yong27/COVID19-analysis-0301.ipynb

## COVID19-analysis-0301.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 코로나바이러스감염증-19 감염현황 데이터 분석 (03-01 16:00 현재)\n",
    "\n",
    "질병관리본부에서 매일 발표하고 있는 일일 집계 현황을 다음의 구글 시트에 별도로 저장한 후, 이를 분석합니다.\n",
    "\n",
    "https://docs.google.com/spreadsheets/d/1nqc9A9M5QJgSnjkErWeyDS4KTiLB1N9VjHd3T6k69hE/\n",
    "\n",
    "활용 가능한 데이터가 추가되는대로 계속 보완합니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from io import BytesIO\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import requests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib 차트 한글폰트 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('font', family='AppleGothic')\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "구글시트에 정리된 데이터를 가져옵니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.get('https://docs.google.com/spreadsheet/ccc?key=1nqc9A9M5QJgSnjkErWeyDS4KTiLB1N9VjHd3T6k69hE&output=csv')\n",
    "df = pd.read_csv(BytesIO(r.content), index_col=0, skiprows=1).iloc[2:, :-4]\n",
    "df.columns = [c.replace('\\n', ' ') for c in df.columns]\n",
    "df.index = pd.to_datetime([f'2020-{i}' for i in df.index])\n",
    "df['대구'] = df['대구'].str.replace(',', '').astype(float)\n",
    "df['확진자'] = df['확진자'].str.replace(',', '').astype(float)\n",
    "df['검사수'] = df['검사수'].str.replace(',', '').astype(float)\n",
    "df = df.replace(np.nan, 0).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>서울</th>\n",
       "      <th>부산</th>\n",
       "      <th>대구</th>\n",
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       "      <th>사망</th>\n",
       "      <th>격리  해제</th>\n",
       "      <th>검사수</th>\n",
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       "    <tr>\n",
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       "      <td>48.0</td>\n",
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       "    <tr>\n",
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       "             서울   부산   대구   인천   광주   대전   울산   세종   경기   강원  ...   전북   전남  \\\n",
       "2020-01-23  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2020-01-24  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2020-01-25  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2020-01-26  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2020-01-27  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "\n",
       "             경북   경남   제주  지역  모름  확진자   사망  격리  해제   검사수  \n",
       "2020-01-23  0.0  0.0  0.0     1.0  1.0  0.0     0.0  22.0  \n",
       "2020-01-24  0.0  0.0  0.0     1.0  1.0  0.0     0.0  25.0  \n",
       "2020-01-25  0.0  0.0  0.0     0.0  0.0  0.0     0.0   0.0  \n",
       "2020-01-26  0.0  0.0  0.0     1.0  1.0  0.0     0.0  48.0  \n",
       "2020-01-27  0.0  0.0  0.0     1.0  1.0  0.0     0.0  57.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "누적값을 별도 컬럼에 추가합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>서울</th>\n",
       "      <th>부산</th>\n",
       "      <th>대구</th>\n",
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       "      <th>강원</th>\n",
       "      <th>...</th>\n",
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       "      <th>전남 누적</th>\n",
       "      <th>경북 누적</th>\n",
       "      <th>경남 누적</th>\n",
       "      <th>제주 누적</th>\n",
       "      <th>지역  모름 누적</th>\n",
       "      <th>확진자 누적</th>\n",
       "      <th>사망 누적</th>\n",
       "      <th>격리  해제 누적</th>\n",
       "      <th>검사수 누적</th>\n",
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       "             서울   부산   대구   인천   광주   대전   울산   세종   경기   강원  ...  전북 누적  \\\n",
       "2020-01-23  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0   \n",
       "2020-01-24  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0   \n",
       "2020-01-25  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0   \n",
       "2020-01-26  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0   \n",
       "2020-01-27  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0   \n",
       "\n",
       "            전남 누적  경북 누적  경남 누적  제주 누적  지역  모름 누적  확진자 누적  사망 누적  격리  해제 누적  \\\n",
       "2020-01-23    0.0    0.0    0.0    0.0        1.0     1.0    0.0        0.0   \n",
       "2020-01-24    0.0    0.0    0.0    0.0        2.0     2.0    0.0        0.0   \n",
       "2020-01-25    0.0    0.0    0.0    0.0        2.0     2.0    0.0        0.0   \n",
       "2020-01-26    0.0    0.0    0.0    0.0        3.0     3.0    0.0        0.0   \n",
       "2020-01-27    0.0    0.0    0.0    0.0        4.0     4.0    0.0        0.0   \n",
       "\n",
       "            검사수 누적  \n",
       "2020-01-23    22.0  \n",
       "2020-01-24    47.0  \n",
       "2020-01-25    47.0  \n",
       "2020-01-26    95.0  \n",
       "2020-01-27   152.0  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for c in df.columns:\n",
    "    df[f'{c} 누적'] = df[c].cumsum()\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['누적 양성비율'] = 100 * df['확진자 누적'] / df['검사수 누적']\n",
    "df['누적 사망율'] = 100 * df['사망 누적'] / df['확진자 누적']\n",
    "df['누적 대구경북'] = 100 * (df['대구 누적'] + df['경북 누적']) / df['확진자 누적']\n",
    "df = df.loc['2020-02-15':]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "차트를 그립니다. 