Railroad_incidents_USA2019.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Railroad Incident Text Analysis\n",
    "\n",
    "This is a notebook to demonstrate how to analyse text using Python. This notebook is meant for people who usually use Excel to anaylse data that mainly consists of text. Some basic Python knowledge is necessary.\n",
    "\n",
    "The initial motivation for this notebook was to demonstrate to someone who is analysing railraod incidents in Britain (when rail tracks were tresspassed). I could not find a similar public dataset, however, found one which describes railroad incidents in the USA for 2019 - data source: https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/on_the_fly_download.aspx\n",
    "\n",
    "Those incidents are of different nature - they describe derailment, whether a train got struck, and other incidents that caused damages and potential hazardous materials to be released. Because I am only interested in the incident text, I deleted most columns in the dataset and only left Railroad, State, Station, County and Narrative (incident text).\n",
    "\n",
    "I am walking through my usual steps of data exploration when I am dealing with a dataset I have never seen before and then set myself some tasks to demonstrate you how to deal with text data in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading in the Data using Pandas\n",
    "\n",
    "pandas is a Python library for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data_df_ = pd.read_csv(\"rail_incident_data_USA.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok - we encounter our first problem. While exporting the data from the website, the incident text got divided into many separate columns. We want the text to be in one column only and therefore need to merge those 15 columns into one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IYR</th>\n",
       "      <th>IMO</th>\n",
       "      <th>RAILROAD</th>\n",
       "      <th>STATE</th>\n",
       "      <th>STATION</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>NARR1</th>\n",
       "      <th>NARR2</th>\n",
       "      <th>NARR3</th>\n",
       "      <th>NARR4</th>\n",
       "      <th>...</th>\n",
       "      <th>NARR6</th>\n",
       "      <th>NARR7</th>\n",
       "      <th>NARR8</th>\n",
       "      <th>NARR9</th>\n",
       "      <th>NARR10</th>\n",
       "      <th>NARR11</th>\n",
       "      <th>NARR12</th>\n",
       "      <th>NARR13</th>\n",
       "      <th>NARR14</th>\n",
       "      <th>NARR15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>ATK</td>\n",
       "      <td>6</td>\n",
       "      <td>CASTROVILLE</td>\n",
       "      <td>MONTEREY</td>\n",
       "      <td>CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...</td>\n",
       "      <td>HE OCCUPANT OF THE VEHICLE WAS A CONFIRMED FAT...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>MNCW</td>\n",
       "      <td>9</td>\n",
       "      <td>NORWALK</td>\n",
       "      <td>FAIRFIELD</td>\n",
       "      <td>PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...</td>\n",
       "      <td>E IMPACT WITH ONE ANOTHER.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>FEC</td>\n",
       "      <td>12</td>\n",
       "      <td>POMPANO BEACH</td>\n",
       "      <td>BROWARD</td>\n",
       "      <td>BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "      <td>G ACROSS THE TRACKS WITH HIS BIKE FROM THE WES...</td>\n",
       "      <td>LOCOMOTIVE ENGINEER SOUNDED HORN AND BELL TO W...</td>\n",
       "      <td>ASSER WAS STRUCK RESULTING IN A FATALITY.  BLF...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>FEC</td>\n",
       "      <td>12</td>\n",
       "      <td>POMPANO BEACH</td>\n",
       "      <td>BROWARD</td>\n",
       "      <td>BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "      <td>MOTIVE ENGINEER SOUNDED HORN AND BELL AND PLAC...</td>\n",
       "      <td>NGERS IN VEHICLE WERE TRANSPORTED TO LOCAL HOS...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>CSX</td>\n",
       "      <td>12</td>\n",
       "      <td>SEVILLE</td>\n",
       "      <td>VOLUSIA</td>\n",
       "      <td>AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   IYR  IMO RAILROAD  STATE        STATION     COUNTY  \\\n",
       "0   19   11      ATK      6    CASTROVILLE   MONTEREY   \n",
       "1   19   10     MNCW      9        NORWALK  FAIRFIELD   \n",
       "2   19    4      FEC     12  POMPANO BEACH    BROWARD   \n",
       "3   19    4      FEC     12  POMPANO BEACH    BROWARD   \n",
       "4   19    4      CSX     12        SEVILLE    VOLUSIA   \n",
       "\n",
       "                                               NARR1  \\\n",
       "0  CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...   \n",
       "1  PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...   \n",
       "2  BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...   \n",
       "3  BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...   \n",
       "4     AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.   \n",
       "\n",
       "                                               NARR2  \\\n",
       "0  HE OCCUPANT OF THE VEHICLE WAS A CONFIRMED FAT...   \n",
       "1                         E IMPACT WITH ONE ANOTHER.   \n",
       "2  G ACROSS THE TRACKS WITH HIS BIKE FROM THE WES...   \n",
       "3  MOTIVE ENGINEER SOUNDED HORN AND BELL AND PLAC...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                               NARR3  \\\n",
       "0                                                NaN   \n",
       "1                                                NaN   \n",
       "2  LOCOMOTIVE ENGINEER SOUNDED HORN AND BELL TO W...   \n",
       "3  NGERS IN VEHICLE WERE TRANSPORTED TO LOCAL HOS...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                               NARR4  ... NARR6 NARR7 NARR8  \\\n",
       "0                                                NaN  ...   NaN   NaN   NaN   \n",
       "1                                                NaN  ...   NaN   NaN   NaN   \n",
       "2  ASSER WAS STRUCK RESULTING IN A FATALITY.  BLF...  ...   NaN   NaN   NaN   \n",
       "3                                                NaN  ...   NaN   NaN   NaN   \n",
       "4                                                NaN  ...   NaN   NaN   NaN   \n",
       "\n",
       "  NARR9 NARR10 NARR11 NARR12 NARR13 NARR14 NARR15  \n",
       "0   NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "1   NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "2   NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "3   NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "4   NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_df_.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing Duplicates\n",
    "\n",
    "Before I merge the NARR columns, let's check whether there are any duplicates in the data and get rid of them. Pandas has an in-built duplicated() method which we can use for that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a new column in the dataframe that stores a boolean value whether that row is a duplicate or not\n",
    "raw_data_df_['dup'] = raw_data_df_.duplicated(subset=None, keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    2401\n",
       "True      284\n",
       "Name: dup, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking how many duplicates there were by counding the True and False values in that column\n",
    "raw_data_df_['dup'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are quite a few duplicates. About 10% of the dataset are duplicates. Let's delete them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's write the data into a new dataframe, only keeping the lines where the value for \"dup\" is False\n",
    "raw_data_df = raw_data_df_[raw_data_df_['dup'] == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "del raw_data_df['dup'] # deleting \"dup\" column since we don't need ot anymore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging incident text into one column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List slicing and lambda functions in python\n",
    "Ok, a lot is going on in the function below but let's break it down\n",
    "1. we are creating a new column (NARR) which will contain the whole narrative of the incident text\n",
    "2. we select all columns starting from columns 7 (NARR1) until the end. That's what [6:] is going: starting from column 6 (column 0 being the first) select all the columns until the end. If we wanted to merge only NARR1 and NARR2, we would write [6:8]. In python the ending column is not included so we would select only columns 6 and 7\n",
    "3. we are applying a lambda function to selected cell (x). A lambda function is a small anonymous function and comes handy when we are doing an operation only once and do not need to define a separate function for it\n",
    "4. the function iterates through each cell in the row starting at position 5 and joins it with the subsequent cells as a type string. If there cell is empty we ignore it (dropna()). The reason why we are expresively defining that the cell should be of type string is because some entries might not have an incident text (all NANs) and would be picked up by python as a float type\n",
    "5. the function goes through each row in the dataframe and does the merging described in 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "raw_data_df['NARR'] = raw_data_df[raw_data_df.columns[6:]].apply(\n",
    "    lambda x: ' '.join(x.dropna().astype(str)),\n",
    "    axis=1\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IYR</th>\n",
       "      <th>IMO</th>\n",
       "      <th>RAILROAD</th>\n",
       "      <th>STATE</th>\n",
       "      <th>STATION</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>NARR1</th>\n",
       "      <th>NARR2</th>\n",
       "      <th>NARR3</th>\n",
       "      <th>NARR4</th>\n",
       "      <th>...</th>\n",
       "      <th>NARR7</th>\n",
       "      <th>NARR8</th>\n",
       "      <th>NARR9</th>\n",
       "      <th>NARR10</th>\n",
       "      <th>NARR11</th>\n",
       "      <th>NARR12</th>\n",
       "      <th>NARR13</th>\n",
       "      <th>NARR14</th>\n",
       "      <th>NARR15</th>\n",
       "      <th>NARR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>ATK</td>\n",
       "      <td>6</td>\n",
       "      <td>CASTROVILLE</td>\n",
       "      <td>MONTEREY</td>\n",
       "      <td>CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...</td>\n",
       "      <td>HE OCCUPANT OF THE VEHICLE WAS A CONFIRMED FAT...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>MNCW</td>\n",
       "      <td>9</td>\n",
       "      <td>NORWALK</td>\n",
       "      <td>FAIRFIELD</td>\n",
       "      <td>PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...</td>\n",
       "      <td>E IMPACT WITH ONE ANOTHER.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>FEC</td>\n",
       "      <td>12</td>\n",
       "      <td>POMPANO BEACH</td>\n",
       "      <td>BROWARD</td>\n",
       "      <td>BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "      <td>G ACROSS THE TRACKS WITH HIS BIKE FROM THE WES...</td>\n",
       "      <td>LOCOMOTIVE ENGINEER SOUNDED HORN AND BELL TO W...</td>\n",
       "      <td>ASSER WAS STRUCK RESULTING IN A FATALITY.  BLF...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>FEC</td>\n",
       "      <td>12</td>\n",
       "      <td>POMPANO BEACH</td>\n",
       "      <td>BROWARD</td>\n",
       "      <td>BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "      <td>MOTIVE ENGINEER SOUNDED HORN AND BELL AND PLAC...</td>\n",
       "      <td>NGERS IN VEHICLE WERE TRANSPORTED TO LOCAL HOS...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>CSX</td>\n",
       "      <td>12</td>\n",
       "      <td>SEVILLE</td>\n",
       "      <td>VOLUSIA</td>\n",
       "      <td>AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   IYR  IMO RAILROAD  STATE        STATION     COUNTY  \\\n",
       "0   19   11      ATK      6    CASTROVILLE   MONTEREY   \n",
       "1   19   10     MNCW      9        NORWALK  FAIRFIELD   \n",
       "2   19    4      FEC     12  POMPANO BEACH    BROWARD   \n",
       "3   19    4      FEC     12  POMPANO BEACH    BROWARD   \n",
       "4   19    4      CSX     12        SEVILLE    VOLUSIA   \n",
       "\n",
       "                                               NARR1  \\\n",
       "0  CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...   \n",
       "1  PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...   \n",
       "2  BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...   \n",
       "3  BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...   \n",
       "4     AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.   \n",
       "\n",
       "                                               NARR2  \\\n",
       "0  HE OCCUPANT OF THE VEHICLE WAS A CONFIRMED FAT...   \n",
       "1                         E IMPACT WITH ONE ANOTHER.   \n",
       "2  G ACROSS THE TRACKS WITH HIS BIKE FROM THE WES...   \n",
       "3  MOTIVE ENGINEER SOUNDED HORN AND BELL AND PLAC...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                               NARR3  \\\n",
       "0                                                NaN   \n",
       "1                                                NaN   \n",
       "2  LOCOMOTIVE ENGINEER SOUNDED HORN AND BELL TO W...   \n",
       "3  NGERS IN VEHICLE WERE TRANSPORTED TO LOCAL HOS...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                               NARR4  ... NARR7 NARR8 NARR9  \\\n",
       "0                                                NaN  ...   NaN   NaN   NaN   \n",
       "1                                                NaN  ...   NaN   NaN   NaN   \n",
       "2  ASSER WAS STRUCK RESULTING IN A FATALITY.  BLF...  ...   NaN   NaN   NaN   \n",
       "3                                                NaN  ...   NaN   NaN   NaN   \n",
       "4                                                NaN  ...   NaN   NaN   NaN   \n",
       "\n",
       "  NARR10 NARR11 NARR12 NARR13 NARR14 NARR15  \\\n",
       "0    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "2    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "3    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "4    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "                                                NARR  \n",
       "0  CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...  \n",
       "1  PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...  \n",
       "2  BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...  \n",
       "3  BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...  \n",
       "4     AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, we see that it has worked - we have a new column with just NARR. But let's double check whether the function has really done what we wanted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECES OF EQUIPMENT, HAD 7 SEPERATE PIECES OF EQUIPMENT MAK'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_df.iloc[1,6] # NARR1 in first row 7th column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'E IMPACT WITH ONE ANOTHER.'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_df.iloc[1,7] #NARR2 in first row 8th column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECES OF EQUIPMENT, HAD 7 SEPERATE PIECES OF EQUIPMENT MAK E IMPACT WITH ONE ANOTHER.'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_df.iloc[1,21] #NARR raw_data_df.iloc[1,-1] another way of selecting last row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great - it has worked! The incident text of the first two NARR columns got combined into the newly created NARR column. Now let's select only this column and the other few"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we want to delete columns in range 6-20\n",
    "cols = list(range(6,21))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why am I not just selecting the columns I need and create a new dataframe?\n",
    "I could: df_new= raw_data_df[['IYR','IMO', 'STATE', 'RAILROAD', 'STATION', 'COUNTY', 'NARR]]\n",
    "But imagine you have many more columns in your dataset. Doing it this way could be very tedious\n",
    "However, we know the range of columns we want to delete - 6th until 19th columns, so we can define that range and tell pandas to delete that range of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data_df.drop(raw_data_df.columns[cols],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>IYR</th>\n",
       "      <th>IMO</th>\n",
       "      <th>RAILROAD</th>\n",
       "      <th>STATE</th>\n",
       "      <th>STATION</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>NARR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>ATK</td>\n",
       "      <td>6</td>\n",
       "      <td>CASTROVILLE</td>\n",
       "      <td>MONTEREY</td>\n",
       "      <td>CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>MNCW</td>\n",
       "      <td>9</td>\n",
       "      <td>NORWALK</td>\n",
       "      <td>FAIRFIELD</td>\n",
       "      <td>PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>FEC</td>\n",
       "      <td>12</td>\n",
       "      <td>POMPANO BEACH</td>\n",
       "      <td>BROWARD</td>\n",
       "      <td>BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>FEC</td>\n",
       "      <td>12</td>\n",
       "      <td>POMPANO BEACH</td>\n",
       "      <td>BROWARD</td>\n",
       "      <td>BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>CSX</td>\n",
       "      <td>12</td>\n",
       "      <td>SEVILLE</td>\n",
       "      <td>VOLUSIA</td>\n",
       "      <td>AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2679</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>TRC</td>\n",
       "      <td>6</td>\n",
       "      <td>TRONA</td>\n",
       "      <td>SAN BERNARDINO</td>\n",
       "      <td>RUN 2 WAS IN THE PROCESS OF PULLING THE HEAD 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2680</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>NCTC</td>\n",
       "      <td>6</td>\n",
       "      <td>OCEANSIDE</td>\n",
       "      <td>SAN DIEGO</td>\n",
       "      <td>METROLINK 858 DERAILED AT STUART MESA YARD.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2681</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>MET</td>\n",
       "      <td>6</td>\n",
       "      <td>MODESTO</td>\n",
       "      <td>STANISLAUS</td>\n",
       "      <td>ON THE MORNING OF JANUARY 15, 2019, CONDUCTOR ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2682</th>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>BNSF</td>\n",
       "      <td>6</td>\n",
       "      <td>BAKERSFIELD</td>\n",
       "      <td>KERN</td>\n",
       "      <td>EMPLOYEE STOPPED AND LINED THE DERAIL TO THE W...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2683</th>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>UP</td>\n",
       "      <td>6</td>\n",
       "      <td>BLOOMINGTON</td>\n",
       "      <td>SAN BERNARDINO</td>\n",
       "      <td>LOQ25-26 WAS MAKING A SHOVING MOVE WITH LIGHT ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2401 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      IYR  IMO RAILROAD  STATE        STATION          COUNTY  \\\n",
       "0      19   11      ATK      6    CASTROVILLE        MONTEREY   \n",
       "1      19   10     MNCW      9        NORWALK       FAIRFIELD   \n",
       "2      19    4      FEC     12  POMPANO BEACH         BROWARD   \n",
       "3      19    4      FEC     12  POMPANO BEACH         BROWARD   \n",
       "4      19    4      CSX     12        SEVILLE         VOLUSIA   \n",
       "...   ...  ...      ...    ...            ...             ...   \n",
       "2679   19    2      TRC      6          TRONA  SAN BERNARDINO   \n",
       "2680   19    2     NCTC      6      OCEANSIDE       SAN DIEGO   \n",
       "2681   19    1      MET      6        MODESTO      STANISLAUS   \n",
       "2682   19    7     BNSF      6    BAKERSFIELD            KERN   \n",
       "2683   19   12       UP      6    BLOOMINGTON  SAN BERNARDINO   \n",
       "\n",
       "                                                   NARR  \n",
       "0     CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...  \n",
       "1     PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...  \n",
       "2     BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...  \n",
       "3     BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...  \n",
       "4        AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.  \n",
       "...                                                 ...  \n",
       "2679  RUN 2 WAS IN THE PROCESS OF PULLING THE HEAD 1...  \n",
       "2680        METROLINK 858 DERAILED AT STUART MESA YARD.  \n",
       "2681  ON THE MORNING OF JANUARY 15, 2019, CONDUCTOR ...  \n",
       "2682  EMPLOYEE STOPPED AND LINED THE DERAIL TO THE W...  \n",
       "2683  LOQ25-26 WAS MAKING A SHOVING MOVE WITH LIGHT ...  \n",
       "\n",
       "[2401 rows x 7 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "Awesome. Finally we have our clean df and can start with our data exploration.\n",
    "Let's get some feel for the data check what Railroad, state, station and county is represented the most and in what month most incidents occur. If you are interested only in the text analysis, feel free so scroll ahead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.17"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_100 = raw_data_df['STATION'].value_counts()[:100]\n",
    "first_100.mean()\n",
    "#mean incidents per year of 100 biggest stations (cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.9849115884877095"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_100.std()\n",
    "#standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fba8961fe10>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#months\n",
    "raw_data_df['IMO'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting - it seems like january and august have more incidents than the other months. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fba87f7e2d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_data_df['STATE'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48    282\n",
       "17    235\n",
       "6     154\n",
       "31     91\n",
       "18     87\n",
       "Name: STATE, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#top 5\n",
    "raw_data_df['STATE'].value_counts()[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I do not know how the states are numbered in this dataset - it does not seem to be alphabetically. Let's check it manually by County\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IYR</th>\n",
       "      <th>IMO</th>\n",
       "      <th>RAILROAD</th>\n",
       "      <th>STATE</th>\n",
       "      <th>STATION</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>NARR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>XTGS</td>\n",
       "      <td>48</td>\n",
       "      <td>ANAHUAC</td>\n",
       "      <td>CHAMBERS</td>\n",
       "      <td>BALLAST WAS FOULING THE TRACK DURING LOADING A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>UP</td>\n",
       "      <td>48</td>\n",
       "      <td>BEAUMONT</td>\n",
       "      <td>JEFFERSON</td>\n",
       "      <td>THE YBM05R-07 HAD A HOLD OF 65 CARS IN HUMP OP...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>TXOR</td>\n",
       "      <td>48</td>\n",
       "      <td>SWEETWATER</td>\n",
       "      <td>NOLAN</td>\n",
       "      <td>TXOR DERAILED 15 LOADED CARS OUT OF 25 LOADED ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>UP</td>\n",
       "      <td>48</td>\n",
       "      <td>EL PASO</td>\n",
       "      <td>EL PASO</td>\n",
       "      <td>MHNTU-24 TRAVELING WEST DERAILED THE TRAILING ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>UP</td>\n",
       "      <td>48</td>\n",
       "      <td>EL PASO</td>\n",
       "      <td>EL PASO</td>\n",
       "      <td>MTUEP-11 WAS SHOVING LIGHT POWER WEST IN 835 T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2658</th>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>XTGS</td>\n",
       "      <td>48</td>\n",
       "      <td>ANAHUAC</td>\n",
       "      <td>CHAMBERS</td>\n",
       "      <td>CREW FAILED TO VERIFY LINING OF CROSSOVER SWIT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2659</th>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>UP</td>\n",
       "      <td>48</td>\n",
       "      <td>TOYAH</td>\n",
       "      <td>REEVES</td>\n",
       "      <td>TRAIN ISINSX-03 STRUCK A SEMI TRUCK LOADED WIT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2660</th>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>XTGS</td>\n",
       "      <td>48</td>\n",
       "      <td>ANAHUAC</td>\n",
       "      <td>CHAMBERS</td>\n",
       "      <td>CREW WAS PULLING CARS OUT OF TRACK 888 IN G-YA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2661</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>UP</td>\n",
       "      <td>48</td>\n",
       "      <td>WHITSETT</td>\n",
       "      <td>LIVE OAK</td>\n",
       "      <td>THE WVJVBR-13 STRUCK A LOADED SEMI OF FRACKING...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2662</th>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>UP</td>\n",
       "      <td>48</td>\n",
       "      <td>SIERRA BLANCA</td>\n",
       "      <td>HUDSPETH</td>\n",
       "      <td>ILXMNX-06, UP7496, HIT THE UNOCCUPIED VEHICLE,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>282 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      IYR  IMO RAILROAD  STATE        STATION     COUNTY  \\\n",
       "307    19   12     XTGS     48        ANAHUAC   CHAMBERS   \n",
       "308    19    1       UP     48       BEAUMONT  JEFFERSON   \n",
       "309    19   10     TXOR     48     SWEETWATER      NOLAN   \n",
       "310    19   10       UP     48        EL PASO    EL PASO   \n",
       "311    19    8       UP     48        EL PASO    EL PASO   \n",
       "...   ...  ...      ...    ...            ...        ...   \n",
       "2658   19    9     XTGS     48        ANAHUAC   CHAMBERS   \n",
       "2659   19    3       UP     48          TOYAH     REEVES   \n",
       "2660   19    9     XTGS     48        ANAHUAC   CHAMBERS   \n",
       "2661   19    2       UP     48       WHITSETT   LIVE OAK   \n",
       "2662   19    3       UP     48  SIERRA BLANCA   HUDSPETH   \n",
       "\n",
       "                                                   NARR  \n",
       "307   BALLAST WAS FOULING THE TRACK DURING LOADING A...  \n",
       "308   THE YBM05R-07 HAD A HOLD OF 65 CARS IN HUMP OP...  \n",
       "309   TXOR DERAILED 15 LOADED CARS OUT OF 25 LOADED ...  \n",
       "310   MHNTU-24 TRAVELING WEST DERAILED THE TRAILING ...  \n",
       "311   MTUEP-11 WAS SHOVING LIGHT POWER WEST IN 835 T...  \n",
       "...                                                 ...  \n",
       "2658  CREW FAILED TO VERIFY LINING OF CROSSOVER SWIT...  \n",
       "2659  TRAIN ISINSX-03 STRUCK A SEMI TRUCK LOADED WIT...  \n",
       "2660  CREW WAS PULLING CARS OUT OF TRACK 888 IN G-YA...  \n",
       "2661  THE WVJVBR-13 STRUCK A LOADED SEMI OF FRACKING...  \n",
       "2662  ILXMNX-06, UP7496, HIT THE UNOCCUPIED VEHICLE,...  \n",
       "\n",
       "[282 rows x 7 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_df[raw_data_df['STATE'] == 48]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### some of the states\n",
    "\n",
    "48: Texas\n",
    "\n",
    "17: Illinois\n",
    "\n",
    "6: California\n",
    "\n",
    "2: Alaska (not surprising)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COOK           106\n",
       "JEFFERSON       65\n",
       "LINCOLN         43\n",
       "ST CLAIR        38\n",
       "HARRIS          37\n",
       "JACKSON         32\n",
       "LOS ANGELES     31\n",
       "TARRANT         27\n",
       "WYANDOTTE       26\n",
       "MARION          26\n",
       "Name: COUNTY, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_df['COUNTY'].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cook County is only the second most populous county in the states after LA - but has almost 4 times as many incidents as LA. However, Illinois has more railway track (9,982 miles) compared to Californias 6863 miles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KANSAS CITY      48\n",
       "CHICAGO          34\n",
       "NORTH PLATTE     34\n",
       "EAST ST LOUIS    32\n",
       "MELROSE PARK     24\n",
       "DAYTON           22\n",
       "HOUSTON          22\n",
       "FORT WORTH       17\n",
       "NASHVILLE        17\n",
       "LINCOLN          15\n",
       "Name: STATION, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_df['STATION'].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow - Kansas City in Missouri (which does not even come up in the top 5 states with most incidents) leads the table with 48 incidents in 2019. NORTH PLATTE in Nebraska is second (#5 in our list above of top 5 states). Chicago is in Illinois (#2). Melrose Park (Illinois), East St Louis (Illinois)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UP      689\n",
       "BNSF    403\n",
       "NS      309\n",
       "CSX     204\n",
       "ATK     119\n",
       "Name: RAILROAD, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_df['RAILROAD'].value_counts()[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it seems like UP (Union Pacific) has the most incidents. But maybe they just have more miles than the other companies. I have googled how many track miles each company has \n",
    "\n",
    "UP      32,100 miles\n",
    "BNSF    32,500 miles \n",
    "NS      20,000 miles\n",
    "CSX     21,000 miles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I am storing the number of incidents and the railroad miles in lists\n",
    "railroad_incidents = [689, 403, 309, 204]\n",
    "railroad_miles = [32100, 32500, 20000, 21000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# if uising a Jupyter notebook, include:\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets plot number of incidents and track miles\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(railroad_miles,railroad_incidents)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that UP is clearly an outlier compared to the other 3 railroads when comparing incident number to railroad miles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kansas City as example\n",
    "Out of curiosity I will have a look at the incidents in kansas city and save it in a new dataframe kansas_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "kansas_df = raw_data_df[raw_data_df['STATION'] == \"KANSAS CITY\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>IYR</th>\n",
       "      <th>IMO</th>\n",
       "      <th>RAILROAD</th>\n",
