{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import cs109style\n", "cs109style.customize_mpl()\n", "cs109style.customize_css()\n", "\n", "# special IPython command to prepare the notebook for matplotlib\n", "%matplotlib inline \n", "\n", "from collections import defaultdict\n", "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import requests\n", "from pattern import web\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Setting custom matplotlib visual style\n", "Setting custom CSS for the IPython Notebook\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fetching population data from Wikipedia\n", "\n", "In this example we will fetch data about countries and their population from Wikipedia.\n", "\n", "http://en.wikipedia.org/wiki/List_of_countries_by_past_and_future_population has several tables for individual countries, subcontinents as well as different years. We will combine the data for all countries and all years in a single panda dataframe and visualize the change in population for different countries.\n", "\n", "###We will go through the following steps:\n", "* fetching html with embedded data\n", "* parsing html to extract the data\n", "* collecting the data in a panda dataframe\n", "* displaying the data\n", "\n", "To give you some starting points for your homework, we will also show the different sub-steps that can be taken to reach the presented solution." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fetching the Wikipedia site" ] }, { "cell_type": "code", "collapsed": false, "input": [ "url = 'http://en.wikipedia.org/wiki/List_of_countries_by_past_and_future_population'\n", "website_html = requests.get(url).text\n", "#print website_html" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parsing html data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def get_population_html_tables(html):\n", " \"\"\"Parse html and return html tables of wikipedia population data.\"\"\"\n", "\n", " dom = web.Element(html)\n", "\n", " # 0. step: look at html source!\n", " \n", " # 1. step: get all tables\n", " \n", " # tbls = [t for t in dom.by_tag('table')]\n", "\n", " # 2. step: get all wikitable sortable tables (the ones with data)\n", " \n", " tbls = [t for t in dom.by_tag('table') if t.attributes['class'] == \"wikitable sortable\"]\n", " \n", " return tbls\n", "\n", "tables = get_population_html_tables(website_html)\n", "print \"table length: %d\" %len(tables)\n", "for t in tables:\n", " print t.attributes\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "table length: 6\n", "{u'style': u'text-align: right', u'border': u'1', u'class': u'wikitable sortable'}\n", "{u'style': u'text-align: right', u'border': u'1', u'class': u'wikitable sortable'}\n", "{u'style': u'text-align: right', u'border': u'1', u'class': u'wikitable sortable'}\n", "{u'style': u'text-align: right', u'border': u'1', u'class': u'wikitable sortable'}\n", "{u'style': u'text-align: right', u'border': u'1', u'class': u'wikitable sortable'}\n", "{u'style': u'text-align: right', u'border': u'1', u'class': u'wikitable sortable'}\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "def table_type(tbl):\n", " headers = [th.content for th in tbl.by_tag('th')]\n", " return headers[1]\n", "\n", "# group the tables by type\n", "tables_by_type = defaultdict(list) # defaultdicts have a default value that is inserted when a new key is accessed\n", "for tbl in tables:\n", " tables_by_type[table_type(tbl)].append(tbl)\n", "\n", "print tables_by_type" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "defaultdict(, {u'Country or territory': [Element(tag='table'), Element(tag='table'), Element(tag='table')], u'(Sub)continent': [Element(tag='table'), Element(tag='table'), Element(tag='table')]})\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extracting data and filling it into a dictionary" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def get_countries_population(tables):\n", " \"\"\"Extract population data for countries from all tables and store it in dictionary.\"\"\"\n", " \n", " result = defaultdict(dict)\n", "\n", " # 1. step: try to extract data for a single table\n", "\n", " # 2. step: iterate over all tables, extract headings and actual data and combine data into single dict\n", " \n", " for tbl in tables:\n", " # extract column headers \n", " # each table looks a little different, therefore extract columns that store data (i.e., table header is a year)\n", " tbl_headers = [ th.content for th in tbl.by_tag('th')]\n", " column_idx_years = [(idx, int(header)) for idx, header in enumerate(tbl_headers) if header.isnumeric()]\n", " column_idx, column_years = zip(*column_idx_years)\n", " \n", " # extract data from table\n", " \n", " # get table rows - but skip the ones that have no td element\n", " tbl_rows = [ row for row in tbl.by_tag('tr') if row.by_tag('td') ]\n", " #print len(trs)\n", " #print trs[0]\n", " \n", " for row in tbl_rows:\n", " \n", " #datarow = [td.content for td in tr.by_tag('td')]\n", " #print datarow\n", " \n", " # get country name - 2nd td, a href, convert unicode to string\n", " countryname = (row.by_tag('td')[1].by_tag('a')[0].content).encode('ascii','ignore') \n", " #print type(countryname)\n", " #print countryname\n", " \n", " # get country data - create a dictionary {1955: 10000, 1960: 14000,...}\n", " # extract data from the columns in column_idx; strip commas from numers; scale number to millions\n", " countrydata = {column_years[i]:int(row.by_tag('td')[idx].content.replace(',', ''))/1000.0 for i,idx in enumerate(column_idx) }\n", " #print datarow\n", " \n", " # append to dictionary\n", " result[countryname].update(countrydata)\n", " \n", " return result\n", "\n", "\n", "result = get_countries_population(tables_by_type['Country or territory'])\n", "print result" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "defaultdict(, {'Canada': {1985: 25.942, 2050: 41.136, 1955: 16.05, 2020: 36.387, 1990: 27.791, 1960: 18.267, 2025: 37.559, 1995: 29.691, 1965: 20.071, 2030: 38.565, 2000: 31.1, 1970: 21.75, 2035: 39.396, 2005: 32.386, 1975: 23.209, 2040: 40.07, 2010: 33.76, 1980: 24.593, 2045: 40.635, 1950: 14.011, 2015: 35.1}, 'Saint Martin': {1985: 0.015, 2050: 0.035, 1955: 0.004, 2020: 0.033, 1990: 0.03, 1960: 0.004, 2025: 0.033, 1995: 0.033, 1965: 0.005, 2030: 0.033, 2000: 0.028, 1970: 0.005, 2035: 0.034, 2005: 0.028, 1975: 0.006, 2040: 0.034, 2010: 0.03, 1980: 0.008, 2045: 0.034, 1950: 0.003, 2015: 0.032}, 'Guinea-Bissau': {1985: 0.885, 2050: 2.895, 1955: 0.592, 2020: 1.893, 1990: 0.996, 1960: 0.617, 2025: 2.061, 1995: 1.143, 1965: 0.604, 2030: 2.231, 2000: 1.279, 1970: 0.62, 2035: 2.4, 2005: 1.414, 1975: 0.681, 2040: 2.568, 2010: 1.565, 1980: 0.789, 2045: 2.733, 1950: 0.573, 2015: 1.726}, 'Lithuania': {1985: 3.587, 2050: 2.788, 1955: 2.614, 2020: 3.435, 1990: 3.695, 1960: 2.765, 2025: 3.356, 1995: 3.674, 1965: 2.959, 2030: 3.257, 2000: 3.654, 1970: 3.138, 2035: 3.147, 2005: 3.597, 1975: 3.305, 2040: 3.031, 2010: 3.545, 1980: 3.435, 2045: 2.912, 1950: 2.553, 2015: 3.495}, 'Cambodia': {1985: 7.841, 2050: 22.339, 1955: 5.049, 2020: 16.927, 1990: 9.368, 1960: 5.761, 2025: 18.038, 1995: 11.234, 1965: 6.602, 2030: 19.031, 2000: 12.351, 1970: 7.394, 2035: 19.945, 2005: 13.297, 1975: 7.5, 2040: 20.809, 2010: 14.454, 1980: 6.889, 2045: 21.618, 1950: 4.471, 2015: 15.709}, 'Saint Helena, Ascension and Tristan da Cunha': {1985: 0.008, 2050: 0.007, 1955: 0.005, 2020: 0.008, 1990: 0.007, 1960: 0.005, 2025: 0.008, 1995: 0.007, 1965: 0.005, 2030: 0.008, 2000: 0.007, 1970: 0.006, 2035: 0.008, 2005: 0.007, 1975: 0.006, 2040: 0.008, 2010: 0.008, 1980: 0.006, 2045: 0.008, 1950: 0.005, 2015: 0.008}, 'Ethiopia': {1985: 42.227, 2050: 278.283, 1955: 21.99, 2020: 120.42, 1990: 48.397, 1960: 24.169, 2025: 140.14, 1995: 55.55, 1965: 26.74, 2030: 162.49, 2000: 64.165, 1970: 29.469, 2035: 187.581, 2005: 74.98, 1975: 32.976, 2040: 215.354, 2010: 88.013, 1980: 36.036, 2045: 245.655, 1950: 20.175, 2015: 103.134}, 'Aruba': {1985: 0.062, 2050: 0.151, 1955: 0.054, 2020: 0.119, 1990: 0.063, 1960: 0.057, 2025: 0.126, 1995: 0.08, 1965: 0.059, 2030: 0.132, 2000: 0.09, 1970: 0.059, 2035: 0.137, 2005: 0.097, 1975: 0.059, 2040: 0.142, 2010: 0.105, 1980: 0.06, 2045: 0.147, 1950: 0.05, 2015: 0.112}, 'Swaziland': {1985: 0.722, 2050: 1.834, 1955: 0.311, 2020: 1.513, 1990: 0.882, 1960: 0.352, 2025: 1.585, 1995: 1.004, 1965: 0.399, 2030: 1.651, 2000: 1.144, 1970: 0.455, 2035: 1.708, 2005: 1.259, 1975: 0.521, 2040: 1.756, 2010: 1.354, 1980: 0.611, 2045: 1.798, 1950: 0.277, 2015: 1.436}, 'Argentina': {1985: 30.672, 2050: 53.511, 1955: 18.928, 2020: 45.379, 1990: 33.036, 1960: 20.616, 2025: 47.165, 1995: 35.274, 1965: 22.283, 2030: 48.795, 2000: 37.336, 1970: 23.962, 2035: 50.273, 2005: 39.181, 1975: 26.082, 2040: 51.573, 2010: 41.343, 1980: 28.37, 2045: 52.663, 1950: 17.15, 2015: 43.432}, 'Bolivia': {1985: 5.935, 2050: 16.004, 1955: 3.074, 2020: 11.64, 1990: 6.574, 1960: 3.434, 2025: 12.463, 1995: 7.375, 1965: 3.853, 2030: 13.262, 2000: 8.195, 1970: 4.346, 2035: 14.024, 2005: 9.073, 1975: 4.914, 2040: 14.739, 2010: 9.947, 1980: 5.441, 2045: 15.401, 1950: 2.766, 2015: 10.801}, 'Cameroon': {1985: 10.191, 2050: 34.909, 1955: 5.211, 2020: 23.471, 1990: 11.884, 1960: 5.609, 2025: 25.522, 1995: 13.603, 1965: 6.104, 2030: 27.531, 2000: 15.343, 1970: 6.727, 2035: 29.487, 2005: 17.261, 1975: 7.522, 2040: 31.371, 2010: 19.294, 1980: 8.762, 2045: 33.174, 1950: 4.888, 2015: 21.387}, 'Burkina Faso': {1985: 7.171, 2050: 47.43, 1955: 4.614, 2020: 21.978, 1990: 8.361, 1960: 4.866, 2025: 25.385, 1995: 9.903, 1965: 5.032, 2030: 29.153, 2000: 11.588, 1970: 5.304, 2035: 33.27, 2005: 13.904, 1975: 5.673, 2040: 37.714, 2010: 16.242, 1980: 6.318, 2045: 42.448, 1950: 4.376, 2015: 18.932}, 'Turkmenistan': {1985: 3.24, 2050: 6.607, 1955: 1.348, 2020: 5.529, 1990: 3.658, 1960: 1.585, 2025: 5.8, 1995: 4.079, 1965: 1.882, 2030: 6.027, 2000: 4.385, 1970: 2.181, 2035: 6.209, 2005: 4.664, 1975: 2.524, 2040: 6.363, 2010: 4.941, 1980: 2.875, 2045: 6.497, 1950: 1.204, 2015: 5.231}, 'Ghana': {1985: 13.229, 2050: 40.243, 1955: 6.049, 2020: 28.784, 1990: 15.408, 1960: 6.958, 2025: 30.919, 1995: 17.704, 1965: 8.01, 2030: 32.989, 2000: 19.752, 1970: 8.789, 2035: 34.984, 2005: 22.062, 1975: 10.117, 2040: 36.872, 2010: 24.34, 1980: 11.011, 2045: 38.63, 1950: 5.297, 2015: 26.585}, 'Saudi Arabia': {1985: 13.33, 2050: 40.251, 1955: 4.243, 2020: 29.819, 1990: 16.061, 1960: 4.718, 2025: 31.877, 1995: 18.755, 1965: 5.327, 2030: 33.825, 2000: 21.312, 1970: 6.109, 2035: 35.614, 2005: 23.642, 1975: 7.208, 2040: 37.25, 2010: 25.732, 1980: 10.022, 2045: 38.781, 1950: 3.86, 2015: 27.752}, 'Saint Barthlemy': {1985: 0.004, 2050: 0.007, 1955: 0.002, 2020: 0.007, 1990: 0.005, 1960: 0.002, 2025: 0.007, 1995: 0.006, 1965: 0.002, 2030: 0.007, 2000: 0.007, 1970: 0.002, 2035: 0.007, 2005: 0.008, 1975: 0.003, 2040: 0.007, 2010: 0.007, 1980: 0.003, 2045: 0.007, 1950: 0.002, 2015: 0.007}, 'Japan': {1985: 120.754, 2050: 107.21, 1955: 89.815, 2020: 125.507, 1990: 123.537, 1960: 94.092, 2025: 123.386, 1995: 125.327, 1965: 98.883, 2030: 120.751, 2000: 126.776, 1970: 104.345, 2035: 117.747, 2005: 127.715, 1975: 111.573, 2040: 114.448, 2010: 127.579, 1980: 116.807, 2045: 110.907, 1950: 83.805, 2015: 126.92}, 'Cape Verde': {1985: 0.317, 2050: 0.742, 1955: 0.169, 2020: 0.583, 1990: 0.34, 1960: 0.197, 2025: 0.619, 1995: 0.385, 1965: 0.232, 2030: 0.652, 2000: 0.43, 1970: 0.269, 2035: 0.68, 2005: 0.471, 1975: 0.28, 2040: 0.705, 2010: 0.509, 1980: 0.296, 2045: 0.725, 1950: 0.146, 2015: 0.546}, 'Northern Mariana Islands': {1985: 0.021, 2050: 0.066, 1955: 0.007, 2020: 0.049, 1990: 0.044, 1960: 0.009, 2025: 0.053, 1995: 0.057, 1965: 0.01, 2030: 0.056, 2000: 0.07, 1970: 0.012, 2035: 0.059, 2005: 0.071, 1975: 0.015, 2040: 0.061, 2010: 0.048, 1980: 0.017, 2045: 0.064, 1950: 0.006, 2015: 0.044}, 'Slovenia': {1985: 1.914, 2050: 1.597, 1955: 1.517, 2020: 1.951, 1990: 1.991, 1960: 1.558, 2025: 1.908, 1995: 2.003, 1965: 1.62, 2030: 1.855, 2000: 2.011, 1970: 1.676, 2035: 1.798, 2005: 2.011, 1975: 1.722, 2040: 1.735, 2010: 2.003, 1980: 1.833, 2045: 1.668, 1950: 1.468, 2015: 1.983}, 'Guatemala': {1985: 7.581, 2050: 22.995, 1955: 3.487, 2020: 16.264, 1990: 8.966, 1960: 4.1, 2025: 17.564, 1995: 10.028, 1965: 4.746, 2030: 18.798, 2000: 11.085, 1970: 5.264, 2035: 19.96, 2005: 12.183, 1975: 5.91, 2040: 21.048, 2010: 13.55, 1980: 6.65, 2045: 22.062, 1950: 2.969, 2015: 14.919}, 'Bosnia and Herzegovina': {1985: 4.275, 2050: 3.892, 1955: 2.974, 2020: 4.592, 1990: 4.424, 1960: 3.24, 2025: 4.535, 1995: 3.709, 1965: 3.493, 2030: 4.448, 2000: 4.035, 1970: 3.703, 2035: 4.335, 2005: 4.43, 1975: 3.98, 2040: 4.203, 2010: 4.622, 1980: 4.092, 2045: 4.055, 1950: 2.662, 2015: 4.618}, 'Kuwait': {1985: 1.733, 2050: 3.863, 1955: 0.187, 2020: 2.994, 1990: 2.131, 1960: 0.292, 2025: 3.169, 1995: 1.664, 1965: 0.476, 2030: 3.331, 2000: 1.972, 1970: 0.748, 2035: 3.482, 2005: 2.257, 1975: 1.007, 2040: 3.623, 2010: 2.543, 1980: 1.37, 2045: 3.751, 1950: 0.145, 2015: 2.789}, 'Jordan': {1985: 2.63, 2050: 11.243, 1955: 0.687, 2020: 7.278, 1990: 3.267, 1960: 0.849, 2025: 7.945, 1995: 4.176, 1965: 1.061, 2030: 8.611, 2000: 4.688, 1970: 