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{
"cells": [
{
"cell_type": "markdown",
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"# 02. Textual geography \n",
"\n",
"This session will walk through some more advanced text processing and data wrangling to produce a map of the locations mentioned in our corpus. Topics covered include:\n",
"\n",
"* Named entity recognition using Stanford's CRF-NER package.\n",
"* Geolocation using Google's mapping APIs.\n",
"* Cartographic visualization, both static (for print publication) and interactive (for online use).\n",
"\n",
"## Named entity recognition\n",
"\n",
"There are several approaches to identifying the places used in a piece of text. We could rely on a dictionary or gazetteer, which would tell us that Edinburgh is a city in Scotland, but would also tell us that Charlotte Brönte is a city in the United States.\n",
"\n",
"We'll instead use statistical machine learning methods. While we *will* get an intro to machine learning tomorrow, for now we'll rely on the [implementation by the Stanford NLP group](http://nlp.stanford.edu/software/CRF-NER.html).\n",
"\n",
"Very briefly, the Stanford named entity recognizer works by learning (from hand-tagged training data) the words and word types that are typically used as locations (and other types of named entities) in context. This means that it can recognize places that were not present in the training data if the context in which they appear strongly indicates a place name. For instance, \"I was born in Xxx\" obviously contains a place name (\"Xxx\"), even though we don't know what that place is.\n",
"\n",
"But this fact does mean that location detection is language dependent. You need a trained entity model for each text language you want to process. In the present case, we're working with English-language texts, and Stanford supplies an English model. There are also models for Spanish, German, Chinese, and others (but not for French, frustratingly).\n",
"\n",
"The Stanford NER is a Java package. It's possible -- with a lot of work -- to invoke Java code from Python. But there's no point in this case; it's much easier to run the NER package from the command line and to read in its plain text output.\n",
"\n",
"Note that there do exist NER, POS, and other NLP packages for Python. NLTK -- the Natural Language Tool Kit, which we met in the last notebook when we used it for corpus processing -- is one of the most diverse and well conceived. But it's not optimized for speed and isn't notably accurate compared to more production-oriented offerings. \n",
"\n",
"There's a guide to using the NER package on Stanford's site. Here's the short version, for reference:\n",
"\n",
"```\n",
"java -mx1g -cp \"*:lib/*\" edu.stanford.nlp.ie.crf.CRFClassifier\n",
"-loadClassifier classifiers/english.all.3class.distsim.crf.ser.gz \n",
"-outputFormat tabbedEntities \n",
"-textFile file.txt > file.tsv\n",
"```\n",
"\n",
"This runs the English-language classifier over a single text file. Note the specification of a particular language model to use ('`classifiers/english.all.3class.distsim.crf.ser.gz`').\n",
"\n",
"The classifier's output follows a tabbed format that looks like this:\n",
"\n",
"```\n",
" Why did the poor poet of\n",
"Tennessee LOCATION , upon suddenly receiving two handfuls of silver , deliberate whether to buy him a coat , which he sadly needed , or invest his money in a pedestrian trip to\n",
"Rockaway Beach LOCATION ?\n",
"```\n",
"\n",
"The thing to notice is that every line begins with two tabs, but only the text in front of the first tab is a recognized entity. The text in front of the second tab, then, indicates the type of entity: PERSON, ORGANIZATION, or LOCATION. Any text following the second tab is body text, presumed not to contain any named entities.\n",
"\n",
"So let's recreate our corpus, then read in the tagged files and get a list of the locations used in the corpus.\n",
"\n",
"### Recreate the corpus"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Suppress compatibility warnings\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>nation</th>\n",
" <th>author</th>\n",
" <th>title</th>\n",
" <th>pubdate</th>\n",
" <th>gender</th>\n",
" <th>wordcount</th>\n",
" </tr>\n",
" <tr>\n",
" <th>file</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>A-Cather-Antonia-1918-F</th>\n",
" <td>A</td>\n",
" <td>Cather</td>\n",
" <td>Antonia</td>\n",
" <td>1918</td>\n",
" <td>F</td>\n",
" <td>97574</td>\n",
" </tr>\n",
" <tr>\n",
" <th>A-Chesnutt-Marrow-1901-M</th>\n",
" <td>A</td>\n",
" <td>Chesnutt</td>\n",
" <td>Marrow</td>\n",
" <td>1901</td>\n",
" <td>M</td>\n",
" <td>110288</td>\n",
" </tr>\n",
" <tr>\n",
" <th>A-Crane-Maggie-1893-M</th>\n",
" <td>A</td>\n",
" <td>Crane</td>\n",
" <td>Maggie</td>\n",
" <td>1893</td>\n",
" <td>M</td>\n",
" <td>28628</td>\n",
" </tr>\n",
" <tr>\n",
" <th>A-Davis-Life_Iron_mills-1861-F</th>\n",
" <td>A</td>\n",
" <td>Davis</td>\n",
" <td>Life Iron mills</td>\n",
" <td>1861</td>\n",
" <td>F</td>\n",
" <td>18789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>A-Dreiser-Sister_Carrie-1900-M</th>\n",
" <td>A</td>\n",
" <td>Dreiser</td>\n",
" <td>Sister Carrie</td>\n",
" <td>1900</td>\n",
" <td>M</td>\n",
" <td>194062</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" nation author title pubdate \\\n",
"file \n",
"A-Cather-Antonia-1918-F A Cather Antonia 1918 \n",
"A-Chesnutt-Marrow-1901-M A Chesnutt Marrow 1901 \n",
"A-Crane-Maggie-1893-M A Crane Maggie 1893 \n",
"A-Davis-Life_Iron_mills-1861-F A Davis Life Iron mills 1861 \n",
"A-Dreiser-Sister_Carrie-1900-M A Dreiser Sister Carrie 1900 \n",
"\n",
" gender wordcount \n",
"file \n",
"A-Cather-Antonia-1918-F F 97574 \n",
"A-Chesnutt-Marrow-1901-M M 110288 \n",
"A-Crane-Maggie-1893-M M 28628 \n",
"A-Davis-Life_Iron_mills-1861-F F 18789 \n",
"A-Dreiser-Sister_Carrie-1900-M M 194062 "
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from nltk.corpus.reader.plaintext import PlaintextCorpusReader\n",
"\n",
"text_dir = '../Data/Texts/'\n",
"corpus = PlaintextCorpusReader(text_dir, '.*\\.txt')\n",
"\n",
"# A function to turn fileids into a table of metadata\n",
"def parse_fileids(fileids):\n",
" '''Takes a list of file names formatted like A-Cather-Antonia-1918-F.txt.\n",
" Returns a pandas dataframe of derived metadata.'''\n",
" import pandas as pd\n",
" meta = {}\n",
" for fileid in fileids:\n",
" file = fileid.strip('.txt') # Get rid of file suffix\n",
" fields = file.split('-') # Split on dashes\n",
" fields[2] = fields[2].replace('_', ' ') # Remove underscore from titles\n",
" fields[3] = int(fields[3])\n",
" meta[file] = fields\n",
" metadata = pd.DataFrame.from_dict(meta, orient='index') # Build dataframe\n",
" metadata.columns = ['nation', 'author', 'title', 'pubdate', 'gender'] # Col names\n",
" return metadata.sort_index() # Note we need to sort b/c datframe built from dictionary\n",
"\n",
"def collect_stats(corpus):\n",
" '''Takes an NLTK corpus as input. \n",
" Returns a pandas dataframe of stats indexed to fileid.'''\n",
" import nltk\n",
" import pandas as pd\n",
" stats = {}\n",
" for fileid in corpus.fileids():\n",
" word_count = len(corpus.words(fileid))\n",
" stats[fileid.strip('.txt')] = {'wordcount':word_count}\n",
" statistics = pd.DataFrame.from_dict(stats, orient='index')\n",
" return statistics.sort_index()\n",
"\n",
"books = parse_fileids(corpus.fileids())\n",
"stats = collect_stats(corpus)\n",
"books = books.join(stats)\n",
"books.index.set_names('file', inplace=True)\n",
"books.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Read and parse NER-tagged files\n",
"\n",
"The taged NER files are in the `..Data/NER/` directory. We need a function that will parse each one and return just the locations for further processing. "
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_loc(line):\n",
" '''Takes a string of NER output. \n",
" Returns a location if found, else None.'''\n",
" line = line.split('\\t')\n",
" try:\n",
" if line[1] == 'LOCATION':\n",
" return line[0]\n",
" else:\n",
" return None\n",
" except:\n",
" return None\n",
"\n",
"def ingest_ner(f):\n",
" '''Take a file handle for an NER output file.\n",
" Uses get_loc() to extract locations.\n",
" Returns a dict of locations and counts.'''\n",
" from collections import defaultdict\n",
" locations = defaultdict(lambda: 0)\n",
" for line in f:\n",
" loc = get_loc(line)\n",
" if loc:\n",
" locations[loc] += 1\n",
" return locations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK, functions are defined and we're ready to ingest the NER data. We'll loop over the files in the NER directory, building three lists of equal length. The lists will hold file IDs (including repeats; one file ID per *unique location* in that file), locations strings, and occurrence counts, respectively. The goal is to construct a table that looks like this:\n",
"\n",
"```\n",
"file1 place1 2\n",
"file1 place2 1\n",
"file1 place3 8\n",
"file2 place1 3\n",
"file2 place4 1\n",
"... ... ...\n",
"file20 place30 3\n",
"```\n",
"\n",
"Once we have the table in place, we can filter and combine locations in database-like ways to produce our summary geographic data."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os # Used to create OS-appropriate file paths\n",
"ner_dir = '../Data/NER/' # Where we keep the NER files\n",
"\n",
"files_list =[] # Lists to hold our data\n",
"locs_list = []\n",
"occurs_list = []\n",
"\n",
"for fileid in sorted(corpus.fileids()): # Loop over fileids in corpus\n",
" file = os.path.join(ner_dir, fileid)\n",
" with open(file) as f:\n",
" locations = ingest_ner(f) # Get a dict of locations and counts in this file\n",
" for loc in sorted(locations, key=locations.get, reverse=True): # Loop over keys in dict\n",
" files_list.append(fileid.strip('.txt'))\n",
" locs_list.append(loc)\n",
" occurs_list.append(locations[loc])"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" occurs\n",
"count 3439.000000\n",
"mean 4.178831\n",
"std 15.361014\n",
"min 1.000000\n",
"25% 1.000000\n",
"50% 1.000000\n",
"75% 2.000000\n",
"max 495.000000\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>file</th>\n",
" <th>location</th>\n",
" <th>occurs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A-Cather-Antonia-1918-F</td>\n",
" <td>Black Hawk</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A-Cather-Antonia-1918-F</td>\n",
" <td>Nebraska</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A-Cather-Antonia-1918-F</td>\n",
" <td>Virginia</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A-Cather-Antonia-1918-F</td>\n",
" <td>Omaha</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A-Cather-Antonia-1918-F</td>\n",
" <td>Chicago</td>\n",
" <td>10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" file location occurs\n",
"0 A-Cather-Antonia-1918-F Black Hawk 21\n",
"1 A-Cather-Antonia-1918-F Nebraska 16\n",
"2 A-Cather-Antonia-1918-F Virginia 13\n",
"3 A-Cather-Antonia-1918-F Omaha 10\n",
"4 A-Cather-Antonia-1918-F Chicago 10"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Turn the lists into a Pandas dataframe\n",
"d = {'file': files_list, \n",
" 'location': locs_list,\n",
" 'occurs': occurs_list}\n",
"geo = pd.DataFrame(d)\n",
"print(geo.describe())\n",
"geo.head()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"14371"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Total number of named location occurrences in corpus\n",
"geo.occurs.sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that there are several thousand unique book-place pairs and that most places are mentioned just one time in a given book. The distribution is Zipf's Law-like: The top few places mentioned account for a large majority of the total location occurrences.\n",
"\n",
"Note that the figure below is log-scaled for better readability."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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krrvuyuzZs9PQ0JAbb7wxS5YsSZIsXrw4nZ2dWbRoUQYGBrJw4cIsXbo0STJt2rSsX78+\na9euTWdnZ9ra2rJhw4YROSEAAIZ3QhF4+eWXZ8+ePYNfNzc358tf/vJxx44dOzarVq3KqlWrjrt/\n/vz5mT9//ok8PQAAI8SfjQMAKJAIBAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgE\nACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAo\nkAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAI\nBAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQA\nKJAIBAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgEACiQ\nCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgE\nACiQCAQAKJAIBAAokAgEACiQCAQAKNAJReD+/ftzxRVXDH7d3d2dT33qU2lra8u8efOyY8eOIeM3\nbtyYOXPmZNasWdmwYUMqlcrgvl27duWqq65Ka2trVqxYkc7OzlM8FQAAqlV1BO7YsSO33nprXn/9\n9cFtd9xxRyZOnJg9e/Zk8+bN+fznP5/9+/cnSbZv357du3dn165defjhh7Nv375s27YtSfL8889n\n3bp1ueeee/Lkk0/m3HPPzdq1a0f41AAAeDNVReCWLVuyffv2rFy5cnDba6+9lkceeSS33357xo0b\nlxkzZuSaa67Jzp07kyQPPfRQbrrppkyePDmTJ0/O8uXL8+CDDyb55auALS0tGT9+fFavXp3HHnss\nXV1do3CKAAD8d1VF4KJFi7Jz585ccsklg9u+//3vZ9y4cXnPe94zuG3KlClpb29PkrS3t2fq1KlD\n9nV0dAzua25uHtx3zjnnpKmpafCxAACMrrOqGXTuueces62npyf19fVDtjU0NKS3t3dwf0NDw5B9\nR44cSX9/f3p6etLY2DjksY2NjYOPHSmVI5URPyZJX1/fkH85/axBbbn+tWcNas8a1N6pXvuqIvB4\nGhsb09/fP2Rbb29vJkyYkGRoEB7dV1dXl/Hjxx+zL3kjGo8+dqT09fXlwIEDI3pMfunw4cO1nkLx\nrEFtuf61Zw1qzxq8fZ10BF5wwQUZGBjIT37yk5x33nlJko6OjsG3eZubm9PR0ZEZM2YkGfoW8NF9\nR3V1daW7u3vIW8Qjob6+PtOnTx/RY/JGXB8+fDhTp0495tVgTg9rUFuuf+1Zg9qzBrV3dA1O1klH\n4MSJEzNv3rxs3Lgx69evzwsvvJBdu3blS1/6UpLk2muvzQMPPJDZs2enrq4uW7duzXXXXZckufrq\nq7NkyZJcf/31mT59ejZt2pS5c+emqanppE/keMaMHTPkLWlGVn19vetbY9agtlz/2rMGtWcN3r5O\nOgKTZP369bnzzjvzgQ98IBMnTsyaNWvS0tKSJFm8eHE6OzuzaNGiDAwMZOHChVm6dGmSZNq0aVm/\nfn3Wrl2bzs7OtLW1ZcOGDad8MgAAVOeEIvDyyy/Pnj17Br9uamrK5s2bjzt27NixWbVqVVatWnXc\n/fPnz8/8+fNP5OkBABgh/mwcAECBRCAAQIFEIABAgUQgAECBRCAAQIFEIABAgUQgAECBRCAAQIFE\nIABAgUQgAECBRCAAQIFEIABAgUQgAECBRCAAQIFEIABAgUQgAECBRCAAQIFEIABAgUQgAECBRCAA\nQIFEIABAgUQgAECBRCAAQIFEIABAgUQgAECBRCAAQIFEIABAgUQgAECBRCAAQIFEIABAgUQgAECB\nRCAAQIFEIABAgUQgAECBRCAAQIFEIABAgUQgAECBRCAAQIFEIABAgUQgAECBRCAAQIFEIABAgUQg\nAECBRCAAQIFEIABAgUQgAECBRCAAQIFEIABAgUQgAECBRCAAQIFEIABAgUQgAECBRCAAQIFEIABA\ngUQgAECBRCAAQIFEIABAgUQgAECBRCAAQIFEIABAgUQgAECBRCAAQIFEIABAgUQgAECBRCAAQIFE\nIABAgUQgAECBRCAAQIFEIABAgU45Ardt25ZLLrkkM2fOTGtra2bOnJl9+/alu7s7n/zkJ9PW1pZ5\n8+Zlx44dQx63cePGzJkzJ7NmzcqGDRtSqVROdSoAAFTprFM9wMGDB7N69eosXbp0yPbbb78973jH\nO7Jnz54cOnQoy5Yty6//+q9nxowZ2b59e3bv3p1du3YlSW677bZs27Ytt95666lOBwCAKpzyK4GH\nDh3K+973viHbXnvttTzyyCO5/fbbM27cuMyYMSPXXHNNdu7cmSR56KGHctNNN2Xy5MmZPHlyli9f\nnr/7u7871akAAFClU4rA3t7edHR05Ctf+Ure//7353d+53fyjW98Iz/4wQ8ybty4vOc97xkcO2XK\nlLS3tydJ2tvbM3Xq1CH7vv/975/KVAAAOAGn9HbwSy+9lMsuuyyLFy/OnDlz8swzz2TlypW5+eab\nU19fP2RsQ0NDent7kyQ9PT1paGgYsu/IkSPp7+/P+PHjT2VKAABU4ZQi8Pzzz89f//VfD37d1taW\nhQsXZu/evenv7x8ytre3NxMmTEgyNAiP7qurqxvxAKwcqQx5nreigYGBPPfcc1WPv+SSSzJu3LhR\nnNHw+vr6hvzL6WcNasv1rz1rUHvWoPZO9dqfUgQePHgw//Iv/5LbbrttyITe/e535zvf+U5+8pOf\n5LzzzkuSdHR0pLm5OUnS3Nycjo6OzJgxI8kbbw8f3TeS+vr6cuDAgRE/7kh6/vnn8+df3ZezJ50/\n7NhXu17M6v+jPdOmTTsNMxve4cOHaz2F4lmD2nL9a88a1J41ePs6pQicMGFC7rvvvlx44YX50Ic+\nlCeeeCIPP/xwtm/fnu7u7mzcuDHr16/PCy+8kF27duVLX/pSkuTaa6/NAw88kNmzZ6euri5bt27N\nddddNyIn9F/V19dn+vTpI37ckdTf35+zJ/00Te+qLoLf+9731vyc+vr6cvjw4UydOvWYt/05PaxB\nbbn+tWcNas8a1N7RNThZpxSBF154Yf7iL/4imzZtypo1a3Leeeflz/7sz3LRRRdl/fr1ufPOO/OB\nD3wgEydOzJo1a9LS0pIkWbx4cTo7O7No0aIMDAxk4cKFx/yKmZEwZuyYIZ89fCs60Runvr7+LXNO\nb6W5lMoa1JbrX3vWoPaswdvXKf+ewCuvvDJXXnnlMdubmpqyefPm4z5m7NixWbVqVVatWnWqTw8A\nwEnwZ+MAAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgEA\nCiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAok\nAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKJAIB\nAAokAgEACiQCAQAKJAIBAAokAgEACiQCAQAKdFatJ0BtDQwM5Nlnn61qbEtLS8aNGzfKMwIATgcR\nWLhnn302t96xPWdPOv9Xjnu168U8cNfHMnPmzNM0MwBgNIlAcvak89P0ruZaTwMAOI18JhAAoEAi\nEACgQCIQAKBAIhAAoEAiEACgQCIQAKBAIhAAoEAiEACgQCIQAKBAIhAAoEAiEACgQCIQAKBAIhAA\noEBn1XoClGtgYCDPPvtsVWNbWloybty4UZ4RAJRDBFIzzz77bG69Y3vOnnT+rxz3ateLeeCuj2Xm\nzJmnaWYAcOYTgdTU2ZPOT9O7mms9DQAojs8EAgAUSAQCABRIBAIAFEgEAgAUyA+G8JZ35Mj/yqFD\nh6oe79fJAMDwRCBvea/9/D/y51/9j5w96afDjvXrZACgOiKQtwW/SgYARpbPBAIAFEgEAgAUyNvB\nFOlE/m7xwMBAxowZk7POGv528UMpALxdiECKVO3fLU6Sn3bsy4Smd/kbxwCcUUQgxar2h01+0fVi\n3nEG/WDKibwK6pVNgDNXzSLw4MGDufPOO3P48OFceOGFWbduXS699NJaTQdO2Yn8PsNq32J+s3F9\nfX1pb29Pf39/6uvrk1QfbNW+CuqVTYAzW00isL+/PytXrszv//7vZ9GiRdm5c2dWrlyZRx55JI2N\njbWYEpyyE/l9htW+xTz8uDee60SDrZpXQf2SboAzW00i8IknnkhdXV1uuOGGJMn111+fv/qrv8qj\njz6a+fPn12JKMCJG+i3masedSLBVO+5Eorb7pR/k/1xyeS666KJhx1YTiyfylnW1xwRgqJpEYHt7\ne5qbh35TmzJlStrb22sxHXjbO9FXId815bKqjnsiUfvnX9037PNXG4uHDh36/483/A/uVHvME/kp\n72Tkw7LasD3Vjwq8GaEM/Hc1icCenp5j3vZtbGxMb29vLaYDZ4QTCbZaPX+1sXg0VEcyQKt9Cz4Z\nPiyPfibztddeS319fVUhVm3YjtxHBX5pNEJ5NGL1RI7Z39+ff//3fx/yudhTee7kzAplPwBGNWoS\ngccLvp6enkyYMKH6g/z08YwZO+ZXDvlfr/0se/bsOZkpnjbf/e5382qV35Rf7Xox+/fvT19f32l/\n/v/63P39/XnxxRfzi1/8IuPHjx/15/6fr/w0lSqPWe01OpHrXu3zn8g8R/qYtXzuEz3mhKZ3VXXM\nE1mfao9ZrZ5XX8r/fe//m8Z3/ur/frz8H3+Vhon/I43v/N+GPebL//HdTP7fW0Zqiiek+vP57gmd\nTzVjR/+Yb35OJ/LcPd0/y/+1YkHe9773DTv27eC73/1uNmx5eNhzP5XzHqnvBaOhtbW11lM4LU61\nB8ZUKpVq/zs/Ynbv3p3169fnW9/61uC2a665JqtWrcpVV1017OP37ds3mtMDAHjbuOyy6j7i89/V\n5JXA2bNnp7+/P1/96ldzww03ZOfOnenq6sr73//+qh5/sicLAMAbavJKYJK88MIL+dznPpfvfe97\nueCCC7Ju3brMmDGjFlMBAChOzSIQAIDaGVvrCQAAcPqJQACAAolAAIACiUAAgAKJQACAAr1lI/Dg\nwYP5vd/7vbS2tuZ3f/d386//+q/HHbdr165cddVVaW1tzYoVK9LZ2XmaZ3rmqnYNli9fnksvvTQz\nZ85Ma2trZs6ceZpnembbv39/rrjiijfd7x4YfcOtgXtg9Ozduzcf+chH0tbWlg9/+MP52te+dtxx\n7oPRUe31dw+MnocffjgLFixIa2trrrnmmnz7298+7riTugcqb0F9fX2VuXPnVv72b/+28vrrr1d2\n7NhRmTNnTuW1114bMu7QoUOVyy67rLJ///5KX19f5Y/+6I8qy5Ytq9GszyzVrkGlUqlcccUVlQMH\nDtRglme+r3/965W2trbK7Nmzj7vfPTD6hluDSsU9MFpeeeWVyuWXX1755je/WalUKpUDBw5ULr/8\n8srjjz8+ZJz7YHRUe/0rFffAaOno6Kj8xm/8RuWZZ56pVCqVyuOPP1655JJLKi+//PKQcSd7D7wl\nXwl84oknUldXlxtuuCF1dXW5/vrrM3ny5Dz66KNDxh2t3paWlowfPz6rV6/OY489lq6urhrN/MxR\n7Rp0dXWlq6srU6dOrdFMz1xbtmzJ9u3bs3Llyjcd4x4YXdWsgXtg9Pz4xz/OlVdemQULFiRJLr74\n4syaNStPP/30kHHug9FR7fV3D4yeCy+8MI8//nguvfTSvP766/nP//zPvOMd78i4ceOGjDvZe+At\nGYHt7e1pbm4esm3KlClpb2//lePOOeecNDU1HTOOE1ftGhw8eDATJ07M8uXLM2fOnCxevDjPPPPM\n6ZzqGWvRokXZuXNnLrnkkjcd4x4YXdWsgXtg9EybNi1333334NevvPJK9u7dm4suumjIOPfB6Kj2\n+rsHRldjY2NefPHFXHrppfnDP/zD/MEf/EEmTpw4ZMzJ3gNvyQjs6elJY2PjkG2NjY3p7e09qXGc\nuGqvbV+4awaMAAAC6UlEQVRfX1pbW3PHHXdk9+7dueaaa7Js2TKfxxkB55577rBj3AOjq5o1cA+c\nHq+++mpWrFiRlpaW/NZv/daQfe6D0ferrr97YPS9+93vzv79+7Nt27b86Z/+aZ588skh+0/2HnhL\nRuCbBd+ECROGbGtoaKhqHCeu2jX44Ac/mC1btqS5uTnjxo3LRz/60Zx33nnH/B+U0eEeqD33wOj7\n4Q9/mI9+9KOZNGlS7r333mP2uw9G13DX3z0w+saOHZu6urrMnj07v/3bv33MD4ec7D3wlozA9773\nveno6BiyraOj45jPGzQ3Nw8Z19XVle7u7mPexuTEVbsG//iP/5i///u/H7Ktv78/48ePH/U54h54\nK3APjK4DBw7khhtuyBVXXJH77rvvuNfVfTB6qrn+7oHR8+ijj+bmm28esm1gYCDvfOc7h2w72Xvg\nLRmBs2fPTn9/f7761a/m9ddfz44dO9LV1ZX3v//9Q8ZdffXV+ad/+qc89dRT6evry6ZNmzJ37tw0\nNTXVaOZnjmrX4LXXXsuf/Mmf5N/+7d/y+uuv5y//8i/T19d3zDhGh3ug9twDo+ell17KsmXLcsst\nt2TNmjVvOs59MDqqvf7ugdEzffr0HDhwIA899FAqlUoeffTR7N69O1dfffWQcSd7D4ypVCqV0TyB\nk/XCCy/kc5/7XL73ve/lggsuyLp16zJjxozceeedGTNmTNatW5ck+Yd/+Ifcc8896ezsTFtbWzZs\n2JBJkybVdvJniGrXYOvWrfmbv/mb/PznP8/06dNz55135td+7ddqO/kzyHe+852sWrUqe/bsSRL3\nQA0MtwbugdHxxS9+MZs3b05jY2OOfqsaM2ZMPv7xj+fll192H4yyE7n+7oHRs2/fvmzYsCE/+MEP\ncuGFF2bNmjX5zd/8zRH5XvCWjUAAAEbPW/LtYAAARpcIBAAokAgEACiQCAQAKJAIBAAokAgEACiQ\nCAQAKJAIBAAokAgEACjQ/we3UK/SPc6icAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119e5d160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import numpy as np\n",
"\n",
"sns.set_style(\"whitegrid\")\n",
"sns.set_context(\"talk\")\n",
"\n",
"plt.hist(np.log10(geo.occurs), bins=50)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>file</th>\n",
" <th>location</th>\n",
" <th>occurs</th>\n",
" <th>nation</th>\n",
" <th>author</th>\n",
" <th>title</th>\n",
" <th>pubdate</th>\n",
" <th>gender</th>\n",
" <th>wordcount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A-Cather-Antonia-1918-F</td>\n",
" <td>Black Hawk</td>\n",
" <td>21</td>\n",
" <td>A</td>\n",
" <td>Cather</td>\n",
" <td>Antonia</td>\n",
" <td>1918</td>\n",
" <td>F</td>\n",
" <td>97574</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A-Cather-Antonia-1918-F</td>\n",
" <td>Nebraska</td>\n",
" <td>16</td>\n",
" <td>A</td>\n",
" <td>Cather</td>\n",
" <td>Antonia</td>\n",
" <td>1918</td>\n",
" <td>F</td>\n",
" <td>97574</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A-Cather-Antonia-1918-F</td>\n",
" <td>Virginia</td>\n",
" <td>13</td>\n",
" <td>A</td>\n",
" <td>Cather</td>\n",
" <td>Antonia</td>\n",
" <td>1918</td>\n",
" <td>F</td>\n",
" <td>97574</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A-Cather-Antonia-1918-F</td>\n",
" <td>Omaha</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>Cather</td>\n",
" <td>Antonia</td>\n",
" <td>1918</td>\n",
" <td>F</td>\n",
" <td>97574</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A-Cather-Antonia-1918-F</td>\n",
" <td>Chicago</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>Cather</td>\n",
" <td>Antonia</td>\n",
" <td>1918</td>\n",
" <td>F</td>\n",
" <td>97574</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" file location occurs nation author title \\\n",
"0 A-Cather-Antonia-1918-F Black Hawk 21 A Cather Antonia \n",
"1 A-Cather-Antonia-1918-F Nebraska 16 A Cather Antonia \n",
"2 A-Cather-Antonia-1918-F Virginia 13 A Cather Antonia \n",
"3 A-Cather-Antonia-1918-F Omaha 10 A Cather Antonia \n",
"4 A-Cather-Antonia-1918-F Chicago 10 A Cather Antonia \n",
"\n",
" pubdate gender wordcount \n",
"0 1918 F 97574 \n",
"1 1918 F 97574 \n",
"2 1918 F 97574 \n",
"3 1918 F 97574 \n",
"4 1918 F 97574 "
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"geo = geo.join(books, on='file')\n",
"geo.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll use Pandas's `groupby` method to aggregate the book-specific location counts into totals for the corpus."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"location\n",
"ASIA 1\n",
"AUGUSTUS MELMOTTE 1\n",
"Abchurch Lane 27\n",
"Abingdon 1\n",
"Abingdon Street 1\n",
"Name: occurs, dtype: int64"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Total location counts for full corpus\n",
"places = geo.groupby('location')\n",
"places.occurs.aggregate(np.sum).head()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"location\n",
"ASIA 1\n",
"AUGUSTUS MELMOTTE 1\n",
"Abchurch Lane 1\n",
"Abingdon 1\n",
"Abingdon Street 1\n",
"dtype: int64"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# How many different volumes contain at least one mention of each place?\n",
"places.occurs.size().head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Build a summary dataframe with total occurrence counts for easier manipulation (rather than recalculating every time we need the numbers).\n",
"\n",
"Note that there are more than 2,300 unique place-name strings in the corpus. Some of these are mistakes. We'll deal with that in a moment."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" occurs volumes\n",
"count 2365.000000 2365.000000\n",
"mean 6.076533 1.454123\n",
"std 30.206909 1.580908\n",
"min 1.000000 1.000000\n",
"25% 1.000000 1.000000\n",
"50% 1.000000 1.000000\n",
"75% 3.000000 1.000000\n",
"max 1042.000000 23.000000\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>occurs</th>\n",
" <th>volumes</th>\n",
" </tr>\n",
" <tr>\n",
" <th>location</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ASIA</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AUGUSTUS MELMOTTE</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Abchurch Lane</th>\n",
" <td>27</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Abingdon</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Abingdon Street</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" occurs volumes\n",
"location \n",
"ASIA 1 1\n",
"AUGUSTUS MELMOTTE 1 1\n",
"Abchurch Lane 27 1\n",
"Abingdon 1 1\n",
"Abingdon Street 1 1"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"place_counts = pd.DataFrame(places.occurs.aggregate(np.sum))\n",
"place_counts['volumes'] = places.occurs.size()\n",
"print(place_counts.describe())\n",
"place_counts.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rather than review all 2,300+ unique places by hand, let's just work with those that appear more than five times and in at least two different volumes. This throws away most of the unique locations, but preserves the majority of the total location occurrences."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>occurs</th>\n",
" <th>volumes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>198.000000</td>\n",
" <td>198.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>35.363636</td>\n",
" <td>4.929293</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>93.222833</td>\n",
" <td>3.837439</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>6.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>8.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>14.000000</td>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>27.000000</td>\n",
" <td>6.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1042.000000</td>\n",
" <td>23.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" occurs volumes\n",
"count 198.000000 198.000000\n",
"mean 35.363636 4.929293\n",
"std 93.222833 3.837439\n",
"min 6.000000 2.000000\n",
"25% 8.000000 2.000000\n",
"50% 14.000000 4.000000\n",
"75% 27.000000 6.000000\n",
"max 1042.000000 23.000000"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lookups = place_counts[(place_counts['occurs']>5) & (place_counts['volumes']>1)]\n",
"lookups.describe()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"7002"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lookups.occurs.sum()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>occurs</th>\n",
" <th>volumes</th>\n",
" </tr>\n",
" <tr>\n",
" <th>location</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Africa</th>\n",
" <td>36</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albany</th>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America</th>\n",
" <td>83</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Andes</th>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia</th>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" occurs volumes\n",
"location \n",
"Africa 36 8\n",
"Albany 7 4\n",
"America 83 20\n",
"Andes 8 2\n",
"Asia 6 3"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lookups.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare the summary numbers here. We've gone from about 14,000 occurrences spread over 2,300 unique strings to 7,000 occurrences over 198 strings. \n",
"\n",
"We could be less aggressive, setting the cull thresholds lower, if we wanted better **recall** and lower **precision**.\n",
"\n",
"## Perform geocoding\n",
"\n",
"OK, this part you won't be able to replicate on your own, unless you sign up for a [Google Cloud Services API key](https://cloud.google.com/storage/docs/json_api/v1/how-tos/authorizing#APIKey). These keys are free for limited use, but if you don't want to get one, you can still execute the rest of the analytical code by loading stored data from CSV as indicated a few code blocks below.\n",
"\n",
"Google API keys are short (40-character) strings of random-looking text (like '`AIzaJNEjnvslrknvslDWNNW9fr6DWojnsvokjtl`', but that one doesn't work). You should store yours somewhere non-public, since it allows applications to run against Google services (potentially incurring charges) on your behalf."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Parameters for geocoding clients\n",
"# NOTE: Per-second query rates will quickly use up daily API quota.\n",
"# Need to be careful not to exceed daily quota\n",
"\n",
"import googlemaps\n",
"\n",
"gc_rate = 50 # Geocoding queries per second\n",
"pl_rate = 5 # Places queries per second\n",
"api_key_file = '/Users/mwilkens/Google Drive/Private/google-geo-api-key.txt'\n",
"\n",
"# Get API key from file\n",
"try:\n",
" api_key = open(api_key_file).read().strip()\n",
"except:\n",
" sys.exit('Cannot get Google geocoding API key. Exiting.')\n",
"\n",
"gc_client = googlemaps.Client(key=api_key, queries_per_second=gc_rate) # For Geocoding API\n",
"pl_client = googlemaps.Client(key=api_key, queries_per_second=pl_rate) # For Places API"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We perform geocoding in two stages. First, we use the Places API to identify the location in question. The Places API is full of Google's best magic; it just *knows* which place you meant. But it doesn't return fully detailed geographic information. So we then use the Geocoding API to look up all the details for the location identified by Places.\n",
"\n",
"The glue between these two steps is the `placeid`, a unique identifier for a location that's shared between the two APIs.\n",
"\n",
"These APIs return JSON data, which looks a lot like a Python dictionary. The `googlemaps` client parses the incoming JSON into straight dictionaries, which can then be addressed as usual.\n",
"\n",
"The functions below extract only part of the full geo data returned by the API. It's enough to give us a feel for the thing, without being overwhelming. But it isn't fundamentally difficult to extract everything.\n",
"\n",
"In any case, we look up each unique string in our lookup set, return the full geocode as a dictionary, then save the output to a Pandas dataframe for easier manipulation."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def get_placeid(string, api_client):\n",
" '''Takes a string and an established googlemaps places API client.\n",
" Returns first place_id associated with that string.\n",
" If not place_id found, returns \"ZERO_RESULTS\" or None, depending on result status code.'''\n",
" try:\n",
" place = api_client.places(string)\n",
" status = place['status']\n",
" if status == 'OK':\n",
" place_id = place['results'][0]['place_id']\n",
" elif status == 'ZERO_RESULTS':\n",
" place_id = None\n",
" else:\n",
" place_id = None\n",
" except:\n",
" place_id = None\n",
" return place_id\n",
"\n",
"def process_id(placeid, api_client):\n",
" '''Takes a Google place_id and an established googlemaps geocoding API client.\n",
" Looks up and parses geo data for placeid.\n",
" Returns int code on error, else dictionary of geo data.\n",
" '''\n",
" # Define all variables, initial to None\n",
" result = {\n",
" 'formatted_address' : None,\n",
" 'location_type' : None,\n",
" 'country' : None,\n",
" 'admin_1' : None,\n",
" 'admin_2' : None,\n",
" 'locality' : None,\n",
" 'colloquial_area' : None,\n",
" 'continent' : None,\n",
" 'natural_feature' : None,\n",
" 'point_of_interest' : None,\n",
" 'lat' : None,\n",
" 'lon' : None,\n",
" 'partial' : None,\n",
" }\n",
" # Perform reverse geocode. Note this needs googlemaps v 2.4.3 or higher\n",
" try:\n",
" data = gc_client.reverse_geocode(placeid)\n",
" except:\n",
" return 1 # Problem with geocoding API call\n",
" \n",
" # Use the first result. Should only be one when reverse geocoding with place_id.\n",
" try:\n",
" data = data[0]\n",
" result['formatted_address'] = data['formatted_address']\n",
" result['location_type'] = data['types'][0]\n",
" result['lat'] = data['geometry']['location']['lat']\n",
" result['lon'] = data['geometry']['location']['lng']\n",
" try:\n",
" result['partial'] = result['partial_match']\n",
" except:\n",
" result['partial'] = False\n",
" except:\n",
" print(\" Bad geocode for place_id %s\" % (placeid))\n",
" return 2 # Problem with basic geocode result\n",
" \n",
" try:\n",
" for addr_comp in data['address_components']:\n",
" comp_type = addr_comp['types'][0]\n",
" if comp_type == 'locality':\n",
" result['locality'] = addr_comp['long_name']\n",
" elif comp_type == 'country':\n",
" result['country'] = addr_comp['long_name']\n",
" elif comp_type == 'administrative_area_level_1':\n",
" result['admin_1'] = addr_comp['long_name']\n",
" elif comp_type == 'administrative_area_level_2':\n",
" result['admin_2'] = addr_comp['long_name']\n",
" elif comp_type == 'colloquial_area':\n",
" result['colloquial_area'] = addr_comp['long_name']\n",
" elif comp_type == 'natural_feature':\n",
" result['natural_feature'] = addr_comp['long_name']\n",
" elif comp_type == 'point_of_interest':\n",
" result['point_of_interest'] = addr_comp['long_name']\n",
" elif comp_type == 'continent':\n",
" result['continent'] = addr_comp['long_name']\n",
" except:\n",
" return 3 # Problem with address components\n",
" \n",
" return result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perform the placeid lookups and save results to a dataframe. Time the whole operation, just for reference, using the magic command `%%time` (FYI, `%%time` times the whole cell; use %time at the beginning of a line to time just that line).\n",
"\n",
"Note that the API can support much faster lookups than what we see here. We've deliberately throttled it (when we set up the API clients above) to avoid going over usage limits."
