210502 기상예측 선형 보간.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install lightgbm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "energy=pd.read_csv('energy.csv')\n",
    "dangjin_fcst=pd.read_csv('dangjin_fcst_data.csv')\n",
    "ulsan_fcst=pd.read_csv('ulsan_fcst_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>dangjin_floating</th>\n",
       "      <th>dangjin_warehouse</th>\n",
       "      <th>dangjin</th>\n",
       "      <th>ulsan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-03-01 1:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-03-01 2:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-03-01 3:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-01 4:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-01 5:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 time  dangjin_floating  dangjin_warehouse  dangjin  ulsan\n",
       "0  2018-03-01 1:00:00               0.0                0.0        0      0\n",
       "1  2018-03-01 2:00:00               0.0                0.0        0      0\n",
       "2  2018-03-01 3:00:00               0.0                0.0        0      0\n",
       "3  2018-03-01 4:00:00               0.0                0.0        0      0\n",
       "4  2018-03-01 5:00:00               0.0                0.0        0      0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Forecast time</th>\n",
       "      <th>forecast</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>WindDirection</th>\n",
       "      <th>Cloud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>309.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>314.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>323.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>336.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>339.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>344.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>256.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>270.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>264.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>34.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>37.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>43.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>138.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>133.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>49.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>130.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>52.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>184.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>135.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>58.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>155.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "      <td>61.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>197.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018-03-01 14:00:00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>314.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018-03-01 14:00:00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>323.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018-03-01 14:00:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>336.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018-03-01 14:00:00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>339.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018-03-01 14:00:00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>344.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2018-03-01 14:00:00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2018-03-01 14:00:00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>256.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2018-03-01 14:00:00</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>270.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2018-03-01 14:00:00</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>264.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018-03-01 14:00:00</td>\n",
       "      <td>31.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Forecast time  forecast  Temperature  Humidity  WindSpeed  \\\n",
       "0   2018-03-01 11:00:00       4.0          0.0      60.0        7.3   \n",
       "1   2018-03-01 11:00:00       7.0         -2.0      60.0        7.1   \n",
       "2   2018-03-01 11:00:00      10.0         -2.0      60.0        6.7   \n",
       "3   2018-03-01 11:00:00      13.0         -2.0      55.0        6.7   \n",
       "4   2018-03-01 11:00:00      16.0         -4.0      55.0        5.5   \n",
       "5   2018-03-01 11:00:00      19.0         -5.0      60.0        4.3   \n",
       "6   2018-03-01 11:00:00      22.0         -2.0      55.0        2.0   \n",
       "7   2018-03-01 11:00:00      25.0          1.0      45.0        0.8   \n",
       "8   2018-03-01 11:00:00      28.0          3.0      50.0        2.3   \n",
       "9   2018-03-01 11:00:00      31.0          1.0      55.0        1.9   \n",
       "10  2018-03-01 11:00:00      34.0         -1.0      70.0        1.6   \n",
       "11  2018-03-01 11:00:00      37.0         -2.0      75.0        2.0   \n",
       "12  2018-03-01 11:00:00      40.0         -2.0      75.0        2.6   \n",
       "13  2018-03-01 11:00:00      43.0         -2.0      80.0        2.7   \n",
       "14  2018-03-01 11:00:00      46.0          1.0      80.0        2.6   \n",
       "15  2018-03-01 11:00:00      49.0          9.0      70.0        3.1   \n",
       "16  2018-03-01 11:00:00      52.0         12.0      65.0        1.3   \n",
       "17  2018-03-01 11:00:00      55.0         11.0      80.0        1.3   \n",
       "18  2018-03-01 11:00:00      58.0          9.0      85.0        1.4   \n",
       "19  2018-03-01 11:00:00      61.0          9.0      85.0        1.4   \n",
       "20  2018-03-01 14:00:00       4.0         -2.0      60.0        7.1   \n",
       "21  2018-03-01 14:00:00       7.0         -2.0      60.0        6.7   \n",
       "22  2018-03-01 14:00:00      10.0         -2.0      55.0        6.7   \n",
       "23  2018-03-01 14:00:00      13.0         -4.0      55.0        5.5   \n",
       "24  2018-03-01 14:00:00      16.0         -5.0      60.0        4.3   \n",
       "25  2018-03-01 14:00:00      19.0         -2.0      55.0        2.0   \n",
       "26  2018-03-01 14:00:00      22.0          1.0      45.0        0.8   \n",
       "27  2018-03-01 14:00:00      25.0          3.0      50.0        2.3   \n",
       "28  2018-03-01 14:00:00      28.0          1.0      55.0        1.9   \n",
       "29  2018-03-01 14:00:00      31.0         -1.0      70.0        1.6   \n",
       "\n",
       "    WindDirection  Cloud  \n",
       "0           309.0    2.0  \n",
       "1           314.0    1.0  \n",
       "2           323.0    1.0  \n",
       "3           336.0    1.0  \n",
       "4           339.0    1.0  \n",
       "5           344.0    1.0  \n",
       "6           345.0    1.0  \n",
       "7           256.0    1.0  \n",
       "8           270.0    1.0  \n",
       "9           264.0    1.0  \n",
       "10           68.0    1.0  \n",
       "11          114.0    2.0  \n",
       "12          140.0    2.0  \n",
       "13          138.0    2.0  \n",
       "14          133.0    2.0  \n",
       "15          130.0    2.0  \n",
       "16          184.0    3.0  \n",
       "17          135.0    3.0  \n",
       "18          155.0    3.0  \n",
       "19          197.0    3.0  \n",
       "20          314.0    1.0  \n",
       "21          323.0    1.0  \n",
       "22          336.0    1.0  \n",
       "23          339.0    1.0  \n",
       "24          344.0    1.0  \n",
       "25          345.0    1.0  \n",
       "26          256.0    1.0  \n",
       "27          270.0    1.0  \n",
       "28          264.0    1.0  \n",
       "29           68.0    1.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dangjin_fcst.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Forecast time</th>\n",
       "      <th>forecast</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>WindSpeed</th>\n",
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       "      <th>0</th>\n",
       "      <td>2018-03-01 11:00</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>20</td>\n",
       "      <td>14.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2018-03-01 11:00</td>\n",
       "      <td>7</td>\n",
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       "      <td>2018-03-01 11:00</td>\n",
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       "      <td>1.9</td>\n",
       "      <td>309</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-01 11:00</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>1.5</td>\n",
       "      <td>318</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-01 11:00</td>\n",
       "      <td>16</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>45</td>\n",
       "      <td>1.8</td>\n",
       "      <td>308</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Forecast time  forecast  Temperature  Humidity  WindSpeed  \\\n",
       "0  2018-03-01 11:00         4          8.0        20       14.0   \n",
       "1  2018-03-01 11:00         7          4.0        20        4.3   \n",
       "2  2018-03-01 11:00        10          3.0        30        1.9   \n",
       "3  2018-03-01 11:00        13          0.0        40        1.5   \n",
       "4  2018-03-01 11:00        16         -1.0        45        1.8   \n",
       "\n",
       "   WindDirection  Cloud  \n",
       "0            298      2  \n",
       "1            298      2  \n",
       "2            309      2  \n",
       "3            318      2  \n",
