project-python-data-toolkit-glimpse.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 結合 Python 基礎資料分析套件的小專案\n",
    "\n",
    "> 使用 numpy、pandas 與 matplotlib 抓出累計確診數趨勢與每日新增確診數。\n",
    ">\n",
    "> 標籤：程式設計，獲取載入，整併轉換，摘要探索\n",
    "\n",
    "郭耀仁 <yaojenkuo@datainpoint.com> from [DATAINPOINT](https://datainpoint.substack.com/welcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 載入專案需要使用的套件\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TL; DR\n",
    "\n",
    "在這個專案中，我們打算使用 numpy、pandas 與 matplotlib 抓出確診數趨勢與每日新增量；首先使用 pandas 將約翰霍普金斯大學 COVID-19 Data Repository 中的時間序列資料 time_series_covid19_confirmed_global.csv 載入、接著使用 numpy  計算每日新增確診人數、最後使用 matplotlib 繪製累計確診數趨勢與每日新增確診數。透過這個專案，我們能夠暸解如何整合運用三個 Python 的基礎資料分析套件。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 取得時間序列資料\n",
    "\n",
    "時間序列資料 time_series_covid19_confirmed_global.csv 可以從約翰霍普金斯大學的 COVID-19 Data Repository 取得；使用 `pandas` 套件中的 `read_csv()` 函式載入為資料框。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(266, 242)\n"
     ]
    }
   ],
   "source": [
    "request_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n",
    "time_series = pd.read_csv(request_url)\n",
    "print(time_series.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Albania</td>\n",
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       "      <td>48734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</td>\n",
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       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
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       "<p>5 rows × 242 columns</p>\n",
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      "text/plain": [
       "  Province/State Country/Region       Lat       Long  1/22/20  1/23/20  \\\n",
       "0            NaN    Afghanistan  33.93911  67.709953        0        0   \n",
       "1            NaN        Albania  41.15330  20.168300        0        0   \n",
       "2            NaN        Algeria  28.03390   1.659600        0        0   \n",
       "3            NaN        Andorra  42.50630   1.521800        0        0   \n",
       "4            NaN         Angola -11.20270  17.873900        0        0   \n",
       "\n",
       "   1/24/20  1/25/20  1/26/20  1/27/20  ...  9/6/20  9/7/20  9/8/20  9/9/20  \\\n",
       "0        0        0        0        0  ...   38398   38494   38520   38544   \n",
       "1        0        0        0        0  ...   10255   10406   10553   10704   \n",
       "2        0        0        0        0  ...   46364   46653   46938   47216   \n",
       "3        0        0        0        0  ...    1215    1261    1261    1301   \n",
       "4        0        0        0        0  ...    2965    2981    3033    3092   \n",
       "\n",
       "   9/10/20  9/11/20  9/12/20  9/13/20  9/14/20  9/15/20  \n",
       "0    38572    38606    38641    38716    38772    38815  \n",
       "1    10860    11021    11185    11353    11520    11672  \n",
       "2    47488    47752    48007    48254    48496    48734  \n",
       "3     1301     1344     1344     1344     1438     1438  \n",
       "4     3217     3279     3335     3388     3439     3569  \n",
       "\n",
       "[5 rows x 242 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_series.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 轉置時間序列資料框\n",
    "\n",
    "從原始載入的資料框中我們可以觀察到日期資訊擺放在欄位名稱，這是一種被稱為寬格式（wide-format）的外型，使用 `pandas` 套件中的 `melt()` 函式可以轉換為長格式（long-format）的外型，讓日期資訊可以轉置到資料列中，改以日期與確診數作為欄位名稱。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(63308, 6)\n"
     ]
    }
   ],
   "source": [
    "id_cols = time_series.columns[:4]\n",
    "time_series_long = pd.melt(time_series, id_vars=id_cols, var_name='Date', value_name='Confirmed')\n",
