{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:02.483662Z",
     "start_time": "2018-11-09T03:43:01.541578Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd, pathlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:02.488698Z",
     "start_time": "2018-11-09T03:43:02.485657Z"
    }
   },
   "outputs": [],
   "source": [
    "data_path = pathlib.Path('../testdata/bmkg/data_jawa_barat_1980_2018.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:10.381205Z",
     "start_time": "2018-11-09T03:43:02.490655Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nama Stasiun</th>\n",
       "      <th>WMO ID</th>\n",
       "      <th>Tanggal</th>\n",
       "      <th>Suhu Minimum (°C)</th>\n",
       "      <th>Suhu Maksimum (°C)</th>\n",
       "      <th>Suhu Rata-rata (°C)</th>\n",
       "      <th>Kelembaban Rata-rata (%)</th>\n",
       "      <th>Curah Hujan (mm)</th>\n",
       "      <th>Lama Penyinaran (jam)</th>\n",
       "      <th>Kecepatan Angin Rata-rata (knot)</th>\n",
       "      <th>Arah Angin Terbanyak (deg)</th>\n",
       "      <th>Kecepatan Angin Terbesar (knot)</th>\n",
       "      <th>Arah Angin Saat Kecepatan Maksimum (deg)</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Keterangan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stasiun Meteorologi Jatiwangi</td>\n",
       "      <td>96791</td>\n",
       "      <td>01/01/1980</td>\n",
       "      <td>22.6</td>\n",
       "      <td>32.2</td>\n",
       "      <td>25.8</td>\n",
       "      <td>90</td>\n",
       "      <td>13.9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3</td>\n",
       "      <td>NW</td>\n",
       "      <td>5</td>\n",
       "      <td>315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>* 8888 : Data Tidak Terukur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stasiun Geofisika Bandung</td>\n",
       "      <td>96783</td>\n",
       "      <td>01/01/1980</td>\n",
       "      <td>20.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>270</td>\n",
       "      <td>NaN</td>\n",
       "      <td>* 9999 : Tidak Ada Data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stasiun Meteorologi Jatiwangi</td>\n",
       "      <td>96791</td>\n",
       "      <td>02/01/1980</td>\n",
       "      <td>21.0</td>\n",
       "      <td>30.2</td>\n",
       "      <td>25.7</td>\n",
       "      <td>88</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>NW</td>\n",
       "      <td>5</td>\n",
       "      <td>315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stasiun Geofisika Bandung</td>\n",
       "      <td>96783</td>\n",
       "      <td>02/01/1980</td>\n",
       "      <td>18.8</td>\n",
       "      <td>27.3</td>\n",
       "      <td>22.8</td>\n",
       "      <td>80</td>\n",
       "      <td>8888.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>270</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stasiun Meteorologi Jatiwangi</td>\n",
       "      <td>96791</td>\n",
       "      <td>03/01/1980</td>\n",
       "      <td>21.6</td>\n",
       "      <td>31.2</td>\n",
       "      <td>26.6</td>\n",
       "      <td>85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>270</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Nama Stasiun   WMO ID     Tanggal  Suhu Minimum (°C)  \\\n",
       "0  Stasiun Meteorologi Jatiwangi   96791  01/01/1980               22.6   \n",
       "1      Stasiun Geofisika Bandung   96783  01/01/1980               20.0   \n",
       "2  Stasiun Meteorologi Jatiwangi   96791  02/01/1980               21.0   \n",
       "3      Stasiun Geofisika Bandung   96783  02/01/1980               18.8   \n",
       "4  Stasiun Meteorologi Jatiwangi   96791  03/01/1980               21.6   \n",
       "\n",
       "   Suhu Maksimum (°C)  Suhu Rata-rata (°C)  Kelembaban Rata-rata (%)  \\\n",
       "0                32.2                 25.8                        90   \n",
       "1                26.0                 23.0                        80   \n",
       "2                30.2                 25.7                        88   \n",
       "3                27.3                 22.8                        80   \n",
       "4                31.2                 26.6                        85   \n",
       "\n",
       "   Curah Hujan (mm)  Lama Penyinaran (jam)  Kecepatan Angin Rata-rata (knot)  \\\n",
       "0              13.9                    3.4                                 3   \n",
       "1               1.0                    4.5                                 2   \n",
       "2               9.4                    1.5                                 4   \n",
       "3            8888.0                    2.2                                 2   \n",
       "4               0.0                    3.8                                 4   \n",
       "\n",
       "  Arah Angin Terbanyak (deg)  Kecepatan Angin Terbesar (knot)  \\\n",
       "0                         NW                                5   \n",
       "1                         W                                 4   \n",
       "2                         NW                                5   \n",
       "3                         W                                 4   \n",
       "4                         W                                 5   \n",
       "\n",
       "   Arah Angin Saat Kecepatan Maksimum (deg)  Unnamed: 13  \\\n",
       "0                                       315          NaN   \n",
       "1                                       270          NaN   \n",
       "2                                       315          NaN   \n",
       "3                                       270          NaN   \n",
       "4                                       270          NaN   \n",
       "\n",
       "                    Keterangan  \n",
       "0  * 8888 : Data Tidak Terukur  \n",
       "1      * 9999 : Tidak Ada Data  \n",
       "2                          NaN  \n",
       "3                          NaN  \n",
       "4                          NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_jabar = pd.read_excel(data_path, skiprows=6)\n",
    "raw_data_jabar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:10.414185Z",
     "start_time": "2018-11-09T03:43:10.384206Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nama Stasiun</th>\n",
       "      <th>WMO ID</th>\n",
       "      <th>Tanggal</th>\n",
       "      <th>Suhu Minimum (°C)</th>\n",
       "      <th>Suhu Maksimum (°C)</th>\n",
       "      <th>Suhu Rata-rata (°C)</th>\n",
       "      <th>Kelembaban Rata-rata (%)</th>\n",
       "      <th>Curah Hujan (mm)</th>\n",
       "      <th>Lama Penyinaran (jam)</th>\n",
       "      <th>Kecepatan Angin Rata-rata (knot)</th>\n",
       "      <th>Arah Angin Terbanyak (deg)</th>\n",
       "      <th>Kecepatan Angin Terbesar (knot)</th>\n",
       "      <th>Arah Angin Saat Kecepatan Maksimum (deg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stasiun Meteorologi Jatiwangi</td>\n",
       "      <td>96791</td>\n",
       "      <td>01/01/1980</td>\n",
       "      <td>22.6</td>\n",
       "      <td>32.2</td>\n",
       "      <td>25.8</td>\n",
       "      <td>90</td>\n",
       "      <td>13.9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3</td>\n",
       "      <td>NW</td>\n",
       "      <td>5</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stasiun Geofisika Bandung</td>\n",
       "      <td>96783</td>\n",
       "      <td>01/01/1980</td>\n",
       "      <td>20.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stasiun Meteorologi Jatiwangi</td>\n",
       "      <td>96791</td>\n",
       "      <td>02/01/1980</td>\n",
       "      <td>21.0</td>\n",
       "      <td>30.2</td>\n",
       "      <td>25.7</td>\n",
       "      <td>88</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>NW</td>\n",
       "      <td>5</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stasiun Geofisika Bandung</td>\n",
       "      <td>96783</td>\n",
       "      <td>02/01/1980</td>\n",
       "      <td>18.8</td>\n",
       "      <td>27.3</td>\n",
       "      <td>22.8</td>\n",
       "      <td>80</td>\n",
       "      <td>8888.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stasiun Meteorologi Jatiwangi</td>\n",
       "      <td>96791</td>\n",
       "      <td>03/01/1980</td>\n",
       "      <td>21.6</td>\n",
       "      <td>31.2</td>\n",
       "      <td>26.6</td>\n",
       "      <td>85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stasiun Geofisika Bandung</td>\n",
       "      <td>96783</td>\n",
       "      <td>03/01/1980</td>\n",
       "      <td>19.1</td>\n",
       "      <td>27.7</td>\n",
       "      <td>22.7</td>\n",
       "      <td>80</td>\n",
       "      <td>8888.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stasiun Meteorologi Jatiwangi</td>\n",
       "      <td>96791</td>\n",
       "      <td>04/01/1980</td>\n",
       "      <td>22.5</td>\n",
       "      <td>30.6</td>\n",
       "      <td>26.6</td>\n",
       "      <td>84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3</td>\n",
       "      <td>NW</td>\n",
       "      <td>5</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Stasiun Geofisika Bandung</td>\n",
       "      <td>96783</td>\n",
       "      <td>04/01/1980</td>\n",
       "      <td>19.9</td>\n",
       "      <td>26.9</td>\n",
       "      <td>23.3</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Stasiun Meteorologi Jatiwangi</td>\n",
       "      <td>96791</td>\n",
       "      <td>05/01/1980</td>\n",
       "      <td>23.1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>26.9</td>\n",
       "      <td>9999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2</td>\n",
       "      <td>NW</td>\n",
       "      <td>2</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Stasiun Geofisika Bandung</td>\n",
       "      <td>96783</td>\n",
       "      <td>05/01/1980</td>\n",
       "      <td>20.6</td>\n",
       "      <td>28.8</td>\n",
       "      <td>23.8</td>\n",
       "      <td>80</td>\n",
       "      <td>8888.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Nama Stasiun   WMO ID     Tanggal  Suhu Minimum (°C)  \\\n",
       "0  Stasiun Meteorologi Jatiwangi   96791  01/01/1980               22.6   \n",
       "1      Stasiun Geofisika Bandung   96783  01/01/1980               20.0   \n",
       "2  Stasiun Meteorologi Jatiwangi   96791  02/01/1980               21.0   \n",
       "3      Stasiun Geofisika Bandung   96783  02/01/1980               18.8   \n",
       "4  Stasiun Meteorologi Jatiwangi   96791  03/01/1980               21.6   \n",
       "5      Stasiun Geofisika Bandung   96783  03/01/1980               19.1   \n",
       "6  Stasiun Meteorologi Jatiwangi   96791  04/01/1980               22.5   \n",
       "7      Stasiun Geofisika Bandung   96783  04/01/1980               19.9   \n",
       "8  Stasiun Meteorologi Jatiwangi   96791  05/01/1980               23.1   \n",
       "9      Stasiun Geofisika Bandung   96783  05/01/1980               20.6   \n",
       "\n",
       "   Suhu Maksimum (°C)  Suhu Rata-rata (°C)  Kelembaban Rata-rata (%)  \\\n",
       "0                32.2                 25.8                        90   \n",
       "1                26.0                 23.0                        80   \n",
       "2                30.2                 25.7                        88   \n",
       "3                27.3                 22.8                        80   \n",
       "4                31.2                 26.6                        85   \n",
       "5                27.7                 22.7                        80   \n",
       "6                30.6                 26.6                        84   \n",
       "7                26.9                 23.3                        82   \n",
       "8                32.0                 26.9                      9999   \n",
       "9                28.8                 23.8                        80   \n",
       "\n",
       "   Curah Hujan (mm)  Lama Penyinaran (jam)  Kecepatan Angin Rata-rata (knot)  \\\n",
       "0              13.9                    3.4                                 3   \n",
       "1               1.0                    4.5                                 2   \n",
       "2               9.4                    1.5                                 4   \n",
       "3            8888.0                    2.2                                 2   \n",
       "4               0.0                    3.8                                 4   \n",
       "5            8888.0                    3.0                                 2   \n",
       "6               0.0                    0.7                                 3   \n",
       "7               1.0                    3.1                                 2   \n",