1.503, 2035: 9.282, 2005: 5.245, 1975: 1.803, 2040: 9.954, 2010: 6.407, 1980: 2.163, 2045: 10.614, 1950: 0.561, 2015: 6.623}, 'Dominica': {1985: 0.073, 2050: 0.065, 1955: 0.057, 2020: 0.074, 1990: 0.07, 1960: 0.06, 2025: 0.074, 1995: 0.071, 1965: 0.064, 2030: 0.074, 2000: 0.071, 1970: 0.07, 2035: 0.072, 2005: 0.072, 1975: 0.074, 2040: 0.07, 2010: 0.073, 1980: 0.074, 2045: 0.068, 1950: 0.051, 2015: 0.074}, 'Liberia': {1985: 2.162, 2050: 8.192, 1955: 0.928, 2020: 4.727, 1990: 2.139, 1960: 1.055, 2025: 5.284, 1995: 1.9, 1965: 1.209, 2030: 5.862, 2000: 2.601, 1970: 1.397, 2035: 6.452, 2005: 2.93, 1975: 1.617, 2040: 7.042, 2010: 3.685, 1980: 1.857, 2045: 7.625, 1950: 0.824, 2015: 4.196}, 'Congo (Kinshasa)': {1985: 33.348, 2050: 144.805, 1955: 14.953, 2020: 89.25, 1990: 39.151, 1960: 16.61, 2025: 99.162, 1995: 46.705, 1965: 18.856, 2030: 108.872, 2000: 52.445, 1970: 21.781, 2035: 118.299, 2005: 60.698, 1975: 25.032, 2040: 127.439, 2010: 69.851, 1980: 29.011, 2045: 136.284, 1950: 13.569, 2015: 79.375}, 'Jamaica': {1985: 2.318, 2050: 3.555, 1955: 1.489, 2020: 3.051, 1990: 2.347, 1960: 1.632, 2025: 3.152, 1995: 2.469, 1965: 1.777, 2030: 3.246, 2000: 2.616, 1970: 1.944, 2035: 3.331, 2005: 2.737, 1975: 2.105, 2040: 3.41, 2010: 2.847, 1980: 2.229, 2045: 3.484, 1950: 1.385, 2015: 2.95}, 'Oman': {1985: 1.497, 2050: 5.402, 1955: 0.54, 2020: 3.635, 1990: 1.794, 1960: 0.601, 2025: 3.981, 1995: 2.139, 1965: 0.682, 2030: 4.305, 2000: 2.432, 1970: 0.783, 2035: 4.601, 2005: 2.697, 1975: 0.92, 2040: 4.879, 2010: 2.968, 1980: 1.185, 2045: 5.147, 1950: 0.489, 2015: 3.287}, 'Tanzania': {1985: 21.618, 2050: 66.843, 1955: 8.971, 2020: 49.989, 1990: 25.214, 1960: 10.26, 2025: 53.428, 1995: 29.753, 1965: 11.87, 2030: 56.53, 2000: 33.712, 1970: 13.807, 2035: 59.397, 2005: 37.771, 1975: 16.148, 2040: 62.068, 2010: 41.893, 1980: 18.665, 2045: 64.548, 1950: 7.935, 2015: 46.123}, 'United States Virgin Islands': {1985: 0.101, 2050: 0.092, 1955: 0.028, 2020: 0.108, 1990: 0.104, 1960: 0.033, 2025: 0.107, 1995: 0.108, 1965: 0.044, 2030: 0.105, 2000: 0.109, 1970: 0.063, 2035: 0.102, 2005: 0.11, 1975: 0.094, 2040: 0.099, 2010: 0.11, 1980: 0.1, 2045: 0.096, 1950: 0.027, 2015: 0.109}, 'Greenland': {1985: 0.053, 2050: 0.049, 1955: 0.027, 2020: 0.058, 1990: 0.056, 1960: 0.032, 2025: 0.057, 1995: 0.056, 1965: 0.039, 2030: 0.056, 2000: 0.057, 1970: 0.046, 2035: 0.055, 2005: 0.058, 1975: 0.05, 2040: 0.053, 2010: 0.058, 1980: 0.05, 2045: 0.051, 1950: 0.022, 2015: 0.058}, 'Gabon': {1985: 0.833, 2050: 3.23, 1955: 0.429, 2020: 1.877, 1990: 0.938, 1960: 0.446, 2025: 2.063, 1995: 1.069, 1965: 0.474, 2030: 2.266, 2000: 1.236, 1970: 0.515, 2035: 2.484, 2005: 1.396, 1975: 0.647, 2040: 2.717, 2010: 1.545, 1980: 0.714, 2045: 2.965, 1950: 0.416, 2015: 1.705}, 'Saint Pierre and Miquelon': {1985: 0.006, 2050: 0.004, 1955: 0.005, 2020: 0.005, 1990: 0.006, 1960: 0.005, 2025: 0.005, 1995: 0.006, 1965: 0.005, 2030: 0.005, 2000: 0.006, 1970: 0.005, 2035: 0.004, 2005: 0.006, 1975: 0.006, 2040: 0.004, 2010: 0.006, 1980: 0.006, 2045: 0.004, 1950: 0.005, 2015: 0.006}, 'Monaco': {1985: 0.028, 2050: 0.03, 1955: 0.018, 2020: 0.031, 1990: 0.03, 1960: 0.021, 2025: 0.032, 1995: 0.031, 1965: 0.022, 2030: 0.032, 2000: 0.032, 1970: 0.024, 2035: 0.033, 2005: 0.031, 1975: 0.025, 2040: 0.032, 2010: 0.031, 1980: 0.027, 2045: 0.031, 1950: 0.018, 2015: 0.031}, 'Wallis and Futuna': {1985: 0.013, 2050: 0.016, 1955: 0.007, 2020: 0.016, 1990: 0.013, 1960: 0.008, 2025: 0.016, 1995: 0.014, 1965: 0.008, 2030: 0.016, 2000: 0.015, 1970: 0.009, 2035: 0.016, 2005: 0.015, 1975: 0.009, 2040: 0.016, 2010: 0.015, 1980: 0.011, 2045: 0.016, 1950: 0.007, 2015: 0.016}, 'New Zealand': {1985: 3.324, 2050: 5.199, 1955: 2.136, 2020: 4.615, 1990: 3.414, 1960: 2.372, 2025: 4.776, 1995: 3.642, 1965: 2.64, 2030: 4.913, 2000: 3.802, 1970: 2.828, 2035: 5.023, 2005: 4.048, 1975: 3.118, 2040: 5.105, 2010: 4.252, 1980: 3.17, 2045: 5.163, 1950: 1.908, 2015: 4.438}, 'Yemen': {1985: 10.54, 2050: 45.781, 1955: 5.265, 2020: 29.727, 1990: 12.416, 1960: 5.872, 2025: 32.65, 1995: 14.862, 1965: 6.51, 2030: 35.473, 2000: 17.407, 1970: 7.098, 2035: 38.227, 2005: 20.345, 1975: 7.934, 2040: 40.901, 2010: 23.495, 1980: 9.133, 2045: 43.436, 1950: 4.777, 2015: 26.667}, 'Jersey': {1985: 0.08, 2050: 0.108, 1955: 0.06, 2020: 0.101, 1990: 0.084, 1960: 0.063, 2025: 0.104, 1995: 0.085, 1965: 0.066, 2030: 0.106, 2000: 0.087, 1970: 0.069, 2035: 0.107, 2005: 0.088, 1975: 0.072, 2040: 0.108, 2010: 0.093, 1980: 0.076, 2045: 0.108, 1950: 0.057, 2015: 0.097}, 'Andorra': {1985: 0.045, 2050: 0.075, 1955: 0.006, 2020: 0.086, 1990: 0.053, 1960: 0.008, 2025: 0.085, 1995: 0.063, 1965: 0.014, 2030: 0.084, 2000: 0.065, 1970: 0.02, 2035: 0.083, 2005: 0.076, 1975: 0.027, 2040: 0.081, 2010: 0.085, 1980: 0.034, 2045: 0.078, 1950: 0.006, 2015: 0.086}, 'Albania': {1985: 2.957, 2050: 2.824, 1955: 1.392, 2020: 3.075, 1990: 3.245, 1960: 1.623, 2025: 3.105, 1995: 3.158, 1965: 1.884, 2030: 3.103, 2000: 3.158, 1970: 2.157, 2035: 3.062, 2005: 3.025, 1975: 2.401, 2040: 2.994, 2010: 2.987, 1980: 2.671, 2045: 2.912, 1950: 1.227, 2015: 3.029}, 'Samoa': {1985: 0.161, 2050: 0.245, 1955: 0.094, 2020: 0.204, 1990: 0.163, 1960: 0.11, 2025: 0.21, 1995: 0.169, 1965: 0.127, 2030: 0.217, 2000: 0.176, 1970: 0.142, 2035: 0.225, 2005: 0.184, 1975: 0.151, 2040: 0.232, 2010: 0.192, 1980: 0.159, 2045: 0.238, 1950: 0.082, 2015: 0.198}, 'Macau': {1985: 0.306, 2050: 0.62, 1955: 0.193, 2020: 0.614, 1990: 0.352, 1960: 0.186, 2025: 0.63, 1995: 0.401, 1965: 0.224, 2030: 0.64, 2000: 0.432, 1970: 0.261, 2035: 0.643, 2005: 0.474, 1975: 0.254, 2040: 0.641, 2010: 0.568, 1980: 0.256, 2045: 0.633, 1950: 0.205, 2015: 0.593}, 'United Arab Emirates': {1985: 1.363, 2050: 8.019, 1955: 0.083, 2020: 6.495, 1990: 1.826, 1960: 0.103, 2025: 7.063, 1995: 2.458, 1965: 0.144, 2030: 7.484, 2000: 3.219, 1970: 0.249, 2035: 7.773, 2005: 4.087, 1975: 0.523, 2040: 7.948, 2010: 4.976, 1980: 1.0, 2045: 8.024, 1950: 0.072, 2015: 5.78}, 'Guam': {1985: 0.121, 2050: 0.244, 1955: 0.069, 2020: 0.204, 1990: 0.134, 1960: 0.067, 2025: 0.214, 1995: 0.144, 1965: 0.074, 2030: 0.223, 2000: 0.155, 1970: 0.086, 2035: 0.23, 2005: 0.169, 1975: 0.102, 2040: 0.236, 2010: 0.181, 1980: 0.107, 2045: 0.241, 1950: 0.06, 2015: 0.193}, 'India': {1985: 759.612, 2050: 1656.554, 1955: 404.268, 2020: 1326.093, 1990: 838.159, 1960: 445.393, 2025: 1396.046, 1995: 920.585, 1965: 494.964, 2030: 1460.743, 2000: 1006.3, 1970: 553.889, 2035: 1519.491, 2005: 1090.973, 1975: 618.923, 2040: 1571.715, 2010: 1173.108, 1980: 684.888, 2045: 1617.238, 1950: 369.88, 2015: 1251.696}, 'Azerbaijan': {1985: 6.845, 2050: 11.21, 1955: 3.314, 2020: 10.206, 1990: 7.497, 1960: 3.882, 2025: 10.534, 1995: 8.051, 1965: 4.567, 2030: 10.781, 2000: 8.463, 1970: 5.169, 2035: 10.974, 2005: 8.825, 1975: 5.696, 2040: 11.117, 2010: 9.302, 1980: 6.198, 2045: 11.201, 1950: 2.885, 2015: 9.781}, 'Lesotho': {1985: 1.552, 2050: 1.92, 1955: 0.786, 2020: 1.969, 1990: 1.703, 1960: 0.859, 2025: 1.971, 1995: 1.848, 1965: 0.952, 2030: 1.952, 2000: 1.916, 1970: 1.067, 2035: 1.926, 2005: 1.922, 1975: 1.195, 2040: 1.907, 2010: 1.92, 1980: 1.359, 2045: 1.905, 1950: 0.726, 2015: 1.948}, 'Congo (Brazzaville)': {1985: 1.942, 2050: 9.599, 1955: 0.904, 2020: 5.444, 1990: 2.266, 1960: 1.002, 2025: 6.162, 1995: 2.65, 1965: 1.124, 2030: 6.884, 2000: 3.104, 1970: 1.272, 2035: 7.594, 2005: 3.604, 1975: 1.454, 2040: 8.284, 2010: 4.126, 1980: 1.674, 2045: 8.952, 1950: 0.826, 2015: 4.755}, 'Saint Vincent and the Grenadines': {1985: 0.104, 2050: 0.094, 1955: 0.075, 2020: 0.101, 1990: 0.107, 1960: 0.081, 2025: 0.1, 1995: 0.109, 1965: 0.085, 2030: 0.099, 2000: 0.108, 1970: 0.088, 2035: 0.098, 2005: 0.106, 1975: 0.092, 2040: 0.097, 2010: 0.104, 1980: 0.098, 2045: 0.095, 1950: 0.066, 2015: 0.103}, 'So Tom and Prncipe': {1985: 0.104, 2050: 0.309, 1955: 0.06, 2020: 0.211, 1990: 0.116, 1960: 0.063, 2025: 0.227, 1995: 0.127, 1965: 0.069, 2030: 0.244, 2000: 0.141, 1970: 0.074, 2035: 0.26, 2005: 0.158, 1975: 0.082, 2040: 0.277, 2010: 0.176, 1980: 0.094, 2045: 0.293, 1950: 0.06, 2015: 0.194}, 'Kenya': {1985: 19.762, 2050: 70.755, 1955: 7.034, 2020: 49.858, 1990: 23.361, 1960: 8.157, 2025: 53.196, 1995: 27.163, 1965: 9.549, 2030: 56.552, 2000: 30.606, 1970: 11.247, 2035: 60.243, 2005: 35.246, 1975: 13.433, 2040: 64.059, 2010: 40.843, 1980: 16.331, 2045: 67.608, 1950: 6.121, 2015: 45.925}, 'South Korea': {1985: 40.806, 2050: 43.369, 1955: 21.552, 2020: 49.362, 1990: 42.869, 1960: 24.784, 2025: 49.372, 1995: 45.105, 1965: 28.705, 2030: 49.003, 2000: 46.839, 1970: 32.241, 2035: 48.172, 2005: 48.005, 1975: 35.281, 2040: 46.911, 2010: 48.636, 1980: 38.124, 2045: 45.284, 1950: 20.846, 2015: 49.115}, 'Tajikistan': {1985: 4.569, 2050: 12.132, 1955: 1.781, 2020: 8.874, 1990: 5.272, 1960: 2.081, 2025: 9.51, 1995: 5.678, 1965: 2.511, 2030: 10.103, 2000: 6.23, 1970: 2.939, 2035: 10.667, 2005: 6.815, 1975: 3.449, 2040: 11.203, 2010: 7.487, 1980: 3.966, 2045: 11.696, 1950: 1.53, 2015: 8.192}, 'Turkey': {1985: 50.997, 2050: 100.955, 1955: 24.145, 2020: 86.757, 1990: 56.561, 1960: 28.217, 2025: 90.498, 1995: 61.94, 1965: 31.951, 2030: 93.743, 2000: 67.329, 1970: 35.758, 2035: 96.468, 2005: 72.674, 1975: 40.53, 2040: 98.601, 2010: 77.804, 1980: 45.048, 2045: 100.101, 1950: 21.122, 2015: 82.523}, 'Afghanistan': {1985: 13.12, 2050: 63.795, 1955: 8.891, 2020: 36.644, 1990: 13.568, 1960: 9.829, 2025: 41.117, 1995: 19.445, 1965: 10.998, 2030: 45.665, 2000: 22.461, 1970: 12.431, 2035: 50.195, 2005: 26.335, 1975: 14.132, 2040: 54.717, 2010: 29.121, 1980: 15.044, 2045: 59.255, 1950: 8.15, 2015: 32.564}, 'Bangladesh': {1985: 102.308, 2050: 250.155, 1955: 49.588, 2020: 183.109, 1990: 112.213, 1960: 54.593, 2025: 197.674, 1995: 121.442, 1965: 60.284, 2030: 211.288, 2000: 132.151, 1970: 67.331, 2035: 223.396, 2005: 144.139, 1975: 76.153, 2040: 233.778, 2010: 156.118, 1980: 87.937, 2045: 242.608, 1950: 45.646, 2015: 168.958}, 'Mauritania': {1985: 1.723, 2050: 6.536, 1955: 1.053, 2020: 4.005, 1990: 1.925, 1960: 1.117, 2025: 4.425, 1995: 2.235, 1965: 1.195, 2030: 4.851, 2000: 2.501, 1970: 1.289, 2035: 5.279, 2005: 2.838, 1975: 1.404, 2040: 5.706, 2010: 3.205, 1980: 1.545, 2045: 6.127, 1950: 1.006, 2015: 3.597}, 'Solomon Islands': {1985: 0.273, 2050: 1.016, 1955: 0.114, 2020: 0.685, 1990: 0.321, 1960: 0.126, 2025: 0.747, 1995: 0.375, 1965: 0.143, 2030: 0.808, 2000: 0.434, 1970: 0.163, 2035: 0.866, 2005: 0.496, 1975: 0.193, 2040: 0.92, 2010: 0.559, 1980: 0.231, 2045: 0.971, 1950: 0.107, 2015: 0.622}, 'Turks and Caicos Islands': {1985: 0.009, 2050: 0.084, 1955: 0.005, 2020: 0.056, 1990: 0.012, 1960: 0.006, 2025: 0.061, 1995: 0.015, 1965: 0.006, 2030: 0.066, 2000: 0.019, 1970: 0.006, 2035: 0.071, 2005: 0.028, 1975: 0.006, 2040: 0.076, 2010: 0.043, 1980: 0.007, 2045: 0.08, 1950: 0.005, 2015: 0.05}, 'Saint Lucia': {1985: 0.131, 2050: 0.162, 1955: 0.086, 2020: 0.166, 1990: 0.138, 1960: 0.088, 2025: 0.169, 1995: 0.146, 1965: 0.094, 2030: 0.17, 2000: 0.153, 1970: 0.103, 2035: 0.17, 2005: 0.157, 1975: 0.112, 2040: 0.169, 2010: 0.161, 1980: 0.122, 2045: 0.166, 1950: 0.079, 2015: 0.164}, 'Gaza Strip': {1985: 0.532, 2050: 3.393, 1955: 0.266, 2020: 2.121, 1990: 0.646, 1960: 0.308, 2025: 2.35, 1995: 0.886, 1965: 0.35, 2030: 2.565, 2000: 1.13, 1970: 0.343, 2035: 2.778, 2005: 1.35, 1975: 0.395, 2040: 2.992, 2010: 1.604, 1980: 0.456, 2045: 3.201, 1950: 0.245, 2015: 1.869}, 'San Marino': {1985: 0.023, 2050: 0.035, 1955: 0.014, 2020: 0.034, 1990: 0.023, 1960: 0.015, 2025: 0.035, 1995: 0.025, 1965: 0.017, 2030: 0.036, 2000: 0.027, 1970: 0.019, 2035: 0.036, 2005: 0.03, 1975: 0.02, 2040: 0.036, 2010: 0.031, 1980: 0.021, 2045: 0.036, 1950: 0.013, 2015: 0.033}, 'Kyrgyzstan': {1985: 4.006, 2050: 8.238, 1955: 1.901, 2020: 6.314, 1990: 4.382, 1960: 2.171, 2025: 6.679, 1995: 4.532, 1965: 2.573, 2030: 7.014, 2000: 4.851, 1970: 2.964, 2035: 7.34, 2005: 5.146, 1975: 3.301, 2040: 7.662, 2010: 5.509, 1980: 3.623, 2045: 7.967, 1950: 1.739, 2015: 5.913}, 'French Polynesia': {1985: 0.175, 2050: 0.325, 1955: 0.072, 2020: 0.295, 1990: 0.2, 1960: 0.081, 2025: 0.305, 1995: 0.217, 1965: 0.095, 2030: 0.314, 2000: 0.236, 1970: 0.114, 2035: 0.319, 2005: 0.254, 1975: 0.133, 2040: 0.323, 2010: 0.269, 1980: 0.151, 2045: 0.325, 1950: 0.062, 2015: 0.283}, 'France': {1985: 56.49, 2050: 69.768, 1955: 44.218, 2020: 67.518, 1990: 58.168, 1960: 46.584, 2025: 68.482, 1995: 59.712, 1965: 49.802, 2030: 69.249, 2000: 61.137, 1970: 51.918, 2035: 69.812, 2005: 62.912, 1975: 53.955, 2040: 70.1, 2010: 64.768, 1980: 55.11, 2045: 70.069, 1950: 42.518, 2015: 66.301}, 'Bermuda': {1985: 0.056, 2050: 0.07, 1955: 0.041, 2020: 0.072, 1990: 0.058, 1960: 0.044, 2025: 0.073, 1995: 0.06, 1965: 0.049, 2030: 0.073, 2000: 0.063, 1970: 0.053, 2035: 0.073, 2005: 0.066, 1975: 0.054, 2040: 0.073, 2010: 0.068, 1980: 0.055, 2045: 0.071, 1950: 0.039, 2015: 0.07}, 'Slovakia': {1985: 5.145, 2050: 4.944, 1955: 3.727, 2020: 5.494, 1990: 5.263, 1960: 3.994, 2025: 5.459, 1995: 5.362, 1965: 4.37, 2030: 5.393, 2000: 5.4, 1970: 4.524, 2035: 5.306, 2005: 5.431, 1975: 4.73, 2040: 5.202, 2010: 5.47, 1980: 4.966, 2045: 5.081, 1950: 3.463, 2015: 5.496}, 'Somalia': {1985: 6.459, 2050: 22.626, 1955: 2.673, 2020: 11.757, 1990: 6.692, 