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NSF historical awards exploration
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nsf_fundings.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploring NSF Historical Awards \n",
"- https://www.nsf.gov/awardsearch/download.jsp"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.ticker import MaxNLocator\n",
"import os\n",
"import xml.etree.ElementTree as ET "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def parse_xml(xmlfile):\n",
" try:\n",
" # initialize everything\n",
" title = None\n",
" date = None\n",
" amount = None\n",
" gtype = None\n",
" dabv = None\n",
" dname = None\n",
" divabv = None\n",
" divname = None\n",
" abstract = None\n",
" name = None\n",
" role = None\n",
" institution = None\n",
" city = None\n",
" \n",
" # create element tree object \n",
" tree = ET.parse(xmlfile)\n",
" root = tree.getroot()\n",
"\n",
" #<Award>\n",
" # 0 AwardTitle\n",
" title = root[0][0].text\n",
"\n",
" # 1 AwardEffectiveDate\n",
" date = root[0][1].text\n",
"\n",
" # 2 AwardExpirationDate\n",
"\n",
" # 3 AwardTotalIntnAmount\n",
" amount = root[0][3].text\n",
" amount = float(amount)\n",
"\n",
" # 4 AwardAmount\n",
"\n",
" # 5 AwardInstrument\n",
" gtype = root[0][5][0].text\n",
"\n",
" # 6 Organization\n",
" # Directorate\n",
" dabv = root[0][6][1][0].text\n",
" dname = root[0][6][1][1].text\n",
" # Division\n",
" divabv = root[0][6][2][0].text\n",
" divname = root[0][6][2][1].text\n",
"\n",
" # 7 ProgramOfficer\n",
"\n",
" # 8 AbstractNarration\n",
" abstract = root[0][8].text\n",
"\n",
" # 9 MinAmdLetterDate\n",
" # 10 MaxAmdLetterDate\n",
" # 11 ARRAAmount\n",
" # 12 AwardID\n",
"\n",
" # 13 Investigator\n",
" first_name = root[0][13][0].text\n",
" last_name = root[0][13][1].text\n",
" name = '%s %s' % (first_name, last_name)\n",
" role = root[0][13][5].text\n",
"\n",
" # 14 Institution\n",
" institution = root[0][14][0].text\n",
" city = root[0][14][1].text\n",
" \n",
" # 15 ProgramElement\n",
" # </Award>\n",
" \n",
" #if dname == 'Directorate For Geosciences':\n",
" # print(title)\n",
" #if title == 'Collaborative Research: Enhancing our Understanding of North Atlantic Deep Water Pathways using Nonlinear Dynamics Techniques':\n",
" # print('%s %s' % (first_name, last_name))\n",
" #if first_name == 'Susan' and last_name == 'Lozier':\n",
" # print('%s %s: %s ($%s)' % (first_name, last_name, title, amount))\n",
" except:\n",
" pass\n",
" \n",
" data = [title, date, amount, gtype, dabv, dname, divabv, divname, name, institution]\n",
" return data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Creating an empty Dataframe with column names only\n",
"columns_name = ['title', 'date', 'amount', 'gtype', 'dabv', 'dname', 'divabv', 'divname', \n",
" 'name', 'institution']"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2010/\n",
"2011/\n",
"2012/\n",
"2013/\n",
"2014/\n",
"2015/\n",
"2016/\n",
"2017/\n",
"2018/\n",
"2019/\n",
"2020/\n"
]
}
],
"source": [
"data = []\n",
"for year in range(2010, 2021): \n",
" folder = '%s/' % year\n",
" print(folder)\n",
" for filename in os.listdir(folder):\n",
" xmlfile = folder + filename\n",
" data.append(parse_xml(xmlfile))\n",
"\n",
"df = pd.DataFrame(data, columns=columns_name)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# remove DataFrame where name are not available\n",
"# Those usually don't have institution either...\n",
"df = df.dropna(subset=['name'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" <th>date</th>\n",
" <th>amount</th>\n",
" <th>gtype</th>\n",
" <th>dabv</th>\n",
" <th>dname</th>\n",
" <th>divabv</th>\n",
" <th>divname</th>\n",
" <th>name</th>\n",
" <th>institution</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LTREB: Female settlement patterns and social ...</td>\n",
" <td>03/15/2011</td>\n",
" <td>339900.0</td>\n",
" <td>Continuing grant</td>\n",
" <td>BIO</td>\n",
" <td>Direct For Biological Sciences</td>\n",
" <td>IOS</td>\n",
" <td>Division Of Integrative Organismal Systems</td>\n",
" <td>Anne Pusey</td>\n",
" <td>Ian</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>SBIR Phase II: Patent End-To-End (PE2E) Exami...</td>\n",
" <td>03/15/2011</td>\n",
" <td>500000.0</td>\n",
" <td>Standard Grant</td>\n",
" <td>ENG</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>IIP</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Rocky Kahn</td>\n",
" <td>Team Patent LLC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>COLLABORATIVE RESEARCH: Latitudinal variation ...</td>\n",
" <td>03/01/2011</td>\n",
" <td>327000.0</td>\n",
" <td>Standard Grant</td>\n",
" <td>BIO</td>\n",
" <td>Direct For Biological Sciences</td>\n",
" <td>DEB</td>\n",
" <td>Division Of Environmental Biology</td>\n",
" <td>James Cronin</td>\n",
" <td>Louisiana State University</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Workshop: SFF Symposium Student Support 2010;...</td>\n",
" <td>05/01/2010</td>\n",
" <td>15000.0</td>\n",
" <td>Standard Grant</td>\n",
" <td>ENG</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>CMMI</td>\n",
" <td>Div Of Civil, Mechanical, &amp; Manufact Inn</td>\n",
" <td>David Bourell</td>\n",
" <td>University of Texas at Austin</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>EAGER: Backbone Selenoamides as Minimal Chromo...</td>\n",
" <td>07/15/2010</td>\n",
" <td>250000.0</td>\n",
" <td>Standard Grant</td>\n",
" <td>MPS</td>\n",
" <td>Direct For Mathematical &amp; Physical Scien</td>\n",
" <td>CHE</td>\n",
" <td>Division Of Chemistry</td>\n",
" <td>Ernest Petersson</td>\n",
" <td>University of Pennsylvania</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title date amount \\\n",
"0 LTREB: Female settlement patterns and social ... 03/15/2011 339900.0 \n",
"1 SBIR Phase II: Patent End-To-End (PE2E) Exami... 03/15/2011 500000.0 \n",
"4 COLLABORATIVE RESEARCH: Latitudinal variation ... 03/01/2011 327000.0 \n",
"6 Workshop: SFF Symposium Student Support 2010;... 05/01/2010 15000.0 \n",
"8 EAGER: Backbone Selenoamides as Minimal Chromo... 07/15/2010 250000.0 \n",
"\n",
" gtype dabv dname divabv \\\n",
"0 Continuing grant BIO Direct For Biological Sciences IOS \n",
"1 Standard Grant ENG Directorate For Engineering IIP \n",
"4 Standard Grant BIO Direct For Biological Sciences DEB \n",
"6 Standard Grant ENG Directorate For Engineering CMMI \n",
"8 Standard Grant MPS Direct For Mathematical & Physical Scien CHE \n",
"\n",
" divname name \\\n",
"0 Division Of Integrative Organismal Systems Anne Pusey \n",
"1 Div Of Industrial Innovation & Partnersh Rocky Kahn \n",
"4 Division Of Environmental Biology James Cronin \n",
"6 Div Of Civil, Mechanical, & Manufact Inn David Bourell \n",
"8 Division Of Chemistry Ernest Petersson \n",
"\n",
" institution \n",
"0 Ian \n",
"1 Team Patent LLC \n",
"4 Louisiana State University \n",
"6 University of Texas at Austin \n",
"8 University of Pennsylvania "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"groups = df.groupby('name')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"At least one person got 27 proposals accepted during the last 10 years.\n"
]
}
],
"source": [
"# array with the number of proposal per PI\n",
"nb_proposal = []\n",
"for name, group in groups:\n",
" nb_proposal.append(len(group))\n",
"np_proposal = np.array(nb_proposal)\n",
"print('At least one person got %d proposals accepted during the last 10 years.' % np.max(np_proposal))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# let's see the amount and the name of the proposals\n",
"for name, group in groups:\n",
" if len(group) == np.max(np_proposal):\n",
" PI_name = name\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" <th>date</th>\n",
" <th>amount</th>\n",
" <th>dname</th>\n",
" <th>divname</th>\n",
" <th>institution</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>43383</th>\n",
" <td>MIT VMS I-Corps Site</td>\n",
" <td>06/15/2014</td>\n",
" <td>299991.0</td>\n",
" <td>Direct For Computer &amp; Info Scie &amp; Enginr</td>\n",
" <td>Division Of Computer and Network Systems</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82468</th>\n",
" <td>I-Corps: An Accurate and Accessible Indoor Pos...</td>\n",
" <td>12/01/2016</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Roman</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91892</th>\n",
" <td>I-Corps: An Objective Clinical Machine Learnin...</td>\n",
" <td>04/15/2017</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93323</th>\n",
" <td>I-Corps: Mobile Augmented Reality</td>\n",
" <td>04/01/2017</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95592</th>\n",
