COVID-19 Time Series Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "from datetime import datetime\n",
    "\n",
    "# Basic packages\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import random as rd # generating random numbers\n",
    "import datetime # manipulating date formats\n",
    "# Viz\n",
    "import matplotlib.pyplot as plt # basic plotting\n",
    "import seaborn as sns # for prettier plots\n",
    "\n",
    "# settings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import plotly.graph_objs as go\n",
    "import plotly.express as px\n",
    "from datetime import date, timedelta\n",
    "from sklearn.cluster import KMeans\n",
    "from fbprophet import Prophet\n",
    "from fbprophet.plot import plot_plotly, add_changepoints_to_plot\n",
    "import plotly.offline as py\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "import statsmodels.api as sm\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM,Dense\n",
    "from keras.layers import Dropout\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>countystate</th>\n",
       "      <th>date</th>\n",
       "      <th>cases</th>\n",
       "      <th>prison_county</th>\n",
       "      <th>frac_released</th>\n",
       "      <th>diff_cases</th>\n",
       "      <th>prison_pop</th>\n",
       "      <th>prison_capacity</th>\n",
       "      <th>prison_fullness</th>\n",
       "      <th>...</th>\n",
       "      <th>age_35to44_perc</th>\n",
       "      <th>age_45to54_perc</th>\n",
       "      <th>age_55to64_perc</th>\n",
       "      <th>age_65to74_perc</th>\n",
       "      <th>age_75to84_perc</th>\n",
       "      <th>age_84plus_perc</th>\n",
       "      <th>lowrisk_agegroup_perc</th>\n",
       "      <th>highrisk_agegroup_perc</th>\n",
       "      <th>land_area</th>\n",
       "      <th>population_density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>abbeville, South Carolina</td>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103663</td>\n",
       "      <td>0.130598</td>\n",
       "      <td>0.146041</td>\n",
       "      <td>0.128357</td>\n",
       "      <td>0.065279</td>\n",
       "      <td>0.024082</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.217717</td>\n",
       "      <td>490.48</td>\n",
       "      <td>50.03466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>abbeville, South Carolina</td>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103663</td>\n",
       "      <td>0.130598</td>\n",
       "      <td>0.146041</td>\n",
       "      <td>0.128357</td>\n",
       "      <td>0.065279</td>\n",
       "      <td>0.024082</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.217717</td>\n",
       "      <td>490.48</td>\n",
       "      <td>50.03466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>abbeville, South Carolina</td>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103663</td>\n",
       "      <td>0.130598</td>\n",
       "      <td>0.146041</td>\n",
       "      <td>0.128357</td>\n",
       "      <td>0.065279</td>\n",
       "      <td>0.024082</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.217717</td>\n",
       "      <td>490.48</td>\n",
       "      <td>50.03466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>abbeville, South Carolina</td>\n",
       "      <td>2020-04-08</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103663</td>\n",
       "      <td>0.130598</td>\n",
       "      <td>0.146041</td>\n",
       "      <td>0.128357</td>\n",
       "      <td>0.065279</td>\n",
       "      <td>0.024082</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.217717</td>\n",
       "      <td>490.48</td>\n",
       "      <td>50.03466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>abbeville, South Carolina</td>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103663</td>\n",
       "      <td>0.130598</td>\n",
       "      <td>0.146041</td>\n",
       "      <td>0.128357</td>\n",
       "      <td>0.065279</td>\n",
       "      <td>0.024082</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.217717</td>\n",
       "      <td>490.48</td>\n",
       "      <td>50.03466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                countystate        date  cases  prison_county  \\\n",
       "0           1  abbeville, South Carolina  2020-04-07      5              0   \n",
       "1           2  abbeville, South Carolina  2020-04-06      6              0   \n",
       "2           3  abbeville, South Carolina  2020-04-09      7              0   \n",
       "3           4  abbeville, South Carolina  2020-04-08      6              0   \n",
       "4           5  abbeville, South Carolina  2020-04-03      6              0   \n",
       "\n",
       "   frac_released  diff_cases  prison_pop  prison_capacity  prison_fullness  \\\n",
       "0            NaN           1         NaN              NaN              NaN   \n",
       "1            NaN           3         NaN              NaN              NaN   \n",
       "2            NaN           1         NaN              NaN              NaN   \n",
       "3            NaN           2         NaN              NaN              NaN   \n",
       "4            NaN           2         NaN              NaN              NaN   \n",
       "\n",
       "   ...  age_35to44_perc age_45to54_perc age_55to64_perc  age_65to74_perc  \\\n",
       "0  ...         0.103663        0.130598        0.146041         0.128357   \n",
       "1  ...         0.103663        0.130598        0.146041         0.128357   \n",
       "2  ...         0.103663        0.130598        0.146041         0.128357   \n",
       "3  ...         0.103663        0.130598        0.146041         0.128357   \n",
       "4  ...         0.103663        0.130598        0.146041         0.128357   \n",
       "\n",
       "   age_75to84_perc  age_84plus_perc  lowrisk_agegroup_perc  \\\n",
       "0         0.065279         0.024082               0.782283   \n",
       "1         0.065279         0.024082               0.782283   \n",
       "2         0.065279         0.024082               0.782283   \n",
       "3         0.065279         0.024082               0.782283   \n",
       "4         0.065279         0.024082               0.782283   \n",
       "\n",
       "   highrisk_agegroup_perc  land_area  population_density  \n",
       "0                0.217717     490.48            50.03466  \n",
       "1                0.217717     490.48            50.03466  \n",
       "2                0.217717     490.48            50.03466  \n",
       "3                0.217717     490.48            50.03466  \n",
       "4                0.217717     490.48            50.03466  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Prison_county=pd.read_csv('prison_county_df.csv')\n",
    "Prison_county.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(115189, 4)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Prison_county1=Prison_county[['date', 'cases','county', 'state']]\n",
    "Prison_county1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HGM0h6fAZTq8zatNNDwAAvI0wmkMS5qHKaMBHNz0AAPA+wmgOSXfTB/0+heimBwAAPQBhNIdkKqMs7QQAAHoIwmgOSbadwOT3KcnSTgAAwOMIozkkbrYPowkqowAAwOMIozmk7QSmENuBAgCAHoAwmkMOGzPKbHoAAOBxhNEc0n7Re8aMAgAA7yOM5pDMBCb/oTGjjkMgBQAA3kUYzSGfHjMqSSa7MAEAAA8jjOaQ9Gz6YGtlVBKTmAAAgKcRRnNIujKa3ptekpImlVEAAOBdhNEckp6wlJ7AJIm1RgEAgKcFsnFR27Y1b948ffDBBwqFQnrggQc0ZMiQzPHq6motXbpUgUBAt956qyZOnKgdO3bo7rvvlmVZchxH8+fP17Bhw7R48WItW7ZMJSUlkqQf//jHGjZsWDaa7bqEaUlKTWBKjxmlmx4AAHhZVsLo8uXLlUgkVFVVpdraWi1cuFBPPfWUJKm+vl5LlizR888/r3g8roqKCo0bN06PPfaYrrvuOl1++eV6/fXX9cgjj+if//mftX79ej388MO64IILstHUnJL41HagEmEUAAB4W1bCaE1NjcaPHy9JGjlypNatW5c5tnbtWo0aNUqhUEihUEilpaXasGGD5syZo6KiIkmSZVkKh8OSpPXr12vRokWqr6/XZZddpptvvjkbTc4JCSYwAQCAHiYrY0aj0agikUjmtt/vl2mamWPp0ClJhYWFikajKikpUTAY1KZNm/Twww/r9ttvlyRdddVVmjdvnp577jnV1NTotddey0aTc8KhMGpkwmiCCUwAAMDDshJGI5GIYrFY5rZt2woEAh0ei8VimXC6atUq3X777frJT36iYcOGyXEc3XDDDSopKVEoFNKECRP03nvvZaPJOSFhOQoFfDIMQ6EAY0YBAID3ZSWMjh49WitWrJAk1dbWqqysLHNsxIgRqqmpUTweV2Njo+rq6lRWVqZVq1bpwQcf1NNPP60LL7xQUqqKOnXqVMViMTmOo9WrV3t67GjCtBVurYjSTQ8AAHqCrIwZnTJlilauXKlZs2bJcRwtWLBAixcvVmlpqSZPnqzKykpVVFTIcRzNnj1b4XBYCxYsUDKZ1Ny5cyVJQ4cO1fz58zV79mxdf/31CoVCGjt2rCZMmJCNJueEhGVllnTKdNMTRgEAgIdlJYz6fD7Nnz+/3X3Dhw/PfF9eXq7y8vJ2x1966aUOrzVt2jRNmzbt5DcyByVMW6HDKqOMGQUAAN7Fovc5JGHaCrVWRkOZHZiojAIAAO8ijOaQZOsEJkkKMoEJAAD0AITRHBLvoJueMaMAAMDLCKM5JGHZmQlMIcaMAgCAHoAwmkMSppVZ2inA3vQAAKAHIIzmkHZjRltDqUkYBQAAHkYYzSFtZ9MfGjNKNz0AAPAuwmgOSZi2gq3d8yF2YAIAAD0AYTSHJCxboYBfkjKhlHVGAQCAlxFGc0jbHZj8PkOGQWUUAAB4G2E0h6Qqo6mPxDAMBf0+xowCAABPI4zmkFRl1MjcDvl9VEYBAICnEUZzSNvZ9FJq3ChhFAAAeBlhNIe07aaXUss7EUYBAICXEUZzhGU7smxHIb8/c1/Q71PCZMwoAADwLsJojkhXQNtWRkMBKqMAAMDbCKM5It66nmiwzQQmxowCAACvI4zmiERrGA0zZhQAAPQghNEckeigm551RgEAgNcF3G5AT3egKaHGuKlt+5okSdE23zuOo2hLMnO7KBxQcUHItbYCAACcbIRRlzXGTa3YuEe7DrZIkv62K6r84B5JqWBqWo5WbEzdvrSsH2EUAAB4Ct30OcK0U93xAd+hj8TvM2Q5dNMDAADvIozmCKt1zGigzWx6v88nyyaMAgAA7yKM5giztQLq97UNo0amYgoAAOBFhNEcYVnpbvpDYTTgM6iMAgAATyOM5oh0BbRdZdQgjAIAAG8jjOaITicwEUYBAICHEUZzhGW3TmD61JhRwigAAPAywmiOSIdOv58wCgAAeg7CaI7ocMwoYRQAAHgcYTRHmB3Mpk8veu+w8D0AAPAowmiOsDqZwCSJXZgAAIBnEUZzRGdLO0lS69wmAAAAzyGM5gjLtmXo8DGjqWNURgEAgDcRRnOEaTvtgqh0KIyalEYBAIBHEUZzhGk7Cvjbh9EAlVEAAOBxhNEcYVmO/L72Hwfd9AAAwOsIoznCsp12yzpJhFEAAOB9hNEcYdp2p2NGWdoJAAB4FWE0R5hURgEAQA9EGM0RdNMDAICeiDCaI6wjLu1EGAUAAN5EGM0RqXVG238cAYPKKAAA8DbCaI4wLfuwdUbT4ZQwCgAAvIowmiMYMwoAAHoiwmiOONJ2oIRRAADgVYTRHEFlFAAA9ESE0RzR0QQmwigAAPA6wmiOONKi9yY7MAEAAI8ijOYIy7bl/9Rs+gCVUQAA4HGE0RzBmFEAANATEUZzhGkdaTa97UaTAAAAsi4rYdS2bd17772aOXOmKisrtWXLlnbHq6urNX36dJWXl+u1116TJO3YsUM33nijKisrdd1112nTpk2SpFdffVUzZszQzJkzVV1dnY3mus6yHTnSYZVRn2HIEJVRAADgXYFsXHT58uVKJBKqqqpSbW2tFi5cqKeeekqSVF9fryVLluj5559XPB5XRUWFxo0bp8cee0zXXXedLr/8cr3++ut65JFH9Oijj+qhhx7SsmXLlJ+fr2uvvVYTJ05U//79s9Fs16TDZsB3+N8Gfp9BGAUAAJ6VlcpoTU2Nxo8fL0kaOXKk1q1blzm2du1ajRo1SqFQSEVFRSotLdWGDRs0Z84cTZgwQZJkWZbC4bDq6upUWlqq4uJihUIhjRkzRmvWrMlGk11ltnbDf7qbPn0fYRQAAHhVVsJoNBpVJBLJ3Pb7/TJNM3OsqKgoc6ywsFDRaFQlJSUKBoPatGmTHn74Yd1+++2dnus1mcqov+MwahJGAQCAR2UljEYiEcViscxt27YVCAQ6PBaLxTKBc9WqVbr99tv1k5/8RMOGDTviuV6SDpt+g8ooAADoWbISRkePHq0VK1ZIkmpra1VWVpY5NmLECNXU1Cgej6uxsVF1dXUqKyvTqlWr9OCDD+rpp5/WhRdeKEkaPny4tmzZov379yuRSGjNmjUaNWpUNprsKss6cmWUMAoAALwqKxOYpkyZopUrV2rWrFlyHEcLFizQ4sWLVVpaqsmTJ6uyslIVFRVyHEezZ89WOBzWggULlEwmNXfuXEnS0KFDNX/+fM2dO1c33XSTHMfRjBkzNHDgwGw02VWZymhHE5gMQxY7MAEAAI/KShj1+XyaP39+u/uGDx+e+b68vFzl5eXtjr/00ksdXmvSpEmaNGnSyW9kDklPYPr00k4SlVEAAOBtLHqfA6xMZfTwMBogjAIAAA8jjOYAM7POKJVRAADQsxBGc4BFGAUAAD0UYTQHmK2z6f1+dmACAAA9C2E0B6Rny3daGWU2PQAA8CjCaA4wrSNtB+qjMgoAADyLMJoDuhozynagAADAqwijOcBkaScAANBDEUZzwKGlnTrZgYkwCgAAPIowmgOOtOg9s+kBAICXEUZzQGY7UD9hFAAA9CyE0RxgWY58huQzCKMAAKBnIYzmANN2OuyilwijAADA2wijOcDqKow6jhwWvgcAAB5EGM0Bpu10OJNeOrT2KLswAQAALyKM5gDLtjtc8F46NMOernoAAOBFhNEc0NWYUYkwCgAAvIkwmgNMy+lwWSeJMAoAALyNMJoDjjiBySCMAgAA7yKM5gDrCBOYqIwCAAAvI4zmANO2uxwzahJGAQCABxFGc0BqaSfGjAIAgJ6HMJoDulr0Pn0OAACA1xBGcwCVUQAA0FMRRnOAZTsK+LuYwMQOTAAAwIMIoznAtDqfwBRgaScAAOBhhNEccORu+tRHRBgFAABeRBjNAUxgAgAAPRVhNAdYTGACAAA9FGHUZY7jyLSdTHf8pxFGAQCAlxFGXZbeWSngpzIKAAB6HsKoy5KWLUnyG11sB8rSTgAAwIMIoy5LWkeujAaojAIAAA8jjLosabZWRpnABAAAeiDCqMsSrd30gS4nMNnd1iYAAIDu0mUY3b17tz788ENt3rxZd999t95///3uaFePkcyE0Y4roz7DkCEqowAAwJu6DKNz5szRnj179Oijj2rcuHFasGBBd7Srx0iPGe2smz59jDAKAAC8qMswapqmLrroIh08eFBXXXWVbLqLT6quKqMSYRQAAHhXl2E0mUzqoYce0uc//3mtWrVKlmV1R7t6jHjrBKaAv/OPwu8zMuuRAgAAeEmXYXThwoUaOnSovve976mhoUE//elPu6NdPUaiNYwGO1naSUpVTamMAgAAL+oyjA4ePFihUEi/+tWv1LdvXxUWFnZHu3qMuJmqNHdVGSWMAgAAL+oyjN57773asWOHVq5cqVgspjlz5nRHu3qMdGW0yzGj7MAEAAA8qMswunXrVv3gBz9QOBzWpEmT1NjY2B3t6jEOddNTGQUAAD1Pl2HUsiw1NDRIkqLRqHydLM6O43NoAhOz6QEAQM8T6OqEf/zHf9S1116r+vp6zZw5U3fffXd3tKvHyFRGjxDy/QZhFAAAeFOXYfTiiy/WK6+8ooaGBpWUlHRHm3qUo6uM+ljaCQAAeFKXYXTp0qWqqqpSPB7P3Pfyyy9ntVE9ydFNYDq0UxMAAICXdBlGf/Ob32jRokUqLi7ujvb0OHHTUsBnyDCOPGa0JcnOVwAAwHu6DKNnn322Bg0aJL/f3x3t6XESln3ELnop1U1v2WY3tQgAAKD7dBlGv/jFL+ryyy/X4MGD5TiODMPQb37zm+5oW48QT9pHXNZJYjY9AADwri7DaFVVlX7xi1+oqKioO9rT4yQs+4jjRaXW7UBZ9B4AAHhQl2F04MCBuvDCC1lfNEvi5lFURlnaCQAAeFSXYTSRSOiaa67RWWedlZlk8/Of//yIj7FtW/PmzdMHH3ygUCikBx54QEOGDMkcr66u1tKlSxUIBHTrrbdq4sSJmWPPPvus9uzZozvvvFOStHjxYi1btiyzrNSPf/xjDRs27NhfaY5KmEczZtRgaScAAOBJXYbRm2+++Zgvunz5ciUSCVVVVam2tlYLFy7UU089JUmqr6/XkiVL9Pzzzysej6uiokLjxo2Tbdu65557tHbtWn35y1/OXGv9+vV6+OGHdcEFFxxzOz4L4qZ9xAXvpfSYUWbTAwAA7+k0jL722muaOHGiNm/efNixiy+++IgXramp0fjx4yVJI0eO1Lp16zLH1q5dq1GjRikUCikUCqm0tFQbNmzQkCFDNG3aNF1yySXatGlT5vz169dr0aJFqq+v12WXXXZc4TiXJUzrqCqjZFEAAOBFnZbkDhw4ICnVLV5fX5/52rZtW5cXjUajikQimdt+v1+maWaOtZ0MVVhYqGg0quLiYn3pS1867FpXXXWV5s2bp+eee041NTV67bXXjv7VfQbETVuBo6qM0k0PAAC8p9PKaDKZ1MyZM5Wfn6/XX39dUmosqGmauuOOO4540Ugkolgslrlt27YCgUCHx2KxWKcz9R3H0Q033JA5PmHCBL333nvtxph+1iVMW5HwkUdL+Ftn0zvMqAcAAB7TaQq65pprNHbsWP3Lv/yLbrnlFkmSz+dT3759u7zo6NGj9dprr+mrX/2qamtrVVZWljk2YsQI/eIXv1A8HlcikVBdXV27421Fo1FNnTpVL7/8sgoKCrR69WrNmDHjWF9jToubtnoXHLkyml76iUlMAADAazoNo6FQSKeffrruv//+Y77olClTtHLlSs2aNUuO42jBggVavHixSktLNXnyZFVWVqqiokKO42j27NkKh8MdXqeoqEizZ8/W9ddfr1AopLFjx2rChAnH3J5cljBtBY9izKgkJS0GjgIAAG/pcjb98fD5fJo/f367+4YPH575vry8XOXl5R0+dvr06e1uT5s2TdOmTTv5jcwRRztmVJJMi8ooAADwFlayd1nCOvrKaMKkMgoAALyFMOoix3FaF70/8seQ3qEpThgFAAAeQxh1UTpcdrU3/aEwamW9TQAAAN2JMOqieDIVRrvamz7YGlZbklRGAQCAtxBGXZSudHa1A1MwQGUUAAB4E2HURelKZ1d706cro58JTPEAACAASURBVIwZBQAAXkMYddHRVkYDTGACAAAeRRh1UcvRjhkljAIAAI8ijLooUxntcjZ96zqjScaMAgAAbyGMuiiztFMXldH08RYqowAAwGMIoy5qaa10drUDU4h1RgEAgEcRRl10aNH7riqjrbPpWWcUAAB4DGHURUdbGfUZhvw+gwlMAADAcwijLjraMaNSKrASRgEAgNcQRl2UqYx2MZs+dY5PcWbTAwAAjyGMuuhYKqMBKqMAAMCDCKMuSldGu9qBSUotfE8YBQAAXkMYdVHctBXwGfIZRxtG6aYHAADeQhh1UTxpKxw4uo+ACUwAAMCLCKMuajEthY46jPpYZxQAAHgOYdRF8aR91GE0QDc9AADwIMKoi1pMS+GA/6jOpZseAAB4EWHURcdSGaWbHgAAeBFh1EVx0zrGCUx00wMAAG8hjLromCqjPtYZBQAA3kMYdVHLMVRGA62L3juOk+VWAQAAdB/CqIuObcxoamF8qqMAAMBLCKMuih/TbPrUR5XeQhQAAMALCKMuaknaCvmPfjZ9+jEAAABeQRh1UfyYdmBKddNTGQUAAF5CGHVRyzHsTR9IV0ZZ3gkAAHgIYdQljuMcZ2WUbnoAAOAdhFGXJC1HtqNjWPQ+dV5zgsooAADwDsKoS9K7KR39ovetlVG66QEAgIcQRl2S7m4/6qWdWkNrnAlMAADAQwijLjn2yihLOwEAAO8hjLokvZPS0c+mZ2knAADgPYRRl6RD5dHPpmcHJgAA4D2EUZekK6PHugNTM930AADAQwijLklXOMNBuukBAEDPRRh1ybFWRn2GoZDfx9JOAADAUwijLolnKqNHt7STlBpfGqebHgAAeAhh1CWZ2fRHWRmVUl36dNMDAAAvIYy6JDOb/ijHjEqpZaCaCaMAAMBDCKMuOa7KaMBPZRQAAHgKYdQl6bGfR7vOqJSqjLIDEwAA8BLCqEsySzsdQxjNY8woAADwGMKoS+KmLZ8h+X3GUT8mFPCrxaQyCgAAvIMw6pKWpKW8oF+GcfRhNBzwZZaEAgAA8ALCqEvipn1MXfQSs+kBAID3EEZdkq6MHgtm0wMAAK8hjLrkuCqjQWbTAwAAb8lKGLVtW/fee69mzpypyspKbdmypd3x6upqTZ8+XeXl5XrttdfaHXv22Wf1s5/9LHP71Vdf1YwZMzRz5kxVV1dno7muOJ7KaF6A2fQAAMBbAtm46PLly5VIJFRVVaXa2lotXLhQTz31lCSpvr5eS5Ys0fPPP694PK6KigqNGzdOtm3rnnvu0dq1a/XlL39ZkpRMJvXQQw9p2bJlys/P17XXXquJEyeqf//+2Wh2tzq+MaN+xU1bjuMc08QnAACAXJWVymhNTY3Gjx8vSRo5cqTWrVuXObZ27VqNGjVKoVBIRUVFKi0t1YYNGxSPxzVt2jTdcsstmXPr6upUWlqq4uJihUIhjRkzRmvWrMlGk7td3LQUDhxbZTS9dWic5Z0AAIBHZCWMRqNRRSKRzG2/3y/TNDPHioqKMscKCwsVjUZVXFysL33pS4ddp6NzvaAlaSt8DPvSS4cWyG9O0FUPAAC8ISthNBKJKBaLZW7btq1AINDhsVgs1i5wHuk6Rzr3sybVTX/ss+klqcUkjAIAAG/IShgdPXq0VqxYIUmqra1VWVlZ5tiIESNUU1OjeDyuxsZG1dXVtTve1vDhw7Vlyxbt379fiURCa9as0ahRo7LR5G4XT1rKO87KKDPqAQCAV2RlAtOUKVO0cuVKzZo1S47jaMGCBVq8eLFKS0s1efJkVVZWqqKiQo7jaPbs2QqHwx1eJxgMau7cubrpppvkOI5mzJihgQMHZqPJ3e54KqPp8MqMegAA4BVZCaM+n0/z589vd9/w4cMz35eXl6u8vLzDx06fPr3d7UmTJmnSpEknv5Euazmuyqg/81gAAAAvYNF7lxzfmFG66QEAgLcQRl1yPJXRUIBuegAA4C2EUReYli3Tdo69Mhqkmx4AAHgLYdQFCSvVzX6864yytBMAAPAKwqgL0mM+845xO9A8xowCAACPIYy6IN5a2Ux3ux8tuukBAIDXEEZdkKmMsug9AADo4QijLshURo9xAlN6Nn0zlVEAAOARhFEXHG9l1GcYCgV8ihNGAQCARxBGXZAOk8daGZWk/KCfMaMAAMAzCKMuaDGPrzKafgxjRgEAgFcQRl1wIpXRvKCfdUYBAIBnEEZdEG+tjIaPcZ1RScoL0E0PAAC8gzDqgnSYzDvGdUZTj/GpmW56AADgEYRRF5xIZTTMBCYAAOAhhFEXpMPkse7AJKVm07O0EwAA8ArCqAtOaMwos+kBAICHEEZdcGg2/fGEUWbTAwAA7yCMuiBu2goHfDIM45gfmxfwqzlBGAUAAN5AGHVBS9I6rqqolO6mJ4wCAABvIIy6IG7ax7Wsk5TupmfMKAAA8AbCqAvipq3wcWwFKqXCaMK0ZdvOSW4VAABA9yOMuqAlaSnvOLYClQ4tlB+nOgoAADyAMOqCE6uMph7HuFEAAOAFhFEXnIzKaDNhFAAAeABh1AVURgEAAFIIoy44ocpo6+PYhQkAAHgBYdQFJ1QZDbWGUXZhAgAAHkAYdUFq0fsTrYwSRgEAwGcfYdQFqUXvT2zMaJxuegAA4AGEURfET6Qyymx6AADgIYRRF7Sc4A5MEt30AADAGwij3cxxHCVM+wQqo+mlneimBwAAn32E0W6W3sbzeMeM5lMZBQAAHkIY7WbpiUcnOmaUpZ0AAIAXEEa7WTpEHm9lNBygmx4AAHgHYbSbnWhl1DAMhQM+uukBAIAnEEa7Wboymq5wHo+8oJ8wCgAAPIEw2s3SldH02M/jkRekMgoAALyBMNrN4iehMpof9DNmFAAAeAJhtJu1nJTKKN30AADAGwij3exkVEbDQb9aTCqjAADgs48w2s1OSmU04FNLgsooAAD47COMdrOTURnNC/pZ9B4AAHgCYbSbnZwxo8ymBwAA3kAY7WbMpgcAADiEMNrN0iEyfJzbgUrMpgcAAN5BGO1mhyqjJ7a0UzNhFAAAeABhtJvFTVtBvyG/zzjua4SDvsxOTgAAAJ9lhNFu1pK0lHcCVVFJygv4lbBsWbZzkloFAADgDsJoN2tJWgqfwEx66dBM/DjLOwEAgM+4QDYuatu25s2bpw8++EChUEgPPPCAhgwZkjleXV2tpUuXKhAI6NZbb9XEiRPV0NCgO++8Uy0tLRowYIAeeugh5efn64EHHtBbb72lwsJCSdKTTz6poqKibDS7WzTEEiopDJ7QNfJbJz+1JG0VhE5GqwAAANyRlTC6fPlyJRIJVVVVqba2VgsXLtRTTz0lSaqvr9eSJUv0/PPPKx6Pq6KiQuPGjdOTTz6pqVOnavr06Vq0aJGqqqp04403av369Xr66adVUlKSjaZ2u73RhPoWhk/oGunKKDPqAQDAZ11Wuulramo0fvx4SdLIkSO1bt26zLG1a9dq1KhRCoVCKioqUmlpqTZs2NDuMZdeeqneeOMN2batLVu26N5779WsWbO0bNmybDS3W+2JxtU3cmLlzHQYZUY9AAD4rMtKZTQajSoSiWRu+/1+maapQCCgaDTarpu9sLBQ0Wi03f2FhYVqbGxUU1OTrrvuOn3729+WZVm6/vrrdcEFF+icc87JRrO7xd5oQv0iJ1oZTXfTE0YBAMBnW1Yqo5FIRLFYLHPbtm0FAoEOj8ViMRUVFbW7PxaLqVevXsrPz9f111+v/Px8RSIRffGLX9SGDRuy0eRu0ZK01Bg31e8EK6PhTDc9yzsBAIDPtqyE0dGjR2vFihWSpNraWpWVlWWOjRgxQjU1NYrH42psbFRdXZ3Kyso0evRo/fnPf5YkrVixQmPGjNFHH32kiooKWZalZDKpt956S+eff342mtwt9sYSkqS+x1kZNS1b2/Y1qbElKUnatq+p068DTYmT1m4AAIBsyUo3/ZQpU7Ry5UrNmjVLjuNowYIFWrx4sUpLSzV58mRVVlaqoqJCjuNo9uzZCofDuvXWWzVnzhxVV1erT58++vnPf66CggJdffXVKi8vVzAY1DXXXKOzzjorG03uFnujcUk67m765qStt+sa9HFDkySpZss+xeIdd9VfWtZPxUy1BwAAOS4rYdTn82n+/Pnt7hs+fHjm+/LycpWXl7c73q9fPz3zzDOHXeu73/2uvvvd72ajmd1ubzRdGT2xkBj0pwraSYtF7wEAwGcbi953oz3pyugJLu0U9Ke2Ek1ajBkFAACfbYTRbrSntTLar+jEKqOBTGWUMAoAAD7bCKPdaG80rvygXwWhExsdka6MmnTTAwCAzzjCaDfaG0uc8HhR6dCY0aYE64wCAIDPNsJoN9oTjZ/wgvdSKowO6Vug2o/3yXaojgIAgM8uwmg32hNNnPCC92lfOrOf9jUl9d6Ogx0eb2xJ6uE/bcisSQoAAJCLCKPdaG80rr4nOJM+7dxBvVRSGNJfPtzT4fF/fX2znvqfOv15Y/1JeT4AAIBsIIx2E9t2TtqYUUnyGYYuGd5XWxuatLV1Efy0nQea9X/e3i5J2lQf6+jhAAAAOYEw2k0ONCdl2c5JGTOaNmZIH+UFfVrZpjrqOI7+sHanIuGA+kXCqquPnrTnAwAAONkIo91kbyy14P3JqoxKUjjg18VnlGjd9gPa17rv/bodB7V5T0zfu3SYzj+1F2EUAADkNMJoN8kseH8SK6OSNHZ4PxmG9OamvUqYtl5+d6cGFefp6s+dquH9I6rbHZNtM+MeAADkpqzsTY/DZbYCPclhtDg/qBGn99ZfP2qQ4zg60JxU+ecHy+8zNHxAoZqTlj452KJTe+ef1OcFAAA4GaiMdpO9rZXRk9lNnzZueD/FTVsr6/ZqxOnFGtqvUJI0vH9EkuiqBwAAOYsw2k32RuPyGVKfgpMfRk/rk6+h/QoV9Bu68oJBmfszYXQ3YRQAAOQmuum7SX00oZLCkPw+IyvXL//8YMXiporzg5n7+kVC6pUXUB3LOwEAgBxFGM2CA00JNcbNdvdt29ekXnlBbdvXfk3QePLk7C9fnB9sF0QlyTAMDR8QoZseAADkLMJoFjTGTa3Y2H5npC17mxTwG4fdP6q0d1bbMrx/RK//jV2YAABAbmLMaDeJxk1Fwt2f/Yf3j2jXwTh71AMAgJxEGO0mMdfCaGpmPduCAgCAXEQY7QZJy1bctN0JowNY3gkAAOQuwmg3iLVOZnIjjJaWFCjgMwijAAAgJxFGu0G0NYwWuhBGg36fhvQtUN1uuukBAEDuIYx2g6iLlVEpNYmJyigAAMhFhNFu4GY3vZQaN/rR3phMy3bl+QEAADrDOqPdINrS/d30pmVnFtgvKQgqaTn660cNGlxScNi5ReGAirOwTSkAAEBXCKPdIBo3FfL7FAp0XyG6OWnr7boGSVJDLLXG6MvvfqJzB/U67NxLy/oRRgEAgCvopu8GsYSlwrDftefvFwlLkuob4661AQAAoCOE0W4QbXFnwfu0/JBfReGA6qOEUQAAkFsIo93Ara1A2+pXFKYyCgAAcg5htBvE4qYra4y21b81jDqO42o7AAAA2iKMZpntOIolTEXyXA6jkbCak5ZiCcvVdgAAALRFGM2y5oQl23FvjdG0/kVMYgIAALmHMJplbm4F2lY6jO4hjAIAgBxCGM0yt3dfSivODyroN/RJY4ur7QAAAGiLMJplbu9Ln+YzDJ3Rt1Cr6vZqxcZ6JjIBAICcQBjNslzpppekiotLdf5pxfrT+k/0u9Vb1ZJkMhMAAHAXYTTLYnFThqSCkHs7MKWFg35de9FgXXXhIG345KCeeO1D7TzQ7HazAABAD0YYzbJo6xqjPsNwuymSJMMwNO7Mfvq7Lw1TwrL1Lys2acd+AikAAHAHYTTLonHL9fGiHTmjX6FumTBckvTof21kDCkAAHAFYTTLYjmwFWhn+hSEdPm5A/Xmpgb9ad0nbjcHAAD0QITRLEt107s/XrQzY4f11VkDIpr3+/VqbEm63RwAANDDEEazyHEcRVtytzIqSX6fobuuOFu7G+N65L82ut0cAADQwxBGs2hrQ5MSlq1TivPdbsoRnXdqL133hSF67o2PtG77AbebAwAAehDCaBbVbNmnkN+nC07r5XZTunTXV85W30hYd//Hu7JsJjMBAIDukbv9x59xcdPS2u0HdOHpxQoHcnfMqCSZlq2DzUndftlwzfv9exo5/z/VtzCkvpGQSgrDGt6/UFd/7lQV5wdVFA6ouCDkdpMBAIBHEEazZN32A0qYtj4/pI/bTelSc9LW23UNCvp9mj7qNO040KzGFlN7GhPavKdJ//XeLj3zl80aVdpH/zj5LF1yZj+3mwwAADyCMJola7bsU79IWKUlBW435agZhqHPn1Fy2P27DrZo5Yd79NaWfap4erUmnt1flWOHaELZAPl9XS/mv2VvTDcvqdF5p/bSd8cP07mDcn/YAgAA6B6E0SzY2tCkLXub9JXzT5GRIzsvnYiBvfI0ffTp+vL5p+iTA8166Z2d+s6zazSoOE/f/PxgzbxosE7r3fEkrf1NCX372b+q/mBcWxua9MJb2zX+rH767vhhGn9WP0+8PwAA4PgxgSkLXn53p3yGNLK0t9tNOaki4YC+86WhevP/m6RfXTdaZw0s0uOv/k1fevhV3fnv7+jgp9YpjZuWvrekRtsamvXMjRfpzbmT9U9fOVsffNKo63/9v7r2X1cpFjddejUAACAXUBk9yUzL1v9d94nKBhapV17Q7eacdKZla9fBFl1wWrEWfL1YOw8064W3tqv6r9v0+t/q9cOvnqvRQ/rIcRzd/4f39b+bG3Tf1efp1N55aown9bXPnaqvnH+K/rB2px5b/jdd98xq/fQbI9Q/EmZiFAAAPRBh9CT788Z67Y0mdOX5p7jdlKxIT3Zq68LTeqt3fkjVaz7W95fWatzwvgr4ffrzxnp9+byBCgf8WrFxT7vH9IuENWPM6fr3NR/rlt++pX+pHE0YBQCgByKMnmTVaz5Wn4Kgzj6lZ03SGVxSoH+YdJb+77qdWlm3V5J0xfkDdelZ/Tt9zMjBvWXZjp5/a5t+9H/W69lvX6xQwCfbdrRxd6PWfLRP+2IJWY4j25Fs20l9bzuyWr+XpLMHFumioSUa1q+w0zGojS1Jrdi4R/+9YZde/9sendY7X7MuGqypnzu1wx2yonFThSF/1se0tiQttSQtFecHj+q5TMvWhk8aFY2buvC0YhXm8O5eAAAcjaz8S2bbtubNm6cPPvhAoVBIDzzwgIYMGZI5Xl1draVLlyoQCOjWW2/VxIkT1dDQoDvvvFMtLS0aMGCAHnroIeXn53d4bq7asb9Z//3+bn3z86cf1SxzrwkFfLpm5Gk6d1Avbdkb099PPFPvbj94xMeMGdJHpm3rxdodunHx/6og5NdfP9qnA83Jw871+wz5DUM+n+QzUt9bjqOmhCVJ6lsY0ufP6KPSkgLFTVstSUtx09bug3Gt2dKgpOWoOD+o8Wf10wefNGruC+9q/h/e09UjTtXFQ0v0t91Rvb/zoN7beVD1jXENLsnXhLL+uqxsgC45s68KQh3/72Lbjt7auk//9f4ufXKgRbG4qcYWU7GEqV55Qc28aLCuvGCQQoFDQ7QPNCW1+I3NWrzyIx1oTioc8OmU4jyd0itPA3vlqU9BUMUFIfXOD6ooL6BNe2J6a8s+rd12QM1JK/N+nDuoSGNK++j8U4u1JxbXR3ti+mhPkzbvjSnoMzR8QETD+0c0vH+hhvQtVO+CoHrlBVNrxuYFFPCf3GHjTQlTqzbtVTRuyZBkGJIhQ30Kgrrg9OJuH7rSnLAU9Bsn/XUCAE6erITR5cuXK5FIqKqqSrW1tVq4cKGeeuopSVJ9fb2WLFmi559/XvF4XBUVFRo3bpyefPJJTZ06VdOnT9eiRYtUVVWlq666qsNzQ6Hc6c7dvCem5e/t0vL3d2nNln0yJF114SBtbWh2u2muKRtYpLKBRUcdAL4wtK+GlBToqT/X6ZTiPI0/q58+N7i3Pnd6sQb0CstnGPK1qRoGfJJpp753HEcfNzTrnW379c62A1q7bb/+vLFe4YBfIb9P4aBPBSG/vjnmdI07s5/OP62XAj6fHMfR+h0H9Ye1O/XSOztUteZjBXyGzuhXqM8P6aPTeufr/U8OalnNNv121VYF/YbKBhaptKRAQ/rm6/Q+BcoL+rXyw71a8bfU0Iyg39CAojwVhv0qCPnVKy+ojxua9IOltbo/8r4qLh6sKy8cpJfe2aElb25RNG5qynkD9YWhJdp1sEUfNzRp54EW1WzZp4MtSUVbTKX3wvL7DJ01IKKrRpyi808tVnFeQGu3H9C72w+oes02NSe3SJJKCkMa3CdfF59RItO2tWVvk2q27MsE9k8b3r9QF51RojFD+uiiM0rUvyisLXub9NHemDbviWn7/mYlTDtTlTZtR30LQzprQETDB0R01oAiBf2Glr+/W6+s/0QrNtYrnv5wPsUwpOH9IxpxerHOOaVIjS2mdh1s0e7GuHYdjMtxHEXCARWGA4qEA8oLpjaLcFrfBUOGBhXnaWi/Qg3tX6hh/QrVu83QDsdxdLDF1JqPGrR6c4NWb9qrd7cfUEEooDFD+ugLw0r0haElOm9QsQxDchzJdhzZjqOg36eg35f5I9KyHe2NxVXfmPra35SU32co6PcpFEj9NxIOqHfrHwy98oNyHEe7GuPavq9Z2/enPsuEaaeq+K1f4aBf/SMh9YuE1a8orF55QR1sSWp/U1L7mhI60JRUOOhTn4KQ+hSE1Lsg9YdDwG8o4PMp5Pcp4Dda22t0WE13HEeOI/mO8AexbafeVZ+hTq9h2o6M1p+99Dnp97i+Ma490dRXY4upWNxUU8JSLGHKth3lB/3KC/mVH/SrMBRQv6KQBhTlaUCvsPoWhtu9z0kr9R519Jps21HCshVP2nLkKBTwKRzwy+8zZNuOtu9v1oe7o/pwd1Sb9kQVDvh15oBI5qtvYernI26mrhG3LAV9qd8LeQG/fD5DjS1JbdzVqPd3NmrDJwe1Y3+LhvQt0Lmn9NI5g4p01oAi5Yf8smxHcdNSS9Ju/SxT12jbZsdxFDdtJS1bjqSQP/WZtf0s0q8padnyGR1/lqZlK2mlzgv4DIUCPgU+9TkkLFsJ05Zppd6XT59jWraak6n22o6jvIBf4aBP4YBPjpNa9SX9x/f7OxsV8Bk6d1AvnXdqL507qEgDe+Vp856YPvikUR980qgPd0fVpzCoswcW6exTeumcU4rUpzAky3bUkrTU3PrHf8if+p2bF/S3+5zTvUCm7Sgc8Ckv6Fc44Mu0N2nZmes4jpQX9CsvmHr/DMPIvLfpc3yGobxg6mcs/f7ZtqOmpKWmhKmWhK1gwFBBMKCCsF/B1n+PEqatWDxVLGhJ2soP+VUY8qswHFDQn/q3oSlhqbHFVDSeVNy0VRQOKpIXUFFe6hzTsnWgOZn5sh1l/l8tzg8q6PepKWFqbzShhlhCDU0Jhf0+lURCKilM/b/tNww1NCW0+2BcuxpbtDeaUK+8gAa2FiT6RUKyHEfb9zVr275mfbyvSftiCQ3slafT+xRocEm+TumVp/3NSW38pFEf7GrUxl2NamwxddaAIp19SkRlA4t0ep8Cbd4TU+3H+/T21tS/kzNGn6a/Gz+s098PbshKGK2pqdH48eMlSSNHjtS6desyx9auXatRo0YpFAopFAqptLRUGzZsUE1NjW6++WZJ0qWXXqpHHnlEgwcP7vDcESNGZKPZx2zBy+9r0YpNkqRzTinSLROG6aoLT1Wv/ECPDqPH48oLB6l/UV67ivKHu2P6cHfssHNHlfbW21v3t7uvOD+kS8/qf9iwgLbnHmg29caH7ce7XjK8n8aU9tGg3nna3RhXwHcoQA/rH9EV552ij/Y2aeOuRu3Y36yVH+7R/113aAWAdEidfM5AnXNKUSZApX1xWB+9tXW/Xnhrux5/9UP98tUPZUiadM4AVY4dojMHRDLnxpOWVm/el7ltO45aEqlfvEV5wXaV1XNPLVaL6ahsYC9NG+loX1OiXYBra+ywPtrfbGrH/mY1xk1FW1KV2wPNCW3cFdUf1u7U0r9+fNjjJKl3fup5/T6jNZRIe6OJDsPtgKKwrv7cIH3pzH7qFwnLUSrwSVJDrEXrdjTq/Z0H9dqG3Xrhre3yGVKfwlQw61sYkt9nqClhqr6xRR/tsdRiWjKU+nkwjNaAGE1khmdIkt8w5Cg1jKOtoN/QeYN66VtfHKLGlqRqt6b+SOmKz5CCfp+Sln3YNbvi9xkdbqVrGFLAl/qDKmHaOpmb7aYCcuo9sm3Jcpx2bQj4DPlaexQcpY6ZtqM2b2G7c9KP//TrCLR+/o4jJayO/9iQ1Bq81BoeO2mzYSjgNzp9jwO+1PFUUO34Kn5f6ifDbHOBksKQWpJWu5/NkN/XZXvbHi8KB3Ran3y9Wbc30wOR/vw6a4vPSPUKHam9wdY/JpKW3a7N7doS8MlnpMJSR6cYRqq9jlLnHKktpuV0+jyffj0+I/W7zrIdvfLeJ5mfjfQfbFLq/R5SUqCGpoT+//899Luiy/c34JOO8DNjGFK4i/amX1PctNv93Lbl9xkK+IxO/xCWUp+BIaPL9ppd/L/f1WtOP1dnPwtt23ykrbfbvv+d8Rlq19Z0z9cf393Z4WOL84P63ODeObnWd1bCaDQaVSRy6B9Zv98v0zQVCAQUjUZVVFSUOVZYWKhoNNru/sLCQjU2NnZ67pFs375d06dPP8mvqHOj099slN5cIb15jI9fxrk5c25Xerd+HeZ96UOlvo70/KPafL9vo/TLl46/LSfzPSg7xufufBSwtKlG2tTF44e2frXV0ub7vNavjpzexbXbeV9a3/ptSesXcDTOdbsB3e2D1H+62i/QW4sVeteR9kiMviP9/A/Sz7utNalc1pWsrkR80AAACMBJREFUhNFIJKJY7FBFy7ZtBQKBDo/FYjEVFRVl7s/Ly1MsFlOvXr06PfdIVq9efZJfDQAAALIlK6P6R48erRUrVkiSamtrVVZ2qPYyYsQI1dTUKB6Pq7GxUXV1dSorK9Po0aP15z//WZK0YsUKjRkzptNzAQAA4A2G43Q1KuHYpWfTb9y4UY7jaMGCBVqxYoVKS0s1efJkVVdXq6qqSo7j6Oabb9YVV1yhPXv2aM6cOYrFYurTp49+/vOfq6CgoMNzAQAA4A1ZCaMAAADA0WDxPQAAALiGMAoAAADXEEYBAADgGsLoSWLbtu69917NnDlTlZWV2rJli9tNymnJZFJ33XWXKioq9I1v/L/27jakqfaPA/h3GUqpPZgGmQlpCEUvojQMLAsyC+zNmG7OZqIvtDQfMBsoZg8aiLEoxVSMBC3twSBFoecXWRmtojKlpZGomWkqMiiz7fq/GO3W0v5u97xX+P288pxz7XCdLx797Tq6nwx37txBZ2cnIiMjoVQqkZOTA6PR9MHCRUVFkMlkUCgUePnypZ1n/mf6/PkzgoOD0dHRwRytVFpaCrlcDqlUiitXrjBHK4yNjSE9PR0KhQJKpZLfjxZ68eIFVCoVAFiU21RjZ6vxOba1tUGpVEKlUiEuLg4DAwMATG3JpVIpIiIicO/ePQDA4OAgYmNjoVQqkZqaii9fZnfzmvE5/lBfXw+5XG7etlmOgmzixo0bQq1WCyGEeP78uUhISLDzjP5sV69eFbm5uUIIIQYHB0VwcLCIj48Xzc3NQgghsrOzxc2bN0VLS4tQqVTCaDSKnp4eIZVK7TntP9K3b9/E/v37xY4dO0R7eztztEJzc7OIj48XBoNB6PV6cebMGeZohVu3bonk5GQhhBBNTU0iKSmJOU5TWVmZCAsLE+Hh4UIIYVFuk42drX7OMSoqSrS2tgohhKiurhYnTpwQnz59EmFhYWJ0dFSMjIyYvz5+/Liora0VQghRWloqzp8/b6/LsLufcxRCiNbWVhEdHW3eZ8scuTJqI79rgUq/2rlzJ1JSUszbDg4OeP36NTZu3AjA1BL24cOHePr0KYKCgiCRSODp6QmDwYDBwcGpTjsr5efnQ6FQYOnSpQDAHK3Q1NQEPz8/JCYmIiEhAVu3bmWOVli5ciUMBgOMRiP0ej3mzp3LHKfJ29sbhYWF5m1Lcpts7Gz1c44ajQarV5t6ahkMBjg5OU1oS+7q6jqhLfmP3+PMcWKOQ0NDOHnyJDIzM837bJkji1EbmaoFKk3O2dkZLi4u0Ov1SE5ORmpqKoQQkEgk5uM/WsKOz/XHfjK5du0a3NzczDc+AOZohaGhIbS0tOD06dM4evQoDh48yBytMH/+fPT09GDXrl3Izs6GSqVijtMUGhpq7lQIWHYfTzZ2tvo5xx9v0p89e4aqqirExMRY1JZ8thqfo8FgQFZWFjIzM+Hs7GweY8scZ6Qd6Gz0uxaoNLne3l4kJiZCqVRi9+7dKCgoMB/7Ny1hZ5Pa2lpIJBI8evQIbW1tUKvVE1aYmOP0LFq0CD4+PnB0dISPjw+cnJzw8eNH83HmOD0VFRUICgpCeno6ent7sXfvXoyNjZmPM8fpmzPnn7Wi/5fbZGPpH42NjTh79izKysrg5uZmUVtyMq3Sd3Z24siRIxgdHUV7ezvy8vIQGBhosxy5Mmojv2uBSr8aGBhAbGwsMjIyIJPJAABr1qzB48ePAZhawvr7+2P9+vVoamqC0WjEhw8fYDQa4ebmZs+p/1EuXLiAqqoqVFZWYvXq1cjPz8eWLVuYo4U2bNiA+/fvQwiBvr4+fPnyBZs2bWKOFlqwYIG5qFy4cCG+f//O+9pKluQ22VgyuX79uvln5IoVKwBY1pacTHk1NDSgsrISGo0Gq1atQlZWlk1z5NKdjYSEhODBgwdQKBTmFqg0tZKSEoyMjKC4uBjFxcUAgKysLOTm5kKj0cDHxwehoaFwcHCAv78/5HK5+RML6PfUajWys7OZowW2bduGJ0+eQCaTQQiBw4cPw8vLizlaKCYmBpmZmVAqlRgbG0NaWhrWrl3LHK1gyX082VgyPV7Oy8vDsmXLcODAAQBAQEAAkpOToVKpoFQqIYRAWloanJycsG/fPqjValy+fNnclpym5uHhYbMc2Q6UiIiIiOyGj+mJiIiIyG5YjBIRERGR3bAYJSIiIiK7YTFKRERERHbDYpSIiIiI7IbFKBHRX2R4eBj19fX2ngYRkc2wGCUi+ou8efMGd+/etfc0iIhshsUoEdEM+fr1K9LS0iCXyyGVSqHVapGRkQGFQoHw8HA0NjYCAFQqFTo6OgAA1dXVKCwsRHd3N+RyOVJSUiCVSpGTkwPA1DCiubkZly5dQkhICIaHhwEAFy9eRHl5uX0ulIjoX2AHJiKiGVJTU4Ply5fj1KlT0Ol0uH37NhYvXoyCggLo9XpIpVIEBgZO+fr379/j3LlzmDdvHrZv347+/n4kJCSgpqYGcrkcfX19aGhoQFRUFOrq6lBUVPQfXh0RkW1wZZSIaIa8e/cO69atAwD4+fmhv78fAQEBAAAXFxf4+vqiq6trwmvGN8Xz9vaGi4sLHBwc4OHhgdHR0QljZTIZ6urqoNPp4O7uDnd39xm+IiIi22MxSkQ0Q3x9ffHq1SsAQFdXFxoaGqDVagEAer0eOp0OXl5ecHR0RH9/PwCgtbXV/HqJRPLLOefMmQOj0QgA8PT0hKurK0pKSiCTyWb6coiIZgSLUSKiGaJQKNDd3Y09e/bg0KFDKC8vx/DwMCIjIxEdHY2kpCQsWbIE0dHROHbsGOLi4mAwGH57Tm9vb+h0OlRUVAAAIiIioNVqsXnz5v/gioiIbE8ixj8TIiKiv0pjYyPevn2LlJQUe0+FiMgq/AcmIqK/lEajgVarRXFxsb2nQkRkNa6MEhEREZHd8G9GiYiIiMhuWIwSERERkd2wGCUiIiIiu2ExSkRERER2w2KUiIiIiOzmf3zsLZWylzV2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count=Prison_county1.groupby(['county'])['county'].count()\n",
    "sns.set_style('white')\n",
    "plt.figure(figsize=(11, 7))\n",
    "sns.distplot(count)\n",
    "plt.xlabel('county')\n",
    "plt.ylabel('times')\n",
    "plt.title('Case Number Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['South Carolina', 'Louisiana', 'Virginia', 'Idaho', 'Iowa',\n",
       "       'Kentucky', 'Missouri', 'Oklahoma', 'Colorado', 'Illinois',\n",
       "       'Indiana', 'Mississippi', 'Nebraska', 'Ohio', 'Pennsylvania',\n",
       "       'Washington', 'Wisconsin', 'Vermont', 'Minnesota', 'Florida',\n",
       "       'North Carolina', 'California', 'New York', 'Wyoming', 'Michigan',\n",
       "       nan, 'Maryland', 'Alaska', 'Tennessee', 'Texas', 'Maine',\n",
       "       'Arizona', 'Georgia', 'Arkansas', 'Kansas', 'New Jersey',\n",
       "       'South Dakota', 'Alabama', 'West Virginia', 'North Dakota',\n",
       "       'Massachusetts', 'Montana', 'New Hampshire', 'Oregon',\n",
       "       'New Mexico', 'Utah', 'Rhode Island', 'Nevada', 'Connecticut',\n",
       "       'Hawaii', 'Delaware'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Prison_county1['state'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "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>date</th>\n",
       "      <th>cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2367</th>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2368</th>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2369</th>\n",
       "      <td>2020-04-28</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2370</th>\n",
       "      <td>2020-04-13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2371</th>\n",
       "      <td>2020-04-21</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115184</th>\n",
       "      <td>2020-05-07</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115185</th>\n",
       "      <td>2020-05-08</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115186</th>\n",
       "      <td>2020-05-09</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115187</th>\n",
       "      <td>2020-05-10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115188</th>\n",
       "      <td>2020-05-11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7508 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              date  cases\n",
       "2367    2020-04-24     16\n",
       "2368    2020-04-22     14\n",
       "2369    2020-04-28     23\n",
       "2370    2020-04-13      3\n",
       "2371    2020-04-21     10\n",
       "...            ...    ...\n",
       "115184  2020-05-07      1\n",
       "115185  2020-05-08      1\n",
       "115186  2020-05-09      2\n",
       "115187  2020-05-10      2\n",
       "115188  2020-05-11      2\n",
       "\n",
       "[7508 rows x 2 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Texas=Prison_county1.loc[Prison_county1['state']=='Texas']\n",
    "Texas=Texas[['date','cases']]\n",
    "Texas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "Texas.set_index(['date'], inplace = True) \n",
    "Texas.index=pd.to_datetime(Texas.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=Texas.resample('D').first().fillna(0)\n",
    "# data=data[:20]\n",
    "x=np.arange(len(data)).reshape(-1,1)\n",
    "y=data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "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>cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-01</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-02</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-04</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-05</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-07</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-08</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-09</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-10</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-11</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            cases\n",
       "date             \n",
       "2020-03-01     11\n",
       "2020-03-02     11\n",
       "2020-03-03     11\n",
       "2020-03-04     11\n",
       "2020-03-05     11\n",
       "...           ...\n",
       "2020-05-07     40\n",
       "2020-05-08     41\n",
       "2020-05-09     41\n",
       "2020-05-10     41\n",
       "2020-05-11     41\n",
       "\n",
       "[72 rows x 1 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1=pd.DataFrame(data)\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "model = MLPRegressor(hidden_layer_sizes=[32, 32, 10], max_iter=50000, alpha=0.0005, random_state=26)\n",
    "_=model.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = np.arange(len(data)+30).reshape(-1, 1)\n",
    "pred = model.predict(test)\n",
    "prediction = pred.round().astype(int)\n",
    "week = [data.index[0] + timedelta(days=i) for i in range(len(prediction))]\n",
    "dt_idx = pd.DatetimeIndex(week)\n",
    "predicted_count = pd.Series(prediction, dt_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-03-01     1\n",
       "2020-03-02     1\n",
       "2020-03-03     1\n",
       "2020-03-04     1\n",
       "2020-03-05     2\n",
       "              ..\n",
       "2020-06-06    39\n",
       "2020-06-07    39\n",
       "2020-06-08    40\n",
       "2020-06-09    40\n",
       "2020-06-10    40\n",
       "Length: 102, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.plotting.register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted_count.plot()\n",
    "plt.title('Prediction of Accumulated Confirmed Count')\n",
    "plt.legend([ 'predicted confirmed case for Perison'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2020-05-07</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2020-05-08</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2020-05-09</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2020-05-10</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2020-05-11</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds   y\n",
       "0  2020-03-01  11\n",
       "1  2020-03-02  11\n",
       "2  2020-03-03  11\n",
       "3  2020-03-04  11\n",
       "4  2020-03-05  11\n",
       "..        ...  ..\n",
       "67 2020-05-07  40\n",
       "68 2020-05-08  41\n",
       "69 2020-05-09  41\n",
       "70 2020-05-10  41\n",
       "71 2020-05-11  41\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prophet= pd.DataFrame(data)\n",
    "prophet\n",
    "pr_data = prophet.reset_index()\n",
    "pr_data.columns = ['ds','y']\n",
    "pr_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>trend</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "      <th>trend_lower</th>\n",
       "      <th>trend_upper</th>\n",
       "      <th>additive_terms</th>\n",
       "      <th>additive_terms_lower</th>\n",
       "      <th>additive_terms_upper</th>\n",
       "      <th>weekly</th>\n",
       "      <th>weekly_lower</th>\n",
       "      <th>weekly_upper</th>\n",
       "      <th>multiplicative_terms</th>\n",
       "      <th>multiplicative_terms_lower</th>\n",
       "      <th>multiplicative_terms_upper</th>\n",
       "      <th>yhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>33.605571</td>\n",
       "      <td>21.719643</td>\n",
       "      <td>43.760108</td>\n",
       "      <td>33.599363</td>\n",
       "      <td>33.611396</td>\n",
       "      <td>-0.810890</td>\n",
       "      <td>-0.810890</td>\n",
       "      <td>-0.810890</td>\n",
       "      <td>-0.810890</td>\n",
       "      <td>-0.810890</td>\n",
