code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Science for Novel Coronovirus\n",
    "\n",
    "11810206 孙涛 (Self Work)\n",
    "\n",
    "\n",
    "## 1. Background introduction:\n",
    "2019 Novel Coronavirus (COVID-19) has infected more than 1 million people in the\n",
    "world.We want to do something against this pestilence by using what we have learnt.\n",
    "This datasets consists of 11 CSV files from the KCDC (Korea Centers for Disease Control & Prevention), a structured\n",
    "dataset based on the report materials of KCDC and local governments was provided by Korean researchers.\n",
    "## 2. Data Analysis\n",
    "### 2.1 Data Statistics and Visualization\n",
    "#### 2.1.1 Data Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# needed imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from sklearn import manifold\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LassoLarsCV\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.linear_model import Ridge,RidgeCV  \n",
    "from sklearn.metrics import mean_squared_error\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy.cluster.hierarchy import fcluster\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "from scipy.spatial import distance\n",
    "from sklearn.cluster import KMeans  \n",
    "from sklearn import manifold\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": 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>patient_id</th>\n",
       "      <th>global_num</th>\n",
       "      <th>sex</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>age</th>\n",
       "      <th>country</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>disease</th>\n",
       "      <th>infection_case</th>\n",
       "      <th>infection_order</th>\n",
       "      <th>infected_by</th>\n",
       "      <th>contact_number</th>\n",
       "      <th>symptom_onset_date</th>\n",
       "      <th>confirmed_date</th>\n",
       "      <th>released_date</th>\n",
       "      <th>deceased_date</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000000001</td>\n",
       "      <td>2.0</td>\n",
       "      <td>male</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>50s</td>\n",
       "      <td>Korea</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Gangseo-gu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>overseas inflow</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000000002</td>\n",
       "      <td>5.0</td>\n",
       "      <td>male</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>30s</td>\n",
       "      <td>Korea</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Jungnang-gu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>overseas inflow</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000000003</td>\n",
       "      <td>6.0</td>\n",
       "      <td>male</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>50s</td>\n",
       "      <td>Korea</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Jongno-gu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>contact with patient</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.002000e+09</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000000004</td>\n",
       "      <td>7.0</td>\n",
       "      <td>male</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>20s</td>\n",
       "      <td>Korea</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Mapo-gu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>overseas inflow</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000000005</td>\n",
       "      <td>9.0</td>\n",
       "      <td>female</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>20s</td>\n",
       "      <td>Korea</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Seongbuk-gu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>contact with patient</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient_id  global_num     sex  birth_year  age country province  \\\n",
       "0  1000000001         2.0    male      1964.0  50s   Korea    Seoul   \n",
       "1  1000000002         5.0    male      1987.0  30s   Korea    Seoul   \n",
       "2  1000000003         6.0    male      1964.0  50s   Korea    Seoul   \n",
       "3  1000000004         7.0    male      1991.0  20s   Korea    Seoul   \n",
       "4  1000000005         9.0  female      1992.0  20s   Korea    Seoul   \n",
       "\n",
       "          city disease        infection_case  infection_order   infected_by  \\\n",
       "0   Gangseo-gu     NaN       overseas inflow              1.0           NaN   \n",
       "1  Jungnang-gu     NaN       overseas inflow              1.0           NaN   \n",
       "2    Jongno-gu     NaN  contact with patient              2.0  2.002000e+09   \n",
       "3      Mapo-gu     NaN       overseas inflow              1.0           NaN   \n",
       "4  Seongbuk-gu     NaN  contact with patient              2.0  1.000000e+09   \n",
       "\n",
       "   contact_number symptom_onset_date confirmed_date released_date  \\\n",
       "0            75.0         2020-01-22     2020-01-23    2020-02-05   \n",
       "1            31.0                NaN     2020-01-30    2020-03-02   \n",
       "2            17.0                NaN     2020-01-30    2020-02-19   \n",
       "3             9.0         2020-01-26     2020-01-30    2020-02-15   \n",
       "4             2.0                NaN     2020-01-31    2020-02-24   \n",
       "\n",
       "  deceased_date     state  \n",
       "0           NaN  released  \n",
       "1           NaN  released  \n",
       "2           NaN  released  \n",
       "3           NaN  released  \n",
       "4           NaN  released  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PatientInfo = pd.read_csv('PatientInfo.csv')\n",
    "PatientInfo.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Using the method duplicated in Pandas to detect duplicate values in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>patient_id</th>\n",
       "      <th>global_num</th>\n",
       "      <th>sex</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>age</th>\n",
       "      <th>country</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>disease</th>\n",
       "      <th>infection_case</th>\n",
       "      <th>infection_order</th>\n",
       "      <th>infected_by</th>\n",
       "      <th>contact_number</th>\n",
       "      <th>symptom_onset_date</th>\n",
       "      <th>confirmed_date</th>\n",
       "      <th>released_date</th>\n",
       "      <th>deceased_date</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [patient_id, global_num, sex, birth_year, age, country, province, city, disease, infection_case, infection_order, infected_by, contact_number, symptom_onset_date, confirmed_date, released_date, deceased_date, state]\n",
       "Index: []"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PatientInfo[PatientInfo.duplicated()] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result shows there is no duplicate values.\n",
    "2. See if there are missing values for each attribute of the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "patient_id               0\n",
      "global_num            1160\n",
      "sex                     94\n",
      "birth_year             464\n",
      "age                    105\n",
      "country                 90\n",
      "province                 0\n",
      "city                    76\n",
      "disease               3110\n",
      "infection_case         819\n",
      "infection_order       3097\n",
      "infected_by           2393\n",
      "contact_number        2539\n",
      "symptom_onset_date    2682\n",
      "confirmed_date           0\n",
      "released_date         2147\n",
      "deceased_date         3072\n",
      "state                    0\n",
      "dtype: int64\n",
      "3128\n"
     ]
    }
   ],
   "source": [
    "index_array = np.sum(PatientInfo.isnull() == True, axis=0)\n",
    "print(index_array)\n",
    "print(PatientInfo.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 3128 rows in the dataset PatientInfo.csv\n",
    "I notice that there are many miss values in many colums, and I decide first observe characteristics of the data by visualization and then deal with the miss value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Outlier detecting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>patient_id</th>\n",
       "      <td>3128</td>\n",
       "      <td>3562811488</td>\n",
       "      <td>2210168227</td>\n",
       "      <td>1000000001</td>\n",
       "      <td>1300000026</td>\n",
       "      <td>2000000576</td>\n",
       "      <td>6001000519</td>\n",
       "      <td>7000000009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>global_num</th>\n",
       "      <td>1968</td>\n",
       "      <td>6234</td>\n",
       "      <td>3297</td>\n",
       "      <td>1</td>\n",
       "      <td>3624</td>\n",
       "      <td>7437</td>\n",
       "      <td>9085</td>\n",
       "      <td>10329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>birth_year</th>\n",
       "      <td>2664</td>\n",
       "      <td>1974</td>\n",
       "      <td>20</td>\n",
       "      <td>1916</td>\n",
       "      <td>1960</td>\n",
       "      <td>1974</td>\n",
       "      <td>1993</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infection_order</th>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infected_by</th>\n",
       "      <td>735</td>\n",
       "      <td>2557285853</td>\n",
       "      <td>1645930848</td>\n",
       "      <td>1000000002</td>\n",
       "      <td>1200000031</td>\n",
       "      <td>2000000205</td>\n",
       "      <td>4100000006</td>\n",
       "      <td>6100000384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contact_number</th>\n",
       "      <td>589</td>\n",
       "      <td>18</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>1160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count        mean         std         min         25%  \\\n",
       "patient_id        3128  3562811488  2210168227  1000000001  1300000026   \n",
       "global_num        1968        6234        3297           1        3624   \n",
       "birth_year        2664        1974          20        1916        1960   \n",
       "infection_order     31           2           1           1           1   \n",
       "infected_by        735  2557285853  1645930848  1000000002  1200000031   \n",
       "contact_number     589          18          76           0           2   \n",
       "\n",
       "                        50%         75%         max  \n",
       "patient_id       2000000576  6001000519  7000000009  \n",
       "global_num             7437        9085       10329  \n",
       "birth_year             1974        1993        2020  \n",
       "infection_order           2           3           6  \n",
       "infected_by      2000000205  4100000006  6100000384  \n",
       "contact_number            4          14        1160  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PatientInfo.describe().astype(np.int64).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results show that there is no outliers\n",
    "#### 2.1.2 Visualization of PatientInfo\n",
    "1. Gender distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2296931a548>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PatientInfo['sex'].value_counts().plot(kind='bar',title='Gender distribution',fontsize=15, rot=360)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Age distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x229693ed588>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PatientInfo['age'].value_counts().plot(kind='bar',title='Age distribution',fontsize=15, rot=360)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Top ten cities with the most patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "city=PatientInfo['city'].value_counts()\n",
    "city= city.sort_values(ascending=False)[:10]\n",
    "city.sort_values(ascending=True).plot.barh(fontsize=15)\n",
    "plt.title('Top ten cities with the most patients',size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Top ten province with the most patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "province=PatientInfo['province'].value_counts()\n",
    "province= province.sort_values(ascending=False)[:10]\n",
    "province.sort_values(ascending=True).plot.barh(fontsize=15)\n",
    "plt.title('Top ten provinces with the most patients',size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Top ten infection_case of patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "infection_case=PatientInfo['infection_case'].value_counts()\n",
    "infection_case= infection_case.sort_values(ascending=False)[:10]\n",
    "infection_case.sort_values(ascending=True).plot.barh(fontsize=15)\n",
    "plt.title('Top ten infection_case of patients',size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. The relation between sex and state among patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22968175c48>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"state\", hue=\"sex\", data=PatientInfo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. The relation between age and state among patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x229694a0048>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3de3xV1Zn/8c8jdxQlQMBASANqLSlIgCA46VA0VS463IxcSiUIyIxjf6KMDrF1OkJvsdIRuYwKRER0REAwDCJWudeKNGKgKGgciCaYAgJRuVhIXL8/9s4xkhCONvucQL7v1+u8zt5rr73Pc85J8mStvfba5pxDREQE4IJoByAiIrWHkoKIiIQoKYiISIiSgoiIhCgpiIhISP1oB/D3aNWqlUtMTIx2GCIi55S33nrrE+dcbFXbzumkkJiYSG5ubrTDEBE5p5jZh2fapu4jEREJUVIQEZEQJQUREQk5p88piIh8U6dOnaKoqIgvvvgi2qEErnHjxsTHx9OgQYOw91FSEJE6paioiGbNmpGYmIiZRTucwDjnOHToEEVFRXTo0CHs/dR9JCJ1yhdffEHLli3P64QAYGa0bNnyG7eIlBREpM453xNCuW/zPpUUREQkRElBRERCdKJZznuvZA8847Z+41dHMBKR2k8tBRGRGjBkyBB69OjB97//febOnQtAdnY23/3ud+nbty+33347P/3pTwE4ePAgN998Mz179qRnz568/vrr0Qz9a9RSEBGpAU8++SQtWrTgxIkT9OzZkxtvvJFf/vKXbNu2jWbNmnHdddfRtWtXACZNmsQ999zDD37wAz766CP69evHrl27ovwOPIEnBTOrB+QC+5xzN5lZB2Ax0ALYBtzqnDtpZo2Ap4EewCFghHOuIOj4RERqwsyZM1mxYgUAhYWFLFq0iB/+8Ie0aNECgFtuuYX3338fgNdee4133303tO9nn33G559/TrNmzSIf+Gki0X00CaiYAh8CHnHOXQEcAcb75eOBI865y4FH/HoiIrXehg0beO2113jjjTfYvn073bp148orrzxj/S+//JI33niDvLw88vLy2LdvX61ICBBwUjCzeOBGYL6/bsB1wDK/ykJgiL882F/H355mdWUwsYic0z799FNiYmJo2rQpu3fvZsuWLRw/fpyNGzdy5MgRSktLeeGFF0L1b7jhBmbPnh1az8vLi0bYVQq6pTAD+HfgS3+9JVDinCv114uAdv5yO6AQwN/+qV//a8xsopnlmlnuwYMHg4xdRCQs/fv3p7S0lKuuuor/+I//oHfv3rRr146f/exn9OrVix/96EckJSVxySWXAF5XU25uLldddRVJSUk8/vjjUX4HXwnsnIKZ3QQccM69ZWZ9y4urqOrC2PZVgXNzgbkAKSkplbaLiERao0aNePnllyuVp6SkMHHiREpLSxk6dCg33HADAK1ateL555+PdJhhCbKlkAoMMrMCvBPL1+G1HJqbWXkyigc+9peLgPYA/vZLgMMBxiciEqgHH3yQ5ORkOnfuTIcOHRgyZMjZd4qywFoKzrn7gfsB/JbCvc650Wa2FEjHSxQZQI6/y0p//Q1/+zrnnFoCInLOmj59erRD+MaicfHaFGCymX2Ad84g2y/PBlr65ZOBzCjEJiJSp0Xk4jXn3AZgg7+8B7i6ijpfALdEIh4REamaprkQEZEQJQUREQnR3EciUqcdfOyZGj1e7B0/CateYmIizZo1o169etSvX5/c3FwOHz7MiBEjKCgoIDExkSVLlhATE1Oj8Z2NWgoiIlGyfv168vLyyM3NBSArK4u0tDTy8/NJS0sjKysr4jEpKYiI1BI5OTlkZGQAkJGRwYsvvgjAxo0bSU5OJjk5mW7duvH5558HFoOSgohIFJgZN9xwAz169Ajdf2H//v3ExcUBEBcXx4EDBwDveoc5c+aQl5fH5s2badKkSWBx6ZyCiEgUvP7667Rt25YDBw5w/fXX873vfe+MdVNTU5k8eTKjR49m2LBhxMfHBxaXWgoiIlHQtm1bAFq3bs3QoUPZunUrbdq0obi4GIDi4mJat24NQGZmJvPnz+fEiRP07t2b3bt3BxaXkoKISIQdO3YsdF7g2LFj/OEPf6Bz584MGjSIhQu9OwgsXLiQwYMHA/B///d/dOnShSlTppCSkhJoUlD3kYjUaeEOIa1J+/fvZ+jQoQCUlpby4x//mP79+9OzZ0+GDx9OdnY2CQkJLF26FIAZM2awfv166tWrR1JSEgMGDAgsNiUFEZEI69ixI9u3b69U3rJlS9auXVupfNasWZEIC1D3kYiIVKCkICIiIUoKIiISoqQgIiIhSgoiIhISWFIws8ZmttXMtpvZO2Y21S9/ysz2mlme/0j2y83MZprZB2a2w8y6BxWbiIhULcghqX8DrnPOHTWzBsAfzexlf9t9zrllp9UfAFzhP3oBj/nPIiKB+Whmeo0eL+Gu0/+0VfbFF1/Qp08f/va3v1FaWkp6ejpTp05l7969jBw5ksOHD9O9e3cWLVpEw4YNazS+swmspeA8R/3VBv7DVbPLYOBpf78tQHMziwsqPhGRaGnUqBHr1q1j+/bt5OXlsWbNGrZs2cKUKVO45557yM/PJyYmhuzs7LMfrIYFek7BzOqZWR5wAHjVOfemv+nXfhfRI2bWyC9rBxRW2L3ILzv9mBPNLNfMcg8ePBhk+CIigTAzLrroIgBOnTrFqVOnMDPWrVtHerrXcqk4dfbSpUvp3LkzXbt2pU+fPoHGFmhScM6VOeeSgXjgajPrDNwPfA/oCbQApvjVrapDVHHMuc65FOdcSmxsbECRi4gEq6ysjOTkZFq3bs3111/PZZddRvPmzalf3+vVj4+PZ9++fQBMmzaNV155he3bt7Ny5cpA44rI6CPnXAmwAejvnCv2u4j+BiwArvarFQHtK+wWD3wcifhERCKtXr165OXlUVRUxNatW9m1a1elOmbe/8qpqamMHTuWefPmUVZWFmhcQY4+ijWz5v5yE+BHwO7y8wTmvdshwE5/l5XAGH8UUm/gU+dccVDxiYjUBs2bN6dv375s2bKFkpISSktLASgqKgpNr/3444/zq1/9isLCQpKTkzl06FBg8QTZUogD1pvZDuDPeOcUVgHPmtlfgL8ArYBf+fVXA3uAD4B5wL8GGJuISNQcPHiQkpISAE6cOMFrr71Gp06duPbaa1m2zBu9dPrU2b169WLatGm0atWKwsLCMx777xXYkFTn3A6gWxXl152hvgPuDCoeEZGqhDOEtKYVFxeTkZFBWVkZX375JcOHD+emm24iKSmJkSNH8sADD9CtWzfGjx8PwH333Ud+fj7OOdLS0ujatWtgsWnqbBGRCLvqqqt4++23K5V37NiRrVu3Vipfvnx5JMICNM2FiIhUoJaCnBMOPvbMGbdF485ZIucrtRRERCRESUFEREKUFEREJERJQUREQnSiWUTqtFeyB9bo8fqNX33WOoWFhYwZM4a//vWvXHDBBUycOJFJkyZx+PBhRowYQUFBAYmJiSxZsoSYmJgaje9s1FIQEYmw+vXr8/vf/55du3axZcsW5syZw7vvvktWVhZpaWnk5+eTlpZGVlZWxGNTUhARibC4uDi6d/duLtmsWTM6derEvn37yMnJISMjA/j61NkbN24kOTmZ5ORkunXrxueffx5YbOo+EhGJooKCAt5++2169erF/v37iYvz7i0WFxfHgQMHAJg+fTpz5swhNTWVo0eP0rhx48DiUUtBRCRKjh49ys0338yMGTO4+OKLz1gvNTWVyZMnM3PmTEpKSkL3XAiCkoKISBScOnWKm2++mdGjRzNs2DAA2rRpQ3Gxd8eA4uJiWrduDUBmZibz58/nxIkT9O7dm927dwcWl5KCiEiEOecYP348nTp1YvLkyaHyQYMGsXDhQqDy1NldunRhypQppKSkBJoUdE5BROq0cIaQ1rTXX3+dRYsW0aVLF5KTkwH4zW9+Q2ZmJsOHDyc7O5uEhASWLl0KwIwZM1i/fj316tUjKSmJAQMGBBabkkIYzjaOORo/VCJy7vrBD36AdwuZytauXVupbNasWUGHFBLk7Tgbm9lWM9tuZu+Y2VS/vIOZvWlm+Wb2vJk19Msb+esf+NsTg4pNRESqFuQ5hb8B1znnugLJQH//3ssPAY84564AjgDj/frjgSPOucuBR/x6IiISQYElBec56q828B8OuA4ov//dQmCIvzzYX8ffnmZmFlR8IiJSWaCjj8ysnpnlAQeAV4H/A0qcc6V+lSKgnb/cDigE8Ld/CrSs4pgTzSzXzHIPHjwYZPgiInVOoEnBOVfmnEsG4oGrgU5VVfOfq2oVVDoT45yb65xLcc6lxMbG1lywIiISmesUnHMlwAagN9DczMpHPcUDH/vLRUB7AH/7JcDhSMQnIiKewIakmlkscMo5V2JmTYAf4Z08Xg+kA4uBDCDH32Wlv/6Gv32dO9OYLRGRGvLEon41erx/vvWVsOqVlJQwYcIEdu7ciZnx5JNPcuWVV57XU2fHAevNbAfwZ+BV59wqYAow2cw+wDtnkO3XzwZa+uWTgcwAYxMRiapJkybRv39/du/ezfbt2+nUqVOtmDo7sJaCc24H0K2K8j145xdOL/8CuCWoeEREaovPPvuMTZs28dRTTwHQsGFDGjZsSE5ODhs2bAC8qbP79u3LQw89xMaNG5k0aRIAZsamTZto1qxZILFp7iMRkQjbs2cPsbGx3HbbbXTr1o0JEyZw7Nixs06dnZeXx+bNm2nSpElgsSkpiIhEWGlpKdu2beOOO+7g7bff5sILL6y2q0hTZ4uInMfi4+OJj4+nV69eAKSnp7Nt2zZNnS0iUhddeumltG/fnvfeew/wJsFLSkrS1NkiItEW7hDSmjZr1ixGjx7NyZMn6dixIwsWLODLL7/U1NkiInVRcnIyubm5lcrP26mzRUTk3KOkICIiIUoKIiISoqQgIiIhSgoiIhKipCAiIiEakioiddptK/rX6PEWDF0TVr1HHnmE+fPnY2Z06dKFBQsWUFxczMiRIzl8+DDdu3dn0aJFNGzYsEbjOxu1FEREImzfvn3MnDmT3Nxcdu7cSVlZGYsXL2bKlCncc8895OfnExMTQ3Z29tkPVsOUFEREoqC0tJQTJ05QWlrK8ePHiYuLY926daSnpwPe1NkvvvgiAEuXLqVz58507dqVPn36BBqXuo9ERCKsXbt23HvvvSQkJNCkSRNuuOEGevToQfPmzUMzoMbHx7Nv3z4Apk2bxiuvvEK7du0oKSkJNLbAWgpm1t7M1pvZLjN7x8wm+eUPmtk+M8vzHwMr7HO/mX1gZu+ZWc3eI09EpJY4cuQIOTk57N27l48//phjx47x8ssvV6pnZoA3dfbYsWOZN28eZWVlgcYWZEuhFPg359w2M2sGvGVmr/rbHnHOTa9Y2cySgJHA94G2wGtm9l3nXLCfgIhIhL322mt06NCB2NhYAIYNG8af/vQnSkpKKC0tpX79+hQVFdG2bVsAHn/8cd58801eeuklkpOTycvLo2XLloHEFlhLwTlX7Jzb5i9/DuwC2lWzy2BgsXPub865vcAHVHHbThGRc11CQgJbtmzh+PHjOOdCU2dfe+21LFu2DKg8dXavXr2YNm0arVq1orCwMLDYInJOwcwS8e7X/CaQCvzUzMYAuXitiSN4CWNLhd2KqCKJmNlEYCJ4H6yIyN8j3CGkNalXr16kp6fTvXt36tevT7du3Zg4cSI33ngjI0eO5IEHHqBbt26MHz8egPvuu4/8/Hycc6SlpdG1a9fAYgs8KZjZRcALwN3Ouc/M7DHgl4Dzn38PjAOsit1dpQLn5gJzAVJSUiptFxE5F0ydOpWpU6d+raxjx45s3bq1Ut3ly5dHKqxgk4KZNcBLCM8655YDOOf2V9g+D1jlrxYB7SvsHg98HGR8IkG6admzZ9y2Kn10BCMRCV+Qo48MyAZ2Oef+q0J5XIVqQ4Gd/vJKYKSZNTKzDsAVQOWUKSIigQmypZAK3Ar8xczy/LKfAaPMLBmva6gA+GcA59w7ZrYEeBdv5NKdGnkkQXtiUfUjn6N1q0aRaAksKTjn/kjV5wlWV7PPr4FfBxWTnJ8+mplefYULIxOHyPkgrO4jM6t009CqykRE5NxWbUvBzBoDTYFWZhbDV//5X4x3gZmIiJxHztZ99M/A3XgJ4C2+SgqfAXMCjEtEJCJuXPFwjR7vpaH3nbXOe++9x4gRI0Lre/bsYdq0aYwZM4YRI0ZQUFBAYmIiS5YsISYmpkbjO5tqu4+cc4865zoA9zrnOjrnOviPrs652RGKUUTkvHLllVeSl5dHXl4eb731Fk2bNmXo0KFkZWWRlpZGfn4+aWlpZGVlRTy2sM4pOOdmmdk/mNmPzWxM+SPo4EREzndr167lsssu4zvf+Q45OTlkZGQAX586e+PGjSQnJ5OcnEy3bt34/PPPA4snrNFHZrYIuAzIA8qHiTrg6YDiEhGpExYvXsyoUaMA2L9/P3Fx3qVccXFxHDhwAIDp06czZ84cUlNTOXr0KI0bNw4snnCHpKYASc45TSshIlJDTp48ycqVK/ntb39bbb3U1FQmT57M6NGjGTZsGPHx8YHFFO4VzTuBSwOLQkSkDnr55Zfp3r07bdq0AaBNmzYUFxcDUFxcTOvWrQHIzMxk/vz5nDhxgt69e7N79+7AYgo3KbQC3jWzV8xsZfkjsKhEROqA5557LtR1BDBo0CAWLlwIVJ46u0uXLkyZMoWUlJRAk0K43UcPBhZBhBx87Jlqt8fe8ZMIRSIitUk4Q0iDcPz4cV599VWeeOKJUFlmZibDhw8nOzubhIQEli5dCsCMGTNYv3499erVIykpiQEDBgQWV1hJwTm3MbAIRETqoKZNm3Lo0KGvlbVs2ZK1aytPFjFr1qxIhRX26KPP+ereBg2BBsAx59zFQQUmIiKRF25LoVnFdTMbgm6VKSJy3vlW91Nwzr0IXFfDsYiISJSF2300rMLqBXjXLeiaBRGR80y4o4/+qcJyKd7NcQbXeDQiIhJV4Z5TuO2bHtjM2uNNg3Ep8CUw1zn3qJm1AJ4HEvGSy3Dn3BH/9p2PAgOB48BY59y2b/q6IiLy7YXbfRQPzMK7xaYD/ghMcs4VVbNbKfBvzrltZtYMeMvMXgXGAmudc1lmlglkAlOAAXj3Zb4C6AU85j+LiATmpmXP1ujxVqWPDqteWVkZKSkptGvXjlWrVrF3715GjhzJ4cOH6d69O4sWLaJhw4Y1Gls4wj3RvABYiXdfhXbA//plZ+ScKy7/T9859zmwy993MLDQr7YQGOIvDwaedp4tQHMzi/sG70VE5Jzx6KOP0qlTp9D6lClTuOeee8jPzycmJobs7OyoxBVuUoh1zi1wzpX6j6eA2HBfxMwSgW7Am0Ab51wxeIkDaO1XawcUVtityC8TETmvFBUV8dJLLzFhwgQAnHOsW7eO9HTvfuMVp81eunQpnTt3pmvXrvTp0yfw2MI90fyJmf0EeM5fHwUcqqZ+iJldBLwA3O2c+8w7dVB11SrKKo1wMrOJwESAhISEcEIQEalV7r77bn73u9+F7otw6NAhmjdvTv363p/k+Ph49u3bB8C0adN45ZVXaNeuHSUlJYHHFm5LYRwwHPgrUAykA2c9+WxmDfASwrPOueV+8f7ybiH/+YBfXgS0r7B7PPDx6cd0zs11zqU451JiY8NurIiI1AqrVq2idevW9OjRI1RW1V0Jyv+BTk1NZezYscybN4+ysrJK9WpauEnhl0CGcy7WOdcaL0k8WN0O/miibGCXc+6/KmxaCWT4yxlAToXyMebpDXxa3s0kInK+eP3111m5ciWJiYmMHDmSdevWcffdd1NSUkJpaSngdS+1bdsWgMcff5xf/epXFBYWkpycXGm+pJoWblK4yjl3pHzFOXcY7xxBdVKBW4HrzCzPfwwEsoDrzSwfuN5fB1gN7AE+AOYB/xr+2xAROTf89re/paioiIKCAhYvXsx1113Hs88+y7XXXsuyZcuAytNm9+rVi2nTptGqVSsKCwurO/zfLdxzCheYWUx5YvCvNah2X+fcH6n6PAFAWhX1HXBnmPGIiNSIcIeQBu2hhx5i5MiRPPDAA3Tr1o3x48cDcN9995Gfn49zjrS0NLp27RpoHOEmhd8DfzKzZXgnf4cDvw4sKhGROqBv37707dsXgI4dO7J169ZKdZYvX16pLEjhXtH8tJnl4k2CZ8Aw59y7gUYmIiIRF25LAT8JKBGIiJzHwk4KEnnVXX5fW/pBReT88q3upyAiIucnJQUREQlRUhARkRCdUxCROm3IsrU1erwX0ytdhlXJuHHjQtNd7Ny5E4DDhw8zYsQICgoKSExMZMmSJcTExNRobOFQS0FEJMLGjh3LmjVrvlaWlZVFWloa+fn5pKWlkZWVdYa9g6WkICISYX369KFFixZfK8vJySEjw5sWruLU2Rs3biQ5OZnk5GS6desWmlk1KOo+EhGpBfbv309cnHdfsbi4OA4c8CaQnj59OnPmzCE1NZWjR4/SuHHjQONQS0FEpBZLTU1l8uTJzJw5k5KSktA9F4KipCAiUgu0adOG4mLvbgHFxcW0bu3dlDIzM5P58+dz4sQJevfuze7duwONQ0lBRKQWGDRoEAsXerevP33q7C5dujBlyhRSUlICTwo6pyAidVo4Q0hr2qhRo9iwYQOffPIJ8fHxTJ06lczMTIYPH052djYJCQksXboUgBkzZrB+/Xrq1atHUlISAwYMCDQ2JQURkQh77rnnqixfu7byNROzZs0KOpyvCaz7yMyeNLMDZrazQtmDZrbvtDuxlW+738w+MLP3zKxfUHGJiMiZBXlO4SmgfxXljzjnkv3HagAzSwJGAt/39/lvM6sXYGwiIlKFwJKCc24TcDjM6oOBxc65vznn9uLdp/nqoGITEZGqRWP00U/NbIffvVQ+sUc7oOLdqIv8MhERiaBIJ4XHgMuAZKAY797P4N3i83SuqgOY2UQzyzWz3IMHDwYTpYhIHRXR0UfOuf3ly2Y2D1jlrxYB7StUjQc+PsMx5gJzAVJSUqpMHN/GRzPTz7zxwpp6FRGR2i2iScHM4pxzxf7qUKB8ZNJK4H/M7L+AtsAVwNZIxiYiddNdKwrPXukbmDm0/dkrAY8++ijz5s3DOcftt9/O3XffXSumzw5ySOpzwBvAlWZWZGbjgd+Z2V/MbAdwLXAPgHPuHWAJ8C6wBrjTOVcWVGwiItG0c+dO5s2bx9atW9m+fTurVq0iPz+/VkyfHVhLwTk3qori7Grq/xr4dVDxiIjUFrt27aJ37940bdoUgB/+8IesWLGCnJwcNmzYAHjTZ/ft25eHHnqIjRs3MmnSJADMjE2bNtGsWbNAYtPcRyIiEda5c2c2bdrEoUOHOH78OKtXr6awsPCs02fn5eWxefNmmjRpElhsSgoiIhHWqVMnpkyZwvXXX0///v3p2rVrtVNiR3L6bCUFEZEoGD9+PNu2bWPTpk20aNGCK664olZMn62kICISBeVdQx999BHLly9n1KhRtWL6bM2SKiJ1WrhDSGvazTffzKFDh2jQoAFz5swhJiamVkyfraQgIhIFmzdvrlTWsmXLqE+fre4jEREJUVIQEZEQJQUREQnROQWRb+nGFQ9Xu91oG6FIRGqOWgoiIhKipCAiIiHqPhKROu3l5z+p0eMNGNEqrHpr1qxh0qRJlJWVMWHCBDIzM2s0jm9LSaEGPLGo3xm3/fOtr0QwEhE5F5SVlXHnnXfy6quvEh8fT8+ePRk0aBBJSUnRDk3dRyIikbZ161Yuv/xyOnbsSMOGDRk5ciQ5OTlkZmaSlJTEVVddxb333huV2NRSEBGJsH379tG+/VfTa8THx/PGG2+wdu1adu/ejZlRUlISldiCvPPak2Z2wMx2VihrYWavmlm+/xzjl5uZzTSzD8xsh5l1DyouEZFoc67y7eWdczRu3JgJEyawfPny0A14Ii3IlsJTwGzg6QplmcBa51yWmWX661OAAXj3Zb4C6AU85j+LRNVtK/pXszUtYnHI+SU+Pp7Cwq/uDV1UVERCQgIzZsxg7dq1LF68mNmzZ7Nu3bqIxxZYS8E5twk4fFrxYGChv7wQGFKh/Gnn2QI0N7O4oGITEYmmnj17kp+fz969ezl58iSLFy9m4MCBfPrppwwcOJAZM2aQl5cXldgifU6hjXOuGMA5V2xmrf3ydkBhhXpFfllxhOMTkTom3CGkNal+/frMnj2bfv36UVZWxrhx42jdujU33XQTX3zxBc45HnnkkYjHBbXnRLNVUVa50w0ws4nARICEhIQgYxIRCczAgQMZOHDg18q2bt0apWi+EukhqfvLu4X85wN+eRFQ8U4X8cDHVR3AOTfXOZfinEuJjY0NNFgRkbom0klhJZDhL2cAORXKx/ijkHoDn5Z3M4mISOQE1n1kZs8BfYFWZlYE/CeQBSwxs/HAR8AtfvXVwEDgA+A4cFtQcYmIyJkFlhScc6POsKnSOD7nDdq9M6hYREQkPJrmQkREQpQUREQkpLYMSRURiYqCGX+t0eMl3n3pWeuMGzeOVatW0bp1a3bu9GYCOnz4MCNGjKCgoIDExESWLFlCTEwMzjkmTZrE6tWradq0KU899RTduwc3E5BaCiIiETZ27FjWrFnztbKsrCzS0tLIz88nLS2NrKwsAF5++WXy8/PJz89n7ty53HHHHYHGpqQgIhJhffr0oUWLFl8ry8nJISPDG7GfkZHBiy++GCofM2YMZkbv3r0pKSmhuLiY4uJi+vTpQ3JyMp07d2bz5s01Epu6j0REaoH9+/cTF+dN+RYXF8eBA961vVVNs71v3z42btxIv379+PnPf05ZWRnHjx+vkTiUFEREarGqptk2M3r27Mm4ceM4deoUQ4YMITk5uUZeT91HIiK1QJs2bSgu9iZyKC4upnVrb77QqqbZbtu2LX369GHTpk20a9eOW2+9laeffrrK435TaimIRMGQZWvPuO3FdN2noS4aNGgQCxcuJDMzk4ULFzJ48OBQ+ezZsxk5ciRvvvkml1xyCXFxcXz44Ye0a9eO22+/nWPHjrFt2zeJ8LAAAAyHSURBVDbGjBnzd8ehpCAidVo4Q0hr2qhRo9iwYQOffPIJ8fHxTJ06lczMTIYPH052djYJCQksXboU8GZTXb16NZdffjlNmzZlwYIFAGzYsIGHH36YBg0acNFFF6mlICJyrnruueeqLF+7tnIL0syYM2dOpfKMjIzQaKWapHMKIiISoqQgIiIhSgoiIhKipCAiIiFKCiIiEqKkICIiIVEZkmpmBcDnQBlQ6pxLMbMWwPNAIlAADHfOHYlGfCJSd+x/9I0aPV6bSdeEVa+kpIQJEyawc+dOzIwnn3ySa665hlmzZjF79mzq16/PjTfeyO9+97saje9sonmdwrXOuU8qrGcCa51zWWaW6a9PiU5oIiLBmjRpEv3792fZsmWcPHmS48ePs379enJyctixYweNGjUKTYoXSbXp4rXBQF9/eSGwASWFM6pumgTQVAkitdlnn33Gpk2beOqppwBo2LAhDRs25LHHHiMzM5NGjRoBhOY/euedd7jttts4efIkX375JS+88AJXXHFFILFFKyk44A9m5oAnnHNzgTbOuWIA51yxmbWuakczmwhMBEhISIhUvIG5ccXDZ9xmtI1gJCISKXv27CE2NpbbbruN7du306NHDx599FHef/99Nm/ezM9//nMaN27M9OnT6dmzJ48//jiTJk1i9OjRnDx5krKyssBii9aJ5lTnXHdgAHCnmfUJd0fn3FznXIpzLiU2Nja4CEVEAlJaWsq2bdu44447ePvtt7nwwgvJysqitLSUI0eOsGXLFh5++GGGDx+Oc45rrrmG3/zmNzz00EN8+OGHNGnSJLDYopIUnHMf+88HgBXA1cB+M4sD8J8j35kmIhIB8fHxxMfH06tXLwDS09PZtm0b8fHxDBs2DDPj6quv5oILLuCTTz7hxz/+MStXrqRJkyb069ePdevWBRZbxJOCmV1oZs3Kl4EbgJ3ASqB8dqcMICfSsYmIRMKll15K+/btee+99wBvIrykpCSGDBkS+oP//vvvc/LkSVq1asWePXvo2LEjd911F4MGDWLHjh2BxRaNcwptgBVmVv76/+OcW2NmfwaWmNl44CPglijEJiJ1TLhDSGvarFmzQucIOnbsyIIFC7jwwgsZN24cnTt3pmHDhixcuBAz4/nnn+eZZ56hQYMGXHrppfziF78ILK6IJwXn3B6gaxXlhwANmRGROiE5OZnc3NxK5c8880ylsvvvv5/7778/EmHpimYREfmKkoKIiIQoKYhIneOci3YIEfFt3qeSgojUKY0bN+bQoUPnfWJwznHo0CEaN278jfarTdNciAhw14rCarfPHNo+QpGcn+Lj4ykqKuLgwYPRDiVwjRs3Jj4+/hvto6QgInVKgwYN6NChQ7TDqLWUFETOMS8//8kZtw0Y0SqCkcj5SEkhYLet6H+WGro0Q0RqD51oFhGRELUUROS8dLZW+oKhayIUyblFSaEOqq5PGtQvLXVDdfcyeWnofRGMpHZR95GIiIQoKYiISIiSgoiIhOicwnmquqtiBxDcrfxEIumJRf3OvPEii1wg5xElBREJ3MHHKt8joFzsHT+JYCRyNrUuKZhZf+BRoB4w3zmXFeWQRETCdq7PXVWrzimYWT1gDjAASAJGmVlSdKMSEak7altL4WrgA/+WnZjZYmAw8G5UoxI5RxTM+Gu125vY3jNui9a9ij+amX7GbQl3LYtgJF+5admz1W5flT46QpFEntWmOcXNLB3o75yb4K/fCvRyzv20Qp2JwER/9UrgvYgHGjmtgOqvNJPaTN/fuet8/+6+45yLrWpDbWspVDVc4GtZyzk3F5gbmXCiy8xynXMp0Y5Dvh19f+euuvzd1apzCkARUPEsTDzwcZRiERGpc2pbUvgzcIWZdTCzhsBIYGWUYxIRqTNqVfeRc67UzH4KvII3JPVJ59w7UQ4rmupEN9l5TN/fuavOfne16kSziIhEV23rPhIRkShSUhARkRAlhQgzsw1mFvGhbtF63XONmf3pW+5XYGbV3p3IzH72LY471sxmf5uY6hoze9DM7o12HNUJ5+ck2pQUAmAefbbnIOfcPwR4+G+cFEQiTX+4aoiZJZrZLjP7b2AbcKuZvWFm28xsqZldVMU+N1RVx8x+YWZ/NrOdZjbXzMwvv8vM3jWzHf4UIJjZhWb2pF//bTMb7Jc3MbPFft3nQfNlh8PMjvrPcWa2yczy/O/hH/3yUWb2F7/soTMc40Uze8vM3vGvwMfMsoAm/vGe9ct+YmZb/bIn/Lm/MLPbzOx9M9sIpEbifZ+rzOznZvaemb2GN8MBZnaZma3xv4PNZvY9v7yNma0ws+3+4x/88jN9D4+ZWa7/PU6t8JpZFX4Pp/tlsWb2gv97+GczS/XLW5rZH/zfzSeo+gLd2sU5p0cNPIBE4EugN94l8puAC/1tU4Bf+MsbgJSz1GlR4biLgH/ylz8GGvnLzf3n3wA/KS8D3gcuBCbjDekFuAooBVKi/TnV9gdw1H/+N+Dn/nI9oBnQFvgIiMUbzr0OGOLXKQBaVfz+8BLxTqBlxWP7y52A/wUa+Ov/DYwB4iq8RkPgdWB2tD+X2vgAegB/AZoCFwMfAPcCa4Er/Dq9gHX+8vPA3RW+00vO9D2c9j3W839vrwJa4E2tUz5ys/z38H+AH/jLCcAuf3lmhd/rG/FmaGgV7c+uuketuk7hPPChc26Lmd2EN8vr6/4/+Q2BN06r27uaOtea2b/j/bC3AN7B+8HdATxrZi8CL/p1bwAGVehLbYz3Q9kH7wcS59wOM9tRw+/1fPdn4EkzawC86JzLM7PrgA3OuYMA/n/8ffjquyh3l5kN9ZfbA1cAh06rk4b3R+3P/vffBDiA90es4ms8D3y3pt/ceeIfgRXOueMAZrYS7+f/H4Cl/ucK0Mh/vg4v8eKcKwM+NW9+taq+B4DhfkuvPl6yTsKbnPMLYL6ZvQSs8uv+CEiq8JoXm1kzvJ+PYf5rvmRmR2ryAwiCkkLNOuY/G/Cqc25UNXWrrGNmjfH+W0lxzhWa2YN4P+jg/afRBxgE/IeZfd8/zs3OufdOOw6cNm+UhM85t8nM+uB95ovM7GHgs7PtZ2Z98f5AXOOcO25mG/jq+/taVWChc+7+0/Yfgr63b+L0z+oCoMQ5lxzm/mf6HjrgtTp6OueOmNlTQGPnXWB7NV5SHwn8FC/ZXID3nZ847ThVxVir6ZxCMLYAqWZ2OYCZNTWz0//bO1Od8j8gn/jnGNL97RcA7Z1z64F/x+squgjv6u//V+G8Qzd//03AaL+sM17TV8JkZt8BDjjn5gHZQHfgTeCHZtbK73ceBWw8bddLgCN+QvgeXouw3Cm/5QFeF0e6mbX2X6+F/5pvAn39vugGwC1BvcfzwCZgqH/+rBnwT8BxYK+Z3QKhQR9d/fprgTv88npmdjFn/h4uxvsn71Mza4N3jxf838lLnHOrgbuB8uTzB7wEgV+vvLzi7+EAIKbmP4aapZZCAJxzB81sLPCcmZU3XR/A6++vto5z7n0zm4fXV1qA140BXr/mM2Z2Cd5/N48450rM7JfADGCHnxgKgJuAx4AFfrdRHrA1qPd7nuoL3Gdmp4CjeP3MxWZ2P7Ae7ztY7ZzLOW2/NcC/+J/7e3jJv9xcvO9pm3NutJk9APzBT/ingDv97scH8boSi/EGLdQL7F2ew5xz2/zutTzgQ2Czv2k08Jj/+TYAFgPbgUnAXDMbD5QBdzjn3qjme3gbr+t2D965HfDOLeX4LXoD7vHL7wLm+N97fbxk8C/AVLzf8W14/0B8FNDHUWM0zYWIiISo+0hEREKUFEREJERJQUREQpQUREQkRElBRERClBRE/g5mdreZNa2peiLRpiGpIn8HMyvAu/r8k5qoJxJtaimIhMm8GWlfMm+GzZ1m9p94k+StN7P1fp1KM2ua2V1V1KtyhlyRaFNLQSRMZnYz0N85d7u/fgnelbKhFoCZtXDOHfanwVgL3OVPSFhQXs+8m6wsBwY4546Z2RS82W+nReN9iVSkloJI+P4C/MjMHjKzf3TOfVpFneH+lAZvA9/Hm1nzdBVnyM0DMoDvBBW0yDehuY9EwuTPS9UDGAj81sz+UHH7mWbWrOJQ4cyiKxIVaimIhMnM2gLHnXPPANPxZk79HG+SNDjDzJq+ivXCmUVXJCrUUhAJXxfgYTP7Em82zTuAa4CXzazYOXftGWbWBG+G1Ir1xlLNLLoi0aITzSIiEqLuIxERCVFSEBGRECUFEREJUVIQEZEQJQUREQlRUhARkRAlBRERCfn/9n3yMntSguEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"state\", hue=\"age\", data=PatientInfo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. The relation between the top five cities with most patients and state among patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2296a829608>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_five_cities=PatientInfo[PatientInfo['city'].isin(['Gyeongsan-si','Seongnam-si','Bonghwa-gun','Bucheon-si','Gumi-si'])]\n",
    "sns.countplot(x=\"state\", hue=\"city\", data=top_five_cities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9. The relation between the top five provinces with most patients and state among patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2296a8ba348>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_five_provinces=PatientInfo[PatientInfo['province'].isin(['Gyeongsangbuk-do','Gyeonggi-do','Seoul','Chungcheongnam-do','Busan'])]\n",
    "sns.countplot(x=\"state\", hue=\"province\", data=top_five_provinces)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10. The relation between the top five infection_case with most patients and state among patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2296b922708>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_five_infection_case=PatientInfo[PatientInfo['infection_case'].\n",
    "                                    isin(['contact with patient','etc','overseas inflow','Guro-gu Call Center','Shincheonji Church'])]\n",
    "sns.countplot(x=\"state\", hue=\"infection_case\", data=top_five_infection_case)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:**\n",
    "\n",
    "After preliminary data visualization analysis, we can find that the number of female patients is about 300 larger than the number of male patients, most patients are aged from 20 to 60, most patients are form the city Gyeongsan-si, most patients are form the provinces  Gyeongsangbuk-do, Gyeonggi-do and Seoul and most infection cases are contact with patient, etc and overseas inflow.\n",
    "\n",
    "By analysing the states of patients(isolated / released / deceased) and other attributes, we can find that male patients may be a little easier to be deceased than female patients because the number of female patients is larger than the number of male patients but the number of  deceased female patients is less than the number of deceased male patients.\n",
    "\n",
    "We can also find that younger patients may be easier to be released than older patients and older patients may be easier to be deceased than older patients.\n",
    "\n",
    "We can also find that contacting with patients around you in your city and province is the main reason people get infected.\n",
    "#### 2.1.3 Visualize the Time Series Data \n",
    "1. Visualize the File Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>test</th>\n",
       "      <th>negative</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>released</th>\n",
       "      <th>deceased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>16</td>\n",
       "      <td>27</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  time  test  negative  confirmed  released  deceased\n",
       "0  2020-01-20    16     1         0          1         0         0\n",
       "1  2020-01-21    16     1         0          1         0         0\n",
       "2  2020-01-22    16     4         3          1         0         0\n",
       "3  2020-01-23    16    22        21          1         0         0\n",
       "4  2020-01-24    16    27        25          2         0         0"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Time = pd.read_csv('Time.csv')\n",
    "Time.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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>test</th>\n",
       "      <th>negative</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>released</th>\n",
       "      <th>deceased</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-20</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-21</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>27</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            test  negative  confirmed  released  deceased\n",
       "date                                                     \n",
       "2020-01-20     1         0          1         0         0\n",
       "2020-01-21     1         0          1         0         0\n",
       "2020-01-22     4         3          1         0         0\n",
       "2020-01-23    22        21          1         0         0\n",
       "2020-01-24    27        25          2         0         0"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We do not care the update time so we drop it.\n",
    "Time.drop(['time'],axis=1,inplace=True)\n",
    "Time.set_index('date',inplace=True)\n",
    "Time.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "Time.plot(marker='o',ms=2,lw=1,ax=ax)\n",
    "fig.autofmt_xdate()\n",
    "plt.legend(bbox_to_anchor = [1,1])\n",
    "plt.title('COVID-19 Data of South Korea',size=15)\n",
    "plt.ylabel('number')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure is not very clear because the accumulated test and negative number is much larger that the rest numbers.\n",
    "Hence we will just focus on the number of confirmed, released and deceased patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Time.drop(['test','negative'],axis=1,inplace=True)\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "Time.plot(marker='o',ms=2,lw=1,ax=ax)\n",
    "fig.autofmt_xdate()\n",
    "plt.legend(bbox_to_anchor = [1,1])\n",
    "plt.title('COVID-19 Data of South Korea',size=15)\n",
    "plt.ylabel('number')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Visualize the File TimeAge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>age</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deceased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0s</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>10s</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>20s</td>\n",
       "      <td>1235</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>30s</td>\n",
       "      <td>506</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>40s</td>\n",
       "      <td>633</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  time  age  confirmed  deceased\n",
       "0  2020-03-02     0   0s         32         0\n",
       "1  2020-03-02     0  10s        169         0\n",
       "2  2020-03-02     0  20s       1235         0\n",
       "3  2020-03-02     0  30s        506         1\n",
       "4  2020-03-02     0  40s        633         1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TimeAge = pd.read_csv('TimeAge.csv')\n",
    "TimeAge.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>age</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deceased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0s</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>10s</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>20s</td>\n",
       "      <td>1235</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>30s</td>\n",
       "      <td>506</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>40s</td>\n",
       "      <td>633</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  age  confirmed  deceased\n",
       "0  2020-03-02   0s         32         0\n",
       "1  2020-03-02  10s        169         0\n",
       "2  2020-03-02  20s       1235         0\n",
       "3  2020-03-02  30s        506         1\n",
       "4  2020-03-02  40s        633         1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We do not care the update time so we drop it.\n",
    "TimeAge.drop(['time'],axis=1,inplace=True)\n",
    "TimeAge.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>age_x</th>\n",
       "      <th>confirmed_0s</th>\n",
       "      <th>deceased_0s</th>\n",
       "      <th>age_y</th>\n",
       "      <th>confirmed_10s</th>\n",
       "      <th>deceased_10s</th>\n",
       "      <th>age_x</th>\n",
       "      <th>confirmed_20s</th>\n",
       "      <th>deceased_20s</th>\n",
       "      <th>age_y</th>\n",
       "      <th>...</th>\n",
       "      <th>deceased_50s</th>\n",
       "      <th>age_x</th>\n",
       "      <th>confirmed_60s</th>\n",
       "      <th>deceased_60s</th>\n",
       "      <th>age_y</th>\n",
       "      <th>confirmed_70s</th>\n",
       "      <th>deceased_70s</th>\n",
       "      <th>age</th>\n",
       "      <th>confirmed_80s</th>\n",
       "      <th>deceased_80s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-02</th>\n",
       "      <td>0s</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>10s</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>20s</td>\n",
       "      <td>1235</td>\n",
       "      <td>0</td>\n",
       "      <td>30s</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>60s</td>\n",
       "      <td>530</td>\n",
       "      <td>6</td>\n",
       "      <td>70s</td>\n",
       "      <td>192</td>\n",
       "      <td>6</td>\n",
       "      <td>80s</td>\n",
       "      <td>81</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>0s</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>10s</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>20s</td>\n",
       "      <td>1417</td>\n",
       "      <td>0</td>\n",
       "      <td>30s</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>60s</td>\n",
       "      <td>597</td>\n",
       "      <td>7</td>\n",
       "      <td>70s</td>\n",
       "      <td>224</td>\n",
       "      <td>9</td>\n",
       "      <td>80s</td>\n",
       "      <td>93</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-04</th>\n",
       "      <td>0s</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>10s</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "      <td>20s</td>\n",
       "      <td>1575</td>\n",
       "      <td>0</td>\n",
       "      <td>30s</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>60s</td>\n",
       "      <td>646</td>\n",
       "      <td>7</td>\n",
       "      <td>70s</td>\n",
       "      <td>260</td>\n",
       "      <td>12</td>\n",
       "      <td>80s</td>\n",
       "      <td>108</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-05</th>\n",
       "      <td>0s</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>10s</td>\n",
       "      <td>257</td>\n",
       "      <td>0</td>\n",
       "      <td>20s</td>\n",
       "      <td>1727</td>\n",
       "      <td>0</td>\n",
       "      <td>30s</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>60s</td>\n",
       "      <td>699</td>\n",
       "      <td>8</td>\n",
       "      <td>70s</td>\n",
       "      <td>288</td>\n",
       "      <td>13</td>\n",
       "      <td>80s</td>\n",
       "      <td>124</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-06</th>\n",
       "      <td>0s</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>10s</td>\n",
       "      <td>292</td>\n",
       "      <td>0</td>\n",
       "      <td>20s</td>\n",
       "      <td>1877</td>\n",
       "      <td>0</td>\n",
       "      <td>30s</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>60s</td>\n",
       "      <td>763</td>\n",
       "      <td>11</td>\n",
       "      <td>70s</td>\n",
       "      <td>340</td>\n",
       "      <td>14</td>\n",
       "      <td>80s</td>\n",
       "      <td>168</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age_x  confirmed_0s  deceased_0s  age_y  confirmed_10s  \\\n",
       "date                                                                 \n",
       "2020-03-02     0s            32            0    10s            169   \n",
       "2020-03-03     0s            34            0    10s            204   \n",
       "2020-03-04     0s            34            0    10s            233   \n",
       "2020-03-05     0s            38            0    10s            257   \n",
       "2020-03-06     0s            45            0    10s            292   \n",
       "\n",
       "            deceased_10s  age_x  confirmed_20s  deceased_20s  age_y  ...  \\\n",
       "date                                                                 ...   \n",
       "2020-03-02             0    20s           1235             0    30s  ...   \n",
       "2020-03-03             0    20s           1417             0    30s  ...   \n",
       "2020-03-04             0    20s           1575             0    30s  ...   \n",
       "2020-03-05             0    20s           1727             0    30s  ...   \n",
       "2020-03-06             0    20s           1877             0    30s  ...   \n",
       "\n",
       "            deceased_50s  age_x confirmed_60s  deceased_60s  age_y  \\\n",
       "date                                                                 \n",
       "2020-03-02             5    60s           530             6    70s   \n",
       "2020-03-03             5    60s           597             7    70s   \n",
       "2020-03-04             5    60s           646             7    70s   \n",
       "2020-03-05             5    60s           699             8    70s   \n",
       "2020-03-06             5    60s           763            11    70s   \n",
       "\n",
       "           confirmed_70s  deceased_70s  age confirmed_80s  deceased_80s  \n",
       "date                                                                     \n",
       "2020-03-02           192             6  80s            81             3  \n",
       "2020-03-03           224             9  80s            93             5  \n",
       "2020-03-04           260            12  80s           108             6  \n",
       "2020-03-05           288            13  80s           124             7  \n",
       "2020-03-06           340            14  80s           168            10  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#processing of Dataframe TimeAge to make it easy to draw clear figures.\n",
    "Age0=TimeAge[TimeAge['age']=='0s']\n",
    "Age0=Age0.rename(columns={'confirmed':'confirmed_0s'})\n",
    "Age0=Age0.rename(columns={'deceased':'deceased_0s'})\n",
    "Age1=TimeAge[TimeAge['age']=='10s']\n",
    "Age1=Age1.rename(columns={'confirmed':'confirmed_10s'})\n",
    "Age1=Age1.rename(columns={'deceased':'deceased_10s'})\n",
    "Age2=TimeAge[TimeAge['age']=='20s']\n",
    "Age2=Age2.rename(columns={'confirmed':'confirmed_20s'})\n",
    "Age2=Age2.rename(columns={'deceased':'deceased_20s'})\n",
    "Age3=TimeAge[TimeAge['age']=='30s']\n",
    "Age3=Age3.rename(columns={'confirmed':'confirmed_30s'})\n",
    "Age3=Age3.rename(columns={'deceased':'deceased_30s'})\n",
    "Age4=TimeAge[TimeAge['age']=='40s']\n",
    "Age4=Age4.rename(columns={'confirmed':'confirmed_40s'})\n",
    "Age4=Age4.rename(columns={'deceased':'deceased_40s'})\n",
    "Age5=TimeAge[TimeAge['age']=='50s']\n",
    "Age5=Age5.rename(columns={'confirmed':'confirmed_50s'})\n",
    "Age5=Age5.rename(columns={'deceased':'deceased_50s'})\n",
    "Age6=TimeAge[TimeAge['age']=='60s']\n",
    "Age6=Age6.rename(columns={'confirmed':'confirmed_60s'})\n",
    "Age6=Age6.rename(columns={'deceased':'deceased_60s'})\n",
    "Age7=TimeAge[TimeAge['age']=='70s']\n",
    "Age7=Age7.rename(columns={'confirmed':'confirmed_70s'})\n",
    "Age7=Age7.rename(columns={'deceased':'deceased_70s'})\n",
    "Age8=TimeAge[TimeAge['age']=='80s']\n",
    "Age8=Age8.rename(columns={'confirmed':'confirmed_80s'})\n",
    "Age8=Age8.rename(columns={'deceased':'deceased_80s'})\n",
    "\n",
    "result = pd.merge(Age0, Age1, on='date')\n",
    "result=pd.merge(result, Age2, on='date')\n",
    "result=pd.merge(result, Age3, on='date')\n",
    "result=pd.merge(result, Age4, on='date')\n",
    "result=pd.merge(result, Age5, on='date')\n",
    "result=pd.merge(result, Age6, on='date')\n",
    "result=pd.merge(result, Age7, on='date')\n",
    "result=pd.merge(result, Age8, on='date')\n",
    "result.set_index(['date'],inplace=True)\n",
    "result.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "result.plot(marker='o',ms=2,lw=1,ax=ax)\n",
    "fig.autofmt_xdate()\n",
    "plt.legend(bbox_to_anchor = [1,1])\n",
    "plt.title('COVID-19 Data of Different Ages',size=15)\n",
    "plt.ylabel('number')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Visualize the File TimeGender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>sex</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deceased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>1591</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>2621</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>1810</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>3002</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>1996</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  time     sex  confirmed  deceased\n",
       "0  2020-03-02     0    male       1591        13\n",
       "1  2020-03-02     0  female       2621         9\n",
       "2  2020-03-03     0    male       1810        16\n",
       "3  2020-03-03     0  female       3002        12\n",
       "4  2020-03-04     0    male       1996        20"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TimeGender = pd.read_csv('TimeGender.csv')\n",
    "TimeGender.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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>sex_x</th>\n",
       "      <th>confirmed_female</th>\n",
       "      <th>deceased_female</th>\n",
       "      <th>sex_y</th>\n",
       "      <th>confirmed_male</th>\n",
       "      <th>deceased_male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-02</th>\n",
       "      <td>female</td>\n",
       "      <td>2621</td>\n",
       "      <td>9</td>\n",
       "      <td>male</td>\n",
       "      <td>1591</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>female</td>\n",
       "      <td>3002</td>\n",
       "      <td>12</td>\n",
       "      <td>male</td>\n",
       "      <td>1810</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-04</th>\n",
       "      <td>female</td>\n",
       "      <td>3332</td>\n",
       "      <td>12</td>\n",
       "      <td>male</td>\n",
       "      <td>1996</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-05</th>\n",
       "      <td>female</td>\n",
       "      <td>3617</td>\n",
       "      <td>14</td>\n",
       "      <td>male</td>\n",
       "      <td>2149</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-06</th>\n",
       "      <td>female</td>\n",
       "      <td>3939</td>\n",
       "      <td>17</td>\n",
       "      <td>male</td>\n",
       "      <td>2345</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sex_x  confirmed_female  deceased_female sex_y  confirmed_male  \\\n",
       "date                                                                          \n",
       "2020-03-02  female              2621                9  male            1591   \n",
       "2020-03-03  female              3002               12  male            1810   \n",
       "2020-03-04  female              3332               12  male            1996   \n",
       "2020-03-05  female              3617               14  male            2149   \n",
       "2020-03-06  female              3939               17  male            2345   \n",
       "\n",
       "            deceased_male  \n",
       "date                       \n",
       "2020-03-02             13  \n",
       "2020-03-03             16  \n",
       "2020-03-04             20  \n",
       "2020-03-05             21  \n",
       "2020-03-06             25  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The processing is the same as the one above.\n",
    "TimeGender.drop(['time'],axis=1,inplace=True)\n",
    "male=TimeGender[TimeGender['sex']=='male']\n",
    "male=male.rename(columns={'confirmed':'confirmed_male'})\n",
    "male=male.rename(columns={'deceased':'deceased_male'})\n",
    "female=TimeGender[TimeGender['sex']=='female']\n",
    "female=female.rename(columns={'confirmed':'confirmed_female'})\n",
    "female=female.rename(columns={'deceased':'deceased_female'})\n",
    "result = pd.merge(female ,male, on='date')\n",
    "result.set_index(['date'],inplace=True)\n",
    "result.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABDwAAAHiCAYAAAAEZtwbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdeXxU1fk/8M+ZSTJL9pnsCUnYwk4IiUEQKptIFakUKQj6U9wQRfstaLWuFagrYKUogpSqLQpK1SKoFASslaUmQBAkbCELgYQkk32WzHJ+f9ybMIQEwhIC4fN+veY1M/eee+85MxNlnnmec4SUEkRERERERERE7YmmrTtARERERERERHSpMeBBRERERERERO0OAx5ERERERERE1O4w4EFERERERERE7Q4DHkRERERERETU7jDgQURERERERETtDgMeRERXGSHEr4UQm4QQFUIIhxDioBBirhAirFG7eCHEX4UQhWq7XCHEW97thBB3CCGkECK1mWulqft/oz7PFULM89r/R3W/FEJ4hBDlQogfhRB/EkJEtXA8XYQQS4QQWUIItxBiSzPtQoQQy4UQFiFEjRDiayFElxacf4tXH51CiJNCiG+FEI8KIXQt6WOj80Wo404832MvhhAiWgjxlRCiUh3L0GbatXi8Qoh71XYBXtt6CCG+F0LUqvsShRAaIcTbQohiddsfW3e0LSeE8FPfj37ncUyyEOIj9W+jTv1MbRRCTBRC+LRmfxv1Y4YQQl6u6xEREV1rGPAgIrqKCCHmA/gUQA6AuwGMAvAmgNsAvOfVrheATADXA3hWbfcKgHEAdgghYtSmawFUA5jUzCUnAahR2zWnEsBAAIPU9p+pffupuUBKI70A3ALgoHprzioANwP4LYDJAMwAvhVCBLXgGpvVPt4I4EEAewC8BmCbECKkBcd7iwDwIoDE8zzuYj0LIBnAnVDGsvMsbVs63nVqO6vXtjcAhAAYq+47AeDXAB4B8Ad127KLH84l4wfl/WhRwEMIcQeAHwFEQxnPSAD3Qfmb+hDAPa3TTSIiIrrcLtuvGEREdHGEELcBmAngfinlcq9d3wkhlkIJakAIIQD8A0A5gIFSyiqvdmuhfPl9B8DtUkq7EOILAL8RQvxeSim9ricA/AbAv6SU3l+IG3NJKbd7PV8vhFgM4D8AVgkhukkp3Wc5/ksp5b/Ua64GENa4gRBioDq+EVLKTeq2HQCOAngIwLzGxzRiadTHfwkh/gZgK5SA0dRzHH8l6A5gh5Tyqxa0bdF4pZQlAEqauM4aKeW39RuEEN0BlDf63F0QIYRBSmm72PNc4LVjAbwPYAWA+7w/7wC+UAOK0W3Rt4vRlq8pERHRlYwZHkREV4/fAdjZ1JdOKaVbSvm1+vQXUH7tnusV7KhvVwhgIYCxXiUZHwOIh5Kh4e0GAB3U/edFSlkB4PcAOgO46RxtPS04ZT8ALgDfeR1XDCV4c+v59k89fg+ARQCm1GeJqGUjy4UQOUIIm1e5kJ+6PxHAT+opNteXjqj7/IUQi4QQB4QQViHEUbUM5JwZKEKIjkKIL4QQVUKIaiHEl97lOuo1RgAYp14z9xKNt6GkRS1dkVDes9+p27eoJUZzAIR6lcokqsfHCyFWqiUhViHEeiFEN69+J6rtpwghPhRCVAD40mv/A0KIfUIpucoTQvy+0evyvhAiQwhxkxBij1pm8181g6letXr/t8b9a8IDUH7smdUo2FH/Gh2QUm5p1IdfqX2wCyGKhBCvCyF8vfb/UQhRKoRIEUJsV1+HXUKIIY3Oo1M/HxXq6/UmAF80IoQwCaXEq1i95lYhxIBGbaQQYqYQ4s9CiBKon0khxGChlCNVqbfdQogJzbwWRERE7R4DHkREVwH1C9YgAN+0oPkv1Pt/NbP/CwACwGD1+QYApTizrGUSAAuAf59XZ0/ZDCVIcf0FHu9NDyWTpHGmiANAj4s47wYoXzr7q8/DoIx5JoDRUMo7pgL4i7r/BIAp6uNHoZR3DFSfGwFooZSe/BLA8wCGQylBapZQ5tX4Vh3HgwDuBdARSkaOSW02EMAunCpVGXcBYwXOHK+3E+q5iwB8pD5+RL39FadKlwYCOKH27b8AugF4GEo2kD+AjUIIQ6Nzz4MSmJgA4GV13E8CWAzl8zhGfTxHCDGj0bHxUN6HP0Ep54kA8ImagQQorzEAzPXuXzPj/wWADCmlpZn9pxHK3DWfAfgflBKfl6BkFL3SqKkRwAcAlgAYD+Vz+bkQwujV5lUoAZc5UD5DCQBmNbqeDsBGKEHCJwHcDiUDZ6M4c06cJ6Fko9wN4HE1iLUWSmnOeAB3APg7lPIkIiKiaxJLWoiIrg5mADoA+S1oGwugQkpZ2cz+PK92kFK61FKSCUKI/5NSuoUQWihfmFZLKZ0X0mEppUMIUQog8kKOb+QwAL0Qoo+Usv7XbAOA3gACL+K8x9T7SABQz/1E/U4hxA8AagEsF0I8po5pj7r7Z++yEbU8ZLrXsT5QSm7+K4SIl1I2995NhfKlPklKmaMeuwPKF9dpAF6RUm4XQlThzFKVixqvNymlA8B2IYQDwAnv6wghjqFR6ZIQ4jkoAY5+9QEE9fXKhTInxttep98upXzU69ggKPNuzJVSvqRu3qAGCJ4TQiz2Cm6ZANwgpTykHqsB8DmUQEs2lPk4AOBIC16bGAC7G28Up09U6pFSetSAyhsAPpRSPuLV1gHgbSHEK1LKMnWzAcD/eZVbnYASoPoFgG+EEGYoQaEXpZTz1TbrAfzcqCt3QflM9/Ia70YAB6AER570alskpZzo1a80AMEAZkgp67NeLjRYSURE1C4ww4OI6OpyKVZ0aOocH0P5Enyj+nyo+vy8y1kaEQ0PhNAKIXy8buJsBzayHkrwYIkQopsQIhrAu1C+4J1tfpAW90/toxBC/J8Q4mchhA2AE8p8DzooQYmzn0yIu9Vyhhr12P+qu5LOclg6lFKlnPoNUspjAH7AqSycS+V8XvNzGQklY6Sq/j2FksWRCSCtUdt1jZ4PhBIs+dT7MwFgE5TPXZxX29z6L/+q+iCBd5uWEmj0+VcDBU6v2yfqriQo7/knTfRRDyUwUc8JYMtZ+thHPaYh60ot5WqchTUSyut31Ot6gFLKda7X9AiUCYY/UstwmNlBRETXPAY8iIiuDmVQ0uTP+aUbQCGAENH83BGJXu3qfQ/l1//6spZJAI5DmXj0gggh9FAyU4rVTd/i9C+WNzZz6BmklHVqnyKh/Kp/HEAnKKtqFJ/l0HOJVe/rz/F/AOZDySD4FZRgRH1mgv5sJxJCjFP7sw1K6cb1OFV6crZjo9H0GIqhZDdcSo3HezHCAEzE6e+pE8AwKHO/eGt8vfqJafc1Onazut37+IpGx9ap92d9P5pRiDMDJT8DuE69ea98U9/Hrxr18WgTfazynotG/bx697G+HOVko2s3fh4G5XPT+DWdinO8plLKcigT+/pCCdqUCCHWCSE6gYiI6BrFkhYioquAlNKplgvcDOC5czSvD1KMhbJaS2NjofzK/b3X+aUQYhWAqUKI/4OyDOkHLZxQtDnDoPx/Zpv6fBpOLz85cD4nk1L+TygTeSZBKa84IpRVZy6mxGMUlC+UmerzCQA+lVI+W99ACNGzheeaAGUVFe/yh5YEdU5AWZq3sUgo84lcSo3HezEsANZAmZOisepGzxtnFdWPawyaDr6c12fjPPwHwNNCiFA1QAB1BaIMABBCePe7vo8PQSlPaexoE9uaU6TeR+D09zSiUTuL2pfpOJOj0fOmJl3dBmC0Wu41EsACKPOxXIp5dIiIiK46DHgQEV09/gxgjRDiHinlB9471HkNRkkpv4HypW43gOeFEP/yqueHWgryWyhLzebhdB9DmSfgDSiZBRdczqKm078GZe6NjYCyAsaFnq+eurLGAfUaXaF8qbvtAvvYF0r2xj+8XiMDzvxiOaXR8+YyDFpybFN2APh/QoiOUsqjat9ioUxS+8cWHN8izYz3YnwLZaLSfRewJOo2ADYAMVLKxqUZ5+t8Mj6WQZkH4w0oE4iezQEoGSGJUsr3Lrx7AJRVVOxQsoaygYa/2V81avctlKBUvpSycfZHi6nvx5dCiN4A/nCh5yEiIrraMeBBRHSVkFJ+KYRYAOCvQogboNT/1wDoDmVCxFwA36jZGndDKQ/YJoR4Xd3XHcoKIpU4Vabhff5MIcRBKL8uH5FS/ti4TTN8hBD1vyAHAkhVz2EEMLqJlVVOo05UeYv6NBZAkBDiDvX5V+ov8BBCPA/ly2IplDkRngewUkq5oQV9NKl91EApsxkGZUWUg1BWZKm3AcqKFzugzIkwBUCXRufKh/Jl/R4hRCUAp5QyQz32bSHEs1CCGLdAWUr2XN4H8BSAr4UQL0CZk+SP6jiXtOD4prR0vBdjAZRJNjcJIf4CJThQPw/Mf6WUzQbMpJQVQog/AnhLCJEAJUingZK9M0xK2eJVaKSUdUKIowB+I4TYCyWwsMerrMS7baEQYiqAFWqpx9+g/G0EQJkjoy+UrBWoE5fOAvB3tTzsayjBlU5QVk+5o/6z2YI+lgkhlgJ4SQjhglLK86B6XW8fQvlb3iKEmAdl4lozlNKqIinlm81dQwhxK5TJYr+A8hmNhZJVtaklfSQiImqPGPAgIrqKSClnCSG2ApgBJVXdAOUL2xooS3/Wt9srhEiFshLGq1DmBjgB5cvQHCllaTOXWAngBfW+pYKh/GIvAVRByer4B4C/SCmLznagKgJnLt1a/7wjlPEByhe/P0MZSwGU8c5vYR+HqX10QZkT4icATwNYpq5OUm82gHAoS5wCypKkjwP4sr6BlNIuhHgQymv7HZQ5EwSU4EQnKBk0eigBkMk4R8mNuvJLffnBX9VzbQHw65Yun3oR471gUspSNajyJwBvQln+9ASUiVr3nO1Y9fjXhRDHAfwOSmaRHUpAZtUFdOdhKJ+HjVAmmPX+3DS+7qdCiENQgkyvQXm/q6FkRf0BwHKvtqvU1XGegRJMcEMJQqzFqcySlvo9lM/KCwA8UP5GFsDrM6x+toZB+Ry+BCWAdBLKsrhrznH+w1D+Bl+G8jdVovbzmfPsJxERUbshlOxgIiIiIiIiIqL2g6u0EBEREREREVG7w4AHEREREREREbU7DHgQERERERERUbvDgAcRERERERERtTsMeBARERERERFRu9Mul6UNCwuTiYmJbd0NIiIiIiIiagWZmZmlUsrwtu4HXdnaZcAjMTERGRkZbd0NIiIiIiIiagVCiLy27gNd+VjSQkRERERERETtDgMeRERERERERNTuMOBBRERERERERO1Ou5zDg4iIiIiIiK4tmZmZET4+PssA9AZ/3L8WeADsdblcD6Smpp5sqgEDHkRERERERHTV8/HxWRYVFdUjPDy8XKPRyLbuD7Uuj8cjSkpKehYVFS0DMLapNox6ERERERERUXvQOzw8vIrBjmuDRqOR4eHhlVAyeppucxn7Q0RERERERNRaNAx2XFvU97vZuAYDHkRERERERETU7jDgQURERERERHQFmTZtWlyXLl16TZs2Le71118PX7Rokbm1r7l27drAYcOGdWluv81mE4MGDUrq3r17z/feey+0tfoRGxvb58SJE5dkvlFOWkpERERERER0BVmxYkV4SUnJboPB0GyJjtPphK+v72Xr09atW41Op1NkZ2f/fNkuepGY4UFERERERETXpN+t2t2h0x/Wpf5u1e4Ol+qcixYtMiclJfXs1q1bz9tvv73jwYMH/QYOHJiUlJTUc+DAgUmHDh3yA4Dx48cn3nvvvR1SUlK6x8XF9fnb3/4WCgDDhw/vYrPZNCkpKT3ee++90JkzZ8a88MILkQCQnp7ebcaMGbHXXXddt7lz50aOHz8+ccqUKfEDBgxIiouL67Nu3bqACRMmJHbq1KnX+PHjE+v79NlnnwX169eve8+ePXv88pe/7FRZWakBgNWrVwd17NixV2pqarfVq1eHNDemwsJCn6lTp3bMzs42dO/evee+fft033//vfG6667r1qtXrx6DBw/umpeX51vfx/vvv79DWlpat06dOvX67rvvjKNGjeqckJDQ+/HHH4+pP+fIkSM79+rVq0eXLl16zZs3L6yp677zzjumPn369OjevXvPyZMnJ7hcrvN6L5jhQURERERERO1O4tPrUlva9vNdhRGf7yqMOFe73FdvzTzb/oyMDP28efOit23blh0dHe0qLi7W3nnnnR0nT55c9thjj5X9+c9/Nk+fPr3Dxo0bjwBAcXGxb0ZGRvbu3bv148aN6zJ16tTyTZs2HTYajSn1mRQzZ840eF+joqJC++OPPx4AlKBJZWWlz7Zt2w5+9NFHIRMnTuy6adOm7NTUVFvfvn17bN261dCxY0fnyy+/HP2f//znYFBQkOfZZ5+NmjNnTuTs2bOLZsyYkbhhw4YDvXr1cowZM6ZTc+OKjY11vfPOO3nz58+P3Lx582GHwyGuv/76buvWrTscExPjeu+990KfeOKJ2E8//TQXAPz8/DwZGRkH5syZEzFhwoQuP/744/6IiAhXYmJin2eeeaY4KirKvWLFitzIyEh3TU2NSElJ6XnXXXeVR0VFueuvuXPnTv3q1atNGRkZ2TqdTt51113x7777rnnGjBll53qf6jHgQURERERERO3OuYITgJLh8a/dhRG/6hd78s2J/Qou9prr168Puu2228qjo6NdABAZGenetWuX/9dff30EAKZPn2556aWX4urbjx07tkKr1SI1NdVeVlbWovqUO++80+L9/NZbb63QaDTo37+/1Ww2O9PT020AkJSUZDty5IguLy/P78iRI/r09PTuAOB0OkVqamrN7t279XFxcY4+ffo4AGDKlClly5YtC29JH/bs2aM7dOiQYfjw4UkA4PF4EB4e7qzfP27cuAoASE5OtnXp0sWWkJDgBIAOHTo4cnJy/KKiomyvvfZa5Lp160IAoKioyHffvn36qKio2vpzfPPNN4F79+41Jicn9wAAu92uiYiIOK8UDwY8iIiIiIiI6Jr05sR+BZci0FFPSgkhRIuXxtXr9Q1tpWzZYYGBgZ6mzqHVauHn59dwEo1GA5fLJbRarRw8eHDVl19+edT7uK1btxqEEC3t6mmklKJLly623bt3Zze1v75PGo0GOp3ujD6tXbs28LvvvgvMyMjIDgwM9KSnp3ez2WynTbkhpRQTJkwoe/vttwsvqJPgHB5EREREREQE5Qu3tc6FmZ/sRuc/fIXnv9jb1l266owePbpqzZo1pqKiIi0AFBcXa1NSUmqXLVsWCgBLliwxpaWl1VzOPg0dOrQ2IyMjYO/evToAqK6u1uzZs0fXr18/+7Fjx/z27dunA4CVK1eaWnrOvn372i0Wi8/GjRv9AcDhcIiMjAx9S4+vqKjQBgcHuwMDAz27du3SZ2Vl+TduM3r06Kq1a9eGFhYW+gDKa3nw4EG/ll4DYIYHERERERFRu+Jye1Bld6HS5kSFtQ4VNieqbE5UWJVbpc2JClsdKhseK9urbE5oNIDdqSQQfLQjH3Nu793Go7m6pKWl2WfNmnViyJAh3TUajezdu7d18eLF+ffcc0/iW2+9FWU2m10ffvhh7uXsU0xMjGvJkiW5kyZN6lRXVycA4MUXXyzs27ev4y9/+UvemDFjuphMJteAAQNq9u/fbzjX+QAlg2PlypVHHn/88fjq6mqt2+0W06dPL05LS7O35Pjx48dXLl26NDwpKaln586d7cnJybWN26Smptqfe+65whEjRiR5PB74+vrKhQsX5iclJdW1dOyipWkzV5O0tDSZkZHR1t0gIiIiIiK6IFJK2JzuUwEKqxOVtrqGx19mHce+41WINxnRwWREha2uoa21zo1AvQ9CDL4INvoh2OCLEIMvQoy+CDYot5D67eq2EIMvggy+0Ptq8fwXe/HRjnxMHhB/xQY8hBCZUso0721ZWVm5ycnJpW3VJ2obWVlZYcnJyYlN7WOGBxERERERUSuoDxz8un8sHhnW5YKyLUIMfggxKsEI76DFvhNVkAAKyq2YO643QgxKACPY6ItAnQ80mgubmwEA5tze+4oNdBCdDwY8iIiIiIiIzoPd6UZZbR1Kqx0oram/1aGk2nHa9kMnlakaPs08hh9zLWpAwk/JvFCDFzEhevSIDkSI0a/JbIvm++BpyMIY0rVFC2sQtchbb71lXrx4caT3tuuuu67m73//e35b9elCsaSFiIiIiIiuebUO12nBi9IaB0qr67y2ndpud7ph9tchLNAPYQE6r5sfwgOVx+YAP/z1+6P4bGfhFV0acrViSQvVY0kLERERERFd06SUKKlxIK/MitzSWuW+TLnPLqqC0y1h9NOia2QgwgNOBTI6h/tjQCfTaUGNYIMvWrKc5xsTkvHGhOTLMDoiagoDHkRERERE1C54PBJFVfaGQIZyq0Wueq/31SLBbESi2R8JZiNG9ohEgtmIOxZvAwA4nB7869Eb2ngURHSpMOBBRERERERXDZfbgxOVSlAjt8yKvNJTAY18ixXBBl8kmv0RbzYi0WzELX2iG54HG3ybPOfkAfEN82EQUfvRqgEPIUQIgGUAegOQAO4DcADAKgCJAHIB/EZKWS6UnLC3ANwCwArgXinlTvU89wB4Tj3tXCnlB63ZbyIiIiIiajs1DhcKLFbkW6zIL1Puv91fjOOVdmgEEBWkR4LZH4lhRiSY/ZGWGIoEsz/iTUb4687/Kw5XJSFqn1o7w+MtAN9IKe8QQvgBMAJ4BsC3UspXhRBPA3gawFMAfgmgq3obAGAxgAFCCBOAFwGkQQmaZAoh1kgpy1u570RERERE1ArcaulJfpn1VGBDvRVYrKitcyHeZES8yYgOJiM6hftjxQ47AEBAYOsfRrTxCIhaZubMmTEBAQHu2bNnF7d1X5oTGxvbJyMjY390dLSrqf1z586NWL58eXjv3r2ta9asOdoafWit16nVAh5CiCAAvwBwLwBIKesA1AkhfgVgqNrsAwBboAQ8fgXgQ6ksG7NdCBEihIhW226QUlrU824AMBrAx63VdyIiIiIiujjVdmdDAONUQMOGAosVhRU2mIx+DQGNeJMRw7qHNzwPD9CdMSloTkkty06I2sBf//rX8K+//vpQ9+7d69q6L+erNTM8OgEoAfA3IUQygEwAvwUQKaU8AQBSyhNCiAi1fSyAAq/jj6nbmttORERERERtqLy2Tp1LoxZHS5V5NP57qARltU74aIAuEYENAY2uEYEY0T0SHUxGxIUaoPfVnte1WHZCreKzhzrgp08j0GfCSfx6acG5Dzi3p556KmrVqlVhMTExdWaz2ZmSkmLdt2+f7uGHH463WCw+er3es2zZsryUlBR7QUGBz3333ZeQn5+vA4BFixbl3XTTTbXvvPOOafHixZFOp1P079+/9sMPP8zz8fHBlClT4rOysvztdrvmtttuK3/zzTePA8AjjzwSu379+hCtViuHDh1atXTp0mPHjx/3mTp1akJhYaEfACxYsCB/1KhRtUVFRdrx48d3slgsvikpKbVKzkHTJk+eHH/s2DHd2LFju0yZMqX0d7/7Xen9998fv3//foPb7RbPPvvs8bvuuqti4cKF5jVr1oR4PB5x4MABw6OPPlpUV1enWbVqldnPz8/z73//+1BkZKR7/vz5YX/729/CnU6nSExMdKxevfpoYGCgx/uazb1WF/JetGbAwwdAfwCPSSl3CCHeglK+0pym1nWSZ9l++sFCPATgIQCIjIzEli1bzrvDRERERER0upo6iWKrB8VWieJaT8Pjk1YPPBKIMmoQYRSI9Ncg0ihgqXUCADwSeLqfB0CNcqsDUAQcK1J+wSRqdX8MTm1x2z2rIrBnVcQ52/2xMvNsu7///nvj559/bvrpp59+djqd6NevX8+UlBTrAw88kLB06dK8Pn36ODZt2uQ/ffr0+O3btx98+OGH44cMGVL9wgsvHHG5XKisrNTu3LlTv3r1alNGRka2TqeTd911V/y7775rnjFjRtmCBQsKIyMj3S6XC4MGDeq2Y8cOQ2JiYt1XX30VmpOTs1ej0aC0tFQLANOmTeswc+bM4ptvvrnm0KFDfjfffHPXnJycfU8//XTMwIEDa+bNm3di5cqVwR9//HFYc+P56KOP8r/77rvg77777mB0dLRrxowZscOGDav69NNPc0tLS7VpaWk9xo4dWwUABw8eNGRlZf1ss9k03bp16/38888X7t+//+f777+/w5IlS8wvvPDCySlTppTPmjWrFAAef/zxmIULF4Y9++yzJ72v2dxr1YJ38QytGfA4BuCYlHKH+nw1lIBHsRAiWs3uiAZw0qt9B6/j4wAcV7cPbbR9S+OLSSmXAlgKAGlpaXLo0KGNmxARERERURO8MzVyS63qvbL6iccjkRjmr9wSjPiFWX1sNsLk73dG6Yn9i70NpSdDhzIjg9rQOYITAC55hsfmzZsDbrnllor6rIVRo0ZV2O12za5duwImTJjQub5dXV2dAICtW7cGrl69+igA+Pj4wGw2u5csWWLau3evMTk5uQcA2O12TUREhAsAPvjgA9P7778f5nK5RElJiW9WVpa+f//+Np1O55k0aVLCrbfeWjlx4sRKAPjhhx+CDh06ZKi/Zk1Njba8vFyzffv2wM8+++wwAEyaNKly2rRp7paOb8uWLUHr168PWbhwYRQAOBwOcfjwYT8AGDRoUHVoaKgnNDTUExAQ4J4wYUIFAPTp08e6Z88eIwBkZmYaXnjhhdjq6mptbW2t9sYbb6z0Pn9lZWWzr9WFaLWAh5SySAhRIIToJqU8AGAEgJ/V2z0AXlXv/6UesgbADCHESiiTllaqQZH1AF4WQoSq7UYB+ENr9ZuIiIiIqD2y1bmRU1qDnJJavPd9Dn46VgmTvy9cHjQENRLMRnQM88cvuobj/w1MQKLZv8mgxtmw9ISuKr9eWnCpSlnqNf578Xg8CAwMdGVnZ//ckuOllGLChAllb7/9dqH39uzsbL9FixZFZmZm7g8PD3ePHz8+0W63a3x9fbF79+79a9asCVq5cmXo4sWLI7Zv335QSomMjIz9AQEBZ1RIaDSaC00ydaIAACAASURBVBqblBKrV68+nJyc7PDe/t///tffz8+v4ToajQZ6vV7WP3a5XAIAHnrooY6rV68+PHDgQNvChQvN3333XaD3edxu93m9VudyYaNsuccArBBC7AHQD8DLUAIdNwkhDgG4SX0OAF8ByAFwGMB7AB4BAHWy0jkAflRvs+snMCUiIiIiolM8HonjFTZ8f6gEH2zNxYv/2ou7/7oDg175Fv1m/xu/W7Ub6/acwE/HKiEBlFud+HbWjdjzx1H48rHBWDS5P2aN6obxqXFITTDB3MTkoUTUvOHDh9esW7cupKamRpSXl2s2bNgQYjQaPXFxcXXLly8PBZQAyLZt2wwAcMMNN1S/8cYb4QDgcrlgsVg0o0ePrlq7dm1oYWGhDwAUFxdrDx486FdeXq41GAwek8nkLigo8NmyZUswoGRFWCwW7cSJEyvffffdgv379xsBYPDgwVWvvfZaQ5nO1q1bDQBw/fXXVy9fvtwMAJ988klQVVVViyfUGTZsWNX8+fMjPR5l2o0ffvjBcI5DTmO1WjXx8fFOh8MhVq5caWq832QyNftaXYhWXZZWSrkbynKyjZ2xjpS6OsujzZxnOYDll7Z3RERERERXp1qHC0dLa3GkRMnYqL/PLauFv84HncL80TkiAJ3C/DG0WwQ6hfsjLtQIrUYJXjzvVXYSFqBr49EQtR+DBw+2jhs3ztK7d+9esbGxjvT09BoA+Pjjj3MefPDBhNdeey3a5XKJcePGWQYOHGhbvHhx/r333puQlJQUptFosGjRoryRI0fWPvfcc4UjRoxI8ng88PX1lQsXLswfMWJEbe/eva1du3btFR8f70hNTa0BgIqKCu2YMWO6OBwOAQBz584tAIClS5cWPPDAA/FJSUk93W63GDBgQPWgQYPyX3311ePjx4/v1LNnzx4DBw6siY6ObvHqK6+++urxhx56KL579+49pZQiLi7OsXnz5sMtPf7pp58+np6e3iM2NrauR48e1pqamjOCLc29Vi29hjdxthlZr1ZpaWkyIyOjrbtBRERERHTB3B6JJz7Nwr92FyI1IRTdo4IaSlLKrXVINPujU7g/OoUFoHOEct8x3B9Bet+27jpRqxNCZEopT/txPSsrKzc5Obm0rfpEbSMrKyssOTk5sal9rZrhQUREREREzbM73ThWbkVemXLLtygThuaXWXGswoY6l5I2npFbjl/2jsaIHhHoHB6A2BADNBqWmhARnQ0DHkRERERErajK7kS+GtDIsyjBjPqgRmltHWJDDIg3GZFgNiLB7I/BXcKQYDaig8mIP63b31B6ct/gjm09FCJqp4qKirRDhw7t1nj7li1bDkRFRbV4FZcrDQMeREREREQX4fkvfsKKHfm4qWckRvWMQp7Firyy2oaMDbvTfVpAo3dsMMb0jUGC2YjoYD18tM2vI8AVT4jocoiKinJfqpVRriQMeBARERERNcPjkSirrUNRpR0nKm04UWnHiUo7itTHRVV25JVZAQDr9xVD76tFgsmIIV3Dcff1RsSbjQjnSidERG2CAQ8iIiIiuia5PRKlNY7TAhiNAxonqxwI0PsgKkiPmBA9ooL1iA42oGtEOKKDlefvfZ+DT348hskD4pmNQUR0BWHAg4iIiIjanTqXB8VVdhRXKQGM4io7/pl5DNlF1TAH+MFPq0FJjQPBBr+GwEW0GszoGR3U8DwySA+97xmrJp7mlV/3xSu/7nuZRkZERC3FgAcRERERXVVqHC4UVdpQVOnAiUrbaUGNoio7iirtqLQ5ERagQ1SwHlFBSkAju7gaEoCltg7fPTkMkUF6+Pk0P38GERFd3RjwICIiIqIrgpQSv1+9B//ceQw3dg3H8J6RKK48FcSov3d75GmBjKhgPZIiAzGk66kyk7AAHbSNlm11uWXDiicdTMY2GiUR0blNmzYt7ttvvw0eMWJEZefOnR1Go9EzY8aMsta85tq1awPnz58fuXnz5sOtcf7x48cnjhkzpnLq1KnlrXH+pjDgQURERESXhUedM+NYhQ3Hym0oLLfhWLkVhRX1j22wOZXVDzcfLEFUiFJScl1iKCKDlHKTqCA9ggw+FzQJKFc8IaKrxYoVK8JLSkp2GwwG2Vwbp9MJX1/fy9mtqw4DHkRERER0Sbg9EkVV9lOBjHIbCuuDGxXKLVDng9hQA+JCDYgNMSApMhDDukUgNtSA2FAD3vjmQEMWBoMTRNTa/vD9Hzp8dfSriFs63nLylSGvFFyKcy5atMi8cOHCSCEEevToYXv99dcL77nnnsSysjIfs9ns+vDDD3O7du1aN378+MTAwEB3VlaWf0lJie+cOXOOTZ06tXz48OFdbDabJiUlpcesWbNO7N+/3xAQEOCePXt2cXp6erf09PSaHTt2BNxyyy0Ve/fuNej1es/hw4f1hYWFuiVLlhx9//33wzIzM/1TUlJq//nPf+YCwGeffRY0e/bsmLq6OpGQkOBYuXJlbnBwsGf16tVBTz75ZAeTyeTq06eP9WzjmjlzZkxubq5fcXGxb25urv7ll18u2LZtW8CmTZuCIiMjnRs3bjys0+nkE088Ef3NN9+EOBwOTVpaWs2KFSvyNJrTywe///5748yZMztYrVZNaGioa8WKFbkJCQnOS/H6e2PAg4iIiIhaxO2ROFFpQ4HFhoXfHsT2HAs6hvkjPFCHwgobTlY5EOrvi7hQI2JDlABGn7hgjO4dhbhQA2JCDDD6nf2fn8zCIKJLpc8HfVJb2nZtztqItTlrI87V7qd7fso82/6MjAz9vHnzordt25YdHR3tKi4u1t55550dJ0+eXPbYY4+V/fnPfzZPnz69w8aNG48AQHFxsW9GRkb27t279ePGjesyderU8k2bNh02Go0p2dnZPwPAzJkzDd7XqKio0P74448HAKVMpLKy0mfbtm0HP/roo5CJEyd23bRpU3Zqaqqtb9++PbZu3Wro2LGj8+WXX47+z3/+czAoKMjz7LPPRs2ZMydy9uzZRTNmzEjcsGHDgV69ejnGjBnT6Vzjz8vL023duvXgzp079cOHD+/+wQcfHHn33XeP3XTTTZ0/+eST4LvvvrviySefPDlv3rwTAHD77bd3XLlyZfDkyZMr68/hcDjE448/Hr9u3brDMTExrvfeey/0iSeeiP30009zz3X988WABxEREREBUObQKLc6UWCxIt9iRUG5FQUWGwrUxycq7TAZ/dDBZEBGbjkkgNyyWsy5vTdiQwyIDtFD53P2FU2IiC6XcwUngEuf4bF+/fqg2267rTw6OtoFAJGRke5du3b5f/3110cAYPr06ZaXXnoprr792LFjK7RaLVJTU+1lZWUtqk+58847Ld7Pb7311gqNRoP+/ftbzWazMz093QYASUlJtiNHjujy8vL8jhw5ok9PT+8OAE6nU6Smptbs3r1bHxcX5+jTp48DAKZMmVK2bNmy8LNde+TIkZU6nU6mp6fb3G63uOOOO6oAoFevXrajR4/6AcDXX38duGDBgii73a6pqKjw6dmzpw1AQ8Bjz549ukOHDhmGDx+eBAAejwfh4eGXPLsDYMCDiIiI6Jpiq3OrgQyrGtiwnfZcqxGINxvRIdSIDiYjesYE4eZekehgUrI26pdoff6LvQ2lJzd0CWvjURERXZhXhrxScKlKWQAlcCyEaHbejcb0en1DWylbdlhgYKCnqXNotVr4+fk1nESj0cDlcgmtVisHDx5c9eWXXx71Pm7r1q2G850PSafTNVzLx8dH1peq1F/LarWKWbNmJezYsePnLl26OGfOnBljt9tPq2eRUoouXbrYdu/enX1eF78ADHgQERERtSNSSpTUOJBXZsWCfx/A9hwLEs1GhPj7ocBiQ7XdidhQA+JN9UENA9I7hiJODXAEG1o2AR5LT4iIzjR69OiqO+64o8szzzxTHBUV5S4uLtampKTULlu2LPTRRx+1LFmyxJSWllZzOfs0dOjQ2lmzZsXv3btX17t3b0d1dbXm6NGjvv369bMfO3bMb9++fbpevXo5Vq5cabrYa1mtVg0AREVFuSorKzVffvll6G233Xbaqix9+/a1WywWn40bN/qPHDmy1uFwiJ9++kmXlpZmv9jrN8aABxEREdFVxjuocbS0FrmltQ2P88pqofPVItFsxK78CkgAeRYrXp+QjHiTEeEBOmg057/CCRERnVtaWpp91qxZJ4YMGdJdo9HI3r17WxcvXpx/zz33JL711ltR9ZOWXs4+xcTEuJYsWZI7adKkTnV1dQIAXnzxxcK+ffs6/vKXv+SNGTOmi8lkcg0YMKBm//79hnOd72zCwsLcU6ZMKenZs2evuLi4uuTk5NrGbfR6vVy5cuWRxx9/PL66ulrrdrvF9OnTi1sj4CFamjZzNUlLS5MZGRlt3Q0iIiKiC9Y4qJFXVovc0tODGglmIzqa/ZEY5q88DvNHgtm/IUvDu+yE2RhE1J4IITKllGne27KysnKTk5NL26pP1DaysrLCkpOTE5vaxwwPIiIiojbi9kg8uToLX+wqxICOJvRPCEVuqRW5ZUrGhp+PpiGokWD2x6hekWcENc6GZSdERHQtY8CDiIiIqJXYnW4UVthwvMKGwnIbCr3v1WVc69zK3HPbcyy4vlMYRvWKRKLZH4lmfwQbWzafBhER0aXy1ltvmRcvXhzpve26666r+fvf/57fVn26UAx4EBEREV0AKSUqbU4cK1cDGl7BjPrnVXYXooP1iA0xIDbEgJgQA67vbEac+jg6RI+5a/c3lJ38dmTXth4WERFd437729+W/fa3vy1r635cCgx4EBERETVS5/KgrNaBkmoHSmuU+4//V4CsggpEh+gRoPNBYbkNGo1oCGbEhir3yR1CEBtqQFyIAWEtmCCUZSdEREStgwEPIiIiuia43B6U1dahpNqBkhoHShvu605/XuNArcMFk78fwgN1CAvQITxAh6wCZcWToko71j0+BLGhBgTpWXJCRER0pWLAg4iIiK5aHo9Ehc2JkmoHTlbb8e6WI9iaU4bukYHoFhV4WkCjyuZEiLE+iKHchwfqEBOiR3KHYCWwoQY4Qgy+Z2Rm6H21DaUnPaKD2mjERERE1FIMeBAREdEVx1bnVjMx7DhZpWRelFSfup1U78tqHfDX+SBcDVZsO1IGCSC7uBoP/qLTaUEMk78ftOcoLzkblp4QERFdXRjwICIiosvG5fbgRKUdf/xyHzZnn0TfuBD06xByRkDD6fY0ZGCEB+gQEaRDeIAefeNCGrZHBOpgDvCDzkfbcP7nv9jbkIXx6/5xbThSIiIiYObMmTEBAQHu2bNnF7d1X5oTGxvbJyMjY390dLTrUp977dq1gfPnz4/cvHnz4Ut97pZgwIOIiIguqTqXB8fKrcgrsyK3rBZ5ZVbkqffHKmwID9ChsMIGAMgqqMBtyTHonxB6KrARqEOgzgdCnH82BrMwiIiIqB4DHkRERHTe7E43CixW5KrBjPrARm5ZLYorHYgK1iPBbESi2R8JZiMGdwlDYpgRcaFG6H21p2Vi3D+4Y1sPh4iIrlGFv3+qQ9XatRFBY8acjH39tYJLcc6nnnoqatWqVWExMTF1ZrPZmZKSYt23b5/u4YcfjrdYLD56vd6zbNmyvJSUFHtBQYHPfffdl5Cfn68DgEWLFuXddNNNte+8845p8eLFkU6nU/Tv37/2ww8/zPPx8cGUKVPis7Ky/O12u+a2224rf/PNN48DwCOPPBK7fv36EK1WK4cOHVq1dOnSY8ePH/eZOnVqQmFhoR8ALFiwIH/UqFG1RUVF2vHjx3eyWCy+KSkptVLKZsdy4MABv9GjR3dNT0+v2blzZ0CPHj2s9913X+ns2bNjy8rKfN5///2cYcOGWTdv3mycOXNmvN1u1+j1es/7779/NDk52eF9rqqqKs39998fv3//foPb7RbPPvvs8bvuuqviUrzmzWHAg4iIiJr07Oc/4eP/5WNI1zAM7BymBDZKlQBHaW0d4kIMSDAbkWD2R5fwAIzoEYlEsz9iQwzw89Gc9dzMxCAiota2v3uP1Ja2rVqzJqJqzZqIc7Xrkb0/82z7v//+e+Pnn39u+umnn352Op3o169fz5SUFOsDDzyQsHTp0rw+ffo4Nm3a5D99+vT47du3H3z44YfjhwwZUv3CCy8ccblcqKys1O7cuVO/evVqU0ZGRrZOp5N33XVX/LvvvmueMWNG2YIFCwojIyPdLpcLgwYN6rZjxw5DYmJi3VdffRWak5OzV6PRoLS0VAsA06ZN6zBz5szim2++uebQoUN+N998c9ecnJx9Tz/9dMzAgQNr5s2bd2LlypXBH3/8cdjZxlRQUKBftWpVTmpqal7fvn17rFixwpyRkZH90UcfhfzpT3+KHjZs2JHk5GT7//73v2xfX1988cUXgb///e/j1q9ff8T7PM8880z0sGHDqj799NPc0tJSbVpaWo+xY8dWBQUFeVryHl0IBjyIiIgIAFBpdSIz34LMvHJk5JZjx1ELAOC7g6XoGhGInjHBuKVPNBLN/ogO1sNHe/agBhERUVs6V3ACuPQZHps3bw645ZZbKgIDAz0AMGrUqAq73a7ZtWtXwIQJEzrXt6urqxMAsHXr1sDVq1cfBQAfHx+YzWb3kiVLTHv37jUmJyf3AAC73a6JiIhwAcAHH3xgev/998NcLpcoKSnxzcrK0vfv39+m0+k8kyZNSrj11lsrJ06cWAkAP/zwQ9ChQ4cM9desqanRlpeXa7Zv3x742WefHQaASZMmVU6bNs19tjHFxsY60tPTbQCQlJRkGz58eJVGo0H//v2tc+fOjQEAi8WinThxYsfc3Fy9EEI6nc4z6lK3bNkStH79+pCFCxdGAYDD4RCHDx/269+/v/3CX/GzY8CDiIjoGiSlRG6ZFZl55cjMsyAjtxzHK2xI7hCCtIRQPDKsCxLNx7E6sxCTB8TjuTE927rLREREl1zs668VXKpSlnqN56DyeDwIDAx0ZWdn/9yS46WUYsKECWVvv/12off27Oxsv0WLFkVmZmbuDw8Pd48fPz7RbrdrfH19sXv37v1r1qwJWrlyZejixYsjtm/fflBKiYyMjP0BAQFn1KxoNC3/0cLPz6/heI1GA71eLwFAq9XC7XYLAHjqqadib7zxxuoNGzYcOXDggN/w4cO7NTEurF69+nDjUpfWxJ9miIiIrgEOlxuZeeVY+p8jeOjDDFz3p42Y/N52bDlwEkmRgVjwm37IenEUPnrweswc1Q03JoXjtTuSceSVW1h6QkRE1ELDhw+vWbduXUhNTY0oLy/XbNiwIcRoNHri4uLqli9fHgooAZBt27YZAOCGG26ofuONN8IBwOVywWKxaEaPHl21du3a0MLCQh8AKC4u1h48eNCvvLxcazAYPCaTyV1QUOCzZcuWYACorKzUqBkWle+++27B/v37jQAwePDgqtdee62hTGfr1q0GALj++uurly9fbgaATz75JKiqqkqLi1RVVaWNi4urA4AlS5Y0WSIzbNiwqvnz50d6PEoFyw8//GBoqt2lxAwPIiKidshSW6eUpuRZkJlbjp9PVKFjmD9SE0Jxa99ovDi2F2JDWv3fGURERNeUwYMHW8eNG2fp3bt3L7UUpAYAPv7445wHH3ww4bXXXot2uVxi3LhxloEDB9oWL16cf++99yYkJSWFaTQaLFq0KG/kyJG1zz33XOGIESOSPB4PfH195cKFC/NHjBhR27t3b2vXrl17xcfHO1JTU2sAoKKiQjtmzJguDodDAMDcuXMLAGDp0qUFDzzwQHxSUlJPt9stBgwYUD1o0KD8V1999fj48eM79ezZs8fAgQNroqOj6y523E899VTRAw880HHhwoVRQ4YMqWqqzauvvnr8oYceiu/evXtPKaWIi4tztPZyteJsM7JerdLS0mRGRkZbd4OIiOiykFLiSEktMvPU+TfyylFS5UC/+BCkJoQiLcGEfvEhCNDxdw4iImofhBCZUso0721ZWVm5ycnJpW3VJ2obWVlZYcnJyYlN7eO/fIiIiK4Cbo/E8Qob8i1W5FusyCuzIt9Si3yLFQdOVMPpkehoNuK+wR1x76CO6BYVCK3mjPnCiIiIiK4ZDHgQERFdIax1rlPBjDI1sGGxIr+sFscr7TD7+yHeZESC2Yh4kxGje0cj3mTE+He2AgDyLTbcPTCxbQdBREREV52ioiLt0KFDz5hodMuWLQeioqLOuorLlYwBDyIiostESomSGsepYIZ6X/+4xuFEh1AlmBFvNqJzuD+GdQ9HvMkfcaEG6H2bnlNs8oB4fLQjH5MHxF/mEREREV1RPB6PR2g0mvY3b0Mri4qKcrd0FZkricfjEQA8ze1nwIOIiKiV1Dpc2Jlfjh05FnySkY+T1XXQ+WjQIzoICWYjEkxGDOpsxp3p8UgwGxEeoIPmAspQ5tzemyupEBERAXtLSkp6hoeHVzLo0f55PB5RUlISDGBvc20Y8CAiIrpEquxOZOaWY/vRMuzIseBgcTV6xQRhQEczSmuUCdBdbokvHr2hjXtKRETU/rhcrgeKioqWFRUV9Qagaev+UKvzANjrcrkeaK4BAx5EREQXqMJah/8dtWDHUQt2HC1DTkktkuNCMKCTCU+N7o6U+JCGMpRKm5NlJ0RERK0oNTX1JICxbd0PunJwWVoiIqIWKq1xKAGOnDLsOGrBsXIbUuJDcH0nM9I7mtA3Lhg6n6bn2SAiIqJLp6llaYkaY4YHERFRM4qr7NieU9aQxVFcZcd1iSakdzThlV/3Qe/YYPhqmTFLREREdCViwIOIiK55UkqUVDuQZ7Fiwb8PYHuOBQE6H2i1AulqgOPO9Hj0iA6C9gImFSUiIiKiy48BDyIiuiY4XG4cK7cpy8CWeS8JW4sCiw3+Oi3iTUbsyq+ABFBb58LhP91yQaumEBEREVHbY8CDiIjajQprHfItXsGMMivy1IBGSbUD0SF6xJuMiDcZkWA2Ir2jCQlmIzqYjAjQKf9LfP6LvQ2TizLYQURERHT14qSlRER01XB7JE5U2vDSmn3YmH0SPaODkGj2V4MctfBINAQz4s1qYMPkjwSzEdHBevhwvg0iIqJ2gZOWUksww4OIiK4o1jpXQ3bGadkaFisKK2wwGf1QVGUHAPx8ogoP/aKTGuTwR6jRF0IwK4OIiIiIGPAgIqLLTEqJ0po65FtqG5WeKI+rbE50UMtO4k1GdAzzx9Bu4UgwGxEXaoTeV3ta2cmv+sW29ZCIiIiI6ArEkhYiIrqkpJSotDlRVGXH698cwOYDJ9E9MhAdTMaGTA2djwbxZn8k1Ac2zEblsdmIyEA9584gIiKis2JJC7UEMzyIiKjFHC43TlY5UFxlR1GVHUWVdvWxsq3+5qvVICpIj0MnawAA2cXVeHxEVyVzw2xEkN63jUdCRERERO1dqwY8hBC5AKoBuAG4pJRpQggTgFUAEgHkAviNlLJcKEXXbwG4BYAVwL1Syp3qee4B8Jx62rlSyg9as99ERNcaKSUstXUobiKY4R3QqLY7ER6gQ2SwHlFBekQG6REVrEfPmCDlsfrc6Hfmiie/7BPdxqMkIiIiomtJq5a0qAGPNCllqde21wFYpJSvCiGeBhAqpXxKCHELgMegBDwGAHhLSjlADZBkAEgDIAFkAkiVUpY3d12WtBARnVJtd6K4yoGTVXYUV9sbghrKzYHsE1WorXPDVyvQKSwAkcF6RAbqEBWsPy2IERmkh9nfj+UmRERE1OZY0kIt0RYlLb8CMFR9/AGALQCeUrd/KJUIzHYhRIgQIlptu0FKaQEAIcQGAKMBfHx5u01EdGWxO9XykupT2Rgnq08FM+pLTzwSiArWIyJQ15CRERtiQP/4UEQG6TFp6TYAgMcDrP/dL9p4VEREREREl0ZrBzwkgH8LISSAJVLKpQAipZQnAEBKeUIIEaG2jQVQ4HXsMXVbc9tPI4R4CMBDABAZGYktW7Zc4qEQEV0+HilRbpcotUmU2Dz4Ns+Jo1USoTrA6CtQ4ZBwuIAQvUCITrmFqo8j9Rp0MwuExAiE6nXQawFlpVaHeqtUCg3LAWs5MDTOB1uOuXBjnJb/7SQiIiKidqO1Ax43SCmPq0GNDUKI7LO0bSpHWp5l++kblGDKUkApaRk6dOgFdJeI6PKQUqKkxoECiw3Hyq04Vm5DgcWKAvXxiQo7Qv190SHUiLhQA3KrjgMAKuuAjx4ejKggPUKMvlCmP7o4/M8lEREREbVHrRrwkFIeV+9PCiE+B5AOoFgIEa1md0QDOKk2Pwagg9fhcQCOq9uHNtq+pTX7TUR0saSUqLA6GwIY3sGMAosVhRU2GP180CHUgDiTER1CjegdG4xf9olGh1ADYkIM0PtqG84XqPdtmPyzR3RQG46MiIiIiOjq0GqTlgoh/AFopJTV6uMNAGYDGAGgzGvSUpOU8vdCiFsBzMCpSUsXSinT1UlLMwH0V0+9E8qkpZbmrs1JS4nocqmyO5FTUosjJ2twpKQGX2YdR0G5DT4aAYOvVg1mGNBBvY8LNaKDScna8NdxZXAiIiKiC8FJS6klWvNf25EAPlfTrX0AfCSl/EYI8SOAT4QQ9wPIBzBBbf8VlGDHYSjL0k4FACmlRQgxB8CParvZZwt2EBFdah6PxPFKG454BTaUWy1qHS50CvdH5/AAdA4PQGGFDQAgJfDTSze3cc+JiIiIiK5drRbwkFLmAEhuYnsZlCyPxtslgEebOddyAMsvdR+JiLzZ6tzIKa05LbCRU1KLo6W1CDb4NgQ2ukYEYHTvKHQOD0BUkP60ZVpLqh0NpSdERERERNR2Wq2kpS2xpIWIzubJT7Pwz53HcF2iCT1jghoCHKU1DiSYjQ3ZGp0jlABHxzB/BOp927rbRERERKRiSQu1BAvIiajdqrI7cai4GgeKanCwuBoHiqpxsLgaZbV1AID/HbVgZI9IDOkahk5hAYgLEQWsbAAAIABJREFUNcBHq2njXhMRERER0aXAgAcRXfVsdW4cOlmNg8WnBzYqbU50jQhAUmQgukUFYnj3CHSLCsSiTYfw0Y4CTB4Qjwd/0amtu09ERERERK2AJS1EdNWoc3mQU1qjBDaKqnGgWAlsFFXa0Sk8AEmRanBDDXDEhhhOm1+DiIiIiNoHlrRQSzDDg4iuSG6PxEN/z8Cm7JOIDzXCz0eDfIsVcaEGdIsKRNeIQIxLiUVSZCASzUaWohARERER0WkY8CCiK8rhk9VYnVmIz3cdQ3GVAwBQUG7F2seGoFO4P/S+2jbuIRERERERXQ34kygRtblKqxP/2J6H29/+AZPf2wEJiX/cPwB3X58ArRCYMiABPWOCGOwgIiIiov/P3p3HR1Xf+x9/fycJSSYr2RfCKkGRnSi4Fm1VtHUvaF1rbbFYtdb2119b76+2Vntr7XL19qrXreK+ILhv1YraCkiCIgoqaxYSkpB9myQz8/39McOQsI6Y5CTD69nHdM58zzKfE6T1vPNdgLAxhwcAR/j8Vv/auEPPFJfrnc9rdWJhpr49c4ROGJ/B8BQAAADsF3N4IBwMaQEwoDbWtOrZ1RVasrpCOclx+vbMEbrlnElKdQ9zujQAAAAAEYTAA0C/a+ro1ksfV2pxSYUqGjp03vR8PXLlLBVmJzldGgAAAIAIReABoF/sHLKyuKRCyz6r0QmFGbr25MN04vhMhqwAAAAA6HcEHgD61KbaVi0uqdDS1duUlRyrb88coZvPOlLDExiyAgAAAGDgEHgA+MqaOrr18sdVWlxSrrL6Dp07PU+Lvne0JuQwZAUAAACAMwg8AHwpPr/V5tpWrd3WpLXbmvTyx5WqaenSyLR43XTmkTqxMFMxDFkBAAAA4DACDwD75PNbbapt1dqKQLjxybYmra9qVkZSrCblp2hyfop2tHZJkrY1ePT1I7IdrhgAAAAAAgg8AEiSvD6/NtW2hYKNtcFwI6tHuHHqxGwdmZ+ilPiY0HnbGjr0+MoyXTRrpIPVAwAAAEBvxlrrdA19rqioyBYXFztdBjBoeX1+bQz23NgZbny2vUXZyXHBcCNZk/JTdGRe73ADAAAAGAyMMSXW2iKn68DgRg8P4BDQ1unVVY8U698b65SeOEztXT7lhMKNFM2dlKsj85OVHEe4AQAAACAyEHgAEcrnt1q+qU5LVlfoH+ur1eLxSpLq27q05qZTlUS4AQAAACCCEXgAEebz7S1a8mGFnv+wUhlJw3Te9BH65RlH6M63NoTm2iDsAAAAABDpmMMDiAA1LR698FGlln64TfVtXTp7Wr7Om5Gvwuwkp0sDAAAA+hxzeCAc9PAAhihPt09vrKvWktUVWl3aoFMm5uhXZxyh2WPTFeUyTpcHAAAAAI4i8ACGEL/fauWWei39sEKvfbJdUwtSdf6MEbrr4hlyD+OvMwAAAADsxBMSMARsrGnV0g8r9NyHlUqKi9Z5M/L1jxu+puzkOKdLAwAAAIBBicADGKTq27r04ppKLVldocomj86Zlqf7LivSxLxkp0sDAAAAgEGPwAMYRH61dK2e/KBMeSnxavJ06+TDs3TDqRN03Lh0RUe5nC4PAAAAAIYMAg9gEOjy+rW4pEKPryyTJFU2dejj35ymxFj+igIAAADAweBXxoCDurx+PbayVCf9aZle+3S75h6ZrShjdPGsUYQdAAAAAPAV8EQFOKDT69MzxRW66+2NGp+dpP++aLpmjBzudFkAAAAAEDEIPIAB1On16eniCt399kYV5iTpfy6eoekEHQAAAADQ5wg8gAHQ6fXp6VXlumvZJh2ek6S7LpmpaQWpTpcFAAAAABGLwAPoR55un55aVa573tmkI3KTdTdBBwAAAAAMCAIPoB94un168oMy3fPOZh2Zl6x7LpmpqQQdAAAAADBgCDyAPuTp9umJD8p0zzubNDk/RfdeNlNTRhB0AAAAAMBAI/AA+oCn26fHV5bpf9/dpMn5qbr/sqM0eUSK02UBAAAAwCGLwAP4CjzdPj22skz/+84mTS1I1QOXH6VJ+QQdAAAAAOA0Ag/gIHR0+fTYylLd++5mTStI1YPfJegAAAAAgMGEwAP4kkpKG3Tp/SvV0e3TGZNz9D8Xz3S6JAAAAADAbgg8gDBZa/XoilL915sb5PH6ZCW99km102UBAAAAAPbC5XQBwFDQ0eXTT59eo8dWlunZhcfq4lmjFGWMLpo10unSAAAAAAB7Yay1TtfQ54qKimxxcbHTZSBClNa16apHSnREbrJ+f+5kxQ+LcrokAAAA4JBmjCmx1hY5XQcGN4a0APvx1vpq/Xzxx/rxN8br0tmjZIxxuiQAAAAAQBgIPIC98Pmt7njzCz1TUqF7L5upmaPSnC4JAAAAAPAlEHgAu2lo69KPn/pInd0+vXDN8cpMinW6JAAAAOBL6fZ3q727XW3dbXt9tXt77wsd623ThoYNauxs1PzC+bpx9o1O3wpw0Ag8gB7WVjRp4WMlOmNyrn5+2gRFRzGvLwAAAPqf3/rV4e3YM4AIhhDt3e1q7W4N7fv3tn9ra/NWZbuzlZuYu8d5PuuTO8athJgEJUQnBN6Dr1B7TIKShiUp253da/8P3/yh/NavZ754hsADQxqBBxD09Kpy/eG1z3TLOZN0xuRcp8sBAADAENTp61R1W7Wq2qp078f3atX2VZowfIKmZE5Rm7dt770qutvk8XkUGxW7K5SIdu8ZUASDi5yEHG1t3iorq5r2Gv3hxD+E9u88NjYq9qDnn5tfOF/PfPGM5hXO6+OfDjCwCDxwyPN0+/TbFz/VB1vq9fRVs3VYVpLTJQEAAGAQstaq3lOv7W3bVdVWFXptb9uuqtbAdnNXs7LcWcpNyFVJdYmsrD5v+FznF56/1/DCHeNWYkyi4qPjFeUKfzXAipaKUCgxM3tmn97njbNvpGcHIgLL0uKQVtHQrqsfW60Rw+P1x29PVWIsGSAAAMChqtPXuSvMaK3aa7ARHx2v3IRc5STkKDchN7CduGs7PS49FFzcuuLWUChBgNC3WJYW4ej3wMMYEyWpWNI2a+23jDFjJD0pKU3SakmXWmu7jDGxkh6WNFNSnaQLrLVbg9f4paQrJfkkXWetfX1/30nggXC8t6FWP3lqja46cay+f8IYlpwFAACIUF6/V/WeetW216q2o1Y17TXa0bEj9L6mdo0aOxtlZJSfmK/cxNw9Qo2dn90xbqdvByLwQHgG4tfZP5a0XlJy8PNtkv5qrX3SGHOPAkHG3cH3BmvtYcaYC4PHXWCMmSjpQklHSsqT9KYxptBa6xuA2hGB/H6ru9/ZpEXvb9V/f2e6jhmX7nRJAAAAOAhev1d1HXWq7agNhRm9toPvjZ5GpcSmKMudpYz4jND7xPSJyojP0Hvb3pMkuYxLr57/qsN3BaCv9GvgYYwZIembkm6VdIMJ/Ar9ZEkXBQ9ZJOk3CgQeZwe3JWmxpL8Fjz9b0pPW2k5JW4wxGyUdLWl5f9aOyNTU0a2fPr1G9W2deuGa45WTEud0SQAAAOjB5/epqatJdR11qvfUh14vbHxB6+vXK8edo9S4VNW016ips0mpcanKjM9Upjsz9D4xfaIy4zNDwUZ6fLqiXft+9GGSTiAy9XcPj/+S9HNJO2eBTJfUaK31Bj9XSMoPbudLKpcka63XGNMUPD5f0ooe1+x5DhC2z7Y364ePlOjEwkzddfEMDYtmyVkAAICB0N7d3iu86Blm1HnqerU1dzYrcVii0uLSlB6frrS4NKXFpWl9/XpZWVW3V+uvJ/9VmfGZSotL22+QES4m6QQiU78FHsaYb0mqsdaWGGPm7Gzey6H2APv2d07P71sgaYEkZWdna9myZV+2ZESw5ZVePb6+UxcePkzHpezQ+/961+mSAAAAhiyv9arN16YWf4tafa1q8bWEtj9u/1jV3moluZI0zDVMLb4WWVkluZKUGJWopKgkJbmSlBQV+JzpytTYqLFKik9SUmKSElwJijI9ViuxkjqkisQK/bv13zou8TjVrq1VrWodu38AQ0N/9vA4TtJZxpgzJMUpMIfHf0lKNcZEB3t5jJBUGTy+QlKBpApjTLSkFEn1Pdp36nlOiLX2Xkn3SoFJS+fMmdMf94Qhpsvr1+9fWa+3t9Xo6atP0BG5yQc+CQAA4BDj9XvV2Nmoek+9GjwNvXpj9Gxr8DSozlOnju4OpcalKi0uTcPjhistIdALY0TcCL354ZuSpDbbpqfOfEpp8WlyR7u/8gTxczSnD+4UwKGk3wIPa+0vJf1SkoI9PH5mrb3YGPOMpG8rsFLL5ZKeD57yQvDz8uD+f1prrTHmBUmPG2P+osCkpeMlfdBfdSNy/OzpNVq8ukIjUuP18o9PUEp8jNMlAQAADKhOX6dq2mtU2x5YmaS6vVo17TV6u/xtlbeUKzkmWS6XSy1dLUqJTdHw2OFKiw+EFzu3Dx9++K62uOFKj0tX0rAkuczehwfXtteG5sMoSC7Y6zEAMBAGYpWW3f1fSU8aY26R9KGkB4LtD0h6JDgpab0CK7PIWvupMeZpSeskeSX9iBVacCBryhu1eHWFJKmqyUPYAQAAIorf+tXgaQgtsbozyNj91drdGpq8M8udpWx3tjLdmapoCfx7Uqu3VW/Pf1spw1IU5Yo6wLeGh/kwAAwWAxJ4WGuXSVoW3N6swCorux/jkbTXaZGttbcqsNILcEDPf7RNv31xneYUZuq9DTt00ayRTpcEAADwpXj9XpW1lOm2D27T8srlGp86XqNTRoeCjNqOWiXEJCjTnRkKMrLcWZqUMUlZ8VmhgGN43PC99sSoaq0K9cJIi0tz4A4BoP8Za/eY/3PIKyoqssXFxU6XgQHm91vd/sbneunjSt13WZEOz2G+DgAAMPg1dTbpi4Yv9Hn954H3hs+1pWmLMuIzVN5SLkkyMvrjiX8MBRxZ7izFRsU6XDngHGNMibW2yOk6MLg5MaQF6HMtnm795KmP1OLx6vkfHa+0hGFOlwQAANCL1+9VaXPpHuFGW3ebCocXqnB4oSZlTNJ5489T4fBCuWPcunXFraGeGHPHzHX6FgBgSKGHB4a80ro2fX9RsY4ak6bfnHmkhkXvfQItAACAgdLoadTnDZ/3Cje2NG1RljtLE9ImaPzw8ZowfIIKhxcqPzH/K69gAhxq6OGBcNDDA0Pa+xt36LonP9KPv36YLj1mtNPlAACAQ0h7d7vqOur055I/659l/9SEtAlKj0/XhvoNave2h3ptTM2aqvkT5uuw1MPkjnE7XTYAHDIIPDAkWWv16IpS3fHWRt154TQde1iG0yUBAIAhzlqrlu4W1XXUBV6ePd/rO+oD7556WWuVHp+uba3bJEmf13+uO066Q4VphcpLyKPXBgA4jMADQ06X16/fvPipVm2p17MLj9Go9ASnSwIAAIOYtVZ1njr9bvnvtKx8maZmTdWMrBl7BBr1nnoNixqm9Lh0pcen93qfmD5RaXFpvdrd0W4ZY3rNs3HSyJOcvl0AQBBzeGBIqW/r0sJHS5QUF62/XjBNSXExTpcEAAAGCb/1q7K1UpubNmtL0xZtbtqsTY2btLlps6JMlBo7GyUFVjy5Zvo1ewQbaXFpiouOc/guAISDOTwQDnp4YMhYX9WsBY8U68wpefrpqRMU5aKbKAAAh6JuX7fKWsq0uWmzNjduDgUcW5u3KnlYssamjNXY1LE6Mv1InTn2TI1NHau0uLRePTEWTFng9G0AAPoZPTwwJLz+6Xb9csla3XTmRJ09Ld/pcgAAwABo727XluYt2ty4q8fG5qbN2tayTbmJuRqTMiYQbqSM1bjUcRqTMkYJMQx1BQ4F9PBAOOjhgUHNWqu//XOjHv+gTH//7lGaWpDqdEkAAKAfeLweraldo78U/0Xr6tfJHe2Wz/o0KnlUKNSYO2auxqaM1ajkUYqNinW6ZADAIEfggUGro8un/7N4jcobOvTcj45TdjJjagEAiBQ7A45V21dp1fZVWl+/XoXDC7W+fn1gv8+j1ZesVpQryuFKAQBDFYEHBqXKxg4teKRY47OS9NSC2YqL4V92AAAYyvYVcByVc5SumnKVpmVNkzvG3WueDcIOAMBXwRweGHRKSht09WMluuK4MbrqxLGsYQ8AwBDk8Xr0ce3HWlUdCDjW1a0LBRxHZR8VCjgA4GAwhwfCQQ8PDCqLSyr0n6+s1+3zpujkw7OdLgcAAIRpfwHHgskLCDgAAAOOwAODgs9v9YdX1+uNddV6csFsjc9OcrokAACwHwQcAIDBjsADjmvq6NZ1T3wor9+v5390nFLdw5wuCQCAQ5a1Vk2dTarpqFFNe41q22sD7x21vT7v6Nghv/w6Iu0I/XjGjzU9azoBBwBgUCHwgKPWVTbrwnuXq8Xj1UWzRhJ2AADQT6y1autuU03HniHGziCjtqNWte21iouOU5Y7S1nuLGXGZyrLnaVxqeN0TO4xgTZ3puY+O1ey0hcNX+i4/OOcvj0AAPZA4AFHWGv18PJS3fHWBrV2emUlPflBuW49d7LTpQEAMCR1+jpV1VqlyrZKVbVWaVvrNr1R+oZKm0uVFJMkr/VKkrLd2cp0Z4aCjPzEfE3Lmqas+KxQe1z0gZeCn1c4L7SaCgAAgxGBBwZcQ1uX/s/ij1Xd7NGShcfqgX9t0eMry3TRrJFOlwYAwKDV3t2uytZKVbZVht6rWqtC202dTcp2Zys/MV+5ibnKS8hTWXOZJKnN26Z/X/hvJQ5L7LN6bpx9o26cfWOfXQ8AgL5G4IEBtXxTnW54+iOdOTVPd108Q8OiXfrdOZP0u3MmOV0aAACOsdaquatZVW3BAKNnsNFaqaq2Knm8HuUk5PQKNCYUTFBeYp7yEvKUEZ+hKFdUr+vWe+pDvTD6MuwAAGAoMNZap2voc0VFRba4uNjpMtCD1+fXHW9t0FOrynX7vKn6WmGm0yUBADCgvH6vqtqqVN5SroqWisCrtUKrtq9SY2ejok20RqeMDgQaCbm9go28xDylxaXJGOP0bQDAoGCMKbHWFjldBwY3enig31U0tOvHT34k97AovXTd8cpKOvC4YAAAhqK27jZVtFSovKU8FGzs3K5ur1ZGfIZGJI1QQVKBCpIKdGr6qXqr7C1JkpXV0rOXOnwHAABEDgIP9KtX1lbp/z33iRacOFY/OGGsXC5+MwUAGLqstartqN0jzKhoDfTYaO9u14ikEYFX4giNSx2nOQVzNCJphPIT8zUsas/VyEqqS5j8EwCAfsCQFvSLji6fbn5pnd7ftEN3XjhdUwtSnS4JAID96vJ1hZZlrW6vDizdGlzCtba9Vuvq1qmlu0WxUbGakDYh1EtjROKuHhsZ8RkMOwGAAcCQFoSDHh7oc+urmnXtEx9qUl6yXrr2eCXFxThdEgDgEOb1e1Xvqd9rkFHTUaOa9sB2a3erMuIzlBWfpSx3YInWLHeWxqWMU6Y7UwvfXBi63mNnPObwXQEAgAMh8ECfsdbqkRWl+q83N+jGM47QeTPy+S0XAKDPebweNXc1q6mzSU2dTaHt5q5mvbL5Fa2vX6/chFylxqWqtr1WDZ4GpcalKjM+c1eQEZ+lKZlTlOUOhhvxmRoeN1wu49rn984vnM/QEwAAhhCGtKBPNLR16efPfqyqpg7deeF0jc1k6TsAwL7dsvwWPbPhGZ0++nRdMvGSPYKLvYUZzZ3Naupqkt/6lRKbopRhKUqJTVHysGQlxyYrJTZFj657VFZWLrn06BmPKtOdqYz4DEW7+B0PAEQShrQgHPy/P76yFZvrdMNTH+mMybn620XTFRsd5XRJAAAHef1e7ejYoer2alW3VWt72/bAdvBzdXu1qtqqJEkvb3lZW5q3KGVYSiC0CIYYmfGZOiz1MCXHJit5WHIo4EiOTVZcVNw+exB2+7pDvTAmZ04eyNsGAACDDD08cNC8Pr/u/OdGPfFBmf54/hSddHiW0yUBAPpZt69btR21+wwztrdvV72nXmmxacpOyFa2O3vXe4/tBz95UEs2LNG8wnm6cfaNTt8WAGCIoYcHwnHAwMMYEyXpdWvtNwampK+OwKP/bWvs0PVPfqjY6Cj9Zf5UZSXHOV0SAKAPdPu7ta1lm8paynTfx/dpTe0a5SXkKTUuVdXt1WrsbFRGfMYeAUZ2QrZy3DnKdmcrw52hGBcTVgMA+g+BB8JxwCEt1lqfMabdGJNirW0aiKIwuL26tkr/8dwn+v4JY3XViWPlcjExKQAMJT6/T1VtVSprLtPW5q0qaylTaXOpyprLVNVWpWx3tkYlj9Ka2jWysqpqq9Ifv/ZH5STkKD0uXVEuhi4CAIDBL9w5PDyS1hpj/iGpbWejtfa6fqkKg1JHl0+/e3md/rVhh+6/vEjTRw53uiQAwD74rV817TUqbS4NhRmlLYHtytZKDY8brlFJozQyeaRGJY/S7NzZGpk8UgWJBYqJCvTOuHXFraH5MKZkTnH4jgAAAL6csObwMMZcvrd2a+2iPq+oDzCkpe9d98RqvbCmSqPT3Xrh2uOVHEdXZQBwkt/61dLVorqOOt2x+g69Xf62Dk87XCOSRmhr81ZVtFQoISZBI5NGanTKaI1MCgQbI5NHqiCpQPHR8U7fAgAAB40hLQhHWD08rLWLjDHxkkZaaz/v55owyLz9eY1eWBOYTb+8voOwAwD6SaevU/Ud9ar31KvOU6e6jjrVe+pDr56fGzob5I52Ky0uTVubt0qSPqv/TN+b9L1QsJEQk+DsDQEAADgorMDDGHOmpD9JGiZpjDFmmqSbrbVn9WdxcJa1Vg+9v1V3Lduk047M1pvranTRrJFOlwUAQ05TZ5N+8/5v9M+yf2pa1jQdlXOU6jx1oXBjZ8DR5etSWlxa4BWfpvS4dKXHpSszPlMT0iYoPS591/64tL0OPZk7Zq7DdwsAADA4hDukpUTSyZKWWWunB9vWWmsH5QL3DGn56rp9fv3mhU+1amu9Hrj8KBWkuZ0uCQAGtabOpsBcGS1lgfkymktV3lKu0uZS+axPbd2BKbCMjBZOXdgr1Ni5nRSTJGOYCBoAgANhSAvCEe6kpV5rbdNu/xJ24KQEQ1JTR7eueXy1olxGzy48VkkMYQEASYFQY+fkn+XN5SptCUwGWtZSJq/fG5onoyCpQMfmHauRySM1Mmmk0uLS9PuVvw/1wlg4baHTtwIAABDxwu3h8YCktyT9QtL5kq6TFGOt/WH/lndw6OFx8Err2vS9h1bphPGZ+o9vHqHoKJfTJQHAgLHW6qb3b9Lzm57X0TlHa0bWjF7hxu6hxs65MnaGGvTOAABgYNDDA+EIt4fHtZJulNQp6QlJr0v6XX8VBWes3FynHz3+oX789cN06TGjnS4HAPpUe3e7atprAq+OGtW216qmvUa1HbW9tjt9nZKklVUrNTljso7JPUYXTLiAUAMAAGCICXeVlnZJNxpjbgt8tC39WxYG2jPF5frDq5/pvy6cphPGZzpdDgCErdPXqdr2WtV2BEOLYHixe6jh8/uU5c5SpjtTWfGB95yEHE3JnKLM+ExlubOUEZ+hv5b8NTT05LoZ1zl9ewAAADhI4Q5pOUrSg5KSgk1Nkr5nrS3px9oOGkNawuf3W93+xud6ZW2VHri8SIdlJR34JADoR92+bjV2NqreU6/GzkY1dDaowdOgRk9gu9HTqJKaEtW01yjGFSMrGwosstxZyozPDIQawe1sd7Yy3ZlKjEmkdwYAABGCIS0IR7hDWh6QdLW19j1JMsYcL+nvkqb0V2Hof+1dXv3kqY/U0NatpVcfp7SEYU6XBCDC+K1fLV0tgcCiZ4jhCYQYDZ2B9kbPrn0er0cpsSkaHjdcw+OGKzU2VcNjA9ujkkdpauZUvV76uiTJZ3368NIP5TLMNwQAAIDewg08WnaGHZJkrf2XMYZhLUPY9iaPrly0SofnJOvO70xXbHSU0yUBGAKstWrualaDp0H1nno1eBpU56nr9bneU9+rzcoqMSZR41LHaXjscKXGBQKM9Pj0QFsw1EiLS1NqXGpYS7N+VPNRaNgJYQcAAAD2Zr9DWowxM4Kbl0pyKzBhqZV0gaQGa+2N/V7hQWBIy/6trWjSDx4u1uXHjtYPvzaWLt7AIcxaq98u/62WblyqkwpO0lnjzgoFFTtfPT83dDYoLipOaXFpGh43XGlxab1eu7edsvgU+axPUSZKH132kdO3CwAAIgRDWhCOA/Xw+PNun2/qsX3gyT8w6Lz2SZV+tfQT/f7cyZo7KcfpcgD0kw5vh3Z07FBdR13g5akLfd7RsUM7PLv2eXweSdJbZW/J6/eGgouchBxNTJ+4R5gxLCr84W/zCueFemIAAAAAAymsSUuHGnp47Mlaq7uWbdKjK0p132VFmpSf4nRJAL6EW1fcqqe/eFrfHPNNfefw7wTCix4hxu6BhtfvVUZ8htLj05Uenx7YjksPtfX83HNVkhtnD8qOewAAAL3QwwPhCHeVllRJl0karR69Qqy1g3K9PgKP3jq9Pv1yyVp9Ud2i+y87SjkpcU6XBGAfunxdqmipUGlzaeDVUqqy5jJ9sP2D0DET0yfuEWCkx6crI25XmMGKJAAAIJIReCAc4U5a+oqkFZLWSvL3Xznoa/VtXbrqkWKlJ8Tq6auOkXtYuH/kAPqL1+9VZWvlrlCjuVRlLWUqbS5VTXuNchNyNTJ5pEYnj1bh8EKdMvIUZbmz9OqWV+mFAQAAAIQp3B4eq621Mw544CBBD4+ADdUtunJRsb41JVc/O3WCXC5+2wsMFL/1a3vb9r2GGlWtVcqIz9Co5FEamTxSo5JHhV55iXmKccU4XT4AAMCgRg8PhCPcX/c/Yoz5gaSXJHXubLTW1u/rBGNMnKR3JcUGv2extfYmY8wYSU9KSpO0WtKl1touY0yspIclzZRUJ+kCa+3W4LV+KelKST5J11lrX/9Sd3kIeueLWt3w1Ef61RlzGOTBAAAgAElEQVRH6PyZI5wuB4hITZ1NqmytVGVrpba1blNlW6XeLntblW2VcskVCDVSRmlkUiDUKMop0ujk0RqRNEKxUbFOlw8AAABEtHADjy5Jt0u6UbtWZ7GSxu7nnE5JJ1trW40xMZL+ZYx5VdINkv5qrX3SGHOPAkHG3cH3BmvtYcaYCyXdJukCY8xESRdKOlJSnqQ3jTGF1lrfl7rTQ8jDy7fqzrc26u5LZuroMWlOlwMMWc1dzbvCjJ7BRnDbL7/yEvOUn5CvvMQ85SXmaXvb9sDJRnpr/lvO3gAAAABwCAs38LhB0mHW2h3hXtgGxsq0Bj/GBF9W0smSLgq2L5L0GwUCj7OD25K0WNLfTGDGvbMlPWmt7ZS0xRizUdLRkpaHW8uh4j+eW6vHVpQpKT5aL15zvEalJzhdEjCoHSjQ8Fmf8pPyewUaM7NnBkKOxHwlD0veY2LQipYKlmEFAAAABoFwA49PJbV/2YsbY6IklUg6TNL/SNokqdFa6w0eUiEpP7idL6lckqy1XmNMk6T0YPuKHpfteU7P71ogaYEkZWdna9myZV+23CHN57d6dEXgj6i1w6sta1dpi8M1AU7yW79afC2q99Wrwdugem99aHuzZ7PabbtcciknJkdp0WlKj05XWnSaxkaP1VGxRyktIU1ul3tXoNERfNVKNcH/7M1xOk7HjTxO8uiQ+98hAAAAYDAJN/DwSfrIGPO2es/hsd9laYPDTqYFl7VdKumIvR0WfN/bjJp2P+27f9e9ku6VApOWzpkzZ3+lRZRun1/XP/mRclP8qmnu1EWzRmrOnElOlwX0q25ft7a3bVdlW6Wq2qpU1VoV2G6tUlVblba3bVfisETlJuQqLzFPuQm5OjzhcOUm5uqGZTdIkowxev0ipgQCAAAAIlG4gcdzwddBsdY2GmOWSZotKdUYEx3s5TFCUmXwsApJBZIqjDHRklIk1fdo36nnOYe8Tq9P1z7+obx+q7d/NkdxMVFOlwT0CWutfvneL/Xqllc1PWu6pmRNCYUa21u3q6GzQVnuLOUk5CgvIU+5ibmaljlNp48+XbmJucpNyFVcdNxerz2/cD7DTgAAAIAIF9aytAd1YWMyJXUHw454SW8oMBHp5ZKe7TFp6cfW2ruMMT+SNNla+8PgpKXnWWvnG2OOlPS4AvN25El6S9L4/U1aeqgsS+vp9mnhoyUaFu3Sf39nhoZFu5wuCThofuvXhoYNKqkuCb3qPHWSJCOj62Zcp9yE3FCPjcz4TEW5CPgAAAAORSxLi3CE1cPDGLNFex9Gsr9VWnIlLQrO4+GS9LS19iVjzDpJTxpjbpH0oaQHgsc/oMDytxsV6NlxYfA7PjXGPC1pnSSvpB+xQovU0eXTDx4uVqo7Rn+9YJpiogg7MLR4/V59Vv+ZircXq6S6RKtrVmt43HDNzJ6prxV8TTcU3aC/r/27Fm9YrHmF8/T9yd93umQAAAAAQ0hYPTyMMek9PsZJmicpzVr76/4q7KuI9B4ebZ1eXblolfJS4vXHb09RNGEHhoBOX6c+2fFJqPfGmto1gVVPsmZqZs5MzcyaqUx3ptNlAgAAYAighwfCcdBDWowx/7LWHt/H9fSJSA48WjzduuLvqzQuM1G/P2+yolx7m9MVcF57d7s+qv0oFHCsq1unsSljNTN7poqyizQ9a7pS41KdLhMAAABDEIEHwhHukJYZPT66JBVJSuqXirBPTe3duuzvH2hyfrJuPmuSXIQdGESaOpv0Uc2ugGND4wYdkXaEZmbP1A8m/0DTsqYpISbB6TIBAAAAHCLCXaXlz9o1h4dX0lYFhrVggDS0demSB1Zq1ph0/b9vHSFjCDsw8Ky1qu2oVXlLuSpaKlTeUq7Xtr6m0uZSRZtoFeUUaWb2TF0/83pNzpi8z1VSAAAAAKC/hRt4nC7pfEmje5xzoaSb+6Em7GZHa6cuuX+l5kzI0v+dO4GwA/2qy9elitaKUKBR0bJre1vrNrlj3BqRNEIFSQUqSCpQWXOZJMnK6r5T73O4egAAAAAICDfweE5So6TVkjz9Vw52V9Ps0UX3r9QZk3P1k2+MJ+zAV2atVXNXs8pbynv11ChvKVdFa4XqOuqUk5CjgqQCjUgMBBtF2UUakTRCI5JG7DEspdHTqGe+eEbzCun0BQAAAGDwCHeVlk+stZMGoJ4+ESmTllY1deii+1bq/Bn5uubk8U6XgyFqe9t2La9crgc+eUClzaWKMTGKjY4NBBrBEKNnuJGTkKNoV7hZKAAAADDwmLQU4Qj3qeZ9Y8xka+3afq0GIRUN7brovpW6ZPZILThxnNPlYAhp7WrVqu2rtLxquZZXLldjZ6Nm5c4KDT3xyaf3v/M+vYUAAAAARLRwA4/jJX3XGLNFUqckI8laa6f0W2WHsNK6Nl1030p9/4QxuuK4MU6Xg0Gu29+ttbVrtaJqhZZXLtcXDV9oSuYUzc6drdtOvE2Hpx0ul3Hp1thbQ0NPCDsAAAAARLpwh7SM2lu7tba0zyvqA0N5SMum2lZdcv9KXXPyYbp41l5/7DjEWWu1pWlLqAdHSXWJCpIKNDtvto7JPUbTs6azOgoAAAAiGkNaEI6wengM1mAj0nxR3aJLH1ipn546QfOLCpwuB4PIjo4doR4cK6pWKMpE6Zi8Y/Stsd/SzcfdrLS4NKdLBAAAAIBBhZkJB4l1lc26/O8f6MYzjtA50/OdLgcOa+9uV0l1iZZXBQKO7W3bdXTO0ZqdO1sLpizQyKSRDEsBAAAAgP0g8BgE1lY06YqHVum3Zx2pb07JdbocOMTj9ejqN6/WqupVijJRmpY1TcfkHqObjrlJR6YfycopAAAAAPAl8ATlsNVlDVrwcLF+f+5knXpkjtPlwAG17bV68vMntfiLxar31EuSrKwemvuQs4UBAAAAwBDmcrqAQ9mqrfX6waJi3f7tqYQdh6B1dev0q/d+pXOeP0dNnU1aNHeRLpxwoaJMlOYXzne6PAAAAAAY0sJapWWoGQqrtLy/aYeuefxD3XHhNJ0wPtPpcjBAfH6flpUv0yPrH9G21m266PCLdN7485QSm+J0aQAAAMCQwSotCAdDWhxw5UOr9NZnNTplYjZhxyGitatVSzcu1WPrH1N6fLounXipvj7y64pxxThdGgAAAABEJAIPB7z9eY0k6Z/raxyuBP2tvKVcj69/XC9uflHH5B6j2068TVMzpzpdFgAAAABEPAIPB1w8a5QeX1mmi2aNdLoU9ANrrUqqS/To+kdVUl2ic8efq8VnLlZOAvO0AAAAAMBAYQ4PoI90+7r12tbX9Mi6R9Th7dDFR1yss8adJXeM2+nSAAAAgIjCHB4IBz08gK+owdOgpz9/Wk99/pTGpY7TNdOv0fH5x8tlWAQJAAAAAJxC4AEcpI0NG/Xo+kf1RukbOmXUKfrfU/5X44ePd7osAAAAAIAIPIAvxVqr5ZXL9ev3f63q9mpNSp+kl859SWlxaU6XBgAAAADogcADCEO3r1uvbHlFi9YtkiTVtAdW2Flfv56wAwAAAAAGISYZAPajqbNJD6x9QHOfnauXN7+sn838mZ4981ldMOECRZkozSuc53SJAAAAAIC9YJUWYC+2tW7To+se1QubXtCcgjm6bOJlmpA2wemyAAAAAIhVWhAehrQAPXyy4xMt+nSRVlSt0Lnjz9WzZz2rnIQcp8sCAAAAAHxJBB445PmtX+9WvKuHPn1IVa1VumTiJfrNsb9RQkyC06UBAAAAAA4SgQcOWR6vRy9uflEPf/qw3DFuXXHkFfrGqG8o2sVfCwAAAAAY6niywyGnwdOgJz9/Uk999pQmZUzSr4/5tYqyi2SMcbo0AAAAAEAfIfDAIWNr01Y9su4Rvbr1VZ066lQ9eNqDGps61umyAAAAAAD9gMADEc1aqw9rPtRDnz6kNbVrNK9wnl445wVlxGc4XRoAAAAAoB8ReCAiWWt19VtX61/b/qXEmERdP+N63XbibYqPjne6NAAAAADAACDwQESx1urt8rd195q79Vn9Z5KkDm+HLjj8AocrAwAAAAAMJAIPRISeQYckLZy6UO9ve1+LNyzWvMJ5DlcHAAAAABhoxlrrdA19rqioyBYXFztdBgbAzqDjnjX3yMpq4dSFOqngJFZcAQAAACKYMabEWlvkdB0Y3OjhgSHJWqtl5ct095q7CToAAAAAAHsg8MCQQtABAAAAAAgHgQeGhN2Djh9O/aFOLjiZoAMAAAAAsFcEHhjUrLV6p+Id3fXRXfJbvxZOW0jQAQAAAAA4IAIPDEp7CzpOKjhJLuNyujQAAAAAwBBA4IFBZY+gY+pCnTSSoAMAAAAA8OUQeGBQ6Bl0+KxPV0+9mqADAAAAAHDQCDzgKJ/fp2v/ea3e2/aeUoal6LfH/pagAwAAAADwlRF4wBGVrZV6buNzWrpxqba3bZcktXa36uujvu5wZQAAAACASMCv0TFgun3demPrG/rhP36o+S/NV2Nno/775P/WhRMuVJSJ0rzCeU6XCAAAAACIEMZa63QNfa6oqMgWFxc7XQaCNjdu1pINS/Ti5hc1LnWczht/nr4x8huKi45zujQAAAAAQ5AxpsRaW+R0HRjcGNKCftHe3a43St/Qkg1LVN5SrrPHna2HT39Yo5JHOV0aAAAAAOAQQOCBPmOt1bq6dXp2w7N6fevrmp41Xd898rs6YcQJinHFOF0eAAAAAOAQ0m+BhzGmQNLDknIk+SXda629wxiTJukpSaMlbZU031rbYIwxku6QdIakdknftdauDl7rckn/Ebz0LdbaRf1VN768ps4mvbz5ZS3ZsESt3a06b/x5WnLWEmUnZDtdGgAAAADgENWfPTy8kn5qrV1tjEmSVGKM+Yek70p6y1r7B2PMLyT9QtL/lXS6pPHB1yxJd0uaFQxIbpJUJMkGr/OCtbahH2vHAVhrVVxdrGc3PKt3y9/V8fnH62dH/UxH5xzNkrIAAAAAAMf1W+Bhra2SVBXcbjHGrJeUL+lsSXOChy2StEyBwONsSQ/bwCyqK4wxqcaY3OCx/7DW1ktSMDSZK+mJ/qod+1bbXqvnNz2vpRuWaljUMJ0//nz94qhfKDUu1enSAAAAAAAIGZA5PIwxoyVNl7RSUnYwDJG1tsoYkxU8LF9SeY/TKoJt+2rHALHW6tp/Xqt3Kt5RjCtGZ407S/95wn9qcsZkBUYiAQAAAAAwuPR74GGMSZT0rKTrrbXN+3lA3tsOu5/23b9ngaQFkpSdna1ly5YdVL3YxW/9+rjjY73R9IbKuwKZk8/v05yuOar/tF7v6B2HKwQAAAAAYO/6NfAwxsQoEHY8Zq1dEmyuNsbkBnt35EqqCbZXSCrocfoISZXB9jm7tS/b/bustfdKuleSioqK7Jw5c3Y/BGHq9nfrlc2v6MFPHlRCTIJ+dtzP9P6297V4w2LNK5ynObPnOF0iAAAAAAD7ZQJTZvTDhQNdORZJqrfWXt+j/XZJdT0mLU2z1v7cGPNNSdcosErLLEl3WmuPDk5aWiJpRvASqyXN3Dmnx94UFRXZ4uLifrmvSObxerRkwxIt+nSRCpIK9P0p39esnFkMWwEAAAAwqBhjSqy1RU7XgcGtP3t4HCfpUklrjTEfBdt+JekPkp42xlwpqUzSvOC+VxQIOzYqsCztFZJkra03xvxO0qrgcTfvL+zAl9fS1aKnPn9Kj657VFMyp+j2r92uKZlTnC4LAAAAAICD1m89PJxED4/w1HXU6bH1j+mZL57R8fnH63uTvqfxw8c7XRYAAAAA7Bc9PBCOAVmlBYNLVWuVHvr0Ib20+SWdPuZ0PfHNJzQiaYTTZQEAAAAA0GcIPA4hm5s268G1D2pZxTKdN/48PXf2c8p0ZzpdFgAAAAAAfY7A4xCwrm6d7l97v0qqS/Sdw7+jl899WSmxKU6XBQAAAABAvyHwiFDWWpVUl+j+tfdrQ+MGfffI7+qW426RO8btdGkAAAAAAPQ7Ao8IY63Ve9ve030f36d6T72unHyl7hx7p4ZFDXO6NAAAAAAABgyBRwT50Vs/0rsV7yp1WKpunH2jThl1iqJcUU6XBQAAAADAgCPwiABfNHyhPxf/We9Xvi9Jaulu0dwxcx2uCgAAAAAA5xB4DGE7Onbobx/+TW+Xv62rplyl/MR8LdmwRPMK5zldGgAAAAAAjiLwGII6vB1a9OkiPbb+MZ1z2Dl68dwXlTwsWZL062N+7XB1AAAAAAA4j8BjCPFbv17a/JLuXH2npmVN0xPffEIjkkY4XRYAAAAAAIMOgccQsWr7Kt2+6nbFRMXoT1/7k6ZlTXO6JAAAAAAABi0Cj0FuS9MW/aXkL9rQsEHXz7hep40+TcYYp8sCAAAAAGBQI/AYpBo8DbpnzT16dcurumLSFfrT1/6k2KhYp8sCAAAAAGBIIPAYZLp8XXp8/eN68JMHddro0/TcOc8pLS7N6bIAAAAAABhSCDwGCWut3ih9Q38t+avGp47XQ6c/pLEpY50uCwAAAACAIYnAYxBYU7tGf1r1J3l8Ht187M06Ovdop0sCAAAAAGBII/BwUEVLhe5YfYdW16zWtdOv1Zljz1SUK8rpsgAAAAAAGPIIPBxw0/s3aemGpYp2ResHU36g3x77W7lj3E6XBQAAAABAxCDwcMDSDUtlZeWzPi2cutDpcgAAAAAAiDgupws4FM0vnK8oE6X5hfOdLgUAAAAAgIhkrLVO19DnioqKbHFxsdNlAAAAAAD6gTGmxFpb5HQdGNzo4QEAAAAAACIOgQcAAAAAAIg4BB4AAAAAACDiEHgAAAAAAICIQ+ABAAAAAAAiDoEHAAAAAACIOAQeAAAAAAAg4hB4AAAAAACAiEPgAQAAAAAAIg6BBwAAAAAAiDgEHgAAAAAAIOIQeAAAAAAAgIhD4AEAAAAAACIOgQcAAAAAAIg4BB4AAAAAACDiEHgAAAAAAICIQ+ABAAAAAAAiDoEHAAAAAACIOAQeAAAAAAAg4hB4AAAAAACAiEPgAQAAAAAAIg6BBwAAAAAAiDgEHgAAAAAAIOIQeAAAAAAAgIhD4AEAAAAAACIOgQcAAAAAAIg4BB4AAAAAACDiEHgAAAAAAICI02+BhzHmQWNMjTHmkx5tacaYfxhjNgTfhwfbjTHmTmPMRmPMx8aYGT3OuTx4/AZjzOX9VS8AAAAAAIgc/dnD4yFJc3dr+4Wkt6y14yW9FfwsSadLGh98LZB0txQISCTdJGmWpKMl3bQzJAEAAAAAANiXfgs8rLXvSqrfrflsSYuC24skndOj/WEbsEJSqjEmV9Jpkv5hra231jZI+of2DFEAAAAAAAB6iR7g78u21lZJkrW2yhiTFWzPl1Te47iKYNu+2vdgjFmgQO8QZWdna9myZX1bOQAAAAAAGDIGOvDYF7OXNruf9j0brb1X0r2SVFRUZOfMmdNnxQEAAAAAgKFloFdpqQ4OVVHwvSbYXiGpoMdxIyRV7qcdAAAAAABgnwY68HhB0s6VVi6X9HyP9suCq7XMltQUHPryuqRTjTHDg5OVnhpsAwAAAAAA2Kd+G9JijHlC0hxJGcaYCgVWW/mDpKeNMVdKKpM0L3j4K5LOkLRRUrukKyTJWltvjPmdpFXB42621u4+ESoAAAAAAEAvxtq9TokxpBUVFdni4mKnywAAAAAA9ANjTIm1tsjpOjC4DfSQFgAAAAAAgH5H4AEAAAAAACIOgQcAAAAAAIg4BB4AAAAAACDiEHgAAAAAAICIQ+ABAAAAAAAiDoEHAAAAAACIOAQeAAAAAAAg4hB4AAAAAACAiEPgAQAAAAAAIg6BBwAAAAAAiDgEHgAAAAAAIOIQeAAAAAAAgIhD4AEAAAAAACIOgQcAAAAAAIg4BB4AAAAAACDiEHgAAAAAAICIQ+ABAAAAAAAiDoEHAAAAAACIOAQeAAAAAAAg4hB4AAAAAACAiEPgAQAAAAAAIg6BBwAAAAAAiDgEHgAAAAAAIOIQeAAAAAAAgIhD4AEAAAAAACIOgQcAAAAAAIg4BB4AAAAAACDiEHgAAAAAAICIQ+ABAAAAAAAiDoEHAAAAAACIOAQeAAAAAAAg4hB4AAAAAACAiEPgAQAAAAAAIg6BBwAAAAAAiDgEHgAAAAAAIOIQeAAAAAAAgIhD4AEAAAAAACIOgQcAAAAAAIg4BB4AAAAAACDiEHgAAAAAAICIQ+ABAAAAAAAiDoEHAAAAAACIOAQeAAAAAAAg4hB4AAAAAACAiBPtdAEAAAAAMNhZayVrJb9f8vsDnw+wXf3nv6j5hReUfOaZyrr+x4HzrQ1eT5KsQh/sntuB7wxV0OuYHXfdrebXXlPSaacp46oFge/2W8kG6/D7Q/Vav18K7gtnu+GZxWpfsUKp8+cr96ZfD9SPGOhzxlp74KOGmKKiIltcXOx0GQAAAHCQ3e0hcvvvblHjM88o9dvfVvavfrnrwdLv3/XwufMh0dpd54eOCT582h7H+K1qf36Zmj/cpuRpecq85Z7AQ6ffF3rwDJzrCz5M+gLn+fzB6+w6Rjb4HT5f6Dvq7vy9WtfXKfGIdKUt/Knk71HXzlplgw+6vkB9oVqDD7/Btl3H+dX4+ENq39wk95gUpcy/aD8PyzbMh3srawPvre+8rc7t7YrNjpd71rGBfTuP9/l2+xz8rp3vwbZdD+m7PndVVMjf4ZUrPlrRmVnBICBYo7WyPbYlBc4P/tnLH/w5hX4W6vXn7O/okHxWchmZmJjgPgV/Jj1+jkaSMZIxMi6za9uYwD5Xj21j5GvuCP3zGD08QaGLmECbCf2XejT0Zvaxv6umLbQdm5sYuOzOC+7cNj2+zthgrXbXtYxkZHsfJ6uWL9oCDS6jI9at29tfL8cZY0qstUVO14HBjcADAADgS+r18BZ8WV938KHNK3l9gXefL3icV/J5gw92PsnvC5274z9/oZZPa5Q4MVPpP/l14FwbfAAMPvhanz/0EGx3XtMfeA98586HaF/gc/ChuXnJE+ooa1bciCQlzT1b1uuV9XllvT7JG3i3vmAtXm9g2xuo23p9of2Bff7g+X7J51V3VZX8Hq9csVGKSs/oEQwEH8hld3vQ7P0w2jtQUOiB0t/ZFWgzRiYmetf+wEmh09Xz32F7be/3T06BRzorE9XziTO4aXp8NnbXLrPrfNNjO9Bu5W2PCl03JtHf4wFz1/k9H0RD1wwd1+MB2PQ+pmP7rprdea59Hhe4lglthx56e+yXTOgem7/oCl035Yi4Xdfo8bC+64E5+HC/87KunsfsfrxR7XsNoWtnn5wuuYL37zK933dey7Xre42r534j4wq2GaOtj1ZK1kjGaswVBYFb2lmLgtfaedMu16773dku7Qokgg/zO/9AN/xlXejaE341Pbg/SsblklxRgeu5XDKuqEBBJljsPl+B/VV3LlLjJrdSx7Ur9yc/UI8b77Ftdtt29fiHZvdjd21X3XKLGje6/3979x4mV1Wne/z7VnXnnhAuAoEgg4MMt6A8eMCBUUBlQBwUuYygB4RReZABkaN4GOUqKM6MF9RROI5cPOLAgOiAyAx6fAAPDB65CoNCDJcIGLlIEnIjJOnf+WOvqtrd6U66053atarfz/Pkye5du1evqrd3df3WXntvZu64nFnnf3a9fRnJ4wtOe39qewWzfjSfTuQBDxsOn9JiZmYdadCpw+nrP1z8BRbfcAObHHEEW33qzLWn/7YaGfEU4ecvuYSXf3QzM971Ll7zt6c0C9TGEdtGoTvYusaRy6KY7SsVp8HC669j+d2/YMo++zDzvYe3ir1mARitI6rR6G/5ebe2axxVbjynJT/9CSt+9RCT5uzO9P3fkn5mKsL7WstFX9e0Cudy8d3XV2xXKqSX338fq/64kt7NJjB5jzes/XqWpmJHv9d58Nc/Stu/MnceqxevomdGLxNft33pKHJrSnXreaajx6Ujrc3lfke7g1WLlhKrAvWKnqmTip/Zr6hubN/IvrGOUl8bi43H0+q+xu9Mo7qKUiEb/eoVBiyXaxWgKPDS/ytfKh5c+sgLrPn0aWtv3ygEy23Vin70q4tSQdr4HglW/G41IF55ZglTH7gO1aBWbxSWQvVGwdkoLIUmCepFgasaqJ7aS+uKbcWT3yva7nt1DTscmgqlWquAUq38daNYLBVYjQJS/b937oV30SiWd7rgHVDvgVoP1Hub/6u8rrm+B+oTiuK03ot6JhTra61tF5x6TKtA/NbN6efWSS9Eqb/1tL424LH6IN9TZ8GHDmHR3fOZ+efbM+uKWxlLC/7m4I3Sdm0jtQuwqtT2Zt8cu7ZnPt5qd9KnxrbPM+9utV077poxa3fWquXMuvdKeNOJ8PZzxqxdgFknP9Zqe68Txrjto1ttm2Usmxkekg4BvgrUgW9HxBeG2tYzPGwsDJwG229K7EWfY9H3v8/MI49kq7P+57rPlxxs2mfEoNu9eOllLPnJT5h+0EFs/uEPtaZa9vWVpm329f/APmBabf+CqFU8LbzmGpbddRdT9tuPTY86qrXdOqbotp73wEKs1YeXf3wLK+6/n8l77smMg/+y1VaaOjuaom7pnXex8tFHmbjTTkx98z6tKcLNaa5rWs+zcaSzWawNKE5LU2Nf+a8HWL3oFXo2mcjEXea0+tHImigtltfTWh6iuH71id+yZumr1KdOoHf7Hfq/DuXXdK1pyLHO12L1wkXEqj7UW6O+yYzSz1xrgbUeXM82a5YsaxaI9WlTB9lmw61ZsoxYHahH1KZMSs+Jfs8xSr8L0e/3AlpTh+l3FDRWNStPapPqa/3coab/DpwqrAFfA6xeuppGsdU7s2eQIjZaB08bX5cf61f8RnN5yfxas90ZO6xubdPsTHOUoHRkOEr9jNJziNYUZOClx6Y22958txWlwpJWQVmrFYUqpUKzUZCmIq/8tWo1FtzyXLPdbd4zu/UiNo70tqr9ZoG99qxJWpQAABXBSURBVDb026ax/Ow1c5ttb/fBOekIqoqjp2m5OLqajqzWy0da+x91Vb2eitoaT5z9PRpHaf/0K6dArRf19JSK5glQ7ykK5kZB3NOLyoVzb7Fe9QnFNvWiWH5sn/2abe/yyCOlI62js7EK2lzb3ph95sefgEYR964vjW3bZtZWnuFhw5HFgIekOjAXOAh4BrgHODYiBj2hrNMHPJ495kBefnABU3fegk1P+bvi6BqRjsT1rX1UrlHArekbcBSu/xG5l2+8gRVPL2Xy7GlMO+SvWudTNou9AcX2mtaRtOaRyMbFikoXN4q+Pl556AFWLVxJ78yJTNptTipK+p//2H954BG0oQcPVj75FKuXrKJnag+9s7elOXU3olSstork5vPoay2XC+rGur5XV6efI9RTa/7o/gu0+lj6r59+BUn6oq+1oSbUm9MuB04FbRVEpWXK62kVCYKVz7XO85y0zZTWz2wWe6U+DGivX0HY3LZYkGDp3MXNtqfvvEmzXaB/++WnWnq88QI1n08qthbdv7j52KZ7zUibpyI//SvqswD6iqIuGusb2xTbK/p/3wsPTKRREG2559Ki3WbxWX6da63zaJvTYGutaau18jY1nrl1TbPd7Q4tTXQbpG4ZsnAufV1+7ebf2CqW/+TIyc3+NfpQ9Kn0daPIbBztHOzIaK3G4//0WLPQ2vH0Oa12h5qOWp4WO9g2pWmx886/udX2Be9e+0UYhXnn3dRs+/UX//WAwrU2SEFbZ/CpwwOm2p59Zuso7cWXlArP0vMecmpweXnt7+t3BPjSHw7yOq5rKvDQ2y54354smje5mB58/cOlXzCNennBe3ZotT2GU49dLLevbTMzGxkPeNhw5HJKy97AvIh4AkDStcB7gM68gs56vPzgAkAse/RF+PuPD1Isl5ZrgxTRzYshica5jajGiqeXA2LFM0uZ8uDNxfbNKbNqnb7YbLv0ebnRDqk2qFGc25oeX7JwJSBWLVrJlps/2fzG1rmS5SKi8Xxqqb1GRwYWGsW/px9aBYjVy1az7VtmrHXUUfVS4dNvuVU0FUf3yst15n7yX9PPCXb6yvubT06DHGmk0ce0rOaLMURxcfE/tAqisz/T/zUYssiqDbHc2mbB33281e4XLh7V79lAC876WKvtz/9967mXj9AOe5nW8hknNtvd+nPfpv90X7HWtOCBBeyAacHl9auO2r1ZxG1+9VOlgnKQkYkRmFkqWqZ9eYynxL7Qanvy58ZwGu99rXZ7T75+zNoFmHnrw622//ofxrbt//hVs+2ewz87Zu3OOvmXram2ux8xZu0WbZem8c7eawzbParV7qQZ6/+GDW17LNu94lZmjWmLG7/tHPtsZmZmG0cuMzyOAg6JiA+nr48D9omIU0vbnAScBLDVVlvtde2111bS1+GYfenZrHroRXr32IJnPnpRx7eba9sbs887zr2MbX9/K89uczDzdjq549vdmG3n2GczMzMzy9uBBx7oGR62XrkMeBwNHDxgwGPviDhtsO07/ZQWMzMzMzMz23A+pcWGo7b+TTrCM8B2pa9nA7+vqC9mZmZmZmZm1uFyGfC4B3i9pB0kTQCOAW6quE9mZmZmZmZm1qGyuGhpRKyWdCpwK8Vtaa+IiEcq7paZmZmZmZmZdagsBjwAIuIW4Jaq+2FmZmZmZmZmnS+XU1rMzMzMzMzMzIbNAx5mZmZmZmZm1nU84GFmZmZmZmZmXccDHmZmZmZmZmbWdTzgYWZmZmZmZmZdxwMeZmZmZmZmZtZ1POBhZmZmZmZmZl3HAx5mZmZmZmZm1nU84GFmZmZmZmZmXccDHmZmZmZmZmbWdTzgYWZmZmZmZmZdRxFRdR/GnKQXgPlV92M9tgBerLoTNirOMG/OL3/OMH/OMH/OMG/OL3/jOcPtI+I1VXfCOltXDnjkQNK9EfGmqvthG84Z5s355c8Z5s8Z5s8Z5s355c8Zmq2bT2kxMzMzMzMzs67jAQ8zMzMzMzMz6zoe8KjOt6rugI2aM8yb88ufM8yfM8yfM8yb88ufMzRbB1/Dw8zMzMzMzMy6jmd4mJmZmZmZmVnX8YCHmZmZmZmZmXUdD3hsRJJUdR/MxjPvg93DWebL2XUH52hmZjnygMdGFL5AStYkfUTSNlX3wzac98H8STpT0uucZb6cXd68D5pVS4WDJE2vui9mOfKAx0Yg6XhJP5Z0vqQ3V90fGxlJx0h6EDgH2L3q/tjISTpO0m2S/lHS0VX3x0ZO0rGS/h/wCeAdVffHRs77Yd68D+ZP0kmSTk/LnqGTIUnvBR4HDgGmVdwdsyz1VN2BbpH+kMwAvgHMAi4C3gkcL+mFiHi8yv7ZukmqAVsA1wJLgdOBUwE1Ho+Ivup6aMMhaSpwMTAHOA/YGXifpCci4r5KO2fDImlT4J+BKcAngcOA5ekx74cdLv0tnIL3wyyl/DYF/hcwFe+DWZI0iWKg6hRgiqQbI+KpantlIyVpJnAccHxE3Fl1f8xy5RkeY0BSPQqLgfuAwyPiNuA7wFbAqko7aOuU8uuLiOeBL0XEuyPiDuAx4EQAf8DLQ0QsAx4E3hMRPwduAhYCEyvtmA1bRCwEvh4Rh0bE/wWex/thFlIxHGk//BXF30Lvh5ko5fcS8A3vg/mRVAeIiFeAeyNiW4oB5Isq7ZgNWyPDZCawLCLulLS1pA9I2qGqvpnlygMeoyTpfOBrko5Kq74OLEtF9CMUAx4+565DDcwvIn6czpWsA/cCz6cRdutQkk6VNKe06tqIeDntg38AdiTN1LHOVM4w5XZHWhbwf4CFkravso+2bpI+Dfxj6W/h1YD3w0yU8jsSICJuT+u9D2ai9HnmyLTqp+n/C4B9JB2YtvNn/w5VyvCItGoS8Np0evz1FKeWXSbp3LS9szQbBu8oo5DemPam+KNymqQzgE3SbIE1knahmAb6aIXdtCEMyO9USWdI2iwd4VoDrATeEBGLquynDU7S9pLuAM4Gvlx6aAVA2ge3psjxoQq6aOsxWIZp3yMtB9ALTAa8H3YgSXuk6zzsDtwDnCPp0IhY2Xgv9X7YuQbJ71xJ70qPyftgHob4PDoTmjMfvwZckDL1LJ0ONCDD0yX9j4h4FHgR+Cfg7Ig4Efg4cIakLZyl2fB4wGMDSeoF/gL4RET8G3AusA1wTGmz1wG/Sx/4dpX05xV01QYxSH7nUeR3bGObiPh3YEtJb6uml7YeLwHfA14P9En6YFpffl/bElgREUskzZH0znZ30tZpYIYnQP8pvRFxD7AD8Lb0mGcJdJYacHlEvD8irqU4Cnk09Dv66P2wcw3M7zqgMUtH4H2w0w3z88zXgTrwXkmvbQxqWWcYoqbYTtKxFNeU2xnoA4iI3wA3A1tX1F2z7HjAYxgG/nFP57muAn5D6w/Kf1IcHZkjaee07k+AHknnUFzPw1dXrsAG5LdT2m4icCvFxUytQoNkqIhYAnw3/X8ZxVGt3jTA2CiYdwMmpH3wSoqjlFaBYWZ4ainDWinHfyXdMcm3xqzOEIXub4GrS4MbtwORvm5k5f2wAwwzvztI+UVEn/fBzrKhn2eSLwLfB35OcWFhq8AIMrwbeDvFgYEvAh+QdKikSygGtJ5qW6fNMucBjw1QmkJ2M8W5dTunN6uHgZdpjboeRnGkZCJwQET8dK3GrO2Gkd82abuVaXlxJR21ITU+cEfEirTqRmAuxbnK5dMi9gUOoDgP9q0R8YP29tSGMowM+0o5Tqa4CKZ1iMaH9ohYFhHLS++r7wT+kPJrFMb7Avvj/bBjDCe/9Lj3wc4yDfpdnHRYn2ck7QWcQ3H3nV0j4vp2d9yaRpLhYoq7XV0E3AAcTnEjhMMiYmm7O26WKw94rIOkQyTdCFwo6U2l9Y0jHr8Gfgd8EJrTzHalmL4LcDmwb0Scnc6htDYaRX6bl5r5YETc2qYu2wDryLBWPkqSPjB8CThY0nRJO0maAPwA+G8R8ZmIWN72J2CjyfBP1bpI4nkRcVN7e24Ng2UYEVHOUFLjNvc7UXxwR9Luad2/AXt7P6zGKPLbVVJjhqP3wYqosKWk24FvQ2sgagSfZ56nuHvZR70Ptt8oMvwz4LURsTodNP3biDjTGZqNjAc8BkhvSpMkXUVxIb3LKUZjPyRp8zQNu3HEYwnFKQ+7SfqYpM2BnrSeiLg+InyRtjYao/yao+alo8/WJsPMsC99YJ+p4txXIuI+iiOQLwFXAbMi4raI+HU1z2T8GqMMv0PrGgIrK3ki49hIM6S4sCUU75/bSboa+LykLSPiZ94P22us8qO47oP3wQqlmVKvpH97KF0DR+n0v7TZUJ9nlqU2no6Ip9vfe4NRZ/hyqZ1V7e25WXfwgMcAUXiFYnr1/umIxg8ARcQf04cDSfomxV0FHgMuBPakOHf5F1Fc7NIqMEb5eUZHhUaQ4TeArwLbAqi4Kv1BwKcjYt+ImF/VcxjvxijDv4iIpyp6CuPeBmQ4Q9LrKI5OngXcExHvjojnK3sS49gY5Xd4RDxX2ZMwoHnx39nAgxTZnAtF8Supdz2fZ/6jkk5bP6PM0J9JzUZJ4WtPASDpYxTnOt4fEdeV1h8NfBP4L+BO4N+B1cApwBkRsTBtVwd60wcMazPnl78xyPDtwAMR8VK7+24FZ5i/0WQoaUvgw8BlzrAazi9/pQzviYgb0rqZwBXAScC3gJ9Q3L50U+BU/HmmozhDs84y7md4pCMcZwDvA+6luE/5CekPP8ALFLdiOwhYAPwNMD8iTkgfEBrTPdf4jan9nF/+xiDDHoA0bd4f0ivgDPM32gwBIuL5iPi8M2w/55e/QTK8KGW4GbAj8GBEvEhxB48vAZdExL3+PNM5nKFZZ+pZ/ybdLU3rPBA4OyJuk7QUOJjidnrfiYjbG9tKehh4C7BMkihmyKwZrF1rD+eXvzHIcHUV/bYWZ5i/Mciwb7B2rT2cX/6GyPAQYAVwF7C/pFuA7dPXT0Dzjjv+PNMBnKFZZxrXMzzUuu/8vRR//EnnO84FdlH/+5cD/CXFm9aKdH6sPyBUyPnlzxnmzxnmzxnmzfnlbx0ZPgbsAbwReJriFIndgGOAAyRt6ww7gzM061zjasCjMVUsjaSW7309D5guaU76+g5gE4qLeE2QdJykhyhGZM/yCGw1nF/+nGH+nGH+nGHenF/+RpDhz4EZFKcknRwR56XtXwL2i4hn29pxa3KGZvkYFwMekvaT9B3gbEmbRRRXalW6FSLwS2ANcJCknihun7ctsFdEvEoxIvvRiDg+fMX5tnN++XOG+XOG+XOGeXN++duADB+hGKDaMyJekVQvFdhLq3gO450zNMtP1w94qLjN2jeB2yjecC6UdCi07mcdEfOAeyguKHRW+taVwPz0+O0RcVebu244v27gDPPnDPPnDPPm/PI3ygyfSo+vaRTY1n7O0CxPXT/gAewN/CYirgI+SXEP7MMkzQKQdJGky4H7gK8Be0u6D3iJ4pZRVi3nlz9nmD9nmD9nmDfnlz9nmD9naJYhddsgo6TDKEZd742IX6TR2O8Cx0bE7yTtChwHPEdxYaFTgHPTiCySpgE9EbGommcwvjm//DnD/DnD/DnDvDm//DnD/DlDs+7QNTM8JM2S9CPgTGBT4EpJB0fEE8DdwNFp08eAX1NcyOvhiHh/RMxTurpyRCz1G1P7Ob/8OcP8OcP8OcO8Ob/8OcP8OUOz7tI1Ax7Am4A7I+KtEXEh8FXgpPTYncAcSftEcVXyZ4G3RsRiKG4lFb4dVNWcX/6cYf6cYf6cYd6cX/6cYf6coVkXyXrAQ9Lxkg6QNBH4GfC/Sw//keIe9AC/AB4AvpKml+0GzJc0BfrdSsrayPnlzxnmzxnmzxnmzfnlzxnmzxmada+eqjswUpIEbA38C9AHPA58BDg9IhZI6o3iSsmzKKahERF/AL4qaXvgCorz8Y6PiOVVPIfxzPnlzxnmzxnmzxnmzfnlzxnmzxmajQ9ZDXhIqkfEGknTgWcj4r9L6gG+AnwLOILiDQvgIIopaEjaMop7zn8KmBwRSyro/rjn/PLnDPPnDPPnDPPm/PLnDPPnDM3GjywGPNIb0GeBuqRbgBnAGoCIWC3pY8DvJe0fEXdImgC8AMyV9DngryQdEBELAb8xtZnzy58zzJ8zzJ8zzJvzy58zzJ8zNBt/Ov4aHpL2p7if9abAPOBCYBVwoKS9ASIiKN68LkjfNgk4geIcvOnAO9Ibk7WZ88ufM8yfM8yfM8yb88ufM8yfMzQbn3KY4dEHfDEivgsgaU9gB+Bc4FJgLxW3f/ohxRvWbGAb4GrgyxHxYDXdtsT55c8Z5s8Z5s8Z5s355c8Z5s8Zmo1DHT/Dg2Ik9jpJ9fT1XcBrI+Iqiulop0VxReTZQF9EPBMRv4yI4/3G1BGcX/6cYf6cYf6cYd6cX/6cYf6codk41PEDHhGxPCJWRnGvayguHPRCWj4R2EXSzcA1FG9kjasuWwdwfvlzhvlzhvlzhnlzfvlzhvlzhmbjUw6ntADF1ZSBALYCbkqrlwCfBnYHnoyIZ6F5/p11EOeXP2eYP2eYP2eYN+eXP2eYP2doNr50/AyPkj6gF3gR2CONwJ5DMeXszsYbk3Us55c/Z5g/Z5g/Z5g355c/Z5g/Z2g2jiingUtJbwb+M/27MiIur7hLNgLOL3/OMH/OMH/OMG/OL3/OMH/O0Gz8yG3AYzZwHMWVkldW3R8bGeeXP2eYP2eYP2eYN+eXP2eYP2doNn5kNeBhZmZmZmZmZjYcOV3Dw8zMzMzMzMxsWDzgYWZmZmZmZmZdxwMeZmZmZmZmZtZ1POBhZmZmZmZmZl3HAx5mZmaZkHS+pE+u4/HDJe3azj6ZmZmZdSoPeJiZmXWPwwEPeJiZmZnh29KamZl1NEmfAY4HngZeAO4DFgMnAROAecBxwBuBm9Nji4EjUxPfAF4DLAc+EhGPtrP/ZmZmZlXxgIeZmVmHkrQXcBWwD9AD3A9cBlwZEX9M21wEPBcRX5d0FXBzRHw/PfYz4OSI+K2kfYCLI+Jt7X8mZmZmZu3XU3UHzMzMbEhvAX4YEcsBJN2U1u+eBjpmAtOAWwd+o6RpwL7A9ZIaqydu9B6bmZmZdQgPeJiZmXW2waZiXgUcHhG/knQCcMAg29SARRHxxo3XNTMzM7PO5YuWmpmZda6fA++VNFnSdOCwtH46sEBSL/CB0vZL0mNExMvAk5KOBlDhDe3rupmZmVm1fA0PMzOzDla6aOl84Bng18Ay4FNp3cPA9Ig4QdJ+wD8DK4GjgD7gUmAW0AtcGxGfbfuTMDMzM6uABzzMzMzMzMzMrOv4lBYzMzMzMzMz6zoe8DAzMzMzMzOzruMBDzMzMzMzMzPrOh7wMDMzMzMzM7Ou4wEPMzMzMzMzM+s6HvAwMzMzMzMzs67jAQ8zMzMzMzMz6zoe8DAzMzMzMzOzrvP/ATh+gjw5+pLoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,8))\n",