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 4.26 s, sys: 282 ms, total: 4.55 s\n",
"Wall time: 2min 6s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"placeids = {} # Store results ina dictionary keyed to placeid\n",
"\n",
"for loc in lookups.index:\n",
" plid = get_placeid(loc, pl_client)\n",
" placeids[loc] = plid\n",
" \n",
"placeids = pd.DataFrame.from_dict(placeids, orient='index')\n",
"placeids.columns = ['placeid']\n",
"placeids.index.set_names('location', inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you don't have an API key, hence didn't perform the actual placeid lookups, uncomment and run the line below to read stored data from disk."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#placeids = pd.read_csv('../Results/placeids.csv', index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>placeid</th>\n",
" </tr>\n",
" <tr>\n",
" <th>location</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>America</th>\n",
" <td>ChIJCzYy5IS16lQRQrfeQ5K5Oxw</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Europe</th>\n",
" <td>ChIJhdqtz4aI7UYRefD8s-aZ73I</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jackson Street</th>\n",
" <td>ChIJw34Q38OAhYARULF5jDe-8cE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hudson</th>\n",
" <td>ChIJbU_L8ymU3YkRzaqMXJfLnmQ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Waterloo</th>\n",
" <td>ChIJ3xjvH7NN5YcRGOs8-41AWeg</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" placeid\n",
"location \n",
"America ChIJCzYy5IS16lQRQrfeQ5K5Oxw\n",
"Europe ChIJhdqtz4aI7UYRefD8s-aZ73I\n",
"Jackson Street ChIJw34Q38OAhYARULF5jDe-8cE\n",
"Hudson ChIJbU_L8ymU3YkRzaqMXJfLnmQ\n",
"Waterloo ChIJ3xjvH7NN5YcRGOs8-41AWeg"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"placeids.to_csv('../Results/placeids.csv')\n",
"placeids.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now perform the second geocoding step, using placeids to retrieve detailed geo data. Again, this only works if you have an API key."
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Bad geocode for place_id ChIJPcTFENIEdkgR94E58rgB69o\n",
"CPU times: user 4.31 s, sys: 262 ms, total: 4.57 s\n",
"Wall time: 30.6 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"geodata = {} # Again, store results in a dict keyed to placeid\n",
"\n",
"for plid in placeids['placeid']:\n",
" if plid:\n",
" geodata[plid] = process_id(plid, gc_client)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Complete the dataframe and examine the results ..."
]
},
{
"cell_type": "code",
"execution_count": 212,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>admin_1</th>\n",
" <th>admin_2</th>\n",
" <th>colloquial_area</th>\n",
" <th>continent</th>\n",
" <th>country</th>\n",
" <th>formatted_address</th>\n",
" <th>lat</th>\n",
" <th>locality</th>\n",
" <th>location_type</th>\n",
" <th>lon</th>\n",
" <th>natural_feature</th>\n",
" <th>partial</th>\n",
" <th>point_of_interest</th>\n",
" </tr>\n",
" <tr>\n",
" <th>placeid</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ChIJ-Y7t-qm02IcRW-C7IsrqOb4</th>\n",
" <td>Missouri</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>United States</td>\n",
" <td>St. Louis, MO, USA</td>\n",
" <td>38.627</td>\n",
" <td>St. Louis</td>\n",
" <td>locality</td>\n",
" <td>-90.1994</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ChIJ-_Xhli0FdkgRwYbM2r7CmPA</th>\n",
" <td>England</td>\n",
" <td>Greater London</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>United Kingdom</td>\n",
" <td>Grosvenor Square Garden, Mayfair, London W1K 4...</td>\n",
" <td>51.5115</td>\n",
" <td>London</td>\n",
" <td>establishment</td>\n",
" <td>-0.15147</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ChIJ-bDD5__lhVQRuvNfbGh4QpQ</th>\n",
" <td>Washington</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>United States</td>\n",
" <td>Washington, USA</td>\n",
" <td>47.7511</td>\n",
" <td>None</td>\n",
" <td>administrative_area_level_1</td>\n",
" <td>-120.74</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ChIJ-yRniZpWPEURE_YRZvj9CRQ</th>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Russia</td>\n",
" <td>Russia</td>\n",
" <td>61.524</td>\n",
" <td>None</td>\n",
" <td>country</td>\n",
" <td>105.319</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ChIJ-ydAXOS6WUgRCPTbzjQSfM8</th>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Ireland</td>\n",
" <td>Ireland</td>\n",
" <td>53.4129</td>\n",
" <td>None</td>\n",
" <td>country</td>\n",
" <td>-8.24389</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" admin_1 admin_2 colloquial_area \\\n",
"placeid \n",
"ChIJ-Y7t-qm02IcRW-C7IsrqOb4 Missouri None None \n",
"ChIJ-_Xhli0FdkgRwYbM2r7CmPA England Greater London None \n",
"ChIJ-bDD5__lhVQRuvNfbGh4QpQ Washington None None \n",
"ChIJ-yRniZpWPEURE_YRZvj9CRQ None None None \n",
"ChIJ-ydAXOS6WUgRCPTbzjQSfM8 None None None \n",
"\n",
" continent country \\\n",
"placeid \n",
"ChIJ-Y7t-qm02IcRW-C7IsrqOb4 None United States \n",
"ChIJ-_Xhli0FdkgRwYbM2r7CmPA None United Kingdom \n",
"ChIJ-bDD5__lhVQRuvNfbGh4QpQ None United States \n",
"ChIJ-yRniZpWPEURE_YRZvj9CRQ None Russia \n",
"ChIJ-ydAXOS6WUgRCPTbzjQSfM8 None Ireland \n",
"\n",
" formatted_address \\\n",
"placeid \n",
"ChIJ-Y7t-qm02IcRW-C7IsrqOb4 St. Louis, MO, USA \n",
"ChIJ-_Xhli0FdkgRwYbM2r7CmPA Grosvenor Square Garden, Mayfair, London W1K 4... \n",
"ChIJ-bDD5__lhVQRuvNfbGh4QpQ Washington, USA \n",
"ChIJ-yRniZpWPEURE_YRZvj9CRQ Russia \n",
"ChIJ-ydAXOS6WUgRCPTbzjQSfM8 Ireland \n",
"\n",
" lat locality location_type \\\n",
"placeid \n",
"ChIJ-Y7t-qm02IcRW-C7IsrqOb4 38.627 St. Louis locality \n",
"ChIJ-_Xhli0FdkgRwYbM2r7CmPA 51.5115 London establishment \n",
"ChIJ-bDD5__lhVQRuvNfbGh4QpQ 47.7511 None administrative_area_level_1 \n",
"ChIJ-yRniZpWPEURE_YRZvj9CRQ 61.524 None country \n",
"ChIJ-ydAXOS6WUgRCPTbzjQSfM8 53.4129 None country \n",
"\n",
" lon natural_feature partial point_of_interest \n",
"placeid \n",
"ChIJ-Y7t-qm02IcRW-C7IsrqOb4 -90.1994 None False None \n",
"ChIJ-_Xhli0FdkgRwYbM2r7CmPA -0.15147 None False None \n",
"ChIJ-bDD5__lhVQRuvNfbGh4QpQ -120.74 None False None \n",
"ChIJ-yRniZpWPEURE_YRZvj9CRQ 105.319 None False None \n",
"ChIJ-ydAXOS6WUgRCPTbzjQSfM8 -8.24389 None False None "
]
},
"execution_count": 212,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"geodata_df = pd.DataFrame.from_dict(geodata)\n",
"geodata_df = geodata_df.transpose()\n",
"geodata_df.index.set_names('placeid', inplace=True)\n",
"geodata_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And uncomment to read stored data if necessary ..."
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#geodata_df = pd.read_csv('../Results/geodata_df.csv', index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Write geo data to disk\n",
"geodata_df.to_csv('../Results/geodata_df.csv')"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>placeid</th>\n",
" <th>admin_1</th>\n",
" <th>admin_2</th>\n",
" <th>colloquial_area</th>\n",
" <th>continent</th>\n",
" <th>country</th>\n",
" <th>formatted_address</th>\n",
" <th>lat</th>\n",
" <th>locality</th>\n",
" <th>location_type</th>\n",
" <th>lon</th>\n",
" <th>natural_feature</th>\n",
" <th>partial</th>\n",
" <th>point_of_interest</th>\n",
" </tr>\n",
" <tr>\n",
" <th>location</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>America</th>\n",
" <td>ChIJCzYy5IS16lQRQrfeQ5K5Oxw</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>United States</td>\n",
" <td>United States</td>\n",
" <td>37.090240</td>\n",
" <td>NaN</td>\n",
" <td>country</td>\n",
" <td>-95.712891</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Europe</th>\n",
" <td>ChIJhdqtz4aI7UYRefD8s-aZ73I</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Europe</td>\n",
" <td>NaN</td>\n",
" <td>Europe</td>\n",
" <td>54.525961</td>\n",
" <td>NaN</td>\n",
" <td>continent</td>\n",
" <td>15.255119</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jackson Street</th>\n",
" <td>ChIJw34Q38OAhYARULF5jDe-8cE</td>\n",
" <td>California</td>\n",
" <td>San Francisco County</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>United States</td>\n",
" <td>Jackson St, San Francisco, CA, USA</td>\n",
" <td>37.793318</td>\n",
" <td>San Francisco</td>\n",
" <td>route</td>\n",
" <td>-122.428007</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hudson</th>\n",
" <td>ChIJbU_L8ymU3YkRzaqMXJfLnmQ</td>\n",
" <td>New York</td>\n",
" <td>Columbia County</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>United States</td>\n",
" <td>Hudson, NY 12534, USA</td>\n",
" <td>42.252865</td>\n",
" <td>Hudson</td>\n",
" <td>locality</td>\n",
" <td>-73.790959</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Waterloo</th>\n",
" <td>ChIJ3xjvH7NN5YcRGOs8-41AWeg</td>\n",
" <td>Iowa</td>\n",
" <td>Black Hawk County</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>United States</td>\n",
" <td>Waterloo, IA, USA</td>\n",
" <td>42.492786</td>\n",
" <td>Waterloo</td>\n",
" <td>locality</td>\n",
" <td>-92.342578</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" placeid admin_1 admin_2 \\\n",
"location \n",
"America ChIJCzYy5IS16lQRQrfeQ5K5Oxw NaN NaN \n",
"Europe ChIJhdqtz4aI7UYRefD8s-aZ73I NaN NaN \n",
"Jackson Street ChIJw34Q38OAhYARULF5jDe-8cE California San Francisco County \n",
"Hudson ChIJbU_L8ymU3YkRzaqMXJfLnmQ New York Columbia County \n",
"Waterloo ChIJ3xjvH7NN5YcRGOs8-41AWeg Iowa Black Hawk County \n",
"\n",
" colloquial_area continent country \\\n",
"location \n",
"America NaN NaN United States \n",
"Europe NaN Europe NaN \n",
"Jackson Street NaN NaN United States \n",
"Hudson NaN NaN United States \n",
"Waterloo NaN NaN United States \n",
"\n",
" formatted_address lat locality \\\n",
"location \n",
"America United States 37.090240 NaN \n",
"Europe Europe 54.525961 NaN \n",
"Jackson Street Jackson St, San Francisco, CA, USA 37.793318 San Francisco \n",
"Hudson Hudson, NY 12534, USA 42.252865 Hudson \n",
"Waterloo Waterloo, IA, USA 42.492786 Waterloo \n",
"\n",
" location_type lon natural_feature partial \\\n",
"location \n",
"America country -95.712891 NaN False \n",
"Europe continent 15.255119 NaN False \n",
"Jackson Street route -122.428007 NaN False \n",
"Hudson locality -73.790959 NaN False \n",
"Waterloo locality -92.342578 NaN False \n",
"\n",
" point_of_interest \n",
"location \n",
"America NaN \n",
"Europe NaN \n",
"Jackson Street NaN \n",
"Hudson NaN \n",
"Waterloo NaN "
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Put all the data together\n",
"placeids_geo = placeids.join(geodata_df, on='placeid')\n",
"placeids_geo.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Join the full geographic data to the corpus data in order to have everything in one place. Remove places not looked up and get rid of \"Charlotte\" (a notorious NER failure in literary texts)."
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(963, 23)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>file</th>\n",
" <th>location</th>\n",
" <th>occurs</th>\n",
" <th>nation</th>\n",
" <th>author</th>\n",
" <th>title</th>\n",
" <th>pubdate</th>\n",
" <th>gender</th>\n",
" <th>wordcount</th>\n",
" <th>placeid</th>\n",
" <th>...</th>\n",
" <th>continent</th>\n",
" <th>country</th>\n",
" <th>formatted_address</th>\n",
" <th>lat</th>\n",
" <th>locality</th>\n",
" <th>location_type</th>\n",
" <th>lon</th>\n",
" <th>natural_feature</th>\n",
" <th>partial</th>\n",
" <th>point_of_interest</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A-Cather-Antonia-1918-F</td>\n",
" <td>Nebraska</td>\n",
" <td>16</td>\n",
" <td>A</td>\n",
" <td>Cather</td>\n",
" <td>Antonia</td>\n",
" <td>1918</td>\n",
" <td>F</td>\n",
" <td>97574</td>\n",
" <td>ChIJ7fwMtciNk4cRxArzDwyQJ6E</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>United States</td>\n",
" <td>Nebraska, USA</td>\n",
" <td>41.492537</td>\n",
" <td>NaN</td>\n",
" <td>administrative_area_level_1</td>\n",
" <td>-99.901813</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A-Cather-Antonia-1918-F</td>\n",
" <td>Virginia</td>\n",
" <td>13</td>\n",
" <td>A</td>\n",
" <td>Cather</td>\n",
" <td>Antonia</td>\n",
" <td>1918</td>\n",
" <td>F</td>\n",
" <td>97574</td>\n",
" <td>ChIJzbK8vXDWTIgRlaZGt0lBTsA</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>United States</td>\n",
" <td>Virginia, USA</td>\n",
" <td>37.431573</td>\n",
" <td>NaN</td>\n",
" <td>administrative_area_level_1</td>\n",
" <td>-78.656894</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A-Cather-Antonia-1918-F</td>\n",
" <td>Omaha</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>Cather</td>\n",
" <td>Antonia</td>\n",
" <td>1918</td>\n",
" <td>F</td>\n",
" <td>97574</td>\n",
" <td>ChIJ7fwMtciNk4cRBLY3rk9NQkY</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>United States</td>\n",
" <td>Omaha, NE, USA</td>\n",
" <td>41.252363</td>\n",
" <td>Omaha</td>\n",
" <td>locality</td>\n",
" <td>-95.997988</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A-Cather-Antonia-1918-F</td>\n",
" <td>Chicago</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>Cather</td>\n",
" <td>Antonia</td>\n",
" <td>1918</td>\n",
" <td>F</td>\n",
" <td>97574</td>\n",
" <td>ChIJ7cv00DwsDogRAMDACa2m4K8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>United States</td>\n",
" <td>Chicago, IL, USA</td>\n",
" <td>41.878114</td>\n",
" <td>Chicago</td>\n",
" <td>locality</td>\n",
" <td>-87.629798</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>A-Cather-Antonia-1918-F</td>\n",
" <td>New York</td>\n",
" <td>9</td>\n",
" <td>A</td>\n",
" <td>Cather</td>\n",
" <td>Antonia</td>\n",
" <td>1918</td>\n",
" <td>F</td>\n",
" <td>97574</td>\n",
" <td>ChIJOwg_06VPwokRYv534QaPC8g</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>United States</td>\n",
" <td>New York, NY, USA</td>\n",
" <td>40.712784</td>\n",
" <td>New York</td>\n",
" <td>locality</td>\n",
" <td>-74.005941</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" file location occurs nation author title pubdate \\\n",
"1 A-Cather-Antonia-1918-F Nebraska 16 A Cather Antonia 1918 \n",
"2 A-Cather-Antonia-1918-F Virginia 13 A Cather Antonia 1918 \n",
"3 A-Cather-Antonia-1918-F Omaha 10 A Cather Antonia 1918 \n",
"4 A-Cather-Antonia-1918-F Chicago 10 A Cather Antonia 1918 \n",
"5 A-Cather-Antonia-1918-F New York 9 A Cather Antonia 1918 \n",
"\n",
" gender wordcount placeid ... continent \\\n",
"1 F 97574 ChIJ7fwMtciNk4cRxArzDwyQJ6E ... NaN \n",
"2 F 97574 ChIJzbK8vXDWTIgRlaZGt0lBTsA ... NaN \n",
"3 F 97574 ChIJ7fwMtciNk4cRBLY3rk9NQkY ... NaN \n",
"4 F 97574 ChIJ7cv00DwsDogRAMDACa2m4K8 ... NaN \n",
"5 F 97574 ChIJOwg_06VPwokRYv534QaPC8g ... NaN \n",
"\n",
" country formatted_address lat locality \\\n",
"1 United States Nebraska, USA 41.492537 NaN \n",
"2 United States Virginia, USA 37.431573 NaN \n",
"3 United States Omaha, NE, USA 41.252363 Omaha \n",
"4 United States Chicago, IL, USA 41.878114 Chicago \n",
"5 United States New York, NY, USA 40.712784 New York \n",
"\n",
" location_type lon natural_feature partial \\\n",
"1 administrative_area_level_1 -99.901813 NaN False \n",
"2 administrative_area_level_1 -78.656894 NaN False \n",
"3 locality -95.997988 NaN False \n",
"4 locality -87.629798 NaN False \n",
"5 locality -74.005941 NaN False \n",
"\n",
" point_of_interest \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"\n",
"[5 rows x 23 columns]"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"geo_all = geo.join(placeids_geo, on='location')\n",
"geo_all = geo_all[geo_all.placeid.notnull()]\n",
"geo_all = geo_all[geo_all['locality']!='Charlotte']\n",
"print(geo_all.shape)\n",
"geo_all.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Make maps"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Setup\n",
"import cartopy.crs as ccrs\n",
"import cartopy.feature as cfeature\n",
"from scipy import stats\n",
"\n",
"figDir = '../Results/'"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Munge data\n",
"\n",
"# Just cities\n",