가능한 한장의 차트에 모든 정보를 담을 수 있도록 노력합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BXCulDJyKGIp6JXuqqb+U8nXUVLdQVLFcSCmfpfaLvd9agBDiGNQNxod6Gi9GfT8SdZxxKJ9wIuoC/HvAGDURSjELmVvDMjOH6p8OvGtF4fvZbLl0nrLkKqxFhrpQCFwohJiIOtbZKJNzbezNcVdDSllRw3GDsv6E2iaUhSYeZQGbHzAr4h8hxA+oQN5wfAHcJYToLPdMyZ0i1BTPpSgry4eoQGa/ef8vlHnfP9vmWSnlO+F2JKX81XpC/1aoXA9/UH1ad2NzHsqttBblgphBkFUNlasilqpBlVeh3FN+C8bDAb99UNbDyUKI5ajfzvNSyplhZIkiIL4okECl2zqPu4PW+/87P7Hndx8YxH00sF7qHBeaZkDIarMoNc2NEOI4VJDklXXsfwrqJn5FPfbxHXCTlHJ5HfvPBS6TUtZn1kvwGPNRN06/0uanEyogMTOo/4moAEcv6iJeYW0fgzLn9pcB0x+FmhI7HhVr4B+/HOWGeaaGuIcmRwjxDsrlsJOqN6MEYIOU8sKg/n1R1qMl7FHc/MftAM4NVlCFEFvY8z35E5XFoBSqf6SUZwf1Px31VJ1o9S1HzRy4R0q5hjAINb33QillqLgYTSMhhPgXSjH+i+q/hXhUcrywWVWtse4BtkspX7WWZ6KuM3lNIbtGE4hWLlohQggh63nihEqhvL0+rpyGYj15lmDlA2ngWPU+5khhPa2XAxWROG4hRFRznmdN89Ga/geafRutXGg0Go1Go2lUmi3mwnAb9wLbTaf5srV8MPAqe+pBOE2nuctaNxQ11zwKlVHuBtNpllnrzmRPAaXZptMc11zHoNFoNBqNJjxNnqHTcBs9DLexCJWoJ5BngStNpzkINY3yIat/LCpq+zTTaR6NCugaY61LRSUAOs50mgOBRMNthJp1oNFoNBqNJkI0uXJhOs1c02kOAe7ytxluwwDWmU5zjdVnBnvS144GZplOc6u1/BJqGhyodMdTTafpn9r4OFVTRms0Go1Go4kwkZqK2hf4M6htm+E2OgWvM51miWXN8G+3MGDdJsNthKzxIFQ1QX+GvWrlyTUajUYTHillfafKtziEELOklHWZdt2iaU3HESnloloSKAtZw7rAxE41ravaqKZf+adg6QBrjUajqSdCiFqrxbYiUiMtQCPRao4jUlVR1wD9gtpSTKe5I3id4Tbi2JPqOnhdd1RhMY1Go9FoNC2EiCgXptP8HehnuI0DAAy3MRpVkhlgFnCG5SIBFVPhLzL1HjDGUjhA1f+oU0IZjUaj0Wg0zUOkLBegFIN3DLcxH7gSK92uFaw5DphluI0FwGFY7g3TaW5CBXguMNzGj0Ch6TS/DDW4RqPRaDSayLBPJNHSMRcajUZTf4QQu6WUIevYtCaEED9LKQdGWo6G0pqOI5KWC41Go9FoNG0QrVxoNBpNK6WipARtldW0RHTJdY1Gs3dsWwszboIjL4XDLwLR6tMhtAgqKiT5RWVs313Ctt0l7NhdwvaCUrbvLlFtBaXs2F3CtoIS/ln9F3ZRwAtZVyH0969pQWjlQqPR1B8p4cu74a8F6vXHx3D6M9CxV6Qla5FUVEg25xexbutu1m8vZOuuYqU4BCgN23eXsr2ghB2FpZRXhLZGREcJOiXG0jExjuRtm+m6KYc+h/XWioWmxaGVC41GU3/+/BJWfgUnPQIxCTDHBS8OgZPGw4ArIWrf87juLilj/bZC1m3bzV9bC1i/bTfrrNf67YWUlFVU6R8XHUXHxFg6J8XRMTGWfl2T6ZgYR+dEtdwpMa5ynXqPo0NCDEIItk2bxuZXH6HTxRfT9Y4LInTEbRSX7V5gOy7fyzWsfwg4AxVWMAGX7+NmlK7VoJULjUZTP0oLYdY9sJ8DhtwA0bHQ72T47FbIHgvLPoEzn4POfSItaaNSUSHJyy+uVBjWbdvNuq0F1udCtuwqrtI/OT6GtM6J9OvanhMdXenVOZG0zon06pxIl/bxJMZF75XFIf/779n86ASSR4yg67j7tNWisXDZegAfAwcCD9TQ51QgBZdvIC5bApCNy/YDLt+W5hO0daCVC41GUz/mPw071oHzc6VYAHRKh8tnwG9vw+xx8OIxMOoBGHw9REVHVNz6sKu4rNLisN56+S0P67ftpjjA+hAloLutHWmdExll70JailIcDrCUiI6JsY1+4y9ctozcsXeQ4HDQ46knEdGt57tt8bh8ucAQXLYrgIQael0D3GL1L8Jlm4oqqDmlOURsTWjlQqPR1J1ta2D+M3Do+dB7WNV1QsBRl8OBo5QFY/Z9yopx1guw38GRkTeIsvIKNvqKApQGZXXwKxPbCkqq9G8fH0OvzokcuF8SI+1dKq0PaZ0T6dGxHXExzef+Kf37bzZcfwPRHW30fOlFohITm23fbYBUIcTPAcuvWvWn6ktXXL6/A5ZXAkMbJlrbRCsXGo2m7nyZqawVJz9acx9bD7joPTA/VEGfLx8HwzPhmFv3WDqakLLyClb/U8DKvHxLaSisVCb+3lFIWUCwZEyUYP+OyvpwSv9u9OrcrlJ56NWpaawPe0N5fj7rx1xPRWEh6W9MI7ZLl0iL1NrY0oTJp/Rc4BBo5UKj0dSNP7+ElbNVEGeH7rX3FQIOuwD6DIcv7oJvxsOyT5UVo/thjSZSWXkFq/7ZhbnBxx+5PsxcH8s37qSodI/7IiUpjp6dEzm8V0fOOLw7vTrtiX3obksgJrplB5/K0lJyb7uN4rVrSZv6KvEHHRRpkfZltuCydcPl22Qt9wNWRVKglopWLjQaTXhKC+HLe2A/uwrirCvJXeACNyyfAdl3wtQRcNxYOP5OiImvlwjhFInEuGj679+BiwalcVhPGwd37UBaSiLJ8a33MielZONDLgoW/kj3iRNJGqot8M2Ky9YFeAiX7yarxQ3cBdyByxYHXA5cGinxWjKt91+n0Wiaj/nPwI6/wDlz71wbh5wF6cNUHMbcJ8AzU1kxeg4I2b2svIKVebswc/coEp4ARSIpLpr++9u4eNABGD07YPSw0Ts1meioyLswGpOtL7+Mb/p0Um+8kY7nnhNpcfZF2gN7pj25fJ/gsg3CZVuMcodMxOXbGinhWjK6cJlG08opLClna0ExW3eprI1bC0rYVlCs3qu0qVdBSRnxMVEkxEbTLjaahNjoyuWEWOs9Rn1uFxdNfHkBCUvfJmG/3iQcfi4J/m3j1HYAFVLl1aqQEol64q5cDm7ftJyKpe8ji3ZS0Xs49BtNRVQs5RWSnK0FLN2gFAn/zIykuGj697BhWK9De9jok5pEVBtTJILxffYZf999D7azzqR7VlZEYj904bKWRWs6Dq1caFov85+GzcvgvNciLUmjIaVkV3HZHoUglMJgvfzKRGFpecix4qKj6JykEjGlJMdVfk6Oj6G4rIKi0nKKSsspLN3zubi0gqKycmu5gsLScooK8imuEJTQ9MGYyfExHLJ/h0pFwuhpo3dK21ckgin46SfWXX0NiUceSdprUxFxcRGRQysXLYvWdBzaLaJpvaz6Bv5aqNJOxydHWpqQVFRIdhSWKsUgQFHYHmRN8CsP2wtKKSmvCDlWQmwUKUnxlUpC3/2S1efkOFL97clxpAQoEQ1+2v1zFrx7BZw0nvKht1JcppQOpZQoZUQIFb8ZJQQCEEJUWY6yltVLECVAoN5Z9yNRs+9D7FxP1JGXYjtlHFHt2jdM5lZO8erVbLj5FuJ69aLnlOcipljUBY/dkQRMAPoCNiAPuNLh9ewM6vckaspmqdWU6/B6LmlOWTXNi1YuNK0X3waQ5ZD7s5qV0EIwN/hwzVzG2i0F7NhdQg1lImgfH0Nny6LQo2MCRo8OdE6KJyUpjk5Je5QEv+UhMa6Z/66lRSoTZ+rBMPgGoqMEiXExJDbmve7QEdBvDnzzCCx+Gcq3tClLVH0p27KF9deNQcTG0uvVV4i22SItUjgSgDcdXs8SAI/dcRNwHfBkUL9U4GqH1+NtZvk0EUIrF5rWSUUF7LRy2axb3CKUi4oKyWvz1zBp9p+kJMUz+tBuVRSESkUhKZ5OSbHEx7Tw7IoLnoXtOXD5ZxDThE/PcUlwapaaPbLgWRh2B3RxNN3+WigVhYWsv/EmyrZu5YC3/0tcz56RFiksDq9nK7AVwGN3xKJSZ38TUaH2cdIzs9OAl4AkIBF4LCcr45P0zOyhwDOomii/AzfkZGWUNZUcWrnQtE52b4Fyq5bDuh8jKwuQt7OIOz5cwryVWzilf1ceP+8wOjbqI34zsz0H5k+G/udAnxOaZ5/H3gb/ex2+fwwu+G/z7LOFIMvL+fvuuykyTXo+P4V2hhFpkfzEhMts6bE7zgFuB3oDS4HxNYx1u8fu6AkkAy86vJ4PmkJgDU8AE3KyMhamZ2anAD+mZ2Z/DjwOnJaTlbE1PTP7NmAM8EJTCaGVC03rxLdBvXfoARv+B+VlEB2Zn/O33s3c9eFSCkrKmHDOoVw8KK1FZHVsELPuBRENJ09ovn0mdlY5NOY+AZtM6NZibrBNTt4Tk8j/eg5d77uP9qNGRVqcQMrCBRA6vJ5PgE8APHbHucDLwIVB3V4DCh1ezy8eu6MD8JnH7liq3SRNQjRgXSDZBawBRgOzcrIy/NNmXwJm04TKRctOTafR1MTOXPXe/xwo2QV5y5pdhKLSclyfLeOqt35mv/bxzLz5OC4ZfEDrVyxWzIY/v4AT7lapvJuToTdCvA2+z2re/UaQbW+/wza3m06XX0bnyy+LtDgNwuH1TAfSQ7TPd3g9v1ifdwLZwCHNK12bIFUI8XPA67oQfe4Gnk7PzP4P8BEwFhVw+6e/Q05WRgk07fQvrVxoWic+S7kwzlfv6xY16+5X5eVzzosLeWthDlcck86nNx3LQV3bwCyH0iJVDyS1Hwy5sfn3364TDL0JvJ/D3781//6bmfxvv2XzY4+RPGoUXe+5J9Li1BuP3ZHisTtODFi+DFjgsTu6eOyOFwLaB3jsjjjrcwcgA1jc7AK3frZIKQcGvEIVX7sEmIWyTjwMPIaKvQgOLW/S/AzaLaJpnfjWQ0wCdD8COvRUysXgMU2+Wykl7/60nvGfLyMxLoY3rhjISHvXJt9vs7HwORVvcdmnTRvEWRtDboBFL8J3j8ElbdctX2j+Qe4dd5LQvz89Jj3RWsun7wYu8NgdE4EC1NPxWKA7gZktYX9gosfuiAYE4HJ4PbnNLWxbJz0z2wH0ycnK8FcW/Dk9M/s9VA2UfgH94oAmC+YErVxoWis7c6HD/ip5Qtpgle9CSrXcROzYXULmxyazlm1i2EGpPPWvw+nSIaHJ9tfsbP8L5j0Fh5wNB46InBwJHeDYW1Wxsw0/Q89WkTOoXpRsyGX9DTcQ07kzvVpx+XSH11OImnoazGrg1IB+M4GZzSXXPsxOwEjPzG6fk5WRn56ZHQ9cgJolMjE9M/uVnKyM7cBVQJNq7totommd+HJVMCdA2lDI3wg71jXZ7hat2cqpz87jG+9m7jvNjvvKQW1LsQAriDMKTmnGIM6aGDQGElPguxYgSyNTvnMn68eMQZaU0OvVV4hJTY20SJo2Qk5WRi4wEZiVnpn9HfAVMD0nK+MHYJzVvgA4DAjlUmk0tOVC0zrZmQu9rSmSaUPU+/rF0OmARt1NWXkFz36zkue/W0V6ShLTbzgWo2eLT2xUf1Z+DX9mw4kusLWA/ArxyXDs7fD1A/DXj3BA26gGKktL2XDrbZSsW0fa1KnEH3hgpEXStDFysjIqZ+8EtX8PDG4uObTlQtP6KC9Tlgr/TIYuh0B8h0bPd7F+224ueOVHpny7ivOP6snntxzXNhWL0iL44i5IOQiG3BS+f3Nx9DWQ1KVNWS82T5rE7kWL6P7IeJKGNNt1XqNpdrTlQtP6yN8IsmKPWyQqGnoerTJ1NhIzfs/l/k/+AOC5i47kzMP3b7SxWxwLp8D2tXDZJ5EL4gxFXCIMGwuzMmHtXOh9fKQlahC+mTPZ/t+36XT5ZXQ8++xIi6PRNCnacqFpffhzXASa79OGQN5yKNzeoKF3FZdx54dLuO293zmoazJf3DasbSsWO9ZZQZxnwYEjIy1NdQZcAe27q5kjrbiycZHXy8YHHiRx4EC63nVXpMXRaJocrVxoWh+B2ROhYRsAACAASURBVDn9pA0BJKz/314Pu3TDDk5/bh7Tf93ArSP78sGYofTq3Dqj+OvMrHvVDJtTJkZaktDEtlO1RtYthDXfRVqavaJ8xw423HwL0R060OOZpxGxTV+6XqOJNFq50LQ+QlkuegyAqBhYv3fJtN79aR3nvbSQkrIK3r12CGNPPpiY6Db+91g5RyWrOv6ulhHEWRNHXa5ymXw3sdVZL2R5Obl33U3p5s30fO5ZPTNEs88Q0ZgLw21Uq95mOs1PDLdRrXqb6TTLrG3OBB60hphtOs1xzS+5JqL4NqgAzoQOe9rikqDbYfXO1FlRIZn01Z+89P1qhh+8H8/++0hsifvAk2VZMXx5F6T0haE3R1qa2omJh+PvhM9vh1Vz4KCTIi1RndnywgsUzJtHN5eLdkccEWlxNJpmI9IBnU8AE0ynudBwGynAj4bbqKzeZjrNrYbbqKzeZriNVOA/wHGm0ywy3MbThtvIMJ1mduQOQdPsBOa4CCRtKPz8OpSV1Ckwsai0nLs+WsrMJX9z8eA0xp/Zv+1bK/wsnALb1sCl01tWEGdNHHGJqtL63QToe2KTJktrDB7+8WH2/y2X416cx5phvfkgzQM/PlynbR8a+lATS6fRND2RvpLWWL3NdJqB1dusAhJcBEw1nWaRtfw4KtOYZl9i54bQBbXSBkNZEWxcEnaI7QUlXPb6YmYu+ZvMU+1MOPvQfUex2LEO5j4JjjOhb4uqwFkzMXFwwj2q3sifX0ZamrC037iTQVMXsS29M79eNrDFK0MaTWMT6avp3cDThtuosXqb6TQDq7cFr9sEhCzsIIS4zl85rolk10QKX27oGIFeVjKtMPku/tpawLkvLWTJBh/PX3wk159wYOuvZFofZt/XsoM4a+KwC6FzHxV7UVERaWlqpHxXAcc8P5+K6CgW3nwsFbGtsmaIRtMgIu0W8VdvexuYh6re9hM1V28Ttayr2qiqxb0KIIRoXVFgmpopLYTdW1SAXzDtu6qbz/qa8138um4717h/RkrJtGsGMzC9cxMKGyGkhCIfFPwDu/KgIA8KtqjPvvXgmQkjH4COvSItaf2IjoETMuGT68A7U02fbWFIKdk4bhztN+Yz984T2J2SFGmRNJqIUKtyYQVcXgQMAlKAImAl8LnpNGc3ZMeG23AAfUynWVm9zXAb1aq3GW4jsHrbGmvdr9a67sDmhsihaWXs/Fu9h3KLgLJerJwdsojZl+ZGbn//d7rZEnjrykH0Tm1FF/6Kcti9TSkMBXmwy/9uKQ6Vn/9Rr/KSEIMISEqFg0+DY25p9kNoFIzzYe4klffCfgZERdr4WpVtb7xB/uzZmBccTt4h3SItjkYTMUIqF1bg5H3W+hnAa8AOIA5IA4ZbisA002l+tpf73gkYhttobzrNfMNtVKneZriNV0ynGVy97T3gXcNtTLfcJbcBr+/l/jWtkVA5LgJJGwJLpsHWVZB6EKCeJl+fv5YJX3g4sldHpl4+kJTk+GYSuBH49CZ1TDKEKyAqFpL2g+T9VKrsLofs+ZzcRSkT/s+JKSqbaWsmKhqGZ8LHV8Oy6UrZaCEU/PgjeU9Npv3o0fw5ug2middo6kFNlotDgMmm09wQ1F6Iinn4E3jFcO/9P9t0mrmG25gIzDLcRgkq/uM102n+YLiNcVZ7GbAEuMXaZpPhNl4CFljrZplOs+VHd2kaj1A5LgJJswpcrVsEqQdRVl7B+M+X898f/+I0oxuTLziChNbkAy/aCUvfV0XaDj7VUiS6KIUhKRXaddr3ggX7n6sCUr/Pgv7ntAiFqTQ3l9z/jCWuT2/2n/AoLH0y0iJpNBElpHJhOs25ddnYdJofNWTnptMMWb3NdJrfU0P1NtNpfkAT16HXtGB8lnLRoYaU3KkHQbvOsG4Ruw+9iFum/cY33jzGHN+He0bbiYpqZTfi1d9CRamaKdFGKoM2mKgoGHEvfHA5mB/B4f+OqDgVxcVsuPU2ZFkZPadMISqpFbnbNJomos4BnYbbeNd0mhc1pTAaTVh2boDEVJUWOhRCQNoQ8nL+4OpXFrHsbx+PnNWfy4amN6uYjcaK2co60fPoSEvSsrCfAV0N+CELDj1PBXtGACklmx4eT9GyZfR88QXie/eOiByaRsBl6wq8BXRGue2duHx/h+g3ETgeNYvxF+BWXL6yav32ccIFdE4B1gLLgYOsttOAnqgcFRWm03ylqYXUaCrx1ZDjIoAVHYdx5ZJEtsXmM/XygYxyhJyt3PKpKIeVX0HfkyJ282yxREXBiPvgvYtg6Xtw5KUREWPH+x/gmz6d1BtvoP3IFlj4rYnx2B1JwARUmgAbkAdc6fB6dobo+xBwBsoFPsHh9XzcnLLWgacAFy7fYly2gcCTwMVVerhso4FUXL7jrOWpwFlASzuWiBMu1PpQYBHQC/A/Kt4KeFEKh7fpRNNoQuDLDT0N1WLhqi2c92M6JcTwwSllrVexAMj9VU277XdKpCVpmRx8Kux/JPzwOJSXNvvuC3//nU0TJpB0/DBSb7qp2fffQkgA3nR4Pac7vJ5hwLfAdcGdPHbHqUCKw+sZCBwD3OixO1pOoRWXrSOQgsun5rG7fD8DNqs9kMDEjwBbgNxmkbGVEe5xaLfpNBcCCw23cY7VVlHXmAyNptHZmQu9jw+56qNfNpD58VL6pCbyZv5YehSch3pQaqWs+BJEtEp3ramOEDBiHPzf+fDbOzDwymbbddmWLWy49TZiu3WjxxNPIKIjH1QaCRxez1ZgK4DH7ogFDgS+CdH1GqzAfIfXU+SxO6ai0hxMaSZRU4MSKr5q5ULy0xtYFbTNGqv9t4C2L4DhuGxPox7Oc3D59q5aYhsnnOUicO5bouE2PgGOakJ5NJqaKfJB8c5qbhEpJc/MWcGdHy5hUO/OfHjDsfTolV7vImYtjhWz4YBjoF3ww5Omkr4nqniUuU+qYmzNgCwtZcPtt1O+cyc9pzxHdMc2fX5i/JmOrVcoq8Q5HrvjB2A1Kg/RghDjdHV4PYHxCytRrpTmYouUcmDA69Wg9aESNBKibQgq1uJZ4BGgJy7bFY0ubRsgnHIRGFpfYDrNc4AlhttoWZlrNPsGlTNF9igXJWUV3PHhEp6Zs5LzB/TkrSsHYWsXq/JdbPwdSnZHSNgGsmM9bP5Du0TCIYSKvdi5AX79b7PscvOkSRT+/AvdHxlPgt3eLPuMIGVhbso4vJ5PHF7PCQ6vJw14A3i5jmO3pMzJOVRXdvpY7YE8BtyNy5eDy7cFuJMQbiBNeOUiOsTncqoqHRpN8xCU48JXWIrzjZ+Y/msu/zmxH5POP4y4GOsn3WsIVJTB379GSNgGstJKgNtvdGTlaA30GQFpx8C8p1R6+CbEN3Mm2//7Np0uvwzbGa3Y5dZEOLye6UB6iFVbPHZHYMrSflR3Q0QOl28bsBuXTVnmXbbDUO6eOFy2FwJ6FqCsF34uQMcehiRczMXagM+BdZmjUEqGRtN8BGXnfGjGH/z81zae+tfhnDcgKMiz1yD1vu5HSD+uGYVsJP6cpeqkpDSn5biV4rdeuE+HX96CITc0yW6KvF42PvAgiQMH0vWuu2rtu+D3A/Z+R60onYnH7kgBjnR4PXOs5cuABR67owvwkMPr8Ue6uoG7gDs8dkcccDkQmSk+NXM78DouWzLgA64E2qMsGH6uB6bgso1HWV5WoCY5aIKoVbkwneaNAYuJVttpTSqRRlMTO3NBREH77pSUVTDHk8d5R/WsrlgAJHaG/RywruYiZi2WkgJYOxeOvmbfy765t/QeBunDYN5kOMoJcYmNOnz5jh1suPkWojt0oMczTyNiY8NvtG+wG7jAY3dMRD3V/4mqbt2dgJuyw+v5xGN3DPLYHYtRN+WJVjBoy8Hl2wAE+yE3A6cG9FkPnN2MUrVa6jN5fnhTCaHR1AlfLrTvDtExLF75D7uKyzixtqmmaYPhj09UvogWkCK6zqz5AcqLdbxFfRkxDt4cDf97DY5tvIdJWV5O7l13U7p5M+lv/5eY1JYzgzLSOLyeQkLHHKwm8Kas+t7bLEJpWgR1Dsw0nWZRcJvhNhIaVxyNphZ86ytdInOWbyYhNopj+9ZyoU8bCsU+yPM0k4CNxIpZEN9hT50UTd04YCgcOBIWPAPFuxplyOKVK/nrcicF8+bRbdw42h1xRKOMq9G0dcJl6JyOmo6aCOxCBXJGA29Z1VC/BEY0tZAaDaDcIt0PR0rJHE8ex/VNpV1cLRaJXlZ5mvWLoNuhzSNjQ5FSTUE9cCTExIXvr6nKiHHw2ij46RUYdsdeD1NRWMiWF19i65tvEp2cTPcJE7Cde074DTUaDRA+5uJcAMNtfGU6zQtCdNH51DXNg5Sw82+wZ+DdlE/ujkJuGRkm2LFTOiR3U/kujr6mWcRsMBt/h12b9CyRvaXnQDjoFFjwHBx9LSR0qPcQ+d9/z+ZHHqU0NxfbOefQ5e67iOnUqQmE1WjaLuEsFz1RZdZXWMtDgV9Mp1lidWlJ85Q1bZndW6GsCDr05BvPZgBG2rvUvo1VxKxVBXWumA0IOOjkSEvSehlxL7w6HBa/DCfcXefNSjdvZvOEieR/9RVxBx5I2n/dJA0a1HRyaqrhsTvSUJk7BwEpQBEq4dbnDq9ndiRl21dIz8yu8RzkZGXU+RyEC+g8DNhsOs2bDbdxPHADcFnAeq1caJoH33r1buvB17/kcXivjnTpUIeQn7QhsPxTq+BZzTVJWgwrZqlptEkpkZak9bL/kWA/HRY8qwqaddi/1u6yrIzt06bxzzPPIsvL2e/220m56kpEnHZLNRdWnZH7UPekGcBrwA5UCoQ0YLjH7ngPmObwej6LmKBtmPTM7LDnID0z+z1gWk5WRthzEE65KAauMdzG5SjrxaWm0wzMb6HnyWmaBys7Z150N5asz+OOk/rVbbs0K9/NukVgnN9EwjUS+Zvg799g1IORlqT1c/Ij8MIQmD0O/vVmjd0KTZNND7koWr6cpGHD6PbgA8T16tWMgmosDgEmO7yeDUHthajprX8Cr3jsjhb+J27VHAJMzsnKqPUcpGdm1+kc1GUq6kqgE2AHkgGf4TZOBvYD9jfcRnSQwqHRND5Wds5vN6v8BSceUsdqp10NiE2C9YtbvnKx8iv1ruMtGk7nPiqg8/uJcNRlKkA2gPL8fP555lm2T5tGTGoqPZ55mvannILQeUUigsPrqVMxTIfX81FTy7KvkpOVUadzkJOVUadzEE65iAG+NZ3m74bbSAfeMtzGtagyu3HAJKoWN9NomgbfBoiOZ86a3fTo2A57t/Z12y46RgX5rfuxaeVrDP6cBbZe0OWQSEvSNjj2Nlj6HmTfCTf+CDHxSCnJnzWLzRMfo2zLFjpdcgn73X4b0cnJkZZWE4TH7jgYeAE1W7EUuMfh9bTyaoSti/TM7GrnICcro07nIJxysRyVgQ3TaeYYbuNB4BzTaU5tgLwaTf3ZmUtR+zTmr9rCBQN71e8JM20ozH0Cinbu1eyBZqG0CNZ8B0dcorNyNhaxCXDak/DOubDgWUr6XMSm8Y9QMG8eCYccQs8XX6Sd0UqmKLdxPHZHssPrCU5O8jhwrcPrWWvFZMwAjm1+6fYCl63GoEhcviYNTE3PzI4CJgLHocp0fJGTlfF4emb2UOAZVH6r34EbcrIyygK2S87Jygh5DnKyMtZaMRl1PgfhpqKuD1o2AbMuA2s0jYpvAwtiBlNUWlF7Vs5QpA0GWQEb/gd9RzWNfA0lZz6U7tYukcam7yik4yz47nHW3+umrLgdXe+7j06XXIyIbkVZW9s+Iz12x5XAywGzQgQq7g9aS9oDly1sUCQu23vANFy+pgpMzQS252RlHAeQnpndOz0zOxalKJyWk5WxNT0z+zZgDMoq4Wdkemb2lcDLAbNC9voc1Cf9t0YTOXy5zJFnkRQXzeA+neu3bc+jVU2SdYtarnKxYhbEJrbOImstmIKffuKfdzfSq385+4+AmFs+J7Zbt/AbapoVh9fzmcfuWA2877E7TgEWAvcC71iFzqKAeyIpYx05BJhs1SkJpEpQJC5bkwSApWdmxwHnAkf72yyrwxnArJysDH89l5eA2QQoFzlZGZ+lZ2avBt5Pz8yucg6scet1DmpULgy38RrKJTIbmGM6zeKa+mo0TUpFORU7N/FNxf6ccPB+xMfU84kzvj10M1SmzpaIPytnnxHKlK9pMGXbt5P3xCR8n3xCbI8elDmuot2q12H7/6CbLpXeEnF4Pcs8dscmh9cz1mN3DEdVKX0HeMfh9ZTUvnULweWrU1AkLt/eBqamCiF+Dlh+VUr5asByb2ApcHN6ZrY/pexEoC9KsQEgJyujxLJmVCEnK2NZemb2ppysjLHpmdnDCTgHOVkZ9ToHNSoXptO8xnAbiagqcZMNtxELzAc+N53mtvrsRKNpEPmb+KOiF3klcYyy19Ml4qfXEPjtbSgvhegWVtEybzn41sHxd0ZakjZBoWmy4ZZbKduyhZTrriP1huuJiouFVxfDl5lKiYvXAZwtDY/dcSbQ32N3LETlULoeNVPxNY/d8QvwqlUorXXhslULisTl29snnS1SyoG1rE8GTgK+zMnKGJmemd0FZSB4h+p5qarlqUrPzD4T6J+emV3tHKRnZv8CvJqTlVGnc1Br4TLTae42neYnptO8ydpJDpBpuI3XDbdxu+E2utdlJxpNg9iZy5zyAUQJGBEuK2dNpA1RMQ2bljaubI3BilnqXVdBbTA7Pv6Yvy65FBEdTe8P3qfL2P8Q1a6dmjWUMRl2boAfHo+0mJrQ3AQc6vB6jkEla8xyeD1zHV7P5cCvwFseu+P0iEoYDpctlNb6OHAtLt8xwHnAU00owWrAk5OV8SFATlZGHrAAFTtRmRzIcnOEiqG4CTg0Jyuj8hzkZGXMzcnKqDwH6ZnZdToH9amKWmE6zbmm07zbdJpXA3MArVxomh7feuZUHMWA7vF0TtrLrImVybRaYCrwFbNVVsn2OhZgb5ElJWx8+GE2jrufxIEDSP/oQxIOCZrSmzYYjrwMFr0Im5dHRlBNbSSx54ZXTsCTtcPrmefwev4N/BAJwerBSFy2T3DZAp8Umi0wNScrYwewKT0zezSoGSDAUcArwBnpmdn+IjlXAR+EGKLGc5CTlTEvJyujzuegzsoFVMZhAGA6zT9Mp/lrfbbXaPaG3I2bWS7TOfGQBtx8O+wPHdNaXr6Lgq2w/ic9S6QBlObl8ZfzCna8+x4p115Dr6lTay40duLDKgYn+w4V66JpSYwFZlhukdeBan5Ch9eT3+xS1Qc1A+R+4ClctslW4Oa9wDu4bPOBL1CzOZqSW4FL0jOzvwc+B+7PycrIB8YBs9IzsxegSnu8GmLbscAMyy0S8hxYY4UlXOGyE1A+IoHKedHLah/CnuRZv5pOs3VME9K0Sr79S8URjTIOaNhAaUNhzffqptJSckms+hqQ2iWyl+z+9Tdyb7uN8oICejw9mQ6nnlr7BkkpSsGYeSsseQ+OuKh5BNWExeH1/AQMj7QcDcblW4bLtgmXbywu23ACgiJx+Zo8MNWyXlwWov17YHCYbRvtHNQ2WyQaOAZlPpkFfAkUWKvfRc3fjUIpHcGJNzSaRuPrzUn0jtnKgV0aGITXazAsfR+2r1XpoVsCf36pysJ3O7zJdyWlZOfMmRQsXkzSMceQfMIJrTYzpZSSHe+/z6YJE4nt3p30118joV8d680ceRn89g58dT8cPBra6XLqe4vH7jgG9WSeDMQDDwdXL/XYHU8CQ1EPqgC5Dq/nkqA+RwCrw1kmPHbHUIfX08LMj0G4bGcC/XHZqgVF4rL9AryKy9fiAlPTM7OPAFaHs0ykZ2YPzcnKCHsOapstUg48ZlVDfRPoyh7/y2rTaU6ou9gazd6xq7iMRflduDzF0/C6D2lD1fu6RS1DuSgrgdXfQv+zIapeHsp6U7pxIxsfeoiCufMQCQn4Pp6OiI0l6ZhjaH/ySSSPHFmzK6GFUVFczKbx4/F9PJ2kE46nxxNPEG2z1X2AqCg4fTK8cjx8Mx5Of7rphG37RAMXO7yefI/d0Qnljw/OQJkKXO3wery1jOMDnvPYHWuAmcASh9cjATx2x37ASOAsYC7QspULKygSl28rLlsfYAouXwYwF5dtGPAWLtvbuHyfR1bMaviA59IzsyvPQU5WhgRIz8yu9zmoSxKtclSARwXQ2XAbM602jabJmbfiH0qI4cRuuxs+2H52SLAp5eKIixs+XkNZ9yMU74R+YUz5DUBKyY4PPiTviSeQFRUqO+VFF1K4dCn5X31N/tdfs+uHHyAqisSjj6b9SSfR/qQTie26l1N+m5jSjRvZcOttFJkmqTfeQOrNNyP2RjHrZsDg62HRS3DEpdBzQOMLuw/g8HrmBSzuAAo9dofwKwb1GGctcKXH7hgIOIEBHrsjFnWP+gulsFwXIkV4S6TGoEhcvnnAPFy2OhZHaj5ysjLWAlemZ2ZXngMrF0aVcxAiRXhI6qJcRKPcHwLYbjrNcw23MWevpNdo6smc5RuxsYuBPRvBfB8VpVwj61pIMq0VsyE6Hvqc0CTDl6xfz8YHHmT3okUkDh5M90cfqSwnnjhgAIkDBtAl8x6Kli8n/+uvyf96DpsffZTNjz5Ku8MPp/3JJ9H+xBOJO6CBsS6NRMFPP5F7+3+QxcX0fH4K7U88sWEDDr8Xln0C2f+Ba7+DKJ0OPAQxYZI2AeCxO6JQhSxfr0GxuN1jd/REuU9edHg9oWYq4PB6fgZ+DrWuFTEWmIHLFodKRHlztR4uX4sNTM3JymiUcxAuoDMdaI8K5Cxnz+ySRomGM9xGtQIrptN83HAb1Qqs+INGDbdxJvCgNcRs02mOawxZNC2P8grJd948RkT9TkzHRiowlTZElTbfvQ0S65lGvDGRElZ8Cb2Ph7ikxh26ooLt/zeNvMmTEVFRdHv4YTpe8K+QbiUhBO3696dd//50uf12ilevJv/rOeR/9RV5k54kb9KTxB98sGXROIn4fgc1e1lyKSXb336HzY8/TlxaGj1feJ74Po3g1kroAKdMhI+uhJ/fgEHXNnzMtkdZmKRNeOyOLsCzwHsOr2dGiC6vAYUOr+cXj93RAfjMY3csDeMmab24fG0jMLWBhLMn3gjMQ1VBW4sK2AFY0kj7z0RZQ44zneYJwAdWJtDHgdNMp3k08AeqwAqG20gF/gMcZzrNgUCi4TYyGkkWTQvjt3Xb2VZYzqjoX8HWo3EG7WXlu1gf4XwXW1fBtjWNPkukeO1a/rr0MjZPmEDi0QPp8/lMOv37gjorBPEHHkjq9WPoPf1jDpwzhy6Z9xCVnMyWF15g7VlnsXr0aPKefJLCpUuRFRXhB2wgFYWF/H3PPWyeOJHkEcNJ//CDxlEs/PQ/R2Xs/GY85G9uvHH3ETx2Rx9UTN5dNSgWOLye+Q6v5xfr804gG1WDo+3hsh1RJ5eHyza0GaSJKOGqot4duGy4DZvVPrahOzbcRrUCK6bTXGu4jTOAWabTDFVg5SJgquk0i6x1j1vt2Q2VR9PymOPJI0ZITohaAh16Ns6gPY6CqFgV73Bw08U6hKWRs3LKsjK2ud3889wURHw83bMew3bWWQ2yMsT17EHKFVeQcsUVlP3zD/nffEv+11+z9S03W197nZhu3Wg/ciTtjjyCBLuduN69ETGNVwuxZEMuG269hWKPl/1uu5WUMWP2Lr6iNoRQZdlfGqpmj5w3tXHHb/u4gKscXk+lZmZZMh5yeD03WcsDANPh9ZRYlosM4JJQg7UBfMBzuGyVQZG4fMpN5LK1tsDUBlHfK0FjOocrC6wYbqPGAium0yyxrBlY6xYGrNtkuI2QkWdCiOuA6xpRXk0zM8ezmcGdC+hQUKiSYDUGse1UNsxIZ+pcMRu6HqoSezWQohUr2DjufopMk+QTR9HtwQeJ7bKXadJrIGa//eh04b/pdOG/Kff52PX99+z8+mt2TJ/O9mnTABBxccT360eCw0683U6Cw0F8v4OJTq6/26dg4UJyx96BLC+n18svkXxC08SlAJDaF469HeY+AUddplxVmrpyDKqSaWDbGCDQvLQ/MNFjd0SjXOouh9eT23wiNiMu31rgSly2yqBIXLZqQZG4fK0hMLVB1Eu5MJ1mafhedaaywIrpNEcabqMuBVZELeuqNqqgo1cBhBA6FV8rI2dLAavydnFJ7w0gUyAusfEGTxsMi1+B0qLIVCEt3AF/LYTjbm/QMLK0lC1Tp7LlpZeJTk6mx9OTaT96dJPHRETbbNjOOgvbWWchy8ooWbuWIq+XIo+XYq+H/K/nsOPDPUUfY9PSSLDbqygdMV27hpRTSsm2N94g76nJxB/Yh55TphCXnt6kxwPAsLFgfqAyd16/AGL2Ms38PobD6+lbw6pTA/rMRD3F1wuP3XEssNbh9fy9l+LVD5etK/AW0BnYCThx+arv22U7CRX3V4JK6/0vXL6Cqn18bSEwFYD0zOxHc7Iy7q/vduECOt+1PkqgHeqL/Ao4AjXdJtN0mv/Ud6cWqwGP6TQ/BDCdZp7hNqoVWLHcJ/5pPWusdb9a67oD2lHaBpljWVlPjPsDOjRSvIWftKGwcAr8/RscEAHX56o5IMsblPK7cNkyNo67n2Kvlw4ZGXQddx8xnZs/QFXExBB/0EHEH3QQtjNUKXMpJWV5eRR5PBRXKh1e8r/6qnK76I4diXfYSbA7KpWO2G7d2PjQQ+R/OYv2p5zC/hMnEJXUuMGuNRLbTrlH/u98+PF5pWxomh2P3dEVuBx4AzWRIM9qS0DdfwY4vJ6mcoM/Bbhw+RZblocngapz1l223qg02mfg8u3AZevGnmzVbYL0zOwD2JOZeyswyGqvPFarIFqthIu5uMhwGxcCi4FXTKd5juE2xqDcGb8CU4AL9+YATKe5w3Abmwy3Mdp0mrMMt+EvsHIvMMtwG6+YXaq0tQAAIABJREFUTnM7VQusvAe8a7iN6abTLAFuQ+U/17QxvvHk0a9rMr0KvdApvXEH72VlwF2/KDLKxYrZkJgCPeqfW6GiuJgtL77E1tdeI6ZzZ3q+8DztR41qAiH3HiEEsV27Etu1K+2HD69sL99VQPGKFRR5PRR7vBR5vWyfNg1ZXLxn46goutx5B52vvrrZZ6Vw0EngOAN+eAIOPQ86tYwpuA//+HCDtn9o6EONJEmzMAmVEXoSqi5GGer6/wvqoRaaIsbOZesIpODyKX+py/czLpsNl60jLt+OgJ53AQ9Utrl8mxpdlgiSnpkdDdwHnIa677/Bnszcv6HOSVR6ZvbYnKwMX21j1cUtUmL1y7emjo4ErjOdps9wGwfu5TH4uRWYYrgNfyGX+02nmW+4jXEoBaMMNTPlFqiMsXgJWGCtm2U6zS8bKIOmheHbXcpPOdsYc3wf+C0XDji2cXeQlAopB0Um30V5maon0m90vfMqFP7+O3+Pu5+S1auxnXsuXe+5u36ZKSNMdHISiUcdSeJRR1a2ybIySnJylHVj1SqSjjmGpMGDIifk6CxYNQhmZcJF74bvr2lsOju8nmkeu8OJekp+GXAAI1BW871N4JgaJl9Hb2BV0DZrrPbfAtoOBYpx2WagUnrPB+7H5WsT1oucrIxyYEx6ZvaXqBjIruyxzCzLycqo83ztcG6Rj4EiwGc6zfOsthSUPwoaWFPEdJohC6yYTvN7aiiwYjrNDwhdKlbTRvh+RR7lFZJRfdvDIh/YGmmmSCBpQ8D7OVRUNHnq7Sps+B8Ubq+XS6SisJB/nn2ObW43Md260WvqVJKHHdeEQjYfIiaG+L59ie9bk+u+mbH1hOH3wNcPgvcLsJ8WaYn2NfzpDmJRN7WbgVccXk+Fx+6Q7L1ysSVMvo5Q8XyEaEtCzXS5DMhH5WO6CpXLoy1RhvquK4BO6ZnZb1BP9084y4UN5XO6yXAbg02nORGV4rUDaspNm9DWNC2LOZ48UpPjOCLZ0mGbSrn47W3YsgK62Bt//JpYMQuiYuDAkXXqXrxmDRtuvoWSNWvoeNGFdLnjjlZbbKyl43c/RFHOmOQU4j4dwwvHOSmLjg2zZatzPbRk/JMG/Bp/b1S8X1OTg5qNGEgfqz2QdcATuHzq4uSyvQ1cUevILpvKExUqOLTlEoNSuATqXn8XqmBpnQn3yCZMp1lsOs3JQIHhNk4HFgGnGG6jDyrYQ/P/7Z13eBzV1YffK8ly99q4YgMWYMwOMISAIdSEDkahJIQklLBAKCEhdPKJBMISmhJICPARWihLaCEhJICA0D6KDRhMHeLZYGOvsWSwkWyvbMtWvd8fd9Zay5J2tdoya533efbRzp07M7/VnXLm3nPPEbJGa3sHr/53OQftOIHSNd5stWw7dEJSErM8TzX/9HkzzDNkVMqqq196idgJ36d91Sq2uf8+trzqKjEs8kBHSSk1Ox3C6PWNfPOzAk9ZHni0ukFrMJ09BscBW7pBS2EeeLmZxhOOrwCaCAd2N8uBXTHPt3LCgduTat4O3EA4kBjTPBbjm9Blf4GJhAOXEQ6MxTimDvfKphIOTCIc8G3wx4qqmhJMz1EZXuqPWHVlAyYVSNqkMi60HbEVgBNybsF48d6DmWZ0A3BJH3ULQq+8u2gFq9e3cehOEyFeawqzFZ0zmS22g2Hj8hupc8Ui+CqackhEt7ez/OY/Unvezynffnu2/ccTDN9nsw/o5ys+H7MVH07eiX1jcxm3Rt6h8shfMX4Mt2OeT7cCj3jrlmB6DnLFhRjDYTZQjXlbH0lyzI5w/CVMrKXXCQdewzyAH+pmXzcCdd7fFjodUy/A/B4/PzsfAb4CLgW+BMorqmoUfZyZmWpY5CU2Hov6JzDaCTmn902