1960: 2.956, 2025: 13.274, 1995: 6.401, 1965: 3.283, 2030: 15.041, 2000: 7.501, 1970: 3.667, 2035: 16.882, 2005: 8.79, 1975: 4.128, 2040: 18.768, 2010: 9.768, 1980: 5.794, 2045: 20.684, 1950: 2.438, 2015: 10.616}, 'Peru': {1985: 19.379, 2050: 36.944, 1955: 8.672, 2020: 31.915, 1990: 21.565, 1960: 9.931, 2025: 33.283, 1995: 23.863, 1965: 11.467, 2030: 34.444, 2000: 25.797, 1970: 13.193, 2035: 35.376, 2005: 27.442, 1975: 15.161, 2040: 36.095, 2010: 28.948, 1980: 17.295, 2045: 36.618, 1950: 7.633, 2015: 30.445}, 'Laos': {1985: 3.657, 2050: 10.069, 1955: 2.077, 2020: 7.447, 1990: 4.21, 1960: 2.309, 2025: 7.972, 1995: 4.846, 1965: 2.565, 2030: 8.472, 2000: 5.397, 1970: 2.845, 2035: 8.933, 2005: 5.836, 1975: 3.161, 2040: 9.349, 2010: 6.368, 1980: 3.293, 2045: 9.726, 1950: 1.886, 2015: 6.912}, 'Nauru': {1985: 0.009, 2050: 0.012, 1955: 0.004, 2020: 0.01, 1990: 0.009, 1960: 0.004, 2025: 0.01, 1995: 0.01, 1965: 0.006, 2030: 0.01, 2000: 0.01, 1970: 0.007, 2035: 0.011, 2005: 0.01, 1975: 0.007, 2040: 0.011, 2010: 0.009, 1980: 0.008, 2045: 0.011, 1950: 0.003, 2015: 0.01}, 'Seychelles': {1985: 0.067, 2050: 0.1, 1955: 0.036, 2020: 0.096, 1990: 0.071, 1960: 0.042, 2025: 0.099, 1995: 0.075, 1965: 0.048, 2030: 0.101, 2000: 0.079, 1970: 0.054, 2035: 0.102, 2005: 0.084, 1975: 0.06, 2040: 0.102, 2010: 0.088, 1980: 0.064, 2045: 0.102, 1950: 0.033, 2015: 0.092}, 'Norway': {1985: 4.152, 2050: 4.966, 1955: 3.427, 2020: 4.836, 1990: 4.242, 1960: 3.581, 2025: 4.917, 1995: 4.359, 1965: 3.723, 2030: 4.978, 2000: 4.492, 1970: 3.877, 2035: 5.008, 2005: 4.593, 1975: 4.007, 2040: 5.008, 2010: 4.676, 1980: 4.086, 2045: 4.991, 1950: 3.265, 2015: 4.754}, 'Malawi': {1985: 7.33, 2050: 37.407, 1955: 3.088, 2020: 20.204, 1990: 9.546, 1960: 3.45, 2025: 22.86, 1995: 10.264, 1965: 3.914, 2030: 25.639, 2000: 11.802, 1970: 4.508, 2035: 28.508, 2005: 13.492, 1975: 5.317, 2040: 31.442, 2010: 15.448, 1980: 6.259, 2045: 34.419, 1950: 2.817, 2015: 17.715}, 'Cook Islands': {1985: 0.017, 2050: 0.005, 1955: 0.016, 2020: 0.009, 1990: 0.018, 1960: 0.018, 2025: 0.008, 1995: 0.018, 1965: 0.019, 2030: 0.007, 2000: 0.016, 1970: 0.021, 2035: 0.006, 2005: 0.014, 1975: 0.019, 2040: 0.006, 2010: 0.011, 1980: 0.018, 2045: 0.006, 1950: 0.015, 2015: 0.01}, 'Benin': {1985: 4.03, 2050: 22.119, 1955: 1.846, 2020: 11.956, 1990: 4.705, 1960: 2.055, 2025: 13.565, 1995: 5.647, 1965: 2.311, 2030: 15.248, 2000: 6.619, 1970: 2.62, 2035: 16.97, 2005: 7.778, 1975: 2.996, 2040: 18.703, 2010: 9.056, 1980: 3.458, 2045: 20.424, 1950: 1.673, 2015: 10.449}, 'Federated States of Micronesia': {1985: 0.091, 2050: 0.074, 1955: 0.036, 2020: 0.102, 1990: 0.109, 1960: 0.042, 2025: 0.099, 1995: 0.106, 1965: 0.049, 2030: 0.095, 2000: 0.108, 1970: 0.057, 2035: 0.09, 2005: 0.108, 1975: 0.066, 2040: 0.085, 2010: 0.107, 1980: 0.077, 2045: 0.08, 1950: 0.031, 2015: 0.105}, 'Western Sahara': {1985: 0.179, 2050: 1.173, 1955: 0.016, 2020: 0.652, 1990: 0.217, 1960: 0.028, 2025: 0.736, 1995: 0.264, 1965: 0.05, 2030: 0.821, 2000: 0.336, 1970: 0.089, 2035: 0.909, 2005: 0.415, 1975: 0.072, 2040: 0.997, 2010: 0.492, 1980: 0.124, 2045: 1.086, 1950: 0.009, 2015: 0.571}, 'Cuba': {1985: 10.065, 2050: 9.161, 1955: 6.381, 2020: 10.932, 1990: 10.507, 1960: 7.027, 2025: 10.785, 1995: 10.847, 1965: 7.81, 2030: 10.575, 2000: 11.072, 1970: 8.543, 2035: 10.298, 2005: 11.198, 1975: 9.29, 2040: 9.961, 2010: 11.098, 1980: 9.653, 2045: 9.578, 1950: 5.785, 2015: 11.031}, 'Montenegro': {1985: 0.561, 2050: 0.578, 1955: 0.432, 2020: 0.639, 1990: 0.583, 1960: 0.461, 2025: 0.636, 1995: 0.671, 1965: 0.491, 2030: 0.629, 2000: 0.732, 1970: 0.514, 2035: 0.62, 2005: 0.699, 1975: 0.549, 2040: 0.609, 2010: 0.667, 1980: 0.56, 2045: 0.595, 1950: 0.396, 2015: 0.647}, 'Saint Kitts and Nevis': {1985: 0.043, 2050: 0.056, 1955: 0.05, 2020: 0.054, 1990: 0.042, 1960: 0.051, 2025: 0.055, 1995: 0.043, 1965: 0.049, 2030: 0.057, 2000: 0.046, 1970: 0.046, 2035: 0.057, 2005: 0.048, 1975: 0.045, 2040: 0.057, 2010: 0.05, 1980: 0.044, 2045: 0.057, 1950: 0.044, 2015: 0.052}, 'Togo': {1985: 3.129, 2050: 16.584, 1955: 1.298, 2020: 8.608, 1990: 3.721, 1960: 1.456, 2025: 9.741, 1995: 4.203, 1965: 1.648, 2030: 10.952, 2000: 4.992, 1970: 1.964, 2035: 12.245, 2005: 5.715, 1975: 2.267, 2040: 13.622, 2010: 6.587, 1980: 2.626, 2045: 15.074, 1950: 1.172, 2015: 7.552}, 'China': {1985: 1058.008, 2050: 1303.723, 1955: 606.73, 2020: 1384.545, 1990: 1148.364, 1960: 650.661, 2025: 1394.639, 1995: 1216.378, 1965: 715.546, 2030: 1391.491, 2000: 1263.638, 1970: 820.403, 2035: 1378.255, 2005: 1297.765, 1975: 917.899, 2040: 1358.519, 2010: 1330.141, 1980: 984.736, 2045: 1333.892, 1950: 562.58, 2015: 1361.513}, 'Armenia': {1985: 3.374, 2050: 2.943, 1955: 1.565, 2020: 3.017, 1990: 3.377, 1960: 1.869, 2025: 3.044, 1995: 3.069, 1965: 2.206, 2030: 3.051, 2000: 3.043, 1970: 2.52, 2035: 3.042, 2005: 2.983, 1975: 2.834, 2040: 3.023, 2010: 2.967, 1980: 3.115, 2045: 2.991, 1950: 1.355, 2015: 2.984}, 'Timor-Leste': {1985: 0.652, 2050: 1.955, 1955: 0.472, 2020: 1.389, 1990: 0.746, 1960: 0.509, 2025: 1.498, 1995: 0.87, 1965: 0.553, 2030: 1.6, 2000: 0.847, 1970: 0.598, 2035: 1.695, 2005: 1.042, 1975: 0.677, 2040: 1.787, 2010: 1.155, 1980: 0.557, 2045: 1.874, 1950: 0.436, 2015: 1.272}, 'Dominican Republic': {1985: 6.379, 2050: 13.69, 1955: 2.737, 2020: 11.109, 1990: 7.084, 1960: 3.231, 2025: 11.703, 1995: 7.759, 1965: 3.806, 2030: 12.239, 2000: 8.469, 1970: 4.423, 2035: 12.705, 2005: 9.164, 1975: 5.048, 2040: 13.097, 2010: 9.824, 1980: 5.697, 2045: 13.424, 1950: 2.353, 2015: 10.479}, 'Ukraine': {1985: 50.944, 2050: 33.574, 1955: 39.368, 2020: 42.561, 1990: 51.622, 1960: 42.644, 2025: 41.038, 1995: 51.245, 1965: 45.235, 2030: 39.492, 2000: 49.005, 1970: 47.236, 2035: 37.981, 2005: 46.959, 1975: 48.973, 2040: 36.513, 2010: 45.416, 1980: 50.047, 2045: 35.054, 1950: 36.775, 2015: 44.009}, 'Bahrain': {1985: 0.423, 2050: 1.847, 1955: 0.13, 2020: 1.505, 1990: 0.506, 1960: 0.157, 2025: 1.58, 1995: 0.582, 1965: 0.191, 2030: 1.639, 2000: 0.655, 1970: 0.22, 2035: 1.7, 2005: 0.916, 1975: 0.259, 2040: 1.758, 2010: 1.18, 1980: 0.348, 2045: 1.806, 1950: 0.115, 2015: 1.347}, 'Tonga': {1985: 0.094, 2050: 0.079, 1955: 0.054, 2020: 0.106, 1990: 0.096, 1960: 0.064, 2025: 0.105, 1995: 0.097, 1965: 0.074, 2030: 0.102, 2000: 0.1, 1970: 0.083, 2035: 0.099, 2005: 0.103, 1975: 0.089, 2040: 0.094, 2010: 0.106, 1980: 0.092, 2045: 0.087, 1950: 0.046, 2015: 0.107}, 'Finland': {1985: 4.902, 2050: 4.82, 1955: 4.235, 2020: 5.272, 1990: 4.986, 1960: 4.43, 2025: 5.251, 1995: 5.105, 1965: 4.564, 2030: 5.201, 2000: 5.169, 1970: 4.606, 2035: 5.123, 2005: 5.223, 1975: 4.711, 2040: 5.027, 2010: 5.255, 1980: 4.78, 2045: 4.923, 1950: 4.009, 2015: 5.271}, 'Libya': {1985: 3.68, 2050: 10.872, 1955: 1.122, 2020: 7.759, 1990: 4.146, 1960: 1.338, 2025: 8.342, 1995: 4.663, 1965: 1.624, 2030: 8.901, 2000: 5.125, 1970: 1.999, 2035: 9.452, 2005: 5.778, 1975: 2.57, 2040: 9.981, 2010: 6.461, 1980: 3.069, 2045: 10.461, 1950: 0.961, 2015: 7.132}, 'Cayman Islands': {1985: 0.021, 2050: 0.091, 1955: 0.007, 2020: 0.062, 1990: 0.026, 1960: 0.008, 2025: 0.068, 1995: 0.032, 1965: 0.009, 2030: 0.073, 2000: 0.038, 1970: 0.01, 2035: 0.078, 2005: 0.044, 1975: 0.014, 2040: 0.083, 2010: 0.05, 1980: 0.017, 2045: 0.087, 1950: 0.006, 2015: 0.056}, 'Central African Republic': {1985: 2.714, 2050: 10.339, 1955: 1.348, 2020: 5.991, 1990: 3.085, 1960: 1.467, 2025: 6.638, 1995: 3.544, 1965: 1.628, 2030: 7.325, 2000: 3.98, 1970: 1.839, 2035: 8.045, 2005: 4.363, 1975: 2.058, 2040: 8.791, 2010: 4.845, 1980: 2.349, 2045: 9.558, 1950: 1.26, 2015: 5.392}, 'Mauritius': {1985: 1.021, 2050: 1.441, 1955: 0.572, 2020: 1.379, 1990: 1.062, 1960: 0.663, 2025: 1.412, 1995: 1.123, 1965: 0.756, 2030: 1.437, 2000: 1.186, 1970: 0.83, 2035: 1.452, 2005: 1.243, 1975: 0.885, 2040: 1.456, 2010: 1.294, 1980: 0.964, 2045: 1.452, 1950: 0.481, 2015: 1.34}, 'Liechtenstein': {1985: 0.027, 2050: 0.044, 1955: 0.015, 2020: 0.039, 1990: 0.029, 1960: 0.016, 2025: 0.041, 1995: 0.031, 1965: 0.019, 2030: 0.042, 2000: 0.033, 1970: 0.021, 2035: 0.043, 2005: 0.035, 1975: 0.023, 2040: 0.043, 2010: 0.036, 1980: 0.025, 2045: 0.043, 1950: 0.014, 2015: 0.038}, 'Australia': {1985: 15.695, 2050: 29.013, 1955: 9.277, 2020: 23.939, 1990: 16.956, 1960: 10.361, 2025: 25.054, 1995: 17.976, 1965: 11.439, 2030: 26.056, 2000: 19.053, 1970: 12.66, 2035: 26.931, 2005: 20.232, 1975: 13.771, 2040: 27.702, 2010: 21.516, 1980: 14.616, 2045: 28.39, 1950: 8.267, 2015: 22.751}, 'British Virgin Islands': {1985: 0.013, 2050: 0.06, 1955: 0.007, 2020: 0.037, 1990: 0.016, 1960: 0.007, 2025: 0.041, 1995: 0.019, 1965: 0.008, 2030: 0.045, 2000: 0.023, 1970: 0.01, 2035: 0.049, 2005: 0.026, 1975: 0.011, 2040: 0.053, 2010: 0.03, 1980: 0.011, 2045: 0.056, 1950: 0.006, 2015: 0.033}, 'Mali': {1985: 7.506, 2050: 32.367, 1955: 4.071, 2020: 17.89, 1990: 8.327, 1960: 4.495, 2025: 20.24, 1995: 9.336, 1965: 4.978, 2030: 22.69, 2000: 10.621, 1970: 5.546, 2035: 25.169, 2005: 12.134, 1975: 6.218, 2040: 27.629, 2010: 13.796, 1980: 6.822, 2045: 30.038, 1950: 3.688, 2015: 15.718}, 'Russia': {1985: 143.938, 2050: 109.187, 1955: 111.125, 2020: 132.242, 1990: 147.973, 1960: 119.632, 2025: 128.18, 1995: 148.49, 1965: 126.541, 2030: 124.094, 2000: 146.71, 1970: 130.245, 2035: 120.215, 2005: 142.776, 1975: 134.293, 2040: 116.553, 2010: 139.39, 1980: 139.039, 2045: 112.92, 1950: 101.937, 2015: 136.01}, 'Bulgaria': {1985: 8.944, 2050: 4.651, 1955: 7.499, 2020: 6.569, 1990: 8.894, 1960: 7.867, 2025: 6.258, 1995: 8.256, 1965: 8.201, 2030: 5.941, 2000: 7.818, 1970: 8.49, 2035: 5.624, 2005: 7.45, 1975: 8.721, 2040: 5.305, 2010: 7.149, 1980: 8.844, 2045: 4.981, 1950: 7.251, 2015: 6.867}, 'United States': {1985: 237.924, 2050: 422.554, 1955: 165.069, 2020: 336.836, 1990: 249.623, 1960: 179.979, 2025: 351.353, 1995: 266.278, 1965: 193.526, 2030: 365.683, 2000: 282.172, 1970: 203.984, 2035: 379.81, 2005: 295.753, 1975: 215.465, 2040: 393.856, 2010: 308.282, 1980: 227.225, 2045: 408.012, 1950: 151.868, 2015: 322.371}, 'Romania': {1985: 22.521, 2050: 18.06, 1955: 17.325, 2020: 21.303, 1990: 22.866, 1960: 18.403, 2025: 20.872, 1995: 22.687, 1965: 19.027, 2030: 20.389, 2000: 22.447, 1970: 20.253, 2035: 19.87, 2005: 22.197, 1975: 21.245, 2040: 19.313, 2010: 21.959, 1980: 22.13, 2045: 18.712, 1950: 16.311, 2015: 21.666}, 'Angola': {1985: 8.39, 2050: 45.888, 1955: 4.423, 2020: 22.484, 1990: 9.485, 1960: 4.797, 2025: 25.673, 1995: 11.0, 1965: 5.135, 2030: 29.155, 2000: 12.683, 1970: 5.606, 2035: 32.91, 2005: 14.77, 1975: 6.05, 2040: 36.948, 2010: 17.043, 1980: 7.206, 2045: 41.28, 1950: 4.118, 2015: 19.625}, 'Chad': {1985: 5.066, 2050: 20.474, 1955: 2.805, 2020: 12.756, 1990: 5.841, 1960: 3.042, 2025: 13.915, 1995: 6.77, 1965: 3.345, 2030: 15.114, 2000: 7.943, 1970: 3.727, 2035: 16.362, 2005: 9.401, 1975: 4.144, 2040: 17.658, 2010: 10.543, 1980: 4.522, 2045: 19.007, 1950: 2.608, 2015: 11.631}, 'South Africa': {1985: 34.254, 2050: 49.401, 1955: 15.369, 2020: 48.53, 1990: 38.476, 1960: 17.417, 2025: 48.714, 1995: 42.228, 1965: 19.898, 2030: 48.854, 2000: 45.064, 1970: 22.74, 2035: 48.965, 2005: 47.483, 1975: 25.815, 2040: 49.071, 2010: 49.109, 1980: 29.252, 2045: 49.21, 1950: 13.596, 2015: 48.286}, 'Cyprus': {1985: 0.679, 2050: 1.392, 1955: 0.533, 2020: 1.267, 1990: 0.745, 1960: 0.579, 2025: 1.33, 1995: 0.847, 1965: 0.6, 2030: 1.375, 2000: 0.92, 1970: 0.627, 2035: 1.402, 2005: 1.011, 1975: 0.627, 2040: 1.413, 2010: 1.103, 1980: 0.63, 2045: 1.41, 1950: 0.494, 2015: 1.189}, 'Sweden': {1985: 8.356, 2050: 9.085, 1955: 7.262, 2020: 9.245, 1990: 8.601, 1960: 7.48, 2025: 9.316, 1995: 8.878, 1965: 7.734, 2030: 9.324, 2000: 8.924, 1970: 8.043, 2035: 9.28, 2005: 9.002, 1975: 8.193, 2040: 9.212, 2010: 9.074, 1980: 8.31, 2045: 9.145, 1950: 7.014, 2015: 9.153}, 'Qatar': {1985: 0.342, 2050: 2.559, 1955: 0.035, 2020: 2.444, 1990: 0.433, 1960: 0.045, 2025: 2.563, 1995: 0.51, 1965: 0.07, 2030: 2.596, 2000: 0.64, 1970: 0.113, 2035: 2.574, 2005: 0.973, 1975: 0.165, 2040: 2.55, 2010: 1.719, 1980: 0.229, 2045: 2.548, 1950: 0.025, 2015: 2.195}, 'Malaysia': {1985: 15.649, 2050: 42.929, 1955: 7.312, 2020: 32.652, 1990: 17.882, 1960: 8.428, 2025: 34.683, 1995: 20.339, 1965: 9.648, 2030: 36.619, 2000: 23.151, 1970: 10.91, 2035: 38.447, 2005: 25.968, 1975: 12.131, 2040: 40.124, 2010: 28.275, 1980: 13.46, 2045: 41.62, 1950: 6.434, 2015: 30.514}, 'Austria': {1985: 7.56, 2050: 7.521, 1955: 6.947, 2020: 8.22, 1990: 7.723, 1960: 7.047, 2025: 8.19, 1995: 8.047, 1965: 7.271, 2030: 8.12, 2000: 8.113, 1970: 7.467, 2035: 8.009, 2005: 8.185, 1975: 7.579, 2040: 7.867, 2010: 8.214, 1980: 7.549, 2045: 7.702, 1950: 6.935, 2015: 8.224}, 'Vietnam': {1985: 60.093, 2050: 111.174, 1955: 27.738, 2020: 98.721, 1990: 67.258, 1960: 31.656, 2025: 102.459, 1995: 73.783, 1965: 37.258, 2030: 105.478, 2000: 79.178, 1970: 42.577, 2035: 107.843, 2005: 84.425, 1975: 48.075, 2040: 109.601, 2010: 89.571, 1980: 53.715, 2045: 110.717, 1950: 25.348, 2015: 94.349}, 'Mozambique': {1985: 13.293, 2050: 58.998, 1955: 6.782, 2020: 28.603, 1990: 12.989, 1960: 7.472, 2025: 32.306, 1995: 15.888, 1965: 8.301, 2030: 36.622, 2000: 17.997, 1970: 9.304, 2035: 41.434, 2005: 20.069, 1975: 10.433, 2040: 46.745, 2010: 22.417, 1980: 12.103, 2045: 