       "      <td>-0.810890</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.794681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2020-06-07</td>\n",
       "      <td>33.916596</td>\n",
       "      <td>22.015514</td>\n",
       "      <td>45.719086</td>\n",
       "      <td>33.909962</td>\n",
       "      <td>33.922737</td>\n",
       "      <td>0.116143</td>\n",
       "      <td>0.116143</td>\n",
       "      <td>0.116143</td>\n",
       "      <td>0.116143</td>\n",
       "      <td>0.116143</td>\n",
       "      <td>0.116143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.032739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2020-06-08</td>\n",
       "      <td>34.227621</td>\n",
       "      <td>23.430331</td>\n",
       "      <td>45.523794</td>\n",
       "      <td>34.220626</td>\n",
       "      <td>34.234057</td>\n",
       "      <td>0.442536</td>\n",
       "      <td>0.442536</td>\n",
       "      <td>0.442536</td>\n",
       "      <td>0.442536</td>\n",
       "      <td>0.442536</td>\n",
       "      <td>0.442536</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.670157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2020-06-09</td>\n",
       "      <td>34.538645</td>\n",
       "      <td>23.055399</td>\n",
       "      <td>45.313184</td>\n",
       "      <td>34.531140</td>\n",
       "      <td>34.545498</td>\n",
       "      <td>-0.571953</td>\n",
       "      <td>-0.571953</td>\n",
       "      <td>-0.571953</td>\n",
       "      <td>-0.571953</td>\n",
       "      <td>-0.571953</td>\n",
       "      <td>-0.571953</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.966692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2020-06-10</td>\n",
       "      <td>34.849670</td>\n",
       "      <td>22.734353</td>\n",
       "      <td>45.552092</td>\n",
       "      <td>34.841872</td>\n",
       "      <td>34.856857</td>\n",
       "      <td>-0.681627</td>\n",
       "      <td>-0.681627</td>\n",
       "      <td>-0.681627</td>\n",
       "      <td>-0.681627</td>\n",
       "      <td>-0.681627</td>\n",
       "      <td>-0.681627</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.168043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds      trend  yhat_lower  yhat_upper  trend_lower  trend_upper  \\\n",
       "97  2020-06-06  33.605571   21.719643   43.760108    33.599363    33.611396   \n",
       "98  2020-06-07  33.916596   22.015514   45.719086    33.909962    33.922737   \n",
       "99  2020-06-08  34.227621   23.430331   45.523794    34.220626    34.234057   \n",
       "100 2020-06-09  34.538645   23.055399   45.313184    34.531140    34.545498   \n",
       "101 2020-06-10  34.849670   22.734353   45.552092    34.841872    34.856857   \n",
       "\n",
       "     additive_terms  additive_terms_lower  additive_terms_upper    weekly  \\\n",
       "97        -0.810890             -0.810890             -0.810890 -0.810890   \n",
       "98         0.116143              0.116143              0.116143  0.116143   \n",
       "99         0.442536              0.442536              0.442536  0.442536   \n",
       "100       -0.571953             -0.571953             -0.571953 -0.571953   \n",
       "101       -0.681627             -0.681627             -0.681627 -0.681627   \n",
       "\n",
       "     weekly_lower  weekly_upper  multiplicative_terms  \\\n",
       "97      -0.810890     -0.810890                   0.0   \n",
       "98       0.116143      0.116143                   0.0   \n",
       "99       0.442536      0.442536                   0.0   \n",
       "100     -0.571953     -0.571953                   0.0   \n",
       "101     -0.681627     -0.681627                   0.0   \n",
       "\n",
       "     multiplicative_terms_lower  multiplicative_terms_upper       yhat  \n",
       "97                          0.0                         0.0  32.794681  \n",
       "98                          0.0                         0.0  34.032739  \n",
       "99                          0.0                         0.0  34.670157  \n",
       "100                         0.0                         0.0  33.966692  \n",
       "101                         0.0                         0.0  34.168043  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=Prophet()\n",
    "model.fit(pr_data)\n",
    "future=model.make_future_dataframe(periods=30)\n",
    "forecast=model.predict(future)\n",
    "forecast.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot(forecast)\n",
    "model.plot_components(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "739.1285637894053\n"
     ]
    }
   ],
   "source": [
    "def mean_absolute_percentage_error(y_true, y_pred):\n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
    "error = mean_absolute_percentage_error(Texas[-102*1:].values,\n",
    "                                       forecast.yhat[-102*1:].values)\n",
    "print(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "Texas=Texas.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c3037cfd0>,\n",
       " <matplotlib.lines.Line2D at 0x1c30389588>]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16,6))\n",
    "plt.plot(forecast.yhat[-288*1:].reset_index())\n",
    "plt.plot(Texas['cases'])\n",
    "plt.plot(forecast.yhat_lower[-288*1:].reset_index())\n",
    "plt.plot(forecast.yhat_upper[-288*1:].reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "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>date</th>\n",
       "      <th>cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>2020-04-29</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>2020-05-02</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110953</th>\n",
       "      <td>2020-05-09</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110954</th>\n",
       "      <td>2020-05-06</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110955</th>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110956</th>\n",
       "      <td>2020-05-08</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110957</th>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3247 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              date  cases\n",
       "1246    2020-05-01      4\n",
       "1247    2020-05-04      4\n",
       "1248    2020-04-29      4\n",
       "1249    2020-05-03      4\n",
       "1250    2020-05-02      5\n",
       "...            ...    ...\n",
       "110953  2020-05-09     10\n",
       "110954  2020-05-06      9\n",
       "110955  2020-03-28      1\n",
       "110956  2020-05-08     10\n",
       "110957  2020-03-30      2\n",
       "\n",
       "[3247 rows x 2 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Michi=Prison_county1.loc[Prison_county1['state']=='Michigan']\n",
    "Michi=Michi[['date','cases']]\n",
    "Michi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "Michi.set_index(['date'], inplace = True) \n",
    "Michi.index=pd.to_datetime(Michi.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=Michi.resample('D').first().fillna(0)\n",
    "# data=data[:20]\n",
    "x=np.arange(len(data)).reshape(-1,1)\n",
    "y=data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1=pd.DataFrame(data)\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "model = MLPRegressor(hidden_layer_sizes=[32, 32, 10], max_iter=50000, alpha=0.0005, random_state=26)\n",
    "_=model.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = np.arange(len(data)+30).reshape(-1, 1)\n",
    "pred = model.predict(test)\n",
    "prediction = pred.round().astype(int)\n",
    "week = [data.index[0] + timedelta(days=i) for i in range(len(prediction))]\n",
    "dt_idx = pd.DatetimeIndex(week)\n",
    "predicted_count = pd.Series(prediction, dt_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-03-17    16\n",
       "2020-03-18    23\n",
       "2020-03-19     7\n",
       "2020-03-20     1\n",
       "2020-03-21     1\n",
       "              ..\n",
       "2020-06-06   -25\n",
       "2020-06-07   -26\n",
       "2020-06-08   -27\n",
       "2020-06-09   -28\n",
       "2020-06-10   -30\n",
       "Length: 86, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.plotting.register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted_count.plot()\n",
    "plt.title('Prediction of Accumulated Confirmed Count in Michi')\n",
    "plt.legend([ 'predicted confirmed case for Perison'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds   y\n",
       "0 2020-03-17  16\n",
       "1 2020-03-18  23\n",
       "2 2020-03-19   7\n",
       "3 2020-03-20   1\n",
       "4 2020-03-21   1"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prophet= pd.DataFrame(data)\n",
    "prophet\n",
    "pr_data = prophet.reset_index()\n",
    "pr_data.columns = ['ds','y']\n",
    "pr_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>trend</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "      <th>trend_lower</th>\n",
       "      <th>trend_upper</th>\n",
       "      <th>additive_terms</th>\n",
       "      <th>additive_terms_lower</th>\n",
       "      <th>additive_terms_upper</th>\n",
       "      <th>weekly</th>\n",
       "      <th>weekly_lower</th>\n",
       "      <th>weekly_upper</th>\n",
       "      <th>multiplicative_terms</th>\n",
       "      <th>multiplicative_terms_lower</th>\n",
       "      <th>multiplicative_terms_upper</th>\n",
       "      <th>yhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>11.119189</td>\n",
       "      <td>-4.749337</td>\n",
       "      <td>25.967709</td>\n",
       "      <td>11.117549</td>\n",
       "      <td>11.120957</td>\n",
       "      <td>-1.129078</td>\n",
       "      <td>-1.129078</td>\n",
       "      <td>-1.129078</td>\n",
       "      <td>-1.129078</td>\n",
       "      <td>-1.129078</td>\n",
       "      <td>-1.129078</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.990111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2020-06-07</td>\n",
       "      <td>11.123602</td>\n",
       "      <td>-1.680157</td>\n",
       "      <td>25.283002</td>\n",
       "      <td>11.121858</td>\n",
       "      <td>11.125462</td>\n",
       "      <td>0.491530</td>\n",
       "      <td>0.491530</td>\n",
       "      <td>0.491530</td>\n",
       "      <td>0.491530</td>\n",
       "      <td>0.491530</td>\n",
       "      <td>0.491530</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.615132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2020-06-08</td>\n",
       "      <td>11.128014</td>\n",
       "      <td>-1.820433</td>\n",
       "      <td>26.533607</td>\n",
       "      <td>11.126193</td>\n",
       "      <td>11.130014</td>\n",
       "      <td>0.987342</td>\n",
       "      <td>0.987342</td>\n",
       "      <td>0.987342</td>\n",
       "      <td>0.987342</td>\n",
       "      <td>0.987342</td>\n",
       "      <td>0.987342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.115356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2020-06-09</td>\n",
       "      <td>11.132427</td>\n",
       "      <td>1.105838</td>\n",
       "      <td>29.641801</td>\n",
       "      <td>11.130502</td>\n",
       "      <td>11.134516</td>\n",
       "      <td>3.762156</td>\n",
       "      <td>3.762156</td>\n",
       "      <td>3.762156</td>\n",
       "      <td>3.762156</td>\n",
       "      <td>3.762156</td>\n",
       "      <td>3.762156</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.894583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2020-06-10</td>\n",
       "      <td>11.136839</td>\n",
       "      <td>-4.544780</td>\n",
