VC Funding using Crunchbase (Demo)

Crunch_IP.ipynb

{
"metadata": {
"name": "VC_Funding"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Analyzing VC funding using Cruchbase API"
]
},
{
"cell_type": "heading",
"level": 5,
"metadata": {},
"source": [
"by Andr\u00e9 R\u00edos (datablast.wordpress.com)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Starting MongoDB service over Ubuntu 12.04.4 LTS\n",
"\n",
"sudo mongod --dbpath /var/lib/mongodb --journal"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib\n",
"import json\n",
"import requests\n",
"import numpy as np\n",
"import pandas as pd\n",
"from pandas import DataFrame, Series\n",
"import pymongo\n",
"import networkx as nx\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import display\n",
"import collections\n",
"from IPython.display import HTML"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 170
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Connecting MongoDB collection called 'cities'\n",
"\n",
" * c_barcelona(companies)\n",
"\n",
" * f_barcelona(funding)\n",
"\n",
" * i_barcelona(investors)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"conn = pymongo.Connection(\"localhost\", 27017)\n",
"db = conn.cities\n",
"c_barcelona = db.c_barcelona\n",
"f_barcelona = db.f_barcelona\n",
"i_barcelona = db.i_barcelona"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 171
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Companies in Barcelona: Create collection c_barcelona"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Searching companies at Barcelona and save on BBDD\n",
"\n",
"location_uuid for Barcelona: eead2c0cb178ad334e6d6c813c955e99\n",
"\n",
"Other Cities:\n",
"\n",
" London: aad17950576b8c448fd4f44dbeb59220\n",
"\n",
" Paris: 67af74be0bfa027795c13cb5853c8bcc\n",
"\n",
" Rome: c5a012b1f2f9a3b5c66aee6be778eda2\n",
"\n",
" Berlin: e61662e5c5c2c4d92a31d0e6d6453375\n",
"\n",
"cs=requests.get('http://api.crunchbase.com/v/2/organizations?organization_types=company&location_uuids=eead2c0cb178ad334e6d6c813c955e99&user_key=< enter your key >&page=1').json()\n",
"\n",
"np=cs['data']['paging']['number_of_pages']\n",
"\n",
"ht = 'http://api.crunchbase.com/v/2/organizations?organization_types=company&location_uuids=eead2c0cb178ad334e6d6c813c955e99&user_key=< enter your key >&page='\n",
"\n",
"lc=[]\n",
"\n",
"for i in range(np):\n",
"\n",
" s=str(i+1)\n",
"\n",
" ht_i = ht + s\n",
"\n",
" cs = requests.get(ht_i).json()\n",
"\n",
" ni=len(cs['data']['items'])\n",
"\n",
" for j in range(ni):\n",
"\n",
" lc.append(cs['data']['items'][j]['path'])\n",
"\n",
"List of organization in the city: organization/< name of organization>\n",
"\n",
"url_list=[]\n",
"\n",
"url_1='http://api.crunchbase.com/v/2/'\n",
"\n",
"for i in range(len(lc)):\n",
"\n",
" rl_list.append(url_1 + str(lc[i]) + '?user_key=< your key >')\n",
"\n",
"Save json file in MongoDB: Companies in Barcelona\n",
"\n",
"for i in range(len(url_list)):\n",
"\n",
" rs = requests.get(url_list[i]).json()\n",
"\n",
" c_barcelona.insert(rs)\n",
"\n",
"Erase some element with errors.\n",
"\n",
"db.c_barcelona.remove({'data': {u'response': False, u'error': {u'message': u'Node 4035881 not found', u'code': 500}}})"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n=c_barcelona.count()\n",
"df1 = DataFrame(columns=['uuid','name','role'], index=list(range(0,n)))\n",
"for i in range(n):\n",
" df1.loc[i] = Series({'uuid': c_barcelona.find()[i]['data']['uuid'],'name':c_barcelona.find()[i]['data']['properties']['permalink']\n",
" ,'role':c_barcelona.find()[i]['data']['properties']['primary_role']})\n",
" \n",
"df1=df1.drop_duplicates()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 172
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df1.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uuid</th>\n",
" <th>name</th>\n",
" <th>role</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> d0647cecacb39a23bc4fd3fad90ecb4e</td>\n",
" <td> strands</td>\n",
" <td> company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> a931b34d31f31d171f3d359875fc02c2</td>\n",
" <td> gsi-commerce</td>\n",
" <td> company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 31af5340f371d4f618156ca86a3a28fd</td>\n",
" <td> akamon-entertainment</td>\n",
" <td> company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 476cdc24e4c314c49b9297032674e2da</td>\n",
" <td> veeva</td>\n",
" <td> company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 78c70050042ba916b8121ab481207ea0</td>\n",
" <td> 3scale</td>\n",
" <td> company</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 119,
"text": [
" uuid name role\n",
"0 d0647cecacb39a23bc4fd3fad90ecb4e strands company\n",
"1 a931b34d31f31d171f3d359875fc02c2 gsi-commerce company\n",
"2 31af5340f371d4f618156ca86a3a28fd akamon-entertainment company\n",
"3 476cdc24e4c314c49b9297032674e2da veeva company\n",
"4 78c70050042ba916b8121ab481207ea0 3scale company"
]
}
],
"prompt_number": 119
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfx=df1[['name','role']]\n",
"h = HTML(dfx[0:7].to_html(index=False))\n",
"my_file = open('role_company.html', 'w')\n",
"my_file.write(h.data)\n",
"my_file.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 96
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n=c_barcelona.count()\n",
"f=0\n",
"df2 = DataFrame(columns=['uuid','name','category'], index=list(range(0,10000)))\n",
"for i in range(n):\n",
" if (c_barcelona.find()[i]['data']['relationships']).get('categories','empty') != 'empty':\n",
" ni=len(c_barcelona.find()[i]['data']['relationships']['categories']['items'])\n",
" for j in range(ni):\n",
" df2.loc[f]= Series({'uuid':c_barcelona.find()[i]['data']['uuid'],'name':c_barcelona.find()[i]['data']['properties']['permalink'],\n",
" 'category':c_barcelona.find()[i]['data']['relationships']['categories']['items'][j]['name']}) \n",
" f=f+1\n",
" \n",
"df2=df2.drop_duplicates()\n",
"df2=df2.dropna(thresh=1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 173
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df2.head(n=10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uuid</th>\n",
" <th>name</th>\n",
" <th>category</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> d0647cecacb39a23bc4fd3fad90ecb4e</td>\n",
" <td> strands</td>\n",
" <td> Artificial Intelligence</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> d0647cecacb39a23bc4fd3fad90ecb4e</td>\n",
" <td> strands</td>\n",
" <td> Reviews and Recommendations</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> d0647cecacb39a23bc4fd3fad90ecb4e</td>\n",
" <td> strands</td>\n",
" <td> Software</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> d0647cecacb39a23bc4fd3fad90ecb4e</td>\n",
" <td> strands</td>\n",
" <td> Personal Finance</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> d0647cecacb39a23bc4fd3fad90ecb4e</td>\n",
" <td> strands</td>\n",
" <td> E-Commerce</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> d0647cecacb39a23bc4fd3fad90ecb4e</td>\n",
" <td> strands</td>\n",
" <td> Personalization</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td> a931b34d31f31d171f3d359875fc02c2</td>\n",
" <td> gsi-commerce</td>\n",
" <td> Enterprise Software</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td> 31af5340f371d4f618156ca86a3a28fd</td>\n",
" <td> akamon-entertainment</td>\n",
" <td> Games</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td> 476cdc24e4c314c49b9297032674e2da</td>\n",
" <td> veeva</td>\n",
" <td> Content</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td> 476cdc24e4c314c49b9297032674e2da</td>\n",
" <td> veeva</td>\n",
" <td> Software</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 121,
"text": [
" uuid name category\n",
"0 d0647cecacb39a23bc4fd3fad90ecb4e strands Artificial Intelligence\n",
"1 d0647cecacb39a23bc4fd3fad90ecb4e strands Reviews and Recommendations\n",
"2 d0647cecacb39a23bc4fd3fad90ecb4e strands Software\n",
"3 d0647cecacb39a23bc4fd3fad90ecb4e strands Personal Finance\n",
"4 d0647cecacb39a23bc4fd3fad90ecb4e strands E-Commerce\n",
"5 d0647cecacb39a23bc4fd3fad90ecb4e strands Personalization\n",
"6 a931b34d31f31d171f3d359875fc02c2 gsi-commerce Enterprise Software\n",
"7 31af5340f371d4f618156ca86a3a28fd akamon-entertainment Games\n",
"8 476cdc24e4c314c49b9297032674e2da veeva Content\n",
"9 476cdc24e4c314c49b9297032674e2da veeva Software"
]
}
],
"prompt_number": 121
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfx=df2[['name','category']]\n",
"h = HTML(dfx[0:7].to_html(index=False))\n",
"my_file = open('cat_company.html', 'w')\n",
"my_file.write(h.data)\n",
"my_file.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 78
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f=0\n",
"df3 = DataFrame(columns=['uuid','name','path'], index=list(range(0,10000)))\n",
"for i in range(n):\n",
" if (c_barcelona.find()[i]['data']['relationships']).get('funding_rounds','empty') != 'empty':\n",
" ni=len(c_barcelona.find()[i]['data']['relationships']['funding_rounds']['items'])\n",
" for j in range(ni):\n",
" df3.loc[f]= Series({'uuid':c_barcelona.find()[i]['data']['uuid'],\n",
" 'name':c_barcelona.find()[i]['data']['properties']['permalink'],\n",
" 'path':c_barcelona.find()[i]['data']['relationships']['funding_rounds']['items'][j]['path']}) \n",
" f=f+1\n",
" \n",
"df3['uuid_fund'] = df3['path'].map(lambda x: str(x).replace(\"funding-round/\",\"\"))\n",
"df3=df3.drop_duplicates()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 174
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df3.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uuid</th>\n",
" <th>name</th>\n",
" <th>path</th>\n",
" <th>uuid_fund</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> d0647cecacb39a23bc4fd3fad90ecb4e</td>\n",
" <td> strands</td>\n",
" <td> funding-round/eb5cbd96fd6c6176199f993a589a4da2</td>\n",
" <td> eb5cbd96fd6c6176199f993a589a4da2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> d0647cecacb39a23bc4fd3fad90ecb4e</td>\n",
" <td> strands</td>\n",
" <td> funding-round/9f1a9a6a5326712b265aa70e78a06aa9</td>\n",
" <td> 9f1a9a6a5326712b265aa70e78a06aa9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> d0647cecacb39a23bc4fd3fad90ecb4e</td>\n",
" <td> strands</td>\n",
" <td> funding-round/eef833bbeabcfeb4c866074eda879500</td>\n",
" <td> eef833bbeabcfeb4c866074eda879500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 31af5340f371d4f618156ca86a3a28fd</td>\n",
" <td> akamon-entertainment</td>\n",
" <td> funding-round/639a184018b1b35cad644c3d11e086c2</td>\n",
" <td> 639a184018b1b35cad644c3d11e086c2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 31af5340f371d4f618156ca86a3a28fd</td>\n",
" <td> akamon-entertainment</td>\n",
" <td> funding-round/32705565ab649c7d969fd42f1d63e3d2</td>\n",
" <td> 32705565ab649c7d969fd42f1d63e3d2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 123,
"text": [
" uuid name \\\n",
"0 d0647cecacb39a23bc4fd3fad90ecb4e strands \n",
"1 d0647cecacb39a23bc4fd3fad90ecb4e strands \n",
"2 d0647cecacb39a23bc4fd3fad90ecb4e strands \n",
"3 31af5340f371d4f618156ca86a3a28fd akamon-entertainment \n",
"4 31af5340f371d4f618156ca86a3a28fd akamon-entertainment \n",
"\n",
" path \\\n",
"0 funding-round/eb5cbd96fd6c6176199f993a589a4da2 \n",
"1 funding-round/9f1a9a6a5326712b265aa70e78a06aa9 \n",
"2 funding-round/eef833bbeabcfeb4c866074eda879500 \n",
"3 funding-round/639a184018b1b35cad644c3d11e086c2 \n",
"4 funding-round/32705565ab649c7d969fd42f1d63e3d2 \n",
"\n",
" uuid_fund \n",
"0 eb5cbd96fd6c6176199f993a589a4da2 \n",
"1 9f1a9a6a5326712b265aa70e78a06aa9 \n",
"2 eef833bbeabcfeb4c866074eda879500 \n",
"3 639a184018b1b35cad644c3d11e086c2 \n",
"4 32705565ab649c7d969fd42f1d63e3d2 "
]
}
],
"prompt_number": 123
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Funding links: Create collection f_barcelona"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"n=c_barcelona.count()\n",
"\n",
"pl=[]\n",
"\n",
"for i in range(n):\n",
"\n",
" if (c_barcelona.find()[i]['data']['relationships']).get('funding_rounds','empty') != 'empty':\n",
"\n",
" ni=len(c_barcelona.find()[i]['data']['relationships']['funding_rounds']['items'])\n",
"\n",
" for j in range(ni):\n",
"\n",
" pl.append(c_barcelona.find()[i]['data']['relationships']['funding_rounds']['items'][j]['path'])\n",
" \n",
"url_list=[]\n",
"\n",
"url_1='http://api.crunchbase.com/v/2/'\n",
"\n",
"for i in range(len(pl)):\n",
"\n",
" url_list.append(url_1 + str(pl[i]) + '?user_key=< your key>')\n",
" \n",
" for i in range(len(url_list)):\n",
"\n",
" try:\n",
"\n",
" rs = requests.get(url_list[i]).json()\n",
"\n",
" except:\n",
"\n",
" print 'Error'\n",
"\n",
" f_barcelona.insert(rs)\n",
"\n",
"Erase some elements with errors.\n",
"\n",
"db.f_barcelona.remove({'data': {u'response': False, u'error': {u'message': u'wrong argument type nil (expected String)', u'code': 500}}})\n",
"\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df4 = DataFrame(columns=['uuid_fund','type','money','currency','year','path_inv'], index=list(range(0,10000)))\n",
"nf=f_barcelona.count()\n",
"f=0\n",
"for i in range(nf):\n",
" if (f_barcelona.find()[i]['data']['relationships']).get('investments','empty') != 'empty':\n",
" ni=len(f_barcelona.find()[i]['data']['relationships']['investments']['items'])\n",
" a1=f_barcelona.find()[i]['data']['uuid']\n",
" a2=f_barcelona.find()[i]['data']['properties']['funding_type']\n",
" if (f_barcelona.find()[i]['data']['properties']).get('money_raised','empty') != 'empty':\n",
" a3=f_barcelona.find()[i]['data']['properties']['money_raised']\n",
" else:\n",
" a3=0\n",
" a4=f_barcelona.find()[i]['data']['properties']['money_raised_currency_code']\n",
" a5=f_barcelona.find()[i]['data']['properties']['announced_on_year']\n",
" for j in range(ni):\n",
" try:\n",
" i\n",
" df4.loc[f]= Series({'uuid_fund':a1,'type':a2,'money':a3/ni,'currency':a4,'year':a5,\n",
" 'path_inv':f_barcelona.find()[i]['data']['relationships']['investments']['items'][j]['investor']['path']})\n",
" f=f+1\n",
" except:\n",
" print 'Companies with errors'\n",
"\n",
"#Total funding in each round is divided by the number of the invertors. \n",
"df4['name'] = df4['path_inv'].map(lambda x: str(x).replace(\"organization/\",\"\"))\n",
"df4['name'] = df4['name'].map(lambda x: str(x).replace(\"person/\",\"\"))\n",
"df4=df4.drop_duplicates()\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Companies with errors\n",
"Companies with errors"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Companies with errors"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Companies with errors"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Companies with errors\n",
"Companies with errors"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Companies with errors"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Companies with errors"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 175
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Investor Links: Create collection i_barcelona"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"li=[]\n",
"\n",
"nf=f_barcelona.count()\n",
"\n",
"for i in range(nf):\n",
"\n",
" if (f_barcelona.find()[i]['data']['relationships']).get('investments','empty') != 'empty':\n",
"\n",
" ni=len(f_barcelona.find()[i]['data']['relationships']['investments']['items'])\n",
"\n",
" for j in range(ni):\n",
"\n",
" try:\n",
"\n",
" li.append(f_barcelona.find()[i]['data']['relationships']['investments']['items'][j]['investor']['path'])\n",
"\n",
" except:\n",
"\n",
" print 'Error'\n",
"\n",
"Remove duplicate elements \n",
"\n",
"a=set(li)\n",
"\n",
"Searching investor uuids\n",
"\n",
"url_list=[]\n",
"\n",
"url_1='http://api.crunchbase.com/v/2/'\n",
"\n",
"for i in range(len(list(set(li)))):\n",
"\n",
" url_list.append(url_1 + str(b[i]) + '?user_key=< your key>')\n",
"\n",
"nl=len(url_list)\n",
"\n",
"for i in range(nl):\n",
"\n",
" try:\n",
"\n",
" rs = requests.get(url_list[i]).json()\n",
"\n",
" except:\n",
"\n",
" print 'Error'\n",
"\n",
" i_barcelona.insert(rs)\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df4.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uuid_fund</th>\n",
" <th>type</th>\n",
" <th>money</th>\n",
" <th>currency</th>\n",
" <th>year</th>\n",
" <th>path_inv</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 421e422d65b93b54d59ba19a465292d9</td>\n",
" <td> venture</td>\n",
" <td> 1250000</td>\n",
" <td> EUR</td>\n",
" <td> 2006</td>\n",
" <td> organization/active-venture-partners</td>\n",
" <td> active-venture-partners</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 421e422d65b93b54d59ba19a465292d9</td>\n",
" <td> venture</td>\n",
" <td> 1250000</td>\n",
" <td> EUR</td>\n",
" <td> 2006</td>\n",
" <td> organization/adara-venture-partners</td>\n",
" <td> adara-venture-partners</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 7a17150c3f14d66358956ac66fe2ea80</td>\n",
" <td> angel</td>\n",
" <td> 73333</td>\n",
" <td> EUR</td>\n",
" <td> 2012</td>\n",
" <td> organization/cabiedes-partners</td>\n",
" <td> cabiedes-partners</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 7a17150c3f14d66358956ac66fe2ea80</td>\n",
" <td> angel</td>\n",
" <td> 73333</td>\n",
" <td> EUR</td>\n",
" <td> 2012</td>\n",
" <td> person/francois-derbaix</td>\n",
" <td> francois-derbaix</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 7a17150c3f14d66358956ac66fe2ea80</td>\n",
" <td> angel</td>\n",
" <td> 73333</td>\n",
" <td> EUR</td>\n",
" <td> 2012</td>\n",
" <td> person/inaki-berenguer</td>\n",
" <td> inaki-berenguer</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 125,
"text": [
" uuid_fund type money currency year \\\n",
"0 421e422d65b93b54d59ba19a465292d9 venture 1250000 EUR 2006 \n",
"1 421e422d65b93b54d59ba19a465292d9 venture 1250000 EUR 2006 \n",
"2 7a17150c3f14d66358956ac66fe2ea80 angel 73333 EUR 2012 \n",
"3 7a17150c3f14d66358956ac66fe2ea80 angel 73333 EUR 2012 \n",
"4 7a17150c3f14d66358956ac66fe2ea80 angel 73333 EUR 2012 \n",
"\n",
" path_inv name \n",
"0 organization/active-venture-partners active-venture-partners \n",
"1 organization/adara-venture-partners adara-venture-partners \n",
"2 organization/cabiedes-partners cabiedes-partners \n",
"3 person/francois-derbaix francois-derbaix \n",
"4 person/inaki-berenguer inaki-berenguer "
]
}
],
"prompt_number": 125
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ni=i_barcelona.count()\n",
"df5 = DataFrame(columns=['uuid','name','role'], index=list(range(0,ni)))\n",
"for i in range(ni):\n",
" df5.loc[i] = Series({'uuid': i_barcelona.find()[i]['data']['uuid'],\n",
" 'name':i_barcelona.find()[i]['data']['properties']['permalink'], 'role':'investor'}) \n",
" \n",
"df5=df5.drop_duplicates()\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 176
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df5.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uuid</th>\n",
" <th>name</th>\n",
" <th>role</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 3eed4fa46c75fb697f5e17db8092b73b</td>\n",
" <td> viewpoint-capital-partners</td>\n",
" <td> investor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 56e8dbb001cdbdc3db01d38b3f8ce7d4</td>\n",
" <td> bullnet-gestion</td>\n",
" <td> investor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 025c7a60c56a47b0ac783b178661ddb7</td>\n",
" <td> pond-ventures</td>\n",
" <td> investor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> e02e9ffcc387327b2339a3f721dbd058</td>\n",
" <td> bbva</td>\n",
" <td> investor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> af37951e36a3ecaa003526db8f967832</td>\n",
" <td> costanoa-venture-capital</td>\n",
" <td> investor</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 127,
"text": [
" uuid name role\n",
"0 3eed4fa46c75fb697f5e17db8092b73b viewpoint-capital-partners investor\n",
"1 56e8dbb001cdbdc3db01d38b3f8ce7d4 bullnet-gestion investor\n",
"2 025c7a60c56a47b0ac783b178661ddb7 pond-ventures investor\n",
"3 e02e9ffcc387327b2339a3f721dbd058 bbva investor\n",
"4 af37951e36a3ecaa003526db8f967832 costanoa-venture-capital investor"
]
}
],
"prompt_number": 127
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfx=df5[['name','role']]\n",
"h = HTML(dfx[0:7].to_html(index=False))\n",
"my_file = open('role_investor.html', 'w')\n",
"my_file.write(h.data)\n",
"my_file.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 81
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Creating relationship table between companies and investors "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfa=df3[['name','uuid_fund']]\n",
"dfb=df4[['name','uuid_fund','type','money','currency','year']]\n",
"dfc=dfa.merge(dfb, on='uuid_fund')\n",
"dfc.columns = ['company', 'link','investor','type','money','currency','year']\n",
"dfc=dfc[['company', 'investor','type','money','currency','year']]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 177
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfc.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>company</th>\n",
" <th>investor</th>\n",
" <th>type</th>\n",
" <th>money</th>\n",
" <th>currency</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> strands</td>\n",
" <td> bbva</td>\n",
" <td> venture</td>\n",
" <td> 24000000</td>\n",
" <td> USD</td>\n",
" <td> 2007</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> strands</td>\n",
" <td> antonio-asensio</td>\n",
" <td> venture</td>\n",
" <td> 25000000</td>\n",
" <td> USD</td>\n",
" <td> 2007</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> strands</td>\n",
" <td> debaeque</td>\n",
" <td> venture</td>\n",
" <td> 3000000</td>\n",
" <td> USD</td>\n",
" <td> 2006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> strands</td>\n",
" <td> sequel</td>\n",
" <td> venture</td>\n",
" <td> 3000000</td>\n",
" <td> USD</td>\n",
" <td> 2006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> akamon-entertainment</td>\n",
" <td> bonsai-capital</td>\n",
" <td> venture</td>\n",
" <td> 700000</td>\n",
" <td> EUR</td>\n",
" <td> 2013</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 129,
"text": [
" company investor type money currency year\n",
"0 strands bbva venture 24000000 USD 2007\n",
"1 strands antonio-asensio venture 25000000 USD 2007\n",
"2 strands debaeque venture 3000000 USD 2006\n",
"3 strands sequel venture 3000000 USD 2006\n",
"4 akamon-entertainment bonsai-capital venture 700000 EUR 2013"
]
}
],
"prompt_number": 129
},
{
"cell_type": "heading",
"level": 5,
"metadata": {},
"source": [
"Convert currency to EUR"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from money import Money\n",
"dfc.money[dfc.currency == 'USD']=dfc.money[dfc.currency == 'USD']*0.79\n",
"dfc.currency=dfc.currency.replace('USD','EUR')\n",
"dfc['money'] = dfc['money'].map(lambda x: Money(x,'EUR'))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 178
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfc.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>company</th>\n",
" <th>investor</th>\n",
" <th>type</th>\n",
" <th>money</th>\n",
" <th>currency</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> strands</td>\n",
" <td> bbva</td>\n",
" <td> venture</td>\n",
" <td> EUR 18,960,000.00</td>\n",
" <td> EUR</td>\n",
" <td> 2007</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> strands</td>\n",
" <td> antonio-asensio</td>\n",
" <td> venture</td>\n",
" <td> EUR 19,750,000.00</td>\n",
" <td> EUR</td>\n",
" <td> 2007</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> strands</td>\n",
" <td> debaeque</td>\n",
" <td> venture</td>\n",
" <td> EUR 2,370,000.00</td>\n",
" <td> EUR</td>\n",
" <td> 2006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> strands</td>\n",
" <td> sequel</td>\n",
" <td> venture</td>\n",
" <td> EUR 2,370,000.00</td>\n",
" <td> EUR</td>\n",
" <td> 2006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> akamon-entertainment</td>\n",
" <td> bonsai-capital</td>\n",
" <td> venture</td>\n",
" <td> EUR 700,000.00</td>\n",
" <td> EUR</td>\n",
" <td> 2013</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 131,
"text": [
" company investor type money currency year\n",
"0 strands bbva venture EUR 18,960,000.00 EUR 2007\n",
"1 strands antonio-asensio venture EUR 19,750,000.00 EUR 2007\n",
"2 strands debaeque venture EUR 2,370,000.00 EUR 2006\n",
"3 strands sequel venture EUR 2,370,000.00 EUR 2006\n",
"4 akamon-entertainment bonsai-capital venture EUR 700,000.00 EUR 2013"
]
}
],
"prompt_number": 131
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfx=dfc[['company','investor','type','money','year']]\n",
"h = HTML(dfx[0:8].to_html(index=False))\n",
"my_file = open('relation.html', 'w')\n",
"my_file.write(h.data)\n",
"my_file.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 132
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Analysis"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Top 10 companies receiving VC funding:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"h1=dfc[['company','money']].groupby('company').sum().reset_index()\n",
"h2=h1.sort(['money'],ascending=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 382
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"h2.head(n=10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>company</th>\n",
" <th>money</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>95 </th>\n",
" <td> privalia</td>\n",
" <td> EUR 395,668,000.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30 </th>\n",
" <td> desigual</td>\n",
" <td> EUR 285,000,000.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td> scytl</td>\n",
" <td> EUR 89,427,998.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td> strands</td>\n",
" <td> EUR 43,450,000.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46 </th>\n",
" <td> groupalia</td>\n",
" <td> EUR 35,899,996.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td> social-point</td>\n",
" <td> EUR 34,845,998.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> arcplan-information-services-ag</td>\n",
" <td> EUR 26,907,400.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82 </th>\n",
" <td> ntr-global</td>\n",
" <td> EUR 26,860,000.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43 </th>\n",
" <td> gigle-semiconductor</td>\n",
" <td> EUR 24,489,998.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td> stat-diagnostica</td>\n",
" <td> EUR 24,100,000.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 134,
"text": [
" company money\n",
"95 privalia EUR 395,668,000.00\n",
"30 desigual EUR 285,000,000.00\n",
"105 scytl EUR 89,427,998.42\n",
"117 strands EUR 43,450,000.00\n",
"46 groupalia EUR 35,899,996.00\n",
"112 social-point EUR 34,845,998.42\n",
"8 arcplan-information-services-ag EUR 26,907,400.00\n",
"82 ntr-global EUR 26,860,000.00\n",
"43 gigle-semiconductor EUR 24,489,998.42\n",
"116 stat-diagnostica EUR 24,100,000.00"
]
}
],
"prompt_number": 134
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = HTML(h2[0:9].to_html(index=False))\n",
"my_file = open('top10_company.html', 'w')\n",
"my_file.write(a.data)\n",
"my_file.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 83
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Top 10 investors"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"g1=dfc[['investor','money']].groupby('investor').sum().reset_index()\n",
"g2=g1.sort(['money'],ascending=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 180
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"g2.head(n=10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>investor</th>\n",
" <th>money</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>62 </th>\n",
" <td> eurazeo</td>\n",
" <td> EUR 285,000,000.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31 </th>\n",
" <td> cabiedes-partners</td>\n",
" <td> EUR 203,989,256.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85 </th>\n",
" <td> index-ventures</td>\n",
" <td> EUR 66,547,928.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71 </th>\n",
" <td> general-atlantic</td>\n",
" <td> EUR 66,547,928.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>180</th>\n",
" <td> vulcan-capital</td>\n",
" <td> EUR 31,600,000.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88 </th>\n",
" <td> insight-venture-partners</td>\n",
" <td> EUR 29,022,928.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>161</th>\n",
" <td> sofina</td>\n",
" <td> EUR 25,000,000.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81 </th>\n",
" <td> highland-capital-partners</td>\n",
" <td> EUR 24,371,500.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td> nauta-capital</td>\n",
" <td> EUR 23,557,527.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>175</th>\n",
" <td> venture-investors</td>\n",
" <td> EUR 22,100,000.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 136,
"text": [
" investor money\n",
"62 eurazeo EUR 285,000,000.00\n",
"31 cabiedes-partners EUR 203,989,256.00\n",
"85 index-ventures EUR 66,547,928.00\n",
"71 general-atlantic EUR 66,547,928.00\n",
"180 vulcan-capital EUR 31,600,000.00\n",
"88 insight-venture-partners EUR 29,022,928.00\n",
"161 sofina EUR 25,000,000.00\n",
"81 highland-capital-partners EUR 24,371,500.00\n",
"126 nauta-capital EUR 23,557,527.14\n",
"175 venture-investors EUR 22,100,000.00"
]
}
],
"prompt_number": 136
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = HTML(g2[0:9].to_html(index=False))\n",
"my_file = open('top10_investor.html', 'w')\n",
"my_file.write(a.data)\n",
"my_file.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 84
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Year vs Inversion"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"l1=dfc[['year','money']].groupby('year').sum().reset_index()\n",
"l2=l1.sort(['money'],ascending=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 137
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"l2.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>money</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>9</th>\n",
" <td> 2014</td>\n",
" <td> 4.057950e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td> 2011</td>\n",
" <td> 3.520405e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> 2010</td>\n",
" <td> 1.015254e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td> 2013</td>\n",
" <td> 9.381874e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 2007</td>\n",
" <td> 7.674140e+07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 138,
"text": [
" year money\n",
"9 2014 4.057950e+08\n",
"6 2011 3.520405e+08\n",
"5 2010 1.015254e+08\n",
"8 2013 9.381874e+07\n",
"2 2007 7.674140e+07"
]
}
],
"prompt_number": 138
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"l2.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 320,
"text": [
"(10, 2)"
]
}
],
"prompt_number": 320
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = HTML(l2[0:9].to_html(index=False))\n",
"my_file = open('top10_year.html', 'w')\n",
"my_file.write(a.data)\n",
"my_file.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 85
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"l2.to_csv('fund_year.csv', sep=',', encoding='utf-8',index=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 321
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Categories"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"co=collections.Counter(df2['category'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 139
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"co1 = DataFrame(co.items(), columns=['Category', 'Number of Companies'])\n",
"co2=co1.sort(['Number of Companies'],ascending=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 140
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"co2.head(n=10)\n",
"# Obs: each company can belong to more than one category."