    "print(time_series_long.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.93911</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>1/22/20</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
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       "      <td>1/22/20</td>\n",
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
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       "      <td>1/22/20</td>\n",
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       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
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       "      <td>17.873900</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "  Province/State Country/Region       Lat       Long     Date  Confirmed\n",
       "0            NaN    Afghanistan  33.93911  67.709953  1/22/20          0\n",
       "1            NaN        Albania  41.15330  20.168300  1/22/20          0\n",
       "2            NaN        Algeria  28.03390   1.659600  1/22/20          0\n",
       "3            NaN        Andorra  42.50630   1.521800  1/22/20          0\n",
       "4            NaN         Angola -11.20270  17.873900  1/22/20          0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_series_long.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 63308 entries, 0 to 63307\n",
      "Data columns (total 6 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Province/State  19278 non-null  object \n",
      " 1   Country/Region  63308 non-null  object \n",
      " 2   Lat             63308 non-null  float64\n",
      " 3   Long            63308 non-null  float64\n",
      " 4   Date            63308 non-null  object \n",
      " 5   Confirmed       63308 non-null  int64  \n",
      "dtypes: float64(2), int64(1), object(3)\n",
      "memory usage: 2.9+ MB\n"
     ]
    }
   ],
   "source": [
    "time_series_long.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 轉換日期時間型態\n",
    "\n",
    "目前 `Date` 欄位是文字型態，在資料排序時不一定會依照日期的先後，因此我們使用 `pandas` 套件中的 `to_datetime()` 函式轉換為日期時間型態。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.93911</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.15330</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.03390</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "  Province/State Country/Region       Lat       Long       Date  Confirmed\n",
       "0            NaN    Afghanistan  33.93911  67.709953 2020-01-22          0\n",
       "1            NaN        Albania  41.15330  20.168300 2020-01-22          0\n",
       "2            NaN        Algeria  28.03390   1.659600 2020-01-22          0\n",
       "3            NaN        Andorra  42.50630   1.521800 2020-01-22          0\n",
       "4            NaN         Angola -11.20270  17.873900 2020-01-22          0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date = pd.to_datetime(time_series_long['Date'])\n",
    "time_series_long = time_series_long.drop('Date', axis=1)\n",
    "time_series_long.insert(4, 'Date', date)\n",
    "time_series_long.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 63308 entries, 0 to 63307\n",
      "Data columns (total 6 columns):\n",
      " #   Column          Non-Null Count  Dtype         \n",
      "---  ------          --------------  -----         \n",
      " 0   Province/State  19278 non-null  object        \n",
      " 1   Country/Region  63308 non-null  object        \n",
      " 2   Lat             63308 non-null  float64       \n",
      " 3   Long            63308 non-null  float64       \n",
      " 4   Date            63308 non-null  datetime64[ns]\n",
      " 5   Confirmed       63308 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(2)\n",
      "memory usage: 2.9+ MB\n"
     ]
    }
   ],
   "source": [