rIKTHS+5yystKOGCHcTCrDlAwcsvsH0gpMzSSz56L+d5UzF6icravWkXdpZexdtYsRp/wPSZecQUlgwf3WF/IHS9O/yY7Lv+Mo9xXeHDG98w5I+QUK+o+gmdMuEHr+0CpFXV/6a0+NacHD8drga4X5zKSYnZ49e7EOJr2xhaE448QDmTbMTXnxKorN5oBWlFVM8pLvd6nVNKppqJWd1l+pKe6gtBftNa8HF3GftuPZVh5GTTWGcMijTHvjNhmH+PUufpLGDkpN8dI5tPnYdx0GNv9JKv10Si15/2ctmXLmPSbqxnz/e/nXpPQI02Dh/Hy9AP49ryX2OWLKJ9MtlJvJGST5zCxLYqRbh1TCcc7CAf645haCDIK1Z62m7wdscvsiP1wJgcRhHRYsHwNixuaOMTyIrrHa3MzJJJgGy+JWT6mpDavhtisHnst4k8/TeyHJ6JbW5n6lwfFsPAJ72+1C7WBScyM/h8z573MXos/YPv6GIF1cZPZVsgZVtRdbUXdlkLryJBCOaZmnVh15eJMtksnQucnGIeVd4Ac9E8LguEl1wR9O8Ty8mI01pn8G7li0q5QNtQYFzsfl7vjAHz2f9Desom/hW5tZdmNN7Lywb8wbMYMpvzxZsrGjcutFiFttCrhqV0O59v/eYldv3AZ0tb5rGstKWXFsDE0DB8DTcrEThk7zeQqETLCDVqvYN70W+kMrw3mIT0cGGJF3YMKJK8vtBIObOKYSjiQcEz17RhbRVXNLZg2KMHoLAFeASowaTtuilVXNqXaTyqfiy2BPwEzMEnA2nqvLgiZ85K7jF2mjGLLwFDzVhiv7VeI7JSUlZsomUvy0HPx6b9hSKAzOijQVl9P3UUX0/Tuu4w59UdMvOwy1KAcDQEJGfPViHHc/40fgtYMb2li7NqVjFu7grFNKxm7diUTVtfD7Fugo/P2+LgaQW3ZeGpLJ7CkbCK1pROoLRvPF6XjaFN9yxc5+8N+RgotLl/gw6yoW0xDBj2RcEz9LZ2OqYlIn0vw97P0GuA7mNwovwdOwGQwH4kJankzJjldr6Q6yxudkPMG8AZwsx2xn+2PYkHoiYY1zbz/+UouOGQHU9C0AtrW52YaajLb7A2zbobmNTA4R9M8Ozpg/r9h2qEb/EfWffQRtedfQHs8zuTf/ZbAMcfk5thC9lCKtYOHs3bwcD7fYuPYK1ft9UtYuRga5kP9fN6c9SxbtS1nr5Z5HLm+c0ZSOyV8UTqWl4fM4B/DDmRdSQES5/mYzcSwgHB8g2Mq4cD3gVLC8fw4pvaTWHVlfUVVTSPQhOm5WInJiH6ht+5X6ewnlXHRdbxrVztiP4PpMhkE4IScmZtsJQh95JXocrSGQxP+Fo2Jaag5CKCVzDb7gL4J6ubCdgfm5hhLP4C1X8F0c6msfPxxll1zLWUTJ1Lx6CMMscRRsOgpHWSGQ8ZNgx1n8sePO+OYDOtYx1bty9mq7Su2al/Ojq2LCa19jmOb3uDR4YfxzLD9aFXSY5XADVrXYTKTJoYUSoAFVtS9qXCq+sWzbPos9S0VVTX/i4kn8h/g8Fh1ZUdFVc1EOuNapRWZO5Vx0XVcyHFCzrcTC4kYGILQX152lzNp1BB2nuzdlONeAK1cOnQCbL0noODzObkzLj59HlQJHVO/ybIrr2TV3/7O8P33Z8pNN1I6enRujin4hqaSoXxaMpVPB3UOb+zYupjT1zzDuWue5Pim/+MvI2by4pA96VB9yzezmXIPJmDT/cCJmBfZuzBZSosL42NxKeF4uNBS+sCOwCmYOB+fYNLQr8H4vKwhzcjcqYyLXqOhOSFH3KWFfrO+tZ3X53/Fd74+pTMTZiKA1qgc91wMCcDEnXMb7+LT5+iYtDuLzzqf9R9/zNhzzmH8+T9HlcqDZKDy30FTqRrzM3Zr/i9nrHmGSxof5YS1rxAZcZTxNxrAMTWsqBsDcINWkxV167zvxfWsCQcOpNOv4gjCgZcwvRdTgXHA3YTjfh0CUrHqymXApRVVNddWVNXsg0netk9FVc2HdGZJ7ZVUxkXXqTPSdydknbcXNtDU0m6iciZorIWSQTB8fO4FbLM3fPSYyVha2jdnu5TE6+BLh4ZPJ9HyWZwpt93KqMMOy+4xhKzQL8fJDJ0mPxy8I+eXT2e/5o85bU0NV8bvh7s/gEN+bfLPDGAjg43fkIvtH7E/JumnBp70lg8D/gsswmQc/Z+CqUtBRVWN8gJnhTG+Iz/D9CSVkKbuVHfSwxNfvHTrwzNSKgi98JK7jGHlpeyz3djOwnidGRLJR8bSrfeGd/8My/8DW34ta7vVWtP04NUMB9at35KKv93H4O22S7mdkB6JRGOZ4ptkY0oxe8jXeGuwzSHr53JZ0xvw0Heh4gA4xCcae8ANWvsCl2OmKA4Grrai7r+7qXcVcDTm4XSdFXWf6GWfN2KSiO3oBq0nvP2uy4H83BGOX7tpWWBn4GrC8WWEAy/mX1TaOJh2ao9VV7ZVVNW8CoyMVVd+u/fNNiZVhM6O5O92xP5mJkoFoSe01rziLmf/aeMYMihpmKCxLvdDIgmSg2llybjoWLeOL678NaPi/6Rt0gimRJ6mdOTIrOxb2DzpUCW8OHQvLjvrZnjvAXj9Rrj3UMKDd+GBEZXEyiYXWmJ3lAInWVF3tRu0xgCvARsZF27QmgmMtaLuDDdoDQFq3KD1mhV167vboRV1L8u56lwTDpwObIXpfdGYZ62NydkBPp6KGquuvKhL0R1eL0afSBVEa0+6hCn1nDgT2ekGOyHnpb4eVBASzPuikaXx9Vx42PSNV8TrYGqeJuiP3tpMef38bfhGyunbKelYv57YySfT8uk8Jn+vHfY6DSWGhZAuZYPNebjbyTDnDr72fzdxZ8PveGXIHjw4fCZflvknyJoVdd9IWlwFrHODlrKibvLD6Ezg51799W7QugfjqHlb/pTmnR8BF9M54+UMTM//cGA1aTpFFpqKqppS4HdkkGgt1bDIYWxsXISAiLddKSaKmhgXQsa8NG85SsHBwQmdhR3tXs9FjmeKJLPN3rD4zaw40636+xM0z3OZetWpqP/+FnbMYSAwYfNl8Aj45mWc6gzj+2tf5rim1/nW+g94bug+PDL8cFaUBvKhokwpNTdp+W6t9d1dK7lBqwSTAfTeLoYFwEQr6i5NWp5PN14qXlr1BzFDIMmRLH8KXI1xhPxVl335lXbC8Q83LIUDS4D3gBMIBz7EZBv1JRVVNTfQGZn7M+DrmewnlXHxVzpjpCuMD8YjGGusFBNpTBAy5uXoMr6+9WjGjUjK/LlmGej23E9DTWabfeCTJ+DWr8O23+z8jJiQetskOlpaaPjznxk6Yw+GjVgO5SNg6n45Ei0MBFaXDOfekcfw5LBvcfLaf3PUujc5fN0c/jnsWzw+/BDWlAzL5eHbtNYzeqvgBq0JwC3AY1bU/Vea+92km92KutoNWglnwb9jIkOC6QVYgXn23EiGibTyTjgwms4eiqGYaNenAAdgejX8ykHAPExv045kOISTyrjo2kf8PibnfeLVLgw0ZnJgQVjWuJ6Pa+NcdsSOG6/YEONi6/yJ2e1k02Oy6DX4z5PwfsSUj7c6DY2K/WDomF53E3/yn7R9+SVbXnMNvHMWbH+Q6eYWhH6yojTAbaO+zxPDDuLUtc/yw6aXqFw3m8eGH8bfhh9SEE1u0NoOM7xxjhV1a3uoVu8GrUlW1E28rU8HFnRXMdEr4QatNcAXwCRMdMgLrKj7lWfIFAOvYMJkt2GcI8uACOH42QVVlR71serKvwB/Aaioqnkuk52kcuj8hR2xA07IiSfK7Ih9LDDECTl/zeSAgpDgZS9R2WHJU1ChMzpnPodFyofB3j8xn/Y2+PIjWPS6+bz/ILxzF6CMw+e234Rtv2WGUpJChuvWVhruvpshu+7K8Gkj4eWlMP2K/P0GYUCwtGw81YEQjw87lNPWPMPWbcsKKScMnGFF3Q0iPAPgKivq/swrimACMl3iBq1yTPjrU3rbqRV1D3eD1m2YHowt6IwOWRxTUsPxGwotoaKq5gDgsVh15RRveUfgbkwvyhIgFKuu7C7aZtc099MrqmruxPTCKIBYdeW5qY6fzqT+hzBTiBJ8BhyfxnaC0CsvucvYeouh7DChS06PRACtfA6LJFNaZhKaTdkD9r8I2lpMePCEsfH2HfDmrVBSBlNmbOjZiL//Ba11dUz81a9Qn/4bULCDxLQQcsPCQVP49ZhzKNUFjcW0L/BXN7hRCPtzgA1zrq2o+6QbtPZyg9YczHDI9VbUbaAb3KB1MGZa60+Bf1hR9zU3aJ0HjMI4jBaHcdGVcOBGwvG8zYKpqKoJYIY1vkgqvgU4PVZdubCiquZY4CqM0ZeKxZhkZmDcIdKK/pdJxKAlmNSrgpAxTS1tzF5Qz0nf2KYzKmeCeB0MGg5DfBIau6wcpu5rPgdWQUuTyaSaMDbeuAle/x2jOkoorwwwtOw9+ORJY5z00WdDEPpKewFDhltRt6f88jO71Ls8zV3awFGYWRWPe2VvAKd76di7nb7qO8KBb2EmQyTe9g8hHNg3qUYz4fh7OVRwI/ArOoc2bODzWHXlQoBYdeW/KqpqftHDtl0jc6+PVVfW9VVAOsbFVDti/5DO/O5lmHEwQciYWfPraW7r6ExUlkxjrUlY5tfohOXDTPTE7Q82y+vjrH3iFtY/cyejvz4K9Ypn5B98ZeE0CkIRYkXdW4Bb3KB1GPCMG7S+jckr8ltMZMhLC6mvD+yN6aVp9/4+6JUpzHM0jpk90lfGpZq9U1FVcxLwXqy6cn5FVU2ieBomOmgyKyqqasbEqitXdinvmqo3o8jc6RgXN2MipCX63hSme0UQMuZldzkjh5Sx17ZbbLoyEZ2zSNDlI1n22LtovTtbXPoUrFsBSz80PR2CIPQZK+q+6Dl1/o/X63FhoTX1iXD8t5uWBQKYKappZRXtgfreZu9UVNVsA8yMVVf+qMsqRTczdHooCyXtT2GGqfpMSuPCCTn3Z7JjQeiJjg7Ny9HlfGv6eAaVdhPeu7HOJBMrEla//DLN8xcw+cYbUSUlMHwc7HBooWVt9vQrFwhknA9EyA9W1H3LDVpzU9csAsKBEcDDwBXAhylq94fjMA6Yr3rLO3nfLyEpnYfH2Fh15aquO4hVVy5N+q4rqmp+kImQbo0LO2LvBnzmhJzVvW1sR+x9nJCTw3SSwubIR7WrqF/TvOksEYC2ZhPnIpCn0N/9RGtN/R13MGjqNoyaKcGyBCEbuEFrH+CbVtTdtAegGDCp1qdhMqGeABwMXLFRYK0cEKuuvBW4NbFcUVXzdqy68kDv+40VVTVTY9WViyuqao7E+LJsQkVVzXg2Dp65pqKqZixJkblj1ZXdTiVOpqeeizhwqx2xFwJPAx8l0qvbEXs85h91LPA6IMaF0CdecpdRWqI4cHo3zo6NntGcz2mo/WDNa6/RPM9ly+uuQ5VlOaOqIAww3KC1HyY2xLeBI7wZJm8B2wLjrKg7q5D6+sAY4CJgB+/7OYTj7xdWEhcAD3lDHXWYkOTdcR0bGxczgefYODJ3yjwJ3d4NnZCzCDjdjtgzMOMve9gRe5BXfzEmMc3ZTsjpz9iRMEB52V3OnhVjCAzrxk+oMRFAy/89Fxt6LSZPJnDM0ak3EAQhFQdjwn+vAB7FOEGOwkTprHWD1p5W1L25gPrSIxxfgZlOm4jUeRXhwDcJx/OqPVZduXfSdwcTHTQVP41VV26IyllRVfN0OnEtupIqiNZcYPMY8xJ8wZIVTUS/XM0VlVb3FeLFY1w0vfUW6z/6mEnhq1CDMnKoFgQhCSvqXtO1zEt0dpEVdWvdoPUiZpJB8RCOrwIuIhz4DeHASYTjjxRaUgoeqqiqSXaGW19RVfM373sJcGY3M0w2QfpxhbzyshfI75DupqBCYaJzZkj9n+6gbOJEAt/9bqGlCMJmgxu0gsCvgTne1NStgYSTYTce4EVCOP5rwoGe4oL4hlh15Q+7llVU1RwBlMWqK2u62aRbirehhKLk5ehyth8/nG3HDe++QrzW5O8oz2kypn7T9O67NM2dy9