvpTpgz4a5LmzEajeP5vd26vB5NLpueO5YFIDDgyR25u5doOt/UMMawps2PrccHc76BFxwXk6IS7d/5IRSZ2OZm7APKB6pzSY0dms0hRmcGJYp6kttQ4yzwSvLLPSvmYHAspsvJQ6b0hqKor3boNtGsvtoJm4WZvtR7LJbAuJhArY4uGp8qLpl8207FwFgMjOnv8ChHUe/08DgefPEQdp0azvh3SVzSD5ddPl1JF4tTHTUzKvW/SGlyO9Ebikw6ttlszqXhcIzKpaehY7YXc+oq8djWkzm/Fuccw2GROq6modHtxIp5dXhyeycSOY5Xp1hcela3+sA58Oye3NxLqRGtGprMZziSxBGKTjiXzCZ65IvUWBxgvu1M4lKJ0h2UxSV1io1ZuKxlBpb43Xhk8zFIOZ48ZEpPMIJ6HcU8i8Dg89DEOLVDS0/nktNmQW2VvaxEDj0ZGI1iRoVd97HWahyxHES9EwNj+MsNe/DJf3od//H6EfzkrY8zfo3BsZju/T9mxanFuWRgLK7RLfe4Tdb5ZzpxiSaXTQtjDHd2NGPr8SEc6R3N6bXGYgnEJU7bXANr2wPoGong3Y8HcnqdIIlLabmgthKtAXfOU+OC4/Gku4O2tfGMxeLgHCnOJdrmRjCqxuKUfdtspd4kLpUoPUFzOpcYY7hnRQuO9o/htYO9eX3vvlBU9+0d8LpMH4vrH9VfXAKoXDodA6Mx3SNxgNxLw1hpO5eO9IbwZ4/twjUPvoGnd53G5y6bhRXzarH7VGaTn8YVR94MA7azGdESizO2c0k+9k/Wu2Q1UfchIMcPjdjG5cZnlgVgFRie2JZbsTe5aLRz/cUNqHFa8cR22uZGc2urD/tOB/FxX/bi6VnOJRI6DGfCUWM9629CX87bzoq4ZJYhXOb4ryxDukeicNksphwj+aklTfC5nXho89G8vSfnXHYu6SyABDwV5FwywLkEyNG4UhY5jGRgLIqZlfpubwCwWwXUVztK0rm0vyuIr/xqB67/17fwu33duPuqFmz5+jX41prF+ORFdTg9PJ4s9ddCsjSdYnG6oPYaRdLG4mIQmDE9KY1Jcan09m29GQ5T/48W6qoduG5hA57c0ZnTkAN1Op+bpsVNi9Nmweo2H363rzunsfYj4yIsAjNdnCUTbm31gTFgUw7F3qFIPCkqUb+V8Zwb1yJxyRjU7aweP6pMtp1JXCpReoIRNLqduvX/lBI2i4Dfv7IF7x8bxN7OzNwE2TIajSMcS6ChRmdxyetCXyhakILyYiEpLuk9uczjQtfwODjPb3yyFDDKuQTIol5XCV2A7zg5hHt+sRWf+uFmvPVRH76yci7efuBa/M0tFydjsG3NHgDIyL00NKb2/+gv4pkRrYXeHgPinsCEc2mqUm8zMUzOJc3cubwZ/aMxvHawJ+vXUCeXUSxOG+vamxGNS3h295msX2NkXESN02rKc2ytNNQ4cXnLTGza3ZX1eVZwXEx2LZFgbTyq4ErOJWMJReKwW4RknJ9icURJ0B2M6C50lBJ3XtqMKoc1b+6lXkUAqa/WORY3o/wKkDOlLxSFRWD6l0t7XYiIUjIqQ0zQPxrVtUA9Fb/HVfTOpYiYwIbtnVjzH1vwmf96B1uPD+H+6y/ClgeuxddunH9eCfcinxsWgWHXqSHN76E6l4wolzYjmsSlMRFegy7A3S4bXDYLxtI4p8yAJHGMjBu3ncuNqy+qQ2ONE49tzT6mpUa0SFzSxtKAG/Pqq7Ahh2jcyLhIThoNrGnz4Vj/GPadDmb8u5xzORbnpFhcvghFzp6CSJ1LxhCKiGcJd2ZzQJK4VKJ0j0TQaLK+pVRqnDbctbwZz+09kxdhRu24qtfduVQBADg1aN7epb5QFDMr7RD0Lpcuw8llehCLSwhG4obFtfxeF7qGI3kv3NfCwGgU//bKR7jy+6/ha0/sxmg0jm+vWYS3H7gWf7xq3pR3Tl12CxY0VmfkXFJFTXIu6YNTicWlmxY3OGaco4YxlnQvmZlgRITEATc5lzRhERjWdwTw5kd9WY9tH6b+n4xgjGFdRwA7Tg5nXaY+Mk7RTy3cvLgJNgvDxl2nM/7diChBTHCaFpdHzu0CMpvokS9Go/GzonBmc4iRuFSCSBJHbyiCBpOf6P7+VS0AgJ+/fczw9+ozyLnUrIhLZu5d6h+N6t63BMjxLKA8JpfpiSp6zDTIuRTwuBBLSMmi9mLgaN8o/ubpvbji+6/h3145jLZmD351z2V45f5P4gufuEDTCVZrswe7Tw1rFs0mxCW6CNcD1bmULkI8FI7Ba+D2Ntt01skYViJa5FzSzrqOZkgc2LC9M6vfV7e5hzqXNHPbJX5YBIYnd2S3zUlc0oa7woZPXlSPZ/Z0IZHhDSU1okWTy/JHkAq980IoEj9r21ba5a/jUvbde6UEiUslyGA4BjHBTe1cAmRnyqeWNOHRD04lrZ5GMTGdT98L8vpqB2wWZmpxqc8gcSngJefSZKiij1Gih1/Z7p0F3u6cc7x/dAD3/XIbVv3gTTyxrRO3X+LHK/dfjXmMORQAACAASURBVEe+tBxXzq3NqE+jrdmDUDSOo/1jmp4/OBaDVWCmHLpgBDaLAKvApu1cmmGgo4bEJXkbA8ZM5CtXmmdU4Kq5tXh826msHJ0j4yLsVgFOG52ya6W+2omVF9XhqR2dGYsegNoFRGKHFla3+dATjOKDY4MZ/V5wXI1oyZ+RRnTlEWeT3OYk6BnKaCSOasfEtlVdTEVo6DcE+qQqQdRCUbOLSwBw74oWhKLxnPoMtNATjKLCbtHdQioIDH6PC51D5o7FGdH/43bZUGm3kLh0DgOKo6bWwEJvAFlHQHJlYDSKn7z1MVb94E3c+ZP38MHxQXz1mrnY8sA1+P4dSzG3vjqr171EKfXedWpY0/MHx2QXDRXC6ofLZsF4bPI7f5xzuXPJQOcSxeJSIlrkXMqIO5c3o3NoHO98PJDx746Mx+B22ehYkiFr2wPoCUax+XBfxr9LziXtXLewHhV2Czbtzmxq3LnOJcJ41KJph1W+/CfnkjEEI+LZsTiTxQ9JXCpBki4aOtHF0oAHl7bMwM/ePo54wji7YW8oivpqhyEndwFvhWmdS5LEDYvFMcbgK4Fy6XwzoDiXjIrF+QsURzzaN4q/emovPvH91/CPzx+Et8KOf1q7FO88cC3+/Ib5OUda59RVocphxW6N4tLAWMywXiuz4rRbpnQujcUSiCUkQ+NajW6XYa9dKgyTcykrbljUAE+FDY9uPZnx7w6HRXhI6MiYVQsb4K2w4YkM44iccxKXMqDCbsX1Fzfg+b1nEItrPw8PjssRrVSHGLmXjEUtmlavZUhcMobR6DmxOBKXiGKnO0jOpVTuXTEHp4fH8fy+bsPeoycYSY4l15uA12VacWlkXISY4KgzSujwusi5dA4TnUvGXBxWO22ocVrzst3DsTg27e7C3T/filU/eBNP7ujE2vYAXv6zq/HkH12B9R3NqLDr86FuERiWBtwZOZeob0lfXDbLlJ1LQ8p+bahziT5zMTSm9v/QhXcmOKwW3H6JHy992JPcV7UyMi7SpLgssFsFrGnz4+UPe5KiqBbCsQTiEidxKQPWtPkwMi5m5BKbcC5NfEZTjNxYzu0ColicMYQi8bPcSlUm269JXCpBekYiYAyGuD1KkVUL6jGnthIPvXUUnBsTaO1TnEtGEPC60D8aTVtUW66o/T9G7ct+j6tg8axipX80BrtFMNSm6/dWGOZc4pxj+4kh3P/YLnR89xX88W92Yl/XCL6yci7e/str8Y+3L8G8huyib9PR2uzBgTNBTf9Wh0hc0p204pJy8Whk51KTh8Sl4XERjIH6aLLgzuXNiCUkPL0zs8law2Fy0WTL2vYAYgkJz2QQ2VJFD9rm2lkxrw7eChs27spgOyc7lya2M21zYwlGxLMEpQplUAahH5xzxbmU0rnkMNd2JnGpBOkORlBb5YDNQv/7ALm36A+uasHe0yN4P8NCQS1wztETjKDBMOeSeSfGqVP4jBKXfB4XhsIiwrG4Ia9figyMRjGzytguIL/HqatziXOOj3pC+Nnbx3DLD7fgjh+/g5f392BNmx+/ufdyvPPAKnztxvmGC+5tzR7EJY4Pu4LTPneAxCXdSReLG0w6l4y7OGmiWByGw3L/D8VXMmdBYw1amz14bOupjG6EyREtOpZkwyJfDRY0VmcUjRsZJ3EpU2wWATcvacLL+3s0n2+dO7kMINHaaEKReLJAHZCvnwh9GRcTSEj8rP26ymGu/ZrUiRKkOxilSNw53LEsAG+FDQ9vPqr7a49G4wjHErpPilNRp5qdMmGpd5/iXDKi0BtImRhnQuFuKgbGYoZF4lT8Hn3iiMf6x/C1J3aj/buv4IZ/fQvfemY/JM7xD7cvxnt/vQrf+8wSfOLCmXm70G3TWOotJiSMjIskLumMyyZgPDa5uKSOazeyC0hLn5NB5tmiYYj6f3LiruXNONQT0hyvBSgWlwuMMazraMaezhEc6g5p+p2RMIlL2bCm1YdxMYGX9/doen5wXITTJsBhnXB10DY3llBEPGuKGaE/IUU0TY3CUSyOKHp6Roxz0ZQqLrsFn798Nl450IuP+0Z1fe1exV2TayHwVJBzydhYHADqXUphYDSKmZXGOnz8XhdCkXgyXpApx/vH8BdP7MZ1P3gTz+05g5Xz6/DPa5di89evwQt/sgKfu2x2QQoSG2qcaHI7py31ViNaVOitL+licapzyUhBL53bT/1RtMzjzcPhGDxU5p01t7b6UGG34PFt2ibcigkJo9E4XXTnwG1tPlgFhg3btW1zci5lx/ILZqDJ7dQcQQxGxPMmxanfxxNlrtIXiHM7lwj9CSUdeSmxOJ26P0sFEpdKkO5gBI1u6ls6l89/4gLYrQIe2XJM19dVp/PVG+Rcqq92wG4R0GlG51IoCrtVMKzE0e8lcelc+kfz4VySBdNMHGPBiIjHt57C+v9+FysffAObdnfhS1dcgLe+fg1+sL4N6zqa0TyjouDjuFsDnmldB2rpsZHl0mbElSYWNxSOQWCFG2vtUrorplpfuTAcJhdNLlQ5rPj00iZs2tWFsej08SFV6KBtnj0zqxy4dkE9nt7ZBVHDVGESl7JDEBhubfXhzY/6NBWoB8fj58Xg1MiWGpkj9CU4LlL00GBCyk3V1F7TSupcIoqZiJjAyLhIsbhJqKt24PY2P57c3pkct64HfQY7lwSBwW/SiXF9o1HUVTkMEwzqq52wCoxicQqccwyMRQ2LIar4lOLjdGXqETGB1w/14h+fP4DV/7EFbd96CV9/cg/6Q1H8xY3z8dbXr8E3Pn1x0Q0uaJvlwcnBcNIpMxkDY/Ixg2Jx+uK0pheXPBX2gnVImEZcGo8ZGj00A3cub8ZYLIHn9pyZ9rkkdOjD2vYA+kejePPQ9NPMRiYpmia0sbrVBzHB8YKG6c2yc+nsG4vqNlfLvgn9SEgcY7EEOZcMJjRJl5jVZB3JtIeVGN0jsouGYnGTc8+KFjy27RT+972T+JPr5unymqpzyajOJUDuBjKluBSKotZA8cAiMDS69S2XLmXCsQQiomR4XGsyx1hC4jjUHcL2k0N466M+bDncj3ExAbtFQFuzB1+9Zi5WLqjHJc2egruT0tEakHuXdp8axjUL6id9jio8GR0/NBtOuwXjscmdB0NjoqZOJL2QzilXciriUniKTqhyYXiMnEu5smyWF3Prq/DYtlNYv7