    "time_series_long.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 計算每日新增確診數\n",
    "\n",
    "從約翰霍普金斯大學的 COVID-19 Data Repository 取得的時間序列資料 time_series_covid19_confirmed_global.csv 中記錄的是「累計確診數」，如果要求得每日新增確診數，必須將 `t+1` 日的累計確診數減去 `t` 日的累計確診數。使用 `numpy` 套件中的 `diff()` 函式可以完成這個計算，以台灣為例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  0,  1,  1,  3,  0,  1,  1,  0,  0,  0,  1,  0,  5,  0,  1,\n",
       "        1,  0,  0,  0,  0,  0,  0,  2,  2,  0,  1,  1,  2,  0,  2,  2,  1,\n",
       "        1,  0,  2,  5,  1,  1,  1,  0,  2,  1,  0,  0,  0,  2,  1,  1,  1,\n",
       "        3,  6,  8, 10, 23,  8, 27, 18, 16, 26, 20, 20, 17, 15, 16, 15,  8,\n",
       "       16,  7, 10,  9,  7,  8, 10,  3,  3,  1,  2,  3,  3,  5,  0,  2,  0,\n",
       "        0,  3, 22,  2,  3,  1,  1,  1,  1,  0,  0,  0,  0,  0,  0,  3,  4,\n",
       "        2,  0,  1,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "        0,  1,  0,  0,  0,  0,  0,  0,  1,  0,  0,  1,  0,  0,  0,  0,  0,\n",
       "        0,  0,  0,  0,  0,  0,  0,  0,  2,  0,  0,  1,  0,  0,  0,  0,  0,\n",
       "        0,  1,  0,  0,  0,  0,  0,  0,  1,  1,  0,  0,  0,  0,  0,  0,  0,\n",
       "        2,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  4,  0,  3,  0,  0,  4,\n",
       "        5,  0,  0,  0,  7,  1, -1,  2,  0,  1,  0,  2, -2,  0,  3,  1,  0,\n",
       "        0,  1,  2,  1,  1,  0,  0,  1,  0,  0,  0,  0,  0,  0,  0,  1,  0,\n",
       "        0,  0,  1,  0,  1,  2,  1,  1,  1,  0,  1,  2,  0,  0,  1,  0])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw = time_series_long[time_series_long['Country/Region'] == 'Taiwan*']\n",
    "daily_increase = np.diff(tw['Confirmed'].values)\n",
    "daily_increase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 繪製累計確診數趨勢\n",
    "\n",
    "累計確診數趨勢圖是線圖，在 `pyplot` 套件中可以用 `plot()` 函式繪製，X 軸資料要傳入日期 `Date`，Y 軸資料要傳入累計確診數 `Confirmed`，以台灣為例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(14, 3))\n",
    "x = tw['Date'].values\n",
    "y = tw['Confirmed'].values\n",
    "axes[0].plot(x, y, label='Taiwan', color='red')\n",
    "axes[0].set_xlabel('Date')\n",
    "axes[0].set_title('Cumulative COVID-19 confirmed cases')\n",
    "axes[0].legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 繪製每日新增確診數\n",
    "\n",
    "累計確診數趨勢圖是長條圖，在 `pyplot` 套件中可以用 `bar()` 函式繪製，X 軸資料要傳入日期 `Date`，Y 軸資料要傳入每日新增確診數 `daily_increase`，以台灣為例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(14, 3))\n",
    "x = tw['Date'].values\n",
    "y = tw['Confirmed'].values\n",
    "axes[0].plot(x, y, label='Taiwan', color='red')\n",
    "axes[0].set_xlabel('Date')\n",
    "axes[0].set_title('Cumulative COVID-19 confirmed cases')\n",
    "axes[0].legend(loc='upper left')\n",
    "axes[1].bar(x[1:], daily_increase, label='Taiwan', color='darkorange') # 因為是每日新增，要扣除資料的第一天 2020-01-22\n",
    "axes[1].set_title('Daily COVID-19 confirmed cases')\n",
    "axes[1].set_xlabel('Date')\n",
    "axes[1].set_ylabel('Daily Increase')\n",
    "axes[1].set_ylim(0, None)\n",
    "axes[1].legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 自訂類別繪製指定國家的累計確診數趨勢與每日新增確診數\n",
    "\n",
    "寫一個類別 `Covid19TimeSeries` 把我們前面寫的程式碼包裝起來。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Covid19TimeSeries:\n",
    "    def get_ts_wide(self):\n",
    "        request_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n",
    "        time_series = pd.read_csv(request_url)\n",
    "        return time_series\n",
    "    def get_ts_long(self):\n",
    "        ts_wide = self.get_ts_wide()\n",
    "        id_cols = ts_wide.columns[:4]\n",
    "        time_series_long = pd.melt(ts_wide, id_vars=id_cols, var_name='Date', value_name='Confirmed')\n",
    "        date = pd.to_datetime(time_series_long['Date'])\n",