52.585, 1950: 6.25, 2015: 25.303}, 'Uganda': {1985: 14.392, 2050: 128.008, 1955: 6.317, 2020: 47.691, 1990: 17.456, 1960: 7.262, 2025: 56.745, 1995: 20.69, 1965: 8.389, 2030: 67.286, 2000: 23.956, 1970: 9.743, 2035: 79.524, 2005: 28.199, 1975: 10.952, 2040: 93.632, 2010: 33.399, 1980: 12.415, 2045: 109.752, 1950: 5.522, 2015: 39.941}, 'Hungary': {1985: 10.649, 2050: 8.49, 1955: 9.825, 2020: 9.772, 1990: 10.372, 1960: 9.984, 2025: 9.615, 1995: 10.281, 1965: 10.153, 2030: 9.426, 2000: 10.147, 1970: 10.337, 2035: 9.211, 2005: 10.058, 1975: 10.532, 2040: 8.983, 2010: 9.992, 1980: 10.711, 2045: 8.742, 1950: 9.338, 2015: 9.898}, 'Niger': {1985: 6.869, 2050: 55.304, 1955: 3.559, 2020: 22.749, 1990: 7.842, 1960: 3.913, 2025: 27.063, 1995: 9.199, 1965: 4.344, 2030: 31.946, 2000: 10.951, 1970: 4.841, 2035: 37.321, 2005: 13.189, 1975: 5.419, 2040: 43.078, 2010: 15.878, 1980: 6.093, 2045: 49.107, 1950: 3.271, 2015: 19.034}, 'Isle of Man': {1985: 0.064, 2050: 0.093, 1955: 0.051, 2020: 0.09, 1990: 0.069, 1960: 0.048, 2025: 0.093, 1995: 0.072, 1965: 0.049, 2030: 0.094, 2000: 0.076, 1970: 0.053, 2035: 0.094, 2005: 0.08, 1975: 0.059, 2040: 0.094, 2010: 0.084, 1980: 0.064, 2045: 0.094, 1950: 0.055, 2015: 0.088}, 'West Bank': {1985: 1.044, 2050: 4.376, 1955: 0.788, 2020: 3.058, 1990: 1.253, 1960: 0.805, 2025: 3.328, 1995: 1.621, 1965: 0.861, 2030: 3.588, 2000: 1.98, 1970: 0.69, 2035: 3.827, 2005: 2.247, 1975: 0.806, 2040: 4.041, 2010: 2.515, 1980: 0.904, 2045: 4.225, 1950: 0.771, 2015: 2.785}, 'Brazil': {1985: 137.382, 2050: 260.692, 1955: 61.774, 2020: 222.608, 1990: 151.17, 1960: 71.695, 2025: 231.887, 1995: 163.544, 1965: 83.093, 2030: 240.173, 2000: 176.32, 1970: 95.684, 2035: 247.359, 2005: 188.993, 1975: 108.879, 2040: 253.261, 2010: 201.103, 1980: 123.02, 2045: 257.722, 1950: 53.443, 2015: 212.346}, 'Netherlands': {1985: 14.504, 2050: 17.907, 1955: 10.758, 2020: 17.28, 1990: 14.966, 1960: 11.494, 2025: 17.572, 1995: 15.476, 1965: 12.301, 2030: 17.797, 2000: 15.93, 1970: 13.043, 2035: 17.935, 2005: 16.299, 1975: 13.665, 2040: 17.982, 2010: 16.574, 1980: 14.155, 2045: 17.959, 1950: 10.121, 2015: 16.948}, 'Faroe Islands': {1985: 0.046, 2050: 0.057, 1955: 0.033, 2020: 0.052, 1990: 0.047, 1960: 0.035, 2025: 0.053, 1995: 0.044, 1965: 0.037, 2030: 0.055, 2000: 0.046, 1970: 0.039, 2035: 0.056, 2005: 0.048, 1975: 0.041, 2040: 0.056, 2010: 0.049, 1980: 0.043, 2045: 0.057, 1950: 0.032, 2015: 0.05}, 'Guinea': {1985: 5.227, 2050: 26.407, 1955: 2.787, 2020: 13.42, 1990: 6.118, 1960: 3.026, 2025: 15.241, 1995: 7.446, 1965: 3.314, 2030: 17.226, 2000: 8.35, 1970: 3.643, 2035: 19.354, 2005: 9.154, 1975: 4.019, 2040: 21.608, 2010: 10.324, 1980: 4.447, 2045: 23.966, 1950: 2.586, 2015: 11.78}, 'Panama': {1985: 2.168, 2050: 4.859, 1955: 1.011, 2020: 3.894, 1990: 2.393, 1960: 1.148, 2025: 4.118, 1995: 2.638, 1965: 1.326, 2030: 4.323, 2000: 2.9, 1970: 1.531, 2035: 4.502, 2005: 3.155, 1975: 1.749, 2040: 4.653, 2010: 3.411, 1980: 1.96, 2045: 4.773, 1950: 0.893, 2015: 3.657}, 'Costa Rica': {1985: 2.644, 2050: 6.066, 1955: 1.032, 2020: 5.098, 1990: 3.023, 1960: 1.248, 2025: 5.353, 1995: 3.445, 1965: 1.488, 2030: 5.571, 2000: 3.883, 1970: 1.736, 2035: 5.75, 2005: 4.209, 1975: 1.992, 2040: 5.891, 2010: 4.516, 1980: 2.299, 2045: 5.997, 1950: 0.867, 2015: 4.814}, 'Luxembourg': {1985: 0.367, 2050: 0.721, 1955: 0.305, 2020: 0.556, 1990: 0.383, 1960: 0.314, 2025: 0.586, 1995: 0.41, 1965: 0.332, 2030: 0.616, 2000: 0.439, 1970: 0.339, 2035: 0.644, 2005: 0.469, 1975: 0.359, 2040: 0.671, 2010: 0.498, 1980: 0.364, 2045: 0.696, 1950: 0.296, 2015: 0.527}, 'American Samoa': {1985: 0.039, 2050: 0.098, 1955: 0.02, 2020: 0.075, 1990: 0.047, 1960: 0.02, 2025: 0.079, 1995: 0.054, 1965: 0.025, 2030: 0.084, 2000: 0.058, 1970: 0.027, 2035: 0.087, 2005: 0.062, 1975: 0.03, 2040: 0.091, 2010: 0.066, 1980: 0.032, 2045: 0.095, 1950: 0.019, 2015: 0.071}, 'Bahamas': {1985: 0.228, 2050: 0.371, 1955: 0.087, 2020: 0.338, 1990: 0.245, 1960: 0.112, 2025: 0.349, 1995: 0.265, 1965: 0.139, 2030: 0.358, 2000: 0.283, 1970: 0.17, 2035: 0.365, 2005: 0.297, 1975: 0.189, 2040: 0.369, 2010: 0.31, 1980: 0.21, 2045: 0.371, 1950: 0.07, 2015: 0.325}, 'Gibraltar': {1985: 0.029, 2050: 0.028, 1955: 0.024, 2020: 0.03, 1990: 0.029, 1960: 0.024, 2025: 0.03, 1995: 0.027, 1965: 0.025, 2030: 0.03, 2000: 0.027, 1970: 0.026, 2035: 0.03, 2005: 0.028, 1975: 0.029, 2040: 0.029, 2010: 0.029, 1980: 0.029, 2045: 0.029, 1950: 0.023, 2015: 0.029}, 'Ivory Coast': {1985: 10.332, 2050: 37.112, 1955: 3.164, 2020: 25.504, 1990: 12.491, 1960: 3.576, 2025: 27.651, 1995: 14.846, 1965: 4.356, 2030: 29.724, 2000: 16.885, 1970: 5.579, 2035: 31.711, 2005: 18.921, 1975: 7.031, 2040: 33.609, 2010: 21.059, 1980: 8.593, 2045: 35.412, 1950: 2.86, 2015: 23.295}, 'Pakistan': {1985: 102.079, 2050: 290.848, 1955: 45.536, 2020: 213.719, 1990: 118.816, 1960: 51.719, 2025: 228.385, 1995: 134.185, 1965: 59.046, 2030: 242.862, 2000: 152.429, 1970: 67.491, 2035: 256.593, 2005: 169.279, 1975: 76.456, 2040: 269.151, 2010: 184.405, 1980: 85.219, 2045: 280.552, 1950: 40.382, 2015: 199.086}, 'Palau': {1985: 0.014, 2050: 0.023, 1955: 0.008, 2020: 0.022, 1990: 0.015, 1960: 0.009, 2025: 0.022, 1995: 0.017, 1965: 0.011, 2030: 0.022, 2000: 0.019, 1970: 0.012, 2035: 0.023, 2005: 0.02, 1975: 0.013, 2040: 0.023, 2010: 0.021, 1980: 0.013, 2045: 0.023, 1950: 0.007, 2015: 0.021}, 'Nigeria': {1985: 84.898, 2050: 402.426, 1955: 35.955, 2020: 207.699, 1990: 96.69, 1960: 41.55, 2025: 234.363, 1995: 109.833, 1965: 48.068, 2030: 263.626, 2000: 124.207, 1970: 55.59, 2035: 295.349, 2005: 141.852, 1975: 64.428, 2040: 329.227, 2010: 161.605, 1980: 74.829, 2045: 364.986, 1950: 31.797, 2015: 183.529}, 'Ecuador': {1985: 9.062, 2050: 21.103, 1955: 3.842, 2020: 16.905, 1990: 10.318, 1960: 4.416, 2025: 17.868, 1995: 11.266, 1965: 5.118, 2030: 18.743, 2000: 12.446, 1970: 5.939, 2035: 19.513, 2005: 13.662, 1975: 6.872, 2040: 20.165, 2010: 14.791, 1980: 7.92, 2045: 20.694, 1950: 3.37, 2015: 15.868}, 'Czech Republic': {1985: 10.31, 2050: 8.54, 1955: 9.366, 2020: 10.013, 1990: 10.31, 1960: 9.66, 2025: 9.844, 1995: 10.324, 1965: 9.777, 2030: 9.629, 2000: 10.27, 1970: 9.795, 2035: 9.382, 2005: 10.241, 1975: 10.042, 2040: 9.115, 2010: 10.202, 1980: 10.289, 2045: 8.833, 1950: 8.925, 2015: 10.13}, 'Brunei': {1985: 0.218, 2050: 0.638, 1955: 0.061, 2020: 0.464, 1990: 0.253, 1960: 0.083, 2025: 0.499, 1995: 0.288, 1965: 0.102, 2030: 0.531, 2000: 0.325, 1970: 0.128, 2035: 0.562, 2005: 0.361, 1975: 0.156, 2040: 0.589, 2010: 0.395, 1980: 0.185, 2045: 0.615, 1950: 0.045, 2015: 0.43}, 'Belarus': {1985: 9.982, 2050: 7.739, 1955: 7.781, 2020: 9.249, 1990: 10.201, 1960: 8.168, 2025: 9.033, 1995: 10.205, 1965: 8.591, 2030: 8.798, 2000: 10.034, 1970: 9.027, 2035: 8.552, 2005: 9.809, 1975: 9.36, 2040: 8.296, 2010: 9.613, 1980: 9.644, 2045: 8.026, 1950: 7.722, 2015: 9.439}, 'Iran': {1985: 48.619, 2050: 100.045, 1955: 18.739, 2020: 86.543, 1990: 58.1, 1960: 21.6, 2025: 90.481, 1995: 64.217, 1965: 25.04, 2030: 93.458, 2000: 68.632, 1970: 28.994, 2035: 95.772, 2005: 72.283, 1975: 33.467, 2040: 97.685, 2010: 76.923, 1980: 39.709, 2045: 99.181, 1950: 16.357, 2015: 81.824}, 'Algeria': {1985: 22.008, 2050: 44.163, 1955: 9.842, 2020: 38.594, 1990: 25.089, 1960: 10.909, 2025: 40.29, 1995: 28.089, 1965: 11.963, 2030: 41.641, 2000: 30.429, 1970: 13.932, 2035: 42.663, 2005: 32.561, 1975: 16.14, 2040: 43.425, 2010: 34.586, 1980: 18.806, 2045: 43.94, 1950: 8.893, 2015: 36.64}, 'El Salvador': {1985: 4.671, 2050: 6.181, 1955: 2.221, 2020: 6.217, 1990: 5.11, 1960: 2.582, 2025: 6.288, 1995: 5.48, 1965: 3.018, 2030: 6.34, 2000: 5.85, 1970: 3.604, 2035: 6.353, 2005: 5.956, 1975: 4.073, 2040: 6.324, 2010: 6.052, 1980: 4.57, 2045: 6.262, 1950: 1.94, 2015: 6.141}, 'Tuvalu': {1985: 0.008, 2050: 0.013, 1955: 0.005, 2020: 0.011, 1990: 0.009, 1960: 0.005, 2025: 0.012, 1995: 0.01, 1965: 0.006, 2030: 0.012, 2000: 0.01, 1970: 0.006, 2035: 0.013, 2005: 0.01, 1975: 0.006, 2040: 0.013, 2010: 0.01, 1980: 0.007, 2045: 0.013, 1950: 0.005, 2015: 0.011}, 'Marshall Islands': {1985: 0.038, 2050: 0.103, 1955: 0.013, 2020: 0.078, 1990: 0.046, 1960: 0.015, 2025: 0.083, 1995: 0.05, 1965: 0.018, 2030: 0.088, 2000: 0.053, 1970: 0.022, 2035: 0.093, 2005: 0.059, 1975: 0.026, 2040: 0.097, 2010: 0.066, 1980: 0.031, 2045: 0.1, 1950: 0.011, 2015: 0.072}, 'Chile': {1985: 12.068, 2050: 19.387, 1955: 6.743, 2020: 18.058, 1990: 13.129, 1960: 7.585, 2025: 18.585, 1995: 14.207, 1965: 8.51, 2030: 18.984, 2000: 15.156, 1970: 9.369, 2035: 19.251, 2005: 15.995, 1975: 10.252, 2040: 19.396, 2010: 16.746, 1980: 11.094, 2045: 19.437, 1950: 6.091, 2015: 17.435}, 'Puerto Rico': {1985: 3.382, 2050: 3.68, 1955: 2.25, 2020: 4.051, 1990: 3.537, 1960: 2.358, 2025: 4.055, 1995: 3.683, 1965: 2.597, 2030: 4.032, 2000: 3.814, 1970: 2.722, 2035: 3.979, 2005: 3.911, 1975: 2.935, 2040: 3.9, 2010: 3.979, 1980: 3.21, 2045: 3.798, 1950: 2.218, 2015: 4.024}, 'Belgium': {1985: 9.858, 2050: 9.883, 1955: 8.868, 2020: 10.465, 1990: 9.969, 1960: 9.119, 2025: 10.453, 1995: 10.155, 1965: 9.448, 2030: 10.41, 2000: 10.264, 1970: 9.638, 2035: 10.327, 2005: 10.364, 1975: 9.795, 2040: 10.206, 2010: 10.423, 1980: 9.847, 2045: 10.054, 1950: 8.639, 2015: 10.454}, 'Kiribati': {1985: 0.062, 2050: 0.14, 1955: 0.037, 2020: 0.112, 1990: 0.071, 1960: 0.041, 2025: 0.118, 1995: 0.077, 1965: 0.045, 2030: 0.123, 2000: 0.085, 1970: 0.049, 2035: 0.129, 2005: 0.093, 1975: 0.053, 2040: 0.133, 2010: 0.099, 1980: 0.058, 2045: 0.137, 1950: 0.033, 2015: 0.106}, 'Haiti': {1985: 6.12, 2050: 13.353, 1955: 3.365, 2020: 10.693, 1990: 6.798, 1960: 3.697, 2025: 11.252, 1995: 7.57, 1965: 4.094, 2030: 11.784, 2000: 8.413, 1970: 4.541, 2035: 12.267, 2005: 9.205, 1975: 4.973, 2040: 12.69, 2010: 9.649, 1980: 5.508, 2045: 13.049, 1950: 3.097, 2015: 10.11}, 'Belize': {1985: 0.166, 2050: 0.544, 1955: 0.077, 2020: 0.38, 1990: 0.191, 1960: 0.092, 2025: 0.411, 1995: 0.217, 1965: 0.107, 2030: 0.441, 2000: 0.248, 1970: 0.122, 2035: 0.469, 2005: 0.281, 1975: 0.136, 2040: 0.496, 2010: 0.315, 1980: 0.144, 2045: 0.521, 1950: 0.066, 2015: 0.347}, 'Hong Kong': {1985: 5.456, 2050: 6.173, 1955: 2.49, 2020: 7.328, 1990: 5.688, 1960: 3.075, 2025: 7.355, 1995: 6.225, 1965: 3.598, 2030: 7.294, 2000: 6.659, 1970: 3.959, 2035: 7.131, 2005: 6.899, 1975: 4.396, 2040: 6.873, 2010: 7.09, 1980: 5.063, 2045: 6.544, 1950: 2.237, 2015: 7.235}, 'Sierra Leone': {1985: 3.703, 2050: 13.594, 1955: 2.233, 2020: 6.625, 1990: 4.228, 1960: 2.396, 2025: 7.5, 1995: 3.881, 1965: 2.582, 2030: 8.5, 2000: 3.809, 1970: 2.789, 2035: 9.61, 2005: 4.708, 1975: 3.03, 2040: 10.831, 2010: 5.246, 1980: 3.335, 2045: 12.16, 1950: 2.087, 2015: 5.879}, 'Georgia': {1985: 5.193, 2050: 3.785, 1955: 3.827, 2020: 4.44, 1990: 5.426, 1960: 4.147, 2025: 4.341, 1995: 5.013, 1965: 4.465, 2030: 4.231, 2000: 4.777, 1970: 4.694, 2035: 4.118, 2005: 4.677, 1975: 4.898, 2040: 4.006, 2010: 4.601, 1980: 5.046, 2045: 3.896, 1950: 3.516, 2015: 4.525}, 'Gambia': {1985: 0.773, 2050: 3.21, 1955: 0.306, 2020: 2.174, 1990: 0.951, 1960: 0.352, 2025: 2.369, 1995: 1.161, 1965: 0.412, 2030: 2.554, 2000: 1.357, 1970: 0.485, 2035: 2.73, 2005: 1.548, 1975: 0.566, 2040: 2.899, 2010: 1.755, 1980: 0.652, 2045: 3.059, 1950: 0.271, 2015: 1.968}, 'Philippines': {1985: 57.706, 2050: 171.964, 1955: 24.553, 2020: 119.329, 1990: 65.088, 1960: 28.529, 2025: 128.921, 1995: 72.597, 1965: 33.268, 2030: 138.333, 2000: 81.222, 1970: 38.604, 2035: 147.466, 2005: 90.436, 1975: 44.337, 2040: 156.188, 2010: 99.9, 1980: 50.94, 2045: 164.384, 1950: 21.131, 2015: 109.616}, 'Moldova': {1985: 4.148, 2050: 2.261, 1955: 2.622, 2020: 3.364, 1990: 4.374, 1960: 2.999, 2025: 3.177, 1995: 4.355, 1965: 3.334, 2030: 2.985, 2000: 4.18, 1970: 3.595, 2035: 2.793, 2005: 3.948, 1975: 3.847, 2040: 2.608, 2010: 3.732, 1980: 3.996, 2045: 2.432, 1950: 2.336, 2015: 3.547}, 'Morocco': {1985: 21.644, 2050: 42.026, 1955: 10.782, 2020: 34.956, 1990: 24.0, 1960: 12.423, 2025: 36.484, 1995: 26.148, 1965: 14.066, 2030: 37.887, 2000: 28.113, 1970: 15.909, 2035: 39.148, 2005: 29.901, 1975: 17.687, 2040: 40.267, 2010: 31.627, 1980: 19.487, 2045: 41.23, 1950: 9.343, 2015: 33.323}, 'Croatia': {1985: 4.458, 2050: 3.864, 1955: 3.956, 2020: 4.427, 1990: 4.508, 1960: 4.036, 2025: 4.374, 1995: 4.497, 1965: 4.133, 2030: 4.301, 2000: 4.411, 1970: 4.205, 2035: 4.209, 2005: 4.496, 1975: 4.255, 2040: 4.104, 2010: 4.487, 1980: 4.383, 2045: 3.988, 1950: 0.004, 2015: 4.465}, 'Mongolia': {1985: 1.908, 2050: 4.34, 1955: 0.844, 2020: 3.535, 1990: 2.218, 1960: 0.955, 2025: 3.725, 1995: 2.447, 1965: 1.09, 2030: 3.89, 2000: 2.664, 1970: 1.248, 2035: 4.036, 2005: 2.866, 1975: 1.446, 2040: 4.165, 2010: 3.087, 1980: 1.662, 2045: 4.269, 1950: 0.779, 2015: 3.318}, 'Guernsey': {1985: 0.055, 2050: 0.067, 1955: 0.046, 2020: 0.067, 1990: 0.063, 1960: 0.047, 2025: 0.068, 1995: 0.061, 1965: 0.05, 2030: 0.068, 2000: 0.062, 1970: 0.053, 2035: 0.068, 2005: 0.063, 1975: 0.054, 2040: 0.068, 2010: 0.065, 1980: 0.053, 2045: 0.067, 1950: 0.045, 2015: 0.066}, 'Thailand': {1985: 51.342, 2050: 69.611, 1955: 23.451, 2020: 69.558, 1990: 55.197, 1960: 27.513, 2025: 70.644, 1995: 58.883, 1965: 32.062, 2030: 71.29, 2000: 61.904, 1970: 37.091, 2035: 71.462, 2005: 64.235, 1975: 42.272, 2040: 