w57XOHqf9HF65ZUI+ZlXZs2N6J6y5uSPvcoDIR0ciJquXKIl8N5tRVYuOu0/i9S2elfW5wXDzP3as6T2MaHGZEZoxOEtci9Gc0StuZjpwlRrcidDS6SVyajHkN1Vg5vw7/895x/OEn5yRP+HOhJxhFhd2CKgNPNAJel+YSxHKiLxRNdk4Zhd/jSuugMRMDo4roYbBzqbbSAbtVwI4TQ4jFJWw+3I/tJ4aSH7p+jwtr2wNYtbAel8+Zqcu/03yxNOCGwORS7+nEJXIu6ct0nUv52N6eChuGw+J5zjWXXd6Hp1pfOSAmJISicXhocllOMMZw1/JmfPe5AzjcE8K8huopnzsyLu9n1HOVGzaLgNsu8eOX7x6f9lgxMi53AdEkrcxhjGF1qw///uphdI9E0l6rBCNxzJ5ZedZj5BYzDrUDk5xLxqLG4sxW4p2KuXxaZYDqoqFY3NTcu2IO+kdj+O3O07q8Xm8oivpq46JbgFzq3T8am3ISUrnSPxo1PPbk97goFqfQr8S1jO5cEgSGgMeF3+7qwnefO4BTQ2HcdokP/3pnK976i2uw5S+vwXduW4yV8+tLSlgCgEqHFfPqq9P2LqnCAzk89MVlk2Nxk41xHwrnJ66lfvaeGTn7mOKwyqdTEbF877ircSFvJe3XuXL7JX7YLAyPbU1fMp3sXKKL7pxZ2x6AmODYuCv9ueHIuEhOsRxY3eoD58Cze9IXe8v9P2dfgNN2Nw5VXCpUL6FZmCwWZzZIXCoxkhEtci5NyRUXzsTCpho8vOUYJCn3iRM9wQjqDRbzAskYkXlKveMJCQNjMePFJa8L3cGIpiLPckd1LtXmIa713dsW43ufWYItf3kNXvvzlfjubUtw+yUBzJpZ+FLuXGlr9mB35/CkIgcgi0tulw02k+XsjcZltyAhcYiTTBLKl7iknjCeG38TSnyf1oJa0ksXgLkzs8qB6y9uwFM7TyMWn/qzSY3FkVCdOwubarDYX4MntnWmfR6JS7kxp64KS/xubEozNY5zjlAkfp7QUWewq9rMqKLHVAN0onSOrAuDYzEwBlSabEJcKnTmW2J0j8gRLcqCTw1jDPeuaMGR3lG8+dH05Y3T0ac4l4xEFZdOmchhMxiOgXPkxbkk8Qlh1swM5sm5BABXzK3F7106y/DYYyFobfZgOCzixMDkYvDAWMzwXisz4pyiNJtzLncu0TY3lCFF6KBCb324c/ksDI7F8MqBqSPxqrhk5v4OPVnX3oz9Z4L4sGtkyueMTOKoITJjTZsPezpHcKx/bNKfR0QJsYR0XgxuXkMVAODPr7/I8DWajYNnggCmPudW41xE9kgSx3N7z2D57BmwmDhWS+JSidETjKCxxlnyd/6N5tNLfWioceChzUdzeh3OOXqCEcML1NUL8M5B8ziX1Cl8dQYLHT6P4gozkXA3Ff2j1AWkB23NSql35+TRuMHR/PT/mA11Itu5vUZjsQRiCQkz8hjXSujgii01hklc0pWr5tbC53bi0TTROLn/x2rqCxU9Wd3qg90iYMP2yd1LksTRE4xSr1iOfHqpD4wBm3ZN7l56YZ88KXH+FH1j5NTTF0ni+MW7J9Da7MHc+qqzfmZVji1q4TeRPVuO9OPEQBifuzx9mX25Q+JSidGdB6GjHLBbBXzpiha88/FA2jtU0zEajSMcSxg6KQ6QrcB2q2CqiXFJcSkPsTgANDEOciyuymEtuZ6jYuOihiq4bBbsPDm5uDQUjpGLxgBcdvmU5dxuuqGx/JUeX6AU0L5/bNB0E426lZ4puvDTB4vAsK6jGZsP9036+RRPSPjg2CANcNERb6Ud111cj427uiaNI/5212mcHh7HDYvST5Qj0tPoduKylhnYuPv0efHxhMTxH68dwcKmGqycX1egFZqL1w724lj/GO6+quU8c0KF3RyTTvPBr94/gRmVdty0uLHQSykoJC6VGNNNXyAm+Oyls1Bht+Dhzceyfo1eRQCprzZ2m6sFyKYUl6qM3bZ+xblEE+OAgbFoXiJx5Y7VImCJ3z2lc4liccbgmiIW9/g22fkxe4bxEcymlM/f7zyz3/D3KxYGRqP44WtHML+hOukGJXJnXUcAAPDEtvPdSz9/5zj2nwniT1ZRREhP1rU3Y3AshtcO9p71eERM4J9fPISlATduXeor0OrKh9WtfhztG8OHXcGzHn92TxeO9o/hT1bNpRRGnnhkyzH43E7cPInooU5FnKpDktBG90gErxzoxbqOABxWc9/ALXpxiTF2E2PsEGPsCGPsgUKvp5BIEkdviJxLWnFX2LC+oxnP7O46b7KPVo72yXnxeoOdS4DssOkcMk8sTs3i11YbexHutFlQW2U3vXOpa3gcO04OUWGmTrTN8uDDruBZd7855/jXlz9CXyh6nvWcyJ3JOpdePdCDH712BOs7Ari0ZUZe13N0kj4RqQxP0Dnn+Kun9mIkLOLf7mqjiJaOBLwVuGpuLZ7Y1nlW1PLUYBj/8tJHWLWgHp9aYu674HqzYl4t6qsd50XjHtlyDGdGIvibTy1MXnAT2XPz4kbYLOysYu+ExPEjRaS+4WLar/PBh10jePfoAL54xQU0ZMRAHt16EgmJ47OXmjsSBwBF3VjHGLMA+E8A1wPoBLCVMbaJc26e24UKpwbDePClQxATHLPycHe2XLj7qhb88t3j+Pk7x/FXNy/U/HuSxPGzd47jn353EDMr7VjYWGPcIhUC3gq81NVt+PsUmnAsjn947gB+9f5JdMz2oiIPExV8BXaFcc4RjUvKnwRi6tdKqWVUTCAalyYej6d+n5h4Xlx+rvw78vfVTisu9tVgka8GCxprUDlJ2f/2E4P4w//ZgYiYwPc/s7QAW6D8aA14EItLONgdxNKAB5xzfO+Fg/jJW0exrj2A37+ypdBLLDuSnUuKff94/xj+9LFdWOyvwbfXLM7rXfA/XjUP//n6ESQkjtPD4aTg8sahPsz96+dhtwpwWAXlb8s53wuwWy0T31sEOGwC7BYBDptF/nuq507ymo4p3sOq04XEY1tP4aX9PfjbWxZiYZPxn4Vm467ls/CVX+/A20f6cfVFdeCc4xsb94Ex4Nu35Xe/NgNWi4Dbl/nx8OZj6AtFUVftQP9oFD9+42PccHEDLpszs9BLLAu8lXZcPa8Oz+zuwgM3LYAgMLyw7wyO9I7iPz57CQl4eeKRLcdQYbfgLhI9DCOekPDoB6dw9UV1mK1E581MUYtLAC4FcIRzfhQAGGOPAlgDoKzFpXAsjh+89FHy+6GwiE27T8MiMPzRygtxR7u/gKsrLZpnVOCmxY349fsnEZ9kfPVU7OkcxtbjQ1i1oB7fu2NJXvpTAl4XBsZi+PYz+1HO55KvHujBicEw7rt6Du7P00QQv8eFdz4ewP2P74IkcUgcSHAOzjkkaeLrhPIziXP5T8rPJC7fdeOcI6H8LPk8DuV15a/jihCkikV6dLMwhkkvIgfHYslCWMbkTpiLm2rQUOMEB0dElPDk9k74PE48et9lmFs/eYEmkRlts+RS7wdf+ghzaivRORTGKwd68YVPzMbf37qITpwNwKV0Q/z07eN4aX8P3jrcB4vA8OPPtee9R8zCGO67eg5+/MbH6BqOYNksL3adGobDKuDuq1om/u2rgnIiVUyWMDIuniUUqyKzKiLrYYASGJLHi/TCVMrjitBlFQRwcEgSxxPbO3HFhTPxBySYGsJ1F9djRqUd//d3B/HawV4MhWN441AfvvHpi5OxbkJf1rUH8N9vHsXXntiNltpK7O8KIiIm8MDNCwq9tLJidZsPrx7sxdc27EaVw4rXDvZibn0Vbl7cNOnz1ePe3z+zHx/3TT5p7uznaztQaj2cZnLc5RpfNbPX1Pg8za/J8czuLnzustlwu9J35f3i3ROIZzioIpuPqew+27L7QMzmvbL5naFwDN3BCL61ZlHmv1yGFLu45AeQGkTvBHDZuU9ijN0H4D4AmDWr9JXZqCjhNx+cTH4vCAx3LAvgT6+7iPqWsuDLK+fi/aODeCzNRJZzqbBb8E93LMW6jkDe7hpe2jID3gpbsj+kXGlyO/HovZfn9e7gqoUN2NM5gg+ODcIiMAiMQWBQ/mYQhJTvla8tys8Yk+90ql9P/P7E71gE+Wfq1xaBnScEpToTHFM4DtI93yqwSfdFzjnOjESwvyuI/WeCOHAmiL2nR/DGoV4IAgMDcPVFtXhwXWteCo/Ngs/txPILvNh5Ygi7Tg7BIjD8n2vn4v7rLyKngUE0eyvg97jw/tEBfMCAKocVP/q9S9CcRzfvdRc34IevHcGqhfWISxw/fuNjXLugHhExgZ++fQwbv3olFuTodOWcIy7x8wWqpLPx7O9T/47FE+cJ22e5Hc8Rs0ajcQyMyt+nvo+Y4Mlj2qwZFfiX9a0kmBqEw2rBPSta8OPXP8apwTAEgeGGixvwpSsuKPTSypa59dW4cVED3jkygJ3K8fv+Gy7CnDqKM+vJ9Rc34MK6Sry8vwdWgcFmEfDNWxdNGa2tUpzXCYnj6Z2nNb2H1o/bTI5eWj/DM3tNzc/U9fX8HhfuvmrqGwNfvWYuvvvcAQDAix9mmp7I7jMhm1OkbD99snuvzH/pspYZWLWgfsqfr2nzYeMU0xPLDVbMBV6MsbUAbuKc36N8/3kAl3HOvzrV73R0dPBt27bla4kEQRAEQRAEQRAEQRBlD2NsO+e8Y7KfFXuz12kAzSnfB5THCIIgCIIgCIIgCIIgiCKg2MWlrQDmMcZaGGN2AHcB2FTgNREEQRAEQRAEQRAEQRAKRd25xDmPM8a+CuBFABYAP+Wcf1jgZREEQRAEQRAEQRAEQRAKRS0uAQDn/HkAzxd6HQRBEARBEARBEARBEMT5FHssjiAIgiAIgiAIgiAIgihiSFwiCIIgCIIgCIIgCIIgsobEJYIgCIIgCIIgCIIgCCJrSFwiCIIgCIIgCIIgCIIgsobEJYIgCIIgCIIgCIIgCCJrGOe80GvQFcZYH4AThV5HDtQC6C/0IghCB2hfJsoV2reJcoT2a8IM0H5OlCO0XxP5ZDbnvG6yH5SduFTqMMa2cc47Cr0OgsgV2peJcoX2baIcof2aMAO0nxPlCO3XRLFAsTiCIAiCIAiCIAiCIAgia0hcIgiCIAiCIAiCIAiCILKGxKXi4yeFXgBB6ATty0S5Qvs2UY7Qfk2YAdrPiXKE9muiKKDOJYIgCIIgCIIgCIIgCCJryLlEEARBEARBEARBEARBZA2JSwRBEARBEARBEARBEETWkLhEEETWMMZYoddAEARBEAShQucmBEEQhYHEpQLAGKPtThAEUcQwxvyFXgNB6A1jbDVj7MJCr4MgCIIgiPKDRI48oZzQ3V/odRCEHjDGbmKMbQTwHcZYR6HXQxB6wRi7jjG2HcD/W+i1EIReKPv1uwAeAdBU6PUQhBEwxm5ljP0GwAOMsdmFXg9B6AFj7DbG2HcKvQ6C0AKJSwbDGLMyxv4SwA8BPMgYa+OcS4wxS6HXRhCZwGScjLGfA/hbyBcpVQDuZozVFnRxBJEDyr5tZ4z9F4AHAXyHc/6N1J8XbnUEkR3Kfl3FGHsG8jH7bwG8B2C28nM6ByTKBsbYdQC+AeAXAKwA/g9j7BblZ7SvEyUHY0xgjN0D+bzkAcbYikKviSCmgw62BsM5jwM4BGABgPsB/LfyeKKQ6yKITOEyEQAbAXySc74JwFMAGOe8v7CrI4jsUfbtGIAKAL/lnP9WOalrVX9