covid19_italy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Libraries and Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import datetime "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utility Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_df_regioni(days):\n",
    "    df = pd.DataFrame()\n",
    "    for i in days:\n",
    "        url =  f'https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-regioni/dpc-covid19-ita-regioni-2020{i}.csv'\n",
    "  #df.append(pd.read_csv(url, encoding= 'Latin'))dpc-covid19-ita-regioni\n",
    "        df = pd.concat([df,pd.read_csv(url, encoding=\"Latin\")])\n",
    "        df.data = pd.to_datetime(df.data , yearfirst=True)\n",
    "        df = df[df['codice_regione'].map(lambda x :len(str(x)) < 3)]\n",
    "    #rename regions\n",
    "        df['denominazione_regione'] = df['denominazione_regione'].mask(df['denominazione_regione']=='P.A. Bolzano', 'Trentino-Alto Adige')\n",
    "        df['denominazione_regione'] = df['denominazione_regione'].mask(df['denominazione_regione']=='P.A. Trento', 'Trentino-Alto Adige')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_regioni_shape():\n",
    "    regioni_url = 'https://gist.github.com/datajournalism-it/48e29e7c87dca7eb1d29/raw/2636aeef92ba0770a073424853f37690064eb0ea/regioni.geojson'\n",
    "    regioni = gpd.read_file(regioni_url)\n",
    "    regioni.rename(columns={'NOME_REG':'denominazione_regione'},inplace=True)\n",
    "    return regioni"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "population size of italian regions as to 2019 , source : https://it.wikipedia.org/wiki/Regioni_d%27Italia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.read_table('https://it.wikipedia.org/wiki/Regioni_d%27Italia',encoding = 'latin')\n",
    "def create_popolazione():\n",
    "    'populate regions'\n",
    "    popolazione = \"Lombardia,Lazio,Campania,Sicilia,Veneto,Emilia-Romagna,Piemonte,Puglia,Toscana,Calabria,Sardegna,Liguria,Marche,Abruzzo,Friuli Venezia Giulia,Trentino-Alto Adige,Umbria,Basilicata,Molise,Valle d'Aosta\".split(',')\n",
    "    nu = [10060574,5879082,5801692,4999891,4905854,4459477,4356406,4029053,3729641,1947131,1639591,1550640,1525271,1311580,1215220,1072276,882015,562869,305617,125666]\n",
    "    popolazione = dict(zip(popolazione,nu))\n",
    "    return pd.Series(popolazione, name = 'popolazione' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_italy():\n",
    "    '''merge population and regions shape'''\n",
    "    italy = shape_.merge(pd.DataFrame(popolazione).reset_index(),left_on = 'denominazione_regione' , right_on = 'index')\n",
    "    italy.drop('index',axis = 1 , inplace = True)\n",
    "    return italy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_regioni(regioni , today):\n",
    "    daterange = pd.date_range(start = regioni.data.iloc[-1],end=today)\n",
    "    days = []\n",
    "\n",
    "    for x in daterange:\n",
    "        dd = '0'+str(x.day)\n",
    "        mm = '0'+str(x.month)\n",
    "        yyyy = str(x.year)\n",
    "        days.append(yyyy+mm[-2:]+dd[-2:])\n",
    "    \n",
    "    df = pd.DataFrame()\n",
    "    for i in days:\n",
    "        url =  'https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-regioni/dpc-covid19-ita-regioni-2020{}.csv'.format(i)\n",
    "    #df.append(pd.read_csv(url, encoding= 'Latin'))dpc-covid19-ita-regioni\n",
    "        df = pd.concat([df,pd.read_csv(url, encoding=\"Latin\")])\n",
    "        df.data = pd.to_datetime(df.data , yearfirst=True)\n",
    "        df = df[df['codice_regione'].map(lambda x :len(str(x)) < 3)]\n",
    "    #rename regions\n",
    "        df['denominazione_regione'] = df['denominazione_regione'].mask(df['denominazione_regione']=='P.A. Bolzano', 'Trentino-Alto Adige')\n",
    "        df['denominazione_regione'] = df['denominazione_regione'].mask(df['denominazione_regione']=='P.A. Trento', 'Trentino-Alto Adige')\n",
    "    regioni = pd.concat([regioni,df],axis = 0)    \n",
    "    return regioni"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_regioni_v21(regioni , today):\n",
    "    daterange = pd.date_range(start = regioni.data.iloc[-1],end=today)\n",
    "    days = []\n",
    "\n",
    "    for x in daterange:\n",
    "        dd = '0'+str(x.day)\n",
    "        mm = '0'+str(x.month)\n",
    "        yyyy = str(x.year)\n",
    "        days.append(yyyy+mm[-2:]+dd[-2:])\n",
    "    \n",
    "    df = pd.DataFrame()\n",
    "    for i in days:\n",
    "        url =  'https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-regioni/dpc-covid19-ita-regioni-{}.csv'.format(i)\n",
    "    #df.append(pd.read_csv(url, encoding= 'Latin'))dpc-covid19-ita-regioni\n",
    "        df = pd.concat([df,pd.read_csv(url, encoding=\"Latin\")])\n",
    "        df.data = pd.to_datetime(df.data , yearfirst=True)\n",
    "        df = df[df['codice_regione'].map(lambda x :len(str(x)) < 3)]\n",
    "    #rename regions\n",
    "        df['denominazione_regione'] = df['denominazione_regione'].mask(df['denominazione_regione']=='P.A. Bolzano', 'Trentino-Alto Adige')\n",
    "        df['denominazione_regione'] = df['denominazione_regione'].mask(df['denominazione_regione']=='P.A. Trento', 'Trentino-Alto Adige')\n",
    "    regioni = pd.concat([regioni,df],axis = 0)    \n",
    "    return regioni"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "regioni = pd.read_csv('covid_regioni.csv')\n",
    "italy = pd.read_csv('italy.csv')\n",
    "italy = italy.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>data</th>\n",
       "      <th>stato</th>\n",
       "      <th>codice_regione</th>\n",
       "      <th>denominazione_regione</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>ricoverati_con_sintomi</th>\n",
       "      <th>terapia_intensiva</th>\n",
       "      <th>...</th>\n",
       "      <th>deceduti</th>\n",
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       "      <th>6316</th>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-12-30 17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>19</td>\n",
       "      <td>Sicilia</td>\n",
       "      <td>38.115697</td>\n",
       "      <td>13.362357</td>\n",
       "      <td>1085</td>\n",
       "      <td>166</td>\n",
       "      <td>...</td>\n",
       "      <td>2381</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-12-30 17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>9</td>\n",
       "      <td>Toscana</td>\n",
       "      <td>43.769231</td>\n",
       "      <td>11.255889</td>\n",
       "      <td>869</td>\n",
       "      <td>160</td>\n",
       "      <td>...</td>\n",
       "      <td>3655</td>\n",
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       "      <td>119696</td>\n",
       "      <td>1872240</td>\n",
       "      <td>1057968.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Positivi diagnosticati solo con test antigenic...</td>\n",
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       "    <tr>\n",
       "      <th>6318</th>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-12-30 17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>10</td>\n",
       "      <td>Umbria</td>\n",
       "      <td>43.106758</td>\n",
       "      <td>12.388247</td>\n",
       "      <td>256</td>\n",
       "      <td>44</td>\n",
       "      <td>...</td>\n",
       "      <td>618</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28660</td>\n",
       "      <td>500003</td>\n",
       "      <td>251329.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6319</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-12-30 17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>2</td>\n",
       "      <td>Valle d'Aosta</td>\n",
       "      <td>45.737503</td>\n",
       "      <td>7.320149</td>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>379</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>20</td>\n",
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       "      <td>2020-12-30 17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>5</td>\n",
       "      <td>Veneto</td>\n",
       "      <td>45.434905</td>\n",
       "      <td>12.338452</td>\n",
       "      <td>2711</td>\n",
       "      <td>358</td>\n",
       "      <td>...</td>\n",
       "      <td>6410</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>3276810</td>\n",
       "      <td>1208044.0</td>\n",
       "      <td>Nei valori riportati per le terapie intensive ...</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
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      ],
      "text/plain": [
       "      Unnamed: 0  Unnamed: 0.1                 data stato  codice_regione  \\\n",
       "6316          16           NaN  2020-12-30 17:00:00   ITA              19   \n",
       "6317          17           NaN  2020-12-30 17:00:00   ITA               9   \n",
       "6318          18           NaN  2020-12-30 17:00:00   ITA              10   \n",
       "6319          19           NaN  2020-12-30 17:00:00   ITA               2   \n",
       "6320          20           NaN  2020-12-30 17:00:00   ITA               5   \n",
       "\n",
       "     denominazione_regione        lat       long  ricoverati_con_sintomi  \\\n",
       "6316               Sicilia  38.115697  13.362357                    1085   \n",
       "6317               Toscana  43.769231  11.255889                     869   \n",
       "6318                Umbria  43.106758  12.388247                     256   \n",
       "6319         Valle d'Aosta  45.737503   7.320149                      72   \n",
       "6320                Veneto  45.434905  12.338452                    2711   \n",
       "\n",
       "      terapia_intensiva  ...  deceduti  casi_da_sospetto_diagnostico  \\\n",
       "6316                166  ...      2381                           NaN   \n",
       "6317                160  ...      3655                           NaN   \n",
       "6318                 44  ...       618                           NaN   \n",
       "6319                  2  ...       379                           NaN   \n",
       "6320                358  ...      6410                           NaN   \n",
       "\n",
       "      casi_da_screening  totale_casi  tamponi  casi_testati  \\\n",
       "6316                NaN        92345  1211824      808160.0   \n",
       "6317                NaN       119696  1872240     1057968.0   \n",
       "6318                NaN        28660   500003      251329.0   \n",
       "6319                NaN         7244    62480       37323.0   \n",
       "6320                NaN       249075  3276810     1208044.0   \n",
       "\n",
       "                                                   note  \\\n",
       "6316                                                NaN   \n",
       "6317                                                NaN   \n",
       "6318                                                NaN   \n",
       "6319                                                NaN   \n",
       "6320  Nei valori riportati per le terapie intensive ...   \n",
       "\n",
       "      ingressi_terapia_intensiva  \\\n",
       "6316                        12.0   \n",
       "6317                        10.0   \n",
       "6318                         8.0   \n",
       "6319                         2.0   \n",
       "6320                        36.0   \n",
       "\n",
       "                                              note_test  note_casi  \n",
       "6316                                                NaN        NaN  \n",
       "6317  Positivi diagnosticati solo con test antigenic...        NaN  \n",
       "6318                                                NaN        NaN  \n",
       "6319                                                NaN        NaN  \n",
       "6320                                                NaN        NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regioni.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "update :\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "daterange = pd.date_range(start = regioni.data.iloc[-1],end='20210215')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "days = []\n",
    "for x in daterange:\n",
    "    dd = '0'+str(x.day)\n",
    "    mm = '0'+str(x.month)\n",
    "    yyyy = str(x.year)\n",
    "    days.append(yyyy+mm[-2:]+dd[-2:])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " '20210212',\n",
       " '20210213',\n",
       " '20210214']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>stato</th>\n",
       "      <th>codice_regione</th>\n",
       "      <th>denominazione_regione</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>ricoverati_con_sintomi</th>\n",
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       "      <th>totale_ospedalizzati</th>\n",
       "      <th>isolamento_domiciliare</th>\n",
       "      <th>...</th>\n",
       "      <th>note</th>\n",
       "      <th>ingressi_terapia_intensiva</th>\n",
       "      <th>note_test</th>\n",
       "      <th>note_casi</th>\n",
       "      <th>totale_positivi_test_molecolare</th>\n",
       "      <th>totale_positivi_test_antigenico_rapido</th>\n",
       "      <th>tamponi_test_molecolare</th>\n",
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       "      <th>codice_nuts_1</th>\n",
       "      <th>codice_nuts_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>13</td>\n",
       "      <td>Abruzzo</td>\n",
       "      <td>42.351222</td>\n",
       "      <td>13.398438</td>\n",
       "      <td>537</td>\n",
       "      <td>57</td>\n",
       "      <td>594</td>\n",
       "      <td>11383</td>\n",