" <td>I-Corps: Point-of-Care Physiological Assessment</td>\n",
" <td>04/01/2017</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95978</th>\n",
" <td>Type II: MIT Innovation Corps Site</td>\n",
" <td>08/01/2017</td>\n",
" <td>299613.0</td>\n",
" <td>Direct For Computer &amp; Info Scie &amp; Enginr</td>\n",
" <td>Division Of Computer and Network Systems</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99195</th>\n",
" <td>I-Corps: Factor graph computing for data-drive...</td>\n",
" <td>09/15/2018</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99279</th>\n",
" <td>I-Corps Teams: Programmable Nanotechnology Vac...</td>\n",
" <td>01/01/2018</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101811</th>\n",
" <td>I-Corps: Improving the Energy Efficiency of Tr...</td>\n",
" <td>01/01/2018</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101937</th>\n",
" <td>I-Corps Teams: Mobile Platform for Collecting,...</td>\n",
" <td>09/15/2018</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102672</th>\n",
" <td>I-Corps: Organ-on-a-Chip Technology for Pharma...</td>\n",
" <td>01/01/2018</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104798</th>\n",
" <td>I-Corps: Machine Learning Algorithms and Tools...</td>\n",
" <td>01/01/2018</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109138</th>\n",
" <td>I-Corps Teams: Photonic Crystal Enabled Thermo...</td>\n",
" <td>01/01/2018</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109250</th>\n",
" <td>I-Corps Node: New England Regional Innovation ...</td>\n",
" <td>08/01/2018</td>\n",
" <td>4200000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Marc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113549</th>\n",
" <td>I-Corps: Heat-stable binding proteins for dive...</td>\n",
" <td>09/15/2019</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113957</th>\n",
" <td>I-Corps: Ultra-clear, transparent aerogel mate...</td>\n",
" <td>09/15/2019</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114746</th>\n",
" <td>I-Corps: Synthetic Matrix Solutions for Neurod...</td>\n",
" <td>04/15/2019</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116925</th>\n",
" <td>Broadening Participation - I-Corps Northeast R...</td>\n",
" <td>10/01/2019</td>\n",
" <td>171116.0</td>\n",
" <td>Direct For Computer &amp; Info Scie &amp; Enginr</td>\n",
" <td>Division Of Computer and Network Systems</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117967</th>\n",
" <td>I-Corps: Electric Reservoir Stimulation</td>\n",
" <td>03/15/2019</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118123</th>\n",
" <td>I-Corps: Decentralized fertilizer production f...</td>\n",
" <td>09/15/2019</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119205</th>\n",
" <td>I-Corps: Robust filtration membranes for harsh...</td>\n",
" <td>03/15/2019</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120929</th>\n",
" <td>I-Corps: Acoustic monitoring of remote pumping...</td>\n",
" <td>03/15/2019</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121225</th>\n",
" <td>I-Corps: Embedding fabric-based sensors into a...</td>\n",
" <td>09/01/2019</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124283</th>\n",
" <td>I-Corps: Label-free optical imaging of the lym...</td>\n",
" <td>06/01/2020</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124441</th>\n",
" <td>I-Corps Teams: IoT Sensor Networks Detecting U...</td>\n",
" <td>02/01/2020</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>125869</th>\n",
" <td>I-Corps: Software Platform for Constructing Mi...</td>\n",
" <td>06/01/2020</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126075</th>\n",
" <td>I-Corps Teams: Machine Learning (ML)-powered D...</td>\n",
" <td>02/01/2020</td>\n",
" <td>50000.0</td>\n",
" <td>Directorate For Engineering</td>\n",
" <td>Div Of Industrial Innovation &amp; Partnersh</td>\n",
" <td>Massachusetts Institute of Technology</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title date \\\n",
"43383 MIT VMS I-Corps Site 06/15/2014 \n",
"82468 I-Corps: An Accurate and Accessible Indoor Pos... 12/01/2016 \n",
"91892 I-Corps: An Objective Clinical Machine Learnin... 04/15/2017 \n",
"93323 I-Corps: Mobile Augmented Reality 04/01/2017 \n",
"95592 I-Corps: Point-of-Care Physiological Assessment 04/01/2017 \n",
"95978 Type II: MIT Innovation Corps Site 08/01/2017 \n",
"99195 I-Corps: Factor graph computing for data-drive... 09/15/2018 \n",
"99279 I-Corps Teams: Programmable Nanotechnology Vac... 01/01/2018 \n",
"101811 I-Corps: Improving the Energy Efficiency of Tr... 01/01/2018 \n",
"101937 I-Corps Teams: Mobile Platform for Collecting,... 09/15/2018 \n",
"102672 I-Corps: Organ-on-a-Chip Technology for Pharma... 01/01/2018 \n",
"104798 I-Corps: Machine Learning Algorithms and Tools... 01/01/2018 \n",
"109138 I-Corps Teams: Photonic Crystal Enabled Thermo... 01/01/2018 \n",
"109250 I-Corps Node: New England Regional Innovation ... 08/01/2018 \n",
"113549 I-Corps: Heat-stable binding proteins for dive... 09/15/2019 \n",
"113957 I-Corps: Ultra-clear, transparent aerogel mate... 09/15/2019 \n",
"114746 I-Corps: Synthetic Matrix Solutions for Neurod... 04/15/2019 \n",
"116925 Broadening Participation - I-Corps Northeast R... 10/01/2019 \n",
"117967 I-Corps: Electric Reservoir Stimulation 03/15/2019 \n",
"118123 I-Corps: Decentralized fertilizer production f... 09/15/2019 \n",
"119205 I-Corps: Robust filtration membranes for harsh... 03/15/2019 \n",
"120929 I-Corps: Acoustic monitoring of remote pumping... 03/15/2019 \n",
"121225 I-Corps: Embedding fabric-based sensors into a... 09/01/2019 \n",
"124283 I-Corps: Label-free optical imaging of the lym... 06/01/2020 \n",
"124441 I-Corps Teams: IoT Sensor Networks Detecting U... 02/01/2020 \n",
"125869 I-Corps: Software Platform for Constructing Mi... 06/01/2020 \n",
"126075 I-Corps Teams: Machine Learning (ML)-powered D... 02/01/2020 \n",
"\n",
" amount dname \\\n",
"43383 299991.0 Direct For Computer & Info Scie & Enginr \n",
"82468 50000.0 Directorate For Engineering \n",
"91892 50000.0 Directorate For Engineering \n",
"93323 50000.0 Directorate For Engineering \n",
"95592 50000.0 Directorate For Engineering \n",
"95978 299613.0 Direct For Computer & Info Scie & Enginr \n",
"99195 50000.0 Directorate For Engineering \n",
"99279 50000.0 Directorate For Engineering \n",
"101811 50000.0 Directorate For Engineering \n",
"101937 50000.0 Directorate For Engineering \n",
"102672 50000.0 Directorate For Engineering \n",
"104798 50000.0 Directorate For Engineering \n",
"109138 50000.0 Directorate For Engineering \n",
"109250 4200000.0 Directorate For Engineering \n",
"113549 50000.0 Directorate For Engineering \n",
"113957 50000.0 Directorate For Engineering \n",
"114746 50000.0 Directorate For Engineering \n",
"116925 171116.0 Direct For Computer & Info Scie & Enginr \n",
"117967 50000.0 Directorate For Engineering \n",
"118123 50000.0 Directorate For Engineering \n",
"119205 50000.0 Directorate For Engineering \n",
"120929 50000.0 Directorate For Engineering \n",
"121225 50000.0 Directorate For Engineering \n",
"124283 50000.0 Directorate For Engineering \n",
"124441 50000.0 Directorate For Engineering \n",
"125869 50000.0 Directorate For Engineering \n",
"126075 50000.0 Directorate For Engineering \n",
"\n",
" divname \\\n",
"43383 Division Of Computer and Network Systems \n",
"82468 Div Of Industrial Innovation & Partnersh \n",
"91892 Div Of Industrial Innovation & Partnersh \n",
"93323 Div Of Industrial Innovation & Partnersh \n",
"95592 Div Of Industrial Innovation & Partnersh \n",
"95978 Division Of Computer and Network Systems \n",
"99195 Div Of Industrial Innovation & Partnersh \n",
"99279 Div Of Industrial Innovation & Partnersh \n",
"101811 Div Of Industrial Innovation & Partnersh \n",
"101937 Div Of Industrial Innovation & Partnersh \n",
"102672 Div Of Industrial Innovation & Partnersh \n",
"104798 Div Of Industrial Innovation & Partnersh \n",
"109138 Div Of Industrial Innovation & Partnersh \n",
"109250 Div Of Industrial Innovation & Partnersh \n",
"113549 Div Of Industrial Innovation & Partnersh \n",
"113957 Div Of Industrial Innovation & Partnersh \n",
"114746 Div Of Industrial Innovation & Partnersh \n",
"116925 Division Of Computer and Network Systems \n",
"117967 Div Of Industrial Innovation & Partnersh \n",
"118123 Div Of Industrial Innovation & Partnersh \n",
"119205 Div Of Industrial Innovation & Partnersh \n",
"120929 Div Of Industrial Innovation & Partnersh \n",
