Earthquake Study in Taiwan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date (mm/dd/yr)</th>\n",
       "      <th>Time</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Mag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1964-01-18</td>\n",
       "      <td>12:04:40</td>\n",
       "      <td>23.1000</td>\n",
       "      <td>120.5000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1965-05-17</td>\n",
       "      <td>17:19:25.900000</td>\n",
       "      <td>22.5000</td>\n",
       "      <td>121.3000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1970-11-14</td>\n",
       "      <td>07:58:19.800000</td>\n",
       "      <td>22.7110</td>\n",
       "      <td>121.3440</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1972-04-24</td>\n",
       "      <td>09:57:21.700000</td>\n",
       "      <td>23.6380</td>\n",
       "      <td>121.5510</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1972-09-22</td>\n",
       "      <td>19:57:27.400000</td>\n",
       "      <td>22.3310</td>\n",
       "      <td>121.1970</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1972-09-23</td>\n",
       "      <td>02:14:26.800000</td>\n",
       "      <td>22.3190</td>\n",
       "      <td>121.3550</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1972-11-09</td>\n",
       "      <td>18:41:14.500000</td>\n",
       "      <td>23.8850</td>\n",
       "      <td>121.5810</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1978-07-23</td>\n",
       "      <td>14:42:36.900000</td>\n",
       "      <td>22.2820</td>\n",
       "      <td>121.5120</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1978-09-02</td>\n",
       "      <td>01:57:33.400000</td>\n",
       "      <td>24.8960</td>\n",
       "      <td>121.9850</td>\n",
       "      <td>109.0</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1986-05-20</td>\n",
       "      <td>05:25:46.960000</td>\n",
       "      <td>24.1250</td>\n",
       "      <td>121.6190</td>\n",
       "      <td>19.3</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1986-11-14</td>\n",
       "      <td>21:20:10.550000</td>\n",
       "      <td>23.9010</td>\n",
       "      <td>121.5740</td>\n",
       "      <td>33.8</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1986-11-14</td>\n",
       "      <td>23:04:37.090000</td>\n",
       "      <td>23.8660</td>\n",
       "      <td>121.7110</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1992-09-01</td>\n",
       "      <td>16:41:13.350000</td>\n",
       "      <td>23.7490</td>\n",
       "      <td>121.6850</td>\n",
       "      <td>54.4</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1994-06-05</td>\n",
       "      <td>01:09:30.150000</td>\n",
       "      <td>24.5110</td>\n",
       "      <td>121.9050</td>\n",
       "      <td>11.4</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1996-09-05</td>\n",
       "      <td>23:42:06.150000</td>\n",
       "      <td>21.8980</td>\n",
       "      <td>121.4980</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>17:47:18.490000</td>\n",
       "      <td>23.7720</td>\n",
       "      <td>120.9820</td>\n",
       "      <td>33.0</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>17:57:16.090000</td>\n",
       "      <td>23.7850</td>\n",
       "      <td>121.2020</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>18:03:44.290000</td>\n",
       "      <td>23.5700</td>\n",
       "      <td>121.2990</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>18:11:53.650000</td>\n",
       "      <td>23.7460</td>\n",
       "      <td>121.1890</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>18:16:18.510000</td>\n",
       "      <td>23.7560</td>\n",
       "      <td>121.2460</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>21:46:42.870000</td>\n",
       "      <td>23.3900</td>\n",
       "      <td>120.9640</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1999-09-22</td>\n",
       "      <td>00:14:39.150000</td>\n",
       "      <td>23.7290</td>\n",
       "      <td>121.1670</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1999-09-25</td>\n",
       "      <td>23:52:48.660000</td>\n",
       "      <td>23.7380</td>\n",
       "      <td>121.1580</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1999-11-01</td>\n",
       "      <td>17:53:00.120000</td>\n",
       "      <td>23.3780</td>\n",
       "      <td>121.5200</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2000-06-10</td>\n",
       "      <td>18:23:29.320000</td>\n",
       "      <td>23.8430</td>\n",
       "      <td>121.2250</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2002-05-15</td>\n",
       "      <td>03:46:05.760000</td>\n",
       "      <td>24.6360</td>\n",
       "      <td>121.9220</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2003-06-10</td>\n",
       "      <td>08:40:30.830000</td>\n",
       "      <td>23.5200</td>\n",
       "      <td>121.6280</td>\n",
       "      <td>44.5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2003-12-10</td>\n",
       "      <td>04:38:11.590000</td>\n",
       "      <td>23.0390</td>\n",
       "      <td>121.3620</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2004-05-19</td>\n",
       "      <td>07:04:11.710000</td>\n",
       "      <td>22.6620</td>\n",
       "      <td>121.5050</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>10:02:19.570000</td>\n",
       "      <td>22.8680</td>\n",
       "      <td>121.2780</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2006-12-26</td>\n",
       "      <td>12:26:21.140000</td>\n",
       "      <td>21.7990</td>\n",
       "      <td>120.5470</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2006-12-26</td>\n",
       "      <td>12:34:13.800000</td>\n",
       "      <td>21.9740</td>\n",
       "      <td>120.4930</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2007-01-25</td>\n",
       "      <td>10:59:17.650000</td>\n",
       "      <td>22.5640</td>\n",
       "      <td>121.9260</td>\n",
       "      <td>36.2</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2008-04-23</td>\n",
       "      <td>18:28:41.880000</td>\n",
       "      <td>22.8810</td>\n",
       "      <td>121.6190</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2008-07-13</td>\n",
       "      <td>14:58:33.010000</td>\n",
       "      <td>21.0140</td>\n",
       "      <td>121.1490</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2009-10-03</td>\n",
       "      <td>17:36:06.120000</td>\n",
       "      <td>23.6300</td>\n",
       "      <td>121.4520</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2009-12-19</td>\n",
       "      <td>13:02:15.570000</td>\n",
       "      <td>23.7960</td>\n",
       "      <td>121.6050</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2010-03-04</td>\n",
       "      <td>00:18:51.290000</td>\n",
       "      <td>22.9180</td>\n",
       "      <td>120.7950</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2013-06-02</td>\n",
       "      <td>05:43:03.800000</td>\n",
       "      <td>23.8318</td>\n",
       "      <td>121.0590</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2013-10-31</td>\n",
       "      <td>12:02:09.350000</td>\n",
       "      <td>23.5436</td>\n",
       "      <td>121.4580</td>\n",
       "      <td>14.8</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2016-02-05</td>\n",
       "      <td>19:57:27</td>\n",
       "      <td>22.9064</td>\n",
       "      <td>120.6061</td>\n",
       "      <td>18.9</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>15:50:43.040000</td>\n",
       "      <td>24.1140</td>\n",
       "      <td>121.7269</td>\n",
       "      <td>12.9</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2019-04-18</td>\n",
       "      <td>05:01:06.490000</td>\n",
       "      <td>24.0374</td>\n",
       "      <td>121.6501</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date (mm/dd/yr)             Time      Lat       Lon  Depth  Mag\n",
       "0       1964-01-18         12:04:40  23.1000  120.5000   33.0  6.8\n",
       "1       1965-05-17  17:19:25.900000  22.5000  121.3000   21.0  6.2\n",
       "2       1970-11-14  07:58:19.800000  22.7110  121.3440   28.0  6.1\n",
       "3       1972-04-24  09:57:21.700000  23.6380  121.5510   33.0  6.9\n",
       "4       1972-09-22  19:57:27.400000  22.3310  121.1970   33.0  6.2\n",
       "5       1972-09-23  02:14:26.800000  22.3190  121.3550   33.0  6.1\n",
       "6       1972-11-09  18:41:14.500000  23.8850  121.5810   33.0  6.1\n",
       "7       1978-07-23  14:42:36.900000  22.2820  121.5120   17.0  7.4\n",
       "8       1978-09-02  01:57:33.400000  24.8960  121.9850  109.0  6.1\n",
       "9       1986-05-20  05:25:46.960000  24.1250  121.6190   19.3  6.1\n",
       "10      1986-11-14  21:20:10.550000  23.9010  121.5740   33.8  7.8\n",
       "11      1986-11-14  23:04:37.090000  23.8660  121.7110   33.0  6.1\n",
       "12      1992-09-01  16:41:13.350000  23.7490  121.6850   54.4  6.0\n",
       "13      1994-06-05  01:09:30.150000  24.5110  121.9050   11.4  6.1\n",
       "14      1996-09-05  23:42:06.150000  21.8980  121.4980   20.0  6.8\n",
       "15      1999-09-20  17:47:18.490000  23.7720  120.9820   33.0  7.7\n",
       "16      1999-09-20  17:57:16.090000  23.7850  121.2020   33.0  6.3\n",
       "17      1999-09-20  18:03:44.290000  23.5700  121.2990   33.0  6.6\n",
       "18      1999-09-20  18:11:53.650000  23.7460  121.1890   33.0  6.1\n",
       "19      1999-09-20  18:16:18.510000  23.7560  121.2460   33.0  6.8\n",
       "20      1999-09-20  21:46:42.870000  23.3900  120.9640   33.0  6.4\n",
       "21      1999-09-22  00:14:39.150000  23.7290  121.1670   26.0  6.4\n",
       "22      1999-09-25  23:52:48.660000  23.7380  121.1580   17.0  6.5\n",
       "23      1999-11-01  17:53:00.120000  23.3780  121.5200   33.0  6.3\n",
       "24      2000-06-10  18:23:29.320000  23.8430  121.2250   33.0  6.4\n",
       "25      2002-05-15  03:46:05.760000  24.6360  121.9220   10.0  6.2\n",
       "26      2003-06-10  08:40:30.830000  23.5200  121.6280   44.5  6.0\n",
       "27      2003-12-10  04:38:11.590000  23.0390  121.3620   10.0  6.8\n",
       "28      2004-05-19  07:04:11.710000  22.6620  121.5050   20.0  6.2\n",
       "29      2006-04-01  10:02:19.570000  22.8680  121.2780    9.0  6.1\n",
       "30      2006-12-26  12:26:21.140000  21.7990  120.5470   10.0  7.1\n",
       "31      2006-12-26  12:34:13.800000  21.9740  120.4930   10.0  6.9\n",
       "32      2007-01-25  10:59:17.650000  22.5640  121.9260   36.2  6.0\n",
       "33      2008-04-23  18:28:41.880000  22.8810  121.6190   10.0  6.0\n",
       "34      2008-07-13  14:58:33.010000  21.0140  121.1490   14.0  6.2\n",
       "35      2009-10-03  17:36:06.120000  23.6300  121.4520   28.0  6.1\n",
       "36      2009-12-19  13:02:15.570000  23.7960  121.6050   43.0  6.4\n",
       "37      2010-03-04  00:18:51.290000  22.9180  120.7950   21.0  6.3\n",
       "38      2013-06-02  05:43:03.800000  23.8318  121.0590   17.0  6.1\n",
       "39      2013-10-31  12:02:09.350000  23.5436  121.4580   14.8  6.1\n",
       "40      2016-02-05         19:57:27  22.9064  120.6061   18.9  6.2\n",
       "41      2018-02-06  15:50:43.040000  24.1140  121.7269   12.9  6.1\n",
       "42      2019-04-18  05:01:06.490000  24.0374  121.6501   20.0  6.3"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing the data for all earthquakes and obtaining all the data for large earthquakes\n",
    "earthquakes = pd.read_excel('./All datasets/Dataset_Taiwan.xlsx', sheet_name = 1)\n",
    "large_earthquakes = earthquakes[earthquakes.Mag >= 6].reset_index()\n",
    "large_earthquakes = large_earthquakes.drop(columns = ['index'])\n",
    "large_earthquakes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      10\n",
       "1      24\n",
       "2      81\n",
       "3      22\n",
       "4      19\n",
       "5       0\n",
       "6       9\n",
       "7      92\n",
       "8      34\n",
       "9     207\n",
       "10     19\n",
       "11      1\n",
       "12    349\n",
       "13     44\n",
       "14     83\n",
       "15    166\n",
       "16      0\n",
       "17      0\n",
       "18      0\n",
       "19      0\n",
       "20     23\n",
       "21     37\n",
       "22     30\n",
       "23     62\n",
       "24     52\n",
       "25    131\n",
       "26     60\n",
       "27     33\n",
       "28     65\n",
       "29    158\n",
       "30     54\n",
       "31      0\n",
       "32     24\n",
       "33     90\n",
       "34     34\n",
       "35     88\n",
       "36     12\n",
       "37      9\n",
       "38    191\n",
       "39     17\n",
       "40    128\n",
       "41    126\n",
       "42    109\n",
       "43     83\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding out the interevent counts for each earthquake\n",
    "interevent_counts = []\n",
    "i = 0\n",
    "for ind, row in earthquakes.iterrows():\n",
    "    if row['Mag'] < 6:\n",
    "        i = i + 1\n",
    "    else:\n",
    "        interevent_counts.append(i)\n",
    "        i = 0\n",
    "interevent_counts.append(i)\n",
    "len(interevent_counts)\n",
    "interevent_counts_new = pd.Series(interevent_counts)\n",
    "interevent_counts_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date (mm/dd/yr)</th>\n",
       "      <th>Time</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Mag</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1964-01-18</td>\n",
       "      <td>12:04:40</td>\n",
       "      <td>23.1000</td>\n",
       "      <td>120.5000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1965-05-17</td>\n",
       "      <td>17:19:25.900000</td>\n",
       "      <td>22.5000</td>\n",
       "      <td>121.3000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1970-11-14</td>\n",
       "      <td>07:58:19.800000</td>\n",
       "      <td>22.7110</td>\n",
       "      <td>121.3440</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1972-04-24</td>\n",
       "      <td>09:57:21.700000</td>\n",
       "      <td>23.6380</td>\n",
       "      <td>121.5510</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1972-09-22</td>\n",
       "      <td>19:57:27.400000</td>\n",
       "      <td>22.3310</td>\n",
       "      <td>121.1970</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1972-09-23</td>\n",
       "      <td>02:14:26.800000</td>\n",
       "      <td>22.3190</td>\n",
       "      <td>121.3550</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1972-11-09</td>\n",
       "      <td>18:41:14.500000</td>\n",
       "      <td>23.8850</td>\n",
       "      <td>121.5810</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1978-07-23</td>\n",
       "      <td>14:42:36.900000</td>\n",
       "      <td>22.2820</td>\n",
       "      <td>121.5120</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1978-09-02</td>\n",
       "      <td>01:57:33.400000</td>\n",
       "      <td>24.8960</td>\n",
       "      <td>121.9850</td>\n",
       "      <td>109.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1986-05-20</td>\n",
       "      <td>05:25:46.960000</td>\n",
       "      <td>24.1250</td>\n",
       "      <td>121.6190</td>\n",
       "      <td>19.3</td>\n",
       "      <td>6.1</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1986-11-14</td>\n",
       "      <td>21:20:10.550000</td>\n",
       "      <td>23.9010</td>\n",
       "      <td>121.5740</td>\n",
       "      <td>33.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1986-11-14</td>\n",
       "      <td>23:04:37.090000</td>\n",
       "      <td>23.8660</td>\n",
       "      <td>121.7110</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1992-09-01</td>\n",
       "      <td>16:41:13.350000</td>\n",
       "      <td>23.7490</td>\n",
       "      <td>121.6850</td>\n",
       "      <td>54.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1994-06-05</td>\n",
       "      <td>01:09:30.150000</td>\n",
       "      <td>24.5110</td>\n",
       "      <td>121.9050</td>\n",
       "      <td>11.4</td>\n",
       "      <td>6.1</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1996-09-05</td>\n",
       "      <td>23:42:06.150000</td>\n",
       "      <td>21.8980</td>\n",
       "      <td>121.4980</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>17:47:18.490000</td>\n",
       "      <td>23.7720</td>\n",
       "      <td>120.9820</td>\n",
       "      <td>33.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>17:57:16.090000</td>\n",
       "      <td>23.7850</td>\n",
       "      <td>121.2020</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>18:03:44.290000</td>\n",
       "      <td>23.5700</td>\n",
       "      <td>121.2990</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>18:11:53.650000</td>\n",
       "      <td>23.7460</td>\n",
       "      <td>121.1890</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>18:16:18.510000</td>\n",
       "      <td>23.7560</td>\n",
       "      <td>121.2460</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>21:46:42.870000</td>\n",
       "      <td>23.3900</td>\n",
       "      <td>120.9640</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1999-09-22</td>\n",