       "4            308      2  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ulsan_fcst.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 예보 데이터 전처리와 선형보간"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Forecast time</th>\n",
       "      <th>forecast</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>WindDirection</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2018-03-01 11:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60</td>\n",
       "      <td>7.3</td>\n",
       "      <td>309</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-03-01 11:00</td>\n",
       "      <td>7</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>60</td>\n",
       "      <td>7.1</td>\n",
       "      <td>314</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-03-01 11:00</td>\n",
       "      <td>10</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>60</td>\n",
       "      <td>6.7</td>\n",
       "      <td>323</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-01 11:00</td>\n",
       "      <td>13</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>55</td>\n",
       "      <td>6.7</td>\n",
       "      <td>336</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-01 11:00</td>\n",
       "      <td>16</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>55</td>\n",
       "      <td>5.5</td>\n",
       "      <td>339</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-03-01 11:00</td>\n",
       "      <td>19</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>60</td>\n",
       "      <td>4.3</td>\n",
       "      <td>344</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-03-01 11:00</td>\n",
       "      <td>22</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>55</td>\n",
       "      <td>2.0</td>\n",
       "      <td>345</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-03-01 11:00</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45</td>\n",
       "      <td>0.8</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-03-01 11:00</td>\n",
       "      <td>28</td>\n",
       "      <td>3.0</td>\n",
       "      <td>50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>270</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-03-01 11:00</td>\n",
       "      <td>31</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>1.9</td>\n",
       "      <td>264</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-01 11:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Forecast time  forecast  Temperature  Humidity  WindSpeed  \\\n",
       "0  2018-03-01 11:00         4          0.0        60        7.3   \n",
       "1  2018-03-01 11:00         7         -2.0        60        7.1   \n",
       "2  2018-03-01 11:00        10         -2.0        60        6.7   \n",
       "3  2018-03-01 11:00        13         -2.0        55        6.7   \n",
       "4  2018-03-01 11:00        16         -4.0        55        5.5   \n",
       "5  2018-03-01 11:00        19         -5.0        60        4.3   \n",
       "6  2018-03-01 11:00        22         -2.0        55        2.0   \n",
       "7  2018-03-01 11:00        25          1.0        45        0.8   \n",
       "8  2018-03-01 11:00        28          3.0        50        2.3   \n",
       "9  2018-03-01 11:00        31          1.0        55        1.9   \n",
       "\n",
       "   WindDirection  Cloud       Forecast_time  \n",
       "0            309      2 2018-03-01 11:00:00  \n",
       "1            314      1 2018-03-01 11:00:00  \n",
       "2            323      1 2018-03-01 11:00:00  \n",
       "3            336      1 2018-03-01 11:00:00  \n",
       "4            339      1 2018-03-01 11:00:00  \n",
       "5            344      1 2018-03-01 11:00:00  \n",
       "6            345      1 2018-03-01 11:00:00  \n",
       "7            256      1 2018-03-01 11:00:00  \n",
       "8            270      1 2018-03-01 11:00:00  \n",
       "9            264      1 2018-03-01 11:00:00  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dangjin_fcst.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 예보시간 컬럼의 데이터 타입을 datetime으로 변경\n",
    "dangjin_fcst['Forecast_time']=pd.to_datetime(dangjin_fcst['Forecast time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 기상청에서는 하루 3시간 간격으로 8회 예보\n",
    "#4시간 후~ 61시간 이후까지예보\n",
    "# 일단 14시 예보만을 사용하여 살펴보자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "fcst_14=dangjin_fcst[dangjin_fcst['Forecast_time'].dt.hour==14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2018-03-01 14:00</td>\n",
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       "      <th>23</th>\n",
       "      <td>2018-03-01 14:00</td>\n",
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       "      <td>2018-03-01 14:00:00</td>\n",
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       "      <th>24</th>\n",
       "      <td>2018-03-01 14:00</td>\n",
       "      <td>16</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>60</td>\n",
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       "      <td>344</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-01 14:00:00</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2018-03-01 14:00</td>\n",
       "      <td>19</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>55</td>\n",
       "      <td>2.0</td>\n",
       "      <td>345</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-01 14:00:00</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2018-03-01 14:00</td>\n",
       "      <td>22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45</td>\n",
       "      <td>0.8</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-01 14:00:00</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2018-03-01 14:00</td>\n",
       "      <td>25</td>\n",
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       "      <td>2.3</td>\n",
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       "      <td>1</td>\n",
       "      <td>2018-03-01 14:00:00</td>\n",
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       "      <th>28</th>\n",
       "      <td>2018-03-01 14:00</td>\n",
       "      <td>28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>1.9</td>\n",
       "      <td>264</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-01 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018-03-01 14:00</td>\n",
       "      <td>31</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1.6</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-01 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>2018-03-02 14:00</td>\n",
       "      <td>10</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-03-02 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>2018-03-02 14:00</td>\n",
       "      <td>13</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>75</td>\n",
       "      <td>2.9</td>\n",
       "      <td>125</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-03-02 14:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Forecast time  forecast  Temperature  Humidity  WindSpeed  \\\n",
       "22   2018-03-01 14:00        10         -2.0        55        6.7   \n",
       "23   2018-03-01 14:00        13         -4.0        55        5.5   \n",
       "24   2018-03-01 14:00        16         -5.0        60        4.3   \n",
       "25   2018-03-01 14:00        19         -2.0        55        2.0   \n",
       "26   2018-03-01 14:00        22          1.0        45        0.8   \n",
       "27   2018-03-01 14:00        25          3.0        50        2.3   \n",
       "28   2018-03-01 14:00        28          1.0        55        1.9   \n",
       "29   2018-03-01 14:00        31         -1.0        70        1.6   \n",
       "170  2018-03-02 14:00        10         -2.0        80        2.4   \n",
       "171  2018-03-02 14:00        13         -2.0        75        2.9   \n",
       "\n",
       "     WindDirection  Cloud       Forecast_time  \n",
       "22             336      1 2018-03-01 14:00:00  \n",
       "23             339      1 2018-03-01 14:00:00  \n",
       "24             344      1 2018-03-01 14:00:00  \n",
       "25             345      1 2018-03-01 14:00:00  \n",
       "26             256      1 2018-03-01 14:00:00  \n",
       "27             270      1 2018-03-01 14:00:00  \n",
       "28             264      1 2018-03-01 14:00:00  \n",
       "29              68      1 2018-03-01 14:00:00  \n",
       "170             90      2 2018-03-02 14:00:00  \n",
       "171            125      2 2018-03-02 14:00:00  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 14시 기준으로 다음 날의 기상예보는 10시간 후~ 33시간 후 예측치이다\n",
    "fcst_14=fcst_14[(fcst_14['forecast']>=10)&(fcst_14['forecast']<=33)]\n",
    "fcst_14.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sungb\\anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\datetimelike.py:1342: PerformanceWarning: Adding/subtracting object-dtype array to DatetimeArray not vectorized\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# 예보시전에 forecast를 더하여 예보 시각을 나타내는 변수 생성\n",
    "def to_date(x):\n",
    "    return pd.DateOffset(hours=x)\n",
    "\n",
    "fcst_14['Forecast_time'] = fcst_14['Forecast_time'] + fcst_14['forecast'].map(to_date)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Forecast time</th>\n",
       "      <th>forecast</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>WindDirection</th>\n",
       "      <th>Cloud</th>\n",
       "      <th>Forecast_time</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018-03-01 14:00</td>\n",
       "      <td>10</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>55</td>\n",
       "      <td>6.7</td>\n",
       "      <td>336</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-02 00:00:00</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018-03-01 14:00</td>\n",
