covid-19FinalForecasting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c5fCBlu-BXfI"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setup complete\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.graphics.api import qqplot\n",
    "import math\n",
    "import time\n",
    "from sklearn.linear_model import LinearRegression, BayesianRidge\n",
    "from sklearn.model_selection import RandomizedSearchCV, train_test_split\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "from matplotlib.pylab import rcParams\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.stattools import acf, pacf\n",
    "rcParams['figure.figsize'] = 30, 10\n",
    "print('setup complete')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv')\n",
    "death_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv')\n",
    "reco_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting all the dates\n",
    "cols = conf_df.keys() #it has all the columns\n",
    "\n",
    "conf = conf_df.loc[:, cols[4]:cols[-1]]\n",
    "death = death_df.loc[:, cols[4]:cols[-1]]\n",
    "reco = reco_df.loc[:, cols[4]:cols[-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we have all the dates\n",
    "dates = conf.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1/22/20', '1/23/20', '1/24/20', '1/25/20', '1/26/20', '1/27/20',\n",
       "       '1/28/20', '1/29/20', '1/30/20', '1/31/20', '2/1/20', '2/2/20',\n",
       "       '2/3/20', '2/4/20', '2/5/20', '2/6/20', '2/7/20', '2/8/20', '2/9/20',\n",
       "       '2/10/20', '2/11/20', '2/12/20', '2/13/20', '2/14/20', '2/15/20',\n",
       "       '2/16/20', '2/17/20', '2/18/20', '2/19/20', '2/20/20', '2/21/20',\n",
       "       '2/22/20', '2/23/20', '2/24/20', '2/25/20', '2/26/20', '2/27/20',\n",
       "       '2/28/20', '2/29/20', '3/1/20', '3/2/20', '3/3/20', '3/4/20', '3/5/20',\n",
       "       '3/6/20', '3/7/20', '3/8/20', '3/9/20', '3/10/20', '3/11/20', '3/12/20',\n",
       "       '3/13/20', '3/14/20', '3/15/20', '3/16/20', '3/17/20', '3/18/20',\n",
       "       '3/19/20', '3/20/20', '3/21/20', '3/22/20', '3/23/20'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "world_cases = []\n",
    "total_deaths = [] \n",
    "mortality_rate = []\n",
    "recovery_rate = [] \n",
    "total_recovered = [] \n",
    "total_active = [] \n",
    "\n",
    "countrywise_cases_conf = []\n",
    "countrywise_cases_reco = []\n",
    "countrywise_cases_death = []\n",
    "countrywise_cases_active = []\n",
    "\n",
    "#j=0\n",
    "for i in dates:\n",
    "    \n",
    "    confirmed_sum = conf[i].sum()\n",
    "    death_sum = death[i].sum()\n",
    "    recovered_sum = reco[i].sum()\n",
    "    \n",
    "    # confirmed, deaths, recovered, and active\n",
    "    world_cases.append(confirmed_sum)\n",
    "    total_deaths.append(death_sum)\n",
    "    total_recovered.append(recovered_sum)\n",
    "    total_active.append(confirmed_sum-death_sum-recovered_sum)\n",
    "    \n",
    "    # calculate rates\n",
    "    \n",
    "    # case studies \n",
    "    #china_cases.append(conf_df[conf_df['Country/Region']=='China'][i].sum())\n",
    "    #italy_cases.append(conf_df[conf_df['Country/Region']=='Italy'][i].sum())\n",
    "#     india_cases_conf.append(conf_df[conf_df['Country/Region']=='India'][i].sum())\n",
    "#     india_cases_reco.append(reco_df[reco_df['Country/Region']=='India'][i].sum())\n",
    "#     india_cases_death.append(death_df[death_df['Country/Region']=='India'][i].sum())\n",
    "#     india_cases_active.append(india_cases_conf[j] - india_cases_reco[j] - india_cases_death[j])\n",
    "    \n",
    "#    j = j+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "days_in_future = 10\n",
    "future_forcast = np.array([i for i in range(len(dates)+days_in_future)]).reshape(-1, 1)\n",
    "adjusted_dates = future_forcast[:-10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "days_since_1_22 = np.array([i for i in range(len(dates))]).reshape(-1, 1)\n",
    "world_cases = np.array(world_cases).reshape(-1, 1)\n",
    "total_deaths = np.array(total_deaths).reshape(-1, 1)\n",
    "total_recovered = np.array(total_recovered).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = '1/22/2020'\n",
    "start_date = datetime.datetime.strptime(start, '%m/%d/%Y')\n",
    "future_forcast_dates = []\n",
    "for i in range(len(future_forcast)):\n",
    "    future_forcast_dates.append((start_date + datetime.timedelta(days=i)).strftime('%m/%d/%Y'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#total confirmed cases in the world\n",
    "world_df = pd.DataFrame(index=dates,data=world_cases, columns=['conf_cases'])\n",
    "world_df['daily_new_cases'] = world_df.diff()\n",
    "world_df['daily_new_cases'][0] = world_df['conf_cases'][0]\n",
    "world_df['daily_new_cases'] =world_df['daily_new_cases'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_death_df = pd.DataFrame(index=dates,data=total_deaths, columns=['total_death'])\n",
    "total_death_df['new_death'] = total_death_df.diff()\n",
    "total_death_df['new_death'][0] = total_death_df['total_death'][0] \n",
    "total_death_df['new_death'] = total_death_df['new_death'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_reco_df = pd.DataFrame(index=dates,data=total_recovered, columns=['total_recovered'])\n",
    "total_reco_df['new_reco'] = total_reco_df.diff()\n",
    "total_reco_df['new_reco'][0] = total_reco_df['total_recovered'][0] \n",
    "total_reco_df['new_reco'] = total_reco_df['new_reco'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "##this is tricky but true as it is daily new active cases being found both way it give same ans\n",
    "total_active_df = pd.DataFrame(index=dates,data=total_active, columns=['total_active'])\n",
    "\n",
    "#total_active_df['new_active111'] = world_df['daily_new_cases'] - total_death_df['new_death'] - total_reco_df['new_reco']\n",
    "total_active_df['new_active'] = total_active_df['total_active'].diff()\n",
    "total_active_df['new_active'][0] = total_active_df['total_active'][0] \n",
    "total_active_df['new_active'] = total_active_df['new_active'].astype('int64')\n",
    "\n",
    "total_active_df=total_active_df.clip(lower =0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "world_full = pd.concat([world_df,total_reco_df,total_death_df,total_active_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this dataset is for regression for univariate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>conf_cases</th>\n",
       "      <th>daily_new_cases</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>new_reco</th>\n",
       "      <th>total_death</th>\n",
       "      <th>new_death</th>\n",
       "      <th>total_active</th>\n",
       "      <th>new_active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1/22/20</th>\n",
       "      <td>555.0</td>\n",
       "      <td>555</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17</td>\n",
       "      <td>510.0</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/23/20</th>\n",
       "      <td>653.0</td>\n",
       "      <td>98</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>605.0</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/24/20</th>\n",
       "      <td>941.0</td>\n",
       "      <td>288</td>\n",
       "      <td>36.0</td>\n",
       "      <td>6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8</td>\n",
       "      <td>879.0</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/25/20</th>\n",
       "      <td>1434.0</td>\n",
       "      <td>493</td>\n",
       "      <td>39.0</td>\n",
       "      <td>3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1353.0</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/26/20</th>\n",
       "      <td>2118.0</td>\n",
       "      <td>684</td>\n",
       "      <td>52.0</td>\n",
       "      <td>13</td>\n",
       "      <td>56.0</td>\n",
       "      <td>14</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         conf_cases  daily_new_cases  total_recovered  new_reco  total_death  \\\n",
       "1/22/20       555.0              555             28.0        28         17.0   \n",
       "1/23/20       653.0               98             30.0         2         18.0   \n",
       "1/24/20       941.0              288             36.0         6         26.0   \n",
       "1/25/20      1434.0              493             39.0         3         42.0   \n",
       "1/26/20      2118.0              684             52.0        13         56.0   \n",
       "\n",
       "         new_death  total_active  new_active  \n",
       "1/22/20         17         510.0         510  \n",
       "1/23/20          1         605.0          95  \n",
       "1/24/20          8         879.0         274  \n",
       "1/25/20         16        1353.0         474  \n",
       "1/26/20         14        2010.0         657  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_full.head(5) #datasetprepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1/22/20', '1/23/20', '1/24/20', '1/25/20', '1/26/20', '1/27/20',\n",
       "       '1/28/20', '1/29/20', '1/30/20', '1/31/20', '2/1/20', '2/2/20',\n",
       "       '2/3/20', '2/4/20', '2/5/20', '2/6/20', '2/7/20', '2/8/20', '2/9/20',\n",
       "       '2/10/20', '2/11/20', '2/12/20', '2/13/20', '2/14/20', '2/15/20',\n",
       "       '2/16/20', '2/17/20', '2/18/20', '2/19/20', '2/20/20', '2/21/20',\n",
       "       '2/22/20', '2/23/20', '2/24/20', '2/25/20', '2/26/20', '2/27/20',\n",
       "       '2/28/20', '2/29/20', '3/1/20', '3/2/20', '3/3/20', '3/4/20', '3/5/20',\n",
       "       '3/6/20', '3/7/20', '3/8/20', '3/9/20', '3/10/20', '3/11/20', '3/12/20',\n",
       "       '3/13/20', '3/14/20', '3/15/20', '3/16/20', '3/17/20', '3/18/20',\n",
       "       '3/19/20', '3/20/20', '3/21/20', '3/22/20', '3/23/20'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_full.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction \n",
    "for next 10 days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solving It as Univariate Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_confirmed, X_test_confirmed, y_train_confirmed, y_test_confirmed = train_test_split(days_since_1_22, world_cases, test_size=0.15, shuffle=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use this to find the optimal parameters for SVR\n",
    "# c = [0.01, 0.1, 1]\n",
    "# gamma = [0.01, 0.1, 1]\n",
    "# epsilon = [0.01, 0.1, 1]\n",
    "# shrinking = [True, False]\n",
    "# degree = [3, 4, 5]\n",
    "\n",
    "# svm_grid = {'C': c, 'gamma' : gamma, 'epsilon': epsilon, 'shrinking' : shrinking, 'degree': degree}\n",
    "\n",
    "# svm = SVR(kernel='poly')\n",
    "# svm_search = RandomizedSearchCV(svm, svm_grid, scoring='neg_mean_squared_error', cv=3, return_train_score=True, n_jobs=-1, n_iter=30, verbose=1)\n",
    "# svm_search.fit(X_train_confirmed, y_train_confirmed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 16278.788744432222\n",
      "MSE: 404306430.1462365\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# svm_confirmed = svm_search.best_estimator_\n",
    "svm_confirmed = SVR(shrinking=True, kernel='poly',gamma=0.01, epsilon=1,degree=5, C=0.1)\n",
    "svm_confirmed.fit(X_train_confirmed, y_train_confirmed)\n",
    "svm_pred = svm_confirmed.predict(future_forcast)\n",
    "# check against testing data\n",
    "svm_test_pred = svm_confirmed.predict(X_test_confirmed)\n",
    "plt.plot(svm_test_pred)\n",
    "plt.plot(y_test_confirmed)\n",
    "print('MAE:', mean_absolute_error(svm_test_pred, y_test_confirmed))\n",
    "print('MSE:',mean_squared_error(svm_test_pred, y_test_confirmed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(svm_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 12))\n",
    "plt.plot(adjusted_dates, world_cases)\n",
    "plt.plot(future_forcast, svm_pred, linestyle='dashed', color='purple')\n",
    "plt.title('# of Coronavirus Cases Over Time', size=30)\n",
    "plt.xlabel('Days Since 1/22/2020', size=30)\n",
    "plt.ylabel('# of Cases', size=30)\n",
    "plt.legend(['Confirmed Cases', 'SVM predictions'], prop={'size': 20})\n",
    "plt.xticks(size=20)\n",
    "plt.yticks(size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM future predictions:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('03/24/2020', 337439.0),\n",
       " ('03/25/2020', 361897.0),\n",
       " ('03/26/2020', 387960.0),\n",
       " ('03/27/2020', 415703.0),\n",
       " ('03/28/2020', 445206.0),\n",
       " ('03/29/2020', 476553.0),\n",
       " ('03/30/2020', 509829.0),\n",
       " ('03/31/2020', 545120.0),\n",
       " ('04/01/2020', 582518.0),\n",
       " ('04/02/2020', 622115.0)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Future predictions using SVM \n",
    "print('SVM future predictions:')\n",
    "list(zip(future_forcast_dates[-10:], np.round(svm_pred[-10:])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 6710.417384342907\n",
      "MSE: 86985470.62226407\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4de3893518>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Also creating Polynomial Features\n",
    "poly = PolynomialFeatures(degree=5)\n",
    "poly_X_train_confirmed = poly.fit_transform(X_train_confirmed)\n",
    "poly_X_test_confirmed = poly.fit_transform(X_test_confirmed)\n",
    "poly_future_forcast = poly.fit_transform(future_forcast)\n",
    "\n",
    "linear_model = LinearRegression(normalize=True, fit_intercept=False)\n",
    "linear_model.fit(poly_X_train_confirmed, y_train_confirmed)\n",
    "test_linear_pred = linear_model.predict(poly_X_test_confirmed)\n",
    "linear_pred = linear_model.predict(poly_future_forcast)\n",
    "\n",
    "print('MAE:', mean_absolute_error(test_linear_pred, y_test_confirmed))\n",
    "print('MSE:',mean_squared_error(test_linear_pred, y_test_confirmed))\n",
    "\n",
    "plt.plot(test_linear_pred)\n",
    "plt.plot(y_test_confirmed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Polynomial regression future predictions:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('03/24/2020', 390009.0),\n",
       " ('03/25/2020', 430680.0),\n",
       " ('03/26/2020', 475468.0),\n",
       " ('03/27/2020', 524656.0),\n",
       " ('03/28/2020', 578541.0),\n",
       " ('03/29/2020', 637429.0),\n",
       " ('03/30/2020', 701641.0),\n",
       " ('03/31/2020', 771507.0),\n",
       " ('04/01/2020', 847370.0),\n",
       " ('04/02/2020', 929585.0)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Future predictions using Polynomial Regression \n",
    "linear_pred = linear_pred.reshape(1,-1)[0]\n",
    "print('Polynomial regression future predictions:')\n",
