subway_project_이찬구_1122.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "metro_all = pd.read_csv(\"C:/Users/82105/Desktop/2학년_2학기/오픈소스_개발_프로젝트/서울시 지하철 호선별 역별 시간대별 승하차 인원 정보.csv\",encoding = 'cp949')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>사용월</th>\n",
       "      <th>호선명</th>\n",
       "      <th>지하철역</th>\n",
       "      <th>04시-05시 승차인원</th>\n",
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       "      <th>07시-08시 승차인원</th>\n",
       "      <th>...</th>\n",
       "      <th>23시-24시 하차인원</th>\n",
       "      <th>00시-01시 승차인원</th>\n",
       "      <th>00시-01시 하차인원</th>\n",
       "      <th>01시-02시 승차인원</th>\n",
       "      <th>01시-02시 하차인원</th>\n",
       "      <th>02시-03시 승차인원</th>\n",
       "      <th>02시-03시 하차인원</th>\n",
       "      <th>03시-04시 승차인원</th>\n",
       "      <th>03시-04시 하차인원</th>\n",
       "      <th>작업일자</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>1호선</td>\n",
       "      <td>동대문</td>\n",
       "      <td>688</td>\n",
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       "      <td>555</td>\n",
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       "      <td>1</td>\n",
       "      <td>221</td>\n",
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       "      <td>2023-10-01</td>\n",
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       "      <td>208</td>\n",
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       "      <td>85</td>\n",
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       "      <td>1</td>\n",
       "      <td>318</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20231103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>1호선</td>\n",
       "      <td>서울역</td>\n",
       "      <td>700</td>\n",
       "      <td>16</td>\n",
       "      <td>8826</td>\n",
       "      <td>9093</td>\n",
       "      <td>13369</td>\n",
       "      <td>60044</td>\n",
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       "      <th>3</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>1호선</td>\n",
       "      <td>시청</td>\n",
       "      <td>60</td>\n",
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       "      <td>5020</td>\n",
       "      <td>4325</td>\n",
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       "      <th>4</th>\n",
       "      <td>2023-10-01</td>\n",
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       "      <td>신설동</td>\n",
       "      <td>388</td>\n",
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       "      <td>2315</td>\n",
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       "      <th>22594</th>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>중앙선</td>\n",
       "      <td>원덕</td>\n",
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       "      <td>2020-10-01</td>\n",
       "      <td>중앙선</td>\n",
       "      <td>중랑</td>\n",
       "      <td>176</td>\n",
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       "      <td>8338</td>\n",
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       "      <td>중앙선</td>\n",
       "      <td>팔당</td>\n",
       "      <td>1</td>\n",
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       "      <td>23</td>\n",
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       "      <th>22598</th>\n",
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       "      <td>회기</td>\n",
       "      <td>838</td>\n",
       "      <td>7</td>\n",
       "      <td>10784</td>\n",
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       "<p>22599 rows × 52 columns</p>\n",
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      ],
      "text/plain": [
       "             사용월  호선명 지하철역  04시-05시 승차인원  04시-05시 하차인원  05시-06시 승차인원  \\\n",
       "0     2023-10-01  1호선  동대문           688            23         11493   \n",
       "1     2023-10-01  1호선  동묘앞           208             1          3065   \n",
       "2     2023-10-01  1호선  서울역           700            16          8826   \n",
       "3     2023-10-01  1호선   시청            60             1          2408   \n",
       "4     2023-10-01  1호선  신설동           388            18          9245   \n",
       "...          ...  ...  ...           ...           ...           ...   \n",
       "22594 2020-10-01  중앙선   원덕            32             0           305   \n",
       "22595 2020-10-01  중앙선   중랑           176             2          5954   \n",
       "22596 2020-10-01  중앙선   지평             0             0             0   \n",
       "22597 2020-10-01  중앙선   팔당             1             0           231   \n",
       "22598 2020-10-01  중앙선   회기           838             7         10784   \n",
       "\n",
       "       05시-06시 하차인원  06시-07시 승차인원  06시-07시 하차인원  07시-08시 승차인원  ...  \\\n",
       "0              2221          9618          6755         14951  ...   \n",
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       "3              5020          4325         26689          8093  ...   \n",
       "4              2315          9988          8178         21304  ...   \n",
       "...             ...           ...           ...           ...  ...   \n",
       "22594             9           441           135           794  ...   \n",
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       "22596             0             0             0           292  ...   \n",
       "22597           101           628           680          1514  ...   \n",
       "22598          3174         18004         12317         48144  ...   \n",
       "\n",
       "       23시-24시 하차인원  00시-01시 승차인원  00시-01시 하차인원  01시-02시 승차인원  01시-02시 하차인원  \\\n",
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       "...             ...           ...           ...           ...           ...   \n",
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       "22597           190            23            84             0             0   \n",
       "22598         20540           390          5270             1            22   \n",
       "\n",
       "       02시-03시 승차인원  02시-03시 하차인원  03시-04시 승차인원  03시-04시 하차인원      작업일자  \n",
       "0                 0             0             0             0  20231103  \n",
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       "3                 0             0             0             0  20231103  \n",
       "4                 0             0             0             0  20231103  \n",
       "...             ...           ...           ...           ...       ...  \n",
       "22594             0             0             0             0  20201103  \n",
       "22595             0             0             0             0  20201103  \n",
       "22596             0             0             0             0  20201103  \n",
       "22597             0             0             0             0  20201103  \n",
       "22598             0             0             0             0  20201103  \n",
       "\n",
       "[22599 rows x 52 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#최근3년만 추출\n",
    "metro_recent_c=metro_all\n",
    "metro_recent_c=metro_recent_c.drop(columns={'작업일자'}) #불필요한 칼럼 제거\n",
    "#사용월을 datetime으로 바꿔준다.\n",
    "metro_recent_c = metro_all[(metro_all['사용월']>=202010)]\n",
    "metro_recent_c['사용월'] = pd.to_datetime(metro_recent_c['사용월'].astype(str), format='%Y%m')\n",
    "metro_recent_c\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>평균 승차 인원 수</th>\n",
       "      <th>평균 하차 인원 수</th>\n",
       "      <th>평균 승하차 합계</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>지하철역</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1호선</th>\n",
       "      <th>서울역</th>\n",
       "      <td>52562</td>\n",
       "      <td>50378</td>\n",
       "      <td>102940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>종각</th>\n",
       "      <td>38456</td>\n",
       "      <td>37283</td>\n",
       "      <td>75739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>종로3가</th>\n",
       "      <td>29201</td>\n",
       "      <td>26706</td>\n",
       "      <td>55907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2호선</th>\n",
       "      <th>강남</th>\n",
       "      <td>89011</td>\n",
       "      <td>87066</td>\n",
       "      <td>176077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>잠실(송파구청)</th>\n",
       "      <td>82314</td>\n",
       "      <td>81086</td>\n",
       "      <td>163400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>홍대입구</th>\n",
       "      <td>69352</td>\n",
       "      <td>73590</td>\n",
       "      <td>142942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">3호선</th>\n",
       "      <th>고속터미널</th>\n",
       "      <td>52902</td>\n",
       "      <td>51301</td>\n",
       "      <td>104203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>연신내</th>\n",
       "      <td>40971</td>\n",
       "      <td>38772</td>\n",
       "      <td>79743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>양재(서초구청)</th>\n",
       "      <td>40053</td>\n",
       "      <td>43539</td>\n",
       "      <td>83592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4호선</th>\n",
       "      <th>수유(강북구청)</th>\n",
       "      <td>38377</td>\n",
       "      <td>38281</td>\n",
       "      <td>76658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>혜화</th>\n",
       "      <td>38238</td>\n",
       "      <td>38576</td>\n",
       "      <td>76814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>쌍문</th>\n",
       "      <td>32631</td>\n",
       "      <td>30020</td>\n",
       "      <td>62651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">5호선</th>\n",
       "      <th>광화문(세종문화회관)</th>\n",
       "      <td>33059</td>\n",
       "      <td>34408</td>\n",
       "      <td>67467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>까치산</th>\n",
       "      <td>32189</td>\n",
       "      <td>30866</td>\n",
       "      <td>63055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>화곡</th>\n",
       "      <td>31616</td>\n",
       "      <td>29464</td>\n",
       "      <td>61080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6호선</th>\n",
       "      <th>공덕</th>\n",
       "      <td>19779</td>\n",
       "      <td>19139</td>\n",
       "      <td>38918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>응암</th>\n",
       "      <td>19619</td>\n",
       "      <td>19423</td>\n",
       "      <td>39042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>망원</th>\n",
       "      <td>16386</td>\n",
       "      <td>17656</td>\n",
       "      <td>34042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">7호선</th>\n",
       "      <th>가산디지털단지</th>\n",
       "      <td>45482</td>\n",
       "      <td>45138</td>\n",
       "      <td>90620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>학동</th>\n",
       "      <td>24626</td>\n",
       "      <td>26082</td>\n",
       "      <td>50708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광명사거리</th>\n",
       "      <td>24467</td>\n",
       "      <td>24158</td>\n",
       "      <td>48625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8호선</th>\n",
       "      <th>문정</th>\n",
       "      <td>22005</td>\n",
       "      <td>23019</td>\n",
       "      <td>45024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>암사</th>\n",
       "      <td>20747</td>\n",
       "      <td>18461</td>\n",
       "      <td>39208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>장지</th>\n",
       "      <td>19131</td>\n",
       "      <td>18213</td>\n",
       "      <td>37344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">9호선</th>\n",
       "      <th>신논현</th>\n",
       "      <td>34682</td>\n",
       "      <td>34261</td>\n",
       "      <td>68943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>노량진</th>\n",
       "      <td>29215</td>\n",
       "      <td>28706</td>\n",
       "      <td>57921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>여의도</th>\n",
       "      <td>28797</td>\n",
       "      <td>27491</td>\n",
       "      <td>56288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 평균 승차 인원 수  평균 하차 인원 수  평균 승하차 합계\n",
       "    지하철역                                          \n",
       "1호선 서울역               52562       50378     102940\n",
       "    종각                38456       37283      75739\n",
       "    종로3가              29201       26706      55907\n",
       "2호선 강남                89011       87066     176077\n",
       "    잠실(송파구청)          82314       81086     163400\n",
       "    홍대입구              69352       73590     142942\n",
       "3호선 고속터미널             52902       51301     104203\n",
       "    연신내               40971       38772      79743\n",
       "    양재(서초구청)          40053       43539      83592\n",
       "4호선 수유(강북구청)          38377       38281      76658\n",
       "    혜화                38238       38576      76814\n",
       "    쌍문                32631       30020      62651\n",
       "5호선 광화문(세종문화회관)       33059       34408      67467\n",
       "    까치산               32189       30866      63055\n",
       "    화곡                31616       29464      61080\n",
       "6호선 공덕                19779       19139      38918\n",
       "    응암                19619       19423      39042\n",
       "    망원                16386       17656      34042\n",
       "7호선 가산디지털단지           45482       45138      90620\n",
       "    학동                24626       26082      50708\n",
       "    광명사거리             24467       24158      48625\n",
       "8호선 문정                22005       23019      45024\n",
       "    암사                20747       18461      39208\n",
       "    장지                19131       18213      37344\n",
       "9호선 신논현               34682       34261      68943\n",
       "    노량진               29215       28706      57921\n",
       "    여의도               28797       27491      56288"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def process_line(line, metro_recent):\n",
    "    # 해당 호선 데이터 추출\n",
    "    metro = metro_recent_c.groupby(['호선명','지하철역']).mean().reset_index()\n",
    "    metro_line = metro[metro['호선명'] == line]\n",
    "    \n",
    "    # 승차 인원 컬럼 추출\n",
    "    metro_get_on_devide = pd.DataFrame()  # 변수 초기화 추가\n",
    "    metro_get_on_devide['지하철역'] = metro_line['지하철역']\n",
    "    for i in range(int((len(metro_recent.columns)-3)/2)):\n",
    "        metro_get_on_devide[metro_line.columns[3+2*i]] = metro_line[metro_line.columns[3+2*i]]\n",
    "    metro_get_on_devide = metro_get_on_devide.set_index('지하철역')\n",
    "\n",
    "    # 하차 인원 컬럼 추출\n",
    "    metro_get_off = pd.DataFrame()\n",
    "    metro_get_off['지하철역'] = metro_line['지하철역']\n",
    "    for i in range(int((len(metro_recent.columns)-3)/2)):\n",
    "        metro_get_off[metro_line.columns[4+2*i]] = metro_line[metro_line.columns[4+2*i]]\n",
    "    metro_get_off = metro_get_off.set_index('지하철역')\n",
    "\n",
    "    # 역 별 평균 승하차 인원 계산\n",
    "    df = pd.DataFrame(index=metro_line['지하철역'])\n",
    "    df['평균 승차 인원 수'] = metro_get_on_devide.mean(axis=1).astype('int64')\n",
    "    df['평균 하차 인원 수'] = metro_get_off.mean(axis=1).astype(int)\n",
    "    \n",
    "    # 추가: 평균 승차 인원 수와 평균 하차 인원 수의 합계 열 추가\n",
    "    df['평균 승하차 합계'] = df['평균 승차 인원 수'] + df['평균 하차 인원 수']\n",
    "    \n",
    "    top3_on = df.sort_values(by='평균 승차 인원 수', ascending=False).head(3)\n",
    "    \n",
    "    return top3_on\n",
    "\n",
    "# 1호선부터 9호선까지 처리\n",
    "result_frames = []\n",
    "for line_number in range(1, 10):\n",
    "    line_name = f\"{line_number}호선\"\n",
    "    result = process_line(line_name, metro_recent_c)\n",
    "    result_frames.append(result)\n",
    "\n",
    "# 각 노선별 상위 3개의 역을 담은 데이터프레임을 생성\n",
    "top3_per_line = pd.concat(result_frames, keys=[f\"{i}호선\" for i in range(1, 10)])\n",
    "\n",
    "# 결과 확인\n",
    "#print(top3_per_line)\n",
    "\n",
    "top3_per_line\n",
    "# 이렇게 노션별 상위3개의 역을 알았다."
   ]
  },
  {
   "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>평균 승차 인원 수</th>\n",
       "      <th>평균 하차 인원 수</th>\n",
       "      <th>평균 승하차 합계</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2호선</th>\n",
       "      <td>240677</td>\n",
       "      <td>241742</td>\n",
       "      <td>482419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3호선</th>\n",
       "      <td>133926</td>\n",
       "      <td>133612</td>\n",
       "      <td>267538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     평균 승차 인원 수  평균 하차 인원 수  평균 승하차 합계\n",
       "2호선      240677      241742     482419\n",
       "3호선      133926      133612     267538"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 승하차 합계 기준으로 각 역의 합계 계산\n",
    "station_totals = top3_per_line.groupby(level=0).sum()\n",
    "\n",
    "# 결과 출력\n",
    "#print(\"각 역별 승하차 합계:\\n\", station_totals)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# 상위 2개 호선 찾기\n",
    "top2_lines = station_totals.nlargest(2, '평균 승하차 합계')\n",
    "\n",
    "# 결과 출력\n",
    "#print(\"상위 2개 호선 (승하차 합계 기준):\\n\", top2_lines)\n",
    "top2_lines\n",
    "\n",
    "#2호선 3호선이 젤 많아!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>호선명</th>\n",
       "      <th>지하철역</th>\n",
       "      <th>사용월</th>\n",
       "      <th>04시-05시 승차인원</th>\n",
       "      <th>05시-06시 승차인원</th>\n",
       "      <th>06시-07시 승차인원</th>\n",
       "      <th>07시-08시 승차인원</th>\n",
       "      <th>08시-09시 승차인원</th>\n",
       "      <th>09시-10시 승차인원</th>\n",
       "      <th>10시-11시 승차인원</th>\n",
       "      <th>...</th>\n",
       "      <th>18시-19시 승차인원</th>\n",
       "      <th>19시-20시 승차인원</th>\n",
       "      <th>20시-21시 승차인원</th>\n",
       "      <th>21시-22시 승차인원</th>\n",
       "      <th>22시-23시 승차인원</th>\n",
       "      <th>23시-24시 승차인원</th>\n",
       "      <th>00시-01시 승차인원</th>\n",
       "      <th>01시-02시 승차인원</th>\n",
       "      <th>02시-03시 승차인원</th>\n",
       "      <th>03시-04시 승차인원</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1호선</td>\n",
       "      <td>동대문</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>688</td>\n",
       "      <td>11493</td>\n",
       "      <td>9618</td>\n",
       "      <td>14951</td>\n",
       "      <td>20657</td>\n",
       "      <td>19909</td>\n",
       "      <td>18030</td>\n",
       "      <td>...</td>\n",
       "      <td>25680</td>\n",
       "      <td>17817</td>\n",
       "      <td>15062</td>\n",
       "      <td>13799</td>\n",
       "      <td>10430</td>\n",
       "      <td>4866</td>\n",
       "      <td>555</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1호선</td>\n",
       "      <td>동묘앞</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>208</td>\n",
       "      <td>3065</td>\n",
       "      <td>3652</td>\n",
       "      <td>7542</td>\n",
       "      <td>12370</td>\n",
       "      <td>11293</td>\n",
       "      <td>13116</td>\n",
       "      <td>...</td>\n",
       "      <td>24843</td>\n",
       "      <td>10039</td>\n",
       "      <td>5844</td>\n",
       "      <td>4793</td>\n",
       "      <td>3530</td>\n",
       "      <td>1141</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1호선</td>\n",
       "      <td>서울역</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>700</td>\n",
       "      <td>8826</td>\n",
       "      <td>13369</td>\n",
       "      <td>42194</td>\n",
       "      <td>75501</td>\n",
       "      <td>70759</td>\n",
       "      <td>67566</td>\n",
       "      <td>...</td>\n",
       "      <td>220828</td>\n",
       "      <td>123025</td>\n",
       "      <td>89108</td>\n",
       "      <td>87580</td>\n",
       "      <td>59608</td>\n",
       "      <td>29305</td>\n",
       "      <td>2333</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1호선</td>\n",
       "      <td>시청</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>60</td>\n",
       "      <td>2408</td>\n",
       "      <td>4325</td>\n",
       "      <td>8093</td>\n",
       "      <td>11102</td>\n",
       "      <td>14674</td>\n",
       "      <td>17236</td>\n",
       "      <td>...</td>\n",
       "      <td>158616</td>\n",
       "      <td>69239</td>\n",
       "      <td>62691</td>\n",
       "      <td>53963</td>\n",
       "      <td>32319</td>\n",
       "      <td>10655</td>\n",
       "      <td>323</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1호선</td>\n",
       "      <td>신설동</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>388</td>\n",
       "      <td>9245</td>\n",
       "      <td>9988</td>\n",
       "      <td>21304</td>\n",
       "      <td>31140</td>\n",
       "      <td>22327</td>\n",
       "      <td>19391</td>\n",
       "      <td>...</td>\n",
       "      <td>49687</td>\n",
       "      <td>20869</td>\n",
       "      <td>13794</td>\n",
       "      <td>12004</td>\n",
       "      <td>8113</td>\n",
       "      <td>3693</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22594</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>원덕</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>32</td>\n",
       "      <td>305</td>\n",
       "      <td>441</td>\n",
       "      <td>794</td>\n",
       "      <td>861</td>\n",
       "      <td>716</td>\n",
       "      <td>822</td>\n",
       "      <td>...</td>\n",
       "      <td>671</td>\n",
       "      <td>334</td>\n",
       "      <td>148</td>\n",
       "      <td>63</td>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22595</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>중랑</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>176</td>\n",
       "      <td>5954</td>\n",
       "      <td>8338</td>\n",
       "      <td>20492</td>\n",
       "      <td>19471</td>\n",
       "      <td>12192</td>\n",
       "      <td>8846</td>\n",
       "      <td>...</td>\n",
       "      <td>6998</td>\n",
       "      <td>4492</td>\n",
       "      <td>3183</td>\n",
       "      <td>2698</td>\n",
       "      <td>1863</td>\n",
       "      <td>920</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22596</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>지평</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>292</td>\n",
       "      <td>155</td>\n",
       "      <td>7</td>\n",
       "      <td>337</td>\n",
       "      <td>...</td>\n",
       "      <td>61</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22597</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>팔당</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>231</td>\n",
       "      <td>628</td>\n",
       "      <td>1514</td>\n",
       "      <td>1589</td>\n",
       "      <td>1225</td>\n",
       "      <td>1414</td>\n",
       "      <td>...</td>\n",
       "      <td>3614</td>\n",
       "      <td>2386</td>\n",
       "      <td>1674</td>\n",
       "      <td>1445</td>\n",
       "      <td>989</td>\n",
       "      <td>179</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22598</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>회기</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>838</td>\n",
       "      <td>10784</td>\n",
       "      <td>18004</td>\n",
       "      <td>48144</td>\n",
       "      <td>61395</td>\n",
       "      <td>40071</td>\n",
       "      <td>32149</td>\n",
       "      <td>...</td>\n",
       "      <td>44829</td>\n",
       "      <td>26174</td>\n",
       "      <td>21545</td>\n",
       "      <td>21644</td>\n",
       "      <td>20408</td>\n",
       "      <td>9451</td>\n",
       "      <td>390</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22599 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       호선명 지하철역        사용월  04시-05시 승차인원  05시-06시 승차인원  06시-07시 승차인원  \\\n",
       "0      1호선  동대문 2023-10-01           688         11493          9618   \n",
       "1      1호선  동묘앞 2023-10-01           208          3065          3652   \n",
       "2      1호선  서울역 2023-10-01           700          8826         13369   \n",
       "3      1호선   시청 2023-10-01            60          2408          4325   \n",
       "4      1호선  신설동 2023-10-01           388          9245          9988   \n",
       "...    ...  ...        ...           ...           ...           ...   \n",
       "22594  중앙선   원덕 2020-10-01            32           305           441   \n",
       "22595  중앙선   중랑 2020-10-01           176          5954          8338   \n",
       "22596  중앙선   지평 2020-10-01             0             0             0   \n",
       "22597  중앙선   팔당 2020-10-01             1           231           628   \n",
       "22598  중앙선   회기 2020-10-01           838         10784         18004   \n",
       "\n",
       "       07시-08시 승차인원  08시-09시 승차인원  09시-10시 승차인원  10시-11시 승차인원  ...  \\\n",
       "0             14951         20657         19909         18030  ...   \n",
       "1              7542         12370         11293         13116  ...   \n",
       "2             42194         75501         70759         67566  ...   \n",
       "3              8093         11102         14674         17236  ...   \n",
       "4             21304         31140         22327         19391  ...   \n",
       "...             ...           ...           ...           ...  ...   \n",
       "22594           794           861           716           822  ...   \n",
       "22595         20492         19471         12192          8846  ...   \n",
       "22596           292           155             7           337  ...   \n",
       "22597          1514          1589          1225          1414  ...   \n",
       "22598         48144         61395         40071         32149  ...   \n",
       "\n",
       "       18시-19시 승차인원  19시-20시 승차인원  20시-21시 승차인원  21시-22시 승차인원  22시-23시 승차인원  \\\n",
       "0             25680         17817         15062         13799         10430   \n",
       "1             24843         10039          5844          4793          3530   \n",
       "2            220828        123025         89108         87580         59608   \n",
       "3            158616         69239         62691         53963         32319   \n",
       "4             49687         20869         13794         12004          8113   \n",
       "...             ...           ...           ...           ...           ...   \n",
       "22594           671           334           148            63            32   \n",
       "22595          6998          4492          3183          2698          1863   \n",
       "22596            61           160             1             2             0   \n",
       "22597          3614          2386          1674          1445           989   \n",
       "22598         44829         26174         21545         21644         20408   \n",
       "\n",
       "       23시-24시 승차인원  00시-01시 승차인원  01시-02시 승차인원  02시-03시 승차인원  03시-04시 승차인원  \n",
       "0              4866           555             1             0             0  \n",
       "1              1141            85             1             0             0  \n",
       "2             29305          2333             2             0             0  \n",
       "3             10655           323             0             0             0  \n",
       "4              3693           289             0             0             0  \n",
       "...             ...           ...           ...           ...           ...  \n",
       "22594            12             1             0             0             0  \n",
       "22595           920            78             0             0             0  \n",
       "22596             0             0             0             0             0  \n",
       "22597           179            23             0             0             0  \n",
       "22598          9451           390             1             0             0  \n",
       "\n",
       "[22599 rows x 27 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#승차\n",
    "metro_recent_c_geton = pd.DataFrame()\n",
    "metro_recent_c_geton['호선명'] = metro_recent_c['호선명']\n",
    "metro_recent_c_geton['지하철역'] = metro_recent_c['지하철역']\n",
    "metro_recent_c_geton['사용월'] = metro_recent_c['사용월']\n",
    "for i in range(int((len(metro_recent_c.columns)-3)/2)):  #맨처음 index는 무시하고 3,5,7,..순서대로 승차인원이므로&24개씩 승차 하차 인원이 있으므로\n",
    "    metro_recent_c_geton[metro_recent_c.columns[3+2*i]] = metro_recent_c[metro_recent_c.columns[3+2*i]]\n",
    "metro_recent_c_geton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>호선명</th>\n",
       "      <th>지하철역</th>\n",
       "      <th>사용월</th>\n",
       "      <th>04시-05시 하차인원</th>\n",
       "      <th>05시-06시 하차인원</th>\n",
       "      <th>06시-07시 하차인원</th>\n",
       "      <th>07시-08시 하차인원</th>\n",
       "      <th>08시-09시 하차인원</th>\n",
       "      <th>09시-10시 하차인원</th>\n",
       "      <th>10시-11시 하차인원</th>\n",
       "      <th>...</th>\n",
       "      <th>18시-19시 하차인원</th>\n",
       "      <th>19시-20시 하차인원</th>\n",
       "      <th>20시-21시 하차인원</th>\n",
       "      <th>21시-22시 하차인원</th>\n",
       "      <th>22시-23시 하차인원</th>\n",
       "      <th>23시-24시 하차인원</th>\n",
       "      <th>00시-01시 하차인원</th>\n",
       "      <th>01시-02시 하차인원</th>\n",
       "      <th>02시-03시 하차인원</th>\n",
       "      <th>03시-04시 하차인원</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1호선</td>\n",
       "      <td>동대문</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>23</td>\n",
       "      <td>2221</td>\n",
       "      <td>6755</td>\n",
       "      <td>12017</td>\n",
       "      <td>20541</td>\n",
       "      <td>23460</td>\n",
       "      <td>26158</td>\n",
       "      <td>...</td>\n",
       "      <td>22333</td>\n",
       "      <td>21418</td>\n",
       "      <td>14927</td>\n",
       "      <td>13295</td>\n",
       "      <td>12184</td>\n",
       "      <td>9061</td>\n",
       "      <td>2415</td>\n",
       "      <td>221</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1호선</td>\n",
       "      <td>동묘앞</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1120</td>\n",
       "      <td>4528</td>\n",
       "      <td>9281</td>\n",
       "      <td>21560</td>\n",
       "      <td>18828</td>\n",
       "      <td>25633</td>\n",
       "      <td>...</td>\n",
       "      <td>11845</td>\n",
       "      <td>8575</td>\n",
       "      <td>6512</td>\n",
       "      <td>6467</td>\n",
       "      <td>7088</td>\n",
       "      <td>4288</td>\n",
       "      <td>1680</td>\n",
       "      <td>318</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1호선</td>\n",
       "      <td>서울역</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>16</td>\n",
       "      <td>9093</td>\n",
       "      <td>60044</td>\n",
       "      <td>111515</td>\n",
       "      <td>226340</td>\n",
       "      <td>157304</td>\n",
       "      <td>85381</td>\n",
       "      <td>...</td>\n",
       "      <td>95463</td>\n",
       "      <td>66792</td>\n",
       "      <td>48809</td>\n",
       "      <td>44886</td>\n",
       "      <td>29468</td>\n",
       "      <td>14003</td>\n",
       "      <td>2475</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1호선</td>\n",
       "      <td>시청</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>5020</td>\n",
       "      <td>26689</td>\n",
       "      <td>70011</td>\n",
       "      <td>186809</td>\n",
       "      <td>91258</td>\n",
       "      <td>53117</td>\n",
       "      <td>...</td>\n",
       "      <td>29295</td>\n",
       "      <td>14841</td>\n",
       "      <td>9941</td>\n",
       "      <td>8943</td>\n",
       "      <td>6830</td>\n",
       "      <td>4416</td>\n",
       "      <td>855</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1호선</td>\n",
       "      <td>신설동</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>18</td>\n",
       "      <td>2315</td>\n",
       "      <td>8178</td>\n",
       "      <td>22493</td>\n",
       "      <td>52860</td>\n",
       "      <td>32651</td>\n",
       "      <td>22218</td>\n",
       "      <td>...</td>\n",
       "      <td>30045</td>\n",
       "      <td>22603</td>\n",
       "      <td>15561</td>\n",
       "      <td>15576</td>\n",
       "      <td>13190</td>\n",
       "      <td>9543</td>\n",
       "      <td>2263</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <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",
       "      <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>22594</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>원덕</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>135</td>\n",
       "      <td>428</td>\n",
       "      <td>699</td>\n",
       "      <td>785</td>\n",
       "      <td>904</td>\n",
       "      <td>...</td>\n",
       "      <td>616</td>\n",
       "      <td>672</td>\n",
       "      <td>415</td>\n",
       "      <td>429</td>\n",
       "      <td>235</td>\n",
       "      <td>231</td>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22595</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>중랑</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>2</td>\n",
       "      <td>505</td>\n",
       "      <td>1956</td>\n",
       "      <td>2904</td>\n",
       "      <td>5669</td>\n",
       "      <td>4484</td>\n",
       "      <td>4177</td>\n",
       "      <td>...</td>\n",
       "      <td>20727</td>\n",
       "      <td>13780</td>\n",
       "      <td>10545</td>\n",
       "      <td>8767</td>\n",
       "      <td>6674</td>\n",
       "      <td>4325</td>\n",
       "      <td>974</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22596</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>지평</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>368</td>\n",
       "      <td>0</td>\n",
       "      <td>446</td>\n",
       "      <td>...</td>\n",
       "      <td>44</td>\n",
       "      <td>141</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22597</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>팔당</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>680</td>\n",
       "      <td>1605</td>\n",
       "      <td>2692</td>\n",
       "      <td>5097</td>\n",
       "      <td>3726</td>\n",
       "      <td>...</td>\n",
       "      <td>1907</td>\n",
       "      <td>1493</td>\n",
       "      <td>648</td>\n",
       "      <td>448</td>\n",
       "      <td>459</td>\n",
       "      <td>190</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22598</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>회기</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>7</td>\n",
       "      <td>3174</td>\n",
       "      <td>12317</td>\n",
       "      <td>25044</td>\n",
       "      <td>46698</td>\n",
       "      <td>30182</td>\n",
       "      <td>26453</td>\n",
       "      <td>...</td>\n",
       "      <td>64716</td>\n",
       "      <td>54571</td>\n",
       "      <td>33024</td>\n",
       "      <td>33629</td>\n",
       "      <td>30621</td>\n",
       "      <td>20540</td>\n",
       "      <td>5270</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22599 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       호선명 지하철역        사용월  04시-05시 하차인원  05시-06시 하차인원  06시-07시 하차인원  \\\n",
       "0      1호선  동대문 2023-10-01            23          2221          6755   \n",
       "1      1호선  동묘앞 2023-10-01             1          1120          4528   \n",
       "2      1호선  서울역 2023-10-01            16          9093         60044   \n",
       "3      1호선   시청 2023-10-01             1          5020         26689   \n",
       "4      1호선  신설동 2023-10-01            18          2315          8178   \n",
       "...    ...  ...        ...           ...           ...           ...   \n",
       "22594  중앙선   원덕 2020-10-01             0             9           135   \n",
       "22595  중앙선   중랑 2020-10-01             2           505          1956   \n",
       "22596  중앙선   지평 2020-10-01             0             0             0   \n",
       "22597  중앙선   팔당 2020-10-01             0           101           680   \n",
       "22598  중앙선   회기 2020-10-01             7          3174         12317   \n",
       "\n",
       "       07시-08시 하차인원  08시-09시 하차인원  09시-10시 하차인원  10시-11시 하차인원  ...  \\\n",
       "0             12017         20541         23460         26158  ...   \n",
       "1              9281         21560         18828         25633  ...   \n",
       "2            111515        226340        157304         85381  ...   \n",
       "3             70011        186809         91258         53117  ...   \n",
       "4             22493         52860         32651         22218  ...   \n",
       "...             ...           ...           ...           ...  ...   \n",
       "22594           428           699           785           904  ...   \n",
       "22595          2904          5669          4484          4177  ...   \n",
       "22596           125           368             0           446  ...   \n",
       "22597          1605          2692          5097          3726  ...   \n",
       "22598         25044         46698         30182         26453  ...   \n",
       "\n",
       "       18시-19시 하차인원  19시-20시 하차인원  20시-21시 하차인원  21시-22시 하차인원  22시-23시 하차인원  \\\n",
       "0             22333         21418         14927         13295         12184   \n",
       "1             11845          8575          6512          6467          7088   \n",
       "2             95463         66792         48809         44886         29468   \n",
       "3             29295         14841          9941          8943          6830   \n",
       "4             30045         22603         15561         15576         13190   \n",
       "...             ...           ...           ...           ...           ...   \n",
       "22594           616           672           415           429           235   \n",
       "22595         20727         13780         10545          8767          6674   \n",
       "22596            44           141             3             0             0   \n",
       "22597          1907          1493           648           448           459   \n",