       "      <th>STATE</th>\n",
       "      <th>STATION</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>NARR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>KCT</td>\n",
       "      <td>20</td>\n",
       "      <td>KANSAS CITY</td>\n",
       "      <td>WYANDOTTE</td>\n",
       "      <td>CREW ON UP TRAIN MNPINP-19 USING KCT MAIN LINE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>UP</td>\n",
       "      <td>29</td>\n",
       "      <td>KANSAS CITY</td>\n",
       "      <td>JACKSON</td>\n",
       "      <td>THE YKC20R-25 WAS HUMPING A CUT OF 100 CARS OF...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>KCS</td>\n",
       "      <td>29</td>\n",
       "      <td>KANSAS CITY</td>\n",
       "      <td>JACKSON</td>\n",
       "      <td>CREW FAILED TO LINE BOTH ENDS OF CROSSOVER FRO...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>KCS</td>\n",
       "      <td>29</td>\n",
       "      <td>KANSAS CITY</td>\n",
       "      <td>JACKSON</td>\n",
       "      <td>CPRS CREW FAILED TO SECURE TRACK 9 &amp; THEY KICK...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>KCS</td>\n",
       "      <td>29</td>\n",
       "      <td>KANSAS CITY</td>\n",
       "      <td>JACKSON</td>\n",
       "      <td>BNSF CREW RAN THRU 126 SWITCH &amp; THEN SHOVED BA...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    IYR  IMO RAILROAD  STATE      STATION     COUNTY  \\\n",
       "18   19    8      KCT     20  KANSAS CITY  WYANDOTTE   \n",
       "36   19    2       UP     29  KANSAS CITY    JACKSON   \n",
       "37   19    2      KCS     29  KANSAS CITY    JACKSON   \n",
       "38   19    2      KCS     29  KANSAS CITY    JACKSON   \n",
       "41   19   12      KCS     29  KANSAS CITY    JACKSON   \n",
       "\n",
       "                                                 NARR  \n",
       "18  CREW ON UP TRAIN MNPINP-19 USING KCT MAIN LINE...  \n",
       "36  THE YKC20R-25 WAS HUMPING A CUT OF 100 CARS OF...  \n",
       "37  CREW FAILED TO LINE BOTH ENDS OF CROSSOVER FRO...  \n",
       "38  CPRS CREW FAILED TO SECURE TRACK 9 & THEY KICK...  \n",
       "41  BNSF CREW RAN THRU 126 SWITCH & THEN SHOVED BA...  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kansas_df.head()\n",
    "#dataframe that only contains incidents that happened in Kansas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fba878df350>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kansas_df['STATE'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not sure why they divide Kansas into Kansas and Missouri if the station is the same - but it does not really matter. Given there are 49 incidents in Kansas, we see in the table with the counties with the most incidents that Jackson and Wyandotte have 58 incidents, hence only 11 outside of Kansas City. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fba878c1c50>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kansas_df['RAILROAD'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ha - we can see that again, UP is overrepresented with incidents. accoriding to this map https://www.ksdot.org/Assets/wwwksdotorg/bureaus/burRail/Rail/publications/KansasRailroadMap2019.pdf  UP only has 300 miles more than BNSF but almost 3 times as many incidents.\n",
    "\n",
    "It will be interesting to check the incidents texts. Let's finally dive in and check the incidents texts. First for Kansas City only. lets subdivide the kansas dataframe further into a dataframe for UP and a dataframe for the rest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "kansas_up_df = kansas_df[kansas_df['RAILROAD'] == \"UP\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "kansas_rr_rest_df = kansas_df[kansas_df['RAILROAD'] != \"UP\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some Text Preprocessing\n",
    "Below you see a function which I have copy pasted from one of my own projects. it's my typical go-to text preprocessing function where I delete all stopwords symbols apart from letters, convert the text into lower case and stem or lemmatise the words.\n",
    "\n",
    "Stopwords: A stop word is a commonly used word (such as “the”, “a”, “an”, “in”). We would not want these words to take up space in our dataset, or taking up valuable processing time. Also, wenn looking for common words in our dataset, finding out that \"a\" is the most common, is not particularly interesting\n",
    "\n",
    "Lemmatising: Lemmatisation (or lemmatization) in linguistics is the process of grouping together the inflected forms of a word so they can be analysed as a single item, identified by the word's lemma, or dictionary form.\n",
    "\n",
    "Loved -> love\n",
    "\n",
    "Stemming: In linguistic morphology and information retrieval, stemming is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form—generally a written word form\n",
    "\n",
    "Loved -> lov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from nltk.stem import WordNetLemmatizer, PorterStemmer, SnowballStemmer\n",
    "stop_words_file = 'SmartStoplist.txt'\n",
    "\n",
    "stop_words = []\n",
    "\n",
    "with open(stop_words_file, \"r\") as f:\n",
    "    for line in f:\n",
    "        stop_words.extend(line.split()) \n",
    "        \n",
    "stop_words = stop_words  \n",
    "\n",
    "def preprocess(raw_text):\n",
    "    \n",
    "    #regular expression keeping only letters - more on them later\n",
    "    letters_only_text = re.sub(\"[^a-zA-Z]\", \" \", raw_text)\n",
    "\n",
    "    # convert to lower case and split into words -> convert string into list ( 'hello world' -> ['hello', 'world'])\n",
    "    words = letters_only_text.lower().split()\n",
    "\n",
    "    cleaned_words = []\n",
    "    lemmatizer = PorterStemmer()\n",
    "    \n",
    "    # remove stopwords\n",
    "    for word in words:\n",
    "        if word not in stop_words:\n",
    "            cleaned_words.append(word)\n",
    "    \n",
    "    # stemm or lemmatise words\n",
    "    stemmed_words = []\n",
    "    for word in cleaned_words:\n",
    "        word = lemmatizer.stem(word)\n",
    "        stemmed_words.append(word)\n",
    "    \n",
    "    # converting list back to string\n",
    "    return \" \".join(stemmed_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sentenc demonstr preprocess function work'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_sentence = \"this is a sentence to demonstrate how the preprocessing function works...!\"\n",
    "\n",
    "preprocess(test_sentence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can see that \"sentence\" was stemmed to \"sentenc\", all stop words and punctuation were removed.\n",
    "Let's apply that function to the incident texts in our kansas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "kansas_up_df['NARR'] = kansas_up_df['NARR'].apply(preprocess)  #Union Pacific dataframe\n",
    "kansas_rr_rest_df['NARR'] = kansas_rr_rest_df['NARR'].apply(preprocess)  #rest dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most Common Words\n",
    "In order to get an idea about a dataset, it's useful to have a look at the most common words. Reading through all incident texts is cumbersome and inefficient. Let's extract the most common key words\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('car', 55),\n",
       " ('derail', 39),\n",
       " ('track', 37),\n",
       " ('cut', 19),\n",
       " ('lead', 17),\n",
       " ('rail', 16),\n",
       " ('train', 15),\n",
       " ('switch', 12),\n",
       " ('shove', 12),\n",
       " ('hump', 11)]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's check the most common words in the UP dataframe\n",
    "from collections import Counter\n",
    "Counter(\" \".join(kansas_up_df[\"NARR\"]).split()).most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xqHIlFly9prAtRfjJMewzn27P9l4JbZDXMI/c86Z65gxzmytzXl/HgNHu6Zo6JSyrSJVnFYc+1XODwjJl8ev5TAoxvvgquWWKrg39rNqMHcDMyJALY/opr6gCKhv2YkUd6dB7DRbmxmxa7iNVZmioz6LVZzE1MZQY656dqtKk41JO7RVinXes7Uf3QRuIiU3in40DZNo/cfYJS9aco6RnfurXSverfYvdRfBX5bG62uLp608aXxuTEv9LGnVrOT+s7QuO0WVUU6Zs6C9xqYxeJhh1ADMLEya2W0xMVDypKSJ8TzzE2NSIM7uu41LUkbfPgug1uTV57S1k2gaYXaq3SjHYytDX0aOqrQfFLQpQzCI/TibW5NE3xUTPkOiUeEISvvEi6gM3wp7xKiaYsETVf2hZ0e/2Ii7VXyh1rqitDSd9elF51RoSUlLY3L4dtQoXUtrWi9AwXK3l++7vBQWRIhLhvWu35Fx5Z9U3VKXhl2kfwNXQp1z48lDtdnKCzG6Y/xKqGvZtNx/Qo4rsRCDoWxTOVvJ/Q1mhlitm3+b03YOXj46TKss4G08jzainsX1Neh03V3upsmYNS9KsYUmpc8mpwTlm1AfN3SP1f4C3wV/p2lizrdtixLS4PE15RRXRQYdm+SrSJn91iuZxVn5BBl5GB3Ey+DbHg2+RJEpRWn95BdlFU7t8gqGJCE1fWzEwTP+6dC0xgROfV3F29w0Ant4IYOjczvw99whtB9Zn3bS9fHz1BYBwBY+jBczseR8bovobA5rnq4x3gVoUNHNQWtfaMA/WhnkobuFC6/zVJOdFYjF/+m3n4pfsSTPv+3CVDi41pc652VjjN3I4YsBNxcgWd1tbAr9GyC0r4+REUmoqe7qotjNUEX6R7ySvE1KTmPZ4i1rXFzF3onPBOjRwLCdZtFRGVHIcR4JusPvdpSwjwmrZl1RY9qujqmH/68QlClpbUcs9PWLnjH8AI3Yfy7k49h/F60+1pI4tzTrgbL1UK22vmehNzQHLWDYmfbZgaKD5x1Hv/HhtDIsJnp1o6lQxW22453HGvVhbRhRrC0CKOJWNr09J7UDNiLOJrcy59dd/p23hkfSe0hprB0uZ8j93DaNl/t+Yu1+4eZer40HFBiV4cjOAOm0rsm7aXjwrutIy/28sPiH/s9lYaTQNL07M8r00dCxHbftScqMmNEVXR4cZJXowo0QPxj/cwO3w5xq1s+LlYSnDvuvRY5oXL4aFkREJycpvqGmUdHQgVSzmZVgY7ra29N5/QKosRSTioL8/bT2FrfgLr15jbM0aao01UZS+SanJJdVE78z1TdhYebTGkUAWBqZ0L1Sf7oUEH3h0SjwLn+394Qu135NQFRdPn80chcf0xRwe2gN3B1tmHDnHnrtPeDRdvbWUNH5qwy4Sp9/lC9huxdxEO4skabjlt82WMU/jS8K3bMsB6OrosKjsIMrkLZLt8WRGX0ePgW7Nae9Sgz+f7uDRtzeSMkWyAgaG+hwMXCK3DKB8XU+OfkzXCB++UAjj6zNVuJn881xwU2SsI9OHruLP3lTPiCklulLd1kthHW0wv0w/ml6aQnxqovLKSrgfHMy0s+fwcnDg9ddwTDJ8t86+esXN9x+5HxTM6/CvhMfFk8fIkOHVhMWxUo6ONNuyFVdra6ISpccysXYtxp44xdb7D3kVHk5ccrLahj2NzwmqrVE5GFvxd5XxGOsZatSPPPLom/BHyZ6c+nSX+c/2IBKLcDFVLRb8VyUuVvXvVc+q5ei39QAnhvuw5+4T+taogKG+nEVNFdARZx12JrewRvuFXNsv6yLZe/w+SzddoGPzcozoU0+jAWXE/4PggjA2LIWrg2ZhXt+D7PiKnU1sGebe6rsLI31JiGDsg3X8UbInruaahVRpg8w7TnsUakDfItnXmVGXnjfnq+0WAvjNvQ3tXTQzstpAne/epfoLVaq/r8Y0bI1kn9K0jUgs5l3sFwqbywZL/FfoXPMvvn1N34B3ym92lvUnHTjNoYf+rOrWmrrFXJU1r9AnprFhT8OzqBPr5naTnL/4zyj6jN1G387VqFPFXdnAsiTNsLs6nMbYUHuP4mm8Df5KoXzWyitmwYj7q3kU8Vqja/9XdiDlrYtmq39t4DlFiGKZ3b4RbctpZ4acmiHZhp5u1tslGl2cyOqKwylirv6ioDZpcmmS2roq+jp6nKs3L4dGpBxtLECnUcTciY2Vxyiv+BPRxEtWKvxnJqNhfxr0RapMRwfymprw940HmBsZSgx7CWeF60kKDbvGfohzO0dgbGRAj5FbJOe83J0w0Ndj2xIfOg/bmG3DnoaRgZvyShrgM3MHl9ZonpkmSZSisVE/V28e+jqaPWZpSt1567k4ob/Mef/Zo+i+7h+t9TP23Cl8P7xjRZOWjDt/igvdFUs8AJypKxvSJo8iu/8C4HXnnEmMcqrOHLUNZYr459H9yQ5n686TiPzl8n3ouHZnluWrLgnaWN9t8dTQQB9jI0HopnoFwSecmipCN8PMLFjFwHxVSEwOwNhQ+yvnJYpo7oKITUnA21czyd/z9eZLMkN9L4IiopRX0hJjqlTnSchnnC0sON2ll9bafd15Mq3PbNJae/Jo5VyVI0E31LpGJBah+53/ntrkQr35ao0/MekBoREjSU55j7XFBCzzCNFyUTGbMDftQHLKWz6FdcBA3xVn+1M5NexfHk0MtqpoZNi9W5Rn2LTdtGpYmu0Hb7H94C3MTY1IShZmL7HxSbgWlI200JSQyLkUsNuhtfbSmDO0JTUHLGNIhxpYmZtIzjetplyvocv1vzQSSzpU8/ccMeq9NuzlTuBHXO2siYxPIDwmDv/Zwhdn5M5jfIuLJzI+gZE7jwGwsFMz9FXQ/fGcspiHfwzHUF+Pv33vs/3GA86O7ZvlNYkpKexs682gE0d4F/mN231kk2xsDbhLm4IlGOZ7kAfhQRxs5CNR2ux9eTc3v7xjTKk69CteWekYb4e+Z8CVvTiZWnCsST/0/t3xOu3uKQ69fUIB87wcbdxXSsTL+9xWnkZ8pqZjYdbWTE9qMbp4e7UN+5nP92nipFwl8Gfkn+pT1L4p6eu7kN9BWBd5F1xCYtgB3n0qjaPtdgrle4nC5Aq55DgaGfZB3WuyZe8Njpx7zNFNQ8hraUpcfBIP/T5I/O/HN2dfUMrZZgVB4cOISbiEWJyCjo52g3hOXn/Gb95CSGV0nOqr12LEWWpvKGJ08fY5lsThTuBH6nkUYUX3VjJlS7q24P67IMbsPsGSri3Uavf0mD6U/X0ZD/8YzrwTlyU3i6xwsbBkl/8T2hTzoLOXfIU+gKqHl+HXUToUsuS+hTxsPxo9HV1G3zjCuaCXNHBW7NKLSIxjyZOrPGwv+IaL7P5L4qqZXLY+f1ZoQkxyIjWPrsC3leB2a35qA0ca90FPR5dUsWzibXN9E7WUOB9GvPolDXsDx7IahTLq6pjzOawbCUn3EYm+ISYFnX9NibXlZM7ftuL41T0MbF+dySuOkZScwrk1Q2kwaCXn1gzl4t0ARCIxv685yYyBTalVvghr9l5jeJfa1Oy7jKsbNQvx+9V5HRrOsccvZJLBj2mo/uK8xpbSp2NVfDqm6xiYmhhSrUIRudEymmJp2pbQyMUkpbzm2ceCmBiWobCDfK0NTfBuoFlyiWmP/1b7Gj0dXVo5a6b7oApjm9Rk8Rlfyv+xglU9WlPZ1UUr7bpYW9KmrBelpy/jykTZHcPymHb5PH/VFSQKJl88K3ktM+ZSdWTOxaUk4f5Pus89NiUxS8M+5+EFboW8k/jfM7L3zSM2v7jD57gokkSppIpF6OnoMqtiU9z/mcvCKq1oW0h2UX6oeyvm+au+5pBx88+vxFQvzZQmA4MK45pf2KEdHNJSqkxPVwhGWD3ZG+8JmxnZtTb+gV8Qi6FbswpExSZQt0JRluy8zNVNI6jeewm+m0diYWbMuv3XOb9Gc+lax/x52XL6++1SV4Xu9eYT9kV5LPuF568ZuvMItuZmhMXEUsgmL2/DI+hXU7M9LVqdAi9Ye5ZxA+X/iOURGbc/y3JdHVOcbZYRFrWY6PhzxCc9xP+DMzo6RliatsbIwBN9PdWiWixN2yssS02VvkMqkya+FvpUpT4zcrx2zqbg61OzAn1qViA6IZFua//hdWg4frO048N7/PEzerq6vAoJxzaPmdL67YunR9a0LabYrZXXSH4MvTqLoyKxmD0NelLeVjoZynK/a0QnJXKxhZACMaPhL2vjzOvOkzkQ+ERqhp9GE6cKahn2D3Ghcs+/jH7NghdL0dfVZ3W5RXLr/Ch+L9lDeSUVSEhSnE3NytwEfX1dWtby4vztl7So6cWF2y9pU7cUXq5CiGPaWl3HhmVpNGQVA9pXU9ieMlwKqx8Tfyr4CU3y5dzOV3snS5UM+5/HL7KuR1tqFi1E+VkrODnCBxAyKH3XGbs8Dp95pJZhDwrX7JFLLE7kW+we5RUzIM+w7zh1j63HbxMRHU9JNyeevPrE9H6NaVFDccjf1yRZSVVlmOkba3WjR1bkMTbiyIieeE5ZjFicLrBopK9PVIL6m3DO+AUQHhPLkz9H4DllcZaumHIb5Ask3e+n+izM3MCIDzHfcDEXMtwoSzQ+tWwD6p9Yw722wrjCEmKxNTYjOimR0jZC+ORKf1+paz7GRpLfzJJ2hUuy3E9WUEvVLfPK+MN/Ljsqr9dKW9qmjr3mWdAcbXcQGOSKiVF17PIq3sSWRpH8tizffYUGld25cCeANnVl3XPNR6zFd/NIxi05zIKRrTUalyqGfemzM2wPvEGvItUZVqwBQXERVDzxB7PLtKdRvhIsenaa3YE32V/nN7pcXc21xlOocXo21xpPod+NTWyomnWEV2bs8lnBQ+XpBcNj4qjqKiQNscsweTLWcAOlWlf9teIUk4c1YfvBWxp19rOx5oAvV9cNp8HQVWyc2gWAUYsPZWnY1ZnJpbG96gSNx6gqnlMWo6MDnvkceB0SjrGBvpRqrpezA/FJyTRZtBl9XV2OjRSiVY48eMbToC8Ehkbwz+3HvPoSjpmRIUPqVSFVJGbkzmMSY759gDdlZgj+dnmkGfCKm1bjltcGLzt7vD3U23/wqP0YRt04zIkPzyhlnY9tdQUZ59dR4TQ6sRaQDnu0MjJhZ91uVD28DJFYTO9ilRjkUZXJZevT5sxmpt49ye563Vn0OH0T1PaAe+x4dR9TfQOml2skO4hscj38FpsChcX+breE8NI0A+8bdouNgdsoZFaAFk6NKZdXMLBnvlyghm0Vlgas4VXMG2Z6TcHZRIjaOhB0lGPBp7EwMGeyxxjsjbK3W7N5PuUL0llhalyPws5vZM5bmAtGr8W/KgvrpqXr2ywZK0h3LBsvTLAaVhESWJ9fI6zFXVonrH9oatQBXIoo/1zOfHrKnWYzJMcRSbHcaTaD8sd/p66jB+WsCzLaozH1z86jjUs5xIjp5VqDyOR4nkWqLxBo56jaRq/qbgU58vgZ7cp6UdvdlRNPXtCsZDG1pCkyopZhf/1eeORcs/0q9avLZjZRlzwmPzapdD5b4UPPGPZ471nWd9dbGuiKKJIl1SaqLGrKq9OqrAetynowuUUdmTI9XR2pa8oVdFZo1DOyu20n9jx7in9oCLN9L/N3K+mnpYvn/OjZQPFiY/BfgZTTNWHX4XTN8yIWNrzuPJkrF59Rq660e6eYlT03WsuO61Cj3pLXGd0tE8vUY2KZ7O+MVkQ1m8pUs6lMt1v9pWbsL6NfczH0CpsqriBRlMjc50uwM7LFxVTYiDf0/lg2V5R+6ln5agMFTPOzqeIKrY1vnEdH5ZV+QVSZsR+vN5oZjw5SwMyGvm61KGEluPCM9Qz4FP+NonmEzUCFze1o41KOs8F+tHYpy9lPfrQtUE6mvZBvMdhYmCrchGfvZKXS2Oe1a8z801dpV9aLSU1rCy6YvZrnnVXLsG+cn+6X+2O0bHTFeV/1jJ6Lbc7GJCtjer/GRETFMW9YSyr5CD7QJaPbarWPXzFaIrvMuHIBU30DKuXLT2cv9f2X/xwdSZfW2hF7+5n4+91OpnoIi3tGukaMdh/KwhfL+cNLSMrSyUVWvvZ6+C2GumlHo/u/jiqG/W74W8wqP04AACAASURBVEYUb0T/m5vp6yYtMljAzIa2l5axvmofguIjcMvjwCL/0zTKV4Jzn/wY6ykrdfHlazQ2FulrRafvvKBxxWLUHrGKy0uHYO+k2ozdwsSYWW3S3djPZo7i+edQijtq9nSmUUC1osiXmpVyZodoTuHl6kheC1OMDPW5vWU0t7eMplopxYluAzTINJOmsPj/iZ4ly5DHyJALb9/Q//ghmfLlC08RFRlPi393nPbtuoaoyHjaN/2f3PZ2bLlGwIvP/Dkl68X2nx0ddLKM7DbXz/lk4l6WBXO8jx+FpbXyhf0KNoWwNjJjf+1hAJKFU98mgjTBwTrDsTUy52Q9IXx2VWXhqXFN5V645VEuFZ0ZVV0x8tDUqIOWF0/nTGijzea+C0GhkWw5JuSg9GlRCWc7xX8IdTeuAJjoqZf89r9AI1c36hdSLGA0YFh9LCxN0DfQIyU5lfdvwxQa9dRUEVs3XKabTw2mzW7PlYvPcmrYOU6/wj3534sVTPMcR5IoiUUvV9K7UNYhhxXyluVw8Ala52sGQIo4Bf1s7Odo6lRJ42t/Rv5YpZ3oHk3R09MlVSQmLZAuPlHQGoqJFwIV7FR0xay8eJOiDjY08tSOdpRG35Ck5BStyN3+aHacuseBi48Y270ehgZ6jFp0kDa1S9K1ifxkG5fUTMyQ3W3m7rMXc214f2osE/y0duZmbOjcltYbtgNQMK8VZ4f0lrpm78OnTDkuneC3gXsRVnVM37gUn5xC6fnLWdWxFQ3cZWWCe2zfy613H3k5Rdon327TTp5+khYuOjPYh0KZsv8o04oxMjaQvNY30KPv4Hp07iE/zE1PTxe7f/XgIzKo5P2KFDIrQFOnBvS+M1R47diAQmYFsrxmlPsQ/vlwAJ87Q7A2tGKm1+RszewbOMr6iX9lKtfO/lpfdvAs6ECzCeuxMDNm9/QenLv3ktWHr1O6iBCRZWElP6Q3M1tvPmBVV9nNhZqikXWu13mJXHdMu4FrObB2oMrt3Hr1gdiEJKHNEtrXIVfGqn3X8N2QnuVpzxwfqvdbqtCwR6uxGxGgqpzkwepSY9l6Xk4ZxYlnLxl54DitN2zn5ZRRbLl9n7/OXpaqe/p5AFOOn6VtKU/mtRQWpq++eUffXQfo/Pc/7O7VCUCiEz58/zH8J0lnuVpx9Sa33n2krLO0jk6d5RsIjormWP8euNsLW/+brPmbRqu38GTCbxjpp3+V1NWKMc9jTNNaf1G1hjvT/+pAh2aLiPwWR9c2y9h5aDibdw+mZf159B1UD2vbnHdXaAt5oY4V8pZlc8WVMucbOShezO3k0k6u/10TjPUMlNbJGCarLaKS43gQ8Ybo5Hgh25hzxWyFla7ZeoUdB25x9dA45ZX/Zfv+W6zddkWta1Tp+8S8dGG9FSNk/07efWvJnMtMXlNjrEyNszWujGh12h0Rqfo2+2Z/bebYJB8p/Y7MxCRcIiruEPmslcfKZiYpJZDw6DUAOFhNR1dH1v82sG01omITsDATPtDwyFj6tqoiU09TshMrnEZtN8Hn38zDnZEcZ3gtYfeqT6VyUob9mN8LRh86gZG+vsSoA9R0LciYujX430XpeO1JDWox59wVmf6WXRHcTZu6pn9B45NTCI6KZnn7FhKjDnBqUC/cZy+my9Y9HOjTVXJemVZM3QZCOOmhM8IPo0WbcrRokz6T3HdCOp2cgYEeR89rFjL6LSmGb8mxRCfHkSRKISo5juiUOKKT4//9fxzRKfHCeck59W7gvzLL917Fzsqczg2FfJuV+ixCLAZjQ32urhUijeYO2MDYFT7o/5saMSU5leVjdzBqaU+F7YKweayT73yC42WTezTLV0Gi6ZOGb+gzQv/NSdsmv/Z+h2k0qu3JroO3td6uMvqMVh79t72vN9027OH0yN5K66qC1gz7kbOPye+ouu7El8joLI06QHT8Sb7F7iU+6SGuDqfQ0VH9jmagl4+IGMFlYWJYFisz2ZyRrWuXpMeM7QxqV52UVBGr919jYq8GnL/zEoD6FbMnO1xIhbycynC2lE5km9ntIUYQZb72RtjWXrOI7OJYqxLF+d/Fa7wJ/4qrjbBTt3VJT+acu8KLkDCK2csKtpkZpm+oevAxGIC6ReX7zf0/SyeoKGQljHFv++zl6VSVZFEqAdEfeRH9kZdRH/kQH0pYQhRhiZH/GVndnOKO/3sWDhdcAGKx8O/Ign60GrdBUuf53UCJUQfBffbkeoDStgffWS3XqCviTewX1gQICXVywrDb2+bh+HbNZbpzEltzM1JEIm4FfqBy4ezLgahl2N9+DGfSPCHKIWOyDQB9PV0u7FZ9C/ucrk1YcPgyJQoIW4ubli0mUyfNMCcmB6hl1AF0dNIXLYO/jpdr2I9efUrH+mUIjxR8t10bl+f9Z/lJhTVB3STU8rA3l37ScLSQdkWIRCL0dHW5+ErYMCLPZ+5kIcTRn33xmoHVBMNubSqoWfbeuZ/rIwX32ZGnQrhq5pvH2ZevACgxd5ncMYoyJWvx/fCO6i45E31xKeQR+z9c48m3wBxpP6c5de0ZTWpk30WnLV59DMM+r/D9+GPjKSb2bICTrQVFnNNv9pHhMTLXhX/KWpa73vmpUnlWVaFHoToSwz7Xfz8TPRXLgPzX8JguJLvx2bxPpizH9dgL5bdh1/K+9By1ha2LfdTuLCONSrvTqLRqM2IdDR8sDPQLkJzyHpA/a+veVPUY8+zmNNWUzBsfFD3jKHv6kcfAapVYez390XT8kVPo6eiwo4f0BhaRSHjvzTxlb77yMDXQjnxCoiiZta+OcyvsOUHxYVppMyfxe/WJ0fMOUNjZhjW/dybgXQhGhgZ0n/A3B5b1x9ZK1h144eZLfl91ghlDmlK/SjFW7brK7pP36NaiAgO9a9Bh5EZsrMz4Eh7NoeWySVKyS4kiToRERHPi+jOO+/rzez8hVvtbTLo7quNvjdi77DQdhwsuhd1LTtJmkOL8w7/dWydl1D0s8tO5YC0Kmtnhc1O1/QlHg27TrVBtXExVl/8eNWMPdx+94/yeURhmeMKYv+o0R8+kJ9DOysc+5o993H4gPWlwtLdk77qsBfAU9a0q2tZm18hiTh3eLNsdfwj/Rs/le3B1sObj10hOT1Gs8W1l1kmjPixMmkn87PIQicRU6SPcKW9vGc3XqDiOXn1Kr+ayIWGhCaplG/9RNCzmxu77jznz/BXtSklLIgRFCkk26mVypYypW52112+z+/5jOpcrhUgsZlWHVthlekqoV9SVXfcfs7htM5WWu7oc3ENiavpW6MBh6qVb2/j6FNvenlPrmp8BLzcnTq8fypOXwaQ9xIR8jebK1pEKr3kSEMyVrSOp2X0x9asU48LNF1L1g758Y9+SrPXvs8PKse2p1n8pujo6DPdOX+RLe4oF6Dq2OfMHbaKpnbBWUq15GaZtkdXYB+h/eyX+kem7t681nKvWImlJq4I8+Sa4FQ9/vMUw9+YqXTd25j7uPnrHhX2jMciUAHr8kMaMH9KYW/cDGTtTdkYsGfvYbTx/9Zl5U9tR7d8EQgkJycTFJ2nc949CI8PuXtg+2x23nb+Nu/MEf1dYdNZhbKbGmvnbjA2zzt/Zbfo2bm8ZLdl1am1hyq7T9+Ua9vCk75eBSBP+aFqfu++DuBAgq+HRar3g0ipqZyNTZm5kyO+nLhCVKMTdNigm68pJW8Ctt2IjF4fJGpkUkQj9DE8WzwePkKmjCsPvreTxL+piARj4+25mDRd2ZIv+1dT2UpKly7OItMrhvqX9mL32NC6OVvRsnT1NF1Uw0NfjzubRMucznxu/pg/j1ygXwMpo1E/V+V3tyJfqdp4Sw34j7IVKhn3CrAPcuh/Ixf1jVEoeo4jnrz4zsEctiVEHMDY2wNhYcSSRtvpOIyQ6hptvPmBtZkoNN83dmRoZ9hevv2Bmakh+J8EX23HweszNjNi8MOtV8ox0qVGai36vcXeyZciGQxwerzgsTlNXjLxImIx8CZdVatTVlf9FjPnJIyV0gBMDe1L8ryW4z15MyXwO5DUx4crrt4AQby6PzV3a03HLLv534ZpMiGNGTg/yofGaLbjPXoyzpQUmBga8CgsHYI9PZ8o4O0nUHSMTEzDQ1UNfT5eE5GReDZU1HBlZ8GwPx4O/f7SCtgn68g0rCxOGzdrDzgU+GrXx4NlHhnSpybBZe7+LYVeVZ3fe8OK+9E23zUDF7hiAPAYmWZbLw9E4fUNPSKLy9JqL153j+t3XXD4wVuFvVxU+hwoTt+7tVf/MtdV3Gml+9owMql2ZEfXVlzLWyGIuWHuWvyYIKmw9R22hXvViJCQk87915xgzoAEVe2etPX1n82jGtKxFcmoqwRHRWRp1gMTkl5oMk8TkV1mW/29kawbP3QvAo4Bgxi49xOqJ3nLrJok0U1n73jyfPJId9x6x2vc2b8O/0alsSf5s1kBh/dLOwoxRDGzuqnixqrBNXl5OGcX/Lvpy4LEfoTGxlMufj/5VK1Dm3xtCmrrj55hoHM2FBbkvsbILb2nEpMTT4vI0dd/iT8ux1YJ74p9Fwsy2aEHZJ9vMC6cNqwkbbM5uFLa4l/UQRKl2zBd+Ezd2qefGUpdnb79gbmKIi4MwSTt14xkz1p9izURvyroLi/9LR28nLCiCBp1zLlFMGhkTaier8Js7cOIBAJPnHGTuFM1j/b+pEaqt7b4BBmw7SA23gqzvmd5Ocmoqpf5Y9v0M++t3odjbCD/cN+/DJAup9TovYcyABpLHuMYj1nB6abovLjoukQOX0hcxDPT0KGirfMttWPRq7CzVz4zyNWZdluVli+VnztAWLPvnCgcvPWbf3D5YmsuPvpGXQi2nybzzU9lxGt3Kl6ZbedVj6BW1I48xdaszpm71LOu037+bOXUbogNMuniWa71kF/1OfrqjkQRyTmKgq0dewzxYGZjzMvrjjx7Od2HO3+ekwh2nrTspCXdM+x2f/+cmR4K0py6ZFW9i0nc2u5gq10q5emgc7z5+pfuwjZy57E+j2p4a9VukoNBXRGQceS1V2y2qrb4BbgV+4P5U6VBMAz3N/fUaGXYvdydCwqM5fdmfZnXT9bb19aV9TG1rSyv75TE1Yu3B6xzxf05whLQb5MZsxckYxOIEhMgW9d5oSqrySAqrPCZM76d8A4FhFskeskv3an/SsH1Feo2RVY/LTBuvSRzymyNzvqlr+o1v8O9tadVT2vj+/b9T7F55TmG5qsSnJmGSIWnIm5jPuJo7StXx7dWf1H99zPKM+s53F1n3SnspDjNS2NyRStbFKWaRn6J5nFUyDvKoc157KdbmjP+Hes3LcPrQPd48/8SWk2PYu/kqh7Zfp6CbA3+t9QHgjxE7cC5oQ+uuVRnfdyOx0QkMmdSCOk0V543NLqqEO/pMbUOgfxCFPbMfvquMbYEXJa/rO6j2vgvmt2ZEv/r8ufg4ZUu4YGejvky2gYFgW3oN38yRv1XP16xK3/1apLtYNhyTP4nqUaUsF1+8poGHdoQUNbJWi2d0pG6nxejq6HBln/CoePKSH/WqSYfDbTx6C6s8prgXsCMxOYUF2y4wpH11ujepQP0/1nN+RtbhW455Z/E5YioA/h8Kkt9mFRamqukpPPuYvgBiayH9h0pbLFXE7S2yPmEzPe1t980OivRnTr4R9hU0LzpebnmvMU3oNaaJwnJVmfFkJ/PL+EiOtwZe5PeSXWTqKdKnPvXpjtaMupdlQfoWaUq5vD+/quiK2UcYPKE583wDGNxhBR6lXRj3Vwcm9t/MjYvPqPqvxvzZww84f/QhY2d3IOhdGHMn7OHFk48MHJ/9SDR5qBLuuH66/EiSk6GKI84AHkS8oWxexWJwmYlLSZS4PK0NzelZWHXN/A4tynHl5kva9V0jE86YkJDMl7AoXr4RngYe+n3AwdYCe9s8Umkwt6/oQ/dhm6jZZgENawl/j3tP3mNnbc6G/yleP8yqb4CPgfInmKsvCwmLdBB0oEbvOUFpF0cqF3bha2w8u+88YnU3zRKPaGTYDfT1ZLRimtbxomkd6SiUO5tHs/vsA87ceo5IDMvHtpeoJyoz6gDW5r0lhh3EfAwfjHHUCgo7HENHR36sdGzCFd6FShsae0vpnJZphnvTkVu0rOWFnZWw6Sc6LpGDFx8jDxsjC7nnvzcHns7+of1nnp3bGKk+O4pKjmNuNt0vZfO6Mad0X5U0T34m4mISqVbfkxlLuzF54BZW7xN86iXLF+LaOT+JYY+MiOXUYyE/bvlqbljmNWPO+H9yzLCrEu6ozIBnZEbJLvzxZBcAw+6u5Xjt6VgZKpfTjUlJoPHF9MxGGysPV3tvxrJZnWnSdSm12i7gykHBwPYcvpnA99KG9bcpuyWvMxrigvltuHpoHPNXnebS9Zfo6eri4e7IuMHKs2zJ6xvgWxbCdVtv3Jc6NjMy4FVIOK9ChKAESxNjJh08zY2Jg5X2n5kcl2hM06DITOlxS3i0QIjX/RIZg4OlfHEn93wPeBmc3kZCsh/PPmbUTNdD0QYkAFdHxfHQG4/cpE+r9FXwNFdRz+aymcHtjVWT3/yvsy3wIoPchFmdSCxi97ur/OYum3RFHq2uTNe43yq2HswtnXPx3DlNEQ9hgTmzPre5hQlxMYpz0dZuUpI543NuLULVcEeA1JRMSd/1ZZ/KGjmWwdXcgV43BH2n5pdnUsDMjsmeHSlpJRu+d+/ra/7y28vnhPQd34XMHLA3zlrHfFDPWgzqKSuudWqndKjt1mXqa6+kxb1nt2+A0Cx26GpisFVFY8Nev8sSEpNSKFncmdWzu7B2x1XcC9tTN4M7puXY9RxdKDszT/O/pv3/VsB7WlWQv/Cgr2cvY9wztaZwjK6OZzE2ULx9e5h3TbwnbWFirwYkJaewYPsFBreX73vW18nZjQcGBnpSfnJbR0u2XU+PGJk5aAsfXn3h45tQidvlR3C89nRqnZtEqliEro4Oh2tNUem6LYFnNOqvio0Hk726YGGg2oLWz4rBLyxzvX/VWfYuO0NkeDQeFV15ducNo5f3oqGCKBk3cyfGebRlwbODALyPDWXQHemUf7XOTZJ7rbGeITuqZR0e+ysR+vnHbGzU6Nv259IT9PauSve2lRk8RXjsGtitJt2Gb5Iy7M2ryTfWerq6GOjp4fdB8HnV9szaD6evZ4+nSxCpoq+8/tyQlNTPWda3MutKPusFSt9Hl0blqFPejb+P3UYshmUZXEXawC/yncoZa7YtOS1lsDMaeYDpa3zknv/eWBmacaWB7OKtMra8Ud+w93FtTM/CDWXOz3zaiukljqjd3q9IfFzWux61QY2By0hMEnzbaTP1ir0XSV5vnXOEwx+W4+0+hkUnhDWaGV1XKjTsIIh4VbZxp8O1eSqPo7Z9Cf4q/WMTZ2ibX8qwn/d9zqURsnfV4BDpN7HzzH0GtZM/A/adNVhKw1sV9HStcc93T3KcmBxASmoIOjp6GOq7oq+n/o5YJxsLJvZSHOedHS6HPFbZsNdvJ6tbExEWTV7bnE+ErS4isVhKO0dPSUKRSyHqJSgBwfUiz6j/fyDoXRjOBYWolCEdVqCrYCFaG0xfd5J+rarg07yS1P4TU6P0NQzHf8dSrFy6C/TRtRdK23Yysca34Tzex4Yyy28Pfhl2paahgw5dC9ViSNGcWUP40fxShr1S2cL4B3zCs2j6TsXTl/1pVFPa7RGfmCx3s9KdzaNpOXcLZ6ZmL0mvkUFRjAy0k0oqJ7gc8pghRVuqVFduCq0fozuWJdXPTqCCtRsxKQkEx3+lsLkDqyrI1w1JY9bTnWr3k9mffiv8COc+b6GWvbRu0MynrehfZBFbA6diY5SPfkWE79vx4NU8/nYRa0NHBrgtlWxtFyNm/atRfE5Il174mWb/9k5WDPVeRUIGfZJj9//Isf7O3n7BjQ2yWjaF8qXLT4xe1otvYdFM2zJQohfz5+5hKvdRwMyOdZVUDyH8L/FLGfb5k9pSo/1CzM2MSE0VUcd7ESmpIplIGXkLMGk4Wv34mWiTMooX8049nClzrriFC8+jPqjc/pcE7UkA/yx0LViLoe7NuRLiRy17L8Y+2Kz0GnU10VdWkN6oEZkcwr2vp5jidYDolK9c/LJDqjxRFM8Ez91kvBM2cuxD83yDSRTFsfRFX0YW2wTAHL+OTPYSwve+l0tn0vz0m5FLYTtJ1AvAjKXSOU/DQ6I4/kD2u5dTVC1RCL83n/FylY528g9Md3cWK1dI8lqdCJlcIOzTL2TYAa7tH0tAYAhPngfh6e5E8SKOyi/KQFhUHCXHpAfuP/mfdmUrVSHNeHepP59d59Pju3s0kZ9YubFTBbUM+3+R4HjhZnUs+A617L3wssw6Z2dEkqwejzIyu6+uhe6neT5hA1sefWuZ+oXM0jbCpYfHPYw4x83ww0Qlh5MqTpeQFWWx2P4zIP7OT2mLRrahYu9F5DEV8hd4T9lCYPBXqdDH1BSR3AiYn4moiDh09XQwt1BfnyYn+aVm7GkULWxPUSVKjyKxGHGmb6ueri6LeqkWIvc9qFZP2oVUra78SBoPi6yNmDy+JcVgZZi9PJ2Pbrxi98rzhAYLRnVg4wXYOVkxaq43No6WXDv5mGPbrxMXnYAoVcT2Jae5fOQBds5WTFzaHYA/B28hNOibwnJVuf1V0O3xDX3G08h37PvgS29XxWJQ72NDFJbJQ54aYJIoHkNd1TeIJaTGcvLTWpo6DcTW2IVtgVMlZeXyNub0pw24mHqgryt/L4QqGiX/Jf7o34QZ608BEBj8FSdbC3pkyFXQwmmIzEx9XKv/seBIzurYqMrvQ7dx85KQJKZxu/KM+jNr3ZbHd76fgmh01I8RD9TIsNdov5D+XWvQq33WcroVey+iomcB7vi/p6SbE09efWJG38a0qOFFcWc7jt57xqtPYQxtor7IjTZ5cvet1PGday8ZPEF2Mae4hfopq2b772JBmaw3Y22/LiuElTFCZsLgrXJdQ2nUaFqKGkq2nU9b7QMI7qcoEfxvn+o+0oycrSuM43Td35n/7CCLy2W9TnI19Kla7XcpVFfmXD4TN97GPsXJRLUdpseDV9HHdT75TYsjyuQGKm5RlfjUKDwtq+NpKX9h/9yXB2qN+VenWTVPmimIYFPE83vyjWPPG4tp7lyRTgVqaGNoSmniJR1ue/rAPa6d9WP/TcXicuN9Nigs+6+gkWG/sHskUxccoUb7hZiZGLJwantKFpfVkejepAIjOtWi/rBVbJoi7AYdteQQLWp4MWjdAVb2a4NeeQ8WHL7MuNa1s/dOskHVeh5S/vZ2PbR3o7kTrjx64Hty6uFMfuuafT+pub4JM0t2VVrvaeRbtdqtZ19G5lxlm1bMfNqKmJSv+EVek3OVNNXt2rP97Qyq27bjSoj05h5LA1u2v53G/g8LsDFyZmjRVWTOS3Xgg/I+coLM/vbvwYeQb7jYyy7cP3n9iZJFnBjfWliMTvs/wMeAz7QbLD+S7HXMZ5a9OMqyF0exM7JkRYWB5DeVzQOQk8RGJ3zX/n5GNDLshgb6zJ8sPO68D/7KhDkH+RAcQYVSBVkyIz2tWnCY4F8qkSHZwN1nQsjT8+BQiZ5IiwryXR/+H4SbhalRVQrZK858ooiQyLlExh1EBz3cnK4rrNf7twb0/k21kMdqtp5cD/NXaxwvoj5QTMlsv1312RzwnUJ8XBJtq83i1MOZLJt1lN+mCC6rgztusGnpWRq0KM2I6en6EVOGbOXx3bf4DKtP2+5VJaFxT+695fcRO7FztGDVniE5GjKXFWGJ6iUoccuTT+75tEXOho595J7PiKOxK2OLbwOgqm1bqbItgRMl14jEqWwJnIxPYem4/IDoILXG/CszceVRFv7WGidbacmM/n/9w82NI5l/eDStXX5j1p70BW1DI9XkHEITI+nkOx+AVs6VmPD/KIfpjybbv/YC+awZO6AhNnnNuPv4nVRZmr90/tCWglum9yLmDRXC/85N60+FicspOWYxV/xzxudlqF+E5JSPJKW8U1r37NGHHNxxQ/JPERM81U/TN/iu/CTQGWnSrjwikYhDO2/gXFCY4Vw4/gidf/UyylR05ejt6YR+ieLZY2EBt221Wcxc3p2jt6fz+vknmpX7HYCob3FsW32R/dcms2bfMMn57JCm41H97ASF/+RJG3/VYPE0JzHQTU9y/jzqBrZG+aXKfwXd/R2vVUsXpwpvgsJljDpA8ULpa2cD/uyIoZGB5F9WDC/WkqJybs5Hgm5T/ewEap+bjG/os+wP/D/EzCmyk9ZOrZZkq02NF0+/fotlzKz9BASGUCCfNRvm98DOWnqRcO5QYbZpZKgvE/qor6fLst6t+BgeSYcq0vK+2sLYsITySgh+52GTW2Bto3yR09JAuaBRZkRiMW9jv1DIzEFhnWbtK+B7/hkHtl2nc18hIiFjLHNhd+Hahi3L8PzJRzxKuRAfl0Tz8r/LtLV+0Wke3w3MMpxTXU7XFWKpZ5bsSn1HWa13kVhMn1vL2FJFWi/DRM+Q2JSf59G4jn1X5vp7o6OjS2WblrTIJx1f/du9ld9lHF8TAzgfPBVTfWtik0Pwdt3Pk4idPAjfRLP8K7E1Lsbpj6MJSXhKc5eVWBsV5dqXeXyI8UVfx0h5BypSsogTgcFfKZxPOtooY7hjcx9ZXRRFdCpQQ+Jfvx72nMmPtkktRqeIUxn/cAsApawKsahcXykZaHVZd3QkA1pKG0FF0riKsHHIOYG/ryHRMsEjqmDvkL0d8Bovnurq6DB9ZHMa1CiusN6uM/flnu/SqBwdF21n8xBvqhUryB97zzGjo/Z3fxrq5VdeCejSvzYtvGXznCqikWN5zny+p7xiBnxuLuBSfcUaL/kL2bJ97UUSE5Jp3rEil08/pXTFwgrrpyFvUVUsFvO/Lf3wKqN+FI8ybBWoXOrq6PBOTgSMpYGZVg372U9+yXdWrgAAIABJREFUNHTyQiQWS6n/ZT5WRCmrepSyUiwH++I7hrNGJwfRsXD6GoCZvj093c6x43UzuhU5QeP8gl97S0AdfIpeIq9hYWoUmcCml9pbmFw6uh21Bi1nQJuqeBRyQE9Pl3lbz2NjmT6BuXb0PhXqe2FsasT66fs4s+s6ewMW8eljBKP6baJAYVs+B31j6xHpm3o12+Jcqi+oka59dZqtgRekyh9/e0uDC8Ii55CizehWSP11tgKudpzyy57i6Y4LE7J1fVb0abqI4Pfhal0jFsNz/+y5AzUy7Jk3Iiki45cjLiGJFfuusme2DyBkBzE3Fu7ULcsrFurKFjqqvb2D22/Qa2jW+RszMtmri9qGHWDSo03MKa04IfDdawG06lwFYxNDzh55QLMOsiqTGTE1M+LTxwic8gtpzSLCY8hrY86AsU3o12oZey5PlDqvDUrnVXyz6VFYNqKloJkDwfGqf7EDooMomkd5Qocyx2bwuOVMhcea0O921jr9OUmqOAlDXXNSxUl0dj0EwJmgcTRyXoBInIJInIqVUSEADHS1J4hmYmTAlTW/MWrJIfZdeESqSET3JhXwyZDQff2MfdRoWY6khGTunH/KLr/5jG+9iGdh8Rz1FaJSIsIVp0AEGOjWmIFujYlPTWLcg808iJBOur4q4ASrAk5gbWjO8goDKWSmvjzIz4ido6VCw7580SmO7L8LQMPqs6TK2qox0ZSHxq6Y1+/CuOD7nBSRtF91cPf0x7ZGlaUTb7SpXZL2kzazf05vPoR/o+b01TLtXp2pPSnLmIQLyishuDwyuy2yCi8EcDaxJSheeYamjNwI8+fO15dUtHaXW56aKqL9vxE5d30D+HNF1jHmB3ynMG/SPq6e9cPdy5nKtYvRqU9NLCxNmb+hN10bLEAsFtOmW1U69anJh8Aw+rcV/P1p71fZ+1SHPq6yT12lrVy5ocZi87HgW4wqJh2HvC7gMjXt3dkQcIUmzsrda8+jPpOYmszcpyfYVXOgSv0GxYfxKjpY5XFqGz0dQy5/nkmHQv8QGHOB4patsTEqSliCEJ+tq6OH75f5tHBZQ4pIu64tEyMD1kzoqLA8IVZwCU7vsoIV56egb6iP361XtJnUlhtXXlDYzYFpI3ewfo9y2QATPUNWVBD+Jk++vWPq4+1SC+xfk2Lodl3YIOjbUHUBsZ8Ve3lSIf/y2+gm/Da6CTOn7GP67A4qtde43WJOH1DuatJR4v+RW7j76F1WbLlEgxrFMc60mDIxCx1jgFbjNnBkgWoaMdmNikm7HsDTRbuRDqliET1uzFNrNprGukojcc+jmpvoZ6H6WcWPq1n9AN/GfsHnpnKlzYxkdFmlikWUPz6Thy1+B9JdMaWOTpeaoWc+7nZtHVNLtsTDMj0iKyuymwovKzdbTqLuuOWNMy4hiWEL9/PqYxhisZjeLSrTp2V6noILe2+xdupekpNSOBAo+LOH1pnFyktTSUlO5cvnSJxdZHcEq8PTyHf8+fQfPsal/55yyrBnjn3PrisnK7YuP8fONenp/uT19ejBO0qXlRULFInF1G0h/dsZP6IJzRtJ9qwo9D1qNGNfv/MaV/eNRZlLU54A2Nnl8mfk32J3kZQiPzomOeUdIZF/qTQ2sVhMYrI/MQmXJOf0dJUvjohFYkQZbnIZU2bJQ09Hl53VJmlkEAbcXsLs0r2pbuulvPJPQtqPrOXlPzlaO33zR9urWUv4ZrVgrIhkUaokW72eji6OJsLfLzwx68f9jBjrGjDo5lYuN1buP9VmftNfjajYBOoPW8WkXg0o4myLGDHL9lxh99n7nFkm/FbrdaxMvY7phl4sFrPi4hQaV/yDAxcnZtuoA7iY2lLMIr+UYf8vYOekfBFUnlEHYd3q8nHNUllqZNgLu9gqr0TWImCZiYw7RGyC/I0hyanBhEVpHq1QNN+dLMublJlOmcquxEUn8inoKwWL2LNwk2rZejq41GTfh6tqj2nKo82Uy+vGonJZKyN+L5a/PMRv7m2U1muVv7LUsaoJh9Whw7WZHK6Vrmh4pO5wKp+YxQiPBtgZC+Jxv5duQ6UTf3K72TSZ4zpn5nGp0QTCEmM4/OEBrV0UJWnJOaN+M2QJLyKPYKRnSWfXA2Qxufqh9Jm1i2trh2NkmG4KNk3pQu1ByxVekxaCq6urw43Lz4mMiKN9d8Xa7IpIFCUz9dF2roc9V3/gvwhZuWLSeBsYSv/ua6XOrdzYF/fisk+bUdHxWORRroejURz7+nndqe39P56+CCY1VST1LyOBwbJ3391nH9B1+jZCIqTjm11st2CTR3keVHVxc/JFVyfrhcMOvaozd60PnfvXYu/lSZiYqh5ONsy9tdq5GdO4H/GKOufH0ktNV4W22PTmNHXOj6XO+bHsV3G35ZY359n73pcXUUHseneFXe+uKL2mqVPWi8CZiUyO5VjQLcmxoa4+t5pNpWvhKjR0Ep5y2hUoJzHqmY8vNRJm6bZG5gqN+uuYTzlm1IPibvMy6hi9il6gs+tBMhr1GyE/boFWHuGRcVJGPQ0Hm6zVV9Pkexs0L00r70p0a6b6+9r85jzVz06g3vmpco36yGKtvpt/PafzHciV485E/+5rOes7Verf0L4bJeXhX9OfVL17r5XXhAwazdhrdhD8dIMmy+psZ4yY8Z7yt+T1zpk9KOpix5nbz9k5sweV+izi9qb0Gb2ujgkOVr/jYPU7AInJL3j9WQhLM9BzxtJM1V1rOhjq58fCpCW6uqr90T4HCeJaZw7dp1pdDzxKqef/PlN3Lg2yETL1LvYLdc6PxcnEmjUVR2gUK68Kj78Fsu71cZ5+e6txG74N57H3vS+731+luEV+lX6AYzw6cPJT1k9NmVn4fC/6uro0UfOmoAr9bi/K0YXS8ISX5DeVr6P07NsBqtr/PKnfhnvXpOv0beycmZ656N3nCAKDv/L1SyTWDpbERsbJvfbkLWEB3sBQjx0nsn5P97++ZtzDLSSkys8IVdG6KPPL+mCo+3/snXV4VFfXxX8jcXfBAgkS3F2CuxSKW3DaQoBSKFCkFFpocSsuxd3doXhxC5oEgiRA3JPJzPfHJZlMxiNA36/refIwc+45994Jk33P2WfttfLfQjCvcuqNGs8EYOzY1rRorrkWx9WAVEzxkuoz86yz9X2Hb9O/l0Bx9SpkmDxDvtIdx/VpQqeGwlK9xcjlHJ0/hPoVvQEY3F63HouZSUnMTIqTkvYUE2lhXO3yj2uasbR8+yqKFhUnIxaL6THYz+DxUpGExVWHMez64lzdx9ukSNqfFyo8va096FioLi09qiHW41CUHQoU3I0K4vS7O5wJv01smuY/zJyic2HN4lnaIBVJMJeYkJyepr9zFsx8uI11QScIKNmB2s7GiVRlx4mwG+x7fdngh5qXlTshCbotGLMjQfaOq+8WEpMWSkp6LKffCKqSjTyn8zbxFoHRuwAy2wH8PH5GbCAtNz/wlV957j5/q7IfJhGLubhyBLOHrGHC6kF87fM9VRoa//tPkCXzw6213NXyOzcVS/mjkj/VHPPGLKdL1yVs32a8ocfChccJCGhm9LjTp8YRMGKjzj7mFvqLr549eavW9vSxsq15ozIE/LiFQgUdWTbPMOvAPP1GzVp+gjFDlHZmt5++zgzskbEJBIaEk/CxmlIi0Z++sLP8mncxxvtrGoufZgkyAct35UzxEKCsnRffFW/Hkqd5Y9zwPP4tswJ3MCtwR56cL6+w+vkJ1gadUrHGM2TWvqX2BL7623gnoLDkSCbcEUwy2haoyVCfNlhJ9Uv4psnTOfL2GsufHTK6QKqRW0Uml+1ldKrGSupKI8/p3IvawvukBzTyVHKTPSwr4WFZiTVP6qq0fwmYMqA5Uwaos9kmrFamRqdvD1A5lpGKyY6XCe85/PYGG4LPaDwO8HWhOowq1S6Hd6sZYbnQPd+772aOAruh2K/HAat8xSI0q/crLdtURKGAo4duU7OOkhJdwNOBeb8JMSo9Xa6X2AF5HNj3Hb+jEtid7Syp1m8uUomYiytGsHzPJS7dC+Hxy3dcvhdCvzY1dJwN7K0+TWD/rttSnj9SfWrmhN/duXB94mRJrA8+kVe39sXheNgtLjSdyY3I51R0KErLs4YFawdTG7ys3AhJCM/xtQ+8vsKB11dyPN4QeFm5M7msUD9gITEjKT0lX6/3ufE09D3FC7no7KPJNWnbY830zu6XNLd7WjiypOoQXM3155yNxc9T9xAbk0RcXBI/T90DwMSf2iOViklKSuWnn3Zy5+5Lihd358mTMNasHoiXlzO377xk794bmefIPN8UQTiuUeOZmJhIKFTIiaCgd0gkYk4cN56lYmqmO8zOXtybN6+j2LnlCiKRiE27huPiqmTyzZh7mOu3QxjYpz4FPewpV0Z/qtiowP7b4qNMGNaCjXuu6u8MjOjagBFdlWXCwzrXY1jnegZfTyoxzpUpp6hUw5slW/OmMKp/sea4mTswK3B7npzvS0PDjyyYkIRwqjh646/DZCM71tUc80VTC13M7FhXU3l//sWasvTpwc94R/mPYbN3cWD2QExNjJvj2ToaVsk8xvcrOhTU7duQW/w85Svu3X/FtGn7MoNyBtq0FVzaTp0cl9nWqPFMTp8aR8UKhalYoTCNGs9UGwdCqiUDT5+GMWTouvz5AMCr0EjKfZQAuf9R5K9hE4EoMKB3PR4/C6Nlk7I0aT+Hk/v0G5wY9b95+Mx9JgxrwbKNxtP7cgpPx1lIxcZzoY3BtfOPGTgq75ZirT2r8+fT/V+U+FVeITRBqLa9GvGEToVqk2Jk3ryOcxkufniQH7eWayyo8q3K+0oOhhl7/JsRn5iiNajvWqJ75dnpu6Zaj9mbWrGoymCKWX+ayZk2KBQKSpfWL1GhD+7uwkojLS0dExNJrs+XFf27LyVUg+xARmC/cecFlSsU4cbtF7i5GiZYZlRgz7ppqmkDtW6nvK++s7fSb+aQW7wMfs/QzkswN1dW0c7fMDhX5zzUYDq/PdiSI02ZLxkZzM43SZHUOfEjYpFYpzVedvxaoR+9L/9OaOL7fLrDnGFPvSk4mKqyqPKjOlgskvI4Zj8l7fI2x5xTdGxYgcjYRBxt1fVnPIsKKRqxRMyC7zcyYHJHJCYS/vptH4tPTdB4vn31f9IqFPe54OurLiMslysQi3Xv87VpOxeJRIyfny/W1gIFOidKjfqQkiLjxMWJWo+3bFKWlk3KkpoqY9NKwyjhOcqx+3jpzsnlBulyeaYBx6eCX8tyjJuhXSsjp5hQpjtDfFrT6cKnc53Pb0wvL+SfN9bKOWVvQ60fGXB1Ls/jP582S1acbvSH0cyjnKJv8dPsDenLlXfzMJc40LXY7k9yXW0Y3cOPXj9vpF29snh5OGKaZTZaq5XgZtWvykQ2P/gjs92vYzX6VZnI2hvqm8BfWlAHCAxUlxPRF9RPn36ISCRi396RAAQHv2fLlvzZ3/Es4MDxw3coWbqASk1MoY++DDduv6BKxSKYaqg30IYcBfZ1c/pqbDeUBpmBuccvsP7STfrVrcpQv+rU+nUpaemCR+W9X0YyaN1uboe+ZcPALpTycCFFJqPlvHWExcTRs2ZFfmrTkEazVlHAwZYNA7vk5KMAcPbIPc4euafSpmvztPG+VZxqb5jejZOZLWcbz2ZD8ElWBx3N8T3+r2F1je9Z9fwIG0NOfbZ7qODgzYLKuvdWGrpV4Ez4HaPOW86hOzhoPiZCzFdeG4w6X36i+QhhY3T1fvWgdWyBwHyRmEhISUrF7CN1LyEmEbEBrLa8giGza+9irnz4oG7q0qljVXbtvs6r11EULODAkiUnccqmdCqVijl48DZt2ihtGd3d7Uj6yOBTKBQMGbo2Dz6JZty+GcKH97G4ZNNg/+OjVaKFuWGOVVmRIxGwrIhKfcX75CAUqFadlrT103vx5nPXcOx7pYztndC3lC3gpjZjLz9lAXenjiBg8wEW9mjLmr+v079eVVrNX8fhkf4A7L31kA6Vcs51jngXi0ym/AxunkJObcKVYxx/+ZRCNnb8XK0JP105xv3IcCQfn6zPe4/l0ItHjLpwkLl12tDGqxQN9izHxcKaNwmxXOqkGjjWBR9nXdDxHN9nfmCwT2t6FFGX3P0USJGn0fLsT8g1uC/lJ3bUnYSLmf7ikdDE9/S+rJ/O+W8WAdMHhUJBG4/vkH+sLBeJRRwK+zOz/iO/0aTp75z8KEKXlJSKhRZu+PbtV1m95jwO9lZs3arcL1EoFIwcuYlHj9/yVYcqDB2qqscvlysYOGg1r19HUaOGN79MFdRFN268xMZNlyhVyoP583oye/YRAgKaYmoqZeg363jyRLXOoW+fuvTta7xWft8uS/hLhzJm06/mkpqqNCvJoh+j9T8gV4F9Y/A3pMmT8LaupXaN+m6G5ah/2n2cIk72DG5QXS2wD12/l2V9OlB64jweTh9FQkoqTWavprZPEeZ0bUWZSfO4OUXwYpSKxWoPhHR5DG8iBYnLQs5rtN5D+5rT6DagPiZZljpf9xWKcNocXEc/36p08i5L12Ob2da8B17rfyekj7Jgatr100yq2gifjbN41muM2nFNCI4PY9ydVYQnRxv0e8prtPCoxhjfzkg+UQpCH57Hv2XQtbkqQmx5DalIwoIq31LGTrPokjYYEjz/lwN7brE26BSrnmufzLiZ2zO/8kAKW6mneP37reRllo1FqVTM8WM5E8b6UjG4zwqCnwsmNVlXJsf+/knbkAzkrbpjBiwktnT3WohEZPxSAeCf4FeMbl6Xfmt2MrhBdbycHTh45xE1vQvjZmtNKQ8X7r9W8p4jExKJSUrm3OMgElPTmNGpBZeevcTH1RFnayssTFWDVJrsBXFJx/Teh5uHPd0HaXZvsTMzx1Qi5B1TPqaJsqOis1D+ayE1/PdQ1NqdbXWUGyaH3lxjU8ipHMkAG4Im7pXoWLAupY0Map8K3tYenG40C7lCwZKn+wzWrtGHqo4lGFmyIwUtDROu04TcBMPGjYQ6jFOnx2s83vGr+ezeM9Kg82g6R14E6tbfr1TTbvpz7NdU882dA9fJsDtMuacuO5Id4cnRdL80G1OxlNONp2d6JQOsWzuIIUPXsXyZf67u5UvGivW6J8Ftuy0iNi6Jc4fGMvm3vfwyQb9YX64Ce5sCk1j0qK1aGgZglK/+dEO1ogLrYN/wPgDYWZjTPks6ZWRTYdb8cPoorgaFMnrbIR5OF2bgs4/9zQ/NdXPi09IN02A3lhUjFYtJkKUSm5qCh2XeiAi19qxOa0+la4pcoeB21DMexL7gadwbwpIieJ8SQ6IshVS5DKlYgrnYBHOJKc5mdnhYOGIttcDLyg1fu8KUtCmUY3GyzwmxSMTwEh0ylSbT5DKC4t9yPfIpD2NfEBIfTpwskXhZEhKRGDOxKc5mtriZO1DcpgBl7byo5lTyi1mJnDo9nm905GcNCer5iTGL91PVtxBTB7XIbEtJlVF3yEKj1Fmz49cHOzj85rpRY1LlMuqeGMfpxtMxEyv/Fpcv8+fWrRckJArFYnXraDaq+Tdj++bLbFh9HgXQo08demRJ6WxaOZC23QS1zYtXnxl0vlylYhY9bkt3r4U4m+n35swLHLv/hPG7juHr4cqmwV319o+MW01YtCBUpMtoY+b4HfnCivnSUK3/XEylEi6uGKG/cy7htVZgUTzr+wPST8xy+hxITExl5IgNvHoVSe06JZg4sX3msW+GrmXpsn4q/TNm8qCczUdGJvD6dSQ/jt3G3n0j6dRxIXPm9qBECXcaN5pB//4NWL/+AgMHNqBzF91V24aizuCFXFwRoNbuP20z6ybljGp88X1gpmE1QGm7wvxesS+OppqLmna8vMj8x6pSHFllKnr1XsaG9UM+WU5fIVcQF5NEaqpxNRqGwFmDSXW39gtw97Bj0LeNUQArl5zi9atIdh4SHqx9hq7mRWgEg/3rY2NtTruWmZu8+ZOK8S+2mn8itlHXdQCmeejDqA3Ny5ageVnDn9aGztiNZcX8h/+QHW3bzMkM0DKZ5pRdVmT0zRrgAVauOMu+/aNo0fwPTp0eT5PGMzh5Sujbuk1FevaqTZ/ey6hbryQeBkjC6sOIrvW5cv8FNcuqpugeBBkngJaBjSFnWfr0SOb7w35TsDPRHRs6F65D58J16H91IY9jhb/Zlc+PM8hbKBp0cLDK16CelJDC6N4rCXqsLsaV19CkLJmYkML8LKmmBcv9addESS9dv2wAabJ0TKSGF0blKrBvDBZ2nh/HnlM79k0J463s8hpp6YbxpP8L4v8hN7h/75UKVU5qxB9gdlSp6oWJiQQfH6HaOuuC2t5eCJDDhzdj8+ZLjB7dKsfXycCdp6+ZtVHdG9jF3pqaA+Znir1dXa3fZxNQCepzKvfXG9SzYk2NgEwLxnVBp6hoX5SZQwSKcKevlcYfu3YON/ic+vDd10t4Hvh56ylc3dS5/y7Z2jKCev9ha1mzuJ9a/+zIVWDPbfBOlQXz7K2QSzIz8cHbXfUBkdWzND+xa/1FVs49hkgsQiFX0PubRvQc4vdJrv0f/v2QKxSkpMj0dzQAGcp9unjbxhSq6MPQjnUY2tE4GWZDUdOppP5O2TCsRGsWPzkEwKaQc+zaOZxFi08wfJggX5CUlMryFWcYMjj39Nyva00nPjYp1+fJLV6EfOB1aGSmxWDoywhehgjSHVnNi+RyBbFxhsmU5IsQ9LzAZgZtngaHK8uqU9IM2xTIDxzeeV1l1j6g/YL/AruBuBYWyk+XjxMaF0O9AkVZ7NcWM4nur1V4Yjx/3DjPmVdBJKal4m3vxJCy1WlXzFeln0wux+ev2VR08WBvm970PLqN5zERbG7RjWJ2jkQmJ9H2wF+YSaQcbu+PuYbrbn58m42PbvMk+gNSkVjrtbLj5ysnORD8iIhkzVr2h9v7U9rRFYDy5QsxauRGxo1vK9y3TI5Umvf7CpGRCTg6WjF//lF+m2F8Qd7A6/6UsS3HqBJKEalCrnmvtpgbNHYrnxnY78e8AGDPnhu4utjy5EkYV64+59DB7xkesIFFCw3TJteEVbOPftKgLpaI6dhH8wN0/8mxtG82C4VcWB1JJGIOnxXSb1HRibx7H5vZvvMvw8QKP5/CP2BpVpm4pJOf8xYAqFZPNW9fva72PH61/nMRieDaas2MgXS5nJoD5+NsZ8WReUMy25ftucjqA+qqmFKJGP/W1RnSQd145I+Np9lx+jb/rPmev+8E8f2CvSrHezWvoqKemYHWo1Xpa2amUi4sU98gyy0a7V5FUExk5vsTL59Scv1ctrTopnVMxqZqVjyICCfg3AECzh0gpJ86R/n2+7fU2PYn4YnxmdcN6TeWyluUy/NS6+eqjL3z/i3tD6pWeMqQ671WcGwkDXetAsDBzIJCNnaExim1vks7ulLE1h53S9WNwL37RtLPfwXh4bE0alSaH8a04uXLCPr5rwBUaY8xMUl0/Gp+5tjGjWbQp29d2ratrOW3BpaWphw+fJsN6y8yaJBfnuTXv0RIxMo0luxj0Zqriy1du9agfYf5mdWgz57lXP4ZYOda3UKGtvaWuBUQyoefPlDdq3PIUrmanJxGUoJ2aee916foNduwsDDluBbOurOTNc5OhilpZkWOAnvGjHxeYO4UEQs5/0Vs4kFSZSE42WinF1qb++ksMNKGlx/8SUjW78m5d9NlavmVokK1oty+GsSeTZcZMqalxr7uTjaERcRp1bSZuPwwAH+OVWXZ7L/wABOphG861qFehWLI0uX8suYYgSHhrNp/hTrli1K2mLpFFsC952/5fsFemlYvSbPqJYmOT2LNgat897U63bPOkIWkpsnwLuDMr0NbkZqWzvilB6nWP2+9Njc8ukVQTCQSkYjn/mMy2/9+E0L3o1u1jlvTpBNRKcl08imj0u67YR5JsjRmXj/HuKrqD6tuJcozqlJdzr8Ops/xHXit/YNjHfpT0sGZHy4cZufT+7xNiMPDSqCfVnDxoLKrJ+ubdcHaxNTga2UE9SD/MSp00YwH0uH2/ho/l42NBWvXqX6HCxd20sg9t7Oz0Mpr79VLmNVlsGgy+h04OFrl+P8qrnx4nPm6hI0g3rV+/WAmT9nNls3fEh2dSMdOCxn3Y5scX+NcNqJEBqo3KMkvf/ZRa+9UcxoJWVIgG06NRapB4TE1RUZA1z8Jeap86HSoOpWDt3/R2D8D4WExuLmrsmUCH7zGt4yQim721VyV8Ye362e15Wi9mDXNMsr3uNqPMbC1bIOz7TBEIu1PNVMTH0QiM6N/TCWFDLqHo7d/4fThO/RvO58Lpx7q3ExdN1GggI1euE/j8ZP/PAGgqIejSvvhOYO5tGIEvVtUxcvDEZ+Czqyf3BMLM4GvO3nFEbVzZaD/r1u4umoUvw1tjV9lHzrUL8f+WQORZnNSCXkbSWqaDGc7K7ZO64N3AWd8vdzY+/sAXByMf+rrwqTLgqTrw96qm2r1PL2o5qZdFbFRIW+1oA5wvZtQUr3h0S2N40ZVEvZi6hdQUmtLOghFRwEVhNXOhTchKmN2t+6lFtQNuZaJWKJWAzCgTFUAzr0O1jhGG9oWHM6715FcOChca3DdqcRGJdC11A/ERsazbcFRkhNT+KnrQgCC7r8i8HoQAc1nGnWdLxFvkyL1d8qGWYFKUbRuRYSJy7V/gmjWrCw3b4UQFPyO3bsCaNCgVI7va+lvB9TaAn7uoDGoA9jYWai8j85iLp0VpmZSlu0NYO1RVb30NhUn61SF7NVpkVrbqG+UftE713/D4e0jMn8MQa4SgS7mxXIz3GCYSXOmi20iNXzzddSUDqw5MJJhE3TPBJzsBKPpi3eDWbnvssqxsAgh/dGipu78bVY0rir4PYa+0y4t0KZOab0iSAC//SUE2xXj1POvi77vaPA9GQNN+fRJ1Rtp6KkbVh8DcEKaZrMod9SWAAAgAElEQVRjXciYpWvLhxt7rYxK46zIyN+nyfVTGbOi4zeNcS3gSN02lZClyQh9GiYE9agEDq49R2J8MhKpBBNT4QFfrGxBlk/aScCs/Jerzg80dFOaOn994XcVC0V9mPlwV+bvt0PBmjT6aOpSt06JzJ+jRzXPto1BdGSCyntTMymtOms3Tbe2zRbYIxK09BTgUciR7RdVUyttKkzW2r+kBlnh4iWVOvY9B63k8PF7nDofyKnzgTqvnYFc5dh7FVW3zALDqk6NgamJd47GmUgMC+xd/Gay/azSLaVzgxnsOKd5qQyCM9SCbedYse8yg9rXymwPmCfMNn4ZpDmNI1co2HriJpfuhfDqXTSxCcnEJeq3XuvoV0Fvn5j4ZG49EXKBhdzUpQW9C+S8pN5YlHfWb66w5/kDjoQ8ITg2ksjkJGJTjbOgy/pAyShB1zQrepsQx1+BN7n57jVRKUkGXUtTwF96T9gfaVLIuEnG09svMl9LTaT0+6kDXQKU/qIhga9JSkjh5w3KTTEzcxMmdlvE1oezNJ7zj8czeBL3mBVV1miUG1agYND1fhSz8maC7ySd9ydTyBh6Q1AqbeLWjG6F1B8oUx9OJjTxpVp7C/eWfF1QtVBwevlezHi4k4Ov/wGg7olx+BdrnMlJ14Tn8WEMurqYFLmyIGiMr7qjEcDwYU10fp6c4I+1upVaDZ2xZ4WtvSU9hjZk8zLB+zU9Xc7eDZfo0Ft9L+2xBrpl1rYDW43fH/tXlARKxY76O2mAWGxYuX/pCqopmyq1df/xtqurnkoACH4j6LxoqqVITE6l9fcrmLf1HFcfvOD1+xiDgjqAjaWZ3j4JyZ/OmzMlPefUPrlCQbsD6xl1/hDHXz7laXQEEcmJRs+EJQYUrBwIDqTW9qUsu3eVa+GvDLqW68dN0WnXlNzu86+DcyxOdu/yUyLCYrh8VJD+3bnkBPHRiRxe/zfJiSmYWZjStdQPdCw2kqSEFC4fvcOoBb0pXV37ZKaxqxDc7sbc1nj8YazgUFXXWb8N5Z/PhDRAO88OGoP63te7NQZ1gOqOmi3vhpdorfJ+XdAp+l6ez67QyzyLf0t0agKvkyL4J+IpMx/uos/leSpBvZpTcZXxp88EZv78Mk1zCjQ3KF5W9wTQzExVAyo5ybCK1M4D6qu837xcu8F3YqJyMpGSIkPb1+3yP88NunauZuyHXv9K6wLqu7l/PR9AX+/VuTk1oFsGwBCYSA1zwCnio2q95+is+4Fga2We+Xrtwav0a1MD+UeqUpVS6nn9d1FxtB69EoDvu/vRrUnlzOA/YdkhTlx7rDYmKwxxJc9LUcR7l55SrnZxrcf10Rm1ITU9nRLr5wBCrn1itYYUs1M+tDUxZnKKFnvX8ijqPSLgj7ot+cq7jIq0gbZrXev6LU33rGb1g+usfqDUOrGQmqjtJxiCA68WA1CrhbDq2v7RBLpVHyHojmr1B0fClwKw+pfdDJgspMwmrxuq9ZxVHIS0wZJni1hZVV2HZvGzBQDUd/FTO5b1eTj78e88igukn9dA6jiry82GJoZy8O1+xCIxy6usVhHnUqBQeZ8V1lIL1tUcgf+VBZltz+LfMvfRXo39s6K0XWHmV1adQTdq6KvxdXbk1KRH39+XjZ1qkdX7tzFaeqrCwtIUkUiUuZKMjdKcKlyz+RvaN1X9Pv65ZgAKhfokcervBzi6U7++UK4Ce3J6LAsetSSg1GFEiEhKj2HZk85Ud9JOdwOEzaMqU9TajwTlraSoqbQYbva6l6IA21afp99wYRYkl8vZveESg0e30Dnmx16N+X3jKf7cfZF+bWowZZVQITdvhLryWsfxwh/fyYXfYmdtrnIsNiFvfFE9nG2xMDMhKcU4fYufui4k8Hows/eNpljZgvSqMI7YyAT2hwozuR5lf8TDy5mo93GsuWpYhe6zaM0KlS32Cb+HX2s1o2epihr75BUeRQnWe8EaKI368Cw6ggLWtlzsrD245hW+m9md9kUC8ClXiDkHx+gf8BG1nepwKeIiafI0TMSqM8o0eRouZq4ax9mZCOyLGY+m8zz+Gd+XGENpW80r0GmBwt9o9qAOaA3qGShu48nfTWbS+twvxKYZtvcxv8pAqjmqTyjevIkmYMQGChd2Jiwsms2bNHO5q4xYwO1Fqg/f+j8u5fzvuTOqd3BWJR68CjHc1rHLgPpsW6UsvHxw6wVlKqnKNxQq4qTRGu/2vVAqlivEpWvKWboszbCVba4Ce6fCvxOREsL8wOaUtW/J/egjfFdyr17dmBEdFuR5ENcEscgCJxv9f5zbzvxI6yo/k54uRyQWsemE/j+wrxtV4HHoO/aeu0eqLJ2jV4RNDQszdenelI8i+dmDOsDVBy/U2nICsUhE50YVWX/kH7acuEn3pqqc6J1nNLsA/bpNyN+1LTiMA68Ws/HOTK6eUN2gmnNwDAq5gjsXHlOhrmo1YZo8HROx6mbjHzc0U0wzOO+agro2hkpOEJMqPCy1qVvqutav/5xBAfka1LPOLOu1q0y9dqr/V3sfB9KhpPrM9MizJ7T0EWos+hcdxKWIi/zxeAY/+So35na82gbA2JLj1MYDOJg4Mvvx7zyPf8bPZaZT0EL7qjbD/ERfENcGsUjEET/h4bDlxXn2vbpKaOKHzOPmEhMaupVnqE8LnZZ6/Qes4ugRQXc+MlL3xmVWpMrSsTUgjakPztmoiI/uvjJ4bMM2FVQC+8m9t9QCuzZULCes/mtnScu1a2XYhCjXBUpOZl7YmLhwP/oIha0qGyQGJsmFlkZ+wM7BikM3fjZ63E99m7L33D2W7b4IwLdaSrPtrS2Ijk/iaeh7ihdSmglMXa1fK94YDO9cj/VH/mHulrN83bBCpr6EXKHg9w2aLegm91zCL5u+0zsTEIlFJGbh8k6q3ohp107ju2Eez/oqzR4eRb3n+MunGs9R2dWTm+/eMOHSMX6r3VxlTAZ9Mi9gZyo8QDXlxfVdK1EmrHh+uHCYqTWaZDJo8gIp6TIqrliSaf/4+LtRKBQKaq1dTmlnV/5q30ltTFp6OhVXLmFUjToUsFFNEdqa2BKcEMRfIWvo6yU4kR0LE2izDqaa96UuRvxNVGoUAFtfbuKHkroNYfIK3YvUp3uR+vo7akCHDlW4dOkpRYu5MGH8DtauVTd0Hv+X8LkrDp+X2eZsa8Whn/ur9TUW3r6q9SXGaMsU8VFdOd27oU6XDQl+z6Bey1XalqweQIlS6nUtAUMMM47PVWCPSAlhfdBgWhUYT0nbhpx8O595gc0YVnI/JmL12WkGZm//jnalxtF/bGscXJRf1gZt83d5nh+wsTRjw1EhF9uvjWYp1R2/+dM0YCk9pgiVkGKxKDMnv2FKL3pP3Zhn9zNtcEsmrThC7cELVNpHdWvAvK3qYm3eZQqqMDcMxYAyVfkr8CYv46LV8tUH2vWh7f71amN2tOqJ97pZbH58h82PVVcQG5p1offx7UbfhzbU8ijM5bcvNebSdV3r11rN2P3sATuf3mfn0/tqxweUqcqEag0N2rzNDjOJlM1fdaG8q9IlzHfZQgK/GUG6XM6eRw/5qpSqvWPjjWt5MDSAN3Gx3AlXVVycUvoXRt8Zyd8fztPXqz/xMoGt0cRNOwMlUZbIiiprmP1EyK8fCTtES/fWWvt/CRg6pCEyWTph4bEagzrAjL4tCX0fzcYfuuf59UuVN6weRhOyq1JGvlf3ZR3Ua7laKqZpneka0zOGIlesmPVBQxhR6jAlbQVBniYeIxngs4HFj9vpHHd23y36jxW+TFHv4zJ/suNhaAEehhYg8JUXb6PGE52wDTSYenxOrP5J+CJl3VDNDntrCy6vHEm9isUwlUpwc7AhoEt9/lnzPaWKuFK5pGGbvIagRU1fTi/+jqqlCmEilVCmqDuH5wymR7MqGvv3ndCe4hWLZG7gAdRoquQib76v1MWu1VKVdnn+68FsadENH3snzCVSmhYuztO+oynnpJnuKBGJCOk3lm4lymNtYoql1IQ2RUvxrO8P1CvgRQHrvHO439KiGzta9aC4vRNikYiito56rxWflorX2j9IkqUxpFx1ptZswtSaTZhYvSFdSwic6tUPruO9TjMNMSdIlglpOolYzOOID2rHX8cJOiGeNur3a2eilBV4kfiChU+F2aomdksGfKxLIBaJGVtyPDZSG3a92kFwQlCuPkN+o1v3P5FKJRQsoMUh/CNyGtQf39OdWtG0uRoXY5jOzNtXqkVamhg1xUuqz8w1zdaNQa7NrPMT+tQdrc39cLDui41F7qQNcoMluy6w7tA11k/uia+Xm/4B/wGAGVP3Mn6K+kbztEm7GTaqOQ6OVrm+RpMdazjZ2fCleMbsXpOGDAj7CcX/mqOzD0CTBr+pvD95bkLm6+jkZE6HBFG7YCHcrW1Y9M8VGnkVY8+jh4ytXQ9TiUQlx77onys09CrK2ts3aVLUOzPHnoELH/5mXchqPMw9eZv8BhOxCUsrr9R4X5pEwAZd74cCBYsqLcVCosrXfhr/hN8f/YZUJGVZlVVq54tNi8XWJO8extoQMGIjCxf0yrPztSijyuSr06QMkxboLgjLPsbb15MlO7UbUGdg1eyjKro0Ds42bDmnuv/RrO50jl+YqLdNA/LHaAMgVZ7E7pfjeJsUiJtFSToWmoG5RH/5euviY5Wu5yIRh5+rz4I8HGYQnbCNpFTNfN345LPEJ5/N0iLCzrIDDtb+WJpVzcnHMRrrDl0D+FcEdblCobKheOPJKyoXL6iRd5+v9yFX8EHDCg1g0rS8q5A1JqhnwMHMQuux7JvEmhATo5sBYm9uTscs6Zbh1QQueBkXZS4268ZpxvE5TTUXvdV1rkd4chhHwgRFxAAf4yiZK6uuZeB1f4bf+gY/l0b0KqIsqy9uXYL6Ln6cf3+Wgdf91cYOLDqYmk7qBTfZce7dfd4mRZEgM44BNsBbkOqNjIynUWOlxMLpU5o3hkeu2M+cgW2QiMVUHD6PQs72DG9bh2aVVR+Gzb6qwvE9NzLfXzz5QO+9lKpQiEd3QjPfPw98Q3JSql6Br+xiY6UrqvvIlq9YhGb1fqVlm4ooFHD00G1q5tL+L5d0xziWPulEY/cRNHYfwdukhyx90pEhxbdhKdW+bBrTdQm77kzH3FL4paQkpTG682Lm7Bim0s/Bug8O1qr6DWmyV0QnbCU6YStp6dkdTxTEJO4hJnFPZotIZIaDVU/cHabl5qNqxPxtQs56mAYxri8R1QbP58ZK5R9+lRK5TwE1rfsr5uYmdOpag01/XeDEBWFms/LPU2zffIWvOlfn2uVntOlQma+71eDOrReEvoggMiKeIweEB3bLj3srt2++YMn8Y8z7sw/WWRhEMdGJfN1mHt171yY46D0Tfu6AhZ4/KIByfy3kXl+B9VNx/SLm+LXiaVQEXUqWZeuju/iXrcKQE3vZ0FIp2BaVon2JPezsfq3HMrBKRxFKfqFTwc6Zgd3XtrSe3upYXGkZw24N5ez705SzK08Fe+VeV58i/vQq3IctoZu4GXWDeFkcbubu1HWupzOoP4l7Q78rC7QeNwQZgX3jBsMYSpcfvUAiFnPwWiDTejWnbY3SNJ6wXC2wDxrTUiWwg2CHJ9Ih2zHql44Maa/6eTpUncqBW1Mx0aKPP7r3CrW2Fl+rTzhnL+7Nm9dR7NxyBZFIxKZdw3Fxzd1KKFeBfWvICIaXOoBUJFCKXMyLUdq+GRuDhuLvrV2N8dmD15lBHcDMwoTnDw0rRjKRFsTF7gdc7JRMDIUildikg8QlHiEu+RQKRUqWYylExq/Js8CeXSXR0tyUvq2060zkF2p+s5C9v/bjQUgYjSsXp/OU9Uwf0IKDlwMJ6FSXVj+uYnLfpgS9iaRdnTI42KjPRP++G0S98oLeT9PRyzP7L99/mUt/DufYP49pXq0kDQL+5NzCb7Xey4GTQlrC0dma40fu0qxlebZvvsKshb2oWLkI345oStO6v/J1txpUqFQE3zIFOHPyQWZAz0DFykVwdlFlfiQmptK1/YLMB0ZOMaGGH40Le9O4sDfHQ54Sl5aKVCTGLMssvKitI8GxkVTcvJBFfu0o7ehKbGoy51+HMP3aGdLk6YiAZY00l7sDHDmkmVaan0iRCzNhbXz0DKyquk5ju7nEXOsxALFITM/CvelZ2DD98+yep7nFkydhDBu+AScna8QiEZs2aQ70voVcSZfLmbjhaCafPUmDb2l2iQCAmWO3MX629vqbIj6uBExpz8KpqpWvbStNwdvXk0FjWlKiTAFiohI4ffA2GxZrZqFllwjPgGcBBwJ+0LwqywlyFdgTZVGZQT3zhCJTEmS6Vd0Cfv2asd2X8vPKfigU8POgNfw4r2eO70MkMsXOsiN2lh1JTPmHmMRdxCYeJl2uuVAmN/BwtiU8Mo5y3h7Uq+D9WYI6QM+mlXF3tMHd0YYX4VEsCOiAp5MtJQu7snCXsPyrV74Y9coX4+yt5/hV0q+3k9F/zeFrObonGxsLoj7qaEilYipWVvJ1dc2GdOHe7ZcqLjI5hYVUtb6gvbcvCWmprGquTP2c6TSQASd3cSr0Ob2PqbNmOvmUYU69L49BsuipMJMMKG58ZWxeY+6jfewKvaTS5mRqQ03nktgaYZOXFcOGb+D4MaG2RBePfd2ornSZuYH1o4UA/SYyls51Ness1WlSRiUFc+7IPZ2BHaBVl+pqgR2EtMy4/vor7bNTH3Vh49q/6dUv55mAXAX2em6D2BA0lN7FlGJgG4KG0NBd96ZCw/aVcSvkyI89liJCxIgZnfEuY7wNXlr6ayLiVhAV/xcKRd47imdHaOI99v8xAB17Fp8MgSFKzeeCLnb8fScYTydb0uVyink65ck1kpKF32l8kvE6NDKZnFs3QqhUxQsg0x0GQCwW65QxzYoy5Qoi+kgPNUThMgNVNi4hLjWFmpuXcqWHeuWh/9GdnO4ykLcJcex9+pBvKgpU1dVN1LnkhuLhg9xJYOQEUx9OIjQxFAuJBVLRZ/XNAVAJ6qZiKWcaq5s3G4Nz5x/h37cuf/11AXcPO1atOseO7cO09t8+Trmq8HS0ZVQHzcFx4vzutCyr3JycvMCwieWcjYMZ3Us9xWIIlu8zTHIX4F14bI6ukYFcfRPK2bfibeJDFcONSo5fUdquqd6xpSt7sWi/4TOMtPQ3JKXcICJuOUmpuqsUpRJ3nGyG4Gjtr1Pn3VgUsiynv9MnQu/mVan1zUJ8i7ixZlxX7oeE8euGkxQv6Myf33diwU51h5hJfZtS57tFXFwynFlbz7Lr3F1MJGJmDNY8Cz154wlL912igre6rKg+DBjakLEjNtGxi5Bj75tFEEkqFXPvTigb1vxNZEQ8I8a0JD1dzo1rQXx4H8fZUw8pWMiJipWLYG1jzpbdw2le/ze+7laDiA/x9OhTB69iLjquDjd6qU4u2nor9bubeRXnu1MHMBVLuPb2FRVdc0cty8DKZeqm0PmBgdf9kYqkyBRKMbaFlf78JNc2BrkN6gAN6qvqrjdvZtzf4LLDlxnaqpZau0gkwtnNjhmr+1GoqO7vUlaUqVSENt1qcHCruhuaLqw+rOq41rTOdL1jvh+X89XhF0N3bFnsBzWZgeDwtiSl3tQ71tayNU42Q7Aw1czV1oT5jzpR3KYWlRxbcyvyECEJN2nhMYIPqS8pZ9+M1c8G06nwz9yLPkFT9+8QiyQ8j7uKt40ws1vypAfpChmDfFax9Elvvvfdx7KnfelddD4bg0cxpPg6Yz7+f/gfQHaaYway0h3zAn88nkFwQhCWEit6Fu5NJYfKOS77z2vUOaGsZL3Y9HcdPT8NKg6fp6Yfkxc4ue8WsyfsNKjvnmuTsbBSTVnrK0CaNX0/Yybqrgciv+iOH1KCcDYz3Gwj8l0sjq62JBhoIqspqJublsPR2h97qy7kpr7K3aI4xWyq4Wnhy6lUoZzX26YG3tTgaZxgoOFp4YunhS+3og5RyUH96dnQbSAWElskIiF/W9/VHysdbKD/dSSHV8Hc7Yb+jv8hVxhbUrtXwP9H+I1fxtkZQ1XkBPIbTdpXokn7SmxZfpb1i0+qpBoBvEt5MGZmZ7yKa6ZB66vTcM2mT2MschXYd70cxwCfjUgNTHcs+2UfExb35uuKk6hSv6T+AVkgQoqpiQ/2Vl2wtWxHbqXkDZ3hpMgTcDDVvFSXilWfwhEpgm51nCzvN23/DRBJiyOLX0T2iYTUWntO9H8FJ47l3tknp3geswlziRMFrHUrkhqLtwlnSJC9xMeub56eN69xdoaSJZN9dp7fwb77ED+6D/Ezetz2A7pXEX0Hqvv+GoNcBXapyMzgoA4wYbFyY2P6OlXNh5bFfsjeHVNpUVJlgmiOAhkpaY8Ii5pEWJRSitfcxBcn22+xs8x767cNwSNIV8jwL7aEU2HLuB11CInIhLYFNRdImIjNmBPYlt5FhS/TqqBtHAlT1WfZVVt3LjQnY3KCA/tusmPrVd68iTJ4jFQqplmL8nTqXJ0iXuqOTGZO2k2ss2Kg/0pCgtWlT93c7Ni0XX81n6HQlhqB3KdH5HIF584EcuLYPa5d1W1+oOs+9KFx07KM178k/w/ATP9Wn/sWvhjkKrB729QmURalsxhJE8YZWB7s43GB2KTDRMQu0Vp9mpwWyOuI4YRHT8fZ5lscbPwRGfCxuhaZkfm6d9H5KseK29TiKPPpXVRZkNDYfSiN3ZUzg+9KbM58PaKUkGtzMC3IaF+lUW5F+9JEp8XyKimM0MTsxVSakZMxxuBdeCxTJu7k6ZMw/Z2zQSaTc/jgbQ4fvE0DP18CRjXHzj5nFLZ/I+Ljkzl5/D7Pn4Vz6sQDUlNz7iSV1xCJJJwK7YBckUaDApswlQg6MhHJN7gaNgILqTt+Bbcj+rjSvfPhV0LjDmJlUgi/glsz20Hx8Twyyjkbr2VfxMqFFwnCQ/tx7GtK2hrPdtOFxORULM01TyZbVFHPAmSdzf9/Qq4Cu5/bN2wK/pYy9s1xMVPlSRewLKt1nCYVR2367LYWrbC1yPoklhMZv56I2CWkpSvlM2Xp4YRFTyEsOsPAQ4yDdW+cbb412Ekpr1HVsRxVHYVd/E6XtBf45HaMIbh75yXfB+SdiuS5s4GcOyto0J84Mx6RWIQscRNSy5zXI3yJuHLpGRPH553qZH7hxrufaFtUqD/YF1SR9sVuk5oezaOopbTyuqDSDlDWaTQVnH9CJk/gxMuWNCssSEjvD6pC66KXkIjMOfqiCT72hhUlZWBz7R/4/uZqrkY8of/VhZxr8htSUd7JdC/Yfp7xfQz3PbW31i4RoQ3NTHtwPHWz/o5GoMHCVRSwt2VzH8FofvbpC6y7epMBtaoyyq82V1+8YtCWPfi4OLF7QA/6b97NrVdv2dy3C75uLhx79JTRe46w1b8rZT30y5fkKrAvfyoY2V79oP5LGFJ8m86xgTdf8Pi2qpdih/6GEPLFOFr742jtn9mSkHyeqPgNxCYdztJPTlT8X0TF/wUYb7M3vKTu+9eEkrbq9mJfAto0n0Vycv7x/DOKj9Jif/ufC+z/FpR2VOdIP4icy4ek6+wLUp9IvYzby/OYTSTJwpErlH6bCuRIRIKcQxnHEaTIdRcbasLcygMYdG0xD2NCaXByAn6u5fi5XDdMxLnn2UfGatfiOXH7KXVLe2FhasKcPefZd+VBrt2TcoIeXsPYHLI4832zP9dxLkCw+9tz9yFflS/NkcAn3B+vNKledP4yd8cNz3y/poeQWi792wIeThjB0/cR3B8fQJkZC3kwXr+5da5+0/qCtzYsmLCDD29jaNIxb4S6rMzrY2WeVcRfQWTcGsKiJ2sd8/8B4eEx9OyyJF+vMe4nZf7X3OUIKRGd1fqYOe3I13v4D2AuUS9KU6Cgnuc6HM1VA/vjqOWkyeNpUkhIG2oK/AAiDQVPWemMhuLsu3v4nTJ+c1kTXbJhZR8mrzxCrbJemW0tawmiaXP3nKdpxeKkpMm48DCYU78NZsCCHaweof6dzC+Ev1DfO3oRGUXKR3nmdmUFXv6p7/oz/sBxvBztGVKnOpUKqtaKDNq6l5XdOiCTC1XXBewE7RhXa8NUTz9Lqdqp3TfY/2im/o56kC6PJDphF7GJ+w3iuwP8dG8Oj+Ke071wW0rYFGXqg4Uqx/sX7Uxrj4YqbdMeLuZ29EO211qMRKTOxhl95zdCEl7lyyZnThEc9I5B/dSlVvMaTZopU24iSeH/uSAuEosMqnhVKBRaDcWNqZjNy7FlHX/g1Kt2tCwibManpEdgJnEiTR6Pg7mQ7nsSrfodESEmTR6HidiGR1FL8LL9dEHREMQnpVK2mAdxierV0Bm6MMOW7mXr2J6YSCTcCspdNfAPTaZhYmbCjENKwoR/qVG8CQqnZFVvHl9/zoZnC3Er7My0rvOJjYwnLiqeaV2FfbvxG4fze/sWXAx+iY+zIy7WVliIxVx78YofGtWl78adDKlTndWXr9O7agXuvg2nSQlvfN1cuP9WWV0++/QF6hYrombcoQ25Duwrn/UgPk1pEGBt4swgH935qf5jWxP86C1FDRSTT5dHE5Owk+S0h8Qk7lUR+dIFW8u2OFj3xcpMvfLsWNh5trw8QK8i7anjXJVn8S+Y83gVa4J34GTqQE2nf5+bUwYSE1IMCuqWlqaM/KEljRrrFo8CIde8euVZgoPeZbaV1PL/l558XOW9xPzz6eXnFjVqenP8jH7e+PSpezl7+qHGY4aMzw+YSuyo47GKYy+aoECBt11Pitv3p6zTaM697sGd99Oo67mGwEhl2qBdsRucDG2HQiHHr+BWXsTt/iz3rg1dGmv/uxzTsQF+45eRlpaOmYkQ2nw81NlbhmJMs+lY21vx805l1eieRUdJTUnTmIOftG0k9y8+5reei5i0bWRme4dy6v611YsI+34HhwjqtQ8nCKm0JjaC5Pn3DQAXV5MAACAASURBVAWbzScTBVrkpOYNcbG24vQww6SocxXYD7yaSiHLirTwVO6eH33zO/tf/Uy7gj+r9ddEacyK7Buo+ow2skIw3eiDjUVz/Z2ByNQYVlebif1HowBXMydq1/6TTpe+ZdbjFV/U7NtYtGs1R+dxG1sLdu0badRssGZtH2rW9gEgISGFaVN2M/kXzboqWQN5atSgf3Vg/zfA2051XyNjgxTA1rQ4zYucVBvToMBmjf1BlJmiAdQ47J+7mjQxJQ3LLIbxz19/wLuAELxbV/OldTVlEFUoYNuPOTPomNZtAbJUmUpQB9j6xz6+Xz44R+f8lMhVYA+J/4fhpQ6qtLXw/JGFjzRrHGhjvhgLC9PKOFr3xc6qI7kpVLLX4P5ia2JNbFo8ChRfTJm2MZg986DO4/0GNqBnb82m21lRd94KxCIR50eqe0xaWZkxc7ZhNmQmtj8b1O8//AdDMHH5IeYGKJ231h68xvQhmvnr2rIWsrR02lScjEgswj+gKV0HqRcDlalVnGVjNvI2+B0eRZWqjOmydCTSvGP5GIoWvsWN6p9rdccXCTcoYqXUaAlNuE1d1wG5OW0WiLGz6oijtT8WppXy6Jy6UdupMkfDznM3+hEV7NWXUF86jh65q/XYN8Oa0KlzdYPOc2HUYOrP12yxpg/J4UopY7FZHUzt5+vo/R/+g+HImJ1nwNleuZkYHZ+E3/hl2YeoVaP++auwIlHIFaydf5y184/jP6Ip3Qb7ZfbpOKIV7b5pRivrPhxL3pTJ/Pp6VBsWDV/DX481f6fNLEyJj9EuLfypkKvA/ibxAWfCBNaFCBGKj5phpmILzocrpS1H+h5VGTd18FomLu2LRCKmZbEf8CjshP+YVtRvraqdXLpQKPkFbRKndiaC0UNUaky+XTu/8OeiE1qPeXo6aA3q++4GUqGAO9HJyVQsoDlv3nrZetb27Eiv9Ttws7FmQ5/ODN26j2Xd2vMqOoaC9kptC3O3K8gStwAipJa6Na4NRUjcYZzMy5KaHoOTeTnuRiyhkHUTQuIOUcFpOAdC2lDNdSKxqcEUtW2LmeTL1uxpN3c9UYlJRMYL9L0HMz+9lnqZcfM+y3Vzg3WHrvFdJ4FWLJcr2HTsBiO7CjPunrO3GCT4dXjHP2ptWYN6BqSmUlbe/oPm5j0zc+rdxrZj75KjNDfrSYmqxXh2K4S5Z6bgW0NIUxavXJTkhBT8fUchkUpYfS9vshTGIleBvbaLP7Vd/I0ed/Pvx0gkYk7tucHoWd1o0qkqPapPVQvs+YmskqdZEZUq6CA7mBouwhOTptm/81Pi1In77N6p/oXNwPot2vm8zXx9qD5rKZv9u2o8HhIRxYpuHXC1scbTzpavypfmRWQ03aqU5/rL11QtrLoXkhYzCRO73z6+npD5OjcoaNWQPcFNaFxQWEUERq2jvNN3OJiV5E7EIgA8rerhaVWP1wlnKWDll+tr5if2fy9smj14FU6XxXlbDPO/jOMLvqHmwPmky+WIRSIOz1Xmu6WSnKVlszNNsm6MFildUG2jdOsL3ftveV3clBPkSknL3tTToJ/s8ClbkPR0ObNHb6FJJ4HLnpyYqtbvc+BShKBOWN5eqQNtLhHEvpLSNatSfgmz+wXzjmk9VqFiEa3HACxMTLg3IYCXUdEajxdysCMwXODnPn0fQZtypdh79yF+xYty5kmQWn+JRacsr7XbyBkDqdiCzt4XiU979fG9IGWgUKRjZ2q4wuh/0A25Il3n8XmPdRMg8hsONhZcWTWSf9Z8z9XVo3Cxt848tnZkF6qPWsimMzc5euNx5o8+dPLPUliYaoh7mO7fkSbI3xmWAs0rfBa645wdw/i21Rzm7RIqrcJfRdK6lzolESBdHoVEbNiy+nXkCGIS9iARW+Fg3RdXO81iXRl4k/QOTwtVu6p4mbA0zrpxWt6uJFcibrE99DD9i6ryer+E2ToIFEdtmD5TNxd51+0H/HLkNFUKF6B1GUFvo9acZUQmJtFgwUrOjRjE3ddhfL/7MPsG90IiEnHg/iNG+NVm2817jGkiVAwnh1fWeH5zN8NqDHQhOHY/N97/gYtFRQpbN6NjsTPsC26OnZkPfp5LuPNhof6TGIDoD1+TLnuCk7v2vYpPiQevwxm75QjhsfF0qFKaie0bqfV5Gh7Br/tOc+vFG1xtrRnVoi6tKqjrpgxctYsbIa/pVK2s2nkWPPmRdymvSP+4kp1Zfhu/Bw7DxsSB6LQPTPBdyvSHg0mVC9+z65FnuR51hubu3dgYMpdJZZT7MXejQyhv72XQ55t+fzvHwm5hKTHjq0I1GeqTc4XKI9cfMaKdEKQjYg3Pczdq+zFTkP4GeVR/xG73AZC/qw2SwiD/gNjlJChSkL+rBh/d2sTugSgSVqGIX4TYcROYlEWRfARFzBhEdn8gMm+FIuYnFClnQGSu7fKkpsow1WKInVN8UrpjVvx5eHTma7eCjgwY10atT3zyOV6+7wFAMfcTmJtodmBPlQXz7K3yqZsuj+VD7CI+xC6iZIG7SMTqVXlWUguG3/qZzgVb0citFsEJofzxSNgXGFtKlc7U3L0+K4K2cujtGcQiMW08GhKW/IE7MYHsfnUMTwtX3iS9U7vGlwILC90KnJ0qlqFTRVUu++XRquJJ3zeqw/eNlGyakx/5tNfHKvVs8iKAa0NR23YUtVVWuYoQ076ocpWS9XVu0jCmZnWxdDbMQCG/MXXPKbZfvcuEdg0p5urI1D2n1PLi+248ZMKOY/jXq8KwprW5GfKaMVsO8zY6lgENlJvYZcbNw8PehhX9O3L52UuqT1GtSB5R4ndeJj6hoIU34o/aLhGp4fzoq+S4Tyy9gsn3lfTHod5TmfVoBG0LKNuuRTxh1E3B/7OotRsba6nSBTMQmviBbhdnZb6PlyWxIfgMG4LPcKjBZOxNDauwzIqeDTVPLPShWMmP+0oST0T2i1SOiZ22AnIUqVcQmdZE7LgeTMoBH5kxEnfEbneQv6uG2PUfSLuN2O0+8rDSiNxbgbQ4YrtfkYdpNrEGaN52DmeOqVf0nj4byNYdV1mxxN/oz/RJ6Y4Z2LtG3bYN1LVi3kYqg7+2oA6oBPXsePy6vEadGCuJJcN8+vL7o2XseKXUmOlftDM1HNWLIBZUmsyIW79w4M0pDrxROpAHFO+LidiEOY9VC4LCkt/z3c0p2U+jIuzlYubIsirTczUG4PKlp2pjMmBmbqL12P93RL7zw955D1Hv6uHkfv9jqyjL8YbYO20lOqIjjq4X+fC2GHZOO5FKiyES2+f7/W2/epfTEwbhZiukG46O6Ue58fMZufEg83sJE6H2VUrTvoryb6Nq0QLcDQ1j0fHLmYF90QnBh/TkOEGvpFqxgsgVClaeyZlpeVZYSW2RZCEizHiofCh6mGtfaWcN6tnR+twvOebLVx4xH/lH0wuRCG4tzIvNYTHI49WbFSkgsgVFCmIXQWgNk/LCvyILIB2kPh/fG/+gcnG2IUiDvLUh+Cx0RwcXm8zXSQkprJ11iGUfXcizIi09Q7ZWO5/85Xul+pxIJKVkgQeIRdYEhbcgOVXQp3gbNQ4PB1UJg6i0WKo7lmed9zVAjI2b7i95QQt3HUVLcmp/PBYXVhkb95u4m7sYXeSUkzEA9+5qZw/Vb1BK67EM7Ln1gK8q6a8+/Z+CIo102VMiwjSrkKbLgrBzWo9Y4oZEIlQJKhQpmJjmbFZoLB68EsrJM4J6BrrVqsDmS6oS1nKFgvlHL/D3kxDCY+KJSUxWOb7j6j08HVRrNvrVq6IW2J3NPLkVfQEf67LYmeTMEP1dsnK/6acyXTT2GX1rTeZrqUjCEb8pWErN6H91IY9jhUnYrMA9jPE1bn+m//ztXJz1HRamwmQmOVWG/7xtrBulmRSQI0iKokg6gMi0JkjcUcSMRuR8EkXyEURqDDAJithJiJy2gSJZ4+l04c/lpylSOGf/D5+F7phdtrdFtxoMbPw7q05pFhhytBmo9R7ik5UGwr4FX2S+LuZ2NLNyNSp+g1pgz7BztXG7Tlx47jY2Yt94Yev5Un/HfMKjh2+0HitTVr9k8YKTl/7/BXaRCVa247HU4u4kkRYmJfkkEkkhZGlPPvHNQXSi5kBgb6maq608cREpMhnda1Vg3aDO2FmaM3nXCXb9cz+zT2xSCsXtVB8QdpbqOV9LiTVVHJTFOn9UUNf9+aWsoJZa1dEPgG99pmn9DNrSKVc+KDc0zzVRMqbW1AjIFBnb++oKfm5lqeZoeGFOYOi7zKAOYG4q5dEr/SnSkCfheJUQpHBFZkqdKLHrpczXIvOPUsFiO0QWHbL0EZhoGUFdZC5kK8QfLSLFLmeEf90DVa559Z8gxk1U/n4bNte8Qjmwa6TGdn34LHRHTZClad9ptrNsr7H9Q6wyT+hora6h4Gr3I+9ijFvSxb9rhJXzLuLf+WHjfoe4sPJYu54jLrwGth5PSHjfGivnvSTH/oy5nWYXdlnyKeSyJ5hYdkGkIb+f13gXrp2VU1SHA/vYnUc4fO8J6XI5vpME16fbU4ZjJhW+FulyOX1W7+D+m3B616rED82EVNnLyGgWnLxE5SKezDr6N/VKeLGoe9s8/ESfBmKRPe/fFsHMrDm2jiuyHZUiS71NXNS3OLhorw/IL1T31vxAvvTkBeYmyj/bFJmMIs72KpuhLyNU2U3lCrlz+4Xqwz/wzefZE9oYcjbz9deF1CugB/s0Z8UzYb/k4Ot/jArsk7o3YcCCHSwc0h6FQsGIFfuY0Ve9KtWntCfPskyG9m+5QsAUzTEmv1CjWjHOHPuR1FQZzdvO4fBe1ZSRiYkUqTTnpMVcBXZNVEZDcO6A6lLy5J7rVKqj/T9QKtYcnKITlLLBmmb15qbljb43M5sRiMQOZMzoTSy//vheYAtIzGqByIT0NO00Kql5Y6AhspQrSM1qG30PxiIlRbuTj4mO3fYUWTpVinhy91UY9UsUBVBRr+y/bhe3Qt9Q2sON1X9fp0PF0vi4Cg+qZ+8iOHzvMWU83Tj58BlXg0OpUbQQx15rFsJqXkD7HsnngrlVL8ytVLVELG2UuuZWtuOwslUyq1w8c6cUaAxMJMLmXFhMHO52ytTlndC3+PmqmtpYm6t67955qeq8VaVoAW6GqN77xScv+Bw49OZ65usuRdQDeylb5QMtJMG4h0+rqqWY8NcR6owRJnxikYjVI9TTQXUal1YJ7FfPPQI+bWDPQAYbRh/BwVjkCccmXZGWmYbJPLEOL9So96oUwe7DmlC6spfW/lKJ5sCeKlN6TZpK1bnaFqZGKjQqUhCJhc0QGzdhiSUxEc4hEglOLOmp14gPr4uVy2GtpxEgBsWnoULGx2vP31laav9/WNCtDTdevGb09sMs6KbKSnoZGc31kNc8/EU5k/CdNI/AacL7J+EfMl+nyGRUnLqIwGmjMgN4wNWdjCvXFE9LO368vveLDOxfAhJShPqNFJksc6WUge+a1KLxjFWMb+tHcXdnpuw+iUIBi/uoeqA+eBXOpacvcLezYdSmg3i5OPDkrZKCPLJ5HVaeuUaz31fza+fm3Ax5zYaLt/L/w2nAywTlZmABC/XVrG+WwB6WpLmuQhcMqTztPrQhfy1SCqNFhMcafZ28hIlJ3mvP5CqwhyU9ZkvIcLX2svYtaOqhmeYEhjolKZEqe4mZieqMXnW2rnmzVmSE0bYwwIykqBFYu/5NWtJBTK3U3YDksiAU8liSo8dg7jAXkcgKRFIUigTh9WeAmZkJaVpSWSkpOXNOWnfxJp2qaLc3VLm+VP1rNKRkHTwtherdPj41cnQP/6v4J+gV/itU89eVJwo0u/ZVSvNbZ0Gh9NsmNWlRvgTD1u/jXWwC7Sr5MnlMP5VxN6YNZ93fNxi+fj/2lhbM7tGKMgXcqDRRldd/f8YoBqzayeA1u+lcvRwXJg2lzLh5+fgp1ZF1tt65sGYhOtMsLkvJ6cYVLb76EENBZ2XFeFp6OonJadhZqe8njP29C3/8qLQ8HOO/ilnrtO/l5SeOH8z7oq9cBfaDr6fxTYldmEtsuPBuDXVd+3M36hAWUnXVxKyYOWIjP8zujvTjk0omS2fRT7sY9bvmXfTohK242U9SaXsTqXxwuNv/onFcmkx9U/HXcqPV2rIyYmw+FqZkBHUTi3Yf2x8gS7mEzUdaXFJUAClxCzC3nYCtR1CW8Uout9TcMAnh3MLaxlzrrD0mRruVmD4otDlHGIBzYc8IuLqDN4kxDPdtQBl7w7T3/z+gWrGCBmu0FHN15P/YO+uwNrKvj39juAR3h0IpUKVu1KlvdevuurWtO/VC3XXr7i11V0qVlhru7gSJvH9MSRhmogTa/b37eZ48JHPvSEhy5t5zz/me67NG0LYtnHcar19FgsvVxdn1/eHkTCTbBSy/iLa5xljw9ynM/LsrBvffji07huHAmD4AgNDXUejdfRO8OWxERaaK9wOAvj0348zFadix9TauXHqD2nXsxUqehQXFmDzhMGJjJLOBHXtGoIa7Yp/tqk+Sm9l09+60fVLKRdUYcpQrlD5261lcXyYZ5HFYLAze8A+uLKH+/9p0rY3rp18hLDQaAPAxJAqPb4WhRQfFBjO/O5WSFODxs6HFIvx/6cVRAAAfoy64nRgoc7/ntz+JjToAsNksPLxKnRqWlebKyCMrtqXnSpIm6FwwZeQXUXWoKwNbsyny0zogL9kHbG1/aBnMV+vxQ1Pn4Eqk8usCNjbS44Ujfsj2U2px2Mgtomatjm3VEOfffFLo/EWlVB//n071EVCvG7Y26osaBuY0e/1HZWjXahUGD22O4LtzcfzMZIwZsQ8pPxfR79/9jC07huHliwgsmncal67PxPjRRNLQ7VthCNpwA+cuT6fsV8b40fsxYHBT3LjzN3qUK1/ZvfNG7DkwGncezoetnTEWL+tFMerlC1fvi5AUXDlSbtHUWttY6vt6mi6JHrHTVa5IRlY+VfIjNYcm/vwnG/4Zg1r1JPYj4K8TOLjpltT+VcXr0Cj4dVwr9aEKlRqx1zBohej8EDjq+SIq/yVEECGZ9wV6HNkfiC1NpIa5NdU42ZseQ0waEYMqreiGi9UjqefJyj8i8zpUQc+sch/8lUgfdHNWb7q6h6c1Ql9H0baFvo5C3/7SXSG1rC3AKylFx6CDYLOYuDaVyCK0NNBDa3dneC4OQi1rC4QlpODM+IHi/cz19VBzURB8bC3xIT4ZB0f0IR33fMw7jHRrAqaCpbz+Q3EifqRAT08LHjWJ4AUmk4F+AxpjbcAVBG6RLAb71LbH6HF+JB/u2oDLuP1gvsz9OnWuDSMjwq3YpCnZBVoWqdGuvRdevfiBlq3JeRIb643EtFBCXuBg5F0cjLyLipxsRs1ZKeNS/Avx8/rGrjL+C1SGt2uAg7dDMKI9kZi179YrDGotW+5745GxOHvgMfZtJEKyT+19iFN7H2Lmqt5o36N6chZmzz+NFs1qYO6sztDR0ZS/gwJUyrB3sJb4hnrYLcemcML1UDEbtSIbTk+Cv/Ms+A9ojJKiUty9EIrTb6juFF2t5uCwrMolKpEx1h8Fhoy3UMKP/vnsf9u4+DZ0xrF/ntK2vX5FFemqSNkiaEW2D6KfLgNARkGh1P0AwMXA9D+jXkWEfYyHQCDEtMn/kLZbWBD+5bLKWCw2E/r6VP/y9Cn0+5Xh6mZBe14zcwNMHn8IPnXscfrECwTfoeadNDB2xcWW89HzEb2iZ1/7ZrR1g8uIL8wAQOTF9LFTLqJsnH9jzDt8A3WmEGsHbWq7InC0/DDcPiNbwK9LbQxqIxkdb5x/DhvnnyOuhcGAuTUXGhpsMFmV+07vvjSNdvvyxeoRyyujUoa9vFCWs15j/FVTsdGstq4mbkRuwKfXURAJRZi1UXo1Hjfr14hPH4NcHjkKxdo4EFxd6RllJXxJOJcFd4HUfm/TFiAp/w6YDBbMdVrBx3QB2ExyMsf7tGVIyL8ObbYFGlpuhS6H7P65EumDrk5vwSg3Db0S6QNrvU6ob74OAPAmdR4SC4LFbWV0cQoBk1H+Ls1AkSANzxPHgMdPgK1+d/iYktcXKuLlbSezvWqQ7X9fGHqF9PppF8naBkvKj6OkRHrYprKIhKqvD/zu1PS0RmkpH5u3DVVp/7UbBkBLBamJtu1roUYNK7Rs7YGx46liZGWYaRriWqvF6PpwBSlabn6tvuhi3UDqfom8TPHziW7+MFDSxw4Aq4f5Y/Uwf5l9uvgsgkAgVPiYIpEIKQlZSl/Lr0S9kmJKUquBk0L9bE2Vr+QjFObAwewkAEBXiz4K50qkD4w0veFrGYRSYT4isg+hoDQOhpo1SX30OM5oaLkVuSVfcS+uGzyMp8CNSy0ZJ4taJjPhYTQJd+M6o4ODJFuWbNQBFkMLd2Lao77FevCFPLxLW4ikgtvo6CDd5SSPL+GJ4ml7dbGsrnS9IF2aKAUAyMpSX+WZWzc/qu1Yvxs13K3A5wtx+1YY2v9c7IuOSoO+vjZMTPVk7rtwSU/07h6Eq8GzwWAyFN4PAE6deIEt2xW7mXA1dPGkfcVsb9nklfKwqf5PPRslEpOURRmjXh0YGGjjybPvaN5Ufe/5lxr2qkTR5KRmNkfA+LmGbK1LLrr8ImkcmAw2/OwuAgBMtRvCQqc17sV1Udqwa7JMwWcUip9LQyDikXzw+hrOeJwwUGr/MhydzBAtRTBo9l/HcSVYfSFV9sZchC2Tnerczlrie53w/BTptbWNET68r1oJhl3b1btwLg9lCoOrgzsP52PDmqsIXHcNGhpsNGzsgr/nS3edldG6jSfs7E0wZOBO5OXyFN4PAFxdLRC4/gZ0dDWQmZGPpMRs3HmovgACdwPFitfPfX8IT9IkiXBP2q2T2T7Psy+6WPvid8XVxRyLlp2X2k6n/CiP/1nDrggGGh64GlkHFjotUd9iI1gVRs9pvOdw5ZJDpXQ5hNsjt+QbDDSkS3GqC66mYuFXS5b3woghu2nbeLwSZKTnKzQqqwoW1SZrbHvUtEbw9fdVes68POVFlyoDnS+7qpk1tytm0chd37o/DwCwPlAyIChvgF1cLXD05ETKfgBw5iK9D3jponMYMKgpabG0XatVEAlF4nqg1cWTtM941Hat1DUcee2/G4YGOvBrpd76yv+vDXsr29PgCwvwPGkMrkf5gsngoItTKKkPR4o8a4lA+ay4qsTO3gTzFnbH6pWXadv7996i1tGVPJpd2yh+3sTcCRt8e4lf+7X1xKaNN2j3u3/3M/zaVi5Lde2qK/I7qZnyseC/E/N2XMXMgX6kos+qYGKih6uX34oN+/dvyQBQ7Ua9DHlGW1b7sh1DpLb9ChYrOGNShv/Xhh0A2ExdtLAhKj4FRzfDndgOaGdPLAKzGJpI4z2jjNoBwFiLHArFF+WDw1C8TmpV0La9l1TDDgC9e2zCuUuqqcUpywP/6bge/wnfclMxzdOP1KarKz2kK2D5xUoZ9uKiUtz+Bf71ho1cpLZFRqTC2eXXGP7VE6kjelks2n0dK8ZRhbOmTO+I/XsfoFunDRCJRGjc1E0cNqkIfzwOwI4GE3EzORTHoh/AXIuL7fUnkBQgS4R8THuzB19y4+Bn7oP+9i3gXk5iYOTLzfieRyQdNr9DFPfx5jpiZ4OJCrWX0UgBKevKEJ/cBLaWz1XaNz+/GHp6lQ95rFSC0r8ZEagLKBoscuJEU+uDSOe9hFAkSW1+mjgcAANMBvme+C5NUiAjuzgMdJTV6eQL1bdIWJGyLEE6crIL0amtcgta8giXIhm8PuwOutp5YbZXO2wMo8YyyyoFFrD8okrXIhAI0aWj9AIOVYm5hfRs64ljD0htUxTfEYE4fD0ETcdsxtFg8qwy+Hk4Wo7firGrT1H28R0RiLxCSQLaiVtvkJlbiN7zDqLHnP3I/pnUc+7+BwxfcRzBL76I9xMIyb+RUWNa40rwLFy9ORsLl/SEsp6OIS82gsNg42br5Whi6oGuj5aJ2woFxWhzbz562jRGcOvlKBKWYtSrLXiYKvktHWg0DY/bESGJT9qtw5N260hGW17770p+frE4Galb703i7X4d1yI2LkOlYyo8Yq+xKghXRg9Bt32SpJ9v8yVxzJ12H0ZkhiRcaW7blhjZSFKAo0HgDkqGY/n9gx4+Q25REW59+YG0AsLw+bk6Y3e/qlFdE4n4uBpFDb3q6iTJgOVqesHDeCqulevHABPdnMnqlI2tduNF0jhxGKMGiwtTbXqNd12OHW5ES+q7qjtZqYGvE6ysuUhKpHcV8flCtGu1Cq3beGLhkp60feRx49p77Nh6GzxeCXzq2CNw82BKn572tcXxyj3sqQvZK9f0xZwZJ2iPf//uZ/B4JVi5ml5igo47t8KwJkD6bKU6YDAZtGGWfL4Q40btx+79sgvQyOOPVt4Y1tkXvf4+AL/6rrAxM4TviEDUc7fFo11TwCsuRaNRQXi5n/hdhRycgakbqYty3WbuxdO9hC/dd0QgQg7OQG8/H/T28xG/rgqMNPQwyLE1AGCyW1ecjHmEAn4RdNlaGPJ8I/yt6qOjFTETXuUzFHeS32HBh38oi6OVQSDMQFyiFwz1p6K09AtMTXaAydBFbv4BMJkGKORdA4tpBiPuYjAZeigoPI/s3EDoaHdFUfEzsFimMDchbtTR8TYw0BuN4pK3pO1lZOYsA0SlMOaupLsUMd16b0LH9l6YO6sLKdPUv6MPNm+/jY1rKhbwkI9Srpg5V4JJxriMrY+fIy47h9RWY1UQhjesJ/Z1hcyYSEoTqrEqCEdev8OQBhIFxmOh7/F21iToaqhXwpIOJkNDIaPqxh0NN65scSAz7SYKG+g2dtekttU3XyeOey+Pssb/yImJaO+3WmYs94N7n/HgHhE5ULeeI5q1qAGPmtYwMNQGh80Cj1eC1JRcREam4cP7GLx8EaFUbPiNhE8Y8PAAigR8TPNszKaxHAAAIABJREFUDU+uJam9Xn3Zoa4vnv1Au1ar8EdvXzRt5oa69R0pfRISsnDk0GPcuUU/Q+ro74ObN2T/79ren4vjTebiS24cCgXFqKFvg8mhO9DJqj6m1eiJoS82ILDuWEwJ3YkTTWVHJ6xa2x/zZp+kbYv4kYJ2rVZh3MS26NOvkdTRLr9UgIiIVIR/TkDYx3gMGNQULq6EG8fgZ5jo7MFtcOjqKywY0R4AEDiNuEFra3LEZeFkMbmvciJ86qKNOfUGXywshS60kFKUjRtJobiRFEqzp/qIT6wDR1v6hMeiogcwNzlI2paWOYnUPzqekFDIyz8Ic5P90NHuRNoOAEyGLrJz1wEigVyjXsbcWdTw4Dq17bE+kH4tSh5KGfbzI6lqhwCw9fELLPdvS9l+8eNn9PIhKvNU/B57mJshJY+s46DFZleLUf//wO3789C+9SooouP19k003r6JVuv5p3v6oZGpI+IKs9HPkT41+9S5qejfewttWxkXzoXgwrkQpc8fuGUwLC25cg17P/uWsNDiwkKLixtJr+GiZwVHXQvU5jojrjAN6+qMgqmmASy1pevxlOHb0FnqqL2M3TvuYvcOqmtKGr37UsP0NCrIvFaUzJaHieGvUSFlM2XL066tPRzNzKpW3lkE6UlwWlp+tNtz8iQFro0MiXUFXvEjsNkOKOV/J20HAIEgBdm5QXCwoZf5UJSc7EIYGmqrtK9SPnaWDKfa0uB7qLl6k/jBYjCQw5OEnPU5dAI1VgXBf89hjDt9CfE51Ko/JrrKZ5r9h3RuP5j/y0Icl727jjrGthjgVB/L3tHPUkxM9TBxSnu1n3vUmNbwqW0v0+9dxtfceMq2slmmtbYJvucRBSqi8pMVOjddmr26yMghXJRr/rmL4V0JVx+TwcCMzZcAEDU+KxvhZ2KoixK+9GpmVcVI5/ZY8ekUZbtQpO5kIiYA5d6fof4U0gMAtDSbg8UwomwHAAZTD462SYhJUCwBE6CX196x5x6GDlKtUI/aFk939OmO8HnTSY8R5XzsHxKTEdSzM26MHYbd/XrAVPfXjBr+v7H3oHKJVOpias3W0ONoip9Lo1cf30obo/IwGMCAwYr/GN5lRSK9OBdP0qhKliwGE3sibiCzJI+kEy4LFosJd4+qkSgetOQodp1/iuikTNiYERFYE3o3w9uv8Qg88QCDlhzB2B7EexcIhXj6IQqpWfm4/eorXodLL3heng6N3DF+zWnsOk+vPVRVDHBohXw+D0ei7yO1OAehmT9wPu4ZAj6flr+zEuhod0ROnkQdViSSXa+AydQHv5w8iUBAJAHq6fRCTv5uyvbyaGk2RiEvmLKdek0amDbrOGnbkeNEvdVO7b3l7k+HWsIdp7RojDlXgvF6Bv0KdHgK8aa7eLqLt0Vn/ru0F/6tGBho487D+Th04BGOHn6i9uMbGpJnWURpPAZWf7iJ3o51YavDReCne3jcWbpg2O0H8zF+9H78+J5SqWup6WmNrTuHK7XPvTZElFBzM0kx7+31J4mfH2lMKBGebqZ4aN/23SOwb899nDymWsibNII3jQMAjO8lKVIxvEtDDO9CjN5nDGgt3s5iMtHMxwnNfMijxgEdyG6xigul5Y9RnWizNPCo7Vos+PAPDkTehrW2MWx1TDHfs69az2NucgBZOasQk+AIFssK1ubXwWBId7PZW39DWuYkFPKugMnkwkBvDAz1p4DJNIKl2TnEJdUBIBRvL4+l2QXEJrjCxuoVWEzpUsXXLv6Fnv22iBdOy/4GrhsgM3pMFgw5xRTEjTVWBdEunJbR//BJvE0gL0pUXEwFCD96EZ+PsU0I3+Esv+YAiKiYy2HhuD+pcpEDvxt+Hdbi/q2qm54ry6MHX7Bu9RUUFalWWQkgyu1NmtoBHf2V146XBY9XgknjDpEKOSiCvYMptu8eTls3clC/7RS9cQDVlqw1c9oxvH+nen3RfYfGwNHJrEqjVf7j90EoFCkjTyG1o8KGvQyBUIghW04hPD4VNW3NcHy6fB2T/8/8boa9PPxSAW7fCsPHD7EI/5yI3BwecvN4YADQ0uLA2EQP1tZG8Khpjfq+TqjlZSv3mOoiN5eH4Gvv8eZNNNLT8pCWmotCXgm4hjowtzBA46Zu6NjJRyE/+u9AUVEpgq+/R8jLCPz4kYLcHB74fAG0dTShr6cFB0dTuNWwRL0Gjqhdh1o85n/JsF+L+4wudvSLpK2vbcWDLtRym/9Bi/oMu8+MIAxpVQ+zurfCx9gk+Dj8V/JMGlevv8fGTcG/rWFXBYFACBZLsjQzaMERHAv4vVK0q5qPT77Cq1kNMNSwOJDPz8f2H1sQWRABA44hWpm1Rlcr9aeYq4vRr4djtvtcuOurnr3Z5EoQnndTrDTg/wcGDtuFpGTqrLIMGSJgUr+AKjlwZvdoBQC/nVHfHfE34gu/yeyzwvuCSsdOTc1F/8E7adsmjPVDvz6ShCS/DuRyVhVf37w6k+I7y84uxB/9tpK2aWlxcP3SDNLiYn5+Mbr12oT7t/5Gr/7bKFK3F85MAddQfnRRy9Fb8GjfVADA4p3XsXxCZ3SfvhdWZgb4a1BreDhaoPesAzAx1EFKZh7OrBuBdhN2oPRnxMTzQ3+hy7Q9KCq3mv8mPA7TN16As40JDi2jD439X8C7ubv8Tgoy/d1k6LP18YdNb+SW5sJUg1pd7N/Azh8zkcijFnVx0vXGSGdJEZ3TUW+RXlSA01FEImBfp7pgAMgpKcKz1ChMfX4O3/suFPd3O7MSXe1r4WrsJ7gYmCKxIAcfehGGzv1sAIa5NcSHzESYaOpge1Pl/PFLwnpXQdSN8jYmKTkHd2/MUatCqFKGnf+b6RhXJ/0H74SOtgbOn54MTU0ORCIRYuMyEbTlJsmoAxCP0I+ffIG9Bx7KHbGXlgrwR7+t4HBYOH18IriGOoiJzcDw0fvQpiO9K6d95/Vo1dIDC+cSFWIWLD6HZy9+4I++WxWaIfRuUxsA8ORdJJaM7YS+cw7i8iYigqb9hB24vXMi4lOzcW7DSPE+O+b2RU0nC/GI/drmsWg7fru4fe+F5+Kbxf8yL4Pfo1En4v93KvAaeoxvhxWDtiHgArVQuizeZRPGLajOVjk9f2+WhvWBQEQfQhhV8BGrPg/BfE8iY72fU10seH0N/ZzIJesMNbTgb0uvcBjU6A98z0nD1Q5jsSP8CVJ4ebid8BXbmvRBexviJut2RrFEoN8Vdcs+K2zY2y3bS3nubW+JoBGEYdl6/Rnyiopw5/0PpOcRo8hWns7YOloiCdB43jYUlhvhBQzsiG4NJL62urM2Q1uDAwtDPUSkZMDejIs+jb0x3E961ZXqZNqUDtDUJCrPMBgMONibYNOGyq0xlJTw0W/QDgDArWsSzXQHexPcuTEb7fzXY/GyC1i+hFw6i8FgiI06AAQs742kpGwMHLYb+w4+wugRLWW/l4GtcPvlV6w9eAd3dk1CXEoWSn4Wpb62ZaxK78Xbjb6YR1pBARrtpUoKR0yfQZpLOm8KROT0GfDdswsZhYXi7W4mJrg5ZJj4dVJeHprtJxdfGVmvPha2bEXaFpuTjdYHqTotbCYT36aSxdA6HfkH3zLS5farSGEuDywWCxxN5SoSiSBCVkmm/I6/OZH5H6Qa9TJ4gnzklmbAgGOi8nm0WMT/V4PJBl8owLPUKNjpchGZR2ipzPKWXtHpdydgaW+ZRaurVI/9zhJiNOczI0j8vCInn7zH89WToKtJjU4YuuUUrI0NcH62pAKLz4wgdK7nARaTiUshnyCCCM9WESGTxx+/w6Zrj38bow4Aq9ddhYG+FhrLUPJTln0HHyEnhwcul+o+KRsZP35KdS8N6EctUG1lRUgMnzz9Uq5hB4DVB26jfwdi5LRkbCe8DIuFk40xQj7F4g8/asSLvZURbj7/ggaedjA31qe0H7v+Gv3a1cHnqBS0rEf8j4QiERrt3Q1zXV28GDNO3Pfmjx+0DsLOR4/g72Yt0KcWEX4YlZUFTTb5a9ps/150cnXDjq7EjS0mOxt+hw7AWl8fI+tKwvlaHzyAVo6OONhTIhlczOfjUxo55vhuZCS+ZaTj29TpYDOZUvvR4de3MXgFRVh6UrGZytWky7iYQNZvGf16uPj5vgaHSG0B4csRVSBxcTAZTOyqtw/McnVD76XewfHYo5R9t3wPwoec9+LtZf0G2Q/BsViJ5hMDDOxtQE6lB4A5H2Yis0QiQjXBZRKlz/Wk/VLfa3mCkw+hn51yMxpZNDZ3RH5pMcZ5qJbA8zuxYOk5DBnYFC2bu0NDQ3Z2rqKoVbZXk8OmNeoA8C46ETcXkTVX9LU1sT34OaZ2boYSvgCccinHOpqcX5IBJ43gKzPQuUcQ5i06CwBo5OuMHt3roUkljfyHj0TiSG0pdUu5XB1kZxdStrvI0P9WtPTXvd2Txc/9m0lmTrbmxA3i5T/kKAwDXS10bk6OZri7S/Jjf3qQGN22NJJku464SBix8kYdADq60legN9fVFRt1AHAyIscYT7tB1L4tM+oA4MDloruHB1Y+fEAy7ADEhroMTTYb9azIa0N8IfE9Y5XrS9cPAP50nY78nEJwOCzMOzgBm6cfxv7QVUhLyMS90y/Q/y+q5G15ulp1Fy+OPk1/goPR+ygGuYz1X9cgqiAS2+rughaL0IlZGDYXY0NHSt1HEU7GHRfvzxfxMT50NNZ/XYPZ7nPFfQ5E7UNmSQaC6myBPpuIPBofStVMSilSrBJWVL5Ez2e4W0P0vXcQTc2d8JdXawDA6/Q4JPNyAQB3Er/B28gKFtrUwUMZQ119UevcaiQW5oDDZOFERCi+9JFe2/h3Z+Qw9er3qNWwm+jJXrQzNSC3m+jrIOOn26ZvEx+sOHMXXQIOwtbEEM+/xSB40e8T066pycHd4DnIyy/CX7NO4GVIJF6GECOpykS9lEVWKFt6ubT097npyeJxTAxMdBSXipjYkF4Vs4wrX79Ah0N1e4yuVx+Xv3whbdvfoydGXboI502BmNa4CXp71oKtATU8sqOrG7TYbLhsCoS9oSGO9u5L2w8ATv7YRHqdk54HjgYHYc++waOBs7y3pxRf875gqMMIsVEHgJVeazD69XA8SnuIlmatZOwtnc11JOsibAYb9joO+Jr3BclFybDUIsTanmU8QS0Db7FRB4DV3usx+4Nq0SzFQp74+YI6HSjtDUyJgU1Xu1qk7WULqWfbEjURRrs3Frd96j1P/HxJXXKVLkWob9QeqcVxSC+ORwE/V+n91cW4Ua3h13EtXJzNYWSkS8nEXheguMppGdVaaOPGm6/o7isZ8UWnZmFmd4nLwEBbC/sn9YElV/qd+lejr6eFfbuIL1nYp3hM+esYJk8/im2bqNK1ilC/niO+fE3COyk1QOlG6wDw9Xsy2rahjwVms9UznVMXpkoYdj0N+UUG9GiE4gw0qfv5OTkjcvoMBDx6iM0vnmPzCyIT9MPEyZRjfJ48FVFZWehz+iRaHtgntV9FrqbtIc7Vt7HMfspSLCQkrqUZ7/tpd1Q27OVvFADA5XARixgUCsgRVi3NyO48Iw1qhiaTwVQossRMs/pyIBSlu814uX2EIiHSi+ORVpyAQkEuXmUEI7koWq3X8e1Hyr+3NN7Fv4eh59rDsDIyQF0na8w5QkypW3lKRjm5vOqtU1lZvGoRX9av3+hlQMsqoRQXl4oXXSsyYmhz3L77Camp1BFDmUulkS91JHjuwmtMHEdeMIpPIGQaBv5Jb2RCYh1pt9Phax+tcF9ZaLBY+JquXCapLOwNDRFLIyD3Jon+MwCABS1bYcHPhVWv7Vvhs2MbIqdTk32cjIwQOm6C3H7VAU/Ak9lewJdfrEVQyVA+bZb8G3J9o/YIybwpt19Ls15y+1QVh6OX40feWyzzOgsmQ7lBD5PBhLmWPcy17AEACYU/FDbsfGEWQuOoyqaNHMiqj1VRGq/aKig5Wxjj0YrxWHPhPprM3w5jPW18CJRM64pK+LAy0keH5fvgMyNI/MjMpx+xVicfw+Ixf9FZxMdLohji4zMRtIUooTdiKL1/rGN7ohD1oGF7xNsq5oOxWEycPEKMHNp33iAeocfFZ6KdP1ENaE0ANT5XKBRh7oIz4tcLFp/DkBF7fl5Pc9rrqWF+WPzwsDgNBoMNc70hcDPbC2eTIHBYFvCwOK02ow4AK9oQcs6pBeqpGrWtC1HqrURAdkXNvX2L1kVTkYWtWit0HkX70VH/+Hb0unoMLc8Qn4dQJELDEzvQ5NQuhY/B5RDrHOnF9DdFVz038XOGlDyVOJ7qUgYAEFso33+uyKgXAGoZ/vsXOZUlLKkHGjlEUR503Lr7CZ26B5KiY6Ki5S/eS0PpEXt5Y1yeKZ2bYkpn2R8eV1cb52bTZyk2nLuVcuyZh69i4+XHCBjYUdnLVCu6Opp4/jICz19GUNo6tveSOkLW1OSga+c6uHr9HSlJqaJPnsFg4Nqlv9C1ZxApSYnDYSH4Cn0kwc6tQzFn/mlK8tPFM9KjMwy1JFP36My5qGF2GAZaEkEpE90/EBLrCDO9AXA0Xi31OMrQt5YXVj9+hMZ7d8ORa4TmDvaIyMzE87g4lUbDXuYWcDMxgcfWzehXyws2BgbYEfIKJQIBPk8mv3fnTYFobGuH5vb2KBEI8C0jA8E/vqOJnT2p3+BzZxGalIg/vbxha2CA0MRE2n7KcL7rIAhFIjxLjEFkbhau9BgKCx3lJZSDvq9HgJfkM04tJoTSBjtIwj/tdQgJAhFEJCOfW6q635jJYOFCwll0svQXb7uTcou2bz+7mTgdt5G2DQCWeKlXofHfAoOhmHmds+A0Ql5HwdhIlyTfu2DpeXRq762SdO9vVcz62OO3GNSCCL97Eh6N2++/4+Lfw+TsVfU4O5upvEA6c3pHzJwu/8ako62Be0rEq2Zk5OPyuWkqXRMApBecg4PRCiltZ9Vm2AHgzfiJeBwTg5UPH+DEx49w5hphuR+1MIui3BwyDI9iorHg7h1kFxWhXy0vLPOjxjGvad8Bh9+9xeaXL6DJYsHGwABHe/dB0woGe0/3Hph/9zYufQlHXnExXE1MaPspC5PBQF5pCQZ71MHH9GQ0PbULESNmyd/xJ1vr7sSUtxMw/d0UdLTshOiCaIRmhcDb0Ac65dwkLnpEhNGY1yPQ1ao7SoQluJUSDB/D2viQ816la1/vsxEz30/HtHeT4G/ZBV/ywvE9jz6r25vbHN7c5riRdAAfc56Cx8+DPscYrc37op6R6p/zvx1PizMIiXWHHfdvcFiSjGIT3W6kfiGvo8Sx6uVH7D271cWZ86//3Yb9zfppmHbgMoKuPAabxYSvqx2eBEyAgbaW/J3VwPHtd3Fm70MYGutizeExsLQzhr/7XIyZ2wWHAm+ibc96mLaiF3rXX4JzocvAKyxBnwZLceLZQhhwddCnwVKcfb20Wq4VUD6KpiJG2v74lNwZXla3xdtKBSk/2yQ3ouSiDEwMXYtRzj3QxaoZ5TjyGPRiERgMBo42Wo6bQ4mb9NjXqzC4NvWGrcwIvqWDIx6PJIffJRQ8QF5pPDy4xEJ2v1pe6FfLS+6xdDgcjG+ciZnN3WGnRzVEKbwQhKSuQFcH1Wqq3o79gfrmNhhSs678zuXQZmljX4ND2Bu5C5cTL8JUwwyLPJfBQYcqEravwSFs+7EFwcnXYalliY21NyG9OF1lw27I4WJr3Z3Y8HUNLideRGuzNpjuNpMUc18Rf6uR8LcaKbW9PLmlGVj/ZTRWeF/As/TLuJtyAh0sh6KRiT/4olJs+z4NOSXpaGzaBR0tJd+VvRHzEFv4BSu8L6BIUIjjMWsQWxgOK21n/Gk/G4YcU6nnZDCYKBUW43TcRkTmf4QWSxfNzf5AExNqWTp1kFFwCXZcwmCX0ui1y8PEWE9q8IQ8qtSw5xReg6GOYv80FkuI7WNUK65cWdbNOgkndytceLec0la3qSt6jWiBRaMPIPxtLDr1bQihQIhLh5/A1dMal488w6DJbdGpn+wwPWn0b7AEU1b2RvNOPvj4KhLeDdUbMicNF9MtCI2rSVlQdTbZBBNdyeew4etRnG+mejHhY41XYPDLxaRtexpUj2SuskjzVVto+0KDyVX4OKEDJbH9HR0kvvCljck3jWamzdHMlH49pDxjnOn92LMmHMaGnRKjN9mVcEWtWXIehsu4MORwSfHubczboY15O8pxprrRu1e1WdpY5LmMtI0ufr7Dwr24tVK1gi6R+R9wI4lIjrqauAdW2k7YGyEJY3ySdhF6bCM0MyUvMEbkv8ehqKXi1/GF37Dhyxi0Nu+Lthb02eCJvAjs+jFb/LpEWITriftwI3E/lntTi35XFksDxW5y0rhx8yPq13NUad9qWzyVRalAsbJjVcWj6x/Qdwx96JiTO5Gk0qhNTSREp8G1lg0SYjLw+W0MnGta4+uHOCTGZsCtlk2lr6O6jHoZXla3wGFZiF+b6f1JMuqT36zHp5xIdHn8F5J46YjMT0ACLxXdn8xEZgnhvx36cglmv5fULa34uiKDXyxGn2dzpbYrjwjXY3rjakwPMECOeLgTPxLnIlsiseCxeNu37BOIzZf4ik/9kNQUZTCYuBrTA9djeqHyc6KqRVbR6rchlau1WZ1cTdyD4U5L0d6SWHt7lHoW2iw9zHDfhcGORAz7u6z7lP1OxW6AAccEY13WYmqNbXDWIzKlH6SeofQt41gM4V78034OZnnsRTsLQqhOBJFcWYTKIIKA9KCDrjRe6NtoNG2sWgKkXMNewo9GSs5G8AWpAIDknNXglYQhKXspRKJShCfWRR7vDtLydoAvzEBq7mbwSj4iNoPINAxPrIvsQqIm4+cEIlYzLN4JpYIE5PCIkMdSQSLpnN+S26BUkIKvSc1/9ndBYckbCITZKr1JeSiSqalvqIP8XB5ad62NB1ffIeThV3Tq64vXj77i3sW3aNmZnILfo9Y8fHkXg7dPiWK3t8+9RnZGPqb23AwAGNYyAOnJOcjLlYS1vbwXLn5+cMN1RHxOwO6Ay+BXUTKSJtsOdWxewtc+Gr720XA0XkNq31ZvNjwNnHGtRRCstIkpbnpxNi433whjDQOMDgnAP42WYX3tqbiT8orymo6jjamzospw+kdjdLQ/hq4OlxCSGiDefuqHL/xsdqK38yOEZx1GTskPucd6k7YBXR0uob3dYZz+od64dFXp2HQFdm2+hb/GHsSyucQi5Ps30bh+8Q0yM/Jx4/Jb3Lj8VhxtdePyW2RlFlC2Tx9zEF8+EfVb/ZuthFAoQlxaNnZee470HEnEUq+V/yAtJx/dlhGj6ODQrwCA5rMJPSP/xfuRnluAzkvopQT+WHkYX+JTsf/WK2Tn8/A1Pg3Tdl9CTiF9KLOJpjVc9GqLwyG/5oVivucRGGlYwF2fKK1JF14oFAkw22Mf7HRqwEzTBiOclsFVrw4A4F32A9pz5ZVmYoX3BdQybAJDjilamfdBL1tC+/1KguIRS4qSlLsXb+Lr41WMKz4n98OrGFek51OVH5cv/gOdugei959Eyb7hY/bBr+NasNlMdO+inPuuDLmGPT5zJiwMZ4LNIlLY03K3Q1vDC1bcpUjJIcLx9LXbwUx/IgqLQ5CaEwhtDW/Ym1BFn8ow1R8LDssGhtrU9OtifiQcTQ+Dw7KABpuIExeJiqGjUQ8sJabDysBgMiBUMA2fwWAg5OFXeNZzgLuPHZw9rPD0dhhFm7ukmA+POg6o24yYikd8TsDUnpvxPSweAoEQUwP6wNTSEBwOfVzt6V334eJpg3ELuuOfIEndRD09Tdy/9TeaN3Wj3U9ZBMJ8lArSKA9p1NCX+HcTeWkoEZaiRFgKP/MGlNfVgQhCsBjEOkxtU3KBBhaDyCNobrUBIamr5B7Ly5hweXCY+hDh1yuZXj4bgiWr+2L8tA4I2jMCTx8QmbW16zmiQ9faMDbRg3/3uvDvXlecrejfva74b/ntm/aOwNRR+7Ftww3MXd4LTCYDS4/dwoQuTWBqSNQfjknNwrYJPWFmqAdrY/rM26ndm8HUgL5ecalAgKjkTPy55hi2Xn6KU48J//7ywR1hqEO/ViYvDFKae6y3HTVwYKgT4fK7kkBve8pG6OWpa0QsukcUfJB5HaoQnx2IerahYDO5qGV5Do0copBZeIPSr0WzGrh+8S84O5mBwQDSM/IxcWwb3L42m+aoiiHXx67JJk8FmAziQxWBD01ODUp/DptQ+OP/NA5Mhi6EImIBQCDMAwDwSsj/RAbYgEgIMAANtj3yeHegwbZDUelXZd+PShx5OA9dPOej14gWyEzLRf/xbeDoZiG1//eweCzaRkwdewxthqD5Z2Uev6SYj0ZtPDF+UQ/4u84Gi8VEXEQq6rdwR0kxn3YfbR3CKAkEQji4War4zqTDF2bibTw1eaIMRWLZZ7oPwtusr7DXsYSRhgHlNYslO2tT3TArvWT0e7lf3oREwcqai9gYQohr5MTKRZhsPTAaU0buw+RZRAijgwW5DqetqSEehUXB2sQAEUnEOcvUWPN5RCZsRBKRy5GanU85PofFwtTuzTGyg8S99TU+DRwZmdBGHOm/MwBgMdjg0xScLhudl6fsJlAipJ8d1DFqLfU8JXISwlRBk03IJOhpSq41t4i+Dq62tgbWr+6vtnPL/SXYGK9DREpXFJV+h4f1C3jafkZ4Yh1ocWrCyewEknMCSP1rWD7Ep/gasOTOA5tpDmfzs0jInImUnPXQ0SRGcab64xEW7wxtDR+4mF+EtoYPviT6gsXkws3yNgpL3iE2YxLcLOnjZtWNibkBbnxdQ9leflurLrVpt3fo3QAdessenWposrF9yXlo62qC87PARtSXJAxsvByePxdHdq24hGvHn4PDYWPu5kE4+3Y5BjRaBicPK6w6rJqMrizCU/rC0mAM7LiyhZM21pGMjJz1yOsIbS18Zb4u42gjsvszAdfJAAAgAElEQVTlbFPq/1pVGGCiVJgHDlMfHzN3wsWwj3i7QFQEFkMLT5Jmob7ZHACAsVYtfM7cD3u9Dijgk7NVP2bugJNBNwhERWD8BstPdRo4orCgGH8OpUYjMZlMCGVXP6MwZeQ+TJ/XFQd23MXIiW2xeEA7DF5/ApHJGbixfDQMdbUQFp2MuQev49RcIrLozttv2HHtGWo7EwM2LQ02fKdtwZFZAwAAfnN3ISufh44L9+LmyjEw0NFEj+WHkM8rxvXl8rWe2Ezl5I7L4DDlS09URJ8tvWh1VeBssh6lggy4me3EyxiiqLi7OVVF8/uPFLi5yr7BKYtCQxwXi6uk1zWt35V7/lb83ECbEOKpZUuOd3U0O0Z6ra/VCl625GorHtYh4ueWhnNhaShZYPO2i1fkMhXmVPhHeJlZ4GFsFP709EGHkwextnVHfM/KQD8PL5wM/4jh3vUwLvgSjnTrg6icLGix2Njw8gk2tvXHjjcvMdy7HjgsJpY9uY+VLamRBjd+rCe93neHHKP+1xqysE/zTj4Yv6gHaduJl0vU9I6plPCTYctV5yLmr6Gf6wtci+kNEQToaHcUEbmXfm5/ibvxo5Fd8g0NzZfASJMo5Waq5QNttjnORDSDj8lE0rG6OlzC1ZjuYICNfq4vAAC5JdG4EUtk/pYttNYzmw03Q+WFmZSlZ9+G6NIiAKlJOWBzWLh6/jWCny0CALDZTIS9i8WRfQ8hEAgxfJyfeL9efzbCtNEHUNfXSbz9zy6BOH1jJrhGuujbaQP6DWkGPX0tHJ09gHTOKd2bYUp3yY1kxySyFICDuRFCNksSwe6vIUfs9Gnugz7NJetN7rZVUxGqSFAILQUkD34lepqSwaC0jFMAGDvpEACgVQt3LJ7fQy1FN36bOPbqolQowLwHt8SjnVIh4Utt6+iCto4uuBX1A3klJWAzmdBkEVNIAw1NDLh0GrnFReALheJ2JoMJlhrqXv4KrAzGITl3D6wMJvzqS6kkDHRxkISqlcWwA0Bb2320e/iaL4CvOTFTcecOIu1XMVbdQMMR/V1D8Ku49lgyoypzoZRx68Xiit0BAONpEuJOXpPkCJwJVjxJqiLt66pnbaeyhOe+RF0jP9K2MjEyDWb15L7Io6DkI8KSeqCii6+ikb9/82+8fBWBxSsuoq0/EVo8Z4Y//DtSayIoyv87w85hsuBgwMX9QZJp4pGwt6Q+PdxqoqC0BPs6E1WLGhzcgaiJs3D2Sxip3UhLG8ta/Dsz60x0eyEsqQPS8o/DWKcLsc5RDhuu6j/+//iPquZiwnaKYT8cvRQA0N3m9xishKcMRCMHah1YOho1dMHNn/Ihh48+xbrAG1gXeAM21kY4elB5V+wvMexxhd8QnHQQsYVf5PY14Jigs9VIhUSENBT0u90fNAp+x/cjr6QYTwZT/2nDr57FvYGjkJSfh4vfPuNw1z5w2xWE4z36kdqLBXz0vXASl/soJtn7OfcFHqScRlKR4nHGNfTro7lZTzjpys+gVIYvKX3AZhpAJCpFRsFFSnt1GfaYgnDcTD6EODlFyMtjo+2KxiadUafCD7u6EUGEt1n38CojGAk8+eGUFdHnGKOhcSc0Me0KTaZ2FVzh/y4MMLDq8xAMcpgPPTYXVxJ3IzL/IwCgNld+9bDqoJ7ta7yMcYKOhicpx8LLSnYG87DBzdCyeQ1s3HwTnz4nqHTuajXsZfKZypBbmoGTsRJ/dQfLoWhh9gdtX1npxBW5P1AyYn89XOJr7eDkikk3r0CDxcKrpHjUsbBCExt7fB8vyc7L4PGgwWKhiM/Hj6wMyOJz7gucjt2gcgLEt7xQfMsLBQDosg0wxW0LdNmGKh2rPLVtXqi0n6zK7hNcN8JaW36SlQgi7I9cgJiCcLl96Ujg/cC5+C04F08kQrnrN0A/+5kKTcFzkjxgaEUMKPIz/oSeyUmFzysUCXA1cS++579FdkmqStdenrzSTNxNOY67KccBABZa9hjjsuaXGvlFH+l/W5VlhTc1frsyLPU6g7XhI7AvUpLFrMHUwqJaJ9R6nsoQGlcXvvZfwWQoFh1WXFyKhUvP4/WbaACAX6ua2BpMDdFUBIZI9sp6peO/ioWFuJn0j0KazcowynklHHXJ1VYepJ4R/0ikoe4vmDTCcp7iVOyGKjk2k8HCfM8jajEABSUfkV/8Chb6ilWrkmXYu1mPRUMTf9q2Ms7EBeFD9iOlr1Me2iw9zPc8Ir8jgJxkH0BU/NPAy18juZNyDA9TZYe0qhMOUwNzax7+Jb7i392wl9eK+RVcjN+O0Kw7MvuUXVtecQjistbCQKs5SenRxnAyqf/Bf57gn2NPAQCGhtoIXDsAzk4KLTpL/fJW6Yj9SdpF3Ew+XCXH3h+5EI66tTDKeaV4m5lm5dP6K4sIIqz8NFBqLK06EIoEWPlpIGro18MQx0UqH6e8TkyZYQ+JdYSj8RqY6f2p9PHiCr9JNewiiLDkY2+IqihWvIu1bK2Sgszh4udsjSbgFz9CQeYI6Bofou1fIizClm9TkVOquia2qpQKS7Di0wA0N+tJEsD6j6qlouxxZdFiO8LNbKfcfv8ce4qxo1phQD/1ZTtXmWE/Gr0SX3+6EKqK6IJPWPFpgHj6VVbl5FeRUhSDbd+nV9v5vuW9wdKwvljqJV0fQxqRGdNhpNMRrqa7KUJgyXl7VTPsPPqEMp4gH6s+0+vwqwt5flVpBpyOc/FbaPVJqpsnaRcRUxCOsS7qi/v/D+k8SH0KP3OJKNvpuEvoZ9dDxh6y4bDMkF/8BvnF70jbK4qD3VdCrltRqiQL42DUkio36mWUCIuw/BORsWX6C0fsrzJuVKtRL0Mg4qs0fc4svAZnk820bcX8OJWuJaOYWp6uSFBY5UZdGXJT5I+KettKL1ZS3cQVfsWF+G2/+jL+XxKWo9oaUBlRGfOQkLMFHJYZ6SENPl+It+9iEBlV+Vmi2kfsj9MuIDJf/boLsigVlmDr92mY4kZvqKqa99kPcSVxj/yOVciij3/Az7wf2lgMkN8ZgK5GbWQVXoeJLrUWJVertVquSQQRAj6rtvijDPL8+uVhsqwV6met7YxEnmKhalXNm6y7aGsxAAYcE9r2rJwAGBlSM4hz8nbCUF+50L/a3FZIL05AenECioXqT7P/HXmeEYLD0aeQU5qLPZH/iLf7W1ETD5UhveA8fO0Vk0Vp02kdKq539u/TEOPHqBb5pVbDXiQowK3kf+R3LIeRhgV62U6hLISWJzTzNq4l7UOpsERqn9SiWGz4ov7Ue3kkFUXhbNwmhfsbcEzQ1XosahrI1m9PKYrB9aQDSt0k76eehqt+XdjreMjt625+DKFx7sgtIhZtsnl3EJkxA3qa9eBqVnmluy+5ITgeo1gVJgddT7jr14eDrif02Fxos/RQJChAanE8ovPDkF6SgC+50pOE/K1GKHxdQmEqshPtxK+51vSzk3Eu67EkrLdCx2QxWGhg3BF1jfxgo+0qs28iLxLXk/YjpuCzwtcMQFyUgo6cvG20hj0rZ7nShr2PnfxZJ19UKjb+acUJuJeifCRKdGImHK2NlW4b46K+yl5lNDHxRRMTX9xPfUJyxVQWO+7fKCz5Ah0N2b/Hdp3Xw9vLFps3SHTkExKzMHjEHnRo56XoQioJtRr2gM+KxXMDhIGb7UGfGViR+sbtUd+4PQAifPBEzFraftW90CWCCDu+K1b1p75xe/S0mSi/408stBwwwokocvAq44bCM4K9EfMUihhgMjRRz/YTwpKJ/+v3tNGwNpwOG8PKu5OEIgGOxchWU+xhMxENfn6mdGiz9EjSrWUU8HNxKWE7wnMlssBshuJ6IwbmTxTqx2QwocPSR6Egj7a9pkFD9LSZBB02vQqiNKy1nTHamdBXKhTkYc3nYQovKGcUJ8FE00qp81UFbAYHllqOsNRyBACVDPvsoEs4s57+hiyrrSpRl1Ev04WRRsXMU4FASDLqAGBjbYSJ49pgycqLOLJf+SImajPsj9LOKdy3ncUgtDLvo9J5PA0aY4X3BawJH4YCvurFeitLWnE8tn+nrzxTHgYYWOp1BkyGdIU7eTQ08UdDE38sDeujUDz86s9DMc9T/syJxdRFbetnKl+XNMrWPOj40352pSrW67INMNCBqLCTXZKKK4nS5aHpyE50BNc6GgAgFCSByZJuKKfW2IY14ZKolE5WIyiVfCqDDksfy73PY0/E3wolaO2J+Jv0uSan9aJ9DgCl/N+z2MbXmFQs2xWM2OQsNBoSKN7+8sgMqW2PD0yFBocwVTtOP8GJ4DcoKeWT9m08NBDHVg2Fiy2RyzJ5zVkY6mkhYHLXanpnEmTpwiiDq7M50tJUs3FqWzx9nKZ4aSlVjXp5Rjurf0qmDJcTdkEgopfcLU9fuxmVMurl+dN+jkL9pI0yq4Oc0nSpN58Gxu0rZdQrwtUwr1S4J4Mpe7St+3M0zmKwMc5lnVqNenmGOS1VqF/Fz7W45C2KigkZ2KLi56SHQJAMTQ2qtO2vpqCwGPZWhMqin6+b+CGrjckkzFR+YTEOX3mFLi08YW6sDyaDgZlDCB/04M6+WLxDonUe8ikW80ZKnxHScSKWsGEiiDAqZBrOxV+p3JutJFHR6TAz1VdpX7WM2AsFeSgSKFZ0VV2JBaaa1hjvup5Uw7C6KOTnIrrgk9x+/e1nw0uNhszDoCHaWQzCnZRjcvuejdukkL9U3Wz4Qj9tnOC6AdbaqpX5Ug9C5KW2BSCQ+NgZbHCtZI+ulnufV2tsMx2aTG0Md1pKquGpCA42xLXHJnrA3lq+PMfvQL2adqhX0w6NhgRizdRuCrcBwNwtV7BwdAd0a0XIazQaEoh+HYjCIpP/bIHz994j9HMcAo8+wMR+zaGno5y0b3oxkUU+9vUM7Ky/AQs+rkRvW+p1KMqP9KlwMdkIxk9XoUjER1TmAjibkF3JDAYwe/5prF8lUQxNTcvF1p13cGgvuWC7oqjFsO/8PlOhfuqOx7XRdoWLXm1E5KtWib2MXpeP4U0KuTxf9BjpN4yNX8fJPaaTrrdajXoZrcz74GHaGZkLyQARqfMrDDsdk902wULLQX7HKoUJffP7yEvrDH2z6wrvVdVGvQwXvdpgMdhyZ4ExBZ/hoOtJ2qanU/USwr8Dns6WePQmAt1aedGWs7y3ZzIaDQmEkb42hnVTvri8uRaxSCkUCaHB5KCxCX19AUXJKrwNhqlk/YfBYCOj4ArFsN+5Pgdt/dfBryN5+/AhzeFgTx8JJQ+1uGKyFVi05DA1YKfjrtDx0hIVj0cfruA0VhZsJhPRY2aTHtIoEhQolFU60lm9tT3LM8eDKtZPh7plHFShrcWA38CoS5Bl1P1m7MSHyCSsPHoHAqEQX+PSMH37JeQUEJ/3xadh+BKXipyCIvjNIDIKuy04gLzCYrwMj6V9fTOECHdrOZ2oGdp48lZ8jEwSt5enbLFcFm9pEqeMuVX3XfudmNivOX7EpaPp8E0YvOAIHu0n5xsUlxA3xZx81bK+LyZcx+CXE7ChNvE5vM2uXNi2Foe6iKrJpobbMpkM3L/5N4Ivz8C6gH7Ys3047t/8G8MGUwusKEqlR+x0XzQ6JrspHhKoLD7clpXSH7HTV7yW6p4I+Vlina0U011RFUULDFxO2AVfY6o2d3XBZLDQ2vzfNZr0cbaCj7MVzjx8Dx9naywb3hGGuoRmy8ojdyhVi1ysTbDq+F2sHt2Z9nVFSkr58HamX7CtOBKnI65Q8XKRIhEPDIZET+hc3Fa8z34MCy07THLbCAC4m3IKD1PPitdFOlkNQwuzHgjJvIWrCfvhrOeNYU4LAQALPvRCgA/hh14bPhrTamyGFou+/qli1wdIK2dA11ZUwoe2JgfPDtHPRFuO2oJrW8Zi26knmLHxIgJn9lTqek40JkeeLatVuUI0nhZn8DLGCeZ6AyAUFSG94ALq20kXQdTU5MC3geyIGkWptGG/kqhYzLOxhuwwLV7BXuTnBkBXjxxpUlryAjmZw8Fi2kBLpx+09ahukD520ytl2BPycjDzwXW0snMST727udDHnqYVy5fRbGJa9SvxHa2G4WZS1ejwqAMWg4Xp7vJ1Mn5H8nnFsDMnbvbl63Xamhni4gpyGN6mSUTKedMpW/Fs6xTK64o1QytLarHiWcGpGaNhYUqsx2z4MgFDHOeit52k4DdPkI/c0gws9z6Dc3Hb8DbrPlqY9UBMQTjSixOxzPsUCvg52PZ9Jia7bcTsmnuw88ffqGfkhxZmPStl1Pt3rIfGQwNRy8UKKRm5uLZ1HKXN1oILNouJU2uHAyDK8kXEp4sjZuq422D3QiICa+yKk+jeygumRnpYOr4TmgwNwreYNNRwUL2CE4tROYcGi6mLRg5RyCt+DYiEcDENpO03cNguJCXnSD2OKpIDlTbs8ny9AJHNJguBIB68gqMws4qGUJCCgjyiiohQmImCvA0wtSQWhnKzpoBZdBOaWuRRaGX9oB0ciVX5dJ7sBWBFZBK4nKopBVaRZqY9FDLskfkf4aznXQ1XRGac63rK/6LXkkOITsmi9H2xdYo4nA0AwmNTMH//DaRm56NbE08M8KsLBwtJvcq5+67DSE8bo/wbYlzQWSRm5KJbE0/MH0gterLj8jOcevAO2hoc1HK0xIZx3aSOEgFgyOrj4AuEOLFwML7GkV2MF1eMwB+LDyEhPQetartg/biu6LHoILLyeDgyj4hDrvj6zptv2Hn5GWq7KJbxqsfmIp+frVBfeQgEknWjrJIUikusSFAoVpDUYGqK4+nvpZzGj/z3eJJG1g3nckxhr+OOh6nnMadm5TKtZwxujRmDWyvV1nhoIG5sGw9jQ2LGevfVN3SbthdXNo/BnkVkbaPn/8gPRa7I2fgrOB9/lZRXUHEUrwr6mrJrIicl5+DujTlqKYlXRqUMO1318IowwEBna9muCV7+NuhziYVVJktS1LUgdwVKi5+TfO4iUT7FsAPEYmVUwUdFL53ESK/68jsBCE6S79vuYq3aKrayKHozC8kM/iWG3UqLOqU8MLs/BELJoldaTgEGBhwDhy35GgYcu4tzjz9gTn8/OFkaI+D4XZx+8B6TejTFKP9G4n43Xn3BmYfvsWZMF/CKS7Hk8E3cffsdd9dLanA2mLAJJga6WDq0I7LyCnHp2SfUnxCE1zumS/0RlRlkAHC3o96kLywfTnp9qcIIvuLr7VPJ8eVvdss2OGaadnINe3S8epKUjDTMEZH/EUs+9kcL855Y6kVo0xtrWmKa9UiYa9lR9nmWfhWGHBOUCotVKihdGUQiiI06ADhaGaOUr1qdAzqepr/E8ca78Sn3Czz0a2DM68oFH7yMcaLEtH9O6Q9Pi1OUvuo06kAlDfvzdPlxno1MOkOHJTsWUyQSQSSim6oKwTW9CI6G/NXpZmY9VDbsFXmeGIsm1lSlyHQF3DAecqQCqpuwnGeQni5UNUgTY+PqkfXj28/ZgyNzB5JG0Ocef0DwmjEw5+oBAC6vGIH4tGx0X3SQZNhzC4vwZpfESDpZGWPoGkkG5Mn77yAUiXBzrST8slcLb9QbH4QJm89h91/UXIr7gb++pJq2gu4NR1uq4FpFEpIlUVnDnRZjadgAtLcYgJjCrxjoQAQIFAkKsMybbGh62IzDgg+90MaiP4qFPBQLCvGH7USsCR+FJV7HocHUIvnbqwsLE320HrMVDWs54FtMKpLSc3F/72T5OypIIxNigBdfmIRaBh7oZat+l2p+MdXHHrC0NyUipjzV7or5nCu/Ck99Y/lCOjr6E5GTMRDG5k8hEkoyrXQNliIrtQVMLMPE20SiYjAY1JFCxdRzRUjnFcBUWxe5JeSbSlROFq1h/18iPnstbLnUL0xy7h5oa7jDUEu2+0wWbRUQIms4aTMGta2HWo6SGdrnmBQAEBv1MmzNCH/394R0uNnQV8nycrQkvd59lUjcqTc+iNI35Ktq6pXVAUONxdFZLMkN1k2/DpZ6ETe+sliLb3lv4KBbE68ybqKAn4s3WfcxwW0tdFj6tEZ7bs394ufVbdQB4PIm5VLrB7tMxdGILQr3v5V8H3/a/YFD0SfgrOuAK4k30dWqg7KXCQAIT/mT9BcAeKURsDKgzugXLD2HIQObomVzd2hoqCeZsVKGPb7wu9w+ZXoSsmCxHKClMwRpSY7Q1ZeEGjKZRjA0OYuMlLqASAQW2wGGJtRpjKosfnoXO9p1R9/Lx2GhK5lVpBTkYWDN2qS+ytQprS502AYoVFFWISl3J61hj8teBQ7LFHVsXqt8XfKySwetOg4LI33M7Eu+eZSFFUojO19xtcGcgiJ42Jtj3oA2Cu/zb0GR0ToA6OvJLtLxT1QAVvpIpEAKBLnILc2UO8NWFX+rSbiRtL1Kjk2HmR29kJg0DvgSN4H9DTZhX9QxzPOYpvK5a1qcREhsTbibS9bBmDQD0jJGDmuh8rno+CXFrOnQ0RsPHb3xP59PEm9nczxgYqFcnVRF2dGOSBFf2rQtaYR+P44q1/o+66Hc42mz9OT2USdaTB0UQv16OQJhfqX2l+X/P3onFF/iUhC6k+prblDDVuZx67oqnt/QoIYt3nxPgLfTrxfN+lXoast2JSz2OoYt36YjrTgBjro14W3YDJa/Uc5BZclKzkEHDbK41q0S2aUzAUCHrYOpbsoLb1XEwWihTGNexrhRreHXcS1cnM1hZKRLWdxfF6B8yPBvY9h/JRXdLi1sHCl9Ygrli+7zBPlVVjNSXUSkT0apgIj2+JJC9r4X82MAAJYGVSN/nJCeg8Czj2iNOkCEFjIZDIzfdA67phOSuUUlfEzach4MBsBmKR5+tmlSTzSftg0PP0SglQ9ZyiCnoEgcm65OsktSEZ4XgvjCb0jkRSi0JqMOBALphbVZLHOpbRpMLUytUXX5JbIYXHcBNlyaAUt7EwSM2YcXNz/Cu4krVp0iwjH71/obB54vxZjmy2Booo8d9+aDwWCgmFeCqZ3WIT0pC237NMLEn2n4dMc49IXqhlOGz7lf4WmgWFIlHeb6itUi+PYjBX6taqp8Hjr+M+w0sJlUA5LIi/gFV6J+XEy3IbfoKb6mDoIGmzya1dZwhz13ManwrjrptvAAAKD+BPIPbvOkHmjh7QwAeL1zOnwnbib5xpkMBl7vUC58TUeTg30z+2H0xtOUtvNLh1XasAtEfNxKPoIXGdcgVEBxsyqJS6ottU1Rt0118qfX39jzeDEMjIiF4gU/9VAeXgzFzePP0HFgU+Rm5mN+/604/mENcjLyMbtnEDZcmoFBdebj7FeiSLxIKAlLpDtGJ+3BCOYdFfcZX38udoUqLmuSWUINza0sdJEyi+erX1zuf8qwq+pz3hT6FJvfPCOpYleUFfjVP151YqDVDPpajeFsUr2jtfJRLLII2SHbt7lmdGesocnsrHj8em42Cp9TESLy3+NYzCqFcjeqEzrjzRckIjGlchWAqoKBtef9H3vnHd9k9f3xd0bTdO+WltIBZVM2sjeyEZChgDJkI6Ig4sDBV0RlgyIyFGWIiOwpW0D2pmwKhTK66d4Zvz8emjbNTtMC/ny/Xn2R3Oc+97kJycl9zj3nc/hyzTiNUQdY+vkGjmw7T0piOv3HF25Wjv2qHwBuXs48uB0LwLIjn9HF/21a9WzAx0veMjiG8mkYZMG/KqWKtCf6XYwTLnyMs1TXjZquyKCFt+UFpvOV8dhJfFGqbOsmHdV9Act2mBeCWaqGvSD5oayQi52s8jlvibxO1MgPOP44mpfKBVJ39felMLvnizDvZ5MVGrb+KyL7f/pMrm0txxO3sztmxbOehkVIJQGoVLZfcZaUtZe+4ZXgd9lwcw4yuR3fjP6Z1yZ0ZvSXfYk4cZvzhwtVKuVOuv5pTz83dsf8gCJfqdmM1TeGRCrBTibl1jlhdSyRilkbpb927KiKQ6jlpptpfiHFuvDpe0/+R2WfHzj7oA5uDsaLrBsiKS4Nd29nJEXcj+YadSilYtYFlHUCg8zK63WtKPjRbicnIhWLmVDP9qqMzxtSsYfpTv/PyVCk8FlE7xfOqFvKoGraVcBGNvykVK+37f5CeoYKRqpeq+osm7aR7b8cZsWMrSbPfTVsEmvm7GT17B3I5HZGx9gYt5zqjcOo3jiMKg0qGhxTn1EHqOVqusSkPir7FEb+VPNdqfUHsG/zeaJuxtK38Zf8OEPIBRrSYRbJiekMfXk2AAl6JAb6NjZf7M3qFbs55bykIpm1w1uFVGzd9VZePc+URi354vgBwr3LseTSaUbVLplk5/OOWq3Q8qXnKR6RlLUFf9e3jZz1/4d/EreUuRaPk9SVHGWmWVWyipKcpus3zszaorfv4Q2naN23MX0Cx7Hx4WKd4+NbTuPBrRi6egxnR+JyxBZsWJuiaKhjwePOg5rReZCwkOoxrLXevgB/XBNkRjZFFuqtDPukp9ExCgy/tdiJS3a+vkpKDSqc59D5KEKrliOoki/hjYQM7aHvdcLDyqIa+rDasJuT0q7ixfBLtw4U3twqHt68uu03JDZMEnleScs9TnLWLkI8BaNwJbYjSlUmatQEuNoum08fSrWa7nuXkZCdweo2b1DdvTBJ6Z+4u0w8uQWZWMKKVgOo6lYY1ZGRn0ufA79wJy1R07a5w3DCPf05+Pg2My7uIyYrlZfLV2VhUyGV/2FmCjMu7qO1fyX+uHuB26kJTApvy1tVCrNY9WFLo+4pK4e3fQBudj642nnhLHXDSeqOTGyPk9QNZ6k7TlI3RIiYf3MsT/JiLRo/NW2h3na5vfHXqI9FR6fR2XUYu5J/Nt35BeTMk+s08jQvAiUtPx1Xu5IYWzUUs5PCnbJg8EVFZAQK9gLUatMLZnMoVR97vhm65ZZw/sZD6lczHutsDQXx7Hv7lqyAbrhbC+p4WJ+xWZY8SplNgJugZx2XvgKJyJX6QVc5Ex1CriKaUM9ZpQwtQN4AACAASURBVHbtxlvncbbX++QoFdTa+C1X+nyEXCJly/0IFlw5zJmek1Cq1VT9cwaHu79DeUc3AOpuns2Nvp8gFYt5efdi3g9vS7inENlzLO4ue7uMRSISMfTIWl4/uJJ17YQEnX2PbtKyXEU2dRA0iyqv/4rmfqFaPxpFsTZkNcixGq19+1HFpb5V51vL8xj58rzyacRS9rQ2L2jgdsZdGngYjjgyRdSTT0nM2IJKnYWrvCm+zgPxcuqmt+8v8/fQoEVljWZM+RBv/t5xiTpNKuLt52bxtUvVsOcqzc8ULEqH0T+wf+nbmn8Pnb3NnFWHSEvP5ugvhiMmcpWZVl1P93fVOmQSB6q6GFZye2XyT2ybUzYiYUXZd/omL7+kHY+blX8dN7lQLzI6+UsaVCiM03+Sub1UDfuf7YUfULlESp+QOky/sIcZDbsx+dRWbj/dWJWIRIys2pTJp7bye9vBmnMLQlF7Bofzd0wknQOF1ddn9QqF4RY2eZUGW+ZoXXNgpULJiR5BNdkZfY2q4bqGffpV03IIRfGTBzG60qwy30+yluxMQT4jM8267+aLTJ5Kv2jhoFNj9Lar1WrWNrGsWHpRQj1nEOo5A4CkzB0kZm4gMvEdXu4trNjnrimUKh47tQce3i78sleIxnNxc6B9z3pa42049bnZ1y5Vw26OH14f3VvVQq2GQV0bkpaRQ9uGlWnbsDL/XNDNCC1KppXp9X/cuMzr1Wprns89+w/vN2xh8Tjp+UlGjz8Low4w57dDOoZdLg0jK/8qDnZVABCLigp02eZ20BChLoWp3tU9/NgeXVg/9vWD2i6QgtU6gL+jK332r+Aln2CW3zzB9b4fa47V2TQLO7GErhVq4Gpn3MjKJXYo1bql1c4l7zerOhYIBa4/q7kOiY0KlZcEtTpfU1cTQKF8SGbWRtxcdBdBR7ecYdWMzdRoHAbAaxUnkJqYzhvVJzFl2Shqt6zGxEXD6Ok3mq1x1hs1WzH35lr2xp7Wause0Jx3KvfTPD+WeJkvr2pvcG9s/g3O0sLPdKfD7+l9vL3lbN4JG0ETL90F2fHE0zptlnDqfihyO6HQhp/LILycSr9OQwHPZRx7j9Y1OXD6Ft1b1eTgmVv0alvb9EnoVnG3ln8e3dcx7K52XqSZMNwPsm7pbU/LzOH0tWg+XryDM78IEQhJqZlMXLAFLzcn/rl0l9AAT2IS0zi6dAJr/jrLkYt3CQv05kTEPdo2CGNCfyFsqvXYRfRvX5cDZ27RuWl1RvVqCkC/T37lXswThnZ7iTuPEvlqTDcc7e3YciSCJ2lZbDkihG71bBmOSASVfZZx+bEwZmUfbX+qi7yphe+YZUSmJRLmKoh5XU2OpVoRl8ivrQbiINW/adUzqBa1PP3pHFidD+sUaq9vj74qJDH1Emrv3kpNYMmN4xbPa8tD83RMvOz9ea+K7sbjs+JJ6meo1Tl4eyxApc7kYUwjJBIfklO/1XHTzNisXZ/4j7u6IlmdBrei02DrwvRszd7Y0+xsNRepgR/QbGUuX15dwbaWs7B/Gjwx58Za+hz7WMvlsqf1ArKVufT650MdV4yh7FJP+5JFjjUMukps2goepy7mYcpc/FyG4O86AjuJrhx0i461SnSt4pRquKO1VAr0ZseRK/h4OHPwjGmhMWvZcfcGDdcs5qOjewhZPlvzV89XV18k2NH0hku2Un8ChKuTnA6Nqui016kcwPz3ehEW6M36GUMZ2r0xCcnCGPWrlGfKG+3YPPMtVu8uFOQ6/ON43u7bgk0z3+KnbYKCYVZOHiq1mjO/TOLtvi2Y924vHO0F49irVbjm316twjU6FPbSIBoFRdEoKAp3hwIjqSLMezGVvPTH+9qKvgcEXftcpYLN9y5r3CgLm75Ko61zNeXnbqUmEJ9d+GO97OYJyjm46owX6ORGlkJIGlKp1byyb7nFc7qRZt7qbHSlmaVq1M29YyhKVvYe3F2ERKyY+K54ecyhgn/J6nU+T8y8vtrgsS+vrsBD5qox6gCTqw002F8fhjZIq7lUtmic4khEjgS4jSXUawYejp2Iz1jHpUdtSjSmuTyXK3aABR8IUQ3fTemjaWtRz3AsqjV0r1iN7hWrsa6YK0Yf1V0bE5H6j02v7+4iFA2QywQjbG8nQfG0+nqYniIP6Vm5tHv7Bz54ox2hAZ4UbKCfv/mQNvXDrJhB8Z0FMR6O+mt12pJzvSbTZc9S4rPT2d5xJHKJ8DHsVqEGFV28aLfrB1LzsmntH8acl3pqzqvu7sfUsztxtrMnPjuDB5nJRPb/lHpegUyo2ZqaG7+ltmcAN/p+Qtj6ryya06aHppPS3O18CHTU/YG2JdlW3HWqVelInq4C8/Nv4eJknkbJi8C2lrMZcOIzOh1+DyepnMUNPqCc3Etz/ELyLdSotdwrBWQosrXcMeagRq1V17Yk5fHOPgjH1/l1fJ0H4Old+t+ropTIsPvaVzBZgzEtPwlXOy+jfZ41pow6QC335qx/MLcMZiNw9W6szir/m5X7WTF1AOFh/lqViGqHBTDv98O83aeFxZVYMvMiyMg9jZ9L6RbgLqAg63R3J93atSAY77+76YZbvn18A2OrN9dsloKQxapSqxGLRLxdowVv1yh0nxVcJ9DJXSfT9ZtGur5OQ3dbRRlfRX9YoS2xNIYdwNlpIHGJQ5CIPZHZGb+z7FL9Y77bMJ6Phi6nfIgP3/0p5C0s+t8WIs5EEfsomcETXqbPsJaa/iM/7Mav8/fQvmc93v3yVWPD2xx7sR2bmgshuYsjNzHk1HTGhPWmd3kh+sxRao+XzI3ljT42NoxZDDg5ilpu1chSZBOfm0igQwBf1PzA9IkGaFjBNoV/rKFErpjqbqbjZM8nHyzJJcwmx8qImAI23rrCiivnNH/FMbcU3bHEbTptF28/Yu8pobr84Qt3NO4WY5y+dp+Zqw7Qf+qvDO4qJEsN6dqI97/bwpq/ztJ+fKE7wNVJjlKlovHw+SxYd5hPl+zizqPCWO8BHevz1le/s2TTMa1rnIkO4VpsD6KTp2u1JWSsM+u1liU+cmd+v3Ne8/xqshDrLS7DnAN7sfmrv/1nbtF85EL2nxb2XVbvPkurMd9z/V6czefl6f4lCsUdsnOPEOBn+vuWlZHLxjPTWLh+nKZt5IfdWLpjIlsvfMmfP2lLVNdrGsa2S9NJjE3l+sVom8/fXMaFvUpb3wYsjSxMvhoU3JnoLPPeUzux8XVsd/+OTK0+id7lu7G84XzkkpJJotyKH4n6aS7PqfuhXHrUhqSsnSUa01xKtGJv5v0Kh+M3GO1zJGEDLX16IyklxcACTiXttvrckOWzmd68Az4OxsuS2YsdyFUZDxPbF7ua5t7aam11Kws64h0ba2/SDO8h/DD+8pkQYvdG54ZFjjWhXUNtH1/VYF/2fjdWpy/A1lmGV9yTBrTRabub9B4ejp0I817KmegQrWOx6cvxcX5d55xnybT6nZkTcYjam2ahRk07/8qa0MiSkqM0XsQcsPjzGxEZw7Hl79J0xAI6vFQFX09njix5h/bjF3Ng0TjTA1hIoP95nTZD8e11GgsuzaLVmvZsPMuZIze5eekB6alZKJUqjU5JaFWhOlWHXvW5efkB1euWTXWxPFU+w898w8CgjlRw9OVaWhSH4s8xJKTQrdEnsA0r7m6n0+H3GFWpF+52TlxKiWRP7CmdTdKCDdh3zs9jdKVeJOam0Ma3MOcgPldYDB1KOEZDz7pUdimZ6zc15ygiJCRmbqaS1xy8nftw/mEjvBz1x7LbkhJZW3MqreSr8jgQt5aO5Qab7FsSDicY/4Exxjv1mvJmjXom+3UPGMnGh8ZLbSnVCqvnURRrQ0XN5UnWTuoH6r9VzFU8n6XjJoe3ZXJ4W5uPm64wLZZV3sGyPYyaFQVjKJfZkZevwMXBnrx8BbvnG9a6P5FUNqu54qxdfIDM9BymLx0KwKQBz0YgrjgysR0tvWuz6t4ukvPSqeDox9y6E6jlpm1wd7aay87Hx/jt/l5S8zMId6/ETwZcMxubf8Pki98z5dIiQp0CtAx7wV15fE4CA06OQiwS82p5642wkywcNUruJE7SyAuoVKYXEbagxMtoESKTRuhowuZSN+z5Kn3FsM3j54izZsWt1/Voa9KwA/x2/2sGBZeukFJJcZLVITlrF15Ouj5Td3kbm17r3sMkQgL177MYO1ZWmPPZcZRan1ous5Py2bLdbJ75FvvP3OLVNvr3dP4qgdhYVvYu4pOEuzZhpa4k4cnb+HguMXluZnoOVWtX0Dx/lu6W4oyq1ItRlXppnv918zYPY3Uj5bpVbU63gOY67cVxljqwpOEUvcfeqyLs+8yuM826yRajRrk/iYjpQs1yQn3YXMVDfF3esMnYpihxuGMnf+N1FQuwtjanOVxNPVGi87MU+VrhjiHLZxvs6yHzM3isgBtpZ0o0nzc6N6R9w9KNvqjq+xt3kyYRlSTENadk7+f8w9o429cnzMe0MbCEj2fqF6QydayscDLDaGcqdNX2jFHgdju0WNicPLBoHK5OcoNGPU+Vg0pP0pQ55OVdJj5pOD6eRcMwJWRmmVZLBMG/vmHFUXrV+5wvxqxk0td9rZpHWdC5amU6V63Mjus36Vy1Mg0DyxOfUbL9taLsjNnHd7eXszNmn03GC/ffjbO94A2wlwYS5FHyTV5zEJkQnTHLH2COtoajxIWPa6wyc1qWYYm2x/TwzSW6VpYynW+umb77KM3XayuUqkyuxL5MnuIxAAFu71Hezbjm8xdX+pg0QAXv8a2oeL76bhd3oxO1jv+zcbLRYwAt+mhLAgAcXPceMjspLfvOYeW8oVQMEpKc3p22HjcXB758v4fReRnD1GdIhIgvwzdZPb4p/nelPwq1/pT34hT/DD+MaYCv90pkdrW499Bf41sv+rg0Mef7V9LvXXGuxsVT009IbAuft4iISSUXrhtwchSDgvtSw7UqN9MjWXXvD35vskyrz5aHP3Aueb/RcWz9Wo1gMHLAJjuacomTyaiULGU68TnR+Mptu/FyK103gqWkxGZmUM5Jf2FqR4mLWZWaspTpbH30Iz3Lj7X5/GxBruIe9tIQ6gRYnqFpLlVCfVk1fygt+szRGGxzjgFabUqlitb95yGzEz6uh/6YRJv+8/hn42TuRidy8dpDDq+fpDOGLSnNPY+jCZvMNur6UKkzkYi9bTij55+/70Tx6sq1+Lu6cO5d23zHuvm/THd/oYJTRadg0vNLVtT9WWKTzNOxYYZdF0X5/rbxkmfWsPqeZYko5hCRaFw2dUo183yhZ5/sLXNN7wJMGYprsb05Ex3CmegQLj1uTmzaMlTqstnYsYS9R67Tuv889q8t/OxIJWKmTezOq6OXMnjirxz6w3bl74wRm3PP5mM+zr7D3ljDmZXm4O76Po/jO2m15St0tcD/TbzdrDHXJk/gwKhhNpPZPpGk7UI9FH/UJuM+C2yyYveU+SMRScxKrvg84lWb3dJaK69aQPPfl3JswGi9PvXiNU+LIhFJaOrVzawohn8St3Az/QwTqpRumj4Idwlr7n3Fg6xbTKy6GE+ZrjRCAfUCL2ge5ysTSczcwM34N8nIFe6AGgXdK+3pmiQ9I4evvtvFnKl9kNtr68d0aFGNafN30KNDuE1i2f3kQcTlGN80XHx7kk3dMbfSz7P63nTTHU3g6jyS9IxfufdQ+P8u+LeC/wVjp73QTNm5h2P3o5nUshnBHu40DCxv9VgDTo7S+/hFxmbB5R9U+5lvrw812U+Nms8ievN5zXVWS50m5j5i4a2S+9SODRB2wX9o/wrdKhbGmG+/c8PQKRq6Boygnkc7Fke+b7JvQu4jPovoTbeAETTxsm0M69GEzeyP+82qYttqFMSnryE2fZnGz+7t1Adne8PSw2VJlyGLOLphMvrs9qwle2lYO5jt+yMYM6gVbq6WpY4Xp1+FSSy6bXx/QY2aFXc/482Qz7CzsloXQHJeHPNu6peKtZby5YTks9y8C0jE3kilFUyc8WIzsWUzImLj6BNek+pzvuP65AlWj1Xcj/5vwGYiYE5Sy8TgF956x6rrZCsz+OG2bW+9PeXaRsFZZt6X1t/BsgSGnY9/YnfMLxadow81ak4n7Wb2jRHsjV1llVF/lDKH8w9qE508DYUyCX/XMdQLPE+o11x8nC3TJDdrzkZc1PqOHT0dSUigl16jDrBt32U+facLnVrXYMYi65PTCvCTB5vVLyrzCr9GfWH1dXJVWaXikizAXlbvX2/UAY7fj6ZZcBDH7kVT3tV2JeX+LdgkKqaAlPwE5t6w7FbGzc6HVwPfoaJzuME+UZkR7Hq8wqSPc1SlmSy786HRPubsWCtUKk1BB3Ow1iXkbV+eWm7NcLPzprxDGI5SV2RiOXmqbHKV2TzJiyUx9zEPs28RlXHFIlliU66YM9EhSMRO+DgPxNf5Deyl5hk2S6JiCli44iB/7jxPjcr+xCWmsfWnsSaPtegzhzZNtEM+Rw5sQXB5T8ZO/Z3g8p58NE7wK7fqO5efZ79J5VD9FZHMZc6NUaTmJ5jd/42QqUYLqxTlWtpJ1kfPMemuHFHxa366azwHogyjLnTIV+WRmp9Aan4iqfmJpOQlcCj+D5PntfcbiJudd+GfzBupqGQ1RQFyFQrspaWT1a5Q55Oal/j0tSaQo8zifPIBk3aore9ruMm8tV6vTFwyeQIDGPRB2tSwA2x/vJTTSX9ZelqJ6VRuCC18epk0sqXxpchX5fHl1ddsPm5JMGXYC8jMu0RM6g8kZ+8FwFXeEhf7hgS46V9VWmPYXySs/ZEOdKyCn30QcokjOcosUhVJRGdet0iGt4v/MJp5v2LxZ/hxXEccHTrg7qo/8cYYa+59RWp+Ein5CeQqs0o949lS7MT2GuM4NHSapr3hdz+Skp1D5IcTGb9lB4t6mS5ikZKfwMYHCzU/Stbc6ZYmUpEdrk9f66CQj83RJirdcMei9AgYzf3MayY3omxJkGM1Wvj0Mt3RAAvOHWPh+eNaH2ljm6fFsRPL+DJ8E59HlK3ynS1wktXB2/l1xGJHUrIPkpZzlLScowYN+7+d3oHj2fzQ8o3uh1m3eGig0Io5NPXuTrOnGkMNPDqYjJUuSr7iNs5O1mWt3iyFcGFbkq/KJTH3EYm5j7Ta948cRsPvBOmD/bfvmDVWtjKDe5lXTXd8RijU+TzJi+FJXgxKVT5YIDpXnFIptDG+8kKLtTWspZw8hJGVvtE895SVs3iMLZHXiRr5AWu7vUbk8PfN9rEXRYSI6eGb8bV/MfybBaGOZ6JDSMxYh7dTX+oHXqZR0L3nIiKmtPn2xhL2xumGs9X3aE9115fKdC4d/AbR1b9QxK2dn2UCbC5Og8nO+dvGs3q+ee03wf2z5OQZvnjZ9vpBLzqlJrk4Jmw2u2NWcDxxe2ldguber9DZf5hWWz2PthyI+92icbo+jYi5nZxIs4AgJtRrZvWc3qnyHZEZF1kZ9T+rx7AFIhO/2Y2CorBNCe8XF3sDkS0Dgz9m88NFnE8+UOpzGBM2W2cRZGn9Ak/3//EwphF20lDk9qb1Uv4N7BkxhHylEjvJs685+zxSqlq6XfzfooVPb3Y+Xl5iPZfiTK62HDc73Ww7awz7yqvnmdKoJV8cP0C4dzmWXDrNqNqNrJ5bmHNdpodvZnfMLxzXo89empgfUvnvNeoDTr7L702MF8X4qJrxcMPegeNp4tXVrHBWa+hXYSK13W1TV7Qgbj02QVfjpSwkBZ4FteZ+r2XUL7xneynkF5lSL43nIvXg9SBhU2f5nY+JzjIdI26MVwPfoZ5HO4PH3ex0S8qZ4upQwZ98ecgEPjm6l9Vd+5k4wzy6+A+ji/8wrqYeZ8ODBSVKGzdEkGM1epQfTTl5iM3HflHJU9nmffZ3qMj0cCFPwFTdAXNp4dOLTuVMC+c19OzI2Sd7zRrz32q8jfHPuJG4O5RKpMm/AptHxZhDVGYEe2JW8Sg70mRfdzsfupcfZXZYmbXEZmagUBXukge6WBaXby4Ps25xPGk7ESmW10+1E9sT7taCuh5tCHWybVXzsiAuJ5Fx5z/XPBchYkOzHwD4KGIWLb0bsSLqTwBa+bzEkQShwPSoiq/TqVwrhp7+gNGVBjLnZmGx6j6BnRkYJGw6jjwrhAk+yUvBU+au6bO84deax1dSb7HuwXZupkfRM6ADbwSbv+melp/Ezsc/cS/zqtmhp1KRHS95daad3wCLKjA9z0SmrORG8hJaBPyMqyyM608WUdNrEtujGtMj9BQA/zweRmreTTpU2IG9xJO90Z1xlAbQImAFalTsvd8JkUhCx6C/dJ4bY8d1oRLZtL0H+bBtKxye6gd1r17V2GlW89GlFVxMvst7VXvT2b9BqVyjBJQ83LF/8xmkpQhaIn9dnWGzmT0PVP9lAePqNsZeUngDUxJXjD5ad51l8TmHd1kevvY80+f4OBbW/YxAR8F1MDViLin5afxQ/398FDGLcvY+vFdlGH2Oj9MY7D7Hx+EsdWLlS7MZevoDspTZrG8qRK0o1ApeOzGBjc0W61yneFtxxpz7lBbeDS0y7Nag7//950VDCasoxNz/GrWPtff/xt3OiQX1RxPgIPjXx55dxJ2MGL6oNYjm3jV4kpfOw6xEJl9czo5WX9Lz6P9YUH80VV0CAbiUcpePLv2Cr9ydX16aiLgERZhN8TBjN4HOXdh9rzVdQg5zK+UnVOo8qrgPRyyy51LiDOp4T9X0P/CgJ+0rCBLCD9K3o1TnUM6pDfKnBbjvpf2p9fx54ae7e1gVVRidFOTky5omhdFy3QctYsdv+jPg+wz7kY2/jNX7vOfgxWxdZRPXkUHDXnr/+y8Q5Z1deadeU0bVbqT5e1EIXfONwWNtttpWV70kJOWlAGiMOsCw0L7E5hQmBNV2rwaAvURGY8+6mvYMRaFyaFf/NprH0qfl6s4n2zaELTampk3HM8SXV9diJ5ayt80M1jf/RGPUWx34gAX1R7O3zQzW3j/E3QzB1bIkchc7W31Jh0Mfs7v1dEadFoq+pOZnsuLuXna3ns7Kxu/T5qDxJL2SoFLnYid2QaXOpVOw4Cqq6DqA2ykrEIsEiZD0PO078Yz8aFTqXFTqXAKduxLi2o8cRQLbo4S78OLPzaHXyrWEzZxP2Mz51JhjuviNNTzIjNd6Hv30+axFe+g5eDH29sLnb/Evf9Oh73wexaaYPfa4KWsZMEq481QoVfQcvJjJ0wR3X8/BhYuSoydvo1KreXXoj/QbvtTs8Uvdx/4iEJmSROeNvyIvksG2pWfZVDr5/8KTXOFD3++E9gqn6MpSJi7MRDRUeFhfgeGihv9F4XJKFPtjL/B5+4F6j9s/fS++rj2Ujy79wozaQ2jkWRk7sZTKLgFAoZTw4ts7uJB8h1YHzM+9KMrEC4JWSge/unQLMB7qKRbZcz5+Kh2CtvMoYy8hrn3ZE/0yPULPciruPRr7LaCOz+fcSvmJYJfeiEX21PedTnz2SVzsKiKXepOQdQpP+7qEuPYHIDbrb63n5vBzv170WrmWo2NH0PPX36x63aaYUKUnh+Iva57/L/xNAEKDvJkyvhOtXplNvkJJnZqBjBvWhl5DFrNlpXkr8cWzBqJSqzl/OZoPv9zIvg0TUSpV7Dmku0jZ/tclls17E29P/VLi+vjPsAOvVKrOd+1MZ649r6y4cYaZFw7h5+DC1i5D8bDX9eWGrvmGqDc+1jze1nUYA/etJdTVk21dhjL/0lFUqHm/jhCpMf/SUcQiEe/WNl0ysAA1CkQGPlKVXUIA+LNpyVQuL6Vc5/UK2v9X4W628a/GxTbC22cbKpVQLSkr8zccnQYR87gq/gE3iY2pga/fMbKyfsfR8TVUqjREInvS02bh7rGAmMdVkTt0wslpCJmZK/HwMLyStCbDU/L0R1Bc7EZbjZofGrxNuHuIxWMCnHsilJqr9tStY4ouIYcBCHEVonC6hQia/o39hOLRLnahuLiP0PSv4Kz9/1XOsQ0A4V5T9D43h7/vRNGxchjH7kWTmZdn9nmW4GXvypH22sqvSqWKkArCnZWjg4y4+DQqBgsupGALSzyKRSIys3LJzRPqJEskYqLuJ+r069mlLjcj4+g/fCkHN5sXpfWfYQe23bnOtjvXtdosyTw1B0P+8nyFktTUbPq8adwnbIxmfsHcHCCMX9SAF6CvLSM/l4jXJmnMy8Q6Lan9xzzG1WyKSCTi15tnudS/UGztSfpiklLnUcF3E3JZbRJSppOcsYLQcoeQSvy5/ag66qdRP1Ur6C+GHeQYwJDTH/B5jfHYi2VEpN3idvo9JlQ2r7wiQGTGfY4lnqOpVz1mXBfeMw+Z7kb3z1HreSu0H3E5iZSTm+O7VeDuPhuJxB/RUw2T1NRPcHQahLOzoH/k6vY5YrEHzs7jyMn5C5msIYmJ/VCrUgEFMllt5PL2yGQNSEs1LhRWx70irXxqsfreAd4MaQ9AvkqJnViCWCQiR5mPXGLHJ5d/ZWJV4xID4yu/wsATM9nRSsidSM5Lx0NmnjDWH9GHzer3vNEnvCZ9wgWtmP2jhpk+wUZIJGJm/7CHxbMGkZObT2CAB2++vYKFX71GTJxl5RMLGD6oBbfvxrPn0FVGD25FfFI6sfFpLFx2gK4danHsdCS1qpWnV9e6pgd7yn+GHdsbcUuwk0rw9jL/Fksf1Tz0i1/5OTjrNeoATf0E0a+iuy+XX5tExac++7vFzpFKAqgcGEnkoxpUCriEg31jfNw/487julQKuEgF3z+Ry+oYXLEDzK/7KSeSLvDtjaXkKHOp4RbG25XetOi1vhHUk6jMB3wfuZIarpX1bpKubbyADyNm8vqJd6nsEsJXtQqrK/U5XnirvPnRXjY/EvzEG5stRqGIxJ42qNVCcWsPz5/JyTmAi6uwShKJtO+EYmPqEFD+EVlZBSJYluUGWyp2yQAAIABJREFUfFV7CEvv7KLDoY/xsXdjaaMJ2Ikd+bvdLN4+9wO30x/zSY3XqOJSnid5hqNwXO0c+a7+GHr/Mx2VWk3/Ci0ZFGJeNubPd/dYNOfnjdISADPG+p8Eue+/twhVvlb/8NbT9kIBxKIbp8WfF904bdmkMi2bVAag8tMN9c/fF+5wvvlU+wd9wsj2Zs/xP8P+L+ZyUgzTGr3MO0e38H1L09Ef+SolDlIhG7OowqVanYtE7IpanUulgAvkKx9ibydsdMqklS2aU1OvejT1qqfT/m144R3N2sYLNI+LG+58tYI3gnsZjWaxl8hYUPczvceMRcvk518lNqYOMpmwiSeXd9C4YfTh5fUbMY+D8fL+0+CYphhdqSujK3XVaf+hwdsMqvkBba4Kxa89ZS4MDu0AwPKXhLyLQ61navpXdPZncwv9r9kYOUrb51b8x7OnRIb9vYFLuHFJ97bbw8uZX/dOxl5uWJbT0vDJzjULw6eK9y84tunU5zg629O97uco8gtj0reem4a93A61Sk2X8E+1zg2tUo4fN+tqw3et/RkqpYpqtSuw4HchS3FUjwVE39WVdS0f7MXPu0q35qY1+Dm6MKRqQ3bdv8G+B7d5uYJxI1x17SzNSr3Sb99yZ9BHAIhE9sQkjSfU/wTpWdtwd36TqNjWVPD5k3ylIPYmk1YiPXMzjvIWSCWmVSWfR9w95mseq9V55OWdxsd3l6bNwaGn5rFc3hkA/4D7mjYv7/Wax94+O2w6t6l9FnDtzB3m7JxChSrl6Bv6ruYzviP2R8QSMW+Gf4hfkBdzdk7hTsQD5I4yxrSYxsqL3+Lpp+uuup3+SKettPn9/t/sjjnLo+xEHCT21HQLplvAS7TysSwvIyLlHrtiznA5JYqY7CdIxRICHbxp4l2NgcFtcJY+3zkDatSsjNrPvtgLxGQ/wUPmTGWXAN4IaUctt5ASj2+VYddnIIuSnJRBzwbTWHNwCt56PlClxZ5N59jwy1Etow7Qs8E0/ro6Q++co27Fsnz2bkZ+0EXvmDcuP0ChUNK9zud6jwM8up9E55pT2Xz6cxycrKsKVZr80fENaq6byz+9x+ndWAUYfuhPlrcpTElf3OpVxh3ZxOJWgmJlWPlrALg7P40MKCf4Ziv6CwkpErE7rk62ydh9HhCJZNjbm79xXNrM2ChUd+ruN4YdcUuYtW0yleuGIJEKd1XDG33K6ghhBb/v9+NUrFWBhEfJbI/5UTPGx5d+5Vii4dDQ3+4f4rf7hwwe7x3YzKS/v2hkznf1x1LXQyhG80XEaq0IE4B8VRYnEq9zIlHY3yq+UVmca2nRjD+7GIUeuV2FUklkxmMiMx6z5t5BANY1+0gTQmqKPJWCDod0XZZFGVGxk+auyRweZScy4LjwfzIwuA1jwrpxLzOOaVd+04SwFpCQm0pCbirHn74XSxq9Qw3XILOvVRyr4tjXLtX+z/fydcU/0FOn3zeTTQvw25LI649Jik9DLBHrzKeosXf3dEJW5G5i15/aRWyL8+PXhasvkVhEUEUfvXcj33xQtq8X0PGfF33+d89CPZSrr7+vMer6fO4/t+1H+8DCFX2nClU0Rv154deXZtM3UP8P8L+dHz9ex5vhH+osWgp4HBVPZ8+RdPYcSfyDJAAq1tKOcjFm1EuD+KchrjHZT3SMuqVkKXMZc+Z7vUbdEMNPLzDdqYy4lS6Unhx95nsdo66Pd879aLKPMaxasa9eJKje/fLX+/hX0Dagl07f5cNhPwNw9fx9nXNLkwPbLtCwRRW+WipEWVw+E8WUoT8B0L3u53j5uvLbocLkjUHtZpIUl0Z2Zq7RcXf+cRoHRxmbz+hGOty5/pi3+wpp8acP3yQjLRvnEtbf/I//KEpebj5NOtdh7Dev09lzJABiiRiVUqVZsQeE+vDzmUIX5Z2IB9jZl7xCUUmISLlHc+8avHZcO4nO1c4RR4k9sTnJmraO5eobHctRov9OuLFXVco7eJOlzOVwfATZysLvcqYih8kXf2JO3RF6zy2KVCSmZ/mmRGfFcz8z3uhmtTWcfXKLvsdmaM3PRepAY+9qOEvlnEm6xaPsJM2xfJWCfsdm8GfzqfqGM4nVPvb1x6bi6u6o017npYrUbVKJiycF8fvtv5+kx4Am1l7GYgqMOkDtRqFax37Zre0HH/dJd6a/u9ascfUZdYBK1QOY/HVf5nwiZI0N6zyXP48bdlP9x3+Yw2tVJpGamM4btaaw5sosFk1eg4OzHLun2Y5V6oXwRq0puHg48ePRL/j5zAyGN/qUrLRsVl76Vu+YxV0dsTnJ9D9WqKMzKLgto8N0N3Kt5UrqPbocFjZ0m3lX59s6b+ntdy8zDm97V5PjfVLjdb6/tZXvG4ylorPuPs4nNV5DpVbT5mDhRvzppJt8FrGK6eGDjY4tFol5v5r2Hert9McMPz3fwBmWE58j3MH4yd0NGuxuhz8nXZENQFxOCkq1SpO/YAlWGXa5g0yvUS/g/Rl9eLO9oJHx967LZWrYjSEr5j6pWT9E89jYSrte00pGx+3Qs57GsKenZpdskv/xH8Aft+ZpPS+6Gi9gzZVZxfp8pXlcKfzZF3y589Tl8GH1fkYzWkOc/Mwar7N/A5NCXGKRiL1tvqbj34V1Yw/HR5g1flmwsvH7hDobLga0s/WXWvsUK+7uZWSlzhZfxyrDPvYT41maPuUKN0xjHyYb6flscfMo/HFKTzVs2AeOMSwTXICXnytJcWkApD7JxM3TyTaTLGMSkzK4dOUBR47f4tqNxyQnZyGzl+Lv50b9OsG82qM+/uXKZkP8n5O3+efEbeIT0rl7L4G09BykEjEuLnKCg7yoXzuYdq2rEVDO3fRgNuDvf26y9+BVHj5KJi4hDYlYhH85d1o1q8LAfo2xs/uv6ENxnKRykzIFtkYuebYuKEPYi+2MGvUCXKQOmlX71kcnys6wt+4Sbnbf/Kfpss8jIlFhQolCYXhTJrxhiMmxmratzo51QoTI37sv03NQ0xLPr6xQq2HEO78SeTde7/F8hZLIu/FE3o1n/WZhozmsoi/LvxuCWGy7gh1R9xP57KstPHj0xGAfpVJFblIGiUkZnLtwn+UrjwAgt7djyYI3CQ3WLb5SEvLyFPQe9AMZBvZhCt6XFWsEGebPP+xB+9bVLb5OpiKPmmvnUdnNm8eZaTjLZJzqK+jqhKz6lj6Vwvn70R3yVErylEpuDpqsOffbc3+z9OpJanj6cfVJHItb96JrsJBn0HD99+zu8RZNNiyiirsP15PjuTf4I4vnZy0/vfRemV2rKGKRCJVx5doy57Na+nWBitPKtxY7Hwvfs7T8LKuuZbUr5j+0qVCxMG09/rH5Km/WkhfXGKnrp/BU4VAsty5a5Jt5u/hr/xWLz4u8G0/b7rPp8nItPppYMr/svB/2sXXnhRKNkZObz9CxK/D0cGLzb28b7Ff11/nYiQtX1lcGTzDYd8mKv/l9w2mL5vHlzO3M/m4Pfz0NT3RylJGZZVrLJPz3+RzqNZpQVw8A+uxezdKrpxhdszEAtb3KMbe5UBmrwfrv2Bp1jZ6hNWi5aQkPMlK0jHXIqm+1ng/e/wd33iw9xUdjlDcz3NDWOEjsyVTkPJNrG8LcWH0vmen9BlP8l3lqI1zcCt06yUkZpX49qcciFKkfIxILhsAaw97p1fnk5JQs83D3viscOxnJ9j8MG0hj7D14tcRGvShPkjNp2302h3bol4k4OWCMwVj+orz70TouXo62ag7Z2Xm07jqLw7umEBLszdXrj02eo1KrNUYd4OMGbRm8/w+NYR9UtTBbd3j1RqyPvEzP0Bo8yBAWESGr9G+YAkxtaNqV+CLyICuBk0k3uJ3+iPicVNIVWaTlZ5Gen02W0nik27+d/wy7jUhLLpSO9fQxT4CpJCjT5yHz2W+6owHa9ZiDUqkyeFwsFlGnVgV8vF3IzMrl6vXHpKTqvy1MS8+hx+vfs32dbgavKTq2q8mMOTuN9hGLRFSpXA5/Pzfy8hXExKZy955uBnABKpWayZ+uZ85XujKwbdb/xNTGbTR+2FcqVdPp89OqoyaNeoXynoRV9EWhUHIzMpb4BN3wuLbdZ9O9cx2zDHtx1GiXL1OqVEglYs2xooS5ebG/50iDYxUtIPOicyQ+gk8jVj3raTz3vBD/4yojBqisri+WGA85irodp3nsF1D6m3lixz4oswvrcEocdAsZG2LAW8v0GvWwir789P1QREbc5vEJ6bw2dImO/zItLZtv5+/SccvcuRmLXG7HmH4/sHLXRDw8nXmj0xwkEjGr/hLEtQb1b8Jv609qzmnSqCKT3+mEj7fxH0i1GgaP+ZnoB0k6x86cv6f3nEtvGv/xycnJZ/U6/YXX27WqxhcfvWLw3Px8JX3eXExqmrDxpVKp2bbrotHrFSAVibmb9oSKrkJeyDfnDvF2eOE+zYrrZxlbS4gu+/n6GaY1EjIgA53diEzVff3/Rtof+ph8lf49O2epAz5yNzxlLnjInNkfa7u7wBeRZ2bY7WTmX/rUYf0iTGXF2X9u81Jr45rfpw4Vyv627VantKeEWFqNvCeDsfc9gTJ7o9nnnTh9h8d6Kr0snDmAumaEyPn6uHBo5we8NmwpscVkSnfvu8LkdzojlWr/CCbEpbL9tCDJsPPPM3y3dgxeRe5qRg1txebt51n23WAqlNfNYDaESASrlw5n5oLd7NqrG9K2Zv1J3uivHWpbd/UiknOzuT/iA8Yc2MqS9j21jvca9IPea82b8RoN6gUbnY+dnYRt697h2/m72L3Psn2LawPfp8pvswlz8yImMx17qZTx4c00xy8lxtBy0xJS8rLJVSp4JbQGAP+8OpalV08Rsupb6nj7E5mSRKYir0w3SMuCdgc/0so6re0eysL6YwzGeP9n2J8RfgHuJMUL4YHxMSn4+hte5f48z3iB29Jm1aL9Jg17ypNCV0xZZJ7mJb2OfbkroM5FIu9m9nkfTdP9EfhoYhezjHpR/vhltN56nv2HLmHTGu0qMlWLpLZ369eI29ceM6TLPHacLUz62r3R+uiJD9/rotewr1p7XMewH+o/nLqrhWIf++5rl3DLyc0nO1t3o7Nzh1omjXpRPprYlVNno3iSbH5lJ5lEYtQYL2ljWKNldM3GGl98cc72t9w99rxxIfmOllH/sHp/ugW8OOUrnwXPrOZp6661NY/nfbrJYD+1Ws3DKN2qImVJ5DXjPtK/NpzVPHYvo/h1qcsEVNnbUeWdIDfBPO3t7bsv6bRJJGK6vGx++GpRpk/Vlc5NemJ84/jk3zfwC3CnW3/bfjE/n9JDpy1XT6ht3+2/A7D40im+bKYt6DR56nqd/gAfT7I86mfjGpsUK/4PYMGtLVrP/zPqpnlmhr1onPfFk3eIOBOl0ycnO48utZ6P9Pwu4Z+iVunGxd65/pgFX2zWPP/lL/NKV5UUidMoxA49EMuaYu933qxz5nyvW1Th+1nmxdbqo1XzKnrb9x26pnlcqWo5HBwLw2ObtKmGq7sjY6fYLnUdoH0b82LHD/R9i8i3JjGuTmMGVdN2mUVc05WxtSYmHYQN3+IuqecNcbHCIEr1s93LMkRURqzmsaud4Yz3/yjkmW6e1qwfrBEK++CpWFdAkBf5eQoSYgv9t4PGtuO3Hw8+kzkC1GoQwpVz9+gS/ikikYjAEG8S41LJLhaf3LJjrTKT7c2NqYy9/20Q2aNWxiKSmM5o00fN6gE2nhmsXneCl9vWsPm4tuCfR/dpUd58t8qU9yzP+ivgrTdasOzXIyb7Rab+QbBLV07EfkhSbgTty6/CVSboHB2NGc/cFue4lRJIFfc3yFdlcujRcDpWWKc5f8+DvrQvvxqp2IGE7PMci52Io9SPlyusQ/R07fbnnQZ0CFzD4cejcbYLpkPganzl2u7Pm+kPrX6tpYmDxF4jnpWnMh2eu+zO7tKe0nPPM11SzF09ikrFDMvj6CQto/7qkOa8Ob49IhtmOFrKnFUjadxaCItTq9U8iErQMeodetZj6vwBZTQjZZF/lajyjps848ixW6Uyk+AKugko9/VEqTwvONnpTzc/bOD9kZdAIbHPK8Z1TYqy/V4nWgUspnfoUY1R3xzVkhblFtKn4gmSc2/wOPNv7MROpObd1px3KWkeaXlRSMUO5CpTuJq8hF6hh+lYYT0b7mi7LPJVmfQKPUKHQP3hgheS71jxKkuflj41NY9NVXyKzUnW6LH/f8bsFfv6Y5bJR5pTFQnghw1vk5+nYOV3+zm27ypxMSl4+7nSsXcD3hhXmFixO+Irg2MYu5apeZg7z/8tfhO1Ss2P3+zg792XyUrPxclVTrvudRg1pauWPEHpIwGRHap8QeNaYm86AWW3nuzSqpWtW+UXpUJ5z+fakBfntZ1/kKss9L3fHyEkMv1z4rahU6xGbqSCWHHCvcbrtClUWWy4W6izolBnEeDUhqrubxKffRZfh4bcSvmNah5CMefLSQtIyD7Hn3f0/6D4OjR8+qjwsyqXyMhRFi5SOv79Cdta/k9Hb0WhVpKlyH0mrpCJVXuzN7bQ3dj9yBeawt1F+SxilUbwy1FiX6IkpaTcNO5lxmq1xeemEpeTgo+9G+Iy/b5bznMRx24nkzJicmdGTLb+trcsEIlFjJvag3FTdTfqyhp7vwsgMt/tc/K07mrs5u1YvZEtzxtKpYobt2KJvBtHfGI6T5IzSU/PIS09h7T0bNIzckhPNy99/NawiXrbI67puiGcy7AalkysPyqsX6VzOm21vd5jS1RreoTsxdO+JuGewo+CGjVty6/AW25+uO2G5lPpfqQwOilHma+ljFiUT2sOMKmbXho4SeVUd63A9TShDGdafpaWAmJxOparz9DQlxl4YqbBPkUZc+Z7rqWZzjLe9ugk2x6d1HvMVPWnsua5MOz/YQUWGHXguRNEMkVySiYbt53n8D83eRSTYjRL1hKe5GSz+voF3q3XjByFAvnTKvfJKbpZtV6ezja5ZmmQr8ogNus4IS6FiwxP++okZJ+1yLC72jniJJU/d7oqxfm2zjB6Hv3SrL5Ta76OiOd7RV3aPN/b9v9hkLx4oR5nbkwVFGnmfeCfdxKTMug/ZAmtu86i18AfWL3uBNEPn9jMqAP8fvMS884dA6D5H8s07fo0cwKM5FYYo18F24Q6Ni83j81RLbmWvJzDj8eQqSgMuw1wasW5hK+p5FZYZzbM7XWuJf/EuYSvuJA4U8fHbojdraczoUpPyjsYV8Y0VMWoLPCQuXC4/Swq6SmwUcCrgc050n72/3ujDi/wiv1MdAgNgyIRvbgvoYQoUSvjkHlvAkn5Zz2ZEqFUqujab2GJBcnMoU1gRWadOQqAq8y4SqmTo7Yhe6Pqe/gF+zBm5iAq1wvhyKbTzBqxhCnLR9OqT2MWvL2Ck7suILNQ/TTM7TW97QFOrekdKswVD20tmObl9Ff26VOxUA6hnnehoqM+l05R+lZoQd8KJS/eXZouCREifmk8yXRHC+eypFHpJHGVd/C26v0YUakzI6zQYC/Kc7tiz1Xc596TZyM1agn92j8bH7XM9xiq3P2I7GqhzjP+pX2euf8giXY95phl1KVSMdWq+NOxXU0G9W/C2yPb8dmU7syb8Rq/LB5m1vWuJMYhFolo8+dPHOpnvBZmUa354XWnsObmAubuncrHPQTf7fXTkex4soKZw5cAEFy9POuivifRiJ78v5VVd3Xff5UFhafNHfM/zOO5Xe4+SPkWqbjkusSlgSJfSd/2M5HLZcie1qD8c9Ux1iw/zJxlQ6lcPYChvb7D08uZhLhUVu+YyCfjV3Pt8kPm/jSMSlVKHo2iSJmM1H0uAGJ5BxO99TNhdHv69DQ/JM/WZGfnMXj0zwaPD+rfhJFDWhkVJTOXlNwc3O3lvFY1nNeqmpdpm51TGC3yKDKOvKc/PmsjvwOgaoOKANg72KNUKAmqLtw5OTjLSz7h55jY7OtsffgJnrJg+gUv4Pd7Y0nOe8B3NzryTrW/UKkVLLnVG6VaeL8mVNvLuSfrOZW4ir5B80jMjeJa6h6aeg9j1+PpjAxbT2T6Uf56/DX9gxfiZR/Cklu9UahzNWOKnt816HOJWYb9THQIjYLu8SDla+LT1yCT+FHZdwVyqXax6My8CO4mTiBPGYe3c1+CPXR9v2eiQ6gXeB61Ws3N+IHkKh7i6diFUK85AMSm/0Rixp9k5wvCXwkZf2jObVDhJuIim4YiJE/n9Bt2Eh+q+P6iM6fSYHifRWw5IkQOdGo4DQBvP1e2Hv2Evu1msuHgh8Q8fMKvWwo1yr9e9CYA3ZpMZ+fJz0o8B6n7PFQ5+8lPnYzU9QskDoa1RAwR+1Sr51nRpe8Cve1dO4bz4XvWFQ4xhFJl3E8vk0nJKyZBEFMkn+KD5aM4f/AKwdXKc/7QVboN15ZxkEglLBi/ggUHPyNXj97Mv4lyDtUZXXkzMdlXATUDQn5k4Y0OTKi2FwCJSEafoDn4yqsgFglFTZyl3oyrsoOlt3vT0ncMfYPmseruMFr5jgEgKfce46vuZtHNzoyv+hd9gubwx/13NGPamv2xV+lQrqbpjk+pNGee6U5mcmey+e4kazH7Z/Dio8YolElU8f0FmbQcEY/bolIXFm6+9+QTrsX2wNdlCJV9lpGafZgz0SF6x0rO2selx43xdR5Eebf3SM05rDnm5diLqr5rhMdOPalb/ozmT1wsEuTioyZP57QCe6m/zpxKi7gi6ogOjvbk5SlwdpGTl6dgrQFJgc/e/Q0wXoLPUsTyDkidhqFMn40ifRa5cXUN9nXU4/fVF+JXVsTEpqIvUKdT+5o2N+oAX58+TN8dv9Nn+1qtvwL0ldS7G1Wo+d5+QHOadK2Hf0VfjVFv008QGNsUI7hiVl+fh5e/B7vTfrX5/J8n/rz/LhkKQb9JZYYMgVKdh73EGaU6jxFhhQs1B4kr4qcVwFzthILWjtKyqbgUnZlI47++4HqqsCG94MZfNPlrGg+znpCnUjD/uiA82OSvaWUyH1tjtmF3lTch1GsuLvaNqeorCCk9TCncGEjIWEud8ifxcxmKq7wFtQMOA2IiE8fqjHXvyYc0rBCJr8tgyrmOpG75M5pjdhJv7CRCmTmxSI6dxEfzZ+mcSsKuy9P56+oMvQlMb45qQ+SNGJIS0snNyUcmkzLz003k5eSzb4d+/e1KVctxy4SYmCXkxlZHmbEEifO7yHyPI3WZgr3vMYP99aX4X78ZY7P5WMrs7/Urdn7yvvlKlZYwt3UXNnQfwMYeA7X+CmjcUPdO70ULES0rUvJjcJC4sy+m8LsmFknJVxUuqtxlgdxMO0iGIgGJSMaex9+gUOVyLVX/CvxYwk9kKZ5oIlrcZYE6Y9qSfJWSU53/x9ATS8lXKannEcLJztMYdmIZMrEUV5kDP946wJGOttOq8ndxYWLzZjrtSkWknt4CJ+M+Z0vUyyTl6KqXGsNsH3ugu66kaL5SKH6cmSdkQMqK6ZX4urxJfPpKsvJv4GhXWKnGx9k2qffG5qSPnISXkfvs02lX5uxGYkFpuUEjWmse736qM77hoLDR2+3VhuTE1WfPWW1hrqHj2gOw5+w0s69jDPty13UbRYaVJfv0bMBWM4s+lAXnLtzXaQsJsn61pq+CUVEKfOyGaNeqOqt+119gw1oske21BZ2cBgOwJ1NbMmBo+GR+jZhjs+uMDBNUMAdX/FXT9k5V7R9qucSF6m4va56PriwI5YW7d9e09QteqHnc2m88jlJPhlVaozm/+Ji2JMBBKEPobe9CTHYKYS7CHUNFZ18AXg9uQtt9XzO2ivC9tcZ9sunqNT7as1fjBjw04i3sJBKdfhkJ3XHzv6F3jCZ+gjtbpVZwPmE29X0MJ2YVxewVu74VcwEKlf7izdKn9TgVyuRi7bapMGRsTvrQZ9RfVCyNY9en6QKw58BVm86rJDSsF2L1uSvWHDV63JSPXZ8rBrC67ilgsBJTabEncxWVS/Ae/tvZH7eF88nCXe2iW3tJys1ALBIR5OTFhLOrSMrN4FGWYKs6HZjJ6S7/Y9K536y+3qs1a3Br0nu8XluQKK82f6HefhKZ4WxehSqbY7EfsufBAKq6DzL72jaJinG1b6q3PS3nCGKRHBf750M/OSe2NvJywt2FImMRYvs2KDIWI3HogTLrD0R2tVDlHkbiOIDchPbIPH5Cmb0BO7fpKLM2FDveEVBg73OQnPhmOJS7Tk58c+y9NqJWpRqfiE2wPI69UqgPd6K0a4V+PXcnndqbv4lUmihKkIhkqmLR16cPcz89BXUx90pRd4w+pk7fzM4/37VqTpu2myenXJze5UazOXYp2Rk59PIbxZ7MVXw34RfeWTiUW+eimDN6OXH3ExjyeR/6TLBsP6KT02DNiv5u4hO6/LjSaP8pHVoxvGnpR05VdmlV6tfYGbOOLv5CHdw97YQ77O1thD2xja2E/+MdbYXn/3QS7sTnNTDfmBpiRscO/BkRgVKtpt/adfw58HWt404eS0iNqYzc9UNE4sIKYjKHV0nKvULzcuZJIxTFJjFEIpEdIOZmfOGboFJnk5F7nqq+axCJLP/9sJP4kpJdeipt+ekLENvVQuaxGIC81E/ITexOfvpslFmrARDL6mPn9jWKrN/1Hrdz+RiR2AMRdoASO7dvBPlckfWKgOZiTRz74nlv6G3/eq7xYtJlxaWIB1adZ44LxZSPHWDMW210zsvIzCUz03IxqUePk013MkCXYa1RKVVsWbyXwKdCbQfWHUckEuEX7M3yc9+wLfEn/pi7w6zxCgTqihr1/yhbPm0nbLiff/yYxadOaR3Ly96E3HUqIEWtStP8AcjEzmyOao9SncedVMMFiYpjszj2RkF3ORsdphUJ4+86Fmf7hoZPMkLNcru5+KiBZjyJ2IX6gZZtIBhDJBFSk9UqYXdfJA1C7nNIc1yR+atwXJ2OWBL7BM/DAAAgAElEQVSi5/hqEBUtgSdBrbgD9q1Abb2qnPmIkTgKP6TmxrHL7e0ICfLiXrS2GuOeA1cJquClU0bOGtLSc3B1MR3H3aBuMOcuavvZo+5bXinr/oMkfl5t3A1jiIMP7tKuQkXN8wF9X2LJir91+nXtt5DDu6ZYNPbAEcutmhNA17fa8s/Ws2z8bjcDpgjFs3Oe/rg4OMv5tPccbpy5Q3pyJkqFEolU129bFC9/9zI16mrUrH+wjMspZ5CIJLzk2ZruAYbvjE4mHeRIwl/E5T7CWepCPfdm9Co/2Og1DsZv45/EvaTmP8HNzpPabi/R1f81ZOLCyLlcVTZHE/bwKPs+l1IE8a7dMevZHaNdKWt+3XWUNi+HVeJ/B4SF6oaIq4xrXFjK0N5pqMHzDj4aTZ+KR1Cq86jgYn6+ilkr9kZB93RS9xsF3aOS9yKttoZBkTQKuqf5C3TXzRw11F4cO4mX1ljFjbq5cyogJ64hanU6OfFNUeYeRO5zgOzYmiizhdWq3OcQOQntyIlrBGpBECk3sSd5Sa8jtm+h93hxVIrr5MQ1Qix7dkk/pli5ZLje9uW/HqHPm4utigQ5eOQGnV6dT+uus4hPMC82ftwI/eX8PtZTk9UQW3deMJrgZIrxB7frtBlK2Or0qv4U/uKo1GraditZZFZgZX+Obz9HbnYe3Ya34/CGU9RpJVRyesV7BF9tnsyGhz9SvXGYWePdOhfFuDlv8vUQ/YW6bcnkS28w6eIATiYdIluZSboilQPx25h48XXS83X34iZefJ0/HiwjJicaESLS8lM4nLCLiRdfZ94tXZVJNWomXnyd7Y/XkpyXiBgJyXmJHE7YxYeXh2j1jcl+wOXU0yTlxRHgIBRXcbPzoIJjRa2/ssCpiHzFw1RDrlpFsT+o6TGS6PQ9xGefZed983NVntvM05IyattWIp884V5KMnffm4Tc76xOH4dy2huHch9t14+991aDx+V+hSGaBX57mdsscLNsnpmZuWRk6V/hP45Nwdfb1eYl1nZteI+uepKDEpMyNEYpuIIXvbrVo3atQDw9nFAqVaSkZhH94AkXrzzg6PFbehURzSWsoq/e9uOn79BvyI+sXjrCoJ75ngNX9bqPnBxlZGbpTw46GfOAJv4VOBBdKF+cp9TNKZgwuj0bt+q6tnJy8mnddRYd2tSgfZvqNHupktbxW5FxzFywm8i72lFZQRW8iLZCr/7Mnkv0HPMycid79q45Srfhupr7108ZDpMriqe/Oz3HvszRLac5seM8TbuXjvTu/66OQ6lWEOwYxntVtOsnTLz4Op9fHaOzOh4c8i713LX36GJyHjDrxgc8yLqrc4110UsBmFvnN03yUwGxOdp5GSFOVZhU5WsAspWZfBIxnGbeL9PR71XrXmAJOHin8LX4uWirhuZmLCMnYzFqVRJSWQMUeedwdJ+HzLEf1TyExEalOo/eoQfMvt6/1rAve6UnABUXWJcxJjezjqgxzl24z6Spf5juaIABby0z3QksdhM4OcrYsnY8vQbqv7sBwcWxcMl+i8a1lB/mDuLt93WjDuIT0s1eIRfwf+yddXRTWdeHnyR1o0ZL0UIpXoo7FHcproMO7u4wMzDADO5ug7t7cfcCLVakQI0Kpe7J98elSdOkTVIB5v3mWYtFc+659560yb7n7LP3b18+NZE9B+5mWIquhkMhABoVVhjkXqXVy9tePD6BRm3Vhwd6XHmOx5Xnao+lZ+n8rgR9juSvZbqXaktJkco3Rh9ceMrcI8Km3twjE2ib91dc65Vh/HqFMNin14H8WlFYCWcU9rjo3HTc7Qex/fli8tiY6zwmTXxN+oJLnqr0L6qapFfTphG3wy4SnhiKlYEiAim9UQdwMCok/zlFloIkjQE3/OZq8YvzpbCJ8sM1n1HBbL+H3GLCacVnoFbhwkrH4qMWksfBh4ggF8xshcLdMV/6YmCiUO6UiHQTlvsuhv22hzcBH8LoOCD3d77/QzusLE24cmoSTdotJikHs2F1oVzpAnRoW4nDx7P3EL18aiJikYiuHaplaNhPv3+l0lYtn3pDoKcn5vSB0bTsrD48TRumT2hFJdciJCYmZ8mwH/2seKinNdBVm5bneMgmlf6FSjhwLuYfbgV9IDpJedWSNoY97XVzkrfRwsPuWcR9xnp2y7Dfm2hvqlq7KbVFJIVzJfgkvrE+fE0MJTZFEf+fLEtSMuwdCvbjeug5lr6ejlgk4ZciI6ig5uHwsxCblETNtetJdXBKRCLGpEtSEkuEB5mefkV5W3LCLY77qhZ8b+t4Wqv7/nQz9thEVZU/E4OcjTLpsHcPB7t2o+amDbQo7szkuvWotmEdv7hWYFJthXRpQFQUw0+dwCs4mGJWVoyqXpNWJUrk6Fh+JCIReBwfz6VrL/l9wfFsX2/0kMYZulgyO6eSaxFmzDmi8/2ciuZly2qFAmBmLquWRUsCMPTicdY2aktoXAwn36ka+1RMTQ25enoS7j1W6exyOrRjGLY2wnLbwOD7fcXcjqznsvugH1K2LSThMwB6Yn3EmWzdpaSTIEj7EChl4YqrZQ1M9cw5HZjxSndphb3cDD3PQb8tbPddznaWU8K8HEOdci5LND05pRVzrHcvHMyVV0smlkuQSUMxsd7A1wDByJva/ENbQ/X7UNrw0xn2in+pugdezVRfziw7bPd8TEhMDP888SQiIYGYxETW3b+nZNgbbNtC0jc/rE9YGIdfPP+fMuypNKxXCiMjfab9fkitfosmihSyYfaUtjgV1S1hLJW6NZ3p7F6FA0dV90Eywi6vOZtW9dX5XrNqCF8WW2NTVnreoW/ZzP3NuzcPon3P1Vprxc//raPcqH9vAmOjflgtTkOJEAnVo/Awte4VddwKE1x9xhJTJpdaRB59K/mxzAw7QG3bphQwdmTju7+ITYnhdZQX10POUjfvz11es3Re1e+IxECh8WRkPh6ZLBL9bBh10MGwtygxmW5DGhL4KQy7Alb0nyD4/1qWmoJ7nzq8evIJSxszZq7+Rd6/+7BGnN1/Fytbcxq207xh8zkqOotvQ3eefA7i3ZhxFFu2hIIWFvKfU3Fduxpna2tO9vwly/eoXLGIzv7vH0Wtak5cOSWMNSVFyokzT7j/yJeXPoFERMYhk8kwNzOiYH4rypUpQM1qTriWK6ThqtozYlBDRgxqyOfgSPYduc+DR74Efo5AJpNhZKiPXV5zXF0K0app+UxXBZp+3x1O7GZBnaa8/fpFKdQxI0yMDTh3WJhYXLz6gguXn+MfEM7n4EgkEjH5HSypV6sEPbpURz+DsMPv9RlYWqc1cx5cooKtEMrbxrH0d7kvgLOZkOT2LOK+1ob9wCfBpTTPJWuRTY6mJfjz27njPLtz2H/bT2vYzQwMeDJKtWB5eozMx6i0+UadpIhZC1JkieiJjdWcpYrWhl1PT0Kfcc2U2k7sus3MVb9Qs7HwR21RQti8OfbPTYbMaEu73rXpPaYpm//Wzi+00CNr8chZoU4hIfzJRF+fZsUVYWMpMhkSkYiohIRsGXVtmXD/MIuqqu7Sn/bzpmXB3M8IlcVfQJbyAbGpouiERCLGvXVF3FtXzOTMjJHGbEIkKYjISPcvmb2dBaMGN8rSfbXhdrfBJEuluBVUFv3a8n4FnQr2xkI/Y7mLRm6laeSmbCy/fIlmxpT9vH7yCWNjA+zsLXj0wBcTUwOWrPiFx498mTR2FxeuTmfk0G2sXNuXiK+xdGy7lDJlC1ChkiOnjj/m0ImxSKUymjecz/kr03j5IoC1qy6wfHWfDEajSqsipWhVpJTmjrmAmZ5QO+Fx+C0s9Cw1xqFnxsvIJzqf08KhC6cD9yGVSRGLVF1BRhITAELisyZ817i4k+ZOaRAhwsHcjCoFCtCqVMlM+8aGD8fEcpkisVGWTGzEFEwsF3HJfxANC2wgRZZIfEoYZmLtNoi1NuzbrkyhU+XZiMVi9t2dhUgs4vFNHxwKWfPprZCm3m+8MIt/9fQT7fvWlZ9bumJhAj9qripzwku9EE5uYKineOsGaYV5ZDJypLKDFqTIZHyOUy9e9eeTs9/FsP9/RE+s+sXvX3SUmp7aUbZcQYaPakr3TiuZ8dtIGtf7k2mz3Pn0MYyDe+/S4JtkwwtvfwC6tF+Ox7XpinsPrA8IFZumzGjHyqXnOHbkgVKffwPjS85n8aupXA05zauop3QpNBALfSuC4/25HXaJZxH3lcId8xkVJCjej0N+W+hYsD8A54MOcyZof0a3YKxnN1zyVKWRfTuKmBRHKpPyIPy63HWjzqgDctXIB+HXqWrtRgnzcsSlxOAf94HiZqrKp+lZ795O69+DriTFn1fOVhfpkRR3HCwXEZOkUISNTHyPmX4OG3YbOwsOPvydxPgkOlf9jYMPf6dCTSdioxPoMri+Ut8S5Qry9N5bnMsJGiZvXwRiYvrjCuFmFe/gYMraabcZWOrwH7QqVI5Tn7xwMrfFPzYCz3ZTAah8fAE9napx1v85bQu5MKK0G/dCfHkXHUZIfDQHfB8D0MmxIiLggO9jwhJiVNoXenlwKfAVdeyc2PH2Hi87CHoWqe117YtzNciHbkUr08+5Jtvf3OVi4Cvym+TBP+YrSdIU9tbvD8hICW0KsiTEFrPk70EauwOxUTtkKR9J+dILkV5RJDbCpqY0chbSuCOIJEWQ2B4HRKRETEKSZyHJQSXRs7sLYkuSP1dEbDYckCCNXoM0ZiViswmITYXEKFnSE6QRk5Gl+CE2GytvT713yteRyJIeI7E5gkgv81nSmjd/4RP1gjb5u9DQXoggGPmoJ+0L9uSE/36q2dShe2FhJSJDxq3Qyxzx30VCipBgtrLSLvk5AH+7bsT428zucvBZqljXYtmrP0iRJTOh1BzM9NSHCFpaCudYWSnUNRs0Kss/W69x984blqz8hQD/cEy/fQcyK87dsHFZ5v1xlHWb1SeSZUTzE1vwj1FODnvWTXVZn5sUNC7KrDKrOOK/nWcR91nhM1vpeH5j5TC/yaUWMelpb26EnudGqELO90+XzSx7PYOQBNXZtbVBXp5F3OdZxH2ldolIj7/Kb8t0fGOc57DMZyZr3ypi7E31zJlbLutZwjmBWE/VJSj+pv/UxvEkpz90JCrpE9Xtf9P6mloZ9pQUKeO6rKZ6g9JcPf1UPhtv+0tt2pabzmf/cPT1JZzae5dTL+bj3rcOLUpMJjw4imtnnuJQ2Jpq9TP39931zZpOSG4xvZ4bbXbvZF7jJrgVccQ7JJjL79/zZ6OM03oXV+2AT0QwxxsPYe3L63yOi8Le2JyHbQV54XFlG1L68B+MKO1GtbyOuFoX5NQnLzo7Krs8OjtWZOajEyrtm1/fkhtzt3zOzH1ylhmuzZXap5VvRqnDf9DPWfBz5je2YEFlYbZR6rCgApkcVAo9e08QGZMcXFNuXAGSQ2ohsdokHEexkyo2n4rY4g+QRZMcUg+9vDeQxZ+CPAsR6ZdFGvsPYrORiE2EwswpUX+gZ3MMsdkwUkIaITNqikhSCJGkIBJbQY41Obiayr317DMX80plgucA/nbdgFgkYbvvGp5GPKR8HiFrtKR5ORpWbMnaN3/zPsaHoqbOjPfsRz6jAixy3czb6Fc8/arYqF1ZaRdr3qgKLc16NoqlFbcBgvFPfRBog0gEd++8oaxLQcq7FmbD2ou0aiP8PUViEVKpTKmmairrVnswfnIrhgzYrNOM/Wyb/lQ/uJq7nYZrfU5uYGVgqzaOfcTsfVzxC2NiOnf63+WFUM6eY7aya5kQ4dR20DrEolIcXa+ayzCzzMosj62IqfN3kQ/QFTPbQ3wNKISBSU+QxZMYd4g8+RSZ9i2LaJ+NnYpWhl0iEbP8kFDJu8cIZcN23EtRiGLYbHf5z2deC1+UX6doVzhh4cWc868vvnWT1fcUQjupm6KeQ4djYajdymFApcrUdyzKyNOnmHXpIs42NsxtqFmrwVhPWFIZSvRIlqUQmRRPtRN/M9O1BU4WtmSndENaF0JteyfmPT2n0g4oRUbUtVeXdp4i17mRmE9CJlVkRorNxiMySNWMUVxHGncQWcxWZNIgkH2Lk/72v8i4I9LopYj0SiAy6Y4s/gJiszHwTalObDEbWfRaRHnmgciUlPAByJKegPSrMBYk8ntrS4I0ntGPFX7chJQ4uWEv8G1mWM2mLr4xbylq6oxUJsXw28aTodiQRKlmPZ+2BTKOx9aG1y8D+W1uJwCOH3nI+q1CQtHegyNpWn+e3Md+4tgjjpwcR8TXWDzOe3Hw2Bhq1HKme8eV7Pn2vdOGH23UAa5dfUk9N1U/f2WXIqz6vWuG56UadYDjG4bgPnh9rozvZ0QkMsMy/yeSE+8DUkys1JeM1IWfJtzxWcDnHLvW+Fq1GV+rdobHD3dTFPrwGj6SpUvPYtSxKu/GKIvpO1lbc7qXYgO1YaMFXLqoWtwjM357fIq99ftTwbqgSgyvRCRGpoOpT06jKX7j8xtq5i2q0g7pKv9o2i9Ip7yZVjZUfr3olSCLQvJNUiE5SHhYiAyqIos/gdikK9LIP5DGn0Bi1PzbO0o7BsXDNPlzOfTyCanwKWGKzLqM7p0ZusygF1fYwuZ3yxnzuDdlLFwZ5KT5IWKhr1kfwtrajJ69hRDZNRsFP3HqTDvtjPvkeUVkjI2tuVofex5LEw4eE9wnVlamOhl1gOdfgml7erv88+DbW7MmU1Z48SKAqVP2U8TRhuXLfyE5OQX3dsvo27cudnbqC9Cn/RievuJNzYpFGT/vMFv+EhRHm/VZxbntmqNGcoqP0Zd5Hr6bkPhnVMs7npKWnTWflMvoGeScvPlPY9h/JGPH5l6I1K8lavPrzZ38WqI2a14qZ0XqicU8CP1I1ytbqGVXlNFlFLGrfYpXV2kfV7YRLS6spq59cf55c1fufkltr2dfnKtBbxhZur6GUYlBGgliC6RRSxCZaNCclkUh0hdibaXRaxRXMe6GNO4IEqM2iE06I41TaOtIo5YgNnQDcV6kkbMQW6lmS8qSHmsYZ8YYSYwJTQjG1lDYA0mSJqEvzjiR7VTAQZ5FPGJFpZ3yjbT/NTqf28WbXkKFnadhQbl2n9Kl83P02Bi8vf2QyaBPnw2cPDWe4OBIXr7QXP7x9ftgNu69SXBY5lWvcpPCZg0obNaAnT6qpep0ISohQedVuDZeg68BhbDMn3X39E9h2Pc+evpD7hsaGsXbt8Fc8PCmVi1nGjYQ9gHWb7jMrVs+VK1ajMOHHyjN0rt0XU2D+qU5cPAeXbtWZ/AgweimGtl99QWfcf9vPu4CJnCr1QSltrSknpeeqeWbqbQNKlmbQSWFlci0NMfTtqc9r0/x6krnp95LL98rUkIaAylIbI4jjcs4CgFAbD6NlLD2yCJnILHeA9GCa0tk3AZZhDDzFZn0gTiFMJde3itIYzYijVmF2Gwiom9p0xKrzSR/dkFkUANxHt0LCKSy0HUT231X8zj8LoVNnBjpPDXT/s8jn2Bn5IBH0AnipfGcDzrGykq7iE6OYurTIQBMeiK4SnRZCfxMVMorbLj1u3iQMtZ2lLfJp+GMrDF61A5mzhLcrlKplM9BglqhnZ2FRsOemJRM7crFGNOvAbU7L86V8eUmH75+pfHmrdmqh5uVMnu68lMY9iWXMi7CnJvY2ppja2uOn59yKOa+fXflxrx6NSdWrrrAyBFC/cb9+wQ/po2NKevWX5Yb9n8XIiR5FUpxqTHsYpOM4/ZTo2MAuSsFxPKfRXrO6Nk/Vr6e2fBvETJp7mzohp69qq5+ZvfOiD6Ow+njqHz9tEa5spXiQZpH34qhxRXukLthwurJTM9crSFvYKe8ivs3GPsdjYXqQFsbdcrV+wQEhGNpacL4cbvZsnUgffrWxccniMOHHlCzZuZSwgb6eizedBETYwP09YW9lda/riU6NgH3wes5un4wrQas4WtkHE16r+Tvye5ULKuaCPc64jBeX7YTnxJOftOa1HdQTBL2vm1MIbN61LZXTJpeft3P/ZCl/OKc9XKFdz5+ouf+A1k+Pz1fAwoh0XfJseul5acw7BFx6vXNfxRpNUeqVCnK6jWqKoeurkW+55D+I5s8j3zCghdTqW3bkKcRD/8n3TFVD6xiZpWG6IkFg9mySOaJMVnlwEEh5n/7P4MB+OUXYbU4eUrrDM/p21FRxGXviv5Kx05uGqr0+tTmYRrH8Cn6Gk0KrsJIYsXpT/3Z4VNTbrS7OXmww6cG5a37Y65fkGRpHPdDltDL+ZYW70496+7eY+H1GyrthfLkQV8i5t0XoWKWrYkJMiAsVllfqEGxYqx3b4skXaCDeV71yZupmjFZ5Ycb9rik5B89BBWSkxWbkfcfvKNSRVUj7uXtp9L2Hz8vaWfcdfM2+YEjyT1Wu7kz5dYZrI2EuPrcMuw/A40KKCJH2hXZzw4f5epfHYoe5fB7d2rnm83NoN9pWnAtomxUAk1r1CUiEa/HK+tXpYqE3R02RKn91MtXjDp5isvv3nHd9wP1iykynjPzoZvbXc7wmDb8UMPuHRjMggtXf+QQ1DLw1/r06buBalWLcSidj71T55W0bOnKzp236NY1+6XkMuNFUAjzL1zVKca/cqEC9KlekWalnXNxZJnzNCCIbXce8fJzCG9DNWccp8XEQJ8uFV3oU70S+fPkvGZ4Wtqu38HxwTkvG/Ex/Cu7Hzxl/6NnxCSqL/yRGY42VrQsU4IulVxwsND+d7D48TUuuQ/U3PF/gMDY+3h92cbXxLckSlU1pkz18lHGqjs3g34nv0kN7I2zJo+hjhfjtE/8alWqJFUKFqDWug0MOHyEZ6NHYqKvWa1WoqdddayM+C6GPUUq5ZT3K44+fcHNdx80n5COknN0K7qgDZkpRnbvXoPu3QWjPXy4InY9rYHv30+9tnxmY9VWpXLMoVOcef5aq77pefjJn4ef/OWvO1Yoy5zWTZDkokxCfHIyM05cyBFJiNjEJLbdfcS2u4JGe14zUzpXLMfo+tmLXkhJFxKaIpPlqAvw3gc/hu8/TmR89uvd+oaFs+b6XdZcV+RitC5XktktGmJhlHE92c7FXTj4VrF/0ckpd/y3OYnLvBVqK1mlx8zQgIeThP2UM58GEJXkR8eiJ+QFKNLP2AGeh+8hj4EjAbF3EMJvc+Y7oOt3yd7MDFeHfDwJDKLu+o3cHjpYWcZEDT9VVIxUJuPcCx+OPn3OFZ/3OXnpXOXK1ZdMnqRdIlVusvXOQxZcUF8oIqsc8vSmrpMjLcrkvNzwggvX2HpHtYxcThISHSM3ct0ru/JbS9UScdogEYv5HBVNYIQQYqcnFnN1TPZnt8su32LtjbuaO2aTk16vOOkl6Md3qeTCnFaqyXIlLfPyi8c+7nYazqG32mXw/kjKzVshl8XODBtTE26NGyx/HRrvTT2HP+VG/V2kqp9679vGlLTsTLW843kTcZydPrWz5WPPLqvatKHuho18jY9n4bUbTG/ghjQlGLHEDplUuzrBupCjhv1tyBfGHFKtRfmz0q37GqKj4xnQ342CBXVLjslpaixeR3hsXK5cOyeNekxiIi3Xbico8vtJLKey5+ET9jx8wrNpozTOeNRhb26mUm8yq9x494EBuw7nyLV0ZWidamrbu57bzfMe40hISaaV449RedSWsn8uV0msU0cBSwsujVTWzamSdzTXAqdjIF5AojQKV5tflY7fDf4LGSlUyyuE4hbP0xav8O1c8B9BkwJCvYergVOISQpChpQnXzbyPuo8pvr5qJtvTg69Q2Xyp3GpXXjzhukN3IiLnI2p1VoigsqiZ+iWydm688M3T38ke/do3n3PbTbdfvBd5Yqzw5fYOGouXvejh4HLvBU8mjxcqfK7tlRfuJavcfG8mjWWUQdOsqJzxpEc6vAJCWPc4dO8Dg7V+d45Rf486rM7R7vW5vj751gYGDHmxgk8u47+ziPTjjJ/LldxjanD2c6Gk4NV5X9LW3antGV3pbby1grjXt1uMtXtlLNu3R2V9VbcHBboMmStufXxo0pN0/QERAmrRlOrtfI2M5udSn2yGxWT9W3i/8gRctuoF7bKWF9cV6xNtBP5/x4M3Ze1Un79a1aW//zwo38mPdXTcdPuH2rUbUxNMjw2uGx1lj65Sb+LB5hVVbOu0Y/gzPPXWhn1sg727Ozd5TuMKGe5+Oadxj7ptZ1MrFbn+Dj+M+zfkfQGoVQubAqnJ6s+6Yz4kdE2abnr+4lLrzV/idJTt7ij/GczI+2lpGUIG+MJyT82PHdXn4yNXXBcNJfdB/K+92Q6FPv5tPxPeL3UylVbw7EQh3/tgaVxxhvFPxL/SFWfePMSwvdi2yP1hdn/uKQIX3SyVnb7Ghi3VemfnY1T+M+wf1c8/RT60rWXrNdJY6KItSXVihSkSani1HAsRF4zU80nAbWLZT2R6l6IagTTik66uS4AXAvkY1Ljeuzq04WHk4bzauZYXs0cy9OpI9nbrysDalZWKnyiLUP3HdPcKR3PA4MRi0Q0XbWVc8P7an1eTjyErUyMcS2QD7fiRXErXpQajoUoYKnerZIRRW2sMjxW5/CPd5NlxNGnz5lw5IzGfs1KO7P9l5zLnL1+2we3ln9n+zq9Kyrqkg4/fkLl+MIWikxlp0VLeByo+K4vvn6T7Y8UukhDqmUk9pWc7l/W0fnbdOnKCxpmoK3ubGeDy3MDRGIRB3ap+q+vXn+FW13lpIlFF2+w8dZ9lb5pyY1i1j8CT/9AulRyYf75q4TGxGbYTywSMb1ZfXpVrZBhn/TsvO/JtTe+XH2jiEbKLCjrnN9Lxt09wr6GfSln5cBfTz3Y8eYBp5sOprCZFQGxEfS7thvvjqoaLM52NvgEh6m5KtiamjCliRttXDRv3hnq6VGxYH4qFszPpMZC+OjyK7eUwvw0sfLqbUa6aVdjE6BTxXJ0qlgOgHu+flRz1FyRpvRc3eKKx3UAACAASURBVGVUJWIxU5rUo3c13eKnP0dFs/b6PfY8VC0P17OKa6bnnmvTn1tBH3D9VvPUVE/3PYjc4JCnN9NOnNfYr2slF/5QE+3zMzCrUUOu+37gfXg4z4JUlWhN9PUxNzQkKkEId+20a4/a64ytXUulVF5C9Abio9cgk4ahZ1CZ5MSHmFguwcAk64qTOb55enDPcDr3XKO5Yxapd3Ei1xotzJFrdb+1gD21dJPhzQ6P/QKJiIuXx2inR08s5vb4IVjo4CJIpVfVCvIHQXRCIv12HqJeGrdDenwiQ/DuOJUyh+bzpP0kqtgWZnL5xtQ6sYxJ5RvhXsSFVbXUz5yODuxF2T+Xy18b6+uzsUd7qhYuoPO40zO6fi1G16+l9Qbbqmt3dDLsaRmy9xiPpmSuYd5x026dBJ/yWZhxceQAteX3tMHe3IzfWjaUu9BOer1i2onzJCQnM6tF5m61uORkLA2M+RD5FYAy1tpV/8pN9j96xsxTqpIc6RlapzpjGqjmKnwJj2HKb4ewtjLF2EgfezsLHjz+wKaVfZg97xgymQyHfJbsPXRPXjR847ZrXL/tQwWXQni/VBYla9BqIZ3cK/PiVSCWlibMndFeq/chAo737sV1X1+aOat3R3qOHC7PQFVHAQsLRtRUjbePj1pIHgcfIoJcMLM9CkDMl77f17AvW3WeKpUc6dJrDWePj6fvwM0sXtCV0RN2s3PrIJX+XX9Zy9zZHdi19zYN3L5f1fSfkXehX6i2aK3aYyPdajKinuKPvuzgNfZdesKB33+hYF5LHr7yY+TyIzgVsGHH9B6cu/+KZlVL4jZqDVdXKK+OzAwNODCge/pbKJHfRNAatzMywz82gpJ5BCPgZGHL64jgTM/VE4sxNzSkUUkn/mqnqkKZEzyfPppSc5Zq5a5Klko1GtLjz16otGnyl3sHfsYrUPs6AaeH9sHJNmfDZluXK0nrctpJA/wMhjwtux884fczlzT2m96sfqYrm3JlCjBqcCM691nL7Clt5a6V36cp6pD6vP2MVCZDLBKxc/8duZFft+UKb94Jn+cjJx/xxwx36tYUDLOuLhoTff0MjXoqbyeMY8zJ05x4qUjWk4hErHFvS2Mn9aUexd+UT/X0Fb+D5ITsxdzrbNiHDmyAhYUxenoS/Py+sGBuJ2xszLC3V/UVpqRImTC6Oc7F7flthjtXr7/K1mBT6XhjLg3tXdn38Zp89n7Y7yamekZcC/bCysCMYc6tMZEYsvbNKW6GeFPNpiQHP91QO9tf7XOSZFkyo0sIUqT1L02iU6G6vIj8iJW+GXPL9+HAp+vcCPHG3siKwLgvJMtSWFsl+4UB8hgbsbF7e1wLKCRWk5JTqOhcgDGd6tF0wgbOLxrE+uO3ubVGt8ILmbHo2SXq5iuGSCTC0cya5ufWscOtF59ivrK5buYPBYAHk3I/VPTptFG4zFuhsd/a63c1ztrbuqhOKu68z3yDqsOm3RrvnUpad+H4HaeY0q4+eS202wfJjHqz13Ht9yFqj13yesvH0K/0rV9Z7fEfyT/3HvPnuSsa+y1yb6HRbWdp8a2mbB7l32ffoVtwyGdJy6YuRMckIJPKQKLsgCxbSrGKfPj4Aw75LPn4SXAjDuqrPns8uyxr3ZJlrVtq3d/EcgkyaSgm1hvkYY6mNv9kaww6rxcNDRU6Bw4Olrx5KzwNfX1VQ8AkErH8lxgeHpPVMapwqM4Mhju3YZhza/Z9VGRq3g97zZ/l+zChVEdMJII7Y8+HK+ysOYlRJdqxqMKvLH99VN7fWGLI5nfnSJFJ5Ub9iN8t5rj0ZoRzG1ZXHs61EEUGn72RJdPKdGVl5aF4R3zgbKCibmZWEItE3JswVMmoAwR+iaJ4AVsAijoIM8DyxfOrvUZCFkXUZlVsRuSHaHaW7U6ban+wu3JP8hqZUWhFAl1qz8fneQANHJzZsvwCbar9wfZVgszvDY/ntKn2Bz7PhSXupqXnaFv9DwI/CZow1y9406baH1w7L/zeZFIZPZsspHdz3bW3DSQSStrbauy395GqDHBGpEil7HnwhJvvPjCvbdMM+x18rH3mZvo9oMW/tMoRow5kaNR/ZrbdfaSVUd/Yvb1WezEZ8f5DKPNnd6BuTWcCg76q7ePzTrHiquRaBAtzI3p2qSH/9zMgMaiASGyLSGSEZf5PWOb/hL5h9uTAsxUVI5GIefkqgGZtFrNsUQ8A2ndZSWhoFF2++dnfvgumRbslXL6WfR2R9FSwdOJF5Ef562o2qktWPZEiQ7GqTQnuf/GRvw5LjGT7ew8GF28hb3vwxYenX9+zy/cyu3wvM9hJ8eRNf/2o5Oxlij6foV5MqLCdJaNXHCUsIoaAEKGIwY5zDwj5Gs0Vz7cA7L7wiICwSKTS7FRRhZDPEZy4NwtrW3MGtFvOjrPjOXp7BtOGbgfg+gUvTtybRZ8RjQD48DaYE/dmMbbPRpKTUihXsQjH785iXD+hQtKLJ584cW8Wf087hOe9d5w+9IAVu4fwz1nt65mmZWfvLipSp+kJjdZ+0vD76Ut0r+JK+QL5cN+wM8N+009e0Op6t8crG16XCUtxmbCUqDiFZsyO64/4Eh1L67+20XzeFsJjlD836y7codq0VTSbtxm/MOHvfcbzlfxaaZHJkF9HXTHsnEbXkMOSc5Yy/7xmYb+9/bplugekDWKxiO17bjF59kEciygmAF3aV6XHrxtZvs6DJ14KFdYObSuxfc8tFq44x7I1HjRonTN7ddklJfmN5k46orMrJjUi5uRhwSj92s+NX/sp0mGP7Fd2F0wa35JJ47VfluiCV8R7SltknuWVLFNoUdwLe01lK4VqmonEkGONZittyFayKk5MSjw9HVWfmDmp4T2sbvVMr3bwjz4AnFggpFPfXSdkEdavIKTEb58muEvurc9admHzgqV5+yqIkuUUUSEBH7+QmCCsAHadF0qsbT05liWzj1CgsA1dB9TDLr+Q8GRta87nwK84OtsDULhoXgBKlhOWvoZG+sTGJNCqc1V8ngfQp8USTj6YrfM4LYwMGV63OiuuZr1AQlraVxDiu80NDTP0ywdGaleyrWGJYipJW88WjWXIRlWpgSZzN/FwgaBj7jJhKc8WCbP8ybvOUDK/LffmKbv1WlQoSYsKJVUMe4XJy7j75wiM9PVo8PsG+rjlrhvG3kJ7CQZtxfpODumNc14brfpaW5nSu7vgZtuwQshCTfWfXz45Ue05wwc2YPhA9TNej2OKCcaYYT9HBE50SGvyOOTsxPdfGcfufv0PNr07x4rXx+laOHM/2WCnlvS6/TcrXh9noucmxpZU3QU/Ue83ml2ZAUDHQrXZ/t6DhS8PsvTVEepfyp2CwNlVK8wNJszpwKM7b0lMSMLjhBB3++yhL/1HN+XyGaF84dblFwgPjUYsFlGgsA2zRuwkPDSaIP9wtde8c+Ul9vktadUl64V6O1Yol+Vz0zNkz1GqL1xL9YVr+RQeQfWFqpvZvf85qNW11nZtp7nTN8a0qqO2/fTjl/RvoP3vRiqVYaQvzMfGZnDNnCSflto62hr1y6MGaG3U/78gMaiU49f812nFpN38/LWYIiKjQ8Haavv3dGwgn32PKqGc4ZUa6phH35Rz9efK2y82mC//OfVB0LlQ3QzHoStNSmVPazmncCqp7Ntv1FoRJ92yk2BsXCo7ArDuoDCjHDq5JVa2Zmw7Jcw4NxwRVmjbTwt1HN2aC1Kxh25MU7r20ElZX7Xl02HWqAk356L4hikeQvvVRA99DFfvr01Lcx2F1WzNc8bnnha9LAih6YqDFpr42hr12+OH/FSyFLpwzdeXYceOs71TJyoXUL/flRG+4eFseiCooE6r76aix25qtY6IQGeMLCYjEiuiqgyMO2R5vP86w/6/wJIOueOayoj2v67jyKZ/3yZcbtC9iisVCzpkeDxeS8mAJe1baO6kBY3KFWfjxXsMbCQoNialpKCficEWi0RExSVgbmzIyrM36Voz86Sl7KJJaE1bo55V0bZURj0eS0SSkMq/vdpmtX0GPRjGhiq5k0Mz+cw54pKS6bJnL2YGBjwZpX1EnKOVFXueCCvet2Ff2NNNWRYiMe4wRhbTAXJMwvc/w/4DyIrkbFYJDI7I0evVbfLzaZDowp4HTwiIUHx5WpVV3hDfce9x+lPUknZDt9jueUhEIu61HUO92UJaf62ZgoFJ9aVnxLK+bVh2+gZVpqzELo8Zu0d3x9JEouRbd5mwlKFNazCsaU08/x5D67+2kiKVcWBsTw7dVY7eKbZ7ntLrdz2UV045ibZGPasyy2lZUVG4V597AzLsI8rFYjLBMYoN+iWtsj4xu+fnx9X373ErqiiRZ2jaNztDU8t/hv07k776yshZ+3js9Ykbhyfw2OsTI2ft48bhCQDU6bCIG4cnsHbHNXYfvYezox2v3wczZ2JbGtQUXAEjZu7j6Qs/ShS1Jzgski9fY+Xnz1h4nIioOCKj45mxUFBD/G1ca/Qk/8qtlRzh4qu3BEVGI/72d0hv2A95emu8RuVC6jNsrUyN1RryX+oq+1DT9xnTsg5jWtbJtE8qIhGcmtJP/jp9DHuqIR976zjHfHOv2Ia2Rv3FjDHy33Vus75yzqskqqORUzGdz2ns5ITHWyGi7ejzF0qGXReun/eiblPNe07/7w17memKD+jYpnUY6Ka6kaVNH21pkk4dsV1TVx57CYkyu4/ep3/XWnwOjcTMxIjKLoWRymTsOnJPbqxBYfABPL0/KR1Ly9yJbXn6wp/Zi08wd6Kqgtz/F2JitmJqKhjDOxOGsOzyLZztbHAvX0al7/sw9ZvAaUkr/fv/EW21c76XxtPN0Fu8ivLhasg1JTdNaEIoC18toYxFae5/eYhYJCIiKVKpT597A7A2sCI+JYFEaSKbq67PlTHWLeooN+z3/XSXi9aV//eG/fmfY5HJoOyMjGcg2vTRlvTJSI1ql2LROiFm+vajd0wd0ZzdR++Rz84C92auXLgmpMLX6bBI7fWG9XajTodFNK9flpYNy1KpXObhnz8DoTGxfI6M5nNUNGExsUTExxMRF09kXAJf4+OJjIv/1pZARFx8tqVyI75Olxv2hR43mNbMjRSplH47D7G1V0edrmUgkdCghOqMTSz6/7EKqrZorVbaOd9TuK+2bS1q29biaohqWcmg+M+UtSjDqkrCw2j6s1lc+HyRJvaNmPBkMlNKTaS0hZAkNe/FXxz4dIjOhXT7TGhD2uiisFhlAcAutf/k99W/cOHoI4bPbMs/Kzyo17wcHscfM2Bccw5svkbVeiXZt/Eq9VpoV8c2Vwy7y7jMDeCzJT+XWqM2K8WcWk2WtFPOpBSJoE2T8nz0/4JYLMLa0oQjZz1xKpKXDX/15MxlbxwL2rBzRT+11+vhXpUe7lU5eu4JSzdewtcvlOuH1M/gvydnnr/miX8QR58+z7WSf2kJ8Neu4oy7q5CHIRGLefVZ94IZdZyKqC1mnMfAiEHXDuDhp0iAy2dizi13VRmI/W+fMOWuqi75o05jsTQQokY6nf+HR6F+cteKZ1gAHc5to7CZJVfaCnIOe994Mu3eaZbWaks7x5wLCc0I9407NRYAn9+2KR1cf659mI4FFdElTmbFCE8UVmUhCaEseKkc3RadHJ0rhv1Lmu+ARE1iWekKhSldoTAn995l36ar9BvbFKfS+dm85ByHtt2g+5AGTF/anevntXOv5YphX95PWPavOXebWiWLUNzBBj2xhJVnbrJ/XM/cuOW/htQY5LSUKm6PX2A4xR0FEaeExGQ++AtSDEUL2+AXqNk94N7MlXZNXanbUf3M/ntxyNObxZduEJaJLHHukIK+fnm1R5KSnsp/nnjkLBu6u/MiKEQu36sLthno4BtK9PDw86G4hS2FzS255P+GoNgonod/poyVvVLfVKMuEYloVMCZp18CCYqNotP5f/BoLRRtdrVx4FGoImvyQbDgrvsYrQjF9A4PAqC8jW7hd1khWSrlRVCIxn4/m1EHMJJknD3rbKYcemxrmDsx9uFxCsNuqp9xdJC6DWCZDuqiqeSKYW/oIqiY3Xzly7g2ivjvlpVK0nzuFs7O6J+t6/uHR9Jk0Wae/zmWlR632HHrMbbmpizq2pIy+RUKd97+n5m4/wyfI6NpX6kMM9rkbDWhrGBhqPoha1irFKNm76NHO8F3rycRExObCEApp3wkp0g5d/U5zdwEn/D6XdcZ3FP4vZ657E2LBsKXKSExSeXaxR3zEvIldwtPH/L0ZvqJ8zoVDskN8tqdVdse4K8wfKeG9sY3LJwGJYrSuJR6tb3MKG2fV227f0yE0owbhAiV1mc2K0Wm1Dq6ElCNVvGPiaDusdXsfvOYHsUr0qtEZba+UtQpWP/iDn9Vb8XkNDP9w++fUTtfUYqa534h9rQyzZkxdN9xVndp8902TLPLxFLjMBTrJpOdmJKic5TP5m9x7ADVC6nWAPB66MulE56MmNmWiPAY3r4I4OJxT/qNbYqxiQFvngdwdOctqtfXTlsnVx2DD976Kb2OiktQuwzJKm4LNvAh7Cuz2jWilENenO0VT9vfjl2k85rd9KxRgVW92nL99QelTdAfhbr3LxLBY69PNK4r/NHaNnXFKo+ituWNwxN49OwjDboupefILZQoqnh4PXnhR9OeK2jYdRn9xv/D3tXKFdtNjA0Y3seNBl2X0mFQzm4MLbx4nZJzljLtJzDq+QsEZHjMzl7hez33wgdHGyskYjHdtu5T6qeN79guk0zMtEYdYEYl1ZT1oNgomhZUTW4qYCrIKM+4J1QZSjXWqWMKi4+hfVHBv5r8Tac+LjmJXs45n7WYHl0KjVx6/TZLhUl+BMOKD2bEozHIvn16/eL8+ZqoOTmt9fYdOt8rrV+9X2XVzfdylR0Z9Zs7YomYHkMa4FQ6P4Mmt0TfQI8eQxpQvEx+JszrpFVEDOTy5mnDcsWV/O1ikQjPReqFr7LC0u6tqFRECD1r7ar8JNt/7ymXJw+Ua12cG9+PcjOWMWb3SZb10L28W26TNrJl3MBGjBvYSOn41BHNmTqiefrTmDKsGVOGZa6J3r1dVbq3y3okT3o+R0VTb9nGHLtebqOnp1huu+RXbF57ByhrrUfGJ6AJc0Ptk2zq5BNC2j5EhVPEXFHSrrEaww5Q1soe73DlMZ399JKWhYV9AT2xGLFIxK43j+hTogoAzQppp9WeFURA1YXabZamp/qitdydMDTHxpI2fj3159TolsyOZTpG62o4GOVj4pMpRCfH4GrpwqBiv6rtO7tRQ36/KGjLv/3yhVMvX6lUQsqIsssU0tODq1WlYv6ME+Ryilw17KNb1WZ0K/Wp/jlBqlFPj5e/8OVIL2DUvboru+548ioolJL5NMvBfk+2PnxEXlNTWmv5YflRbLr9gIUe13Pt+sb6+tiamWBjYoKNmQk2piYY6+uzPYOqU9oQ4J9fPqNvumor/WpUwivwM2u7KWu9xGshgaxLDkDqTDCrXgkbI1NOfHiOoUTxNe3qVIGtL+/RvFDW5W61RQZExme+WZoRX+Pi6b/rMFt6Zj0tPi2ZGeqMjtka2qoc61+0r9LrwiaFWeT6l8b7965YAYlIxCwPQb561MlTrLt3j0M9e2Tolrnx4QN9DhxSaptUr65Kv/03p2u8v67kqmGPSUjEVIcZTk7xNVb9h9HSxOjb8dyP0vhfZMmlm6y/eS9L5xaztaZThXI0LulEEWtLnc/XZNhTUj4jkdgj1ZCS7TV9VIbH8hhr9rVGxSdq7JPKtUCh/mxhM+UC1Oc+vaJTMdWN3vSz9V7OlVjldROZTCafmQ8oVY09bx5z5qNqRaifjZvvPrDzvqdOtXt/ZnpWcGXD/fv4fctcfh4cQumlir0HiUhESiarm1N9fsn1MaaSqz72xcdzb2aXGdWLqS9QfPPNB4z09aicwUz/RyMWiWiyZSv1N25W2kU/9uIFLstX0m2vwie88f4DvsbFU3/jZhpt3qrk43ZatIT19+5TaulyufhQKv0OHqZ0uvZ/HnsSmZBA7wMHcVm+Evedu1TG9jQgSCejXsDSgmF1q/Nq5lhezRzLmaF9GFCzMkFJkfKxvo0Iw3lnzmhiR0bMAiAosBTh4UOU/mmLcTpxJrX3yWQGGxSrLPc7//FFlT7OeWy56O+j0u4fI0g/TK+kcMH1cq5EikzKeb/XDCkjSNcWsxD2kS4HvKFNEdUEq+9BWQc77k/Uzs0y5+xl3oZ+yeURfT+uDvyVig7qXSmZGfWTfX6hVF71G++5Qa7O2MN0KICQk+hLJIhFIvpvOcSW/kJManxSMp4fA9k5qOtPm1I//vQZXowV9NWdFi3h7YRxPAoIYN/TZzwbPZLYpCRKLF7K6/FCHkC7nTu5OvBXIhMSKLF4KZd+7U+hPMImXNfyLgyuVpWGm7bQzLk4hfLkwWX5SjxHDkciFjPu1Bk83rylcXEhMqTm2vV4jxFms+o+np03q6+6nh4HC3OujFbvpwSobq9IoHLKY8PftXJGEM3KWrExbGOjXNIubVRMdnn1ORTU5IjUtHek1tGVOJpbUzyPjTye/WzLgUr9zrUaRPE98+X6Mo0LluBpWCCBsZHkMzFnQKnqivdhpAitdE0X0ngryJc9jXsptQXEROITGYp/TAQvvwqVzTa8uEN+Ewvym1pQyVb9hEcXLo7sT0FL4TO2s08Xem3fr/Gclmu34zVtVKbiZv8mDvbsTnhcHK227+BzdOYRZ11dXJjXrMl3GpmCXDXsjV2cmbrrLGUK2WFlqogYaF0594tae80dg8vM5UqRMAPdqlKpiOIL4u3/meE7jxMcKfxxlp6/waqLt8mXx5xz4/tp7NPatSQjG+ecrro6/9tvFy+xu6ugBmeir680KxhbW9i/sDA0RCqTsfbOPfmHyNJIcDv91qihvD02KYkSSxQRCzGJiXLDPqGuQqskvUt42P7jWo1/fMM6DKqd+SbtRb83NCqoXrZ435snlLPOx1X/d3QrUQFrQ90lXtVFx+RzyDnNlOdB6gt9j3etRyXbHgy9fohL/m+oZFuQfxp2x0RPdRXwpvtUdvo8xMPPh0v+byhsZsmuRj2oae+o1RhKWdrx8mswVfIqJ2X9/vA8F/xeK7UteKwoJp0dQbD2rmVY0FZ5k75q4QIMqVONdTc0r+TKzVvxXbNRcxsrY2NuDRkkf/0m7AshMdFIxGKKWlmR1zTnZZp1IVcNe3R8AuWLCFEIaUuFZZcCVhY8/1Pzh+TZHGH2m6p4N7apstDSw5hPfCwUgkQkwqf7VJXzKx9axsOOY7gyeaDKsfN+rxly7SAjyTnDru7DIEKUYYJCathbZhjqKc+S3k4Yp7aflXHGRvTiq7ca79OziqtGo66JqbfPyiMwkmRSRpfXfeNdJotFJDJRahOLcy7O+9b7j8QmJmFioDDYaQ3m2rodWel9gw0vbtPizAZ2NuhJITNL/vF5gHuRcoy4eYTHYf4cadqXXs5C2NuxD14Mun6Aslb52NtI4Yc96uuFtbEJJnr6nPn0khbfNkwHlanB3Mce1DuxmqkVGsnb19frRCeP7bz6Gsyymu40KuBMdFICXS/u4FRzxSrK5eBCnnVSX30oPXpiMY8mD8dQT72pGNugNrfefeRpQJDGa1VYsArPKdkvAA/w4KYPM4b9w9knc3LketmluI01xW20+5wlJSbTpurvuTp2nQ372vv3GFq1mlJbcEwML0JDcCviqNTevc7PsWnyrsc02p7dotLeu0QVepeogvOe+WrOgocdcy40M6vMb9aEQUeOsadbF+KSkpVm04uv36BTubLEJSUjFokYWkPxdwmJiSGvqSkzL1xkc0ehWIiZgQGfIiLk7pqE5OQMv7CpPPbLOD48LbNaZD/5q4i5JZfdB6u0J6akqOmtnpDgFiQn+2BmPhILC9WHdU5w5vlrOlZQn2E59vYxSlnaqTWcNY+twLvzJKW2R6F+7HvrybNOE4lNTqLEvvm87iqMe+5jDx60Fz6DscmK5LOM2p32zsO78ySMJHp08fiHgqZ5KPltdp/Kn489WF+3s9bvdVzDOho/IwcGdKfSX6uJScx8YzkuKYnOm/dwQE1xk+/FgikHmLJA+/efG+gb6OX6A0lnZ/OOp54qbWYGBkw8rz7rTyqTkSKVKv3LLjFJibQ6s4ky+/5m9M2j8vZjvl6U27+Qrh66JxCk5cSH5xTbPU9F21oGNDqxDrfja9TqheQGZezs6F+lEuWWr6DPwYO8STPjntO0MW4bN9Fq+z+8Hj9WbrAB9j/zotTS5XQt7yJvfzJqBEtu3KTkkmV03r1Xq6SiBedVhZXSo21I2+/3PRhy5Qhldy/hkt9bJt46zcRbpyi7Zwl3gj5y2X0wDY5uoMr+lcSnKEIPtUllT8XO/ir5CwRgaFiLwABnAgOKEB9/TuvzS+fTvMG15vrdDI8d/+DN4NI11R6bUL6+SttvD8+z4ZuhNdFTdrUlpiTT89Iu4lOSlVw6GbUDGH0LjVxftxPT7wvJTgNLVedO8AcAtry6Ry0tXT668GjycK36PQ0IyrH6tVnhypmnmjv9D5AjrpiYpESi1TytXcYtpbpzYe76fMTV0YEnvoHM7d6UdlWzpyfhcmCRfPmbJBVmc49C/dj7xhOvLsLMx3nPfLXuFW1oU6QMbYqUUTHsJfbM52nnCRjr6VP9sHYp1trSr7JyBmFal0mT4sXxGq0appeUksLVgeo3KofXqM7wGtVV2pe2asnSdIUCelfMeGXl6R+Y6bgBahcrorEPwOyqjZldVZGN2bCgEwvTbZ5edh+U/jRuvf+g1fXTYmjoRj4Hb75+ncCXsH6ZZqampWOFcsw9eznTPn5fs1a8xMrQRKVNJFK/WQ3wtNME4lOSaXduC24OTkyr2CjT9oyYUqERFQ4t5o77aJVN2Jzk+YwxlNEi63T1tTu4FS+qonSqiQVTSrMyBgAAIABJREFUDnDDw5vGbSrQqLXqZ/bortvsXHcZYxMDZi3tgXMZxXtdv/AMF44LRVSau86Ut6edOScmJDN10FZeeftTr2k5Js3rpHT97as8MDI2oGm7Skzot4mIr7G496xJryENGNNrPUv/GUSPJn9Tt0lZBoxpRreGf9G2a3X6jRb2vU4duM/KucfV3hugZ5OFLNk+kIsnPTmw9Tq29nlYtHUAeax099drPWPvfnA/3Q/uJywuTv5z94P76XpwH9U3rmdgpSoq5/RtUJlNQzuSx8SInaO68WzJWM4/UQ310oUHIZ/oXryi/LW+WPAh//bgPBvd1M98cooUmQzjbzOkyRV/vO7M/xd23n+iU//IiLkE+OcnNKQ1efLM1tqog7BXoA2hGUR8NS1YkjXPb8lfp048MmJ+1VYMunYAgLiUJLmrLfXTayTR42TzARx8/yTTdhDCZeNSBNfM4OsH+b2KYrMzNjmJGffPsMWtq1bvLytIRCIujRyAjanqAyw9XbbsIVaNtlFGtKv+B+9eBXH09kzqNy/P1EHblI4PaLOMfVuusf7QCH5b3pOR3ddy8+Jz+fHBE1tw8LowGTz7ZI78XypxMQm0rfY7LTtX5fDN6cTHJSk9AFK5e+0V4/ttZPbyHvy+oieOxRXyHu1rzWXz8TEc33OXXk0XsttjEvu2KFa8rTpXVblvegZ3WImenoRDN6dTrW4JutZfoPXvKC1aG/bt7TtSzt4ePZGIfObm8n8FzC1Y1rwlY2uqbiL6hwmB/C5FFE/me28+KfXR1aMhlcmIT1H/gfieeiX64h8bMjmwahValFCfmp7RBmlukNsuqU23H2RoRNURGFAMQ8Pa5C8QQF6784jFuqn1aSte1TWdzkwqa+t0JCYpgTIH/qbBybXEJGXudy5jZU//ktUod2AhfS7v4U03xUZs67ObKLFvPn2u7OFRh3Ea2326TqXP5T24HFxIv5LVKGul+N6tqOXOEd9nKno2OU0BSwtujVPdJ1FHxb9WaX3dhPgkNhwZiZ6+hArVi9G8o7Leiv/HMPZcnIyNnQVOpRyYsqAzc8ZpF6ILMLjjSpq0rUij1hUwMNRn1lJhH8DnufKk4LnnR7acGEvhYnaUrViEOo0V3oceg+pjYmqISCRiwtyOGJvonpxpaW1Kl/51EYvFDBwvSIjExugeeKK1K8ZAImF6XTdOvX7F0mbaFfJN/Y4s69tGrhmzdmB7pT4WRropq1WzK0w3j50srilIAydLpeiJxcyv3oqBVw+wt3Ev4pKTVEL2cgKxSERkYjwWBkYsfnI1F+7w78PCOGNJ1JxgkY7yBfkcvFSiYnSlumMh7vp+yrSP39cIZKiGhgJMdG3ARNcGSm29nVVXtKk0KVgCr87Km60i4GRzVTdbRu2p7G/cW21780KleNst9+qfpufVzLFalc8r++dyvKePzrTPp/eqeyyVaxbn5D4hzPKVlyA2qG6GrS3BgRFcOP5Y7q5J5dlDXyWXTt58edKfKsfOQThmaKSPfX5FdnVKihSJlrkz6kS+EuKSMDHVzU7q7GPf2UH7HeXFfQSxLUN9vQyLa1ib6P4lfNRpLE1OricgNpLWRcrwV/VWlLWyp3+papTdv5AyVva8/eaDfxsZRpOTQvJKqs/8typN6V2iipIPvdjueRm2j3apy2iXuvh0n0rDE4Io0onm/alwcInOY/9fQ1PhhezwJTZO51VYalSMufkYzC0maT5BDZt6tMdl3gqN/aovWsu9HBS60oX30aEc/vCYsWUaIxaJ8Ah8QR59Y4bc2YWTeV521u3P8heXmFi2KZVOzOVRmxnffYy3xw+h5uJ1mfZJlkppsXY7Z4b2ybCPeR5VGxEXq1gJWVoLmlDZjTT5bUVParhlrsGTWcHsnCimraeXM54AnQ17MSsrzZ2+0XP5Xp5+EDbfqhUvROdaLjRzLankfimvxQaKVCZTWiJbGhhzobXqcq9pwRJ4d1Ge+ThZ2GSYmKFruwi43Gaoxn4/itmnLrL34VNujhuUYUGInCYryn/aUnuJ7jLDdvbCSio+/iKBAU6AFGvrLRgaNcj8xDRoq7UdERfP3kdP6VZJfYGP3MQj8AV3Q99z1t+L2nbFaexQmr43t/GwtUJQKo++MStfXuZ2qynffXwA1ibGbO7ZgQG7Dmfa713oF34/c4nZGYTMWloLn+WwkChs8poDsHmpItIpdXYc8PEL+QsrYsmlUiliNS5TmVSGKJ18dq8hDVg0/RAHb+S8INePIFcdxbtGd+Pk1H4s7tMaCxNDZu49T63pa5T6OFiYa7yOV+BnjX3+43+LhOTkbD00jIwakc/hKUZGLQgL60lk5DxSUrT/HDUppT47Nj1zz17J4gizx/uoUOyNLHgREcSbKCFO3dVKWTJggHNtNr6+jqH4x5U2rqNlxNTuB5lvkOsb6DFt8DaSk1LwfvyBqEjllaJjcXtG91rHa29/Pr4L5sTeuyyeeUTttdb9fRqZTEbgJ4WGTcc+dYiOimff5muEfo7E8+47pg3ZptXYf0Z0NuzFlmvvfrjxwpcFRy8z56AHl7ze0rpyaXaMUt6VN9NC/XHo3mO6DvM/cohSGVQMSktILmgClZ+/Mlvnx8TsIDDAmRRpEA75P2BoWJvwL4OJjl6j+WRgVec2WvVLSkmhhgZ3Q27wIOwDnR0rczvkHVeCBBkB76/KG30zHx9nXiV3viT8GM2mVF7M0C7Rr8rfGf9tTtyfTREnO9xrzsHjhCfH781SOr7u0AhGzWzH72N2M/aXjTy89YbBE1X3Ao/emYnnvXe0qfo7i2cpVhLGJgacefwHr7z86Nd6Kavnn6ReU+0KR2tLc9eZ8n9pX/u+yfmJq0hDPT2Vg8WWL+HdaO2iLipNXEG7qmUoWSAvnWq4ZCi+pc0my81xg7HVIozqR6PpvZwZ2oditrlTyiw3XDFHnjxnyvHME3zMDQ15MGlYjtwPoMyfy3VOZEvVIQn+XJ/k5NdYWi3FxEQ1tC8wwBGH/L5aXXP5lVuZJiOlRYQQx53bJeHehX6hmK01vz05wW+ubVjy3IP9vg+403IKN4PfMOLuXspYOrCyWjfOBzynW9Gq1DrzF7daTFZ7rRZrt2d6v0mN6zGgpmrFH10JjIyi/vJNGvtVKVyAXX26ZPt+/0/I8MOWq66YRwtHMbtLY7rVduX8Ex+GbjiiVFEplcYlNdeerL1kPbNOeeTGMP8jE9q7apaGjUpIYJeGpbQ2+H2NoOScpdnKTs5rd5L8BQLUGnUAh/yadW9SGV2/ltaKhDKEEnIzc+Ez+j4snG5b91JyzlK5If7NVVhRjCvTmDstBR96bbviPG4zg111B2BtaEq3ooJ2jzqj/r1xsDBnRSfNlcsefPRn4637Gvv9R+bo7Hw73fMXWu/ewbHuvTTGMHdZvIsX/oL/r7JTATrXKM/qge4q/ZZ2bKVVFMK+R8+4/f4TF0b003XY/ypKz1mGVCbj1ayMhc5K/iE8IDPrk5a+Ow5x+/1HahYtzLZfOuo0HgsjI42VdP44c4l3oV+Y2Vz7TcpUUmQymq/exsdwzfUmNSESmREff47YmD1Y22xDJksAZIhEqWGZuknHPp06UqcanvsfPWP/o2cUs7Xm73bNcclvr9P9AHxCwthx7zEHHnvl6ub096ZZaWc6uJbl8BPvTPstuniDOsUctZJ3+A/16GzYW+4SdFicV6jOvNO7aPaP76nVNQ0kEoz19YjTojTZx/CvlJyzFFMDA4bXq6FSkScuKYnPkdF8+hrB+7BwXgeH4h0YzIugYCW/0s8sIbqma1uG7D3G0ks3GdtQVeHw8ut3APxaK+PY6LQM3H2E2+8/Uq+4Ixt7tNd8QjrODOujVYTKzvue7LzvybC61RlVv1amuQQJyclsvv2QFVduaQxp7F65PAZ6elqVx4uOXkdSkhfx8ecBEIkMCQ1pi21e7aSH0yMWibg5brDOETrvQr/QabOyLryznQ3WJibkMTIkLimZpJQUAiOj+Pjl6w8vBv69mN+2KQ8/+fPhS+YPcfeNO3HJb8/BAT2+08j+t9DZsGvrXwc4dMeLjjWUA+77rt7PtuGqPrRb44bolIkWk5jI3x7X+NtDs0jVv40GJYoBsO7GPbWGffTBUwBMbKyq356e4fuPc+2NLw1LOrG2a9ssjcfW1ITieW14ExKmVf811+9q7ZvWhOeUERjr6xMZn6CVYY+KXIBDfl/iYhUbY0lJ2XMT2ZqacG5YX5qt2Zat6/gEhwHa/Q7/lzk/vB9l/1yuUXb6WcBnVly9zSg39aJqGZEoTaHmseV8TVRfAvNtV+WQxt8enWOHzwPedp3OpQAfBl5XLh7ya8nqTK3QmPSs8L7Oci9l+5PHwIhH7cfLX3f02IZnmL/KPZ32/QnAm67T5ROgh6F+dLm4ndddpiIRZc9L/t3z4p/6qtdtNjHQ13oG+v+BZmWcAWHTKT0JyckYZSKlamooZKmNPXQaj5dvaVbGOctGPZVTQ9RnNOYmdycMlZer0zZD2cCwFqBY+aUkf8DIWLNvVxOONlZal4P7D81oyjZNZfW1O1oJ0aUSlZRA6QML+JoYRyFTSxZUa836Op1ZUK015awcVOrPpuVxmD8Dr++nVeEyrKvTiXlVW1HANA8Ty6vG1/e5upvlXtcoam7N4Sb9uNRqGL2dqxKRGC832gDdnCqqnJtaBhHgiK9CbfLwt5+za9QhlwptTN55huCIaEIiYzj5UFF0933wF3rXr5TheRMb1cUr4DN3NKRz/39geafWlPpjKd237Pu/9s47rKnk6+PfQOi9g4iASLGAYhdQLFhxUUCxYWMtiBV11XWtWF5BRdkVEex1BUXBrriuCiiiYsWCIqiASFXpJZn3jwsJ4SYhweju+svneXjMnXtm7k1Mzj0zcwquL+CGkIfXVavZNW6EwL5KckysOncVF1Jf4qcONtjiIVoKiKYQNUxcEjz+dS4tD3hTxYIBqizehxzqoZiT3QIMhhqMWryUyD2pKyri5Up/JL/NwsRDJyQy5v8yD5fNRadNTbu1jtl3HA+WzuEpbiKITqe2AACiBkxGl0alAEebC0/wNurqAaR5LefZOxzTmp5Fsry2Ggm5Geiqa4LIAVyDZ3XnQeimZ4K5t05hVuJJhDmOgrupLZYln8PjohzYaVOpCfa8pGazsgwGDqTdhYcZFeB25q3kKn2Jrdg3xt/AnpT7fM/VL9MEeg8Fm01wMukJRnTnelU0lbAfAA5OHIUtfyX8z++MM0B9Xo0t9uBriQCAXuat+PSiOJ/6EpEpTwBAYkq9npcr/dFp0x8i7Yc0B301VcQvoFesAoApPTtj723+372GGLX4ugyiTdHdtCVervSH98Eo3H2X/U2v9SOjJMfE6ekT4L6bXjy9MfaBO5rcF6t/5MvLyNKUuih4mNmJlNTOLzEaAHCs/0TauWEmbTEXwJUsyphg1kW+nnmbCjvtFqhg1eDo6/twMjSHvqIax0oHqAyc9jrGYt83P8S2+fek3MeFCRPxZv5CDLO0wut5/pCTlaWtvcvIMGCurw0FJpPzJyqLBzjh4bI5YvX5ETlY571SX1Oy3lrtbib8S7sw+gKGtbcGANhuaNrbSFweLpsrkYpJjTk2xUugUgeA2X16Cjz3+RPl8ldT/fVul6JyZLIXXqz0Rz/L1t/legv6OfyrN/2bQztDfSwd2Eck2fYbhNdAuJz1AgAw2ap5JRrHtxG8mtCQ+FzKecEqaiMsIjfQ/vgRW2eN73+ZDBZhY5R5R77XG2ch2j00RbM0p40u5YZUUF4OGQYD1yf74OczMdjrxuvK2K1N86uiK8nJ4fGvc1FaVY2Zx2NwTwKWkYWuNib1sEcnY6OvHut7YG9CTd22XUuEr1N3LI2hqlRFjKO7jDbkxoLpMFRXhaG6Kvbdvg+P3UdxarpoHkqiMqFrR0zo2lGkACZhGKmrIXzsSFgb6DYpqyIvOEq5rOwYNDQ3IT9/qFj5178WBoBdY6llsWe5eVh+9opYFZ+EoaGkiPl9HUTOEf9fxadnF9x8nYnbGe+EyjWVMCyrbu3aVMg6ujC0+RRCkQQdtIzwtJjaJ9iXRhlpw1u152yaFlaWQbbOsh9pJplo168yiR/mUjdrpKaG+LeZtPPbziXAfzhVQHpz7E3EJKcicYN4G1CqCvI8kWhfKitx9ulL3H+XjWe5eSgsq0BpVRUU5ZjQUFSEprIirPX10M5QD3bGhrBv+e0qxvBD0haVV2dbRKU8QTWLhbNPKIukfkNREMy6BEdLB/bBjVcZSP2Qh50378CvD72i0tfi3rEdJ4jpffFnxD55jpT3OUjLK0BVbS1KKqsgJysLLWUlmGhpoL2RAfpYmMHJQrQcIo0R9PkWFtxCyZdAAEB52THaeWWVb+82185QHzHTvTnH74o/ITkzCylZOXiWm4dP5ZX4VFGJ6tpaKMgxwZSRhZ6qMow11WGlr4uOxkZwMG8lUpoNSdBaV/tfMwM44C1ebEXfoUEw0FdH5EFfTpuREpV3Kqf8i0TvTRCNPV0EMdaiE1bco3RlcVU5AN6Q0aPpKdCpe6hIqr7BVyn2ahYLx548RlTqUzia0Nd8PxRTH3BVTS0SXmTgesAMTA09gf2zm19MVl1RkWMt/i+wbrgLolKeIORvqioPP/dHYVzwm4x260MQcv0WBlhbiGQZi0tU+iO01zbEzZx0TOjRCf1tzbHt8U34tXdAeW01HA3N0TYyCCEDhtUdm2Lw+Qgc6j8eY68ext8/zULbyCAcGzCBIy8uOronUFVZV9KOIV4QkqiYRWzmOc6cQS9Y3ZBWWppopaWJUfb0HNtSJM/gllTK3YNpd7HItu83u04vAzPc/piJjJIimKs1nR7E07wjVty7yDlumHZCXU4Rf6anwEZDn1/XZiO2Yn/qN5fzOtFnOhz37YaynBxPez0tdajE8367YxC1cALkZGWRkiHdbBIXdUUF7Ll1DwDg69Rd7P7PVsyHdcA2uIUfRuqK+ZwNHUmxPPkCJ0KyhrDhYmyJoJ7DoaXArdZTxaqFvS61MZRRUoS9fcfAQEkVxioatPPNRUGxH5hMK4HpBL6WekV+MDUFqxP/+ibXkNJ86r/XZbXVePU5H5Ya3yZyNcJpNGyjN2PwxXCkeTVdV1m+rnzng0JK93k18LSZaNkVoc8SUFhZhs7N2PAVhNi/cOUGywBGamp4M38hX6UOALuvJsP+lxCkvv8IBTnqGWJpKHmL8UfnuM9YANRspbkk1/lgt18v2SLcANBKVQtvxi/Hm/HLMa8DtfSmICTHSitVTTwrpjLavfokmfXoevQNrkt0PCn/LW67Uf7xQy5FYNil3Yh9+xQ3PqQj5u1TzEw4IXBzUxyUmfJwN7MFi7BhEbkBwU+u42bdNebfPg2LyA24k0cvwO6fFAMA+NmauyQ6pW6jl0UIxvHxeW8u39TtpHHVJEKAE4u8BUj/+Ez32omstwVgswl8Zg/AmClOIvWLfUzFAuz39hAqt9Z1ANa68q9Yr6GkKHJeGXG59pMvBpzdhZKaKtwcMbtJeVmGDB4V5mB+YgzOD/1ZIvfw+dMyaGhuQk31I8jJi79M9yAvB6sSruJ5UT5M1TXhYdkefvY9m11i8UVRPub9dQ6ZX4oxoJUFdg4cwXesG+8zEHwvAamFeTBSUcOCLg7wtKIv3ZhFbEbmjF9QXFmBSRdOIK24EL1amGD/0FFN3uOcRUfx9Bl9pjxrWj+M8eR6kJSWVWH4qBBcv7gE7uNDUVzMTffbuaMpgjfxnwkN89iO8gre2q7iykcf9YNOXSWkhmS+LcAU3308bXtCp/AdFwD0lVTx0GMxesZux8vPeViYxJvyW1Jr2Ft6uKGfURvMu30aoc8SaedbqPCW0GuraYDnnz6ir1EbtFbj1uFtuGE7wlSCaYIJIcL+aPTaE05re/upmHidOM5P/JuR876IBK+L/a7XlBSDuq4hx/fHiyxvtTaYWK0N/oZ3JJy9ifeI9Zp/5vpb4uKJ9ZpgEvvomVC57CyTun+NxL5G98M7iWl4EOl5NIz8Fn+FeMYeJabhQQLlDzy9L/S8X1wsMQ0PIguunSM7HyQR+4M7iGl4EPlcVckjl/m5mJiGB5GJ50+QXQ/vEO/zUcQ0PIiMPUv/LZmGB5H04kJiGh5EZsXFcq7RFDvC/yLOQwLJhcuPudd9V0Dm/XKMJltSWkmchwSSAcM3k3WBZzntv66OJs5DAknwH1dofZyHBJJ+w4JIXv4XQgghpWVVJDI6mTgPCSQ+fvtFkh/ktpU4Dwkk1TW1PLJsNiXvPCSQFH8qI4QQci8lk9PmNSmsyff/b8XeVyK/J4G6WyKLrbrKKsgoLuZ7jk0IWGw2z58kSEl+I5Fx/u3U+65rfOOi0f91ZGSa56rGJgQfy0uhr6yC2+N9sd5pIE66jUf4IOEupcI4/+YlerYwwbZ+rpjVqQfix1G++WtvXeORM1XXxE4XNxwaNgozO3bH4WGjIctg4HYOf7c/v6tnsNl5CHa6uCHUxQ1/jxFc1Lqe5HsZAIChDYpGmJroICRonMA+tbVsrFjCTcOwfjWVOO7shYc8clnZ1G/+t1+GQ0+X8khRUZaHlwc1C0h/kyeS/LqV1PgXLz/hkU+4TQWaeY/tCc26uqdd7E1h0vLb1DP4kZCIYi+rqUZpdTWt3XbhNszYdQqdFodg8o4odFocwpNi4GvYH/rjbl4lpL9FzucvuPz8Fdqto1LGJiyc8Q/f1b8bbZ0jyMmmXFtzslvQ/gQx+eJJAECyN2+hkMFmls26j3NvqIjD48PHctpU5OQx2MwS0Wn0kPFhra15jtc5DRQ4tr6yKkZbcxW0uUbT/toKCtRqa22t6AZVj668AVcyDAY62prQUgjPnEflhh/Qty1tjJk+zgCAogbLOYLku3elvKCCd1zhtNXUshDwf1RGzmmTeQOY9of5iPxe6snK/4SAI3Fi9/uvIvIa+7iTVMazwooKzmsAYIPgbnY25nanRwVO6dcFi37qA6cVYTgyj/qiz94TgxHd2jf7hoc7rkdNNQsAcCn2AS7FPuCcu3x3Nef14G5rEXbMFyvnH0VBfglfme0bzuJiDG/GwC49LbDxD2of4Nnj9/D/eR9PHwAI33YZp44lIfTITLSxpopxJ/79HAFLeLPCRV9bClU18S3tOVFnUVFTwzk+PnWMyAWW/1eRl++KFsY5+JBjLVZumPisTOgoSS4w5XAq9X1s7BopiLKaamy9m4BbOe+QW1aCL9VVAmXn2AuOvBVEaLA3BrpthctPW9DCSBPbA8dBX094neE+Tla0tuFD7PDoyXu8zy6CiTFlMZeVU8Zc36FBAsdKfZ6D3g6WIsvXk3DrFWpqWXzPMZni26MhpxOgpixaIrkfAZEV+0F3T2y+lYDHH3NhqMb9YjAATBjSEW7WNrQ+2YWUH7utqSGnLfn11yX4Ope4AgCluIeMsIf/CsFZC+dN2Y3QwzNh2ppye2KzeS2OOUtdseA3bm3L3JxPmDyC6zXSzs4EAPA45S3sOnMDak4dS8KsRUM4Sr2ivBoBS6JwJn45FBQpr6Eta2Pg2T+Q9lAQhYe/zhG7z/fgj+u3EXojiXNsZ2yIqGm8U3rn4N34WFJK6xu/aAb0GpXrKygtg9PWCJrs89X+TeZy77jhDzhZmGJPow1lTc1NIrwTXvSUJFNGEACKK6lUsat6NZ1ywTcuFpcy0tC/VWuscuiP1hrauP7+DZbd5B/Jqyok8lYQcnKyuH5xCYK2XcSFK0/gNSkMALA9cCw62fHPN6SupkRrq9/YLCws5Sh2AGAwAMs2hjT5ehQVeYPpmpKvp7CI/h1qDiv2X0RaVj5e51DpkmMSubOmlDCuM0HnWdtwbbMv2ITAd/tJ5BR+gUtnK6yZNAgAsGzvBVy59xJ3Q+dzokTr+w3qao1NPw/jue626Js4l/QMbEJg38YYS8f0h4EWfXO4nufvPmLC/x3D8d+8YdXy6900RVbs8rKy+K23M86nvcS2waIllqrfgN4+5SdOSbyw6eIXemgurcz0OEodoPLXNKTxk9+wBVWwo6aaBTl5ykLu7mSJVf7HEHOD8lctK6UsqpFjuS5LAUsioaWjylHqALB49UjEnfs2eUssjm8EAAT1GA5Pczux+69NuYLI9IfQlFdC4oi5Int+nHn8HC9WUz+GV3mF+CnsEA7deYBJPbhuWuO62WFqry48eX7aB2xH760RnL4AtbbttDUCeqoqiF/EXWaKe/Fa6P1Us1jouOEP9LNqjTA+GS6VlMX7fsnLyuJFkeRcLu0NjJBWXAAfW+F1QmdeicHlzFdY2r0PZnXifpdKhFjsX8MS/6FY4j8UL9I+wG/BESxYehwbVrnDsRd9yamomK5U8wqoWa+uDq+1TwgQ8bvoKZ1Fldfm4yHTHPw9qWWcgUsjMLSbDRaOEpyX5vqjdGz88y8sHt0XVTW1OBTXdMK5xhACdPHbBjmmLBZ49IasjAxO3HgEPQ3BxsPTzFxMCvwTZ9f5wFhXQ6CcOIjt7njEQ/So0a2TqQ0YBTkmzfXxe9C5h/DkTM+fZGGBz14AAFNOFjp1GzqkQT2b1ZvHwrXXOs7xygVHodNoKvsg+Q0IoWYRjSktqWzWcoww0scuh9fVQ83qG5+bgZNvHuHZ6CW0c2vuX8aaLoMF9o2bx13btNTXgY6KMjZeus6j2H1709MWrHLtj9XnePdEph89Td3PIt69g4E2bfheW11JETUsFuzW/45BbS3xu5fgHOs52SYA6qfxDLQwFhwUt95pIJbcuIS88jLoK3+95b7WwQWRL57gUkYahpjTlzTqKaykQstN1TV52oOS47/6HoRhY2WEaxd+Qd+hQVi9MRZXzy6mycRde4YRrrw+1ecuUkZKS2Puur6SkjwqKuh7a4IQR753L0swmTJ89wbEqRaoo879P1WQZ/IcNyYXfUpzAAAO+ElEQVTgSByPFT/RRfwi3sNX7IUcUxZ3/pjHafNyFux+++hNDqZujkRc4Ayh9yYuYiv21lqiJ9ghBAg4cRWFJeX4/Wc3VNXWAgScYKVvDUNGuC26wGcv1mwZi17O3A2sxsq53qqPOpgIr8mOSH30HnujeZdKlFUUoKOnht1RTftw/9OkFueijxH/4uGHX90Xqtgb42nfHhEJ9PTKF1PTcCLlCQpKy/GxpBQllXQrNDH9LXRURF/bNlRXha0ISr0g3x1GLV6CwaDGJqQCBfkjoKsXy1fey9oWG5Ouo/uRnTDX0IKTsRlefyrE7Zx3PCkDCIDcshJkl3zB6+IiAEBybhaMVdVhpKLGCRNXZDIxsZ09fONioa+sgsFmViioKMPlzFcYYGqB3YOoGcV6p4EYFn0QflfPYJ2TC+RlmFiZGAd3y3aIevmEdp/Npbi4DFpa/BWGsRH/3/LTZ9lgE8J5T2xC8PRZNm3Gu3fnFIyfGoFJM/biUAQ9HqGgsBS6Oqoiy5eUVkJNlTKC5ORksWLJT1izMRb7Dydg6kRuzMfshUeaeNfNw8Pp6/3IPxR9wbKxomU+Tf9QiKmbI3Ez2A+qSpJd/xdbw+aVlUFfhftFOfjoATobtoCtAb1or92ibQidNhKz91ARVwpMJib+HonD8yQT8l1aIrzAsig0VOrFhfzX9WYvGYbQoAvo2YeSbdlKh+f8hGnOiNh+hV/X707go2vY/TwJbbUM8Kz4I3Y4emCoiQ0+lH/B+gdXkVFShE9VFZidSJWOC3X0wJ28tzj0ipp21rcDQEivkULTD/ArfGCzdhtkGAwsH9IXbfR0YK6rhZuvMrHyLN0jQRzFPnLXEWgrK+HK81c8SqcxNTVPOEodABgMJdTUCC9g8HDyXMRnZSLg9jX8+eIRzDW0sM6JtxSa9d5gVLN4N/O8zvzJed3wIbDOyQULuzpizl9ncfzFY2gpKmKGXTcs6+HMkbHR1sOb6Ysx/9o5rL11DSZqGogeMQG2ugYSVezu40MBUNaqSUttfMz7gpJS6nezZ+cUvn38pvdD/2GboaWpDHl5Jj7mUXtl507yVj1qYaiJKd6OOHAkUeCG6PWLS0SW37nNG+1suB5MfXtbQ0NdCQeP3cKRyCS0MtFGRmZBk5u/zUVdWTIza31N0ZaRRgdQs+7f9l1EyOzmu9fyQ2zF3nNPOCf3eo/d4SitrkJFbS1OjxmPjoa8myLTXbqjTzvehE5P3/MvjScurp5dcT76Hv6+/BSaWsp4/7YQbqPFz8O8wGcvft3giYd3MxC8jn/BY7fR3RAadAFbA2IwaDi9oornhF7Yt+MqBndbixkLBkFTSwWP7mfi8pkHtM1TFouaWlZV1dDG+VrYhCDieRLSxy7ntFkc34j0scthpKyOUEcP7H5xBw8LsxHqyN107KFvih76prA4vpGnvSlSP/D6Kc+NPAsAeLZqAU97aRXdYpeXlUVaXoHI1wp0H4IRdm3RbdNOtAvYzrNe3xBNzSAUFHhCR+cACCEoKpoCLe3QJsfv3dIMcaMFu9Gl/cxbbyAw6SZG2XSAhSZ/n2otRSUcdaXX9m1MSP/hCOnPOwPhl1ysqYRjgjgTNQ+h4X8hOSUTbzLyoa2tAnc3B/hMFBz1bNZKF6f/nINfV0cjPSMPfRytELCCv+KZMsERk8Y5YEvIJSTcfoXqGhY01JUwsH87+Eyi1+TlJ9+hnTEmju3Fo9TriY2cizPnH+LA0UR8+VKJXxYMgetgO7iOknxqDGHUGzilFdXQUBH+ALj28DX6duQ/K25ISpg/allsdJ8TglMJTyQyY6jnq9ZE8svL8Gb+Qjz5+BH+ly/g6qSpPOfjHr/CvGHcbIRZhZ8xqKPgdUdxmLfMFeoaSti+4Qzk5ZlwHih+Br0z8csx32cvpo0KRS9na1y+uxquDuv5yuoZqOPF02yE7OcfFHL+9kqcj76Ho3tv4vOncth2aoU9J7hLM4/vZ+IX34Oc46N7buLoHqoQ7sDhHbF49dc/sc+8TQXA3VyVNNdfZaCvJfdBHff8Ncx0uNP5wrJyvv2C/6KHXK92HYDfzlxBfmkZzVuGH/X2+d1lfrBZuw1OWyOQsIju26+k7AFZZisU5I8CwICm1hbIyf07sitGPLyLGZ2aVwSiuWOrqyni18WuYo1VXVMLLU1l7AqhVwjih4wMA0v8hyKJFODMwqb71Msv8RfNCcPNtRPcXCmDqj7A8fxJ0Wqm1qOroYL4Jxli9WmIQ3tTXEh+joDDV7DVl/LES337kSY3z90Jv59OwNyRjtDT4FruZZXVUFGkezUxZWVwJmAq3FbtRxfLljA1aF4uedq4ze24If4GPNpSebhtDQyQ+ekTTWbWoJ4cbxjbhdugqiiP2xubtw7Nqk5C9Zc1UNK9xGnz8vwVU2bd4SsvipuhgqIcdh3z5Wk7f2sFX9kj55re/HX17ApXT/4Fue26mDXL9VEc2ISgjbouLg/7NsFMvsdi4GJjATtjI+y4fhsAcH42t+jBatcBGLHrMLz3R2Fyr874XFGJgPPX4GZng+gHqTxjedq3R1DcTfTeGgFTbU04WpgiPb8IdzLfC7TG63m6cj46rAuB/8nz2DaKrrTk5btCT7/5xT8as/3uLYSm3MEwCyuEuHCvV1FTg077dsBKWxdRI6k4jYraGridPIIPZSXwsGqPgN4DsOtBMrYkJ6CWzcbG2zcwp0tPLO5OWcyORyIwy747AhL/xrSOXbGkR2+YhW1B5ixqU3PU6T9x0p1yKU3/VATvsydQXlOD+V0d4GPXGUOiDuB1cRHfsb8n/dftRnk1dxbqHBAOEx1NFJaU4+KyqWATApf1eyArw0Dcb9PgtDoMJjqaqGGxcNKfihtx2bAHxlrqOOhHzXTupmfBd+9ptDHQwaHZXnBYFYaauuWwx4EL6DchhOO/ecNlSTg6z6L0kaqSAm4G+zXRi8uw7m2hraYMv99PccbQUFFEN2sTHrkpg7rh5ft8DF62m6c9bL4netjwdy9tqaeJ9VOHwn3NASTvmA+mrATiRoXlG+CXnMB8+1Zy6/07Yr59K6ftcW4u6b1vt1hJDso+9iQVBaMIIYSwqlMJqyadlH5oQ9isj4RVnULKcu1IRYEnIYQQdm02Kf3QhpR9aN9ojO4iXauwqpjUslli3Z+4fI9rNGR03EFy8s0jnjYWm01a/7lBaL+I50nELyGa77nWf24gbD7texPvEffwI4QQQhZHXyAd1oWQSQdOEBabLv04O5c4B0eQzht3kEG/7yNPc3IJIURgrpmE15nENfQgaRewnbiGHiRHkx/ynBeUK+Z1fiGxXhNMLj9L42kvKvQlbHY155jNribFRf58ry0K+x7fJ6H3kwghhLz/8ol4nDpKCCFk0+0bxP/qeUIIIQ8/fiBTz1Ofqe3e3zl9G34+yTlZJPxBMm18052bSUVNDa2tHs9TVE6XGhaLWIVv43uPgsYWl/pcMfG30poW5kOPFaGc133W7iKEUJ9B0qt3JPLWI/LxcwnnvOOqndQ1K6pI4stM4hq4n3Mu5m4qWRt9lUzeGcUz/sPMHFLL+n6/sf8AAnW32Bb7vhHumBB9Auv7czeX1t74Gz72orsGVeT3g7I+ZfHVVpyEDLMdCDsXKoZUbgiGjD6UDR6BVX0fAAFDtgUUtXahqli86Vc9BVXF0JBT5xxPu7sUe7pR1XaSix5BX0EHke/PYnabSVBlqmDa3aWY1cYbWeUf0E/fAWdzrsJBtwtu5CXB28wdvvd+4/SffGchDvYIpl3jW3E68ymeFH3Am5IiHHudgrTP+RjS0gb2usaQYTCwrFN/WBzfCDttI7z+Uojy2mqeNXdhTLHqhjbHN8JUVQtMGRlcGTYTAODj0AU+DtT/72aPodgspEC2bQsDXPen1y0VZIU7WpjinJ9gv+ZFLk5Y5EK3QC10tfmOWVl5GQwGd1OXwZBDRUUsNLWCBV5DGDtT7uDuZCrlcUs1DdzPzUH8+0wAwEx7Kjd+R31DXHtL5S66OtYHZmFbMLyNDXYMFOy90xBFEWr7xr56jhUOfcV/A/8wMgwGSiur4dXLDs+yPmLQxr14uIn7O1ZVlEdW4We8LSimvOYADO9sg+jkp+hiLpnCzv+LiK3Y+5qZ0wpXn/QaK0CaP+zaDIBQG2pMpZFg16RBRo7r61lZ6AkFzfoNL1ZzblNs/OqUej1dtGzRRcsWyUWPEJN9GRNMR8Lc3ARH3p7+5vciDHezDnA364BVnfnnFJlu0xPTbQSHnk+3EVweb2XngVgpYNz/CkwmPXZBVrb5CqK1pjYqamugxOQ+LMw0tHAr+x2ySj7DWpuqL1CvnPWVVZA5azFq2WyeJZXmZovN+EwlzjJR18CF9DS+MhLKRAtVFQUeLxZJ8ndqOjqZtcCYXtzf+f2MbJxLeY6V7gOgJC+H22nvYGGgDV01FawZ5QKP4MMY59gRT99/RP/2FjDT08L5By/Qo00rGGhIJoDpR+X7OJQ3QkEzGKyqeDDkLCEjQw+fZde+BUNGB9VF46Ck9/WJe2QZMmATNmQZ1NqVkizlM5pXyfXKkGMI/igU6+RZhA0TZSNOfwAoZ1XwvYaUfwZdvRjkZLeAsoo3CKlERflJGBo9a/Z4x9y8YLFrK5b3ckbYg2Rc9JoME3UqOjAoKR5pRQUIe5CMkyOpdfD2e0IwvWM3VLFYPJa4vUELjD8TBQUmE5ZaOnAw5r/eCgA6SspYk3AN5TU10FKkwvu7G7WE99kTqGaxYKCiindfPmFr/6Fij/0tSVrHXbO+sWom5/WADlwPkV9H9OW87mJuzLHKf+rCmxistb42x7Lv355S4hrKinDr0k7i9/0jwiDCw7jEiPH6d+N7bzlUmcrY0mkFiqo/Yf6DNRjfagR0FLShr6ADQ0U9jgJvvFTTVcsWM+4tg6myMVa2n8/prySrCH0FHay3/YV2DSn/LNXVyQBhQ15B/MRZUr49TqvDkLBWvML2UmgInKv9zyh2KVKkSPnBEKjYpesGUqRIkfKD0dQau4S2ZaRIkSJFyvdCarFLkSJFyg+GVLFLkSJFyg+GVLFLkSJFyg+GVLFLkSJFyg+GVLFLkSJFyg+GVLFLkSJFyg/G/wN7mQT/EtGJpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#nice library to produce wordclouds\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "kansas_up_words = '' \n",
    "\n",
    "#looping through all incidents and joining them to one text, to extract most common words\n",
    "for arg in kansas_up_df[\"NARR\"]: \n",
    "\n",
    "    tokens = arg.split()  \n",
    "      \n",
    "    kansas_up_words += \" \".join(tokens)+\" \"\n",
    "\n",
    "wordcloud = WordCloud(width = 700, height = 700, \n",
    "                background_color ='white', \n",
    "                min_font_size = 10).generate(kansas_up_words) \n",
    "  \n",
    "# plot the WordCloud image                        \n",
    "plt.figure(figsize = (5, 5), facecolor = None) \n",
    "plt.imshow(wordcloud) \n",
    "plt.axis(\"off\") \n",
    "plt.tight_layout(pad = 0) \n",
    "  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The word cloud above shows the most common unigrams (single words). This is useful but might also mislead. For example, we can see that \"car\" is the most common one. But what car? An automobile? Do trains hit cars? Let's check for most common bi-grams (two words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('cut', 'car') 6\n",
      "('track', 'crew') 3\n",
      "('yk', 'kick') 3\n",
      "('west', 'end') 4\n",
      "('car', 'cut') 3\n",
      "('car', 'derail') 6\n",
      "('rail', 'car') 3\n",
      "('derail', 'derail') 4\n",
      "('yk', 'pull') 3\n",
      "('train', 'shove') 3\n",
      "('track', 'damag') 4\n",
      "('speed', 'mph') 3\n",
      "('cut', 'empti') 3\n",
      "('track', 'derail') 4\n"
     ]
    }
   ],
   "source": [
    "from nltk.util import ngrams\n",
    "n_gram = 2\n",
    "n_gram_dic = dict(Counter(ngrams(kansas_up_words.split(), n_gram)))\n",
    "\n",
    "for i in n_gram_dic:\n",
    "    if n_gram_dic[i] >= 3:\n",
    "        print(i, n_gram_dic[i])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aha - we see that \"car derail\" is the most common bigram. A railroad car or railcar (American and Canadian English) is a wagon. That makes more sense to me now. Seems like wagons are derailing a lot at Union Pacific - let's check the dataframe that contains the incident texts for the other companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('track', 29),\n",
       " ('switch', 19),\n",
       " ('car', 19),\n",
       " ('railcar', 13),\n",
       " ('derail', 12),\n",
       " ('yard', 11),\n",
       " ('pull', 10),\n",
       " ('kck', 10),\n",
       " ('materi', 10),\n",
       " ('train', 9),\n",
       " ('damag', 9),\n",
       " ('releas', 9),\n",
       " ('crew', 8),\n",
       " ('line', 8),\n",
       " ('east', 8)]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Counter(\" \".join(kansas_rr_rest_df[\"NARR\"]).split()).most_common(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kansas_rr_rest_words = '' \n",
    "\n",
    "for arg in kansas_rr_rest_df[\"NARR\"]: \n",
    "      \n",
    "    tokens = arg.split() \n",
    "      \n",
    "      \n",
    "    kansas_rr_rest_words += \" \".join(tokens)+\" \"\n",
    "\n",
    "wordcloud = WordCloud(width = 500, height = 500, \n",
    "                background_color ='white', \n",
    "                min_font_size = 10).generate(kansas_rr_rest_words) \n",
    "  \n",
    "# plot the WordCloud image                        \n",
    "plt.figure(figsize = (5, 5), facecolor = None) \n",
    "plt.imshow(wordcloud) \n",
    "plt.axis(\"off\") \n",
    "plt.tight_layout(pad = 0) \n",
    "  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('kct', 'main') 3\n",
      "('mp', 'pittsburg') 3\n",
      "('pittsburg', 'subdivis') 3\n",
      "('car', 'track') 4\n",
      "('east', 'end') 3\n",
      "('equip', 'damag') 4\n",
      "('ran', 'switch') 3\n",
      "('rco', 'kck') 7\n",
      "('derail', 'railcar') 5\n",
      "('railcar', 'pull') 4\n",
      "('pull', 'yard') 4\n",
      "('yard', 'track') 9\n",
      "('hazard', 'materi') 6\n",
      "('materi', 'releas') 9\n",
      "('kck', 'railcar') 3\n",
      "('hump', 'retard') 3\n",
      "('result', 'railcar') 3\n",
      "('railcar', 'derail') 3\n",
      "('releas', 'rco') 3\n",
      "('track', 'track') 3\n"
     ]
    }
   ],
   "source": [
    "from nltk.util import ngrams\n",
    "n_gram = 2\n",
    "n_gram_dic = dict(Counter(ngrams(kansas_rr_rest_words.split(), n_gram)))\n",
    "\n",
    "for i in n_gram_dic:\n",
    "    if n_gram_dic[i] >= 3:\n",
    "        print(i, n_gram_dic[i])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok - it seems like UP has a derailment problem where wagons are getting off track. although there are fewer incidents in the other dataframe, the word \"derailment\" only comes up 11 times compared to 39 in the UP one. However, the bigram \"hazardous material\" and \"material released\" comes up a lot, so lets actually have a peak at the actual data to make sure we make the right conclusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREW ON UP TRAIN MNPINP-19 USING KCT MAIN LINE AS HEAD ROOM TO SET OUT BAD ORDER IN UP ARMOURDALE YA RD, TRAIN PULLED EAST OUT OF ARMOURDALE INBOUND TO KCT MAIN 3.  CONDUCTOR MADE CUT AND INSTRUCTED EN GINEER TO PULL EAST TO CLEAR SWITCHES ON LEAD, ENGINEER BROUGHT TRAIN TO STOP USING INDEPENDENT BRAK E CAUSING BUFF FORCES IN THE TRAIN CAUSING MTNR175009 TO DERAIL ON KCT MAIN LINE.  KCT MAINTAINS TRA CK AND SIGNAL WITH DAMAGE OF $3900.  UP OWNS CONSIST WITH DAMAGE OF $7852.  #12 SUBDIVISION NAME IS LS 1001.\n",
      "\n",
      "CREW FAILED TO LINE BOTH ENDS OF CROSSOVER FROM WORK LEAD TO 901 & DID NOT PROTECT SHOVE THRU SWITCH RESULTING IN A RUN-THRU SWITCH & WHEN PULLED FORWARD THEY DERAILED.  MP 2.5 PITTSBURG SUBDIVISION\n",
      "\n",
      "CPRS CREW FAILED TO SECURE TRACK 9 & THEY KICKED CARS INTO TRACK 9 & CARS ROLLED OUT THE EAST END & DAMAGED 2 CARS. CPRS CONSIST INCLUDED 2 KCS CARS WITH EQUIP DAMAGE OF $36,000.  MP 4 PITTSBURG SUB\n",
      "\n",
      "BNSF CREW RAN THRU 126 SWITCH & THEN SHOVED BACK THRU & DERAILED 2 CARS.  BNSF EQUIP DAMAGE $2,746 M P 3 PITTSBURG SUBDIVISION\n",
      "\n",
      "RCO Y-KCK3012-13 DERAILED 9 RAILCARS WHILE PULLING INTO YARD TRACK 6097 DUE TO A PREVIOUSLY RUN THRO UGH SWITCH. NO HAZARDOUS MATERIALS WERE RELEASED.\n",
      "\n",
      "FOREIGN TRAIN F-NS 1121-10 DERAILED 2 RAILCARS WHILE PULLING INTO YARD TRACK 6012 DUE TO A SPRING/PO WER SWITCH MECHANISM MALFUNCTION. NO HAZARDOUS MATERIALS WERE RELEASED.\n",
      "\n",
      "Y-KCK311-1-25A TRAIN RAN THROUGH SWITCH DUE TO FAILURE TO COMPLY WITH RESTRICTED SPEED OR ITS EQUIVA LENT NOT IN CONNECTION WITH A BLOCK OR INTERLOCKING SIGNAL AND SWITCH IMPROPERLY LINED.  NO HAZMAT R ELEASED.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#because we already preprocessed the incident texts in the UP and Rest dataframes above, \n",
    "#we need to create a new one where it is not preprocessed so we can read the raw text\n",
    "kansas_raw_rest = kansas_df[kansas_df['RAILROAD'] != \"UP\"]\n",
    "kansas_raw_rest_list = kansas_raw_rest['NARR'].tolist()\n",
    "\n",
    "\n",
    "for g in kansas_raw_rest_list[:7]:\n",
    "    print(g)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NO Hazardous materials \n",
    "Aha - we see that the other railroads have a different style or reporting. They report if no hazardous materials were released. Since \"no\" is a stopword, it was daleted from the text and one could draw the wrong conclusion that because \"hazardous materials\" comes up often - that it might be an issue and hence reported. But infact the opposite is the case\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fba718fae50>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u+ZblC5JcM/t2pKeY9f7p82EXOUYtSY3z0/MkqXEGtSQ1zqCWpMYZ1JLUOINakhr3/zxGLNPG+HNPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's check the month with the most incidents\n",
    "kansas_df['IMO'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fun fact - August 2019\n",
    "https://apnews.com/8f3be8700efc4a488fafc6e0d8a564f7\n",
    "so apparently there were strong winds in Kansas in August 2019 and caused the derailment of over 140 cars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing and key word analysis of whole dataset\n",
    "Ok - I am not a railroad expert so any conclusions I have drawn above may be completely wrong. I just wanted to take one city as an example and show you how to plot some graphs and do some basic text preprocessing. Let's try the same with the whole dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "prep_data = raw_data_df['NARR'].apply(preprocess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets create a column for the preprocessed data and store the original text in NARR\n",
    "raw_data_df['PREP'] = prep_data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>IYR</th>\n",
       "      <th>IMO</th>\n",
       "      <th>RAILROAD</th>\n",
       "      <th>STATE</th>\n",
       "      <th>STATION</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>NARR</th>\n",
       "      <th>PREP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>ATK</td>\n",
       "      <td>6</td>\n",
       "      <td>CASTROVILLE</td>\n",
       "      <td>MONTEREY</td>\n",
       "      <td>CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...</td>\n",
       "      <td>camera equip train struck occupi sanit truck m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>MNCW</td>\n",
       "      <td>9</td>\n",
       "      <td>NORWALK</td>\n",
       "      <td>FAIRFIELD</td>\n",
       "      <td>PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...</td>\n",
       "      <td>product gang travel north piec equip seper pie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>FEC</td>\n",
       "      <td>12</td>\n",
       "      <td>POMPANO BEACH</td>\n",
       "      <td>BROWARD</td>\n",
       "      <td>BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "      <td>blf travel southbound east main approxim mph n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>FEC</td>\n",
       "      <td>12</td>\n",
       "      <td>POMPANO BEACH</td>\n",
       "      <td>BROWARD</td>\n",
       "      <td>BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "      <td>blf travel southbound east main notic vehicl f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>CSX</td>\n",
       "      <td>12</td>\n",
       "      <td>SEVILLE</td>\n",
       "      <td>VOLUSIA</td>\n",
       "      <td>AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.</td>\n",
       "      <td>amtrak struck semi privat cross</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IYR  IMO RAILROAD  STATE        STATION     COUNTY  \\\n",
       "0   19   11      ATK      6    CASTROVILLE   MONTEREY   \n",
       "1   19   10     MNCW      9        NORWALK  FAIRFIELD   \n",
       "2   19    4      FEC     12  POMPANO BEACH    BROWARD   \n",
       "3   19    4      FEC     12  POMPANO BEACH    BROWARD   \n",
       "4   19    4      CSX     12        SEVILLE    VOLUSIA   \n",
       "\n",
       "                                                NARR  \\\n",
       "0  CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...   \n",
       "1  PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...   \n",
       "2  BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...   \n",
       "3  BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...   \n",
       "4     AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.   \n",
       "\n",
       "                                                PREP  \n",
       "0  camera equip train struck occupi sanit truck m...  \n",
       "1  product gang travel north piec equip seper pie...  \n",
       "2  blf travel southbound east main approxim mph n...  \n",
       "3  blf travel southbound east main notic vehicl f...  \n",
       "4                    amtrak struck semi privat cross  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('car', 2640),\n",
       " ('derail', 2167),\n",
       " ('track', 2070),\n",
       " ('train', 1314),\n",
       " ('switch', 977),\n",
       " ('shove', 748),\n",
       " ('damag', 705),\n",
       " ('yard', 680),\n",
       " ('pull', 667),\n",
       " ('crew', 628),\n",
       " ('locomot', 594),\n",
       " ('lead', 559),\n",
       " ('caus', 474),\n",
       " ('engin', 458),\n",
       " ('end', 449),\n",
       " ('line', 441),\n",
       " ('railcar', 438),\n",
       " ('rail', 427),\n",
       " ('releas', 423),\n",
       " ('cross', 404)]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(\" \".join(raw_data_df[\"PREP\"]).split()).most_common(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_words = '' \n",
    "\n",
    "for arg in raw_data_df[\"PREP\"]: \n",
    "      \n",
    "    tokens = arg.split() \n",
    "      \n",
    "      \n",
    "    all_words += \" \".join(tokens)+\" \"\n",
    "\n",
    "wordcloud = WordCloud(width = 500, height = 500, \n",
    "                background_color ='white', \n",
    "                min_font_size = 10).generate(all_words) \n",
    "  \n",
    "# plot the WordCloud image                        \n",
    "plt.figure(figsize = (5, 5), facecolor = None) \n",
    "plt.imshow(wordcloud) \n",
    "plt.axis(\"off\") \n",
    "plt.tight_layout(pad = 0) \n",
    "  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('train', 'struck') 58\n",
      "('train', 'emerg') 93\n",
      "('main', 'track') 130\n",
      "('line', 'switch') 136\n",
      "('crew', 'shove') 58\n",
      "('shove', 'yard') 78\n",
      "('car', 'derail') 368\n",
      "('derail', 'car') 372\n",
      "('set', 'truck') 58\n",
      "('derail', 'railcar') 236\n",
      "('railcar', 'shove') 63\n",
      "('hazard', 'materi') 303\n",
      "('materi', 'releas') 310\n",
      "('lead', 'locomot') 145\n",
      "('locomot', 'car') 63\n",
      "('car', 'track') 139\n",
      "('caus', 'damag') 55\n",
      "('yard', 'track') 266\n",
      "('improperli', 'line') 119\n",
      "('shove', 'move') 63\n",
      "('turn', 'run') 57\n",
      "('switch', 'hazard') 55\n",
      "('train', 'oper') 68\n",
      "('track', 'car') 74\n",
      "('train', 'derail') 67\n",
      "('track', 'failur') 58\n",
      "('crew', 'pull') 53\n",
      "('east', 'end') 61\n",
      "('derail', 'locomot') 80\n",
      "('broken', 'rail') 75\n",
      "('result', 'derail') 54\n",
      "('cut', 'car') 116\n",
      "('car', 'roll') 50\n",
      "('equip', 'damag') 89\n",
      "('ns', 'train') 85\n",
      "('shove', 'back') 52\n",
      "('track', 'damag') 141\n",
      "('caus', 'car') 85\n",
      "('west', 'end') 76\n",
      "('track', 'switch') 61\n",
      "('track', 'derail') 86\n",
      "('switch', 'point') 58\n",
      "('maintain', 'track') 56\n",
      "('derail', 'track') 54\n",
      "('track', 'track') 90\n",
      "('shove', 'track') 52\n",
      "('railcar', 'pull') 74\n",
      "('pull', 'yard') 80\n",
      "('shove', 'car') 64\n",
      "('car', 'train') 55\n",
      "('load', 'empti') 61\n",
      "('singl', 'main') 53\n"
     ]
    }
   ],
   "source": [
    "\n",
    "n_gram = 2\n",
    "n_gram_dic = dict(Counter(ngrams(all_words.split(), n_gram)))\n",
    "\n",
    "for i in n_gram_dic:\n",
    "    if n_gram_dic[i] >= 50:\n",
    "        print(i, n_gram_dic[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Derailing and Hazardous Materials\n",
    "Ok, derailing is no doubt the most commin incident, followed by train strike. I want to label the rows where derailing occured and those were the train strucked something. I further want to check whether hazardous material was released and/or whether anything was damaged - negation will be an issue (eg. no hazardous materials were released, nothing was damaged). I also want to extract the damage cost so we can have a look at regular expressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing the words in a list, in case I want to add any later\n",
    "derail_words = ['derail']\n",
    "struck_words = ['struck']\n",
    "\n",
    "\n",
    "#checking whether any of the words appear in the incident text and store \"true\" or \"false\" in columns DERAIL and STRUCk\n",
    "raw_data_df['DERAIL'] = raw_data_df['PREP'].apply(lambda x: any([k in x for k in derail_words]))\n",
    "raw_data_df['STRUCK'] = raw_data_df['PREP'].apply(lambda x: any([k in x for k in struck_words]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IYR</th>\n",
       "      <th>IMO</th>\n",
       "      <th>RAILROAD</th>\n",
       "      <th>STATE</th>\n",
       "      <th>STATION</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>NARR</th>\n",
       "      <th>PREP</th>\n",
       "      <th>DERAIL</th>\n",
       "      <th>STRUCK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>ATK</td>\n",