    "list(zip(future_forcast_dates[-10:], np.round(linear_pred[-10:])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 12))\n",
    "plt.plot(adjusted_dates, world_cases)\n",
    "plt.plot(future_forcast, linear_pred, linestyle='dashed', color='orange')\n",
    "plt.title('# of Coronavirus Cases Over Time', size=30)\n",
    "plt.xlabel('Days Since 1/22/2020', size=30)\n",
    "plt.ylabel('# of Cases', size=30)\n",
    "plt.legend(['Confirmed Cases', 'Polynomial Regression Predictions'], prop={'size': 20})\n",
    "plt.xticks(size=20)\n",
    "plt.yticks(size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ARIMA Model\n",
    "##### Using Pyramid ARIMA \n",
    "It has auto arima "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
      "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/pyramid/__init__.py:68: UserWarning: \n",
      "    The 'pyramid' package will be migrating to a new namespace beginning in \n",
      "    version 1.0.0: 'pmdarima'. This is due to a package name collision with the\n",
      "    Pyramid web framework. For more information, see Issue #34:\n",
      "    \n",
      "        https://github.com/tgsmith61591/pyramid/issues/34\n",
      "        \n",
      "    The package will subsequently be installable via the name 'pmdarima'; the\n",
      "    only functional change to the user will be the import name. All imports\n",
      "    from 'pyramid' will change to 'pmdarima'.\n",
      "    \n",
      "  \"\"\", UserWarning)\n"
     ]
    }
   ],
   "source": [
    "import pyramid as pm\n",
    "from pyramid.arima import auto_arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/pyramid/arima/auto.py:379: UserWarning: stepwise model cannot be fit in parallel (n_jobs=1). Falling back to stepwise parameter search.\n",
      "  'Falling back to stepwise parameter search.' % n_jobs)\n"
     ]
    }
   ],
   "source": [
    "tsarima = auto_arima(world_cases,start_p=1, d=None, start_q=0, max_p=5, max_d=2, max_q=5, \n",
    " start_P=1, D=None, start_Q=1, max_P=2, max_D=1, max_Q=2, \n",
    " max_order=10, m=1, seasonal=True, stationary=False, information_criterion='aic', alpha=0.05, \n",
    " test='kpss', seasonal_test='ch', stepwise=True,n_jobs=-1, start_params=None, trend='c',\n",
    " method=None, transparams=True, solver='lbfgs',maxiter=50, disp=0, callback=None, \n",
    " offset_test_args=None, seasonal_test_args=None, suppress_warnings=True, \n",
    " error_action='warn', trace=False, random=False,random_state=None,n_fits=10, \n",
    "return_valid_fits=False, out_of_sample_size=0, scoring='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsarima.fit(world_cases)\n",
    "arima_pred = tsarima.predict(n_periods=10)\n",
    "arima_pred = pd.DataFrame(arima_pred,index = future_forcast_dates[-10:],columns=['Prediction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2ZmT/MR3p6yCBEUt4T8OYq7OaWXl8AqA9fp69KvihjdHUirF/LH5RDYCryU5shrs6ovyBmlMAbeTHDHyCtRJ+nqiHgLcOlV5FwW/M5fihd43wwxvLkp34CRftfQ5NpG/Af4K5zV508edHC3dm8LgBP7daXuXTwh6Pwg9bBp8o2Y6fNy/T/MrDY6L1eCrixpLdY/lqov+OhHrTbcMPA44UuqergPMTuKeh3oV1zaxUnN6yB/u7VRji9cgNn7txsXNuX4JlI39Hzwx7nmZmsf5RIiS85+RxeZQVETnkKTEnIiKHmqZk/yEaOUwulJj7Ko9ETH7VCHv+dQLlv8Yn5kJ1CysxFz4UKK9hpInan2uNVjfSWhd/cYjCPPeaeI0453aG/aFeMloZMzsbeBE/fDgR5WOc6zsz+wDf67OtmR0RDBkLnacq2QsbzHPOrUjwfPEU1BDnRD0IXIhPSjfC37e9ZvYpPpk+Cz98LubQ4AQUxmcfMzsJvzDJMQlWyfU+O+e+MLMH8atPlsHPSdbfzH7E/2a9j09Ufh+jzYzgMZ3oSaZ4shIizrl1ZnYHfn7MFPxQ17+b2W9BHHOAqc65ZVFbSsy6sOf7+z6E14s2vHE28BM+CXq5mfUKHzIbDP0N9XR9PRjiS9jxsmT3xGvI/t3TWJ/Vg/3dKgzxhjCHD7POa6hzeNnI39GMsOeZCcQULuY/loiIHC6UmBMRkSIr+IOrTsTu0DDLXcCxZha+qmFo2NGaKP8iP9c5FzdBE0e5sOd5DcEBv2BCtLoF7Vf85PDFgJJmdmQBDKsqrGtNJAlTWOeO18sjT2Z2DH5V19CccF8BU/FzKq3DTyYf8gx+0vR4Q5hfwCfmUvBzhIUP8b2C7P8/K6hVdg8kAZZvzrn1ZnY6fr7AG/E9zkKrXp4C3AZsNrPH8HP/5dUrM5rwz/nRMUvlQ7AS6jtkr6b6I34hiy+B3/Hvc2hew9DKylGnhXHO9TWzj/D3IDTctTZ+/rIuwJNmNg3oFQydDHcgw39LRMTxtJl9iZ/78uwg3qr44dKdgCFBoviO/Zzv76ew5xlmlrof88w1CHueK9HlnNtnZuPw97IKfiGfN8OKhPcwjfadOdDh1CXiHDuo363CkEcvuHAH8jtaYJ9pEZHDkRJzIiJSlN1G9oT6kUoA/41xrCXZE4GHnI3vebE/Noc9jzV0MVzZsOebY5Y6QM65LWa2BGgc7GpB/DmAEpHMay2S9xnf8ymUlBsM3OOcc9EKmlnc+asCLwOP4z/DV5MzMRdKMuwi9rDgZMtzjuJgHrZ+ZnYvvtfcGfjv5Ln45Eo5fLLoNDO7INb9jGMecFnwPNa8Y/nVg+yk3BjgRufcnmgFzexfeTXmnHsdeN3MjsQP5WsBtMbPtWX4XrUtzKx5RK+1LfheZD845+ru57WEx/Eefkhx5SCO5kAr4FT8e9kCmGtmbZ1zs/PZ/Jf45HQlfC+pJsQeUh5L+PsXOT1ByAtkLwxzNUFiLpjD84pg/6/4xGqk8AT++865VvmMTw5c+HtwjnNuVtIiEREpgrT4g4iISN7CV/hLZIhbeJnCnhg8fC62aCtE5lcyr7Wo3uc2weNvwL1xknLlyDkZflTBirahCftPC3rkYWYN8MkS8EMdC3I4dl7Ch6Hl1UOlSqKNOuf2Oec+cc496Zy7At977hKyh0C2wy+ukl/hn/vzzKzafrQRKfQ+78H3ZIualAsknDBzzv3inJvgnLvdOdcI30MslECqANwXUSXUa6yqmaUmep4E4lgbrGJ9t3PudHxv5BeDw6nAI/vRpgPeDduVr98gM6tB9n2H6Ik1nHNLgU+Dlx2D+R7BJ3pDw9hfirbybLCCdigxFGvuRylc4T0h9R6IiERQYk5ERIos51w355yFNrJXdQNoHHEsNKH+yvD9YdvsAwglvAfIeTFLAWZWiuzeepvJnoi9sAzHJxIAOphZ0wNs7zOykzStE0gMhK9auD9D4cIlfJ8L4dzxhJI+K/IY9tWGxP/fKnzI3dURj5HHD4bwlTGPzKNsrBVn8xQk6iYC94btjuzdmkg7y/CLTIBPJPbd35jChN7ntc65mCuMmlkT/HDl/RIsAtGZ7AUGIq//veCxJHDW/p4ngTh+xvdIDq0kfErw+5Vfw8KeX2dm+enl14/sETwz8pjvLvSdKAVcGjxP9DvzfvB4lJkVyNBnyZf3wp7ntdKtiMgfjhJzIiJyKDk7eFwLRK4sGDo2sxDO+xbZyaprgwn6Y/kb2RPCT4rWg6MgBRPIPxO8LA68GPRCSYiZdTGz0B+5OOd24q8XfM+obnHq1gauDF5uAd5OPPLcnHMrgY+Dl43MLGZyLkhAnhO8/B5YfCDnzsO24PEoi7GcYzCkLj/JobeAUI+4rkG7oZUl15H9HhwUwUIMK4OXpwYT5ucSJGr/VgCnXBn2fH+nVskkO7nV08wui1c4nJnVNbOHI3aH3ueqQe/HWO6NcywhQS+u0Psfef3Phz3PDD5bhSLoFRg+T1y+3wvn3Fyyv/tlgLFmVjqvemZ2Mdmfpb3kvSjAi2S/31cH57gkeL3UOfdJnLpjwp4PzCs2KXCLgKXB8y5mdkIygxERKWqUmBMRkUNJKPn2fvhwwqCXR6gXT4HPXROsmvlc8LIi8HLYUKosZnYuflJ48L3Y8j00bD/1JnuY1zHAfDM7P14FMzvWzMYD48m9cMLDZE/0PcTMzohSPx0/n11oLrj/BMmGA/VQ2PMxZnZslHPXwccd+v+Yhws5AfpR8HgE0CtKPKnACPyKwQkJEqCvBC/r41fMPCp4/cp+LohwoKYFj6XJ7oGaxcxSgKeB4+I1YmbPmNmJcY6nADeF7fos/6GCc24+vscV+DnbxptZfzOLOT+hmZU2s38An5A9bDgk9D4b2d/j8LpmZvfhF02IycxuM7NL4/U2DZKIoSHBOa7fObcQvzIs+DnhxkX7vQlrK8XM/mxmt0bs72pm18XrBRcs0tEkePldMDfg/gjvedcSmBEaoh3lnMWCWF/B32uAAcH7GZNz7leyh822Bm4le57JsXnE9yrZ7++VZvaYmcUcsm1mpcysm5ldEauMJC747/U/g5epwBQzi/z+5WBmx5vZU4UenIhIEaDFH0RE5JBgZsXwE5ZD7uRbcyAtxrGCcjd+PqMGQRxfmNlzwBf4REYb/GqLoWRRpnNuvxIO+eWc22FmFwCvA6fj57+aamb/wydbvsEPVawQHGuLTyLFWlFygZk9hP9Dqhx+4viX8L0RtwMnkr3aJvjeiwfciyg498tmdgl+QvcawMdmNhqYj+8t0xS4gexeidPxw3kL0xNkD6191Mxa43sIrcUnQq8JHmcFj4nOofQC0D14fn/E/mQYBlyPHxr69yAp+l/8kOyj8dfZEJ8UjZewuAm4ycyW4u/J5/hegGXwyccryJ4f8GsObMGSh/BDPu/Ff54zgR7Biqcf4ldTLQZUxy9y0Ibsz06k4fjrLw7cZmaN8df/K35F1avwSawv8N+DU2K0czJ+cY/1ZjYd35vzZ3yyuzr++9cuKOuAB6K0cT3+t+Yk/O9KOzN7Gd/zaD1+OGfNIJ7zgHTg2Yg2jgnuxxNmNgOfmPoR3/u3Kj7p14nsFYTvZz8551YHPVyn4O9VC+Dz4H2YhZ8/sjT+8/Nncs4P+ShREqExvIC/f8XC6jhgXB7x7Qt6Bs/H37fbgcvN7BV8YnQjPslXB/8bcy7+83pPgnFJHpxzb5rZQPx3tQ6wMPh+vIPvtemAyvjVjlsDx+N/8wuih66ISNHmnNOmTZs2bdqK/Ib/Y9cF24kRx+4L9n9VyDFUx/9h5+Jsu4F/5tHO7FD5Ao4vDb9q6OY8YgxtG4B/AaVitDcQ3/MvXhuzgcpxYloZlFuZj+tIwfdAyyv+V2LFHrSTFWMC54xbFp+0iBfLXHyPuoSvF99b6LuIdr5N8B7F/QzhhyCH2uyWj3t/A/6P4VjXOQKfXAu9Hh3nXua1fQZkFNBnvwM+AZjIeffhk9gNo7Tztzyu/wt8kjLm/QdGJRjHFuAvca6pPD4Jmuj9HBhRPzPBeruAuwvofagRxLwvgfP+FO/6Y7RfJrhv4e3MzGd87yR4X/bgV+aNbKN/WJnWBXHf4sQ7OuxcCX2PgYywOrm+n0GZmJ/f/LaV3/uC/0edjQm+BysL8/5q06ZNW1HZ1GNOREQOFaFhrL+TPVdN5LFZhRmAc+5XM2sBXIbv9XMqPhmzE98TZQYw3PnJ3Q8654dH/svMHsVPLt8Gv2DGEfieb5vxq5cuwvf4muj83GKx2rs3GO76V3wPktr43lS/AwuBF51z/y2E69iD73H1LP6PuFb4P6iL4XsvzQNGOecKYz7BWDH1NbP3gR74YdMVgDX4xT1ewv/RuifGFHSx2nRmNpacvXLi9vwpbM65Z4Oelr3xPaqq4HsGLgaecs69ZWYZeTRTEzg/qP8noB4+ybQLWI0fRvoqMMEV0BDkIK6pwXnPB87AL2JRCZ8sXwv8D59Afcn5uRmjtfOUmX2CH1p8Jr4Hz3pgeRDz0865bXm8z38DJuB/l07F93yrgu+ZtgH4Ev9bMdI5F3M1YefcJuCKoPfqNfjvQR38Z28HvhfaUvzCBpOcc99FNDEYn4A5FzgN31utGn4o4WZ8T9pZQRzL411Qopxzq4KYB+N/J88LYq6C72X4G34eySn4Idsxf39itL/VzP5LztVf8xrGGhlfGzNrhZ8fsyX+81oO2IpPFv4Pf98mBeWlADnnRprZq/heoe3wPbArB4fX43vRLsT39p6djBhFRA42c84lOwYREREREREREZE/HC3+ICIiIiIiIiIikgRKzImIiIiIiIiIiCSBEnMiIiIiIiIiIiJJoMSciIiIiIiIiIhIEmhV1j+YKlWquIyMjGSHISIiIiIiIiJy2Fi8ePEa59wR+a2nxNwfTEZGBosWLUp2GCIiIiIiIiIihw0z+35/6mkoq4iIiIiIiIiISBIoMSciIiIiIiIiIpIESsyJiIiIiIiIiIgkgRJzIiIiIiIiIiIiSaDEnIiIiIiIiIiISBIoMSciIiIiIiIiIpIESsyJiIiIiIiIiIgkgRJzIiIiIiIiIiIiSZCS7ACk6Nq5cyfr1q1j8+bN7N27N9nhiEghKF68OOXKlaNSpUqkpaUlOxwREREREZE/FCXmJKqdO3fyww8/kJ6eTkZGBqmpqZhZssMSkQLknGP37t1s2rSJH374gTp16ig5JyIiIiIichBpKKtEtW7dOtLT06lSpQolSpRQUk7kMGRmlChRgipVqpCens66deuSHZKIiIiIiMgfihJzEtXmzZspX758ssMQkYOkfPnybN68OdlhiIiIiIiI/KEoMSdR7d27l9TU1GSHISIHSWpqquaSFBEREREROciUmJOYNHxV5I9D33cREREREZGDT4k5ERERERERERGRJFBiTkREREREREREJAmUmBMREREREREREUkCJeZEDmHDhg3j+OOPp1SpUpgZjz32GODnC2vdunVygytgGRkZZGRkJDsMERERERERkQKjxJxIAr788kt69uzJiSeeSIUKFShRogRHHnkkHTp04Nlnn2Xnzp0HPabx48dz++23U7JkSXr16kVmZiann376QY+jqPvxxx/p06cPp5xyCunp6aSmplK1alXatGnD448/zsaNG5MdooiIiIiIiPxBpSQ7AJGibuDAgQwYMIB9+/bRvHlzrr32WsqWLcvq1auZPXs2N954I0899RSLFi06qHFNnjw56/HII4/McWzZsmWULl36oMZTFI0cOZIePXqwc+dOGjVqxJVXXkl6ejpr165l7ty59OrVi/vuu481a9YkO1QRERERERH5A1JiTiSO+++/n8zMTGrXrs0rr7xCs2bNcpWZPHkyQ4YMOeix/fLLLwC5knIAxx577MEOp8gZN24cN910E+np6bz22mt06NAhV5l58+Zx6623JiE6EREREREREQ1lFYlp5cqV9O/fn9TUVKZMmRI1KQdw4YUXMm3atFz7X375Zc466ywqVKhAqVKlOOmkk3jggQeiDnsNzZ+2detW7rrrLurUqUNaWhpHH300Dz30EM65rLL9+/fHzJg1axbg55MLbSHR5pgL1Zs9ezaKI35sAAAgAElEQVQvvvgizZo1o2zZslnztq1cuRIzo1u3bnz77bd07tyZypUrU65cOdq2bcvnn38OwO+//0737t2pUaMGJUuW5NRTT82KJdKePXsYPnw4p59+OuXLl6d06dI0adKEJ598kn379uUq75zjySef5IQTTqBkyZLUrFmTHj165Hu46ebNm7ntttsAP+Q3WlIO4IwzzmDhwoU59k2cOJGrr76aBg0aUKZMGcqUKcMpp5zCsGHDosa8evVq7rzzTho2bEiZMmWoWLEiDRs2pFu3bnz33Xe5yr/99tu0b9+eKlWqkJaWRv369bnrrrvYsGFDrrJLlizhyiuvJCMjg7S0NI444ghOPvlkevXqxe7du/N1T0RERERERKToUY85kRhGjRrF7t27ueKKKzjxxBPjlk1LS8vxum/fvjzwwANUqVKFq666irJlyzJ16lT69u3L22+/zfTp0ylRokSOOrt376Zdu3b88ssvXHDBBaSkpDBx4kT69OnDjh07yMzMBMhKuI0ePZrvv/8+a3+ihgwZwowZM+jYsSNnn312rqTXypUradasGccddxzdunVj5cqVvP7667Ru3Zr58+dz/vnnU758ebp06cK6desYP348F1xwAV9//TV16tTJcT0dO3bk7bffpmHDhlx11VWULFmSWbNm0bNnTxYuXMgLL7yQ49y9evVi2LBh1KhRg+7du5OamsqkSZNYuHAhu3btynXPYnn11VdZt24dp59+Om3bto1bNvK969OnD8WKFaNZs2bUrFmTjRs3MnPmTG6//XY++uijHDFv27aNM844g2+//ZbzzjuPjh074pzj+++/Z9KkSXTu3Jmjjjoqq/yAAQPo378/lSpV4sILL6Rq1aosWbKERx55hClTpjB//nzKly8P+KRcs2bNMDMuuugi6tWrx6ZNm1i+fDnDhw9n0KBBpKamJnQ/REREREREpGhSYk7ybcCbS/nil03JDiOu448sT2bHEw6ojblz5wJw7rnn5qve/PnzeeCBB6hduzYffvgh1atXB+CBBx7gkksuYfLkyTzyyCP07ds3R71ffvmFRo0aMWPGDEqVKgVAZmYmDRo0YOjQofTt25fU1FRat25N69atmT17Nt9//z39+/fPV3wzZ85k/vz5NGnSJOrx9957j0GDBvGvf/0ra999993HvffeS7Nmzbj88ssZPnw4xYr5DrfnnXce11xzDUOHDmXo0KFZdQYPHszbb79Njx49eOyxxyhevDgAe/fupXv37jz33HN07tyZiy++GIAPPviAYcOGUb9+fT788EMqVaqU1c7ZZ5/NqlWrqFu3bkLXuL/vHcBbb71F/fr1c+zbt28f1113Hc8//zw9evTI6j357rvv8u2339KrV68c1w6wa9euHL0jZ82aRf/+/WnevDlTpkyhYsWKWcdGjx7NddddR2ZmZlY7Y8aMYceOHUycODHrHoWsX79ecwiKiIiIiIgcBjSUVSSGVatWAVCrVq181XvuuecA6NevX1ZSDiAlJYUhQ4ZQrFgxRo4cGbXusGHDspJyAFWrVuXiiy9m48aNfPXVV/m9hKi6d+8eMykHflhtnz59cuy79tprAdi5cycPP/xwVlIO4KqrriIlJYVPP/00a9++fft44oknqF69OkOHDs1KygEUL16cIUOGYGaMGzcua/+oUaMA+Ne//pWVlAMoWbIkDzzwQL6ucX/fOyBXUg6gWLFi3H777YAfihop/D0LKVGiBOXKlct6PWzYMABGjBiRIykH0K1bNxo3bpzjfsRrOz09Pcd7ICIiIiIiIocm9ZiTfDvQnmiHu48//hiAc845J9exBg0aUKtWLVasWMHGjRupUKFC1rEKFSpw9NFH56pTu3ZtwPeSKginnXZa3OONGzfOkUiD7AUmGjRokCPZBD7RVq1aNX766aesfV9//TXr1q3jmGOOYdCgQVHPU6pUKZYtW5b1OnTfWrVqlatsy5Ytc8VUWNauXcvDDz/MlClT+O6779i6dWuO4z///HPW81atWlGzZk0efPBBPv74Y9q3b88ZZ5wR9R7Onz+f1NRUXnnlFV555ZVc5921axe///47a9eupXLlynTp0oXHH3+cTp060blzZ9q0acMZZ5wRNXEoIiIiIiIihyYl5kRiqFGjBsuWLcuRiElEaM62GjVqxGz3hx9+YMOGDTkSc5G9qEJSUvzXdO/evfmKI5bwXnzRhMcUGUO0Y6Hj4YsRrF27FoBvvvmGAQMGxDzXli1bsp6H7lu1atWitl+lSpW4cYcL3fv8vncbNmzg1FNPZcWKFZx22mlcc801VKpUiZSUFDZs2MDjjz+eY3hq+fLlWbBgAZmZmbzxxhtZvemqVKnCLbfcQr9+/bLmgVu7di179uyJez/A35PKlStz2mmnMWfOHAYPHsyrr76aNbddw4YNyczM5Morr8zXtYmIiIiIiEjRo7FQIjG0bNkS8POI5UcoefXrr79GPR4aZhkryVXYwldvLSyha7vkkktwzsXcVqxYkavO6tWrc7W3Z88e1qxZk/D59/e9GzlyJCtWrCAzM5OFCxdmLbLQv39/unTpErVOrVq1ePbZZ/ntt9/4/PPPGTZsGJUrV2bgwIEMHDgwq1yFChVIT0+Pez+ccznm0WvevDmTJ09m/fr1zJs3j3vuuYfVq1dz1VVX8c477+Tr2kRERERERKToUWJOJIbrrruO1NRUXnvtNb744ou4ZcN7UYXmb5s9e3aucsuXL+enn36iXr16MXvIHQ6OPfZYKlasyIIFC3L0pIvn5JNPBvziE5Hmzp2brx6DnTt3plKlSsyfPz/PBFb4e7d8+XIALr300lzlosUVzsw44YQT6NmzJzNmzABg4sSJWcdPP/101q9fz9KlSxO+jpC0tDRatGjBwIEDs+aqmzRpUr7bERERERERkaJFiTmRGDIyMujfvz+7du2iQ4cOLFq0KGq5adOmccEFF2S9vv766wEYNGgQv//+e9b+vXv3cuedd7Jv3z5uuOGGwg0+yVJSUujZsyerVq3itttuY/v27bnKrFq1KkfCs1u3boBfhXXdunVZ+3fs2ME///nPfJ2/XLlyWQmsLl26RF2wAWDBggU0b94863VGRgaQO6n6ySefRF2AYunSpVF7+IX2ha+cescddwBw00038csvv+Sqs3XrVhYsWJD1+oMPPoh636K1LSIiIiIicihZvWQ1S19einMu2aEkneaYE4mjb9++WfOCnXrqqbRo0YKmTZtStmxZVq9ezfvvv88333xD06ZNs+q0aNGCf/zjH/z73//mxBNPpHPnzpQpU4apU6fy+eef07JlS+66664kXtXBcc899/DZZ5/xn//8hzfffJNzzjmHmjVr8ttvv/HNN98wb948Bg8ezPHHHw/AGWecQc+ePXniiSey7ltqaiqTJk0iPT095px9sXTt2pXt27fTo0cPzj//fBo3bkyLFi1IT09n7dq1zJ8/n88++yzH3HXXXHMNDz/8ML169WLWrFkcc8wxfPPNN0yePJk///nPTJgwIcc5ZsyYwV133UXz5s1p0KABVatW5aeffmLSpEkUK1Ysx/t87rnn8uCDD/LPf/6TY445hvbt21OvXj22bNnC999/z3vvvUfLli2ZNm0aAP/+97+ZOXMmZ555JvXq1aNs2bIsXbqUqVOnkp6eTvfu3ff3rREREREREUmafXv38caNb7Dx+40cff7RpJVPS3ZISaXEnEge7r33Xi677DKGDx/OrFmzGDVqFDt27KBy5co0btyYu+++m6uvvjpHnYceeogmTZrw5JNP8vzzz7N7927q16/PoEGD6N27NyVKlEjS1Rw8qampTJw4kbFjxzJ69GgmT57Mli1bOOKII6hXrx733XcfXbt2zVHn8ccfp0GDBvzf//0fTz/9NJUrV+aSSy7h/vvvp1GjRvmO4cYbb6Rdu3Y8+eSTzJgxg3HjxrF161YqVqzIiSeeyNChQ7N6OIJffXbOnDn06dOHuXPn8vbbb3PssccyfPhw2rRpkysx165dO3744Qfef/99Jk2axKZNm6hRowbnnXcef//732nRokWO8nfffTdnnHEGw4YNY+7cuUyaNIkKFSpQs2ZNunfvzlVXXZVV9pZbbiE9PZ2FCxcyd+5c9uzZQ61atbjlllvo3bt3jrnoREREREREDhUfPvkhv3z0C5e+dOkfPikHYOo2+MfStGlTF2tIZrhly5Zx3HHHHYSIRKSo0PdeREREREQK04bvNzD8hOFktMrgyslXHpTFCQ8WM1vsnGuad8mcNMeciIiIiIiIiIgUKuccU26ZAkD74e0Pq6TcgdBQVhERERERERERKXSNrm3EsX8+lop1KyY7lCJDiTkRERERERERESlUZsYJl5+Q7DCKHA1lFRERERERERGRQjOl5xTmD52f7DCKJCXmRERERERERESkUKyYtYKPnvyIrau3JjuUIkmJORERERERERERKXC7t+9mcvfJpNdPp1Vmq2SHUyRpjjkRERERERERESlw7w96n3XL1/GXd/5CaqnUZIdTJKnHnIiIiIiIiIiIFKgtv25h/iPzaXRtI44696hkh1NkqceciIiIiIiIiIgUqLLVy3LtrGup3LByskMp0tRjTkRERERERERECsy2NdsAqN2iNqUrl05yNEWbEnMiIiIiIiIiIlIgNv6wkWH1h7H4mcXJDuWQoMSciIiIiIiIiIgcMOccb93yFvv27KN+2/rJDueQcEgl5sxspZm5GNuvMeq0MLMpZrbOzLab2RIz62VmxeOc50Izm21mG81si5ktNLNr84jtWjP7MCi/Mah/YZzyxc3sjiCe7UF8U8ysRZw6pcxsgJl9ZWY7zOw3M3vZzI6LF5uIiIiIiIiISGH74pUv+Oatbzh70NlUzKiY7HAOCYdUYi6wERgQZXsksqCZXQy8D5wFvA48CZQAhgLjozVuZj2AN4ETgbHACOBIYLSZ5TpHUOcRYDRQIyg/FjgJeDNoL7K8Bed/NIjnySC+s4D3g7gj66QBM4B7gU3A48A7wCXAIjNrFi02kT+i2bNnY2b0798/x/7WrVvjv36FY/To0ZgZo0ePLrRziIiIiIiIFEXb129n6m1TqXFKDZr1VIoiUYdiYm6Dc65/lC1H0szMyuOTZHuB1s65G5xzdwGNgflAZzO7IqJOBj7Btw5o6py71Tl3B/An4Fugt5k1j6jTAugdHP+Tc+4O59ytwClBO48E7Ya7AugMfAA0ds7d5Zy7ATg7iHeEmZWLqPN34AzgVaCZc+5u59xVQTulgefM7FB8Pw8ZgwcPxswwM7766quY5ULJmfAtLS2NunXr0rVrVz777LOo9fr37x81mRTab2Zcc801Mc/73nvvZZXLyMiIey033XQTZkbp0qXZsGFD3LLRrFy5Mtc1pqSkUK1aNTp06MDUqVPz3eahIFbCT0RERERE5I/u5w9/Zve23Vw08iKKpSg9kajD+U51Bo4AxjvnFoV2Oud2AP2Cl3+LqHM9kAY86ZxbGVZnPXB/8PKvEXVCrwcH5UJ1VgL/F7R3XUSd0Hn7BfGE6nwETAji7hzaH/SwC53nH865fWF1JgFzgOOBVkihcM4xcuTIrN5WI0aMyLNOo0aNyMzMJDMzk1tvvZXq1avz4osv0qxZM+bNm5fvGFJSUnj11VdjJtJGjBhBSkpKnu1s3ryZ8ePHY2Zs376dsWPH5juWkAoVKmRd45133snJJ5/MlClTaN++PcOGDdvvdgvL888/