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Category</th>\n",
" <th>Number of Companies</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>183</th>\n",
" <td> E-Commerce</td>\n",
" <td> 93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>234</th>\n",
" <td> Software</td>\n",
" <td> 90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87 </th>\n",
" <td> Mobile</td>\n",
" <td> 86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>152</th>\n",
" <td> Games</td>\n",
" <td> 59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td> Internet</td>\n",
" <td> 45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>138</th>\n",
" <td> Enterprise Software</td>\n",
" <td> 45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>210</th>\n",
" <td> Curated Web</td>\n",
" <td> 44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td> Apps</td>\n",
" <td> 41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44 </th>\n",
" <td> Technology</td>\n",
" <td> 41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>153</th>\n",
" <td> Advertising</td>\n",
" <td> 30</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 141,
"text": [
" Category Number of Companies\n",
"183 E-Commerce 93\n",
"234 Software 90\n",
"87 Mobile 86\n",
"152 Games 59\n",
"270 Internet 45\n",
"138 Enterprise Software 45\n",
"210 Curated Web 44\n",
"124 Apps 41\n",
"44 Technology 41\n",
"153 Advertising 30"
]
}
],
"prompt_number": 141
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"co2[co2.Category =='Startups']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Category</th>\n",
" <th>Number of Companies</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>323</th>\n",
" <td> Startups</td>\n",
" <td> 15</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 410,
"text": [
" Category Number of Companies\n",
"323 Startups 15"
]
}
],
"prompt_number": 410
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = HTML(co2[0:9].to_html(index=False))\n",
"my_file = open('top10_category.html', 'w')\n",
"my_file.write(a.data)\n",
"my_file.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 408
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Graph Analysis"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Relationships (Company - Investor) as Undirected Graph"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x1=list(dfc['company'])\n",
"x2=list(dfc['investor'])\n",
"y=list(dfc[['company','investor']].itertuples(index=False))\n",
"G=nx.Graph()\n",
"G.add_edges_from(y)\n",
"pos=nx.spring_layout(G)\n",
"nx.draw_networkx_nodes(G,pos,nodelist=x1,node_color='b')\n",
"nx.draw_networkx_nodes(G,pos,nodelist=x2,node_color='r')\n",
"nx.draw_networkx_edges(G,pos)\n",
"nx.draw(G)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
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I7z33JHvZMqZj2+sGmBjmqDTa9WBb7tlhx3YHHsFiobfCRB1vAOcCL2FiiWTw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Jq107zzX6adgn8HoY0bN6rY49EczOkZIj07dCx/jPkPkgmZK7A6tZne4xXIqPD4tbjkZ5xgpfxU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\n4TcMwwgY/w9HGLemxkY8tgAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 144
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"# View connected components into Graph"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"h=sorted(nx.connected_components(G),key=len,reverse=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"H=G.subgraph(h[1])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nx.draw(H, with_labels=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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+y5GkJ5LhK5V6CoWCyZMns2LFCt544w327Nmj75JKDRm+sHz5cubPn8/x48ep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\nSUVORK5CYII=\n"
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"H=G.subgraph(h[2])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nx.draw(H,with_labels=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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Ryu6g5XfmtQ0oLC7m8OH/LV3Z1dXCw8Mr1BoWFlbhdXZ29uUcvkito/AVcZJz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\n"
}
],
"prompt_number": 29
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Relationships (Company - Investor) as D\n",
"irected Graph"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x1=list(dfc['company'])\n",
"x2=list(dfc['investor'])\n",
"y=list(dfc[['investor','company']].itertuples(index=False))\n",
"DG=nx.DiGraph()\n",
"DG.add_edges_from(y)\n",
"pos=nx.spring_layout(DG)\n",
"nx.draw_networkx_nodes(DG,pos,nodelist=x1,node_size=50,node_color= 'g')\n",
"nx.draw_networkx_nodes(DG,pos,nodelist=x2,node_size=80, node_color= 'b')\n",
"nx.draw_networkx_edges(DG,pos)\n",
"plt.axis('off')\n",
"plt.legend([\"Company\", \"Investor\"])\n",
"plt.savefig(\"direct.png\",dpi=1000)\n",
"#nx.draw(G)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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Bo/mOtesZ5/qBgLtdZD+3C77R/l4UAQMBJwKg0ejLkSNHvpDoPC02aWlptFr9KAiLi6jL\nZep0AVSrTQSuF1PfXOr1AaxQoRp1umYETuUrO0mdrilr127K7OzsZ9YtICCAO3bsKHFpw8ePH9No\nNPLcuXPS52JiYrhq1SqS5MCBA9myZUvev3+faWlpbN68OUeOHEmSHDFiBHv37s2cnBzm5OTw22+/\nLXTuPM6cOUODwcDt27czJyeHb731FkNCQqTv4e/vz/Lly/PKlSvMzMx87utf0m/xRyALv8wfxvHj\nx9m2bVeq1XoqFHqKFn0YgZ4UrVcnAivyPfD9CIwtQZS/J+BqbwSK22eoXbzfsjciD54q/5qAD4Gf\nivjsKbtQ7iHgQmAEgdyn9jlIwERgFsUGypXAmCIEOY1ATQLj7a+3ENASPUEMAKEHMaIo4V9o3z/A\n3nDkHW8An927iCWwg2LvyZ3A9nz1yiDwAQE9xYY3mJ6eoXz//ff57rvvcteuXdLyg0XxtNjMmzef\nBkPLEuoyigpFjWfUtznV6lgC2UWUZdNgqM6PP/74mfdZfuEvaWnDpKQkTpgwgSR59uxZGo1GZmRk\n0Gaz0WAw8MKFC9Jn9+7dy8DAQJLkG2+8wZYtW/L8+fPFnjuPCRMmsEOHDtJrm81Gb29vfv3119L+\nixYteuZ3Kok/Q/j/vvFkMn97cnJy8OOPP6Jduw5QKk2IiqqMNWtKITv7Z3tM+1kAzQF8DiAVwC6I\nA6l5KyftB9CohDNUgOh/v1nCPj9B9L+vhegbbwz7iKmdOIjO9GiIg7xHII4dDIc48KsEcNtet48h\n+trHAJgBoB6A6hAHXf8NMZKnIsQB4RiIkT5fAJhkL/Owf6Y7gATAIxNYA3HMNwrimPBliM6YWwBU\nDwAst9ezLICv8tX7kf17lYQrxAFuBcQB5br4dYxDC6APxEgjA4D/4Pp1I1599S0MG3YOzZu/Cn//\nSOzbt+8Z5xBZunQdHj/uWsIeEbDZTM84ymlkZ7+BoocWVXj8eDjefnvec9Unj5KWNuzcuTNWrBAz\ngC5fvhytW7eGVqvF7du3kZ6ejooVK0pLJjZu3Bh37twBAAwdOhQhISFo0KABgoODMW3atGLPf/36\ndfj5+UmvBUGAr68vrl69Kr3n6+v7Qt/pL+EPb0pk/vHs2LGDQUFRBJQU3QtVKfqXPy/G0ptpt4Zp\nt1BL2S3TWLtFXpyFmGu3WA8XU37VbjW3JlDf/jqagI4QylCrbUC12kqz2Z9169ZlaGgUxV6HSbTG\nYRD3hcluNeeNOyjFciWImiAagmgBohmISJ39syDgaD+e1n4cR/v1UNrLQZOziQZnA3VmHZUaJQWt\nIJY5goiB/XNb7Fu4vedAAm9TdIsVd20y7VY+CLQrYb9sij0hE4G99r/Z9uu/ghqNIxs2bM2OHV/h\n0qVLJT/309JQtmwNiu6n4s7zH4q9oaKs+bxNQ+BhCeW/UKMxPPP+y2/xV6tWrUCZIAiSJZ+VlUWL\nxcKjR48yPDycW7duJUnm5uZSr9fz2rVrzzzXiRMn6Obmxp07d5IkAwMDC1j8EydOZPv27aXXRVn8\n+ff/LfwZMi1b/DIvxLJly1C3bnP8+GNLiDHw1SGGIIZAtLaLoh9E6/8kgNoQLdQDEKNt1pRwtp0Q\nrdXOAK4/VXYTYm/hCcRQzwwAwVAofoJanQOFcApPnmyDWv0YLi4qHDlyBLdvX0ZwsAXAQ4hrAz62\nf+4hxBm72fbj5QIOmUBbe3XjIHY+YgAEZIhlAESr/Bf7sTLsr9OQf9FZs7MZjes1RmJCIsaNGYdF\n8xahdv3aUOeqoT6vtn8uAWLPoSxEq303gK4A1kPs0RTFIohRTkYAtUq4hip75R9C7OEoIPa0xDCj\nrKx4fPFFI6xcWRnJyR/DbPbGjh07Ch2lfPkIKJV7SjiPKwQhB4KwqJjyQxB7V/dLOMYDaDS6Esp/\nRdTDklGr1WjXrh2GDBmC+/fvo359MbpLoVCgR48eGDhwIG7fvg0AuHr1Kr788ksAwObNm3H+/HmQ\nhMlkglKplCbbubu748KFC9I52rdvj82bN2Pnzp3Izs7GjBkzoNVq/9JlFH8Tf3hTIvOP4fLly2zc\nuBmVSmeKfmJHu4X6JoFf7Nb4DxQHVSeWYMmRQBKBvIHB5gTW8Vef+f4irT+xZ+BCsTehJdCG4phA\nWwJaKpUGKpVKli5dmh07duT69ev5888/89GjR7TZbDxw4ACjoqKoUqkYGBjITz/9lOPGjaNaraaH\nhwdNJhODg4PZqlUrvvLKK3R1dWVwcDDnz59PrVZLOIDoCOINEGNAJIBQgwEBAVSpVIyJiWFkZCQd\nHR0lC//prX79+mzWrBlLlSpFo9FIpVJJnU5HtVpNABQEgWaz2X5d9fZeh9FumTsScCPwBX8de0gj\n8I79/eP261LSOAkp9oJg/99CMYLJSnFM4Ol959jrUFAaDh8+TL3em8C9Is+hUg1ky5btaTK5UxCm\n8tdIqkwCqdTp3Fm9egMqlZOLradSOYmJicnPvC/zrOjFixezevXqBcoUCkUB3/3u3bspCAL79+9f\nYL/MzEympKQwKCiIJpOJERERfO+990iSM2fOZEBAAA0GA318fDhp0iTpcxs2bKCfnx+dnZ05Y8YM\nkuS6desYGRlJJycn1qpVi6dOnSpU19/DnyHTsvDLFMnnn39OQTASaEngO7vgnCLQ3y5QQylGupDi\nYOfoZ4hPW7vg2AhEENhnf38TRXfN6xRDE88R+ICC4E1BcGJ4eAT79u3LlJQUNmjQgCqVigkJCdy5\ncyfNZjN/+OEHhoSEcPLkydJg5fnz59m5c2e6u7tz9uzZTE9P59q1axkbG8uQkBDOnz+fGRkZ3LNn\nD4ODg5mTk0OS9Pb2Zo8ePUiS7du3Z0BAAKEFoYLo9tGCDRo04M2bNwmADx48YEZGBo8ePcoFCxYw\nMTGRHh4eFASBgiBQp9PRbDZz4sSJXL9+Pc+dO8ecnBx+/vnn9Pf3Z1JSEvfv38+kpCRGRUUxPDyc\nPXr0YMOGDenn50elg1I8r2CkIFipUETYr70fIbSgOHAOitFSTw9K521n7Y3KfALHKLq3ehbRWFwn\nUJeAl728sDT07z+Yen15+/2QN4h8iyrVEHp5hfDmzZs8c+YMW7ToRAcHJxqNYXRwcGFsbD1+9dVX\nPH78OPV6K8UB86freZB6vZXHjx//627y/xFk4Zf508nOzmZSUg+KvutBxYjJcrul6Wl/vdsuQsWF\nHv5iF/erFH38/naB302gAwEd3dwCaTB4UKl0piCIlnH16tX56NGjAvXr0aMHhwwZwtGjR/PVV18l\nSV67do2lS5dm37592bt3b7q6unLChAl8+PBhgc/abDZ+9dVXbNy4MT09Pfnmm2+yYsWK3LBhA+/d\nu0eNRsOBAweSJPv168d3332XX375JQMDA+nl5cV169Zx5cqV7NatGxUKBQMCAqjVahkZGcl27dpx\n/PjxXLNmDXfu3MkePXrQxcWFbm5uTE1NJUneunWLiYmJDAwM5JdffkmSfPLkCT08PNi9e3dOnDix\n0O/x4MED/uc//+H8+fM5YsQIVqtWjU5OTnQwOPDXnoWRYoTU0+J/n6K1r6foe69PMUTWlcClfPul\nUQxpHUMgy/5eYWmw2WycM+cDengE09ExmE5O5anVOrNjx//jjRs3Cux7//59nj59utD7GzZsoF5v\nplbb3d7ob6JW2516vZkbNmx4kVv1pUEWfpk/hVOnTjE5uT99fCKo13tSEDztluGjEiz4POvzmF3w\nK1EMqXx6v1wCPShOfjpEcaBRT9GNYaJarWV0dDT1ej3VajUbNWpELy8vqtVq1qlTh25ubpw8eTLv\n379Pkrx+/TrNZjNdXV2lwbr79+9z4MCBVCqVLFu2rBTOVxLHjh1jYmIiDQYDfX19OWfOHJYvX57t\n27fn6dOn2b59e9aoUYNt27alUqmkIAj09vZmQkICa9euTQ8PD7799tslTjo6f/4869evT0EQ2KZN\nG1qtVg4ePLhAY7Zq1SrWrFmTtWvXliYflcStW7fo5OTEx48f85dffmG3bt0oCHmD7MEUw05XExhu\nb7wNFF1sVSn20NIouu3yT7J6n0CLp3634qUhNzeXx48f58GDB6Xf5UW4efMmJ058k3FxjRgX14gT\nJ775XL/Zy4os/DJ/GNu3b7dbiy52a9xMIJRi5EYcgSYliD4JTLZbjjUIPLFbkCEEEij6pH8g8BnF\nyB0/5kXbmExOnDx5Mnv37s02bdqwVKlSVKlUbNeuHcuWLcvo6GgGBATQ29ubgiAwIiKC4eHhdHR0\nZK9evXj8+HGGhYkujhYtWnDatGm0WCzs3r07T548yRo1arBz5848ffo0169fz8mTJ3Pw4MGFvn9O\nTg7PnTvHDz74gBqNhoIgUKvVUhAEBgcHMyoqiuXLl+eyZcvYvn17xsTE8P333ydJNmrUiJ06deLw\n4cOfeZ1/+uknurq6UqvV0tXVlbNmzZIiZ0iyVq1aXLFiBU0mE2/duvVcv139+vW5YsUKJiYmMjY2\nlh988AFDQ0PzWf+u9sYVBASK4wVBBG7bf7twAt/m+y1j7b/Z8wm/zF+LLPwyfwivvfaaXSCiKfrd\nDxJYabfa8/z6TZ9D+CsTCCRQhsCnFCcgvWEXGROVShdGRESwU6dO/OijjwpYxw8ePKC3tzd3797N\ngQMHUhAEajQali5dmo8ePRLrqBH96tCCKpWKRQ2exsTEcO7cuZw6dSq7dOnC6OhoKhSKAvsolUqu\nXbtW2qd8+fLU6/X09/dnkyZNWL16dSqVSrZq1YoKhYKtW7fmpEmTmJCQQJL85ptv6O7uzmHDhjEn\nJ4dOTk5ctmwZGzRoUOw1zsnJ4cyZM+nq6sqhQ4fSxcWFX3zxBZs1a0YfHx/Onz+fR48epYeHB0+c\nOEF/f//n/v3Gjx9Po9FIV1dXGo1Gtm7dmm3atCEAjhgxgm3atOGQIUM4ZswY3r59m/PmzWN4eEU6\nOFioVvckUItAA/7qmvOnmLoi/2CtS5HXW97++s3FxeU3P+vFIQv/S0RmZiY3bdpE0QL8Pxb2Cdso\nzhjNcxGU5OqJpBipc5qimyAvMkWM/GnevDmXLFnCgwcPFvLT57FmzRpGRERw+/bt1Ol0VCqVrFy5\nMjUaDaEGUQNED4iRNV4QG4Lf+PDEx8dz0KBBXLhwIffv31/A///ZZ59RqVTy0qVLVKlUnDVrFt3d\n3enk5MTNmzczJyeHLi4ubNSoEQ8fPszw8HBeuXKFVqu1yNmvx44dY6VKlVizZk2ePXuWJJmSksLE\nxESS5Hfffcd69erRZDKxRYsWTE1NZdu2bYv93Ww2Gw8fPsyxY8eydOnSVCqVVCgUXL58OdPT03nu\n3DkajUY6ODgwNTWViYmJ3LZtG6tUqVLgOGfOnOE777zDfv36UatzpZjm4haLtvjzbz/b74miBpCn\nEehi/2siYKZabWWHDh2Ynp7+wveozF+DLPwvATabjTNnvkej0Y1iiKSBwONiHvIn9oc8L9KmqAHb\nZfZj2EMOlc5ENIi2onXu4ODAbt26sVOnTixbtiy1Wi0DAgLYtGlTDhs2jIsXL+bBgwf58OFDNm/e\nnGazmX5+flyyZAmdnZ1FgU9EwfQGY0B4/HbhX758ebHXp3///oyNjeUbb7xBDw8PXr58mUeOHKGn\npyfLli3LqKgoVqlShV5eXnz33XfZo0cP2mw2WiyWApOAMjIyOGrUKFqtVi5YsKBAbpy0tDR6e3tz\nz5490muj0ciKFSvSbDYzMTGxwP5ZWVncsWMHBwwYQD8/PwYHBzM5OZm+vr5MSUlh48aNuXz5ct65\nc4ehoaGMi4ujh4cHFyxYwO7duzMjI4NGo5F3794t8jtPnz6dWq0LxUH6EIoDv8UNzg8j8GoxZT/Z\nG/sYiuknviHwEYFwKhROPHPmzO+9fWX+BGTh/wdz4sQJvvHGOMbF1aJGE0YxHLMzgY4lWHck0Iui\nn9jZbs3voTjj8iTF6BHROq9ZsyY1Jrt1rgFVBhXHjBnDb7/9lm5ubpLYZmdn88yZM1y7di0nTZrE\nTp06MTo6mjqdjgaDgQDYoUMHTp8+nW5uboQTiLEonNSsndiwPI/QOzk5MTg4mE5OTlQqlTQYDKxb\nty5Hjhys0AYIAAAgAElEQVTJdevWSYJts9no5+fHTZs20c3NjRUrVuTevXt58+ZNurq60mazccuW\nLYyKiiIARkdHc8GCBSTJevXq8fPPPydJfv311wwLC2ObNm2KnRGamprKmJgY5ubm8sMPP2SLFi1o\ns9lYunRphoSEMCoqiiNGjGBSUhLNZjMrVarESZMm8cSJEzx69Ci9vb05e/ZskuTixYvZvHlzVqtW\njcOGDWNoaCj9/Pw4e/ZsKWa9SZMmUlKy/Fy4cIEWi4U//PADW7ZsQ3Gw15XifIwn+e6DXLuIuxG4\nWMy90oZA1yJ6AzkEkqhSmXnlypU/9saW+d3Iwv8PJC0tjfXrt6JW60Glsp/dIvs534Pa4xnCP5Si\n62ax/a8LxUk9BgIOTEpK4vXr16XzHT9+nN9//30Bi/X48eP08fHhu+++W2w9Z82aRb1eT09PTzo4\n5AtP9CpC9MeB+Fdh4c+bnNW9e3d26tSJnp6erFSpEv/973/TZrPRZrNx9OjRDAkJ4ZIlS/jGG2+w\nUaNGNJvN9PHxYb169Wg2m7lr1y42bNiQFSpU4KpVq5iTk0OlUinF+GdnZxMA1Wo1zWYz33jjDfbr\n149vvPEGe/XqRW9vb65du7bE38VmszEuLo4LFy5kuXLluHXrVl69epUODg5s0KCB1BAGBgZy5cqV\n0ue++uorurm5FRDxe/fuUa1Ws1WrVrxw4QLNZjO9vLz41ltvcciQISTJ2bNn85VXXilQh9zcXNas\nWZPTp0+X3hs/fjzVaq29p+dE0Q3Yh2IUkJFiyo2i7pPz9gYjvZjyxwQcqVIZWaVK3WcmhpP565CF\n/x/Gf/7zH1qtQRRzzijswp0/l8v7FKNsiuvW54Vq1iRQhhDCKGa3NDIhoQ1v37793HW5ePEiw8LC\nOGrUqAIP/O3bt9mtWzfRl1+Uxa4CMawI4Y8FoRQHa+Pi4jhjxgyuWLGCFotFcmlkZ2dzxYoVjIqK\nYnR0NFeuXMmcnBxOmzaNgYGBUsZFm83Gc+fOsW3btixXrhyrVKlCBwcHKpVKVqpUifPnz6eTkxOv\nXr0q1dtkMtFgMPD06dPs0aOHtH/nzp2fO6zxwIEDdHFxodlsZlxcHI1GIx0dHblixQo+ePCAOTk5\nXLZsGUNCQli7dm2++eabtFqt3L59e4HjpKSk0MXFhQsWLODs2bPZuXNnGo1GTpgwgWPGjCEp+vS9\nvLwKXPv33nuP8fHxUoO2YsUKenl58bvvvqNCoWCPHj1YtmxZBgUFMTg42N4bqFSERU8C77LkfEK0\nNyLvUBz0d6SXVyiXLk197ntI5s9BFv5/EFu3bqVKZaSYtOwDAjftD2b+afK5dgt+WTEP6ia7ZV+a\ngI4uVheGhISwdOnSdHR0ZJ06dTh27Fhu37692EHb/Ny6dYsxMTHs2rUrP/zwQzZo0KBQ1E2hTQ3C\nF8QQu+CPtY8fqMDk5GRaLBYajUaazWYGBQVRqVTSaDSyUaNGHDx4MBcuXMjvvvuOq1atYlxcHEND\nQ/nRRx/xvffeo7e3N0+cOCHVLyYmRkrAlZmZSVdXV0ZHR7Nr167UaDTU6XSsXr06Bw8eTDc3N2o0\nGl6+fJkJCQn09fWV6tG5c2cePXq0wHfPC9vMzc3lvn37OGLECEZERFCpVNLT05Nbt27lggUL2KlT\np0LXLSsri126dKFSqWR8fDy///57qWzBggUMCQnhvHnz2KRJE9arV4+rV6+mQqHgiBEjOHnyZJJi\n4xYYGCjNhj1//jxdXV0lv/vGjRvp5ubGw4cPs2rVqnR2dmZCQgKtViv9/f1Zr149Ojk52WfbdiBw\nN999kk6gIYHXniH8AwnMsP8/mEArarWhnDhxyovc2jJ/MLLw/wO4efMmu3btau+WB9qtrAR7t70a\ngeSnHsblFOO8p+R7mB/YH1A9ASUdHLT09/enWq2m0Wjk6NGjefbsWf773//m8OHDGR8fT71ez8qV\nK3Pw4MFcv34979y5U6BeDx8+ZGpqKhs1akRBEEoWe/tmsVg4btw4KnQK0fJ3g5jL3gEMCgoiSV65\ncoUBAQHs06cPy5QpwwoVKtDR0ZFz587llClTmJSUxAoVKlCv19PHx4eVKlWiv7+/FJljtVp56NAh\nXrlyhWazmVlZWVKde/fuTQ8PD5Jk9erVuWnTJm7bto2TJk2iXi/GxguCwNDQUI4ePZoODg48efIk\np02bRk9PTzZs2JCLFi1i165dqdPpWKZMGbq5uTEyMpIpKSn88ssv6eTkRBcXF545c4b9+/eXcr7k\nYbPZOH78eAYHB/PUqVN877336OnpyYSEBH744Yd0d3fnmTNn+PDhQ5pMJjo6OjItLY06na7Q8Xr3\n7s23336bubm5rFGjBt955x1mZGRw5syZ1Ov1rFKlCo1GIwGwYcOG/OSTT3jp0iVmZmYyLCyMXbt2\nZXh4OBMSkujg4GzvCTam6OKpRKB8Cb1HG8UFeLbZX1+hOG40l4ADZ8+eLfU8ZP5aZOH/H+bJkyfs\n3r0fVSodxVDKZU91ye/aGwATCy9QsplihI+DvQegYd6KTVqtlseOHWPVqlVZsWJFVq9enVqtllqt\nlomJiTxy5AhJMj09nV999RUnTpzIBg0a0GQyMTIykr169WKDBg0K+u1L2CpUqMBp06bx5MmTDAgI\n4BdffEGS3LJlC+vUqcOOHTsSALt16ya5Lc6ePUtPT09++umnXLlyJS0WC11dXbljxw5pn5ycHF64\ncIEbN27k1KlT2aRJEzFqyC7ePj4+DA8P50cffcS9e/fywYMH/Oqrr6hSqfjDDz8wISGBn376qXQ+\nlUpFvV7PFi1acPXq1RwyZAgdHR2p0+kYFhbGtm3bMj4+XkrAln8LDAzk0KFD2atXL3bs2JFvvfUW\nmzVrxipVqkgpfPPq3KdPH5YvX75AuoPHjx9z0KBBFASB9evXl1aWio2NZZkyZUiSbm5u7Nq1Kz/4\n4APpc+vXr2etWrXYq1cv+vj4sFq1atTpdNKkuXXr1rFBgwb08fEpcG+NHz+e0dHRDAoKktxdt27d\nYmpqKqOiytkNBK397+ZihH8DxUmB+e9JM4F4ijO5w+nq6sfVq9f8cQ+FzHMhC///MI0bJ1ClKmW3\nvt4p5uF7QnEqf0UWzoV+lYAftTojhwwZwipVqnDQoEHU6/Vcv349c3JyOGPGDLq6unLUqFFs3bo1\nDQYDTSYTq1WrxrVr1xaw2LKzs3no0CHOnDmTgYGBJYq9Wq1mcnKyFOeex5YtWxgUFMTHjx9L761e\nvZrx8fGsUqUKu3fvLp3z8OHDtFqt3LZtG9PS0ujq6kofHx/WrFmzgJg+zfHjx1mtWjUCoJ+fH9u1\na8eKFStSr9fTw8ODCoWCZcuWZY0aNThw4ECOHTuWZrOZarWaLVq0oKOjI2vWrMl+/foxJiaGtWrV\nYkxMDLVaLf38/J6rwYuMjKTRaKRKpeK3337L7OxsZmRksE2bNqxTpw5/+eUXqb4bN27kyJEj6ebm\nxoULF3LChAl0dXVlcnIyK1WqxIiICJJkcHAwExISOHPmTK5evZoDBgxgdLSYmVOlUrFfv36cN28e\nLRYLN2/eTFIUc4VCwTVrfhXfs2fP0tHRkZ6envzxxx+LvIZPnjzhtGnT6OzqbBf/Ofx1LYE0Au9R\nzASaf4ZwJsVe6Q8UM7zWJlCRKpUz58x5/3lve5k/AFn4/we5dOkS69ZtYLfyE+yWV0kLXLxH0Wfv\nSDGiZyqhEFMbq9U6KWFYamoq69evL6UryOP06dOMjY1l7dq1uX37dnbq1IlGo5G+vr708/PjjBkz\neP/+fd66dYtz585lzZo1JfdB/i0vJbG7uzuVSiU/++yzItdY7dixY4F0CPXq1eOyZcuYlpbGunXr\nsl27dtIs4K+//ppWq5X79+/n0qVLWblyZS5atIhBQUGsV68e9+7dW+Q1TEtLo4ODA7VaLQ0GA/v2\n7csLFy7w3LlzVCqV1Gq1DAwMpEqlolKppJOTE00mE729vYsVdovFwlq1akkhqi+y5WX0dHZ2Zmpq\nKn/66Sfu2LGDWictoQPhLYbM6px1rFGjBg8dOsThw4dLjejkyZNpNpup1+tpMBjYpEkTTp06lbt3\n76bJZGLPnj155swZenp6FogOatGihbROLSm6maKjo2k0Gnny5Mnnuh/FmeB5C9J4U+xhtqKYbTX/\nfZhK0TVkIdCd4uIzH9iNEheWKlWJK1eufO5F12V+O7Lw/w9x9+5dNm/eikqlg130dxPYSDHqpqQB\ntp327rUjARWhVFPpoGRQUBBnzJhBNzc3nj9/npmZmXRzc+OCBQuoVqsllwtJKTLGYrFw3rx5/OGH\nH9i1a1cajUZ6e3tTpVJRo9GwWbNmXL9+PdPT0xkcHEwfHx86OjpKaZMvX77MUqVKMSIigiqVigaD\ngfXr1+eECRO4a9cupqen88aNG7RarTx27BjPnz9Pi8UiDZRmZGSwVatWbNSokdQr2LhxI93d3Xni\nxAlWqFCBK1euZFZWFhcsWEA/Pz82atSI+/fvL3AtP/vsM9avX1/qNTRt2pRms5ldu3alu7s7Q0ND\n6eDgwHr16vHJkyds1KgRQ0NDnz0w/Vs2B3uYqiekORHSIHdDiOsBjAMxCuKi7RowMTGRderUoVqt\nlmbyOjk5sUyZMgWs97zc8t26daOfnx8XLlwold25c4cKhaJAQ5CSkkKVSsV9+/a90L359ddf0+xu\ntot/UWsFn6C4BoALxUleuRTTP/vZ9/+SwFLqdNUZEBDJy5cvv9D5ZV4MWfj/B8jNzeXw4WOoVhsJ\nNKOYLrkBRX/pSHsjkFnEw5a3LSIQRL3ejW+++SZPnz7NdevWURAEdujQgVOmTGGZMmWYlpbGkSNH\nsn///jQYDPT09Cw0YHvy5ElWqFCBpUuXZp06dejo6Eh/f3/q9XrGxcXRarWySZMm/OKLL3jw4EH6\n+/tz/vz50ue/++47BgQEMDs7m2vWrKGrqysnTZrEIUOGMDY2VjpOntAOGDCAgwYNKlCH7OxsdunS\nhdWqVeODBw9IkkuXLqWvry9XrlzJgIAAZmZmkhRdEnPnzqWPjw+bNWvGw4cPkyS7devGOXPmkBR7\nNL6+vnzrrbfYrVs3AmJ+FK1WHOC2WCzU6XT817/+RZVK9dwD1c+1qUHUzyfuKSAqgBBABKFwSOtY\nEI6g1Wplw4YN+eqrr3Lt2rWMi4sTc/jbw0sfPHjAM2fO0GKxcPHixVSr1Zw5c2aB69iqVSt6enpK\nr7/44gsqFArOnTv3N92n4gzxmRQEA9XqaIrhngsprsXgSHEy4GD7PTmJYuBB4Z6qUjmZoaHlCswL\nkfljkYX/f4DBg1Oo18dSXCwj/0NyluLgmRt/Xd2q6MgKi8WngN98x44djI2N5ZAhQ2i1WlmjRg22\nbt2aP/30E81mM5s1a8bGjRuzbdu2tNlsfPToEVetWsXWrVvTZDJJGTPnzJlDm83GCxcuMDk5mS4u\nLmzSpAnDwsKo0WjYpk2bAudt27ZtgUldO3fupNVq5WeffUaSfPToEXfs2MGxY8fSyUkcbA4LC2Pf\nvn25cuVKaaAxNzeX/fv3Z/ny5aWslrNmzWJoaCgbNmzIt99+u8A1zMjI4OzZs+np6clWrVrR2dmZ\nly5dksq///57GgwG6vV6yZ+flzju448/pl6v55MnT+jl5cVdu3YxPDycXbt2ZefOneng4FD8nIRn\nbYFFiPsb9l5AsyLKxtmtfoBGo5GzZ8/mw4cP6ezszObNmzM4OJgNGzakxWKhn58fx48fzzJlytBg\nMBTw19+9e5cKhUKaKHb06FHqdDo2bdr0N9+n6enpLF26NOfNm2ef6WyiGMUTSjGsOJzi+smZ9nv2\nzFP36gGKEWkVCFjp5ub9u1evkikaWfj/5hw5csQem3+rGGE/wV8X+f72qbJsAn0pCMZCKxuNHTtW\n8qMfPnyY5cuXp8lk4oABA9iqVSt27NiRnTt3pq+vL2NjY+nk5MSGDRvy448/5r1790iKk8UqVKjA\nRo0aSV3zn376SVqopHTp0mzcuDEtFguHDx/O3bt309XVlWlpaQXqcvjwYXp6eko9g7yonHfeeYcq\nlYqbNm3i9OnT2aJFCyl2v1u3blywYAH79OnDUqVKSecfM2YMIyIi6OrqWqi3Qori1L9/fymq5fPP\nP2dSUhI1Gg3VajUdHR0ZHBzM0aNH8+2336ZaraaXlxdNJhM3bdrE+Ph4fv3113zvvfeYlJREkmzX\nrh1TU1N59uxZKRV0YmIiy5UrJy7hWJzoa0oQd0cQ1YspCyl4HLPZzNDQUEZERNDf35/79u3jsGHD\n6OrqSrVazVq1arFz586cO3cu586dy2vXrjEhIUEKWz179iytViudnZ1/U379PAYMGMDWrVuzZs2a\nrFy5sn2sQ0ugkf3+DKA423cXxcRw+Y2TFIouy6n2BmAnxVQQOg4YMOA310mmaGTh/5vy+PFjtmvX\nzR6q2akY0c/balLMv6OnmEt/AsWJM2bq9Z40Go3U6/Vs3LgxN2zYwOzsbNauXVuK7CBF98n48eMp\nCALLli1Lo9FIQRBYqVIlGgyGAhOI8pOVlcUJEybQarVy8eLFfPjwIePi4vjKK6+wT58+dHFxYXJy\nMnv27EmtVsvw8HB+++23habunzt3jkFBQZw4cSI/+eQTVq5cmREREWzZsiVbtWol7Zebm8sTJ05w\n7ty5UsOU9/1GjRrFQ4cOsXfv3vTy8mKfPn24b98+pqSkSJPNbDYbk5KSWLVqVVqtVgKgRqPh8OHD\nmZGRwdu3b9PX15dlypThxYsXqVQq2aVLF7Zu3ZrR0dF0dnZm3759eeXKFTo5OTE9PZ2TJ0/m0KFD\ni7w+OTk5nD17NkuXLs0333yTiYmJ4pKOgLisYo1ixN3d7vd/egZzP4jzG4ppTBQKBZs2bUqTycTY\n2FgmJCSwWbNmNJvNDAkJIQDqdDoC4Lx583jp0iX6+/vT19eXy5Yt+83369atW+nt7c1y5crR3d2d\n8fHxPHfuHN966y37felNwJdi7p/NFCd/5R/0jeSv6wXk37YR0HHRokW/uW4yhZGF/2+IzWZj5cq1\nqVC0ITCEz17IvDvFNVXTKK6J60hvbx/JfXL9+nWGh4ezZcuWjIuLo7e3N9VqtRS1kZ2dzW3btrF7\n9+6S4OcNFp4+fZrTpk1jjRo1Spxsc+TIEZYtW5YWi4WdOnWShP3y5cvs378/nZ2dqdFoOGzYMIaE\nhDAmJoapqakFIjiuXbvG6Ohoenl5SUKWF0lTUh6cixcv8pVXXqFer2dgYCD1en0hS3vs2LHs168f\nfX19qVKpWLlyZTo5OXHUqFEcP348LRYLu3btynPnznHZsmVSxkxBEGixWPjFF1/QZrOxffv29PX1\nZUhICMPDw7lixQpu3ryZ9evXL7Z+tWrVklwqs2bNoo+PD1NSUsSEdMWJu9K+GUE0B/EKiLqiC0jt\nUHieQFGbTqfj66+/zv3799vXUC44NuHo6Eiz2SzN/v2teXRu375NDw8PKefS9OnTmZOTw4MHD7JU\nqVLs1KkT+/btSycnN4qDuScoRvak2639cgS2lnB/JxFQFxqgl/ntyML/N+PixYsMCAi0PyDZFEMx\nuzxD+GtQjO45TkGoQ0dHt0Jhkjdv3mTp0qU5atQoLlmyRFrEIyAggM7OzqxUqRKnT5/OS5cuccmS\nJTQajVQoFKxWrRrv3r3LGjVqcNq0acXWOysri82aNWNkZCTd3Nz4ySefFBCSYcOGMSIigi4uLnz1\n1Ve5ZMkS1qtXj56enhw/fry09N6BAwcKCdjSpUvp4+NTILY9P/fv3+fy5csZFxdX7MCrSqWSYu9V\nKhWrVavG06dPS8d48OABx48fT1dXV7Zs2ZLh4eFs2LChFMWTl+Zh3rx5TE5O5tdff83SpUtTp9Nx\n7NixtFgsRQrnqVOn6OHhwczMTI4YMYLh4eFSQzV37lwKDoIo7i1AdAdRz+7fz1//vKiffOsRKBQK\n1q5dm2az+bkaAU9Pz2LLBEHgoEGDnisFx9PYbDbWqlWLGo2G7u7uPHXqFHNycjh58mS6ubkVSDZn\ns9mYkNCZGk1FiktBTqc4bmVm8YvF51n9LhQErbR+sczvQxb+vxFdu/4fxSyYRoprp5Kib9+JwI1i\nHopjFHPreNLBwYVarSNjY2OLPP7NmzcZHBxMb29vGgwGRkVFMSEhgVFRUfT39+ekSZOklML9+vWj\nQqGgl5cXvby8OHfuXFqtVmnWbn5yc3PZuXNnNm3alE+ePOGhQ4dYpkwZtmrVijdu3GBmZiY9PT15\n/PhxXrt2ja+//jpdXFzYv39/btu2jT169KCzszP/9a9/MSkpqYAoxcfHkySTk5OldMOk2JOYM2cO\n69evX+zqXPk3o9HI6tWrU6FQUKFQsFy5cnz11Ve5evXqAjNk7927x9dff52CIDA5OVny/b/22msk\nxQlmedb93bt3aTAY2LJlSwqCwKFDh0rjH3kMGDCAI0eO5P/93/8xNjZWSnJXtmxZHjx4kCQZFBQk\nCrtOFHl/f39+++237NSp0zO/m5eXF5OSktiuXbsiZwy/yObm5saZM2cWWBqyJGw2G9u1ayf9TtnZ\n2fzxxx9ZtWpV1qlTp8iQzNzcXM6ePYdWqx9FF9BAim6gkgybvfbGoSwBAx0dfZ6ZCVWmZGTh/5sw\nbNgIihEQThS7wXm++msExlCc5HL5qQfiFAFPCoKOrVu34ZMnT5icnExXV1fJLWOz2Xjo0CEOHTqU\n/v7+DA0NpUajYZ06dQpYqN9//z179uxJFxcXJiQkcMuWLfT09KRSqeTOnTsZERHB8uXLMywsrIAw\n2Gw29urVi7Vq1Sqw4lJmZiZTUlLo7u7O3r17s2HDhgW+740bNzh48GC6uLiwT58+PHLkCMePH18o\nTj4oKIi3bt3i3bt3abFY2LNnT8bExDy3mDk4OFAQBBqNRjZv3pwxMTHcuHEj9+7dy6lTp7Jp06Z0\ndnZmaGgoX3nlFS5atIjnzp2jWq3m4MGDqVAoaDAYqFQqeeXKFZ46dYqhoaHS92jatCk/+eQTVq1a\nVUrxPHToUF67do2PHj2ii4sL69aty8aNG0sW9ePHj6nT6aSQU5vNxsTERCYkJEhRVRcvXiQpuukm\nTJhQwP1V1JYXzw+A9evXl/IKFbeV1KB4e3tz3rx5JU6kunjxIitVqkRAXPs4NzeXixcvpsVi4YwZ\nM54Zipmbm8t169ZRoRRXbBPv5eKEfxzFNSJI0T00h4C+yLUGZJ4PWfj/BuTm5lIQHChGQLQnsI6i\n66YXxQkvK+yNgDPFGZED7Q2DloMHD+XWrVtZqlQpaZKVt7c3R48ezVGjRjEkJITBwcFMSUnhsWPH\nmJ2dTZPJxDJlynDQoEGF3BMPHz7kvHnzWK5cOck9MGDAAGZmZnLcuHHUaDSsVasWc3JyaLPZOGTI\nEFauXLnAUob52bdvHzUaDWvUqFHkYuI3b97kgAEDxLj1PHeGw6/ujHLlytHZ2Zl+fn7PLfbu7u5M\nSEigk5MTa9WqRZVKRZPJxGnTptFkMkmCm//6Hzt2jHPmzGGHDh3o5eVFpVLJxo0b02AwMDIykoIg\n0NHRkQcOHKBWq5WE7ZNPPmGzZs04bNgwTpo0iRcvXpTGNKpWrUqTycQuXboUSAS3Z88eVqxYUXo9\nZswYxsbGSmGv48aNY/v27QvUMSsrix06dJAGpJ/VC/jggw+YmprK1q1bFznT2Gw2S9lHiztOYGAg\nlyxZUmBsx2azccGCBVLIa+3atXn79m22bduWZcqU4bFjx57nlpdYunQpxTxRrVl0srfrBDwIHH3q\n/QXU6Txe6FwyvyIL/3+Zs2fP0t3dn2K395sibvz/EHCnGOL2gMASAh2pUOoZHx/PxYsX02azsVq1\nanz77bcZHx9Ps9lMhULBvn378uDBg4Us+8jISN67d48VK1bka6+9VqRv2mazcf/+/ZIw1KtXjzt3\n7uTevXvp4ODAUqVKsV+/fixTpkyxy/uR5Oeff86oqCgOGzaMHh4eBWaVkmJ8vaAVxJQEXUAMgBji\n+JwrbeVtpUqV4vDhw/ndd98xNzeXmzZtooeHB/V6PXv37k1vb286OTmxcuXKz/xNbDYbY2NjJYvf\narXSYDBIvRGNRiNNBDt79iwVCgVr1arF2NhYyVV26NAhKpVKajQaJiUlFUh/8O6777JXr14kyUWL\nFjEwMFAa4yDFHoGfnx+/+uqrAvUqVaqU9H0VCoVk4Re3ubi4cMiQITx69Cjnz58vvR8bG8vk5GRG\nR0czPDycRqOxRDdReHg4U1NTRZeWTiB0oKASpNTSPj4+fP3115/bRUSSV69elaKy5syZQ9G92ZLi\nwC8pruC1gUAYxcleTz8XWQScmZTUrVBDLvNsZOH/L3L16lU6OXlQTKU8pISu7iz+ulziDgKO9PDw\noMFgoE6nY82aNenh4UGlUsnw8HCmpKRwyJAh7Nq1a6Fzzpw5k7179yYpDopWqlSJr776arERHaNG\njaJOp6OzszNLlSrFsLAw9ujRgzqdjgqFgv379y8wQetp6tSpIw3I7d27l2FhYezUqZMUY1+tWjXC\nFeKauuPybX1RYtiiIAiMi4vj1KlTCwzS3rhxgx06dGBISAh37tzJH3/8kUFBQYyMjGRQUBBNJhO3\nbt36zN+mS5cuHDduHM1mMydOnMhr164xODhYCokExARvzZo1K1Q3X19faTWtAwcOcNKkSXRzc2Pr\n1q154MABdunShR999BG3b99Oq9VaoP55rFq1itHR0ZK1febMmQLneJHUEYIg0MXFhSqVilOmTGHt\n2rVptVqlsY0TJ05w0KBBdHZ2Ln5WsgaEi71R7gAiCoRaXOJy27Ztz7yeeWRlZXH69Ol0dXXlyJEj\n+ejRI06YMEEc55CyfZrs/5cisLqE56I2VapgVq1aX87v84LIwv9fwmazMSwsmmI8s5HierbF3eB3\nKdcuxBUAACAASURBVHaHYwmYGBNTmTNmzGD58uWpVCoZHBzM2NhYqlQqqlQqxsbGcuzYsbRardy0\naVOB87Zu3brAwuMPHjxgbGws+/btW6Rf9pdffqEgCOzevTvj4+O5a9cuxsXFEQA9PDxYp04dBgYG\nSone8nP48GF6e3sXcHOkp6dz0KBB9PT05Pr166nQK4qfxORT2C+tVCqp1+up0+k4d+5cqdGx2Wz8\n+OOPabVaOXz48ALjDXmiDYDLli2jxWIpNnlbHikpKWzSpAljY2OlCUTLlv0/e98dFdW5fv1ML5Rh\nmMLQRgYQKRaaKFUREVRAJTSjYjSixgYWNHZAoyb2hprYYouxRhNL1KgxxsTyu/bEqNGAFRXsipTZ\n3x+HOTLMgCXe+617w17rXcqZ0+bMOft9zlP2sxaRkZHw9fWFtbU1wsPDX2l1G65Tly5dkJiYCLVa\nDalUitzcXMjlcqhUKkyaNMkkKKzX6xEREYHFixcDYJqjv8q9Zfhdahtz587Fn3/+CT6fb1aWQa/X\nY/fu3fDz8zPdXk2gcTV+n47Mm9nrpoEeOHAA3t7eaNeuHdsMZs2aNWwzmxYtWoBJbggCk9f/qjTm\nJiD6FBJJLKZPn/mKo9ejOuqJ//8DTp8+DZnMCYwWvsHNc6OOG7wSRBwQNQeHYw0bGxv07t0be/bs\nwebNm+Hv7w+9Xo99+/ZBKBRi0qRJGDFiBLy9vcHlcuHu7o5u3bph1qxZkMlkrJa7AQ8fPkRwcDD6\n9etnlvxdXV0RExODLl26oE2bNtBoNPjxxx9hZ2cHe3t7aLVayOVyJCcnG/nxu3Xrhs8++8zsNThw\n4ADjr5YQ013LHPE3JKOCspSUFKxfvx5qtRpqtRr+/v5swDc0NBT+/v6sC6Ym1q1bB4FAAB8fH3z3\n3XdQq9Um1czVsWjRIuh0OvTt25ftkFVWVgZHR0d0794dqamp0Gg0VZbqm7mlDFZ49VoDKysrjBw5\n0qiX8cmTJ6FWq1FSUoJWrVrVui+pVIr169dj1qxZICIEBQWZfSNIS0vDyJEjIRaLcezYsdpvUDDd\nunx9fZltxQRKMfP7TGA+s7S0RHR0NLKzs7F27VqcP3/eKJ34+vXrSEtLg1arxebNm9mJYsOGDeBy\nuaze07x581BQUAC1WgeiKDDZPrWleV6oem4egugXaDRu9f183wD1xP8fxunTp8HlWoERWiuquonb\ngOjrOoj/RxBZQSqVY9GiRUavtZWVlfDx8WELjEQiEduBSa/XIzY2FkOHDsXy5cuRmpoKoVAIqVQK\nX19f9O3bF1988QVOnz6NkpIShIaGok+fPibkP3r0aIhEIqxcuRI8Hg+jR48GwMQLVCoVvv76ayQn\nJ7Myx+PHj8dff/0FW1tbVkStOr7//nv4+Pi8JCUPM6QymnElGCzehw8fYvLkyaySplKphIWFBTIz\nMyEWiyGVSpGUlGS2KhgA+vbtizFjxkAgEKBDhw5YvXo1HB0d8eeff5r9nb777juIRCKsWLECbdu2\nZZdPmTIFQUFBGDBgALZs2QKFQoFp06YxxVhvMQHUHIY+BVevXgUAxMfHQyypkmY2E/fg8/nYt28f\nsrKyjJZbW1sjKCiI9d3PmjULkyZNAo/Hg42NDTp37lyri+7WrVtISEhAkyZNsHnzZua4/WqZnFXm\nv4erqyvKysowffp0tp+D4XgPHjxAly5dYHgb2rRpk1EAuaioCJGRcWDehDNhGvS9X2Xtx1VNAHkg\nEmPOnDm11nrUwxj1xP8fhlbrjZcKhYbxNZh0TXMKm5Ugag0rKxlEIhFEIhHCw8OxbNky1j2wZs0a\ntGrViiV+V1dX1uIy9FktKChAfn4+PvjgAzx//hy//vor5s+fjx49esDT0xMWFhZsVW+rVq3wxx9/\nsAR64sQJVud98+bNUKvVOHDgAACGCCMjI1FZWYk7d+5gyJAhEIlEEAgECA4ONnoQf//9d3Ts2NGU\nKAQEakOM7HAOgYYRyIXAk/JMrt+jR48wZcoUyGQycDgcSCQSXL58GY8ePcK8efPMVgVXVlbC3t4e\nFy9exNSpU2FnZ4eOHTtizpw5cHNzM7KyDdixYwf4fD5OnDiBZs2ascsNufuGyWD37t2QSqWwsbGB\ns7MzOnfuzHYra9iw4SvTKl/lviEBgbwIlEagRAJpiC3k4nA42LZtGwBg3LhxRts2aNCAnUjEYjGK\ni4vRo0cPDB8+HJMnT4atrS0UCgWGDx9u1IB+3bp1UKvVGDt2LEpLS1FQUMBkWbU1Q/ojqdY4jFar\nhVarRZs2bdhmO1euXMHAgQMhFArB5XLx/vvv1/qcnD59uqqngyUYkbe5YPz9o8CkPAeAKBlM4sNQ\nEE2DQNAJEokN8vMXv8aT+M9GPfH/B1FQUADGV/8+GH38aDD9R++Dka6NBpPFYyD9SyDqBAsLDR4+\nfIiLFy9iwIABsLCwgKOjI6RSKeLj47F69Wq4uLhgz549bLD3yy+/ZI+bm5uLLl26IC0tDcuXLzd7\nbg8ePMAPP/yAvLw81pq2trZGdHQ0evXqxRJRZWUl9u7dC41Gg6tXr6KiogJhYWGYMWMGu687d+6w\n5C8Wi/H+++8jNTW11txxgUDAWJUCAlkzZCK2FrPFTtXx7NkzjBw5Ekqlkp1E1Go1qyVkyOipXhW8\ne/dueHp6AmDqC3Q6HaKiohAREYExY8agWbNmJuJkCxcuBJ/PR2FhoVGjEoBRGFWr1aisrERmZibc\n3Nxga2sLiUQCnU6HZs2a4ezZs0hOToZKpXqtAjOzQ0SgSDPuFVcCcQjz589nz+nTTz812rZm7MHD\nwwNarRaPHz/G06dPYWlpiVOnTiE7OxtKpRJRUVEIDQ2Ft7c36wY6c+YMq1hKQgL1qXYeY4gRixOa\nP3ehUAhvb29YW1tDrVbD3t4eQqEQFhYWUKlURm00a+LMmTPQaDTYsGEDPpn6CYjLA5E9iNzBxLl2\ngElr7gqmsHE+mNTnLDCa/g2wdu06s/uuB4N64v8PQa/XIzKyPZhmFJ+CaaLyTdUNrK0i/E+qPncA\nI2glgUymQWFhodG+Hj58iDlz5kCn08HFxQVNmzaFWCyGWq2GSqXC999/D3d3d9bqf/78OUtOBuuu\nLjx58gQBAQHg8Xjw8vKCVCqFTCZjUxsTExPRsWNHuLm54caNG7hy5QqUSiWbwz19+nR07doVFy9e\nRMOGDevUr+/ZsyeuX78OgGmxmJqaCjs7O/Tp08fktf2HH36Au7s70tLS2IyUVatWQSAQQCQSISEh\nwcjHf/bsWWRkZEAsFqNx48Zs1fH69evh7++Pfv36ISAgABkZGQgNDcXTp09RXl6Ox48fo3fv3hAK\nhbh9+zaEQqERSR09ehQcDgfJyckIDw/H/fv38euvv0IsFoPD4Rj1vP3ggw9MvrNBPqJLly5wdnau\nnfj5VQRb09LuzUwK1ZGfn2+0bc1rrtFo2LcDAIiOjmarX9esWQNra2vY2dlBp9Nh5syZWLp0KStt\nMXnyZCgUCuZ8NFWEX71hTNUwFMsRERo3bowBAwYgMDAQTk5OaNGiBSQSCdRqNUQiEaysrBAaGoqB\nAwfiiy++wPHjx/H8+XOcPXsWGo3GSOphzJgxYIq8fqkyiE6AiY2tBFPnkgqmk9cUMJ3mfKBW6+r1\n/OtAPfH/hzBjxmyIRAEwbXoOEC0HI1lbCiZP3xpEEsTGxrJW0vTp0018stWtW5VKxVrYcrkcGo0G\nw4cPZ8l/xYoV4PF4daZeGnD69Gm2KTkRk8c9d+5cBAUFISoqCl999RWysrKgVqvB4/HQqFEjhISE\nwMHBAT/88AMcHBwwe/Zso7zzmkOj0dSqBvnw4UP07dsXWq0We/fuRUlJCXr37g1nZ2eTLCWAyVTy\n8fGBTqeDRqNBQkICTpw4wX7u5eWFfv36wdHREREREdi8eTOCgoLw5Zdf4uOPP4aXlxcSEhKgUqkQ\nHh6OyMhIuLm5QafT4ezZs7C0tDSKVTx69AgcDgdNmjRhs4fat2+Phg0bgogwfPhwAIy8g5eXV63X\nQCQSITMzE8ePH8fq1avx4YcfGmvqSM2QvsEVJiCj/PXVq1fXehy5XI74+HijazZz5kykp6cjJSUF\njRo1wq+//gq9Xo9Dhw6xKateXl74+uuvoVQqcfXqVdy5cwfdu3dHhw4dMG/ePLN9lVUqFQYNGgSF\nQgGFQsEaJG5ubhg/fjw8PDxQUlKC4uJi7N+/H7NmzUJ6ejqaNm0KkUgEPp+P0NBQTJ8+HXv37sXd\nu3dRWVkJnpgH4klAvD5gUpu7VxlINbPh9CDKAYdjg8OHD7/yXv+nop74/wMoLy+HhYUaRP9nhvQN\noy2I1oIoAlyuFHPnMt2hnj59ioyMDAgEAlhYWKBPnz5mA5jnz59Ho0aNwOFw8N577yElJQUikQgq\nlQofffQRRo0aBWdnZ4wfP77Oc/3jjz/MBiqnT58OtVptJCfw/PlzNG/eHEOGDMHnn38OnU73yn6z\nPB4P/fr1Q25uLqv1v3z5crMT0q5du6BUKiGVStG3b99aA3c3btyAQqFAeno6vL29WYmDjh07YuvW\nrVCpVKioqEBZWRnWr1+P4OBg2NvbQyaT4ebNm5g0aZJJdatAIEBUVBR27twJnU7HZkIVFRUhICAA\nVlZWcHNjMkkuX75sYm0b1EKtrKwgkUjg6OhY6zWRSCQYMWIEmxF169YtDBgwgLGwB5kh/k6MxS+R\nSDBr1iyUl5djzpw5te7fwsLCqOkMwKiE8ng8jBgxgp28fvvtN3h5eUEikSAnJwcTJ04En8+Hh4cH\nvvrqK5Nc+X/9619mReL4fD5mzpyJvXv3wt7eHnFxcQgMDASHw0H79u2xadMmo3Rbw/2r0WjwySef\nYPny5RgyZAgiIiJYdVYfHx/memh5IIEMTLrnplqeJT2IAo36NtfDGPXE/x/A9OnT8eq+uCvA+P0t\nsGyZqR/+5MmTcHFxQcuWLeHp6Ql3d3fk5eWxJAwwfmmRSISRI0dCq9XC2toa77//PiZNmgS5XA5r\na2uIxWJs2rTJrH/1r7/+Mut6cHd3R1JSEtzc3NC9e3d8/PHH7DbXr1+Hg4MDVq9ezbYtrG24uLgg\nKSkJDg4OaNeuHa5evYpvv/2W1YsfPHgwq4J57do1JCQkwMPDA3FxcXBxccH+/ftrvcb5+fkIDg7G\n1KlT4eLiwkow2NjYwNHREb/88gsApgHNokWL8NNPP8HZ2RlisbjWlEwPDw8sXrwYQUFBOHLkCK5c\nuQJ3d3dMmDABcXFxcHZ2xr59+5CdnW1C5NX9+nw+HytXrkRiYiKsra3rJOiPP/6YLW6zVlgzfXir\nyzZ/REyGT7XtFAoFk74pIhC3aoheTkKDBw9mr9O9e/fw/vvvo2HDhmzxmF6vZ6+VjY0Nli5dCgAY\nNGgQUlNTsXHjRkRGRsLe3h4TJ07E1atXMW3aNHayNacjxOFwIJPJsG/fPtaY+Oabb/D5558jKioK\nNjY26NatG7799lucPn2avYdqQq/X48qVK9iyZQtSU1MhthaDOFwwAd7yOp6nVeDxlKwQXj2MUU/8\n/0ZUVFTgyy+/BF8gBxEPjIrme2D8+zVv1E0gssFXX31V6/4ePHiA9957D35+ftiyZQsGDBgAhUKB\nyMhIfPnll8jNzUVISAj69OmD8vJy5OXlQSwWw8nJCba2tlizZg2io6MhkUjg5uaGsWPHskR769Yt\no6pUw8jKysLz58/RsWNH6HQ6jBw5EiqVii3PLy0txcCBA2v143M4HGRkZODChQvYu3cvPvnkE8TH\nx7O6/97e3hg/fjyWL1/OFna5urrCysqKzSwBmCwbR0dHDBo0yKx8cGVlJUJCQrB48WIsW7YMGo0G\nx44dQ2RkJPr16wetVovw8HCWkJ2cnJCTkwOZTMaKjZkbLVu2RIcOHTB79mw4ODhg4cKFABj10tTU\nVLRv357xf9cx4RERFi5ciPfeew+rVq3C4sWL4eTkVOu6VlZWGD9+PG7evMkQHZ9ALsRMArVVMwsJ\nlExMBfQYAsUy6xrE5gBg27ZtcHBwQFZWFp4+fYoPP/wQeXl5aN++PRo3bgy1Wo2VK1cCYKQ2tFqt\nUdD73LlziIuLA4fDYYXtdDodZs+ejcGDB5s9rwEDBsDNzY2dTAy4ffs2FixYwL4JRERE4PvvvzeR\nEzeHr7/++jUMqX0gcoJAYPnKYr1/IuqJ/9+EiooKJCSkwsKiBRjBtedgKnAXgvFNLqlxo34Ij0be\nJm0Ja0Kv12POnDlQqVTYunUrSktLsXHjRsTFxUEkEqFZs2awsrJiA8Jt2rRBVlYWRCIRZDIZevfu\nDXd3d0ydOhUjRoyAk5MTPD09mdTBGg/thx9+yL4ZlJaWIjAwEGq1Gu3atcPKlSuxadOmOguY2rRp\ng1OnTtX6XQ4cOAAvLy84ODigRYsWsLKygkgkYgt6ZDIZBg4ciAsXLgBgJJPT09Ph5uaGQ4cOmezv\n7NmzUCqVuHnzJrZt28Y2SX/y5AlevHiBiIgIs1Z2aGgoJkyYUOv38PDwgJWVFTZu3Mgea/r06Rg8\neLCJBW8ug8fHxwfl5eVwdHRkYw+lpaWYP39+nTr5MpkMubm5mDZtGvh8PkJCQrB8+XLT4iwRMeme\nOTVGGwJHzIGTkxN73X788Uf2O4wcORJCoRD9+vWDvb09a3EXFRXB3t7eSCuooKCAbX2pUqng6uoK\ne3t7NGnSBEuWLMGTJ0/MBrKJCA0bNjTryvvjjz/g6OiIWbNmYdasWQgKCoJKpUL//v1x4MCBWhv/\nlJSUgMezAJMNVxvxfwaicBAFQCCQG70Z16Oe+P9tmDt3PqTSCJjPzf8TTPaOQYr2Cvh8K8TExEAu\nl2PgwIGsJV4bfvnlF2i1WgwbNoyVREhKSkJaWhqUSiWsra0xYcIErF+/HiqVCp07d0ZRUREmTZoE\npVIJoVCIdevWobi42GwAMj4+3uTBu337Nvh8PgICAtC0adNahb0Mmv/VpSFqQq/X4/Hjx6isrER+\nfj4rwzB69GisWrUKmZmZ8PPzY+WGNRoNevXqhZ9//hmbN282slyrY8yYMUhOTgbAuHWEQiHWr1+P\nx48f1+lmUSqVrLa8uREWFmZ0nA0bNiAxMbFO3z0RoXPnzoiOjkZUVBQ4HI5Rc3iASU+dPXt2nQVg\nPB4PSUlJePToEbZv384GQtl1+MSkedYk/mHMZ3w+H0lJSeyb0uPHj9GnTx80aNAAYrEYGo2GfdPU\n6/WIj49n/eOlpaXIzs6GWCyGRCJBUlISfv31VwDMW9aePXvQoUMHCIVCyOVyjBs3zmycp3379kbX\n7+LFi3BycjJJL/7zzz8xdepUVh12yJAhOHLkiIlrslOnruBwapN0eAoiV7zMAvoUdnau9WJu1VBP\n/P8G6PV62NpqQRRYZd07gpFbflbt5hwLogEg2gqptAEbzC0sLMT48eNhb2+P8PBwrFu3rtYb9t69\ne+jQoQOCg4NRWFiI2NhY7NixA9euXYO1tTUyMjKgUqkgFArRvHlzNjj64sULtGrVCnZ2dmabgTs5\nOcHGxgatWrXCokWLjIiqefPmCAsLM+sbF4vFcHd3x4gRI3D8+HGoVCqzzTgKCgrQoUMHxMTE4Jdf\nfkHjxo3Rtm1bxMXFwc3NzUj0q6ysDEePHkXfvn3h4ODA6hF5eXnBw8MDdnZ2WLt2LZu69+zZM+h0\nOkRERMDPzw8ffPAB7O3tMX/+fNy4cYOt9K2NZOsKTk+aNIkloKNHj9aZsUPEuImePXuGwsJCo2N6\nenqyKawGPHnyBNOnT4dSqax1fxwxh6l3aE6gQMbS50l4jE/fHPFnMcSfnp6OrKwsAIxMtru7O3r1\n6oWffvoJAoEAY8eOZc9j8eLF8Pf3x4sXLzBnzhxYWlqy1cTmrOYTJ06gYcOGSE1NxbBhw6BSqRAc\nHGwk98zlco2E8S5dugRnZ2cT909NXLhwAbm5ufDy8oJWq0V2djZOnDjBBtQZgcM5NYyrq2DkHrqD\nKfpKAVFPCIV+dRoi/zTUE/+/AcxNLwETrJ1d9drZGIyPf17VDXoMRDYQiZVG+dUGlJWVYePGjWjT\npg3UajVGjx7NlvFXR2VlJVuN6u7uzhbfZGRkYMKECXjx4gWbOy2TydCtWzfs2bMHBQUFEAqFJuQS\nFBSEZ8+eobS0FNu2bUNaWhqsra0RExODlStXIjMzE61btwaPx4OFhQXkcjkrP/zHH3/g7t270Ol0\nWLduHSZNmoSoqCiWlA2Nx6uTq0wmw/r161lC/fbbb6HVapGenm62gOvixYvIzMyEjY0NPDw84Onp\nyR4/IiICI0eORLdu3UwsZqFQCD8/PyxevBh79uxBZmZmnVr0tQ13d3esWrUKhYWFZnXuzZFd3759\nTT7X6XRm5SIeP36MKVOmQC6XG2/DJUa0zlDdnEOML9+BmN685vSOIpjJ4dy5c3B0dMTEiROhVqux\nadMmHD9+HGq1miVsgCFahUKBiRMnwtbWFnw+32w9heG+mzFjBlQqlVHO/fPnzzFkyBDw+Xz2jTA4\nOBiBgYG4desWLl++DK1WiyVLlrzqMWKh1+tx5swZjB07llVIHTt2LLZv3w57ew8wufytwbQgVYCR\ncrABkyLdB0xcTQqRSG32nvonop743zGYXG4piPaYeQVdWfXZYRD9Ag7PxqjCtjZcuHABWVlZsLW1\nRceOHfHdd9+ZuGEOHjwIHo+HwYMHo6KiApcuXYJSqcSVK1cgk8nQtm1bZGRkwKmBE/iWfHD4psFY\nBwcHKJVKxMbGYteuXSxhP3nyBOvWrWODoAKBAHl5eRAIBGjcuDF69eqFgQMHsudy+vRpKJVKHD16\nFMHBwZg9ezbOnTuHli1bmhzT1tbWpEHL48ePMXToUNjZ2eHLL780m4FUWlqKdevWISIiAmq1Gg0b\nNoS9vT369u1bZ/1A9beTwMBA+Pv710ng5twuAQEBdbpmLCwsoNPpWEJ98uSJ2QYq9vb2Rjr91XH3\n7l3Y2dm9lHwQEKi/GXLPICawKyCmb+8YYrKAIokNBPv5+UEikSAwMBA3btzA0aNH2Sybo0ePwtvb\nG8XFxXBycoJUKgWfz0dqamqt8abbt28jNjYWLVu2NDFG/vWvf0GpVOLYsWPYv38/wsLCIJPJ4Ofn\nB7VaDY1Gw+ovvQ0MHeVGjBgBZ2dn6HQ68HhyMEWQu0G0puoZ2wRjjZ9HIEqGtbXDawWQ/9dRT/zv\nGIywVK4Z0jeMdDDaI1lw9/B+o+rCp0+fYtmyZQgMDISLiwumTJnCNvDQ6/Vsd6zIyEjcunULaWlp\n6NGjB9q3b4/omGiGIEIJ1IVAfmSUIeLo6IgrV67g+fPnWLFiBXx9fdGoUSMsWLAAhw4dQnh4OJo1\na4ZNmzZBLBYjMjISAoEAMpkMPB7PRBVzw4YN0Gq1+Omnn0zSG6uPsLAws28yAHD8+HH4+voiKirK\nRFG0On777TdkZWXB0tISQqEQGo3mjS35dzkSEhJgb28PR0dH9ve1t7dHdHS0yboKhYKVm6iOcePG\nISEhASUlJRgxYgTToawm6ecQaCKzHy8vLybIy6l6O6hRVSuVStG1a1ccOXLESK77ypUrEIvFbCwl\nLCwMV65cqfVaf//997C3t8fYsWON5LYBpp7C2dkZGzZsMFpeVFSE7OxscDgccLlcjB079p3o51dW\nVuLnn3+GnZ0OzNt0MZjeFnNrefbKQdQQkydP/tvH/m9HPfG/Qzx58gSMnvitOoj/VzA5yFJMmTLl\nraVkjx8/jt69e8PGxgZpaWmsWFhFRQXGjx8PBwcHLFu2DBYWFozWjoCY7lbVSaPXS/KPiYmBTCZD\namoqdu7cibKyMmzevBkNGjQAh8NBdHQ0K7YVGxuLTZs2sccUCoXg8/no2bMn9u7dy76N9OzZs1Z3\nipWVFRYtWvTKia+8vJxVeJw6daoJ2VTHs2fPMG/ePDaIa2gv+E6JnV9FqgEECiaQhSnJGoa1tTUW\nL17MFpi9ePHCxA1lWO+nn35iv8eJEyegVqtZ8biKiorai7k+qrL2iQniGqqHaxuWlpbYsWMHjh49\nitTUVNjY2EAikYDD4WDNmjW1XtsXL14gOzsbTk5OZuspnj59isDAQLOk+tdff8HFxQXz5s1DXl4e\nhEIhZDIZxo8fbxLreBsUFRVBILAFEzMTg+hxHc/fIiiVbn/7mP/tqCf+d4jPP/8cjG6+ud6hhnEN\nRBZITU1D06ZNkZiY+Lf8jiUlJZgzZw5cXV0hEAiwYMECPHz4ELt374adnR1EIhEcnB1ALWqxGLVM\nsDEjIwPFxcXIz89HYGAgrKysIBaL8eGHH+Ls2bPIzs6GQqFAp06d8NFHH6F///4oLy+HSCTCtGnT\nkJycDFdXV/j5+UGlUqFJkya1kk+nTp3e+IG/cuUKYmJi0KRJEzarpDacOnUKcrmcJTQPDw/k5eUh\nLy8PCQkJf++NQEmgj6tdv/EEakh1dgtr1KgRmjZtirKyMlRWVqJ///4m60gkEnz//fcoLS2Fj4+P\nkZzF2LFjIbWWMho542scW1dl4b/Bd1AqlXB2dkb79u3Z6xQcHFzr9bx06RICAwMRHx9v9l6trKxE\nUlISunfvbmLIFBQUQKfTYd68eeyy3377DU5OTggMDISNjQ2SkpJw8ODBVxpBZWVlKCoqwu+//47D\nhw9j+/btmDBhAjQaTVVdhABMMkVd+f0HQaT4x6d31hP/O4Jer4ednSsY/+LZOm68b0Fkg08++QRn\nzpzBiBEj4Ojo+FrtAOvCkSNH4OnpiaSkJNjY2KBv377YvHkzU1glotq7XDUndOzYEfb29vjuu+8Q\nHR0NR0dHxMTEoH///nBwcEBgYCDmz5+PgoICLF68GK6urmz+t5ubG2JiYlBZWYnevXvDy8urVv83\nl8tFbm7uW7/lGGSDNRoNBg0aVKuEw6RJk5CZmYmbN2+iVatW4HK5rDrlpEmTcOPGDRQWFiI1otOV\nuQAAIABJREFUNRXW1tYQCoWv5+cXEdMXuOY1HFo38RuGo6MjPvnkE9y5cwcjR440e306deqEzp07\ns9fos88+g6enJy5fvgwbtQ1IRoz/vjUxSqa1vG0Y3gDq+iw8PBydOnVCeno65HK5Wd/3qlWroFQq\nMX/+/Drbc4aGhppknxUWFsLV1RVz5swxWl5eXo7z58+jadOmiIqKQkZGBuzt7aHRaNC+fXtkZGQg\nNTUV0dHRCAgIgIuLC6ytrcHj8aBUKtGoUSO0aNECbm5uEIvFiI2NRV5eHlxcXaos/urZczXHMhCp\n4OtrnJ77T0M98b8jHDp0CAKBDoyPv1stN10liJj2eJaWlrC2toZOp0NycjLUajUGDhxoomHyuti+\nfTs6duwIgGk1mJeXB4VCAWtra/AFfEbTvSZhTSSQLWHKlClGvWMTExPZ/VZUVGD37t3o2rUrZDIZ\nEhMTsXXrVtjY2MDKygqWlpaQSqXYunVrnXnwHA4Htra2kMlkr+z+9CoUFxejd+/ecHJywjfffMMu\n37VrF3JycuDt7Y19+/YBYCaL5ORkdqLq06cP5HI5EhMTMW/ePPbNxuB/Dg0NhYeHB9RqtWk1Mo9A\no2qZQN+gObxYLEafPn0wZMgQs5+HhYUhPz8fS5YsgYuLC5sSW1lZiSFDhkAoEUKulLMS2m/79iIQ\nCODm5oanT5+iadOm+Pnnn9lr+ejRI3Tv3h1eXl6s6mpNVFRUYOHChXBycsKuXbuwY8cOrF69GnPm\nzEFWVhasra3h5+eHmJgYBAYGwtXVlY0JKRQKuLm5QS6Xw9bWFsnJyUhMTGTVYGNjY7F06VIcP34c\nf/75J+7fv8+6BQ8fPoxGjRohKSmJVWkFmEmKSAmiL+p4/hqDSbOWsffIPxH1xP+OkJubC6Zh+lYw\ngaZRMPY13quaEKxY687W1hYCgQAODg5wcnJifZ+fffYZHj169EbHX7p0KXr16mW0LDs7G2lpaYyW\nCp+MqzsnEqgt0+zE39/fhKSrq1sa8ODBAyxatAienp4QCASQSCTYunUr2/TD3DA0PTcIrnl6ekIi\nkaBZs2aYOnVqrYHd18GBAwfg4eGBLl264Pr160hJSWGP6+TkxKbJlpWVoVGjRvDz84Ofnx+OHDmC\nwYMHg8/nQ6PRmLh+DHr7Jr1nRQTqaYb0h1dNCjW++6sE64iYquDaPrO2tmaD2k+fPsXQoUPZXsXH\njh17pT//dYj/xIkT7IQyePBgHD16FHPnzoWdnR1CQkIwduxYDB48GN26dUNsbCyCgoLg7u7OpvFy\nOBw4OzsjKCgIsbGx6NatG3r16gVbW1t07twZa9aswa5du3D06FFcunQJJSUlRnGdiooKDB06FI0a\nNWLTWwsKCjB69Gi2Snz79u2oqKjAkydPkJmZCXt7e2zatMnoXigrK8OWLVsgFDqDad7yQw3SLwWj\n2S8DkReI4tGkSeBb33v/7agn/neErl27gmhk1U22HIYALlNMEgHmFdQKRDyUlJQgKysLcrmc1d4R\nCATgcDiwtrYGl8sFn89H69atsXDhQhM9fnOYOnUqRo4cabQsODgYa9euhaWlJfOwC4hpmt2UQDIC\nV8xlNXFqFmM1b97cbMn8hg0bWNKwsLCo1UXC4XCg0Whw8+ZNdtvPPvsMzZo1g5ubG3JyctC/f38o\nlUq0bNkSc+bMMVr3dfH8+XOMHz+elQCufg7VYwGHDx+Gra0t5s+fDysrK1hYWODgwYPo1auX2f60\nMTEx5mMBGjLWyJ9AIB9iA6zVB4/Hw9q1a0FEb92JKycnB0eOHIGHhwe6du2K27dvsy0U/w7pG4ZI\nJGJrMkQiEWuAGLTyJ0yYgDlz5mDVqlXYsWMHfvnlF1y8eBEnTpyAnZ2diYvy+vXraNiwIaZPn/5G\nv+OCBQug0Wiwf/9+FBYW4tSpU9i9ezeGDBkCFxcXWFpaskWDCQkJaNOmDXx9fdGgQQNYWVmxbxE8\nnjWIPgJjfPmCaHQ1wteCkTzPBpEN+HzxW91z/wuoJ/53hPfeSwaTplk9sPsZmK5BjcDkF9siPT2d\n3ebEiRPw9fVFu3btcOXKFWzfvh2tW7eGUChkJwKJRAKhUAhHR0f06tWr1p6yQ4cONeqC9fTpU0il\nUrRu3droQefz+exEwOVyMWLECNy8eRO7du0yIYVFixYZHUOv1yMoKOiVZKLRaBAUFASpVAoHBwf8\n/vvv7Pbvv/8+YmJioFarcf36dZSVlWHXrl1IT0+HjY0NIiMj8fnnn7MKla8LJrD+8hwM3cIAJiMl\nLS0NhjcQe3t7hISEvLSYDWmQHGJVLYnIJCMoPDyc8alLCRRGjJ/dhur0swcHB0MoFMLf3x8uLi5v\n3I3LQPAcDgeNGzeuu3HLGw5bW1ukpKTAzc0N27dvB5/PR1BQ0CsDnw8ePIC3tzcWLFhgtPzGjRvw\n8PDA1KlTce/ePVy8eBG//vordu7cibVr12L+/PnIzc1FVlYW0tPTERcXh5CQEHh5ecHOzo69NgqF\nAk2bNkWrVq0QFxeHRo0aQSqVws3NDRKJhDWI/u///g9Xr17FgwcP2N96/fqvIRI5gOjnKtKPAlPQ\ntQqMmzW76tnMBJerqFPx9X8Z9cT/juCkbQBGmmFZNeKvPqaAyBE2NjYICwtDz549kZeXhy+//BID\nBgyAra0tZs2ahYqKCty9exefffZZlQXDQ2hoKDp06MD6nQUCAXx8fDBs2DCcPHkSlZWV6N69u1Ex\n2A8//FBro4yQkBD069cPHA4HYWFhbGZFTREzGxsbtk4AAH766ac6iYTD4SAlJQURERFQKpV4//33\n4ebmBi6Xi5SUFBw/fhzPnj2Dv78/oqOj0a5dO6PX/ufPn2PLli1ITk6GtbU12xT9ddxeAwcONDoX\nX19fPHv2DI8ePULbtm2NPhs3bhwePHjATIB8AsUQ09x9HIE6m7feiZhsmE6dOjF/G/Ll3xEJmx1C\nYgK4YcSkjwrptYLIhlGXNpFh9OzZE6NGjQKXy2Wboufl5aGyshJPnz7F9evXcebMGRw8eBBbt27F\n559/Dg8PDwQEBKB///5ISUlB27Zt0aRJE7YREI/Hg62tLdzd3dG8eXPExMSga9euGDhwIMaNG4dZ\ns2Zh5cqV2LZtG3766SecP38et27dQmlpKU6cOAFHR0fMnDkTO3bsgLOzMzIyMthGOHfv3sW0adOg\n1WrRsmVLrF692iSovHq1oYgrBESDwHS5swHRBDB+foDoOojE+OijweZup/951BP/O8Dz588ZsuA5\ngEknGwxGgO05iE6CqBeYwhIxgoKCsGfPHnzxxRcYPXo0UlJS2MYePB4PUqkUUVFRyM7ORn5+PgYP\nHgwLCwsIBAL06tULR48exbp16xAfHw+lUgkOhwM+nw8LCwt06tQJ27Ztw+3btzFkyBATETWBQABv\nb2/2jSEiIgIODg5ISUnBzJkz4eHhYRLQFAgEsLOzw9atW83KNhtGVFQUXFxc2Hz0K1euIDc3F66u\nrrC1tYW1tTVraS9cuBAajQaenp5GaX7V8ejRI6xZswZxcXGwtrZGUlKS2QYeAPMmUdMSDgkJgYuL\ni1kfOp/PR79+/RgijSJTn/17ZGT5/38ZQgI1IWMNnpHEvGG842MJBAKThiqGdpYODg5o3LgxmwHk\n5eWFBg0aIC8vD/n5+Vi/fj2++uorNGjQAMOGDTPx4b8NDB3grKysas12q6iowDfffIPo6GjY2dlh\nzJgxOHfuHH777Tfs27cPlpaOYN6454Cx9s11vnOARCJ/64SK/2bUE/87wKVLl5jgno0FmKrdj8FY\n/wIweiE5EAh6wt8/HHK5HBEREUaWtAH37t3DmDFjYGVlhcjISHzwwQdo3bo1HB0dweVyWWvfzs4O\naWlpWL9+PQ4cOIDp06ezk4ONjY1R71PDEIlEOHv2LLRaLev7PnbsGBwcHNCjRw94eXnh3LlzTOcn\nA/FYEMifmPzxWixNPp+PFStWsHLRaWlpRt9Jr9fjxx9/RNOmTcHlctGkSRN4enqyzcltbGxqlS0w\noLi4GF988QXbwKNHjx7YsWMHW8x18uRJo3MSi8VYv369UftIs4NHoBFmiH88mQ3W/kcHn4wbsBhG\nCr1RBtHrjprNVD744AOT32H+/Pnw9vY2akN5+/ZteHl5IS8v742fG3PYsmULK70RGRmJjh074vbt\n27h06RIOHjyIdevWYcaMGRg2bBjS0tIQERGBBg0asG4iS0tL+Pn5wdbWBYwcem1pnc9BJIJQ2MhI\nbvufgnrifwfIy8tjHtRGBBIJQbwEMIUiV6puvpbw8QnC3bt32Ztaq9WazZwBGF9ply5d4OnpyVrQ\npaWlWLp0KRQKBZo0aQKtVguhUAiFQgGRSAQulwtfX1+0adPGbJMPPp+PZs2aISQkBH5+fjh79iwq\nKioQFxeHGTNmIDExEXK5HCtWrGCsXd8a1uYgU8JJTEyEVCplLab79+/DxsbGbMBMr9ez+v+urq7g\ncDhsAxO5XG5WtsAcbt26hXnz5iEoKAgymYwN8r0V4f1/Iv66Gr+ww8rMeeUQo7hZiyvq74yaFdYi\nkQjDhw9npRV27doFjUZjJCxXVFQEb29v5OTkvNHzYkBpaSmuXr2Kn3/+GUuXLoWfnx/kcjliYmIQ\nFRUFLy8vCIVCcDgcuLi4IDw8HKmpqRg6dCimT5+OtWvX4sCBA/jjjz/w6NEjPHr0CPn5+fD29oZG\nYw8uN6YO4l8JopYg8kZ4eNu3ri35b0U98f8NPH36FElJ6SCSgjhdQZQFEjQBCSQgSylIYgESWMPG\n1obtWDV27FgMGTIEGzduhFKpxKpVq2rdv0F3fsCAAWyx0r1795CYmIgmTZpgx44dbEcsDoeDiRMn\nMvIMZh5sLpcLtVoNLy8v8Pl8qFQqSKVSIz35iIgIJgVRQMaZK4YRQ2wgc8SIEQCA0NBQfP/99+w5\nG/rpmoNer0dsbCx7PEN2jeFNJjg4GFu3bmWziR4/foyTJ09iw4YN+OSTT/DBBx8gNDS0ToG0Nxpv\n6erJzMzEqFGj/i2TgkAgYOIIfDJfM5Bm/twMgeiAgICXWVxVw+DG6dixI9q0afNa5yEUCtGhQwcE\nBARgx44dUKlURrISd+7cgY+PDyZMmGDyO5eXl+P69es4duwYvvnmG+Tn52PcuHHo3bs3YmNj0bRp\nUygUCgiFQmi1Wri7u0MkEsHf3x+5ublYuXIl9uzZg3PnzqG4uBiTJk2CVqvFmTNnXuu51Ov12Llz\nZ1WGz3wzpH8aRBowxtkDiEQNjBrU/BNQT/xvCb1ejzZt4iAUJoNoI5gMgjFgVDn3gziWDLGojQui\n/vrrLygUCjx9+hRnz56Fm5sbhg4dWqti4P3799GnTx84OTlh+/bt7LGXL18OpVKJWbNm4datWxAK\nhWZdG1ZWVjh37hx27tyJPn36wMPDA1wulw3EmX3w7WqxNjMIJGaCuK1atcLGjRsxfvx4ZGdns+d7\n5swZODo6mtXU2bdvn8mx7Ozs4ObmBj6fD7FYDJlMBpVKZbZb1rsYfD4fPB4PPj4+TAyET6B29DK4\n24leaVFLpVKMHj0aLi4u7/TcmjVrxnYsIxExQnoTq13/UQSyrXsf9vb2SEpKMvuZIfheV6Fd9bF9\n+3ZMmzYNXC4XGRkZqKioQFFREfbv348GDRqgY8eOyMnJQd++fREXFwd/f39oNBoIBALY29sjICAA\n8fHxrDHwxRdfYMeOHTh58iTu3LmDwsJCxMfHw8fH55VFfevWrYNKpcKePXvqXK+8vJwtZmTqKCzA\nNGafDaawKxVMoHc9OxFwOHMRH59W537/11BP/G+JQ4cOQSzWgsgNRM1BlAeiHBA1BVMgMgOksgJJ\nyaQBRIcOHbBixQoAjP+6Xbt2aNOmTZ2aPfv374e7uztSU1PZasU///wTQUFB0Gg04HA4LzNOqo3M\nzExUVlZCr9fjt99+Q35+PuLj42vtkUtUZVFW14QxjM4M8ffr1w8LFixAREQEFAoF7OzsjBquhIeH\nm/WbGmQdah5PqVTCxsYGKpUKs2fPho+PT93n9zeGQCBAmzZt0Lhx45eplUJi0zmtVFYYPnx4nS0l\niZg4wpYtW7Bs2TJIpdK/lVfP4XDY3gkGjB07ljkvW2LSRltW/S58eiMZ6ZrDx8enqmftq9dt3Lgx\n22qRz+ezRYeGfPrevXtj3LhxyM/Px7Zt23D8+HHcuHHjlbLHer0ey5Ytg1KpxIQJE167M9ahQ4dg\nZ2dntoFLYWEhJk6cCCcnJ7Rs2RLLly/H9evXweEJQCQEUX8wSRbzYBroPQtHR6/XOof/FdQT/1ui\nU6dUMA0g1te4ifQgWgomuGsBEple4u3bt6Nly5bs3xUVFRg1ahRcXFxw8uTJWo/57NkzjBo1Cmq1\nGsuWLcPs2bOhVCrh7e0NHo9nQj6NGjWCr68vdDod4uPjX58gRARqW4P0RxGjE1NFVAYZX4POvEAg\nQJMmTZCXl4fp06cjPDwc165dw/79+438pxUVFejRo4fJMQ3nLhQKERwcjJCQkFcSeKNGjf6WZEHN\n49+/fx93795FeXk5Hj9+bJIiao6wlyxZAqFQiIKCAmRnZ5sWg/EYsiZbMqvkyeFw2Abu1ZGQkPBy\nPc7L9XNzc7Fo0SKoVCqkpKSYNmwxN7jEvMVwCVwpFwEBAbW2zaw+uFwuAgMDcfjwYfz+++/o3r07\nhEIh0tPT39on/tdffyE6Ohp+fn519mOuDRcuXICbmxvGjBmDFy9eYPv27YiLi2NbltbcZ7u4diDi\ng+hFjee0+jgCmUz7Vt/nvxX1xP+WYDr/DK/jZuoHIjUiIiJMti0vL4eTk5PJTfrVV19BqVSy/U/N\nwZA9IxKJoFAosHv3bnzzzTcICwszeZhnzpwJvV6P7du3v3kAVEAgFwJ1JFArAkmYZUOHDsX9+/ex\nZ88eTJgwAa1atWLdNAa/rYH8DJapSCRidXoMzdTNNSaxsLAAj8fD5MmTWReDi4sLWrRoAWdnZ3A4\nHAiFQnTu3BnXrl1DSUlJrSQ+Y8YMTJs27Y2+d5s2bdC0aVP4+fmxwebu3bu/cjsrKysUFBQAAFup\nyxKukkCZ9FIm430y60pKSUnBvHnzMGjQoDolMN577z1otVqcO3cOf/75J/bv349mzZrVfn5CAnkT\naCAxcZsUZgKysDKWk4iIiMDu3btNtre1tcWSJUtw9+5d+Pn5oVOnTlCpVJg6deobpW1WVlZi4cKF\nUCgUmDJlSp3y2q/CyZMn2aYxQUFBWL58OdtPuCauXLkCRsLhqzqe1QHg8SzeuGjwvxn1xP+W4POt\nQXS5jpvp/0BkZdKgxICcnBx89NFHJstPnToFnU6H7OxsE8mEM2fOoG3btvDy8sK3336LTz/9FAqF\nAsnJyejRo4eJxZ+UlARXV1colUokJiaauAg4HM6rXSpV7oXqBPX7779Dr9fj/v37OHbsGHr06AEf\nHx+EhYXBzs7utdweBhKvubz6xFF9QmjSpAkSEhIQEhLCFiaZ214gEJjouPz222+YOHFinbo45kZK\nSgrCwsIYoubTSy1+M+tGRUVBoVBAKpW+JO7aNPSjat9PXcPwVmdQFHVxcUFISAi0Wm3t22nJOE6Q\nUzUJ1EjPVSgUKCsrM9H/GT16NJo3bw6pVMrKLhcUFCAsLAxRUVGvJXlw8eJFREREoGXLlvjtt9/e\n4Cl7iYqKCnz77besdd+3b1+0bdsW4eHhdRL2s2fPwOFywARzC808p/tBpIRI1OGVPYD/l1BP/G8J\nIi6Iyuog/nvgcCS1bn/9+nXI5XKz7e3u3buHqKgoREdHo7i4GEVFRejXrx/UajXmz59vZC1dunQJ\nWq3WrN+3bdu2OHnyJPtanpqaitjYWIwfP95EmO11hqHZOZfLZfWE3N3dER0dDWtrayxatAibN29G\nUVERZs+e/cb7f1dDLBYjISEB3333nZFVqtfrsW/fPljZWL20ul9VqMUnUDgxaZRDyailocl6amL0\n8Q2fW5sh/ZyqyeAtiJ+I0LRpUxQUFLC/aXl5ee1N3yUESq7lHDRkMsHu3r0bgwYNMlrWvn17+Pv7\no127drCzs8OAAQNQUlKC8vJy5OTkQKPR4LvvvjN7j1dUVGDGjBlQKBSYPXu2We2nV+HatWvIycmB\nk5MTWrRogWXLlrHWfWVlJbKzs+Hh4VFnhzanBk4gagUiFZgEjB9B9D2IeoNR89wPosEYPPifU8Vb\nT/xvgcOHD4MRXDtZB/EfhJWVY5376dSpE7744guzn5WXl7N9dmUyGYYOHYqSkhIAjHrhqlWrkJ6e\nDjs7O7P+2i5duiA+Ph5+fn5GVtbevXtNinVed/Tq1QuzZs1CQEAAmjRpgmHDhqFx48aQSCTgcrng\n8XiIjY0FwHQIc3JyMiuA9k6GiF76z+sgby6Xy/YXGDBgADhiDsiVmP6144lR21TUQuZCYgKrNUkz\ntsYxhQT6sNrno4nJwxeRcS2EYfSq+5zrGlZWVujRowd27dqFq1evIjg42Ox6Hh4esFBaGCuyVh9O\nZFKtm5CQYJIqy+VyMWjQIOj1ehQXF6N///7QaDRYuXIl9Ho9Dh06BK1Wi8zMTKMg7blz59CiRQu0\nbt0aly9ffqPny2Ddx8fHQy6XY8CAAXXGAxYtWgSNRoMjR46Y/TwmJgZMzv7vIBoCotCqiWAqiIqq\nntdWcHX1eaPz/G9GPfG/BRISuoKoI4h61kL6ehDFwN7BwWyFrgE7d+5EQECAyXK9Xo9NmzbB1dUV\n/v7+kMlkGDRoEHr16gWdTgeVSoXk5GRMmDChVoIYNmwY9Ho9lixZAqVSiU8//dRs2z/DMGRuEBFj\nDVsTE8ytsoyjo6PZ86uoqEBOTg7s7e2xdOlSk31FR0dj4cKF2LhxI9zd3TFu3DgTkvlbg0+geGLS\nLycSqDsx1u3rbCs3Q8YjayF+Lpmvnh1DLwOuQjINhOcQUxgmIiY9tPryicR07OKYOd6rhkGrR0Z1\nTnju7u4oKytDp86dmKLCmuc2jNl+9erVL3/vKlcWV8I1KeaqKWR27NgxBAYGIiwsDKdPn0ZxcTES\nExPh6+uLM2fOYPLkyVAoFK/VWrM6DNa9Qea5unX/KuzcuRMqlcqk3y8ADBw8EMQRg8nfN/e8ngKR\nGmKx2qhA7X8Z9cT/FlAqG4BoCRirfyyY8m/DTfQETNDXApGRkVAoFPj000/NpqxVVFTAxcXFqIL3\nxIkTCAoKgrOzM2JiYqDT6SCXyyGVStGuXTucOXOGfc2vSzTN0tIS3t7eiIiIqLPVoI+PD9q1a4dZ\ns2Yxec9iAvWuRhL9CGRB0LnqTM5/7969sLOzMykYmjRpEtLT01mp5O7du+OHH37AihUr3jw/v7pl\nb3DPRJohs3R6tftEQEzOfs1tc4gJgNZcn0PmC9km0EuBNjExbw/m9mmIC4QQUwPRg5i3jbdx8xi0\newxFXaMJ1Nz8vgy57teuXQNXzGWOb9gug0AKglsjNxw4cIC5JmHEuLKGEyiaTILPGRkZZu/dxYsX\nQ6VSISsrCw8ePMDYsWPZGonXbW1YUVGB7777jrXuP/roozoz2+qCIej76aefQq/Xo6KiAteuXcPE\niRNBfAkYV8+RqudUDyZGt6pq+UrIZBH/GLVODgBQPd4IcrkDPXhQRkQ2RPQnEQmIqIKImhLRFSKS\nE1EhPX78mG7evEnZ2dl09uxZ+uyzz+i9994jDofD7mvKlCl07tw5atGiBS1YsICuXr1KUqmUYmJi\nKDIyklq3bk1eXl507949SklJIZFIRJMmTaI7d+7QunXraN26dSbnJxAIqFevXnT+/Hn6448/aPLk\nydS2bVsKDQ2loqIiIiLicDj08ccfU05ODp0/f55iY2PpzsM7RPFE1LjGDv8kog1EIzNH0kcffUQu\nLi7sRzdu3CA/Pz+6e/cuu2zChAmUm5tLFRUV5OvrS+fPn3+7Cy0goigialJ1eY8T0dGqyxxfY10Q\n0TQielHH/oRV+2th5rPNRHS2xjIREcUSkV+N5eeJaHvVscRE1JmIPGusc4+IFladF6dqX0REpXWc\nX12wIqKhRMSttgxElE9Ed41XPXbsGDVv3pyIiE6dOkUdEzvSzcKbL7etIGrWtBk9ePaACuwLiNrU\nONb/EdEeIiqr+n56IieVE82dO5fi4uJIKBSyq969e5eys7Np8+bNxOFwaNSoUfT111+Tl5cXLVmy\nhGxsbMx+nevXr9Py5ctp6dKlZG9vT3379qW0tDSysLB4rcsBgB48eECFhYVUWFhI165do8LCQrpw\n4QLt27ePOBwOlZaWklqtJkdHRzp+/Hcisq/6UkTMD1FORJZEdIeI7EksrqBff/2WmjVr9lrn8N+M\neuJ/CwiFKiovf0FEeiLqRkQ6IjpBRDuq1mhARIX04sUD9iH54YcfaNiwYSSTyWjUqFFUXFxMBw8e\npH379tG1a9dIIBBQ69atafLkyeTn50eFhYV06dIlk/HXX38Rh8OhoKAgunz5Mt25c8fk/Lp06UJb\ntmwhIqIff/yRunbtSiUlJTR48GC6ePEi7dy5k2JiYuirr74iKysrIiJycHCgW7dvEY0hhnCrA0Q0\nmSi+QzwdOnSIGjduTO3btyc3NzcqKSmhPXv20LZt29jVuVwuCYVC0uv1VF5eTm91i4mIqAMR1XwG\n9xHRMWLOszr0RDSVmGe5LqiJ6CNiyNiAF0Q0g4hbySW9Xm+8vpCIOhGRV9U2F4loCxlPMBoiyiAi\nXtXfq4mogIiciOGVy8Rcw7ompVchmIhizCw/TEQHiKjy5aKOHTuSTqcjuVxONjY2JJfL6cGDBzRs\n2DDjbflENISIrGvss4KIphBRQ2ImvadE9DMR5xmHZGIZpaSkUNeuXSk8PJxOnDhBvXv3JoVCQSUl\nJaRWq2nmzJm0bNky2rFjB61du5ZCQkKIiKiyspJ2795Nn3/+Of3000+UlpZGffv2JV+cJN1UAAAg\nAElEQVRfX5Ov9eLFC7p+/TpL7NXJ3fB/LpdLWq2WtFotOTs7s/9XKBQ0bdo0kkgktHHjRrp69So1\na9aSiK4T0SIiWklEk4noETE/cAgRLSQOZxldv/4HOTg4vMEP89+JeuJ/Q9y6dYscHBoRURARfUPM\nk23ANSKKIKJbxOHwqbS0hABQYWEh/fjjj3Tw4EHavXs3lZSUkJOTE3l6etLx48dJr9eTh4cHyeVy\nunLlCl2/fp2USiXpdDrS6XTk4uLCDgcHB/r2228pNzeXANCzZ89MznHkyJGUmppKRUVFNHPmTDp3\n7hy5urrSmTNnKDg4mFQqFR09epRu375NOp2Obt++TcXFxQxxDSEiWY0dlhLRZ0SkZ0gdjIuQiJi/\n+Xw+lZWV0TsFjxhy59VY/oCIFhDRuBrLzxHRt0T0gkilUtHw4cPJ39+fxGIxSSQSKigooK1bt9La\njWuJPIgouup73q7a7g6ZThr8audSSQzxc8mUwIXEkGcoMSR/mYh6E5Fd1eeVxEwWl+ilwfmmaEZE\nXcws30NEvxAzsVTh008/JZFIRPfv36cHDx7Q/fv36f79+3T48GG6f//+yxW5RDSCiKQ19qknok+I\naBAxL69EzGTwBZGfgx/Fx8fTli1b6OrVqwSAxo4dSx9//DHp9XrKz8+nSZMmUe/evcnPz48yMzPp\ngw8+IIlEQsuXLyeNRkN9+/alyMhIKi4uNiLz6uR+//59cnBwYMm8Jrk7OzuTTFbzRn2J8vJyGjRo\nEB09epQcHR1p504bIsoh5nXPiZgfw4WYH/0WMc9zY0pLe0pffbX8dX6R/2rUE/8bQiQSUVmZgBjG\nsDSzxnlibqIy4nAqWYLkcDisi6e6VcnhcIjLZSxNqVRKPB6PeDweS67Vh16vZ/998eLvmI9EPB6P\n3R8LATEWXocaKx8kol+IQgNDSSAQEJ/PJ4FAQE+ePKGCggK6desWAaCKioq/dU5G4BPRWDK2zImI\nHhPRXCJqTswzLCCG9PfRq6396vs2fG0uMcSsN7NOD2Je3oiIfiXGi3exjv2Kq/bbihgjsjpKiWgG\nMQT6BuDz+TRx4kQanzueKJMYl0/1fc4hE/fRtWvXyMnJyWRf33zzDXXpUm32qM319QcRbSOikTWW\n/84st+Rb0pMnT4jP55NUKqXS0lLi8XjUoEED8vb2JrlcTocPH6aCggKSSCR0//59EgqF5OLiQqWl\npXT79m2SyWQmZF79bzs7O+Lxas76bwYANH36dJowYQK9eJFNRCVEtJaIEojxCxos+4tENICILpFQ\n+ICKigpqdVH9r6Ce+N8QHA6XiPoT41ytDX5EdIqImAdXIpEQn8+Yj0+ePKHy8nISi8UEgMrKysy6\nQrhcLvF4POLz+cTn80kkEpFQKCSxWEzFxcWsK6W4uNhoO6FQSC1btqRTp05Rz549KSgoiEQiEWup\nP3r0iObOnUtnzpwxf+oCYvzVgcRYuv8iorNEyZ2TKTw83GQCAkBPnz6lL7/8kq5evfpG17JOiIjo\nPWKs8+r4mYh+JONwJp+IX8l/txOPmNkvORJjaW8nxpInYiYKHpm33sVElE4vOaU68ol5s3gD9O7d\nm5YtW0Y2Kht6+OIh86biSERFRLSXmImwxte+fPkyubm5meyrvLyctFot3b59++VCATHxEh9iJsFL\nxMQ7Qol5ea2O20S0kkhhoaCsrCyys7OjS5cu0dWrV+nChQtUUFBAjx8/Zlc3ELdQKCShUEiPHz9m\nJwqBQEByufz/sXfd4VFUe/vd3pJN2/SekJCEhJAAoUgKvSWUgPQmKE1AvaAC0ptIsSCIIhcRREC9\nKEUpFiAoVYqFKkoNBEILkLBp+35/nOyQzW5CsHzfp5f3ec6TzOzMmTO7M+/5nV+1UUdVZ9vV1RUG\ng8HGTvYgpKWlYedOPYQ6NgliiVfx/CIAiVCrr2LPni1ITEysdv9/Rzwi/oeETKYDMB1ijVwZMgF8\nWumncrncpgGQJHgnJydotVq7Y6wTQUFBAa5fv46wsDBcvHgR+fn5Nn0rFAq4uLggKioKOp1OmjwA\n4Pz58zh16tQD1TJylRwWRZkIXATAAgwePBgqlQpyuVxapZT/+9tvv0l2BSv8/Pxw9epVyGQyFBff\nF8dbtmyJxMREyGQyKBQKu+9i9erVOHHihCD/TAhdswXAjwA2w06yDwkJQadOnXDs2DF8+eWXv8+m\nYIUagDvExAcIg/JNAAYA3SDsg2YI3fo++7FAC6A9hEG6PEoh1GVlCzWZTAYqysbpAuAeBIGX+2kU\nCgVOnTqFsLAwAEBKSgp2Hdglvgs5KjUUBwYGYtWqVUhOTrb7bNy4cZg9e7a0rdFoUCQvAosouNA6\noY0pu+fy+BZAFqCiComJiQgJCUFQUBACAgJw7do17Nq1C0eOHEFycjJUKhV27tyJiIgI+Pj4ICsr\nCx06dMA333yD9PR0TJkyBWaz2U4dVf7/yrZLSkqkyaA6k0ZRURHatesMMcttx/0ftyLWABiOY8e+\nQ3R0dCXH/DPwiPgfEkLS6A3gg0qOIISYehopKSkwm8346aef4O3tLS11b968iby8PNy+fRtmsxl6\nvR46nQ7Xrl2DXC6H0WhEjRo14OLiArVaLalWSkpKsHXrVjRp0gQGgwHrN68XJKCFIAE5oJVrERYW\nhuLiYhQWFqKoqAj5+fm4e/dutQjRSuRKpdJGndS8eXMEBgZCrVZLq4/y/5PExIkTbdRYmZmZyMrK\nwo0bN2z2R0REYNOmTdBoNNL51r+TJk2yISZoIAiREO/tH9NwSROhddIqP+HcLrgtVjtdcF8gJIC1\nAC4BqGAbxRoAJxxcxB3AENz35AGA3RArFev4lQA8AfSAIH5CqFI+hTSZeHp64vTp0zAaba2vhYWF\nqFevHn7++edK71Mul2P8+PGYNGkSVCphrS8oKEC3bt3w+eef2xwbHR2N48eP39+hBhAIoHvZ/4Cw\ni64Apk2YhkOHDsHHxwcTJ07Ev//9byxduhReXl4YMmQIevToAScnoQItKirCl19+iQ8//BAbN26E\nXq9HSUkJIiIikJ+fjzVr1iAmJqbSe6gMhYWFDieGqiaNH36wflfFsJf2rTgHoBYsljsPtaL4O+IR\n8T8khPdCAYAzEO4cFbEDQAYMBme4uMhQUFCA4uJihIaGIi0tDWlpaUhJSYGnpycAsfy+efOm5HUT\nGBiImzdvYtu2bYiPj0d0dDRu3bqFQ4cO4ebNm5J65W7RXSAIQl1phCD+HQAOAckNktGzZ094eHjA\nw8MDBoMB6enpdmohV1dXuLu747fffpP