"121225 Div Of Industrial Innovation & Partnersh \n",
"124283 Div Of Industrial Innovation & Partnersh \n",
"124441 Div Of Industrial Innovation & Partnersh \n",
"125869 Div Of Industrial Innovation & Partnersh \n",
"126075 Div Of Industrial Innovation & Partnersh \n",
"\n",
" institution \n",
"43383 Massachusetts Institute of Technology \n",
"82468 Roman \n",
"91892 Massachusetts Institute of Technology \n",
"93323 Massachusetts Institute of Technology \n",
"95592 Massachusetts Institute of Technology \n",
"95978 Massachusetts Institute of Technology \n",
"99195 Massachusetts Institute of Technology \n",
"99279 Massachusetts Institute of Technology \n",
"101811 Massachusetts Institute of Technology \n",
"101937 Massachusetts Institute of Technology \n",
"102672 Massachusetts Institute of Technology \n",
"104798 Massachusetts Institute of Technology \n",
"109138 Massachusetts Institute of Technology \n",
"109250 Marc \n",
"113549 Massachusetts Institute of Technology \n",
"113957 Massachusetts Institute of Technology \n",
"114746 Massachusetts Institute of Technology \n",
"116925 Massachusetts Institute of Technology \n",
"117967 Massachusetts Institute of Technology \n",
"118123 Massachusetts Institute of Technology \n",
"119205 Massachusetts Institute of Technology \n",
"120929 Massachusetts Institute of Technology \n",
"121225 Massachusetts Institute of Technology \n",
"124283 Massachusetts Institute of Technology \n",
"124441 Massachusetts Institute of Technology \n",
"125869 Massachusetts Institute of Technology \n",
"126075 Massachusetts Institute of Technology "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pi_df = groups.get_group(PI_name)\n",
"pi_df[['title', 'date', 'amount', 'dname', 'divname', 'institution']]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"PIs_with_n_proposal = np.bincount(np.array(nb_proposal), minlength=np.max(nb_proposal))[1:]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[30074 11053 5185 2724 1415 815 460 257 151 76 47 45\n",
" 29 20 11 8 5 2 3 2 1 1 1 0\n",
" 0 1 1]\n"
]
}
],
"source": [
"print(PIs_with_n_proposal)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(4,4))\n",
"ax = fig.add_subplot(1,1,1)\n",
"ax.plot(np.arange(1, 11), PIs_with_n_proposal[:10])\n",
"ax.set_xticks(np.arange(1, 11, 3))\n",
"\n",
"ax.set_title('How many proposal per PI in the past decade?')\n",
"ax.set_xlabel('Number of funded proposals')\n",
"ax.set_ylabel('Number of PIs');"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax = fig.add_subplot(1,1,1)\n",
"ax.set_title('PIs with more than 10 proposals in the last decade')\n",
"ax.bar(np.arange(10, len(PIs_with_n_proposal)+1), PIs_with_n_proposal[9:])\n",
"ax.set_xlabel('Number of funded proposals')\n",
"ax.set_ylabel('Number of PIs')\n",
"ax.xaxis.set_major_locator(MaxNLocator(integer=True))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# get the institution where the PIs more than 9 proposal funded in the past decades\n",
"institution_count_top_pi = {}\n",
"for name, group in groups:\n",
" if len(group) >= 10:\n",
" for inst in group['institution']:\n",
" # when there is no institution's name NSF puts the name of the Pi\n",
" if len(inst.split(' ')) > 1:\n",
" if inst in institution_count_top_pi.keys():\n",
" institution_count_top_pi[inst] += 1\n",
" else:\n",
" institution_count_top_pi[inst] = 1"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# get top n institutions\n",
"n = 20\n",
"n_institutions_top_pi = {}\n",
"for key in sorted(institution_count_top_pi, key=institution_count_top_pi.get, reverse=True)[:n]:\n",
" n_institutions_top_pi.update({key: institution_count_top_pi[key]})"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax = fig.add_subplot(1,1,1)\n",
"\n",
"ax.bar(range(len(n_institutions_top_pi)), list(n_institutions_top_pi.values()), align='center')\n",
"ax.set_xticks(range(len(n_institutions_top_pi)))\n",
"ax.set_xticklabels(list(n_institutions_top_pi.keys()), rotation = 90);"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# get top instittion regardeless of the number of funded proposal per PIs\n",
"groups_i = df.groupby('institution')\n",
"\n",
"# get the institution where the PIs more than 9 proposal funded in the past decades\n",
"institution_count = {}\n",
"for inst, group in groups_i:\n",
" # when there is no institution's name NSF puts the name of the Pi\n",
" if len(inst.split(' ')) > 1:\n",
" nb_proposal_per_institution = len(group)\n",
" if inst in institution_count.keys():\n",
" institution_count[inst] += nb_proposal_per_institution\n",
" else:\n",
" institution_count[inst] = nb_proposal_per_institution"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# get top n institutions\n",
"n = 25\n",
"n_institutions = {}\n",
"for key in sorted(institution_count, key=institution_count.get, reverse=True)[:n]:\n",
" n_institutions.update({key: institution_count[key]})"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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EA0Qo/SAIggEilH4QBMEAEUo/CIJggAilHwRBMECE0g+CIBggQukHQRAMEKH0gyAIBohQ+kEQBANEKP0gCIIBIpR+EATBABFKPwiCYIAIpR8EQTBAjLnSl7S1pHslzZR08FifPwiCYJAZU6UvaRzwfWAbYH1giqT1x7IPQRAEg8xYj/Q3BWaa2f1m9ixwOrDDGPchCIJgYJGZjd3JpJ2Arc3sw+n1XsDrzewTBbn9gP3Sy1cA9/axG8sBT85D8mNxjkG8hrE4R1zDvHGO+aFP3VxDHU8CmNnWI94xszHbgJ2BH+de7wV8d4z7MH1ekp8X+zQ/XMO82Ke4hnlDfl7sUzfX0O021uadh4HVcq9XBR4d4z4EQRAMLGOt9G8E1pG0pqSFgd2AaWPchyAIgoFlwbE8mZk9L+kTwIXAOOAkM7tzLPsAHD+PyY/FOQbxGsbiHHEN88Y55oc+dXMNXTGmC7lBEATB3CUicoMgCAaIUPpBEAQDxHyt9CWNk/SZ0T7HaB5/fkbSIn0+3oR+Hq/k+NtJ6uo3I2kBSeP73adg3kDSq3poO6b3xnyt9M3sBVpG/Er6hqQNWjSZKenrbdJJSDpb0rZNFYikN0i6WNIfJN0vaZak+zu02ULS7pLen201sosk2UMlfSnbOhx/uqT9JS3T8BpOKrx+GfCbJm1bcL2kMyW9S5L6fGxwb7P7JB0t6ZWdhCX9XNJ4SUsAdwH3SjqwQ5tPNP1Mk3zb76Ht8fcp2XdUhzaNf0NpYHZJ0/7k2q2QHsLbSVq+gXyr6861W17S6tlWI3qcpBskfVzS0g2O2/re6BfztdJP/F7S9yT9l6SNs61G/h7geEnXS/qopKU6HH9D4A/AjyVdJ2m/Bk/tHwK74wrkKEnrdZA/ETgGeCOwCTA5/S1F0qnAN3LyWZsqzsUfjs8D/8xtdewGrAzcKOl0SVt1ULSPSPph6t8ywEXAz2qu4b2S7pP0d0lPSZoj6akOfVoX94LYC38Yf0XSuv06h5ntCbwW+CNwsqRr0/e9ZEWT9c3sKWBH/AG3eupbHSvin+lUeXLCTg+vtt9D2+PvJGmP7IWkHwATO7Rp/BtKA7N/NfidvYSkXYAb8GDPXfCH/U4dmrW6bknbS7oPmAVcCTwAXFBzHW8E9sDjkKYnpf6OmlN0c2/0h7GKAptbG3B5yXZZg3avAI4C/gT8HNiyQZs3AY/gCvMU4OUd5JcCPgo8BFwDfBBYqETu+pbXfDfJM6uh/B09fL4LANun634IOAKYUCH7NeA4PF7jfR2OOxN4ZQ/92jL16W/4j3bzfp0DD5n/NEOK4D7gkyVydwILAWcCb077bmtwfAFb4bmpZgJfAdbu4/fQ+PjAYsDFwBTgp8C3W3xOjX5DwFTgQXxw851sqznubcDyudcT+/25pnMsC9ySu5+Ob3COccD70vdwN/4AfG+/7o1+bPP9SN/MtizZ3lrXRm6nXy9tT+I3wGclnV4mm0YF5wDHAt8E1gLOo8Z8IWlZ4APAh4FbUtuN8R9YkcvlJqTNG85W7sBHNk25RtKrW8gDIGlD/Hq/DpwN7AQ8BVyWk3lvtuGjs83w67W0r4rHzezulv1ZVtIBkqYDnwc+iSvoz+FKp6dz5L7ny/Af7KZmtg2wUTpfkR/hD4YlgKskrYF/PrWYa4A/p+15YBngLElHV/Sr4/fQ9viSJsjXSBbD79EvpGN+WQ3WTtr8hoBfA/8PuAq4KbdVsYCZzc69/gsNrBYtP9fnzOwvwAKSFjCzy4HXVB1b0oaSvoUr+rcC7zazV6b/v1XSpKt7ox/M9376adp4GD4KBx/1fdnM/l4hfww+YroUONHMbsi9d6+ZvaIgfz8+ezjRzK4pvPcdM/tUyTl+if8YTgV+YmaP5d6bbmaTC/KXl3TVqh5eSf41uJJ9Jtdg+wr5u4CX41PZZ/ARkZnZhmXyqc1N+Cj6ROBsM3sm994vzey96f+Tq46RzvGhiuMfiz+4flW4hl/W9OkP+Gd6spk9XHjvIDP7Wi/nkPRTPHfUVSXvvc3MLq3qW05uQTN7vub9TwF744ryx8CvzOw5+frPfWa2dkG+0ffQ9viSZgF55ZA3h5iZrVVzDccA78YfOh1/Q2n/YsDqZtYxuaKkr+Nm1V+kXbsCt5vZQTVt2n6ul+Cml6/iA4fZwCZmtkXF8a8CTgDOMrOnC+/tZWanNriu2nujb4zFdGJubvjI5wh89L0W/gD4ZY38h4DFK95bqmTfG0v2vaFDn95Vsm+RPl7zm8u2Gvk1yrYO51irRX/GAZ9peQ0nl2wndWizS8m+nft1DuBrTfbl3lsBV8YXpNfrA/t0uIYvV332lJiiyr4HYM1+HB8fPdfeyxXHafsbejeeSXdWev0aYFqHc7wPX+f6FvCeBn1q+7kuke7bBfGHxaeAZWuO/+mSfQf0897o1zbqJ5jbG3Brk3259xYA9gS+lF6vjk/jq+RvbrKvlzbAZ0u2fYDXdLiptkvb8nX9SfIbAZ9I20YN5Fv1Cbh8DL7r1t9FH45/e438BfhC423p9YLAjA7nOLXJvg59uqmPx7+2i8/p0ib78v3F17duye2r/Zy66FOr6+7TvXFLjXzre6Nf25jm3plLPC3pjWZ2Nbj7I/B0jfz3gRdxW9yXgTn4bGGYt4ykzYEtgImSPpt7azw+QhiBpBWBVYDFJL2WoSnzeGDxmj5NTtt56fW2+GLoRyWdaWbDbJLJu+HrwBXpHN+VdKCZnVXRrwOAfYHMrPEzSceb2Xf71Sd83eB7wBnkPIPM7OZCX75gZkdL+i7DzQuZfJm5bBvgXcAqkr6Te2s8brstRdKqwHeBN6RzXY2PzoqmoY8BHwfWlnR77q0lgd9XHR9YzsymSjok9f15SS/UyAMMc3VMtvHXlfR9vSS7VGFtZDywaK/Hz3GRpPfhs+NaW7CkRfH7eDm5h1b+/l65punzZvb3gkNN5bkkzSl5/+/AdOBzZlbmztzqutNn+jVgefw6MpPn+ILcFNwTb01J+eSRS+JrDVV0c2/0hUFQ+h8DTkm2fQH/h0/Xqni9mW0s6RYAM/urPCNokYWBl+GfYd5l7yl8Ia2MrfDF21XxqWnGHODQmj4tC2xsZv8AkHQYcBa+TnETUFSwX8Ttj7OT/ETgktSmjH3w6/5nkv8acC2uEPvVp8wW+uXcPsMfrnmyhdXpNecu8miS357hC4BzgLrgvJPxBd6d0+s9076iq93P8ZHZV4F8Xec5ZvZ/Ncf/Z1qwNwBJm+HKaQTpx38oPiDIFvQEPEt5Mq5X4LO4pXHzyEt9wh/gvR4/47O4qeN5Sf+mQvklPoJ7Na0M5B/mT+GDqSrukLQ7ME7SOrgp5Zoa+WPw7/znqT+74Wsz9wInAW/JBHu47qPxxdhOC/3XAI/hdv9v5vbPAW4vbeE0vjf6zlhMJ+aFDR9tjG8gdz0+Ur85vZ5I/TRtjS76UuuuWCJ/N7Bw7vUiwN3p/xF9ozBNxE1WlVNHYAawaO71onXy3fSpi89ohC2+bF/h/QVbnqOR6S+7b4AJZVvN8TfGZwJ/T3//