    "        time_series_long = time_series_long.drop('Date', axis=1)\n",
    "        time_series_long.insert(4, 'Date', date)\n",
    "        return time_series_long\n",
    "    def get_plot_data(self, country):\n",
    "        ts_long = self.get_ts_long()\n",
    "        country_df = ts_long[ts_long['Country/Region'] == country]\n",
    "        group_by_date = country_df.groupby('Date')['Confirmed'].sum()\n",
    "        date = group_by_date.index\n",
    "        cumulative_confirmed = group_by_date.values\n",
    "        daily_increase = np.diff(cumulative_confirmed)\n",
    "        return date, cumulative_confirmed, daily_increase\n",
    "    def show_plots(self, country):\n",
    "        date, cumulative_confirmed, daily_increase = self.get_plot_data(country)\n",
    "        fig, axes = plt.subplots(1, 2, figsize=(14, 3))\n",
    "        axes[0].plot(date, cumulative_confirmed, label=country, color='red')\n",
    "        axes[0].set_xlabel('Date')\n",
    "        axes[0].set_title('Cumulative COVID-19 confirmed cases')\n",
    "        axes[0].legend(loc='upper left')\n",
    "        axes[1].bar(date[1:], daily_increase, label=country, color='darkorange')\n",
    "        axes[1].set_title('Daily COVID-19 confirmed cases')\n",
    "        axes[1].set_xlabel('Date')\n",
    "        axes[1].set_ylabel('Daily Increase')\n",
    "        axes[1].set_ylim(0, None)\n",
    "        axes[1].legend(loc='upper left')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mtBB4xpftx3cX4YLeUVSjeVc8Zx1HOI+d5RX0h7GgMWEgAGLZslLW+j/EZSub2amECzRDKnja24FTY5lqZrZefP2Z2qfT4zl3O0K/1uG5vp48egOYZ2ZnmFmT+Fl0iYkthPLlrFhetCP0n6nIY0BbMzsxltlNzax7XNeU0AH+x3gx/OjMThWVE1WVfRamQcg0g/6e0M9F5Uue1FuCYqHz7dbAA2Y2kdAeMFPl2pDQ2XY7QhvJ2yq44ppa7j6A8APz34SRKaYCxwEPx03uJnQK+5xQI3F/LZ7re0Jnr9sJV0TmU3EV8F2EKtKvCCNivFZm/WCgs4XqzIfL7hw9Svh8vvGszoLu/hBwOTDcQhXqu4T+BhXZFxhFeO3fx+27Ea5+4e73E66UnURIqN4njOjRw91/6+gck5m7CLUPuTTv+p+ZzSN8JucQCrOqhgX+iPDjeU3CKFE/s7S2Y3PCqB/zgP8QRpcqd0hGd/8S2I1QWzCbkKhuElcfT/jsphCuNt4L3JHD66kPZxCqsl+Ln+0zxE6f7v4EYXSWZ+M2z1ZxrEMJtUUfEq4sZpLzawmf73eE7+WTWfusQPh/+prwvv2ZWMBU8b1rRihg5hC+97MI1fIikh9VnV+PAS6M25zH0mF9qxT7ILxJuGL/YiXbzSb8vlgIvB6fawyhnJkcNzuOUGbeQ7jg8SRhdKp9yhzrc8Koi6tQcZOybHPNbD6hTNiNMJpnZefxtQjlSabM+JlQ3mQcSqhFmEHo2L2Tl99RHXd/gNB3715CefQwYdTNBYSEZFfC+fUmoLe7f5jD68mreHFud0Kfl88I8d1OaOEBoeP+F3HdU1Tc74jYR2Qnwmv9htDs+i9x9amEZHgeoUzI/s1VWTlRYdlH+B30DOH78ypwk7s/V823QHJk+ezfY2ECvsfcvYuF9n0fufty7UDN7BZCh/A74+MxwJnuPi5vwYmIiEiqmdkdhE7Z/046FhGpP/VWg+LuPwCfZTXFMDPLXEl+mFB7gpmtRmjyNaW+YhMREZF0iRc596aeJ34UkeTlc5jh+whVYBuY2TQz60sYcamvmb1NqN7MzLI+mjB29/vAc8Bp2U16REREpHSY2UWE5ptXuvtnSccjIvUrr028REREREREqkMzyYuISF6Z2UkWJuF918zui6MUrW1mr5vZZDO738wyoxqtFB9Pjus7Zh3nrLj8o6xR5TCznnHZZNPcBCIiBU8JioiI5I2ZrUmY3LSbu3cBGhBmZr4cuMbd1yOMptM37tIXmBOXXxO3w8w6x/02JkwCfFMcorQBYcK1XQlzGhwYtxURkQJV25nPy7Xaaqt5x44d83FoERHJ0YQJE75z94omca1PDYEmZraQMB/TdGB7ls6JMRToT5hcrVe8D/AgcEOcd6AXYd6jXwkDrkwmzPQNMNndpwCY2fC47fuVBaRySkQkeRWVU3lJUDp27Mj48ePzcWgREcmRmX2RdAzu/pWZXQV8SZjz4SlgAjDX3RfFzaYR5hwi/p0a911kZt8DrePy7HmcsveZWmZ5d6qgckpEJHkVlVNq4iUiInljZi0JNRprA/9HmACvZ0Kx9DOz8WY2fubMmUmEICIiOVCCIiIi+bQj8Jm7z3T3hcB/gR5ACzPL1OK3A76K978C2gPE9c0JMz3/trzMPhUtX467D3L3bu7erU2bNLR8ExGR8ihBERGRfPoS2NLMVo59SXYg9A95Dtg3btMHeCTefzQ+Jq5/1sN4+I8CB8RRvtYGOgFvAOOATnFUsBUJHekfrYfXJSIieZKXPijlWbhwIdOmTeOXX36pr6esU40bN6Zdu3Y0atQo6VBERAqGu79uZg8CbwKLgLeAQcDjwHAzuzguy8wWPhi4O3aCn01IOHD398xsBCG5WQQc6+6LAczsOMKEvw2AO9z9vZrEqnJKRCQd8jJRY7du3bxs58PPPvuMpk2b0rp1a8JFtMLh7syaNYt58+ax9tprJx2OiBS7Dz+Ec86Bm2+G1Vev8WHMbIK7d6vDyIqGyikRkeRVVE7VWxOvX375pSBP+gBmRuvWrQv2qpqIFIh58+D00+H3v4cxY2DSpKQjKikqp0SK3IDC+98uVfXaB6UQT/oZhRy7iKScO9x3H2y4IVx5JfTuDR9/DDvumHRkJaeQz/WFHLuISLaS6ST/+eef06VLl2WW9e/fn6uuuorXXnuN7t2707VrVzbaaCP69++fTJAiUnrmzIF99oGDDoLf/Q5efRUGD65V0y4pXCqrRETqsZN8mvXp04cRI0awySabsHjxYj766KOkQxKRUvDKK3DggfD116Hm5KSToEGDpKOSjLpuDnJK7fp8qqwSkVJRMjUolZkxYwZt27YFoEGDBnTu3DnhiESkqH3zDRx5JGyzDTRsCC+/DKeequREKqWySkRKRTI1KCeeCBMn1u0xu3aFa6+t0a4nnXQSG2ywAdtttx09e/akT58+NG7cuG7jExGZPh1uvRWuvhp++SXUmJx3HjRvnnRkUgBUVolIqSiZGpSKOg+aGeeddx7jx49n55135t5776Vnz571HJ2IFLU334QDDoAOHeCCC2CHHeC992DAACUnsgyVVSIiSdWg1LCmozZat27NnDlzllk2e/bs38aLX3fddTn66KM56qijaNOmDbNmzaJ169b1HqeIFJF58+Doo2HYsJCIHH98eNypU9KRSUqprBIRKaEalFVXXZW2bdvy7LPPAuGE/+STT7LNNtvw+OOPk5mw8pNPPqFBgwa0aNEiyXBFpNB98AFssQUMHx4mXfzii9C0S8mJVEJllUhKaM6URJXUKF533XUXxx57LCeffDIA559/Puuuuy7nnHMOJ510EiuvvDINGzZk2LBhNFBnVRGpCXcYOjTUljRpAs88A9ttl3RUUkBUVolIqcspQTGzFsDtQBfAgSPc/dV8BpYPnTt35rnnnltu+fDhwxOIRkSKzqefwhFHwAsvQI8eofakXbuko5KaquWwwDWlskpESl2uTbwGAk+6+4bAJsAH+QtJRKTAuMMtt8Amm8Dbb8Ptt4ckRcmJiIhItVWZoJhZc2BbYDCAuy9w97n5DkxEpCC8/TZsv33o/L711vDOO9C3L6xQMl38REQKi/qXpF4uJejawEzgTjN7y8xuN7NVym5kZv3MbLyZjZ85c2adByoikipz5sA//gGbbQaTJoUalNGjoX37pCMTEZGaUvKSCrkkKA2BzYCb3X1TYD5wZtmN3H2Qu3dz925t2rQp90CZ0UcKUSHHLiJ1bOJE6NYNBg+G446DTz4JyUoFc1hI4Sjkc30hxy5SUJTE5F0uCco0YJq7vx4fP0hIWKqlcePGzJo1qyBPoO7OrFmzNGOvSKlzDzPBb7kl/PorvPgiDBwIrVolHZnUAZVTIiLpUOUoXu7+jZlNNbMN3P0jYAfg/eo+Ubt27Zg2bRqF2vyrcePGtFOHV5HS9cMPoZZk+HDYZRe4+26ooLZYCpPKKRGRdMh1HpTjgWFmtiIwBTi8uk/UqFGj32bCFREpKG+9BX//exhG+NJL4Ywz1Am+CKmcEhFJh5wSFHefCHTLcywiIuny009w8cVw5ZWw+urw3HOw7bZJRyUiIvmkPiaJ0yVAEZGy3OGhh6BzZ/jPf+Dgg8NwwkpOREQEQhKjRCZvcm3iJSJSGmbMCLPBP/44/P738PzzSkxERArBAINTyhnkQolEwVENiohIxosvwqabwjPPwNVXw5tvKjkREZGKKfnJCyUoIiJLlsDll8Nf/gIrrwyvvQYnnQQNVcksIlLSlIAkQqWviJS2WbOgd28YNQr23x9uuw2aNUs6KhERSSMlLPVCCYqIlK5XXw3DB3/7Ldx4Ixx9tGaDFxERSZiaeIlI6fn11zCfybbbhmZcr7wCxxyj5ERERCQFlKCISGl5+WXo0gXOOQf+9rfQEX7zzZOOSkREkqSmW6miBEVESseAAaHWZNEiePJJGDECWrRIOioRERElSVmUoIhI8VuyBE49Ndz23jtMurjLLklHJSIiSVEykGrqJC8ixW3hQujbF+6+G447DgYOhBV0bUZEpKgo4SgqSlBEpHjNnw/77QdPPAEXXwxnn62O8CIiIimnBEVEitOsWfDXv8K4cTBoEBx1VNIRiYhImmRqXU7xZOOQ5eTUzsHMPjezd8xsopmNz3dQIiK18v77sPXWMHEijByp5CRhZtbCzB40sw/N7AMz28rMWpnZ02b2SfzbMm5rZnadmU02s0lmtlnWcfrE7T8xsz5ZyzePZdTkuK+qyUQkd2oeljrVaYj9F3fv6u7d8haNiEhtjRwJ3bvD3LnwzDOw115JRyQwEHjS3TcENgE+AM4Exrh7J2BMfAywK9Ap3voBNwOYWSvgfKA7sAVwfiapidsclbVfz3p4TSIikifqKSoixWHRIjjzTNh33zDPyZtvwjbbJB1VyTOz5sC2wGAAd1/g7nOBXsDQuNlQIJNJ9gLu8uA1oIWZtQV2AZ5299nuPgd4GugZ1zVz99fc3YG7so4lIiIFKNcExYGnzGyCmfXLZ0AiItU2b17ob3L55fDPf8LYsbDmmklHJcHawEzgTjN7y8xuN7NVgDXcfXrc5htgjXh/TWBq1v7T4rLKlk8rZ/lyzKyfmY03s/EzZ86s5csSkcRV1DRLTbYKXq4Jyjbuvhmh6v1YM9u27AY68YtIImbNgh12gDFj4Lbb4OabYaWVko5KlmoIbAbc7O6bAvNZ2pwLgFjzkfdequ4+yN27uXu3Nm3a5PvpRESULNVQTgmKu38V/84AHiK0/y27jU78IlK/pk+HP/8ZJk2Chx6CI49MOiJZ3jRgmru/Hh8/SEhYvo3Ns4h/Z8T1XwHts/ZvF5dVtrxdOctFpJQNMCUHBazKBMXMVjGzppn7wM7Au/kOTESkUp99FvqYfP45jBoFe+yRdERSDnf/BphqZhvERTsA7wOPApmRuPoAj8T7jwK942heWwLfx6Zgo4Gdzaxl7By/MzA6rvvBzLaMo3f1zjqWiBS7NCQiST9/EcplHpQ1gIfiqI0NgXvd/cm8RiUiUpn334eddoKffw5Nu7p3TzoiqdzxwDAzWxGYAhxOuEA2wsz6Al8A+8dtRwG7AZOBn+K2uPtsM7sIGBe3u9DdZ8f7xwBDgCbAE/EmIpIOA0xzrVRTlQmKu08hDAspIpK88eOhZ09o1AheeCGM2CX1wszWAjq5+zNm1gRo6O7zqtrP3ScC5Q1Rv0M52zpwbAXHuQO4o5zl4wF9EUQkHZSQ1JqGGRaRwvH887D99tC0Kbz0kpKTemRmRxH6j9waF7UDHk4uIhERKVZKUEQk/dxh4EDYcUdo1y4kJ+uum3RUpeZYoAfwA4C7fwKsnmhEIiL1Rf1M6pUSFBFJt3nz4IAD4MQTYbfd4OWXNcdJMn519wWZB2bWkHoYGlhEpGiUl+Qo8SmXEhQRSa/Zs0OTrpEjwySMDz8MLVsmHVWpet7MzgaamNlOwAPA/xKOSUREilAuo3iJiNS/778PTbreey8kJrvvnnREpe5MoC/wDvAPwmhbtycakYhIfVAtR71TDYqIpM+vv8Jee8E77yg5SQl3X+Lut7n7fkA/4PU44paISP4oOShJSlBEJF2WLIHevWHsWBgyBHbdNemIBDCzsWbWzMxaAROA28zsmqTjEhGR4qMERUTS5bTTYMQIuOIKOPjgpKORpZq7+w/A3sBd7t6dcuYxERERqS0lKCKSHtdcA1dfDccfD6eemnQ0sqyGZtaWMOP7Y0kHIyIlRk29SooSFBFJhxEj4OSTYe+9Q6JiKoxS5kJgNDDZ3ceZ2TrAJwnHJCKSXkqqakyjeIlI8p58Eg49FHr0gHvugQYNko5IynD3BwhDC2ceTwH2SS4iEREpVkpQRCRZ990XOsV36QKPPgpNmiQdkZTDzBoThhneGGicWe7uRyQWlIhIsVBtyzLUxEtEknPjjaEjfI8eYdSuVq2SjkgqdjfwO2