2jR49GkuWLIHZbLaR0rt27Yp27dpJk4n1b/n/16xZg8uX\nL0vnJCQkwMfHR1IFlIevry8UCoXNBHXv3j27yUmlUkGlUjk0Yv/pUAAYCeGlWx7XIZxBKhqUT0LY\n9+9V2K+CIP2GEGagowDO4r5qysopjjxqvoKwJ5RT31hXgAAkT6nS0nJuPBrcXymoYLNq0Ov1MJlM\nuHfvHm7cuAGFQgGdToe8vDzpmFatWmHbtm33h69SQemkxL2798Qzdld8B5kdMvHRRx/hs88+w4AB\nA1BaWop+/fph8ODBD1SN5OfnY9OmTXjjjTewd+9eODs7o7S0FLNnz8bTTz/9lxJtaWkpNBpPlJbe\nhdC1Vaa/PwSZrBksllt/2Vj+3+CvCQ/456J16y4EdAQiCZyqEAG4k6LIAwgoGRUVxf3797O4uJj7\n9+/nnDlz2K5dOxqNRsbGxnLEiBH85JNPePXqVZLkjh07pGLoFy9eZP/+/enj4yOlVJbL5Zw/f77I\ng6OFiFwtHzA0GYQfpAReHTt2ZL169ejs7GyTjE0mk9HT05ONGjVihw4dqNFoGB0dTR8fH7q5uTE6\nOtoudL9FixYP/G6mTp1qc075+rudO3e2+cyaxtmax3/Pnj12Sde8vLy4f/9+Tpw48eEDnn5Pk8E+\noZn1e4WDYK7HUXX9W1W5z91BDAAxFiJYzOTgOtYAq4epqauByLg5qazvSnIJxcXF0dPTkydOnGBc\nXJzNZ9biN+X3tW/f3q6PadOmMTg4mPXq1ePUqVPp4eHBffv2PfQ7dPnyZWZmZlKpVFImkzEwMJBr\n1679Qxk7q8L27dupVLpSRNu/XuGdtc2o6+Li+5eM4f8bHkn8D4lmzTph+/ZOEM7ggPCzC4Ew5mYD\nKABQEzLZCXh5eYEkWrZsiVmzZiEoKAgAUFJSgsOHD2PHjh3YsWMHvv32WwQGBiI1NRXr16/H4sWL\nkZEhIpS++OILpKen20jCarUaRYai+5KmDMLtbiCAQ4BhrwHtmrfDli1boNPpMHnyZPTv3x8NGzZE\nixYtMHDgQADA9evXsX79emzZsgUDBgzA9evXkZOTg127duHs2bN29+7n5yetItzd3e3+5ubmYty4\ncdLxnp6eyMnJgVwuxzfffIPmzZvb9SmXy5GRkYHvvvsO165ds7nHrKwsNGjQAMuWLcMnn3wCALh2\n7RoOHDjwcD9adaGGMMxWdIg5B+BDAOPK7SOAZRAevA+CDiL4yqo22Q1hpB4De62DNU6gOoFeaghV\nUViF/VshbBMObN0qlQpqtRpms9lm1eDkJDx1rIiIiMCZM2dsDOZt27ZFz549kZCQAJ1Oh507d2LS\npEnYvXs3AgMDH1pqP3v2LAYPHoxvv/0WxcXFMBgM6NmzJ3r27IkmTZpIq5w/iiFDhmD9+u24cqUd\nxI+WBaBi7MBmAI8DyP9jNqK/CR4R/0OiT58BWLVKCWAphJK3N4SiXQPgPMRaOwBt27bBTz/tR25u\nLjp16oQvv/wSgwcPxrhx4+x0tiUlJThy5Ah27NiB9957D7/88gsiIyORlpaGX3/9FVu2bLEfiAbC\nbTAEQhXxDcREkASovlfB1eCK5ORkZGVl4f3330e7du1QUlIiGXqteOyxxzBmzBhbNz8AI0eOxKJF\ni2xegt69e+PZZ5/F7du3cf36ddy4cQM3btyQ/s/NzcXmzZttCEWr1cLLywtubm745Zdfqq2ykclk\naNeuHYYOHYqVK1fio48+kj5Tq9UPFTeg1+shl8ttiK0iIiMjcePWDVzjNeAJ3Pdtz4fgihsQuv8a\nAPIgbIS/oXp++Y9BeONYYTX0doIIDLPCAhFbdL4afQLCtvMi7CePqxCPZyVja9SoEXJycmy8sBQK\nhc3vplRW7iVVmWtyeScEqzeadaIpbxPSarVSfIXV3fPAgQMoLCyEn5+fpPpr1KgRmjdvjjp16sDV\n1RUuLi5wcXGRbGLl3T0tFgtu3LiBy5cvIycnBzk5Obh8+TKys7Px9ttvw8/PH2fP3oHwA94L4crU\nHkLn/wlEMMRzAF4G+c9X9Twi/ofEqlWr0KfPEIj4/lAHR4wDsAtNm7pi27bP0KNHD3z66afo1asX\n5HI5tm7dikmTJmHw4MF2JAwAN27cQFhYGD755BMcOXIE77zzDk6fPm17kBIiUtS73L5CAG8CuAc0\nSmqELVu2wGg04rvvvkO3bt0wYsQIjB071kYqO3ToEDp37oxff/3Vbiy7du1CWlqazYsdHh4Ob29v\nrFy5UvI0qYhOnTqJKF41AAWgUWowfsx4pKenY9myZVi0aFGl321lsMY5OIQKQvr2F/eOG3hoX3kJ\nCtzPvmH1hvwVgliLcd/QrCj7W8mQbCCDcItsWmH/VgjvwkYQBuV8ALsgXCarO6f9TuIHgHr16uH7\n77+v9HN/f3+YzWYbu9D27dsRGhoqCQMWiwVFRUV46qmn4O/vj0GDBuHOnTu4ffs27ty5g7t370ot\nPz8fBQUFKCgowL1792A2m6VWWFgIs9mMa9euIT8/X4r8LikpeWgX3YqTj0wmQ2FhIVxdXZGbWwwh\n2dcA8B7EBKCACGboDbE00+Onnw4iNrZi3pJ/Fh4R/0PiypUr8PcPR2mpB0QkUToEE1+CiNBZD8j8\nkdZUie1ffwMAWLp0KYYPH46wsDAsWLAAc+bMQXZ2NubOnYv27dvbLZH79++P2rVro3///ggNDbWX\nVEMADHAwuDJ3u5KCEhtpKDs7G5mZmQgKCsJ7770neV0MHDgQkZGRGDt2rE03Fy5cQHBwsN2S193d\nHUOHDsWSJUswd+5c9O/f327srl6uyCvIA5Ih0iOcBbAX6NCmA1auXAl/f/8qJe8qSb4ilBDulb0g\n3llAeJ+shCBqJR4uUtYAYDSEJL+rbF8qgC8gcu/8XrhABF+V11yUQsQQAfejkx82j48awvOmosv+\nFohJpQrO1Ol0cHFxsfXpLwer8bX8Cs3Pzw8kUVhYKLXS0lKoVCoUFxfDyckJLi4ukreWVbovv13e\nC6ykpATFxcUoKiqSJoHr16/j119/RXFxsUTc1uf17t270Ov1qFmzJhITExEYGAgnJycYDAZotVqU\nlJSgoKBA8mK7e/cuNmzYADc3N5w+fQU3bliXavYpIgQIwIB9+3YgKSmpOr/A3xaPiP93oG3bLtiy\npQhCvDwB4SVwE+ItbAfIH8cTA3th2bv3Q7+PHz+Opk2bIi8vD2vWrIFSqcTzzz8PHx8fzJ8/HwkJ\n9zOB7d69GwMGDECdOnVw5swZHDt2DAEBATh37pxwsYwF0NXBwI4Asm0yWPLtidNsNuPpp5/G/v37\n8dlnn8HV1RU1atTAqVOnJA+j8nj88cclvboVWq0Wer0e/fr1w9dff43IyEi888478PDwAADMmTMH\nL05+UXjGlPcBvwzg38Bvp37DvHnz8NZbVQW/lfm4V+exrCzXzB4IHXds2f/VjehtAKCtg/0O8uE8\nFNQQ5NwWYqy3IALCzj18nwqFAm3btkVISAh2796NQ0cPCa1FNMSqbx+EDaEa91zVJKvX6xEXF4d9\n+/ZJ+3bv3o1GjRrZHGeNIj906BA6dOiAuXPnQq/X4/Lly7h8+TKuXr2K3Nxc5ObmSqrB27dvQ6fT\nwdnZGc7OztDr9dDr9dIkoVQq8euvv+LYsWMwGAxISkqCSqWC2WxGbm4url69ilu3bkGlUkkeT+W9\nw1QqlTTB3Lx5E4GBgTh//hru5wGxFXTuYz+A5igsvG6TiO4fif9lY/I/Ahs3bqRMYSDQjaJ6zykC\nFwnZXEKpJ/Rg2/S2dufl5+ezVatWVCqVHDx4MAsKCqQiEv3795c8XCwWC/38/KjRaFivXj2eOnWK\nJHnv3j326NFDeH1MdOAREgEGBAZUOm6LxcKFCxfSy8uLgwYNYv/+/Ss99uDBgw69SNavX8+WLVsy\nPj6effr0ob+/P7du3UqS9PDxIFIq8VYJAVu1asWff/652h4roaGh1Gq1lR/jWsm1ni7zjJkCIhQP\nLi9pbXUq6a8Jfl/+/IoePtY0zdbi5yqIlNO/I01zz549uWDBAvH9WPPyyyvvS6q1UK4pFAoppbaz\ns7NIy13u88cee8xme+bMmXzvvfc4a9Ysjho1it26dWNycjIjIiLo7OxMlUpFpVLJ+vXrs3Pnzhw+\nfDinTZvGJUuWcOPGjTxw4AAvXLhQbe+dS5cuMSYmhgqFgi+88AKLi4ulz8xmM9evX8/u3bvTaDSy\nbdu2XLFiBfPy8mg2m5mXl8d3332XKSkpPH36NBUKDYEoAu4Echx49BQTSKObm2e1xvZ3xyPi/x04\nc+YMNUYN4a0gZDpCpiBkSsJTJ8rvOYFqvZqFhYV251osFs6bN49qtZqRkZE8e/Ys8/LyOH78eLq7\nu3PChAl86623KJPJGBoaKr0kZrOZCxYsoK+vr3jRa5W5700pmwRaCSKxFgmvCt988w0VCgVHjRol\n5fZ3hDZt2tiRxWuvvUaLxcLly5fTy8uL3bt3p7+/P0eNGkWtm5boUAl5JoCJiYm8ePGindumXC5n\n165dHRJWeHg4X3jhBUZFRdl/robjCbCP+A0wBUQn0DPQk+PHj68eOT9foa9xqH6Rl+o0BYRr51Nl\n/Y+FKO2oKptcrPUHHBRkL0/iOp3O4e/ze5paraZMJrOrlVxxwvTw8GBUVBSTkpLYsmVLPv744xw6\ndChfeuklvv7661y+fDkzMjIYHx/PL774gtu3b+eePXt46NAhHjt2jL/++iuzs7N57do13r1714bI\nK4PFYuHLL79MlUpFf39/fvvtt3bH3Llzh6tWrWJ6ejqNRiO7du3KdevWMSMjg++88w5PnjxJuVxL\nUTujBYEAAh9TlE61UOToTyXgzg4dOjxwTP8EPFL1/E4kNEjAEe0R8UrshzCw+UHogmUihe21nGtw\nd3d3eP7+/fvRpk0bmM1mrFixAl27dsXRo0fRoUMHnDlzBgkJCfjtt9/w888/Y+vWrZg2bRpiY2Mx\nffp0qNVqxNaNFWoCdwgvEwAoBLKyshyG6pfH+vXrMW3aNMhkMkRERGDp0qXQ6/U4c+aMjdE2KysL\nqampNuempaVh+/btAIS945lnnsH+/fsRHByMPXv3oDCwUNjJyoMA5gPPD3se27dvtzMqqtVqXLly\nBV9++aVkICwPrVaLRYsWISwsDIsXL8a6deuE0U8DkUu+fJKxUgi7nRuEF85eANuBxFqJOHz4cNUq\nJAVEwFVrCJfOKxDZL2+hatVJ+ShfR9CUjasEQj01EvYZUNdCGGVHQmQL3oo/ls3zAVAoFAgICIC7\nuzuys7ORl5cHnU6HW7fue7QEBwfj3Llz0vaAAQOQkJBgY5Ct+NdsNmPHjh1wdnZGYGCgzX5Hx8rl\nchtPn/J2gfJ/i4uLsXfvXpjNZikY0s3NDQqFAvfu3ZP0+rm5uThz5gwuX74Ms9kMmUwGZ2dn3L5N\nCH1bNoQlXQ0RmaaAsJKnAriNYcOiH6iK/Efg/3ji+dtiwoQJ95frSiMBBQE1AT3hriQUYG5ubpV9\nXLt2jY0aNaJGo2G7du0YEhLC5s2bMzAwkE2aNKFer6ezszMjIyP52Wef2ZwbFBTkUHrLzMx84Nhb\ntGjBDz74gAUFBezXrx/j4+M5ZcoUajQaLl++XDrOYrGwTp06dtJhxXJ6GzZsYEBAAOvWrXu/LKI1\nEGoCiCRQppXxs88+c6x2UYNyrZwdOnTg0aNHWbt2bYf3NmDAAJ47d47r169nZmamKBGohKhM1UtI\n9/CCKE04vuzabqCrqyuvXLnCmjVrUiaT2ak07JoOtsFXAAMDAx2vOAxlEnsahOqpoqpFB7ECmVw2\nTj84XhH1BqEvtz2prL8/a6VR1vz8/BgfH8/k5GRevHhR+p0nT55sd2xkZKTN9ltvvVWtdyM3N5fB\nwcEOyyCWh8ViYVFREe/cucPc3FxevHiRp0+f5tGjR3nw4EHu3r2bn3/+Od9++21OmDCB/fv3Z3Bw\nMOVyOWUyGTUaDRUKBd3c3BgUFMSYmBjWrVuXjz32GKOiouju7s7IyMiyFaYrgV68H3RpJvAWAR8C\nbxPIJqDluXPnqnWPf3c8Iv7fiddee43QyQi4EFhMII/AZooCzm0IOLFnz54P7OfevXts3LgxARGp\n6u7uzrlz57JmzZo2JQ2HDRsmnXP9+vVKX2y5XM7ffvut0usdO3aMPj4+khrKYrFw5MiRNn2MGjVK\nUjFt3LjR7hobNmyw6zcvL4/Dhg27T4hOIEIgVBcaER1qNpuZmZl5X6WgBuELoh1E3VoXMUGcO3eu\nUtWPXC5nXFwc+/bty+nTp/Opp56ii7uLIFgtiHAQIyDKHPrdJ2KVSsXA4EAxUQSXHacEdUad3TVq\n1arFO3fu8IUXXqBcLudLL73kuEC9P4iXKpB1FO5HzqohSkJaP08sm5gcEX/3CsQ/BSIKV45Kf+uH\nbY0bN+bdu3dZUlLC6dOn08fHh5s3b5Z+w4iICJvjK5bsnDNnTrXfj4MHD9JkMvGnn36q8ri8vDz+\n+OOP3LBhAxcsWMDRo0ezS5curFu3Lk0mE7VaLWvWrMnWrVtz8ODBnDVrFseOHUsXFxf6+fmxbt26\n3L9/v12/LVq04Nq1a0myLGr3yTLCr9h+IeBGoDYBA7Ozs6t9j39nPCL+34nNmzcTsBZwHlgmUZgI\nhJXt96ZMpq+yj2PHjjExMZHt27fn6tWrKZfLCYABAQF86qmn7AhvwIABzM7O5jfffFPlC/6vf/2r\n0ms+/fTTnDhxorR969Ytu/QMAJiWlsarV6/SYrHQaDTafNaoUSOHfUvFuytp3bt3Z3h4uKgVrAFR\nF7YpEiaACBDHhoeHMz4+3qFR0snJiWvWrCEppMs7d+7w7NmzbNq0qSB/FRwXIjdC1Ja1Xu95QcS1\nE2rbFIM3Go188cUXeefOHR45coTFxcUcPHiwbV9q2NYmtrahFY4pf39jyiaFYRXOmVw2GflX2P9n\nGJUB1qxZk/3792fTpk1t9Oo7duygv78/x44dy6KiIr7xxhsEhNFXoVAwOjrapp/Jkyc/xBtCvv/+\n+wwLC2NWVhY//fRTvvbaa3z22WfZqVMnJiQkSLWkY2Ji2LZtWw4bNoyvvPIK16xZw7179zInJ6dS\nG1ROTg5btWrFGjVq0NPTk0OGDOH169dJipQQLi4uzM/P5/79+8vex0uVED8JDCKgo1brx19++eWh\n7vHvikfE/zvRrFlrAn0JNCsj+50UhiISuEpRcF3PJUuW2J1rsVj45ptv0mQy8e233+bu3bsZGxsr\nGbAMBgO7du0qVBnlXrzMzEy6u7szIyOjSk8VFxcX3r592+66eXl5dHNzk5b4VqxcudKh90xQUBAP\nHjzIXr162eyXyWQOl8T/+c9/6ObmViUJhYWFCZJVgHjBAXEOEKRtxU8//eTYsAtQrpcLiVgOevh5\ncMuWLbxx4wZDQ0Ptj9dAqIMqXm8IKNPIeOHCBQ4ZMoQDBw7kpUuX2LdvXwYEBHD16tXcsmULXV1d\nbfuTwzZ3T3rZNeS4b6QFxCqg/PWCIVYnXSEMu8MhcveoQTxX7rjxECuAP0D4CoWCY8eO5b1791hS\nUsIWLVpw3LhxNr/Z1atX2aZNGzZu3JgnT56km5sbJ0+ezPbt29tN+KNHj7Z7jtIxz9cAACAASURB\nVK9du8bvv/+en3zyCefPn8+RI0cyIyODtWvXptFopEqlorOzM9u3b88RI0Zw3rx5/Pjjj3ngwAHm\n5uZW6VzwIJSWlnLevHn08PBgq1at6O3tzX//+998/fXX2adPH5Ism7BrVEH6JPAZAW/qdG7Mz8//\n3eP5O+ER8f9OuLmFERhHoeq5UskDNZoajYfNeZcuXWKbNm1Yv359rl+/nhkZGfT396fRaOS3335L\ns9nMwYMH02g02qh6AJE468yZM+zZsyc9PDzsXvLy22+++abdmBcsWMBu3bo5vJ+DBw861GNrtVpO\nmjTJbv+oUaOkcy0WC3Nycrhz507OmzevUqK2aToHJDwFQgpXwsaOcOfOHfbs2dP2fC2IHmXEOg5E\nSxAqcNeuXczOzmZAQIDt8TLYqmXKN6WQ8gcNGsQtW7ZIUvHGjRsrn8g0IPrjvppGBeHK+iKEBN8b\nQv/v7OB6pnKThNWl0xVCPTUWYvLzxe9y8yw/OWu1WjZt2pTHjx8nKSRhX19fvvzyy1y5ciWnT5/O\nJ598ksePH+fs2bPp5eXF9PR09unTh8nJyXz33XelCS86OpqPP/44hw8fznbt2rFWrVo0GAx0dXVl\nfHw8O3bsyGeeeYavvvoq161bx0OHDvH69essLCxkcnLyQ68WHgYHDhxgjRo12KFDB9avX59OTk7S\n8//kk08S8H8A8a8i4MNBg57+y8b4/w2PvHp+J0ymCFy/LoNwHXm5kqOuAgiGXF4Eo9EoVc8KDw+H\nSqXChQsXMHLkSPzwww+Ijo62yUO/atUqDB482CZyUi6X48yZMwgKCsLLL7+MmTNnorS0FG3atAFJ\nm4LnNWrUwMmTJ23S+UZHR2Pp0qWVev1cvXoV3bp1w86dO+0+S05Oxq5du6RtlUqFzp0749y5czhx\n4gQUCgXCw8Ph4uKCoqIi/PLLLzYpmu2gBPA07td0teI4gM+AFsktsGLFCvj6+gIASOKdd97ByJEj\nUSIvAXrCPmPGl0DNvJo48dMJbNq0CR07drwfoKSCSHPhVeGcfACvwiaQymQyIS4uDgcPHsTt27cr\nvwcviMR4b0CkXehY4fPLEHl+GkOkiCmG8ADbU0l/1pQQStjXHbBm1NBBqr1QWW0CtVqNnj174uzZ\ns9izZw+Ki4uhVqtRWlrqMAXCyy+/jJCQEOzcuRPvv/++VIfB6lFz+/ZtyOVypKSkICMjA6GhoQgO\nDkZwcHC1ShTm5OSgfv36WLRoETp06PDA438P7ty5gxEjRiArKwuXLl2CQqGQ0nmXlGggQrETKjm7\nJWSynbhyJdthMOM/Ev+3887fF3369CXgSWDLA6SJELZo0YKJiYk0GAx0c3OjXC6nRqORvBNQJln7\n+PgwPj6eHTp04OjRozljxgw7HbdVPz9jxgyOGTOGNWrUYEBAAJs2bWp37MaNG6Xxbtu2jbVr137g\n0rqwsJCDBg2qllTZunVrzps3jyNHjmRSUpLdqqPKpoQw/paXwkdD6OHLjjGZTNy0aZPN+BYvXixW\nC47SJ48QapsPP/yQJpOJQ4cOlewmkIOoWeG8ySDqoUqf+Sqb1ZagBTGoktWES9m1Fbhv9LX66lcR\ncGV3HR8Qo3DfFtLmD4y7QgsMDGTXrl05ZswYvvLKKzQYDJTJZNyxYwdJsnbt2ly3bh0TEhLYsWPH\nB3qrOcKePXuktNB/Ni5fvsx33nlHSvUMiJgD4c2jotDxNyJwx8H7+QkBHYcOHfqnj+v/Mx4R/+/E\nlStXKLwBVldB+haKSEEwJiaG7u7ufOmll3jz5k2S4oE1mUxcunQp33rrLQ4fPpwtW7ZkVFQU3dzc\nqFAo7hNXWZPL5WzUqBGDg4PZo0cP+vj48IMPPpDcMcsf27x5c2m8GRkZfPfdd6Xte/fu8ccff+TH\nH3/M6dOns0+fPqxfvz6NRiNNJhNr1KjxQCJ/oFvkg5q6jATjcd8bRmF/3KhRo3jv3j2S5M6dOwXR\nOiL+4aLP8nni33rrLdvr+YLoCCITRLCYKCrqsitrFVVvUtPCsaF3CmwmMsggvJ36lY1/LIjUahC4\nEsIwXLHvOvhTvH7Kq+1IctmyZdTpdHR1deW6devYuHFjSQ35/PPP08/Pj9u2bXvod2bJkiWMjo52\naH96GJSWlnLfvn2cPHky69WrR1dXV8bFxVGj0VAmk9Hf358hISFs1KgRAWcCMym8eiIIvEngEIGv\nCPSjUNW62rlL/9PxiPj/AJKTkyki/ioj/m8ICFLp3r07r1y5Ip1rsVjYuXNnjh07ttL+LRYLT58+\nTb1eb/OiWlcN1klBr9dTq9U6NPgOHz6czzzzDLVaLfv378/WrVszJCSEGo2GUVFR7NSpE/v27ctZ\ns2bxm2++4bVr16Tr79+/n/7+/n+IVGrWrGkX+l9Za9WqFVNSUhx+Fh8fz2PHjrG0tJRKg1Lo0CsS\nYQNIUnXt2rW5d+9ekuRzzz1n259VSoeIS9i+fTs3b97MJ554ouoUEZU1OYjaDsbzZAVSd+TRMwVE\nwgMIvKK3j7X1xUNHFTs7O0tCCCBsQ5mZmfzxxx+l3/3u3btUq9UcOnQoQ0JCGBQUZOPC+9VXX9Hf\n35//+te/aDabH+qdeeqpp5iZmfnQRt2bN29y7dq17N+/P728vBgTE8MxY8Zw/vz57NKli52QUq9e\nPQIyCok/l0II206gB4F4ihXA3LJ31MXm/v8b8Ij4/wCEP72ewBIHpH+ZQAiBFKrVXuzXr5/NuR9+\n+CFjYmKq9eIMGTLE5qG2PuQXL16kQqHgli1bOGvWLD7++ON2kwQAKRxfrVZTLpfTw8ODCQkJfPzx\nxzlmzBgbjxUfHx82btyYvXr14ksvvcT58+ezVq1a9iSiAFEDQurUQVJZREdHMzMzk0ajkdu3bydJ\n3rhxw+G4HLXQ0FA+99xzDlcbOp2OS5Ys4YwZMwR5d4ZQFY2BcH1U2d/3yJEjefPmTXbs2LHSa7q4\nuHDr1q3MyMhgVFSUFLT2UKorFYiGEOqqiRBVsXSg0dXIefPmiWPcKiHwgai66pZHJec9oAKYi4sL\n69WrRxcXFw4bNowffPABW7VqRZPJxOeee47z58+nUqmki4sL3d3d2bBhQy5fvpwFBQVs0qQJ69ev\nzxs3btDX15dhYWE8ffq09Exeu3aNnTt3Znx8PI8ePVrtd8ZsNrNBgwacNWtWlcdZLBYePXqUc+bM\nYWpqKp2dndmuXTsuXLiQhw8f5muvvcaaNWuyVq1aHD16tJ3QI543Qxn5W73tHLWLlMudqj3+fwoe\nEf8fQGFhYdmD5kwh+a8i8DmB58v2tSBwjoDwfrBK05cvX5bKClYHhw4dEtdRQ6gOAsqkxzKy1Wq1\njImJYefOne1KHMpkMspkMg4ZMoTTpk1jv3792KhRI/r6+lKpVFZbzWHTtBBBUlYCmggiFoQGvHDh\nAv38/OyCvCr6wYeFhTn00S8vmUJXNqlUkIa7du0qdNEeBqE+UYCuXq6V3ktAQADXrl0rIosruZ5M\nJuOzzz5Ls9nMK1eu8L333uPSpUsfbsWjxn1VlUb8Ljqd7r66zqUSAh/wAOJXwd6GMAliJfAQv5tc\nLpeiWSMjI6nX6+np6Um9Xs+QkBAGBgYyISGBHh4ebNq0KVUqFUtKStinTx/27t2bnp6eNtG4FouF\n7777Lk0mExctWlRtKf7ixYv09fXlli1bbPYXFBTw888/5/Dhw6WVxrBhw7hp0ybevXuX+/bt44AB\nA2g0GtmoUSO2bduWISEhdipHT09Pbt++nYAThar1+yqIfx3DwxOqNe5/Eh4R/x9AcXExATmBCwSe\novDnDyaQRBHFSwInCTgxISGBL7/8Mi0WCzt16mTnT21Ffn4+Dx8+zNWrV3PKlCns2bOniKhUQrgv\nWnXbY0FECLKZNGkSS0pKSJIlJSUMCQmRXoIaNWowJiaGoaGhTE9P57Fjx2zGv3PnzocjfQ2Itg7I\na6yYjEwmE0eNGiUFf1nx448/2vW1efNmu5QQEoEGlt1vf4hArwp1ZL29venj48Phw4dL+v+rV6+y\nb9++lY69bdu2jiNwy1p4eDgvX77Mr776yi6K9U9pKgj1T8XvLgYPDtRSQxh0R5VNFCH4Q+6ecrmc\nU6dOZUZGhhQv4uzsLE1SVpWXk5MTfX192bx5c44ZM4be3t7s2LEjf/zxR+bl5dFisfDEiROsW7cu\nMzIypPrRD0JWVha9vLyYlZXFRYsWsV27dnR2dmZKSgpfeeUV/vzzz7RYLLx79y5feeUVhoSE0MnJ\niW5ubnR3d2fXrl25cOFC/vzzz3YT+vz587lixQqK2tjPU2TRdST1lxBI4MqVK38vBfxt8Yj4/yCM\nxiACy6uQKKYRSCdgoLe3N1euXMmYmBieOnWK27Zt45tvvskRI0awRYsWDAwMpFarZWxsLLt06cLx\n48fz/fffZ81aNYX+uiJhjBOEGBQUxNTUVCncfMGCBezTpw9r1KjB8PBwbtmyhWazmfPmzaPJZOKQ\nIUO4ZcsWvvLKK0xOTqZCoaBKpao+8Q91MJYpIDyFzjw+Pp7u7u7UaDQMDw9namoq+/TpY5dfqF+/\nfiwtLeWbb75pe/0w2Btv28EuGlcul3Py5Ml2WR63bdvGsLAwh+M3GAx2RvDyrU6dOg+MjP5DTQsR\nvPUSRBRxfVTfO0dTRvYPU4y9kqbVajlr1izm5ubSbDYzNjaWqamp9PDwYM2aNWk0GqVC6B4eHlSp\nVIyPj2ezZs1oMpmoVqup1+up0+kYGhrKBg0aMCIiggaDgX379uU777zD9evXc9++fTx37pyk0iwq\nKuKOHTv4/PPP09fXlwqFgr169eLatWslp4fz58/z5ZdfZkxMDOVyOdVqNZs0acIFCxZIE4IVmzZt\nsrkvmUzG8+fPU+esIxBO4DaBOgSGUQRWWt/L8wQyqVC4/mVF3v8/45Ef/x/E7NmzMW7cqwAOQ9T/\nK48jEOkjFwAYB4XiMiwWC1QqFdzd3REVFYWaNWtKf2vWrIng4GCb6lkAoPfQ4177e/aVlgBgGeBl\n9oKPjw9ycnKwfPlytG0rqom89NJLeP3113Hr1i388MMP2LFjB7788kvs2LEDpaWlSEpKwqhRo9Ci\nRQuYTCYUFRXhwoULOHv2LM6cOWPz9+zZs7h06ZJIZJgOUeSkPEoAzAHaNm+LL774AgCQn5+P7Oxs\nXLhwARcuXMDWrVuxZs0am9M0Gg0CAwPh5eWFEydO4EbBDeGjH1yhf2vFKgdZMps0aYJVq1ZJxewB\noKCgANOmTcO8efNsaslWB+7u7sjPz5f82RUKhVTs40+BBiLrpgL3yzqWQafToXPnzujXrx+ioqLw\n3nvvYerUqQ/V/cKFC7FhwwZ8//33CAoKwpkzZ5CXl2d3nLUQi06nQ2hoKM6cOYMxY8bAw8MDa9eu\nxb59+2CxWBATE4MGDRrAx8cH7733HiIjIxEeHo4NGzZgzpw5SE1NlWrcZmVlYfny5QgKCkJgYCBy\nc3ORnZ2N3NxcyOVylJaWwmAwIDg4GHFxcTh58iQAoHnz5jh16hS+++473Lp1C0qlEqmpqRgzZgya\nNWtmV3j93LlzSEpOwtXLV8X3WRbb0Lh+YwQGBmLtx58BlhCIQkm3IEqrrQNQu+wLPwGgCRIT83Dw\noH3cyj8dj4j/T0BISE2cO3cTolhzOkTx1xcgykAFQ5SjOg6gBDJZEUji008/RadOnarVv0eAB27U\nv2FfMY4A3gAWTFuA77//Hrt27UJBQQF69+6Nbt26YfDgwTh69Cj0ej2CgoKQlpaGtLQ0pKam4tat\nWxg7diy+//57zJo1Cz179rR7uSrCbDYjMjISF+5eAIZCvHBWZAH4DkAhMGHCBEyfPt3u/JKSEoSG\nhuLixYvSvlmzZqFz5864ePEiLly4gIFDB4q+HWWzng/I7jquzuXq6op3330XXbvaliYbOnQo1q5d\na5Nu+EHQarUYOnQoFixYgPDwcJjNZly4cKHa51csXF5deHp64ujRozZBRDNmzMDEiROl7bp16+L4\n8eN2ResTEhJw+vRp5OfnIzY2Frm5uYiLi8PRo0exefNmuLm54bvvvsN3332HrKws3Lt3D6GhoXjs\nscfw8ccf4/jx45DL5SgqKoJcLgdJKJVKWCwWlJaWQiaTIT4+HqNGjcLVq1fxxRdf4McffwQAtGzZ\nEsuXL4deLyrU5+bmonv37jh27Bi8vLxw/vx5tGjRAikpKYiLi8Ply5exa9cuHDx4ECdPnsTt27eh\nUqmk65CEt7c3fH194ePjY9dcXV3RtlNblEaVAi0A6AHcAfA5ID8nh+WeBYAJoijyYQBWgeAGgB8g\not/qQqHoj7lzU/Dcc8889G/1t8f/3WLjn4Np06ZTLu9JkewpmoAXgaYETpRbWl6i8BsW3i0tW7as\ndv8vvPCC8OyomHKgl1ATnD17locOHeLjjz9OtVpNhUJBjUZDtVrNjh07VllpKysri/Xr12e9evW4\nc+fOB46lqKiIMq1MpCNIg0jBHGKvrhg4cKBDY9/MmTOlY5ycnDhv3jybz2U6mdBlV1QjjYKk55fL\n5ZUahv39/dmnTx9OnDiRS5YsYWxs7EOrQVQqFevWrctu3boxPT39oc6tdrWvStrs2bOl78JisdjZ\nGhYtWsSFCxfa7FMoFJKeXKFQUCaTMT09nU888QT1ej01Go1dCo+CggK2adOGXbp0YWFhIW/fvi0Z\ns9VqNdu3by+lZQBAjUYjfedOTk50cXGhRqOhq6sr5XI5FQoF4+Li2KhRI7q5uTE0NJTNmjWj0Wjk\nlClTuGLFCg4aNIjh4eH08PBgZmYmhwwZwpSUFLq6ulKv10spwQsLC3nhwgUeOHCAGzdu5Lvvvsvp\n06fz6aefZnp6ulDVmWCvDpwIQg+q1c4UKtZnKVIxl5Z7D61tD7VaV0m99N+GR8T/J+DkyZPU6bwI\nFBD4lEACRb5vRwFdHSgMwpBKKj4IxcXF9A/1F+TfGcLg2eg+ESqVSkZGRnLo0KGcNWsWAwMDGRwc\nTK1WK+VbsWYudITS0lJ++OGHDA4OZqdOnXjy5MkHjsnd3V3SN1fm9timTRvJ6GzFlStXWL9+ffbs\n2ZOdO3e263fw4MGi3yfKvdCjQXjDLrirYhI7a/Px8eHgwYPZtWtXuwC4//Vm9fSRw2HG0Ir34OTk\nJP1We/bssZtUrly5wj179thNfG3atCEpspWmpaVRJpNRpVKxR48ezMjIoFwuZ2hoKN9//31J3242\nm9mpUye2a9dOMpCXlJSwYcOGUjCf1ZgaGxvL8ePHMyYmRprcXFxcmJSUxKioKOkZkMvl9PHxoZeX\nF5VKpXSsWq1mUlISBw0axC5dutDb25u1a9fmkiVLmJ+fz6+++oo+Pj5V5sPfsWMHAwICaPIxidxM\nUxy0SFAEZX1I4C6BZIo06dvL3slsArMIOD/QpfSfjEfE/yehc+fe1Gq7EGhNYGU5sj9HYApFJs8R\nBP4tyjWqwJEjR1ar79LSUh46dIjNmjUTGSl1sPNyKZ8r/fLly1KAVlBQECMjIzl9+vQHXufevXt8\n5ZVX6OHhwZEjR1YZmv/SSy/Rx8eH06dPp8ViYbdu3RwSX2JiIgsKCuzOv337Nt3c3KQ6w+WRkpIi\nVhBuEJG2Svv7fZCUbXVj/T8jfGuUbn8IyXQciGb295GWlmbjhqrRaDhq1CgeOHCAQ4cOtTnW09OT\nc+bM4dtvv10WlWrb14cffih9h4sWLaJGo6Gnpyfj4uLo4eFBnU4niNNk4vjx46X6tz169GDz5s15\n9+5dksI7KjAwkG+//TZdXV0ZEBBAmUzGoUOHctGiRRw+fDg9PDxsUo5oNBr6+vpKgYXW0qLTpk3j\nrl27mJqaSqVSSZVKxfDwcCYkJDAkJIRqtZpeXl6sV68eY2Nj6eXlxdmzZ3Pt2rXcvXs3L1y4wMLC\nQk6dOlWqH5BQL0EUv6lI+uNAEbDlSeFO7UzhadeNQCwBJUX69IHUar2k5HX/jXhE/H8S7t27xw4d\nelBE6p6mkO4nU/gRjyTwHkWRlkgCBsIopCNHhSpKS0v5008/8c0332RmZiY