AGzYoU9fbXkNI1xR+3n8Lr+7Ee6rHeQXB47EZ4rT0/+L1siPcGEGrkt/S90eu/hcfz/Kn1Hre6Nf23w/0k9P08PwQCWTdDXuvVM19foOcA6wvKQj8VH7f5cc99tm9mnge5LKzBAjPGUk7WlmPwMmFUxCWZtjivsSPweuk3Ruev1u4BcaiuYr8ltJFzLcu6Eu+vVkPMDlnPR6R3zEVEe+T8JHnXV9QtK2+DT7JfODmX25TBY4BPdh7rQPSVPNbBfglsJ30ckL6UlJezL0OU2hfEr+c/z6bsJHZsM8WXAHgRGY2c2S3oyPygXca2bPlclKWs/M7gHOVIk7rlWYwYDdk4mhKP+pgnyr4xfaLgOsw/DvrcyD6a1mdhkeiDfCHdcqvKLM7F/47PSLVX0o8GIyYWYz1/zMethvsYfrni7pDDp4dkm62szeWGJyqpsRtbo3+s0guGxejPv/ZtGfewBvMbO317RZD3gb/mVcaiVTPEmvM7Ob0hc3AjO7sqTNR8zsR8kUUtbmiJo+vQ5/cAm42sxqzR/JDvuGJH+VmZ3TQX7j3PGvMrNb6uTb9knScfiIbkvcZW4n4AYz26cgl9nnd8Ht/xnj8SjGTUuOvZKZPSb3dR6Bmf2pok+rA98DNk+7fo/b9Evlm5IpvzLFl/ozQvmlNZT91NA9V9K7zew8SaWmSjM7pZfj59p9GDgAN0neisdZXFsmL+kIMztM5W66ZgX3XEnnUWO7Lxs4pXZr4XEtm6f21+FmvEeA11lav0uy3V53o2toS9U9kTtBpUtyvxgEpX+Tmb2usK/MF7424MTqbbfZMZYBVjOzOlteYySNN7OnqvrWpE8Nz3Oqme3VaV9Ju41wG74BvzOz22pkbzezDXN/X4YvDr6z5JivwW3/+fw/c3APoL/WnGMJ4Gkze1GefmE93CWuLyMouRPArWb2zzRD2BiPUH2wINdK+fULuc/5y8zD+/t1zBm4E8N1ZvaaNCA6wsx27cOxswHTe3GbfDYwmwI8YGZ161zzFJLWBh42s2ckvQWPI/ipmf2tIFd2T2SM2r1RPMt8veE5aHbD7doL4CPII0rkZgH3p7+zCq/vrzn+FfgodAIeSn4TcEyHPh2d2iyEB4E9CexZInd+oW/31/UJH22DK8inctsc4Kma/txceD0OuKvDNRyAR/4egSvoGdTYcvFRPfiobGV8DeC+GvmFcv8vQwN7Z/rsF8c9pB7CzXSn9fo95ORvx2c1G6X/DwCurJEf4S9ftq/w/s7Akun//8Y9ql5bI//zdA1L4CH/jwEH9vH4N6a/t5JiSahxec7dG+PTZ/VjfFH3nTXyVzXZl3tv3fR93ZFebwj8d58/11XT/TMbeBz34Fu1Rv5W3Knj5Xhupm8Bv+l0z86Nba53YNQubEjxzcFdMJ9L24t1CrCL82S5OT5MephQ47ud3SDp73vwHD0TGKO8G4V+HJI+n+dzn9Uc3K5du/CVlN4SuddL1F03Hma/NB5U8+eknL5cI38F7R+m2eL7J4Ev5L+ffnwPueN/iRRIQ318RSsf+vy9g5vNfocnwqvMvZS7hj1wr5aFOnwPbY9/TvreDsfNpOd2UmYM+Z5vhdfA3qjD53Q3uSAzYE2SU0CF/JXApgz366/NH9XFdV+M58JaMG0fAC5ucG8cSBr8dLj3IjhrVC7ORxqrd9FmT+D/pdedgrNmACvhWSM3yd9gNW3uTH9PALZO/9cqfXy0uyluTnkT8KYa2bYBOK09Omjh8YNnptyJFPmIj/JHRGYW2nTzML0Ft/NeB2yQ9bNf30NSNofgnhYr4jOiEcfHzUrvw0d8781tH8jO2eC6vwrsnt9XdQ20SNzVxfGXy/3/ZtwtduEq+fz3hNvd39PgHFvjD/Yr0vYAsFWN/I3FY9J59tH2utsGdV6Pm6XuIM3mqHkQEcFZo4OZWfJIqQs+KfIDhoKz/oeK4KwcR+CF3q82sxvTItN9Hc4xTdI9eJDYx5Mf/b+rhKsW0xjp455RDERZkPrPoMyz4e/An6w6F8jJjPT4ObGk71/CH6I3AUdL+qqZnUDOI6KCBSWthP8wmnp1HIAr5XPM7M70XVxeI39em+8B94LaHR+R/TktBH+9RK6VD32BRyT9CHg78DV5oZm6ZGLH4UryNpol7mp0fEnvxj24npP0Ip7iYoRzQgU3SboIH7EfIk89/WKVsJn9NvnnZynG77FcDqESnkw2dEt93QmfOdbR9nNt6tmV8UE8Y+6RZjZL0prUpA5nLgZnjfpTZW5veFDIJi3ks2lafhRR5fvbTU6ZBfBApWWAcWnfEsCKNW1m4CPpbCq/HnBGiVzRXJOZbGrNNfjI+FncR/qm9P+N+PpBnS12YzyQ5gAq7KP4SHTx9P+ypFFag89pZ9yE9IP0ei08oViV/Djg613cH/nvYfGq7yEd/5KWx27lQ5/rw3uBddLrlaq+g3Qv7VLYJ2riFZoeP33266X/X0/N2kXJ+VdL98bSue99xJoMw2dBI7aac6yFBxv+C/fYuRqY1K/PNb2/Om6aegK36/+K6tw944Cftfyer0ifS6ZvNmv6GclFbD0AACAASURBVPe6DYL3zl34ws+f8PD/Wt9tSdfjSvlG88jcicBFZvbaCvnLzWzLln261sw27yz5kvyNZraJpFvxyNlnJN1qZqWpXtNo+pAWxz8d+B8zuzO9Xh+3Tf4P7mHzmpxsKy+novdUmTdVv5B0mXVIm53kWrtUpnbTgL2sIkNrTq51KonUbgHcNNK49J6kq8zsTZ0l2x1f0s1mtnHV6w5tG33HvXqyJG+tBcxsTge5bj7X1czsocK+Fc3szxXyF+IRvM82PP7GeMT7q3CT0ERgJ+uT518d87V5J7FNS/lGwVk5GuWUKdA4n0niYXkJtl8BF0v6Kx6GXoqZHaKGATWJ9TKFn+TukvRaM7tfIwsMlQUovdSUkYFKa2soJ4kKr7FqX+yTKVeYdYrglnTsMxn+XRSV+JvxtL/vZiTGUA6iIv8GZshjP/LHLyrxblJJYO5qepuk1a3gBlrDxZI+z8j7b4Q7b8vjL6/hAYTDXlt1ICF40N4mZnZj3QnM7IMd+jAMpeDGQr/I7tGqPnX5uc6SdCbwIRtKlfwbfAZTxgN4lb5pDP8eqvo014Kz5nulb7lAmzQy2BG3y25b0eQsXLFlwVk74i5bVTTNKZMny2fygqSn6Ry995707+EpyGQp4LdVB+9iDeBeeSnDrMDFrsAfkt1z2I1oZmvWXFcZxRrF32jY7vzc/4viHjaVD7rEBNyUlb/OEUrczA5Lf1spHbzYx68byF0saaKNDJBans6FMlYC7pR0A8OVR+nDEU9jDLB/bl/Zw7ft8U9geE6p4us6tsQT7z1As9l1aT1mGxmtvUT627Qfedp+rjNwL5+rJe1iZn+kfKCT8WjaFmjSv5JZ5rqS/o4v5s4ua9MvBsG8szAe4bk77iVwNj7CPq9C/tfAjtlTNy0mnj9aJommaChi1vC8IHVh860CauQFLD5OLroWX9D+N26P/0dFu+0ZKk5zhZmdXybXD9IU/ZIm5psGxxqRAiNP3ShWDYp9SDoe+G1xhiGvNftGM/tYTds3V/Sp6SJqLaN9/HSONSrOURUZ/bncy0XxRfC7O5l3Wvap1XVn5ix5QN4JwEH4b6jWxCVpCUuJCzvI/Rr3NMscDd6Cr62ti7sydyy60jWjvWgwtzY8U+JJ+ELPz/Cp/AMN2u2Lm1HGAZPwBa26BZ9uCmUU3UJXo94t9Ev4yOOItN1GTTAKXQTUdPH5HoUHyHwobRczism88GnwzA4yjYJ28FxMh+GBTffh2RG/SSpwX3P8RsU+qAlso4PLZpJZA3h7+n9xUlBRheziuPnx+PR6HWC7fh2/h+/rjcAH0/8T6RCUVmi7CHBhxXtb4gO3O9N2Fp5Wpclx23yueUeOlfAYhX/VyG+O55x6ML3eiOSEUCF/HrBC7vUK+Ix0Aj3UrG70OYzmwefmhruIXZm/2aiJrC203T99KTOALTrIdlMo44e4V1GWlXIZarxacBtx3id+MeqDV1oF1OA5ei5OSu+lyN8O13A7voiWvR5HBz/6lt9fMar4D3Qupt4qaAePrVgy93pJfIReJd+o2EeH76byvfT+vrjn1B/T63WoL0ByBl6/NnvQLUa9P3mr43f53R2Wfj9/SK9XpkXWyvR7GBGtjZtkZ+HukVm6jg+l+3VENboeP9eVCq8XpD425np88Nb03itmwlXuO+w5S23dNj/b9F+Hp1+4RF7H9nQqipvAiCl/5nZ2K7CZpM2sesrfjb9t05z9GQ/g097Mh3wRPPCnFGu5BoDPVD6DK7U2vsJL4/UJSOeoRNKrzOyOpgc2s27stoub2Q2Fxee6mqOr4+6pGc/is7sqmhb7mC1pU8vVhgWQtAnuAljH/viD63oAM7svrQVUsbaZ7aqUadPMnlbJ6nsPx++G9+ABeTenczyafPVLSebI7HMch88MyrKvHoibXvM5nm6VNB33hKnLJNvoujWUCXdKxcdY5QyBmT1UaFP3W/qdpPMZyhr7PjzOYgm85vGoMd8qffMskbcAByW73BRgYUkX4ME7xQIKxZvynIr9RbophvCcvHJP1mYiNcEreCDTnclrxHDT1dVKVaIs5z1SdE+zZrbav5vZBQ3k8nwV95a5HH9IvgmPE6jiuPRg+wnwcyskoqpDHogzBdjN6t3u2gbtnArcIA8wM1xZ/bRGvmmxjwOBqZJ+wlBRl8nA+/GBSB3PmNmzmfKQB9bVLbw9m9YZsmtem/rAt1bHl7QC8BVgZTPbRu7Ou7mZjQjEy/fJzEwpzXVSZHVsl/v/eeBxKw8KXNFKkvqZ2e2pn3U0ve5uF4sfkrQFnr59YfzeqCvAsj+u6LNMuD/F41AMN2GNHqM5jZjXNnxlfSvg5Ir3Wwf44DOKtoUy9sADPx7GC0bcS30B773rthL502iRfgK3z38dt0tunG0N2q2Eh+XvQE1wWU5+HfxhMRO3p7+jw7E/A9yAz3AOA17d4fhlQTtrNPj+DqAmwCwnmy/2cSPwv1QU+8DL7B2B25/PxkeuTWoVH41XeroHf7ifg0d5Vsm/EzdrPZG+9weosXF3cfxuzJefB36Em132xT3HagurMLxGc+nvh/rav51yGjW+broLulwuff6P48FcP6OmkPrc3OZ77522SLrUzN7Wss2CtPS3VYOc/d0i6TLce6eRe5pa5hrPtVsFXxx7acZo1bEAWZtxuBvsd3BbvYBDLXm6SNoXH9WvCkxN27nWwFVU0jgze0ENg3Zy7ZZneDxDqS+3PHahY52BXkgztX1wZS48xcePreaHmmaamyX568zsyX4dX0OBgbdYClBUTWBgrt078ucws4trZIs1mt+DL0x/tyD3N8rNK8K9opapOUfb677cWgRdJhfdTqa7vHyjGryjQSj9ApK+iY9IOwX4ZPK34YtpZ5j78jY5x7FJvq4OaF5+Ozw6NlOwtTfIGLnlfQ3357+TIdOU1TxYNsQX4LbFF41PNA9QWRkvyrFGknsWHxl+zlJRFkn3m1mV33n+HA/iaxdnAJfVKcokvz3utbMyPjpbHc/7skGF/OX4DORM4HTLBbT1C0nvwRfdO+UmyuSn4flhplkzV8G2x78CN0NcbL4OtRnwNTMrvcdSm88AZ1qhwHyN/O24ySir0bwEfk9sWJCrPCfU399dXPeR+DpVo6BLSffhi8xn4GaaWvOlpJk0q8Hbd0LpF1DLwhfJJ3nXtL2If+lTq0aLqc3eSX5dfJp5htVXnZqJ5w2Z0UmR5dqswFCSuBusJOBDFRGOGVbvr34vPg1v+iO6Cvd3PsuGIhyz9/ay5JcsaTk8784U3I1tKvABM1utwTkWw90qd8NNVOfjyvnqCvnb8ECuS8zstZK2BKaY2X4151gRN3fsiqd+PsPM/rdT35qS7r+34iPa0/FRcuVidFKEu+IP0xvw++98MytNHNfF8VunC5BXhtsFX+Q/Hf/OKwMc00LuJlmfJS2Ke7O9uqpNW7q47tazX0mb4vfejrj75unmi8Jlsr83sze0uIT+MbftS2Ox4Ta6lfGR3Oq0TLfc4jzr4AsyLzSUn4BPay+lvqDI5eTcIxscdxc819ApqT+z8B9qUe4j6e9hZVuHc1yAV2lq2qdPl+w7oEObVXH78E34othXWpxvmU7fBTA9/b0t+3xJxV4aHP/V+ELws6NwHy2Er5Wclr7HytiBXJtxuK16Kh3qRbQ9Pj673ABX/Au1uI4N8TWQe6hJVodHqN+GuxgfjnvNjbhf5sbnWmi/QkO55Rrce8fiD+gpNEgy189tvvXeyZD0SVyJPU7ODIHfkGXy6+J+9CuY2auSWWJ7qxnNSZrE0OjvBdxvugkvxzNmTqKimHjiC8BvJF3J8CLNVSPxL+Ijp9mpfxPxBc6z8kJm9qP0t7I2bw3/wt3lLi30qTSZGO658u3Cvg/gN38p5uaBbwDfkPQKOnu+5Ee+2+CLrbvUiP9NXrbxKuA0SbOpcfGU9Mp07J3wdA+nA5+rkV8X9+QprnvUrpWY2XPJy8xwv/sd8LoCVefJZji74jOcU6pk2x5fvaULmI0XzPkLbruu6s8xyYyURYR/0EZh7aTt5wogaSncvLU78Eq8KluZ3Hh8LWI3YG18Bj+innOO8fhvKF8u1KjO+9Q35nvzTjKNvN7M6nJh5+WvxH+oP7Khhas7rMJVUJ6VMyticYaZ3d/gHF/Dn+x/xEdmv7QaG6A8N/k/8GCxl1w7q5S1pBmWmxqnRazbrGK6nB4K++IPn7xyqgyDV/OC3FPwH0xWsShjSXwkVFmgvi2SZuGjxKk0sHEn2/G/cUWzB27DPa3qXpF0HW4/P9PMOuUBysxHx1GIfzCzm2rabI0rji3x9Ltn4FleSx9Gks7AUx//Fr/uK8ys0v23i+O3Thcg6WP4A2giPtA4w8zqBjVZu8XxiPY/WYtF0Sa0ue70EN0ev283xu/VHfESjqWfbbr3foWbdq/tso8dk9T1g/l+pI/XSu3kN5+nbYDP3mZ2T8s+zcIXriq9LApMsEIB8Q78Vp7qNSsAsSv1gSvn4gr5EhoGZxWVew3X4L7yy+GLphlz8KjefrKRtSgKXngodLweM9usZX+eN7MftmzzAXwG8RFrtl5yMl4JqmlQXdvjv4hXPHscXlor+iH+oLkKN3EVWQM3z9xad+C0kP4d3Pb/33iU+uPAJEkHVd1jXc6gPkCD65Z0Gh5zchHwPTwb60wzu6LuWvByj61H0PK4h91wM8/f8XiOUWUQRvon4u6Uv6aBaSRN/z6Bj+Y2lgf47GNm2xTkWi+CSlrPzO7RyCpVWZsqz4CjcG+Ui0ovsrzNexmaLl9lZufUyHZ0wStp8wbc/lr0KOroZdNvNJS//jtl7xdNTpLmMDwwRwylizYreEVJmmpmu2h45Cg5+SpT4eG4ieMcht97I9Iet0Vd1gTo4jzFWaNw086rlHPjTO+NN7OnVFFzoXjdaSa0Mz7Duhx3DLhf7kJ7ac3MtPUMqinp2Fmw1BnmUbaV3mOSvm1mn5Z0HuWpwEd4syXnjylpex7/DU02swd67X8TBmGk/2DaFk5bJ/YHjgfWk/QIPirfo0Sum8i9zwL7MXzEm2FUpz7eH/iCpGfwVMdNfHqvwX8QL+K27TrOl/QuM6ubDRRplLpB0tVm9sYqRVt1DSqJlyjbl8jc3pr+6C/Fa9z+Evew6JRj/YD0d7taqZFkJrADc/uMkrTHJZ9T7YOIljUBujh+RjFdwE5Upwv4Of4ZldVcKLvuF83sD6l/szLTqJnNllQ3u248g2p73Wa2kTyGZnc8hctsYElVF1DJZjqNUoZLugZ/yJ2OO1fcl679gSbt+8F8P9LvFrUM8JmXkOfT/xKuFIQriC+b2UkFufwPYQl8NNrooSLpejN7/Sj0fVE88vVy3H6cKY7xeCbTV/bpPEvh6yq74YFZZ+APgJ5H4fMTaWSfnzVezVC6gF6PfRv+HS+A36tvYej7vtzMNqpodzijNIMqOddkfES+M/CwmW3RoUmn452L5yWahqcjuaZuJjEazPdKP9n/Ps/IRcoRo+rk+fFX81weu+C2vZnAD6vsgN0sgqZ2W5S0qcz7ohaVsOQ+9FtkC5LyiM1rzOwVdX1qQs40tQvuJvhLhv/wqkxUa+M/mmckvQX3nvqpFRaw5dGZn8ZdbB9hSAk8BZxgZt+r6Vvj7zrXZgF8zeO7uEtoXWxCoyjKXk0v8qjlFQrXUBUlvAjuXTKpIF+WsKz18ZP8Gnht2UvSYuu4ToMhNYjWlhdZeZHhM4KceKVJZVYb+Vy7VtddaCs8y2ZV/v3G5k4NeQRNwT34lga2skKCvtFiEJR+I/ufpO/jimgRPH/Oy3CPiC3wm7zMxJNN135Xcvyza/p0Ku7WdWuujRVtzzn50kpYVcpM7ka5jaV6nfIEUL+xCk+ZdMPeamb/lLQn7rHw7bIfhMqDVjKspk+34otUk/AQ+GnAK8zsXRXyn7RCGH4n2th600N3CvBf+Oj1DDP7XVGu0KZRFKWkI8zsMLUM9EttS12Ma9YNfosvABavucyE2M3x98VNkhPMbG15ornjKsxsWZssWvsuht/fVVWqRp22193F8e+hxNxpHbwG0/rFrvi9uJo1CELslUFQ+k2LNN9lZusn88IjeHKsF9IT/vaaRaVuFkHvBtZvOkVW+0pYP8WDh87FzTc74NGaf4CRi8zyMPiN8Ifeqbi9/r1WE2rfFg1VIjoQ+LeZfbe4EFjS5lW4C19+dlM3G2r6XT+A26NPx80Kw+zHNbOVUY+iVHsX40p34j4d/1ZSSmIbcmGeUfV7SO+3itbuhi7ujVbX3UV/ejZ3SlrDKqqL9ZNBWMg9T9LH6Wz/+3fa/29Jf7LkAmdmJqkugVo3i6B34AuJdWl/h/Ut9QtJi5h7ANWZav7I8Hz756a/VYvOz6fr3AE41sxOVIUffoakrwBHZ+aZZH76nJlVFZF/Tu6zvzdDi48L1Rz/MNzGuz7ubroNPiKvS33c9Lt+AH8YbsVQAq6XxKleUJ8u94v/VeH4VXmZ/oj7tP8O96Dq6KtOexfjayS92sxmNJRve/y2qZ7Bs2suRH2K567p8t5odN2SDjCzYyW9wcx+36Jbl0v6Og3NnWWMhcKHwRjpN7L/SXoYOAZXAJ9J/5Nef7pq2pUWQ9sugl6OV/25geE3SFWysnPwZGWfxhXSX/Fw+BGmkWS3PMrMDiy+V9OfK3FT1gfxdYwncHNP3WhuxCg9G81XyK8PfBQ3S/1C0prArmZ2VIX8DHz2cYu5R8UKeNh8mbdK1qYrW29T2pprkr399bgJ6Q149PVtNlTkpqxNWxfju3C78Kwk38mNtO3xj8ZnRe8HPonXUr7LzL5Ycw1n499d02jtVnR5bzS67mzmXncvVxy/da6eucV8P9K3kpS8Kq9SdQJDI+H8/wA/rjl+NxWeDm8jbC0qYSWTVOObNbEr7qK2j5n9WdLqeH79OsalWcczAPIoxkXKBNOD6FAz2zPXz1l4Hv8qnjazFyU9Lw9xn02Jq2Oesu+6KZKOt5pEa+n4H2x52BfwgUDmOpvlWq+jrYvxNp1Fejr+wXhK4hnAR/CRdeXvITEtbY2QdKqZ7dVpX47W9wbNr/vuZP6bmMyeL3WJmoeptUjDPLeZ70f6Gck2vyWu3N5tZp0q7SBpOzM7v+K9omI14Ekze6jnzpafr41HR6v00F325wt4qPrJ+LV/CE99cHSF/IX45/5s2fsl8j/Ai17shue3+Qc++xiheDXSS8aAJ5N805z6dbOU7zLcpJEd/3KryOCZ2v0LV5bH4AnH+mZP1sgAKAP+1nSdaF6i+Nmne32Gma1fId/43uiyPyvizgYjZt5FE4xGBmdm98bVaWBTdY6j8SI8T+MDuI1wi0JpVs5+Mt8rfUmvxxX9e/CslvvjyumvDdrWKYKy6dwEfBQxxUpC0DUyQOkl5QEcVKUUuvC4aGuG2Ax3WXxl6v844B9m1qnu7dbA2/FR0EVmdmGN7I9wr6BpDH8QVbpI5tpOAsZbRTrfiuudgC9M72NmlzU4x2/NbOuK98rWNybgbqtnmFkxkVzWbgfcv31TvP7uNbht/9IS2WJEZ/7BMkIRJFNWMQDqZXi2yg9bIdini+MXo4+HUXbvlbTJ39/fsEK6Z3ld6UPx5Gf/ynbjn9XxZlZXfjM7xiTq741W111ouzCeYwgqiiOl9YUiE/D1osPN7PSKY2dmpPfgeX0+Q01sQj+Zb5W+vAjCLviU7hf44t70NiaATt4lFW0mA8eY2Zsayi+D5wXZwsx2rpAZbc+D6fio6UyGarmuY2aHdmjX2H+74sfRbYbPRqT+TbVRCCJLx18Mj3+ovUfk3lbb4Gsyy5vZYiUyZZ5SE4A98bTbBzfs03uB/YoPsLbHT59dJWWLjhVtJuCL90uY2b4Vff5qQwW/Bj6b+Xt6vSWuMP8EfK9sFtnt55ra/RRf9BewGp5nq7YyXK79BHx2VzVovNPMNpB0Ah7s9ltJt42F0u9LfuZ5ccMXI6/Gw8YXTfvub3mMTbs89839bIOPlBZscaxV8YfcbHx2cDawao18llf+9ty+azqcY188vcMf0+t18Hwpnfq2xBjfB3Wf67r4+s1FuOvmZXiOozbHv6Vk30Xp79m4F9WFeEKxN1NRU7fm+ONw08Wo3H/dHL/L72HE51R4fxl8RvSmbCuRuR4v0A7uCPEkbt45hfa58WuvG/e3f0XhXqmtw9vmmvH1rHuAW3BPp4m4W+yofg9m83c+/RVxd7wpwLeTOWYxSQtaTcUcGO4DnEZpWI0PcKHtCnR2aSu2WYiSRfWcvfB+4Ap5mtsm+fRPxvOgZDOHPdO+d1TI/ytNZW9NtsbHGMotVMX+JP/t1Jf75IEmpUjaHPf/fxmwuqSN8IyHH+9wnq6Ru7XWuQ2eiQdznUDD7KK5Yy8I7IUXty+yXPp7FK6AWx07j/nCfJt+vQxPa9DT8UtMkU1z9VRR2SdVBB8y0nV2MRtKab0ncJKZfVMeVV2b0bNIg891ITO7Nyf/h/Q7bYSkzMuu6vwHy4PYnkp9+SceTzPqzLdKP/3QLgAukAdcbYfndHlEnrhr97J2augDXLK4Bz5t3IKhBF3FY5eF5S+De8+cVfJe5hnU1uNiopnl7dw/kfTpGvm98B/lJ3Db4mp4mHgdbf23v43bOacBmNltkipNYGqYtiHJlmU4nIDXs92zKJ+jUeKuEgUIvgB3Je7RUmTp3He9WlG5WMmCesnCLPi98X68DnFRviy76zL44uOIVBVtj29deKWVODdk59iT8oLmGQcwFHy4ZRpolZn98h/kW4FDUl9frFLgba87x3S5m2eWUG0PShL6Vax9TAAeTeeo6tdC+O/uTanvV+IDkFFnvlX6ecwXkM4CztJQhZsqdmLIB/iDaeRe5qJWrGlreIWgz1p1RaGiH3HW5lgz+3WJ/NHAksXjpT7VBZo8KU+nkOXTn5LOMwxJf8GDh64Bfo+XCqy1sUv6hHn+myslHYrPnt6B+2+fV9fWPE1tflfdCPhsYLKkl+MzhGn47KUsbUMxw2H2ud5n9d5CjYK5ulCAS+GDjNKcMpRXRypmpswWHK8APlYiX+yT4VWq9rTyQK22x3+JNCv7r/TyKquuj1tM/ZB9D1fgmWuraBp8eJmkqfhMdBncHIeklfDF3zK6ve6P4bPZT6W2VwE/KJErZl414C/WuUj9D3GzTnbMvdK+2kpe/WC+XcjtFkk3mNmmkm7CXTznAHeY2QZzoS/HA78tjgwl7QG80cxKb1q5n/338IpHhiv1A2yku9l4fCq9Rdpeh5uSrgF+b2ZTS46dpVNYAPffziJaL8TtqqU3lKSzcNfF76VzfgrPIV5aAlFdpG1oi0YpmEstA3vmZeQJ8PZl6EH1HtyzplVepA7naBR8KB8x7IrP4Kaa2SNp/2vxBfJK77F5jbJF21jInUsb/uRdGo8evQ9faDl5LvXlrpr37hyF8y2Bm3hmUlHUmS4WqVO75fCC1FmA0s+AZWvkr8dnKHcAa6Z9d8zt+6PhtZYt7m48t/vV5bXcTm7xPd0jt4/i+d6Mm6gWbii/3dz+jLq8zpuBtXOv1+r2t9V2i5F+DZ18gMfg/HdbRf74svfSIuz9ZnZcYf9ngBXN7KDC/pUZGuVvknbfhJt8rrVyt7znGfKpHvYWNQt8kiZai7qnapm2oQ0a5apTkl5lZncU9v1Hjv6TzXoTSz72aX3sRqtJ0TGW/Ad/rm/DnSvux387a+AF4cvif/rKQNj0M1QTaq+aUoaSNrYWiZP6yGxJm1ohz7akTXCX1CLbAWUZF4/FR2wHFfY/jI84vgUcbM2iZWdYdyaWa5I55QzcL7myEDyAeXKyT+Vez6I+bUMb3kyLqlNFkpdSPrvjsMjoosLPmrXv5jzBycD1yQQD7hd/4lzsT5FR+VzLHtwN2uTjVhbD3axL41bM7FJ5mupX4NdwD+0rs3XFQI3060YF2QNBDRMnVXiM5BtU5g5Xw8IXkjYFpgI/YchzIAue2s3Mri/I32kVaw9l7yU3ys3xkf6aeCDKtWmbbiWpcXuxq6fr2Q1XHHfhlapKoyLTD+KrjEyfW2lv1yjX7ZUX8v4mXuBldjrP3VWfeaHtjmb2qwZywj1F1jKzL6f1mRWLD/6c/Lr4AuAK5nVrNwS2N7P/LcjVjobrBjWpbb7e8i0drmFx3H9+dTPbN1NuVpHSpBfKBkU1sm0KEV2Ne8r9BK9wVTtIURd1B0qO8aCZrd5UvlsGTelXhtp3caws0u+9eExAprymAA9YTTSrWhS+SKPK/Rkawd8BfN9KPIQk3Qjsbmb3FfavA/zCzCZ3uKZJ+Oj3ADyYa9ESmUPN7Ct1x+mEpOXwRd09zGxchczVeOqJb6U+fRC/X0sje1ObVoUs0iLlyfhi/Ql4moiDraIAvbxIy1vxSMvXyiNCp1TNHlObxoomyf8QT7XxVjN7ZWp/kZltUiF/JV6D90c2lO9+RI79isFMrksjBjWbAMuZ2QWF/dsDj1hNEXJ5+umbgPenB9FiuJmusu5E8kjLrvGGsvu7IN82n36rQkSpzTp4Tqmd8Yy4J5vZxRWyresOlBzjIRuDIipzfUFjXtzwke/u+Ij6/fjNWyV7VZN9hfe7XpCkZkEQjymYiad1eHXaPogXT3lXRZv18Bv7x3iB8T/jLoyf7/NnOh4Px78g9edrwOtq5G9Kf2fk9v2uwzlaRTTiaY5hKH5gI+ojeLPI5dvw+sngCqpK/sN4wrW/4lHVT9Mh4jc7P7nF4KyfFfI3lsj3HGGLuzROKtn/8gbXML2kT3XXsAueSuEUPB5mFl40vEr+sPR5Po4/tP8MnNWhTzPwB8St6fV6eN6kTp/DOHxW/kj6fdyDFxgqvfeya8Znmq0WvIEHe/3emmwDZdNvgipKGVJdoGGipLXM7P7Ufk08pLqOtoUv8vwYH5GOwMwukLQjPvL7ZNp9B/C+snNJOvUgSgAAIABJREFUehL3ec5KPh5lZjO76FMTbsOLj3zZzK5tIP/v5BZ6n6RPkKqZdWjTtpBFZg9+Fz6Kuy2ZV6r4mzzi9SrgNEmzKVTdKtA06CjPc/Iskwa+AM5Qkr0ynpQHsmXyO1FSnKdq0TrDRi5eL2uFpG1Jbqa85nIdz6bRfdantamPjP4ivlg8O8lPBC6hPGARmsfS5GlViCiZyT4IbAtcjGeIvTk5P1zLyHWfK9UgbkXVieyEZ9EddULpj2QyLUoZ4r7FV0i6P72ehNv26ngj8IG0sNmx8EWB2oUr88WnvRscB9xlrE0VJdR9ZaG1Wnym4J/r4vhi7v/gZpVO15UlVsubsYzqSlg3SboIX884RNKS1CvYHfAKa5/B7e5LAZUFyGlf8QzgO/hMa3l50sCd8Lw9VeyPBz6tJ+kRfJRcVs+5ssAI5YvXI5LC5eiUouNwPF3wapJOwwvI1KU9XsCGm3P+Qn0qiW7y6T8saWl84HGxpL/iUbNVfA83+R1qZk9nO83sUUll38eIugNmdkKJ3Jgs1tYyFtOJ/6QNz8eyUkPZBfCp6SL4yGMjYJEG7dYo2xqec8e5/Plk0+NGPsV4gXXwUc+04lbTbkRiMtzGXCU/DvhMy2tZAJ81LZ1eL4vXdu3XZ3UOHvNxOD47OBdXBp3arYcr808Ar+wgm8UwLIFHb7+0r8e+HwccSVr3y+0/Ag/O6tR+WXyUvF3d95Zkv44H930gbRcAX6uR7ymWhgaxAHhu++K+A2rkR7xXJ1+QG9NYg/l+ITdNFfdlpKdMVW75y2lXyvAqa5hGudAuH9r+OzO7rYN8qwXB0ULSL3CPn4kMr8NbOluR9Dozu0nlKW4xsysrzjMD2NfMrkuv3wd81czWLZNPMpdbywpGklZhyNsn69NVBZmrzeyNGsrB0zr5WLr+pYALrCQve06u1HvDqgvmjPBIU02B+GQK+QqerXIbeTzE5mZ2YkFuCdxksilDycw2wtOPfNjM/lFzDZdawWulbF/h/ffhM4LMQ+icKtlCu0k0jKVRu0JEZZ9rpedaW/lObUeTQVD6mb266NFxdoV8W+X0//AFujMYXhykWIw736ZVaHs3ngdN6NZUoxaVhXro26uBk/AFxZXxkeOHzawsq2XW5khcsRa/i1KbvjzL4a64++gLQ+LV7rZtUPsygHmbr/AH/Jp4AY+iu+16wAZ4fqZ8PeTxwIFF+Vy7C/DFzy+a15ddELeNl3qZSFornQc8Cvz+MrkkuyhukrscT1qYmSLH4w+70kDDpqgmlgY6up02KkQkaQruxPFGXG9kLIlHqb+9F/mKvvU1vUjH8w2A0r/ValzFCrIL4CvuZQFOVW1mlew2q/cnvx0fXf0zvV4CV+JVlbBmMLQg+JpsQdDMdq2QbzS7UZdFoHPtO1YWysm29qFPi9Kn4i6Vb7IOi8xqWZxa0r24OadukbHYJvNZN7wkXqXPevFzVYcygDXn+4iZfaSwfwc83mF7htejnYPHP1xTcbwbzWyTvKJp8xvp0NcD8LWYlfGF90zpPwWcYJ6oLy9flr30JYozKLWMpSm0bVSISB5gtSYeI5IvsDIH1w3P9yJfcc7GsQb9YBAWcs+X9C4z+00nQfPFodskrV417Stp000xbjE8w+QL1C/Qtl0QPBcfdVxCfSbLropAw0szomGVhSTtXWNyOpESH/qa45+Ie1FtiD9YzpP0PTP7flWbtqYdPAR+Ieo9S/J9+hLus53N0H4i6UwbGQj1UhlASU8x9N0+S322yRGYe4yM8NE3s3OBcyVtbs28oTL+Kfe+yTxrNqM+Y2ubvh4LHCvpk1Wz1oL8kqkPX8bdLk/FP6s9GJlFFEvxEF18zwAP0eA600z1T7gJsyNt5fOoh7odvTAII/05+CLXM8BzdLDDSroMH1XfwHATQZVNfyE8DWtm178CD5SpG/V+FvdEyYe2/8Sqa602ykKYk28zu+nKVCPPQrq7pUIT8sjQX9TYkq+3FmUL5fmCvm3pBpW0FF6Gcp+aNo3s1Tn5s3E79aUMX7/5VIX83cBrbSgPzWL4gnZVfqRGZQALbfJ58rOF5mXNbKsK+UVxr5ENGL7eU7VmtTFeD/lVuDvvRNwnvq/5pdQieKrs3uh0v0jagpEz2brgrBPxlAe1hYhK1m9eeot6vVFWZ/qfNfKHUVK3w8x2qrqGfjHfK/22dGHT/zE+Wjwl7doLt+XV5sVWy9D2Qv+WwlMul+bKkfS/eLnDjrObXJvGppokf3uJPXTEvtx7R+E/hKY+9NnUuVEukyTf1l5d6gJqZqeU7U/Hn2IpJF/uAvgzM6t0w5NHsL40ILAOqQg0vJbw8/hM6mwrFBXPyZ+JBwztjruP7oGnhigt5JPaLMhQzpeqgt9lxUdewurXrFopNPm62/eB03FFOwXY38y2qJAvjaWpeljn+lR2HX2p0azyOtMvN7MvVsjPYCjWYKM0YPmxmdW51vaFgVD6ah8K3zgkXC3yYvf4Q+roeaDh3iVtZjcjTDV0KAIt6aR0rnxloQXNrNQfuwt7e+tcJt3Yq5s87DRUJW11/L64OL1+B67MqmoCfBX3fjkt7ZqCR6u2Gv3XkV1r9sBNM88LO9i3O46S5WtV2b20Oj67FO4q+WCdWbOtQpN74ByLe+8YXtDn01YSHJbk76ZdLE1r1KJyW5KfbmaT8wMfSdfUPLjmWt2O+d6mr+b1NzP5XXC/4Svwm/y7kg40s6rowBckrW1mf0zt16LaZl1WxYf02qgIMFGF5wF+I76EdVHiLnEM8M6iqQYvqlJF08pCWd/a2mFb1eBNtLJXpx/zKXRel8iqpN3EkEkO/B6pY1vgNWb2YjrfKbhP+Qilr+4T+GUPqb8lk8qfcYVeStUomULEeabUJR2Hx1P8Jr3eBujkkdIqeCop9zb1Ye/A812NiDyuQu7c8AVGmsGqHo5tKrdB+zrT09NM8QT8vvoHblIefWwMgwLmxkbLnBt4uoDlc68nUp835G14/dor8DqXDwBb9vkaZlJTcKRE/j3AUrnXS1MT1EVJjpCyfV32/bOF7TO4Caw2gIgucpng9u/f44r+93iOn41q5G/Csz9mr9cl5fzp07Xfjs9UstcTqq4BDxh6Mz7iPQOPoH03rmi+UnOOD+OlA9+EL0zPxr19quTvhuEBVx2uYcTnQcqtU9OmVfBU+n3un9qdlG018pfjM48LaRDol9pchK993J0+55OoDwDLciAdCHwyfy9WyK+RrmM8PkA7hlyRlA59m0QfgwI7bfP9SJ/2ni+NQsIl7WxmZ+I/tGF5sa3CBVDdp7dt5HmQ4zDLBbeY2d+STbMqtW+jItBdUjb7mAR8UdLhZnZ6/g31UIMXL3T9ZnL2aurD+ReyNLsBMLM/JPNIKZK2w1NCFN1Oq4KzvgrckkxbwhVzqWnH0pqRpP+x4cF+50mqC8K71Mz+is+01krHqPMoaztKflKeduBn+IxgT0rqLecxs4+nf4+TZ5TtFDx1Kr4usRW5dYka+cObdX0Yy5rZifLYlCvx+6t0nS7xnNwHf2+GUlhU3hv4oOpYPE3HEUDmwnpsXkjzQN2O+d6mr/aeL1/HzSZZUfFd8dFZsepUVsO1sY97hV07w6zavt3I8yAnX7bIWpnmVZ7ff39yC8vAD6oeXv0grW9cUvzs1GUN3nzbTvty77Vdl5iJp9KeUdePQpuV8HUA4bOXP3eQvxvY1oYn8PuNVXsItY3IbRtxPgEfuWYPoqvwGJG69adWEblt1iXURSxNanedmW0m6UI8v9GjeGbOtSvkW1Vuq/geRgRdqYdYg34x34/0zew96d/D0we9FJ4MahhpFvCMmR0oz0iYKcDjrTwk/C/peGtKmlZ8s+xHZN35F4Objx7EXcEWbiA/XdIxuEeE4Rk3K0fuSbkfk7ZWSFrCUpBZG8zs/6TqjJbmdvAT0tapDysCq+CzgtcytGYyHo8QraLVugQ+47qjhcJ/A25WnCZpT+ALko61+qjlRgn8NBSRu5SGZ9AcT85mXcLhTfqekZR7pSdQoU9ZRO5ycueJ/Pewck3TxusS1kUsTeJ/5W6/n8NdK8fjpsay6xiHJ1rbM3feWZRUbtNQRG5RD4ynZEZkvcUa9IX5XulruMdMll647Ed7LbCxhsLkO9VJ3Ra3IZ+KV1Nq06fFcfv26umpX1tZyNq7lX0S+H+4bRjcnlmXqbE1cg+QHwMvA1aX5xL6SG5q36l9NusqsqE8oGlEE6pNKVvhibpWxb+LTNnMwYOkqlgQODabMaUf+yI18l8AfpPMAh1nXHhFq43SZ3Mgbkf+KW6CGkEaxS6FmwvXS7urzIWvwJOZLc3wDJpz8GjsquN/v80oOS3qf56R3j5lI9KPMBSRexND38NT+ACkiuPTQ+K/cfv8y/D7t4qVgDslNYqlSe9lv62/494ylZjZC5ImSlrYOpcQvQY3lS3HcD0wB1/TqUQtYw36xSCYdx7AXRDzLmeP4Qte+1qqACTpDtxr50sMz2UCgFUUy1bLgt+pTavKQm08D5LiutAa5PzoBUnX42l/p1l9xaay/OET8On1+83snoJ8L+UY32cVOZUq5K8D3m4peZg8V/5FVu1mdxHuZTGDXArmqodyzlT1Jbza1ImdzIFqmcBPLSNy5amOD2k6SpZXCzuOkbmr6ipnNYrITbIL4MFhU5vIpzaNY2kkfcHMjtaQ222xTVUg3o/wQd00hj9YamfDcu+xN+FurXWfUetYg34x34/0cVPOOWZ2IYCkdwJb47Vnf8BQDvaP4jbd4sgJqC2WvYw80dckOo+EMtY2s13T1BAze7rO1IH7eZ+Bj+w+ii8ulT5o0ijlX5KWsoa58tVFEeh0rocK3S5zVS0GLhnwl25MQg1YVe4i2Kj8IZ6++aVskWb2jzQLq2KCmb2zRX/myFMy7AX8V3og1y0Ggud6/zwdEvjJ4xiuMLNr071zIl7h6U/AB2oWBNuOkp83sx926HPWp02AhzKFL+n9uT4dXrYOkMw1n8B/j40wsyvVPJYmWxCeXvF+FY+mbQHKnREAkHQ+fo/dkdZvbk7nWjvZ70uj7Glft6N/2Bi5Cc2tjRL3MobKuY0oKwfs0/L4t+G24U1xv/bXUVMGMLW5Bi9SkbmFrU192b2sdODtuX1X1shPxdcATsQXrb4DfKdG/mp8Ye/jpNzyDa77LLys5M34OsPn8URfvX5fh/bQtm35w9+TKz+Zvrtra+SPwuMZmvZnRdyM91/p9erUlN5MMrNKtvtL5P5/e2ceLzlVpv/vQ7M3NNqKiCibLI4iu8qmIIqjghs7AgoqIwMjDSrqD3RoEfcFBHdlkFFEgWZnQHawAVkautkUQcABcRRQaZRFG97fH+9JV25ukqqkkqq6qTyfT33ureQkOVWVvOecd3me2/GEBHCf8jycifSN5MhK0kkNnfDKaT873Ber4iu0mcTSUBNtb4724bPdh3Cj/xly5AxxV85H8RV57jVC+0Lyin3ej9O77L8jfu/iBVzgA0VmijEFdDuqfo3DTP/Pkj6Ol3iDZ+P8Jcy6Fi/RJW1vZpeHfZOk5SzDvUOBmVAMRzFZWWi/nPZRoOsPknbEH6YX57S/ILx6gjnXSCQCfVOYBZ5kGSLQAQfi6WirAQ/icYOe/Pld+tKP6HpR+cNDgdMlRQpKq+L3RxYOxoOxPVU6m9n/yfl91g2bHmFicVfaMb0S+C2yTvXwTrixeRS4VF4clHX+IrNk6KiVxV2eWYWE06wzm98DT4KYA8yRC4dnIeIJOriHa0ABeUWVLHqTtCU+aeoWs4pXcL+BkHhgZo9LylNhez5wZ3jWumZRVYlxMPrvxo3s2fhDOjdsm4bPGCJsC1xOuqxcnnvnPEkH4Q9z/MfLTGkzs0sk3YxXBwtX2Hkk5zP0nHkQzp/KHZMH84rXT+JL0+OBTYLBPCJjwFvfzCbI8oVslSISilWjkPyhmd0YsmDiNRaZnENWsOJZMSoJfDW3Gu4fz6OSeE/GtZMBvmeDO+Ev4Xyfje3LlDpUwYrzAoMQwDRJS5rTCb+BiVlHmbam4DWgmLziV8LfnfGV14/D+73wQsosHEdnxUiYQKTFWh6QV8w/iLsTLwIIcbo8V97snH21ovGB3LqhAnz6Kl+cVbRP6+KFQUmWwyyah6QI9IkWE4E2szVSjimaE38YcLrliKCEdmU1eKOg4Ma4O+SvIai2mmUUBknaDSeuezwMeJsCx2T9DhkPPZbBURRmt6/G8/OjYHdmvUTYHw+ALosbz5stQVYmLxT7Lj55Oc/MDgjbtwU+ZmY7Zpx/AbBDcpZsKVxRsWN6YsyUdCS+ynoEd2VtamYmpzI42cy2zjh/IaZa9VhLkzhmUoA8L2iuwPKpiTxOaTxbL8ALylbFM6MuDttfj7t5v5Jy7lK1BlWhsTN9SceZ2aFZy7vkMkoTKW0nwTKi9gVnKTfhVaNREDbuejCy+YDWxl0pW+Iz1+twPdgsFaOT8NXNsXh62v6JayXRswh0WPZuhXPwx7+zGbgBysIM4OeS/oy72s4wsz+mtNsf/6wn4Ea4CAw3TjvhD+J08nPWP2Vmp0vaBp/VfQVPs8yi9I27OJbFDfo8soXXnzazf0QeJjm7Ze4sy8w+FH8fVng/Sml3vpyFdEXzitwIN5HvoiokQq4MxkwSXD2hT5+VdBluAC+2zoxyCTyNOAvfxmfFUY3EvmHbBKZaFa+liWNlSWvbxKK3lXPaPyBPqTQ5p84hpFQJh+/ywJTtV+B0EZNg5WsNKkFjjT6dB2XSSJuBsmRlPc+EcPfMLri84k/xrKJMrdEYfoLnOUeFZnvis5ws47ScmV0mSeaFQLMl/QIfCNJwpplNMCzRjDu5HQ/aroDfO/HvbCGewpkK87TGT4dVxR54GfyDNjm1tLSwC240nsWN8NF4Fs8cOv7rJKJsox2Bb5vZOZJm53yGCa4/SS/B5QqzcJWKU0kk8QSdmECyP4tI1DpY96yoi+RVqfFZ8oU57Xelw5i5f4gH/CCrsQVN48S233Tp06sSM+jLw4okiaK1NHEcxuSitw9mN0+NWR2c074oCtcaVIXWvdMnsmZCyeV44pi1cJ/iO/AshM+ZWWagS+kiE780sy0y2l+Di66fgccpfg98wcxSOYcyXDW5+fKS1rASerjy6tnd8IFrxTQjrvLCLlFefO6SPNb+fPy7eSOeufMkHtjMdHUkjhe+TM+ityhDJRFfmS6B31enmdknso4pisQsOVeEXAOgAA7xrd1sIlPtGSn3ZKlamtjxy9C96C1qW7j+pghUULejSjR5pg8sDi7OpkdtVhVUIqLgTCic6z5J5+ABt31xdsdJRl+dauIrJH2CjsjEHuRn5xyKl8MfgqfLvZ5OFkb8/Fkl5CvShVQLlwpMc5tluaj+PfR7ZXwwOsDM7kxra85Ps5EKCrvgJFnT6FArr0xOIBcP5L8Z+EqIAaxKijGJfYZ4gU8UP0ibkUafo2cqiRjiK9NFwO+6xUF6QfCrr2Jm1wTjeGbY/jrFqMFTMAgK4MPxe/xe/PlcA3fzJVG2libCZnTqaTaSlLUiB7hWHq/7GS5ik8qjXxZWPIuqMjR+pi/p16Ros1qGQLIKKhEVmQmFGcye+Az/AdyIn2/Zqkj3wQT+/TgyB67Y8bm8OOpD1FlSnNBrWdxttcjMPpbR/gt4Hn9e6l68/bYUF3bZGx9YNsVzuHcFPmnOhhpvN8PMFipD1MYyMq80UWlrEXC/5QSbi0w4wmTjQGAdvOL3xLzvP3FsV5GgsKo5whJBbUmb46ysXRWb5GIn3RgzSyHMwrsy1Ya277cMCcycY8qobb0af17fCdyJ378/zmi7Mk5/sSYTizSzZCuTWVSvBfJ0OyrDOBj9otqshZSIJH0LL8rYE/fZ/w0v+po0U5Hn7d6KC5cvJBHUs5RgcXARbJlnXFKOWZxjbGaFeXHKQtJVZrZtYltZA1tIgzd23MvwjBfhtMOTgm+SzjeznTIG1a6Daa8oMuGQU3P8Exe0fws+w+9KdKYMkaDk/aoUiozYvkkZRSqRaabJurLJYybUMyilHibRfsLMXaGWJuu4PPeO+lDbkvR8nIxwbzNLTVaQSz7+gsm/dSotiEpkUVWFxrp3YjftFfIUr161WQspEVkx7vCj6TwUK3T9ECyO9H8Fz9zpFT3lGKukCHQ4Nm7El8CXzi9MafoTPJsmqRoG+QU4hbjuQ5+Oxh+8H+atcCxo2lqPmVdK5w+C7sHlx8wsL0gax8sjwyun0u7VhTILdxH80sxeHwa9NC6gvCymtLz+PBLB1EwzC3UM4Xf4PzyZQvhqOS1RIm91keauKVtLAwV1BOR0Hu/CJ3MvxetwXp1zyPKWkzKagkJZVFWisUafyTft5rH/M9MjSWf8+8+siyjGE25B01MZ3OFmNrvXzidwsaRd8CybnmYq1gMvjpltE/6WyVyKG/FFeCn8+1OuUcjAxlBG2OV+PEB+fBjIfoEHKs+JN5L0KPBLnA7jGtyf+kTOeTOFz7ugyITjn7H9i5RbSDwBvYoE3SjpADObEF+Q9H5Svlfrj/r3XxOr62/LCfomZDqlrYbzYGZHlTkuoGgF7AK8oPNo643Q7nxJb7UgK9kD0rKoej22LzTevVMX1OEOvwLP3olzh19oGaIXKefpKsISDNh03Lg+RZeZuKQz8OXoN/Dl/iHA5pYt4F1IBDocs2wyFhGMTp4vdjU6/m0gt7CptLCLPPtnd5zP5bnJQS3M4rbA6w22wlcp9xIGActhfCwSfFMBoQxJz9BJ3RM++36C7r91TyJBod9nAf+gY+Q3x1Nw32UZ4i6SbsIpoU+1ifUAmQiujm/SSTzYCzjYstlLV2KiUMtVuLF9LNGuVC1NOLZQtowkFXEFxZ7Rf9AZwCf9bvFnRAWyqKpE442+pM8BX4oMWJjFf8TMkkVHH8aX4ycmtn8I5xQ5LrF9Fh3u8N/TMfoLge+bS/710r/SVMI553w+nmP8xtCvi3Gqh6zg9XzcAKxJR3d0/aThSBxTtCL3i/hs5k4mBtIqy0uW9AM8xfGP+Cx/Ll7NmhsQlTSdjuFcK8dvO7TgWy8Ihm0lvMo4lQdeXika+fbvMOebyjvnOvh3swde+HUSEwuv0o5ZE7//tsaN/jXAodFKOKX9HNz9EtGH7ItrG++caJdVZwIsrgXpCypY1Fni/FFacVRrMHCMg9FPkyxLM1i342Xj/0hsXwa4MctvqwLc4RnHH5McgGL7kgbUgEfM7IGy18u4TnQjHo67C07IGozUUan6MZ7hFF/hfMfMXpY8Jhx3Fy7+XKcE41n4IHwnPlu82lKqluX0EtEsP5q1z8NdPtdZRi1A0eBbuHd2YXJGx9FlPl8W5GmqqySuUWmlpzyhYCe8UvZZfPb/dcvhmCpw7vmW0JJI21by3FnB5dQVlKTNzGxe0ZVBOPbtxKgkLEUUSX3WGlSBJvv0I0xLLKmWI10dydJmR8HlMcnBqhLc4Ynj1wDWNbNPhj4taWaPJ5qlBdNmyvPX97KM9Ec5y+IxeLHRRXgdwaGWkW5GMRHouEpVfDndTaXq3nDO2oy+BWlMSf+C9/MKSdPMLMlI+iBOA3wszoXeTR0pQtHg2zm4UtM8avrcYSV6FL66iWoSDHfRVXWNiJvprXiF8ym4W+JyvFYh2b5Q+iLwpKRtzGxuOH5r/N7N6k/PtTRF41UWhE/yjHtGn76ATyBOCZtmhc+ULKrrt9agf9gQ+JwH+cIVp+biN8n7wv8fS2l3G168kty+Ci6Endxeijs8tD8AuBH4bXi/Lp5e2Otn2hyfxWbtnx/+vgtfMs8kcM1ntH85zqy5V3i/Fm4M8/qwS8HfYQ5wD04S1gvH/wYlfuudgC/i5fq/xl0R70tptyXOc38GbpDn4P7/rYFlcs7/Zdz9tV94XYi7DrPa3z6A+/se4Hk1nn8ecBm+qlsmse/MjGOuDb/D7uGZ2CXvfsEnJQvwQPz9wC24eyer/enhOfstPlG5GF91VPm5t8bJB3+DT1juI0XXINb+VnxSEL2fRj6ffiHdjipfjXfvAEh6C53c7YstqGgl2rwHD3h+BDfo4AG+L+HseScn2i8u75f0TeBhC9k53ZamKsG+mHKOPP/5HWb2Cknfx6sJL1IGHUFwDZxsMRHoLtfdx8x+LOkjpPs8U4NpmljYFG+fSgMtaS4eZPwh8BProSIy/A5X4yIiD3VrHztuTXzWNQt4sZllpjcWCb5J+h5wgpndltWmX4Rg8Q7WYyFXwXMvgQ/+hTQOyrpmQoAdM0vTSI63K1RLUwYqXtR5K7CdhRW+PKX5Sku4hdVHrUFVGAf3Dua50rn50mb235IexnPpN8AN2h14tWLasaW4wwMKsy/GETIx8tqfF27aJ4GDwnI7terXiolAg2coQHqdQV5hTiGOfysh7GJmPRNiyfPZI7/+1sBz8RXCd1LalqUw2AbYT14E9jR0zesvg3txIrEL6E2svWeY14i8GSgqbNNT+mIyeSIy9lnJEzEUqqUpiSI1FuBV7beEQVj46v//pbTrp9agEjR2pq8+Co96PH8p7vBw7JeAvwLvwSlnDwLuNLMjE+3SxJxn4oZqlpllMjbKs5QWBqM+HSc3y0rLKywCrRS++4xtycImw7+zK3DOm9TBKHb8NLwM/ng8MypT2CXMnr4IvCC0ywrWPYIX6VxLJ03znpw+lKIwCHGbSbASRHU5fUvNaLEKMlnC+T+FTx5yNXsTx0Tpi7kKY30kT3wAd8m9El8JrgD8p5lNGrDLIvjop9F7USdy7qZX4Z/3+qznbdhorNEfBCRtQYc7/O9h23o4/UHezdET+2KKS8Tw4OGNlp8fvjzus17dzP4tzJjXt5RsgtC+sOFIcy9lbEszfDNxX+x0C+IfKecvI+xyD/A2S6FeSLTrWTQ+tC9KYVCKemIUoQIiQSXOnenSLOrurBoqUGN/gjdHAAAgAElEQVQR2qe5Wh/D6TQWxdqVrjWoCo1172Q9cBGqePCsHHc41iP7YlGXSAwn4b7IqBjmQTz4lWr0I+OuLgRtoU0hEZWMWe3v8KXwLTmX6lnYJYY/djP44Rw9G/yAohQGZakneoZqziePnadoJTWweKWZSwIX2q1iCTGd4L5MO2ehWpp+YMUrkr+Fr5ZvxX/vDcL/z5N0oAVFLfrQ7agKjTX6uAvhQbyKFbo8eOpDpq9XSDrNzHZPcXl4p6rz9b7UzPaQp2FiZk9K2XX96l0EGkqKqGQgL92xZ2GXWFDsJjlx2dlMXJL36yctSmFQlnqiCIqKBJWGehcJitqnksAxmfrky8AFISkgmTyR9rneR7qa2vfwbLi+jX7KTDxyR841s7RVT4T78YycO8J5Xo7n4X8GdxFdDNW53fpBY907kr6O0yNcg/NbzE26TxLt55vZxmkuigr7tKqZ/aFuX6+8DP4NuK96UznNwqlmlkoYJedF2RU41zrZRJkujbB/jai/wV21gqVkXWQse58L7AP8zRLygLHjehZ2kXRSVj/xJfmE/O2iA7wKUhhkfOZ4hyrRQh4EVE4k6DY6JHAbh6D5p81skoyjPLPuE0xMnvhCWhB1EO6gDFfnTLzuY7aZ/TTjuMwCs4x9RXU7KkNjZ/pmNivMbrfDy7pPkHQxLouXNmL3I9PXa5/+EP4WMu6SdrPJnPCTtsVwFF6U9RJJp+DZKft16VtXgrYEPi/pwNBuHrCSpK+Z2ZcT7ZIFZlFc4kp8hjYBKiHsYoGAKyu4nHJIIR3e4H7YShMpDC6wbAqDUlrIRZC1WqT6DKHCIkH0TgLXU2ZdHEXcQWWQNRMP7uJLcT6hNNwl6dux/XsAvwlB6TTxnx/htST/Sky3o4+u9w4bUoHAIF949duB+EN4QE67F+JFImskXxX353HcHRJ/PYDPJtdOaX9zL9sS+5+HB0F3whkGMz8DXqS0Fb7EXhovVPppl/NHBWB745W5S5FTjFLgu1kDH6ivw9PbotemeNVy3rE9fU/4yu9+PBvl1tjrtoo+w2F4EeAF+IRjhRru6Un3aB33K04qBz6wz8AHlTu6HHNWeOZm43UT5wD/U0Ff3oMPqNvik4AVw71yAy6wU+l3nHL9W3L2LYfX+JyFuxc/ihMyRqvg1HNF91t4fi6v+zOYWXNn+vI0xXfQkeg7E08Py