       "8               0.0                    4.3                                 2   \n",
       "9            8888.0                    6.4                                 2   \n",
       "\n",
       "  Arah Angin Terbanyak (deg)  Kecepatan Angin Terbesar (knot)  \\\n",
       "0                         NW                                5   \n",
       "1                         W                                 4   \n",
       "2                         NW                                5   \n",
       "3                         W                                 4   \n",
       "4                         W                                 5   \n",
       "5                         W                                 4   \n",
       "6                         NW                                5   \n",
       "7                         W                                 5   \n",
       "8                         NW                                2   \n",
       "9                         W                                 4   \n",
       "\n",
       "   Arah Angin Saat Kecepatan Maksimum (deg)  \n",
       "0                                       315  \n",
       "1                                       270  \n",
       "2                                       315  \n",
       "3                                       270  \n",
       "4                                       270  \n",
       "5                                       270  \n",
       "6                                       315  \n",
       "7                                       270  \n",
       "8                                       315  \n",
       "9                                       270  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_jabar = raw_data_jabar.iloc[:,:-2]\n",
    "data_jabar.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:10.517127Z",
     "start_time": "2018-11-09T03:43:10.415184Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 52376 entries, 0 to 52375\n",
      "Data columns (total 13 columns):\n",
      "Nama Stasiun                                52376 non-null object\n",
      "WMO ID                                      52376 non-null int64\n",
      "Tanggal                                     52376 non-null object\n",
      "Suhu Minimum (°C)                           52376 non-null float64\n",
      "Suhu Maksimum (°C)                          52376 non-null float64\n",
      "Suhu Rata-rata (°C)                         52376 non-null float64\n",
      "Kelembaban Rata-rata (%)                    52376 non-null int64\n",
      "Curah Hujan (mm)                            52376 non-null float64\n",
      "Lama Penyinaran (jam)                       52376 non-null float64\n",
      "Kecepatan Angin Rata-rata (knot)            52376 non-null int64\n",
      "Arah Angin Terbanyak (deg)                  52376 non-null object\n",
      "Kecepatan Angin Terbesar (knot)             52376 non-null int64\n",
      "Arah Angin Saat Kecepatan Maksimum (deg)    52376 non-null int64\n",
      "dtypes: float64(5), int64(5), object(3)\n",
      "memory usage: 5.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data_jabar.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ubah nama kolom, untuk lebih mudah digunakan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:10.587087Z",
     "start_time": "2018-11-09T03:43:10.519127Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Nama Stasiun ',\n",
       " 'WMO ID',\n",
       " 'Tanggal',\n",
       " 'Suhu Minimum (°C)',\n",
       " 'Suhu Maksimum (°C)',\n",
       " 'Suhu Rata-rata (°C)',\n",
       " 'Kelembaban Rata-rata (%)',\n",
       " 'Curah Hujan (mm)',\n",
       " 'Lama Penyinaran (jam)',\n",
       " 'Kecepatan Angin Rata-rata (knot)',\n",
       " 'Arah Angin Terbanyak (deg)',\n",
       " 'Kecepatan Angin Terbesar (knot)',\n",
       " 'Arah Angin Saat Kecepatan Maksimum (deg)']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nama_kolom_asli = data_jabar.columns.tolist()\n",
    "nama_kolom_asli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:10.756982Z",
     "start_time": "2018-11-09T03:43:10.593081Z"
    }
   },
   "outputs": [],
   "source": [
    "nama_baru = [\n",
    "    'name_stat', 'id_stat', 'date', 'suhu_min', 'suhu_max', 'suhu_mean',\n",
    "    'lembab_mean', 'curah_hujan', 'lama_sinar', 'cepat_angin_mean',\n",
    "    'arah_angin_mode', 'cepat_angin_max', 'arah_cepat_angin_max'\n",
    "]\n",
    "\n",
    "# Simpan informasi satuan\n",
    "satuan_kolom = dict(suhu_min='Celcius', suhu_max='Celcius', suhu_mean='Celcius',\n",
    "                    lembab_mean='%', curah_hujan='mm', lama_sinar='jam', cepat_angin_mean='knot',\n",
    "                    arah_angin_mode='deg', cepat_angin_max='knot', arah_cepat_angin_max='deg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:10.997303Z",
     "start_time": "2018-11-09T03:43:10.763305Z"
    }
   },
   "outputs": [],
   "source": [
    "data_jabar.columns = nama_baru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:11.103239Z",
     "start_time": "2018-11-09T03:43:11.004297Z"
    }
   },
   "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>name_stat</th>\n",
       "      <th>id_stat</th>\n",
       "      <th>date</th>\n",
       "      <th>suhu_min</th>\n",
       "      <th>suhu_max</th>\n",
       "      <th>suhu_mean</th>\n",
       "      <th>lembab_mean</th>\n",
       "      <th>curah_hujan</th>\n",
       "      <th>lama_sinar</th>\n",
       "      <th>cepat_angin_mean</th>\n",
       "      <th>arah_angin_mode</th>\n",
       "      <th>cepat_angin_max</th>\n",
       "      <th>arah_cepat_angin_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stasiun Meteorologi Jatiwangi</td>\n",
       "      <td>96791</td>\n",
       "      <td>01/01/1980</td>\n",
       "      <td>22.6</td>\n",
       "      <td>32.2</td>\n",
       "      <td>25.8</td>\n",
       "      <td>90</td>\n",
       "      <td>13.9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3</td>\n",
       "      <td>NW</td>\n",
       "      <td>5</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stasiun Geofisika Bandung</td>\n",
       "      <td>96783</td>\n",
       "      <td>01/01/1980</td>\n",
       "      <td>20.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stasiun Meteorologi Jatiwangi</td>\n",
       "      <td>96791</td>\n",
       "      <td>02/01/1980</td>\n",
       "      <td>21.0</td>\n",
       "      <td>30.2</td>\n",
       "      <td>25.7</td>\n",
       "      <td>88</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>NW</td>\n",
       "      <td>5</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stasiun Geofisika Bandung</td>\n",
       "      <td>96783</td>\n",
       "      <td>02/01/1980</td>\n",
       "      <td>18.8</td>\n",
       "      <td>27.3</td>\n",
       "      <td>22.8</td>\n",
       "      <td>80</td>\n",
       "      <td>8888.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stasiun Meteorologi Jatiwangi</td>\n",
       "      <td>96791</td>\n",
       "      <td>03/01/1980</td>\n",
       "      <td>21.6</td>\n",
       "      <td>31.2</td>\n",
       "      <td>26.6</td>\n",
       "      <td>85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       name_stat  id_stat        date  suhu_min  suhu_max  \\\n",
       "0  Stasiun Meteorologi Jatiwangi    96791  01/01/1980      22.6      32.2   \n",
       "1      Stasiun Geofisika Bandung    96783  01/01/1980      20.0      26.0   \n",
       "2  Stasiun Meteorologi Jatiwangi    96791  02/01/1980      21.0      30.2   \n",
       "3      Stasiun Geofisika Bandung    96783  02/01/1980      18.8      27.3   \n",
       "4  Stasiun Meteorologi Jatiwangi    96791  03/01/1980      21.6      31.2   \n",
       "\n",
       "   suhu_mean  lembab_mean  curah_hujan  lama_sinar  cepat_angin_mean  \\\n",
       "0       25.8           90         13.9         3.4                 3   \n",
       "1       23.0           80          1.0         4.5                 2   \n",
       "2       25.7           88          9.4         1.5                 4   \n",
       "3       22.8           80       8888.0         2.2                 2   \n",
       "4       26.6           85          0.0         3.8                 4   \n",
       "\n",
       "  arah_angin_mode  cepat_angin_max  arah_cepat_angin_max  \n",
       "0              NW                5                   315  \n",
       "1              W                 4                   270  \n",
       "2              NW                5                   315  \n",
       "3              W                 4                   270  \n",
       "4              W                 5                   270  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_jabar.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ubah tipe data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:11.266192Z",
     "start_time": "2018-11-09T03:43:11.107237Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 52376 entries, 0 to 52375\n",
      "Data columns (total 13 columns):\n",
      "name_stat               52376 non-null object\n",
      "id_stat                 52376 non-null int64\n",
      "date                    52376 non-null object\n",
      "suhu_min                52376 non-null float64\n",
      "suhu_max                52376 non-null float64\n",
      "suhu_mean               52376 non-null float64\n",
      "lembab_mean             52376 non-null int64\n",
      "curah_hujan             52376 non-null float64\n",
      "lama_sinar              52376 non-null float64\n",
      "cepat_angin_mean        52376 non-null int64\n",
      "arah_angin_mode         52376 non-null object\n",
      "cepat_angin_max         52376 non-null int64\n",
      "arah_cepat_angin_max    52376 non-null int64\n",
      "dtypes: float64(5), int64(5), object(3)\n",
      "memory usage: 5.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data_jabar.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`id_stat` diubah menjadi `object`; `date` jadi `TimeStamp`;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:11.640082Z",
     "start_time": "2018-11-09T03:43:11.267191Z"
    }
   },
   "outputs": [],
   "source": [
    "data_jabar.id_stat = data_jabar.id_stat.apply(str)\n",
    "data_jabar.date = pd.to_datetime(data_jabar.date, format='%d/%m/%Y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ambil data Per stasiun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:11.807994Z",
     "start_time": "2018-11-09T03:43:11.642080Z"
    }
   },
   "outputs": [],
   "source": [
    "# cek jumlah stasiun\n",
    "data_jabar = data_jabar.sort_values(['id_stat','date']).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:11.825979Z",
     "start_time": "2018-11-09T03:43:11.808989Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name_stat</th>\n",
       "      <th>id_stat</th>\n",
       "      <th>date</th>\n",
       "      <th>suhu_min</th>\n",
       "      <th>suhu_max</th>\n",
       "      <th>suhu_mean</th>\n",
       "      <th>lembab_mean</th>\n",
       "      <th>curah_hujan</th>\n",
       "      <th>lama_sinar</th>\n",
       "      <th>cepat_angin_mean</th>\n",
       "      <th>arah_angin_mode</th>\n",
       "      <th>cepat_angin_max</th>\n",
       "      <th>arah_cepat_angin_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stasiun Meteorologi Citeko</td>\n",
       "      <td>96751</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>18.1</td>\n",
       "      <td>24.7</td>\n",
       "      <td>20.3</td>\n",
       "      <td>90</td>\n",
       "      <td>9.0</td>\n",
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       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stasiun Meteorologi Citeko</td>\n",
       "      <td>96751</td>\n",
       "      <td>1985-01-02</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.1</td>\n",
       "      <td>20.7</td>\n",
       "      <td>88</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stasiun Meteorologi Citeko</td>\n",
       "      <td>96751</td>\n",
       "      <td>1985-01-03</td>\n",
       "      <td>17.6</td>\n",
       "      <td>25.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>86</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stasiun Meteorologi Citeko</td>\n",
       "      <td>96751</td>\n",
       "      <td>1985-01-04</td>\n",
       "      <td>17.8</td>\n",
       "      <td>25.6</td>\n",
       "      <td>21.7</td>\n",
       "      <td>88</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stasiun Meteorologi Citeko</td>\n",
       "      <td>96751</td>\n",
       "      <td>1985-01-05</td>\n",
       "      <td>17.5</td>\n",
       "      <td>23.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>87</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name_stat id_stat       date  suhu_min  suhu_max  \\\n",