e2BUSROYo+/UogP/lnK/knL8K4EUAa5SfSwVdIEHoy3UAnuWc/w7yuXY1gD9gjFXSvk6UIsp+exjAJQC+DIDcS0TRw+icWX8YY38MwAdgB+f8ccaYjXMuKj87BuBvOOe/Tn2cIIqVc/fnlMfXA/hPAB8C2AzgRc75lsKskiAyJ2Xf3sk5f0zpovkJgJ2QL1ROATgD4EnO+YuFWylBaCdlv97OOX8i5XEBwO8BWAbgrznn0QItkSByZpJz7dUAvgJgDec8whj7d8guvVc55z8q5FoJQiuMsbUATnHO31e+T72G3Arg/+OcP8IYE0g0JYoRci7piGJB/zMAdwLYBuBbjLEvAfCmPO1+AP8MACQsEcXMVPszY6xBeUovgGshX4R3AfgSY6yuMKslCO1Msm//PWPsbs75xwB+C9lpeieAzwLYB+B2in4Sxc4k+/W3lWN2HZC8C34MwC0kLBGlyhTnJl8EcBDyucjjjLHXAdRAdlpXUyyOKHYYY/WMsTch16j8Vco+G0/5+u8A3M8Y85KwRBQrdLDVESU6cQ2Av+WcbwDwZwCWArgx5TlPA/iIMfY1IJkRJ4iiY4r9uRXATcrP3+Cc71Win3shR4rGC7VegtDKVPs2Y2y9cof7Ls75Ic55CMAuyBcp4cKtmCCmZ7pjtvKcdwB0Ki4Pgig5JtnP7wfQBnlfvwfANwE8yDn/fQAxAC10IU4UO5zzXshi6E2QHdN/qPyIKV29jHP+AoADAO5jjFUzxtYVaLkEMSUkLulEiqq8DcAKAFBy34cBLGKMzU95+h8B+CfGWDcAGndNFB1p9uePACxkjF10zq/cAFlYInGJKGrS7NsHALQzxuYrPTUq10MWliJ5XShBZMA0x+xFjLEFyvNqIDs8yDlNlBxT7OcvQN7PlwOYyznfyf//9u4u1LK6jOP499eMmeGMFZFOTJYWSJRijlkUpkUWaEIYwSA61EVhkXojaVZiTdFVkkVJL6aivYIU9nIhhek4mjONlYrROJVmY2GabyEMOvN0sdZ2jnTOzDn7HPbaZ63vB+Zi77X38Gz4nf85+1n/l6pftK9bB9wx8UKlBZiR668B9wI3AqclWdM2ll7A3u/sFwJfovl+edjEi5X2w+bSmEanvY1OEZpxV2QHzRTco9vHNwOH0GwsSJJjgW8D1wPHVdU1k6xbms0YeV7dnq51dpK7gNcAn3Kjek2bBWZ7NXvH6vVJ7qHZs+Ni73xrmowxZh/cvu5JYC1wKNKUW2DOV7F3/D41yRaa8fv6iRYt7cdcua6qZ9rVALfR3AQ4b3S9qna3+0JeQbN8/zj3EtM0srm0QEnenuQa4DNJXjY6RSjJAe1LtgDPAu9JsrKq7qWZnXR8e/1R4ONV9cGqemjS9UszLSLP69rTtR4EPlZVG9opvdJUWIKx+gHMtqbMEuQammWfV0+ybmkhFpHzN7fX7wPOqaoPVNVjk65fms0+cr1i1GhqPQLcAByVZG2Sl7ezTh8BPlFVZ/gdUtPK5tICJDkS+AZwE83dkI1JToW9m3NX1Q6a6bqvBS5q37qL5osKVfVgVd094dKl/7NEef5NVW2ecOnSPi1Rtm+vqk0TLl2a0yJzff/o/6kql3hqai1Fzqvqvqq6c7KVS3PbT653V1UlOTDJge3jW2hOY76H5kTmQ6vqiara3tVnkObD5tLCnAD8qb3jdwHNRq+nJ1kDkOQLSa4EttHs9n9Ckm3AfwCPsda0WUyeb+ymZGleHKvVR47ZGgLHb/XR/nL9eeA7wOjxOTSben8TOKaq7uuiaGmhVnZdwDRLcjpNd/l3VfVbmmm45yY5vKr+nmQzzV2T9Um2AkcCl1TV/e37zwRWVtXj3XwCaS/zrL4y2+ojc60hMOfqozFy/Tpm5JpmX7G3tbP0pGXDmUuzSLImyc+ATwIvBa5K8t6q+itwOzA6+vHPNFMWVwN3V9WZVbUj7a7/VfVff9mpa+ZZfWW21UfmWkNgztVHS5DrFQBV9SsbS1qObC7NKTJfmwAABElJREFU7nhgU1WdWFUbgcuBj7bXNgFHJ3lLNSdj7QTeUVVPQHOcZHmqkKaLeVZfmW31kbnWEJhz9dFic+2py1rWbC61kmxIcnKSA4FfA9fOuPwoMNpA7Q7g98BlSQ4G3gA8kOTF8LxjUqXOmGf1ldlWH5lrDYE5Vx+Za2mvQe+5lCTAYcD3gT3AX4CPAOdX1T+THNCeTLGGZmojVfUv4PIkrwa+S7OedkNVPd3FZ5BGzLP6ymyrj8y1hsCcq4/MtTS7wTaXkqyoqt1JVgE7q+qsdp3rV4BvAWfQDBYAp9BMayTJK6rqYZq1tAdV1VMdlC89j3lWX5lt9ZG51hCYc/WRuZbmNrjmUvvDvxFYkeSXNBup7QZoB4rzgYeSnFRVNyd5IfBvYHuSLwLvS3JyVT0GOCioU+ZZfWW21UfmWkNgztVH5lrav0HtuZTkJGAbzfTEHTQDxDPAO5OcAM+td70U+Fz7thcBH6JZQ7sKeHc7KEidMs/qK7OtPjLXGgJzrj4y19L8DG3m0h7gy1V1LUCSNwFHAJcAVwDr0hxt+lPgXUnWAq8ErgMuq6o/dFO2NCvzrL4y2+ojc60hMOfqI3MtzcOgZi7RdJx/3E5rBNgMHF5VV9NMcTy37TqvBfZU1T+qaktVbXBQ0BQyz+ors60+MtcaAnOuPjLX0jwMqrlUVU9X1a6q2t0+dQrNWliADwOvT/Jz4Ac0g8joNABp6phn9ZXZVh+Zaw2BOVcfmWtpfoa2LA54bkO2Ag4Fbmiffgq4GHgj8Leq2glQVdVJkdI8mWf1ldlWH5lrDYE5Vx+Za2nfBjVzaYY9wAHAI8Axbaf5szTTGG8dDQrSMmGe1VdmW31krjUE5lx9ZK6lfchQm6pJ3grc1v67qqqu7LgkaWzmWX1lttVH5lpDYM7VR+ZamtuQm0trgbNpdvDf1XU90mKYZ/WV2VYfmWsNgTlXH5lraW6DbS5JkiRJkiRp8Ya655IkSZIkSZKWgM0lSZIkSZIkjc3mkiRJkiRJksZmc0mSJEmSJEljs7kkSZLUgSSXJrmg6zokSZIWy+aSJEmSJEmSxmZzSZIkaUKSfDrJ9iS3Ake1z52X5N4kdyX5YcclSpIkLdjKrguQJEkagiTrgPXAsTR/g90JbAMuAo6oql1JXtJhiZIkSWNx5pIkSdJknAj8pKqerqongRva5+8CvpfkLODZzqqTJEkak80lSZKkbp0GfB04DtiaxJnlkiRpWbG5JEmSNBm3AO9PclCSVcDpNH+LvaqqbgIuBA4BDu6wRkmSpAXzzpgkSdIEVNWdSX4E/BF4GNgKFHBdkkOAAF+tqsc7LFOSJGnBUlVd1yBJkiRJkqRlymVxkiRJkiRJGpvNJUmSJEmSJI3N5pIkSZIkSZLGZnNJkiRJkiRJY7O5JEmSJEmSpLHZXJIkSZIkSdLYbC5JkiRJkiRpbDaXJEmSJEmSNLb/AU+YSuhKa0pSAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5fZPz4-FuuPo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "import itertools\n",
        "p = d = q = range(0, 2)\n",
        "pdq = list(itertools.product(p, d, q))\n",
        "seasonal_pdq = [(x[0], x[1], x[2], 12) for x in list(itertools.product(p, d, q))]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVPmK7_Tuykc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e2dc02c4-5b9f-497d-cfc2-8c5adad72266"
      },
      "source": [
        "import statsmodels.api as sm\n",
        "for param in pdq:\n",
        "    for param_seasonal in seasonal_pdq:\n",
        "        model = sm.tsa.statespace.SARIMAX(y,order=param,seasonal_order=param_seasonal,enforce_stationarity=False,enforce_invertibility=False)\n",
        "        results = model.fit()\n",
        "        print('ARIMA{}x{}12 - AIC:{}'.format(param,param_seasonal,results.aic))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning:\n",
            "\n",
            "pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(0, 0, 0)x(0, 0, 0, 12)12 - AIC:8537.250031136558\n",
            "ARIMA(0, 0, 0)x(0, 0, 1, 12)12 - AIC:8204.596472030975\n",
            "ARIMA(0, 0, 0)x(0, 1, 0, 12)12 - AIC:8353.552405291965\n",
            "ARIMA(0, 0, 0)x(0, 1, 1, 12)12 - AIC:7918.284565018257\n",
            "ARIMA(0, 0, 0)x(1, 0, 0, 12)12 - AIC:8218.125541224268\n",
            "ARIMA(0, 0, 0)x(1, 0, 1, 12)12 - AIC:8182.85973001213\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(0, 0, 0)x(1, 1, 0, 12)12 - AIC:7992.116143917598\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(0, 0, 0)x(1, 1, 1, 12)12 - AIC:7905.465522928476\n",
            "ARIMA(0, 0, 1)x(0, 0, 0, 12)12 - AIC:8453.224400859988\n",
            "ARIMA(0, 0, 1)x(0, 0, 1, 12)12 - AIC:8130.608222411256\n",
            "ARIMA(0, 0, 1)x(0, 1, 0, 12)12 - AIC:8302.838437934946\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(0, 0, 1)x(0, 1, 1, 12)12 - AIC:7875.03601672245\n",
            "ARIMA(0, 0, 1)x(1, 0, 0, 12)12 - AIC:8176.233032331013\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(0, 0, 1)x(1, 0, 1, 12)12 - AIC:8121.03326535124\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(0, 0, 1)x(1, 1, 0, 12)12 - AIC:7969.545076406099\n",