       "      <td>...</td>\n",
       "      <td>Oggi sono risultati 533 nuovi positivi. Ma dal...</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NB: Oggi sono risultati 533 nuovi positivi. Ma...</td>\n",
       "      <td>48299</td>\n",
       "      <td>0</td>\n",
       "      <td>680147</td>\n",
       "      <td>172729</td>\n",
       "      <td>ITF</td>\n",
       "      <td>ITF1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>17</td>\n",
       "      <td>Basilicata</td>\n",
       "      <td>40.639471</td>\n",
       "      <td>15.805148</td>\n",
       "      <td>70</td>\n",
       "      <td>6</td>\n",
       "      <td>76</td>\n",
       "      <td>3359</td>\n",
       "      <td>...</td>\n",
       "      <td>IL NUMERO TOTALE DEI DECESSI COMPRENDE 11 PAZI...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14280</td>\n",
       "      <td>0</td>\n",
       "      <td>223172</td>\n",
       "      <td>6158</td>\n",
       "      <td>ITF</td>\n",
       "      <td>ITF5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>18</td>\n",
       "      <td>Calabria</td>\n",
       "      <td>38.905976</td>\n",
       "      <td>16.594402</td>\n",
       "      <td>195</td>\n",
       "      <td>22</td>\n",
       "      <td>217</td>\n",
       "      <td>6529</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35558</td>\n",
       "      <td>6</td>\n",
       "      <td>547283</td>\n",
       "      <td>7971</td>\n",
       "      <td>ITF</td>\n",
       "      <td>ITF6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>15</td>\n",
       "      <td>Campania</td>\n",
       "      <td>40.839566</td>\n",
       "      <td>14.250850</td>\n",
       "      <td>1281</td>\n",
       "      <td>107</td>\n",
       "      <td>1388</td>\n",
       "      <td>67668</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>240766</td>\n",
       "      <td>3116</td>\n",
       "      <td>2615310</td>\n",
       "      <td>64083</td>\n",
       "      <td>ITF</td>\n",
       "      <td>ITF3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>8</td>\n",
       "      <td>Emilia-Romagna</td>\n",
       "      <td>44.494367</td>\n",
       "      <td>11.341721</td>\n",
       "      <td>1972</td>\n",
       "      <td>172</td>\n",
       "      <td>2144</td>\n",
       "      <td>35426</td>\n",
       "      <td>...</td>\n",
       "      <td>Sono stati eliminati 2 casi, positivi a test a...</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>237049</td>\n",
       "      <td>74</td>\n",
       "      <td>3186022</td>\n",
       "      <td>263000</td>\n",
       "      <td>ITH</td>\n",
       "      <td>ITH5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>6</td>\n",
       "      <td>Friuli Venezia Giulia</td>\n",
       "      <td>45.649435</td>\n",
       "      <td>13.768136</td>\n",
       "      <td>412</td>\n",
       "      <td>63</td>\n",
       "      <td>475</td>\n",
       "      <td>9284</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68395</td>\n",
       "      <td>3793</td>\n",
       "      <td>1183593</td>\n",
       "      <td>64116</td>\n",
       "      <td>ITH</td>\n",
       "      <td>ITH4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>12</td>\n",
       "      <td>Lazio</td>\n",
       "      <td>41.892770</td>\n",
       "      <td>12.483667</td>\n",
       "      <td>2090</td>\n",
       "      <td>254</td>\n",
       "      <td>2344</td>\n",
       "      <td>36086</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>218342</td>\n",
       "      <td>1723</td>\n",
       "      <td>3243557</td>\n",
       "      <td>477514</td>\n",
       "      <td>ITI</td>\n",
       "      <td>ITI4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>7</td>\n",
       "      <td>Liguria</td>\n",
       "      <td>44.411493</td>\n",
       "      <td>8.932699</td>\n",
       "      <td>594</td>\n",
       "      <td>61</td>\n",
       "      <td>655</td>\n",
       "      <td>4396</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in Regione Liguria la positivitÃ  al test anti...</td>\n",
       "      <td>74009</td>\n",
       "      <td>0</td>\n",
       "      <td>875098</td>\n",
       "      <td>72601</td>\n",
       "      <td>ITC</td>\n",
       "      <td>ITC3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>3</td>\n",
       "      <td>Lombardia</td>\n",
       "      <td>45.466794</td>\n",
       "      <td>9.190347</td>\n",
       "      <td>3572</td>\n",
       "      <td>366</td>\n",
       "      <td>3938</td>\n",
       "      <td>44692</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>560291</td>\n",
       "      <td>3732</td>\n",
       "      <td>5868307</td>\n",
       "      <td>257016</td>\n",
       "      <td>ITC</td>\n",
       "      <td>ITC4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>11</td>\n",
       "      <td>Marche</td>\n",
       "      <td>43.616760</td>\n",
       "      <td>13.518875</td>\n",
       "      <td>537</td>\n",
       "      <td>83</td>\n",
       "      <td>620</td>\n",
       "      <td>7524</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60936</td>\n",
       "      <td>0</td>\n",
       "      <td>714838</td>\n",
       "      <td>43352</td>\n",
       "      <td>ITI</td>\n",
       "      <td>ITI3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>14</td>\n",
       "      <td>Molise</td>\n",
       "      <td>41.557748</td>\n",
       "      <td>14.659161</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>85</td>\n",
       "      <td>1481</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9524</td>\n",
       "      <td>0</td>\n",
       "      <td>150107</td>\n",
       "      <td>288</td>\n",
       "      <td>ITF</td>\n",
       "      <td>ITF2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>21</td>\n",
       "      <td>P.A. Bolzano</td>\n",
       "      <td>46.499335</td>\n",
       "      <td>11.356624</td>\n",
       "      <td>258</td>\n",
       "      <td>39</td>\n",
       "      <td>297</td>\n",
       "      <td>8034</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40240</td>\n",
       "      <td>8544</td>\n",
       "      <td>462024</td>\n",
       "      <td>149249</td>\n",
       "      <td>ITH</td>\n",
       "      <td>ITH1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>22</td>\n",
       "      <td>P.A. Trento</td>\n",
       "      <td>46.068935</td>\n",
       "      <td>11.121231</td>\n",
       "      <td>174</td>\n",
       "      <td>27</td>\n",
       "      <td>201</td>\n",
       "      <td>2482</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26164</td>\n",
       "      <td>4193</td>\n",
       "      <td>533924</td>\n",
       "      <td>35133</td>\n",
       "      <td>ITH</td>\n",
       "      <td>ITH2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>1</td>\n",
       "      <td>Piemonte</td>\n",
       "      <td>45.073274</td>\n",
       "      <td>7.680687</td>\n",
       "      <td>1933</td>\n",
       "      <td>144</td>\n",
       "      <td>2077</td>\n",
       "      <td>10249</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230424</td>\n",
       "      <td>3420</td>\n",
       "      <td>1976643</td>\n",
       "      <td>343387</td>\n",
       "      <td>ITC</td>\n",
       "      <td>ITC1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>16</td>\n",
       "      <td>Puglia</td>\n",
       "      <td>41.125596</td>\n",
       "      <td>16.867367</td>\n",
       "      <td>1345</td>\n",
       "      <td>159</td>\n",
       "      <td>1504</td>\n",
       "      <td>38960</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135028</td>\n",
       "      <td>473</td>\n",
       "      <td>1401198</td>\n",
       "      <td>37177</td>\n",
       "      <td>ITF</td>\n",
       "      <td>ITF4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>20</td>\n",
       "      <td>Sardegna</td>\n",
       "      <td>39.215312</td>\n",
       "      <td>9.110616</td>\n",
       "      <td>320</td>\n",
       "      <td>25</td>\n",
       "      <td>345</td>\n",
       "      <td>13600</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>41746 antigenici praticati come screening \"Sar...</td>\n",
       "      <td>Si segnala il decesso dei paz.:  - 1 donna 81 ...</td>\n",
       "      <td>40247</td>\n",
       "      <td>16</td>\n",
       "      <td>606333</td>\n",
       "      <td>97559</td>\n",
       "      <td>ITG</td>\n",
       "      <td>ITG2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>19</td>\n",
       "      <td>Sicilia</td>\n",
       "      <td>38.115697</td>\n",
       "      <td>13.362357</td>\n",
       "      <td>1035</td>\n",
       "      <td>165</td>\n",
       "      <td>1200</td>\n",
       "      <td>33349</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>146076</td>\n",
       "      <td>0</td>\n",
       "      <td>1636810</td>\n",
       "      <td>497508</td>\n",
       "      <td>ITG</td>\n",
       "      <td>ITG1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>9</td>\n",
       "      <td>Toscana</td>\n",
       "      <td>43.769231</td>\n",
       "      <td>11.255889</td>\n",
       "      <td>713</td>\n",
       "      <td>132</td>\n",
       "      <td>845</td>\n",
       "      <td>11491</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143496</td>\n",
       "      <td>307</td>\n",
       "      <td>2296489</td>\n",
       "      <td>169266</td>\n",
       "      <td>ITI</td>\n",
       "      <td>ITI1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>10</td>\n",
       "      <td>Umbria</td>\n",
       "      <td>43.106758</td>\n",
       "      <td>12.388247</td>\n",
       "      <td>464</td>\n",
       "      <td>84</td>\n",
       "      <td>548</td>\n",
       "      <td>7604</td>\n",
       "      <td>...</td>\n",
       "      <td>Si fa presente che 24 dei ricoveri NON UTI, so...</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41068</td>\n",
       "      <td>0</td>\n",
       "      <td>648493</td>\n",
       "      <td>84405</td>\n",
       "      <td>ITI</td>\n",
       "      <td>ITI2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>2</td>\n",
       "      <td>Valle d'Aosta</td>\n",
       "      <td>45.737503</td>\n",
       "      <td>7.320149</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>126</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7918</td>\n",
       "      <td>0</td>\n",
       "      <td>71669</td>\n",
       "      <td>2265</td>\n",
       "      <td>ITC</td>\n",
       "      <td>ITC2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2021-02-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>5</td>\n",
       "      <td>Veneto</td>\n",
       "      <td>45.434905</td>\n",
       "      <td>12.338452</td>\n",
       "      <td>929</td>\n",
       "      <td>110</td>\n",
       "      <td>1039</td>\n",
       "      <td>23771</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>319390</td>\n",
       "      <td>2326</td>\n",
       "      <td>3895438</td>\n",
       "      <td>686627</td>\n",
       "      <td>ITH</td>\n",
       "      <td>ITH3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   data stato  codice_regione  denominazione_regione  \\\n",
       "0   2021-02-15T17:00:00   ITA              13                Abruzzo   \n",
       "1   2021-02-15T17:00:00   ITA              17             Basilicata   \n",
       "2   2021-02-15T17:00:00   ITA              18               Calabria   \n",
       "3   2021-02-15T17:00:00   ITA              15               Campania   \n",
       "4   2021-02-15T17:00:00   ITA               8         Emilia-Romagna   \n",
       "5   2021-02-15T17:00:00   ITA               6  Friuli Venezia Giulia   \n",
       "6   2021-02-15T17:00:00   ITA              12                  Lazio   \n",
       "7   2021-02-15T17:00:00   ITA               7                Liguria   \n",
       "8   2021-02-15T17:00:00   ITA               3              Lombardia   \n",
       "9   2021-02-15T17:00:00   ITA              11                 Marche   \n",
       "10  2021-02-15T17:00:00   ITA              14                 Molise   \n",
       "11  2021-02-15T17:00:00   ITA              21           P.A. Bolzano   \n",
       "12  2021-02-15T17:00:00   ITA              22            P.A. Trento   \n",
       "13  2021-02-15T17:00:00   ITA               1               Piemonte   \n",
       "14  2021-02-15T17:00:00   ITA              16                 Puglia   \n",
       "15  2021-02-15T17:00:00   ITA              20               Sardegna   \n",
       "16  2021-02-15T17:00:00   ITA              19                Sicilia   \n",
       "17  2021-02-15T17:00:00   ITA               9                Toscana   \n",
       "18  2021-02-15T17:00:00   ITA              10                 Umbria   \n",
       "19  2021-02-15T17:00:00   ITA               2          Valle d'Aosta   \n",
       "20  2021-02-15T17:00:00   ITA               5                 Veneto   \n",
       "\n",
       "          lat       long  ricoverati_con_sintomi  terapia_intensiva  \\\n",
       "0   42.351222  13.398438                     537                 57   \n",
       "1   40.639471  15.805148                      70                  6   \n",
       "2   38.905976  16.594402                     195                 22   \n",
       "3   40.839566  14.250850                    1281                107   \n",
       "4   44.494367  11.341721                    1972                172   \n",
       "5   45.649435  13.768136                     412                 63   \n",
       "6   41.892770  12.483667                    2090                254   \n",