       "      <td>25.074115</td>\n",
       "      <td>11.134829</td>\n",
       "      <td>11.139030</td>\n",
       "      <td>-0.116597</td>\n",
       "      <td>-0.116597</td>\n",
       "      <td>-0.116597</td>\n",
       "      <td>-0.116597</td>\n",
       "      <td>-0.116597</td>\n",
       "      <td>-0.116597</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.020242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds      trend  yhat_lower  yhat_upper  trend_lower  trend_upper  \\\n",
       "81 2020-06-06  11.119189   -4.749337   25.967709    11.117549    11.120957   \n",
       "82 2020-06-07  11.123602   -1.680157   25.283002    11.121858    11.125462   \n",
       "83 2020-06-08  11.128014   -1.820433   26.533607    11.126193    11.130014   \n",
       "84 2020-06-09  11.132427    1.105838   29.641801    11.130502    11.134516   \n",
       "85 2020-06-10  11.136839   -4.544780   25.074115    11.134829    11.139030   \n",
       "\n",
       "    additive_terms  additive_terms_lower  additive_terms_upper    weekly  \\\n",
       "81       -1.129078             -1.129078             -1.129078 -1.129078   \n",
       "82        0.491530              0.491530              0.491530  0.491530   \n",
       "83        0.987342              0.987342              0.987342  0.987342   \n",
       "84        3.762156              3.762156              3.762156  3.762156   \n",
       "85       -0.116597             -0.116597             -0.116597 -0.116597   \n",
       "\n",
       "    weekly_lower  weekly_upper  multiplicative_terms  \\\n",
       "81     -1.129078     -1.129078                   0.0   \n",
       "82      0.491530      0.491530                   0.0   \n",
       "83      0.987342      0.987342                   0.0   \n",
       "84      3.762156      3.762156                   0.0   \n",
       "85     -0.116597     -0.116597                   0.0   \n",
       "\n",
       "    multiplicative_terms_lower  multiplicative_terms_upper       yhat  \n",
       "81                         0.0                         0.0   9.990111  \n",
       "82                         0.0                         0.0  11.615132  \n",
       "83                         0.0                         0.0  12.115356  \n",
       "84                         0.0                         0.0  14.894583  \n",
       "85                         0.0                         0.0  11.020242  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=Prophet()\n",
    "model.fit(pr_data)\n",
    "future=model.make_future_dataframe(periods=30)\n",
    "forecast=model.predict(future)\n",
    "forecast.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mzfNkWSIicszhijpWHVt9uyqviJKapkUZ3ePDuXFg0356w9OtpNtCCTL7e7pcETkFtwW/pKQklixZctz3ZsyYcdzXTzzxRPN/P/LII+4qRURETlO9w8n6vWXNd/X+fbhpUYYtNICsVAtDUi0MTbfQJyEKa2igh6sVkTPlFUO9IiLiObuKq1mRW8TKPDsf7yqmusGJ2c9En4RI/ndYWnPYS4oKweyvrVZE2jIFPxERH1NZ52DNrmJW5hWxMtfOntIaAJKigrm0WxxD0iyMSLfSNS6CiGD9mhBpT/R/tIhIO+dyGWwpKG8+KWP93lIcLoOQAD8GJkVzbZ+ODEm1kJVqIS48CD+dfyvSbin4iYi0Q0WV9Xy4w86KvCJW5dkpqmoAoEtsGFP7JzIk1cKITBvp1lBCArQoQ8RXKPiJiLQDjU4Xn+87tigjz87Xh8oBiA4xk5XSdDdvWLqVfolRWEMDtNWKiI9S8BMRaaP2ltQ0zdPLs7N6ZzGV9Q78TdCrYyR3DE1t3molKTqEAC3KEBEU/ERE2ozqegef7D52/m1eETvsTScedYwIYmznmKZFGRk2esSHExms829F5IcU/EREvJRhGGw7Utm81cq6PSU0OA2CzH4MSIriih7xZB1blNEhMvjY+bciIien4Cci4kVKqhv4aIedFcdOyiioqAcg0xbKpL4JZKVaGJFhJdMWRmigPsJF5Myc9FPjyy+/POmTzj//fLcUIyLiaxxOF5sOHGXFsbl6Xx48imFAZJCZwSnR3DI4haFpVvonRRETpvNvReTcnDT4vfHGGwAcOHCAxsZGevXqxfbt2wkLC2Px4sWtVqCISHtzoKymeU+9j3bYKa9z4GeCnh0iuHVwCkNTLQxLt5FiCSHQrEUZItJyThr8nn76aQBuu+02nnvuOcxmM06nk9tuu63VihMRaQ9qG518uruk+aSMnKIqAOLCAxmdaWtafZth5bwOkUSHaFGGiLjPKSeI2O325v92Op2Ulpa6tSARkbbOMAy2H6ls2lMv187aPSXUOVwE+pvonxjFzJHpTeffplnoGBmiRRki0mpOGfyuvfZaLrvsMrp06cKuXbuYMWNGa9QlItKmlNU0sHpnMSvz7CzPPszhqkYA0iwhXNWrQ9NJGRk2OseEERakRRki4hmn/PSZOnUqEydOZM+ePSQlJWG1WlujLhERr+Z0GXx18Cgr8+ysyC1i44EyXAaEBfrTNz6EmwY3baB8foqF2HAtyhAR73DK4JeTk8Nbb71FfX198/fmz5/v1qJERLxRQXld80kZH+6wU1rTiAnoHh/Oz89PPraBspWA2nLSU5I8Xa6IyA+cMvjNmTOH66+/ng4dOrRGPSIiXqPe4eSzPaWsOHZSxreHKwGwhQYwNNXCkDQLw9Js9E6IwBIa2Py8goJKT5UsIvKjThn8YmJi+MlPftIatYiIeJRhGOwsrm4+KeOTXSXUNDox+5nolxjJjOFpDE21MjTNQkJUMGadfysibcwpg19iYiIvvfQS3bt3b56jMnz4cLcXJiLSGirqGlmzs7jprl5uEfvKagFIjg5mQo+45q1WusVFEK5FGSLSxp3yU6yxsZG9e/eyd+/e5u8p+IlIW+VyGXyTX958Usbn+8pwuAxCA/wZmBzF5L4JZKVZGJxiIS48CD9ttSIi7cgpg9/8+fPZu3cvBw4coGvXrsTFxbVGXSIiLaawsp5Vx4Leqjw79uoGALrGhjFtQBJZqdEMz7CRYQ0lOMDfw9WKiLjPKYPfa6+9xocffkh5eTlXXXUV+/fv58EHH2yN2kREzkqDw8WGfaXHjkUr4pv8CgAsIQFkpUYzJNXKsHQLfROjsH5vUYaISHt3yuC3fPly/va3v3HDDTdw4403cs0117RGXSIiZ2R3cXVz0Fuzs5iqBif+fib6dIzgf4amNs/VS4wKIUCLMkTER50y+BmGAdC8sCMwUP86FhHPq6p38PGu4uawt6u4BoCEyCDGd41tPimjW3w4kcE6/1ZEBE4j+E2YMIGpU6dSUFDArbfeytixY1ujLhGR4xiGwb8PV7Aitynofba3lEanQbDZjwFJUVzZs+lYtKxUCx0igrUoQ0TkBE4Z/IYOHcqQIUPYsWMH6enpdOvWrTXqEhGhuKqeD3cUN5+WcaSy6QShTjGhTatvUy2MyrCRERNGiBZliIic0imD33333ccbb7xBZmZma9QjIj6s0eli4/6y5pMyNh8qxzAgKtjMoJRopmelMjTNQv+kKGxhOv9WRORMnTL4hYaGMm/ePNLT0/Hza5oQPWnSJLcXJiK+YX9pDSvz7KzIK2L1jmIq6h34maBnh0imZ6WSlWpheLqV5OgQAs1alCEici5OGfw2bNhAv379KCkpAaC+vt7tRYlI+1XT4ODT3SVNYS+3iDx7NQDxEUFc2DmmafVtupXzOkQQFaJFGSIiLemkwe/vf/87b7/9NqGhoaxbtw4Al8uFw+Hgl7/8ZasVKCJt3057FR/kFvF+ThGf7C6h3uEiyL9pUcYvRsYzJM1CVmo0HSND8NeiDBERtzlp8Js4cSJDhgzhxRdf5PbbbwfAz88Pm83WasWJSNtU2+jk090lvJ9TxAe5hc1braRaQri6VweGpVkZnm6lU0wYYTr/VkSk1Zz0EzcwMJCkpCQeffTR1qxHRNqoPSXVx4Je0wbKdQ4XQWY/zk+O5upeHRmaZiEr1UpcuBZliIh4ilv/qb1161aeeuopFi9ezP79+5kzZw4mk4nOnTvz0EMPNS8WATh69Ci//vWvqaqqIjo6mrlz5+ruoogXq3c4Wbu7lPdzC/kg5z9z9VKiQ7iyZweGplkYmWGjc2wYoYG6qyci4g3c9mm8cOFCli1bRkhICADz589n5syZDB48mAcffJDVq1czbty45se/+OKLDBgwgNtvv50NGzbw9NNP89hjj7mrPBE5C/tKa/ggt4gPcopYvbOYmkZn81y9X42OZ1ialaxUC3HhQdpAWUTEC7kt+KWkpLBgwQJmzZoFQHZ2NoMGDQJg5MiRrF+//rjgt2vXLu655x4A+vfvzyOPPHLKazgcDgoKCtxQ/emz2+0evX57pt66z+n2tsHpYlN+FWv2VLBmbzk7S+sA6BAewJj0CAYmhpOVGEGGNYjQAH/AwFVVypEqNxbfBui96z7qrXupv+7jLb11W/AbP348hw4dav7aMIzmeT1hYWFUVlYe9/ju3buzZs0aevTowZo1a6irqzvlNcxmMwkJCS1b+FnwhhraK/XWfU7W24LyOj7ILWJ5TiEf5tmpanAS4G+iX2IU9/RMYFiahSFpOhbtVPTedR/11r3UX/fxht622sSb78/nq66uJjIy8rif33bbbTz22GP87Gc/Y8SIEXTo0KG1ShPxaU6XwRf7y3g/p5D3c4rYUlABQHx4IBd1jWVYmpWRmVa6xUUQrhW4IiJtWqt9ivfo0YONGzcyePBg1q5dS1ZW1nE//+qrr5g4cSJZWVmsXLmS/v37t1ZpIj6ntMbBms2HeD+niJV5RZTWNOJvgt4JkfzvsDSGp1sZmmYhIUr76omItCetFvxmz57NAw88wNNPP01GRgbjx48H4KabbuKFF14gPT2d2bNnAxAXF8e8efNaqzSRds/lMvgmv5z3j22ivHF/GQZgDQ1g6LGTMkZk2HRahohIO+fW4JeUlMSSJUsASE9P57XXXvvBY15++WUAUlNTefPNN91ZjohPqahrZFWevXlvvSOV9ZiAHvERTO0dw0XnJTEiw0pSVAhmf52BKyLiCzRhR6Qd2V9aw7vbC1mWfYRPdpfQ6DSICDIzJNXCHUMtjMiw0jchitqjxV4xyVhERFqXgp9IG+ZyGWw+VM6724+wLLuQrccWZqRZQpjcN5GRGU0LM9IsYQSa/3NXr/aopyoWERFPUvATaWNqG52s2VnMsuwjvLu9kMMV9fiZoG9CJDNHpDMiw8rQNCvxEUE6Gk1ERI6j4CfSBhRV1rM8p5Bl2YWsyrNT0+gkNMCfIWkWbh+SyujMGPokRGphhoiI/CgFPxEvZBgGuUVVLMtumq/3+f4yDAPiI4K4rEccozJsjM60kRkTRnCAv6fLFRGRNkLBT8RLOJwu1u8rbQ57u4prAOgeF85tg1MYkWFjZIZVe+uJiMhZU/AT8aDKOgcr84pYmn2E5duLKKttJMDfxPnJ0VzbO4GRGVYGpViwhQV6ulQREWkHFPxEWtnBstrmLVc+3lVMg9MgKtjM8HQrIzNsXNDJRo/4CMJ0PJqIiLQw/WYRcTPDaDo147sh3G/ym7ZcSYkOYVLfBEZm2BiVaSPNGkqANlIWERE3UvATcYN6h5OPd5U0bbmSXcih8jpMQJ+ESO4ans7IDCtD06100JYrIiLSihT8RFpISXUD7x/bcmVFbhFVDU6CzX5kpVq4eVAKozpZ6ZcYTbS2XBEREQ9R8BM5B7uLq1ma3XRqxmd7S3G6DGLCArmoaywjM2xc2NlG55hwbbkiIiJeQcFP5Ay4XAZfHTrK0m1HWLqtkOzCSgA6xYTys4FJjMywMTLTSnJ0qLZcERERr6PgJ3IK9Y6mI9KWHluccbiiHn8T9E2M4hcjMxiZ2XREWkxYoObriYiIV1PwEzmB0poGlm8vZGl2ISuPzdcLCfBjSGrTEWkXdoqhd0IkkcGaryciIm2Hgp/IMXtLaliafYSl246w7r/m643KsHGB5uuJiEgbp+AnPsswDDYfKm+ar5d9hG8PN83Xy7CFcsOAJEZl2hil+XoiItKOKPiJT6l3OPlkV0nzStz88jr8TNA3IZKZI9MZnWEjK81KXLjm64mISPuj4Cft3tHaRt7PKWTptkI+yC2ist5BsLlpvt6tg1MYlWmjX2IUUdpfT0RE2jkFP2mX9pfWsCy7kKXZR/h0dwkOl4EtNIAxnWMYlWHlgk4xdI3TfD0REfEtCn7SLnx3Hu7SbU1hb2tB03m4aZYQpvZPZFSGjdGdbCRHh2DWebgiIuKjFPykzWpwuPh093fz9Y5w8Oh/zsO9e0TTebjD0m2aryciInKMgp+0KeW1jXyQW8TSbUd4P6eIinoHQWY/slIs/Pz8ZEZl2uifpPNwRURETkTBT7zewbLa5v31Pjk2X88SEsDoTram/fU62egWH0GI5uuJiIj8KAU/8TqGYfDvwxW89nkBaw7s4utD5QCkWEKY0i+RUZlWRmfGkGrRfD0REZEzoeAnXqHR6WLdntLmO3v7y2oxAb06RjJjeBqjMmwMS7cSHxGk+XoiIiJnScFPPKayzsGKvKb5estzCjla6yDI349BqdFMG5BEz2i4qE8GltBAT5cqIiLSLij4SavKL6/l3WP7663ZWUyD0yAq2MyIdCujMm1c0CmG7vHhhAaaKSgoUOgTERFpQQp+4laGYbDtSGXzebhfHWyar5cUFcxP+iQ076+XZg0lQPP1RERE3ErBT1qcw+nis73fzdcrZG9pDQA9O0Rw59A0RmVaGZZupWNksObriYiItCIFP2kRVfUOVuYVsXRbIctzCimtaSTQ38T5ydFM6dd0Z29gSjRWDd2KiIh4jNuC39atW3nqqadYvHgx+/fvZ86cOZhMJjp37sxDDz2En99/hvUqKyu55557qK2tJSAggCeffJLY2Fh3lSYt5HBFXfN8vdU7i6l3uIgMMjM8w8qoDBsXdrbRPS6CsCD9+0JERMQbuOU38sKFC1m2bBkhISEAzJ8/n5kzZzJ48GAefPBBVq9ezbhx45of/84779ClSxdmzZrFkiVLWLRoEXPmzHFHaXIODMMgp7CqecuVjQeOApAYGczVvTo0babc2Ua6NUzz9URERLyQW4JfSkoKCxYsYNasWQBkZ2czaNAgAEaOHMn69euPC35dunRhz549AFRVVWE26w6Rt3C6DNbv/c/+ertLmubr9YgP5/YhqYzOtDH82Hw9Pz/N1xMREfFmbklY48eP59ChQ81fG4bRPIk/LCyMysrK4x5vsVhYv349l156KeXl5bz++uundR2Hw0FBQUHLFX4W7Ha7R6/vDjWNTj7dV8HK3eV8tPsoZXVOzH4m+nYI5X8HxZOVFEGfDmFYQ469fWrKOFLT8nW0x956C/XWvdRf91Fv3Uv9dR9v6W2r3PD7Q7UAACAASURBVFr7/ny+6upqIiMjj/v5s88+yy233MLkyZPJzc1lxowZvPvuu6d8XbPZTEJCQovXe6a8oYZzVVhZz7vZR1iaXchHO+zUOVxEBJkZnm49tuVKDOd1iCC8lefrtYfeeiv11r3UX/dRb91L/XUfb+htq/wW79GjBxs3bmTw4MGsXbuWrKys434eGRlJREQEADabjerq6tYoy+flFlayNLuQpduO8MWBMgwDOkYEcWXPDozKtDE600aGLYxAs+briYiItAetEvxmz57NAw88wNNPP01GRgbjx48H4KabbuKFF17g7rvv5v777+dvf/sbDoeDRx99tDXK8jlOl8EX+8uaN1PeYW8K2N3iwrltcAqjMm2MyLCSEBmi+XoiIiLtkNuCX1JSEkuWLAEgPT2d11577QePefnllwGIj49n4cKF7irFp9U0OPhoRzFLs4/w3vZCiqoaMPuZGJgUxewLMhmZYWNwqgVbmPbXExERae+0fLYdKqqs573tTfvrfbjDTm2ji/BAf4amWRmdaeOCzjH09MB8PREREfEs/eZvJ3bYq5qGcLcdYcP+pvl68RFBXN4jvmm+XqcYMm2hBJn9PV2qiIiIeIiCXxvlchlsPFDG0m1Nd/Zyi6oA6BIbxi2DmubrjcywkhAVgr/m64mIiAgKfm1KbaOT1TuLWbrtCO9mF1JYVY+/n4kBiVH8anQGozJsZKVaiAkP8nSpIiIi4oUU/LxcSXVD83y9lbl2ahqdhAX6MzTVwqjMNC7oFEOvjpFEBOuvUkRERH6c0oIX2l1c3XxE2md7S3EZEBceyKXd45rOw+1ko1NsmObriYiIyBlR8PMCLpfBV4eOHlucUUh2YdORdp1iQvn5+cmMyrQxKsNGYrTm64mIiMjZU/DzkLpGJx/vKmZpdiHLso9wuKIefxP0S4zil6MyGJlhY0iahZiwwOZzjkVERETOhYJfKyqtaeD9nCKWbjvCitwiqhqchAb4MyTNwh1D0hidaaNPYiSRwQGeLlVERETaIQU/N9tbUtM8X2/d3lKcLoOYsEAu6hrbNF+vs43OMeEEB2i+noiIiLiXgl8LMwyDzYfKm8/D/fZw03y9DFsoNwxIapqvl2klOTpU8/VERESkVSn4tYB6h5NPdpWwNPsIy7ILyS+vw88EfRMimTkyndEZNrLSrMSFa76eiIiIeI6C31k6WtvI+zmFvPnlPj7Zv5XKegfBZj+GpFq4dXDTyRn9EqOICtF8PREREfEOCn5nYH/pd/P1Clm7pwSHy8AS7M+YzrGMyrByQacYusZpvp6IiIh4JwW/U6h3OHl8zW7e+fYwWwsqAEi3hnB9/0RGZtjoHulgYJc0zP5+Hq5URERE5Mcp+J3CVwfLmfvRDs6Lj+DuEemMyrAxNP0/8/UKCgoU+kRERKRNUPA7hWHpVuwPj8cwQbTm64mIiEgbpuB3GqJCFfhERESk7dMYpYiIiIiPUPATERER8REKfiIiIiI+QsFPRERExEco+ImIiIj4iDa9qjc/P5+rr77a02WIiIiIeI38/PyT/sxkGIbRirWIiIiIiIdoqFdERETERyj4iYiIiPgIBT8RERERH6HgJyIiIuIjFPxEREREfISCn4iIiIiPaNP7+P2YxsZG7r33XvLz82loaOCOO+6gU6dOzJkzB5PJROfOnXnooYfw8/Pj8ccf5+uvv8bhcDBp0iSuu+46SktL+dWvfkVdXR1xcXHMnz+fkJCQ466xf//+E77eM888w4YNGzCZTNx///307t37hDXu37+fO++8k/feew+AgoICZs2ahWEYREVF8X//938/uKa38GR/AWpra5k8eTK//OUvGTly5A/qe/bZZ/nkk08wm83ce++9x/0dzJs3j/T0dKZMmeLeJp0lb+/td8///nv36NGjjB8/ni5dugAwduxYbrzxRjd26ex5e39PdM2DBw8yZ84cDMMgISGBRx991Cs/GzzZ29tvv52jR48SEBBAUFAQf/7zn09Y49atW3nqqadYvHgxALt27eKBBx7AMAy6devGAw88gL+/v9t7dTa8ub8nqm3MmDHcc889FBcXA017y/Xp04dnnnmm1Xp2ujzZ23feeYc33ngDp9PJmDFjuPPOO09Y439/7p7ONU/IaKfefvttY+7cuYZhGEZpaakxatQoY/r06cYXX3xhGIZhPPDAA8aqVauMzz//3Pif//kfwzAMo76+3hg7dqxx9OhR49FHHzX+8Y9/GIZhGC+++KLxl7/85QfXONHrZWdnGzfccIPhcrmMgwcPGpdffvkJ6/vnP/9pXHXVVcbQoUObv/fYY48Zr732mmEYhvH0008bf/3rX1umGW7gqf5+Z86cOcbEiRONTz/99AfP27ZtmzFt2jTD5XIZ+fn5xtVXX20YhmGUlJQYN998szFmzBjjb3/7W8s1o4V5c28N48Tv3fXr1xuPPPLIuf/hW4E39/dk15wxY4axbNkywzAMY8mSJcaf/vSnFupGy/Jkby+55BLD5XL9aH0vvfSSMWHCBOMnP/lJ8/fuuOMOY9OmTYZhGMbs2bOP+7vyNt7c3xPV9n1Hjx41rrjiCqOwsPDsG+BGnurt/v37jWuvvdaora01nE6n8cwzzxgNDQ0/eO6JPndP55on0m6Hei+++GLuvvvu5q/9/f3Jzs5m0KBBAIwcOZINGzbQr18/5s2b1/w4p9OJ2Wxm8+bNjBgx4rjH/rcTvV6PHj1YtGgRJpOJgoICYmJiTlhfVFQUr7322nHf6969OxUVFQBUVVVhNnvvDVlP9Rdg