gf/5iS8q4xZwRB6Ac3Y6JLDnKD1gxgLSZkNXQm0sw+4cBEwoHnCAfmEA68SDgwuZe6gwkHPiYc6JsXdzie0hHSD1RU1fyjS9Fy4Gt92Ycvei7siH0A8JgTcqZ4y5vEQE/4d9gRex/gjxjD6EPgXCfkZJS1TcgvTS1tvL2wgdP327bnSvG6ohgSqb/jTkrHjmX0Cd8rtBRB2Nwot6Jugxu0ZgNBjEPnUW7Qeh3jj5Erfg+ECcfnEA7MAG4CTuqh7rUY/8PNla5vTJ+TFNI9HQrec2FH7B5joDshZy9MZLOrvLqDMJHajnJCzp6YnPMpvVYFf/DOohW0tmsO2GFcz5Ua62CUv42LdR99xNo332TsGadTMnRo6g0EQegLJW7QGgLsisme+r+YkOB3AD2FrO4fxulyLOH4HLMcnwsEvPKudQ/HOJzmMnx3oRlfUVWzd0VVzV4VVTV7AzvRx8jcaRkXdsQe4/29vu8aU5KIgd7mHcMGPndCzkIAJ+T8C5McB+BI4Hkn5CSS3twByKtjkTB7QT3lZSXsWdFNVM4E8VrfR+esv+NOSgMBRv9gk6FJQRD6z1WYvCIVVtR924q6662oe7oVdb9rRd35Ge5znFJqbtKna+rzbdk0DfxCr7yTcGAscCbwuwx1FAvPA0dgAm8dChwIPNGXHaQ7LHK9HbHPo/MhnxXsiH0S8J4TcubbETtR3G0MdM/A2WidE3JavN6MTfBOnq4nkFBA3phfz4ypYxgyqIdES81rYP0qXztzrp83jzWvvsr4C86ndEQPfiOCIGSMFXVfx8RQyia9hs0m/fDYfwD+h3C8nXCg572FA79iY/+QRD6RMd73UsLxq9PQXRBi1ZVX9XcfKXsu7IhdBpQ4IacdE20sK9gRextgphNy7uqyqrdG7m5dd3XRWt+ttZ6R4oQS8sRXq5uJfrma/VMNiYCvfS7q77yLkpEjGXPKKYWWIghC9ohhXl6T2c4rNxgHzxnA/YQDrwKnAb/zDImuPA+8jInO+RLwIiZWR433eTqL2n1JOj0XVZjhB8iup+5xwHQ7Yr/qLe/kfe82BroTclZ5EUODiUI7YpfjDacI/ubNz0ym5P2n9WJcxL1pqD41Lprnz2f1Cy8w9tyfUDpyZKHlCIKQLcLxFYQDTYQDuxOOv084sCvQAJQTDtxOOP4zwvGlQGfSo3AgDLxNOP58N/t7z6uz2tvfMGAd4fhHefg1vqBH48KO2CXAC0C1E3IS8dA77Ih9B6bHYzDQ4YScnkKI9ooTcjaKgW5H7LedkHOg9/1GO2JPdULOYjtiJ8dAfx64zI7YdzkhZyUmfOnjmRxfyC+zF9QTGDqInSf30pWY6Lnw6bBI/Z13oYYNY4tTTy20FEEQss+FwL1ekrE4cDowkj7OkgAgHHiFcPxg4BTCgcOAw4D1WdTqe3o0LpyQ02FH7NuBH9sR+9Wk6Z5X0jkU0e2QRBa4AHjIjtgbxUB3Qs56O2L/CnjejthtwEeYmSaCj9FaM2t+PftuP5bSEtVzxXgdoGBUz9PLC0XzokU0PvccW5x+GmVjxhRajiAI2SYcr8U4MSazDJNbo7v64V72pggHpmOGRXYAzgL+2X+RxUOq8N9P2hF7CWb6T2KmSNwJOVkPZOKEnL2TvvcYA90JOa8C38j28YXcEWtoYml8PT89qJchETDDIiMmQqn/Qmk33H0PqrycsaefXmgpguBL3KB1ObDSirp3drPuJkyS+8Szo86Kuid3Uy+lI6QVdX3rCJnEthh3gvHAUYTjrYQDvogrlYqKqpqUbRCrrkzZBil/rBNy5toR+8ykonJyGSVN2OyYNf8rIIW/BZjQ3z70t2iprSP+1FOMOfkkysal+A2CMMBwg9YUzDTF7TE9290xDvixFXWjKXaX8F+4ExPDSAG3AediHmy9dH36inmE40cRDljAA4QDlwONhRaVJllpg3QtqWfsiD3E+z4YE45VENJi1oJ6thozlKljU4T0jtfBxJ3yI6oPNNxzD6qkhLE//nGhpQiC77Cibh2wtxu0TgOGpKieal/vAbhBa7UVdd93g9YwYJ0VdYvNEdIYUeG4SzhwAmZ2SDoZSQtOrLryPYCKqprVserK9yuqaoYB62LVlX1qg7SCaDkh5xkn5KwHfgKkzIYmCAnaOzRvftbA/tPGoVQvBq/WvozO2frll8T/8Q8Cx3+XQRN7SLYmCJsvZSmCT/WFC92g9YwbtF51g9b3u6vgBq1XvK+nuEHrMCBMMTpChuMXJ31fCRxHOJ4rH8WsUlFVs6ENKqpqMm6DPo0BOSGnrq8HEAY2Tl2c1evb2C/VkMi6ldDa5LvonA333ofWmrFnnlVoKYJQCNqyFCvoz5geiPfcoDUKeMoNWh93M0yi3KC1OTpC7gk8V2gRaaIqqmr63QYFzy0ibN7MXmDiW+y7/djeK8b9l2q97auvWPX44wSOOYbyrfyjSxCKDSvqzkoMeVhRtxETSKq7MdCEI+QPgCusqNuKTxJsZkSnE2d3gbb8ykZtEKuuzKgNxLgQcsqs+fXstOUoxo4Y3HvFDdE5t869qDRpuP8BdGsr486WXgtB6Atu0JrgBq3bk5b3cINWufd9FFAJzOlm03lW1D0E82B7wA1ae1I8jpDdUeP9LSY/xXmx6soNbVBRVZNRG/TJuLAj9qF9PYAwcFnX0s57i1f2HvI7wYbonP7oIWhbuZKVjz3GqKOOoryiotByBKHY6Bp8ajLwtBu0XgKeBMKeI2hXogBW1HUx3fI3A936ZxQJiRTxxTTDMgoQq67sVxv02tVhR+xSL6dIgkswcdIFISXvxFbQ0t6RegoqmJ6LkkEwfELuhaXBikgE3dTEuJ+cU2gpglAUWFH3gaTvn5EUfMqKuk+TRj4NK+penPR9pRu0jrOiblE4QqZgKOHAIXTmyFKE4758lsaqKy9O+r6yoqrmuFh1ZZ/bINU4ylNetlIFlAIldsR+CWj3tlWrG+EiAAAgAElEQVROyDm4rwcVBgazF9RTXpoixXqCeC2M2hJKCj9S197YyMqHHmbk4YczeFrXXEaCIOSRYnKE7I5ECugSjHNkh/cplngdkGEbpDIuvg3s4kXM3IAdsQ93Qs4LfT2YMLCYNb+ePaaOYWh5DynWk4nX+cbfYsVDD9GxZg3jzv1JoaUIwoDEDVplVtRtwzhCFrNxkXjGNhOObxK51M9UVNWUxaorM26DdF4Tr09esCP2NcCkvh5IGFg0rGlm3heN6flbgInO6YOZIu1r1rIy8iAjDjqIIZZVaDmCMFApRkfITsKBGwgHbgBGeCXFOOOlX22QKreI9oZFsCN2EPgf4HUn5DyYycGEgcObnzUApI5vAdDRAY1f+MKZc9Vjj9Iej0uvhSAUlmJ0hEzmLqAN+KO3nGK6nC/pVxukY03tZkfsp4CvA79wQs6jmRxIGFjMXlDPyCFl2FN6SbGeYO1y6GgteM9Fx7p1NNz/AMP324+hu+5aUC2CIAAw1A1aGzlCWlHXl46QGxGOx7qU3FwIGVliaEVVzUZtEKuuTNkG6QyLOE7IOQYTWGOMHbH/4KVCF4Ru0VrzRjop1hNsmIZa2NDfqx5/nPaGBum1EITC09URchrmGbRtwRT1h3D8H4WWkAH9aoN0ei7KAZyQ0wb8yY7YM4FbgPP7LFUYECxuaKJu1Tp+cuD26W3gA+Oio7mZhnvvY9ieezJsRjaiHQuC0A82OEJ2l8JdyAsb2iBWXdnnNkgn5fqhXZafsyP25309kDBwmOWF/E4rvgV0Rucs4LBI/R130LZ8OZOrbyiYBkEY6LhBK3EBFrMjZFFTUVWTlTbIaCMn5Pwnk+2EgcHsBfVMGT2UilQp1hPE62DQMBg6JrfCeqDx+edpuPMuAt/9LsP22acgGgRBADYPR8hiJyttkCpC5+WYgFlgxl/KvE+zE3KutyP23U7I6U8KXmEzI5Fi/YidJ/aeYj2Z+BLTa5Fu/SyyPhpl6eW/ZOjXvsak8FXpaxYy4uq3ru7X9lftc1WWlAh+xIq6sS5FxewIWZTEqitjXYoyaoNUPRfvAC2YiGJgPEXLkpa3624jYeDyn6Vx4uta05uCmqCxriD+Fm0rV1L7059ROmoUU267lZLy8rxrEAShZ6yoW4yOkJsVserKjNoglXGxlRNyIokFO2LvCyxyQs5ir6gpk4MKmy9vzDf+Fn0yLuJ1sEN+A1bp1lbqLriQtvp6pj78EIMm+COniSAIwuZAKuPi+0AEwI7Y04HfAscmrV/X3UbCwGX2gnqsLUcxLlWK9QRtLbBmGYzKb8/FshuqaXrnHSb/tpqhtp3XYwuCIOSKiqqafYHLMQ6Zg4GrY9WV/66oqtkRuBsYCiwBQrHqyjW50pEqzoW2I/aBnu/FH4ATnZCzIml9sUZPE3LAupZ25sZWsv+0selvtPoLQOc1OufKxx9n5SOPsMXppxM49tjUGwiCIBQPpcBJserKg4BK4Eav/Bbg9Fh15V7Ag0BOHZhSGRclwGne52kn5NT2cXthADF3sUmx3rchEe+UytM01Kb33+fLa65l+H77MeGSi1NvIAiCUETEqivfiFVXrvYWVwHrKqpqbODzWHXlQq/Ov4B9c6kjpXHhhJzTgJ0x0TlvB7Aj9i/tiP02cFguxQnFxawF9QwqVey1bRop1hMkYlzkISNq6xdfUHv+BQyavCVT/vB7VJlMoRcEoagYp5Sam/TpcbZmRVVNCXATcC8muuZ/u1RZUVFVk7P5/6nurg2wITpntR2xr7AjdqUTcq6nS7ZUQZi9oJ7dtxnDsPI+PLQ3ROfMbc9Fx/r11J73c/S6dWz9wP2UBtLIeSIIguAv6rXWKUMIV1TVTMAMgzwWq678V0VVzXcxuUG60l1ZVkiVFfXkLkXVuRQjFC8r1rbwn6WNXHzo9L5t2FgHQ0ZD+fDcCMPkOvniyl+zft48trr9dgZPm5azYwnCQMUNWpcDK3sK1+0GrauAozE95tdZUfeJfOobKFRU1WwH3AacE6uuTLgyLAQO71J1bKy6clWudKT1imlH7KHAFCfkLMiVEKG4efOzerSG/Xbog78FmJ6LHMe4WHHffTQ+/TTjL7yAkQcflNNjCb0z+8Op/duBBFD1HW7QmgI8AWwPXNlDnZnAWCvqznCD1hCgxg1ar1lRtz6PUnsnHJgIPABsATQCIcLxpV3qjMb02lvAEOBN4FLCcT+9dIeBM2LVlcsSBbHqyg8rqmqmV1TVTI1VVy6uqKo5EngjlyLS7b8eBexrR+yFTsjpSFlbGHAkUqzvmk6K9WTiuQ2gteaNN1h+0+8ZeeSRjD3nnKzuuz/RJnMdadLP2oTNCyvq1gF7u0HrNMwDtzvOBH7u1V/vBq17gBMxb9h+4fdAmHB8DuHADIy/wkld6gwH7iIc/wiAcOBuYCbwbD6FpmBf4K8VVTXJZWcCFwAPVVTVKKAOOCOXIlKF/34ME6GzBDO95Sg7Yr8B/Ap4DRjihJzv5FKgUBzMWlDPPtuNpay0jxOIGmth671yoql50SLqLr6EwTvuyOTrr5PQ3oLQd8qUUnOTlu/WWt+dwX4mWlE3uRdgPn7qhzI9EmMJx+eY5fhcwoEA4cBowvHOoYNwvA7zYE6wGC9zuF+IVVf2Nu57QL50pPK5+KEdsWc6Iec5ADtiHwu8D3zbCTkn5kOg4H8+b2hiyYp1nHVAH6PBtzTBupU5ceZsX72a2p+dhyorY6v//V9KhqWZRE0QhGTa0nEgzJB8DiWMS2EkbQt0HfZf6JV/0O0ew4EDMAbSb7Ooc7MhnWGRS+yI3Qwk+q53JgvBs7xQ4htFEXNCzr/tiL1JFDEn5KzxttkHk6mtBPgQONebySIUkESK9T7Ft4CkVOvZHRbR7e0svfQyWj7/nG3uu5fyrQqXyl0QBADq3aA1yYq6X3rL09n0YZ7T46cwkhTpzqYIBxRwEbAN8D3CcXkGdUOvfdh2xL4FWA9MwYzPfAFMIsNU7V0oBU5yQk63UcSckLNRFDE7Yg/CWIhHOSFnT+ATILuD6EJGzF5Qz5aBIWw3ro8zPuJLzN8s91x8dettrHntNSb+8nKG75WbIRdBEHrGDVoT3KB1e1JRBLjMW1cOnAo8WghtPRDDxIJIZjuvvCv3AYsJxy8kHF+fY11FS6oB8kQvRWLM6UJMj0JG+d2TcULOG07I2SiKmB2xbeBzJ+Qs9OokRxE7EnjeCTkN3vIdwPf6q0PoH+0dmtmf1bPftHF992mIJwJoZa/novHZZ2m46y5Gn3ACY06UkTtBKBAjScqabUXdJ4EWN2jNAV4H7rCibkNPG+edcHwF0EQ4sLtZDuyKifNUTjjQaSSFA8cAcwnHZRptClL1QNwP/IDOIZGLge+QBeMigR2xSzC9Fj1GEbMj9piu65yQ0+L1ZnSLF7msx+hlQnaYt7SRVU2tHNDXKajgDYsoGDk5K1rWz5vH0l/+iqG7786kK6/IuQNnv6ZV+seVTRCyghV1H0j6/hlmFkXy+svzramPXAjcSzgwAogDp9PFSAL2Bw4kHDghqezvhOP/mz+ZxUEqh86H7Yh9NmYO8ytAG8ZH4uhsHNyO2BuiiDkh5192xO4tilh3Y2I9OgR5zjp3Ayil/DQHebMi4W+x7/YZGBfxWhgxAcr672zd1tDAkvPOo3T0aLa69RZUua8cuPOOGD6C0EfC8VrgiC6ly0g2ksLxX+RTUjGT0nfCCTnf8r4utyP2KOB1J+Ss7O+B7Yi9IYpYUkK0bqOIOSFnlR2xFwLBpO3LMcaOUEBmL6gnOGkk40dm0JnVWJeVhGW6pYXaCy6gvWEFUx9+mLJxGRg6giAIQtboU1ACJ+Q0OiHnhSwdOwyckZxp1Qk5HwLT7Yg9FcCO2MlRxJ4HjvaGSMA4mD6eJS1CBqxvbeed2Iq+zxJJkKXonF/ecAPr5r7Hltddx9Bddu73/gRBEIT+Uci0kPsCf7UjdnLZhihidsTeKIqYE3LW2xH7V8DzdsRuAz7Ci/gmFIb3Fq+kpa2D/TMxLrQ2Dp3TDu2XhpWP/ZVVjz7G2LPOJPDtyn7tSxAEQcgOBTMunJDT5yhiTsh5FfhGTgQJfeaN+fWUlfQxxXqC9augdW2/hkWa3n2XL6+9luHfPIDxF16Y8X4EQRCE7FLInguhyEmkWB8+OIPTaMM01MyMi9WvvkrdRRdTvvXWTLnpJlRpaUb78Qv9yQUCkg9EEAR/0cdEEIJgWLm2hU+Wxtk/kymoYPwtIKPonKueeILan53H4G23ZepfHqR01KjMNAiCIAg5QXouhIx4a2GDSbGeqTNno2dc9MGhU2tN/R13UH/rbQzff3+m/PGPlI7oY1RQQRAEIeeIcSFkxKwF9YwYXMbXtupjivUE8TooKTNxLtJAt7fz5W+uYdVf/0rg2GPZ8tprUIN6jKEmCIIgFBAxLoSMmL2gnr0zSbGeoLHOROYsSe0r0bFuHXWXXsaal19m7NlnM/6iCyV9uiAIgo8R40LoM0tWNLG4oYnT963IfCfxurScOdtWrqT23J+y7qOPmHjFFWxxysmZH1PIiP44m4qjqSAMTMS4EPpMIuR3xs6cYDKibt17xtKW2jqWnHUWrXV1TPnjHxl1RNfgrZsP/QrXDRKyWxAEXyHGhdBnZi2oZ9KoIWw/fkRmO+jogMalvca4WO+6LDn7HDqam9nmvnsZNmNGhmoFQRCEfCNTUYU+0dGheXNBhinWE6z9Cjpae5wpsvatt1h8yo+gtJSKhx8Sw0IQBKHIEONC6BPzvmhkZVMr++8wNvOdJKahdtNzEX+mhs/PPodBkydT8dijDN5hh8yPIwiCIBQEMS6EPjHb87fYL5MU6wkSAbS6OHQ23Hc/Sy+9lGG77cbUhx9i0KRJmR9DEARBKBjicyH0iVkL6pk+cQQTRg3JfCcbQn9vDYDu6GD5b3/HikiEkUceyeTfVlMyOIMU7kJO6JezqTiaCsKARIwLIW3Wt7bzzqIVnPSNbfq3o8Y6KBsKQ8fQ0dLCF1VVND77HGNO/RETq6pQJdKhJgjFghu0JgIPAFsAjUDIirpLu9S5CWNqtnpFdVbUlXnlmzFiXAhp8/7ilTRnmmI9mXgtBKbQvmYNtT87j6Z33mHCZZeyxRlnSHAsQSg+fg+Erag7xw1aM4CbgJO61BkH/NiKutG8qxMKghgXQtrMWmBSrH9ju344cwI01tExZDyLTz6F5kWLmHzj7wgcfXR2RKZAAkIJQvZwg9ZoYKwVdecAWFF3rhu0Am7QGm1F3VUFlicUEDEuhLSZvaCer28zmhGZpFhPQjfEWL2wg9baMWxz150M33ffLCkUBCHLlCml5iYt3621vjtpeVtgQZdtFnrlH3Qpv9ANWlsBI4A/WVH38ayrFXyDGBdCWsSbWvm4Ls4Fh/RvamjTnLcY2vQVbesmMPWhvzBkp52ypFAQhBzQprXuLdCMAnQ35V3L/gyss6Lue27QGgU85Qatj2WYZPNFjAshLd5aWI/WZORv0d7YSONzzxN/8klaP53LDsdA4NSfUVYAw0JmPghCVokB07qUbeeVb8CKurOSvje6QasG2AkQ42IzRdzyhbR4Y349w8tL+drWo9Oqr9vbWTNrNnWXXMr8A77Jl1ddRfua1Uw48wcAlFXskku5giDkASvqrgCa3KC1O4AbtHYFGoByN2jdnqjnBq093KBV7n0fBVQCcwogWcgT0nMhpEUixfqgFCnWmxcuIv7PfxL/179oW7aMkkCA0ccfT+A732HILjujPnkCngBGdR/6WxCEouNC4F43aI0A4sDpwEhMD0aCycD1btAqxQylhK2oW5d3pULeEONCSMmSFU3EGpoI9ZBivX31ahqffY74k0+y7sMPoaSEEQccQODyyxlx8EGUlJd3Vo4vMX/TSLcuCIL/saJuLXBEl+JlwMykOk8DT+dTl1BYxLjIkI6WFtbOmoUqLYWyMlTZIFRZKaqsDErLUIPKUGVl3vrOdaqszKtf1rmtz2M7vPmZl2I9yd9Ct7ez9u23iT/5T1a/+CK6uZnyadsz4bJLGXX00QyaMKH7ncXrYEgABo/Mh3RBEAShAIhxkSHtK1dR+9OfZWdnnqGhysvNZ3A5JeWDUYMH97w8eDBqQ9mgTZeHDqN0dIDS0aMpDZi/JSNGZBT9ctaCBiaMHMy0CSNoXrSI+D//ZYY9vvySklGjGH38d71hj11SG0qNdTIkIgiC/wgHNok0Sji+tJt6VwFHY3wWryMcfyKPKlNSUVWzye+IVVdu+jtyjBgXGVI2ZjQVf/87tLeh29rQbe3otlZoa0O3t6Nb2zrXtbahve+0tZuy9jZTt9Wr39aKbm1FN7egm5vRLS10tDRvWO5oaqJj1Uqz3OLVaW6mw/tOR0dq0SUlxtAIbGx0mE8XQyQQoGz0aBgVYPb8r9hnSBOLTzqZdR98ACUlDD9gfyZW/Q8jDjqob3lAvOic6dKfoFcgga8EQUib3wNhwvE5hAPdRxoNB2YCYwnHZxAODAFqCAdeIxyvz7/cHvk9EI5VV86pqKrpKWJqzhHjIkO+ql/G/g/NT7N2CVDufdIkUX1En6Ul0d30826Ie5/FAM2Y4dJl3kpFC4M4sPnPbLPDLNSOpVBaguJJePdJeLePktqbYaveps1vTL+mjoJMHxUEITXhwGiM0WBmsITjcwkHAoQDownHkyONngn83KuznnDgHuBE4LY8K+6Wiqqa0cDYWHXlHIBYdeXciqqaQEVVzehYdWVeI6aKcZEhQ4cN54ypXxVaRvbQ2utBaYON/rYzRLdwxLQK1MQ96Ld3iFKwm+QrEgQhr4zLUqTRiV2GSuaT31eYbP2OnCPGRYaMHL0FVeeeVWgZgiAIQmrqsxRptDvS7CLOCrn8HVlFgmgJgiAIA50YaUQaBeoJByYlLU9n056CQhIjvd+Rc8S4EARBEAY24fgKoIlwYHezHNgQaZRw4PakmhHgMq9OOXAq8GhetfZCrLpyBdBUUVWzO0BFVc2uQEO+/S1AhkUEQRAEAbxIo4QDPUcaDcefJBzYi3BgDmao4XrC8YZCiO2FC4F7K6pqkn9H3hHjQsg6h9/xQL+2f+Hc07KiQxAEIW3C8ZSRRr16l+dLUibEqiu7+x15pyiNCztibxIkxAk5eQ8SIgiCIAjCphSrz8XvgbATcr4BXI4JEiIIgiAIgg8oOuPCjtijgbFOyJkD4IScuUDAKxcEQRAEocAorfM+/bVf2BH768AZTsj5eVLZbcB9TsjZECREKXU2cLa3uEd+VQqCIGweaK39nVkxDZRSz2utjyy0jv5STL+jGH0u0goS4kUtu7ubetkVo9TcFEFNCoJfdYF/tflVF4i2TPCrLsiuNj//Tr9QLA/kVBTT7yi6YRF8FCREEARBEIRNKTrjwgk5K4AmO2LvDmBH7F2BBifk5D1IiCAIgiAIm1KMwyLgBQmxI3ZBg4R45HzoJUP8qgv8q82vukC0ZYJfdUF2tfn5dwoDlKJz6BQEQRAEwd8U3bCIIAiCIAj+RowLQRAEQRCyihgXgiAIgiBkFTEuihSl1ClKqRO9775qR6XUuKTvvtImbD7INSAI/kUcOntBKXUIsEhrvbDQWhIopfYGLsIkbRumtd6vwJI2oJTaF6jCZBIcDlwMfKW1bi+oMA+l1HCt9VqllNI+O/GVUgGtddz77ht9cg30jVxcA34+bwWhJ8Si7gal1D5KqSeBHwJrC60HQCk1SCn1B+DXwMNa68OAJ5RSWxVYGgBKqbGYm+o9WuuzgOeBHwMFvfEn3hqVUj8CzgHw0w1aKbW3UuoZ4HdKqUvBH/r8dg0ow4C5Bvx+3gpCKoo1zkVOUEqVATcAOwN/0lo/U2BJG9BatyqlntFaXwyglJqIyZlya2GVbWAG8JHW+mlv+d/AP4BSpdR/tdbLCiFKa93hPXwuBj5RSu2qtf64kG+BSimFMezPBA4D7gLmAT9VSu2gtZ5fQF2l+PAa8NrK79fAnsDH2bgG/HjeCkJfkJ6LJLTWbZiu1n9rrZ9RSpUppX6slNpNKTUSNtyA84JSanel1MzEcbXWr3jfB3k3qmXAwfnS05M2Dxc4VCm1pbe8F+bNbQ0wLN/6unAk8ApGz3FQ2LdAbWjHtN8vvIfRemAy0KSUGgr5PdeSdLUBY4EXfHINnKiU+pNS6gyl1GCfXQMJbYkgfp8Ah2TxGvDVeSsIfWHA91wopXYHJmqtn/OKfgM8pJSKAd/D3Bi+hjE6TsnzxX0csINS6hWtdXPSW0ubUmoQUAcs8H5Hvt9oNtKmtf5cKfUscLVSajymK/2XwB+BucCifIjyHPwOAN4BHtdaN2mt/+yt2w6YrpQ6Smv9bL7/Z0na5gIPAs9orduUUsdhzrv/Ar8AxgCn5ktbkq73tNb3AlcBjyilFlGga8AbYrgBCAB3AudhovE+4bVba6Guge60KaXWaq0fV0o9TwbXgFLqVMz/932t9esAfjlvBSETpOfCPCRPVUoNBtBaLwaeA34K3Kq1PldrfT4wTCl1YL5EKaV+ApwAtALnJq/z3jBbgQmYbvW8vtH0pE1rfQ2mG/dGrfUpWuvPMd396/KgaaxS6m5Me/4NOBpI9PqUetW+BN4FDvAcKPP18O6qrRI41jMsRmIeKmdprU/QWl8AbKWU2rMAuo5SSn1fa70E06V/LoW7BoYAr2mtf6C1/j8ggue7kGi3Al4DvWm7mjSvAc+PZKhS6hbgB8BC4Aql1PFJvR9QoPNWEPrDgDYuenmA3+B9Pkoqmw0MzoOmRJdzDbATcAGwl1JqW621VkqVqM6pbc8DeUvY1ps2b32Z1nqN1vpNb/kSYHcgHz4Evd3w272/TZg2rQeOz4Om3rTt72larbW+T2s9J6n+y0A+HiA9/s+A64BqCnANeDRhjPwELZieHZRSpUnnYl6vgV60zfO0DUr3Gkj0QgIdwMla66eAS4FJGAM0Ua9Q560gZMyANC5SPSS9noFXMRc9SqlzgcPZ+EabE5LeSmq976uBWXR6jHdorTu8OntiDKO8kIa2NgCl1HSl1M3ArsBPtNb5uPn3+jBKKq8DPgR2UkptkQddPWmLetrKvL9ben9/ihmrz8fUzx4fkoW8Brzjr9Rar/CM6RHAtxPH1lq3J52Le5HHayANba