       "7   44.411493   8.932699                     594                 61   \n",
       "8   45.466794   9.190347                    3572                366   \n",
       "9   43.616760  13.518875                     537                 83   \n",
       "10  41.557748  14.659161                      74                 11   \n",
       "11  46.499335  11.356624                     258                 39   \n",
       "12  46.068935  11.121231                     174                 27   \n",
       "13  45.073274   7.680687                    1933                144   \n",
       "14  41.125596  16.867367                    1345                159   \n",
       "15  39.215312   9.110616                     320                 25   \n",
       "16  38.115697  13.362357                    1035                165   \n",
       "17  43.769231  11.255889                     713                132   \n",
       "18  43.106758  12.388247                     464                 84   \n",
       "19  45.737503   7.320149                      10                  2   \n",
       "20  45.434905  12.338452                     929                110   \n",
       "\n",
       "    totale_ospedalizzati  isolamento_domiciliare  ...  \\\n",
       "0                    594                   11383  ...   \n",
       "1                     76                    3359  ...   \n",
       "2                    217                    6529  ...   \n",
       "3                   1388                   67668  ...   \n",
       "4                   2144                   35426  ...   \n",
       "5                    475                    9284  ...   \n",
       "6                   2344                   36086  ...   \n",
       "7                    655                    4396  ...   \n",
       "8                   3938                   44692  ...   \n",
       "9                    620                    7524  ...   \n",
       "10                    85                    1481  ...   \n",
       "11                   297                    8034  ...   \n",
       "12                   201                    2482  ...   \n",
       "13                  2077                   10249  ...   \n",
       "14                  1504                   38960  ...   \n",
       "15                   345                   13600  ...   \n",
       "16                  1200                   33349  ...   \n",
       "17                   845                   11491  ...   \n",
       "18                   548                    7604  ...   \n",
       "19                    12                     126  ...   \n",
       "20                  1039                   23771  ...   \n",
       "\n",
       "                                                 note  \\\n",
       "0   Oggi sono risultati 533 nuovi positivi. Ma dal...   \n",
       "1   IL NUMERO TOTALE DEI DECESSI COMPRENDE 11 PAZI...   \n",
       "2                                                 NaN   \n",
       "3                                                 NaN   \n",
       "4   Sono stati eliminati 2 casi, positivi a test a...   \n",
       "5                                                 NaN   \n",
       "6                                                 NaN   \n",
       "7                                                 NaN   \n",
       "8                                                 NaN   \n",
       "9                                                 NaN   \n",
       "10                                                NaN   \n",
       "11                                                NaN   \n",
       "12                                                NaN   \n",
       "13                                                NaN   \n",
       "14                                                NaN   \n",
       "15                                                NaN   \n",
       "16                                                NaN   \n",
       "17                                                NaN   \n",
       "18  Si fa presente che 24 dei ricoveri NON UTI, so...   \n",
       "19                                                NaN   \n",
       "20                                                NaN   \n",
       "\n",
       "    ingressi_terapia_intensiva  \\\n",
       "0                            9   \n",
       "1                            0   \n",
       "2                            1   \n",
       "3                            6   \n",
       "4                           12   \n",
       "5                            4   \n",
       "6                           12   \n",
       "7                            1   \n",
       "8                           19   \n",
       "9                            6   \n",
       "10                           3   \n",
       "11                           4   \n",
       "12                           1   \n",
       "13                           7   \n",
       "14                           8   \n",
       "15                           2   \n",
       "16                           9   \n",
       "17                           6   \n",
       "18                           8   \n",
       "19                           0   \n",
       "20                           4   \n",
       "\n",
       "                                            note_test  \\\n",
       "0                                                 NaN   \n",
       "1                                                 NaN   \n",
       "2                                                 NaN   \n",
       "3                                                 NaN   \n",
       "4                                                 NaN   \n",
       "5                                                 NaN   \n",
       "6                                                 NaN   \n",
       "7                                                 NaN   \n",
       "8                                                 NaN   \n",
       "9                                                 NaN   \n",
       "10                                                NaN   \n",
       "11                                                NaN   \n",
       "12                                                NaN   \n",
       "13                                                NaN   \n",
       "14                                                NaN   \n",
       "15  41746 antigenici praticati come screening \"Sar...   \n",
       "16                                                NaN   \n",
       "17                                                NaN   \n",
       "18                                                NaN   \n",
       "19                                                NaN   \n",
       "20                                                NaN   \n",
       "\n",
       "                                            note_casi  \\\n",
       "0   NB: Oggi sono risultati 533 nuovi positivi. Ma...   \n",
       "1                                                 NaN   \n",
       "2                                                 NaN   \n",
       "3                                                 NaN   \n",
       "4                                                 NaN   \n",
       "5                                                 NaN   \n",
       "6                                                 NaN   \n",
       "7   in Regione Liguria la positivitÃ  al test anti...   \n",
       "8                                                 NaN   \n",
       "9                                                 NaN   \n",
       "10                                                NaN   \n",
       "11                                                NaN   \n",
       "12                                                NaN   \n",
       "13                                                NaN   \n",
       "14                                                NaN   \n",
       "15  Si segnala il decesso dei paz.:  - 1 donna 81 ...   \n",
       "16                                                NaN   \n",
       "17                                                NaN   \n",
       "18                                                NaN   \n",
       "19                                                NaN   \n",
       "20                                                NaN   \n",
       "\n",
       "    totale_positivi_test_molecolare  totale_positivi_test_antigenico_rapido  \\\n",
       "0                             48299                                       0   \n",
       "1                             14280                                       0   \n",
       "2                             35558                                       6   \n",
       "3                            240766                                    3116   \n",
       "4                            237049                                      74   \n",
       "5                             68395                                    3793   \n",
       "6                            218342                                    1723   \n",
       "7                             74009                                       0   \n",
       "8                            560291                                    3732   \n",
       "9                             60936                                       0   \n",
       "10                             9524                                       0   \n",
       "11                            40240                                    8544   \n",
       "12                            26164                                    4193   \n",
       "13                           230424                                    3420   \n",
       "14                           135028                                     473   \n",
       "15                            40247                                      16   \n",
       "16                           146076                                       0   \n",
       "17                           143496                                     307   \n",
       "18                            41068                                       0   \n",
       "19                             7918                                       0   \n",
       "20                           319390                                    2326   \n",
       "\n",
       "    tamponi_test_molecolare  tamponi_test_antigenico_rapido  codice_nuts_1  \\\n",
       "0                    680147                          172729            ITF   \n",
       "1                    223172                            6158            ITF   \n",
       "2                    547283                            7971            ITF   \n",
       "3                   2615310                           64083            ITF   \n",
       "4                   3186022                          263000            ITH   \n",
       "5                   1183593                           64116            ITH   \n",
       "6                   3243557                          477514            ITI   \n",
       "7                    875098                           72601            ITC   \n",
       "8                   5868307                          257016            ITC   \n",
       "9                    714838                           43352            ITI   \n",
       "10                   150107                             288            ITF   \n",
       "11                   462024                          149249            ITH   \n",
       "12                   533924                           35133            ITH   \n",
       "13                  1976643                          343387            ITC   \n",
       "14                  1401198                           37177            ITF   \n",
       "15                   606333                           97559            ITG   \n",
       "16                  1636810                          497508            ITG   \n",
       "17                  2296489                          169266            ITI   \n",
       "18                   648493                           84405            ITI   \n",
       "19                    71669                            2265            ITC   \n",
       "20                  3895438                          686627            ITH   \n",
       "\n",
       "    codice_nuts_2  \n",
       "0            ITF1  \n",
       "1            ITF5  \n",
       "2            ITF6  \n",
       "3            ITF3  \n",
       "4            ITH5  \n",
       "5            ITH4  \n",
       "6            ITI4  \n",
       "7            ITC3  \n",
       "8            ITC4  \n",
       "9            ITI3  \n",
       "10           ITF2  \n",
       "11           ITH1  \n",
       "12           ITH2  \n",
       "13           ITC1  \n",
       "14           ITF4  \n",
       "15           ITG2  \n",
       "16           ITG1  \n",
       "17           ITI1  \n",
       "18           ITI2  \n",
       "19           ITC2  \n",
       "20           ITH3  \n",
       "\n",
       "[21 rows x 30 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for d in days:\n",
    "    url = f'https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-regioni/dpc-covid19-ita-regioni-{d}.csv'\n",
    "    pd.read_csv(url, encoding=\"Latin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "HTTPError",