0aJF9OvXj27dup2wts2bNzN8+HBMJhMJCQk4nU5KS0uprq5mxowZTJw4sWWa4Cbe3Fs48Xt327ZtZGdnc/3113PXXXdRVFR0ln969/Pm/p7smrt27Wq+O9i/f382b958Li1wG0/1tri4mIqKCm6//XamTJnCxx9/fML6UlJSWLBgwXHfW7BgAeeffz4NDQ3Y7XZsNtu5NcGNvLm/J6rt+xYsWMD1119PXFzcOXTAfTzV2w0bNtCzZ09mz57N9ddfT//+/QkICPjBc0/0uXs61zyRdhv8wsLCCA8Pp6qqirvuuouZM2diGAYmk6n555WVlQQFBREVFUVjYyNz5sxh0qRJhIWFUVVVRURExHGP/W8nej0As9nMM888w/Tp05kwYcIJ67vgggsIDQ097nsdOnTg9ddf57LLLmPt2rVcfPHFLdaPluap/n7++efs37+f66677qS1VVVVER4eflytlZWVJCcn06dPn5Zsg1t4c2/hxO/djIwM7rrrLl577TXGjh3L3LlzW6IVbuHN/T3ZNbt3786aNWsAWL16NbW1tS3dlhbhqd42NjZy00038ac//Ylnn32W+fPnU1JS8oPnjh8//gf/oPb39yc/P58JEyZQVlZGenp6S7elxXhzf09U23dKSkr4/PPPvfqIVU/1tqysjK+++orHHnuMBQsWMHfu3OYbQN93os/d07nmibTb4Adw+PBhbrjhBiZOnMjll1/ePMcGoLq6msjISADKy8u55ZZbyMzMZPr06QCEh4dTXV193GNXrFjBtGnTmDZtGtu2bTvp6wHcc889rFu3jkWLFnHgwAGmT5/OtGnTePTRR09a7xNPPMH8+fNZvnw59913H7Nnz27RfrQ0T/T37bffZseOHUybNo1169bx5JNPkpOTc1x/v//a3z33u/852gpv7e3JZGVlMXjwYADGjRvH9u3bW7wnLcmb+3uia86ePZs1a9Zw88034+fnh8ViaZU+nQ1P9DYmJobJkydjNpux2Wx0796dvXv3Nj/v+eef/9GaExMTWbVqFVOmTOF3v/tdS7ekRXlzf/+7tu+sWLGCCRMmeO3cye94orfR0dEMGjSI8PBwbDYbmZmZ7Nu377Q+d090zdNyWgPCbZDdbjcuvvhiY8OGDc3f++/x9eXLlxu1tbXGlVdeaSxduvS45z/yyCPHjZ2/+OKLP7jGiV5vw4YNxsMPP2wYhmE0NDQYV1xxhXHgwIGT1vn98fobbrjB+OabbwzDMIzs7Gxj0qRJZ/NHbxWe6u/3zZ49+4TzpL799lvjhhtuMJxOp5Gfn/+DeZZ//OMfvXqOnzf39vu+/969++67m19j9erVxl133XW6f9xW5839Pdk1//nPfxo5OTmGYRjGokWLmucCextP9faTTz4xbr31VsMwDKOqqsq45JJLjJKSkhPWePDgwePm+E2fPt3Yu3evYRiGsXz5cmPOnDln+ad3P2/u74lq+86dd95pbNu27Rz+5O7nqd7m5uYaEyZMMOrq6ozq6mpj3LhxRllZ2Unr/P7n7ulc80RMhmEYZxSJ24i5c+fywQcfkJGR0fy9++67j7lz59LY2EhGRgZz585l8eLFPPvss3Tv3r35cfPmzSMkJITZs2dTXV2NxWLh//7v/35wm3Xv3r088MADx70ewCOPPEJeXh4ul4trr732R4d2hg0bxvr164Gm1WWPPPIILpcLwzC477776NGjR0u2pcV4qr/f/xfjnDlzuPTSS0+4MnLBggWsXbsWl8vFb37zGwYOHHjcz2JiYrx2Va+39/Y733/vHjx4kHvvvReAkJAQ5s6d67Vzeby5v6+88soJr1laWspvf/tbAgMD6dy5Mw8++OAJ5wF5mid7+9hjj7F161b8/Py45ZZbGDt27AlrPHToEL/4xS9YsmQJAF9//TVPPPEEAQEBeu9y9v09UW0LFy4kODiYyy67jDfeeOP070h5gCd7+8orr7Bs2TIMw+DGG2/kyiuvPGmd3//cLS4uPuU1T6TdBj8REREROV67nuMnIiIiIv+h4CciIiLiIxT8RERERHyEgp+IiIiIj1DwExEREfERCn4iIuegvr6eCy+80NNliIicFgU/ERERER9hPvVDRETk+6qrq/nVr35FRUUFKSkpALz++uv861//ws/Pj/79+3v9kYsi4pt0x09E5Az985//pEuXLrz++utMnjwZgHfeeYf77ruPt956i+TkZBwOh4erFBH5IQU/EZEztHPnTnr16gVAnz59MJvNzJ8/nzfffJPrr7+egoICdCiSiHgjBT8RkTOUkZHBli1bANi+fTsOh4MlS5bw29/+ltdee42cnBy++eYbD1cpIvJDmuMnInKGpk6dym9+8xumTJlCRkYGAQEBdO3alWuvvRaLxUJ8fDx9+vTxdJkiIj9gMjQeISIiIuITNNQrIiIi4iMU/ERERER8hIKfiIiIiI9Q8BMRERHxEQp+IiIiIj5CwU9ERETERyj4iYiIiPgIBT8RERERH6HgJyIiIuIjFPxEREREfISCn4iIiIiPUPATERER8RFeF/xKSkoYNWoUu3fv9nQpIiIiIu2K2dMFfF9jYyMPPvggwcHBp/X4wYMHk5iY6OaqwOFwYDZ7VavaHfXYvdRf91OP3Uv9dT/12L1as7/5+fls3LjxhD/zqr/hxx9/nMmTJ/PSSy+d1uMTExN555133FwVFBQUkJCQ4Pbr+DL12L3UX/dTj91L/XU/9di9WrO/V1999Ul/5jXB75133sFqtTJixIjTDn4Oh4OCggI3VwZ2u93t1/B16rF7qb/upx67l/rrfuqxe3lLf70m+P3jH//AZDLx+eefk5OTw+zZs3n++eeJjY096XPMZnOrpWf9K8j91GP3Un/dTz12L/XX/dRj9/KG/npN8Hv99deb/3vatGk8/PDDPxr6REREROTMeN2qXhERERFxD6+54/d9ixcv9nQJIiIiIu2O7viJtHMvbNjH1W/mkV9e6+lSRETEwxT8RNoph9PFjHe+5Y5/fMvG/CrGPP85VfUOT5clIiIepOAn0g6V1zYyYdEmnl2/j6n9E3liXAo7i6uZ8OdNOF2Gp8sTEREP8co5fiJy9nYXV3P5y5vYYa/m/rGdmTkyg/ryYswh4fxi2Xam/30rf57U19NlioiIByj4ibQja3eXcPUrX9LoNPjTVT2ZNjCJ0EAzBeVwz6hMcouqeOmLA3SNDefXF3bydLkiItLKFPxE2om/bDrA9Lf/TcfIYH5/xXlcfl48Zv/jZ3M8d01vdhVX85v3c+gSG8bEXh09VK2IiHiC5viJtHFOl8Gsd7dz01tb6ZcYxSuT+nBV744/CH0A/n4m/vXzQWTGhDH1b9/wTX65ByoWERFPUfATacOq6h1c/cqXPPnJbq7t3ZFXJ/flgs4/fuJNRLCZ1bcPISTAn0sXbuSwtnkREfEZCn4ibdSBshqGP7ue97YX8qvRGTx7VU+6xUec1nOTokN4/5ZBHK1tZNyLG6ltdLq5WhER8QYKfiJt0Mb9ZQz6/WfsKq7m9xPP46GLuhIfGXxGr3F+ioW//rQf2wsrueovX+LSNi8iIu2egp9IG/PmN/mMem4DZj8Tf5nUh9uHphEedHbrtH7SJ4FHL+nKyjw7d/9rWwtXKiIi3karekXaCMMweHjlDh75cAd9EyJ5ckJ3xnSJxWQyndPr3jumM3lF1Ty7fh9d4sKZMTy9hSoWERFvo+An0gbUNjr52RtbWLK1gAnd45h/WXd6doxskdc2mUwsmtSHPSXV3LM0m062UC7pHt8iry0iIt5FQ70iXu5wRR2j/rSBv28tYMbwNF76Se8WC33fCfD3471bBpMaHcLkxV+TfbiiRV9fRES8g4KfiBf75lA5g36/jm8PV/DkhO7MvaQbHaNC3HKt6JAAPrp9CP5+Ji5euBF7Vb1briMiIp6j4Cfipf717WGGP7ueRqeLRdf1YcaIDCKDA9x6zXRbKMtuGkRRVT3jX/qCeoe2eRERaU8U/ES8jGEYPL5mF1e/+hXp1lBendKPKf0TCTS3zv+uwzOs/Pm6PnyTX8F1f92MYWibFxGR9kLBT8SL1Duc/PzNLcxZnsPYzjH8dUpfxneLO+eVu2dq2sBk7hvbmWXZhcx6L6dVry0iIu6jVb0iXsJeVc/Vr3zFZ3tLuXVwCg9e1Jmk6FCP1fPoxV3JK6riqU920zU2jFuyUj1Wi4iItAyvCn5Op5P777+fvXv34u/vz/z580lJSfF0WSJul32kkssXbaKgvI7HLunK/wxLJzrEvfP5TsVkMvHa1H7sK6vhzne+JTMmjAs6xXi0JhEROTdeNdT78ccfA/Dmm29y1113MX/+fA9XJOJ+K3KLGLrgMyrqGnnx2l78cnSmx0Pfd4LM/qy4NYv4iCCueeUrdtqrPF2SiIicA6+64zd27FhGjx4NQEFBATExP353weFwUFBQ4Pa67Ha726/h63yxx4Zh8PI3dh7+5CBp0UE8PDqJCxL9KSkqbPFrnWt/X78qg0tfz2XMc+tZOa07UcFe9dHhFXzxPdya1F/3U4/dy1v663Wf3mazmdmzZ/Phhx/yxz/+8ZSPTUhIaJW6Wus6vsyXetzodHHXP7fxwucHGZVh4/EJ3RmcanHrNc+lvwkJsPTnkYxfuJGfLdvPZzOGEeDvVQMGXsGX3sOeoP66n3rsXt7QX6/85H788cdZuXIlDzzwADU1NZ4uR6RFldU0cMnCjbzw+X5uGJDEK1P6uD30tYQLu8Ty3DW92HTwKNe//rW2eRERaYO86o7fv/71LwoLC5k+fTohISGYTCb8/f09XZZIi9lpr2LCok3sKanhoXFduGtkOtbQQE+XddpuzUolt6iKpz/dQ7e4PH57cTdPlyQiImfAq4LfRRddxG9+8xumTp2Kw+Hg3nvvJSgoyNNlibSIj3cVc80rX+EyDJ6/phdTByQREtD2/mHz5IQe7LBX8+iHO+kSG87UAUmeLklERE6TVwW/0NBQ/vCHP3i6DJEWt/CL/fzPP74lOTqYZyaex4QeHfD3a91NmVuKn5+JJTcMIOsP67h5yVbSbSEMTbN5uiwRETkNXjnHT6S9cLoMfrE0m9v+/m/OT47mlcn9mNizY5sNfd8JCfBn1fQh2EIDuGLRl+wv1VxcEZG2QMFPxE0q6hqZ+PImnlm7h0l9E3h1Sl9GZrafO2PxEUGsvC2LOoeLcS9+QWWdw9MliYjIKSj4ibjBvtIahi1Yz4rcIuZckMmCq3rSOTbc02W1uJ4dI1lywwD2lFRz6Z834nC6PF2SiIj8CAU/kRa2YW8pg36/jn2lNfzhyp7cP64LseHtd5HSpd3jefqK85rOGF6y1dPliIjIj/CqxR0ibd1rmw9x81tbiQsP5PlrejGxZwfMPrDR8V0jM8gtquL5z/fTNS6cOWM6e7okERE5AQU/kRbgchk8sCKXeat3MSApiscv686FnWMwmdr2Io4zseDqXuwsrua+D3LpHBvGNb09v0O9iIgcr/3fihBxs+p6B9ct3sy81bu4smcHXp3SlzFdYn0q9AH4+5n418/Pp0tsODf8bQubDx31dEkiIvJfFPxEzkF+eS0jn9vAO/8+zMyR6Tx/TS/O6xDp6bI8JizIzEe3ZxEe5M9lf95EQXmtp0sSEZHvUfATOUubDx5l0O/XkVtYxdNX9OCR8d3oEBns6bI8LjEqhPdvGUxFXSPjXvyCmgZt8yIi4i0U/ETOwttbCxjxp/W4DFh0XR/uHJ5ORLCmzH5nQHI0r/20PzmFVUx8+UtcLsPTJYmICAp+ImfEMAwe+2gHP/nrZjrHhPHq5L5M6pdAgA+s3D1TV/fuyLxLu/HRzmLufOdbT5cjIiJoVa/IaatrdHLLkq28/nU+l3SLZf6l3eiTGO3psrza7As7kVdUxQvHtnmZOTLD0yWJiPg0BT+R01BYWc9Vf/mSz/eXccfQVO4b05nE6BBPl+X1TCYTC6/rw57SGn717nY6x4RyWY8Oni5LRMRnaXxK5BS+PVzBoN+v4+v8cn53WTfmX9pdoe8MmP39ePfmQaRbQpi8+Gu+LSj3dEkiIj5LwU/kR7y3vZChf/yM2kYnC3/Sm5kjM4gKCfB0WW1OZHAAH90+hECzH5cs3ERRZZ2nSxIR8UkKfiInYBgGT3+6myte3kRSdAivTunL1P5JBJn9PV1am5VqDeW9mwdRXN3AuBc3Utfo9HRJIiI+R8FP5L80OFzc9vd/88tl2xmdaePVyX25pHs8fn6+dRKHOwxJs/KXyX349+EKrn31KwxD27yIiLQmBT+R7ympbmD8S1/w540H+Pn5yfxlUl8GpVo8XVa7MqV/Eg+N68LynCJ+uSzb0+WIiPgUr1nV29jYyL333kt+fj4NDQ3ccccdjBkzxtNliQ/JLazk8pc3sb+slkfGd+V/h6dhCQ30dFnt0kPju5Brr+KZtXvpFhfObUPSPF2SiIhP8Jrgt2zZMqKjo3nyyScpKyvjqquuUvCTVvPRDjvXvvoVfiYTL1zTm5/2TyQ4QPP53MVkMvHXKf3YW1rD//5zGxm2MMZ2ifV0WSIi7Z7XDPVefPHF3H333c1f+/vrl660juc37OPihRuJCQvk1cl9ufH8ZIW+VhBo9uODWwfTMTKYa1/9iryiKk+XJCLS7nnNHb+wsDAAqqqquOuuu5g5c+Ypn+NwOCgoKHB3adjtdrdfw9d5oscOl8FvPznIy9/YGZQYxm+GJzLA6qLwyOFWr8XdvPk9/PqV6Vz2tzzGPLeeVTd0J7qNnnnszT1uD9Rf91OP3ctb+utVn7CHDx/mzjvv5Kc//SmXX375KR9vNptJSEhohcpotev4stbscXltI5MWb2Zlnp2f9kvkkYu7khkT1mrX9wRvfQ8nJMDSm6K46KUvuHHpPtbPGE6g2WsGI86It/a4vVB/3U89di9v6K/XfLoWFxdz00038etf/5prr73W0+VIO7anpJohCz5j9c5i7hvTiT9e1bPdhz5vN7pTDC9c25uvDpUz9fWvtc2LiIibeE3we+GFF6ioqOC5555j2rRpTJs2jbo67e4vLWvdnhIG/X4d+UfrePaqntw7tjO2MK3c9QY3DUrh16Mzefvfh7nvg1xPlyMi0i55zVDv/fffz/333+/pMqQde2XTQW57eysdI4P5/RXncfl58Zj9vebfPgI8PqE7O+xVzF+9i25x4dwwMNnTJYmItCv6rSftnstlMPu97fz8rS30S4jilUl9uKp3R4U+L2QymXhz2gD6JkRy25J/89neEk+XJCLSrug3n7RrVfUOrn7lS574eDfX9OrAq1P6ckFn7RfnzYID/Fk1PYuYsECufPlL9pZUe7okEZF2Q8FP2q2DZbUMf3Y9724v5FejMvjT1b3oFh/h6bLkNMSGB7Fq+mDqHS7GvfgFFXWNni5JRKRdUPCTdmnTgTIG/WEdu4qreeaK83hofFfiI4M9XZacgR4dIvnHjQPZV1bLJQs34nC6PF2SiEibp+An7c6b3+Qz6k8b8DeZeHlSH+4YlkZ4kNesY5IzcFG3OP4w8Tw27CvjZ29u0TYvIiLnSL8Npd0wDIPfrtrBb1ftoG9CJE9O6M6YLrGYTCZPlybn4M7h6eQWVfHs+n10iwvn/nFdPF2SiEibpeAn7UJto5Ofv7mFt7YUMKF7HPMu7U6vhEhPlyUt5A9X9mRncTUPrsijS0wY1/VL9HRJIiJtkoZ6pc07XFHH6Oc2sGRLAf87LI0Xf9Jboa+d8fMz8c7PBtIjPoKfvbWFLw+UebokEZE2ScFP2rQt+eUM+v06/l1QwZMTuvPYpd1IiArxdFniBqGBZj68PYvIIDMTFm3iYFmNp0sSEWlzFPykzVq67QjDn11Po9PFn6/rw4wRGUQGB3i6LHGjjpHBrLgti6p6J+Ne/IKqeoenSxIRaVMU/KTNMQyDJ9bs4qpXviTVEsKrU/oxpV8igWa9nX1B38QoXp/aj53F1Vy+aBNOl1b6ioicLv2mlDal3uHkpre2Mnt5DmM7xfDXKf0Y3y0OPz+t3PUlV/bqyOOXdeeT3SXc8fa/PV2OiEibcc6rerdt20bPnj1bohaRH1VcVc/Vr37Fuj2l3Do4hQfGdSbZEurpssRDfnVBJ/Ls1SzceICuceH8cnSmp0sSK5wptgAAIABJREFUEfF65xz8Fi1aRH5+PldccQVXXHEF/9/enYdFVbYPHP8Oq2zCsAsCghuaC6KAilu+WGZhSWKaYub2Vr9cUHNJLXPfzSVIyy1UUnvVTM1Sy11xzyVlzRVkUURB2ef3BzppaakwzMDcn+vyUmfOPOc+N8Mz9zzPec6pWlVWU4qy9/v1O7y29AjXsu4x5ZW6fBDoiY2ZnM+n7yLfbEhCRg6jtvxObXtzOjeopu2QhBBCp5V6qnfevHl89dVXKBQKhgwZwvDhw4mJiSmL2IQA4KcLabRYuJ/buQUs6dqIYW1rStEnADAyNGBzX3+87Cx4e/VJTl3L0nZIQgih08rkHL+MjAySk5PJzMxEqVSyfft2xowZUxZNCz23aP8fdPo6BmdLU1Z29yGsmRtVjA21HZbQIVZVjNj5XnOqGBnQ6asYrt/O1XZIQgihs0o91RsaGkqVKlUIDQ1lyJAhmJiYANCvX79SByf0V0FRMUM3nSPi4EXaeNky49X6NK+h1HZYQke5K83Z2j+AthEHeWnxYWKGtsZMviAIIcTflLrwmzVrFjVq1Pjb40uXLi1t00JP3bpXQOjKY+yMzyCsaXUmvFQHL3sLbYcldFyAh5KVPXzoEXWCkOVH2do/QFZ7CyHEXzx34ffWW2+hUDzaqapUKhQKBd9++22pAxP6KSEjh9e+jiHxxl0+6VCbwa29sLMw0XZYooJ4y8eVuPSSe/oO/f4sC7o01HZIQgihU5678Js7d+5jHy8oKHjuYAB+++03Zs+eTVRUVKnaERXP7oQM3lx5jKJiFREhDenVrLpM14lnNi6oNhdSs1m4/yLejpZ8EOip7ZCEEEJnPPfiDldXV1xdXfnxxx/V/87JySE8PPy5g/nqq68YN24ceXl5z92GqJiiz2TQYfFhrKsYsbK7D30D3KXoE89FoVCwoocPLTyUDNl0jp9j07QdkhBC6IxSn+MXFxdHdHQ0d+/eZdOmTXz22WfP3Za7uzsLFy5k5MiRpQ1L6LDiYhVXs+4Rm5ZDXHo2By9msubkNZq72zD9tXq0rWmv7RBFBWdsaMDW/v74zt1L6MrjHBocSH1nucaoEEKUuvCbPn06I0aM4ObNm/zvf/9Tr+p9Hi+//DJXr1596u0LCwtJTk5+7v09rfT0dI3vozLKvFdIUmYuSZl5JGbmkpSZS+LNXP7IzCOv6M/7q5oZGfCKpzlj2rlS0yy/XH6m+kZf38NrQmrScdV5Onx5kJ961cPWXHPXf9TXHJcXya/mSY41S1fyWyaLOwoKCoiNjaV3794A5ba4w8jICBcXl3LZV3ntp6LJLSgi8cZdYtOyiUvPJi49h9j7f2fk5Ku3M1SAq7UZHkpzmrrb4aE0w93GjDqOltSyMyf/9k1qelTX4pFUfvr4HnZxgS39qxL05SF6b77IocGtMDXS3CkE+pjj8iT51TzJsWbpQn7LfHGHqHyKi1VcuXWPuPSSqdnYB3+nZXPp1j1Ufw7eYW9hgruNGa09bUuKO6UZXnbm1HO0wsHShKpVjDF8zCU2ku/dKscjEvqktZcdS0Ib0+fbU3SPOsGGPs3+dkUCIYTQF89d+Lm6ugKQmprKrFmzyMzM5OWXX6Zu3brq50TFcvNufsmI3cOjd2nZxGfkkFtYrN7O3NgQd6UZdR0t6VDHAQ+lGR5KM+o7W1Hd2gxrMyONjqoI8aze8XMjNi2bab8kMGbreaa/Vl/bIQkhhFaU+hy/8ePH8+677xIREUGzZs0YPXo069ate+72qlevXqrXi3/216nZB6N3j52atTHDw8aMNxtVU0/N1nW0pKadOUpzEyxMDGXkRFQYUzp5E5uezYxfE6nraMm7/u7aDkkIIcpdqQu/vLw8WrRoQWRkJF5eXpiampZFXKIUymNqVoiKRqFQsKaXL4ELD/Ded6fxtDWnXS1ZQS6E0C+lLvxMTEzYt28fxcXFnDp1qlSresWz+evUbGx6DnHPODXram2GjUzNCj1hamTI9gEBNJm7l5AVxzgytDW15HaAQgg9UurCb9KkScyYMYPMzEyWLVvGhAkTyiAs8UBuQREJGTmPHb27cffPu6TI1KwQT8fe0pQd/22O//z9vLT4ECeGtcXGTHOXeRFCCF1S6sLP2dmZwYMHc/nyZerWrYuTk1NZxKVXHp6afXAplH+bmm3jZSdTs0I8J28nKza805SOXx+h45LD7PswEGPD576RkRBCVBilLvxWrVrFjh07yMrKokuXLly6dIlPPvmkLGKrdGRqVgjdEVTXkUVdGvD+/87wTvRJVvf0lRFxIUSlV+rCb+vWraxZs4bevXvzzjvv8Oabb5ZFXBXWw1Ozfx29e2Rq1kCBq3UVmZoVQovea1mDC2nZzN/3B3UcLJnwcl1thySEEBpV6sJPdX8e8kGBog+LOx43NftgFO/fpmY9lGZ4ytSsEDpjbucXiM/IYeLPcdR1sKSHr1yHVAhReZW68OvUqRO9evXi2rVrDBgwgKCgoLKIS2cUFavYeP4myaey/nFq1kOmZoWokAwMFHz3TjMC5u+j79pTeNqa07yGUtthCSGERpS68Nu0aRPu7u707NmTmjVrUrdu5Zoq2f/HDT7c9scjU7NdG1XDXaZmhag0zIwN+Xlgc5rM3UvwsiMcH9oad1tzbYclhBBlrtSF34YNG0hMTOSXX37hm2++wd7enkWLFpVFbDqhbU17dvepTzUnJ+wtTbCWqVkhKiXnqlX4aWAALRceoMPiwxwf1gZL01J3kUIIoVNKff2CCxcusHv3bg4fPgyAl5dXqYPSNbXtzKjjaImtuYkUfUJUYo1crPk2zJeEGzm8+nUMRcWqf3+REEJUIKX+OtuzZ0/c3NwIDw+nbdu2ZRGTEEJozWv1nZkdXJ9hm39nwLrfWNbdR9shCSFEmSl14RcTE8Px48fZv38/y5Ytw87Ojrlz55ZFbEIIoRXhbWsSm5bN4sOXqetgwaj/1NZ2SEIIUSZKPdV7+/ZtUlNTSU5OJjc3FxcXl7KISwghtOqLNxvRvpY9H/94gU1nUrQdjhBClIlSj/j179+foKAg3nvvPWrXlm/FQojKwdBAwfd9/Wg2bx+91pxk7/+Z4VvdRtthCSFEqZR6xG/Dhg188MEHUvQJISodS1Mjdr7XHDNjQ179+ggpWfe0HZIQQpSK3JVcCCH+QXUbM7b19+fWvQKCFh/mbn6htkMSQojnJoWfEEL8Cz93JVFvN+F8ajZvLD9KsVzmRQhRQUnhJ4QQT6FrYxemvOLNjrgMBm08q+1whBDiuejUZemLi4uZMGECsbGxmJiYMHnyZDw8PLQdlhBCADD6P7W4kJZNxMGLeDtaMKh15btgvRCictOpEb+dO3eSn5/P2rVrGT58ONOnT9d2SEIIoaZQKPj6rcYE1lASvvl3tp1P1XZIQgjxTHSq8Dt+/DitW7cGwMfHh7NnZTpFCKFbjA0N2NI/AA8bM7pHneBsym1thySEEE9Np6Z6s7OzsbS0VP/f0NCQwsJCjIweH2ZhYSHJyckajys9PV3j+9B3kmPNkvyWvdUhXnSMOk+HLw/yc1g9inNuaTukSk3ew5onOdYsXcmvThV+lpaW5OTkqP9fXFz8xKIPwMjIqNzuFCJ3JNE8ybFmSX7LlosLbB1gTfvIg4R9f5GNoTUlxxom+dU8ybFm6UJ+dWqq19fXl7179wJw6tQp6tSpo+WIhBDiyQI9bfm6W2N+S77NK6vOc/CPm9oOSQgh/pFOjfh16NCBAwcO0L17d1QqFVOnTtV2SEII8Y/CmrlRUKTiox/OEbjoACENnZnb+QU8bM21HZoQQvyNThV+BgYGTJw4UdthCCHEM+kb4E6gI8w9lsnKo1fZ+nsaQ9p4Mi6oDlZVdKqbFULoOZ2a6hVCiIrKytSIxaGNOf1RW9rVsmPmr4l4Td3F0pjLcqcPIYTOkMJPCCHKUB0HS7YPbM5PAwNwtjKl/7rfaDR7D3sSM7QdmhBCSOEnhBCa8FJdR34b3pYFbzQg424+7SIOEbw0hsSMnH9/sRBCaIgUfkIIoSEGBgoGtfYkYXR7Pgyswc64DOrN/JVh358j616BtsMTQughKfyEEELDLKsYsTCkIedGtuPlOg7M25uE55RdRB64SGFRsbbDE0LoESn8hBCinHjZWfBD/wB+fb8FHkozPthwhgaz9rAzTjeu6C+EqPyk8BNCiHLWrpY9J4a1YXHXRtzJK6DD4sN0XHKYuPRsbYcmhKjkpPATQggtUCgUDGzhQcKY9gxr48n+P25Sf+ZuPtxwhsy7+doOTwhRSUnhJ4QQWmRmYsSc1xtwfuSLdK7vRMSBi3hO2cX8vUkUyPl/QogyJoWfEELoADelGRve9WP/h4HUtrdg6PfnqDfjV348n6rt0IQQlYgUfkIIoUNaetpyZGhrlr/VmPyiYjp9fYT2EQc5d/2OtkMTQlQCUvgJIYSOUSgU9PF3J350e0a3r8nRK7doPHs3A9f9RkZ2nrbDE0JUYFL4CSGEjjI1NmTaq/WJG9OekIbVWHbkMp5TdjHr1wTyC+X8PyHEs5PCTwghdFy1qlVY904zDg1uRQNnK0ZuOU+d6b+w6UwKKpVK2+EJISoQKfyEEKKC8HNXcnBwK1a/3QQF0GXFMdp8cZDTybe1HZoQooKQwk8IISoQhULB202rEzemPZ90qMOZlNs0mbuHd6NPkXpHzv8TQvwzKfyEEKICMjY04LOOdYkb/SLdfVyIOnGVmlN3MXVnPLkFRdoOTwiho6TwE0KICszRqgqrezXl2NBW+Fa3ZuyPF6g97RfWn7om5/8JIf5GCj8hhKgEfFxt2Pt/gazv3RRTIwO6RZ2gxYL9HL9yS9uhCSF0iM4Vfjt27GD48OHaDkMIISqkro1duDDqRSZ1rEtceg5+n++j1+oTJGflajs0IYQO0KnCb/LkycyZM4fiYrk+lRBCPC8jQwPGdahDwpj29G5WnbWnkqk1bRcTtsdyN79Q2+EJIbTISNsBPMzX15egoCDWrl37VNsXFhaSnJys4aggPT1d4/vQd5JjzZL8ap6u5nhqWyd616/KJ79e5rMdcUQeTOKTttV5w9sWhUKh7fCemq7mtzKRHGuWruRXK4Xf+vXrWbly5SOPTZ06lU6dOhETE/PU7RgZGeHi4lLW4T1Wee1Hn0mONUvyq3m6mmMXF2jfuCabz15n5Jbf+XDbRZb9lskXbzakuYdS2+E9NV3Nb2UiOdYsXcivVgq/0NBQQkNDtbFrIYTQW50bOPNqfSfm7klkxi8JtFiwn66NqjG38wu4Kc20HZ4Qohzo1FSvEEIIzTI0UPDRi7UYGODBR1t/Z+XRK/zweyrD2nrx8X9qY2kqHwtClIV7BUXsT7rJrvgMdsanY6gq4nB4Na2fYiG/4UIIoYeszY1ZEtqY4W1rMnjjWabtSmBpzGWmv1qPd5q5YWBQcc7/E0IXFBYVc/xqlrrQO3gxk7zCYgwNFDR0tqKVmyV384uw0PKXK50r/AICAggICNB2GEIIoRfqOlry03+bs/1CKiM2n6fv2t+YtzeJiJCGtPKy03Z4QugslUrFhbRsdsZlsCs+nV8TbnA7r2TVfB17C7o2qoafmw2tPJXUcbDizs00rRd9oIOFnxBCiPLX0duJl+o4snD/H0zdFU/rLw7y+gtOzHu9AZ525toOTwidcPXWPXbFZ5SM6sWlk3L//tiuVavQvrY9/m42NK+hpFG1qtiaGz8yrXtHW0H/hRR+QgghADAwUDCkjRd9/d0YvfUCy45cxnvGLwxu7cn4DnWoWsVY2yEKUa4y7+azO/GGelQvNj0HABszI/zcbOjr74afuw0B7kocLU0rxCkSUvgJIYR4hFUVY754syHD2noyeNM5Zu9OYtmRK0zrVI9+Ae4YVoAPNyGex72CIg788eeCjBNXsyhWgZmxAU1crRnq7Yi/uw0taihxtTbD2FCn7oPxVKTwE0II8Vg17S3Z2j+AX+LTCf/+d/773Wk+35vEF2825MVa9toOT4hSKypWcfzqrftTtxkcuHhTvSCjgbMV/fzd8Xe3IdDTlhq25pgZG2o75FKTwk8IIcQ/al/bgVPD27D40CUm/hxH+8hDvOLtyPw3XqC2g6W2wxPiqalUKmLTstl5/zy9XxMyyMotWZBR296CNxtWKyn0aiip42hZKU9vkMJPCCHEv1IoFLzXsga9m1Vn7I8XWHLoMvVn7ub9lh5M7OiNjVnl+4AUlcO1rEcXZCTfLlmQUc3KlHY17e5P3do+dkFGZSSFnxBCiKdmbmLEvNcbEN7Gi8Ebz7Jo/0Wijl9jUse6vNfCA6MKeM6TqFxu3Stgd0KGelTvQlo2ANZVjPB3s6GPnxv+bjb4udvgZFVF785ZlcJPCCHEM3NXmrOprz/7km4Q/v05Bm08y/x9f7CoSwNe9nbUdnhCj+QWFHHwYiY749PZFZ/BsSu3KFZBFSMDfF2tGdLaE383G1p6VtwFGWVJCj8hhBDPrbWXHUeHtmb50St8uj2Wjl/FEFTbnoVdGuDtZKXt8EQlVFSs4sTVLHbFp7MzPoMDf9wkt7AYQwW84FyVfv7u+LnZEOhlSw2lGeYmUuo8TLIhhBCiVBQKBX393Xm7iQufbI8j4uBFGszazYDmHkx+xRs7CxNthygqMJVKRVx6jvoSK78mZHDrXsmCjFr25nRp6Iy/W8nK27qVdEFGWZLCTwghRJmoYmzEzOD6DGnjyZCNZ1ly+BJrTlzj05frMKiVp95PsYmnl5yVy677U7c74zO4lpULgLOVKW287PB3K7mWXqNqVbGzMKn0CzLKkhR+QgghypSrtRnf9fHjyKVMBm86y/DNv7No/0Xmv/ECr9V3kg9p8TdZ9wrYnXhDvfL2/EMLMpq52dC7aXX83W3w19MFGWVJCj8hhBAa4e+h5NDgVqw+cY1xP16g87KjtPWyY1FIAxpUq6rt8IQW5RYUcehSJjvjSkb1jt5fkGF6f0HG4Fae+Lvb0PL+HTJMjGS0uKxI4SeEEEJjFAoFvZpWp1vjakzaEc+CfX/QeM4e3vVzY9qr9XCwNNV2iKIcFBWrOHkt6/719NLZl/Twggwr3vVzu3+HDDs8bWVBhiZJZoUQQmiciZEhk17xZlCrGgzZdI4Vx67y7alkxgXVJrytF6ZGFf9WWOJPKpWK+Iwc9dTtrwk3yLxXAICXnTldGjjj525DYI2SBRnWcgHwciOFnxBCiHLjaFWF6LCmfHTlFkO+P8eYbReIOHiRz19/gS4Nq8n5fxVYyu1c9R0ydsWnc+VWyYIMJytTWnna4u9uQ3MPG3xcrGVBhhZJ4SeEEKLc+brZsO/DQNadusaYbRd4c+VxWngo+SKkIU2qW2s7PPEUsu4VsCfxBrsSSkb1fk/9c0FG0+rW9PStjr9byYIM56qyIENXSOEnhBBCa7r5uBLSsBrTdsUzd28STeftpWdTV2a+Vp9qVatoOzzxkLzCIg5dzFTfCu3olVsUFaswNTKgiUtVBrWqUXKHjBq2VLeRBRm6SmcKvzt37vDRRx+RnZ1NQUEBo0ePpkmTJtoOSwghhIYZGRow/qW6fBDoSfj354g+eY0Np68zun0tRrxYEzNjOf9PG4qLVZxKzmJnXEmht++PG9wrKMZAAS84WfFOs5IRvVaetnjamcuCjApCZ35Ky5cvp3nz5vTp04ekpCSGDx/Oxo0btR2WEEKIcmJnYcI3bzfho3ZeDN50jk9+iuXLQxeZ0/kFWsvtfzVOpVIRn56tPk/vl4QMbt69vyDD1pzXX3AuuRWapy3esiCjwtKZwq9Pnz6YmJTc1qeoqAhTU1niL4QQ+qihizW/ftCSTWdSGLXlPD1WnaCGjSnVrP9Qb/Pw2WIPrxF4eMFAqbZ56JlHH3/89v/4mmdu92nie8L2z9lmUbGKmIs3uXYnHwAnSxMCa9ji52ZDgIcNTVytsZcFGZWCVgq/9evXs3Llykcemzp1Ko0aNSI9PZ2PPvqIjz/++F/bKSwsJDk5WVNhqqWnp2t8H/pOcqxZkl/NkxyXPX872BlWly+PXWfLhQxy8/NKnlD9uc1D/3zk30/cRqV67OOPvPQJr33yNo9u9XRxPKHdh7Z69jietM3jj/mv27tYKHijnhM+zhY0cTbH0cLk/oKMQgpu3yDl9hN2IJ6KrvQRWin8QkNDCQ0N/dvjsbGxDBs2jJEjR+Lv7/+v7RgZGeHi4qKJEP+mvPajzyTHmiX51TzJsWZMqe7K/yUnazy/qidVYzy5UFM//5zt/vtr/22//9L2M8Sddv06Hm6u//wCUSq60EfozFRvQkICQ4YM4fPPP8fb21vb4QghhNAz/zSNWboZzooxPWpsWDHiFKWjM4XfnDlzyM/PZ8qUKQBYWloSGRmp5aiEEEIIISoPnSn8pMgTQgghhNAsubqiEEIIIYSekMJPCCGEEEJPSOEnhBBCCKEndOYcv+dx7do1QkJCtB2GEEIIIYTOuHbt2hOfU6j+7QJDQgghhBCiUpCpXiGEEEIIPSGFnxBCCCGEnpDCTwghhBBCT0jhJ4QQQgihJ6TwE0IIIYTQE1L4CSGEEELoCb0p/JYsWUKfPn3o27cv/fr14+zZs8/dVmJiImFhYWUYXcUWExND3bp12bZt2yOPBwcHM3r06OduNzo6moULF5Y2vApr+vTphIWF0bFjR9q1a0dYWBiDBw8u032Eh4cTExNTpm3qkt69e3P69GkA8vPzadq0KUuXLlU/36tXLy5cuPCPbeTl5dG+ffsyj62y9yOPe/82b96c8PBwjexv7969pepvKpurV6/i6+tLWFiY+s+iRYse2SY8PJz8/PxHHpM8/t2z1A9r166loKDgufYTGBj4vCE+kwp9AeenlZCQwC+//EJ0dDQKhYLz588zatQoNm/erO3QKg0vLy+2bNlCp06dAIiNjeXevXtajqpie9D5btiwgaSkJEaMGKHliCqeVq1acezYMRo1asTx48dp1aoVu3fvpl+/fuTl5ZGSkoK3t7e2w6yUHvf+jYmJ4dtvv9VyZPqjVq1aREVFPfH5efPmlWM0FdOz1g+LFy/mjTfeKOcon41eFH62trYkJyfz3Xff0aZNG+rVq8d3331HWFgYEyZMoGbNmkRHR5ORkUGXLl0YPnw4zs7OXLlyhYYNG/LZZ5+RlpbGiBEjUKlUODg4qNvevn07q1evVv9//vz5rFixAicnJ3r27ElWVhbvvvsuGzZs0Mahlxtvb28uXrzI7du3qVq1Kps3byY4OJiUlBQ2b97MypUrMTExoUaNGkycOJEffviBPXv2kJuby+XLlxkwYAAhISEcO3aMqVOnYm1tjYGBAT4+PgDMmTOHs2fPkpOTQ82aNZk2bRrdu3dn0qRJ1K5dmz179rB7924+/fRTLWdCsx58cD7osAMDAzlw4AApKSmMHz+evLw8TE1NmTRpEra2tgwZMoTs7Gxyc3P56KOPCAgIYPXq1axfvx4HBwdu3LgBQHZ2NmPHjuXOnTtkZmYSGhpKcHAwXbp04aeffsLQ0JBZs2bRoEEDXnnlFW2m4Jm0bNmSiIgI+vbty549ewgNDWX27NncuXOHc+fO4e/vz5EjR5g3bx6Ghoa4ubkxceJE8vPzGTFiBLdv38bd3V3dXlhYGN7e3sTHx5Odnc38+fNxdXUlKiqKLVu2oFAo6NSpE7179+bnn3/mq6++wsjICFdXV2bOnElGRobe9yOXLl2if//+3Lx5kxdffJFBgwY9sS9+//33sbGxoU2bNpibm7Np0yYMDAzw9fVl1KhRJCYm8vHHH2NmZoaZmRnW1tYArFq1ip9//pnCwkKsrKxYuHAhY8aMITg4mHbt2pGYmMiMGTNYsmSJlrNRvmJiYpg9ezbGxsZ069aNBQsW8OOPP3L16lXJ4xM8qX44cuSIegQ1NzeXGTNmcOzYMdLT0wkPD+edd955bF89evRobt26xa1bt4iMjGTWrFkkJCTg5uamHn2Ni4tj+vTpFBcXc/v2bcaNG8fdu3dZt24dCxYsAKB79+4sWLAAR0fHZz4mvZjqtbW1JTIykhMnTvDWW2/RsWNHfv311yduf/HiRaZMmcL69evZu3cv6enpLF++nNdee42oqCiCgoIe2XbJkiVERUXh6enJ/v37CQ0NZdOmTQBs2bKF4OBgjR+jLujQoQM7duxApVJx+vRpmjRpwq1bt1i4cCErV64kOjoaKysr1q5dC5QUG4sXLyYyMlLdcUybNo05c+awfPlyqlevrt6uatWqLF++nG+//ZZTp06RmppKaGgoGzduBOB///sfXbt21c6B64AZM2YQFhZGVFQU/fr1Y/bs2Vy+fJmMjAy+/PJL5syZQ25uLnfu3OGbb75h3bp1REREqKckLl26xKuvvsqyZcv48ssvWbFiBVZWVjRt2pT9+/dTVFTE3r17+c9//qPlI3029evXJykpCZVKxdGjR/H396dFixYcPHiQI0eO0Lp1a8aPH8+iRYtYtWoVTk5ObNy4kY0bN1KnTh1Wr15N9+7dH2mzUaNGrFixgsDAQLZu3UpCQgLbtm1jzZo1rFmzhp07d5KUlMSWLVvo06cP0dHRtGrViuzsbOlHKJk6j4iIYPXq1axateoft01PT2fp0qUMGDCADRs2MHbsWNauXYubmxuFhYXMnz+fwYMHs2LFCpo0aQJAcXExt27dYsWKFaxZs4bCwkLOnDnzSH/x3Xff6UV/kZCQ8MhUb2pqKnl5eaxZs+aRUSnJ45M9qX6Ij49n1qxZfPPNN7Rv357t27cTGhqKg4PDv46kNm/eXP1ZlpeXx7p16xg+fLh6liwhIYFRo0axYsUK9Re+wMBA4uLiyMrKIiEhAaVS+VxFH+jJiN+lS5ewtLRk2rRpAJw5c4aBAwdib2+v3ubhO9e5u7tjaWkJgIODA3l5ecTHx/P6668D4OvrS3R0NAAi4lihAAAKx0lEQVR2dnaMGjUKCwsLkpKS8PHxwc3NDQsLCxISEvjhhx+IiIgor0PVquDgYCZMmICbmxvNmjUDSjqPWrVqqfPp5+fH/v37ady4sXqKrVq1aupvOqmpqXh6egIleb58+TKmpqbcvHmTYcOGYW5uzt27dykoKKBTp0506dKFfv36cf36dV544QUtHLV2PXjfxsXFsXjxYr7++mtUKhXGxsbUrl2bnj17MmzYMAoLCwkLCyMpKYlatWphYmIClBQxAPb29qxcuZKff/4ZS0tLCgsLAQgNDSUqKori4mJatmypfl1FYWBggLe3N3v37sXBwQETExPatGnD7t27uXDhAj179iQtLY2hQ4cCJd/cAwMDyczMpHXr1gA0btwYI6M/u8r69esD4OzsTEZGBnFxcSQnJ9OnTx8AsrKyuHz5MmPGjGHx4sVER0fj5eVFUFCQ9CNA7dq11e+jh/P6wMN9cfXq1dXbTps2jWXLljF79mx8fHxQqVTEx8er38O+vr4kJSVhYGCAsbGxur+4fv06hYWFBAQEMGXKFG7cuMGBAwcYNmxYORytdv11qjcmJkbdvz5M8vhkT6ofRo4cyZQpUzA3Nyc1NRVfX99/bOfh9/WDn8HDeXdxcaFatWoAODo6EhERQZUqVcjJycHS0hKFQkHnzp3ZsmULV69eLVXBrRcjfrGxsUyYMIG8vDygJOlWVlbY2NiQnp4OwO+//67eXqFQ/K0NLy8vTp48CZT84AHu3LnDggULmDdvHpMnT8bU1FT9w+3WrRuRkZE4OTlha2ur0ePTFW5ubty9e5eoqCg6d+4MlOQyMTGRu3fvAnDkyBH1m/5xeXZwcCAxMRH4M8979+4lJSWFuXPnMmzYMHJzc1GpVJiZmak7oQcfppWdqamp+j177do1srKygJL354gRI4iKiuKzzz7j5ZdfJjY2lpycHJYsWcL06dOZNGkSbm5uJCQkkJubS1FREefPnwdg2bJl+Pj4MHv2bDp27Kh+Hzdr1owrV65U6G/2gYGBLF68WF3INW3aVP37rlQqcXZ2JiIigqioKN577z0CAgLw8vLi1KlTQEnf8KAQfhwvLy9q1arFN998Q1RUFCEhIdSpU4e1a9cyaNAg9ajWjh07pB/h8b/3JiYmj+2LDQz+/Ihat24dn332GatWreL8+fOcPHnykXw+OOH+woUL7Ny5k88//5zx48dTXFyMSqVCoVAQHBzMlClTCAwMxNjYWJOHqbMezukDkscne1L9MHXqVKZOncr06dNxdHRU/84qFAqKi4uf2Fc/2AZ4pJ9JTU0lNTUVgClTpjB48GBmzJhBnTp11G2/+eabbN++naNHj9K2bdvnPia9GPF76aWXSExMJDQ0FHNzc1QqFSNHjsTY2JiJEydSrVq1fx0yHTJkCOHh4Wzbtk09BWlpaYmvry9dunTB3NycqlWrkpaWBkBQUBATJ05k1qxZGj8+XdKpUye+//57PD09uXLlCkqlktdee43evXtjYGCAu7s7I0aMYOvWrY99/axZs9QjHxYWFlhbW9OoUSMiIiLo1q0bJiYmuLm5kZaWhpubG926daNHjx5MmDChfA9USxo0aICVlRWhoaHUrFlT/V4cNWqUunPKzc1l7Nix1KhRgy+++IJNmzZhbGzM4MGD1ef9de/eHVtbW8zMzAB48cUXmTBhAj/88AM2NjYYGhqSn5+PiYkJwcHBbN++ndq1a2vz0J9by5YtGTduHDNnzgRKigwrKyvq16+PgYEBY8eOZeDAgahUKiwsLJg5cyZ+fn6MGTOGHj164OXl9Y8fbt7e3rRo0YIePXqQn59Po0aNcHJyolGjRrz77rvY2NhgYWFBu3bteOmll6QfeYzevXv/a19ct25dunbtilKpxMnJicaNG/Ppp58SHh7O0qVLsbW1xdTUFA8PD8zMzAgJCcHExAQHBwd1PkNCQmjXrh3ff/99eR6ezpM8PtmT6oejR4/SrVs3qlatir29vTo3zZo1Y+DAgSxbtuyxffXDgoKCOH78OKGhobi4uKBUKgHo3LkzH3zwAXZ2djg7O5OZmQmAk5MTFhYW+Pj4PHa0/GkpVA+PP4oyc+/ePXr16sX69esf+w1LlI3Tp0+zatUq9Ye6KHtfffUVSqWywo74VWTSj5St1NRURo4cycqVK7UdSoUmedSe//73v3z88cd4eHg8dxvSk2jAiRMn6NatGx988IF01hq0atUqPv30UwYNGqTtUCqt0aNHc+zYMfXUvSg/0o+UrZ9++on+/fszfPhwbYdSoUketSM3N5eQkBC8vb1LVfSBjPgJIYQQQugN+RophBBCCKEnpPATQgghhNATUvgJIYQQQugJKfyEEJVSTEwMLVq0ICwsjF69etG9e3e2bdumkX2NHDmSbt26qa9BWZY2bNjA7Nmzy7xdIYR+0ovr+Akh9FPz5s3Vt0/KyckhLCwMT09P6tWrV6b72b9/PwcPHizTNoUQQhNkxE8IoRcsLCx466232L59O0VFRYwdO5Z+/foREhLC559/TnFxMR06dODWrVsArFmzhq+//vqRNg4cOEBoaCi9evXiww8/5Pbt20yYMIHbt2/z/vvvq7fLzMxU303m5MmT+Pv7U1RUxPXr1+nXrx8FBQV8/PHH9OzZkx49ehATEwOU3NmmR48e9OrVizFjxqjvpQxw8+ZNunfvzqFDhzSdKiFEJSaFnxBCb9jZ2ZGZmUlKSgo+Pj4sXbqU6OhooqOjMTAwIDg4WH1Xmc2bNz9yI3uVSsX48eNZtGgRq1atws/Pj8jISCZMmIC1tTWRkZHqbZVKJTY2NqSkpLBv3z6cnZ05d+4cu3btIigoiPXr16NUKlm9ejURERFMnDjxb+07OTmxceNGAG7cuMH777/PmDFjaNGiRfkmTQhRqchUrxBCbyQnJ+Ps7IyNjQ1nzpzh8OHDWFpakp+fD0DXrl0JDw/Hz88Pe3t77O3t1a/NzMzE0tISJycnAPz8/Jg7d+4T99WhQwf27NnDyZMnGTBgAAcOHODkyZNMmTKFL774guPHj3P69GkACgsLuXHjBmlpaQwdOhQouWBrYGAg7u7u7Nu3DwcHB4qLizWVGiGEnpDCTwihF7Kzs1m/fj3z589nw4YNWFlZMXHiRC5dusS6detQqVS4uLhgZWXFl19++bdb1CmVSrKzs0lLS8PR0ZEjR45Qo0aNJ+4vKCiIESNGoFQqadOmDX379sXKygoHBwe8vLxwdnbmvffeIzc3l8jISGxtbXF2diYiIgIrKyt27dqFubk5KSkpvPHGG7zxxhsMGTKE9evXY25uruFsCSEqKyn8hBCV1uHDhwkLC8PAwICioiIGDRqEl5cXRUVFDBs2jOPHj2NmZoaHhwdpaWk4OTnRrVs3Jk+ezKxZsx5pS6FQMHnyZAYNGoRCocDa2ppp06Y9cd/Ozs7k5eXRvHlzrK2tMTIyol27dgB0796dcePG0atXL7Kzs3n77bcxMDBg7NixDBw4EJVKhYWFBTNnziQlJQWAWrVq0blzZ6ZNm8akSZM0ljMhROUmt2wTQoiHbNu2jfj4eIYMGaLtUIQQoszJiJ8QQtw3d+5cjh07RkREhLZDEUIIjZARPyGEEEIIPSGXcxFCCCGE0BNS+AkhhBBC6Akp/IQQQggh9IQUfkIIIYQQekIKPyGEEEIIPfH/vEJQwYfD934AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot(forecast)\n",
    "model.plot_components(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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>date</th>\n",
       "      <th>cases</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>2020-05-07</td>\n",
       "      <td>1938</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>2020-04-21</td>\n",