    "result.plot(marker='o',ms=2,lw=1,ax=ax)\n",
    "fig.autofmt_xdate()\n",
    "plt.legend(bbox_to_anchor = [1,1])\n",
    "plt.title('COVID-19 Data of Different Genders',size=15)\n",
    "plt.ylabel('number')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Visualize the File TimeProvince"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>province</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>released</th>\n",
       "      <th>deceased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>16</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>16</td>\n",
       "      <td>Busan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>16</td>\n",
       "      <td>Daegu</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>16</td>\n",
       "      <td>Incheon</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>16</td>\n",
       "      <td>Gwangju</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  time province  confirmed  released  deceased\n",
       "0  2020-01-20    16    Seoul          0         0         0\n",
       "1  2020-01-20    16    Busan          0         0         0\n",
       "2  2020-01-20    16    Daegu          0         0         0\n",
       "3  2020-01-20    16  Incheon          1         0         0\n",
       "4  2020-01-20    16  Gwangju          0         0         0"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TimeProvince=pd.read_csv('TimeProvince.csv')\n",
    "TimeProvince.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "scrolled": 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>province_x</th>\n",
       "      <th>confirmed_Gyeongsangbuk-do</th>\n",
       "      <th>released_Gyeongsangbuk-do</th>\n",
       "      <th>deceased_Gyeongsangbuk-do</th>\n",
       "      <th>province_y</th>\n",
       "      <th>confirmed_Gyeonggi-do</th>\n",
       "      <th>released_Gyeonggi-do</th>\n",
       "      <th>deceased_Gyeonggi-do</th>\n",
       "      <th>province_x</th>\n",
       "      <th>confirmed_Seoul</th>\n",
       "      <th>released_Seoul</th>\n",
       "      <th>deceased_Seoul</th>\n",
       "      <th>province_y</th>\n",
       "      <th>confirmed_Chungcheongnam-do</th>\n",
       "      <th>released_Chungcheongnam-do</th>\n",
       "      <th>deceased_Chungcheongnam-do</th>\n",
       "      <th>province</th>\n",
       "      <th>confirmed_Busan</th>\n",
       "      <th>released_Busan</th>\n",
       "      <th>deceased_Busan</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-20</th>\n",
       "      <td>Gyeongsangbuk-do</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gyeonggi-do</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chungcheongnam-do</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Busan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-21</th>\n",
       "      <td>Gyeongsangbuk-do</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gyeonggi-do</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chungcheongnam-do</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Busan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>Gyeongsangbuk-do</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gyeonggi-do</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chungcheongnam-do</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Busan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>Gyeongsangbuk-do</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gyeonggi-do</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chungcheongnam-do</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Busan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>Gyeongsangbuk-do</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gyeonggi-do</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chungcheongnam-do</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Busan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  province_x  confirmed_Gyeongsangbuk-do  \\\n",
       "date                                                       \n",
       "2020-01-20  Gyeongsangbuk-do                           0   \n",
       "2020-01-21  Gyeongsangbuk-do                           0   \n",
       "2020-01-22  Gyeongsangbuk-do                           0   \n",
       "2020-01-23  Gyeongsangbuk-do                           0   \n",
       "2020-01-24  Gyeongsangbuk-do                           0   \n",
       "\n",
       "            released_Gyeongsangbuk-do  deceased_Gyeongsangbuk-do   province_y  \\\n",
       "date                                                                            \n",
       "2020-01-20                          0                          0  Gyeonggi-do   \n",
       "2020-01-21                          0                          0  Gyeonggi-do   \n",
       "2020-01-22                          0                          0  Gyeonggi-do   \n",
       "2020-01-23                          0                          0  Gyeonggi-do   \n",
       "2020-01-24                          0                          0  Gyeonggi-do   \n",
       "\n",
       "            confirmed_Gyeonggi-do  released_Gyeonggi-do  deceased_Gyeonggi-do  \\\n",
       "date                                                                            \n",
       "2020-01-20                      0                     0                     0   \n",
       "2020-01-21                      0                     0                     0   \n",
       "2020-01-22                      0                     0                     0   \n",
       "2020-01-23                      0                     0                     0   \n",
       "2020-01-24                      1                     0                     0   \n",
       "\n",
       "           province_x  confirmed_Seoul  released_Seoul  deceased_Seoul  \\\n",
       "date                                                                     \n",
       "2020-01-20      Seoul                0               0               0   \n",
       "2020-01-21      Seoul                0               0               0   \n",
       "2020-01-22      Seoul                0               0               0   \n",
       "2020-01-23      Seoul                0               0               0   \n",
       "2020-01-24      Seoul                0               0               0   \n",
       "\n",
       "                   province_y  confirmed_Chungcheongnam-do  \\\n",
       "date                                                         \n",
       "2020-01-20  Chungcheongnam-do                            0   \n",
       "2020-01-21  Chungcheongnam-do                            0   \n",
       "2020-01-22  Chungcheongnam-do                            0   \n",
       "2020-01-23  Chungcheongnam-do                            0   \n",
       "2020-01-24  Chungcheongnam-do                            0   \n",
       "\n",
       "            released_Chungcheongnam-do  deceased_Chungcheongnam-do province  \\\n",
       "date                                                                          \n",
       "2020-01-20                           0                           0    Busan   \n",
       "2020-01-21                           0                           0    Busan   \n",
       "2020-01-22                           0                           0    Busan   \n",
       "2020-01-23                           0                           0    Busan   \n",
       "2020-01-24                           0                           0    Busan   \n",
       "\n",
       "            confirmed_Busan  released_Busan  deceased_Busan  \n",
       "date                                                         \n",
       "2020-01-20                0               0               0  \n",
       "2020-01-21                0               0               0  \n",
       "2020-01-22                0               0               0  \n",
       "2020-01-23                0               0               0  \n",
       "2020-01-24                0               0               0  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#It will be a mass if we draw the imformatin of all provinces, so we choose to draw the figure of the top 5 provinces with most patients.\n",
    "TimeProvince.drop(['time'],axis=1,inplace=True)\n",
    "p1=TimeProvince[TimeProvince['province']=='Gyeongsangbuk-do']\n",
    "p1=p1.rename(columns={'confirmed':'confirmed_Gyeongsangbuk-do'})\n",
    "p1=p1.rename(columns={'released':'released_Gyeongsangbuk-do'})\n",
    "p1=p1.rename(columns={'deceased':'deceased_Gyeongsangbuk-do'})\n",
    "p2=TimeProvince[TimeProvince['province']=='Gyeonggi-do']\n",
    "p2=p2.rename(columns={'confirmed':'confirmed_Gyeonggi-do'})\n",
    "p2=p2.rename(columns={'released':'released_Gyeonggi-do'})\n",
    "p2=p2.rename(columns={'deceased':'deceased_Gyeonggi-do'})\n",
    "p3=TimeProvince[TimeProvince['province']=='Seoul']\n",
    "p3=p3.rename(columns={'confirmed':'confirmed_Seoul'})\n",
    "p3=p3.rename(columns={'released':'released_Seoul'})\n",
    "p3=p3.rename(columns={'deceased':'deceased_Seoul'})\n",
    "p4=TimeProvince[TimeProvince['province']=='Chungcheongnam-do']\n",
    "p4=p4.rename(columns={'confirmed':'confirmed_Chungcheongnam-do'})\n",
    "p4=p4.rename(columns={'released':'released_Chungcheongnam-do'})\n",
    "p4=p4.rename(columns={'deceased':'deceased_Chungcheongnam-do'})\n",
    "p5=TimeProvince[TimeProvince['province']=='Busan']\n",
    "p5=p5.rename(columns={'confirmed':'confirmed_Busan'})\n",
    "p5=p5.rename(columns={'released':'released_Busan'})\n",
    "p5=p5.rename(columns={'deceased':'deceased_Busan'})\n",
    "result = pd.merge(p1,p2, on='date')\n",
    "result = pd.merge(result,p3, on='date')\n",
    "result = pd.merge(result,p4, on='date')\n",
    "result = pd.merge(result,p5, on='date')\n",
    "result.set_index(['date'],inplace=True)\n",
    "result.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "result.plot(marker='o',ms=2,lw=1,ax=ax)\n",
    "fig.autofmt_xdate()\n",
    "plt.legend(bbox_to_anchor = [1,1])\n",
    "plt.title('COVID-19 Data of Different Provinces',size=15)\n",
    "plt.ylabel('number')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:**\n",
    "\n",
    "Through these time series figures, I find that from February 22, 2020, South Korea began to increase the number of daily tests, and the number of newly diagnosed people in South Korea also increased rapidly. The most newly confirmed patients are in the age group 20s and 50s. There are more newly diagnosed female patients than male patients.A large number of newly confirmed cases came from the province of Gyeongsangbuk-do, and the number of newly confirmed diagnoses in other provinces gradually increased a few days later.\n",
    "\n",
    "### 2.2 Prediction of the Recovery Time of the Patients\n",
    "#### 2.2.1 Data Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "patient_id               0\n",
      "global_num            1160\n",
      "sex                     94\n",
      "birth_year             464\n",
      "age                    105\n",
      "country                 90\n",
      "province                 0\n",
      "city                    76\n",
      "disease               3110\n",
      "infection_case         819\n",
      "infection_order       3097\n",
      "infected_by           2393\n",
      "contact_number        2539\n",
      "symptom_onset_date    2682\n",
      "confirmed_date           0\n",
      "released_date         2147\n",
      "deceased_date         3072\n",
      "state                    0\n",
      "dtype: int64\n",
      "3128\n"
     ]
    }
   ],
   "source": [
    "#count miss value of each column\n",
    "index_array = np.sum(PatientInfo.isnull() == True, axis=0)\n",
    "print(index_array)\n",
    "print(PatientInfo.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to build a regression model to predict of the recovery time of the patients.\n",
    "\n",
    "Firstly I do data preprocessing to the data. \n",
    "It is easy to define the ‘recovery time’ as the difference between ‘released_date’ and ‘confirmed_date’.\n",
    "For the ‘deceased’patients,I choose to simply delete it in this task and I will find the rows that released_date is not null\n",
    "and calculte the recovery time and join the table with the table Region to get more information.\n",
    "\n",
    "Finally I will delete some useless column and do some data coding and discretization and normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "patient_id                    0\n",
      "global_num                  357\n",
      "sex                           0\n",
      "birth_year                  130\n",
      "age                           2\n",
      "country                       0\n",
      "province                      0\n",
      "city                          0\n",
      "disease                     952\n",
      "infection_case              451\n",
      "infection_order             926\n",
      "infected_by                 759\n",
      "contact_number              724\n",
      "symptom_onset_date          857\n",
      "confirmed_date                0\n",
      "released_date                 0\n",
      "deceased_date               952\n",
      "state                         0\n",
      "recover_days                  0\n",
      "code                          0\n",
      "latitude                      0\n",
      "longitude                     0\n",
      "elementary_school_count       0\n",
      "kindergarten_count            0\n",
      "university_count              0\n",
      "academy_ratio                 0\n",
      "elderly_population_ratio      0\n",
      "elderly_alone_ratio           0\n",
      "nursing_home_count            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data_released = PatientInfo[PatientInfo['released_date'].notnull()]\n",
    "data_released=data_released[data_released['state']=='released']\n",
    "data_released['recover_days']=(pd.to_datetime(data_released['released_date'])-pd.to_datetime(data_released['confirmed_date'])).dt.days\n",
    "Region=pd.read_csv('Region.csv')\n",
    "result = pd.merge(data_released,Region, on=['city','province'])\n",
    "#count miss value of each column of the new table\n",
    "index_array = np.sum(result.isnull() == True, axis=0)\n",
    "print(index_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that some columns with too much miss value should be ignored. Since birth_year is more accurate than age, we choose to fill the miss value in age and then use age to fill birth_year with media value i.e. 20s will be considered as 25 years old and then \n",
    "we use 2020-25 to calculate his birth_year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20s    251\n",
       "50s    195\n",
       "40s    165\n",
       "30s    119\n",
       "60s     95\n",
       "10s     43\n",
       "70s     39\n",
       "80s     21\n",
       "0s      13\n",
       "90s      9\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age=result['age'].value_counts()\n",
    "age= age.sort_values(ascending=False)[:10]\n",
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "patient_id                    0\n",
      "global_num                  357\n",
      "sex                           0\n",
      "birth_year                    0\n",
      "age                           0\n",
      "country                       0\n",
      "province                      0\n",
      "city                          0\n",
      "disease                     952\n",
      "infection_case              451\n",
      "infection_order             926\n",
      "infected_by                 759\n",
      "contact_number              724\n",
      "symptom_onset_date          857\n",
      "confirmed_date                0\n",
      "released_date                 0\n",
      "deceased_date               952\n",
      "state                         0\n",
      "recover_days                  0\n",
      "code                          0\n",
      "latitude                      0\n",
      "longitude                     0\n",
      "elementary_school_count       0\n",
      "kindergarten_count            0\n",
      "university_count              0\n",
      "academy_ratio                 0\n",
      "elderly_population_ratio      0\n",
      "elderly_alone_ratio           0\n",
      "nursing_home_count            0\n",
      "age_int                       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#fill the miss value of the column age with mode '20s'\n",
    "result['age'] = result['age'].fillna('20s')\n",
    "result['age_int']=result['age'].apply(lambda x: (int)(x[0:1]))\n",
    "result['birth_year']=result['birth_year'].fillna(2020-5-result['age_int']*10)\n",
    "index_array = np.sum(result.isnull() == True, axis=0)\n",
    "print(index_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I find that there is no miss value in the columns that will be used later. Then I will normalize the data and encode the column sex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>birth_year</th>\n",
       "      <th>elementary_school_count</th>\n",
       "      <th>kindergarten_count</th>\n",
       "      <th>university_count</th>\n",
       "      <th>academy_ratio</th>\n",
       "      <th>elderly_population_ratio</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>elderly_alone_ratio</th>\n",
       "      <th>nursing_home_count</th>\n",
       "      <th>age_int</th>\n",
       "      <th>sex</th>\n",
       "      <th>recover_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.410526</td>\n",
       "      <td>0.293578</td>\n",