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       "      <td>2018-03-02 03:00:00</td>\n",
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       "      <th>24</th>\n",
       "      <td>2018-03-01 14:00</td>\n",
       "      <td>16</td>\n",
       "      <td>-5.0</td>\n",
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       "      <td>344</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-02 06:00:00</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2018-03-01 14:00</td>\n",
       "      <td>19</td>\n",
       "      <td>-2.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>2018-03-02 09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2018-03-01 14:00</td>\n",
       "      <td>22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45</td>\n",
       "      <td>0.8</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-02 12:00:00</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2018-03-01 14:00</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>270</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-02 15:00:00</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2018-03-01 14:00</td>\n",
       "      <td>28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>1.9</td>\n",
       "      <td>264</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-02 18:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018-03-01 14:00</td>\n",
       "      <td>31</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1.6</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-02 21:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>2018-03-02 14:00</td>\n",
       "      <td>10</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-03-03 00:00:00</td>\n",
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       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>2018-03-02 14:00</td>\n",
       "      <td>13</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>75</td>\n",
       "      <td>2.9</td>\n",
       "      <td>125</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-03-03 03:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Forecast time  forecast  Temperature  Humidity  WindSpeed  \\\n",
       "22   2018-03-01 14:00        10         -2.0        55        6.7   \n",
       "23   2018-03-01 14:00        13         -4.0        55        5.5   \n",
       "24   2018-03-01 14:00        16         -5.0        60        4.3   \n",
       "25   2018-03-01 14:00        19         -2.0        55        2.0   \n",
       "26   2018-03-01 14:00        22          1.0        45        0.8   \n",
       "27   2018-03-01 14:00        25          3.0        50        2.3   \n",
       "28   2018-03-01 14:00        28          1.0        55        1.9   \n",
       "29   2018-03-01 14:00        31         -1.0        70        1.6   \n",
       "170  2018-03-02 14:00        10         -2.0        80        2.4   \n",
       "171  2018-03-02 14:00        13         -2.0        75        2.9   \n",
       "\n",
       "     WindDirection  Cloud       Forecast_time  \n",
       "22             336      1 2018-03-02 00:00:00  \n",
       "23             339      1 2018-03-02 03:00:00  \n",
       "24             344      1 2018-03-02 06:00:00  \n",
       "25             345      1 2018-03-02 09:00:00  \n",
       "26             256      1 2018-03-02 12:00:00  \n",
       "27             270      1 2018-03-02 15:00:00  \n",
       "28             264      1 2018-03-02 18:00:00  \n",
       "29              68      1 2018-03-02 21:00:00  \n",
       "170             90      2 2018-03-03 00:00:00  \n",
       "171            125      2 2018-03-03 03:00:00  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcst_14.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "fcst_14 = fcst_14[['Forecast_time', 'Temperature', 'Humidity', 'WindSpeed', 'WindDirection', 'Cloud']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>25</th>\n",
       "      <td>2018-03-02 09:00:00</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>55</td>\n",
       "      <td>2.0</td>\n",
       "      <td>345</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2018-03-02 12:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45</td>\n",
       "      <td>0.8</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>162085</th>\n",
       "      <td>2021-03-01 09:00:00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>90</td>\n",
       "      <td>3.9</td>\n",
       "      <td>64</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>162086</th>\n",
       "      <td>2021-03-01 12:00:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>90</td>\n",
       "      <td>4.8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>162087</th>\n",
       "      <td>2021-03-01 15:00:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>85</td>\n",
       "      <td>6.6</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162088</th>\n",
       "      <td>2021-03-01 18:00:00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>85</td>\n",
       "      <td>6.5</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162089</th>\n",
       "      <td>2021-03-01 21:00:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>85</td>\n",
       "      <td>5.9</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8768 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Forecast_time  Temperature  Humidity  WindSpeed  WindDirection  \\\n",
       "22     2018-03-02 00:00:00         -2.0        55        6.7            336   \n",
       "23     2018-03-02 03:00:00         -4.0        55        5.5            339   \n",
       "24     2018-03-02 06:00:00         -5.0        60        4.3            344   \n",
       "25     2018-03-02 09:00:00         -2.0        55        2.0            345   \n",
       "26     2018-03-02 12:00:00          1.0        45        0.8            256   \n",
       "...                    ...          ...       ...        ...            ...   \n",
       "162085 2021-03-01 09:00:00          8.0        90        3.9             64   \n",
       "162086 2021-03-01 12:00:00         10.0        90        4.8              7   \n",
       "162087 2021-03-01 15:00:00         10.0        85        6.6             11   \n",
       "162088 2021-03-01 18:00:00          7.0        85        6.5             34   \n",
       "162089 2021-03-01 21:00:00          5.0        85        5.9             27   \n",
       "\n",
       "        Cloud  \n",
       "22          1  \n",
       "23          1  \n",
       "24          1  \n",
       "25          1  \n",
       "26          1  \n",
       "...       ...  \n",
       "162085      4  \n",
       "162086      4  \n",
       "162087      4  \n",
       "162088      4  \n",
       "162089      4  \n",
       "\n",
       "[8768 rows x 6 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcst_14"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 태양광 발전량 예측은 1시간 간격으로 해야함 \n",
    "### BUT 예보는 3시간 간격으로 나온다\n",
    "### SO, 간단한 선형보간법을 활용하여 비어있는 값을 채운다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "      <td>2018-03-02 03:00:00</td>\n",
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       "      <th>4</th>\n",
       "      <td>2018-03-02 04:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>26276</th>\n",
       "      <td>2021-02-28 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26277</th>\n",
       "      <td>2021-02-28 21:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26278</th>\n",
       "      <td>2021-02-28 22:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26279</th>\n",
       "      <td>2021-02-28 23:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26280</th>\n",
       "      <td>2021-03-01 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26281 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Forecast_time\n",
       "0     2018-03-02 00:00:00\n",
       "1     2018-03-02 01:00:00\n",
       "2     2018-03-02 02:00:00\n",
       "3     2018-03-02 03:00:00\n",
       "4     2018-03-02 04:00:00\n",
       "...                   ...\n",
       "26276 2021-02-28 20:00:00\n",
       "26277 2021-02-28 21:00:00\n",
       "26278 2021-02-28 22:00:00\n",
       "26279 2021-02-28 23:00:00\n",
       "26280 2021-03-01 00:00:00\n",
       "\n",
       "[26281 rows x 1 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 우선 한시간 간격의 데이터 프레임을 생성한다\n",
    "\n",
    "fcst_14_=pd.DataFrame() # 1시간 단위로 기상예보 값을 넣어줄 데이터 프레임 형성\n",
    "fcst_14_['Forecast_time']=pd.date_range(start='2018-03-02 00:00:00', end='2021-03-01',freq='H')\n",
    "fcst_14_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
       "      <td>2018-03-02 03:00:00</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-03-02 05:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-03-02 06:00:00</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>344.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-03-02 07:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-03-02 08:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-03-02 09:00:00</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Forecast_time  Temperature  Humidity  WindSpeed  WindDirection  Cloud\n",
       "0 2018-03-02 00:00:00         -2.0      55.0        6.7          336.0    1.0\n",
       "1 2018-03-02 01:00:00          NaN       NaN        NaN            NaN    NaN\n",
       "2 2018-03-02 02:00:00          NaN       NaN        NaN            NaN    NaN\n",
       "3 2018-03-02 03:00:00         -4.0      55.0        5.5          339.0    1.0\n",
       "4 2018-03-02 04:00:00          NaN       NaN        NaN            NaN    NaN\n",
       "5 2018-03-02 05:00:00          NaN       NaN        NaN            NaN    NaN\n",