z7Jlywqt/UsuuYRly5ZxySWXFNo5REREREREiqKj2x3NHT/eQfXG1ZMdyiHlUEzMpZnZ1WbW18xuN7OzY8wXd07wOC3KsfeBbUCLYIhoInWmRpTZrzpmVhJoEZx/ToLnqQ/UAb52zq3IR2xSQKZPn87KlSu59tprqV69OmPGjGHXrl1x6zRu3Jj+/fvTv39/Hn30URYuXMjNN9/Mzp076devX9y60Vx44YVs376dcePG5Tq2fv16XnvtNTp27JhnOy+++CJbtmzhjjvuoESJEgklGWOpWLFi1jU++OCDTJ06leeeew6Avn37sm3btv1uuzDUqVOHY489ttDar1ChAsceeywVKlQotHOIiIiIiIgUJXt27OHLSV/inKNkhZLJDueQcygm5qoDLwCDgceAmcA3ZhbZW6xh8Ph1ZAPOuT3ACiAFOCrBOquArUAtMysNYGZlgJrAluB4pG+CxwZh++oDxYHvgjgSqRMzrjh1pACFklc33XQTXbt2Zc2aNbz++uv5bueGG24A4KOPPsp33fPPP59atWoxcuTIXMdeeOEFduzYwU033ZRnOyNGjKBYsWL06tWLjh07smTJEhYuXJjveGLp1q0bZcqUYevWrSxduhTIOQT0ww8/pEOHDlSqVAkzY+XKlVl1f/rpJ3r06MFRRx1FWloalStX5qKLLop5v1avXs0NN9xAtWrVKFWqFI0bN2bMmDExY4s3x9z06dPp2LEjVatWJS0tjdq1a3PxxRfzzjvvZF3X2WefDcCAAQNyDOOdPXs2EH+OucWLF3PppZdmtV+3bl1uueUWVq3K/dPRrVu3rHvz9NNPc9JJJ1GyZEmqVatG9+7d2bhxY646S5Ys4corryQjI4O0tDSOOOIITj75ZHr16sXu3btj3hMREREREZED8d597zGh0wR+/STqmpySh7zHvRUto/C9zJYCm/FJtR5Ad2CqmTV3zoUm8Ap1Wcn9F2zO/eHLhCRSp0xQblshnqMg6mQxs+74e0SdOnViNCGxrF69mjfeeIMGDRrQokULypcvz5AhQ3jmmWfo0qXLfrWZmpqa7zrFixfn+uuvZ+DAgSxatIimTZtmHRsxYgT16tWjTZs2cdv45JNPWLx4Meeddx61a9emW7duvPbaazzzzDM0a1Zwk3M65wByJcHmz5/PAw88QMuWLbn++utZs2YNJUqUAODjjz+mbdu2rFu3jnbt2vHnP/+ZNWvWMHHiRFq2bMnrr79O+/bts9pas2YNLVq04LvvvqNly5a0bNmSVatW8de//pW2bdvmK97MzEwGDhxI2bJl6dSpE7Vr1+aXX37hgw8+YOzYsbRp04ZOnToBMGbMGFq1akXr1q2z6uc1p9/kyZO59NJLcc7RuXNn6taty+LFi3nqqaeYNGkSc+fOpV69ernq/eMf/+Dtt9+mY8eOtG3bllmzZjFixAiWL1/OzJkzs8otWbKEZs2aYWZcdNFF1KtXj02bNrF8+XKGDx/OoEGD9uszJyIiIiIiEs+vn/3KB//+gEbXNqLGyTWSHc4h6ZBKzDnnBkTs+hz4q5ltwS/A0B+/SqmEcc49AzwD0LRpU1cQbY5uPTrXvhMuP4FTbzmV3dt2M6597uGWjbs1pnG3xmxbs42XO7+c63jTvzXlxC4nsvHHjbz+l9y90Zr3bk7Djg1Z89UaJt88Odfxs/qdxVFtjuLXT38t0DHto0aNYvfu3XTr1g2AE088kVNOOYVZs2axfPlyjj766ITbeuaZZwBo2bLlfsVy/fXXM2jQIEaMGJGVmFuwYAGff/45gwYNynPF0aeffhqA667z0x6ef/75VK9enQkTJjB06FDKly+/X3GFGzVqFNu2baNMmTKccMIJOY5Nnz6d//znP9x888059u/Zs4fLL7+cLVu2MGvWLFq1yu4A+8svv3Dqqadyww03sHLlStLS/Ojzvn378t1339GrVy+GDh2aVb5Hjx40b55jjZa4pk+fzsCBA6lXrx5z5syhZs2aOY7/9NNPAHTq1ImKFSsyZswYWrdunfACEFu2bOHaa69lz549zJ49mzPPPDPr2EMPPUSfPn24+eabmT59eq66CxYs4H//+19WQn3Pnj2cc845zJo1iw8//JDTTjsN8MnCHTt2MHHiRC6+OOeizuvXr6d06dIJ3w8REREREZFE7NuzjzdueINSlUrR7tF2yQ7nkHUoDmWN5j/B41lh+0K9yGJN9hTaHz6TfqJ1NkY8FsY5DrSOFIDQog/FihXLsSJqt27dcM7FnZ/t008/zZp/7e9//zunnnoqI0eO5Mgjj2TIkCH7FU/dunVp27YtL730Elu3bgV8b7nixYtnJdti2bp1Ky+++CIVKlTIWpwgJSWFrl27snXr1qhz1+Vlw4YNWdfYp08f2rdvnzVc9/7776dUqVI5yjdu3DhXUg7grbfe4ttvv6Vnz545knIARx55JP/4xz/49ddfeffddwHYvXs348aNo1y5crkSZE2bNqVr164JX8MTTzwBwJAhQ3Il5QBq1aqVcFvRTJo0iXXr1tGlS5ccSTmA3r17k5GRwYwZM/jhhx9y1b333ntz9HJNSUnJep8//PDDXOUj7zdAeno6xYodLj/1IiIiIiJSVCx4bAGrFq/igicuoFSl3H+LSGIOqR5zcfwePJYJ2/cV0BQ/79ri8MJmlgLUA/YA30XUqRLUmR9Rp0bQ/k/OuW0AzrmtZvYzUNPMakSZZ+6Y4DF8brhvgb3AUWaWEmWeuWh1vgoeY80hF61Ooeo2u1vMY6mlU+MeL12ldNzjFWpXiHu8SsMqcY8XZG+5mTNn8u2339KuXbscSZurrrqK3r17M3r06JjDBD/77DM+++yzHPvq1KnDnDlzDmhI8U033cS0adMYP348l112GRMmTKBDhw4ceeSR7NkTbdpCb/z48WzevJmbb76ZkiWzJ+Ts1q0bQ4YMYcSIEfztb5ELFce3ceNGBgzwHVmLFy9OpUqVuOCCC+jRo0eOYachoR5ekebP91+377//PmpPtG++8dMoLlu2jPbt2/Pll1+ybds2zjzzzKgLLbRu3TruXHPhFixYgJlx/vnnJ1Q+vz7++GMAzjkn99osKSkpnHXWWaxcuZJPPvkk1+cifLhySO3atQHfEy6kS5cuPP7443Tq1InOnTvTpk0bzjjjDOrXr1+QlyIiIiIiIpIl/ah0mtzYhOMvOz7ZoRzSDpfE3OnBY3iSbSbQFTgfeCmi/FlAaeB959zOiDpnBHXmR9S5IKxMuJnAX4I6o/Kq45zbYWYfAGcG26wEzvMt8APQwMzqRVmZNVZscoBCQ09Dw1hDKlWqRMeOHXnttdeYNGkSnTt3zlX32muvZfTo0Tjn+O2333j22Wfp168fHTt2ZP78+fs9vLBjx45Uq1aNkSNHsnv3brZu3ZrQog+xriU0NHfx4sW55q7LS926dXMs3pCX6tWjJ03Xrl0LwCuvvBK3/pYtWwCyFj+oVq1avs4TzYYNG0hPT4/a26wghGKtUSP6fAuh/Rs25O7wWrFi7mkjU1L8z/bevXuz9p122mnMmTOHwYMH8+qrr/LCCy8A0LBhQzIzM7nyyisP7CJEREREREQiHPfn4zjuz8clO4xD3iEzvsnMjgtWQY3cnwE8GbwcG3boVWANcIWZNQ0rXxIYFLx8KqK5UcBOoEfQbqhOOtA3ePmfiDqh1/8KyoXHdWvQXmTCLnTeQUE8oTqnAl3wPQBfC+13fib90Hn+bWbFwupcjE/wfQG8hxSY33//nYkTJwJw5ZVX5liF08x47TX/FoUSXrGYGdWqVaNv37707t2bJUuW0K9fv/2OKzU1leuuu44FCxYwePBgatWqxQUXXBC3zpIlS7KGPjZv3jzXtSxevDihazlQsebAC/V6mzRpEs65mFtmZmaO8qtXr47a3q+/Jr4aUMWKFVm/fj3bt2/Pz6UkLBRrrJhCq7JG6/mXH82bN2fy5MmsX7+eefPmcc8997B69WquuuqqrJVlRUREREREDtRnz3/GvH/PY9/efckO5bBwKPWY6wL0NrP3ge/xq7LWBzoAJYEpwCOhws65TWZ2Ez5BN9vMxgPrgIuAhsH+CeEncM6tMLO7gGHAIjObAOwCOgO1gCHOufkRdT4ws0eBvwNLzOxVoEQQbyWgp3NuZcS1jAf+HLT7iZm9CVQO6hQHbnLObYqo8yhwYVBnoZm9C9QBLsOvEHu9c07figI0ZswYdu3axSmnnELjxo2jlnnjjTd45513WLFiRdRVNSPde++9jBkzhieffJKePXsmVCeaG2+8kYceeoiffvqJe++9l+LFi8ctH0q4tW7dOubwxhdffJGXXnqJRx99lLJly+5XXPvr9NN9p9c5c+Zw0UUX5Vn+2GOPpXTp0nz66ads3LgxV1Jr9uzZ+Tr35MmTmTZtWtbce7GE7nN4b7W8NGnSJCum0Px7IXv27GHOnDkAnHzyyQm3GU9aWhotWrSgRYsWHHPMMVxzzTVMmjQpzxV7RURERERE8rJ51Wam3jaV6o2q0+LOFskO57BwKCXmZuETak3ww03L4Bc7mAu8ALwQ9CzL4pybaGatgH8Bl+ITeMvxSbRhkeWDOk+Y2UrgTuAafK/CL4B+zrmok1Y553qb2f/wPeS6A/uAj4GHnXO5lg91zjkzuxL4ALge6AnsAN4HBjnnPohSZ6eZnQf0Aa4E7gA2AROBTOfcFzHum+yn0MIOw4cPjzk32j333MOgQYMYOXIkgwcPzrPNcuXKcffdd3PnnXfSv3//hOdBi1S/fn2mTZuWNc9aPNu3b2fcuHEUL16ccePGceSRR0Ytt3PnTsaOHctLL72U0NDYgnTxxRdTv359/u///o+zzz476vx08+fPp1GjRpQuXZrU1FS6du3KiBEj6N+/f45VWRctWpSvhSx69uzJ5MmT6d27N6eddlquBSB+/vnnrH2VK1cGiLpQQyydOnWiUqVKvPTSS9x6661ZSUiAxx57jBUrVtCmTZsDmnfwgw8+oEmTJrmG44Z6FGpVVhERERERKQhTe0xl7869dBzRESsWfUSU5M8hk5hzzr3HfgzVdM7NA3L/lR+/zpvAm/msMxoYnY/ye4ChwZZonW3AvcEmhWj27Nl8/fXXnHTSSTGTcgA33HADgwcPZtSoUQwYMCBr/q94brnlFh555BHGjh1Lnz59OO64/RuT32eGju0AACAASURBVLZt24TKTZgwgQ0bNtCxY8eYSTnwvfDGjh3LM888c9ATc6mpqfz3v/+lXbt2dOjQgRYtWtC4cWNKly7Njz/+yEcffcR3333HqlWrspJM999/P++++y6PPfYYixYtomXLlqxatYoJEybQvn173njjjYTO3bZtW/r168egQYM47rjj6NSpE7Vr12b16tXMnTuX008/ndGjRwN+zraaNWsyfvx4UlNTqVu3LmbGX/7yF+rWrRu1/bJly/Lcc89x2WWX0apVKy677DLq1KnD4sWLmT59OtWrV+fpp58+oPv373//m5kzZ3LmmWdSr149ypYty9KlS5k6dSrp6el07979gNoXERERERH54rUvWPbfZZz74LlUblA52eEcNg6ZxJzIwRTqLXfjjTfGLZeRkUGbNm2YMWMGb775Zp5DIQFKlSpF3759ue222+jXr1/WXHWFJdFradWqFQ0aNGDRokV8+umnMYfvFpY//elPfPbZZzz66KNMnjyZUaNGUaxYMWrUqEGTJk0YMGAAVapUySpfpUoV5s2bR9++fXnzzTdZtGgRDRs25KmnniIjIyPhxBzAfffdR/PmzRk2bBiTJ09m69atVK1alaZNm3LNNddklStevDivv/46ffr04ZVXXmHz5s0452jZsmXMxBz4HoHz5s3j/vvv5+2332bjxo1Ur16dv/71r9xzzz1xE6aJuOWWW0hPT2fhwoXMnTuXPXv2UKtWLW655RZ69+4dNzYREREREZG87Nm5h2m3TaN6k+q06K0hrAXJoozmlMNY06ZN3aJFi/Ist2zZsv3uySUihyZ970VEREREJJYf5/9IiTIlqPanaskOpUgys8XOuaZ5l8xJPeZERERERERERCSqXVt3UaJMCWo3r53sUA5LxZIdgIiIiIiIiIiIFD27t+3m6cZPM/fBuckO5bClxJyIiIiIiIiIiOQy695ZrFu+jtot1FuusCgxJyIiIiIiIiIiOfz80c8sGLqAU24+hbpnaUG5wqLEnIiIiIiIiIiIZNm7ey9v3vgmZauXpc1DbZIdzmFNiTkREREREREREcmy6uNVrP1mLR2e6kDJCiWTHc5hTauySkzOOcws2WGIyEHgnEt2CCIiIiIiUkTUalaL21fcTtlqZZMdymFPPeYkquLFi7N79+5khyEiB8nu3bspXrx4ssMQEREREZEkcvscy6ctxzmnpNxBosScRFWuXDk2bdqU7DBE5CDZtGkT5cqVS3YYIiIiIiKSRB899RHjLhjHd+98l+xQ/jCUmJOoKlWqxPr161mzZg27du3SMDeRw5Bzjl27drFmzRrWr19PpUqVkh2SiIiIiIgkycYfNvJun3ep37Y+R7U5Ktnh/GFojjmJKi0tjTp16rBu3TpWrlzJ3r17kx2SiBSC4sWLU65cOerUqfP/7N13mFblnf/x9xeGXhUQUZoiKhZERVGMJvaGNRpjEmPPL8XsJpuyWdM3WWOyxkTXTdyYRM3aMBpQ1CR2BUFxsIAaxUIHBQu9zsz9++M5Y2Ynw5SHgTPPzPt1Xc91eM4533N/B5Xr4uN9n5tOnTrl3Y4kSZKkHKSUuP8L95NSYtz/jPN989uQwZw2q1OnTgwYMIABAwbk3YokSZIkSdpKZt4yk9cfeJ3jf3k8vYf2zrudNsWlrJIkSZIkSW1Y516dGXHmCMZ8eUzerbQ5zpiTJEmSJElqw/Y4dQ/2OHWPvNtok5wxJ0mSJEmS1Ab9bcLfmPLTKVRVVOXdSptlMCdJkiRJktTGrH1vLfd//n5e+eMrebfSprmUVZIkSZIkqY3561f+yrr313HeQ+fRrsx5W3nxd16SJEmSJKkNmX3/bGbeMpOPXP4R+o/sn3c7bZrBnCRJkiRJUhtRuamSB774ADvsswNHfPuIvNtp81zKKkmSJEmS1Ea079Ces8afRfuO7WnfsX3e7bR5BnOSJEmSJEltwMbVG+nYvSMDDxmYdyvKuJRVkiRJkiSpldu4ZiPXj7qeyT+ZnHcrqsFgTpIkSZIkqZV79NuP8sGbHzD4I4PzbkU1GMxJkiRJkiS1YvOfms8z1z7DQZcdxJDDh+TdjmowmJMkSZIkSWqlKtZXcO/F99JrcC+O+ckxebejWtz8QZIkSZIkqZVaPGMxKxes5JyJ59Cxe8e821EtBnOSJEmSJEmt1ODDBvOVeV+ha9+uebeiOriUVZIkSZIkqZWp3FjJq/e8SkrJUK4FM5iTJEmSJElqZaZcOYXxp49n4dML825F9TCYkyRJkiRJakWWvrSUJ3/8JPucuw+DDh2Udzuqh8GcJEmSJElSK1FVUcU9F91D516dOeGaE/JuRw0wmJMkSZIkSWolpv1iGoufXcyJ151It37d8m5HDTCYkyRJkiRJaiX6DO/DAZ87gL0/sXferagRyvJuQJIkSZIkSc1jz9P3ZM/T98y7DTWSM+YkSZIkSZJK3PTrpjPlyimkqpR3K2oCgzlJkiRJkqQS9t7r7/HQNx5iwdQFEHl3o6YwmJMkSZIkSSpRVZVV3HPhPZR1LmPc9eOIMJkrJQZzkiRJkiRJJWr6ddNZ8NQCTrjmBHrs1CPvdtREBnOSJEmSJEklaP3y9Tz2nccYfvJwRp43Mu92VAR3ZZUkSZIkSSpBnXt35ryHzqPnoJ4uYS1RBnOSJEmSJEklZvU7q+nevzsDDxmYdyvaAi5llSRJkiRJKiHvv/k+1w67lhm/mZF3K9pCBnOSJEmSJEklIlUl7r34Xtq1b8fwk4bn3Y62kMGcJEmSJElSiXj2188y74l5HP+L4+k5sGfe7WgLGcxJkiRJkiSVgA/e+oCH//Vhhh0/jFEXjsq7HTUDgzlJkiRJkqQS8M7Md+jUoxOn3HCKu7C2Eu7KKkmSJEmSVAL2PH1PdjthN8o6G+e0Fs6YkyRJkiRJasGWz13OzFtnklIylGtl/KcpSZIkSZLUQlXvwrro2UXsesyudO/fPe+W1IwM5iRJkiRJklqoGb+ZwZxH5zDuN+MM5Vohl7JKkiRJkiS1QMvnLuehbzzErsfsygGXHJB3O9oKDOYkSZIkSZJamJQSky6dBOAurK2YS1klSZIkSZJamIhg9BdHM/KzI+k9tHfe7WgrMZiTJEmSJElqQVJKRAQjzhiRdyvaylzKKkmSJEmS1EKklLj9lNt55tpn8m5F24DBnCRJkiRJUgtRfn05r9//OmWdXeTYFhjMSZIkSZIktQAfvPUBD33jIYYdN4wDLnUX1rbAYE6SJEmSJClnqSpxz0X30K59O075rbuwthUGc5IkSZIkSTlbMG0B8yfP54RrTqDXoF55t6NtxAXLkiRJkiRJORt82GC+MOsL9B3RN+9WtA05Y06SJEmSJCknVZVVLHx6IQD99urnEtY2xmBOkiRJkiQpJ9OunsbvDv0di8sX592KcmAwJ0mSJEmSlINlryzjse8+xp5n7MmAAwfk3Y5yYDAnSZIkSZK0jVVVVDHxgol06tGJcdePcwlrG2UwJ0mSJEmStI1N+ekUFj+7mJN+dRLdduiWdzvKicGcJEmSJEnSNta1T1dGXTiKvc/eO+9WlKOyvBuQJEmSJElqa0Z/fjSjPz867zaUM2fMSZIkSZIkbSNTr5rKzFtm5t2GWgiDOUmSJEmSpG1g8YzFPPyth3nzwTfzbkUthMGcJEmSJEnSVlaxoYKJ50+ke//unHDNCXm3oxbCYE6SJEmSJGkre/wHj7Ps5WWc8ttT6LJdl7zbUQthMCdJkiRJkrQVvf/G+0z92VT2v3h/hp84PO921IK4K6skSZIkSdJWtP1u23POxHMYcsSQvFtRC+OMOUmSJEmSpK1kzbI1AOxxyh507tU5527U0hjMSZIkSZIkbQXzp8znl4N/6S6s2iyDOUmSJEmSpGa2cfXGwi6sA7ozaOygvNtRC2UwJ0mSJEmS1Mwe/MaDfDDnA06/+XQ6du+YdztqoQzmJEmSJEmSmtEbf3mDGdfP4NCvHcqQw93wQZtnMCdJkiRJktSMlr60lP4j+3PUj47KuxW1cJFSyrsHbUOjR49O5eXlebchSZIkSVKrVrGhgrJOZXm3oW0kImaklEY3tc4Zc5IkSZIkSc1g9n2zmfPoHABDOTWKwZwkSZIkSdIWWv32aiZeMJFH/u0RXJ2oxjKYkyRJkiRJ2gIpJSZdOolNazZx+s2nExF5t6QSYTAnSZIkSZK0BZ7//fPMvm82R//kaPru2TfvdlRCDOYkSZIkSZKKtHLRSv76lb8y9GNDGfNPY/JuRyXGNxFKkiRJkiQVqceAHhx95dHsPm53op1LWNU0BnOSJEmSJElFqNhQQVmnMg7+0sF5t6IS5VJWSZIkSZKkJlr2t2Vcs8s1zHlsTt6tqIQZzEmSJEmSJDVB5aZKJn52IpUbK+k3ol/e7aiEuZRVkiRJkiSpCSZfMZnF5Ys5+49n033H7nm3oxLmjDlJkiRJkqRGWly+mMk/nsy+n96Xvc7aK+92VOIM5iRJkiRJkhrptUmv0a1/N078rxPzbkWtQKSU8u5B29Do0aNTeXl53m1IkiRJklSy1r67lq59u+bdhlqQiJiRUhrd1DpnzEmSJEmSJDVg4TMLeWfWOwCGcmo2BnOSJEmSJEn12LBqA3d/8m7u/uTdpCpXHqr5GMxJkiRJkiTV48GvPcjyecsZ95txRLvIux21IgZzkiRJkiRJmzH7vtk8d8NzjP3GWAYfNjjvdtTKGMxJkiRJkiTVYc2yNdx78b30368/R/77kXm3o1aopIO5iPhMRKTsc8lm7hkXEY9HxIqIWB0Rz0TE+Q089/yImJ7dvyKrH1fP/e0j4qsRMTMi1kXE+xHxQESMraemS0T8MCJei4j1EbE0Iu6MiBH11GwfEb+MiLkRsSEiFkfE7yNiYH0/jyRJkiRJarpOPTsx6qJRnHnLmZR1Ksu7HbVCJRvMRcQg4DpgdT33XAZMAvYBbgFuAHYCboqIqzZTcxVwEzAgu/8WYF9gUva82vcHcAdwNdAx62kCcATwZEScVkdNJ+Ah4HvASuAa4GHgDKA8IsbUUdMHmAb8M/Am8AtgOnAhMCMidt3c74MkSZIkSWqaVJUo61TGMT85hh322SHvdtRKlWQwl4VhNwLvAddv5p6hwFXA+8DolNKXUkpfBUZSCLa+FhGH1qoZC3wtuz4ypfTVlNKXgAOz51yVPbemTwJnAVOBUSmlb6SULgaOBCqBGyKiR62afwEOA+4CxqSU/jWl9KnsOV2B30dE7X82VwC7A1enlI5OKX0rpXQ6haBuB+BX9fyWSZIkSZKkRvrgrQ/49chfs7h8cd6tqJUryWAO+CfgKAqzxdZs5p6LgE7AdSmludUnU0ofUAi5AD5fq6b6+39k91XXzAX+O3vehbVqvpAdv5NSWl+j5llgPNCPQuAGfBgqVo/zzZRSVY2ae4DJwF7AR2vUdAfOy37WH9Qa/zpgHnC8s+YkSZIkSdoyVZVVTPjsBFYuWEm3Hbrl3Y5auZIL5rJ3sF0JXJNSerKeW4/Kjn+p49qfa91TVE1EdAbGAmspBGqNGWcYMBiYnVKa08iaQ4AuwFMppVU1b86Cvb9mX30TpSRJkiRJW2Dqf05lwVMLOOm/T6LX4F55t6NWrqSCuYgoA/4XmA9c3sDte2TH2bUvpJSWUJh9NjAiumbP7gbsDKzOrtf2enbcvca5YUB74K2UUkUjazbbVzPXfCgiPhcR5RFRvmzZss08QpIkSZKktm3J80t47HuPsdfZe7Hvp/fNux21ASUVzFHYLGF/4IKU0roG7q2OtVds5vqKWvc19v7eRYyRR82HUkq/SSmNTimN7tev32YeIUmSJElS2/bcDc/RtW9XTv71yRTeRCVtXSWz12+2U+nlwM9TStPy7keSJEmSJLUuJ113EmO/MZaufbrm3YraiJKYMZctYf0DhaWc321kWe0ZcbXVnoXW2PuXFzFGHjWSJEmSJKkRFpcvZuWilUS7YLtdtsu7HbUhJRHMAd0pvD9tBLA+IlL1B/h+ds8N2blfZt9fy47/8N61iBgAdAMWppTWAqSU1gCLgO7Z9dqGZ8ea73l7E6gEds3Cw8bUbLavZq6RJEmSJEkNWL98PePPGM8fz/4jKaW821EbUypLWTcAv9vMtQMovHduCoUAq3qZ66PAYcAJNc5VO7HGPTU9CpyX1dzYUE1KaX1ETAUOzz6PNWKcNylsXrF7ROxSx86sddU8DawDDouIHjV3Zo2IdsBx2dfa40uSJEmSpHo8cNkDrH57NedMOMf3ymmbK4kZcymldSmlS+r6APdmt92cnRuffb+RQqB3WUQMrX5WRGzH33d0vb7WUNXfv53dV10zFPhS9rzagd2vs+OPI6JzjZqDgHOAZcDdNX6WVGOcn2XBWnXNaRQCvleAJ2rUrKawG2034Ae1xr8MGAr8NaX0FpIkSZIkqVFeGv8Ss26dxRHfO4KdRu+Udztqg0plxlyTpZTmRMQ3gGuB8ogYD2wEzgIGUscmEimlqRFxNfAvwMyIuAvoSCFg2x74ckppbq2h7gDOzJ77fERMAvpkNe2BS1NKK2vVXA2My2qeiYhHgMHA2cBa4KKUUlWtmsuBjwH/EhGjgOkUlvaeBiylEBxKkiRJkqRGWLloJfd//n52HrMzh//b4Xm3ozaqJGbMFSul9F/AqcDLwGeBzwFvAxeklL6+mZqvARdm930uq3sZOCWldF0d9yfgXAphXgXwZQpB3ZPAESmle+qo2QAcC/wI6A18Nfs+ETgopfRMHTXvAYdSCBp3A74GjKEwg+/AlNKbjfpNkSRJkiRJdOzWkT3P3JMz/vcM2pW16nhELVj4YsO2ZfTo0am8vDzvNiRJkiRJyk1KyffJqVlFxIyU0uim1hkJS5IkSZKkNmPZ35Zx40du5P033s+7Fan1vmNOkiRJkiSppsqNlUz4zASWz1tOx+4d825HMpiTJEmSJEltwxM/eoIlzy3hE3d/gu47ds+7HcmlrJIkSZIkqfVbMHUBU66YwqgLRjHizBF5tyMBBnOSJEmSJKkNeOpnT9FrSC9OuOaEvFuRPuRSVkmSJEmS1OqddcdZrFiwgk49O+XdivQhZ8xJkiRJkqRWa+HTC9mwcgNlncvoM7xP3u1I/4fBnCRJkiRJapVWLlzJrSfdyr0X35t3K1KdDOYkSZIkSVKrk6oSE8+fSOXGSo664qi825Hq5DvmJEmSJElSqzPt6mnMeXQOp9xwiktY1WI5Y06SJEmSJLUqb7/wNo9c/gh7nr4n+1+8f97tSJtlMCdJkiRJklqVLn26MOKMEZxywylERN7tSJvlUlZJkiRJktRqpJToNagXZ40/K+9WpAY5Y06SJEmSJLUKr//5dW476TbWvrc271akRnHGnCRJkiRJKnlrlq7hngvvoVu/bnTs1jHvdqRGccacJEmSJEkqaSkl7r3kXtZ/sJ4zbz2Tss7OQ1JpMJiTJEmSJEkl7bkbnmP2pNkcfeXR9B/ZP+92pEYzmJMkSZIkSSWrqqKKaT+fxq7H7Moh/3xI3u1ITeLcTkmSJEmSVLLalbXj4mkXU7mxkmgXebcjNYkz5iRJkiRJUkl686E3qdxYSZftu9B9x+55tyM1mcGcJEmSJEkqOfMmz+OW429hypVT8m5FKprBnCRJkiRJKinrV6xnwnkT2G7X7Tjkq75XTqXLYE6SJEmSJJWUB770ACsXruTMW86kU49OebcjFc1gTpIkSZIklYxZt89i1q2zOOK7RzDwkIF5tyNtEYM5SZIkSZJUMvrt1Y9RF4ziiG8fkXcr0hYry7sBSZIkSZKkhqSUiAh23G9HTrvxtLzbkZqFM+YkSZIkSVKL98QPn+Cei++hqqIq71akZmMwJ0mSJEmSWrT5U+bz5I+eJFUk2pUZZaj18N9mSZIkSZLUYq1fvp4/feZP9B7amxOvOzHvdqRmZTAnSZIkSZJapJQS933+PlYuXMmZt51Jpx6d8m5JalYGc5IkSZIkqUVaMW8Fb/z5DY789yMZOGZg3u1Izc5dWSVJkiRJUovUe2hvvvjyF+k+oHverUhbhTPmJEmSJElSi1K5sZJZt88ipUTPgT1p1974Qq2T/2ZLkiRJkqQW5bHvP8afPvUn5k+Zn3cr0lZlMCdJkiRJklqMOY/O4amfPsUBlx7AkMOH5N2OtFUZzEmSJEmSpBZh7XtrmXDeBPrs3ofjf3F83u1IW52bP0iSJEmSpBbhvs/dx5plazj3vnPp2K1j3u1IW53BnCRJkiRJahEO/PyBDDthGAP2H5B3K9I2YTAnSZIkSZJyVbmpkvYd2jPs2GF5tyJtU75jTpIkSZIk5aZiQwW/H/t7pl09Le9WpG3OYE6SJEmSJOXmkcsfYXH5YrYfvn3erUjbnMGcJEmSJEnKxRt/fYOnr36ag750EHucskfe7UjbnMGcJEmSJEna5tYsXcPE8yfSb+9+HPufx+bdjpQLgzlJkiRJkrTNzZ8yn01rN/Hx2z9Ohy4d8m5HyoW7skqSJEmSpG1uxJkjGPqxoXTZvkverUi5ccacJEmSJEnKhaGc2jqDOUmSJEmSJCkHBnOSJEmSJElSDgzmJEmSJEmSpBwYzEmSJEmSJEk5MJiTJEmSJEmScmAwJ0mSJEmSJOXAYE6SJEmSJEnKgcGcJEmSJEmSlAODOUmSJEmSJCkHBnOSJEmSJElSDgzmJEmSJEmSpBwYzEmSJEmSJEk5MJiTJEmSJEmScmAwJ0mSJEmSJOXAYE6SJEmSJEnKgcGcJEmSJEmSlIOyvBuQJEmSJEn5qqisYsob77KhooqUABIpQYLsWPN7glrn9925F8P798jxJ5BKk8GcJEmSJElt3O3PLuC7E18quv7yk/Y0mJOKYDAnSZIkSVIbllLi1qfnMWJAT/7zrJFEQBCFY81fU/jO//keBLBdt465/gxSqTKYkyRJkiSpDXtx4QpefXsV/3HGPuyzc6+825HaFDd/kCRJkiSpDbv9mfl07dieU/fbKe9WpDbHYE6SJEmSpDZq1fpN3PviYk7dbyd6dO6QdztSm2MwJ0mSJElSG3XPC4tZt6mScw8enHcrUptkMCdJkiRJUhuUUuK2Z+az14CejBzou+WkPBjMSZIkSZLUBs1atIJXlqzk3DGDicJ2q5K2sa2yK2tEdAIuBj6SjfEi8JuU0rKtMZ4kSZIkSWqa256ZT5cO7TltlJs+SHlpcjAXEfsBNwIJ+FJK6ela13sATwD71Tj9ceCfIuLYlNLMLehXkiRJkiRtoepNH07ZbwA93fRByk0xS1k/AYwCdgKeqeP6Fdn1qPXpB0zIZtNJkiRJkqSc3PviYtZudNMHKW/FBHOHUJgt92BKKdW8EBE9gUuy63OAccA+wK+yW4YC5xXbrCRJkiRJ2nK3T5/Pnjv2YNSg3nm3IrVpxQRzO2fH5+u4dhJQPSPuopTSAymlV1JKlwEvZOdPK2JMSZIkSZLUDGYtXMFLi1byKTd9kHJXTDDXNzsuqePax7LjwpTSE7Wu/ZHCktaRRYwpSZIkSZKawW3T59O5QztOG7VzwzdL2qqKCeZ6ZsdNdVwbS2EZ6yN1XFuQHfsVMaYkSZIkSdpCqzdUcO8Lixg3cid6dXHTBylvxQRza7LjDjVPRkQfYO/s61N11G3YgjElSZIkSdIWmvTiYta46YPUYhQTkr2ZHY+odf50CktVoe5grjrIW1HEmJIkSZIkaQvdPn0+e/TvwQGD3fRBagmKCeYepxDAfTwiPg4QEQOBy7Prc1NKr9ZRV/1uubeKGFOSJEmSJG2BlxatYObCFZx78CA3fZBaiGKCueuBjUAZcGdEvA/MAYZSeL/cf2+m7pjs+nNFjClJkiRJkrbA7dPn06msHWfsPzDvViRlmhzMpZTeAC6jELIF0Bton/36ceDa2jURMQbYJfs6ucheJUmSJElSEdZsqOCeFxZz8sgB9Orqpg9SS1FWTFFK6bcRMQO4CNgNWAs8BPwupVRRR8nZwDwKYd5fi+xVkiRJkiQV4b6Zi1m9oYJPuemD1KIUFcwBpJSeB77cyHu/Dny92LEkSZIkSVLxbpu+gOE7dOfAIdvl3YqkGop5x5wkSZIkSSoRLy9ewYsLlnPuwYPd9EFqYQzmJEmSJElqxe6YvoCOZe0484Cd825FUi1FL2UFiIj2FN4fdxwwAtgO6JBSGlbrvn2AnsCKlNLLWzKmJEmSJElqnLUbK5j4/CJO3ncAvbt2zLsdSbUUHcxFxMeAm4Ga+ywHhQ0eajsd+CGwKiIGpJTWFTuuJEmSJElqnPtmLmHVhgrOddMHqUUqailrRJxCYRfWgRTCuEpgRT0l/wNUAT2Ak4sZU5IkSZIkNc3t0+czrF83Dhrqpg9SS9TkYC4i+gK3AO2BlcDFQG/gws3VpJSWAU9lX49pepuSJEmSJKkp/rZkJc/Pd9MHqSUrZsbclynMfNsIHJtSujGltLYRdU9TmF23fxFjSpIkSZKkJrhj+nw6tm/Hxw8Y2PDNknJRTDB3IoX3yN2ZUipvQt3s7LhrEWNKkiRJkqRGWrexkj89v4gT992R7bq56YPUUhUTzFXvuPpYE+uq30HXs4gxJUmSJElSI90/awmr1rvpg9TSFRPMdcuOK5tY1yU7ri9iTEmSJEmS1Ei3T5/Prn27MWaX7fNuRVI9ignm3suO/ZtYNzw7LitiTEmSJEmS1Aivvb2KGfM+cNMHqQQUE8y9nB0/1sS6Uym8m+7ZIsaUJEmSJEmNcHv1pg8HuumD1NIVE8zdT2F31VMjYq/GFETEecB+2dd7ixhTkiRJkiQ1YP2mSv703EKO32dHtnfTB6nFKyaY+y2wFOgA3B8R+9Z3c0RcAvwPhdlybwHjixhTkiRJkiQ14IFZS1i5voJzDx6UdyuSGqHJwVxKaQ1wIVAFDAZmRMSfgU9X3xMR34+IGyLiLQqhXGdgI/Dp2WV7nwAAIABJREFUlFJVsc1GxE8j4pGIWBAR6yLi/Yh4Phuvz2ZqxkbEA9m96yJiZkR8JSLa1zPOuIh4PCJWRMTqiHgmIs5voLfzI2J6dv+KrH5cPfe3j4ivZv1U/ywPRMTYemq6RMQPI+K1iFgfEUsj4s6IGFFfb5IkSZKktuGO6QsY2qcrh+5a51+RJbUwxcyYI6X0Z+CTwCqgDDgO+DiFWXEA3wMuAoZQWPa6HDgtpTR9C/v9KoVdYR8CrgFuBSqAHwAzI+L//C+BiDgNeBI4ApgAXAd0BH4B3FHXABFxGTAJ2Ae4BbgB2Am4KSKu2kzNVcBNwIDs/luAfYFJ2fNq3x/Z+Fdn/VyX9XcE8GTWd+2aTtnP/T0KO+JeAzwMnAGUR8SYunqTJEmSJLUNby1bzfS57/OJgwa56YNUIiKl1PBdmyuOGAh8E/gUUNcezCsphGdXpJQWFT3Q38frnFJaX8f5/wAuB36dUvpidq4n8AbQCzgspVRe/QzgUeBQ4NyU0h01njMUeBVYAxyYUpqbnd+OwqYVw4CxKaVpNWrGAk8BbwIHpZQ+qPGsGRSCxD2rn5VdOxe4DZgKHF39M0XEQcAUYAUwLKW0qkbNvwFXAHcB51TPPMxCvInAK8C+Dc1IHD16dCovL6/vFkmSJElSCbryz69yw+S3mPato9ihZ+e825HalIiYkVIa3dS6ombMVUspLUwp/VNKqS+FGWKnAJ8BTgcOArZPKX2pOUK5bLx/COUyd2bH4TXOnQX0A+6oDuVqPOM72dcv1HrORUAn4LqaQVoWtl2Rff18rZrq7/9RHcplNXOB/86ed2Gtmupxv1PzZ0opPUvhHXz9sv6BD2fYVY/zzZrhW0rpHmAysBfwUSRJkiRJbU5FZRV3P7eQI/foZygnlZAtCuZqSim9nFK6P6V0W0rp3pTSjC15n1wTnZIdZ9Y4d1R2/Esd9z8JrAXGZktEG1Pz51r3FFWTzdgbm40/uZHjDKPwPr/ZKaU5TehNkiRJktQGPPbaMpat2sAnRrvpg1RKyvJuoBgR8XWgO4VlqqOBj1AI5a6scdse2XF27fqUUkVEzAH2BnYF/taImiURsQYYGBFdU0prI6IbsDOwOqW0pI5WX8+Ou9c4NwxoD7yVUqpoZM1m+6qn5kMR8TngcwCDBw/ezCMkSZIkSaVq/LML6Nu9E0fuuUPerUhqgq0WzEXESRQCszLgReCulNKGZnr814H+Nb7/BbggpbSsxrle2XHFZp5Rfb53E2u6Zfet3YpjNEfNh1JKvwF+A4V3zG3mGZIkSZKkErR05Xoee20plxy+Cx3aN9vCOEnbQJP/i42I3SLiT9nnH15qFxEdIuIBCjub/ivwNeAPwPMR0SzTtVJKO6aUAtgROJPCrLfnI+KA5ni+JEmSJEml4u7nFlFZlVzGKpWgYqL0T1LY3OFj/N93ulX7DnACELU+ewIToxn3bE4pvZNSmgAcB/ShEABWq55F1usfCv/v+eVF1KyoddwaY2xpjSRJkiSplUspcWf5Ag4auh3D+nXPux1JTVRMMHdYdnwopbSx5oVsY4OvAAl4H7gMGAfcm92yH/CJ4lrdvJTSPOAVYO+I6Judfi07/sN71yKiDNgFqADeqnGpvpoBFJaxLkwprc3GXQMsArpn12ur3iW25rvh3gQqgV2zPhpTs9m+6qmRJEmSJLVy0+e8z5x31zhbTipRxQRzgykEb+V1XDse6JH9+qKU0q9SSg8AH+fvGxScXcSYjbFTdqzMjo9mxxPquPcIoCswtdZ77+qrObHWPUXVpJTWA1Oz8Q9v5DhvAvOB3SNilyb0JkmSJElqxcaXL6B7pzJOHlnXXBFJLV0xwVz1jLSFdVw7Mju+m1KqniVHSqkSuIPCktb9ixiTiNg9Iv5hKWdEtIuI/wB2oBC0fZBdugt4F/hkzXfhZbP6fpx9/XWtx90IbAAui4ihNWq2Ay7Pvl5fq6b6+7ez+6prhgJfyp53Y62a6nF/nPVTXXMQcA6wDLi7+nxKKdUY52cR0a5GzWkUAr5XgCeQJEmSJLUJK9dv4oFZSzhlvwF07bjV9naUtBUV819udfi0vo5rh1GYTfdIHdeql4z2r+NaY5wE/CQipgBzgPeyZ32UwuYPbwOXVt+cUloZEZdSCOgej4g7KCyvPRXYIzs/vuYAKaU5EfEN4FqgPCLGAxuBs4CBwM9TStNq1UyNiKuBfwFmRsRdQEcKAdv2wJdTSnNr/Sx3UNi04iwKm1ZMovCOvHOA9sClKaWVtWquprAs+CzgmYh4hMLsxbMp7BB7UUqpqjG/kZIkSZKk0jfpxcWs31TlMlaphBUTzK0DulMInT4UET2AUdnXKXXUrcmOHYsYE+BhYDfgIxRm3fXOnjkb+F/g2pTS+zULUkoTI+KjwLcpLKftDLxBIUS7NpuJRq2a/4qIucDXgc9SmFX4CvCdlNLNdTWWUvpaRMyiMEPuc0AV8Bzwnyml++q4P0XEuRSWtF4EfJlC0Pkk8OOU0tQ6ajZExLHAt4Bzga8CK4GJwPdTSq9s5vdNkiRJktQK3fnsAvbo34NRg3rn3YqkIhUTzM0D9gYOAX5X4/zJFGZ7JeCpOuqqg7zaM8EaJaX0EoXNJJpa9xSF2XZNqZkETGpizU3ATU24vwL4RfZpbM1a4HvZR5IkSZLURr369kpeXLiC747bi4jIux1JRSrmHXNPUXhX3LkRMQY+nC33r9n1d1JKL9ZRt092nFPEmJIkSZIkKTP+2QV0aB+csf/OebciaQsUE8z9hsKsuC7A5Ih4nsL740Zm53+7mbojs+szixhTkiRJkiQBGyoqmfD8Io7ba0e271bs26IktQRNDuZSSs8DP6Iwa64M2I/CxgVB4V1sP61dExF7U1j+CjC52GYlSZIkSWrrHnrlHZav3cQnDnLTB6nUFbWfckrpB9lMuUsobMiwFngIuDKltKaOkn/KjgH8pZgxJUmSJElSYRnrTr0685Hd+ubdiqQtVFQwB5BSuge4p5H3/j/g/xU7liRJkiRJgoUfrGXKG+/y5aOG076dmz5Ipa6Yd8xJkiRJkqQc3DVjIQBnHzgw504kNQeDOUmSJEmSSkBlVeKP5Qs5bFhfBm3fNe92JDUDgzlJkiRJkkrAU2+8y6Ll69z0QWpFin7HXLWIGAuMAQYCPYH2DZSklNLFWzquJEmSJEltyfjyBfTq0oHj9uqfdyuSmknRwVxEnAZcBexaRLnBnCRJkiRJjfTBmo089PI7fGrMYDp3aGg+jKRSUVQwFxFfBP6r+msDt6da96RixpQkSZIkqa2a8PwiNlZWcY7LWKVWpcnvmIuIXYFfZl/fAS4CRmTfE/A5YB/gZOBaYHV2/mZgGMXNsJMkSZIkqU1KKXFn+QJGDuzFiAE9825HUjMqZsbcF7O6KuC4lNJLABEfTopbmlJ6BXgF+HNE/ByYCHwWWJtS+tIWdy1JkiRJUhsxc+EKXn17FT8+fZ+8W5HUzIrZlfVICjPg7q0O5eqTUloAnASsAD4fEUcXMaYkSZIkSW3S+PIFdO7QjlNH7ZR3K5KaWTHB3NDsOHUz1zvWPpFSege4kcK75i4pYkxJkiRJktqcdRsrmfTCYk7aZwA9O3fIux1JzayYYK5HdlxQ6/z6Wtdrey47jiliTEmSJEmS2pwHZi1h1YYKPuGmD1KrVEwwt2Yztcuz49DN1FVH+zsWMaYkSZIkSW3O+PIFDO3TlTG7bJ93K5K2gmKCuTnZsX+t869SWKr60c3UHZQdNxYxpiRJkiRJbcqcd9cwfc77nD16UM0NFyW1IsUEc89RCOBG1jr/eHY8PCKOq3khIg4CLqKwaUSDG0ZIkiRJktTW3Vm+gHYBZx04MO9WJG0lxQRzj2bHY2qdvxnYkP16UkSMj4grImI8MBnolF373yLGlCRJkiSpzaiorOLuGQs5co8d6N+zc97tSNpKyoqomURhOerAiDgupfQgQEppXkT8G3A1hffJnVWjpnrO7WPADVvQryRJkiRJrd7jry1j6aoNbvogtXJNnjGXUlpFYefVLsDDta79EjiPwnvoosZnDYXA7uSUUtUW9ixJkiRJUqs2vnwBfbt34qg9d8i7FUlbUTEz5kgpbarn2q3ArRGxC4UNItYCf6uvRpIkSZIkFSxevo5HX13KJYfvQof2xbyBSlKpKCqYa4yU0hz+voOrJEmSJElqhFuenkdKic+MGZJ3K5K2MqN3SZIkSZJaiPWbKrnj2QUcPaI/g7bvmnc7krayBmfMRUQX4Ozs66qU0oSmDBARZ1B4Jx3A+JTShvrulyRJkiSprbpv5hLeX7ORC8YOzbsVSdtAY5ayfhP4PpCAc4sYowNwU1a/E3BlEc+QJEmSJKlVSylx89S5DN+hO2OH9cm7HUnbQL1LWSOiK/A1CqHanSmlO5s6QFZzJ4XdWb8ZEZ2KaVSSJEmSpNbsufnLmbVoBZ8dO5SIyLsdSdtAQ++YOwPoDlRRmDVXrO9nz+gFnL4Fz5EkSZIkqVW6eepcenQq48z9d867FUnbSEPB3LHZcUZKaXaxg6SUXgPKs6/HF/scSZIkSZJao6Ur1/PArCWcNXog3To15q1TklqDhoK5AyksY32wGcZ6kMJy1tHN8CxJkiRJklqN26bPp6Iq8dlDh+bdiqRtqKFgbkB2nNcMY1U/Y6dmeJYkSZIkSa3Cxooqbn1mPh/box+79O2WdzuStqGGgrme2XF5M4y1Ijv2aIZnSZIkSZLUKvz5pSUsW7WB88cOzbsVSdtYQ8Hcyuy4fTOM1Ts7rmqGZ0mSJEmS1CrcPHUuQ/t05aPD++XdiqRtrKFgbll23K0Zxhpe65mSJEmSJLVpsxau4Ln5yznv0KG0axd5tyNpG2somHuRwoYNJzTDWCdQ2EhiZjM8S5IkSZKkknfT1Ll07dies0cPzLsVSTloKJh7ODvuExHHFztIVrtvrWdKkiRJktRmvbd6A5NmLubMA3amZ+cOebcjKQcNBXN38/d3wl0fETs0dYCI2BG4Pvu6Grirqc+QJEmSJKm1uePZBWysqOL8Q4fm3YqknNQbzKWUPgCuobCcdTDwREQc0NiHZ/c+DgyhsIz12uyZkiRJkiS1WRWVVdz69DwO260Pw/v3yLsdSTlpaMYcwI+AaRTCud2BpyPiTxFxZkQMqH1zROyYXZsAPM3fN314Gvj3ZupbkiRJkqSS9dAr77B4xXpny0ltXFlDN6SUNkXE6cBfgP2B9sBp2YeIWA8sz27vDXSuUV69pcwLwBkppU3N1LckSZIkSSXrpqlz2bl3F44e0T/vViTlqDEz5kgpLQPGAr8CKigEbtWfLsCO2adLrWsVWc2hKaWlzd28JEmSJEml5m9LVvLMnPc579AhtG8XDRdIarUaFcwBpJQ2pJQuA3YDfgY8D1Rll6uDOLJzL2T37JZSuiyltKH5WpYkSZIkqXT9Ydo8OpW145zRg/JuRVLOGlzKWltKaQHwLeBbEdGNwky5Ptnl94C3U0prmq9FSZIkSZJahxVrNzHx+UWcPmpntuvWMe92JOWsycFcTVkA92b2kSRJkiRJ9bizfAHrNlVy/tihebciqQVo9FJWSZIkSZJUvMqqxB+ensvBQ7dnr5165t2OpBbAYE6SJEmSpG3gsVeXsuD9dXx27JC8W5HUQhjMSZIkSZK0Ddw8bS479uzM8XvvmHcrkloIgzlJkiRJkrayN5auZvLr7/LpMYPp0N6/iksq8E8DSZIkSZK2sv+dNpeO7dtx7pjBebciqQUxmJMkSZIkaStatX4Td81YyLiRA+jbvVPe7UhqQQzmJEmSJEnaiu6esZA1Gys5f+zQvFuR1MIYzEmSJEmStJVUVSX+MG0e+w3qzX6DeufdjqQWZrPBXESMzD49tmVDkiRJkiS1FpPfeJe33l3DBWOH5N2KpBaovhlzLwDPA0fWPBkR38s+u2/VziRJkiRJKnF/mDqXvt07ctK+A/JuRVILVFZEzQ+ARCG4m92s3UiSJEmS1ErMe28Nj762lC8fuRudytrn3Y6kFqi+GXNV2bHDtmhEkiRJkqTW5PdT5lDWLvj0IS5jlVS3+oK55dnRP0EkSZIkSWqC5Ws3cmf5Qk7db2f69+ycdzuSWqj6grmXgQAui4iDIqL2zLm09dqSJEmSJKl03frMfNZtquTSI3bJuxVJLVh975i7Azicwoy5pwEiovpaABNrfG+KlFIq5t12kiRJkiS1eBsqKrlp6lwOH96XPXfsmXc7klqw+mbM/Q/wAIUQruanWu3zTflIkiRJktQq3fPCYpat2sClh++adyuSWrjNzlxLKVVFxCnA6cBJwCCgE/BRCstYXwHe3RZNSpIkSZJUClJK/G7yHPbcsQeHD++bdzuSWrh6l5SmlBIwIfsAEBHVu7V+O6V071bsTZIkSZKkkvLk6+/y2juruOrs/Sjy9U+S2pD6lrJKkiRJkqQmuOHJt9ihRydO3W+nvFuRVAKK2YThyOz4UnM2IkmSJElSKXtl8UqmvPEu3zxhDzqWOQ9GUsOaHMyllJ7YGo1IkiRJklTKfjvlLbp2bM+nDx6SdyuSSkQxM+b+QRQWzu8FDAB6AKuAxcDfsvfUSZIkSZLUar29Yj33vrCYzxwyhF5dO+TdjqQSsUXBXETsC3wDOJVCIFfbqoiYCPw8pTRrS8aSJEmSJKmlumnqXKpS4qLDdsm7FUklpOhF7xHxLaAc+DTQE4g6Pj2B84DyiPjXLe5WkiRJkqQWZs2GCm57Zh4n7LMjg/t0zbsdSSWkqBlzWch2BZAoBHCrgCnAbGA10B0YDnyEQjjXAbgiIlJK6WfN0LckSZIkSS3CneULWLm+gksO3zXvViSVmCYHcxGxG/Dv2dcNwHeBX6WU1tVxb2fgi8CPgC7AjyLi7pTSm8W3LEmSJElSy1BRWcXvpsxh9JDtOGDwdnm3I6nEFLOU9QsUZsBVAqemlH5eVygHkFJan1K6msI76KooBIFfKLZZSZIkSZJakr++/A4LP1jnbDlJRSkmmDuGwhLWP6aUHm5MQUrpEWA8hWWvxxUxpiRJkiRJLUpKid9Mfoshfbpy7F79825HUgkqJpgbnB0famJddYg3qIgxJUmSJElqUcrnfcCLC5ZzyUd2oX27yLsdSSWomGCuc3Zc28S66vs7FTGmJEmSJEktyg1PvkXvrh0460Dnn0gqTjHB3NLsuHcT6/bKjsuKGFOSJEmSpBZjzrtreOhv7/CZMUPo0rF93u1IKlHFBHPTKbwr7qKI6NGYguy+iyi8m+7ZIsaUJEmSJKnF+N2Ut+jQrh2fHTsk71YklbBigrk/ZscBwKSI6FffzRHRF7gH2Dk7dUcRY0qSJEmS1CJ8sGYjd81YyOn778QOPTo3XCBJm1HW1IKU0p0R8RXgEOBwYHZE3Aw8CMwG1gDdgOHAscD5QG8Ks+WeTind1Uy9S5IkSZK0zd3y9DzWb6riksN3zbsVSSWuycFc5nRgMoXwrSfw5exTl+qtaWYDZxQ5niRJkiRJuVu/qZKbp83lY3v0Y/f+jXq7kyRtVjFLWUkpLQUOBP4H2EghfNvcZwPwa2B0VidJkiRJUkm654VFvLt6I5c6W05SMyh2xhwppdXAFyLiu8DJwMEU3jvXA1gFLKGwUcT9KaV3m6FXSZIkSZJyU1WVuGHyHEYM6MnYYX3ybkdSK1B0MFctC91uzj6SJEmSJLVKT8xexhtLV/OLc/YjIhoukKQGFLWUVZIkSZKktuaGyW+xY8/OjBu5U96tSGolDOYkSZIkSWrAS4tWMPXN97jgsKF0aO9fpSU1D/80kSRJkiSpAb+d/BbdOrbn3IMH592KpFbEYE6SJEmSpHosWbGO+2Yu4ZyDBtOrS4e825HUihjMSZIkSZJUj58/OJsEXHjY0LxbkdTKGMxJkiRJkrQZf561hLtmLOQLHx3GoO275t2OpFbGYE6SJEmSpDq8vWI9/zZhFiMH9uKfjxmedzuSWiGDOUmSJEmSaqmqSnz9jy+yYVMVvzxnlDuxStoq/JNFkiRJkqRabpw6lylvvMt3x+3Frv26592OpFbKYE6SJEmSpBpefXslP/3LqxwzYgfOPXhQ3u1IasUM5iRJkiRJyqzfVMlX7niBnp3LuPLjI4mIvFuS1IqV5d2AJEmSJEktxVV/fY1X317FjRccRN/unfJuR1IrV9SMuYh4KyLejIjDm7shSZIkSZLyMOX1d/ntlDmcd8gQjtxzh7zbkdQGFDtjbiiQgC61L0TE9sA+ACmlJ4vuTJIkSZKkbWT52o187Y8vMKxfNy4/aUTe7UhqI7bGUtbDgQlA1VZ6viRJkiRJzSalxOUTZvH+mo387vyD6NKxfd4tSWoj6l3KGhGPRMTlETE2Ipr6J5NvyJQkSZIktXh3P7eIB2a9zb8cuwf77Nwr73YktSENvWPuSOBHwGRgeUQ8EBHf3Ppt/aOI6BMRl0TEhIh4IyLWRcSKiJgSERdHRJ0/SxYqPhAR72c1MyPiK/UFjRExLiIez56/OiKeiYjzG+jv/IiYnt2/IqsfV8/97SPiq1k/67L+HoiIsfXUdImIH0bEaxGxPiKWRsSdEeE8a0mSJEkqwvz31vL9e17i4F2253NH7Jp3O5LamIaCuRuBeRRmv3UDTgB+UuP6DyPiBxFxfERs7f+tcDZwAzAGeAb4JXA3hffZ/Ra4M2rtYx0RpwFPAkdQWF57HdAR+AVwR12DRMRlwKTsubdkY+4E3BQRV22m5irgJmBAdv8twL7ApOx5te+PbPyrs36uy/o7Angy67t2TSfgIeB7wErgGuBh4AygPCLG1NWbJEmSJKluFZVVfPXOF2jXLvjFOaNo386FX5K2rUgpNXxTxFDgqOxzJIUAKlEI7KofkIDXgHcpvGcupZSabWF+RBxFIRy8P6VUVeP8jsB0YBBwVkrp7ux8T+ANoBdwWEqpPDvfGXgUOJT/z959h+lV1vkff5/pNTOZmjJpk04ICSGhKlURLMuuYkHFuoB97WX1d1lW/W1R9yeW3VUXRFxARayrgAiIFIEUIKT3nplJpveZZ+7fH88zk2FMQgJJzpT367qe636ec873Od8zXFfCfHKfc8M1IYQ7Bn3XdGA90AacFULYnto+HngSmAmcH0J4bFDN+cAjwBZgWQihYdB3rUj1PK//u1L7rgFuAx4FLgshdKa2LwMeBpqAmSGElkE1nwG+CtwJvLH/Z5AK8X4JrAUWDv7ZHM7SpUvD8uXLj3aIJEmSJI0JN/5xE9/4w0a++abFXLV4ctztSBrBoihaEUJYerx1zzdjDoAQwvYQwk0hhLeGEAb/afUwsI1kQJcGzAdeMqipLVEUfT+KojdHUTTxeJsb0sP9IYTfDA2eQgj7gf9Mfbx40K6rgXLgjv5QLnV8J/C51Mf3DjnNu4Bs4NuDg7RU2PbV1Mf3DKnp//yV/lAuVbMd+E7q+945pKb/vJ/rD+VSNU8CP0n1fXX/9tQMu/7zfHLwzyCE8CuStxqfBlyEJEmSJOl5PbWrkW/+cRNXLZ5kKCcpNscUzB3Fl0MIs4BK4G+BfwGeGrR/Bsmw61ZgdxRF66Io+s6LPOfh9KTG3kHbLk2Ndx/m+IeAduD81C2ix1Lz+yHHvKCa1Iy981Pn//MxnmcmMBXYGELYdhy9SZIkSZKGaOvq5cN3rGLCuBy+dNXpcbcjaQx7vlVZS47lS0IIdSGEX4cQPgN8adCuL5KcVddNclbdXP56xtmLEkVRBvC21MfB4djc1LjxMP32kpzplwFUH2PNPpK3uFZFUZSXOnc+MBloTe0falNqnDNo20wgHdia6uNYao7Y11FqJEmSJEmH8eX/XcuO+na+/oZFFOVmxt2OpDHs+WbM1UZRtDKKom9EUfSa1HPbjlkI4YshhIuA8cBlwFdIPlftRPpnkgs1/C6EcM+g7f2LUTQdoa5/e/ELqCkaMp6Mc7zYmgFRFF0fRdHyKIqW19XVHeErJEmSJGn0u3fNfm5/Yhc3XDiTc6tL425H0hiX8Tz704DFwCLgH4C+KIpWDdqffdiqIVLPUXsg9Tphoij6EPAxkgs2XHsiv3s0CSF8D/geJBd/iLkdSZIkSYpFbUsnn75rNQsmjeOjL/eGI0nxe75g7u9ILqhwMXAGyVswl3JoRdZfRFG0keQsuEdS4ylZXzqKog8A3yS5GullIYT6IYcMnd02VP/2xiE1Zal9B49S0zRkPN5znIoaSZIkSVJKoi/wsZ8+TVtXL99802KyMl7sI9cl6cU76p9EIYRfhRA+EkI4k2Rg9XfAjSTDt5Cqn0dy1dEfkAzJbu+vj6LopDxFM4qiDwPfAp4FLkmtzDrUhtT4V/8Mknou3QySi0VsPcaaiUA+sDuE0A4QQmgD9gAFR1h1dnZqHPxsuC1AAqhO9XEsNUfs6yg1kiRJkqSUr927gT9vOsAX/mYBsyoK425HkoDjWJU1hNCQCuo+PGjzvwBfB/7CoQUesjk0o+7pKIpqoyi6M4qi90dRdNqLbTiKok8B/05y9ddLQgi1Rzj0/tR4xWH2XQjkAY+GELqOsebKIce8oJrUbb2Pps7/0mM8zxZgJzAniqIZx9GbJEmSJI15v31mL//x4BbefM5Urjl7atztSNKAFzt394EQwidCCBeQvJ3yJcD3OTSjLiI50+61JGfarY6i6HCrlx6TKIr+D8nFHlaQvH31wFEOvxM4ALwpiqKlg74jB/hy6uN/DKm5GegCPhBF0fRBNeOBf0x9/M8hNf2fP5s6rr9mOvD+1PfdPKSm/7xfTvXTX7MMeCNQB/y8f3sIIQw6z79GUZQ2qOYqkgHfWuBPSJIkSZIGrNvXzCd+9gxnTRvPF16zIO52JOk5nu8Zc8cshNANPBpFUTlwXWrzGcAlqVf/6qwVL+T7oyh6O/AlkreB/hn4UBT91ePstocQfpjqpzmKouuRKyjpAAAgAElEQVRIBnQPRlF0B1AP/A0wN7X9J0OuYVsURZ8gGSIuj6LoJyRnAl4NVAFfDyE8NqTm0SiKvgF8FHgmiqI7gSySAVsJ8MEQwvYhfd5BMqy8GlgVRdFvgNJUTTpwXQiheUjNN4BXp2oej6Loj8BU4PVAO/CuEELfUX+IkiRJkjSGNLZ3c/2tyxmXm8F/vGWJz5WTNOycsGDucEIIz5J8Dty3omSKtphkSPdC9N/CmQ58+AjH/An44aDz/zKKoouAzwKvA3KAzSRDtBtTM9GG9vytKIq2Ax8H3kZyVuFa4HMhhFsOd9IQwseiKFpNcobc9UAfsBL4txDCbw9zfIii6BqSt7S+C/gg0Ak8BHw5hPDoYWq6oih6OfBp4BrgI0Az8Evg8yGEtUf4mUiSJEnSmNOb6OODt6+ipqmLn9xwLhXjcp6/SJJOsegw2dTzF0VR/8ysK0II9w7ZdxXwC5L5U/qLb1En0tKlS8Py5cvjbkOSJEmSTqr/+7t1/NdDW/mX1y3kjct8rpykkyuKohUhhKXPf+RzvaAZcyEE5/9KkiRJkoalXz+9l/96aCvXnjvNUE7SsHYybmXdCRz2lk9JkiRJkk6mNXub+OSdT7Ns+nj+z6tPi7sdSTqqEx7MhRBWAe880d8rSZIkSdLRNLR1c8OtKyjOzeK7bznLxR4kDXsndfEHSZIkSZJOhd5EHx+4fSW1LV387IbzKC/MjrslSXpe/vOBJEmSJGnE++ffr+eRzQf5yt+ezqIpxXG3I0nHxGBOkiRJkjSi/eqpPfzg4W28/bxpvH7plLjbkaRjZjAnSZIkSRqxnt3TxCfvfIazZ5TwORd7kDTCGMxJkiRJkkakg61d3HDrCkrzs/juW5aQme6vuJJGFhd/kCRJkiSNOL2JPj5w2yrqWru48z3nUVbgYg+SRh7/OUGSJEmSNOJ89XfreWzrQf7v3y3kjCoXe5A0MhnMSZIkSZJGlLtW7uamR7bxzgum87qzquJuR5JeMIM5SZIkSdKIsXx7PZ++azXnVpfwj6+cH3c7kvSiGMxJkiRJkkaEzbUtvPuW5UwuzuW7bznLxR4kjXj+KSZJkiRJGvZqmzt5+01Pkpmexi3vPJuS/Ky4W5KkF81gTpIkSZI0rLV09vCOm5+kob2bm9+xjKmleXG3JEknhMGcJEmSJGnY6u7t470/XsnGmha++5YlLKwqirslSTphMuJuQJIkSZKkwwkh8KmfP8PDmw/wtdcv4uK5FXG3JEknlDPmJEmSJEnD0r/es4FfrNrDxy+fw9VnVcXdjiSdcAZzkiRJkqRh55ZHt/MfD27hLedM5f2XzIq7HUk6KQzmJEmSJEnDyt3P7uMLv1nDy0+r5EtXnU4URXG3JEknhcGcJEmSJGnYeHJ7PR+64ykWTynmxjedSXqaoZyk0ctgTpIkSZI0LGyubeHvb1lOVXEu//32ZeRmpcfdkiSdVAZzkiRJkqTY1TR38vabniQzPY1b3nU2JflZcbckSSedwZwkSZIkKVYtnT284+YnaWzv5ofvXMaUkry4W5KkUyIj7gYkSZIkSWNXd28f7/nxCjbVtHDTO5Zx+uSiuFuSpFPGYE6SJEmSFIu+vsAn73yaRzYf5GuvX8SFc8rjbkmSTilvZZUkSZIkxeJf79nAL5/ayydeMZerz6qKux1JOuWcMSdJkiRJOqVCCNz4x83855+28NZzp/K+i2fG3ZIkxcJgTpIkSZJ0yiT6Al/49Rpu/csOXrtkMl/8m9OJoijutiQpFgZzkiRJkqRTorMnwUd/+hS/W72fGy6s5tNXzjOUkzSmGcxJkiRJkk665s4erv/Rcv6ytZ7PvWo+f//S6rhbkqTYGcxJkiRJkk6q2pZO3nHTk2ysaeHf37iIvzvThR4kCQzmJEmSJEkn0fYDbVx70+McbO3mB29fysVzK+JuSZKGDYM5SZIkSdJJsXp3E++4+Qn6QuC2685l8ZTiuFuSpGHFYE6SJEmSdMI9vOkAN9y6nOK8LH707rOZWV4Qd0uSNOwYzEmSJEmSTqjfPL2Xj/70KarLCrjlXWczoSgn7pYkaVgymJMkSZIknTA/fGQbX/ztWpZNK+H7b19KUW5m3C1J0rBlMCdJkiRJetFCCHzt3g1854EtXH5aJTdecyY5melxtyVJw5rBnCRJkiTpRelN9PHZXzzLT5bv4pqzp/BPV51ORnpa3G1J0rBnMCdJkiRJesE6uhN88PZV3Leuhg9dOouPvHwOURTF3ZYkjQgGc5IkSZKkF6S+rZvrfrSclTsb+NJVC3jbedPjbkmSRhSDOUmSJEnScdt+oI133PwE+5o6+c6bl/DKhRPjbkmSRhyDOUmSJEnScVmxo56/v2U5URRx23Xncta08XG3JEkjksGcJEmSJOmY/e8z+/jIT59iUlEOP3zn2Uwvy4+7JUkasQzmJEmSJEnPK4TA9/+8la/+bj1nTRvP99+2lJL8rLjbkqQRzWBOkiRJknRUvYk+vvibtdz6lx28auFEvv6GReRkpsfdliSNeAZzkiRJkqQjau/u5YO3reKP62u54cJqPnXFPNLSorjbkqRRwWBOkiRJknRYtS2dvPuHy1mzt4l/umoB1543Pe6WJGlUMZiTJEmSJP2VTTUtvOPmJ6lv6+b7b1vKZfMr425JkkYdgzlJkiRJ0nM8uuUAN9y6gpzMdH56w3ksrCqKuyVJGpUM5iRJkiRJA+5auZtP/fwZppfmc/M7l1E1Pi/uliRp1DKYkyRJkiQRQuBb92/mG3/YyHnVpfzntWdRlJsZd1uSNKoZzEmSJEnSGBdC4B9/8Sy3P7GT1545mX9+3RlkZaTF3ZYkjXoGc5IkSZI0xt36lx3c/sRO3nPRTD51xVyiKIq7JUkaE/wnEEmSJEkaw57d08SXf7uOS+aW88lXGMpJ0qlkMCdJkiRJY1RrVy8fuG0l4/Mz+fobFpOWZignSaeSt7JKkiRJ0hgUQuAf71rNzvp2br/uXErys+JuSZLGHGfMSZIkSdIY9JMnd/Hrp/fy0ZfP4Zzq0rjbkaQxyWBOkiRJksaYDftb+Pyv1/CSWWW89+JZcbcjSWOWwZwkSZIkjSHt3b28/7aVFOZk8u9vXEy6z5WTpNj4jDlJkiRJGkM+/6s1bKlr5cfvPofywuy425GkMc0Zc5IkSZI0Rty1cjc/W7GbD14yiwtmlcXdjiSNeQZzkiRJkjQGbK5t5XO/fJazZ5Twoctmx92OJAmDOUmSJEka9Tp7EnzgtpXkZKZz45vOJCPdXwUlaTjwGXOSJEmSNMr902/Xsn5/Cze/cxkTinLibkeSlOI/k0iSJEnSKPbbZ/byP4/v5IaLqrlkbkXc7UiSBjGYkyRJkqRRasfBNj7989UsmVrMxy+fG3c7kqQhDOYkSZIkaRTq6k3wgdtWkZ4WceM1Z5Lpc+UkadjxGXOSJEmSNAr98+/Xs3pPE9+79iyqxufF3Y4k6TD8JxNJkiRJGmXuWbOfmx/ZzjsvmM7lCybE3Y4k6QgM5iRJkiRpFNnd0M4nfvY0Z1QV8Zkr58fdjiTpKLyVVZIkSZJGgfbuXtbta+affruOEOBb15xJVoZzMSRpODOYkyRJkqQRprWrl7V7m1m9p4k1e5pYvaeJLXWt9AVIi+Dbb17CtNL8uNuUJD0PgzlJkiRJGsaaO3tYs6eZNXuTAdzqPU1sO9BGCMn9FYXZLJxcxJULJ7JwchGLqoqoGJcTb9OSpGNiMCdJkiRJw8xDG+v46fJdrNnbzLYDbQPbJxblsGBSEVctmszCqnGcPskQTpJGMoM5SZIkSRomth1o4yv/u5b71tVSXpjNkqnFvG7JZBZMLuL0SUWUF2bH3aIk6QQymJMkSZKkmLV09vDtBzZz08PbyM5I5zNXzuMdF0wnOyM97tYkSSeRwZwkSZIkxaSvL/Dzlbv513s2UNfSxevPquITV8ylotDbUyVpLDCYkyRJkqQYrNzZwBd/vYandzdx5tRifvC2pSyaUhx3W5KkU8hgTpIkSZJOoZrmTv7l9+u5a9UeKgqz+cYbFvG3iyeTlhbF3Zok6RQzmJMkSZKkU6CzJ8F/P7yN7zywmd5E4H0Xz+T9l8wiP9tfyyRprPJvAEmSJEk6iUII3Lu2hq/87zp21rdz+WmVfPZV85lWmh93a5KkmBnMSZIkSdJJsnZvM1/93Toe3nyA2RUF/Pjd5/CS2WVxtyVJGiYM5iRJkiTpBOrsSfC/z+zjtid2smJHA+NyMvjCa07jredOIyM9Le72JEnDiMGcJEmSJJ0Am2tb+J/Hd3LXyj00dfRQXZbPZ185n9cvraI4Lyvu9iRJw5DBnCRJkiS9QJ09CX7/7D5uf3wXT2yvJzM94hULJvDmc6ZyXnUpUeRKq5KkIzOYkyRJkqTjtLm2lduf2MnPV+6msb2H6aV5fObKeVx9VhWlBdlxtydJGiEM5iRJkiTpGHT1Jrj72f3c9vhOHt9WT0bac2fHpaU5O06SdHwM5iRJkiTpCBJ9gZU7G7j72f3ctXI3De09TC3J45NXzOX1Z02hvNDZcZKkF85gTpIkSZIGae7s4aGNdfxxXS0PbKilsb2HzPSIy+ZV8uZzpvKSWWXOjpMknRAGc5IkSZLGvF317dy3rob71tXwxLZ6ehKB8XmZXDq3gsvmV3LhnDIKczLjblOSNMoYzEmSJEkacxJ9gVU7G7hvXS33r69hY00rALMqCnjXS2bwsvmVLJk6nnRnxkmSTiKDOUmSJEljQltXL3/aWMd962p4cEMd9W3dZKRFnD2jhDcum8rL5lcwrTQ/7jYlSWOIwZwkSZKkUau+rZv71tVwz7P7+fPmA3T39lGUm8klc8tTt6iWU5TrLaqSpHgYzEmSJEkaVfY2dnDvmv3cvWY/T2yrpy/A5OJc3nLOVC4/bQLLpo8nIz0t7jYlSTKYkyRJkjTyba5t4Z41NdyzZj/P7G4CYHZFAe+7eBZXnD6BBZPGEUU+L06SNLwYzEmSJEkacUIIPLO7iXvW7OeeNfvZUtcGwOIpxXzqinm8YkEl1eUFMXcpSdLRGcxJkiRJGhESfYEnt9dz97P7uXfNfvY2dZKeFnFudQlvP386l582gQlFOXG3KUnSMTOYkyRJkjRsdff28djWg9z97D7uXVPDwbZusjPSuHBOOR+7fC6Xza+gOC8r7jYlSXpBRkwwF0XR1cBFwGJgEVAI/E8I4a1HqTkf+BxwLpALbAJuAr4VQkgcoebVwMeBM4F0YA3w3RDCLUc5z9uB9wOnAQlgFfC1EMJvj3B8OvAh4J3AbKAD+Avw5RDCo0eoyQU+DbwJmAY0Aw8Cnw8hrDtSb5IkSdJI09mT4E8b67jn2f3ct66G5s5e8rPSuXR+JVeePoGL5pSTnz1ifpWRJOmIRtLfZp8jGci1AruBeUc7OIqiq4CfA53AT4B64DXAvwMXAK8/TM0HgG8BB4EfA93A1cAPoyhaGEL4+GFqvgZ8LNXT94EskuHZb6Io+mAI4dtDjo+AO1LfuwH4NlACvBF4KIqi14UQfjWkJhv4Q6rv5cA3gSmpa3hVFEWXhhAeP9rPQ5IkSRrOWjp7eGBDHXc/u48H1tfR0ZOgKDeTyxdM4MrTJ3DBrDJyMtPjblOSpBMqCiHE3cMxiaLoEpLh12aSM+ce4Agz5qIoGpc6rgi4IISwPLU9B7gfOA+4JoRwx6Ca6cB6oA04K4SwPbV9PPAkMBM4P4Tw2KCa84FHgC3AshBCw6DvWgHkA/P6vyu17xrgNuBR4LIQQmdq+zLgYaAJmBlCaBlU8xngq8CdwBtDCH2p7VcBvwTWAgv7tx/N0qVLw/Lly5/vMEmSJOmka2jr5r51Ndz97H7+vOkA3Yk+yguzecWCSq5YMJFzqkvITE+Lu01Jkp5XFEUrQghLj7duxMyYCyE80P/+GJY5vxooB37UH8qlvqMziqLPAX8E3kty5lq/dwHZwL8MDtJCCA1RFH0V+G/gPcBjg2rekxq/0h/KpWq2R1H0HeD/kLxd9fODat6bGj/XH8qlap6MougnwLWp/m9OXWs06DyfHBy+hRB+FUXRn4GXciislCRJkoatXfXt/GFtDX9YW8MT2+tJ9AUmF+dy7XnTuOL0CSyZOp70tOf9/31JkkaFERPMHadLU+Pdh9n3ENAOnB9FUXYIoesYan4/5JhjOc/vSQZzl5IK5lIz9s5Pnf/PR6i5NlVzc2rbTGAqsDGEsO0INS9N1RjMSZIkaVgJIbB2XzN/WFvDvWtqWLuvGYA5lQW896KZXL6gkoWTi47lH98lSRp1RmswNzc1bhy6I4TQG0XRNmABUA2sO4aafVEUtQFVURTlhRDaoyjKByYDrSGEfYfpYVNqnDNo20ySC0psDSH0HmPNEfs6So0kSZIUm95EH09ub+Detfu5d00Nexo7iCJYOm08n33lfF5+WiXTy/LjblOSpNiN1mCuKDU2HWF///bi46zJTx3XfhLPcSJqniOKouuB6wGmTp16pMMkSZKkF6y9u5eHNh7g3rX7uX99LY3tPWRlpHHh7DI+dNksLptfSVlBdtxtSpI0rIzWYE6DhBC+B3wPkos/xNyOJEmSRoHOngSr9zSxckcDT2yr5+HNB+jq7aMoN5PL5lVw+YJKXjq7nPxsf+WQJOlIRuvfkv2zyIqOsL9/e+OQmrLUvoNHqWkaMh7vOU5FjSRJknTChBDYVd/Bql0NrNzRwKpdjazd20xvX/LffKeV5nHN2VO5fEEly6a7kqokScdqtAZzG4ClJJ+7tmLwjiiKMoAZQC+wdUhNWarmsSE1E0nexro7hNAOEEJoi6JoDzA5iqKJh3nO3OzUOPjZcFuABFAdRVHGYZ4zd7iaDanxSM+QO1yNJEmS9IK1dfXyzO4mVu5sYNXORp7a1cCB1m4A8rLSWVRVzPUXVrNk6ngWTy32FlVJkl6g0RrM3Q+8BbgCuH3IvguBPOChQSuy9tdckKp5bEjNlYOOGXqea1M1Nw/Z91c1IYTOKIoeJbmK6kv561VUD3eeLcBOYE4URTMOszLrkXqTJEmSnldHd4KNNS2s29fM6j1NrNrZyPr9zaQmw1Fdls9Fcyo4c2oxS6aOZ05lARnOiJMk6YSIQhh5jxyLouhikqHW/4QQ3nqY/eNIBlrjgAtCCMtT23NIBljnAdeEEO4YVDOD5AqtbcBZIYTtqe3jgSdJrqh6fgjhsUE15wOPpM61LITQkNo+neRMvXxgXv93pfZdA9wGPApcFkLoTG1fBjxM8tbVWSGE5kE1nwG+CtwJvDGE0JfafhXwS2AtsLB/+9EsXbo0LF++/PkOkyRJ0igTQmBvUyfr9zWzbl8z6/Ynw7jtB9oGQriC7AwWTykeCOEWTylmfH5WvI1LkjQCRFG0IoSw9HjrRsyMuSiK/hb429THCanxvCiKfph6fyCE8HGAEEJzFEXXkQyyHoyi6A6gHvgbYG5q+08Gf38IYVsURZ8AbgSWR1H0E6AbuBqoAr4+OJRL1TwaRdE3gI8Cz0RRdCeQBbwRKAE+ODiUS7kDeG3qe1dFUfQboDRVkw5cNziUS/kG8OpUzeNRFP0RmAq8nuQKse86llBOkiRJY0Nnz6FZcOv2Jcf1+1to6ugZOGZKSS7zJ4zj1WdM4rSJhcybMI6pJXmkpUUxdi5J0tgyYmbMRVH0BeDzRzlkRwhh+pCaC4DPkpwhlwNsBm4CbgwhJI5wntcAHweWAGkkZ6N9O4Rwy1F6ewfwfuA0oA9YCfxbCOG3Rzg+A/gg8C5gFtBJ8vbZL4cQHj1CTR7waeAakqFcM/Ag8PkQwtoj9TaUM+YkSZJGl47uBGv3NfHM7iZW725i9Z4mttS1DsyCy8tKZ+6EQuZPHMf81Dh3QiGFOZnxNi5J0ijyQmfMjZhgTieGwZwkSdLI1dmTGHgWXH8Qt6m2ZSCEKy/M5ozJRSyYNC4ZxE10FpwkSafCqL+VVZIkSRpLunoTrN/Xwuo9yQDumT1NbKxpIZFK4Urzs1hYVcQrFlSysKqYM6qKqByXE3PXkiTpeBjMSZIkSTFqaOtmS10rW+va2FLXOvB+R337QAg3Pi+T0ycXcem8ahZOToZwE4tyiCJnwkmSNJIZzEmSJEknWW+ij5317QPh2+AQrqH90IIMWelpTC/LY05lIVcunMCCSUUsnFxE1fhcQzhJkkYhgzlJkiTpBKpt6WTt3mbW7mtm7d7kaqg7DrbRkzj0bOeygiyqywq44vQJzCwvoLo8n5nlBVSNzyPd58FJkjRmGMxJkiRJL0CiL7D9YNtzQrg1e5s50No1cEzV+FzmTRjHy+ZXMrM8n5kVBcwsK6AozxVRJUmSwZwkSZL0vNq7e9lY05oK4ZoGZsK1dycAyEiLmF1ZyMVzyzlt4jhOS62KWpRrACdJko7MYE6SJEkiOQNuT0MHWw8knwG39UAr2w60sbWujX1NnQPHFeZkcNrEcbxx2ZSBEG5WRQHZGekxdi9JkkYigzlJkiSNKQ1t3Ww90MqWurZU8JYM4LYfbKe7t2/guHE5GVSXF3BedSnV5fnMqihgwSQXYpAkSSeOwZwkSZJGjZbOHvY3dbKvqfPQ2Nwx8HlvYwfNnb0Dx2emR0wtyaO6vIBL5lZQXZ5PdXkBM8ryKc3PMoCTJEknlcGcJEmShr0QAg3tydBtcNDWP+5vTo6tXb1/VVtWkM3EohymlOSxbHoJ00rzkgFcWQFV43PJSE+L4YokSZIM5iRJkhSzRF/gQGvXoZCtqYN9zf3vk6HbvqbO59xmCpAWQeW4HCYU5TCnsoALZ5czoSibCUW5TCzKYcK4HCrH5ZCVYfAmSZKGJ4M5SZIknVC9iT4aO3poaOvmYFs3DW3d1LenxrYeGtq7qW/rpqG9m7qWLmpbukj0hed8R1Z6GpVF2Uwcl8uiqmKuWJAM4CakgrgJRTmUF2Q7202SJI1oBnOSJEk6rK7eBE0dPTR39NDU0UNje3JsGvS5f19DezcN7T3Ut3XT1NFzxO/Mz0pnfH4WJflZjM/LYlZFQXJ2W1EuE1Oh28SiHEp8vpskSRoDDOYkSZJGid5EH129fXT2JGjrStDW3Ut7dy+tXQnau3pp607Q1tWb3N6VoLUruX9ge1cvzR29ydCto5vOnr6jnq8wO4NxuZkU5WZSkp/F5PF5lORlPid4GzwW52WSk5l+in4akiRJw5/BnCRJ0jDQk+hjd0MH2w+2seNAG9sPttPQ3k1XTx9dvQm6evtSr0Rq26DtqWOG3A16VGkR5GdlkJ+dQV52OvlZGeRlpTOtNI+iVNhWnJccx+VmUpyXNbC9KDeTcTkZ3kYqSZL0IhnMSZIknSKdPQl2N7Sz/UB7MoA7eGjc09jxnOes5WWlU16YTU5GOtmZaWRnpJGTmUZRbibZGWmp16F92RnpyTEz+T4/O4P8rHTysjMoyE4nLysjGb5lp1OQnUF2Rpq3ikqSJMXMYE6SJOk49CT6kreBdidv/WztOnRbaP9tom1diYF9rV297GnoYMfBNvY1dxIGzWobl5PB9LJ8Fk0p5qrFk5hWms/00jymleZTVuAz1iRJkkY7gzlJkjTqJPoCHT0JOroTdPYkaO9ODHzu6EkGZx3diYHnq3V0J5/HltyW3N7enUjt601tS4Zv3b1Hf+5av7QI8rMzKMjOYGJRDudWlyaDt7K8gQCuOC/rJP8kJEmSNJwZzEmSpGGvubOHbXVtbDvQxta6VrYeaKOpoycZuA0N33oSxxye9UuLIC/1jLW8rHRys5K3gY7LyWDiuJzUtuQtoPmpV//toYe2Jff3b8vJ9FZRSZIkHZ3BnCRJGha6e/vYWd8+EL4lxza2HmjjQGvXwHFpEVSNz6OsIIvcrHTG5yXH3Mw0cjOToVpy/OvPOZnp5GYmn7+WDOGSo89bkyRJUhwM5iRJ0inV2N7N5tpWNtW2srm2dSCE29Xw3MUPygqymFGWz2XzKphRns+MsnxmluczpSSP7Iz0GK9AkiRJOjEM5iRJ0gkXQqCupes5Adym2hY21z539ltOZhozygpYMLmI1yyaRHV5PjPKCphRlk9RbmaMVyBJkiSdfAZzkiTpBQshsK+pkw01LWypbWVTTSub61rZVNNCc2fvwHGFORnMqijg0nnlzK4oZFZFAbMqCphcnEtamreQSpIkaWwymJMkScekqaOHDftb2LC/mfX7W5Lva1poGRTAleZnMauigNcsmsTsigJmVyZDuIrCbJ/hJkmSJA1hMCdJkp6jqzfB5trWVAiXDN827G9hX1PnwDGFORnMm1DIVYsnMbeykDmVhcyuLKQkPyvGziVJkqSRxWBOkqQxKtEX2HGwjY01LWysaR0I4LYdaBtYhCErPY2ZFQWcW13K3AmFzK0sZO6EQiYW5TgDTpIkSXqRDOYkSRrlQgjsbepkY2r2W/+4ubaVrt4+AKIIppbkMaeykCtPnzAQwk0vyyczPS3mK5AkSZJGJ4M5SZJGkRACK3c28vSuRjbWJAO4TTWttHYdeg7cxKIc5lQWcsGsMmZXFDB3QvI5cHlZ/m+BJEmSdCr5f+CSJI0ST26v59/u2cAT2+oBKMnPYk5lAa9bMpk5qRlwsysLKcrNjLlTSZIkSWAwJ0nSiLd6dxNfu3cDf9pYR3lhNl+6agGvXDiRsoLsuFuTJEmSdBQGc5IkjVCbalr4+r0buXvNforzMvnMlfN423nTyc1Kj7s1SZIkScfAYE6SpBFm58F2/t99G/nFU3vIz8rgwy+bzbtfMoPCHG9RlSRJkkYSgzlJkkaIfU0dfOv+zfz0yV1kpEdc/9Jq3nPRTMbnZ8XdmiRJkqQXwGBOkqRh7mBrF999cAu3/mUHIQTefM5UPnDJLCrG5cTdmiRJkqQXwWBOkqRhqqmjhx/8eSv//fA2OnsSvHZJFf9w2WymlOTF3ZokSZKkE8BgTpKkYejxrdG6B8wAACAASURBVAe5/tYVNHX08KozJvKRl81hVkVB3G1JkiRJOoEM5iRJGmZ2Hmznhh+voLQgi//5+3M4fXJR3C1JkiRJOgkM5iRJGkZaOnt49y1PEgLc9PZlTC/Lj7slSZIkSSeJwZwkScNEoi/w4TueYuuBNm5919mGcpIkSdIolxZ3A5IkKelf71nPH9fX8oXXnMb5s8ribkeSJEnSSWYwJ0nSMHDXyt3815+28tZzp3LtedPjbkeSJEnSKWAwJ0lSzFbubODTP1/NedWlfP41C+JuR5IkSdIpYjAnSVKM9jZ2cP2PVjChKIfvvmUJmen+1SxJkiSNFS7+IElSTNq7e7nuR8vp7Elw+3XnMD4/K+6WJEmSJJ1CBnOSJMWgry/w8Z89zdp9zdz09mXMriyMuyVJkiRJp5j3y0iSFIMb79/E71bv5zNXzuOSeRVxtyNJkiQpBgZzkiSdYr9bvY//d98mXrekiuteWh13O5IkSZJiYjAnSdIp9OyeJj7606dYMrWYr772dKIoirslSZIkSTExmJMk6RSpbenkuh8tpyQvi/+89iyyM9LjbkmSJElSjFz8QZKkU6CzJ8ENt66gsb2Hn73nPCoKc+JuSZIkSVLMDOYkSTrJQgj84y9Ws2pnI//xliWcPrko7pYkSZIkDQPeyipJ0kn2vYe2ctfKPXzkZXO4cuHEuNuRJEmSNEw4Y06SpGPQ1tVLU0cPXb19dPYkBsbB77t6+ujqTdA5aGzq6OHHj+/gVWdM5EOXzYr7MiRJkiQNIwZzkiQN0tjezebaVjbVtrKpppXNda1srmlhb1PncX9XWgQ5mem8ZFYZX7t6kSuwSpIkSXoOgzlJ0pgTQqCutYvNta3JEK6mdSCMO9DaNXBcTmYasyoKOHtGCbMqCigryCYnM53sjLTkmJlGdkY6OUcYM9MjwzhJkiRJR2QwJ0ka1UIIbD/YztO7Gnl6dyPP7mliY00rTR09A8cUZmcwq7KAS+aWM7uygNkVhcyqKGBycS5paQZrkiRJkk4OgzlJ0qhS09w5EMI9s7uJp3c10tzZCyRnwC2YVMSrzpjI7IpDAVzluGxntkmSJEk65QzmJEkjVlNHD6t3N/H07saBMK6mOXkranpaxNzKQl51xiQWVRVxRlUxcyoLyEh3QXJJkiRJw4PBnCRpRAghsLO+nce31fPEtnpW7mhg64G2gf0zyvI5t7qURVXFLJpSxGkTi8jNSo+xY0mSJEk6OoM5SdKw1NcX2FzXOhDEPbHt4MBsuPF5mZw1rYTXnVXFGVVFnDG5mKK8zJg7liRJkqTjYzAnSRoWehN9rN3XnArh6nlyez0N7ckFGirHZXPOjFLOnlGSXCG1vMBFGSRJkiSNeAZzkqRYtHf3snp3E8t3NPD4tnpWbK+nrTsBwLTSPF42v5KzZ5RwzoxSppTkujiDJEmSpFHHYE6SdNL19QW2HWxj1c5GntrVwKqdjazf30KiLwAwt7KQ1y6pYtmMEs6eXsKEopyYO5YkSZKkk89gTpJ0wjW2d/PUrkae2tWYCuMaaepI3pZakJ3B4inFvO/imZw5tZjFU8ZTkp8Vc8eSJEmSdOoZzEmSXpQQAuv3t7BiR3Im3KpdDWytS66WGkXJ2XCvXDiBM6eMZ/HUYmaWF5Du8+EkSZIkyWBOknT8mtp7+PPmOh7cUMefNtZR15JcLbWsIIvFU8bzuiVVnDmlmDOmFFOQ7V81kiRJknQ4/rYkSXpeIQTW7G3mwQ21PLihjlW7Gkn0BYpyM3np7DIumlPOudWlVI13kQZJkiRJOlYGc5KkwzrSrLjTJ4/jvRfN5OK55SyeUkxGelrMnUqSJEnSyGQwJ0kCkiunrt13aFbcyp0N9AUGZsVdPLeCC+eUUVHoiqmSJEmSdCIYzEnSGBVCYFNtK49tOcijWw7w+LZ6GtuTK6cunFzE+y+ZxcVzy1lU5aw4SZIkSToZDOYkaYwIIbD1QBuPbTnIY1sP8vjWgxxo7QZgcnEuL5tfyXnVpVw4p5zywuyYu5UkSZKk0c9gTpJGqRACO+vbB4K4x7YcpDb1nLgJ43J46exyzqsu5byZpUwpyYu5W0mSJEkaewzmJGkUaWzv5sENdTy0qY6/bDnI3qZOAMoKsjlvZulAEDe9NM/VUyVJkiQpZgZzkjSChRDYUtfKfetquX9dLct31NMXoCQ/i3OrS3hvKoibWV5gECdJkiRJw4zBnCSNMN29fTy5vZ771tXwx3W17KxvB+C0ieN4/yWzuGx+JWdMLiItzSBOkiRJkoYzgzlJGgHq27p5YH0t96+v5aGNdbR09ZKVkcYFM0u5/sJqLp1XwaTi3LjblCRJkiQdB4M5SRqGQghsqm0dmBW3cmcDIUBFYTavXjSRS+dVcsGsUvKy/GNckiRJkkYqf6OTpGEihMAzu5u4e81+7nl2P1sPtAFw+uRxfOjS2Vw2v4LTJ3mLqiRJkiSNFgZzkhSjRF/gye313P3sfu5ds5+9TZ2kp0WcP7OUd71kBi+bX8mEopy425QkSZIknQQGc5J0inX1Jnh0y0HueXY/f1hbw8G2brIz0rhwTjkfu3wul82voDgvK+42JUmSJEknmcGcJJ0C7d29/GlDHXev2c/962pp6eqlIDuDS+dVcMXpE7hoTjn52f6RLEmSJEljib8FStJJ0NcX2FDTwuNbD/LIloM8tLGOrt4+xudl8sqFE7ni9AmcP6uU7Iz0uFuVJEmSJMXEYE6SToBEX2DdvmYe31bP41sP8sT2ehrbewCYXJzLNWdP5RULJrBs+ngy0tNi7laSJEmSNBwYzEnSC9Cb6GPtvmYe31rPX1JBXEtnLwBTS/J4+fxKzqku5ZwZJUwpyYu5W0mSJEnScGQwJ0nHINEXeGZ3I49vSwZxy7c30NqVDOJmlOXzqoUTOae6hHNmlDKpODfmbiVJkiRJI4HBnCQdxbYDbdy5Yhc/X7GH/c2dAMwsz+dvFk/inBklnFtdSuW4nJi7lCRJkiSNRAZzkjREa1cvv3tmHz9bsYsntzeQFsFFc8r5zCvncf7MMsoLs+NuUZIkSZI0ChjMSRIQQuDxbfX8bPlufv/sPtq7E1SX5/OpK+bx2iWTnRUnSZIkSTrhDOYkjWl7Gjv4+Yrd3LliNzvr2ynIzuCqxZO4+qwpLJlaTBRFcbcoSZIkSRqlDOYkjTmdPQnuWbOfO1fs5uHNBwgBzqsu5SMvn80VCyaSm5Ued4uSJEmSpDHAYE7SqNbXF9jV0M7GmlY21rSwYX8LD2yopaWzl8nFuXzo0tlcfVYVU0ry4m5VkiRJkjTGGMxJGhX6+gJ7GjvYVNsyEMJtrGlhc20rnT19A8dNKsrhZfMref1ZVZxbXUpamreqSpIkSZLiYTAnaUTp7u1jd0M7O+rb2TwogNtU20p7d2LguAnjcphdWcBbzpnGnMoCZlcWMruigMKczBi7lyRJkiTpEIM5ScNOU3sPO+rb2Fnfzo6D7exKjTvr29nX1EFfOHRseWE2cyoLeMPSKcypLBwI4YpyDeAkSZIkScObwZykWDS197CxtoUtta3sqE+GbjsPtrPjYBvNnb3PObasIJupJbmcPaOEqSV5TC3JY1ppHjPLCxifnxXTFUiSJEmS9OIYzEk6qerbutmUutV0c23rwG2ndS1dA8dkpEVUjc9lamk+i6cUJ8O30ryBEC4/2z+qJEmSJEmjj7/tSjohDrR2sammlc2pxRc21SYXXjjQ2j1wTH5WOrMqC7loTnnyltOKQmZVFDCxKIeM9LQYu5ckSZIk6dQzmJMEwMHWLlbvaeJgazft3b20dSdo70qN3b20dQ0Zh+zvSRx68FthdgazKgu4bF4lsysLmFWRfO7bpKIcoshVUCVJkiRJAoM5aUzq6E6wZm8TT+1q5KldjTy9u5Fd9R1/dVwUQX5WBnlZ6eRnp8asDMbnZ1E1/rnbSwuymV1RwJzKQirHZRvASZIkSZL0PAzmRpgoiqqALwFXAKXAPuCXwBdDCA1x9qbhKdEX2FzbytO7Glm1q5GndzWyoaaFRGpp08nFuSyaUsRbz5nGoinFTCrKJS87GcDlZKYZsEmSJEmSdJIYzI0gURTNBB4FKoBfAeuBs4F/AK6IouiCEMLBGFtUDEIItHUnaOroobmjh6aOHg6kbkt9amcjq/c00d6dAGBcTgaLphTzvvkzWVRVzBlTiqgozIn5CiRJkiRJGpsM5kaW75IM5T4UQvhW/8Yoir4BfAT4CvCemHrTC9ST6KO1s5fWrl5aUmNrVw8tncnPTR09NHcmQ7fmjkOf+4O45s7egdlvg2WlpzF/0jjesHQKi6YUsaiqmOml+aSlOQNOkiRJkqThIArhr3+h1/CTmi23GdgOzAwh9A3aV0jyltYIqAghtB3pe5YuXRqWL19+krsdXUIIJPoCPYlAR08i+epO0DnofUdP6nPqfUdPgs7uBO2pz+3diVTo1pMM3gYFcV29fc/bQ1ZGGkW5mYzLyUiOuZmpz6kxN+M5n4vzsphZkU92Rvop+AlJkiRJkjS2RVG0IoSw9HjrnDE3clySGu8dHMoBhBBaoih6BLgcOBf446lu7lS75dHtrNvXTF8I9AXoC4GQGg99DvT1HdoWQqAvBBIBehN99CT66E4EenqT75OvQHfqfe+g9y8kv44iyMtMJyczuUBCQXYGBTkZVBbmMLP80OfCgX2ZFGRnUJjz3H3jcjPJyTRgkyRJkiRptDGYGznmpsaNR9i/iWQwN4cxEMw9vbuRRzYfIC2KSIsioojUe4Z8HvQ+rX9fRFZ6RGZ6GnlZaWSmp5GVkfzc/+rfn5lx6HNGehq5menkZqaTk5U+8D43K42c1Pu8rIzU/jSy0l04QZIkSZIkHZnB3MhRlBqbjrC/f3vx0B1RFF0PXA8wderUE99ZDL7xhsVxtyBJkiRJkvSipMXdgE6+EML3QghLQwhLy8vL425HkiRJkiRJGMyNJP0z4oqOsL9/e+Mp6EWSJEmSJEkvksHcyLEhNc45wv7ZqfFIz6CTJEmSJEnSMGIwN3I8kBovj6LoOf/doigqBC4A2oG/nOrGJEmSJEmSdPwM5kaIEMIW4F5gOvD+Ibu/COQDt4YQ2k5xa5IkSZIkSXoBXJV1ZHkf8ChwYxRFlwHrgHOAS0jewvrZGHuTJEmSJEnScXDG3AiSmjW3FPghyUDuY8BM4JvAuSGEg/F1J0mSJEmSpOPhjLkRJoSwC3hn3H1IkiRJkiTpxXHGnCRJkiRJkhQDgzlJkiRJkiQpBgZzkiRJkiRJUgwM5iRJkiRJkqQYGMxJkiRJkiRJMTCYkyRJkiRJkmJgMCdJkiRJkiTFwGBOkiRJkiRJioHBnCRJkiRJkhQDgzlJkiRJkiQpBgZzkiRJkiRJUgwM5iRJkiRJkqQYGMxJkiRJkiRJMTCYkyRJkiRJkmJgMCdJkiRJkiTFwGBOkiRJkiRJioHBnCRJkiRJkhQDgzlJkiRJkiQpBgZzkiRJkiRJUgwM5iRJkiRJkqQYRCGEuHvQKRRFUR2wI+4+TpAy4MAprIvjnF7j8DrnWOh1LFxjHOf0GofXOb3G4XVOr3F4ndNrHF7nHAu9joVrjOOcXuPwOqfXePJqh5NpIYTy464KIfjyNSJfwPJTWRfHOb3G4XXOsdDrWLjGkdTrWLjGkdTrWLjGkdTrWLjGkdTrWLhGex1edfY6vOrsdXjVjZVeR8vLW1klSZIkSZKkGBjMSZIkSZIkSTEwmNNI9r1TXBfHOb3G4XXOsdDrWLjGOM7pNQ6vc3qNw+ucXuPwOqfXOLzOORZ6HQvXGMc5vcbhdU6v8eTVjngu/iBJkiRJkiTFwBlzkiRJkiRJUgwM5iRJkiRJkqQYGMxJkiRJkiRJcQgh+PJ1TC/gauBbwJ+BZiAAPz6Guq+njl07qG4N8CRQB3QB24AfALMOU7cOaE+97wQSqfd9QANwyxHq1gIdqff9x4dB31ML7AO6gZpUPwH4Xeoa+8/ZAdQPqk0M+q5u4C/AT1OfGwcdd7RXF/An4NZBvbYM+o66Qdc5tO4h4Aupzw8c4/n6f1a/GtTrWqBtyDX1DTq+v3boNQ7u9f+3d95hlxVF/v80WRSQIIgYQBFJBkRAQFFEBcwBRFgR2