       "0  Stasiun Meteorologi Citeko   96751 1985-01-01      18.1      24.7   \n",
       "1  Stasiun Meteorologi Citeko   96751 1985-01-02      18.0      24.1   \n",
       "2  Stasiun Meteorologi Citeko   96751 1985-01-03      17.6      25.5   \n",
       "3  Stasiun Meteorologi Citeko   96751 1985-01-04      17.8      25.6   \n",
       "4  Stasiun Meteorologi Citeko   96751 1985-01-05      17.5      23.7   \n",
       "\n",
       "   suhu_mean  lembab_mean  curah_hujan  lama_sinar  cepat_angin_mean  \\\n",
       "0       20.3           90          9.0      9999.0              9999   \n",
       "1       20.7           88         11.0      9999.0              9999   \n",
       "2       21.0           86          7.0      9999.0              9999   \n",
       "3       21.7           88          7.0      9999.0              9999   \n",
       "4       21.0           87         13.0      9999.0              9999   \n",
       "\n",
       "  arah_angin_mode  cepat_angin_max  arah_cepat_angin_max  \n",
       "0            9999             9999                  9999  \n",
       "1            9999             9999                  9999  \n",
       "2            9999             9999                  9999  \n",
       "3            9999             9999                  9999  \n",
       "4            9999             9999                  9999  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_jabar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:11.950907Z",
     "start_time": "2018-11-09T03:43:11.826978Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['96751', '96753', '96783', '96791', '96793'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cek jumlah stasiun yang ada berdasarkan id_stat\n",
    "data_jabar.id_stat.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:12.127808Z",
     "start_time": "2018-11-09T03:43:11.955902Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>name_stat</th>\n",
       "      <th>date</th>\n",
       "      <th>suhu_min</th>\n",
       "      <th>suhu_max</th>\n",
       "      <th>suhu_mean</th>\n",
       "      <th>lembab_mean</th>\n",
       "      <th>curah_hujan</th>\n",
       "      <th>lama_sinar</th>\n",
       "      <th>cepat_angin_mean</th>\n",
       "      <th>arah_angin_mode</th>\n",
       "      <th>cepat_angin_max</th>\n",
       "      <th>arah_cepat_angin_max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_stat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96751</th>\n",
       "      <td>11934</td>\n",
       "      <td>11934</td>\n",
       "      <td>11934</td>\n",
       "      <td>11934</td>\n",
       "      <td>11934</td>\n",
       "      <td>11934</td>\n",
       "      <td>11934</td>\n",
       "      <td>11934</td>\n",
       "      <td>11934</td>\n",
       "      <td>11934</td>\n",
       "      <td>11934</td>\n",
       "      <td>11934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96753</th>\n",
       "      <td>12330</td>\n",
       "      <td>12330</td>\n",
       "      <td>12330</td>\n",
       "      <td>12330</td>\n",
       "      <td>12330</td>\n",
       "      <td>12330</td>\n",
       "      <td>12330</td>\n",
       "      <td>12330</td>\n",
       "      <td>12330</td>\n",
       "      <td>12330</td>\n",
       "      <td>12330</td>\n",
       "      <td>12330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96783</th>\n",
       "      <td>13817</td>\n",
       "      <td>13817</td>\n",
       "      <td>13817</td>\n",
       "      <td>13817</td>\n",
       "      <td>13817</td>\n",
       "      <td>13817</td>\n",
       "      <td>13817</td>\n",
       "      <td>13817</td>\n",
       "      <td>13817</td>\n",
       "      <td>13817</td>\n",
       "      <td>13817</td>\n",
       "      <td>13817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96791</th>\n",
       "      <td>13807</td>\n",
       "      <td>13807</td>\n",
       "      <td>13807</td>\n",
       "      <td>13807</td>\n",
       "      <td>13807</td>\n",
       "      <td>13807</td>\n",
       "      <td>13807</td>\n",
       "      <td>13807</td>\n",
       "      <td>13807</td>\n",
       "      <td>13807</td>\n",
       "      <td>13807</td>\n",
       "      <td>13807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96793</th>\n",
       "      <td>488</td>\n",
       "      <td>488</td>\n",
       "      <td>488</td>\n",
       "      <td>488</td>\n",
       "      <td>488</td>\n",
       "      <td>488</td>\n",
       "      <td>488</td>\n",
       "      <td>488</td>\n",
       "      <td>488</td>\n",
       "      <td>488</td>\n",
       "      <td>488</td>\n",
       "      <td>488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name_stat   date  suhu_min  suhu_max  suhu_mean  lembab_mean  \\\n",
       "id_stat                                                                 \n",
       "96751        11934  11934     11934     11934      11934        11934   \n",
       "96753        12330  12330     12330     12330      12330        12330   \n",
       "96783        13817  13817     13817     13817      13817        13817   \n",
       "96791        13807  13807     13807     13807      13807        13807   \n",
       "96793          488    488       488       488        488          488   \n",
       "\n",
       "         curah_hujan  lama_sinar  cepat_angin_mean  arah_angin_mode  \\\n",
       "id_stat                                                               \n",
       "96751          11934       11934             11934            11934   \n",
       "96753          12330       12330             12330            12330   \n",
       "96783          13817       13817             13817            13817   \n",
       "96791          13807       13807             13807            13807   \n",
       "96793            488         488               488              488   \n",
       "\n",
       "         cepat_angin_max  arah_cepat_angin_max  \n",
       "id_stat                                         \n",
       "96751              11934                 11934  \n",
       "96753              12330                 12330  \n",
       "96783              13817                 13817  \n",
       "96791              13807                 13807  \n",
       "96793                488                   488  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ada 5 stasiun, lihat juga jumlah data yang dimiliki\n",
    "data_jabar.groupby('id_stat').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:12.220751Z",
     "start_time": "2018-11-09T03:43:12.129807Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'96751': 'Stasiun Meteorologi Citeko',\n",
       " '96753': 'Stasiun Klimatologi Bogor',\n",
       " '96783': 'Stasiun Geofisika Bandung',\n",
       " '96791': 'Stasiun Meteorologi Jatiwangi',\n",
       " '96793': 'Pos Meteorologi Penggung'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# buat dictionary id dan nama stasiun (hal ini bisa menggunakan database dalam metadata_station)\n",
    "info_stat_jabar = dict()\n",
    "for i, j in zip(data_jabar.id_stat.unique().tolist(), data_jabar.name_stat.unique().tolist()):\n",
    "    info_stat_jabar[i] = j\n",
    "info_stat_jabar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diketahui bahwa untuk \n",
    "- 96751: Stasiun Meteorologi Citeko - `11934` hari\n",
    "- 96753: Stasiun Klimatologi Bogor - `12330` hari\n",
    "- 96783: Stasiun Geofisika Bandung - `13817` hari\n",
    "- 96791: Stasiun Meteorologi Jatiwangi - `13807` hari\n",
    "- 96793: Pos Meteorologi Penggung - `488` hari\n",
    "\n",
    "__catatan: angka tersebut masih memiliki nilai `9999` dan `8888`__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:12.397698Z",
     "start_time": "2018-11-09T03:43:12.222749Z"
    }
   },
   "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>suhu_min</th>\n",
       "      <th>suhu_max</th>\n",
       "      <th>suhu_mean</th>\n",
       "      <th>lembab_mean</th>\n",
       "      <th>curah_hujan</th>\n",
       "      <th>lama_sinar</th>\n",
       "      <th>cepat_angin_mean</th>\n",
       "      <th>arah_angin_mode</th>\n",
       "      <th>cepat_angin_max</th>\n",
       "      <th>arah_cepat_angin_max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1985-01-01</th>\n",
       "      <td>18.1</td>\n",
       "      <td>24.7</td>\n",
       "      <td>20.3</td>\n",
       "      <td>90</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-01-02</th>\n",
       "      <td>18.0</td>\n",
       "      <td>24.1</td>\n",
       "      <td>20.7</td>\n",
       "      <td>88</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-01-03</th>\n",
       "      <td>17.6</td>\n",
       "      <td>25.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>86</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-01-04</th>\n",
       "      <td>17.8</td>\n",
       "      <td>25.6</td>\n",
       "      <td>21.7</td>\n",
       "      <td>88</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-01-05</th>\n",
       "      <td>17.5</td>\n",
       "      <td>23.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>87</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            suhu_min  suhu_max  suhu_mean  lembab_mean  curah_hujan  \\\n",
       "date                                                                  \n",
       "1985-01-01      18.1      24.7       20.3           90          9.0   \n",
       "1985-01-02      18.0      24.1       20.7           88         11.0   \n",
       "1985-01-03      17.6      25.5       21.0           86          7.0   \n",
       "1985-01-04      17.8      25.6       21.7           88          7.0   \n",
       "1985-01-05      17.5      23.7       21.0           87         13.0   \n",
       "\n",
       "            lama_sinar  cepat_angin_mean arah_angin_mode  cepat_angin_max  \\\n",
       "date                                                                        \n",
       "1985-01-01      9999.0              9999            9999             9999   \n",
       "1985-01-02      9999.0              9999            9999             9999   \n",
       "1985-01-03      9999.0              9999            9999             9999   \n",
       "1985-01-04      9999.0              9999            9999             9999   \n",
       "1985-01-05      9999.0              9999            9999             9999   \n",
       "\n",
       "            arah_cepat_angin_max  \n",
       "date                              \n",
       "1985-01-01                  9999  \n",
       "1985-01-02                  9999  \n",
       "1985-01-03                  9999  \n",
       "1985-01-04                  9999  \n",
       "1985-01-05                  9999  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_citeko = data_jabar[data_jabar.id_stat == '96751'].drop(['name_stat', 'id_stat'], axis=1).copy()\n",
    "stat_citeko = stat_citeko.set_index('date')\n",
    "stat_citeko.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:12.476653Z",
     "start_time": "2018-11-09T03:43:12.399698Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "401"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hitung jumlah data curah hujan yang memiliki nilai 9999 dan 8888\n",
    "stat_citeko_ch = stat_citeko.curah_hujan\n",
    "mask = (stat_citeko_ch == 9999) | (stat_citeko_ch == 8888)\n",
    "mask.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:12.615792Z",
     "start_time": "2018-11-09T03:43:12.480648Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1985-01-01', '1985-01-02', '1985-01-03', '1985-01-04',\n",
       "               '1985-01-05', '1985-01-06', '1985-01-07', '1985-01-08',\n",
       "               '1985-01-09', '1985-01-10',\n",
       "               ...\n",
       "               '2017-12-23', '2017-12-24', '2017-12-25', '2017-12-26',\n",
       "               '2017-12-27', '2017-12-28', '2017-12-29', '2017-12-30',\n",
       "               '2017-12-31', '2018-01-01'],\n",
       "              dtype='datetime64[ns]', name='date', length=11934, freq=None)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pada tanggal berapa saja datanya memiliki nilai 8888 atau 9999\n",
    "mask.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:14.726381Z",
     "start_time": "2018-11-09T03:43:12.619784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ed0ebc46a0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Coba plot curah hujan dengan mengabaikan nilai 8888/9999 pada durasi tertentu\n",
    "stat_citeko_ch[~mask].plot(figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:15.318325Z",