9PDxYo0YNPvXUU6xV\nqxaXL1/O1NRUm5deqVTywIEDJEVwWLNmzTht2jT6+vqyXr16VKlU1Y5OvHr1Kp9++mmaTCbOmTNH\nkgatsFgsbN68OT08PGzUOSNGjHBIgqGhoQ5XHCNGjOCECRPsvscRI0YwICCArVu3Zrt27bh7927+\n8MMPNm6q1WkNGjTgxo0buWrVKg4aNIg+Pj50cnJiaGgoPT09/1riV8FxlG5d2MUl6HQ6enh4SBJy\nZaqsevXqUa1WMzQ0lI0bN7ZbLeh0On733XfSd7llyxYaDAa6uLhwxYoVHD16NDUaDQ0GA+vWrUuj\n0cguXbrw66+/5hNPPMEmTZowLy+PJLl79256eXlRq9XyiSeeoFVqV6vV9PHxYceOHdmnTx+J7K0q\nRh8fH+r1espkMoaFhUlR5u7u7uzXr59Un9n6TJWWlvLSpUvcu3cvP/roI9apU4cxMTHs0qULk5KS\n6OXlJVXbqlu3LuPj48XqQg/btNhTQHgqKKT9mRRu1L8SeL1sImjF+8LYR6xVq2G13oV/Kh4R/58M\nb+8IAnsJLCQQRyCn3ANHCn3jk4TCWUrJq9AruGzZMi5cuJBdu3alp6cnw8PDOWjQIH7wwQeSVGyx\nWOjh4cFLly7x/PnzdkXLIyIieOfOHTZu3Jjr1q0jKVwbvb29aTKZ6OTkJOlRL126ZJeXvyJOnDjB\nDh06MCQkhKtXr5ZIfubMmaxbty71ej3v3Lljc86MGTMcEqHJZOLZs2dtjj1+/Di9vb0l1cOJEydY\np04ddunShTdu3OD58+e5efNmzp8/nwMHDmRSUpLdPVfWFAoFBw4cyP/85z9MSkpiTEwMV69ebaN6\nMpvNXLt2LYODg/984nd1QPpTIPz4/wR3TEdNpVLR09PTJpL2hx9+oKenJ52cnPjaa6/x1q1bbNiw\nIcPCwujr60s/Pz96enoyMjKSqampTExM5KZNm/jiiy/S19eXgEiXodVqmZiYyPDwcDo5OdHPz486\nnY4ZGRn8+uuvuXz5cgYFBTE2NpZBQUGSy6yXlxfDw8OpUCioVCoZEBDAyMhI1qxZ06HQc/fuXcbG\nxnLRokXcunUrfX19OX78eE6ePJnu7u6sXbu26FsFUc2sN0QdgwBrNbzTFd43lr2DvgSmEviKWq1J\niir/b8Uj4v+TMX78JKpUT1HkAD/i4CG0kr+f0P8+XyYFqsFu3bpxxYoVPH/+vMO+L1y4QG9vb2m7\nfv36di9/hw4dGBQUZOPbfu7cOUZGRtJoNDIyMpK9e/dmSkoKTSYTP//88wfe0/bt25mYmMgGDRpw\n5syZDAgIYHZ2Nps0aeIwWdeSJUscqlkMBgMPHz5sc2zLli35+uuvc/To0TQYDGzcuDGTkpLo7OxM\nHx8fNmvWjCNHjuTbb7/NrKwsXr9+nXl5eWzVqlWVJFinTh26urpSo9Hw1VdftasTXB5ms5nTpk1z\nOKnUqFGDLi4unDVrFtPS0qpvMzBWQvxP/HXEb53w5HI5PT09GRQUxBo1ajAiIkKqT+vq6sqQkBA6\nOzvTaDSyYcOGDAkJsUkIaPXfj4yMlFRmKpWKBoOBL730EpcuXcoPPviA7777Lp944gkGBgbSz8+P\nKSkp9PT0pFwuZ3BwMOvXr0+5XE5/f38uW7aM//nPf5iZmSmlCJHJZIyPj+c777zDo0ePSr/R8ePH\npajicePGMTQ0lK1bt+aePXvYoEED23vWgpCpy0i/H0Ud3Y8J5Fd451YQcKNM5sylS5c+8Jn/p+MR\n8f/JyM7OpkZjpKjhWVW65umETCNC7yeD8Afj4uKq7Hvjxo1s3bq1tJ2SkuLQ68RRrd+CggI6OzvT\n39/frlDK6NGjpRq8laG0tJQzZsygXC5ns2bNePr0aY4fP95OVWPFunXrHJKkUqnk4MGDOXDgQDZs\n2JA6nY4ymYx6vZ49e/a0IfgHYd26dVUGY2m1Wg4aNIgeHh5cvHjxA1MKbN68mXq9XjIKy2Qy1q5d\nm3FxcZTJZDSZTFWS7uLFi/n++++LojEq2Bqora0W/pRyihWbWq3m4MGD6eXlxeeff56RkZE8cuQI\nf/jhBy5atIgdO3a0CZKTy+WsVasWlUolNRoNQ0JC6OfnZ3OMk5MTw8LCqFKpqNVq6e7uzvT0dA4Y\nMIC9evVi165d2bRpUwYHB9vkgnJ2dqaLiwvlcjmdnJwktZVMJqNSqbSZYKzXsk4wWq2WSqVSOsdg\nMDAhIYGtWrVyWFnNZDJRJtNTeMs9RmAwhVrHg8B83o/YzScgp0bj4tDm9N+GR8T/F2DgwIEEGj+A\n+N8V6p7HygihgyglWBWmT5/OF198UdquXbs29+zZY1cb1dnZmWfOnLE7f/78+YyLi3NIHElJSfz1\n118rvXZOTg6Dg4O5YsUKzpw5kx4eHuzcubNd/V2LxcLz589zy5YtHDZsWKUG1gEDBvDf//43w8LC\n6OTkJOV+rw5KS0u5ZMkSmkwmvvDCC/zXv/5VpSQeFRUlZSItn320PL777jt6e3tzyZIl3LlzJzMy\nMqhWq6nT6WgwGNirVy8p2V1FCRsQHi6lpaXS9wRAePFkQhgdn4FN7v9ly5axZ8+enDFjBs+ePctT\np07x559/5qFDh8qyvt6/ho+PDzt16kRnZ2c2btyYTZo0YadOnVijRg3269ePHTp0kCT4Jk2aMDU1\nlU2bNqXRaKwyH5K3t7ckfaekpPD111/nkSNHePv2bck12GpzAMARI0awV69eLCoq4kcffcSmTZvS\n29ub48aNk563vLw8LlmyhA0aNKCPj4+UNjkzM1PyAjIajezVqxe/+uornjt3jq1bt6ZKpaJer6dS\nqaROp5MmEZ1Ox65duzp0HjAYDIyKiiPgR+BwhffrFwqD7uyy7SICMj7195Zg2QAAIABJREFU1Ihq\nP2f/ZDwi/r8An3/+OUWu/qJKSN9CoCuhlAsvj3Eg2gldf1Xo0qULV69eLW0HBgby7NmzPHLkiJ2a\nonHjxnapDG7dukVXV9eyicmeCIxGo00SLivMZjMbN27MSZMmSfsuX77M7t27ExAlIfv378+GDRvS\naDTaqGjGjx9fqVSu1+v54Ycfcu7cuezbt2+1vtvjx48zJSWFSUlJPHToED/88ENGR0ezTp06jIyM\nrJTkrNK7v7+/3SSzbt06uru7s1evXgwNDWWtWrU4Z84cnjt3junp6ezSpYuk9ijf57PPPksXFxc2\nbNiQCxYs4L179+y9bawlFit4Jmk0Gr7xxhvs0qWL3T1OnDjR5loKhYIbNmzg4cOH6e7uzujoaP7y\nyy+sX78+lyxZwqKiIn7++edUq9UPlVZaoVDw2Wef5caNG+np6SnZhax444036Ovra5dZ1Wg0MiUl\nhatXr65ypfjjjz/ymWeeoZubG729vens7ExfX19mZGRw5syZrFmzJmNiYjhv3jwpHbdSqaRer2d6\nejrXrFnDxo0bMzY21m4Ck8vlnD9/fpmkf6iS9+w8hRfPTYq6ulW7Nf834RHx/0XQ6bzpOIfPGgK1\nxMSgDCGgJdz0hKuQ1CsaS8sjLCzMxgXN2dlZ8sJ49dVXbV6MkJAQh1L/yJEjOW7cOG7durVSP/ih\nQ4dKy+HS0lJ27dqVTZo04dy5czlo0CCJ4L29vWkwGCTj8bRp0xy6gJ48edK+WHlZGzZsGHNzc+nq\n6mpTr6AizGYzp06dSg8PD7722mtcvnw5IyMj2bhxY27dulVS43z00UdVZhzV6/V0cXHhhAkTePXq\nVXbv3p0qlYru7u585plnePDgQRuV0J07dxgREUGVSiVVTrOS0BdffMHExESmpaXx66+/titK/6AW\nHh5Od3d3h/aHDh06SNIvtCC0omiMyWTi008/TVdXV6alpUlG04e5rrXpdDpGRESwW7du/Pbbb+nn\n58c33njDZhzlC9lYPXdkMhnVajXT09O5a9euB6rQzGYz16xZw4YNG1KlUlGlUtHNzY179+7l7Nmz\nJfVSQkICmzZtypiYGE6dOpVJSUn08/Oj0Wi0E2zi4uLKvKPqVEL61tadwBsEojlw4MAqx/nfhEfE\n/xdh5cqVZdLIMt6X/F8lEEpgG+9HE14nMIEik6ComlXRfZIU0rper5e8UoqLi6lQKCTS+PTTTyVy\ndXFx4VtvveVwXKdOnaKnpyfz8/OZm5tbaWSrm5sb4+PjJaJLTU3lyJEjuXjxYmZlZUkqkzFjxnDa\ntGn88ssvGR8fz8aNG3PPnj385ZdfmJSUxK+++oqkMExXLBBvbZMmTeKTTz7JGTNmOBzzrl27GB0d\nzfbt23PevHkMDw9nSkoKv/76a4ekc/fuXQ4cOLBSNVPHjh3p7e1NmUxGg8HApUuXOkzUVVJSwmnT\npkkF1zt27MiLFy9KEnBGRgbHjx/PJk2aVKtcpSN1lIuLC48cOWJz3cLCQvFbaiA8Vx6HSMucIlRF\nfyQozWg0sl27dvz000/51Vdf0cvLi4899hjj4uK4Y8cORkVF8V//+hdzcnI4e/ZshoaGSqS7adMm\n1qxZk56enpw7dy79/f2lSfO5555z6KVTEb/99hv79u1rozJr164dz549yzlz5jAsLIwBAQF0cnLi\nyy+/zEOHDklqHusEl5mZyfoN6hPQUMTHVEX8UwiYGBQUU6WB/78Nj4j/L8TKlSup0/lSLDfrUhSG\nOO/g4bxJoGPZgwx26tTJTk2TlZXFBg0aSNvXrl2jm5ubtN28eXO+9dZb/OCDD/jDDz/QZDI5jAy2\nWCxs0aIFn3nmGb766qscOHAgXVxcHJKESqWii4tLldGUGzdulEo8lpSUcNmyZfT392dAQIDNiz1q\n1Ch6enpKLoLWZjAYeOXKFR45coT+/v42BHzz5k0OHTqUvr6+HDp0qJQGoLr2gMOHDztMr+zh4cH4\n+Hgp8+SaNWvszr1w4QJTU1OZlpbGPn36UK/X02Qycc+ePWzevLlkjGzTpo1Do2PF9M9qtZp9+/Z1\nSNpPPvkkb968yRMnTnDHjh0cM2aMIFsTRH3dKeVaPzxUrd2IiAiOHTuWAQEBnDt3Lr/44gv279+f\nbm5uTE5O5oQJExgUFMS2bdtKqzZPT0+qVCr269eP+/btY+/evWkwGGgymfjZZ59RoVBIgsW+ffuk\nNAoGg4ERERGcNWuWw9UmKdyIhwwZQpVKJdkQZDIZW7VqxV27drGkpIRffvkl27RpQ4VCwYCAALZp\n04bh4eGSkVmvd6eIj+lOkRqlKuLvxho1Ih6RfgU8Iv7/BXz++edUqDSE7MkKD+UZiuASfdnkYKC1\nVGPv3r1tHtYFCxZwyJAh0vbp06cZFhZGkjx27Bi9vb1t9K1vvvkma9euzY0bN/LVV1+1UdG4ublR\nr9dz5MiRXLBggVSGr7LWo0cPLly4kLVrN6GnZyhjYhpy4cJFvHPnDm/evEknJyeba2/bts1hP337\n9uUvv/zChIQEaWLR6/Xs0qULzWYzk5OT+fHHH9NisfCTTz6hr68vk5OTGRAQwJYtW3LXrl0P9b1n\nZ2dz9uzZ9PHxkcbQtGlTKbfM66+/zn379jEiIoJPPPGEpGb79NNP6eXlxZkzZ/LEiRPUaDScMGEC\nN27cSB8fH0ZFRdFgMFChUDhM/+zh4cF+/frZ7NPr9XzyySeZlpZWPdLWQqTnmOKguVd+nrWs5oYN\nG5iVlUUfHx9evXqVv/32G/38/PjRRx/xzp07XLRoERMTE9mrVy8ajUZqNBpqNBrK5XK2adOGHTt2\nZHJyMq9fv85Zs2bRzc2NixcvpqenJ9u3b0+9Xs+cnBzpu7569SqnT59OT09P+vj40NnZmQ0bNuSb\nb77JnJwc3rhxg2PHjqWLiwtNJhM7derEvLw83r17l8nJyZJnV0hICOfMmcOcnByeP39emlRVKlVZ\nbYDAsvfFjaLQirFMqNrtgPSvUal0thnnIwg8Iv7/JURHJxFYW+6hvEARUt6GwA9l+0oJfElrdO+C\nBQuk8wcOHMjFixdL2wcOHGBiYiItFgv79+/PXr162RG8RqNhUFCQnYrGYrEwNjZW8sE/duyYnWeQ\nbdMTaEJgI4W3xFYqFG0ZGBjF7Oxs1qlTh7t375bGNnXq1Er7cnZ25tSpU/n666/z8OHDfOaZZyRv\nlPfee48NGjRg+/bt6e3tTS8vL7Zt29am7wchPz+fq1atYuv/ae+845sq9z/+zR5NmqZJuvde0EWp\nbWkLBQotSJE95bIFWSJQ0AKyRKSiMmUoyOayvOBCGSKi/JhyQUBAliwF2rJLRz6/P57k0DRpwTsc\nt8/79TovaE5yzknOOZ/zPN/ZogX0ej369u2L3bt348qVK/jss88QFRWF559/HgkJCdBoNIiIiMCO\nHTvwt7/9DSEhIejYsSMCAgLw7bffCnVrVCoVrl27BgAoLCwUbP2BgYF2I3iRSCRks/6r5hgiYmU5\netYg/H6P96XVaqFSqfDMM88gNjYWRqMRCQkJGDVqFABmiuvYsSMAYMOGDVAqlTYdq5o1ayZE2xiN\nRkgkEmi1WsTHx6Nfv36IiIjAokWL4OzsjAMHDuCLL76A0WiETCZDenq63cy0vLwcGzZsQHp6ulDj\nR6FQQCaTITw8HHq9HosWLbIz0a1duxZOTk5QKpUIDQ0VmrLY/zbOIHIH63i3Eqy71hDLNTrbRvRl\nsmQMGzb6qa+dugQX/t+Jtm27gei9KhemO1hrRkeVA4tBxNLircTHx+Mf//gHtm3bhlmzZiEnJwc6\nnU5omJGWloYhQ4bYCPyNGzfg7e2NL7/80u54lixZgtzcXOHvu3fvClE69qI/GI47GE1AQkIGhg0b\nZlNVsqysDD169Kg1y9bX1xcrVqxARUUFpkyZAq1Wa4nJZiO/Vq1aPXVrysrKSnz11Vfo3bs3XFxc\n0LJlS6xevRr3798X3nPkyBF4e3tj1qxZAJjJ64MPPoCzszOcnJzQqlUreHl5QS6XY+rUqaisrMSb\nb76JqKgoNGnSRAiP/LfE/LcsYmLhn9VF39LpzNoKcePGjThw4ACSkpKgUqkwbtw4tGjRAmKxGNu2\nbUNxcTE8PT1rfLAHBQXh6tWrAJipbvDgwfDx8YGvry/EYjGCgoKg1Wohk8mEbNfdu3dDoVDA29u7\nxk5ypaWlGDp0KFQqFSQSCVxdXYXQ2Pbt22PDhg12vqzz588jMTERwcHB0Ol01UJn1WD+sbkg2gai\nOSCKAlEuWIz+l2B+suFgDdWdkJmZbVckkMPgwv87sWnTJmg0KRbBLAebpu53IKbW5V0Q6YQRPBFL\nf8/KysKQIUPQr18/ZGZmYvr06ejUqVON+92+fTu8vb3tWuI9ePAAbm5uNpU4KysrkZKSUs0pqgHR\nwxqOsQJyuTdmzpyJnJwcAMx5Zx21//rrr9iwYQOCg4NrFLjExETMnz9fcKBKJBI0a9bsqX7TH3/8\nEQUFBfD390e9evVQWFgoiFhVvvjiC7t+sVZ+/fVXNGzYUEgemjBhApKTk5GWlga9Xo/MzEyMHDmy\n1u/wn1oCAwOh0WiQmpqKrKwsFv7Znh6XHx5DrDuZnHDy5EkcPnwYHh4eWLVqFSorK1FQUACxWIye\nPXsiLi5OyEOobZ8SicSudMc777wDLy8vjBo1ClqtVgiTDQkJwYcffoiSkhLMmTMHEokEBoPBpr9y\nRUUFFi9eLOQQvP322wgLC0NSUhICAgIQHx+PPn36oHHjxnBxcUGvXr2wbds2Yebw6NEjjBw5slqk\nktoi5hXVrr9yEHUFUS/L38+BSAmiRCgUOqEjHcceLvy/E+Xl5fD3jwTRTDB7pKIW0QeIfgARayq9\ndOlShIaG2mxv0aJF6Nu3L8LCwvD111/Xuu8xY8bg2WeftZteFxQU4MUXXxT+fuedd1CvXj18+eWX\nVZqMd3rCcQ7F+PHjodPpsHbtWphMJsyaNctmX48ePcLbb78tiLujRafTYfjw4dBoNBCJRPj0008d\nfpdbt25hwYIFSElJgbu7O1566SUcOXKkxpDCZcuWCb1dzWYz7ty5gzNnzmDPnj344IMPEBsbC3d3\nd2RmZgoJRFVHmiKRCM8884ydU/q3LCKRCDExMejfvz8KCgowd+5crF+/HikpKXBxYU3iJRIJ1q5d\nCw8PD0EEX3jhBVbWw4lAHmykb+2zm5aWhsrKShw/fhxeXl54//33UVFRIdjfn3RMWq0Wer0eBw8e\ndPi7bdq0CUajUYjesWb4xsXFQavVok2bNnB1dYWzszOcnZ1x9uxZbNiwASEhITYzI4lEIpRIqKio\nELLP3dzcMHToUEyYMAFJSUlwd3cXErUSEhKqHa8T7EswWJc7YPb+yyDaACJXSKXR6NlzQK33RF2H\nC//vyPnz56HX+4DFHktB9KgWQf0/EIWCSA1XVy9kZ2fbiNuMGTPQoUMHxMbGPjGO+tGjR2jQoAHm\nzp1r8/qVK1fg4uKCoqIifP755/Dw8BAKqf3yyy+Wkd6TwuVGIycnBy4uLvD29hYqhDri1q1bGDly\nZI39faVSKXJzcyGVSqFUKoWEorKyMvzjH/9A+/bt4ezsjE6dOuHjjz8WIoAePHiAc+fO4bvvvsPm\nzZuxYMECTJw4EUlJSVCr1YiNjUVgYCDUajXUajWCgoIQHR0NlUqF+vXro6CgAO+99x7Wr1+P/v37\nQyaTQSKRIDo6GnK5HCNHjhQqVFrt+1ZRswr7mDFjMGXKFOh0OoSGhmLAgAGCLV0kEuGdd96x+z3K\ny8tRWVkp9PjVaDQYPHiw3XtGjhxpEyVlXazn85tvvoFOp3uik56IZTGvW7cOjx49QkFBARo0aFBj\n7si+ffvg4eGB6dOnC2WXAwICMGbMGCxbtgwRERHCg1IikcDf3x8ajcZun9W/E8AK8g0bNgx6vR55\neXkweBlYtFIwPS7FTWQZIPV+wjX4PJgZdQuIDMjNbS8U/uM4hgv/78zt27fh7x8GFsWztpaLeQSI\nxoFoiuVBoYZer0dOTg4mTJiADh06ICQkBIsXL36q/Z4+fRpGo9GuNHP37t0xevRomEwmu6iZffv2\nQSLxhP0U27qYweqds6iLnj171voQKi4uRn5+vpAIVJM4Wc0TarUaiYmJ0Ol0CAkJQV5eHvr3748O\nHTogPT0doaGhNk7shg0b4tlnn0Xfvn0RHx8PHx8fLF68GHv27MGZM2dw9+5dlJeXY8KECXB3d8fH\nH39sd4wHDx6Eq6srUlJSoFAooFQqERMTg5iYGOzbtw8pKSlCeeju3btj9erVEIlEwkNq1apVEIlE\n2L17t/A9lEql4Dh2ZIoCAA8PD4jFYmRkZNiFHt6/fx+hoaGsF0OVAm9KpRKtWrV6YgKXSCRCeHg4\nMjIyIBaLsXTpUgDMz9GvXz9kZ2fXmIH7008/ITw8HB4eHvD29kZwcDCio6PRu3dvbN68WbDf17Rv\nLy+vWju73blzB+6+7syEVbXMcl8Ca1jkBKJRTxD+YSB6G0R9IRaratwX5zFc+P8A7t69Czc3TzAH\n788OLuQ9IDKC6AKIfrFc/C9CLNbi5ZdH4ZVXXhFCFD09PZGXl4epU6fi888/r7EWDcDMHtHR0TZF\nqrZv3w6pVFrjAyQqqiFYxUNHN9zfYW0laV0aNWqEQ4cO2Wzj1q1bmDBhAjQaDRQKBVq3bo2NGzfi\nlVdegZ+f3xNFy9fXF71798a4cePwzjvvYO3atfjqq69w8uRJFBcX22Xa5ubmokWLFrhz547NcVy4\ncAFpaWlo1qyZQwF++PAhoqOjsWrVKpSUlEClUsHV1RW+vr5wd3eHs7MzZDIZRCIRNBqN4FsxGAxw\nd3fHuXPnMG/ePKSlpcFkMsHb2xtKpRLJycl48OABXnnlFZhMJixcuNBO3Nu3bw+ZTIbAwEDMmDHD\n5rdzcnUCeRGr+9OZQBH0VLH8BoMBoaGhmD9/PkaPHg2j0Yj4+HhoNBp07NgR169fR3l5Odq0aWMX\nPlyVmzdvCtFDCxcuhIuLC3Q6HZRKpdDsx9H+PT09sXTpUocJiVYuXrzIRvejqoj+RAKFSC3XljeI\nYmsRfbNlYLQIRKoaExc5tnDh/wNxdfUGC08bD2b33wmigRbR31bl4laD6DSIXCCVumLv3r2IiIhA\nq1at8NNPP2HdunUYPXq00M0pMDAQHTt2xIwZM7Bz506UlJQAYCO8rl27ClPvsrIyZGVlwcvLC+vX\nr3d4jKdOnYJO5wGikXicfHYNQraxhFjFyTRi8eUWG3RUVBRyc3MREBBg06nJWlM9OzsbPXv2xKhR\no9ClSxdotdoaBUyhUKBly5YoLCyEyWSCi4sLunXrZhdKeP36dTRo0AC9e/e2y8Rdv349TCYTZsyY\nUaPAjRkzBu3bt4fZbMaKFSvQqlUr3Lp1C/3794erq6tgxnB2doavr6/ghHZ3d8fUqVMRERGBtLQ0\nfPTRR+jatSukUil0Oh2ef/55YR///Oc/8cwzz6BRo0Y4ceKE8HpWVhb8/f2h1+thMBgEv02z7Gag\nEGJVXF+rsmQ8/q2rLkqlEi4uLujRowf8/PwwefJk1K9fH2azGSdOnECTJk0gl8uRlpYGNzc3LF++\nHPfv30daWhpGjhxp95t8/fXXaNSokc3DRK/Xw8XFBf7+/g6zo8ViMSZMmIB58+ahcePG0Ol06N69\nO7Zs2WJnglm6dClrKWr9XgMIZFCBKBys8Fo5iHxBtKMG4f8ErNa+Mzw9fR2eV449XPj/YMRiHYha\ng6ghWEXPyWCN2a0X9jmwLMVKyw0QAqnUCLFYjEWLFtltr7KyEidPnsSKFSswfPhwpKamwsnJCeHh\n4ejevTumT58ODw8PrF27FoMHD0Zubi7Wrl2LRo0aCdswm824desWTpw4gZ07d2LOnDlISWkMiUQN\nIjlYezslE55h1UZqjcimt6zBYIBGo0FBQYHNTMNq2/7b3/4GFxcXtGjRAj169Kgxi1hwbCZaHjJa\ntp/jx48DYBE+QUFBmDhxos0M4P79++jfvz+CgoLwf//3fzWeh7179wrJTgDQunVrLF++XFi/c+dO\nIdPUWrs+PDwcABvx//rrr+jfvz+kUilu376NYcOGCfZva0y9lYqKCsybNw9GoxETJ07EzZs34ezs\nDLVajVmzZiE8PBxubm6YPn06+y37k31Y5ziy6UEcHh6Od955B0VFRfj1118RFxeHdu3awdvbGzEx\nMULfBbPZjGnTpkEsFqNx48aIiopCTk4Ojh49iujoaMycORMAcPjwYeTk5NidB7lKDo1Gg9zcXIfn\nydXVFVKpFMOHDxfOw7Vr1zB37lykp6dDr9ejV69e+OSTT3D58mWkpqay7zieQM0kIIkTWG39C1Xu\ngS/AEh2X4nGE2QMQLbYMipyQnZ39FHcbxwoX/j8YVuMlBY7j5K22/pEW4XcF0Sqw+j8x8PePqtW0\nY6W8vBxHjx7F3Llz0bVrV6FssEQiQUxMDJ555hkoFAqEh4fDx8cHcrkcOp1OsAt36tQJQ4cORU5O\nDjQaDRMEOYGaORCk8WTXVNxkMmHx4sWoqKjAqVOnBBNPbGws3nrrLSE5CmBmheHDh9vbrfXEwhmt\n+5nAZhpSJ6lQUrl6g42jR48iMjIS3bt3F4rZOeLevXsICQnB5s2bAQBFRUU2BfCsSXIRERGQy+U2\nRcOsD66ioiLMnj0b3t7e6NOnD5KSkuDt7Q2RSITg4GCH5o7Lly/jueeeg6enJ5KTkxEZGYmuXbva\nJFjZmUGqLirmD/n888/tfCtFRUVCI/SsrCykp6fbrB8+fDiioqLg6uqK7OxsGI1GTJs2TTgWhw/e\n+pZz7u/Y1JSeno6ioiLhNxkyZIjd7Ory5ct45513bJq2k4JAUQQSuViEnI3giXqAtVAE2Iy4ueUe\nCACL5IkAkfqJ1z/HHi78fzDFxcVQKIwg6gMWmmYV/EdgjSR8LTOAT0DUoMp6M4iGwMXFDzt27MDe\nvXuxadMmzJ8/HxMnTsTAgQORl5cndFiyJs9YnXNWp1zr1q2RlpYGV1dXiMVixMTEoF+/fli8eDGO\nHDmCsrIy3LhxA61atULDhg1x7tw5AGCZpX+rQZB8HZtsVCoV9Ho9Xn75ZbvCZNU5ffo0WrZs+VgY\nujjYzygmSCqVCitXrhQ+azabMWfOHBiNRptRe00MGTLEpiz0Bx98gLZt2wp/T58+HQkJCWjevDmM\nRiPOnj0Ld3d3odcsEWHHjh3IyMjA2rVrER8fD7lcjjZt2sBgMCAmJgbt2rWzSyYqKSnBunXr4OLi\nUnNjeGtN/+rf/UUmvs2bN6+xGc7t27eRkpICtVoNDw8PfPPNN8K6Bw8eIDw8HHPmzEGrVq3g7e1d\nYwVVkhFocLX95z0Wf6ujXqPRYM2aNSgtLUVcXBwCAgLQr18/u3aXI0eOdLAfJ7DInHuW6/sWiF4H\nkRuI/lnlur8CokgQ5YBI7TA3g/NkuPD/Cbh48SLc3IIto53nwEIoPUHUGMzU8zOIgsFayllvgCMg\nWmMZ+ShQv3595OXlYcCAAYJ9dePGjdi7dy/Onj0rhOydPn0abm5uglBZM26Lioqg0+mwZcsWzJ49\nGz179kRkZCQUCgXkcjkSEhLw/vvv44cffkBFRQXEKjGopQNBmsBGorU5HVu2bGnTF7Y2MjMzmcCM\nrOEh4/R4uzExMXjhhReQnJyMuLg4h0XqqrN9+3b4+PigqKhIeK1FixZC34ONGzfC29sbly9fFmLN\nAWDgwIFQqVQ28f0ymQwlJSU4evQoiJgoJycnY/DgwWjevDn69euHU6dO4a233kKTJk2evpyyqprw\njiaQB8E3wBfPP/88TCYTjh496vD73bt3T+hTW7V7G/C4ofrAgQNrPhYJgVIc/O4TCeTKnO/Dhw9H\neno6XFxcoNVq8cYbb+DcuXNC+Yju3bujvLwcJ06cQFxcnP1MglQg+qrKtV11WQqipGoDHhOIlFi9\nevVTXUMce7jw/4lYu3YtZDINWK2e6WBxyUPB2sgVWi78/ZYbwQ9EHUD0DIic4OER8sQKhMXFxQgP\nD8fChQsBAJcuXYKbm5tg+x48eLAweqyoqMDEiRPh7u6ON998E4WFhejcuTOCg4Ph5OTEYtlVDgS5\nKdmZehwt69ate6rf5KuvvmIhjN0ciM8YsmluYmOLlsvRuHFjvP766zhw4IDD1P2SkhL4+fnh888/\nF167ceMGnJ2dcffuXRw6dAhGoxEHDx5EaWkpxGKx4AR/7bXXIJfLUVJSInSKEolEcHZ2FhqpKBQK\nPPvss8jMzMSQIUN+U5MUIlYaOzo6+vGo24tAAew7JzRMwC+//AKTyYSJEyeiQYMGds5uKw8ePGC+\nEzFB46pBkyZN8OOPP2L8+PE15lRYF5FaxLKHHT1067FS1TExMcjNzUVCQgIaNWoEjUaDvn37YuvW\nrfDy8hJMTXZZxCLLQ02cWIPoAyyUOACsJg9AtAtEzvjss8+e6vrhOIYL/5+M+/fvw8PD3yLs9cFC\n2c5ZLvpDYBE/K2EbW/8TiOJhNPnUWNCsvLwcLVq0wNChQ21et5ZUuHPnDk6dOgU3NzecPXsWGRkZ\nyMrKsgl7/Omnn9CvXz/o9Xp07twZSq2SCVIDAjUnkDc9VZihNZX/SX1+AWa2EUoUj6024owlh5Et\nNTkdO3bsiMWLFwtJan369LGpeAoACxcuRMeOHXHlyhX4+PgIQv/tt99CLBYLIaILFiyASCRCZWWl\n0DTc399fMJdYe9D+FqF3cXGB0WjE6NGj8eqrrwpVVN3c3JCcnIyJEydi7NixOHXqlHC8c+fORZMm\nTdC4cWMUFhY6/A3DosLY75RMbJbmV/t58vDweJz9KyZQwxpG/HrC22+/jZKSEjRr1gxKpRLffPMN\nhg0bBqVSicaNGyM/Px+ZmZnIzc21PGSsYZrhYIXWXEEUgtrLl/z79veUAAAgAElEQVQNREtA9DNk\nsgDMnj3nidcNp3a48P8JuXbtGqRSDZgZx6OKyDcDi2RwdHOUgEQ6kJQw8IWBdtscPnw4mjdv7nBU\n2L9/fyHkMDExEc7Ozpg6daowSj5z5gx69+4NV1dXFBQU4ObNmzh06JDg7CQRQawQ11qSobqtPzg4\nGIGBgdi8eTNWrFiBw4cPO2yGAgDz5s2DSCFiI/8UAjUhm9DRf2Xx8vKCVqvFmjVrbBy/WVlZWLVq\nFRITEzFt2jTh9ZEjR8JkMgl/r1+/HlKpFMXFxcLo/N69e5g5c2bN9noHi0wmw+jRo7F7926Ul5dj\n/PjxGDt2LABmlrMmdjVs2FB4vSrl5eWIjo7G/PnzYTAYcPbsWZv1Y8aMcTwza0F2MzNXV1eHvQVI\nRqCB1T7fiiBVSYXrqaysDHFxcTCZTLh69SpWrlwJhUKB4OBgpKenIzExESwLNwRE31e5bsstAxlj\nLeKfB6JMyGQ6vPGG44cb57fBhf9PyqZNm6FUGsCcux+C1e43gqi0lpHRGJCC2WpTU1Mxb948oTF5\nWFiYjR27Kvfu3UN4eDhycnJgMpkQFBQEs9mMU6dOoWfPnjAYDELY4WeffYa4uDiIRCJER0dj7dq1\nNhEr69ate+qSxEql0ibrU6FQIDk5GS+++CKWLl2K48ePo6KiAvfu3YNer4dOp4NcIYdKo6qxHo27\nu/vjZudPuUgkEuzevRvXr1+HTqdD27Zt7bKQ4+PjbUIGt2/fDrlcjnPnzgmF0SorK7F//34h3LMm\noU9JSYFWq0Xjxo0xefJkm3Oxc+dOm4Y7HTt2REREBJRKJTQaDTZs2GB3/r788ksEBgbi9ddfR1ZW\nlnDcpaWlkGvloGwHI/YJJPhHrFVea/2dpMQSxzIJ5EUQK8U2JjKAlSRRqVTw8/PD8ePHcfToUaF6\nLBvpq0B0vYZrdzlY6e/qrxeBSIX69ZPw888//4Y7iFMbXPj/xBw5cgRNm7a23DCDweL8axL9G2C2\nfx3Y1HgwiHwhkbAiYFWrcFbnzJkziIyMhFwux8GDBxEUFISsrCwYjUZMmTIF+/fvx7hx4+Du7g6l\nUomoqKgai3uZzWYYjcbfLL41LU5OToiPj4dMJkNUVBROnz6N27dvo2fPnjWaUnQ6HQoLC/Hee++h\nXbt2NUerWBaxWIwdO3Zg9uzZiI6ORlpaml2ikVqttqm3c/jwYSgUCuzZswdEzJHr4eGBkJAQEJFd\n03WxWAxXV1ccPnwYAKuvIxKJ7KJSHj58CI1GI8xCAgMDcfDgQfj7+yM+Ph5isRjLli2z+93z8vIw\nbdo0JCYmIj8/HwMHDmQzMBWxrl3Vhf81AgXQE6t3tmvXDkeOHLGpjS+Xyx2WuwCAnJwcDBw4ECaT\nCcOGDYNK5QxWMdMJRP1ruX7LwbJ0T9i8JhK1R0pK0yfWo+L8Nrjw/wU4duwYVCoTWGibo3j/B2Bp\n6yNhOyMwg80W1Bg1apRNApWV1atXw2g0Yvbs2Rg1ahQMBgO0Wi1CQ0Mxc+ZMJCUlwcPDQ2jy8fe/\n//2JN2GnTp0we/ZsJCUl2YiIwWCoOUHrNyxisRienp4YMmSIILRExEw/LgQKZP93cXfBxYsXUVFR\ngX379mHKlCmIiIiw256Xlxeio6OFAmwzZ87ETz/9JHyfs2fPQiwWC6Gs58+fx6RJk4RSzkSEwsJC\nnDlzBrNmzRLKNDz//PPYuXOn4EBVKBRQqVQoKCjAmjVrUL9+fbi5udlF5DRp0gQff/yxUCqhsrIS\nu3fvhqenJ7p37w6FQoHOnTvbdJbatm0bVCqVTbcx4Tdp4kD0x5NN3Z/qS9u2bXHkyBFh+3fv3gUR\n2dRH6tChg