     "evalue": "HTTP Error 404: Not Found",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-588cd22791d7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mregioni\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mupdate_regioni\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mregioni\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;34m'20210215'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-6-c461cb898e27>\u001b[0m in \u001b[0;36mupdate_regioni\u001b[1;34m(regioni, today)\u001b[0m\n\u001b[0;32m     12\u001b[0m         \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m  \u001b[1;34m'https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-regioni/dpc-covid19-ita-regioni-2020{}.csv'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[1;31m#df.append(pd.read_csv(url, encoding= 'Latin'))dpc-covid19-ita-regioni\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m         \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Latin\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m         \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0myearfirst\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m         \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'codice_regione'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\data\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    674\u001b[0m         )\n\u001b[0;32m    675\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 676\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    678\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\data\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    428\u001b[0m     \u001b[1;31m# though mypy handling of conditional imports is difficult.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    429\u001b[0m     \u001b[1;31m# See https://github.com/python/mypy/issues/1297\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m     fp_or_buf, _, compression, should_close = get_filepath_or_buffer(\n\u001b[0m\u001b[0;32m    431\u001b[0m         \u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m     )\n",
      "\u001b[1;32m~\\anaconda3\\envs\\data\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_filepath_or_buffer\u001b[1;34m(filepath_or_buffer, encoding, compression, mode)\u001b[0m\n\u001b[0;32m    170\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mis_url\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 172\u001b[1;33m         \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0murlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    173\u001b[0m         \u001b[0mcontent_encoding\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Content-Encoding\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    174\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcontent_encoding\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"gzip\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\data\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    139\u001b[0m     \u001b[1;32mimport\u001b[0m \u001b[0murllib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 141\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0murllib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0murlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    143\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\data\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \u001b[0mopener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    223\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\data\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    529\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mprocessor\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprocess_response\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    530\u001b[0m             \u001b[0mmeth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprocessor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 531\u001b[1;33m             \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    532\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    533\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\data\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttp_response\u001b[1;34m(self, request, response)\u001b[0m\n\u001b[0;32m    638\u001b[0m         \u001b[1;31m# request was successfully received, understood, and accepted.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    639\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m200\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mcode\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 640\u001b[1;33m             response = self.parent.error(\n\u001b[0m\u001b[0;32m    641\u001b[0m                 'http', request, response, code, msg, hdrs)\n\u001b[0;32m    642\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\data\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36merror\u001b[1;34m(self, proto, *args)\u001b[0m\n\u001b[0;32m    567\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhttp_err\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    568\u001b[0m             \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'default'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'http_error_default'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0morig_args\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 569\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_chain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    570\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m \u001b[1;31m# XXX probably also want an abstract factory that knows when it makes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\data\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    500\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    501\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 502\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    503\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    504\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\data\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttp_error_default\u001b[1;34m(self, req, fp, code, msg, hdrs)\u001b[0m\n\u001b[0;32m    647\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mHTTPDefaultErrorHandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBaseHandler\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    648\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mhttp_error_default\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhdrs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 649\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfull_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhdrs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    650\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    651\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mHTTPRedirectHandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBaseHandler\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mHTTPError\u001b[0m: HTTP Error 404: Not Found"
     ]
    }
   ],
   "source": [
    "regioni = update_regioni(regioni , '20210215')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "regioni = update_regioni(regioni , '20201122')\n",
    "regioni.to_csv('covid_regioni.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if data not on disk create :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "shape_ = create_regioni_shape()\n",
    "popolazione = create_popolazione()\n",
    "italy = create_italy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>denominazione_regione</th>\n",
       "      <th>geometry</th>\n",
       "      <th>popolazione</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Piemonte</td>\n",
       "      <td>MULTIPOLYGON (((8.44975964324396 46.4617640290...</td>\n",
       "      <td>4356406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Valle d'Aosta</td>\n",
       "      <td>MULTIPOLYGON (((7.588571915813588 45.970750850...</td>\n",
       "      <td>125666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lombardia</td>\n",
       "      <td>MULTIPOLYGON (((10.24929849041003 46.618381793...</td>\n",
       "      <td>10060574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Trentino-Alto Adige</td>\n",
       "      <td>MULTIPOLYGON (((12.20554426101755 47.086368287...</td>\n",
       "      <td>1072276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Veneto</td>\n",
       "      <td>MULTIPOLYGON (((12.50590532016695 46.678391366...</td>\n",
       "      <td>4905854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  denominazione_regione                                           geometry  \\\n",
       "0              Piemonte  MULTIPOLYGON (((8.44975964324396 46.4617640290...   \n",
       "1         Valle d'Aosta  MULTIPOLYGON (((7.588571915813588 45.970750850...   \n",
       "2             Lombardia  MULTIPOLYGON (((10.24929849041003 46.618381793...   \n",
       "3   Trentino-Alto Adige  MULTIPOLYGON (((12.20554426101755 47.086368287...   \n",
       "4                Veneto  MULTIPOLYGON (((12.50590532016695 46.678391366...   \n",
       "\n",
       "   popolazione  \n",
       "0      4356406  \n",
       "1       125666  \n",
       "2     10060574  \n",
       "3      1072276  \n",
       "4      4905854  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "italy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "italy.to_csv('italy.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### determine which daterange you want to include "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "daterange = pd.date_range(start = '20200306',end='20201201')\n",
    "days = []\n",
    "\n",
    "for x in daterange:\n",
    "    dd = '0'+str(x.day)\n",
    "    mm = '0'+str(x.month)\n",
    "    days.append(mm[-2:]+dd[-2:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If this file does not exist "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 27s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_regioni = create_df_regioni(days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_regioni.to_csv('covid_regioni.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                                       20\n",
       "data                            2020-11-22 17:00:00\n",
       "stato                                           ITA\n",
       "codice_regione                                   22\n",
       "denominazione_regione                        Veneto\n",
       "lat                                         46.4993\n",
       "long                                        16.8674\n",
       "ricoverati_con_sintomi                        12077\n",
       "terapia_intensiva                              1381\n",
       "totale_ospedalizzati                          13328\n",
       "isolamento_domiciliare                       155066\n",
       "totale_positivi                              164406\n",
       "variazione_totale_positivi                    10263\n",
       "nuovi_positivi                                11489\n",
       "dimessi_guariti                              186680\n",
       "deceduti                                      20524\n",
       "casi_da_sospetto_diagnostico                 276322\n",
       "casi_da_screening                             95288\n",
       "totale_casi                                  371610\n",
       "tamponi                                     3798851\n",
       "casi_testati                            2.24881e+06\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regioni.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "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>nuovi_positivi</th>\n",
       "      <th>terapia_intensiva</th>\n",
       "      <th>deceduti</th>\n",
       "      <th>dimessi_guariti</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>251.097022</td>\n",
       "      <td>50.787587</td>\n",
       "      <td>1446.565186</td>\n",
       "      <td>8158.462101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>785.112152</td>\n",
       "      <td>129.223418</td>\n",
       "      <td>3307.061468</td>\n",
       "      <td>16047.424596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11489.000000</td>\n",
       "      <td>1381.000000</td>\n",
       "      <td>20524.000000</td>\n",
       "      <td>186680.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      nuovi_positivi  terapia_intensiva      deceduti  dimessi_guariti\n",
       "mean      251.097022          50.787587   1446.565186      8158.462101\n",
       "std       785.112152         129.223418   3307.061468     16047.424596\n",
       "max     11489.000000        1381.000000  20524.000000    186680.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regioni.describe().loc[['mean','std','max'],['nuovi_positivi','terapia_intensiva','deceduti','dimessi_guariti']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#regioni.data = pd.to_datetime(regioni.data)\n",
    "#regioni.set_index('data',inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Charts : Hospitalized , New Positive , Deceased , Dismissed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hositalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def barchart(title = 'nuovi_positivi' , color_map = 'viridis'):\n",
    "    data = regioni.groupby('data')[title].sum() \n",
    "    \n",
    "    clr = sns.color_palette(color_map , n_colors =1+ data.values.max())\n",
    "    colors = [clr[x] for x in data.values]\n",
    "    labels = [x[8:10]+x[4:7] for x in data.index[[30*i for i in range(1+(data.shape[0]//30))]]]\n",
    "    fg , ax = plt.subplots(figsize = (16,5))\n",
    "    #plt.xticks(ticks= [31*x for x in range(0,1+ospedalizzati.shape[0]//31)],rotation = 45)\n",
    "    plt.grid(color = 'grey')\n",
    "    plt.xticks([30*i for i in range(1+(data.shape[0]//30))] , labels=labels)\n",
    "    plt.bar(data.index , data.values, width = 1.0 , color = colors)\n",