     "start_time": "2018-11-09T03:43:14.728379Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ed10355cf8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2em5Bjp3mfgh+IYBDold9dJ382ntvazobAAAAjXnZh+6UC9/D/RD8QgCHRLsmzjSdBQAAgEYdmpptOgvAGAI4ACgpaQQloyoBAIBLBHAAAAAAEAgCOAAAAAAIBAEcAAAAAASCAA4AStIJzxFIeg0AAMAWAjgAAAAACAQBHAAAAAAEggAOAEpKGi3JAEoAAOASARxgAfOeAAAAUAcCOMAC4jcAAADUgQAOsID4DQAAAHUggAMsYAglIhQFAADgEgEcYAH37AAAAKgDARxgAb0uqAPlDAAAEMABFmj64DqJgAoAANSNAA6wgBt59FEWAACAQwRwAGDBp+7cJe//zmNNZwMAALQcARwAlDQ8dPayG7fKp9furm17AACgmwjgAAsYQgkAAIA6EMABFtAzgghlAQAAuEQAB1hADxzqQDkDAAAEcIAF3Fd3EwEVAACoGwEcYIHmTh49FAUA6JYzcwvyzm9skZlzC01nBR1BAAdYwD07AADddOXa3XLtvfvk6nV7ms4KOoIADrCAXpduqvuwU8wAwD9LvZuAJSpp1IQADrCBShsAAHTE3mNn5Jq76XFsyvlNZwBoA5aOR4SSAABou9d96l45dnpOXv/8Z8j3P+m8prPTOfTAARYwhLKb6l68hsVyAAA+mJ6dbzoLnUYAB1jAbTUAAOga2hWbQQAHWEDPCAAAAOpAAAdYQPiGCME8AKArlGo6B91EAAdYwD17N/EYAWDZHVuPyNFTs01nA0DNuP9pBgEcAAAoTWstf3rNRvnDK9c3nRUA6AQCOMACHiOACCUBXRO1wO85dqbZjAAN6XIvFEMom0EAB9jQ4cq7y+q+aHf5JgEA6nCK5fEL4brUDAI4wALqLwBdRf2HNvlfX9hc+DP0QqFuBHCABbRAAQAQvq2HTxX+DPcAqBsBHGABc+C6avy4cyFH1/DoDLQL3Wkm6HVsFgEcYAH3L6gHBQ0AfNPFYCa67+nid/cBARxgAbfVALqK+g/oLhqwm0EAB1iQNYSI4UUA2sxGFae1lp1HT1dPCKiIHiUz7KdmVQrglFJ/qZR6RCm1RSl1nVLq+5VSz1RKbVBK7VBKfVkp9WRbmQV8RYzWTUnHnfmQQHFX3bVHXvqhO+XBAyebzgpQWBfvAbr4nX1SOoBTSj1NRP5CRC7UWj9HRM4TkdeLyD+JyIe11heIyAkR+TMbGQWAruOCCR/ZaLR44MAJERE5cGKmclpAFXQsFUOjZTOqDqE8X0R+QCl1voj8oIgcEpEXi8gNvb9/XkReU3EbgPeavLE+ceaczC8uNZcBAABaoszlvIvDCbv4nX1SOoDTWj8uIh8Ukf2yHLhNicgmETmptV7ove2giDytaiYB32W1QLkO7p737lvlL657wO1GkIh2R4CeYaDL50CXv3uTqgyh/DERebWIPFNEflpEfkhEfjfhrYmHVil1sVJqo1Jq48TERNlsAF5ougK7ccvhZjOAAYdlgesk2koxcA2eoCQiBFWGUL5URPZorSe01vMi8jUR+XUReUpvSKWIyNNF5ImkD2utr9RaX6i1vnDlypUVsgE0jxtrAKiu6cYw35w9t9h0FmCA4YSoW5UAbr+IvEAp9YNKKSUiLxGRR0VkjYi8rveei0TkG9WyCPiPRwUA6Cor1R83wGM27z8hv/jOm+SOrUeazgpydPkWoMNfvVFV5sBtkOXFSjaLyMO9tK4UkbeLyF8ppXaKyI+LyGct5BMIFpVbeyU/RgAAqtu8b3llznU7JhvOCQDfnJ//lnRa60tF5NLYy7tF5PlV0gVCw0076tDlVl74y+Yy4hRxAMhX9TECAIQbawAA2oD5bMUwhaQZBHCAFVmPEaByayseYArQgIV2KVOeCfpQNwI4wAJuYBBxWRYIGOEj1jBB13EPgLoRwAEWUHcDQHWMWEDT6E0rxtcz9kO3bJOffcfqprPhTKVFTAAsa+qeg5udZrH7Aeoh1wgo6lXmofIcI/985I6dTWfBKXrgAAuyhrZxawMAQBjKDFXvchtGl797kwjgAAua64FrZrtI53KeGscbPrIyB44ujDGc7wDSEMABFnChBQC4QGjrP9ofUDcCOMCCplYHJG5sFoE7YOc84P4XvigzB67T14Iuf/cGEcAh19tueFA+dseOprPhtazKu9MVewdxvNE5vTJPL4QbVCkA4liFErmu33hQRET+94svaDgnQLcRHMJnNsonZRxN43mbCAE9cIAFPEagm7jQA3bOA3rv0rFr6lVmCOXgs93DdbAZBHCABVRgAACEr0pjAncCqAsBHGBB5hw4l8vKO0sZZbk8JjQUwEcMBECbUJ6LYX81gwAOsID6CwCqo5FigH0Bn1UZaorqCOAAC5YaaoKi5atZ7H/A0oO8LaTRVm2fH/jpO3fJ+t2TTWejkrYfoyQ0MDSLVSgBC7IWE+EmH0CbRfVfF29iUd37b9wqIiJ7L3tFwzlZVqYcd/k63+Gv3ih64AALulx5Y5TLlUEpZ0D3cN4DiCOAAyxYauoxArR9AWiYzVqIYAV1ODw1K1fctj2xwY0yiBAQwAEWNDUHDgB8UaUaVIy/TMWuse8t122WK27bIY88cWrsb1X2dxcPFc+jbQYBHGABi5gA6CrqIYTm7PyiiCRfu6sEYZwKqAsBHGADtXYnJd24cjOLromGctvoKeL8QR2ylsCnCBbD/moGARxgQVNz4ACgDbo49CwPwax7tvdxl8oxz4FrFgEcYAFz4AB0FtWfU8wPtC/apbaLbpdOBRZRaxYBHGBBVjVGbAdbKEv+2RD4A4iBLopC4qQFOAiXi+G61AwCOMCCqAduRc01PxVns2iBxB9eub7pLDTO6mMELKbVFqzy54CjXs0uBX8MoWwWARxgQXSBZagLAJRA1YkGEBqXRwNmswjggAwHjs/IA/tP5L4vaiCt+x6ECrRbih5vrbXctOWQLCwuOcoR6nT01Kzct+d409kYQweRWzQM2jcYQtloNlqB+5BmEMABGV54+Rp57SfuyX1ftArlioQLLZVbe/n+GIFbHz0ib/rCZvn4ml1NZwUWvPxf7pL/99P3Np2NMdRxaJMqAXOXzgSGUDaLAA6wYKk/hLLhjABDJs+cExGRQ1NnG84JbDjeO55txnwvIDCcso0ggAMs0A0FcF271zl2ek62HZ5uOhsAhtioh6LW/I5VaZnYF+4MrtV29zJtuKgLARxgwWAOHNW3S7/1we/K71yxtuls9NV9g9W1gB0AQYELrubAUUWjLgRwgAWDOXDjf3N50921i8X07ELTWcjl43wgAj+4RPFCaFgYxh7O/2YQwLXU/skZOTU733Q2WunoqVk5Oj078toSjxGAhyiN9Zg6Oy8Hjs80nY3G2Ji3RtWZjhtkd2zvW4ox6kIA11Iv+sAaec3H7246G630/PfdLs9/7+0jr0UXgdofI0DXCtC43/uXu+SFl69pOhsADPEYge5o630SAVyL7Z4403QWOiNrEZN2Vh0Qqf/CQFny0+Mnu73KZ0vvj7xBr4599Pjaw/nfDAI4ZGpry4VtDKFEhFMGqIDzB5ZNTM/J7Pxi4t+4x2m/th5iAjjAgqWl5Z+1P0ag3s0BgBM0fcGVX3vvbfInn7t/5DVWjLbHx4W7uoAADrAgqr5W0APXKfU/RoALJdAVnO723Lt7suksAFYRwCETFxAz0RDK5McIuNuJHB//+HhIaCGFS9RDjtEu6AxFt/3aeowJ4AALBkEaV1oA3WKzgYDGBtSid6mm8aE69mEzCOAAC6IKjBGUAFCc4oYaNeo/RoAGAwSKAA6ZQqjatNaNzw1a6m0+aQilUyEcoBaru9hxuOEjgi6ERg0iOLRc0/eHrhDAIXhv/+pD8sx3fKfRPPQfI5AwhLKdVQfStPViAQBtk1RbM5KmGK54zSCAQ/Cu33iw6Sz0K7D6HyNA1QmgWTZqIZZ1R52i8rZEg1vrtfUIE8AhE70JZnR/FUpuQrql3vOD0xFtRxEfoIHOvSV2MQJFAFfBxPScrLpktax+6FDTWUHDlnpXgcdPnpVVl6yWbz74RC3b5YYeWWhPQB1o6HMrtN7J+cUlWXXJavnM2t1NZyUXZbc69mEzCOAq2HZ4WkREvnjfvoZzgqbFW/G+cO+gTFC3dQuHG11DmcewmblFERH56B07Gs5JOlY97Y62HmMCuAqyFq5At4zVDxSJTmjrhQGoG73FqFNU3pgDVx27sBkEcBU0tXBFnTgvzcSHENRVJDg+XcMRh3+4gXOD/erOYBGThjMSsFDufds6l5QAroJ+D1wopRjOxFvxKBLdxU0XAITR3MT8rfLYdc0igKuiqYc3wzvximxkNUqHlRwXH5igmMAtewWMsjpAQ6A7gyGU6X8DfFYpgFNKPUUpdYNSaqtS6jGl1H9RSv0HpdStSqkdvZ8/ZiuzvhnMgWsvLqZm4hcBLgDd4Pvpwfxc1CG6TlQpbdSZaAKNoOWFcs629RBX7YH7FxG5SWv9CyLySyLymIhcIiK3a60vEJHbe7+3ku73wAVSiuHM2BBKbpzhQFsvRGgHiqddnO/u2drFbZ1nlYXy2azSAZxS6kdE5EUi8lkREa31Oa31SRF5tYh8vve2z4vIa6pm0leDOXANZwSNG1vEZGQEpbtajvrTRxwVdIvNEt/FG+E83GPYF61dwCqU1bELm1GlB+5nRWRCRD6nlHpAKXWVUuqHROQntdaHRER6P3/CQj69NCiz7a1duZiaoQLrJo47YEt7r6PwVzT9YfL0nLz2E3fL4anZUul0cdQNDQvNqhLAnS8ivyIin9RaP09EzkiB4ZJKqYuVUhuVUhsnJiYqZKM5/XH/FOLOG58DV0+hIIDoFg43RPybt2MzO559NbRcdC5dv/GgPLD/pHzunj0N5yg8NPQ3o0oAd1BEDmqtN/R+v0GWA7ojSqmfEhHp/Tya9GGt9ZVa6wu11heuXLmyQjaatFxoWYUS8WEYlAkAAPwUXaJpMGi/th7j0gGc1vqwiBxQSv1876WXiMijIvJNEbmo99pFIvKNSjn0WNTr0uau87YWfNviu2m4RLjch7R8Nev2rUfGXvPxnPEwS6jAtzLW5Xro6nV75KYth5rOBgoaPEagu2UXYau6CuVbROTflFIPicgvi8j7ROQyEXmZUmqHiLys93srMYQSEd+GNKEen75zd9NZABrX5ccI/OO3H5U3fWFz09lAQVFxS3wOXIsb5V3w/fanrQ1M51f5sNb6eyJyYcKfXlIl3VDo/hBKTvauGx9CSZmAfb5fKFGPNheDNn83+IfGV4Sqag9cp/Vbbhq8V//qpoOydnuYi8C0SfwaUFv8xrUHQMO4B8aIANovo4XGbJXdtvbywF+VeuC6Lmq5abK35a+/8qCIiOy97BWN5QFJwzAGZYJqvVs43nBt+drj310yZd8N/450+AZDKGOllkJcmO+7rK0NTPTAVWBj3D/aIetB3k63W89m4AmG+8BHNnofuI6iCUklN+36nVX/Mm8OdSOAq0DzGAGvNHlzy2MEmrV+96Tc9uj4ipBAGxHG++feXZNNZ8EfHhXQvPsCW6tQdnkIpe8Ni37nrjwCuAqWlpZ/1vXQ5iZ4fl56Y2wOXE2tcRyfZa+/cr38+bUbm86GiHh2TNpbNcEjVsu8VyeQuTd8Zr31NH2/MQ7Z4DECzeYDKIsAroL+GibcJHmhyWvd2HPghsoEF2EANvlapXTxMQIYF0ZvVLSIiXles97KEErUjQCugqjrnRMXja1CiU4J4bYIqIIyPtDm0T11yYvPWIWyOt+/eVsb0QngquiViTbPdwqpUmoyp/H9VNeFN6TjA8AO3877lt4foaQi5aGpm+vBEEoKL8JEAFdBvweuxQEczIzPgUNX+XZzDSBMbe05qFPaHhw8RqD3u8FFm6MBnxDAVTB4jje36z5Iu9ht2D0pB47P1JqX4R44l5U+13eYoJyIbNx7XPYeO+N0G1294bbzGAGuo2lCayQuUhqaPmW6es7a5Psu9Dx7pfEg7wp0gZabUPl+Ypr4wyuXVwer82HnLS4SaFAbzsemvO5T94qI23pA63quB76VA5v58e27IWx5AVrSn8ucwjRAoG70wFUQtToy0dgPjc6Ba+hB3kAWiiGAuoXQqxXPoUmWs75mBfrAAAAgAElEQVRXt4fO+/3dAyiOpRDAVVBk7DTCltuKF/t9uEi4rDxaWi8Fra0XC+Tr6qHP+t57jp2RPY6Hrvrmgf0n5MSZc5XT6UJd4vorJqV/em5B7t97vPf3DuxkR7j1bRYBXBW92rXdq1CGw2mgVHAp4hVE9XCAmw2/hdDzULff+uB35bc++N3c97WpynztJ+6R133qnsrpMM/ejbde94CcnJkXEXv3DV08RtR2zSKAq6DfA9fBExejxm6sayoS3DAC/qjrbPTttLdZD7WlTts1Ya/XMbSGm2KLmNT/3R47dMp6mqEdI5u8P2V9z19JBHAVaE974NpyASzKZQWal/L4YwQ8KxQo7PTcguw4Mt10NoBOoMYc19FLuVWJi5SUXCXat8Px8MEpmV9camz7nLPNIoCrYDAHjmLcdkWD4uEi4TSw9O2K0iL/39X3ycs+vLbw53w8Jl1uHa5TXcfet+PpV27aw7fjbMrHOrBtdhyZlld9bJ1cftPWxvIQymEO9TzKQwBXga9Fwu6Szr5+y3FNLhaStYgJwrRx34mmszAunNOxk9p6o5DH6jXHXlLB6z+qqMVXFPeLmJg9RsBWO3xd7fkTp+dEROThx6fq2WAGztlmEMBVMBhC2d7KFWbGhlBaKBIHjs80OjwCgJ8CalfzxsmZczLZu+kNhe7/DOuAh5bfuLRRVWaPGrCTh3MLS3Lg+IydxBzhzrdZBHAV+Pog77CrTj/lV8qx58BVrNqmZublhZevkb//+pZK6aC6or3Qod+8oLzuBlb+f/Ff/sdb5Vffc1vT2SimV6DaXK5cf7fkOXBDf/e07L79qw/JCy9fIzPnFjLf12TZ8HPPjWvr+UMAV0F04vu2iIlNLS33hZkOw4isGD6zSuzEmfnlSvvO7RPFPwyrfKr8PcoKGtTGcsBc8nHBHmfPMx5CUVuz7aiIiMzN+z8Kx6drZJcQwFXg6yImIc1bs0lrkaUlLcctPEC1zLZHVSsT5/XK1MJSscDRxOTpueDLyNzCYm3bWgp8XwGu2Z13bS+tNHMLi3Jqdt79hiqK9gU1kDtFyltWQ25T8xRNbj9PnDknizn3EqW2XeC9WuvGhjC39fwhgKvA1yGUXfahW7fLr7z7VjlmuaLIfZB3fAhlxTKxotetazvQ2vL4lPzqe26Tr2w6aDXdur3+yvW1bautlT/sq20VSs8aFfzKTb43XLlenvuuW5rOhjHPDneuYkvz1//lXARbvg7FnJ6dl+e9+1Z57+rHnG3D5Ltfddce+dX33Cb7Ju09H7HrCOAqiFrmW71ClJ91UiItWm5+5LCISCO9cMOqlohoYZzFnANQ9KKxvfdcs3t3TZbLmCce2H+ytm0VPQd8Omd8Gx3Qdr7exGHUZof1h9WHmlOeGtGmWnN6dnk6xo1bDjWaj2hI6METZxvNR5sQwFng2z0SVX79slahLHM8oo8vLto9mj4FF6HIG0LpYmhKGo6f3+p7DpxfeIzAQNV9sbikZaG3+vBgCGXoeyVd44uYxN6gpew127MbwZKWlnTrVr/2bcSCLQRwFeh+Dxx84PQ5cLlDKEdVrcyj9GzPv4pSo8za84qP3NV0FoDWaOvNlqmXfehO+c9/d6OIDF1XAtslvh/Cste/rO/VliD7Tz9/v1zQK3+m6ny8AgYI4CqICqRvz4GzeqJw0olIiVUoLRWJ3CGUZY+PX0XWa3n7eOvh6XoyAu/VVV36djNkI+jy7DJaWtU9sfvYYI4Qi5hUV/TaLdKuy2PRsvPdbe1b+bqt5w8BXAWDVSibzQeWNXmSji9iUrEHrndVWWrXSIYg+dSyWjov/nyFVut67xGYAyfif76ZG1w/drl9BHAVRJWUb5WB75WnK1q7++a51+SsoRUlMhV9JH8Rk4LptvwGc9Ulq+Vd33zEapp1TXH72uaDsuqS1TIx3cxSywiIZ6ex1UEfnn23RkU9cKHuFA+yXXT6Q1V13w76UDR8yEMXEcBV0O+BazYbqEHR+M1WJe5qgYy2TLhOcs09e62mV9fN03X37RcRkT3HWGY5VF29j7FxikR1UugNkC5mMIR2g1zo2WoNfDeXVz9fj1V7r/j5fD0mVRHAVdErFW2eAxfSxdSnnFYtE64qHJ/2USiK93I6yUYpftVM7VffKpQeFTKMcFEGONqWjaxCmfqnVnFZhqiPmkEAV8KeY2dk1SWr+8+S8Sx+C86LLl8jl3z1IatpVj0kS0taVl2yuv97Xi9M/O/D2y9TuZl+pmzvEGXWnK5pHiIreSFUNm/gKOMDoQ6dLJJr1zf/SalnXf50xhtMDgfXVtSFAK6EO3sPJFy385iIiDzpPHZjFfuPz8iX7j9QOZ3hyrXqJWEhNnSxqSGU1oV5P9CopBuMlT/8fQ3kBN6rqweuxedx6F/NRTDb5uPdtMTjVWF/132sTO81mr4labIMt7WHkMjDgrYWDhEuHJHcidC295NhejxFwL2kY2vrMRHDvA36YazN14JMNoftB74LXTzUPLRy5VPPoU95aSt2cTMI4Eoo/lyNo/JL/3CLzJxbcJIf9AwdmLrvhRuK31CDog9TL3Kz9eX798sLL7+jaJac+6ebtsp/++yGprMRnPrmwKELutAD18giJkOtZYnbTxtC2fCZ9/E1O+W1n7i70TyU1WgDZUvPn/ObzkAb5FVAl9+0TabOzsvuiTPynKf9aOP5Qb6xijq3By425FIn/9u2omk3fQEKUdIes3VM3/7VhyvnxcXnPvndXSW3hC6ycTpEN3jUUQPsi+ry4rOQ9vAHbt5W+DP0QLYXPXAldGmkU0invsuJ9Hlpx/9aNSfOVqGMHn3RpUJcUW29KiGdbEhU1yH07aZsUK9Ur1g8+2qFWR1CGei+KPQYAXfZSFWkmBY917i2oi4EcCXUVeG87YYH5Zq79xT+XJdb7Wzd2FRN5rPr9sj63ZNW8uJCm58DV8V/++wGuWnLoZHXfLtZhr/+x7Ubm85Co6qcK77XSA/sPyGv+ug6mZ1fTH3Ppn0nnFx/qYPKy911lvet7UNVNfs2GlXivrb5oFwcUF3X1rOHAM4CV5Xr9RsPyru+9aiTtNso6TCUPTbxi3D+RWD8pb/9WrHhcVnbL7RhlHbXjmPypi9sHnktcQhlRhplqwOT6yw3cn7btO9ELdvxrRR0odHwXd96VB5+fEoeO3Qq9T1//+9brG4zOt/bv3frldSAaRLmhFj9urhm/NX1D8otjx4Z2ob1TcAAAVwL2R3CEdaZGW9tKpv98SGUOe9Pekd/Tkf17dsS1tH0Qx2ngNaaiyAg4V1zhi0sLlk9j5fC3RXGmjjew7cJBdYwKZy2DT6fD0V69xp9jIC/u7ASAjgL6iwc33zwCfnI7Tvq22BAtCQsJlIinR1HpuUt1z1QbNsWysCHbtkmNz58KP+NFbbbpjlw5xaW5M3/tll2T5x2up2iq1ACrvlWJG3kp7+IiWffrc8gYwtLdvsio9S83ScpvMpvibxUGXZo67v3VyC1k5wTPgeXXcAqlIH5i15g8RcvuSD1PZxSA2UqmL+8/nuy5fHRYTJ56dioxz5yx04REdl72SucH8M2BHCb9p2Q1Q8fkonTc063U8f5xHUQIYuKr4v5NiFZWFqymt5S/ya+vRVE098sqe597tN+VHYePT3296bzWoXLc9OkfHa8anCCHjgLQj6p2yQpyLJ1bMoMoaxSX5kGnl0ue+f1nqbtuhUwsVxlbLLUkNmhf2dd6IqmzUWznbihb4DBybSwqK3WR/05cN7ulGQ+lc9SealQb9qqc233SLu4TobSYONTebSJAM4CrUU+cPNW2Xl0uumsWBdqsa97OE7SdqLKLV5xLi1ped93HpN9k2fqyNqINlVkvfjN+TyRum+efLpZW+zCJBxUZjdosZaUXQYZm1+0W8Na7tBDz8iDvAO/JjYZQzGEslkEcBZMnpmTj6/ZJW+8akPTWRGR7p5Uw996MH7cj8cKDNtx9LRcuXb32GqHrraXLIyWsyzRRdjWHLW086auRUx8NL/IHaSX/Cwu1R4jEDV4+frlDNgfQhnmKpSFngPXQAOcyvl7elr1Hwlb54PTIZQGWWQRE/sI4CyICkdaa3UgvcytZG34QcEHeYukh0hRWksWejfKLmLSBrZ74NL2TfKxT9+o04t8zcdvgR44GOhEKTEeQmlvk/05cJ3YwfXJHKaudUcKdHWhDKFsKwI4C3Jv7usegmUzrYAqsuG8Wq9XcvZD8hBK8+S3PD5VLD8VtaHeXRH1wDkOMuqIYVxvomxQubgYUAXQUvfumpTVD8UeLt9QXupQpqgeOTUrH719h+PGE5NVKJesHpzB9xkk+uk7d8mB4zP2NtK0hgtz/vz2hpW496hb3VnYvP+EfHXTwZq36h8COLRafZVbVo9M/qdf+dF1hT+zvNViX9CDut6aaBETa0Mo016voRA5bXyoYJ5JOI17w2fWy5u/mD7c2gs2g5YSn3nLdQ/IP9+6XR7NeMh2HRYsN3jE67aj07Py/hu3ykVX32d1O7b5dJ1JHB3jUR3rmg8Bnm3/9RP3yF9/5UHj97dwF4gIAZwVeSdI3ZVFG09YE0nBTJnx4yph8GNuK13iOPsKz5JxXOW04fql6lrExG3y49vz6Py1fUMKO3wqIyKD+srKkKoSX27m3IKINL/ox/Jz4OwdnPgQyuj7nZ5bsLaNrksqbqmNeSXTq8KzU720LgXNdSGAs2BwgiWXUB2rhEMS2oTyeG5tP1Rz2DV37xks85zwGSost1akrPJZVpFFTMo+RuDM3IJcddfusWGfpufZtffuLfR9q5ZBFjEpztcFaXxXZb/5tMvtzoHTiWn6fm0pcixd32Mk5SWrcdW3eV0+le00tuq86zcekENTZ62kNaytdTIBnAUuykaleT3tLKv5CrSkZSdj9ql3fetR2bjvRIktGOTBdAhlV4+1DM2BC2gI5Xu/85i8Z/VjcvvWo6nvybp/WLNtQh47VN/jSljEpLhaVi31rJK3+Z39+mbFWZ2D3v8Za/AJfSc1bLiOTRy5U2k11dIfdSKUsjI9Oy9vu+EheeNn/FjNPQQEcDWITugiJ7atm9Kuie9ia70zKZflcwtLVrfjnI6GOjWcDwuiVShdP6vMZupTZ+dFRGR2fnF0GwU2Umev2AI9cIW5Ko3B1DElVRmp0lR95vqYxNMPpd4uslecP0ag6ueHMtjEKehbY01ZefsuuoxPTM+5z0xLEMCVMF4QzU6wIif/YpXhJE3PKHfg6PSs3PLI4cz3aLGT3cQ5cHkrQSWlU+Fqa/o9Cj9GoPezyvw837gcJmszfVvqzE5be+C+9eATMjUz7yTtOhrfvCuTKfnZvN/NCIW07T9wwP32Rh4CnTi82uIcuKX07YTA12yPXP1imQytoSTvNqOOANDKfZeyl1ZcYIfUWOUATil1nlLqAaXUt3u/P1MptUEptUMp9WWl1JOrZ9NvboZQ2k8zZH/0mQ1y8b9ukrmFxfw3D6nrvM17WGjx9Fpa41gU7SF7QyiT00lKP6sXrGp2fDr0rns3m7D32Bl5y3UPyFu//ICT9F0dP5/KRZp4vfVfP3GP+Wf7P8t/0Xd+45HSnzWVVzfbPExpD/L2vSj4VFYT85IT9XiU/VZdT7LYntPeBTZ64N4qIo8N/f5PIvJhrfUFInJCRP7Mwja8FpW3vJaQIh0yVXrg2mh/77k3mYtHFFxsooi8ZLL+7va5zt0tJ/2V2VwPwUlooc1eBS6rkJptI0uZhoGyu6iN1dBsrxHo0MlZJ+mH0uJtk9V5XyENoXScfnwVyvaMmxhooiwP78dC2/ftxPOEjetEdExa2GboTKUATin1dBF5hYhc1ftdiciLReSG3ls+LyKvqbKNEJhesAsNoaxQitt405Xk/r3H5djpnPHS1obX5bS6Jq10VeFq624ejaOEGxCdd9Z64NKCq9jRODu/aP0iM7wNn+a5tLGBwPmcG1c9cG6StSKq/yoNG4+ClQqfte3hg1Ny8MToQ7NHh1COb9jqgi7BVtjh5LvsPj49tyB37ZiwnJtxuY3HJaZ3NCF/qGf005cc++/8ip+/QkTeJiI/3Pv9x0XkpNY6ap4+KCJPS/qgUupiEblYROQZz3hGxWz4rcw1rdIqlBb5kYtB68xwZfUHn7pX/p8f/8H+77aeA+cDd8OwohstN+k3wfW5Ej8W07PZz2AyOXZt2v+hcnUMalmFMtgb+7C86mPrRERk72Wv6L82sqhFwmfsPgcuGkI5mmabDr/zhWASjkduMGGQpb/88vfk1kePyD2XvFh++ik/UDJ37oVWVwSW3UaV7oFTSr1SRI5qrTcNv5zw1sTDobW+Umt9odb6wpUrV5bNhhdyW0BKFMhqi5i0T1qFu29y0Do6vMtsP3vP10qlbL7aED80NYQyN4CzsA1f+JovG9z1lLlJ2OcbMRs5i/ZbUEMoHR+Sft3m76FP5HFRHVOkB2v43N559LSIjK8obJvP5/1A9Tz2n6nr4OsGsQtLqDKE8jdE5PeVUntF5EuyPHTyChF5ilIq6tl7uog8USmHAYjKhs1riC89cL4xHq4a++la4iIm/VWVSt3SV8pPFwwCuHqHUGbPf6u2Ld8Eks1CXN/s19MD534brmitZcPuydQb0+HzbePe494tpJM7TNRiduN1m197IhBJ1+biHxl/T00nYeVFTOxkozLTQDnUUVNNKB3Aaa3fobV+utZ6lYi8XkTu0Fq/UUTWiMjrem+7SES+UTmXnssrmGVuGCr1wFmsWHy5UYiWvc+6licOZ3F8cz/YdsIwjQohfRfn0RQ1mAPndjvx9M/0ArgnnZd8fDOPXUqRaNNx8Z37XhNHPXBOUrWjyFf+9kOH5A+vXC/XbzyQktjyj037jsvrPnWv/MvtO6xuf/Rz1fZq8rB9+/o3tzr+Svia+CYj8xgtpeXLvVKcr/mKsz1qqgtcPAfu7SLyV0qpnbI8J+6zDrbhlfwFLoqn6Uuroy+tIf3eLMOdWWVCfBmhVTpVFhvwxaDCtxSkp5SWePrRs9GedF5y9ZmZn7RePk8LkK/5ssHZHDg3yXrO/FtHKwrvnRxdHCRe1KIH+m47fKpa1hyqqzGgP7wskNLlUy5HhkDabNy2llL4rC7cYy+poTTbebSqLmIiIiJa6++KyHd7/94tIs+3kW4onBS4Com2saiaLDHrckWwvAogawhlue0NHJ6alf/4o98/9p7J03P9mxzjdFtYOCbPnLOSTvoQylGLvYc0nr+i/AFWSuTxk2dLf954OxUHdrewuDjnbgEiN+nWrb+QUtrfez+j50JlPHKxr2xdq7X9QN7mceo/yNtB2r5o4juNPEagt/3hcpA6vFeP/zv6mL9tooEUGssNsl1gJYDrmnjxyhsyU2oIpS89cH5kY8AwP/0J8SUqr6TjVW4IpdlnE9Mb+swL3n/7yCpokV99z23FE0Zh8QvK/GJOD5xhur9x2R2FP1M3785/T2TdZNRxA+LbcSmSn6Qb5tG/L7/hvBVRAJcfwZUeQlnuYzlp2ks1fm/haniubfHgxhdlAva8udG+z6kNpMgM3bM5SDuQfVCUiyGUnZN3QSqj2hw4e/nwpdxHQ/6yLmBJrWOuv0CVIK0JgWTTiO19npZcfDtR48p5FXrg0F6hrW7ZlLTe4Wj/rViRP++5aa7r/fi8oFCuMz7JGx1T9rwaBKnNXgfcPQ7FfL8U2YOpPe+U8cII4EqIF8AoqDhyqthwtiysQjmqHyhFP03nwlnaflo6M+cW5eRMyhC+Kg+1tZRzrbUcnpode93f4R7jTs8tyKnZ+bHX67qhjW8lfw5ciW14e9MfZj109NTsyCgGm71ih6dmM49XHXss6bjOzi/KCUvDiYsq8p3TG0pG/7LCoNEuUn4IZbWjlbiIic0hlP25b9nbmJqZl5lz1VfHtaVIvVNnw0S0pbygK7WMJrzmvAeuxPSN0c/7xbf8hIwAroSxynTo39uPTFvZRpUeOJu8GY/cqyQHk7qz3267NSdtP/z5tRvll//x1uSK3c6mK/nc3XvlBe+/XbYdtlMum/CcS2+W577rlrHXrffApSQYb0zpz4FLW4WyzCWqppb8uj7XpONnzsnz33e7vP87j1lPe/uRaXnB+2+Xq+/ek/qepurM137iHnneu29tZNtF5A6h7P08rz8HLn9/NlVOk7ZrMytji5ikJP5L/3iLvPiDd1rcsh0+VB/D9bHNefKh1I11fD8b+8Ll7gzkUBVGAGfBcKWwL7ayVllV5sDZbNHyrZLKXV1Sx3+t6QtYbpG3td/v2TUpIiL7Js/00vXsgHrEtNV1oTcHrswiJlnlkbH/dpzo9YjfsfVo/7XE4dUl7Dm2fB6t3z2Z+h5Xgyfy8v3YoeZWayx0sxfNHRp7fTStFb27E5fzwV30T9usY+NfPav+OHxqfKRFUwrtgibqqLx57h7Vm4mNBHXMszV5j81VPbt4saqIAM6C0ZuDrBs08wJqMG/bqrmFRTm3UPNGCxgMoRxtkRw2chws18B5qSUuYlJlFcpYcrPzi7JgshybDJ5TlqXpcftZTPIvUt81Nm0OXNoQSpOMxW9KfZ3f5GeuzLi4HxuUhfTzJ+1YmpbrYvnwQ6nym1NBnpcwhPLsucXEgK7KKpTG7y23ib4y19jB4wOi3+3kpUtG7wuWFSkueT14kTNzi8UyZrz9hNdKNJi4EEo5bGtwSABngYuVoSqlWeKjz3rnzfL89/m7qmH8YZmmX9HeEMrif7cZIv3C/71J3vSFTbnv23l0Wp596c1yw6aDme/zdQ7c+t2T8uxLb5a12ydy32u7Us5bbSyykLOISVauosA5WslyeNs+HpIQr3t5+7Faw4rOTyNhn925fUKefenNmT13XZF6nvXr9tHzazhe+8V33iR/+7WHjdN0rciQvOdcerP8+mW3F0s/9o9QTkff642kRwaUrReUErnx4UNyzT17q2fMUKEOzt6bi6++ab4VG9diz4uMlwjgLEhq4UlSpNej7uWCF5e0nJxJWCjCs7Mqa27baEtZ9Fo9srZTpnJLajW77bGjCe8cte3waRERuWPrkcLb9MHGvcdFRGTDnvwbXevHNufGMhL1hJ6f1gNnYD6hN9XHIZRtabkc/h42vlJWTZ404i8K3DbtO1F94+LfzU6Znqy81ehWrBidAxcdwy9vPFAih2l5Mc94Un6LHIf5RS3HThdbZGawiEn6yJPQuf5GIz3xhhszKRfD8xLX7shvcCwruZGgxD1FwY+YDaEsnI1a0uoKAjgLhi/Y2auT2e4xSE7P5lZ8GdoVtR7FL2h57F3wstNJ2o6qsgplyWz7crzqUFeFPxbA9U74tDlwJvmKD4ftzlFrRj21QPQeN0ezNTc4hj0eRRYxqZgVs/dW/HwZ8akUg6GUbSkMzRhuTC/Um5Xyb5+5LCqlnrNrMS1ToRyrogjgrLBfPNpa4Moaf4zA+HtMXyuj7uvlKz+6rtLn856x5ONwvWFN3J+kXUDiry/mBXAGZ++5xXq+oI79LPt5LDMpl3WUXd9u4MvkJl5Hxc+bsUY7v75yKpv5jH/3YPZBgRJR53cqEyS87pP3JqdVVwOi4Wvpny83RNTV/NBAinAQCOAsGG0gtFM8q9wotLFbu/8gb8PW2Cr5LhPcJG3OxyCpbGVel2K9lvUUzniR6/fApTxGIEu0/+NDKH27IY94mi0jtodNipidP652WVt61/O+RXzJ/P4QSod5MpF4yJMaDRNeLHt+jwduTe+F8CSPjhn+e9JnBv9++PGp5HRlUE7rricLBVehjOZx2VPY0tOGAM6CJcMbhWKFyI8S50cuxiXPgTN7X27ahq+ZbsfXfRg625VyeoNIWg9c+Qd5Jw2h9DGmDjFoSGoEsPU9Bj3Y6UfLtJGpUj6cb6EYkwAlHpjFD1PavOX4s9BsqjyE0vBInJ0vt0Lh+By49Lz4xNcbZhf5cl1Hps3392ElaZs9w54WGa8RwFmQV3ijE61QN7NJD1zq6+07FaKqynQOXP+CV1OPaGIPnEH9es+uY7LqktWl8pSdn5ThgJ4XjUIrX9nedsbr33rwCVl1yWo5ceZc4iqURY9h0hBKF4cmb3/+4v+9Sf726+Or+iFZtDeb6MH2/dwd9qFbto2dE/FVJvMWMYm+ruseuKvX7ZFVl6w2XuJ/ON+mw/ZPz5Z7hER8MbOAioCxRgKgodeSe0zz0xoOXprogWtiv429p+btYRQBnAUjPXAJfy9zopl8opaHOXp2VmWuQqnH97TtoVPpb0gYpmHQQva1zY+XzVIlVRZYqYNJ9upbxETLZ9ftERGR3cfOyOLSkpy/Iv3ommRrrAeuodPs7PyifHHD/vQ3+HX6l2ZvLqxJT5OdbTW9jSLic2s/csfO8ffEfh/rgRtLc/kVlx2aWrRccdt2EVl+xpzZZ5L/nfXafMkvEb/e+XbcQ5V1PXexWmM11bZAmRFpzYUshgDOsuwhlOEVIl9yHF3so/yYDmksk39bc+Bcbcss3eLPKAuN/efApfdaDve8LCxqOf88lRpkmuRrbA6coyNTNdWQy4vLEQpZDQyuHgET8rEQGR9Cmfq+/uiJZS4XMcnribG3nXJpD0acjP7eJrUuYpIQCJefIxZ9XjsuOzkZaFKsV71aUj58obAQwJUQr4yHK9U3f3GzXHXX7pG/1z6E0uJ54M/1Inqoa3QxTx6CNr5apZ0vUCqZKOjM+GxTHWF+9781I/N80oNhXwtLOnX+mynT4Vooro6yndW7Xk+V6U3FLCJmN1/xxrf4KIC0XqbBEEo337noaITRIZRJw+/Mh+TlGVzHqqVTN1v53Lz/hKy6ZLUcOTVrJ8Ge/F7UlMa8pOGWInL9xoNW8mWq2NzNar2/kXd87SH546s2VE77jbE00rZnUyjnTVEEcBbEC8d7Vj9m9L7sNP0YquPdjUJOz9rYMBzL2y3y97w5HqvxkFgAACAASURBVMvvqTeUalNFVtdXWRoamquUksUl3Zv/Vv7YjY2ostAinKhiWm0pL7bOOaOGNVc9cB4fDLP9YvbeeNAyeJB3ubyZbMvm521mc3wOnL9lwIXP37NXRETu3TVZOo3EXlYLu7G2IfyJr5mXhLRFg/K3O7qF6+47IOt2HiuWCJwigCsh3mLnYlhDlRTbWMX3h1BmDKcZHRbh7qKfpOyFtbGpaC3ogrN9bFMnrg/9bbkHbin1GXDR+3O3FchZGko+i6jynZaGC0Jq+u75FsuZ3CSmPedt+B1Jv7scNpgVFKcOqR55T9LnzF4zMXiQ9+j1zLfjH1foOXAO85G+zcFWS/eO1nyPMbrt+reZpJ8PGwFx9SQ6hwCuhHjFbn4yZb/x0SdOyW9cdoecnDlnOITSfZH3paKID400/+p2vkDuqpdJPXCpc6Ty31PWeD48OYAFNVH+s1buHH7+1+KSlhUr0ufAlcnW6E1h88dMGQz/9V3e/KYyC/kYxG/ueuCcpGpHvNcs8T39zg+zunTQA1ctb2WlNugM/cG0Dio/jK3+IGFhcUlefsVauWPrkfo2WpNoP2atKTM9uyA3P5Ly3RODc7cHJ7FBIOX1YTdtOSSv/OhdpRtAqkzjyfPHV22Q6+4bXTzL5X70ue6sggDOAtNyl/e+j63ZIY+fPCt375w0m1NgcIFpi+heK/sxAnrsX7Z2RbkhlGNhZ+83PfIuJ9KCR8+rskI31XX1rmo9dOPeG0KpslahLH7uujplbc1/CMlgwaPqrexl1LEt3w5PdM0xOn2HzqXMN/Q4XcSk5N+Kplq6B64f9FZLp4jjZ87J1sPT8vav1vN4EecBUOLxGGkyG/nb/fuOG6a7rIbHPo5v22Cf/cV135Mtj58qPd+6yNcqugvW7Twm7/jaaPkK+ZrTFAI4C1w/a6zJgMy3c8p4HkXswuda2e24H0KZvFCADw8B9U7a+Tf0b6WW92HV4+bmxtE+n/JiymXZNtkfrvaZzzc4RvslVienPcg7nqbrRUzSpPVc6NRfei+l9JjYyMPggd7u90WVTfhaVnXs57B+vVEw72PHyPJ3L7KgStLffX9kEMojgLPAtAXG9LxWavTNRSsEmxWILxVxVLnGl1Ue1mReky6oJkMokxw4PmMhR9naUKfXdbiXhnrgou0qKX98k97jqreobFrxOaeh+NCt2+ULG/aNvW7rW0T7Y0XGCRTVUUtLWt70r5vk/r1mLfpFfGXjAetpujY2By7tfbFGuqWERUymzs6nbuePPrNepmfT/560rfi/k35P/IzRVszFV7Du98BpLecWluRPr7m/dNr37Tku//MLm/r7M5Wja8OZuQV541XrZd/kGTcbSJE3Nz5+nHcfM8vf4DmF9deRJlus2jNotpBeGNeHQLJZGAGcBaaFuEghSu/g76b4nJz8fW532I2r1sika+UVt+0ovI02BGRF2W/pNPvbcg9cxiImGQk1dSEpe6ENre75yO075Mq1u8det/04kczFOnrvOTFzTm565LBcfO1GK9sePhifuWuPnTRtKdBokfW8xaT3JfWarH7oUOp27tk1KTdtORxLO717Pe1YmtyYm/a2mZa/+ArWw/tg+5FpOXb6nFE6Sf7s8/fLjVsOy/TsQuk0TCV92zXbjsrdOyfl8pu2jb63gUqm7CabaDhOLGPafLul58AVea+FfdDWIMslAjgLjHvgCpTQpFajrPe44sucqf5sstgwnGHJr/mR/zRJNw/nVTgrx7+t39+/CuuLmGS0uA/P8ekPTanUVJ0+7Mbm92rv0S+vUmNMhbnJbVboOXA5QXD6YkLlrp9Jvydv1zj1Qtsulvao4fl/w/urUhFr7Nmjo6No6pLUGD6yEE3/nqJYvqJ3jw9ztStxd+n87dTRQxhKVef7fWBZBHAWuCgaI0OqDN7jim83I4M5AAl/SxgWUSr/CXcWZVahNHlvUhBwXsYS9abShyd5dkArqOurDG9Gieq32qffgBqkGb/BLJs5x9bvnpSP3VG8R9gHLoe6ZW43PlTQUve4zzchJudifPh7vO6Lf7/sHvHsDZreVF999x45ObM83DJeN5rMbTJehbLkofP3iGdLus6siI2iKZReTY11ZdJoZBGTIvujbNkr0Ktug8v67ZKvPiyHp+w+DN4HBHAWGA+hLJRmubzY5ks+opugvNay8o94yP5QXjo2nwNnd9Jx2iImiMsqU8O9BlryhtCll4X4aqopGfHGp+/cLR+8ZXvT2ajM9lDq7McI2NlWSMo0WuTNIa3SyBF/b9o5+S+3Dxonxj+TknZCQ+Hotk3Hh+Trz/8r9Wm/GNV9jkWb/oX/+MOD14r0vY4cez3y05XkVTTztzvoISy94QJvrb4PXO7G7x04KX//71vcbaAh5zedgTYwLXilW+EMLiQ2tpOYlmeXjn7ln3Ph1AmvVZE/XMH8s8N5SrqHOc/hhLaoMm/DnLlahqr0t7X8x+VVKLUoyXgOXIltjT5bKuezFbaDavsk+mjWIiau9rnPx9IobzmjIuJV+1iPXE7gNKzMsLZ4mvE0Bo+nyE43b3RIEcPzAUd6LGspC+U3kvxJVTrVKsPVk3sDlfzkj3yfrFCqcu9oPEDy5Twd9BB6kqEcYeTSLwRwFqSdIItLWj54yzaZPD1XOM3Ri0RKz0DhVMOnYz/jxnrqSl0tSjzgt8h7c95sYwjl0NZiv7Wn1NQ1HHR4wrgSldsDl5eWiHlrf3Ia7TmGtRm++a9wDhgtbBFL/9TZefnkd3eV3mYIzObA6ZGfqe9La9zIamAZC76y/56Vv8Hv+Wm4GH0zzOrNt2FSNh/DMbx/0oZQ1lqd9YtSrxFu6E9lv3djD/I2/XzZ7ZbsmUR9COAsSCu8d+88NnLhLnZCDLXIFzw5rC6C4PjENK384suap7VyuqpMc4crlNxu0nDJrNb9qtpU0VrvgUt9fXBGaVkeTqkk/YJfNV8uetBbdNiNjc6BG69PXZ1l8eBhwdIkmRCOYWYvdqxRLV73jX00fqOfMMLCNCNG18T49lKef5w7nN5iD1xq0alQeOsYfZH0fQeNq/WW5LTG8KQe1fy0RioVEUloLKjhTC2yD6NGgKIBat33CjRMFsccOAuyeuBGFGllN3lPCwq86VcYe4xAbitueqBXJlM293ReWlVWoRyXc5MUspq+zHDDgNbS64GrfwhdP323yYtIu+dIVtl//WNbcg5kW5n1cI1KXWipRKND/HQs1wOXnI/4NvIaVxPnLJUsdUtp17EKRayp4tlfSTr2ehOjQtIaEcpo7DlwhpsdPIrC73qp3K2a39/JNQI4C1LH9OcMychOND/9Yffumiz0fuNseHJ+DJYgXv7dtJXTdqW1ZuvRxNezthLP153bJvr/Trp+2OqBu3rdHpmYjq28VCWwtWRxScvH1+yUM3Pun0dURNbjOvTwv7VeLo0W58CN/K23tYXFJfn4mp1y9tyi8WeLbMeW1Q8dki2PT7nfUAWj86fK75RB/JYRwJdOPWfbvlTGCaw2OKa0e458PN7DFvtMmZtqG8Mw095XeQ6caHs9Z3k9iFZK8HgaK3p3m7X3wKXcS8UfTVQ43d7PRgK4EteAA8fPFtr3xXomq8vK2uGpWfn8PXstbKVdGEJpgYuWjdHhIik3lkP/fsNn1svey15hPR+uFd1z8Ye75qVraw5clM6fXHN/9gYNfPi2wap+STeCKyzMgds9cUa+nfCw234AXHkL5a1++JB84OZtcvTUrPzDq59TOp3angPX/58MhlOqrPgtP18mCzR8ZdNB+cDN27wLdOPe/MXNIiJB1j+FRcOR6IEbZTQ3MHpr8j6MT3UbmytVIDtW5sClNegUTLfI++JSe+AqyK03HRXfeCNsk8pmIakhyPWcvrSG6TKPsLh396T8+n96quHnPDhQPX9+7f2y5fFT8tJn/aQ87Sk/0H9d63YsyFYWPXAWpFVItk7s1BvLrBvOlhksQbz8M6lySZrnYm9fZKeU9FeT4534IG8LNdLCUvIEDh+GUczOL/conYn1LBVV1/VFaz16U6ktLCM/dnM6/qFoP82M7afmj2FodMq/y6bTxGMEfD7qZj1wo7/n1XJZAVV8e+PB4Og7jBafyemBS/yM6U10xSGU4+mVlzuHr0LamVLmnJUJrotJLjf9OXkl066rB67qNXs4f/OLYdYiJ84sP6txKXZS+vxt6kAAZ4H5SlQFuq9LlMx44bbB9Q1/fN9NnZ2Xr2w8kJsf85bPek7x0ouYJLxWpQOu37qdtsCGHv3ZCEvbri2Ak/jNoxalVOr8iaxslZqLkzNcrEhaXVVlUajRdJZ/fmXTwfT3xH52gelN+LmFJfnChv2pf19OK6Vno8D2Sz1GIPZ76nD53ht3Hp2Wj96xMzcvaa+ZSOuNrCIvKRvbSkpjMGTR7Zlx9NSsfPPBJzLfM5gDV347jV5LdYF7oKF/F/m6Rb6WyTG9d/dk5t896vALBkMoLTDtISs7dyX1Y7E/TM8tyI/+wJPMN1IwHy7Ek/+brzwotz56RJ790z8qz/rpH+m/rtI+MJzWyD7TeW8v5NxC8R64wSpX6Z9NnANnYQhl6kNy+z+bry0zezEMPm/7G6SWdT16I6VzeuCMtpWw7fhFMH2RB1RTfg+a3Ki4urEL4QYnM4ta5NN37uov7mXaAFLme49fe02O2+h73vbVh5Lf18vhu7/9mNy5fSLxPTZora3OgRvUYXnXMjcFLW1ut8nWiqygeNHn7pfHDp2S3/z5lfIj3z96P5S4oEfFr1umsaCI5CGU7huxbdY3h6dm899UwvJ36+4YSnrgLHBR3Q2nmT4Wf/T1qAfOZgtX3fcMR6eXn5k3tzA6bCy62GctYpLE1q7Im4dUttU16SbG7YO8+3cEjfMgC0bi81GjcfepAVbGFxsMBU5/01iPm2lGi2amQ0br0+WfZU4zk725lHST2HJGAZKIHJ851/99bP/n9LglzRNNS2vss7m5K36qTJ5Jfr5r4iqUFoLQ0b9VaIQw3G6lnuqE10zqPhuihbuiIehpm1OqeB0wUo8UHA1kU5FtDg/MaqpGit/PJSmTtyKfaeNcOQI4C0wrJC3LK/B968En5PDUrNyz69jo30cuUHrkc7c9ekSmZ+cz01+0UJPc8sjhWJ7cnvJjyRu2DtY9EXt6Lnvfl5U4hLJED9zYvZBhr3AjLFWktstm6mJBevSmJnoIbNF04mmOfmZcag9FDcfQxvLaPrE1rM1oqOCgmzvRgwdOyu6J08W37XFAaBogDe+/vAaQ8YaMrEaP0d/HekUK7Lp1O47JxHRycNbLSKbkHpPix274O1gdQmna+GlvkyIy6EVzXX89qfccnqT5XnrsH8W+58a9x+XA8ZmR19zPgUt6LbtEHR1afbr88F3zD+a902g+qb/Vm7cYQmlB6s3y2Pu0fHbdbnnfd7b2X0tauU3FPnvg+Iz8+bUb5aW/+JOZ2x20/JZ38b9ukhvf+sIKKbgRXezzeuAyquxKTs/m9MBZvNydVyKAy9t6vwdzqXoZ8UVd30HLcMOB9HvgqqY58nuswSb+Wtp7XfFpBTLbqnwzo0AlZ/j2qz9+t4i0a9XOfq9m1nty9l487h17d0oDZ1Z+kj6b95k//uwGeeZTfyj9fflJ5efHQFM9Jza2lfR9o8tameGtRfQDuIXlhbxGes2GGxDUclBZZPtv/dL3RGT53I0+NhaceFB3vuZjd/f/XTrAtNpoYNCoWWKDHuzqRtEDZ0GRE+TIqYyWvRTRKnT7Js/0X0s6IVIWHixseLig6/Nj7KRNuzOOPUQ1sVUq4YJX5gRPysF0mSGUJkMsEp8Dl7kpI2mrsuUFByGpc47RcK+Ajc0m1RlqMGnSGltJhVhe4gvPDF5f/llqCGWBOXDWmfacNHCsTLY4dqOb2sOc0nCRsb2xB3mPrVZX7AZyz7EzGe/MS8fstTxLsQvacK9/uSAyugbkBb9uys9gGoTb8vmk85a3k7YSs8hg/1VaxKT3c9HxcxESV9zW2cfpiaE5Z7U8py5nE7ZyMD5UOrzrkk0EcDlmzi3I/XuPZ77n4Imzia8nzWUxqS+2PDElx88M5gqY9jZFQyibGG7hOv34QzfTKq+x95XJU8Jr03k9cAkfMqnXE58DV8McON/vx0+dnZfvHTiZ8y67XyIttZEhlBL1wGUMoTQIBJPm+4wtYlJh0n/WhxYWl+SencfS35uw7aLl5fGTZ2Xn0eliH3IppfdmcUnL3Tn7ovCm9OjPujUzJ8cksI2V77w0TM6jFKWeA5dzU2yaVvINd/qHth4+lbjIQ/wjtT3I20L5Sfq+Kq0HrvrmRkQ9cHNRD9xIHDz4pT+kM+FvRTifZmL4Wh3bLZ2Wo4Yv3+9jXCOAy/G2Gx6SP/jUvXJoKjlIK8SwsH18zS55x9ce7v8e3XQPV+BJFxsXjxGod/BGvqzhSUnjwm2d4DNlHqZsEsAlPQfORhdcilAqvH/bsF9e8/G7899Yg/HeG21hBc1Y407KTUb8b0m/Z24n4b0fvm27/NFVG3IbpkbSMd+kiIj8xmV3yEs/tLbgp+ox/F0+decueeNVG+S725KXjC+XvllPR/F07b6vbqYBSdoNtVHDSE+818HoOXA52xi8r/gezvrEy6+4S37rg98de31kDpyFo5rSdlSbfuNqqf1n/pknn58+B66fXnRPVTgnI5kSkYTGgippmm66wEiQWnrgcpg2oKAYArgcjx46JSIiZ+aKP3R4LJiQcksBJw/PGzfoym/fmTCYw7X8e24raH/4YvF9kXSI8lLJerB4VhaSnwNXfRGTNDbmSfrC/hDK5ARHG0sGq1BWYTLcOf0Gt9oX33V0eXjYsayFGuLbDPzqmpb7vb2hckdOmS1zXeRGpLkeuOaOVZl6Mi2R8Z4aHX+LcT6MGlW0tnKzm9i4mJLswuJyRXB2fvz+oqlFTKz0wGX8rUg7c5lq9sm9Hrhz/TlwI61jg7RjPYJFv3eUrvNFTJLu/yo24tn+XN41yWgRk4w0Qr/+uEIAl6PaqPOE9Erc+aU9nDmeIxurUI5v23qSpQxa72TsX/1XDAPdMvIDxnFGQygTikPq8FCDopM/tCg/qCxifnFJHn3ilJ3EEmRV3FlfYW+JOSzD6cUXFemHb3r531nH4pEnpkZ+n51flO1HRocTjl/002/WqgRsZfdfnONpHk6MHs/kf0eNJabng8nNmqtGkvim5xYWZevhU7L9yHR/yfSmGA9RHPo97Xo21vNm0hCWMuc3KX8nhqYnxLdvFuiNp5m2rdHUxx3NaESJL2Jiq87OX0zGzck+qEPTj03aZ4p4UiyAy0p3+H6s6LbSpmnUdc9kup3h96VdtrYdHq9DbJaDsmk9fHBqpLxUHdrfNgRwOVTCBb7MiRP9bm0Yu9Zj6Q+eA2dpI+K+p8Y0r/FnyJi2ItraF2Va2cq2GqXdLNsoO4OLjp0dc9mNW+X3PnJXqWXRTWQFDlm79zcThiQVMZL20LmmZbksZPWSfvrO3SO///X1D8pvf3itnBp6DIjJ90rdgq0bOYstrCEZ/i4reldA0wDV6AY/+un47uLvv75FXn7FXfLbH14rb/3SA2Pbr1PRRUKM0hxryDB/7/jDlQe/v+jyNalpuDpkaekeO70cwH3/kxJuxSw25JjkJf53649pSQl4TBR5kPeTekMozy2ON2oMN8SNNwqX4753KC19s+3m3btMnp6T37lirfzt0LQd20xGnMSzuWbbUXnVx9bJF+/b7yZTLUAAl8PWSS7SK6CWHh6b9JqtHrg6HwE1dlHK+Q5ZF4HE1yztk7xUyvb+JV2Y0j5n0nub9xbbPSkP7D8hIjKy6I5N2T1Ibm4wltMe/fdwL4BpQ8yP/9CTRURk/e5JERGZmx9cxQq1Qhe4kS2i0HN+Ao/f0oZRRUfS6jAoy41Hg2RHE9y470T/3+t3D+YzNnGsjHvgRg5D8jlgcqOfV6+PL+0++Gf6isJ5T9fKz1faO9I+E017OH/F+K3Y6BDK0QbbKsc491pWPulBGonXw+UXx69Bdgvsk3urUEb1bdq+Umr06lt8COWyJuaY2RxCGU0Pun/f6Jxom9soc62ORtHsOHI6vfe2RQ2LZRDA5Yj3/Ay/VpSWcvObkhYxWU5vtPDaWs7W1oWiiiOnZmVuYdCCpmKhtMmQxv2TM9aCWtOhiaOvRXkZ/G3/5OhDQAsNoczOgpH+UtI1H1et9dh3N/qcg7yYiM8/GW6V1iJGlUA878MfGe8hEHni5Ozo5yy0pBTptchMx7Pr5NTZ+dShcHkOnFguh0oNPZvK8LMm+6HpRQOauKkx2eJYTJXyobQeoKygbXwIZYn8abPjW/gZdCmv5eVrdBETe+dgfv4dlZ/BXfjIy2XWF0gzO7/Yb0w8tzje7ZPUOFD2+0afi/cuuWxYHN6G8UiwhPxMTM+NPC4q+XP22Cu75dOtsV+iNgRwOfpLzZYKasbfWKYQJcVlWo8n31/go8Q20jhfIjcl+Td9YbP87y8OhgUNAune55JaOYcS+9zde+RFH1gjDx2cGntfyZxm/jUxdk74ci/6wJqR3xMXTEltMczMQqa6nsGT5ov37ZcXfWCNbBrqNTBRpGequtGgbfDvQWnTvd+LHIrEohH7ffL0OTlscSENo3QKvdevCO6X/uEWed67b818T1qd/favPtx/bTAHzuz7GfXQ6NGftsTT8+mGxDTwyVyooP8zHrjlb2P8xi45jcz85b/FKM3o5dFVo5PfnFXulmLld3QRl+KFy7Rc2ii2idfnlPRfbXG14Tf/22bZvH/58TP5jxFY/l9auTPlew9c0r3Jr733NnnVR9eVTrNKfkzT8K3R0EcEcDnSnl1ShtZlV6E023jTLb+23