+StsfIyfUXxNbyC9yf4Q7QnPuDchXOabBc+w1vwtNDVJB0fO34GnVjFBAT//Gp44dAFIQvmeJwc7CUZ/SkjAr2UvCjmncA3zOyfSpFPLArzVdDvKKAfUCK4vJdydHj7hZkdCxyrjhbyZZK6aiEXvMbi1aLqld0rLJdogQ4DmB2yYFbCV559wYrX0lQGSRGDaVbfnpQLKp1vMR2IgL+lHDKIWoNUNNmn/3fgbnxWdw+JpbBlBPdUQqavRN8yhc41sdJ3I5zb5Ggmcvo/DlxhCapbOXf7TnjwbB08W+cgvLjmu5Yty1hYBFrSIcDH8ZXRjnitwo/N7LVFzpNy3sL1FeE32w4fvOK52o8D55nZ3TnXq3WAl/QKfFDfF6f/yKRtLnn+gTF/qmK5xEEkTxRFhttsJj5Je4+Z/TrjuLfjv8PSZraWpI1xeujUSYUGUGuQhSYb/R+SXSFqlq1dWUqmr2DfrsPJvqIHc1fgw8HopwV9lurFGEm6E1/NPCVPmXsIZ7bMNHrhuLtxbpG+RKDVqVAeOORFXD+znABjyjG1DPAqqIXc57Vqkd0bRDB6EMkTRZGSZGHAo9Y9lXkeHqu50jrJELdatdXXlaCx7h0z26/koYVl+kpgb9yd8i38pvolsI+cbfM/UtqvKenzTE6bS+Z7PxkZFjP7i6S7uhn80HZddUSgjwyDR6YINBBVTU6iDcZXJWntl8d9nqub2QHqXjBWSNjFvPI4tzYjBV8D3pQc4PG0wX5wDxO1kFfH6TCivlZZgFOX7F7twWgGkDxRFFY+g26RmT2mLopnGmCtQWYfmjrTLwtJ/4Xf1HGZviWtnERbVX2ai2fkHItzduyP/3ZHJdr9FQ+cRXhd/H3WUjNxjq4i0KHdRXRog+OEVKnaqvL8+Xn4EnmDMMBdl1zVxNqXEXb5Kl4QdDoTKQOyXHmTZmJVzM4kzSafh6iyXO3g0tuQibJ7t1oxvda08x6GD+qP4SuVs8wszTeddfwaOHX4pcqmDicvtpI0wKPoDoogl/a8DE8/3QUnb1zKzA5MtCtFPVFpX1ujPxHqQ6avwDXWw8UoVgkGcEPg7WZ2TEb7eWa2mWK5yJJ+kfSfK0P4IYJlS8OliUCfZoFbPOOY3Dz+lPY3mdnmiuXaK4P5M+zrWdgldkxavn6eK2/kBvgikE8rX4wHcWuR3YsFo9+BB9i7BqMlHYATEM40s5eGVd13LEU3OnZM19jKKLqDIoSV7JE4tQr4QHZM0qWnUaCesAGkCA3jBewW/q417L6k9O0qnFr5lti2TO51vMBsCTwD6T9wA31Xhf25D19FbFngmO8BryzQ/lo8re3m8P6lhHTAjPbX48bm9ug3zPuOSn7uZXCOojPxge4wcvj0R/EFzBvANV6BV5beD+zeQ/v5eOpv/P6epEkR27ctPqBchU+y7gNel9Ku1lTbPr6faXgcpZe2hXQ7aunvsL6oAfwQN8f/jtILX8aReCjm57R/FR7dfzHOqzMH2KLC/qjEMXfiFap39fLg4TOgq3Af8Snh4X19Tvsywi4vDsb7T7iS1BycH3/ov3mN99I3gVfVcN61cSW06/GEg12BZXs89vrwN8pFX7LLvTEPd91F79fLGswYUC1Nie/rXGClHtoNtdbArMEiKpIuwW+2jXGR7AmwCgW5i0LShfiM/XRzF8auOG/HWwbcj9KkXSlZDtExmYEwSc/DeViEl+g/ktGukLBL7LhL8NqHyF2zDx6b2KHIefrFIH3PIei+Hj5T/jsVBUElPcvEYHQy5TmPdrsn6vBY+8KxlbpTbYtC0mn4vX0JE+NJh6S07Zl6opa+NtjoL41Xcf4I+EByv2X7tzcws9tr7tvauHtkK7zg4z5gHzO7v87rpvSjsAh0twwZy2AvlXSZJXy6adti+36O0yT3ql+72OfbbVvdGKTvuczg2+N5Z1MyGK0U6nBLkNUl2heKrdSVatsPVFAZbphorNGPoFB4JGlFfAaUm4GgEjJ9ffRtOp5yNymroc/zrocz/K3BRGHqKjhf7mMyXXDsEhPTSOXEUsvjoinbxY6bAVxoZv+ScZ0ywi6X4r9blMmyF7B/zsBSywAv6VS8onhlXMd18S5qSkWU9AImpvP+b9XXKNCXWWb29W7bYvsKJU9oALU0RRHu83XwZ+O3VkNNRlUYB6O/AT6DmInfUA/js4LMh10dmb7dcF/bSZYj01eiT6k57maWmuNe4vwL8MrUZDplajaOnJRsNp1BIjJOVfC+zwIOBV4E/J6O0V8IfN/MvpFx3FFp27vMMFfHefi3xB++a3GFsdRZb50DfJFUxD6v83ac1O5FeCxjDeBXZvaKqq5Rok89M6SWPH8tqbYl+7IkXvH+PtzFtgSd2NuRw3Y7pWEcjP61+Jd/RXi/HZ52tlWX43qW6SvRp6I57l8CjsFl6y7CWQ8PtYziqSjFs0B/ColAl4GkD5nZCSWO6yrs0g8GMMDXTfOwAC+UutRcLPz1ePD737ocWjnUYUjdholxtBWBZ8zsjRVdZ2RSbSUdi3++w6IVe0iB/gpeLDkr0X7otQbjYPQn5YJ3yQ8vLNNXok9Fc9wjH/G78IHoMJx7J/m5In/7Ifis7ywmiolk+dsncQHVARUQ41BM2MXMcoVdJP3npBN0YGb2mS79qmWAH4TvOVb/sADYxFzM/AbL0E4ocN7CxinEF9bCRcI/Edv1OJ69UwlFR1F3UJ2QU5isZwlDGu6pX5vZuontQ681aCwNQwz3ysWd4xkd9+W0LyPTVxTXSnqlmd3WY/uIBuKtuO/yz0ov907K8x0e22dky/RdIa/s7FkEuiiUIcaBG8U0HIdzjZ8b+rJA0usy2qatBKbjwcTn4TnmaX1KDvBviw/w+PfRD+qieYjjr5JWwA3fKZL+RAYDa0EU0hyAcgypUDy2Eoz718Jr2LCkwQ8bn1E66+zwqSdsiLmtg3jhKk3H41zxN+PG5Lk57Q9N2Tar4j4VzXH/Ai64cAs+AKxMyIWuqD9XpLxyub3x4qplwv/b4auL5+S0vw33dy4I71fBGTCz2k/I9Q7/L+jhs6wIfBIf2L8IvCCn7dU4++VyKfv2reB7nfSb5v3OJa8xPXyvS+Ji84cAz6vgvKULoZioF/EU7jJcmNN+Lu5aOyjvHhrFF86d/56U7fvgSnRpxwy11mDoX9qovUgX3cgUTyhxfuGcOIV+8DB4TQv/Lw+8MKftwfGHJxx7UMXf0/xgaNbBM1SOJUcog4JiHBQUdsED9ccEYz+bnIE9dkytAzyujXAiPihuh68gT6ro3OsAW6dsfx2ukVzFNSoxTrjr7HNd2qyLu4XuwWstdqjyfq3rhWtQXI9TW38V9+VfFQax1bocuzSeq78BztMzkD433qffKwYVhArX6inQKml7M7tcHeHvCbBsIrG0fPVJ2RMqLwJdmBtHLjBxBM7v8xFcWGK+ZediPx93L7wRHyAuxg3ypOBycE3tjNc+fNN6JAYbQJZJbb5nSefjLshbE9s3x0VF3tbvNWLn7DsYraAX0aVNT7GVulJt+4FcZOUVdCYzl3VpP7Rag9boBwwqCBWu9U3gh2Z2Y5d2nzazo1ScSOxWYCMLP254mG61RBpfRlpkVxHocOz1uKvsSNwXfl+vAWr1IMahAsIu8urRp3FfdvyGThVeGeQAXxfyvmtVSNxVxjglJilL4Gyp25pZqq+/aPJEnam2g8Iwaw0ab/QlzbSMrJVhQTWVzsfO/2W8BuA7uBE8EHjAzD7S4/Ez8RTAzACepJeH815nZqfK2Rj3MLMvZLQvWpFbibBLxrkHNsDXBUn3mNk6RfeVuE5h45SYpCzCB4zvW4aMo6SrcdfXGRZLngj79jWzH6UcU2uqbd0Yaq3BoPxIw3rhkomn45kvmcRiuEsDJouWP05OEKpknyb5SMkXLl8Jz1S4Kby+Sg65Ez67+nfcLz4H+CAhHlCgj7lxDFJ83xnblsVXDwvw2MLM8FoTLyLKu8arw+e+F5d+3GfY99OovPBA6wEp29+PK4hVdZ3CwWjSYw2TtsX2lYqt4OyWu+BFf7/Ckx12HvZv0+P3Wlu8p9trHGb6wv3C78ONyM9w18pvhtox6Ll0XtIcnGI44vHYF3ffpPr6wzHL4SpVSZHmXvq1PfBJy6Ft6NUfrpIVuYlz9CTs0itUQoe35HVq8z3LxdDPwrPAokrrzXG3x7vM7P8quk7hQqiMeyMzL71obGUQtTR1Y5i1Bo03+nHIqxV/jKe5LcCpeq9LtCkk01eyH4VK5zMCs5lEYupRpFklRKDL+sNVsCJXJYRdRg2D8D2Hezry7d9hZpdXfP6ejZO8oG4rfJA/NrZrBj4QJYsJy95Lhd1BLWIY9jJnAMuo5wGzcLfIBXiWx5L4rOi+lPaFUhFL9mlB6FfEN/564Hs57a8Dtom93xqf0WS1n4e7hOI57mnL9KSLaXVgepe+r4EvR6/DOcGj16b4DDDZ/lXE0ktxut1z8AyNmTnXuY+Cwi4lfodCtQYlrzElUxFLftZtcVnPP4S/0evDuHRiX/dS7Ljaa2ma/Bp6B2r/gPAb4FOkiGkAH0/ZFomvHA58KPxfWZ5+ON9N4e8CnGUT8lWkNgpt78eDv7fg7p2s9mmFTVUXBX2xx203R8YdzyF/CPfDfgafqWWdv7CwS4nPUPsAH64zZX3PJT/vGjWfv9Zamqa/xoGG4ZNmdlp8g6TdzOx0M/tiSvt/hmXne3ERcujQIFSFQqXzZrYA2Ci4PDCzhWntJO1sntN8u6R3A9NClsMhOONkldgBSIpvvyVl2zTrZE/tga9o5gBz5OLnE6Ag7AKcm1bGbtWK3zxrZovknEbHWag1qOrkKb7nqmkeRgqx3+4bvfx2RWMrMXfQWpLOje1aEaiMHHAQGGatQeN9+iWCSoVSEUv2aTrOmLkEHhhbCTjFMlgt1SMVszoFU3GRZuEUv5+xCji+Jf07Xi7/UtxlEWFF4BpLqF1Juh3YOBjXXwP/ZiHHOy3XXCWEXfr4LKVrDXo8/5T3PRcxTnX/dk1ItY0wzFqDxhp9uSTZW4Hd8YydCDOAl1sKC6FKyvQV6NM6uCjyNYntrwN+b2a/zTiuJyrmvMGsKkhaCU+9nPTgWUo9hKQj8d/hETxmsKmZWfguTjazrevsbx7qHuAlHWpmxyW2ZYqJlLzGzgSOIXyArzoDqbBxSvuMeZ97EMkTo4hh1Ro02ehvhOvjHg3EqXcfx2mJ/5JxXGGZvgJ9KlU63+vsU9ITTJx9T4BVXPghaRs8QHdSSKtc0VLoGyRtAawKXGyBGz8U+axgGUyeqlHYJZy/1gE+XKNWmodwvnvw+/VXVZ0z5RqFjFOJFMz5eGLFmviq9FxcKP2tiXYDSbUdJFSjbkfmNZtq9CNIWrLIsk8lZPoKnLtU6byk7wEnWBcqZkl34LPqVFi1ik1H4Q/q+ma2XvBTn17VzF2DEXapZYAvm4pY8lrXDGK11ItxyvncM4BFWZ9bBXmcmoCUeM/Aag0aG8iVdJqZ7Q7ckggqdaM8eCi8lsAf0iqxbM6+5XL2bQPsJ9enfZrsz/CPKg17F7wL2ATPzsFcb6DK7+sxM7uwwvOl4X7gmhAUrHKAvxZPW3w+Xo8R4XGcnrhvqMNvc5Okn+EUv3EthEpmigWD0WU/d6HkiYa4gwah25GKxhp9PDcfYKciB1nQYFU9Mn03SjrAzL4f3yjp/XSqKtPwlh7PP0j5tX8E33xE6ja94vPXLuxCTQO8lRQTKYi4K/AJPGi/uAtUlxnUs3GKPrekN+JSgc8GN97LcB7+LOyPx1Y+G4Lpa+FFlFmYA2we4kIn4qvyn5Czyh1BnJkM5Edxj7oD/OPg3pnO5BvwQsugh1UBmb4SfSlUOi9phpktVEcGcQLSAqeDgqSP4oVHO+BB3ffhgb7COrgZ578iZbNZDjVEH9eqdIAfpO9ZKXKGadv6OH/hYLScpO21eMD/l3hh5BNmtndK28KxlSa4gwYR78m89hgY/Z5vwND+emBXXPVmk7CtsjS+cL6eSuclnW9mOwW3jsFi3hqoMKhZFpJ2IJYW2iW4twUuvfcv+CA3Dfj7MINvdQ7wg0KG8agsi6uMcYoZ5Q/hqmRf6hLILRRbUc2ptnVikPGeLDTZvRNBZvZEcKGcEN2AeQeY2QOaqEH7TFbbMjCzK3BJwm7tdgp/16ry+lUgrKAuN7NLJK0PrC9pqawVFO4m2BNnPN0cp2OYRP+rPoRdSqCIDm9h1Ol7VofnZuXEdzYDH1D7PX8/hVAK/dsbZ/2EfFtzP8ViK0XdQaOE2uM93TAWRr/gDfiApK0AkysGHYKXzg8cknJna3n+bUkb4CLkcRbPLBHyMrgaeK2k5wKX4iuoPfDvORVmdo+kaWb2DHCSpLQq4TTf+prAkZJmW46wSxnUPMDX6XteGlgBv5fj39lCfKXaL/oxTrOA/wecZWZ3SFqb/ElOz7GV4A46Iu4OCpOByoon68SA4j25GAejX/QGPBCX6VsNeBCX6Tu49l6m46s5+wxI9W+HdMrtcKP/P3ggeC6ugFQViq6gngiD6HxJX8INyqTgbxRIn3SxIOwCVGn06x7ga6N5MLOr5IVTr8z6zvo8f2njZF5xfXXs/b34d5vVvufkCTN7RtLKkpauOtV2EBhkvCcLjTb6YVbwNotxfnS7AXFjljlbHSTM7PUlD90VJ2m7xcz2DwHkH1TXM6D4Cmpf3O3wH3j+/UtwaomeYGZ/VmJKXgHqHuBr5XEKBjA1yN8v+jFOIWHio0ymDcmapCyOrQC9xFbup55U29phZtuEv1Wng/eMRhv98FAU1Zy8NgROK5fpKwplCKJHsOxc7ChbaZGcpO1PQNVB30MpsIKyTv3Ak0Dhmalc2CW1iroP1D3AD8L3fEswfqcz0QD2lbLZp3E6HZfq/AG9ucuKxlbqrKUZCOqM93S99hhk73wVTy3s+aGQ9Go86PhO4E7gp2Y28ECR0gXRI5hlC6N/CzgC/wwfAf4GzLcctaO6oFAkp3TBlknUEBntcoVd+uhbnTq8tdM8hOuk3SOZ90aJ8xc2TpLmWQGBb0nXm9lr4hk+khZYQnQl5bg6amkGAvVIPVHLtcfA6Jd+KFSxTN8wIGlNYIYl+H4qOO8VpBvx7RPtVjWzP8gZEifBEhXEKe0MeLSuh7vOAb5oKuIoooxxkjQbX12excTCutS6Ekln4M/ZN4AtcPfr5ma2Z0b7xqTaagi1Bo03+kWhEZLpS0lfnICkD1PSy8zs11lZP3nZPiX6Fp/JLYv75xeZ2cequsYgUccArxp5nGLXeDFe/7A1PkDOxVWkHqzo/IWNU3CPJmGWUVcSvvuv41rWwmMrsyybarz2Wpq6oSHWGjTapw+Lg0rfximNN5BzibzdzI7JOGQBzmNytCX0c4eAov7KDwP/RnrWT2a2TxmkDILXSMrkS1fNFMBlkDHAT6Lc7gOD8D2fhKeB7hbe7xO27VDR+QsHo614XUnh2ErNqbaDwNBqDRo/0w+G6HDgu73MCiTJmv6lVIBE1sgSwGbA8Wa2fkb72imAiyLMSM/GV3K1DfB1+p4lzTezjbtt6+P8PWsOlE08KBpbKeoOGjUMKt6ThcbP9IHlzeyGxKxgEtWyBivTVysyHr7HgNvM7E8VXWYeHWqIRfhD+/6c9n8cJYMfsHadA3yJVMQyeETSPsCp4f1eVCQdqOKFUKl6ENGhZJDAmdm6sdjKkZK6xVZGqZamMGzItQbjYPQfCRkIERvkrnhhUBIRs91XBtWxGvF+vKgmSqHcDucdWk/S0VYBi1+JJXytFMBFMMABvlaah4D34TPeY/F7/NqwrW8UNU79ZIeZ2Q3ADZI+h8/iTybb3TEytTR94H6GVGswDkb/YOB7wMsk/R6fkU66YSIftVWowdovFNgMVZw18VngX8zsj+E8q+BxjdfglZKVULfKq1nXZGIBTlbV7wzqpQAugoEN8HX7ns3sf4E6V6H3U7NxKhFbGZlamj4wtFqDcfDpTwszlunAEmb2eJf2tcr0FUHkm1VB1kQlVLjkVue2EMiuJC1M0o/wB3Q+HUNmZpZX7TxWqNP3LOk/c3abmX2m32uE6xyVcYHKqB/KxFZGpZamXwyj1mAcjP7/Ahfhs4LLu/lwNQCZvl4h6VTcTbMyEBdNz1X/CsVZq+MFaeDplA/iAe3zrTy9Q/wav8IF5nu6gSQti7udXsFEErhKXBFlUPcAXzQVseC5P5KyeTr+HT/PzFbo9xqJ69UZjC6dPDFVa2mGWWswDkZ/OTzAtCeeM30+PiuYm9H+ejN7zQC7mAtJL8SLYiYt4S1HGlHSLjhnt/Dc7TlVBi0lnQ4cYmZp8ZGs9r/G6XqPxl1svzKzWbkH1oi6B3hJK5vZw1Wcq8t1VsSJBd8PnAZ8taqAfRnjJOlg4JTI7SJnYt3LzL6VaHecmR0q6TzSC/1S3VajVEtTFsOsNWi80Y8j3HxfJ2dWIOkLODFYnTJ9hSFngVwvvL3LspW/lgBurevmiT2gKwIbAzcw8XvKelBvMbNNJN1qZhtKWgoXXqlcCatX1D3AF01