"cities = geo_all[(geo_all['location_type'] == 'locality')].groupby('location')\n",
"\n",
"cities_tot = [int(i) for i in cities.occurs.sum()]\n",
"cities_lon = [i for i in cities.lon.max()]\n",
"cities_lat = [i for i in cities.lat.max()]"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def bubblemap(lats, lons, sizes, color, name):\n",
" import cartopy.crs as ccrs\n",
" import cartopy.feature as cfeature\n",
" import os\n",
"\n",
" plt.figure(figsize=(12, 6))\n",
" ax = plt.axes(projection=ccrs.Robinson())\n",
" \n",
" ax.set_extent([-170, 170, -60, 80])\n",
" #sizes = [i/50 for i in sizes]\n",
"\n",
" plt.scatter(lons, lats, s=sizes, linewidths=0,\n",
" color=color, marker='o', alpha=0.4, transform=ccrs.Geodetic())\n",
"\n",
" ax.add_feature(cfeature.COASTLINE, linewidth=1.5, alpha=1.0)\n",
" ax.add_feature(cfeature.BORDERS, edgecolor='gray')\n",
"\n",
" plt.tight_layout()\n",
" plt.savefig(os.path.join(figDir, name)+'.png', dpi=300)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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8HI2aP38+/P39AbzsJR0xYgScnZ3h7OzMKOMVFRUhLCwMffr0wYEDB1BQUIDKykrEx8cj\nNjYW9+/fh5OTE7Zu3Yq1a9cCAE6cOIGkpCRUV1dDRkYGtbW1ePLkCXJyciASiSAlJQVJSUlwOBzI\nyMiAx+OhuroapaWl4HK5sLe3x8CBAzFgwAAMGDAAurq6rbquV69exdKlS5mcK63BwMAAM2fOhIeH\nB6ytrT/ohsG/yczMxL59+3D48GFUVlaCy+Wycf0fEUSEu3fv4uzZszh37hzz/PTs2RM6OjrQ0tKC\nlpYWJCQkUFJSAj09PTx58gQxMTGora0FAGhpaSEiIkJsPmFnUlVVhRs3bqCkpARlZWXYunUrysvL\nm91fUVERc+bMgY6ODmpra6GpqYkVK1a89jkQiUS4dOkSrK2tGykn1tfXIzExEQ8ePEB0dDSio6OR\nkJAAkUgES0tLHDx4EAMHDmy3+r4tFy9exOTJk18rsa+iogJzc3NGQKfh/zY2Nu88J1lHQkQIDQ1F\nQkIC801i+bhhHaaPGD6fj9OnT6O0tBTu7u6s+lMHk5ubi+PHjyM9PR16enowNDREt27d8M8//zCT\nzJWUlDB79mx4eHhARUUFXC4XlZWVSEpKwmeffQYfHx/s2LFDrNyCggLo6urC09MTvr6+zPqkpCQm\nMev+/fvFxBZcXV1x8+ZNiEQi7N+/Hz/88APq6uowaNAgZGRkICsrC8BLtbDIyEiMGDECmZmZiImJ\nQa9evVBdXY2DBw9i27ZtYmIRzaGgoABlZWUIBAKUl5dDKBRiyJAhCA4OFpMmbyt8Ph8RERHMuRQU\nFCAnJwcZGRmm11RaWhoCgQDV1dXMCFlAQADTU37s2DF4eHi8tS3vCyKRCOHh4dizZw8j8MKG4n74\nFBQU4O+//8bhw4eRnp4OaWlpjBgxAlOmTMHEiRNhYGCAqKgo/P333wgPD8fjx48BgBnpblAbc3R0\nRM+ePTu1k4GIkJycjMrKSoSFhWHbtm0oLS0V20dKSgqXLl2CmpoalJWVUVlZiVu3boGIsGDBAmhq\nar5TGwsLCxEUFITt27ejpKQE0dHRXSalQUpKClxcXMDj8bBx40YmyXd9fT1KS0uRlpbGLKmpqcjO\nzmacqyFDhmD27Nn4+uuvAQC9e/eGqakp9PX10a1bN3Tv3p1ZLCws3qvOqOjoaNy5c4eJ6mD5OGEd\npo+QBkeprKwM06ZNYx2lLkRiYmKL8xD17NkTy5cvx9atW5GXl8es79u3LyoqKlBcXAwej4dhw4Zh\n586dsLa2ZvYpLy/HhQsXMHv2bAAv50vp6+ujsLAQxcXFTFJP4GUsv52dHfbv3w/gZcjNzp074enp\niVWrVsHY2Jj5+JWWliI6Oho1NTWMDVwuF6amprCwsICpqSlUVVWZHtvKykrcu3cPS5YsQUFBAQoL\nC5m8TZ3FsmXLsGfPHkydOhXHjx9vFwfufSA6OhqOjo4AXo5wNigSsny4EBGMjIyQm5uLgQMHYsGC\nBZgyZQqTjDcjIwPr1q1DYGAglJSUMHDgQAwZMgRDhgyBg4PDa9MAdAYnTpzAzJkzmb/Hjh2L1atX\nw8zMDBoaGpCXl+/0hnpKSgp8fX3x119/oa6uDqGhoRgwYECn2vQqsbGxGD16NEpKSsDhcGBnZ4fh\nw4djxIgRUFJSws2bN5GTkwNJSUnw+XxkZ2fj0aNHKCoqwowZM3DixIk3nmPr1q1Ys2ZNB9SmfWEd\np48b1mH6iODz+Th16hTKysrYEaV3TINoRkVFBSorK1FZWYmKigoAL2PZnZ2dYWhoCDk5OcjKykJe\nXh4WFhaQkpLCs2fPcP/+fWRmZkJOTk5MKKEp1NXVoaOjAxUVFaiqqkJFRQUqKirQ0NDA9OnTm801\nQUQ4dOgQTpw4wUjnampqYvjw4aisrMSiRYvE9peQkMCOHTtQX1+PdevWMXOMVFVVYWpqim7dukFB\nQQEyMjIQCoUQCASoqKjAixcvmDlLlZWVTPLI7OxsZo6Wuro6fv31V8ybN+9tL/1bk5+fj5UrV+LE\niRPw8vLCunXrOtukd0pDKFZZWRm+++47pKamYvfu3Vi6dGlnm8byjhGJRHBzc8PNmzdx6NAhZnSA\nx+Nh7dq1+PPPPyEtLY3vvvsO3333HSPF3lUZOXIk7t27hz179sDBwaHFnU8dgUAgwMSJExEcHAxp\naWm4u7tjw4YNXSZ88VXq6uoQGRmJmzdvIiQkBPfv34dAIGC26+joMLn5RCIR86+NjQ0uXLiAsLAw\nxMXFMcurc3EB4NatWxg6dGhHV6vdaHCc7OzsMHjw4M42h6WDYB2mj4AGR+nFixdwd3dv9ZwQltbT\no0cPRmlMXl6ecWS4XC5qampQVlbW6BhbW1tcvHhRTJ1HJBLB1dUV4eHhzOhMnz590K9fP1hZWcHK\nygr6+vqt6jUtKipCSEgI+vTpg759+6K8vBzFxcWQlpZGz549wefzERAQgHPnziEiIgJlZWWMc6So\nqAgrKyv0798fNjY2kJCQwMOHD5GVlYVnz56htrYWXC4XQqEQysrKUFdXZ5YGhTEej4dnz55BVVUV\nJiYmsLa2xvjx46Gtrd2lQsDs7e2hpKSEW7dudbYp75SmEhfb2NggNja2kyxi6QgyMzMxfPhwZGdn\nY/z48Thy5AgTjvbzzz9j8+bNmDdvHn755ZcupRhWU1ODx48f4/Hjx+DxeJCRkWGWpUuXMkqic+bM\nwV9//dXJ1v4fV69exbhx4wC8DGccNmwYrKys0KdPHzx8+BCGhoZYunQp5OXlUVtbi4yMDEhKSkJN\nTQ1qamotGh0rKiqCUCiEtrZ2u84/rK6uxu3bt1FbW4uhQ4eKKZsWFBQgKioKeXl5MDIywoABAxqp\nJFZWVjIdZ1JSUh+MkMKDBw9w+/Zt2Nvbs47TRwDrMH3AEBGuXLmCjIwMTJ8+nR1R6gAaeud9fX2x\nZs0abNq0qdFEWJFIhKSkJJSVlaGurg5FRUW4cOECAgMDmY9nSz92DSM5v/76Ky5evIisrCwUFxej\nT58+CA8Ph4aGBoD/k40+evQoQkJCGAeIw+GIqdEdP34cP/30EzIzM9GtWze4ublBR0cHSkpK4HK5\n+Oeff/D06VMAwJgxY7Bq1Sr8888/iI2NRXZ2Nl68eMGUNXLkSAwdOhTx8fGIj49HWloaRCIRfv/9\nd/z111949OiRWF1kZWWxcuVK/PTTT10iDG716tXYtWsXysrKoKio2NnmtCsZGRnIz88HEaG0tBSz\nZ89GZWUlOBwOM6euK/wGLO+OkJAQjBw5EgDw8OFD9O/fHwDw/Plz9OrVC5MmTWpReFVHcuPGDYwZ\nM0ZMxa45Bg8ejPDw8A6wqmXw+XycOHEC6enpyMzMRFJSEh4/foy6ujrIyMgwanrNIS0tDTU1NWhr\na2PZsmWYP3++WAcTn8+HmpoaamtrweFwoK2tDV1dXcjIyICIIC8vjyFDhmD06NFwdnaGjIzMW9eJ\niKCrq8s4qQ188sknGDp0KL7//vtGqR4+RKKjoxEeHo6xY8fik08+6WxzWN4RrMP0gRIZGYk7d+5g\n/PjxXXLI/0Pj3r178PLywuXLlyEjI4OZM2dix44dTU4grqmpQVhYGE6dOsXEg/+bffv2QUFBgVGv\nA17mkYmKisIXX3yBx48fY9GiRYiPj4dAIACHw2EkoRu4efMm+vXrBy6Xi9LSUlhbW6N79+748ssv\nMWHCBMTFxWHx4sXM/jo6OigqKoKVlRW8vb0xbtw4lJSUID4+HlFRUdi/fz+ys7PRq1cvODo6IiQk\nBAUFBVBWVsbgwYORlZWFpKSkRnUxNTVFv3790K9fPyYktKCgAN988w169+6NuLg4vHjxAgkJCUwu\npQcPHjDOXmcRGhqK4cOHIygoCFOnTu1UW9oTgUAAVVVVRtmsAX19fZw6dYpVg/oIICL89NNP8PLy\nAvBy7qGGhgaICO7u7rh06RKePHnSro3diIgIBAQEQFVVFT169ECPHj1gZGQEY2NjpkOirKwM2dnZ\nsLKyarLTqCHlRV1dHa5fv46ePXuivr4e9fX1qKurg4KCApP24H1AIBAgMzMTurq6ePjwIWbNmoXc\n3Fx88803GDZsGDgcDsrLy8WWuLg43L9/nxFUUFRUhJKSEhQVFXHjxg1mPuvEiRMhISEBPp8PCQkJ\nvHjxAlFRURAKhVBSUoKrqyu+/fZb5vvSFoRCIdzc3BAaGtrsPmVlZVBXV2/zOd4XiAjBwcFITU3F\n9OnTP6hUDCz/n3eUEJelk0hPT6cdO3bQ7du3O9uUj4KoqCgaO3YsASAtLS3asGED5efnN9ovIyOD\nfvnlF+rfvz9JSEiIZYyXl5cnGxsbsXWvLvPmzaN+/foxf3/66adkb29POjo6tHz5clq+fLnY/v/5\nz3/owYMHdPny5UZl/fnnn4xNJSUlpKKiQgBo6tSppKioSIMHD6aamhq6e/cujR49WuzYQYMG0d69\ne2nYsGHM36dOnaLa2loiIjpy5Aizr6GhIR09erRR5vTt27cTADI1NW22vnJyclRaWvpuf7gWUF9f\nTwDIxsaGRCJRZ5vz1vD5fPL19aVVq1bRl19+ydx7ERERXeJ6s3Qcx48fZ5776urqRuu3bt3arufL\nysoiAKSoqEjS0tKNnvdz584RETHvwd69e1NSUlKTZYWHh5O0tDQ5OTlRbm5uu9rZ2QiFQsrLy3vt\nPiKRiPz9/WnixIk0bNgwcnBwoE8++YR69OhBmpqaJCcnR1JSUnTp0qVGx5aXl9O5c+do0aJFZGBg\nQABozJgxlJCQ0Gabnz9/ThcvXiQ7Ozux31VKSoo0NDTIwcGBxo8fT1999RWtXr2atm3bRkeOHKEr\nV65QdHQ0ZWdnM9+QDwE+n0/Hjx+nffv2UVVVVWebw9KOsA7TB0JxcTHt3r2bzp49+0E07ro6MTEx\nNHHiRAJAGhoatHXr1kYvx7q6OgoMDKSRI0eKfUikpaVp9OjRdPDgQUpNTSWRSEQCgYDGjx9PMjIy\n5O3tTefPn6fExESaNGkSASBnZ2fas2cP85EDQEeOHCGilx/QQYMGkYqKCsXExDDnnzJlCmPfunXr\naMOGDY3qcfDgQQJAJiYmBIA8PT1p1KhRjAO4ceNGunHjBhUWFtK1a9dIXl6eVFRUaN++fSQUCsXK\nSk5Opu7du9NPP/0k1ghrICkpibFdWVmZ+b+WlhYNGTKEZGVlCQCZm5tTVFRUO/xKbaeuro7Wr19P\nAMjMzIzq6uo61Z724Pvvv2/SQc3Jyels01g6EKFQSOvWrSMAtHr1arFtZmZmZGBgQHw+v13PWVpa\nSgBox44dJBAIKDc3l+7evUv//PMPOTo6kpSUFMXGxlJgYCBzX8rIyFBoaCht2bKFevfuTcrKyo2c\nLQC0ceNGEggE7Wrv+05L2gBcLpe2b99O6urqpK6u/lbvuNDQUObbNmvWLPLy8qLly5eTh4cHjRo1\niqytrcnQ0JBkZGSa7ShTVFQkExMTcnJyovXr11NmZmab7ekKVFVV0b59++j48ePt/jyxdA6sw/Se\nc+3aNVq6dCl5eXl9EI2694Fr164xL/mFCxdSRUVFo33Cw8NJX1+f+fADoO7du9PevXtb1eskEAjo\n+fPnREQUEhLCnNfBwYFpJFy6dKnR6BERUVlZGX311VdMj23DyFd9fT1FRkbSL7/8QhwOhwwNDUlJ\nSUlsxOvfDmB1dTUZGRmRpaUlPXv2rNXXjIhoz549BIBkZWXJ3d2dNmzYQBEREUw9Kioq6MCBA9St\nWzeSlJSk48ePU3Z2Nt28eZO5Bh1BQUEBqaurEwBycnKimzdvdti53xUikYgSExNp8uTJZGRkxPzW\nbm5ubAfLR8bDhw8JAKmqqtLp06fFtnl7exMA2r9/f7ue89GjRwSA1q9f32hbREQEAaDTp09TYGAg\nKSoqNmpMDx06lL799lv64Ycf6JdffqEFCxY02odtlLaNoKAgAkDGxsa0ZcsWKiwsZLbduHGDevTo\nQdOmTaM//viDEhMTqaamhq5evUobN26kQ4cO0d27d6msrIzWrl3L/BYcDofOnz/f6FwikYjKy8sp\nLS2N7t69S+fPn6eDBw/Sli1baMWKFeTh4UFDhgwhDodDEhIS5ObmRv7+/u/1SGJeXh79/vvvH8R3\n5GOHdZjxgdesAAAgAElEQVTeY8LCwsjS0pJ5STk7OzfZs9+RCIVCio6Opi1btpCrqysNHDiQ1q5d\nS6mpqZ1qV3tSUlJCHh4eJCkpSdLS0rR3716KjY0lPz8/WrZsGQ0ePJikpaXJ3Nyc1NTUmuxNu3fv\nXqvPe/bsWQJAPj4+xOVyiehlL5axsTGZm5s36zBfvXqVOBwO9erVi4YNGybWIJGSkiIANHz4cPr5\n55+ZMLxt27aJlbFmzRrmmJ9++qnNjex/j0o1RXh4eKPr1adPnw5rEIWFhTU6v7u7OxUUFHTI+dub\nhgZRw4gkEdGxY8dIRUWFjI2NWYfpLeByuZSbm0slJSVUU1ND+fn5dOvWLdq/fz/5+fl1yYaeSCRi\nRsfV1dVpyJAhtGTJEkpLSyOBQEDDhw8nADRw4EAKDAx86+cuMzOT9PX1ydDQkLKzsxtt9/PzIwA0\nePBgZpTi3r179MUXXzDPX7du3RqNIgmFQoqMjCRTU1OaPHkyO8rURkQiEZ06dYpcXV2Z6z9jxgwK\nDAyk69evNwoh//ffr1vaGuqbk5NDGzdupO7duzNlmZqa0tdff03+/v7NjooLhUIqLy+n3NzcLhfm\n98svv9DYsWPp6dOnnW0KSxthHab3kMDAQDI3N6c+ffqIvZzk5eWprKysQ2yoqqqiW7du0a+//kru\n7u40YMAAGjduHGlqajL2WFtbk4uLC/O3k5MTOTo6kqKiIoWGhnaIne+S1NTUJsMKBgwYQN9++y2V\nlJSQtbV1kx+SwMBAKikpeWsbfvvtNwJA33///WsbvgcPHqRRo0aRo6MjLVq0iAYOHMg4Aq+Gv4lE\nIurXrx+NHj1a7Ph/2/8uRzNzc3Np1qxZ5OnpSfv27aNt27Yxvcw+Pj4UGRn5zp2n5ORksXu5Yfnx\nxx/fOwejIewSAH355ZdUXV1NXC6XCTl8tUeZpeVERkaSnp7eGxuNVlZWtHr1arp582aXiQKoqqoi\nX19f+uabb8jFxYXk5eVJX1+ftmzZQlu3bm00p3LVqlVtvu8bwoofPnzY5Pbc3FwmfLhhkZOTo2nT\nppGzszOzLj09/W2qzNICkpOTafny5Y06+mRlZSk8PJz8/Pxo/fr1FBwcTNXV1ZSRkUEXL14kHx8f\n+vrrryk4OJiSkpLowoULdOXKlbd+VwoEAoqNjaWdO3fS5MmTmZF/4GUYuZubGzk4OJCZmRlpaGgQ\nh8MRs1tVVZUsLCxo+PDh9Ouvv3aqs9LQITBgwIAu0bnN0npYlbz3iPr6ehw7dgzp6ekICQmBqqoq\nDAwMYG9vDzs7O9jb2zeSsG4PeDwebty4gdDQUDx9+hSpqalITk5m5Kh79uwJIyMjlJWVwcbGBqNG\njcLIkSOZ5Hb6+vooLCzEyJEjcePGDaZcOzs7PH/+HAKBAEOHDoWbmxv69+8PDQ0NZuFwOCAilJeX\nQ0VFBZKSku1eP5FIhGfPnkFFRQXq6uqIjo7G6tWroaSkhD/++AM9evQAABARuFwuFBQUmL9/+OEH\nSEhIwNbWFtbW1jA1NRWzsaX5kcLCwjBkyJBW2/706VPMnDkTkZGRGDBgAP773//C1dVVbB962THC\nXMuFCxfiwIEDzWZbHz16NMrLyxEZGclcHwMDAxQWFkJLSwurVq2CjY0NHBwcWqVkx+PxIBQKW61g\nRUTYvHkzTpw4geTkZACAlpYWzp07904U3YgI8+fPx+HDh9GzZ09GarsBPz8/fPXVV+1+3vamuroa\nU6ZMQUhICADA2NgYWVlZjfZLTk5G7969O9i694uqqipERESAx+NBQUEBOTk5WLZsGfT09PD9999D\nIBCAx+MxCajNzc1RVVWF4OBgXL16VUxF7I8//oCnp2cn1qYxiYmJGDt2bKMEo6/y/PnzNuVjCggI\ngIeHB8aNG4eTJ08y+dj+TUREBMaNG4fq6mqMGTMGwcHBmDNnDg4fPsxcW5aOgc/nIy4uDuHh4bh9\n+zbi4+Nx7tw5Rnq+sxCJREhISMCtW7dw69Yt5OXlQUNDg8n11/B/RUVFRo21oKAAqampePjwIQBg\n4MCBOHjwICwtLTvU9qysLHh7eyMoKAhycnJYsmQJbG1t4ebm1qF2sLQd1mF6TwgNDUV8fDxmzZrV\npFR1e0NEuHr1Ko4ePYpLly6huroa8vLyMDExQc+ePWFnZwdHR0c4OjqKJbFrCh6PBykpKUhJSSEz\nMxN+fn7IyspCaWkpDA0NIRAIEBIS0uhjraWlBUNDQzx9+hSVlZXo1q0b5syZg1GjRsHW1rbN0rE5\nOTm4ceMGwsLC8PjxYyQnJzMSy5999hlOnToFXV1dlJWVYd68efjpp5/g7++P1atXA3iZHPDatWvg\n8/nQ0tLC8OHDGSfqVdLS0mBubt4im96mESUSiXD48GFs3LgReXl5+OKLL+Dv7w+hUIigoCB4e3sj\nOTkZCxcuhKmpKZYuXYpZs2bh6NGjYuU8e/YMu3btwr59+2BmZsZ8YAAgJiYGnp6eSE5ORmVlJbNe\nU1MTxsbG0NPTg7GxMSwsLGBoaAgdHR3Iy8ujuLgYhYWFSE1Nxf79+yEvL4+dO3fC2toaZmZmra5r\nYWEhwsLC8OOPP6KwsBC5ublQVlZu03VrjsuXL2PChAlYu3YtvLy8ICEhAS6XiwsXLmDu3Lnw9fXF\n7Nmz2/Wc74Ldu3fj22+/fe0+r+bfYREnKysLx48fx7Vr13D37l0IBAKx7QMGDMC5c+egra0ttr6k\npATZ2dmQlpaGhYUFZGVlYWdnxyQC3r9/PwQCAQwMDDBhwoR2TTD6NhAR6urqUFVVJbbweDxYWVk1\nmfA8JCQEZWVlMDY2hrGxMbS0tJrsJNq/fz8WL14MW1tbhIWFNfm+BID79+9j0KBB8PT0ZN4/YWFh\n7VtRlo+SzMxMnD59Gtu3b0dtbS327duHzz///K2Spf/xxx9YunQpdHV10atXL+jr6zOLnp4eJCQk\nUFNTg9raWkhLS8PS0hKWlpbo1q0bJCQkEBcXh5CQELi7u8PY2Lj9KsvyTmAdpi5OcXEx/P39MWTI\nEDg4OLzz8wkEAkRERGDDhg0IDw+HtrY2pkyZgmnTpsHV1bVdkt01BREhNTUVmZmZKCsrQ2lpKaKi\nolBeXo6ePXuiW7duCA0Nxf/+9z9oaWmhqqoKvXv3hrOzM1asWCHmmIhEIkRFRTG9Y48fP2ZeYg0J\nVIGXeYesra1haWmJ48ePo6SkBLq6upg+fTo2bdqEOXPm4Nq1a6ivrxdLlNiQr6iB7t27w9vbG5Mm\nTRJrvB85cgRz5szB+PHjYWlpib59+6JHjx6QlZVFTU0NOBwOzMzM0K1btza/tK9cuYKjR4+isrIS\nz58/x6NHjzB9+nRoamoiKCgIhYWFMDc3h62tLU6cOAEnJycUFBTg+fPniIyMhK2tLYCXPej6+vqo\nqakB8HKU6cyZM1BQUBBrANH/T3R66dIlLFy4sFHup9cxduxYxMTEoKioCJKSkoiIiICzs3Ob6n3p\n0iVMnDgRd+7cwYABA9pURnNMmzYNZ8+exYULF6CgoID79+/j/PnziIqKAgB4eHjg2LFj7XrOd4FA\nIMC9e/cgEAhw+fJl+Pr6gsvlMtv/+ecfzJgxoxMt7JqIRCLs2bMHa9euBZfLha2tLUaNGoVRo0ZB\nQ0MDNTU1EAgETSb/DAsLg5GREYyMjFBfX4/U1FRwOBzw+XzMnTsXiYmJkJeXR3V1NQDAyMgIixYt\nwsyZM9GtW7d3MoL+roiLi2PeHw0oKCjA2NgYZmZmGDRoEFxdXWFjYwNJSUn89ddfmDt3LlatWoXB\ngwfDzc2tScdp/vz58Pf3R58+fVBSUoLk5OT3Jq8SS9cnLy8PM2bMwO3bt6GmpgYHBwf069cPCQkJ\nePDgAXg8Hvh8PgDAwMAA3bt3h5GREbS1tVFRUYHS0lLU1dXBzc0N+vr6mDVrFlO2oaEhKioqmOe7\nOZSUlBjnqXfv3qiqqoKRkRHmzZvXZTpQWBrDOkxdFCJCYGAgamtrMWvWrHYPtSsuLkZeXh6T9C8l\nJQXBwcG4fv06ysvLoaOjg40bN2LevHnvJMyvrcTExCAxMRHp6emIjIzE3bt3weFw4OXlBQsLC9y4\ncQMBAQF4/vw5AMDCwgL9+/dHcXExcnNzYWFhgZEjR2LEiBHo06cP4wzU1NTg2bNnMDc3Z5yXixcv\nYtWqVZg0aRJ27NgBCQkJ9OjRAzY2Nli2bBmMjIyQkpKCtWvX4tGjR5CUlISTkxOGDx8OJycn8Hg8\nfPrpp5CUlIRQKMTgwYOxbNkyuLu7N1s/gUCAlJQUxMbGIjY2FsnJySgrK0N1dTX69evHlG1iYgJl\nZWVYWFggLS0N1tbW0NTUxJAhQ/Do0SMEBQVBUlISVlZWcHd3x5dffokdO3Zg165d+Pvvv7F8+XK4\nuroiKCgIwMv7zd/fH3fu3MGhQ4dARHB2dkZSUhLCwsJgbW3N2FhZWQknJyeUlJTg9u3bkJOTQ2Fh\nIZ48eYKbN2/C398f5ubm8Pb2hp6eHnR1daGjowNlZWVUVVXh1KlT+Prrr+Hk5IT79++36vevqanB\nrVu3EBgYCH9/fxw+fBhz585tVRlv4vDhw/jhhx9QUlLCrDM1NUVGRgYAoKCgoMne9q4EESE2NhYB\nAQE4efIknj9/Di0tLXz55ZeYN29eh4ejvC+kpqZi7ty5TNJvX19fdO/eHVVVVXjy5AmkpaWbTapa\nXl6O4uJi9OrVi1knFApx79496OjooKysDGFhYXjy5Am++eYb5Ofn448//sDNmzcBAJKSktDX10f3\n7t3RrVs39OnTB9OnT+/w36q2thZ8Ph8qKirNhhRXV1cjKiqKSXqqr6+PefPmobq6mklgnZKSAgBQ\nV1fH1KlTMXjwYCxevJhx2vv374+9e/fCxcVFrOxnz56hV69e4HA44HK5cHR0xJgxY2BmZoZZs2a1\nOMyZhaU5BAIBAgICcOfOHURGRiIxMRG9evXC0KFDoaSkBBkZGYhEIjx//hzPnj1DTk4OSkpKoKam\nBk1NTQiFQiQkJDQq99Wk9S9evACHw4GCggIUFRVRW1uLJ0+eICkpCcnJycy/DYmGJSUlYWhoiOHD\nh+P333+HqqpqR18WljfRYbOlWFpMWloamZubk5aWFqOG1l5ER0fT559/zqijvboYGhrS119/TadO\nnepyCddEIhHFxcU1UlnKzs4mBwcHMdW3CRMm0PHjx9tV1ayysrLZ30IgEFBISAitW7eOnJycGk08\nBUAuLi6MzPj27dtp/fr15OnpSTNmzKBRo0aRnZ0dmZiYiOWp4HA4JCsrSzIyMtS3b1+xHEwAmKSz\nLi4uYgpRxcXFTDLdhqVB2UhFRYVWrFhBHA6HRo4c2aguQ4YMYY5RV1cnDQ0NsrOzY9Tt8vPzadiw\nYSQpKdmkTGpCQgLp6emRtbV1k9dKJBKJ2bVx48YW59v4+uuvmesjLy9PY8eOpbS0tBYd21p4PB6d\nPn2afvnlF1q0aBGZmJiQtLQ0k2CzK8Llcunhw4e0ceNGMjc3ZxSvJk2aRIGBgcTj8TrbxC6LQCCg\n7du3k5ycHKmrq5O/vz8zYf3JkycUFxdHd+/epbt371JERAQ9ePCAYmJiKDo6mhITEyk7O5vu3LnT\nSIyksrKSwsLCqLy8vNlzP378mHx9fWn9+vU0e/ZsGjFiBJmbmzPvkf79+9PWrVspKyvrtXW4d+8e\n7dmzh+7fv99qdTI+n09XrlyhmTNnkry8PCPaQ/RSFjklJYUSExPp7t27NH/+fJKVlSUdHR1as2YN\njRs3jgDQunXrxMrMy8ujgIAAmjVrFpN3TUVFhcaOHUtTpkwhVVVVAl4qdP77HdAgSPLvJS4urlX1\nYmFpCW1RWExPTydvb+8mk87Hxsa2uJwXL15QcHAwrV27llxcXEhBQYF27tzJikJ0QViHqQtRX19P\nhw4dotOnT5O7uzsBL/PttBebNm1iHugVK1bQmTNn6NKlS3Tt2jVKSEjosupffD6f7ty506wCoFAo\npNTUVLpx4wYVFxd3sHWNqaqqooiICNq6dSuNGjWKScj6qhPD4XBIQ0ODzMzMyMnJiUaPHk0aGhqM\nM9Cw3t3dnUaMGEEA6MCBA3T48GHy8vIiHx8fWrx4Mfn6+jZ62S9cuFDs5a2srEz29va0YMECWrRo\nEQGg8ePHN3KKKysrmWN8fHwoOjqaDA0NSV1dnWpqaujSpUukra1N8vLydPToUbFjq6uraeHChcTh\ncEhNTa3JxLN8Pp8OHjxI48ePp27dupG5uTlJSEiQjIwMrV69+o0fiKVLlzKNyFmzZtGDBw+ovr6+\njb/S67l16xbp6Ogwv9WAAQMoLCzsnZyrNeTl5dGRI0fo0KFDtGvXLlq6dCmNHj2aTExMGKdYQkKC\nXF1d6eDBg22W9f2YePToEdPpMmnSJMrLy6Pbt28zTtG1a9eYzpL6+np6+PAhRUVFMbLFNTU1zeYm\nEwqFFB4e3qZ3a35+Pu3atUtMKW7AgAG0Z8+eJjuDBgwYIPbcq6urk6OjI3333XdNdvaIRCKKjY2l\nFStWkK6uLgEvk1w3KHtqaGjQsmXLGjUG5eTkaP78+Yz8OADS1dV9ba40LpdL58+fF3OeGt6LAOjw\n4cONbLt+/TpTp4bcYRs3buyy3ymWj5fU1FTy8vKiSZMm0bVr196qLB6PR1VVVbR79+63LoulfWEd\npi5CdHQ07dixQ6zB39BQbqrx2RZu3rzJfKAiIyPbpcx3TXV1NYWHh793veO///57I1nWtWvX0rJl\ny2jRokU0Z84ccnFxIVlZWfr+++8pJyeHFBUVSUJColFPcl1dHRkbG4uV5+zsTP7+/nTgwAFav349\nffXVVzR37lzy8PAgALRgwQKKi4tjksW+unh6ejYryx0fH0+1tbVUWFhI2trapKOjQxcvXqTZs2cz\nvd1JSUmNjmvIEeXh4dGkXHppaSmNHDmSAJCWlhZpamqShIQERUVFMWWvXr36jdc1PT2dFi9eTHJy\nckzjzcXFhWJiYlr4y7yZoKAgkpWVJUtLSzp58mSHSfW/jrS0NPLw8CBpaWmx31JJSYns7Oxo5syZ\ntGHDBjp+/HiXzP3TVfH29iYpKSnS1tamEydOUGxsLMXExDR5j78Kn8+nuLi4ZqWyX6WoqIju3btH\n6enpbW7sZ2RkkJeXF/Xt25dx4ufNmyd2b+bl5TGj0E5OTrRo0SLGERw4cCDjPOfm5pKPjw9TlrS0\nNE2dOpXOnj1LdXV1dObMGTFnZtasWXTs2DE6efIknT17loqKiphzhoaG0qeffkp3795tcV24XC6d\nO3eOPDw8SElJiRQVFZtNsSASiWjWrFmkrq7O5IibNGnSe/c9YGFpCw8ePKDt27d3iY5gFtZh6nR4\nPB79+eefTfYkVFZWkqamJo0ZM6bdzldQUEAAaMuWLe1W5ruioKCAIiMjW5TstKtx+fLlJkNKGkZ8\nDAwMaODAgWRjY0PS0tLMyMmoUaPoxYsXjcpLSUmhU6dOUXh4OP35559iDhSHwyEDAwMyNDQkHR0d\nmjRpEtXX11NJSQmzT+/evSksLIxiY2Nb1GjLy8sjCQkJsra2Jjk5OZKRkaE1a9Y0G5YYEhJCAOjP\nP/9scvv48eNJSkqKvL29mdxRtra2xOfzSSQSkaWlJU2cOLHF17eoqIj++ecfWrVqFenp6ZGFhUW7\nhK8eOHCAOBwOOTs7t0uerPbg7NmzpKKiQsrKyvTtt9/Sw4cPKTs7mwoKCtje9regIT/V9OnTqaSk\nhDIzMykvL69VZcTFxbU4J1hOTk67hJQlJCTQsmXLSFJSknR1dcXyE927d48A0IkTJ6ioqIgUFBSY\nd8Dff/9NZ86cYUYiXVxcaO/evU2OQopEIiosLKTU1NR3eo9xudw3hk67u7uTuro6CYVCsrGxIQ6H\n0+IwXhaW9536+no6cOAAXbhwobNN+ehhHaZOJCoqin777bdm49uTkpKYj52JiQmNGTOGvvjiC1qx\nYgXduXOnTedsaNju2LHjbUx/56SmplJiYmJnm/HWiEQiys/Pp2fPnlFFRUUj5+/x48dkb29P69at\na1Vixvr6eoqOjqasrKxmG2xhYWFMg7AtTueIESNIQkKCvvjiiybnTwiFQrp9+zYtXLiQVFRUyMjI\nqMkG57/nLQGgqVOnUnp6OmVmZjJzIDZu3NhqG4mI/ve//xEAmjx5Mnl7e5Ovr2+zIVKvIz8/nwCQ\nsbFxl4gf5/P5tGbNGgJA9vb2b5zDwtJybt26RVJSUjR69Gji8/kkFArp3r17rS4nPz+/xSN6d+7c\nee1ciZqamlaFUMbExJCsrCwtWrSIWRcVFUVSUlJkaGhIV69eJR0dHdLR0aGTJ09ScXExLV++nAC0\nakSoM3n69ClxOBxavXo11dbWEgCx+rKwfCzEx8eTj4+P2AgvS8fCOkydQMOo0vXr15vdJyAgQKyB\nOXbsWLK3t6cePXqQtLQ0OTs7v/YcQqGQUlJS6Pjx47R8+XKyt7cnTU1NCg4OJltbW5KUlGxXUYT2\nQiQSUUxMTCNxB5a28Tajc0VFRfT48WPasGED+fr6EpfLpZycHAoNDaUffviBevTowcy5mjlz5msF\nGPbu3Us///wz7dixg2JiYqiuro62bNlC8vLypKSkRDt37nxtT31NTQ0lJyc361R+9913YkImP//8\nc6vrKxKJyMLCgiQkJDpV9KSkpIR27dpFVlZWTHhle4ye8Xg8SktLo5CQEMrPz3/j/tnZ2bR582Za\nuXIlbdy4kQ4dOvTWNnQF0tPTSVNTk3r37s2M5iYkJFBFRUWryxIIBC2a4C0SiSg0NLTZOXevzjs0\nMTGh6dOn0/bt2yk8PLxZ5z0lJYX09PTIyMiIkpOT6ffff6cjR46Qv78/Mz9w8+bNYqNMDc9ra0fS\nOouVK1eSpKQk3bp1i5kzxTpMLB8r7GhT58I6TB1IXV0dHTt2jMaPH08jRox4bVjB1q1bmTkj/w7R\nWrZsGSkqKoo1hqurqxmllREjRjAKRP9ezM3NqV+/fqStrd3iUJKOgs/nU0RERJeYM8LyUm3Pzc2t\nyftIUlKSxowZQ0ePHm21c1FYWEiOjo4EgKZNm/ba0aCamhoaNGiQ2LnPnz/f5L7BwcGkoqJCffv2\nbVPjd8eOHQSAvLy8Wn3s21BbW0txcXF09uxZ+vTTTxklQFtbWwoICHjr8uvr62nmzJlMKFbD3Kfd\nu3eTQCCgmJgY+vLLL0lLS4vGjx9Pv/32G02YMIE4HA5JSEiQkpISc1xXEL54G8rLy8nS0pI0NDQY\nB7+uro6io6PbVB6fz6fAwEAKDQ19o9JWXV0dhYeHU11dXaNtXl5ejZ6vV0NuraysaO7cueTr60sb\nNmygkSNHkry8PKmrq9OYMWPE5hwBIE1NTVJWVqa6ujoqLi4mHx8f+vXXXykkJKTJkN+uioGBAROu\nLCMjQ5s2bWLnc7B89CQkJNCvv/7KjjZ1MKzD1EEkJycz6kCvxpQ3B5/Pp+vXr1NtbS3l5eVRQkIC\n+fn50fjx40lGRoYkJCSotLSUfv31Vxo4cCDTuy4lJUW2trb0zTff0KFDh+jRo0c0Y8YM5pxqamqk\nra3d4Y3CN1FbW/teijt8iIhEIjp69CipqqqShIQEffbZZ3T58mVav3497d+/n65fv97mF7VQKKRP\nPvmE5OXl6dSpU2/cf8mSJQS8lCw+duwY9e/fn7S0tMRGSLKzs2natGlMh8DTp0/bZJu7uztJSkpS\nampqm45vCQKBgJKSkujo0aP07bffkpOTk5iQg6amJjNPqb3O13BtPv30U/Lz86OrV68yE+j19PQY\nB+qzzz4jExMTZv2PP/7IhAHu3buXANDVq1fbxa7OoK6ujtzc3EhKSopCQ0OZ9Xfv3m3SiXkdISEh\n5OrqSoqKiqSoqEhqampkbGxM3t7erw2rq6+vp/Dw8CY7q3JycmjRokUkLS1NKioqFB8fT5cuXaKf\nf/6ZxowZQ5qamgS8VEHs168fjR49mrp3704AaMqUKZSRkUEpKSnk4+NDpqam9M0337SqTl2Ra9eu\n0erVq2ny5Mm0devWzjaHhaXL0KA6e/ny5c425aOBdZjeMdXV1bRx40bS0tJi8ua8uqiqqlJKSkqj\n465du8Y0Xl5devToQStWrKCoqCji8XgkKSlJBgYGtGbNGgoODu5y+ZNaQkVFBUVERLQpFwJL+5KT\nk8M0pl8dkXBzc2uxCMKtW7fIw8OD3N3daerUqTRp0iQaOnQo9enTh2n0rV+/vkVlDRkyhBQVFSk5\nOZmIXs75kpOTI1tbW4qMjCRvb29SUFAgeXl58vLyeiuHOycnh9TU1MjBweGdyJVHRESIiXXIy8vT\nkCFD6IcffqDAwEC6fft2qxvubyItLU3sd5SRkaGJEyeSSCSi48eP08iRI2nHjh3MPEqRSERPnz5t\nVP+oqChSUVEhAwOD9zIXjlAopJkzZxIA+uuvv5j1KSkpLQpPfJXIyEhSUFAgY2NjWrJkCQUEBFBA\nQAANGzaMALwxXDo6Ovq177rY2FgCQNu2bWPW1dTUUEJCAj19+pTKy8tJJBIxUtt//fUXK/7BwvKR\nEhcXRzt27GhTVAVL62AdpneIUChkpFv79u1LN27coJ07d5Knpyd5e3szjZhXJWx5PB6tXLmSAJCl\npSVt3ryZfH19KTAwsEmFs969e9OgQYOataG6upoiIiLIy8uLTpw48c7q2laKi4vp/v377Ae/CxAa\nGko6OjokLy9PUlJSZGVlRZ9//jkBLxNOysvLk5+fX7OhnEKhkLy8vIjD4ZCWlhZZWlpS3759qX//\n/uTi4kITJ06kBQsW0Lp161o8CpSVlUXa2tqkrq5Oe/bsIT6fT2fPniVtbW0xAYn2EkTYtWsXAe0n\n5d+Aj48PcTgcMjU1JT8/P0pISOiwkNgff/yxUbhXW563ffv2EfBSovp9QiQSMfmEXh2lePHiRZtG\n8rV5yXQAACAASURBVHr06EHGxsaN5oBevXqVDA0NCcBr54e2JKWDvb09OTo6EtFLtdSGXEzdunWj\nxYsXU3BwMP33v/8lAOTu7t7lwqtZWFg6Di6XSytXrqSzZ892tikfNKzD9I7Izs4mb29vOnv2LEVE\nRDRqoFRXVzMZoocOHUrPnz+noKAgJmng4sWLqaam5rXnaFD1AkBz5syhzz//nIYPH04uLi60bt06\n6tevX6MRqjeV2ZE8f/78veyt/tC4e/cuM6rUo0cPUlFRIUtLSyoqKiKRSEQCgYDi4+OZeUfGxsZN\nTjptaJSOHTuW4uPjKSYmhm7cuEEnT54kKysr0tHRIT8/v1YLUSQnJzMTvidPnkwikYjKysrIx8en\n3UPEli5dSnJycu36nGRnZzNhU53RCzhnzhyxd4CtrW2r1NgKCwtp3bp1xOFwqGfPnu9NDjeilyGJ\n69atIwC0cuVKsfdwVFRUq0fkRSIR9e7dmwYPHsysq6mpIU9PTwJeyve/ad7ZgwcPXpvklej/5rBe\nvXqVTp48SQBIUVGRRo0axYg4KCkpMfPdEhISWlUPFhaW95+GUfOGxd7envbs2cNObXhHsA5TOyMS\niej06dN09OjRNzYMhUIhLV26VOyG19fXb7ECikgkooCAAJo6dSppaWlRz549ycXFhczNzQl4mWej\noVwPDw+Kj49vjyq2C0+fPv0gZMPfNyoqKmj37t3k6elJs2bNYgQVtLS06Pvvv6fevXuThoYGZWRk\nMMdERkbS/PnzKS4ujvz9/Zl76t/Oiq+vb5MCEf9eHB0d6cGDB62yWyQS0XfffUcA3mli1n79+tHI\nkSPbtcyff/6ZALRKNr69yc3NJTs7uzcKZxC9vNYPHz6kzZs3k5OTExPS99VXX1FlZWUHWv125Obm\nkqurKwGguXPnMu9jkUhEPB6PSktLW+1o+Pv7/z/2zjsqqqtr488MMwMMHUFBQDqiIIoK2HuNwY7i\np0aNxm7EmGiMURNNNPZYkmDU2KLBij2xiyKKICgCSi/S+9AGptz9/eHLfcMLSmdQ57fWrKW3nLPv\nMPfes8/Z+9mkoaFBlpaWRPRa2tve3p4A0LJly2qtZhgZGflWJdDExETS1dVlHaOKv9umTZtILBbT\nlStXaO7cuTR58mS6efNmna5BiRIl7wf/W5i+R48eFBAQQNu2baNnz54p2rz3DqXD1IhUzHpX5FvU\nhsLCQjp+/DgtXryYPD09aerUqTR16tRKScm1RSqV0p07dyggIIA2btzI1reZP39+ndtqSl6+fPlW\nCWoljY9MJqO1a9ey6ol6enpkYWFBzs7OtGrVqkoPXW9vb5LJZPTPP//QkCFD2O39+vWjLVu2VDr2\n7t27lfq5e/cubdu2jf744w86f/48+fn50fPnzykzM5MYhqGjR49S69atCai+ePK+ffvIwsKi2of9\ngQMHCAAlJyc32fc0fPhw6ty5c6O1d+rUKeJwODRhwoRGa7M+SCSSSn83XV1dcnV1penTp9MPP/xA\np06dolOnTtG8efNYIYEK53bDhg3v3Mv3/PnzpK+vT0KhkA4ePFhpZSkkJITCwsIoLCys1qudBQUF\n7Epd37596dWrV+Tr60t8Pp9MTEzeWiKiOkpKSmqcMCooKKAzZ87Q7Nmz2dy/L774ok79KFGi5P2G\nYRgKDAykhQsXkpaWFnE4HPL09KTffvutXhEdSt6M0mFqJO7fv0979uypV9J2ReHN6j63bt2q9pz4\n+Hj6/fff6fvvv6cTJ07Q6tWrqW3btpXONTMzo2+//bZFiSmEhYUpaywpgK+//prN9/nf/Jz/XeXk\ncrlkbGxMAKhNmza0ZcsW+uabbypJHVcICNR1pYiIaMWKFewK6P9SkZcBgKZPn14p12ndunUEoMZw\npoZQkVt49erVBrUTHx9P06ZNIw6HQ717924RobDp6el09epV+vnnn2nBggU0aNAgNuem4qOlpUVj\nx46lgwcP1lkMQdFIJBLy8fGh3r17s6GHL1++rHSMXC6vc36ar68vtW3bljgcDn377bfsxJRAICA3\nN7c6hTdWkJubS/7+/rVekSorK6MrV6406W9fiRIl7za5ubm0atUqEgqFxOVyafz48TR37lzlc6OR\nUDpMDUQqldJvv/1Gt2/frncbycnJNHLkSLZQ5b8/ly9frnRsbm4uLVu2rJIUccUg96OPPqLTp0/T\nmTNnKCgoqEUJKTAMQ0FBQe/cIOxdRSwW0+7du8nDw4O6d+9OwOsCqNUhkUgoKiqKbt++zf6eRo8e\nTcuXL6eBAwdSr169yMjIiFauXEk3b96kHTt2NEio4+HDhwS8Volbt25dpQR5uVzO5kpVOGWenp60\nefNmtvZTU/6uxWIxdezYkdq2bVuvemBZWVm0ePFi4vP5pKamRitWrGjx6kVFRUUUGhpKjx49ahJ1\nwKYmIyOD1q9fz04YWVtb086dO6uN409OTq71MygnJ4fGjx9PAMjJyYl1tGQyGdnY2FD79u3r5SxV\nIJFIKCQkhJ48efJOfu9KlChpmWRmZtIXX3xBGhoaBIBMTExo27ZtijbrnUfpMDWA+Ph42rRpU6MW\nD7t//z5t2LCBrl+/XqnCe0REBC1cuJA0NTWJy+XSnDlzKDo6msRiMd24caNJw5QaCsMwFBAQoCxI\n2wzIZDI6dOgQG1ZlbW1Nw4YNoxUrVtSYCCqVSunatWuUk5NDY8aMqeK8e3h4ENHrsLtdu3ZRWloa\nyeVyOnz4MA0fPpxcXFxo6dKldPr0aYqLi6OysjK6e/curV69ukqIaWpqKo0ePZp1imbMmEG3b9+m\noqIiysjIIGNjY+Lz+eTp6UkGBgYEvC7i3Bw5NEFBQaSiokKffPJJnc/9+OOPSUVFhebNm9ekuVZK\nXnPhwgVSV1cnADR8+HC6fPnyW0NQcnJyaq3Q6OXlRTwejzZt2lTJofHz8yMA9McffzTUfCJ6/R5R\nTiQpUfL+8/TpUxo9ejR17dqVhg4d2qAJl7eRmppK06ZNIzc3t0qRIe7u7rVe1VZSFaXDVE/OnTtH\nf/75Z5Ov4gQEBLAKYaqqqvTJJ5+8U4pIMpmM7t+//07Wh3rXuHnzJjk4OBAAcnFxeWM4Z02cO3eu\nkqNkbW1Nu3fvptLSUlq6dCm7fc6cObR7924CQFZWVtSvXz928Frd5/PPP68SHhoVFUWLFi1iZ8K4\nXC516tSJleP//vvvSSqVkr+/f5O9XKrDy8uLANT5dzt69OhGzYFS8mZOnz5NKioq5OrqWiX07k1I\npdJaS4l37NiRhg8fXmV7ZmYmaWlp0ccff1wne6tDJpORn59fi4oGUKJESdNw6tQpdsVHIBDQsGHD\nGjVlgmEYOnz4MOnq6pKamhq5ubmxxckBkI2NDW3duvWdGkO2JJQOUx0pKSmh7du3N/kP7tWrV+Tp\n6UkAyMjIqNFXspqD8vJyunfvnnJGo4kpLS1lJb2tra3p9OnTDRqAZWdn06pVq2jRokXk6+vLPtBz\nc3MrOUAnT54kOzs7cnNzY/srLy+nR48e0cGDB2nt2rXk4+NDWVlZrH0TJ06sNs+voKCALl++TGvX\nrqXhw4ez4hQHDhyo93U0hDNnzhAACgkJqfU55eXl5OTkRA4ODk1omZIKXFxcyNbWts6rjg8fPqzx\nmNTUVAJAW7ZsqXZ/hfhJQ0KxpVKp8vmoRMl7QnFxMd26dYs2btxIY8aMIQsLC5o+fXql/CGGYWjo\n0KGkoaFBa9asIQC0YsWKBvfNMAx999131KFDBwJAffr0oejoaHZ/aWkpvXjxgl69ekVErydF//rr\nL+VETR1ROkx1ICIigrZv314pVK6pcHd3J4FAQGvWrGmW1ZmwsDBavnw5rV27tlHaKykpoXv37ikL\nKjYxwcHBrKzxkiVLmlRcgGEYmjlzJnXp0oXu3LnDipX8+eeftTq/orbMrFmzyM/Pj0JDQ98YJsgw\nDKWmpipM4ef58+cEgL799ttan1NRcPrs2bNNaJmSCsaMGUMdO3as83mvXr2qUfWvovbRm9RKs7Ky\nCADt2rWrzv0T/XcySVkvRYmS94OKqAgAZGtrS2PGjCGBQECampq0efNm9l6vEDYKCAigBQsWEADy\n8fFpUN+BgYEEvC4qvm/fvlq9N6OiomjLli3vVKkIRaN0mGpJhZhCcxAfH0+WlpZNPlOdkpJC8+bN\nq7RqoKOjQ0Svk9+vX79OP/30E02bNo1OnjxZ63YLCgrowYMHSjnLJmbXrl3E4/HIxMSErl+/3uz9\nu7u7U+vWrWs96MvPz2cLbVZ8Pv300ya2sn4wDEOTJ09mBTD+7//+j6ZOnUrTp0+vVkHv6tWrBIAW\nLVqkAGs/TL788ktSVVWt13OmJnXHqKgo0tTUJCcnJ8rPz6+yPzMzkwDQnj176tx3aWkp3bt3Tyn0\noETJe4S+vj5NnDixUuh4bGwsm6tra2tLf/75J2lpaZG7uzsRvVa/1NHRod69ezeo79mzZ5O6unqd\nBYbEYjHt3Lmz1mHKHzpKh6kGiouLaevWrRQZGdkk7YtEInrw4AH5+vrS8ePH6fHjx2w+R1MVJMzK\nyqJly5ZVyTGZPHkyRUZGUklJSSWlsooCit988w3l5OTU2Pbjx4+VS71NCMMwtHr1agJAY8eOVYiY\nRlFREXG53DqHE2RkZFBQUBAbonfs2LG3Hv/s2TOFqcxJpVKaP38+2djYkLW1NVlZWZGBgQFxuVw6\ncuQIe1xGRga1bt2aOnXqpAyvaka8vb0JQL3KFNRGDv/69evE5/PJwcGBQkNDK+0rKCggLpdLQqGQ\nPvvss2pFG/Ly8sjHx4eWLVtGM2fOpHHjxtGIESPou+++UwrgKFHynqGnp0dLliypdt/ff/9NdnZ2\nBID4fD4bLufj41Or92AFqamp5O7uTj179qSff/6Z0tPT6cWLF6SiotKgybrz58/TiRMnlOO2GlA6\nTG/h+fPntGPHjiYLc3r69GkVp6VLly7E4/HIzMyMwsLC6PTp0/TXX381Sn9RUVG0cuXKSv2Zm5tT\ndHQ0MQxDSUlJdOXKFXJ3dycOh0MHDhygvLw8Kisro6lTp7JS0LNnz6YvvviCZsyYQZMmTaKDBw+S\nSCQihmHo77//puDgYIqIiFDefE3El19+yYouKKrG1v379wkAXbp0qc7nMgxDdnZ21Llz5zeuDmRm\nZtKUKVMIaFnFOouKimjw4MEEgDw9PWny5Mlkb29PampqFBERoWjzPihu3bpV74ml2tYPu3btGhkb\nGxOPx6PDhw9X2hcSEkKffvopqaqqkr29Pfn7+9OxY8do5cqV1KtXL+Jyuewz09TUlBwcHKhr167E\n4XBIX1+fjh8/Xme7lShR0jJ5m8NE9DoMd9euXXT06FF229ChQ8nW1rbSezw/P5/y8/OJYRi6cuUK\nde/enX799Ve6fPkyGRoaklAopM6dO7MiSRVjuYZOsMfExNCWLVtafBkMRaJ0mN7AmTNnmjwXoSKs\n49+f27dv06JFi6psr+8KV35+Pu3bt4969uxZpc1ly5bR1q1bafDgwaSnp1dp386dO6u09fz5c5o9\nezapqqqSUCgkMzMzMjU1JQCkpqZGkyZNomvXrhHR69nV6pZ5i4uLKTExsV7XouR1EU0AtGDBAoU5\npOnp6azTkJaWVufzw8PDCQDt27ev2v2hoaFkbGxMAoGADA0NqUePHg01uVERi8U0depUMjIyIjs7\nO3JxcaFTp04p2qwPjuTkZAJAv/32W43H/jsPNDw8vFLtr5oIDQ1ln5fVce/ePRIKheyzk8/nk7Gx\nMbm5udGUKVNoz5499PDhQ0pPT6f4+Hhyd3dnV4eVKFHS8klJSaGvvvqKvvvuOzp48CBdv369Sm65\nnp4eubq6Unh4eK3btba2psmTJ7P/l8vl7FhMU1OThEJhpTB2BwcHdiwYGRlJa9asYQWSFixY0ODr\nrAjRCwsLa3Bb7yNKh+l/KCsrox07djTbDyYsLIwWL15Me/bsobi4OCIiiouLox9//JE8PDzYGyU4\nOLjOqwl//PEHqampsTfap59+yrbH4/HYfzs5OdHcuXPp119/pfv371NmZuZb2/33qgDDMPTw4UNa\nuHAhWy+nQs66YhZXLBZTSEgIBQUFUUREBAUEBNTpOpS85tWrV6Svr09du3atVmmuOfD19SUDAwNS\nU1Mjb2/verWxadMmAlBtnaJ//vmHNDU1yczMjJ49e0bLly8nVVVVKi0tbajpSt4z5HI56evrv7Ve\nFsMwtHv3buLxeOTl5UVJSUnsc7a2LFiwgPh8/ltD/168eEF79uyhjz/+mGxsbN4orQ+A2rZtSwsX\nLlT+ppUoURAZGRkUEBBAf/75J61fv55mzpxJ/fr1o759+9KYMWNo5syZtGzZMtqwYQPt3buXOnXq\nVKmeEQCyt7evFIq7fv16duLk448/rnEVWy6Xk0AgqBLW/tVXX7HtT5o0iQICAuju3bu0Z8+eagXH\nGIahkJCQOk0C1cSZM2fo3Llzjdbe+4LSYfoXSUlJtGnTJiooKFC0KWx9mwpZ8X+vQF24cIGCg4Np\nw4YNxOFwqp3dlslkNG7cOBIKhRQUFEQMw9CKFSvYdj766CPasmULxcbGNprNpaWl1K5dO+rWrRvJ\n5XLy8/OjwMBAevr0aaUB/uPHjxutzw+J8ePHEwAaP348nThxollFNaRSKc2ZM4cAkLOzc4Ny+vr3\n709CoZB2797NqihKpVL69ddfSUVFhbp06cJKsd68eZMANJvgipLmpaGrpFOnTiUDA4NqJ5NKS0vp\nk08+YeX2AdCGDRtq3fbDhw/pyy+/JIFAQPPmzavx+Aqp8b59+9LOnTvJz8+PMjIyKDExkc6fP0+/\n/PIL/f7778rVSCVKFMjGjRurTGKYmJhQnz59aMCAAeTk5ERmZmakqanJ7hcIBHTjxg0qLy+n+Ph4\nOnXqFGloaJC9vT1t2bKFli1bRh4eHmzBeAA11uOTSCTVPpOkUin16NGDbG1tm/JrqJGnT5/Szp07\nFTY52xJROkz/4fbt23Tw4MEWkXeTlJT01hnK//3s3buXPbesrIz279/PznL279+f3SeRSBp1FqI6\njh49SgBo7dq1lJaWVu33+eLFixrFI5RUZfv27eTs7MyuGv67zkJTEx0dTQDI1dW1wQ/Q3bt3swmw\n69ato7Vr15KJiQkBoGHDhlWSOZVKpdS6dWvy8PBo6CUoUTCXLl0iQ0NDateuHZmYmJCenh7p6+tX\nq0JXW/766y8CQF5eXpWebTExMdStWzcCXhc/lslk5OHhQVpaWpScnFxjuxW/dz6fTyNHjqxV6Km/\nvz8BoLlz59Lff/9dbTmIgICAFvGOUaLkQ6VDhw7UtWtXunz5MkVGRr51pbe8vJwyMzOrfUbdu3eP\ntLS0CAAJhUKysbGh0aNH05o1a+jMmTM1jrUYhiEul0urV6+usq9CelzR+UT5+fm0ceNGtn7Th84H\n7zDJ5XLav38/3b17V9GmsMhkMvrrr79oz549FBoaSv7+/jR9+nQCQMbGxpWWhF+8eEEMw1BwcDCt\nWrWKHXja29uTj49Ps0t7y+VyNkZfXV2d2rRpQ6ampmRtbU09evSgFStWEMMwFBgY2Kx2vU988skn\n1KpVq2YXfJg/fz5xOJw31qapCwzDkJubGwEgDodDI0aMqFQk998sXLiQ1NXVm6X+mZKmISYmhrS1\ntaljx470ySefsDmV/fr1a9AzSiwWk6enJ3E4HFJVVaVRo0aRhYUFASBtbW26ePEie2xiYiLx+Xxa\nuHBhje3evXuXANA///xTa1vkcjlbVwUA/fzzz1WOefnypVIhT4kSBREbG/vGe7M+lJSUUGFhYZ0n\nQcRiMd27d4/4fD4tX768yv6KMhWbNm1S+ARLSxwjK4oP2mEqLCykTZs2vRPeM8MwdPnyZWrVqhVp\na2vT8ePH6cqVK7R06VIyNzdnB57/jrOdOnWqwux98OABeXl50dy5c2nWrFk0ZcoU4nK5ZGtrSwUF\nBRQVFaUw295lGIYhIyMj8vT0bPa+i4uLSV9fn2bNmtUo7YWFhdHGjRspISHhrcdVqKH9e/Cr5N2B\nYRhycXEhfX19SkhIoLy8PPYZNWHCBFq0aBGtW7euQSuXUVFR9Nlnn5G1tTVNmDCBduzYwYrLyGQy\n8vHxoc8//5xmzZpFAoGgxmf+hQsX2NzRmhCJRLRhwwaaO3cuicVi8vf3J01NTfr+++/p8ePHFBkZ\nyTqFcrlcOVmkRImC2LlzJwGocx5jYxIaGkrq6ursM/DXX3+tcoxEIqExY8YQALKzs6P/+7//o+3b\nt1eq8dTc3Lp1iw4dOqRwB06RfLAOU2xsLG3durXF103JyMigDRs2kK2tLQEgCwsLGjp0KKucoqqq\nyjpJmpqaxOfz2RuxJYUxXbt2jQCQt7c3PX/+vMV/7y2VCmXF+fPn06FDh2jdunV08+bNZltt6tq1\nK3300UfN0lcF5eXlpKmp2SgqQEqan8jISAL+W+R1z5497DNKX1+fDTG9cCGYrlyJI1/faPL1jaZL\nl2Lp/v1X9PJlLuXm1l0gISsri4KCgqhr165sfxcuXCAej0efffbZW8+tCC2OiYl563HPnz8nfX19\ntv0RI0ZQYWEhcblcNjehoKCAHj16REFBQXT//n0KCQmp87UoUaKk4QwaNIg6duyoUBsyMzPJ3Nyc\nuFxulfpu/0Yul9OePXtozJgxrBpxx44d66VM21jExcXRli1bPljBmg/SYbp9+zYdOXKkxXrKUqmU\nrl+/TrNmzWJVVypkvA0NDYnD4dDcuXPp4sWLbFFZQ0NDUlVVJQDk6OhIe/furZQLomjmz59PfD6f\nxGJxrWugKKnK48ePq81jMzU1pa+//pqysrKarG+GYcje3p4GDx7cZH28iVGjRpGVlVWz96uk4fz4\n44+VVBFLSkro0aNAevIkgS5ciKEuXQZRq1bG5O0dSvv2PX3j59SplxQenk0SSc2TA6dPn2YnkgQC\nAVlaWrIO0BdffEEA3hpaumPHDgJQ4/00adIk0tbWpuDgYDpw4EClsOmtW7dWOZ5hGAoNDaXs7Owa\nr0GJEiWNR15eHvF4PFq5cqWiTWFDfi9cuFDrc27fvk0aGhpkY2NTr2LdjUVRUdE7E5nV2HDxAUFE\nOHLkCADgk08+AYfDUbBFlQkKCsLixYthYmKCYcOG4dChQygtLQUAlJaWIjU1Fe3bt4e/vz/27duH\nIUOGsNfA4/EwZ84c+Pv7IywsDIsWLYKWlpYiL6cSvXr1glQqxdOnTxVtyjtN586dceDAAZw/fx7R\n0dEoKirCyZMn0blzZ2zduhWOjo64du1ak/R97do1vHz5ElOmTGmS9t+En58fbt68CSsrq2btV0nj\n8OzZM2hra0NDQwMAkJcnR1ycFgIC8nDr1i08e3YXTk4Danwe5+eX4cGDVBw//gIvX+a+8TiZTIal\nS5eiS5cuOHfuHCIjI2FnZwcACA4OxoYNG2BtbY3Zs2ejpKSk2jauXr0KKysrGBgYvLWfM2fOYPr0\n6ejWrRtmz54NHx8f9O3bF7NmzcK4ceOqnMPhcODk5IT4+Pi3XqsSJUoal7Nnz0Imk8HDw0PRpkBX\nVxcAEB4eXutzBg4ciBs3biArKwv9+vVDXFxcU5n3VjQ1NbFixQpcvXoVjx49UogNioJDRKRoI5qD\nsrIy7NmzB+PHj4e1tbWizQEASCQSpKen4+XLl9i+fTtu3LgBNTU1ODk5ISgoCEQEfX19jBgxAqNG\njcLw4cPRqlWrSm08f/4cxcXFcHNzA5fbcv3fyMhIODg4YMuWLRg8eDCMjY1hbGysaLPeK54/f44p\nU6YgOTkZL168gImJSaO1ffXqVXh4eMDIyAjh4eFQV1dvtLZrwsLCAsnJyQgJCUGXLl2arV8ljcOj\nR4/Qp08fzJ07D56eX+Plyzykphbj2bNo/POPF7S19fDttyegpqZRp3bNzLTQr58pNDQElbZnZmbC\nyMgIO3bswLJlywAAW7duxYoVKwAARUVF2Lt3L1atWoW1a9fi+++/BwBs27YN/v7+aNOmDfbv3481\na9aw+96Ejo4OZsyYgQULFqC0tLSS00dE4HA46NKlS5Vnc2RkJAwNDWFoaFina1aiREn9GDRoEFJT\nU/Hy5UuFT5YTESZMmIBLly7hzp076NOnT63PXb9+PdatWwcASElJadT3fF25evUqiouLMWnSJIXZ\n0Jx8EA5TRkYGDh06hMWLFyt81SUtLQ1Hjx7FkSNH8PLly0r7WrVqhTZt2iAyMhJqampYu3Ytli9f\nDoFA8IbW3h3Gjh2L69evIyIiApaWloiIiICWlhbatWunaNPeK2JiYuDo6Agul4uRI0fCy8sL/fr1\nq3d7paWl2Lp1KzZs2IDOnTvjypUrMDIyakSLa+bPP//E/PnzwefzceLECYwcObJZ+1fScFxd3RAU\n9BgTJ36BIUOmIzg4E3fu7EBc3HUAgJ1dN3h57YOKCq9O7WpqCvDRR5bQ1VVjt0mlUggEAnz//fdY\nu3YtcnNz4eTkhLS0NJibmyMrKwtisRgAYGhoiMzMTLx69QrW1tYwMDCAVCqFTCZDSEhIjauazs7O\n6NixI7y9vSu9WyqcJZFIhPj4eJSVlcHR0RGamppISkpCRkYGBAIBGIaBhoYGbGxswOVyoaKiUqfr\nV6JESc2kpqbCzMwM69atY50NRSMSidC9e3eUlJQgNDQUbdq0qdV5AwcOxN27dwG8Xr13cnJqQitr\nJjw8HLdu3cKiRYvA49Xt+f2u8d47TGFhYbh//z4WLFigsBWYrKwsXL9+HSdPnsTly5ffemz37t0x\nduxYTJ06FRYWFs1jYBPj5+eHAQMGYMmSJdi9ezeICI8ePUKXLl2adaXiQ+Hp06f4448/cOrUKWRl\nZWHp0qXo3r07NDU1oampCWNjY5ibm7MhUtXBMAyOHz+OVatWITU1FZMnT8aBAwegqanZjFfym7uY\nVQAAIABJREFUX2JjY+Hh4YHo6Gjcv38fXbt2VYgdSmoPwzAoLy+HiooAqqr/fZHOnLkBurq9kZCQ\ngIyMZwAyEBh4Cp6eX2PgQM869yMU8jF6tDW0tVXZbbq6uhgzZgyOHDmCWbNm4fjx4zh06BCmTZsG\nFxcXrF+/Hh4eHiguLsbkyZMRHx+PkJAQxMfHo127dqzDUxPffPMNtmzZgoyMjLeG7zEMg+fPnyMn\nJwcODg6VJh1SU1ORnZ0NhmEgk8nQvn176Ojo1Pl7+JCRy+XgcrkKXzlQ0jLZsWMHli9fjqioKDY8\ntyUQFhaGHj16wNXVFVevXoVQKKzxnMDAQPTq1QvTpk3DoUOHWkRkUXZ2Ng4cONAiFiWakvfaYbpx\n4wby8vIwefLkZu87Pj4ePj4+OHfuHJ48eVJlv7q6OgYMGIChQ4eiQ4cOyMnJQf/+/WFmZtbstjY1\nqampGDx4MOLi4hASEoLi4mLY2tq+dYChpOGUlJRg2bJl2L9/f7X7T506VSmem2EYBAYG4vz58zh3\n7hxiY2PRvXt37NixA3379m0us99Ieno6XF1dkZKSAjs7O/Tq1QseHh746KOPFG2akn8RHx+PGTNm\nIDAwEFKpFCNHeiIrKw9PnrxeTfr662MwN3dEVlYpVFS4MDBQw88/z0dy8gts2HARmpq6de5TT08N\n48fbQkXl9eBh3rx5OHjwIJ48eYJ9+/bhwIEDiI2NxYQJExAcHIyxY8fi/PnzMDQ0BI/Hg5aWFiZP\nnoz169dXaVssFiM2NhZ2dnZQVX3tlKWlpeHIkSM4ceIE0tLSsHnzZri7u6N169ZvHLSXlZXhyZMn\n6N279xuvg2EYhISEoHv37nX+Dj5UsrKyEBMTA3V1dfx7OOPs7NwiBpNKFE/37t3B4XAQFBSkaFOq\ncOLECUybNg1du3bFhQsXahVi9+233+LHH3+Es7MzduzYgQEDBjS9oTVQVlaG3bt3w9PT872NHHpv\nHaY///wTpqamzf5D+uOPPzB79uxq97Vt2xZjx47Fxx9/jAEDBnxQqyu3bt3CxIkTsX79eixcuFAZ\netKMZGdnIz8/HyUlJSgsLMTixYvx4sULPH78mF2p8fX1xcKFC5GRkQEej4dBgwZhxowZ8PT0bFGD\njqSkJPj4+CAgIAABAQHIycnBzz//jKVLlyraNCUAzpw5gzlz5kBDQwOzZs1CXFwqfHwOw8TEFlZW\nTrh//yz09NrA1LQ9evQYD3v7XlBT4yEiIgK//TYTVlauGDToU3Tt2h1cbt1WC7p0aQ1X19d5kfn5\n+bCxscH8+fPh5uaGzz//HBMmTICXlxf27NmD7du3Q1dXF0+ePKl2JZ9hGNy9exfHjh3D2bNnUVRU\nBD6fDzc3N+zatQuzZ8/G06dP4erqirFjx2LhwoUQiUSIjo7G4MGDqzhN2dnZSEhIgLOzM/h8/huv\ngYgQEBDwVqfqQ0YulyM7OxsZGRmQy+UAAC0trSqrBkVFRYiNjYWzs7MizFTSgnj16hXatWsHQ0ND\ndOnSBUZGRujXrx8+/fTTFvNuu3z5MqZMmQItLS1cvHixxgkThmHw+eef45dffoG2tjZycnLe+lxp\nLogIv//+O7p16/ZeTvq8dw6TTCbD3r17MXz4cHTo0KFZ+66Inf83rq6u6N27Nzw8PFq8MENjcOfO\nHUgkEhgYGCAnJwd9+/YFl8uFg4MDxGIxnjx5ohR7UCCXL1+Gu7s71q1bh++++47dvn37dnz55Zf4\n6quv8M0337AqPi0ZiUSCPn36oKysDGFhYYo254OmsLAQS5cuxeHDh+Hi4oKjR4+ipKQM4eE8nDv3\nGy5e/BWTJ68EEYO4uHBERT1BaWke3Ny+B49nCX19NSQknEN4uA8YRobhwzfBza0vTExqHwLK4XAw\nbpwNDAxeh7XY2NjA3t4e586dQ8eOHVlVqQULFoDH4+GPP/5AeXk5PDw84OXlBVdXVzbf9eDBg4iL\ni4OWlhYmTpyIAQMG4N69ezh48CAAQF9fH19++SU+/vhjCIVC0OsSHUhMTISDgwOMjY0rOU2hoaHo\n1KlTrWL8K+xsKeJEiqasrAwFBQXIz89Hbm4uLCwsYGRkVON3GR0dDW1tbQgEAqSkpMDc3PyNoY7l\n5eUIDAxsUL6nkpaJWCzGunXrEB0djYyMDKSkpCA1NRX9+/fHH3/8ASsrK9y/fx/79++Hjo4O2rRp\ng2nTprETKS9evEC3bt0wefJk7Nu3r8lyyp8/fw53d3dkZWXhyJEjb1XzO3LkCGbOnAlzc3P88ssv\nGDVqVJPYVF98fX0hEAhanF0N5b1ymIqLi7Fnzx7MmTNHYepD58+fB4fDQadOnWBhYfHeO0j/5uTJ\nk/D0rJyDcPToUXh4eMDExAQ9e/asMYdLSdMyYcIEBAYGIiEhodKMVFFREXR0dPDVV19h8+bNCrSw\nbowfPx5Pnz6Fv78/2rZtq2hzPkhevXqF/v37IykpCatXr8aaNWuQkpKC4OAYpKSU4sCBDcjNzcW3\n356EQKCGJ08ykZeXh6tXl0Emk6BDh+3g81VgYaENDqcMFy/Og6FhB/Tv/y0sLLTRrp12rW0xNuZD\nVzcXRUVF8Pb2xqVLl5CSkgJjY2OEhISgd+/ekEgkAIAhQ4bAwcEBhw4dQmFhIcaMGYNr166hrKwM\n/fv3x2effYbx48dXigS4ceMG0tPTweFwMH78ePB4PIjFYnA4HPaTn5+P5ORkWFpaQi6XIy0tDbq6\nurC3t6/1dcTFxUEkEn1wuXoJCQnIy8urtE1NTQ26urrQ1dWFUCisU57S06dPoampCTMzMyQlJaGw\nsBAaGhqVJlOjo6MhEonA4/GgqqqKDh06KHOh3mOICIcOHcKyZcsgkUjQr18/pKSkIDo6Gpqamigo\nKACfz8fcuXOxevVqPHz4EBMmTGDPz8rKarLxZVZWFsaNG4eAgACsX78e3377bbW/xZs3b2Lo0KGY\nMWMGDh061CJ/rw8fPsTLly8xc+bMFmlfvWimek9NTmZmJm3cuPGDrUCsaIqKisjAwICcnJxoyJAh\nBIDs7OyovLyciIjWrl1LACguLk7Bln64SKVS0tHRoTlz5lTZ9/TpUwJAf/75pwIsqz+LFi0iAKSi\nokLXr19XtDkfJA8fPiQA9NNPP7Hb5HKGjh2LoCVLzpOOjg65uIynffue0pgxX5KV1WCytR1LGhpW\nxONpk7m5N1lZ/U5DhpykqVMvk4PDZOJwuDRmzEGaOvUy/fxz8BuL2Xp7h9LKlUepb98J1LnzAFJV\nFVYq6Dxu3DiSyf5b6Pb69et04sQJtoguEVFhYSF99tlnZGlpSVOmTKGoqKgGfyeBgYGUkpJCGRkZ\n9S6QnpycTEFBQR9MgUipVEp+fn5N3k9wcDBlZ2dTUlIS+fv7U2ZmJrsvPz+fAgICFFoY9EOGYZh6\n3y91JTk5mRYtWkSdOnUiDodDY8eOJSKiV69e0bx584jH45G6ujqtWLGCzp49yz5TrKysKCIiosns\nEovFNH36dAJAnp6ebxzTrlmzhgDQ/v37m8yWhhIdHU07d+6s9Ax+l3kvVpji4uJw8eJFfP7558rc\nGAURFhaGzp07o1WrVsjNzcWUKVNw5MgR8Pl8EBGGDx+OR48eIS4uTll7REE8efIE3bt3h4+PTxUh\nlF9++QWLFy9GYmIizM3NFWRh3ZHL5Xj69ClGjx6Nrl274tKlS4o26YODYRiYm5vD2dkZFy9eBAAk\nJopw/Xoi7t1Lxl9/jYej4xjMnbsSXl59AKiAYRgwTDm0tXtBX38GpFIGrVqpwd6+FSSSXFy4MBvW\n1sPg4rIAZma6sLauGkr18uVjnDq1FampMVBVVYeBgSksLc3Qt+9QaGkR8vLysHHjxlrH9j9+/Bh2\ndnaNEo5aWFiIhIQEdO7cud5tZGVlQSaTISwsDEOHDv0g3m2JiYkQiURwcnJqslnpsrIyZGRkQCgU\nwtDQsNp+UlJSkJKSAldX1w8qSqS5iY+PR35+PgBUqWEGACoqKjAzM6tSf7KxEYlEUFdXrxRuFxcX\nh++++w7Hjx+HmpoaxGIxpk6dips3b6KsrAxnzpzBkCFDmsQeIsJPP/2Eb775Bq6urrhw4UIlZc38\n/Hz89ddf+OKLLwC8zu2trSx5c5ORkYHDhw/Dy8sLampqNZ/QgnnnRdNDQkIQEhICLy+v92fZ7x2k\nffv2AIDc3FxMmjQJx44dY1/wZ8+exY0bN/DTTz8pnSUFUlpaCgDVqhP6+/vD1NT0nVO3UVFRgYOD\nA/Lz898bGf53DS6Xi2HDhuHYsWOQy+VQUVFBenoJACA7+xYYRgZra3ukpERBJpOgZ08vcDguKC8X\nA+CjrEyOwkIJGIZQUFAGQ0ND2NqORHT0FSQm3kWrVtaYOtUL7du7sH0yjBxHj34HAJg69Vu4uo6E\nmpoGysuzYWJigPHj617gWC6XN5oQj7a2Nhv6V19CQ0Ph5OSE3r17fxDOEvD62RQXF4fU1FSYmpo2\nSR9qamo1PitMTU2hr6+PBw8ewNXVlVVHVNJ4lJaWQiQSoVu3bm88RiaTITk5GXFxcVBTU4ODg0OT\n3AvV5bZZW1vj2LFjWLlyJZYtW4abN2/iwoUL+Oeff7BgwQKMGDEChw8fxrRp0xrdHg6Hg1WrVsHe\n3h7Tpk2Dm5sbLl++jE6dOgF4Xc7A29sbbdu2hbu7e4uW8jYyMsLChQuxY8cOLFq06J0umfBOT53c\nvn0bsbGxmDNnjtJZUjAFBQXsv/v06VPpoaaurg5VVVVs2rRJmcOkQCruEYZhKm1/8eIFrl69in79\n+r2T99Hdu3chFovfuwTTd4WEhAScPHkSAwcOZO/77OxSxMU9w5073nB07IPhwyfCyMgC2tqtEBnp\nA6m0BCoqqlBR4UJDgw9dXVVoawugqSkAEeDsPAc9e34BK6vByMyMQHj4g0p9Pn16B7m5aZg4cTn6\n9ZsINbXXNcXk8hIUFdVvHtDFxQWhoaEN+zL+Q0JCQoPb6Nq1K0pKSioNhv733n3fiIqKgqOjY5M5\nS3VBKBSiZ8+eCA4OhkgkUrQ57x08Hg8MwyA0NBRvCnTi8XiwsrKCq6srbGxsEBAQgJKSkma109HR\nEf/88w8GDx6M4uJiTJ8+HTdu3ED//v0xa9Ys3Llzp8n6HjduHO7fvw+ZTIZevXrh6tWrAF6XTRAI\nBHB2dsZvv/1Wq/pNikRbWxtffvklvL29kZqaqmhz6s076zD5+vpCJpNh0qRJijZFCYDWrVu/Mel+\n1KhRWLlyJUQiEfz8/JrZMiUVVCyHR0ZGstvS0tIwYsQIqKur44cfflCUafWCiHDhwgV8/vnnEAqF\n6N+/v6JN+uBgGAazZ88GwzD4/fffUV5ejhMnTmD58knYsmUG1NW1MGPG98jJScH27Z+hsDAXpaX5\naNVKDnV1HlRVVaCvrwYHh1Zo21YTBQVliIsrQEJCMVRVXaCpaQUAMDT87wA6M7MEFy4cgqamERim\nAzIy/juAImJQXi5HcXHdV3cq6jEVFxc3+HvR1tZ+a2HomigtLUV2djbCwsLg5+eHJ0+e4MmTJwgO\nDq50/75PFBYWQiwWQ19fX9GmsPB4PPTq1QuxsbFIS0tTtDk1UlRUBLFYrGgzaoVAIEC3bt1gZmaG\niIiIGo8XCoXo3bs3IiIimv1voaKigkuXLsHOzg4JCQlYsmQJTp48CTs7O0yYMAHR0dFN1nfXrl3x\n+PFj2Nrawt3dHXv27EGXLl2wbds2XLlyBb/++muT9d2YCAQCfPXVVzh79ixevHihaHPqxTuZw3Ti\nxAlYWlqiZ8+eijZFyb+IjY1FTEwM+vfvX2nG49GjR+jVqxdGjBiB8+fPN5ksp5K3I5fLMXz4cDx4\n8ACBgYFQU1PDpEmTEBcXBz8/v3dGkUsul+PmzZtYvXo1njx5AjU1NUyePBmHDx9WtGkfHNHR0Ww4\nrqqqKlRVVVFYWIjWrc3Qv/8k9Ow5GhoaOoiLe4otW2ZCXV0TAwdOgapqW3A4NlBX1wPDELKySpGW\nVgwejwuJhAHDMFBXL0FYmBf09c2xZs0xCIXaiI8X4eHDa7h3byO6dfsM9vZjAACmpgxat5ZAINCD\nQNAK48fbsvLidSE5ORmqqqqNkg8QFBQEU1NTJCYmQlVVtdIsOofDgbGxMXR1dSEQCMDlcsEwDLtC\nFxAQgK5du0JVVbXSduB1vkJZWRn7vSuSV69eITc3FwzDgIjQpk0bmJiY1GulOi0tDcnJyTAyMoK5\nuXmLW+1+8eIFeDwebG1tFW3KGwkKCmLzbXR0dGBiYgINDY0W913+G7lcjqCgIPTo0aPW50RFRUEi\nkcDR0bFZr83b2xsLFiwAAPTq1QubN2/G+PHjIRQKcfXqVXTs2LHJ+i4pKcG0adNw/vx5zJkzB7t2\n7UK7du0wePBgnDx5ssn6bQoOHjyIbt26oUuXuodOK5J3zmE6cuQIHB0d3xr3qqRlsWrVKmzbtg25\nubnQ1q69RLCSxiczMxPOzs5s/LiamhouXLiAYcOGKdq0txIeHo5//vkHd+/exf3791FYWAgOh8MO\nQq2trRETE9OiBwbvKw8fPkRYWBji4uKQn5+PcePGIznZqEqy/PHjP+DevTPs/7W1TeHu7o3U1CKk\nphZDKn096JZIGKiqqkBHR4CYmLWQSHIxceJ2JCQ8wcuX96GvL4GWlhHc3GaBz1dBebkQcjkfHTtq\nQkfndWL02LE2aN267is8DMMgMDCwUSbjKpz5jh07VvldMgyDtLQ0FBYWoqysDOXl5ZDL5WxNp8LC\nQgwcOLDadrOzs5Geng4rKyuFDYaJCKGhoWjVqhVMTU3Zv3V6ejpSU1PB4XBgYWFRbb5kTWRkZCAp\nKQm6urotwin8N8nJycjLy0Pnzp3x/Pnzt9Z2UgRPnjxhx0YikQjp6ekoKSlhn5MGBgYtLtdTJBIh\nJyenznXH8vLyEBkZiW7dujVa7mFNPHv2DF26dMGCBQtw7NgxqKmpYcWKFdixYwfEYjHOnj2LwYMH\nN1n/DMNg7dq1+PHHH2FtbY24uDh4e3tj3rx5TdZnU3Hs2DHY29vDxcWl5oNbCO+Mw0REOHDgAHr0\n6MEmvilp2fj6+kJHRwfffPMNsrOzERgYWK8XqJLGxd/fH0uWLMG4ceMwd+7cSuo7LQ2RSISvv/4a\n3t7eAAA7OzsAr1c2rKysEB8fDzMzM9y7d6/FDQQ+VBiGcOBA1ULCz57dxf79X0Mul8HS0gEDB36G\njAxVZGSoIiurABLJK0gkryCXl0NdnQ+xOBki0SNwuUIwTClsbGyQni6Dvr4rrK2HQlNTBzo6QgiF\nIvD55dDXV4eZ2ev6OhMm2KFVq7oNojIzMxEVFYXy8nIYGRk1+D1DRHj06FG9nC8ieqsjlJqaiqKi\nIhQXF6N79+4NMbPOSKVSPH78GA4ODm9UFGQYBomJicjNzQWPx4OdnR00NDQgk8kQHh5eKReLYRjY\n2dlBW1sbUqkUDMOAz+cjNDS0RU6M5ubmIiIiAvr6+uDxeP9ZUW0NHR0dJCcnQyqVQkVFBe3bt2/2\n3JIHDx6gd+/e1e4jIqSmpkIkEsHBwaFZ7XobxcXFSEtLY5/tdUEulyMwMBDt27dvciW9iv4cHR0h\nEolw+vRpzJs3D5GRkfjhhx9w4sQJREVFYd++ffj000+b1I5r165h+vTpyMnJQVJSEszMzJq0v6bi\n1KlTMDExeeNvtqXxTjhMRITffvsNgwcPbnEzTkqqZ9++fZg/fz6A17GrEokEKioqGDhwILZt29Yg\nuV0l7xZEBJFIhOzsbJSUlIDL5YLD4UBdXR1mZmbVKlARES5duoSFCxciPT0dXl5eWL58Ob7++msc\nO3YM06dPx7Vr18Dn83H37l3Y2Ngo4MqUvInjxyNRUiJl/+/ndwp//fUTOBwuFi36GdrareDtvRx5\neRlQUzOCWJyO12VO/g0Henr9kJ/vB0PDoXB2HgNtbUAq5SI9/bUQgp6eGgwM1MHlytCuXTHatXME\nAMyc6QiBoHZqWiKRCM+fP4epqSnMzNqBy+UgJycHycnJkMvlcHFxgVwuB49XdzGJuLg46OjoNNlE\nUUxMDIgItra2TbrSVOHAMQyDe/fuoWfPnrVWjpNKpYiKikJpaSmICE5OTpVWBORyOaKjo1FYWIii\noiIYGxtDKpVCTU2tTsV+mxOxWAw+n8/+JjIzM1FcXAwzMzMIBALIZDIEBwfDxsamXn97uVyOiIgI\ntiiyUCiEkdHrVVtNTc03hrW/evUK+fn5cHJyemPbUVFRUFNTazHlI8RiMeLi4uDo6Fiv84kIT58+\nRZs2bZqlePmzZ8/g5uaG/v3748yZMxg9ejQePXqE69ev44cffsD169fx+eefY9u2bbUuaVAfMjIy\nEBMTg759+zZZH83B+fPnoaOj88YV9ZZEi3eYiAi7d+/G6NGjYWlpqWhzlNSSkSNH4sWLF9i4cSN8\nfX3h6+sLuVwOALCwsEBkZGSzLaMrURzBwcHw9PREXFxctfs5HA7atm0LS0tLmJmZIS8vD8nJyUhO\nTkZJSQk6deqEAwcOwNXVFT/99BNWrVqFuXPn4vfffwcAeHl5YevWrfUazCppOq5dS0BSUiEAICDg\nAo4cWVflGC0tfXTrNhTJycmQSIwgEJhDKLSEqqoQAEEiESE11Rd5eQEYMmQfdHXbIDm5CLq6ZdDQ\nkCA1VQsAB0ZGGtDR4cLeXgYDAxtoa6vC07N2A+2AgADo6OigY8eOKC2V4uzZGAgEKpg40Q48Hhex\nsbGIj4+HgYEBpFIp3Nzcav0dxMXFITc3F87Ozk02cCIiZGZmIiUlhXVqysvL37r6Ux+Cg4MhkUjQ\nsWNHxMXFNdnKD8Mw703dIyLC8+fPIZVKYWpqWuu8uMTERGRlZcHBwYEVDikpKUFOTg4YhkFeXh4E\nAsEb83cePXqEbt26vfU3l5SUhKysLABAmzZtQETIzs6u0h4RwcrKql5CHFKpFGFhYdDV1YWlpeVb\n/64BAQFwc3NrkGR4WFgYWrVqBRMTk3q3UVsqJoQ3bdqEWbNmoWvXrlBXV0dgYCB+/PFH7Ny5E/37\n98fp06eVpVRqwZUrV8Dj8TB8+HBFm/JWWrTDRETYtWsXJk6c2CJkRpXUnkGDBuHOnTvgcDi4ePEi\nBg4ciK1bt+L7778HAIwb54F5836CSCSBrq4anJwMYWRUf1UpJS0LIsL+/fuxZMkSGBkZYcmSJWjT\npg00NTUxdepUiMViCASC/8zqm4FhGKSkpMDAwABmZmZo164dHBwc8Mknn0AgEMDX1xfjx4+Hp6cn\nfHx8KvXl4+ODXr16wcTE5L0ZbL3rhIRkIjg4AwAQGfkQu3a9TpQeOXIO2rXrgLy8dDg7D0KrVm0R\nFRWECxeOoHXrj2FgYIvY2BBkZt5AXl4wAIKp6WD07+8FAEhPL0FxsQRCoQRt2pSCYYDSUiE6dFCB\nsbEQmpp2sLbWxeDBtZs9f/z4MVxcXMDhcJCTU4pz52LA5XIwdWoHqKtXHnBmZGQgLS0NEokEbm5u\nb13Ryc/Px6tXr946099UEBGePXv2n+LADLS0tGBubt6gopFBQUHo3r07IiMjUVpa+k7lHSgaIkJC\nQgLy8vLQoUMH5Ofno7i4GO3bt69SrPXZs2fQ0dGpcXJYJBIhJiYGXC6XzU/i8XjQ09NjnfTa2paV\nlQUOh1NtEV8iQkxMDPLy8tCuXbtar+BIpVI8evQIbm5uKC4uRkxMDAQCAYgIZWVl0NbWho2NDdTU\n1CASiRAeHo6ePXs2+PkdHh4OoVAICwuLJn0XEBEmTZqES5cuISYmBsnJyRgwYABGjRoFX19fHD9+\nHJ999hkMDQ3h6+vbIkNLWxo3btyAXC7HiBEjFG3KG2mxDhMRYe/evRgzZsw7V0xTyeu45BMnTmDe\nvHno1KkT/P39ER8fD2dnZ7i69sLjxwEYOvQTTJz4ulI1h8PBsGHmMDdvOQm0jY1YLEZZWRn09PQU\nbUqTkZWVhePHj+Pw4cMICwuDq6srtm3bBoZhUFhYiH79+mHQoEEICQmpdN7IkSNx7NixauPQQ0ND\n0adPHzg6OuLu3bs4ceIE8vPzsWnTJnYQ8uLFC9jZ2WHFihWYPXt2c12ukjdQ4XxUIJGUQSIRQ1Oz\n6m/fz+80Tpz4EQAHQmFrlJZmQkVFA61b94e19UcwN7dgjyUi5OWVQSQqh1xOUFHhwMREis6dAVVV\nFWhrd8KgQeawta3dPZaamorc3Fx06tQJHA4HaWnF4PO5MDR8c+5JXFwcIiMjYWhoCGdnZxQUFIDH\n40EsFqO4uBh2dnYICAhA7969W4QISVFRERITE1FSUlInJbIK8vLykJeXpwx7bSDFxcVISUmBvr4+\n+Hw+IiIi2BXL1NRUpKSkwMHBod7vB6lUiuzsbLRp06ZJirsmJCQgOzsbXbp0qTYkUCwWIyUlBYWF\nhSgvL4eLi0ulVS6pVAoej4fk5GRwOByIRCKUl5cDALp169Zo90p6ejoyMjLYPDk+nw97e/tGV+dN\nSkqCnZ0dpk2bhoMHD+Lnn3/GsmXLcPLkSbi5ueGrr77C6dOnoaamht9//x3Tp09v1P7fR/7++2+o\nqqpi0KBBijalWlqsw+Tt7Y2hQ4fWWTlFScuBiLB582asXr0ay5cvx+bNmzFjxgwcO3YM5uYdkZQU\niY8+moFRo+YAAHR0VDF8+LsTdlnXW6eiVsO7kuBYF+RyOb755hvs2LEDMpnsjcc5ODjg77//xtGj\nR7F79242LAQATp8+jYkTJ1Y6/u+//8bMmTMhEAjw+PFjGBsbs/uePXuGbdu2ISgoCB4eHti3bx+y\ns7ORm5vbomq5fKicPx+DrKzSKtsLC3ORmBiB/PxMmJjYgGEYbN8+GxYWDpBIeGjdug9MTPpCS0sI\nFZXqZ4mJKsK3OOBwOLC01IaxMR9yeQZ69DAFn89DmzZtKv1e3kRBQQHCw8Ph5ubGDvDGQYxmAAAg\nAElEQVRycnKgqamJoiIG2dmlaNNGA61aqbMqcN26dYNcLkdISAhat24NhmEgFAqhrq6OqKgoPHv2\nDLNmzWpRoaJBQUH1WhkqKSnBs2fP0KtXryaw6sNFIpHg1q1bkEql6NmzJwwMDFqEg/02JBIJIiIi\nQESV3n8Mw0BVVRXm5ubQ1tZ+63UUFBQgJyen2RzwsrIyREREgMPhwMHBoda5d7Vh2bJl2L17N8LD\nw2FnZ4fOnTtDJpNh5cqVVYQf1q9fjzVr1jRa3+8rly5dgo6ODvr166doU6rQIh2mAwcOoE+fPi02\n4VNJ3bCzs0NKSgo+/fRTfPrpHAwbNgpt29rg+fN76NSpO+bO3cweO3KkJfj8xp8dawno6ekhKCgI\njo6OrMLS+0B+fj48PT1x/fr1StvNzMwwe/Zs2NnZwdDQEOHh4Vi2bBl69+4Nf39/yGQy+Pn54dat\nW9DU1ISXlxerKsUwDJYuXYq9e/fC0dERp06dQocOHd5ow9OnT9kwlDNnzmDChAlNd8FKakVMTD7u\n3Elm/19YmItt2z5FZmZStccPGTIddnbTIRLVvehsu3ZasLDQQZcureHqagyJRIKAgEfo379vrQah\nYrEYd+7cQfv27VFQUABdXV1ERKQiLi4XAAccDhdWVtowM9NB586dq4T7VISYqqioIDU1FWKxGFZW\nVi0mRJSIEBQUBFdX13qdGxwcrAzDa2Sys7NRWFgIQ0NDxMbGvjN18BpKeXk5YmJi6i3y0JB+IyIi\nwOVy0blz50ZxTnNycmBlZYUhQ4bg3LlzuHDhAsaOHcvKjrdv3x7u7u548OABHB0dsWPHjka4kvef\ns2fPom3bti2u1mqLG7EdOXIEbm5uSmfpPeLSpUuYOnUqfvnlF/zyyy8AAHt7N3A4XLRr1xUCwesw\nLD6fC0tLC3C5LXuWrSH06NEDubm5CA8Ph0wmg4qKCpu8KxKJoKenxw6yKhJxK4pbtkQiIyMxatQo\nJCYmstsGDRqExYsXw93dnXUKjx8/ju+++w5CoZAt/Mfj8TB48OBq61akpaVh7969AP5bjPFt3Lt3\nj/13aGio0mFqAdjY6CI8PAfZ2a9XmYqK8pGZmYSePUejd++x0Nc3QlpaLJKTXyIlJRr29i7gcKo6\nGCJROUpKpNDSEkBLq/r7QEWFC6GQj86dDUFEOH3aH4WFmsjOjsbo0dZQVX3zq65CJnvgwIEoLCyE\nnp4etLV14edXCg0NHRARioujkZdnhY8/dqj0fKoId5PJZGAYBnK5HFlZWejevftbnSWpVIqMjAxk\nZmZCLpfD0tISrVu3ru1XW2cqFO7qI6pQsdqmpHGomCjS09ODs7MzW8T44cOHcHBweO9rFVao5jY3\nqqqq6Nq1KwoKChAYGFhtHqJcLkdaWhoyMzPBMEyNEwwGBgZYvHgxNm3ahJycHIwZMwbz58/Hb7/9\nBk1NTYSHh+PatWtYt66q6I2SNzNhwgT4+PhAIBC0qPyvFuUw+fj4oFOnTso6S+8Z1tbWCAsLg76+\nPuRyOUQiER488AUAdOs2lD2ufXv999pZAgChUAihUMjWTSgrK0N4eDg4HA50dHSQmJhYScLX0NAQ\niYmJ0NfXb3E5BBWzaRVMnz4dK1eurFLjIywsDNOmTUOvXr1w5MiRGq9DLBazDo+7u3slZ4mIwDAM\nG6NPRLh9+zZmzpyJWbNmQVtbu1FDLpTUHw6Hg/79TXHuXAwYhqCl9TpMMjMzETk5KWjb1hqdOvVD\np07/Db1ISytGXl4Z+3+JRM6G9ZWWSiEU8qoN09PXV0OfPiZQVeWhoKAM2dlFEApbIT+/DElJhbCz\ne3OIZnl5OVq3bg11dXVWubOsTAaZjGGvg8PhQiRKhFTaHgIBD+Hh4SgrK4NQKISJiUmlEFC5XI4X\nL14gNjYWampq/zmfg/bt20MikSAtLQ25ublQV1cHl8uFi4sLEhISkJycDD6fDycnp0YNzSIiVp5a\nIpHUWfzB0NAQWVlZiImJga2tbaPZ9aHC4/GgqalZaUXJ2NgYbdq0QWRkJMRiMfT09GBubt6kstSK\ngsPhoKioiA3drphUq5Ceb+r6Vbq6uuj4/+ydd3hU1fa/30nvvRLSKAFCCBBCDV2KohS5gMJVLihS\nxMZFFAt4fxYUAWmiIgiogBUp0lRESkjvJCEhjdRJQuqkTd+/P/JlNCaQEAhByPs8PI+e2WfvfSZz\nztlrr7U+y9eX0NBQhg4dqrvXSktLSUlJoVu3bgwYMIC4uDg0Gk2z+WDX7nFLy/pSB1u2bCElJYUz\nZ84AsHbtWtauXXvXh1vebTz++ON8/fXXGBkZ3TU2wV0Tknfs2DFsbGzuyfyO+51r4VJffvklM2fO\n1D0Me/cexgsvfIJEIqFrVxtGjep83ZyF+53CwkIyMzPx9/dvt91etVqLSqVBX1/w2muvNQgv2L9/\nP7Nnz27yvAMHDjBjxgzi4uJaVH+ruLhYJ8FrZ2eHkZERhoaG1NXVUV5ejqmpKdOmTeOJJ55g27Zt\n/Pzzz4waNYo//vgDe3t7ZsyYoZMd76D9SUi4SlhYAUIIDh/+mLCwo5SXF6Gnp8/QoVOYPfs1DA3r\nPUcajZawMCkaTf1rSanU6OTJJRLw9rZu9IywtjZm1qwejBrljlwup6ZGzldfHUMisUCrldO/f2fs\n7S0xNzenc+fO5ObmYmJiQnl5OQqFQmeE/10y/OTJLHJyZNTWZqPRyOnVqw/OzvXGm6ur63UVw6RS\nKTk5Ofj4+OgS+LVaLRcvXsTU1BQPDw+d0VJSUkJhYaEuPCkjIwNLS8vb5m26lp9lZ2eHtbX1LUku\n5+XlIZVK0Wq1eHh4tCg/rIOmSU5Oxt3dXbfI/jvl5eVkZ2cjl8vx9fW957xOcrmcuLg4jI2NdZ7Z\nTp064eTkRGpqKjU1Nfj7+7ep4VRVVUVqaqrO42piYkKvXr10hk1BQQFAs8qACxYs4Pjx47r2UG98\nXdsIAZgwYQI7d+78xxaYbU+2b9/Ogw8+eFfUDbsrDKbz589TXV3NQw891N5T6aANWLVqFWvWrEEq\nlSKVSunXrx8AJ0+ews9vENbWxlhY3J0hZ3cTWq2WhIQEjI2N6dmz5x3dsSooqOaXX7IoKMhh8+Zn\nuHpVikQi4eGHH2bevHl4enpiamraZAX5bdu28dxzz5GcnHzdPCSNRkNISAjDhg1DX1+frVu3kpGR\ngUql0v0zNTXFysqKLVu2UFdX1+D8BQsWMHHiRGbOnMmmTZt48cUX2+R76KB1hIYWcPHiVaDe45Gd\nnURY2DH++OMbuncfwNKlmzA1rV88XrlSSU5Ole5cmUxBTY0aS0vDRs8JiQQmTvRmzpxe6OlJSEhI\nQK1W4+bWhaIiFc7OZjg7m5Ofn8/Vq1cpLy+nd+/eCCGwsrLCyMgIrVZLVVVVI6EQpVJDbGwxqalJ\ndO3alcGDPTEwaH5D51q4T0u5fPmyroizUqkkLCyMgQMH3lKdOqlUyoEDB8jNzSU4OJiQkBA2bNjA\nf//731b3+VcSEhJwcHBosJgUQvD7778jk8mYOHGirobQ/U5ubi6FhYWYmpri4uKCnZ0dOTk5GBsb\nN2t0CiGIjo7G3d29xXWc7gU0Gg3x8fEYGRnRu3fvdvHO5Ofno6en1+zfaMKECchkMsLCwhoc//33\n3xk3bhy2trYoFAoMDAzYvHkz//nPfzq8TTeBEIKNGzcyd+7cNisA3lLaPSQvPj6e/Px8Hn/88fae\nSgdtgEaj4dtvv2X06NE4OTlRVVVFz549Wb58ORMnNs5d6eD66Onp0a9fP0pKSggNDb2jqlUREVIK\nC/N4881HgHqp1ueee47XXnsNGxsbDA0NCQsL04UT/pWSkhIAfH19GThwIBEREQ0+V6vV+Pj4kJWV\nRUxMDP379+f5559vch6fffaZzliysLBg+PDhnDx5End3d5YuXUpAQABLly693ZffwS0ydGgn9PUl\nxMXV13zx8vLDy8uPLl382b37TfbsWc3ixR/932fWKBQaiorqvTlWVsZYWTUOs5RIYMwYD52xlJqa\nSUpKOX5+vXF2tuGv60sLCwtyc3MxNTWlsLAQlUoF1Nez6d+/f5OqikZG+gwe7IpMlsiwYV4tWuQU\nFRXd9Evdx8eHhIQETExMcHR0ZMiQIURGRjJs2DBqamo4c+YMycnJBAUF3TACIzY2lrCwMAoLC9m6\ndSvl5eUYGhrqCoanp6ff1LxuhL+/P+Hh4XTq1AmtVsv58+dZvXq1LpfQ1NQUb29vKisr8fPz4/jx\n43eNAMadRi6X07NnTwwNDSkqKiInJ0f3LG8OiURCYGAgSUlJOi/MvY4QgpqaGpydnVGr1cTExLRL\nHou9vT2pqanNGkyVlZVNhrk+8MADzJ07l/3797Nr1y7mzp3L/PnzOXfuHLt27Wqrad9zSCQSXnjh\nBdatW8cLL7zQrhsx7WowZWZmEh4ezsKFC9tzGh20Id9//z3p6em89957QH0+06VLl9p5Vv9sHBwc\nSE9PJz4+nj59+tyRhYi+vh4mJub07Tsae3tX5s59mNmzZzYQo9DX129yUflXNcDIyEh69eqFQqEg\nKyuLgwcPsn//fl3oQnM5EjNmzKCiogIzMzP+85//cOrUKWJjY3nrrbcwNzdn165d94z64L3GoEGu\nuLiYc/58HjU1qv879hAyWSk//LCeX37Zw4MPzgfq8xktLIzIz69CLtc06svW1pjp07szZsyfYRpR\nUWnIZNYEB+fj5WWNmdmf+R8KhYLAwEBiYmJwc3PDwMAAa2trtFotKSkpZGZm6kLzrK2tqaqq93BJ\nJBKcnZ05d+4cI0aMuOG9JoQgPT29VWHl/v7+REVFYWNjQ3Z2Nl5eXsTHx7Ns2TJdLoS3tzdbtmwh\nICCgwcK5srKS//73vw0WYUFBQToxC09PT7Zv3864ceNuel43wtTUlA8//JCPPvqIoqIinJyc2LZt\nG76+vhw4cEBX5Pfo0aMcOXKkQb7j/YS5uTk1NTW4uLjg6enZqtCi3r17Ex0dfU8bTDKZjMTERExM\nTLCwsMDKyoq6urp2C0E3MTHRhezeaLNk5MiRbN68GZlM1ih0csOGDZw5c4a5c+fqju3evZtBgwax\nePHiNpv7vYaBgQEvvfQSmzZt4uWXX2633L52C8krLi5m7969LFu2rMM9eY+i0Wjw8/PDwMCA+Pj4\n+3aHsS1QKBRERUXh4OBAjx492ny8srI6Tp3KprZWjaNjNYMGdcXR0VH3eUVFBVevXm3S4Pniiy9Y\nsGBBk/2OGDGC8+fPA/UetD179tx0gT+VSkVKSgru7u7Y2Njc1Lkd3HmUSg0REVIuXy5HrdYihGDn\nzpVER//Gyy9/Qbdu/XVthRCUl8uRyZRoNAJDQ33693di4kSvRuF5J05c4NIlKc7OXXj88X5otRrS\n09MxMzOjpKQEfX19nJycrrvoFEIQExOj8wYYGRkRHx+PiYkJTk5OJCcn06NHD50HSaPRkJqaSl1d\nHTU1NZiYmNCjRw+srVtXfDsmJgYPDw8uX76MpaUl5eXljBo1ijfeeAMfHx/mz5+vK8bp7OzMihUr\nWL58OZ999hlLlixh6dKlrFy5EkdHR4yNjSkuLmb06NFcunSpTWqTFRcX0717d/z9/VmyZAlTpkxp\ntLhVq9V0794dX19fjh07dlvH/6dQWVnJ1atXb1m0Jzo6+q5SDLvdhIeHM2jQoLtqPVhYWIhSqcTD\nw+O6bfbs2cP8+fNJSEhoUpwgKSlJl6M4ZMgQXeieVqu9q671n0B5eTk7duzg5Zdfbp/1pGgHampq\nxJo1a4RarW6P4Tu4Q+zbt08A4ocffmjvqdyTaLVa8euvvwqtVnvHxiwqKhIJCQmNjkdHR1/3flYq\nlWLt2rXC3NxcAI3+vf766+I///mPAMTAgQPb+hI6uEtQKNQiIaFY/Phjqtiw4YwAxLRpz4vt2+Ma\n/fvmm0siMlIqqqsVN+yzulouDh06LCIjI0VoaKgoKioSmZmZTd4jKpVGnDiRKd5770cxatQ40bNn\nTzF37lyRnZ0ttFqtuHr1qggLC9O112q1IiUlRURERIjw8HBx7NgxIZPJhBBC5OTkiLi4uFv6PsLD\nw0VkZKTQaDRCqVSKwMBAYWdnJ0pKSoQQQshkMnHu3DkxcOBA3b2Tm5srQkNDBSDef/99UVpaKi5f\nviyqq6tFenq6MDY2FrNmzbqlef2d5ORkMWnSJGFraysAkZiYeMP206dPF76+vrd1Dv8kVCrVLf82\nhBC638a9SHl5uYiNjW3vaTRCq9WK6OjoG7Z5+umnhY2NzQ3Xs8eOHRNOTk7i22+/FREREeLbb7+9\n3VO9b8jNzRXbtm1rl7HvuIdJo9Hw4YcftnssYgdtS3l5Of7+/tjZ2REbG9vhXWojZDIZCQkJDBgw\n4JaSxJujpqaGrKwsVCqVrkDsX0lPT+fy5ctcuXKFrKwsSkpKUCgUKJVKDA0N2bBhAwYGBmzbtg0h\nBK6urhQUFJCUlMSpU6eora1l0aJFvPXWW21aj6aDu5PMzCt07erN229vZMqUOQAYGOhha2uCg4Mp\nRkYtL2Z94cIFhg0b1uzubXZ2JWvX7uKzz5ZjYWFNVVUF/v7+9OjRgwcffBA3NzcmTJhw3X7y8/Op\nrq7WeXiTk5MxNzfHw8OjVTvHWVlZJCQk4OXlxdGjR1m9ejXfffcdM2bM0LU5dOgQjz76KFAvV/zK\nK6+gUqkYNWoUoaGhjfq0tLQkJSXltoVyCSEICgri0qVLzJgxg6lTp/LII480aFNTU8Nrr73GL7/8\nQm5urk5YQyaT3bDfwsJC8vPzgXrPlIeHxz8+BE2tVnP58mVMTEzo0qXLLfVVW1tLdHQ0gYGBbfqs\nv5OkpqZSWVmJtbU13bp1a1bC+05RWlqKubk5JiYmxMTEXLeosBACLy8vAgMDOXDgwA37nDFjBgcO\nHGDOnDls2rSpQYRGBzdHfHw8KSkpPPbYY3d03Dse7L9t2zaeeuqpDmPpHkYIwZIlSygsLOTgwYMd\nxlIbYmVlhYuLCwqFotUvUbVaTXp6Okqlkmv7J3/dR1EqlUgkEnr37n1dmVeZTMaiRYvIy8vD2NgY\nlUqlCx9ydXVFLpfj4uLCgAEDOHLkCJ9//jlFRUXo6+szZcoU3n///TsSWtjB3Ul5eSkA/v7e9O3b\neoP5WnHWlmBvb4pCUY0Qgs8++5GMjBBWr15NQkICP/zwA08++STDhw/n9OnTWFlZNQo3s7Gxoaio\nSPe79fX1RSqVtjpJ3dvbGysrK1avXs3u3buxsbFpYCwBuLu74+npSXZ2Nq+++ipubm78+9//ZtWq\nVUyaNKlRn4sWLbqtRsfBgwcJDQ1l586dPP300022yczMZOvWrQQGBrJgwQLi4+OxsLBoMhekrKyM\njIwMXfHWAQMG6NpkZ2cTFhZG586d6dy58227hramvLycK1eu6Gr4eHt735ZQYTMzMwYPHszly5dR\nKBQNPjMyMsLb2/uuLTBcU1NDQkIC5ubmdOrUCXt7e+Li4nBzc7vrnvsRERHY2NgglUpRKBQ3XKum\npaWRk5PDypUrm+13+vTpHDhwgP379/PLL7+wfv36DsW8VtK3b19KSkr4/fffmyx832bcSXfW119/\nLZKTk+/kkB20A19//bUAxHvvvdfeU7kviIqKalVYnlarFUlJSSIiIkLIZDKhVCqFSqUSarW60b/r\nIZPJxIcffijMzMwEIJYvXy5GjBghAOHq6io2b94s4uPjxdKlS3UheVZWVuKxxx4T+/btE6Wlpbdy\n6R3cIxw/flwAIiQk5Jb60Wq1IjIyssGxkpISUVtb22T7Tz75TAAiKSlJlJaWii5duohHH31UjB8/\nXtjb24uIiAiRkpLS5LlSqVQcOHCg0fG4uDiRnZ0t4uPjRU1NzU3N/5dfftGF2+3cufO67a59XwsW\nLBCTJ08WgPD29hbPP/+82LNnjwgLCxN9+/YVgHBwcBDLli0TlZWVNzWXppg2bZoAhEqlavLzmTNn\nil69eglArFixotn+MjIymn0G5OTkiNDQUJGdnd2qOd9pIiMjxdWrV+/omHK5XCQkJIjw8HBRVVV1\nR8duCVlZWaKkpEQoFAqRlZUlIiMjRWFhYXtPq0lCQ0NFdXV1i9pu3LhRACIzM7PZtgqFQjg6OgoP\nDw8RFBQkADF69Ghx6tSpBqGWeXl5dzTM/p/Mt99+K+Lj4+/YeHfMYDpx4oQ4e/bsnRqug3biypUr\nwsrKSgwfPrwjR+0mUCjUQi5XNfugVKk0Qqls+L3+fYH4d7RarZDJFEKl+vOhXFFRIYKDg3X5ETeD\nWq0Wv//+u5g3b56wsLAQgBg/frw4ffq0GDVqlLCzsxObN28W2dnZYs6cOQIQRkZGYt68eeLUqVNC\nobhxHkoH9x9ffvmlAERaWtot9xUeHi7Ky8uFXC4Xc+bMEXp6esLBwUGsWbNGSKVSUVkpF0ePposf\nf0wVX311UABizZo1Qoj6RY1KpRJJSUkCEPr6+uLSpUuioKCgybHOnTsnlEplo+O5ubkiJCREXLly\npdn5arVaXR8//vijAJrNeSkoKBA9e/YUgLCwsBBr164Vcrm8QZu6ujoxe/Zsoa+vLwAhkUjEK6+8\n0ux8bsQbb7whgOvmEBgaGuoMvpbkGSiVyhbnruTl5Yng4GBRV1d3U3O+02i1WhEcHNzk76KtUavV\n4uLFiyI8PFzEx8df17C900il0iZzX+9GNBrNde/rvzNu3DjRq1evFvf9/vvvC0CEhoaKzz//XNjb\n2wtAdO3aVaxevVqsWrVKt/HYQcv4+OOPRV5e3h0Z644YTNHR0eLHH3+8E0N10I6o1WoxatQoYWFh\n0aIdl/ud2lqliIkpFPv2JeuS27/8MlGEhRUImUzRqO2vv2aJzz+PF9u3x4njxzNEVVV9m6ioqOuO\nkZ9fJfbvr+9/164EERdXJBISksTRo8Hi/PlcceZMjggJyRcpKaUNDCqFQi2Sk0tEdHShSEsrE2q1\nRvz222/i6aefFk5OTjpP0dNPPy0iIiKEEEJUVlYKAwMD8corr4g9e/YIOzs7YWRkJF5//fW7djex\ng9uHQqEQu3btErt37xYxMTGirq5OqNVqoVQqm908mTt3rtDX178tu+NKpVLs3r1bPPzwwwIQy5Yt\nEw899JBuIf/ss+/o7rcNGyLE8OETBCAefni6KCiQ6vqZOHGiAMTu3btFaGhok3NTKBQiNDS0yXnE\nxsaK06dPi7S0NFFUVNRoM+Ty5csiMjJSREVFicjISBEbGyu++OKLFu9YazQaERYW1uy9pVAoxLvv\nvitMTU0FIHr06CGkUukNz7lRX1OnTr2uQXTixAnh7e0tAHHixIkW9dnchs9fUavVIjg4WJSXl7f4\nnPZAqVSK4ODgdvUU1NbWtvsc/kpRUVGzAgp3CwqFQoSHh9+wTU1NjTAyMrop40Ymkwl7e3vx0EMP\nCSHqNzX27t0rRo8eLfT09ASgE1P5/PPPb+ka7hc0Go344IMP7ohntc1FHwoKCjh06BDPPvtsWw7T\nQTsjhGDp0qV8+umn7Nq1i/nz57f3lNoduVxNWlo5tbVq3Nws6NzZUvdZUVENJ09moVDU15hRqbSo\n1RqMjQ3Q05Ogr6/H2LHueHvXx74fOpRGcXFtg/5tbIwZN86JkpISfHx8Go1fUFDIgQMRaDT1t3hd\nnYaKijpsbOyxt29cjM/ISB8fH1v09CA5uQy1WotWKygpqSMiYh8//7wNKysrHn74YaZNm8bkyZMb\n5E1duHCB4cOH6/4/KCiIHTt20KtXr1v4Fjv4J3D27FmeffZZkpOTm/xcX1+fXr16ERAQgJ6eHuXl\n5ejp6WFsbIxGo+GHH37g9ddf19Vra46KigpOnz5NXFwchoYO2Nl5MWvWCGxsLHj55ZfZv38/MpmM\nTZs2sWTJEgASExOZO3cupaU1PP/8XtLTy6muVuHvb8v5819z8uQXWFnZceRICEFBXairq2Pq1Kmc\nOnWKhQsXMm7cOIYMGdIgnyY7O5vCwkIGDx7caI5SqZTy8nJcXV2pqqrSFXCGevEjT0/PBgInZWVl\nLF68mHPnzpGWloalpWWjPm+F6upqxo8fT1hYGG5ubnz11VeMGDHipmuaKJVKpk+fzokTJ5os1CuX\nywkICKCmpobExMRmryM6Opr+/fu3ONdVCEF0dDQGBgao1WokEokuD0QIgVqtxt7e/pZlvG+V8vJy\nsrOzW1Sgtq2orKwkPT39rpAj12g0nD17lpEjR/4jauUlJSXRuXNnrK2tyc/Px9bWtkEO74kTJ5g0\naRLHjx/noYcearY/tVrNoUOH2Lp1K+fOndPJqF+jpKSEzMxM+vXrp3vunDx58s7m6PxDqaurY9Om\nTbz66qttmjPfpgZTXV0dmzdv5pVXXulI/L/H+d///sf/+3//j1deeYW1a9e293TanfJyOT//nIFc\nrtYd8/GxZfRoDyoq5Bw6lI5SqUGt1pKWVk5oaAGVlQr8/R144AEvAPT0JDz0kDfGxvr89FNaozGE\n0NK5cxmTJo1tMnH02LEz5ORYo6+vR25uFVlZlQC4uJjj42PboK1GI9BotMTEFJGcXIqrqwWuruak\np5eTlvYdV678xMCBkzh4cC9ubraNxoL6F8KBAwdISUnB09OTuXPndtz39zhqtZqFCxeye/duvLy8\n2Lp1K927dyc+Pp7Lly/rEv1ra2uJi4sjLi4OfX19bG1tEUKgVCpRKBRMmDCBrVu3YmxsfMPxwsLC\nWL58OeHh4Wg0TRW0taW8vJxly5bx3nvv6Qz6+Ph4KioqiI6OZvny5UyfvhtTU0fs7Ezo3dsBiQTS\n02NZt24+kyY9wxtvvMWwYW7U1dXx4osv8vXXXyOXy3nyySdZuXIlvr6+XLlyherqasrLy+natSsW\nFhakpqbSv39/3YIwMTERBwcHXFxcmv0uN2/ezEsvvcQ333zD448/3oq/RsvYtLge2KUAACAASURB\nVGkTa9euxcjIiGnTpjFixAi8vb2B+mKdPj4+zRpR19T6oqKimlyMh4aGEhQUxIgRI1i8eDGPPPLI\ndQ0nqVSKVqvFzc3tpq5DLpdjYmLS5GeFhYVkZmbSu3fvVtfFukZ2djZFRUWYmprSrVu3mxLXuXTp\nEpaWlu0qWlFQUEBRURH9+vVrV4GByMhI/Pz8/jEKf1qtlsjISIQQjB07Fjs7Oz766CNmzpzJqVOn\nmDVrFsbGxi3e3AgLC2Po0KFA/QbS4cOHefjhhxu0qa6uZuvWrRw6dIiIiAgCAgKIjo5uk+u715BK\npRw6dEi3QdYWtJnBJIRg3bp1PPvss3etcksHt4dvv/2W2bNnM3/+fL744osO1Rfg5MkscnIayugK\nAR4elmRlVaKvL0EIiIsrpqCgmoyMSmprVdjYGDN1ajc6daq/ZxwdzRgwwJmTJ7MAyM+vpri4Fmdn\nU6yspEyYMBw/v8beosuXczh4MJykJDMsLY2orFTqPuvc2YIuXf5UbUpPr+DSpVKUSg25uVXU1amR\nSOo9TkVFUVRUfIaDwyjeeGMd/v7OjB17/SJ+HdxfFBcX07lzZ9zc3EhKSrquimJzVFdXc+nSJQAc\nHR3x8PCgsrISfX19rKysdO0WLlzIvn37WLZsGRMnTmTgwIGcPh1PVFQ8Wm0hmZnpTJ48mZkzZ+rO\n+eabb/jPf/6DiYkJUVFR9OzZE1/f4Tz55PvY2Fjw18fVzp0riYs7w4oVu3juucm4utbfhxUVFWzf\nvp3Vq1fTv39/HnzwQUaMGMEDDzyAEIIrV64QGxvLuHHjiI+Px9/fH2tra0JCQhg8eHCL5JLHjx+v\nk9pva3Jzc3nppZf4/fffqaqq4rXXXuOdd96htraW1NRU1Go1Xbt2xdbWFrVajZFRwyLBly9fpmfP\nnkycOJFDhw41aeh+/PHHrFmzBqlUiomJCdu3b2fu3LmN2l3zGAUGBjb6rKqqqtWeNiEEwcHBDBs2\nrMVy1VevXiU3Nxdra2vc3d0xMjIiIiKCQYMGIZfLSU9Pp66uDuC6fWq1Wnx8fDAwMCA8PJwRI0a0\nu0elvLyc5ORkunfvrttosLKywszMrM3f11qtltTUVPT09O46RbzrceLECbp160ZkZCQvvvgi1tbW\nWFlZERsbi42NDZWVlfj5+XH48GHdZkNzpKen0717dz7++GMWL16MQqFg/fr1jBo1ipEjRyKRSNi7\ndy9PPvkk/v7+TJw4kSeeeAJ/f/82vtp7h5iYGLKzs3WlF243bWYw7dy5kwkTJtywQnIH9wZTp04l\nMTGR1NTUdn8xtAdVVUo0Gi02Nn/udu7efRGVqqG8cXm5nLIyOVJpDb6+9mg0Wi5dKtOFvdXUKHF2\nNsfGxoTBg110L7JJk7rw669XuHq1lpCQfIqLa+jWTUb//gEsXDgAC4v6xUxNTQ2ZmZnU1dVRXm5A\nVpYe58/nUVBQjZtb/aJDX19CQIATpqb1O8glJXWcPZtLRYWC/PxqDA3/9AhVViqQy/cgl6fh7v4O\ny5YFYWNjwrx5fm36fXbwz2LVqlW8++675OTk4O7ufkt9CSEoKSkhJydH5z2orq7G09OTuro63nzz\nTTIyMggLC2tRf5mZmfTv319XA+j06Uh27jzE/v3v4ec3nGee+RATkz+NvLKyQtavn49MVs5rr23i\n//2/RQ36S0hIYNWqVZw4cYLBgwczYcIEZs2aRVpaGt26daNnz54IITh58iQTJkwgPDycYcOGtWiu\n7u7u9OzZk99++61F7W8HVVVVPPfcc3z11VcNQouEECQlJSGXyzE0NESlUmFhYUHPnj115+7cuZNn\nnnmGKVOm8MMPPzQyqqB+sXzhwgVef/11YmJiKCwsxMLCAq1W28DgiIqKamQwJSUloVKpUCqVWFlZ\n4ePjc9Me68LCQtRqdYs8PEIIzp8/z/Dhw6moqCAvL0/nAQ0KCmrxmEIILl68iEKhoKysjIkTJ97U\nnNsKpVJJYWGh7h0tk8moqam54TlarRYPDw+cnZ1bNEZpaSk5OTkNylNoNBp8fHxui7T6nUAul3Px\n4kVqamr46KOP+Pnnn0lLS8Pb25vdu3cTExODs7Mzy5cvvylnwLXf8fjx4zly5Ihu0wHAx8eHZ555\nhsrKSt59911KSkqwt7dvq0u8pzl69ChOTk4Nwh1vF22yuj127Bh9+vTpMJbuAzQaDSEhIUyePPm+\nM5bUai2nTmXrPElOTmZMmOCFmZkhFhZGlJfLG7Q3NzekulqFiYk+FhaGFBRUA/Whd05OZkD9wk2p\n1KBSaXXFOktK6hg0yIWDB9NRqQSenpVkZVnz2GMOFBfncelSKXp6epibm9O1a1fMzMyQSqvJzc3E\nwsIILy8rDA31MTU1wN3dSmcsAaSmllFRoUCrFRgY/LnTqFZrMTTUo6qqAiGqyMl5id9+28WDD3bs\ndnXQEGtra5ydnUlLS0OtVrd4x7UpJBIJjo6ODYo6arVa8vLysLa2RiaToVQqb9BDQ2JiYpDJZKxb\nt44VK1YQEhLDqFEz0dPTY9++91i/fj7PPrsZO7v6kDk7OxdWrtzLp58u4+23F5Oa+geWlpY8+eST\njBw5En9/fw4fPkxycjKrV69m9erVVFVV8e6773Lp0iViYmLQaDSYmJgQHx+PUqmkoqLihovFqqoq\n0tLSWLRoER9//DGnT59m7NixN7yuiooKjh8/zpEjR7hy5QpqtRq1uj789+GHH+aFF15o0SLX0tKS\nHTt2cObMGd577z2dwSSRSPDza7gxcuXKFRISEujduzf6+vosWLAAhULBc889x5w5c/j2228bvQP0\n9PQYMWIEw4cPJyoqinfffZfw8HB69erFvHnz6NKlC46OjhgaGqJUKjEyMkKj0XDx4kVsbGzw8vIC\n6nNxzp07x7Bhw5o0zK6Hk5MToaGh2Nra3rCejkwmIzk5merq+rpcdnZ22NnZtXicvyKRSPD390cu\nl3PmzJlW9dEWGBkZNViTtaQ+lxCC7OxsIiIiMDc3p0ePHo3+xhqNhvj4eDQaDfb29vj7+981BWhv\nhtLSUjIyMjA2NqZv374sWLCAkJAQevToocuHW7BgQav7NzIyYs2aNSxfvpytW7fy3HPPYWNjg5ub\nG9bW1qxYsULX9lbDSO9nHnnkEXbs2IGrq+stb+D9Hf3//e9//7udHSYlJSGVSpkwYcLt7LaDu5T9\n+/ezb98+Vq1adV8l92dlVfDll4lcvHgVGxsTJBIJNTUqamvVeHtbY2CgR3Z2w5A8a2sTxo/3RK0W\n6OvroVJpKSmpa9S3kZE+Hh6WOg+Ts7M5/fo506OHHTKZAj29UoYPd6Z7d3B2dsbHx4dOnTrh4OCg\nyz2wtDTC0dGU4uJaunSxoVMnC5yczDA2rn+RlZXJycurIj29AqVSgxDg4GCKWq2lrk6NUlmOEOnU\n1EQA9UUSbWx86d27F0OH3r5CmB388zl+/DgXL15ky5YtpKSkUFhYSEFBAVeuXLktm2YSiQRra2vO\nnTvHunXr8PT0bLGojJOTExs2bODXX38F4JlnXkWhMMbT0xcvLz/On/+J0NDDuLp2wdnZEwBjYzMG\nD55EVVUZsbEXOHfuHHv27OGll17Seb0cHR2ZOXMmUqmU/fv3U1dXx6xZs0hMTGTkyJF4e3vj6uqK\np6cnSUlJusXptZ33a/f22bNnKS0tJSAggEGDBrFjxw4iIiIICgpqJKYA9cbj+vXrmTRpEj/88AMl\nJSV4enpibW2NnZ0dBgYGfP3112zdupWcnBx8fHya7Oev6Ovrk5aWxsmTJ3njjTeaLDAL9YV6TUxM\nOHbsGNXV1Tg5OTFkyBCsrKx0BW0fffTRJhfLCQkJpKenc+zYMczNzTl+/Dhjx47VhfI5ODgglUqx\ns7PjzJkz+Pv7NxDEMDExwcnJifT09AbHm0MikWBkZERiYiJubm5Neqjy8vLIycmhR48eaDSaWy70\nq1QqiY+Pp7i4mMGDB/+jNxIlEoluUW9mZsalS5eQSqVIpVKuXLmCQqEgJSWFvn374unpia2t7T8q\nb1WpVBIWFoazszO5ubk4OzvTrVs33YbAxIkTOXjw4HVz5W6WIUOGEB0dzWeffUZpaSmpqakYGxsT\nExPDzJkzMTQ0JDAwkEceeeS2jHe/EhAQwGeffUZgYOBtvf9ua0ieTCbjiy++YNmyZberyw7uYlQq\nFb169cLCwoKYmJh/1IPyVpDL1ezdm0xMTBEymZIuXax1CngmJgbMndsbgPT0chISrupU8gYOdKGs\nTK7LR9JqBXFxxVRXqxr0362bjS6HCWDYsE74+f25465Wq1GpVM0mz4aE5JOYWNLgmFYLKSml/2eo\nCXJyqqirU6Cnl0xOzi9cvZqKRGKBWl3a4DxTU3cmTtzEM8/0ZdKkrjf3hXVwT/PZZ5+xZMkSYmNj\nGyiCJSYm0qlTp1bv1APk5+cTFRVFcHAwGzduxM/Pj2PHjt2UQMCTTz7J3r17cXFx4fjxOCIjC3Wf\nSaWZ7NjxKvn5afTpMxJX1y4MHfoInTp1w9LSiNmzezFo0CAiIyMZNWoUb775Jg888IDOoFAoFJiY\nmLBy5UrefPNNrly5Qu/evXX9CyH47bff8PDwQKlUIpfL0Wg0uLm5YWxsTGJiIkZGRowYMQKATz/9\ntIGi7PLlK3Bzc8XR0RF7e3s2bdrEr7/+yqOPPsqKFSsYPHhwo+duWloaGzZsYM+ePSiVSsaMGcOj\njz7K1KlTcXd3p6KigpSUFMzMzLC2tua9995j586d/Otf/2LlypVoNBr09fXx9/fXbcDU1dWRmJhI\njx49kEql6OvrI5VKGTBgAKmpqfz++++sWLGCRx99lB9//LHRnKqrq3W5SBERETz33HNER0ezceNG\ngoKCsLGxIT09HW9vb1JSUpgwYUKTeVFhYWF0794dExMTXYhTS7wZtbW1pKWl0bdv3wbHMzIy0Gq1\ndO/enUuXLqFSqejduzcZGRkYGhri5eV10zk+oaGhBAQENCtgci9QWFiIs7PzPzZvOSsrCxMTEwoL\nCxFCUFtbq1N67dGjB/r6+sTFxd2UV7M5SktLmTBhAqmpqahUKgwMDHBzc+PYsWN07979to1zv9MW\n9shtM5iEEKxdu5Zly5bdFw+KDv7MXfj555/vmx2R/Pwqjh3LICNDhkqlobi4Fi8vKzw86hPTnZzM\nmDbt+g89jUbLvn2XdOp5KpWW/PwqysrkGBrq4epqgYPDn4aQnp6E2bN7Ym7e/AO7uLiGwsJajI31\n8fa25tSpbPLyqhq0uXKlkpCQAl0YYEbGcaKiPmvUl63tdIyNu6Gvb4iVlQd2dpb861/dWLy4X4OQ\nvg46KCgowM3NjU2bNvHiiy8C9UnmmZmZBAQEtHox9f777/P6668D9aFdU6dOZc+ePQ1EIG6ERqPh\noYce0uUEmZiYcOFCBNHRgr++9lQqJceObScy8hcqKoowNjZj0aL1zJ79MIGBrtTU1LBjxw7WrVtH\nQUEBQ4YMYevWrQQGBlJRUYGtra3u2i9cuICfnx91dXVkZmZiaGiIp6cnFhYW5Obm8vvvv/Pbb79R\nV1eny+sICgpi/PjxvPTSS2zbtg2tVnu9S8LExIRNmzaxcOHCZr/XoqIiPv30U77//nudoIaDg0MD\neXOo9zC9+OKLvPXWW7rvVqFQEBERgRACc3Nz9PT0qKqqws7ODoVCgVwuZ+jQoVy8eBFLS0u6dOnC\nxo0b+e9//8ubb77JO++802g+14zB6dOns3TpUl555RWio6NZv349c+bMwdjYGENDQyQSCebm5k1e\nnxCCjIwMnRCFVCrFz8+vRSFMaWlpVFXVPw/r6uoYMmQIcXFxOpW/2tpa8vLyqKyspGvXrqhUKrKy\nsrC2tqZHjx433BBMSEhAparf+HJ1db1lL1UHrSclJYXly5czbNgwVq5ceUNxjuDgYEaMGNHkb+3n\nn39mypQprFu3jpdffvm2z3PFihWsX7+eqVOncuHCBczNzQkODm5XRcV7jaSkJJKSkpg1a9Zt6e+2\nGUy7du1i3LhxHXlL9wm//vorDz74oE4Z725Fra4PezMzM8DK6tYN+ZiYIqKiCqmsVGBpaURKShnd\nu9tiaKiHvr4eDz7opRNYuB4REVLi4opbNJ63tzXjx3vdsI1KpeG33xoaRwYGeigUavT1/3zJCyH4\n6ac0CgqqMTbWJz5+CWp1baP+/Pweo1u3x5HJFBgY6OHqas7zzwfQt68TBgb3hxexg5aTkZFBt27d\n+PTTT1m8eDFQr8JmbGx8U+FTf6W0tJSBAwdiYWHB9u3b6du3700r8KlUKt3O8KZNm3jnnXdwd3dn\n3bqfyMyUNXlOSUk+69bNp6KiGB8fH4KDg3X5VJmZmcyePZuIiAgkEgnvvvsuixcvxt7enrVr1/LK\nK6+g0Wg4deoUvr6+uLm5oVar2blzJ2PGjKFfv34olUo8PT1xd3cnOTkZlUrFnDlzkMlkfPPNN0B9\nbklBQQEAZ84k4O/fmeLiYoqLi/H09GzVOzY1NZXDhw+TmppKjx496NWrF3K5nPz8fMaPH9/AK3YN\nIQSRkZG65Ona2lpiYmIa1Fr7e/sFCxawa9cuJkyYwAsvvNBINtnDw4Pc3Fzd/zs4OLBnz55G7VqK\nEIILFy4QFBR0U4Z5XV0dkZGRWFlZYW1tTWlpKWZmZpSXlzcQeFCr1Zw9e5ahQ4c2+v1dqyVmZWVF\nTEzMXVHr6H7n0KFDPP7440gkEuRyOWPHjuW7775rFJaqVqsJCwsjICDgus+VawbNBx98wKuvvnrb\n53qtFEtNTQ2XLl1izJgxdO7cmXPnzjUbRttByzly5AidO3cmICDglvu6LQbT2bNn0Wq1jBkz5pYn\n1ME/Az8/P1QqFbGxsa2WEm5rlEoNR46kk59fbyCMGeNBjx6tDw+CegMsNbWMTp0ssLU1obZWRXp6\nBWq1lm7dbFpklF2bV1mZ/IbtzMwMmTatm04F73qcO5dLSkpZo+OJiSV06WKNmVm9R+jy5TIuXCig\nsLAGPT0VmZnPAdCr13SsrNwYNWoUXl5duHq1jqtXa9FqBdbWxkyc6MWoUR0bIR00zTXPQnp6Ol27\n1odrJiYm4unp2SpJaKVSyejRo4mJieHXX39l5MiRrZ7bU089xcGDBykvL2fDhg28/PLLXLmSw8WL\nSp3oyt/RaOpQKOJYseJ5Ro4cyebNm/Hz82PatGkcPny4QduPPvqIzZs3Y2dnx9GjRykpKcHNzU2n\ncLVjxw4WLlxIly5dcHNz4/z58zz33HNs3boVgMcee4zvv/++yXk89NAkjh8/1uprvx0EBwczcOBA\nXdRIXFwclpaWur/z35HL5Q1ChcPCwhoU9fXz88PQ0JBHH30UJycnnnjiiVsuO1JWVkZhYSG+vr6t\nOj8mJka3mLomXmBhYYGNjQ2pqakMGjSoQdTM1atXSU9P1xnSMpkMFxeXDq9SOxMXF0dQUJBO7nvn\nzp2sWrWKzz//nGeeeUbXTghBbGwsvr6+TeYmyeVydu/ezcqVKxk7diwHDx5sk/meP3+ekSNHsnTp\nUj7++GPOnj3LxIkT8ff35/fff7/thavvZ7Zs2cK///3vW1YevOXtYqlUyuXLlzuMpfsMV1dXDA0N\n71pjCeoNhMTEEiIjC4mOLiI0tOCW+zQw0KN3bwdsbesftGZmhvj7OxIQ4NxiD5aRkT6PPNK1QZ4S\n1D/Ir+1f2NmZMGVK12aNJaVSQ1paRZOfGRvrI5X+6UG6erUOFxdzbG1N0NfPAaBfv3kEBDxFnz4P\n07VrN/T19XBxMadPH0f69nXCy8saZ+frq0t10MHZs2fp3r17g0W0QqFo9Qv/moDAzp07b8lYAvD0\n9KSiogK1Wq0LqbK2tmTSpC6MHNm5QfirsbE+/v6OPPlkAC+9tJiPPvqIiIgIHnjgAaKjozl8+DDz\n5s3jyy+/1IWAFRYWsnbtWi5dukSfPn0oLS1t8FK+9t+ZmZmcP38egNjYWN3n1wwngDlz5lBcXKxT\nAmxvYwlgwIABpKSkEBkZiUajoXfv3hQVFV23vYmJCfb29ri4uDB37txGiokODg7ExcXx1ltvMX78\n+NtSo9HOzg49PT2SkpJozf6vWq3m8uXLhISEoFarqaiowNHRkZqaGoYNG9bAWEpPT6e4uJihQ4fS\nrVs3unXrRkBAQIex1M5oNBr+9a9/YWtry6FDh3BxcSEjIwMzMzOmT5+OVqvl6NGjLF68GE9PT5Yu\nXconn3xCZmZmo9/M/PnzefbZZ/H09OTDDz9sszmPGDGCZcuWsW3bNo4fP86oUaP44YcfiImJ4aWX\nXmqzce9HlixZwo4dO1r1fPgrt2QwabVavvzyS5566qlbmkQH/zxMTU11tU3uVvT0JFRV1UsQ19Wp\n0Whub8mx+pox9Z6Ym8XExIBHHunKo492p2dPOywtjUhOLiM7W8bYse7MmNGjRQZYba0KjabpnAdn\nZ3Oysyt14hQXL37F5cv7MDTMITPzYywsXOjZcwpGRvr06tW0583QUI+uXf8Z9TM6uPN88803HD58\nuEGtmejo6FuSxd25cyeenp7MmTPnlud3Tc67srKSrKx6sRULCwv09CT07GnP9Ok+zJvnx9y5vZk7\ntzdDhnTSbVI8//zznD59muLiYt577z1sbW3Zu3cvUqmU8PBwZs2axZIlS3jsscfYt28fACdPnmww\n/uTJk3WL6TVr1vDbb79x9uxZ3efV1dWYmpoyaNAgvvjiCxwdHbG0tNSJLbQ3pqam9O7dG7Vazfff\nf09iYiKGhoaEh4fr8oFkMhmRkZG6gq4GBgZMmTKFL7/8slFI5pEjR3R5abdTKKBnz5506tSJkJAQ\nrl692mz72tpaIiIiiIiIwMbGBltbWxQKBRKJBCsrK65evUrnzp0b5L9otVqqqqpaXJOog7bnWt22\no0ePkpmZybp163B1rS/kLpVK0Wq1XL58mZdffpnJkyezb98+AgMDMTU1Zfny5XTt2pWRI0fq8s8A\nzp07x+DBg0lISGhzEYY1a9bQp08fnnrqKSoqKpg8eTIvvPACe/bs4eLFi2069v2EoaEhc+bMYe/e\nvbfUzy0ZTF999RVPPPHEP1Jzv4PWU1JSwokTJ5g69V/U1qqaP6Gd8PGxY9AgFzp1MqdXL3vGjLm9\nmvzh4VJ++imNCxfyb9guL68KqbTp8B9HRzNGjnRn+HA3eve2x8vLukUCD9cwNze8bl6RlZURdXW1\nfPvtM1y48Cuxsd+RnPwjERGrsLGxZ8GCbfj5uTJokMt1PVn1+Vkd93cHTbNlyxb69OnD+vXrdcds\nbGxuKZwkMjKSvn373hbVTVtbWwBGjx6t8+b8XWbWyEgfExODJhfwAwcOZN68eRw5coQDBw7g5eXF\n/v376dGjB999952uTtD06dMJDAzkk08+ITExUXe+oaEhe/bsAeCPP/5g3LhxDd6X+vr6WFhYcPny\nZcLDw2/5etuCqqoqbGxsGD58OBqNBo1Gg6+vL6mpqRQVFZGamkpAQAAxMTFUV1cjk8muW/PIysoK\nT896+fbHHnvsthbptbW1ZdiwYZSVlREbG3vD3eTs7Gy8vLwYNGgQPj4+ODo6Mnr0aAICAggMDMTZ\n2ZmkpCSioqKIiYkhOjqa+Ph4ampq7hpjtgNYtGgRjo6OTJs2DSMjI10dMYANGzYgl8sZNmwYGzdu\n5Nlnn6WkpISffvqJ06dPEx8fz9KlSwkODiY6Olp33rRp0wgPD2fRokUUF7cs17i1mJiYsGHDBoqK\ninT3/5tvvolWq+XEiRNtOvb9hoeHB9bW1iQkJLS6j1a/kWJjY3FwcOhQ9LgPOXEi+P+KJHZh795k\njh7NoKLixvk47YGBgR4zZ/bk9deHsHx5IF263F5PybUF1o02SuPjizl+PJOff84gNbVxntE1One2\nZPBgV4YO7YSra8vDVAwN9enZs2nvkL6+HmVl+6iszKGkJIGuXf+UfF65cg+9enXF0dEUPb2mL0BP\nT0Lv3h3Jpx1cn4yMjEY5Hm5ubjcM22qOp59+miNHjjQwPFrL5MmT6devH0lJSbz66qt89913N93H\ntGnT0Gg0jB07lvT0dF0Ry7+za9cuLCwsmDp1KmVlf97rcnn9szE8PByNRtPgHE9PTy5cuEBlZSWf\nfdZYrfJuwNbWlqqqKtzd3XF2dkYIwY8//ohUKqW4uJiBAwfqivW+8MILKBQKpk6det3+nnrqKXbs\n2EFJSQlTpky5qULEzSGRSOjRowddu3YlJCREJ57xd7p3705paSkXLlxoVBsL6guH9uvXj8DAQAIC\nAhgwYAD9+/dn2LBhpKSk3Lb5dtB6rl69yp49e5gwYQJfffUVFy5caFAgOiYmRvffr732Gps2bWrw\nnPL392fJkiUAnDp1qkHbBQsWsGfPHnr27HnLXonmuFa/8poH3NbWFhsbm47fWRswZcoUnUppa2iV\nwVRbW8sff/xx30hJd/AneXlVlJbao6enT0ZGAjKZkvz8ao4ezUShULf39BqhpyfBxsYEE5PbXzxw\n0CAXZszwISjo+jVh/lqYtqkitdeQSCT07etEnz6O121zPQYPdm1kDNZfbxLBwT//X5uJWFjUt9my\nJRQrq+aTH0eM6KzL1eqgg6bw9PTkl19+0XlRAJ0SW2vIzMzku+++0xVJvVVsbW0JDQ0lJSWFDz74\n4Lrysmq1usFLVAhBUVERISEh7Ny5U3d88eLFfPLJJ0324ebmxsGDB8nNzWXu3Lm6hfgjjzzCypUr\nkclkjBo1hpycnAbnXbx4ESEEs2fPvtXLbTMsLS2RyWS4u7szdOhQZsyYwZgxY+jTpw9Q70nT09ND\nT08PJyenG+aeGRgYsGDBAl577TXkcnmb7OJbWVkRFBSESqUiNDSU7OzsBh4nAwMDevXqRe/evZtc\nmBYWFpKRkdHIS6Wnp4eDgwPh4eFERkZSU1Nz2+feQcswNDTEzc2NkJAQxvvzVgAAIABJREFUXF1d\nCQwMbPD5rFmzCA8PR61Ws2bNmiY9g+7u9VEncXFxumOdO