    "    plt.title(label= title , fontdict ={'fontsize':'xx-large'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barchart('totale_ospedalizzati',  'rocket')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New Positives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barchart('nuovi_positivi',  'viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cumulative Deceased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barchart('deceduti', 'Oranges')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cumulative Dismissed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barchart('dimessi_guariti','cool')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Possible colors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, icefire, icefire_r, inferno, inferno_r, jet, jet_r, magma, magma_r, mako, mako_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, rocket, rocket_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r, viridis, viridis_r, vlag, vlag_r, winter, winter_r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deceased per day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "deceduti = regioni.groupby('data')['deceduti'].sum() \n",
    "#ospedalizzati = regioni.groupby('data')['totale_ospedalizzati'].sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2020-03-06 17:00:00', '2020-03-07 18:00:00', '2020-03-08 18:00:00',\n",
       "       '2020-03-09 18:00:00', '2020-03-10 18:00:00', '2020-03-11 17:00:00',\n",
       "       '2020-03-12 17:00:00', '2020-03-13 17:00:00', '2020-03-14 17:00:00',\n",
       "       '2020-03-15 17:00:00',\n",
       "       ...\n",
       "       '2020-11-22 17:00:00', '2020-11-23 17:00:00', '2020-11-24 17:00:00',\n",
       "       '2020-11-25 17:00:00', '2020-11-26 17:00:00', '2020-11-27 17:00:00',\n",
       "       '2020-11-28 17:00:00', '2020-11-29 17:00:00', '2020-11-30 17:00:00',\n",
       "       '2020-12-01 17:00:00'],\n",
       "      dtype='object', name='data', length=271)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deceduti.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.insert(deceduti.values[1:] - deceduti.values[:-1],0,deceduti[0]) \n",
    "color_map = 'copper'\n",
    "clr = sns.color_palette(color_map , n_colors =1+ data.max())\n",
    "colors = [clr[x] for x in data]\n",
    "labels = [x[8:10]+x[4:7] for x in deceduti.index[[30*i for i in range(1+(data.shape[0]//30))]]]\n",
    "fg , ax = plt.subplots(figsize = (16,5))\n",
    "plt.grid(color = 'grey')\n",
    "plt.xticks([30*i for i in range(1+(data.shape[0]//30))] , labels=labels)\n",
    "plt.bar(np.arange(len(data)) , data, width = 1.0 , color = colors)\n",
    "plt.title(label= 'Deceased per day' , fontdict ={'fontsize':'xx-large'});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Map plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "#taday is automatically set to yesteday\n",
    "#gdf[str(today)]\n",
    "\n",
    "def region_plot(df, day = '2020-11-01' ,save = True):\n",
    "    \n",
    "    df.data = pd.to_datetime(df.data)\n",
    "    df = df.set_index('data')\n",
    "    df = df[day]\n",
    "    df = df.groupby('denominazione_regione')['nuovi_positivi'].sum()\n",
    "    df = italy.merge(df, on ='denominazione_regione')\n",
    "    gdf = gpd.GeoDataFrame(df, geometry = df.geometry)\n",
    "    gdf['totale_casi per 1m abitanti'] = 1e6*gdf['nuovi_positivi'] // gdf['popolazione']\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize= (12,8))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.title(f'Nuovi positivi per 1m abitanti \\n {day}')\n",
    "    \n",
    "    gdf.plot(column = 'totale_casi per 1m abitanti',\n",
    "                               cmap = 'inferno',\n",
    "                               vmax = 1000,\n",
    "                               vmin = 0,\n",
    "                               legend =True,\n",
    "                               ax = ax)\n",
    "    if save:\n",
    "        plt.savefig(fname = f'./images/it_cv{day}.png' ,format = 'png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mYw94DngO7tb7cbfeP+QYvINbiL/4a4JnvGfI1yql1GRbIVaTEzVhua/9nfhTP8Kr3X5cUjLmuhox7bUYJ4oEQqmNRSml0pyuc6ImLGfrA3j710M8nOpQAHCe+hHh763GdDXhbLwHr+7VVIeklBoHBIN4TtIf6UxbTlTKGdfBO7QZu+JUTDwCdgbulvvw9q/DO/AK9uLLCV7w8SHV6e55Fnfjn0Yp4hHobCD87RXgOUhhBZmfeAYJZqU6KqWUSiuanKiUc7c/SOyum8HOADuA5JRiWvYdOe8d2ID7+mPYS64geM7Ng6s0HhmlaJMg8ReLaT2A+9ojBJZfneKAlFJpbRIOiNVuHZVSXnM1zst/8J+4MYh1H5OYHClXvwPJn4rpbsZ47gnrdF7+PdE//ctohJtcOSXYi69IdRRKqbRnjgzKT+ojjWnLiUotN45X/cLA5cKtxB74PIRbseaeixRMx158OZJfjrfzKTAuUr4I3Dixh76YNuNMTigeIfq/VyIFFQTO+wj2zFNTHZFSSqUFTU5UypjuZn+Rs8HOpOluBsDb/TQA7it3jlZoYyPW5a+rUrMJjId9w69THZFSKi0ZZJJNJdZuHZU6nouz6c8wQDfNZOC+9gjOuj8O2GWllFKTgbacqJSJ3vvJI60hE5YdBDc+cDnjEfvrvyJPfB9r8aXYCy/Eyp2CTD0ZsfS/qVKTmmHS/RGnv/VUylil8/FefyzVYYwqa85KxMRxa/dB1wCJmIA1ZTpmyy9xNv3MPzR9NfYpN0G4GRNpQXKngedgLbgMKZgKsWbInIGINoIqNXGZtF+XJNk0OVGjxsTq8Do3gNuFZM3Dyl15zPngeR8hcNqNmHAbsb9+GlO/I0WRjhLLRrwI5tA6rEAmMmsBxg3i1WzDqljul/FcJCcH09UFbhOm5tljqjAH1+EcXHd83c98E/v8+RCtRfJXYU1/J9aUy8bgRSml1OjT5EQlnXPgh3gd6zDhXUeO2TM+cVxyInllSF4ZAIEV7yD+6NfHNM7RZpXNxRxKJBZOBNOwFQorsafNwDStP1LOtAyj8mgnkONf374et3sPXs2vMG4EvAiSORMyp4PxkOx54LSCFUJylyOhcsheoLsjKzVuGO3WUWokvI51uE0Pgdt+7PHWx4lH9xOY+Wn8cdguIkd//Ny9z41toKMtmInYEUzv461Vxx8bth5dOU4LpuNolmMiR9eK6et+kr8KKTwTq/xtmFgDkjkdovV4DQ9gul7HqvwkEiwBtxtMHMldkrSolVJqIJqcqKTxOl4hvvuz4HUdd850bcF0bSHeuRHsHEx4J5KzDLFykMjpePteTkHESRTKQfKmYBWUAg54MUzthlG5lbXoNGR6EdA+YNn+mPb1mPb1ePt+gp++HG5F8VMZd+PzRwsHCsDK8ltgggVIxlSssjdDziJtfVFqLEzCFWI1OZkEjBfFdG7CuO1IxnSsnCUYLwLxZiQ0PXn3idX1mZgcUyay5+jXHev8j8KW6tTvGjwcgQzsyhWYSB20HYCuHZiu0Rs3I5XLsOYHIbYfnP2QlN9Vpte/fXDagDZMrPZISa/mVxAsxpp5M/aMdyUjEKXUCYh266iJJr7rXzGd/hgHyVpIYM7XcA/8EMmcQ6DiY0m7j4nXD+/CoiqsN00DBJxC2GHhVW+H2IkTnVSTgqn++A4rA+OM3l4+UrkU09aEZGaCVwwcGrV7DUm8GclbkeoolFITkCYnY8BtqSO69h7c2j04u18h6403k3n+dWNz79anMZGqI89N+HXi268BwM5ZltR7iTX83XXFSrScZDTBcrBOWoD30Na0bso0Tftwm/yv7TmnYGpfSWr9Mmsx1sJ8iFXjjy85AE5NUu8x9KCC/tRlOwcTb0bylqc2HqUmA6MDYlUSeV1thB/8H6Iv/x+mo+no8fbGMYtBrBDYWX13Adg5Sb2XNeVt2MZBJIhkzsQ58CNMZO/wKgu0QTAzMSslfUnJLETakp6YEMjAmhtNJCYAh7u9kjecdsgC+QRWP4wEi1IXg1JqUtCVm0aRZGSBZR2TmAC4DcfvujtarPwzsPLP7POc1/L4sOs1iX0ejDn6YSkSIFB+PXbZPyGZlchIWma8HCS3ZPjXjxGrqBy6htmd1Z/CcuyLzgKvO7n1DpcEkaLzsJfcqomJUikinpv0RzrTlpNR4jbV0P3XHxDb3EcCEBvbHXMlOKXP4yayh/j+7xGY8XHEyui7jDFgHMQKYrwYXtODmFgNbuP9SNZcQAjM/AxW1tyj1zhtxPd8DtP92pDiNPFcpH4K7qb1EDuQ1l061rSFSHY2pn2YLUP9ySvGXrYCopuTW+8wSNlVSM5CrOILkey5A1+glFJJosnJKAg/9huia+/Frel75oY9bd6YxmOXvg334G1Ar0zZ7cBruAc3NJNA2bV9Xus13Ydz6NdYeafgtT59zF/zpnNjoswDWBWfOHpN65NDTkx84j+Ml9aJCYB3aAf2vNPAiyWlPimfjZRXIOVWWiQmSBCr+HysKVekOhKllC7CpobDeB7xzY8juUU4VVvovvc7/f4gZax8A9lv+eSYxuc2/R/HJSY9zx/4McQOYRWci4RmIhnleB3rkNBMvK6tEK/Da3643+u97lePeW4VX44dq8et+x2YQWx6lyDBDpjRgRU5FW/T04O+LjUEd/c67AWrMV0NI67NWl4Csd0QTUJoIySF52DP+TSSuzjVoSilADEm7bthkk2TkxEykS667ryF6At/GVT5+N6NuC112EXloxyZz+tYh1tz6wClXNz6O3Hr7wQEgmUQrxv0PUz368T3fgm75HKs/DMRK4PA9A9AIB/3wH8NPWiTwkGfQ+WBzDgLumoxrcPr4rEvWQOxbUkObPgkZ74mJkqplNIBsSNkol1ITsHgy3e14rz+wihG1ONexsFtvI8TtZr0cdWQEhMAvG68lkeIV3/zyABZYwzEB9iFtz9Tx09y4u5eh7vrFTwnE5l6yvAq6bZA+h7zkwpe/QOY7j0DF1RKjR3PTf4jjWnLyQhZBWVkv+Nz2FPn0XX7fw58gROn89f/j657v0vG0vPIueEWxE7+t8HEW4jv/XdMZ5KnuJ6IF8bZ+wVMeA9IABPZPeQqjJON2TTE5CgNmIY9ULh6WNe6ax+F4mlI4RSseXkQr03tTJ14M179A+B2Yrp2IAWnI6FpSOmlSCAvdXEppSYNTU6SQEQInfN2Ik/djntgcANBTVsD0bX34rU1kvv+/8LKyk1qTG79nWObmAC47XitT4yoCukow6t7MjnxjDF3z0as2WfDobVDv7j5EKb5EO4esNdcCl5qB8V6+3925GvT5u97JE2PIzkL/KnFWbOwyq5MVXhKTS6TcMyJduskixPDPTT0loL4tqfp+t0X8NpGPqgSwG1bS3Tr2/zBqOOQyWuEjOQuDjdW7DmnQN3INzB0166FaPrtAmyaH8fb/3O8fbdi2jemOhylJhft1lFD5VRvpevOr4I7+JkpPcU2PILkFpJx6hsJnnQGYtkYL+qvLzLIVVy98C68xvtxG/8ypBky6UY6SyC2NdVhDE0ghFWxBFPzbHLqC3fibliLfba/d0+6saZegzX7o6kOQyk1gWlyMkxeRzPhv/3U3y+neiumq3VE9UWfuYvoM3dhFZaTseoNBJfvA7Gwp7wVEKyiS0+4Pb2J1eM23D2iGFLNuEHMnrZUh9EvmTIXq6CEI0vJGwfTXQ+t1VCXxEHOBVOwV50CThqsd9JTRjn2gq9gFV+Q6kiUmmQM4o3DndtHQJOTYYpve5rI48nvOvFa64isvYfAgnLw2nG6NgEgTQ+RseBHx5U3xmAiezCdm5Iey5jqnobZ1Iw5lL7dBVZhCeZAklpH+mGfdjFkbU+vxCTnJOwZ70bK3oyI/spQSo0+/U0zTGY0s9hwBBOdhQQT3RsSIDDjw30Wdfb+O17r8PfISRfmUBBzqO8VddNCKAeMC3YI3FFaKS2UAzlVR/f4SxNW+duwyq9OdRhKTV6GtB8jkmyanAySMcZfUt0zuId2EX7oJ6N2LynMR4KJ5duDpWQsvA0JTe+zrJV32rhPTkz7LLydW1IdBmCQKfOwCkr8vX/D3Xg1icXRnCimdc/oJSYA0S6QBUD6tIJJ3gqs0jemOgylJjldvn7ScJ76DuRNxZ5/CcaJAgaruO/NzbzWOjp++WmcnSOfiTGQjDOWk3FqF7iJKcnGIb7vG9hFl2CVXHVk3InXtRVDEK/tmVGPabRJh526exdMxZoy01+VVlxMzQuYzsTJYDbklIAThmg3UjgX0zW6a7CYhnakeFRvMSSmYyte48NYU98O0Xrcqh8gwWKseV9ArFCqw1NKTVCTKjkx3U3EH/48GA9v/wvQcYgj28tllxA466MEzv44xnMR6+gHZmTtvaOfmFgW2deeg5W3DdweK6Q6rZiOdTgd65DmRwhM+yBW3ilIsBzsbJAJMBt8xl5sayXu8yNbI2VYLBtz6EXw+pjhFO/GohvyyqCgBHPwxbGJKVQJ0aqxudeAXLw938Sr+iF4/m7aJmMq1vwvpzQqpSYbMWnW3zvKxl1y4lY9A8bDntP/jAHvwMuQkYtkFSJ50zCeg/vyL3Be+Am01/R9UXcTzlPfwXQcwl3/G+xV74bMQiSrCKdqdAdpWmUlZL9tDngnnkJrOjfgtT2F5C5HMqYgANM/jIQq/M393M4TXp/OzJR6pGwepn7oa8UMiWVjVSxFMkJ4LXVIIAyRAaZed9WPbkw9eFufh1cDWCvOQUociI7y+zFYicQEwJpy2Qlnjiml1EiNq+TEa91H/PdvhUAIufFerJlnHDln3Djetr9gOg7iPPcjiLYDgjXvYryDGyA8iH1enDDuS7cBHPkXwIuN7oJY2VcvBG9wK8u69XcimfOwS/3VOa2seVgVn0RCM3H2f3c0wxxVEs/Ba98/6vexZp0MdS9ggLT9eHUdvFeeQhasxJqRmVZrnUjxRViVn0p1GEpNLkbHnKQlEw/jvvgz3N2PAQacCLE/vI3gVT/Bef6/scqXY6IdeNt77wxs8HY/NuL7B6eW4jaM3l/P8e1hgkPIf7zOjVglbz7y16vb/AjOcHb/TSMm1DgGuxEbxG1lvGwraHZuhJJLIOMELWrWGCYvEsSu/ISONVFKjbq0TE7cTXfi7X8Br2kXiIVp3g0dtccWciLE//xev/zBDaMajxd1Bi40AtG1mwguLQcziJkgEkDsXJx938LKWYJV8iacgz8FM7oxjjaxPKwZC/D2bQUnduS4VbkCKSuGTDCuIBaYsAdhF69qG3QNcudjO4hduQxzcGx2hE4W94XHsN9wDmCDCUNsn38iYzqYUtx1G7BXnYppDeO9ugH7zOl+wkI5WB0Q3Zu8YOwsjBdL3xYnpSYyXYQt9dwtd+PtfSrVYRzhdXaNWt32rGlkXTETvJ2Du8A4uA13+XE13Qf7vgNMkOa+Fc1IeA6mfg/WglWQFYKZjRDwP2APfyhKZCpm3Q6sLIMXz4JYuP86E+zKlZiDo7uA2ugwuI8+C9l50N3hbwpoxXGfWwfd/saO7uOPHCntPrED7CB4W7BXXQDZSQpDguC04+35Dsz+GBgXyV+B2Mm6gVKqX8ZocpJqxo3hVT+X6jCOkTGtAOdgPwNpR8gqKQRvzwhqmCCJCSACsqIY7DwI1fZZxjj58OIW6G5GCueA1z2Imo3f6jCedXcA4D7294HLJvZ48uoPYc0vAadpxLe3Fv0AEcG0rMWr+j6mYzMECrDnfAaZ8ibEzhzxPZRS6rC0m4cqdgZStjTVYRxD6teSvWoJVmFR0uuOb3gVZ//8pNc7bmUfglBjn6eMF0Q2d0J3MyAYu/iYLqD+CaY1id0b44TZ9xrEZ0ASlpyXYCFWyRrs+f+JveJO7PlfAcDd+R84r1yFs/OLmI5tI76PUqpv4rlJf6SztEtOAKz5a1IdwrE8B6l9mozZfa/SOlKRv71A9MUynOqFfvO5Oo7xBNmRhal71T+QW4ZkZmHNWApHhrgapGye/8gt9Q9l5mHPXT642VoTkLt5A9h9JPvZ85GSS7CmXosUnAaBwr4rsLOR4oshWofx/ERQRLCmXYO14h7IXAiRfZjaP+Fs/zBe6xitBaOUmiW49g8AACAASURBVNDSrlsHIHDB55CCCtwXf47prIVIGuxUW76ayCtbGa0JqPFNOyCwhEClDjfszRiDVJdjqp4+erCzDtPpr9ZqVawCO4AEg5iaxLiSYDZW5SLwXMyhdSmIOk20NeDtrMaan+uvgxMowl78Q6TgtGPWKjFeDNP0OCZcjVfrj2myyt6CNetfECujz6rt7ApYeTsc+h1e9X9DrAGv+r+R/FP6vUYpNRyTb8yJmCFM31y9erVZt25sf9G7e5/GefBTkFOGOfDSmN77GIEQUbMUZ9++Ub2NPaeCrEuj4372TbIYT5DqMsxrTw9cWPXLWnkuMi2TwPLbkGByuyc9N4a76Z3Q5e+kLEXnYS/4ChKaltT7KJUKIrLeGLM6lTGsWhgyL/5P8v8/BS+tTvlr609atpz0ZM85H/uj6wGIP/L5YxZHG1MFc5C20e8FCy6qAKse3JZRv1e6M17Q78qp0sRkqKSwEilfhuRNh6wiJJCJTDkz6YkJgNe9D8mejUkkJ6blGZxX3kZg2S+QvPQaP6aUGh/SPjk5zHgu7rbei6yNQFYRxMPgnGABq+IFmFA5APGmKPHq0R9UGfnbC2RddQ52uSYncrAMUzW+d1weU6F8Amd9HHv1B5BQ/pjc0sQakI4X8axcCM6C+D6kZA32wm8hgdwxiUGpiW/ydeuMm+QEsbDmrcHbfOewqzDlFxDd14BdVEBsz14ku5Csk2YgeNC0GWKdIDYUzcdxpxDd8how9kmC1xbBLh/z26YfOy3Ha6cdmb4K+9T3Yi++CsnIGdt7Z0zBnn4DlheDyH7AgswKxNKB3Uqp4Rs3yYmIIHlTh3fxlGXEmkPEX/GnOrqN/lL0pqOD7nX+njaSVYSJ5yI5uZgDjcDI14YYrujLrxJcNAW89pTFkBZCmpyciMw6m8A5n8aac1HKN+ITKwOy56U0BqUmLIPurZPO7AWX4T73w6MHgjlQtBDqeyxfnzsVL+skjAdOUxuSESS+qWrAfVtMOLEdfFsrKd8SrqsbvBnA5E5OJOiNm31wxoQVxJp7MdaiK7HnXIDkV6Q6IqXUGBAMot066cuaeTqhz+7DefIbuK89CNkVOF02VtkFWFY3GJeu7fUQGf+LQXXethYpLiT7LcuRE238NoGZYDzVIaQB8bttllyNffL1SFbyB7QqpVS6GVfJCYBk5BC89OsEL/067T98D/HX1qY6pFFjmlsJP/Qq2W+fDu7BVIcz5ow9zpecHy4riDVvDdaci7AXXo4UzEx1REqpVNOWk/Ej551fI7r2z0Qe+y0m0pnqcEaFV99I5OlCMs+xmUj76AxKoA05eQ3iuHj7Xzt+Z+oJxj7r4+BEsU//MFbh7FSHo5RSKTOukxO7ZAbZV36cwMwldPzsI6kOJ/kyQ0gw4GfM1hzwdqU6ojElAsw4gGcMEp6FmcDJSeAN3yRw+odSHYZSKh0ZtOVkvDFOjK67v57qMIZNCvMxre0QCkL06BiL4MpFhM7MT+xY3Abe5F33RJwSTM3EHXdjzXsD9mk3pzoMpZRKG+M6OXFb6uj4yc14zeNzPIbk5ZJzXR5QAF4npns5XXc8B66LiR/uwplkXTl9cTIhpwRiHamOZFTYJ1+X8qnASql0NvkWYRvXC0m4+7fh7n811WEMT042OdedDF4UPH+8jGRvIXTOCqyppWSenwfezhQHmSayDiInz/F3Ij79UmTqklRHlDQyZRHW4remOgylVDozgGeS/0hj4zo5CS6/CHv2slSHMTzhCG6jAMdmw8FFVWS/xQV39JfKH09MZi2cUw4l1ZiTLaTy9FSHlBT24rdqq4lSSvUyrpOT+NanMOFxOkvH84i9vBvs3gtppXc2mypieYgV87+245iFUeSsNyBzzgAZvz/GMmXitAIppUaR5yX/kcbGzZgTE+kivmsdXkstbl0V2AEij6Roh+JkCQaAOEgQjC44NhRiR6BwHxSCzDkb8+ymcTkmxbRWpToEpZTql4h8Cng//l/OW4D3ANOAO4Fi4BXgncaYmIiEgN8Bq/D3gLnWGFM1nPuOi+TEuHHa/+dDODtfSnUoSeU1tuI2zsEudcFtSHU441eoAS6ehtV5Bt6z/0h1NEPiVT2NOe1mxNaN8pRS/UnNgFgRmQF8HFhijAmLyN3AdcAVwH8ZY+4UkZ8B7wN+mvi3xRgzX0SuA74NXDuce6dVe3h8z0ZiW5/CRLoA8NoaaP/x+2j96psnXGICYDo68Wo7gcxUhzLuiQheTi1SthCKZqU6nEHzdj+Ku/VPqQ5DKZXOUjsgNgBkiUgAyAYOARcD9yTO/xa4OvH1VYnnJM6vkWEOqktpy4kxhuhz9xB+5Daswqk41VsgFkay88m88Ea81lriu9ZDbOIuYx59ZgOx7UXk/NM88HanLo7qpWTM3IYXz8dpqCBUsb3fsm40n/CmuWRURLDyW4jtnUbW0o0pH/ohVhxWCfJqOaZln38wsxDsIHSlb8uUZJekOgSllDqOMaZGRL4H7APCwN+B9UCrMcZJFDsAzEh8PQPYn7jWEZE2oARoHOq9U5acGGNo++bbcff5m/R5DfuOnutuJ/zQT1IV2pgzTS1E14UJrRq7sSfGCeK0VRA/VEBGRRPxuiCRHUsxnsEuChLqMU6365WVhObXEcg/RLx5FhIK4zREMF6I7hfaMJFanIaTyb94y5jEPqB5QSTvYsgOQMjBZB9Ads3H7Ho+1ZH1yRzaCAsuS3UYSql0ZkalW6dURNb1eH6bMebIYE4RKcJvDZkDtAJ/Ai7vK7rDl5zg3JCkLDkREXJvvIX2Wz+IaR9yUjXhxNdtIzD1bOzpJ1q3ZeT76xhjiO5bRsej9biNewCw8gsQuxO3pdm/S3EJTs08ss8KYBc0Ed3ZSudTzeSctxLnYDdOcwwJdBLZcjTWWFUdbrgIOysNVrLNaIKek6CMQGdbysIZiPPsdyBvGvbKG5FUNz8ppSaTRmPM6hOcvwTYa4xpABCRe4GzgUIRCSRaTyqAwyuhHgBmAgcS3UAFQPNwAktpt05g1lICs5cR3/JkKsNIG25DB/Z0G6wcwAGvG7AxziLcQw7O7loyz58BVv9dLgOJ7V9G6x3HLgXvtR/7we02N+E2NxF9/fARv0uk69nXMPE4uA69OfV1tPxpBjmnzSJr8Sa8WC7RPfPIXLAN44awMrqGHfNIiQCnuEj0bAjnYHbshOaqlMVzHOPhPPQJvO1/JvjmW3UXYqVULylbNG0fcKaIZON366wB1gFPAO/An7FzE3Bfovz9iefPJ84/bowZXy0nh2Vf/a9451+PiUfo/O3nINqd6pBSJvbiFrzm2bgt7ViZIULnLMPrjBN5bB1EIgC4i2dglw+vfmMM8frhzwoxkROP/XEO1tB2Xw3d62cSP3gQvI20iYDlkXXqMvIv3A6Wm7pFx0JNEGpCls6D+pmYHc+kJo5+eFVPE/3VRQTf8A2sJW9HLDvVISml0sHhAbFjfVtjXhSRe/CnCzvABuA24EHgThH5WuLYLxOX/BL4vYjswm8xuW6495ahJDWrV68269atG7jgMJhIF23fv2H8Lkc/VrIyyb7qdCQDJHNoYzy6Nq6k4+ENoxTYINgBQgvnkXd+BCu3nuiukwjN3Y0V6n8hPeNaOK2zie0rILTwAIGcJHYBdlXAoQhm3xaItg/5cifrJMQNg9h4VgFCmDjzMJ5N0G5ETDt2eAcYh6HmY1JYiTXrbKzl12FXnjfk2JRSySEi6wfo+hh1q+dY5sWvhJJeb+CmSMpfW39S3nJymGTmEDrtSro1OTmxcITuO5+GzBC5NxWBiQ7qMi+WS/cL1aMc3ABch+irO4jusLFycvA6NpJz0UryztjUZ3HjCa0PLcCpbcRt2gPWKVhLurCCfbfgxJsqceoLkEyXeK0NAlkntRAo8gdbu5ECvO4iTDxAZFsmkm1j5wimtIJQoBO7+sFBvYxI1mVE6i2im3ZzdBr44Ra/Xb1KzyVQPo3CWbuxogcAv5vJGMHYOVhe34mZaa3Cba2CrCJNTpRSab8XTrKlTXICEH9tbapDGD8iUbzWBVgFWwcs6nSW03KXh9taPwaBDYLn4nX4LRVdT2wk/FIeWafMJevkRuycWhAPt20WnS/kEN12dHxNx9820PGwYOXlE5pXQcbsIIEpbTit+cSqPMLrth03or3rmQCB8ul4HR14Ha34A86PF5lRgZV5OU5bByJCoKSA7MIDBLo3+skEAdysxcS82YT3duDUHRr0y3XqDtHKPKyspQQKMrEzHcJ7O3A72ildvAfL7bvVxj77UwQv+uKg76OUUhNFWiUnWVd8GOPGcXa8mOpQxoXuPz9P1tVnYk+pP+EKs07DFNyGNJnm2wevq4OuZzfR9SwggmRmYcL9tPIYg9feRnhDG+HB9FC5Ds7BmgGLxWsOHPPcaagjAhSefSEgdO50cBrqgNf7uHpgh5OZWI9jwekVWO7Gvi8I5hA482PDupdSamIxZrRmEqevtJq3GJy/mvxP/Ibc9/8X9oyTjhy3Zy4GHRx4PMchfM+zdP5iL9jF/RYLzd5G6YdmkX3GyWMY3DAZgwmnz6Do1rUHaV1bk0hMkstpbcENzaavYV+Bcz6FZBUl/Z5KKTUepFXLCYBYFqHVV5Cx6nK8pgN4bQ0E5qzEaz6Is2cDnb/5HHjHT2WdzOxZU4H+02qxDIHC/WSvcul+Sejz01CNudCCZXgrL6LzhUfJN3f4g2ZzyrCXX4OtrSZKqZ4m2ZiTtGo56UlEsEtnEpx3KmJZ2KUVZKx+E4HZy1IdWtpxd+8n8g8P99BiTGQ5yEnHlTFOEKexBMnMSkGEqi+heUvIOfsNBKZW0mldhSlYSHDNVwmuuQWxM1IdnlIqXRj8vz+T/UhjaddyciImPPTpnpOFs7MaZ6c/TiN4ymJCpx0954aL6Fw7g/DLm1MUnerNLp1K/ltuxMrOpeT9/4/4oX2YaAS7cmGqQ1NKqZQbV8mJlVNIcPHZOHv7GUSoAIhveBVYTOiMbvDawbPBnVxNguku56w1WNm5R54Hp42fnZSVUimQ5i0dyZa23Tr9ybzgnwmuuCTVYaQ9r6EVLxrHuBZ2TiNZK6NIKHPgC9WYCM6oTHUISimVtsZNy4mJdhNd9yCmu4PsN/0LHbW78er2pjqstOVJFvG6qcRrQmRUdhHZk4OJRlIdljosMPxtBJRSk9Aka/weF8mJcWK0ffMduLW7Ux1KynlWBjsaL8ZzPIpmZRMKRShpe/i4cqbbouUPw98gUI2urmf+Ru75fe08rpRSvRh/xezJZHx069hBJK//dTwmC08y2Bt+I51VdXQfaKBmbTV7nqijo/Bc4pnH7gZoQoX+F6naZE+dUGTzy0Re07FTSinVl3GRnMQ3P46z8+VUh5Fylokxtez4xcB2PBVh00sz6Sg8F/CXMenuttlRM4/w9LNh7mqsOUcXYLMKS3T8SaoZj46//SnVUSilxgudSpxevM5mwo//LtVhjLr2gvOorcknmOV/S7LzDeVd9wN+suEFcjFWBpmd27Czl+N2Hx0/YlwXgL2bA0w56So6m1xaN/hLtlc9d/BIudLFp2FZQsOWRuaffxKhaB1eax2mu/9dgdXocZvTZK8jpZRKM2mbnJhYmK67vk5s0z8wnS2pDmdUGYSWthLaXj92P5msi88nv/VpugtPY/sTDnZ2JqHCHNzupj7riTa2caCxrd/7NL7aeOTrnU/4e71MW7mMwu4XkvAq1JBlZac6AqXUeDHJxpykbXLi1lcTfW58Nnu3Fl5Cc0M2FfkbCIYPIIlh1tGcBYgXoT24nAyrC4Cg10JD51zq1x2/0d3ul12WLZvC9if85frd7gjd3cmdcdO0q4XCchsSrS9q7Fi6X5RSajAm4YDYtE1OAhWLyLz4XUTGWZdOPHMaB3fZdO3bTxOl5M1dxpRZUBheS3XdQtpePwhWbY99Emyg7x14nc4w1e1nAftHJdZAVpBZq0ugaseQrz28PY+Otx2+6I7NGM9DrHEx9EsppcZM2iYnADnX/DuBuafQ+at/A298/GUfC02na9/RsQQde2rp2CsE85YTb0+M/xjsBk7G0LJtdBITgBmnTCFY9dygy0crzqR2WxteLI6I4MXiZJYXU150CKvtkCYqQ+R1d+J1tGIX6Ew0pdSJyKTr1kn/P9ksm9AZV6U6ikHLat9C9ozSYw8aQ7y9a0j1FC5fQOUNb6Lo1MVMfeO5ZMyYcvSkNfIf0qziLHJDndjT5+HNPZPIzHPoKD2b8IxzsApLjisfrjiPA8/X4LR34kWiuOEIxnUJH2ygemcWJrtoxDFNOsbQ8cifUx2FUkqlnbRuOQHovu+/xsVKsMZAuOAUGrtm013TdzfNQIL5OeQtmM1Jn7iRgkVzE/X6rSyxlnY69h7Aae/Ci8XZf+8/cLrCdOzeB+7Q54SFm8NseyKM2BbG7TU9WfJZdNF8ZM+LAEQqzuXg81X91pU5pQiCYQgPOYxJr+Phuyn4p/cj2uyklDoRM7l+R6R1cmJiEcQeH8t8R/KXse1J6G/8yIlkzSij+NQlzP/gO8gqP7bV5fCHVqi4gFBxwZHj0y87BwDPcYg2ttKxs5q2V/fQ8OwrtL82+GTO9JXYGKjbG6d4/sU07mgj8vKBE9YRrqmnqjaDgpPOozBUi924c9D3n+y8znacg9W6145SSvWQ1smJZGRS8B/30fa1t+AeTN8PvGjuSbQ48xlqYpI3fxYVV19MxVUXY4cyhnVvKxAga2opWVNLKTtvFfPf93aevPIjRBtbmH39FdihEPVPvkzn3hMnGL217G2hZW8LJacshLq+py73ZFyP1u1VtAKZ5SuJNrYy7fRZZB14elivazLpev4xCt/xvlSHoZRKVzpbJ/2IZVHw2btp+8G7cKu3pDqcY7hWFi05F9PRGqBx0+ATEysjyJx3XsmCm69JflACCz58LU0vbaHiyovImzeThR++lpbNO9j/l8eo/cfzeNH4oKtr2vA62TPKkHAn8baOQV0TSSQzB5/fS+HS8ygM1mA37RnWy5kMOv7+Zwre/l7t2lFK9c9L/yGiyZT2yQmAhLLJveGrhP/2U2Ib/p7qcI4Qz8HxbBo37Rv0NcGCPM76/TfJnlo6cOHhxGRZVFx5IRVXXnjM8aKTT6Lo5JNY+KFreeGDXyJyqLHvCvrQXdOAFQxQuGwxxKN07hh8otG6rYr2zAxmLluCEyzC8YJkRaqRSDtWP4vJTTZeaxPx6p1kVC5MdShKKZUWxk0qFpi1hODJF6c6jGOIiRPtGtpb6HSHSeXfx5nlJaz+0edZ9Ml3YoUysAbZneTFHZo37aR5ezWhsuNn85zw2kiM6nUd1Dy/j7oXd1O1yWHvzhw6pl50ZL2UVEt1HG33/yG1ASil0pdJTCVO9iONjYuWk8NCZ16NPXUuxMIY1yXy1B+Jb/pHyuIRga6GwU9REdvi3Lu+T9YotZoMVm7lDHIrZzDrHZcS7+zmlX/7Lm1bdw3yaiE0rYx4WwdeNDb8IDxD/cu7CM8/C7GEgmKHjJqXhl/fCMRmnE79nihTZ7sEaremJIbuZx+h+9xLyT713JTcXyml0sm4Sk5EhOCcFUeeZyw5B7euCjB03f0N4tvGfvDlgpmb2VgzbcCF1SRgkzd/FhkFuWMU2cCsjCCh4gLO/OUtRJtaefHmrxDeXzvgdcYIrh0ie1Yp4X0HByx/Ih27/IG67uyplBVXYjVXjai+wTIGGnPOwemKENnYhBeJ0TljHvl55Vgdx+/8PBYavv0ZSj/1dXLOTK8WQqVU6plJNpVYzBDas1evXm3WrVs3iuEMn3EdnJ0v03n7F/EaBjEGRCwktwjiUfL/3114zTV03PrBQd3Ls0LEcuYSan+VpoI3sffJvj+g7ZwsSlYvo+y8Uym/8DSC+emTmPTFjcbY84cH2P3zgfc0yq2cTjA/GxGIHawl1tw64vtnz5rK1NAOJNIOgJdXjgnlYQLZYFzECQ97YK1nBSGrEKewEgGaDgld1YeOKxfIz6V8aQm200bMKiFgRcg48OKR/ZFGnQiF13+E/LfcqANklUoDIrLeGLM6lTGsmhEwz32oYOCCQ5T1xeaUv7b+jKuWkxMRO0Bw0VkUfulB3PpqEIvY+r8Reew3GM8lMHMJVsEUMlaswcovxZ65GCun8Mj1kd3r/X9zT6I+vJiMkEcxmwl2VR23LHtD1mXsf+4g2dMupXtr39ve586fxek/+U8yCvNG7TUnmx3KYP573krBgtmESot4/X/uoOnlvrs5OquOJmQlyyqTkpx076ulOruc8hUryDrwDC2ykNYNVUD0SJlpZ15AVtOGIwlMT27xHLxQEcFDrxxfeWYeHdlLad64Hy/Sf3eU095JzfOdiWetlK6aR4ZlgediMgsAg8nIwWo/PrFJCmNovf1WnNr9FH/gs4huDqiUmoQmTMtJX4wTA8/DxCOIHUAy+2+5MNFuOm//Ijuet46dfSNCXmU5uWUhMkIu8ZgNAg3bm4i3dfZZV6i0iLP/8K1jFk0bj4wxNK/fzvpPfQsvGieYn0vFu9/CwbseIdpj7ZP8BTOJtnYQyM7EikeI1g1+JlB/MooLiDW39XkuWJBH7qxS8rI6CTTuQOJdeIUVVG0PUH7abLI6dyNdDXjFc+liKk7MJTNHCBDGldAJV7s9hggZhXkE83PILc+mbt0e8AzTz6ok68AzI36NA8ladR6lH/0SVs74SXCVmmjSoeXk1OkB89zNhQMXHKLsLzel/LX1Z0InJ0PleR77v/xRdq2NDnkvnJ7mvuetLPzwtUmMLLVire3s+f0D1DzwJEs/+z6KVy2hZcNrbPv2L49JIIqWzSVSvQ+3OzJmsRUsmk1+YYy6HV3EWo5vTUm20lPnkdf0HJY3+LViRsIumkLORW+m4C03YmWnd7egUhORJiepMWG6dUbK6eym+k+PsPPh5hHVI7ZN7pwZSYoqPWQU5rPoYzew8OZrOPjIcwQL8ii/6HQKli/g9f++nUBuNpllxey/474xTUwA2l6rpu/2leTLnjmV/Ponx3T3ZbelgfZ7f01k80uUfe4H2PnJ/wWllEp36T/1N9k0OUnY8rWfU/f4iyOqI6O4gJO/8hGKVy1NUlTpxcoIMu2N5xwZqJlZWsTJX/nokfNzbryShmdeZucPf5OUrp10UrB4NsX2HmSsMqFeYru2cejz72bKJ79GaMGy1AShlEqZyTZbZ9wswjaaPMclkoQP0yWffR+lZ5yMFZi4gxjtYP8bMYptU3bhmZxx+w8ov3RirddRmN2I1TayadODJkJoyalkn37hMYfdhkPU/scHaP7tD3HbRz4AWSml0tWkbTkxxtC8bhstm3Zw4P4niNSOLDnJKC7A7Rr8gmwTWSAnmyVf/gR5i+ez68e/Tf3yq0mwb0uMilNPI6Pm5dG7STCDnHMuJe+SqwktXE7r3bfBS08eW8Z4dDx4Bx0P3UVD5WU8tb2QklklnP/uc5mzulKnHys1ERl0b53Jwo1E2fj5HxJv73vGzVAUr1rKSR+/gYLFc5MQ2cQgIsy67s0EsrN47Zs/TXU4I2ZicTo6MiksmTt6mxjGYxRcfRPB6bPwXIdodf+r9hrP4/7HDI3797L7pb28dM86Zi6v4KIPXsDSi5eQV6qDZ5VS49ekTE6iTa0c/NszI05MMooLmPueq6m89vIkRTbxTH/LGtq37+LgfY+mOpQRa91WRd6ZM7AZneQkbmfzwu3P0B0qw3MN2x8t5LxZKyhv34QxHDMQ12DR2XJsS93+LQf43cduJ78sn/f+/CYWnj1/VOJUSo09owNiJ7aWzTt4+SNfw4sObypoICeLKeecQsXVayhZPTEHvibbgk+9h6bnXyFaP/53Ie7szKCwYHpSx5+4EuDv0StpaIiR37aPHeuP7jE0vbySJxpnsXJuhEXtfoJnDDQWLCUedfqsr72+nR+9/VYu+/gbuPxTlxLM7H+ckFJKpaNJl5xkFOZjHHfQ5QM5WRSvWkpmWTGFyxdQePJCsmeUj2KEE48dyqDw1KXUPTz2ex8lW8vWvbRgkzP7dKYUNSali6c1dx6bn/RXnO297uwzf/f3+eloKWDu4lxsJ8KTciXrHjgE9P9zbDzDwz/8O8/87jku/egaLnz/BQRDk+6/u1IThEy62TqT7rdVzqxplJ65gobnNhx3zs4KAVB89kpKLz6dwlnTCRUVkFlWPNZhTjgzr7liQiQnh3VVHyJ8KEjZKRfiOpCR4ZDRtBUr3P8sGiM28emrEOMSaN6JRNppy6nk3ldmAydeQK75YBv/Gzmd7vYIxht4c8YjcTZ38Zev3s+Ld7/MB3/9Xsrmlg36WqVUmtABsZPDon+9ieJVS5hy9il0Vh+ka28N3TV1LLj5GkIlhYg9uX4IxkLeSXMJ5GbjdHanOpSk8WJxal/cfeR5IG8qU5auIKvmGQTvuPJiXMLxbAIBsK0A4VApP3toKgMlJod1Ng//vTv42iG+uea7vOOWt3HW9WdgWfozrpRKX7p8vRozmz/zLRqfnfg/P8HCPOxQBlll+eRnNOBkFANCd5dNobeLjtACBKFlfytN2TN4tcbiwI6+N5AcDXNWVXLD969l+uLpY3ZP9f/Zu+/wtqrzgePfq70see8VrzixE8fZezPCCHuVlE2hFNpS6K/Qli5KB1CglBZo2RTKpmwIIwvSDGcnTuLEjh3vvW1Z6/7+sLOInXhIurJ9Ps+jp5Z0dc4rGsuvzniPMFwFRPn6aJ285rveX05gfbhM8ffWl1E5ciIowxAToXQIfuFsasUJ2KvraQTg2K6wZoMBj/3YaIsuNpjp6kqCpmezd1OZX+I7tKWYP57xMMvuPJOzfngGau3ILRooCMLwJMZ2Bb/RR4YpHYLiPHYHALJKhYxElSqCQ2ETqCj2b118t9PNhw9+wh+WPMj+rw/4tW9BEAZGprt8WdzuZQAAIABJREFUvbdvgUyMnAh+Y6+sVToExckyyFodRZGTyd9ShaPoMCabkY5mZaoLV+6v4q+XPEHm/AyW3LqIcYsyxXoUQQg0siQWxAqCr4TPnUL5O58pHYaiOhLH8cWGDpwFpcceUygxOd6+tQXsW1tAcGwwMy6bxtLvL8IcYgag9lAtoYmhqNVi+kcQBP8QyYngN4YYsY3VUlfCjIVT+ObzQxiDDOiMWjLGBuP0SGxbV0LG1HgK8vyz9qQ3TRVNfPbXz/nyqVWMnZdBeGIYG17fRHxWLDc9ewO2SKtisQnCaDbaKsSOrnEiQVGmpDjMqYlKh6EcjQYpJhnDri0sjmtmpqWSqUF1WA/swKDu3nqsZGJyPFeXiz1f5LPmuXV0tXdRuOkQr9/zJo5Oh9KhCYIwCojkRPC547ere7pG6R83lRpD5nga8osBkNxuVLIMHpmS6AlsWNudlFjCzKgCtM7O9o92cvfYe1nz3DqlQxGEUUcsiBUEL5N6TqyTJImkay5i3x+G/ynFA6EbP5H6/MM0bzt08pOdHcRHtJGYpaLeFk9YWT6ucckgSdQ6DORvPOz3eE/F1eWiq71L6TAEYXQZhQtiR9e7FRQXfdb80bf2pLGGkMwEZPnkqrEA6sOHkOrrCC/ajuRwoC0qQFu4n+iynWRMTfBzsKe36/M9eNy9vxdBEARvEMmJ4FcqnZbs++8k6uz5SofiN5LJhLO9Czz9r8YMoJJlEip2kTM36aTnQmJszF4YT8I4/x9CWbixiPWvbvB7v4Iwmskeyeu3QCamdYRBqV23mYh50wb1WmtWOllZ6bg77NSt3eTlyAKDLINp/Hi6HNBQUo2zbXDbhSVkIoq2syQnglpLHCHqTjpVRkzONjQFuzDFTvRy5P3z/h8/ZMqFkzEGGRTpXxCEkU0kJ8KgDDYxOV7GT27A43DQsGG7FyIKPB61FrVOHnRicjyptpbI2u4idtqex2RbMGq1Mt9+2urb2fDaRhbdvECR/gVhNDlSIXY0EdM6gmIMUeGMv+8OGIEVSSUJunbvQOVoJ2JyGjqbxfudqFSUFNR5v91+WvWvNWLtiSAIPjHy/ioIw4omOIi4C89QOgyf6SosxJm/k9DkEK+3LTU2MCNVwmQzer3t/qgrqWfbhzsU6VsQRhV59K05EcmJoCiVSkXCFediHZ+mdCi+pemejLGmxqEP9V6VVVVFGRkTldv99MbP36J8b4Vi/QvC6CAhyyqv3wJZYEcnjAqmxFgmP3k/QeNSlQ7FZ5ylh4meloY5xIA5zrunM9vtyk2ttNa18cbP3z6h0J4gCMJQieRECAgqnZakay5SOgyv0qemoouJRm0NQps0hs5mO5V5hTTs6qUY2xDEle0kfXK8V9sciAPrD7Ljk12K9S8Io4JH8v4tgInkRAgY4XOmoNLplA7Dazw6MzWHWmhqVlG1+QDNBb45N0eSIDrIfbQSrxL+e//7YvREEASvEcmJEDBUWi3mMcqNAHiTymyiqbASAHeX0+f96Q/kM39ehM/76UtNUS371xYo1r8gjHSj7WwdkZwIAUUfFa50CIMmaXUYsrJxhiXR0CDhbGn3W9+yDK0os2vniI/+8qkYPREEXxC7dQRBWWrD8J3W0WZkUrWliPby2u5swY+c6ePI31rl1z6/rXBjEfvW7lc0BkEQRgaRnAgBxW13KB3CoKkk5UYNtAf2MtdaRVqustNib933Lh6PKMwmCN4ki63EgtA3V3snjsZmn/bhaGjyafs+5XKitZq6V6j62ZEuY6zKJgaV+6toqvTtvxFBEEY+kZwI/bbzZ3/m8H8+8Fn7HpeL9qJSn7Xva/aCA9iCJSImKVevRZKVH7U4+L9CpUMQhBFHrDkRhF64uxzYq+uImDvVZ33UfLked8fQD8lTjMeNs7aelpIapSNR1J6v8pUOQRBGFnn07dYRpxIL/aLW65j5n8dQaXzzT6Z23WbK3vrUJ237kzE1lebd5Yr1r21tQKVWKXog366Ve3A73ai1asViEARheBMjJ0K/+SoxAbBX1tCyuwBJq8E6Po3QGTmETMnGkpY0rAqzefQmZQNwuUjOjlY0BHurnZ0rdysagyCMNGLkRBAUELt8Ke1FpejCQ0m56fITnnN3Oaj/31bKXv+Ypu2BO2Ugy9ByuFbRGKT6esbI9cQvyCK/qJO60sbuyrESyB7/7Sb64u9fkbNsAiqV+P4jCMLAieRECAhqg57Me27t/Tm9jsiFM4lYMIOif75GyQtv+zm6/lGZDJiiQ7DXKrvjSJJAd2APpuBxTJmfhNXRjKxWU9iopTTfH7VQZLrqmyn4cheZZ+T4oT9BGPkCfQGrt4nkRFBMe3E5kk6DKTYKj8uFSqNB9niQ+vi2LUkS9opqP0fZf9qQENAGzq9UUoQK7cEdqHoKwo0FIudPYsvakqPXGK0GOlvsQ+xJxmTRk5EdidXVQWdNI47mSgpfXy2SE0HwgiN1TkaTwPkkFUa0I2XNJUnq/tnjIf+3f8XZ0sbM1//W//UsATZNoDKZ0ERHo7ZYqC+owlF0QOmQjtIf2HPSY8EHtjN26kQ62p3ERenQu+2sWTf45GT+4kQ6Cg7jbG9B3t/C8RVOqrcV4XY4Ueu0g25fEITRSSQngk+07i/C0diCx+kkdHr3t+eCR55FkiRSvn81hX//N637iohetgCV5tiujr5GTWRZpmlbPvXfbPFL/KeiMpuREtNQu7uo2luNu64GSV2POTJI6dBOS5IgoWJn951mkDUarJGxtNS0nvJ1k2bFc7ioibAIM0FWHc2NXcRH62nZVYjH5e71Nc52O+Ub9pM4P9vbb0MQRhdZTOsIwpCVvv4RBx57/uj9oMwU3HYHHcVlAFR//jXuju5v6x5n/0/s1YVYCc7NouNwBZ3lVchOl3cD7ydNRAQ1+ytwdznx9MQgu90YIsOQZBl7TaMicQ2UrNHiTkii5Zv6U16XmRODpqyMBIcdyutwl4MFaOrH8pVdL3whkhNBEAZMJCeC19myM5DUKuSeWhut+4qwZIxBawsi5vzFhM3KZe8D/8AYG8X4X/+wX21KkoR5TAIT//x/AHhcbtoPldK8fS+1azbSuMV/W1cdxcVEJibiNMZSs+1YNdS6HYWgkgjPSqF5T5Hf4hkM2WhkY1MYrp3tgAz0/a0sOlhF4+HBTf2Urd9L8Vc7SF4s1p4IwlAE+tZfbxPJieB11qx0pv/7UTqKyzDERdG6t5CoM+bgbG1HE2RGbdCT+pMbKHjgSXbf+zATH7pnwH2oNGqC0pMJSk8m/rJltOw5wO77HsVe6Z/qrI7Dh9FlBZ8Yk05D+IQUGncW+CWGobCmJTDfLdEp61CXHkKXGM/m3W3UVrUfvWbBokTo7KRpz6Eh9bXhwbdJXDABlTqw1gsJghC4RHIi+IQ5OQ5zchwAQenJAKiNhqPPR82ZStTHz+LqHOpOkW7WrHSyH/gJeTcMPNEZDF1CAtV7Kk58zGqms7IWFKzO2l8qoH139+JdD+DcXcDkyBDassdQV9tJTLCa9sJSHK1DP06gubianS98zqQbzxpyW4IwWo22kRPxVUZQlOa4hGWorOPSCJszxWvtnYqruRnbmKgTHrPXNdPZ2EFQZgpBaQkAqI16zMkx6MODCc4ZS0huJhqz0S8x9kYfHow2yER7VcNJz9lrGjG7OjAcLqZxZ6FXEpMjNj3yXyo2Bf6IkiAEJNn7h/71d4GtJEnBkiS9JUnSPkmS9kqSNEuSpFBJkj6XJOlAz/+G9FwrSZL0uCRJByVJ2ilJ0uTBvmWRnAgjysQHf8bkp+4netkCn/WhjYtDkzAGR9vJoz6uzi4a8otpKChHGxVBl91NU1E1HU0d1G47QM2WAqQgC/rw4F5a9j59eDBBafFobRZCcjPpbO3CKavorD45OQFwHCwiNiMMvdW7Zfhlt4cv7vwnXc3tp79YEIRA8lfgU1mWM4EcYC9wD/ClLMvpwJc99wGWAek9t+8BTw62U5GcCF7VvPcgsrv3raX+IKlUBOeMY+zPbsGWk+nVtjXh4WizJtHmNFK+uZDWklMXhOuorD+6KNjjOLazqKOiHq3V4tXYjgjJzSQkNxNDUhymlAQwGGkoqKCjsYOaLQW4O7twtnT0+XrZ5UJjs9J1imsGq7O+ldW/eAnZE/jTXoIQSGSUOVtHkiQrMB94FkCWZYcsy03ABcCLPZe9CFzY8/MFwEtytw1AsCRJMYN5z2LNieA1zbsLKP3PB8RefBahU3y/fVT2eJBlGZX65NNv1Xod6T+8jrwbh7AGRa1BE2zD09mJJj6R5rou2jYfHELExzVt1Hulnd7UbDn99EnQ+DScaNBqwO2RkLo6UJm6R0taan03ulH8xXY2PPg2s+65zGd9CILgNSlALfC8JEk5wBbgR0CULMuVALIsV0qSFNlzfRxQetzry3oeqxxoxyI5EbzGlp2B7YG7/NafpFKd8lu4dXwaU5//M+6OTnbc+QAeR/9rqgBoIsKpKukAtMh1ZUOM9kT1+SVE5GbSuG3fkNuSZbCmx+NBTd2u029h1lhMNFa101bZ+9SOr+184QvUei3TfnxB96GEgiCclo/K14dLkpR33P1/yrL8z+Pua4DJwB2yLG+UJOmvHJvC6U1vv9CDOnFUJCfCsHa6U2+tmakAxF+6jMOvvj+gtt22KGR34ekvHIQj0z2oVYPa3SNLEsbwYIzxUdTtKqKhoOL0L+qhiwihtdL70zYDse3pT2gprWXRn65HrRMfQ4JwOh7f7Napk2V56imeLwPKZFne2HP/LbqTk2pJkmJ6Rk1igJrjrk847vXxQP8/nI4j1pwIo4IuPGTAr/Hs20H8lERUet+cDVOzpYDgCemE5GaiNhuwTkjHlpXar9eGZKfSWtNCzZaCE9az9EfHoXL0Qd5d8DoYhR/n8dkP/iEWyQpCgJJluQoolSRpbM9DS4B84H3g2p7HrgXe6/n5feCanl07M4HmI9M/AyWSE2HEOnLYIEDDxu0Dfr0kQdfefLRm7213/rbard07eLo63dTvKEQ6RaGy4NxMVHotapMeSaNBHzLwRbXG+GhME8bRUn7qkvX+UrpuD6+f+xsOr9mldCiCELgU3EoM3AG8IknSTmAS8AfgT8AZkiQdAM7ouQ/wMVAEHAT+Bdw22LcsxlOFEUmWZXb97M9IGg2SJNGwcQcAklbTPaXSx1oV/bgsnLIGZ4cDtU6DpFbh2ufd9Sa9xttzeJ6kVmHNHEPz/mKk45IrWYbG/BJ0NhuOlo5+LXrtjcqgo76wprvBANFZ18IntzzB+O8sYPqdF6EPUq4OjCAIJ5JleTvQ29TPkl6ulYEfeKNfkZwIw5YsyyDLfZ5k7GrroGlb/tH7WX+4i8gFM2gvOkzL3kI6K2roKCmndW8h9qpadKlp1Byow9Gi3DRDzZYDBCVHEzIxHdnpojm/CBmJ0NwMancU0tFL8bSBaD94GFtEKKEpKVRsCazzf/JfXUPRp1uZ8v1zGHfFPNQ630ynCcJwc2Qr8WgikhNh2Oosq2LHTx5g8pP3o//WmhJJkpj8j9/RvOcAhU+8jDU7nbDpOUgqFZa0ZCxpySe2VVFNTd4+nO9voCZvn6IjC63FVbQWgy09npDcTFpKqqnZesBr7Xu6HOAKzFoj9oZWvnngdXY8t5LsFYuZcN1ScSaPICCSE0EYElmW+9we6mhqoeChf5H2w2sxRIUPuS9Jo0ZjtXD4tQ9Iv/2aXq+xZaUz+cnfnbYtY2wUScujSFq+gI7qBoreXcuB/3yBo6ltyHEOVvMB30wnSToNsidwpnV601bZyIaH3kZSq5h43VKlwxEEwc/EVxLBq05Vt0JSqzGnJCJpTi6aNhjGmEgmP/Ebos+Y65X2jjBFhZJ964Wc8/6fSLlkgdfiDRSOuiYcBQXEZUYoHcppbX7svxz6fJvSYQiC4pSoEKskkZwIfqMNMjPmxsvQhw18W29f1EYDQWNTvNbe8fQ2C9N+dT1nv3k/QUnRPulDSW1FZZjCrUqHcUouu5OVdzzF5z/+J/YmseVYEEYLkZwII1bdujyvnONiTYll0TM/Qx8S5IWoAockgYl2oiYmKR3KaRV9uoWND7+Ne4A1XQRhRJAlPLLK67dAFtjRCcIQBE/J6nMnz0AZI0NY+vJ9aHx4Jo4SXK0dOEtKSJiaTFROstLhnNK+t77hlcX3sveNdbgHeBSBP7md7hNq7AiCMHAiORFGLI3Ju/UyLAmRpF150tb+Yc9j76J1136k1ibCxsZ1D6kEqM66Ftb+6t+8suheNjz0NlVbDwbcKcdqrVqcGSR4lQxKFmFThNitIwgDMOH2Szj86UY6KgOjwupAWHKycDlcSG3NOBtbcLWdeL6O2mTA6QJJJSG7A/ubf2d9KzueXcmOZ1diirCRdv50Zv3fpUqHJQg+E+gLWL1NjJwIwgCoNGpSLpyvdBgDZoiLonZfBVXbi6k82EhDM2hT0zAlxRI0YSyWnPGU76mmfl/5sUMJh4mO2mbK1u3xW38t1c243W6/9ScIo5EYORGEARp/y3Laymoo/uAbpUPpF2PWOKp3luBxHfuD6nG5qc0v7bk3/EaBvq2rtRPZ4/HaGqO+1BfX0tXWhTXK5tN+BOHbxMiJIAinJEkS0359PelXBX5xMLXZQOW2QyckJiNRe1UjH1z7KGVf78Hj8t2OnrDkCGKz433WviAI3URyIgiDoNJqyL7tIgxh3XVCDBHBqPWBdxaMq81O+Ng4pcPwi8rNBRx+6S02X3UHlR98gburS+mQBME7ZPDIktdvgUxM6wjCIOmsZpa89Eu0ZiP6kCDs9c3sefp9Dr7+pdKhHSVJINdUYEuORGcxoNVrqDtQiaOlU+nQvE6jVyMjYa+opuBP/6DwiRcInz+D4MnZWNLHYEqKQ6UNvARSEE5HJvArunqbSE4EYQgs8ZFHfzaE2Zjy8+8SNXM8let2cOi9rwNicam7vROpvRQn4ASsVgvGGanYWx3UFZQjB+ghgAORPCkaU81+XHuqjj7mbuug+uNVVH+8qvsBlYq4S5aRdMMVaK0WhSIVBKE/xLSOIHhZ/OIpTPv1DUz7zQ3orGalwzmJq6WN1u35OAsPEpVoJXZKKobgwIuzv5JzozHVFnSftnwqHg/lb37E5qtup2Hjdv8E1we3y43H4xHF2oR+G21n64iRE0HwkTHL55JwxjQKXllJyccbaCksVzqkk9jLq6G8Gr0MEdPHUXewls6GVqXDGgAZY+VePM7+L4J1NrWw6+7fE7lkDrYpE5DGjCU4PgyDrbton8fVnTRodKf+eJRlmY6GNmoP1mAKNROeGonquN1CTruTttoWWqubaa1pobWmhfpDtdQeqKJkcxFaoxa9xUD6gnGcce/5GK2mwf0nEIQRSBpI5j516lQ5Ly/Ph+EIwsjkcbr43z1PUfbF6X9/dFYzjhaFDrlTq/GERdNSWufnjmXSZ8ThdHioOtiIvfX0i1mtURbGjLXQtWfX4HvVGfmqKPzoCIakkpA93T8brEYmXjiFuImJSJJEV5ud1tpWmsobaCiupb6ols7mY4XsbLEh6Ew6HJ0O7M0ddLX1f0GuJcLK+GUTOfd3l56Q4AjKkyRpiyzLU5WMYbzVJr88bbbX25361aeKv7e+iJETQfADlVbDjN/fTMuhytOOoCiWmACW1ATKdlX6tU9JLTFpRgid+3egBjLTE6huD8dg0VKWX4uz8+RREaPNQKKpjq49h4bUt8dsQWvU4ujonhI6kpgA2Fs62fTS1/1uq7micdBxtNW2sOmlrwmOD2XerYG/RV3wv0CfhvE2kaILgp9ojHqm3ned0mGchoTaoPXLGpTgWCvj5sSRM8lA5/6Co4/bS0qx1e1FX7yT8akeIsaEnPTajJxgXC1Dm37yGIPY1RJ7NDEJBMUbC/EE2FlBgqAEkZwIgh9F5KYTNTNL6TD61F54mJj0CIyOZhImJWAMt/Z6Xfj4eHQWw5D6Co0xI+/bjr34cJ/XdFVWEx977Buj0aZn0vyoIU3lHCF1tKLRqYfcjjcVfLmHmgL/jlwJgU+WR9+CWJGcCIKfpV62UOkQ+iS73bQVHEJ2u2ndexB9ay3xWdEnXBMxPgFP6WFCgtXET4onIitxUH0VbalAFxF22uuc5eWotWqCY61kZuqw79k9qP6+TTYF4XIGVuVclVqFaRjvnBIEbxFrTgTBz6zJsT3V0QaxjXSwrxuCtoMlxKTHogkJxllTi73wIABdtQ101TagC7URkhKN7PHQWtWI2+48bZtqrZqwBBv2YCOa1jY89r4Xj3qcTibMDMVZWkLnQe+tx5ENRtqqlFvf821hKZFc9fQNWKODlQ5FCDiBX9HV20RyIgh+ZkuLY9k7D1C2aivF731Na0nV6V/UQ63X4rb7f41E5+EKOFzR63OOhmagGVmGsIhgDDHxyCoVdQdrTtqWPGF+LCo8SHiw79kDzXC6FRYeexf2/HzvvJHjSE11JGSmcmBT6ekv9iFbXAjLH7icMbMz0BpEBVtBAJGcCIIirCmxjE+JZdz151D59U72PPUeDf3YeaJEYtJfkgSOuiYcdU0AWKPCCIuPwWiUqC1tIzHdgqtwP86uLiS18h89ksdDpL6VzqwYyvYos85j3NkTufyJ605bU0UQAn2NiLeJ3whBUJCkUhE7fxIxcydy8M1V7PjLa7i7Tj8tMhx0VdcjdXViTbehdhbh2H1sOkr2KPseXWGxVBmT2LeuFGjya98qtYpxZ01k6ndmkzpvLJI0uv7oCIMjkhNBEPxOUqlIv2IJUdPG8c1dT9BS1PsUynBjb+qgeJeT6GAj7raO07/AR1xRiTSZ4ggr3YI9fizfrGsE/DudozPpmH3zYqZeNQtb7MnbowVBOEYkJ4IQQKwpsSx56Zd8c+ffqNm8V+lwvMJtd6Idk4Z7107FYqjRxZO/9jAGayLOcv+X55ckids+/RlhyRF+71sY/mQYdQtixVZiQQgwuiAT8564k5h5E5UOxWtUp1326ltWrR0Ae4sdt6P/5/B4iyUiSCQmgjAAIjkRhACkMeiY++gPSVw2U+lQvKJiXz0aa5Bi/TfY9Yr1DdBe34ajM3AXMwsBThRhEwQhUBw5jyd2Ya7SoQxZSGIoHrtduf4NDkITlFvn4XF72PlfcWiqMFjddU68fQtkIjkRhACm0qiZ+cdbsKbGKR3KoEVlxSIX7sHj6N6hozZoSZ0XhjUlnJT5UaTMiyBxbgJBSWF0z657X1DRFiYZi8iaO7hqtt7w1SOf0NEYOEXfBCGQieREEAKc1mRg7mM/RGsxYstIQB/a+3k3gSg4zkzsGAl9yLGS7MlTg3AX7SS0aweewq14irajOrSBMNdOUuZFYk0JR1IP9qNJRtKeeF6OxqgjJCOStGkWZrneZNKCmCG8o8FrrW7mxav/TlNZgyL9C8OXDMhIXr8FMrFbRxCGgaDEKM79+CH0Ngset4eyzzez/eH/0Fnr3xod/aHSSISnWIlO1GKu3YJ8oIPYCdM4tLYNAFnVdxVUT9F2QoGwZCOqyCTcWhvN1U4a91dBPz5M42Ynoavahit2Ei2VdsLD23CXHYR28BR2X5Pr+IjIc+ZS1mQjf71/i69V7C7jb2f8kZnXL2DqlbMISTz92UKCMBpJ8gDO6Zg6daqclyfmTQUhEDhaO2gvr0VSq/E4XTTmF3Pwja9o2t/3Kb/+EDU2mDGaPHCdWGjNlTSL9jo7HrdMiHMXuPt/6J46MZPC/x3ZAixzJFExRVmJyjTjcmtQq2Skynzkjv5tFd4TdB7/+7y23zH4QtzERKZ+ZzZZ507CaDMpGovQO0mStsiyPFXJGMZaQuSnJy72eruL/veO4u+tLyI5EYQRRJZlSj/dyLaHXsVe36JIDJIKZs1owNNQ3fsFWh14POAe2JZedewY0Bqho5kOXQIarYS6+H+DirE6ZAZf/M9EZ5NyheGOpzPrmXjBFGZcN5+osTGiamwACZTk5MkJS7ze7pINbyv+3voipnUEYQSRJInEZTOJnD6etXc8SmM/zuvxNpVGhdx2iukm5+C21Lorjr0XPeWDauMIF7qASUwAHO1d5L26nrxX1xOcEMqZ9ywn+7xckaQIo5ZYEDuKdDR10FqnzLdpwb8MYVYW/OMuTLH+X9Pgdnjoig3IL2NHxTas48olVeQuVGZx7Kk0lTbwxg9e4PHFD/Df/3uVHe9uFotoRz3v1zgJ9DonYuRkFNmzag+SSiJjTgbW8OGz40MYHH2whem/vYnVN//5pOcsiVEEZyQgqVUkLZuJzmahtaSKuu0HqFizna7GwZV4j0y3kZgKuq4qhWvCnpokgaW9hElSOY4556NSyZQX22kobVQ6tKPqCmuoK6xhy2sbkCSJrPNyyblwCmOXZosRFWHEE8nJCON2upFUEk1VTRSs3c/Uy6ah0Whwu9zEj4sjMj2Kpoom1r2wjrnXzhUfciNc1PRxRM/Opmr9bgBi5k5kwh2XEJKZdNK1EZMzSLloPh6ni/JVW9n34qc07C464ZqQrDHEL8qlo6qB0s83Y46PPDp1JKkgLFaH5tDagE5MjqeWXcxyvwtucCWZ6MoIZ7djMrvW+XcXz+nIsszuD7ay+4OtJE1PZcL5uYxdmk1wXKjSoQl+IMuj72wdkZyMMHXFdWx49X+senoVAO/97j2ueuQ7TFw2kdJdZbTVt/PKD/+N3qKnpqiGzPmZjF04Fo1G/FMYqVIvW4TH6SZm3kQyr1122utVWg0JZ04nfulU6ncWUvzBN5R+kUd4ThrzHv/x0eum/PJaJEmiYu12nKv+A/lroGr4HlaocXVgN6ioqXSe/mIFlWwqpGRTIR//5h1isuKZ94MzGHfWBFQqMUsvjBziL9IIE5UeRdnusqP3O5s7WfOv1ZhDTDRXNRMUFsQZd56JLcrG6qdXMWfFHJGYjHDxi6cQv3jKgF9gLcKtAAAgAElEQVQnqVSET0onfFI6Wd+/kNaSapztnWjNxu7ne0bdYudPwj05hfonZTo3r/Fq7P4myS7sbcPjDByP20P5zsO8dsuzjJmdTtayHKIyY4nMiMF0XNE7YWQI9DUi3ia2Eo8gHo8HlUrFB3/4gC+f+KLXa9RaNdlnZnPWXWdjjbQiqSRUahXGIKOfoxVGGtnjofGFR2j99E2lQxmU2pCpvP+ZBtnjmxL6/qI16sg8I5u4nEQScpOJz01GNeiKu0IgbCXOMIfKj2ed6fV2l21+XfH31hfxlXmYc9qdlO0uIyk3iQNfF/DKj16hpabvHTlup5sdH+1Ao9Nw0e8uZuVjn1G5t5LL/nQ5IQkhYhRFGDRJpSLk2h/jKC2ia88WpcMZMJ27lTGTMinaWuW1NjVaFQazltBIC4cL6r3W7qk4Ox3sen8ru97fCkDyzDRyL5tBZEY0YckRotibMCyIkRMfKtlWwr7V+1h6x1LUGvXpXzBAVQVV/O3ix2lvGPhhYkERQYxfMp6Nr20EIGNeBmf/dBkpU1O8HaYwyrjbmqn+1S04y/xfY2WoPKjYpFnOnm+q0Jl1dLV1oTfrUWnVfdRFkZEkieM/RjU6FS6Hh+AIE9mJEu0lFai0akxpydhVBratr8DlVGbJcHhaFBkLxzF2aTbJM9LEiEo/BMLISbo5VH58/Fleb/ecvNcUf299EV+TfcRpd/LRnz6kYF0BerOOhd9b5NX2a4tqeOqqJweVmAC01rYeTUxUGhVJuUkExwXTVNVEcHSwN0MVRhm1xUb4jx+g8qdXwwC+/AQCFR5muv7L5HkhqJ1tNNmy0LuqMXRWs0t/JvX1ULKzGo1OxfTZUdj3FYAMppR40GjB46HjYAmmtCQ87e20F3ePwngcLtryDwIwc1I83+S1KfKfpu5gNXUHq1n/zGp0Jh3JM9OInZBI8vRUUuZmiN17QsAQyYkPNJQ1sOaZ1Sz+/hJyzs0hZbp3RyOKNhXxwi3P01LtnYJqYUnhnPOzc2mtbxX1TwSv0CWmYpy2gM5Nq5UOZVB0ju56J2FN248+ltvxHhjBPj+cOssE6ncW4XF0l+Bv2198wuvb9hb22XZ7URlz56ZSWidRvLfO+8H3k6PDQcFX+RR8lQ9AWEoEi350NtnnT/bJSK8wFNKo20osxvR84Kt/fEXKtFQyF2Yy55q5xGTGeqXdst1lPHLuI/zt4se9lpgAZC4Yy3M3PoveqKdkW4nX2hVGN9vyFUg6vdJheJ2hq474+lUkTI4YdBtt+YXERwXWd8P6olre+tHL/GXmr3n37lcCqry/wKirECuSEy/rau+iYl85xXnen2/f8eF2Dm8r8fpugvUvr+fA+oM8ccnfKNxQyKqnv6Lg6wKv9iGMPvqMCUTe9wSScWRua7U4i4f0+k53YI5OtNa0sPWNjTx7+eOsf2YVTvvw2FotjCyBlbqPAFvf28qiWxcx4cyJXm33m5e+5vPHP/dqm0e4nW7czk7sbXYW3LQAJMSw7gji7nJgr2/BHBvu974NYycS/dunqP7tbXjaB1cSP1C16xJhEAcQmhKjadSEkL+uDAjcb6/V+yr45HfvsuaJlSy561ymrZgj1qQoRIZhU3XZW8TIiRe1N7bz3m//yzcvrvdam067k3d/8y5v3uO72hFJU5KIGRfDefeeh0qjQlId+wByOVwMZEeXoAyPx0N9wYlTcg35xbjsXaj1OkUSkyN0yRmE3nyPYv37giNsLEXrBncycsfhKkyVRcybF864qdHoDN3fEbV6NcGRgTfK1NHQzge/eIP3730dt8utdDjCKCFGTrzIHGJmxd++y5dPfIHT7kRr0A64jdqiGj599DNqD9XisjtprmymvXFwO3L6y2Az0lrTyuFdpez4aAfRWTEsuXUJ+1fvo6mymckXTMYQZPBpDMLQSJJEcGr80ft7n/+YjBVnotYGxq+4adYSDF++h33XJqVDGRKPpMEemUNVrY3BjJoc4e7soi2/EAOQE2nAk5SGuvQQztZ6zAvH8vXqaq/F7C15r65Ho9Mw77alWMWOPv+SR1+F2MD45BpBss/MZu1za/nXNf/k2qevwzyAMtJv3PMG61/6xofR9W7/V/sA+PLxL1BpVEzVa6gvqSdpcjIdPSv5ZVkWQ7oBTJIk1OpjU3Gx83K6j94NEJIkEXT2pcMuOemKyKZdFY3Dqcao76KlRUvN14eBNq/14Wq3Q/7uo8P27bv2E5syhoqiwDkh+YgNL6xl40vrOOvnFzDrpoXiPB8/Gm27dURy4mWFGw5SsHY/AA8tfZCldywlPDmckIRQPG4PkSmRJ63nqC+t5/PHVrLhPxuUCPkEHpeHTa9vonx3OWPnj2X8GePRmXUiMRlmbGlxJz2mdIKpT8/u+0lJwjhlHl17twXM2hQZCXVnLY2t4TQdLPVr3ylJeiqKZAJxTYrskfn09/9l29ubOOfXF5MyO0PpkIQRSCQnXuRxe/jwjx8evd9U2cRbP3/rhGvCksLIXZ6L7JFpqmyicl8VFfmDHx72lYr8Ci554FKSJieJb0cjhNIJpuxyIZksxPzhORxF+7Dv30XbF++inbaQqBt+gjo4DE9HGw3PPUz72k8UjbWbRHvQODqL/Z8ste/cx7QFWWxeE3ifDUdU763g39f/k0mXTGPy5TOJy0lU/N/YSCYHYKLqSyI58aKPH/yIQ5tPvYW4vqSeL/7W+6F8gSRhUqLXi8cJw0fzwXJMMaFHTyD2BtnjxrLwPFBrMM89C+PU+RinzUeblI7aFgKAymQh7LZfoYlOoPnNfylaYVbCg7VyLcHJS6lu9N40Tn+5du9h3rw08g/aqa/0f//94ex0sPnf37D5398QnBBK+oJxpMzJIGlqCkFRNqXDE4YxkZx4idvlpnhLsdJheM3hbSW8ee8bLLv7HCxhFqXDEfyst2mh/nJWleEsL0aXlI46NAJJpaIjbx2G7KmEXnfn0etUBiOmidNPer2kUhF86Y3o07Jofvd5ZJcTx4E9g45nKCQ86BU8sLst/yDp4cGotTZqDjef8NzYyVHUlLcREWPBZtPS0OCgsbaDhqpWlJgOaiptOJqoAMRkx5N1ziSmrZiDKTjwdiENJ/IorBArkhMv2fbeVg6uP6h0GF71zYvfMHvFHJGcCAPSmbeOxpceA0ATm0TMQ6+gHzsRlWFgf+WNk2ZinDQTWZapfuBHdO3c6ItwT0vpPwlddU3Eq1tIW5TJ+lUVSBJMnR+Ha/cessen4aisxFXdgdXhIjzUii4llPJmLUV7ahWNu3J3GZW7y9jw/BrO+sWFZJ+Xi0Yn/uQI/SMWE3jJ1Eumcc0/rsUcOrK+IWx6cxNdHV1KhyEMI+rwKILOupSgZZdju/h6JGTUQYMf4pcdXejHTfJihAMTVrOSzHkhaIzKleKX3R46d+Yzd1EMcyabce3uHklqyz+Io7Hl6Bk/joYW2vYXExcuodUHRiHFttpW3v7xyzw841esf34V7Q2BOUUV6Dyy92+BTBpIga2pU6fKeXl5Pgxn+Fv52Gd8/ODHSofhVRf//hLmXT9PLHYT/M7VUEv1b2/DVXn4lNepQyIwL1iGymKj9ePXcDd4f9TArTPTGjqDygOddFQ1eL19b6uwJFNd0qR0GL0yhwdhjbZhibBy0UPfwRIRFLCfL5IkbZFleaqSMaSawuU/pJ/n9Xav3Pmi4u+tL2KMzYuaq5rZ9ObwquPQH4liFb7gZ572VtxN9VT96nt4WptPel5lDsK6fAXIMh0bVxH2/V+iS+7e0hq05ALqHv8Vndu8V6kZQO1oR0s7w2HA2ZQYTfW2wExMANrrWmmva8UabePdn76KJTqIZT+/CKPNpHRoQoAQyYmXHFh/gL9f+gTLfnoOa55ZTUfjyDnRs3xPGclTkpUOQxglug7mU/XLm8DjRpeejeXy76HPmIDsdND65Xtoo+KwnHHx0aki28XXn/B6lTmIiP97iPp/3E/7uk+9FpdbF0ThDgfOlsD/3fY4XOgMGhx2l9KhnFJLVTMtVd3Jp9Fs5KxfXihKF/RCRhRhEwYpPDmcc+45l9TpKXz+xEoW3bqY8WeMZ+Ujn3HgmwNKhzckX/79S6LSo0mblaZ0KMIo0Lb6Q6znfQfj1Hlo07NBllFruj+q9BkT+tWGpNYQdtt9uFuasO/wTnFDtaOV8alVtFrGU7ypGk+X0yvt+oTsweUM8EUF37L5lfUs/OHZGIPF6IkwHMYnh4ngmGDmrJhD6sw0HjzwEBf86gLSZ6WTe0Gu0qENWUNpAy/c8jwuZ2B/CxOGp9qHf0bTey/hKO8+uDD0xp8SsuJ2DJk5qNXqo4nJQElqDeE/+h3q4DCvxappqySk6ktSpkd4rU1f0ISH4XEPr+TE43JTtL5AHDTaG7m75I+3b4FMJCdeIknS0Z06xw9LTjovF1PI8P8m0FbXxv0zfoejw6F0KMIIE3bbfVjPX4EuLgnwbiVbtcWG7YpbvNbeEcauUy/QVVr7vkJs4f3/3AmODyVtQSapc8eiM+l8GFnf3E43r936HF/+5WNx+nEvPEhevwUyMa3jY6ZgE7e9/gPe/dU7FG4oVDqcITGHWtAaB37SsiCcisrk2zo6lgXn0vL+v0+742cgJI8LldaAJ0BHE2W3h9RxIWz9uu/1MTqznhnXzmPKlbMISz42ElS1t5z/PbuarW8oU1dmzeOfUbh2H8v/dCXR42LFYvxRSoyc+EF8djzn/OxcpcMYsqV3LBUfFMKwI2k0hH3/F6DyXt0PbXMJ2TltGCICt0S7SeMmd04cGZOiTnpu4oVTuHPtfZx5z/ITEhOAyLExnPfA5fxs6wNc+dQNqLX+r5dStr2Ef5z9Zx5f/ADrnvwClyMwk0B/kQFZlrx+C2QiOfGTlOkpjJk2RukwBm3a5dOZdL5yhbAEYSgMmZOwXXaTV9vUNpeQmt6JdczJf/wDQfuufbBvD+bS/UQmWAFQaVRc/Jeruezxa7FEWHt9XUd9G26nG3OYhdgJCZjDg/wZ9gnqCmtY+cf3eWzB/dQUVCoWh+B/IjnxE0mSiEqPVjqMQVtw0wKxxU8Y1mzLV6AOi/Rqm4baXaSp1zJmbgIqfeBOeaYGtZEzO46LH11B7mUzTnmtJcKKwWKgbHsJH973Fm21LX6Ksm/N5Y386+LHKFy3X+lQFNJ9to63b4FM/LXxo+G45iQ+O54Vf1tBfHa80qEIwpBIWh3G3Nneb1eCkKovyEkpJHpKktfb9wZ7dQOpE6LIuaD/xUATcpP57gu3cPeG3zHv+0vR6JVdomhv6eSFq//O3pU7FY1DKbIPboFMLIj1k5rCajqbO1CpVXjcHqXD6ZfpV8zg8j9drviHkiAMlaejjY5Na+jc4ZtFnpIEkqsTrTIbXU4rYvp4cv5vxaBeGxRp5cx7lzP/B2dQvPEgG55fS+HXyo1gvHrTM+RcNJULH7wKTQCPVglDI/7q+ElkahS/3/UAh3cc5rNHPmPP57uVDumUJp2fy5V/uVJM5QgjQtMb/6L149d83k946waaEifSeljZE4GPFzwumRkP34FKO7SPe4PVSOYZE8g8YwLt9a201ray6tFPOJxXRFttq5ei7Z8d7+bhdrm59LFrFFmw62+iQqzgc4k5iVz4mwsp3XmYlmrl53J7k5CTwJUPi8REGDk04f5Z76XqaiHNugv73LHUNAZRv6fML/32RWMyMOuxO9EFefe0dHNYEOawIK56+kZaqpopXLcPc3gQeosBg9WIOcyC1qjD0WanvriOprJ6OhrbqdhdRnNFI6VbD+F2DK2Wye4PtlG9r5Ir/nE9UWNjvPTOhEAhkhMFhCWFIQfwedUX/OpCDEEGpcMQhCGTZZm2tZ/Q8sErfutT1dWMqXoTUZG51Put195l//hKjJEhPu3DGm3rc5Gt3qwnKMoGM1JPeLxwfQEf//ptavYPbQdO7YEqvnjoQ65+5uYhtTMcDI/FAN4jvhoroGhjEa1+HgYdCL1Zr3QIguAVkiShCQnHvOAcNDGJfutXlqHJoWyJe3NCJGMuXaRoDH1JnZ3BWfcu90rdpH0rd7Hn4+1eiEoIJGLkRAF1JXVKh3BKHU2Bf+qqIPSXceJ0jBOnE/Kd2+jYtJraR34OHt+VR++ImkZllZmmzSU+66M/Mq47N6CLJiZM9l7dJ0d7l9faClSBXjTN28TIiQKqC6qVDuGUVj29Shy+JYxIpukLCb3hboy5s7FecA3BV9+OytJ7MbLBsEfmsn9TB00HlS0YlnLFUsZcHJijJkcYg00s+em5SKqh/9H9+Lfv4OgYuQmKLCPqnAi+F5ka2Cea7lu1lx0f7VA6DEHwiaAzLyby3kcJufoH2C74LlG/fRqVzTvrMlo8EXh8WGrdbTLjslpx2oJPeNyj1SH3LGCPnp9L9o+u8FkM3rTg9jO58qkbsMUO7b+/vaWT1297nk4x6jtiiGkdBcy4aiY7PtrBvtX7lA6lTy/f/hLmUDNps9ICemhYEIZKl5BC7COvY9+xgfonH0B2Du4buCxDZ6tvly3WRo3hw5UlgJugkCii4mxUlTbR0WpHa9Dym6euYOJFM4bV7+z4s3MwhwXx7KV/HdKI7YHVe2mpbsIYPPxPge+NUmPZkiSpgTygXJbl8yRJGgO8BoQCW4HvyrLskCRJD7wETAHqgStkWS4ebL9i5EQBKpWKsDFhSodxSm6Hm79f+gTPXPsvpUMRBJ9TB9kwzz2LqPv/iX7c6c+QUpmD0CaloYlLBq0Oj8ZIfeSZ1O0u9Wmc7uOOuW9t7ODg7kramjvxeGR++sy15Fw8c1glJkfEZMWh0g7tz9Hc7y8lIm34HhESwH4E7D3u/p+BR2VZTgcagRt7Hr8RaJRlOQ14tOe6QRMjJ37icXtoqWmhs6WTHR/tYP0L65UOqV+SJgdmOW5B8AV9SiYRdz9I2ffOAXfv0zNht92HecGxxaaumgpKf3MnrY2+W2QL4FFr2Lij983Jao2KaWdl+bR/X+hqs+Nyutnx9qYh1T3RmXUsvnMZKvXI/b6txBoRSZLigXOBB4CfSN3/6BcD3+m55EXgN8CTwAU9PwO8BTwhSZIkD3I4bOT+Pxlg6kvq2PDq//jzoj/x6cOfDIsFpyHxoSy5fanSYQiCX3k62rBdeiOmmYtPeFyXOp7Yv76JZeF5J4xOaCJjiX/oBQyxPi4EplLRVNfe61OSJNHS0PtzgUxvMWAOMTP7pkWkzMkYVBvJM9P43n/vQqMbud+1ZbrrnHj71g+PAf933OVhQJMsy0cy9zIgrufnOKAUoOf55p7rB2Xk/r8ZINrq27CEWYhIiSQ6Y3gNOWYuGItaM/JLQwvC8bRRcQRfcgOyy0nbpFnI7a04igsIvfGnqEyW3l9jNpJ1+6W0H66i+UD31I5HpaYwLgunG+J0djRuJ3XqIKI665DVamSVClNZ/7cb1yWk48k/3OtzLqebvRuKmHvR5IG/4QBQsauUmoKqAb/OHGbhnN9cIirEDl64JEl5x93/pyzL/wSQJOk8oEaW5S2SJC3seb634Ru5H88NmEhOfMwSduzDbNv72xSMZOAmLc9FluVhOYctCEMlabQELV7e7+uDkmOY9+wvWHvD72mobmGrOp6dXx361lXdNY6soWYuHNv/z21nSBgff1V+ymvee3L1sE1OItKjiMmK48DqgR3pMf27c4kZH3f6C4c9yVd1TupkWe7rqOo5wHJJks4BDICV7pGUYEmSND2jI/FARc/1ZUACUCZJkgawAQ2DDUxM6/hRWGJgL4L9tk2vbaShdND/tgRh1NEFmcj83oU0RCWzc3Pfi2NdLjeatt6rRHvUalzmIJqSx9IePwa3ycya5mBczr7XZKhUEpffdeaQ41eK1qBj+R+vIDg+dECvGzMr3UcRCbIs3yvLcrwsy8nAlcBXsixfDawCLu257FrgvZ6f3++5T8/zXw12vQmIkRO/OnJ6pi3axrhF42iubmbvV3tP8yrlTDw3h+DY4NNfKAjCUfFnTCd4WwN88u1Rk2M6Wux83hnNkqBaXBYrHUYrzR4tDjckepp4ZU0jR76QxiSHk5Vmpnh/720Zgwz85KnvMu2sbB+8G/8Jjgvlji9/zhs/eIH9X/Tv1PbwlEgfRxU4AuhsnZ8Br0mS9HtgG/Bsz+PPAi9LknSQ7hGTK4fSiUhOfOjbUyLn3nMe8RPiSZ2ZhiXMgizL/O2ixynaVKRglH1LzEkUa04EYRDOv3UBn77wDTWH+x55LD1YywsAtPTceldZ3EBlce/tGIMM/PHDH5IxZWTsqtMZdVz97M2U5BXx7l2v0lBce8rrG8saug8WHOFklC1fL8vyamB1z89FwPRerrEDl3mrTzGt40O9rdXImDf26DoUSZI4++5lqHWBmQDs+WKP0iEIwrDU1eHAFtH74llv+vHfrx4xickRkiSRPC2V7390N+c/cDkRaVG9XmewGonPHVnvXThGjJz4mdFqPOF+9YHqIe3v9xW9Wc+MK3o/Bl0QhFNzOd3UlPh2vdZld53JvIuH5wLY/jAEGZn+3blM/+5cnHYHHpeHHe/mseqxT2mrbcEYbEKlGj3frz2BX33Cq0RyorD9a/uYSFZY/IR4tAat0mEIwrAUmRDKpEWZrHkz7/QXD9LZ1872WduBRmvQAd27cyZdMo2tr28geWaawlEJvjR60s4A4epysfKvKwHY8k4euz/bpXBEfRDbhwVhSHLmD66oWH+5XQG0RNKPdCY9M69fQPS40bCF+BjZB7dAJpITP1Pr1Kx9Zg07Pt7BNy8Hbgn75sompUMQhGFt+jLf7p55+Hsvcmj3qWufCMJwJaZ1/EySJEITQnnlh//G0eFQOpw+xU+IVzoEQRjWLKFmDGYd9nbf/J4X5JWwbdU+xmSPrhGE0UiWlTlbR0li5MTP3E43zi5nQCcmABPPyVE6BEEY1nQ6DefePN+nfYSLOkSjhkJn6yhGJCd+ptaqA74cvFqrJkckJ4IwZCt+cS46Hy4sf+SWl3n5/g9pqum92qwgDFdiWkcBk86bREV+xekvVIjb6aamqIYYcZiWIAyJ3qhjyXdm8MlzX/ukfYfdyX/+/AlvPfY5E+dnEBptxeVwozNqsdiMVBTWcf39FxCf3nutEGH4ULIImxJEcqKA8DERSodwWq01LSI5EQQvOOva2T5LTo5wdrnY8nl+r8/lfb6HX7xyM9PPHt7l7YXRRUzrKCDvrc1Kh3BKGr2G2FFx0qcg+F765ESSximX6Du7XDzxo/+QvyEwj8kQTk9GrDkRfGz/2v0BfdgfwOwVszGFmJQOQxBGBEmS+MuXd/HjJ1dgtOgViaGuvImfnvEID97wPAVbShSJQRgaWfb+LZCJaR0/qj5QzYu3vsAQTpH2C0mtGlVloQXB10xWI2d+dxZ6o44/X/ecIjHIsszqN/JY/UYeU88cz9nXzSFhbDQxKRFotIF5vpcweonkxE86mjp49LxHsLfalQ7ltA58XaB0CIIwIs2/ZDIHtpbwzuNfKhpH3sp88lZ2r1GZe1EuP3/5JkXjEU7Pw+haECu+HvuJwWpg2U+XKR1Gv6TNTlc6BEEYkSRJ4qY/XMzs5ZOUDuWo9e9t56Nn1uF2B/oqBGE0EcmJn6hUKnRGZeabB8IQZODsu85WOgxBGNEuv/tMpUM4yuOR+fuPX+POBQ/y2YvrKd4TuGUORiuZ7lOJvX0LZCI58ZOiTUW8/tPXlA7jtOZeNxeTTSyGFQRfypicROb0MUqHcYKD20v56w9eYWuAL9gfrUbbgliRnPhJYk4ipuDA/6OfOkscQy4I/nDOjXOVDuEkZpuRc24IvLiE0UckJ36i0Wv4zZbfkpCToHQop1S2sxS3y610GIIw4s1YNkHpEE5y4Q8WYTAH/vTz6CPh8cEtkInkxI90Rh0zvzNL6TBOqbG8kZqiGqXDEIQRLyjUHHBTO2PEaeRCgBDJiR8d2nyI1U+vVjqMPmUuzOSyP11OTIYoWy8I/hCfHql0CCeITg5TOgShNz5YbyLWnAh4PB42vraBJy77G7V+GpUwBZsYM21g38rmXDM34E9MFoSRxBZuUTqEE7z5l5UBXyRSGB1EETYfczlcaHQadCY9QWFBNFU2+aSfudfNY+KyCbxz3zskTkrksj9djtagpf5wPW31bexeuYt1z3+NvaWzzzZiMsWIiSD4U1BoYCUna97aQnJ2HFfcfZbSoQjHOXK2zmgikhMf6GzpZN/qvexdtY8pl0xh7Nyx5C7PpavNzgd/+ID2hnav9SWpJK5+fAVTL54KwO1v30FLdTNagxaAsMQwwhLDSMpNIuecSaz+1+o+Dx7cv3Yf4clipb4g+EtIVJDSIZxAo1Uz+/wcpcMQehHodUm8TUzr+EDe23n85yf/YdPrG3F2OI8+nn3WBK58+Cqv9rXg5gVHExMAS5ilzxOF4yfEs+LxFSy9YylGmxEAfc/KfLVWTebCcbQ3ey9xEgTh1GzhgZWcLL5qOgljo5UOQxDEyIkvzLt+HiVbi8l7O+/oqqPaQ7WEJYVhsBpY/P3FfPXkV17pK2V66oBfc9695zP/xgV43B5s0Tbev/99zvv5eag14vAvQfCn7NmpGC16Otu6lA4FgBnnBN72ZqHbKBs4ESMnvvKdx67mJ5/cxbjF46krqeMvZz/ML7J+zju/fJupl00jMTfJK/2U7Sod1OuskVaCY4KRJImFty6kqzMwPhwFYTQxWY1MPTNL6TCOWv1GHh2nWJcmCP4ikhMfUalVJOYkotaqCQoPwtHpoLO5k8p9lax7bh1zrpnDefeeR1DE0IZ1P3/8cwo3HBxSG7ZIG6agwK9eKwgjUc6CDKVDOGrdO1s5tLtc6TCEb+k+W0fy+i2QieTED/RmPakzjk2/pM5IYcYVM1h6xxn8bvv9TDo/d9BtB8cEEzMu1hthCoKggClLx6PrWcAeCO4993Eeuy76HYkAABjXSURBVO3fVB6qUzoU4TiizongE5c/eMXRHTQJOYlHH5ckifI9g/+mknNuDkarccjxCYKgjKikMB744I6ASVBcTjc5CzKIGROudCjCKCaSEz+JGBPBtMumkT43nYiUiBOeO/snZxEcEzyg9lQaFVc/voILfn2hKJwmCMNc1qxUzr9lvtJhHJU1WxwAGmg8PrgFMrFbx48u/eNlqFQqXE4XKtWxvHDyRVPIXDSOX2b/4mh1xvAx4dT1MawakRLJtU9eS7w4B0MQRowr/28ZlhAzHz+zjtqyRp/0IUkSkYmhJGZGExJtY9uXe0/qa9kNc7HYxGisoCyRnPjRkYREoz3xP7skSRTnHUKWZWLHx3Lmj89i/JLxVO6r4J1fvcvhbSXIPRV4MuaP5eYXbj46RSQIwshgthm54u6zWH7rAi6Jvstr7UYnhzF7+SQmLRrL+BkpmI6bBi7eU8GqNzaTOS2ZDR/tJD03iXNvnidGYwOMTOCvEfE2kZwEiOixMVz71HWkTE/BFm0DICk3mdvfvJ22xlb+++v/MuPKmWQuzDxh1EUQhJFFb9J5pZ1JizK59M6l5C7K7DPZSM6K5frfXgBA7uJxGLzUt+B9gT4N420iOfEjt9ONWtt7obOwxDAsYRZ03/pw0Bq0hMSEcv0/b/BHiIIgKEySJFQqCc8g65Wn5SZy8x8vZsLc9AG9TiQmQiARyYkf9ZWYHHGklLwgCP7n6nKi0qhRqZUdmaw53DCoxGTexZM585pZTF4yTkzLjDSyOFtHEARh1OlobOO5Cx5ky7/XKh0Ka9/ZOuDXzDxvIve8eANTlo4XiYkwIoiRE0EQRj1TiIULH72OsNQoReOwdzh4/x+rBvSasBgb9754g0hKRjCZ0Xe2jkhOBEEQgP9v786jo67uPo5/7kwmZCEQQkIISYCwKYnsi+AGCloQl4qiUqtoETlWrbZ2sS6nPX26uPVRn7pV664VrSta3OtGo4hBZA8KIZAQIAECCSHLzNznj0RKSoCEzMzvR+b94swx85vf3PsdOSTffO/WY3DLp3mHmrVW1TtrtK1kh3ofmyFfbOO34aIVpbrv2ue0vWxXq9vyeIyuvW+mfJ1YvdfRRduwDskJEGVWzv9Sq/+5ROc/dKUMK78iKuAP6O4rn9LHLxVIkmJ8XvUfni1rrdYvK5W/3t+m9vrk9tKYKe45OBAIFZITIMocO3WEBk8bSWKixkmwMWGuOtRU1erDFxZryfurtXzhWlVX/ufUX39DQIWLNxxx20UrSvXSPe/rwhvPCEGkcDP2OQHQoR1u1Vg0KMovVO+xAxQMhnf3iF3lVbr2hD+1aaimrfwNgbC1DTiF5ARA1Omc1kXeGK+8MeFN1LZu3BH2pckla7eEtX04zyr6NmGjrgvgqFFdvltr31sme4iKR82Oam3IL1Tp0g37zqr6b2kDM8IVYjODRvXRPR//QiMnDQ5bHys/Wxe2tgGnkJwAcJWgP6DSr4pafM0X59Omxd+qsmTHQd+/4bO1evW6x/Xsxfcq0MYJpuGQkt5Vv3v1x5o25xR5PKFf7rt9866DJmHoOII29A83Y1gHgKv46/2qLNmuzBE5B7zWKSlek26efsj3504bqUGTh2j9J6vljY1R1dZK1e+pU9fMlLBPfj0Yj8ejq26frg9f+EI1u2tD27i1CgaCYR+igrNcnkuEHJUTAK4Sm9BJeWePblcbMZ18GnT6UNlAUE+ce6f+Ovl/tLusMkQRHpnF76wMfWIiKRi0KlpRGvJ2ASeRnADosKyV0nOzlZiapLqqvYd/Qxh9egTb0rfWtk07w9Y2nGfFsA4AdBhen1cXPX61lr+ySCl90xyLY21BcViTk7SsbmFrG3AClRMAHV7Nzj1a+kK+gv7I7wnSUNeg2y9//IhOGm6Nmb+aqoEjeoelbbiEbawChvrhZiQnADq8jCG9VfDMJ1r6Qn7E+86f/7W2FFWEvF2P16Nr7r1Yl952VsjbhvsEw/BwM4Z1AHR4vccO0FXv3hrxftcvL9Gf5z4T8na790rWTx/+oUaeFr79UwAnkZwAiApOLCNe+2Vxmw/zO5guKYkad9ZQjZw0WGOnHKe4xE4haRfu992E2GhCcgIAbbCtcLMWP/Gh0o7ppZE/OOmgSc+OLbs07663293fydNHavpPJqn/sGzFcC4SogTJCQA0KVmyXlkj+7X4WqAhoIX3v6XP//r+vp1nF963QCMvPUWDp45Qem6Wamvq1VDn13vP5OvFu9/V7h172hXPxAtH66cPXypfLN+qo12UFU5ITgCo8awaY2RM6LdXP5psXVmizBE5+/4/WGtVuWm7Vrz6hZa/skiVm7bvuzc+OUGzXv65uvVN03vPfKZ/3fiSln36TchiGTPlON34yGXs/ApJDOsAiCI7isu1/dsteu93L+mkn0zV0PPHOR2SY/x1DSp852vV7t4rY6TNy4pVXrhZO4tbXmkz4LTj1K1vmgL+oO798XMhjWXMlON0y7NXkpggapGcAEehte8vU9nyjRo0aYgyhvY57P3+uga9fPXftLO4XDknHasRM0/U5q+Llf/gO/uqAW/+4lkVLSzUuffMCnf4YWGDQRnPf3ZHqNpaqYpvtuhft7+mcXMnH3JLfH9dg9785bPakF+oDfmFreovPTdbkuSN8ahX/zRtXlfevg/QZMz38nTrc1fK59A5QHAnt+9LEmokJ8BRKHvMAL11yzxVlVVq2u3ZzX4ot2TNO0tV9O81CjYEtKNomwqe+aTF+zZ+EbphiUiqr6nTP+b8VR6vR3HJCSopWK+q/c7Sef36J1W0cI26ZnZXQ02dxlw+UUk9kxUMBLXh32u08C9vq6Rgfav7O2bKcI25YuK+4Z9Lbj5Td81+qt2fY8KM0brhwUtITBD1SE6Ao8jussYzVEqWFGlP+W4te+lzTbr5PMUnJx7yfRWFZQo2HH531NiETqou363OaV32XStdukGJqUlKzurevuDDpK5qr16Y/bBKvlx3yPuW/ePzfV+v/2S1+k/I1eq3vlLlxrZtkOaNjdEZv7mg2fycUJxtE9+5k37x2Cx5DpNoIvpYuX/TtFAjOQGOEtZaLXr0Ay1+8qNm1z+6a76m/P7iQ05mzRyZ06o+tq/bqocm/laZI3IUn5KoyuIKlS3fqOEzT9SZf5jZnvDDoqRgvd66dZ7KCze36X3b1pRq25ojO8l37BWnKik9ed/zqh179NRv5x9RW/vLHJhOYgI0ITkBXCzgD+iNnz0tGam8cLPK15YdcM9Xz/9bMkbj505WcnZqs9dKl27Qx3e/oeJFrR+uadhbf8C8i8pNFQo0BOR1eJ+Nuqq9WvHaYlWX79Y3HyzXtjWbIzoYnzWqn068bkqza0kpibrw52foxbvfbVfbx53Qv13vR8cWjLJJJyQngMOC/oA8LazKqKvaq9euf1LrPlp52Da++vtCrVmwRGf/eZaCgYC2rizRt/9aobLlG0MS44aFhXp25r06fvZpOmbK8FYtOQ76Ayp892tVbdml6m27VLu7RqfcMK1xCMoYbV1VotSBPRWb0Hyn02AgKFmrqq27lP/gO6qrrlXntC4qX1umsuUbVburJiSfqa36nTJY5z80R7742GbXrbVKTktqd/t5JwxodxvouKIrNSE5AcJmV+kOJaYmHXbb9Nd/+pTOuvOH8sXHKuj3yxMTo4Dfr/yH3m1VYvKdvZU1enH2Q+0N+6BKlxTplSWPqdfwvhpz+UT1PC5bXTNTDvh8eyqqtOK1L1Tw7KcHzOdYOi9fHp9XHq9H/toGSVJSRrK69UmTrFViWheVfV2sPdur1FBTH7bP0lY9j8vWufdefkBi8p33n1vUrvb75PbSuGlD2tUG0JGQnABhsvTFfHm8Xp38k6mHvG/aHZcoJq7pB3zTnANjPAddUeO0zUs36PUbnpQk9Z+Qqz4nDJINWFWWbFfpV0UqL9wse4gdo4INgWaTc6vKKputrHGbHoMzNfOZ6xTfNaHF140xuvXvc3T9KXeq6gh2hDXGaO6dF7CnCQ7KWjZhAxAi4+eerg35harfU6fYFg5pKylYr+WvLNKYK05VSk4PGa/ZNyGy+LO1qt9TF+mQ22zdx6u07uNVTocRNsm9U3XxEz8+aGLynZ59U3Xvx7/U83e8pQ+eWyTbyvkBHq9H1z/wAw2feEwowgU6DJITIExiEzpp0OShB309PiVRPXKzVPpVkdZ/ulpjrzhVklRdvlvzb3w6UmHiIHwJsZr+wGx17tG1Vfdn5KTqZw9fqktvO0ufzf9asXE+xcb71D0jWb36pykpJVHbirdr3bJNqt1Tr755vdQ1tbN69e8R5k+Co5+VjbJZJyQngEO656Sre056s2s1O6u15u2lyh7TX2sWfOVQZIhPTtDFT16jnnnZbX5vWmY3nXP1xBZf6z04Q70HZ7QzOkQbq+gb1mFRPeAiCd06K3VAz+jbq9plRs2a2KpjAQCEB5UTwGXSczM79DwOtxt+0Qk65foznQ4DaIYdYgE4ZufGCj1x3l2uWkYbTbJG9dNpvz7P6TCAqMewDuAi3Xqn6qzbL3E6jKiUmNZF593/I8V1iXc6FOAA1tqQP9yM5ARwmYGTh6j7wJ5OhxF1xlw+sdmZOYBbfHfwX6gfbkZyAriMMUZJrVy+itBZ/+lqp0MA0ITkBHCh7//fFUrPy3I6jKgyfu7pTocAHFS0DeswIRZwoYRunXXZiz9T5abtathbr88ffZ99T8IoMa2LMob2djoMAE2onAAu5YuPVdqgDPUa1kfT75+tjCH88AyXM34zQwndOjsdBnBQzDkB4ErT7rhEsZ3jml2LT05Qtz6pSumbpm59UmW8/JM+EoF6v9MhANgPwzrAUaLHsZn64fPXa8WrXyg9N0u9jx+orpkpze5pqK1XoN6v0iVF+udNz6l6226Hoj16ZI3qp34Tc50OAzioxu3rIz9HxBiTLelpST3VWGx5xFp7nzEmRdILkvpK2iDpQmvtTmOMkXSfpDMl1Ui63Fq75Ej6JjkBjiI987IPed6LLy5WvrhY9Z+Ypyte/6WennGPdpVsj2CER5e8c0br9N9coITkRKdDAQ7JoYP//JJutNYuMcYkSSowxrwn6XJJH1hrbzfG3CTpJkm/kjRV0sCmx/GSHmr6b5uRnAAdVFJ6ss78w8X6/NEP1GfcQFVXVKm0YL0kqWz5Roejc16vEX017Y5LFNPJ53QogCtZa8sklTV9XWWMWS0pU9K5kiY23faUpI/UmJycK+lp27gU6HNjTLIxJqOpnTYhOQE6sJyTByvn5MEHXF/1ZoHevnWeanfvdSAqdxg3ZzKJCY4aTk9gNcb0lTRC0iJJ6d8lHNbaMmNMj6bbMiVt2u9tJU3X2pycMHsOiEK5Z43SVe/dpi4Z3ZwOxTH11bVOhwA4LdUY8+V+j6tauskY01nSy5JusNYeaiKbaeHaEY1HkZwAUapzWhfNXvBrJfWMzi3bd2+pdDoEoFWsrIJheEiqsNaO3u/xyH/3bYzxqTExec5a+0rT5a3GmIym1zMkbWu6XiJp/0lxWZI2H8lnJjkBolh81wRNf2C2hs0Y73QoEeeva3A6BKB1bONqnVA/Dqdp9c1jklZba/93v5fmS5rV9PUsSa/vd/0y02icpF1HMt9EYs4JEPUyR+So1/C+6nviMVr/ySqteG2xbNDdW1uHQuP3XQCHcKKkSyUtN8Ysbbp2s6TbJb1ojJktaaOkGU2vLVDjMuJv1biU+Ioj7ZjkBICMMco7Z7Tyzhmt0bMmasHNf9fWlSVOhxVW5WuP6Bc6wBFOLCW21i5Uy/NIJGlSC/dbSdeEom+GdQA0kzGkt2Y+da2Ov/I0ZY3qp17D+siXECvj6ViVhrXvLdP9J92m/IfeVcW6LdqxYdvh3wQgIqicADhAQkpnTbp5+r7nwUBQJQXr9foNT6qqo0wktVa7N+/UR3fN16f3LVBsQqzOe+BK9R0/yOnIgGas9N0E1qhB5QTAYXm8HvUeO0Bz3r5FQy8YJ3Ww+RrBQFBDZ4xX8edr1VBb73Q4wAHCtFrHtaicAGi1uC7xOuvOHyptUIY++OOrTocTMr3HDtBJ102V1+dlYzbABaicAGiz0bMmasajc9VrWB+nQwmJratKVLurhsQELmXD8sfNqJwAaDOvz6uBk4Zo4KQhWvnGl1r1RoH8tQ3aubFClRsrnA6vzWp31ejzR9/X6MsmqHu/dKfDAaIeyQmAdsk7e7Tyzh4tSQr6A3r5mr/pm/eW73vdE+ORJ8Yrf22DOiXFq666VnLg+PfDWTovX9ljBqhrZoo83saYATeIxgmxJCcAQsYT49X5D85R0cI12ruzWnFdE9X3hEGK6eRT0B+QJ8ar+po6bf66WBXflOnbD1eq6NPVrtj0LVDv12vXPS6Pz6thM8Zr6u8vdjokIGqRnAAIKY/Xo/4Tcg+83lSJiE3opL7jB6nv+EEafdkE7SrdodULlmjJs5+qctP2SId7gJScHhp0+lCnwwD+w0hB4/S5xJHFhFgAjuqamaJxcyZrzju3aNxVk50OR/XVtSp8e6m2rSl1OhRgn2hbSkxyAsAVfHGxOu2m7+vce2bJ43NuvkfqwAz1m5CrooVrHIsBiHYM6wBwlbxzx6hTlwS9NPevCvojV8qO6eRTvwmDNW7u6coakROxfoHDaVz4G13DOiQnAFxnwKl5OuM3M/T2bS9EpL+sUf103l9+pKSeyRHpD8ChMawDwJVG/OAkDZ42Muz99D5+AIkJXI85JwDgAsYYnfHbGeqc3jWs/Uy+9XwSE7he0ARD/nAzkhMArpXYPUnj554etvZPvHaK0gZmhK19AEeG5AQIkW1rSvXszHu16G8faNOX65wOp8MI59DO8VeeJm8sU+/gbo3TYUP/x81IToAQqd9Tp60rS/TBH1/VMxfeowW3PO90SB1CYmqSZjw6V3HJCSFtNz0vSzFxsSFtE0Bo8CsDECJZo/pp+gOz9doNT2rvzj1aOi9fU353kTxefgdoD2OMBk4aop8tuVO7y3aqYW+9Ni1ep28/XKl1H61UoN5/RO364jsphqoJjhJur3SEGv8ygRDKOXmwzvzTD/TV8//WoMlDSExCrEtGN0lS937pGn7RCSpfW6a3bp2nkhaG0eK6JsiXEKuqssoW2yr5cp0K3/1ax5wxLKwxA+3HPicA2umYM4bxAy9C0gZl6MK/zVXZso3yxcdq+SuL1LC3XsdMGa5Bk4fIeDzasnKT/vmr57R1VYm6D+ipyo0V+6otxZ+t5e8KcCGSEwBHtbguCco56VhJjUNr/61nXrZmvXyj9lbuUVJ6sio3VeiJ8+7W3h3VWvfRKlVeuUPJmSmRDhtoNavoO/iP5ARAhxfTyaek9Ma9TJKzU3XRY1dr4xffaMzlp8rr4Dk+AFpGcgIg6vQa1ke9hvVxOgyglWzUTYhlth4AAHAVKicAALicVcDpECKK5AQAABezDOsAAAA4i8oJAAAuR+UEAADAQVROAABwNcuEWAAA4B5WDOsAAAA4isoJAAAuF22nElM5AQAArkLlBAAAV7MKMiEWAAC4hRXDOgAAAI6icgIAgKtZBW10DetQOQEAAK5C5QQAAJdjzgkAAICDqJwAAOBqnK0DAABcxEoKWoZ1AAAAHEPlBAAAV7NMiAUAAHASlRMAANzMSjbKNmEjOQEAwMUaB3UY1gFwFPrX7a+psmS702EAQLtROQE6iNNu+r7TIQAIE8tSYgAAAOdQOQEAwNXYIRYAALgMwzoAAAAOonICAICrsUMsAACAo6icAADgYlbRt0MslRMAAOAqVE4AAHA1y2odAJFlgy1/07HBoHZs2CYbDCroD8haG+HIALiFVTDkDzejcgI4zHha/h3BX+fXzuIKrXqzQGsWLFX22P763m8vjHB0ABB5JCeAS/niY9V/Qq76T8jVSddOVaAhuibEAWhi2YQNgEt5fV6nQwCAiKByAgCAq0XfJmwkJwAAuBj7nAAAADiMygkAAK5mpSgb1qFyAgAAXIXKCQAALhdtS4lJTgAAcLXoW63DsA4AAHAVKicAALgelRMAAADHUDkBAMDtomxCLJUTAADgKlROAABwtehbrUNyAgCA60VXcsKwDgAAaJExZooxptAY860x5qZI9UvlBAAAt7M24l0aY7ySHpB0uqQSSYuNMfOttavC3TeVEwAA0JKxkr611q631tZLmifp3Eh0TOUEAABXs7KKfOVEUqakTfs9L5F0fCQ6blNyUlBQUGGMKQ5XMAAAuEwfpwOQ9I7kTw1Du3HGmC/3e/6ItfaR/Z6bFt4TkSypTcmJtTYtXIEAAIADWWunONR1iaTs/Z5nSdociY6ZcwIAAFqyWNJAY0yOMSZW0sWS5keiY+acAACAA1hr/caYayW9I8kr6XFr7cpI9G2sA8uTAAAADoZhHQAA4CokJwAAwFVITgAAgKuQnAAAAFchOQEAAK5CcgIAAFyF5AQAALgKyQkAAHCV/wduZ4nToFwHFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "region_plot(regioni , '2020-11-21', save = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save map images into a gif "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "images = []\n",
    "for d in date_range[-7:]:\n",
    "    day = str(d.date())\n",
    "    im  = Image.open(f'./images/it_cv{day}.png')\n",
    "    images.append(im)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "images[0].save('./images/sample.gif',\n",
    "               save_all=True, append_images=images[1:], optimize=False, duration=700, loop=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Positives / Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "positivi = regioni.groupby(['data','denominazione_regione'])['nuovi_positivi'].sum().unstack()\n",
    "tamponi = regioni.groupby(['data','denominazione_regione'])['tamponi'].sum().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_im_positivi():\n",
    "    M = tamponi.iloc[1:,:].values-tamponi.iloc[:-1,:].values\n",
    "    M = np.insert(M, 0, M[0,:], axis = 0)\n",
    "    pos_test = 100*(positivi/M)\n",
    "    pos_test.replace(to_replace= np.inf , value = 0 , inplace = True)\n",
    "    reg = pos_test.columns.values\n",
    "    labels = [str(p)[8:10]+'-'+str(p)[5:7] for p in pos_test.index.values[::30]]\n",
    "    plt.subplots(figsize = (18,5))\n",
    "    plt.yticks(ticks = np.arange(21) , labels = reg)\n",
    "    plt.title('Number of positives over Tests in %')\n",
    "    plt.xticks(ticks = [0,30,60,90,120,150,180,210,240],labels = labels )\n",
    "    plt.legend(labels = [0,50])\n",
    "\n",
    "    plt.imshow(pos_test.T ,aspect = 'auto', vmin = 0 , vmax = 50 , cmap = 'viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_im_positivi()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deceised / Hospitalized for each region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "deceduti = regioni.groupby(['data','denominazione_regione'])['deceduti'].sum().unstack()\n",
    "hospitalized = regioni.groupby(['data','denominazione_regione'])['totale_ospedalizzati'].sum().unstack() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "deceduti.iloc[1:,:] = deceduti.iloc[1:,:].values - deceduti.iloc[:-1,:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deceased_hospitilzed(df, day = '2020-11-01' ,save = True):\n",
    "    ''' '''\n",
    "    \n",
    "    \n",
    "    df.index = pd.to_datetime(df.reset_index().data)\n",
    "    df = df[day].unstack()\n",
    "    df.name = 'dec'\n",
    "    df = italy.merge(df, on ='denominazione_regione')\n",
    "    gdf = gpd.GeoDataFrame(df, geometry = df.geometry)\n",
    "    #gdf['totale_casi per 1m abitanti'] = 1e6*gdf['nuovi_positivi'] // gdf['popolazione']\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize= (12,8))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.title(f'Deceased over Hospitalized \\n {day}')\n",
    "    \n",
    "    gdf.plot(column = 'dec',cmap = 'Oranges',vmax = 5,vmin = 0,legend =True,ax = ax)\n",
    "    if save:\n",
    "        plt.savefig(fname = f'./images/it_cv{day}.png' ,format = 'png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For every 100 hospitilized how many die on the same day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = 100*deceduti/(hospitalized+1)\n",
    "deceased_hospitilzed(df, day = '2020-11-20' ,save = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}