            "ARIMA(0, 0, 1)x(1, 1, 1, 12)12 - AIC:7873.656027528029\n",
            "ARIMA(0, 1, 0)x(0, 0, 0, 12)12 - AIC:8529.32714785884\n",
            "ARIMA(0, 1, 0)x(0, 0, 1, 12)12 - AIC:8198.65360917797\n",
            "ARIMA(0, 1, 0)x(0, 1, 0, 12)12 - AIC:8448.94594329097\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(0, 1, 0)x(0, 1, 1, 12)12 - AIC:7946.576699237356\n",
            "ARIMA(0, 1, 0)x(1, 0, 0, 12)12 - AIC:8222.116508646743\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(0, 1, 0)x(1, 0, 1, 12)12 - AIC:8199.51508222582\n",
            "ARIMA(0, 1, 0)x(1, 1, 0, 12)12 - AIC:8040.485938997725\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(0, 1, 0)x(1, 1, 1, 12)12 - AIC:7926.566823056084\n",
            "ARIMA(0, 1, 1)x(0, 0, 0, 12)12 - AIC:8361.632851627157\n",
            "ARIMA(0, 1, 1)x(0, 0, 1, 12)12 - AIC:8043.778082617008\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(0, 1, 1)x(0, 1, 0, 12)12 - AIC:8299.281205040978\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(0, 1, 1)x(0, 1, 1, 12)12 - AIC:7849.341887202343\n",
            "ARIMA(0, 1, 1)x(1, 0, 0, 12)12 - AIC:8092.0084075305185\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(0, 1, 1)x(1, 0, 1, 12)12 - AIC:8043.878442212377\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(0, 1, 1)x(1, 1, 0, 12)12 - AIC:7948.648858586903\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(0, 1, 1)x(1, 1, 1, 12)12 - AIC:7851.257369080118\n",
            "ARIMA(1, 0, 0)x(0, 0, 0, 12)12 - AIC:8449.876315977825\n",
            "ARIMA(1, 0, 0)x(0, 0, 1, 12)12 - AIC:8127.714577149503\n",
            "ARIMA(1, 0, 0)x(0, 1, 0, 12)12 - AIC:8326.910461461694\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 0, 0)x(0, 1, 1, 12)12 - AIC:7887.382090256488\n",
            "ARIMA(1, 0, 0)x(1, 0, 0, 12)12 - AIC:8126.6719502674705\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 0, 0)x(1, 0, 1, 12)12 - AIC:8122.527266771205\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 0, 0)x(1, 1, 0, 12)12 - AIC:7938.722675946269\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 0, 0)x(1, 1, 1, 12)12 - AIC:7888.351749699074\n",
            "ARIMA(1, 0, 1)x(0, 0, 0, 12)12 - AIC:8382.097952538712\n",
            "ARIMA(1, 0, 1)x(0, 0, 1, 12)12 - AIC:8064.823957943412\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 0, 1)x(0, 1, 0, 12)12 - AIC:8304.577370250168\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 0, 1)x(0, 1, 1, 12)12 - AIC:7845.407529045582\n",
            "ARIMA(1, 0, 1)x(1, 0, 0, 12)12 - AIC:8088.917973506932\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 0, 1)x(1, 0, 1, 12)12 - AIC:8065.799375815624\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 0, 1)x(1, 1, 0, 12)12 - AIC:7924.735261687327\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 0, 1)x(1, 1, 1, 12)12 - AIC:7847.362989297835\n",
            "ARIMA(1, 1, 0)x(0, 0, 0, 12)12 - AIC:8475.908810708159\n",
            "ARIMA(1, 1, 0)x(0, 0, 1, 12)12 - AIC:8149.279107903309\n",
            "ARIMA(1, 1, 0)x(0, 1, 0, 12)12 - AIC:8409.863937987151\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 1, 0)x(0, 1, 1, 12)12 - AIC:7939.310642299919\n",
            "ARIMA(1, 1, 0)x(1, 0, 0, 12)12 - AIC:8149.225901727422\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 1, 0)x(1, 0, 1, 12)12 - AIC:8149.228592344309\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 1, 0)x(1, 1, 0, 12)12 - AIC:7996.483367336291\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 1, 0)x(1, 1, 1, 12)12 - AIC:7941.171464705169\n",
            "ARIMA(1, 1, 1)x(0, 0, 0, 12)12 - AIC:8359.976998644897\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 1, 1)x(0, 0, 1, 12)12 - AIC:8041.904735640861\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:512: ConvergenceWarning:\n",
            "\n",
            "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 1, 1)x(0, 1, 0, 12)12 - AIC:8283.074558788827\n",
            "ARIMA(1, 1, 1)x(0, 1, 1, 12)12 - AIC:7840.684719470208\n",
            "ARIMA(1, 1, 1)x(1, 0, 0, 12)12 - AIC:8065.623739545958\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 1, 1)x(1, 0, 1, 12)12 - AIC:8042.345635541777\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 1, 1)x(1, 1, 0, 12)12 - AIC:7929.299229751905\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA(1, 1, 1)x(1, 1, 1, 12)12 - AIC:7842.639536539859\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZavxBJqPu2tb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "7a051704-f5f7-4e91-ae94-fc9f7ebef520"
      },
      "source": [
        "model = sm.tsa.statespace.SARIMAX(y,\n",
        "                                order=(0, 1, 1),\n",
        "                                seasonal_order=(1, 1, 1, 12),\n",
        "                                enforce_stationarity=False,\n",
        "                                enforce_invertibility=False)\n",
        "results = model.fit()\n",
        "print(results.summary().tables[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning:\n",
            "\n",
            "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "ma.L1         -0.8027      0.060    -13.454      0.000      -0.920      -0.686\n",
            "ar.S.L12      -0.0255      0.156     -0.164      0.870      -0.331       0.280\n",
            "ma.S.L12      -0.9929      0.034    -28.818      0.000      -1.060      -0.925\n",
            "sigma2      4.249e+09    8.6e-12   4.94e+20      0.000    4.25e+09    4.25e+09\n",
            "==============================================================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_9yk6omrvAle",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "a577d688-6a84-4d20-9f40-ff422d57092a"