       "      <td>1260</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>380</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>291</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>304</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>California</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>115010</th>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>18</td>\n",
       "      <td>Yuba</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115011</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>19</td>\n",
       "      <td>Yuba</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115012</th>\n",
       "      <td>2020-05-02</td>\n",
       "      <td>19</td>\n",
       "      <td>Yuba</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115013</th>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>19</td>\n",
       "      <td>Yuba</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115014</th>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>19</td>\n",
       "      <td>Yuba</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2820 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              date  cases   county       state\n",
       "1002    2020-05-07   1938  Alameda  California\n",
       "1003    2020-04-21   1260  Alameda  California\n",
       "1004    2020-04-01    380  Alameda  California\n",
       "1005    2020-03-29    291  Alameda  California\n",
       "1006    2020-03-30    304  Alameda  California\n",
       "...            ...    ...      ...         ...\n",
       "115010  2020-04-30     18     Yuba  California\n",
       "115011  2020-05-01     19     Yuba  California\n",
       "115012  2020-05-02     19     Yuba  California\n",
       "115013  2020-05-03     19     Yuba  California\n",
       "115014  2020-05-04     19     Yuba  California\n",
       "\n",
       "[2820 rows x 4 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cali=Prison_county1.loc[Prison_county1['state']=='California']\n",
    "Cali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "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>date</th>\n",
       "      <th>cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>2020-05-07</td>\n",
       "      <td>1938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>2020-04-21</td>\n",
       "      <td>1260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115010</th>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115011</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115012</th>\n",
       "      <td>2020-05-02</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115013</th>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115014</th>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2820 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              date  cases\n",
       "1002    2020-05-07   1938\n",
       "1003    2020-04-21   1260\n",
       "1004    2020-04-01    380\n",
       "1005    2020-03-29    291\n",
       "1006    2020-03-30    304\n",
       "...            ...    ...\n",
       "115010  2020-04-30     18\n",
       "115011  2020-05-01     19\n",
       "115012  2020-05-02     19\n",
       "115013  2020-05-03     19\n",
       "115014  2020-05-04     19\n",
       "\n",
       "[2820 rows x 2 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cali=Prison_county1.loc[Prison_county1['state']=='California']\n",
    "Cali=Cali[['date','cases']]\n",
    "Cali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cali.set_index(['date'], inplace = True) \n",
    "Cali.index=pd.to_datetime(Cali.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=Cali.resample('D').first().fillna(0)\n",
    "# data=data[:20]\n",
    "x=np.arange(len(data)).reshape(-1,1)\n",
    "y=data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1=pd.DataFrame(data)\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "model = MLPRegressor(hidden_layer_sizes=[32, 32, 10], max_iter=50000, alpha=0.0005, random_state=26)\n",
    "_=model.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = np.arange(len(data)+30).reshape(-1, 1)\n",
    "pred = model.predict(test)\n",
    "prediction = pred.round().astype(int)\n",
    "week = [data.index[0] + timedelta(days=i) for i in range(len(prediction))]\n",
    "dt_idx = pd.DatetimeIndex(week)\n",
    "predicted_count = pd.Series(prediction, dt_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-03-01       0\n",
       "2020-03-02       0\n",
       "2020-03-03       0\n",
       "2020-03-04       0\n",
       "2020-03-05       0\n",
       "              ... \n",
       "2020-06-06    3096\n",
       "2020-06-07    3133\n",
       "2020-06-08    3171\n",
       "2020-06-09    3209\n",
       "2020-06-10    3246\n",
       "Length: 102, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.plotting.register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted_count.plot()\n",
    "plt.title('Prediction of Accumulated Confirmed Count in Cali')\n",
    "plt.legend([ 'predicted confirmed case for Perison'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2020-05-07</td>\n",
       "      <td>1938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2020-05-08</td>\n",
       "      <td>1982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2020-05-09</td>\n",
       "      <td>2044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2020-05-10</td>\n",
       "      <td>2085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2020-05-11</td>\n",
       "      <td>2122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds     y\n",
       "0  2020-03-01     1\n",
       "1  2020-03-02     1\n",
       "2  2020-03-03     1\n",
       "3  2020-03-04     1\n",
       "4  2020-03-05     1\n",
       "..        ...   ...\n",
       "67 2020-05-07  1938\n",
       "68 2020-05-08  1982\n",
       "69 2020-05-09  2044\n",
       "70 2020-05-10  2085\n",
       "71 2020-05-11  2122\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prophet= pd.DataFrame(data)\n",
    "prophet\n",
    "pr_data = prophet.reset_index()\n",
    "pr_data.columns = ['ds','y']\n",
    "pr_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>trend</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "      <th>trend_lower</th>\n",
       "      <th>trend_upper</th>\n",
       "      <th>additive_terms</th>\n",
       "      <th>additive_terms_lower</th>\n",
       "      <th>additive_terms_upper</th>\n",
       "      <th>weekly</th>\n",
       "      <th>weekly_lower</th>\n",
       "      <th>weekly_upper</th>\n",
       "      <th>multiplicative_terms</th>\n",
       "      <th>multiplicative_terms_lower</th>\n",
       "      <th>multiplicative_terms_upper</th>\n",
       "      <th>yhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>3208.760368</td>\n",
       "      <td>3073.673923</td>\n",
       "      <td>3340.093000</td>\n",
       "      <td>3074.774888</td>\n",
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       "      <td>2.843756</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>3211.604124</td>\n",
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       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2020-06-07</td>\n",
       "      <td>3251.100956</td>\n",
       "      <td>3117.378878</td>\n",
       "      <td>3397.532459</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>3261.010700</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2020-06-08</td>\n",
       "      <td>3293.441545</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>3295.280297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2020-06-09</td>\n",
       "      <td>3335.782133</td>\n",
       "      <td>3171.682458</td>\n",
       "      <td>3482.845891</td>\n",
       "      <td>3178.981938</td>\n",
       "      <td>3494.895448</td>\n",
       "      <td>-6.272866</td>\n",
       "      <td>-6.272866</td>\n",
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       "      <td>-6.272866</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2020-06-10</td>\n",
       "      <td>3378.122721</td>\n",
       "      <td>3203.655550</td>\n",
       "      <td>3529.816637</td>\n",
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       "      <td>-8.761572</td>\n",
       "      <td>-8.761572</td>\n",
       "      <td>-8.761572</td>\n",
       "      <td>-8.761572</td>\n",
       "      <td>-8.761572</td>\n",
       "      <td>-8.761572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3369.361150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds        trend   yhat_lower   yhat_upper  trend_lower  \\\n",
       "97  2020-06-06  3208.760368  3073.673923  3340.093000  3074.774888   \n",
       "98  2020-06-07  3251.100956  3117.378878  3397.532459  3109.133062   \n",
       "99  2020-06-08  3293.441545  3137.211196  3446.996268  3146.353559   \n",
       "100 2020-06-09  3335.782133  3171.682458  3482.845891  3178.981938   \n",
       "101 2020-06-10  3378.122721  3203.655550  3529.816637  3212.674817   \n",
       "\n",
       "     trend_upper  additive_terms  additive_terms_lower  additive_terms_upper  \\\n",
       "97   3339.324958        2.843756              2.843756              2.843756   \n",
       "98   3389.132961        9.909744              9.909744              9.909744   \n",
       "99   3441.244248        1.838753              1.838753              1.838753   \n",
       "100  3494.895448       -6.272866             -6.272866             -6.272866   \n",
       "101  3540.699310       -8.761572             -8.761572             -8.761572   \n",
       "\n",
       "       weekly  weekly_lower  weekly_upper  multiplicative_terms  \\\n",
       "97   2.843756      2.843756      2.843756                   0.0   \n",
       "98   9.909744      9.909744      9.909744                   0.0   \n",
       "99   1.838753      1.838753      1.838753                   0.0   \n",
       "100 -6.272866     -6.272866     -6.272866                   0.0   \n",
       "101 -8.761572     -8.761572     -8.761572                   0.0   \n",
       "\n",
       "     multiplicative_terms_lower  multiplicative_terms_upper         yhat  \n",
       "97                          0.0                         0.0  3211.604124  \n",
       "98                          0.0                         0.0  3261.010700  \n",
       "99                          0.0                         0.0  3295.280297  \n",
       "100                         0.0                         0.0  3329.509267  \n",
       "101                         0.0                         0.0  3369.361150  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=Prophet()\n",
    "model.fit(pr_data)\n",
    "future=model.make_future_dataframe(periods=30)\n",
    "forecast=model.predict(future)\n",
    "forecast.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot(forecast)\n",
    "model.plot_components(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9766.784915114302\n"
     ]
    }
   ],
   "source": [
    "def mean_absolute_percentage_error(y_true, y_pred):\n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
    "error = mean_absolute_percentage_error(Cali[-102*1:].values,\n",
    "                                       forecast.yhat[-102*1:].values)\n",
    "print(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure(figsize = (16,6))\n",
    "# plt.plot(forecast.yhat[-288*1:].reset_index())\n",
    "# plt.plot(Cali['cases'])\n",
    "# plt.plot(forecast.yhat_lower[-288*1:].reset_index())\n",
    "# plt.plot(forecast.yhat_upper[-288*1:].reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}