AX4HmgHTAv0YhEpLjpR3vJUoIiIskYMgSOOw723DM08WrePOmIpHLrufu5wHx3Hwr8FeiecEwiIlIbKU0Cc05QzKyBmb1lZhq9RURqZ8wYOOqoMFP8Aw9A48ZV7yNJWxj/zjWzLkBzYPUE4xERkZlGbJkAAB8PSURBVCJVnRqUE4AP8hWIiJSId98NI3VtuCE8+CA0apR0RJKbQWbWEjgXeBR4H7gi2ZBEpOSk9Ip/tQyw4ngdeZRTJ3kza0eozr8EODmvEYlI8fr6a9htN1hlFRg1Ss26Coi73x7vPg+sk2QsIiJS3HKtQbkWOB1YUtEGZtbPzMab2fiZM2fWSXAiUkR++AF23x1mz4bHH4f27ZOOSKrBzNYws8Fm9kR83NnM+iYdl4hISSmR2pcqExQz2x2Y4e4TKtvO3Qe5ezd379amTZs6C1BEisDPP4fO8JMmhQkZN9006Yik+oYQJmr8v/j4Y+DExKIRESlGJZB85CKXGpQewJ5m9jkwHNjezO7Ja1QiUjwWLoT99oMXXoC77w5NvKQQrebuI4g16e6+CFicbEgiIilSrMlFAq+rygTF3c9y93bu3hE4AHjW3Q/Je2QiUvgWLw6TMD7+ONx8Mxx4YNIRSc3NN7PWgAOY2ZbA98mGJCJSYOrzx34BJ0yaB0VE8sMdjjkGhg+Hyy+Hf/wj6Yikdk4mjN61rpm9DNwFHJ9sSCIiBaw6CUQBJxs1kdMoXhnuPhYYm5dIRKR4uMMZZ8CgQXDWWXD66UlHJLVgZg2AP8fbBoABH7n7wkp3FBGR+lcEyYxqUESk7v3nP3DllaEG5ZJLko5GasndFwMHuvsid3/P3d9VciIieVXej+wi+OEtuVGCIiJ168Yb4Zxz4JBD4PrrwVSgFImXzewGM/uTmW2WuSUdlIgUuJomHWlNVuo6rrS+zjyrVhMvEZFKjRoFxx8fhhS+805YQddAikjX+PfCrGUObJ9ALCIipW2AwSmevmPVESUoIlI3PvwwjNLVtSvcdx801OmlmLj7X5KOQUSkpFVVm5LCRKOmdHlTRGpv7lzo1QtWWgkefhhWXjnpiKSOmdmlZtYi63FLM7s4yZhERKQ4KUERkdpZsAD23x+mTIGRI6FDh6QjkvzY1d3nZh64+xxAs26KiBSKAVYwfVqUoIhIzS1ZAkccAU8/HYYU/tOfko5I8qeBma2UeWBmTYCVKtleRCQ3BfTDWeqHEhQRqZklS6BfPxg2DC69FA4/POmIJL+GAWPMrK+Z9QWeBoYmHJOISHEq8YRNCYqIVN+SJXD00TB4MJx7bpiMUYqau18OXAJsFG8XufsVue5vZg3M7C0zeyw+XtvMXjezyWZ2v5mtGJevFB9Pjus7Zh3jrLj8IzPbJWt5z7hsspmdWTevWEREflPPtVxKUESketzhuOOWzhJ/wQVJRyT1xN2fcPdT4210NXc/Afgg6/HlwDXuvh4wB+gbl/cF5sTl18TtMLPOwAHAxkBP4KaY9DQAbgR2BToDB8ZtRUSkQClBEZHcucMJJ8DNN8Npp4VZ4jURY0kws73N7BMz+97MfjCzeWb2Q477tgP+CtweHxth/pQH4yZDgb3i/V4sbTr2ILBD3L4XMNzdf3X3z4DJwBbxNtndp7j7AmB43FZEClGJN22SQAmKiOTGHU4+OcwOf/LJcPnlSk5KyxXAnu7e3N2buXtTd2+W477XAqcDS+Lj1sBcd18UH08D1oz31wSmAsT138ftf1teZp+Kli/HzPqZ2XgzGz9z5swcQxcRkfqmBEVEcnPuuXDttfCvf8FVVyk5KT3fuvsHVW+2LDPbHZjh7hPyEFO1uPsgd+/m7t3atGmTdDgiUra2RLUn9aOu3uc8fl5VTvVsZo2BFwjDSTYEHnT38/MWkYikz003heZcRx4ZkhQlJ6VovJndDzwM/JpZ6O7/rWK/HsCeZrYb0BhoBgwEWphZw1hL0g74Km7/FdAemGZmDYHmwKys5RnZ+1S0XEREClAuNSi/Atu7+yZAV6CnmW2Z37BEJDUGD4Zjj4U99gh9T5SclKpmwE/AzsAe8bZ7VTu5+1nu3s7dOxI6uT/r7gcDzwH7xs36AI/E+4/Gx8T1z7q7x+UHxFG+1gY6AW8A44BOcVSwFeNzPFrbFysiIsmpsgYlFgw/xoeN4s3zGZSIpMTgwaHWpGdPGDECGlZ5ypAi5e51PdHNGcBwM7sYeAsYHJcPBu42s8nAbELCgbu/Z2YjgPeBRcCx7r4YwMyOA0YDDYA73P29Oo5VRCTdiqx5XE6/NuIwjhOA9YAb3f31vEYlIsnLTk4eeggaN046IkmAmV1PJRel3P1fuR7L3ccCY+P9KYQRuMpu8wuwXwX7X0KYi6Xs8lHAqFzjEBEpKkWWnECOCUq8StXVzFoAD5lZF3d/N3sbM+sH9APo0KFDnQcqIvXo9tvhqKOUnAjA+KQDEBGR0lKt9hruPtfMniNMkvVumXWDgEEA3bp1UxMwkUK0YAGcfjoMHKjkRABw96FVbyUiIgVlgMEp6f25XmUneTNrE2tOMLMmwE7Ah/kOTETq2VdfwV/+EpKTE0+ERx9VciIiklGEzWhE0iqXGpS2wNDYD2UFYIS7P5bfsESkXj33HBxwAMyfD/ffD/vvn3REIiIikrSEEvMqa1DcfZK7b+ruf3D3Lu5+YX0EJiL15I47YKedoFUrGDdOyYmUy8xaJx2DiBQB1UTlLo0TKtbT56eZ5EVKlTtceCH07Qs77ABvvAEbbZR0VJJer5nZA2a2m5kmw5ECoB/C6aXPpm4U8fuoBEWkFP36axil6/zzoXdveOwxaNo06agk3dYnDIRyKPCJmV1qZusnHJNI/ajOD8Ei/tEoBazAvpdKUERKzddfw3bbhXlOzj0XhgyBRo2SjkpSzoOn3f1A4CjCbO9vmNnzZrZVwuGJlK/AfpSJFIR6+L9SgiJSSp59FjbfHN55Bx58MDTxUmsdyYGZtTazE8xsPHAqcDywGnAKcG+iwUnpUgIikh8J/28pQREpBfPmwTHHhL4mzZrBa6/BPvskHZUUlleBZsBe7v5Xd/+vuy9y9/HALQnHJlI7asKVP3q/pAaqNVGjiBSgl16CQw6BL7+Ek0+Giy6ClVdOOiopPBu4e7mzern75fUdjIiIFC8lKCLFavFiuOQSuOAC6NgRXnwRevRIOiopMGb2P8Dj/eXWu/ue9R2TCFDxlXldsRcpeEpQRIrRu+/CP/8JL78cak9uvDE07RKpvquSDkAkbwYYnFJuxaDUBSWLhSNl/wtKUESKybx50L8/DBwIzZvDXXfBoYcmHZUUMHd/PukYRHKmH8Tpps9HcqRO8iLFYMkSuO8+2HBDuPpqOPxw+OgjJSdSa2Y2Iv59x8wmlb0lHZ+UoJr8yNUPY5GCohoUkUK2YAHccw9ccUVISDbdFEaOhC23TDoyKR4nxL+7JxqFSFWSTkKSfn6RIqIaFJFCtHhxSEw22AD69oUmTeD++2HcOCUnUqfcfXr8+0V5t6TjE6mxfCYUSlZEakUJikghWbQIhg6Fzp1D862WLWHUKHjzTdh/f2jQIOkIpUiZ2ZZmNs7MfjSzBWa22Mx+SDoukZwpaRApGEpQRArBlClw7rmw9tpw2GGhxuTBB2H8eNh1V80GL/XhBuBA4BOgCXAkcGOiEYnUJQ1bLJIaVSYoZtbezJ4zs/fN7D0zO6GqfUSkDvz0U2jGtf32sO66YU6TLl3gkUfgrbfCTPAr6BqD1B93nww0cPfF7n4n0DPpmEQSSSDKPmc+ZqIv5MSokGOXVMilk/wi4BR3f9PMmgITzOxpd38/z7GJlB730I/kjjvCqFw//ADrrAMXXwy9e0P79klHKKXrJzNbEZhoZlcA01EtvBSqqn5A53tOiJTNOSGSNlUmKLGDZKaT5Dwz+wBYE1CCIlJXpk4NTbbuuCNMstikCey3HxxxBPzpT6opkTQ4lJCQHAecBLQH9kk0IhERKUrVGmbYzDoCmwKvl7OuH9APoEOHDnUQmkgRcw/NtB59NNzeeiss794dbr0V/v73MNGiSEq4+xdm1ibevyDpeKQE1Uetg5om5YdqjApLCv4Pcr4sa2arAiOBE919uZFb3H2Qu3dz925t2rSpyxhFise778KJJ0KHDrD55nDhhbDKKmEekw8/hNdeg379lJxIaljQ38y+Az4CPjazmWZ2XtKxiaReLk3JCpUGFZA8yilBMbNGhORkmLv/N78hiRSZ774LtSI9esDvfw833wzdusGdd8K338KLL8Jpp4U5TUTS5ySgB/BHd2/l7i2B7kAPMzsp2dCk5NT2x28+OrPn6/kz21e2T13HWNXz5et5RcqosomXmRkwGPjA3a/Of0giReCbb0LTrZEjYcyYMLHihhvCgAGhs/tqqyUdoUiuDgV2cvfvMgvcfYqZHQI8BVyTWGQiaaAf6yJ1Lpc+KD0IBdQ7ZjYxLjvb3UflLyyRAjRvHowYESZSfOml0M9knXVC7cgBB8Af/qD5SqQQNcpOTjLcfWasXReRquSzD0YmQarq+LluJ6UtJQl3LqN4vQSkI1qRtFm8GF54AYYMCaNw/fRTqCnp3x/+9rcwb4mSEilsC2q4TkTyKSU/JH+TtnikoFVrFC8RARYuhLFjQ/Othx6CGTOgWTM45BA4/PAwEpeSEikem5jZcgOjEC5cNa7vYEREpPgpQRHJxZIl8NxzcO+98PDDMHt2GH1r993DjO5//SusvHLSUYrUOXdvkHQMIlJLNemcX1FTMNWUSD1QgiJSmS++CM237rwz3G/WDPbcE/bdF3beOUyoKCIipSfXfiXVHTlMfUREcp8HRaRk/PxzqCnZaSdYe2244AJYf324774wLPDdd0OvXkpORHJgZu3N7Dkze9/M3jOzE+LyVmb2tJl9Ev+2jMvNzK4zs8lmNsnMNss6Vp+4/Sdm1idr+eZm9k7c57o4+qRI8anPIYdFEqQERQRCv5LRo+GII6BtWzj4YJg8OXR2/+wzeOqpMBJXYzW5F6mmRcAp7t4Z2BI41sw6A2cCY9y9EzAmPgbYFegUb/2AmyEkNMD5hDlYtgDOzyQ1cZujsvbrWQ+vS+qLfnjXD73PkiJKUKS0vfEGHHUU/O530LNnGIlrzz3D3CWffgrnnQdrrZV0lCIFy92nu/ub8f484ANgTaAXMDRuNhTYK97vBdzlwWtACzNrC+wCPO3us919DvA00DOua+bur7m7A3dlHUuk8NQmUahJX5N8HVukFtQHRUrPggXwwANw/fXw+uuhs3uvXrD//rDLLqolEckTM+sIbAq8Dqzh7tPjqm+ANeL9NYGpWbtNi8sqWz6tnOUixUn9VKQEKEGR0uAOEyaEiRTvvjvM9L7++iFJ6dMHmjZNOkKRomZmqwIjgRPd/YfsbiLu7maW919cZtaP0GyMDh065PvppDZKaVLBJBKOss9Zn7U2IjlQgiLFyx0mTgxJyYgRMGUKNGwYmnIdd1zoBL+CWjmK5FuccX4kMMzd/xsXf2tmbd19emymNSMu/wpon7V7u7jsK2C7MsvHxuXtytl+Oe4+CBgE0K1btxL45VsE9ONXpCTp15kUn59+gltugY03hs02gyuvhE6dYPDgMArX//4XmnIpORHJuzii1mDgA3e/OmvVo0BmJK4+wCNZy3vH0by2BL6PTcFGAzubWcvYOX5nYHRc94OZbRmfq3fWsUQKwwCrOhmrbn8RJXdSwFSDIsVj+nS48Ua4+eYwkeLmm8Ott8Lee8NqqyUdnUip6gEcCrxjZhPjsrOBy4ARZtYX+ALYP64bBewGTAZ+Ag4HcPfZZnYRMC5ud6G7z473jwGGAE2AJ+JNCpV+WNedumo+ps9E6pkSFClsS5bA00/DbbfBI4/A4sWhw/vJJ8M224CmQxBJlLu/BFT0j7hDOds7cGwFx7oDuKOc5eOBLrUIU0TKUmd8SZASFClMc+aEmpLbboPPP4fWreFf/4Kjj4b11ks6OhERqQldqa8/ub7X+kwkAVUmKGZ2B7A7MMPddYVKkvXdd3DNNWH0rXnzYIcd4LLLYK+9YKWVko5ORERERGoplxqUIcANhMmvRJLx7bcwYADcdFPoBL/ffnDOOfCHPyQdmYiIiIjUoSoTFHd/IU6uJVL/PvgAbrgB7rwTfv0VDjwwJCYbbZR0ZCIiIiKSB+qDIumzeDE8/nhITJ5+OjTdOuggOOusMFywiIiIiBStOktQNEOv1NpPP4W+JTfdBF9+CWuuCRdfDP36QZs2SUcnIiIiIvWgzhIUzdArNeYOw4fDGWfA1Kmh4/u118Iee4SZ30VERESkZOjXnyRnyRJ46CH4z39gwoQw6/uwYfCnPyUdmYiIiIgkZIWqNjCz+4BXgQ3MbFqc9Vek5hYsCJ3eO3eGffeF778Pj8eNU3IiIiIiUuJyGcXrwPoIRErA/Plw++1w1VUwbRp07Qr33w/77AMNGiQdnYiIiIikgJp4Sf7NmRNG5Bo4EGbNgm23DTPA77ILmGaoFREREZGlqmziJVJj06fDaadBhw5w3nmw1Vbw0kvw/PPQs6eSExERCQaoPBCRpVSDInXv00/hiitgyBBYtAj+/nc480zN+i4iIiIiVVINitSdt98OM72vv35ITg4/HD7+GO69V8mJiIgsTzUnIlIO1aBI7b38Mlx6KYwaBauuCqecAiedBG3bJh2ZiIiknZIUESlDCYrU3JtvwjnnwJNPwmqrwUUXwbHHQsuWSUcmIiIiIgVKCYpU38cfw7//DQ88AK1ahf4mxxwDq6ySdGQiIiIiUuDUB0Vy9/XX8I9/hAkWR42Cc8+FKVPCSF1KTkREJFdq1iUilVANilRt1iy48kq47rowKtfRR4calDXWSDoyERERESkySlCkYrNnw9VXhwkW588PI3RddBGss07SkYmIiIhIkVKCIsubOxeuuQauvRZ++AH22w/OPx823jjpyERERESkyClBkaW+/Rauvx5uuAG+/x723jskJprDRERERETqiRIUCcMF33IL3HUXLFgAf/tb6ADftWvSkYmIiIhIiVGCUqq++AIefhiGDoW33oLGjaFPnzDJ4vrrJx2diIgUI43eJSI5yClBMbOewECgAXC7u1+W16ik7rnDxInwyCPhNnFiWL7JJqFJ18EHQ4sWycYoIiIiIiWvygTFzBoANwI7AdOAcWb2qLu/n+/gpIYWLoQvv4RJk5bexo2DqVPBDLbeOgwb3KsXdOqUdLQiIiIiIr/JpQZlC2Cyu08BMLPhQC+g7hOURYvg3nvDffely3V/+fuLFoWhf3/8MfydOzckIFOnwjffLN3WLCQhW24J/fvD7rvD6qsjIiJSbwYYnOJVbyciQm4JyprA1KzH04DuZTcys35AP4AOHTrULJqFC0M/CMlNgwaw6qrh1qwZtGsHXbpA+/bQoUO4v/HGmuVdRERERApGnXWSd/dBwCCAbt261ewyyUorwaefLn1spvsV3W/QAFZccdl1IiIiIiIFLpcE5SugfdbjdnFZ3VthBc1SLiIiIiJSwlbIYZtxQCczW9vMVgQOAB7Nb1giIiJSFDS0sIhUU5UJirsvAo4DRgMfACPc/b18ByYiIpIrM+tpZh+Z2WQzOzPpeEREpOZy6oPi7qOAUXmORUREpNo0HH4KqdZERGohlyZeIiIiafbbcPjuvgDIDIcv9UlJiYjUkTobxUtERCQhOQ2HL3Uok4yc4ssnJkpURKSWzL3uJ04ys5nAF3V+4MqtBnxXz89ZHWmPL6MQ4iyEGKEw4iyEGEFx1tRa7t4m6SDyzcz2BXq6+5Hx8aFAd3c/rsx2v83XBWwAfFSvgabv+5EtzbFlK4Q4CyHGjLTHmvb4shVKrGmLs9xyKi81KEkUiGY23t271ffz5irt8WUUQpyFECMURpyFECMoTqlSTsPhZ8/XlYQ0fz/SHFu2QoizEGLMSHusaY8vW6HEWihxqg+KiIgUOg2HLyJSRNQHRURECpq7LzKzzHD4DYA7NBy+iEjhKqYEJbFq+xylPb6MQoizEGKEwoizEGIExSlVKJDh8NP8/UhzbNkKIc5CiDEj7bGmPb5shRJrQcSZl07yIiIiIiIiNaE+KCIiIiIikh7unsiNMOLKc8D7wHvACXF5K+Bp4JP4t2VcfjAwCXgHeAXYJOtYPQnDRU4GzqzkOfvE434C9InLVgYeBz6McVyWpvji8ieBt2MctwAN0vY+lln/KPBuGmMExsb9J8bb6imNc0VCNezHhO/mPmmKEWia9R5OJAxZeG1K38sD43EnEf6XVktpnH+Px34PuLy651Td6v6Wlu8H5ZRTaYktLlcZVXfv5VgqKKNSGOty5VRa4qOKMiqF76XKqbIx5PPglT4xtAU2y/oifQx0Bq7IvGnAmZk3ANg6683fFXg93m8AfAqsE/9Z3gY6l/N8rYAp8W/LeL8l4cT/l6x/thfj8VMRX1zXLP41YCRwQNrex6z1ewP3suzJPzUxEk7+3dL8nYzrLgAujvdXIJ6s0hRjme0mANum7b0k9LObkfX+XQH0T2GcrYEvgTZxu6HADvV9XtYtnecEyi+nDkpDbHGdyqi6ey/HUkEZlcJYlyun0hRfme2WKaPS9F6icqr873q+DlztQOARYCdCZtc260P5qJxtWwJfxftbAaOz1p0FnFXOPgcCt2Y9vhU4sJztBgJHpTE+oBHwP+DvaXwfgVWBl+I/zrspjXEslZz8UxTnVGCVNMeYtWz9GK+lLU7C/8xMYC3Cj6dbgH4pjPOPwJis5YcCN+XyPdWt/m5p+H+Ly5crp9IQGyqj6rWMSkGsVZZTKfleVllGJRkrKqfKvaWiD4qZdQQ2BV4H1nD36XHVN8Aa5ezSF3gi3l+T8MXLmBaXlVXldmbWAtgDGJO2+MxsNCHDngc8WM7+aYjzImAA8FN58aUkRoA7zWyimZ1rZpa2OOP3EOAiM3vTzB4ws+WeMyXvJYQ5J+73eMZKU5zuvhA4mlDV/TXhh8ngtMVJqG7fwMw6mllDYC+WnXhQEpaW/7fyyqk0xKYyqs5ihBzKqKRjzaWcSsl7CVWUUUnHqnKqfIknKGa2KqFK+ER3/yF7XfwyeZnt/0J4w8+o4zgaAvcB17n7lLTF5+67EDLklYDty4k/0TjNrCuwrrs/VMk2aXgvD3b33wN/irdDUxhnQ8JM2K+4+2bAq8BVKYsx2wGE/53lJB2nmTUinPg3Bf6P0Hb2rLTF6e5zYpz3E5rvfA4srotjS+0l/f3IOu5y5VRaYlMZVWeqLKNSEmul5VQK4stWYRkVnzvp76bKqXIkmqDED2UkMMzd/xsXf2tmbeP6toQrMpnt/wDcDvRy91lx8Vcsm8G1A74ys+7xCsREM9uzou2yHg8CPnH3a1MaH+7+C6Fqr1f28pTEuRXQzcw+J1Shr29mY1MWI+6e+TuP0A55ixS+l7MIV/gyz/8AsFnKYswcexOgobtPoIyUxNkVwN0/jSfwEYT2uWmLE3f/n7t3d/etCFX3H5d9T6X+peX7ES1TTqUsNpVR9VBGpSjWCsuplMSXOXaFZVSKYlU5VR7PU9uxqm6EdnZ3sfyoCleybKefK+L9DoTqpa3LbN+Q0IFnbZZ2+tm4nOdrBXxGaI/XMt5vFdddTPjgV0hbfIQ2s22zjnU/cFza4iyzTUeW7YCYihjj/plOaI0IzRD+mbY447rhwPbx/mHAA2mLMa6/DLggrf/fhKtR01naqe8iYEDa4ozrVo9/WxJGnVm/7P661e8tZd+PZcqptMSGyqi6fC8rLaPSFGtct1w5lab44vpyy6g0vZeonCr//JuvA1f5xLANoTpqEkuHgduNMErAGMLQZs9kvSm3A3Oyth2fdazdCFncp8A5lTznEfFDmwwcHpe1i3F8kHXsI1MU3xrAuBjHu8D1hKsBqXofy6zvyLIn/1TECKxCGMkjM0TeQJYdDjMVccblawEvxFjGAB3SFmNcNwXYMK3/33H5Pwn/35MIHXhbpzTO+wjDSL5P1ihIuiV3S8v3g/LLqctSEpvKqHoqo9IUa1y+XDmVpvjiunLLqBS+lyqnytw0k7yIiIiIiKRG4p3kRUREREREMpSgiIiIiIhIaihBERERERGR1FCCIiIiIiIiqaEERUREREREUkMJipQMM1scJyJ6z8zeNrNTzKzS/wEz62hmB9VXjCIiUrpUTokESlCklPzs7l3dfWNgJ2BX4Pwq9ukI6MQvIiL1QeWUCGgeFCkdZvaju6+a9XgdwgRjqxEmnLqbMFEWhJmQXzGz14CNCDOpDgWuI0yQth2wEnCju99aby9CRESKlsopkUAJipSMsif+uGwusAEwD1ji7r+YWSfgPnfvZmbbAae6++5x+37A6u5+sZmtBLwM7Ofun9XrixERkaKjckokaJh0ACIp0Qi4wcy6AouB9SvYbmfgD2a2b3zcHOhEuHIlIiKSLyqnpGQoQZGSFavOFwMzCG18vwU2IfTN+qWi3YDj3X10vQQpIiIlS+WUlCp1kpeSZGZtgFuAGzy0c2wOTHf3JcChQIO46Tygadauo4GjzaxRPM76ZrYKIiIidUjllJQy1aBIKWliZhMJ1eSLCJ0Nr47rbgJGmllv4Elgflw+CVhsZm8DQ4CBhBFT3jQzA2YCe9XXCxARkaKmckoEdZIXEREREZEUURMvERERERFJDSUoIiIiIiKSGkpQREREREQkNZSgiIiIiIhIaihBERERERGR1FCCIiIiIiIiqaEERUREREREUkMJioiIiIiIpMb/A4wFMJPvv3vdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cts = Covid19TimeSeries()\n",
    "cts.show_plots('US')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('ggplot'):\n",
    "    cts.show_plots('India')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('ggplot'):\n",
    "    cts.show_plots('Brazil')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python Data Analysis",
   "language": "python",
   "name": "pyda"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}