      },
      "source": [
        "results.plot_diagnostics(figsize=(18, 8))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fWstrFvhvF-x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "outputId": "3dab339a-cd98-4c26-81da-3674e9d21347"
      },
      "source": [
        "pred = results.get_prediction(start=pd.to_datetime('2020-3-31'), dynamic=True)\n",
        "pred_ci = pred.conf_int()\n",
        "ax = y['2020':].plot(label='observed')\n",
        "pred.predicted_mean.plot(ax=ax, label='One-step ahead Forecast', alpha=.7, figsize=(14, 4))\n",
        "ax.fill_between(pred_ci.index,\n",
        "                pred_ci.iloc[:, 0],\n",
        "                pred_ci.iloc[:, 1], color='k', alpha=.2)\n",
        "ax.set_xlabel('Date')\n",
        "ax.set_ylabel('daily_cases')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nlufkb6tvG-e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from fbprophet import Prophet\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "plt.style.use('fivethirtyeight')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-inbSk4OvNCk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "9d04c2e6-3be8-4ab8-d43f-22b1237b5993"
      },
      "source": [
        "def mean_absolute_percentage_error(y_true, y_pred):\n",
        "  y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "  return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
        "\n",
        "MSE = mean_squared_error(y_true=y['2020-3-31':],\n",
        "y_pred=pred.predicted_mean)\n",
        "MAE = mean_absolute_error(y_true=y['2020-3-31':],\n",
        "y_pred=pred.predicted_mean)\n",
        "MAPE = mean_absolute_percentage_error(y_true=y['2020-3-31':],\n",
        "y_pred=pred.predicted_mean)\n",
        "print ('MSE :',MSE)\n",
        "print ('MAE :',MAE)\n",
        "print ('MAEP', MAPE, '%')\n",
        "y_pred = pred.predicted_mean ;y_true = y['2020-3-31':]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE : 2805311490.076694\n",
            "MAE : 22768.27357591518\n",
            "MAEP 1562.863980757251 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xBloeR0vWRU",
        "colab_type": "code",
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          "height": 1000
        },
        "outputId": "626732f2-58fc-4343-8265-abea2a655393"
      },
      "source": [
        "y_forecasted = pred.predicted_mean\n",
        "y_forecasted.head(60)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ds\n",
              "2020-03-31    3141.117958\n",
              "2020-04-02    1685.702578\n",
              "2020-04-03    1667.196936\n",
              "2020-04-04    1698.684872\n",
              "2020-04-05    2028.490380\n",
              "2020-04-06    3352.296978\n",
              "2020-04-13    2017.710122\n",
              "2020-04-14    3176.900263\n",
              "2020-04-15    1613.805341\n",
              "2020-04-16    1525.269760\n",
              "2020-04-17    1526.955120\n",
              "2020-04-18    1790.185492\n",
              "2020-04-19    2920.030814\n",
              "2020-04-20    1503.060631\n",
              "2020-04-21    1487.223848\n",
              "2020-04-22    1519.593151\n",
              "2020-04-23    1843.606514\n",
              "2020-04-24    3134.550732\n",
              "2020-04-25    1837.136556\n",
              "2020-04-26    2968.511031\n",
              "2020-04-27    1449.420393\n",
              "2020-04-28    1365.699621\n",
              "2020-04-29    1368.286843\n",
              "2020-04-30    1630.591966\n",
              "2020-05-02    2779.853081\n",
              "2020-05-03    1361.901087\n",
              "2020-05-04    1345.996146\n",
              "2020-05-05    1378.342941\n",
              "2020-05-06    1702.504224\n",
              "2020-05-13    2994.287679\n",
              "2020-05-14    1695.924189\n",
              "2020-05-15    2828.009019\n",
              "2020-05-16    1307.794602\n",
              "2020-05-17    1223.950870\n",
              "2020-05-18    1226.515060\n",
              "2020-05-19    1488.843813\n",
              "2020-05-20    2637.609088\n",
              "2020-05-21    1219.682167\n",
              "2020-05-22    1203.778967\n",
              "2020-05-23    1236.126337\n",
              "2020-05-24    1560.283842\n",
              "2020-05-25    2852.045865\n",
              "2020-05-26    1553.706619\n",
              "2020-05-27    2685.773308\n",
              "2020-05-28    1165.587589\n",
              "2020-05-29    1081.746997\n",
              "2020-05-30    1084.311776\n",
              "2020-05-31    1346.639925\n",
              "2020-06-02    2495.417863\n",
              "2020-06-03    1077.490302\n",
              "2020-06-04    1061.587058\n",
              "2020-06-05    1093.934412\n",
              "2020-06-06    1418.092014\n",
              "2020-06-13    2709.854585\n",
              "2020-06-13    1411.514719\n",
              "2020-06-13    2543.581872\n",
              "2020-06-13    1023.395420\n",
              "2020-06-13     939.554748\n",
              "2020-06-13     942.119512\n",
              "2020-06-13    1204.447676\n",
              "dtype: float64"
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          "metadata": {
            "tags": []
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          "execution_count": 16
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    {
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          "height": 1000
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        "outputId": "48874276-2c07-49ac-8fac-eae00a5a169b"
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      "source": [