       "      <td>00:14:39.150000</td>\n",
       "      <td>23.7290</td>\n",
       "      <td>121.1670</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1999-09-25</td>\n",
       "      <td>23:52:48.660000</td>\n",
       "      <td>23.7380</td>\n",
       "      <td>121.1580</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1999-11-01</td>\n",
       "      <td>17:53:00.120000</td>\n",
       "      <td>23.3780</td>\n",
       "      <td>121.5200</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2000-06-10</td>\n",
       "      <td>18:23:29.320000</td>\n",
       "      <td>23.8430</td>\n",
       "      <td>121.2250</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2002-05-15</td>\n",
       "      <td>03:46:05.760000</td>\n",
       "      <td>24.6360</td>\n",
       "      <td>121.9220</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2003-06-10</td>\n",
       "      <td>08:40:30.830000</td>\n",
       "      <td>23.5200</td>\n",
       "      <td>121.6280</td>\n",
       "      <td>44.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2003-12-10</td>\n",
       "      <td>04:38:11.590000</td>\n",
       "      <td>23.0390</td>\n",
       "      <td>121.3620</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2004-05-19</td>\n",
       "      <td>07:04:11.710000</td>\n",
       "      <td>22.6620</td>\n",
       "      <td>121.5050</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>10:02:19.570000</td>\n",
       "      <td>22.8680</td>\n",
       "      <td>121.2780</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2006-12-26</td>\n",
       "      <td>12:26:21.140000</td>\n",
       "      <td>21.7990</td>\n",
       "      <td>120.5470</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2006-12-26</td>\n",
       "      <td>12:34:13.800000</td>\n",
       "      <td>21.9740</td>\n",
       "      <td>120.4930</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2007-01-25</td>\n",
       "      <td>10:59:17.650000</td>\n",
       "      <td>22.5640</td>\n",
       "      <td>121.9260</td>\n",
       "      <td>36.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2008-04-23</td>\n",
       "      <td>18:28:41.880000</td>\n",
       "      <td>22.8810</td>\n",
       "      <td>121.6190</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2008-07-13</td>\n",
       "      <td>14:58:33.010000</td>\n",
       "      <td>21.0140</td>\n",
       "      <td>121.1490</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2009-10-03</td>\n",
       "      <td>17:36:06.120000</td>\n",
       "      <td>23.6300</td>\n",
       "      <td>121.4520</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2009-12-19</td>\n",
       "      <td>13:02:15.570000</td>\n",
       "      <td>23.7960</td>\n",
       "      <td>121.6050</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2010-03-04</td>\n",
       "      <td>00:18:51.290000</td>\n",
       "      <td>22.9180</td>\n",
       "      <td>120.7950</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2013-06-02</td>\n",
       "      <td>05:43:03.800000</td>\n",
       "      <td>23.8318</td>\n",
       "      <td>121.0590</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2013-10-31</td>\n",
       "      <td>12:02:09.350000</td>\n",
       "      <td>23.5436</td>\n",
       "      <td>121.4580</td>\n",
       "      <td>14.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2016-02-05</td>\n",
       "      <td>19:57:27</td>\n",
       "      <td>22.9064</td>\n",
       "      <td>120.6061</td>\n",
       "      <td>18.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>15:50:43.040000</td>\n",
       "      <td>24.1140</td>\n",
       "      <td>121.7269</td>\n",
       "      <td>12.9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2019-04-18</td>\n",
       "      <td>05:01:06.490000</td>\n",
       "      <td>24.0374</td>\n",
       "      <td>121.6501</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date (mm/dd/yr)             Time      Lat       Lon  Depth  Mag    0\n",
       "0       1964-01-18         12:04:40  23.1000  120.5000   33.0  6.8   10\n",
       "1       1965-05-17  17:19:25.900000  22.5000  121.3000   21.0  6.2   24\n",
       "2       1970-11-14  07:58:19.800000  22.7110  121.3440   28.0  6.1   81\n",
       "3       1972-04-24  09:57:21.700000  23.6380  121.5510   33.0  6.9   22\n",
       "4       1972-09-22  19:57:27.400000  22.3310  121.1970   33.0  6.2   19\n",
       "5       1972-09-23  02:14:26.800000  22.3190  121.3550   33.0  6.1    0\n",
       "6       1972-11-09  18:41:14.500000  23.8850  121.5810   33.0  6.1    9\n",
       "7       1978-07-23  14:42:36.900000  22.2820  121.5120   17.0  7.4   92\n",
       "8       1978-09-02  01:57:33.400000  24.8960  121.9850  109.0  6.1   34\n",
       "9       1986-05-20  05:25:46.960000  24.1250  121.6190   19.3  6.1  207\n",
       "10      1986-11-14  21:20:10.550000  23.9010  121.5740   33.8  7.8   19\n",
       "11      1986-11-14  23:04:37.090000  23.8660  121.7110   33.0  6.1    1\n",
       "12      1992-09-01  16:41:13.350000  23.7490  121.6850   54.4  6.0  349\n",
       "13      1994-06-05  01:09:30.150000  24.5110  121.9050   11.4  6.1   44\n",
       "14      1996-09-05  23:42:06.150000  21.8980  121.4980   20.0  6.8   83\n",
       "15      1999-09-20  17:47:18.490000  23.7720  120.9820   33.0  7.7  166\n",
       "16      1999-09-20  17:57:16.090000  23.7850  121.2020   33.0  6.3    0\n",
       "17      1999-09-20  18:03:44.290000  23.5700  121.2990   33.0  6.6    0\n",
       "18      1999-09-20  18:11:53.650000  23.7460  121.1890   33.0  6.1    0\n",
       "19      1999-09-20  18:16:18.510000  23.7560  121.2460   33.0  6.8    0\n",
       "20      1999-09-20  21:46:42.870000  23.3900  120.9640   33.0  6.4   23\n",
       "21      1999-09-22  00:14:39.150000  23.7290  121.1670   26.0  6.4   37\n",
       "22      1999-09-25  23:52:48.660000  23.7380  121.1580   17.0  6.5   30\n",
       "23      1999-11-01  17:53:00.120000  23.3780  121.5200   33.0  6.3   62\n",
       "24      2000-06-10  18:23:29.320000  23.8430  121.2250   33.0  6.4   52\n",
       "25      2002-05-15  03:46:05.760000  24.6360  121.9220   10.0  6.2  131\n",
       "26      2003-06-10  08:40:30.830000  23.5200  121.6280   44.5  6.0   60\n",
       "27      2003-12-10  04:38:11.590000  23.0390  121.3620   10.0  6.8   33\n",
       "28      2004-05-19  07:04:11.710000  22.6620  121.5050   20.0  6.2   65\n",
       "29      2006-04-01  10:02:19.570000  22.8680  121.2780    9.0  6.1  158\n",
       "30      2006-12-26  12:26:21.140000  21.7990  120.5470   10.0  7.1   54\n",
       "31      2006-12-26  12:34:13.800000  21.9740  120.4930   10.0  6.9    0\n",
       "32      2007-01-25  10:59:17.650000  22.5640  121.9260   36.2  6.0   24\n",
       "33      2008-04-23  18:28:41.880000  22.8810  121.6190   10.0  6.0   90\n",
       "34      2008-07-13  14:58:33.010000  21.0140  121.1490   14.0  6.2   34\n",
       "35      2009-10-03  17:36:06.120000  23.6300  121.4520   28.0  6.1   88\n",
       "36      2009-12-19  13:02:15.570000  23.7960  121.6050   43.0  6.4   12\n",
       "37      2010-03-04  00:18:51.290000  22.9180  120.7950   21.0  6.3    9\n",
       "38      2013-06-02  05:43:03.800000  23.8318  121.0590   17.0  6.1  191\n",
       "39      2013-10-31  12:02:09.350000  23.5436  121.4580   14.8  6.1   17\n",
       "40      2016-02-05         19:57:27  22.9064  120.6061   18.9  6.2  128\n",
       "41      2018-02-06  15:50:43.040000  24.1140  121.7269   12.9  6.1  126\n",
       "42      2019-04-18  05:01:06.490000  24.0374  121.6501   20.0  6.3  109\n",
       "43             NaT              NaN      NaN       NaN    NaN  NaN   83"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adding the intervent counts data to the large magnitude earthquakes dataset\n",
    "earthquakes_new = pd.concat([large_earthquakes, interevent_counts_new], axis=1)\n",
    "earthquakes_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "earthquakes_new.rename({0 : 'Interevent Counts'}, axis='columns', inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date (mm/dd/yr)</th>\n",
       "      <th>Time</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Mag</th>\n",
       "      <th>Interevent Counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1964-01-18</td>\n",
       "      <td>12:04:40</td>\n",
       "      <td>23.1000</td>\n",
       "      <td>120.5000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1965-05-17</td>\n",
       "      <td>17:19:25.900000</td>\n",
       "      <td>22.5000</td>\n",
       "      <td>121.3000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1970-11-14</td>\n",
       "      <td>07:58:19.800000</td>\n",
       "      <td>22.7110</td>\n",
       "      <td>121.3440</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1972-04-24</td>\n",
       "      <td>09:57:21.700000</td>\n",
       "      <td>23.6380</td>\n",
       "      <td>121.5510</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1972-09-22</td>\n",
       "      <td>19:57:27.400000</td>\n",
       "      <td>22.3310</td>\n",
       "      <td>121.1970</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1972-09-23</td>\n",
       "      <td>02:14:26.800000</td>\n",
       "      <td>22.3190</td>\n",
       "      <td>121.3550</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1972-11-09</td>\n",
       "      <td>18:41:14.500000</td>\n",
       "      <td>23.8850</td>\n",
       "      <td>121.5810</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1978-07-23</td>\n",
       "      <td>14:42:36.900000</td>\n",
       "      <td>22.2820</td>\n",
       "      <td>121.5120</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1978-09-02</td>\n",
       "      <td>01:57:33.400000</td>\n",
       "      <td>24.8960</td>\n",
       "      <td>121.9850</td>\n",
       "      <td>109.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1986-05-20</td>\n",
       "      <td>05:25:46.960000</td>\n",
       "      <td>24.1250</td>\n",
       "      <td>121.6190</td>\n",
       "      <td>19.3</td>\n",
       "      <td>6.1</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1986-11-14</td>\n",
       "      <td>21:20:10.550000</td>\n",
       "      <td>23.9010</td>\n",
       "      <td>121.5740</td>\n",
       "      <td>33.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1986-11-14</td>\n",
       "      <td>23:04:37.090000</td>\n",
       "      <td>23.8660</td>\n",
       "      <td>121.7110</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1992-09-01</td>\n",
       "      <td>16:41:13.350000</td>\n",
       "      <td>23.7490</td>\n",
       "      <td>121.6850</td>\n",
       "      <td>54.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1994-06-05</td>\n",
       "      <td>01:09:30.150000</td>\n",
       "      <td>24.5110</td>\n",
       "      <td>121.9050</td>\n",
       "      <td>11.4</td>\n",
       "      <td>6.1</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1996-09-05</td>\n",
       "      <td>23:42:06.150000</td>\n",
       "      <td>21.8980</td>\n",
       "      <td>121.4980</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>17:47:18.490000</td>\n",
       "      <td>23.7720</td>\n",
       "      <td>120.9820</td>\n",
       "      <td>33.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>17:57:16.090000</td>\n",
       "      <td>23.7850</td>\n",
       "      <td>121.2020</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>18:03:44.290000</td>\n",
       "      <td>23.5700</td>\n",
       "      <td>121.2990</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>18:11:53.650000</td>\n",
       "      <td>23.7460</td>\n",
       "      <td>121.1890</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>18:16:18.510000</td>\n",
       "      <td>23.7560</td>\n",
       "      <td>121.2460</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>21:46:42.870000</td>\n",
       "      <td>23.3900</td>\n",
       "      <td>120.9640</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1999-09-22</td>\n",
       "      <td>00:14:39.150000</td>\n",
       "      <td>23.7290</td>\n",
       "      <td>121.1670</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1999-09-25</td>\n",
       "      <td>23:52:48.660000</td>\n",
       "      <td>23.7380</td>\n",
       "      <td>121.1580</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1999-11-01</td>\n",
       "      <td>17:53:00.120000</td>\n",
       "      <td>23.3780</td>\n",
       "      <td>121.5200</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2000-06-10</td>\n",
       "      <td>18:23:29.320000</td>\n",
       "      <td>23.8430</td>\n",
       "      <td>121.2250</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2002-05-15</td>\n",
       "      <td>03:46:05.760000</td>\n",
       "      <td>24.6360</td>\n",
       "      <td>121.9220</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2003-06-10</td>\n",
       "      <td>08:40:30.830000</td>\n",
       "      <td>23.5200</td>\n",
       "      <td>121.6280</td>\n",
       "      <td>44.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2003-12-10</td>\n",
       "      <td>04:38:11.590000</td>\n",
       "      <td>23.0390</td>\n",
       "      <td>121.3620</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2004-05-19</td>\n",
       "      <td>07:04:11.710000</td>\n",
       "      <td>22.6620</td>\n",
       "      <td>121.5050</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>10:02:19.570000</td>\n",
       "      <td>22.8680</td>\n",
       "      <td>121.2780</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2006-12-26</td>\n",
       "      <td>12:26:21.140000</td>\n",
       "      <td>21.7990</td>\n",
       "      <td>120.5470</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2006-12-26</td>\n",
       "      <td>12:34:13.800000</td>\n",
       "      <td>21.9740</td>\n",
       "      <td>120.4930</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2007-01-25</td>\n",
       "      <td>10:59:17.650000</td>\n",
       "      <td>22.5640</td>\n",
       "      <td>121.9260</td>\n",
       "      <td>36.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2008-04-23</td>\n",
       "      <td>18:28:41.880000</td>\n",
       "      <td>22.8810</td>\n",
       "      <td>121.6190</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2008-07-13</td>\n",
       "      <td>14:58:33.010000</td>\n",
       "      <td>21.0140</td>\n",
       "      <td>121.1490</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2009-10-03</td>\n",
       "      <td>17:36:06.120000</td>\n",
       "      <td>23.6300</td>\n",
       "      <td>121.4520</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2009-12-19</td>\n",
       "      <td>13:02:15.570000</td>\n",
       "      <td>23.7960</td>\n",
       "      <td>121.6050</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2010-03-04</td>\n",
       "      <td>00:18:51.290000</td>\n",
       "      <td>22.9180</td>\n",
       "      <td>120.7950</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2013-06-02</td>\n",
       "      <td>05:43:03.800000</td>\n",
       "      <td>23.8318</td>\n",
       "      <td>121.0590</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2013-10-31</td>\n",
       "      <td>12:02:09.350000</td>\n",
       "      <td>23.5436</td>\n",
       "      <td>121.4580</td>\n",
       "      <td>14.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2016-02-05</td>\n",
       "      <td>19:57:27</td>\n",
       "      <td>22.9064</td>\n",
       "      <td>120.6061</td>\n",
       "      <td>18.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>15:50:43.040000</td>\n",
       "      <td>24.1140</td>\n",
       "      <td>121.7269</td>\n",
       "      <td>12.9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2019-04-18</td>\n",
       "      <td>05:01:06.490000</td>\n",
       "      <td>24.0374</td>\n",
       "      <td>121.6501</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date (mm/dd/yr)             Time      Lat       Lon  Depth  Mag  \\\n",
       "0       1964-01-18         12:04:40  23.1000  120.5000   33.0  6.8   \n",
       "1       1965-05-17  17:19:25.900000  22.5000  121.3000   21.0  6.2   \n",
       "2       1970-11-14  07:58:19.800000  22.7110  121.3440   28.0  6.1   \n",
       "3       1972-04-24  09:57:21.700000  23.6380  121.5510   33.0  6.9   \n",
       "4       1972-09-22  19:57:27.400000  22.3310  121.1970   33.0  6.2   \n",
       "5       1972-09-23  02:14:26.800000  22.3190  121.3550   33.0  6.1   \n",
       "6       1972-11-09  18:41:14.500000  23.8850  121.5810   33.0  6.1   \n",
       "7       1978-07-23  14:42:36.900000  22.2820  121.5120   17.0  7.4   \n",
       "8       1978-09-02  01:57:33.400000  24.8960  121.9850  109.0  6.1   \n",
       "9       1986-05-20  05:25:46.960000  24.1250  121.6190   19.3  6.1   \n",
       "10      1986-11-14  21:20:10.550000  23.9010  121.5740   33.8  7.8   \n",
       "11      1986-11-14  23:04:37.090000  23.8660  121.7110   33.0  6.1   \n",
       "12      1992-09-01  16:41:13.350000  23.7490  121.6850   54.4  6.0   \n",
       "13      1994-06-05  01:09:30.150000  24.5110  121.9050   11.4  6.1   \n",