3dmx44dxMXF4e/vz1tvvcVbb711y/kv\n16NTp04YGRnpDKbc3FwqKioaXU8Ht4eFCxfy+eeft+rcVhlMn3/+OYsWLWrVgB38s4mLK8bMzApv\n7z6Ehf1CTEwBV65UUlurIjW1vL2nd0eRSCTY2ZneMBa/b19HrK2NsbMzwc+vbbw1+vp6jBvnyaxZ\nPRg2zA1XV3M6ddJnwYI/F2AbNjxNfPwZHnzwKYyNb1zwViKRMGJE51tWFOzg3mfjxo1IJBLmz59P\nZWUlUJ/Yn52d3ar+Nm3aRGlpKefOnbuuJ+dmMTExwcfH54ZtxowZg5mZGTY2NnTp0gULCwtcXFwI\nCgri6NGjAJibm7NmzZob5rAMGTKEt99+m2PHjukW4hKJhNGjR9O37zBCQi7w4osrGpxz7tw5oF5K\n/W7F0dGxQf0mS0vLBoINEomE/v37M2TIEGprazlw4ECzfbq4uAD1xsk1VCoVKpWKjIwMoqOjyczM\nvGFdqubw9PRk6NChGBsbExUVRVRUVIPrsLGx0eViXUMIQWZmpk4u/O907dqVwYMH6wQxOmgfbGxs\nuHDhAt7e3kyaNImJEyfy9ttv63Krr5U1CA4Ovu5vqLq6PlS+tLSUrVu3snnzZoqKirh48SJ1dXVs\n2bKFMWPG8Pbbb/Pss8828hDfDnJzc5FIJDrjOzMzE+CWFd06aBpLS0uGDRvGL7/8ctPn6v/vf//7\n382ccOrUKbp3766L3e7g/iIkpACNRlBZWUJc3Clsbbvg6dkdGxtjTEwM8PZufbL3vYiZmSF+fg74\n+jq0iZfrr5iYGBATU8S5c3mEhBTj6upNevp53eezZs1l/vzXqalpOu9MIpHg5WXNqFGd8fLq+Dt2\n0DweHh44OTnx008/MW/ePBwcHKiqqkIIocsfaglVVVV88803bN26laFDh/Lf//63DWfdEIVCwZIl\nS3Bzc2PatGk4OTkxevRo5syZg0KhIDs7GzMzM9auXdsi1T4vLy82btxI586ddTWLZsyYQXx8FCAw\nNTVn0aI/ZY4HDRpEZGQkmzdvxsrKiqFDh7bVpbYapVLJ1atXmxU86Nu3L/v37+fEiRO4ubnh5OR0\nXSVVrVbLtm3b8PX1ZciQIVRUVBAbG4tMJsPOzo5u3bohhCAxMRGZTIa9vX2rhSIsLCxwcnLC3t6e\nxMTEBrWsCgsLdcIcSqUSpVJJRUUF3bp1o7CwELVa3aSIiUQiIS8vD1dX19sqYNFBy7G0tGT27NlU\nVVWRlJTE999/T1lZGY888gj5+fkMGzaMrVu38t133+lEOywsLHQ5dhYWFhgaGvL9999z+PBhTp48\nycmTJ3FycmLKlCk4OzszefJk5HI5mzdvJjk5malTpzbKg2wtWq2W6dOnU1RUxN69e7GxscHV1ZW9\ne/cSFRXVogLVHdw8bm5unD59Gm9v7wZlEJpF3ASVlZXi448/vplTOrjH+P77FLF9e5z417+WCUC8\n9dZRsX17nNi+PU6EhRW0ut+QkHzx88/porJSfhtne29QXl4nsrIqRE2Nstm2q1cHi3//+6j497+P\niqlTtwhAAMLY2ESoVCohhBClpbXiwoU88csvWeLo0XTx669ZIipKKqqrFW19KR3cg2zZUv87y8vL\nEwqFQvzwww+iurq6xefX1dWJgIAAAQgPDw9x+vTpVs1Dq9WKkJAQcfToUXHkyBGRmpoqhBBCrVaL\nH3/8UQQFBYmuXbuKxx9/XOTk5Ij09HQhhBAKhUI3vpOTk1i1apVIS0sTWq1WDBw4UAQGBt70XIKC\ngoSnp6dQKpXiwoULwsHBQQDC2dlF7Ny5s1F7pVIppk6dKoyMjHTzak/UarVITk4W0dHRIj09XVRU\nVIhLly6JwsLCZs89ffq0sLS0FICQSCRi2bJlQqFo+tkyZMgQ4evrKy5duiRCQkKEVqttsl1FRYWI\njIwUUVFRIj4+/rrtrodWqxUXL14U586dEydOnBBhYWEiOTlZyOVyERUVJZRKpcjOzhbnz58XSUlJ\nora2VgghhEajEdHR0bp+CgsLRWhoqIiKihLR0dEiODhYREVF3dRcOmg7lixZIgDRr18/4evrKyws\nLMRHH30kBg4cqHsX2tvbi/z8/Abn5eXliYULF4rPP/9cjB49WgDigw8+aPA7W79+vQDEY489dtO/\nv7+ya9cuMW/ePJGdnS3WrVsnALFr164Gbfbt2ycA8fXXX7d6nA5ujEKhEBs2bLipc27KYNq0aZPu\nQdLB/cnFi8Vi+/Y4MWfO6wIQH354SmzfHid27IgXFRWtM3Zqa5U6oys6uvkX8r2IUqkWYWEFIiys\nQCiVat3xjIxysWNHvNi+PU7s2XNRlJXVXbePqiqFeOedEPHEE8fE7Nk/C3//BbqXxPffx92Jy+jg\nPuTJJ58UgOjVq5fw9/cXrq6uQk9PTwwePLjRArusrEyUlZWJ6upqIZPJRFlZmXj66acFIL799tvr\nLkRUKpXIysoSp0+fFnv27BEfffSReOutt8T/Z++8w6Oo1j/+3WwaBFJIoYeE3kNIIHQRrnCpiiAo\nIiqIKCiCF9vFhl4LoBQRBZF2VVSKICoXBQ3pZXfTSe892SQkpGd35/v7g1/msqYnmyJ3Ps+zz5PM\nnPOed3ZmZ+Y95y0vvPACt23bxuPHj9PNzU283gHQ2NiYc+bM4aBBgwiAQ4YM4cKFC/Xa/PrrryRv\nv1Bfu3aNS5YsoUwmIwCOGzeOU6dOpZmZWYtfkC5dukQAPHjwIC9fvkwAfOihhxo1JLOystijRw8u\nXryYxcXFVKvV4iTHnd/frVu36myvRRAEXrhwgaGhoRQEoV69y8vL9f6vqqpiaGgolUql+Pnxxx9Z\nVFREnU7Hmzdv8saNG/Tz8+PVq1ep1WrryPwzNTU19Pf359NPP00AnDhxIt9++21+8803DAgI4A8/\n/MBXX32VcrmcAwYMYHZ28yfbSkpK6O3t3ex3kYqKCv7xxx9MSUnR+z5KS0sZFhZGLy8vBgUFMT09\nnRkZGXX6+/j4sLq6mhEREXrGU3V1NUNDQ3nlypVWGXEShqeyspIHDx6ku7s7TUxM+PPPP4v7fvvt\nN65YsYIAuGLFigZlVFVVcdasWQTAnTt36u17//33CYAfffRRq3VcvHgxAbB79+40NTXlsmXL6lw7\nOp2Oo0aN4uzZs1s9jkTTqFQq8RnQHJptMHl5edHPz69VSkncPQiCQC+vdD755HsEwHfe+ZHHjkUw\nIaGoTXL9/DJ56dL/7gpTQECWaDQGBPx39uuHH+LF7UeOhNHXN7NBGRkZt3jkSBg//TSEu3cHceHC\nTwmAU6as4Fdf3eiIw5D4H2Tv3r0cO3Ys77vvPjo7O3P//v18/fXX///+8A5JMj8/nw899JCesXLn\n57XXXqsjNz09ne+++y5HjRpFuVxeb7+ePXvS3NycAOjk5MSjR48yODiYQUFBfOqppzhp0iQ+9dRT\nPHfuHLVaLfPz8/X6+/v71xk3OTmZn376KQcPHkwAvOeee1r8neh0Ok6bNo0AeP/999PCwoIeHh5N\n9qudca79DBgwgBcuXCBJnj59Wm/fgw8+yMLCQr3+H374obi/f//+BMD169fzl19+YXBwMIODgxkZ\nGcng4GCqVCoqFAqGhoaysvK/EzHFxcUMDQ2tV7+ysrIG9zXEuXPn6OzsXOfcmZiY0N3dXbxGWoJW\nq2VAQADT09NZWVnZqLFSVlbGmJiYFo9RS2VlJT09PRkeHi6ucimVSoaFhbGq6vbzqri4mD4+PtKE\nchfizmv6xIkT4nXXu3dvvv/++w32Ky8vp7GxMWUyGXfs2KG3TxAEPvDAAzQ1NWVOTk6r9Fq0aBEH\nDx7MBx98kCNHjqRara633erVq+ns7NyqMSSaz6FDh1hcXNysts1yxCwvL0dYWBi2bNnSnOYSdzEy\nmQyzZg1EVtYInDgBjB5tgSVLRsPUtG21eqZNazjT3P8CdybgufNvExP9vCx//v9OrKzMxDZWVmaY\nPHksLl+WwdbWFtbWTRfBlZBoDdu2bcO2bdvqbA8ICMCBAwfg7u6OTZs2ISsrCy+//DL69u2Lmpoa\nyOVyGBsbw97eHqtWrRL7kcSuXbvw+uuvQ6fTYfbs2XjwwQfh7OwMJycnDBo0CLa2trC0tIRcLkd5\neTnCw8Ph7u4OU9P/1hObPHkySCIkJARubm4Abicv+P333zF37lz84x//qDdeyNnZGZs3b8b69etx\n8uTJVmWrMjIygqenJ/bu3Yt33nkHlZWViI6OhiAIjdaX2rp1K+zs7FBUVAS5XI7jx49j2bJl8PDw\nQGJiIgYNGoTnnnsOWVlZOHToEFxcXLB06VLk5eUhNzcXfn5+WLVqFe677z5s2HA7Tmrw4MGwt7fH\npEmTmtQ7Ly8PmZmZcHV1rXe/hYUF5HI5bt26BUtLy2Z9F8uXL8fy5ctRWVmJ5ORkJCUlwcHBARMm\nTGh1JkS5XI4pU6YgMzMTSUlJqKysbPA8lZWVtamml0wmg5GREQYOHAhra+t6Y0qsrKwwZcoUqFQq\neHh4tHosCcNRe22RxJEjRzBq1Ch8//33GDt2bKNxQd27d8fGjRvF+Lo7kclk2LNnD3788UccOHAA\nH3zwQat0s7GxaTIxyuDBg/H9998jMjJSzEYpYXjWrVuHzz//vN5nWB2aY1UdPHiwRT7pEnc/MTEx\nBMC9e/d2tip3BdXVWvr5ZdLPL5PV1f91eSkoqOBXX93gkSNhPH8+jlVV9bvi1PLbbyniatRbb537\n/xnmD5iWVtLehyAhoUdCQoLeisLVq1cbbFtZWcnPPvuMa9eu5ahRowiAK1euZHJycpv1+HOMSU5O\nDgFw9erVHeJGdfPmTSYlJbG4uJh+fn7MzGx4lfjP1NTUcM+ePRw3bhxXrFih5+WhVCo5ZswY2tjY\ncOTIkbznnnv43HPP8fr16ywvL2diYiKtrKy4YsUK3rx5s1njlZaWNrkac9vLwKvO6lZnEhwcXGdb\nQkICFQoFIyIi2nSeo6KiWFBQ0Ky2CQkJvHHjRoMxWxIdS0VFBVetWkUALYpXqa6u5owZM9itW7d6\nV1RXrFhBKysrlpQ0/lwVBIGJiYk8c+YM9+/fT09PT86ZM4dubm5N6pCTk8O+ffty+PDh1Ol0zdZd\nouUEBATQ09OzyXZNGkxhYWG8cuWKIXSSuMvw8PDgmDFjJN/tdkanE1hR0XTCB5LUaHT08krnl19G\n8MEHtxIAL1zwbF8FJSTqISAgQM9gGj9+PPft28fXXnuNe/bsoVKppFarpVKp5OjRo0V3mQULFvDI\nkSMGu6/UF5S/adMmAmB0dLRBxmgOarVa/C5efPFFbtiwgVu2bOFHH33UJj0EQeAHH3zAl19+WZRf\nGyy+ZMkS9u/fv0XympPEQBAExsbGMj4+vlU6G5rQ0FAGBASIE7s6na5eI6o1CILQonCEmzdvMiws\njAqFggqFotnGloTheemllyiTyeokcGgOubm5dHBw4NKlS+vs8/HxIQCePn263r7Xr1/n7NmzaWVl\nVa8r8aRJk5qlw44dOyiTySQDvAPYt2+f6GLbEI265AmCgKtXr2L79u0tXeWS+B/gySefxDPPPIOI\niAi4uLh0tjp3LUZGMnTrVrfoXn0YGxth1qyBSEy8hgsXDmDBggVYurTpVMgSEoZmz549sLa2RkZG\nBvz8/LBy5Ups27YNxsbG0GpvF7k2NzeHXC6HlZUVLl++jAULFhhcD9ZTcLLW5aw2vXBHcOPGDfHv\nvXv36u3bvn07goODm+U2V0tVVRUef/xx/Pzzz+jfvz+Kiopgbm6OqqoqsUBvZmYmRo4c2SI9m5PG\nWCaTYcSIEVAqlS2S3V7IZDJMmDABSUlJqKiogEwma7L2Vktk9+nTB2q1Gvb2TRcWt7a2hrX17SLh\noaGhiImJwYgRI5rVV8KweHl5YdasWXjllVda3Ld3795YtGgRLl26BJLi7yIhIUGszVZ7nu9Ep9Nh\n/fr1qKysxCOPPIKJEydi4sSJ6NevH8LDw6FQKDBx4sQmx8/IyMDVq1cxYMAAPVdjifZh7dq1+Pe/\n/y26MtdHowbT2bNnG6yMLiFR6yNsqJoEEm3j5s2b+Omnn3Dx4kX8+OOPmDt3Ls6fP98m/30JidYQ\nEBCAH374AW+99RZ69OiB+fPnIzs7G9XV1bC2tkZeXh6uX78OlUoFCwsLPP/887CzM3xh5ztfdGoR\nBAF//PEHZDIZHBwcDD5mQ8yaNQtXrlyBQqHAvn37UFRUpLe/tohmSzhz5gzMzc0xbtw4pKamIiMj\nAxcuXBBfsCZNmiTWoGnOfaC6urrZL2dpaWl6xWs7E51OB3Nzc4wZM6Zd5Ofn56N///rjbIuLi2Fh\nYQETE5M6fbp37w5XV1eEhIRAEIQm61hJGJakpCQAtycoNm7c2OIJkpkzZ+LEiRO4evUq7rvvPnz0\n0Ud45ZVXQBIuLi71TnAcO3YMSUlJOHv2LFasWCFu1+l0GDZsGEiKMZW1BAUFISwsDOXl5SgrK0NO\nTg5OnjwJkti3b18rjlyipfTq1QuWlpZISUmBs7Nz/Y0aW3765JNPDLTYJXE3sm/fPjGd8IoVKxgb\nG9vZKnV5qqo0VKvLqdMZ1o3x2rVrtLGxEbNjbd26VYo7lOgUBEGgh4cH+/bt26xrsLS0lFFRUe2i\nS1RUFPPz8/W2bdy4kQD46quvtsuYzaG6upp+fn4MDw9nZmZmq7Orfffdd7SwsCAATp8+nVevXtVz\nPXrqqac4dOhQ+vr6NkteZmYm09PTm9W2K9UfujPdd3vQkHtfTk6OmDHvzrTsSqWSERERem1DQ0Nb\nlD5dou34+/tz7ty5BEB7e3v+8MMPLepfWVnJwYMH09LSUny+PvTQQ0xNTa23/bvvvksAnDVrFjUa\nDSsqKvjuu+9y9OjRNDIyEl3y7rvvPrGPSqUSSxnUfszMzPjII48wJSWlLYcv0UJ0Ol2jdk+jBtPR\no0cNrpDE3UNlZSXfeOMNLlmyhFZWVrSysuL8+fOlF/UGiIpS88svI3jkSBi/+SaaBQVtT0Hr5eXF\n119/nXK5nGPGjGFAQIAUICrRaZSWlvKZZ56ptxhjY7THy3dqaiqTkpLqbHdycqK7u3udekR/VSor\nKxkfH18nRiMvL4/m5uZct24dfXx8GqzdRN5+UQgNDWV4eHiz7x93u8Gk0WiYm5vLwsJCBgYG1tsm\nLCysRc+7huRItC++vr6cOHEiu3fv3uKJXS8vL95///189tln+eWXXzb4+1AqlaLBs3//fn788cd0\ndHQUay7V7qstkeDn50edTscpU6bQwcFBLBDd2O9Uov1pzO6RDCYJg6BUKsUAx0WLFrGmpnlJCroa\n5eU1DAnJZWysYTNAVVTUiAVoaz8//ZTYanklJSVcvXq1eBNesmRJkxl7JCTaC51Ox2+++YaDBg2i\nTCbjCy+80KwCp7UoFAqD6xQaGlrvy8eWLVvEOkBd6aXf0Bw8eFDMTtjYykZ6erpewoTm4u/v32Xu\n8wEBAQZPPlRdXc2ffvqJycnJvHXrVr1tautBNZe7+Xrr6mRmZtLW1paurq5NBve3lKqqKnHl+s6P\nvb09AdDZ2Zn3338/jxw5wt69e9PExIQWFhZ84IEHCIAnTpwwqD4Sracxu0cKbpAwCG5ubkhMTMTa\ntWvxyy+/4IsvvuhslVoMSfz0UxIUilx4eWVApco1mOySkhoIgn7weVFRVatkCYKAp556Ct9//z3e\neecdqNVqXLp0qdl1USQkDElkZCTc3Nzw6KOPwtraGt7e3ti/fz/k8pbVZmM9yRnagrW1dZ04IQB4\n8803ceTIEfTo0QPPP/88wsLCDD52V+D333/HkCFDYGFhgb59+9bZX1lZievXr4MkpkyZ0uL4Djc3\nNygUCkOp2yYGDx4MX19fg8o0NTXF2LFjIQgCevbsWW8buVwOBwcHZGdnN0umg4MD0tLSDKmmRDPp\n378/jh8/jtDQUDz33HMG+82HhITAyckJR44cwfLly3Ho0CH07NkTcrlcrON07tw5eHp6YuPGjcjP\nz8fDDz+MxYsX4+LFi5g2bRrWrl1rEF0k2hfJYJIwGHZ2djh58iRmz56Nf/3rX52tTouprtahpKRa\n/D8/v8Jgsu3susHMTP8FcsCA+h/CjRESEoJp06bh7Nmz2LlzJ9544412CZaXkGgOubm5WLhwIXJz\nc/HNN98gJCQEM2bMaLGcXr161WvctAVHR0ekpKQgPz9fb7utrS2efvpp7N27F8nJyZg1axbGjBmD\nt956Cy+//DIOHDgAlUoFQRBaPfbNmzdx8eJFschlSkpKm+S1lLKyMly/fh1Lliyp98UwNTUVUVFR\nMDc3h6OjY6vGMDU1xbBhw6BUKjv02OpDo9HAycnJoDJzc3OhVqsxcODARtsNHjwYqamp0Ol0Tcoc\nOHAgSkpKUFhYaCg1JVrA0qVLsWPHDnz55Zd47bXXDCJTo9FArVZj7NixOH36NDZt2oSYmBjMnz8f\nXl5eAG5nFL516xa2b98OBwcH/OMf/8B3332H+Ph4/Pzzz1Jipr8KrV2akpBoiGeeeYZOTk7Myspi\nQkICw8LCqFKp6OXl1eXja37+OVF0mYuLM6xbXk5OGc+fj+OxYxG8ejW1ySK0d1JRUcHNmzdTJpPR\nwcGBp06dkupfSXQqgiBw+vTp7N69e73FHVuCRqOhj4+PgTTTJzg4uEF3s8jISH7yySecNWsWZTIZ\njY2NRXeawYMH87333mtxoP5PP/1ES0vLOu45vXr1opeXlyEOqUl+++03AuDly5d5+fJlsXCtRqNh\nYGAg09LSSJLx8fFtLkBbUlLCwMBARkZGtsgN05CUlJQwMjLSoDKDgoKafY+tqKhgQEBAk8k7CgoK\nqFarJde8TkQQBD777LMEwPfff98gMo8fP04AvPfee+np6UlBEMQ6dDNnzqSRkRGNjIxYWlpqkPEk\n2g8phkmiQ1m7di0nT57M7OxsvSDG5ORkBgYGdulMQTqdwLS0EqrVXScgPD09nW5ubgTA559/Xnz5\nkZDoTGJjY8UA57ZQUVHBjIyMdguILysro4+PT524BUEQGBwcLManFBYWsrq6mhkZGTx58iRnz54t\nBmkfPny4WWN99dVXlMlknDhxIr29vcWEAV988QWHDh1KBwcHbtiwgaGhoaysrDT4sdZy9OhRWltb\n89SprxgeHsXY2FgGBQXR399fb1ydTsegoCCDjHnr1q1OnRQLDg42aMB8S+PqtFotVSpVvYlGaklI\nSGBwcLBosEp0Djqdjo8++qiYLdMQk4+HDx8WY5Y+++wzxsXFEQC//vprpqWl0dvb2wCaS7Q3ksEk\n0aFs2LCBffr00dt29OhR/vDDDxQEgampqQwMDGR0dHSnzUj+VcjOzqaTkxN79uzJS5cudbY6EhIi\ntWUF4uPjW9U/NTWVAQEBjIqKYm5ubrtmh9JoNPT399fbplQq66Qc/zPx8fGcOXMme/Xq1WDgfy3V\n1dXs378/PTw86l1pCA0N5YwZM9itWzcCoEwm45gxY5iSkkJBEKjVag22avzMM89w9OjR/OSTazx4\n8D8sL6+bnKGoqIgqlYq+vr5UKBRUKBRMSEhokw5lZWWdtnpy69YtRkdHG0xeS1aYyNsGuEKhoFqt\nbrSNlCmva6DVasVEDY8//rhB7j8VFRV0dnbm8uXLqVarCUAqz/MXozG7R6o4KmFwunXrhrKyMrFo\n5G+//SZWT7a1tcXq1avx8ccfo7KyEqGhoQCAESNGNBhYm52djdTUVLFQLnA72NbFxaX9D6YTqaio\nwN///neo1Wp4enrWWyRPQqIzIIkvvvgCHh4eGDZsWIv6ZmVlIScnBzY2NpgyZUo7aaiPsbExTE1N\nQRJarRahoaFwdHSEvb19o/2GDRuGjz76CB4eHnjjjTfw6quvok+fPvW2zc3NRVZWFrZu3Ypu3brV\n2T9hwgT4+PggKysL169fR2JiIj799FMMHz4cGo0GAGBjY4MlS5bgwQcfxPz58/XueWlpadi+fTvi\n4uKQl5eHoUOHYtGiRXjuuef0Er6QxOHDhzF79r3QagtgamqN2tq9RUVFYjFPGxsbjB8/Xq/weEFB\nAYKCguDg4ABnZ+c6RX+bwsLCAnK5HNXV1TAzM2tR37bSs2dP5Ofno1+/frCysmqVjNjYWLGAsKmp\naYuOPykpCQMGDGg0plQmk8HCwgK3bt2SkvR0MnK5HJ9//jn69OmDnTt3Yu7cuXjsscfaJLNbt24Y\nN24cwsPDxXuAoWMzJToPyWCSMDhOTk4oKytDQUEB7O3t8fXXX8Pe3h4vvfQSAgICcPDgQRQVFeHr\nr7+Gu7s7dDod4uLiUFlZWe8DSqPRYOrUqXr74uLikJqaavBA367EV199hYiICFy6dEkyliS6FAqF\nAjExMTh27FiL++bl5cHNza3FL+NtxdraGsXFxYiLi4Orq2uzX+gnT56M5cuX48CBAzhw4ADGjRuH\nVatW4eGHH8aQIUPEdgMHDsSwYcNw7do1bN++vUF5/fv3x6OPPgoAGDlyJCIiImBsbAy5XI7ExERc\nunQJ//73v2FpaYkHHngAjz76KBITE7Fz505UVVVh9uzZmDRpEiIjI7Fjxw7s2LEDY8aMwbJly+Ds\n7IyAgABYWlpi+fIHMXZsXwwYMBDdupkAuJ1hUy6XY+LEifXqZmdnBzs7O+Tl5SEwMBBOTk71Zthr\nDCMjoxZnSDQUU6ZMQXp6OuLi4jB58uRm9wsLC4MgCDA3N4e7u3urxu7VqxeKi4ubbDd69GiEhYWh\nW7duGDlyZIf/DiT+i0wmw1tvvYUTJ07g+++/b7PBBAALFizApUuXMHjwYADAqFGj2ixToovQ2qUp\nCYmGuHz5MgGIPruLFy/m+PHjSd52SbC1teWKFSvaPM6NGze6dDyeVrISAAAgAElEQVRUWxk/fjwn\nTJggJXeQ6HJ4e3sTAK9cudKifiUlJbx27Vo7adU4VVVVjIiIoEqlanGcjVarpUKh4O7duzljxgwx\nkcPkyZN54cIFCoLAsrIyjhw5kkOHDm223ODg4DrbampqeOXKFT755JO0trYWx5oxYwajoqL0dLKz\nsxMDy2UyGQHQyMiIr776qlh4NT09XU9+dnY2w8PDm6VfXFwcIyIiWnQPysjIaFFtovYgODi42Tqn\npKQwIyOjzWOWlZUxIiKi2e3VajV9fX0bdeGTaH+8vb3p4OBAOzs7g8k8c+YMe/fuzTfffNNgMiU6\nBsklT6JDGTlyJIDbq0AzZ85EWFgYZs2aBeC2u8jNmzcxYsSINo8zevRoBAYGtngGtD2JjIzEuXPn\nkJ6ejvLycgwZMgS9e/dGRkYGUlJS0L9/f7zyyisYMGBAo3IEQUBERATeeOMNaQZSostR64aXkJCA\n+fPnN9n+5s2biI+Ph6WlJWbPnt3O2tWPmZkZKioqUFlZ2eI0vnK5HO7u7nB3d8dLL72E9PR0nDlz\nBkePHsWyZcswdepUaDQaMU1wWzAxMcH8+fMxf/58fPbZZ7h8+TJsbGwwe/ZsvXuBXC5HdHQ0qqur\nMWDAAFRUVCA/Px8mJiawsLBAUlISNBoNhg4dqie/b9++uHXrFpKTk8VZ8IYYPnw4CgsL4e/vj0mT\nJsHU1LRJ/QcMGIDc3FwIggAvLy+UlpZiyZIlHXYfI4mamppmty8oKGj1qtKdREREwMPDo9nt7ezs\nYGtri8TERCQmJmLUqFHo2bMnSkpKYGNj02Z9JJrHt99+i/z8fBw/ftxgMh966CGsWLFCenbfZUgG\nk4TBcXR0hLm5OaKjo1FcXIzMzExxWdrIyAi2trYoKCgwyFjW1tYoKytDjx49DCKvMQRBaPBF6/Ll\nZAQHB2LnztuuNv3790f37t1x8eJFaDQadOvWDYMGDcLPP/+MkydPQqFQQCaTYffu3UhISMCbb76J\nv/3tb3pjAWjWC4qEREfTu3dv9OjRA4mJiU22FQQB0dHRmDZtWqe/QFRUVGDq1KltluPo6Ijt27dj\n69atOHHiBN5++21oNBqcPXsWCxYsaLacpr4Pc3NzPPjggw3ut7e3B0nk5uaiT58+ei7K8fHxkMvl\nenFQwG2DoqCgoNnfg62tLSZPngyFQoFhw4Y1GvdFEvn5+YiLi8OFCxdw4MABlJeXw8XFBcnJyXjh\nhRfw7rvvNmvc1hIbG4vhw4c3+1ozNjaGVqvVi+VqKWlpaRgwYECLDXGZTIZhw4Zh6NChiIyMRF5e\nHgYOHIj4+HjY2dnpuXxKtA9vv/02fvjhB+zYsQMajQbr1q1r07VQS2ff6yQMj1QtS8LgyOVyjBs3\nDiEhIbCyssKIESNw4cIF0QgYMWIEgoODDTKWra1tnaDKgoIChISEQKlUorS0tNWyCwsLcezYMdx/\n//3o168fjI2NMXfuXLz33nt48sknsX79ehQXF0OrFZCdXYbKytuB24cOHUJmZibi4+NRUVGBgoIC\nlJeXIyYmBjExMTAxMcGECRMwatQofPvtt0hLS8OyZcuQm5srji2Xy2FmZoaSkpJW6y8h0V5cvXq1\n0ZWa6upqZGRkQKlUQqVSYcKECV3iBWLIkCEG/U0ZGxtjw4YNSE9PR25ubqPGTX0YouDrRx99hL59\n+8Lb21tvu42NDdLS0vS2KRQKhISEYOTIkS16uTcxMcG0adOQn5+P2NjYBtslJyfj5s2bOHXqFPbu\n3Yt+/frh448/Bkk4Ojriww8/RHR0dMsOsAWQRGRkZLPbZ2dno7i4WEy60VpqDZ22UFJSgnvuuQcj\nR46Eh4cHLCws4Ofnh/Ly8jbJlWgcBwcH/PLLLxg0aBA2btyITz/9tLNVkuiqtNaXT0KiMV5++WUC\n4Jo1a3jixAkC4JEjR0iSu3btIgCD1KKoqamht7e3mBK0oKBATGur0+kYHBzMvLy8Fsv9/fffxZoK\njo6OfPzxx/niiy9y0KBBBMC+ffvSxMSEU6ZM4fXr1/nJJ0dpbn47XfD58+cblf3TTz/Rzc2NO3bs\nYH5+PuPj42liYsJ169bptRs2bBhXrlzZYt0lJNqTsLAw9uzZk+PHj2dJSYnevtpaa5GRkczLy+ty\nhaprUz93FSIiIposdtoUp06dEuOc7mTz5s2cOnUqIyMjKQgCExMTmZqa2qaxSDInJ4cBAQF10jAL\ngiCmbv/kk08IQC9GS61W08bGhvfee2+7xmUmJibSx8eHwcHBVCgUTEpKYkhICJVKJUNDQ3n9+nUq\nlUoGBQUxPT29zbqUlpbyxo0bbZJx69YtxsXF1dmu0+kYEhJChULRZAp8Q3BnmY+cnJw68W93M4Ig\n0NnZmatWrepsVSQ6EakOk0SHU1NTw3/+858EwIMHD3L27Nm0srJiSUkJL126RAD08/MzyFhVVVUM\nCgoSH4J/fgDWF1jdEDqdjh988AGNjIw4atSoOsHDWq1WrMfyww8/UC6Xiy8rtR8vL68WH8OLL75I\nmUym94BasGABhw8fLiV9kOgSCILAw4cP08rKigMGDGBmZqa4Lysri/7+/szJyelEDZtHZ9UJqo/S\n0lLGxMS0SUZNTY147/H09CR5+1wBYPfu3enn50eVSmUQY6mWyspKBgcHU6VSUalUUqlU0tPTk15e\nXgwNDaWnpycB8NNPP9Xrd/jwYQLg6dOnDaZLfURFRTE/P586nY6FhYWi4V5TU2Pw2n+hoaGsrq5u\nk4yUlJRGkz/odDqGh4czNze3TeM0RlxcHAMCAhgcHCzWR4uIiGjXMbsaCxcupIuLS2erIdGJSAaT\nRKcgCALvu+8+9uzZk+fPnycAPvXUUwwNDSUAvvXWWwYdr6qqql7jIiwsjFVVVU32PXbsGEeNGkUA\nXLVqFUtLS5scMzk5mdeuXaOnpyfLy8tb/XAJCQkhAH7zzTfitkOHDhEAIyMjWyVTQsJQCIIgToDM\nmTOHiYmJ4r6MjIw2v/R3JF3JYNJqtQwNDW2znFdffZUAaGNjQ0EQmJmZKRpRNTV1i9a2J9XV1QwM\nDOT06dPp5OSkZ0xotVpOmjSJdnZ27ZrhVBAE+vn5saCgoN3GIP9rOLaVtLQ0BgcHs6ysrNF2ERER\nVCgUjIuLM9hEmiAIDAoKYlZWVr37fX19DTLOX4HHH3+cgwYN6mw1JDqRxuweKYZJot2QyWRYtGgR\nSktLMWbMGADAl19+iQkTJuChhx7Crl27EBcXZ7DxzMzM6o2TcHJyquPLfydKpRLDhw/H+vXrYWpq\nitOnT+Pbb79tViIJZ2dnzJ07F7Nnz0b37t3Ru3fvVumel5cHAHr1S2bOnAkAYnFfCYmO5saNG/j2\n22/x1FNP4f3338fTTz+Nq1ev6gWjV1dXN1jMVaJx5HK5QeKYXnrpJQC3sxHu2bMH4eHh4r7MzMw2\ny28JpqamcHR0xMaNG8V4plrkcjlOnTqF8vJyrF271iDHXh8ymQxTp05FUVERAgMDcevWrXYZJzQ0\nFK6urm2W4+joCFdXV6SkpCAwMLDBOLtx48bB3d0d9vb2CAwMRFpaGkg2KlsQBCQlJaGqqqre/bUx\nbf369auzT6fToaKiAiqVClFRUQgNDTVYwqauxtWrV6FSqVBdXd3Zqkh0VVpraUlINMavv/7KHTt2\n0MLCgm5ubiTJNWvWiH722dnZtLGxoZ2dHdetW9fubme1vvV/5qeffqK5uTkHDRrEX3/9tdPc3+bN\nm8f+/fvrzcZeuXKFAHj9+vVO0UnifxdBEPjGG2/ouZpu2bKl3pik+Pj4OrFMXRVBELrUChNJqlQq\ng8j5/PPP67gHA6CZmVmnrFILgkAPDw86OjrWWeE/evQoAXD16tW8fv26wd3k/qyHoc55Tk4OlUol\nVSoV/f3922WVTKfTMSoqisHBwSwvL2+0bXZ2NpVKJRUKhRijFR8fz8jISPr4+FCn09HLy4t5eXkM\nDw8X3dYLCgpYUVHBsLCwZtWgEgSB0dHRzMrKop+fX4evWrY3tTXNrKysuGXLls5WR6ITkVzyJDqc\ngQMHEgBdXV3Fh0qt//rZs2dJkj4+Ppw6dSoBtLvrRHZ2tp4bEXn74WdjY0NXV9dWJYYwJC4uLly4\ncKHeths3bhAAv/76607SSuJ/lY8//pgA+MQTTzAyMrLRFzdPT08xru+vwN1qMOl0Ok6YMEE0lDZu\n3Mjff/+dDg4OHDt2bJuTS7SG2kmf4cOHMzAwUNwuCAJffPFFdut2O1GOk5OTQVzbGsIQ57yiooJB\nQUEkb+vf1rilptBoNIyIiGBQUBCLioqa1Uer1bKkpIQBAQGMjY1lYGBgHddyQRAYEhLCy5cvs7Cw\nsNn6+Pr6sqKigtXV1fTx8en0Z6Yh8fPzIwB+9913na2KRCcjueRJdDjbtm0DADzwwANiEb7169dj\n0KBBOHLkCEhixowZ2LBhA4D/uqS1F3379q3j5nD06FEUFxfj9OnTcHBwaNfxm2LixInw8/PTcwdw\ndHSGXG6Mq1frpmAvKiqSXAck2oWCggK88847WLBgAY4fP46xY8eie/fu9batrq5Gfn4+EhMTm3QN\n6grodLoW18r5q2BkZITdu3cDAFasWIHPPvsMc+bMwcmTJxEVFYWXX365w3WaP38+fvzxR1RUVOCJ\nJ55Afn4+gNsucx9//DHy8/Px3XffAbjtgnzy5Mk6MoqLi3Hy5EmsWbMG27dvb9S9uiF69OgBpVLZ\n6muUJFQqFdzc3ET927tGnrGxMcaNG4dJkyYhPz8fKpVK/Ny4caPeY5HL5bC0tIRcLoezszNu3bpV\nx7VcJpPB1dW12TWeSkpKoFKpUFVVBWNjY5iammLGjBmtOg9dlZ9//hlyubxZRbgl/odpraUlIdEY\nGo2GS5YsEWc7a5MZ1KYU37t3L0kyISGB3bt35/Tp09t9lvrPs4zz5s3juHHj2nXM5nL58uX/X006\nS43mtttTbGwhHRwc6eo6l/n5ZXzzzTfZo0cPPXeb5OTkTtZc4m5j8+bNlMvldVIlR0ZGUqlUMjg4\nmEqlkj4+PoyIiODvv//OwsJC+vj4NJlcpbPRaDQGSbJgSAy1wkTeXj2YMmUKHR0d9VZAtm3bRgC8\ndOmSwcZqCZcvX6apqSnt7Ox4+fLlOvvVajXnzJlDAFy8eDFPnDjBvLw8Hjt2jHZ2dgRAe3t7Ghsb\n09HRsU5K8+aQnZ3dLPez+khJSWFsbGyr+rYHRUVF9PPza9CVMTw8nJWVlYyOjmZgYCDz8/OZnJxM\nlUrF6OhoJiYmMigoqMnMibGxsQwLC6NWq2VqaiorKyvFfaGhoa06D12RCRMmcNasWZ2thkQXQHLJ\nk+g0vvzyS/Hl/qOPPqJOp+N9991He3t7sc13331HmUzG/v37N5ipxxD8uf6Kg4MD169f327jtYSb\nN8vYo4cVJ09ewNOno1leXsPi4gqam3fn4sWP8aeffq43PmHu3LmdrbrEXYJarebq1asJgJs2bdLb\nV1xc3OALY+0Lv0aj6fKpxQVBaFfXr9ZgSIOJJH/55RcC4Jdffiluq6qqoqurK+3t7TvNfTIqKoqj\nRo1qMAuZRqPhG2+8wf79++vd46ZPn05/f3/u2rWLTzzxBAHw3LlzzR73Tte2thx7bGwso6OjW93f\n0FRUVIhp3WtjmNLT01lYWMiwsDCq1WqWlZVREAQmJSVRrVZTEASWl5fz5s2b4r6GCA0NbfS3nJ2d\nrVda4K9Kbm4uAfD999/vbFUkugCSS55Ep7F+/Xqo1WqsXLkS27dvx65du+Dh4YGCggIxQ9KqVauw\nfv165Obmtqtbj7OzMwICApCVlQVBEKBWq2Fvb99u47WE9PQKTJx4H8LCPKFWFyMxsRipqfGoqqrA\nypX3wd3dDUuXLsWGDRuwf/9+jB07FgAwbdq0TtZc4m5h9erVOHv2LN58803s3btX3K7RaBAREYGh\nQ4c22t/Y2BhTp05FcXExoqKiuqSLXlhYGIYPH97ZarQrCxYsgJubG/71r3+hoqICwO0MoocPH4Za\nrca+ffs6Ra8xY8Zg1apVSEtLw40bNzB79my8+OKL4nVibGyMd955BxkZGVCpVHj33XfxzTffwNvb\nG/n5+Xj11Vdx8uRJDBo0CIcOHWrWmPn5+VAqlRg8eDAmT56Mnj17tlr/ESNGoLy8vMtc1926dcPE\niRPh5uYGd3d3uLq6wtTUFCUlJbCxsUFxcTHCw8MREhKCAQMGwM7ODjKZDN27d4e1tTUsLCzqzSpb\nC8lGn492dnZ3Rca8q1evAgDmzZvXyZpIdHWMO1sBibsfOzs7nD59Gmq1GkePHsWWLVtAEkVFRbCz\nswMA/Oc//4Gbmxv69+/fbnrY2tpi6tSpyMzMRGBgIKZPn47k5OR2G68lWFiYwMNjEby9z+HUqTex\nYMH3OHPmZwDAPffcgz59+uDHH38U2wcGBiIqKgqPPfZYZ6kscRfh6emJq1evYu/evWL8YS0+Pj4Q\nBEEvVfWdlJeX6/0/cuRIFBQUICAgAO7u7u0e69ESTE1NodVqO1uNdkUmk2HPnj2YM2cO3nrrLezZ\nswcAMHnyZCxbtgy7du3ClClTOuUFURAEyGQybN++HV5eXvDy8sKECROwdu1aPf0nTpyIiRMnAgDS\n0tLw5JNPwt3dHeXl5Xjsscfw2muvYe7cuRgyZAhGjBiB6dOnw83NDSYmJnrjZWdnY9KkSXrlGtqC\ng4MD8vPzW10+oj0xMjLS0yswMBBTpkxBTU0NYmNjUVVVhcmTJ7dInlarbfC7q6qqatTg+qvwn//8\nB7a2tgZJDy9xdyMZTBIdglwuR9++fZGcnCwGzl65cgVr1qwBAMyYMQPff/89Vq9ejQMHDrTrys+A\nAQNw7tw5+Pr64ueff263cVrCkCHWePjhBUhNfRZnz36O6Ggf7Nu3D/Pnz4ejo6PYLjs7G5mZmfj3\nv/+NDz/8EIMGDepErSXuFi5fvgwzMzM8++yzettJQqPRwM3NTZzcaA52dnawsrKCQqHAsGHDusxK\n7ujRo+Hv74+pU6d2meQP7bFice+992LDhg3Yt28f1q9fj5EjRwIADh06hPnz52PNmjVISkpq04pL\na1izZg0+/PBDXLlyBQ888AD++OMPKJVKPYPpTkpLS7Fs2TLodDrExsbC1dUVmzZtQmpqKsLDw/Hj\njz+KiSRqV1xcXFzg4uKC8ePHo6amBgkJCUhPT0dGRgYqKiqg1Wqh0+lQU1ODrKwspKWlIS0tDRkZ\nGfj73/+OU6dOwczMrF59Bg4ciKCgIJiamorJjLoqY8eOhb+/v/hd1Ca9aK6RU11d3eD3oNPpEBYW\nhhkzZhhS5Q4nPz8f58+fxxNPPKF3P/jss89gY2ODhx9++K4wCiUMRGt9+SQkWsrQoUO5dOlS6nQ6\nDhgwgA888IC4r6qqim+//TZNTU25ePHidtWjqKiI1tbWnDdvXqfVXWqImJgYymQyAqCxsbFevMX1\n69dFv/72jPWS+N9j5cqVHDBgQJ3tmZmZDA4OblH6YfJ2rFBBQQHz8/P5xx9/MDw83FCqtpmkpKRm\np2nuCAwdw1RLfn4+e/bsyXnz5unVzwoICCAAfvrpp+0yblN8++234n2sb9++fOyxx+ptV11dzXnz\n5lEul/OXX37htm3b2L179zop7nNycnj27Flu2bKFM2bMoKWlpSh/6NCh9cZ+1n6sra3p4uLCpUuX\nivF7S5YsaTRluCAI9PHx6XLPjvrIyclhQEAAydtxiEqlUvyoVCoqFArGxMTUOZby8nJGRUU1KFep\nVHZKmnpD88477xAAY2JixG2XLl0Sr4+nn366E7WT6AykpA8SnU5RUREBcObMmSwoKODq1avZv3//\nOu1qb2Dtmcnq4MGDBNDl6rHUEh8fzyNHjtR5YD3wwAPijbwlQc8SEo2RmppKc3NzPvPMM3rbdTod\nvb29mZiY2GIDo6CggP7+/kxLS2N6ejrj4+Pp7+/fJbJqVVRUMDQ0lIIgdImX3va8D3322WcEwA8+\n+EDcVllZSVNTU65atardxm0MnU5HAHR2dmb37t358ccf19tu8+bNBMBjx45Rq9Xy6NGjtLGx4Wuv\nvdaofEEQmJyczIsXL/Ljjz/m119/TW9vb6akpLCgoIA3b97krVu39DK+1XLo0CEC4Nq1axsdIzMz\ns8snPKisrNSrfdUQgYGBdQpSV1dXNzrJ0V5GfkdSWlrK3r17c8GCBeK2wsJC9u7dmy4uLvz73/9O\nR0fHTtRQojNozO6RXPIkOgRra2t8+OGHePPNNzFjxgyYm5tDrVbjH//4Bz766CNx2Xvz5s04ePAg\nli1bBj8/P/Tr18/guhw/flwMlu2KDBs2DMOGDauzXaPRiH8rFAosX768I9WSuAsJDw8X3Wq2bNki\nbtdoNAgKCoKrqytyc3Nb7L5mYWEBS0tLPXdSR0dHBAUFYeTIkbC1tTXMAbSCbt26wcTEBMHBwdBo\nNBg3bhysrKw6TR8jIyMIgtAuLoLPPPMMvLy8sGPHDkydOhX33HMPzM3NsXXrVuzZswcjRozAli1b\nOvR8GBkZQa1WIyQkBE8//TT+8Y9/QCaToaioCHK5HN27d0dRUREOHTqEF198EevWrYNWq4W7u7te\nDNbSpUvrlS+TyeDs7AxnZ+cW67Zp0yakpaVh9+7deOWVVzB69GhUVVUhKipK7/yQhFarbdeY27ZS\nWVkJrVaLwsLCRs+vXC6vc+2p1epGXXDZRRJftJaioiIsXLgQarUar776qrj966+/Rl5eHi5fvox/\n/etfHe6yKtHFaa2lJSHRGry8vESXifHjx9dbG0ShULBHjx4cO3aswV1nCgsLCYDvvfeeQeV2BMXF\nxeIK09tvv93Z6kj8xSkqKuLgwYPZr18/JiYmitvLy8vp7e0tuiXFx8ezpKSkRbIFQah3FloQBEZE\nRHSZmja1eubl5XWaDlFRUfWudhiKW7duEQD79Okj1skqKSnh0qVLCYBOTk6Mj49vt/EbY/fu3XR1\ndSUAGhkZ6bnL3XvvvaypqSFJZmRk0NfXl8XFxeJzY9myZdy3bx8jIiIMulKoVqvZvXt3jh8/nqdO\nnaKnp2eXWBltDYIgMCgoqN59ZWVl9PX11XPvDg8PZ2hoKL29vRv9TpVKJaOioqhUKjvt2mkt2dnZ\nHDt2LE1NTXnhwgW9fXPnzuXo0aNJkrNmzeLMmTM7Q0WJTkRKKy7RZZg1axZ+++03mJmZwcbGBsOG\nDcNrr70GnU4ntnF3d8fFixcRGxtr8Or0wcHBAIApU6YYVG5HYGVlhdmzZwMAXFxcOlcZib88b7/9\nNtLT03Hu3DkMGTIEwO2ZY4VCgenTp4vZ7Wozm7WEhtrLZDKMGzcOlpaWCAwM7PSMdbUZ2RITE8Uy\nBx2NiYmJ3uqxoenZsydGjRoFY2NjGBvfdiqxtLTEjz/+iKCgIJSXl2PBggXtqkNDvPTSS9i6dSt8\nfX1RUVEBnU6HsrIy5OTk4Nq1a2LWu7y8PEyfPh1WVlYICgrCyy+/jODgYGzbtg3jx4/H5MmTDZbi\n2s7ODsePH0d5eTm2bt2Kf/7zn10mm2pLkclkDa5cJiUlwd3dHfn5+WL6eY1GgzFjxmDEiBGN/ubH\njRuHIUOGwM3NDTdv3vzLrDhFRERg2rRpSElJweXLl/HAAw+I+8rKyuDl5YUlS5YAANLT0/VWyCUk\nJINJosPx8PDA7t274eXlhV69euHGjRuIi4vTazN37lxs2bIFx44dg1KpNNjYwcHBkMlkcHd3N5jM\n1kASoaGh+Pe//43t27dj5cqVWL58OTZs2IBNmzbh4MGDuHXrVp1+586dQ0xMjN6NXkKipdy8eRPH\njh3D6tWrMXXqVHG7Wq2Gk5MTjIyMkJSUBJVKBbVa3WC2rNbSt29fTJgwAYGBgbh586ZBZbeUgoIC\naLVaKBQKKBQKpKWl1TGedDodQkJCEBAQIGZlMxSmpqaoqakxqMw/M2DAANjb29d5eZ48eTJOnDiB\npKQknDp1ql11aIh58+Zh4MCBMDMzg5GRESwsLNCnTx9RV/5/psZazM3NsWvXLmRmZiIrKwsHDx5E\nZGQkHnnkEb2Jt7awatUqJCQk4MSJE4iPj4e7uzvOnTtnENkdiVpdAbW6EkVFpXX29erVCwkJCaip\nqUF0dDSA2+6SRkZGcHBwaFSuqakpzM3NAQCDBg1Campqq/SrqKiAn59fndIE7cG5c+cwdepU1NTU\n4Pr165g7d67efmNjY5iZmaG4uBg6nQ5ZWVmSwSShT2uXpiQk2kJycrJeJfda14s7KSkpYe/evTl1\n6lSDuVzMmzePY8aMMYis1qDT6XjmzBlOmDChTsYmS0tL9unTh7a2tuL/7777bpcITJe4u7h48SIB\n0NPTU297aGgoa2pqqNVqqVAo2jRGcwLDBUFgeHg44+Li2jRWa9BqtVSpVHpZwgRBYF5eHv39/UVX\npaSkJPr5+bG8vJyCIDAhIYEBAQEtdlNsiMzMTObk5BhEVkPUJtNZt25dnQxwgiDQw8ODjo6Ooste\nRxMUFCS6eRUUFOjd8yoqKhrN2EaSX375JQHwxRdfrPdZ0hbS0tLo4eFRJ3lGVyc5+Sa/+CKchw8r\nefDgJZaV3T7vCQkJXLhwIRcsWMBNmzZRoVBQoVBQo9EwLCysVdfAjRs36OXlVSd5RENUVlYyNTWV\nAQEB1Gg0VCqVVCgUjI2NNcj5EwSBpaWlPH78OF9//XWuWLGCADh16lRmZ2c32G/VqlW0s7NjfHw8\nAfDzzz9vsy4Sfy3+0lnyysrKmJub29lqSBgYQRD43HPPcdu2bY367x8+fJgA6Ovr2+Yxi4uLaWJi\nwpdeeqnNslrL1q1bCYD9+vWrYzAtWrRIbBccHCxmxZs5cyZHjhzJiRMn8saNG52mu8Tdw/79++vN\nFFlrJN28eVMvrqk1tCSTVlZWlvjy1BHk5OTQz8+PZWVlDW31QoMAACAASURBVLZRqVT09fWll5cX\nb926pbdPp9MxMjKSwcHBbTYy8vPzmZaW1iYZTaHT6fj666+LsUF/jg29fPkyAfDs2bPtqkdTVFZW\nMiUlhSqVSkx/7ePjQ29v7yb7bty4kQDo7u5ucL2qq6u5cuVKymQy+vn5GVx+e3D1aiqPHAnjkSNh\n3Lv3AmNiCkiSv//+u/jMMTc3p4eHBwsLCxkVFaVXxqKlNJTtUaPRMCIigkFBQeJ5jYqKqneSICMj\ng0lJSa3WQRAEPvbYYxw5ciQnT54sxsYNHDiQzz33XJO/VW9vb8pkMo4ZM4YAeP369VbrIvHX5C+b\nJS89PV0szFlYWIhevXp1skYShkImk+HgwYNNtluzZg22bt2KH374AdOnT2/TmLdu3YJGoxHjNTqS\n8vJyPPvss/jqq68AADk5OTA2NsaHH36IuLg4HD16FL/88gvGjRuHoUOH4oknnsD58+fx/PPPw9fX\nF6NHj4afnx9mzpyJ//znPy2q2C4hcScBAQHYuXMn5syZg4kTJ4rby8rKxBgXCwsLpKend5hO/fr1\nQ69evRAYGIgxY8a0W1FQrVaL0NBQ2NjYYNq0aY22dXV1BXDbfbGgoEAvY5aRkRHGjh0LjUaDiIgI\nGBsbY+zYsZDL5S3WydTUFCkpKWK8TnuxadMmDBo0CO+88w7mzJkDf39/dOvWDcDtIqcAUFJS0q46\nNIW5uTmcnJzg5OQkbhMEAcnJycjJyUHfvn0b7PvZZ5/By8urXeKN5HI5YmNjYWZmJsb2dXVsbP7r\nRpuTk4V9+96Aqel/r89jx47BysoKK1aswP79+7Fo0SKUl5ejpqamVceo1WrrLYz7+++/w9LSEiNG\njGjyHc7c3BylpXXdB5tDfn4+du7cia+++gpmZmbQ6XT4/vvvsWzZsmb/tmbOnIlXX30VH3zwAbp1\n6/aXjHWWaD+6tMFUW2m6uroaZWVl8PT0hJmZGTw8PLpM5XiJ9sXCwgIODg5ISkpqs6x+/frBzMwM\n4eHhBtDsv5BEfn6+6AOdmpqKPn366KVlPXTokGgsAcCjjz6KLVu2YNKkSSCJ+fPnw9/fH4mJiQgJ\nCcHFixcxa9YsrFy5Eh4eHnBxcYGlpSX+9re/Ye7cubh06RLuvfdegx6HxN2PIAhYtGgRbGxscOTI\nEb2Xm/DwcDGeycTEpMMTMpibm2P69OmIjIyEWq3G8OHDDSo/IyMDWVlZmDBhghh/0Ri13421tTVS\nU1PrTVNtYmICNzc3lJeXQ6lUwsrKqsmA+fpkdNR3vWjRInTr1g1r1qzB+fPnsWbNGgC377MAOj2e\nrD6MjIwwdOhQKBSKRg2mvLw8JCUlYfPmze2ih7GxMQRBQE5OTrvINyTJycmIjQ2Ej48PkpKSkJoa\njfz8XDHGSy6XY8mSJbC3t8fKlSuxf/9+LFmyBOPHj2+1QTh8+HAEBgaid+/ecHZ2RmZmJuRyOWxt\nbeHm5oaAgAC4u7s3Kt/Ozg5FRUVQqVQYOnQo0tLScOjQIQC345rnzJmj91wtLS1FZGQkjh8/jq+/\n/hrV1dV45pln8Nprr6GoqAgTJkxo8XHs3LkT/v7+cHBwMHjspsRfnNYuTXUU2dnZjIuLE4vd4f+L\nyl28eLFOxW+Ju5OXX36ZRkZGBikU+Mgjj9DKyqrNftIVFRU8deoUH3nkkXrd62xsbPTStZaVlfHw\n4cPctWtXk65KlZWVfPfddzly5EhR3vDhw0ne/j2MGTOGZmZmfP/99++KAoISHUd2djatra25e/du\n+vr6ih9vb29GRUWxpKRE/LTFPYckw8LCWu1il52dbTAXPZ1Ox+DgYKakpLRaRnOLyxYWFtLf35+p\nqanNll1TU9NokVBDo9PpOHDgQD0XYJJ0dHTstGK2TVFVVcWIiIgG9+t0Oq5bt44AmJCQ0C46FBYW\nctKkSTQ2NuaZM2f09hUXF/Odd97hW2+91S5jtwSFQqHncjd27FiOGTOGe/bs4bVr13j16lW92EVf\nX18C4GeffcaMjIw2j5+bm8vAwEBmZGTQy8uLISEh1Gg0rKmpaTDF+Z+prq7mp59+ShcXF1pYWIil\nSABw8eLFTEhI4LBhw8Rt3bp147PPPmuwcgU6nU6KHf4f5S8dw3QnTz75pN5L6cSJE3ny5Enpwr7L\neeWVVwjAIDfzL774ggDaZHydOXOGvXr1IgD27duXDz/8MPfv388DBw7www8/5EMPPUQAvHbtWpt0\nrQ0wf+211whAjF8qLCwUg5AB8J577ml2sK3E/zbBwcEcP348jxw5wsTERCYkJNT5xMfHMz4+nmq1\nuk1jJSQktCkxQlVVFX19fVlYWNhqGbU1pRqLVWoO0dHRLTK4MjIy6Ofnx/z8/CbbNlSzqj1Zvnw5\nTUxM9M7PunXraGpq2iXjJFNSUhq9Hmtj8l5++eV21aOkpIQzZsygkZER9+zZw9DQUO7cuZPW1tbi\n/bi9E3g0RVxcHAFw586dzZoYrK6uZo8ePfj000+zoqLCIDoIgsDg4GCGhITQx8dH/A3Hx8c3+ZvQ\narXcvn07AXDevHksLCykRqNhQEAABw4cyFGjRvHkyZMEwFdeeYUXL15kQUGBQfSWkLhrDCby9g/x\n9OnT7N27t3iD+qsWlZNomtpsen/7298MIu/8+fME0OoMYMXFxezZsyfd3Nzo6elZx1ivqqrinDlz\naGJiYrCbeHp6Ou3s7CiXy+nk5MQjR45Qo9EwOjqaVlZWBEBra2tu3LjRYJm7JO5OCgsL2adPHzo4\nODAmJqZdx8rOztZbZW0NbSl0m5WVxaCgIINNJgQEBLRocu7OjHp/ThrxZzraYJoyZQotLS31Eu7k\n5ubS1taWHh4e1Gq1HapPUyiVynq/e61Wy19//ZXDhw/ntGnTOmTytKysjH/729/0Jm/vv/9+8SX+\n5MmT7a5DY5SWltLIyIjbt29vdp8PP/yQ48eP5/fff28QHSIjI+s1cAVBoJ+fH1UqFVUqVb0rq+vX\nrycAbt68uc673eLFi2lvby9OonZWVkeJu5e7ymCqJSYmhs7Ozuzbty8PHDjQ5W7wEoZh1qxZBMDj\nx48bRF56ejrNzc25YsWKVvW/cuVKo6tHjzzyCAHwq6++aouadYiJieGOHTvEB3Sti1BERARff/11\nrlmzhgA4aNAg5uXlGXRsibuL2NhY2tjYcNmyZe06TklJCePj4w0iKyYmpk5mt8a4ceOGwdxzaiko\nKGBoaGiL+92ZUa+hjKCdscI0dOjQOttPnz5NAPz73//eoW6CTdGQS2RtunQAPHHiRIfpU3tOv/nm\nG/GaEASBffr04Zo1azpMj/r02rx5MwE0K7NgLdXV1Zw6dSrNzMzo4+PTJh2ysrL466+/NmuiIi0t\nTc9zpDaDX0OZbL28vMTz3atXLxYXF7dJVwmJP3NXGkwkuXv3bvHHs3DhQsk17y6k1i+9sdTjLeW9\n994jAP7yyy8t7vvWW29RJpPVO2McHBxMAHzjjTcMoWYdHn/8cfF6HzNmDMeOHcvFixdzyZIldHV1\nJQCamJgwPT2dGzdu5JIlS7hp0yYeOHCAS5cu5cKFC7l27VoGBgZKv5X/cbZs2UIjI6N2XZ3XarWt\nMjDqQ6fT0dfXt0m3XK1WS39//3aZNCgtLW2yHlBj1NTUUKVSMTQ0lFqtVkyfrVKp2lzzqqW8/fbb\nlMlkdVwVBUHg+++/T1tbW9rY2DAyMrJD9aqPgoKCBuPpRo0axVGjRvHXX3/tEpOm99xzD2fMmNEp\nYwcFBXHKlCkEwOeff77F/dVqNYcPH04bGxseOnSoVS7wKpWKKSkpLCsro7+/f7P6+Pv7UxAEVldX\nc+TIkRw8eHC9roHR0dF87LHHaG5uLj4HnZycJK8KCYNy1xpM/fr147Bhw/jCCy/UW4RR4q/PH3/8\nQQDcu3evwWRWV1fT3t6ea9eubVG/mpoaOjs7c9q0afXurzXu2mvWa/HixQTASZMmcdGiRVy6dCnH\njx9PFxcXzps3j6+88gqDg4O5a9euOkkoHBwc6OLiQhsbGwJgjx49qFKpJHfW/1FWr17NgQMHtvs4\nhl45SU9Pp7e3d72uOHl5efTx8THo5MqdxMbGsrS0tM1yysvLGRQU1KoJG0Px008/EQCvXr1a7/7k\n5GT269ePffv2bfcaUfWh0Wh448YNBgUFMSEhocHVCjMzM77wwgsdrF3DrF69ms7Ozh06ZmVlpRjf\n3bt3b544caLVbqjJyckcPnw4AdDFxaVZfaqrq+nn58egoCA9NzylUtnk8yUyMpIhISEUBIE//vgj\nAfDChQt12uXn53PgwIG0tLTkY489xjNnzojPuX379rXsIP+PvfMOj6ra/v53kkkvJCQESAidFFJJ\nI6Fa4FIVBRFRuAgqXpSmovhiw4aKly6ICgiCFJGqdIOEtElmJr33XkkvM5OZOev9I7+cy5A2SWaS\ngOfzPDwPc84+e68zmVPW3mt9FwdHBzy0dZg6Q0dHB+np6Th8+DAAsJKZHI8Ojz/+OGbOnImPP/4Y\n5ubmWLVqVZcke9tCX18fAQEBuH37tto1J+rq6rBhwwZkZ2djz549bbapr6/vsW0d8ccff6jVztzc\nHDdv3kRgYCDOnTuH8ePHo7q6Gv7+/qirq8P27dvxxRdfwNvbG9bW1sjJyWFlhTn+GRgaGkIqlbZZ\nN6U/Y29vD5lMBqlUCn19fdTW1iIrKwsKhQKDBg3C5MmTtXY+9fX1GrlOjI2N4efnB6FQqAGruscT\nTzwBY2NjnD9/HjNmzGi1f9SoUbh58yYmTJiAPXv2YMeOHb1iV3l5ObKzs6GrqwtHR0eYmpq221Yu\nl2PYsGGIiorqFdvUwc7ODoWFhWAYBjo6Or0y5pYtW/Dzzz/j3XffxYcffghzc/Nu9zVq1CikpKTA\n09MT5eXlqK+v7/BvAACFhYVwdHSElZWVynZHR0ckJiaipqYG06ZNa3XcvXv3wDAMW++spXRIW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kZWXB09MTurq6Gu03JSUF/v7+kMvl4PP77hHcVk7WsmXLUFdXhzfeeAOTJk3C2bNnMXbsWHZ/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LhwIatWreLBgweoVCoURcHOzo4+ffrQr18/evfujZ2d3RPvt7e3x9/fn6CgIG7dusUXX3zB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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a7ef9b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"bubblemap(cities_lat, cities_lon, cities_tot, 'darkblue', 'map-cities-corpus')"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"location\n",
"London 1042\n",
"New York 338\n",
"Paris 205\n",
"Chicago 167\n",
"Leicester 141\n",
"Rome 135\n",
"Boston 83\n",
"Wellington 80\n",
"Providence 73\n",
"Brighton 71\n",
"Name: occurs, dtype: int64"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cities.occurs.sum().sort_values(ascending=False).head(10)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>file</th>\n",
" <th>location</th>\n",
" <th>occurs</th>\n",
" <th>nation</th>\n",
" <th>author</th>\n",
" <th>title</th>\n",
" <th>pubdate</th>\n",
" <th>gender</th>\n",
" <th>wordcount</th>\n",
" <th>placeid</th>\n",
" <th>...</th>\n",
" <th>continent</th>\n",
" <th>country</th>\n",
" <th>formatted_address</th>\n",
" <th>lat</th>\n",
" <th>locality</th>\n",
" <th>location_type</th>\n",
" <th>lon</th>\n",
" <th>natural_feature</th>\n",
" <th>partial</th>\n",
" <th>point_of_interest</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1031</th>\n",
" <td>A-Melville-Moby_Dick-1851-M</td>\n",
" <td>Leicester</td>\n",
" <td>1</td>\n",
" <td>A</td>\n",
" <td>Melville</td>\n",
" <td>Moby Dick</td>\n",
" <td>1851</td>\n",
" <td>M</td>\n",
" <td>260961</td>\n",
" <td>ChIJc2y3SatCd0gRuJy0byodFZo</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>United Kingdom</td>\n",
" <td>Leicester, UK</td>\n",
" <td>52.636878</td>\n",
" <td>Leicester</td>\n",
" <td>locality</td>\n",
" <td>-1.139759</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1756</th>\n",
" <td>B-Dickens-Bleak_House-1853-M</td>\n",
" <td>Leicester</td>\n",
" <td>140</td>\n",
" <td>B</td>\n",
" <td>Dickens</td>\n",
" <td>Bleak House</td>\n",
" <td>1853</td>\n",
" <td>M</td>\n",
" <td>435141</td>\n",
" <td>ChIJc2y3SatCd0gRuJy0byodFZo</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>United Kingdom</td>\n",
" <td>Leicester, UK</td>\n",
" <td>52.636878</td>\n",
" <td>Leicester</td>\n",
" <td>locality</td>\n",
" <td>-1.139759</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" file location occurs nation author \\\n",
"1031 A-Melville-Moby_Dick-1851-M Leicester 1 A Melville \n",
"1756 B-Dickens-Bleak_House-1853-M Leicester 140 B Dickens \n",
"\n",
" title pubdate gender wordcount placeid \\\n",
"1031 Moby Dick 1851 M 260961 ChIJc2y3SatCd0gRuJy0byodFZo \n",
"1756 Bleak House 1853 M 435141 ChIJc2y3SatCd0gRuJy0byodFZo \n",
"\n",
" ... continent country formatted_address \\\n",
"1031 ... NaN United Kingdom Leicester, UK \n",
"1756 ... NaN United Kingdom Leicester, UK \n",
"\n",
" lat locality location_type lon natural_feature partial \\\n",
"1031 52.636878 Leicester locality -1.139759 NaN False \n",
"1756 52.636878 Leicester locality -1.139759 NaN False \n",
"\n",
" point_of_interest \n",
"1031 NaN \n",
"1756 NaN \n",
"\n",
"[2 rows x 23 columns]"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"geo_all[geo_all['location']=='Leicester']"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Aggregated places by nation\n",
"nations = geo_all[(geo_all['country'].notnull())].groupby('country')\n",
"\n",
"countries_tot = [int(i) for i in nations.occurs.sum()]\n",
"countries_lon = [i for i in nations.lon.apply(np.mean)]\n",
"countries_lat = [i for i in nations.lat.apply(np.mean)]"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create labels for country counts (used later)\n",
"countries_lab = [i for i in nations.country.groups]\n",
"countries_lab = [str(i) for i in countries_lab]\n",
"countries_lab = sorted(countries_lab)\n",
"for i in range(len(countries_lab)):\n",
" countries_lab[i] = countries_lab[i] + ': ' + str(countries_tot[i])"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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MN+S/AJ/Pp+PHj9P+/fupsrKyo81haUdYh+k/QmFhIf3+++90/vz5dmncsbye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\n5