        "y_true= y['2020-3-31':].head(48)\n",
        "y_true"
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "      <th>2020-03-31</th>\n",
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              "      <th>2020-04-02</th>\n",
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              "      <th>2020-04-15</th>\n",
              "      <td>12322.0</td>\n",
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              "      <th>2020-04-16</th>\n",
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              "      <th>2020-04-17</th>\n",
              "      <td>14352.0</td>\n",
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              "      <th>2020-04-18</th>\n",
              "      <td>15722.0</td>\n",
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              "      <th>2020-04-19</th>\n",
              "      <td>17615.0</td>\n",
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              "      <th>2020-04-20</th>\n",
              "      <td>18539.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-04-21</th>\n",
              "      <td>20080.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-04-22</th>\n",
              "      <td>21370.0</td>\n",
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              "      <th>2020-04-23</th>\n",
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              "      <th>2020-04-24</th>\n",
              "      <td>24530.0</td>\n",
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              "      <th>2020-04-25</th>\n",
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              "      <th>2020-04-26</th>\n",
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              "      <th>2020-04-27</th>\n",
              "      <td>29451.0</td>\n",
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              "      <th>2020-04-28</th>\n",
              "      <td>31324.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-04-29</th>\n",
              "      <td>33062.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-30</th>\n",
              "      <td>34863.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-02</th>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-03</th>\n",
              "      <td>30.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-05-04</th>\n",
              "      <td>3588.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-05-05</th>\n",
              "      <td>49400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-06</th>\n",
              "      <td>236184.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-05-13</th>\n",
              "      <td>78055.0</td>\n",
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              "      <th>2020-05-14</th>\n",
              "      <td>81997.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-05-15</th>\n",
              "      <td>85784.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-05-16</th>\n",
              "      <td>90648.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-05-17</th>\n",
              "      <td>95698.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-18</th>\n",
              "      <td>100328.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-19</th>\n",
              "      <td>106475.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-20</th>\n",
              "      <td>112028.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-05-21</th>\n",
              "      <td>118226.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-05-22</th>\n",
              "      <td>124794.0</td>\n",
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              "      <th>2020-05-23</th>\n",
              "      <td>131423.0</td>\n",
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              "      <th>2020-05-24</th>\n",
              "      <td>138536.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-05-25</th>\n",
              "      <td>144950.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-05-26</th>\n",
              "      <td>150793.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-05-27</th>\n",
              "      <td>158086.0</td>\n",
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              "      <th>2020-05-28</th>\n",
              "      <td>165386.0</td>\n",
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              "    <tr>\n",
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              "      <td>173491.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-05-30</th>\n",
              "      <td>181827.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-05-31</th>\n",
              "      <td>190609.0</td>\n",
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              "2020-03-31    1397.0\n",
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              "2020-04-04    3082.0\n",
              "2020-04-05   46437.0\n",
              "2020-04-06  226713.0\n",
              "2020-04-13   10453.0\n",
              "2020-04-14   11487.0\n",
              "2020-04-15   12322.0\n",
              "2020-04-16   13430.0\n",
              "2020-04-17   14352.0\n",
              "2020-04-18   15722.0\n",
              "2020-04-19   17615.0\n",
              "2020-04-20   18539.0\n",
              "2020-04-21   20080.0\n",
              "2020-04-22   21370.0\n",
              "2020-04-23   23077.0\n",
              "2020-04-24   24530.0\n",
              "2020-04-25   26283.0\n",
              "2020-04-26   27890.0\n",
              "2020-04-27   29451.0\n",
              "2020-04-28   31324.0\n",
              "2020-04-29   33062.0\n",