       "      <td>0.268421</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.212042</td>\n",
       "      <td>0.243018</td>\n",
       "      <td>0.860991</td>\n",
       "      <td>0.134585</td>\n",
       "      <td>0.143713</td>\n",
       "      <td>0.339691</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.452632</td>\n",
       "      <td>0.293578</td>\n",
       "      <td>0.268421</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.212042</td>\n",
       "      <td>0.243018</td>\n",
       "      <td>0.860991</td>\n",
       "      <td>0.134585</td>\n",
       "      <td>0.143713</td>\n",
       "      <td>0.339691</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.652632</td>\n",
       "      <td>0.174312</td>\n",
       "      <td>0.136842</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.089005</td>\n",
       "      <td>0.324991</td>\n",
       "      <td>0.872790</td>\n",
       "      <td>0.216556</td>\n",
       "      <td>0.215569</td>\n",
       "      <td>0.211115</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.410526</td>\n",
       "      <td>0.082569</td>\n",
       "      <td>0.063158</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.353403</td>\n",
       "      <td>0.383750</td>\n",
       "      <td>0.865619</td>\n",
       "      <td>0.178304</td>\n",
       "      <td>0.209581</td>\n",
       "      <td>0.204209</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.431579</td>\n",
       "      <td>0.082569</td>\n",
       "      <td>0.063158</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.353403</td>\n",
       "      <td>0.383750</td>\n",
       "      <td>0.865619</td>\n",
       "      <td>0.178304</td>\n",
       "      <td>0.209581</td>\n",
       "      <td>0.204209</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   birth_year  elementary_school_count  kindergarten_count  university_count  \\\n",
       "0    0.410526                 0.293578            0.268421               0.1   \n",
       "1    0.452632                 0.293578            0.268421               0.1   \n",
       "2    0.652632                 0.174312            0.136842               0.1   \n",
       "3    0.410526                 0.082569            0.063158               0.3   \n",
       "4    0.431579                 0.082569            0.063158               0.3   \n",
       "\n",
       "   academy_ratio  elderly_population_ratio  latitude  longitude  \\\n",
       "0       0.212042                  0.243018  0.860991   0.134585   \n",
       "1       0.212042                  0.243018  0.860991   0.134585   \n",
       "2       0.089005                  0.324991  0.872790   0.216556   \n",
       "3       0.353403                  0.383750  0.865619   0.178304   \n",
       "4       0.353403                  0.383750  0.865619   0.178304   \n",
       "\n",
       "   elderly_alone_ratio  nursing_home_count   age_int  sex  recover_days  \n",
       "0             0.143713            0.339691  0.555556    0            13  \n",
       "1             0.143713            0.339691  0.555556    0            10  \n",
       "2             0.215569            0.211115  0.333333    0            32  \n",
       "3             0.209581            0.204209  0.555556    0            20  \n",
       "4             0.209581            0.204209  0.555556    1            19  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col=['birth_year','elementary_school_count','kindergarten_count','university_count','academy_ratio','elderly_population_ratio',\n",
    "    'latitude','longitude','elderly_alone_ratio','nursing_home_count','age_int']\n",
    "temp=result[col]\n",
    "#using min-max normalization\n",
    "data_pca=temp\n",
    "temp = (temp-temp.min())/(temp.max()-temp.min())\n",
    "Map={'male':0,'female':1}\n",
    "temp['sex']=result['sex']\n",
    "temp['sex'] = temp['sex'].map(Map)\n",
    "temp['recover_days']=result['recover_days']\n",
    "temp['birth_year']=temp['birth_year']\n",
    "temp.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2.2 Data Prediction\n",
    "It is well known that how long a confirmed patient will recover mainly depends on his physical health condition and whether he has other underlying diseases. We do not have any data on the health indicators of patients, so it is very hard to build an accurate model.\n",
    "\n",
    "I will compare some models to see how they work on this dataset.\n",
    "\n",
    "Firstly I will divide the samples into training set and test set and the test set accounted for 30%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = temp['recover_days']\n",
    "X = temp.copy().drop(['recover_days'], axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3,random_state=33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_square_value: \n",
      "0.077\n",
      "MSE_value: \n",
      "52.682\n"
     ]
    }
   ],
   "source": [
    "#1.LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "predicted = lr.predict(X_test)\n",
    "print('R_square_value: ')\n",
    "print(round(r2_score(y_test, predicted),3))\n",
    "print( 'MSE_value: ')\n",
    "print(round(mean_squared_error(y_test, predicted),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_square_value: \n",
      "0.0785\n",
      "MSE_value: \n",
      "52.6262\n"
     ]
    }
   ],
   "source": [
    "#2.RidgeRegression\n",
    "model = RidgeCV(alphas=[0.5,0.6,0.7,0.8,0.9,0.4,0.3,0.1,0.2,1])\n",
    "model.fit(X_train,y_train)\n",
    "predicted = model.predict(X_test)\n",
    "print('R_square_value: ')\n",
    "print(round(r2_score(y_test, predicted),4))\n",
    "print( 'MSE_value: ')\n",
    "print(round(mean_squared_error(y_test, predicted),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_square_value: \n",
      "0.0804\n",
      "MSE_value: \n",
      "52.5148\n"
     ]
    }
   ],
   "source": [
    "#3.LassoRegression\n",
    "model = LassoLarsCV()  # LassoLarsCV自动调节alpha可以实现选择最佳的alpha\n",
    "model.fit(X_train,y_train) \n",
    "predicted = model.predict(X_test)\n",
    "print('R_square_value: ')\n",
    "print(round(r2_score(y_test, predicted),4))\n",
    "print( 'MSE_value: ')\n",
    "print(round(mean_squared_error(y_test, predicted),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_square_value: \n",
      "0.055\n",
      "MSE_value: \n",
      "53.9639\n"
     ]
    }
   ],
   "source": [
    "#4.Assembled model: GradientBoostingRegressor\n",
    "gbr=GradientBoostingRegressor()\n",
    "gbr.fit(X_train,y_train)\n",
    "predicted=gbr.predict(X_test)\n",
    "print('R_square_value: ')\n",
    "print(round(r2_score(y_test, predicted),4))\n",
    "print( 'MSE_value: ')\n",
    "print(round(mean_squared_error(y_test, predicted),4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:**\n",
    "\n",
    "I find that no model is accurate because the most important information i.e. physical health condition and whether he has other underlying diseases is unknown.\n",
    "\n",
    "Comparing the four models, I find that the Assembled model did worst because it may be overfitting in this dataset.\n",
    "\n",
    "In the other three models, LassoRegression did best because it uses LASSO Regression with L1 form which has a very good variable selection effect.\n",
    "\n",
    "RidgeRegression uses Ridge Regression with L2 form which has a good variable selection effect so it did better than LinearRegression\n",
    "but it did worse than LassoRegression.\n",
    "\n",
    "The LinearRegression did worst in the three models without assembling because it did not use Regression to solve multicollinearity.\n",
    "\n",
    "#### 2.2.3 Feature Selection and Model Selection\n",
    "\n",
    "We know that not all features in the dataset will affect the recover time of a patient and some columns may have multicollinearity\n",
    "and some columns maybe unrelated to the recover time.\n",
    "\n",
    "A method to choose feature is using iteration and the greedy algorithm. In other words, we choose subset of the features and build\n",
    "a model on the training set and then test the model on the test set. If the model works better than the prvious one, we keep the \n",
    "selection, otherwise we reselect features and build models aga until the model will not be impoved by selecting features.\n",
    "\n",
    "However, greedy algorithm can find a better model but the result may be not a global optimum solution because it may be \n",
    "stuck in a local optimum solution.\n",
    "\n",
    "The followings are the new model I built after variable selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Divide the samples into training set and test set. \n",
    "col1=['elementary_school_count','elderly_population_ratio',\n",
    "    'latitude','elderly_alone_ratio','nursing_home_count','age_int','recover_days']\n",
    "temp1=result[col1]\n",
    "y = temp1['recover_days']\n",
    "X = temp1.drop(['recover_days'], axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3,random_state=33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_square_value: \n",
      "0.09\n",
      "MSE_value: \n",
      "51.953\n"
     ]
    }
   ],
   "source": [
    "#1.LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "predicted = lr.predict(X_test)\n",
    "print('R_square_value: ')\n",
    "print(round(r2_score(y_test, predicted),3))\n",
    "print( 'MSE_value: ')\n",
    "print(round(mean_squared_error(y_test, predicted),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_square_value: \n",
      "0.0902\n",
      "MSE_value: \n",
      "51.9557\n"
     ]
    }
   ],
   "source": [
    "#2.RidgeRegression\n",
    "model = RidgeCV(alphas=[0.5,0.6,0.7,0.8,0.9,0.4,0.3,0.1,0.2,1])\n",
    "model.fit(X_train,y_train)\n",
    "predicted = model.predict(X_test)\n",
    "print('R_square_value: ')\n",
    "print(round(r2_score(y_test, predicted),4))\n",
    "print( 'MSE_value: ')\n",
    "print(round(mean_squared_error(y_test, predicted),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_square_value: \n",
      "0.0903\n",
      "MSE_value: \n",
      "51.9525\n"
     ]
    }
   ],
   "source": [
    "#3.LassoRegression\n",
    "model = LassoLarsCV()  # LassoLarsCV自动调节alpha可以实现选择最佳的alpha\n",
    "model.fit(X_train,y_train) \n",
    "predicted = model.predict(X_test)\n",
    "print('R_square_value: ')\n",
    "print(round(r2_score(y_test, predicted),4))\n",
    "print( 'MSE_value: ')\n",
    "print(round(mean_squared_error(y_test, predicted),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_square_value: \n",
      "0.1109\n",
      "MSE_value: \n",
      "50.7721\n"
     ]
    }
   ],
   "source": [
    "#Assembled model: GradientBoostingRegressor\n",
    "col2=['elementary_school_count',\n",
    "    'latitude','elderly_alone_ratio','age_int','recover_days']\n",
    "temp2=result[col2]\n",
    "y = temp2['recover_days']\n",
    "X = temp2.drop(['recover_days'], axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3,random_state=33)\n",
    "gbr=GradientBoostingRegressor()\n",
    "gbr.fit(X_train,y_train)\n",
    "predicted=gbr.predict(X_test)\n",
    "print('R_square_value: ')\n",
    "print(round(r2_score(y_test, predicted),4))\n",
    "print( 'MSE_value: ')\n",
    "print(round(mean_squared_error(y_test, predicted),4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:**\n",
    "\n",
    "By using feature selection, It is obvious that the R_square_value of the four models all increase and the MSE_value of the four models\n",
    "all decrease which means the four models are all improved.\n",
    "\n",
    "The best model is the Assembled model: GradientBoostingRegressor because its R_square_value is maximal and its MSE_value is minimal.\n",
    "However, the assembled model has poor interpretability.\n",
    "\n",
    "#### 2.2.4 Using PCA to Do Data Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>birth_year</th>\n",
       "      <th>elementary_school_count</th>\n",
       "      <th>kindergarten_count</th>\n",
       "      <th>university_count</th>\n",
       "      <th>academy_ratio</th>\n",
       "      <th>elderly_population_ratio</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>elderly_alone_ratio</th>\n",
       "      <th>nursing_home_count</th>\n",
       "      <th>age_int</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1964.0</td>\n",
       "      <td>36</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1.17</td>\n",
       "      <td>14.39</td>\n",
       "      <td>37.551166</td>\n",
       "      <td>126.849506</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1080</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1968.0</td>\n",
       "      <td>36</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1.17</td>\n",
       "      <td>14.39</td>\n",
       "      <td>37.551166</td>\n",
       "      <td>126.849506</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1080</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1987.0</td>\n",
       "      <td>23</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>0.70</td>\n",
       "      <td>16.65</td>\n",
       "      <td>37.606832</td>\n",
       "      <td>127.092656</td>\n",
       "      <td>6.9</td>\n",
       "      <td>689</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1964.0</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>1.71</td>\n",
       "      <td>18.27</td>\n",
       "      <td>37.572999</td>\n",
       "      <td>126.979189</td>\n",
       "      <td>6.8</td>\n",
       "      <td>668</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1966.0</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>1.71</td>\n",
       "      <td>18.27</td>\n",
       "      <td>37.572999</td>\n",
       "      <td>126.979189</td>\n",
       "      <td>6.8</td>\n",
       "      <td>668</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>1969.0</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>1.25</td>\n",
       "      <td>27.01</td>\n",
       "      <td>35.686526</td>\n",
       "      <td>127.910021</td>\n",
       "      <td>17.4</td>\n",
       "      <td>127</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>1963.0</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>1.25</td>\n",
       "      <td>27.01</td>\n",
       "      <td>35.686526</td>\n",
       "      <td>127.910021</td>\n",
       "      <td>17.4</td>\n",
       "      <td>127</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>1998.0</td>\n",
       "      <td>113</td>\n",
       "      <td>123</td>\n",
       "      <td>4</td>\n",
       "      <td>1.53</td>\n",
       "      <td>15.10</td>\n",
       "      <td>33.488936</td>\n",
       "      <td>126.500423</td>\n",
       "      <td>6.4</td>\n",
       "      <td>1245</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>1998.0</td>\n",
       "      <td>113</td>\n",
       "      <td>123</td>\n",
       "      <td>4</td>\n",
       "      <td>1.53</td>\n",
       "      <td>15.10</td>\n",
       "      <td>33.488936</td>\n",
       "      <td>126.500423</td>\n",
       "      <td>6.4</td>\n",
       "      <td>1245</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>1974.0</td>\n",
       "      <td>113</td>\n",
       "      <td>123</td>\n",
       "      <td>4</td>\n",
       "      <td>1.53</td>\n",
       "      <td>15.10</td>\n",
       "      <td>33.488936</td>\n",
       "      <td>126.500423</td>\n",
       "      <td>6.4</td>\n",
       "      <td>1245</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>952 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     birth_year  elementary_school_count  kindergarten_count  \\\n",
       "0        1964.0                       36                  56   \n",
       "1        1968.0                       36                  56   \n",
       "2        1987.0                       23                  31   \n",
       "3        1964.0                       13                  17   \n",
       "4        1966.0                       13                  17   \n",
       "..          ...                      ...                 ...   \n",
       "947      1969.0                       17                  16   \n",
       "948      1963.0                       17                  16   \n",
       "949      1998.0                      113                 123   \n",
       "950      1998.0                      113                 123   \n",
       "951      1974.0                      113                 123   \n",
       "\n",
       "     university_count  academy_ratio  elderly_population_ratio   latitude  \\\n",
       "0                   1           1.17                     14.39  37.551166   \n",
       "1                   1           1.17                     14.39  37.551166   \n",
       "2                   1           0.70                     16.65  37.606832   \n",
       "3                   3           1.71                     18.27  37.572999   \n",
       "4                   3           1.71                     18.27  37.572999   \n",
       "..                ...            ...                       ...        ...   \n",
       "947                 2           1.25                     27.01  35.686526   \n",
       "948                 2           1.25                     27.01  35.686526   \n",
       "949                 4           1.53                     15.10  33.488936   \n",
       "950                 4           1.53                     15.10  33.488936   \n",
       "951                 4           1.53                     15.10  33.488936   \n",
       "\n",
       "      longitude  elderly_alone_ratio  nursing_home_count  age_int  \n",
       "0    126.849506                  5.7                1080        5  \n",
       "1    126.849506                  5.7                1080        5  \n",
       "2    127.092656                  6.9                 689        3  \n",
       "3    126.979189                  6.8                 668        5  \n",
       "4    126.979189                  6.8                 668        5  \n",
       "..          ...                  ...                 ...      ...  \n",
       "947  127.910021                 17.4                 127        5  \n",
       "948  127.910021                 17.4                 127        5  \n",
       "949  126.500423                  6.4                1245        2  \n",
       "950  126.500423                  6.4                1245        2  \n",
       "951  126.500423                  6.4                1245        4  \n",
       "\n",
       "[952 rows x 11 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.340437</td>\n",
       "      <td>0.439450</td>\n",
       "      <td>0.577860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.340445</td>\n",
       "      <td>0.437860</td>\n",
       "      <td>0.536146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.211365</td>\n",
       "      <td>0.436380</td>\n",
       "      <td>0.309048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.204034</td>\n",
       "      <td>0.502386</td>\n",
       "      <td>0.531499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.204038</td>\n",
       "      <td>0.501591</td>\n",
       "      <td>0.510642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>0.026261</td>\n",
       "      <td>0.365837</td>\n",
       "      <td>0.490679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>0.026249</td>\n",
       "      <td>0.368222</td>\n",
       "      <td>0.553250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>0.396894</td>\n",
       "      <td>0.121135</td>\n",