71.216, 2010: 66.336, 1980: 47.026, 2045: 70.584, 1950: 20.042, 2015: 68.119}, 'Switzerland': {1985: 6.564, 2050: 7.296, 1955: 4.98, 2020: 7.751, 1990: 6.837, 1960: 5.362, 2025: 7.774, 1995: 7.157, 1965: 5.943, 2030: 7.756, 2000: 7.267, 1970: 6.267, 2035: 7.691, 2005: 7.489, 1975: 6.404, 2040: 7.586, 2010: 7.623, 1980: 6.385, 2045: 7.451, 1950: 4.694, 2015: 7.698}, 'Grenada': {1985: 0.093, 2050: 0.114, 1955: 0.085, 2020: 0.113, 1990: 0.094, 1960: 0.09, 2025: 0.115, 1995: 0.098, 1965: 0.093, 2030: 0.116, 2000: 0.102, 1970: 0.095, 2035: 0.116, 2005: 0.105, 1975: 0.096, 2040: 0.115, 2010: 0.108, 1980: 0.09, 2045: 0.115, 1950: 0.076, 2015: 0.111}, 'Iraq': {1985: 15.694, 2050: 56.316, 1955: 5.903, 2020: 36.889, 1990: 18.14, 1960: 6.822, 2025: 40.387, 1995: 19.564, 1965: 7.971, 2030: 43.831, 2000: 22.679, 1970: 9.414, 2035: 47.207, 2005: 26.076, 1975: 11.118, 2040: 50.459, 2010: 29.672, 1980: 13.233, 2045: 53.516, 1950: 5.163, 2015: 33.31}, 'Portugal': {1985: 9.897, 2050: 9.933, 1955: 8.693, 2020: 10.842, 1990: 9.923, 1960: 9.037, 2025: 10.806, 1995: 10.066, 1965: 9.129, 2030: 10.731, 2000: 10.336, 1970: 9.044, 2035: 10.615, 2005: 10.566, 1975: 9.411, 2040: 10.447, 2010: 10.736, 1980: 9.778, 2045: 10.219, 1950: 8.443, 2015: 10.825}, 'Estonia': {1985: 1.538, 2050: 0.862, 1955: 1.154, 2020: 1.203, 1990: 1.569, 1960: 1.211, 2025: 1.149, 1995: 1.447, 1965: 1.288, 2030: 1.092, 2000: 1.38, 1970: 1.363, 2035: 1.034, 2005: 1.333, 1975: 1.432, 2040: 0.978, 2010: 1.291, 1980: 1.482, 2045: 0.921, 1950: 1.096, 2015: 1.249}, 'Uruguay': {1985: 3.019, 2050: 3.495, 1955: 2.353, 2020: 3.388, 1990: 3.085, 1960: 2.531, 2025: 3.432, 1995: 3.15, 1965: 2.693, 2030: 3.467, 2000: 3.22, 1970: 2.824, 2035: 3.485, 2005: 3.265, 1975: 2.844, 2040: 3.5, 2010: 3.301, 1980: 2.93, 2045: 3.503, 1950: 2.194, 2015: 3.342}, 'Mexico': {1985: 76.767, 2050: 147.908, 1955: 32.93, 2020: 124.654, 1990: 84.914, 1960: 38.579, 2025: 130.199, 1995: 92.88, 1965: 45.142, 2030: 135.172, 2000: 99.927, 1970: 52.775, 2035: 139.457, 2005: 106.203, 1975: 60.678, 2040: 143.026, 2010: 112.469, 1980: 68.347, 2045: 145.856, 1950: 28.485, 2015: 118.689}, 'Lebanon': {1985: 3.171, 2050: 4.155, 1955: 1.561, 2020: 4.243, 1990: 3.44, 1960: 1.786, 2025: 4.307, 1995: 3.654, 1965: 2.058, 2030: 4.335, 2000: 3.791, 1970: 2.383, 2035: 4.33, 2005: 3.892, 1975: 2.691, 2040: 4.298, 2010: 4.125, 1980: 2.899, 2045: 4.24, 1950: 1.364, 2015: 4.151}, 'Uzbekistan': {1985: 18.215, 2050: 35.116, 1955: 7.232, 2020: 30.565, 1990: 20.53, 1960: 8.531, 2025: 31.824, 1995: 23.067, 1965: 10.206, 2030: 32.855, 2000: 25.042, 1970: 11.94, 2035: 33.653, 2005: 26.54, 1975: 13.988, 2040: 34.278, 2010: 27.866, 1980: 15.994, 2045: 34.768, 1950: 6.293, 2015: 29.2}, 'Tunisia': {1985: 7.364, 2050: 12.18, 1955: 3.846, 2020: 11.494, 1990: 8.207, 1960: 4.149, 2025: 11.85, 1995: 8.947, 1965: 4.566, 2030: 12.086, 2000: 9.508, 1970: 5.099, 2035: 12.222, 2005: 10.013, 1975: 5.704, 2040: 12.284, 2010: 10.525, 1980: 6.443, 2045: 12.277, 1950: 3.517, 2015: 11.037}, 'Djibouti': {1985: 0.382, 2050: 1.396, 1955: 0.09, 2020: 0.922, 1990: 0.499, 1960: 0.111, 2025: 1.017, 1995: 0.553, 1965: 0.142, 2030: 1.109, 2000: 0.669, 1970: 0.18, 2035: 1.194, 2005: 0.666, 1975: 0.227, 2040: 1.27, 2010: 0.741, 1980: 0.327, 2045: 1.337, 1950: 0.079, 2015: 0.828}, 'Rwanda': {1985: 5.987, 2050: 27.506, 1955: 2.698, 2020: 14.327, 1990: 6.999, 1960: 3.032, 2025: 16.081, 1995: 5.473, 1965: 3.265, 2030: 17.983, 2000: 8.398, 1970: 3.769, 2035: 20.08, 2005: 9.611, 1975: 4.357, 2040: 22.378, 2010: 11.056, 1980: 5.14, 2045: 24.859, 1950: 2.439, 2015: 12.662}, 'Antigua and Barbuda': {1985: 0.064, 2050: 0.123, 1955: 0.051, 2020: 0.098, 1990: 0.064, 1960: 0.055, 2025: 0.104, 1995: 0.069, 1965: 0.059, 2030: 0.109, 2000: 0.075, 1970: 0.066, 2035: 0.114, 2005: 0.081, 1975: 0.068, 2040: 0.117, 2010: 0.087, 1980: 0.069, 2045: 0.12, 1950: 0.046, 2015: 0.092}, 'Spain': {1985: 38.535, 2050: 52.491, 1955: 29.319, 2020: 50.016, 1990: 39.351, 1960: 30.641, 2025: 51.415, 1995: 39.765, 1965: 32.085, 2030: 52.445, 2000: 40.589, 1970: 33.876, 2035: 53.139, 2005: 43.704, 1975: 35.564, 2040: 53.45, 2010: 46.506, 1980: 37.488, 2045: 53.27, 1950: 28.063, 2015: 48.146}, 'Colombia': {1985: 29.748, 2050: 56.228, 1955: 13.588, 2020: 49.085, 1990: 33.147, 1960: 15.953, 2025: 51.195, 1995: 36.532, 1965: 18.646, 2030: 52.965, 2000: 38.91, 1970: 21.43, 2035: 54.344, 2005: 41.488, 1975: 24.125, 2040: 55.335, 2010: 44.205, 1980: 26.631, 2045: 55.956, 1950: 11.592, 2015: 46.737}, 'Burundi': {1985: 4.922, 2050: 27.149, 1955: 2.576, 2020: 13.429, 1990: 5.536, 1960: 2.815, 2025: 15.465, 1995: 6.165, 1965: 3.171, 2030: 17.65, 2000: 6.823, 1970: 3.522, 2035: 19.95, 2005: 8.162, 1975: 3.676, 2040: 22.321, 2010: 9.863, 1980: 4.298, 2045: 24.729, 1950: 2.363, 2015: 11.574}, 'Taiwan': {1985: 19.337, 2050: 20.161, 1955: 9.486, 2020: 23.278, 1990: 20.278, 1960: 11.209, 2025: 23.214, 1995: 21.29, 1965: 12.978, 2030: 22.978, 2000: 22.183, 1970: 14.598, 2035: 22.532, 2005: 22.701, 1975: 16.122, 2040: 21.887, 2010: 23.025, 1980: 17.848, 2045: 21.083, 1950: 7.981, 2015: 23.212}, 'Fiji': {1985: 0.699, 2050: 1.014, 1955: 0.332, 2020: 0.936, 1990: 0.74, 1960: 0.393, 2025: 0.956, 1995: 0.773, 1965: 0.463, 2030: 0.972, 2000: 0.805, 1970: 0.521, 2035: 0.986, 2005: 0.837, 1975: 0.576, 2040: 0.998, 2010: 0.876, 1980: 0.635, 2045: 1.008, 1950: 0.287, 2015: 0.909}, 'Barbados': {1985: 0.257, 2050: 0.282, 1955: 0.227, 2020: 0.295, 1990: 0.262, 1960: 0.232, 2025: 0.297, 1995: 0.268, 1965: 0.235, 2030: 0.298, 2000: 0.274, 1970: 0.239, 2035: 0.296, 2005: 0.28, 1975: 0.247, 2040: 0.293, 2010: 0.286, 1980: 0.252, 2045: 0.288, 1950: 0.211, 2015: 0.291}, 'Curaao': {1985: 0.154, 2050: 0.15, 1955: 0.112, 2020: 0.151, 1990: 0.145, 1960: 0.124, 2025: 0.154, 1995: 0.142, 1965: 0.134, 2030: 0.155, 2000: 0.134, 1970: 0.145, 2035: 0.155, 2005: 0.136, 1975: 0.149, 2040: 0.154, 2010: 0.143, 1980: 0.148, 2045: 0.152, 1950: 0.101, 2015: 0.148}, 'Madagascar': {1985: 10.029, 2050: 56.514, 1955: 5.003, 2020: 28.374, 1990: 11.633, 1960: 5.482, 2025: 32.431, 1995: 13.532, 1965: 6.07, 2030: 36.797, 2000: 15.742, 1970: 6.766, 2035: 41.433, 2005: 18.312, 1975: 7.604, 2040: 46.296, 2010: 21.282, 1980: 8.691, 2045: 51.344, 1950: 4.62, 2015: 24.651}, 'Italy': {1985: 56.718, 2050: 61.416, 1955: 48.633, 2020: 62.403, 1990: 56.713, 1960: 50.198, 2025: 62.591, 1995: 57.295, 1965: 51.987, 2030: 62.623, 2000: 57.784, 1970: 53.661, 2035: 62.531, 2005: 59.038, 1975: 55.572, 2040: 62.319, 2010: 60.749, 1980: 56.451, 2045: 61.956, 1950: 47.105, 2015: 61.855}, 'Bhutan': {1985: 0.529, 2050: 0.952, 1955: 0.186, 2020: 0.782, 1990: 0.615, 1960: 0.212, 2025: 0.82, 1995: 0.566, 1965: 0.254, 2030: 0.855, 2000: 0.606, 1970: 0.309, 2035: 0.885, 2005: 0.655, 1975: 0.373, 2040: 0.912, 2010: 0.7, 1980: 0.446, 2045: 0.934, 1950: 0.164, 2015: 0.742}, 'Sudan': {1985: 23.145, 2050: 97.165, 1955: 9.233, 2020: 56.292, 1990: 25.888, 1960: 10.589, 2025: 63.117, 1995: 29.953, 1965: 12.086, 2030: 69.996, 2000: 34.109, 1970: 13.788, 2035: 76.882, 2005: 38.363, 1975: 16.156, 2040: 83.745, 2010: 43.94, 1980: 19.482, 2045: 90.536, 1950: 8.051, 2015: 49.78}, 'Nepal': {1985: 16.57, 2050: 45.985, 1955: 9.479, 2020: 34.209, 1990: 18.918, 1960: 10.035, 2025: 36.623, 1995: 21.877, 1965: 10.862, 2030: 38.886, 2000: 24.818, 1970: 11.919, 2035: 40.939, 2005: 27.094, 1975: 13.156, 2040: 42.776, 2010: 28.952, 1980: 14.665, 2045: 44.447, 1950: 8.99, 2015: 31.551}, 'Malta': {1985: 0.347, 2050: 0.396, 1955: 0.314, 2020: 0.419, 1990: 0.359, 1960: 0.329, 2025: 0.421, 1995: 0.377, 1965: 0.319, 2030: 0.42, 2000: 0.39, 1970: 0.326, 2035: 0.416, 2005: 0.399, 1975: 0.328, 2040: 0.41, 2010: 0.407, 1980: 0.364, 2045: 0.403, 1950: 0.312, 2015: 0.414}, 'Maldives': {1985: 0.177, 2050: 0.444, 1955: 0.08, 2020: 0.392, 1990: 0.217, 1960: 0.092, 2025: 0.389, 1995: 0.261, 1965: 0.098, 2030: 0.401, 2000: 0.3, 1970: 0.115, 2035: 0.415, 2005: 0.336, 1975: 0.133, 2040: 0.427, 2010: 0.396, 1980: 0.153, 2045: 0.437, 1950: 0.079, 2015: 0.393}, 'Suriname': {1985: 0.394, 2050: 0.718, 1955: 0.24, 2020: 0.61, 1990: 0.416, 1960: 0.284, 2025: 0.637, 1995: 0.428, 1965: 0.336, 2030: 0.662, 2000: 0.464, 1970: 0.372, 2035: 0.683, 2005: 0.504, 1975: 0.363, 2040: 0.7, 2010: 0.546, 1980: 0.354, 2045: 0.711, 1950: 0.208, 2015: 0.58}, 'Anguilla': {1985: 0.007, 2050: 0.027, 1955: 0.005, 2020: 0.018, 1990: 0.008, 1960: 0.006, 2025: 0.02, 1995: 0.01, 1965: 0.006, 2030: 0.021, 2000: 0.011, 1970: 0.006, 2035: 0.023, 2005: 0.013, 1975: 0.006, 2040: 0.024, 2010: 0.015, 1980: 0.007, 2045: 0.026, 1950: 0.005, 2015: 0.016}, 'Venezuela': {1985: 16.998, 2050: 40.256, 1955: 6.17, 2020: 31.276, 1990: 19.325, 1960: 7.556, 2025: 33.189, 1995: 21.549, 1965: 9.068, 2030: 34.958, 2000: 23.493, 1970: 10.758, 2035: 36.543, 2005: 25.269, 1975: 12.675, 2040: 37.942, 2010: 27.223, 1980: 14.768, 2045: 39.173, 1950: 5.009, 2015: 29.275}, 'Israel': {1985: 4.068, 2050: 10.828, 1955: 1.772, 2020: 8.479, 1990: 4.478, 1960: 2.141, 2025: 8.984, 1995: 5.353, 1965: 2.578, 2030: 9.459, 2000: 6.115, 1970: 2.903, 2035: 9.898, 2005: 6.743, 1975: 3.354, 2040: 10.28, 2010: 7.354, 1980: 3.737, 2045: 10.593, 1950: 1.286, 2015: 7.935}, 'Indonesia': {1985: 166.119, 2050: 313.021, 1955: 90.255, 2020: 267.532, 1990: 181.77, 1960: 100.146, 2025: 278.503, 1995: 197.764, 1965: 110.754, 2030: 288.678, 2000: 213.829, 1970: 122.292, 2035: 297.585, 2005: 228.896, 1975: 135.272, 2040: 304.686, 2010: 242.968, 1980: 150.467, 2045: 309.799, 1950: 82.978, 2015: 255.759}, 'Iceland': {1985: 0.241, 2050: 0.351, 1955: 0.158, 2020: 0.329, 1990: 0.255, 1960: 0.176, 2025: 0.338, 1995: 0.268, 1965: 0.192, 2030: 0.344, 2000: 0.281, 1970: 0.204, 2035: 0.349, 2005: 0.297, 1975: 0.218, 2040: 0.351, 2010: 0.309, 1980: 0.228, 2045: 0.352, 1950: 0.143, 2015: 0.319}, 'Zambia': {1985: 6.716, 2050: 38.372, 1955: 2.869, 2020: 18.065, 1990: 7.858, 1960: 3.254, 2025: 20.672, 1995: 9.021, 1965: 3.694, 2030: 23.491, 2000: 10.345, 1970: 4.248, 2035: 26.611, 2005: 11.71, 1975: 4.897, 2040: 30.121, 2010: 13.46, 1980: 5.643, 2045: 34.04, 1950: 2.553, 2015: 15.644}, 'Senegal': {1985: 6.378, 2050: 27.244, 1955: 2.927, 2020: 15.736, 1990: 7.348, 1960: 3.27, 2025: 17.581, 1995: 8.378, 1965: 3.744, 2030: 19.485, 2000: 9.469, 1970: 4.318, 2035: 21.43, 2005: 10.804, 1975: 4.989, 2040: 23.39, 2010: 12.323, 1980: 5.611, 2045: 25.336, 1950: 2.654, 2015: 13.976}, 'Papua New Guinea': {1985: 3.229, 2050: 10.11, 1955: 1.545, 2020: 7.259, 1990: 3.683, 1960: 1.718, 2025: 7.823, 1995: 4.216, 1965: 1.941, 2030: 8.359, 2000: 4.813, 1970: 2.214, 2035: 8.859, 2005: 5.44, 1975: 2.504, 2040: 9.317, 2010: 6.065, 1980: 2.846, 2045: 9.733, 1950: 1.412, 2015: 6.672}, 'Trinidad and Tobago': {1985: 1.189, 2050: 1.024, 1955: 0.721, 2020: 1.209, 1990: 1.255, 1960: 0.841, 2025: 1.184, 1995: 1.264, 1965: 0.939, 2030: 1.152, 2000: 1.252, 1970: 0.955, 2035: 1.118, 2005: 1.237, 1975: 1.007, 2040: 1.085, 2010: 1.229, 1980: 1.091, 2045: 1.054, 1950: 0.632, 2015: 1.222}, 'Zimbabwe': {1985: 8.56, 2050: 25.198, 1955: 3.409, 2020: 15.832, 1990: 10.156, 1960: 4.011, 2025: 17.37, 1995: 11.159, 1965: 4.685, 2030: 18.82, 2000: 11.82, 1970: 5.515, 2035: 20.281, 2005: 11.639, 1975: 6.342, 2040: 21.839, 2010: 11.652, 1980: 7.17, 2045: 23.491, 1950: 2.853, 2015: 14.23}, 'Germany': {1985: 77.685, 2050: 71.542, 1955: 70.196, 2020: 80.16, 1990: 79.38, 1960: 72.481, 2025: 79.226, 1995: 81.654, 1965: 75.639, 2030: 78.022, 2000: 82.184, 1970: 77.783, 2035: 76.589, 2005: 82.439, 1975: 78.682, 2040: 74.984, 2010: 81.644, 1980: 78.298, 2045: 73.276, 1950: 68.375, 2015: 80.854}, 'Vanuatu': {1985: 0.135, 2050: 0.312, 1955: 0.059, 2020: 0.251, 1990: 0.154, 1960: 0.066, 2025: 0.264, 1995: 0.173, 1965: 0.074, 2030: 0.276, 2000: 0.19, 1970: 0.085, 2035: 0.287, 2005: 0.206, 1975: 0.1, 2040: 0.296, 2010: 0.222, 1980: 0.117, 2045: 0.305, 1950: 0.052, 2015: 0.236}, 'Denmark': {1985: 5.114, 2050: 5.575, 1955: 4.439, 2020: 5.642, 1990: 5.141, 1960: 4.581, 2025: 5.698, 1995: 5.233, 1965: 4.758, 2030: 5.73, 2000: 5.337, 1970: 4.929, 2035: 5.728, 2005: 5.432, 1975: 5.06, 2040: 5.691, 2010: 5.516, 1980: 5.123, 2045: 5.635, 1950: 4.271, 2015: 5.582}, 'Kazakhstan': {1985: 15.999, 2050: 22.237, 1955: 7.977, 2020: 19.092, 1990: 16.775, 1960: 9.982, 2025: 19.809, 1995: 16.39, 1965: 11.902, 2030: 20.378, 2000: 15.687, 1970: 13.106, 2035: 20.886, 2005: 16.123, 1975: 14.157, 2040: 21.401, 2010: 17.085, 1980: 15.0, 2045: 21.877, 1950: 6.693, 2015: 18.157}, 'Poland': {1985: 37.226, 2050: 32.085, 1955: 27.221, 2020: 37.949, 1990: 38.119, 1960: 29.59, 2025: 37.35, 1995: 38.601, 1965: 31.262, 2030: 36.531, 2000: 38.654, 1970: 32.526, 2035: 35.556, 2005: 38.558, 1975: 33.969, 2040: 34.481, 2010: 38.464, 1980: 35.578, 2045: 33.322, 1950: 24.824, 2015: 38.302}, 'Eritrea': {1985: 2.941, 2050: 11.381, 1955: 1.499, 2020: 7.26, 1990: 3.138, 1960: 1.615, 2025: 7.987, 1995: 3.565, 1965: 1.746, 2030: 8.71, 2000: 4.197, 1970: 2.16, 2035: 9.42, 2005: 5.07, 1975: 2.421, 2040: 10.109, 2010: 5.793, 1980: 2.569, 2045: 10.765, 1950: 1.403, 2015: 6.528}, 'Ireland': {1985: 3.54, 2050: 6.334, 1955: 2.916, 2020: 5.177, 1990: 3.508, 1960: 2.832, 2025: 5.418, 1995: 3.614, 1965: 2.876, 2030: 5.631, 