              "2020-04-30   34863.0\n",
              "2020-05-02       3.0\n",
              "2020-05-03      30.0\n",
              "2020-05-04    3588.0\n",
              "2020-05-05   49400.0\n",
              "2020-05-06  236184.0\n",
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              "2020-05-25  144950.0\n",
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              "2020-05-30  181827.0\n",
              "2020-05-31  190609.0"
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          "metadata": {
            "tags": []
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          "execution_count": 17
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      "metadata": {
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      "source": [
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>-124714.848057</td>\n",
              "      <td>130997.083973</td>\n",
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              "    <tr>\n",
              "      <th>2020-04-02</th>\n",
              "      <td>-128665.123276</td>\n",
              "      <td>132036.528431</td>\n",
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              "    <tr>\n",
              "      <th>2020-04-03</th>\n",
              "      <td>-131132.772556</td>\n",
              "      <td>134467.166428</td>\n",
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              "    <tr>\n",
              "      <th>2020-04-04</th>\n",
              "      <td>-133507.529567</td>\n",
              "      <td>136904.899311</td>\n",
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              "    <tr>\n",
              "      <th>2020-04-05</th>\n",
              "      <td>-135543.721982</td>\n",
              "      <td>139600.702741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>-136548.194494</td>\n",
              "      <td>143252.788450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-13</th>\n",
              "      <td>-140175.788257</td>\n",
              "      <td>144211.208502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-14</th>\n",
              "      <td>-141276.740774</td>\n",
              "      <td>147630.541299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-15</th>\n",
              "      <td>-145061.286049</td>\n",
              "      <td>148288.896731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-16</th>\n",
              "      <td>-147375.332084</td>\n",
              "      <td>150425.871604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-17</th>\n",
              "      <td>-149548.453620</td>\n",
              "      <td>152602.363859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-18</th>\n",
              "      <td>-151437.272854</td>\n",
              "      <td>155017.643838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-19</th>\n",
              "      <td>-152066.724148</td>\n",
              "      <td>157906.785777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-20</th>\n",
              "      <td>-155516.900780</td>\n",
              "      <td>158523.022043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-21</th>\n",
              "      <td>-157540.861446</td>\n",
              "      <td>160515.309142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-22</th>\n",
              "      <td>-159492.744902</td>\n",
              "      <td>162531.931203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-23</th>\n",
              "      <td>-161130.313911</td>\n",
              "      <td>164817.526940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-24</th>\n",
              "      <td>-161779.490742</td>\n",
              "      <td>168048.592207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-25</th>\n",
              "      <td>-164996.803497</td>\n",
              "      <td>168671.076609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-26</th>\n",
              "      <td>-165766.398610</td>\n",
              "      <td>171703.420673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-27</th>\n",
              "      <td>-169154.846445</td>\n",
              "      <td>172053.687230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-28</th>\n",
              "      <td>-171144.364174</td>\n",
              "      <td>173875.763415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-29</th>\n",
              "      <td>-172992.546873</td>\n",
              "      <td>175729.120559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-30</th>\n",
              "      <td>-174568.372318</td>\n",
              "      <td>177829.556250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-02</th>\n",
              "      <td>-175387.102075</td>\n",
              "      <td>180946.808236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-03</th>\n",
              "      <td>-178640.420333</td>\n",
              "      <td>181364.222507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-04</th>\n",
              "      <td>-180473.964330</td>\n",
              "      <td>183165.956622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-05</th>\n",
              "      <td>-182242.301738</td>\n",
              "      <td>184998.987619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-06</th>\n",
              "      <td>-183702.659812</td>\n",
              "      <td>187107.668260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-13</th>\n",
              "      <td>-184180.053432</td>\n",
              "      <td>190168.628790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-14</th>\n",
              "      <td>-187233.124344</td>\n",