       "      <td>0.330485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>0.396894</td>\n",
       "      <td>0.121135</td>\n",
       "      <td>0.330485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>0.396846</td>\n",
       "      <td>0.130752</td>\n",
       "      <td>0.582834</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>952 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2\n",
       "0    0.340437  0.439450  0.577860\n",
       "1    0.340445  0.437860  0.536146\n",
       "2    0.211365  0.436380  0.309048\n",
       "3    0.204034  0.502386  0.531499\n",
       "4    0.204038  0.501591  0.510642\n",
       "..        ...       ...       ...\n",
       "947  0.026261  0.365837  0.490679\n",
       "948  0.026249  0.368222  0.553250\n",
       "949  0.396894  0.121135  0.330485\n",
       "950  0.396894  0.121135  0.330485\n",
       "951  0.396846  0.130752  0.582834\n",
       "\n",
       "[952 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The dimension of the final data is 3\n",
    "pca=PCA(n_components=3)\n",
    "X_pca=pca.fit_transform(data_pca)\n",
    "df = pd.DataFrame(X_pca)\n",
    "df = (df-df.min())/(df.max()-df.min())\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = temp['recover_days']\n",
    "X = df\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3,random_state=33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_square_value: \n",
      "0.051\n",
      "MSE_value: \n",
      "54.216\n"
     ]
    }
   ],
   "source": [
    "#1.LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "predicted = lr.predict(X_test)\n",
    "print('R_square_value: ')\n",
    "print(round(r2_score(y_test, predicted),3))\n",
    "print( 'MSE_value: ')\n",
    "print(round(mean_squared_error(y_test, predicted),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_square_value: \n",
      "0.0117\n",
      "MSE_value: \n",
      "56.4408\n"
     ]
    }
   ],
   "source": [
    "#2.Assembled model: GradientBoostingRegressor\n",
    "gbr=GradientBoostingRegressor()\n",
    "gbr.fit(X_train,y_train)\n",
    "predicted=gbr.predict(X_test)\n",
    "print('R_square_value: ')\n",
    "print(round(r2_score(y_test, predicted),4))\n",
    "print( 'MSE_value: ')\n",
    "print(round(mean_squared_error(y_test, predicted),4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:**\n",
    "\n",
    "After using PCA to Do  Do Data Preprocessing, we did not get a better model as we have expected. The result is much worse than the result of using the Greedy Algorithm to do the Feature Selection.\n",
    "\n",
    "I guess the reason is that maybe the dataset is not suitable for the method PCA to do data preprocessing.\n",
    "\n",
    "\n",
    "### 2.4 Clustering of Risk Levels of the Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "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>city</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gyeongsan-si</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cheonan-si</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gumi-si</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chilgok-gun</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yeongcheon-si</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city  cnt\n",
       "0   Gyeongsan-si  439\n",
       "1     Cheonan-si   89\n",
       "2        Gumi-si   49\n",
       "3    Chilgok-gun   35\n",
       "4  Yeongcheon-si   22"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=result['city'].value_counts()\n",
    "df=t.to_frame()\n",
    "df = df.reset_index()\n",
    "df=df.rename(columns={'city':'cnt'})\n",
    "df=df.rename(columns={'index':'city'})\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>city</th>\n",
       "      <th>cnt</th>\n",
       "      <th>code</th>\n",
       "      <th>province</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>elementary_school_count</th>\n",
       "      <th>kindergarten_count</th>\n",
       "      <th>university_count</th>\n",
       "      <th>academy_ratio</th>\n",
       "      <th>elderly_population_ratio</th>\n",
       "      <th>elderly_alone_ratio</th>\n",
       "      <th>nursing_home_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gyeongsan-si</td>\n",
       "      <td>439</td>\n",
       "      <td>60010</td>\n",
       "      <td>Gyeongsangbuk-do</td>\n",
       "      <td>35.825056</td>\n",
       "      <td>128.741544</td>\n",
       "      <td>31</td>\n",
       "      <td>61</td>\n",
       "      <td>10</td>\n",
       "      <td>1.34</td>\n",
       "      <td>16.18</td>\n",
       "      <td>7.0</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cheonan-si</td>\n",
       "      <td>89</td>\n",
       "      <td>41120</td>\n",
       "      <td>Chungcheongnam-do</td>\n",
       "      <td>36.814980</td>\n",
       "      <td>127.113868</td>\n",
       "      <td>75</td>\n",
       "      <td>112</td>\n",
       "      <td>6</td>\n",
       "      <td>1.91</td>\n",
       "      <td>10.42</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gumi-si</td>\n",
       "      <td>49</td>\n",
       "      <td>60040</td>\n",
       "      <td>Gyeongsangbuk-do</td>\n",
       "      <td>36.119641</td>\n",
       "      <td>128.344295</td>\n",
       "      <td>50</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>1.96</td>\n",
       "      <td>9.08</td>\n",
       "      <td>4.3</td>\n",
       "      <td>616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chilgok-gun</td>\n",
       "      <td>35</td>\n",
       "      <td>60220</td>\n",
       "      <td>Gyeongsangbuk-do</td>\n",
       "      <td>35.995529</td>\n",
       "      <td>128.401735</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>1.48</td>\n",
       "      <td>15.17</td>\n",
       "      <td>6.7</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yeongcheon-si</td>\n",
       "      <td>22</td>\n",
       "      <td>60150</td>\n",
       "      <td>Gyeongsangbuk-do</td>\n",
       "      <td>35.973268</td>\n",
       "      <td>128.938603</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0.83</td>\n",
       "      <td>27.32</td>\n",
       "      <td>15.3</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city  cnt   code           province   latitude   longitude  \\\n",
       "0   Gyeongsan-si  439  60010   Gyeongsangbuk-do  35.825056  128.741544   \n",
       "1     Cheonan-si   89  41120  Chungcheongnam-do  36.814980  127.113868   \n",
       "2        Gumi-si   49  60040   Gyeongsangbuk-do  36.119641  128.344295   \n",
       "3    Chilgok-gun   35  60220   Gyeongsangbuk-do  35.995529  128.401735   \n",
       "4  Yeongcheon-si   22  60150   Gyeongsangbuk-do  35.973268  128.938603   \n",
       "\n",
       "   elementary_school_count  kindergarten_count  university_count  \\\n",
       "0                       31                  61                10   \n",
       "1                       75                 112                 6   \n",
       "2                       50                 104                 3   \n",
       "3                       21                  32                 2   \n",
       "4                       18                  23                 1   \n",
       "\n",
       "   academy_ratio  elderly_population_ratio  elderly_alone_ratio  \\\n",
       "0           1.34                     16.18                  7.0   \n",
       "1           1.91                     10.42                  4.5   \n",
       "2           1.96                      9.08                  4.3   \n",
       "3           1.48                     15.17                  6.7   \n",
       "4           0.83                     27.32                 15.3   \n",
       "\n",
       "   nursing_home_count  \n",
       "0                 427  \n",
       "1                1069  \n",
       "2                 616  \n",
       "3                 151  \n",
       "4                 192  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = pd.merge(df,Region, on='city')\n",
    "t.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>cnt</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>elementary_school_count</th>\n",
       "      <th>kindergarten_count</th>\n",
       "      <th>university_count</th>\n",
       "      <th>academy_ratio</th>\n",
       "      <th>elderly_population_ratio</th>\n",
       "      <th>elderly_alone_ratio</th>\n",
       "      <th>nursing_home_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gyeongsan-si</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.495141</td>\n",
       "      <td>0.772432</td>\n",
       "      <td>0.247706</td>\n",
       "      <td>0.298429</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.256545</td>\n",
       "      <td>0.307943</td>\n",
       "      <td>0.221557</td>\n",
       "      <td>0.124959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cheonan-si</th>\n",
       "      <td>0.200913</td>\n",
       "      <td>0.704956</td>\n",
       "      <td>0.223707</td>\n",
       "      <td>0.651376</td>\n",
       "      <td>0.565445</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.405759</td>\n",
       "      <td>0.099021</td>\n",
       "      <td>0.071856</td>\n",
       "      <td>0.336074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gumi-si</th>\n",
       "      <td>0.109589</td>\n",
       "      <td>0.557579</td>\n",
       "      <td>0.638511</td>\n",
       "      <td>0.422018</td>\n",
       "      <td>0.523560</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.418848</td>\n",
       "      <td>0.050417</td>\n",
       "      <td>0.059880</td>\n",
       "      <td>0.187110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chilgok-gun</th>\n",
       "      <td>0.077626</td>\n",
       "      <td>0.531273</td>\n",
       "      <td>0.657875</td>\n",
       "      <td>0.155963</td>\n",
       "      <td>0.146597</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.293194</td>\n",
       "      <td>0.271309</td>\n",
       "      <td>0.203593</td>\n",
       "      <td>0.034199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yeongcheon-si</th>\n",
       "      <td>0.047945</td>\n",
       "      <td>0.526555</td>\n",
       "      <td>0.838865</td>\n",
       "      <td>0.128440</td>\n",
       "      <td>0.099476</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.123037</td>\n",
       "      <td>0.712006</td>\n",
       "      <td>0.718563</td>\n",
       "      <td>0.047682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    cnt  latitude  longitude  elementary_school_count  \\\n",
       "city                                                                    \n",
       "Gyeongsan-si   1.000000  0.495141   0.772432                 0.247706   \n",
       "Cheonan-si     0.200913  0.704956   0.223707                 0.651376   \n",
       "Gumi-si        0.109589  0.557579   0.638511                 0.422018   \n",
       "Chilgok-gun    0.077626  0.531273   0.657875                 0.155963   \n",
       "Yeongcheon-si  0.047945  0.526555   0.838865                 0.128440   \n",
       "\n",
       "               kindergarten_count  university_count  academy_ratio  \\\n",
       "city                                                                 \n",
       "Gyeongsan-si             0.298429               1.0       0.256545   \n",
       "Cheonan-si               0.565445               0.6       0.405759   \n",
       "Gumi-si                  0.523560               0.3       0.418848   \n",
       "Chilgok-gun              0.146597               0.2       0.293194   \n",
       "Yeongcheon-si            0.099476               0.1       0.123037   \n",
       "\n",
       "               elderly_population_ratio  elderly_alone_ratio  \\\n",
       "city                                                           \n",
       "Gyeongsan-si                   0.307943             0.221557   \n",
       "Cheonan-si                     0.099021             0.071856   \n",
       "Gumi-si                        0.050417             0.059880   \n",
       "Chilgok-gun                    0.271309             0.203593   \n",
       "Yeongcheon-si                  0.712006             0.718563   \n",
       "\n",
       "               nursing_home_count  \n",
       "city                               \n",
       "Gyeongsan-si             0.124959  \n",
       "Cheonan-si               0.336074  \n",
       "Gumi-si                  0.187110  \n",
       "Chilgok-gun              0.034199  \n",
       "Yeongcheon-si            0.047682  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col=['cnt','latitude','longitude','elementary_school_count','kindergarten_count','university_count','academy_ratio'\n",
    "     ,'elderly_population_ratio','elderly_alone_ratio','nursing_home_count','city']\n",
    "a=t[col]\n",
    "a.set_index(['city'],inplace=True)\n",
    "#min-max normalization\n",
    "a = (a-a.min())/(a.max()-a.min())\n",
    "b=a\n",
    "a.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate the linkage matrix\n",
    "Z = linkage(a, 'complete', 'minkowski')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate full dendrogram\n",
    "plt.figure(figsize=(25, 10))\n",
    "plt.title('Hierarchical Clustering Dendrogram')\n",
    "plt.xlabel('sample index')\n",
    "plt.ylabel('distance')\n",
    "dendrogram(\n",
    "    Z,\n",
    "    no_labels = True\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Hierarchical Clustering Dendrogram (truncated)')\n",
    "plt.xlabel('sample index')\n",
    "plt.ylabel('distance')\n",
    "dendrogram(\n",
    "    Z,\n",
    "    truncate_mode='lastp',  # show only the last p merged clusters\n",
    "    p=3,  # show only the last p merged clusters\n",
    "    show_leaf_counts=False,  # otherwise numbers in brackets are counts\n",
    "    show_contracted=True,  # to get a distribution impression in truncated branches\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#We use the MDS method to reduce the dimension of the distance matrix, and use different colors to represent the clustering results. \n",
    "seed = np.random.RandomState(seed=3)\n",
    "mds = manifold.MDS(n_components=2, max_iter=3000, eps=1e-9, random_state=seed,\n",
    "                n_jobs=1)\n",
    "pos = mds.fit(a).embedding_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k=3\n",
    "clusters = fcluster(Z, k, criterion='maxclust')\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.scatter(pos[:,0], pos[:,1], c=clusters, cmap='prism')  # plot points with cluster dependent colors\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>cnt</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>elementary_school_count</th>\n",
       "      <th>kindergarten_count</th>\n",
       "      <th>university_count</th>\n",
       "      <th>academy_ratio</th>\n",
       "      <th>elderly_population_ratio</th>\n",
       "      <th>elderly_alone_ratio</th>\n",
       "      <th>nursing_home_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.166667</td>\n",
       "      <td>36.474173</td>\n",
       "      <td>127.457965</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>135.166667</td>\n",
       "      <td>4.916667</td>\n",
       "      <td>1.912500</td>\n",
       "      <td>12.203333</td>\n",
       "      <td>5.425000</td>\n",
       "      <td>1249.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.098039</td>\n",
       "      <td>37.150067</td>\n",
       "      <td>127.067092</td>\n",
       "      <td>29.098039</td>\n",
       "      <td>43.843137</td>\n",
       "      <td>1.980392</td>\n",
       "      <td>1.483922</td>\n",
       "      <td>15.057255</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>664.137255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.694444</td>\n",
       "      <td>35.938680</td>\n",
       "      <td>128.591659</td>\n",
       "      <td>20.750000</td>\n",
       "      <td>29.250000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.388611</td>\n",
       "      <td>21.550556</td>\n",
       "      <td>11.011111</td>\n",
       "      <td>279.305556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               cnt   latitude   longitude  elementary_school_count  \\\n",
       "cluster                                                              \n",
       "1        16.166667  36.474173  127.457965                76.000000   \n",
       "2         4.098039  37.150067  127.067092                29.098039   \n",
       "3        18.694444  35.938680  128.591659                20.750000   \n",
       "\n",
       "         kindergarten_count  university_count  academy_ratio  \\\n",
       "cluster                                                        \n",
       "1                135.166667          4.916667       1.912500   \n",
       "2                 43.843137          1.980392       1.483922   \n",
       "3                 29.250000          1.250000       1.388611   \n",
       "\n",
       "         elderly_population_ratio  elderly_alone_ratio  nursing_home_count  \n",
       "cluster                                                                     \n",
       "1                       12.203333             5.425000         1249.666667  \n",
       "2                       15.057255             6.333333          664.137255  \n",
       "3                       21.550556            11.011111          279.305556  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=b.copy()\n",
    "b['cluster'] = clusters\n",
    "b.groupby('cluster').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:**\n",
    "\n",
    "We use the MDS method to reduce the dimension of the distance matrix, and use different colors to represent the clustering results.\n",
    "The figure above shows that the results is good.\n",
    "\n",
    "The cities are divided into three different risk levels: high, middle, or low.\n",
    "\n",
    "In the high risk level cities(cluster 3), the number of confirmed patients, the elderly_alone_ratio and the elderly_population_ratio\n",
    "is the largest but the nursing_home_count and the academy_ratio is the smallest. It is very reasonable to classify the places with high number of diagnosed patients, serious aging, small number of nursing homes and people with low education level as high-risk areas.\n",
    "\n",
    "In the mid risk level cities(cluster 1), although the number of confirmed patients is large, the nursing_home_count and\n",
    "the academy_ratio are maximal and the elderly_population_ratio and the elderly_alone_ratio is minimal which means there are\n",
    "more medical resources for every patient.\n",
    "\n",
    "In the low risk level cities(cluster 2), the number of confirmed patients is fewest and the academy_ratio, elderly_population_ratio,\n",
    "elderly_alone_ratio and nursing_home_count are all middle valuse.\n",
    "\n",
    "If I were the policy maker, I would distribute more medical resources to cities with high level of risk, and send medical teams to assist high-risk cities.\n",
    "\n",
    "## 3. Conclusion:\n",
    "\n",
    "By analyzing these data of South Korea, I found COVID-19 is highly transmitted and people of all ages are easily infected. The main transmission route is contact transmission, so reducing aggregation and paying attention to wearing masks are good protective measures. At the same time, we should assist medical resources and send medical teams to high-risk areas to help them fight the epidemic together and overcome difficulties."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}