       "6 2018-03-02 06:00:00         -5.0      60.0        4.3          344.0    1.0\n",
       "7 2018-03-02 07:00:00          NaN       NaN        NaN            NaN    NaN\n",
       "8 2018-03-02 08:00:00          NaN       NaN        NaN            NaN    NaN\n",
       "9 2018-03-02 09:00:00         -2.0      55.0        2.0          345.0    1.0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Forecast_time을 기준으로 기존의 예보 데이터프레임과 병합\n",
    "fcst_14_ = pd.merge(fcst_14_, fcst_14, on='Forecast_time', how='outer')\n",
    "fcst_14_.head(10)  # 기존의 예보 데이터 프레임에 없는 시간은 값이 비어있음을 알 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pandas에서 지원하는 interpolate함수를 활용하여 선형보간한다.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "inter_fcst_14=fcst_14_.interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Forecast_time</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>WindDirection</th>\n",
       "      <th>Cloud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-03-02 00:00:00</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>336.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-03-02 01:00:00</td>\n",
       "      <td>-2.666667</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>337.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-03-02 02:00:00</td>\n",
       "      <td>-3.333333</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>338.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-02 03:00:00</td>\n",
       "      <td>-4.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>339.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-02 04:00:00</td>\n",
       "      <td>-4.333333</td>\n",
       "      <td>56.666667</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>340.666667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-03-02 05:00:00</td>\n",
       "      <td>-4.666667</td>\n",
       "      <td>58.333333</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>342.333333</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-03-02 06:00:00</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-03-02 07:00:00</td>\n",
       "      <td>-4.000000</td>\n",
       "      <td>58.333333</td>\n",
       "      <td>3.533333</td>\n",
       "      <td>344.333333</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-03-02 08:00:00</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>56.666667</td>\n",
       "      <td>2.766667</td>\n",
       "      <td>344.666667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-03-02 09:00:00</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>345.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Forecast_time  Temperature   Humidity  WindSpeed  WindDirection  Cloud\n",
       "0 2018-03-02 00:00:00    -2.000000  55.000000   6.700000     336.000000    1.0\n",
       "1 2018-03-02 01:00:00    -2.666667  55.000000   6.300000     337.000000    1.0\n",
       "2 2018-03-02 02:00:00    -3.333333  55.000000   5.900000     338.000000    1.0\n",
       "3 2018-03-02 03:00:00    -4.000000  55.000000   5.500000     339.000000    1.0\n",
       "4 2018-03-02 04:00:00    -4.333333  56.666667   5.100000     340.666667    1.0\n",
       "5 2018-03-02 05:00:00    -4.666667  58.333333   4.700000     342.333333    1.0\n",
       "6 2018-03-02 06:00:00    -5.000000  60.000000   4.300000     344.000000    1.0\n",
       "7 2018-03-02 07:00:00    -4.000000  58.333333   3.533333     344.333333    1.0\n",
       "8 2018-03-02 08:00:00    -3.000000  56.666667   2.766667     344.666667    1.0\n",
       "9 2018-03-02 09:00:00    -2.000000  55.000000   2.000000     345.000000    1.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inter_fcst_14.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 선형 보간 결과 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1af8aef5340>]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.title(\"Temp\",fontsize=25)\n",
    "\n",
    "\n",
    "days=5 #5일치만 시각화\n",
    "plt.plot(inter_fcst_14.loc[:24*days, 'Forecast_time'], inter_fcst_14.loc[:24*days, 'Temperature'], '.-')\n",
    "plt.plot(fcst_14_.loc[:24*days, 'Forecast_time'], fcst_14_.loc[:24*days, 'Temperature'], 'o')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1af8b8a5be0>]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.title(\"Humidity\",fontsize=25)\n",
    "\n",
    "\n",
    "\n",
    "days=5 #5일치만 시각화\n",
    "plt.plot(inter_fcst_14.loc[:24*days, 'Forecast_time'], inter_fcst_14.loc[:24*days, 'Humidity'], '.-')\n",
    "plt.plot(fcst_14_.loc[:24*days, 'Forecast_time'], fcst_14_.loc[:24*days, 'Humidity'], 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1af8c13f5b0>]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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REREREREfoGTPSYqPCOKqsX3478qdbCw86HQ4IiIiIiIiItLFKdnTBm4Yn0pYoD+/eXc1j8/NZXl+sdMhiYiIiIiIiEgX5e90AJ1BdFgg5w7pzpvLd7B4y34C/V28cm0WmSnRTocmIiIiIiIiIl2MKnvaSM/IEADcFmpq3WTnFTkckYiIiIiIiIh0RUr2tJEz+yfg7zIA+Pm5yEqNdTgiEREREREREemKlOxpI5kp0bx4zamEBPgxsEeEhnCJiIiIiIiIiCOU7GlD49LiuGNKOisLSlmyZb/T4YiIiIiIiIhIF6RkTxv7XlYf4iOCePCTDVhrnQ5HRERERERERLoYJXvaWEigHzdP6MfiLfv5YrOaNIuIiIiIiIhI+2pxsscYc54x5jNjTJkx5oAxZpkx5ixvBtdRfffU3vSMDFZ1j4iIiIiIiIi0uxYle4wxNwDvAsuBi4FLgTeBUO+F1nEFB/hxy1lprNhWwrwNe50OR0RERERERES6EP/mVjDG9AH+Btxtrf3bEQ997J2QOodLM5N5av5mHpy1gQn94zHGOB2SiIiIiIiIiHQBLansuQZwA095OZZOJdDfxa1npbN6xwE+XlPodDgiIiIiIiIi0kW0JNkzDlgPfNcYs9kYU2uMyTXG3Ozl2Dq8i0cmkhoXxsOzNuJ2q3ePiIiIiIiIiHhfS5I9vYB04K/An4CpwCzgMWPMbV6MrcPz93Nx2+R0NhQe5P1Vu5wOR0RERERERES6gJYke1xABHCDtfZZa+0ca+2PgI+An5tGmtEYY66vn61r2d69XbtB8bRhvcjoHs7Dn26kts7tdDgiIiIiIiIi0sm1JNlTVH8765jlnwDdgZ7HbmCtfcZaO8paOyo+Pv4kQ+zYXC7DnVMyyNt7iHe/2ul0OCIiIiIiIiLSybUk2bOmieUNFT0qV2nG2YN7MLhXN/7y0Xr+PmcTy/OLnQ5JRERERERERDqpliR73q6/PfuY5WcDBdba3W0bUudjjOGC4b0oPFjFQ59s5MrnspXwERERERERERGv8G/BOh8Ac4GnjTFxQB5wCZ5GzT/wYmydSq3bUwBlgZpaN9l5RWSmRDsblIiIiIiIiIh0Os1W9lhrLXAR8DrwW+B/QBZwpbX2RW8G15lkpcYR6Of5cRtjyEqNdTgiEREREREREemMWjKMC2vtAWvtzdba7tbaQGvtMGvtq94OrjPJTInmtevGkBQdQkSwP4N7dXM6JBERERERERHphFqU7JG2kdknhgcvHU5xeQ0vZ+c7HY6IiIiIiIiIdEJK9rSzMamxjEuL44l5mzlUVet0OCIiIiIiIiLSySjZ44A7p2aw/1A1L36x1elQRERERERERKSTUbLHAaf0juasAQk881keByprnA5HRERERERERDoRJXsccueUDEoranh+wRanQxERERERERGRTkTJHocMSYzknME9+MfCLZSUVzsdjoiIiIiIiIh0Ekr2OOiOKRmUVdfyzGd5TociIiIiIiIiIp2Ekj0O6t8jgmnDevHC51vZV1bldDgiIiIiIiIi0gko2eOw2yanU1Vbx1PzNjsdioiIiIiIiIh0Akr2OKxffDjfPiWJl7LzKTxQ6XQ4IiIiIiIiItLBKdnjA26blE6d2/L43FynQxERERERERGRDs7f6QAEkmNC+X2/dZyx4sfYL4swkUkw6R4YNt3p0ERERERERESkg1GyxxfkzGT6rr/iMhWe+6Xb4b1bPd8r4SMiIiIiIiIiraBhXL5g9n24aiuOXlZTAbPvcyYeEREREREREemwlOzxBaUFrVsuIiIiIiIiItIEJXt8QWRS65aLiIiIiIiIiDRByR5fMOkeCAg5alGVCfIsFxERERERERFpBSV7fMGw6TDtUYhMBgylgT24u+qHrIs/x+nIRERERERERKSD0WxcvmLY9K9n3iqvYe5f5lA5ayPPfH+Us3GJiIiIiIiISIeiyh4fFBkawLXjUvlkbSE5BSVOhyMiIiIiIiIiHYiSPT7qmnF9iAoN4KFZG50ORUREREREREQ6ECV7fFREcAA3jO/HvA17WZ6/3+lwRERERERERKSDULLHh101NoW48EAe/ETVPSIiIiIiIiLSMkr2+LDQQH9+NCGNLzYX8cXmfU6HIyIiIiIiIiIdgJI9Pu7KMb3p3i2Ihz7ZiLXW6XBERERERERExMcp2ePjggP8uOWsdJblF/PZJlX3iIiIiIiIiMjxKdnTAVw2KpnEqBAe/GSDqntERERERERE5LiU7OkAAv1d3DopjZyCUj5dt8fpcERERERERETEhynZ00F8+5Qk+sSG8vv31/LYnE0szy92OiQRERERERER8UHNJnuMMROMMbaRr5J2iE/qBfi5uHBEL/KLynnwk41c+Vy2Ej4iIiIiIiIi8g3+rVj3VmDpEfdr2zgWaUaAnyc3Z4GaWjfZeUVkpkQ7G5SIiIiIiIiI+JTWJHvWWWuzvRaJNOu0fnEE+G2ips7ichmyUmOdDklEREREWiJnJsy+D0oLIDIJJt0Dw6Y7HVXrdZbnISLSyalnTweSmRLNa9dlER8eSGxYIMOTIp0OSURERESakzMT3rsVSrcD1nP73q2e5R1JZ3keIiJdQGuSPa8YY+qMMUXGmFeNMb29FpU0aVSfGO6/eCi7D1Tx1oodTocjIiIiIs2ZfR/UVBy9rKbCs7wj6SzPQ0SkC2hJsqcUeBC4FjgL+B0wGVhkjElobANjzPXGmGXGmGV79+5ts2DFY8qg7gxLiuSR2ZuornU7HY6IiIiIHE9pQeuW+6rO8jxERLqAZpM91tovrbU/sda+Z62db639G3AO0B1P0+bGtnnGWjvKWjsqPj6+bSMWjDHcOSWDHSUVvLFsu9PhiIiIiMhxuLslNv5AZFL7BnKymoq3oz0PEZEu4IR69lhrVwAbgdFtG4601JkZ8YxKieaxOZuorKlzOhwRERERacL85Jsot4FHLwwI8TQ37kgm3UOVCTpqke2Iz0NEpAs4mQbNBs8s4OIAYwx3Ts2g8EAVryze5nQ4IiIiItKI8upa7t6QwfPRd0BkMm4MO4mj8ty/dbhZrJZHTuHuqh9yIKgHFkOBO44vh/+2wz0PEZGu4ISSPcaYUUAGsLhtw5HWGNsvjtNSY3lyXi7l1bVOhyMiIiIix/jnF/nsK6vmtItuhDtW89U1Wxhb+SjPl45yOrRWe2jWBr4IPQv/u9bgvqeY74Y9y192DnM6LBERaUSzyR5jzCvGmN8bY75tjDnLGHMX8BGwA/i71yOU47pragb7yqr55xf5TociIiIiIkc4WFnD059t9gy/7xMDwCm9ozlrQALPfJbHgcoahyNsuUWbi/g8t4gbz+xHaKA/fi7DFWN6k523n9w9B50OT0REjtGSyp7VwAXAC8DHwO3AW8AYa+0+74UmLTGqTwxnZsTz9GebOdiBThhEREREOrsXPt9KSXkNd03NOGr5nVMyKK2o4fkFWxyKrHWstTw0awPduwUxIyvl8PLpo5IJ8DO8nK2WAiIivqYls3H90Vo7zFobaa0NsNYmW2uvt9buao8ApXl3TsmgpLyGFz7f6nQoIiIiIgKUltfw7II8pgzqzrCkqKMeG5IYyTmDe/CPhVsoPlTtTICtsGDTPpZuLeaWiWkEB/gdXh4XHsS5Q3rynxUFaikgIuJjTqZBs/iI4clRTBnUnWcX5FFaruoeEREREac9uyCPg5W13Dklo9HH75iSQVl1Lc8syGvnyFrHWsuDn2wgMSqE6aOTv/H4jKwUDlbW8t7KnQ5EJyIiTVGyp5O4c0oGBytredbHTxhEREREOruisir+8fkWzh/Wk4E9uzW6Tv8eEXxrWC9e/Hwr+8qq2jnClpu9bg8rC0q5dVIaQf5+33h8dJ9oMrqHayiXiIiPUbKnkxjYsxvnD+vJC59vociHTxhEREREOrunP8ujsqaOOyanH3e92yenU1Vbx1PzNrdTZK3jdlsemrWRlNhQvn1KUqPrGGOYkZXCqh2lrNxe0r4BiohIk5Ts6UTumJxOeXUdN768nOX5xU6HIyIiItLl7DlQyb8WbeWiEYmkJUQcd91+8eFcPDKJl7LzKTxQ2U4RttxHa3azdtcBbp+cToBf0x8bLh6ZSGigHy9na3ZYERFfoWRPJ1JaUYvLGJZuLeaKZ7OV8BERERFpZ0/M20xNneW2Zqp6Gtw2KZ06t+Xxublejqx16tyWh2dtJC0hnAuGJx533YjgAC4ckch7OTvVP1JExEco2dOJZOcVYbEAVNe6yc4rcjgiERERka5jZ0kFry7exqWZSaTEhrVom96xoVw6KpnXlmyjoLjcyxG23P9ydrJpTxl3TM7Az2WaXX9GVm8qa9z8e0VBO0QnIiLNUbKnE8lKjSXQ34UBLJCWEO50SCIiIiJdxt/n5GKx3HJWWqu2+/FZaRgMj83xjeqe2jo3f/t0EwN6RHDukB4t2mZwr0hG9o7ilcX5WGu9HKGIiDRHyZ5OJDMlmleuzeL68an4uWD+xr1OhyQiIiLSJXyQs4s3lm5j8sDuJEWHtmrbXlEhXDGmNzOXbef+99c6PhT/4U83smXfIS4c0QtXC6p6GswYk0Le3kMs2qzqchERpynZ08lkpkTz8/MGcsWpKcxcup1tRb5TDiwiIiLSGS3PL+bHr32J28Kc9XtOKFkzLi0Ot4VnF2zhyuec6724OK+IJ+Z6Zgd7ZPamVsVx/rCeRIUG8JIaNYuIOE7Jnk7q5olpuFyGR+dscjoUERERkU7tg1W7qKsfulRbd2J9EzcUHqShhsbJ3ovPLdxCwyCsmlbGERzgx6WZSXyyttAnZxcTEelKlOzppHpEBvO9rBTeWlFA3t4yp8MRERER6bQ27D4IgJ+BAH8XWamxrd5HQ+/FI++3t8qaOpZvLcaYE38uV4xJoc5teX3Jdi9FKSIiLeHvdADiPT+a0I9XF2/jb59u4tHLRzodjoiIiEins2H3QT7fvI9vj0ykX0I4WamxZKZEt3o/mSnRvHpdFn/9eD3ZefsJDfTzQrTH9+ribewvr+beCwZxqKruhJ5L37gwzkiP459fbMXlgrH94k7o5yEiIidHlT2dWFx4EFef3of3cnYevuIkIiIiIm3n4VkbCQ/0555pg7h5YtpJJTYyU6J5akYmEUH+PDxrYxtG2byK6jqemLeZ01JjuXps35N6LqelxrK/vJqHPtnoaP8hEZGuTMmeTu76M1IJC2z/EwYRERGRzm71jlI+WrOba8b1JSo0sE32GRUayLVnpPLJ2kJWFZS2yT5b4l+LtrKvrIq7pmac9L4a+hdZWt/3R0RE2oaSPZ1cdFgg14zry0drdrN6R/udMIiIiIictJyZ8PAQuDfKc5sz0+mIjvLQrI1EhgTwwzP6tul+rxnXh6jQAB6ctaFN99uUsqpanpq/mTMz4hnVJ+ak9ze2Xxz+9VO2+/udWA8jERE5OUr2dAE/HNeXyJAAVfeIiIhIx5EzE967FUq3A9Zz+96tPpPwWbGtmDnr93D9+FS6BQe06b4jggO4YXw/5m3Yy/L8/W2678a8sHALxeU1bVLVA18PRzMGzhnSQz17REQcoGRPFxAZEsD141OZvX4PK7ZpzLSIiIh0ALPvg5qKo5fVVHiW+4CHZ20kNiyQq8f28cr+rxqbQlx4IA9+4t2LdaXlNTyzII8pg7ozLCmqzfY7eVB3zhncg/kb91JZU9dm+xURkZZRsqeLuHpsH2LCAlXdIyIiIh1DaUHrlrejxXlFLNi0jx9N6EdYkHcmtw0N9OfGM/vxxeYivti8zyvHAHhuYR4HK2u5c0rbVPUcaUZWCiXlNbyfs6vN9y0iIsenZE8XERbkz4/O7Ef05neo+utAnx37LiIiIgJgIxMbfyAyqX0DOYa1lgdnbSQhIogZWSlePdaMrBS6dwvioU82YuubHrel/Yeq+cfCLZw/rCcDe3Zr8/2P7RdLalwYLy/Ob/N9i4jI8SnZ04VcFb6EPwc+T9Chnfji2HcRERGRBnMTf0S5PXqGq3IbyJJ+tzgUkcfnuUUs2bKfmyemERzg59VjBQf4ccvENJblF/PZprav7nl6/mYqauq4Y3J6m+8bwBjDFWN68+W2Etbs1EQhIiLtScmeLiRw/u8JoerohT409l1EREQEoKS8mjvWZfDP2DshMhkwuLsl8Y/oO7hsUTIzl253JC5rLQ98soFekcF899Tkdjnm9NHJJEaF8NAnG9q0umfPwUr+uWgrF41IJC0hos32e6xLM5MJDnDxcvY2rx1DRES+ScmersSHx76LiIiINHhk9iYOVtZw1vRb4I7VcG8JrjvXcO3NP+OM9Hh++p8cXs5u/6FBczfs4avtJfx4UjpB/t6t6mkQ5O/HrZPSWFlQyqfr9rTZfp+Yu5maOsutk7xT1dMgMjSAacN68e5XOzhYWePVY4mIyNeU7OlKmhrj7vDYdxEREZEGW/Yd4qVF+Vw2Opn+PY6uOAkO8OOZ72UyaUACv3pnNS98vqXd4rLW8uAnG+kdE8olme177vTtU5JIiQ3loVkbcbtPvrpnZ0kFry7exiWnJNEnLqwNIjy+GVkplFfX8faXO7x+LBER8VCypyuZdA8EhBy9LCDEs1xERETEB/zpw3UE+bu4o4nZoYID/HhyRiZnD+7Ob99byz3vrubxubkszy/2alxPzstlzc4DXDiiFwF+7XsKHeDn4vbJ6azbdYDb3/jqpJ7r8vxifvTycuqsmx9PSmvDKJs2PDmKoYmRvJyd75VG0yIi8k1K9nQlw6bDtEepjUjCbQ0Hg3vCtEc9y0VEREQclp1XxMdrCvnRhH4kRAQ3uV6gv4vHrjiF0/vF8q9F+Tzw8QaufC7bawmfZVv389ePNwLw7II8ryeWGpMUHYoB/rty5wk/1+X5xVzxbDYrC0oBQ+GBqma3aSszsnqzsbCMJVv2t9sxRUS6MiV7upph0/G/aw2TI97h1u7/UqJHREREfILbbbn//XX0jAzmh+NSm10/wM9FVr9YACxQVeMmO6/tZ6wCePGLrTTUo9TUusnOK/LKcY7nyCRJ9QnGkJ1XRFWt23PH2nZ9HtOG9yIi2J+XF6tRs4hIe1Cyp4salx7H4i37qW54wxcRERFx0Lsrd7BqRyk/Pac/IYEta348tl8cwQEuDJ6Ez6bCQ20+TKi2zs2K/GIM4GcgwN9FVmpsmx6jJbJSYwkK8Jy6Wwuj+0S3eh+9YzzD+Q3t/zxCA/25JDOJj1bvYu/B9qsoEun0cmbCw0Pg3ijPbc5MpyMSH6FkTxd1eloc5dV1rNjW/mXIIiIiIkeqqK7jLx9tYFhSJBcOT2zxdpkp0bxybRZ3Tc1g6qDuvPPVDn7//ro2Tfi889VOdpZWcvfZ/blzan9euTaLzJTWJ1pOVsNzvXhELyywdV95q/fx6bo9BPq5uGliP0eex5VjUqips8xctr1djyvSaeXMhPduhdLtgPXcvnerEj4CgP+JbGSM+Qg4G7jfWvurtg1J2sNp/WJxGfg8d58jV6dEREREGjy/MI9dpZX87bIRuFymVdtmpkSTmRKNtZbfvreW5xduoabOzb3TBrd6X8eqqXPzyOyNDEnsxo8m9MOYk9vfycpMieaU3lFs3neIR2Zv4qKRiQT6t+za7YbdB/nvyp3ceGY/7j57gJcjbVxaQjinpcby6uJt3HhmP/xO8vcj0uXNvg9qKo5eVlPhWa52HV1eqyt7jDGXA8O9EIu0o27BAQxPjmLBJu+MbRcRERFpiT0HK3ly3mbOHtydMSdxAcoYw2+mDeKG8an8a1E+v3h71UlPU/7msgK276/grin9HU/0NDDGcOeUDHaUVPBGKypk/vbpRsIC/bn+jOb7IXnTjKwUdpRUMH/jHkfjEOkUSgsaXWybWC5dS6uSPcaYKOBh4E6vRCPt6oy0OHIKSiitqHE6FBEREemClucXc+0/l1FZU8f/nTvwpPdnjOH/zh3Aj89K4/Wl27n73znUnWDCp7Kmjr/P2cQpvaOY0D/+pGNrS2dmxDMqJZrH5myisqau2fVX7yjlw9W7uWZcX6LDAtshwqZNHdyd+IggXlqU72gcIp1CZFKji3fYWC5/JpuXFm1lz4FKlucX8/jcXEdmEhTntLay5y/AGmvta94IRtrX6WlxuC0s2tz+M0qIiIhI19YwDXhOQSkYw/5D1W2yX2MMd03tz51TMvjPigLueOMrautaPyHF60u2sau0krum+k5VTwNjDHdOzaDwQBWvtGB2q4dnbSQyJIAfjuvbDtEdX4Cfi++OTmbexr1s39/6vkMicoRJ91Blgo5a5PYPYWXGrewtq+LX767h1D/M5pKnvuCBjzdwxbPZSvh0IS1O9hhjxgHfB27yXjjSnkb2jiY00I/PczWUS0RERNqXt6cBv3VSOj87ZwD/XbmTGc8v5u9zNrX4Q05FdR2Pzd3MmL4xjO3nm70Nx/aL47TUWJ6cl0t5dW2T6325rZjZ6/dw/fhUIkMC2jHCpl1+am+w8JM3V+qDp8jJGDad+10/Yr9/d8BAZDKuCx7l/Ctv49M7z2TWHeMZlxaHtZ4ZC6tq3dz8ynKemJfLln2HnI5evKxFyR5jTADwNPCAtXaDd0OS9hLo72JM3xgWKtkjIiIi7SyovrGwN6cB/9GEflw9NoXsvP08+MlGrnyuZVe1X8reyr6yKp+s6jnSXVMz2FdWzb+OMyTqoVkbiQkL5OqxfdovsGbsKq3EGFi8ZX+Lfyci8k17DlTyr0On8vaEj+HeErhj9VGNmdO7R3DHlAyCA1y4DPi7DOHBAfzlow1MfGAe5/ztMx75dBPvfLmDx+e2PCEuHUNLZ+P6GRAC3N+SlY0x1wPXA/Tu3fvEIpN2MS49nrkb1lJQXE5SdKjT4YiIiEgXUFvnZuay7fToFsQVY1I4PS3Oa9OAx0cEY/Bc1a6scbNw097jHqusqpYn523mjPQ4Tu0b45WY2sqoPjGcmRHPU/M3c+WY3kQEH125s2TLfhZs2scvzxtIWNAJTcLrFUdWcVXXusnOK3JkOnuRji6noBSA4UmRTa6TmRLNK9dmkZ1XRFZqLJkp0ewoqeCj1bv5aPUuHv504+F1/V2b+OO3h3JJZpJPJ7qlZZqt7DHG9AZ+CfwaCDLGRNU3auaI+35HbmOtfcZaO8paOyo+3rca2snRzkiPA9BQLhEREWk3byzbzsbCMu69YDC3Tkr36gf9rNRYggJcNHxsmb1uDxXVTTc1fvHzLRSX13DX1P5ei6kt3TU1g5LyGl74fOtRy621PPDJBuIjgpiRleJMcE3ISo09asp4b1R1iXQFOQUluAwM6tXtuOtlpkRz88S0w/9rE6NC+OG4vrx541humtDv8P/HWrfl7n/nMP6vc7n//bUszy/2zGqYMxMeHgL3Rnluc2Z694lJm2jJMK5UIBh4GSg+4gvgJ/XfD/VKdOJ16QnhJEQEsTBXTZpFRETE+w5W1vDwrI2c2ieGswf38PrxGq5q/+Ts/vz4rDRW7yzl6heWcKjqm31uSitqeOazPCYPTGBEcpTXY2sLw5KimDKoO88uyKO0/OsZVj/PLWLJlv3cPKEfIYF+x9lD+2v4nZyZEY/bQrgPVR2JdCQ5O0rJ6B5BaOCJv4YmDexOUIALP+MZXnvThFT6xYfz4hdb+c6TX3Dv/fdS/fYtULodsJ7b925VwqcDaMlfxVfAxEaWz8WTAHoeyG3DmKQdGWMYlxbHvI17cbstLpfK9URERMR7npq/mX1l1Tx/1cB2GyaQmRJ9+Ip2WkI4d85cyff/sYQXfzD6qKFPzy/I40BlLXdMyWiXuNrKnVMyOPeRBTy7II+fnN0fay0PztpAr8hgLh/jmy0VMlOiefiyEWT9cTavLM7nvguHOB2SSIdirSWnoJRJAxJOaj+NDfMCT/J7zvpCxr9/O4G26uiNaipg9n1H9QcS39NsZY+1tsRaO+/Yr/qH8+vvl3k3TPGm09Pi2H+omnW7DzgdioiIiHRiO0oqeG7BFi4a0YvhDlXOXDgikccuH8nK7SXMeH7J4WqY/Yeq+cfnWzlvaA8G92q6/4UvGtizG+cP68kLn2+hqKyKeRv28uW2Em45K50gf9+q6jlSTFgg5w/tyVsrdjRaaSUiTSsormD/oWqGtcH/0mOHeQFEhgRw8cgkYmv3Nr5RacFJH1e8q8VTr0vnNa6+b8/CTerbIyIi0uH5cG+Fv360HoC7zxngaBznDu3JkzMyWbfzAE8//kfqHhpM9F8T+Iib+HXvNY7GdqLumJzOlLr5+D06jDNfzyA75DamBy1yOqxmzcjqTVlVLe9+tdPpUEQ6lFU7mm/O3CYik1q3XHzGCSd7rLXGWvurtgxGnNG9WzDpCeGagl1ERKSjy5np6aXgg70VVm4v4Z2vdvLDcX1JjApxOhymDOrOO+N3cEvZ3/E7UIDBkmT20fOzn/rEz6u10nZ/yF8CnyeqejcuLD3sXvzfv83nn8spvaMZ0COCl7LzsdY6HY5Ih7GyoIQAP0P/HhHePdCkeyDg6P/Z5TaQxam3ePe4ctJU2SOAp7pnyZb9VNY0PTuFiIiI+LjZ93l6KRypobeCg6y1/P79tcSFB/KjCf0cjeVIg9b+jVBTffRCH/h5nZDZ9zXdV8OHGWOYkZXCul0HWLGtxOlwRDqMVQWlDOzZzftDNYdNh2mPQmQyYLCRSbwQcwdXLu7Ngk1NDPESn6BkjwAwLi2Oqlo3y/OLm19ZREREfFNTPRQc7q3w8ZrdLN1azB1TMo5qiOw4H/15nZAO/FwuGplIWKAfr2TnOx2KSIfgdltWFZQyNLGd+osNmw53rIZ7SzB3rOF7N9xNWkI4P3p5BWt3qu+rr1KyRwAYkxqLv8toKJeIiEhH1kQPhUMh3p/ivCnVtW7++OF6MrqHc9moZMfiaFRn6kXRgZ9LeJA/F5+SyP9W7aL4UHXzG4h0cVuLDnGwqpbhSVGOHL9bcAAv/GA04UH+/ODFJewsqWh+I2l3SvYI4HmTPaV3tJo0i4iIdGST7qHaBB21qMoE8fPSi/nNu6upqXO3e0j/WrSV/KJyfnHeQPz9fOzUs5FeFASEeJZ3NB38uczISqG61s2by7c7HYqIz8sp8DRnHurt5szH0TMyhBevGU15VR1Xv7CE0ooax2KRxvnYO6446fS0OFbvLNUVFRERkQ7KDr2UP/j9iH1+CYCByGT8L/w7PcZ9n38uyud7zy9mfzu+z8/fsIe/fryB4UmRTOif0G7HbbFjelEQmey5P2y605G1Xgd/LgN6dGN0n2heWbwNt1uNmkWOZ2VBCcEBLtITwh2NY0CPbjz9vUy27DvElc9m8+jsjWoL4kOMt7vejxo1yi5btsyrx5C2sTy/mO88+QWPX3EK5w/r6XQ4IiIi0kprdpZy/qML+ct3hjF99NFDpt7+soCf/WcV8eFBPPv9UQzq1c2rsSzPL2b604uoc1uC/F28el0WmSnRXj2mdGzvfrWD217/in9dcyrjM+KdDkfEZ13y5BdY4D8/Gut0KAA8PGsDj8zOBSA4wMUr1+r/fXsxxiy31o5q7DFV9shhw5MiiQjyV98eERGRDmrW2kKMgbMGfrOK5uKRSbx5w2nUuS3fefIL/j57E4/PzfXaVdgPV++irr5Co7bOTXZekVeOI53HOUN6EBsWyMudqVFzzkx4eAjcG+W5zZnpdETSwdXWuVm9s5RhDg7hOlagvx+m/vuqGv2/9xVK9shh/n4usvrFsjBXU+iJiIh0RLPWFpLZO5q48KBGHx+eHMV/f3w6vWNDeXDWRh74eANXPpftlYTPqoISAPwMBPi7yEqNbfNjSOcS5O/HpaOS+XRdIbtKO0HD15yZ8N6tULodsJ7b925VwkdOSu7eMipr3I41Z25MVmosQQEuDGBB/Xt8hJI9cpRxaXFs31/BtqJyp0MRERGRVthRUsGanQeYMqj7cddLiAjm/KGe2bksUFnj5rONe9o0luy8IhZvKeaKU5O5c2p/lfRLi105pjcWeG1JJ2jUPPs+qDkmaVVT4VkucoJytjvfnPlYmSnRvHJtFndOzSAzJZrnFuTx6dpCp8Pq8pTskaOMS48DYIGqe0RERDqU2es8J9aTm0n2AJyeFk9w/VVYgH+v2EHe3rI2icPtttz//jp6Rgbz628N5uaJaUr0SIslx4RyZkY8ry/Z5sjscW2qtKB1y0VaIGdHCRFB/vSNDXM6lKNkpkTz47PSeemHpzIkMZIfv/YlK7eXOB1Wl6ZkjxwlNS6M2LBA/vnFVnVSFxER6UBmrS0kNT6MfvHNz87ScBX2J2f357cXDKa8qpYLH/+ceRtOvsLn3ZU7WLWjlLvP7k9IoN9J70+6nhljUsg6NJvaBwZ37F43kUmtWy7SAjkFpQxJjMTlMs2v7IDQQH+ev2o0cRGBXPPiUvKLDjkdUpelZI8cZcW2EkrKa9hYWOa1MfwiIiLStg5U1pCdV9TsEK4jZaZEc/PENK4a24f/3jKOxKgQrnlxKU/P38yJztZaUV3HXz7awNDESC4akXhC+xA5q2Y+fw58npCKnXTkXjd20j1UcnT/LOsfApPucSgi6eiqautYt+sAw5J9ZwhXY+IjgnjxB6dSZy1Xv7CU/YeqnQ6pS1KyR46SnVeEu/4Er7pWndRFREQ6gnkb9lJTZ5naimTPkZJjQnnrprGcO6Qnf/xwPbe/8RWVNXWt3s/zC/PYVVrJr84f6LNXncX3uebcRwhVRy/sgL1uZvmN56fVP+RQSE8shgJ3HJ+m/xKGTXc6NOmgNuw+SE2dZVhilNOhNKtffDjPfX8UO0oquPafS0/oPUVOjpI9cpSs1FgC/T1/Fi5jNHOGiIhIBzBrbSFx4YGMSD7x3jihgf48dsVI7j67P/9duZPzHvmMP324rsVVvnsOVvLkvM1MHdSdMTp/kJPRCXrduN2Wh2ZtZFXM2QT9ZC3m3hLu7PUyf9w+9IQr50RWFniaM/vStOvHM6pPDH+7bARfbi/h9te/os6tv/32pGSPHCUzJZpXr8siPiKIAT0i1FBRRETEx1XXupm3fg+TBnTH7ySraYwx3Dwxjf87ZwB5+8p5an4elz/TsmHdD8/aSFWtm5+fN/CkYhDpDL1uPli9i/W7D3LbpHT8/TwfuaaPTiZv3yGWblWbBDkxqwpKiAkLJCk6xOlQWuy8oT355XkD+WjNbm59bQWPz81Vq5B2omSPfENmSjQXjejFxsIyKqpVbiciIuLLlmzZz8Gq2lb162lOrdvSkDeqrnPzzGebj7v+ht0HeWPpdr53Wgp943xrhhjpgCbdAwFHf5i1AR2n102d2/LwrI2kJ4QzbXivw8vPG9qD8CB/Zi7rBNPKiyNyCkoZmhiJMR1rmOy1Z6Ry/tAevL9qNw98vEG9YduJkj3SqHHp8VTXuVmydb/ToYiIiMhxzFq7m+AAF6enxbXZPhuGdfsZcBn4eE0hv35ndZNTYd//wTrCg/y5bVJ6m8UgXdiw6TDtUYhMPtzrZsXw33aYXjfvfrWDzXsPcceUjKOq7UID/Zk2vCfv5+ziYGWNgxFKR1RRXcfGwoMM7yBDuI41sGc3ACzqDdtelOyRRp3aJ4ZAPxef5+5zOhQRERFpgrWWWWsLOSM9vk2nOW+Ymv3Oqf1544bTuGF8Ki9l53Plc4vZV3Z049x5G/bw2ca93DopnajQwDaLQbq4YdPhjtW47ynm2pgXuG1Neodo8FpT5+aR2ZsY1LMb5wzu8Y3HLx2VTEVNHe/n7HIgOunI1uwsxW1haFKU06GckNP6xRFc3xvWbSEyJMDhiDo/JXukUSGBfmSmRLNgk5I9IiIivmrNzgPsLK1s0yFcDRqmZh/dJ4afnzeQv102gpXbS7jwsc/ZPv9FeHgI9t4oBr4+lmu6LeX7p/Vp8xhE/FyGX50/iILiCl78YqvT4TTrrRUF5BeVc+eUjEZnpBuZHEV6QjhvaCiXtFJOB2vOfKzMlGheuS6LWyam0SsymL9+vIHcPWVOh9WpKdkjTRqXHse6XQfYe7Cq+ZVFRESk3c1aW4jLwKQBCV4/1kUjE/n3jWOZXDOfuDl3Q+l2DJbudi+/qHuSwLX/9noM0jWNS4/jrAEJPD4nl6Iy3z0vraqt49HZuQxPjmLSwMZfk8YYpo9K5sttJWwqPNjOEUpHllNQQvduQXTvFux0KCcsMyWan5zdn9evP40AP8PVLyxhz8FKp8PqtJTskSaNqx/7/8VmVfeIiIj4ok/XFZKZEk1seFC7HG9oUiT3hP6bEFN91HL/ukqYfV+7xCBd0y/OG0B5TR2PzN7kdChNmrl0OztKKrhrSsZxG+hefEoi/i6jRs3SKjkFpQzroEO4jtU7NpR/XD2aorJqfvjiMg5V1TodUqekZI80aUhiJJEhASzUUC4RERGfs6OkgjU7D3hlCNfx+B3c0fgDpQXtGod0LWkJEVxxam9eWbyN3D2+VxFTWVPH3+fkMrpPNGekH79Zelx4EJMGJvDWih1U1zbe9FzkSAcqa8jbd4hhiR1zCFdjhiVF8dgVI1mzs5RbXl1BbRMTAMiJU7JHmuTnMoztF8vnufuw1jodjoiItEbOTHh4CNwb5bnNmel0RNLGPl1bCMDkge2b7CEyqXXLRdrI7ZPTCQ3w448frHc6lG94OTufPQeruHNK/xZNi33Z6GSKDlUzZ/2edohOOrrVDf16kqOcDaSNTRrYnd9dNIS5G/by7xcfwuq8pU0p2SPHNS49jp2lleTtO+R0KCIi0lI5M+G9W6F0O2A9t+/dqhOnTmbW2kL6xYeRGh/evgeedA8EhBy9LCDEs1zEi2LDg7j5rDRmr9/jUzPGfp67j4dmbWRoYjdO6xfbom