2000: 3.822, 1970: 2.95, 2035: 5.832, 2005: 4.199, 1975: 3.177, 2040: 6.023, 2010: 4.623, 1980: 3.401, 2045: 6.195, 1950: 2.963, 2015: 4.892}, 'Mayotte': {1985: 0.071, 2050: 0.267, 1955: 0.024, 2020: 0.229, 1990: 0.087, 1960: 0.028, 2025: 0.236, 1995: 0.119, 1965: 0.032, 2030: 0.244, 2000: 0.157, 1970: 0.037, 2035: 0.253, 2005: 0.186, 1975: 0.043, 2040: 0.261, 2010: 0.206, 1980: 0.053, 2045: 0.265, 1950: 0.022, 2015: 0.22}, 'Montserrat': {1985: 0.011, 2050: 0.006, 1955: 0.013, 2020: 0.005, 1990: 0.011, 1960: 0.012, 2025: 0.006, 1995: 0.01, 1965: 0.012, 2030: 0.006, 2000: 0.004, 1970: 0.012, 2035: 0.006, 2005: 0.005, 1975: 0.012, 2040: 0.006, 2010: 0.005, 1980: 0.012, 2045: 0.006, 1950: 0.013, 2015: 0.005}, 'New Caledonia': {1985: 0.153, 2050: 0.371, 1955: 0.065, 2020: 0.29, 1990: 0.169, 1960: 0.079, 2025: 0.307, 1995: 0.192, 1965: 0.09, 2030: 0.324, 2000: 0.211, 1970: 0.112, 2035: 0.338, 2005: 0.232, 1975: 0.133, 2040: 0.35, 2010: 0.252, 1980: 0.139, 2045: 0.361, 1950: 0.055, 2015: 0.272}, 'Macedonia': {1985: 1.845, 2050: 1.991, 1955: 1.34, 2020: 2.113, 1990: 1.861, 1960: 1.366, 2025: 2.12, 1995: 1.954, 1965: 1.47, 2030: 2.114, 2000: 2.015, 1970: 1.574, 2035: 2.096, 2005: 2.045, 1975: 1.684, 2040: 2.069, 2010: 2.072, 1980: 1.792, 2045: 2.033, 1950: 1.225, 2015: 2.096}, 'North Korea': {1985: 18.831, 2050: 26.969, 1955: 8.863, 2020: 25.643, 1990: 20.451, 1960: 10.448, 2025: 26.242, 1995: 22.107, 1965: 11.964, 2030: 26.688, 2000: 22.785, 1970: 14.062, 2035: 26.955, 2005: 23.621, 1975: 16.014, 2040: 27.074, 2010: 24.326, 1980: 17.39, 2045: 27.073, 1950: 9.471, 2015: 24.983}, 'Sri Lanka': {1985: 15.847, 2050: 25.167, 1955: 8.694, 2020: 22.889, 1990: 16.862, 1960: 9.913, 2025: 23.563, 1995: 17.941, 1965: 11.261, 2030: 24.12, 2000: 19.041, 1970: 12.619, 2035: 24.584, 2005: 20.103, 1975: 13.779, 2040: 24.935, 2010: 21.084, 1980: 15.056, 2045: 25.132, 1950: 7.533, 2015: 22.053}, 'Latvia': {1985: 2.606, 2050: 1.544, 1955: 2.002, 2020: 2.077, 1990: 2.664, 1960: 2.115, 2025: 1.993, 1995: 2.488, 1965: 2.254, 2030: 1.903, 2000: 2.376, 1970: 2.361, 2035: 1.814, 2005: 2.29, 1975: 2.462, 2040: 1.728, 2010: 2.218, 1980: 2.525, 2045: 1.638, 1950: 1.936, 2015: 2.152}, 'Guyana': {1985: 0.762, 2050: 0.888, 1955: 0.491, 2020: 0.754, 1990: 0.772, 1960: 0.571, 2025: 0.786, 1995: 0.769, 1965: 0.64, 2030: 0.819, 2000: 0.786, 1970: 0.715, 2035: 0.846, 2005: 0.776, 1975: 0.768, 2040: 0.867, 2010: 0.748, 1980: 0.759, 2045: 0.88, 1950: 0.428, 2015: 0.739}, 'Syria': {1985: 10.466, 2050: 33.658, 1955: 3.938, 2020: 24.744, 1990: 12.5, 1960: 4.533, 2025: 26.536, 1995: 14.449, 1965: 5.326, 2030: 28.224, 2000: 16.471, 1970: 6.258, 2035: 29.804, 2005: 18.563, 1975: 7.407, 2040: 31.257, 2010: 22.198, 1980: 8.752, 2045: 32.551, 1950: 3.495, 2015: 22.879}, 'Sint Maarten': {1985: 0.019, 2050: 0.053, 1955: 0.002, 2020: 0.044, 1990: 0.029, 1960: 0.003, 2025: 0.047, 1995: 0.032, 1965: 0.004, 2030: 0.049, 2000: 0.031, 1970: 0.006, 2035: 0.05, 2005: 0.034, 1975: 0.01, 2040: 0.052, 2010: 0.038, 1980: 0.012, 2045: 0.052, 1950: 0.002, 2015: 0.041}, 'Honduras': {1985: 4.077, 2050: 12.949, 1955: 1.662, 2020: 9.465, 1990: 4.794, 1960: 1.952, 2025: 10.144, 1995: 5.551, 1965: 2.329, 2030: 10.785, 2000: 6.359, 1970: 2.761, 2035: 11.389, 2005: 7.193, 1975: 2.858, 2040: 11.95, 2010: 7.989, 1980: 3.402, 2045: 12.468, 1950: 1.431, 2015: 8.747}, 'Myanmar': {1985: 36.766, 2050: 70.673, 1955: 21.05, 2020: 59.126, 1990: 40.464, 1960: 22.839, 2025: 61.748, 1995: 43.994, 1965: 24.937, 2030: 64.103, 2000: 47.439, 1970: 27.393, 2035: 66.155, 2005: 50.572, 1975: 30.33, 2040: 67.927, 2010: 53.414, 1980: 33.336, 2045: 69.444, 1950: 19.488, 2015: 56.32}, 'Equatorial Guinea': {1985: 0.325, 2050: 1.428, 1955: 0.226, 2020: 0.836, 1990: 0.371, 1960: 0.244, 2025: 0.936, 1995: 0.426, 1965: 0.253, 2030: 1.037, 2000: 0.491, 1970: 0.27, 2035: 1.138, 2005: 0.567, 1975: 0.213, 2040: 1.238, 2010: 0.651, 1980: 0.256, 2045: 1.335, 1950: 0.211, 2015: 0.741}, 'Egypt': {1985: 50.052, 2050: 137.873, 1955: 23.856, 2020: 96.26, 1990: 54.907, 1960: 26.847, 2025: 103.742, 1995: 58.945, 1965: 30.265, 2030: 111.057, 2000: 65.159, 1970: 33.574, 2035: 118.256, 2005: 72.544, 1975: 36.952, 2040: 125.242, 2010: 80.472, 1980: 42.634, 2045: 131.822, 1950: 21.198, 2015: 88.487}, 'Nicaragua': {1985: 3.18, 2050: 7.234, 1955: 1.277, 2020: 6.203, 1990: 3.644, 1960: 1.493, 2025: 6.494, 1995: 4.402, 1965: 1.75, 2030: 6.753, 2000: 4.866, 1970: 2.053, 2035: 6.956, 2005: 5.267, 1975: 2.395, 2040: 7.101, 2010: 5.604, 1980: 2.803, 2045: 7.191, 1950: 1.098, 2015: 5.908}, 'Singapore': {1985: 2.75, 2050: 8.61, 1955: 1.306, 2020: 6.21, 1990: 3.047, 1960: 1.646, 2025: 6.733, 1995: 3.567, 1965: 1.887, 2030: 7.223, 2000: 4.063, 1970: 2.075, 2035: 7.66, 2005: 4.606, 1975: 2.263, 2040: 8.036, 2010: 5.14, 1980: 2.414, 2045: 8.351, 1950: 1.022, 2015: 5.674}, 'Serbia': {1985: 7.72, 2050: 5.869, 1955: 6.313, 2020: 7.012, 1990: 7.786, 1960: 6.659, 2025: 6.846, 1995: 7.691, 1965: 6.959, 2030: 6.672, 2000: 7.604, 1970: 7.248, 2035: 6.486, 2005: 7.502, 1975: 7.431, 2040: 6.287, 2010: 7.345, 1980: 7.588, 2045: 6.08, 1950: 5.956, 2015: 7.177}, 'Botswana': {1985: 1.08, 2050: 2.871, 1955: 0.461, 2020: 2.312, 1990: 1.265, 1960: 0.497, 2025: 2.425, 1995: 1.478, 1965: 0.538, 2030: 2.519, 2000: 1.68, 1970: 0.584, 2035: 2.605, 2005: 1.84, 1975: 0.705, 2040: 2.691, 2010: 2.029, 1980: 0.9, 2045: 2.78, 1950: 0.43, 2015: 2.183}, 'United Kingdom': {1985: 56.584, 2050: 71.154, 1955: 50.946, 2020: 65.761, 1990: 57.411, 1960: 52.372, 2025: 67.244, 1995: 58.187, 1965: 54.35, 2030: 68.451, 2000: 59.14, 1970: 55.632, 2035: 69.394, 2005: 60.487, 1975: 56.215, 2040: 70.148, 2010: 62.348, 1980: 56.314, 2045: 70.742, 1950: 50.127, 2015: 64.088}, 'Greece': {1985: 9.923, 2050: 10.036, 1955: 7.966, 2020: 10.742, 1990: 10.13, 1960: 8.327, 2025: 10.671, 1995: 10.458, 1965: 8.55, 2030: 10.583, 2000: 10.559, 1970: 8.793, 2035: 10.485, 2005: 10.668, 1975: 9.047, 2040: 10.366, 2010: 10.75, 1980: 9.643, 2045: 10.217, 1950: 7.566, 2015: 10.776}, 'Paraguay': {1985: 3.633, 2050: 8.84, 1955: 1.683, 2020: 7.192, 1990: 4.2, 1960: 1.91, 2025: 7.603, 1995: 4.826, 1965: 2.17, 2030: 7.974, 2000: 5.418, 1970: 2.477, 2035: 8.279, 2005: 5.926, 1975: 2.848, 2040: 8.517, 2010: 6.376, 1980: 3.172, 2045: 8.699, 1950: 1.476, 2015: 6.783}, 'Namibia': {1985: 1.204, 2050: 2.15, 1955: 0.522, 2020: 2.263, 1990: 1.471, 1960: 0.591, 2025: 2.284, 1995: 1.681, 1965: 0.671, 2030: 2.281, 2000: 1.893, 1970: 0.765, 2035: 2.258, 2005: 2.028, 1975: 0.915, 2040: 2.223, 2010: 2.128, 1980: 1.058, 2045: 2.185, 1950: 0.464, 2015: 2.212}, 'Comoros': {1985: 0.383, 2050: 1.17, 1955: 0.164, 2020: 0.846, 1990: 0.43, 1960: 0.183, 2025: 0.906, 1995: 0.483, 1965: 0.206, 2030: 0.964, 2000: 0.545, 1970: 0.237, 2035: 1.022, 2005: 0.623, 1975: 0.276, 2040: 1.078, 2010: 0.706, 1980: 0.34, 2045: 1.128, 1950: 0.148, 2015: 0.781}})\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating a dataframe from a dictionary" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# create dataframe\n", "\n", "df = pd.DataFrame.from_dict(result, orient='index')\n", "# sort based on year\n", "df.sort(axis=1,inplace=True)\n", "print df\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Index: 227 entries, Afghanistan to Zimbabwe\n", "Data columns (total 21 columns):\n", "1950 227 non-null values\n", "1955 227 non-null values\n", "1960 227 non-null values\n", "1965 227 non-null values\n", "1970 227 non-null values\n", "1975 227 non-null values\n", "1980 227 non-null values\n", "1985 227 non-null values\n", "1990 227 non-null values\n", "1995 227 non-null values\n", "2000 227 non-null values\n", "2005 227 non-null values\n", "2010 227 non-null values\n", "2015 227 non-null values\n", "2020 227 non-null values\n", "2025 227 non-null values\n", "2030 227 non-null values\n", "2035 227 non-null values\n", "2040 227 non-null values\n", "2045 227 non-null values\n", "2050 227 non-null values\n", "dtypes: float64(21)\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Some data accessing functions for a panda dataframe" ] }, { "cell_type": "code", "collapsed": false, "input": [ "subtable = df.iloc[0:2, 0:2]\n", "print \"subtable\"\n", "print subtable\n", "print \"\"\n", "\n", "column = df[1955]\n", "print \"column\"\n", "print column\n", "print \"\"\n", "\n", "row = df.ix[0] #row 0\n", "print \"row\"\n", "print row\n", "print \"\"\n", "\n", "rows = df.ix[:2] #rows 0,1\n", "print \"rows\"\n", "print rows\n", "print \"\"\n", "\n", "element = df.ix[0,1955] #element\n", "print \"element\"\n", "print element\n", "print \"\"\n", "\n", "# max along column\n", "print \"max\"\n", "print df[1950].max()\n", "print \"\"\n", "\n", "# axes\n", "print \"axes\"\n", "print df.axes\n", "print \"\"\n", "\n", "row = df.ix[0]\n", "print \"row info\"\n", "print row.name\n", "print row.index\n", "print \"\"\n", "\n", "countries = df.index\n", "print \"countries\"\n", "print countries\n", "print \"\"\n", "\n", "print \"Austria\"\n", "print df.ix['Austria']" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "subtable\n", " 1950 1955\n", "Afghanistan 8.150 8.891\n", "Albania 1.227 1.392\n", "\n", "column\n", "Afghanistan 8.891\n", "Albania 1.392\n", "Algeria 9.842\n", "American Samoa 0.020\n", "Andorra 0.006\n", "Angola 4.423\n", "Anguilla 0.005\n", "Antigua and Barbuda 0.051\n", "Argentina 18.928\n", "Armenia 1.565\n", "Aruba 0.054\n", "Australia 9.277\n", "Austria 6.947\n", "Azerbaijan 3.314\n", "Bahamas 0.087\n", "...\n", "United Arab Emirates 0.083\n", "United Kingdom 50.946\n", "United States 165.069\n", "United States Virgin Islands 0.028\n", "Uruguay 2.353\n", "Uzbekistan 7.232\n", "Vanuatu 0.059\n", "Venezuela 6.170\n", "Vietnam 27.738\n", "Wallis and Futuna 0.007\n", "West Bank 0.788\n", "Western Sahara 0.016\n", "Yemen 5.265\n", "Zambia 2.869\n", "Zimbabwe 3.409\n", "Name: 1955, Length: 227, dtype: float64\n", "\n", "row\n", "1950 8.150\n", "1955 8.891\n", "1960 9.829\n", "1965 10.998\n", "1970 12.431\n", "1975 14.132\n", "1980 15.044\n", "1985 13.120\n", "1990 13.568\n", "1995 19.445\n", "2000 22.461\n", "2005 26.335\n", "2010 29.121\n", "2015 32.564\n", "2020 36.644\n", "2025 41.117\n", "2030 45.665\n", "2035 50.195\n", "2040 54.717\n", "2045 59.255\n", "2050 63.795\n", "Name: Afghanistan, dtype: float64\n", "\n", "rows\n", "\n", "Index: 2 entries, Afghanistan to Albania\n", "Data columns (total 21 columns):\n", "1950 2 non-null values\n", "1955 2 non-null values\n", "1960 2 non-null values\n", "1965 2 non-null values\n", "1970 2 non-null values\n", "1975 2 non-null values\n", "1980 2 non-null values\n", "1985 2 non-null values\n", "1990 2 non-null values\n", "1995 2 non-null values\n", "2000 2 non-null values\n", "2005 2 non-null values\n", "2010 2 non-null values\n", "2015 2 non-null values\n", "2020 2 non-null values\n", "2025 2 non-null values\n", "2030 2 non-null values\n", "2035 2 non-null values\n", "2040 2 non-null values\n", "2045 2 non-null values\n", "2050 2 non-null values\n", "dtypes: float64(21)\n", "\n", "element\n", "8.891\n", "\n", "max\n", "562.58\n", "\n", "axes\n", "[Index([u'Afghanistan', u'Albania', u'Algeria', u'American Samoa', u'Andorra', u'Angola', u'Anguilla', u'Antigua and Barbuda', u'Argentina', u'Armenia', u'Aruba', u'Australia', u'Austria', u'Azerbaijan', u'Bahamas', u'Bahrain', u'Bangladesh', u'Barbados', u'Belarus', u'Belgium', u'Belize', u'Benin', u'Bermuda', u'Bhutan', u'Bolivia', u'Bosnia and Herzegovina', u'Botswana', u'Brazil', u'British Virgin Islands', u'Brunei', u'Bulgaria', u'Burkina Faso', u'Burundi', u'Cambodia', u'Cameroon', u'Canada', u'Cape Verde', u'Cayman Islands', u'Central African Republic', u'Chad', u'Chile', u'China', u'Colombia', u'Comoros', u'Congo (Brazzaville)', u'Congo (Kinshasa)', u'Cook Islands', u'Costa Rica', u'Croatia', u'Cuba', u'Curaao', u'Cyprus', u'Czech Republic', u'Denmark', u'Djibouti', u'Dominica', u'Dominican Republic', u'Ecuador', u'Egypt', u'El Salvador', u'Equatorial Guinea', u'Eritrea', u'Estonia', u'Ethiopia', u'Faroe Islands', u'Federated States of Micronesia', u'Fiji', u'Finland', u'France', u'French Polynesia', u'Gabon', u'Gambia', u'Gaza Strip', u'Georgia', u'Germany', u'Ghana', u'Gibraltar', u'Greece', u'Greenland', u'Grenada', u'Guam', u'Guatemala', u'Guernsey', u'Guinea', u'Guinea-Bissau', u'Guyana', u'Haiti', u'Honduras', u'Hong Kong', u'Hungary', u'Iceland', u'India', u'Indonesia', u'Iran', u'Iraq', u'Ireland', u'Isle of Man', u'Israel', u'Italy', u'Ivory Coast', u'Jamaica', u'Japan', u'Jersey', u'Jordan', u'Kazakhstan', u'Kenya', u'Kiribati', u'Kuwait', u'Kyrgyzstan', u'Laos', u'Latvia', u'Lebanon', u'Lesotho', u'Liberia', u'Libya', u'Liechtenstein', u'Lithuania', u'Luxembourg', u'Macau', u'Macedonia', u'Madagascar', u'Malawi', u'Malaysia', u'Maldives', u'Mali', u'Malta', u'Marshall Islands', u'Mauritania', u'Mauritius', u'Mayotte', u'Mexico', u'Moldova', u'Monaco', u'Mongolia', u'Montenegro', u'Montserrat', u'Morocco', u'Mozambique', u'Myanmar', u'Namibia', u'Nauru', u'Nepal', u'Netherlands', u'New Caledonia', u'New Zealand', u'Nicaragua', u'Niger', u'Nigeria', u'North Korea', u'Northern Mariana Islands', u'Norway', u'Oman', u'Pakistan', u'Palau', u'Panama', u'Papua