FLHH+mXhtxt544PPrZlYpP1EZ45SRRjrJrShpMzObJ2nbtPNkxdcGlWpbJ1SSeqIKjEMgl3BT7YFTDN8I7J7TPDICm8e2VVrYVBSh//+NB9UEvETSe80pbCfAnGhtgaTVQ5CvapQNfkaD1F/lXP//hweBh4ladXhLpCL2jNDvnfEkhVea2d/6PWcWSgSjl4i7bELq59Ip5y2bPFFrqu2AMDTdjsbP9MOsYD6+7D130EGTKiCXfHy3md0V3q8HnGoZpFaSLgdehc/C4xkXlWV5hCKdP5jZU+H9cnjV8/0Z7T+Ai4psiFeMrgB8ysy+W1WfikKD1eGt1Pcs6Vl8oFpEjbzsZYLRYUBaE2faNDyv/gEzS4tD9BxbKesOGkXUGe/peu0xMPozzGxhD+0GKdNXCJFLpNu22L5Cy+WSfboJ2MpC/naYrVxjZq/KaB8pZo0NGuJ7Lmycgovxg8AbYsf8IOv37zW2UtYdNIoYVLwn9dpNNfqSPmau5nR82v5kamFGatpMvLimcpm+IpD0X/ggFOWX7w0saTnFMCE3PzLAN1QV2IudP81vm+mTLJpFVScGNcA3xPdcu3EateSJQaDueE/utRts9N9mZudJem/afjM7ucfzzAQutQJ58lVD0jJ4kVmUjXM18C0zezqj/e7Al4ErQ/vXAoeb2RkV9ukSXCbx3PD+HXg2zxsy2sezqDYDziMni6pODGqA7ycVcVRQxDipoHZC7LhCyRN1p9oOChpSrUFjjX6VSMs8GGVIWgDsEM3uJa2MD1yVZQbIqS1OAV6EP3QP4CIn9/RwbNcsqmGgqgG+Sb5n6N04qaB2Quy4QrGVulNtB42q4z3d0PjsHRXU60w5vg6ZvrqxRMKd8yhe7l0ZzOy3wBaSVsAnD7mVzlA4i2rgsOp0eJvE49QzL46Fmo0s455z/qLFgo+Z2YUFjxkpZMR7qqT1zkTjjT496nVmLEkXy/TV1rt6cJFctSliXtwDqPwhkbQjocI2spVmdnRG23gW1eGjmEVV1QDfRyriyKGMcVKP2gl9xFZqTbUdEIam29F494561OtMWZLWKtNXBJI2MLPbCx6zM7EYgJmdVXGfvgMsD7weH1B3xQPGqfTKvWZRDQLdBnirSIe3Cb7nMsFo9aidUDa2MshU27owzHjPOBj92RTQ6xxFSJqLF7f8EPJGj3AAABDZSURBVPhJL5H+MIita2aXSloemNaLC6ZAn6LK2ujvCsCZZvamRLsTSPFrR0hmUQ0Cgxrgm+B7LmOcJF1jZlv3cc2hJ0/UhVGI94yDeyfK3jk8ts2AKTPbMrNtJK2L86TfJOkG4CQzuyStvaQDcNnEmfiSfDXcxZWaWVMST4a/T0h6ER43SOPYv6nCa1aCoj7nPjBlfc/qT3OgL+2ErNjKKNfSFMDQ4z2NN/pWXOxjJGFmd0v6JG5Ejwc2CQ/GESkP08G43/X62LEvqLhL50t6Dp4aejP+AP4gpd8nQzkXVQMwlX3P/RinvrQTcmIrabzza+IUF0OtpekVoxDvaax7R5MlA6NZwfwq3RyDgFzMfX9gR+AS4EQzuznMsK8zszUS7SeQOUlaErg5K0+6gv4tAyxrZo/ltCnsoprqaILvuU5UFVuZiu6gYcZ7mjzTT9PrnAlsKOn9Znb5oDvUB74BfB+f1UduFczsoTD7T+IqSUcAy0naATgIL4aqDJJ2Ay4KA+jhwKaSPmNmt6S1z3BR/dDMLq6yX6OEEqmII4cyxinUhRzA5DTppHbCTon3pWIrFabaDhInkhLvGQQaO9PPQgjinWZTqOxb0qFmdlxi2ywz+3pG+yVwbvU34Q/pz3Hukyr59KMA7jbA53E3wBHdvlc54+I7cRfVwtC/NBfVlEVDfM9AuWC0pGuBX6QcM6emPm4PfHIqraA0ROqJsTP6ACooPzhspPV32FXCMdfR53Epxp/k9amoi2oqo2wq4iiijHFSCi9TRX0ZSKrtIKAh6naMndGXtD7uVthy2H3pBkl74UpT2+AzpwgrAs+Y2RsT7d8BvNjMvhneX49LLQJ83MxOpyJIOh/4Pc6+uBmezXODZROuXY27qM6Iu6jCvn3NrBKx9lHGFPU9FzZOko4BrjWz/6m4LyNbS1MUw4z3NNboZ+TBzgRWBfYZdBVcGYSbfC3cffKJ2K7HcXWsRYn21wB7mtkD4f18PE1zOp7iWVnKZsj9fzM+y79b0qq4mEdXH72ce+clZnZrVf2ZKhj2Cq0oihgnSY/jz5zwe+5pXDynUo7/Fv2hyYHcZKqZ4bnkd1vggB91hHzy3+Hi6L1g6cjgB8wNvtdHJU2vuHurAheY2dOStsPFUf47q7GkK3Gd3yVxOoaHJV1lZkn/d2ORk4o4sigSjDaztJTKFjGMQrynsTP9JkDS3JD1Es2gFu8incvkHjNbJ+NcvzWzl1bYt/m4pOSaeKD4XGB9M3trRvsoBvABfJZ/lHKEYKYymuB77sc4SXoXrpnwWHj/HGA7Mzu7ls5OIYxCvKc1+g2CpFOAK83s+4ntH8Qfur0qvNbNZrappI8BT5rZCV0Cubfh2UQnA0ea2Y0NNvpT3vfcj3FKC+RONbfWoDHIeE+T3TuNgZy7/sGkKyWlwOkw4GxJ78arZMGDrMvgaZJV4p8h0PweOjURS+W0PxpfEcwNBn9t4O6K+zQSsMHRPNQGM/t02vbIOAF5M9I0Gu/W1uRgkLUGYzHTl6s2rW5BWHyqoYQrZXuc8hjgjjoK0SS9HBe8vs7MTpULpe9hZl+o+lotRgvdZu1yec+/At/EVzkfAp5rZvsNpodTD4OsNWi80Zf0Njyou7SZrSVpY5zDesqoF8VcKYcDT3VzpYwiClRpthhh9GKcQtLAp/B0XnBh9M9OJfdWXRiFeM84LLlm4+RjVwKY2XxJaw6vO6UQuVLeS2+ulNoRKBU+D7wcWDbanlOefw5ea3ApAy47b1Ec3YxT3rHBuH9C0gpm9reaujhVUQn1RD8YB6O/yMwem3rUHBOwP+5K+ayZ3RdcKbULKHfBScBRwLG4kMr+eFZRFpY3s48PomMtKkFp4yRpK5xxdQVgdUkbAR80s4Oq7+bUwijEe8bBvXMicBle3LQLcAiwlJkdONSO9YjAVXOyme0z7L7EoaBIJuk2M3tl2PYLM3ttRvtaqjRbjB5CJfiuwLmRC1LS7Wa2wXB71gIqFsseUXwID2o+jWvGLgQOHWqPCsDMngFWlrT0sPuSwFOB2O1uSf8RcrPzOPtn4Rz8T0paKOlxSSMhn9iieiSKBKF16Y0MGu/eMbMngCPDa6rifuAaSecCi5fXZva1ofXIB87l8ZXTZ4Dt6aiUTUJbrTlWeCC4eCxMVg4BcvVyWwwO4+DeWQ/4KJOzRqYSDWtaoUxmLvWoQtJqdHjZATCzq4fXoxZ1QNLzga/j2TvCs3cOsSmkS91kjIPRX4Drwya5vecNrVMlIWn6sNPewmojE1mpsJK+COwB3Ennd7CplDrbojdI2trMrum2rcVwMA5Gf56ZbTbsfvQDSVviSjsrmNlQsyEkPQw8gMdHrieRsWMZ2p+S7gI2NLOn0/a3aA6Urv8wpTQsmozG+vRDuTjAeZIOAs5iIh/4VFpqHodznpwLYGYLJL1uSH15IbADEHH9XwCcamZ3dDnuXry2oDX6DUWYnGyFJx7ECdtm4Jz8LUYAjTX6uDsn4vYG13GNYEDtAsRVwsweSNQaDCUbImQTXQRcJBdE3wu4UtLRZnZCzqFPAPMlXcbEwfeQWjvcYpBYGs/NXxIX+omwEE/hbDECaKzRN7O1ACQta2ZPxfdJWjb9qJHFSGVDBGO/I27w18T1brtp3J4bXi0aiuDau0rSmWZ227D70yId4+DTn/L+xYxsiFmWI05dY19OBjYALgR+ama3Fzh2ShPftegNkubis/4fAj9JYYNtMUQ01uhLeiGwGk5X8G46bp4ZwHfM7GXD6ltRSFrZzB4edj8AJD1Lp1agq7BL7LgpT3zXoneEVOn9gd2AG3C5zkuG26sW0Gyj/15gP5yS+EY6Rn8hTmvQzR0xMpB0N3Af8DNgzlScOUmahxdwXRkrzV9M4dCieQgUIu/E3X8L8WfwiKn07DURjTX6ESTtYmZzht2PfiHp1cCe+EN0J+5aGTbpWs+QdL2ZvSZOCd1U5axxh6QN8Vn+jsAlwIlmdrOkF+H6C0llsRYDROONftMQ/PtfA/Y2symTBjfVie9a9A5JV+Msm6eb2ZOJffua2Y+G07MW0Br9KQFJM4B34TP9l+I1B6dNpapiScvj/EdvCpt+DhyTzKxq0aJFvWis0Ze0m5mdLmktM7tv2P3pB5LuA87GDf11w+5PEYT02AOBdYDb8KX+ouH2qkUdyBBegU6Qv3XljQCabPQjicEplZ6ZBkmyKfpDSfoZ8E9cNestwP1mNmWorVv0Dkm5vvpREBBp0WyjfwlefLYxbnAmYCqkCko6zswOlXQeKTOoKfIZ4iIrSwI3TPVBuEV3SFoFeFV4e4OZ/WmY/WnRQWMrcvHMgU2BHwFfHXJfyiIKeH1lqL3oD/+M/jGzRVNctrJFD5C0O/BlXJdawAmSDjezM4basRZAg2f6EaLCJkkr4n7FVqh5gJD0DJ1iLgHL4Tw8ucVcLaYuAp35DtHsXtLKwKVmttFwe9YCmj3Tj7CKpIuBmbh7/GHgvUXoA4YNSVsDs+kIkEQGc+RJ46ZSWmmLyrBEwp3zKOMhzTolMA5G/3vAh83sCgBJ24VtWw2zUwVxInAYCSGYFi1GFBdJ+jmuuQAunvM/Q+xPixjGwb2zILmsTNs2yoiqWYfdjxYteoWknYFt8FXp1WZ21pC71CJgHGb690r6FJ2g6D44j81UwhWSvozTF8e56G8eXpdatJgISeviSQcvxWsyPmpmvx9ur1okMQ4z/ecCn8ZnHQBXA582s78Mr1fFIOmKlM02lcTdWzQfkn4B/Df+jL0N2MrMdh5ur1ok0Xij36JFi8FA0nwz2zj2fsoXRjYR4+DembJI6IyCF2g9Asyd6tQSLRqJZSVtQofGfLn4+9YdORpoZ/ojDElHpWyeiYukzzaznw64Sy1aZCLDDRmhdUeOCFqjPwUhaSZe7NIunVu0aFEIjS+YkPQlSTMkLSXpMkmPSNpn2P3qB2b2ZzpL6BYtWrToGY03+sCbzGwhsBPwILAecPhwu9QfJG0PTJnsoxYtWowOxiGQu1T4+1bgVDP781Qh/crgJ58JPAS8Z/A9atGixVTHOBj98yT9GngSOCiQP00VtaadEu8NeNTM/p7WuEWLUUGoj1kXWDbaZmZXD69HLSI0PpAraRlgeWChmT0jaTqwgpn9cchda9GikZD0AWAW8GJgPrAFLojeZu+MAMbBp3+dmf3FzJ4BCLPkC4fcpxYtmoxZuIDK78zs9cAmwMPD7VKLCI1170h6IbAaiQIRYAY+82/RokU9eMrMnpKEpGXM7NeS1h92p1o4Gmv08QKm/fAl5tdi2x8HjhhGh1q0GBM8KOk5wNnAJZL+gicftBgBjINPfxczmzPsfrRoMY6QtC2wEnCRmf1j2P1pMR5GfxlgF2BNYisbMzt6WH1q0aKJkDTDzBaGivFJCEWFLYaMJrt3IpwDPIarTj3dpW2LFi3K4yd4mvE8PL04XhBjwMjLe44DxmGmf7uZbTDsfrRo0aLFKGAcZvrXSnqlmd027I60aDEukLQasAYTXaptcdYIYBxm+ncC6+ASiU/jS04zsw2H2rEWLRoKSV/ExdDvBJ4Jm83M3j68XrWIMA5Gf4207Wb2u0H3pUWLcYCku4ANzayNoY0gGl+RG4z7S4Dtw/9PMAafu0WLIeJeOkSHLUYMjffpB/WpzYH1gZPwm/HHwNbD7FeLFg3GE8B8SZcRy5gzs0OG16UWERpv9IF34dwfNwOY2UOSVhxul1q0aDTODa8WI4hxMPr/MDOTZACBZbNFixY1wcxOHnYfWmRjHIz+aZK+CzxH0gHA+4DvD7lPLVo0DpJOM7PdM8R/aDPmRgONz94BkLQD8CY8XfPnZnbJkLvUokXjIGlVM/tDmzE32hgLo9+iRYsWLRyNd+9IepzJS83HgJuAj5jZvYPvVYsWzUX7zI02Gm/0cS79h3AyKAF7Ai8E7gL+C9huaD1r0aKZaJ+5EUbj3TuSrjez1yS2/dLMtpC0wMw2GlbfWrRoItpnbrQxDpWpz0raXdIS4bV7bF+zR7wWLYaD9pkbYYzDTH9t4OvAlvgN90vgMOD3wGZmNneI3WvRonFon7nRRuONfosWLQYHSdOAL5jZ4cPuS4t0NN69I2k9SZdJuj2831DSJ4fdrxYtmggzewbYbNj9aJGNxs/0JV0FHA5818w2CdtaNa0WLWqCpK8C6wKnA3+PtpvZmUPrVIvFGIeUzeXN7AYpLtfJomF1pkWLMcBM4FFg+9g2A1qjPwIYB6P/iKSXErIGJO0K/GG4XWrRorkws/2H3YcW2RgH987awPeArYC/4LKJe7c8IC1aVAtJHzOzL0k6gXTCtZZPfwTQ+Jl+KPl+Y6BUXgJ4EtfvbI1+ixbV4s7w96ah9qJFLhpr9CXNAA4GVgPOAS4N7z8KLABOGV7vWrRoJN4s6c8tn/5oo7HuHUnn4O6c64A3AM8FlgZmmdn8YfatRYsmQtIsnGdnVeBnwKntszZ6aLLRv83MXhn+nwY8AqxuZo8Pt2ctWjQbgU9/z/BaFjgV+KmZ/WaoHWsBNNvo32xmm2a9b9GiRf2QtAnOrLmhmU0bdn9aNNvoP0OnMETAcsAT4X8zsxnD6luLFk2GpKWAN+Mz/TcAV+GunrOH2rEWQIONfosWLQaLIEu6F7AjcAPwU+BsM/t77oEtBorW6Ldo0aISSLoCF06ZY2Z/HnZ/WqSjNfotWrRoMUZoPMtmixYtWrTooDX6LVq0aDFGaI1+ixYtWowRWqPfokWLFmOE/w8/UzluqCciZwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax = fig.add_subplot(1,1,1)\n",
"\n",
"ax.bar(range(len(n_institutions)), list(n_institutions.values()), align='center')\n",
"ax.set_xticks(range(len(n_institutions)))\n",
"ax.set_xticklabels(list(n_institutions.keys()), rotation = 90);"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The University of Miami received 162 awards over the past decade.\n"
]
}
],
"source": [
"print('The University of Miami received %d awards over the past decade.' % len(df[df['institution'].str.contains(\"University of Miami\", case=False)]))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"df_oce = df[df['divname'] == 'Division Of Ocean Sciences']"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3630 proposal in Ocean Sciences\n"
]
}
],
"source": [
"print('%d proposal in Ocean Sciences' % len(df_oce))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"df_oce[['date', 'title', 'amount']].to_csv('oce.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"At least one person got 20 proposals accepted during the last 10 years.\n"
]
}
],
"source": [
"groups = df_oce.groupby('name')\n",
"\n",
"# array with the number of proposal per PI\n",
"nb_proposal = []\n",
"for name, group in groups:\n",
" nb_proposal.append(len(group))\n",
"np_proposal = np.array(nb_proposal)\n",
"print('At least one person got %d proposals accepted during the last 10 years.' % np.max(np_proposal))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
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}
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