       "22598         64716         54571         33024         33629         30621   \n",
       "\n",
       "       23시-24시 하차인원  00시-01시 하차인원  01시-02시 하차인원  02시-03시 하차인원  03시-04시 하차인원  \n",
       "0              9061          2415           221             0             0  \n",
       "1              4288          1680           318             0             0  \n",
       "2             14003          2475           184             0             0  \n",
       "3              4416           855            12             0             0  \n",
       "4              9543          2263             0             0             0  \n",
       "...             ...           ...           ...           ...           ...  \n",
       "22594           231           117             0             0             0  \n",
       "22595          4325           974             0             0             0  \n",
       "22596             0             0             0             0             0  \n",
       "22597           190            84             0             0             0  \n",
       "22598         20540          5270            22             0             0  \n",
       "\n",
       "[22599 rows x 27 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#하차\n",
    "metro_recent_c_getoff = pd.DataFrame()\n",
    "metro_recent_c_getoff['호선명'] = metro_recent_c['호선명']\n",
    "metro_recent_c_getoff['지하철역'] = metro_recent_c['지하철역']\n",
    "metro_recent_c_getoff['사용월'] = metro_recent_c['사용월']\n",
    "for i in range(int((len(metro_recent_c.columns)-4)/2)):\n",
    "    metro_recent_c_getoff[metro_recent_c.columns[4+2*i]] = metro_recent_c[metro_recent_c.columns[4+2*i]]\n",
    "metro_recent_c_getoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>호선명</th>\n",
       "      <th>지하철역</th>\n",
       "      <th>사용월</th>\n",
       "      <th>출근시간6-9</th>\n",
       "      <th>점심시간11-14</th>\n",
       "      <th>퇴근시간17-20</th>\n",
       "      <th>야간시간22-24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1호선</td>\n",
       "      <td>동대문</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>45226</td>\n",
       "      <td>72198</td>\n",
       "      <td>71973</td>\n",
       "      <td>15296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1호선</td>\n",
       "      <td>동묘앞</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>23564</td>\n",
       "      <td>78579</td>\n",
       "      <td>72411</td>\n",
       "      <td>4671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1호선</td>\n",
       "      <td>서울역</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>131064</td>\n",
       "      <td>277083</td>\n",
       "      <td>518332</td>\n",
       "      <td>88913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1호선</td>\n",
       "      <td>시청</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>23520</td>\n",
       "      <td>80217</td>\n",
       "      <td>326434</td>\n",
       "      <td>42974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1호선</td>\n",
       "      <td>신설동</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>62432</td>\n",
       "      <td>69486</td>\n",
       "      <td>110299</td>\n",
       "      <td>11806</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22594</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>원덕</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>2096</td>\n",
       "      <td>2040</td>\n",
       "      <td>2022</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22595</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>중랑</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>48301</td>\n",
       "      <td>24504</td>\n",
       "      <td>18090</td>\n",
       "      <td>2783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22596</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>지평</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>447</td>\n",
       "      <td>0</td>\n",
       "      <td>222</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22597</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>팔당</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>3731</td>\n",
       "      <td>4926</td>\n",
       "      <td>10332</td>\n",
       "      <td>1168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22598</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>회기</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>127543</td>\n",
       "      <td>105174</td>\n",
       "      <td>122866</td>\n",
       "      <td>29859</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22599 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       호선명 지하철역        사용월  출근시간6-9  점심시간11-14  퇴근시간17-20  야간시간22-24\n",
       "0      1호선  동대문 2023-10-01    45226      72198      71973      15296\n",
       "1      1호선  동묘앞 2023-10-01    23564      78579      72411       4671\n",
       "2      1호선  서울역 2023-10-01   131064     277083     518332      88913\n",
       "3      1호선   시청 2023-10-01    23520      80217     326434      42974\n",
       "4      1호선  신설동 2023-10-01    62432      69486     110299      11806\n",
       "...    ...  ...        ...      ...        ...        ...        ...\n",
       "22594  중앙선   원덕 2020-10-01     2096       2040       2022         44\n",
       "22595  중앙선   중랑 2020-10-01    48301      24504      18090       2783\n",
       "22596  중앙선   지평 2020-10-01      447          0        222          0\n",
       "22597  중앙선   팔당 2020-10-01     3731       4926      10332       1168\n",
       "22598  중앙선   회기 2020-10-01   127543     105174     122866      29859\n",
       "\n",
       "[22599 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#시간 4분할 (승차)\n",
    "metro_recent_c_geton['출근시간6-9']=metro_recent_c_geton.iloc[:, 5:8].sum(axis=1)\n",
    "metro_recent_c_geton['점심시간11-14']=metro_recent_c_geton.iloc[:, 10:13].sum(axis=1)\n",
    "metro_recent_c_geton['퇴근시간17-20']=metro_recent_c_geton.iloc[:, 16:19].sum(axis=1)\n",
    "metro_recent_c_geton['야간시간22-24']=metro_recent_c_geton.iloc[:, 21:23].sum(axis=1)\n",
    "\n",
    "#4분할만 남기고 드롭\n",
    "\n",
    "metro_recent_c_geton_4time=metro_recent_c_geton.drop(metro_recent_c_geton.columns[3:27], axis=1)\n",
    "metro_recent_c_geton_4time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>호선명</th>\n",
       "      <th>지하철역</th>\n",
       "      <th>사용월</th>\n",
       "      <th>출근시간6-9</th>\n",
       "      <th>점심시간11-14</th>\n",
       "      <th>퇴근시간17-20</th>\n",
       "      <th>야간시간22-24</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>동대문</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>39313</td>\n",
       "      <td>86251</td>\n",
       "      <td>66071</td>\n",
       "      <td>21245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1호선</td>\n",
       "      <td>동묘앞</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>35369</td>\n",
       "      <td>115163</td>\n",
       "      <td>36323</td>\n",
       "      <td>11376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1호선</td>\n",
       "      <td>서울역</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>397899</td>\n",
       "      <td>251946</td>\n",
       "      <td>257839</td>\n",
       "      <td>43471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1호선</td>\n",
       "      <td>시청</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>283509</td>\n",
       "      <td>146698</td>\n",
       "      <td>77339</td>\n",
       "      <td>11246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1호선</td>\n",
       "      <td>신설동</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>83531</td>\n",
       "      <td>60342</td>\n",
       "      <td>77606</td>\n",
       "      <td>22733</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",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>22594</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>원덕</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>1262</td>\n",
       "      <td>2565</td>\n",
       "      <td>2252</td>\n",
       "      <td>466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22595</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>중랑</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>10529</td>\n",
       "      <td>16095</td>\n",
       "      <td>47582</td>\n",
       "      <td>10999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22596</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>지평</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>493</td>\n",
       "      <td>1</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22597</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>팔당</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>4977</td>\n",
       "      <td>9605</td>\n",
       "      <td>5693</td>\n",
       "      <td>649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22598</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>회기</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>84059</td>\n",
       "      <td>83163</td>\n",
       "      <td>164645</td>\n",
       "      <td>51161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22599 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       호선명 지하철역        사용월  출근시간6-9  점심시간11-14  퇴근시간17-20  야간시간22-24\n",
       "0      1호선  동대문 2023-10-01    39313      86251      66071      21245\n",
       "1      1호선  동묘앞 2023-10-01    35369     115163      36323      11376\n",
       "2      1호선  서울역 2023-10-01   397899     251946     257839      43471\n",
       "3      1호선   시청 2023-10-01   283509     146698      77339      11246\n",
       "4      1호선  신설동 2023-10-01    83531      60342      77606      22733\n",
       "...    ...  ...        ...      ...        ...        ...        ...\n",
       "22594  중앙선   원덕 2020-10-01     1262       2565       2252        466\n",
       "22595  중앙선   중랑 2020-10-01    10529      16095      47582      10999\n",
       "22596  중앙선   지평 2020-10-01      493          1        186          0\n",
       "22597  중앙선   팔당 2020-10-01     4977       9605       5693        649\n",
       "22598  중앙선   회기 2020-10-01    84059      83163     164645      51161\n",
       "\n",
       "[22599 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#시간 4분할 (하차)\n",
    "metro_recent_c_getoff['출근시간6-9']=metro_recent_c_getoff.iloc[:, 5:8].sum(axis=1)\n",
    "metro_recent_c_getoff['점심시간11-14']=metro_recent_c_getoff.iloc[:, 10:13].sum(axis=1)\n",
    "metro_recent_c_getoff['퇴근시간17-20']=metro_recent_c_getoff.iloc[:, 16:19].sum(axis=1)\n",
    "metro_recent_c_getoff['야간시간22-24']=metro_recent_c_getoff.iloc[:, 21:23].sum(axis=1)\n",
    "\n",
    "#4분할만 남기고 드롭\n",
    "\n",
    "metro_recent_c_getoff_4time=metro_recent_c_getoff.drop(metro_recent_c_getoff.columns[3:27], axis=1)\n",
    "metro_recent_c_getoff_4time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>호선명</th>\n",
       "      <th>지하철역</th>\n",
       "      <th>사용월</th>\n",
       "      <th>출근시간6-9</th>\n",
       "      <th>점심시간11-14</th>\n",
       "      <th>퇴근시간17-20</th>\n",
       "      <th>야간시간22-24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2호선</td>\n",
       "      <td>강남</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>116856</td>\n",
       "      <td>249917</td>\n",
       "      <td>732645</td>\n",
       "      <td>220645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2호선</td>\n",
       "      <td>잠실(송파구청)</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>323016</td>\n",
       "      <td>289048</td>\n",
       "      <td>615476</td>\n",
       "      <td>133236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>177562</td>\n",
       "      <td>228290</td>\n",
       "      <td>551300</td>\n",
       "      <td>275156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>2호선</td>\n",
       "      <td>강남</td>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>113285</td>\n",
       "      <td>244934</td>\n",
       "      <td>713297</td>\n",
       "      <td>219564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>2호선</td>\n",
       "      <td>잠실(송파구청)</td>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>311846</td>\n",
       "      <td>267958</td>\n",
       "      <td>556111</td>\n",
       "      <td>125340</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21443</th>\n",
       "      <td>2호선</td>\n",
       "      <td>잠실(송파구청)</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>287827</td>\n",
       "      <td>219032</td>\n",
       "      <td>474179</td>\n",
       "      <td>84737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21450</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>152445</td>\n",
       "      <td>146248</td>\n",
       "      <td>372483</td>\n",
       "      <td>166395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22004</th>\n",
       "      <td>2호선</td>\n",
       "      <td>강남</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>107003</td>\n",
       "      <td>221380</td>\n",
       "      <td>732822</td>\n",
       "      <td>237046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22046</th>\n",
       "      <td>2호선</td>\n",
       "      <td>잠실(송파구청)</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>273314</td>\n",
       "      <td>219080</td>\n",
       "      <td>463658</td>\n",
       "      <td>92690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22053</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>144853</td>\n",
       "      <td>145548</td>\n",
       "      <td>384347</td>\n",
       "      <td>189355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       호선명      지하철역        사용월  출근시간6-9  점심시간11-14  퇴근시간17-20  야간시간22-24\n",
       "10     2호선        강남 2023-10-01   116856     249917     732645     220645\n",
       "52     2호선  잠실(송파구청) 2023-10-01   323016     289048     615476     133236\n",
       "59     2호선      홍대입구 2023-10-01   177562     228290     551300     275156\n",
       "627    2호선        강남 2023-09-01   113285     244934     713297     219564\n",
       "669    2호선  잠실(송파구청) 2023-09-01   311846     267958     556111     125340\n",
       "...    ...       ...        ...      ...        ...        ...        ...\n",
       "21443  2호선  잠실(송파구청) 2020-11-01   287827     219032     474179      84737\n",
       "21450  2호선      홍대입구 2020-11-01   152445     146248     372483     166395\n",
       "22004  2호선        강남 2020-10-01   107003     221380     732822     237046\n",
       "22046  2호선  잠실(송파구청) 2020-10-01   273314     219080     463658      92690\n",
       "22053  2호선      홍대입구 2020-10-01   144853     145548     384347     189355\n",
       "\n",
       "[111 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2호선 승차데이터\n",
    "metro_recent_c_geton_4time_2ndline = metro_recent_c_geton_4time[metro_recent_c_geton_4time['호선명']=='2호선']\n",
    "\n",
    "\n",
    "#2호선중 강남, 잠실, 홍대입구\n",
    "metro_recent_c_geton_4time_2nd_line_123 = metro_recent_c_geton_4time_2ndline[\n",
    "    metro_recent_c_geton_4time_2ndline['지하철역'].isin(['강남', '잠실(송파구청)', '홍대입구'])\n",
    "]\n",
    "\n",
    "metro_recent_c_geton_4time_2nd_line_123"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>지하철역</th>\n",
       "      <th>사용월</th>\n",
       "      <th>출근시간6-9</th>\n",
       "      <th>점심시간11-14</th>\n",
       "      <th>퇴근시간17-20</th>\n",
       "      <th>야간시간22-24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2호선</td>\n",
       "      <td>강남</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>473405</td>\n",
       "      <td>312660</td>\n",
       "      <td>401785</td>\n",
       "      <td>60838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2호선</td>\n",
       "      <td>잠실(송파구청)</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>302868</td>\n",
       "      <td>376302</td>\n",
       "      <td>575669</td>\n",
       "      <td>128641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>165975</td>\n",
       "      <td>445131</td>\n",
       "      <td>610358</td>\n",
       "      <td>126225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>2호선</td>\n",
       "      <td>강남</td>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>470334</td>\n",
       "      <td>299000</td>\n",
       "      <td>398639</td>\n",
       "      <td>60157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>2호선</td>\n",
       "      <td>잠실(송파구청)</td>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>273201</td>\n",
       "      <td>342624</td>\n",
       "      <td>549461</td>\n",
       "      <td>124336</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21443</th>\n",
       "      <td>2호선</td>\n",
       "      <td>잠실(송파구청)</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>257676</td>\n",
       "      <td>249385</td>\n",
       "      <td>465063</td>\n",
       "      <td>100017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21450</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>134661</td>\n",
       "      <td>254912</td>\n",
       "      <td>434960</td>\n",
       "      <td>66157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22004</th>\n",
       "      <td>2호선</td>\n",
       "      <td>강남</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>469010</td>\n",
       "      <td>320257</td>\n",
       "      <td>423875</td>\n",
       "      <td>56832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22046</th>\n",
       "      <td>2호선</td>\n",
       "      <td>잠실(송파구청)</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>240683</td>\n",
       "      <td>252741</td>\n",
       "      <td>451434</td>\n",
       "      <td>108003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22053</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>130301</td>\n",
       "      <td>278584</td>\n",
       "      <td>450294</td>\n",
       "      <td>75360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       호선명      지하철역        사용월  출근시간6-9  점심시간11-14  퇴근시간17-20  야간시간22-24\n",
       "10     2호선        강남 2023-10-01   473405     312660     401785      60838\n",
       "52     2호선  잠실(송파구청) 2023-10-01   302868     376302     575669     128641\n",
       "59     2호선      홍대입구 2023-10-01   165975     445131     610358     126225\n",
       "627    2호선        강남 2023-09-01   470334     299000     398639      60157\n",
       "669    2호선  잠실(송파구청) 2023-09-01   273201     342624     549461     124336\n",
       "...    ...       ...        ...      ...        ...        ...        ...\n",
       "21443  2호선  잠실(송파구청) 2020-11-01   257676     249385     465063     100017\n",
       "21450  2호선      홍대입구 2020-11-01   134661     254912     434960      66157\n",
       "22004  2호선        강남 2020-10-01   469010     320257     423875      56832\n",
       "22046  2호선  잠실(송파구청) 2020-10-01   240683     252741     451434     108003\n",
       "22053  2호선      홍대입구 2020-10-01   130301     278584     450294      75360\n",
       "\n",
       "[111 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2호선 하차데이터\n",
    "metro_recent_c_getoff_4time_2ndline = metro_recent_c_getoff_4time[metro_recent_c_getoff_4time['호선명']=='2호선']\n",
    "\n",
    "\n",
    "#2호선중 강남, 잠실, 홍대입구\n",
    "metro_recent_c_getoff_4time_2nd_line_123 = metro_recent_c_getoff_4time_2ndline[\n",
    "    metro_recent_c_getoff_4time_2ndline['지하철역'].isin(['강남', '잠실(송파구청)', '홍대입구'])\n",
    "]\n",
    "\n",
    "metro_recent_c_getoff_4time_2nd_line_123"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "      <th>출근시간6-9</th>\n",
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       "      <th>퇴근시간17-20</th>\n",
       "      <th>야간시간22-24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>3호선</td>\n",
       "      <td>고속터미널</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>82597</td>\n",
       "      <td>249450</td>\n",
       "      <td>412985</td>\n",
       "      <td>77320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>3호선</td>\n",
       "      <td>연신내</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>302921</td>\n",
       "      <td>177119</td>\n",
       "      <td>137973</td>\n",
       "      <td>27726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>3호선</td>\n",
       "      <td>고속터미널</td>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>77329</td>\n",
       "      <td>230246</td>\n",
       "      <td>387442</td>\n",
       "      <td>76361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>3호선</td>\n",
       "      <td>연신내</td>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>296752</td>\n",
       "      <td>162000</td>\n",
       "      <td>131815</td>\n",
       "      <td>27356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>3호선</td>\n",
       "      <td>고속터미널</td>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>77368</td>\n",
       "      <td>236613</td>\n",
       "      <td>389737</td>\n",
       "      <td>70683</td>\n",
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       "    <tr>\n",
       "      <th>20871</th>\n",
       "      <td>3호선</td>\n",
       "      <td>연신내</td>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>294103</td>\n",
       "      <td>126782</td>\n",
       "      <td>102474</td>\n",
       "      <td>10121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21454</th>\n",
       "      <td>3호선</td>\n",
       "      <td>고속터미널</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>66732</td>\n",
       "      <td>175645</td>\n",
       "      <td>330110</td>\n",
       "      <td>51136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21474</th>\n",
       "      <td>3호선</td>\n",
       "      <td>연신내</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>334412</td>\n",
       "      <td>153224</td>\n",
       "      <td>128359</td>\n",
       "      <td>24540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22057</th>\n",
       "      <td>3호선</td>\n",
       "      <td>고속터미널</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>63605</td>\n",
       "      <td>179962</td>\n",
       "      <td>339382</td>\n",
       "      <td>60179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22077</th>\n",
       "      <td>3호선</td>\n",
       "      <td>연신내</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>314508</td>\n",
       "      <td>159033</td>\n",
       "      <td>138226</td>\n",
       "      <td>28328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       호선명   지하철역        사용월  출근시간6-9  점심시간11-14  퇴근시간17-20  야간시간22-24\n",
       "63     3호선  고속터미널 2023-10-01    82597     249450     412985      77320\n",
       "83     3호선    연신내 2023-10-01   302921     177119     137973      27726\n",
       "680    3호선  고속터미널 2023-09-01    77329     230246     387442      76361\n",
       "700    3호선    연신내 2023-09-01   296752     162000     131815      27356\n",
       "1296   3호선  고속터미널 2023-08-01    77368     236613     389737      70683\n",
       "...    ...    ...        ...      ...        ...        ...        ...\n",
       "20871  3호선    연신내 2020-12-01   294103     126782     102474      10121\n",
       "21454  3호선  고속터미널 2020-11-01    66732     175645     330110      51136\n",
       "21474  3호선    연신내 2020-11-01   334412     153224     128359      24540\n",
       "22057  3호선  고속터미널 2020-10-01    63605     179962     339382      60179\n",
       "22077  3호선    연신내 2020-10-01   314508     159033     138226      28328\n",
       "\n",
       "[74 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3호선 승차데이터\n",
    "metro_recent_c_geton_4time_3ndline = metro_recent_c_geton_4time[metro_recent_c_geton_4time['호선명']=='3호선']\n",
    "\n",
    "\n",
    "#3호선중 고속터미널, 연신내, 양재(서초입구)\n",
    "metro_recent_c_geton_4time_3nd_line_123 = metro_recent_c_geton_4time_3ndline[\n",
    "    metro_recent_c_geton_4time_3ndline['지하철역'].isin(['고속터미널', '연신내', '양재(서초입구)'])\n",
    "]\n",
    "\n",
    "metro_recent_c_geton_4time_3nd_line_123"
   ]
  },
  {
   "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>호선명</th>\n",
       "      <th>지하철역</th>\n",
       "      <th>사용월</th>\n",
       "      <th>출근시간6-9</th>\n",
       "      <th>점심시간11-14</th>\n",
       "      <th>퇴근시간17-20</th>\n",
       "      <th>야간시간22-24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>3호선</td>\n",
       "      <td>고속터미널</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>162058</td>\n",
       "      <td>304651</td>\n",
       "      <td>300658</td>\n",
       "      <td>37358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>3호선</td>\n",
       "      <td>연신내</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>72098</td>\n",
       "      <td>121432</td>\n",
       "      <td>321347</td>\n",
       "      <td>85793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>3호선</td>\n",
       "      <td>고속터미널</td>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>168590</td>\n",
       "      <td>299138</td>\n",
       "      <td>304542</td>\n",
       "      <td>39980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>3호선</td>\n",
       "      <td>연신내</td>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>70294</td>\n",
       "      <td>115316</td>\n",
       "      <td>305064</td>\n",
       "      <td>82284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>3호선</td>\n",
       "      <td>고속터미널</td>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>158560</td>\n",
       "      <td>295027</td>\n",
       "      <td>289157</td>\n",
       "      <td>36657</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20871</th>\n",
       "      <td>3호선</td>\n",
       "      <td>연신내</td>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>52715</td>\n",
       "      <td>94565</td>\n",
       "      <td>287942</td>\n",
       "      <td>44435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21454</th>\n",
       "      <td>3호선</td>\n",
       "      <td>고속터미널</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>137147</td>\n",
       "      <td>230462</td>\n",
       "      <td>257648</td>\n",
       "      <td>28634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21474</th>\n",
       "      <td>3호선</td>\n",
       "      <td>연신내</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>64066</td>\n",
       "      <td>105991</td>\n",
       "      <td>331143</td>\n",
       "      <td>83562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22057</th>\n",
       "      <td>3호선</td>\n",
       "      <td>고속터미널</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>134222</td>\n",
       "      <td>235768</td>\n",
       "      <td>255142</td>\n",
       "      <td>29799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22077</th>\n",
       "      <td>3호선</td>\n",
       "      <td>연신내</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>65204</td>\n",
       "      <td>111787</td>\n",
       "      <td>322270</td>\n",
       "      <td>88252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       호선명   지하철역        사용월  출근시간6-9  점심시간11-14  퇴근시간17-20  야간시간22-24\n",
       "63     3호선  고속터미널 2023-10-01   162058     304651     300658      37358\n",
       "83     3호선    연신내 2023-10-01    72098     121432     321347      85793\n",
       "680    3호선  고속터미널 2023-09-01   168590     299138     304542      39980\n",
       "700    3호선    연신내 2023-09-01    70294     115316     305064      82284\n",
       "1296   3호선  고속터미널 2023-08-01   158560     295027     289157      36657\n",
       "...    ...    ...        ...      ...        ...        ...        ...\n",
       "20871  3호선    연신내 2020-12-01    52715      94565     287942      44435\n",
       "21454  3호선  고속터미널 2020-11-01   137147     230462     257648      28634\n",
       "21474  3호선    연신내 2020-11-01    64066     105991     331143      83562\n",
       "22057  3호선  고속터미널 2020-10-01   134222     235768     255142      29799\n",
       "22077  3호선    연신내 2020-10-01    65204     111787     322270      88252\n",
       "\n",
       "[74 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3호선 하차데이터\n",
    "\n",
    "\n",
    "metro_recent_c_getoff_4time_3ndline = metro_recent_c_getoff_4time[metro_recent_c_getoff_4time['호선명']=='3호선']\n",
    "\n",
    "\n",
    "#3호선중 고속터미널, 연신내, 양재(서초입구)\n",
    "metro_recent_c_getoff_4time_3nd_line_123 = metro_recent_c_getoff_4time_3ndline[\n",
    "    metro_recent_c_getoff_4time_3ndline['지하철역'].isin(['고속터미널', '연신내', '양재(서초입구)'])\n",
    "]\n",
    "\n",
    "metro_recent_c_getoff_4time_3nd_line_123"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "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>호선명</th>\n",
       "      <th>지하철역</th>\n",
       "      <th>사용월</th>\n",
       "      <th>출근시간6-9</th>\n",
       "      <th>점심시간11-14</th>\n",
       "      <th>퇴근시간17-20</th>\n",
       "      <th>야간시간22-24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1호선</td>\n",
       "      <td>동대문</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>84539</td>\n",
       "      <td>158449</td>\n",
       "      <td>138044</td>\n",
       "      <td>36541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1호선</td>\n",
       "      <td>동묘앞</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>58933</td>\n",
       "      <td>193742</td>\n",
       "      <td>108734</td>\n",
       "      <td>16047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1호선</td>\n",
       "      <td>서울역</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>528963</td>\n",
       "      <td>529029</td>\n",
       "      <td>776171</td>\n",
       "      <td>132384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1호선</td>\n",
       "      <td>시청</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>307029</td>\n",
       "      <td>226915</td>\n",
       "      <td>403773</td>\n",
       "      <td>54220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1호선</td>\n",
       "      <td>신설동</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>145963</td>\n",
       "      <td>129828</td>\n",
       "      <td>187905</td>\n",
       "      <td>34539</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22594</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>원덕</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>3358</td>\n",
       "      <td>4605</td>\n",
       "      <td>4274</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22595</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>중랑</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>58830</td>\n",
       "      <td>40599</td>\n",
       "      <td>65672</td>\n",
       "      <td>13782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22596</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>지평</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>940</td>\n",
       "      <td>1</td>\n",
       "      <td>408</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22597</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>팔당</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>8708</td>\n",
       "      <td>14531</td>\n",
       "      <td>16025</td>\n",
       "      <td>1817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22598</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>회기</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>211602</td>\n",
       "      <td>188337</td>\n",
       "      <td>287511</td>\n",
       "      <td>81020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22599 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       호선명 지하철역        사용월  출근시간6-9  점심시간11-14  퇴근시간17-20  야간시간22-24\n",
       "0      1호선  동대문 2023-10-01    84539     158449     138044      36541\n",
       "1      1호선  동묘앞 2023-10-01    58933     193742     108734      16047\n",
       "2      1호선  서울역 2023-10-01   528963     529029     776171     132384\n",
       "3      1호선   시청 2023-10-01   307029     226915     403773      54220\n",
       "4      1호선  신설동 2023-10-01   145963     129828     187905      34539\n",
       "...    ...  ...        ...      ...        ...        ...        ...\n",
       "22594  중앙선   원덕 2020-10-01     3358       4605       4274        510\n",
       "22595  중앙선   중랑 2020-10-01    58830      40599      65672      13782\n",
       "22596  중앙선   지평 2020-10-01      940          1        408          0\n",
       "22597  중앙선   팔당 2020-10-01     8708      14531      16025       1817\n",
       "22598  중앙선   회기 2020-10-01   211602     188337     287511      81020\n",
       "\n",
       "[22599 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 승하차 데이터를 합친 데이터셋\n",
    "metro_recent_c_total=pd.DataFrame()\n",
    "metro_recent_c_total['호선명'] = metro_recent_c['호선명']\n",
    "metro_recent_c_total['지하철역'] = metro_recent_c['지하철역']\n",
    "metro_recent_c_total['사용월'] = metro_recent_c['사용월']\n",
    "metro_recent_c_total['출근시간6-9']=metro_recent_c.iloc[:, 7:13].sum(axis=1)   \n",
    "metro_recent_c_total['점심시간11-14']=metro_recent_c.iloc[:, 17:23].sum(axis=1)\n",
    "metro_recent_c_total['퇴근시간17-20']=metro_recent_c.iloc[:, 29:35].sum(axis=1)\n",
    "metro_recent_c_total['야간시간22-24']=metro_recent_c.iloc[:, 39:43].sum(axis=1)\n",
    "metro_recent_c_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>호선명</th>\n",
       "      <th>지하철역</th>\n",
       "      <th>사용월</th>\n",
       "      <th>출근시간6-9</th>\n",
       "      <th>점심시간11-14</th>\n",
       "      <th>퇴근시간17-20</th>\n",
       "      <th>야간시간22-24</th>\n",
       "      <th>월평균승객</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1호선</td>\n",
       "      <td>동대문</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>84539</td>\n",
       "      <td>158449</td>\n",
       "      <td>138044</td>\n",
       "      <td>36541</td>\n",
       "      <td>104393.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1호선</td>\n",
       "      <td>동묘앞</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>58933</td>\n",
       "      <td>193742</td>\n",
       "      <td>108734</td>\n",
       "      <td>16047</td>\n",
       "      <td>94364.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1호선</td>\n",
       "      <td>서울역</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>528963</td>\n",
       "      <td>529029</td>\n",
       "      <td>776171</td>\n",
       "      <td>132384</td>\n",
       "      <td>491636.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1호선</td>\n",
       "      <td>시청</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>307029</td>\n",
       "      <td>226915</td>\n",
       "      <td>403773</td>\n",
       "      <td>54220</td>\n",
       "      <td>247984.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1호선</td>\n",
       "      <td>신설동</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>145963</td>\n",
       "      <td>129828</td>\n",
       "      <td>187905</td>\n",
       "      <td>34539</td>\n",
       "      <td>124558.75</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22594</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>원덕</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>3358</td>\n",
       "      <td>4605</td>\n",
       "      <td>4274</td>\n",
       "      <td>510</td>\n",
       "      <td>3186.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22595</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>중랑</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>58830</td>\n",
       "      <td>40599</td>\n",
       "      <td>65672</td>\n",
       "      <td>13782</td>\n",
       "      <td>44720.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22596</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>지평</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>940</td>\n",
       "      <td>1</td>\n",
       "      <td>408</td>\n",
       "      <td>0</td>\n",
       "      <td>337.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22597</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>팔당</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>8708</td>\n",
       "      <td>14531</td>\n",
       "      <td>16025</td>\n",
       "      <td>1817</td>\n",
       "      <td>10270.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22598</th>\n",
       "      <td>중앙선</td>\n",
       "      <td>회기</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>211602</td>\n",
       "      <td>188337</td>\n",
       "      <td>287511</td>\n",
       "      <td>81020</td>\n",
       "      <td>192117.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22599 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       호선명 지하철역        사용월  출근시간6-9  점심시간11-14  퇴근시간17-20  야간시간22-24  \\\n",
       "0      1호선  동대문 2023-10-01    84539     158449     138044      36541   \n",
       "1      1호선  동묘앞 2023-10-01    58933     193742     108734      16047   \n",
       "2      1호선  서울역 2023-10-01   528963     529029     776171     132384   \n",
       "3      1호선   시청 2023-10-01   307029     226915     403773      54220   \n",
       "4      1호선  신설동 2023-10-01   145963     129828     187905      34539   \n",
       "...    ...  ...        ...      ...        ...        ...        ...   \n",
       "22594  중앙선   원덕 2020-10-01     3358       4605       4274        510   \n",
       "22595  중앙선   중랑 2020-10-01    58830      40599      65672      13782   \n",
       "22596  중앙선   지평 2020-10-01      940          1        408          0   \n",
       "22597  중앙선   팔당 2020-10-01     8708      14531      16025       1817   \n",
       "22598  중앙선   회기 2020-10-01   211602     188337     287511      81020   \n",
       "\n",
       "           월평균승객  \n",
       "0      104393.25  \n",
       "1       94364.00  \n",
       "2      491636.75  \n",
       "3      247984.25  \n",
       "4      124558.75  \n",
       "...          ...  \n",
       "22594    3186.75  \n",
       "22595   44720.75  \n",
       "22596     337.25  \n",
       "22597   10270.25  \n",
       "22598  192117.50  \n",
       "\n",
       "[22599 rows x 8 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metro_recent_c_total['월평균승객']=metro_recent_c_total.iloc[:,3:].mean(axis=1)\n",
    "metro_recent_c_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>9895</th>\n",
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       "      <td>2022-03-01</td>\n",