       "      <td>6</td>\n",
       "      <td>CASTROVILLE</td>\n",
       "      <td>MONTEREY</td>\n",
       "      <td>CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...</td>\n",
       "      <td>camera equip train struck occupi sanit truck m...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>MNCW</td>\n",
       "      <td>9</td>\n",
       "      <td>NORWALK</td>\n",
       "      <td>FAIRFIELD</td>\n",
       "      <td>PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...</td>\n",
       "      <td>product gang travel north piec equip seper pie...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>FEC</td>\n",
       "      <td>12</td>\n",
       "      <td>POMPANO BEACH</td>\n",
       "      <td>BROWARD</td>\n",
       "      <td>BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "      <td>blf travel southbound east main approxim mph n...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>FEC</td>\n",
       "      <td>12</td>\n",
       "      <td>POMPANO BEACH</td>\n",
       "      <td>BROWARD</td>\n",
       "      <td>BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "      <td>blf travel southbound east main notic vehicl f...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>CSX</td>\n",
       "      <td>12</td>\n",
       "      <td>SEVILLE</td>\n",
       "      <td>VOLUSIA</td>\n",
       "      <td>AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.</td>\n",
       "      <td>amtrak struck semi privat cross</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2679</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>TRC</td>\n",
       "      <td>6</td>\n",
       "      <td>TRONA</td>\n",
       "      <td>SAN BERNARDINO</td>\n",
       "      <td>RUN 2 WAS IN THE PROCESS OF PULLING THE HEAD 1...</td>\n",
       "      <td>run process pull head load saltspur track trc ...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2680</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>NCTC</td>\n",
       "      <td>6</td>\n",
       "      <td>OCEANSIDE</td>\n",
       "      <td>SAN DIEGO</td>\n",
       "      <td>METROLINK 858 DERAILED AT STUART MESA YARD.</td>\n",
       "      <td>metrolink derail stuart mesa yard</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2681</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>MET</td>\n",
       "      <td>6</td>\n",
       "      <td>MODESTO</td>\n",
       "      <td>STANISLAUS</td>\n",
       "      <td>ON THE MORNING OF JANUARY 15, 2019, CONDUCTOR ...</td>\n",
       "      <td>morn januari conductor switch car industri set...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2682</th>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>BNSF</td>\n",
       "      <td>6</td>\n",
       "      <td>BAKERSFIELD</td>\n",
       "      <td>KERN</td>\n",
       "      <td>EMPLOYEE STOPPED AND LINED THE DERAIL TO THE W...</td>\n",
       "      <td>employe stop line derail work posit pull locom...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2683</th>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>UP</td>\n",
       "      <td>6</td>\n",
       "      <td>BLOOMINGTON</td>\n",
       "      <td>SAN BERNARDINO</td>\n",
       "      <td>LOQ25-26 WAS MAKING A SHOVING MOVE WITH LIGHT ...</td>\n",
       "      <td>loq make shove move light locomot track conduc...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2401 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      IYR  IMO RAILROAD  STATE        STATION          COUNTY  \\\n",
       "0      19   11      ATK      6    CASTROVILLE        MONTEREY   \n",
       "1      19   10     MNCW      9        NORWALK       FAIRFIELD   \n",
       "2      19    4      FEC     12  POMPANO BEACH         BROWARD   \n",
       "3      19    4      FEC     12  POMPANO BEACH         BROWARD   \n",
       "4      19    4      CSX     12        SEVILLE         VOLUSIA   \n",
       "...   ...  ...      ...    ...            ...             ...   \n",
       "2679   19    2      TRC      6          TRONA  SAN BERNARDINO   \n",
       "2680   19    2     NCTC      6      OCEANSIDE       SAN DIEGO   \n",
       "2681   19    1      MET      6        MODESTO      STANISLAUS   \n",
       "2682   19    7     BNSF      6    BAKERSFIELD            KERN   \n",
       "2683   19   12       UP      6    BLOOMINGTON  SAN BERNARDINO   \n",
       "\n",
       "                                                   NARR  \\\n",
       "0     CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...   \n",
       "1     PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...   \n",
       "2     BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...   \n",
       "3     BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...   \n",
       "4        AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.   \n",
       "...                                                 ...   \n",
       "2679  RUN 2 WAS IN THE PROCESS OF PULLING THE HEAD 1...   \n",
       "2680        METROLINK 858 DERAILED AT STUART MESA YARD.   \n",
       "2681  ON THE MORNING OF JANUARY 15, 2019, CONDUCTOR ...   \n",
       "2682  EMPLOYEE STOPPED AND LINED THE DERAIL TO THE W...   \n",
       "2683  LOQ25-26 WAS MAKING A SHOVING MOVE WITH LIGHT ...   \n",
       "\n",
       "                                                   PREP  DERAIL  STRUCK  \n",
       "0     camera equip train struck occupi sanit truck m...   False    True  \n",
       "1     product gang travel north piec equip seper pie...   False   False  \n",
       "2     blf travel southbound east main approxim mph n...   False    True  \n",
       "3     blf travel southbound east main notic vehicl f...   False    True  \n",
       "4                       amtrak struck semi privat cross   False    True  \n",
       "...                                                 ...     ...     ...  \n",
       "2679  run process pull head load saltspur track trc ...    True   False  \n",
       "2680                  metrolink derail stuart mesa yard    True   False  \n",
       "2681  morn januari conductor switch car industri set...   False   False  \n",
       "2682  employe stop line derail work posit pull locom...    True   False  \n",
       "2683  loq make shove move light locomot track conduc...    True   False  \n",
       "\n",
       "[2401 rows x 10 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if no derailing and no striking occured, label OTHER as true\n",
    "raw_data_df['OTHER'] = (raw_data_df['DERAIL'] == False) & (raw_data_df['STRUCK'] == False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Negation\n",
    "Negation is a complex problem. What he have done so far is simple text preprocessing and analysis. Checking for negation involves dependency parsing and relationship extraction. However, judging so far the incident texts are quite simple so we might be able to get some good results with easier techniques. Let's try out these first!\n",
    "\n",
    "\n",
    "Examples:\n",
    "No Hazardous materials were released - check for bigram \"No hazardous\" and we know its a negation\n",
    "No train damage - that's already more difficult. What if instead of \"train\" we have another word?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets take the NARR column and create a list of sentences "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_narr_list = raw_data_df['NARR'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SANITATION TRUCK AT MP 110.68, A PRIVATE RD CROSSING.  T HE OCCUPANT OF THE VEHICLE WAS A CONFIRMED FATALITY.',\n",
       " 'PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECES OF EQUIPMENT, HAD 7 SEPERATE PIECES OF EQUIPMENT MAK E IMPACT WITH ONE ANOTHER.',\n",
       " 'BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN AT APPROXIMATELY 79 MPH AND NOTICED A TRESPASSER COMIN G ACROSS THE TRACKS WITH HIS BIKE FROM THE WEST SIDE OF THE TRACKS TO THE EAST SIDE OF THE TRACKS. LOCOMOTIVE ENGINEER SOUNDED HORN AND BELL TO WARN TRESPASSER AND PLACED TRAIN INTO EMERGENCY.  TRESP ASSER WAS STRUCK RESULTING IN A FATALITY.  BLF COSTS WERE $15325.',\n",
       " 'BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN AND NOTICED A VEHICLE FOULING HAMMONDVILLE ROAD.  LOCO MOTIVE ENGINEER SOUNDED HORN AND BELL AND PLACED TRAIN IN EMERGENCY.  TRAIN STRUCK VEHICLE AND PASSE NGERS IN VEHICLE WERE TRANSPORTED TO LOCAL HOSPITAL WITH MINOR INJURIES.  BLF COSTS WERE $10793.',\n",
       " 'AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_narr_list[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Dummy', 'sentence'), ('sentence', 'to'), ('to', 'demonstrate'), ('demonstrate', 'bigrams')]\n",
      "[('Dummy', 'sentence', 'to'), ('sentence', 'to', 'demonstrate'), ('to', 'demonstrate', 'bigrams')]\n"
     ]
    }
   ],
   "source": [
    "from nltk import word_tokenize, bigrams, trigrams\n",
    "\n",
    "\n",
    "word_data = \"Dummy sentence to demonstrate bigrams\"\n",
    "nltk_tokens = word_tokenize(word_data) \n",
    "#using tokenize from NLKT and not split() because split() does not take into account punctuation\n",
    "\n",
    "print(list(bigrams(nltk_tokens)))\n",
    "print(list(trigrams(nltk_tokens)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# looping through all the incident texts and storing the bigrams and trigrams in separate lists\n",
    "all_narr_bigrams = []\n",
    "all_narr_trigrams = []\n",
    "\n",
    "for narr in all_narr_list:\n",
    "    narr = word_tokenize(narr)\n",
    "    all_narr_bigrams.append(list(bigrams(narr)))\n",
    "    all_narr_trigrams.append(list(trigrams(narr)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('CAMERA-EQUIPPED', 'TRAIN'),\n",
       " ('TRAIN', '14'),\n",
       " ('14', 'STRUCK'),\n",
       " ('STRUCK', 'AN'),\n",
       " ('AN', 'OCCUPIED'),\n",
       " ('OCCUPIED', 'SANITATION'),\n",
       " ('SANITATION', 'TRUCK'),\n",
       " ('TRUCK', 'AT'),\n",
       " ('AT', 'MP'),\n",
       " ('MP', '110.68'),\n",
       " ('110.68', ','),\n",
       " (',', 'A'),\n",
       " ('A', 'PRIVATE'),\n",
       " ('PRIVATE', 'RD'),\n",
       " ('RD', 'CROSSING'),\n",
       " ('CROSSING', '.'),\n",
       " ('.', 'T'),\n",
       " ('T', 'HE'),\n",
       " ('HE', 'OCCUPANT'),\n",
       " ('OCCUPANT', 'OF'),\n",
       " ('OF', 'THE'),\n",
       " ('THE', 'VEHICLE'),\n",
       " ('VEHICLE', 'WAS'),\n",
       " ('WAS', 'A'),\n",
       " ('A', 'CONFIRMED'),\n",
       " ('CONFIRMED', 'FATALITY'),\n",
       " ('FATALITY', '.')]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first incident text in bigrams\n",
    "all_narr_bigrams[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AMTRAK CALLED TO INFORM THAT THEY FOUND WHAT APPEARED TO BE DERAILMENT MARKS NEAR A TURNOUT.  CSO 1 REPORTED NOTHING OUT OF THE ORDINARY WITH THEIR TRAIN IN THAT AREA AND AFTER INSPECTING THE CARS BEI NG DELIVERED TO CSX FOUND THAT THE FICX 8735 MAY HAVE DERAILED BUT HAD NO DAMAGE AND WAS BEING SHIPP ED TO BE SCRAPPED.\n",
      "176\n",
      "YTU24R-29, IN AN ATTEMPT TO COUPLE THE UP4590 AND UP4509, THE DRAWBARS CROSSED CAUSING DAMAGE TO THE UP4590.  NO DERAILMENT AND NO DAMAGE TO THE UP4509.\n",
      "608\n",
      "TRAIN #X123 OPERATED IN ELECTRIC MODE DEPARTING HOBOKEN ENROUTE WEST ON TRACK #3 M&E AND DIVERTED ON TO TRACK 3 ML AT WEST END AND OPERATED OUT FROM UNDER T E WIRE CAUSING DAMAGE TO THE PANTOGRAPH ON E NGINE #4514. CONFIRMED NO DAMAGE TO CATENARY.\n",
      "948\n",
      "CLNA TRAIN 115-03 TRAVELING WESTBOUND DERAILED AT 2:50PM. A 2 FOOT SECTION OF RAIL HEAD BROKE UNDER REAR CAR OF TRAIN, DROPPING 1 WHEEL (R4) ON THE GROUND. CAR RODE THE BASE OF THE RAIL FOR 628 FEET U NTIL TRUCKS SLEWED ENOUGH TO CAUSE L4 WHEEL TO WALK OVER THE RAIL IT WAS RIDING ON. CAR TRAVELED 118 FEET UNTIL ROLLING ONTO ITS LEFT SIDE. CAR ROLLED FORCED THE CAR AHEAD OF IT OFF THE RAIL. CREW SHO WED NO SIGNS OR SYMPTOMS OF ALCOHOL/DRUG USE. NO DAMAGE OR SPILLAGE OF FUEL FROM EITHER LOCOMOTIVE I N CONSIST. 25 OTHER CARS IN TRAIN RECEIVED NO DAMAGE. 14 EMPTY HAZMAT CARS IN TRAIN (UN 2448 - SULFU R, MOLTEN) WERE NOT DERAILED AND RECEIVED NO DAMAGE.\n",
      "967\n",
      "ZMQG4-14, LEAD LOCOMOTIVE UP5382, STRUCK UNOCCUPIED VEHICLE FOULING MAIN TRACK 50 FEET SOUTH FROM TH E CROSSING AT MP 73.10 ON THE JOLIET SUB.  NO INJURIES.  NO DAMAGE TO THE LOCOMOTIVE.  OCCURRED 50 F EET FROM CROSSING.\n",
      "1109\n",
      "HOBOKEN EXTRA CREW OPERATED ENGINE #4517 WITH PANTOGAPH RAISED INTO UNELECTRIFIED TERRITORY WHILE AT TEMPTING TO FUEL VIA THE UNWIRED OLD WESTBOUND TRACK. CONFIRMED NO DAMAGE TO CATENARY.\n",
      "1248\n",
      "CAR DERAILED IN CROSSING AND SHOVED 5 CAR LENGTHS. TRUCKS CAME OUT FROM UNDER CAR. FOULED BNSF TRACK WITH CAR, CAUSING NO DAMAGE.\n",
      "1572\n",
      "AMT2-15 STRUCK AN EMPTY CEMENT TRUCK AT MP404.65, BEAUMONT SUB.  MINOR INJURIES TO DRIVER OF TRUCK A ND ONE PASSENGER WAS TRANSPORTED TO NORTHEAST HOSPITAL WITH SEIZURES/ANXIETY.  NO DAMAGE TO TRACK OR UPRR STRUCTURES.  SIGNIFICANT DAMAGE TO THE AMTK173.  ATK EQUIPMENT DAMAGE - $45,062\n",
      "1773\n",
      "UP'S MADIT TRAIN STRUCK A DUMP TRUCK AT MP446.1, NO DAMAGE REPORTED TO TRACKS OR CROSSINGS.\n",
      "2348\n"
     ]
    }
   ],
   "source": [
    "no_damage_bigrams = [('NO', 'DAMAGE')]\n",
    "index = 0\n",
    "\n",
    "for narr in all_narr_bigrams:\n",
    "    for bi in no_damage_bigrams:\n",
    "        if bi in narr:\n",
    "            print(all_narr_list[index])\n",
    "            print(index)\n",
    "    index += 1\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there are no that many cases of no damage, and that some incidents specify that no damage occured to one thing but may have occured to something else. However, given we know the indexes, we can update them later manually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_damage_indexes = [176, 967, 1109, 1248, 1572, 2348]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's check for trigrams. This loop requires another nested for loop since we are only interested in the first and third word (NO and DAMAGE) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSAFW-09 CREW WAS TRAVELING NORTH ON THE WACO SUB WHEN THEY WENT INTO UDE. THE CREW WAS TRAVELING AT 20 MPH WHEN DERAILMENT OCCURRED. IT WAS DETERMINED THAT THE ENGINEER HAD TOO MANY DYNAMIC BRAKES CU T IN ON THE LEAD LOCOMOTIVE OF CONSIST. THE EDBA ON THE CONSIST WAS 48.5 COMPARED TO A MAX ALLOWABLE OF 28. NO TRACK DAMAGE.\n",
      "299\n",
      "MAINTENANCE OF WAY WAS INSTRUCTED TO DUMP THE AIR ON DUMP ROCK CARS AT COBURN SO THEY COULD BE SCRAP ED.  DUE TO FROZEN MATERIAL IN THE CAR CAUSED BY WINTER WEATHER, INSTEAD OF THE MATERIAL DUMPING FRO M THE CAR, IT TIPPED THE ENTIRE CAR OVER AND OFF THE RAIL.  NO TRACK DAMAGE.\n",
      "536\n",
      "H02H030 WITH 7 LOADS AND 6 EMPTIES 698 TONS OPERATING NORTHBOUND FROM DUNDALK MARINE TERMINAL INTO B AYVIEW YARD ON THE PRESIDENTS STREET I.T. RECIEVED INSTRUCTIONS FROM THE BALTIMORE YARDMASTER TO ENT ER ON THE TRAPPE ROAD TRACK SUBSEQUENTLY STRIKINGOVERHEAD CSXT RAILWAY BRIDGE AND DAMAGING THE BRIDG E AS WELL AS 7 LOADED MULTILEVEL CARS. (NO LADING DAMAGE).\n",
      "977\n",
      "BK42-28 WAS IN THE PROCESS OF SWITCHING OUT TRACK #3 AT THE WEST END OF KNOCHE YARD WHEN THE CARS TH AT WERE SWITCHED INTO TRACK 9 HAD RUN THROUGH THE EAST END RESULTING IN A CORNERING INCIDENT.  BK42 SWITCHED 3 LOADED CARS INTO 9 TRACK MAKING A JOINT WITH TWO CARS AND STALLED.  THE FOREMAN THEN RELE ASED THE HANDBRAKE AND WATCHED THE 5 CARS ROLL INTO 9 TRACK.   BK42 THEN SWITCHED MORE LOADED CARS I NTO 9 TRACK AND WATCHED THE CARS CLEAR TO ENSURE THEY DID NOT STALL.  VALIDATED BY CAMERA SYSTEM THE 8 CARS CAUGHT UP COUPLING TO THE REMAINDER OF THE TRACK AND PUSHED THE CARS FOUL AT THE EAST END. UPON INCIDENT DRILL DOWN WITH THE CREW IT WAS DETERMINED THAT THE ASSIGNMENT FAILED TO VALIDATE THAT TRACK 9 WAS SUFFICIENTLY SECURED IN ACCORDANCE WITH GOI SECTION 4 ITEM 1.3 G, PRIOR TO COMMENCING S WITCHING OPERATION.  KCS CONFIRMS NO TRACK DAMAGE.\n",
      "1217\n",
      "YARD CREW #MM-06 OBSERVED BUFFER PLATE DAMAGE ON THE B-END OF CAR #6026 AND C-END OF CAR #6028 DURIN G SHOP MOVE WEST OF THE S&I BUILDING AS A RESULT OF ONE CAR HAVING MORE AIR REMAINING WHEN DRILLED A ND A REPORTED TRACK CONDITION AS A CONTRIBUTING FACTOR. CONFIRMED NO TRACK DAMAGE.\n",
      "1250\n",
      "LHX07-27 WAS PICKING UP TRACK 9 IN ROBINSON YARD TRACK 9 AND FOUND A LOAD OF FLAT STEEL LMIC7001 NOT SECURE AND SHIFTED INTO SOXX620851.  NO DERAILMENT AND NO TRACK DAMAGE.  INDUSTRY MAINTAINS TRACK\n",
      "1772\n",
      "AMTRAK TRAIN 763 WAS DAMAGED WHEN IT STRUCK A TREE THAT FELL ACROSS THE TRACKS FOULING THE UPRR RIGH T OF WAY.  NO TRACK DAMAGE.  AMTRAK EQUIPMENT DAMAGE = $43,311\n",
      "1913\n",
      "PROGRESSIVE RAIL CONDUCTOR LINED SWITCH UNDER LOCOMOTIVE.  WHEN THE LOCOMOTIVE PULLED AHEAD, IT RAN THROUGH THE SWITCH, CAUSING EXTENSIVE DAMAGE TO THE SWITCH.  NO DERAILMENT.  TERMINAL SWITCHING SERV ICES REPORTS NO EQUIPMENT DAMAGE.\n",
      "1981\n",
      "AMTRAK TRAIN 59 OPERATING ON IC'S MAIN LINE TRACK STRUCK A TREE AT MP 778.6 ON THE MCCOMB SUB CAUSIN G DAMAGE TO THEIR LEAD LOCOMOTIVE. THERE WAS NO TRACK DAMAGE. AMTRAK ADVISED EQUIPMENT COSTS OF $19, 119.39.\n",
      "2200\n"
     ]
    }
   ],
   "source": [
    "no_damage_trigrams = [('NO', 'whatever', 'DAMAGE')]\n",
    "index = 0\n",
    "\n",
    "for narr in all_narr_trigrams:\n",
    "    for tri in no_damage_trigrams:\n",
    "        for t in narr:\n",
    "            if t[0] == tri[0] and t[2] == tri[2]:\n",
    "                print(all_narr_list[index])\n",
    "                print(index)\n",
    "    index += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, we identified a few more - but again I did it manually and added the indexes to the list above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_damage_indexes.extend([299, 536, 977, 1217, 1740, 1772])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we have a list of indexes for incident texts where no damage occured. Now we can loop through the incident texts and check whether the word \"damage\" is present and the index of that incident text is not in our \"no damage indexes\" list. This way we weill identify those incidents where real damage occured"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = 0\n",
    "damage_indexes = []\n",
    "for narr in all_narr_list:  \n",
    "    n = word_tokenize(narr)\n",
    "    if ('DAMAGE' in n or 'DAMAGED' in n) and index not in no_damage_indexes:\n",
    "        #print(narr)\n",
    "        #print(index)\n",
    "        damage_indexes.append(index)\n",
    "    index +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "394"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(damage_indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fixing No derailment\n",
    "\n",
    "Remember, above when we extracted key words we extracted \"derail\" and labeled the column \"Derailed\" as True?\n",
    "Now we know that some incidents report that NO derailment occured. We have not accounted for negation above, so we need to fix that\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_derail_bigram = [('NO', 'DERAILMENT'), ('NOT', 'DERAILED')]\n",
    "index = 0\n",
    "no_derail_indexes = []\n",
    "\n",
    "for narr in all_narr_bigrams:  \n",
    "    for bi in no_derail_bigram:\n",
    "        if bi in narr:\n",
    "            #print(all_narr_list[index])\n",
    "            #print(index)\n",
    "            no_derail_indexes.append(index)\n",
    "    index += 1\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(no_derail_indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hazardous materials\n",
    "now we want to do the same as above but with hazardous materials\n",
    "\n",
    "Code will throw error - use all_narr_bigrams for now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_hazard_bigrams = [('NO', 'HAZARDOUS'), ('NO', 'HAZMAT'), ('NO', 'LEAKS'), ('O', 'HAZMAT'), ('O', 'HAZARDOUS')] #<<- insert  ('NO', 'LEAKS'),('O', 'HAZMAT'), ('O', 'HAZARDOUS') here\n",
    "index = 0\n",
    "no_hazmat_indexes = []\n",
    "\n",
    "for narr in all_narr_bigrams:   \n",
    "    for bi in no_hazard_bigrams:\n",
    "        if bi in narr:\n",
    "            #print(all_narr_list[index])\n",
    "            #print(index)\n",
    "            no_hazmat_indexes.append(index)\n",
    "    index += 1\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "352"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(no_hazmat_indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's print put the incident texts where there should be hazmat releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THE MANISTEE-21 CREW WAS SWITCHING PACKAGING CORPORATION OF AMERICA WITH 16X12 WHEN FOUR LOADED ASPH ALT TANK CARS (SEQUENCE: 13, 14, 15, 16) DERAILED NEAR THE GATE BETWEEN MQT AND TONDU (PACKAGING COR PORATION OF AMERICA).  4 HAZMAT (CLASS 9) CARS DERAILED, UPRIGHT AND NOT LEAKING.\n",
      "229\n",
      "THE MRENL-20 DERAILED THREE LOADED AUTO RACKS AT MP 351.06 ON THE REISOR SUB.  THERE ARE NO INJURIES OR HAZMAT RELEASES ASSOCIATED WITH THIS DERAILMENT.  1 DRUG POSITIVE - NOT DETERMINED TO BE A CAUSA L FACTOR.\n",
      "362\n",
      "AT 1240 ON JUNE 9TH 2019 THE 0759 EH WAS SHOVING WEST UP 3 EAST APPROACH AND COLLIDED AT THE TOP OF THE HUMP WITH THE 0630 EAST HUMP ASSIGNMENT WHO WAS IN THE PROCESS OF HUMPING WESTWARD OFF OF 01EAPR . NO ONE WAS INJURED FROM THIS INCIDENT HOWEVER THERE WAS A TOTAL OF FOURTEEN CARS DAMAGED OF WHICH 10 OF THE CARS WERE DERAILED OF THE DERAILED CARS SIX OF THEM WERE ON THEIR SIDE JUST TO THE NORTH O F THE EAST HUMP LEADS AT THE TOP OF THE HUMP. MOST OF THE DAMAGED EQUIPMENT WERE TANK CARS IN WHICH TWO OF THEM CLASS 9 HAZARDOUS HOWEVER THERE WAS NO PRODUCT THAT WAS EXPOSED FROM THE DAMAGE. ALL EMP LOYEES WERE CLEARED OF THE AREA AND THE HAZARDOUS MATERIAL TEAM WERE CALLED OUT AS A PRECAUTIONARY, AND A THOROUGH INSPECTION OF THE DAMAGED EQUIPMENT WAS PERFORMED. HULCHER WAS CALLED OUT TOCLEAN UP AND RERAIL DAMAGED CARS.\n",
      "571\n",
      "498-27 WAS TRAVELING EASTWARD ON THE CARRINGTON SUBDIVISION WHEN THE 96TH CAR TRAVERSED A BROKEN BOL T HOLE CAUSING 35 CARS TO DERAIL. TRAIN WAS TRAVELING IN IDLE AT 49MPH AT THE TIME OF THE DERAILMENT . LINE 96 (CRYX 7084) TO LINE 130 (CGTX 64480) DERAILED. TRAIN WAS OPERATING WITH DISTRIBUTIVE POWER WITH THE REMOTE LOCOMOTIVE PLACED IN THE 56TH POSITION OF THE TRAIN. THERE WERE NO TRAIN HANDLING E XCEPTIONS NOTED. THE MAXIMUM SPEED OF THIS LOCATION IS 49MPH. DERAILMENT ALSO RESULTED IN 3 HAZARDOU S CARS RELEASING PRODUCT - PROX 93752 STCC 4905421, PROX 105499 STCC 4920359 & PROX 105275 STCC 4920 359. HAZMAT RELEASE NOT MEASURABLE. PRODUCT WAS FLARED. VAPOR LOSS ONLY.\n",
      "620\n",
      "YDU51-10 WAS SHOVING SOUTH TO TRACK 303 WITH ALL CARS OFF AIR. THE ENGINEER WAS INSTRUCTED TO BRING MOVE TO A STOP AT THE NORTH END OF TRACK 303 TO ALLOW CONDUCTOR TO REPOSITION AT THE SOUTH END OF TR ACK 303 AND PROTECT THE SHOVE. THE CONDUCTOR WAS UNAWARE THE TRAIN BROKE IN TWO 53 CARS AHEAD OF HIS LOCATION AND WERE ROLLING SOUTH. WHEN HE REPOSITIONED, HE GAVE ENGINEER A FRESH CAR COUNT TO CONTIN UE THE SHOVE, AT WHICH TIME THE 83 CARS ATTACHED TO THE LOCOMOTIVE SHOVED INTO THE 53 CARS THAT HAD BROKEN AWAY. THE IMPACT INTO THE 53 CARS CAUSED DERAILMENT OF 14 CARS, INCLUDING ONE HAZMAT CAR THAT CAUGHT FIRE. 1 DRUG POSITIVE - NOT DETERMINED TO BE A CASUAL FACTOR. AT THE TIME OF THE INCIDENT TH E RAIL EQUIPMENT WAS MOVING ABOVE THE MAXIMUM FOR THE TRACK CLASS REPORTED.  #11 - 2158 PERSONS EVAC UATED\n",
      "706\n",
      "YDU51-10 WAS SHOVING SOUTH TO TRACK 303 WITH ALL CARS OFF AIR. THE ENGINEER WAS INSTRUCTED TO BRING MOVE TO A STOP AT THE NORTH END OF TRACK 303 TO ALLOW CONDUCTOR TO REPOSITION AT THE SOUTH END OF TR ACK 303 AND PROTECT THE SHOVE. THE CONDUCTOR WAS UNAWARE THE TRAIN BROKE IN TWO 53 CARS AHEAD OF HIS LOCATION AND WERE ROLLING SOUTH. WHEN HE REPOSITIONED, HE GAVE ENGINEER A FRESH CAR COUNT TO CONTIN UE THE SHOVE, AT WHICH TIME THE 83 CARS ATTACHED TO THE LOCOMOTIVE SHOVED INTO THE 53 CARS THAT HAD BROKEN AWAY. THE IMPACT INTO THE 53 CARS CAUSED DERAILMENT OF 14 CARS, INCLUDING ONE HAZMAT CAR THAT CAUGHT FIRE. 1 DRUG POSITIVE - NOT DETERMINED TO BE A CASUAL FACTOR. CAR#: UTLX 200035 METHYL ISOBU TYL KETO, WHOLE CAR. AT THE TIME OF THE INCIDENT THE RAIL EQUIPMENT WAS MOVING ABOVE THE MAXIMUM FOR THE TRACK CLASS REPORTED.  #11 - 2158 PERSONS EVACUATED\n",
      "707\n",
      "MITDM-14 WAS TRAVELING SOUTH ON THE ALBERT LEA MAIN WHEN IT DERAILED 9 CARS IN MANLY, IA.  NO INJURI ES OR HAZMAT RELEASES REPORTED.  1 SET OF WHEELS DERAILED SOUTH OF THE IANR MAIN LINE SWITCH IN MANL Y AND WAS CARRIED ON THE GROUND FOR 0.2 MILES UNTIL IT HIT THE NORTH SWITCH OF THE MANLY SIDING.  AT THIS POINT, THE OTHER 8 CARS DERAILED.\n",
      "879\n",
      "CLNA TRAIN 115-03 TRAVELING WESTBOUND DERAILED AT 2:50PM. A 2 FOOT SECTION OF RAIL HEAD BROKE UNDER REAR CAR OF TRAIN, DROPPING 1 WHEEL (R4) ON THE GROUND. CAR RODE THE BASE OF THE RAIL FOR 628 FEET U NTIL TRUCKS SLEWED ENOUGH TO CAUSE L4 WHEEL TO WALK OVER THE RAIL IT WAS RIDING ON. CAR TRAVELED 118 FEET UNTIL ROLLING ONTO ITS LEFT SIDE. CAR ROLLED FORCED THE CAR AHEAD OF IT OFF THE RAIL. CREW SHO WED NO SIGNS OR SYMPTOMS OF ALCOHOL/DRUG USE. NO DAMAGE OR SPILLAGE OF FUEL FROM EITHER LOCOMOTIVE I N CONSIST. 25 OTHER CARS IN TRAIN RECEIVED NO DAMAGE. 14 EMPTY HAZMAT CARS IN TRAIN (UN 2448 - SULFU R, MOLTEN) WERE NOT DERAILED AND RECEIVED NO DAMAGE.\n",
      "967\n",
      "YPD11-07, LEAD LOCOMOTIVE UPY635 PULLED EAST OUT OF TRACK 19 WITH FOREMAN RIDING THE PLATFORM. THE F OREMAN OBSERVED A BROKEN RAIL ON THE SOUTH RAIL OF TRACK 20 AND IMMEDIATELY TOLD THE ENGINEER THAT W ILL DO. MOVEMENT OVER THE BROKEN RAIL RESULTED IN THE DERAILMENT OF TWO LOCOMOTIVES AND THREE LOADED HAZMAT CARS. GOING STREET OVER PASS WAS STRUCK BY LOCOMOTIVE UPY635 AND UP699 RESULTING IN DAMAGE O N THE NORTH CENTER LANE PILLAR.\n",
      "1317\n",
      "SOUTH PLAINS LAMESA RAILROAD WAS WORKING INSIDE VESTAS OFF THE VESTAS LEAD, PULLED OFF TWO CARS.  A SINGLE BNSF ENGINE ROLLED OUT DOWN THE VESTAS LEAD AND STRUCK A SPLIT POINT DERAIL RESULTING IN A BN SF ENGINE ON ITS SIDE.  NO INJURIES OR HAZMAT RELEASES.  SOUTH PLAINS LAMESA RAILROAD WAS PERFORMING THE WORK, BNSF ENGINE CAUSED THE IMPACT, UP MAINTAINED TRACK.  SOUTH PLAINS LAMESA RAILROAD EQUIPME NT DAMAGE = $100,000\n",
      "1431\n",
      "YSP51-05 WAS PULLING INTO EAST MINNEAPOLIS YARD WITH THE MINNESOTA COMMERCIAL TRANSFER.  THEY STARTE D INTO THE YARD AND DERAILED AT THE TK5 SWITCH.  SEVEN TANK CARS DERAILED, SIX BEING LOADED, ELEVATE D TEMPERATURE HAZMAT (ASPHALT).\n",
      "1983\n",
      "THE RUT 212 CONDUCTOR FAILED TO PROPERLY PROTECT A SHOVE THAT RESULTED IN THE MOVEMENT RUNNING THROU GH THE TRACK 6 SWITCH IN EAST PROVO YARD WHILE BUILDING THE OUTBOUND PVOSTO1.  WHEN THE OUTBOUND CRE W FOR THE PVOSTO1 BEGAN TO DEPART PROVO YARD WESTBOUND, THE CREW FELT A TUG THEN THE TRAIN WENT INTO EMERGENCY. THE CONDUCTOR INSPECTED THE TRAIN DISCOVERED THEY HAD DERAILED THE REAR LOADED HAZMAT TA NK CAR CONTAINING (BISULPHITES, AQUEOUS, SOLUTION N.O.S.) DUE TO MOVEMENT THROUGH THE PREVIOUSLY RUN THROUGH SWITCH. THE DERAILED HAZMAT CAR ALSO STRUCK AND DERAILED A NON-HAZMAT CAR ON THE ADJACENT T RACK. THERE WAS NO RELEASE OF THE HAZMAT COMMODITY AND NO INJURIES.\n",
      "2048\n",
      "MOGWC-29, LEAD LOCOMOTIVE UP8905, EXPERIENCED A UDE AT MP 686.1.  UPON INSPECTION, TRAIN WAS FOUND T O BE DERAILED.  CREW EVACUATED WESTBOUND ON HEAD END POWER (CUT AND RUN) DUE TO HAZMAT INVOLVED.  UP ON INVESTIGATION, 25 CARS WERE FOUND TO BE DERAILED DUE TO TREAD BUILDUP FOUND ON THE MP650466.  CAR #: TILX 303199 PROPANE - 80% OF CAR WAS RELEASED.  CAR#: TILX 305830 PROPANE - VAPOR DISPERSION.  CA R#: TILX 308673 PROPANE - CAR#: BRGX 537 BIO-DIESEL, <500 GAL.\n",
      "2055\n",
      "YHO32R 03 WAS PUTTING BRAKES AND CARS INTO 311.  AS THEY WERE DRAGGING OUT THEY DERAILED 2 CARS UPRI GHT.  NO INJURIES OR HAZMAT RELEASES.\n",
      "2370\n"
     ]
    }
   ],
   "source": [
    "index = 0\n",
    "for narr in all_narr_list: \n",
    "    n = narr.split()\n",
    "    if ('HAZARDOUS' in n or 'HAZMAT' in n) and index not in no_hazmat_indexes:\n",
    "        print(narr)\n",
    "        print(index)\n",
    "    index +=1\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we encounter a problem. it seems like either during typing the incident texts, or during merging of the columns, \"NO\" becamse \"N O\". However, this is an easy fix. Let's insert another bigram into our list above and rerun the cells from there\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THE MANISTEE-21 CREW WAS SWITCHING PACKAGING CORPORATION OF AMERICA WITH 16X12 WHEN FOUR LOADED ASPH ALT TANK CARS (SEQUENCE: 13, 14, 15, 16) DERAILED NEAR THE GATE BETWEEN MQT AND TONDU (PACKAGING COR PORATION OF AMERICA).  4 HAZMAT (CLASS 9) CARS DERAILED, UPRIGHT AND NOT LEAKING.\n",
      "229\n",
      "THE MRENL-20 DERAILED THREE LOADED AUTO RACKS AT MP 351.06 ON THE REISOR SUB.  THERE ARE NO INJURIES OR HAZMAT RELEASES ASSOCIATED WITH THIS DERAILMENT.  1 DRUG POSITIVE - NOT DETERMINED TO BE A CAUSA L FACTOR.\n",
      "362\n",
      "AT 1240 ON JUNE 9TH 2019 THE 0759 EH WAS SHOVING WEST UP 3 EAST APPROACH AND COLLIDED AT THE TOP OF THE HUMP WITH THE 0630 EAST HUMP ASSIGNMENT WHO WAS IN THE PROCESS OF HUMPING WESTWARD OFF OF 01EAPR . NO ONE WAS INJURED FROM THIS INCIDENT HOWEVER THERE WAS A TOTAL OF FOURTEEN CARS DAMAGED OF WHICH 10 OF THE CARS WERE DERAILED OF THE DERAILED CARS SIX OF THEM WERE ON THEIR SIDE JUST TO THE NORTH O F THE EAST HUMP LEADS AT THE TOP OF THE HUMP. MOST OF THE DAMAGED EQUIPMENT WERE TANK CARS IN WHICH TWO OF THEM CLASS 9 HAZARDOUS HOWEVER THERE WAS NO PRODUCT THAT WAS EXPOSED FROM THE DAMAGE. ALL EMP LOYEES WERE CLEARED OF THE AREA AND THE HAZARDOUS MATERIAL TEAM WERE CALLED OUT AS A PRECAUTIONARY, AND A THOROUGH INSPECTION OF THE DAMAGED EQUIPMENT WAS PERFORMED. HULCHER WAS CALLED OUT TOCLEAN UP AND RERAIL DAMAGED CARS.\n",
      "571\n",
      "498-27 WAS TRAVELING EASTWARD ON THE CARRINGTON SUBDIVISION WHEN THE 96TH CAR TRAVERSED A BROKEN BOL T HOLE CAUSING 35 CARS TO DERAIL. TRAIN WAS TRAVELING IN IDLE AT 49MPH AT THE TIME OF THE DERAILMENT . LINE 96 (CRYX 7084) TO LINE 130 (CGTX 64480) DERAILED. TRAIN WAS OPERATING WITH DISTRIBUTIVE POWER WITH THE REMOTE LOCOMOTIVE PLACED IN THE 56TH POSITION OF THE TRAIN. THERE WERE NO TRAIN HANDLING E XCEPTIONS NOTED. THE MAXIMUM SPEED OF THIS LOCATION IS 49MPH. DERAILMENT ALSO RESULTED IN 3 HAZARDOU S CARS RELEASING PRODUCT - PROX 93752 STCC 4905421, PROX 105499 STCC 4920359 & PROX 105275 STCC 4920 359. HAZMAT RELEASE NOT MEASURABLE. PRODUCT WAS FLARED. VAPOR LOSS ONLY.\n",
      "620\n",