New Guinea', u'Paraguay', u'Peru', u'Philippines', u'Poland', u'Portugal', u'Puerto Rico', u'Qatar', u'Romania', u'Russia', u'Rwanda', u'Saint Barthlemy', u'Saint Helena, Ascension and Tristan da Cunha', u'Saint Kitts and Nevis', u'Saint Lucia', u'Saint Martin', u'Saint Pierre and Miquelon', u'Saint Vincent and the Grenadines', u'Samoa', u'San Marino', u'Saudi Arabia', u'Senegal', u'Serbia', u'Seychelles', u'Sierra Leone', u'Singapore', u'Sint Maarten', u'Slovakia', u'Slovenia', u'So Tom and Prncipe', u'Solomon Islands', u'Somalia', u'South Africa', u'South Korea', u'Spain', u'Sri Lanka', u'Sudan', u'Suriname', u'Swaziland', u'Sweden', u'Switzerland', u'Syria', u'Taiwan', u'Tajikistan', u'Tanzania', u'Thailand', u'Timor-Leste', u'Togo', u'Tonga', u'Trinidad and Tobago', u'Tunisia', u'Turkey', u'Turkmenistan', u'Turks and Caicos Islands', u'Tuvalu', u'Uganda', u'Ukraine', u'United Arab Emirates', u'United Kingdom', u'United States', u'United States Virgin Islands', u'Uruguay', u'Uzbekistan', u'Vanuatu', u'Venezuela', u'Vietnam', u'Wallis and Futuna', u'West Bank', u'Western Sahara', u'Yemen', u'Zambia', u'Zimbabwe'], dtype=object), Int64Index([1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050], dtype=int64)]\n", "\n", "row info\n", "Afghanistan\n", "Int64Index([1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050], dtype=int64)\n", "\n", "countries\n", "Index([u'Afghanistan', u'Albania', u'Algeria', u'American Samoa', u'Andorra', u'Angola', u'Anguilla', u'Antigua and Barbuda', u'Argentina', u'Armenia', u'Aruba', u'Australia', u'Austria', u'Azerbaijan', u'Bahamas', u'Bahrain', u'Bangladesh', u'Barbados', u'Belarus', u'Belgium', u'Belize', u'Benin', u'Bermuda', u'Bhutan', u'Bolivia', u'Bosnia and Herzegovina', u'Botswana', u'Brazil', u'British Virgin Islands', u'Brunei', u'Bulgaria', u'Burkina Faso', u'Burundi', u'Cambodia', u'Cameroon', u'Canada', u'Cape Verde', u'Cayman Islands', u'Central African Republic', u'Chad', u'Chile', u'China', u'Colombia', u'Comoros', u'Congo (Brazzaville)', u'Congo (Kinshasa)', u'Cook Islands', u'Costa Rica', u'Croatia', u'Cuba', u'Curaao', u'Cyprus', u'Czech Republic', u'Denmark', u'Djibouti', u'Dominica', u'Dominican Republic', u'Ecuador', u'Egypt', u'El Salvador', u'Equatorial Guinea', u'Eritrea', u'Estonia', u'Ethiopia', u'Faroe Islands', u'Federated States of Micronesia', u'Fiji', u'Finland', u'France', u'French Polynesia', u'Gabon', u'Gambia', u'Gaza Strip', u'Georgia', u'Germany', u'Ghana', u'Gibraltar', u'Greece', u'Greenland', u'Grenada', u'Guam', u'Guatemala', u'Guernsey', u'Guinea', u'Guinea-Bissau', u'Guyana', u'Haiti', u'Honduras', u'Hong Kong', u'Hungary', u'Iceland', u'India', u'Indonesia', u'Iran', u'Iraq', u'Ireland', u'Isle of Man', u'Israel', u'Italy', u'Ivory Coast', u'Jamaica', u'Japan', u'Jersey', u'Jordan', u'Kazakhstan', u'Kenya', u'Kiribati', u'Kuwait', u'Kyrgyzstan', u'Laos', u'Latvia', u'Lebanon', u'Lesotho', u'Liberia', u'Libya', u'Liechtenstein', u'Lithuania', u'Luxembourg', u'Macau', u'Macedonia', u'Madagascar', u'Malawi', u'Malaysia', u'Maldives', u'Mali', u'Malta', u'Marshall Islands', u'Mauritania', u'Mauritius', u'Mayotte', u'Mexico', u'Moldova', u'Monaco', u'Mongolia', u'Montenegro', u'Montserrat', u'Morocco', u'Mozambique', u'Myanmar', u'Namibia', u'Nauru', u'Nepal', u'Netherlands', u'New Caledonia', u'New Zealand', u'Nicaragua', u'Niger', u'Nigeria', u'North Korea', u'Northern Mariana Islands', u'Norway', u'Oman', u'Pakistan', u'Palau', u'Panama', u'Papua New Guinea', u'Paraguay', u'Peru', u'Philippines', u'Poland', u'Portugal', u'Puerto Rico', u'Qatar', u'Romania', u'Russia', u'Rwanda', u'Saint Barthlemy', u'Saint Helena, Ascension and Tristan da Cunha', u'Saint Kitts and Nevis', u'Saint Lucia', u'Saint Martin', u'Saint Pierre and Miquelon', u'Saint Vincent and the Grenadines', u'Samoa', u'San Marino', u'Saudi Arabia', u'Senegal', u'Serbia', u'Seychelles', u'Sierra Leone', u'Singapore', u'Sint Maarten', u'Slovakia', u'Slovenia', u'So Tom and Prncipe', u'Solomon Islands', u'Somalia', u'South Africa', u'South Korea', u'Spain', u'Sri Lanka', u'Sudan', u'Suriname', u'Swaziland', u'Sweden', u'Switzerland', u'Syria', u'Taiwan', u'Tajikistan', u'Tanzania', u'Thailand', u'Timor-Leste', u'Togo', u'Tonga', u'Trinidad and Tobago', u'Tunisia', u'Turkey', u'Turkmenistan', u'Turks and Caicos Islands', u'Tuvalu', u'Uganda', u'Ukraine', u'United Arab Emirates', u'United Kingdom', u'United States', u'United States Virgin Islands', u'Uruguay', u'Uzbekistan', u'Vanuatu', u'Venezuela', u'Vietnam', u'Wallis and Futuna', u'West Bank', u'Western Sahara', u'Yemen', u'Zambia', u'Zimbabwe'], dtype=object)\n", "\n", "Austria\n", "1950 6.935\n", "1955 6.947\n", "1960 7.047\n", "1965 7.271\n", "1970 7.467\n", "1975 7.579\n", "1980 7.549\n", "1985 7.560\n", "1990 7.723\n", "1995 8.047\n", "2000 8.113\n", "2005 8.185\n", "2010 8.214\n", "2015 8.224\n", "2020 8.220\n", "2025 8.190\n", "2030 8.120\n", "2035 8.009\n", "2040 7.867\n", "2045 7.702\n", "2050 7.521\n", "Name: Austria, dtype: float64\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting population of 4 countries" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plotCountries = ['Austria', 'Germany', 'United States', 'France']\n", " \n", "for country in plotCountries:\n", " row = df.ix[country]\n", " plt.plot(row.index, row, label=row.name ) \n", " \n", "plt.ylim(ymin=0) # start y axis at 0\n", "\n", "plt.xticks(rotation=70)\n", "plt.legend(loc='best')\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"# people (million)\")\n", "plt.title(\"Population of countries\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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zHemjYlMqJa5eyeDo4XiyMgsAsLDUp1tPB9q2M6tyDrGGRM7XrjaoLVGkpKQw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\nqatRHfUEtnqEsykfGBW5GtU6f95Zj3DWqZujTr1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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot 5 most populous countries from 2010 and 2060" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_populous(df, year):\n", " # sort table depending on data value in year column\n", " df_by_year = df.sort(year, ascending=False)\n", " \n", " plt.figure()\n", " for i in range(5): \n", " row = df_by_year.ix[i]\n", " plt.plot(row.index, row, label=row.name ) \n", " \n", " plt.ylim(ymin=0)\n", " \n", " plt.xticks(rotation=70)\n", " plt.legend(loc='best')\n", " plt.xlabel(\"Year\")\n", " plt.ylabel(\"# people (million)\")\n", " plt.title(\"Most populous countries in %d\" % year)\n", "\n", "plot_populous(df, 2010)\n", "plot_populous(df, 2050)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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IE7bp5ORUaGz8gwcPSld4PbJo0SKmT5+ubzFKhcqsGwj9KjqVXT9bW9vnvrZMQ3Xfeust\nVq1axd27d1m+fHmBG5sJBAKBoPxTpsbj7NmzvPrqqwC89tprnD17FtCOKB5fDBYVFYWzszNOTk66\n9QWPyv+7WvlFoDJvKV2ZdQOhX0WnsutXHMrUeHh4eHDkyBFAu1nco4U7ffr0YcuWLahUKsLCwggJ\nCcHLywtHR0eUSiVnzpxBkiQ2btxIv379ylLkckHDhg31LUKpUZl1A6FfRaey61ccSm2dh5+fH0eO\nHCEhIQEHBwfmzp2Lp6cnY8eOJTs7G1NTU4KCgmjatCkAn3zyCevWrcPQ0JCVK1fSvXt3QBuq6+/v\nT2ZmJr6+vnl2DtUpIZNV6jkPgUAgKA1sbW2fe8K8UiwSLMx4uLu767b+FlR+rK2tRVZDgeAZKI7x\nKNNoq7ImOTlZjEheIIoTOVJaHD9+nLZt2+pbjFJD6PfiIjZGFAgEAsEzU6ndVra2tmLk8QIh7rdA\n8GwUx20lRh4CgUAgeGaE8RAISpHjx4/rW4RSRej34iKMh0AgEAieGWE8yimLFi1i9OjRhdb7+Phw\n8uTJMpRI8DxU9kgdod+LS6UO1a0IbN++naCgIEJDQ7GwsKBhw4ZMnjy5yOuE4RAIBPpEjDz0SFBQ\nELNnz2by5MncvHmTq1ev8vbbb/PHH3/oWzRBCVHZfeZCvxeXF3rk4fnrohJr62q/Z9u2OTU1lYUL\nF/LZZ5/xyiuv6Mq7detGt27dWLRoESqVioCAAHbv3o2TkxNBQUE0adIEgMaNG7N69Wrat2/PokWL\nuHHjBqampgWeu2LFCjZu3EhCQgLVq1fn/fffz9OnQCAQPCti5KEnzp07R1ZWFr169Sr0nD179jBg\nwADCw8Pp2bOnLvMi5E+utHfv3kLPrVmzJr///jsRERFMnz6d0aNHExsbW/JKCfJR2X3mQr+SRdJo\nkLIflmmfz8sLPfJ41tFCSfLgwQPs7OyQywu3361bt6ZLly4AvP7666xZs+a5zu3bt6/uc79+/Vi+\nfDkXL16kZ8+exVVDIBAUE0mSyL33N1kXfyb74s8YNeyJ5YCF+harSMTIQ0/Y2tqSmJiIRqMp9Jyq\nVavqPpuZmZGVlVXo+U86d8uWLXTo0IGaNWtSs2ZNrl+/LlZilxGV3Wcu9Ht+cuPvkL5vKUmL2pC0\ntBOZB1ejSY4mJ+J8qfVZkrzQIw990rJlS4yNjdm1axd9+vQptX4iIyP53//+x44dO2jZsiUymYwO\nHTo895YEAoHg+clNvU/2X7+SffEncu7+pSuXmdth3KQvJs36Y+jmpUcJnx5hPPSEUqlk5syZTJs2\nDUNDQzp27IhCoeDIkSMcP34cU1PTEuknPT0dmUyGra0tGo2GLVu2cP369RJpW1A0Yk6gYlMS+mky\nksm+8hvZF39GHXoc/nlxkxmbY+T5CibNBqCo0x6ZgaLYfZUlwnjokYCAAOzt7fn000959913sbCw\noEmTJkyaNImDBw/mmxT/7/GT6h4d16tXj7Fjx9K9e3fkcjlvvPEG3t7eJa+MQCDQIakyyL62l+yL\nP6G6fgBy1doKAyOM6nfFpFl/jOp3Q2ZUMi+J+qDUdtUdOXIku3fvxt7enqtXr+rKV69eTVBQEAYG\nBrzyyissWqQNlw0MDGTdunUYGBiwatUqunXrBvybSTArKwtfX19WrlyZXwmxq66A8nm/K3s+CKFf\nXnKir5J58luyL2xDyk7XFsrkKGq3w6TZAIwavYLc1KqUpH12ymUyqBEjRjB+/HiGDRumKzt06BA7\nd+7kypUrKBQK4uPjAQgODmbr1q0EBwcTHR3Nyy+/TEhICDKZjDFjxrB27Vq8vLzw9fVlz5499OjR\no7TEFggEgmdCUmWQ/devZJ7cQM7di7pyQ9fmGDcbgEmTvsiVDnqUsHQoNePRrl07wsPD85R98cUX\nzJw5E4VC69t7FCG0Y8cO/Pz8UCgUuLm54eHhwZkzZ3B1dSUtLQ0vL+0E0rBhw/j1118LNB5jx46l\nRo0agHY+wdPTs7RUE5RzHkXIPHpj1Odx27Zty5U8Qr+S08+7VhWyTn3LkR2bkFTptHKUITNR8pey\nPUb1u9Gh7+DHzg8pF/ocP36czZs3A+iel89LqSaDCg8Pp3fv3jq3VdOmTenbty979uzBxMSEpUuX\n0qJFC8aPH4+3tzdvvvkmAG+//TY9e/bEzc2NGTNmsH//fgCOHTvG4sWL+e233/IqIdxWAsT9FpQ+\nkjqL7Cu7yDq5AfWd07pyQ9fmmPr4Y9ykLzIjMz1K+GyUS7dVQeTk5JCUlMTp06c5d+4cAwcO5M6d\nO2UpgkBQpog5gYrNI/1y4kLJOvUdWee2IKVrX1BkxuYYN38dU5/hGDq9eJ6OMjUezs7O9O/fH9Cu\nc5DL5SQkJODk5ERkZKTuvKioKJydnXFyciIqKipPuZOTU1mKLBAIXlCk3BzUt0+SfOVT1CHHdOWG\nTo0waTMc46YDkJtY6FFC/VKmK8z79evHwYMHAbh16xYqlYoqVarQp08ftmzZgkqlIiwsjJCQELy8\nvHB0dESpVHLmzBkkSWLjxo3069evLEUWCIpFZX4rh8qpn6TKJPPEeh4EetPw0mKt4VCYYtLqTaz/\ntx/ryQcwbT38hTYcUIojDz8/P44cOUJiYiIuLi7MnTuXkSNHMnLkSDw9PTEyMuK7774DoH79+gwc\nOJD69etjaGhIUFCQbp1CUFAQ/v7+ZGZm4uvrKyKtBAJBqaDJSCbz+Foyj32N9DABAHmVmpi1H4Vx\ni4HlKsS2PFCqE+ZlhZgwF0D5vN8vypxARSY3KZrMI2vIPPUdqLRrMwxdGmPWeQJn02xo1669niUs\nPSrMhLmg+CxatIiwsDDWrFlDVFQUPj4+REREPHH1uUAgyE/O/ZtkHFxN9oXtoMkBQFG3I2adJ6Co\n3Q6ZTIaskm/8WByE8dATjRs3ZtWqVXTo0OG523B2dubu3bslKJWgpKnob+VFURH1U985TcbB1aiu\n7dUWyOQYN30V087jUDg3znNuRdSvrBDGQ0/IZDIxWhAIyghJo0EVvI+MA6vICT+rLVSYYOLlh1nH\nAAyq1NSvgBWQF9p4xP+vSom1VXV5wjNfI0kSP/zwAxs3bqRly5Z8//33WFlZsXTpUl1ip4iICMaO\nHcvVq1dp0aIFHh4euuvv3r1L06ZNiY+PRy6Xs2nTJj777DPu3buHnZ0dEydOZPjw4SWmo+DZqQxz\nAk+ivOsnSRKqq7tJ/2MhufdvACAzs8a0zVuYtnsbuWXVJ15f3vXTJy+08dA3j0YeFy9eZPDgwdy+\nfZsNGzYwYcIErl27BsA777xDq1at+OWXXzh//jyDBg3C19e3wPbs7e3ZsmULrq6unDx5koEDB9K0\naVMaNWpUZjoJBOUBSZJQ3zxE+u8LyIm8DIDcujqmHcZg4j30hQ+zLQleaOPxPKOF0sDFxYWhQ4cC\n8MYbbzBlyhTi4+PJzs7m0qVL7NixA4VCQevWrenevXuh0RFdu3bVffbx8aFTp06cOnVKGA89Utnf\nWsujfuo7p0n//RPUt08CILd0wKzbJEy8hyAzNH6mtsqjfuWFF9p4lBfs7e11n83MtPvipKenEx8f\nj7W1dZ7EUC4uLkRHRxfYzp9//snixYu5ffs2Go2GzMxMGjRoULrCCwTlBHXUFTJ+D0R1XbsXnszM\nGrMuEzFt+1aF2m+qoiBymJdjHB0dSU5OJiMjQ1cWGRlZ4ER7dnY2w4cPZ/z48dy6dYuwsDC6du0q\n0s3qGZHju/TJiQ0h9du3SP60M6rr+5EZm2PWbSq271/ErPP4YhmO8qBfeUUYDz1S1IPdxcWFJk2a\nsHDhQtRqNadPn2bv3r0FnqtSqVCpVNjZ2SGXy/nzzz85dOhQaYgtEJQLch9EkrZ5AkmL2pB9aQcY\nGmPaMQDb9y9g3nM6clOlvkWs1Ai3lR55FK77pHSzX3/9NQEBAdSqVYuWLVvi5+dHSkpKvnMtLS1Z\nuHAhI0eOJDs7mx49etCzZ8+yUURQKJXdZ64P/TSpsaTvX07WqW+16V3lhpi0HopZ18kYWFcv0b4q\n+/0rDmJ7EkGlQdzvyo0m6yGZB1aQceRLUGeCTIZxswGYd5+GQVV3fYtXISnO9iTCbSUQlCKV3Wde\nFvpJGg2ZZzbx4BMvMv5cAepMjDx9sZl6FOWQNaVqOCr7/SsOwm0lEAjKLeo7p3n4y2xyorRrNQzd\nWmLRdx4KtxZ6lkwg3FaCSoO435WH3KQo0n+bQ/ZfvwDaBX7mvT7CuFl/sa1PCSJ21RUIBJUCKTud\njIOryTj0GaizQGGCWafxmHUeh8zYXN/iCR6j1OY8Ro4ciYODA56e+XP7fvrpp8jl8jxviYGBgdSu\nXZt69eqxb98+XfmFCxfw9PSkdu3aTJw4sbTEFQhKhcruMy8p/SSNhqzz23gQ6E3GvqWgzsK4aX9s\nZ57GvOd0vRmOyn7/ikOpGY8RI0awZ8+efOWRkZHs378fV1dXXVlwcDBbt24lODiYPXv2EBAQoBtK\njRkzhrVr1xISEkJISEiBbQoEgoqLOuIiyat8Sds0Bk1KDIYujbEevwvlsK8wsHHWt3iCQig149Gu\nXTtsbGzylU+aNInFixfnKduxYwd+fn4oFArc3Nzw8PDgzJkzxMTEkJaWhpeXFwDDhg3j119/LS2R\nBYISp7KvEyiOfrnJMaRuCiB5RTdyIs4jt3TActAqrN/bj8LduwSlfH4q+/0rDmU657Fjxw6cnZ3z\nbdR37949vL3//WNxdnYmOjoahUKBs/O/bx5OTk6F7us0duxYatSoAYBSqSzQXSZ4MXjkanj0jy+O\n9X+s1uRSt0UTErPTOXroEMbBe+iVcgiDnCxOxxmQVKcTbv4zMTRVcv2PXzGUy/Fq3RojuQGXz55H\nIZPTtm1bjOQGXDx9FplMVq70qyjHx48fZ/PmzQC65+XzUqrRVuHh4fTu3ZurV6+SkZFBp06d2L9/\nP0qlkpo1a3L+/Hns7OwYP3483t7evPnmmwC8/fbb9OzZEzc3N2bMmMH+/dqNzo4dO8bixYv57bff\n8irxgkZbTZ48mWrVqjFlypQSaa8kshvqk/J4vyt7PojfD/2JU+OXSMxOJzErQ/s7O53E7H8+Z2mP\nH+aoAGiUHMn/QvbhmqG9T8eq1GaNe0diTK2fuk+F3ABHU0uqm1rhaKakmqmSamaWVDNV4vjPj6mh\nokT0q+z3r0JEW92+fZvw8HAaN9ameYyKiqJ58+acOXMGJycnIiMjdedGRUXh7OyMk5MTUVFRecqd\nnJzKSuRSx87OjgsXLuDm5qYrezxHeVF8+umnus/Hjx9n9OjR/P33388tz5OyG0ZHRzNr1ixOnjyJ\nWq3GycmJcePG4efnly8p1dPQuHFjVq9eTfv27Z9bXkHZkqLKJDj5Pn8n3eda8n2uJccQ8dc1jFMu\nFHmtbU4W48KO0fHeJQCSlI4c8x5BmMNLNNXk0CA3F5VG+6PW5JCdm4tKk/PP8T91udrjzFw1kenJ\nRKYnF9qfjZHpP0bFimr/GJga5jbUtqpKdVOlCPctAcrMeHh6ehIbG6s7rlmzJhcuXMDW1pY+ffow\nePBgJk2aRHR0NCEhIXh5eSGTyVAqlZw5cwYvLy82btzIhAkTykpkwWOMGTMGT09Prly5grGxMdeu\nXSMuLi7POc/yBiOTyV6IHX8r6ltrujqb6ymxXEu+z99JMVxLvl/gw9qmoQc1LWypYmKOnfGjHzPt\nbxNz7IxMsf57N5rfP0FKfwAGRph1fY8qnSdQR2HyXLJl5KiIzUwjJjOVexmp3M9MJSYjlZh/ft/P\nTCVJlUmSKpPglNh815sbGlHLsgp1lFWpraxKbSvtb2sj03znVtT7VxaUmvHw8/PjyJEjJCYm4uLi\nwty5cxkxYoSu/nHLX79+fQYOHEj9+vUxNDQkKChIVx8UFIS/vz+ZmZn4+vrSo0ePEpMxcO6JEmtr\n5odtSqytRzwaTQQEBLBy5UoMDAx4//33GTx4MKCd53FycuK9995j4MCBqFQqatSogUwm4+zZs9jb\n27Ny5Uo2btxISkoK7du3Z9myZVhba10EW7duZcGCBWRkZBAQEPBEWS5dukRgYKAut8jjc0qvvPIK\noH0hAPh1TDVAAAAgAElEQVT555+xs7Pjvffe49q1a8hkMjp37sySJUtQKpWMHj2aqKgoBg8ejIGB\nAdOmTWPcuHGcO3eO999/