       "14      1996-09-05  23:42:06.150000  21.8980  121.4980   20.0  6.8   \n",
       "15      1999-09-20  17:47:18.490000  23.7720  120.9820   33.0  7.7   \n",
       "16      1999-09-20  17:57:16.090000  23.7850  121.2020   33.0  6.3   \n",
       "17      1999-09-20  18:03:44.290000  23.5700  121.2990   33.0  6.6   \n",
       "18      1999-09-20  18:11:53.650000  23.7460  121.1890   33.0  6.1   \n",
       "19      1999-09-20  18:16:18.510000  23.7560  121.2460   33.0  6.8   \n",
       "20      1999-09-20  21:46:42.870000  23.3900  120.9640   33.0  6.4   \n",
       "21      1999-09-22  00:14:39.150000  23.7290  121.1670   26.0  6.4   \n",
       "22      1999-09-25  23:52:48.660000  23.7380  121.1580   17.0  6.5   \n",
       "23      1999-11-01  17:53:00.120000  23.3780  121.5200   33.0  6.3   \n",
       "24      2000-06-10  18:23:29.320000  23.8430  121.2250   33.0  6.4   \n",
       "25      2002-05-15  03:46:05.760000  24.6360  121.9220   10.0  6.2   \n",
       "26      2003-06-10  08:40:30.830000  23.5200  121.6280   44.5  6.0   \n",
       "27      2003-12-10  04:38:11.590000  23.0390  121.3620   10.0  6.8   \n",
       "28      2004-05-19  07:04:11.710000  22.6620  121.5050   20.0  6.2   \n",
       "29      2006-04-01  10:02:19.570000  22.8680  121.2780    9.0  6.1   \n",
       "30      2006-12-26  12:26:21.140000  21.7990  120.5470   10.0  7.1   \n",
       "31      2006-12-26  12:34:13.800000  21.9740  120.4930   10.0  6.9   \n",
       "32      2007-01-25  10:59:17.650000  22.5640  121.9260   36.2  6.0   \n",
       "33      2008-04-23  18:28:41.880000  22.8810  121.6190   10.0  6.0   \n",
       "34      2008-07-13  14:58:33.010000  21.0140  121.1490   14.0  6.2   \n",
       "35      2009-10-03  17:36:06.120000  23.6300  121.4520   28.0  6.1   \n",
       "36      2009-12-19  13:02:15.570000  23.7960  121.6050   43.0  6.4   \n",
       "37      2010-03-04  00:18:51.290000  22.9180  120.7950   21.0  6.3   \n",
       "38      2013-06-02  05:43:03.800000  23.8318  121.0590   17.0  6.1   \n",
       "39      2013-10-31  12:02:09.350000  23.5436  121.4580   14.8  6.1   \n",
       "40      2016-02-05         19:57:27  22.9064  120.6061   18.9  6.2   \n",
       "41      2018-02-06  15:50:43.040000  24.1140  121.7269   12.9  6.1   \n",
       "42      2019-04-18  05:01:06.490000  24.0374  121.6501   20.0  6.3   \n",
       "43             NaT              NaN      NaN       NaN    NaN  NaN   \n",
       "\n",
       "    Interevent Counts  \n",
       "0                  10  \n",
       "1                  24  \n",
       "2                  81  \n",
       "3                  22  \n",
       "4                  19  \n",
       "5                   0  \n",
       "6                   9  \n",
       "7                  92  \n",
       "8                  34  \n",
       "9                 207  \n",
       "10                 19  \n",
       "11                  1  \n",
       "12                349  \n",
       "13                 44  \n",
       "14                 83  \n",
       "15                166  \n",
       "16                  0  \n",
       "17                  0  \n",
       "18                  0  \n",
       "19                  0  \n",
       "20                 23  \n",
       "21                 37  \n",
       "22                 30  \n",
       "23                 62  \n",
       "24                 52  \n",
       "25                131  \n",
       "26                 60  \n",
       "27                 33  \n",
       "28                 65  \n",
       "29                158  \n",
       "30                 54  \n",
       "31                  0  \n",
       "32                 24  \n",
       "33                 90  \n",
       "34                 34  \n",
       "35                 88  \n",
       "36                 12  \n",
       "37                  9  \n",
       "38                191  \n",
       "39                 17  \n",
       "40                128  \n",
       "41                126  \n",
       "42                109  \n",
       "43                 83  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earthquakes_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "        text-align: right;\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>Date (mm/dd/yr)</th>\n",
       "      <th>Time</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Mag</th>\n",
       "      <th>Interevent Counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1964-01-18</td>\n",
       "      <td>12:04:40</td>\n",
       "      <td>23.1000</td>\n",
       "      <td>120.5000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1965-05-17</td>\n",
       "      <td>17:19:25.900000</td>\n",
       "      <td>22.5000</td>\n",
       "      <td>121.3000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1970-11-14</td>\n",
       "      <td>07:58:19.800000</td>\n",
       "      <td>22.7110</td>\n",
       "      <td>121.3440</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1972-04-24</td>\n",
       "      <td>09:57:21.700000</td>\n",
       "      <td>23.6380</td>\n",
       "      <td>121.5510</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1972-09-22</td>\n",
       "      <td>19:57:27.400000</td>\n",
       "      <td>22.3310</td>\n",
       "      <td>121.1970</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1972-11-09</td>\n",
       "      <td>18:41:14.500000</td>\n",
       "      <td>23.8850</td>\n",
       "      <td>121.5810</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1978-07-23</td>\n",
       "      <td>14:42:36.900000</td>\n",
       "      <td>22.2820</td>\n",
       "      <td>121.5120</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1978-09-02</td>\n",
       "      <td>01:57:33.400000</td>\n",
       "      <td>24.8960</td>\n",
       "      <td>121.9850</td>\n",
       "      <td>109.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1986-05-20</td>\n",
       "      <td>05:25:46.960000</td>\n",
       "      <td>24.1250</td>\n",
       "      <td>121.6190</td>\n",
       "      <td>19.3</td>\n",
       "      <td>6.1</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1986-11-14</td>\n",
       "      <td>21:20:10.550000</td>\n",
       "      <td>23.9010</td>\n",
       "      <td>121.5740</td>\n",
       "      <td>33.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1986-11-14</td>\n",
       "      <td>23:04:37.090000</td>\n",
       "      <td>23.8660</td>\n",
       "      <td>121.7110</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1992-09-01</td>\n",
       "      <td>16:41:13.350000</td>\n",
       "      <td>23.7490</td>\n",
       "      <td>121.6850</td>\n",
       "      <td>54.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1994-06-05</td>\n",
       "      <td>01:09:30.150000</td>\n",
       "      <td>24.5110</td>\n",
       "      <td>121.9050</td>\n",
       "      <td>11.4</td>\n",
       "      <td>6.1</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1996-09-05</td>\n",
       "      <td>23:42:06.150000</td>\n",
       "      <td>21.8980</td>\n",
       "      <td>121.4980</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>17:47:18.490000</td>\n",
       "      <td>23.7720</td>\n",
       "      <td>120.9820</td>\n",
       "      <td>33.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>21:46:42.870000</td>\n",
       "      <td>23.3900</td>\n",
       "      <td>120.9640</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1999-09-22</td>\n",
       "      <td>00:14:39.150000</td>\n",
       "      <td>23.7290</td>\n",
       "      <td>121.1670</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1999-09-25</td>\n",
       "      <td>23:52:48.660000</td>\n",
       "      <td>23.7380</td>\n",
       "      <td>121.1580</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1999-11-01</td>\n",
       "      <td>17:53:00.120000</td>\n",
       "      <td>23.3780</td>\n",
       "      <td>121.5200</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2000-06-10</td>\n",
       "      <td>18:23:29.320000</td>\n",
       "      <td>23.8430</td>\n",
       "      <td>121.2250</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2002-05-15</td>\n",
       "      <td>03:46:05.760000</td>\n",
       "      <td>24.6360</td>\n",
       "      <td>121.9220</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2003-06-10</td>\n",
       "      <td>08:40:30.830000</td>\n",
       "      <td>23.5200</td>\n",
       "      <td>121.6280</td>\n",
       "      <td>44.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2003-12-10</td>\n",
       "      <td>04:38:11.590000</td>\n",
       "      <td>23.0390</td>\n",
       "      <td>121.3620</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2004-05-19</td>\n",
       "      <td>07:04:11.710000</td>\n",
       "      <td>22.6620</td>\n",
       "      <td>121.5050</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>10:02:19.570000</td>\n",
       "      <td>22.8680</td>\n",
       "      <td>121.2780</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2006-12-26</td>\n",
       "      <td>12:26:21.140000</td>\n",
       "      <td>21.7990</td>\n",
       "      <td>120.5470</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2007-01-25</td>\n",
       "      <td>10:59:17.650000</td>\n",
       "      <td>22.5640</td>\n",
       "      <td>121.9260</td>\n",
       "      <td>36.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2008-04-23</td>\n",
       "      <td>18:28:41.880000</td>\n",
       "      <td>22.8810</td>\n",
       "      <td>121.6190</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2008-07-13</td>\n",
       "      <td>14:58:33.010000</td>\n",
       "      <td>21.0140</td>\n",
       "      <td>121.1490</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2009-10-03</td>\n",
       "      <td>17:36:06.120000</td>\n",
       "      <td>23.6300</td>\n",
       "      <td>121.4520</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2009-12-19</td>\n",
       "      <td>13:02:15.570000</td>\n",
       "      <td>23.7960</td>\n",
       "      <td>121.6050</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2010-03-04</td>\n",
       "      <td>00:18:51.290000</td>\n",
       "      <td>22.9180</td>\n",
       "      <td>120.7950</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2013-06-02</td>\n",
       "      <td>05:43:03.800000</td>\n",
       "      <td>23.8318</td>\n",
       "      <td>121.0590</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2013-10-31</td>\n",
       "      <td>12:02:09.350000</td>\n",
       "      <td>23.5436</td>\n",
       "      <td>121.4580</td>\n",
       "      <td>14.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2016-02-05</td>\n",
       "      <td>19:57:27</td>\n",
       "      <td>22.9064</td>\n",
       "      <td>120.6061</td>\n",
       "      <td>18.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>15:50:43.040000</td>\n",
       "      <td>24.1140</td>\n",
       "      <td>121.7269</td>\n",
       "      <td>12.9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2019-04-18</td>\n",
       "      <td>05:01:06.490000</td>\n",
       "      <td>24.0374</td>\n",
       "      <td>121.6501</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date (mm/dd/yr)             Time      Lat       Lon  Depth  Mag  \\\n",
       "0       1964-01-18         12:04:40  23.1000  120.5000   33.0  6.8   \n",
       "1       1965-05-17  17:19:25.900000  22.5000  121.3000   21.0  6.2   \n",
       "2       1970-11-14  07:58:19.800000  22.7110  121.3440   28.0  6.1   \n",
       "3       1972-04-24  09:57:21.700000  23.6380  121.5510   33.0  6.9   \n",
       "4       1972-09-22  19:57:27.400000  22.3310  121.1970   33.0  6.2   \n",
       "6       1972-11-09  18:41:14.500000  23.8850  121.5810   33.0  6.1   \n",
       "7       1978-07-23  14:42:36.900000  22.2820  121.5120   17.0  7.4   \n",
       "8       1978-09-02  01:57:33.400000  24.8960  121.9850  109.0  6.1   \n",
       "9       1986-05-20  05:25:46.960000  24.1250  121.6190   19.3  6.1   \n",
       "10      1986-11-14  21:20:10.550000  23.9010  121.5740   33.8  7.8   \n",
       "11      1986-11-14  23:04:37.090000  23.8660  121.7110   33.0  6.1   \n",
       "12      1992-09-01  16:41:13.350000  23.7490  121.6850   54.4  6.0   \n",
       "13      1994-06-05  01:09:30.150000  24.5110  121.9050   11.4  6.1   \n",
       "14      1996-09-05  23:42:06.150000  21.8980  121.4980   20.0  6.8   \n",
       "15      1999-09-20  17:47:18.490000  23.7720  120.9820   33.0  7.7   \n",
       "20      1999-09-20  21:46:42.870000  23.3900  120.9640   33.0  6.4   \n",
       "21      1999-09-22  00:14:39.150000  23.7290  121.1670   26.0  6.4   \n",
       "22      1999-09-25  23:52:48.660000  23.7380  121.1580   17.0  6.5   \n",
       "23      1999-11-01  17:53:00.120000  23.3780  121.5200   33.0  6.3   \n",
       "24      2000-06-10  18:23:29.320000  23.8430  121.2250   33.0  6.4   \n",
       "25      2002-05-15  03:46:05.760000  24.6360  121.9220   10.0  6.2   \n",
       "26      2003-06-10  08:40:30.830000  23.5200  121.6280   44.5  6.0   \n",
       "27      2003-12-10  04:38:11.590000  23.0390  121.3620   10.0  6.8   \n",
       "28      2004-05-19  07:04:11.710000  22.6620  121.5050   20.0  6.2   \n",
       "29      2006-04-01  10:02:19.570000  22.8680  121.2780    9.0  6.1   \n",
       "30      2006-12-26  12:26:21.140000  21.7990  120.5470   10.0  7.1   \n",
       "32      2007-01-25  10:59:17.650000  22.5640  121.9260   36.2  6.0   \n",
       "33      2008-04-23  18:28:41.880000  22.8810  121.6190   10.0  6.0   \n",
       "34      2008-07-13  14:58:33.010000  21.0140  121.1490   14.0  6.2   \n",
       "35      2009-10-03  17:36:06.120000  23.6300  121.4520   28.0  6.1   \n",
       "36      2009-12-19  13:02:15.570000  23.7960  121.6050   43.0  6.4   \n",
       "37      2010-03-04  00:18:51.290000  22.9180  120.7950   21.0  6.3   \n",
       "38      2013-06-02  05:43:03.800000  23.8318  121.0590   17.0  6.1   \n",
       "39      2013-10-31  12:02:09.350000  23.5436  121.4580   14.8  6.1   \n",
       "40      2016-02-05         19:57:27  22.9064  120.6061   18.9  6.2   \n",
       "41      2018-02-06  15:50:43.040000  24.1140  121.7269   12.9  6.1   \n",
       "42      2019-04-18  05:01:06.490000  24.0374  121.6501   20.0  6.3   \n",
       "43             NaT              NaN      NaN       NaN    NaN  NaN   \n",
       "\n",
       "    Interevent Counts  \n",
       "0                  10  \n",
       "1                  24  \n",
       "2                  81  \n",
       "3                  22  \n",
       "4                  19  \n",
       "6                   9  \n",
       "7                  92  \n",
       "8                  34  \n",
       "9                 207  \n",
       "10                 19  \n",
       "11                  1  \n",
       "12                349  \n",
       "13                 44  \n",
       "14                 83  \n",
       "15                166  \n",
       "20                 23  \n",
       "21                 37  \n",
       "22                 30  \n",
       "23                 62  \n",
       "24                 52  \n",
       "25                131  \n",
       "26                 60  \n",
       "27                 33  \n",
       "28                 65  \n",
       "29                158  \n",
       "30                 54  \n",
       "32                 24  \n",
       "33                 90  \n",
       "34                 34  \n",
       "35                 88  \n",
       "36                 12  \n",
       "37                  9  \n",
       "38                191  \n",
       "39                 17  \n",
       "40                128  \n",
       "41                126  \n",
       "42                109  \n",
       "43                 83  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discarding all those large magnitude earthquakes for which the intervent count is zero\n",
    "earthquakes_final = earthquakes_new[earthquakes_new['Interevent Counts'] != 0]\n",
    "earthquakes_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date (mm/dd/yr)</th>\n",
       "      <th>Time</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Mag</th>\n",
       "      <th>Interevent Counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1964-01-18</td>\n",
       "      <td>12:04:40</td>\n",
       "      <td>23.1000</td>\n",
       "      <td>120.5000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1965-05-17</td>\n",
       "      <td>17:19:25.900000</td>\n",
       "      <td>22.5000</td>\n",
       "      <td>121.3000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1970-11-14</td>\n",
       "      <td>07:58:19.800000</td>\n",
       "      <td>22.7110</td>\n",
       "      <td>121.3440</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1972-04-24</td>\n",
       "      <td>09:57:21.700000</td>\n",
       "      <td>23.6380</td>\n",
       "      <td>121.5510</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1972-09-22</td>\n",
       "      <td>19:57:27.400000</td>\n",
       "      <td>22.3310</td>\n",
       "      <td>121.1970</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1972-11-09</td>\n",