frokbHXTC9C9+9ZHg5Q5YGsSdtN/3v6BStTQoFIu5CYzANIb9XutRamtG5XlVeuo8Ct6Fy5rDJtu7SnXeiHW7+jn2XO4+GPxC+qU2eHn/sYu/mM57PE0MeiN3/jz+gy3qQ3TFYt7DcMGY5eKBIIuA5601JvZAil4cDC0XMs//3l8xP/3SzwdhHoZX0mOygtHoTa4KwDrpduqfnkhnv99q2Dx4FkLmISjWoqnJPhPEVpVUikwHbir5luNm1/7+7dG6U+n8/iFzN7jEf2dS8pnQAvS1YRwOWYOru88IAWLadm5/vL/ppIGsYxtvKWUcFOem28SnPyIG97SVlJP2sRE53wb1vPxis3hLL4jXLmtiw0uRctG1Nn58f24Zm5hf7wVtPkikxCN1VmpUlT8d6bKMCKhq4U6oEbSuxklE7GzVm/Bd8gb3Vp44VyeQjl6MN8bXA1TDkrOV+W2c6cszoWVcXPgdH9ltUiP9YbGTtZ4nWWWWBmNgcu7y3xbc2cW+j3BhVJKr4KpS35+8JGa3XSS8XPiypXjbmExwiMbLuX+PTsgiwsLpU+X8eCCoN0igz/TrzXKZDZst/L6n2kyWmVEkAOn9unzi7IyZnRxs7FJT2yQFiXEMBl2D85I4emevNStMhz33WLvO2Gh6xuwyi+SDyBE9KyFKzEHxjukmlFpGI3WqYtcrbmwOUFY0nzD016C5N6ctyuQtm7iBq8d2pmXn7pH26Ry2/eNvL6sy+9WV75kXW99ErkrsAhydp3H7p1u3lCJttK2e7JmXODh8Lq5bJnsqBMfF9/7u69/cUmirRCVxrakvDaYFi4ecKh9+4XXvSiZDrDadW5x9LKbm3bN9rmaK2d9hGd+4787cePp1EPgLaz7+Jl5FnvvFkuuvq+xL8PrhHjGx65dmmbi5hkf9h1D1xd5dPkMQJaRNZun5C//XrxFRgHPYrFPvfA/hPyvHffKt9+6InC20zafv77st9oY9G6vKxUuX4MN/6+6mPr5Jf/cXTo/P/P3nfH61XU6T/zvrfl3vRKCklIIxA6iHQVBEER7F1g17Wt4uq6q+wqbljdVbHwU8EudlFXUUqQIr23QBJCSA+k56bcm3tz6/u+8/vjvPOeOXOmnnPe9703mefzSe55z5kzM2f6t//nzStw3OK7M/MBMZzgCTgNnt/SUbkulAfHzc9ttX5fxnA0xauRIXRioo+BwQbwUIjfkTUqLkx0EjgZUZtR9U3Z2NZHhNwLpfzFLBZalzhw+8qcrjtWbI89W7srWdgACQPUkL52449vE37DYVL4oD6wlsCJY49XCZZJ58Vn6kDe2cBlTx3m9Juy/qH6kN0H2hIqLnnaIks7vXrAVH0TQeeiBpxEKkKpm6pX0laOEmLqXHiGLIX6m13HWdJ+cCpDc8+JeZWiDjICjnIXBGEb37zU/lwXz9Otlo+u2w0AWLHVzj4/sXlGGfw+VEsP49E62MwrzTPFQwrgz0u3WJdxsMETcBrs7AydCvSXgxzmc2lmQNz5QZQjqVqgg7/xd6Ppa+VxMUvEijKsMPq6xTmbSaSScs+Q+ndki4dNyS5luaghqvKoF5Oq8p2O5ddrTeaLbWoIl8lCiaJE7WxZxbHHjxG3A6aYNqN57iQKzaTImsKmmVy9Ats0Q6FEUSplT0YN5fOJi2QyfEeVl/y5G8PBvbFMCpS2PeoijWBtIpPo236/66faJs9iuJUox1jOSAojgjdr4Y9nLA6cmnlDQnX1FMe62Lg2pGfOqg4b3ZK4TEodxqM1c8qd6WELm3NHojlLOQbPEF4fq4XEBBwh5HBCyP2EkFWEkJWEkH8p3x9PCLmHELK2/HdcdtWtLXZ1hQRcX5mb0+BAwEkX4ASR5KXcLMnNihfKLAfyEJkUroG8GdjmkZbzZFosZcSzzYIkt4GTv8eHVVAh5A7L86i0n0XHurSZKa1tVjFblhquynybRMOGhJW6/Man8MSGvVbEtGiTyo8RG4ItC7tBOffWvU0PVu5mTlhXTLBJ9snfPYc3fvfh+jEf6lBuKDVWj1nR9bl6fZRLuCLMTsM3iuuxrQTOK13B7AAAIABJREFUNl2a51HbNnXiWCgTvgzFtbZe1gf+6N8kYO/2DhYx9z/vKJefPl8Rty3bhnlf+FvFORY//vi4m2G91Ptf2nACtmjvCoK3jy/HCTXm73BXhjjjJHpDNE9JAlMb2Kg3xuZ7ZU3Rv+dypjnYkEYCVwDwWUrpUQBOA/AJQsjRAK4CcC+ldD6Ae8u/hyUK3KBjErimfDqhpUmKJoPaBXH0d1YSuEgZmeco5K+atQJET0nyBZi7Lj+vqJ+mqVOQoRbKUA8JoFrrXKRnrtxtlzwiaeyrlAj1WpIj6pSyhreRwFUIuDjTIa5CqSbudOqWaeAk0ciozHpB9a1MAmdtA2fZEi/t6LLL0AHa/qLSy5rBpkzjOBbWJlHzJGI7ZiirdyB6eLc+3GmSyUIKJYEsqLzJBk4bfsGR+WCMA1flEZQlM+hvLwTq/au2B/MtKoHTO5wjyIaYdHWYw+ypbeyoVbBlNgDxIV2x55bkGX0vu35K43mU6Oqi2VMPBSSmRiil2ymlS8vXXQBWAZgO4FIAvywn+yWAt6St5FBARQKXd5DAiapPMAcclecjz1u8X6qoKlhXUYqa6kk71tXExRPvs4UjSQB1WbnK5wklHXInJulXIlUOae03VLBtXddNoZaLcqQsnoBLRr/FArzqVChlUA7ZjNqk2ippwwKOEjg3FeRs2yweH011EKt9X9kzhJIc5NzR3R/1SmctgdMRStYqlAYCyZLYFp2dZKc6bXhereFTzrea0nx+n5fawAnMAVHKmQSuZgkiY88EWTIK+3khllMQNiZbAYG2DMNzcS90KU93dOOl+gfrFqVDJjZwhJDZAE4E8CSAKZTS7UBA5AGYnEUZ9UZfxQYueZNRmkwtqiRRA5QN1mp44an3pPjy7S8CCNstFJfHIbvH2kRFwD33yj7MvmoJXuAMipMQVbKmX7OzGz98cL32PRkXLpndnj48Bftls5BWA4njKaYYf2mM+03EllFllMTf4+enzgZOLK1ac9Al25O/8vfqVMIRHT0DmH3VEuv0s69agm/etVp6qCYglXXh2jtXx57LkNZ99zfusivHNr+hBl37PLimHTc9tZlLK7wrMJd0caFMbXGgP6pubtN0ooqnCv/0q2ewvbNXlxGAgDcw5z/iY3Xh1Xfi+w+sC5JqyhMFSGqmnBvSqoBalSHTkEnBPLzq5uVW815GwOkIg1DK63Yu+8Rvl1auxbVct+ev2r4fD6xu19Yra4jliBK4WtTDjmhPVxEX5jDbF4Y7UhNwhJCRAP4M4NOU0v0O732EEPIMIeSZ9vb2tNWoCvgxxwi4RgcJXCw/xJ0f2Izr0NBZfFc4IFZFhbK6s9uU/88e2Qgg7gTD9iBVVLQdA/MM+OAa/Rg0Facinr9//zr9i7K8FIWdPX+i8h2TAbLIcXQZKvXyXAWkG39ppgP/qpSAM2z4DTkS2sCxfCIEnLkOagFcujmZJIzAUMHLe3qs07Kvu/7+dQouNoWrT6paedCzyU+lglWPXrUZk38VPDgrCRLFGuUyXLv6C9I8dQgkcHZYtd3uqKOa5z94YH35OWPOxvuS31N0KnPu01j/QrX2/CSSEtYqfRJ7Nml6rhlVsfdUdXD56iWcZ2aX73l4bXjOsLa7lRHD1H791jESWV6ycrIcBUnOphGmpsXYdw0Fc32Cs9lQQyoCjhDSiIB4+y2l9Oby7Z2EkKnl51MB7JK9Syn9MaX0FErpKZMmTUpTjZqgd4ARcPZNJht0SWzgpA4ypOWxA+PwO5iZwNpNK4GTTHjWJioJnL3tS3K4rl0OseIdygoehN+b0RipMhGQRqjszJnm3uA3PRlxTog+/3yOxDbZiPMCTbtlySFXES4m1JNozwo27eTKec/CDjUp4lJb7oBfxXJtYKWiaFABDQ+S0b98Dqq8RBwQCThz9QK1tAwazyYH0SmvrNxSrH/lOWetlp6JBE627jgyX5OA3+dNgbwJSCZ1cWHq8OuN9VqScA1XlVMoyu1D4wwTB2aVcUzZMVCi9Qqgd4zkVgZQHU21eiGNF0oC4GcAVlFKv809uhXA5eXrywHckrx69QU/bhgHqCFPEhNIsvGV1L0qpXF1jzQHf3XZ2eeZJv9ww7NLF6pQqsrXE3hiuqxh2rijacPr25ZFg4DGVCgN5WX9OSaD7IrUxzHfNO3uamuh4vhJbeAM5/6Gsqp1iaLy0fEDGaS/45xQ+UHXBWk5zup8h/JmqG5vgKlQhr/P/dYDWHzrSn2OLgScoYVnX7UEf5PEWExddj0IOIuiEw+VysHf/pXuviQSOLedPRPbIU3iqH2WsN8r1iqrMt2SZwabMaJ6xxa2Tkxi6pwk+fh0eY3fN9LYAv704Y34l98/b5VWHNWDSglc9aDqil1dfZh91RI8tm53fE8s37BlsYn79DpFrNqCJ+AAAGcC+CCAcwkhz5f/vRHA1wCcTwhZC+D88u9hj96yCqVNGIFpY4L4HrJDWk543yWQtwmi2/IsUO2hbp1/xVuchj+rIYbUEjg9gVfJ27KarpAS9YoFhl+Ir/v7GkO+6RfpLL85qUQnTR3SqVCGL8v6wyS5YfEiVXZvqZyYOEB7JK0Sp32owGqf5hp5Q/sB/OKxTdrkWXKlAeB797mo8diVXRcNjAz2HlHFTmXHa1NOojAClukA9fwf0ZivtL+OqWXjut01vpgJ1szPLNYFab7VYR7y4Ntctm7z0iYiEG1J501SQszaiYnk3p+e3eJQTnhNQCQSOPtyk0LVRktf7gAA/PyxTepxodF2odEOjOCulTuk75iY+sMJDUlfpJQ+AjVxfF7SfIcq+ioEnJ7mfdcpM/Dw2t3W+dpMEjbf+E1D9l4S5xcmDBUOe+hKOvrXBOa0Q7WXsnyMXiozIgZsss3CllG1+Sexgasn0tQzDYczKoGTEHBGCZzIcBCIOdFBQYTbrq4LkN3GmsVBfyjHhxMlGDK47uFONnAZN80QbmprFcXIb8VL1fhMq/rRdPvdsdPHYO0uu/ARoVMndXkic1HZXo5VNnrJrBIDQNyDqoGcRMKlKy6Lb1UxYUVmPRAlMGs1n8VzoejExEbjxwx9YtXZNH18Xq4MywoXKgTc8KfgMvFCeSiAd2KiGyc5QpRuYilN6sSEcfSi78VVKOWZHegv4JSv/B2PrrMnLGsFHZeVh7jhSTl8kh+MGJItpoDaQUw87yptaorF3zatKo3KyyHLOqvvcc0l68NGtcDXU0VQ64ZMniPgwoMLl7+Q3sVRSlZSIOe+kLyQVe9cfuNT+O69azPKLQA/j1T1dN3EnWzgMh67urJ1DIBaIKp+bCtZENf+6L5pYmQAwP6+QZz05XvwosGpiF2V0vWYidDiITIkZShxB02RuMzaudMfn96MN1z3UOR5GmJWbvahLj8r5AwEkqiJUtn/U9RLtl+r1PSiBKZd/kaJqel94bfSiUkV+0X1rR/99bNcPeTrAWDHaIsziKJ37n5xJx5bv7sigVSdCYcTPAFnCaZPbbT1IUQ7MWOHMhsCzkKlDlAfNNfu6sbu7n5ce+dL5sIgqhUMLbD6WEvgDCqUNuouQPXc78s2YlU/2qoB6cpJosZis8yZ0lQIcMcRVUsJnErqJsuGEKL9kgZOhZK1eYSgEDLlx1dsI9LWunbIMli9iAfXtOPb9+jVgl1hUzfXPdzlezOXwFmrUNYXakmRvmZqiRyN/OWxbHMH9h4YMOZl03YypqgLCCO0LNOyMlVg8y2XC+zuTWu7LWRlfu7Py7F6Z1c5v2qh+iMzokKpYfaytEn7mzelka2Lqr2HWKTJGmI5gzGDNCr8Fe+bYfoUG62iOAEW/NWZK+g0ZWRFfuOu1RUCNu8lcIcOWKdT6Id1jqg3KkoloQAsJol08MsWDUUgb9HjlQlU+SN72GZf8UJZ+UY5h09sz5CLqSif48Dp61m7Y5HK4NfuEKIn0NxUwPQbIF9OktaxeSfNHpdG2se/KpeO6ZEvhxsplXhub5iPmKdU3U81Zg1l28I1n6TB6usFmyC9rnt4PVVG40SJ4roOdVTNHe07QsK42rf5QKarkeu7VFKmEpJxkyP2/WCzJ4sHTTVhrMlElt6kQpnB+JHloNIOyRL8Pi+tg1AXJBi3ImTfo5LARVUobRky6dorypCnnAph9LmNxDt5HZIxbwC9x2eVh1Ed2PfnvQTu0EGhZDdQckYJXBR9gyW84wePYfmWDuU7vJif4V//+Dz+IsTVMXE5bAc4n+4ffvG03UsG/M+SF/GThzYkrlMlvfBXnS5IwdpEGTOJhovZNbetxM8f3ahI51ZP23dlz1KpUJb/bu2IBpoVF2kr4skiTZi/Q2qHdTPN5pXmoHDxdx+uXKvCCOjADl0RGzgNQcH//sqSVQCAz/1puTGtCTbEty2qKYFLit89+Qr+4+YV0mfR+poPVDbI2omJ2xxzSFxjRA6JlhoEYiqR+aQb96Z+iBGDNn1B1el++nB874q9jyB49JMb9kjrwMPGBo7fm0TmZBotGVNbVGuYxYknM8TZ+fHfPCtNx5CTEEhR9eLwmlebdGXkRM1Z4h+kMmdJokKZFnwxJRqGEWAETC2qYeO6X8lksyzDNo4dq8tBIIBL7sTkUEORGX4aVp8cURvPygJ5v7C1E8+8vA9fumUlpoxuluYpO9Df+9Iu3PtSNMSeyr7JVX2tGhyynzwcEEYfPmeONp1qTokqJ3KhJOWCFAf3mGqaitnCq6n8/NFNAICTZo6V5F0dyPJN1f4qLq2Qt92BprzQWRRrXp+5fnEhQLJvCquy9vUMautglMAxFUpe6sZJVmPqHlxt27v6TTU1PLeDbj0I5pG6jkMF//mXgHj76tuOjT0zzSNC6m8D50QQiuu6Ml3tQRXXqjRAfF6FKm/2a5SyPpr5pXtHleorS1bhmOmjhfTRNLv2B/P2++Ug3XowBo86BW+/TQH12m7NmbVIQuPhiRJBw+xJs4787QW5Z0EGewKJltNEuAKJ6iQrR0mwJJHAGZIZtYcEAjb0whhnMkbes6qdXVqrcFlJxoWm+1Tt5iVwhyAiEjjdwYeQUM3PQY1DN3QrEyxhrDKRqDGhlpxe20lbUaGk8rZV5WcbRoB/Kss5aZsYOfwy7p3Ngqok1BTvUrZh6dMZqqZLrX0qa4YkhskuoI52iyaOHQ9Tv4Zx4MIDkU6lrxoxHPlypJJe7XoUfyhlmgw9mq4Cm7OZKxfW5ZCRdduYJFjVKtcGNm3tKjUT83NRl0okgYP9oVo2bFwOhOG4U5fHviGfI1riMg2jSv6sOgOIfYEbE8QNJhVFWnmmV81zgYwAUhFwURs4u/zT1lGMPyo6blNp5WS5jlh5SI/NWfN8jzCOLOtbLHNSvQ3cIQTW6cEir07HPEYBcscXokHmZ/5YDsao4XzZTnSTq1ZKge2dvbjypucqXjVlGIqc9hikVFb8siIuN2RjVqWq1qYWh3KxK9/++aMbsWH3AXkSQzVdOP4uXLNqHRrThMbIahwnsoFjXigVUjedDZyILAJ5ZwGV1HuoQhc4ncE9jIB92qxbxnzwZuXWvk+SlKlitmVxoFTFwdS/k25uORFwXJkqsLVPZgNXrR62mTNJoWMmZQU+ylNJMY4Y4nHgksFFAqcK5L2jsw9X3vQcegfU57KkiLQ3DeeGyNivpu1sEq0iV/VW1zACrurzQxGegNOAHw+s001jJKJCKclPHDNdfQVpWh62h1ieu8QjVKEMVEFuW7YN97y405xRLWBZFvsGk2cpEbY2cPxjMWVDTm/XaILuVdl4KipeYAeea257MVFZALfZOBBn2jSGTTKepx6mANkucO0z1QaWKA5cPlShlHGe0wTpdUubbODK+sFWKjdUUI2qudnAZVsDfTzJGpzuLaHWjhB+xzju4t/42qzKK/Y8Nr8s1rKUDeeikcXvyfxfHkXuoEkRrV/EWYfrOqcdR9nMaelaIbnKGhEbOMlzcb8SiRlbRGLyShqsoHBbrQpz8NW/BecyWfDptOtINHwNVToLis1PgzZP9J6+DmYvlPHS2G9bL5Q2rUQAFMoHrPxBQP0cBJ9QG9gYYQKBvjpLGrdzUUPLiZOo+cnT6Z+rjcvt65k1bMuKc4vs8jJxcdhznS0MU2GpBmSLZBZx4FT3XQjgMAi6eXMz1S3UGHJrxzTNnpUtp7w79G0SSuBUh1lhzmnFK9qiksOxfaROTDKqSjVg44XStf7VCidig6FMLEfox4T1NAUUtlHTlFTHvk7UUconlOImgbMJI8AdNIV0kTXZss422hJ83MpqTe5qOu+QOzGRpyWIhoLJctyq1ol6hBEQVRHFuMKmM4PtfR2stHlU/UQA1WDk56C416qYCAVBsj2c4Qk4DfiBX5HAgUbui2OAcBI4cfwEgbzdB41NDA2W/3fvXYvVO7oi902G+ipOaD2gLLoigWPp9JVkfSQjvCml+Pbdq7FuV5cVcdyQ08f8SgNZWy9ZsT15foaFzkVdwoUjbeMwIgn4Oqzd2aVJKXmXAuvbu3HtnS9Zfq8ciSRwfCBvScbiBq+Tst/83NZIrKus5qdrNnLO69ClKviqLX1lnzSNc6xAh1bLumVs86tHj1jRR4ZErC+e3rQXv3h0o57haSiR79f17d345t2rzfWDfjzwj2T7uIsUR9Qokb1ZcbDFJHBc+VF3+a5jWPPMkYjV5RO/F9ysJuGiUlHkahGpS+LJYnCWIpPA/fDB9VjGeRtn7+3c34dbnt+mLMpURZfnEQKOu+eCJE1m0iKjNDqOk+4rd6/cgb88t6WSpwzfKq8FB0Mgb++FUgN+zBUVKpQ5QiIEVsQGTnbwU5SlVWsoPzIdGvsLJWkwXCOnRfhdy1hHSdU/5BtE/J6s3/b1DOK7963D75/ejNcsmBSk01QkkMC51dMWLtmmOSS5EG6mvNKmdQE/B9/z4yec3qWU4tO/fx4rtnbibSdNx7zJowzpzXVgMK39FS+USmlq9L6JQ9nD2UZk5UjDtc+GmwSOhyrUQBZtoELWc8ImUG01yrWBjcMB3TtAyNQolCgW3/Yirn37cdH0Ccf9ZT97KhZWRfWOS9OlUfeuHJ619QmeMgYsn1aliqeDzR4QEHDVHUD6NSnK5Ja1sI4RLm8XeYG8E5PgOtl3SyVwkntf+9tLkd+svM//eXmicm3BE088I6Di2VtxplKvLxJGngNDxQa2jAQ+TYlSfOTXQZiJt544Q5r+QH8Bz70SENGuHoiHIrwETgN+0PE2cCpOGPuttIHTlKUbrLbqm4MGV3a2Os1D8VAmcotsuU66zWigWKocyAoqwzMADflcqjYxbZhZ5FNJY3huMuyOlmefxnbzM/ebeEgNf/cX3PTXKIC25jwAYEenyTW/Jh8pI8ZShVJxKBwKTBPnMi2ZJgcz+Db770sXGVJn2zhiW6tUROvixMSifJMzHl1ojVgZNH5PlVevxmGXWJ42+HaG570wLI78rADwYQSCdHzdePUv197WpY+oUKaAjsGqW3dsjjq6NKYwAuL5ISmxGvFaLclCFchbVhfd2SNIaF8vGSI2cJQqGUG2oTeSVMc1DhzlyidQz3P+dowANZThwwgc5OAHQJETifP3RU5QEMg7SBFbqDQcviwIuO/cu1abt5jNSzv24/dPvRInNGt5mLRcDsTAp7ZcIJlkjScG2Ts64rfWNnDqtDaJVER68NcpDpzLUm1Iasut1i3Crn3wmydexmGjWwAAO/b32ZQuvZsokDcXRkCWrUmaolM5qRfRJD14DXMCztXLKd8E5vhL7vUx5Bj5lcZDa9bQHaRsYYpH5ZItn9aWUfHw2t24T4itGskztjZVt/3ZupOvODEJYQokrYNWAmZ4ngaV9tKVb8Ok1KQx2cBRAJt2H8ADq9sjkS57Bor463NqNUYdZOPLSJRBP3/Xt3ejg4tFmgYR1URwYQS4e/xfY34awtjlnVga7jpiemDJOYmft+OFrt3VXbk+CARwXoVSh4gNnGJCimOAkNCJiWzBVzoSsahH0gODagO78P89DAB4+8lRcXMtjwXOQgDzHhABo8tkdot80NJBzYLbWFahTELEZRmdQFe8bTEuErhquExP2t8uZTB87751uOKM2QCA/b3mzdBl/pj6tdGgQineFsvQqfQmmZ/8Jp7cHjGOWkoOVXWoPEtQF9c33AJvZ4u4xMouXS1gM09NHHJT7DapJFzpnCpMa8sA/fFDG6zSAQrVPoceN5k18M9yORJ8A5c2nf2OutBAApd+AMlysNm700rgZOWJeOePHgcQaHTw65eNmi2DydbOZl3Ufcdbrn+0cp22P0TJdYnlJzDFde9F7ieoj8mPg/hUZJTZzHPXWnknJgc5eDvUii0VooNL1KPNRYiD+JBSToqUagU6qETkyjrV4QBgApPg6NzgB0RW9J5sIeU3Evacl8CJUtV8npSJ70RV1yKrLE1cNJ3kUpmnQ9pqOTHhv6hukieLeI4icpwTExlMh3HdoTMRoZKAaypCOpeca5IOtk4mbKBTzVGXb5826/EaX6bdmYG1gO0+YyLQtMS6sQ7hdVaSymxVKIO/OicmbA1gdvURJgyXzp0JoXlWSpChY7lOMS8laXTv83u3au/v5Jh5WcxRWR42KpS67+jqL2jzd0F0XoXnUtZSLqGFktbHhqCNEmPRkaA+N6vTmEr0KpQHOfghFDrDiA6tuA2c2vaFUuD25e5i+j89G3jVSRp4MCTgFM9ji2b1jwB9g0Xc+MhGa+4o+/Q/PrMZgLmO4mbBp+a9hLLiBzj7KvEg0ZDLBZyrhCup7q2bl27NJJ9KGkMiEzHPwy6Qt125Yfn6hHcIHjjFGDbVhCp3ab/beqEsyesdd2Ki/50Uoee3+DNtGbLYW1IisLbkgvbwmSQ/x7dc+iXzOHAxIp97VsVybSCqabm+A1hI4HQPBfD9lJWmKV/kmp1dWLOzW53YgFhYHADtXVE73UoYARJnIFIA/YUifuawh/Lvqp9lZQOnZvbYSB31eQfq8R09A/p0wl9ZPkm/lWfgSddW0aV9FZlfLko+lIbrRnieDFPcukx+PjWtKaZvMXuhVBNsumPvK3t7InkwPLim3TiWDgYnJl6FUgN+zKk4KioJnMx9OAWwbEunNB/dYLOz31HDdOiISQNqEOvou/euxfcfWI+egYI5MYetHb3Y1dUnP0wiPtllmxuvRsjaZsBoA5fwgKh56cBAAbu7HZxr6DiPlSTyNOyuWyB0M+GhThmF7VK5ctt+obzwulrB1E3Q2VGqkDdI4OI2cNHfrocyE2T9loUL/XqQCsonzt/jPqZcisi6bUxjJryfccEW0HHCbaGWVtN4Gab6WOSbBl9ZsirV+2IgbwD4+G+ejaRh9c5VVPi5ZyWKHz6wAdf9fQ16+u32UBsCqkSrN35Chy26OWzOZ9mWDnzxry/goTXtaMjLz1+ASmOARp9n8K3yQN565oTqPXn+iarFlR1lrqjiwJUoxaduek5abtr5bbXOimVYFPT+nz4pex2X3/gUPvm6edp3DwL6zUvgdODHD3+g0nEHCAkPbrW2D1HB7dBem0MZU2Po7o96CFOGWYhsXhpponBfFkYgok5Yvq8zOpZxQLPA9k43wtxxDZQ+UMUolEE0dgYkXHG+LS3r5+a4xeHUJnvfoX6qJLLNxyQNZxK4ooSRI8szxkTRVNeJiBD7PXLPPh9VubVe4rTtkiTDNESsY3zNtNBLpLItKxWUa7NIgEafx+aERGk0TKv/4CjjpzaN41KMjOHW3i1K4IK/OQmxRwHsK0ugeiy9bIb1VFeUt4HLstV4CYtuDtv0FbNn3t8Xt2uOOneJ/g0fhGUlHRsmGziRAZfUTi4LRL1QxtV22WPRy7NKqp7IBs5ZShyWIgZcV8HkxVbEwSCB8wScBvxCxxu48sNCNCauxGyRcLK0qgMWQzTpcAs5b/IyVIfyWsBG5902nQ0nDIguaGzS815GYzZwubgNgi2KJYq/KNQknRc1i+RKwpZtyuXnj6zbnSgvdf8AD61pxzaFITjfpkpCSRp0nXueYFyyLF118E31Mkvgcsp3ZfURpXxZexh0VqGUQD4eaks5aNdQx6rwXuhs4aKdkIkzCO6jntq4N1qXIcIgBKL1tLXNi/02EHiuUoBH1u4GpVTroMoFonZAGoQxuLh7QpqKBI5InGhR3kYu+t6q7fuxbHMHVNC1huzckgRiFiXK3XRgwsjWWaYt05iPHl+3dvTiha1hH7H2Wt8eqrq+sodXuVPXwwQ+LqdsbTURcI15Ys1AMyUzPo8Mm5ChSLizKqC321OFLAnvmRgqFvsv9yUPrWmvnLlt6azbl203J+JwENBvnoDTQekAg7uvU6F0sWuppkdoXayZ4L4dETWUYKtTHjqfCZ/yEknZ4hWzgcsTCN1uje7+An79xMvuLyaEso9p9Hln7yAKhriBLlxDSikuu/EpXPSdhxPXUabGmp6Ao+W/zq9qyzWHEWDvqpgNYhnR33ovlPYfozszuTZnkjACWTODXBwgpM0vdfosDsJcHtf9fU20Lsog8enLdQVfpK12hHgjzkFXw2Y+f+BnT2K5wmShGnA5EJpU3oGwfxmTmE9ZorSyZ4ne9C76zsO49IZHoYKeCVIdlgzvO0BXgs386h+UE3AXXveQkFfw9z9uXlG5xwI9A2XtCGNpZsjqHJNmcUkIYd7KazNRI8wVam9v7cow0cHGCyWf5GO/WerkHwAArr9/nVP6g4B+8wScDtJDj3BX58RERFq7lsTuv8vFqiaqWK+aHgBsuVAiN9Yye5kKJR8LTRmzj0NDOQ5cvRneNtuN6cDMf6dxUZVy2vRpOw3u+nUcXhkBl9YZQShhteEAyiGNA2fIi0ngVOXG+slBApdkHKrCaDjlYXmPR9aMqSwlcEneqUZojaR5mKTttYQgHJKnMdQr1rYags72G2vpac5pLEnCCIjMYNYeeYLY/kMRao2kCykQRXC4PGUtAAAgAElEQVSQTj9+ZAyq8Bxi/54MjDhqEgi4LsEW0DzesjFzkWVxQKgLX06ekODcaH32SVfHuA1ccM3Hw5WWG7mm0vv2dTCnUdUj8BbsXqrxlYNABOcJOA2UcXa4a16UDkTdA8fUpAyuwau17ZomT1wtqM6UigTiYiKdnI7EYIlrc94GTsyGeaGsx8GIR5p1nL1qUoWIlhcmCNRTOtXqUQ51U22ajLOaJUqSOIAAsLu7H8++vC9yz1pyALMNXGM+tIW1kRY7SeC4Ry9s7cR9L+3EZs4bl+kdVZkmmFRc5eWmZzokTWuVX4o3ahHIW/e9xcjBbOit2SbE54B+v0wiEbh3lTow91BA5DMUDrhCIjS6djN+l2vAZ1sJWJZzjbdp0uVrU+ZAIThziQ5MRJjUnXXMRBfI9rMuwT6PH8q5HEGuphI4/jo8l4aOdGzW6GgersjaJCALDH/yzXuh1EIVx42/LRJwFRs4iaMNvfpP9cBLnGSI2d/UcK4lKSqortubEQKQhvdYH6uc1ADBBlosDgEJXAouVoVolcQ2VObFXZ/5tfsAAC99+UJjWhNYWpEGMqlQJgHbnMRPfcsNj2LLvl5s+tqbjHlIVSgN74SSePkHmNRYbKX1F3/vkcq17Ftk0nd2qSvDdnMzhvOwycOhj1VJk0rI3VUo7dO6HoxcYRtjsBaIcOgt62X6LZquyYJzm75VVDsdKhClH5SqbeAIiavwlyitSOB+8dgmt8I1baZS+XZHnPjm913lW2KfS9LwEri+MjFnE6pFRFZEhawYURoYl8BlZwNnQtxhlkUjC+lEKZ57HSzWQsX9xJpnhucHgQDOS+B0kI0506CI2sBFn2kPZRazwhQ8WJk1jf4VUU8VSmtuTmzzkiWxy4v3xMgudQa8eaZCaVfTqkFXfoVxoJKQIU6ouoaXUN2zySushzqTgYKEgEvZ6uxzxTG+ZV/c2YqqrDRx4FRzXrwd+63hHidneoj33BhKSptgx3JtylIhc6LF8b1aOw5xOezavFMtRDj0tu8IKeNMDNErXoi+wWLs3nACMazXQNge+bITEx4UcQLXBJMtfCVNFRrVVnpqZQPHCLiG8PjqclZTMfWSQi6BE9Q5uaGczxEQ4rBnpqxnlLnCf3fctlKFaJxcSRmGTAzm9uU8sl3b681wrwU8AaeB/MCiD3WZ4+I/ien0jgmqB5krdb48V/erSctPlQdXp1LJbL8Vr0N4zcd+Y66II+pIQt6EoKw7PnRXhIFiCZ09g8ZFq8AdioyqtbLxryJyDJmt3tEVvK8hhKUEXMomL5XiY98VmyXEnomZkudVKCXP4x73HNYKh28JDyvxd1RdtnN/n3VfiLe27OuJeCK1WUvcgmPL769r78aeAw4xFQGA2Lmn5uEkgbMiXoNEe7r7saE9HhhadIZgk79ufHT0DGDdri5zxRzBl5iUsBSZHezQJ3uvt0zADeU1WYeQ0Rv8lUkDKjZwOYId+/uiawKNE7gmsLd1TUZp1ON2UsQJTvm5SRyrm/YcwK4ufXgdRrzzTkxsGU4uz22xQxIOqFsk4HjVaxJ6Fq0JIsMmrkKpZIpx15F1SJJ8+ZZO9BfU4SySMIv54qrRVAdDGAGvQqmBSR1NBsI5MRHT6R0TWAzRlKJkvoTblm2rXFdbAqe3/bPLQyTAVOqtNuCr8/SmwA6qFJFMRdMTEg+kWg+YxsjbfvAo3nnK4Yp3g7+8pNFEdMk3RXO+sXdKFI9v2FN+X63qJtsA0m6yg+V6WRH8kiQ5IicsTWs/k8CpYhbGJXDi4TVrJoqsDvIyXv2/98rzkOYbvXvW1+83lmtbD9s6AMAF1z0U4cjbwlWNSuYMRpnWId9zrr0fBwaKMTXYe1ftVL6jsmfVlfum7z6CrR12qsMuiJZv9+WxQ75hTvA/mb1sLUPeZAki8WIi2tWyNeDpTUH4iB89uKHyrESpNnapDqZA2l/86wuJ8tVBFSBcvPf2HzwOIFQHl00xRrzzNnAu5wF2P6sl9hnBlhoIvE/z4MvKc0x+O6SrqLhOWDsx4e5HJHCS+tz46EYc6C/g6+84zlgHFVQpEkvgDO02/Mk3L4HTQi6B0KMSdFNCZOjEyNXchmQ2cC/vOVC5jhFwmZdvn9ZmshYpTXDA5Rex+LuRMAJCC1S8IA3xs8L69gNG7nexSCMONnSoPI8ERxXHCjXmxRNPwUYuTytXoUwHxqVWDZdSpN/jGNGYl75n7YVSqRYSvW8i6CLvGsqOlsPeiRMezk5MEjBNrOhmh3ro2kU2fhgaZQ4PNNJgFVzq6kJcHBiQc6/7JEGaz54/EUAyu5QspCtymCnIuFQmCqMTE+4NdogfSvSbS1VCBxLlvzS+prDxw6Qfq3eGklOK5Mwt3WtpbZ1U76o0L1w1aYCwPfjwCTZMMhHVJP57hfnMt2tTPpepBM60F4mBvON7uBn9BhVKAFi2RR170C4Oq+J+0pFoeO0gEMB5Ak4Haf8bzvE5jQTOVeUhK7AJbGvjU9vYTSE2tHer3a7z1zRuXxjcV9chKsGLP9c6dCBMjF/f04KLSpbq5UKJVlRPjJunrI0VSXXBckUHMaqU1VChZFxqZQgNQwGNCqmOMQ5cOYHKO2QqCVyCNhE38SAbt4xsOOix5xZlOPVxwvGgDuegf6+zZxC79ocqUi6H5k27D5gTQT1GADnTr7UpX66L/J16EDW2EkDVO4B5DvBBmNkBeQg6t3MC/8nimhIPq8C/l4SJGcsmhmqppAaBvON5JylP5q04kROTKk6UPk6bpFSiWLszVI9uzOcwWChh0x679SHBNq18ThEyLc3hZML7UQmcO2xs4FQ5J5fA6ZHUp8RQgleh1EAlqdENKK0TEx0hYzFIkw630AZO/lyn/pYFtAQc9+jcbz2oSRcmLJbSeZCS1acgEBk8CBjnKnGRmSDNYZjdLpYoWpvz6BkoGr/HReKiY05EY7mp54/MC2VaGRzrV9V4KZYomJBNVi/VnDMt/ky95ytLVlnVM87s0TE93NtEtpalUR+0rUsqpoMESQ9d0k+1cOV92lfvRe9gqNroUv7i2160qtfZ196vfC7bM8yxzYYqo8l0oBZ/h/tWz0ABV3EBmUMJ3NCh4JLsz6I9Eg+dBLJEk+/brs6LMoGCcZeEp81U7SOaHVKnZsqqBO9UcejwErgfPLge37hrdeV3Y56gq7+Ah9fuzlxVXgYxNESoQll2YmLBCOLNG5LFZDPsEwnfbWrIKTUvatG29YaXwGngKqkBeBs4Gtt8dVyIanKDQo676nn0SdZ1yWIi8TnI2lZMY3pfhNEGDuagn994x3FYevX52jT1RqFUqkjgknmhVBFC6nyiEjj1cV02TpIMxQsXHVa5rhBwinyiKkPxRKp4b4SEdm4y6J6Z6gEAg3YsSyN0hxXXaSlLnpY77FqPWp/XewdFVahs8zeqMUsKNMUgrL8Ezq4CYrq4yUH4W5S69A0OPQmcS1UqXig1L8UdHUWfu9rAmc4BsjKTQiZdlas5upfHDuyRfUWSrlZOTGTgVZ/FeKO885WCBQWbtpai87okceDSS+DSM59lmDSyWfnM2L/DXwDnCTgddu6Pexcqlig6eweU7+S4hVncfE2SqPYuvRe15PEwQk6mDOJZMet1rRqHHllbdvcPSt3Di5B9H7+QxrxQQq/6x9DUkMP4tiZj+UmRpl/YeCzRQAcfMC+qlYVeUwf2WyuB4x1YUbVEWFafJGNnwsiwDwrF+Gbvkr9SAkf0XqxcPVyJbaFTSU0yDuReKN0ySmITbHMgdKlH1ocu5wNr1muZYQDKxq1sbKk8DNcK/CFQxRGPNXXskB/9zX+7+Ml9Q1AC5wIxNDel8X4VmZTil8rGRs9AIXYvBi3RaH49CSjkfWVSYZc9ZZoaBYExGHtXkbXJeUcWeGVvj3Js8gRcGsJGRO9AEZ298aDukfdpfF+3IZx47Zhk+4/+uY6xq9N+GtmsViI0ra0HAf3mCTgV1uzswspt+2P3CyWKm57arHyPV6EUVRx0k3VrRy+e36w2Ak2D8ADNL3jqemW9sOlVNiy5tVyykkKF8jN/WGZVB9khkG8DZmPCYOuF0sQZT4OpY1qsWkodSyUcj8xbn630hEruidCNbdGJiTKdVAIXv2dqZj4b9s1qyaF8TpjLIshrVk/XoSAeZAoaCZzT9Cznm8QBiU36COGQgMBzrUfWZ64sHLlUs3xVKJuhBr5KH/nVs/I0ht86GzixnfqYF0qXSg4hmO2PzHZDMunN6V+9z1i2Pvac8XUryGL8ybKmSVQoy31fMqzdJvf41ZTA7esZxJ+XbpU+4+2qszRfOf+6B3H8NXfH7osmDBUVSoMUmL9v8kJpQrWcmNg6T5PBOzE5iLFRYYAuShq+fOmiyG/eiUnWrsEbcsm6S+aFUpzUPLJe1jIJI8BdB4G8k9dS9iq/kI5saYxI0ljQTVu3tM988fVYeNioxPUTsWjaaJw0c5zVwU3HTWNtZiuBc9kUdRuRGDxcKQmWSoni6UzqiXw7VSRwikJNXihVfDpCQk+TAPCTy06JPHeXwEVLH8xwYwfkNiJZS+BkMctsinCKa5e1BM5xtau1enmS8mxeSWNDLC2Tu+a9JUbSmFTaRIYnl15sp1A1eniScDI7RpEBqIoV+bojJwGQB/KWSWBE6JqsWu2pYoAm8UIplcBJnZjo86n22Fm5rRNAfAdp5Pq+KHSi9Du4e7qtT6V9xKvaUiQ775niwJmQpq11r+rGj4kBcjA4MfEEnAKqA5h4UJ0yuiXymw+OKG46aReMnKNdDQMV/gImCVxtucxW4DdzKvdCqUOxRDm7CQmhIKhjzBzfWvldaXVDmWzMTBzZjLGtjW4V1GDupJEAsZRmaJ4xji2TwJlt4ILn/KhLQsCJUpoeicv0vsGitQTO5MShJDn4qaqXlEtHgIgEbu6ktshz16kqVkMrgXMY+zy3mVIasc1wXY/kErjwWuodLgXTAQjUgoolWjGiT7M0ycaNa35Zq4OLjntiREwCCQUFxUChpB1D2auiWqSJvSPuO9HnJYHxw4MxUocp/YYcIegdKDo5MWGYWLb7SUqE69osq/YU81GpyCmlZOW1SraMsrVAZ7fO8tCh2k7BxbMhQ9QGTjgjGuqUJPg0X0Yv57zMGMibu18TGzhFziWq7kvdHDB5ffcSuIMYqgOYOBAbBT2q0AYuTsClFZe7OkZgYINfFePFpGufFtWwW3HNc39fAQuvvrPyvgixDfjJTUiwIRntpaq4IBAA0CxkDDp1CDYebePAyZ6KhvPsl60K5dJXOnDd39dEng8WS1h49Z24RuK1T5Zto0ESzZ9bTSqUIuEuQmcDx0vER7VECXZXZovYF7qYZpXYew7rCaXA9x9Yj4VX34mOMpfe2YmJNH14s08SiN1KGqRItGT5dhz1pTvxmm/cjyO/eGc5rU1N5cgLE5QkyC/rtVHs59iBziD1lN2jFFjwxb/hou88rCw3ieRDhyQecmMEm0ikHcQSuOc3d+CoL92JFVsDMw1KgdEtUXsecWyyn+yMUe0wAlk2LYV8fVWtYT9+aAMWXn0n9hyI+xtgjCJ+rjy5YU88b+VeWJuxM75Vbg8fVaGMzn/p3OZuJmHi8+Pkv29/ES9uj5oG6bR2GAYs4sDpUD0VSnV+GfkBG9LwBJwCqsO4eIBl7sJnTWjFw597nVaFUscRtapTwvfYIOclHxEVyqpL4NT52ZbFpyrRFJuXQoVP59GLIPBCaTqkJKSvK+ClfgBw8qxxYR0svJYB6sWOAjEbuCT2N6p20hJw3LNlEjtPJhXaK9msZXUY0SQPrs0QVaGk2voll8CRikTnbSdOx6RRzfiftx7DvedKwEV/75A4UBLhcggvUYqbl24BAOzuDpwluc5zk5qSzHOmTQmqoXPvSzsBRFWD0sRiFOl+yv1vC5rxAVdsM3GcplEVX7urW/msHiFJTQGEdU5MYtosFcZMZtVLjST75gou+PFkQWIT8xZYzp6N42oQIJlJ4Az5lioEuPz9W57fBgDY0RlfBwckjqme3rQvli4LL5SfOneeMY0J4lbQlA9viPupqU6yM4ZppxHXmJVbO4V6mdshrfAh1XqjJdLUD00ePocr84eHJ+AUUB3ARJUXdoibOLIZh49v1Tox0XHVbZB0uLFxypcvUzMT02eFLNSORJXPpHmq3hWdWfC9zyRw5nYJ30rShsfNGBP5ffyMsZHfFDZqj6r7lJPAJQ8jEOMYlhPpFnj+lebGOPGl87gounEHgDaN5ykg+l2svkoVSoMNnEpPPrCBC54tKNs7njJrfOW5KzEv9sVWjTdVltSFqylXG7avH59X5B53LSPurVQoHVa2VCqUEhsjdxXKbBdH8XA1WO4U5k1QekCR3aLyaxWylsBlIZrU2YyrJHBpCPqhAF6iovJaLarNhUzi5ExM5bMqtado+8zqnsgGrhDOEVGrhIcqZ3bf5gxxnLAHuyDcE6N103mhNDmCEtcw8bkM8XiCrFb2Tkx4dcQkY8TobZTq+0v1TEvAGcJsHAT0myfgVLDVNWZqVGwgVeLASTwl6g6qNkjC4Zt91RLj4U2cXHzAVBGX3vAoTv7yPU510Kl62XzRv/7h+ciioQojYINCSf6uuBDwBHyOEKxv78YZX9N7+EorgRMJfCbdpWChDMyEq+5xRQIncWIy+6olWHzrykh6U8BzHjp9c358NTfElxwdY+Ojv457tBO9hIqIEBWM08vVeznH9Y5UW/Jpehu4qCrTCI44dbVVuFnwWLZdwnkWq+lCgMmmSyZOTLhbsrFhU4JLNdIQUKL6UaFov46s2NKJ2VctwQtbQ/WjLPZ/cU8oFil++dgmHP2lu/CJ3y3Fd+5d65ynzQEr6yC3Sfr5Z49sxAOrdymfFzV7FOu3ekgSswS/Tqhs5sV9pboqlPHyE5Uhkbby47JI4+syD6bmJ5MiM4ca969ux10rAym9aMoiq0MS5CWEoS3YN4pbQYPOBs5Q5SQqlKJDrJf39AAIQh184KdPagin8Am/TiVpVjtbaHmaHz+0AV198vAYujFqiqXqCbiDGLbTpEGwJ+IlcOICy3vy+ez5C5zrxE92UV9eB74WYewPjiBy2ASXbe6Q6qXrYIp/Z8LNz0UPt6owAgDQZjjcKwk4XjWq3GIPf+51uONTZwPEjluXNoyAKN1tKRM7g4VSIAWExaaqec5UeFVhBH7x2CZjHVVcLVsvlDynlDWXq2S6rckkgePqVa4v324PrG7n0urbU20DRyrcUPZ9LY3hcmo7Eq44Y7b0fle/Jp6Tgw1HxV5OxsRx3MBkySOHMqkTGnO+tVJlaRIOeTqPqCIeWbe7CjWKo1CilcC/S5Zvl6YxEWhWbZ41AWdRqKyf71gRfqOKSAPi9XW1gRuqDgt4iUqR0oijHbYdiPtKeMZIVqauyfh+zJLIL5WoINFx6z8e/RKtDDkB55x1DC5+Bz7z+gW48YpTcOenzwYAFBUERGOe4MqyaqaNozu+T5I4MdExVx9Zt1vZByoJeJJmzZphxFAS5gwPIwE3zKX3gCfglLAdcMyhAjso8oG8xQ2JP6CfMNNdNM8fdF81e7wmZRT8BGaDmv+8zNVpYuWnz19Uv1DV+az5E/V1KZpt4Eql4AB++PhWHD1ttPVhPK0ETlxwWsuqgn2FYhhM3ES/qe7TuA1cZRNV9E9og6HmElfua6TL/AYhk0IPFOMbsg4mGziTF0qe2JQR7jx0RLkogWvh6mW7z77tpOnS+32SQ4oINxs47kf52tkGziCBk9rAOah5mlBSMF9s0SRIf4sl+y1cpqJVDWQZ2FdbTsZrvk1upjJ1KpQig4jSMlFgWb8kB99aQFSh5Ak6NndizndSSuB0vaVaJ7PGYDG+LttCZHQC8i9Se1e0L0umsqjCsTNG49yFUzB1zAgA6vZryudwzPQx5TSCF1pD3ZKcMZKqEvLvmYKmm1CtoVQs6Qg403pTjRrVFp6AU8B28RpVloRNGxsYIPMGxuLBeIDz0pYkBgV/QGputO86nvBjougo59xdD+ULf1mBV//v363S6prS9kAW4d5J2pbBFCuvUCoZVSgf37AnIi21laylJVRFSRSTJvYNFkMnJgm57xShRLhJsIEbVPS/LDC6mJYVp4/HEj6TESay+GE6tDXrCbixI0KPkMwZSCSYOFefXz/+MmZftQQDhZKzKl+MgGvgCTi7MaNaB3QEHKumixQlQtQ6SPAi5UqS83nYHqx0eehQdJCYySBy6fM5Yp1fUg/ArvifO1YZ07i0gcpxVvYSOHMaXZEye0SdF0r23PYwWaPucwY/JOMSuDIBp6j8K3t7tHmr2sZWApclxEDeBYkjElvIDuYyqVwWQ9wUsoZHS1mFnq0VFQaokK4hTypp3vXDx6PSfUP7q55+jDM1mH3Vksizl3bI4zKa8uTPmxEbOId2PfebD+Azf3jewuN1srW9WKLKtVm2H0XKPAh0KD0Bp4AtUTN7Yhu+//6T8K13nQCAs4GjcScm/MKThCHIv9/coD/E8uAJA3bNV22g4D6Qf/vkK9i5v98qrU6UbU3A8ddUbQtmWnBVTkzEvuI9Itp2VV/KWCkDwsbUWlYV7Cu7TaYw28CpiDFKQ45ao+CF0mUTFbl57F1bL5QyYs2VgGs1qFC+7aTpMckWv1jzbcTURrv6zAFw/+2CBXjNgnIQXY7zxw6aTQ25io1fWo6/loArf4pNt7G0/DQrJOR+S/1pcPfkceDM+druo2mcFwFxImzy6BbrTdzlIJcGty3bljqPqFRU/n3V1rqQQWsLLWMOcOlVTDebz/jdh1+dWr29WohqlkTHKPt8sea267Uqme7tao0KUXuEfUNWh2jVHpI0/3eePAN3fvps63nfkCM4fc4EAOFaoRICtDU3VNIcEGKimtZYFe5cucOqnjKo2kgnAbfFht0H8Jfntjqp+7ugRNVSUpMKpZfAHcRwGbBvPHYqxpS5/rowAvyASrKd8O+3OEjg+MWN5cFP2n5J/KYs0R+JISInAFxQKqkPICZOucoGTrwnxoGzgYwL6AJRAtfKS+BQVhsyLISyQzSDUgLn4FxHVPlgxIDWCyVXZxlh4m4Dp2deEELwjpNnRO5F9Pll3hIh3yj5vn/rSTNw5rxgk+YPW3zerz9qCgB7jr9qbPVp+pHBZe7IPXO6SuD06aVx4BLEB1OhULKXuMjAH8ZmT2iFKriwDNWy4agG+K9ScaHr4cSkRCkmj2pWPhMRUXWWSeAsCfojp4xyUoVLiiQtGlXdpxWbeiBokxyJ73W2xHcSFcKsJLNiGYEEjmeilRl/GRFwKoZX0uyPnTEGCw8bbU3AvfXE6RUmAXuHtaU49EY1N0ht9gDzmlxLvsugYv4llZTZ4kQH86JiiSoduxSKVOo0jWH4rOhqeAJOgaQbnM6JSeSgmkgCx6lQukjgijwBF+e+20pAZIenXfv7sOALf4t49ouVHwlfEH1mGxovYlytU6E02Kp86ZYXpN8hqhrxSWy7ylWSJGJAOAAzW6/+QmCURylADUWs2SlXl6AIJU+VQN7lNnSJT/iThzZEflckcFobuPCaP5T98ZktoJQ6E3CthjACBHGunMnm02QnAARzO19W0S1xErjIu4SlTXdglIVPYHAJRMtSyOwCXTfiAwMFHLv4Lpz5tfvw0V8/E8tDxsCwU62zq4jKftUWvPe30FY5nqFsLetLObdrCZNdIp+mv1DEif99N/62Qu4wxb5Mc8eUqHpeyJbzkoGAO//bD1qNnRwhNVGhnDhSTpzqwPqnd7CIZ1/eV1lfAEbAkbhtoCXDTXWG0bEtXKfXt+5ejXf96HFjOpFBxvacrLyIKgm4hPmxtjOZZDDwhB7bewqlYG9jnjIZRrU0KgnDEgXe++MnIvf4/qqG2t+TG/dK77M+6hss4nv3rZPWxxamfn503R48vDZQJXXZO4tUo0JZKGkZ+l6F8iCGzYFOBjbhB4ulGHEyEJHAue8oAxECzkECN8gTcHHuexrC4+G1uzFQLOHnj25SplHFn5P9tkGgQil/L29YcP++apd0MdER7LYLSqQdEwwfURLG3NIHErioXr0Kj63fI71PaTgORpRVEBkh4yJtvp/z4Bi8WzLmoWvbgWIpcwkcEHe3bDoMDhZLci+LEdqMgPEHcjkijcfESk3L8O8bsLCBc5g7/Frk6sGPYUP7AXT1FbC1o7dyKOE3cxnRaVOCbS0KpVIqz2G8I5JcxU4lnk62lvXK+qNOBwCXNlDtY2wOtHf1Y1/PIL6yxGx7p6+THVTzQkZommzgtnX2WZWby5FE7tdd8d33nIiZ41ud3uHVuXsHi5ExWiwxAi76jq3USpVMK4ETHpoOud+7bx2ekhAA4hgVsxmUeAdOA9UZxiX/tqZ8REUeCH0amBAJO5QjICTIo6M37rF7ZHODkrCglOLxDfI9vFr43ZOvSO+ztWPLvqitZZIu23PAbG7DQum4TFWekSpisFjSSlAPAvrNE3AqJJXAMU9nA4W4s4yBiGMM97wHOVs1FwIuKoFjKpTyeukgtVMo39Qf0sPDj87TmG3ZxZJacmfjbMAURiAGWxXKlKqoMS+UZUKrvxxGAEjOUaSgFRW3kWUnIOyTTbriOthsxLpnA4WS0dhYhMkGjkg47lEVQgUBZ1Ct5ZWAmvKkIu1No46mVKHUjCVWJRePhREJdtE8Z2UwxYGTqX3aqGRZS+AsbZ5U4NeGildXRTkidP2RNdKeK6JeXxUqlFmfXiyzUzHDZHtQRO1ZqQ5oLjifIzWxYRzX1ogPnjbL6Z1Bwf6cryelCELYCOPRVs1R2Waad8RXsvJEGTip4dfgZGrcKqjq6ZL/G4+dioWHjYrkZyuBE1u1IUeC8SspflRLg3I8yqrL36slzaG0p0+Qly6uqQgXe1WdBG6wVIpoXYjwYQQOYqgGrwmMsOovlCROTIgcXOgAACAASURBVLKzgRNdYuswELGBix+4bQmPa+9aHbv3739aHstPV/6KLZ2RZ0kWcF0gb5uNWrbWi4fPiA2cZW8lkaryiNnAlQmtlkrctuRu1K+9czX+8NTmIF8mgbNwQGICe1c8KPYMFHDpDY/iBw+s1+bfX3CXwJkCeQPxg2JEhVIilVDZAUZiUdHwOxvzuUoZ/DzPylmCjV2iuEQ9+/JeXHL9I1J1IhkBm9aJyQXXPYh9PSGHOakNqDikl76yD5fe8GjMnrOQ2olJVIUy8Hxmx8yRSeCqtf2bHGiZlgB+PKqYIyVh7idVJ/ry7S/i2/esSX0YkklQmEoVoFbBstGUyRNSkzACBO6EomhTzL+/ZMV2KSPYlqhSqlBq+lp85Lo2q/Khwr1CMZh77/vJk4nyT1oPHqIfgRwJJbUmD6AixLbOlwk42bge2dKgJAxla8+371mDL9/+YvCjhjTHF/7yAjbuPhDvS1sbTK5NejQaJSJcZhCl6uDmgwW1dA7ITn23nvAEnAJsQl7/vhOd3mvmJXAxAk590HvXKTOwYMpIbd78ZqzjLIjgCTR2AI14BrRwmAAAP3xwvfKZbk7zi9gqwaWtvRdK/iCdjoCzWQX5Dd927/+HM2fbJRTw6dfPBxDfLEc1N2Dxm4/Gbz98WiAxgBvBO33siMhv5qmKueFP4sREBDuAiBvYto5eLNvcga/f+ZKWYzxQKMUIjg+ffQTedcoMxRvA2NYmbZ0IZASchQROkheftFSilbZqbMiF7qJ5Aq7817abkhD9LGtxLHzplpVYvqUTa3d2x96J2iGycebW72Ifr9nZjXtX7ar8ltmJ2bQDfyCglOK/blmJZZs7YvacQdy25GO1QaJC2TdYjAX4lh1QDvTHDyBp5o0Kpx4x3hy3yZDHoETjQgQ7KKa12/3ZIxvx3XvXWo93lUDDRCiIhA6DTDL6uQuPjJWZloBbeNgovPNk9Zp0zSWL0NSQc44XqFKb5xG3G7fcM4V5ZQNxTUk7Pvh8+ZwLJYq+wRK6+wuZ5K+C7rO///6TIr9zuVBKz+ag7jxx/OFj8fHXzgUQ76M8IYENnGT+tTU1aJxXxcfzln29+NkjG5X1qCZ+8vCG2DfYrnqumjUMrlNVKYErlrTmFsNf/uYJOCXYBD573qTYs89fuFD5Hq9CqXNiIo65f3vDkbjr0+dY1QlwCzDJl8sOr3xeWSzSOsKCz1+07UmiQilzEMNg0yo2RfL9Y5NnQ45U4sAA9uL5GeNG4K0nBi7vxQWPEIIrzjwCR0xsA2FOTBxWnfOPniK9zyRwLC/V4YhBd7gqKNTxeImmTl1roFCK2E41N+TwhTcdjXeecrjynUmj9AQcEN94TWplA0V5HDieOKM0bIvGfE7qLjpUdbXrqCTnSnYYE9uVbWS89gCrRxYSONM6ITt82LSD6FBJVmcg6Lc0Eri8RIWyq6+ACSOj40k2HWRhJmyCrbuCwCxhMUrg+AC8RX78htdsvrLxXCs3+yqGBa9mL4NqfxGZj5NHNWPOxCgjNJ+BE5PXHjkZX3zT0crnl58xG4AbYxWIr0WjuRiWKsj2vtcfNaXiIVeWriCsY8q8NWYfOohMurjUJu7ExCZ0S1rw68+5CydXrq84YzbOXRjdHwkhsdAwOpOMqy5ciGljgvi/4vdXJHAS5nhLY165dpgkVfUgOsQxYHsGSXqudGW2qIjsgWIJYzTMXu/E5CBGRYQu4aiN1AQSrhBwxZJ2MRTHaI4Q4ybKH/BdjLL/79ktsTrweWURRkBrA8cTcIMiAWeX/9pdoVShVAoOfTI1UlkT6g7zKhBHCZx46LLVnS+VaGTM8IgSkaQcB85+0VHZSbaVCbhfPLYJv358k5Hjrxsf7LvF/ueJMpMTE57YY32ls8uz