5AVbmXTpk1ISkpiZazFchBFWwQ9FMxOX5PwA0TpIBoDou9AtAQaTTwaNcq26avA+c/A\nhf8vwtWrVyGR6MEaTlS/Yd4HUQsHD4WDYJUNvcFmAk4ICAgA8LgheWhoKNasWYN27drB3d0dXl5e\nCA4OhkgkwrPPPovx48fDy8sLAwYMqNFUVJ158+ahd+/euHfvHlq3bg0iFuPt7e2N/fv3Y/LkyXYR\nLk+KLnniIiXWlNxau76AQHFM/K1cu3YNbm5u2LlzJ7Zu3Yphw4YhIiICkyZNwubNmyESiTBp0iR0\n794d7u7uCAoKwgsvvIDu3btDIpEgPz8f0dHRMJlM6NatG8RiMXr06AGZTCbso0mTJggPD4darRZC\naKOiorBw4UKh5HRUVBTUajUGDx6MtWvXwtvb2+ZBM3nyZLz88sv44osvkJmZKbw+atQotGvXDi1b\ntkSDBg1gMBjQtWtXNGjQoPbfRkEs7t8q+g4yrK1LcHAwmjdv7vC8yuVy+Pv7IyoqCm+++SaUSiW0\nWq1Nz+KVK1datuUC5rg1Wq69RLCCg7UJfzBIoYZS5YEWLdrjk08+4Y1U/ktw4f8LsXr1GsjlPmB1\ne6reMBlgoZ9VX5sBIi8QvQGWAHMIRPlgU24RIiMjkZubi9zcXBgMBqSlpcFgMCA1NRU6nQ6tWrVC\nUFAQIiIinljvvzrHjh1DcHAwAOZ8HD58OA4dOoQPP/xQqI1fUlKC7t27QyQSITc3F2vWrEFwcDAi\nIiKEOjNPLfoiYhE+NYxqvby80KxZM/j4+CA9PR0LFy7Ep59+imPHjqG4uBinT5+GVqu1aQBTVFSE\nGTNmICEhQXDWent7o3fv3ti1axcePXokZOVqtVoAzMyl0WjQqlUrJCQkCNtq3LgxduzYgbKyMjRp\n0kSYtYSFhSE7OxsTJ05EcHCwkMH8zTffID4+Hq+//jpefvllYTsPHz5EZGQk+vbtC5VK9VS+FIPB\nAC9vL/ZgDCfmHNeTnWNcrVZj69atQmKXo9pNBoMBGzduhEwmw8KFC3H9+nWhKXrDhg0t/YS1YJFo\ny0G0FyzLPArM3OOFmiu9HgKRE1IzU3Hr1q3fdL1xfjtc+P9izJ+/ECqVC9TqriB6B6x0sw5Ep6rc\nRJ+Btam74uAGOw7WVYs5V63O3MmTJ+P8+fNC+z1rHPovv/zym4+xsrISBoPBrrMTYN8N6/vvv0d8\nfDzatWuHW7du4cMPP4SzszM0Gg3Wr1+PESNGQCwWQ6fTISkpybHYKS2j/erC/xoxh+QTxFEsFgs+\nhIYNGwoNyxs2bIhXXnkFcrkcPXr0wPbt25Gfn4/4+HjBaSmTyeDq6gqz2YyjR49Cq9WiY8eOGDJk\niPCdO3XqJCSEARDq3Dg5OaFPnz4wGAxo1KgRYmJiUFJSgkePHkGr1aJ169ZYvXo1zGYztm/fjl69\nej1VkxWRSISsrCzo9XrhYWLttyAWi23MY1qtFnv37kVhYSECAwNx7tw5fPvttzZ1i6z4+fnh5MmT\nkMvl8PT0FEpczJo1y5JP4QxWZLB6O9EKEHWzrO/rQPyvgigQLVq0/s3XGudfgwv/X5Di4mLMnj0H\n/fsPQWbjppZp9dYqN1I2iFbUMqUebxH/VCgULpgyZTrMZrNN5chevXrBx8cHb7311r90jG3btq0x\ns7J6/9vS0lIMGjQIwcHBaNSoEbKzszFo0CDBlvzWW2/h3XffhclkwjPPPIOmTZti9erVGDlyJHM6\nKojlETgSfvd/w3xUZcnIyLD5Dr/++itkMhl0Oh3EYjF8fX2RlJQErVaL1NRUm+8+ZMgQvPvuu8Lf\nSUlJGDBgANRqNSQSCbKystChQwdoNBpERkbi/v37Qv2c06dPw2w2IyYm5olibzAY4OnpiZCQEKxc\nuRJDhgxBly5d8PLLL0Ov12P48OFCXsaUKVOQmpqKyMhITJ8+HQDLCfD19cWPP/6IUaNG2bX0jIqK\nwrFjxxASEoKlS5dCo9EgODgErHCf3jIAqSnq7K5l1O8GlpX+Glgp5T4gUiE6OvGJCYic/xxc+P/i\nmM1mS6u6TDAbfwVY6FxNfXIBFjmhtTwglCCSQqPxgkajwRtvvIHy8nKUlpYiLCwMBoPhX7Kzvv32\n23bJUVW5ePEioqKiMGLECFRWVuL69esIDAyEQqFAly5dQERCVJG/vz+ys7Oxbds2DBkyBCKRCKNH\njxacfu+++y4LTcyvJvq9qMbM3t+6JCcnY+PGjdi/fz+uXr2KyspKyOVypKenCyPh8PBwiEQiIdQ1\nPz8f27dvx4QJE/Dqq68CAE6cOAEvLy9UVFRgxIgRiIyMFEI1R40aBZ1OB6PRiAEDBkAul+Phw4f4\n6KOPUK9ePYfH5ezsjOTkZFy6dAlmsxnr16+H0WiEWq1Gv379IBaLkZeX53D2tWfPHvj5+cFgMAgl\not9//314eXnh4MGDdiafhg0bYu/evQgOjoJMpoVI1Aoss7w5WFJWRi3XHEDUGaze1Ptg1WgjQOQM\nvd7rN19fnH8PLvz/A9y6dQtSqR5Eo8GKXElgP92uupy3jPhXgBWGuwtW9dMHRHKIVWL07tMbJ0+e\nhFQqxezZs3/zMR06dAiRkZG1vqeoqAgZGRlo0aIF/P39MXHiRLz00ks2wubj44Ovv/4a7733Hnx9\nfZGbm4uWLVsiKioKPj4+WL58OcrLy1E/sT4r1ZxDoB4WW7aMhFBOJycniEQiiMVi+Pn5wd3dHTKZ\n7KnLKGRkZCAvLw8JCQlwc3MTQkl1Oh2cnJzw0ksvCWYfo9GIzZs3o6CgAMnJyVAoFPDx8UFhYSH6\n9OmD0aNHA2BlMdq0aYMOHTqgXr160Ol0SExMFHoYiMViGAwGZGRkYMaMGYKvwdPTE3369IFOp8OB\nAwcQGhoqFKm7efMmXnrpJcF8NWTIEAQEBODevXsOz0FaWhp69+6NlJQU4QG/atUqeHh4YNmyZTYm\nn5SUFCQlNQKLILtW7Zr6wXL9LKvluhtgmR10BFE8iFQICanHHbh/AFz4/0c4fvw4tFofMLOPFxxH\n/1iX+WDTcgfJMlJvUCizm2ucNQgJCYFMJsN7772HHTt24OzZs09VaqGiogI6ne6JPoJdu3YJGZ6d\nOnVyaMOWyWSYPXs2Hj58iLlz58JkMkGhUGDRokVITk5GYmIidu7cicLCQngGekJtVCMwNBBNmzYV\nKmjK5XIEBwdDpVIhISEB7u7uOHPmjNCS8sCBA9iyZQvmzZuHcePGoUePHkhNTRV8Cl988YXNcb/5\n5pvQ6/WIiIiAu7s7+vbtKyRCWUsvKBQKBAYGIjw8HAaDAbGxsRCJRNBqtcjNzcWcOXNw5swZJCQk\nYMqUKRg8eLBQ898q8kajEStXroTZbMb06dPxxRdfCEI5d+5cNGzYEAcPHoTBYMDAgQPh6uqKAQMG\n4LvvvoOzszOCg4Ph4+ODfv36Ofz9P/74Y8TGxiI9PR1vv/228PqGDRvg5uaGDh06WKpv6sBaIYrB\n8kVWwT6K7BBYBFmZg2vLDCJ/EPlDJFIgMTHJphw35/eFC///GFu2bLEIZjwcN3MvArOx+tXwUPgQ\n5KRh4X4uBA8/D4hEIvj4+KBRo0YICAiAXC6Hl5cXUlJS0KVLF4wdOxYLFizAp59+ih9++EEYXebm\n5jrMNLWyceNGGAwGTJo0SRDMnTt3Ii0tzeGoOy4uDk7OThCpRRDJmPP5ueeew4wZM+Dt7Y3Y2Fhk\nZWVBq9UKxzp27Fjcu3cP6enpMJlMiI2NBREhMDAQN2/exLRp0zBo0CCHx/fBBx8gLS0NOTk5dqPS\nlJQUJCQkICIiApGRkUIHtISEBMF38eDBA5w5cwazZ89GSEgIevfuDaPRiKysLKHvgdW5TMRqGIWF\nhUGj0Qjr6tevj7i4OGRkZNj1SzabzUKdHicnJ+j1epuqp5MmTUL79u0xduxYiEQijBw50u57mM1m\n1KtXD0uWLBFKPty4cQNffvklevbsaRF8A4jqgWgWiG6C6COwyJ1BDsS/AVhwQfXraj2INJg4cTIX\n/D8BIgAgzv8UZ8+epdDQBCKKIKKZRJRBRGYi+pSIRhDRr0R0t4ZP3yaSmIjGlxNdJKL1GqJ7FUSk\nJZnsIeXltaNp014lpVJJFy9etFsuXLhAly5dIicnJ1IqlaRUKqlNmzbk7+9vs6xcuZLeeOMNSklJ\noU2bNpFMJiMXFxe6c+cOtWzZkm7cuEHffvut7aHJiCiBiLyI6GciOkqkVWvp4f2HJBKJKDg4mC5f\nvkzdunUjb29v2rlzJ+3cuZPEYjHdv3+fmjZtSgcPHqTAwEC6cuUK6fV60ul0tGDBAsrMzLT7JTp3\n7kxlZWUUERFB06dPF17/+eefKS4ujpo1a0bfffcdubi4kEQiIb1eT+fOnaN169ZRcnKy8P4zZ85Q\nTk4OpaamUmJiIg0YMIC2bt1KK1asoD179lD9+vWpvLyc9u/fT2KxmIxGI/3yyy9ERASAxGIxubq6\n0u3btyk4OJhatWpF3t7edOjQIdqyZQuVlZXRunXraNOmTUREtGzZMhKJRPTgwQOKjIyk5cuX06FD\nh2j8+PEUHR1NS5Ysofr16wvHt3r1alq4cCE1bdqU5sx5n+7evU2VlVqqqJAQ0StElGS5ZpYS0bdE\ntI2IfIgojYgmElH7Kr9aOyJSE9F7RKRh1xO9T0TjqXfvzvTBBx/UcN1xflf+4AcP57/EmTNnQCQC\nC6GTgzl8rVmzlxyMyKzLLZBEBupKIKkGrB76zyCaBKIgsPIRWrRunYfNmzc7NOWYzWZcv34d77//\nPvz9/TFz5ky8+OKLaN26NerVqyeYM9Rq9VOFJxJZnLSDqjlvB7DXCwsLMW3aNJhMJrRr1w65ubkQ\niUQYN26cUHrh0aNHSEtLg7e3N/z9/ZGamgqtVguRSIQDBw7YfYfy8nLo9Xq0atUKq1atslk3a9Ys\n9OnTB0OHDoXRaERMTAx0Oh1CQ0OhUqnsTGElJSXQaDRwcnJC165d4eLigubNm2P58uU2DeE//fRT\nuLu7Y+bMmVAoFDZ1jORyObp06YK0tDTI5XJIpVJ4eXkhLCwMarVayClwcnJCWloaCgoKsHDhQuTn\n5yM8PBw3btxAbm4u2rRpA5PJhPz8fME5bg0fFYn0YHkeGjBHraMAgQ8s10EZWD+IJtXWJ1iuOQVY\nBI8CIpEe+fn5//Y1zfnPwYX/f5y7d+8iPz8feXnPQa0OB1FLsE5HNQn/EpCvGiRXguj/wEo++4OF\n3R0C0W0QHQCL0NBBInFC48Y5OHToEG7duoWtW7fi2LFjAFjFRo1GI2T8lpaWonPnzoiKioLJZMKA\nAQOECJ4nLvVqCNcMZ0XW+vfvj7lz52Lw4MEQi8WIjY1FkyZNEBQUhHXr1qFPnz7Iy8tDcXExUlNT\nERYWJlToVCqVdvXdv/32W9SrVw/h4eF2JpZnnnkGn332GaZOnQqNRoOgoCC0bt0aMpkMaWlpNu89\nduwYq5BJLALnrbfewpUrV2o8X/Pnz4ePjw8aN26MyspKaLVaGAwGEJFg9/fw8IC/vz+Sk5Nx+PBh\nmM1mtG3bFr169cLixYuh0WgwaNAg9O3bFy1atIBarYZSqRTCRxs0aICgoCBoNBp07twZarUbWKOf\n5SA6A5Zxe7aWayQDRBvAAgNUVV632vgvQqHwR/v2HWssE875Y+HCX4eYOfNtyOUasFLPVx3c0L+A\nZG6gVALJreGhDcESxaq/97ZltMdmAGxWoWMx9TKCs8kZu3btQrNmzbBlyxYUFRUhPT0dISEh8Pb2\nxq5duwAA48ePf3JlSDE57u/7GoEy7N8vkUgQEBAApVKJuLg4GAwGqNVq7NixAwBrSWiNtgkKCkLD\nhg2FbFQrEyZMwEsvvQSlUmkzgr9w4QIMBgPKysqwePFiyOVymEwmjB07FiaTCWPGjMGVK1cwc+ZM\nxMbGwtfXF2PHjoVMJsOCBQue6jylpKTAz88Pjx49Ql5eHjp27Mga3xAhKysLTk5O8PPzg0wmg0wm\nQ1JSEtauXQsvLy/s2rULK1asQFhYmFDo7dChQ4Izu2vXrpZtuYDNAsVg/qD7lvP6teWc1xaWOR+s\nFMgty+zAGtXjBYmkKZRKPSZPfv0/dNVy/htw4a9jlJSUoE2b9pDJPEE0Fyy796rlZjaBnpGBkmRg\nHb/2gWUAVw8NLQZz7j2PxxnDdyyzAmv0hwJEWiQkJKB///4IDg6GyWRC27Zt7RrEFxUVYcmSJcjN\nzYVWq4WXlxeSk5PRqFGjx6aggBqE3+vp4vBlMhn0ej26dOmC8+fPY+/evZDL5UhKSkJoaCjat28P\nqVSK/Px8mM1mJCcnY8GCBahXr57NsRYWFqJv374AmGnGGjU0depU6HQ6xMbGQq/Xo0+fPti1axcq\nKytx8eJFSCQS7N2796nOUatWrZCYmIikpCRoNBoEBATg+PHjGDNmDMRiMQYMGCCUtcjPz0dcXByk\nUinkcjlUKpXQLa1z584wm8346quv4ObmAbFYDmbKMYHF0hdbRvrfVTm328FaftYm/B+CNUJ/D6wc\nQ3MQqRAVlYgJEyY7zBng/Lngwl9H2b17N3JyOkCjMUGjMaJFi3bo2asn5Bo5yF0Mlt07E0TDHdz4\nQ0HUD48jOu6DJZC1ANFOsOzNKyCaZhEaNmrPzs5GRUUFzGYzTp48iZkzZyIzM1Oob7NgwQJcunTJ\n5jjv3LnDonykBHquiuBPJNCz9FQdv6yLWq1GgwYN4OLigpSUFPTv3x8NGjRAZmYm/Pz8MGLECEil\nUrRt2xbOzs5YtGgRunXrZnM8ycnJ2LZtGwBWmpmIhKqeYrEYixcvtquCOm3aNPj6+uKTTz554nl5\n8OABtFotjEYjXFxc0K1bNwQGBgrr33jjDSiVSqSkpGD16tUIDQ1Fu3bt8Omnn6JBgwbQarVwdXWF\nWOxcZTamsZybJmAJe5FgcfgXwEJ/q0bmXLfMBkpqEf4eIJpo+WwW5HJXzJ4971+9FDl/AFz4OTb8\n+OOP8PDysNzUM0A0otpNfw/MBvxzlddeBlEXBzMDgPkJlGDmBBOINBCLdXByckGXLl2wdevWJ5bd\nLSsrY71nZQQyESvIZmCiHxMT81Qdr8RiMXx8fKBUKoVGKU5OTpgwYQLi4+PRokULeHh4YMaMGZBI\nJNDr9Rg6dKjQjB5g5ZhdXV3x3XffYdiwYTAajbDa7lNSUuDp6Wl37GazGWFhYcjJycHSpUtr/I4P\nHz7Eu+++C3d3d8jlchw+fBhXrlyBr68vdDodzp8/L7x38uTJ8PDwgMlkwtq1a/Hqq68KDwlXV3eL\n2OeDhexOASvbXcV5T91A5A5WvM9RWG9nEL1Sg+gfs2xfByIFkpOb2eU4cP78cOHn2PHgwQMYTQFg\nDV2Cqwn6UbAa69a/71seBBdqGSF2sDxIQiyzgOUgGgax2BmDBo14qhotZrMZ+fn5UCqV8PLyQm5u\nrtD569VXX4VEIkGTJk2E7FwPDw+4u7tDKpUKhclu376Na9euYdKkScIIXSQSQSqVwsnJCdHR0XBx\ncUFycrKQ1WstOXz+/Hm0bNkSOp0OwcHBmDhxIn744QcQESIjI+Hq6ornnnvO7rj37duH0NBQjBo1\nCjNmzLBbX1pairlz58Lb2xvPPvssZs2ahZYtWwrrjx49CoVCgXHjxgFgdZq2b9+O9PR0qNVqiCUK\nSCQaSKWBYPkZTmCF+MaANeNxdD4qweLtB4E5Y/9Zbf01y3l/ESzLG5aHx1KL4CsRERFh07KS89eC\nCz/HIVevXkVAQDREIjcQzakiCifB7P5W88B+y2i+NpvwOhCFwb4qYxFIVA/R9eLQJLsJ3Lzd4Obl\ng8TETAwePAKHDh2yO64lS5bA3d1diBa5d+8egoODodfrcezYMVy+fBkpKSno1q0bzGYzSktLsW7d\nOmRnZ0On06Fbt27o2LGj4IQdPHiwEFYqlUqh1WpBxEo5W30D0dHRMBqNcHNzw7vvvmvT2pCI0KBB\nA0gkEsyfP9/ueAcPHoypU6fizTfftCmx/OjRIyxYsEAoQ7F//34AzNldUFAAAMLxP//885BIJAgN\nrQ+ZTAuJJB4soUoJogCwvgsA0UsWwa8Eq4NfW2TORovoTwFRawfn5lcwB64SRM6QStXIyMjF7t27\n/+PXGuf3hws/p0ZKS0tRWFgIqVRnEYGjYHV9/MBMOAAL7Yx9gvCvBdFzNaz7CSRWgiQKkMQZJHoZ\nRMsgEo2HVOYJk7s/8p7LQ3p6OjIyMjB8+HCMHj0azs7O2LBhA4YMGYKePXvitddew8CBAzFhwgSk\npKTY9AG2cuPGDcyePRtyuRxGoxEvv/wyjh49ikuXLgnVMq1RQVIp611s/VelUkEmk2HMmDHYtGkT\nrly5gl27doGIEBUVBYVCYRf2WVpaCoPBgAsXLmDZsmXo2bMnysrKsGjRIvj5+aFly5bYt28fAFbK\n+tatW0hMTES7du2QldUCGo0JrO6SzDKr6gnbnrVXLa+lg8Xcx1nOyx2womm1nZMzltH7IzD7f1Mw\n/0yl5fPzQaRHWloGLly4YJNvwPnrw4Wf80R++eUXvPLKBOj1PnjcNDsGLKTzIZjtvrbRZVsQvVvz\nekkCSBRmeahUXfcIRK1AIhUomEAyJ8sINwdSaQysyUaenuGYMmUKFAoFDAYDpkyZgj179uDs2bPY\nuXMnvv/+e2GUvm/fPoSFheHEiRN45ZVX4Ovri/r16+PFF19Eu3btLHVpSKiXM3fuXGRnZ4OIEBsb\ni9dee01oXmOdHWi1WkgkEhQXF9v8bhs3bkTjxo2F/4eFhcHX1xepqalYv349zGYzfv75Z2RkNINI\npLGIvNjyvfrgccTUFDjusGY12zQHi7CJBRv9l1uE/2Yt52SX5bwBLBmrEMypKwKRGAZDEObN4w7b\n/1V4yQbOb6KsrIw2bdpE/fu/RPfuiYgon4iOEdENItpIRNJqn9hNRHnE6j/oathqLhE9S0SDHKy7\nTyQyEEmURBUfEVEmEYks644QUQciiidWSqCYiMREVGlZNERkJKWynIgekJeXO0kkYvLzM9ELL7xA\njx49oo8//pg+//xzevjwIVVUgGQyMTVs2JC+/fZbqqioIFMIK0IAAAikSURBVCKi7Oxs+uabffTg\nQQWlpiZRt24dqaKigkaMGGF3tE5OTiQSaYlITKWlxWQ2E4lEYqqsFBHzM5eTWKoksfQBkZSo4p6S\niLKJaDwRxRFRUyJqQkQFVbYaQ0TziZXecMSXRDSOWGkFD2JlFHpZPje6hs90JFZOYTmxUh4TSaF4\nSEVFl0ipVJJYLK7hc5z/Cf7oJw/nr0llZSXmzJkDD48wiMUGsDT9BLCGMDdB9COIxlpGnpm1jDwr\nwWzN+2tYXw7W5GNvDevPWkaqcSDKA9H3eOyM/ADMqfwBiHaAyBfM5OQCEjmDyAiRKNnyHg+waJc2\nYHZtA1j0itIyAh8E1j3KC4/DJL3AbOoTwBzeasuMZDaYqSTL8t4Cy4jaA0QLwUoXO4PkGrBSxdaR\n/I9gZQ6qNzNRwDYyp/pSZPn9/2n5/Hmw6BsjWFx+1feaLcfmZPluShC5oGnTpigvL/+jLyvO7wQX\nfs5/hBs3biAsLMwiwgqLsLiCle/VoWZT0HqwaBRHZgyAVXp8kg8hAszW7WgbP1qO4zJYr2JrAtNH\neBytZAYrY+0DFnHUBsxh3QtEKWCJTm+BRSUVWrYxHfbhqx9ZxL9qdcqvLK99ZDkWE1gjnE8tovtr\nlfeuAAuLrf4djKg9auoU2MMTYI54HzDTz8dgD5vGluOfBqJQiERaDBz4Am7cuPFHXzacPwhu6uH8\nVygrK6OoqCT66acLxEwQp4loFRE1ImaqqSCidUSSAUTkSlR5iR6bcKqykIi+IaIVNezpPhF5EtFx\nIvKr4T0vEpGJmDnFjYg2EDOnVOefxMwp4cTMH6FEdJSY+aieZX09Ikonoi017GuSZfvHqrw2jYjW\nWI6xgJiJJYyIlli2b2UNMXPZhmrbHGw5/kk17HMsEZUR0SzL34uJmXjuETN5qcnFxZn8/HypS5fn\naPTo0SSVVjfJceoS3JDH+a8gl8vp7Nmj9Nln6ygpqYxcXMwkleaRUhlKKlUGkciNRJIXSa6sIJG5\nhIh21rCle0R0vpY9nSEid6pZ9ImIWhLRfiLaQaycsCPRJyKqT0SxRNTYcjzJRORLRB8QUVfLZ4mI\nhteyrxeI6EdiQmylH7EHn5mIniOir4n5QsqrfTbDst871V4fQczGv8vB/rZZ1oUS0T+IqBsRDSex\n+BHVq5dMmzZtIuA+FRdfo6NH99O4ceO46HPsPHEczn+Uli1bUsuWLYmIyGw20+HDh6m4uJh8fHxI\nIpGQTCajH3/8kdq160YPH75HRG2ISELMEbqT2Gj5ATHnsL+DPZQSG/XXRimxYv5niM0+aiOd2G1R\nQqzwPxHRCSJqbfl/ZQ3HYcWd2MzlZpXPuxGb4ZRZ1oGYc/VlYg+1QMv7vIk9pMYS0Tx6PAMKI6L1\nxB4aicQeQiClcgPJ5UeoR49edPz4p2Q2g7KzU6ldu1fJy8uL9Hr9E74rp67ChZ/zuyEWi6lBgwZ2\nrwcGBtLHH6+hF18cSz///BJJpWFUWXmB1GoztW/fhU6e/Im+/ro1mc07iZk8rNwjFsFSSkTfE4uK\nccTfiUXOaIjo0hOO8mdi5pxgYiNpEJETERVZ1kuI6CQRhdTw+QvEJtJuVV773rJvJbFReSMiMlr+\nHkREW4k9mMiyzxZE1IzYgyHGcswLiEhEcvkJEoleoYSEWOrXrwt16bKJ1Gr1E74Th2MLt/Fz/lQc\nP36cLl++TG5ubhQfH08ikYgA0JgxBTRnznyqqHiOKivrE9E5YjbxtsRs/NstS3UR/IyIehMT6xHE\n7OfniI3Mq3OfWDjk68T8AqFEtIiIHhF7wOy37CuEiPaQY5/EcGKhpQcsf4OIOhObQSwgolRiM5k9\nRPQSkUhGBD9i/oeGxMJiFxDRavL2DqYHD+6Ss7OeGjWKo6SkRIqJiaEmTZrwcEvOvwUXfs5fhqtX\nr9KKFStp8+aP6dCho1RRUUZErkR0i9iI2UDMedqIWEz/UmLO0s7E7OM+xMT8ODHnrabK1h8Rs4/f\nI6KzRPQdER0ior8RE/+JRNSJWGvBhsTMLW9V2UY5MfNMARF9RUQNiIn9RCL6kIheItaOsBM5OV0l\ntfoAffHFRySXy2nevHn04Ydb6MGD2yQWyyglJY7mzHmL4uJqmsFwOP8eXPg5f1n++c9/0tWrVykm\nJoYMBgO5ubnRvXtSYk5UERGVksHgQWYz6P7926RQ+FBZ2WUyGj3o5s1iqqzsQxUVEcRs/4uIyJmY\nKedXIlIQi4xxJaI3iUXilBFz9mYQE/NyYiYkJRF9QcyO/5BYQpmMiA4TkYyk0grS6z1JJhOTyeRJ\n/ft3pp49e5Czs/Pv9EtxOLZw4efUCS5dukQlJSXk7e1NBoOBTp06RUuWfEg//XSZPDxcKSwsgDQa\nDV27do3OnLlMV65cphs3iun69V9IIpFQcnIcZWam0OHDh+nLL/dTaWk53b9/jSorHxERkUKhoNLS\nUrp48SKtWLGCKisrqX379hQTE/MHf3MOxx4u/BwOh1PH4B4iDofDqWNw4edwOJw6Bhd+DofDqWNw\n4edwOJw6Bhd+DofDqWNw4edwOJw6Bhd+DofDqWNw4edwOJw6Bhd+DofDqWNw4edwOJw6Bhd+DofD\nqWNw4edwOJw6Bhd+DofDqWNw4edwOJw6Bhd+DofDqWNw4edwOJw6Bhd+DofDqWNw4edwOJw6Bhd+\nDofDqWNw4edwOJw6Bhd+DofDqWNw4edwOJw6Bhd+DofDqWNw4edwOJw6Bhd+DofDqWNw4edwOJw6\nBhd+DofDqWNw4edwOJw6Bhd+DofDqWNw4edwOJw6Bhd+DofDqWNw4edwOJw6Bhd+DofDqWNw4edw\nOJw6Bhd+DofDqWNw4edwOJw6Bhd+DofDqWNw4edwOJw6Bhd+DofDqWNw4edwOJw6Bhd+DofDqWNw\n4edwOJw6Bhd+DofDqWNw4edwOJw6Bhd+DofDqWP8P8Fu11IPLstVAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 423
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"h=sorted(nx.connected_components(G),key=len,reverse=True)\n",
"#connectec components only work over undirected graphs\n",
"H=DG.subgraph(h[1])\n",
"nx.draw(H,with_labels=True)\n",
"plt.axis('off')\n",
"plt.savefig(\"direct_case.png\",dpi=1000)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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fxYdIvtXoI6FQKOjVqxc9e/YkJCSENQEBHL52jcTERCwsLKhaty6nhwzB2dn5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\n"
}
],
"prompt_number": 158
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#The out-degree centrality for a node v is the fraction of nodes its outgoing edges are connected to.\n",
"co3=nx.out_degree_centrality(DG)\n",
"co4=DataFrame(co3.items(), columns=['investor','Degree Centrality'])\n",
"co5=co4.sort(['Degree Centrality'],ascending=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 91
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"co5.head(n=10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>investor</th>\n",
" <th>Degree Centrality</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>130</th>\n",
" <td> caixa-capital</td>\n",
" <td> 0.057402</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78 </th>\n",
" <td> cabiedes-partners</td>\n",
" <td> 0.042296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183</th>\n",
" <td> wayra</td>\n",
" <td> 0.039275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>311</th>\n",
" <td> inveready-technology-investment-group</td>\n",
" <td> 0.033233</td>\n",
" </tr>\n",
" <tr>\n",
" <th>179</th>\n",
" <td> nauta-capital</td>\n",
" <td> 0.027190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td> highgrowth</td>\n",
" <td> 0.027190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35 </th>\n",
" <td> active-venture-partners</td>\n",
" <td> 0.021148</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16 </th>\n",
" <td> kibo-ventures</td>\n",
" <td> 0.018127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>235</th>\n",
" <td> debaeque</td>\n",
" <td> 0.015106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>158</th>\n",
" <td> la-caixa</td>\n",
" <td> 0.012085</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 92,
"text": [
" investor Degree Centrality\n",
"130 caixa-capital 0.057402\n",
"78 cabiedes-partners 0.042296\n",
"183 wayra 0.039275\n",
"311 inveready-technology-investment-group 0.033233\n",
"179 nauta-capital 0.027190\n",
"190 highgrowth 0.027190\n",
"35 active-venture-partners 0.021148\n",
"16 kibo-ventures 0.018127\n",
"235 debaeque 0.015106\n",
"158 la-caixa 0.012085"
]
}
],
"prompt_number": 92
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = HTML(co5[0:9].to_html(index=False))\n",
"my_file = open('d_central_inv.html', 'w')\n",
"my_file.write(a.data)\n",
"my_file.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 93
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#The in-degree centrality for a node v is the fraction of nodes its incoming edges are connected to.\n",
"co3=nx.in_degree_centrality(DG)\n",
"co4=DataFrame(co3.items(), columns=['company','Degree_Centrality'])\n",
"co5=co4.sort(['Degree_Centrality'],ascending=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 192
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"co5.head(n=10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>company</th>\n",
" <th>Degree_Centrality</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>93 </th>\n",
" <td> geenapp-internet</td>\n",
" <td> 0.030211</td>\n",
" </tr>\n",
" <tr>\n",
" <th>160</th>\n",
" <td> era-biotech</td>\n",
" <td> 0.027190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63 </th>\n",
" <td> groupalia</td>\n",
" <td> 0.024169</td>\n",
" </tr>\n",
" <tr>\n",
" <th>178</th>\n",
" <td> scytl</td>\n",
" <td> 0.021148</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28 </th>\n",
" <td> tiendeo</td>\n",
" <td> 0.021148</td>\n",
" </tr>\n",
" <tr>\n",
" <th>218</th>\n",
" <td> nubelo</td>\n",
" <td> 0.018127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td> privalia</td>\n",
" <td> 0.018127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td> infantium</td>\n",
" <td> 0.018127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51 </th>\n",
" <td> uvinum</td>\n",
" <td> 0.018127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43 </th>\n",
" <td> bidaway-com</td>\n",
" <td> 0.015106</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 193,
"text": [
" company Degree_Centrality\n",
"93 geenapp-internet 0.030211\n",
"160 era-biotech 0.027190\n",
"63 groupalia 0.024169\n",
"178 scytl 0.021148\n",
"28 tiendeo 0.021148\n",
"218 nubelo 0.018127\n",
"116 privalia 0.018127\n",
"270 infantium 0.018127\n",
"51 uvinum 0.018127\n",
"43 bidaway-com 0.015106"
]
}
],
"prompt_number": 193
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a=co5[co5.Degree_Centrality != 0]\n",
"b=a.merge(h2, on='company')\n",
"x=list(b['Degree_Centrality'])\n",
"y=list(b['money'].astype(float))\n",
"plt.scatter(x,y)\n",
"plt.xlabel('Degree Centrality')\n",
"plt.ylabel('Total Fundraised (EUR)')\n",
"plt.title(r'Degree Centrality vs Total Fundraised')\n",
"plt.show()\n",
"\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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ZejtDhw7FrFmzMHDgQLRv3x6DBg0y+DcBgK+++gr/+Mc/YG9vjw8++ABPP/10ue0Dxb//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\niIjIoP8DgJXGl1AhYhgAAAAASUVORK5CYII=\n"
}
],
"prompt_number": 220
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = HTML(co5[0:9].to_html(index=False))\n",
"my_file = open('d_central_comp.html', 'w')\n",
"my_file.write(a.data)\n",
"my_file.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 95
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Save DataFrames "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfc.to_csv('barcelona_link.csv', sep=',', encoding='utf-8',index=False)\n",
"\n",
"dfd=df1.append(df5)\n",
"dfd=dfd[['name', 'role']]\n",
"dfd.to_csv('barcelona_role.csv', sep=',', encoding='utf-8',index=False)\n",
"\n",
"dfe=df2[['name','category']]\n",
"dfe.to_csv('barcelona_cat.csv', sep=',', encoding='utf-8',index=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 185
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b.money=b.money.astype(float)\n",
"b.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>company</th>\n",
" <th>Degree_Centrality</th>\n",
" <th>money</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> geenapp-internet</td>\n",
" <td> 0.030211</td>\n",
" <td> 103000.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> era-biotech</td>\n",
" <td> 0.027190</td>\n",
" <td> 10469198.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> groupalia</td>\n",
" <td> 0.024169</td>\n",
" <td> 35899996.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> scytl</td>\n",
" <td> 0.021148</td>\n",
" <td> 89427998.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> tiendeo</td>\n",
" <td> 0.021148</td>\n",
" <td> 834995.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 223,
"text": [
" company Degree_Centrality money\n",
"0 geenapp-internet 0.030211 103000.00\n",
"1 era-biotech 0.027190 10469198.42\n",
"2 groupalia 0.024169 35899996.00\n",
"3 scytl 0.021148 89427998.42\n",
"4 tiendeo 0.021148 834995.00"
]
}
],
"prompt_number": 223
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b.to_csv('deg_central.csv', sep=',', encoding='utf-8',index=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 224
},
{
"cell_type": "heading",
"level": 5,
"metadata": {},
"source": [
"Normalization link weight to apply PageRank Algorithm"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfc['money']=dfc['money'].astype(float)\n",
"dfc['money'] = (dfc['money'] - dfc['money'].min()) / (dfc['money'].max() - dfc['money'].min())\n",
"y=list(dfc[['investor','company','money']].itertuples(index=False))\n",
"x1=list(dfc['company'])\n",
"x2=list(dfc['investor'])\n",
"dfc.money[dfc.money == 0]=0.00000000000001\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 265
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"DG1=nx.DiGraph()\n",
"DG1.add_weighted_edges_from(y)\n",
"pos=nx.spring_layout(DG1)\n",
"nx.draw_networkx_nodes(DG1,pos,nodelist=x1,node_size=50,node_color= 'g')\n",
"nx.draw_networkx_nodes(DG1,pos,nodelist=x2,node_size=80, node_color= 'b')\n",
"nx.draw_networkx_edges(DG1,pos)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 255,
"text": [
"<matplotlib.collections.LineCollection at 0xad2c36c>"
]
},
{
"output_type": "display_data",
"png": 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/oSeBlgRW2trtJ7bfnhQJZAq32/9SqIaSCYwhpMGUJB+Ghsbxyy+/rKqueNei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\n"
}
],
"prompt_number": 255
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pr=nx.pagerank(DG1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 309
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"m=list(set(x2))\n",
"for i in range(len(m)):\n",
" if m[i] not in x1:\n",
" del pr[m[i]]\n",
"\n",
"\n",
"\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 310
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"co6=DataFrame(pr.items(), columns=['company','pagerank'])\n",
"b=co6.merge(h2, on='company')\n",
"x=list(b['pagerank'])\n",
"y=list(b['money'].astype(float))\n",
"plt.scatter(x,y)\n",
"plt.xlabel('PageRank')\n",
"plt.ylabel('Total Fundraised (EUR)')\n",
"plt.title(r'PageRank vs Total Fundraised')\n",
"plt.show()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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3vZ8yZQqeeuopU4VjdAEBAaYOwaBqc/tqc9sAto9Kq7EX3CkUCqM9qIaIqLYwxL7zkbmF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\nlAAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 315
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b.money=b.money.astype(float)\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>company</th>\n",
" <th>pagerank</th>\n",
" <th>money</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> akamon-entertainment</td>\n",
" <td> 0.006464</td>\n",
" <td> 2800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> ebdsoft</td>\n",
" <td> 0.002360</td>\n",
" <td> 3594500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> compring</td>\n",
" <td> 0.005577</td>\n",
" <td> 300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> bettymovil</td>\n",
" <td> 0.002279</td>\n",
" <td> 1580000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> tiendeo</td>\n",
" <td> 0.009005</td>\n",
" <td> 834995</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 317,
"text": [
" company pagerank money\n",
"0 akamon-entertainment 0.006464 2800000\n",
"1 ebdsoft 0.002360 3594500\n",
"2 compring 0.005577 300000\n",
"3 bettymovil 0.002279 1580000\n",
"4 tiendeo 0.009005 834995"
]
}
],
"prompt_number": 317
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b.to_csv('pagerank.csv', sep=',', encoding='utf-8',index=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 318
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Category Score"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dcat=df2[['name','category']]\n",
"m=list(h2.company)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 383
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dcat['name']=dcat['name'].apply(lambda x: x if x in m else 'false')\n",
"dc=dcat[dcat.name != 'false']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 384
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"h2['money']=h2['money'].astype(float)\n",
"h2['money'] = (h2['money'] - h2['money'].min()) / (h2['money'].max() - h2['money'].min())"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 385
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rt=dict(zip(h2.company, h2.money))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 387
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dc['score']=dc['name'].apply(lambda x: rt[x])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 388
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dc.head(20)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>category</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> strands</td>\n",
" <td> Artificial Intelligence</td>\n",
" <td> 0.109814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> strands</td>\n",
" <td> Reviews and Recommendations</td>\n",
" <td> 0.109814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> strands</td>\n",
" <td> Software</td>\n",
" <td> 0.109814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> strands</td>\n",
" <td> Personal Finance</td>\n",
" <td> 0.109814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> strands</td>\n",
" <td> E-Commerce</td>\n",
" <td> 0.109814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> strands</td>\n",
" <td> Personalization</td>\n",
" <td> 0.109814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> akamon-entertainment</td>\n",
" <td> Games</td>\n",
" <td> 0.007077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> veeva</td>\n",
" <td> Content</td>\n",
" <td> 0.007986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> veeva</td>\n",
" <td> Software</td>\n",
" <td> 0.007986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> veeva</td>\n",
" <td> CRM</td>\n",
" <td> 0.007986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> veeva</td>\n",
" <td> Life Sciences</td>\n",
" <td> 0.007986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> veeva</td>\n",
" <td> Enterprise Software</td>\n",
" <td> 0.007986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 3scale</td>\n",
" <td> Payments</td>\n",
" <td> 0.009983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> 3scale</td>\n",
" <td> Software</td>\n",
" <td> 0.009983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> 3scale</td>\n",
" <td> Infrastructure</td>\n",
" <td> 0.009983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> 3scale</td>\n",
" <td> Developer APIs</td>\n",
" <td> 0.009983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> 3scale</td>\n",
" <td> Services</td>\n",
" <td> 0.009983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> 3scale</td>\n",
" <td> Enterprise Software</td>\n",
" <td> 0.009983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> trovit</td>\n",
" <td> Employment</td>\n",
" <td> 0.003255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> trovit</td>\n",
" <td> Cars</td>\n",
" <td> 0.003255</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" name category score\n",
"0 strands Artificial Intelligence 0.109814\n",
"1 strands Reviews and Recommendations 0.109814\n",
"2 strands Software 0.109814\n",
"3 strands Personal Finance 0.109814\n",
"4 strands E-Commerce 0.109814\n",
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"7 akamon-entertainment Games 0.007077\n",
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"9 veeva Software 0.007986\n",
"10 veeva CRM 0.007986\n",
"11 veeva Life Sciences 0.007986\n",
"12 veeva Enterprise Software 0.007986\n",
"13 3scale Payments 0.009983\n",
"14 3scale Software 0.009983\n",
"15 3scale Infrastructure 0.009983\n",
"16 3scale Developer APIs 0.009983\n",
"17 3scale Services 0.009983\n",
"18 3scale Enterprise Software 0.009983\n",
"19 trovit Employment 0.003255\n",
"20 trovit Cars 0.003255"
]
}
],
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},
{
"cell_type": "code",
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"input": [
"dc1=dc[['category','score']]\n",
"dl1=dc1[['category','score']].groupby('category').sum().reset_index()\n",
"dl2=dl1.sort(['score'],ascending=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 390
},
{
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],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>category</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>61 </th>\n",
" <td> Fashion</td>\n",
" <td> 1.721666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48 </th>\n",
" <td> E-Commerce</td>\n",
" <td> 1.152117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td> Retail</td>\n",
" <td> 1.095642</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41 </th>\n",
" <td> Design</td>\n",
" <td> 0.720301</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td> Printing</td>\n",
" <td> 0.720301</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145</th>\n",
" <td> Software</td>\n",
" <td> 0.581629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50 </th>\n",
" <td> Electronics</td>\n",
" <td> 0.226776</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86 </th>\n",
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" <td> 0.226018</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>125</th>\n",
" <td> Reviews and Recommendations</td>\n",
" <td> 0.120252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
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" </tr>\n",
" <tr>\n",
" <th>66 </th>\n",
" <td> Games</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td> Personalization</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td> Personal Finance</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td> Social Media</td>\n",
" <td> 0.101359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96 </th>\n",
" <td> Mobile</td>\n",
" <td> 0.097313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td> Price Comparison</td>\n",
" <td> 0.090733</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59 </th>\n",
" <td> Facebook Applications</td>\n",
" <td> 0.088069</td>\n",
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" <tr>\n",
" <th>139</th>\n",
" <td> Social Games</td>\n",
" <td> 0.088069</td>\n",
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"</table>\n",
"</div>"
],
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"prompt_number": 393,
"text": [
" category score\n",
"61 Fashion 1.721666\n",
"48 E-Commerce 1.152117\n",
"124 Retail 1.095642\n",
"41 Design 0.720301\n",
"115 Printing 0.720301\n",
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"109 Personal Finance 0.109814\n",
"140 Social Media 0.101359\n",
"96 Mobile 0.097313\n",
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]
}
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},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dl2['pct']=dl2['score']*100/(dl2.score.sum())\n",
"dl2.head()"
],
"language": "python",
"metadata": {},
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{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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" <thead>\n",
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" <th></th>\n",
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" <th>score</th>\n",
" <th>pct</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>61 </th>\n",
" <td> Fashion</td>\n",
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" <th>48 </th>\n",
" <td> E-Commerce</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td> Retail</td>\n",
" <td> 1.095642</td>\n",
" <td> 11.907894</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41 </th>\n",
" <td> Design</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" category score pct\n",
"61 Fashion 1.721666 18.711779\n",
"48 E-Commerce 1.152117 12.521690\n",
"124 Retail 1.095642 11.907894\n",
"41 Design 0.720301 7.828530\n",
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]
}
],
"prompt_number": 397
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dl2.to_csv('cat_score.csv', sep=',', encoding='utf-8',index=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 398
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
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"outputs": [],
"prompt_number": 398
},
{
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}
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}
]
}