       "      <td>18:41:14.500000</td>\n",
       "      <td>23.8850</td>\n",
       "      <td>121.5810</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1978-07-23</td>\n",
       "      <td>14:42:36.900000</td>\n",
       "      <td>22.2820</td>\n",
       "      <td>121.5120</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1978-09-02</td>\n",
       "      <td>01:57:33.400000</td>\n",
       "      <td>24.8960</td>\n",
       "      <td>121.9850</td>\n",
       "      <td>109.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1986-05-20</td>\n",
       "      <td>05:25:46.960000</td>\n",
       "      <td>24.1250</td>\n",
       "      <td>121.6190</td>\n",
       "      <td>19.3</td>\n",
       "      <td>6.1</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1986-11-14</td>\n",
       "      <td>21:20:10.550000</td>\n",
       "      <td>23.9010</td>\n",
       "      <td>121.5740</td>\n",
       "      <td>33.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1986-11-14</td>\n",
       "      <td>23:04:37.090000</td>\n",
       "      <td>23.8660</td>\n",
       "      <td>121.7110</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1992-09-01</td>\n",
       "      <td>16:41:13.350000</td>\n",
       "      <td>23.7490</td>\n",
       "      <td>121.6850</td>\n",
       "      <td>54.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1994-06-05</td>\n",
       "      <td>01:09:30.150000</td>\n",
       "      <td>24.5110</td>\n",
       "      <td>121.9050</td>\n",
       "      <td>11.4</td>\n",
       "      <td>6.1</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1996-09-05</td>\n",
       "      <td>23:42:06.150000</td>\n",
       "      <td>21.8980</td>\n",
       "      <td>121.4980</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>17:47:18.490000</td>\n",
       "      <td>23.7720</td>\n",
       "      <td>120.9820</td>\n",
       "      <td>33.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1999-09-20</td>\n",
       "      <td>21:46:42.870000</td>\n",
       "      <td>23.3900</td>\n",
       "      <td>120.9640</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1999-09-22</td>\n",
       "      <td>00:14:39.150000</td>\n",
       "      <td>23.7290</td>\n",
       "      <td>121.1670</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1999-09-25</td>\n",
       "      <td>23:52:48.660000</td>\n",
       "      <td>23.7380</td>\n",
       "      <td>121.1580</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1999-11-01</td>\n",
       "      <td>17:53:00.120000</td>\n",
       "      <td>23.3780</td>\n",
       "      <td>121.5200</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2000-06-10</td>\n",
       "      <td>18:23:29.320000</td>\n",
       "      <td>23.8430</td>\n",
       "      <td>121.2250</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2002-05-15</td>\n",
       "      <td>03:46:05.760000</td>\n",
       "      <td>24.6360</td>\n",
       "      <td>121.9220</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2003-06-10</td>\n",
       "      <td>08:40:30.830000</td>\n",
       "      <td>23.5200</td>\n",
       "      <td>121.6280</td>\n",
       "      <td>44.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2003-12-10</td>\n",
       "      <td>04:38:11.590000</td>\n",
       "      <td>23.0390</td>\n",
       "      <td>121.3620</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2004-05-19</td>\n",
       "      <td>07:04:11.710000</td>\n",
       "      <td>22.6620</td>\n",
       "      <td>121.5050</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>10:02:19.570000</td>\n",
       "      <td>22.8680</td>\n",
       "      <td>121.2780</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2006-12-26</td>\n",
       "      <td>12:26:21.140000</td>\n",
       "      <td>21.7990</td>\n",
       "      <td>120.5470</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2007-01-25</td>\n",
       "      <td>10:59:17.650000</td>\n",
       "      <td>22.5640</td>\n",
       "      <td>121.9260</td>\n",
       "      <td>36.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2008-04-23</td>\n",
       "      <td>18:28:41.880000</td>\n",
       "      <td>22.8810</td>\n",
       "      <td>121.6190</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2008-07-13</td>\n",
       "      <td>14:58:33.010000</td>\n",
       "      <td>21.0140</td>\n",
       "      <td>121.1490</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2009-10-03</td>\n",
       "      <td>17:36:06.120000</td>\n",
       "      <td>23.6300</td>\n",
       "      <td>121.4520</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2009-12-19</td>\n",
       "      <td>13:02:15.570000</td>\n",
       "      <td>23.7960</td>\n",
       "      <td>121.6050</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2010-03-04</td>\n",
       "      <td>00:18:51.290000</td>\n",
       "      <td>22.9180</td>\n",
       "      <td>120.7950</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2013-06-02</td>\n",
       "      <td>05:43:03.800000</td>\n",
       "      <td>23.8318</td>\n",
       "      <td>121.0590</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2013-10-31</td>\n",
       "      <td>12:02:09.350000</td>\n",
       "      <td>23.5436</td>\n",
       "      <td>121.4580</td>\n",
       "      <td>14.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2016-02-05</td>\n",
       "      <td>19:57:27</td>\n",
       "      <td>22.9064</td>\n",
       "      <td>120.6061</td>\n",
       "      <td>18.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>15:50:43.040000</td>\n",
       "      <td>24.1140</td>\n",
       "      <td>121.7269</td>\n",
       "      <td>12.9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2019-04-18</td>\n",
       "      <td>05:01:06.490000</td>\n",
       "      <td>24.0374</td>\n",
       "      <td>121.6501</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date (mm/dd/yr)             Time      Lat       Lon  Depth  Mag  \\\n",
       "0       1964-01-18         12:04:40  23.1000  120.5000   33.0  6.8   \n",
       "1       1965-05-17  17:19:25.900000  22.5000  121.3000   21.0  6.2   \n",
       "2       1970-11-14  07:58:19.800000  22.7110  121.3440   28.0  6.1   \n",
       "3       1972-04-24  09:57:21.700000  23.6380  121.5510   33.0  6.9   \n",
       "4       1972-09-22  19:57:27.400000  22.3310  121.1970   33.0  6.2   \n",
       "5       1972-11-09  18:41:14.500000  23.8850  121.5810   33.0  6.1   \n",
       "6       1978-07-23  14:42:36.900000  22.2820  121.5120   17.0  7.4   \n",
       "7       1978-09-02  01:57:33.400000  24.8960  121.9850  109.0  6.1   \n",
       "8       1986-05-20  05:25:46.960000  24.1250  121.6190   19.3  6.1   \n",
       "9       1986-11-14  21:20:10.550000  23.9010  121.5740   33.8  7.8   \n",
       "10      1986-11-14  23:04:37.090000  23.8660  121.7110   33.0  6.1   \n",
       "11      1992-09-01  16:41:13.350000  23.7490  121.6850   54.4  6.0   \n",
       "12      1994-06-05  01:09:30.150000  24.5110  121.9050   11.4  6.1   \n",
       "13      1996-09-05  23:42:06.150000  21.8980  121.4980   20.0  6.8   \n",
       "14      1999-09-20  17:47:18.490000  23.7720  120.9820   33.0  7.7   \n",
       "15      1999-09-20  21:46:42.870000  23.3900  120.9640   33.0  6.4   \n",
       "16      1999-09-22  00:14:39.150000  23.7290  121.1670   26.0  6.4   \n",
       "17      1999-09-25  23:52:48.660000  23.7380  121.1580   17.0  6.5   \n",
       "18      1999-11-01  17:53:00.120000  23.3780  121.5200   33.0  6.3   \n",
       "19      2000-06-10  18:23:29.320000  23.8430  121.2250   33.0  6.4   \n",
       "20      2002-05-15  03:46:05.760000  24.6360  121.9220   10.0  6.2   \n",
       "21      2003-06-10  08:40:30.830000  23.5200  121.6280   44.5  6.0   \n",
       "22      2003-12-10  04:38:11.590000  23.0390  121.3620   10.0  6.8   \n",
       "23      2004-05-19  07:04:11.710000  22.6620  121.5050   20.0  6.2   \n",
       "24      2006-04-01  10:02:19.570000  22.8680  121.2780    9.0  6.1   \n",
       "25      2006-12-26  12:26:21.140000  21.7990  120.5470   10.0  7.1   \n",
       "26      2007-01-25  10:59:17.650000  22.5640  121.9260   36.2  6.0   \n",
       "27      2008-04-23  18:28:41.880000  22.8810  121.6190   10.0  6.0   \n",
       "28      2008-07-13  14:58:33.010000  21.0140  121.1490   14.0  6.2   \n",
       "29      2009-10-03  17:36:06.120000  23.6300  121.4520   28.0  6.1   \n",
       "30      2009-12-19  13:02:15.570000  23.7960  121.6050   43.0  6.4   \n",
       "31      2010-03-04  00:18:51.290000  22.9180  120.7950   21.0  6.3   \n",
       "32      2013-06-02  05:43:03.800000  23.8318  121.0590   17.0  6.1   \n",
       "33      2013-10-31  12:02:09.350000  23.5436  121.4580   14.8  6.1   \n",
       "34      2016-02-05         19:57:27  22.9064  120.6061   18.9  6.2   \n",
       "35      2018-02-06  15:50:43.040000  24.1140  121.7269   12.9  6.1   \n",
       "36      2019-04-18  05:01:06.490000  24.0374  121.6501   20.0  6.3   \n",
       "37             NaT              NaN      NaN       NaN    NaN  NaN   \n",
       "\n",
       "    Interevent Counts  \n",
       "0                  10  \n",
       "1                  24  \n",
       "2                  81  \n",
       "3                  22  \n",
       "4                  19  \n",
       "5                   9  \n",
       "6                  92  \n",
       "7                  34  \n",
       "8                 207  \n",
       "9                  19  \n",
       "10                  1  \n",
       "11                349  \n",
       "12                 44  \n",
       "13                 83  \n",
       "14                166  \n",
       "15                 23  \n",
       "16                 37  \n",
       "17                 30  \n",
       "18                 62  \n",
       "19                 52  \n",
       "20                131  \n",
       "21                 60  \n",
       "22                 33  \n",
       "23                 65  \n",
       "24                158  \n",
       "25                 54  \n",
       "26                 24  \n",
       "27                 90  \n",
       "28                 34  \n",
       "29                 88  \n",
       "30                 12  \n",
       "31                  9  \n",
       "32                191  \n",
       "33                 17  \n",
       "34                128  \n",
       "35                126  \n",
       "36                109  \n",
       "37                 83  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earthquakes_final = earthquakes_final.reset_index()\n",
    "earthquakes_final = earthquakes_final.drop(columns = ['index'])\n",
    "earthquakes_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "# After final preprocessing of the dataset, we convert the dataset into an excel file\n",
    "earthquakes_final.to_excel('Dataset_Taiwan_Preprocessed.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      10\n",
       "1      24\n",
       "2      81\n",
       "3      22\n",
       "4      19\n",
       "5       9\n",
       "6      92\n",
       "7      34\n",
       "8     207\n",
       "9      19\n",
       "10      1\n",
       "11    349\n",
       "12     44\n",
       "13     83\n",
       "14    166\n",
       "15     23\n",
       "16     37\n",
       "17     30\n",
       "18     62\n",
       "19     52\n",
       "20    131\n",
       "21     60\n",
       "22     33\n",
       "23     65\n",
       "24    158\n",
       "25     54\n",
       "26     24\n",
       "27     90\n",
       "28     34\n",
       "29     88\n",
       "30     12\n",
       "31      9\n",
       "32    191\n",
       "33     17\n",
       "34    128\n",
       "35    126\n",
       "36    109\n",
       "Name: Interevent Counts, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removing final interevent count as that is responsible for finding EPS Score of the country of Taiwan\n",
    "from scipy import stats\n",
    "interevent_counts_final = earthquakes_final['Interevent Counts'][:37]\n",
    "interevent_counts_final "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generating best fit curves of candidate distributions on the histogram plot of Interevent Counts Data\n",
    "\n",
    "def make_pdf(dist, params, size=10000):\n",
    "    \"\"\"Generate distributions's Probability Distribution Function \"\"\"\n",
    "\n",
    "    # Separate parts of parameters\n",
    "    arg = params[:-2]\n",
    "    loc = params[-2]\n",
    "    scale = params[-1]\n",
    "\n",
    "    # Get sane start and end points of distribution\n",
    "    start = dist.ppf(0.01, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.01, loc=loc, scale=scale)\n",
    "    end = dist.ppf(0.99, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.99, loc=loc, scale=scale)\n",
    "\n",
    "    # Build PDF and turn into pandas Series\n",
    "    x = np.linspace(start, end, size)\n",
    "    y = dist.pdf(x, loc=loc, scale=scale, *arg)\n",
    "    pdf = pd.Series(y, x)\n",
    "    return pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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feg01atTI9ebWDz/8kHnz5jF79mwaN26Ms7MzH330UbYVOh587x4mr1iVUg/9XMHDP1tKKTp37oy3tzfz58+nSpUqGAwGmjVrlm3sOnfuzPLly1m1apXNijSZMS1cuJCAgIBsMWSWJOXXo7xHZrOZnj17Mn369Gz7sr5vDg4ONuOmlEIpxfvvv0+/fv2yHVu5cmXA8stxvXr1WLp0KZ988gnLli1jyJAh1r7MZjO+vr58++232fp48EbyrNeX9fiCqFu3LmBJwr29vfNsm9PnJOvnJ7NcJj//D3qU96ZFixZcunSJTZs28dtvv/H666/zwQcfsGvXLhwcHHI8ZtiwYXz55ZecPXuW+Ph4zp8/z5AhQ6z7x4wZQ69evdi4cSO//fYbgYGBDBs2jPnz5+cZS36dOHECOzs7qlWrxrVr14D8fb4fNj6ffvopI0eOZOPGjWzZsoUPPviADz74gClTphQovtdee43Bgwfz2WefsWjRItq3b//Qz4EQmSRJF+KeevXqsWfPHptltfbu3WvTpkKFCqSlpREXF2ddDeXw4cP56t/Pz49jx45Rq1atfCVtpWH79u2MGDHCuopM5ix/w4YNcz3Gx8cHT09PZsyYgYODAw0bNrSO0YIFC2jQoIF1hv/YsWPcvn2b6dOnW2eWN23aVPwXds/27dvp06cPQ4cOtW7L77cYBoMhW918fjRo0IDLly8TExND1apVAYiNjc02+12hQgWuXLlifW02mzl69Kh1Lf/Lly9z8eJFli5daq2pPnfuXI716N27d6d///7069eP9PR0az3uc889h7u7O+fPn2fw4MEFvpaCymnM/Pz8+OWXX6hevTp2dnb57kuv19OkSRNOnTrFpEmT8mw7ZMgQvvzyS3r37s3Zs2dt3m8/Pz/WrVuHu7s75cqVK9gFZZGZ5D3sM9GyZUtq1arFJ598Qq9evbLVpd++fZsyZcrQoEEDMjIy2L17t3XW32w2s2vXLjp37gzcX1HkypUrNG3aFLB8OxIfH/9I8ecUu6urKwMGDGDAgAGMHz8eb29v9u7da/1F+0EBAQHUrVuXpUuXcuvWLev9Bll5eXkxevRoRo8ezaJFi3jttdeYN29eoVe0iYuLY9GiRXTv3h0nJ6ci/3x7e3vj7e3N2LFjeeedd5g/f36uSXpu49mjRw/Kli3LggULCAkJ4X//+1+h4xLPDil3EeKeiRMn8u233/Lf//6XiIgIFi9ebJ1ty0yqW7RogZOTE5MnTyYiIoINGzbw0Ucf5av/qVOncvz4cYYNG8aBAweIiopi69atjB07Ns9ykvj4eMLDwzl69Chg+Uc5PDyc6Ohoa5sff/wRHx+fAn8V/6C6deuycuVKDh8+zIkTJxg+fDi3bt166HEdOnRgyZIl1n/IDQYDrVu3ZsmSJTalLjVr1kSv1zNnzhwiIyMJCwtj8uTJhYr5QdevX+fatWs2P8nJydbr27RpEzt37uTMmTNMnDiREydO5KvfGjVqsH//fqKiorh582aeZQBZ9ejRg1q1ajF48GAOHTrEoUOHGDx4MPb29jbtgoODWbp0qfUGu7///e/cuHHDur9SpUq4uroyf/58IiIi2LlzJ8OGDcu1LKdLly6sXbuWd955xzprrdPp+PDDD5k+fTozZszg1KlTnDlzhpUrVzJ27Nh8XU9B1KhRgx07dhATE8PNmzdRSjFhwgRu3bpF//792b17NxcvXmTHjh1MnjyZI0eO5NnfRx99xPLly5kyZQpHjx7l/Pnz/PLLLzZJOFiS9NjYWF5//XVatWpl823aiBEj8PDwoEePHmzZsoWLFy+yd+9ePvzwwwKtp+/p6YnRaCQsLIw//viD27dv59hOp9OxdOlSTp06RevWrVm7di0RERGcPn2aefPm0bhxY0wmE02aNKFr166MGjWKrVu3curUKV577TUuXbrEhAkTAMs3bk2aNOHjjz/mxIkT7N+/nxEjRhSoNCtTTu/NtGnTCAkJ4fTp01y4cIHFixdjMBge+m3k0KFDWbZsGSEhIdnei1dffZVNmzYRGRnJ8ePHWbNmDd7e3tYE/c0337Te1JyX1NRUrl27xpUrVzhx4gQLFy7E398fg8HAnDlzrGNdFJ/vGzduMG7cOLZt28bFixetN/rXr18/12NyGs/MmEaOHMnUqVMxGo05fgskRG4kSRfinhdeeIGPP/6Yjz76iEaNGhESEsK//vUvAGtC5eHhwXfffcfOnTtp1KgR06ZNY8aMGfnqv2HDhuzatYvbt2/TsWNH6tevzyuvvEJaWhplypTJ9biffvqJpk2b4ufnB1gewtS0aVPef/99a5vbt29z9uzZXEtv8uuLL77Azc2NNm3a0LlzZxo2bEjXrl0felxQUBAZGRk2CXlgYGC2bV5eXnzzzTf89NNP1K9fn3feeafIn9pXt25dKleubPOzZMkSwJLk+fr60rVrV9q2bYtSitGjR+er3ylTpmAwGGjYsCHly5fP91NG9Xo9a9euRavV0qpVK/r27ctLL71kLYPI9H//93+0b9+evn370qFDB5577jmbsTcajaxYsYLw8HAaNmzIq6++yjvvvJPnuvVBQUGsX7+ejz76iA8//BCA119/ncWLFxMaGoqvry8BAQF8/PHHNjXzReXjjz8mOjqa2rVrU758eeLi4vDy8mLPnj04ODjQs2dP6taty9ChQ7l27dpD157u3r07GzduZM+ePbRu3ZqmTZsyefJkypcvb9OuatWqBAYGEh4eni1pdHFxYefOndSrV4+XX36ZOnXqMGDAAI4dO1agMbC3t2fOnDl89dVXVKlShTZt2uTatlWrVhw5coR69erx97//nfr16xMcHMyGDRv43//+Z01Yv/32W1q3bs3AgQNp2rQpx44dY+PGjTb3LyxfvhylFP7+/gwdOpSJEyc+0rMLcnpvnJ2d+eSTT/D39+cvf/kLW7ZsYc2aNQ8dl8xfipKSknjhhRds9qWnp/P3v/+dBg0a0KFDB7RaLWvWrLHuj42Nzde9EBs3bqRy5cpUq1aNdu3asXjxYkaNGsXRo0dtyliK4vNtNBq5evUqw4YNo27dunTv3p3atWvn+RC3nMYz0+jRo0lPT8/zl2ohcqJRDxa3CSGs3n33XRYsWFDoGWohHuTn50ebNm3k0eJCPOX2799PQEAAp0+ftpavCZEfUpMuxD0pKSl8/vnndOnSBUdHR7Zu3cqsWbNsnsophBBC5EdKSgoxMTG88847dO/eXRJ0UWCSpAtxj0ajYcuWLfz73/8mISGBGjVq8O677zJx4sTSDk0IIcQTZtGiRbz55ps0btyYVatWlXY44gkk5S5CCCGEEEI8ZuTGUSGEEEIIIR4zkqQLIYQQQgjxmJEkXQghhBBCiMfMM3njaNan+j0oKkpHmzYVqVIlgx17oqn5dU3stHZcHHkRAI3mTypXrofZ7Mi1a/l7UqGw8PDw4ObNm6UdxjNLxr90yfiXHhn70iXjX7pk/EuPp6dnoY6XmfQHeHmZ0OsVsbF6MlItj6xON6dbnx6mlAtKGdFqk9BokkozVCGEEEII8ZSSJP0Bej1Ur2553PflSwb0GsuXDenmzCc5ajCZPADQam/k1IUQQgghhBCFUmLlLuHh4XzzzTeYzWaCgoLo06ePzf709HTmzZtHZGQkLi4ujBs3jgoVKpCQkMCsWbOIiIigffv2jBw5Mlvf06dP5/r16/znP/8pklhr1jRx4YIdkZF67HR2ZGRkkG5Ox6AzAGA2lwdi0WpvYDJVz7szIYQQQgghCqhEknSz2cxXX33FO++8g7u7O1OmTMHPz4+qVata22zduhUnJyfmzp3Lrl27+Pbbbxk/fjx2dnYMGjSIy5cvEx0dna3vffv2YW9vX6Tx1qxpmUmPjNRjLGMkOSOZNFMaTnZO967HMpOu090kPT3XboQQQgghSoxSipSUFMxmMxqNBoA//viD1NTUUo7s6aWUQqvVYm9vbx3zolIiSXpERASVKlWiYsWKALRq1YoDBw7YJOkHDx5k4MCBALRo0YKvv/4apRT29vb4+Phw7dq1bP2mpKSwbt06Xn31VWbPnl1k8WZN0u18LXXpaeY0636TqQIg5S5CCCGEeHykpKRgZ2eHXn8/vdPr9eh0ulKM6umXkZFBSkoKDg4ORdpvidSkx8fH4+7ubn3t7u5OfHx8rm10Oh2Ojo4kJCTk2e8PP/xAz549MRgMRRqvTZKuvXfzqOn+lHnmTLpWK3dLCyGEEOLxYDabbRJ0UTL0ej1ms7no+y3yHnOQuTJKVg9+JZCfNlldvHiRa9euMXz4cK5fv57n+Tdv3szmzZsB+PTTT/Hw8MizvZ+f5b+RkXaU0VtKaZzKOOHhlpmcP2fZ5pSAvX3efYn79Hr9Q8deFB8Z/9Il4196ZOxLl4x/yTGZTDkm6ZK4Fz97e/si/5yXyLvm7u5OXFyc9XVcXBzlypXLsY27uzsmk4mkpCScnZ1z7fPcuXNERUUxZswYTCYTd+7c4b333uO9997L1jY4OJjg4GDr64etF2pnB2XKVCI+XkuZhIpAJNdvXqec2RKzvb0Dbm6QlhbNrVsym55fslZr6ZLxL10y/qVHxr50yfiXnNTU1GylLXq9noyMjFKK6NmRmpqa7XP+RKyTXqtWLa5evcr169fJyMhg9+7d+GVOV9/j6+vLtm3bANi7dy8NGjTIcya9U6dOLFiwgC+++IIPPvgAT0/PHBP0R6HRQN26lvIW8x/1gaxLMGau7iI16UIIIYQQoniUSJKu0+kYMWIE06ZNY/z48bRs2RIvLy9CQkI4ePAgAIGBgdy9e5exY8eybt06Bg8ebD1+zJgxLFmyhG3btvHaa68RExNT7DHXqWP5rTPjmg9ge+NoZpKu00mSLoQQQgiRaeXKlXTv3p2OHTsyadIkYmJiaN26NfHx8ZjNZvr27cvvv/9OdHQ07dq148033yQ4OJjRo0eTnJwMwI4dO+jUqRNBQUFMmDDBujpNQEAAM2fOpHPnzgQFBREREVGal1rsSqxIqVmzZjRr1sxm26BBg6x/NhgMTJgwIcdjv/jiizz7rlChQpGtkZ7Jx8cyc552rTY0gjRT1tVd5MZRIYQQQjy+PKtUKZZ+r8TG5rrv/Pnz/Pzzz6xevRo7OzumTJnCnj17GDNmDJMnT6ZZs2Z4e3vz/PPPEx0dzYULF/jPf/5D8+bNmTBhAkuWLGH48OGMHz+ekJAQatWqxRtvvMHSpUsZPXo0AG5uboSFhbF48WLmz5/PzJkzi+U6HwfP5BNHNUlJOISG4pzHso1161pm0lOu1gZsZ9KVKoNSBrTau0ByscYqhBBCCPEk2LlzJ8ePH6dbt2507NiRnTt3cvnyZV566SUSExNZtmwZ7777rrW9p6cnzZs3B6Bfv37s37+fCxcuUK1aNWrVqgXAwIED2bdvn/WYrl27AtC4ceMcn5/zNHkmb/fVJCVRdsIE0OlIHDECVaZMtjaZSXrSlRqgbJdgBA0mkwd6/RV0upuYTF4lFLkQQgghxMNlzniX5I2jSikGDhzIlClTbLYnJydz9epVABITE60Lgzx476FGo8lxtb+sjEYjYCmlNplMRRX6Y+mZnEk3e3iQ1qIFmvR07MPCcmzj7m7Gw8OEKcUJ7njZ3DgKYDZbHsyk1WZ/yJIQQgghxLOmTZs2rFu3zrrKya1bt4iJiWHatGn07duXf/zjH7z11lvW9rGxsdZ7E9esWUPz5s2pXbs20dHRREVFAZYa9xYtWpT8xTwGnskkHSC5Z08AHNaty7VN5s2j3GhgU5MOYDJZknSd7o/iCVAIIYQQ4glSp04dJk2axIsvvkhwcDAvvvgi0dHRhIeHM2bMGPr164fBYCAkJAQAb29vQkNDCQ4O5vbt2wwbNgx7e3tmzZrFq6++SlBQEFqtliFDhpTylZWOZ7LcBSClWzfU1KkYt29Hc+dOjiUvPj7p7N5thOsNbWrSAczmSoAk6UIIIYQQmXr37k3v3r1ttq3LMiG6aNEiAKKjo9FqtUyfPj1bH23btmXTpk3ZtmetTW/SpAk//vhjUYX9WHpmZ9LNHh6ktWyZZ8mLdSb9esNcZ9K1WknShRBCCCFE0Xpmk3SA5B49AHBYu8GZtJ8AACAASURBVDbH/fXq3atDv/YXUk2pNvvul7tcLb4AhRBCCCGeQl5eXmzdurW0w3isPdNJekq3biitFuOOHWhu3862v379DNCY4UZ9EpNt74yWchchhBBCCFFcntmadLhf8mLctQv7sDCSszxcCcDRUVGuynVuxVQiNqoc+N3fl99yl23n0vPcX5Ta17ErsXMJIYQQQoji80zPpEOWkpdcVnmpWPMKAFfOV7DZbjLJTLoQQgghhCgez3yS/rCSF8/aliT86oXKNtstTx21R6tNQKNJLJFYhRBCCCHEs+GZT9IftspLldo3ALgRWeWBPZosJS/yQCMhhBBCCFF0nvkkHfIuealeNx6AuEtVefDps/JAIyGEEEIIURye6RtHM1kfbHSv5EWVLWvd51ZOgetlTH9WIypKT+3a91d5MZslSRdCCCGEyDR79mxWrVqFp6cnbm5uNG7cGBcXF7799lvS0tKoUaMGn3/+OQ4ODowbNw57e3siIiKIjY1l1qxZhIaGcujQIZo2bcpnn30GWJ5MOnz4cHbs2EGZMmV4++23mTZtGrGxsbz//vt06tSJ6Oho3njjDZKSkgD46KOPaN68eWkORaFJkk7eq7wYdUaofAT+rMaJE3Y2SXrmzaPyQCMhhBBCPE48PR8s0y0aV67E5rrv6NGjrF+/nrCwMEwmE507d6Zx48Z07dqVwYMHAzB9+nS+//57RowYAcCdO3cIDQ1l06ZNDB8+nNWrVzNz5ky6devGiRMnaNiwIUlJSbRs2ZKpU6cycuRIZsyYwffff8+5c+cYN24cnTp1wsPDg++//x57e3siIyMZM2YMGzZsKJYxKCmSpN+T3LMnxl27cFi3ziZJN+gMUOkInO3N8eN29OmTbN13f4UXeaCREEIIIZ5t+/fvp3Pnzjg4OADQsWNHAM6ePcuMGTP4888/SUxM5Pnnn7ce07FjRzQaDT4+Pnh4eFCvXj0A6tSpQ0xMDA0bNsRgMNChQwcAfHx8MBgM2NnZUa9ePWJiYgBIT09n6tSpnDp1Cq1WS2RkZEleerGQJP2elK5dUf/8Z7aSF8tM+mEAjh2zXYdcyl2EEEII8TjKnPHW6/VkZGQ8pHXRUErluH38+PF89dVXNGjQgJCQEPbs2WPdZzAYANBqtRiNRut2rVZrjVuv16PRaLK1y9pm4cKFlC9fnl9//RWz2UzNmjWL/gJLmNw4ek9uq7wYdAaosh+wJOlZbx7N7wONhBBCCCGedv7+/vz666+kpKSQmJjIli1bALh79y4VK1YkPT2dVatWFcu5//zzTypUqIBWq2XlypWYHlzt4wkkSXoWyT17AuCwdq11m73OHlz+wOB2lbt3tURE3P/yQR5oJIQQQghh8Ze//IVOnTrRsWNHRo0aRZMmTXBxceGtt96iR48evPjii9SuXbtYzj1s2DB+/PFHevToQWRkJI6OjsVynpKkUbl9N/EUu3LlSo7btXFxVGzaFIA/jhzB7O7O0RtH6ba6G65rwvjzSCf+859b/PWvlrp0jSaJypW9UcrI1asXAE22PredSy+263hQ+zp2D29Uijw8PLh582Zph/HMkvEvXTL+pUfGvnTJ+JecpKSkbMlpSZa7ACQmJuLk5ERycjL9+vVjxowZNGrUqMTOX1pyGntPT89C9Skz6VmY3d1JbdcOjcmE/b010406S92TwesIAEeOGKztlXLEbC6LRpOKVhtX8gELIYQQQjxGJk2aRMeOHencuTPdunV7JhL04iI3jj4guXdv7H/7DYc1a0gaNsxSkw7oqx0CbJN0AJPJE632NjpdLGazR4nHK4QQQgjxuPjiiy9KO4SnhsykPyClSxeUvT3GffvQxsZaZ9LNlQ+i0ynOnNGTnHy/rMVksqxDqtPlXEIjhBBCCCFEQUmS/gDl4kJKYCBguYE0M0nP0N2hbt0MTCYNx4/fr/02mSz1Rjpd7ov7CyGEEEIIURCSpOcguU8fABxWr7Ym6ammVJo2TQPg8OGsSXrmTLok6UIIIYQQomhIkp6DlMBAzM7OGI4fx+GS5UlWaaY0mjWzJOnh4ffr0u/PpEu5ixBCCCGEKBqSpOfEwYGULl0AKLN2PQDp5nQaN0kFZCZdCCGEEEIUL0nSc5FZ8uK4eo215KVazURcXc3Exuq5csUydPeT9KulE6gQQgghxBPsH//4B+fOnQPA29u7QMfu3r2boUOHAhASEsLUqVMLHc+QIUO4c+dOofspLEnSc5Hapg0mNzfsLlzAeG+lygxS8fOzlLwcOGApeTGZKqKUFq32DyCttMIVQgghhHgizZw5kzp16pR2GFbLli2jTJkypR2GrJOeKzs7Unr0wGnpUoxpZtBZ6tKbN09j61Z79u830rt3CqDHbK6ITncVne4PTCav0o5cCCGEEM+4KgurFEu/saNzL+/973//i9FoZOTIkfzf//0fp06dIjQ0lB07drBixQoGDBjAzJkzSUtLo3r16syePRsnJycGDBjAv/71L5o0aQLA+++/z+7duylTpgxffvkl7u7uNm3i4+Pp2rUr+/btK1Ds48aNw97enoiICGJjY5k1axahoaEcOnSIpk2b8tlnnwEQEBDAhg0bSExM5OWXX8bf35+DBw9SqVIlvv76axwcHB59AAtAZtLzYF3lJckyQ55qSiUgwPLn/ftzunlU6tKFEEII8WwKCAiwJs7Hjh0jKSmJ9PR0Dhw4gI+PD3PmzCEkJISwsDCaNGnC//73v2x9JCUl0ahRI8LCwmjZsiWzZs0q0hjv3LlDaGgo7733HsOHD2f06NH89ttvnDlzhhMnTmRrHxUVxbBhw/jtt99wdXVl/fr1RRpPXmQmPQ9pzZtjqlwZY5ql3jzVlEqTJmkYDIrTp/XcuaOhTBl1ry79kCTpQgghhHgsZM546/V6MjIySuScjRs35vjx49y9exeDwUCjRo04evQo+/bto1OnTpw7d47evXsDkJ6ejq+vb7Y+tFotvXr1AqBfv36MGjWqSGPs2LEjGo0GHx8fPDw8qFevHgB16tQhJiaGhg0b2rT38vKybmvcuDHR0dFFGk9eJEnPi1ZLcu/e2GfMByxJur09NG6czsGDBg4dMhAYmCpPHRVCCCHEM8/Ozo6qVasSEhKCn58f9erVY/fu3Vy6dAkvLy/atWvHf//73wL1qdFYnvKu0+kwm80ApKSkPHKMBoOlEkKr1WI0Gq3btVptjr/MZG2j0+kKde6CknKXh0ju0wfjvfcsLS0ZAH9/y1KMmSUvUu4ihBBCCAEtWrRg/vz5BAQEEBAQwLJly2jQoAG+vr4cOHCAqKgoAJKTk7lw4UK2481mM7/88gsAq1atwt/fH7DMaB87dgzAuv9pJ0n6Q6Q3bIhRbw+AOfwAAM2bP7jCi8ykCyGEEEL4+/tz/fp1/Pz8KF++PEajEX9/f9zd3Zk9ezZjxowhODiYnj175pikOzo6cvbsWbp06cKuXbsYP348AK+99hrLli2jV69exMfHl/RllQqNUkqVdhAl7cqVgiXTL33Zkt/1l/k5sjW+n6zg1i0NDRtWxmhUnD59FWfn45Qv34X0dB9u3Nhic+y2c+lFGXqe2texe3ijUuTh4cHNmzdLO4xnlox/6ZLxLz0y9qVLxr/kJCUl4ejoaLOtJGvSn2U5jb2np2eh+pSZ9Hywq1QVAPPhA2gSEylXTlG3bjqpqRqOHbMjI8OyX6eLBp6533mEEEIIIUQRkyQ9HwzOZQFIN6dhv2EDcL/kZe9eI0qVxWx2QatNRKu9VWpxCiGEEEI86+bMmUPHjh1tfubMmVPaYRWYrO6SD0ad5c7eFD04/PQTyQMG0KpVKsuXO7F7t4GxYzWYTNXQak+i013CbHYr5YiFEEIIIZ5Nb775Jm+++WZph1FoMpOeDwad5QbRFIMO444daP/4g1at7j/UKDUVMjKqAaDTXS61OIUQQgghxNNBkvR8yJxJT2hQB43ZjMOqVZQvb8bHJ52UFC1HjhgwmbwA0OtLbpF7IYQQQgjxdJIkPR8yZ9ITm1ieOOW4ciUArVtb1kvftcsoM+lCCCGEEKLISJKeD/Y6yzrpd2tVw1y2LHanTqE/fZrWrS0lL7t2GTCZLEm6Xi9JuhBCCCHE4+7atWuMHj0agJCQEKZOnVrKEdmSJD0fMmfS0zRmknv0ACw3kAYEpKLRKA4fNpCQ8ByQuQyjEEIIIYR4nFWqVImFCxeWdhi5kiQ9HzJr0lMzUkkeMAAAx59+oqxLBo0apZOermHv3loA6HQxgKm0QhVCCCGEKDUrV66ke/fudOzYkUmTJhETE0Pr1q2Jj4/HbDbTt29ffv/9d6Kjo2nXrh1vvvkmwcHBjB49muTkZAB27NhBp06dCAoKYsKECaSmWsqLAwICmDlzJp07dyYoKIiIiAgAgoKCuHPnDkopGjRoQGhoKABjx45l+/btmEwmPvzwQ7p160ZwcDDLli0DIDo6msDAQGvsV65cYfDgwbRt25ZZs2aV5LDlSJL0fMicSU81p5Lm50dGtWrorl3DsHt3lpIXF0ymCmg0Geh0V0szXCGEEEI846pU8aRKFU8qVqxg/XNR/OTl/Pnz/Pzzz6xevZpff/0VnU7Hnj17GDNmDJMnT2bBggV4e3vz/PPPA3DhwgVefvllNm/ejIuLC0uWLCElJYXx48fz5ZdfsmXLFjIyMli6dKn1HG5uboSFhTFkyBDmz58PgJ+fHwcOHODs2bNUr16d/fv3A3D48GF8fX35/vvvcXFxYf369fzyyy989913XL6cvTw5PDycuXPnsmnTJtatW8fRo0eL6u14JJKk50PWmXQ0GpL79QMsN5BmvXk0c4UXuXlUCCGEEM+anTt3cvz4cbp160bHjh3ZuXMnly9f5qWXXiIxMZFly5bx7rvvWtt7enrSvHlzAPr168f+/fu5cOEC1apVo1YtS4XCwIED2bdvn/WYrl27AtC4cWOioy0lxgEBAezbt4+9e/cydOhQTp8+zdWrVylXrhxOTk78/vvv/Pjjj3Ts2JEePXpw69YtoqKissXftm1b3NzccHBwoGvXrtZkv7TIw4zyITNJTzNbZs2T+vfH5bPPsF+/noB37qDXu3H0qB03b9bD0/OQ1KULIYQQolTFxl4BQK/Xk5GRUSLnVEoxcOBApkyZYrM9OTmZq1ctVQaJiYk4OzsDoNFobNppNBqUUnmew2i05GQ6nQ6TyVJeHBAQwOLFi6latSqTJ09mw4YN/PLLL/j7+1uP++ijj2jfvr1NX5lJftbz5/W6pMlMej5YZ9JNlllzU82apDVtijYxEfedG2nWLA2zWcO2be0BWeFFCCGEEM+eNm3asG7dOm7evAnArVu3iImJYdq0afTt25d//OMfvPXWW9b2sbGxHDx4EIA1a9bQvHlzateuTXR0tHWme+XKlbRo0SLP81apUoX4+HiioqKoXr06/v7+zJ8/n4CAAACef/55li5dSnp6OmAps0lKSsrWz44dO7h16xbJycmEhYVZZ/lLiyTp+WBT7nJP0r0bSB1WruT55y3bN22yvJlS7iKEEEKIZ02dOnWYNGkSL774IsHBwbz44otER0cTHh7OmDFj6NevHwaDgZCQEAC8vb0JDQ0lODiY27dvM2zYMOzt7Zk1axavvvoqQUFBaLVahgwZ8tBzN23alJo1awLg7+/PtWvXrEn2Sy+9hLe3N126dCEwMJDJkyfn+O1C8+bNeeONN+jUqRPdunWjSZMmRTg6BadRD/te4Sl05cqVArXfGr2VIRuH0L5qe77t+i0A2vh4KjZtCkoRtuQkXV+ui5dXIpcuOZOe7sfNm2sA2HYuvcjjz037OnYldq5H4eHhYf3tWpQ8Gf/SJeNfemTsS5eMf8lJSkrC0dHRZltJlrsURHR0NMOGDWPr1q2lHUqRyGnsPT3zvtH2YWQmPR8yH2aUnJFs3WZ2cyMlKAiNyUTAme9wczMRHe3E2bN1ZSZdCCGEEEIUSondOBoeHs4333yD2WwmKCiIPn362OxPT09n3rx5REZG4uLiwrhx46hQoQIJCQnMmjWLiIgI2rdvz8iRIwFITU1l1qxZ/PHHH2i1Wnx9fRk8eHCxxG6vtyTpKRkpNtuTX3gBh7AwnENDeL7dZFatdmTjxu74+MxCo7mLUs7FEo8QQgghxJPMy8vrqZlFLy4lMpNuNpv56quv+Oc//8ns2bPZtWsXMTExNm22bt2Kk5MTc+fOpXv37nz7raWsxM7OjkGDBuVYj9SzZ08+++wzZsyYwdmzZzly5EixxJ85k55isk3SU4KCMLm7Y3f2LIE1zwOwcWNvAHS6i8USixBCCCGEePqVSJIeERFBpUqVqFixInq9nlatWnHgwAGbNgcPHrQujdOiRQtOnDiBUgp7e3t8fHwwGAw27Y1GIw0bNgQs9VY1atQgLi6uWOJ30DsA2WfSsbOzrpneLforALZv9yclxYheH1kssQghhBBC5OQZvM3wsVEcY18iSXp8fDzu7u7W1+7u7sTHx+faRqfT4ejoSEJCQr76T0xM5NChQzRq1Kjogs7CWu7ywEw6QNILLwDw3KalNKifSnKyPTt2tEWvz75IvhBCCCFEcdFqtY/lTaJPu4yMDLTaok+pS6QmPaffLh5cID4/bXJiMpmYM2cOXbt2pWLFijm22bx5M5s3bwbg008/xcPDIz9hW2mdLAOfak7Nfmy7dpibNkV75Ajd2kZw8lQDNm7sQmDgUeztPeDc1QKdqzAKel0lTa/XP/YxPs1k/EuXjH/pkbEvXTL+JUcpRXx8vE2ibjabZYa9mNnZ2VGxYsUif/hRiSTp7u7uNqUocXFxlCtXLsc27u7umEwmkpKSrE+kysuCBQuoVKkS3bt3z7VNcHAwwcHB1tcFXQoqc1WXpLSkHI917N+fskeOEBy9kH/zGWFhnfnkkx9KfMmpx32JK1mGq3TJ+JcuGf/SI2NfumT8S55Op7P+Wca/+Cmlciy5fiKWYKxVqxZXr17l+vXrZGRksHv3bvz8/Gza+Pr6sm3bNgD27t1LgwYNHvobyQ8//EBSUhLDhw8vpsgtMm8cTTOnYTKbsu1P7tMHZTDw/NEvcXLM4OTJhsTGpmZrJ4QQQgghRH6USJKu0+kYMWIE06ZNY/z48bRs2RIvLy9CQkKsj4MNDAzk7t27jB07lnXr1tkspzhmzBiWLFnCtm3beO2114iJiSEuLo6ffvqJ2NhYJk+ezFtvvcWWLVuKJX6NRmNN1FNN2ZNvVa4cKZ06YSSNDlXOALBxY2s0mjvFEo8QQgghhHi6ldg66c2aNaNZs2Y22wYNGmT9s8FgYMKECTke+8UXX+S4fcWKFUUX4EPY6+1JMaWQYkrB0c4x2/6kF17AYd06esUvZR0zWLu2JwMGRAENSixGIYQQQgjxdJAnjuZT5govWZ86mlXq889jqliRXnFL0GjMbN0aSGpqdEmGKIQQQgghnhKSpOeT9YFGD66VnkmvJ2nAACpynebex0lNtWf7dkPObYUQQgghhMiDJOn5lPlAo2RTzjPpAMn31kzvWXYlAGFh1Yo/MCGEEEII8dSRJD2fHjqTDmTUrk1as2b0ylgNQFhYE8zmEglPCCGEEEI8RSRJzyfrU0fzSNIBkgYNolHMcapVu8Qff3gQecauJMITQgghhBBPEUnS8ymz3CXFlHeSntyrF+pPIz07rwXg2L6iffqUEEIIIYR4+kmSnk/5KXcBUK6upPTsRc+GliT9yB6HYo9NCCGEEEI8XSRJz6eHLcGYVdLgwbR33IazcwKR58ty45ruoccIIYQQQgiRSZL0fLLOpD+k3AUgzc8P/S0XOncOA+Dw7uwPPxJCCCGEECI3kqTnU35vHAVAoyGlakf69l0FwIEdkqQLIYQQQoj805d2AE+K/N44minJ92W6VxiMnV0aZ48a+fO2Fteysh6jEEIIIYR4OJlJz6f83jiaKd25EWUcEwgO3oxZaTi0S2bThRBCCCFE/kiSnk/Wcpd8zqSDHlNSVfr1+wmAA7/LKi9CCCGEECJ/JEnPp8yZ9Pys7pIp3dCE3r3XoNWYOHnYSOJdWTNdCCGEEEI8nCTp+VSgG0fvSc+oQ/nyN2lXYzsZJh1H9kjJixBCCCGEeDhJ0vOp4OUukJFRG4C+QZaSl8ObZb10IYQQQgjxcJKk55OD7t7qLgWYSc/IqANAz1EbATh6yImUZCl5EUIIIYQQeZMkPZ8epdwlI6MGZqXlOb8LBGj2kmIycGyfsbhCFEIIIYQQTwlJ0vMpM0kvyI2jYORucnU0WkXPGpanjx77uSDHCyGEEEKIZ5Ek6flkXSe9ADXpAHeSLHXp7YZdAmD/sQqkpUrJixBCCCGEyJ0k6fn0KOUuALfv1gOgwkATfhwg0eTIyc1pRR6fEEIIIYR4ekiSnk+POpN+O9EHAOfysXR57jAA4T8mFm1wQgghhBDiqSJJej456Au+ugvA7bt1ASjrfIYGL3kAsPNiXVISzEUboBBCCCGEeGpIkp5Pj7JOOsDd5OfIMBlxso9F17k2LQ37ScGeyGWXiiNMIYQQQgjxFJAkPZ8yy10KtroLKHTcSbw3m+5ynnb+1wDY96uhaAMUQgghhBBPDUnS88moM6LVaEk3p5NuTi/Qsbczk3SnM3iPqokWE7/f9sN87kpxhCqEEEIIIZ5wkqTnk0ajwVHvCEBSelKBjs28ebSs82kcqrvQyv046RiIWnS+yOMUQgghhBBPPknSC8CapGcUMEm/twxjWaczADTvYrlpdNuRamjTClbjLoQQQgghnn6SpBeAo92jzqRnlrucA8zUe8EDO9LZltEW519+L+owhRBCCCHEE06S9ALIXIaxoDePpqa7k5xaHjv9XZzsY3FyVQTUvIgZHSdW3C6OUIUQQgghxBNMkvQCeNRyF8hSl+50GoCmAyx9rbnWlrIRp4ooQiGEEEII8TSQJL0AHrXcBeDWvbr0cs6WuvSmHUw46pPZS0v0320uuiCFEEIIIcQTT5L0AiiSmXRny6y50V7RqkU8ANu2l0efmFBEUQohhBBCiCedJOkFUJgk/VZCQwDcnE9Yt/n30wHwvWkQ1cNWFUGEQgghhBDiaSBJegEUptzlz+SaZJjscXaIwaC33Cxar0kqFVwTuEgN4kJOgdlcpPEKIYQQQognkyTpBVCYmXSl9Nb10ss5nwRAq4UW3SxPL111PZBKh3YWUaRCCCGEEOJJJkl6AWTOpBd0CcZM8XcbAeDmcty6rU0XS18reIFqP35XyAiFEEIIIcTTQJL0ArDOpD9CuQtAfGZdusv9uvQq1TOoXTuRPynDwf1lcI69WOg4hRBCCCHEk02S9AIoTLkL3E/SM8tdMrXqmgrAcl6mzuqlhYhQCCGEEEI8DSRJLwDrjaOPmKTfSfLGZDbg6hiFne5P6/aWgYnotGY20gWn9dvQJ90tkniFEEIIIcSTSZL0AnDQOwCPXu6ilB2371rWSy/nfP8po65lzTRpkYIJPT8m9aJG2E+FD1YIIYQQQjyxJEkvgMKWuwDE371X8pKlLh2gbadEAL7hb3j/tFiWYxRCCCGEeIZJkl4AhV3dBXK+eRSgWaskXMtmcJzGnI9xo/LBHY8eqBBCCCGEeKJJkl4AhV3dBeDW3exPHgXQ20HrjpZ+FzGKOquWPPI5hBBCCCHEk02S9AIoinKX23frYjLb4ep4Ab3O9gbR9t0sr7/nRcru3YtLTNSjByuEEEIIIZ5YkqQXQGFXdwEwKyO3E33QaBRuLsds9lV9Lp3a9VNJwJUfGYC3LMcohBBCCPFMkiS9AIqi3AUg7s+mAHi4hmfblzmb/hUjqbUhVJZjFEIIIYR4BkmSXgBFceMowM0//wKAu0v2JL1Fh0SM9mZ20I7IJE9qrV9RqHMJIYQQQognjyTpBWCvs0eDhhRTCiaz6ZH7ibuXpHu4HgGUzT4HR0WLDpaZ+q8ZQd2VX6MxPfq5hBBCCCHEk0eS9ALQaDTWBxoVZjY9IbkGqellcDDewNF4Jdv+zJKXxdoRGK9do+rOsEc+lxBCCCGEePJIkl5ARXHzKGiIS2gCgHsOdeneDVLxrJbOdXN51tMNn9BFhTiXEEIIIYR40kiSXkBFsQwjZC15yZ6kazT3Z9MX6F6n/MnDeJw8VKjzCSGEEEKIJ4e+pE4UHh7ON998g9lsJigoiD59+tjsT09PZ968eURGRuLi4sK4ceOoUKECCQkJzJo1i4iICNq3b8/IkSOtx0RGRvLFF1+QlpZG06ZN+dvf/oZGoynW6yjqFV5yunkUoG3nu4QsKsvGjE5coho+oV+xs4Fvoc4phBBCCCGeDCUyk242m/nqq6/45z//yezZs9m1axcxMTE2bbZu3YqTkxNz586le/fufPvttwDY2dkxaNAghgwZkq3fhQsX8uqrr/L5559z7do1wsNzTniLkoOdpSY9MSOxUP1klru4uRxHo0nPtt+1rBn/dkkotMzXvE7VHRtxuhpdqHMKIYQQQognQ4kk6REREVSqVImKFSui1+tp1aoVBw4csGlz8OBB2rdvD0CLFi04ceIESins7e3x8fHBYDDYtL916xbJycnUqVMHjUZDu3btsvVZHJz0TgAkpxduGcbUdDcSkp5Dr0uhrNPZHNsE904AYKHda2SYddRd+XWhzimEEEIIIZ4MJZKkx8fH4+7ubn3t7u5OfHx8rm10Oh2Ojo4kJCQUqs/i4GRnSdIT0ws3kw5wM+HeeumuR3LcX7dRKlWfSyMurSyr6UOt9Suwu3un0OcVQgghhBCPtxKpSVdKZdv2YO14fto8rH1uNm/ezObNmwH49NNP8fDwyPexD3J3tvxioLHX5K+fc1dz3RX3Z1NqVFxNedfDRFzJXs6j0UBQrwSWfO7OHOe3eeFuKLXX/cDpv76aY3+Fua6SoNfrH/sYn2Yy/qVLxr/0yNiXLhn/0iXj/+QqkSTdNITGIwAAIABJREFU3d2duLg46+u4uDjKlSuXYxt3d3dMJhNJSUk4OzsXqE83N7cc2wYHBxMcHGx9ffPmzUe9FPRmy5Bdi79WqH4Art/xA6B8mdzLdNp0SuSH/5Vj991mnMaHOj8t5syAESi9Xba2hY2nuHl4eDz2MT7NZPxLl4x/6ZGxL10y/qVLxr/0eHp6Fur4Eil3qVWrFlevXuX69etkZGSwe/du/Pz8bNr4+vqybds2APbu3UuDBg3ynEkvV64cDg4OnDt3DqUU27dvz9ZncXC2s/zicDf9bqH7unPXh7QMF5wdYnAw5jzj7uikaB1sKa2Z4/I2TjeuUm3b+kKfWwghhBBCPL5KJEnX6XSMGDGCadOmMX78eFq2bImXlxchISEcPHgQgMDAQO7evcvYsWNZt24dgwcPth4/ZswYlixZwrZt23jttdesK8OMGjWKBQsW8MYbb1CxYkWaNm1a7NeSWZNeFEm6QsfNO80AqJDHbHrQvRtIv00bRCKO1FuxEApQ7iOEEEIIIZ4sJbZOerNmzWjWrJnNtkGDBln/bDAYmDBhQo7HfvHFFzlur1WrFv/5/+zdd3RUdfrH8feUTHqdNBJCC0UpAoI0K0VXxIIFde2CDSWCiOiCHVCUBVSaurLYUFEQ1wXLiogFLFQhdBSkBUJ6mZRpvz9CQH8WQpi5k4TP6xzPIeTe+33mLufsJ98897mTJ/uuyBqIsFXtpPviwVGA7MJupNi/JCF6Fb9kX/qHxzRr6aRl2wp2bArhjdDbuGv7CyStWcHBLmf6pAYRERERqVv0xtHj5Mt2F4BDNehLB+h3adVu+rSwUQC0fedFn6wvIiIiInWPQvpx8mW7C0BecUfcniBiwrcQZP3z8Yrde5cSEeVmU24ay4PPpdGqr4ndlumTGkRERESkblFIP05HdtIrfRPS3Z4Q8opPw2TyEh+15k+Ps9ngvIuq1nw66RkA2r49yyc1iIiIiEjdopB+nHzd7gK/bnlZ9ZfHnT+wGJPZyyf7zmCPpQlpX31MxL5dPqtDREREROoGwx4crWuWbXPW6rzdJcEAZJeU1Poa/9+hwjOAl/5ywgtAfJKbrmc5WPlVOM+kT2H6T1dx6ryXWTnyKaD2n6k2zmv9+zntIiIiIuIb2kk/TiGWqp30MrdvprvA0Z10e9Q6zKaKvzz2wiurHiB9M/sSygihxSfzCcnL9lktIiIiIhJ4CunHKdRa9eBoudt37S6VrhgKSltjMVdij/rxL49t06GCZq0qKCy2MavVOCzOStosmOOzWkREREQk8BTSj1OIpTqkl+L14QuFDub3BCAp5tu/PM5kgguuqNpNn1l2G16g1X/exFpa7LNaRERERCSwFNKPU5A5GKvJhtvrwun969aU43Gw4HBIj11xzGN79iklKsbNT3tj+DD9DmylxbT671s+q0VEREREAkshvRaO7Ka7fNeXnl3QA6/XRHzUWizm8r881maDvpdU7ZxPCf0HAG3mz8Zc6bsfGkREREQkcBTSa+FoX7rvQnqlK4b8knZYzJXER60+5vF9Ly3BYvHy9aamrG/Sl7DcbJp/ttBn9YiIiIhI4Cik18LRCS++e3gU4EB+L6BmLS+x8W66n+fA6zHxTEr1y41exOR2+7QmERERETGeQnot/PrhUV/KPtyXnhxz7JAOcOGVRQB8sKET+5NPJXLfLposW+TTmkRERETEeArptRB6OKSXuXy7k55deAYer4W4qPVYLcee1pJ+aiVtTivHUWrh2VOmAdDuzRng8fi0LhERERExlkJ6LVS3u/h6J93ljiC3qCNmk/uYbx+tNuDqqt30uZvOoSCxKTG7tpH29ac+rUtEREREjKWQXgv+eHC02sGCmvelA3TuWUajNCc52UFM6zwVgHZvTgMfznAXEREREWMppNdCdU+6rx8cBTh4+OHR5NjlNTrebIaLDu+m//un/pTGJRK3YxMp3y31eW0iIiIiYgyF9FrwV7sLQE7R6bjcwcRGbCY4KKdG55x1QQnRsW527Qjh9bMmAtD+9Re0my4iIiJSTymk18LRB0d9H9LdnhCyC7sD0Cju6xqdY7PBBVdU7aa/tG8Q5TF24rf8SPKqmp0vIiIiInWLQnothFqr56QfewJLbWTlnQNAo7gva3xO30tKCA7x8OPqCP7T92EA2r+h3nQRERGR+kghvRZCLZEAlLn8FdLPA6BR7NdAzcYpRkZ7OLd/VY/8zIJbqIiMJnHDShJ//N4vNYqIiIiI/yik10KYNQoAh5920osc6ZSWpxBiyyU2YmONz+s/qBiT2cvyZdF8deF9wOHddBERERGpVxTSayHMWrWT7vDTTjqYyMo7F4BGcV/V+KzERi66nePA7TbxgvNuKsMjSV6znPiNq/1Up4iIiIj4g0J6LVS3uzhcRX5bo7ovPeU4+tIBLrm2qqbPPrHzw0VDgcOTXkRERESk3lBIrwX/76TDgYJeeLwW4qPWEGSp+Q8DzdtUctoZZVSUm3nBPAJnaDgpP3yp3XQRERGRekQhvRaqe9L9Nd0FwOmKJqeoM2azq8ZvH6122Q2FAHz0USJrLr0LgA6vPufzGkVERETEPxTSayHEEo4JE+XuUtxel9/WqU1fOsApp1XQpkM5pcUWpoeMpDI8kkarviZh/Q/+KFNEREREfEwhvRbMJjOhh986WuYq8ds61SE9Je4L4PjmnVfvpv/3wyR+HHgnAKfNmeLT+kRERETEPxTSaym0egyjH/vS84rb46hIJDwki5iITcd17mlnlNO8dQWF+RZejBxBZUQUSeu+I3Ht8bXOiIiIiIjxFNJrKbz64VG3/ya8gJn9uX0BaGxfclxnmkxw2fVVu+n/WZjE+quqd9On6i2kIiIiInWcQnotHd1J92dIh725/QBIjT++kA7Q5awyUppWknvQyr9j7qUiKobEDStJXv2Nr8sUERERER9SSK+lMIv/xzACHMw/E5c7BHvkBkJtB47rXLMZLr2u6oeIDxYkseHqqkkvp82Zot10ERERkTpMIb2Wqmell/k5pLs9IRzIPwuAVPvnx31+r76lJCS7yNoTxNz4eyiPsRO/aS2Nfljm40pFRERExFcU0mupela6w4+z0qvtzTkfgNT44w/pFgtc8veq3vT58xLJvLbqLaTqTRcRERGpuxTSa+noW0f925MOsD+vNwDJsd9gMTuO+/xz+5dgT3Kxd6eNN2Pvoiw2HvvW9aQu/8zXpYqIiIiIDyik11KoQT3pAOWVieQUdcJiriQ59vgf+rQGwcDDk17mv5XAhmvvAQ73pns8Pq1VRERERE6cQnotHd1J939Ih6MtL43ja7f7fc6FJcQnudj3i403Y+6gNDGF2J+30OzzD31ZpoiIiIj4gEJ6LVX3pJcZ0JMOsO9XId1kch73+dago28hXTA3gXU33QfAaXMmY3ZW+q5QERERETlhCum1VL2TXmpATzpAoaMVhaXpBAcVkBjzfa2ucc6FJSQku9i/O4i3g26ksEk6EVl7SF/0jo+rFREREZEToZBeS9Vz0ssMCulgYs+hiwBoEv9xra5gtcLAw7vp778Zx5pbRwPQ/o0XsJQd/wOpIiIiIuIfCum1dPSNo8a0uwDszrkQgMYJn2LCXatrnPW3EhIaOcnaE8R85+XkntKR0Pwc2iyY48tSRUREROQEKKTXUvjhkG5UuwtAQUlbisuaEmrLISF6Za2u8evd9IVvxLB68EMAtH3nRWyF+T6rVURERERqTyG9lsKt0QCUugrxGvZSIBO7D/UHIC2hdi0vAGddUEpiStVu+sK8fmR1PRtbaTFt337RV4WKiIiIyAlQSK8lmyUEmzkEt9dJhafMsHWr+9KrQnrtZpxbrTDwxsOTXl6LZtUtVb3prRe+SuihAz6pU0RERERqTyH9BIRbYwAodRYYtmZecQdKylMJC84mPmpNra9z1vmlpDRxcigriPe392T3uRdhrayg/evP+7BaEREREakNhfQTEBFUFdJLXMaF9N9MeUn4qNZXsVhg0OCquj94I5rvr38Aj9lC+kfvErnnZ59UKiIiIiK1o5B+AiIO96WXGLiTDhzpS2+S+FGtp7wAnHGOgxZtKijIs7JgZUd+7j8Is8dNx1cm+apUEREREakFhfQTEH54J73UVWjourlFnSkpa0xY8AESYn6o9XVMJrjm9qofMP77dhTfXnk/ruAQmnz1MfGZq31VroiIiIgcJ4X0E1A94cXYdhcAE7uyLwOgWeJ/TuhK7buU065zGY4SC+8tacXmq+8A4PRZ48GwqTUiIiIi8msK6ScgIgAPjlb75WBVSG+S8BFmU8UJXevq26rq/3RBJCsuuJuy2HjiN60l7cva97yLiIiISO0ppJ+A8KBA7aRDoaM1+SWnYgsqIsW+7ISu1bJtJV3PclBZYWb+eylsuOU+ADr96xnMlSf2A4CIiIiIHD+F9BNQvZNe4jS2J73arsO76U0TPzzhaw0aXIDJ5OWLxRGs6Hw9hU1bErl/N63+88YJX1tEREREjo/VqIXWrVvHnDlz8Hg89O3bl4EDB/7m+06nk+nTp/Pzzz8TGRnJiBEjSExMBGDhwoUsXboUs9nMrbfeSqdOnQBYtGgRS5cuxWQykZaWxt13343NZjPqIx0ZwWj0g6PVfsm+lM7pE0m1L8FqKcbljqz1tRo3d3LWBaV8/WkE770WT6c7x3DemMG0f2MaO/92FZVRMT6sXERERET+iiE76R6Ph9mzZzNmzBimTp3K8uXL2bt372+OWbp0KeHh4UybNo0BAwYwd+5cAPbu3cuKFSuYMmUKY8eOZfbs2Xg8HvLy8vj444+ZOHEikydPxuPxsGLFCiM+zhFHXmYUgHYXAEdFCgcLumG1VJAW/+kJX+/KWwqwBnlZ8Xk4y+P+xoHOvQguLqTdm9N9UK2IiIiI1JQhIX3Hjh0kJyeTlJSE1WqlV69erFy58jfHrFq1ivPOOw+AHj16kJmZidfrZeXKlfTq1YugoCASExNJTk5mx44dQFX4r6ysxO12U1lZSWxsrBEf54hAzUn/tV0Hq34j0SzpxKa8ACQku7ng8iIA5r4Yx5q7xuA1mWj9weuE7999wtcXERERkZoxJKTn5eVht9uPfG2328nLy/vTYywWC2FhYRQXF//u3Li4OPLy8oiLi+OSSy5h6NCh3HHHHYSFhdGxY0cjPs4R1Q+OBqrdBWDPoYtwe4JIil1OqO3ACV9v4A1FhEe62bwuhKW5Z7Dz/MuxOCvp9MqzPqhWRERERGrCkJ507x/M2zaZTDU65o/+HqCkpISVK1cyY8YMwsLCmDJlCl999RXnnHPO745dsmQJS5YsAWDixInEx8fDtqzafJTfCD/y4GjgdtIrXTHsy+1Hk4SPaZ68gE277zmh64VHerj8pkLenBHH2y/G0nPiKJosW0zTLxax5arB5LY9HaDqHh4nq9Vaq/PEN3T/A0v3P3B07wNL9z+wdP/rL0NCut1uJzc398jXubm5v2tNqT7GbrfjdrtxOBxERET87tzqXfQNGzaQmJhIVFQUAN27d2fbtm1/GNL79etHv379jnydk5Pjk88VaonAjIUKjwOnp4Igc7BPrnu8fs66miYJH9MieT6bdt8NmI55zl85/7JiPlsYyf7dQSxe2ZpTrhpCu7dm0mX6k/xv+vtgNtfqHsbHx/vs3svx0/0PLN3/wNG9Dyzd/8DS/Q+clJSUEzrfkHaX9PR0srKyyM7OxuVysWLFCrp27fqbY7p06cKyZcsA+O6772jXrh0mk4muXbuyYsUKnE4n2dnZZGVl0bJlS+Lj49m+fTsVFRV4vV42bNhAamqqER/nCJPJFPAJLwBZ+WfjqEgkKmwnCdGrTvh61iC45o6q3w4seDWGlZcPoywugfjN62i25IMTvr6IiIiI/DVDQrrFYmHw4MFMmDCB++67j549e5KWlsa8efNYtaoqVPbp04eSkhIyMjJYtGgR119/PQBpaWn07NmTkSNHMmHCBIYMGYLZbKZVq1b06NGDBx98kFGjRuH1en+zW26U8CMPjwYupHu9VnYevAKAFsnv+eSa3c5x0KpdOUX5Fj74Twrrbn8QgE4vT8RaVuqTNURERETkj5m8f9b03YDt37+fZducPrnW42uvYHvRGh7t9B5tos/wyTVrIzL0Jy7p3henO4yFK1bicoef8DW3ZQbzREYyQTYPk1/fy3WPX4p9y49kXn8Pcc+OOe7r6VdugaX7H1i6/4Gjex9Yuv+BpfsfOPWi3aUhOzIrPYA76QDFZekcKuxCkMVBWsJHPrlm6/YVdDu3FGelmffmxLF62KMAnPruv7D88otP1hARERGR31NIP0HVPeklAXqh0a/9lHU1AOk+ankBuOb2AixWL9/8L5yVQT2PjGSMGj/eZ2uIiIiIyG/VOKSvWrUKt9vtz1rqpXBr1XSZQI5hrLb70ABc7lASY34gMnSnT66ZnOrigsuL8XpNvD4tlrW3PYgzJIzQjz7C9s03PllDRERERH6rxiF93rx53HHHHcyePZvt27f7s6Z6JTIoDoBiZ94xjvQ/lzuCX7IHAJDe6G2fXffymwqIinGzLTOELzJbsPH6qlns0Y89Bi6Xz9YRERERkSo1DumTJk3ikUcewWazMXnyZIYPH86CBQvIzs72Z311XnVIL6oDIR1gx/6qqTgtGr2H2Vzuk2uGR3i5+raq3xS89WIs6y69DVdaGkFbthD25ps+WUNEREREjjqunvRmzZpx4403MmvWLIYMGcJ3331HRkYGjz32GF9//TUej8dfddZZUUF2AIqducc40hi5xZ3IK25HSFA+TXz0ACnAuReW0KxVBXmHrHw4P5GiR6seIo2aNAlTfr7P1hERERGRWjw4euDAAebPn88rr7xCZWUl11xzDX379uWTTz5hypQp/qixTqtrO+lgYvv+GwBolTLXZ1c1W+CmjKowvmheFNvbX0xFr16YCwqImjTJZ+uIiIiICFhreuAnn3zC119/zYEDB+jZsyfDhg2jdevWR77fvXt3brvtNr8UWZcd3UmvKyEddh28jM7pT5EQvZqY8M0UlJ7qk+u26VBBzz6lfLs0nHHjo/n3uHEkXHABYa+/juPaa3GedppP1hERERE52dV4J33dunVcfPHFvPTSS9x2222/CegAwcHBjBo1yucF1nVRtuqd9LrR7gLg9oSx80DVG0hbpfi2Z/zvd+UTHOJh8eJQvsw9jdIhQzB5vUSPGQMnYbuTiIiIiD/UOKS3bduWnj17YrX+dvN90aJFR/7csWNH31VWT4RbYzBhxuEqwuXxzVtMfaH6AdJmSR9gtZT47Lr2BDeX/L0IgMceiyZ/+P24k5OxrV1L2Nu+mygjIiIicjKrcUhfsGDBcf39ycJsMhMZFAtAsbPuPEBZ6GhNdkE3gqylNEv6wKfXHnBNEWlpLjZvDuKNDxIprH6I9KmnMOfVnbYfERERkfrqmCE9MzOTzMxM3G73kT9X//f5558TGhpqRJ112tFZ6XWn5QVg++Hd9NaprwNen13XFuzlkUeqdtMnTYpib6+BVJx5JuaCAiInTvTZOiIiIiInq2M+ODpr1iwAnE7nkT8DmEwmYmJiGDx4sP+qqyeiguLYR12a8FJlz6H+OCqeIiZ8G8mx33Ag/2yfXfuii8o599xyvvwyhAlPRTPtqadI6NePsLfeqnqI9PTTfbaWiIiIyMnmmCF9xowZAEyfPp1hw4b5vaD6KLKOzUqv5vHa2LbvJjq1mESbxv/2aUg3mWD8+EL69g3m3XfDuPbatvS7804ip08neswYchYvBovFZ+uJiIiInExq3JOugP7njk54qVs76QA/Zf0dlzuYVPsXRIb+5NNrt2jh5p57qh5KHTMmmvy7h+NKScG2YQNhb7zh07VERERETiZ/uZN+3333MXXqVACGDh36p8f9ug3mZHRkVnpl3QvpFc44dh68glYpb9Om8RxWbR/v0+vfc08x778fypYtQcx+J4HhTzxB3O23E/XMM5QPGIAnIcGn64mIiIicDP4ypN95551H/pyRkeH3Yuqr6naXujQr/de27h1Mq5S3aZG8gPU7R1HpivHZtUNDYdy4Qm66yc7kyZFc8sUAwnr3JuSLL4h68kkKpk3z2VoiIiIiJ4u/bHc55ZRTjvy5bdu2f/rfyS4qqO62uwAUOVqRlXc2VksZ6Y3e8fn1+/atoH//MkpLzTzxZAyF48fjDQkh7P33Cf7yS5+vJyIiItLQ1bgnfdGiRezatQuAbdu2MXToUIYNG8a2bdv8VVu9UVdHMP7alr1VU3hap76GyeT7ly498UQRoaEeFi0K5fNdbSgeORKA6IcewlRW5vP1RERERBqyGof0xYsXk5iYCMDbb7/NxRdfzBVXXMGrr77qr9rqjShbdbtL3dxJB8jKO5fC0nTCQ7Jomrjo2Cccp9RUNyNHVj1EOnZsNDk33YHz1FOx7t5NxJQpPl9PREREpCGrcUh3OByEhYVRVlbGrl276N+/P3369GH//v3+rK9eiDqyk153QzqY2bznDgDapr2IL19uVO2220po3drJrl1Wpr0YR8GkSXhNJiJeegnrxo0+X09ERESkoapxSLfb7WzdupXly5dz6qmnYjabcTgcmM01vkSDFREUiwkTJc58PF53oMv5U7sODsRRkUxMxFZS7Et9fn2bDZ55phCA6dMj2BB6BqW33orJ7SZm9Ghw1917IyIiIlKX1Dhh33DDDUyZMoWFCxdy1VVXAbBmzRpatmzpt+LqC4vJSkRQLF68dXbCC4DHG8yWPbcB0LaJf8ZmdutWyQ03lOJymXjggRgKRj2Iu1EjbOvWYT7JR3WKiIiI1FSNQ/rpp5/OSy+9xIwZM2jRogUAPXr0YPTo0X4rrj6JtlXNAy+oPBTgSv7ajqxrqXBGkxi9ioToH/yyxtixRSQluVmzxsZr7ydS8NRTAFgeewzzvn1+WVNERESkITmuXhWHw8GOHTvIzMwkMzOTLVu2sGXLFn/VVq/E2Koeqi2s4yHd5Y5g276bAf/tpkdFeZkwoart5emno/i53YWUXXQRppISYsaOBa/v++FFREREGpK/fJnRry1btozZs2cTEhKCzWY78vcmk4np06f7pbj6JObITnp2gCs5tm37buHUtJdJtX9BTPgmCkp9P+u+f/9y+vcv4+OPQxkzJobXn36SkG++IeSzzwhZvJjyiy/2+ZoiIiIiDUWNQ/rbb7/NyJEj6dy5sz/rqbdi6km7C0CFM46fsq6lTeNXadtkFis2++etoOPHF/LNN8EsWRLCf65szm3jx2O9916ix46lslcvPHFxfllXREREpL6rcbuLx+OhY8eO/qylXqsO6XW93aXa5j234/YE0TRxEVFhO/yyRnKyh7FjiwB49NFocq+6nYqePbHk5BD16KN+WVNERESkIahxSL/ssstYsGABHo/Hn/XUW/XlwdFqjopUfj4wCJPJS/umL/htneuvd9C9ewWHDll4aEwQBZMm4QkJIWzhQoL/9z+/rSsiIiJSn9W43WXx4sUUFBTw4YcfEhER8ZvvzdJovSMPjtaXkA6w8Zd7aJH8Hk0T/0vmL/dS5PD9OE2zGZ59tpDzz0/g1VctXHDBKVz00ENEP/44MQ89RHa3bnhjYny+roiIiEh9VuOQnpGR4c866r361u4Ch3fTs66mVepc2jd93m+96S1buhg5spiJE6MYNSqa0z8bQuiiRdhWrSL6iScomDrVL+uKiIiI1Fc1bndp27btn/4nv253qfvTXX5t4+57jvSmR4dt89s6Q4eW0LWrh/37rYx7Kpb8yZPxhoQQ9u67BC/1/dtPRUREROqzGod0p9PJ22+/zbBhw7j55qo52z/++COffPKJ34qrT8IsUQSZgyl3l1LuLg10OTXmqEjhp6xrqnrTm/mvN91qhVdecWGzeZk7N5yl+9pRNGoUADGjR2MqKvLb2iIiIiL1TY1D+muvvcaePXu49957MZlMAKSlpfE/PfwHVM2LP9rykhPgao7Ppt134/bYaJKw2K+76aeeCvffXwzAqFHRZF17J5WdO2PJyiJq/Hi/rSsiIiJS39Q4pP/www/ce++9tG7d+khIj4uLIy8vz2/F1TfRQfWz5eXXu+kdmk/x61p33VVC586VR9peCiZPxmuzET53LsFffeXXtUVERETqixqHdKvV+rvxi0VFRURGRvq8qPoqJrj+TXiptvGXe3C5g2mS8An2yLV+W8dqhSlTCrDZvLz1VjhLsk6jeMQIAGJGjsRUWOi3tUVERETqixqH9B49ejB9+nSys6t2ifPz85k9eza9evXyW3H1TX2c8FKtrDKZrXsHA9CpxTOA129rtW7tYtSo6raXGPbfOOxI20v0I4/4bV0RERGR+qLGIf26664jMTGR+++/H4fDwb333ktsbCyDBg3yZ331Sn1td6m2ac9dVDijSYr9jkZxX/p1rTvvrGp7ycqy8MSEOPKff77qJUcLFhCyeLFf1xYRERGp62oc0g8cOEBqaiqXX345gwYNYsKECdxyyy1YrTUetd7gxQYnAZBfcTDAldSO0xXNpt13A9W76f57u6zVClOnFhAc7OWdd8JZvL0dRYd30aMffBDzwfp5D0VERER84Zgh3ev1MnPmTO6//34WLlzI6tWr+fzzzxk9ejQzZ87E6/VfW0R9ExecDEBeZf0NmNv23UxpeSNiIzbTLPFDv67VqpWLMWOqRi8+8EA0u/rfSvk552DJzyfmgQdA/7ZERETkJHXMkL5kyRI2bdrEhAkTmDlzJhMmTGDWrFmMHz+ezZs389lnnxlRZ70QF9wIgLyKrABXUntuTwgbdt0HwGnNJ2M2Vfp1vcGDSzn77Ary8izcPyqW/MlT8ERHE/L554S99ZZf1xYRERGpq44Z0r/66ituvfVWWrZs+Zu/b9myJbfccgtff/2134qrb47spFcd5/9vAAAgAElEQVQcCHAlJ2bnwSsoLG1JROgeWqW+4de1zGaYOjWfmBgPS5eG8Opn6RQ+9RQAUY8/juWXX/y6voiIiEhddMyQvnfvXtq2bfuH32vbti179+71eVH1VZglimBzGOXuEhyu+vsGTa/XyrqfHwKgQ9PnsVnz/bpeo0Yenn66AIAnn4xiQ/srKbv0UswOBzHDh4Pb7df1RUREROqaY4Z0j8dDaGjoH34vNDT0d7PTT2Ymk6nB7Kbvy+1LVt5Z2IKKOK35VL+vd+ml5VxxhYPycjP33hvLoSeewp2URPDKlUTMmuX39UVERETqkmOGdLfbTWZm5p/+p5D+Ww2hL72KiTU/PYzHa6Zlylyiw7b5fcUJEwpJTXXx4482pr7WhILJkwGInDSJoHXr/L6+iIiISF1xzPmJ0dHRzPqLncyoqCifFlTfNZSddIDC0lPYsf86Wqe+yektx/HF+tcBk9/Wi4ry8vzzBQwaZOeFFyLovfAC+gwZQsTs2cTecw+HPv0Ub0SE39YXERERqSuOGdJnzJhhRB0NRvVOem6930mvsmHXfTRL+g+N4r4mJe4L9uf18et6PXtWctddpcyaFcGwYbF8+uHDtPz2W4I2bSL64YcpeO45v64vIiIiUhfU+GVGUjMNaScdoMJpZ8Ou4QCc3nK830cyAjzwQBEdOlSyZ4+VBx9NIm/GzKq3kb73HqEffOD39UVEREQCTSHdxxpOT/pR2/fdRJGjBVFhP9M69XW/rxccDDNn5hMe7uG//w3ljVUdKXriCQCiH3oIy+7dfq9BREREJJAU0n3s6FtHG8ZOOoDHa2PNjkcA6NBsKqE2/3+2Fi3cPP10IQCPPBLFujNupuyiizAXFxM7bBi4XH6vQURERCRQFNJ9zN4Ad9IB9uf1Zs+hCwiylnJ6y3GGrHnllWUMGlQ1lnHo3XFkPTkJd6NG2FavJnLKFENqEBEREQkEhXQfi7DGEmQOxuEqotxdGuhyfGr1jsdwuUNpmriY4OAvDVlzwoRCWrRwsWVLEI8/35T8adPwmkxEvPACtm+/NaQGEREREaMZFtLXrVvH8OHDycjI4IM/ePjP6XQydepUMjIyGDNmDNnZ2Ue+t3DhQjIyMhg+fDjrfjUvu7S0lMmTJzNixAjuu+8+tm3z/yzvY6l6odHhCS/lDWs33VGReuQh0ujoMUC539cMD/cya1YeNpuXN94IZ2Feb0oyMjB5vcQOG4Y5N9fvNYiIiIgYzZCQ7vF4mD17NmPGjGHq1KksX76cvXv3/uaYpUuXEh4ezrRp0xgwYABz584FYO/evaxYsYIpU6YwduxYZs+efeQFSnPmzKFTp04899xzTJo0idTUVCM+zjHFB1fVkVOx9xhH1j9b9w6moLQVVusuIiKMeRNo+/YuHnmkCIBRo2LYNOgBKrt2xXLgADH33gt6oZaIiIg0MIaE9B07dpCcnExSUhJWq5VevXqxcuXK3xyzatUqzjvvPAB69OhBZmYmXq+XlStX0qtXL4KCgkhMTCQ5OZkdO3bgcDjYvHkzffpUze22Wq2Eh4cb8XGOKSGkMQCHyhteSPd4bazaVtWTHhk5DYtllyHr3nprKRdcUEZRkZmh9yZy4PlZuGNjCVm2jIjp0w2pQURERMQohoT0vLw87Hb7ka/tdjt5eXl/eozFYiEsLIzi4uLfnRsXF0deXh7Z2dlERUUxc+ZMRo8ezYsvvkh5uf/bL2oi/nBIz2mAIR0gu7AHDseVmEwVh9tevH5f02SCyZMLSE11sXatjSdmn0LBCy8AEDlpkvrTRUREpEE55htHfcHr/X2IM5lMNTrmj/4ewO12s3PnTgYPHkyrVq2YM2cOH3zwAddee+3vjl2yZAlLliwBYOLEicTHx8M2//WLJ4akAZBdvsdvawSa1focXu8XhIR8SWLiJ3g8N9bgHGvVva+l+HiYN89L795e/v3vCPr0uZZrR2/A8uyz2DMycH7/PSQl1fr6Dd2J3n85Mbr/gaN7H1i6/4Gl+19/GRLS7XY7ub96wC83N5fY2Ng/PMZut+N2u3E4HERERPzu3Ly8POLi4rDb7djtdlq1agVUtcj80QOpAP369aNfv35Hvs7JyfHlx/udhr6TDpCTYyY09DFiY4djNo8iJ6cLHk/iX54THx9/wve+eXN4/PEwxo6N4c47zaR9OJzuX31F8Hff4b3uOnLfegsslhNao6Hyxf2X2tP9Dxzd+8DS/Q8s3f/ASUlJOaHzDWl3SU9PJysri+zsbFwuFytWrKBr166/OaZLly4sW7YMgO+++4527dphMpno2rUrK1aswOl0kp2dTVZWFi1btiQmJga73c7+/fsB2LBhA40bNzbi4xzT0Z70fQGuxL/Kyq6kvLw3ZnMB0dEPG7buzTc7GDjQQWmpmduHJrDvn7Nw2+0Ef/MNEc8/b1gdIiIiIv5iyE66xWJh8ODBTJgwAY/HQ+/evUlLS2PevHmkp6fTtWtX+vTpw/Tp08nIyCAiIoIRI0YAkJaWRs+ePRk5ciRms5khQ4ZgNlf9bDF48GBeeOEFXC4XiYmJ3H333UZ8nGOKsSViNdkocuZQ4S4j2BIa6JL8xERh4URstj6Ehi6mrOwjyssv8v+qJnj22UI2bgxi27YgHpjSmhenTSf++uuInDKFyjPOoPLss/1eh4iIiIi/mLx/1vTdgO3fv59l25x+XeP+H3pzoGwnz3T9jMbhrfy6ViCc1zroyJ/Dwl4lJmYsbnci2dlf4PXG/OE5vv6V2/btVi66KB6Hw8zTTxdwT/aTRE6dijs+nkOffIKnUSOfrdUQ6FeegaX7Hzi694Gl+x9Yuv+Bc6LtLobspJ+M4kMac6BsJznlextkSP/tDzl/p1+nD0iMWYmDx/h+2z//+CSfP6zr5NaRucwYn8Ajj0bjfm4kQ07/geQ1y7HedDtLpr6DxxZc46v9+gcPf/L3D4i/ZtRnEhEREd8y7I2jJ5uGPCv998x8v3Uibo+N9EbzSbUvMWzlXn0dnD+wCJfTxNQnkvkoYyalSanEb1pLlxlPGlaHiIiIiC8ppPvJyRXSobgsnR9/Hg1AtzYPERyUe4wzfOf6ofm0aldObraVfz7Xmi8efRF3kI1WH86lxUfvGlaHiIiIiK8opPvJ0ZDecGel/39b9g7mYH4PQm05dGttzEuOAIJsMPzxHGLsLjb/GML0z89h5X3jATjjuYeJ27rekDpEREREfEUh3U8Sgg/PSq84OXbSq5j5dss/cboiSEv4lOZJ7xu2cmy8mxFPHsIa5OXT96OYY7qV7Zdej8VZydmP3kVwgXE7+yIiIiInSiHdT+Kr3zpadvLspAM4KhqzasdjAHRp9ThhwcbNim/VtpJbhucBMGeKnXn9niKnbWfCs/dz5pMZmNwuw2oREREROREK6X4SY0vAZg6hxJWPw1UU6HIMtfPAVew5dAE2azE9ThkFeAxbu/eAEvpdWozTaWLKkyl8OOIVymLjSV67go7/mmRYHSIiIiInQiHdT0wmE0mhzQA4ULYroLUYz8QP256mrDKe5NhvOSXtFUNXv3FYHm06lJOfY+WZF9ryxcOz8FistJ33Ek0//4+htYiIiIjUhkK6HyUfCek7A1tIAFQ47Xy/9RkAOjV/lrjIHw1b2xoEwx8/RGy8i22ZIUxe9jdW3/0IAN2fHU3cFuNqEREREakNhXQ/Sg5tDkCW4+QL6QD7c/uyZe+tmM0uzmybQZDFuLaf6DgP9407RFCQl6X/jWS69x52DPg71soKznnkDkJzDhpWi4iIiMjxUkj3o+qQfvCka3c5at1PD5FX3I7I0N10a2PcWEaA9FMquX101auQ35gZxys9J3HwtG6E5RzknEfuwFJRblgtIiIiIsdDId2PTuZ2l2oebzDLN03H6QqnaeIiWiQb+3KhM/s5uPymArweE88/1Yi3hrxKSXJj7Ft+pPuzo8Fr3A8NIiIiIjWlkO5HjcIOt7uU7cR7EofB4rLmrNxe9XKhrq0eIypsu6HrX3lLIT16l1LuMPP00634z4Ov4wwNp9nSD2k7d6ahtYiIiIjUhEK6H0UFxRNiicDhKqLElR/ocgJq18HL+fnAlVgt5ZzVdhgWs8OwtU0muPPBXNJPqeDQAStPvNKTL0ZPw2sy0Wn2JFK/+Z9htYiIiIjUhEK6H5lMpqMtLyfpw6O/tmr7kxQ6WhATsZVurcdiZH+6LdjLyAnZ2BNdbN8YwpMrBrFuyAMA9JowgpifNhtWi4iIiMixKKT7WfJJOyv991zucL7JfBGnO4zmyQtplfKGoevHxHm4f0I2wSEeln8WwdOeh9jZbyBB5Q7OHTME84EDhtYjIiIi8mcU0v2sUWgLoKovXaDQ0Zrvt1TNTz+95Tjio1Ybun7Tlk6GPZKDyeTlvX/HMqXrDA6160J49n7ibr4ZU2mpofWIiIiI/BGFdD9LCmsGnNwTXv6/3YcuYcuewVjMTs5qdzchQYcMXf/0XmVcf3fVMwKz/tmImde+TXFqM2yZmcQOHQoul6H1iIiIiPx/Cul+pjGMf2ztz/8gu+AMwoIPcmbbDEwmY4Nx/6uKufCqItwuE89OTOe1e97FHRtLyOefE/3ooxrNKCIiIgGlkO5nKWHpAOx3/ITH6w5wNXWH1xvEN5tmUFaRQFLsd3Rq8YzhNVw/NJ9u55biKDXz2NTObJr0Fl6bjfDXXiP85ZcNr0dERESkmkK6n4Vbo4m1JeP0VJBdvifQ5dQp5ZWJfLNpBh6PlVPT/kXzpPmGrm82w9AxObTpUE7eISvXTO7LL09XzU2PGjeOkI8+MrQeERERkWoK6QZIDW8FwL5SY1/iUx8cKuzGqu1PANCtzRjDHyS12WDk+EOkNHGyeXMQNy68gZzRD2PyeonNyCBozRpD6xEREREBhXRDNA6rCul7HdsCXEndtCPrerbtuwmLuZKz299JWPA+Q9ePiPIw+pmDJCa6+eabYO7c/g9KrrseU3k5cbfcguXnnw2tR0REREQh3QBHd9J3BLiSumv1jkc4kH8mobYczml/u6FvJAVISHbz+ut5hIV5eH9hGKMjZ1DeuzeW3Fzs11+P+eBBQ+sRERGRk5tCugEah7UGYJ920v+U1xvENxtnUOxoRlzkJnqeej/gMbSGDh2cvPxyPlarl1kvRTOuy7tUdu6Mdfdu7DfcgKmoyNB6RERE5OSlkG6A1PCWAOxz7NCEl79Q6Yrhy8xXqHRF0iThY05rPsXwGnr3ruD55wswmbw89c94Xrj0vzjT0wnatIm4wYOhvNzwmkREROTko5BugKoJL0k4PRUcKt8b6HLqtCJHS5ZvmobHa6F90+mkN3rH8BoGDixj/PhCAB4cl8Lrt32MOzmZ4G+/JTYjA9z6QUtERET8SyHdIKlhmvBSU1l557Fy2zgAzmg9lkZxXxhewy23OBg1qgiPx8Tdj7Xk/fsW44mKIvSjj4geM0YvOxIRERG/Ukg3SONwTXg5Hj9lXUfmL/dgNrk5q909xEZkGl7DiBElDBlSQmWliVue6MRnY/6DNySE8DffJGLqVMPrERERkZOHQrpBqnfS95YqpNfU+p2j2HngcoIsDs7rcCvhIca+DMpkgscfL+KKKxw4HGaunXgO3459C6/ZTNTkyYTNmWNoPSIiInLyUEg3SJOIUwH4pXRzgCupT0x8v/UZDuT3JDT4EOd1uJUga6GhFZjNMGVKAeefX05BgZkrpl/K2tEvAxDz8MOEzptnaD0iIiJyclBIN0ha+CmYMLO/dAeVHk0IqSmP18bXG1+koLQ10eE7OLf9bVjMZYbWEBQEs2bl0bNnBQcPWrjk9ZtZP7yq3SVm1ChC/vtfQ+sRERGRhk8h3SDBllAahTXHg1sPjx4npyuaZetfpbS8EYkxKzm73VDMpkpDawgNhddey6Nr10r277dyycJhbL5zHCaPh9hhwwhessTQekRERKRhU0g3UNOIdgD8UrIpwJXUP46KFL5Y/wbllXGk2JfR89SRmDB2FGJ4uJc33silU6dKdu+2MuB/D7HjxtGYXC7i7rgD2zffGFqPiIiINFwK6QZqGt4WgF0lGwNcSf1U5GjJF+tfw+mKoGniIrq2ehQwdhRiVJSXuXNzadfOyc6dVi76bjw7r7obU0UFcbfeStCqVYbWIyIiIg2TQrqBmkZUhXTtpNdefkkHvtzwCm6PjVapc+nYfJLhNcTEeHn77VzatHGyfXsQAzZOZc/FN2N2OLDfeCPWTOPHRYqIiEjDopBuoOqQvrt0Mx6vJ8DV1F/ZhT34ZuNMPF4L7ZrO5NS0Fw2vwW738M47ubRo4WLzZhsDdr1M1vmDMBcVYf/737Fu1hQfERERqT2FdANF2+KJtSVR7i4lu3x3oMup1/bl9uO7zZMB6Jw+kTaNZxteQ2Kih3ffzaFpUxcbMm30P/AG+8++DEteHvarr1ZQFxERkVpTSDeYWl58Z1f2QL7f+hQAXVqOo3Xqa4bX0KiRh/fey6VpUxc/bgjmb3nz2Hf2wKNBfcsWw2sSERGR+k8h3WDVE152Fq8PcCUNw09Z1/HDtnEAdG31GC1T3jC8htRUN/Pn59CsmYvMjcGcf+gd9px5OKgPGqSgLiIiIsdNId1g6ZEdAfhZId1nduy/kZXbngSgW+tHSG/0luE1pKR4WLAgp6pHfUsw52fP45czr9COuoiIiNSKQrrBWhwO6T8Vr8fjNXbOd0O2ff9NrN7+KADd24yhRfI8w2tITq4K6q1aOdm63cb5B99mZ88rsOTmVgX1rVsNr0lERETqJ4V0g8UGJ2IPTqHcXcJ+x8+BLqdB2bpvMGt2PAxA9zYP0bLRXMNrSEz0MH9+Lqec4mT7Dhv9DrzNTz2vqgrqan0RERGRGlJID4D0yE4A/FS8NsCVNDxb9t7Gmh1jMZm8dGszljaNXzG8hvj4qodJ27Z18vNOG/32vcXW7tdUBfWrriJ26wbDaxIREZH6RSE9ANKjDre8FP0Y4Eoapi17b2fl4YdJu7QcT7um0zD6zaRxcR7mzcuhQ4dKdu0O4rxf3mRdz8FY8vPpe/91xGeuNrQeERERqV8U0gMg/UhfukK6v2zffyPfbpmEx2umY/PJdGz+LMYHdS/vvptL9+4VHDhgpe/Wl1lx9nBspcX0fuBGktasMLQeERERqT8U0gOgeWQHTJjZU7qFSnd5oMtpsHYeGMSKTc/j8Vhp13QWXVo+ARj7pteoKC9z5+bRp085eXkWLlw3hXe6PkxQuYPzHrqFlG+XGlqPiIiI1A8K6QEQYgmncXhr3F4XO0syA11Og7b70CV8vXEWbo+NNo1fpXub0ZhMLkNrCA31Mnt2HpdeWkZxsZmb1j/Bv7o/i8VZydmP3knalx8ZWo+IiIjUfQrpAdIqqjMA2wpXBbiShm9f7vl8uWE2Lnco6Y3mc077O7CYywytwWaD6dPzuf76UpyVZoauGsWUHi9hcTk588lhNPvf+4bWIyIiInWbQnqAtInuBsDWwpUBruTkcCD/bD5f9xblzlhS7Uvp2/E6bNZ8Q2uwWOCZZwoZcE0hbreJUd/fzqM95mH2eOj19EhaL5hjaD0iIiJSdymkB0ib6DMA2Fq0Eo/X2D7pk1VucWc+WzOfkvJU4qPXcn7nqwgL3mdoDSYT/P3OAq6+LR+v18S4767m9i6f4wW6Tn+C016ZBF5jH3AVERGRukchPUASQhpjD07F4Spib6neRGmU4rJ0PlvzPvklpxAd/hMXnH4F0eHGvmDIZILLri9iyP25mMxeXlndh0s7rKfCFEz7uTPo9s+HMLmN7ZsXERGRusWwkL5u3TqGDx9ORkYGH3zwwe++73Q6mTp1KhkZGYwZM4bs7Owj31u4cCEZGRkMHz6cdevW/eY8j8fD6NGjmThxot8/g6+dcng3fYtaXgxVVpnEknXzOFjQjbDgg5zfeRCJMcaPQ+xzcQkjxx3CFuxh0YYOnNt6O4W2OFp+NI+zHhuKpUKTf0RERE5WhoR0j8fD7NmzGTNmDFOnTmX58uXs3bv3N8csXbqU8PBwpk2bxoABA5g7t+qV7nv37mXFihVMmTKFsWPHMnv2bDyeo+0hH330EampqUZ8DJ9rcySkfx/gSk4+Tlc0X6x/nd2H+mOzFtPntJsIDZ1neB2n9ypj7JSDRES5+X5rGt2St7MnPJ205Z/Re/RNBJUUGl6TiIiIBJ4hIX3Hjh0kJyeTlJSE1WqlV69erFz5293jVatWcd555wHQo0cPMjMz8Xq9rFy5kl69ehEUFERiYiLJycns2LEDgNzcXNasWUPfvn2N+Bg+d0p0d6Dq4VGv+pAN5/GEsHzjdDbvuR2z2UVs7EgiI5/G6FnqLdtW8vj0AyQ0crJtdxzdw9ezIbYHiet/oN/wawjNOWhoPSIiIhJ4hoT0vLw87Hb7ka/tdjt5eXl/eozFYiEsLIzi4uLfnRsXF3fk3FdffZUbbrgBk8lkwKfwvZSwdCKD4iiozCa7fHegyzkpebGw9qex/LB1Al6vhcjI6cTGDgWMHdHYKM3F49MP0KxVBVnZYZzl/oqliQOJ/XkL5w+7guid2wytR0RERALLasQif7RL/P+D9Z8d82c7zKtXryY6OpoWLVqwcePGv1x/yZIlLFmyBICJEycSHx8P27JqWr7fmEwm2kSfwaqcT9lc8B1JoU0DXZJfmYC6+vuCHVnX07lFR6zW6wgNXURwcDYu13wgyfeL/cm/vZg4Dw8/d5DnH0tgw6pQ+pcvYGbqgwzZ90/Oz7iSr598iYOn9zqupeLj44/82Wq1/uZrMZbuf+Do3geW7n9g6f7XX4aEdLvdTm5u7pGvc3NziY2N/cNj7HY7brcbh8NBRETE787Ny8sjLi6OVatWsWrVKtauXUtlZSVlZWW88MIL3Hvvvb9bv1+/fvTr1+/I1zk5OX74lLXTLqYXq3I+JTP/G85rdE2gy/GruhrQqx061Bmr9QPi4m7Gav0Bs7kXeXn/xuVqZ1gNoWFeRj2dzZzn4li2OJLb9k1ia9N2PPPLrfQefRPfj5rIzguvqvH1fv1vPT4+vk792z/Z6P4Hju59YOn+B5buf+CkpKSc0PmGtLukp6eTlZVFdnY2LpeLFStW0LVr198c06VLF5YtWwbAd999R7t27TCZTHTt2pUVK1bgdDrJzs4mKyuLli1bct111/Hiiy8yY8YMRowYQfv27f8woNd17WPPAiCzYLnmpdcBLlcbcnIWUVl5OlbrXuLjLyMk5ENDa7Ba4bb787jurnxMJi+TfrmFK5otx+k20/OZUXSYM0Wz1EVERBo4Q3bSLRYLgwcPZsKECXg8Hnr37k1aWhrz5s0jPT2drl270qdPH6ZPn05GRgYRERGMGDECgLS0NHr27MnIkSMxm80MGTIEs7nhjHdvFNoCe3AKuRX72V2yiWaR7QNd0klr2Tbn4T9FYza/RbfWY2mRvIC4uKFs3P0j639+AC8WQ2oxmWDANUUkpTqZOSGeD3b1omfKNj7N6kqH118gImsv3496Go8t2JB66pKj/zv533mtgwxby6jPZeRnEhGR2jN5T8KxIvv37zf0/+iP5eWto/nywLtc2/whLmlyV6DLkSO8tE59jdNbjsNscrM/7xyWb5qG0xVtaBU7t9mYPDaB/BwrqfZCPi0+h3aV6znYsTtfjXsZZ+Sf1/PrQNZQfuVZX0P6se6/Qrr/NJR/+/WV7n9g6f4HTr1od5G/1iH2bAA25H8d4Erkt0xs23cLS398k/LKOFLivuLCLpcSHWbspJXmrSt5cmbV5Jd9udH0sK5kUeRVJP34PX+753Iid/9kaD0iIiLifwrpdUD72DMxYWJb4Uoq3XrLZF2TXdCTT1Z/SF5xOyJDf+GCLgNpkrDI0BriEtw88vxBTu/loMRh47KSd5kQ9zSRe37mb3cPpNEPXxpaj4iIiPiXQnodEBkUR9OIdji9lWwp/CHQ5cgfcFQ05rO189l5cCBBFgdntRtGl5aPYTZVGFZDSKiX+548xGU3FOLxmng47yGuSvgMd6mTc/9xK23mz9YDpSIiIg2EQnodUd3y8mPessAWIn/K7Qnl281TWbntSdyeINo0fo3zO19NeMgew2owW+DqIQXc+/ghgkM8vH+oH2fYt7DPk0KXGePoPulBzJXG/eAgIiIi/qGQXkd0tvcFYG3u53/6AiepC0xs338Tn62dT0lZY+xRP9K/ywBS7Z8ZWkX3cx08Pv0ACY2cbMxtSsfwrSyz9ib943fpc//1hOQdMrQeERER8S2F9DqiVVRnIoPiOFj+C/scOwJdjhxDXnFHPl69mL05/bAFFXFuh9vp1OJpTCbjJo80SXcybtYB2p1eRn5pGOd7lzAt/AESM1fxt6GXEbs907BaRERExLcU0usIs8lCp7jeAKzNXRLgaqQmnK5ovsr8F2t+GoPHa6Ftk5fo1+kaQ9tfIqM9PPhsNv0HFeFym7m39Fmui16IOTuP84ddSeh77xlWi4iIiPiOQnod0sV+PgCrFdLrERNb9tzBkrXzKC1vREL0Gi7q2p9mSQsNq8BigRvuzueuf+QQZPPwduFAukRuZldlY2JHjCB6zBiorDSsHhERETlxCul1SIe4s7GabOwoWkNhpV48UJ/kFHXl41Ufs/tQf4KsJfQ69T56njqCIEuRYTWcfUEpT848QFKqk83Fzehky+R961WEv/Ya8VdeCfv2GVaLiIiInBiF9DokxBJO25ieePGyLm9poMuR41TpiuGbjTP5bsszuNyhNE/6gP5dLyI+arVhNTRJdzL+xSzOOLuU0soQrnS9x/3hMzGtWU9Qjx7YVqwwrBYRERGpPYX0OqZLfFXLy8pDnwa4EqkdEz8fuIaPVy0mt7gDEaF76df5ato3fQ6TyWVIBWERXoY/kcMNd+dhtXqZUjqU86JWkZVtwX7ttYS/+KLmqYuIiNRxCul1zBnxf8OEmaFqzloAACAASURBVPX5X1HqKgx0OVJLxWUt+GzNAjbtvhMTHk5r/hwXdL6SqDBjJveYTNB/UDHz5+eSnOxmRVFHOodt5XP3eUSPG0fsHXdgKtS/LxERkbpKIb2OibYl0DamB26vk1U5/wt0OXICPF4b637+B0t/nEtpeWrVTPWuF3FK2suYcBtSwxlnVPLpp4c466wKsh2RXGD6jIds/8T60f9IuPBCgtatM6QOEREROT4K6XVQj4SLAfgue1GAKxFfOFjQi8UrP2FH1jVYzJWcnv4U/TpfTWToTkPWj4/38NZbuTz6qAuTCZ6pvJ+zwlazZ7eZ+IEDCX/5ZbW/iIiI1DEK6XXQGQkXYsbCxoLlFDvzA12O+IDLHckPW59h2fo5OCqSSIheTf+u/Wmd+irg8fv6FguMHeth/vxcUlJc/OA4jU5BG3nXeTnRTzxB3K23Yvo/9u47zo6y7v//a2ZO33POlrMt2Wx6I42WRmhJCIQSEFFAxIKo91dpN/au3HqjEUVRyg9UROEGAZWAdAghlARIQhJKSG+bzZaz7Zw9vczM749ztqUv2d2Ts/k8H495zJyp11w5mfOe2WtmWlv7vRxCCCGEODIS0o9BHmsJk4vnoJtp1jTLDaSDSV3rPJ5d/RI7Gz6JRYszfdwtnHPSVXicOwZk+7NmJXnppSYuuCBGe8rFZ3iMa60Por+8grKFC7GtXj0g5RBCCCHEoUlIP0bNLrsYgBWNT+a4JKKvpdKFvLXp97z+4X3EkqVUFL3DhTPOZ/Lwu1CV/n/pUHGxyZ//3MavfhXA4TB5IPV5TrFv4MO6Mnyf+hTuP/4R9IFpMy+EEEKIA5OQfoyaWXY+NtXBxuDb+GMD95p5MXBqmxfy7KqX2V7/aTQ1yYmjf8v5p16Mz7Ou37etKPCFL0R59tkmxo9PsSUxkpnqGm7Tv0XBr3+D7/LL0Wpr+70cQgghhDgwCenHKJfFy4zS8wF4o/FfOS6N6C/JdDHvbP4tr6x/mFBsBEXuzZx3ymWcOvYWLFq437c/cWKa555r5vOfj5AyLHyfX3OWdSV73/FTtmABziee6PcyCCGEEGJ/EtKPYWdVXg7AG43/xjD7/+ZCkTuNgdN5bvULbKj5GiYqE4b9jYtmnEeVb2m/b9vpNFm8OMhDD7VQUaGzMjWLadoG7g9dSdGNN1J0/fXyTHUhhBBigElIP4ZNKjoNn72KpngtGwNv57o4op/phpP3dnyfF979Dy2hqRQ46jh76lc4a8pXKHDU9Pv2589PsHSpn0WLYkR0J//Fn7lYfZbQkyspW7AA21tv9XsZhBBCCJEhIf0YpioqZ1V8CoDXpcnLcSMQnsxLa5fw7rafkEq7GVa6lEUzFzBlxB1oarxft11SYnLvvW3ceWcbXq/Bs8aFTLFs4um6mfguvxzPrbdCItGvZRBCCCGEhPRj3lmVnwZgVdNzRFLS5OB4YZoWNtd+madXLWNn46VoapJpo+7gwhnnMbRkWb9uW1HgsstiLF3q54wzEjSni7mMJVxtPkzinscou+ACrO+9169lEEIIIY53EtKPceXO4UwpOoOkEef1xn/mujhigMWT5by18Q6WrnuUQGQ8HmcNc6ddm20C079P/amqMvjHP1r4xS+COBwG/+AqJqmbWLJ5Gr5FF+NZvFiuqgshhBD9REJ6Hji36gsAvLz3IbmB9DjlD87m+TXP8u62H3drAnMOJ466rV+fAqOqcO21EV55pYnTTkvQZJRyJY/zaeNxInc+TtmFF2J9//1+274QQghxvJKQngdO8Z2Dz15FY3w377e+luviiBwxTSuba7/SownM5BH3cPGsuYwZ8igK/fcCopEjdR5/vIXFiwO43QZLuIxJ6ib+sWkGvosW4bntNkj2/4uYhBBCiOOFhPQ8oCoa5w79HAAv1z2Y49KIXOtoAvPi2idoDp6M09bMrAnf5/zpi6goWtlv21VV+Pznoyxb1sS8eXHajCK+yIMsMv5D4A//lqvqQgghRB+SkJ4n5g65Eqti473W5TTEduW6OOIY0NJ+Ci+te4IVH/2RSLyKYvdGzjnps5w15St4nDv6bbtVVToPPdTK73/fRmGhwfNcyCRlI3dvPI+iCy/B+z//gxKJ9Nv2hRBCiOOBhPQ84bGWMKfiE5iYPF/7l1wXRxwzFHb7L+GZVa+wfsd3SKULGFa6lItmnMf0cT9FVZv6Z6sKXHFFjFdf9XPhhTHCpptvcAezzLfZ+Ke1lM2fj31Z/z6FRgghhBjMJKTnkQuH/RcAr9U/TjDZP+FL5CfdcPBRzfU8vepVttV9BkXRGV/1IOXlc/B4foOihPpluxUVBn/+cxt/+1sLVVVp1nEKs3iHm2u/h+Xz11N03XWoTfJdFUIIIXpLQnoeGVYwjlN955Iyk7xQ+0CuiyOOQfFkOau2LOa5NS9Q27wAVY3i8dxBeflpqOofgP55GdK55yZYvryJ664LoWoKd3MDJ7CJZ56yUXb2XJyPPgqm2S/bFkIIIQYjCel55uLhXwfg5bqHiKbbc1wacawKRibw+od/obn5SRKJmWhaGxbLdykvPxOn8zHohyfBuFwmP/pRiBdfbOLUU5PUM4QreZxFwUdo/dbd+C6/HMvmzX2+XSGEEGIwkpCeZ8Z5T+GEwlnE9BCv1D2c6+KIY1wyOYOWlidoafk7hjEFi6WO4uJvUla2AIfjaaDvn7t/wglpnnyymV//OkBhocELXMAkPuLnb12E69xL8f785yjh/nu2uxBCCDEYSEjPQ5cMvw6AZ2v/TCwtYUccjkIisYB0ejVtbX8kna7Gat1CScnX+i2sqyp87nNRXnvNz+WXR0li55f8iIn6Bp65L0DZmWfhXLJEmsAIIYQQByEhPQ9NLT6Lcd5TCaVaeWGvtE0XR0olFvsUfv/rBAKLSaeHYrVuzob1c3E4nqGvw3pZmcEddwR46qkmpk1LspdhXMWjzPc/Ts0Nf8o0gdm0qU+3KYQQQgwGEtLzkKIoXDHq2wA8t+dPRFLBHJdI5Bcb0ejn8fvfJBD4VTasb6Kk5P/1W1ifPj3Fs88285vfBCgp0XmDsziFtXzzravRzr0S7y23oIT65wk0QgghRD6SkJ6nJhWdxuSi04nqIZ7Zc1+uiyPykp1o9AudYV3Xh/QI607nk0C6z7amqvDZz0Z54w0/X/5yGEVTuYfrGW9s4oE/2yk+fS6uhx8Gve9vahVCCCHyjYT0PNZxNf3FvQ/QlmjMcWlE/sqE9cbGFQQCv+wM68XF11NefhYu14P05aMbi4pMfv7zdl56qYk5cxK04uMm7mRay2ss/+4KSs9biO311/tse0IIIUQ+kpCex8Z6T2ZG6fkkjBiP7fx1rosj8p6daPSL2bB+G+n0SCyW3RQV/YCKitNwu+/p05ciTZyY5vHHW/jrX1sZPTrFVsbzSZ5kwaZ72XnV7ZR88Yto27b12faEEEKIfCIhPc99dvQPsSg23mh8gu3t63NdHDEo2IlGr8bvf53W1v+PVGoymubH672VioqZeDyLUdXmPtmSosDChXGWLWvi1lsDlBRn2qvPZDVfXnoNsflfwvvTn6K0tfXJ9oQQQoh8ISE9z5U7h3PBsGsBeHDb/2DKI+1En9GIxy+hqelFWloeJpE4DVVtx+O5k4qKWRQWfh9N65sr3VYrXHNNlBUr/Vx/fQi7zeARrmaivoFf3D8O25yLKLjvPoj3zxtThRBCiGONhPRB4BPDb6DQWsq20DpW+p/KdXHEoKOQSMylpeVfNDU9RSx2HooSp6DgISoqzqak5AvYbG8AR3+C6PWa/PCHIV5/o4nLLouSwMFtfI+x7eu45+dJCs5YyKgX/okiN5cKIYQY5CSkDwJOi5srR38XgIe3/688klH0m1RqOm1tD+D3LycSuRrTdOBwvEJp6WeyT4R5DEgc9XaGDdO5884Azz3XxBlnxAlQzA/5FRPq32DNr3cz/9qLqXrzJXkZkhBCiEFLQvogcWbFp5lQOJNgqplHdvwy18URg1w6PY5g8DYaG1fT3v4ddL0cq3UjxcXfpKJiFm7371HVlqPezoknpnjssVYefbSZk09K0EglN3EnM2ueY8dPVjLvhispe39VH+yREEIIcWyRkD5IqIrKV8b/EotiY3nDY3wUeCvXRRLHAcMoIRy+mcbGt2lr+z2p1CQ0rQmv97dUVMygqOi/sVrXcbRNYc48M8nTz2SeBDNsRIIaRnAtDzD/o78R/O/HOPN711K0bUPf7JQQQghxDJCQPogMdY3lE8OvB+D+LT8kqctNdmKg2InFrqCp6SWamx8nHj8XSOJy/YuyskWUll6I0/koihL72FvoeBLMr+5v4LofNlNRmWQTJ3A5/2LRqt8R++r9zPnJ1ynavrHvdksIIYTIEQnpg8zFw79GlWscDbGdPLbztlwXRxx3FJLJ02lt/Rt+/5uEw1/HMIqw2d6nuPhbVFRMx+v9HzRt58fegqrB6edGuO3Ber70jRZKSpK8z4l8mn9z8Zu/JvyVvzLnZ9dTuHNzH+6XEEIIMbAkpA8yVtXO1ybejqZYeGHvX/mg9Y1cF0kcp3R9JO3tP6ahYQ1tbb8nmTwZVQ3gdv+JioozKCm5GofjRSD9sdZvscKCS8Lc/o96vnhTCz5fkg+ZypU8zkWv/5rgtX9j9i03UrhzS9/umBBCCDEAJKQPQqM907hsxM0A3Lf524RS8iIYkUtOYrEraG5+hqam54hGr8w+FWY5JSXXZl+Q9KuPfXXdZoPzPhnm9kfqufYbLZSWJviIyVzNI1z02q9pu/bvzLrlJry7tvbxfgkhhBD9R0L6IHXJ8K8z3judtmQj92/5gbzkSBwTUqkTCQR+R0PDGoLBn5BOj0bTGvF47qKi4gx8vstxOp8Aet923WqDcy4Jc/sjDXzl2y2Ul8XZxAl8nv/j/Nd+y94v/ZMZP76Bkk3v9f2OCSGEEH1MQvogpSoaX5/4exyam9XNL/By3YO5LpIQnUyzmEjka/j9r9PcvIRo9HIMw4HdvpLi4huprDwVr/fHWCwf9nrdFivMuyjMbx5p5L++20xlRZxtjONr3MdZK/7E+q+vZPrN/0XF2pXynHUhhBDHLAnpg1i5s5qvjl8MwP9t/wVbgu/muERC7EshmZxJIHAHjY3rCAR+RTJ5IqoaxO1+gPLyhZSWXoDL9QCq2tqrNVsscPYFEW57uJEbftLEqFERGhjCD1jM9Pf+yYvf2sO0r36VYW++CIbRT/snhBBCfDzaLbfccstAbGj9+vX86le/4rnnniOZTDJx4sQe01OpFH/84x955JFHePPNN5k6dSoFBQUALFmyhHvuuYcXX3yRIUOGUFlZSXNzM7/5zW946qmneOmll9B1nXHjxh1RWUKhELtajo8f5WEF44mlQ2xpf5f3W19jTsUncGgFuS6WGCAjfVrnsMvlIhqN5rA0h2MnlTqRaPRqYrHzMU0rFssOLJYaHI5lFBT8GZvtPcIJC+F4NaZpOaK1qipUj0ox75Io4yYnCDaa1DZ6Wcnp3N92Jbz6AectvQ2vJ0VwxNjM42Oyutff0Tpc/Q/UMakv9ylfHPvf/cFN6j+3pP5zx+PxHNXyAxLSDcPgl7/8JT/60Y/45Cc/yQMPPMCkSZPwer2d8yxdupRoNMpPfvITHA4HL7zwAqeddhq1tbX861//4rbbbmPGjBnccccdnH/++SSTScaPH89VV13FWWedxX333cfUqVN7rPNgjqeQDjCpaA4bg+9QG93C9tB7nF7+CVTl+PuhPh7lV0jvYhjlJBLzCYevJZWaiKLEsFh2YbVuY0T5s4yv+jtuxx6SaS/RxFBAOew6FQUqqtKceUGMk2bHiARM9uxxspZT+UvoaprebOKMp25npLmD4MjxGHaHhPRBIp+++4OR1H9uSf3nztGG9AFp7rJt2zYqKyupqKjAYrEwZ84cVq9e3WOeNWvWMHfuXABmz57Nhx9+iGmarF69mjlz5mC1WikvL6eyspJt27ZRXFzM6NGjAXA6nVRVVdHa2rs/hx8vLKqVmybdRZGtnM3BVfxFbiQVecNJPP4JWlsforHxXYLBW2gNTcFmCTF26D849+QruGTWWUwd+Ts8ziN/OsyYiUn++3/b+M2D9cy/IIhFM/gPn+DC9n9z7Z8/Q/tldzPl9/+LtvPjP89dCCGEOBoDEtJbW1vx+Xydn30+336Buvs8mqbhcrkIhUL7LVtSUrLfsn6/n507dzJ27Nh+3Iv8VmQr51tT/oJddfJG4795subOXBdJiF4xjDIika/ywrvP8Oyql9hQ8zUi8SG4nXuYOvKPXDxrHuefuogTqu+lwLHniNY5pDrNl78b4A+P13PZF9sodsd4j5P4Svo+zv3P/3LPGUtJfu5b2FatkptMhRBCDKgja9R5lA501VZRlCOa53BXfOPxOLfffjvXXHMNLpfrgPMsXbqUpUuXArB48WJKS0thS/2RFn/QGO2ZxnUn/IE7Nvw//rXrd5Q7hnN6xaUDtn0FkJgzsEpLSzuHLRZLj895a0s9weh43tvxfd7f8R3Ki95mVOUTVJe+SInnQ0o8H3LymMU0B09md9MiavwXEUtWHnKVhSUGn7qmnUs+285bywp4+REbO/ZU8HN+xuJXE1z16j+4adJ3mfa9hRiXXZZ5OHsvHbb+B+iYNCi+A700aL77eUrqP7ek/vPXgIR0n89HS0tL5+eWlhaKi4sPOI/P50PXdaLRKG63e79lW1tbKSkpASCdTnP77bdz5plnMmvWrINuf8GCBSxYsKDzc3Nzc1/tWt6ZXnoenxvzEx7a/nPu2/xtXBYvJ/vmD8i2JaAPvO7f9dLS0kH33TfRaAycTmPgdFaptzK0ZDkjyp+hyvcKpYXrKC1cxylj/hd/cAY1/oupabqAROrgP1ZWG5x1foQzF0bY9F6UNf9x8OKrXv7ONfz9o2s4/Ytv8v9u+A4XfMFF+pqrMIYOPeKyHiv1fyyUYaAdK3V/vJL6zy2p/9wZ2ovfiAMZkJA+ZswY6uvr8fv9lJSUsHLlSm666aYe85x66qksX76c8ePH8/bbbzN58mQURWH69On88Y9/ZNGiRbS1tVFfX8/YsWMxTZN7772XqqoqFi1aNBC7MWgsrPoSrYkGnq39E3/Y8DW+M/VvTC6ek+tiiX6wfEuq68Mg/+uRYTiobT6f2ubz0dQoVb5ljCh/mqG+V6koWkVF0SpOHfcz/IHT2NN0PrXN5xFLVhxwXYoCJ5yU4OtXGOzeneCBP9l59B9OViTOYEXoDErvbuJL9/yNa87eRPnXF5I8/fTMQqKHHt+/XOrD7/7c8dY+W5cQYmAN1DGpr44TijlAdxCuXbuWv//97xiGwbx587jssst47LHHGDNmDNOnTyeZTHLXXXexc+dO3G43N998MxUVmR/QJ554gldffRVVVbnmmms4+eST2bRpEz/96U8ZPnx4Z9OZq666ilNOOeWwZamrqzt2fjxyxDRNHtj6Y16pfxi76uIHJ/4f47yHrzsh8o1FCzGsdCkjyp+msvgNNLXr/35z8GT2NJ/PnuaFhGMj91u2+4E2ElF4comDB+9V+HBnEQAKBgt5ka8OeYqzvz6c5BWfwjzI3fyHu5qVbz8eR2IwHmclpPeeXMnNLan/LgN9nD3aK+kDFtKPJRLSMwzT4N5N32SF/0lcmofvTv0b4wpPzXWxhOg3VkuQYb6XGVb2IkOKX8eiJTqntYUnUtt8HnuazicQOQFQDhjITBPWrrXy0J9U/vO8l4SemaeaGr5q/RufubiZoq9eSGrq1B5X1yWkDw4S0ntPQmJuSf13kZCeBySkd9HNNHd+dCOrm5/Hrrr49tT7mVR0Wq6LJUS/09QoQ0teo7rsBYb6lmGzhDqnhWPV7GleSJnzQpLJUzlYy8DWVoXHH7Xzf3/S2NlUCICKzgU8zxeqlzL3K5Xol38Cs7BQQvogISG99yQk5pbUfxcJ6XlAQnpPupnmvk3fYYV/CVbVzs2T7uUk37xcF0uIAaMqSSqKVzKs9EWGlb6M09b1g2YYRcTj80gkFhCPn41pFu+3vGHAG2/YeeRenRffLCFlZEJ9GX4+p/2Dq+fvZNr3LqZp4sSDtl2XkJ4fJKT3noTE3JL67yIhPQ9ISN+fYRo8sPXHLKt/BE2x8tXxizmz8lO5LpYQA05Bp7RwLcNKX2TckGVYLDs6p5mmRjI5g3h8AYnEAtLpsez7ttOWFpV/P2blsb8qbKov6Rw/i7e5pvQ/LLrGieXqizHKy3ssJyE9P0hI7z0Jibkl9d9FQnoekJB+YKZp8o8di3m29j4ALhvx31w24ub9nmkvxPFi7ngrmrYdh+MVHI6l2GzvoCjpzunp9Aji8XNIJBaQSMwCHJ3TTBPWr7fy+F90nnyukPakEwAnUT6lPMEV0z5g1ldGkb7gXHA6JaTnCQnpvSchMbek/rtISM8DEtIP7aW9D/LgtlswMTij4jK+Mv5XWFV7roslxIDbN5ApSjt2+2s4HEux25ehaV1vPzYMB8nkHBKJs0kkzu5xlT0WU3j91RL+8ts2Vm7uerFSJfV8xvpvPj2/jsj5J9E8bQao/fsiaAnpR0dCeu9JSMwtqf8uEtLzgIT0w1vb8gp3fXQDCSPGOO8p3DTpHkrsh35roxCDzaEDmY7Vug6HYykOxytYrR/1mJpODyWRmEsicRaJxJn4fGNpbm5m1y6NJx+GJx6zsL3F1zn/CXzE5e6nmXNujOQnzyZUPToH+9S3BuNxVkJ670lIzC2p/y4S0vOAhPQjszP0Ib/b8FVaE/V4raXcNOkuTiianetiCTFgehPIVLURu/31bPcamtb1pmTTVDHNGUQic4jHzyaVOhnTtLB+vZUlf03w1POFNMe6nq9+Jq9zaeVrnHiRlfbz5xMrPfBLl/p7n47WYDzOSkjvPQmJuSX130VCeh6QkH7k2pMt3LXxRjYEVqKicdXoH3DBsC9LO3VxXPj4gczAat2A3b4cu/01bLY1KErXMccwPCSTs0kk5pBInE4sdgJ3P2Jh9T8TvPXeEBKGDQCNNAtYysJh7zJlkZ328+aRKC7N0T713mA8zkpI7z0Jibkl9d9FQnoekJDeO7qZ5p87f8vTe+4F4MSSufzXhNsospUfZkkh8lufvdpZCVNa+iGJxDM4HMuxWHb2mK7rxextnU1j2xx21p7B8mfHs+bJNGt3VKGjAWAhxTm8wsKR65i8yEX7uXNJeotytk9HYjAeZyWk956ExNyS+u8iIT0PSEj/eNY0v8ifN3+PcDqA1+rjq+N/zSmlC3JdLCH6TV8Gsu4/lJq2F5ttBXb7Suz2N9G0+h7zRhPl+AOnsWnHOTz3r3m8/YSHtbuGdQZ2K0kWKEs5b9T7TLrEQ3D+maQ8hQO+T4czGI+zEtJ7T0Jibkn9d5GQngckpH98rYkG7tv0bT4MvAnA/CFXcdXoH+KyeA6zpBD5p79Cek8mmraLXYHXqSh6i4rit3q8TAkgEh/Kpu3nsOTRy1j66CRWbxuD0S2wz2U584d/wJTzLKQWnnbINuwS0o+OhPTek5CYW1L/XSSk5wEJ6UfHMA1eqP0rj+28jbSZpNhWyTXjfs700vNyXTQh+tTAhPSMrmOSide1jcrilVQUraS86G3s1mCPeffsHcvDD32N/zy8gHc2TMEwtc5pM3mHc8tXcfLcNNZLTiFcNbLHshLSj46E9N6TkJhbUv9dJKTnAQnpfaMmvIm/bPke20PvATCz9EK+MPYWiu3SVl0MDrkJ6fsyKCrYTFnhasoKV1NetBqXvaFzalNTKU899UmeePQKXn39DOKprhcqTWAT5xe+wfQ5IYo/MYHg+MnMnWDrq106rMF4nJWQ3nsSEnNL6r+LhPQ8ICG97ximzkt7H+Txnb8hYURxah4+OeImFlZ9EYs6cGFAiP5wbIT0fZkUOGopL1yVCe5Fqyh07QAgEnHx0kvn8eSST/L0fy6mLVjcuVQl9Zxnf5WFM/3MuaoM+7mzMF2uo9mlwxqMx1kJ6b0nITG3pP67SEjPAxLS+15zfC9/2/pT1rW+AkClcxRXj/kxJ5fMl8c1irx1bIb0/dmtzZQVrskE96LVFLs/wtDhjTfO5MknL+XJJy+lpmZE5/xWJcmZlhWcO3wT8y5SGHHVyRjDqz/29g9mMB5nJaT3noTE3JL67yIhPQ9ISO8/61te5f+2/4L6WObK3tTiM7ly1PcY5ZmS45IJ0Xv5EtL3palRfJ738XnXUVq4Fp9nHVs2VvHccxfy3HMX8tZbp2EYXe3YR7l3cL7vDc6ZGmLmZ4ehzTkZrEe/74PxOCshvfckJOaW1H8XCel5QEJ6/0obKV6ue5Andt1BVA8BMKP0Aj498psMKxiX49IJceTyNaTvL9NEptS7lhOHv084vJVly6p5/vmFPP/8BbS0dL0gyWGLMW/scuaN+oAzTlYYtXA2xqjR8DH+IjYYj7MS0ntPQmJuSf13kZCeBySkD4xQqpWna+7lpbq/kzISKKicXnEpnxx+I5WuUbkunhCHNXhCepeufYphtX6Ipq3jg/eCvPxiFS8sncu7707vMX9lRT3nnPQq8yd9wBmzDIpOXUDaNQk4fGgfjMdZCem9JyExt6T+u0hIzwMS0gdWW6KRJ2vu5NX6R9HNNAoKM8su5OLqrzHKMzXXxRPioAZ3SN+fqjbT2rqFFS+HeG25j2VvzaC+cUiPeSZP/pAFZ73C/BPXM3tmCkvVHFLmiej6CEDtMe9gPM5KSO89CYm5JfXfRUJ6HpCQnhv+2B6eqrmLNxqfQDcz9T+l+Ewurv4ak4vmyA2m4phzvIX0fZkmbNvcwspnanl9RSFvfDCNSMzdOd1qTTJr1jvMnbucs+esYMYUP5bCqaSMqaRSk1m2cTS64TjEFvKPhPTek5CYW1L/XSSk5wEJ6bnVmmjg+dr7WVb/CHE9AsCwggmcW9TSpQAAIABJREFUN/QLnF5xKQ6tIMclFCLjeA/p+0omYe2baVY+UcdrawtZWzO+x4uUrNYkM2euyoT2s19j9uy3MZRK2iKTaQtP6uwSKV9f7EpOSEjvPQmJuSX130VCeh6QkH5sCKcCvFz3EC/v/TvBVOYA4tQ8nF35aRYM/TxDXKNzXEJxvJOQfmiBgMKapXHe+U+Qle97eb95RI/QbrGkmDlzFWef/Rpz5y5nzpyVuN0RookKAuGJBCITCEbGE4hMoD06Ft1w9lnZ+ouE9N6TkJhbUv9dJKTnAQnpx5a0kWRV8/O8tPdBtra/2zn+hMLZnF15OTPKLsCh9e9LV4Q4EAnpvRMMKqx5McI7z4R5a52X9a0jMOgK7ZqW5qRp65lzxkpOP30Fc+aspLq6FgDTVAjHRhCIjCcYmdDZb4+NwjSPnWAsIb33JCTmltR/FwnpeUBC+rFrV+hDXq57iBX+J0kZCQAcWgGzyi7krIrLmVA4Q9quiwEjIf3oPLc2zc4VUXYtD/PhxmI2BEegY+kxT7WvhtNmvc3p577J6Weu4MQT38Ni0Tun64aVUHR0j/DeHh1HOF6NaVr23WS/k5DeexISc0vqv4uE9DwgIf3YF02383bTs7ze8E+2tq/tHF/mqGZ22UXMKlvESPdkCeyiX0lIPzr77lM8prB7TZLa14Ns2eDg/cbhtBueHvO4tAgzR61h1px3OW3h25y2YDnl5U37rVs3rIRiI2mPjqU9OqZHl9b7774WCem9JyExt6T+u0hIzwMS0vNLXXQ7bzT8mzcan6At2dA5vsIxglllFzGr/CJGFEySwC76nIT0o3O4fTIMqN9mUrusjR1rTT7YU8HueNV+842y7+DkkR9w0uxNnHrue8xc8BalFbsOut5IfEi30N4V4mPJco7k+e6HIiG99yQk5pbUfxcJ6XlAQnp+MkydzcHVvN30LKuanqc91XXQqXCM4GTfOZziW8CEwhlYVPkhFUdPQvrR+Tj7FGxRqH0twM63k2zd7uajtuFEzZ73pKjoTNY2MG34VqbO3M20BTs5Yd4miir24HHtRFOTB1x3Mu0hFB1Ne2wU4dgIQtFRhGIjCcVGkkwXHVH5JKT3noTE3JL67yIhPQ9ISM9/hqmzMfAO7zQ9y+rmF2hPtXROc2oeppWcxcm+czipZB4ea3EOSyrymYT0o9MX+6TrUL/FpOHNFnatN9hc42NzePh+bdudRDlR+5CJvj2Mm9rMuLnNjD2nAc8IP96CHRS6tmG3Bg66nUSqKBPYo6MIxUYQinUF+FS6sHM+Cem9JyExt6T+u0hIzwMS0gcXw9TZ2r6WdS2vsLblFfZGt3ZOU1AZ7ZnKlOIzmFx0OuMKT8GmDq6Xq4j+IyH96PTXPiXiCnXrY9S/GWDHBgsb6yvYlRi233xWkkxSNnJCUQ1jhrczYkaSkQtDeCbF8BTU4HHuwu3chce1C6sWPej24qliwtGRhGKj8LlGo+sjSaer0fXhGEYZR9uEZrCTkJhbUv9dJKTnAQnpg5s/VsPalldY17qMjYG3O99uCmBV7UwonMmUotOZUnwGI9wnoCraIdYmjmcS0o/OQB5nQwGFuncjNK4OsXuzha2NpeyIDcNE7TGfgsE4tjHJs4OxVa1UjzcoP9lNwawCHJVteJy78Th34nHtyvSdu7FosYNu1zCc6HomsKfTw9H14dnhzDjTdB902eOFhMTckvrvIiE9D0hIP37E9QibAqv4MLCCDW1vUhPZ1GO6U/MwznsKEwpnMKFwOqM9J2LXjv0XqoiBISH96OT6OBuPKTSsi9KwKkjNRypb60rYGqkmhW2/eYtpZbJlM2NLGhgxPErlCRZ8M3zoE4djLwjgce7E7dzN5KoaLJbdaFoNFksNqnrwJjQAul7SLbj3DPC6PgSw99PeHzskJOaW1H+XfAvpA/+QWSEGkEMr4CTfPE7yzQMgmGzmo8BKPmxbwYbACpritbzf9hrvt70GgKZYGOmewoTCGYwvPJXRnhMpsVXKk2OEyEMOp8nIOU5GznEyOzsulayn8aMEjW+1sWejws69HrYGqmgzSngzfRpv+gE/sAZ4CIazm4nOKGPKNapHDSM5fwKjzihFG1MNFguK0p4N7HvQtN3Zfk12XC2a1oqmtQLr9yufaSoYRjm6PhRdr+rWDSOdrkLXh2KaxUhzGiGOT3IlXRzXWhL1bAmuyXTta9gd3oiJ0WOeIlsZo9zTGO3p6rw2X45KLAaSXEk/OvlynDVNaPMrNL8boPG9KHu3a+xoLGZbuJrEAa50a6QZww4muGuYUBlgzJgUY060MXpOCc6pI8DRcd+Lgar6OwO8pu3BYqnpDPGa1oii6Putv7tMc5pMcM/0h/YI87peCQf4y8CxRK7k5pbUf5d8u5IuIV2IbqLpENva17E5uJpt7evYEX6faLp9v/lK7VWM9pzICPckhhdMpNo9kVJ7lVxxH2QkpB+dfD/O6jo07dBpWdVCw4YUNbvt7GyrYEds6H5t3TsMZS8THTsZV9bCuJFxxkyxMGaWF9/0Kije9zGPaVS1EYtlL5p2oK4WVQ0fsoyZq/Gl6PoQdL0Sw6hE1yu7fc70TdNzyPX0JwmJuSX130VCeh6QkC6OlGmaNMZ3syP0Hjva32dH+AN2hT4gYex/I5lL81DtPoHhBRM7g3t1wQQcWv+9/VD0LwnpR2cwHmfnjrcSi8HODSl2rmxl23tJtm23sLW+mC2RKhLmgduYewkyXtvGWK+f0ZUhRo7SGTHZxsiZxXimDcN0H/gG00xzmq7Qrml1+wT5BhTFOOCy3RlGAbo+pFuIr8x+HtL52TBKgb6/kV5CYm5J/XfJt5AubdKFOARFUah0jqTSOZI55Z8AMo983Bvdxo7Q++wJb6ImsomayEZCqVY2B1exObiqa3kUSh1VDHGOYahrDFWusQxxZYa9Vp9ceRciDzmdMGm6lUnTK3qM1/UWanfo7FjRyvZ3o2zborJ1r5fNwaG0GYWs0U9lTRvQBmwEnsssV0ILYyy7GFPoZ/SQCKNG6YyYYmfEjCI8U6pIF5xAOn3CQUqTRlX9aFoDmlaPpjWgqg3dPtejqg2oagRV3QZsO+h+maYFwyhD18uzbeU7+mUYRkW3fikgj7IVor9JSBeil1RFo7pgAtUFEzrHmaZJMNVETTgT2GvCm9gT2cje6Haa4rWdN6h257YUdQb2KtdYKpwjKHeMoNw5HIfm2nezQohjnKbBiHEaI8aVMe+arvGmGaGlOcqONe3UrA2xa1Oanbtt7PAXsj1cSavpozXtY3UL0AJ8CDydWbaEFsZadjLK08Tw0jDDq9NUj9Womuyi8pQy1OFDMCxDMYyhpA56kdBEUQLZ4N6QDfL1+wT7ejStrTPYH45hFGVDexm6XrFPvyvkm+aRvclVCLE/CelC9AFFUSiylVNUUs60krM6x6eNFP74buqi27PdNuqi29kb3U44HWBr+7tsbX93v/UV2cood4ygwjk828+E9wrHCDzWErkCL0QeURQoLTMpvcDDzAt6tg03zSD+hiA17wbZtTbE7s1pdu62s6OpiO2RTIBflfaxquMK/FZgWWZZFZ1q9jDSvpvhha0Mr4gxfLhO9XgrVSd6KZ1WBmU+UFVMs5h0uvgQV+QB4mhaE6ramO370TQ/qtqEpjV26zejqoHs4ye3HmJ9YJo2oIzS0mIMoxTD8HX2db3758ywacojcIXoICFdiH5kUa0MdY1lqGtsj/GmaRJI+rvCe2w7jbFd+GM1+ON7CCSbCCSb2NK+Zr91OjQ3pY4qSu1DKXVU4bMPxWev6hxXZC9HU+S/thD5QFGgYghULCpkxqLCHtM6AvyutSH2fhBmz5YUe/ZYqPG72NXuoy5Zxm5GsjsxMvPYSD/wAfBsZnk7cUYoNYywN1DlDTKsLMrQoQZDRmoMmeCkcmoRtlGVmAUd9804si9mqj7EVXnIPLWmrVuI93eG+0yQ93ebFgL2YrPtPaL6MAxXjzCv6759wn0pul6SHS7meHjOvDh+yS+5EDmgKArF9gqK7RVMLp7TY5ph6rQk6vHHamiM78Yf201jrAZ/vAZ/bDdRPURtZDO1kc0HXLeKRrG9sjO0++xDKbEPodheTrGtgiJ7BYXWUizqwN1AKITovc4Af5EHLtr36Sw6iUQDdTvT7F0XoPajGHu26+yptbG72cPucClNuo8t5ni2xMdDnEyI39BzLT6aqdbqGOZspqo4RFV5kqpqk8rRVoZMcFF2QiFqVXm3x0oCqNnQ7DvMlXlQlBg+n0EwuC17Bb4ZTWtBVVuyn1uy4zqGo6hqDVBzRHWUCfUlGEZxtssMm2Yxul6CaRYfYJoLefa8yAcS0oU4xqiKRpljGGWOYUymZ4A3TZNwOkBLfC/NiTqa43tpSdTRkqijOV5LS6KOQLKJlsReWhJ7OXCMz9zQ6rX6KLZXZJrp2Co6Q3yxraJzvMdaImFeiGOU3Q6jJloYNbH0AFMTRCL11G5NUfdBO/VbYtTv1tm710Jdi5PaUBG18TJaKKVFL2V9GAgDe4BuLfBUdCppoEqrp9IZYIg3TKUvTmWFTvkwlfLRDsrHu/GO82GWlWYa5neTab5SSip1JE+5MlGUcLcw39o5nAnwLd3CfHP2an4UVY0CtUdcb6Zp6xbsi/YJ+R1hvhDTLMIwCjs7kKY4YmBJSBcijyiKgsdajMdazEjPlAPOkzIStCYaaI7vpTmxl+b4XgLJRtoSftqSjQSSjQSTzQRTmW6/S2v7cFuK8Np8eK0+vNbSzuFCWymF3T57bT5cmlfaywtxjCgoMJlwkoUJJ5UccLphNNPcpNCwMUzdhhAN2xPU1cDeBht72wrYGymhPlVKHVXU6VWZEB8G6sg0q+nGRoKh1DHEWs8QV5DKwgiVpUkqKg1GjnXhqdDxjS7AO6oIo6x0nyvzHRRM04Oue9D1UYdpcgNdob41G9jbDjusKG2oarzzBtreME17NtQXZkN8R4Av6jZcKAFf9BkJ6UIMMlbVToUzc7PpwehmmvZkC23JRtoSjZnwng3xHeMCST+hVBvhdIBwOkAd2w+7bU2xZgO8D7elGLe1KNNZinFbC7P9YtyWwkzfWkyBxYuq9P2zmYUQh6aqUF5hUl5RwLS5B7rSnSaZbKCxXqFpaxj/1gj+XUkaaw0a/RoNrQ4aQh7qY8UEjEJ2MYpdqVEQJNMdoMWKlSTl+KlQ/ZTbA5QWRCj3xij1pSkrNykdquGrtlEyqoDiMYVQcbBADz1D/cGPd/stpcRQlNYDBPnuXebGWEUJoqqZTlESaFojmtZ4xNvqkAn4+4Z7L6bp6db3YJrebn13t88eOMgLtMTgJSFdiOOQplg628RziBcRGqZOOBWgPdVCMNlMe6ol0yVbaE81055sIdhtXEwP0ZZsoC155FeoFBRcFm/P8G7JhHuXxUuBxYvL4sWlZfsWLy6Lp7MvN8kK0X9sNqgeYVI9ogAWHKzJSoRYLErjnjRNm0P4t0Xx707RuNekodmKv91NXbsbf7yQoOFlL8PYawyDGJmuGdix/1pVdEpppkJtotwWoNwVotQTpawoQUmJSVGpQkmFRnGVnaJqJ97hHpSyEkyPJ9Og/yBM04lpVmEYVb2oCTMb7gOdoT0T3APdhjvGB/b53BHwMzfTflyZ0L5vqO8Z7nsGfzeG4QWGo6opDKOAzI228tfOfCG/bkKIg1IVLdOcxeZjWMH4w86fNOKEkq0EU82EUwHC6bZMv/twOkA41TUcSQc7u95fnwK76uoZ5jsDvBeX5umc5rAU4NDcOLVufUsBTs2DTXVIMx0hjoLTaTJyvMbI8UVAz2ejd73xMkw8HqalSaF5Z5SWnVGaa+I01xk0N0Jzq4WmoJ2mSCbQt+jF+KnAb1RkbnyNA63A7gOXQcGghFZKaaHEGsRnD+NzRfF5EpQUpSnxGRSVa5RUWigaYqO4yo6nqgCKCzG93v3a0++7dtN0YZouDKO3b5E0UZT4AQJ9O4oSQlXbUdUQitLRD/eYlumHUdVMe6MjeY79viorsyUxLdnw7sI03ZhmQfbzvsOuA4wvyC6TGdcV+kV/kZAuhOgzNtWBzzEUn+PIf8QMUyeSDmbDfBuhdIBINtRH0u3E0iEi6Xai6Xai6RBRvdtwOkTCiJJIRnt19X5fCmo2tLtxaK7OEP/gLg8F1gLcVjduqzszbOs2nO27LC4cFgcuiwuX1YXT4sSm2iT4C7EPhwOqqk2qqp1w1qHaacdIpWK0NGcCfdvOCM17kjTXGzT7IdCm0hK00hp20BIroCXpoU0vzNwISymkyHRhMk+1OQgVnWLaMp3WTpE1QpE9SpErQVFBgiKvTmGRgbdEpahUo7DCirfChrfKhbPSDSXFmE7nIa/cZwK+E9N0YhhDPla9gZ4N7+FuYX7ffqhbvyvkWyxRDKMdVY2gKMnsyULgY5ajJ9O0ZgN79/DeEeydnSc2XSc4rgOO75ruxDQLABtyxV9CuhAix1RFw2MtwWM98M1th2KYBnE90jPI66FsiO8I8u2ZsK+HiOsR4ukIMT2cGdYzwykjkVlOD/VY/4aj+B3TFA2XJRPYXVYXHocHK9YeQd5lcXXO0xC049Cc2FQnds2FXXNizw7bVCc21YFVtWPT7NhUBzbVgaZY5URADFpWK1QOMakc4oQ5h7vxMkI6HSEYVGmtT9FWEyWwJ0ZbXZJWv0FbC7S2arS222iN2GmJu2lJegka3q5gr5Pp4mTa1B+GhRRFBCgmQLElSHFHwHfG8LpSeAp0vB4Dtxc8RQqeEg23z4q71Iq73EFBhRO12Ivh8Ryi3X0HDdMsRNcLgd400+n+lwyAZDbsR1CUMIoS6Ta87/hMP9OFu33uPl+qT0N/B9PUuoV35z4hf//x+58EODFNR+cwZIbtVg3dcKDrDkyO/XuhJKQLIfKWqqjZ5i0efPT2T9BddDPdI7zH9DDxdIQxFXHCqTCRVIRwKpwZTkZ6jIumo0RTUWLpGLF0jGg6M5wyUoRSIUKpUKbdbXvf7XcHBSUb3jsCvKMrzKv2bsMObFpm+LVWFw7NgcPiwK7ZcVgcODVn57BDc2C32HFoDmyaDbtmx6basGnZLjtsVeUEQRxbLBbw+Qx8Pg2meDjkDTcAhEmlwgSDKoFWk/a9Udrr4wQbkrQ36wRbdIJtZKaHrAQidgJxB4FEAW1pN1HTRTNlNFMGaTJdDOhFXnUTwks7hQTxWCIUWqJ47HG89kRn0Pe4dTxuA4/XxFOk4CrUcBdpuEpsOIutuEod2EoKMFwuTLc7cyPBQdkwzRJ0vfcXRQ4scdCQryjRbBdDVaPdPkey7fu7xnVN7xifQlFCQOiwJeiNT1V0DeuGDd2wk9admeBuONB1J2nDjt5tXNf0nvN2H59ZrmsdmubOnhx8/N8lAMU0TfPodjn/1NXVsXzLYZ/tJIQ4zs0d//GfEZ8yUp3hPZqOYnfbqWuq6/zcEeajqczwZn+EhB4lrsdIGjESepSEHss059FjpIwESSOR7cdJGXF0M92He9s7CkqP0H6gQB9LWLGoNqyqHYtiw6rasp9tPT73mKZ0m0e1YVXsWFQrmmLBoljRVGumr1g7x3cftmSn99cTg47mO3G86nkld/BIJKC9XSXoTxHcG6O9MUnQn6K9Rae9zSAUVAiHoD2sEYpaCcWstCcctCcdtKcLCBkFmH30xBYrSdyEcRPGQxi3FqXAEqfAmsBrT+GyJihwpilw6LgLDFwFJgVuKPAqFHhVnIUaBV6VgiINZ7ENZ5EVa6ETXE5MlyvTpMcykNd1Uz1C/P5BP3qQkN/RxbOBP95jOGXEsagxLFp8gPbj6CK2hHQhhDiIvgxkhwsqH+eYpJvpTGjX49ngngnwncN6vFu4z4wf7ksTS8eI63ES6QRxPU48Hc981hOZ4XRmOGkkM309mem6fdZN/Wiqo98pqFgUC5pqw5IN75qSDfiqZb+gb1FsBx6v2rpOEBQrY0rtWFUrFtWCTbNhUS2ZTsn0NUXDqlrRVC27/Z6fDzhN0brWk13XvtM0Rcvbv1wM1pB+tAwDIhGF9naFUHOKSGOMkD9BpCVFqFUn1GYQCpqE2hXCEY32qIX2mI1IwkokaSecthNOOwnpLvR+aBihouMiiosoBURwKTFcahynmsBlSeK0JCmwJXFa0zjteqZzmjgdBk4XuFwmDreKs0DB6dFweDScXgtOr4bDa8FRaMXusYLTgelwgCPbV/vvUZNdx1kTVU1gUeNoahxNy4T3ruGu8Zoay3zWsuPUOBYtts9wosf4AnsCRYmhaU1HVV5p7iKEEHlKUyxomgWHdiRvc8zoqxMP3dAPGOI7hhNGglW7oqTNJCkjSdpIkjKz/c7PiX0+d5tuJkkZCdJGCt1MkzZT6EYq0zfTpI3kQcanSJtJTAxSZpKUnuyT/e105C+27HOdJwLZkN8R6g93YrBv+N93WU3V0BQNVVE7x6mK2nmC0DF8wHmy6+sY7piv+zpLWkqIhCNd6+k+3z6fDzjPPsOqonbuS/ey5dtJjKqCx2Pi8ZhQpQHubHekdCCMaYZJJjOBPxJWiLSliLUmibQkibYmMVM2mutDRIImkZBJNAKRiEo4qhGJa0TjVkJJG5GUnUjaQVh3EtXtpLBlr8tnmw2ZdLXb78PrnA5iOLt3ShyHksShJXBqSRxaEocljdOSZpp3B9ePfhrT4cC02zGdzky/I+RnhyNXXw3OQ93HoGAYDpLG4e4F+Hg6jrNDj661i4R0IYQQvaepGk7VidNy8B/CSDA3f7E0TRMDHd1IkzaT3cJ7R6DPjjfS2aCfzM6bQjdT3U4M9h9fXWKQMlKkjTRJI0naSJM20uim3jleN/XO8ft9NtPoRmbefefrmJY2919WN7Pj9XQmJIn9dA/s3U8Sup8AdHQdob5juPu07tM1RUNFPfT0A0xTFRUFJXMiQeYERqHn9rovf8jydFu+Y32HLE+BiupWUUdkphUVFqGEIpR3X15V91uPoiidZVQVFSOlkUxqJBMWklELibBJqt0kGTJIhU2SYZNkGBJhSEYU4lGFREwhEVNJxDVicY1EwkI8YSGeshJL2YinbcTSdqK6g5jhIGHaieMkjpO2jn9IM9sZ7HcycGHLs3xr5yuH/S5Er7zyKBuaHBsGLKSvX7+eBx54AMMwOOecc7j00kt7TE+lUtx1113s2LEDj8fDzTffTHl5OQBLlixh2bJlqKrKl770JU466aQjWqcQQojjj6IoaGT+ymCjb6+U5apNumEahw3/aSN7AmCmMmH/EOG/+4lFx7p1Q8cwjc75u2+zc57sfB3Dhml0rqv7ejrnNXUsVgvReDQzvfs0o9s6ernNA43r6FKk5ERmIBRku4rDzXgIhgppB6ScmX7amR12oaYLUNIOlLQLJeVESbt4xVOPd5wTBVBRUM3MgxpVs6NTUEwTnp7fdfKB0nkCFE8pZBrDqSjdpinZE5bM3TZKdrmDTFfUzJSO5cmc4KiokJ33HzWZE62nPv/UUVXxgIR0wzC4//77+fGPf4zP5+MHP/gB06dPZ9iwYZ3zLFu2jIKCAu68805WrFjBww8/zDe+8Q1qa2tZuXIlv/vd72hra+MXv/gFf/jDHwAOu04hhBBiMOgIHFY1/25cHYg26aZp9gzx2ZOS7icGhmlgYmZOBjB6BPvO6dn1dIwzTbPH9M6OzHpNDjK92/IHmzZQ5bFYLcQT8YOWp2OZjvVg0jncUYbu83QfBvZbV0cZDYzO4Y5lMn/l6rZezcSwpDDsic55OhgH+bdOHOgSuULPx6pHon311fp4+ujrPiAhfdu2bVRWVlJRkTndmjNnDqtXr+4RqNesWcPll18OwOzZs/nrX/+KaZqsXr2aOXPmYLVaKS8vp7Kykm3btgEcdp1CCCGEGPwURcm0tZdWvPvJtxt39w32Bwr/H+fkwTAN3tmZwqRjfV0nEnScRGBgmka2xU3HusxuZcj26TrhINvvGGdickKlgtkHDW4G5Nvc2tqKz+fr/Ozz+di6detB59E0DZfLRSgUorW1lXHjxnXOV1JSQmtra+d6DrVOIYQQQgiRPzra1veH2oKBuU9m7pi++YvXgIT0Az3lcd+7sA82z8GeEHkk6+ywdOlSli5dCsDixYsZOnQonz3KO26FEKK3hh7iVv/BeEwajPskPp5DffdF/5P6z8i3Y1L/PYyyG5/PR0tLS+fnlpYWiouLDzqPrutEo1Hcbvd+y7a2tlJSUnJE6+ywYMECFi9ezOLFi/n+97/fl7smekHqPrek/nNL6j93pO5zS+o/t6T+c+do635AQvqYMWOor6/H7/eTTqdZuXIl06dP7zHPqaeeyvLlywF4++23mTx5MoqiMH36dFauXEkqlcLv91NfX8/YsWOPaJ1CCCGEEELkowFp7qJpGtdeey233norhmEwb948qqureeyxxxgzZgzTp09n/vz53HXXXdx444243W5uvvlmAKqrqznttNP45je/iaqqfPnLX0bNvo3qQOsUQgghhBAi3ynmwRp9D1JLly5lwYIFuS7GcUnqPrek/nNL6j93pO5zS+o/t6T+c+do6/64C+lCCCGEEEIc6wakTboQQgghhBDiyB03T/1fv349DzzwAIZhcM4553DppZfmukiD3vXXX4/D4UBVVTRNY/HixYTDYX7/+9/T1NREWVkZ3/jGN3C73bku6qBwzz33sHbtWgoLC7n99tsBDlrfpmnywAMPsG7dOux2O9dddx2jR4/O8R7krwPV/eOPP84rr7yC1+sF4KqrruKUU04BYMmSJSxbtgxVVfnSl77ESSedlLOyDwbNzc3cfffdBAIBFEVhwYIFXHjhhfL9HwAHq3v5/g+MZDLJz372M9LpNLquM3v2bK644gr8fj933HEH4XCYUaNGceONN2KxWEilUtx1113s2LEDj8fDzTffTHn/eoaPAAANdUlEQVR5ea53I28drP7vvvtuPvroI1wuF5DJQyNHjuz9scc8Dui6bt5www1mQ0ODmUqlzG9/+9vmnj17cl2sQe+6664zg8Fgj3EPPfSQuWTJEtM0TXPJkiXmQw89lIuiDUobNmwwt2/fbn7zm9/sHHew+n733XfNW2+91TQMw9y8ebP5gx/8ICdlHiwOVPePPfaY+dRTT+037549e8xvf/vbZjKZNBsbG80bbrjh/2/vzmOiON8Ajn93gFXxwGVBN2LUeKBCtEXYqLgoVNs/esSUECy93CilHtFWo1VLbWqq6SHQaqXRBohIrTaaRtMmLbHEI6i0oMUDUhXrhUulsKjIIezu/P4gTjzYiv1xrPh8/trZnZ195uHd4Zl335lXdTqdnRlut2O329Xz58+rqqqq9fX16uLFi9UrV65I++8E7nIv7b9zuFwutaGhQVVVVW1ublZXrVqlnjlzRk1NTVXz8/NVVVXVLVu2qLm5uaqqquovv/yibtmyRVVVVc3Pz1fT0tK6JvBuwl3+N23apB49evSB9R/12PNEDHcpKyvDZDIxcOBAvL29iYyMpLCwsKvDeiIVFhYybdo0AKZNmyZ/h3YUEhLywK8S7vJdVFTE1KlT0el0BAcHU1dXR01NTafH3F20lnt3CgsLiYyMxMfHhwEDBmAymSgrK+vgCLs3g8Gg9Ub16tWLoKAg7Ha7tP9O4C737kj7b186nY6ePXsCLXPMOJ1OdDodJSUlTJo0CYDo6Oh72n50dDQAkyZN4vTp024njRQP5y7/7jzqseeJKNLtdjtGo1FbNhqN/3oQEe1n3bp1rFixQpvx9caNG9qkUwaDgZs3b3ZleN2eu3zb7XYCAgK09eQ70TFyc3NZtmwZX3/9Nbdu3QIePB75+/tL7ttRZWUlFy5cYOTIkdL+O9nduQdp/53F5XKxfPlyEhMTGTduHAMHDsTX1xcvLy/g3hzfnX8vLy98fX2pra3tsti7g/vzP2rUKAB27NjBsmXL2Lp1K83NzcCjH3ueiDHprZ0l/tuZjmgfH3/8Mf7+/ty4cYO1a9fKtMQeRL4THe+5554jLi4OgO+//55t27axYMEC6bXqQI2NjaSmpmK1WrWxoK2R9t/+7s+9tP/OoygK69evp66ujpSUFK5evep2XWn77e/+/F++fJlXX32V/v3743A42LJlC3v37iUuLu6R8/9E9KQbjUaqq6u15erqaq13RXQcf39/APz8/DCbzZSVleHn56f9tFNTU6NdVCQ6hrt8G41GqqqqtPXkO9H++vfvj6IoKIrC9OnTOX/+PPDg8chut2vfFfHfORwOUlNTiYqKYuLEiYC0/87SWu6l/Xe+3r17ExISwrlz56ivr8fpdAL35vju/DudTurr6+XmDe3kTv6Li4sxGAzodDp8fHyIiYnRhnQ96rHniSjSR4wYQUVFBZWVlTgcDo4cOUJERERXh9WtNTY20tDQoD0+efIkQ4YMISIigoMHDwJw8OBBzGZzV4bZ7bnLd0REBIcOHUJVVc6ePYuvr68UKe3s7nGGv//+uzYjckREBEeOHKG5uZnKykoqKiq04QHiv1FVlc2bNxMUFMSLL76oPS/tv+O5y720/85x8+ZN6urqgJY7jZw6dYqgoCBCQ0MpKCgA4MCBA1rNEx4ezoEDBwAoKCggNDRUetL/D+7yf6f9q6pKYWHhPe3/UY49T8xkRsePHyc7OxuXy0VMTAyxsbFdHVK3du3aNVJSUoCWs3WLxUJsbCy1tbV88cUXVFVVERAQwNKlS+Usvp18+eWXlJaWUltbi5+fH/Hx8ZjN5lbzraoqmZmZnDhxAr1ez4IFCxgxYkRX78Jjq7Xcl5SUcPHiRXQ6HYGBgSQlJWkH4x9++IH9+/ejKApWq5WwsLAu3oPH259//smHH37IkCFDtIIjISGBUaNGSfvvYO5yf/jwYWn/neDSpUukp6fjcrlQVZXJkycTFxfHtWvXHrgFo4+PD01NTWzatIkLFy7Qp08f3n33XQYOHNjVu/HYcpf/NWvWaNfADB06lKSkJHr27PnIx54npkgXQgghhBDicfFEDHcRQgghhBDicSJFuhBCCCGEEB5GinQhhBBCCCE8jBTpQgghhBBCeBgp0oUQQgghhPAwUqQLIYQQQgjhYaRIF0KIdrRw4UJOnjzZpnU/+ugj8vLyOjii9ldZWUl8fLw2o6E7NpuNtLQ05s6dy+zZs1m2bBk//fQTLperQ+NLT09n586dHfoZQgjR0by7OgAhhBD/jcvlQlE8s6/l77//Jjk5mejoaFJSUjAYDNhsNnbt2kVDQwO9e/fu6hCFEMKjyWRGQgjRjhYuXMjbb7/N+PHjOXDgAHl5eYwaNYr9+/fj6+tLYmIiYWFh7Nixgz179uDt7Y2iKERHRzN37lyuXr1KVlYWf/31F/369WPWrFlERkYCLT3Eer2eqqoqSktLWb58OWPHjmXHjh0cPXoUh8OB2WzGarWi1+tZsmQJr7/+OuHh4UDL7L9JSUkkJyczfPhwzp49y7Zt2ygvLycwMBCr1UpoaCjQ0ss/ZswYSkpKuHTpEsHBwSxevJh+/foxf/58qqur6dGjBwCrV68mODj4njxs3LiRuro6Vq1a5TZXRUVFfPfdd9jtdoYNG0ZiYiKDBw8GID4+no0bN2IymbR9NxqNvPLKK5SUlPDVV1/xwgsvsHfvXhRFISEhgZiYGH799VcyMzMB8Pb2JjQ0lJUrV7Jnzx5+/vlnGhoaMBgMJCYmMm7cuHb8ywshRPvyzC4YIYToJsrKyhg0aBCZmZnMnDmTzZs3o6oqCQkJjB07ljlz5pCTk8PcuXNpbGxk7dq1WCwWMjIyeOedd8jMzOTKlSva9vLz83n55ZfJzs5mzJgxbN++nYqKCtavX8/GjRux2+3s3r0bgClTpnD48GHtvSdOnKBv374MHz4cu93Op59+SmxsLFlZWbzxxhukpqZqU1kDHD58mPnz55ORkYHD4eDHH38EYM2aNQBs3bqVnJycBwp0gFOnTjFp0iS3ebHZbGzYsAGr1UpGRgZhYWF89tlnOByONuX1+vXr1NfXs3nzZubNm0dmZia3bt1ixowZWCwWZs6cSU5ODitXrsRms5Gbm8snn3zCtm3bSE5OJjAwsE2fI4QQXUWKdCGE6EABAQHMmDEDRVGYNm0aNTU13Lhxo9V1jx8/TmBgIDExMXh5eTF8+HAmTpxIQUGBto7ZbGbMmDEoioKPjw95eXnMnj2bPn360KtXL2JjY7XC3GKxUFRUxO3bt4GWAt9isQBw6NAhwsLCmDBhAoqiMH78eEaMGMHx48e1z4qOjmbQoEHo9XomT57MxYsX27zft27dwmAwuH39yJEjhIWFMX78eLy9vXnppZdoamrizJkzbdq+l5cXcXFxeHt7M2HCBHr27InNZmt1XUVRaG5upry8HIfDwYABA7QeeiGE8FQyJl0IITpQ//79tcd3hoc0Nja2uu4///zDuXPnsFqt2nNOp5OpU6dqy0ajUXt88+ZNbt++zcqVK7XnVFXVLsw0mUwEBQVx7NgxwsPDKSoq4vPPPwegqqqKgoICjh07ds9n3Rnu0lrs7uJuTZ8+faipqXH7ek1NzT292YqiEBAQgN1ub9P2+/bti5eXV5viM5lMWK1Wdu3aRXl5OU899RRvvvkm/v7+bdwbIYTofFKkCyFEF9HpdPcsG41GQkJCWL16dZve07dvX/R6PWlpaW4LzilTppCfn4/L5WLw4MFaD7LRaCQqKop58+b933G3Zty4cfz222/ExMS0+rrBYODy5cvasqqqVFVVafvRo0cP7RcAaBnecvcJyqPGZ7FYsFgs1NfX880337B9+3YWLVrUpu0JIURXkOEuQgjRRfz8/Lh27Zq2HB4eTkVFBYcOHcLhcOBwOCgrK6O8vLzV9yuKwvTp09m6das2hMZut1NcXKytM2XKFE6ePMm+ffu0oS4AUVFRHDt2jOLiYlwuF01NTZSUlFBdXf3QuPv164dOp7sn9vvFx8dz5swZcnJyuH79OtByx5c7F5RGRkbyxx9/cOrUKW28u4+PD6NHjwZg2LBh2slFcXExpaWlD43rjvvzarPZOH36NM3Nzej1evR6vcfeFUcIIe6QnnQhhOgizz//POnp6ezbt4+oqCjmzJnDBx98QHZ2NtnZ2aiqytChQ5k9e7bbbbz22mvs3r2b5ORkamtr8ff359lnn+Xpp58GWnqsg4ODKS0tZcmSJdr7AgICeO+99/j222/ZsGEDiqIwcuRI3nrrrYfG3aNHD2JjY1m9ejVOp5P333//gYtHTSYT69atY+fOnSxduhSn08mAAQOIjo6mV69e9O7dm0WLFpGVlaXd3WXFihV4e7f8W7JaraSnp5Obm4vZbMZsNrc5r8888wxpaWlYrVZCQkKYNWsW27dv5+rVq3h5eTF69GiSkpLavD0hhOgKcgtGIYQQQgghPIz83ieEEEIIIYSHkSJdCCGEEEIIDyNFuhBCCCGEEB5GinQhhBBCCCE8jBTpQgghhBBCeBgp0oUQQgghhPAwUqQLIYQQQgjhYaRIF0IIIYQQwsNIkS6EEEIIIYSH+R8IHCa7GsnORwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib inline\n",
    "import warnings\n",
    "import matplotlib\n",
    "from matplotlib.pyplot import cm\n",
    "matplotlib.style.use('ggplot')\n",
    "matplotlib.rcParams['figure.figsize'] = (12.0, 8)\n",
    "\n",
    "DISTRIBUTIONS = [        \n",
    "stats.expon, stats.gamma, stats.weibull_min, stats.exponweib\n",
    "]\n",
    "colors=iter(['red', 'yellow', 'green', 'blue'])\n",
    "\n",
    "# Estimate distribution parameters from data and then plotting curves to show bestfit curve\n",
    "for distribution in DISTRIBUTIONS:\n",
    "   \n",
    "    with warnings.catch_warnings():\n",
    "        warnings.filterwarnings('ignore')\n",
    "        params = distribution.fit(interevent_counts_final)\n",
    "        dist = getattr(stats, distribution.name)\n",
    "\n",
    "        pdf = make_pdf(distribution, params)\n",
    "        ax = interevent_counts_final.plot(kind='hist', bins=25, density=True, alpha=0.5, color=list(matplotlib.rcParams['axes.prop_cycle'])[2]['color'] )\n",
    "        \n",
    "        # Save plot limits\n",
    "        dataYLim = ax.get_ylim()\n",
    "        ax.set_ylim(dataYLim)\n",
    "        ax.set_xlim((0,350))       \n",
    "        \n",
    "        pdf.plot(lw = 2, label=distribution.name, legend=True, color = next(colors))\n",
    "        interevent_counts_final.plot(kind='hist', bins=25,density=True,alpha=0.5,ax=ax, color = 'lightblue')\n",
    "        \n",
    "        #Setting title and labels\n",
    "        ax.set_title(u'Figure 1: Taiwan Earthquake Interevent Counts vs. Density')\n",
    "        ax.set_xlabel(u'Interevent Counts')\n",
    "        ax.set_ylabel('Density')\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "from shapely.geometry import Point, Polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "taiwan_map = gpd.read_file('./data/Map 1/TWN_adm1.shp')\n",
    "taiwan_map_0 = gpd.read_file('./data/Map 0/TWN_adm0.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1264cc43248>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize = (10, 10))\n",
    "taiwan_map.plot(ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date (mm/dd/yr)</th>\n",
       "      <th>Time</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Mag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1963-02-13</td>\n",
       "      <td>09:30:39.200000</td>\n",
       "      <td>24.6</td>\n",
       "      <td>121.8</td>\n",
       "      <td>77.0</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1963-03-04</td>\n",
       "      <td>13:38:42.500000</td>\n",
       "      <td>24.5</td>\n",
       "      <td>121.8</td>\n",
       "      <td>33.0</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1963-04-04</td>\n",
       "      <td>13:43:24.500000</td>\n",
       "      <td>21.1</td>\n",
       "      <td>121.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1963-05-25</td>\n",
       "      <td>01:35:54.100000</td>\n",
       "      <td>24.6</td>\n",
       "      <td>121.8</td>\n",
       "      <td>69.0</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1963-09-14</td>\n",
       "      <td>02:13:41.200000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Date (mm/dd/yr)             Time   Lat    Lon  Depth  Mag\n",
       "0      1963-02-13  09:30:39.200000  24.6  121.8   77.0  5.4\n",
       "1      1963-03-04  13:38:42.500000  24.5  121.8   33.0  4.8\n",
       "2      1963-04-04  13:43:24.500000  21.1  121.0   27.0  4.2\n",
       "3      1963-05-25  01:35:54.100000  24.6  121.8   69.0  5.4\n",
       "4      1963-09-14  02:13:41.200000  22.0  121.3   90.0  5.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earthquakes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<shapely.geometry.point.Point at 0x1264f4ef348>,\n",
       " <shapely.geometry.point.Point at 0x1264f4ef0c8>,\n",
       " <shapely.geometry.point.Point at 0x1264f4ef388>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geometry = [Point(xy) for xy in zip(earthquakes['Lon'], earthquakes['Lat'])]\n",
    "geometry[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\arshk\\anaconda3\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date (mm/dd/yr)</th>\n",
       "      <th>Time</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Mag</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1963-02-13</td>\n",
       "      <td>09:30:39.200000</td>\n",
       "      <td>24.6</td>\n",
       "      <td>121.8</td>\n",
       "      <td>77.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>POINT (121.80000 24.60000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1963-03-04</td>\n",
       "      <td>13:38:42.500000</td>\n",
       "      <td>24.5</td>\n",
       "      <td>121.8</td>\n",
       "      <td>33.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>POINT (121.80000 24.50000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1963-04-04</td>\n",
       "      <td>13:43:24.500000</td>\n",
       "      <td>21.1</td>\n",
       "      <td>121.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>POINT (121.00000 21.10000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1963-05-25</td>\n",
       "      <td>01:35:54.100000</td>\n",
       "      <td>24.6</td>\n",
       "      <td>121.8</td>\n",
       "      <td>69.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>POINT (121.80000 24.60000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1963-09-14</td>\n",
       "      <td>02:13:41.200000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>POINT (121.30000 22.00000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Date (mm/dd/yr)             Time   Lat    Lon  Depth  Mag  \\\n",
       "0      1963-02-13  09:30:39.200000  24.6  121.8   77.0  5.4   \n",
       "1      1963-03-04  13:38:42.500000  24.5  121.8   33.0  4.8   \n",
       "2      1963-04-04  13:43:24.500000  21.1  121.0   27.0  4.2   \n",
       "3      1963-05-25  01:35:54.100000  24.6  121.8   69.0  5.4   \n",
       "4      1963-09-14  02:13:41.200000  22.0  121.3   90.0  5.0   \n",
       "\n",
       "                     geometry  \n",
       "0  POINT (121.80000 24.60000)  \n",
       "1  POINT (121.80000 24.50000)  \n",
       "2  POINT (121.00000 21.10000)  \n",
       "3  POINT (121.80000 24.60000)  \n",
       "4  POINT (121.30000 22.00000)  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crs = {'init': 'epsg:4326'}\n",
    "geo_earthquakes = gpd.GeoDataFrame(earthquakes, crs = crs, geometry = geometry)\n",
    "geo_earthquakes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Figure 3: Taiwan Large Earthquakes Scatter Map')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minx, miny, maxx, maxy = geo_earthquakes.total_bounds\n",
    "fig,ax = plt.subplots(figsize = (10, 10))\n",
    "ax.set_xlim(minx-2, maxx+2)\n",
    "ax.set_ylim(miny-0.5, maxy+0.5)\n",
    "taiwan_map.plot(ax = ax, color = 'grey')\n",
    "geo_earthquakes[geo_earthquakes['Mag']>=6].plot(ax=ax, markersize=20,color='red', marker = 'o', label = 'large')\n",
    "# geo_earthquakes[geo_earthquakes['Mag']<6].plot(ax=ax, markersize=10,color='green', marker = '^', label = 'small')\n",
    "# plt.legend(prop={'size': 15})\n",
    "plt.title('Figure 3: Taiwan Large Earthquakes Scatter Map')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Figure 2: Taiwan Earthquakes Scatter Map')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minx, miny, maxx, maxy = geo_earthquakes.total_bounds\n",
    "fig,ax = plt.subplots(figsize = (10, 10))\n",
    "ax.set_xlim(minx-2, maxx+2)\n",
    "ax.set_ylim(miny-0.5, maxy+0.5)\n",
    "taiwan_map_0.plot(ax = ax, alpha =0.5, color = 'grey')\n",
    "geo_earthquakes.plot(ax=ax, column='Mag', legend = True, cmap='viridis',markersize=10, \n",
    "         legend_kwds={'label': \"Magnitude\",\n",
    "                      'orientation': \"vertical\"})\n",
    "plt.title('Figure 2: Taiwan Earthquakes Scatter Map')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}