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       "      <td>281674</td>\n",
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       "      <td>2022-02-01</td>\n",
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       "      <td>139509</td>\n",
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       "      <th>12950</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2022-01-01</td>\n",
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       "      <td>490614</td>\n",
       "      <td>817366</td>\n",
       "      <td>123000</td>\n",
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       "      <th>13560</th>\n",
       "      <td>2호선</td>\n",
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       "      <td>2021-12-01</td>\n",
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       "      <td>882931</td>\n",
       "      <td>201184</td>\n",
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       "      <th>14169</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>302193</td>\n",
       "      <td>455328</td>\n",
       "      <td>902429</td>\n",
       "      <td>279968</td>\n",
       "      <td>484979.50</td>\n",
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       "    <tr>\n",
       "      <th>14778</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>263150</td>\n",
       "      <td>471593</td>\n",
       "      <td>822826</td>\n",
       "      <td>239510</td>\n",
       "      <td>449269.75</td>\n",
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       "    <tr>\n",
       "      <th>15387</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>244054</td>\n",
       "      <td>409824</td>\n",
       "      <td>711181</td>\n",
       "      <td>165741</td>\n",
       "      <td>382700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15994</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>248022</td>\n",
       "      <td>412167</td>\n",
       "      <td>687234</td>\n",
       "      <td>135059</td>\n",
       "      <td>370620.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16601</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>269116</td>\n",
       "      <td>366794</td>\n",
       "      <td>647862</td>\n",
       "      <td>155344</td>\n",
       "      <td>359779.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17208</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>297360</td>\n",
       "      <td>449801</td>\n",
       "      <td>831762</td>\n",
       "      <td>260477</td>\n",
       "      <td>459850.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17816</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>268156</td>\n",
       "      <td>479564</td>\n",
       "      <td>846460</td>\n",
       "      <td>266747</td>\n",
       "      <td>465231.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18424</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>292571</td>\n",
       "      <td>406865</td>\n",
       "      <td>797458</td>\n",
       "      <td>237886</td>\n",
       "      <td>433695.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19032</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>287338</td>\n",
       "      <td>426458</td>\n",
       "      <td>833187</td>\n",
       "      <td>244927</td>\n",
       "      <td>447977.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19638</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>216503</td>\n",
       "      <td>398934</td>\n",
       "      <td>679194</td>\n",
       "      <td>137491</td>\n",
       "      <td>358030.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20243</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>231372</td>\n",
       "      <td>324225</td>\n",
       "      <td>577341</td>\n",
       "      <td>64485</td>\n",
       "      <td>299355.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20847</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>240936</td>\n",
       "      <td>287781</td>\n",
       "      <td>524386</td>\n",
       "      <td>61868</td>\n",
       "      <td>278742.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21450</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>287106</td>\n",
       "      <td>401160</td>\n",
       "      <td>807443</td>\n",
       "      <td>232552</td>\n",
       "      <td>432065.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22053</th>\n",
       "      <td>2호선</td>\n",
       "      <td>홍대입구</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>275154</td>\n",
       "      <td>424132</td>\n",
       "      <td>834641</td>\n",
       "      <td>264715</td>\n",
       "      <td>449660.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       호선명  지하철역        사용월  출근시간6-9  점심시간11-14  퇴근시간17-20  야간시간22-24  \\\n",
       "59     2호선  홍대입구 2023-10-01   343537     673421    1161658     401381   \n",
       "676    2호선  홍대입구 2023-09-01   325527     604851    1083730     385776   \n",
       "1292   2호선  홍대입구 2023-08-01   331840     652365    1104224     390661   \n",
       "1908   2호선  홍대입구 2023-07-01   335378     672601    1128928     413242   \n",
       "2524   2호선  홍대입구 2023-06-01   343571     633569    1084964     411166   \n",
       "3138   2호선  홍대입구 2023-05-01   350782     644670    1127759     413510   \n",
       "3752   2호선  홍대입구 2023-04-01   346553     616825    1105234     398155   \n",
       "4365   2호선  홍대입구 2023-03-01   361650     630759    1179666     413571   \n",
       "4982   2호선  홍대입구 2023-02-01   300813     601898    1061409     367336   \n",
       "5595   2호선  홍대입구 2023-01-01   303763     581521    1033795     360746   \n",
       "6207   2호선  홍대입구 2022-12-01   331162     646228    1151734     414261   \n",
       "6820   2호선  홍대입구 2022-11-01   342477     562446    1065229     349768   \n",
       "7433   2호선  홍대입구 2022-10-01   335542     621260    1096150     377405   \n",
       "8048   2호선  홍대입구 2022-09-01   325255     535046     995475     343320   \n",
       "8663   2호선  홍대입구 2022-08-01   321191     564666    1002163     340366   \n",
       "9280   2호선  홍대입구 2022-07-01   330311     595129    1039038     369193   \n",
       "9895   2호선  홍대입구 2022-06-01   321218     533793     943954     336078   \n",
       "10510  2호선  홍대입구 2022-05-01   337464     572929    1043152     362190   \n",
       "11125  2호선  홍대입구 2022-04-01   296405     470185     912682     308899   \n",
       "11733  2호선  홍대입구 2022-03-01   263944     425029     807166     281674   \n",
       "12342  2호선  홍대입구 2022-02-01   220664     416947     696567     139509   \n",
       "12950  2호선  홍대입구 2022-01-01   263231     490614     817366     123000   \n",
       "13560  2호선  홍대입구 2021-12-01   294388     483487     882931     201184   \n",
       "14169  2호선  홍대입구 2021-11-01   302193     455328     902429     279968   \n",
       "14778  2호선  홍대입구 2021-10-01   263150     471593     822826     239510   \n",
       "15387  2호선  홍대입구 2021-09-01   244054     409824     711181     165741   \n",
       "15994  2호선  홍대입구 2021-08-01   248022     412167     687234     135059   \n",
       "16601  2호선  홍대입구 2021-07-01   269116     366794     647862     155344   \n",
       "17208  2호선  홍대입구 2021-06-01   297360     449801     831762     260477   \n",
       "17816  2호선  홍대입구 2021-05-01   268156     479564     846460     266747   \n",
       "18424  2호선  홍대입구 2021-04-01   292571     406865     797458     237886   \n",
       "19032  2호선  홍대입구 2021-03-01   287338     426458     833187     244927   \n",
       "19638  2호선  홍대입구 2021-02-01   216503     398934     679194     137491   \n",
       "20243  2호선  홍대입구 2021-01-01   231372     324225     577341      64485   \n",
       "20847  2호선  홍대입구 2020-12-01   240936     287781     524386      61868   \n",
       "21450  2호선  홍대입구 2020-11-01   287106     401160     807443     232552   \n",
       "22053  2호선  홍대입구 2020-10-01   275154     424132     834641     264715   \n",
       "\n",
       "           월평균승객  \n",
       "59     644999.25  \n",
       "676    599971.00  \n",
       "1292   619772.50  \n",
       "1908   637537.25  \n",
       "2524   618317.50  \n",
       "3138   634180.25  \n",
       "3752   616691.75  \n",
       "4365   646411.50  \n",
       "4982   582864.00  \n",
       "5595   569956.25  \n",
       "6207   635846.25  \n",
       "6820   579980.00  \n",
       "7433   607589.25  \n",
       "8048   549774.00  \n",
       "8663   557096.50  \n",
       "9280   583417.75  \n",
       "9895   533760.75  \n",
       "10510  578933.75  \n",
       "11125  497042.75  \n",
       "11733  444453.25  \n",
       "12342  368421.75  \n",
       "12950  423552.75  \n",
       "13560  465497.50  \n",
       "14169  484979.50  \n",
       "14778  449269.75  \n",
       "15387  382700.00  \n",
       "15994  370620.50  \n",
       "16601  359779.00  \n",
       "17208  459850.00  \n",
       "17816  465231.75  \n",
       "18424  433695.00  \n",
       "19032  447977.50  \n",
       "19638  358030.50  \n",
       "20243  299355.75  \n",
       "20847  278742.75  \n",
       "21450  432065.25  \n",
       "22053  449660.50  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2호선의 top3 강남, 잠실(송파구청), 홍대입구역만 추출해서 시각화\n",
    "metro_line2_top1=metro_recent_c_total[(metro_recent_c_total['지하철역']=='강남') & (metro_recent_c_total['호선명']=='2호선')]\n",
    "metro_line2_top1\n",
    "metro_line2_top2=metro_recent_c_total[(metro_recent_c_total['지하철역']=='잠실(송파구청)') & (metro_recent_c_total['호선명']=='2호선')]\n",
    "metro_line2_top2\n",
    "metro_line2_top3=metro_recent_c_total[(metro_recent_c_total['지하철역']=='홍대입구') & (metro_recent_c_total['호선명']=='2호선')]\n",
    "metro_line2_top3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2호선 top3역 3년간 승하차 평균 월별로 나타냄\n",
    "\n",
    "#폰트 깨짐 해결\n",
    "from matplotlib import font_manager, rc\n",
    "\n",
    "font_path = \"C:/Users/82105/Desktop/2학년_2학기/오픈소스_개발_프로젝트/나눔 글꼴/나눔고딕/NanumFontSetup_TTF_GOTHIC/NanumGothic.ttf\"\n",
    "font_name = font_manager.FontProperties(fname=font_path).get_name()\n",
    "rc('font', family=font_name) \n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title('2호선 승객수 top3 추세')\n",
    "plt.plot(metro_line2_top1['사용월'],metro_line2_top1['월평균승객'],color='red', label='강남(2)')\n",
    "plt.plot(metro_line2_top2['사용월'],metro_line2_top2['월평균승객'],color='orange', label='잠실(송파구청)(2)')\n",
    "plt.plot(metro_line2_top3['사용월'],metro_line2_top3['월평균승객'],color='blue',label='홍대입구(2)')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>호선명</th>\n",
       "      <th>지하철역</th>\n",
       "      <th>사용월</th>\n",
       "      <th>출근시간6-9</th>\n",
       "      <th>점심시간11-14</th>\n",
       "      <th>퇴근시간17-20</th>\n",
       "      <th>야간시간22-24</th>\n",
       "      <th>월평균승객</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>452939</td>\n",
       "      <td>254283</td>\n",
       "      <td>578584</td>\n",
       "      <td>90545</td>\n",
       "      <td>344087.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>441393</td>\n",
       "      <td>240966</td>\n",
       "      <td>554514</td>\n",
       "      <td>86599</td>\n",
       "      <td>330868.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>468505</td>\n",
       "      <td>228720</td>\n",
       "      <td>581576</td>\n",
       "      <td>85899</td>\n",
       "      <td>341175.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>479018</td>\n",
       "      <td>244188</td>\n",
       "      <td>591358</td>\n",
       "      <td>92033</td>\n",
       "      <td>351649.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2547</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>484545</td>\n",
       "      <td>251551</td>\n",
       "      <td>598167</td>\n",
       "      <td>95723</td>\n",
       "      <td>357496.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3161</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>483669</td>\n",
       "      <td>259096</td>\n",
       "      <td>595590</td>\n",
       "      <td>97203</td>\n",
       "      <td>358889.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3775</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>477576</td>\n",
       "      <td>258830</td>\n",
       "      <td>589902</td>\n",
       "      <td>92243</td>\n",
       "      <td>354637.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4388</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2023-03-01</td>\n",
       "      <td>515515</td>\n",
       "      <td>264326</td>\n",
       "      <td>639255</td>\n",
       "      <td>99626</td>\n",
       "      <td>379680.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5005</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2023-02-01</td>\n",
       "      <td>445434</td>\n",
       "      <td>245467</td>\n",
       "      <td>574836</td>\n",
       "      <td>88649</td>\n",
       "      <td>338596.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5618</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>437679</td>\n",
       "      <td>232197</td>\n",
       "      <td>560039</td>\n",
       "      <td>86938</td>\n",
       "      <td>329213.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6230</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>462324</td>\n",
       "      <td>267170</td>\n",
       "      <td>616603</td>\n",
       "      <td>106972</td>\n",
       "      <td>363267.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6843</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>491526</td>\n",
       "      <td>255289</td>\n",
       "      <td>624548</td>\n",
       "      <td>87313</td>\n",
       "      <td>364669.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7456</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>459400</td>\n",
       "      <td>263508</td>\n",
       "      <td>572540</td>\n",
       "      <td>89503</td>\n",
       "      <td>346237.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8071</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2022-09-01</td>\n",
       "      <td>447997</td>\n",
       "      <td>237589</td>\n",
       "      <td>557826</td>\n",
       "      <td>85177</td>\n",
       "      <td>332147.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8686</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>461279</td>\n",
       "      <td>226738</td>\n",
       "      <td>559154</td>\n",
       "      <td>83637</td>\n",
       "      <td>332702.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9303</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>474852</td>\n",
       "      <td>243151</td>\n",
       "      <td>575921</td>\n",
       "      <td>87272</td>\n",
       "      <td>345299.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9918</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2022-06-01</td>\n",
       "      <td>466253</td>\n",
       "      <td>245688</td>\n",
       "      <td>569277</td>\n",
       "      <td>88603</td>\n",
       "      <td>342455.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10533</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2022-05-01</td>\n",
       "      <td>492791</td>\n",
       "      <td>262378</td>\n",
       "      <td>610004</td>\n",
       "      <td>99023</td>\n",
       "      <td>366049.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11148</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>452733</td>\n",
       "      <td>236541</td>\n",
       "      <td>565372</td>\n",
       "      <td>83812</td>\n",
       "      <td>334614.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11756</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>425267</td>\n",
       "      <td>208484</td>\n",
       "      <td>506686</td>\n",
       "      <td>70145</td>\n",
       "      <td>302645.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12365</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>360061</td>\n",
       "      <td>194646</td>\n",
       "      <td>452943</td>\n",
       "      <td>46311</td>\n",
       "      <td>263490.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12973</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>428669</td>\n",
       "      <td>226102</td>\n",
       "      <td>546461</td>\n",
       "      <td>45630</td>\n",
       "      <td>311715.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13583</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>462883</td>\n",
       "      <td>243685</td>\n",
       "      <td>591487</td>\n",
       "      <td>67969</td>\n",
       "      <td>341506.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14192</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>475637</td>\n",
       "      <td>245970</td>\n",
       "      <td>611830</td>\n",
       "      <td>83453</td>\n",
       "      <td>354222.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14801</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>419673</td>\n",
       "      <td>237936</td>\n",
       "      <td>525128</td>\n",
       "      <td>74219</td>\n",
       "      <td>314239.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15410</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>391589</td>\n",
       "      <td>204700</td>\n",
       "      <td>476338</td>\n",
       "      <td>54788</td>\n",
       "      <td>281853.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16017</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>407119</td>\n",
       "      <td>202605</td>\n",
       "      <td>494160</td>\n",
       "      <td>47429</td>\n",
       "      <td>287828.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16624</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>447061</td>\n",
       "      <td>204588</td>\n",
       "      <td>531710</td>\n",
       "      <td>59238</td>\n",
       "      <td>310649.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17231</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>486904</td>\n",
       "      <td>229974</td>\n",
       "      <td>583140</td>\n",
       "      <td>84600</td>\n",
       "      <td>346154.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17839</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>433421</td>\n",
       "      <td>230986</td>\n",
       "      <td>535414</td>\n",
       "      <td>83585</td>\n",
       "      <td>320851.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18447</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>476445</td>\n",
       "      <td>228316</td>\n",
       "      <td>586047</td>\n",
       "      <td>83178</td>\n",
       "      <td>343496.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19055</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>477766</td>\n",
       "      <td>229231</td>\n",
       "      <td>589899</td>\n",
       "      <td>84009</td>\n",
       "      <td>345226.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19661</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>364348</td>\n",
       "      <td>184958</td>\n",
       "      <td>463781</td>\n",
       "      <td>51523</td>\n",
       "      <td>266152.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20266</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>391897</td>\n",
       "      <td>177546</td>\n",
       "      <td>486132</td>\n",
       "      <td>36567</td>\n",
       "      <td>273035.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20870</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>395442</td>\n",
       "      <td>176890</td>\n",
       "      <td>474115</td>\n",
       "      <td>31775</td>\n",
       "      <td>269555.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21473</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>455199</td>\n",
       "      <td>230514</td>\n",
       "      <td>577737</td>\n",
       "      <td>72607</td>\n",
       "      <td>334014.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22076</th>\n",
       "      <td>3호선</td>\n",
       "      <td>양재(서초구청)</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>427216</td>\n",
       "      <td>221073</td>\n",
       "      <td>539499</td>\n",
       "      <td>78175</td>\n",
       "      <td>316490.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       호선명      지하철역        사용월  출근시간6-9  점심시간11-14  퇴근시간17-20  야간시간22-24  \\\n",
       "82     3호선  양재(서초구청) 2023-10-01   452939     254283     578584      90545   \n",
       "699    3호선  양재(서초구청) 2023-09-01   441393     240966     554514      86599   \n",
       "1315   3호선  양재(서초구청) 2023-08-01   468505     228720     581576      85899   \n",
       "1931   3호선  양재(서초구청) 2023-07-01   479018     244188     591358      92033   \n",
       "2547   3호선  양재(서초구청) 2023-06-01   484545     251551     598167      95723   \n",
       "3161   3호선  양재(서초구청) 2023-05-01   483669     259096     595590      97203   \n",
       "3775   3호선  양재(서초구청) 2023-04-01   477576     258830     589902      92243   \n",
       "4388   3호선  양재(서초구청) 2023-03-01   515515     264326     639255      99626   \n",
       "5005   3호선  양재(서초구청) 2023-02-01   445434     245467     574836      88649   \n",
       "5618   3호선  양재(서초구청) 2023-01-01   437679     232197     560039      86938   \n",
       "6230   3호선  양재(서초구청) 2022-12-01   462324     267170     616603     106972   \n",
       "6843   3호선  양재(서초구청) 2022-11-01   491526     255289     624548      87313   \n",
       "7456   3호선  양재(서초구청) 2022-10-01   459400     263508     572540      89503   \n",
       "8071   3호선  양재(서초구청) 2022-09-01   447997     237589     557826      85177   \n",
       "8686   3호선  양재(서초구청) 2022-08-01   461279     226738     559154      83637   \n",
       "9303   3호선  양재(서초구청) 2022-07-01   474852     243151     575921      87272   \n",
       "9918   3호선  양재(서초구청) 2022-06-01   466253     245688     569277      88603   \n",
       "10533  3호선  양재(서초구청) 2022-05-01   492791     262378     610004      99023   \n",
       "11148  3호선  양재(서초구청) 2022-04-01   452733     236541     565372      83812   \n",
       "11756  3호선  양재(서초구청) 2022-03-01   425267     208484     506686      70145   \n",
       "12365  3호선  양재(서초구청) 2022-02-01   360061     194646     452943      46311   \n",
       "12973  3호선  양재(서초구청) 2022-01-01   428669     226102     546461      45630   \n",
       "13583  3호선  양재(서초구청) 2021-12-01   462883     243685     591487      67969   \n",
       "14192  3호선  양재(서초구청) 2021-11-01   475637     245970     611830      83453   \n",
       "14801  3호선  양재(서초구청) 2021-10-01   419673     237936     525128      74219   \n",
       "15410  3호선  양재(서초구청) 2021-09-01   391589     204700     476338      54788   \n",
       "16017  3호선  양재(서초구청) 2021-08-01   407119     202605     494160      47429   \n",
       "16624  3호선  양재(서초구청) 2021-07-01   447061     204588     531710      59238   \n",
       "17231  3호선  양재(서초구청) 2021-06-01   486904     229974     583140      84600   \n",
       "17839  3호선  양재(서초구청) 2021-05-01   433421     230986     535414      83585   \n",
       "18447  3호선  양재(서초구청) 2021-04-01   476445     228316     586047      83178   \n",
       "19055  3호선  양재(서초구청) 2021-03-01   477766     229231     589899      84009   \n",
       "19661  3호선  양재(서초구청) 2021-02-01   364348     184958     463781      51523   \n",
       "20266  3호선  양재(서초구청) 2021-01-01   391897     177546     486132      36567   \n",
       "20870  3호선  양재(서초구청) 2020-12-01   395442     176890     474115      31775   \n",
       "21473  3호선  양재(서초구청) 2020-11-01   455199     230514     577737      72607   \n",
       "22076  3호선  양재(서초구청) 2020-10-01   427216     221073     539499      78175   \n",
       "\n",
       "           월평균승객  \n",
       "82     344087.75  \n",
       "699    330868.00  \n",
       "1315   341175.00  \n",
       "1931   351649.25  \n",
       "2547   357496.50  \n",
       "3161   358889.50  \n",
       "3775   354637.75  \n",
       "4388   379680.50  \n",
       "5005   338596.50  \n",
       "5618   329213.25  \n",
       "6230   363267.25  \n",
       "6843   364669.00  \n",
       "7456   346237.75  \n",
       "8071   332147.25  \n",
       "8686   332702.00  \n",
       "9303   345299.00  \n",
       "9918   342455.25  \n",
       "10533  366049.00  \n",
       "11148  334614.50  \n",
       "11756  302645.50  \n",
       "12365  263490.25  \n",
       "12973  311715.50  \n",
       "13583  341506.00  \n",
       "14192  354222.50  \n",
       "14801  314239.00  \n",
       "15410  281853.75  \n",
       "16017  287828.25  \n",
       "16624  310649.25  \n",
       "17231  346154.50  \n",
       "17839  320851.50  \n",
       "18447  343496.50  \n",
       "19055  345226.25  \n",
       "19661  266152.50  \n",
       "20266  273035.50  \n",
       "20870  269555.50  \n",
       "21473  334014.25  \n",
       "22076  316490.75  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3호선의 top3 고속터미널, 연신내, 양재(서초구청)역만 추출해서 시각화\n",
    "metro_line3_top1=metro_recent_c_total[(metro_recent_c_total['지하철역']=='고속터미널') & (metro_recent_c_total['호선명']=='3호선')]\n",
    "metro_line3_top1\n",
    "metro_line3_top2=metro_recent_c_total[(metro_recent_c_total['지하철역']=='연신내') & (metro_recent_c_total['호선명']=='3호선')]\n",
    "metro_line3_top2\n",
    "metro_line3_top3=metro_recent_c_total[(metro_recent_c_total['지하철역']=='양재(서초구청)') & (metro_recent_c_total['호선명']=='3호선')]\n",
    "metro_line3_top3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FXb8l2fF3bYc2YcIEFi5cmJ1hER8fn2IMwcHBdOrU6Z3JlZGRUaangKfWL0CRIkWYO3cuvr6+tG/fnmnTpuHj48O3336baoKtSeKvXr36ziUGKpVK+6AkPUJCQpg5cyaNGjXC3t4eY2NjTE1NKVOmDD169GDnzp3vvH/gwIHa/lJ72djY6LTzZtX2tyu4v13V/c3Xu6SUtJcrV44bN26keP3NmzepUKGCzrEHDx4ku+7+/fuUK1funX0nJSWRmJiY6s+7WLFixMfHp/iaN28eLVq0SPX8+PHjAZg+fTrXr1/nt99+A9QPo+rVq0f37t0zNbtFCCGEYUgiL4QQwmCeP3/OypUr+fPPP1m1ahUrVqzg0qVLnDt3jq+++oouXbpQu3Zt7O3tmTdvXqrtpJb864u3tze3bt2iefPmyc6Fhoayffv2dyZBp0+fpkGDBhnuNygoiM2bN+sk3AkJCSm+FEUhKSkp1fMptX358uVU+zY2NiYyMjJdW6E9f/4cDw8Pzp07x4QJE7h+/TpxcXGEhoayc+dOGjZsyIgRIxg7dmyqbaxcuVL7c0zt1bVrVy5evJhmPFlhYWGRbKS/R48e3L59O9n68dDQUNatW0f//v11jr+9v/uNGze4cOECrVq1emfff//9NwD169dP9RpjY+MUH04YGRm98+GFSqXC39+fBQsWsGvXLuzs7LRtrly5kooVK0oiL4QQeYhMrRdCCGEwwcHB7Nu3j6SkJO0IcMmSJalatSpFihShXr16ODs7U6FCBQoVKsT27duzNZ7o6GimT59O27ZtKVmyJFFRUezatYvJkyczceLENEdUU1O8ePE0r/n1119xc3PDxcUFExMTzpw5w3fffYebmxufffYZABs2bKBnz56ptrFjxw7tyOvbTp06pbMuPj3SOxrv5eVFfHx8sqJvJiYmuLm54ebmRvHixRkwYACzZ8/OUAxvMjMzy9YHNgAlS5bk6dOn7Nmzh/Lly5MvXz5KlCjBmDFj6NmzJwsWLOCjjz7i3r17DBkyBA8PDzp06KDTxpUrV/j555/p378/T548YeDAgfTo0YNKlSoB8PjxY9auXUurVq0oXrw4wcHBbNiwQVvsLn/+/Nny3hwdHXn48GGymhM2NjYsWLAgW/oUQgiRPSSRF0IIYTAuLi5s3Lgxy+0YGRmlul46IxITE7l69SorV64kKCgIU1NTatSowaZNm/D09EzxHs1U7Pj4+AyvvX7T8+fPWbp0KQEBASQmJlK+fHm+/vprBg8ejLGxMaAeGc5IkbZ3UalUJCYmpmste1qaNWuGkZERPXv2ZMCAAVSpUgV7e3vi4uJ4+PAhx44dY+bMmclGrnOjYsWKMWHCBPr06YOxsTErV67ULlsoVqwYkyZN4t69exQqVIgePXowZcqUZN+/9evXM2fOHCpVqoSxsTG9evXSeYChKArHjh1j3rx5BAcHY2lpSYMGDTh27Bi1atXK1veXWuFIIYQQeYsk8kIIIfK8mjVrMmLECKZPn/7O68qUKcO9e/dSPW9tbc22bdsy1HeRIkVwdHTEysrqndc1bdqUgwcPpnp+1qxZzJo1K0N9Z0XZsmVJSEhIc+3+oEGDWLZs2TuvKVq0KBcvXmTRokX8+OOPXL9+nfDwcFQqFY6OjtSqVYvffvuNTp066fEdpK5UqVJERUVl+v5p06Yxbdq0ZMeHDx/O8OHD07y/UKFC/PPPP6meL1myJHv37s10fCkxMTHRPvDJClNTU8zNzfUQkRBCiOykUrJ7jpoQQgihJzNmzOCjjz6iYcOGyc4lJiamOSqvKcCWHWJjY9857Ts9hdZymqIoxMbGvvMaU1PTTCWICQkJGBsbZ3m0/03r16+nYMGCtGnTRm9t6puZmRkPHjxI13IKfUtKSsp0UcU3xcfHY2pqyqNHj/jf//6Xru3uhBBC5CxJ5IUQQgghhBBCiDxEqtYLIYQQQgghhBB5iCTyQgghhBBCCCFEHpK7FuvlEklJSTx58gQbGxu9ru0TQgghhBBCCCFSoigK4eHhFC9ePM2aJ5LIp+DJkyc4OTkZOgwhhBBCCCGEEB+Yx48f4+jo+M5rJJFPgY2NDaD+BubPn9/A0QghhBBCCCGEeN+FhYXh5OSkzUffRRL5FGim0+fPn18SeSGEEEIIIYQQOSY9y7ul2J0QQgghhBBCCJGHSCIvhBBCCCGEEELkIZLICyGEEEIIIYQQeYiskc+CxMRE4uPjDR2GeI+ZmppibGxs6DCEEEIIIYQQuYgk8pmgKArPnj0jJCTE0KGID0CBAgUoWrRouopeCCGEEEIIId5/kshngiaJL1y4MFZWVpJgiWyhKApRUVEEBgYCUKxYMQNHJIQQQgghhMgNJJHPoMTERG0Sb29vb+hwxHvO0tISgMDAQAoXLizT7IUQQgghhBBS7C6jNGviraysDByJ+FBoPmtSj0EIIYQQQggBkshnmkynFzlFPmtCCCGEEEKIN0kiL4QQQgghhBBC5CGSyIsMuXPnjqFDEEIIIYQQQogPmiTyIt0eP35M1apVs7xWu3v37ty4cSNL9zx58oQFCxa88x5PT08uX76cqRiFEEIIIYQQIrfKUiLv6+uLubk5U6ZMASAyMpKvvvoKd3d33N3dqV27Nl5eXjr3TJs2jaJFi+Lh4aF9dejQQeeamzdv0rhxYzw8PKhWrRpbtmzROR8fH8+oUaNwc3PDzc2NESNGEBcXp3PN9u3bqVatGh4eHjRq1Ijr169n5a2+Nzp16oSLi0uKr9KlS/PFF1+keF9SUhLDhg3D1dWVcePGpdr++vXrqVSpkk67VatWJSEhQXvN8+fPiYqK0n59+PBhXF1dk8UzadKkVO958uQJmzdvfud7jY+PT/Ghw8mTJ1m0aBEA8+fPp2rVqri7u+Pi4kLfvn0JCAgA1Nu/DRgwgNjY2Hf2I4QQQgghhBA5KUvbz40aNYqmTZtqkyVFUWjbti3z58/HyMiI8+fP07p1ay5fvqzdAzshIYGBAwcybdq0FNuMiYmhY8eOrFixgsaNG/Ps2TMaN25MuXLlqFq1KgCTJk0iNjaWq1evAjBy5EgmTpzIrFmzALh+/Tpjxozh6NGjFC9enBMnTtCpUyeuXLmi3c7rQ7Vt27ZUzz1+/Ji2bdsmO/7y5UsGDRqEg4MDW7dupVevXvTp04f//e9/FCpUSOfa06dPM3bs2FQfCKTkwoULdO7cmZ9//ll77NSpU0yYMCHdbQAcOHAAb29v7dcPHjxIdk1MTAzTp0/Xfh/at2/PoEGDsLCwICEhgSlTptCuXTsuXryISqWif//+TJ48mV9++SVDsQghhBBCCCFEdsn0iPzmzZspUqQIderU0R6ztramdevWGBmpm61Rowb169fn1KlT6W53//79VKtWjcaNGwNQtGhRvv32W/744w9APTK8du1afvnlF4yMjDAyMmL69OmsX7+exMREAFauXMk333xD8eLFAWjYsCG1atVi3759mX2776QoCpGRcQZ5KYqi1/dhYvL62c7Dhw/55ptvqFWrFh999BGrVq3CxMSEjRs3Ur9+fWrVqsWAAQO4du2aThuan39G+jU1NdU5ZmpqmuH3VqRIEZ0R/ZS2CFyxYgUdOnTQvs/SpUtjYWEBgImJCVOmTOH+/fs8efIEUH92zp49S2BgYIZiEUIIIYQQQojskqkR+aioKCZNmsSBAwdYvnz5O6999eqVNlFKDy8vL20Sr9G4cWPmzZsHwKVLlyhevDgFChTQns+fPz8lS5bkwoUL1KpVCy8vLwYOHJisjQMHDtCpU6dkfcbGxupMnw4LC0t3vABRUfFYW8/I0D36EhHxPfnymemprQid5PfFixc4Ozvj4+ODvb09oE58/fz8GDZsGP369WPnzp2Ymen2HxoayrNnzwD199bU1JQCBQpo17iHh4dnOdaUtmSrWrUqRYoUISgoCFdXV5YuXZrsmhUrVrzzwVJUVBQqlUr7fgF69uzJypUr+f7777MctxBCCCGEEEJkVaYS+enTp9O7d2/tiHdqrl+/jp+fH02bNk1320+ePMHT01PnmJOTE/fv39eed3JySnaf5ppatWqleI2Tk1Oq08pnzJihXef/ITl37hxRUVHaBycvX77EwcFBe75mzZrUrFlT554XL15o/9/Kyoru3bsna/f3339n9+7dKIqCubk5np6eeHp6MnfuXAD8/f2zHLujoyPXr1+nevXqxMbGEh8fT0JCAnZ2dnz88cfMnj072T1+fn7Y29uTL1++FNu8fv0648aNY/LkyZibm2uPe3p68vnnn0siL4QQQgghhMgVMpzI37t3j82bN3Px4sV3XpeUlMTgwYOZOnWqzoi8SqVi48aNeHl5ERoaStWqVZk8eTKurq4AhISEJBvBt7CwICYmBkVRUjyvuUZTDC21Nt4slvam77//nm+++Ub7dVhYWIoPC1JjZWVKRIRhkjwrK9O0L0qFt7c3L1++1CbyT548wdHREYCZM2eyatWqZPdERUXh4uKS7HiPHj346aefAPj222/p169fsmvWrVsHQJMmTTIds0aRIkV48uQJkZGRGBsbY21tnWyUvlKlStja2mq/9vX11X7O3jR27FjWrl3L8+fPGThwIKNGjdI57+zszKNHj7IcsxBCCCGEyL1iYhIYNWoP9eo50a+fh6HDEeKdMpzIjxo1imnTpqU5Xf6HH36gWLFi9O/fX+f46NGjGTduHJaWliQkJLBu3To8PT25evUqdnZ2mJubExMTo3NPdHQ05ubmqFSqFM9rrtEUstNc8+a66zfPv83c3FxnBDajVCqV3qa3G5Kvry8VKlQAYPz48YwfP97AEb2bZsp+apYsWaLzdXBwcIrXz549m9mzZxMUFMRPP/1E//79Wb16tc41+qxFIIQQQgghcp8NG66xfPkF1q69wiefVCJ//sznB0Jktwwl8nv37iUqKoouXbq887q//vqLPXv26FQQ13hzhNTExIR+/fqxbt06Tpw4QYcOHXB0dEw2+vn48WPtSHFK51O7xs3NLcXzImUjR45Mtt49o0xNTTl//jwODg7ExsYSGhrKvXv3ePDgAYsWLUoxkTYzM2P//v06x/z8/NK1w8Dnn3/O+fPnUzz34sULfHx8tLMrChYsSHBwcKpt2dvbM2/ePAoUKMD8+fN1PqsprckXQgghhBDvjzVrLgMQHZ3Axo3XGTiwuoEjEiJ1GUrk/fz88Pf3x8PDQ3tMU9Rs7969HDt2jPPnz/PDDz9w7NixVNcivy0xMVFbRbx+/frs3r2b4cOHa88fPXqU+vXrA+Dh4cGdO3cICQnRJoVhYWH4+vpSvXp1bRtHjx7VSeSPHj2abO290GVnZ5fsWN26dQkJCUnx+oiICD777DOmT5+uPTZw4ECWLl3Knj17sLa2xsHBgSpVqvDpp5+SP3/+FNvp378/pUqV0tnzvWrVqtqf57u8PXL+poYNGxIYGKhN5CtWrMjMmTPf2V5sbCxxcXHaHRBAvY1dyZIl04xFCCGEEELkTQ8fhnD48APt13/+eUkSeZGrZSiRHzp0KEOHDtU59tNPP5GQkMC0adO4ffs2PXv2ZNu2bamuMdeMjKtUKhITE1m2bBmPHj3i448/BqBr165MmjSJo0ePaveRnzNnjnZ9taWlJZ9//jnfffcdixcvRqVSMWHCBHr37q2tuD58+HC6d+9Op06dKF68ON7e3nh7e7NixYoMf4PeF6mteQfYtGlTsmOaNe+nT59Otc29e/eybNkynWNubm4sWLAgQ7HZ2NjQsWPHDN2jMWTIEPbv35/iVnMFChTA2dlZ+3WZMmUICgoiIiICa2tr4uLiCAwM1M7UCAkJYdCgQXTt2lXnocaBAwdo2bJlpuITQgghhBC53/r1VwGoWrUI164F4u39mDt3gihf3j6NO4UwjExVrX+TqampdtrxokWLiIiISLb12yeffMKkSZMA+PPPP1m/fj3m5uYoikLt2rU5cuSIdhp1vnz52LFjB8OGDSMiIoKkpCSmTJmis1/9zJkzGT16tHbEvWHDhsyfP197vmbNmkyfPp1WrVqhUqmwsrJi+/btWFtbZ/Xt5lnZtebd0GvHfX192bhxY7Lq+qkZOHAga9asYdiwYbx48YKOHTsSGRmJhYUFRkZG9OrVK1mxu/Xr17Nhw4bsCF8IIYQQQhiYoijaafVff12XjRuvs2fPXVavvsy0aenffUuInKRSDJ2J5UJhYWHY2toSGhqabDp4TEwMfn5+lC5dOs2Cf++7vXv3snTp0lS39UuNp6cnc+bMwd3dPcv3vGtEHtTFGb/88kvt19HR0XTs2JHdu3frFENMzdGjR/nvv//SnJKfneQzJ4QQQgiRfc6eDaBOnd+xtDTh+fMx7Nlzl+7dN+HomJ8HD0ZhbGxk6BDFB+JdeejbsjwiLz5cDRo0oHTp0hm+78CBA3q7Z+nSpRlqx9LSksmTJ7N48eJkI+9vUxSFP/74g+XLl2eoDyGEEEIIkXdoRuM/+aQSNjbmdOhQkQIFLPD3D+PQIT88PcsaOEIhkpPHSyLTbGxsqFixoqHDyLAGDRqkmcSDulL96tWrs7Q1oRBCCCGEyL3i4hL5++9rAHz2mXrmp4WFCb16VQbgzz8vGyw2Id5FEnkhhBBCCCHEB+m//+7w6lU0xYpZ06zZ65mm/fp5ALBly01CQ2MMFJ0QqZNEXgghhBBCCPFB0kyr79Onqs5a+Jo1i+PqWoiYmAT++ee6ocITIlWSyAshhBBCCCE+OEFBUezadRt4Pa1eQ6VS0a+f+tiff17K6dCESJMk8kIIIYQQQogPzj//XCc+Polq1YpSuXLhZOfVo/QqTp3y59atlwaIUIjUSSIvhBBCCCGE+OBoptW/PRqvUayYDa1alQNg9WopeidyF0nkhRBCCCGEEB+UW7decuZMAMbGKnr2rJzqdZqid2vWXCYxMSmHohMibZLIf4Bu3rzJ1q1buXjxYrJznp6eXL6c/Inj5s2biYuLS/f1Kfnqq68AOHr0KGPGjMlg1EIIIYQQQujH2rVXAGjVqhxFilinel379hWws7MkICAcL6/7ORWeEGmSRP4DoigK/fr1o2fPnvz3338MGDCA9u3bExsbq70mPj6e+Pj4ZPeOHDmSyMjIZMffvn7x4sXMnj07xf43bNgAQHh4OC9fJl9ndPLkSRYtWgTA/PnzqVq1Ku7u7ri4uNC3b18CAgK072PAgAE6cQshhBBCCJEeSUmKNpFPbVq9hrm57CkvcidJ5D8gK1euJDg4mAsXLrBixQrOnz+Pra0tzs7OuLi44OLiwtmzZ5PdFx4eTlRUFAULFkyzj6ioqBQT/rTExMQwffp0Bg8eDED79u05e/Ysly9f5tq1azg7O9OuXTtAXUW0f//+TJ48OcP9CCGEEEKID9uxYw959CgUW1tz2revkOb1mun1W7feJCRE9pQXuYMk8nqhAJEGeinpjnLHjh0MHDgQIyP1j12lUjF8+HDKlCmDr68vvr6+1K5dO9l927ZtIywsjHv37qX/W5JBK1asoEOHDpiYmABQunRpLCwsADAxMWHKlCncv3+fJ0+eANCwYUPOnj1LYGBgtsUkhBBCCCHeP5oid926uWFpaZrm9dWrF6Ny5cLExiayYcO17A5PiHSRRF4vogBrA72i0h1lUlISKpVK55hKpcLY2DjVexRFYenSpfTs2ZPx48enu6+MWrFiBb179071fFRUFCqVCnt7e+2xnj17snLlymyLSQghhBBCvF+iouL5998bQNrT6jVkT3mRG0ki/wFp164dq1evRlFej+IvXryYR48e0aNHD3r06MGNGzd07pk3bx4WFhasXbsWgOnTp2e6/4iICPr06cOvv/6qc9zPzw97e3vy5cuX4n3Xr1+ne/fuTJ48GXNzc+1xT09P9u7dm+l4hBBCCCHEh2XbNl8iIuIoXboADRo4pfs+zZ7yZ84EcPPmi2yMUIj0kUReL6yACAO9rNId5aBBg1CpVDRs2JCxY8fy0UcfERAQwJYtW/juu+/47rvvKFmypPb6FStWsHjxYtasWYNKpWLt2rUcP36cPn36pFjBPi0WFhaMHj2ajh076hz39fXF1dU12fVjx46laNGiVK5cmeLFizNq1Cid887Ozjx69CjDcQghhBBCiA/Tm3vHvz1T9V2KFLGmTZvygIzKi9xBEnm9UAH5DPRK/19ARkZGbNy4kblz51KlShWmTZuGl5cX1atXx8PDAw8PD6yt1dtvPH78mPXr13Pw4EFKlCgBgKWlJbt27aJ9+/aYmZml/J1I4S9Ef39/YmJiMDExoWbNmpQrV07nfHBwMAUKFEh23+zZs3n27BkvX77EwsKC/v37J7vmzdkFQgghhBBCpObJk3AOHFBvIde3b9UM368perd27RUSEmRPeWFYksh/gEqXLk2XLl1o3Lhxqk8inZycOHLkCE5OTgQGBhIdHQ2AsbEx3bt3T7XtsmXLsmTJEmrUqEGlSpVwcnKie/fu3L+f+r6bBQsWJDg4ONXz9vb2zJs3j61btxIaGqpzLiNPUoUQQggh9Gno0F0UKTKHR49C075YGNxff10lKUmhQQMnypa1y/D97dpVwN7ekqdPIzhwIPuKQAuRHpLIf4CmTJnCpk2bUjw3f/58KleurHNs/Pjx/PfffyleP336dFxcXLRfd+rUidu3b+Pl5cWFCxd4/Pgx3t7eKU6d16hYsWKytflvi42NJS4ujsTERO2xBw8e6CwFEEIIIYTIKc+fR7BixQUCAyPZtOndv8cIw1MUhdWrX0+rzwwzM2N6964CyJ7ywvAkkf8AKYqS6pT0qlWrard9S8/19evX107H17C1taVgwYJYWlqmK54yZcoQFBREREQEAHFxcfj7+2vPh4SE8Pnnn9O1a1fs7F4/PT1w4AAtW7ZMVx9CCCGEEPr099/XSExU/36kma4tcq/Ll59z7Vog5ubGfPpp6gNMadFMr9+2zZdXr6L1FJ0QGSeJ/AdIpVKRkJCQbddnxsCBA1mzZg0AL168oGPHjri4uODh4UHTpk2pXbs2q1at0rln/fr1fPHFF9kalxBCCCFESjRF0wCOHn1AbGz2/q4kskbz8+rQoSIFC6ZvsCklHh5FqVq1CHFxsqe8MCwTQwcgcl7jxo2ZNm0ac+fOTfF8t27dmDRpkvbrpk2bMmPGDKZNm5au61Pzyy+/AGBqaoqpqanOuUGDBtGxY0e+/PJLSpQowfnz59/Z1tGjR6lTpw5FixZNs18hhBBCCH26di2QixefYWpqRP785gQFRXPy5GM+/ri0oUMTKUhISGL9+qtA5orcvUmlUtG/vwdff72PP/+8xLBhtfQRohAZplKk7HcyYWFh2NraEhoaSv78+XXOxcTE4OfnR+nSpZNNQRdZ4+3tjY+PT7Jt5t6mKAr9+vVj+fLlOvvKv6/kMyeEEELkLuPHH2DWrJN07FgRGxtz1q27wvffN2T69GaGDk2k4L//7tC27V84OFjx5Mk3mJoaZ6m9wMBISpT4jYSEJK5dG4qbW2E9RSo+dO/KQ98mU+tFrtGgQYM0k3hQPwldvXr1B5HECyGEECJ3SUx8Pbr72WfueHqWAWSdfG6mmVbfq1flLCfxAIUL56NtW9lTXhiWJPJCCCGEEMJg+vXbRs2aywkJiTF0KOly5MgDAgLCKVjQgrZty9OsmXo6/fnzT6T4WS4UEhLDtm2+QOar1adE9pQXhiaJvBBCCCGEMIhTpx6zevVlzp9/ysqVFwwdTrqsWXMFgG7d3DA3N6FEify4uhZCUeDQIT8DRyfetmnTDWJjE3F1LUT16sX01m7btuUpVMiK588j2bfvrt7aFSK9JJEXQgghhBAGMXv2Se3/L17sQ2Ji7h7ZjIyMY/Nm9Z7xb47uvp5ef88gcYnUaabVf/ZZVVQqld7aNTWVPeWFYUkiL4QQQgghctytWy+1U56trc24fz+YPXty98jm1q2+REbGU7ZsQerVc9Qel3XyuZOfXzDHjz9CpYLevbNWrT4lmun1O3bcIigoSu/tC/EuksgLIYQQQogc9+uvp1AUaN++AkOG1ABg4cKzBo7q3dauVU+r79tXd3S3cWNnTEyM8PML4d69V4YKT7xl3Tr1z6tZszI4Or67AnhmuLsXxcOjKHFxifz9t+wpL3KWJPJCKzw8nEuXLhk6DCGEEEK85549i2D1avV05HHjGjBsWC1UKti37x63br00cHQpe/IkHC8v9Yh7nz66o7vW1mbaEXrNNcKwFEXR1jP47DP9j8Zr9O/vAUj1epHzJJH/wPj4+NC7d28++ugjhg8fjr+/v/bc+fPnmThxYrra+eqrrwDYunUrM2bMSHf/169fT/F4ixYtePbsWYrnJkyYgIuLi/a1du3aVNtv2rSpzrVvvtzc3Fi2bFmye548eYKnpyfNmzenefPmHDlyROd8hw4duHZNnrIKIYQQ+jJ//hni4hKpV8+RBg2cKF26IO3aVQBg0aJzBo4uZX/9dZWkJIX69Z0oW9Yu2XmZXp+7nD7tz927r8iXz5TOnStlWz+9elXB1NSI8+efcvXq82zrR4i3SSL/ATl37hxdu3alV69erFu3jlq1atGwYcNUE+hPPvmEunXral8tWrQgNDQUgA0bNgAQGhpKUFBQsntPnjzJokWLdI4lJiZSo0aNFPt68uQJMTEpbzvz888/c+3aNe2rT58+qb7HQ4cO4evrm+Jr7ty57Nu3L9k9xYsX588//9S+6tevr3M+LCxMG9vly5eZOXNmqv2/+b7nz59P1apVcXd3x8XFhb59+xIQEAConxIPGDCA2NjYVNsSQggh3kfh4bEsWeIDqEfjNVPUR4yoDahHNsPDc9+/j5pp9amN7np6lgXUletze9G+D4GmyF2XLq5YW5tlWz8ODlbah1AyKi9ykiTyH5DZs2czc+ZM2rZtS6lSpejXrx8DBw5k1qxZ2muOHTtG5cqV6datG+vXr8fLywsvLy82b97M8ePHMTExSbOfmJgYpk+fzuDBg3WOBwcHY2Vlle54f/nlF1xcXKhUqRKVK1fWvlxdXalfvz6PHj1K/5v//2xsbHS+9vb2xsXFhWbNmmlH5J2cnFi/fn2K97u7u/Pw4UOuXr2a7Nzb77t9+/acPXuWy5cvc+3aNZydnWnXrh0AKpWK/v37M3ny5Ay/ByGEECIv+/33C4SExFChgj0dOlTUHm/evAwVK9oTHh6nTcJyi8uXn3HlynPMzIzp1s0txWtq1iyOra05wcExnD//NIcjFG+KjU1gwwb1LNDsnFavoSl6t27dVeLjE7O9PyFAEnm9UBSFyLhIg7wURUl3nPfu3aNKlSo6x6pVq8a9e6+3SmnUqBHXrl1j48aNWFpaYm1tjbW1NUeOHKFZs2bky5cvzX5WrFhBhw4dkiX9L1++xMnJKd3xfvfdd8lG1a9fv07btm2JioqicOHC6W4LIDAwEDs73alwDRo00Gn/1KlT2NvbU7NmzVTbGTlyJD/99FOy42+/79KlS2NhYQGAiYkJU6ZM4f79+zx58gSAhg0bcvbsWQIDAzP0PoQQQoi8Kj4+kd9+Ow3A2LH1MTJ6XTBOpVLx1VfqUfmFC89l6Hec7KYZjW/fvgIFC1qmeI2JiRFNm5YGZBs6Q9u16zYhITE4OuanSRPnbO+vdetyFC6cj8DASPbuNdzOCzduvODx41CD9S9yVtrDqyJNUfFRWM+wNkjfEd9HkM8s7eQawNnZmdu3b+Pq6qo9dvXqVUqVKqX9OiYmBn9/fywsLHBwcNAeX7VqFaNHj05XPytWrODUqVPJjp86dYr8+dUVQ5OSkti8eTOJieqnlpop++9y6dIlhg8fjo2NDYcPH9Ymyenl5+dHhQoVUj0fFBREx44dGTt2LImJibi4uAAkG/l3cXEhMDCQgIAASpQooT2e2vvWiIqKQqVSYW9vrz3Ws2dPVq5cyffff5+h9yKEEELkRRs2XMPfP4wiRfIlKxgH8Pnn7kyYcBBf35ccPOhH8+ZlDBClroSEJNavV8/E69v33aO7np5l2LrVFy8vP374oVFOhCdSoCly16dPFYyNs3/c0tTUmD59qvDbb6f588/LtG9fMe2b9Ozff6/To8dmHBysuHXrKwoUyNjvye+7lSsv4ORkS4sWZQ0dit7IiPwH5Ouvv2b8+PH4+PgQExPDjh07WLBgAWPGjNFec/bsWVq1asXQoUO1x7y8vDh+/DiNGzfWHouIiGDgwIGsWrVKpw8/Pz/s7e1THLk/ePAgZ86cISAggKSkJJ2R8HetFT9+/Dg9evSgVq1aPHr0iOHDh2NpmfLT8He5dOlSshkJb8bWqFEjAgMDiY2NpVKlStrYateunez6Zs2asX///nS9b1AX+evevTuTJ0/G3Nxce9zT05O9e/dm+L0IIYQQeY2iKMyadRKAUaPqYGGRfDzJxsZcO015wYLcsRXdwYP3efYsAnt7S1q3Lv/OazUPHry9HxEZGZcT4Ym3vHgRyX//3QGgb1/3HOtX87ndufMWL1/m7J7yu3ffplevLSQlKQQGRvLLLydytP/cbtOmG3z55U7atfuLK1fen4KEMiKvB1amVkR8H2GwvtOrYcOGLF26lB9++IF79+7h6urKgQMHKFmypPaaRo0asWvXLu3XoaGhjB49mi5dujB9+nRthXoLCwsGDhzI7t27iY6O1l7v6+urM+Kv8fTpUw4cOMDPP//M5MmT+f333/nxxx+15zdt2qRz/fnz5/nzzz/Zv38/Tk5ODB48mFWrVuHt7c3OnTuZNGkShQoVYvjw4XTs2JGZM2cme6jwtocPH3Lx4kVMTU3p0aMHP/30E5cvX2by5MkEBQWxefNmihcvzqhRo1i+fDnLly9PdYp9pUqVuHjxYprve+zYsaxdu5bnz58zcOBARo0apXPe2dk5U2v9hRDiQ5SYmERCQhLm5vLrS160d+9drl0LxNrajCFDUl/CNnx4LRYsOMvOnbfw8wumdOmCORhlcppp9T16VMbMzPid15YrZ0epUrY8fBjKsWMP00z8hf5t2HCNhIQkatYsjqtroRzrt0qVItSoUYzz55/y119XGTmyTo70e/Dgfbp02UhCQhK1ahXn3LknzJ17mqFDa1KqVIEciSE3O3DgHr16bUZR4IsvqlGlSsaW5uZmMiKvByqVinxm+Qzy0lR6Ta+PP/6Yffv2cffuXXbs2KGTfBYsWJCKFV9PBYqPj6dnz54MGTKE1atXc/DgQXbv3g2o13zXrVuXsmV1p6cEBwdToECBZP1OnTqVgQMH8s0333Djxo00R6Hj4uJo0qQJZ86cwcvLi08//ZRZs2bRvHlz5s2bx8WLF1m5ciXVq1cHYPz48SlWqn/w4IH2/6Ojo7l37x6+vr7aNe7Hjh1j8ODBHD9+HBcXF/Lnz8+qVav4888/KVKkCACmpqaYmprqxFewYEFevXqV5vuePXs2z5494+XLl1hYWNC/f/9k1+SmNYBCCJFbJSQkUaXKEjw8lhEbm2DocEQmaEbjBw2qnuo6c4CKFR1o0aIsioK2ur2hhIfHsmXLTSDtafWg/p1QtqEzrJzYOz41mlH5nKpe7+39iA4dNhAbm0inTi54e39BkybOxMYmMnHi4RyJITc7ezaAzp3/IT4+ia5dXVm0qE2Gc6fcTBL5D9CzZ88YNWoUbm5uVKpUiUqVKvHRRx/h4+PD7NmzAYiMjKR9+/ZUqVKFr776ClNTUzZu3MjIkSNT3Qse1AlucHCwzrGNGzdy7NgxJkyYgLGxMX/99RcjRozg3LnU94mtV68eXbp00UmONbFpODk5Zah4XkpGjBhB69atAfXU+5EjR+Lp6cngwYMZOXIky5cvZ/v27bi7607NCg4O1imcl9L7fpO9vT3z5s1j69atyeoBvE9/oQghRHa5d+8VN2++xNf3pXbarMg7zp0L4MiRB5iYGDF6dN00r9dsRff77xeIiorP7vBStWXLTaKjE6hQwZ7atUukfQOvt6GTRD7n3bjxAh+fJ5iYGNGjR+Uc779nz8qYmhpx8eIzLl9OeXtnffHxeUKbNn8RFRVPq1bl2LChC6amxsyZ4wnAunVXuHDhw9094ebNF7Rps57IyHiaNy/DunWdc6ReQk56v96NSFN0dDSNGzemYsWK+Pj4cPPmTW7evMkff/zBpk2b+O677wD1fuytW7fW2TPd2dmZAwcOUKZM6oVnKlasyI0bN7Rf37t3j7Fjx7Jlyxbt+nFnZ2f+/vtvtmzZkk3vMuO2b99O3759ad26NRs3buTIkSPMmDGDu3fvUq9ePZ3lAwA3b97Umb3w9vtOSWxsLHFxcdoCfwAPHjzQWdoghBAiZTduvND+v6bwmMg7Zs9Wj8b36lUFJyfbNK9v3bocpUsXIDg4hr/+MtzPWzO627dv1XQ/eG/atDQqFVy7FsizZ4ZZevmhWrtWvW1hmzblKVQofcWg9cne3kq7pWJ2jspfvfqcli3XERYWS+PGpdi8uZt2yVGNGsXp3VtdE2rMmP0f5MzPR49CadFiHUFB0dSuXYKtW7u/l0uyJJH/wNy4cQMzMzOGDRumUzCufPny/O9//9Ouj2/fvn2y9dwAZcqUeWehuTJlyhAUFEREhPofrrJly3L69GmdpBegZs2a2vX2b5s5cyYuLi7JXo6OjikeT2kruIxatmwZU6dOpXXr1hQsWBBzc3NcXFyYNWsWhQoV4vTp0zrXe3l54enpmer7jouLw9/fX3s+JCSEzz//nK5du+qM5B84cICWLVtmOX4hhHjf3bz5Uvv/u3bdJjQ0xoDRiIy4e/cVmzerp6ePGVMvXfcYGxsxfHgtABYuPGuQZOTx41AOH/YDSLHCfmocHKyoVq0YAF5eMiqfUxITk1i3Tv3QxxDT6jX69/cA1HvKx8Xpf0/527eD8PRcy6tX0dSpU4KdO3tiZaW7BPTnn5tibm7M4cMPPrgZTC9eRNKixVr8/cNwcXFg9+5eWFubGTqsbCGJ/AfG1dWV+Ph4Vq1apVMp/uHDh3z//fd07Ngxy30MHDiQNWvWaL8uVqxYhu5Pbb17ai99JPItW7Zk5cqVPHv2ehpUQkICmzZtws/PT2dq/c2bNylcuDCOjo46bbz5vl+8eEHHjh1xcXHBw8ODpk2bUrt27WQF+davX88XX3yR5fiFEOJ992YiHxubqE0MRe7322+nSEpSaN26HFWqFEn3fV98UQ1LSxMuX37OiRM5Xxh2/fqrKAo0alQKZ+cCGbpX1snnvCNHHuDvH0aBAha0a5f6dsPZrWXLchQpko+XL6PYs0e/SfSDByE0a7aG588j8fAoyp49vbGxMU92XalSBbTF9saN8yIhIUmvceRW4eGxtGnzF7duBeHklJ/9+/vg4JD+wuB5jSTyHxhLS0sOHTrE+fPnqVGjBq6urri5udGnTx/atm3L9OnT09XOL7/8AqRcCG7QoEFs27aN+Pj0r2kzNTXFxET/U17q1Uvfk/9Ro0bRtWtXunbtSrVq1fDw8KBatWocPHgQLy8vnVH0//3vf0yePDlZG2++7xIlSnD+/Hl8fX25dOkSFy5cYMyYMTrfq6NHj1KnTh2KFi2a9TcqhBDvOc3Ues06ZZlenzcEBkayatUlAMaNa5ChewsWtNSOhOf0VnSKomir1aenyN3bXify9z64qc2RkXEMG7abRYvOkpiYcwmkZhlEjx5uBp1GbWJipP3M/PnnZb21GxAQRtOmq/H3D6NSJQf27+/zzqKREyZ8hJ2dJTduvOCPPy6met37IjY2gU6d/sHH5wkODlbs3983Xct48jKV8qH97ZIOYWFh2NraEhoaSv78+XXOxcTE4OfnR+nSpbGwsDBQhLmft7c3Pj4+KU7Pz8suXrzIvn37tLUE3pbe960oCv369WP58uU6+8qnRD5zQogPXVKSgo3NDKKi4tm3rw8tW65DpYLHj7+mRIn8aTcgDGbSpMP83/8do1at4pw5MzDDBV6vXn1O1apLMTZW8fDh6Bz7eV+48JQaNZZjbm7M8+djsLXN2L+/MTEJFCw4k5iYBK5dG4qb2/uz5VVa5s49zddf7wOgRo1irFjRXrvUILtERMRRtOgcIiPjOXnyC+rVy1ox5Ky6fj2QypWXYGJiREDANxQunLX1+oGBkTRu/Ce+vi8pW7Ygx471p3hxmzTvmzfvNKNH76NIkXzcvTvyvZ1inpiYRLdum9iy5SbW1mYcPvw5NWsWN3RYmfKuPPRtMiIvskWDBg3euyQeoFq1aqkm8ZD+961SqVi9enWaSbwQQgj1WuWoqHjMzIxp2rQ0DRo4oSjq/aJF7hUZGceiReodasaNa5CpXVqqVClC48alSExUWLo057aiW7NGPZLasaNLhpN4AAsLExo1KgV8eNPrt271BUClgvPnn1Kz5gq+/XYfERFx2djnTSIj4ylXzo66dR3TviGbubkVplat4iQkJGW5WOOrV9F4eq7F1/clTk75OXjws3Ql8QBDh9aibNmCPH8eyZw5J7MUR26lKApDhuxiy5abmJkZs317jzybxGeUJPJCCCGEyNU00+rLl7fDxMRIW5FZptfnbn/8cZFXr6IpW7YgnTu7ZLqdr75Sb0W3fPkFYmMT9BVequLjE/n7b/VDoqwUTdNMr/+QCt69eBGprWdw6tQAunVzIylJ4bffTuPmtpjdu29nS79v7h2fW7b11cee8mFhsbRqtY4rV55TtKg1Bw9+RqlSBdJ9v5mZMTNmNAPUO0c8eRKe6VhyqwkTDvL77xcxMlLx999daNq0tKFDyjGSyGeSrEgQOUU+a0KID52m0J2rayEAPv3UDRMT9V7Nb25LJ3KPhIQkfv31FABjxtTP0v7NnTq54OiYn8DASP79991bverD/v33CAyMpFAhK1q0KJvpdpo3VyfyR448yJbq5bnRjh23SEpSqF69GHXqOPLPP13ZvbsXpUrZ8uhRKO3a/U337pv0ui2fv38YBw+qH5ZkZHeB7NajR2XMzIy5fPk5ly5lfE/5yMg42rX7i3PnnmBvb4mXV1/Kl7fPcDtdu7pSt64jUVHxTJ58OMP352a//nqSX37xBmDZsnZ88kklA0eUs7KUyPv6+mJubs6UKVO0x54+fUrbtm1xd3enSpUqLF26VOceRVGYNm0abm5uVK5cmZ49exIWFqZzjbe3N3Xq1MHDw4M6depw/PhxnfPh4eH06dOHypUr4+bmxtSpU5MlOytWrKBKlSq4u7vTunVrAgICsvJWtTTFyqKiovTSnhBp0XzW3i4qKIQQH4qbN9XJeqVKDoB6e69WrcoBsH79FYPFJVL377/XefgwlEKFrPj8c/e0b3gHExMjhg6tCeRM0TtNkbuePStjamqc6XaqVi1CoUJWREbGc/q0f9o3vAc00+rfnIHRpk15rl8fxrff1sPISMXGjddxcVnIsmU+JCVlfbBi/for2t0FSpcumOX29MXOzpKOHdXbL69albFiczExCXTu/A/Hjz/C1tac/fv7ZrrOgkql4tdfWwDwxx+XuHYtMFPt5DZ//nmJMWMOAPDLL80YOLC6gSPKeVkq6Thq1CiaNm2qU528S5cuDB8+nN69exMeHo6npyclS5akTZs2ACxfvpzTp09z4cIFzM3NmTVrFgMHDmTjxo0ABAYG0qtXL/bs2YOrqyu3bt2iZcuWnD59Wlvde9CgQVSpUoV169YRFxdHt27dWLJkCcOGDQNg3759LF++nBMnTmBra8vGjRv55JNPOHPmTFbeLgDGxsYUKFCAwED1HwIrK6tcM4VHvF8URSEqKorAwEAKFCiAsXHmf5kQQoi8TDMiX6lSIe2x3r2rsGvXbf766xrTpjWVf4tzEUVRmDVLvR535Mg6WFpm/UH0wIHVmTLlKGfPBnD2bIB29wJ9Cw2NYds2dTL62WdZewBhZKSiefMy/P33NQ4cuKddM/++Cg+P1dYDeHspRb58ZsyZ04JevaowaNBOzp9/ypAhu1m79grLlrXLdJKqKIrOtPrcpn9/D/799wbr119l9uwWmJml/btcfHwi3br9y4ED98mXz5Q9e3pTvXrWigXWr+/EJ59UYsuWm4wbd4D//uudpfYMbft2XwYO3AHAmDH1Mrwjxvsi01XrN2/ezPbt2ylTpgwJCQlMmzaNK1eu8OWXX+okzPv372fx4sVs27YNgOrVq7N+/XoqVVJPfUhKSqJ06dJcuHABe3t75s+fT0BAADNnztS2MWHCBAoXLszo0aN59eoVHh4e+Pn5aROb27dv061bNy5dugTAJ598wpdffknr1q21bdSvX5/Fixfj4eGR5ntLq1qgoig8e/aMkJCQDH7XhMi4AgUKULRoUfklVQjxQVIUBXv7WQQHx3Dp0mDc3dUP9aOi4ilSZA4REXGcONGfBg1KGjhSoXHgwD1atFiHlZUpjx6Nxt5eP/s4f/75NtasuUzfvlVZs6azXtp828qVFxg4cCeVKjlw/fqwLP/bu2rVRb74Ygd16pTg9OmBeooyd9q48Trdu2+ifHk7bt36KtXvXUJCEgsXnmXixENERsZjamrE+PEN+OGHRlhYZGyM8fz5J9SsuQILCxOePfs2U4UJs1NCQhIlS/6Pp08j2LKlG507v3vqd2JiEr17b+Gff65jYWHCf//14uOP9bPm+86dIFxdF5OQkISXV1+aNSujl3Zz2tGjD2jZch2xsYn06+fBH390eK9+R85I1fpMjchHRUUxadIkDhw4wPLly7XHvby8aNy4sc61H330EV27dkVRFF69ekVAQIA2iQcwMjKifv36HDp0iE8//RQvLy+GDBmi00bjxo2ZP38+o0eP5siRI9StW1dndLJChQoEBgYSGBhI4cKFOXjwIGvXrk3WxoEDB1JM5GNjY4mNjdV+/fZU/7epVCqKFStG4cKFM7RXuhAZZWpqKiPxQogPWmBgJMHBMRgZqahQ4fX6UCsrUzp3dmHt2iusX39VEvlcRDMaP3BgNb0l8QAjRtRmzZrL/PPPdebMaZHlLb1SoplW/9ln7npJDjTr5M+de0JISAwFCuSuRFOf3pxW/67vnYmJEaNH1+WTTyrx1Vf/sXPnbaZNO84//1xn6dJ2GSpWptldoFOnzO0ukN00e8rPmnWSP/+8/M5EPilJYeDAnfzzz3VMTY3YsqWb3pJ4gPLl7Rk6tCYLFpxlzJgDnD8/CCOjvJUAX7z4lPbt/yY2NpEOHSqyYkX79yqJz6hMJfLTp0+nd+/eFC+uW9r/yZMnlCqlO23I0tISCwsLAgMDefHiBY6OybeEcHJy4v79+9o2nJycMnQewNHRET8/P6ysrDAxMSFfvnzJ2rh6NeXqtjNmzNBZ559exsbGkmQJIYQQ2Ugzrb506QLJpmj37l2FtWuvsHHjdebNa5Wl9cxCPy5ceIqX132MjVV8/XU9vbZds2Zx6tQpwZkzASxffp6JExvptf0HD0I4evQhKhXanRGyysnJlooV7bl1K4jDh/3SHJHNq2JjE7QV6dP7HkuWtGX79h5s2XKTESP2cOfOK5o1W8Pnn7szZ04LHBze/RAoPj6Rv/7K+u4C2a1fPw9mzTrJ7t23ef48giJFrJNdoygKI0b8x59/XsLYWMWGDV1p3bq83mOZNKkxq1df5tKlZ6xbdyXLy0dy0p07QbRqtZ7w8DgaNy7Fhg1dMDH5sOu2Z/jd37t3j82bN/PNN98kOxcSEoKFRfKnYRYWFkRFRaV5PrU20jqf0T7e9v333xMaGqp9PX78OMXrhBBCCJGzNFXp31wfr9GsWRkKF85HUFA0+/bdy+nQRAo0e1V3714ZZ+cCem9/xAj1VnRLl/oQH6/fSvCawolNmjjj5GSrt3Y129C9z/vJHzrkR3h4HMWKWWeofoFKpaJLF1du3hzO0KE1Ualg9erLuLgsZO3ay+/cuWfv3ru8fBlFkSL58PTM/O4C2a1SpULUqVOCxEQlxS0zFUVh/HgvFi/2+f/vv1O2VV93cLBiwoSGAEyceIjo6LwxszggIAxPz7UEBkZSrVpRtm/voZfaG3ldhhP5UaNGMW3atBSTZXNzc2JiYpIdj46OxtLSMs3zqbWR1vmM9pFS3Pnz59d5CSGEEMLwNBXrXV0dkp0zMTGiRw83QPaUzw38/ILZuPE6AGPH1s+WPj791I0iRfIREBCuLUqnD7pF0/Q7SqlJMt/nRF4zrb5TJ5dMTde2tbVg8eK2eHt/QeXKhQkKiuazz7bh6bmWu3dfpXiP5ufVu3eVXD8yq9lTftWqS8keTkydepTZs9UPwJYta0fv3tk7u2DkyDo4OeXn8eMw5s3LeiHw7PbqVTQtW67j4cNQype3Y+/ePrlyGYUhZOhTv3fvXqKioujSpUuK5x0dHXn06JHOsejoaCIiIihcuHCK5wEeP36snXKf0jVpnX/zGgcHB22fqbUhhBBCiLwhpYr1b9L80rt9uy/h4bEpXiNyxv/+d5rERIUWLcri4VE0W/owMzNm0KAagH63ojt37gm3bwdhaWlCly76HQ1t0sQZY2MVd+++4sGDEL22nRskJiaxffstIHm1+oyqV8+JCxcGMX16UywsTDh40I8qVZYwffpx4uJez8AIDo5mxw51n3lhenj37m6Ymxtz7VogFy++3lN+zpyT/PTTUQDmzm3Jl1/WyPZYLC1N+fnnpgBMn36cFy8is73PzIqMjKNt27+4fv0FxYvbsH9/32ypjZFXZSiR9/Pzw9/fHw8PD+1r6dKl/P7779SsWZP69etz9OhRnXuOHTtGrVq1MDIyolixYlhbW3Pjxg3t+aSkJE6cOEH9+uontym1cfToUe35evXq4e3tTWLi6z/Mt27dwszMDEdHR1QqFXXq1OHYsWOptiGEEEKIvOF1Ip98RB6gVq3ilCtnR3R0gl5HaEXGvHwZxe+/XwBg3Ljs/X1ryJCamJgYcfz4Iy5ffpb2DemgKZrWuXMlbGzM9dKmRv785tStqx5M8vJ6/0blT53yJzAwkgIFLGjSxDnL7ZmaGvP99x9x9epQmjcvQ0xMAj/8cIjq1Zdx8qR6+evGjdeJi0ukatUi2p0scrOCBS3p1En9kEOzp/zixecYO1a9D/rPPzdl1Ki6ORZP795VqVatKOHhcUydejTtGwwgLi6Rrl3/5fRpfwoWtGDfvj7ZslwnL8tQIj906FBu377NpUuXtK8hQ4YwcOBAfHx8aNSoEfHx8axfvx6A8PBwJk+ezIgRI7RtjBw5knHjxhEXFwfAnDlzcHd3p0wZ9fqhAQMGsH79em2yf+vWLdauXcuAAQMAcHZ2platWtrt6eLj4xk/fnyyPiZNmqStPr9x40YiIyNp0qRJZr5HQgghhDCA0NAYnjwJB8DFJeVEXqVSaQuTyfR6w1m8+BzR0QlUr14sQ1XHM6N4cRvtqPnChVkflY+LS2TDBnXRtL59s2das6Z6/fs4vX7r1psAtGtXQa8FJ8uVs2P//j6sXdsZBwcrrl9/QcOGfzB06C5+/12dDOfmIndv69/fA4C//rrG8uXnGT78PwAmTGjIhAkf5WgsRkYq5sxpAcDSpee5fTsoR/tPS1KSwuefb2Pv3rtYWZmye3cvKlcubOiwcp0sLygxNTXF1FRdbEClUrFt2zbWrFlDlSpVqFOnDt26dePTTz/VXj969Gg8PDxwd3fHzc2Ns2fPsnr1au15R0dH1q5dS9++fXF3d6dnz56sWrVKpxr+qlWruHz5Mm5ublStWhUXFxe+/fZb7fnOnTvz2WefUa9ePapUqcKKFSvYvn07Rka5e/2MEEIIIV7TjMaXKGHzzjWRmkT+wIH7PH8ekep1hrR//z3WrHl38a68KioqXjvNfezY+jmyHdRXX6mL3q1ff5VXr6Kz1NbevXcJCoqmaFFrbcKtb5qCdwcP3icp6f35DCiKorPtnL6pVCr69KmKr+9w+vf3QFHUiaePzxOMjFT06qWf3QVyQvPmZShe3IZXr6IZPHgXAKNG1WHatKYGiadp09K0aVOehIQkvv/+oEFiSImiKIwcuYcNG65pt+GrVy/5jmUCVMr7+C9KFoWFhWFra0toaKgUvhNCCCEMZNWqi3zxxQ6aNy/DgQN933lt7dorOHfuCfPmtWLkyDo5FGH6+PuHUbbsfOLiEvnvv17Zsq2UIS1Zco5hw/7D2bkAd+6MyJHCY4qiUL36ci5desbs2Z6MGZP56fxdu25k8+abfPNNXX79taUeo3wtPj4Re/tZhIfH4ePzJTVqFE/7pjzg8uVneHgsw8LChJcvx5Ivn1m29nf4sB+DB+/izp1XtGtXgZ07e2Zrf/r2/fde/PKLNwBfflmdZcvaGXQf9OvXA6ladSlJSQrHj/enYcOSBotF46efjjBlylFUKvjrry706FHZ0CHlqIzkoTJELYQQQohc6fXWcylPq39Tbp5eP3PmCW2hrgkTDr1XI7KJiUnMmXMKgG+/rZdj1cNVKpV2K7pFi86RmJiUqXaCg6PZuVO9/3nfvtlXNM3U1JiPP1YvOXifptdrRuNbtiyb7Uk8wMcfl+bKlaHs2NGDNWs6ZXt/+jZ0aC3Kli3IkCE1WLKkrUGTeAA3t8IMGFANgLFjDxh8xtDChWeZMuXo////Nh9cEp9RksgLIYQQIlfSTK13dU25Yv2bunevjJGRirNnA7hzJ/es93zyJJwVK9RF4MzMjLl06Rn//nvdwFHpz5YtN7l/Pxh7e0vtGuCc0rNnZezsLHnwIITdu+9kqg1N0bQqVQrj7l5EzxHq0kyvf58K3mXntPrUWFiY0L59RQoWTHlb6dysZElb7t4dyZIl7TA2zh1p2JQpTciXz5TTp/3ZtOlGmtdnh7i4RMaO3c+IEXu0MQ0bVssgseQlueMTJIQQQgjxlrQq1r/pzfXNf/2Ve0blZ83yJjY2kQYNnJg4UV3QauLEw8THJ6ZxZ+6nKAozZ6qnCX/1Ve0cGZF9k6WlKQMHqkcTM1v0bu1a9V7kfftWzfbRUU0if+LEI6Kj47O1r5xw/34wV648x9hYRfv2FQ0djsikYsVsGDtWvTTlu+8OEhubkKP9374dRL16K7Uze8aNq8+PPzbK0RjyKknkhRBCCJHrREfH4+cXDKS+h/zb3pxeb+gpogBPn4azbNl5ACZPbszo0XUpVMiKu3dfsWrVJcMGpwdHjjzg/PmnWFqaMHy4YUbPhg6thZGRigMH7uPr+zJD99679wpv78cYGano3Tv7q59XqGCPo2N+YmMTOX78Ubb3l9001eobN3bGzi7vjY6L1779tj5Fi1pz/34wS5b45EifiqKwatVFqldfxoULT7Gzs2Tbtu7MnOlp8CUHeYUk8kIIIYTIdW7dCkJRwM7OkkKFrNJ1T+fOLlhamnDnzit8fJ5kc4Rpmz37JDExCdSt60jz5mWwsTHnhx/Uo/JTphzN86Oys2adBOCLL6pRqFA+g8Tg7FyA9u0rABkflV+3Tj0a36xZaYoXt9F7bG9TqVTaUfkDB+5le3/ZzRDT6kX2sLY2Y+rUJgD83/8dIzg4aztBpCUkJIYePTbzxRc7iIyMp2nT0ly5MoSOHeWzlBGSyAshhBAi17l5U13oztW1ULpHZ2xszOnQQT3F19BF754/j2DpUvXI1uTJjbXvYciQmpQsacuTJ+EsWnTOkCFmyZUrz9m79y5GRiq++aaeQWPRFL1bvfoyYWGx6bpHURSdafU55XUin7fXyT9/HsHJk48B6NRJkq/3Qf/+1XBzK8SrV9FMn3482/rx9n6Eh8dSNm68jomJETNmNGP//j6UKCE7hWWUJPJCCCGEyHUysj7+TZrp9Rs2XCMhIXOVzPVh9uyTREcnULt2CVq2LKs9bm5uwpQpTQCYMeMEoaExhgkwi+bMUY/Gd+3qSpkyBQ0aS9OmpalUyYGIiDhWr76UrntOnfLn3r1g8uUzpXPnStkb4BuaNVMn8pcvP+f584gc61fftm+/haJArVrFcXSUBOx9YGJixKxZngDMn3+WBw9C9Np+QkISU6cepVGjP3n4MJSyZQvi7f0F333XMNcU/str5LsmhBBCiFwnI1vPvally3LY2Vny/Hkkhw75ZUdoaQoMjGTxYvVo+5uj8Rp9+1alUiUHXr2K5tdfTxkixCx59CiUv/++BqAtkmVIKpWKr75Sj8ovXHguXdv7rV17GYBPPqmEtXXOFekrXDgfHh5FAQz2+dQHmVb/fmrduhxNm5YmLi6RH344pLd2Hz0K5eOPVzN58hGSkhT69q3KxYuDqV27hN76+BBJIi+EEEKIXCcjW8+9yczMmG7dXAHDTa//9Vf1aHzNmsVp3bpcsvPGxkZMm9YUgN9+O5XnRmbnzj1NQkISTZuWpmbN4oYOB4DPPnMnf35zbt8OSnN7t9jYBP7557r2vpzWvHne3k8+NDSGgwfVsefkbAaR/VQqFbNnq0fl//rrql5qjWzadAN396WcOPEIGxsz1q3rzJo1nbGxMc9y2x86SeSFEEIIkavExydq94JPb8X6N2kqkG/ZcpOoqJwtKPfyZZR27XtKo/EanTu7UKtWcSIj47N1Paq+BQdHs3y5uhL/uHGGH43XsLY2o18/dVK+YMG7i97t3n2H4OAYihe34eOPnXMgOl2enuqlFgcO3M8Vuytk1H//3SE+PgkXFwdcXDI2Y0bkftWrF9PWjRgzZn+mP6ORkXEMHLiDTz/9l5CQGOrUKcGlS0NyZIeID4Uk8kIIIYTIVe7dCyY+Pol8+Uxxcsr4+tv69Z0oVcqWiIg4du68lQ0Rpu7XX08SGRlPjRrFaNu2fKrXqVQqpk9vBsDSped5+DAkhyLMmiVLfIiMjKdq1SK0aFE27Rty0PDh6un1u3ff5v794FSv0xS56927ikHW5n70UUnMzY3x9w/j1q2gHO8/q2Ra/ftv2rSmmJsbc/ToQ3btup3h+y9ceEr16stZufIiKhX88MNHHD/e3+D1NN43ksgLIYQQIlfRVKx3cXHI1H7CRkYqevV6vad8TgkKimLhQvVo/KRJqY/GazRvXoZmzdTrUX/66WhOhJglMTEJzJt3BlCPxue2vZ4rVLCnVatyKAraGgVvCwqKYvdudWJiiGn1AJaWpjRsWBLIe9vQxcQksGfPXUAS+fdZyZK2jB5dF4Bx47zSXTg0KUnht99OUbfu79y+HUSJEjYcOvQ506Y1xdTUODtD/iBJIi+EEEKIXCWz6+PfpKlev2fPXYKCovQSV1p+++0UERFxeHgU1e5tnhbNqPyaNZe1Bf5yqzVrLhMYGEnJkrZ06+Zm6HBS9NVXtQBYufIikZFxyc7/88914uOT8PAoSuXKhXM6PC3NNnReXnmr4J2X130iIuJwdMyfa+ojiOzx/fcNsbe3xNf3Jb//fiHN6589i6BNm/V8++1+4uOT6NTJhcuXh9CkiXP2B/uBkkReCCGEELlKZreee5ObW2Hc3YuQkJDEv//e0FdoqXr1Klq7NnvSpEbpHq2uXbsEnTu7kJSkMHGi/qpE61tiYpK2wv7XX9fNtaNrrVuXp2zZgoSExKQ4G2PNGnW1+s8+M+w6Xc06+cOH/YiPTzRoLBmxdetNADp1qpjrZmQI/bK1tWDy5MYATJ58hPDw2FSv3bPnDu7uS9m37x6WliYsXdqWLVu6YW9vlVPhfpAkkRdCCCFErvJ667nMj8jD61H5nJhe/7//nSI8PI6qVYvQsWPGphxPm9YUIyMVW7f6cvZsQDZFmDU7dtzi9u0gCha0YODA6oYOJ1VGRiqGD1ePyi9ceFanUNft20GcOROAkZGKnj2rGCpEADw8imJvb0l4eFyu/Zm/LSEhiR071MsSpFr9h2Hw4JqUK2dHYGAks2Z5JzsfG5vA11/vpU2bvwgMjKRq1SL4+Axi8OCa8qAnB0giL4QQQohcIylJwdc361PrAXr2rIJKBSdOPMrWYnLBwdHMn/96NN7IKGO/wLq6FtJWiZ4w4aDe48uqpCSFmTPVv8QPG1YrR/ddz4z+/athZWXK1auBHDv2UHtcs3d8y5ZlKVrU2lDhAeoHDs2aqafX55Vt6Ly9H/HyZRR2dpY0alTK0OGIHGBmZszMmc0B+PXXUwQEhGnP3bz5gjp1fmfuXHXdjJEja3PmzMAs/70t0k8SeSGEEELkGo8fhxIVFY+ZmXGWKxw7OuancWNnQL0ncnaZN+8MYWGxVK5cONMjlT/91AQzM2MOHvTT7tGdW0yZcoQzZwKwsDBhxIjahg4nTQUKWGgfjGiWOyQlKaxbp/4MaM4ZmmadfF5J5DXV6tu3r4CJiaQQH4rOnV1o0MCJ6OgEJk06jKIorFhxnho1lnP58nMcHKzYtasn8+a1xsLCxNDhflDkT6EQQgghcg3NtPry5e30kiz06fN6en127NkdEhLD3LmngcyNxms4OxdgyJAaAEyYcCjX7C/+119XmTr1GACLF7ehSBHDjmSn11dfqR84bNvmy+PHoXh7P+LBgxBsbMwyvPQhu2gS+TNn/AkLS339cW6gKIpsO/eBUqlUzJ7tCcCqVZdo3Xo9gwbtIjo6AU/PMly5MoS2bdNX3FPolyTyQgghhMg1Xhe608/0zC5dXDEzM+b69RdcufJcL22+af78M4SGxuLqWoguXVyz1NYPPzQiXz5Tzp4NYNs2Xz1FmHmnTj3miy+2AzB2bH36969m4IjSr3LlwjRp4kxiosLSpT7aInddu7piZWVq4OjUSpUqQPnydiQmKhw58sDQ4bzTxYvPePQoFCsrU1q0KGvocEQOq1fPiU8/dUVRYN++e5iaGjF7tid79/ahWDEbQ4f3wZJEXgghhBC5hmYPeVfXzFesf1OBAha0a6ceLdJ30bvQ0Bj+9z/1aPyPP2Z+NF6jcOF8fP21eu/mH344RGJi+vZuzg4PH4bQqdM/xMYm0rFjRWbMaGawWDJLswxg+fIL2p0Lcsu0eo3mzTXT63P3fvKaavWtWpXD0jJ3PAgROWvGjGYUKmRFxYr2nDo1gDFj6mf57zyRNZLICyGEECLX0PeIPLyuXv/339dIStLflPUFC84SEhKDi4sDn36atdF4jTFj6mNnZ8nNmy9Zu/aKXtrMqPDwWNq1+5vAwEjc3Yuwbt0nGBvnvV8ZO3SoiJNTfl6+jCI0NBYnp9c1E3KLvLJOXqbVi7Jl7Xj06Gtu3hxOjRrFDR2OQBJ5IYQQQuQSiqK8sfWcfkbkAdq0KY+trTn+/mE6VcyzIjw8lt9+U++r/uOPjfSW6NraWvDddw0A9d7NsbEJemk3vRITk+jZczPXrgVStKg1O3f2zPVV6lNjYmLE0KE1tV/36VM1140gfvxxaYyMVNy6FcTjx6GGDidFd+4Ecf36C0xMjGjbtryhwxEGZGFhItvK5SKSyAshhBAiVwgMjCQ4OAYjIxUVKtjrrV0LCxO6dlWPmK9fr59R7oULzxIcHEPFivZ07+6mlzY1vvqqNsWL2/DoUSjLlp3Xa9tpGTv2ALt338HCwoQdO3rg5GSbo/3r25df1sDS0gSVKvdNqwf10o/atUsA4OWVO0flNaPxH3/sTMGClgaORgihIYm8EEIIIXIFzbT60qUL6H0drmZ6/aZNN7M8yh0REcevv6pH4ydO1N9ovIalpSmTJzcGYNq0Y0RExOm1/dQsX35eu+Z/9epO1KpVIkf6zU4ODlYcPPgZe/b01utyDX3K7dPrZVq9ELmTJPJCCCGEyBVeT6vXf8LVuLEzJUrYEBISw3//3clSW4sWnSUoKJry5e3o0aOyniLU1b+/B+XK2fHiRZR2e7vsdOiQH8OH/wfA1KlN6NZNv7MMDKlePSdatixn6DBSpSl45+V1X681HPThyZNwTp/2B8g12/YJIdQkkRdCCCFErqCpWK/P9fEaRkYqevZUJ91ZqV4fERHHnDmvR+P1sdd9SkxNjfm///sYgNmzTxIUFJUt/QDcvh1Ely4bSUhIolevKkyc2Cjb+hLJ1a3rSL58prx4EZUtWyRmxfbt6tH4unUdKV5cthkTIjeRRF4IIYQQuYJmar2ra/ZMge7dW71Geteu24SGxmSqjSVLzvHyZRRlyxakV68q+gwvmW7d3PDwKEpYWCy//HIiW/p49Sqadu3+IiQkhnr1HFm5soMUs8phZmbGNGniDOS+behkWr0QuZck8kIIIYTIFbKjYv2b3N2L4OpaiNjYRDZvvpnh+6Oi4pk9+yQAP/zwUbaNxmsYGamYPr0pAAsXniMgIEyv7cfFJdK160bu3HlFqVK2bN3aHQsLE732IdInN66TDw6O5vDhB4Ak8kLkRpLICyGEEMLgQkNjePo0AgAXl+xJ5FUqlbboXWam1y9d6sOLF1GULl2APn1ypgJ6q1bl+OijksTEJDB16lG9tasoCsOH7+bw4QdYW5uxc2dPihSx1lv7ImM8PcsCcPz4I2JicnbLwdTs3n2HhIQk3NwKUb68/naREELohyTyQgghhDA4zbT6EiVssLW1yLZ+NNPhDx/2y9AId1RUPLNmeQPq0XhTU+Nsie9tKpWKGTOaAbBy5UXu3AnSS7v/+99pfv/9IkZGKjZs6EKVKkX00q7InEqVHChe3IaYmAS8vR8ZOhxAptULkdtJIi+EEEIIg3td6C57twhzdi5AgwZOKAps2HAt3fctX36e588jcXYuwGefuWdjhMk1aFCStm3Lk5io8OOPh7Pc3s6dtxgzZj8Av/3WgrZtK2S5TZE1KpVKW70+N0yvj46OZ+/euwB07lzJwNEIIVIiibwQQgghDC6718e/KaPT66Oj45k5Uz0aP2FCwxwbjX/Tzz+r18r/8891Ll58mul2Ll9+Rs+em1EUGDy4BiNH1tFXiCKLctM6+f377xEVFU+pUrZUq1bU0OEIIVIgibwQQgghDE4ztT4nEvlPP3XDxMSIixefaWcCvMuKFRd49iyCkiVt+fxzj2yPLyXu7kW1ywJ++OFQptp49iyC9u3/JjIynmbNSrNgQWupUJ+LaEbkL158ysuX2bfdYHpoptV36uQinxEhcilJ5IUQQghhcNm99dybHBysaNWqHJD2qHxMTILOaLyZWc6PxmtMndoEExMj9uy5y/HjDzN0b3R0PJ06beDx4zAqVLDn338/NcjMApG6okWtqVKlMIoChw75GSyOhIQkdu68Dcj6eCFyM0nkhRBCCGFQ0dHx+PkFA9m/Rl5DM73+r7+uoihKqtetXHmBJ0/CcXTMT79+HjkSW2rKlrVj4MBqAHz//cF3xv0mRVH44osdnDkTgJ2dJbt29aRgQcvsDFVk0uvp9YbbT/7YsYe8ehWNg4MVDRuWNFgcQoh3k0ReCCGEEAZ161YQigJ2dpYUKmSVI3126FARa2sz/PxCOHXKP8VrYmMT+OUX9Wj89983xNzc8Hus//hjYywtTfD2fsx//91J1z1Tpx5lw4ZrmJgYsXlzN9lKLBd7s+Bdeh/U6NvWrTcB6NChAsbGkioIkVvJn04hhBBCGJRmnbqra6EcW49rZWWqnTa8fv2VFK/544+L+PuHUaKEDQMGVMuRuNJSvLgNI0bUBmDChEMkJb072fv776v89JN6//mlS9vSpIlzdocosqBRo1KYmRnz8GEod+++yvH+FUVh27ZbgFSrFyK3k0ReCCGEEAaVk4Xu3qSZXr9x4w3i4xN1zsXGJjBjxgkAvvsud4zGa4wf3xBbW3OuXHn+zi30Tp/2p3//7QCMGVOPAQOq51SIIpPy5TOjfn0nwDDV6318nuDvH4a1tZl2doAQIneSRF4IIYQQBpWTW8+9qVmzMhQunI+XL6PYv193TfKff17i8eMwihe3YeDA3JUA29lZMm5cAwAmTTqc7CEEwMOHIXTsuIHY2EQ6dKjIL780z+kwRSZp1sl7eeV8Iq+pVt+6dTksLDL38CouMY4fDv7Ayccn9RmaEOItksgLIYQQwqBej8jnTKE7DRMTI3r0cAN0q9fHxSUyfbp6NH78+AaZTmiy06hRdShSJB/37gWzcuVFnXPh4bG0b/83gYGRuLsXYf36T2Stcx6iSeQPHfIjISEpR/vWJPJZqVb/99W/mX5iOj029SAxKflDJiGEfsjf6kIIIYQwmPj4RO7cCQJyZuu5t/XuXRWA7dtvERERB8CaNZd59CiUokWt+fLL3DUar5EvnxkTJzYC1MXsoqLiAUhMTKJXry1cvRpI0aLW7NzZE2trM0OGKjKoevViFCxoQWhoLD4+T3KsX1/fl/j6vsTU1Ig2bcpnup0Tj9QPwR6HPWb3nd36Ck8I8RZJ5IUQQghhMPfuBRMfn0S+fKY4OeXP8f5r1SpOuXJ2REXFs22bL/Hxifz883EAxo2rj6WlaY7HlF6DBtXA2bkAT59GsHDhWQDGjTvArl23sbAwYfv2Hjg52Ro4SpFRxsZGNG1aGsjZbeg01eqbNSuDra1Fpts56f96Sv3ic4uzHJcQImWSyAshhBDCYDQV611cHHKsYv2bVCqVtujd+vVXWbv2Cg8ehFCkSD4GD66Z4/FkhJmZMVOnNgHgl19OMGfOSX777TQAq1d3onbtEgaMTmTF6/3kc26dvD6m1QdHB3PjxQ3t1/vu7ePeq5x7GCHEh0QSeSGEEEIYjGZ9vCGm1WtoEvkDB+7x009HABg7tj5WVrl3NF6jV68quLkVIjg4hrFjDwAwZUoTunVzM2xgIks8PcsCcOqUv3bJR3by9w/j3LknqFTQsWPFTLdz2l/9IKm8XXlalWsFwFKfpXqJUQihSxJ5IYQQQhiMobaee1P58vbUqlWcxESFx4/DKFTIiiFDcvdovIaxsRE//9xU+3XPnpX58cdGBoxI6EOZMgUpU6YgCQlJHD36INv727ZNPRpfv74TRYpYZ7odTaX6+k71GVZzGAB/XPqD6PjorAcphNAhibwQQgghDOb11nOGG5GH16PyoB6Nz5cv7xSI69ChIkOH1qRXryr88UdHgyxREPqXk9Pr9TGtHl6vj6/vVJ825dtQ0rYkr6Jf8e+Nf7McoxBCV4YT+fnz51O1alXc3d1xcXGhb9++BAQEADB16lQ8PDx0Xs7OznTv3l17/8CBAylZsqTONUOGDNHpw9vbmzp16uDh4UGdOnU4fvy4zvnw8HD69OlD5cqVcXNzY+rUqSiKonPNihUrqFKlCu7u7rRu3VoboxBCCCFyh6QkBV9fw4/IA/ToUZn8+c1xdMzP0KG1DBpLRqlUKhYvbsv69Z/kyq3yROY0b54ziXxQUJR21L9z50qZbichKYEz/mcAdSJvbGTM4BqDASl6J0R2UClvZ8Bp8PPzo1ixYlhYWJCQkMCUKVPYtWsXFy9eTPH6oUOH4uHhweDB6j/I/fr1o2HDhgwcODDF6wMDA6lVqxZ79uzB1dWVW7du0bJlS06fPk3RokUB6NmzJ1WqVGHChAnExcXRrVs3WrRowbBh6ik8+/btY+LEiXh5eWFra8vGjRv59ddfOXPmTLreY1hYGLa2toSGhpI/f85X0BVCCCE+BA8fhuDsPA9TUyOion7AxMSwEwUDAsIwMzOmUKF8Bo1DCIBXr6JxcJiFokCdOiWoVq0o1asXo1q1YlSuXFhvD21Wr75Ev37bqVq1CJcvD0n7hlRceHqBGstrYGtuy6vxrzBSGfE84jlO/3MiPime84POU71Y7tzOUYjcIiN5aIb/xSxdujQWFuotKUxMTJgyZQr379/nyZPk+1xGRESwbds2evXqle72N2zYQI8ePXB1dQWgYsWK9OrViw0bNgDw6tUrvL29GT9+PABmZmbMmjWL5cuXa9tYtmwZU6dOxdZWveVKt27dMDY25tKlSxl9u0IIIYTIJppp9RUq2Bs8iQcoUSK/JPEi17Czs9SOkJ85E8DSpecZNGgXtWqtwMZmBu7uS+nXbxvz5p3m2LGHhIXFZqofvU2r///r4+s51cNIpf7zXMS6CF1duwKw5NySLLUvhNCV5X81o6KiUKlU2NvbJzv3119/0aZNG2xsbNLdnpeXF40bN9Y51rhxYw4cUFdiPXLkCHXr1sXY2Fh7vkKFCgQGBhIYGAjAwYMHadSoUaptvC02NpawsDCdlxBCCCGy1+tCd4ZdHy9EbvXvv5/i6zucv//uwtix9WnevAz29pYkJCRx5cpzVq++zOjR+2jc+E9sbX+hfPkFdOv2L7/8coJ9++4SGBj5zvYjI+PYt0+9PZy+Evn6jvV1jg+tORSA9VfXExITkqU+hBCvZWlOzvXr1xk3bhyTJ0/G3Nw82flly5axaNGiDLX55MkTnJycdI45OTlx//79VM8DODo64ufnh5WVFSYmJuTLly9ZG1evXk2xzxkzZjBlypQMxSmEEEKIrNHsIe/qatj18ULkVkZGKipWdKBiRQd69KgMgKKod1e4ePEpFy8++/+vpzx+HMbdu6+4e/cV//77ei/3EiVsqFat2BtT84tSsqQtKpWKffvuEROTQOnSBahatUiWYn2zYv2bGpZsSOXClbkWeI3Vl1Yzqu6oLPUjhFDLVCI/duxY1q5dy/Pnzxk4cCCjRiX/A3nu3Dni4uKoW7euznGVSsWCBQtYtmwZ0dHR1KlTh59++kmbnIeEhGin7mtYWFgQFRWV6vk3r0nrfEq+//57vvnmG+3XYWFhKT4sEEIIIYT+3LghI/JCZJRKpaJkSVtKlrSlY8fXo+gvX0Zpk/sLF9T/vXMniICAcAICwtm167b2Wjs7Szw8ivLypfp3486dXbK020FAWAAPQx9ipDKidonayeIdVnMYw/4bxhKfJYysM1J2VhBCDzI1tX727Nk8e/aMly9fYmFhQf/+/ZNds2zZMgYNGpTs+Jw5czh37hznzp3Dx8eHsmXL0rJlSxISEgAwNzcnJiZG557o6GgsLS1TPf/mNWmdT4m5uTn58+fXeQkhhBAi+yiKoh2RN3TFeiHeBw4OVnh6lmXcuAZs2NCVW7e+IjT0O06c6M/8+a3o398DD4+imJoa8epVNIcO+XHlynMga9Xq4fVovHsRd2zMky+p7VO1D9Zm1twKusXhB4ez1JcQQi1LU+vt7e2ZN28eBQoUYP78+dricmFhYWzbto3Zs2eneI+GhYUFEyZM4I8//uDGjRtUrVoVR0dHHj16RJUqr/dzffz4MY6OjoB6Cv3Zs2eTtau5xsHBgejoaCIiIrC2tk6xDSGEEEIYVmBgJMHBMahU6mJ3Qgj9s7Exp0GDkjRoUFJ7LDY2gevXX2hH74sVs6ZBg6zNRE1tWr02DnMb+lbtyxKfJSzxWULT0k2z1J8QQg/F7mJjY4mLiyMxMVF7bO3atbRu3ZqCBQumq43ExERMTNTPFOrXr8/Ro0d1zh89epT69dV/MdSrVw9vb2+d/m7duoWZmRmOjo6oVCrq1KnDsWPHUm1DCCGEEIalKXRXpkxBLC1NDRyNEB8Oc3MTqlcvxoAB1Vm4sA0//NAoy1PdT/q/O5GH10Xvtt7cypPw5LtdCSEyJkOJfFxcHP7+/tqvQ0JC+Pzzz+natSt2dnba48uWLePLL79MsY2HDx9q/z82NpaJEydStGhR7XZzAwYMYP369dy4oS7ScevWLdauXcuAAQMAcHZ2platWsycOROA+Ph4xo8fz4gRI7Ttjhw5kkmTJmmrz2/cuJHIyEiaNGmSkbcrhBBCiGyi2XpO1scLkbdFx0dz4ekF4N2JfJUiVWhYsiGJSiIrzq/IqfCEeG9laGr9ixcv6NixI5GRkVhYWGBkZESvXr10it2dPXsWRVGSbf+mMWPGDI4cOYKFhQVJSUl4enqye/du7XlHR0fWrl1L3759SUhIwNjYmFWrVlGqVCntNatWrWLo0KG4ubmRlJREx44d+fbbb7XnO3fuzOPHj6lXrx5GRkYULVqU7du3Y2Rk+D1qhRBCCIGsjxfiPeHzxIeEpASKWRejlG2pd147rOYwTjw6wfILy5nw0QRMjWU2jhCZpVIURTF0ELlNWFgYtra2hIaGSuE7IYQQIhs0b76Ggwf9WLWqI/36eRg6HCFEJv1y4he+P/g9XSp1YVO3Te+8NjYhlpJzSxIYGcjmbpv5pNInORSlEHlDRvJQGaIWQgghRI57PbVeRuSFyMs0he4aODVI81pzE3MGVFMvl118bnG2xiXE+04SeSGEEELkqNDQGJ4+jQDAxUUSeSHyKkVR0qxY/7bBNQajQsVBv4PcenkrO8MT4r0mibwQQgghcpSmYn3x4jbY2loYOBohRGbdeXWHoOggzI3NqVasWrruKVWgFO0qtANgqc/S7AxPiPeaJPJCCCGEyFGaQneurlKxXoi8TDMaX6tELcyMzdJ9n2Yruj8v/0lUfFS2xCbE+04SeSGEEELkKFkfL8T7wfuRNwD1HdM3rV6jZbmWlC5QmpCYEDZc25AdoQnx3pNEXgghhBA5SjO1XhJ5IfK2k/4ZWx+vYaQy0o7KLzq3CNlES4iMk0ReCCGEEDlKk8jL1Hoh8q7g6GBuvLgBZDyRB+hfrT/mxuZceHqBc0/O6Ts8Id57ksgLIYQQIsdER8fj5xcMQKVKksgLkVed9j8NQHm78hTKl/E/yw5WDnRz6wbAEp8leo1NiA+BJPJCCCGEyDG3bgWhKGBnZ0mhQlaGDkcIkUkZ3XYuJcNqDQNgw7UNBEUF6SUuIT4UksgLIYQQIsdoKtZXquSASqUycDRCiMzK7Pr4N9UpUQePoh7EJMTw56U/9RSZEB8GSeSFEEIIkWM0FetlfbwQeVdCUgJn/M8AWUvkVSoVw2qqR+WXnl9KkpKkl/iE+BBIIi+EEEKIHJOVivWKosgv+kLkAleeXyEyPhJbc1tcC7lmqa1eVXqR3zw/d1/dxeu+l54iFOL9J4m8EEIIIXLM60Q+YyPy8YnxuC91x2OpehquEMJwNOvj6znVw0iVtXQin1k+Pnf/HIDF5xZnOTYhPhSSyAshhBAiR8THJ3LnjrqgVUan1h95cISrgVe5GniVDdc2ZEd4Qoh00ha6c8z8tPo3afaU33l7J49DH+ulTSHed5LICyGEECJH3LsXTHx8EvnymeLklD9D9265uUX7/3NPz0VRFH2HJ4RIJ31UrH9TpUKV+Nj5Y5KUJJafX66XNoV430kiL4QQQogcoalY7+KSsYr1SUoS225t0359+flljj48qu/wxHtIURS+2P4FlRZVot1f7Ri9dzQLzixgz5093Am6Q3xivKFDzHMCwgJ4GPoQI5URtUvU1lu7mlH5FRdWEJcYp7d2hXhfmRg6ACGEEEJ8GDK7Pv60/2meRTwjv3l+ulbqyh+X/mDu6bk0cW6SDVGK98nmm5tZdWkVAL4vfZOdN1YZU6pAKcrZlaNcwXLq/9qVo6xdWcoULIOFiUVOh5zraUbjqxapio25jd7a7eTSiaLWRXkW8Yxtvtvo5tZNb20L8T6SRF4IIYQQOeL11nMZq1i/9eZWANpVaMfYBmP549If7Li1g3uv7lHWrqze4xTvh9iEWMYdGAfAoOqDqF6sOndf3eVu8F3uvrrLvVf3iE6I5n7wfe4H32c/+3XuV6HCMb+jNrnXJvkFy1LWrizWZtaGeFsGp0nkGzg10Gu7psamDKo+iKnHprL43GJJ5IVIgyTyQgghhMgRmRmRVxSFrb7qRP4Tl09wcXChdbnW7Lm7h/ln5jOv9bxsiVXkfQvPLsQvxI9i1sX4reVv5DPLp3NeURSeRjxVJ/cpvMLjwnkc9pjHYY85/OBwsvaLWhelnF05ahWvxS/Nf8HM2Cyn3ppBnfTX7/r4N31Z40t+Pv4zRx8e5XrgddwKu+m9DyHeF5LICyGEECLbJSUp+PpmfA/5q4FXuRd8DwsTC1qVawXA6Lqj2XN3D39c+oOpH0/F1sI2W2IWedfLqJf837H/A+Dnpj8nS+IBVCoVxW2KU9ymOI1KNdI5pygKL6Ne6ib3wa///1X0K55FPONZxDNOPDpBreK16FmlZ468N0OKjo/mwtMLQPYk8o75HelQsQNbfbey1GcpC9os0HsfQrwvpNidEEIIIbLd48ehREXFY2pqRNmydum+TzOtvmXZltpkzLOMJ5UcKhERF8EfF//IlnhF3jb16FRCY0NxL+LOZ+6fZfh+lUpFoXyFqOdUj77ufZny8RTWf7KeMwPPEDQuiFfjXnHuy3P0rtIbgB23d+j7LeRKPk98SEhKoJh1MUrZlsqWPobVGgbA6suriYiLyJY+hHgfSCIvhBBCiGynWR9foYI9Jibp//Vji69627nOLp21x1QqFaPrjgZg/tn5JCYl6i9QkefdenmLJT5LAPi1xa8YGxnrvY+ClgWpWbwmw2sNB2DPnT0fRAV878fegHo0PiM7T2RE09JNKW9XnvC4cNZfWZ8tfQjxPpBEXgghhBDZLjPr4++9useV51cwVhnTvmJ7nXN9qvbBztKOByEP2HHrwxgNFekzzmscCUkJtK/QnmZlmmVrX7VL1KZwvsKExoZy/NHxbO0rN9D3/vEpMVIZabeiW+yzGEVRsq0vIfIySeSFEEIIke00e8hnZH28pshdE+cm2FnqTse3MrViSI0hAMw9M1c/QYo875DfIXbc2oGxyphZnrOyvT9jI2Palm8L8N4/UFIUJdsq1r+tn0c/LE0sufL8Cqf8T2VrX0LkVZLICyGEECLb3bihHpF3dU3/iLy2Wn2lT1I8P6zWMEyMTDj28Ji2AJf4cCUmJfLt/m8BGFpzKC4OLjnSb4eKHQB1Iv8+jx7feXWHoOggzI3NqVasWrb2VdCyID0q9wBg8bnF2dqXEHmVJPJCCCGEyFaKomR4RP5p+FPt6F/Hih1TvKZE/hLavabnnp6b9UBFnrb2ylouPbuErbktk5tMzrF+Pct4Ym5sjl+IHzde3MixfnOa5s9jrRK1cmSrPU3Ru39v/MuLyBfZ3p8QeY0k8kIIIYTIVoGBkQQHx6BSqYvdpcf2W9sBqOtYlxL5S6R63ag6owDYcG0DT8OfZj1YkSdFxkUy4eAEACY2moiDVfqXcGRVPrN82rX47/P0eu36eMfsWx//pprFa1KreC3iEuNkdwohUiCJvBBCCCGylabQXZkyBbG0NE3XPVtuJq9Wn5LaJWpT36k+8Unx2krl4sMz5+QcnkY8pXSB0oyoPSLH++9Q4f9Pr3+Pt6F7s2J9TtEUvVt2fpnsTiHEWySRF0IIIUS20mw9l96K9cHRwRx+cBhIO5EHGF1nNABLfJYQkxCTuSBFnvUk/AmzTqoL281sPhNzE/Mcj6FdhXYAnPE/w/OI5znef3YLjg7WLhuo51Qvx/rtXrk7BS0K4hfix757+7K9v5dRL1l8bjHzz8xn9+3d3HxxU/5OEbmWiaEDEEIIIcT7LaPr43fd3kVCUgKVC1emvH35NK/vXKkzJW1L8ij0EX9d/Ysvqn2RpXhF3jLx0ESi4qOo71Sfrq5dDRJDifwlqFGsBuefnmf3nd3v3WfwtP9pAMrbladwvsI51q+VqRX9Pfrz2+nfWHxuMW3Kt8mWfi4+vciCswv46+pfxCbGJjtfwqYEZe3KUqZgGcoW1P2vg5UDKpUqW+IS4l0kkRdCCCFEtnq9h3z6EnlNtfr0jMYDmBiZMKL2CMYeGMvc03Pp79FffrH+QFx8epE/L/0JwG8tfjPoz71DxQ6cf3qeHbd2vHeJfE7sH5+aITWH8Nvp3/jvzn88CHmAcwFnvbQbnxjP5pubWXB2gfb9AVQvVp3SBUpzL/ge917dIzwunIDwAALCAzj28FiydmzMbNSJvV1ZyhQoo5Pwl7Qtialx+pYTCZFRksgLIYQQIltpptanZ+u5qPgo9t7dC6S+7VxKBlQbwE9HfuJq4FUO+R3SFh8T7y9FUfh2/7coKPSs3JM6jnUMGk+Hih2YfGQy++/tJzo+GktTS4PGo08n/Q2XyJe3L49nGU8O3D/AMp9lzGg+I0vtPYt4xvLzy1nqs5SnEeoCmaZGpnzq9ilf1fqKuo51tQ+EFEUhKDqI+8H3uffqnvq/wa//GxAWQHhcOJefX+by88vJ+jJWGVPStqTuSL5dWZqWboqdpV2W3ocQksgLIYQQItuEhsbw9GkEAC4uaY/I77u7j+iEaJwLOONexD3d/RS0LEg/j34sOreIuWfmSiL/Adh1exeHHxzG3NicGc2yltzpg3sRd5zyO/E47DGH/A7RtkJbQ4ekFwlJCZzxPwMYJpEH9VZ0B+4f4PeLv/NTk58yXAdBURTOBJxhwdkF/Hv9X+KT4gEoal2UITWGMKjGIIrZFEt2n0qlwsHKAQcrB2qXqJ3sfExCDA9CHqSY6N8Pvk90QjR+IX74hfhx0O+g9r4CFgWY2mQqQ2sNxcRI0jGROfLJEUIIIUS20UyrL17cBltbizSv3+Krrlb/icsnGZ4mPbLOSBadW8Su27u4E3QnXevrRd4UnxjPmANjAPi67teUKlDKwBGpk772Fdqz2GcxO27teG8S+SvPrxAZH4mtuS2uhVwNEkO7Cu1wzO+If5g/m29upleVXum6LyYhho3XN7Lg7AJ8nvhoj9dzrMeI2iPo4toFM2OzTMdlYWKBi4MLLg4uyc4pisKziGevR/Bf3eN+yH3OBZzjVtAtRu4dybLzy5jXap48eBSZIlXrhRBCCJFtMjKtPj4xnl23dwHqAnYZVcG+Am3Lq5On+WfmZ/h+kXcsO7+M20G3KWRViO8/+t7Q4Wh1qKjehm7n7Z0kKUkGjkY/NOvH6znVw0hlmNTBxMiEQdUHAbD43OI0r/cP8+eHgz9Q8n8l+Xzb5/g88cHc2Jx+Hv3w+dKHkwNO0rNKzywl8WlRqVQUsylGw5IN+cz9M6Z8PIW1nddyfdh1lrZdir2lPddfXKf52uZ02diFByEPsi0W8X6SRF4IIYQQ2SYjFeuPPDhCSEwIRfIVoZ5j5ra4+rru1wCsurSKkJiQTLUhcreQmBB+OvITAFM/nkp+8/yGDegNTZybYG1mzdOIp5x/ct7Q4eiFttCdo2Gm1WsMrD4QEyMTvB97c+X5lWTnFUXh2MNjfPrvpzjPdWb6iem8iHqBY35HpjedzuOvH7Oq4ypqFK9hgOhfMzYyZnDNwdwZcYcRtUdgrDJmy80tVFpUicmHJxMVH2XQ+ETeIYm8EEIIIbJNRirWb7mpnlbfsWJHjI2MM9Vf09JNqVy4MpHxkay8sDJTbYjc7edjPxMUHYRrIVcGVh9o6HB0mJuY07JsS0A9Kv8+MGTF+jcVsymm3cliybkl2uNR8VH8fuF3PJZ50PjPxmy6sYlEJZEmzk3Y9Okm/Eb58f1H31MoX9qzgnJSQcuCzG89n0tDLtG0dFNiEmKYemwqLgtd2Hh9I4qiGDpEkctJIi+EEEKIbPM6kX/3L9FJShLbbm0DMlat/m0qlYrRdUYDsODsAhKSEjLdlsh97r26x/yz6mUTczzn5MpCYZrp9Ttu7TBwJFkXEBbAw9CHGKmMUiz2ltOG1RoGwNora7n87DJj94/F8TdHvtz5JVeeX8HSxJJB1QdxZcgVDn9+mC6uXXLlZ+RNlQtXxquvF5s+3UQp21I8DntM903d+Xj1xynOPBBCQxJ5IYQQQmSL6Oh4/PyCgbTXyJ/2P82ziGfYmtvycemPs9Rvryq9cLBy4GHoQ7b5bstSWyJ3+e7gd8QlxtGibAtalWtl6HBS1KZ8G4xURlx+fpmHIQ8NHU6WaEbjqxapio25jYGjgcalGlPJoRKR8ZF4LPNgzqk5BMcEU7pAaeZ4ziHgmwCWtV9GlSJVDB1qhqhUKrq4duHm8JtMaTIFSxNLjj48SrVl1Ri+ezhBUUGGDlHkQpLICyGEMJj/7vzHiP9GEBoTauhQRDa4dSsIRQE7O0sKFbJ657Vbb24F1NWps1qAytLUkiE1hgAw9/TcLLUlcg/vR95surEJI5URczznZHhXg5ziYOVAA6cGANrijXlVblkfr6FSqRhRe4T26xZlW7Cz507ujLjDt/W/paBlQQNGl3WWppZMajwJ36986ebWjSQlicU+i6mwsAKLzy2WGUZChyTyQgghDEJRFIbtHsbCcwvpu7Xve1PhWbz2ZqG7dyVdiqJot53TrIHNqmG1hmFqZIr3Y2/OBZzTS5vCcJKUJL7Z/w0AA6oNyPUjru0rtAdgx+28Pb3+pL86kW9QsoGBI3ltSM0hbOu+jZvDb7Kvzz7aVWiX6ZoauVVJ25L80/UfDn9+mCqFq/Aq+hXD/xtOjeU1OPrgqKHDE7mEJPJCCCEMwi/Ej4eh6mmnO2/vZPrx6QaOSOhbereeuxp4lfvB97EwsdDbdOliNsXoUbkHAPPOzNNLm8Jw/rn2D2cDzmJtZs3Uj6caOpw0adbJH/Y7TFhsmIGjyZzo+GguPL0AGL7Q3ZtUKhUdXTqmuHf7+6aJcxMuDL7AojaLKGhRkCvPr9BkdRO6b+rOo9BHhg5PGJgk8kIIIQzikN8hAApYFABg0uFJ7Lmzx4ARCX1Lb8V6TbX6lmVbks8sn976H1VnFAD/XP+HJ+FP9NauyFnR8dF8d/A7AL5r8B1FrYsaOKK0VXSoSAX7CsQnxbP/3n5Dh5MpPk98SEhKoJh1MUrZljJ0OB8sEyMThtUaxp0RdxhWcxhGKiM2Xt+Iy0IXph6dSnR8tKFDFAYiibwQQgiD0CTyI2uPZHCNwSgo9N7Sm/vB9w0cmdCX9Fas3+qrXh+vr2n1GjWK1+Cjkh+RkJTA4nOL9dq2yDnzzszjUegjnPI78U29bwwdTrppp9fn0er13o+9AfVofG6tR/AhsbeyZ1HbRVwYdIFGpRoRnRDN5COTqbSoEptvbJbt6j5AksgLIYTIcYqiaBP5pqWbMq/VPOqUqENwTDBdNnYhKj7KwBGKrIqPT+TOHXWl5XeNyN97dY8rz69grDKmfcX2eo9jdN3RACz1WSojV3lQYGSgdtnN9GbTsTS1NHBE6aeZXr/7zu48WaQst+wfL3S5F3XnyOdH+KfrPzjld+Jh6EO6/tuV5mubcy3wmqHDEzlIEnkhhBA57ubLmzyPfI6FiQV1HetibmLOpm6bKGRViEvPLjFk1xAZXcjj7t0LJj4+iXz5THFysk31Os1ofBPnJthZ2uk9jo4VO+JcwJmg6CDWXVmn9/ZF9pp8eDLhceHULF6TXlV6GTqcDKnvVB87SzteRb/SJsV5haIoksjnYiqVim5u3fD9ypdJjSZhbmzOIb9DeCz1YMLBCfLv5wciw4n8/PnzqVq1Ku7u7ri4uNC3b18CAgK0501MTPDw8NB5/ffff9rziqIwbdo03NzcqFy5Mj179iQsTLcIiLe3N3Xq1MHDw4M6depw/PhxnfPh4eH06dOHypUr4+bmxtSpU5N9YFesWEGVKlVwd3endevWOjEKIYQwrIP3DwLQsGRDzE3MAXDM78jGTzdirDJm7ZW1MhU6j9NUrHdxccDIKPVpuZr18Z9U+iRb4jA2MtZuVzX3zFz5BTcPuR54neUXlgPwW4vfMFLlrfEnEyMT2pZvC8DOWzsNHE3G3Hl1h6DoIMyNzalerLqhwxGpsDK1YsrHU7g5/CafVPqERCWRGSdmMGLPCPm77gOQ4b8R27dvz9mzZ7l8+TLXrl3D2dmZdu3aac8nJibi4+PDpUuXtK82bdpozy9fvpzTp09z4cIFrl27RrVq1Rg4cKD2fGBgIL169WLVqlVcunSJNWvW0LdvX549e6a9ZtCgQbi6unLt2jUuXrzIhQsXWLJkifb8vn37WL58OSdOnODy5cv079+fTz7Jnl8QhBBCZNyhB/9/Wr1zU53jTZybMMtzFgCj943G+5F3jscm9CM96+Ofhj/llP8pQD1ynl0GVBuAtZk1N17cwOu+V7b1I/RrzIExJClJfFLpEz4q9ZGhw8mUvLoNnWY0vlaJWpgZmxk4GpGW0gVLs7nb5v/H3nmHRXF+bfieXToCCnZAwYqiYu81xqixJxpLNLGk/hJNM/VL78UkxjQ1xRhjYknsib333gt2kY5I7+zu98fLLKB0tgHvfV24y87szAHZ3Tnvec5zWDBiAQoK3x/+nhc2viCT+UpOqRN5f39/nJycAFF9f++997h69Srh4SVzg503bx5ffPEFjo6iAjNz5kwOHjxIbKzoo1uyZAnjxo2jZcuWADRv3pwJEyawZMkSAG7fvs3evXt59dVXAXBwcODzzz9n/vz5+c7x/vvv4+EhpHwPPfQQWq2WEydOlPbHlUgkEomJ0el17Li+AxD98XfyQtcXGBs4lmx9NmOWjyEyOfKufSS2T+7oucL741ddWAVAV5+ueLt7my0WDycPpradCoiqvMT22Xh5Ixsub8BeY89n935m7XDKzMAmA7HX2HMx9iLBt4KtHU6JMcrqfaSsviIxue1kfh7+MyBMImdumimT+UpMuTVKqampKIqCl5dXsfvGxsYSFhZGixYtcgPQaOjevTvbtonqzJYtW+jTp0++5/Xp04fNmzcDsGPHDrp27YpWqzVub9asGdHR0URHRwOwdetWevfuXegx7iQjI4PExMR8XxKJRCIxDyciTxCfHo+7ozsd6ne4a7uiKPw8/GcCawUSkRzBmOVjyNJlWSFSSXkoSUXeXG71BTG9y3QUFP679F+FSqiqIjq9jpmbZwLwbOdnaeLZxMoRlR13R3f6+fcDYO3FiiOvz+tYL6lYTG03lflDc1pSDnzFq1telcl8JaVcifzZs2cZO3Ys77zzjrHCXhQRERH4+Pjc9bivry9Xr4pxQ+Hh4fj6+pZqO4CPjw/Xrl0jOTkZOzs7XF1dCz3GnXzyySd4eHgYvwo6vkQikUhMg+pW36dhH+w0dgXuU82hGivGrsDd0Z09IXuYuWmmJUOUlBO93sCFC0XPkI9Li2P79e2AZRL5Jp5NjK743xz8xuznk5SdX4//ypnoM3g6e/JW77esHU65qWhj6OLS4jgXcw6Abr7drByNpCw83uFxfhwi2o6/2PeFNMCrpJQpkX/55ZepW7curVq1on79+jz33HP5tg8aNIjWrVvTpUsXZs+ejV6vByA+Pt4oy8+Lk5MTqamphe5T3Pa8+5TkHHfy+uuvk5CQYPy6efNmCX4LEolEIikLxv74AmT1eWnm1YxFoxYBMOfQHBafWmz22CSmISQkgdTULOztNTRuXLAT/bqL68jWZ9OqdiuaejW1SFzPd3kegIUnF3I77bZFzikpHUkZSby5/U0A3u79NjWca1g5ovKjJvJ7b+7lVuotK0dTPAdCDwBi8au2a20rRyMpK091fIrvBn8HwKd7P+Xt7W/LZL6SUaZE/osvviAyMpJbt27h5OTElClTjNsiIiLYsmULp0+fZunSpaxYsYJPP/0UAEdHR9LT0+86XlpaGs7OzoXuU9z2vPuU5Bx34ujoiLu7e74viUQikZieTF0mu27sAopP5EHMYX6zl7iof3zt45yMPGnW+CSmQXWsb9bMCzu7gi81VlzIcasPsJwZbV+/vrSp04bUrFR+Pvazxc4rKTmf7f2M6JRomno25elOT1s7HJPQsHpDguoEoTfo+e/Sf8U/wcqo/fE9fHtYORJJeXmm8zN8M0gokD7c/SHv7XzPyhFJTEm5pPVeXl588803rFy5koSEBADq1q1r3O7n58fHH3/M33//DQj5e0hIyF3HuXnzplFyX9A+xW3Pu0/NmjVJS0sjOTm50GNIJBKJxDocCjtEalYqNV1q0qp2qxI9592+7zKw8UDSstN4YNkDxKXFmTlKSXkprj8+NSuVjZc3AjCqhfll9SqKovBC1xcA+PbQt9J7wcYISQjhy/1fAvD5gM8rlVv68ObDgYrRJ78vVM6Pr0zM6DKDr+77CoD3dr7HBzs/sHJEElNRbrO7jIwMMjMz0el0BW7X6XTY2YkeyHr16lGtWjXOnTtn3K7X69mzZw/du4s3i+7du7Nz5858x9i5c6dxe7du3di7d2++8wUHB+Pg4ICPjw+KotClSxd27dpV6DEkEolEYh3U/vh7/O8p8UxorUbLnw/+iV91P67GXWXiyonoDXpzhikpJ2pFvrD++I2XN5KWnYZfdT+C6gRZMjTGtRpHbdfahCaGGs32JLbBG1vfID07nT4N+5h1HKE1UOX1Gy5vICM7w8rRFE62PpuDoQcBmchXJl7o9gKf3ytGu769420+2f2JlSOSmIJSJfKZmZmEhoYav4+Pj+fRRx9l9OjReHp6kpqaSkxMjHH79evXmTlzJlOnTjU+NmPGDF555RUyMzMBmDVrFkFBQTRq1AiAadOmsXjxYmOyHxwczKJFi5g2bRogqvydOnXis8/EKJKsrCxeffVVpk+fnu8cb7/9ttF9ftmyZaSkpNC3b9/S/LgSiUQiMTHGRN6veFl9XjydPVnx0Aqc7Jz479J/vL/zfXOEJzER586JinzLlgVX5PPK6hVFsVhcAE52TjzdUUi2Zx+YbdFzSwrncNhhFp9ejILCVwO/svjfhbnpUL8D9arVIzkz2Th+0xY5FXWKlKwU3B3daVmrpbXDkZiQl3u8zCf9RQL/xrY3+Hzv51aOSFJeCrYLLoSYmBhGjBhBSkoKTk5OaDQaJkyYYDS7i4uLY+jQoWRlZWFnZ4ezszPPPfccEydONB7j+eefJzY2lqCgIDQaDS1atGDhwoXG7T4+PixatIhJkyaRnZ2NVqtlwYIFNGzY0LjPggULePrppwkMDESv1zNixAheeukl4/ZRo0Zx8+ZNunXrhkajoW7duqxevRqNptwCBIlEIpGUkdSsVPaH7gdK1h9/J+3qtWPe0Hk8uupR3tv5Hp3qd2JIsyGmDlNSTgwGQ5EV+UxdJusurgMsK6vPy1Mdn+KTPZ+wP3Q/B0MP0sWni1XikAgMBgMvbnoRgElBk2hfr72VIzI9GkXDsGbDmH9sPmsvrmVgk4HWDqlA1P74bj7dSqyaklQcXuv5Gjq9jje3v8mrW15Fq2h5qftLxT9RYpMoBmlfeBeJiYl4eHiQkJAgje8kEonERGy5uoUBiwbg4+5DyPMhZa64Pfvfs3x/+HuqO1Xn8OOHK/SM6cpIVFQydet+iaJASsobODvb59u+6comBv4xkDqudQh7MQytRmuVOCevmszCkwsZ12ocfz34l1VikAhWnF/Bg8sexNnOmYvTL+LjXjk9jdZdXMewv4bh6+7Ljedv2KTqYMI/E/jrzF+83/d93upT8Uf/SQrmvR3v8e7OdwH4euDXPN/1eavGI8mlNHmoXGqTSCQ2j8FgYMzyMbT5sQ1JGUnWDkdSRrZe3QqIanx5LmC/GvgV3X27E58ezwNLHyAlM8VUIUpMwLlzohrv71/jriQeYOV50Zc+ovkIqyXxgPHCdfnZ5YQmhha9s8RsJGYk8tImURGc2X1mpU3iAfr798fZzpmbiTc5GWWbEzjUirzsj6/cvNP3Hd7qLRZqXtj4At8e/NbKEUnKgkzkJRKJzXMk/Ah/n/ub09Gn2XB5g7XDkZQR4/z4UvbH34mD1oHlY5ZTx7UOp6NP88S6J+RsXBtCdawvqD9eb9CzKngVAA+0sNzYuYJoW7ctff36ojPo+P7Q91aNpSozY/0Mrsdfp6FHQ17p8Yq1wzErzvbO3Nf4PgDWBtuee31YYhg3Em6gUTR09u5s7XAkZua9vu/xRs83AJixYQY/HP7ByhFJSotM5CUSG+L1La/Tbl47xv8zng92fsCK8ysIvhVMtj7b2qFZlR+P/Gi8LxP5iklCegJHwo8AZeuPv5P6bvVZPmY5dho7/jz9J3MOzin3MSWmoaj++AOhB4hMjsTD0YN+/v0sHdpdPN/leQDmHZ1HalaqyY8fmxrLX6f/4tFVj1JnVh38ZvtxI/6Gyc9TUVl6ZikLTy5Eo2j444E/qOZQzdohmR3VvX7NxTVWjuRu1Gp8mzptcHN0s3I0EnOjKAof3vMhr3QXC2jP/PcM84/Ot3JUktJQKrM7iURiPrJ0WXy+73P0Bj0nIk/k2+aodaR5zeYE1goksFYgLWu1JLB2II1rNLaqNNUSxKXFseTMEuP3G65swGAw2GRvoaRwdt3Yhd6gp6lnU3w9fE1yzF4NezFrwCye3/g8MzfPpH299vRq2Mskx5aUndwZ8ncn8ivOC7f6oc2G2sSM8KHNhtKoRiOuxl1l0clFPNnxyXIdT2/QcyT8CBsub2D95fUcCjt016jEEUtGsHfqXlwdXMt1ropOSEIIT/37FAD/1+v/6Nmgp5UjsgxDmw1FQeFI+BHCEsPwdve2dkhGjLJ6HymrryooisKn936KzqDjy/1f8uS6J9EoGh5r/5i1Q5OUAJnISyQ2QmhiKHqDHgetA+/3fZ+zMWc5G3OW8zHnSctO41TUKU5Fncr3HEetIwE1AwisHZgvyW9Uo1GlSfB/P/k7adlptKzVkmtx1whPCudM9Bla12lt7dAkpSDv/HhTMqPLDA6FH+LP038yZvkYjj15jPpu9U16DknpUHvk75TWGwwG49z2UQHWcau/E61Gy4zOM3h+4/PMPjibxzs8Xmqn7piUGDZd2cT6y+vZeGUjt1Jv5dveqnYrBjcZTHff7jy57klORp3k0VWPsmzMsirrCq7T63hk5SPEp8fT2buzsVe3KlCnWh26+HThQOgB1l1cV+7FI1OyL1T2x1dFFEXhiwFfoNPrmH1wNk+sfQKtomVKuynWDs2krAleQ8taLSuVQa5M5CUSGyEkIQSABh4NeLXnq8bH9QY91+Ovczb6rDG5Pxdzzpjgn4w6eZdpjpOdk0jwc5J7NdH3q+5XoRJ8g8HA3KNzAXi207Osu7SO/y79x4bLG2QiX8HYei3X6M6UKIrC/KHzOR11mtPRpxm9bDQ7Ju+wiWpvVSQhIZ2IiGQAAgLyV+RPRZ3iatxVnOycGNRkkDXCK5Ap7abw1va3uHDrApuubCo2Np1ex+Hww6y/tJ71l9dzJPwIBnI9Gtwc3BjQeACDmwxmYOOB+RQotVxq0W9hP/45/w8f7vqQt/u8bbafy5aZtW8WO2/sxNXelcUPLMZee7cpYmVmeLPhHAg9wNqLa20mkU/LSuNYxDEAejToYeVoJJZGURS+GvgVOoOObw99y7Q109BqtDwS9Ii1QzMJZ6LPMO7vcdhp7Ng/bT+BtQOtHZJJkIm8RGIj5E3k86JRNDSq0YhGNRoxrPkw4+M6vU4k+DFnjUn+uZhznL91nvTsdE5EnrhLou9s58zQZkNZMnpJhagE7byxkwu3LlDNoRoT20wkW58tEvkrG3i5x8vWDk9SQqJTojkdfRqAfn6m74t2dXBlxdgVdJzfkf2h+3lx44t8d/93Jj+PpHjUanz9+m54eDjl26ZW4wc2HmhTsnJ3R3emtZvG7IOzmX1gdoGJfFRyFBuvbGT95fVsurKJ22m3820PqhPE4CaDGdRkEN19uxeamPZo0IO5Q+cybc003tnxDoG1Anmw5YNm+blslaPhR3lz+5sAfDv420pVHSspw5oP441tb7Dl6hZSMlNs4vVwJPwI2fps6lWrR0OPhtYOR2IFFEXhm0HfoNPr+OHID0xeNRmtouXhNg9bO7RykZyZzJjlY0jLTuO+xvfRolYLa4dkMmQiL5HYCGoiX9IPUK1GS2PPxjT2bMzw5sONj+v0Oq7FX8uX3OeV6C8/t5xJFyflWxSwVVSTu4dbP4ybo5vxAnv3jd0kZyZXCWOkysCO6zsAYaBUy/VuJ3NT0MSzCYsfWMzQv4by/eHv6ezdudJUEioSp05FAdCmTZ27tqn98dZ2qy+IGV1mMOfQHDZe2ci5mHM082rGwdCDrL+8ng2XN3A04mi+/T0cPYxV90FNBpWqnWNqu6mcijrFNwe/4ZFVj9DEswlBdYNM/SPZJCmZKUxYMYFsfTajW45mctvJ1g7JKgTWCsS/uj/X4q+x+epmRgaMtHZI7L25FxCyeulBU3VRFIVv7/8WnUHHvKPzeGTVI2g1Wsa1Gmft0MqEwWDgyXVPcuHWBeq71eePUX9UiEJWSZGJvERiIxRWkS8tWo2WJp5NaOLZhBEBI4yP6/Q6Zm6ayeyDs/n6wNc2n8hHJUcZL/yf7vg0IJI11Zhq+7XtNv8zSATG/vhyjp0rjiHNhvBOn3d4b+d7PLnuSVrXbk27eu3Mek5Jfk6eVBP52vkev3z7MqejT6NVtAxtNtQaoRWJfw1/RjQfwcoLKxm1dBTRKdHEp8fn26dd3XYMbjKYwU0H09WnK3aasl9CzbpvFudizrH56maGLxnO4ccPU9u1dvFPrOC8uPFFLsZexNvNm3lD51XZhFFRFIY3H843B79hbfBam0jk5fx4iYpG0fDDkB/Q6XX8fPxnJq6YiFbRMiZwjLVDKzU/HfuJP0//iVbRsuTBJWYrJliLyrMkIZFUcG4kiJFE5U3kC0Or0fJitxfRKlq2X99+l+ze1vjl+C9k67Pp5tPNWK1SFIVBjUVVfv3l9dYMT1IKzGV0VxBv93mb+5veT3p2Og8se+AuCbTEvBRWkV95Xsjq+/r1xdPZ0+JxlYTnuz4PwMXYi8Snx1PDqQZjA8fy24jfiHgpgmNPHuOj/h/Rs0HPciXxAHYaO5aOXkoTzyaEJIQwetloMnWZJvgpbJfVF1Yz/9h8FBR+H/W7zf4dWAp1DN3ai2vR6XVWjcVgMMhEXpIPjaJh3rB5TGk7BZ1Bx/h/xvPPuX+sHVapOB5xnBnrZwDwcf+PK+VUG5nISyQ2gqkq8kXh6+HL6JajAfj6wNdmO0950el1xlmmT3V8Kt+2wU0HAyKRNxgMdz1XYlvcTLjJpduX0CgaejfsbfbzaRQNf4z6g0Y1GnE9/joT/plg9YvkqoLBYDAm8kFBdfNtU/vjbVFWr9KrQS++G/wdb/d+m71T9xL9cjRLRi/h0baPUrda3eIPUEpqONdgzbg1uDu6sztkN8/+92ylfU+LSIpg2pppAMzsPtMii3q2Tu+GvfFw9CAmNYZDYYesGsul25eITYvFUetIu7pSxSQRaBQNPw37iUeCHkFn0DHun3GsurDK2mGViIT0BB76+yEydBkMbTaUmd1nWjsksyATeYnEBjAYDKXukS8rL3R9AYC/Tv9FRFKEWc9VVjZc3sCNhBt4OnsypmV+KVdfv744aB24Hn+dS7cvWSlCSUlRq/Ed63fEw8nDIues4VyDlWNX4mznzMYrG/n1+K8WOW9V5/r1eJKSMrG319C8uZfx8fCkcPaH7gdgRPMRhT3d6iiKwjOdn+G9fu/R3bd7uavuJaFFrRb89eBfKCj8dOwnfjj8g9nPaWn0Bj2TV08mNi2WdnXb8UG/D6wdkk1gr7U3LkyvCV5j1VjUanwn70442jlaNRaJbaHVaPl1+K883PphsvXZPLT8Iav/vRaHwWDgsbWPcfn2ZRp4NGDhyIWVqi8+L5Xzp5JIKhhx6XGkZKUA4OPuY9ZzdfHpQnff7mTps2z2olE1uZscNBlne+d826o5VKNXAyGP2nB5g8Vjk5SObdct0x9/J23qtOGdPu8A8Pup3y167qqKWo1v2bIW9va5Yy5XX1gNQFefrni7e1slNlvm/qb389m9nwHw3IbnjItflYU5B+ew6comnOycWPzAYpko5mF4M2FUu/biWqvGYZTV+0hZveRutBotv438jXGtxpGlz2L0stGsDbbu32xRfHfoO/4+9zf2GnuWjV5Wqdt4ZCIvkdgAN+JFf3xt19p3Ja7mQK3Kzz06l7SsNLOfrzTciL/Bf5f+Ayh0vq7qXi8TedvGYDAYk5L+jfpb/PwT20xEQWFPyB6j4kViPgqT1a+4IEwrRwWMsnhMFYWZ3Wcysc1EdAYdY5aP4crtK9YOySScijrFq1teBeCr+76qVGOfTMGgJoPQKlrOxpy16v95Xsd6iaQg7DR2LBq1iIcCHyJLn8WDyx60ycr8obBDvLTpJQC+GPAFXXy6WDki8yITeYnEBrBEf3xeRgaMpKFHQ26l3uKPU39Y5JwlZf7R+Rgw0N+/P828mhW4j5rI77i+w+YWIiS5XL59mdDEUBy0Dla5QPR29zaa2yw9s9Ti569qFORYH5cWZxw/KBP5wlEUhZ+G/URn787cTrvN8CXDScxItHZY5SItK42HVzxMpi6Toc2G3uV3IhFtQKp3iLWq8nFpcZyLOQdAN99uVolBUjGw09ix+IHFjA0ca6zMq4orWyAuLY6HlouFhgdaPMCMLjOsHZLZkYm8RGIDWKo/XsVOY2d8g5t9cLbNGCxl6jL55fgvQO7IuYIIrBWIt5s3adlp7A7ZbanwJKVErcZ38+mGi72LVWIY32o8AEvOLrHK+asSBVXk111cR7Y+m1a1W9HUq6m1QqsQONk5sXLsSupVq8e5mHNMXDERvUFv7bDKzGtbXuNM9BnquNbhl+G/VNlRc8UxvLl15fUHQg8AYrxrVRiBKCkfdho7/njgD6PMfszyMTaRzBsMBh5d9Sg3Em7QqEajKvOeIxN5icQGsHRFHmBau2lUc6jGuZhzbLqyyWLnLYpVF1YRlRJFvWr1jBc3BaEoirEqv/6SHENnqxj7463oUD265WjsNHYcizjGxdiLVoujspOSksnly2LUX97Rc6qs/oEA23WrtyXqu9Vn1bhVOGodWXtxLW9te8vaIZWJDZc3MOfQHAAWjFggE8QiUMfQ7by+k7i0OIufX+2P7+Hbw+LnllRMVJm9sWd+ufUr81/u/5K1F9fioHVg+ZjlVHeqbtV4LIVM5CUSGyAk0fKJvIeTB9PaiXFAtjKKbu6RuQA81v4x7LX2Re5r7JO/IvvkbRG9QV/s/HiDwcCMGetp1Ogbliw5Y5Y4arrUZECjAYCY1CAxD2fORGMwQJ06rtSu7QpASmYKGy9vBGBUCymrLymdvTvz8/CfAfh4z8cV7u82JiWGyasmAzC983SjM7ukYBp7NqZlrZboDDqr+L7I/nhJWVCT+fGtxpOtz2b08tFWG023N2Qvr215DYBvBn1D+3rtrRKHNZCJvERiA6hmd5ZM5AFmdJmBgsLGKxuNPXLW4sKtC2y/vh2NouHx9o8Xu/+9je5Fq2i5cOsC1+Ovmz9ASak4E32GW6