3Gp8fTvVuQhnJJi+TsqE/2dKLKngZXjknh70M2ccG2P2N03tKmlOyR42ro2+NLb6giItKM2fdBTcXRy2oq1FOlEymtqCE7r4gpg745tbPXDZsO0x6FyGTAeG6nPepZLuJlV4/tQ2JUCL9/fx11bucrz5fnF3PVP5ZQXl3HhsIylucXt2g7fz8X3zkliXkb9lB4QA1q5fhyCkpIjgkhOizQ6VC84lt7nyP0mF5wOm85eUr2yHGlxIaRHBOiKdhFRHzQ8vxiHp29kbdWFPDeyp08NGsjN7+yAndTvVPUU6XTmLdhD7Vu2+79eg4bNh3uWA33lnhuleiRdhIc4MfPzh3Aul0H+M8K5/+nfbbR81oEqKtzk51X1OJtp49Kxm3h38udfx7i2zpTc+bGGJ23eIW/0wGI7xuXFsf/Vu6its6Nv5/ygyIi7WF5fjHZeUVkpcaSmRLNoapaNu8tY1NhGbl7y1i6ZT/L84s58rq2y0DvmFD2+8UTV9dIHwj1VOk0Zi4rIDTQTz31pEuaNqwnL3y+hT9+sI6dJRWckR5PZkq0I7HsLvVMBe8yEODvIiu1ZcO4APrEhXFq3xjeXLadmyb0a1GvH+l6isqqKCiu4HtZKU6H4j2RSfVDzxtZLidMyR5p1ri0eF5bsp2VBaWOvZGKtLucmZ7S0dICzxvNpHt05VraznH+vkrKq3k/Zxf3vreG2jqLMRATFsi+sq/LmwP8DN2CAw4nelwGvpfVh5+fN4DgAD/Iud8z1v2IoVw1rmAC1FOlU1icV3R4ePWM5xfzyrVZen+WLsUYw6WZSfzi7dU88ukmnpq/2ZHXQUl5NR+s2sWpfaI5s3/C4eR8a1w2Kpm73lzJki37GdOKRJF0HYf79XTiyh4m3fON8xb1gjt5zSZ7jDFnAz8DBgHRwF7gC+Bea+1a74YnvmBsv1iMgYWb9ulkUrqGhua2DW84DU3iQAkfOXmN/H1Vv30LT8/ZxD/LxrCvrOqo1a2F+Iggrh7bh7SECNISwkmJDSWnoJQrn8umptZNgL+LC0b08iR64Ou/0/qE0v6ABO4r/w5XRU1hZDs+VfGOfx8xdKWm1jNsRO/P0tUUl9cAYHHudfDsgjzKqmu576IhDOjR7YT2ce7QHvzmv2uYuaxAyR5p1MerdwNQ6+7EU5Mfcd7iLi2gyC+e+Gn367z7JLWksicGWA48gSfR0xv4PyDbGDPUWpvvxfjEB0SHBTKkVySf5+7jtsnpTocj4lV5e8uIe//XdGuqua3edORkNdI8OdBWcdmBF9k28Fukd/fMrPTgJxuprfMkcn5/0dBvfIjJTInmlWuzjhrqdZRh0w//vfpX1rD0bwvImbmS9289g5BAP+89P/G6sspaAPxOYNiISGeRlRpLkL+Lqlo3YNr9dVBUVsULn2/l/KE9TzjRAxAa6M+04b34z/ICkqKDGZ+RoOStHLY8v/jwjG3X/WtZ567krD9vuf9/a3k5O5+cQVMJcjqmDq7ZZI+19jXgtSOXGWOWAOuBS4AHvROa+JJx6XE8+1keZVW1hAdp9J90fIf7ofSNISzYnw9X7eaj1bvZUHiQvKDd0NiweTWJkzZgSwsa/fNKcO/lr5cOP3w/MyWm6UTO4XWiW3TS1y04gL9eMowrnlvMnz9az70XDD7R8MVh1lpWbi85qWEjIp1BZko0r16XxZ8+XMfSrcUEB7RvX8mn5m+msqaO2ydnnPS+hiVF8tqSbTw6O5enP8vr3B/opVWy84pomHSuq1Ryjukbw/MLt7Byeymn9o1xOpwO7UT/Kza0ma9pq0DEt41Li6PWbVmypeUzDIj4quVb93P5s9k88PEGLnlqEef8bQGPztlEZEgA93xrEO5ujTeDqwzt2c6RSmfz5bZidtHE1edjmhBmpkRz88S0NjupG5sWx9Vj+/DiF1sP93uRjmfNzgPsLK3k0lHJbfr3IdIRZaZE89xVo4kODeD+99e1W8PyPQcq+deifC4amUhaQvhJ72//Ic/w3SOHpIkAZCREAJ5rkF2lkvPUvjEY4+lPJyenxckeY4yfMSbQGJMOPA3sBl73WmTiUzJTognyd2kKdumw3G7L8vz9/P5/a/nBi0uprnVj8ZxYTRnUncW/mMTMG0/jmnF98Z/yG09TuCNUEsT/lV7EM59t1uw3ckKWbt3P955fwnOB38Ptf/TfV3s1IfzZOQNIjQvj7jdXcqBS12s6ok/WFuIyMGmgQ1Oui/iYyJAAbp+cwRebi5izvpFZCL3g8bm51Lktt01qm/YGWalx+Ls8NZ/+fl3jA720jK2fiuGKMb27TMVXVGggA3p0Y/GW/U6H0uG1prJnMVAFbASGAWdZa9vnP6o4LjjAj1P7xuhqsHQYy/OL+fucTbz4xRbueXc1p/1pNt95chH/WpRPevcIAvwMfgaCA1zceGY/EiKCv9542HSY9ihEJgPGc3vBo1QPuoQ/fLCeO974isqaOseem3Q8izYXcdU/lpDQLYjrb/4/XBcc8/c17dF26QcVEujHg9OHs/tAJfe9pzkWOqJP1xYyKiWGmLBAp0MR8RlXjOlNanwYf/hgHTV13m1iu6OkgteWbOfSUUmkxIa1yT4zU6J59vuZuAxMHdS9S3ygl5bJKSjFz2X41fmDutTfxZi+MSzPL6a6thM3pW4HrWm+8j2gG5AK/ASYZYwZZ63deuyKxpjrgesBevfu3QZhii8YlxbHHz9cT+GBSrp3C25+AxEH1NS5+deirfzhg/XU1Q9yDvQzTByQwLlDenLWwAS6BQd83bOnqX4XRzS3BQgGHh9peXxuLg98spHNew9xy1lp5O4pU88MOa6Fm/Zx7b+WkhwdyivXjfEkFo/5+2pPI3t7hof9fU4uZw/uwZRBqhDpKAqKy1m76wC/OG+A06GI+JQAPxc/P3cg1/1rGa8t2cb3T+vjtWM9NicXgFvOattJSyYO6M7Zg3vw+eYiqmvdBPq3bw8i8U05O0rJ6B7R5SZWyEqN4cUvtrJqRwmZKerbc6Ja/F/EWrvOWru4vmHzJCAcz6xcja37jLV2lLV2VHx8fBuFKk4blx4HeD64iDgmZyY8PATujfLc5syksqaOWWsLuXPmV2T+bha/+9+6w4kel4GbJqbx9PdGcdHIRLoFBwAn1g/FGMMtZ6Xz7PdHkbunjBteWs6Dn2zgyueyWZ5f7I1nKx3c3PV7uOafS+kTG8br12cdXUHmoB+flc7gXt34+Vs5FB0z1bv4rk/XFgIwZVAPhyMR8T2TByZwWmosf/t0k9eGqW4rKufNZdu5/NRkEqNCmt+glaaPSmb/oWrmrC9s831Lx2OtJaeghGGJkU6H0u5O7esZypidp6FcJ+OEUsbW2hIgF0hr02jEpw3s0Y3YsEAN5RLn5MyE926F0u2AhdLtVL19C7/+3T1c969lfLq2kMmDuvN/5w4g2N+Fn4FAfxdnpLdt0nnKoO5cPiYZALeFqho1U5SjLc8v5o7Xv+Tafy0lo3s4r12XRWy470wgGujv4qHpIzhQUctNr6zg8bmblLDsAGatKyQtIZy+cW0zdESkMzHG8MvzB1JcXs3jc3O9coxHZm/Cz2W4eaJ3PgKNz4inR7dg3li63Sv7l46loLiCkvIahiV3vWRPTFggGd3D1bfnJJ3QHNrGmO7AAOCVtg1HfJnLZRibFsfC3H1YazGmscmDRbxo9n1QU3HUoiBbxS+C3uRbM27ntNTYw2XPo/s0P231yTh/aC9eyd5GVX2j51UFpdTUuQnwU9l1V7c8v5jvPrOImjqLMfDTswcQ7YP9Vfr3iOCy0cm8lJ3Pki37CQrI7TLNHzui0ooaFuft57rxqU6HIuKzhiRG8u2RSbywcCszxqSQHBPaZvvevLeMt78s4Ifj+pLgpXYGfi7DdzITeXLeZnaXVtIj0jeqQcUZKwtKABiWGOVoHE4Z0zeWt1YUUFvnxl/n1yek2Z+aMeZtY8yvjTEXGmMmGmNuAOYDtcCDXo9QfMq4tFj2HKxi054yp0ORLsiWFjS6PLpmD2dmxB81vr2tp60+VmZKNK9el8VPpmZw4YhefLRmN1c+t1hDYoQXPt9CTV39MEJg1Y5SZwM6ju7dPNVGFqjWdL8+bd6GPdS6rXosiTTj7rP743LBnz9a36b7/dunmwgO8OPGM/u16X6PdWlmMm4L/1nR+DmPdB05BaUE+rno3yPC6VAcMSY1hkPVdazeecDpUDqslqTIsoGLgH8C7wN34kn2jLDWbvReaOKLxtUPh9EU7NLedpZUsNcV1/iDkUntG0y9zJRobjkrnUe+O5K/XTaCldtLuOCxz1mz03c/3It3/Xt5Af/L2YXLgJ+BAH/fnkL3tH5xBB2RJM3qqyaIvuqTtYXEhQcxIinK6VBEfFqPyGCuH9+P/+XsarPhqet3H+B/OTv5wel9vD4kt09cGGP6xjBz2XastV49lvi2nIISBvbq1mWbdZ9af06yWBeiTlizfznW2j9bazOttVHW2lBrbX9r7Q2NzcIlnV9iVAh948LUt0fa1dKt+7ngsYU8UPdd6vyOKWkOCIFJ9zgT2BEuGpnImzeehttavvPkF/wvZ6fTIUk7e23JNu7+90rOSI/j5R+O4c6p/X1+WFRDhdqUQd1xW1i7+6DTIUkjqmvdzN+wl8kDE3C5NIRapDk3jE8lPiKI37+/9qQTJsvzi/nxq18S4u/HdWe0zzDKy0Ynk19Urn4lXZjbbVm940CXbM7cICEimNT4ML0OTkLXTBPKSRmXFsfnuft4dLYaeor3vbZkG1c8m01EcADX3/wz/C78O0QmA8ZzO+1Rx6awPtawpCjeveV0hvSK5JZXv+QvH61n6db9PD43V6+VTu5fi7by87dWMSEjnme/P4qxaXFeHUbYljJTonnme5mMz4jnD++vY+u+Q06HJMfIziuirKpWQ7hEWigsyJ+fTM3gy20l/H1O7gm/Dy/PL+byZ7PZtKeM6jo3m/e2z//Hc4f0JCLIn5lq1Nxl5e07RFlVLcOSum6yBzx9e5Zu2X94ll1pHSV7pNV6RgVTVevmb59u1JTT4jXVtW5+9c4qfv7WKk7rF8c7N51OWkKEJ7Fzx2q4t8Rz6yOJngYJEcG8el0Wl5+azBPzNnPZ04s0PXsn99yCPO55dw1TBnXnqe9lEhzg53RIrWaM4S/fGUaAn+GuN1fqpMrHzFpbSEiAH6enNTGUVUS+4ZLMZFJiQnl41sYTfh/OziuiutYNeKbBbq++ZiGBfkwb0YsPVu/y2jTy4ttyGpozd/Ghu1mpMRysqmXdLvXtORFK9kirVVbXAZ4pp2vU0FO8YM76Qs56cB4vZ2/jhvGpvHD1aCJDA5wOq8UC/V384eKhTK4fGuO2an7bWT0+N5ffv7+O84b24IkrTyHIv+Mlehr0iAzmdxcNYXl+Mc98lud0OFLPWsun6woZnxHXIROJIk7xcxnGpMZgOfFz1uhQz0yKhvbvwXbZqGQqa9y8t1LDwruinIJSQgL8SEsIdzoUR43p63nN6Rz6xCjZI612Zv8EGmZd9/Xmo9LxvLlsOz98cRkFxRUE+BmmDu6BXwfsUWGM4Udn9iOwfqpIt6XLNtjrjJZv3c8Vz2bz1483cOGIXjz63ZEEdIJpQS8Y3ovzhvbgoVkbdBXNR6zecYBdpZVMGdTD6VBEOpzLRvem4RTC36/156wfrNpFZIg/t01Ob/cebMOSIunfPYKZyzQrV1eUU1DCkMRuHfIcuC31iAwmJTZUfXtOUMc/M5V2l5kSzXdOScQAz35/VIfoSSEdw3srd/Lzt1bRMIDE7W6/kmlvyEyJ5rXrs7hhfCp9YkP5wwfreGr+Zs2u0cEt37qfy57J5ovNRfgZw4ysFPw7QaIHPEnK3180lMiQQO6cufLw8AVxzqy1u3EZOGtAgtOhiHQ4mSnRPDx9BKb+NdSac9bsvCIW5u7jx2elc/vkjHY/3zXGMH10Miu3l7BBzfO7lNo6N2t2HujyQ7gajOkbw5It+3FriHmrdY6zU2l3l2YmY4Hy+iFdIiejzm3580fr+fFrX5KWEE6Qv6tDTFvdEpkp0fz8vIF8cNsZnDe0J3/6cD23vf4VFXrtdEjWWv700XpqD59wWJZ0sqtNMWGB/OnbQ1m36wCPzN7odDhd3idrCxmVEkNMWKDToYh0SBeOTOTKMb35ZG0huXvKWrSNtZaHPtlI925BzMhK8XKETbt4ZCIBfoaZy9SouSvZWFhGVa27yzdnbjCmbyylFTWsV9Kz1ZTskRMysnc0oYF+moJdTtqByhqu/edSnpy3mctPTea/t4zj1euyOsS01a0RGujPY5eP5O6z+/Nezk4ueeoL9me/DA8PgXujPLc5M50OU47DWstv31vL0q3F+LlMp0lINmbyoO5MH5XEk/M2s2KbGos7Zfv+ctbvPqhZuERO0u2TMwgN8ONPH65r0foLc/exZOt+bpmY5mivrJiwQKYM6s7bX+5QpWUXoubMRxuTGgPA4i0dt9rfKf5OByAdU6C/izF9Y1i4SckeOXGb95Zx3b+Wsa2onN9dNIQZY3pjjCEzJbrTJHmOZIzh5olpDOwZwcev/Z2Qj54Gqj0Plm6H9271fO9jM4yJZ0jhL99ZzWtLtnHtuL6cO6QH2Vv2k5Ua2yn/VgF+/a1BfJ5bxM2vrOCy0cmckR7faZ+rr/p0XSGAkj0iJykuPIibJqbx54/W80XuPsYeZ2Y7ay0PfLKRxKgQpo9ObscoG3fpqGQ+WLWb2esKOXdoT6fDkXaQs6OUiGB/+sSGOh2KT0iKDiUpOoTFefv5wel9nQ6nQ1Flj5yw09PiyNt3iB0lFU6HIh3Q3PV7uOixzykpr+GVa8fwvawUjOkaTejOGtCd30e8RUhDoqdBTQXMvs+ZoKRJdW7LT/+Tw2tLtnHThH788vyBZPaJ4eaJaZ06+RERHMB14/uyq7SSv3266YSmLZaTM2ttIekJ4fSJC3M6FJEO7wen9yExKoTfv7+OuuP0/pizfg8rt5fw47PSfGKGxfHp8fSMDOYNDeXqMnIKShiWFNllzotbYkzfWJZs3a++l62kZI+csDPS4wH4XNU90grLt+7n+88v4QcvLiU5JpT/3nI6YzrhMJjmBJQ1PpWqLS0gb2/LegqI99XWublr5lf8e3kBt09O5+6z+3epk69DVXU0PNvqE5i2WE5caXkNi7fsZ7KqekTaRHCAHz87dwBrdx3grRWNz3DldlsemrWRlNhQvpOZ1M4RNs7PZbgkM4nPNu5lV6kusHZ2lTV1bNh9UEO4jjEmNYb9h6rZ1MK+W+KhZI+csIzu4cRHBLFAfXukhb7I3celTy/is017cRn41fkDSYruoiWqkY2fRO5wx3LWg/M5++HPeHjWRtbvPsDyrft5fG6uqiraS85MeHgI9t4oSv84AHfOTO4+uz+3T87oUokegKzUWIL8PacKbouaRbajuRv2UOe2GsIl0oamDevJiOQoHvhkA+XVtd94/OM1u1mz8wC3TUonwIdmWbw0Mxm3hZ++maNzgU5u/e6D1NRZhiXq/fZIWX09F4YX66JTq/jOfzHpcIwxjEuL44vcfZoKT5q1o6SC29/4ioY/FQN8ub3EyZCcNekeCAg5ellACGHn/ZZ7vjWIyJAAHp2ziXP+toBLnlrEAx9v4PJns1m+tXPN/ORzcmZ6eieVbsdgia0t5MHgf3Bz7AqnI3NEZko0r1yXxYysFFwG3vmy8Yo0aXuz1hYSHxHECF3dFWkzxhh+/a2BFB6o4pnP8o56rM5tefjTjfSLD+PCEYkORdi4vWVVuAwsyN2nIbWd3KqG5szJUY7G4WuSY0LoGRlMdiebAdXblOyRkzIuLY6iQ9Ws233A6VDEhy3OK+KCvy+krLKWAL/OPYtRiw2bDtMehchkwHhupz1KdNYMrhnXl5k3nsbiX0xiyqDuWMDiGUbzgxeX8vv/rWV5/n4lWb1h9n2e3klHCHBXduleSpkp0fz+oiHcMjGN/6wo4OM1u50OqdOrqq1j3oY9TB6YgMvVtarJRLwtMyWG84f25On5eRQeqDy8/H85O9lYWMYdUzLw87HXXXZeEQ2tSjSktnNbWVBKXHggvSKDnQ7FpxhjGNM3hsV56tvTGpqNS07K6fWzGXyeu4/BvVRuKN/0cnY+9/53Db1jQpl542mUlNeQnVfUqWcxarFh048781ZCRDA3ntmPBZv2UlPrxuUypCWE889FW3lu4Ra6dwvi7ME96BcfzsHKGk7rF6ef6ckqbbyPQ5PLu5Bbzkpn9vo9/OKtVWSmRBMXHuR0SJ3WS4vyOVRdR181Zhbxip+dM4BZawt54OMN/PXS4dTWufnbp5sY0COC84b43oxXDUNqK2vdGEzXvljWya0qKGVoopozN2ZMaizvfLWTvH2H6Bcf7nQ4HYKSPXJSekQGk54QzoJN+7h+fD+nwxGn5cz0VECUFmAjE5nZ7Rp+tWkAE/vH87fvjiQyJABACYlWyEyJ5pVrs45KkJVW1DBnfSEfrtrNa0u2UVPnucLh59rEL84bwPdP6/N1r4EjfidEJnmGj3ljavf2Oo6X2chETGOJnSZ6LHUlgf4uHpo+gml/X8gv317FUzMydTLqBcvzi/njh+sBeOiTjWSmxOh/pkgb6x0bylVjU9jzxUtU519OQNlOXnLHsj/r/3C5xjsd3jc0DKm97701rN11gPTu+qDbGZVX17Jpz0HOGdLD6VB80pi+MQAsztuvZE8LaRiXnLTT0+JYunU/lTV1TociTjqi1wlYTGkB07b9iUcHb+K5q0YfTvRI62WmRB81zXdkSAAXj0zime+P4uYJaYdnS6pzW373v3Vk/m4Wd81cyaoPn8X+9+vfCaXbPb+jnJnfOMby/OLjNoE+7uPH/O6Pdxxft7Tfjym3gUcvDAjxJK+E/j0iuGtqBh+vKeTtL3c4HU6nlJ237/C00DV1Gq4h4i13dF/JnwKeI7BsBwZLkmsfQ7/8tc++d2WmRPO7i4ZQU2f571fqn9YZrdl5QJMhHEffuDDiI4L0vtgKquyRk3ZGehwvfrGVFfnFjK0f1iVdUCO9TkJNNRfsew5ctzsTUxdwRkY8T322mZpaNwF+Lm6fksHGwoPMWrubO9x/wriOmaa1poKKj37DZ35fX7ncvLeMh2dtpLbO4u9nuGNKxlFXTJp7fPxHvyGk5pvHYfZ9Haq6x+22/DJ3IJNCf8zPAmd6Knw6cJWSt1x7RiqfrivkN/9dQ1ZqLL2iQprfSFosvn54nEG9zUS8KXTB/UD1UcuMj793DU2MZECPCN5ctp0ZWSlOhyNtbGX9xCVDlexp1OG+PVuKsNaqurgFlOyRkzYmNRZ/l2Fh7j4le7oy9TpxRGPDvMDTwDHg941f+Qg6tIsbXlre6GM1dZa/fLShyeM19nhe0E5o7P22g/3uP11XyKY9Zdx82Q8xI1XJ0xQ/l+HBS0dwziOf8bP/5PCva07VCVcb2l5cgQFunpjGxAEJGsIl4i0d8LzFGMP0Ucnc97+1rN99gAE9ujkdkrShnIJSekYGkxCh5sxNGZMay/9ydvGHD9ZxzpCeeo9shpI9ctLCg/wZ2TuKhbn7+KnTwYhj1OvEOZkp0d94swv0d3l+9qXbv7F+bUQv3r9h3OH763Yd4Bdvr6a2zo2/n4s/XDyEgT27tfjx2lcTCSxrZEhPB/rdW2t5fN5mkmNC+NYw32vO6Wt6x4byy/MH8su3V/Nydj7fO62P0yF1GrPWFjK6bww/Obu/06GIdG5NvEf6+nvXRSMT+eOH65i5tIB7pg1yOhxpQ6t2lGoIVzO6BXnSF88t2MJL2fm8cm2WEj7HoWSPtInT0+J4ZPYmig9VEx0W2PwGHUUnaTrbHv4d9UPOL/kjoeaIkmj1OnHWpHs8vXOOHGIVEELg1HuPmj1vcK9I+saFNzlLWnOPM/XeRo/TkX73izYXsXJ7Cb+/aAj+fmpn1xJXnNqbT9YUsvLDZ7n8i7fxP7hT/ydP0vb95azffZBfnT/Q6VBEOr8m3iN9/b0rJiyQqYN68PaXBfzs3P4E+fs5HZK0gdKKGrbsO8Qlmb6dbHRaQUk5ABaoqfX0tVOyp2k6o5U2cUZ6HNbCos7UMKsTNZ31tne/2sHdG/rzceovIDIZMJ7baY/qQ5+Thk33/A5a8Ds5tgl0qx4/4jgWQ4E7jm2n/6lD/e6fmLeZ+IggnWS1gjGGRwZt4neuZ/A/uAP9nzx5s9YWAjB5YHeHIxHpAlrxHulrpo9Opri8htnr9jgdirSR1TtKATVnbk5Wahyu+pHj6mvXPFX2SJsYlhRFeJA/Czbt47yhnWQIRCMNhzti01lv21R4kP/7zypG94nmWzNuA787nA5JjjRsevv8vdYf52BlDd/6y1xGbonihQneP2xbyCkoYWHuPv7v3AEEB+gKaWtELfojxzY41f/JEzdrbSHpCeH0iQtzOhSRrqG93iPb2Li0OHpFBvPG0u2d57y7i1tZUALAsMQoR+PwdZkp0fxgbB+e/3wrD08foaqeZqiyR9pEgJ8ns/p57j6nQ2k7TTTosz7cuK+9lVXVcuPLywkL8uOxK04hQMNfurxuwQFcPz6VuRv2NjmNu695Yu5mugX7c+WY3k6H0vF0wAanvqqkvJolW/czZZCqekTk+Pxchksyk/hs0152llQ0v4H4vFUFpaTEhhIZGuB0KD7vklHJAByqrnM4Et+nT2bSZs5Ij2Pb/nK2FZU7HUrbaKJB325ieT9nF9badg7It1hr+b//5LBl3yEevXwk3btp5gDxuOq0PsSGBfLQrKZn9fIVuXsO8tGa3Vw1tg8RwTrBarWmGpn6eINTXzR3wx7q3FbJHhFpkUsyk7EW/rO8HZPrOTPh4SFwb5Tn1htDdtvjGD4op6CUYUlRTofRIfTvHkFUaACLO1P7EC9RskfazOn1064v7CTVPbtH/5Rye3Szabd/CK+EXc3Nr67ghpeWU3ig0qHonPfPL7byv5xd/OTs/oztF+d0OOJDwoL8+dGEfnyeW8Sizb79RvzkvDyCA1xcPbaP06F0TJPu8TQ0PYL19/0Gp75o1tpCEiKCGK6TfRFpgd6xoYztF8ubywtwu9vhAmQjvSztf2+lbOmr7D9UfdTX3A17eODjDczdsOcbjx3v8bKlr2L/2/X6Ze4rq2JHSQXDEtWvpyVcLsOpfWJYvGW/06H4PPXskTbTLz6MnpHBLMzdyxWdYDjEU/szKa27jgdi3sXvwA6ITMI16R5uH3wJEQu38NCsjUx+aD7fy+pNWJA/WalxXWbc6Iptxdz/wTomD0zgxvH9nA5HfNCMrBSeXZDHQ7M2MDP1NIwxTof0DQXF5bz71Q5mZKUQGx7kdDgdU0Ovi9n3YUsL2OGOZV367UzpgD0wnFRVW8f8DXu5YEQiLpfvvVZExDdNH5XM7W98RfaWIq9eeKuqrcP90W8IOaaXpamtoOS9XzPuP22TpFgY+GvCXd/sl1n83q949+Bo0hIiSO8eTkJEECu2lTQ9S2gHs6pAzZlba0xqLJ+sLWRXaQU9I0Oa36CLUrJH2owxhtPT4pi1tpA6t8WvA5+wllfX8p8VBUwcfCl+l//hqMf8gRvO7MeUQd25+dUVPDEvD4Ag/1xevS6rw7/hNGfu+kJufe0rokMDePDSEfpgIo0KDvDjlolp/PrdNSzYtI/xGfFOh/QNz37mee1ePz7V4Ug6uPoGpwb49QtL+HJDCQsqazQsrhUWbS7iUHUdUwYlOB2KiHQg5wzpQcS7/sxcur3Nkj3L84vJzitiZHIUBypr+Wj1Lmav28NKdkIjp3yJriJ+e8Hgw/c/27SXOev2YPGsftbABManx7fo8cRPGq8GjqzZw73vrT18PzTQj4qaOqwFf5fhZ+cM4JwhPUiMCjl8XtrwPDpCMmhlQQnGwBBV9rTYmL4xACzO289FIxMdjsZ3KdkjbeqM9Dj+vbyANTs79rjT91bu5GBlLTOyUppcJzU+nPOH9mT9roNYoKrWzWNzc3nu+6M6dKLreJZu3c8P/7kMt4WqOhe5e8t8/g1UnDN9dDJPzc/jwVkbOSM9zqeqe/aVVfH60u1cPDKRXlG6ItRW7pzSn2mPLeQfC7dy2+R0p8PpMGatLSQ00E9DYkWkVYID/LhwRC/eXFbAbytqiAw5uST7wtx9/OCFJdTUfT0sLCo0gHOH9qAqrxch5Tu/sY2JTOKqI4ZCD0mM5PPcfdTUugnwd3HThLSjzhWP+/jipPohXN88xpK7JpFbWEbu3jLeWrGDr7aXAFDrttz/wTru/2AdIQF+9EsIIyY0kC82F1HntgT6u3jph6dyat9jpujOmemZObK0wNNnbtI9js3MtqqglLT4cMKC9NG8pQb27EZEsD+LtxQp2XMczf5FGWMuAS4HRgEJwDbgLeAP1tqD3g1POpqGE9UFm/Z16GTPy9nbyOgezug+x09knNYvjqCAXKpr3QDMXb+HS576gr98Zxjp3SPaI9R29dCsjTQMC6+rc5OdV6RkjzQpyN+PWyel8bP/rGL2uj1M9qHGsy98voXqOjc3TtAwxLY0NCmSqYO689yCPK4am0JUaGDzG3Vxbrfl03WFjE+PJzjAz+lwRKSDuWxUb17O3sZ/V+7ke8e5SNmU0vIaPl1XyIerdzF3w17q6k/0DHDpqCT+cPFQ/P1ckPNbT/+cI4dyBXyzR1tmSjSvXJvVZFXNcR+fdE+jxzCT7iEhIpiEiGDGpsUxuFckVz6XTU2tG38/F/d8axAul2FTfTLoy/xiauufR1Wtm+8+k01aQjjpCRH0SwjnzKq5jPzqN7hq64/T0BsI2j3hY61lWX4xSdEhLM8v1nl1C/k19O3JU9+e42lJ+vAneBI8vwAKgJHAvcBEY8xYa63be+FJRxMfEcSAHhF8nruPmyemOR3OCVm5vYRVO0q578LBzVYiHPWG1TeG7cUV/Pa9NZz36AJ+fFY6N57Zj0D/ztEHfe6GPSzaXISfMYAlwN9FVmpss9tJ1/btU5J4Yt5mHpq1kbMGJPjEsL8Fm/by7IItZPWNoV98uNPhdDp3Ts3g3EcW8OyCPO4+e4DT4fi8VTtKKTxQpVm4ROSEDEnsxoAeEby5bHuzyZ6GoU2Dekaw+0AVH67ezRe5+6h1W3pGBnPO4O7MWruHOren6uay0b09iR44qkdbc9UwmSnRx01aNPl4C4/RXEJpeX4xVz6bTXWdGz+X4fyhvSirqmHNzlI+XL2L6QF/wdVIbyBm39fuyZ5ZawsprajhQEUNVz6XzSvXdv6WEG1lTGoMs9fvYc+BShI0K3CjWpLsmWat3XvE/fnGmP3AP4EJwBxvBCYd17i0OP61KJ+K6jpCAjveVcqXs/MJDfTj4haWBB75hpXZB8alx/Hb99by0KyNfLBqF3/+zjCGJ0d5L+B2UFBczh1vfMWAHhHc861BfLm9pEOMgRbnBfi5uG1SOnfOXMlHa3Zz3tCejsazPL+YH7ywlFq3Zfm2El1F84IBPbpx/tCevPD5Vq45va+aXzdj1tpCXAbOGqB+PSLSesYYLhudzG/fW8u6XQcY2LNbo+t9uraQG19efrjiBaB3TCg/HNeXc4f2ZHhSJMaY4/e6qe/R5lUtPMbxEkqZKdG8cl3jyaDKmjqC7m9iptDSdpzGvt4Hq3YBYIGaWlXNt8aY+qF52Vv2c8HwXg5H45uaTfYck+hpsLT+VgPk5Bu+HfAFV/v9keA/FDk+Bra1SstreC9nJxePTDrh5qJx4UH8/fKRXDC8F796ZxUXP/E504b3IjUujHHp8R3uH3hVbR03vbKCujrLUzMy6RMXxtg09ZWQlrtwRCKPz83l4VkbOXtwD0d7Wi3ctPfwia6GInrP7ZMz+GDVLp7+LI9fnDfQ6XB82qy1hYzqE0N0mIa8iciJuWhEIqs+fI6eL9wB1XsOn39vT/oWH6/ZzYerd7M8v/jw+gb4/mkp3HvBN6vYm6vK6Siaeh7BAX6en08jvYGITGqHyI7WcE7kZ1DVfCsN7tWN8CB/FucVKdnThBMdX3Jm/e26tgpEOomcmQxc+iuSXPsw2K/HwObMdDqyFvn3igIqa9zMyDr5qeOnDOrOrDvPZNKA7rz71U4e/nQTlz+TfdSbbUfwu/+tJaeglAemD6dPXJjT4UgH5Ocy3DElg017yrj1tS8dfQ2UVdUC4NJJlVelJYRz0chE/vnFVvYcqHQ6HJ+1raicDYUHmaohXCJyEqI3v8MfA54lqroQ6s+/K9+6hb8+8Dt+//46yqvr+O7oZIL8XfgZCApwccGIRJ+aOKFdTbrH02/oCLV+wd/oP9Qe9hysom9cKHdO7a8hXK3k7+ciMyWaxVvUt6cprU72GGMSgfuAT621y9o+JOnQZt+HqW1iDKyPs9byyuJ8TukdxeBebTP1YbfgAEb0jqKhkKG6zs19763hQGVNm+zf2975cgcvZ2/jhvGpnD24h9PhSAfWo1swBnh/1S6ufM6ZpGdNnZsPV+8mo3s4d03N0EmVl902KZ1at+WJeZudDsVnzVpXCKB+PSJycmbfR5CtOmpRMFXcH/E28++ewIe3ncGfvjOMV6/LUlIBPCMOpj0KkclYDIWueH7LDZT1/3a7hmGtJaeglKzUWG6emNa1fycnaExqDLl7ythXVtX8yl1Qq5I9xphw4F2gFvjBcda73hizzBizbO/exkaBSafV1FhXB8bAttaizUXk7T103OnWT0RWaiyB9VdS/FyGnIJSpj70GXPWF7bpcdraxsKD/PytVZzaN4a7z+7vdDjSwR151aWqxjN8qr29t3InBcUV/PTsAdw8MV0nVV6WEhvG9FFJvLp4GztKKprfoAuatdaTfEyJVdWkiJyEJs6zI6p2H/X/JTMlWkmFBsOmwx2rMfeWsPPqpbxcPoaHPtnYriFs219OaUVNh57B2GkNfXuWqLqnUS1O9hhjgoH/AqnA2dbaJj+9W2ufsdaOstaOio+Pb4MwpcNoaqyrA2NgW+ul7HyiQwPavIFsw4wBd07tz8wbTuPtm08nMiSAa15cxm2vf8nBJa/Cw0Pg3ijPrQ8MeTtYWcONLy0nLMifxy4f+fVMDCInKCs1lqCAr/+ORrXziabbbXly3mb6d49QI9x2dMtZ6QA8NifX4Uh8T0l5NUu3FquqR0ROXgc+//YFI3tHc8WpvXnxiy2s3lHabsddWeA51tDEthlR0BUNS4okJMCPxQ5cROwIWvQJzhgTAPwHOBU4z1q7yqtRScflQ2NgW6PwQCWfrC3k0lHJnsZtbezIKykjkqN478fjuH1yOq7Vb+L/wW31TeJ8o8eRtZaf/SeH/P3lPHbFSE1lKG2iIel5SWYiFti0p6xdj//pukI27Snjpon9fGL6964iMSqE756azMyl2/jD++s6XM8yb5qzfg91bsuUQRoiKyInqZHzbwJCfP7825f89OwBxIQF8st3VlN3xIxl3rSqoIQgfxf9e0S0y/E6owD17TmuZpM9xhgX8AowCbjQWpvt9aik4zpmDOxuE88DgTfjHnKp05Ed1+tLtlPntlxx6sk3Zm6JQH8Xt0/O4C9R7xBC9dEPOtjjaHl+Mdf8cxkfrNrN3Wf3V/NaaVOZKdH89ZLhZKZE89icXCpr6trluNZaHp+3md4xoZzv8NTvXdH49HjqLDyzIM+xfk2+6NN1hSREBDFMV3RF5GQdcf4NxnM77dEOMxuuL4gMDeBX5w9i5fYSXluyrV2OubKglEG9uhGgCvqTMqZvDOt3H6T4UHXzK3cxLfnLehy4FHgAOGSMyTriS7WB8k1HjIFdctFnPFWcyf9W7XI6qibV1rl5bck2zkiPa/fZpgLKdja63DrQ42h5fjGXP5vN3PV7cBkYrfHc4gXGGO6amsHuA5W8urh9TqYWbS5i5fYSrh+fqiGJDthQeJCGWqrqWmf6Nfmaqto65m/Yy6SB3VVpJiJto/78m3tLPLdK9LTahSN6MbZfLH/+aD17D3q34W+d27JmR6kS/m1gTP3F6SVbVd1zrJac9Z5bf/tLYNExX9d6KS7pJL41tCf9u0fwt083UlvndjqcRs1ev4fdByrbvDFzizQxlnqHO5YZzy1u1/Gnc9YXUl3r+R0ZIFvlkOIlY/vFcVpqLE/M20x5da3Xj/fEvM3ERwRxSaauTzihoUn9kfe7tJyZuB8awipzGffkTveJPm0iIuK5IPW7i4ZQVePmDx+s8+qx8vaWcai6Ts2Z28Dw5EiC/F0sztNnl2M1m+yx1vax1pomvu5thxilA3O5DHdMSSdv7yHe+arxKhYnLc8v5o8frCM2LJBJTjRtbWSMtfUPYcOQ21m/+yCXPZPN9KcWsWDTXpZv3c/jc3O9MgSizm35bKNn5jyXgQB/lz6QiVfdNTWDfWVV/GtRvlePk1NQwsLcfVw7rq9X+nFJ8zJTonn1uizG9I3BbSE8yN/pkJyTMxPeu5WQ8p24DISU73S8T5uIiHytX3w4N56Zyttf7uCL3H1eO05OfXPmYUmq7DlZQf5+jOwdxeItqhw+lurZxevOHtyDwb268ejsTdT4UHXP8vxirng2m61FnmkPGzrit6tGxlibCx5l0vQfs/BnE/nNtEFs21/O955fwiVPL+KBjzd4pefFw7M2smrHAW6e0I+7pvbnlWuzNC2neNWoPjGcmRHP0/M3U1blveqeJ+ZupluwP1c6Ubknh2WmRPP09zKJCPLn4VntO7WtT5l9n6cv25Ec7NMmIiLfdNPENHrHhPKrd1dTVeud/oI5BSWEBfqRGh/ulf13NWP6xrJ21wFKK2qcDsWnKNkjXtfQo2Pb/nL+vbz9e9E0JTuv6PCwJWutc30kmhhjHRzgxw9O78v8n05g6qDuWAsWqKxx89KirbjbaKaAOesLeWxuLtNHJXH3OQMOzxom4m13TsmguLyGFxZu8cr+c/cc5KM1u7lqbJ+uXU3iI6JCA/nhGX35aM3udp3a1pc02Y/NgT5tIiLSuOAAP+67cDB5ew/xwxeXeaWqfmVBKYMTI/FT37Y2kZUai7WwTH17jqJkj7SLif0TGJEcxd9nb/Jahry1TukdRUO6xJeHLQX5+3HDmf0IDnBh8PTTeeernZzzyGe8+9WOk5oecvv+cu54YyWDenbjvguHtFnMIi0xPDmKyQO788yCPErL2/5KzJPz8gipT5qKb7hmXF8iQwJ4qAtW95RX17LPL77xB5vo3yYiIs6ICA7AZWBh7j6ueLZtq+pr6tys3XWA4RrC1WZG9o4i0M+lKdiPoWSPtAtjDD+Z2p+dpZW8vmS70+EAsLOkEoDLRiX5/LClzJRoXrk2i5+c3Z+ZN57GI98dgbVw2+tfMeWh+fx7eQFLthS1qqdPZU0dN72yAre1PDUjU/1MxBF3TsngYGUtzy3Ma9P97iip4N2vdvDdU5OJCQts033LiesWHMANZ6YyZ/0eVmzrOlOwl1XVcvU/lnJ/5SXU+gUf/WBAiKd/m4iI+IwjK/6rat1k57Vd/54Nuw9SXetmqJozt5ngAD9GJEe16+Q2HYGSPdJuTk+L5dS+MTw2N5eKauere15enE9qfBh/+s4wn070NMhMiebmiWmM7hPDhSMS+fj28Tx55SkEB/jxkzdXctnT2a3q6XPf/9ayakcpD00fQe/Y0HZ4BiLfNKhXN84f2pN/LNzC/kPVbbbfZz/Lwxi47ozUNtuntI2rTutDbFggD33SNap7DlTW8L3nF7N8WzGTpv8Y/wv/flSfNqY9qimSRUR8TMNMkg2DrNxt2HZ0Vf1QZlX2tK0xqTGs3nmAg5Xq29NAyR5pN8YY7pqSwd6DVbyc7d0ZeJqzZmcpX24r4coxKRjTMcfKulyGc4f25P1bx/HtUxKxfN3T54m5uVTWNJ1Qe2tFAa8u3saNZ/ZjyqDu7RazSGNun5xOeU0dT8/f3Cb721dWxWtLtnHxyER6RYU0v4G0q7Agf340oR8Lc/c51yutnZSUVzPjucWs3lHK41ecwrThvZrs0yYiIr6joar+zqkZpMaF8lJ2fpslEXIKSogMCaB3jC62tqUxfWOpc1uWeaHHUkelZI+0qzGpsZyRHseT8zdzyIsz8DTn5extBAe4uOSUjt8nwRjDlWNSCA5w4TJgDMxev4cz/jKXZz/Lo7y6/uecMxMeHoK9N4ox757JXT2+4idTM5wNXgRI7x7BRSMS+eeirew5WHnS+/vjB+uoqnUzLj2uDaITb5iRlUJCRBAPfbIRa9um2byv2X+omiueXcz6XQd5akYm5wzp4XRIIiLSCpkp0fz4rHQevmwke8uqeLCNKlJzCkoZlhTZYS84+6pTUqLwdxkW56lvTwMle6Td3Tklg/2Hqrn/g3Wt6jHTVg5W1vDuVzuYNqwXkaEB7Xpsb2m4+nDX1P78+8axvH59Fhndw7n/g3WM+/NcPnn979j/3gql2zFYEs0+bin7O/5r/u106CIA3DYpnZo6y73/XXNS/xcWbNrLf1bsAOCn/85p9/8v0jLBAX7cclYaS7buZ2Fu2/VB8AXL84v5y0frufCxhWzeW8azV41i0kBVUIqIdFTDk6OYMSaFfy3aetKzSVbW1LFh90GGaQhXmwsN9GdYUiSLt3TuquHW0Fy00u5G9o5mVEo0ry7ehstAoL+rXRskv/3lDsqr65iRldIux2svmSnRR/0Ms1JjWZ5fzGNzNjFo7d8wroqj1je1FTD7Pg0hEJ/QJy6MCRnxfLBqNx+t3n3C/xee/ezradxrat1k5xV1iJ5cXdFlo5N5en4eD36ykXFpcZ3iCufy/GKueDabqlpPc4d7pw3izIwmZuASEZEO4ydn9+fD1bv55dureOum0094yvR1uw5Q67YMTYxq2wAF8IwiaRjZEBqoVIcqe8QRg3p1A8Btv/5A1h6stbycnc/QxEiGJ0e1yzGdlJkSzQs/OJVEVxM/39KC9g1I5DjSEsKBE/+/UFlTx8qCYlwG/AwE+LvISo31RqjSBoL8/fjxWWl8tb2EOev3OB1Om5i1dvfhRI/LwCEfmIxAREROXmRIAL/+1kBWFpTy6uIT7z2aU1DfnDlZlT3eMKZvDLVuq8ruekr2iCMuHJFIQ0Lc36/9PpAt3VrMxsIyZmT1bpfj+QoT2URvoqaWizhg6uAeh6+Uncj/hTeXbae0opZ7LxjMnVP7t2vFoJyY72Qm0TsmlIdmbcTt7ti9e7bvL+etFZ4EekPVqpKNIiKdxwXDe3F6Wix/+WjDCfcYXFlQQlx4ED26BbdxdAIwqk8MLoMjrUJ8kZI94ojMlGgevHQEAOcN7dluH8heys4nItjfMyNKVzLpHgg4ZlaigBDPchEfkZkSzVNXZuLnMpzeL7ZV/xdq6tw8/Vkep/SO4ntZKdw8MU2Jng4gwM/F7ZPTWbPzAB+v2e10OCds675DXPb0IqpqLX/5zlDuUrJRRKTTMcbwuwuHUFXr5v73153QPlYVlDJczZm9ZsPug1ggO28/Vz6X3eUTPkr2iGMuPiWRC4b34qPVu9lXVuX14+09WMVHq3dxSWZS1xvDOWw6THsUIpMB47md9qj69YjPmTK4O1ed1od5G/eSt7esxdu9t3InBcUV3DwxTSdQHcyFIxLpFx/GHz5Yx2NzNnW4E7PNe8u47JlFVNTU8ep1Y5g+ureSjSIinVRqfDg3TujHu1/tZOGm1k0wUFZVS+7eMoaqObPXZOcVQX2hcHu2CvFVSvaIo26bnE5VbR1Pztvs9WPNXLadmjrLlWM6V2PmFhs2He5YDfeWeG6V6BEf9aMJ/Qjy9+OR2ZtatL7bbXly3mYG9IjgrAEJXo5O2pqfy3DhiES2F1fw4CcbO9SVuI2FB7ns6Wzq3JbXrz+Nwb10Ai8i0tndNKEfKbGh/Prd1VTWtLw325odpVgLw5OivBdcF5eVGou/n+ein59Lw6mV7BFH9YsP59unJPFydj67S09s7GtL1Lktry7exmmpsYebwIqIb4qPCOKqsX3478qdbCw82Oz6n64rZNOeMn40oZ+qejqohh5ulo5zJW7tzgN895lsXAZev/40+veIcDokERFpB8EBfvzuwiFs2XeI37y7psX9YRqaM6uyx3syU6J5/qrRAFw0sleXr7JVskccd9ukdOrclsfn5nrtGPM37mFHSUWnm25dpLO6YXwqYYH+PDxr43HXs9by+LzN9I4J5fyhPdspOmlrp/WLI6D+SpzLZXz+StyqglIufzabIH8Xb9xwmi4iiIh0MeMz4jm9XyxvLNvOg59saFFVas6OUhKjQogLD2qnKLum8RnxDO7VjW37y50OxXFK9ojjkmNCmT46mdeXbqOg2DsvypeztxEfEcTUwd29sn8RaVvRYYFcM64vH67ezeodpU2ut2hzESu3l3DDman4++ktraPKTInm9euy6N4tiKjQAIYm+u5Vz9eWbOM7T31BoL9h5g2n0TcuzOmQRETEAcOTowBw25ZVpeYUlPj0+1tnMqZvLF9uK6GqtuXD7DojnRmLT7hlYhoGw2Nz2r66Z/v+cuZu2MN3RycToA+DIh3GD8f1pVvw8at7npi3mfiIIL5zSlI7RibekNknhj99Zxh7D1bz5vLtTofTqFey8/n5W6uornVzoKKWPQe9P7mAiIj4pkkDu+PvallVakl5NflF5QxLVrKnPWSlxlBV62bl9qYvGHYF+uQrPqFXVAhXjOnNm8sL2LrvUJvu+7Ul2zDA5af2btP9ioh3RYYEcMOZ/Zi9fg9fbvtmafTK7SUszN3HteP6Ehzg50CE0tYmZMRzSu8oHpuT26qml+1h0eYi7n1vzeH7tXUdo7eQiIh4R2ZKNK9d76lKDQvyJ71700N6V9VXKQ9LjGqn6Lq2U/vGYAws7uLv00r2iM+4aUI/AvwMj7ZwBp6WqKqt442l2zlrQHd6RYW02X5FpH1cPbYPMWGBPNRIdc8T83LpFuzPlerF1WkYY/jJ1P7sKq3ktSXbnA7nsAWb9vKDF5fQvVswQf4u/AwE+GuWDxGRrm50nxie/f4oSitqePDjDU2up+bM7SsqNJD+3SNYvGW/06E4Sske8RkJ3YL5/ml9eOerHeTuaX4GnpZ4ct5mig5Vk5Ua0yb7E5H2FRbkz4/O7MeCTftYcsQbdu6eg3y8ppCrx/YhPMjfwQilrY1NiyMrNYbH526motr56p656/fww38uo29cOO/efDqvXpfFnVP788q1WV1+lg8REYFhSVF8PyuFl7LzySkoaXSdnIIS+saFERkS0L7BdWFZqbEszy+mps7tdCiOUbJHfMoN41MJDvDj4U9PvrpneX7x4SqhBz7Z0KIpEUXE98zISiE+IogHPtmAtRaAJ+flERLgx9Wn93U4OvGGu6b2Z19ZFS9lb3U0jk/W7Ob6l5bRv3sEr103htjwIDJTorl5YpoSPSIicthdZ/cnNjyIX769mjq3/cbjOQWlas7czsb0jaGipu5wVVVXpGSP+JTY8CCuOb0v7+fsYt2uAye1r/+t3EnD/9qWdMgXEd8UEujHLRPTWLJlP5/nFlFQXM67X+3gu6cmExMW6HR44gWj+8QwPiOeJ+dtpqyq1pEY3s/ZxU2vrGBwr0hevnYMUaH6WxMRkcZ1Cw7g198axKodpbycnX/UY3sOVrKrtJJhGsLVrk7t6xnZsXhL1/0MqGSP+JzrzkglIti/0R4drbH7QCWAeiuIdALfPTWZq8KWkPHaaSQ+0ov5AT/m1vgvnQ5LvOjOKRkUl9fw4udb2ueAOTPh4SFwbxTlfx7ArDceZWTvKF764akquxcRkWZNG9aTM9LjeODjDeyp/xwCsKq+smRYUpRDkXVNseFBpCeEk53Xdfv2KNkjPicyNIBrx6Uya20hv35n9QkNvzpUVcuCTfsYnxGn3goinUDQ2v/wa/sUCe49GCyJZh/Rs3/i+YAundKI5CgmD0zgmc/yKK2o8e7BcmbCe7dC6XbAElqxiz8HPs9Lp24jIliJHhERaZ4xhvsuHEJVnZvfvb/u8PKcglJcBgb36uZgdF3TmNQYlm/dT20X7dujZI/4pFNSogB4KTufK5/LbnXC592vdlJWVcttk9LVW0GkM5h9H/7uyqOX1VTA7PuciUfaxR1TMjhQWcvzC/LaZH/L84t5fG4uy/OLcbst2/eXM3fDHg6+f4/n7+kIQbaK4Pm/b5PjiohI19A3LoybJvTjvZU7WbBpL+BpzpyWEE6YJpRod2P6xnKouo7VO0+uPUhHpb848Uk5BaUYwALV9f12WpqwsdbycnY+A3pEcEpvJXlEOoXSgtYtl05hcK9Izhvag398vpWrT+97Qj2aauvcbNtfzkdrdvPQJxupdVuMgQCXi+r6K315QbvANLKx/r5ERKSVbjyzH+9+tZNfv7Oaj24fT05BKRMHJDgdVpc0pn5G5sV5RYxIjnI2GAco2SM+KSs1liB/F5W17sP3W+rL7SWs3XWA3180BGMaO3sXkQ4nMql+iE0jy6VTu31yBgFr/43fI7dCdaHndz7pHhg2/aj1svP28dHqQmLCAqlzW3L3lJG7p4wt+w4dTuo0sBaGJkVySWYSaQnh2LeS4EAjiR39fYmISCsFB/jxuwuHMPOFB6n4yw0srS2kfHNPyLnvG+9d4l0JEcGkxoexeMt+bjizn9PhtLsWJXuMMUnAz4BRwHAgBOhrrd3qvdCkK8tMieaV67J4bG4uc9fvoaq2rsXbvrwon7BAPy4amejFCEWkXU26x9NT5cihNgEhnuXSqWUUfshfA58nsLrKs6B0O+53b2Xplv3MC5rApsIy1uwsZVfp0cP8UmJDSU8IZ+KABNISwqlzu/nNu2uoqXMT4O/iF+cN/LpidPJv9PclIiJtZlzFHE4Nep7AmiowEF65y/M+A0r4tLMxfWP538qd1Lktfq6uVQjQ0sqeNGA6sBxYAEz1WkQi9TJTonnyylOY8Nd5PPTJRk67MbbZSp3iQ9X8b9Uupo9KIlzjYkU6j4YTo9n3eYbWNFHdIZ3Q7PsItFVHLXLVVZC4/K88W9ubvnFhRAT7s7vUM/TXZeC2SencNjnjG7tKS4ggO6+IrNTYo4cG6+9LRETaUiPvXYd7Deq9pV1lpcbw2pJtrNt1gCGJkU6H065a+mn4M2ttdwBjzLUo2SPtJDjAj5vPSuPX76zms037ODMj/rjrv7l8O9W1bmZkpbRThCLSboZN1wlSV9RE35xEVxHrfncOAX4ulucXc+Vz2dTUeqp2xqU3/l6RmRLddP83/X2JiEhbUa9BnzGmr6cdSHZeUZdL9rRoNi5rbdecq0x8wmWjkkmMCuGhTzZgrW1yPbfb8sribYzuE82AHpraUESkU2iib46JTCLAz3Mak5kSzSvXZnHn1P68cm2WZmAUERFnNdXzTb3g2l2PyGBSYkNZvGW/06G0O029Lj4v0N/FbZPSWVlQyqfr9jS53sLcfeQXlauqR0SkM5l0j6d/zpEa6aeTmRLNzRPTlOgRERHntfC9S9rHmL4xLN26H7e76cKBzkjJHukQvn1KIn1iQ3lo1sYmX6QvZ+cTGxbIOUN6tHN0IiLiNcOmw7RHITIZMJ7baY9qyJWIiPguvXf5lDF9Yykpr2FD4UGnQ2lXXulga4y5HrgeoHfv3t44hHQx/n4ubp+cwe1vfMWHq3dz/rCeRz2+q7SCT9cVcv34fgT5+zkUpYiIeIX66YiISEej9y6fMSY1BoDFeUUM7Nl12n14pbLHWvuMtXaUtXZUfPzxG+qKtNS04b1ITwjn4U83UndMdc9rS7ZjgSvHKLkoIiIiIiIiHknRoSRGhXS5vj0axiUdhp/LcMeUDHL3lPHeyp2Hl9fUuXl9yTbOzIgnOSbUwQhFRERERETE14xJjWHJlv3HnfCns1GyRzqUcwb3YGDPbvzt043U1nkmiZu1tpA9B6v4nhozi4iIiIiIyDGy+sZSdKia3D1lTofSblqc7DHGXGKMuQTIrF90bv2yM70Tmsg3uVyGO6dksLWonLdW7AA8jZkTo0KY0D/B4ehERERERETE12SlxgKQ3YWGcrWmQfObx9x/ov52PjChTaIRaYHJAxMYnhTJ2o+fo2b+m7x8cAdlwT3wW/07NUETERERERGRoyTHhNAzMpjFeUVdZkRIi5M91lrjzUBEWsoYwx/TN9DniycIqK0GA92qdsN7t3pWUMJHRERERERE6hljGNM3hs83F2GtxZjOn95Qzx7pkAaufZhQU330wpoKmH2fMwGJiIiIiIiIzxqTGsveg1Vs2XfI6VDahZI90iGZ0oLGH2hquYiIiIiIiHRZY/rGAHSZKdiV7JGOKTKpdctFRERERESky+obF8aM0MWc/clkuDcKHh4COTOdDstrlOyRjmnSPRAQcvSygBDPchEREREREZEjmFVv8mv7FDG1hYCF0u2evq+dNOGjZI90TMOmw7RHITIZMJ7baY+qObOIiIiIiIh80+z7CLJVRy/rxH1fWzP1uohvGTZdyR0RERERERFpXhfr+6rKHhERERERERHp3LpY31cle0RERERERESkc+tifV+V7BERERERERGRzq2L9X1Vzx4RERERERER6fy6UN9XVfaIiIiIiIiIiHQiSvaIiIiIiIiIiHQiSvaIiIiIiIiIiHQiSvaIiIiIiIiIiHQiSvaIiIiIiIiIiHQiSvaIiIiIiIiIiHQiSvaIiIiIiIiIiHQiSvaIiIiIiIiIiHQixlrr3QMYsxfI9+pB2k8csM/pIEQ6AL1WRFpGrxWRltFrRaRl9FoRaZnO8lpJsdbGN/aA15M9nYkxZpm1dpTTcYj4Or1WRFpGrxWRltFrRaRl9FoRaZmu8FrRMC4RERERERERkU5EyR4RERERERERkU5EyZ7WecbpAEQ6CL1WRFpGrxWRltFrRaRl9FoRaZlO/1pRzx4RERERERERkU5ElT0iIiIiIiIiIp2IY8keY8wlxpj/GGPyjTEVxpgNxpg/GmMijlkv2hjznDFmnzHmkDHmU2PM0Eb29wdjzCfGmCJjjDXGXN3EcWONMY8YY/Lqj7vFGPOYMabR6coa2T7YGPNXY8yu+u0XGWPGH7NOhDFmpjEmtz7mEmPMYmPMjFb8fFpynIz655JjjCmrX/e/xpjhLT2O+D69VnzjOOL79Fo5+eM0ss3l9c+9oKXHEd+n18rJH6d+va31z/fYr4taeizxbXqtnPxxjlg30RjzD2PMbmNMVf1z+mNLjyW+S6+TNjnO1U28nzR89Wjp8Y5irXXkC8gGZgJXAmcCtwMl9ctd9esYYAFQAFzO/7d3trF2FGUc/z1SijUVS4EQbFEjjYai4UVDEKJtbUOVpkgVSVEuF4JCaSFG/UCMSvD9gy8BLlHBCLWCAUGJ1EARhIovYNuYaG2IxtiKQDFtb62aUhT6+GGeK+tyzj17z+7tnl3+v+TJ5Mx5Zv6zM/vce86cmVl4F/AzYCcwO1ffP8P3O4ADF3bQNOCXwA7gMmA+sBLYBTxCbGvr0e5bo50fBhYCPwSeAU7M+BwOfA+4OHzOzLTrowX7p4jO5cDvgI8DC4BlcR37gLfUNbayak2xMhg6ssE3xUp5nZz/DOBpYDvwRN3jK6vOFCvldcJvG7AOODVnh9U9xrJqTLFSXif8Xgc8CfwCODf6chj4XN1jLCtvipNKdI7kxf9L3hb9s6HvsanxpjiyQ94F0XHvjNfvidcLMj6vAkaB63Jlx26kOePcFG+I9y7J5a+I/Df2aPMJ4XdRJm8K8Afg7gLX/AiwuYBfIR3giPyNHP2zG1hT19jKqjXFSv06smaYYqVaHdLBhfcBq9FkT6tMsVKNDmmy55a6x1M2eaZYqUaHNCm6ATi47jGVVW+Kk8nRAd4eZVf1Oza1beNy9x0dsjdGOivSs4Cn3P2hTLk9wFrSDZOtb38B2amR/iOX//dIe/XHWcB/gNszus8BtwGLzeyQHuV3RfleFNJx950ed0LGbw/wR17oQ9FwFCsDoSMagGKlOh0zOx04H1hVoG7RMBQrk6ojWoRipbyOmR0LLAZG3F2fuVqI4mTSdIaBf4dvXwzaAc3zIn0s0uOB33fw2wK8xsymT7D+LcDDwKfN7K1mNt3MTgGuAu5198fGL87xwFZ339uh3qmk2cf/YYkpsZ/wEtIfumsKtHNCOjnNmcCbeKEPRTtRrBxYHdFcFCsT1DGzg0mrer7s7n8qULdoB4qVPnSApWa2N84gedR0Xs9LAcXKxHROj/QZM7s/YmW3ma0xs8ML6IhmojjpQyejNw14P/Bjd99VQKcjAzPZY2azgM8CD7j7psieSdqSlGc00sMmohGrYM4kLZvaSNoP+Gvgz8D7ClTRqz0zc/mrSDN5O4HrgY+4+5pJ0MkyQtrDeE0BHdFAFCu16IgGoljpW+dK4BBAB2e+RFCs9K2zFriC9KH/g6QzE++ayMGdolkoVvrSeXWkN5F2H7yb9H9mCXCfmQ3M91FRDYqTUjpjnA0cSjofqG+mlClcFTGT9yPgOeCi7FukfWovKlJC7lukA49WkGYajwM+A9xpZkvdfb+ZHZTT2B/LySbanttJB1MdQVrCNWJmz7v7DQAV6hD1fQL4AHCxfo1tJ4qVydUR7UGx0p+Omc0BPgksc/d941yzaAmKlf513P2K/3Mwuys0vwTc0qVdoqEoVvrWGZvMWe/uY1uDHzSzPcRWFuDeLm0TDUNxUlpnjGHS4dP39PAbl9pnUs3s5cDdwOuBxe6efbzrKJ1nu8Zm/jrNko2ntYR0+veQu9/g7g/HAA2RZgaXhutPSTN3Y3ZVwfaMZjPdfYe7b3L3de6+Evgu8JVYIl+ZTlzbCuCLwKfc/aYuXSAajGLlgOiIFqBYKaVzHfAg8KiZzTCzGaRlxhavp43bIaJRKFaq+QyW0XweuAOYbWZHd/MTzUOxUkpnbAvK/Tm/n0R6Uoc6RANRnFT2vf5oYBFwq6fzffqm1pU90Tk/AE4BFrn75pzLFuCMDkXnAo+7+78mKPnmSDfm8jdEehxpJvJS4JWZ95/KtGeZmb0it+9uLunwpF6raTaRZumOIj12rhIdMxsCvg581d2/0KMNooEoVg6Yjmg4ipXSOnOB19L5Q9du4FrSI1VFw1GsTJrO2K+1nX7JFQ1EsVJaZ0uk3WKiyGG8YsBRnFSqcz5wECW3cAG1Pnr9ZcD3SfubF3bxOZv0h2FeJu9Q0gzxSJcy4z2i7cJ4b1Eu/4zIH+rR5hPDbziTN4W0bGxtgWu+g7SfcGpVOsAy0jK5G+saS9nkmmKlfh1ZM0yxUl6HtBx6fs7WkZYSzwfm1D3OsvKmWJkcnfDbBPyl7jGWVWOKlfI6kbeddNBstvx5Ub5jv8qaY4qTanWAzcBvKxmbGm+Kb8SFf5704TJrszM3zq+AvwLLSXs615OWOx2Tq28ecA5wedR7fbw+J3dDPUmaabsMWBDp08DjwPQC7b6N9Ovmh4CFwJ1xY5+c8bkUuJl0WN884L1RzoErC/ZPEZ13RN5vgNNyfXhSXWMrq9YUK4OhIxt8U6yU1+lSbjXwRN3jK6vOFCuV6JwXfhfEtSwHfh46y+seY1k1plgprxN+w1HvN0lfxldGuYcAq3ucZeVMcVJeJ+N7ctT9sUrGpsabYltcSCe7OuM3k3R6+yiwl7Qf7oQO9a3vVl/O7xjg28DW6OStpMOdZhVs9zTga3Ej7SOd+j0/53Ma6TCl7cCzcSM+ACyZQP8U0bl6nD7cVtfYyqo1xcpg6MgG3xQr5XW6lFuNJntaZYqVSnROJZ1v9TfS+Qx7Qmdx3eMrq84UK+V1Mr5DpEdvPxuaIxT4Qi4bfFOclNfJ+F4b/1OOqmJsLCoVQgghhBBCCCGEEC2g9qdxCSGEEEIIIYQQQojq0GSPEEIIIYQQQgghRIvQZI8QQgghhBBCCCFEi9BkjxBCCCGEEEIIIUSL0GSPEEIIIYQQQgghRIvQZI8QQgghhBBCCCFEi9BkjxBCCCGEEEIIIUSL0GSPEEIIIYQQQgghRIvQZI8QQgghhBBCCCFEi/gvLIm702fMeZwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.title(\"WindSpeed\",fontsize=25)\n",
    "\n",
    "\n",
    "\n",
    "days=5 #5일치만 시각화\n",
    "plt.plot(inter_fcst_14.loc[:24*days, 'Forecast_time'], inter_fcst_14.loc[:24*days, 'WindSpeed'], '.-')\n",
    "plt.plot(fcst_14_.loc[:24*days, 'Forecast_time'], fcst_14_.loc[:24*days, 'WindSpeed'], 'o')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1af8c4b5d00>]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.title(\"WindDirection\",fontsize=25)\n",
    "\n",
    "\n",
    "\n",
    "days=5 #5일치만 시각화\n",
    "plt.plot(inter_fcst_14.loc[:24*days, 'Forecast_time'], inter_fcst_14.loc[:24*days, 'WindDirection'], '.-')\n",
    "plt.plot(fcst_14_.loc[:24*days, 'Forecast_time'], fcst_14_.loc[:24*days, 'WindDirection'], 'o')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1af8c8e1b20>]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.title(\"Cloud\",fontsize=25)\n",
    "\n",
    "\n",
    "\n",
    "days=14 #5일치만 시각화\n",
    "plt.plot(inter_fcst_14.loc[:24*days, 'Forecast_time'], inter_fcst_14.loc[:24*days, 'Cloud'], '.-')\n",
    "plt.plot(fcst_14_.loc[:24*days, 'Forecast_time'], fcst_14_.loc[:24*days, 'Cloud'], 'o')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "############3  csv로 내보내기\n",
    "inter_fcst_14.to_csv('new_dj_fcst.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}