8fYm9tmj6/ZU8pRtnoMWRvAlSit5NOWJfPwZa5YdxA2IHR5lh66IHUX52lUdV+plkjtSyGzgZEWs29WNK296rlLPiCMU7gAj5hfYlCTfdHnX3blcSMCNb4tu8jKGQ1dfXFJQDQKuUKKpbFKB6HyTeSAGgjX0pw9vwG+ffDlVWQzWEjjFBDEd9lRrlEwCJ879fC69CmWxVLKa3K4B38XvGt1iQcBJGjufi9vTsXQPrWnH4ltXVu7rmCpifcS15KcPb8Afn9kce+9Dv3w6EjtVRCyMQJGiq8rSNyA6F/gxIJtjOcKHhgme685Zo1rCcABinzACTiaFGtGYV0qnfix4eRZRD6JDHAO6fta9Zwt3CZz8nDNYLGFcq3o+vbSjCx/8WXVVeKsNT8ApEBqxRkfT5afP0koHmDpO/2AxxpWJTtpovjYbDL8YifU6fPwIXP++E/Hd98ZVPiMOS2TShypL4Pj82cFepQZmAxZGQCYelxHBIiFjw8Xnc0my+X/73cdbpdvW2VcZM2JTyNQ4XZpLRcAx27oH17Tj6ltWGg+Msya0GVVTRQKWP4hrpbODUQlcxQWzhqgUD9xvOzEatLtnoBiRhjLsKBtSy7wlBvWPl8k+a3RLA2ZNaI3YwF10zFRcccZsfOFNR8Xe0/rEIfJrV4jdxtaEbp7Y0BBrrtIsuW2d/rmsiM/9aRluW7YNy8ru+nmJF983+wUOfXobuLCx2Tjr7o8TcLJDElP1eu+pM8O6Oth1iBjf1iSNdzRYLFnYdemfR1QoJWsvEKyhX1myCjc9FT+MJ4G9xFk+4BkB96GzjqiopfFQMYFECQch8fWal6wkRaFErebqGIkETVTp5CHa2o8eoXfQBMjbIkdIXNWtnOyyG5/CbxWeBuN5C7bMg1Ei6ytLVuFzZbt3HvevbseNnJqfWMOYBK5UkjJFskaUORje/8z5CwAAP7nslMq9HCEVL9ZsP9NpOo1qaQhD/QjjMJ/LoUjlIXJamnI4dfZ4xy8J1qWsVFpff9Rkc6IyxHF1w/3rFCn179mixSFEFiCfc0BwhhhlYIjwdrbDEZ6AU4AtZOLCf82lx0gPhwzN5WcDxbgTEx5xCZxb/UTO0HkLp+Di46bhvIX6iSmL+WUTNNgE3cGKTeSGHAkJOCpXk7IBpRRFSqX2Any7MhswcUNnZTOPUzKkPWRPHTPCnKiMZsV4itfbrb1Ujm7EBVJ2IIgwH6jZcUhchZKXwKnfGygWI2lzFbVE9Utiv3/+oqhKc3d/AaMkseIYQSANJq5y9lBu76suOgqEkNAGLp9DU0MOiy9ZFCEA2AFV101ppQEsb7HNGTe4qz+umiQl4BypIZOETSapteEYR6V4YR7iAS/wQpmcghNjRFJQdPUNxiS6smZhBNz/vvWYsK4pNBcuPm4qLlx0WOz+QKFkVGk2gZe08dd9llLxJLC2+VQMfXbQPW/hZHzk7Dmx56q9VDaXZUW4TjnmTp7Btr1k6/4/v3aeMj2lAaONbedWEjgZAZcjMUac2omJOm/RJi8rIovSKFFXKNEoo6lK4NU0+bMcm/PnHz0FFx0TzMMcIaEErtwOOkWakc0NGFneZ3qEcdiQIygWFQRcYx4N+RyuPFc9LmSQOcbTYf7kUJX4iIltkWeqQOIyiN9ga7eYVDAwqsXMxOAhqsAzFEoUjYaDtY65MhzgCTgFbDgwMjRxaky6w7aYq6szA1ECxzYyUyBr2fzPQoWSfetty7bh908F3L5H1+3GDfevq3CnRo9orBwC2RklyUHi6r+uxJ+e3YJtEte0fDvOmiCPycPqqmsrnlMsc2BgA9tQD6IThUoduNuEBBz/i77zsHX5KgJOZErInDHwnOGdXX3SjZw/TD+yLuRk7e8bjHD7b1u2TVnHfsGJiWhELqu7aGci9uKB/oKU8/bp3z9fdpoS31gGC3obOFYkI/QaFW1bcWIifRpAVI11xe3loMvfuOulyH3WB39+dit+9fimSD36BksVCSQDvz79x83LsXN/n5ZAkq0T/D1pGAEdY4epUHJpdAHMs/RCScvldvcXMEFUoZQU0tU3iHwuquaehvFVKMk9pL20owt7uu1UlNR5cxI47pqfZ2//wWOpygCi8//uF3dqUoYQv/ilHV34yu0vViQLTQ05KYNUtU/ICDjZodtVAicmHyxSq5k6ZYx7MG9COEl/Qhu4PHEn4PZ09+OqPy/HPVzfXXnTc5G0WRFZP3hwHR5ZG8YQLRSpdl/ICp29IQHHGGeioxk2pQnhGYg2ErjGyj7TIxA1+RzBSzu7cPUtL8TeYwxIFyIKiGskVKBYE3UaWy6EoEiI2douJjlXNuaJVkAig25um0yNTBK6oY6qEXCEkAsJIasJIesIIVdVq5xq4bDRLTh51jjkcgRff/ux1u+xxUHmxISHqErCDus2ngxnTWjFpSdMw+cuPLLCxa0QcNZxS0JkIZY/UF7ArrzpOVx18woAwPt/+iS+cddq7OsZQFtTHm3NofFuMYEEjnFmeGnJFWfMxrjWRlx6wjQAwIS2JpwxdwKOmT664sRjhCA9Ymeai46ZilfNHicti5/3t3IbzXXvPt5a/eB/33qsVVp+Q+GHRVTSRNDVX3DiiLZJpFBA/HAi4/jzRN3Le3piz4P35H33mydejqiU3KrZqAcKpYgqGltwX3PkJBw9dXQs/WgL7twbFh0WcxENBHaUT27cIyUSBouhHOj1R03GqbPH48YrTsGBARY7L1joP/P6Bbj0hGkxtU2Gz1+0EBcfNxVvOnaqsn4EBD+/4lX4l/PmR/r7XafMwOuPkjueufz0Wfjq24J1qKNnAKUSjQVVZ3hk3W586ZaVsfsHBJU/fu7d9NRmfPWOVdI1K1xj4uNk1/4wQKvcJkw9v9lY5uvRN1iM2fIwFEtyT6G24A8xIxrz6OwdxGCRYsrolki6AxIO8/6+QuWgcPXFR+O6dx+vJTZNKBapUhK7YfcB6f15k0fiTcdNxYfOikuoePDMD5UKZRZIsm+MFNak5Vs68dNHNuKVvcEa09SQkzK+1BI4O7vLtI4RiqVSbM++6cOnxd6bNLIZV5QdmsjwzXfGVet5Js5pc+KqdX/86OlCXWQqlHFGnOrAz1J95961+P3Tm/HhXz2jrC+fh4vEXmQE3bFiB5a+0lH5XSiWsFExzrPEto6QacX6VDwjsTEZkcCVv1U2bj7z+gX40FlHoKkhV/HoLK6t+RzBss0d0m9khJtOq2W2hPmssj1T2RLycybmB8ChL8V5bkvUd/REx5/N2XZkc4NSzXpUSwM+8bq4erWOgGvIEfzHRQuV0k6Zps5wQlUIOEJIHsANAC4CcDSA9xJC1C6chiDec+pM/PnjZwAA3v2qmYbUIQgJnBv0Gwg4kVvG1rv/evOiirj7o6+Rb9Tff/9JaG1qwD+/dh7efHxAuLBJlkTVn5+gbz9J7SpZB5FrzC/gK7d1YurYERjRmK8c1m0CgIv4nMT75+JLFuG5L12ABVMCdcjd3f343YdPw+1Xnl05sEwZLapIBWVecvw0/N/HzpCWpZKMvOWE6bj2HeEmrIvH9/aTZ+Cnl79K80XlsnhbN+4+z6FLonXXqCDmxQWSJ7aYeoTKqQcPle2c6FlSh/6C3AauuSEvDeExeVRL7B7faP92wQKMaMorN4ED/UX0DhTxxmOj6mv8t/z7Gxbijx87PeKlbOrYoNxJo5rxnfecqCSOp4xuwfXvOynGNBDr+7qFkyt2GAzXvuN4XKewnbzm0mPw3lNn4iPnzMH2zj60d/dL0/GQqUvzEA+6fYNxx0sA8Mny5idTGeQPKFIbOMn0ZrcYJzcigRsoYVeXPOgrix2VFGyjv/SEaSAkCHUBhH3LsEdyUBoolCrqOB866wi89cQZMY+6DH/6WPTAffuVZ8XSFKk+RpEMvQNF3PC+kzCuTc81jkjguEN9Gps9GZI4cVGplr+yJxhHTQ05KddcFRtPNiZl/ee6L7L1g2lxyKbS6XMnxO4RQrD4kkXKfN9xcpxJwy9V8yfH1fpPPSJK1KlUKEVJyfYO+Txic0jXf8wOkWcYdg/oD+4uweEHSxTbOnsrvz/5Ojd1QlvwoUjY3iJq3hxWHpNFTirOiB/ZWDxiUhuuvjg4zjKHYD1C29g4s5k0Si2t/YNAtANAe1ew5osaA1v3yRmsurOVS1+J42q/JQHH1leGD5w2y/iOTiL26w+9Gv/+hvgZUMec6S+U8NHXzMVnLwhVJTd97U2VM7aruuZQQ7VqfyqAdZTSDQBACPk9gEsBvFil8oYUmhtyuPvFndjdpT5k7dwvqDNFvCUFf1Xx2fiDPZM0sMU4iTvolVvD4NoqfWITtuzrxSd+t7Ty++O/Ca+Xbe7Eq+eMR2fvIJa+sg+f+N1SPF/mxq2RxKxSQcctYVz0XVybMw7QYaNb8MLW/ZX7v37iZQB6okj1jAiqKmndU8vyl516ExWjeEk8OF7PGSV/6qbnMKIpbxUb8DN/eF56f8ny7dbutH/04PoIl5Rvd5mKiW7TA8LNWIXvP7AOu7v7Mba1CeNaG7GvPEZ+/ujGSrvI+n6ag02jCboQFSb1kaljWjBQKCnbnsfHf7sU63ep59da4dlTm/bi07+P58va4wVunWDYym3SKyTPv7xkVWyTfHFbMBd/+fgmPLCmHR1cMPDv3Lum0icivn3PGuvDgwxsTBZLFASkkpdIVGxlaxmVv8+gsoETD04yRkfR0ikGD7ZnmKRJv3kidFjxk4c34I4XApXbvRrVzK0d0fXbBrbxQ3mIxDLDHSuCGJXNCgcG/DfxkI3JnZyqMBt7SVUowwN9ycgQsYXYfUTzTAaZxoRMhfJ7963FX57fGkv7mydexgNr2it7sAxMKvXbJ1/B4xv2AIh6of3Eb5fGFq87X9hRkaRu2SsnKhhufGQjtnP9tFzSj1ngz0tDD9zsbCTaULNQAHsPDFS0YVQ+EABgB0d4Modgovt/m37UeVOWPbvunjUAgj2QZ1Ko1kR+nIhHClvG+UNr2rFiS7RvVDZwL+3oiqwhG9uj0kcb1chAAid/dtho+dpBSHDmlmkEyJg5QEiUihoBww35xYsXZ57pNddccxqAyYsXL761/PsIAEctXrz4DpaGEPKRa6655kfXXHPNRxobG6d95jOfybweWWJkcwOOP3wszpg7EUAg6XnXKYfjuBlxT2Kbdh/Ajs4+tDU3oCGfw8zxrTh7/qTAsUmR4m0nTccHT5+FZ1/eh0+8bh4GiiW8+1UzKxvz2NZGbO/oxecvXIinN+1DiVIsvuRobNnXi9kT2nDZ6bMrkp8po1vw5Ma9+NKbj65M+rU7u/HZC47EqJZGHD11FFZu24+z50/EzPGtaMyT2AFp2tgWEEIwZ1IbPnz2HOw90I8vXnwURrc0YtaENuzq6oupqjAD2bbmBjQ15DB93Ai0d/VX8g5c3gcckNkTW/HuV83E1NEt2Lj7AHZ396OhXI9pY1uwr2cQLY05FEoU8yePRD5HcPXFR2PX/n786/kL8PdVuzChrQlXnjsPU8a04NF1wYZyxRmz8dojAxXFw8ePwNKX9+E/33gUxpU5VPMmj8QLWzux+JJFeHz9Huw9MICGHEFrUx4LpozC+189Cw35HHZ09uGFbftx5JRRmDtpJA4MFPC9955YOdgdOWUUlqzYjkuOn4aLjp2KloYcntvcgV1dffjRB09BqURx5XnzKlJAEet2deNT583HtDEj8MZjp+Kx9XsqNg+ffN08nDFvIl7e04OPvWYu3vWqw3HL89vw+QsX4lWcp6rewSJW7+iqtO/EkU2VTeMtJ0zDS+UA6YumjcapR4xHnhB88LTZmDWhNeJp6TvvOQHHzxiLpzfuxcmzxgW2dYNFNDXkMGlkM/b3DWJ3dz86egYwZXRLZdzu6xnEte84Djs6+7Crqx8jGvMYLJYqErGjpo7ClefOx70v7cK41sbKBjFrfFtlEWV9/M6TZ+C4GWPwwrb9aG3Ko6khh4Z8DoeNbsF5R02p9GlLYw5Pb9xbIRIWTBmJT503H0dMbEN7Vz82tHfj8xcuxBlzJ+L/ntmM7v4Cvvmu4yuHwP29Bazd1Y1Pnz8fOULwyt4ejGxuwJjWJrz7VYdjbGsT2rv6sb+vgJHNDegvlDBn0sjI/BoolNDW3ID3nTrTmTmyvaMPr+zpwawJrciRIObfQKGEH3zgZMye0Fb+xjye2rgXHz5nDk6cOQ75HMHdK3eivbsfX750ER5bvwfnLpyMS8qS9tamBjz78j7s7x3EiMY8uvoLGNXSgDEjGtEzUMTEkU2VeGkEweaWz+XQM1DErAmt6OwdxIIpI1GiFOPbmrCvZxCjW4Jvnza2BXsODGB/XwGUBo42zpg7ERcecxieLB/ixLVj7qQ2nHrEeIwe0QgC4JjpY3D4+BHY0dmHEg040bu7+yP/RrUEhPOolgbs7u5Hd38BY1ub0NKQR7EUqDSeMXciBkvBuBvbGtjOtjTmUCxRJYH3jfL4/OwFC7C/r4A5k9pw0syxeGlHFw4fPwL/fekxWL6lE1968yJQSrG9sxdzJ43EP509Bx29g5g5vhXbOnoxc0Ir2ruCuk4a1QyKYI08Z/6kSGzFow4bjdU7uvAPZx6Bw8e3Yv6UkfjHM4/AOQsmVZgiJ84ci4+9Zi427+vB6h2hNOD77z8JE9qa8adnN6NEgWOmj8ZnLwjWujmT2tDckMcnXjcPOQK89cTpGNfahC9cfDRmT2hDS2MeT27ci386ew5KJYqegQKmjR2BQrGE/kIJo1oaML6tCflcDoSg0u4HBgpoa27A6BGNlUNfQ46gRIGZ41ux78BArK90/zp6BiJ98dHXzMH7Tp0ZsYcbM6IRcyeNRJFSzBg3AlecORuFUtD2A4USpo8dga6+AiaPasb8KaNw2enBmrxq+36sLx8Aj5wyCv2FIggJnGCdNW8iKCj29xYwdUywf8ybPBL9g0Xc8P6TcM78Sbh31S60NjXgqouOwtHTRmPR9DHYuPsAjpo6CosvWYRCieLKc+fhHSfPwK3LtmF0SwPamhswfdwIHDGxDYsvWYSlr+zDDe87uTxmjsaU0S14fnMH9h4YwGWnzcI5CyZhR2cf3n/aLBw7fUxkLM6a0IqH1uzGW06cjgsWHYbewSIuOuYwnDRrHDp7B7F5bw9IeVydOX8iXn/UFFy46DCcNmcC/vj05soh+f2vnonzjpqCnfv78cLWTpw9fyL+682LcMeK7Zg2dgTyOYLDxrTgHSfPwLtOORx3rdyB42eMwdxJI7Gn3J/5HEHPQBHzJo9ErhwTj9+Dx7c1oXewiAnlv80NOXz2giOxVxgPHT2D2NczCEICAmB3dz8GCqWKlHdCW1Ml7UCxhAVTRmHOpDZ09AxUxuU58ydi4qhmdPQMYOLIZnzuwiOxv6+Ar7/9OPz80U0AApMCFX1x2OgWHD6+Ff/+hgV45uV9mDGuFXsPBCYa1737BIxrbcL69m786/lHAqDoGyyiMZ/DYWNa8OW3HINlWzrxsdfMxaJpYX+1NuXx7Cv7cMUZs3HO/Ml4bP1ufOniRRjb2oSGHMFzmzsqZgRzJrXh6osXVTQwWhrzWL6lA1e/6WgcPj5Ue9ze2YedXX3Y31vAaXPG45/OOgIPrGnHRccchouPC9byyaOa8esnXo4QHpeeMA2nzh6P846ago6eQTy/uSNSz1kT2vCB02Zh054e5MraXrPGt2HCyKYIsTJnUhs+9pq5uO+lXQCAfzzrCIxtbcSWfb24/n0n4aSZ47Bx9wGcNmcCCqUScoRg1oRWnDF3Ik6ePQ7HzhiDFVs7MWlUMwbL+/8/v3YuJrQ1gyJcg+dOasM5CyZh9Y4uzJ7QGhkz7Axw+LhWHDt9DN536kw8t7kDHzlnDta3d2NsayM6e8P954iJbbj4uGm47PRZ+Pmjm3D4+BE4e94k7O7ux5lzJ+K95T14fFsTmhvyWNfejTEjGvHB02bhg6fPxt4D/Zg6ZgQ++bp5eO6VDvQOFnHVhQsxv3wuK5Uozpw3Ea+eMwGjWxqwq6sf/3jWEXptmTrhmmuu2b548eIfm9KRasSVIIS8E8AbKKX/VP79QQCnUkqvlKU/5ZRT6DPPqPWwPTw8PDw8PDw8PDw8DmYQQp6llJ5iSlctJyZbAPDB0mYAqL7LIQ8PDw8PDw8PDw8Pj4MY1SLgngYwnxByBCGkCcB7ANxapbI8PDw8PDw8PDw8PDwOCVTFgo9SWiCEfBLAXQDyAG6klMb9Wnt4eHh4eHh4eHh4eHhYo2ouWCildwC4w5jQw8PDw8PDw8PDw8PDwwpVC+Tt4eHh4eHh4eHh4eHhkS08Aefh4eHh4eHh4eHh4TFM4Ak4Dw8PDw8PDw8PDw+PYQJPwHl4eHh4eHh4eHh4eAwTeALOw8PDw8PDw8PDw8NjmMATcB4eHh4eHh4eHh4eHsMEnoDz8PDw8PDw8PDw8PAYJvAEnIeHh4eHh4eHh4eHxzCBJ+A8PDw8PDw8PDw8PDyGCTwB5+Hh4eHh4eHh4eHhMUzgCTgPDw8PDw8PDw8PD49hAk/AeXh4eHh4eHh4eHh4DBN4As7Dw8PDw8PDw8PDw2OYwBNwHh4eHh4eHh4eHh4ewwSegPPw8PDw8PDw8PDw8Bgm8ASch4eHh4eHh4eHh4fHMIEn4Dw8PDw8PDw8PDw8PIYJPAHn4eHh4eHh4eHh4eExTOAJOA8PDw8PDw8PDw8Pj2ECQimtdx1ACOkCsFryaAyAzhpXh8dEALvrWH49v7/ebV/v8n3f1w/1Lr+efV/vbz/Uy/d9f+iW7/v+0C3f9/2hW/5Q7PsjKaWjjG9TSuv+D8Azivs/Hor1qmH5dfv+IdD29S7f9/2hW37d+n4IfPuhXr7v+0O3fN/3h275vu8P3fKHXN/b1mmoq1DeVu8K1Bn1/P56t329y683fN8fmqj3tx/q5dcT9f72Q738eqLe336ol19P1PvbD/Xy64lU3z5UVCifoZSeUu96iBiq9fKoPnzfH7rwfX/owvf9oQvf94cufN8fuhiKfW9bp6EigftxvSug7KnAhgAABytJREFUwFCtl0f14fv+0IXv+0MXvu8PXfi+P3Th+/7QxVDse6s6DQkJnIeHh4eHh4eHh4eHh4cZQ0UC5+Hh4eHh4eHh4eHh4WHAIUXAEUIOJ4TcTwhZRQhZSQj5l/L98YSQewgha8t/x5XvE0LIdwkh6wghywkhJ3F5XV5Ov5YQcnm9vsnDDhn3/Z2EkA5CyO31+h4Pe2TV94SQEwghj5fzWE4IeXc9v8vDjAz7fhYh5FlCyPPlfD5Wz+/yMCPLNb/8fDQhZCsh5Pp6fI+HPTLe74vlef88IeTWen2Thx0y7vuZhJC7y3m9SAiZXZ+vUqCe7jvr4LJzKoCTytejAKwBcDSAawFcVb5/FYCvl6/fCOBvAAiA0wA8Wb4/HsCG8t9x5etx9f4+/6/6fV9+dh6ANwO4vd7f5f/Vru8BLAAwv3w9DcB2AGPr/X3+X036vglAc/l6JIBNAKbV+/v8v+r3PZffdwD8DsD19f42/692fQ+gu97f4//Vre8fAHB++XokgNZ6fx//75CSwFFKt1NKl5avuwCsAjAdwKUAfllO9ksAbylfXwrgVzTAEwDGEkKmAngDgHsopXsppfsA3APgwhp+iocjMux7UErvBdBVy/p7JEdWfU8pXUMpXVvOZxuAXQAm1fBTPByRYd8PUEr7y2macYhprwxHZLnmE0JOBjAFwN01/ASPhMiy7z2GF7Lqe0LI0QAaKKX3lPPqppT21PJbTDhkN6GyKPREAE8CmEIp3Q4EnQ9gcjnZdACbude2lO+p7nsMA6Tse49hjKz6nhByKgKpzPrq1tgjK6Tt+7JqzvLy86+XiXiPYYA0fU8IyQH4FoB/r1V9PbJDBmt+CyHkGULIE4SQt8Bj2CBl3y8A0EEIuZkQ8hwh5BuEkHyt6m6DQ5KAI4SMBPBnAJ+mlO7XJZXco5r7HkMcGfS9xzBFVn1f5sz+GsA/UEpL2dbSoxrIou8ppZsppccBmAfgckLIlOxr6pE1Muj7fwZwB6V0s+S5xxBGRmv+TBrE5HofgP9HCJmbcTU9qoAM+r4BwNkA/g3AqwDMAXBFxtVMhUOOgCOENCLo1N9SSm8u397JqUpMRaAaBQSU+OHc6zMAbNPc9xjCyKjvPYYhsup7QshoAEsAfLGsbuExxJH1vC9L3lYi2Nw9hjAy6vvTAXySELIJwDcBXEYI+VoNqu+RAlnNeyZpp5RuQGATdWLVK++RChme85+jlG6glBYA/BVAxLFRvXFIEXCEEALgZwBWUUq/zT26FQDzJHk5gFu4+5eVvdScBqCzLHq9C8AFhJBxZU82F5TveQxRZNj3HsMMWfU9IaQJwF8Q6Mv/X42q75ECGfb9DELIiHKe4wCcCWB1TT7CIxGy6ntK6fsppTMppbMRcON/RSm9qjZf4ZEEGc77cYSQ5nKeExHM+xdr8hEeiZDhWe9pAOMIIczO/VwMtb6ndfaiUst/AM5CIBpdDuD58r83ApgA4F4Aa8t/x5fTEwA3ILBzWQHgFC6vfwSwrvzvH+r9bf5fTfv+YQDtAHoRcGneUO/v8/+q3/cAPgBgkMvjeQAn1Pv7/L+a9P355TyWlf9+pN7f5v/Vpu+FPK+A90I55P9lOO/PKP9eVv77oXp/m/9Xm74vP2Pr/goAvwDQVO/v4/+RciU9PDw8PDw8PDw8PDw8hjgOKRVKDw8PDw8PDw8PDw+P4QxPwHl4eHh4eHh4eHh4eAwTeALOw8PDw8PDw8PDw8NjmMATcB4eHh4eHh4eHh4eHsMEnoDz8PDw8PDw8PDw8PAYJvAEnIeHh4fHIQNCyGJCyL9pnr+FEHJ0Levk4eHh4eHhAk/AeXh4eHh4hHgLAE/AeXh4eHgMWfg4cB4eHh4eBzUIIV8AcBmAzQDaATwLoBPAR/D/27l/1SiDKAzjzyuJEshCGovcg2DAIpAgSHpBUSsR0ghegRcQBC9AQgIWbikYECSNhY0XIIgggoIIgqQQ/IOBNB6LjLDEIFjsyizPrzxzvo8z5QszAyeBd8ANYAnYbWtfgSvtF5vAaWAfuFlVbyY5vyRJowxwkqSpleQcMASWgRngBbANPKiqz63nDrBXVfeSDIHdqtppa8+AW1X1NskycLeq1ia/E0mSDs387wEkSRqj88DjqtoHSPKk1c+04LYAzANPj36YZB5YAR4l+V0+NfaJJUn6CwOcJGnaHXfUZAhcqqqXSdaBC8f0nAC+VNXS+EaTJOnf+IiJJGmaPQcuJ5lLMgAutvoA+JRkFrg+0v+9rVFV34D3Sa4B5NDZyY0uSdKfvAMnSZpqI4+YfAA+Aq+BH8DtVnsFDKpqPckqcB84AK4CP4EtYBGYBR5W1cbENyFJUmOAkyRJkqROeIRSkiRJkjphgJMkSZKkThjgJEmSJKkTBjhJkiRJ6oQBTpIkSZI6YYCTJEmSpE4Y4CRJkiSpEwY4SZIkSerEL4V4yWqnwsaSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Periode tertentu\n",
    "ch_citeko_00_05 = stat_citeko_ch[~mask].loc['20000101':'20051231']\n",
    "ch_citeko_00_05.plot(figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:15.623987Z",
     "start_time": "2018-11-09T03:43:15.321893Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ed10e2f588>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ch_citeko_00_05.plot.hist(figsize=(15,10), bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-09T03:43:15.884068Z",
     "start_time": "2018-11-09T03:43:15.625985Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ed10eb6518>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stat_citeko_ch[~mask & (stat_citeko_ch > 0)].plot.hist(figsize=(15,10), bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python [conda env:hidrokit]",
   "language": "python",
   "name": "conda-env-hidrokit-py"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
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   },
   "types_to_exclude": [
    "module",
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    "builtin_function_or_method",
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    "_Feature"
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   "window_display": false
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 "nbformat": 4,
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}