0ASqkdlFJ/JPU1MA0o01qvUkqVaq0/Bj4Axiql7KR6hThvBSFjBnSci8TYpXeDPxvYRmtdlbS+EjgZ82Z0g9ddnK1jD/PeSNKpOwHzFnmv1nq293bU6vUUtGVLUza0eWWDgC10AWaIeL06wzxND2mt3+6mzkjM83ONX7QppX4MnAgsAa7UWtf6RNdM4FRgJQW6BpRSuwD7a63v9Ja3BgZrrRckroVsacqCtm0w59YSpdTEdK4BpdSLwHWeMZcIwHUaxqn2Y6VUiTazRwpy3gpCJmzWDp2pbhBJY7dtSqm/A9VKqf0SD0ngLWC+1vrTLGoaDvwKmKiUehx4R2u90utNKdFJwXYSxo/WerlS6mlMqvnZiZtptg2LbGjzdLVivPizSjo3fO8mXAF8knhIeg+jMq31Iq/Oah9pm4oZj38CeEtrPc9HupoxPVOfFeoa8DgS+NjrgboWM+35Mk97Vg2LLGpbksqw8Hoq2oEbMX4ar3q/qd7rybIxU1s7vPKsn7eCkCs2y2ERpdRwpdT1wG1KqSOUUmO8ctWli3zDEInWejmQeEjila3I8k11X4zjXJP393DMzShxvHav3m7dbP4C8GiX8eas4XNtabenx5HAQk/PDcB9GM/+rJMFbX8GpmitV2XTsMiSrkna+IQU7BrwtK4BvoVxMm3GOMN+mC1NudLmtcE1SqkTvB6NDUN0SYYFWusXgLVKqXM94wbMvTkvQ1CCkAs2O+MiWw/JHMlrA27RWl+rtX4eM7Uu4OnSSqlDlFL/As7ybj4bxqy01mu11v/oMt682WsbIA+jD3yqK+v/M/p2ng3ytLZipuf+RGsdTnfIrpDalFLHA3/H9A5PAe7w9tOe/FcptYd37Msxs4ZuVEq9gbk3R3P0OwUh52yOwyKJG8S/AZRSe9HlBgGcD9Qqpc5P7ubUWq/FRNTLCkqpKcAYrfUnXtF/MTfuBGvw5r4rpYKY6Wh3eDe2nOJnbV3oS3teqI0vSvINf8EA1OYbXVk6z+7TJv5GVsmVNm9IY3vgfK31fKXUMGC8Uqpca93i1Ulug4+11jHgOqXUdKBZmynxglC8aK2L+oN5K9glaTkADElavhjYw/seBO4GjsyxJgVcjYnY9xBmDHa3LnVmeFpG9bCPkoGmLVvtieeoPFC0+VGXn8+zXGjz2sBOWt7F+7s9ZmrxC8BtmCisQUzI95m5+H3ykY8fPgUXkLFwf9+8dgIe9L6XYYI5PQhsl1TnNODgpOV9MDMscv1/86U2n7enL7X5VZefz7Nsa+umDX4B7Oqt2wJ4HbjIW/4NxtAblI82kI98CvkpZp8LC9hWa70L5kbwCXCxMqFyE+wCPKa1bgRQJtPjhjniujNeRLbpoLN7tV2b0OKzMN2gCWYAHymlvqaUegozDTEf+FWbn9vTr9r8qgv8e55lW1vXNnCAS5VS07TWK4BKrfXNXt0/YIZWhsOGKcC5bANBKBjFbFz4+eY1AS/fgdY64eD4MDBUKbWzMnE1lgD3YGIL3Ke1Pt+7GQ1UbX5uT79q86su8O95lm1t3bXBbEwuFPTG00enA69oL6CWGBXC5kwxGxd+vnl9COytlNoJNkRhLAWWe58OYBDwotZ6ptb6n3nQ5Hdtfm5Pv2rzqy7w73mWbW3dtcFDmDYIKqUmK6WOUUrdhYmD8UZOfpEg+IxiNi4KdvNSSu2nlLK6KS/xpmk2YmJmnK2UmqS1bvPKtgZGem8s1VrrO7KlqRi0pWCgPIw2C11+Ps/yrK23NqgHvsDcZz/QWh+utX42Kz9SENTu43cAAAerSURBVHyOb8N/K6X2A1Zord0u5SUY3e1KqTMwsfurtdZfeusfAH6jtV6oshweWyk1GfNW0oSJQPk+Jm324uRpZkn1f45xHvsMk6jqC+AynYPwvX7W5h3Pd+3pd21+1OXn8ywX2vrZBldrrRcpJSnShYGH74wLn9+8vodx3rpRKTUek7nwMK31yd76MRjP8fe11g94ZdMwKaEbtdYPZ1uT37X5vD19qc2vurxj+fI8y7Y2P7eBIBQF2gdTVpI/wPcwFyXAeIwH9sNJ68cAtwKnJZVNw6RMPzkHekYmfb8oSVvCMHsSOMbT8BFwJSaPRT7+V77V5tf2LAZtftPl5/MsV9r81gbykU+xfXwRoVMpNVJ3elVvnbSqXmv9gFLqWKXUMZh00E9gwuo+lKikTUTBrEY7VEodCVwCuEqpFVrrMLAU6FBKDdZaJzzEf4u5qZ2JCYqz1Ns+Z12hftbm7d937el3bX7U5efzLBfa/NgGglCsFNS48OPNyxtLPQX4Dsa7ey5wr1LqGxjP+2OBt4ElyuRk+Az4D+ZNaam3vc7FTdXP2jx9vmtPv2vzoy4/n2e50ObHNhCEYqcgs0WU8do+FTgHc4O4HAgm3SBmYKZ40dMNIlcXtDae4iuA87TWr2mTb6QO+EJr/SawHjhYKbWDNnlJJmOiHzYmts/Vjcav2vzcnn7V5ldd4N/zLNva/NwGglDsFMS48NvNSyl1olLqG0qpcV7Ra5g3lwSDgQbv+wOABv6klLoIk5p6nrefXKQb9622BH5rz2LQ5jddfj7PcqXNb20gCJsTeRsWUUqdCCwEPtNa12NuEMme1F1vEAdgbhDPAifhpSzO5puCUqrCO1YDpiv1AqXUeTopyJBS6jve+iYArfUiYJFSagGwM/BjrfXH3rps3ux9q807tu/a0+/a/KjLz+dZLrT5sQ0EYXMk51NRu7lBfB3zptD1BrEbEE6+YJVS+2JuEHMSN4gsazsE2FFr/Sdv+RfAllrri7zlQcD1wI1a6+VKqanA4Vrre7KtpVi0+bw9fanNr7q8/fvyPMu2Nj+3gSBsjuRjWGR74HGt9fFa6xsxEe2uTKz0bhD7Ard7bxZTlVJnAWit39Ra35OtC1opVaqUukIpdYZSagqwFaarM8GNwASl1AHe8hRgjXfjugK4DxiSDS3FpK0LvmnPItLmG11+Ps9yrM03bSAIA4GsGxd+vXkppQ4CngPGYoaDngLe8tZNgA1dun/FeISjtY4BxyulPgBGAd/TWt82wLT5sj39rM3Huvx8nmVVm1/bQBAGClk1Lvx681Jm+tkk4Eqt9UVa67uBD4A9MI5h+yfqaa2fAhqUUtO9za8BztRa/0JrvTKbuopAmy/b08/afKzLz+dZVrX5tQ0EYSCRNYfOLjeIOV7ZXmx8g/hH4gahlDpaKTVda/0p5gaxUGv9Xrb0JKO17lBKzQFinq5hwFqMM9cuwIFKqS+11m8qk/BopacLrfXfcqHJ79r83J5+1eZXXeDf8yzb2vzcBoIwkMiaceHnm5d3jIVJiwcBy7SZq96A+T9cpZR611v3iPcb8uIR7kdtfm5Pv2rzq64kfb47z7Ktze9tIAgDhaxORfXzzavLsWzgUU9zs1LqFcDBBM25U2td663L21QzP2rzc3v6VZtfdSXw43mWbW1+bwNBGAhkPc6Fn29eWmutlBoCbKdNKuRdgd8A84HLtdZP5ktLsWjzc3v6VZtfdSWO5cfzLNva/NwGgjAQyLpx4eebl8cIoE0pdRlwOHC/1vqRAmtK4Dttfm5Pv2rzq64kfHeeJZEVbUXQBoKwWZOrCJ2+vXlpreuVUgFMyuRKrXVLoTUl8LE237Yn/tXmV11+Ps+yrc23bSAImzs5MS78fPPy+JE2eQX8iO+0+bk9/arNr7qS8N15lkRWtBVBGwjCZkvOwn97U738evMS+oif29Ov2vyqayAhbSAIhSHnuUUEQRAEQRhYFCTluiAIgiAImy9iXAiCIAiCkFXEuBAEQRAEIauIcSEIgiAIQlYR40IQcoRSqkwpZfew7hGlVGmXMqVM4i2UUiVKqZqudQRBEIoBMS4EIXcMBi7pYV2J1rpdKTVJKfWKUqoW+CkwWykVA04BWrTW7XnSKgiCkDXEuBCE3NGtYeCFpVZKqZnAcEz0yBe01rcDpwEPaa0f7Gl7QRAEv5Or8N+CIJjr6zCl1EsYI2IkoIAnvb8tQBswDdjJ2yYIDMm/VEEQhOwhxoUg5I4m4OtAXGvdnLxCKXWP1vpl7/sVwDyl1AnAt4AKr9oQpdSLwLVa69fyJ1sQBKF/iHEhCDnCCzu9XCl1mlJKYYY5OjC9FgEApdSOwHbAWcCPvPUvKaV+gImge1hBxAuCIPQDMS4EIff8ECgHWgGNMS5289YNBi7ynDv3AK4A1gAnID5RgiAUKZJbRBByjFLqX8Bxuo8Xm1LqBa314TmSJQiCkDOk50IQco8NvKKUasMMe5RieiWu1Vq/0st2EuNCEISiRHouBCHHKKXKtNZthdYhCIKQL8S4EARBEAQhq4jDmCAIgiAIWUWMC0EQBEEQsooYF4IgCIIgZBUxLgRBEARByCpiXAiCIAiCkFXEuBAEQRAEIav8P+bwhO+iirB9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(8, 4))\n",
    "\n",
    "color = 'tab:green'\n",
    "ax1.set_xlabel('날짜')\n",
    "ax1.set_ylabel('확진자 수 (대구경북/전국)', color=color)\n",
    "ax1.set_ylim(0, 1000)\n",
    "plt.bar(x=df.index, height=df['확진자'], color=color, alpha=0.6)\n",
    "plt.bar(x=df.index, height=df['대구']+df['경북'], color=\"tab:blue\", alpha=0.6)\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "color = 'tab:red'\n",
    "ax2.set_ylabel('누적 양성비율 (%)', color=color)  # we already handled the x-label with ax1\n",
    "ax2.plot(df['누적 양성비율'], color=color)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "color = 'tab:orange'\n",
    "ax3 = ax1.twinx()\n",
    "ax3.spines[\"right\"].set_position((\"axes\", 1.15))\n",
    "ax3.set_ylabel('누적 사망율 (%)', color=color)  # we already handled the x-label with ax1\n",
    "ax3.plot(df['누적 사망율'], color=color)\n",
    "ax3.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "color = 'tab:blue'\n",
    "ax4 = ax1.twinx()\n",
    "ax4.spines[\"right\"].set_position((\"axes\", 1.3))\n",
    "ax4.set_ylabel('누적 대구경북 (%)', color=color)  # we already handled the x-label with ax1\n",
    "ax4.plot(df['누적 대구경북'], color=color)\n",
    "ax4.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "#plt.xticks(rotation=90)\n",
    "fig.autofmt_xdate()\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "plt.title('코로나바이러스감염증-19 감염현황 (03-01 16:00 현재)')\n",
    "\n",
    "plt.savefig('covid19-stat-0301.png', dpi=200, format='png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "사망율 감소, 대구경북 증가, 양성비율 증가. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}