m3cLV3pbN35wL3+fzzvXz77SGuXYtn/Ph/GDv2b2JjU00eiyqv/+vMXzbTRlLZKEhWv/HKRtKy0/Cv7k9QnSBrhVYhmdhmIq90fwWAqWumciT8iJUjKhkGg4Fpa6YRlRJFYK1Aoxu/pGhU9/o1Fy1rHjb7wGy2X98OQM8GPS16bknFx05jx++jfmdC6wlk67MZs3wMK8+vtGgMMSkxjP17rJhz32ocT3Yo2CS5siITeYnEBrCGtB6gUY1GjAwYCYgPdGuiVuOHNhuKr4dvsftXd6puNOZRq34S20Gtxvdq2AsHrcNd29euDeb117cCMGJEc7RahWXLztKq1Y/8+69pJfAjAkbgZOdEcGwwJyJPmPTYEkFBRncrL4gLulEBo6pEr6Kp+bj/x9zf9H7Ss9MZuWQkEUkR1g6pWOYdnWeUt/754J8WmcJSGVBbydZfWk+WLsvs5zMYDLy25TVe2Cgm2LzY9UVa1mpp9vNKKh92GjsWjlxoTOYf+vshVpxfYZFz6w16Jq2cRFhSGM28mjF/6Pwq91kjE3mJxMpkZGcQkSwu0CxldpcXdRTdolOLuJV6y+LnB0jNSmXhyYVA0SZ3dzKosZTX2ypGWX0B8+PPno1mwoQVGAzw1FMdWLVqHAcOPEaLFjWJjExm6NC/eOyxNSQmZpgkFndHd4Y0HQKIqrzE9KgVebU/PlOXaZwzLGX1ZUOr0fLnA38SUDOAsKQwRi0dRXp2urXDKpTzMed5ceOLAHza/1Pa1Glj5YgqDp29O1PLpRYJGQlmN3DN1mczdc1UPtsr1BKf9P+EWffNMus5JZUbO40dv4/MrcyP/XusRZL5T3Z/wsYrG3Gyc+LvMX/j5uhm9nPaGjKRl0isTFhSGCAci2u61LT4+Xs26EmHeh1Iz043VsUtzdIzS4lPj8e/uj/3Nb6vxM9T++S3Xt1Kpi7TXOFJSkm2PpudN3YCd/fHx8amMnz4EpKTM+nb1485c0T/bMeO9Tl69AlefLErigK//HKcNm1+ZMeO6yaJSZXXLz27tEI7gdsiBoPhLmn9jus7SMhIoI5rHbr5yJFWZcXDyYM149ZQ3ak6B8MO8tS6p2yyPSRTl8nDKx4mLTuNAY0G8FzX56wdUoVCq9EytNlQALPO5k7NSmXU0lH8duI3NIqGX4b/wms9X6tyVUyJ6dFqtPw+8ncebv2wMZn/59w/Zjvfjus7eHvH2wD8cP8PtK7T2mznsmVkIi+RWJm8/fHW+DBVFMVYlf/u0HdkZJumCloa5h4VCwhPdngSjVLyt6V29dpRy6UWSZlJ7L+531zhSUrJsYhjJGYkUt2pOm3rtjU+npWlY8yY5Vy9Goe/f3WWLx+Dvb3WuN3Z2Z4vvxzI9u2P4udXnRs3EujXbyEvvLCBtLTyyU3vb3o/bg5uhCSEyL8VExMSkkBCQgZ2dhoCAsRipFqNGdF8BFqNtqinS4qhqVdTlo1ehkbRsPDkQpsxJ83LW9ve4njkcbycvfht5G+leh+XCFR5/ZrgNWZZrLmddpsBiwaw7uI646jDqe2mmvw8kqqLVqNl4ciFTGwz0azJfGRyJOP/GY/eoOfRoEeZ0m6Kyc9RUZDvtBKJlbFWf3xexgSOob5bfaJSolhyxrLzto9FHONQ2CHsNfalvqjQKBoGNhkIwPrLcgydrbD1quh97+vXN18S9/zzG9i+/TrVqjmwZs14atYseLZ8nz5+nDr1FI8/LpxnZ88+SPv28zl8OKzMMTnbOxsl3lJeb1rUanyLFjVxcNCi0+uM7sUPtJBj50zBgMYD+Oq+rwB4efPLVnE3L4xt17bxxb4vAPh5+M/Ud6tv5YgqJgMaDcBR68i1+GsmN58NTQyl14Je7Lu5j+pO1dk8aXORI14lkrKi1Wj5bcRvTGozCZ1BZ/JkXqfXMeGfCUQmRxJYK5Dv7//eZMeuiMhEXiKxMmoib43+eBUHrQPPdnoWEKPoLCndVOX8o1uOppZrrVI/39gnb0MXtlUddX58f//+xsfmzj3CDz8cQVFg8eIHaNWq6LnSbm6OzJ8/jH//nUC9etW4cOEW3br9wttvbyczU1emuFR5/fJzy8nWZ5fpGJK7uVNWfyD0AFEpUXg4etDPv581Q6tUzOgyg6ltp6I36Bn39ziCbwVbOyRup93mkZWPYMDA4+0fN5qnSkqPq4Mr/RuJ90xTyuvPx5yn+y/dORdzjvpu9dk9Zbd0qJeYFa1Gy4IRC/Il83+f+9skx35/5/tsv74dV3tXlo9ZjquDq0mOW1GRibxEYmVsoSIP8GTHJ3G2c+Zk1El2XN9hkXMmpCew+PRioHQmd3m5r/F9KCicjDpJeFK4KcOTlIGM7Az2hOwBcvvjd+y4zvTpQjHx0Uf3MHx48xIf7/77m3LmzP8YN64VOp2BDz7YRdeuP3PmTHSpY+vv3x8vZy+iU6KNZnyS8nOnY73qVj+02dACJxZIyoaiKPww5Ae6+3YnISOB4UuGE58eb7V4DAYDT6570ugY/fVA25P8VzTUMXRrL641yfEOhB6g54Ke3Ey8SXOv5uybuo9WtVuZ5NgSSVGoyfwjQY+I0XB/j2P52eXlOuamK5v4YNcHAMwbOo8WtVqYItQKjUzkJRIrE5JoG4m8p7Mnk9tOBrBYD+aiU4tIzUolsFZgmSsEtVxr0bF+R0C8yUusy4HQA6Rnp1PHtQ4tarbg6tU4HnxwGdnZeiZMaM1rr5X+/9nT05m//nqQpUtH4+npzPHjkXToMJ8vvtiLTldy4zp7rT1jWo4BpLzelOR1rDcYDMb++FEB0q3e1DjaObLioRX4uPtwMfYi4/4eh05fNoVKeVl4ciF/n/sbO40dix9YXOUrY6ZANbw7EHqAqOSoch3rv0v/cc/Ce7iddpvO3p3ZM3UPDatbT/knqXpoNVp+Hf4rjwY9is6gY/w/41l2dlmZjhWWGMbDKx7GgIEn2j/Bw20eNnG0FROZyEskViav2Z21ea6LcBped3Edl2Ivleq5Bw+GMnfukRKbkhkMBqOs/qmOT5XL6E91r5fyeutjHDvnfw/JyZkMH/4Xt2+n0bFjfX7+eVi5/p8feiiQM2eeZsiQpmRm6njllS307buQK1dul/gY41sLef2K8ytsepRXRSE1NYtLl8TvPyioLqeiTnEt/hpOdk7G16XEtNSpVoc149bgbOfMxisbeXXLqxaP4fLty0xfPx2A9/u+b1xMlZQPb3dvOtTrgAED/176t8zH+f3k7wz/azhp2WkMajKIbY9ss8pUHIlEq9Hyy/BfmNx2MjqD6G8vbTKfrc9m3D/juJV6i7Z12/LN4G/MFG3FQybykkJZeX4lbee25WTkSWuHUmkxGAw2I60HaF6zOUOaDsGAgW8OlvyN8ty5GO6553eefvpf2rSZy7Zt14p9zp6QPZyNOYuLvQuT2kwqT9jGhGHTlU1Wq05JBFuvqUZ3/Zg4cSVnz8ZQr141Vq0ai7OzfbmPX6+eG2vXjufnn4dRrZoDe/aEEBQ0l7lzj5TI26Fng574uPuQmJEoF35MwNmz0ej1BmrVcqFOHVf+OS9MjQY2HigrtGakXb12LBy5EIAv93/JwhMLLXbuLF0WE1dMJDkzmd4Ne/NKj1csdu6qQF73+rIwa98sHl0lKqAT20xkzbg18rUosSpajZafh/2cL5lfemZpiZ//5rY32ROyBzcHN5aPWY6TnZMZo61YyEReUiC3Um8xbc00Tkad5I9Tf1g7nEpLbFosadlpAPi6+1o5GoE6im7BiQXEpcUVu39KSiZjxiwnNTULRYHLl2/Tv//vTJ26mtu30wp9njpybkKrCXg4eZQr5s7enanuVJ249DgOhx8u17EkZSc5M5mDYQcBOP6PE2vWBOPoqGXVqnF4e7ub7DyKojBtWntOn36aPn0akpKSxdNP/8vgwYsJC0ss8rkaRcPYwLGAlNebgrxGd4qiGBP5B1s8aM2wqgRjAsfwVu+3AHhi3RMcCD1gkfN+sOsDDoYdxMPRg0WjFsnxgiZGTeQ3X91MWlbhn6F3ojfomblpJi9vfhmAF7u+yMKRC7HXln8BVSIpL2oyP6XtFHQGHQ+veLhEyfy6i+v4bO9nAPw64leaeDYxd6gVCpnISwrkzW1vEpcukrjgWOs741ZW1Gp83Wp1cbRztHI0gnv876FNnTakZqXy07GfitzXYDDwv//9x7lzoup68eJ0/vc/IbFcsOAELVp8z5IlZ+6qlMakxBgdTJ/q+FS5Y7bT2DGg0QBAyuutyZ6QPWTrs6llV5+5n1wF4JdfhtO5s7dZzufnV51t2x7l668H4uRkx8aNV2jV6kcWLz5VZHVeda9fG7yW5Mxks8RWVchrdHfh1gXOxZzDXmPPsObDrBxZ1eDdvu8yMmAkmbpMRi0dRWhiqFnPtzdkLx/t/ggQZlO2oCSrbATVCcLX3ZfUrNQSm3Jm6bKYvGoyX+7/EoDP7/2cLwd+iUaRl/kS20Gr0fLz8NxkfsKKCUWOPL4Rf4NHVj4CwPTO0xndcrSlQq0wyFe45C6ORRxj/tH5xu8v3LpgxWgqN7bUH6+iKArPd3kegG8PfUuWrvCe9wULTvD77yfRaBT++utBmjTx5Pvvh7BnzxRatKhJdHQK48f/w7BhfxESkpD7vBMLyNRl0ql+JzrU72CSuFV5vZwnbz3Ui87bR8UYsldf7cHDD7cx6zk1GoXnn+/K8eNP0qlTfeLj05k4cSVjxiwnJialwOe0r9eeJp5NSMtOY/WF1WaNr7KT1+hONbnr36g/1Z2qWzGqqoNG0bBo1CJa125NZHIk7ea1o8P8DvRb2I8RS0YwaeUknvn3GV7b8hof7/6Y7w59x+8nf2fl+ZVsvbqVw2GHCb4VTHhSOMmZyUUugCWkJzBx5UT0Bj2T2kxibKuxFvxJqw6KojCsmVgIK4m8PiUzhRFLRrDo1CK0ipaFIxfyco+XzR2mRFImNIqGn4f/bByl+fCKhwtM5jN1mYz9eyxx6XF0qt+JLwZ8YYVobR87awcgsS0MBgPT10/HgIF+fv3Yfn07V+OukqnLlGOEzIAt9cfnZXzr8by29TVCE0P55/w/jGs17q59Tp+O4pln/gPgww/70aePn3Fbjx4NOH78ST79dA8ffbSbf/+9RGDgD3z00T08/b8OzDs6Dyj7yLmCGNh4IACHww5zK/WWNPaxAhsvbgFAd9mPoUOb8dFH91js3AEBNdm3bxqffLKb99/fxT//nGf37hB++mnYXePuFEVhfKvxfLDrA/4685d0vy0jBoMhn7R+ziEpq7cG1RyqsXrcarr90o2olChupd4q87E0igY3BzfcHN1wd3Q3frk5uHEz8SbX46/jX92f7+7/zoQ/geROhjcfzg9HfmDdpXXoDfpCK+uxqbEM+XMIB8MO4mznzN8P/c39Te+3cLQSSenQKBp+Gi4Un7+e+FW40RsMRjNagFc3v8rBsINUd6rOsjHLbEa1amvIRF6Sjz9O/cG+m/twsXfh91G/0+L7FiRnJnPl9hU5r9EMqIl8Qw/bGgnjZOfE/zr+j3d3vsvXB75mbODYfG7jSUkZjBmznPT0bAYPbsKrr949UszR0Y533unLQw8F8vjja9m79ybPPbeBHzb9xdVOV6nuVN2kFR1vd2/a1GnDqahTbL6yOd8HgsT8hMdFcyrmBCjQ1L4Dixc/gFZrWdGXnZ2Gt97qw5AhzXjkEWG0N2LEEubNG8oTT+RXfqiJ/MYrG4lNjcXLxcuisVYGQkMTiYtLR6tVcKqXxLGIY2gUDSOaj7B2aFUO/xr+XJp+iRORJ0jMSCQpM4nEjETjV1JGzveZd3yvbs9MQm/QozfoSchIICEjocDzaBQNfzzwB+6OpvO8kNxNX7++VHOoRnhSOMcijhU4FSAkIYSBfwzkwq0LeDp78u+Ef+nq09UK0UokpUdN5hVF4ZfjvzBx5UQMGJjQegIrzq9g9sHZACwcuRC/6n5WjdWWkYm8xEhiRiKvbBHus2/2ehMfdx8CagZwJPwIF25dkIm8GbCVGfIF8XSnp/lkzyccCjvE/tD9dPftDogq3JNPriM4OBYfH3d+/30UGk3hI8VatKjFrl1TmD//KK++uoVgN9HD3jTlHjQ6BzChD8+gxoM4FXWKDVc2yETeghgMBsa/9iXUN6C5XYv1y57C3d16q+ft29fjyJEneOWVzXz77SFmzFhPly7eBAXVNe7TolYLguoEcTLqJCvOr+DxDo9bLd6KilqNDwioyb9XhAS4d8Pe1HKtZcaz/gLoAfn/dSdujm70atirTM81GAykZqUWvQiQkUiH+h2MnwUS8+Fo58jAxgP55/w/rAlec1cifzb6LAP/GEhYUhi+7r5snLjRBq/RdgKtAU9rByKxUTSKhvnD5qOg8PPxn5m0chI3E27y8Z6PAZjZbabR/FFSMLJHXmLkg50fEJkcSRPPJrzY7UUAAmoGALJP3lzYqrQeoLZrbR5uLSTHX+3/yvj4/PlH+euvM2i1CkuWPEjNmi7FHkujUXjqqY5sOTQcml8E4PCP9QgKmsvOnddNFrPaJ7/x8kb0Br3Jjispms8/38uumzsAGBY4iMaNrX/h5uRkxzffDGLo0GZkZOh46KG/SU7OzLePanon3evLRl5Z/YoLoj/evLL6U8BjwBPAHjOep+qhKAquDq7Uc6tHM69mdKzfkXv872FkwEgmBU3imc7P8Hqv17mv8X3WDrXKUNgYur0he+m5oCdhSWG0rNWSvVP32mASvxXoC9wLyJGwksLRKBrmDZvH4+0fR2/Q89rW10jMSKS7b3c+7v+xtcOzeWQiLwFEoq7KWGYPnG3sRWnuJXpLL8TKRN4c2KLZXV6e7/o8ACsvrORa3DWOH4/guedERf2TT/rTo0fp4l4XvhQUA4Gunaln78/Fi7H07buQxx9fQ1xcycfsFEaPBj1wtXclKiWKk5Eny308SfGsW3eR11/fCv7XAHik10jrBpQHRVH47bcR+Pi4c/FiLE8//W8+My+1tWPH9R2EJ4VbK8wKi+pY79daw76b+wAYFTDKjGf8Ns/914DCjdkkkorO/U3vR6NoOBl10rjovzZ4Lfcuupf49Hi6+3Zn95Td+HrYxuja/GzKuT2OUNFIJIWjUTTMHTqXx9sLpZWXsxdLRy+VoxNLgEzkJRgMBp7b8BzZ+myGNB3CkGZDjNvUinzwLTmCztSkZ6cTlSIuhG01kW9dpzX3NroXvUHPl3tm89BDf5ORoWPo0Ga89FLp5JVZuizjOLu3B7/EuXPP8OSTom/555+P06LF9/z997kiXZOLw0HrQP9G/QE5hs4SnD0bzYQJ/2BwTYLaMSgo9GnYx9ph5cPLy4W//noQrVbhjz9O8dtvJ4zb/Kr70c2nGwYMLDu7zHpBVlDUinxc3eMAdPPphre7eUYNwm1gcc59LbAXWGemc0kk1qemS01jG8Pa4LUsOL6AUUtHkZ6dzpCmQ9g8aTOeztZXPxXMvjz33wTirRSHpKKgJvMrx67k0OOH8HH3sXZIFQKZyEtYHbyaTVc24aB1YPag2fm25ZXWlyfBktyNOu/Xxd4FL2fbNdp6oesLAMw7/BOXb4bToIEHCxeOLLIvviDWXlxLRHIEdVzrMDJgJNWrOzF37lB27pxM8+ZeREWlMGbMckaOXEpoaGKZ4x3UWI6hswSxsakMH76EpKRMWgwWs9jb1m1rk6ZxPXs24IMP+gHwzDP/ce5cjHGblNeXjfT0bIKDYwE4mbkDgAdaPGDGM/4CpAFtgZk5j72OlO1KKjPDmwl5/fu73mfqmqnoDDomt53MyrErcbEvvq3NOmQCR3Lu1wFigA+sF46kwqBRNIwMGEmjGo2sHUqFQSbyVZy0rDRe2CgStZe6vUQTzyb5tjfxbIJG0ZCQkWCsHktMQ97++LyO8LbGoCaDqKNtSLY2DU2HEyxdOhpPT+dSH+fHIz8CMK3dtHyjDHv3bsiJE0/x1lu9sbfXsGZNMC1bfs/33x9Cry/94pHaJ7/v5j4S0gt2XpaUj6wsHWPGLOfq1Tj8/avTYUwGAPf4W27cXGl59dWe3HdfY9LSsnnooeWkpmYB8FDgQ2gUDYfCDnHl9hUrR1lxOHs2Gr3eQA1vHQciRb+6+frjdcD3OfenA68C1YGzwB9mOqdEYn3UPvnolGgAXu3xKr8O/9XGJccngHSEyd1vOY/NAaSyUyIxNTKRr+J8se8Lrsdfx9vNmzd6vXHXdic7J+PYB2l4Z1psvT9e5fixSG6tCwKg+sBTdOpcr9THuBR7iS1Xt6CgFOgO7uRkx/vv9+PYsSfp2tWHpKRMnn12PT17/srZs9GlOpd/DX+aezVHZ9Cx9drWUscqKZ7nn9/A9u3XqVbNgTVrxrM3fCdg24m8RqOwaNEo6tatxtmzMTz3nFBs1KlWxxj30rNLrRlihUKV1dfuHYreoKdd3Xb41/A309nWAjcAL2A8UANRjQd4G5E0SCSVj+Y1m9OubjsAvrrvKz6991ObXvgX7M+57QYMAoYC2cBLVotIIqmsyES+CnMj/gaf7PkEgFn3zaKaQ7UC95N98ubBWJF3t91EPj4+nTFjlqM71gr77GrcNoSzOnh1qY8z7+g8QJj3FDUPtFWr2uzZM4Vvvx1MtWoO7N8fSrt283j77e1kZGSX+HxqVV72yZueuXOP8MMPR1AUWLz4AVy9U7gWfw07jR29GpRt9JWlqF3blT//fABFEb4Mf/55GpDy+rKgGt1lNBK/Q/O61asmd48DqhpoOuANhAA/mvHcEol12TxpM+f+d44Xur1g7VBKiNof3y3n9kvEnNl/AdnyJpGYEpnIV2Fe2vQS6dnp9GnYh7GBYwvdL8BLjqAzB2oi37B6QytHUjAGg4GpU1dz7Vo8ft61md71aQC+PvB1qY6TlpXGghMLAHi649PF7q/Vanj22c6cO/c/hg1rRlaWng8+2EVQ0Fz27g0p0TnzJvLS28F07NhxnenTxYXYhx/ew/Dhzdl+fTsAnb074+boZs3wSkS/fv68/bYw5HvyyXVcvBjLqIBR2GvsORN9hjPRZ6wcYcXg1KkocErjpv0JwJz98WeAbYjLlbzvH87Auzn3PwLK7qshkdgyXi5eNjherijUirxqiNsMmJFz/0Ugy+IRSSSVFZnIV1G2Xt3KP+f/QaNomDN4TpFSLaPhnRxBZ1JCEm13hjzAnDkHWbnyAvb2GpYtG81LvZ/HXmPPnpA9HAk/UvwBcvj73N/cTrtNQ4+GxgS7JPj6erB69TiWLRtNnTquBAfHMmDAIiIikop9bp+GfXCyc+Jm4k3O3zpf4nNKCufq1ThGj15Gdrae8eNb8frrPQHYdm0bAPf42a6s/k7eeqs3ffv6kZycydixf+OsuDG46WAA/jotq/LFYTAYRCLf7CI6smlRs4UZE43vcm5HAXe+V04GAoBYYJaZzi+RSEpOKHATkV50yvP4W0At4ALwgxXikkgqJzKRr4Jk6bKYvn46AP/r+D/a1GlT5P7Na+bMkpcVeZOS1+zO1jh0KIyXX94MwJdf3kenTt7Ud6tvnLtdmqq8anL3RIcn0Gq0pYpDURTGjAnk/PlnCAioSVpaNrt23Sj2ec72zsYxaFJeX36SkjIYMWIJsbFpdOxYn19+GY6iKBgMhtxE3ob74+9Eq9WwePED1KzpwokTkcycuckor19ydolUcRRDeHgSsbFp0EJ8JphPVh8HLMq5P72A7XaIajzAV4A0ZJVIrItajW8D5G3X9CD3tfoOwsleIpGUF5nIV0G+O/Qd52+dp6ZLTd7v936x+6sV+RvxN0jLSjN3eFUCg8Fgs4n87dtpPPTQcrKy9Iwe3ZJnn+1s3KaOolt2dhlhiWHFHutk5En2h+7HTmPH1HZTyxxTjRrO3HuvMNI6cCC0RM+RffKmQa83MHHiSs6ciaZevWqsWjUWZ2fhmBwcG0xEcgROdk508+1WzJFsi/r13Vi0aBQA339/GN35JrjYu3A17iqHwg5ZOTrb5tSpKLDPRGl6GYAHW5orkf8VSAVaA70L2WcU0BlIQY64kkiszZ2y+rxMRYyPTECYVEokkvJS6kR+zpw5tGnThqCgIAICApg0aRJhYeKCPiIigilTphi39+nTh2PHjuV7/mOPPUaDBg1o27at8eupp57Kt8/evXvp0qULbdu2pUuXLuzevTvf9qSkJCZOnEirVq0IDAzk/fffv6uC8tNPP9G6dWuCgoIYPHiwMcaqTlRyFO/ufBeAj+/5mBrONYp9Ti2XWtRwqoEBA5duXzJzhFWDmNQY0rPTUVDwcfexdjhGDAYDU6as5saNBBo3rsHPPw/L13bRvl57ejfsTbY+m+8OfVfEkQRzj8wFRP9s3Wp1yxVb167i93TgQMley2oiv/PGTlIyU8p17qrMp5/uYc2aYBwdtaxcORZvb3fjtq1XxVSAHr49cLJzslaIZWbQoCa8+moPAJ55fAv9vYW8fsmZJdYMy+Y5dSoKml7CYJdFoxqNCKoTZIaz5B05NwMorP1LAT7NuT8PkCMEJRLrcafRXV60wDc59+cDpywSkURSmSl1Ij9s2DAOHTrEyZMnOXPmDH5+fgwdOhQAvV7PlClTOHXqFCdPnuSVV15h5MiRpKfnjobJzs7m7bff5sSJE8avuXPnGrdHR0czYcIEFixYwIkTJ/j999+ZNGkSkZGRxn2eeOIJWrZsyZkzZzh+/DjHjh3jxx9zXWs3btzI/Pnz2bNnDydPnmTKlCk88IC5jHgqFq9vfZ3EjEQ61OtQ4gqpoihSXm9i1Gp8Pbd6+WaqW5uvvtrPmjXBODhoWbZsDB4edydnalV+3tF5RSbISRlJ/HFazHguicldcaiJ/LFjESVysG/u1ZyGHg3J1GWy4/qOcp+/KqLXG/j++8MAfPvtYLp0yb/otO16xZPV38kHH/Sje3dfEhIyuPC3WGxaenYpOr3OypHZLidPRkEL4T3xQMADZhqH9R9wDTFqbkIx+/YDBiJGXMlKn0RiHdIBtXhXUEUehLJmDKAHngdkG5NEUh5Kncj7+/vj5CQu7u3s7Hjvvfe4evUq4eHheHt707t3rvxtyJAheHp6cu7cuRIff8mSJYwbN46WLVsC0Lx5cyZMmMCSJaJCcvv2bfbu3curr74KgIODA59//jnz5883HmPevHm8//77eHh4APDQQw+h1Wo5ceJEaX/cSsXB0ING9/Dv7v+uVP3KcgSdabFFWf2+fTd57TVRYZ09eyDt2xc8L35Ys2E0qtGIuPQ4Fp5cWOjxFp9eTHJmMgE1A4z96uWhUaMa1KzpQmamjhMnIovdX1EUBjcRFVYpry8bhw+HER6ehJubA488kr/qqjfo2X5NONZX5ETe3l7LX389SI0aTlxaXwNHQzUikiPYdWOXtUOzWU6cCYVmFwFzyurn5Nw+BriUYP9Pcm7/BE6YIyCJRFIkRxGO9LUB/yL2+xxwArYDKy0Ql0RSeSl3j3xqaiqKouDl5VXg9ri4OGPiXxK2bNlCnz75L/r79OnD5s3CeGvHjh107doVrTY3CW3WrBnR0dFER0cDsHXr1nwLCnce404yMjJITEzM91URyM7WExNTMsmw3qA3Gtw9GvQoXX26lupcxhF00rneJNyIF4ZttpLI37qVytixf5OdrWfs2ECeeqpjoftqNVqe6/IcALMPzEZv0N+1j8FgMJrcPdXhKZNU7BRFySOvL2Wf/BWZyJeFlSvF6/3++5vi6GiXb9vJyJPEpcfh5uBGx/qF/71UBBo08OC330aCzo6M400BOVO+MDIysgnOOgSOmdR1qU9n787FP6nUnAe2IC5R/lfC57QDxufcf90MMUkkkqLJ2x9f1Ge+H/Byzv2XEJV8iURSFsqVyJ89e5axY8fyzjvv4OjoeNf2//77j9q1axur6yUhPDwcX1/ffI/5+vpy9erVQrcD+Pj4cO3aNZKTk7Gzs8PV1bXQY9zJJ598goeHh/GroOPbIkuWnMHP7xteeWUz0dFFJ/QLji/gcPhh3Bzc+PTeT4vctyCMI+iktN4kGCvy7tZP5PV6A488spLQ0ESaNvVk/vxhxSbeU9pOwd3RnUu3L/Hfpf/u2n4g9ACnok7hbOfMI0GPmCzWrl29ATh4sGR98vf434Odxo7Lty9z+fZlk8VRFTAYDKxYIeTTo0YF3LVddavv3bA3dhq7u7ZXNIYPb87zz3eB060BWHZmOZm6TCtHZXucOxeDvrlQ2Y1u9QAaxRyeuar/xnDERX9JeR/hZL8B2GHakCQSSTEU1R9/J68C3sB1oORTcCQSSX7K9An88ssvU7duXVq1akX9+vV57rnn7tonNTWV5557jk8/zZ80KorCt99+S6dOnWjVqhXTpk3j5s2bxu3x8fF3VfCdnJxITU0tdHvefYrbXhCvv/46CQkJxq+88dgy//57idTULL74Yh/+/t8wc+YmoqKS79ovPj2e17eKCsW7fd8tk+mY2iMffCtYjmYyAeoM+YbVG1o5Evj8872sX38ZJyc7li8fg7v73Ytyd+Lm6Mbj7R8HCh5Fp1bjx7UaVyJDxZJS2oq8m6MbPRuIeecbL280WRxVgXPnYrh06TYODloGD2561/at10QbRkWW1d/JZ58NoINXd0iqRkJmPOsvSiXHnRw7GQoB5hw7lwCoLTsFjZwriibAEzn3X0X230oklsJA0Y71d+IKfJZz/yMg3BxBSSSVnjIl8l988QWRkZHcunULJycnpkyZctc+jz32GCNGjKB///75Hp81axaHDx/m8OHDHDlyhMaNGzNw4ECys4V5laOjYz5zPIC0tDScnZ0L3Z53n+K2F4SjoyPu7u75vioCf/75AOvWjadjx/qkpmbx5Zf78ff/hpde2khkZG5C/872d4hJjaFFzRZM71zaCyNB4xqNsdPYkZKVQliSnABQXgrrkU9NzeLmzQSLLZbs3n2DN98UldVvvx1MUFDJF3mmd56ORtGw7do2TkaeND4emxrLsrPLAHiq41OFPb1MdOrkjaLAtWvxBS5aFcSgxlJeXxZUWf299za6a3EnS5dl7CHv79//rudWVBwctCxdMgaHS20A+L+/5hTzjKrH+nNbwDkdZ70HvRr0MsMZFiBGyQUiTOxKy1uInvpDyP5bicRSXAciEYqYDiV8zgRE9T4F2Q4jkZSNcmnivLy8+Oabb1i5ciUJCQnGxz/55BNiY2P57LPPCnyOg4Nw6XZycuKNN94gMzPTaIjn4+NDSEhIvufcvHkTHx+fQrfn3admzZqkpaWRnJxc6DEqC4qiMGRIMw4deox//51A587epKVl89VXB/D3/4YXXtjA9rMH+f6wGOEzZ/Ac7LX2ZTqXvdaexjUaA1JebwoKSuQNBgPduv1CgwazqVfvS0aNWsoXX+xlz54Q0tKyTB5DdHQK48b9g05nYOLENkyb1q5Uz29YvSGjW44GYPbB2cbHfzvxGxm6DNrXa0+n+p1MGTLu7o60bFkLKLm8Xu2T33ZtG+nZshevpKiJfEGy+sPhh0nJSsHL2YvWdVpbOjSz0rixJ2+NEn3ZZ3W7WLP+jJUjsi0OJAqvmU5u95TKMLVk6MmV1U+n6D7bwqgLvJhz//8QTvYSicS8qNX4dkDBRbO7UcgdR/c7YvFNIpGUhnI3t2VkZJCZmYlOJ0b1LFmyhD///JNly5blM6QrCp1Oh52d6LHs3r07O3fuzLd9586ddO8upDrdunVj7969xvMBBAcH4+DggI+PD4qi0KVLF3bt2lXoMSobiqJw//1NOXBgGuvXP0yXLt6kp2cze/YB7v1yPDqDjvv9hnNvo3vLdR7ZJ28a0rLSiE4Rxox5E/mQkAQxnxmIikph1aoLvPLKFnr1WoCHx6d06fIzL7ywgWXLzhIaWj5DRr3ewKRJKwkPTyIgoCY//jikTIZ06ii6P0//SWRyJHqDnrlHxTjJpzs+bZaxVKWV17ep04Z61eqRmpXKnpA9Jo+nMnLjRjzHjkWg0SgMH978ru1qf3w//35m6pG2Lv/36ATcsmuDQxaTPviMiIgka4dkE2Trsgl3F+MIRweONsMZ1iPmwFcHJpbjODMBL+ACuTJ9iURiPkojq89LJ+DRnPszEIt5EomkpJTqCiwzM5PQ0NyL5/j4eB599FFGjx6Np6cne/fu5ZVXXmHt2rXG0W93cuPGDeP9jIwM3nzzTerWrWs0xJs2bRqLFy82VuiDg4NZtGgR06ZNA8DPz49OnToZq/1ZWVm8+uqrTJ+eKxmfMWMGb7/9ttF9ftmyZaSkpNC3b9/S/LgVDkVRGDSoCfv3T2PDhodpOjISfcNrkGXHlleaMGPGesLCyp4ANveSs+RNwc1E4cHgau9KDafc/vHDh0WPWFBQHfbsmcLnn9/LqFEB1KnjSlaWnkOHwpg9+yBjx/6Nr+/X+Pp+zdixfzN79gEOHQojM7Pkc68//ng3mzZdwdlZ9MVXq1a2WfZdfbrS1acrmbpMfjz8I9uubePy7cu4O7ozvtX44g9QlnOWMpFXFCXXvV6OoSsRq1aJ13iPHr7Uru1613Y1kb/Hr/L0x+dFURSe7CEuLhMbHOHhh1eg08kLzHUnt2JwTYZ0Jyb1HGGGM3ybczsN0UNbVjyAN3LuvwOklScoiURSLKUxuruTT4BqwEHE+EiJRFJSSmU1HBMTw4gRI0hJScHJyQmNRsOECROMZneff/456enpjBw5Mt/znn32WR577DFAyO537NiBk5MTer2eAQMG8O+//xr39fHxYdGiRUyaNIns7Gy0Wi0LFiygYcNcU7AFCxbw9NNPExgYiF6vZ8SIEbz00kvG7aNGjeLmzZt069YNjUZD3bp1Wb16NRpN5ascFYSiKPTsV4+0c/9CIviG3M/NaHe+/fYQ8+cf5bHH2vPaaz3x8SmdF4BxlnysnCVfHlRZfcPqDfNVrA8dElLxrl196NGjAT16iGq9wWDg+vV49u27yf79oezbd5NTp6IIDU1k2bKzLFt2FgAnJzs6dqxP9+4+dOvmS7duPtSpU+2u82/ffo133tkBwA8/DKFVq9rl+nle6PoCY/8ey49HfuRoxFEAHmnzCK4O5bkQLxw1kT90KAydTo9WW/zrelCTQSw4sYANlzcw675ZZomrMlGUrD4tK419N8VFW2UyuruTSe0mMuvgF9D0EttXn+ejj3bz9tt9in9iJWbhYTGSzz2iNdXdTP36DgY2IuS2JR05VxT/A2YDNxFy/ZeL3FsikZSVFED1ySmL8rUeYuHtDYRJ5UhEYi+RSIpDMUgL8rtITEzEw8ODhISECmN8dydvbnuTj3Z/hF91P84+fZb9uyN5992d7NkjkkgHBy2PPdaO117ria9vweqJO9l/cz/df+2Oj7sPN1+oGM7+tsivx39l2pppDGoyiPUPrzc+3q/fQnbsuM4vvwxn6tSi+9WTkzM5fDjMmNjv3x/K7dt3V50aNapB9+4iqe/e3ZdatVzo2PEnIiOTmTy5LQsWlL+qlq3PpvGcxsYFCoAzT58hsHZguY9dEDqdnho1PiMpKZOTJ5+iTZs6xT7ndtptan1RC71BT8jzIfh6VIwRk9YgJiaFunW/RK83cO3ac/j5Vc+3fevVrdy76F683by5+cJNs7RP2AIGg4FWP7biXMw5WDUCzan2bN36CH37+lk7NKtgMBjw/KAu8YZoeoS8wp5f7vbAKR/TEQn3MGCNiY75GzAFqAFcRUj2JbbJMUSPdAPAH2iIMC2U2D47EMaU3kDJlHJ3kw60BK4BbwIfmCQyiaQiUpo8tGqUqKsYV25f4Yt9XwDw1X1f4eLgQv/+jdi1azJbtz5C794NyczU8cMPR2jS5Fv+979/CQlJKOaouSPoQhNDSc4smWO45G5uxIv2krwz5HU6PUePCml9p071iz1GtWoO9Ovnzxtv9GLdugncuvUyFy48w4IFI3j88fa0alUbRYGrV+P4449TPPPMf7RrNw9f36+JjEwmMLAW339/v0l+HjuNXb5pCL0a9DJbEg+g1Wro3FmdJ1+yiwZPZ0+6eHcBYOMVOYauKNauvYheb6Bt27p3JfGQR1bvf0+lTeJBKJvGBY4DoP7AG+j1BiZM+IeYmBQrR2YdjoQfId4QDZn23OtfPr+Vu0lEJN0g+mRNxSSE+30c8LkJjysxLSFAX+BpYAgioXNFGBd2Q7ibvwH8BGxB+ChkWiNQSYGUtT8+L07Alzn3v0C44EskkuKQiXwl5IWNL5Cpy2RAowGMDBhpfFxRFO65x5+dOyezffuj9OkjEvoffzxCkyZzeOqpddy4EV/ocT2dPanlIhzDL8ZeNPNPUXlRZ8jnNboLDo4lKSkTFxd7WrSoVepjKopC8+Y1mTy5LfPnD+P06aeJi3uVjRsn8s47fbjvvsa4uztiMIhFgOXLx+DiUrYJBgXxWPvHcLUXUtunOz5tsuMWRpcuIpEvaZ88IPvkS0hRsnqAbddzE/nKzvjWwuchyuU0Tds6EhGRzCOPrEKvr3pCtn/O/yPuXGxGh6AGRe9can4DkoEWgCnHGWqBj3Puz0bOqrZFDMDjQBKiCh8EqBWoKOAA8Beij/oJYADQBOGM3gDoA0wG3kUYG+5CtFOU3DNGUl7K0x+fl5GIyn4G8Eo5jyWRVA1K1SMvsX3WX1rP2otrsdPYMWfwnEIrZn37+rFjx2R27rzOe+/tZPv268ybd5Rffz3OlCltef31XgVW4wJqBhATEsOFWxdoX6+9mX+aykneHnmVw4dFf3yHDvWwszPN+pqHhxP33deY++4TYwN1Oj0XLtzCw8Op1P4IxVHdqTpLRy/leORxHgp8yKTHLohcw7uSjaADkci/s+MdNl/dTJYuq8yjGCszSUkZbN58BSg4kU/MSORwmHAt7+dXlhnfFYsmnk3oWL8jR8KPMOZd+GqcHRs2XGbWrH288koPa4dnMQwGA3+fy0nkz7cgKKiuCY+ed+Tcs5Rt5FxRDENUCvcB7wNzTXx8Sfn4DdgEOCI8Epojkvs4RFX2Wp6v63lu0xAJ+01E8n4n9uTK9P0BP4T0u3qeL4+cW3dkXausGDBNRR7Ea382YoTdcmAnYqHG0oQDH+bE8Rimf0+SSEyHTOQrERnZGTy3QRgPPtflOaM5XVH06ePHtm1+7N59g/fe28nWrdeYP/8Yv/56gsmTg3jjjV74++c6qwfUDGB3yG7pXF8OCpohrxrdlURWX1a0Wg2BgeUztiuKIc2GMKTZELMdPy9duohE/ty5GOLj06le3anY53So1wEvZy9i02I5EHqAXg17mTvMCseGDZfJyNDRuHGNAk0Qd9/Yjc6go3GNxvkWoioz41uN50j4EXbGruPbb+fw+ONreeONrfTs2YDu3auG18KZ6DNcibsM2Vrco1rh62vKhcBNwCVEMvWICY+rogCfAr2BnxEz5puZ4TyS0hMGvJBz/31EEg/i/8wz56uggoEBUa2/TsGJ/g0gCyHBv1KCOBTE35+a2Fcv4X31ew+gbJNfKj6XgFjEQkzR3j4low3wJPAj8BxwFKGssRRLEEaZcTnfn0TMurdkDBJJyZFLkJWI2Qdmc+n2JepWq8vbfd4u1XN79WrIli2PsHv3FO69txHZ2Xp+/vk4zZp9x5YtV437yRF05UNv0BeYyKuj5zp18rZKXBWN2rVdadRILDCpaobi0Gq03Nf4PkDK6wtDldU/8ECLAtU8efvjqwpjA8eioLD35l4GjPZi/PhW6HQGxo//p0CDycqIUVZ/uQlBLRqY2BthTs7tVMznVN0L0XutQxhpSayPAXgKSEDMEn+xFM9VEP3zXYHx5PbPbwYuI4zTbiAqur8hRhA+AgwEugABOc9XF4ANOXGEAKcQFf61wCLESMQPgZmI6uwYhLy/E9AUqI1IYv2pmq0bajW+I6ZbzHgfsUhyEvjVRMcsjlvAWMTfUxzi/xbge2AUwplfIrE9ZCJfSQhLDOODXcLl87N7P8PdsWwVk549G7B58yT27p1Ku3Z1yc7Ws3HjZeN2OYKufESnRJOpy0RBwdtNJO0ZGdmcPBkFYDRxkxRPaefJAwxuMhiADVdkIn8nmZk6/v33ElB4f/zWa1sB6O9vyj5m28bb3ZveDXsDsPTsUubOHUqTJp6EhCQwZcpqqsLgF2Mif74FQUHFT4koOZeA9YjE7BkTHrcgPsk5z3LgiJnPJSmeP4F1CAn8r5hWIKpFyOp7A4+S2z+/AdFzfx6IQMjz0xHV/WDEHPNNwDJgPsJ07f8Qf5sPA0OBnkArwBdwy3PO68BiE/4MFQU1kS9vf3xeaiL+z0D8/uNNeOyCWIf4P12G+Nt5FziLeK9wRCzq9EP8nUgktoVM5CsJr2x5hZSsFLr5dGNim4nlPl737r5MntwWgKtX442Pq4n8xdiL6PTSTKa0qNX4+m71jT3ap05FkZmpw8vLGX//6laMrmLRtatqeFfyPnm1In8s4hhRyfJDOS/btl0jMTGDevWqGVsX8nIr9RYno8Ss4L5+fS0cnXUZ10q41y85swR3d0eWLh2Ng4OWNWuCmTPnoJWjMy8XYy9yJvoMil4Lwc1LNO6x5Hyfc3s/wsDMnLQG1M/G1818LknRRJI7neBtRBJlLRwRVfVmQGdEtX0MwoBvJqIa/x3wByKh2w2cRlTvE4FsRF83wEoLxm0rmMro7k7+h1BOxGC+UXSJwDSEj0YUYlrCQYSCwx4YDWwFvIDDiJ9RFrEktoVM5CsBu2/s5s/Tf6Kg8N3936FRTPPfqkqXr16NMz7mV90PB60D6dnp+eaGS0pGwUZ3Qo7XsWN9FOV3hOHTXoTcT1IYakX+4MHQEldF61SrYzRp3HRlk9liq4isXHkegBEjmqPR3C2d3nF9BwCtareiTjVTJnO2z+iWo7HT2HE88jjBt4Jp374eX34pFoVefnkzZ89GWzlC8/FPjsmdfWhjSHc2YSKfRK5sdnpRO5qQ9xAX6FtyviSWx4CocN8G2gKvWjWa8qNFJHwgqtMRVozF0iQCZ3LumzqRtyd3gWQOpk+gtyP68X9FKHVeQvTjd7hjvx6IxYpGCP+FbojFHInENpCJfAVHp9cxfb24CHq8/eMmdZJXq8NXr8YZEyWtRktTT9E7JPvkS09R/fFdu9ZBjNf5nlz53jeICx7JnQQF1cXRUUtsbBpXrsQV/4QcBjXOGUMn5fVGdDo9q1eLC6VRo1oUuI+xP96v6vTHq9R0qcmARgMA+OvMXwA880wnBgxoRFaW3ugtUBlZcWEFAJknm6EoFGiCWDZ+RyTzzRFVUEvgj5hVDvAawjFfYln+BlYgpPQLEAlbRccb0XsPsNqagViYg4iFGT+gnhmOPxDhbZGNSLRNQRrwPHAPwkfBH9gBzCLXM+FOmiFaMrog+ufvBZaaKB6JpHzIRL6CM+/oPE5GnaSGUw0+6v+RSY+tutUnJmYQF5dufFz2yZedG/E3AGjgfrdjfe/eeiATscLvApxDfOB4I4x69iCr9Lk4OGhp315cPJRlnvzGyxtle0gOBw6EEhWVgoeHI337+hW4T1U0usvL+FZipvxfZ/7CYDCgKAqDBgk5+NGjlbMKdyP+BkfCj6CgwIUAmjTxxNXVFIZWd46cs+SlyP8hTPWOIpJKS2FAmK9VZSVQDLleCK8jKvKVhVE5t6usGYSFMdXYuaL4CrHo8y/CT6M8HEI463+T8/0TCEO93iV4bi1gG2LWfSYwDvgceU0msTYyka/A3Eq9xZvbhAPvB/0+oKZLTZMe38XFnrp1hYtwXnm9msjLinzpCUnMX5FPSsrg/PkYANq2Tc7ZqwXC/fZ7hPQrHeGe2wsIRMjNZJUeymZ419WnK+6O7sSmxXI04qi5QqtQqBXloUOb4eBw95idsMQwgmOD0Sga+vhZY66v9RkZMBInOycuxl7keORxQLTDABw9WjndqlecF9V4f00QpFQz4fz4LcAFhFnYoyY6Zkmpjeh9BpHUZ5n5fAaEm3pPoC+iylgVTdFA9MXHINRmlW16gJrIb0M44FcFzNUfn5dmiDF0ICYblOX1mgm8hVhwCEaoB/4D5pHfsLA4XBCLf6q/w6uIhansMsQkkZgGmchXYN7c9iZx6XG0qdOGJzs+aZZzqPL6a9dyE3k5gq7s3Nkjf+xYBAYD+Pq64+mpJqNNEXNp/wecQMjXpiE+RM4j5u7WRxg37aIqrwiXJZG319pzb6N7ATmGDsBgMBgT+cLc6tVqfId6HajuVN1SodkUbo5uDG02FIC/Tgt5fbt2dVEUuHkzkejoyjeeSHWr94wWfaNt2phKVv9tzu0USnchbSpeRFTYLmO+8VZ5E/j7EEmPesn1OHDcTOe1VVYhZnRrEL/zyjZ3vRliET4LUT2u7OgRcnMwb0UeRBJeC7H490Mpn3sGMabwQ8T4yfE5jw0uYyxaREX/a0Rv/Y/I8XQSayIT+QrMgEYDaODRgG8Hf4udxpSjW3IpyPBOSuvLzp098qqsXsyPv5SzV9M8z1AQTro/I0x0fkTIETMQVZ0+CKfVr4FY8wZvg6iJ/MmTUaSmlnyl3jiGTibynD4dzdWrcTg52Rml4ney7XrVltWrjAsU7vVLzy5Fb9Dj5uZI8+ZCCVXZqvIRSRHsuykqbimHGwOYyOjuCrmJjrlHzhWGG7kV4feAVBMeu6AE3gnRJhWCSCDSEBf/t0x4XlvmNrneBC8jZrBXRtSqfFVwrz+PUB64IJSD5sQDUFtH30GoOopDh5C+d0Asmnkhxsv9CXiaIKbnEePpnBDj6/oipjFIJJZFJvIVmAdbPsil6ZeMM47NQUGJfPOaoiIfmRxJfHq82c5d2UjJTOFWqrhwUxN51eiuU6f6FJzI58UdeAo4huj1egxwRaxSv4io0j+M6MOsGlV6X1936tWrRna2nmPHSt6nPLDxQAAOhh3kdlrVblNQ3ervu69xgf3PBoOhyvfHq9zf9H7cHNy4mXjTmOR26CB8Go4cqVyJ/KoLqzBgoHP9zlw+JqSjppHWf494fxqEqGJaiycRJl0R5PbMloeiEviriMVWb8QCbGOE0dY4qoYs9wVEktOc3PnglRE1kV+PWKypzKj98Z0RPezmZiqiiJGAGFlYFJcRRY5XEbL6oYgq/BgTx/QgopXCCziCaDE4b+JzSCRFIxP5Co6D1rzyNDWRv3Yt3viYu6M79d1Eb2jwLVmVLyk3E28C4ObghoejB5CbyHfuXFhFviAUREXjJ0Qv/VyEgUsmYrW5L0Li9xWVveKjKEqZ5PW+Hr4E1gpEb9Cz5WrVHkNVnKz+atxVQhJCsNfY08O3hyVDszmc7Z0Z1UJcrKvy+tw++cpleKfK6nt4DiQrS4+bmwMNG3qU86jJ5ErZZxS1owVwJHc+9WeU3XeksAT+OXIT+LyO3jUQMnNXxIzq18p43orCf4gJBQri/74wZ/DKQAfAFyGzruyfK5boj8+LKmkHmA+cKmAfA0K1GIQY4esG/AKsAUzl73En3RCLGk2A64hxdbvMdC6J5G5kIi8pkrwj6PIi++RLT15ZvaIoxMSkcP16PAAdOnghKjRQfCKfF3dEZekYYkX4CYQjczBiXIs3MAExXqVyVunLkshDrnt9VZbXX70ax8mTUWi1CsOGFVwdVavxXX264urgasnwbBLVvX75ueVk67ONFfnKlMjHpsay4/oOAHySxFitNm3qoChKOY/8B6Ki1hRh+mZtJiBkwQnAJ6V8bkEJvCO5CfxsCh/J1Qr4Lef+l8BfpTx3RSEB8ZkEQplg7l5qa6MgXM2h8svr1Yq8pRJ5EO7yYxD9+c+T/5omFKHy+R+iVaYvItmfivh/MSdNEa//rojxdAMQfhASifmRibykSNSK/I0bCeh0uTN3ZZ986bnT6E6txjdv7oWHRwTiw8mVss9j7YBwYQ3Pue2AqNL/BfQDAhCzUitXlV5N5A8eDCvV8/Im8gZD5VzkKA5VVt+7d0O8vFwK3Gfrta0A9Pfvb7G4bJn+/v2p6VKTmNQYtl7dSrt29VAUCA1NJCoqufgDVADWBK9BZ9ARVCeIqPNC9RUUVN7+eAO5JnfPYBuXHxpyE/hvEclAcRgQ1dZelD6Bz8tocqvx0xDGppWNl4EwRCvBh1aOxVKo8vo1VN62iduIlj6wbCIPou/dCdiOWCwxIBYIWyFGOzohKvdbEa0zlkIdTzcKcd01HqH0qZrXFhLLYQufpBIbpn59NxwctGRn6wkNTTQ+LkfQlR5jRd5d7Y8XiWd+WX0Tyr967IaoghzJ+XoSUaW/iLiwaob4EKwcdOhQD61WITQ0Md/faHH0bNATF3sXIpIjOBVVkEyv8lOcrD5Ll2VULAxoPMBicdky9lp7RrcYDYiZ8tWqORAQoBreVY6qvCqrf7DFg5w8GQWYwuhuG3AO8V40uZzHMiWDEUl5BkX3b+dN4AcgpLt3JvD1S3nuDxHKBNX8rjIZlm5BtH+BkDcXvFBY+eiF6JmORfyNVEZUt/qmgGnHHhePH7njI2ciFsQmIdQfnRHGdjOwTnrjjDDAez7n+9cQCoHKuqAjsQVkIi8pEq1WY+yLzGd4J6X1peZGgpDO5zrWl8borqx0QPTQRyAuqlohpF/3AQtMfC5zEoO40O3OnWNeXF0daN1aJBkHD5ZcXu9k50Q/v35A1ZTXR0Uls2+f8G0YObLgRH7njZ0kZCRQ27U2Xby7WDI8m2Z8ayGvX3lhJenZ6XToIBK4ymB4l5iRyOarmwFhqHrqlKkSebUa/yjChdpWUBCVMxDviXeaVRWWwM+g7Am8ihbha9II0V9bWczvkhBmrCDUF32sGIulsQOG5dyvrPJ6VVZvrVaJ1xBtg9eAFYjf+YeI12bBn2WWQ4vwxZiNeG+Zi2i3qBxqLYntIRN5SbEUNYLu8u3LZOsrw4WH+cnbI28wGAqpyJs6kVephriwOgSMRVwsTgXeQEj6bZmDQHuEQdR+xIp3frp29QZK3ydvHEN3peol8qtXB2MwCLM2X9+CE6vVF1YDMKzZMLQarSXDs2l6NuiJj7sPiRmJrL+0no4dK0+f/LqL68jUZdLcqzk1DQ2IiBAXoOpiWdm4hpAaAzxb3hDNQDdgBOK98P9yHisugf+GsifwefFEvLe55JzvDRMc09q8jvB8aQh8auVYrMHInFtV+l3ZsLTR3Z24kjvHvRXiuub/sIx7fkl5DvgHIfX/FzmeTmIuZCIvKZaCnOt9PXxxtnMmS5/FtbhrVoqsYpG3R/7GjQRiYlKxs9PkjHQydyKv4oyoAKkzlD9B9HLZ4qgcA/AD4kI6lFy349/u2jPX8K5sffJ7QvaQlJFU1kArJMXJ6g0GA6uDRSI/ovkIM0YyH3HhG2LGc5gWjaJhbOBYQMjrK1NFPq+s/vTpaAAaN65BtWrlmZDyA+L1fB/Wr5gVxseIS6KVwBzMn8DnpTW5CqkvgKUmPr4l2YkYMQjwM2IRuapxH2JhJgQh9a5MZCMSZ7CueeEYhP/CccTUHltkFKKNsSZwFGGGJ8fTSUyLTOQlxVKQc71G0RjnyUt5ffHoDXpuJggZcwOPBsZqfJs2dXByssNyiTyIl/0HiITYHliGMMOLssC5S0oKou/tGSALMa/1KGIFfifigjoXNZE/ciScrCxdic/S2LMxTTybkK3PNrqzVwUSEtLZulX8DgtL5I9HHudm4k1c7F24t9G9ZopkA8LDYTXCcbisI8Asj+pev/biWhq3dEVRIDw8icjIiiuhTM1KZf2l9UB+WX355senIBI6gOnlis+8tETI/kFU0yyRwOflIeCVnPtTgZNmPJe5SEUY9wE8DpjrfcPWcUa8n0Hlk9efQcjE3RCvGWtSD9uqwhdEV3LH091ALH7stGpEVZvz2GbhquzIRF5SLAVJ60H2yZeGyORIsvRZaBQN9d3q55kfXx9IJ7caaYlEXuVRxPikGgj5elfgrAXPXxgXEbEsRvSbfYmQ07ck98Lw93zPaNrUixo1nEhPzzYmHyVlUOOqN4buv/8ukZWlp3lzL1q0qFXgPqqsfmDjgTjbO5shijDEYg2I/+fzwHAqyods+3rtaerZlPTsdHaGbzb+Ho8erbhV+Q2XN5CWnYZfdT/a1W2Xx+iudjmOuhiIRziX31/+IM3Ke0B1RAI/HbiC+RP4vHyMqOamIqp5FWdhS/Am4nfmg1AWVGVU9/rKlsir/fFdEe/bkuJpgvi9dUO8F96HUEZKLMttxO++E6Ldq3IgE3lJsRSWyMsRdCVHldV7u3ljp7Hj0CFRke/UyRtR7TEgVrjLc8FcFvogHGibIMyWuiOSe2uxAuiIWPWvi5ClvUiuk//knNuF5O3t12gUunQp5xi6K1VnDF1xsnrAzLL6bERLxy2gLeJv0ANRBZ0AlFxVYS0URTF6LOy7uc84T74iy+tVWf0DAQ+gKIoJjO5sceRcUfgCwYgRnnMQhlqWRIsYF+qPuNAcT0V4LQj2IQy+QLTL2JKhoTUYgqgWnyVXcVcZsHZ/fEWlJmIk3oOI8XQPI6YL7aNy+ijYGgaESigUofK09LW2+bD1T1WJDeDvLxL5mJhUkpMzjY/LEXQlJ6/RnU6nN5pi3e1YX97Rc2WhGSKR6gUkIsYxzbdwDNmI0XgPIhyPewHHcm7zMhJwRyw67Mq3pUuXshne9fXri4PWgevx16vEolR6ejbr118GYNSoFgXucz3+OiejTqJRNAxpNsQMUbwL7Eb0zy5DLN6sBhwQxl/TqQgXN118hJP/wbCDdOwoqrYV1fAuIzuDdRfXAUJWn52t5+zZGKA80vqdiEU5F2CKKcK0ALURBnTWIq/53SZyzfdsmTREO4ABofQabN1wbIIaiJY1EP+flQW1Ii8T+dLjjPi8ezHn+5+AHoipFf+HbSgiKys/IwpF9ojFUlfrhmNCZCIvKZbq1Z2oUUMYjV27drdzvUzkiyev0V1wcCzJyZm4utrTsmUtLNsfXxheiEr8REQF6EnEjFZLVIMigf7ArJzvZyJWrusVsK8LwnUf7jS9yzW8K10i7+rgyj3+9wDwy7FfSvXcisiWLVdJTs7E29vNmHzeyZpg4TDes0FParqYek7wJoSEGMSFjPp33wchw1aAH/PsY7uoI/lORJ6gdTuR/FXUivzWa1tJzEikvlt9uvp0JTj4FpmZOqpVc8DPr3oZjzon5/ZRhGRdUjLaIGavgxiNt8yKsZSE9xBKhrrAV1aOxZaobPL6aETrBAhpvaT0aBDtglsQrWXVEIWJjxEO/G2Bz4Gb1gmvUnIe4XsCwuC5vRVjMT0ykZeUiILk9U09xQV4bFost1JvWSWuioKxIu/ewCirb9++HlqtBttI5EH0hf4OvJ/z/ZfAaO6c225a9iDeVHchWgv+RvRW2hfxHLWy9zd5Z7OKMX5w6dJtYmNTSxXFs53ESKx5R+eRkJ5QqudWNFauFK65I0cGoNEUrAAxn6w+HLFYZEAsFo27Y/toRE8yiH7b30x8ftPSqEYjvJy9yNRlotSLQqNRiIhIJiKi4k1A+OeckNWPChiFRtEYZfWtW9cu9O+kaG4gVBZgmyPnbJ1xCJUSiPe801aMpSgOk9sPPxfrqhlsDfX9cz9QMZU6+VGr8S2RC3PlpT/ieisKWILwh7FHmFy+CjRALG7Pp+J5ZdgSGeROZroPeMG64ZgBmchLSoQqr887gs7VwZUGHg0ACL5V+SXJ5eFGwg0gv2O9kNWD7STyIKqhbyGMWFSZcx9EAmZKDIg5sH0RFziBiAvCB0vw3K6IdoAURDIv8PR0pnlzL6D0ffKDmw6mRc0WJGUm8fOxn4t/QgUlO1vPmjUXgcL74+PS4th5XbjqmjaRz0b0v8cgKo5fF7LfdMSFDMBjwHoTxmBaFEWhs3dnAE7FHqNFC6FeqGjy+mx9tnHx5sEW4jWY61hf1v74HxA+Fv2xvrt1ReVjhMFnKqKtyNYu6DMQiwx6xMWyOcdUVkTqA11y7q8uascKgprIW3PsXGVDVRmuRlwLzUNcc4EocDyJULoMR4ylLF2RQvIaYnGkFsJbqfKlvZXvJ5KYhUaNqgOFG95JeX3R5O2Rz3WsV42UbCmRVxkPbCN3/mkXTDcOKQnxwfUiQro/AeGa37yEz1fINb37Ld+WssrrNYqGl7q9BMDsg7PJ0mWV6vkVhb17Q7h1K5UaNZzo3bthgfv8d+k/dAYdgbUCaezZ2IRnfx/RM632xRflhP8JQnaoQ1TpD5swDtOiyusPhh2ssPPkd17fSWxaLF7OXvRqKHwpch3ry5LIp5E7cm6GSWKsmtghqnV+CFPUh7Et87uPEH29tchto5DkpzLJ66XRnXnxQhjg7UBMMvoMCEKYs61FqHTqAI8gRrdmWyXKisN6cg04f0MsiFQ+ZCIvKRFyBF35UBP5ei7enDgRCaiO9akIF00QzvG2RA+ECV4AIsaewL/lPOY5xOiP5QgZ2bfAH5TeeGQSBc2UL2siD/Bwm4ep41qH0MRQlp219Z7UsqG61Q8f3hx7+4JHB5lHVr8F+DDn/jyKX7RRED3C6iiuIcBlE8ZjOlTDu0Nhh+jYUfg6VLSK/IrzKwAYGTASO42Yy1w+x/o/EdVjP8T/naTseCGSQGfExftb1g3HyAnEghvA94hFX8ndqIn8NsTosYpKFrkLqrIib358gVcQr7MzwBuI99NkYBHCULI+QsF2gIpgDmtZosgt+MzA9keflh2ZyEtKhJrI55XWQ56KfKxM5AsjOTOZ22lCEpkQ4kRWlh4vL2f8/auTaxzjgW1eCDVGrMLfg/gAGU7uOKnSsgTojDBF8kYk4c9SNqd+H2BAzv3cmfJqIn/wYBh6fek+2JzsnJjeeToAs/bPqnSj6AwGQ7Fj5zKyM1h/WUjZRwSYKpGPJLcv/nGEAqMk2CNaJzog5PgDER/OtoUqrb98+zJNWguVQUWqyOsNelZeENVCVVYfG5tKWJjo82/durSJvIHc6uwzyFnTpqAtueZ3n5C3pcg6ZCEk9dmIdqgx1g3HpmmGaC3JpvwL4dbkBJCOcONvZt1QqhyBCPXLVcSI1mcQ14sxwHcIhUQTxCLfeSvFaEvoEUl8NKKN7zOrRmNuZCIvKRFqj/zVq3H5EhzjLHnZI18oajXew9GD8yfExXGnTt4oioL1R8+VhBqIStA0xBvkjJyvkko8MxGOoeMRfe39geOUX543Oec2d6Z8q1a1cXGxJzExg+Dg0hswPtXxKVzsXTgReYJt17aVMz7b4vjxSEJCEnBxsee++wqWzG+/vp3kzGTqVatHx/odTXBWtXUiCmhNrpFdSXFDXPz6Iy5ihpDX4NAW8HT2NBp/pte8gUajEBmZTHh4xTC8239zPxHJEbg7utO/UX8gtxrv718dd3fHUh5xL3AK0fs5zZShVnHGAy/l3J+MqNJZi88QiZ0nohovKZrKIK/PO3ZOpg7WQUGoIb5D+Bb9h2i3cUV8Pn6IWDRqh3hdZlgnTKvzDeKa1Qkxas7JuuGYGflqrNBEISq6txDJkvlo0MADjUYhPT2byMjcC2k1kb8ad5WM7Kr6plE0efvjVcd62zS6Kwp7xKgwdWXzW4SxUXHJShhilq5aoXsD2IjoqSwvI7lzprydncY4Uq0s8novFy+mtp0KiKp8ZUJ1qx80qAnOzgVPBVh9QcjqhzcfjkYxxcfDh8B2xIVGcX3xhVEH8Tej+jWMRlQEbQdVXn/y1tGckZIVpyr/z3nhVj+s2TActA5AeWX1P+bcjkcsAkpMx6eIhdAUxPtfXJF7m4cz5E42mYN4fUqKRk3kNyD8Iyoisj/etrBHyOv/QOQCfwJDEb4aJxBqx2bAr1StXvrj5Jrlfk1VMFqViXyFZjZCTlMLMTrMCfGh2hQhR70H8WH/COJF/X+IROxHxIt+HSIBOglcA2Ip7ALZwUGLr687kF9eX69aPao5VENn0HEl7kqBz63qFGR0V/ESeRCrwa8gZJ1OiEppLwqfd7oNsTK8D9E6sAYhDzOV1NaZ3PFlvxkf7dpVmAiWJZEHeL7r82gUDRsub+BMtDWrXqalOFm93qA39sePDBhpgjNuQ8yXBjGWquDzloymiL83F0RS/xi21BOY1/BOXUg6etT2E3mDwWDsj1dl9ZBrdFd6x/oYcmXfT5c/QMkdqOZ3DRGL+JY2v8sGpiKuE4ZR8jaZqk57xDixFIRfSEUkb0VeYlu4IhZO1yJa2eYg+udDEKqoQITjvd5aAVqIFMTvIQuR+zxp1WgshUzkKzzV8tzPQPSEXAaOISphqxHGGN8jRtm8BvwPcQEwDDHmoi3QCFHxckBcLNdFrOb1BcS4qrzyehVFUaS8vhhyje58OH8+BlCN7qBiJfIqDyL62+sgFoG6ICqlKgbEgtEAxIV9UM72YWaIZXLObe5M+VzDu9KNoFNp7NmYB1o8AMCX+78sZ3y2waVLsZw9G4OdnYYhQwr+WzsSfoSI5AjcHNzo59evnGeMQrzHGBAX/hPLeTwQ/grLEQtBvyMWJm0DNZE/FHaI9u2FM25FMLw7FnGMGwk3cLF3YWCTgcbHy16R/w2hDuuIWEyWmJ6aCIm2E8KV+R0LnTcVobA5jFiY/RHbbQezNRREYgEVU14fhkgKNYj3YYnt4oUwwLsMzMr5/iKi6NEesSBuO4vgpuUFcj2YfqaqvD/JRL5C8wlC2pyNkNjdQPQm7kH0zvyFcIj+AngT0dc8GSHz6o9wD2+GSNpd8hw3DXEhfgmRsIkxQnIEXdlQE3lNYnUMBvD1daduXXUBRnXirkiJPIgP84OIld4IoDdi5nw84u/rNXINR/YjTPPMwd0z5bt0EYn8mTPRJCWVrd1jZreZACw+tZjwJNuvrBaHWo3v18+PGjUKlrersvpBTQbhaFfavui86BCJeyTi76Os5ogFcT+ixQPE+993Jjx22WlTpw0OWgdup92mTgvxN3fkSLjNGyaqsvr7m96Pi734DMjO1nP2rFhwDAoqzbgePeLzBmQ13ty0I3e830fAChMfPw3x/v49wtSuDaKNSVXYfI24WJaUnJE5t2uoeFJntRrfGuFbIrF9nBGeGlcRr1s3ROFlKGIi0Q6rRWYe/kZcGyiI4qWXdcOxIDKRrxRogeoI6VZrxIt0MGIF7glgJvABwgBiAeJDfwtwCLF6FYFIhDIR8vpriB4bteIl5ocX6lzvJZ3ri+JGwg0AksKE4UZuNT4FYVgCFS+RByHv3ItwE08FHkAkbqsRyo75iP6ssvRFl5S7Z8rXr+9GgwYe6PWGMvcpd/HpQs8GPcnSZ/HtQVMmotahOFk9mHLs3MeI9xcXRAXdpejdS80UxPsZiMXJf0x8/NLjaOdIu7rtAEjyuIpWqxAVlWLThncGg8GYyD8Q8IDx8UuXYklPz8bFxd74nl8ytiDk3h7AWFOGKimQhxEVKIBHEaM9y0I6osr+I0KG2xZx0d8V0ZL3G3AasUBXF9F/OrmM56rK9EIkF7GIYktFQk3k5di5ioc78Dbiuv4VxPXYfoR30QBEHlDRuYmYiAOiiFReRWHFQibykjzYI1xo/RByaPWC/jhgKFBaD9C8ppwlXxRqRT7ygugN79xZ7Y9Xq/GeOV8VEQ+E18LTCLlWOLkJ/uNYRtp090z58syTV1Gr8nOPziUpw3YTsuIID08y/h5GjCg4kb9y+wpnY86iVbTc37Q881Z3AO/m3P8RaFGOYxXF/wFPIf7mHgZ2m+k8JUeV15+8daxCGN6djTnLxdiLOGgdGNIsd9a7Kqtv3bo2Gk1pXr9zc24fQfRsSszP54iL1mRExTe+mP0zEG1O8xCL/O0RSXtnRMvdr4iFex1QG6GAeRuxOBuGWPT/lKoiWTUtduS2l1U0eb00uqv4eCFaHq8gxtfZIxZfuyDeO05bLbLyoSoA4xHvY+8VuXdlRCbykiJohaj2xwARxupMYdL64FvBNi8ltTQ6vY7QRJFEXTwsTIkqdn98QdghJJi/IMbMHUX0yFqKu2fK5xrela1PHmBY82E09WxKfHo8vx7/tZwxWo/Vq8UCW9euPtSvX7AsUq3G9/HrQw3nsjqNRyPMr9SWikfKeJySoCBk9SMRyclw4KwZz1c8qnN9fsM72+2TV03u7mt8H+6O7sbH1US+dEZ3YQjJMFQVgyHbwA5hYtUA8XkykVxDq0zEIvxPiEWvjoikvWPO9z/lbM9G9N0PQiyQrURUuCIR/bTvIV5f6gK0pOyo7vWrqDh9yukIzyWQFfnKQD3EZ+dFxOe0BrFQF4R4/7hc6DNtk08Qpt3VECbeBU/kqczIRF5SBM7kOk2fMCbyYWGJZGTk9ng18WyCRtGQkJFAVEqU5cO0YSKTI8nWZ6NVtIRdEB/cHTrUy9laWRJ5EInVVMQkBWv0Jk3OuRUz5dWK/MGDoWVeXNIoGl7qJuY2zz44m2x9RetrFFhGVq9HKCMiEONeLNG7rkV8cHdHrMYPAsquwCgvakX+ROQJ2rQXrwFbrsirsvq8bvWQ61hfOqO7XxCVkd6I9hqJ5ahFrvndv4jXQWdE0t4eUXmfh1hgzUKov+4DXke0pVxHLMKtR5jZjUQsjsqqu+kZgFCrhCAWUSoCxxCLQrUQpsiSyoEfotX2DGKkqwFYjLjmf4LCpxHZEvvJVQD+gPm8mGwbmchLiqFtzu1xatVywcXFHoMBbtxIMO7hZOeEX3U/QMrr70Ttj/eyrwsGDQEBNfHwcMrZWpkSeWszkrwz5du1q4e9vYaoqJR8f6ul5ZGgR6jpUpPr8deNFcyKRFxcGtu3XwcKT+Rvpd5iT4jo2Sx7Iv8psAmx+LcMy0mrnREjdwIQSfxgipcXm4dGNRpR06UmmbpM3JoK1dLRoxE2qVK6fPsyp6JOoVW0DG8+PN+20jvWZyP8MEBUeiWWpz25/webET3vmQjvnHsRfe3LEa1HtxAjHD9G+Jo0RCbtlsIZsdACFUden7c/Xv6dVD5aIN4bjiI+P3UItU5T4EWEItcWSUAoAHWI9rpJ1g3HishEXlIMbXNuT6AoSrHyepnI50ftj3fKEBW63PnxIBN5U5J/pryTkx1t2wrH7fL0yTvbO/Nsp2cBmLVvlk0mZUWxbt1FsrP1BAbWomnTgpUS/178F71BT1CdIBpWb1iGs+wC3sq5/z2Wr8h6AhsQ0t8zCG+PdAvHIEZxdvYWo5niXS+j1SpER6cQFmZ7/gr/nBPV+H7+/fB0zvXniItL4+bNRKA0ify/CGl9LURiKLEOkxDKiFcQcvvLwG1EYv8pourmj0zGrI0qr68oibzsj68atEdMu9qNMGbMQEyn8Ed8vsdbLbK7MSB8ma4j4vvBqtFYG5nIS4qhbc7tCSCvc/0dibyXnCVfEGoinx0repM7d847skcm8qZlcs6tmClvCsM7gP91+h9Odk4cDj/M7hDrm6qVBvPL6mOA8Qhp/SNYz826IUIa7I5YWJiEWKm3LKq8/lj0EQIDawO2Ka9fcUGoS+6U1avV+IYNPfIoh4pDNbmbCpRnbKGk/ExFGFo9hJCZyqTd9hiC8DY4S+41gK1iQCbyVY2eCOPgDUAHxHSlDxFtFZ/mfG9tFiHGa6vtde5F717JkYm8pBja5txeBpLw968OFFGRlyPo8qEm8nHXxQVubkU+EVD9BGQibxryz5Q3VSJfy7UWjwY9CoiqfEUhNTWLDRuEcc2oUQW7x6dlpbHxykYARgSUNpFX++LDEdL277Fu4tAGYSLlgFjMeQFLG0qpFfmDwulbQAAAY2JJREFUoQfp2FF4YRw9aluJfHhSOIfCDqGgMDJgZL5tpZfVX0XItEH0VUokkqKpTu54LFuvyt9AmB7aYVkDW4l1URBjhQ8jfDRaAnEIX43GwLeIir01uIxw3QdhxNnVSnHYDjKRlxRDTYTxDcDJPNL6+Hx7yRF0BaMm8mkRLtjZaQgKqpuzRXUGrYUY4SYpP3lnyi8wJvLHj0fmM2csCy90fQEFhbUX11aYv/FNm66QlpZNw4YetGtXt8B9tlzdQmpWKr7uvsY56CXnc0QS54zosatWvoBNQj/UyQXiYuMLi55dTeSvxF2heTsXAI4csS3n+p3XdwLQvl576lbL/3ehGt2V3LF+PmKxZCDSCEsiKSkVRV6v9se3BVysGIfEOiiIdqlTiM9Vf0QBagbCLO89xEKPpchEKACTgT6ImfESmchLSkDbnNsThUvrcyryN+JvkJaVZsHYbBvV7I4ED4KC6uDkZJezRcrqzYM6U34X/v6x1KrlQmamjuPHy/dh07xmc6Mp2Ff7vyp/mBZAldWPHBmAohRcKVdl9cObDy90n4LZA7yZc/9bxKhKW2EsorcPhMnXIoud2dPZk6ae4jXt4C/+5o4eDbcpbwW1PaRXg153bStdRT4DMXccpMmdRFIaVPXTAYSiyVbJa3QnqbpoEddWF4AfAW9EAv8uYvTlROCQBeJ4GzgC1EB8rmstcE7bp9SJ/Jw5c2jTpg1BQUEEBAQwadIkwsJyZzWfP3+ePn360LZtW9q1a8eKFfmdnrOysnjuuecIDAwkMDCQ6dOnk5mZmW+f1atX065dO9q2bUvv3r05ezb/fOCIiAiGDBlCUFAQrVu3Zu7cufm2GwwGPvzwQwIDA2nVqhXjx48nMTGxtD+qxIhaqctN5K9cict3cVrLpRY1nGpgwMCl27be92U51Io8CR7S6M4i5M6UV5RFJpPXA8zsPhOA30/+TlSybY9ZzMrSsXat8KsorD9ep9ex9uJagLsk1kVzC2EsqEN8gE8tR6Tm4nlgZs79qeT2eZofdZ78LcdL2NlpiIlJJTTUdj5/jIl8w/yJvE6n58yZaIA8yqGiWInwSPAGhpo0RomkclOfXEnwamsGUgyyP16SFwfEou1VRG96N8RIy8VAl5yvxYjKuanZilABAvwM+JrhHBWTUifyw4YN49ChQ5w8eZIzZ87g5+fH0KHiQzw9PZ0RI0bw/vvvc+LECdavX8/rr7/OqVOnjM9/++23ycjI4PTp05w+fRqDwcCbb75p3H727FlmzpzJv//+y4kTJ/j4448ZOXIkaWm5Vd4HH3yQCRMmcPLkSfbt28dvv/3Gf//9Z9w+f/58Dhw4wLFjxzhz5gzt2rXjscceK9MvSAJ5R9D5+VUHIDExg7i4XGdoRVGkc/0dJGYkEp8eL75J8KBTJ2l0Zxkm59wupEsX0ad88GBYoXuXlB6+Peji3YUMXQbfH/6+3MczJ7t23SAuLp2aNV3o2bNBgfscDDtIdEo0Ho4e9GnYp4RH1gOPIlzKmyNW523VUOszYAxiPNr7Fjuranh3NOowgYG1ANsxvLuddpsz0WcA6NmgZ75tly/fJi0tG2dnOxo3rlGCo/2Yc/s4oodWIpGUHFVev8qaQRRBCqrJsazIS/LjgJC470P00T+S89ghxOJ+A0S13lRtZbcQigADwotFTkfJS6kTeX9/f5ychJutnZ0d7733HlevXiU8PJxNmzbRrl07+vQRF4V169blpZde4tdfhfxOr9ezaNEiPv30UzQaDRqNho8//pjFixej0wmH4V9++YUXX3yR+vVF9bJnz5506tSJjRuFoc6pU6fQ6XQ8/PDDALi5ufH+++8zf/58Y4zz5s3jiy++wNFRGIzNnDmTgwcPEhsbW6ZfkqRtzu0ZXFygbl3RC3unvF72yefnZsJNcSfNGTIdC3Gsb2LpsKoAIxEupjcYPFgk8KaoyCuKYqzKf3/4e1KzUst9THOhyuqHD2+GVlvw2/zqC6ISdH/T+7HX2pfwyLMQI2qcEPPibaEvvjA0CJddBdHLf84iZ1UT+UNhh+hgNLyzjT75vSF7AWju1ZzarrXzbVNl9a1a1S70byaXc4jpAFpALpJLJKVHTeS3YVujvVSOIFRX9ZHVT0nhdAQWAjeBDxB/L1GI/vmGiBnvByi78awBmIZYFAgAKkZroyUpd498amoqiqLg5eXFli1bjEm8Sp8+fdi8eTMAJ06coH79+lSvXt243d3dnQYNGnDs2DGAYo9R0PZevXqxbds2DAYDsbGxhIWF0aJFrkuzRqOhe/fubNu2rcCfISMjg8TExHxfkrz4IRKjTOBC4bPk1RF0sXIEHeTtj3fH1dWeFi1q5tkqK/LmI3emfKtW61EUuH49nsjI5HIfeVTAKPyr+3M77Ta/nfit3MczB3q9gVWr1LFzBbvVQ1nGzu0D3si5PwfhEm/rNCL3gnm2Rc4YVDcIR60jt9Nu0yBImCzaSkW+JP3xJTO6m5dzOwwhrZdIJKWjKRCIUAz9a+VYCiJvf7ytqq4ktkNthG/OdWAJ0AMhu1cl+F0Qfe2ldbufC6xBVPz/AlxNE24lolyJ/NmzZxk7dizvvPMOjo6OhIeH4+ubf+XO19eXq1evAhS4vST7FLfd2dkZJycnoqOjiYiIwMfHhzvJe4w7+eSTT/Dw8DB+FRRj1UZDXsO7YkfQyYo8kL8/vn37enmqXPEIqRDIRN5cTAbAwWElnTqJGaMHD5a/Kq/VaHmx24uAML3T6S0/q7w4jhwJJywsiWrVHLj33oKdxINvBRMcG4y9xp7BTQeX4Kix5PbFj6diVWFfyLn9HdHTbV4ctA60qyd8RRRfoQg5ejTCJgzvCuuPh1zH+uKN7lIRFRiAp00XnERS5RiZc2uL7vWyP15SFuwRhrN7gKOIazEHciX4DRCmdSVZ3D4DvJhz/zNy8xBJXsqUyL/88svUrVuXVq1aUb9+fZ577jkA4uPjjbJ7FScnJ9LT0zEYDAVuV/dJTU0t8hhFbc+7T0nOcSevv/46CQkJxq+bN2+W4LdQ1Wibc3u80Ip8Xmm93qC3YGy2Sd5EvmBZfR3AzcJRVRXUmfKpPPXUNcA08nqAKW2nUMOpBlfirhir2rbEypXnARg8uEmeKQn5UePu598Pd0f3Yo5oQHwY30QsPM2jYlVoeiDkfxmI1X3z07m+GEMXoQ3Gzk7DrVuphIQkWOTchZGalcqR8CNAeR3rlwAJCLXDvSaNUSKpWqhqofWALU37MSAd6yXlpz2wAAgFPkKot6IREvyGiKLAfgqW3aflbE8HBgPPWSDeikmZEvkvvviCyMhIbt26hZOTE1OmTAHA0dGR9PT0fPumpaXh6OiIoigFblf3cXZ2LvIYRW3Pu09JznEnjo6OuLu75/uS3EnbnNu8I+ji8+3RuEZj7DR2pGalEpZYfnOxio4xkY+vLh3rLU7uTPlBg0Rf8IEDpvmbdHVw5X+d/gfArH2zTHJMU6L2xxfmVg+lldXPAdYBjoh58RVt8Ukhd1X/e0ov7Ss9qnP9sagjtG4tetGt3Sd/MPQg2fpsvN288avul29bQkI6N26IhYbiE3l1MeRJ5ARbiaQ8tEdUKFOBzVaOJS+XEapBB3KnFkkkZaUWoi3vGsJbpyeipWQJYqGoE0Ixl/ez+RVERb4OYjGgIhUPLEu5PoW9vLz45ptvWLlyJQkJCfj4+BASEpJvn5s3bxql7gVtL8k+xW1PS0sjOTmZ2rVrl+gckrKQO4LO398DuLsib6+1p3GNxoDskwe4Hpc7Q1461lsDMVO+Xr1jNGp0m8OHw9DpTKMUebbzszhoHdgfup99Ny032qw4zp+PITg4Fnt7DfffX/DfV1RyFPtvimrL8ObDizliJvBJzv0vgSBThWphRiNGE0Yh+uzMi2p4dyLyBG07CG+Mo0et2yefV1avKPkvitRqvK+vOzVqFLzgLTiKkEg6AFPMEqdEUnVQsE15vVqN74BYwJVITIE9YpLMbuAY4jPEEfG58ijCVPFN4Ffgu5zn/IZI5iWFUe7l9IyMDDIzM9HpdHTv3p2dO3fm275z5066dxfSnLZt23Lp0iXi4+ON2xMTE7lw4QLt27cHKPYYBW3ftWsXnTp1QqPRUK9ePapVq8a5c7kOxXq9nj179hiPISkLLREvwjiaNhWmYTduJNyVGMk++Vyu3BKSbndqG30FBDKRtwy5M+Uff/wMKSlZnD1rmh7putXqMqnNJMC2qvJqNf7eexvh4XF3ixHAuovrMGCgQ70O+LgXt7i5GpH81kOMfamo2APP5tz/mrI76JaMRjUaUdOlJpm6TGq1SQLgyBHrVuRLYnRXfDVeNbkbjaiySCSS8qHK69ciqpS2gJTVS8xNO0TCHgp8jLhei0FI8Kfl7PMCMMgq0VUkSpXIZ2ZmEhqa22caHx/Po48+yujRo/H09GT06NEcPHjQmGhHRkYya9YsnnnmGUCY0j366KO89tpr6PV6DAYDb7zxBg8//DAuLi4APPPMM3z55ZeEh4vqxd69e9m7dy8PPfQQAL179yYrK4vFixcDkJSUxDvvvMP06dONcc2YMYNXXnmFzMxMAGbNmkVQUBCNGhVs/CQpCQ6IZB7q1LmMg4OW7Gw9oaH5Hf6be8kRdADZ+myi0yMBaN8o4I4K2OWcW5nIm5/J4t/Jp1AUvcn65AGj6d2qC6u4FHupmL0tg+ll9aqM+jFEMlyReQJwAU4B2816JkVR6Owt+uR1dYXnytGj4VYzvMvSZRlVGGV3rE8AFufcf8q0AUokVZaegBfCUHSPlWNRkUZ3EktRE3gdIbtfDvTOebwjuWpASVGUKpGPiYlhxIgRBAQE0LZtW+655x46d+7MggULAHB1dWXNmjW88cYbBAUFMWDAAN577z26dOliPMZnn30GQGBgIC1btiQjI4NZs3IrWh07duTjjz9m0KBBBAUFMXPmTFavXk21amJesaIorFq1it9//53WrVvTpUsXHnroIcaMGWM8xvPPP0/btm0JCgoiMDCQQ4cOsXDhQiTlpS0AWu0pGjYsWF6vVuSrurQ+IikCPTrQaejZtuUdW2VF3nKMBNypW/cWffrcMGki37JWS4Y0HYIBA18f+Npkxy0rN28mcORIOIoCw4c3L3CflMwUNl8VvZgjAopL5IMRM441VCyX+sKoQa4c3PyzaFV5fSjnsLfXEBubZuxDtzTHI4+TkpVCDacaBNYOvGt7yRzr/0D08gYikg+JRFJ+7AC1xckW5PWJiN5kkIm8xHLYIZReO4EQYBeyraNkFGxpXAje3t4cPXq0yH2CgoLYu3dvodudnJyYO7do5+CxY8cyduzYQrc3bNiQjRs3FrpdURQ+/PBDPvzwwyLPIykt7RBjh07QqNGDXLp0m6tX4+jXz9+4h5TWC4xGd4nudLk37zjD2zlfAE0sHFVVRJ0pP5/Jk0/w2WedTHr0md1n8u+lf1lwYgHv9X2PWq7Wkxurs+N79GhAnTrVCtxn05VNpGen41fdj9a1WxdzxPk5t0MQhkyVgeeAHxBzm4OBghc8TIGayB+JOEzr1v04diyCo0fD8fOrbrZzFsbuG0JW36NBDzRK/vV7vd7A6dPRQFGJvIFcdcZTSOMhicSUjEIYeq0CZmPd19chQI9wFa9fzL4SiTmQI8BLg7SclZSCtjm3xwt1rldH0IUmhpKUkWS50GyMC5E58vkEj0Ic6+sDrhaOqqoyGYDRo89x82YY8fF3T7UoK30a9qFDvQ6kZ6fz45EfTXbcslAaWf3I5iPvMjzLTxrCZAYql4y6KTAs5/43Zj1TJ2+xaHQl7gqBncRr3VrO9UX1x1+5cpvU1CycnOxo2tSrkCPsQ1TpXBAmkhKJxHTci7geCEGYgFkT2R8vkVQkZCIvKQWqY/UNAgLEn86d0npPZ09quYiq5MXYi5YMzqY4eEGYLbpk1bqjOipl9ZanK9AcV9csRo8+x6FDphuNqCgKM7vPBOC7Q9+RlmWdWcCxsans2iWmJBSWyGfrs1l3cR1QEln93wjlSENgoMnitA1eyLn9DdGXah48nT1p6ile59VbivMcOWJ553q9Qc+eENF7W1R/fGBgLezsCrskUBepxgMepg9SIqnSOJNr6mVteb3sj5dIKhIykZeUguqAkNEHBYmLvzsTeZB98gCnQ8QiRgOPOyXJMpG3PLkz5SdPPmHSPnmA0S1H08CjATGpMSw6tcikxy4pa9deRKczEBRUB3//GgXus+/mPmLTYvF09qRng+J6nFUZ9ROA1pSh2gB9EOqiNHLbB8yDOk8+vaZYZDl6NMLihncXbl0gNi0WZztnOtTvcNf24o3ubiFMiKByqTMkEltCda+3ZiKvBw7k3JcVeYmkIiATeUkpaQtA48biwvROaT3IPnmA63GiR75l/cZ3bJGJvHWYhF6voU+fG1y/XrTPR2mx09jxfJfnAfhy/5foDaaZVV8aSiSrvyBk9UOaDsFOU5Q9yilEVcYOmGqyGG0HBXgx5/53QKbZzqT2yd/QncXeXsPt22lcvx5vtvMVhNof38WnCw5ah7u2F2909xvid9Qx50sikZieIYj33HOAtdSMF4B4hEKgjZVikEgkpUEm8pJS0haA2rVFQhodnUJycv4LYTmCDmKzRS9s54A7HaJlIm8dvElKEmNNAgL+M3lV9LH2j+Hh6MHF2ItG+bqluHz5Nv/+Ky78HnzwzgkJAoPBUIqxc+qs8FFAXdMEaXOMBeoB4cAys50ln+Fdm9qA5fvki+qPh+JmyOvJb3InkUjMQ3Xgnpz7q6wUg9of34mKP25UIqkayEReUkraAuDgcJoaNZwAuHat4BF0VTWRj45OIctFONP3ax+UZ4sBmchbD1fXxwF46KEjXL58y6THdnN048kOTwIwa9+sYvY2LR99tBudzsDgwU1o1ap2gfuciznHlbgrOGodGdikqJ73ZEBtD3jS1KHaEA7AMzn3v0a8Nk1PUN0gHLWO3E67TVMxVt7iffJFJfKJiRlGVVXBifxW4AqiL36cuUKUSCSA9eX10uhOIqloyEReUkra5dyeo3lzYeJ2p7xeTeQv3b6ETq+zYGy2wY79F8ApA4BA77wJ+y1AnSN9p+ReYm7s7EaRlOSMn18C166tNvnxZ3SZgZ3Gjt0huzkYetDkxy+Iy5dvs2jRSQDefbdvofup1fj+jfpTzaHg0XSCv4AkxEJTP1OFaaM8iZCQHkPMrDU9DloH2tUT75muzcWIN0tW5EMSQghJCEGraOnme7d51enTohrv7e2Gl5dLAUdQq/GPIKdsSCTmZgSi9ecAQi1kaaTRnURS0ZCJvKSU+ACeQDa9eycDdxve+VX3w0HrQHp2eu489SrE9uMisXLUueHqkPfiV63G+yISCIllceb0aZGcurv/ZfKje7t7M6H1BED0yluCDz/chU5n4P77m9K5s3eh+5VMVm8g1538SSr/x0NNRIIKoipvHlR5fUr1awAcPRpuMcM7tT++fb32BS7gFC2rDwfUBa/KrM6QSGyFeogpK5D72rMUccD5nPsykZdIKgqV/UpNYnIUVHl9x46iwnRnIq/VaI1jl6qivP7wRTF6rpZD/Tu2SFm9tUlLexiAoKA9iMqzaXmp20sA/HP+H67FXTP58fNy+fJt/vjjFADvvNOn0P3Ck8I5FHYIgGHNhhW6HxwBjgOOwKMmi9O2eT7ndg1w2SxnUBP5KxmncXDQEheXXqBJqDkoaX98wY71PwM6oBdwp9eHRCIxDyNzbi0tr1fd6psAtSx8bolEUlZkIi8pA0IqGhAg5nEX5Vxf1UbQGQwGLkRcAaCRV8M7tspE3to0a3Y/Fy544eycSUbGUpMfv02dNtzX+D70Bj2zD8w2+fHz8sEHJavGrw1eC4iEsp5bvSKOqMqoxyCq1VWBAOB+hBrhG7OcobO3aI4/GXWSVm09AVGVtwTGRL5hwYl84Y712cBPOfelyZ1EYjnUPvntiCq5pZD98RJJRUQm8pIy0BYAHx+RsBY1S76qVeRv3EggxS4GgEDfOxN2NZFvYtGYJLn4+HiwYkXObO/0n4rZu2zM7DYTgF+O/8LttNtmOcelS7HGavy77xZejQdYFbwKKE5WH4/oj4eql7i9kHO7APF7MC2NajSipktNsvRZNOicBljG8C42NZZzMUId1LNBz7u26/UGTp8Wqqq7E/n/gFDEgs6DZo1TIpHkpSlCAZMN/GvB88r+eImkIiITeUkZaAuAh8cFFEXPtWtxd/V8VtURdIcOhYGHMLRr7Ol/x1ZZkbc2iqJw7dowdDoFD49DCEdu03Jvo3tpU6cNKVkpzDsyr/gnlIEPPtiFXm9gyJCmdOpUeDU+KSOJbde2ATAioKhEfhGQBrSi6lVk+gOtgRRyq9CmQ1EUY1XeqXEkYBnDuz0hewBoUbMFNV3uVlhcuxZHcnImDg5amje/c7uqzpiKaLWQSCSWw9Lu9TpANWitau//EknFRibykjLQHHBEo0mmceME0tKyiYpKybdHVa3IHz6cm8g38GiQZ4scPWcrNG3ams2bG+V897vJj68oirEqP+fQHDKyM0x6/IsXY1m8+DRQtFM9wIbLG8jUZdLUsyktarYoZC8D+WeFKyaKtKKgkFuVnwNkmfwMap98QjWxcHT0aITZDe9K2h8fGFgLO7u8lwLXgA05958wX4ASiaQQ1ER+A2KB1dycQYwedUP6YUgkFQuZyEvKgD2iggX9+4uk9U55ffOaoiIflRJFfHq8JYOzKocPhxeSyEchPigVoFEBz5RYiq5dffjtt7Y53y0E9CY/x9hWY/F28yYyOZI/T/9p0mOr1fihQ5vRseOdhor5yetWryiFJeh7+P/27ju+yvL+//jrZA8gBMJOWLIJEPYQQWsdIIqTVfCnIohaV23ha1Wso7W2jlrrQNviRMGJA6GiBQWlzDBkCWEECAQkZJJ57t8f17lPErKTc5Kc5P18PDCHe1znyiXc5HOu6/O5YCcQBkz3ZFd9yFSgNWY5+Yceb90O5PdkbiM42J8zZ7JLTUnypIry4wsL3bU958yrmA93LkXbZIrUhYFAJyAL+KoW3s/Ojx8G+NfC+4mIpyiQl2qKA+D8838GSgbyzYKb0b6pCTL2nGocBe8KCpxs2JwITU019OKBvD0b3xEIqe2uSRGDB7fj8897c+ZMMHAIWO3x9wjyD+Ke4fcA8PQPT3ts9nXPnlMsWmTPxpefG59XkMcXP5kcy/KX1duz8dOAiJp30ieFAHe4Xj+LCWQ9x15an3BmP70Hm/3avbm8PjM3k81Jm4GyZ+QLC921LnI0F/iX6/XtXuufiJTHQe1Wr7fz47WsXsTXKJCXaooDoH9/88PogQMlZ5caW5787t2nyPI7DX4WQf5BtGlStICUltXXF+HhQfToEc1778W6jiz0yvvMHjybpkFN2XlyJ8v3La/4hkqwZ+OvvLIHgweXPxv/3eHvOJN9hlZhrRgZXVYBo5PAB67Xja3I3blux+SDb6DwB1vPiAyNpEfLHgC0G5IGeLfg3boj68h35hPTLIZOzc/dPcMofQ/5jzF/JtoDE7zWPxGpiL28/jNM4TtPOwO8j6mD8ZHrmArdifgaBfJSTXEAdOlyCICEhDMlrmhsefJFl9XHNIvBz1H0r5cC+fqk+PL6D4A0j79HREgEswbNAsysfE3t3n2Kd9/dAVScGw+wdLdZVj+hxwT8/cpaLvk6ZhZ2CDC4xn30ba0pTC14zuOt27PyAZ3Mh5/enJGvaFl9RkYu+/ebD1+LB/Ivu77OAgK81j8Rqcj5QEvgZ+A7D7RnAVuAPwEXYHakmIT5IDsTiAZK7m4hIvWbAnmppv6Ag2bNThIVlVnuFnSNZS/5ohXriy+rBwXy9cuIEdH873/RHDrUFlNM6IOKbqmWe0bcg7/Dn28OfONe6lxd9mz8VVf1ZNCg8vaDB8uyiuXHl84J2FX1G/tsvM0uevcxpuib59h58j+HmmfBpk3HvFbwrqJCd9u3m9n4du2a0KpVuOvoLkyaiT9wq1f6JSKVFQBc5Xpd3eX1KcAS4GbMKptBwIOYuigFmMLF9wIrMD+jNK1+d0WkTiiQl2pqir0felzc8VKX1jfKGfnmZ4DSAvl9rq8K5OuDESOiAQf//Gc/15HXvfI+HSM6Mjl2MgDP/PBMtdsxs/GVy40H2HZiG4dSDxEaEMol511SxlVfY7bfawZMqXbfGpa+mCJvTkwFe8+xA/nd6VsJCvYjNTXHPSvuSXkFeaw7sg6ouGJ98dl4+0OdKzGzcyJSt+zl9Z9QubodTmAz8EfM7HorYDLm37fjmIKmVwIvAQnAbszqo0tR7R4R36RAXmogzvw37jhHjqSRk1M8j8vOkd93eh/5Tm/keNUfOTn5bN16vJyt5xTI1yfdu7cgMjKEf/+7H5blh1m6uK+i26rl/pH3A7B4x2L+ufmfFDgLqtzG449/i2XBxIk9GTiw/Nl4KKxWf8l5lxAWGFbGVXaRuxuB8DKuaYzsWfl/4cmUiwFtBxDsH8zps6fpNdL807tpk+fz5DcnbSYrL4sWoS3o3ar0LQcLK9bbgXwWZgcH0OoMkfriEsyzOREToJfmNLAYuAkz6z4YeAhYi5l17w38BlP9/jTwKaYeSBcv9ltEaosCeamBgQAMGZKMZcGhQ6nFzsZExBAaEEqeM48DKZ5dplrfbN16grw8J4FRGQB0iihaYCoJk4Pmh/7xrB8cDgfDh0dz7FgzDh8e5jrq+T3lAQa1G8TVva6mwCpg1mezGPbPYaw5vKbS9+/addI9G//IIxXPxgOVWFZ/DFjqen1bpfvSOFwG9AHSKazgXnNB/kEMbGeema0Gmpl4bxS8+/bQtwCM7jj6nDodhQor1tuB/GJM8auumOBBROpeCDDO9dpeXu8ENgFPYPLoW2FWVL2B2eY2HLMk/2VMetBO4Bngl5hiniLSkCiQlxqIA2DIEPND4bnL6/0cfu795Bv68voNG44CENw6Eyhr67nOQFBtdkvKMWJEBwA+/9zecsc7e8oDLL5+Mc9e+iwRwRFsTtrMBQsvYOqHUzmcerjCe+3Z+Kuv7lWp2fjE1EQ2J23GgYMJPcqqPP4vzGzNaCC2jGsaKwcmbxTgeTxZMdpeXm91OAJ4p+BdRfnxlmWVsrTeXp1xG/qxQKQ+sZfXvwP8P6Adpjjpw5jdNZyYDx7vB1ZiiuMtxays6VzLfRWR2qZ/saUG4gDo3PkEoaG5pRa8ayxb0K1ffwywyA39GSgrkNey+vrE5MnDyy+3x+yffhhvFb0L8g/ivpH3sfeuvcweNBsHDt7b8R69/tGLR1c9SlZeVqn37dx5kvfeM5XqqzobPypmFK3DW5dyRT7wquu1llGXbjqmqvMhTH6qZ9iB/IkAUwB006YknE7PFbxzWk73ao/y8uPT03MJDPSjV68ozJLd9UAgpiiWiNQfV2D+bh7ErBpLBppg9plfgHlG/Qg8DVyMZt1FGhcF8lIDbYHW+PtbxMYml1u5vqEH8hs2HIWQbHIdZwGTVlBIgXx9NGyYmZH/8ccMsrLs5eW3YX5g8o7W4a1ZcOUCNt+2mTGdxnA2/yx/WP0Hev2jF4t3LC5Rxdyejb/mml7ExbWt1HtUvKz+S+AIZmuj66r9vTRsoZg8UoBnPdbq8GgTyO9N+5HgcIu0tBz27z/tsfZ3ntxJSnYKYYFhDGo3qNRrnnvOFMK78sqeBAb6Uzgbfz1mma6I1B8RwHxgGPA74BvMrPvHwGzg3MK6ItKYKJCXGnBg58kPHHi83L3kG/IWdGlpOezefcpd6C4qLOqcAmMK5OujyMhQ14wkrFo1AxiOyROeCuR59b3j2sax6v+tYsn1S+gY0ZHEtESmfDiFMa+PcW9T9+OPySxeXLXZ+DPZZ1h1cBUAE3uVFcjbgdvNqFJxee7ApML8APzPIy12ad6FqLAo8px5dDs/F/Bsnvx3h8yy+hHRIwj0DyxxPjExlXfeMfUW5s4dBaQCi1xntTpDpH56CPMM+gtwEUrRExGbAnmpoTjz30a8BZ3ZDxqiupngr3ihO1AgX3/Zy+t/+CEZeA8z+7EO84OTdzkcDm7oewO779zNYxc+RmhAKGsOr2HIq0OY9eksHvzTZ1gWXHttbwYMqNxs/Jc/fUm+M5/eUb3p0bJHKVccxMzIg5nNkbK1xXyoA2aLpppzOBwM62CKK0b2NzPxnsyTryg//rnn1pGf72Ts2E4MHx6NybvNxOTYln6PiIiI1E8K5KWG4sx/446zf39KiaXB3VuY4PXnsz9zKutUbXeuVmzYYGbUovuY7714frwTbT1Xf9kF79atO4opDPRv15m/UBjweldoYCgPj32YPb/ew7R+07Cw+OeWf7K0450w8nt+//DISrdV8bL61zDbIf4S/XmsDHsrug8wNRRqzs6Tz29t2vNUIG9ZVrmB/OnTZ3n11U0AzJt3PubPgb06Yw5mhZWIiIj4CgXyUkNmaX3//ifIyDhLSkp2sbPhQeHuwLahzsrbgXyzjiY/vnggfxTIBgJQBdn6x56RX7/+qKvo2LXAr11nb8T8/6sdMRExvHPtO6y5eQ2RZ7tCSA5c9h+mfncxX+z9osSHZOfKLcjly33mw4fSl9XnUridmpZRV84A4BeYCv8veKRFO5A/6tgFmBU9nih4dyj1EEfSjhDgF8CI6BElzr/88gYyM/Po378Nl1/eDVPxejumHsCMGr+/iIiI1C4F8lJD3YAwwsPz6N7953KX1+851TDz5NevN8Gef4t0oKyK9V0wwbzUJ337tiYsLLCwzgEAf8WsNDkFTMOT249VRkRad1L+Mh0+mUjL4Fb8dPonJrw7gfGLxrPr5K4y71t1cBVpOWm0bdLWvXy7uKWYfYbbYvYZlsqxZ+Vfw+wtXzP2/5vEzIMER+aQnp7Lvn01L3hn58cPajeI8KDwYufOns3j+edNnv/cuaNwOBwUzsZPBZrX+P1FRESkdimQlxryB/oDZnl9qZXrWzbcPPnk5EwOH07F4YDMABMIFs+RtwP5brXeN6lYQIAfQ4e2B2DduiOuoyHAYswWP98Cj9dqnx57bDVYflzf7Vck3LePuaPmEugXyPJ9y+n/Sn/uW34fKWdL/j1butssq7+yx5X4OUp7tNuB262Y7YykcsYDPTCF4RbWuLXI0Eh3/YLO52cCnil4Zy+rH9NxTIlzr78ez8mTWXTsGMGkSX0xH1K97zp7e4nrRUREpP5TIC8eEGf+W0Yg3zPKtZf8zw0vkN+wwczG9+oVxdGMREB7yPsae3l9YSAPJnBb4Hr9OGbLH+/bvv0E77+/E4D588fQLLgZT13yFD/e8SNX9byKfGc+f/vf3+jxjx4s2LiAAmcBYPKjP937KVBWfvwe1/fgB8yqle+l4fAD7nW9fh6zzL5m7OX1zfqcBMzy+ppy58d3Kp4fn5/v5OmnfwDg/vtHuracewPIAQYDQ2r83iIiIlL7FMiLBxRuQXfgwJkSZxty5Xp7Wf2goa05lm5+GFcg71tKD+TBLKufiSkK9ivMsnTveuyxbwG44YY+9OvXxn28e8vuLJ2ylBXTV9CnVR9OZZ1izhdzGPzqYFYdXMXmpM0cSTtCeGA4F3e9uJSWX3V9HY/2Ha6OG4FIIAH4rMat2YF8dotDQM0L3p3MPOl+vp4fc36xcx9+uJOEhBRatgxl5syBmAKcRYvciYiIiC9SIC8eEAfAwIFJJCSUzPW0A/kDKQfIyc+pzY55nV3ortugQCwsgv2DaRXeqsgVCuTru+HDTeX6HTuSSU8/98/n3zFbcx3HBHNOr/Vj+/YTfPDBThwOmD+/9H3jLz3vUuJvi+fvl/+d5iHN2XpiKxe9cRHXLbkOgMu6XUZIwLl7w2cDr7teK3CrnnAKx+7ZGrdm58kfdu4CLDZvTqpRwbs1h9cA0LdVX1qGtXQftyyLv/zlewB+/ethhIcHAfGYnTSaULi9noiIiPgaBfLiAbFYlh+tW2eRnn6oxNl2TdrRJKgJBVYB+1P210H/vMOyLHcg37aHKYgWExFTJD+5ALC/XwXy9VW7dk3p1CkCy4Inn1xzTkAVBizBVPb+D2ZbOu949NHVANxwQ19iY1uXeV2gfyB3Db+Ln+76iTuG3IGfw49DqebvXenL6j8ATmNm4i/3eL8bjzsxBSu/AzbVqKUBbQcQ7B9Mam4Kwe1TSU/P5aeffq52e2VtO/f11wfYvDmJ0NAAfv1ruwDiGtfXCzAfUIiIiIgvUiAvHhBGfr4p3tSy5V4KCorPWjocjga5vP7gwTOcOpVFYKAfQa0zgHML3SVitvwKRMuZ67c5c0ye8JNPrmHcuHc4eTKzyNm+wD9crx8C1nr8/bdtO8GHH+7C4YCHHy5ZrKw0UWFRvHjFi8TfFs8V3a9gbKexXNPrmlKutJdRz8YUp5Tq6QBMdr1+rkYtBfkHMbCdSUmKGWEq4dek4F1Z+fFPPWX+rM6cOZCoqDDX0aKBvIiIiPgqBfLiEf7+9n7yxzhyJK3E+Ya4BZ09G9+/fxuSMk2ufOn58V3R1nP12//932gWLpxIaGgA//nPfgYOXMCaNYeLXHEzJk++ALMcufqzp6Wp7Gx8afq16cfn0z5n1U2raBrc9Jyz2zEfPAQAt3iiq42cvRXdYuBojVqy8+TDuicD1c+Tz8jNYEvSFqD4jPymTcdYuTIBf38H998/ynXUwqwoABhdrfcTERGR+kGBvHiEn58J5Cvcgq4BVa63K9YPG9bBvbRZhe581003xbF+/SyzA8HRdC688HX+8pe1rqX2DuBlzP/LRExgX/2c5qK2bj3ORx/tcuXGV242vvLsyvtXA+083HZjNBgYA+RTuEqjeuxAPj0iAaj+jPwPiT9QYBXQKaITMREx7uN//avJjZ88OZbOnZu7jiZg6j0EAUOr13ERERGpFxTIi4fEmf/GlV653r0FXQNaWr9+vfnBe+jQ9hxONbO3CuR9W2xsazZsmMW0af0oKLCYN28lEye+x+nTZ4GmmHz5YEzl8uc98p72bPykSX3p27dqs/HlywDedL1WkTvPsWflFwCZ5V1YruHRJpBPzNsLAXls2XK8WgXvSltWv3//afc2hnPnjipytb2sfghwblFEERER8SUK5MVD4gDo0eM0R46UXHJaNEfesjwzk1mXCgqc7r2fhw7t4A7ki+fI73N9VSDvS5o0CeLtt69hwYIJBAf78/nnexk4cAH/+98RzJ/zZ1xXzgU21Oi94uOP8/HHu8utVF997wHpmD9/F3m47cbsSuA8IAWzH3v1dGnehaiwKPKtPII7nSIjI5e9e6ueslFaobtnnvkBp9PissvOY8CAtkWuVn68iIhIQ6FAXjykFenpZts1p3NribPdWnTDz+FHWk4aJzK9vx+3t+3adYrMzDzCwwPp1aulZuQbGIfDwezZg1m37la6dWvB4cOpXHDBQp5/fh2WdTtwLZCHKX6WWu33sWfjJ0+OpU+fVhVcXVV2kbvb0KPek/yBe1yv/0Z1tyR0OBzu5fXth5q6IlVdXp9bkMu6I+uAwkA+OTmThQvjAZg37/xz7lB+vIiISEOhn+7EYzIy+gDQpMnOEudCAkLo0rwL0DCW19v58YMHtyc19wyZeWaJbXSzaNcV+Zh8VFAg77vi4tqyceMsrr++D3l5Tu69dwXXX/8BqakvAp2BA8AsqpMvv2VLEp98sttLufEbMVukBQP/z8Nti6mREIH5sG5ZtVux95MP6mIK3dmrfCpr07FNZOdnExUW5V719MIL/yM7O5+hQ9tz4YWdi1x9ErCLjY5CREREfJsCefEYhyMOgLZt95V6viHlyX/7rZmBL5of3zq8NaGBoa4rDmNmbIOBmNKaEB8RERHCkiXX88IL4wgM9OOjj3YxaNASdu/+B6Ya/PvAq1Vu156NnzIllt69vTUbfwMQ5eG2BZpgtvMDeLbardgz8inh+wHYuLFqlevtZfWjO47G4XCQkZHLiy+adI95887H4XAUudreNrEv0KLafRYREZH6QYG8eEzTpuaH0l69EsnIyC1x3l253scD+ezsfD7+eBcAV13Vs4Jl9eehv2a+z+Fw8OtfD2Pt2lvo3Lk5CQkpDBiwhe+/v9N1xT3Atkq3t2VLEkuX7vFSbvwZ4F3XaxW58567MMvs/wuUTCeqDHtGPjk/EcIy2bIliYKCyi/V//bQt0DhsvrXXttESko23bu34Oqre51ztfLjRUREGhJFGOIx4eHmh9LY2GQOHjxZ4rx7L/mffXsv+S+//InU1Byio5sxenTHMgrdKT++IRo6tAObN8/mqqt6kptbwOjREWzePAjIASZhKsVX7A9/MLPxU6f2o1cvT8+Yvw1kAbFoCbU3xWC29QNTWLDqIkMj6dGyBwDBXU+QmZlX6YJ3TsvJ2kQzy35BxwvIzS3g2WdNvvxvfzsKf/9z/3m3A3nlx4uIiDQECuTFg7qQkRFCSEgBJ09uLnG2oSytX7RoBwBTpvTFz8+hQneNTGRkKJ98Mpmnn74Ef/8ALr30Yo4fb47JP76zgrth8+YkPv10D35+Dh5+2NO58RaFy+rnAI5yrpWau9b19fNqt2Avr2896AxQ+YJ3O5J3cCb7DOGB4QxsN5D33tvBkSNptGkTzo03Djjn6kxMzQRQIC8iItIwKJAXD/Lj8GFT0C4nZ2OJs/aM/KEzhzibd7ZWe+YpaWk5fP75XgCmTesHwKHUQ4AC+cbE4XBw//2jWL36JkJD23HDDddQUOAA3sSyXi/3Xjs3furUWC/Mxq8FfgTCgOkebltKuhzzz+gO4FC1WrADeb8YU0Bz06bK5cl/d8jkx4+MGYkf/vzlL2Z2/t57RxASEnDO1esxBTijgY6IiIiI76tyIL9s2TIuvvhi+vfvT2xsLHPmzCErKwuAxx57jLi4uGK/OnfuzOTJk93333rrrXTs2LHYNXPmFM/jXLt2LcOHDycuLo7hw4fz3XffFTufnp7O9OnTiY2NpW/fvjz22GMl9iZ/7bXX6NevHwMGDGDcuHEcPVpyb3PxvJQU1zLR4O0lzrUKa0VkSCQWFj+d/qnEeV/wySe7yc7Op1evKOLizP7MmpFvvEaNimHLltto0uRi5s83e7Xn5s4hK6v0nOlNm455cTYeCmfjp2Gqqot3taAwfeGLarUwPNoE8qeCfwKsSs/IF90/ftmyn/jxx5M0bRrEnDlDSrm66LJ6rdIQERFpCKocyDdp0oQ333yTbdu2ER8fT3p6OvPnzwdg/vz5xMfHF/s1btw4fvGLX7jvz8/PL3HdK6+84j6fnJzMtGnTWLhwIfHx8bz55pvMmDGD48ePu6+ZPXs2ffr0YceOHWzZsoXNmzfz8ssvu8+vWLGCV199lTVr1rB161Zuvvlmrr32WsT78vL6A9Cixd4S5xwOh3tW3leX1y9aZD6gmDo11l0RumSOfB5mWzJQIN/wRUWF8cUX02ja9GFWruxKcHAOR46MZ/fuwyWutWfjp03rR8+enp6NP4WpoA8qclebrnB9rV4g379Nf4L9g8l0pkGL02zZcrzCgneWZRUL5J96yszGz5kzhObNQ0q5Q4XuREREGpoqB/JjxoyhQ4cOAAQEBPC73/2O//znP6Vem5GRwSeffMK0adMq3f57773HlClT6NPH7Enes2dPpk2bxnvvmWJCp0+fZu3atcybNw+AoKAg/vKXv/Dqq4XbPy1YsIDHHnuMiAgzIzVp0iT8/f2Jj4+v6rcrVRQSMhiATp0OUtre2r6cJ5+cnMnKlWZv+KlTYwHIyc8hKcMshS2ckT8IFAChQPva7qbUAT8/B//3fxcSFvYeJ082oUePY6xdez1vv11YyX7jxmN89tleL87Gvw7kAkOAwV5oX0o3wfX1G0yRwaoJ8g9iYLuBAASfd5ysrDx27z5V7j0HzhzgWPoxAv0CsY50YM2awwQG+nHPPcNLuTof+N71WvnxIiIiDUWNc+RPnz5NSEhpMwCwaNEixo8fT9OmTSvd3sqVKxk7tvh2TGPHjuWrr74CYNWqVYwYMQJ/f3/3+R49epCcnExycjIAX3/9NWPGjCmzjXPl5OSQlpZW7JdUT1TUMPLy/GjePBPLKjkj6ctb0L3//o8UFFgMHdqe7t1bAnAk7QgAIQEhRIXZM6zaeq6xGjVqKAEBi3A6HcycuYHPPnuc2bM/4+zZPPds/K9+1Y8ePVp6+J2dwALXa83G166+mLzzbEwwX3V2nnyL/qeBivPk7fz4we0H8/dnTGHRGTP606FDs1Ku3obZTSHC1VcRERFpCGocZbzyyivceOONpZ5bsGABs2bNqlJ7x44dIyYmptixmJgYEhISyjwPEB0dzYEDB8jIyCAgIIDw8PAy2zjXk08+SUREhPtXae1L5XTs2JqdO1sBcObMuhLnfXkLOrtavV3kDornx9tL7ZUf37hFRl4JPADAa699ytdfryQubgGff25m4x96yBuz8d8A+4BmwBQvtC9lc1A4K1+96vV2IO9slwiYWgrlsZfV9wkfzNKle3A44He/O7+Mq+1l9aMw+96LiIhIQ1CjQH7FihXEx8eXGqxv2LCB3NxcRowYUey4w+HghRdeYOjQocTGxjJz5kwSExPd58+cOVNihj8kJMRdUK+080Wvqeh8aR544AFSU1Pdv4r2R6omKMifffvMByHp6etLnC+aI++0ys8DrU8OHEjh++8TcThg8uTCWS0VupPS+Pk9CoymWbNcPvzwQw4ePAHA9On9azgbn4+pjv4tZr/4PwKzgdtd52cA4aXfKl5UNE++ZEpRRdwF7wISICCPjRsrmJF3BfKHvjPpYxMn9ipnBwTlx4uIiDRE5+5RU2mJiYnMnj2bDz/8kODg4BLnFyxYwOzZs0scf/rpp2natClBQUFkZ2fz7LPPctlll7Ft2zYCAgIIDg4mOzu72D1nz54lNDQUgODgYFJSUkq0a19T2v3ntnGu4ODgUr8HqZ6kpO7ARmBLiXNdI7sS4BdAVl4WR9OOEhPhG6sf3nvPzMZfdFEX2rUrTBUpWegOFMiLebS+C8QRF3eU997bwH33Xc4jj4yt4L5M4DAmWD90zutDwFHMMvrS+FMY0EvtughTE+MIZin7ufu4l69L8y5EhUVxKusUtD1OfHwY+flOAgJKftZ+IuMEe3/eiwMHq98yHxrMnTuqxHWGBdi7vig/XkREpCGp1ox8ZmYmV199NU888QRDhpTc6iYtLY1PPvmE6dNL7mPcsmVLgoKCADNL/vvf/57c3Fx27twJmCXyhw8Xz61OTEwkOjq6zPNFr4mKiuLs2bNkZGSU2YZ4V1aWKVTYtOmuEucC/QM5L/I8wHt58sfSj/HC/17g1k9vZcW+FR5ps3BZfWyx45qRl7JFA28AcM01KzhwoBNdu+YDm4CPgOeAe4FrMcXpooAmQB9gHCbX/U/AO5hZ1URMEB+Iqb9wEXAT8AjwL1e7yoGuG6HAL12vq7683uFwuJfXB3Utv+DdmsNmhr1lQWfy04O54IKOjBxZ1geiCcBxIAgYWuV+iYiISP1V5UC+oKCAKVOmMG7cOGbMmFHqNW+99Rbjxo0jMjKy0m0GBJjFAaNGjWL16tXFzq9evZpRo8yMw8iRI1m7di0FBQXu83v27CEoKIjo6GjzA9Hw4Xz77bdltiHe5e9vKjBHRh4HzpQ47408+RMZJ3hpw0uMfX0s0c9Gc/fyu/nXln9x+TuXM3PpTFKzU6vd9vbtJ9ixI5mgIH+uvbZ3sXOHUg8BRQP5XMzMKSiQF7Pk+n4AHI5rgdaYqvLXAb8Bngc+BjYDP7vuaQb0w+Rd3wE8BbwH/ICZkc/G5MN/AywE/gDcQlVngcXTarYNnR3IR/Qxfw7KypO3l9Wnbm8NwLx5ZeXGQ+Gy+iFA6UVpRURExDdVOZC/5557CA0N5fHHHy/zmvKK3B06dMj9Oicnh4ceeoi2bdu6t5ubOXMm77zzjnuGfs+ePbz11lvMnDkTgM6dOzN06FCeeuopAPLy8pg3bx533XWXu927776b+fPnu6vPL1myhMzMTC688MKqfrtSDe3bd+TgwQjX77aWON+zpWe2oEvOTOaVja/wizd+Qftn23Pnsjv59tC3WFiMjB7J9P7TceDg3/H/JvblWL786ctqvc+775rZ+PHjuxMZWTw9o+SMfAJm1jQcaFe9b0wamD9hCo3Z2gHDgUnA74AXgE8xf1fOAKmY5dmfAS8Cc4HJwAjMdobaCaF+sgP5dcDJKt9t58nntjL/RpZVud4O5PP2RRMb25rx48v7wFD58SIiIg1VlXLkU1JSePHFF+nZsycDBw50H3c4HCxfvpw2bdqwfv16LMsqsf2b7cknn2TVqlWEhITgdDq55JJL+OKLwhmM6Oho3nrrLWbMmEF+fj7+/v4sXLiQTp0Kc5AXLlzI7bffTt++fXE6nUycOJH777/fff6aa64hMTGRkSNH4ufnR9u2bVm6dCl+fvoBuDZ06RLJli3t6Nw5FZMnXzwvuGjBu6o6lXWKj3d9zOIfF/Pfg/8tVjBveIfhTOo7iev7XO8OrGcNmsUtS29hf8p+xi8azy1xt/DMZc/QPKR5pd7PsiwWLdoOFO4dX/RcyRx5e1l9N0w1a5Eg4L/AMUwQr3ocDVM0ZlXEVmA5pvBg5Q1tb5a+p/onQVgmGzeWnJFPy0kj/ni8+c2hTsx9cVSR3TJKo/x4ERGRhsphWVbVS+w2cGlpaURERJCamkqzZqXtyyvlSU7O5MUXJ/Doo6soKJiBv/+bxc7/kPgDo/49iuhm0STeV/EOAafPnubjXR+zZOcSvk74mgKrMK1iSPshTOoziRv63kDn5p1LvT8rL4vff/17/v6/v2Nh0aFpB1678jXGdR9X4Xt//30i55//b5o0CeLEid8SFhboPncq6xSt/mq22st+MJvggGDgWcxS6uuB9ytsX0QakocwOwlMAhZX+e6e/+jJ3p/3wjvTCD3Sh7S0B4oVvFuxbwWXv3M5pDQn5qP57N9/N4GBZW0pdxKTygEmbaNFlfsjIiIitasqcWi1q9aLlKVVqzB27+4AQF7eFvzP+TmzZ5RZWn8k7QjpOek0DW56bhOknE1h6Z6lLP5xMSsTVpLvzHefG9RukDt47xrZtcL+hAWG8bfL/8Z1va/jlk9vYd/pfYxfNJ6b427m2cueLXd23p6Nv+aaXsWCeIBDZ8wS2LZN2rqCeDC5y6D8eJHG6ApMIL8CyMMUJqy84R2Gs/fnvQR1Pc7Zn3qwa9dJ+vVr4z6/+qCr9suhTvzmNyPLCeIB1rq+9kVBvIiISMOjtebicQ6Hg9OnTVG4oKDdQE6x8y1CW9AqzMxk7/15r/t4anYqb259kwmLJtDm6TbcvPRmlu9bTr4znwFtBvDHX/yRvb/ey6bZm5g3el6lgviiLuh0AVvnbOXe4ffiwMHC+IXEvlR27nx+vpMlS34EYNq0fiXOq2K9iBQ3DLP7QCqFgXTl2QXvwnskAyXz5D/Z8h8Awk6dx623DqqgNTs/XsvqRUREGiLNyItXhIV15fTpEFq0yAZ2AgOLne8V1YuTh0+y/uh6dp3axZIfl7Bi/wpyC3Ld1/Rr3Y9JfSdxQ58b3LP4Ne5XYBjPXf4c1/W5jpuX3lzu7PzXXydw8mQWrVqFcfHFXUq0pUBeRIrzx2wd+Bamev2FVbrbLniX1eIgYLFx4zFuuikOgOy8bHanx4M//L+LrqJJk6AKWrPz41XoTkREpCHSjLx4RdeukcTHt3X9Lr7Eebvg3R3L7mDGxzP4bO9n5Bbk0juqN38Y+wd+vONHtt2+jYfGPOSxIL6o0R1Hs3XOVu4bcV+x2fllPy1zX2PvHX/DDX1KXcJastBdNnDY9VqBvEjjNMH1ter7yfdv059g/2ByHBnQ4nSxGfnXvvgMyz8fMsN55M7rKmgpE7OlIWhGXkREpGFSIC9e0aVL+YG8XaEZoEfLHjw85mF23L6DnXfu5JELH6FPqz5e72NYYBjPXvYs3978Ld1bdOdo+lGuWHQFNy+9maSUk3z00S6g9GX1AIfTStt6zgKaUlhkSkQal0sxi912A/urdGeQfxCD2rmWzEcfIT7+OPn5ZmeOFz79AICu/gNo06ZJBS2tB/IxlfQ7VnCtiIiI+CItrRev6No1ksWL7X3Ut5Q4f/PAm4kMjaRHyx70a92vgi2UvGt0x9HEz4nn4W8e5rl1z/F6/Oss3bGMjHa/pFPuUEaOjCn1PrvYXWEgX3RZvbaeE2mcmmNmwVdhltffXaW7h3cYzg9HfiCwy3Gyt+Wzc+dJnE6Ln3LMc3Ta6CsqaAGK58frWSQiItIQaUZevKLk0npnsfMBfgFc3+d6+rfpX6dBvC0sMIxnLnuG727+ju4tupOSnwy/WkSzGV+Slpta6j0lc+SVHy8iUJPl9cM6DAMg5LzjAGzadIyn/voddDTPm6sHXlaJVuxAXvnxIiIiDZUCefGKzp2bs3t3FNnZ/kA6cLCOe1Q553c8n1VT1uH3v1FgwfaA/9D3pb58sfeLYtdl52dzIvMEUDRHXoG8iIDZhg5gNeb5V3l2wbvMpokQkMcHH+xiyapvICSHMP9wBrQdUEEL+cD3rtfKjxcREWmoFMiLV4SFBRIVFcGOHXaueMnl9fXV8s8O4fzyUrr89356tOzBsfRjTHh3Ajd9chMpZ1MAOJJ2BDAz+S1C7T2aFciLCEBP4DwgF1hZpTu7NO9CVFgUTkc+tD3OsmU/4Yw2aTyjO59PgF9FGXHbgAwgArOHvIiIiDRECuTFayqqXF9fLVq0HYBbL5tI/G3x3D/yfhw4eGPrG8S+HMsXe78olh9fmBqgQF5EwOSl27PyX5R3Yck7HQ73fvJ0OGq+djLPmws6VmapvL2sfhRmOzwRERFpiBTIi9d06dLc5wL5pKR0/vvfgwBMnRpLaGAoT1/6NGtuWVNsdv7u5aaAVWF+fBZwxPW6W213W0TqHTtP/gvOrRFSETuQD+ySBFgEnGeeLVUL5JUfLyIi0pApkBev8cUZ+SVLfsTptBg5MpouXSLdx0fFjCL+tnh+O/K3OHCw8+ROADo2swN5e5upCCCqVvssIvXRGCAcOE5VU4vsPPmgLsehxWnyQ9II9At0F8IrmwV853qt/HgREZGGTIG8eE3XrpFs3WoH8keAU3XZnUpZtGgHYGbjzxUaGMpfL/0ra29ZS4+WPQCIaxvnOqut50SkqGDMnvJQ1er1dsCeGXyCe15uDsDQDkMJDQyt4M4EzAcHQcDQKr2niIiI+BYF8uI1XbtGkpERzMGDrVxH4uuyOxXat+8069cfxc/PwaRJZReJGhkzkq1ztvLDzB+YM2SO66jy40XkXNXLk28e0pyeLXsC8OHRhUBVl9UPAUKq9J4iIiLiWxTIi9d06dIcgI0b7cr18XXVlUp5911T5O6Xv+xKmzZNyr02JCCEEdEj8Pezi0kpkBeRc413fd2AmSmvPHtW3t4ho2qBvJbVi4iINHQK5MVr2rdvSlCQP1u2tHEdqb9b0FmW5V5WP21ayWX1FVMgLyLnaoeZHQdYVqU73ZXrAQcOzu94fiXusvPjVehORESkoVMgL17j7+9H587N2bKl/he827r1BLt3nyI42J9rruldjRYUyItIaaq3vN4ueAfQr00/moc0r+COk8Ae1+tRVXovERER8T0K5MWrim9Btxs4W5fdKZO9d/yECT1o1iy4indnAEmu1wrkRaQoexu6/wA5lb6rf5v+BPubZ1HlltWvdX3tC7SoQv9ERETEFymQF6/q2jWSpKSmZGQ0x+ylvL2Oe1SS02nx7rv2svp+1Whhn+trC/QDtIgUNwhog/nA77sKri0U5B/kXk5/SddLKnGH8uNFREQaEwXy4lVdu0YCDhISOrmOxNdhb0q3du1hjhxJo1mzYMaPr86MupbVi0hZ/ChcXl+1begWTlzIBzd8wFU9r6rE1cqPFxERaUwUyItX2ZXrt25t5zoSX1ddKZO9rP6663oTEhJQjRYUyItIeYoG8lal7+oY0ZHr+lyHw+Go4MpMYLPrtWbkRUREGgMF8uJVZkYe1q61l5zH11lfSpObW8CSJTsBmDq1OtXqoXBpvQJ5ESnNJUAgsB/Y64X21wP5QDTQ0Qvti4iISH2jQF68yg7kV6+OdB3ZChTUWX/O9dVX+zl9+ixt2oRz0UVdqtmKZuRFpDxNgbGu11WrXl85RfPjK5q9FxERkYZAgbx4VURECJGRIezd2xKnMxTIonAGu+7Ze8dPntyXgIDq/nVQIC8iFbGr11ctT75ylB8vIiLS2CiQF6/r2jUSp9OPM2d6uI7E12V33DIzc1m6dDdQ3Wr1AGnACddrBfIiUhY7kP8OSPVgu/nAD67Xyo8XERFpLBTIi9fZy+uPHLGXrm+pu84U8dlne8nMzKNr10iGDetQzVbs1QWtgAgP9UxEGp7zgJ6YwPs/Hmx3G2ZruwjMHvIiIiLSGCiQF6+zK9fv2hXtOhJfV10pxq5WP3VqbCWqQpdFy+pFpLK8sbzezo8fBfh7sF0RERGpzxTIi9fZM/Lr17d2HYmvs77Yfv45iy+/NLPp1V9WDwrkRaTy7G3ovsRzRT+LFroTERGRxkKBvHidHcivWtUU80fuBHC8LrvEhx/uIj/fyYABbejTp1UNWlIgLyKVNRpoBpwENnigPQsVuhMREWmcFMiL13XpYgL5XbsysSy74F3d5sm/+66pVl+z2XgoDOS71bAdEWn4AoHLXK89sQ1dAuZD0SBgqAfaExEREV+hQF68rmPHCPz8HJw9m092th04x9dZf44cSWP16oMATJkSW8PWNCMvIlXhyTx5e1n9ECDEA+2JiIiIr1AgL14XFORPTEwzAE6csGeu4+usP4sX78CyYPTojnTsWJNK82eAU67XCuRFpDLGAQ7MM/BoDdtSfryIiEhjpUBeaoWdJ79/f0fXkbpbWr9okb2s3lOz8W2ApjVsS0Qah1bAcNfrmi6vV368iIhIY6VAXmqFvQVdfHxb15F9QHqt92PPnlNs3pxEQIAfN9xQ0z2XtaxeRKrDrl5fk0D+JLDH9XpUzbojIiIiPkeBvNQKe0Z+xw4H0B5TbXl7rffDLnJ3ySVdiYoKq2FrCuRFpDrsPPmVQHY121jr+toXaFHjHomIiIhvUSAvtcIO5A8cSAHiXEfja7UPlmWxaJH58KDm1epBgbyIVM8AoAOQBayqZhvKjxcREWnMFMhLrbC3oEtIKBrI126e/KZNSfz002lCQwOYOLGnB1pUIC8i1eGgcHl9davXKz9eRESkMVMgL7XCnpE/ciSNvLz+rqPxtdoHezb+qqt60rRpsAdaVCAvItVVdBs6q4r3ZgKbXa81Iy8iItIYKZCXWtGqVRjh4YFYFhw92tV1dDuQXyvvX1DgZPHiHwFPLav/GUhxve5W3oUiIqX4BRAMHAJ2VvHe9ZhnZzTQsYJrRUREpCFSIC+1wuFwuJfX79kTATQBcoDdtfL+3357iGPH0mnePITLLjvPAy3as/HtgXAPtCcijUs4JpiHqi+vL5of7/BYj0RERMR3KJCXWmMvr09ISMUUe4LaWl5vL6u//vreBAcHeKBFLasXkZqq7jZ0dn68ltWLiIg0VgrkpdZ07docgAMHzgADXUf/CPwZ2EbV80QrJycnnw8+2AV4Yln9QeBl4G+u3yuQF5HqsgP5tcDpSt6TD/zgeq1CdyIiIo2VAnmpNcUr11+B+eO3G3gAM0PfEbgNWApkeOx9ly/fx5kz2bRv35QxYzpV8e5c4Bvgt5j9mrsAd1BYaOpCj/VTRBqbzpjnihNYUcl7tmGejxGue0VERKQx8sQaY5FKKVxanwJMAg4AyzDLSr8GjgCvun4FAWOA8ZigvzvVzQV9990dAEye3Bd//8p8dnUE+NLVt5UU/1DBHxhZpF/9S9wtIlJ5E4AfMXnyUytxvZ0fPwrzPBIREZHGSIG81Bo7kDdL68HMwM9x/coGVmOC+i+ABEwQvRL4DXAehcHzWCCkUu+Znp7Dp5/uAcpbVp+HWaq6DBPAbzvnfGtgnOv9LwEiK/XeIiIVmwA8BSzHLJuv6J9l5ceLiIhINZbWL1u2jIsvvpj+/fsTGxvLnDlzyMrKcp8PCAggLi6u2K9ly5a5z1uWxRNPPEHfvn2JjY1l6tSppKWlFXuPtWvXMnz4cOLi4hg+fDjfffddsfPp6elMnz6d2NhY+vbty2OPPYZlFc+vfu211+jXrx8DBgxg3LhxHD16tKrfqnhY587NAThzJpuUlLPnnA0BLgP+DuwD9gDPAr8EAoH9wAvA5UAL4EpMrvqhct9z6dI9nD2bT/fuLRg8uF2RM0nAQszKgFaYDweewgTxDmAE8Biw0XXt665rFcSLiCeNwDxXTgPrKrjWonBGXvnxIiIijVmVA/kmTZrw5ptvsm3bNuLj40lPT2f+/Pnu8wUFBWzcuJH4+Hj3r/Hjx7vPv/rqq6xbt47NmzezY8cOBg4cyK233uo+n5yczLRp01i4cCHx8fG8+eabzJgxg+PHj7uvmT17Nn369GHHjh1s2bKFzZs38/LLL7vPr1ixgldffZU1a9awdetWbr75Zq699toqD454VlhYIG3bNgHs5fVlcQA9gPuArzB7tn8CzAI6AGcxy1DvwOSYxgJzgVWY2fVCdrX6X/2qDw7HOuBhYDBm27hbgPeBVKAl8CvgHSAZM0NvX6tSEiLiLQGYFT9QcfX6BOA4JvVoqDc7JSIiIvWcwzp3KruK4uPjufHGG9m2zSxHdjgc5OXlERBQ+vLAQYMG8c4779C7d28AnE4nXbp0YfPmzbRs2ZK///3vHD16lKeeesp9z+9//3tat27Nvffey+nTp4mLi+PAgQP4+5v8wL179zJp0iTi4+MBuPbaa5k1axbjxo1ztzFq1Cheeukl4uLiKvye0tLSiIiIIDU1lWbNmlVnWKQM55//b77/PpH337+B66/vU40WLGA75gfeZcD3mEJRtmbApcB4Tp+O4957n+Dyy/cyefJR/P3P/fBgCGa5/DjMD8XKNxWRurAI80FiLOb5VpY3gJsw+fFrvd8tERERqVVViUNrPNV4+vRpQkIql6/8888/c/ToUXcQD+Dn58eoUaP45ptvAFi5ciVjx44tdt/YsWP56quvAFi1ahUjRoxwB/EAPXr0IDk5meTkZAC+/vprxowZU2YbUneKF7yrDgemwNwDmFzRk8C7wAwgCkgDPgBuoUWLQbz55kdMm7bDFcQ3ByZjfhg+DmwAHsUsbVUQLyJ15XLMP8c7KD9dyF5Wr/x4ERGRxq7Gxe5eeeUVbrzxxkpdm5SURHR0dInjMTExJCQkAHDs2DFiYmKqdB4gOjqaAwcOEBYWRkBAAOHh4SXa2L699JmOnJwccnJy3L8/N2dfPKdLl+ZATQL5c7UAprh+FQCbsGfrnc5NbN3ahpycSxkxYhYmYFd9RxGpb1pgZtnXYJ5fd5RxnV0vRvnxIiIijV2NZuRXrFhBfHw8s2bNKnb88ssvp1+/fgwfPpy//e1vOJ1m6fOZM2dKnb0PCQlxF8wr7ZqKzhe9pjLvca4nn3ySiIgI96/SPigQzyhZud6T/IFhwKMcOvQVAQEPM3jwHKKjX8DMYCmIF5H6aoLr6+dlnD+JKQIKJugXERGRxqzagXxiYiKzZ89m0aJFBAcHu48nJSWxcuVKtm/fzuLFi/noo4/485//DEBwcDDZ2dkl2jp79iyhoaFlXlPR+aLXVOY9zvXAAw+Qmprq/pWYmFjJUZCqqvnS+oqtXn2Qa69dgmX5MXZsZ6KjVedAROq7K1xfvwEySzlv58T3xczgi4iISGNWrUA+MzOTq6++mieeeIIhQ4YUO9e2bVv3686dO/OnP/2JDz74ADDL3w8fPlyivcTERPeS+9Kuqeh80WuioqI4e/YsGRkZZbZxruDgYJo1a1bsl3iHvbT+4MEzFBQ4y7+4inbvPsXEie9x4YVvsHlzEk2aBPHII2MrvlFEpM71BToBOZhg/lzKjxcREZFCVQ7kCwoKmDJlCuPGjWPGjBmVut6uYN+uXTuaNGnCzp073eedTidr1qxh1CizVHDUqFGsXr26WBurV692nx85ciRr166loKDAfX7Pnj0EBQURHR2Nw+Fg+PDhfPvtt2W2IXWnffumBAX5k5/v5MgRz9QiSE7O5M47vyA29iU+/XQP/v4Obr99CPv23cWFF3b2yHuIiHiXg8Ll9aVtQ2fnxyuQFxERkWoE8vfccw+hoaE8/vjjJc5lZWVx8uRJ9+8PHjzIb3/7W2655Rb3sbvvvpu5c+eSm5sLwNNPP82AAQPo2rUrADNnzuSdd95xB/t79uzhrbfeYubMmYCZ5R86dKh7e7q8vDzmzZvHXXfdVew95s+f7y5at2TJEjIzM7nwwgur+u2Kh/n7+9G5c3Og5nnyWVl5/OlP39Gt29956aWNFBRYXHllD7Zvv52XXrqCNm2a1LzDIiK1xl5e/wVmq01bJrDZ9VqF7kRERKSK1b9SUlJ48cUX6dmzJwMHDnQfdzgcLF++nPz8fCZMmODeRz40NJR77rmH6dOnu6+99957+fnnnxkwYAB+fn707t2bN954w30+Ojqat956ixkzZpCfn4+/vz8LFy6kU6dO7msWLlzI7bffTt++fXE6nUycOJH777/fff6aa64hMTGRkSNH4ufnR9u2bVm6dCl+fjXebU88oEuX5uzd+zMJCSnVmjF3Oi3eemsrDz30X/es/uDB7Xj66Us1Ay8iPuwiIAw4AmwDBriOrwfygWigY910TUREROoVh2VZVsWXNS5paWlERESQmpqqfHkvuOOOL3j55Y08+OAFPPHEL6p078qVCfzud18RH38cgI4dI3jyyYuZMiUWPz+HN7orIlKLrgI+A54AHnQdexyYj9lm89066peIiIh4W1XiUO3HJbWuOlvQ7diRzNy5X/Hll/sAiIgI5ve/v4C77x5OSIj+GItIQzEBE8h/TmEgr/x4ERERKU4RkNQ6u3J9ZbagS0pK55FHVvGvf23B6bQICPDjjjuG8PDDY4mKCvNyT0VEatt419f/YfaOjwR+cB1TfryIiIgYCuSl1lVmL/nMzFyefvp7/vrX78nMzAPguut68+STF9O9e8ta6aeISO2LBuKAeOBLIBbIACIwW9SJiIiIKJCXOmAH8snJmWRm5hIeHuQ+V1DgZOHCeObP/y9JSRkADB/egWeeuZTzz1eRJxFpDK7ABPJfAGdcx0YB/nXUHxEREalvFMhLrYuICCEyMoSUlGwOHDhDbGxrLMti+fJ9zJ27kh07kgET8P/5zxdz/fV9cDhUyE5EGosJwB+B5UCO65jy40VERKSQAnmpE127RrJpUxIJCSnk5zv53e++YuXKBAAiI0N4+OEx3HHHUIKD9UdURBqboUAUcApT+A6UHy8iIiJFKUqSOmEH8vPmrWTPnlNYFgQF+XPXXcN48MELiIwMresuiojUEX9M0bs3AScQhAnuRURERAwF8lIn7Dz53btPATBlSix/+tMv6NIlsi67JSJST0zABPIAQ4CQOuyLiIiI1Dd+dd0BaZwuuqgzfn4ORo/uyLp1M3n33esUxIuIuF1K4Wftyo8XERGR4jQjL3Xissu6kZ7+AGFhgXXdFRGReigCs7z+U0wVexEREZFCCuSlziiIFxEpz5vAIaB/XXdERERE6hktrRcREamXIlAQLyIiIqVRIC8iIiIiIiLiQxTIi4iIiIiIiPgQBfIiIiIiIiIiPkSBvIiIiIiIiIgPUSAvIiIiIiIi4kMUyIuIiIiIiIj4EAXyIiIiIiIiIj5EgbyIiIiIiIiID1EgLyIiIiIiIuJDFMiLiIiIiIiI+BAF8iIiIiIiIiI+RIG8iIiIiIiIiA9RIC8iIiIiIiLiQxTIi4iIiIiIiPgQBfIiIiIiIiIiPkSBvIiIiIiIiIgPUSAvIiIiIiIi4kMC6roD9ZFlWQCkpaXVcU9ERERERESkMbDjTzseLY8C+VKkp6cDEBMTU8c9ERERERERkcYkPT2diIiIcq9xWJUJ9xsZp9PJsWPHaNq0KQ6Ho0ZtpaWlERMTQ2JiIs2aNfNQD6UyNPa1S+NddzT2dUPjXvs05nVHY183NO61T2NedzT2ZiY+PT2d9u3b4+dXfha8ZuRL4efnR3R0tEfbbNasWaP9A1nXNPa1S+NddzT2dUPjXvs05nVHY183NO61T2Nedxr72Fc0E29TsTsRERERERERH6JAXkRERERERMSHKJD3suDgYB555BGCg4PruiuNjsa+dmm8647Gvm5o3GufxrzuaOzrhsa99mnM647GvmpU7E5ERERERETEh2hGXkRERERERMSHKJAXERERERER8SEK5EVERERERER8iAJ5ERERERERER/S6AP5ZcuWcfHFF9O/f39iY2OZM2cOWVlZ7vO7du1i7NixxMXFMXDgQD766KNi9z/77LMMGzaMAQMGEBsby/PPP1/iPZYuXcrAgQOJi4tjzJgx/PjjjxX2KykpiSuuuIIBAwbQr18/XnnllWLnMzMzufvuu+nfvz89e/Zk4sSJJCUlVXMUal9tjDvAl19+SbNmzVi1alWl+lXRuNsef/xxAgICcDqdlfuG65ivjvcTTzxB27ZtiYuLc/+66qqrqvbN1zFfHXtff8aA98d+3bp1XHHFFfTv359+/foxZcoUTp48WWG/9JypX+Ot50zdjb2vP2e8Pe6LFy9m0KBBDBgwgN69e3P11Veza9euCvvVUJ8x4LtjrudM3Y29rz9nymU1cqtXr7aOHDliWZZl5eXlWdOmTbPuv/9+y7Is6+zZs1b37t2tVatWWZZlWUlJSVaPHj2srVu3uu//9NNPrfT0dMuyLOvkyZNWnz59rM8++8x9fseOHVa3bt2so0ePWpZlWd99953VrVs3Kysrq9x+jRw50nr77bcty7KstLQ0a/jw4dYXX3zhPj9p0iTrvvvuswoKCizLsqzFixdbY8aMqdFY1CZvj7tlWdYrr7xiDR8+3IqNjbW++uqrSvWronHPy8uzbr75Zuumm26yACsvL6+aI1C7fHW8H3nkEevBBx+s5nddP/jq2Pv6M8ayvD/2GzdutPbu3ev+/dy5c63rrruuwn7pOVO/xlvPmbobe19/znh73BMTE620tDTLsizL6XRar7zyitWuXbsa/wzpq88Yy/LdMddzpu7G3tefM+Vp9IH8ubZs2WL169fPsizLWrp0qTVp0qRi5xcsWGDdc889Zd7/3HPPWXfddZf79/fdd5/10ksvFbtm6tSp1scff1xmG1u3brWGDRtW7NiKFSusiRMnWpZlWSdOnLCioqJKPHgvu+wya/v27WW2W595etztezIzM62xY8dWKripaNwty7IyMjKs1157zbIsy+f+8SvKV8a7IfzDdy5fGPuG+IyxLO+MfVEpKSlW06ZNy+2DnjOF6st46zlTUm2MfUN8znh73C3Lsvr27WutXbu2zPON6RljWb4z5nrOlFQbY98QnzNFNfql9ec6ffo0ISEhAKxcuZKxY8cWOz927Fi++uqrSt1f3TZKu+eCCy7gm2++wbIsEhIS6NSpEwEBAcWu6du3Lxs2bCj/G6ynPD3uALNnzyYsLKzSfaho3AHCw8O59dZbK91mfeUr490Q+cLYN8RnDHhn7Ktyvqz31XOm4vurc76s99Vzpn6MfUN8znh73J1OJ2lpabRv377MaxrTMwZ8Z8wbIl8Y+4b4nClKgfw5XnnlFW688UYAjh07RkxMTLHzMTExJCQklHpvTk4Ob7zxBtOnT3cfq2obZd0TGhpKSEgIycnJtGrVisOHD1NQUFDsmn379nHixImKv8l6yNPjXh0VjXtDovGuO74w9g3xGQPeH/ui7ZelMf2513jXHV8Y+4b4nPHmuCckJDBz5kyuueYaOnfuXGYfGtufeY153fGFsW+Iz5miFMgXsWLFCuLj45k1axYAZ86cKfFJUUhICNnZ2aV+wvboo49y4YUX0r9/f/exstqwi0P8+te/dhe9uOSSS8q8p+h95513Hn379mX+/Pnk5eVRUFDAm2++yffff+9TBUts3hj3ilRn3BsKXxpvh8PBkiVLGDFiBL1792by5Mns3Lmz0u9b3/jK2De0Zwx4f+y3b9/O22+/zbx589zH9JzxjfHWc6a42hr7hvac8da4P/fcc8TExHDeeedx8uRJ/vznP7vPNeZnDPjWmOs5U1xtjX1De86cK6DiSxqHxMREZs+ezYcffkhwcDAAwcHBZGdnF7vu7NmzBAcH43A4ih3/8ssv+fDDD1m/fn2x43YbgYGBxdoIDQ0F4B//+EeJvpT2vufe9/HHH/Poo48ybNgwCgoKuOKKK5g+fTotWrSoxndfd7w17hWp7rj7Ol8b73vvvZe5c+cSGhpKfn4+b7/9Npdccgnbt2/Xn/VKauzPGPD+2KelpTF58mQWLFhAq1at3Mf1nPGN8dZzplBtj31Dec54c9zvu+8+7rvvPjIyMnj++ecZP34833zzDQ6Ho9E+Y8D3xlzPmUK1PfYN5TlTGs3IY7YluPrqq3niiScYMmSI+3h0dDSHDx8udm1iYiLR0dHFju3atYvbbruNjz/+mIiIiGLnKttGRfecPXuWjIwMWrduDUDz5s157rnn2LJlC9u2bePJJ59k7969xMbGVv4br2PeHPfqqMy4+zJfHO+IiAj3gzggIICbbrqJ3r17s2bNmhq/f23yxbFvCM8Y8P7YFxQUMGXKFKZNm8aVV15ZYX/0nClUX8ZbzxmjLsa+ITxnauv53qRJEx588EGSk5PZvn17mdc19GcM+OaY6zlj1MXYN4TnTFkafSBv/8M0btw4ZsyYUezcqFGjWL16dbFjq1evZtSoUe7fnzhxgquuuopXX32VPn36lGi/Mm1U5p5vv/2WoUOH4udX+v+yxMREduzYwciRI8tstz7x9rhXR3XG3Vc0pPEuKCgoUbSkPmsoY+9rzxionbG/6667aNmyJQ899FCl+qTnTKH6PN56zpRUG2Pva8+Zuni+p6WllbskuCE/Y6BhjbmeMxXzxtj72nOmXLVbJL/+ufPOO60bbrjBcjqdJc5lZGRYHTt2LLYnYrdu3ax169ZZlmVZWVlZ1tChQ61//OMfZba/YcMGq2vXru595NesWWPFxMS491EsjdPptOLi4krsibhkyRL3Nfn5+e7Xu3fvtgYNGmS9//77VfjO65a3x72oym7JVZlxLwof2rLFV8f78OHD7j7n5+dbL774otW1a9cK9xStT3x17H39GWNZ3h/7v/71r9b5559v5eTkVLpPes7Uv/HWc6buxt7XnzPeHvf9+/e7X2dlZVl33323NXr06FLfz9aQnzGW5btjrudM3Y29rz9nyuOwrAa8L0IFUlJSaNGiBT179ixWKMHhcLB8+XLatGnD1q1bueOOO8jIyMDpdPLAAw8wbdo0wORc3HDDDSWWZnTs2JFPP/3U/fvFixfzxz/+EYfDQVhYGC+99BIDBw4st2+HDh1i9uzZHDt2jIKCAm699VZ+85vfuM/fcccdbNiwgdzcXCIjI/m///s/Lr/8ck8Mi9fV1rjbLr30Uh566CHGjBlTYd8qGveiwsLCSE9Px9/fvyrffq3z5fF+/PHHeeeddwgODsayLIYNG8YjjzxSokJpfeXLY+/LzxionbGPiIigZcuWNGvWrNg1r7/+OnFxcWX2Tc+Z+jXees7U3dj78nPG2+Oem5vLRRddxKlTp9xLsidMmMC8efNo2rRpuX1riM8Y8O0x13Om7sbel58zFWnUgbyIiIiIiIiIr/H9ZBkRERERERGRRkSBvIiIiIiIiIgPUSAvIiIiIiIi4kMUyIuIiIiIiIj4EAXyIiIiIiIiIj5EgbyIiIiIiIiID1EgLyIiIiIiIuJDFMiLiIiIiIiI+BAF8iIiIiIiIiI+RIG8iIiIiIiIiA/5/6S3xHJQ/iOqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3호선 top3역 3년간 승하차 평균 월별로 나타냄\n",
    "\n",
    "#폰트 깨짐 해결\n",
    "font_path = \"C:/Users/82105/Desktop/2학년_2학기/오픈소스_개발_프로젝트/나눔 글꼴/나눔고딕/NanumFontSetup_TTF_GOTHIC/NanumGothic.ttf\"\n",
    "font_name = font_manager.FontProperties(fname=font_path).get_name()\n",
    "rc('font', family=font_name) \n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title('최근 3년 3호선 승객수 top3 추세')\n",
    "plt.plot(metro_line3_top1['사용월'],metro_line3_top1['월평균승객'],color='navy', label='고속터미널(3)')\n",
    "plt.plot(metro_line3_top2['사용월'],metro_line3_top2['월평균승객'],color='yellow', label='연신내(3)')\n",
    "plt.plot(metro_line3_top3['사용월'],metro_line3_top3['월평균승객'],color='green', label='양재(서초구청)(3)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 예측도구(SARIMA) 임포트\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "\n",
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf #(p,q)의 차수를 결정하기 위해\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose      #ETS 분해를 위해\n",
    "from pmdarima import auto_arima                              #자동 ARIMA 사용을 위해\n",
    "#자동 ARIMA(자동회귀누적이동평균)모델\n",
    "#자기 회귀-현재 값이 이전 값들의 선형조합으로 표현됨을 나타냄.p는 이전 값들의 개수\n",
    "#현재의 값이 과거의 값들에 의존한다는 가정\n",
    "#yt=c+ϕ1yt−1+ϕ2yt−2+⋯+ϕpyt−p+εt,      ϕ는 각각의 과거 값에 대한 가중치,εt는 모델의 오차항 \n",
    "\n",
    "#누적 이동 평균-새로운 값이 들어올 때마다 평균을 최신화함\n",
    "#차분-추세와 계절성을 제거\n",
    "\n",
    "# 평가도구 임포트\n",
    "from sklearn.metrics import mean_squared_error #평균과 데이터의 차를 제곱후 합함\n",
    "from statsmodels.tools.eval_measures import rmse #위 값의 제곱근을 구함\n",
    "\n",
    "# 경고 무시\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### 강남역 예측 모델\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.a 원본 데이터 플롯으로 그리기(계절성, 추세 확인)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>월평균승객</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>사용월</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-10-01</th>\n",
       "      <td>642187.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-01</th>\n",
       "      <td>629802.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-01</th>\n",
       "      <td>680279.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-01</th>\n",
       "      <td>710963.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-01</th>\n",
       "      <td>678488.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                월평균승객\n",
       "사용월                  \n",
       "2023-10-01  642187.75\n",
       "2023-09-01  629802.50\n",
       "2023-08-01  680279.25\n",
       "2023-07-01  710963.50\n",
       "2023-06-01  678488.25"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metro_line2_top1_pred=pd.DataFrame()\n",
    "metro_line2_top1_pred['사용월']=metro_line2_top1['사용월']\n",
    "metro_line2_top1_pred['월평균승객']=metro_line2_top1['월평균승객']\n",
    "metro_line2_top1_pred.set_index('사용월',inplace=True)\n",
    "metro_line2_top1_pred.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('최근 3년 강남역 월별 평균 이용 인원')\n",
    "plt.plot(metro_line2_top1_pred['월평균승객'],color='red', label='강남(2)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.b ETS 분해"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = seasonal_decompose(metro_line2_top1_pred['월평균승객'], model='add')\n",
    "result.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.c 자동ARIMA로 추천모델 도출."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>37</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(0, 1, 0)</td> <th>  Log Likelihood     </th> <td>-447.212</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 23 Nov 2023</td> <th>  AIC                </th>  <td>896.424</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>02:06:49</td>     <th>  BIC                </th>  <td>898.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th>  <td>896.977</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> - 37</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td> 3.512e+09</td> <td> 5.78e+08</td> <td>    6.073</td> <td> 0.000</td> <td> 2.38e+09</td> <td> 4.65e+09</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.07</td> <th>  Jarque-Bera (JB):  </th> <td>6.88</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.79</td> <th>  Prob(JB):          </th> <td>0.03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>7.14</td> <th>  Skew:              </th> <td>0.51</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th> <td>4.88</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &        y         & \\textbf{  No. Observations:  } &     37      \\\\\n",
       "\\textbf{Model:}                  & SARIMAX(0, 1, 0) & \\textbf{  Log Likelihood     } &  -447.212   \\\\\n",
       "\\textbf{Date:}                   & Thu, 23 Nov 2023 & \\textbf{  AIC                } &  896.424    \\\\\n",
       "\\textbf{Time:}                   &     02:06:49     & \\textbf{  BIC                } &  898.008    \\\\\n",
       "\\textbf{Sample:}                 &        0         & \\textbf{  HQIC               } &  896.977    \\\\\n",
       "\\textbf{}                        &       - 37       & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &       opg        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{sigma2} &    3.512e+09  &     5.78e+08     &     6.073  &         0.000        &     2.38e+09    &     4.65e+09     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 0.07 & \\textbf{  Jarque-Bera (JB):  } & 6.88  \\\\\n",
       "\\textbf{Prob(Q):}                & 0.79 & \\textbf{  Prob(JB):          } & 0.03  \\\\\n",
       "\\textbf{Heteroskedasticity (H):} & 7.14 & \\textbf{  Skew:              } & 0.51  \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    & 0.00 & \\textbf{  Kurtosis:          } & 4.88  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SARIMAX Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                   37\n",
       "Model:               SARIMAX(0, 1, 0)   Log Likelihood                -447.212\n",
       "Date:                Thu, 23 Nov 2023   AIC                            896.424\n",
       "Time:                        02:06:49   BIC                            898.008\n",
       "Sample:                             0   HQIC                           896.977\n",
       "                                 - 37                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2      3.512e+09   5.78e+08      6.073      0.000    2.38e+09    4.65e+09\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.07   Jarque-Bera (JB):                 6.88\n",
       "Prob(Q):                              0.79   Prob(JB):                         0.03\n",
       "Heteroskedasticity (H):               7.14   Skew:                             0.51\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         4.88\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_arima(metro_line2_top1_pred['월평균승객'],seasonal=True,m=12).summary()\n",
    "#계절성주기를 12(1년 12개월) 입력하여 SARIMAX를 결정받는다.\n",
    "#seasonal=True 는 계절성을 갖음을 나타냄\n",
    "#SARIMAX(0,1,0)을 추천"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.d 훈련 데이터셋과 테스트데이터셋으로 분리."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = metro_line2_top1_pred.iloc[len(metro_line2_top1_pred)-24:]       #3년전부터 1년전까지\n",
    "test = metro_line2_top1_pred.iloc[:len(metro_line2_top1_pred)-24]      #1년전부터 올해"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.e 자동 ARIMA가 제공하는 차수를 통해 예측값을 얻는다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                <td>월평균승객</td>             <th>  No. Observations:  </th>    <td>24</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(0, 1, 0)x(1, 0, 0, 12)</td> <th>  Log Likelihood     </th> <td>-289.602</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Thu, 23 Nov 2023</td>        <th>  AIC                </th>  <td>585.204</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                       <td>02:13:22</td>            <th>  BIC                </th>  <td>588.610</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                         <td>0</td>               <th>  HQIC               </th>  <td>586.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td> - 24</td>             <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>               <td>opg</td>              <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>drift</th>    <td>  144.9375</td> <td>  899.965</td> <td>    0.161</td> <td> 0.872</td> <td>-1618.961</td> <td> 1908.836</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L12</th> <td>   -0.0709</td> <td>    0.232</td> <td>   -0.305</td> <td> 0.760</td> <td>   -0.526</td> <td>    0.385</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td>  5.16e+09</td> <td>    0.000</td> <td> 2.42e+13</td> <td> 0.000</td> <td> 5.16e+09</td> <td> 5.16e+09</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.08</td> <th>  Jarque-Bera (JB):  </th> <td>1.54</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.78</td> <th>  Prob(JB):          </th> <td>0.46</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>2.18</td> <th>  Skew:              </th> <td>0.38</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.29</td> <th>  Kurtosis:          </th> <td>4.01</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 8.01e+28. Standard errors may be unstable."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &             월평균승객              & \\textbf{  No. Observations:  } &     24      \\\\\n",
       "\\textbf{Model:}                  & SARIMAX(0, 1, 0)x(1, 0, 0, 12) & \\textbf{  Log Likelihood     } &  -289.602   \\\\\n",
       "\\textbf{Date:}                   &        Thu, 23 Nov 2023        & \\textbf{  AIC                } &  585.204    \\\\\n",
       "\\textbf{Time:}                   &            02:13:22            & \\textbf{  BIC                } &  588.610    \\\\\n",
       "\\textbf{Sample:}                 &               0                & \\textbf{  HQIC               } &  586.060    \\\\\n",
       "\\textbf{}                        &              - 24              & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &              opg               & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                  & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{drift}    &     144.9375  &      899.965     &     0.161  &         0.872        &    -1618.961    &     1908.836     \\\\\n",
       "\\textbf{ar.S.L12} &      -0.0709  &        0.232     &    -0.305  &         0.760        &       -0.526    &        0.385     \\\\\n",
       "\\textbf{sigma2}   &     5.16e+09  &        0.000     &  2.42e+13  &         0.000        &     5.16e+09    &     5.16e+09     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 0.08 & \\textbf{  Jarque-Bera (JB):  } & 1.54  \\\\\n",
       "\\textbf{Prob(Q):}                & 0.78 & \\textbf{  Prob(JB):          } & 0.46  \\\\\n",