FVxQVREZUX3B7prWnEBUUFFCZJzlCGDJMlxgGFgAswwTmJyeOcN/fujuqbr9D3n3vu+M8M7w9b3ec5zzz3n1Onq7uqq7jrV3b1AX0OaSqe8fj7Vz+HpfxNdBOYC5wHvTjQ7Ar9M6S00zy2gNZ8LDf+bAxul/J0MHJKuN5XvImA8Uv/XA8+kNGYjsqLPWJo+YHpKdyZVGe0HZqXzSelds4A7gInAY8DqwLgiDwOpriMwJvFRpltXx8rPjJo056U0f4K0v0VFPfeR5VfpBjqkqemWvGqZ9Zo6W1RT5wNI24vAPqYNj0banLY72+Ysv4vrOdGtmvI+AjiFLKPt+Le6Y31D92KRZpn+hJTv6cANiHxF4N5Ufv0NtMr3QKqXvwFXktu+pesv6t3qnfsNndZXTOV+X6rrOvku6dq1QyuP05H2HIF7Uj1rW+wr3mPrqwe4y6TZ7dEujypTdfWjeueXBa91cjBQHDOK+phElu12OrIHuBO4qIGuj1Y+ldcHEXmNwD8TXW/NszZ9lZ0HTR4nU6/TtA12IztzgecHUR916ZWH2rrjivooy7WuXl4s6mMwslOxPWQ78CJVG1Km+zzw6aIP87l0fwHV/oel7UvlZ23PDun8KOByUx5NPNfZHrXRM2m1AWrvxpN1prU9SjuN3I9ZgMjK+sDGiC2076yjm9uhvLW+VM5nm/QeSmlMAR5N7+qhqr+V5+fT0Y3daZfHhen6kymtOjnVPFm7szmiA3rb1PMAVdu4OdnuXGXqueyblMeehYztm66Pq6lne9yWnrG25x5T7qUerrMfvYjuuMOU3SLzTNkmS916XPr/aKovbU/aByrlwvY/7zDPdpIpqz9uKeo5In30F6m3YdpX1vRe7JCeTXcGeTygeeyhmid93sqClo8t157i3aUdaaLT8rH2oIlfzdtIpG13sumWVvunJwLnkOWmTi+XtH2I3rktXZtJ7vc10VrepiML/fcDpyYeuq0nLZuxyBhQ66tTf8/WwxRgFPV9pXaH6lyl66FZr3eis3Xdjt+piC6NwPbA6xP/ndKcj9jPs1Oe9yXri0683oTos35gQ/LYbUqb+tX2qmO3VwL/Qu7jdMrj4nFfon9vuq9j9SZeb7B0iXbTlId5tPbdFgDXAbvU+CxWBb4CPE21LzYN+B9grTb+jtcDfwFeSGUxDpHrdbv2tQy3s8ePFecAHk4COgfp8ES6c8zNqKEbQDqgJwLHI4OaiHSWdi7otOOlx/PAI+Z+E92cgnYqMrCwncw+RKlfWiiaOeSO2YykFMpB0lO0KuPSKIyvUSb9Nc/NITt+lK86A1M3wOsx5wuSQqhTlpp2NM+W5Vrmp+zYKa+zCrq6TqjldRGwJuKEUtr5NTTl0YsMik4010rD3U/zwGFz4GvpfHfEaWLLr67DPtXcvw74OVk+NV/3FnxMBR4o3jOhyOPz6V1/ITuonyB35DXNJ5AOi33Xc8DVxbUn0mFl7R9FXTyV0ry4oJ1k0puT8mQ7B8rrP8gyoUazNMilM/o54Pe0djasPJfOighcBgTyIDhSlbNYk/biek5t/6Pp/5ld0kWSwzjRf724166De3Eqo3OLvGpnqclp0Y90oFUGLG+TaT8Ifh6Rs7Kdng9c24ZuVhu6R2v40/N5tH7g0DZ4DvBsQ3oDKR+P1JTh40hnvonXPmTQWMfr/W3oVEZLukl07oha57KtjwtNPTXJUp0+Px/p8Del1dQG/lbzfKknrTPC1u8/qXY8S14HqLdZnWRH0yztwPm0DgzalY3em4TITqcBlA7u7DvrZKdsZyWvant+a66Vzqg6/v+f0Ql6/eaa5+c0vGtz4GfpfLOCz35a+R5IdajnantsG1uE2BUrNwuQDyL6/0VEb1u9PA74BflDYEQGhyPN/742dDcU6V1h/vcgTsTSZt2LfKyw7eA2ch9Qy+F+qrIwFrgR0R16bXbxPyIftep4vbTgrZTJ8mPiCMTuvIese8cUNJHWNmfrWe3OITX12mQH5gM7GdtzblEXTe3iOeB06m1PJ9pnUr1YnTKA2Muy/krZHE2r7rgX6c882EDXg7RZtR/W2flMm/QiIjcP0upYPJP2+mpuqquyr3we4via3Ia2z/CqcjMekf92fYjptNrXAeDuDnnUMqnL4z+6oC15WoDo+Kb7nY6Fhq5dOdXRRKSf/KdBpGftx7ep6oFOju2Y6rnsd8+js23Rvr+VkR46f/xWWkunHwEGU86av993wWtdXf47reOgurq2Hxgi4ti7ZQi8PpN0VFOfpo6PfqQvv5cpX32mSUfZDwefS2meYe5PrSmvHvO+PkP3FsSRptetnbZ6qI/WD3EfMc9OQtriBHNtNLBBja/jLeR2czlij7TP8BSwfle+luF29vix4hyIY+OtSCfmg0nY2jrmgNUQQ3lDQXdJzbPHpHuPFXSbACeUdOkZ66wo6QK5w9QDrJzoDqY6ALd0A4hy3hjpAEWkQ2gV9qOGblOy86AfuMA8dwmwSzofD6xr8viUURSLUvr63hmGv4eBNQydjZzqT+8Phtfr0u9k5AtJNOlNp+qgs0r2UsPrBKoG86kij+8kK7YRhu454MfpfH56Zrah2xj4Qfo/EziCrGyPJCvQctAyDek0X5Tu71PU3RrIV4pP09qZ+DTSiZuWntMvJzNT3R0N/LfhUXnQyJfHksx8m6rh+Dzww+Kadgg+Ro5eObDIi0YN/lfBpxqWCen+WoZOjYtGZPWTjcQ8RB7eY9rFx4s0twd+VPDam+pms0SzCmJErXGv43VWSu/U9P/BBl5tx1k7/HNTuuocPYLW6JzPkx3LvzY0vcgXLP1Cuw1VQ3944vWU9MyB5EG9DtSuRmSndMCMB1ZK9B8ly+504Jr0voNp/apqI+3eYa7vDRyGDEJnInozFsfJiD6wg5N+RCavRTpdnyXLs3ZGRqf0NiUPIrVtWTq9p3J1eRu6M9L/52vS29zQ2YHbNOBd5M7tLuS6j8DWwKqJdgfzvjpeJ5t7EfhLG15VZ/7Q0Kk+1PR3oaqv3kWOapgFfMrweb05Vx1p9as6Vhak96jjRN8/2dDqYFf1+YXpHXtQ7SCW+tx2NPdCZOdpxA5MNOlb5+JIsuyoU3HTxMeLwOtqeFV9WVeu1yK65Pfm2ZjyYG1WSaeOkb1MelNptnW9yMeZbcmd4qNNev9RlE2PoS153c6kqQOjO0yaswo6fc8+VG3W28y9Z8kDTbV1Kjszyf2XhcAf0vlfkCiEciC5M+KEepjcv1B9sy5V26PHbLLd1n6JypXqrS+aMus393oR+dX8rkV1APR5Q3eaua7vsLanpDuYqs6cn9L7mtGBlk553pWqE/SvSPuYjTgrV0l51IjudrwuNO9/sA2vd6XzBwxdL7ATEj0RkY91AwWddfx9gGw/fpPSGIm0ZW2TWm6Hk+3OxikvvSmf6gy+hdyHtcdIZEmh1VKZqN35GGKv+hAZtDSbF/1ma3t+jui0F1Me/ljQnpVo1iX3IQYS39cZOo2csmX7BK393Y3T+25G2kf5Qezywn7Y/qf2lZ8G3l+TprU9ZV/59vR/FFmHL7ZZSMTLDlR1ecnruVTrMQL/S9XWqW7VvvIc4NVUnQX67Btrykd1xwKkj6p0Wg4qq3X6SvvgV5lynUNVTq1u1XxchIwl+oBDzbNXprJWHV3nMH4eaYevR9pLWT4R0WnaVrUNqW67CnifeVbzMAn4lXmX0j1AHrvYj98XUbXNddHnI02a+sGhj9zGlS99fl5Bp/3QF6jmcxpiey+imm8t/4g47nsLOj3G15Sp/W+DKJoct3Uzr2w6dQ7Enza8S8dNWg+Wd3vcgMh/3TtUt40zaY9NvwsRfWLH4mpDX0TGbl8ydXiyoR9Jax61HUwj24YInATsT5ab7xm6C0yepie668y9g8kydlJRTxo5uZbRqbeme0cVutYGiJxc48PQNI8ormu5ntqVr2W4nT1+rJgH3Tvm1Ft+UCc6YGVyh27fbujIHeyBBrqPpf/P1qRnI+6U7rH0+yWqof0PmWfPMXyuT+5QRKqDp7OBs9L5ITV5tIp/N7LivRIJwY/A7jV0txq6zwNrG171S+9XyFFROmD4laErlXuFV/LUIaW1vP6xDd3KiGJUQ2CN6+fJndoTasrm7+bZB4r0t0cM6C1kmVpcPqZOj6Gax+MRg3B6QfdjQ/MPxEhcYu7vW9Txjelcw+VPNnQ2iuLM9M7L0v/vFPycbNK08qcdrwlFu1EDFslRC08W+Tyzoc3pcRR5qqyNzvtxBzrLq/06d6Ypq0MbeNX0Jpa8mjI5KtVLpLUc+hEHV1muyv9uVAeXZyEOapURNYR3kA2wOiFPM2lU5KjIy4/1fQ2ytXlD2R2FtEmVu58UdJHs8P3X4rp2YE5P9zUSQct/kUnznDo6pCOk+lA7kpc30H2eLKsn1KS3eQ2ddsLVCXJpuj+WmrJJ926kcx6V54+2yePiMqjhVdPYvqD7nanv08l6JyIDZ+twLHWkvvuEIs0byfpKO6FWRy7WV0XZLOigz0vZUd1yD622Tp1oqkeayvVGqrqmtFlKdyXSubUR12fTbLO+ac43tel1sHXbkyMDL6Wq908u6M4v6qMpj9rp37ohzVJ2Sttjp8qeYc5L2VEZO41s6+YiEXl/MM9Gcrs/lqp+OKSwPaXDfwda+0GappaR2p6rzP0zyW15V2r0uaHbpaCL1NsepTs4ndtIsnZ2R2VII+tmFPQ/rqE7jXperWNKdfmhbXjVQe1+Ja9k21M6rLQMFqU0F9uPGrvzlaKez6K+b3Km4XczWh1sem93Wu3OYluWI+3VvAAAIABJREFU0rd0pW61tMdQbR8qk9o+Rhk6Owgv25XKlTozZpHbTmk/VF9dSWvUjrU7pR3UvvIJDWnW2R6tAy37rch9xTk1dNbuWF5Pr0kvAlsW6ZUff8ryiSaN7Wt4VTkp6UaSdUcn3bqZoVPHmo1Ss3TzU34mIfJodavaa62j26nyqTLy18TrNen/LaYMlFfbfiwvc6jaV83/IuQjeSyORSZd6yzav3hO7YB+6F9geJlDa3Sh1tcvyP2VJ4rynlOUj63vYxv41cCCviLNmSZdS2f51KMpus7OlPgYrTMn9D0ziuta/o+aNG07fAqJWq2rL3tshnzomEtuX7ZOvlk8rx/fn07yspZJ10aXbQ8clM7HmTLfHTigeKempx/c9H5Per+lXYk8xn2KqtxpHYxNz1k6zaOOLbU8tS/wFktb6Nq1TD08XtzrRKeRvK9s5zOJMfrmD45ljs8hSuyqLp5VhQcS9dMNXa+hraPbOf0+b4lCCO9DvnwNFHST0v/tkK9uIIOz1zTw2Y/MoVdsYM63RqIzQIxiSbumeVYVDUgD3gVR8juGED6BfFlWumDo+oBPGF43Mum8FlFIWkbXU8WLxf8Ppd/7kDXdJhlay+tWDXQjYoz9yEBjZfKUEcUGSCQHSMdoMV36/Xv67UccoSD5BomQ2hgZeOxr8nh7wUtv8f/NyNeTy8hTJBenGULYJOX1MqSzZN9j6/i96fyW9Lt6A92IEMKGyNfGHqQzrtOSAaJJs8fSFXxru5mPfIkF6VSCdB52Ms/e20Cr/K8FvCmdP2Ce2zCE8C8hhPULOkWf4fVxc/1u4JOInJ7bwOus9DuOap1chzjcBhCng8qStsP10u8UJJqqUl/IwA4kcmG0eW9E2rrKyCfT9b8lnp6OMY5N15TO2j+VRSsjE837oFW2LDTaEHKbVLn7UM3zWs7XFte3M3Qg8ot599PmWat3lO4a5OuxPm9lrI6uj1xn82ht23V0qn92Sb9nJH36JvPsqlRhdVZTHvWZMebZMo8Aq6b0lG5h+n0norOeKOi03axEa330IgNkRakj102/pxe8jiHXnfJtdeRifRVCWINcxlrv1g7U6XMtn83S9bG02jptt2uk36ZynUfV1pU2S+m2Q6K7bD5KXu29Hcz53mTZ6Q8hrN2GbnXEmQAyiN0l8QTwBqq27oaCl6Y8robYj6ca0ixlZ7HtSfp6R3K5RkNXyo7agV8nWzceKUurjxXvSL9qexSl7RlX0Fnbo/2H+WS9ZW3PfEN3F9nujCLrZWvDle63JT8GJV1EopCgKqtzQwivr6GbT+6HvCf9jkq/2p4GauzOr827bB4/aa5vQrY7dbyuCayTzr9i8xhCWIlse9YntweAV6TfVYs0z0i/1u5oH0btx3pkO2Htx0NUbY/Nh33nh2jtm1hb1gnW9ryNavvQdjYv/Vr7bvu0ZbvaLf3OTL9rIx+todV+qL7arnh/idLWaV95XgjhaODD6f/sGlqbpvaVxyB68V3puuoQa3va6VbNo9rIe2OMTxXprZZ+ta+s95UWqnan5BWy7rB0t5N1h9a71Veqn8Yn2VG6F9Lvo+ZZS6f6+7XIGGgXc6+UAavnQCKUILdVpVX7qOsXQm7HIE4URZ/hfW76XQn5mPyn4lmQutIyXjn9TgdeVTyn+kdt3Shym+0jy4B9L0g5a31smPiwdCqTWhZT0u9lwGfMcwrVWQNUy31R+l0Jma2h125P6dmybvK7TDV01yPRXRZaPq8ork9Iv9sguvd2qnK/MrlfDRJxWWJBjHFsjHFOSld5XNk8Y+39LHKZzwBItFq/tg3qjBOQqLpdDJ+qt1U29b/Ws471dLmDxbQxRp0qDlImVr+ovb8ekS9Lp3nUNHSsonK7u9KmNBYj0Wr5rU4VnejuRGTmvXRCJ8+dH37UHXQRMYc07smkL+id6MhfOO/ulo68UHsT3a1kB85xyILgl1GdIqN0d5DXC7ic7L3Xr/vzzbuUbiWqERdN63TMRqY82Dyqp76fatRE3RpxUxvo1kW+mNlpNHPIIbW/MXSvMc/Mo/rFTL/eLkppjSZ/Hbq7yJeNHjyP/EXjv5GOrH41Obeg28+k/coaOq2P+6munTYBiXyLSEdXp4X1FrKwCjniUY+/Ix2EV6Q61i9p6ycaXcz0A1TX5tCpxHcjnQQdZOr7f2vobBTBVeSIwcNSmjeRv1J9xaRpj+1NXhe3G6pfuu6toVO+TkUMjdLeQpZfnXb6ItV1DPWYC/yb4VW/ln3K8GrXOdJQ9D+24fXPxfv1fCwiv4cgxlt51HrRyJXp6b0axq5fozVy4YKirn9AjnZ6o3nvt7Re2siIvk/zorxoPt/QQLet0XNTyOsZbYW0ybmpTurWs/q5obVt8vJEtz95GmUkt6l9DJ1tW0r3W6pfRLXeLm+gW5fqFNTyOLbIY6mP+mhdvzAiTs0dDK3VWcrrGsiAr6TdvE0e5yMyYfOoz4xDOo8lnU5D70U6qMpHRAaudnqK1ZGaxrzEj+V1T6pT6ku9rPrqDVSnmk2tsXU2GtXKzivJkcf7UG/rNB/tZGcRVVtXV66LUn53NNcjEjFnebV0D9K6/p4es8k6oCybPciyc30NrbV1O5jrNo91snNAG17rZKcHGSzONeVT5sfKzvrKd6rD75H7BHVrJup0Y9UrMaXzXaQvYiOt1Yb0I1NVNQ/aNh8j6x+1A08X6c1MZfrvJk1rezSiYpzJf6Te9li6K6lGw9pDbY/a1xup2u52a1xZu3MjomNVnnUtSZ22Z6NO/lj0L+vyaKcjR6SfMhnpJ11PdTZEJC9LEZFplWo/tk/1pX0DlU3b39L+4Rup2iy1VWPIfa3HTJrani+g6JuQ+4N/ojWyaduij21tz1PpPb8p6FT+TjV01i6NNHSa1wGy/M1AnIV19uMRWtfr0vKxEXNl/7MpcqicylraD7Vpz1K/UcBEsh2wdP9p8nm3ua7PHNlg6+aR22cfrfZ8HFVbV44B+lO53mn+b1g8V+orLfcxBZ32O28zz9k8/s68w9rlRVT7j7bsdcq2jgGOQD7k6H3VO5NraCNV3XCXOZ9l0p5IVT7sUU6r/LPJc9P6a1YfXGbObfRxD+3XqTsP+HJxbRaiR+0Yx/Y1NJLrgSI/dXxOIbelug1YmnTjZMR+P1Ncb3r+viLN0i7cYc77qF8nbrppp7+tud+H2Hu1k3ad0DsMrbYNuzzJyuR2qvZUZ4xo1J2OS+x6gRPIMnky4nhcTJvoVd7uJkd195D7X0c10Nk8np5+70n3dBxRmcZqaLU9jCiud6LTNP+9o39luB08fqyYB9055nStgcM70SGRAVMQBXdoN3SI40AX8WyhQzrvqsDHFYpmTFIWlk4N/g+oKn6dtqdKR9cs2Znq+lGqqNWoqMHQnYoieceiIw1dL1UDokr8biQK5Q/m3vfN+ZSUxzlUHXu6UcWLpkx3Rr5I6jM/SHR1zp7ZSckorXUk9VHdee6mGno9/ljQfSOdj0S+OpfPq8EfW1zfFzFS91Fdv6KnkCFVxrZTNRcZ6L7P8BGBVRLNjYgRKdd/mWbKTcOi1UBfY+iOpzUfs5GvZpqmnU6xM9XweXVabU42RovbDbmjcSfZUOmAV8tLjde5htYaOO1klB2CDZGB0RxzzfK6cfqv6cwt0ntPG15vpL6TMIDI8xvITl3rGLqPaodHDfzmyFcvvVfKyD7Uy4i2s7MLGSnX6LiOVhl5BrivoLPHzoWei0jHWNvkRem+Dg61UzOJtEMTVf3xU6WjdQqf6qPNa+j6Et1tVBecXkDesOfyBro3pPMryeu0LKTaKfxakcdIdvgPIAOKuo70VCQqwqY5pSgb62wq17Wry+NFSCRy3QeQ41PZl3RbGF7ttK9rkDWZ9P8pVHWk6u+RBa86HfVA895Sn1vZsYu6T6De1kVkgLVYdqjK3Geot3VaZu1kx9q6OpulsvV9k0d12lxGsx2ISMf6C1Rl5yiyrPfVlI1OK9U1//TezVRt3V7m2Ui2WXWyMw2JFmri1cqO/QCjfNj/MwydlZ2j9H5Kf05BV+dk/BXVtjOBPLjT40Zy36OXetnQqUHtdu+bAxxY6KQbG56dYtKssz1NdLeRnRRHkPWELrNwOPWO/gHko6jq3a9StTuHU69jVcbsh6H3dJnHspzLw/K5M9nOnWSub06rrrmW6geu8VTtjtqs0sl7LdVNM3Rqp07pXNw3odmJEUl2p8b2nEqrLJf/312jW+ueK4/PNOjkNWqeXUj+SGcdc1a3lmsr6jHL5P2Hic4uadHkqLmf6npdU6lOKZxCvTzYNYm3rinTiOidduVzPLBGQ/nUPf8C1X58P/W6tY5O+9Qqq9ZB0kvzOmN1H/ttP/RUss7/AVW9W3dMLtLVeq/Tgfao2xirn6ou/UwNnXW+2Y/Hi6iu+3VVm7QXUV2XdcuCtjzsOsl2TKFO3ia6OmegXYe6ia6bjRXqjjs73LfO0onUr083xbTTC2vuq/7Vvr8dX+h0/2+Ya5pGL7mPGcnTYe8wz48iy7xdSsSuA//f5OUXND0d/0ck8n6syaO2k6+UdEUeZ5I/TI1K9xbT1vg07PrE5VJAjXTpvrbNH3T0rwy3g8ePFfOgO8fcr1PD2aQdHeIg0M7yYd3QpcY2j6ygW+iQqQOxoFsbmbaqToazqK61cT75i7V2htUxZztyhyFfAqwyVQN1eEE3AdkuWp87kuoc/LKzoV+J7yjKJlL9WjfK5NEu0K8dQv1KUfLab+h0UKYOtklUBzxHNOTxFvOuiBjWW1L5131V7iGv3TOC7JibgHTutqK6m5ry1Qe8PZ0fY+o4YhxzZIVfN2A/wNCpwVgF+drbS9VwWYfsYendp5prTyID616yQbSdoz3JHftHDK/63o+aa6PJHQU7OFJerfHXnWWvMmkq7ftMXdt1FnTQazt6M2wZpPwdQbW8tI63NPVr04vAA0Ubt7x+Lr1DOyHPkQdzP0LaXjlosfWti17b61cnvh8q6JQ/XTtIZcS2tYhE/rSTketolRF9n9I9WdCoY06jO/6BhLdr2zqA6rocyqvuGlXqjy830E019fv9Gjq76O1Yc30ixjFXQ9dj6v6Egu7WIv3fmP99VDtQZ5hzrdcRpvxsmqr3yjxG8iLJm7fJ48+odm5foLrWy501ebR2QOWqn2q7j8jGA1ZHar5GFLxORPSyLlr9FFV9rvJzDK15fIGqrbO7zD5teC0XUS9tXblIc5PsTKNq65rKdTQ5ulTpIlmHHN5At0tD2WhdzinKpoeq7Og6RvMRJ5i1dceQ9cYcmttVRNbbU9omXn+GtJFbDa87kNvWOKqDnEXUy85cxF4OkG31X6nfnXUXqjZL13FT23MTrTtkah1bukjV9vwVmepvo7XPSL+nFrRWtz6X+H/G5KfO9lg67bucTXUQtTky6NLyG6D6Mc72eWYisq91sQpVu6P2o9f8ah5HkWX/8Zr+ZZnHS8mLlCvdexFHp67zU5b3R8kbT9n6K+3Og9TvVmn7JlonansuTvnQctLyU8fcOKp2513mnRuRl3TQwzrmrO3R5w6oobN1ZnWA7aMpna572k+2eT1IlHupW7WvPNrwrZs1RfIHobL/qXW/S02aGn06HVmGo11f2c7Y0LJQ29PUV/66Sc+2L/0gZHWr7StrH/e4goeI6I3VqNc7yp+1UwNkJ/vlbeguLsrm6iLdhUUa2v6uQ8Y5Nh9qmy9B1vyyDjUrKz+gut6y5ecYsiO5dFaeRe7XD1B1kj1Edf1t+85+qpvLrE6OfP2vdM/u6rsF2Yk4g2zfZtC6dqQdUyyiOnvjYLJ9s/3RT6XysfJjo4CbPj5ovrROHiDLpx5W99Rt8BARB/L9tOqiAZNGKXt6fhfV9VJt249U25I9pphxdZ3jTvWAtrGKY47qMgaWzvYxI9W+dB/i8Jxq8qYOwOkFXcUxl87nmrK2H0pG0cYxV+Txd8iGlpEOjjmT5mIHdnHfHXN+DO9Bd465MaTw0CY6pBOvX9e+2Q0d8nVTd7BqpENCVBe1SW8iovBVWV6AdBg1ykedIapcVXnch3Q09PrihdmTgti9oJtLdZqfhh5rB28BVWWog5UnDN1fzX1V6PekPPZQNcLWYH6z4FXT1QVq1fg08aqdCc3jgiI9+yXpbmTRUhtOP9ekqcb2KvJU1jmmvk4zdJbXY9P5VqmO9T29iU6/vKixtEajB1lzZkwqL83v+uQFSfW4lixTRxm+1JHTj6zdYelGIlEY+l+nXJ5aPKODRuVtWnqXyoKdTjSGHOGphufk4n0lre7kp4bxNqqdCT2+aMsg8fr64t3aHjTSZ3yRXiQvvl3H63+Y556p4VWnWAzQWj4R+UI+gRzlExGH05FUp8iOMvlVh6TKiMqdOhs0/FxlpPxyeQGtMhLJDm8tc0uzLfA/6Xw28Cqjd3qQKVAqN5HWwVGpV84q6LT+NBpOy1inSNk2qQssl3TqmLuCVn2lU55n1dDdms4nmXRtm9Qv48q7Om213DW6SB00qrOmFnm0zk7tuG9Rw6vqHa1vlRfLq/1ab/Oo9VE6u/RdGjlgB05WX6nsaPmUOnKKobFloLJjHdoqz98ky47KoupzfWakKbd+Q7eySUOfbZIdpVNbV8qOyuQvCzrbwf5WTX2orTuC5rKxA4/J5reUHW3LSqdt2e5mZ21dneyoza7jVWVnBqJXNqjh1e50qAP7PpPmgVTbgNo6nZ5SDrpVLnQtLNV/c5KOOCj9VxmzgzDbn9Eo5IVUbc/e6ZlPGLrtyXZHB5qlbo2IE/dNhk9re6bV0Knt2aqkSzyo7VE9ovJsP9YcmMpR5W19st3pN2lavax5fJO59lNjl+t4tWW6Zw2v1vn8tLmvNusbVGcEjEXszheo6qjyULtzj6nrxcsokNv6gHm36oe5VO3OL/SdJq82LV1CoWJ7yLK6Trq/s0lLZdfqVt18IBZ0rzRpaX3o2o22XU0mt7vtDZ0tP/0gVPY/b0H0x0pt0oy07ysPAO8250p3lKGv6yuvU6Snh9pl67yxfeWJyBpRJa3qs6a+8oGIjbGOe9vfL+kWmfOrivS0DmxfzOpWlc8fk6fwlcdRZNnRMjqdqo2xDls7NVvrsS5K9d/J0/17yR9g7DTnW4t8xJT2YrokD1pv36UqOzottnRaxVS+9qPPJKrjoljQWSeV6o/ZZJn8Sk0aF5P7C+PMdbXDz5A3LLAbDFhbp3QaxaiOnnLmSJ2DzNKVbSQijq2eGhqtw6nUR2ROR/TFrCJd7QdMTuWpDltre58g9we03myf6BZTDjqVdSDd0yn8D1Ll5x6qfVM7lfXZdH021XHW2YZ28bRSQ/dYTR7/HZn5E2kzldXQzSaPXypTUuvoivs+ldWPZXvQwTFH/np2dBMdMlXuidRID+uS7jpEAfe3o0MM7DSyoWpJj+qUhYcSzcq0n0ZQd/zenJ9S5FGV6ACtXyvUgdVLNULl3MSDduK+QXW9kPPM+6chDiVN0yrlw5B1vMrQ5NvJCu826nm9iPo89hbpnW3ytXFKzzo2dFB7tylX/TJh6b5dpKeG8nbEufCkqWNdD2aAHCmlRu1mqo6z2wzd0eT1FnamumbY1eRO5gwjd7Zzobwq3eOIo3dn8579E51+qZuXaGzES7eHDlR6yQO0x4ANUxpqiPanGvH0DPKleR65Q6CGaCdbBuk9SjuAOJsi1XVLNAJVZWJBev+7TJqPGV51C/S+Gl7LHbsmmfLRqW1qKI8u8vjtGjob0WdlRA29pVEZuaUom0j+2mVlRDvHtswt738x5z8q9I46Kp7FyH363ZLWNvl8QfcYIlvaRsrorrJNtpvi1kR3tym/pvRsB0zL9Haqa/s8hkTO2IiTQ4s0VWctLPJ4sHlG0/wb9XnUCLVZDbyOrKG7vaZcdUqT2oFyDSctG2sHtHysjiyjF1SfzzVp2LLR5w6j2l4vSL9nkweFjyGRGn1UeV2VPBBUPVQnO9uSBwv/QdXW2fIZ7GHp7jDplWVzJNUBrpZNKTuPU7V1h1GdLqKH2rom2VE7aduVlR390n9JaqcvFHQq26OQHU31HdMRe6d6Re3mxuk9Vnauo8rzOVRtj7V3Nir4t2S7Paboz+gxD/loeHniYbX03P7mma9Rnb5X6ta6AVvT8TayPn8EeNLYRK2/rxW6PZJ1ay/VXTp3ohrtvDMSxWbbl9oeXQtQ87ihee6Iwi7X2Y9IWjOphtfTyTJh7ccIqrZH5UHtud1R9DGqtvyZop61PNT2KA83U3UA28gsa3eetuVdY3d2oKo/fkS2O9em599PXqzf0lrdqhEc/Upn0iv7qmPNudWtg+0rR7JuPaVDmvao6yvPR2ZblJGO1lHR0lc26ZVRT1uSdav2BW1f+ZIGXn9dvKfSVy7yaJ0qYxroItkZOKqGLlKN8LK6Va/vYcqmLMtHi/+9qSy0364fyLVMy2imc5C1lZV+lknTTkM9qibtpsM6vO2UZ03T6tYeWtf71Y/n/89ce5IcDaxH6QCKxftHmrr6XoeyszZVna63Uz+9u7+GTuVU69YGJuiSR4NtW2VagzkWIo41O6NE7eYAMrbVdcdnkj/a6tjWLoukH7e0rWtAwvmGZnKqy/3IDkB1+F6Y6DT67zqq8jwT2axBebiWbAv/Snaq/sHQLUjlOpPsDN/D0qV6X0xb6NKZiM1SWdmjaNsVuhqfSC1d3eG7sjqWFT6bfpt2lVoTcZpsCXw9xvj7Lun2SLShA91uyFcY3bWzkh6iBPcx7/1ilF3WdkM6OXcgnRho3Y1Rd5DRHWgGzD3L95rk3YUuRjqCIEoBpJGCdLjPJe+4tD7iSAmIEv8wMl1RobvJTEzP2jR1N5y/pzxehKzRZnfiGUPebe7VNbyejUwfhbxTkOZxFVrr5ub0+3Ek2m8Dcpn1p98pSLlOAbYIIWxm6E5COpq2HHU3sAeRXa8uI9fxRek3IJ2iaUg53JB42MO853qqsqFp/gwZCIAo/y3IOz9dCZB2CvtuuqY71/0u0c0APhhjnIJEeCn2SnSfNe/aCilfXeh/LBL59WeyfOqOe1oGM5Cvnmsixud1iCHbPaWJycteSCSc4rNI513byQxymW1r6RKv307/xyOLv0Ne/PfyGONEqng+yi5Dx6f/TyLREWsiBnrrdF0NqOX1QMTwKz5jyici8qO719n6+i4iI/rePRKd2rAtqZeRA01aKiO/TOnZ3d90h6XLEJkDmW72MNUytziEvLPehelX9c5W5GhBbQuaxsm0tsknDJ1NU/O/vnl2XPq1bXJ1pFzPRuTqmXRvnqF7saBbGym/Z2vS0128tjD0d6XfMVR3ZTskxvh8uq/1oe1G61p11uo2j0inTKF53JtcNjaPb0jnIxvKRuXO0o2hWq4HkHdTOyu9Q3WrrQ/VV4qDkKlKqiPPI++UpzvkqT5/JTI9S/MYybu2zUW+4H6bLDu6K93rE+3zSHvSDUlA2oXq848YHqBVdg5APgzojtbTyLZuXPq1uhay7JxH1d61s3W7mjyWZbM/1d0JtWxK2fk+2dadkPL4c8OTQm2dlZ0vm/tnI3bStisrO/el89eknURfVdDpLm5XI5FWivWo6hWrO7cky85ZyODS4gbqbc8XEBlfGZki/lGyDKjesrr1bkRmP5yevSbGqPJkbc/u5N3voFW33ke2O38m1/OzBd9XIFNm10R04Tuo2nxtF7uHEALiDAT5mKa69R+J37Hp3rbk3UxBbNZv0vmixLvantWKPB5i6HYp7HKZR93Fd+0QwmoFr0eTy+cZqvZjN6q2R+thrxDCqojDGMSR/J+GDprreTukva+L9Cc/TtZhUO3TWbq30n431u9R1R8Xku3OZSGEDyH9lT7yrrIKq1vtrplNfTrFpubc6lbtK6tMqd3R9q67BtvdOFW3dkoTmvvKY5ANRzahancg7wQM7fvKPUVaJ5N16/Xp1/aV7S62llfdcbltX7nQO5B33C71FUh093NIX3lnWncpXcuca9k8lq73IuW2AdnuRPP826nufN6PlM1aiHPmoHRd5ePW9PtmRM/9ktRHTlgdqWedTq35WI0qbiTv+Gp510h2pTvC3Nc0J5hrRyA2VHnvQZwekPv0EZFZ3ewJqmuGQv54Btkurp92b4asY0DKdAApu0dTmlYeVM+8FRmvlHnUd/YZOtWNOmb7cEFXZ6MhjxMVfR3odM1bxQPUow+RIbuDq/IWEFnWfu46yHgEpA6mkDdqsrxqWz8//d+PbO/XRdYyPBppy7NM3uYkutMQh9quSJ9fd5s9Dvmw8Nr0/3CyLbwZ+UAEYi8XIR9tVRd8GpETlVlLh6UNIexB1qUfSe/Z1dBaWLqKby2EsFYbulYMd+SVHyvmQeeIuUcxXx9q6DTq7eAu6XT6ykA3dEinr58cubE4PcSQ3Gfe91QN3UbkHd4GaF2s9rSU5tOIEtI1HDatyaP9ivFMQddn0tNQVzUCkfy1Vb8ovIAYhSlIGG/JazTpqIf+NPKc/oXksPVew5fl1U5jGtvA68e0/slz+O0UBP2yMTuleQPiINJot4uoRiaUu6gpnU732MHWsaGzO+eukZ7rMe/ZqaDbjOqXqHuQTko/eUrAzuRpOPORAccuVMv3EGQgsWkqm4WmjFRmYionjfTRjsC+Rt7WJk/v03cvQJy4ZZRUGTqttAMmzadSOTxHldftyeu3bJ/41XrWstI1mCL5i/C+Ka1A/nq5CLMQfgOvGuH3X4m+brHn6YlXS7sv0jkYafJYTjs9sYauIiMFnYbRT0IcjkqnZTSaqozcSpbhd5ryXrdI71bLa3pGF6B+NpXvNGRAphvGWP0x0tS7nS65nnmfyp2NfBxJVe9ExFlRR6f3x1PVO8pLmUelK9eHVLqF5K/OWscXI4MQG72jx7Pk6RUatfkw0vl7U+KvnK55Ia26NaZ8q876aE0eVYdMrOH1McQ9leZIAAAgAElEQVTBMMY8P8akPYKqjtTpp9oOriloNS9TGnh9wuRxjKk/vf+3xJOWjY0uO4Q8PVn19HTyLp4vIu1cebSyY/OobU7Lpa3sGF7LL+6lrdO8PIJ0cEs6G705h/ayY6e93UV1p9IJ6d4U8td45bXUx9rWS17VZr2DLDuTDK+ljjiE6vSdfuAtZL2idraUnTcbOrtGp9JtgXTsrexcRVU2BhB7orZnQcqrrlWm0as/SG11R7K96aUaHfNcqhsbiaK6fCVytJSlU/th6TTC+cs1dD3kKNUBJLJA6TSK6OhUhtqPuZ+q7YmII/Y9VCOILK922rVde7TkVe2HRlVqNKPNo9b7r8ltW2XxS6a+tkN0dw95urN+IFM6rcfPFvX8esQu23Z0CXm6uD0Wku3Oh8iy8ymjk9eroVP9oWlqX/ALZLvzblp3WFTd+iR5Ifd+RFbfbMr8jIJO+93PIO2qB+n79QMbGV53pZVX2//sQQawM4FNDV1dmtZmzaDa/7Tr9+n0fG1H2pZfSHU01ZTPRim9TWnVyapbVXfYPFq7U8drJPeVLa9vSemVdkCPEUUe+xEHxark6CSrIy19b8GrjmkuaUjPro1oZ7WMNs/p2EPtTy/VqY2/pH6N3nOQPsB0cmSs7bctpNo/sLsol3SlPfyDOX+A6oZBCxHb/CWTZm9NOrF4Z7/5/wTiaFS6I6jOhrDHcYnXBxOfPbRGtrWLIn2CPH77m6EdWzxXt9vwokT3eJEXPZo2lhhBXgZE208Z3V3KRKS6U/kMYNUky08Xz6nsqH3SqDs77vs38/wC87xdY1cjx7VsZiDtQOu/lxwRaqeFjyCP+6YAayc+NY+/I/ezHyZHLJ5aR1fQLkJ06XbpukY5n9rg91C6I4rrbela3jPcDh4/VpwD+Tp5Rjo0VPVZc+349JyGD/93G7qYBP5hpEN+HHmB8b8jhlPpLqVqPOcjTo7nkcHxPTV0c2k1INphKRXpeGSwfxyi3OYmPrXzuDDxaZ0nGkKr71LH3ABVpVpOMbPbSFsjdCVVZVm3ELEqwQEkKmEeorzOoNrRtee9VHej+pvJ42hay8caFbvgquV1BNmQ6MC6jle9dl3i4wbEKWoX0a4zJEo3OtXhLPIOPY9QP/VsXuK37MA8aOjeR+u6cqUjTb/a2Gv3UN0VKiKdnj7zzBhyR9caNmtAtLP+M0TW9FBnsD73Aq3TLFT2dO2rOxCjVi62+nyRZj/SeT2OvGGHnWpn32/fVfKqXzV1sNqO136qUwHrOhkRaS+a5nxERn5keDyb+sWO59E60Oohr39xZk09N8lYJE+bfoRWB+KCVBYLaJVxLecpSEfOyogNuf8n1aknA0VZjy7uTUi8TE5lazvXc81zlpeLyPL0cDpK51od3eR0bTzS1iYXdLGgs1OxziE7h7ScLI11/Np0lVedRj+lSHMGzXm8xfy3O/h14tUupjyHaie2n2p7sNNkLqHq5LYfMCJZ9kvZOJvqFMWynep/1ed6fSZV+zSHqh3TdDTfpez0GbpyLR4t19L+aX3o/ReK+phEfR5vrUnTPlO2UVsfJxo+emvKx9aPXb9NeVX+7GL+AzW8Lkj190Gq05zK9Cy/EdEzcxBZ0ClGDyGRNLa++qnKvc2/9kseQQYDC4pyLWV3PNWp4xNSOY2oSdMOlhdSXY8vpv92HcIBZLrPX8gfWCZRXbu2pJtLdZ2kHnO9tMFTCzp1/N5C1v/9qf7senCLUv0uMP8tr2qLdTOIbvJo+5jWRtt20m/oRqV0da3ax5DonlLerS6271B9YevZ6pOyvZUOIe2bfNvc6yE7Bks5HSA7hR82+V1o3jmWep1ffgDVNqI7euuOwGWaJe92PdI/Is7V0bRu6jRAVX9p3c9P955HBvrt1sWy/C9AdPIvaD/1VelUP/UicvhzRK7qyqbsCz5h0rQyW1c+PcU7lO5ZpG00rRdWp1vnkNcl7HaqsNWBlzXkr+6w6z6Wzzb1z8tDHYeqB8d3SNfeu4vs/H+2KKdOedc8j0KckvYjRadpoNb+qs6ZQX3fx/Jdlkk3dE313o9E6tWNnZrKTDfUuJdsr+vK1R7a/5qH2MJ9kDbUKV3VcX3ImOODiJ6qG3/ZQ+tBx33/qEmrrFs73r080T2KLBGwk8nbFKoyotF6msfzkP7Bu5APanZsYnXTfETvat1/uvB12OnUE1Oex6b/0xAH9XE1PpK3kMf4lyP6Rj82jiKt693R1zLczh4/VpyD+t2I7DEuPadz89/dJV3d8cEloCt35vFj+TguSPJwFNIZ68Yg6XHzUkj/YIYmU8v6uJXqpgaDOYayxkukc4et6Ri1BLx2c+ig8Sak0z6U/B27BPV87UtY73744cdLcxyXbM9g6a4j6zvdjGYo64X+EHHUNg3Qh3Ko06GHahRJu2MWMog9DonEOq5Luh5kYDOHHPHWzcB9UUrvAmQQXrfuWTte1alz5CB4nYVElTyCOMi6sXU62IqJZhGtUc6Dqeemj1FNx8HIul0DNH9wWJaHOsoGm+7sVHZD4VfLdrB9kTkpvW5l3sriUOi0fAbb5iMiQ0Ppa5UfyQdTNkPJ39I87IyfwR4PL4X0h5p2JDuihkI3mDU8La9DsScRmUX2zBDofjEEmojo34OHQHfDEtTHweQZaIOiS/Z+326er/F1dJVmg5/kDcistEmIztHAn3W79rUMt7PHj5ffgXTAxg0XHRLlE4HNlgVdSYuseTEfWHOQdENKbwl5fUnpBls+xXt0V9zdByMbltc6OmQ6QkTWxhpMPoZE1y1tA93iMlgBeF3ich2sDhiqjLSR867KuyEfL7s2uYLn8aXW58tcdpaD+hhS2SzvslPznrY2pA3d4vKpo2MYdetg6LqV55dBHodke1jKdqfb8l5SOV8adEuY5nLbV16KeRwO3brEsoMsVzIRuK2Ld1TyOFTakq7btlSXXrflV5PmS0rXLW0DXbflo+sFnjnINlDSDWnstiS0S5Lmy+UYdgb8eHkdyHpTA8CJw0WHTAd5eFnQlbTIAqezkEXyB8XrUNJbEl6Hia7r8ql517XI19hNBiMbymuTTCHro/UBGwyyzIdE1w1tGzotg5VXAF6XtFy3GUw9L4mMtJHXjuXdJh8vqza5IudxqLTdls1wyM5w18eSlM3yLDsN72prQxpoFpdPEx3Dp1uHYgfayvPLJI9Dsj0sZbvTTXkPV/tYWm2Z5bivvLTyOBxls7RkB5kyGIFvd8FDJY9DpbV0TW204R2V9AZZfjbNl5RuMLzW0A2mfHQt1M8MUiYX0w0mjzXvGRLtkqT5cjpCKgyHw+FwOBwOh8PhcDgcDofD8RJipc6POBwOh8PhcDgcDofD4XA4HI6lDXfMORwOh8PhcDgcDofD4XA4HMMAd8w5HA6Hw+FwOBwOh8PhcDgcwwB3zDkcDofD4XA4HA6Hw+FwOBzDAHfMORwOh8PhcDgcDofD4XA4HMMAd8w5HA6Hw+FwrGAIIdwaQoghhDjcvDgcDofD4XA4ho5VhpsBh8PhcDgcyyfaOH0WAbOBWcB44EHgHuCaGOOCl4i9FRYhhI2ALwMfAbYF1gVWRsr0OeBJ4D7g5hjjo8PF54qOEMLGwHuA7c3va9Pt8THGTYf43rWAacBqwPdijMebe28D9gLeD7wDeB2wKjATeBy4HvhzjHFaF+m8Edgb2A14J/BGYA2k3Y0CbgZOizE+N4Q8nAd8ERgZY9zWXF8H2BPYHXg3sDmwNjAXkc07gdNjjPcNMr29gEOA9wIbIbI+GrgY+GOMcV4H+lWADwIfBnYEtgLWA3qByUh7OR+4KsbYPwi+tgUORdri64F+JJ/XAKfEGMd3nUmHw+FwOIaIEKN/aHU4HA6Hw9GKIURjzQTOBI6NMc5aBiyt8AghHAocD7yqS5LDYoyn1LznVuADADHGsNQYfJkghPBJ4Mo2jyyJY+4LwAXp79tijE+n6w8B7+riFbOBw2OMZ7dJ43LgU0Cnuu0BfmSdg50QQlgVmAqsA/w8xnhMuv594CfA6l285mzg0Bjj/A5prQ6cgTgBm/As8LkmJ3QIYXfEgbdeF3zdB+wfY3y204MhhO8CP0Mcp3WYA3wtxnh+F+k6HA6HwzFkeMScw+FwOByObvBZcx6QQf26iCNiN2BT4NXAt4DPhxD2jzHe8VIzuTwjhPBN4CRz6WHgKiRyaAFSflsDuwI7IOW8ct27YowfXJa8vgxQllsvErG23VJ496fS7yh1yiW8Pf0OAHcAtwFjgPnAZsABSBTd2sBZIQTaOOe2JTvl7gNuAZ5GnHqvBz6PyMnqwK9CCKvHGH/aJf8fQNovVJ2XW5CdcmOAGxEZnYa09T1SuisDXwI2DCHsHWMcaJPWmcB+6Xw68EfgMWCD9I4dgbcAI0IIO8UYn695xyZkp9yLia9/AJMQp9qOSATq2ki7uSWEsGOM8Z9NTIUQvg78Kv3tBf6K1NeqSMTgPsBawF9DCDNjjCPa5NHhcDgcjiWCR8w5HA6Hw+GohY2YaxeVFUIIyJS7E4G3psuzgF1jjCOXKZMrCNK0ymeBVwARiYQ7tc3zr0Om/j0VY7zkpeHy5YMQwi7AwcAD6Xg0xrjIyPSQIubSlMrJiKPoVzHG75t7LwKnAKfWOZhCCCshzqDvpEszgbfEGF+sefZx4Fbg5BjjqAZejgT+N/3tA7YpHIVNefgNcETKx8YxDQZCCH8CNk75uq2B9v3A38gRn/8aYzy94dlPA5env88B77fTblN5nIbIOcDFMcZ9a97zJeD7wM+BS2OMPTXPbAJcB2yTLp0VYzyoga+NgWeANZFy2zvGeGPxzMGA5ut5YIsY48K69zkcDofDsaRwx5zD4XA4HI5adOuYM8+vjazN9L506Ulg2w4RNf8nEEI4HPht+ntBjLHd1D7HMsJScMx9EIleA3E03WHurRtjnNGBPiDrMe6QLtU6trp5V3ruYiSKDeAnMcZju6AZi0S4/jnG+JUhpPkN4OT09+8xxg80PGen9n48xvi3mmdeATyFrJ8H8PYY4+PFM68GZsUOg5a0Xtxj6e8CYIO6qbYhhBOAb6e/Fedq8dyFgDoKvxFj/F279B0Oh8PhGCp8V1aHw+FwOBxLBTHG2cAXkEggkAXa96t7NoTwihDCZ0MIvwsh3BNCmB5C6A0hzAohjAwhnBJCeGdTWiGEr+uupCGE7zQ9V9BcaWi2qrn/wRDCmSGEUSGEuSGERSGEf4YQHk+0302ROUPBlua8NhppMOi0K2sI4WCT14PTta1DCH8IITwbQliQyvymEML+yWHUTbrrhhC+H0K4MYTwQgihJ4QwL5XZuSGEfwkhrNHhHXuFEM4IIYwOIcwJIcxPPJ0RQnhfO9rlADqNdRpwl73RjVMrOZdsBOTbG57r+K6Eizq9yyKE8A7EKQfFGnxLM80QwlvJTrnRdU65lOYC4E/m0hdqnpnZySmXnnsccfKBRKZuXsNXIDvbItnBWIffmPNaPeZwOBwOx9KAO+YcDofD4XAsNcQYJyHrSCn+teHRJ4BLgcOQNaLWQ9a+XRtZZ+3rwMMhhJ810J+D7BQJ8G+d+EoOtY+lv3fEGJ8091ZK0/huQdaq2gJ4JbLe1EbI9LhPItMQv9cprQbYNc9eM8R3DBnJOfcg8DXgzcjunusBHwLOJU/ba/eOg4BxwC+R9cY2RnYmXRMps/2RTQGObqB/TQjhJuBa4CDEcfIqxIny5nTt9hDCaUE2KFge8cn0e80SRILOMeevWEJ+BvsudSwuAG5Yhmnuac6v6/A+u37bXkPiKKMTb9sga9aB7Ehbt6ad4i5kTT+AXYPsxutwOBwOx1KHb/7gcDgcDodjaeNcZE0ogF1CCKvGGHuLZ16BLOR+A/AQMBFZhH0T4N1I5MyqwA9CCFNijCda4hjjnBDCuYijaesQwq4xxjvb8HQI2Tn2p+LeEYBO6ZuJOJceQtbJWxOJMNoJ2L1DvtvB7hJ5UAjhxBRh+FJgb2Qx+1nA75C8RWTTjkOQcj4ohPD3GONf6l4QQjgK2U1WcSeyccV4pFw3RTYV2J2anURDCOsBdyML/QM8gjhmn0E2S9gWWRNuE8TRukr6v9wghLA1OQqr3Y6vnbCtOR+/BO8ZyrvUMXdjilZbVmnaZx7o8L6HgX5EjrYOIYRuIuRKhBBWI69x2cRb13zFGAfSdNwPIMEMWwH3DpYvh8PhcDg6wR1zDofD4XA4ljYeB+YhUWdrIhFwjxTPHIw4B/rqXhBC+CESSbMl8JMQwp9jjHOKx/6AOOYAvoo4i+reFchRdTOpTsVTWhDH1U5NC+inNfTeXHevC1yCLF6/anrHgyGEk4CrY4xjh/jObvEFxPmxZ4xxirl+TghhBOIgA/gu0OKYCyHsjETJASwEDo4xXlCTzk9TZOLra+79GXHKReDIGONJ5QMhhF8g5fRRxFF4/nK2G6Y6tXroHAVWixDCOlSnRV4zVGaCbERxiLnU9l1p04P3pL9L4lj8mjlvSnMLcz6u3ctijH0hhInIOnOvRJyzE4bA1xeQnY0BHmzYlbVrvhKsc28L3DHncDgcjmUAn8rqcDgcDodjqSLG2E91YN0ydTPGOKLJKZfuj0emuQKsBXy65pkHgfvS332T46wOHyavq3VOTaSQRkHd1m5Xyxjj7Bjjw0332yHGOA74D3PpLcgaVmNCCJNDCNeEEI4LIXw4Rf4sTfQC+xROOeXrMrJDc6sQwhtq6H9Mjjb8VoNTTt83McZ4j70WQng38Jn094Q6p1yinQt8EXGQQt69dHmBOuZujjHOG+I7folMIQaZDvtYu4c74LvktQsfpbOT75NINGNEoh0HjSC73aozcCFwQsOjrzbn07p49fQG2m75Whf4H3Pp58sDXw6Hw+FwdAN3zDkcDofD4VgWsAvJrz/Ed9jF9XdqeObU9Lsm8C8Nz3zVnP+x5r7u3PjWZbm2WYzxf5EdNMcUtzZE1r87Fpna+88Qwi9TdNXSwNUxxmfb3L/ZnG9tb4QQXgN8JP0dA5w2hPQPTL8R+HW7B9MGBLpRwG4hhNWHkN5SRwhhQ7IMDinaLIRwIHBo+jsb+NYS8LM78F/pbx/w9S7WvFPH4r0xxslDSPO1wIXk8cN/xhibItteZc4XdvF66ywf1FpuIYSVgfORNQ9BHJ4XDzdfDofD4XB0C5/K6nA4HA6HY1nAfvxr2jl0Q2SzhY8iDqF1EQdbHeqmR4IMyP8XWAdxwJ1SpPEacrTdvTHGR2vecQMyDW4r4MYQwvEs2RpcjYgxXhpCuALZPOFTwPuQda/s5hDrImv07RtC2KtdFF+X+EeH+xOLtC3sLqlXD3HDg/en35nAjl1sALu6+X0z8GSbZ18qfAKR6SFFm4UQ3k92Ckfgqx2cpe3etSUyHVv78cfEGO/uQLMmInMwBMdiCOGVwBXkjROuoYOT9SXESYgOAXiO5WxtQofD4XA4OsEdcw6Hw+FwOJYF7LSvF8ubIYT9kDXiuo0Kq52mGmOcH0L4K/ANYLsQwrvTFFfFl5GdQ6F10wfF0YgD6nXIhgi7AT0hhPuRaZ43I9MXyw0shoQ01ff6dBBCeAWwHbLI/AHkBeo3A64IIbxjCdPuNGWvx5yvUdyzDtGhOsg2Tb/rApcNkrZ0FA4XNNrsgRjjxLZPFgghvAe4mly2R8YYLxwKEyGEzYAbyVGoJ8YYf9UF6UdN+lcMMs01EGfejunSncB+HTZomGvOS5mqg91BtVxLsh1vPwUOT38nAx+JMbaT95eEL4fD4XA4BgOfyupwOBwOh2OpIk0tsw6dqcX93ZCdW9Up9yCyPtShyBpjnzWHwkaUlfiDOf9qcU93W52LRNe1IK3/th3wWySqCyRaa1ckcm0EMCGE8K3QRbjXYBFjXBBjvCvG+HPgHeQdbUHWENuvnrJrDCXKTWEdonMbn2qPJZmSu7TX2xs0kuNUp/MOKtoshPAOZKMILccfNK2x18W73oA4iTVq7dQY45FdkmvU6JgY48hBpLkasjnIh9Kle4GPdbHG3kxzvkEXSdnp7jMbn6ry9iPgmPR3GvDhLqJLlzlfDofD4XAMFh4x53A4HA6HY2nj7eQpqfOA0hFwHPnj4NdijLWRbGn6XEfEGB8PIdyJONIOCCEclSLp3k9eHP+8tLlA0zumAEeEEL6D7Fy5CxJF9yHEqbIhcCKwDdVdKZcqUhTSr0IIewB7pst7AGcvqzQ7YLY5f1XjU+0xF4mgfC7G+KYlZ+klx4fJ8ty1Yy6EsA1wE3mzh2NjjL8YCgMhhNchTrlN06W/kDdH6US7EvDx9Hcw/K+KTJndO116CNgrxji7mWoxngZ2T+ebdkhnFbKzcR7VqdVNNN8nr7E3A4mUe7xLvhRt+Uqw8rqkU8odDofD4aiFR8w5HA6Hw+FY2jjAnN9ld19NETi65tj9TU65hME4cXQTiLWR9eIgR8tB/aYPLYgx9sYY744x/jrG+FlkR9l/JU/3/GoI4e2D4GuouMmcv+4lSK8JdnH/rYb4DnW0bLgsN9dYhtBprONjjI90Q5DWgbuJHJX18xjjT4aSeNp04Wby7sFnI2vUtZtKarEzeWfkrhxzyVl2HjnvjyHOrxnNVBVYJ9n2HZ59Fzki9olO+QohHInsbguyg++eg9gtuWu+kkNzu/R3gOVjrUOHw+FwvAzhjjmHw+FwOBxLDSGEjalOJ/1z8cj65Ij9Tovf79nhvsXFwPR0/tUQwquBfdP/h2OM9w/iXYsRY1wUYzwdONlc3nUo7xokFpnzoU4hXRq4w5x/IjkrBovb0u8ayPp9KwzS1OVPpL9dbfoQQtgccaRtlC4dH2M8pg1Ju3e9BnHwvS1dugA4eJCbcKhzbQZwexdprow4/z6fLj2BTBOd3kzVguvMead2vJc5H9GBt8ORzV5A1nzbO8Z43yD4Gkl2Nm8TQmjaVAYkalanIN8ZY/Q15hwOh8OxTOCOOYfD4XA4HEsFIYS1gAvJGz88iUyFs5hvzt/S4V3drp9FjHEhcGb6uwvwU/LC7e2i8rrFOHM+6KVA0g60g8EnzfkTg01vaSHGOJXsZHkz1SjEbnGWOT82OX5WFOwIvDadd4w2S5sz3AxsnC6dFGP83lASDiGsh2z0sHW6dCnwpbR5yGCgjrlrbfRqQ5orIdNkdV3DUcAeaap314gxjkamvgK8NYSwd91zaWMJ68hv3BQjhPBVsoN8HrLWXdvdaGv4imSdFIAj2jz+TXN+wWDScTgcDodjMHDHnMPhcDgcjiVCEOwN3I+sywayNtm+ZWRPjHEWMDr9fU8IwW7woO97FTJ4fsMgWbGbQOj6W/OBc9rwvnEI4fjkUGl6Zk3gIHOpq+mMBb4TQrg3hLBfckY0pbVK2mlyj3SpH5lSOJw4LvEBcFII4QtND6by3NFeizHeA1yS/r4fOCeEULvLbnrHKiGEz6XoqOGGOrVmA7e2e9BszqBy+/sY47eHkmgIYR1k1953pEtXAF/s5Firec/m5HUW2zoWU3TgH5CdjAGeAT4UY/znYNI0+LE5PyWE8MYivZWA3wF6/eKmdeJCCF9OvAWkTX8ixnhH3bNd4HjyB4LvpPUcy/QOJkfcPk9r5K/D4XA4HEsNvvmDw+FwOByOjgghfMb+BdZCFrV/FzI90Tq2JgD7t9n98WTgN+n84hDCOciUyTnAtsDByLpqZ5GdBB0RY3w6hHALedF5gAuTM7AJqwNHAUeFEO5Dpvo9iezAuA4yhfAA8uL0t1Od3jkY7IDsDDs7hHAbcA9SVnORKXPbAJ+jWpa/iDEOW8QcQIzxHyGEoxGHxhrABSGEbyKOnueQD71vQpxuHwZ+huzeafGvwBbIxiD7AXuGEC5EnLkzkOjGTZA1vT4CrMsSOkNCCEel99Th1SGE/y6ujY0xlmmqY25EjLG3TVqvQqacbpoujQRuLNpNHaY1OJiuJa+B9gKyi/HHO2wKPD/GeH1xTXdj7U3vbIefkiMie4GTgB272Ij4+hjj/PJijPGKEMIFSH2/CXgwhPAHZL269ZG2rU7cScB36l6enP5/QfQO6fzVXZTtgzHG52r4eiHJxinIWOjaEMJZyJTrVZDNLvZJj/chG9Qs7JCWw+FwOBxDhjvmHA6Hw+FwdIPLunhmJuJMOzbGOLPNc78FdgL+BXHqHJgOiyuArzMIx1zCqVQdc52msdqF5ndIRxNuQaIAu1103+IZZPrdKxEn3CepTlctMQ84LsZ4/BDSWuqIMf46hDALOAHZnXVXmtfaa1n/LMY4O4TwPmQTjv2Q6c5fo/0Oty8sEdMyTbFpA5F1gB8W127DOANTFOW26W+naawbAG81/7dBpp52wm3AB2uu72zOX0d3UynH07rTqDoWb+tiN9VdzPmqVNdVbIfNqE71tjgIaWNfRJxxdWvtPQt8Lsb4fMM7diJvDgHwjXR0wiHAGXU3YoynJmfqz5C8/ls6LOYgTrm26945HA6Hw7GkcMecw+FwOByOwaIXmdo3GxmQP4hEf10dY1zQiTg5tr4UQrgGWV9qO2BNYArwMPDXGOOFAF1E65S40Zw/EWO8qwMv49N0vz0RR9Pbkal1r0J2Yp2IRHWdG2O8erDMmHROCyGcDXwI+ADwbsSR8xokam8+MBXZNfIm4PzBruu1rJHycAVwKLJg/xZIRFoPMt3vfsSBdUUD/WzgiyGEXyIO1w8gZb0OsBCJmhoJ/B24IsY4ZplmqDPUqdUH/G04GRkK0hp16jztajfWpY0YYw+wfwjhTCRq8r3AhojTazQyZf2PMcZ5w8Db8SGEEcgHgI8gEZsDiIPzGuCUGOP4l5ovh8PhcPzfQxjaR1+Hw+FwOByO5Q8hhK+Qo+SOjDGeOJz8OFZchBBuQhypt8YYd+/0/PKGEMKB5I03NnUnk8PhcDgcyyd88weHw+FwOBwvJ3w9/S6guhuow9E1QgivRtbMg2GKNhZGIdoAAAEtSURBVFsK0Ii/R90p53A4HA7H8gt3zDkcDofD4XhZIITwKfKC+efEGF8cTn4cKzT2RtYegxXQMRdCWA2Zng0rIP8Oh8PhcPxfgk9ldTgcDofDsUIihPAKZJ2yVYB3At9D1itbBGy1HKxR5nA4HA6Hw+FwtIVv/uBwOBwOh2NFxUbAtTXX/8Odcg6Hw+FwOByOFQHumHM4HA6Hw/FywEzgSeD4GOOlw82Mw+FwOBwOh8PRDXwqq8PhcDgcDofD4XA4HA6HwzEM8M0fHA6Hw+FwOBwOh8PhcDgcjmGAO+YcDofD4XA4HA6Hw+FwOByOYYA75hwOh8PhcDgcDofD4XA4HI5hgDvmHA6Hw+FwOBwOh8PhcDgcjmGAO+YcDofD4XA4HA6Hw+FwOByOYcD/BzJKcNGsJPu8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 12))\n",
    "plt.plot(world_df[\"conf_cases\"])\n",
    "plt.plot(arima_pred, linestyle='dashed', color='purple')\n",
    "plt.title('# of Coronavirus Cases Over Time', size=30)\n",
    "plt.xlabel('Days Since 1/22/2020', size=30)\n",
    "plt.ylabel('# of Cases', size=30)\n",
    "plt.legend(['Confirmed Cases', 'ARIMA Predictions'], prop={'size': 20})\n",
    "plt.xticks(size=20)\n",
    "plt.yticks(size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA future predictions:\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>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>03/24/2020</th>\n",
       "      <td>353160.312549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03/25/2020</th>\n",
       "      <td>370599.031017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03/26/2020</th>\n",
       "      <td>388320.155405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03/27/2020</th>\n",
       "      <td>406323.685713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03/28/2020</th>\n",
       "      <td>424609.621941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03/29/2020</th>\n",
       "      <td>443177.964089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03/30/2020</th>\n",
       "      <td>462028.712156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03/31/2020</th>\n",
       "      <td>481161.866143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04/01/2020</th>\n",
       "      <td>500577.426050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04/02/2020</th>\n",
       "      <td>520275.391877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Prediction\n",
       "03/24/2020  353160.312549\n",
       "03/25/2020  370599.031017\n",
       "03/26/2020  388320.155405\n",
       "03/27/2020  406323.685713\n",
       "03/28/2020  424609.621941\n",
       "03/29/2020  443177.964089\n",
       "03/30/2020  462028.712156\n",
       "03/31/2020  481161.866143\n",
       "04/01/2020  500577.426050\n",
       "04/02/2020  520275.391877"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Future predictions using ARIMA\n",
    "print('ARIMA future predictions:')\n",