      "YDU51-10 WAS SHOVING SOUTH TO TRACK 303 WITH ALL CARS OFF AIR. THE ENGINEER WAS INSTRUCTED TO BRING MOVE TO A STOP AT THE NORTH END OF TRACK 303 TO ALLOW CONDUCTOR TO REPOSITION AT THE SOUTH END OF TR ACK 303 AND PROTECT THE SHOVE. THE CONDUCTOR WAS UNAWARE THE TRAIN BROKE IN TWO 53 CARS AHEAD OF HIS LOCATION AND WERE ROLLING SOUTH. WHEN HE REPOSITIONED, HE GAVE ENGINEER A FRESH CAR COUNT TO CONTIN UE THE SHOVE, AT WHICH TIME THE 83 CARS ATTACHED TO THE LOCOMOTIVE SHOVED INTO THE 53 CARS THAT HAD BROKEN AWAY. THE IMPACT INTO THE 53 CARS CAUSED DERAILMENT OF 14 CARS, INCLUDING ONE HAZMAT CAR THAT CAUGHT FIRE. 1 DRUG POSITIVE - NOT DETERMINED TO BE A CASUAL FACTOR. AT THE TIME OF THE INCIDENT TH E RAIL EQUIPMENT WAS MOVING ABOVE THE MAXIMUM FOR THE TRACK CLASS REPORTED.  #11 - 2158 PERSONS EVAC UATED\n",
      "706\n",
      "YDU51-10 WAS SHOVING SOUTH TO TRACK 303 WITH ALL CARS OFF AIR. THE ENGINEER WAS INSTRUCTED TO BRING MOVE TO A STOP AT THE NORTH END OF TRACK 303 TO ALLOW CONDUCTOR TO REPOSITION AT THE SOUTH END OF TR ACK 303 AND PROTECT THE SHOVE. THE CONDUCTOR WAS UNAWARE THE TRAIN BROKE IN TWO 53 CARS AHEAD OF HIS LOCATION AND WERE ROLLING SOUTH. WHEN HE REPOSITIONED, HE GAVE ENGINEER A FRESH CAR COUNT TO CONTIN UE THE SHOVE, AT WHICH TIME THE 83 CARS ATTACHED TO THE LOCOMOTIVE SHOVED INTO THE 53 CARS THAT HAD BROKEN AWAY. THE IMPACT INTO THE 53 CARS CAUSED DERAILMENT OF 14 CARS, INCLUDING ONE HAZMAT CAR THAT CAUGHT FIRE. 1 DRUG POSITIVE - NOT DETERMINED TO BE A CASUAL FACTOR. CAR#: UTLX 200035 METHYL ISOBU TYL KETO, WHOLE CAR. AT THE TIME OF THE INCIDENT THE RAIL EQUIPMENT WAS MOVING ABOVE THE MAXIMUM FOR THE TRACK CLASS REPORTED.  #11 - 2158 PERSONS EVACUATED\n",
      "707\n",
      "MITDM-14 WAS TRAVELING SOUTH ON THE ALBERT LEA MAIN WHEN IT DERAILED 9 CARS IN MANLY, IA.  NO INJURI ES OR HAZMAT RELEASES REPORTED.  1 SET OF WHEELS DERAILED SOUTH OF THE IANR MAIN LINE SWITCH IN MANL Y AND WAS CARRIED ON THE GROUND FOR 0.2 MILES UNTIL IT HIT THE NORTH SWITCH OF THE MANLY SIDING.  AT THIS POINT, THE OTHER 8 CARS DERAILED.\n",
      "879\n",
      "CLNA TRAIN 115-03 TRAVELING WESTBOUND DERAILED AT 2:50PM. A 2 FOOT SECTION OF RAIL HEAD BROKE UNDER REAR CAR OF TRAIN, DROPPING 1 WHEEL (R4) ON THE GROUND. CAR RODE THE BASE OF THE RAIL FOR 628 FEET U NTIL TRUCKS SLEWED ENOUGH TO CAUSE L4 WHEEL TO WALK OVER THE RAIL IT WAS RIDING ON. CAR TRAVELED 118 FEET UNTIL ROLLING ONTO ITS LEFT SIDE. CAR ROLLED FORCED THE CAR AHEAD OF IT OFF THE RAIL. CREW SHO WED NO SIGNS OR SYMPTOMS OF ALCOHOL/DRUG USE. NO DAMAGE OR SPILLAGE OF FUEL FROM EITHER LOCOMOTIVE I N CONSIST. 25 OTHER CARS IN TRAIN RECEIVED NO DAMAGE. 14 EMPTY HAZMAT CARS IN TRAIN (UN 2448 - SULFU R, MOLTEN) WERE NOT DERAILED AND RECEIVED NO DAMAGE.\n",
      "967\n",
      "YPD11-07, LEAD LOCOMOTIVE UPY635 PULLED EAST OUT OF TRACK 19 WITH FOREMAN RIDING THE PLATFORM. THE F OREMAN OBSERVED A BROKEN RAIL ON THE SOUTH RAIL OF TRACK 20 AND IMMEDIATELY TOLD THE ENGINEER THAT W ILL DO. MOVEMENT OVER THE BROKEN RAIL RESULTED IN THE DERAILMENT OF TWO LOCOMOTIVES AND THREE LOADED HAZMAT CARS. GOING STREET OVER PASS WAS STRUCK BY LOCOMOTIVE UPY635 AND UP699 RESULTING IN DAMAGE O N THE NORTH CENTER LANE PILLAR.\n",
      "1317\n",
      "SOUTH PLAINS LAMESA RAILROAD WAS WORKING INSIDE VESTAS OFF THE VESTAS LEAD, PULLED OFF TWO CARS.  A SINGLE BNSF ENGINE ROLLED OUT DOWN THE VESTAS LEAD AND STRUCK A SPLIT POINT DERAIL RESULTING IN A BN SF ENGINE ON ITS SIDE.  NO INJURIES OR HAZMAT RELEASES.  SOUTH PLAINS LAMESA RAILROAD WAS PERFORMING THE WORK, BNSF ENGINE CAUSED THE IMPACT, UP MAINTAINED TRACK.  SOUTH PLAINS LAMESA RAILROAD EQUIPME NT DAMAGE = $100,000\n",
      "1431\n",
      "YSP51-05 WAS PULLING INTO EAST MINNEAPOLIS YARD WITH THE MINNESOTA COMMERCIAL TRANSFER.  THEY STARTE D INTO THE YARD AND DERAILED AT THE TK5 SWITCH.  SEVEN TANK CARS DERAILED, SIX BEING LOADED, ELEVATE D TEMPERATURE HAZMAT (ASPHALT).\n",
      "1983\n",
      "THE RUT 212 CONDUCTOR FAILED TO PROPERLY PROTECT A SHOVE THAT RESULTED IN THE MOVEMENT RUNNING THROU GH THE TRACK 6 SWITCH IN EAST PROVO YARD WHILE BUILDING THE OUTBOUND PVOSTO1.  WHEN THE OUTBOUND CRE W FOR THE PVOSTO1 BEGAN TO DEPART PROVO YARD WESTBOUND, THE CREW FELT A TUG THEN THE TRAIN WENT INTO EMERGENCY. THE CONDUCTOR INSPECTED THE TRAIN DISCOVERED THEY HAD DERAILED THE REAR LOADED HAZMAT TA NK CAR CONTAINING (BISULPHITES, AQUEOUS, SOLUTION N.O.S.) DUE TO MOVEMENT THROUGH THE PREVIOUSLY RUN THROUGH SWITCH. THE DERAILED HAZMAT CAR ALSO STRUCK AND DERAILED A NON-HAZMAT CAR ON THE ADJACENT T RACK. THERE WAS NO RELEASE OF THE HAZMAT COMMODITY AND NO INJURIES.\n",
      "2048\n",
      "MOGWC-29, LEAD LOCOMOTIVE UP8905, EXPERIENCED A UDE AT MP 686.1.  UPON INSPECTION, TRAIN WAS FOUND T O BE DERAILED.  CREW EVACUATED WESTBOUND ON HEAD END POWER (CUT AND RUN) DUE TO HAZMAT INVOLVED.  UP ON INVESTIGATION, 25 CARS WERE FOUND TO BE DERAILED DUE TO TREAD BUILDUP FOUND ON THE MP650466.  CAR #: TILX 303199 PROPANE - 80% OF CAR WAS RELEASED.  CAR#: TILX 305830 PROPANE - VAPOR DISPERSION.  CA R#: TILX 308673 PROPANE - CAR#: BRGX 537 BIO-DIESEL, <500 GAL.\n",
      "2055\n",
      "YHO32R 03 WAS PUTTING BRAKES AND CARS INTO 311.  AS THEY WERE DRAGGING OUT THEY DERAILED 2 CARS UPRI GHT.  NO INJURIES OR HAZMAT RELEASES.\n",
      "2370\n"
     ]
    }
   ],
   "source": [
    "index = 0\n",
    "hazmat_released_indexes = []\n",
    "for narr in all_narr_list:\n",
    "    n = narr.split()\n",
    "    if ('HAZARDOUS' in n or 'HAZMAT' in n) and index not in no_hazmat_indexes:\n",
    "        print(narr)\n",
    "        print(index)\n",
    "        hazmat_released_indexes.append(index)\n",
    "    index +=1\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "above are the few incidents that actually involved hazardous materials to be released. There are only a few so we can go through them \"by hand\" and identify that there are some others where nothing was released but was not picked up by our bi-gram identified because there were several words between \"no\" and \"hazmat\". We can therefore create an\n",
    "index list for those incidents where hazmat was released and delete those that are wrong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[229, 362, 571, 620, 706, 707, 879, 967, 1317, 1431, 1983, 2048, 2055, 2370]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hazmat_released_indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[229, 362, 571, 620, 706, 707, 967, 1317, 1983, 2055]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_hazmat_add = [ 879,1431, 2048, 2370]\n",
    "hazmat_indexes_final = [x for x in hazmat_released_indexes if x not in no_hazmat_add]\n",
    "hazmat_indexes_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Putting everything together before final thing\n",
    "so we have a list of indexes for\n",
    "- hazardous material release\n",
    "- damages\n",
    "- where no derailment occured\n",
    "lets update our dataframe with these"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2401"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# populating boolean lists\n",
    "len(all_narr_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets populate three lists with boolean values\n",
    "hazard =[False] * 2401\n",
    "damages = [False] * 2401"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#the derail column already exists so lets extract that into a list so we can update it\n",
    "derail_old = raw_data_df['DERAIL'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index in no_derail_indexes:\n",
    "    derail_old[index] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index in hazmat_released_indexes:\n",
    "    hazard[index] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index in damage_indexes:\n",
    "    damages[index] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "del raw_data_df['OTHER']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "del raw_data_df['DERAIL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data_df['DERAIL'] = derail_old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data_df['OTHER'] = (raw_data_df['DERAIL'] == False) & (raw_data_df['STRUCK'] == False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data_df['HAZMAT'] = hazard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data_df['DAMAGE'] = damages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>IYR</th>\n",
       "      <th>IMO</th>\n",
       "      <th>RAILROAD</th>\n",
       "      <th>STATE</th>\n",
       "      <th>STATION</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>NARR</th>\n",
       "      <th>PREP</th>\n",
       "      <th>STRUCK</th>\n",
       "      <th>DERAIL</th>\n",
       "      <th>OTHER</th>\n",
       "      <th>HAZMAT</th>\n",
       "      <th>DAMAGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>ATK</td>\n",
       "      <td>6</td>\n",
       "      <td>CASTROVILLE</td>\n",
       "      <td>MONTEREY</td>\n",
       "      <td>CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...</td>\n",
       "      <td>camera equip train struck occupi sanit truck m...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>MNCW</td>\n",
       "      <td>9</td>\n",
       "      <td>NORWALK</td>\n",
       "      <td>FAIRFIELD</td>\n",
       "      <td>PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...</td>\n",
       "      <td>product gang travel north piec equip seper pie...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>FEC</td>\n",
       "      <td>12</td>\n",
       "      <td>POMPANO BEACH</td>\n",
       "      <td>BROWARD</td>\n",
       "      <td>BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "      <td>blf travel southbound east main approxim mph n...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>FEC</td>\n",
       "      <td>12</td>\n",
       "      <td>POMPANO BEACH</td>\n",
       "      <td>BROWARD</td>\n",
       "      <td>BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "      <td>blf travel southbound east main notic vehicl f...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>CSX</td>\n",
       "      <td>12</td>\n",
       "      <td>SEVILLE</td>\n",
       "      <td>VOLUSIA</td>\n",
       "      <td>AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.</td>\n",
       "      <td>amtrak struck semi privat cross</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2679</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>TRC</td>\n",
       "      <td>6</td>\n",
       "      <td>TRONA</td>\n",
       "      <td>SAN BERNARDINO</td>\n",
       "      <td>RUN 2 WAS IN THE PROCESS OF PULLING THE HEAD 1...</td>\n",
       "      <td>run process pull head load saltspur track trc ...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2680</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>NCTC</td>\n",
       "      <td>6</td>\n",
       "      <td>OCEANSIDE</td>\n",
       "      <td>SAN DIEGO</td>\n",
       "      <td>METROLINK 858 DERAILED AT STUART MESA YARD.</td>\n",
       "      <td>metrolink derail stuart mesa yard</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2681</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>MET</td>\n",
       "      <td>6</td>\n",
       "      <td>MODESTO</td>\n",
       "      <td>STANISLAUS</td>\n",
       "      <td>ON THE MORNING OF JANUARY 15, 2019, CONDUCTOR ...</td>\n",
       "      <td>morn januari conductor switch car industri set...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2682</th>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>BNSF</td>\n",
       "      <td>6</td>\n",
       "      <td>BAKERSFIELD</td>\n",
       "      <td>KERN</td>\n",
       "      <td>EMPLOYEE STOPPED AND LINED THE DERAIL TO THE W...</td>\n",
       "      <td>employe stop line derail work posit pull locom...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2683</th>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>UP</td>\n",
       "      <td>6</td>\n",
       "      <td>BLOOMINGTON</td>\n",
       "      <td>SAN BERNARDINO</td>\n",
       "      <td>LOQ25-26 WAS MAKING A SHOVING MOVE WITH LIGHT ...</td>\n",
       "      <td>loq make shove move light locomot track conduc...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2401 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      IYR  IMO RAILROAD  STATE        STATION          COUNTY  \\\n",
       "0      19   11      ATK      6    CASTROVILLE        MONTEREY   \n",
       "1      19   10     MNCW      9        NORWALK       FAIRFIELD   \n",
       "2      19    4      FEC     12  POMPANO BEACH         BROWARD   \n",
       "3      19    4      FEC     12  POMPANO BEACH         BROWARD   \n",
       "4      19    4      CSX     12        SEVILLE         VOLUSIA   \n",
       "...   ...  ...      ...    ...            ...             ...   \n",
       "2679   19    2      TRC      6          TRONA  SAN BERNARDINO   \n",
       "2680   19    2     NCTC      6      OCEANSIDE       SAN DIEGO   \n",
       "2681   19    1      MET      6        MODESTO      STANISLAUS   \n",
       "2682   19    7     BNSF      6    BAKERSFIELD            KERN   \n",
       "2683   19   12       UP      6    BLOOMINGTON  SAN BERNARDINO   \n",
       "\n",
       "                                                   NARR  \\\n",
       "0     CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...   \n",
       "1     PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...   \n",
       "2     BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...   \n",
       "3     BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...   \n",
       "4        AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.   \n",
       "...                                                 ...   \n",
       "2679  RUN 2 WAS IN THE PROCESS OF PULLING THE HEAD 1...   \n",
       "2680        METROLINK 858 DERAILED AT STUART MESA YARD.   \n",
       "2681  ON THE MORNING OF JANUARY 15, 2019, CONDUCTOR ...   \n",
       "2682  EMPLOYEE STOPPED AND LINED THE DERAIL TO THE W...   \n",
       "2683  LOQ25-26 WAS MAKING A SHOVING MOVE WITH LIGHT ...   \n",
       "\n",
       "                                                   PREP  STRUCK  DERAIL  \\\n",
       "0     camera equip train struck occupi sanit truck m...    True   False   \n",
       "1     product gang travel north piec equip seper pie...   False   False   \n",
       "2     blf travel southbound east main approxim mph n...    True   False   \n",
       "3     blf travel southbound east main notic vehicl f...    True   False   \n",
       "4                       amtrak struck semi privat cross    True   False   \n",
       "...                                                 ...     ...     ...   \n",
       "2679  run process pull head load saltspur track trc ...   False    True   \n",
       "2680                  metrolink derail stuart mesa yard   False    True   \n",
       "2681  morn januari conductor switch car industri set...   False   False   \n",
       "2682  employe stop line derail work posit pull locom...   False    True   \n",
       "2683  loq make shove move light locomot track conduc...   False    True   \n",
       "\n",
       "      OTHER  HAZMAT  DAMAGE  \n",
       "0     False   False   False  \n",
       "1      True   False   False  \n",
       "2     False   False   False  \n",
       "3     False   False   False  \n",
       "4     False   False   False  \n",
       "...     ...     ...     ...  \n",
       "2679  False   False   False  \n",
       "2680  False   False   False  \n",
       "2681   True   False    True  \n",
       "2682  False   False   False  \n",
       "2683  False   False   False  \n",
       "\n",
       "[2401 rows x 13 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting Amount of Damages in \\$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['$400,000']"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = \"sentence to check whether it finds the damage of $400,000 \"\n",
    "\n",
    "amount = re.findall(r\"\\$[-0-9.,]+[-0-9.,a-zA-Z]*\\b\", test)\n",
    "amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['$18K']\n",
      "137\n",
      "['$15K']\n",
      "257\n",
      "['$45K']\n",
      "564\n",
      "['$3K']\n",
      "1482\n",
      "['$8500K', '$81660']\n",
      "1816\n"
     ]
    }
   ],
   "source": [
    "damages_amounts = []\n",
    "index = 0\n",
    "\n",
    "for narr in all_narr_list:\n",
    "    amount = re.findall(r\"\\$[-0-9.,]+[-0-9.,a-zA-Z]*\\b\", narr)\n",
    "    if len(amount) == 0:\n",
    "        damages_amounts.append([0])\n",
    "        \n",
    "    else:\n",
    "        #piece of code explained below\n",
    "        for a in amount:\n",
    "            if a[-1] == 'K':   #K\n",
    "                print(amount)\n",
    "                print(index)\n",
    "        \n",
    "        damages_amounts.append(amount)\n",
    "    \n",
    "    index += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By manually inspecting the incidents where there are several amounts, there is one at index 508 that lists all the different damages and then totaling it (the other incident reports don't do that) and another incident at index 1235 shows a range (800,000 - 1,000,000). I will therefore manually change the amounts for those incidents and for the rest I will create separate columns for each damage and totaling them\n",
    "\n",
    "\n",
    "508  $11071.46  (total)\n",
    "\n",
    "1235  $1,000,000  (range)\n",
    "\n",
    "\n",
    "Also, I ran into an error below so I had to insert the loop above that checks for a K at the end of the damage amount string because some incident reports write 5K instead of 5,000. I am therefore fixing those amounts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "damages_amounts[508] = ['$11071.46']\n",
    "damages_amounts[1235] = ['$1,000,000']\n",
    "damages_amounts[137] = ['$18.000']\n",
    "damages_amounts[257] = ['$15,000']\n",
    "damages_amounts[564] = ['$45,000']\n",
    "damages_amounts[1482] = ['$3,000']\n",
    "damages_amounts[1816] = ['$8500000', '$81660']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to store all the damage amounts reported in the incident text in separate columns. In order to know how many columns we need, we need to find the list with the maximum entries. this is the maximum number of columns we need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def find_max_list(list):\n",
    "    list_len = [len(i) for i in list]\n",
    "    print(max(list_len))\n",
    "\n",
    "#print output#\n",
    "find_max_list(damages_amounts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['damages_1', 'damages_2', 'damages_3', 'damages_4', 'damages_5']\n",
    "damages_dff = pd.DataFrame(damages_amounts,columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>damages_1</th>\n",
       "      <th>damages_2</th>\n",
       "      <th>damages_3</th>\n",
       "      <th>damages_4</th>\n",
       "      <th>damages_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>$15325</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>$10793</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>$615.33</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  damages_1 damages_2 damages_3 damages_4 damages_5\n",
       "0         0      None      None      None      None\n",
       "1         0      None      None      None      None\n",
       "2    $15325      None      None      None      None\n",
       "3    $10793      None      None      None      None\n",
       "4         0      None      None      None      None\n",
       "5   $615.33      None      None      None      None\n",
       "6         0      None      None      None      None\n",
       "7         0      None      None      None      None\n",
       "8         0      None      None      None      None\n",
       "9         0      None      None      None      None"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "damages_dff[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a total column\n",
    "I on purpose left the dollar sign in the pandas dataframe, to imitate another problem you might encounter with raw data and did not convert the numbers into float. imagine you get numbers in different formats e.g. 1000 vs 1000.00 vs 1,000\n",
    "\n",
    "I don't know how to deal with that problem, since you cannot convert 1,000 into a float. I guess it is another regular expression I need which deletes the $ sign and the commas from the numbers. Let's google it!\n",
    "\n",
    "This is what I found when googling\n",
    "\n",
    "https://stackoverflow.com/questions/32464280/converting-currency-with-to-numbers-in-python-pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "damages_dff[damages_dff.columns[0:]] = damages_dff[damages_dff.columns[0:]].replace('[\\$,]', '', regex=True).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a total damages column\n",
    "\n",
    "I want to sum up the damages, if there were several amounts reported. Let's sum up all the damages in a row and store the total amount in a new column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "damages_dff['damages_total'] = damages_dff[list(damages_dff.columns)].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>damages_1</th>\n",
       "      <th>damages_2</th>\n",
       "      <th>damages_3</th>\n",
       "      <th>damages_4</th>\n",
       "      <th>damages_5</th>\n",
       "      <th>damages_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15325.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10793.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10793.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2396</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2397</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2398</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2399</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2400</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2401 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      damages_1  damages_2  damages_3  damages_4  damages_5  damages_total\n",
       "0           0.0        NaN        NaN        NaN        NaN            0.0\n",
       "1           0.0        NaN        NaN        NaN        NaN            0.0\n",
       "2       15325.0        NaN        NaN        NaN        NaN        15325.0\n",
       "3       10793.0        NaN        NaN        NaN        NaN        10793.0\n",
       "4           0.0        NaN        NaN        NaN        NaN            0.0\n",
       "...         ...        ...        ...        ...        ...            ...\n",
       "2396        0.0        NaN        NaN        NaN        NaN            0.0\n",
       "2397        0.0        NaN        NaN        NaN        NaN            0.0\n",
       "2398        0.0        NaN        NaN        NaN        NaN            0.0\n",
       "2399        0.0        NaN        NaN        NaN        NaN            0.0\n",
       "2400        0.0        NaN        NaN        NaN        NaN            0.0\n",
       "\n",
       "[2401 rows x 6 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "damages_dff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inserting total into our original dataframe\n",
    "\n",
    "In order to join two dataframes in pandas, we need a common column. Our damages and original dataframe do not have a common column so we need to create one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we need a common column to merge two dataframes - so I am creating an index column\n",
    "damages_dff['i'] = damages_dff.reset_index().index\n",
    "raw_data_df['i'] = raw_data_df.reset_index().index\n",
    "\n",
    "damages_final = damages_dff[['damages_total', 'i']]\n",
    "\n",
    "\n",
    "\n",
    "#del final_df['PREP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = pd.merge(raw_data_df, damages_final, on='i', how='outer')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>IYR</th>\n",
       "      <th>IMO</th>\n",
       "      <th>RAILROAD</th>\n",
       "      <th>STATE</th>\n",
       "      <th>STATION</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>NARR</th>\n",
       "      <th>PREP</th>\n",
       "      <th>STRUCK</th>\n",
       "      <th>DERAIL</th>\n",
       "      <th>OTHER</th>\n",
       "      <th>HAZMAT</th>\n",
       "      <th>DAMAGE</th>\n",
       "      <th>i</th>\n",
       "      <th>damages_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>ATK</td>\n",
       "      <td>6</td>\n",
       "      <td>CASTROVILLE</td>\n",
       "      <td>MONTEREY</td>\n",
       "      <td>CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...</td>\n",
       "      <td>camera equip train struck occupi sanit truck m...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>MNCW</td>\n",
       "      <td>9</td>\n",
       "      <td>NORWALK</td>\n",
       "      <td>FAIRFIELD</td>\n",
       "      <td>PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...</td>\n",
       "      <td>product gang travel north piec equip seper pie...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>FEC</td>\n",
       "      <td>12</td>\n",
       "      <td>POMPANO BEACH</td>\n",
       "      <td>BROWARD</td>\n",
       "      <td>BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "      <td>blf travel southbound east main approxim mph n...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>15325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>FEC</td>\n",
       "      <td>12</td>\n",
       "      <td>POMPANO BEACH</td>\n",
       "      <td>BROWARD</td>\n",
       "      <td>BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "      <td>blf travel southbound east main notic vehicl f...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>10793.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>CSX</td>\n",
       "      <td>12</td>\n",
       "      <td>SEVILLE</td>\n",
       "      <td>VOLUSIA</td>\n",
       "      <td>AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.</td>\n",
       "      <td>amtrak struck semi privat cross</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IYR  IMO RAILROAD  STATE        STATION     COUNTY  \\\n",
       "0   19   11      ATK      6    CASTROVILLE   MONTEREY   \n",
       "1   19   10     MNCW      9        NORWALK  FAIRFIELD   \n",
       "2   19    4      FEC     12  POMPANO BEACH    BROWARD   \n",
       "3   19    4      FEC     12  POMPANO BEACH    BROWARD   \n",
       "4   19    4      CSX     12        SEVILLE    VOLUSIA   \n",
       "\n",
       "                                                NARR  \\\n",
       "0  CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...   \n",
       "1  PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...   \n",
       "2  BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...   \n",
       "3  BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...   \n",
       "4     AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.   \n",
       "\n",
       "                                                PREP  STRUCK  DERAIL  OTHER  \\\n",
       "0  camera equip train struck occupi sanit truck m...    True   False  False   \n",
       "1  product gang travel north piec equip seper pie...   False   False   True   \n",
       "2  blf travel southbound east main approxim mph n...    True   False  False   \n",
       "3  blf travel southbound east main notic vehicl f...    True   False  False   \n",
       "4                    amtrak struck semi privat cross    True   False  False   \n",
       "\n",
       "   HAZMAT  DAMAGE  i  damages_total  \n",
       "0   False   False  0            0.0  \n",
       "1   False   False  1            0.0  \n",
       "2   False   False  2        15325.0  \n",
       "3   False   False  3        10793.0  \n",
       "4   False   False  4            0.0  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "del final_df['i']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "del final_df['PREP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\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>IYR</th>\n",
       "      <th>IMO</th>\n",
       "      <th>RAILROAD</th>\n",
       "      <th>STATE</th>\n",
       "      <th>STATION</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>NARR</th>\n",
       "      <th>STRUCK</th>\n",
       "      <th>DERAIL</th>\n",
       "      <th>OTHER</th>\n",
       "      <th>HAZMAT</th>\n",
       "      <th>DAMAGE</th>\n",
       "      <th>damages_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>ATK</td>\n",
       "      <td>6</td>\n",
       "      <td>CASTROVILLE</td>\n",
       "      <td>MONTEREY</td>\n",
       "      <td>CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>MNCW</td>\n",
       "      <td>9</td>\n",
       "      <td>NORWALK</td>\n",
       "      <td>FAIRFIELD</td>\n",
       "      <td>PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>FEC</td>\n",
       "      <td>12</td>\n",
       "      <td>POMPANO BEACH</td>\n",
       "      <td>BROWARD</td>\n",
       "      <td>BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>15325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>FEC</td>\n",
       "      <td>12</td>\n",
       "      <td>POMPANO BEACH</td>\n",
       "      <td>BROWARD</td>\n",
       "      <td>BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>10793.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>CSX</td>\n",
       "      <td>12</td>\n",
       "      <td>SEVILLE</td>\n",
       "      <td>VOLUSIA</td>\n",
       "      <td>AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IYR  IMO RAILROAD  STATE        STATION     COUNTY  \\\n",
       "0   19   11      ATK      6    CASTROVILLE   MONTEREY   \n",
       "1   19   10     MNCW      9        NORWALK  FAIRFIELD   \n",
       "2   19    4      FEC     12  POMPANO BEACH    BROWARD   \n",
       "3   19    4      FEC     12  POMPANO BEACH    BROWARD   \n",
       "4   19    4      CSX     12        SEVILLE    VOLUSIA   \n",
       "\n",
       "                                                NARR  STRUCK  DERAIL  OTHER  \\\n",
       "0  CAMERA-EQUIPPED TRAIN 14 STRUCK AN OCCUPIED SA...    True   False  False   \n",
       "1  PRODUCTION GANG, TRAVELING NORTH WITH 22 PIECE...   False   False   True   \n",
       "2  BLF 604 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...    True   False  False   \n",
       "3  BLF 606 WAS TRAVELING SOUTHBOUND ON EAST MAIN ...    True   False  False   \n",
       "4     AMTRAK P09811 STRUCK SEMI AT PRIVATE CROSSING.    True   False  False   \n",
       "\n",
       "   HAZMAT  DAMAGE  damages_total  \n",
       "0   False   False            0.0  \n",
       "1   False   False            0.0  \n",
       "2   False   False        15325.0  \n",
       "3   False   False        10793.0  \n",
       "4   False   False            0.0  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh well, the \"damage\" column is redundant since the $ amount indicates whether there was any damage or not - even if we could not find the \"damage\" keyword in the incident text. But it was just for the sake of practicing anyway, so I will leave it now as it is. It is not the most exciting dataset to play around"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.to_csv('railroad_incidents_analysis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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