       "\\textbf{Heteroskedasticity (H):} & 2.18 & \\textbf{  Skew:              } & 0.38  \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    & 0.29 & \\textbf{  Kurtosis:          } & 4.01  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SARIMAX Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using the outer product of gradients (complex-step). \\newline\n",
       " [2] Covariance matrix is singular or near-singular, with condition number 8.01e+28. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                     SARIMAX Results                                      \n",
       "==========================================================================================\n",
       "Dep. Variable:                              월평균승객   No. Observations:                   24\n",
       "Model:             SARIMAX(0, 1, 0)x(1, 0, 0, 12)   Log Likelihood                -289.602\n",
       "Date:                            Thu, 23 Nov 2023   AIC                            585.204\n",
       "Time:                                    02:13:22   BIC                            588.610\n",
       "Sample:                                         0   HQIC                           586.060\n",
       "                                             - 24                                         \n",
       "Covariance Type:                              opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "drift        144.9375    899.965      0.161      0.872   -1618.961    1908.836\n",
       "ar.S.L12      -0.0709      0.232     -0.305      0.760      -0.526       0.385\n",
       "sigma2       5.16e+09      0.000   2.42e+13      0.000    5.16e+09    5.16e+09\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.08   Jarque-Bera (JB):                 1.54\n",
       "Prob(Q):                              0.78   Prob(JB):                         0.46\n",
       "Heteroskedasticity (H):               2.18   Skew:                             0.38\n",
       "Prob(H) (two-sided):                  0.29   Kurtosis:                         4.01\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 8.01e+28. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SARIMAX(train['월평균승객'],order=(0,1,0),seasonal_order=(1,0,0,12),trend='t')\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.f 예측값과 테스트값을 플롯에 표기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24    650353.146371\n",
       "25    652486.010464\n",
       "26    653266.787904\n",
       "27    652083.788735\n",
       "28    658616.781436\n",
       "29    661307.668443\n",
       "30    664240.290102\n",
       "31    679326.491317\n",
       "32    686596.693314\n",
       "33    693171.352805\n",
       "34    684150.531150\n",
       "35    690377.147234\n",
       "36    695006.749288\n",
       "Name: predicted_mean, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start=len(train)\n",
    "end=len(train)+len(test)-1\n",
    "\n",
    "predictions = results.predict(start=start, end=end, dynamic=False, typ='levels')\n",
    "#구하려는값이 차분되는 값인지 실제값인지를 결정하는 파라미터\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>예측값</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>사용월</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-10-01</th>\n",
       "      <td>650353.146371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-01</th>\n",
       "      <td>652486.010464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-01</th>\n",
       "      <td>653266.787904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>652083.788735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-01</th>\n",
       "      <td>658616.781436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-01</th>\n",
       "      <td>661307.668443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-01</th>\n",
       "      <td>664240.290102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-01</th>\n",
       "      <td>679326.491317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-01</th>\n",
       "      <td>686596.693314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-01</th>\n",
       "      <td>693171.352805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-01</th>\n",
       "      <td>684150.531150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-01</th>\n",
       "      <td>690377.147234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-01</th>\n",
       "      <td>695006.749288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      예측값\n",
       "사용월                      \n",
       "2022-10-01  650353.146371\n",
       "2022-11-01  652486.010464\n",
       "2022-12-01  653266.787904\n",
       "2023-01-01  652083.788735\n",
       "2023-02-01  658616.781436\n",
       "2023-03-01  661307.668443\n",
       "2023-04-01  664240.290102\n",
       "2023-05-01  679326.491317\n",
       "2023-06-01  686596.693314\n",
       "2023-07-01  693171.352805\n",
       "2023-08-01  684150.531150\n",
       "2023-09-01  690377.147234\n",
       "2023-10-01  695006.749288"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#인덱스 달아주기(예측값이 데이터 프레임이 아니기 때문에)\n",
    "\n",
    "data=predictions\n",
    "dates = ['2022-10-01', '2022-11-01', '2022-12-01', '2023-01-01',\n",
    "         '2023-02-01','2023-03-01','2023-04-01','2023-05-01',\n",
    "         '2023-06-01','2023-07-01','2023-08-01','2023-09-01','2023-10-01']\n",
    "predictions_df = pd.DataFrame(data.values, index=pd.to_datetime(dates))\n",
    "predictions_df.reset_index(inplace=True)\n",
    "predictions_df.rename(columns={'index':'사용월',0:'예측값'}, inplace=True)\n",
    "predictions_df.set_index('사용월',inplace=True)\n",
    "predictions_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#예측치와 test 데이터 비교\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title('최근 3년 강남역 월별 평균 이용 인원')\n",
    "plt.plot(test['월평균승객'],color='red', label='강남_테스트데이터')\n",
    "plt.plot(predictions_df['예측값'],color='blue', label='강남_예측치')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.g 평균제곱근 오차를 통해 모델 평가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "671498.2115384615"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#test데이터의 평균\n",
    "test['월평균승객'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "평균 제곱근 오차: 28590.61488\n"
     ]
    }
   ],
   "source": [
    "#평균 제곱근 오차\n",
    "error = rmse(test['월평균승객'], predictions_df['예측값'])\n",
    "print(f'평균 제곱근 오차: {error:11.10}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "오차가   \n",
    "(28,590/671,498)*100 = 4.25% 로 구해짐"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 이후 1년 후 데이터를 예측한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SARIMAX(metro_line2_top1_pred['월평균승객'],order=(1,1,0),seasonal_order=(1,0,0,12), trend='t')\n",
    "results = model.fit()\n",
    "fcast = results.predict(len(metro_line2_top1_pred),len(metro_line2_top1_pred)+11,typ='levels').rename('미래_예측값')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>미래_예측값</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>사용월</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-11-01</th>\n",
       "      <td>641950.428993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-01</th>\n",
       "      <td>642893.142221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-01</th>\n",
       "      <td>644195.902919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-01</th>\n",
       "      <td>645987.043439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-01</th>\n",
       "      <td>646082.269383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-01</th>\n",
       "      <td>647090.008377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-01</th>\n",
       "      <td>648088.618777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-01</th>\n",
       "      <td>646389.833392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-01</th>\n",
       "      <td>646500.390163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-01</th>\n",
       "      <td>646813.343205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-01</th>\n",
       "      <td>650691.313210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-01</th>\n",
       "      <td>651173.664988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   미래_예측값\n",
       "사용월                      \n",
       "2023-11-01  641950.428993\n",
       "2023-12-01  642893.142221\n",
       "2024-01-01  644195.902919\n",
       "2024-02-01  645987.043439\n",
       "2024-03-01  646082.269383\n",
       "2024-04-01  647090.008377\n",
       "2024-05-01  648088.618777\n",
       "2024-06-01  646389.833392\n",
       "2024-07-01  646500.390163\n",
       "2024-08-01  646813.343205\n",
       "2024-09-01  650691.313210\n",
       "2024-10-01  651173.664988"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#미래_예측값 데이터프레임 만들기\n",
    "data=fcast\n",
    "dates = ['2023-11-01', '2023-12-01', '2024-01-01',\n",
    "         '2024-02-01','2024-03-01','2024-04-01','2024-05-01',\n",
    "         '2024-06-01','2024-07-01','2024-08-01','2024-09-01','2024-10-01']\n",
    "predictions_fcast = pd.DataFrame(data.values, index=pd.to_datetime(dates))\n",
    "predictions_fcast.reset_index(inplace=True)\n",
    "predictions_fcast.rename(columns={'index':'사용월',0:'미래_예측값'}, inplace=True)\n",
    "predictions_fcast.set_index('사용월',inplace=True)\n",
    "predictions_fcast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#미래_예측치\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title('최근 3년 강남역 월별 평균 이용 인원')\n",
    "plt.plot(test['월평균승객'],color='red', label='강남_테스트데이터')\n",
    "plt.plot(predictions_df['예측값'],color='blue', label='강남_예측치')\n",
    "plt.plot(predictions_fcast['미래_예측값'],color='green', label='미래_예측치')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### 홍대입구 예측모델\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('최근 3년 홍대입구역 월별 평균 이용 인원')\n",
    "plt.plot(metro_line2_top3['사용월'],metro_line2_top3['월평균승객'],color='blue', label='홍대입구(2)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>월평균승객</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>사용월</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-10-01</th>\n",
       "      <td>644999.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-01</th>\n",
       "      <td>599971.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-01</th>\n",
       "      <td>619772.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-01</th>\n",
       "      <td>637537.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-01</th>\n",
       "      <td>618317.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-01</th>\n",
       "      <td>634180.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-01</th>\n",
       "      <td>616691.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-01</th>\n",
       "      <td>646411.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-01</th>\n",
       "      <td>582864.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>569956.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-01</th>\n",
       "      <td>635846.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-01</th>\n",
       "      <td>579980.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-01</th>\n",
       "      <td>607589.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>549774.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>557096.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>583417.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>533760.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>578933.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-01</th>\n",
       "      <td>497042.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>444453.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-01</th>\n",
       "      <td>368421.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>423552.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-01</th>\n",
       "      <td>465497.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-01</th>\n",
       "      <td>484979.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01</th>\n",
       "      <td>449269.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>382700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01</th>\n",
       "      <td>370620.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-01</th>\n",
       "      <td>359779.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01</th>\n",
       "      <td>459850.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-01</th>\n",
       "      <td>465231.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>433695.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-01</th>\n",
       "      <td>447977.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-01</th>\n",
       "      <td>358030.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>299355.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01</th>\n",
       "      <td>278742.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>432065.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>449660.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                월평균승객\n",
       "사용월                  \n",
       "2023-10-01  644999.25\n",
       "2023-09-01  599971.00\n",
       "2023-08-01  619772.50\n",
       "2023-07-01  637537.25\n",
       "2023-06-01  618317.50\n",
       "2023-05-01  634180.25\n",
       "2023-04-01  616691.75\n",
       "2023-03-01  646411.50\n",
       "2023-02-01  582864.00\n",
       "2023-01-01  569956.25\n",
       "2022-12-01  635846.25\n",
       "2022-11-01  579980.00\n",
       "2022-10-01  607589.25\n",
       "2022-09-01  549774.00\n",
       "2022-08-01  557096.50\n",
       "2022-07-01  583417.75\n",
       "2022-06-01  533760.75\n",
       "2022-05-01  578933.75\n",
       "2022-04-01  497042.75\n",
       "2022-03-01  444453.25\n",
       "2022-02-01  368421.75\n",
       "2022-01-01  423552.75\n",
       "2021-12-01  465497.50\n",
       "2021-11-01  484979.50\n",
       "2021-10-01  449269.75\n",
       "2021-09-01  382700.00\n",
       "2021-08-01  370620.50\n",
       "2021-07-01  359779.00\n",
       "2021-06-01  459850.00\n",
       "2021-05-01  465231.75\n",
       "2021-04-01  433695.00\n",
       "2021-03-01  447977.50\n",
       "2021-02-01  358030.50\n",
       "2021-01-01  299355.75\n",
       "2020-12-01  278742.75\n",
       "2020-11-01  432065.25\n",
       "2020-10-01  449660.50"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metro_line2_top3_pred=pd.DataFrame()\n",
    "metro_line2_top3_pred['사용월']=metro_line2_top3['사용월']\n",
    "metro_line2_top3_pred['월평균승객']=metro_line2_top3['월평균승객']\n",
    "metro_line2_top3_pred.set_index('사용월',inplace=True)\n",
    "metro_line2_top3_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = seasonal_decompose(metro_line2_top3_pred['월평균승객'], model='add')\n",
    "result.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>37</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(0, 1, 0)</td> <th>  Log Likelihood     </th> <td>-442.314</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 23 Nov 2023</td> <th>  AIC                </th>  <td>886.628</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>00:55:32</td>     <th>  BIC                </th>  <td>888.212</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th>  <td>887.181</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> - 37</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td> 2.675e+09</td> <td> 5.61e+08</td> <td>    4.773</td> <td> 0.000</td> <td> 1.58e+09</td> <td> 3.77e+09</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.13</td> <th>  Jarque-Bera (JB):  </th> <td>3.76</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.72</td> <th>  Prob(JB):          </th> <td>0.15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>3.05</td> <th>  Skew:              </th> <td>0.71</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.07</td> <th>  Kurtosis:          </th> <td>3.70</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &        y         & \\textbf{  No. Observations:  } &     37      \\\\\n",
       "\\textbf{Model:}                  & SARIMAX(0, 1, 0) & \\textbf{  Log Likelihood     } &  -442.314   \\\\\n",
       "\\textbf{Date:}                   & Thu, 23 Nov 2023 & \\textbf{  AIC                } &  886.628    \\\\\n",
       "\\textbf{Time:}                   &     00:55:32     & \\textbf{  BIC                } &  888.212    \\\\\n",
       "\\textbf{Sample:}                 &        0         & \\textbf{  HQIC               } &  887.181    \\\\\n",
       "\\textbf{}                        &       - 37       & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &       opg        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{sigma2} &    2.675e+09  &     5.61e+08     &     4.773  &         0.000        &     1.58e+09    &     3.77e+09     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 0.13 & \\textbf{  Jarque-Bera (JB):  } & 3.76  \\\\\n",
       "\\textbf{Prob(Q):}                & 0.72 & \\textbf{  Prob(JB):          } & 0.15  \\\\\n",
       "\\textbf{Heteroskedasticity (H):} & 3.05 & \\textbf{  Skew:              } & 0.71  \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    & 0.07 & \\textbf{  Kurtosis:          } & 3.70  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SARIMAX Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                   37\n",
       "Model:               SARIMAX(0, 1, 0)   Log Likelihood                -442.314\n",
       "Date:                Thu, 23 Nov 2023   AIC                            886.628\n",
       "Time:                        00:55:32   BIC                            888.212\n",
       "Sample:                             0   HQIC                           887.181\n",
       "                                 - 37                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2      2.675e+09   5.61e+08      4.773      0.000    1.58e+09    3.77e+09\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.13   Jarque-Bera (JB):                 3.76\n",
       "Prob(Q):                              0.72   Prob(JB):                         0.15\n",
       "Heteroskedasticity (H):               3.05   Skew:                             0.71\n",
       "Prob(H) (two-sided):                  0.07   Kurtosis:                         3.70\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_arima(metro_line2_top3_pred['월평균승객'],seasonal=True,m=12).summary()\n",
    "#계절성주기를 12(1년 12개월) 입력하여 SARIMAX를 결정받는다.\n",
    "#seasonal=True 는 계절성을 갖음을 나타냄\n",
    "#SARIMAX(0,1,0)을 추천"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = metro_line2_top3_pred.iloc[len(metro_line2_top3_pred)-24:]       #3년전부터 1년전까지\n",
    "test = metro_line2_top3_pred.iloc[:len(metro_line2_top3_pred)-24]      #1년전부터 올해"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                <td>월평균승객</td>             <th>  No. Observations:  </th>    <td>24</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 0)x(1, 0, 0, 12)</td> <th>  Log Likelihood     </th> <td>-284.557</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Thu, 23 Nov 2023</td>        <th>  AIC                </th>  <td>577.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                       <td>00:55:32</td>            <th>  BIC                </th>  <td>581.655</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                         <td>0</td>               <th>  HQIC               </th>  <td>578.256</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td> - 24</td>             <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>               <td>opg</td>              <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>drift</th>    <td>   63.6247</td> <td>  869.515</td> <td>    0.073</td> <td> 0.942</td> <td>-1640.594</td> <td> 1767.843</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>    <td>    0.0272</td> <td>    0.342</td> <td>    0.080</td> <td> 0.937</td> <td>   -0.642</td> <td>    0.697</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L12</th> <td>   -0.1026</td> <td>    0.322</td> <td>   -0.319</td> <td> 0.750</td> <td>   -0.733</td> <td>    0.528</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td> 3.549e+09</td> <td>    0.000</td> <td> 7.63e+12</td> <td> 0.000</td> <td> 3.55e+09</td> <td> 3.55e+09</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.01</td> <th>  Jarque-Bera (JB):  </th> <td>2.82</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.91</td> <th>  Prob(JB):          </th> <td>0.24</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.63</td> <th>  Skew:              </th> <td>0.76</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.50</td> <th>  Kurtosis:          </th> <td>3.79</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 5.76e+28. Standard errors may be unstable."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &             월평균승객              & \\textbf{  No. Observations:  } &     24      \\\\\n",
       "\\textbf{Model:}                  & SARIMAX(1, 1, 0)x(1, 0, 0, 12) & \\textbf{  Log Likelihood     } &  -284.557   \\\\\n",
       "\\textbf{Date:}                   &        Thu, 23 Nov 2023        & \\textbf{  AIC                } &  577.113    \\\\\n",
       "\\textbf{Time:}                   &            00:55:32            & \\textbf{  BIC                } &  581.655    \\\\\n",
       "\\textbf{Sample:}                 &               0                & \\textbf{  HQIC               } &  578.256    \\\\\n",
       "\\textbf{}                        &              - 24              & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &              opg               & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                  & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{drift}    &      63.6247  &      869.515     &     0.073  &         0.942        &    -1640.594    &     1767.843     \\\\\n",
       "\\textbf{ar.L1}    &       0.0272  &        0.342     &     0.080  &         0.937        &       -0.642    &        0.697     \\\\\n",
       "\\textbf{ar.S.L12} &      -0.1026  &        0.322     &    -0.319  &         0.750        &       -0.733    &        0.528     \\\\\n",
       "\\textbf{sigma2}   &    3.549e+09  &        0.000     &  7.63e+12  &         0.000        &     3.55e+09    &     3.55e+09     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 0.01 & \\textbf{  Jarque-Bera (JB):  } & 2.82  \\\\\n",
       "\\textbf{Prob(Q):}                & 0.91 & \\textbf{  Prob(JB):          } & 0.24  \\\\\n",
       "\\textbf{Heteroskedasticity (H):} & 1.63 & \\textbf{  Skew:              } & 0.76  \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    & 0.50 & \\textbf{  Kurtosis:          } & 3.79  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SARIMAX Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using the outer product of gradients (complex-step). \\newline\n",
       " [2] Covariance matrix is singular or near-singular, with condition number 5.76e+28. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                     SARIMAX Results                                      \n",
       "==========================================================================================\n",
       "Dep. Variable:                              월평균승객   No. Observations:                   24\n",
       "Model:             SARIMAX(1, 1, 0)x(1, 0, 0, 12)   Log Likelihood                -284.557\n",
       "Date:                            Thu, 23 Nov 2023   AIC                            577.113\n",
       "Time:                                    00:55:32   BIC                            581.655\n",
       "Sample:                                         0   HQIC                           578.256\n",
       "                                             - 24                                         \n",
       "Covariance Type:                              opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "drift         63.6247    869.515      0.073      0.942   -1640.594    1767.843\n",
       "ar.L1          0.0272      0.342      0.080      0.937      -0.642       0.697\n",
       "ar.S.L12      -0.1026      0.322     -0.319      0.750      -0.733       0.528\n",
       "sigma2      3.549e+09      0.000   7.63e+12      0.000    3.55e+09    3.55e+09\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):                 2.82\n",
       "Prob(Q):                              0.91   Prob(JB):                         0.24\n",
       "Heteroskedasticity (H):               1.63   Skew:                             0.76\n",
       "Prob(H) (two-sided):                  0.50   Kurtosis:                         3.79\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 5.76e+28. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SARIMAX(train['월평균승객'],order=(1,1,0),seasonal_order=(1,0,0,12), trend='t')\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24    458399.138215\n",
       "25    461281.448397\n",
       "26    464093.157870\n",
       "27    455585.270691\n",
       "28    456862.278334\n",
       "29    461994.234431\n",
       "30    462488.416616\n",
       "31    473746.771419\n",
       "32    481860.547802\n",
       "33    486132.828822\n",
       "34    472616.679023\n",
       "35    473097.828817\n",
       "36    474553.478711\n",
       "Name: predicted_mean, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start=len(train)\n",
    "end=len(train)+len(test)-1\n",
    "\n",
    "predictions = results.predict(start=start, end=end, dynamic=False, typ='levels')\n",
    "#구하려는값이 차분되는 값인지 실제값인지를 결정하는 파라미터\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>예측값</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>사용월</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-10-01</th>\n",
       "      <td>458399.138215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-01</th>\n",
       "      <td>461281.448397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-01</th>\n",
       "      <td>464093.157870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>455585.270691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-01</th>\n",
       "      <td>456862.278334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-01</th>\n",
       "      <td>461994.234431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-01</th>\n",
       "      <td>462488.416616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-01</th>\n",
       "      <td>473746.771419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-01</th>\n",
       "      <td>481860.547802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-01</th>\n",
       "      <td>486132.828822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-01</th>\n",
       "      <td>472616.679023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-01</th>\n",
       "      <td>473097.828817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-01</th>\n",
       "      <td>474553.478711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      예측값\n",
       "사용월                      \n",
       "2022-10-01  458399.138215\n",
       "2022-11-01  461281.448397\n",
       "2022-12-01  464093.157870\n",
       "2023-01-01  455585.270691\n",
       "2023-02-01  456862.278334\n",
       "2023-03-01  461994.234431\n",
       "2023-04-01  462488.416616\n",
       "2023-05-01  473746.771419\n",
       "2023-06-01  481860.547802\n",
       "2023-07-01  486132.828822\n",
       "2023-08-01  472616.679023\n",
       "2023-09-01  473097.828817\n",
       "2023-10-01  474553.478711"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#인덱스 달아주기(예측값이 데이터 프레임이 아니기 때문에)\n",
    "data=predictions\n",
    "dates = ['2022-10-01', '2022-11-01', '2022-12-01', '2023-01-01',\n",
    "         '2023-02-01','2023-03-01','2023-04-01','2023-05-01',\n",
    "         '2023-06-01','2023-07-01','2023-08-01','2023-09-01','2023-10-01']\n",
    "predictions_df = pd.DataFrame(data.values, index=pd.to_datetime(dates))\n",
    "predictions_df.reset_index(inplace=True)\n",
    "predictions_df.rename(columns={'index':'사용월',0:'예측값'}, inplace=True)\n",
    "predictions_df.set_index('사용월',inplace=True)\n",
    "predictions_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#예측치와 test 데이터 비교\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title('최근 3년 홍대입구역 월별 평균 이용 인원')\n",
    "plt.plot(test['월평균승객'],color='red', label='홍대입구_테스트데이터')\n",
    "plt.plot(predictions_df['예측값'],color='blue', label='홍대입구_예측치')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "614932.0576923077"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#test 데이터 평균\n",
    "test['월평균승객'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "평균 제곱근 오차:  150307.079\n"
     ]
    }
   ],
   "source": [
    "#평균 제곱근 오차\n",
    "error = rmse(test['월평균승객'], predictions_df['예측값'])\n",
    "print(f'평균 제곱근 오차: {error:11.10}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### 새로운 노선 그리기\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1~9호선까지 승객 승하차 상위 3개역씩 뽑아서 지도에 표시\n",
    "\n",
    "지하철 역의 좌표 csv\n",
    "출처: https://developers.kakao.com/docs/latest/ko/local/dev-guide#address-coord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: folium in c:\\users\\82105\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (0.15.0)Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Requirement already satisfied: branca>=0.6.0 in c:\\users\\82105\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from folium) (0.7.0)\n",
      "Requirement already satisfied: jinja2>=2.9 in c:\\users\\82105\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from folium) (3.1.2)\n",
      "Requirement already satisfied: numpy in c:\\users\\82105\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from folium) (1.25.1)\n",
      "Requirement already satisfied: requests in c:\\users\\82105\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from folium) (2.31.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\82105\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from jinja2>=2.9->folium) (2.1.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\82105\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->folium) (3.2.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\82105\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->folium) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\82105\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->folium) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\82105\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->folium) (2023.7.22)\n"
     ]
    }
   ],
   "source": [
    "%pip install folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>지하철역</th>\n",
       "      <th>x좌표</th>\n",
       "      <th>y좌표</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.19민주묘지역</td>\n",
       "      <td>37.649457</td>\n",
       "      <td>127.013506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>가능역</td>\n",
       "      <td>37.747906</td>\n",
       "      <td>127.044358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>가락시장역</td>\n",
       "      <td>37.492915</td>\n",
       "      <td>127.118215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>가산디지털단지역</td>\n",
       "      <td>37.482414</td>\n",
       "      <td>126.882240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>가양역</td>\n",
       "      <td>37.561758</td>\n",
       "      <td>126.853997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>회룡역</td>\n",
       "      <td>37.713908</td>\n",
       "      <td>127.046619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>회현역</td>\n",
       "      <td>37.557688</td>\n",
       "      <td>126.976720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>효창공원앞역</td>\n",
       "      <td>37.539622</td>\n",
       "      <td>126.960984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>흑석역</td>\n",
       "      <td>37.508502</td>\n",
       "      <td>126.964009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>서울역</td>\n",
       "      <td>37.554722</td>\n",
       "      <td>126.970833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>526 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          지하철역        x좌표         y좌표\n",
       "0    4.19민주묘지역  37.649457  127.013506\n",
       "1          가능역  37.747906  127.044358\n",
       "2        가락시장역  37.492915  127.118215\n",
       "3     가산디지털단지역  37.482414  126.882240\n",
       "4          가양역  37.561758  126.853997\n",
       "..         ...        ...         ...\n",
       "521        회룡역  37.713908  127.046619\n",
       "522        회현역  37.557688  126.976720\n",
       "523     효창공원앞역  37.539622  126.960984\n",
       "524        흑석역  37.508502  126.964009\n",
       "525        서울역  37.554722  126.970833\n",
       "\n",
       "[526 rows x 3 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#지하철 좌표 CSV 읽어오기\n",
    "subway_location = pd.read_csv(\"C:/Users/82105/Desktop/2학년_2학기/오픈소스_개발_프로젝트/지하철_승하차_인원_분석/지하철 역 위치 좌표.csv\")\n",
    "subway_location.drop(columns={'주소'}, inplace=True)\n",
    "subway_location.drop_duplicates(subset=['지하철역'], inplace=True)\n",
    "subway_location.reset_index(drop=True)\n",
    "\n",
    "#좌표없는 곳 (서울역) 서울역 좌표 추가해주기\n",
    "seoul_station={'지하철역': ['서울역'],'x좌표': [37.554722],'y좌표':[126.970833]}\n",
    "seoul_location=pd.DataFrame(seoul_station)\n",
    "subway_location=pd.concat([subway_location,seoul_location], ignore_index=True)\n",
    "subway_location\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>평균 승차 인원 수_x</th>\n",
       "      <th>평균 하차 인원 수_x</th>\n",
       "      <th>평균 승하차 합계_x</th>\n",
       "      <th>평균 승차 인원 수_y</th>\n",
       "      <th>평균 하차 인원 수_y</th>\n",
       "      <th>평균 승하차 합계_y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>지하철역</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>서울역</th>\n",
       "      <td>52562</td>\n",
       "      <td>50378</td>\n",
       "      <td>102940</td>\n",
       "      <td>52562</td>\n",
       "      <td>50378</td>\n",
       "      <td>102940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>종각</th>\n",
       "      <td>38456</td>\n",
       "      <td>37283</td>\n",
       "      <td>75739</td>\n",
       "      <td>38456</td>\n",
       "      <td>37283</td>\n",
       "      <td>75739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>종로3가</th>\n",
       "      <td>29201</td>\n",
       "      <td>26706</td>\n",
       "      <td>55907</td>\n",
       "      <td>29201</td>\n",
       "      <td>26706</td>\n",
       "      <td>55907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강남</th>\n",
       "      <td>89011</td>\n",
       "      <td>87066</td>\n",
       "      <td>176077</td>\n",
       "      <td>89011</td>\n",
       "      <td>87066</td>\n",
       "      <td>176077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>잠실(송파구청)</th>\n",
       "      <td>82314</td>\n",
       "      <td>81086</td>\n",
       "      <td>163400</td>\n",
       "      <td>82314</td>\n",
       "      <td>81086</td>\n",
       "      <td>163400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>홍대입구</th>\n",
       "      <td>69352</td>\n",
       "      <td>73590</td>\n",
       "      <td>142942</td>\n",
       "      <td>69352</td>\n",
       "      <td>73590</td>\n",
       "      <td>142942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>고속터미널</th>\n",
       "      <td>52902</td>\n",
       "      <td>51301</td>\n",
       "      <td>104203</td>\n",
       "      <td>52902</td>\n",
       "      <td>51301</td>\n",
       "      <td>104203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>연신내</th>\n",
       "      <td>40971</td>\n",
       "      <td>38772</td>\n",
       "      <td>79743</td>\n",
       "      <td>40971</td>\n",
       "      <td>38772</td>\n",
       "      <td>79743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>양재(서초구청)</th>\n",
       "      <td>40053</td>\n",
       "      <td>43539</td>\n",
       "      <td>83592</td>\n",
       "      <td>40053</td>\n",
       "      <td>43539</td>\n",
       "      <td>83592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>수유(강북구청)</th>\n",
       "      <td>38377</td>\n",
       "      <td>38281</td>\n",
       "      <td>76658</td>\n",
       "      <td>38377</td>\n",
       "      <td>38281</td>\n",
       "      <td>76658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>혜화</th>\n",
       "      <td>38238</td>\n",
       "      <td>38576</td>\n",
       "      <td>76814</td>\n",
       "      <td>38238</td>\n",
       "      <td>38576</td>\n",
       "      <td>76814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>쌍문</th>\n",
       "      <td>32631</td>\n",
       "      <td>30020</td>\n",
       "      <td>62651</td>\n",
       "      <td>32631</td>\n",
       "      <td>30020</td>\n",
       "      <td>62651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광화문(세종문화회관)</th>\n",
       "      <td>33059</td>\n",
       "      <td>34408</td>\n",
       "      <td>67467</td>\n",
       "      <td>33059</td>\n",
       "      <td>34408</td>\n",
       "      <td>67467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>까치산</th>\n",
       "      <td>32189</td>\n",
       "      <td>30866</td>\n",
       "      <td>63055</td>\n",
       "      <td>32189</td>\n",
       "      <td>30866</td>\n",
       "      <td>63055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>화곡</th>\n",
       "      <td>31616</td>\n",
       "      <td>29464</td>\n",
       "      <td>61080</td>\n",
       "      <td>31616</td>\n",
       "      <td>29464</td>\n",
       "      <td>61080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>공덕</th>\n",
       "      <td>19779</td>\n",
       "      <td>19139</td>\n",
       "      <td>38918</td>\n",
       "      <td>19779</td>\n",
       "      <td>19139</td>\n",
       "      <td>38918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>응암</th>\n",
       "      <td>19619</td>\n",
       "      <td>19423</td>\n",
       "      <td>39042</td>\n",
       "      <td>19619</td>\n",
       "      <td>19423</td>\n",