    "arima_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solving as a multivariate problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>101.0000</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>36.0000</td>\n",
       "      <td>138.0000</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1.2833</td>\n",
       "      <td>103.8333</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>28.1667</td>\n",
       "      <td>84.2500</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>2.5000</td>\n",
       "      <td>112.5000</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region      Lat      Long     Date  Confirmed  \\\n",
       "0            NaN       Thailand  15.0000  101.0000  1/22/20        2.0   \n",
       "1            NaN          Japan  36.0000  138.0000  1/22/20        2.0   \n",
       "2            NaN      Singapore   1.2833  103.8333  1/22/20        0.0   \n",
       "3            NaN          Nepal  28.1667   84.2500  1/22/20        0.0   \n",
       "4            NaN       Malaysia   2.5000  112.5000  1/22/20        0.0   \n",
       "\n",
       "   Deaths  Recovered  \n",
       "0     0.0        0.0  \n",
       "1     0.0        0.0  \n",
       "2     0.0        0.0  \n",
       "3     0.0        0.0  \n",
       "4     0.0        0.0  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = conf_df.columns[4:]\n",
    "\n",
    "#making new dataframes for confiremed ,death and recovred cases\n",
    "conf_df_long = conf_df.melt(id_vars=['Province/State', 'Country/Region', 'Lat', 'Long'], \n",
    "                            value_vars=dates, var_name='Date', value_name='Confirmed')\n",
    "\n",
    "deaths_df_long = death_df.melt(id_vars=['Province/State', 'Country/Region', 'Lat', 'Long'], \n",
    "                            value_vars=dates, var_name='Date', value_name='Deaths')\n",
    "\n",
    "recv_df_long = reco_df.melt(id_vars=['Province/State', 'Country/Region', 'Lat', 'Long'], \n",
    "                            value_vars=dates, var_name='Date', value_name='Recovered')\n",
    "\n",
    "#full table combinig all confirmed,recovered and deaths \n",
    "full_table = pd.concat([conf_df_long, deaths_df_long['Deaths'], recv_df_long['Recovered']], \n",
    "                       axis=1, sort=False)\n",
    "\n",
    "full_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# full_table = pd.read_csv('covid_19_full_updatedNew.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>101.0000</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>36.0000</td>\n",
       "      <td>138.0000</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1.2833</td>\n",
       "      <td>103.8333</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>28.1667</td>\n",
       "      <td>84.2500</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>2.5000</td>\n",
       "      <td>112.5000</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18051</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Dominica</td>\n",
       "      <td>15.4150</td>\n",
       "      <td>-61.3710</td>\n",
       "      <td>3/22/20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18052</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Grenada</td>\n",
       "      <td>12.1165</td>\n",
       "      <td>-61.6790</td>\n",
       "      <td>3/22/20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18053</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozambique</td>\n",
       "      <td>-18.6657</td>\n",
       "      <td>35.5296</td>\n",
       "      <td>3/22/20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18054</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Syria</td>\n",
       "      <td>34.8021</td>\n",
       "      <td>38.9968</td>\n",
       "      <td>3/22/20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18055</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>-8.8742</td>\n",
       "      <td>125.7275</td>\n",
       "      <td>3/22/20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18056 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Province/State Country/Region      Lat      Long     Date  Confirmed  \\\n",
       "0                NaN       Thailand  15.0000  101.0000  1/22/20          2   \n",
       "1                NaN          Japan  36.0000  138.0000  1/22/20          2   \n",
       "2                NaN      Singapore   1.2833  103.8333  1/22/20          0   \n",
       "3                NaN          Nepal  28.1667   84.2500  1/22/20          0   \n",
       "4                NaN       Malaysia   2.5000  112.5000  1/22/20          0   \n",
       "...              ...            ...      ...       ...      ...        ...   \n",
       "18051            NaN       Dominica  15.4150  -61.3710  3/22/20          1   \n",
       "18052            NaN        Grenada  12.1165  -61.6790  3/22/20          1   \n",
       "18053            NaN     Mozambique -18.6657   35.5296  3/22/20          1   \n",
       "18054            NaN          Syria  34.8021   38.9968  3/22/20          1   \n",
       "18055            NaN    Timor-Leste  -8.8742  125.7275  3/22/20          1   \n",
       "\n",
       "       Deaths  Recovered  Active  \n",
       "0           0          0       2  \n",
       "1           0          0       2  \n",
       "2           0          0       0  \n",
       "3           0          0       0  \n",
       "4           0          0       0  \n",
       "...       ...        ...     ...  \n",
       "18051       0          0       1  \n",
       "18052       0          0       1  \n",
       "18053       0          0       1  \n",
       "18054       0          0       1  \n",
       "18055       0          0       1  \n",
       "\n",
       "[18056 rows x 9 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Will be adding Soon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Growth Factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This functions smooths data, thanks to Dan Pearson. We will use it to smooth the data for growth factor.\n",
    "def smoother(inputdata,w,imax):\n",
    "    data = 1.0*inputdata\n",
    "    data = data.replace(np.nan,1)\n",
    "    data = data.replace(np.inf,1)\n",
    "    #print(data)\n",
    "    smoothed = 1.0*data\n",
    "    normalization = 1\n",
    "    for i in range(-imax,imax+1):\n",
    "        if i==0:\n",
    "            continue\n",
    "        smoothed += (w**abs(i))*data.shift(i,axis=0)\n",
    "        normalization += w**abs(i)\n",
    "    smoothed /= normalization\n",
    "    return smoothed\n",
    "\n",
    "def growth_factor(confirmed):\n",
    "    confirmed_iminus1 = confirmed.shift(1, axis=0)\n",
    "    confirmed_iminus2 = confirmed.shift(2, axis=0)\n",
    "    return (confirmed-confirmed_iminus1)/(confirmed_iminus1-confirmed_iminus2)\n",
    "\n",
    "def growth_ratio(confirmed):\n",
    "    confirmed_iminus1 = confirmed.shift(1, axis=0)\n",
    "    return (confirmed/confirmed_iminus1)\n",
    "\n",
    "# This is a function which plots (for in input country) the active, confirmed, and recovered cases, deaths, and the growth factor.\n",
    "def plot_country_active_confirmed_recovered(country):\n",
    "    \n",
    "    # Plots Active, Confirmed, and Recovered Cases. Also plots deaths.\n",
    "    country_data = global_data[global_data['Country/Region']==country]\n",
    "    table = country_data.drop(['SNo','Province/State', 'Last Update'], axis=1)\n",
    "    table['ActiveCases'] = table['Confirmed'] - table['Recovered'] - table['Deaths']\n",
    "    table2 = pd.pivot_table(table, values=['ActiveCases','Confirmed', 'Recovered','Deaths'], index=['ObservationDate'], aggfunc=np.sum)\n",
    "    table3 = table2.drop(['Deaths'], axis=1)\n",
    "   \n",
    "    # Growth Factor\n",
    "    w = 0.5\n",
    "    table2['GrowthFactor'] = growth_factor(table2['Confirmed'])\n",
    "    table2['GrowthFactor'] = smoother(table2['GrowthFactor'],w,5)\n",
    "\n",
    "    # 2nd Derivative\n",
    "    table2['2nd_Derivative'] = np.gradient(np.gradient(table2['Confirmed'])) #2nd derivative\n",
    "    table2['2nd_Derivative'] = smoother(table2['2nd_Derivative'],w,7)\n",
    "\n",
    "\n",
    "    #Plot confirmed[i]/confirmed[i-1], this is called the growth ratio\n",
    "    table2['GrowthRatio'] = growth_ratio(table2['Confirmed'])\n",
    "    table2['GrowthRatio'] = smoother(table2['GrowthRatio'],w,5)\n",
    "    \n",
    "    #Plot the growth rate, we will define this as k in the logistic function presented at the beginning of this notebook.\n",
    "    table2['GrowthRate']=np.gradient(np.log(table2['Confirmed']))\n",
    "    table2['GrowthRate'] = smoother(table2['GrowthRate'],0.5,3)\n",
    "    \n",
    "    # horizontal line at growth rate 1.0 for reference\n",
    "    x_coordinates = [1, 100]\n",
    "    y_coordinates = [1, 1]\n",
    "    #plots\n",
    "    table2['Deaths'].plot(title='Deaths')\n",
    "    plt.show()\n",
    "    table3.plot() \n",
    "    plt.show()\n",
    "    table2['GrowthFactor'].plot(title='Growth Factor')\n",
    "    plt.plot(x_coordinates, y_coordinates) \n",
    "    plt.show()\n",
    "    table2['2nd_Derivative'].plot(title='2nd_Derivative')\n",
    "    plt.show()\n",
    "    table2['GrowthRatio'].plot(title='Growth Ratio')\n",
    "    plt.plot(x_coordinates, y_coordinates)\n",
    "    plt.show()\n",
    "    table2['GrowthRate'].plot(title='Growth Rate')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "    return \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CurveFitting Technique\n",
    "### The Math: Exponential vs Logistic\n",
    "The spread of infectious disease can be modeled using a logistic curve rather than an exponential curve. The growth starts exponentially, but must slow down after some point called the inflection point. The inflection point is essentially the midpoint of the spread. We will model the number of confirmed cases using a logistic curve. Let's look at the equation for such a curve, the differential equation for which this curve is a solution, and the graph of the curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import curve_fit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>conf_cases</th>\n",
       "      <th>daily_new_cases</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>new_reco</th>\n",
       "      <th>total_death</th>\n",
       "      <th>new_death</th>\n",
       "      <th>total_active</th>\n",
       "      <th>new_active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1/22/20</th>\n",
       "      <td>555.0</td>\n",
       "      <td>555</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17</td>\n",
       "      <td>510.0</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/23/20</th>\n",
       "      <td>653.0</td>\n",
       "      <td>98</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>605.0</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/24/20</th>\n",
       "      <td>941.0</td>\n",
       "      <td>288</td>\n",
       "      <td>36.0</td>\n",
       "      <td>6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8</td>\n",
       "      <td>879.0</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/25/20</th>\n",
       "      <td>1434.0</td>\n",
       "      <td>493</td>\n",
       "      <td>39.0</td>\n",
       "      <td>3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1353.0</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/26/20</th>\n",
       "      <td>2118.0</td>\n",
       "      <td>684</td>\n",
       "      <td>52.0</td>\n",
       "      <td>13</td>\n",
       "      <td>56.0</td>\n",
       "      <td>14</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         conf_cases  daily_new_cases  total_recovered  new_reco  total_death  \\\n",
       "1/22/20       555.0              555             28.0        28         17.0   \n",
       "1/23/20       653.0               98             30.0         2         18.0   \n",
       "1/24/20       941.0              288             36.0         6         26.0   \n",
       "1/25/20      1434.0              493             39.0         3         42.0   \n",
       "1/26/20      2118.0              684             52.0        13         56.0   \n",
       "\n",
       "         new_death  total_active  new_active  \n",
       "1/22/20         17         510.0         510  \n",
       "1/23/20          1         605.0          95  \n",
       "1/24/20          8         879.0         274  \n",
       "1/25/20         16        1353.0         474  \n",
       "1/26/20         14        2010.0         657  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_full.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = world_full.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05543641806494608 280.74181215153914 58948871190.67571\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3/19/20    242708.0\n",
       "3/20/20    272166.0\n",
       "3/21/20    304524.0\n",
       "3/22/20    335955.0\n",
       "3/23/20    336004.0\n",
       "Name: conf_cases, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#country_data['GrowthFactor'] = growth_factor(country_data['Confirmed'])\n",
    "\n",
    "# we will want x_data to be the number of days since first confirmed and the y_data to be the confirmed data. This will be the data we use to fit a logistic curve\n",
    "x_data = range(len(data.index))\n",
    "y_data = data['conf_cases']\n",
    "\n",
    "def log_curve(x, k, x_0, ymax):\n",
    "    return ymax / (1 + np.exp(-k*(x-x_0)))\n",
    "\n",
    "# Fit the curve\n",
    "popt, pcov = curve_fit(log_curve, x_data, y_data, bounds=([0,0,0],np.inf), maxfev=5000)\n",
    "estimated_k, estimated_x_0, ymax= popt\n",
    "\n",
    "\n",
    "# Plot the fitted curve\n",
    "k = estimated_k\n",
    "x_0 = estimated_x_0\n",
    "y_fitted = log_curve(x_data, k, x_0, ymax)\n",
    "print(k, x_0, ymax)\n",
    "#print(y_fitted)\n",
    "y_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6b81dfa278>]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(x_data, y_fitted, '--', label='fitted')\n",
    "ax.plot(x_data, y_data, 'o', label='Confirmed Data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Graph suggests that it has not still reach its Inflection point !! Hence more dreadful days are yet to come! \n",
    "## But Ray of Hope!! \n",
    "#### There are various parameters which cant be incoporated like lockdown, preventive measures, medical facilities etc. This all will effect the growth rate!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "GujaratPred2.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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",
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