              "      <td>190624.972722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-15</th>\n",
              "      <td>-187842.220928</td>\n",
              "      <td>193498.238966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-16</th>\n",
              "      <td>-191072.309655</td>\n",
              "      <td>193687.898859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-17</th>\n",
              "      <td>-192924.105271</td>\n",
              "      <td>195372.007010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-18</th>\n",
              "      <td>-194626.174501</td>\n",
              "      <td>197079.204622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-19</th>\n",
              "      <td>-196059.934539</td>\n",
              "      <td>199037.622164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-20</th>\n",
              "      <td>-196716.798802</td>\n",
              "      <td>201992.016978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-21</th>\n",
              "      <td>-199831.973347</td>\n",
              "      <td>202271.337681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-22</th>\n",
              "      <td>-201531.633510</td>\n",
              "      <td>203939.191444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-23</th>\n",
              "      <td>-203170.112342</td>\n",
              "      <td>205642.365016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-24</th>\n",
              "      <td>-204504.437056</td>\n",
              "      <td>207625.004740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-25</th>\n",
              "      <td>-204859.440065</td>\n",
              "      <td>210563.531795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-26</th>\n",
              "      <td>-207793.509648</td>\n",
              "      <td>210900.922886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-27</th>\n",
              "      <td>-208286.895770</td>\n",
              "      <td>213658.442386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-28</th>\n",
              "      <td>-211400.337160</td>\n",
              "      <td>213731.512339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-29</th>\n",
              "      <td>-213152.454265</td>\n",
              "      <td>215315.948260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-30</th>\n",
              "      <td>-214746.963506</td>\n",
              "      <td>216915.587059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-31</th>\n",
              "      <td>-216075.661412</td>\n",
              "      <td>218768.941263</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  lower y        upper y\n",
              "ds                                      \n",
              "2020-03-31 -124714.848057  130997.083973\n",
              "2020-04-02 -128665.123276  132036.528431\n",
              "2020-04-03 -131132.772556  134467.166428\n",
              "2020-04-04 -133507.529567  136904.899311\n",
              "2020-04-05 -135543.721982  139600.702741\n",
              "2020-04-06 -136548.194494  143252.788450\n",
              "2020-04-13 -140175.788257  144211.208502\n",
              "2020-04-14 -141276.740774  147630.541299\n",
              "2020-04-15 -145061.286049  148288.896731\n",
              "2020-04-16 -147375.332084  150425.871604\n",
              "2020-04-17 -149548.453620  152602.363859\n",
              "2020-04-18 -151437.272854  155017.643838\n",
              "2020-04-19 -152066.724148  157906.785777\n",
              "2020-04-20 -155516.900780  158523.022043\n",
              "2020-04-21 -157540.861446  160515.309142\n",
              "2020-04-22 -159492.744902  162531.931203\n",
              "2020-04-23 -161130.313911  164817.526940\n",
              "2020-04-24 -161779.490742  168048.592207\n",
              "2020-04-25 -164996.803497  168671.076609\n",
              "2020-04-26 -165766.398610  171703.420673\n",
              "2020-04-27 -169154.846445  172053.687230\n",
              "2020-04-28 -171144.364174  173875.763415\n",
              "2020-04-29 -172992.546873  175729.120559\n",
              "2020-04-30 -174568.372318  177829.556250\n",
              "2020-05-02 -175387.102075  180946.808236\n",
              "2020-05-03 -178640.420333  181364.222507\n",
              "2020-05-04 -180473.964330  183165.956622\n",
              "2020-05-05 -182242.301738  184998.987619\n",
              "2020-05-06 -183702.659812  187107.668260\n",
              "2020-05-13 -184180.053432  190168.628790\n",
              "2020-05-14 -187233.124344  190624.972722\n",
              "2020-05-15 -187842.220928  193498.238966\n",
              "2020-05-16 -191072.309655  193687.898859\n",
              "2020-05-17 -192924.105271  195372.007010\n",
              "2020-05-18 -194626.174501  197079.204622\n",
              "2020-05-19 -196059.934539  199037.622164\n",
              "2020-05-20 -196716.798802  201992.016978\n",
              "2020-05-21 -199831.973347  202271.337681\n",
              "2020-05-22 -201531.633510  203939.191444\n",
              "2020-05-23 -203170.112342  205642.365016\n",
              "2020-05-24 -204504.437056  207625.004740\n",
              "2020-05-25 -204859.440065  210563.531795\n",
              "2020-05-26 -207793.509648  210900.922886\n",
              "2020-05-27 -208286.895770  213658.442386\n",
              "2020-05-28 -211400.337160  213731.512339\n",
              "2020-05-29 -213152.454265  215315.948260\n",
              "2020-05-30 -214746.963506  216915.587059\n",
              "2020-05-31 -216075.661412  218768.941263"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H_uPPB2Bvgp9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}