       "      <td>39042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>망원</th>\n",
       "      <td>16386</td>\n",
       "      <td>17656</td>\n",
       "      <td>34042</td>\n",
       "      <td>16386</td>\n",
       "      <td>17656</td>\n",
       "      <td>34042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>가산디지털단지</th>\n",
       "      <td>45482</td>\n",
       "      <td>45138</td>\n",
       "      <td>90620</td>\n",
       "      <td>45482</td>\n",
       "      <td>45138</td>\n",
       "      <td>90620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>학동</th>\n",
       "      <td>24626</td>\n",
       "      <td>26082</td>\n",
       "      <td>50708</td>\n",
       "      <td>24626</td>\n",
       "      <td>26082</td>\n",
       "      <td>50708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광명사거리</th>\n",
       "      <td>24467</td>\n",
       "      <td>24158</td>\n",
       "      <td>48625</td>\n",
       "      <td>24467</td>\n",
       "      <td>24158</td>\n",
       "      <td>48625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>문정</th>\n",
       "      <td>22005</td>\n",
       "      <td>23019</td>\n",
       "      <td>45024</td>\n",
       "      <td>22005</td>\n",
       "      <td>23019</td>\n",
       "      <td>45024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>암사</th>\n",
       "      <td>20747</td>\n",
       "      <td>18461</td>\n",
       "      <td>39208</td>\n",
       "      <td>20747</td>\n",
       "      <td>18461</td>\n",
       "      <td>39208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>장지</th>\n",
       "      <td>19131</td>\n",
       "      <td>18213</td>\n",
       "      <td>37344</td>\n",
       "      <td>19131</td>\n",
       "      <td>18213</td>\n",
       "      <td>37344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>신논현</th>\n",
       "      <td>34682</td>\n",
       "      <td>34261</td>\n",
       "      <td>68943</td>\n",
       "      <td>34682</td>\n",
       "      <td>34261</td>\n",
       "      <td>68943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>노량진</th>\n",
       "      <td>29215</td>\n",
       "      <td>28706</td>\n",
       "      <td>57921</td>\n",
       "      <td>29215</td>\n",
       "      <td>28706</td>\n",
       "      <td>57921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>여의도</th>\n",
       "      <td>28797</td>\n",
       "      <td>27491</td>\n",
       "      <td>56288</td>\n",
       "      <td>28797</td>\n",
       "      <td>27491</td>\n",
       "      <td>56288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             평균 승차 인원 수_x  평균 하차 인원 수_x  평균 승하차 합계_x  평균 승차 인원 수_y  \\\n",
       "지하철역                                                                 \n",
       "서울역                 52562         50378       102940         52562   \n",
       "종각                  38456         37283        75739         38456   \n",
       "종로3가                29201         26706        55907         29201   \n",
       "강남                  89011         87066       176077         89011   \n",
       "잠실(송파구청)            82314         81086       163400         82314   \n",
       "홍대입구                69352         73590       142942         69352   \n",
       "고속터미널               52902         51301       104203         52902   \n",
       "연신내                 40971         38772        79743         40971   \n",
       "양재(서초구청)            40053         43539        83592         40053   \n",
       "수유(강북구청)            38377         38281        76658         38377   \n",
       "혜화                  38238         38576        76814         38238   \n",
       "쌍문                  32631         30020        62651         32631   \n",
       "광화문(세종문화회관)         33059         34408        67467         33059   \n",
       "까치산                 32189         30866        63055         32189   \n",
       "화곡                  31616         29464        61080         31616   \n",
       "공덕                  19779         19139        38918         19779   \n",
       "응암                  19619         19423        39042         19619   \n",
       "망원                  16386         17656        34042         16386   \n",
       "가산디지털단지             45482         45138        90620         45482   \n",
       "학동                  24626         26082        50708         24626   \n",
       "광명사거리               24467         24158        48625         24467   \n",
       "문정                  22005         23019        45024         22005   \n",
       "암사                  20747         18461        39208         20747   \n",
       "장지                  19131         18213        37344         19131   \n",
       "신논현                 34682         34261        68943         34682   \n",
       "노량진                 29215         28706        57921         29215   \n",
       "여의도                 28797         27491        56288         28797   \n",
       "\n",
       "             평균 하차 인원 수_y  평균 승하차 합계_y  \n",
       "지하철역                                    \n",
       "서울역                 50378       102940  \n",
       "종각                  37283        75739  \n",
       "종로3가                26706        55907  \n",
       "강남                  87066       176077  \n",
       "잠실(송파구청)            81086       163400  \n",
       "홍대입구                73590       142942  \n",
       "고속터미널               51301       104203  \n",
       "연신내                 38772        79743  \n",
       "양재(서초구청)            43539        83592  \n",
       "수유(강북구청)            38281        76658  \n",
       "혜화                  38576        76814  \n",
       "쌍문                  30020        62651  \n",
       "광화문(세종문화회관)         34408        67467  \n",
       "까치산                 30866        63055  \n",
       "화곡                  29464        61080  \n",
       "공덕                  19139        38918  \n",
       "응암                  19423        39042  \n",
       "망원                  17656        34042  \n",
       "가산디지털단지             45138        90620  \n",
       "학동                  26082        50708  \n",
       "광명사거리               24158        48625  \n",
       "문정                  23019        45024  \n",
       "암사                  18461        39208  \n",
       "장지                  18213        37344  \n",
       "신논현                 34261        68943  \n",
       "노량진                 28706        57921  \n",
       "여의도                 27491        56288  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top3_per_line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[102940,\n",
       " 75739,\n",
       " 55907,\n",
       " 176077,\n",
       " 163400,\n",
       " 142942,\n",
       " 104203,\n",
       " 79743,\n",
       " 83592,\n",
       " 76658,\n",
       " 76814,\n",
       " 62651,\n",
       " 67467,\n",
       " 63055,\n",
       " 61080,\n",
       " 38918,\n",
       " 39042,\n",
       " 34042,\n",
       " 90620,\n",
       " 50708,\n",
       " 48625,\n",
       " 45024,\n",
       " 39208,\n",
       " 37344,\n",
       " 68943,\n",
       " 57921,\n",
       " 56288]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#최근 3년간 지하철 노선별 승하차인원이 제일 많은 역을 리스트로 추출\n",
    "top3_per_line_list = top3_per_line.index.get_level_values('지하철역').tolist()\n",
    "top3_per_line_list_mean = top3_per_line['평균 승하차 합계'].tolist()\n",
    "\n",
    "#좌표 데이터셋의 지하철역 이름을 맞추기 위해 ()를 없애준다.\n",
    "top3_per_line_list=[station.replace('잠실(송파구청)', '잠실') for station in top3_per_line_list]\n",
    "top3_per_line_list=[station.replace('양재(서초구청)', '양재') for station in top3_per_line_list]\n",
    "top3_per_line_list=[station.replace('수유(강북구청)', '수유') for station in top3_per_line_list]\n",
    "top3_per_line_list=[station.replace('광화문(세종문화회관)', '광화문') for station in top3_per_line_list]\n",
    "\n",
    "\n",
    "#좌표를 사용하기 위해 마지막에 '역' 붙여주기\n",
    "for i in range(1,len(top3_per_line_list)):\n",
    "    top3_per_line_list[i] += '역'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>지하철역</th>\n",
       "      <th>x좌표</th>\n",
       "      <th>y좌표</th>\n",
       "      <th>평균 승하차 합계</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울역</td>\n",
       "      <td>37.554722</td>\n",
       "      <td>126.970833</td>\n",
       "      <td>102940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>종각역</td>\n",
       "      <td>37.570233</td>\n",
       "      <td>126.981416</td>\n",
       "      <td>75739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>종로3가역</td>\n",
       "      <td>37.570517</td>\n",
       "      <td>126.991117</td>\n",
       "      <td>55907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>강남역</td>\n",
       "      <td>37.497057</td>\n",
       "      <td>127.028181</td>\n",
       "      <td>176077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>잠실역</td>\n",
       "      <td>37.514063</td>\n",
       "      <td>127.102052</td>\n",
       "      <td>163400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>홍대입구역</td>\n",
       "      <td>37.556890</td>\n",
       "      <td>126.923674</td>\n",
       "      <td>142942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>고속터미널역</td>\n",
       "      <td>37.504850</td>\n",
       "      <td>127.004875</td>\n",
       "      <td>104203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>연신내역</td>\n",
       "      <td>37.618933</td>\n",
       "      <td>126.920853</td>\n",
       "      <td>79743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>양재역</td>\n",
       "      <td>37.484779</td>\n",
       "      <td>127.033384</td>\n",
       "      <td>83592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>수유역</td>\n",
       "      <td>37.639822</td>\n",
       "      <td>127.027223</td>\n",
       "      <td>76658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>혜화역</td>\n",
       "      <td>37.582133</td>\n",
       "      <td>127.001815</td>\n",
       "      <td>76814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>쌍문역</td>\n",
       "      <td>37.649058</td>\n",
       "      <td>127.035086</td>\n",
       "      <td>62651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>광화문역</td>\n",
       "      <td>37.572432</td>\n",
       "      <td>126.976902</td>\n",
       "      <td>67467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>까치산역</td>\n",
       "      <td>37.532540</td>\n",
       "      <td>126.846261</td>\n",
       "      <td>63055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>화곡역</td>\n",
       "      <td>37.541234</td>\n",
       "      <td>126.840796</td>\n",
       "      <td>61080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>공덕역</td>\n",
       "      <td>37.544487</td>\n",
       "      <td>126.951195</td>\n",
       "      <td>38918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>응암역</td>\n",
       "      <td>37.598807</td>\n",
       "      <td>126.914482</td>\n",
       "      <td>39042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>망원역</td>\n",
       "      <td>37.556057</td>\n",
       "      <td>126.910034</td>\n",
       "      <td>34042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>가산디지털단지역</td>\n",
       "      <td>37.482414</td>\n",
       "      <td>126.882240</td>\n",
       "      <td>90620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>학동역</td>\n",
       "      <td>37.514301</td>\n",
       "      <td>127.031775</td>\n",
       "      <td>50708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>광명사거리역</td>\n",
       "      <td>37.479803</td>\n",
       "      <td>126.853075</td>\n",
       "      <td>48625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>문정역</td>\n",
       "      <td>37.488149</td>\n",
       "      <td>127.121157</td>\n",
       "      <td>45024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>암사역</td>\n",
       "      <td>37.551167</td>\n",
       "      <td>127.128209</td>\n",
       "      <td>39208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>장지역</td>\n",
       "      <td>37.477628</td>\n",
       "      <td>127.126553</td>\n",
       "      <td>37344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>신논현역</td>\n",
       "      <td>37.506457</td>\n",
       "      <td>127.031325</td>\n",
       "      <td>68943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>노량진역</td>\n",
       "      <td>37.513585</td>\n",
       "      <td>126.940904</td>\n",
       "      <td>57921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>여의도역</td>\n",
       "      <td>37.522813</td>\n",
       "      <td>126.923035</td>\n",
       "      <td>56288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        지하철역        x좌표         y좌표  평균 승하차 합계\n",
       "0        서울역  37.554722  126.970833     102940\n",
       "1        종각역  37.570233  126.981416      75739\n",
       "2      종로3가역  37.570517  126.991117      55907\n",
       "3        강남역  37.497057  127.028181     176077\n",
       "4        잠실역  37.514063  127.102052     163400\n",
       "5      홍대입구역  37.556890  126.923674     142942\n",
       "6     고속터미널역  37.504850  127.004875     104203\n",
       "7       연신내역  37.618933  126.920853      79743\n",
       "8        양재역  37.484779  127.033384      83592\n",
       "9        수유역  37.639822  127.027223      76658\n",
       "10       혜화역  37.582133  127.001815      76814\n",
       "11       쌍문역  37.649058  127.035086      62651\n",
       "12      광화문역  37.572432  126.976902      67467\n",
       "13      까치산역  37.532540  126.846261      63055\n",
       "14       화곡역  37.541234  126.840796      61080\n",
       "15       공덕역  37.544487  126.951195      38918\n",
       "16       응암역  37.598807  126.914482      39042\n",
       "17       망원역  37.556057  126.910034      34042\n",
       "18  가산디지털단지역  37.482414  126.882240      90620\n",
       "19       학동역  37.514301  127.031775      50708\n",
       "20    광명사거리역  37.479803  126.853075      48625\n",
       "21       문정역  37.488149  127.121157      45024\n",
       "22       암사역  37.551167  127.128209      39208\n",
       "23       장지역  37.477628  127.126553      37344\n",
       "24      신논현역  37.506457  127.031325      68943\n",
       "25      노량진역  37.513585  126.940904      57921\n",
       "26      여의도역  37.522813  126.923035      56288"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#최근 3년간 지하철 노선별 승하차인원이 제일 많은 역의 좌표정보를 담은 데이터 프레임.\n",
    "top3_per_line_location=pd.DataFrame()\n",
    "top3_per_line_location['지하철역']=top3_per_line_list\n",
    "top3_per_line_location = pd.merge(top3_per_line_location, subway_location, on='지하철역', how='left')\n",
    "top3_per_line_location['평균 승하차 합계']=top3_per_line_list_mean\n",
    "top3_per_line_location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
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       "                tile_layer_c669c6876955745fe205552d416ecac1.addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "    \n",
       "            var circle_marker_4fee3ce83975ba424a8946fd42523167 = L.circleMarker(\n",
       "                [37.554722, 126.970833],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 6.862733333333333, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
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       "    \n",
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       "\n",
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       "            \n",
       "                var html_ef9096fe81219a30924b46c3687659fd = $(`&lt;div id=&quot;html_ef9096fe81219a30924b46c3687659fd&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;서울역&#x27;, 102940]&lt;/div&gt;`)[0];\n",
       "                popup_b4769a4e1370d7256dae5ba5a0f24e4b.setContent(html_ef9096fe81219a30924b46c3687659fd);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_4fee3ce83975ba424a8946fd42523167.bindPopup(popup_b4769a4e1370d7256dae5ba5a0f24e4b)\n",
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       "            \n",
       "                var html_fad7382aee1af1fd7dd770697a94ef35 = $(`&lt;div id=&quot;html_fad7382aee1af1fd7dd770697a94ef35&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;종각역&#x27;, 75739]&lt;/div&gt;`)[0];\n",
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       "        \n",
       "\n",
       "        circle_marker_9db4267c1aab2a41d7c6f008ad882d4a.bindPopup(popup_b03617f7a3d3331cb77e1fdb4b39ff7e)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_5ae74aa5dab0d25b158484038ee7ab78 = L.circleMarker(\n",
       "                [37.570516997425, 126.991117384357],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 3.7272, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_c37fa882800e482277be6c1d8cdd5559 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_d45792141b711547b4164a4ae00fefa2 = $(`&lt;div id=&quot;html_d45792141b711547b4164a4ae00fefa2&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;종로3가역&#x27;, 55907]&lt;/div&gt;`)[0];\n",
       "                popup_c37fa882800e482277be6c1d8cdd5559.setContent(html_d45792141b711547b4164a4ae00fefa2);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_5ae74aa5dab0d25b158484038ee7ab78.bindPopup(popup_c37fa882800e482277be6c1d8cdd5559)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_fe0a469ef2c33f8824d45527a6d732ab = L.circleMarker(\n",
       "                [37.4970572543978, 127.028180714381],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 11.738533333333333, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_d198f2c98e531dafc96f379e6959ed9d = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_4dda68573970e4d51c7b52dbedacb5b9 = $(`&lt;div id=&quot;html_4dda68573970e4d51c7b52dbedacb5b9&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;강남역&#x27;, 176077]&lt;/div&gt;`)[0];\n",
       "                popup_d198f2c98e531dafc96f379e6959ed9d.setContent(html_4dda68573970e4d51c7b52dbedacb5b9);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_fe0a469ef2c33f8824d45527a6d732ab.bindPopup(popup_d198f2c98e531dafc96f379e6959ed9d)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_f7a9f0aefe7c2df98922b4a750559214 = L.circleMarker(\n",
       "                [37.514062738295, 127.102052316075],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 10.8934, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_d976ae8a99dcea6e21e770bd8c152164 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_f99e26bdb52948f393386f3dc690e574 = $(`&lt;div id=&quot;html_f99e26bdb52948f393386f3dc690e574&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;잠실역&#x27;, 163400]&lt;/div&gt;`)[0];\n",
       "                popup_d976ae8a99dcea6e21e770bd8c152164.setContent(html_f99e26bdb52948f393386f3dc690e574);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_f7a9f0aefe7c2df98922b4a750559214.bindPopup(popup_d976ae8a99dcea6e21e770bd8c152164)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_6bc2d8be98dd2f8110028ee611f82133 = L.circleMarker(\n",
       "                [37.5568904093718, 126.923674307594],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 9.529533333333333, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_e38ff7c89d612913db85956c0320686c = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_40307916515bbc0084294851a3578678 = $(`&lt;div id=&quot;html_40307916515bbc0084294851a3578678&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;홍대입구역&#x27;, 142942]&lt;/div&gt;`)[0];\n",
       "                popup_e38ff7c89d612913db85956c0320686c.setContent(html_40307916515bbc0084294851a3578678);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_6bc2d8be98dd2f8110028ee611f82133.bindPopup(popup_e38ff7c89d612913db85956c0320686c)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_fc68e2f7e69b49f2410e35a36253d3aa = L.circleMarker(\n",
       "                [37.5048497116637, 127.00487538273],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 6.946933333333333, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_b64ebf71f751749bca75049e960f87dc = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_2545098da6cf1a24484782c103405a0d = $(`&lt;div id=&quot;html_2545098da6cf1a24484782c103405a0d&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;고속터미널역&#x27;, 104203]&lt;/div&gt;`)[0];\n",
       "                popup_b64ebf71f751749bca75049e960f87dc.setContent(html_2545098da6cf1a24484782c103405a0d);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_fc68e2f7e69b49f2410e35a36253d3aa.bindPopup(popup_b64ebf71f751749bca75049e960f87dc)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_0c76e390b3b01fd11a7a58471ad95818 = L.circleMarker(\n",
       "                [37.6189326042759, 126.920853043578],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 5.3162666666666665, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_c5db869525125648d72e68456210f0ae = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_5cdf03fd3942e32160c02768b887477f = $(`&lt;div id=&quot;html_5cdf03fd3942e32160c02768b887477f&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;연신내역&#x27;, 79743]&lt;/div&gt;`)[0];\n",
       "                popup_c5db869525125648d72e68456210f0ae.setContent(html_5cdf03fd3942e32160c02768b887477f);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_0c76e390b3b01fd11a7a58471ad95818.bindPopup(popup_c5db869525125648d72e68456210f0ae)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_220a24c39046a57b6d4bbeec9a67847a = L.circleMarker(\n",
       "                [37.4847788607946, 127.03338417886],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 5.572866666666667, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_b75a77bfbfd43691c0b2a27dcf7466bd = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_68569718594caa6302adc22a0febb560 = $(`&lt;div id=&quot;html_68569718594caa6302adc22a0febb560&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;양재역&#x27;, 83592]&lt;/div&gt;`)[0];\n",
       "                popup_b75a77bfbfd43691c0b2a27dcf7466bd.setContent(html_68569718594caa6302adc22a0febb560);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_220a24c39046a57b6d4bbeec9a67847a.bindPopup(popup_b75a77bfbfd43691c0b2a27dcf7466bd)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_3daae80a248df348e6bed014fd0c5aa3 = L.circleMarker(\n",
       "                [37.6398219485414, 127.027222545302],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 5.1106, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_ac6cd78ec3426ac038976c29e0245955 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_fc100698d3efc72272fb0695cee56efe = $(`&lt;div id=&quot;html_fc100698d3efc72272fb0695cee56efe&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;수유역&#x27;, 76658]&lt;/div&gt;`)[0];\n",
       "                popup_ac6cd78ec3426ac038976c29e0245955.setContent(html_fc100698d3efc72272fb0695cee56efe);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_3daae80a248df348e6bed014fd0c5aa3.bindPopup(popup_ac6cd78ec3426ac038976c29e0245955)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_0720930953e0548fdf9cbbbc25f91e21 = L.circleMarker(\n",
       "                [37.5821325777078, 127.001814703064],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 5.121, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_02e3f01cc540fd9ecf6ae6e59f186530 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_5e844bb2cdaadea56d3e0f84ec471d21 = $(`&lt;div id=&quot;html_5e844bb2cdaadea56d3e0f84ec471d21&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;혜화역&#x27;, 76814]&lt;/div&gt;`)[0];\n",
       "                popup_02e3f01cc540fd9ecf6ae6e59f186530.setContent(html_5e844bb2cdaadea56d3e0f84ec471d21);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_0720930953e0548fdf9cbbbc25f91e21.bindPopup(popup_02e3f01cc540fd9ecf6ae6e59f186530)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_9a61b532ed8d4963ffa6b00d1d26aef9 = L.circleMarker(\n",
       "                [37.6490575706429, 127.035086481532],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 4.1768, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_30c620dd3cec57ea2eef0f85b50b28df = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_185aa0ad512d149e7551695a1f1def22 = $(`&lt;div id=&quot;html_185aa0ad512d149e7551695a1f1def22&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;쌍문역&#x27;, 62651]&lt;/div&gt;`)[0];\n",
       "                popup_30c620dd3cec57ea2eef0f85b50b28df.setContent(html_185aa0ad512d149e7551695a1f1def22);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_9a61b532ed8d4963ffa6b00d1d26aef9.bindPopup(popup_30c620dd3cec57ea2eef0f85b50b28df)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_fa79cc18b7234b379dff8787f9b8b585 = L.circleMarker(\n",
       "                [37.5724324798009, 126.976901845925],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 4.497866666666667, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_b4f1ad1aa2a5c7a9c528b54b02e70988 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_0ec1cc4391852c274a85bbcf18c5b665 = $(`&lt;div id=&quot;html_0ec1cc4391852c274a85bbcf18c5b665&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;광화문역&#x27;, 67467]&lt;/div&gt;`)[0];\n",
       "                popup_b4f1ad1aa2a5c7a9c528b54b02e70988.setContent(html_0ec1cc4391852c274a85bbcf18c5b665);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_fa79cc18b7234b379dff8787f9b8b585.bindPopup(popup_b4f1ad1aa2a5c7a9c528b54b02e70988)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_48f43ee9696fe61b483345137ff75ea6 = L.circleMarker(\n",
       "                [37.5325400173123, 126.84626068468],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 4.203733333333333, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_d7a18712f03db8cc52d9ed9a729dcd42 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_9053061dd8ea38cc944f323d6cc9fd86 = $(`&lt;div id=&quot;html_9053061dd8ea38cc944f323d6cc9fd86&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;까치산역&#x27;, 63055]&lt;/div&gt;`)[0];\n",
       "                popup_d7a18712f03db8cc52d9ed9a729dcd42.setContent(html_9053061dd8ea38cc944f323d6cc9fd86);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_48f43ee9696fe61b483345137ff75ea6.bindPopup(popup_d7a18712f03db8cc52d9ed9a729dcd42)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_3ad7c67acac0393e3936e7cbd856482a = L.circleMarker(\n",
       "                [37.5412338197326, 126.840795822727],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 4.072066666666666, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_f6727ff4c718e4b694c276889c03a08b = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_5e70dd28e9073a83f35ac77c017d29ae = $(`&lt;div id=&quot;html_5e70dd28e9073a83f35ac77c017d29ae&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;화곡역&#x27;, 61080]&lt;/div&gt;`)[0];\n",
       "                popup_f6727ff4c718e4b694c276889c03a08b.setContent(html_5e70dd28e9073a83f35ac77c017d29ae);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_3ad7c67acac0393e3936e7cbd856482a.bindPopup(popup_f6727ff4c718e4b694c276889c03a08b)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_51d9854aac5b912d2b7d09b009b9fb98 = L.circleMarker(\n",
       "                [37.5444872539487, 126.95119513916],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2.5946, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_ebb589a17ea696329d4ca9c46773488d = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_cead139ef143139acac5ce576941ba7f = $(`&lt;div id=&quot;html_cead139ef143139acac5ce576941ba7f&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;공덕역&#x27;, 38918]&lt;/div&gt;`)[0];\n",
       "                popup_ebb589a17ea696329d4ca9c46773488d.setContent(html_cead139ef143139acac5ce576941ba7f);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_51d9854aac5b912d2b7d09b009b9fb98.bindPopup(popup_ebb589a17ea696329d4ca9c46773488d)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_a564b1fe5389271e8f7c89e49c0998a7 = L.circleMarker(\n",
       "                [37.598807448195, 126.914481913318],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2.602866666666667, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_37a44531eb8e64c643aa18b1a78a87da = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_43282d9e1cfd0102cc51a6e3fba273c1 = $(`&lt;div id=&quot;html_43282d9e1cfd0102cc51a6e3fba273c1&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;응암역&#x27;, 39042]&lt;/div&gt;`)[0];\n",
       "                popup_37a44531eb8e64c643aa18b1a78a87da.setContent(html_43282d9e1cfd0102cc51a6e3fba273c1);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_a564b1fe5389271e8f7c89e49c0998a7.bindPopup(popup_37a44531eb8e64c643aa18b1a78a87da)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_d7f8a620f5a33ec30246a3ba9736311f = L.circleMarker(\n",
       "                [37.5560572923314, 126.910033925408],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2.2695333333333334, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_92d051a7f0e77aefce96d0c6ad53a103 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_8ce346242178a222a083bcaee94ed55a = $(`&lt;div id=&quot;html_8ce346242178a222a083bcaee94ed55a&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;망원역&#x27;, 34042]&lt;/div&gt;`)[0];\n",
       "                popup_92d051a7f0e77aefce96d0c6ad53a103.setContent(html_8ce346242178a222a083bcaee94ed55a);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_d7f8a620f5a33ec30246a3ba9736311f.bindPopup(popup_92d051a7f0e77aefce96d0c6ad53a103)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_688635ea6becd65340d064fea006307f = L.circleMarker(\n",
       "                [37.4824136254197, 126.882240156863],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 6.0414, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_04642c8625ade79817eb9fd2e14fbe75 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_fdf9466885451bd8bb0960ef709d33f1 = $(`&lt;div id=&quot;html_fdf9466885451bd8bb0960ef709d33f1&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;가산디지털단지역&#x27;, 90620]&lt;/div&gt;`)[0];\n",
       "                popup_04642c8625ade79817eb9fd2e14fbe75.setContent(html_fdf9466885451bd8bb0960ef709d33f1);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_688635ea6becd65340d064fea006307f.bindPopup(popup_04642c8625ade79817eb9fd2e14fbe75)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_3ed5d11b2e52cc9ec10f17f8703310e8 = L.circleMarker(\n",
       "                [37.5143011318846, 127.03177451842],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 3.3806, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_0ab28d67bb97d4e95a41db03dab1652e = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_0d7a8b40f7fc683a05934e5dfad43d97 = $(`&lt;div id=&quot;html_0d7a8b40f7fc683a05934e5dfad43d97&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;학동역&#x27;, 50708]&lt;/div&gt;`)[0];\n",
       "                popup_0ab28d67bb97d4e95a41db03dab1652e.setContent(html_0d7a8b40f7fc683a05934e5dfad43d97);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_3ed5d11b2e52cc9ec10f17f8703310e8.bindPopup(popup_0ab28d67bb97d4e95a41db03dab1652e)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_c60abdb7cb9c7a3aacd74a8f5c70d7c1 = L.circleMarker(\n",
       "                [37.4798031725412, 126.853074780215],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 3.2417333333333334, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_23c6ad81bd24b559955024aea9bd7aa8 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_639ead3e0ffcca1a4ebc71d99d08bc3d = $(`&lt;div id=&quot;html_639ead3e0ffcca1a4ebc71d99d08bc3d&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;광명사거리역&#x27;, 48625]&lt;/div&gt;`)[0];\n",
       "                popup_23c6ad81bd24b559955024aea9bd7aa8.setContent(html_639ead3e0ffcca1a4ebc71d99d08bc3d);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_c60abdb7cb9c7a3aacd74a8f5c70d7c1.bindPopup(popup_23c6ad81bd24b559955024aea9bd7aa8)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_b8047253f4e54620e9b0845509342916 = L.circleMarker(\n",
       "                [37.4881491599168, 127.121157265226],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 3.0016666666666665, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_6cad64796ba61f9babb6d4ef7795c19b = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_2b505e970b79eb965f39b5be5dac4f72 = $(`&lt;div id=&quot;html_2b505e970b79eb965f39b5be5dac4f72&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;문정역&#x27;, 45024]&lt;/div&gt;`)[0];\n",
       "                popup_6cad64796ba61f9babb6d4ef7795c19b.setContent(html_2b505e970b79eb965f39b5be5dac4f72);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_b8047253f4e54620e9b0845509342916.bindPopup(popup_6cad64796ba61f9babb6d4ef7795c19b)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_09af1e9e5faa52fb7212271b46ec3da3 = L.circleMarker(\n",
       "                [37.5511672958111, 127.128208775415],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2.613933333333333, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_f5578eb4ef6b417877403664c9d60bc7 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_92a4d14d48833b095fe6a717d9f0e449 = $(`&lt;div id=&quot;html_92a4d14d48833b095fe6a717d9f0e449&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;암사역&#x27;, 39208]&lt;/div&gt;`)[0];\n",
       "                popup_f5578eb4ef6b417877403664c9d60bc7.setContent(html_92a4d14d48833b095fe6a717d9f0e449);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_09af1e9e5faa52fb7212271b46ec3da3.bindPopup(popup_f5578eb4ef6b417877403664c9d60bc7)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_e6a2d5fd57046020873aa643fea02a36 = L.circleMarker(\n",
       "                [37.4776281970976, 127.126553391079],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2.4896666666666665, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_6765bb4f172d7ea23e685b70763c3a51 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_fd28819cf6f412e8a360c6adb75bc446 = $(`&lt;div id=&quot;html_fd28819cf6f412e8a360c6adb75bc446&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;장지역&#x27;, 37344]&lt;/div&gt;`)[0];\n",
       "                popup_6765bb4f172d7ea23e685b70763c3a51.setContent(html_fd28819cf6f412e8a360c6adb75bc446);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_e6a2d5fd57046020873aa643fea02a36.bindPopup(popup_6765bb4f172d7ea23e685b70763c3a51)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_a84c773cb9a43125dbefa40c35b953a3 = L.circleMarker(\n",
       "                [37.5064573770425, 127.031325016674],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 4.596266666666667, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_faef08b36467fa8890fc16ca1b27488c = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_41b52f1d7a8ffe73a72033225f10e17e = $(`&lt;div id=&quot;html_41b52f1d7a8ffe73a72033225f10e17e&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;신논현역&#x27;, 68943]&lt;/div&gt;`)[0];\n",
       "                popup_faef08b36467fa8890fc16ca1b27488c.setContent(html_41b52f1d7a8ffe73a72033225f10e17e);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_a84c773cb9a43125dbefa40c35b953a3.bindPopup(popup_faef08b36467fa8890fc16ca1b27488c)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_20a4f6414e3cff96809a63aa8dee326c = L.circleMarker(\n",
       "                [37.5135851365497, 126.940904491209],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 3.861466666666667, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_5c4988a91467a5497ec5f637a840ed5b = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_400ee1a91a29e3e8fbb9a4cf9bbe2b01 = $(`&lt;div id=&quot;html_400ee1a91a29e3e8fbb9a4cf9bbe2b01&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;노량진역&#x27;, 57921]&lt;/div&gt;`)[0];\n",
       "                popup_5c4988a91467a5497ec5f637a840ed5b.setContent(html_400ee1a91a29e3e8fbb9a4cf9bbe2b01);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_20a4f6414e3cff96809a63aa8dee326c.bindPopup(popup_5c4988a91467a5497ec5f637a840ed5b)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var circle_marker_08188c949b4ed1e10c0cdca197616812 = L.circleMarker(\n",
       "                [37.5228127393231, 126.92303459092],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 3.7526, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_bf40c5d35527146c07ad7559ad477704);\n",
       "        \n",
       "    \n",
       "        var popup_3e8351051033d34683eaa1b8e594e32f = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_9069256c3868e7ebc7a98e0b803dd645 = $(`&lt;div id=&quot;html_9069256c3868e7ebc7a98e0b803dd645&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;[&#x27;여의도역&#x27;, 56288]&lt;/div&gt;`)[0];\n",
       "                popup_3e8351051033d34683eaa1b8e594e32f.setContent(html_9069256c3868e7ebc7a98e0b803dd645);\n",
       "            \n",
       "        \n",
       "\n",
       "        circle_marker_08188c949b4ed1e10c0cdca197616812.bindPopup(popup_3e8351051033d34683eaa1b8e594e32f)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "&lt;/script&gt;\n",
       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x2297c6cee10>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "\n",
    "# 서울의 중심에 위치하는 명동역의 위도와 경도를 중심으로 지도 출력합니다.\n",
    "latitude = subway_location[subway_location['지하철역']=='명동역']['x좌표'] #위도설정\n",
    "longitude = subway_location[subway_location['지하철역']=='명동역']['y좌표'] #경도설정\n",
    "map_osm = folium.Map(location = [latitude, longitude], zoom_start = 12)\n",
    "\n",
    "# 각 지하철 역의 위치 별로 마커를 지도에 추가\n",
    "for i in top3_per_line_location.index:\n",
    "    marker = folium.CircleMarker([top3_per_line_location['x좌표'][i],top3_per_line_location['y좌표'][i]],\n",
    "                       radius = (top3_per_line_location['평균 승하차 합계'][i]+1)/15000, # 인원 수가 0일 때 계산오류 보정\n",
    "                        popup = [top3_per_line_location['지하철역'][i],top3_per_line_location['평균 승하차 합계'][i]], \n",
    "                        color = 'blue', \n",
    "                        fill_color = 'blue')\n",
    "    marker.add_to(map_osm)\n",
    "map_osm"
   ]
  }
 ],
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