n1u3buHi4kJgYCBt2mi/1x9++IGlS5eSmJiIra0ts2fP5rXXXivZL/wF\nRSNJ3EiJ5VJiNH8naw1FWFoi/zXtxnJD6lnb08C6Gg2sHWlo44irhS0GsoJHmzmxITz8bjzq29r/\nM0Xtdli8tgRDe48Cz39azAyNqGlpR01Lu0L1ScxOz2NQ7mWmEJaWSEhqAonZ6VxJuseVpHt5rqtq\nYqE1Jsoq//yuirulHSYGJeMCq2yUmvF4NClTGHfu3MlzPGvWLGbNmpXvvObNm3P16tUSla0iERcX\nR1paGsHBwRw6dAh/f3969eqFUqnUuZnMzMzYtm0b7777bh631Zo1a/jjjz/YtWsXVapUYfr06Uyd\nOpWvv/6aGzduMHXqVH788UeaNWvG3LlzuXfvXqFytGjRgilTpjBq1ChatmyZJ5Dh999/p0mTJoSH\nh+vcVmFhYUyaNAkfHx9SU1MZPnw4ixYtYsGCBaxZs4bTp0+zatUqndvq3r17+Pn58eWXX9KlSxcO\nHz7M8OHDOXv2LMbGxsycOZODBw9Sq1Yt4uLiyt3cRmGUV595YnY6p+LCOR57h1Nx4TxQZeSpN5TJ\nqWNlrzMSDayr4W5ph0JukOe8gvSTVJlk/LmCjIOrIFeNzKIKFn3nYtz89TJxF8llMqqaWFDVxIJG\n5N9l90F2BiGp8Xl+QlMTiM96SHzWQ07GhenOVd2IpIFXUxrZVKexrRONbKpT09IOuXB7vdgrzEtj\ntFDSKBQKpk6dilwu5+WXX8bc3JyQkBCaN28O/OsqKsgF9O2337Jo0SKqVasGwLRp02jcuDFr1qxh\n586ddO/eXRflNmvWLL755ptC5Vi/fj0rV65kyZIlhISEUL9+fVasWEHTpk0L7LtmzZq6kYidnR1j\nxoxhyZIlhba/bds2unbtSpcuXQDo2LEjTZo0Yd++ffTp0we5XE5wcDDVq1fH3t4+T/ZFQdGoNblc\nfhDNibgwTsaG5XPnOJpa0qqqG5422lFFHWVVjAye/fGgunmYtO1T0SRoH8Am3kMx7/UhcvP8Yfv6\nwtbYjFZVXWlV9d+1ZhpJIjoj+R9jkqAzKjdlkf8cJ/BTxBUALAyN8LSpTiPb6jS2qY6nbfUCXV6V\nnRfaeOgbAwMD1Gp1njK1Wo1C8e8w2cbGJs8ktKmpKenp6U/V/t27dxk6dGie6w0NDYmLiyM2Npbq\n1f99KzMzM8PW1rbQtqysrPjwww/58MMPefDgAR9++CFDhw4tdII+Li6OmTNncubMGdLS0pAkSecu\nK4jIyEh27NiRZxFobm4u7du3x8zMjLVr1/L5558zYcIEWrVqxbx586hdu/ZTfQ/6RJ+jjqj0ZE7E\nhXEiLoyz8RGk/xPxBFoXVIsqLrRxqEkb+5rUtLB7rlHBI/00aXE8/PUDsi/+BICBYz0sX19abtZr\nFIVcJsPF3AYXcxs6V6ujK8/umMONlFguP9C6uS4/iOZ+Zhqn4sM5FR+uO8/V3IZGttVp9I9Rqa2s\nmm+UVtkQxkOPODs7c/fu3TwPwYiIiGd6KD76hy/oH9/Z2ZnPPvuMli1b5qtzcHDg1q1buuOMjIyn\ndgXZ2toyduxYNm/eTHJycoF9z58/HwMDA06cOIGVlRW7d+9m+vTp+eR+XNaBAweyYsWKAvvs3Lkz\nnTt3Jjs7m/nz5/Pee++xe/fup5L3RSEzR835hLscjwvjROwdItKT8tTXsrTDx74mbR3caWbnXCK+\nfEmjIev0RtJ3zUXKTAGFCebdpmDaMQCZoVGx29c3xgaGNLZ1orHtv1GesZlpXE26x5V/DMq15PtE\npCcRkZ7Eb5HXADAxMKSJrRPeVd3wtnfjJSuHSufqEsZDj7z66qssXbqUl156CUdHR44ePcq+ffuY\nPHnyU10vSZLOZVS1alWSkpJITU1FqdSm3/T392fevHkEBQXh7OxMQkIC586do2fPnvTp04du3bpx\n5swZmjZtSmBgIBqNptC+Pv74YwYNGoSHhweZmZmsW7eOWrVqYW1tjZGREXK5nLCwMGrVqgXAw4cP\nUSqVWFpacu/ePVavXp2nvapVqxIeHq6b83j99dd5+eWXOXjwIB06dECtVnP+/Hnc3d1RKBScO3eO\nDh06YGpqirm5OQYGFeOtrrTnPO4+TOJ47B2Oxd7mXEIk2ZocXZ2lwpjWVd3wsdeOLhzNSjYta05c\nKPsD/WnBDQAU9bpg+doiDOzcSrQffVLQ/XMwtcTBtC4vV68LaF2CIanxOmNy5cE9ItKTOB0fwen4\nCAg+grWRKa2quuJd1Y3W9m44mVnpQ50SRRgPPTJ16lQCAwPx9fUlOTkZd3d3vvrqK+rVq6c750mu\nhMfXZdSpU4cBAwbQrFkzNBoNp06dYvTo0UiSxIABA7h//z5VqlShf//+9OzZk3r16rF48WLeeecd\nXbTVk9bQZGVlMXToUGJjYzExMaFFixZs2rQJ0Lq8Jk+eTM+ePcnJyWHbtm1MmzaNgIAA3NzccHd3\nZ+DAgXzxxRe69v73v/8xffp0PvroI6ZOnUpAQADff/89H3/8Me+88w4GBgY0b96cpUuXotFo+OKL\nLwgICEAmk9GoUSOWLl1a3K+/QpKdm6MbXRy7fzvf6KKBtSNtHdxpa+9OQ5tqGD7luptnQdLkknn4\nC9L/CCT3fhby2o6Yv7oA4yZ9X8j1Ewq5AfWtHalv7cggmgHaSfmz8RFa91ZcODGZqeyNvsHeaK2h\nrWFug3dVV1rbu+FV1RXlc4Yt6xORz0NQaais9zs6I4Vj929zPO4OZ+Pvkpn77zyZUmGCj70b7Rxq\n0ca+JnYmpbv7bM79m6RtHk/O3YsAGHv5YdF3HnKzp18h/qIhSRJ3/xmJnIoL52xCBGnqbF29HBkN\nbBxp/c+opLGtU5nNlxRnhbkwHoJKQ2W532pNLhcTozj6j8G4k5aYp/4lKwfaOrjTzsEdT5vqpTK6\n+C9Sbo42z8beJZCrQm5dHcuByzB66eVS77uykaPREJx8XzcqufwgmhzpX5dxXSt7tnca8YQWSo4K\nsT2JQPAi8rRzHqnqLE7EhnH4fgjHYu/keTO1MDTSTXS3sa+JvallaYqcj5x710jbPEGXCtbEeyjm\nfeYgN1WW23UsJUVp6Gcol2sjs2yr825dHzJyVFxIiORUfDin48NpYlsxtmASxkMg0BOR6ckcuR/K\n4ZhQLiRG5nn7dLe0o6OjB+0c3MvUjfE4Uq5au9hv/zLIVSO3ccbyjRUY1e1Y5rJUZswMjWjnWIt2\njtpgk1yp8MCV8oRwWwkqDeX9fudKGv5OiuHwPwYjNC1BV2cgk9HMzoWOjh50dPSghoV+F9XlRF8l\n9Yfx5N7TruMx8fHHvPfHyE0s9CqXoGQRbiuBoJySlavmZFwYh2NucyQ2lAfZ/24DYmFoRFsHdzo6\n1qatQ02sysEqZSlHRcb+ZWT8uQI0OchtXbEctAKj2u30LZqgnCGMh0BQCkSmJ/Nj2F9s3LOTXA9H\nXbmTmZV2dFHNg+Z2LuVqFbI68hJpmyeQGxMMgEnbt7Ho9T4y48JHG2LO48VFGA+BoITQSBIn4sLY\ncucix2JvIwHZOSqaWjnwcvW6dKzmgYdllXK3FkLKUZGxbykZB1aCJhd5lZpYDlqJUS0ffYsmKMeI\nOQ9BpUFf9ztFlcWOu1fZGvYXd/9ZtGckN6CH00sMcm+Kp03+nV3LCzn3b5L2/Rhyoq+ATIZp+9GY\n+85EZmSmb9EEZUCpzXmo1Wr27dvH0aNHCQ8PRyaT4erqSvv27enevTuGhmLgUtY8S6bB0mD58uWE\nh4ezcuVKvfRfnriZEsfmOxfYHRVMVq52W5BqpkreqNmUV10bYWtcfh/AkkZD5vFvSN81F9RZyG1r\nYDn4c4xqtda3aIIKQqFP/3nz5vHTTz/RunVrvLy86Ny5MxqNhpiYGH777TddMp7333+/LOWtNFTU\nfOH/+9//9C2CXlFrcvnz3i0237nAXw+ideXeVd3wc29GB8daeZIjlUefeW5yDGlbxqO+eRj4Z5X4\nq58gN3n29SPlUb+SpLLrVxwKNR6NGzfm/fffL9A/O3LkSDQaDbt27Sq04ZEjR7J7927s7e11yZym\nTp3Krl27MDIyolatWqxfvx4rK+0GYYGBgaxbtw4DAwNWrVpFt27dALhw4QL+/v5kZWXh6+tbad54\nn5QvXFD+SMxOZ8udv9gefomEbO2W+OaGRvSt4cmgmk0LzWpX3si+tIO0bZORMpKRmdtiOXAZxo16\n6VssQQWk1OY8jh07hoWFBcOGDdMZj/3799OlSxfkcjkzZswAYOHChQQHBzN48GDOnTtHdHQ0L7/8\nMiEhIchkMry8vPjss8/w8vLC19eXCRMm5EtF+7xzHtltCk9+9KwYn3j7mc5v0qQJK1euJDo6mo0b\nN9KyZUu+//57rKysWLp0qS4pUkREBGPHjuXq1au0aNECDw8PUlJSdG6rP/74g7lz53L//n08PT1Z\nunQpdepo8xE0btyYUaNGsWXLFiIjI+nSpQtBQUEYGxsDsHfvXhYsWEBkZCR169Zl2bJl1K9fH4CV\nK1fy9ddfk5aWhqOjI0uWLKF9+/b53GYjRozg9OnTZGZm0rBhQ5YuXZpnY8eypDTmPHIlDT+GXWL1\n9aO6Vd8ellUY5N6MXs71MVcYl2h/pYUmM5WHP00n+8I24J8dcP1WYqB0LOJKQWWmOHMeRW6Kc/Pm\nTd555x26du1Kp06d6NSpE507dy6y4Xbt2mFjk3ehU9euXXWJiVq1akVUVBQAO3bswM/PD4VCgZub\nGx4eHpw5c4aYmBjS0tLw8vICYNiwYfz666/PrGR55dHI4+LFi9SuXZvbt28zYcIEJkyYoDvnnXfe\noWnTpoSGhjJlyhS2bNmiuy40NJRRo0axcOFCQkND6dq1K4MHDyYnJ0fX/o4dO9i+fTuXLl0iODhY\nlx74ypUrTJgwgRUrVnDnzh38/f0ZPHgwarWakJAQvvnmGw4cOEBERAQ//fQTNWrUKFCHrl27cv78\neUJCQmjcuDHvvvtuaX5lZcrlB9H4Hf6OT67sJ02djY99Tda19ePnziN5o2bTCmM4VKEnSFrSXms4\nFKZYDFiM1agtwnAIikWRM96vv/46Y8aM4e2339blUCgJd8u6devw8/MDtPmrH6VDBW1ioOjoaBQK\nRZ5c2U5OTkRHR+drC2Ds2LG6B5xSqcTT07NIGZ51tFBauLi4MHToUADeeOMNpkyZQnx8PNnZ2Vy6\ndIkdO3agUCho3bo13bt31133yy+/0K1bN928ybhx4/jyyy85e/YsPj7aMMtRo0bh4OAAQPfu3XWj\nwG+//RZ/f3+aNdNuIT1o0CCWL1/OuXPnqFatGiqVihs3bmBra5vnHvyXwYMH6z5PmzaNNWvWkJaW\nhqVl2e6/9DjHjx8H/s1y96zHfxw6wE/hlzhjozXC5mEJDHZvzvjW2hzcz9Leo8/Fked5j9t4tyT9\n90AOb/kMJIk2LZtiOWQNp2/dhxMnSqQ/fepXFseVTb/jx4/rXiALeyF8Woo0HgqFgjFjxhSrk/+y\nYMECjIyM8jx4isvnn39eYm2VNY/n4zYz00bopKenEx8fj7W1Naam/648dnFx4d69ewDcv38/z4Nd\nJpNRvXp1YmJiCmzb1NSU+/fvA9q0r1u3buWrr77S1efk5BAbG4uPjw+ffPIJixYt4saNG3Tu3Jn5\n8+fj6Jj3TTU3N5f58+ezc+dOEhISkMvlOheiPo3Hfyc4n/ZYI0n8FHGZlRl/k2KTg6FMjn9tL97p\n1Rqzx7LiPW/7ZXmcE3OdpOXdyb33N60c5Zh1nYRZt8nIDBS0tffQu3ziWD/Hbdu2zXO8aNEinpci\njUfv3r35/PPP6d+/v85XDjwx3/WT2LBhA7///jsHDhzQlTk5OREZGak7joqKwtnZGScnJ51r61H5\nkxIWVTYcHR1JTk4mIyNDZ1QiIyN1I8Bq1aoRHBysO1+SJO7du0e1atUKbfPRqNHZ2ZlJkyYxadKk\nAs8bMGAAAwYMIC0tjUmTJjFnzpw8yZwAtm/fzp49e/j1119xcXEhJSUFd3f35/ah6pNryfdZcHkf\nV5O0hte7qiuzGnUt9kR4WUfqSBoNmUfXkL5rvnbr9Co1Ub4ZhMItfyrikqCyRyJVdv2KQ5FzHhs2\nbGDp0qX4+PjQvHlzmjdvTosWLZ6rsz179rBkyRJ27NiBicm/mbP69OnDli1bUKlUhIWFERISgpeX\nF46OjiiVSs6cOYMkSWzcuJF+/fo9V9/lkaIesi4uLjRp0oSFCxeiVqs5ffo0e/fu1dX37duX/fv3\nc/ToUdRqNZ9//jnGxsa6OaIn9Tls2DDWr1/PhQsXkCSJ9PR09u3bx8OHDwkNDeXo0aNkZ2djbGyM\niYlJgWlf09PTMTIywtramvT0dObNm/ec34T+SFFlseDyPvwOf8vVpBjsTSxY0qIPX/m8UWEiqB6R\nm3qflC9fJ33Hh5CrwsR7KLZTDpWa4RC82BQ58ggPD3+uhv38/Dhy5AgJCQm4uLgwZ84cAgMDUalU\ndO3aFYDWrVsTFBRE/fr1GThwIPXr18fQ0JCgoCDdG3JQUBD+/v5kZmbi6+ubL9KqIvMoXPe/c0iP\nH3/99dcEBARQq1YtWrZsiZ+fHykpKQDUrl2bNWvWMH36dGJiYmjUqBE//PDDExdvPmq7SZMmrFix\ngunTp3P79m1MTU3x9vbGx8cHlUrFvHnzuHXrFoaGhrRq1Yrly5fna+ONN97g4MGDNGzYEBsbG2bO\nnMmGDRtK6uspVTSSxM7Iv1n+92EeqDIwkMkYUqsFY+q2KdGJ8LJaJ6C6/iepP4xDepiAzNwOy0Er\nMW5Y+v8rlX0dRGXXrzgUGaqrUqn44osvOHr0KDKZjA4dOjB69GgUCkVZyVgkYnsSATz9/b6VEsf8\ny/t0i/ya27kwu3FXaiurlrhMpf3wkXKySd81n8wjWpeionZ7LIcElVkkVWV/uFZ2/Uo1De1bb71F\nTk4Ow4cP17mODA0N+eabklsjUVyE8RBA0fdbI0msuXmCr26eJFeSsDM2Z0rDTrziXL9CLtjMib9N\n2nejtBn+5AaY95yFaefxyMogLa2gclCq+TzOnTvHlStXdMddunShUaNGz9WZQKAv0tTZzLzwG0fu\n30aOjMHuzRn7UluUCpOiLy6HZJ3/kYfbpyJlpyO3rYFy6Fco3J5vLlIgeB6KfEUxNDQkNDRUd3z7\n9m2xIaKgQhGWlsjgI99x5P5trBQmrPEZyMxGL5eJ4Xh8nUBJoMl6SOqmANI2BSBlp2PcpB82Uw7p\nzXCUtH7ljcquX3Eo0gosWbKEzp07U7NmTUA7gb5+/fpSF0wgKAkOx4Qy48JvpOeoqK2syspW/XEx\nt9a3WM+FOvISad+NIjfhjnaleP9PMGk1pEK63AQVn6fa2yorK4ubN28ik8moW7dunvUe5QEx5yGA\nvPdbI0l8dfMkn9/Qvjl2d6rH3KY98yz2qyhIkkTmkTXa7dNz1RhUb4By2NcYOtTRt2iCCk6pzHkc\nOHCALl268NNPPyGTyXQdPHJh9e/f/7k6FAhKm4fqbGZf3M3BmBBkwMT6HRhZu1WFfEPXpMWTtnkC\nquv7ATBp+xYWfeYgq6BzNYLKQ6HG4+jRo3Tp0oXffvutwH86YTwE5ZHwhw+YeOZn7qQlolQYs6hF\nH9o6uOtNnuKEeqpuHSXt+zFo0mKRmVljOWgVxp6+JSxh8ajsoayVXb/iUKjxmDNnDkCFWfQlEAAM\nPvIdaepsPCyrsLJVf2pY2BR9UTlDys0hY+9iMv5cDpKEwr01lkPWYGDz4mzNIyj/FDrn8emnn+Y/\n+R/3lUwmK3RPJH0g5jwKx8fHR7e9jL5T2JY2tra2OK2bzsvV6jC/mW+F2TL9cXJT7pO2cRTq2ydB\nJses2xTMuk5CZiAiHAUlT6nMeaSlpRXornpkPATFo3HjxiQkJGBgYIChoSFeXl58+umnJb7x48mT\nJ0u0vfLO+Jfa8U6d1hXyb1R18zCp349GepiA3NIBy2FfYeTRRt9iCQQFUqjx+Pjjj8tQjBcPmUzG\n5s2bad++PdnZ2UydOpUZM2awcePGfOdqNBpdEi3BkxlV10ffIuThaXzmkiaXjL1LyNj/qdZNVbs9\nyqFrkFvaP/G68kBlnxOo7PoVh0KNx/jx4wu9SCaTsWrVqlIRqCyZsadJibW1sMel577W2NiY3r17\nM3v2bECb2MrExITIyEhOnTrFpk2byMrKYsGCBYSHh6NUKhkyZAjTp08HtEmYtmzZomsvKyuLKVOm\nMG3aNBo3bszq1atp37598RQUlAq5qfdJ2/gu6tATIJNh1mO61k0lz7+LsUBQnijUeDRv3jxPiO7j\nVESXQHnk0XebkZHBL7/8QsuW/26d/dNPP7Ft2zZatmxJdnY258+f58svv6RevXoEBwfTv39/PD09\n8fX1ZfHixSxevBiAq1evMmDAAHx9tVE54l7plye9tapuHdW6qdLikFnaoxz6JUa125WhdMWnsr+V\nV3b9ikOhxsPf378MxdAPxRktFBdJkhg6dCgGBgZkZGRQtWpVtm3bpqt/5ZVXdMbE2NiYNm3+9X3X\nr1+f/v37c+LECZ2RAEhISGDIkCEsXryYhg0blp0ygmdC0uSSsf9TMvYu0bqpPNqiHPolcqWDvkUT\nCJ6aQo3HxIkTWblyJb17985XJ5PJ2LlzZ6kKVtmRyWR8//33tG/fHkmS2L17N7169eLUqVPIZLJ8\n2QDPnz/P3LlzuXHjBiqVCpVKlScxllqtxt/fn4EDB1aqhFkVnf/6zDVpcaRuHI065KjWTdVtKmbd\np1RYN1VlnxOo7PoVh0KNx7BhwwCYPHlymQnzoiKTyejVqxeTJk3i9OnTurLHGTVqFKNGjWL79u0Y\nGRkxe/ZsEhMTdfXTp0/HyspKN28iKH+oQk+Q9t0o7aI/iyooh6zBqG5HfYslEDwXhYbwNG/eHICO\nHTsW+FMUI0eOxMHBAU9PT13ZgwcP6Nq1K3Xq1KFbt24kJyfr6gIDA6lduzb16tVj3759uvILFy7g\n6elJ7dq1mThx4vPoWG55NOchSRK///47KSkp1K1bt8B5pvT0dKytrTEyMuLChQts375dZ2A2bNjA\nqVOnKu36jYpM27ZtkTQa0vd9SkrQq2jSYlHUaoPNlMOVwnBU9rfyyq5fcSgy/vO3336jadOm2NjY\nYGlpiaWlJUqlssiGR4wYwZ49e/KULVy4kK5du3Lr1i26dOnCwoULAQgODmbr1q0EBwezZ88eAgIC\ndA/QMWPGsHbtWkJCQggJCcnXZkVm8ODB1KhRAzc3Nz755BOCgoKoW7dugalplyxZQmBgIK6urixd\nujSPa+rnn38mIiKCBg0aUKNGDWrUqMGKFSsK7FNMoJctmrR4Ur4aSMYfgYCEWddJWI35CQOrssn0\nJxCUFkXuqlurVi1++eUXGjZs+MxrDcLDw+nduzdXr14FoF69ehw5cgQHBwfu379Px44duXHjBoGB\ngcjlcl3oaY8ePfj4449xdXWlc+fOXL9+HYAtW7Zw+PDhfG/YYoW5AMrf/VbfOc3+T4bS0jIJmbkd\nyiFfYFSvs77FKlEq+5xAZdevVDMJOjs706BBgxJZpBYbG4uDgzaixMHBgdjYWADu3buHt7d3nj6j\no6NRKBQ4Ozvryp2cnIiOji6w7bFjx1KjRg0AlEplHneZ4MXiUQKfR//0ZX187NgxVJd+pVH492jS\nc7igrI9Z2ym0/8dw6Fs+cfziHh8/fpzNmzcD6J6Xz0uRI4/Tp0/z4Ycf0qlTJ4yMtLkQnnZvq/+O\nPGxsbEhKStLVP3pTHD9+PN7e3rz55psAvP322/Ts2RM3NzdmzJjB/v3a7aiPHTvG4sWL+e233/Iq\nIUYeAsrH/dZkJJO2eRyqv7XuVdNO4zB/ZTYyA4Ve5RIICqJURx4ffPABlpaWZGVloVKpnquTRzxy\nVzk6OhITE4O9vXb7BScnJyIjI3XnRUVF4ezsjJOTE1FRUXnKn2XvJ2tra2xtbYsls6DiYG2t3wyB\n6shLpG54C82DCGSmVlgO/hzjhj30KpNAUFoUaTxiYmJ0b/7FpU+fPnz77bdMnz6db7/9Vjfp26dP\nHwYPHsykSZOIjo4mJCQELy8vZDIZSqWSM2fO4OXlxcaNG5kwYcJT93fnzp0SkVvfVCa/a45Gw9aw\ni3x+4zhp6mxyb0Xxbq/XGFXHB1PDivl2LkkSWSc38PCX2ZCrwtClMcrh6zCwc61U964ghH4vLkUa\nD19fX/bu3Uv37t2fqWE/Pz+OHDlCQkICLi4uzJ07lxkzZjBw4EDWrl2Lm5sbP/74I6BdMT1w4EDq\n16+PoaEhQUFBuqigoKAg/P39yczMxNfXlx49xJtcReV8wl0+ufInIanxALR1cOdl80YMqN9Bz5I9\nP1L2Q9J+nEz2xZ8AMGkzAou+80SmP0Glp8g5DwsLCzIyMjAyMkKh0L4ZymQyUlNTy0TAp6GwOQ9B\n+SA2M41l1w7xe5Q2as7ZzIrpni/TwbFWhQ4dzrl/k9QNI8iNvQVG5lgOXIZJ8wH6FksgeGpKdc7j\n4cOHz9WwQKDW5LLx9nnW3DhBZq4aY7khb9fxxr+2FyYVfAI56/w20rZNBlUGBo51Ufqvx9Chjr7F\nEgjKjELjb2/fvl3kxU9zjqD4PAq1q0icjAuj/8F1LL92mMxcNV2q1WHHy28zul6bPIajoukmqbNI\n+3ESaZvGgCoD4xYDsXlvX6GGo6Lp96wI/V5cCh15zJo1i/T0dPr06UOLFi2oVq0akiQRExPD+fPn\n2blzJ5aWlnnySAgE9zJSWHL1IH/G3ALAzcKWGZ5daOPgrmfJik9uQhipG94iJ/oKGBpj0T8QE++h\nFa3dDiMAACAASURBVNr1JhA8L0+c8wgNDWXLli2cOHGCiIgIAFxdXWnbti1+fn64u5ePB4KY89A/\nqtwc1oWc4Ztbp8nW5GBqoGB0vTYMrdUCRQXdMfZxsq/sIm3zBKSsVORVaqIcvhaFcyN9iyUQFIvi\nzHkUOWFeERDGQ78kqzKZeOZnLiZq1+T4Or/EpAadcDC11LNkxUdSZfJw54dknVgPgJHnK1j6rUZu\nWvT+bgJBeac4xkMkxq4AlGe/692HSQw5spGLiVE4mFiyrq0fi1r0eWrDUZ51y4m9RdKK7lrDYWCE\neb8FKEdseCbDUZ71KwmEfi8uRUZbCQSFcflBNONP/0SSKpO6VvZ87v1a5RhtSBJZZzbx8OeZoM7E\noGotLId9hcK5sb5FEwjKDcJtJXgu9kbfYNaFXag0ubS1r8nSln0xVxjrW6xio8lM5eG2yWT/9QsA\nxi0GYjFgMXITCz1LJhCUPKW6zkOj0bBp0ybCwsL48MMPuXv3Lvfv38fLy+u5OhRUbCRJYkPoWZZd\nOwzA625NmNWoK4YlsOuyvlFHXCT1u3fQPIjQLvp7bTEmLd/Qt1gCQbmkyP/4gIAATp06xQ8//ABo\nV5wHBASUumCCfykvftccjYb5l/fpDMekBh35oHG3YhmO8qCbpNGQcfAzklf5onkQgaFTI2wmHygR\nw1Ee9CtNhH4vLkWOPM6cOcNff/1F06ZNAe0wR61Wl7pggvJFujqbqed3ciz2DkZyAz5p3ovuTvX0\nLVax0aTFkfrDONQ3DgJg2mE05r0+QGZY8V1wAkFpUqTxMDIyIjc3V3ccHx9fIomhBE+Pvnf1jM1M\nY9zp7dxIicPayJTVrQbQxO7pt8Z/EvrUTXXrCKnfj0FKi0NmbovloNUYN3y2DUCLQt/3rrQR+r24\nFGk8xo8fz6uvvkpcXByzZs1i+/btzJ8/vyxkE5QDbqXEEXB6O7GZabia2xDU+nVqWNjoW6xiIeWq\nSf9jIZkHV4EkoajVBsshazCwrqZv0QSCCsNTRVtdv36dAwcOANClSxdeeumlUhfsWajs0Vb6yilw\nIvYOk8/tID1HRTM7Z1a26o+1kWmJ9lHWuuU+uEvqd6PIiTgPMjlm3adi1nUSslJaBV/Z80EI/So2\npRJt9fjD2MHBAT8/P+DfB7XI0Fe52R5+mfmX95IrSfR0eol5zXwxNqi4y4IkSSL73FYe/jwDKfsh\ncuvqWA5Zg1EtH32LJhBUSAodebi5uRW64ZtMJitXWfoq+8ijLNFIEquvH+WbW/9v78zjo6yu//+e\nfSaZZLJAEhbZ0bAjsgQBQREQVERUEK24FFfEftWf0NraWlslaKui1rZaQdDWKoICsilV0AjKjuwg\nSBICCUsyyezr/f3xTCYJQhKyTWa479drXs9z72RmzsmTPJ8559zlOwCmXZrFjG5XoY7ixf+C9jPY\nFv0/vD8sB0JLjEx+BXW8/AIkubhplMjj6NGjdbWnRmbPns3777+PWq2mV69ezJ8/H4fDweTJk8nN\nzQ3vMli+J/Xs2bOZN28eGo2G1157jdGjRzeabRcz/mCQZ3esYmnebjQqFb/rM4ZbO0T3rGrvvv9h\n++AxgrYiVAYz5omzMQy4Xa6EK5HUkxqHTQkhWLx4MY8//jhPPvkkn3zySb0+8OjRo7z99tts27aN\nXbt2EQgE+O9//0t2djajRo3i4MGDjBw5kuzsbAD27t3Lhx9+yN69e1m9ejWPPPIIwWCwXjZEG00x\n1twT8PPE5k9Zmrcbk0bH37JubRLhaCzfhNeJbfEsSt+aTNBWhK5TFslPrcc4cEqTCkeszxOQ/l28\n1JjEfuSRRzh8+DBTpkxBCME//vEPvvjiC9588806fWBiYiI6nQ6n04lGo8HpdNK6dWtmz57N+vXr\nAbj77rsZMWIE2dnZLF26lClTpqDT6ejQoQNdunRh06ZNZGVlVXnf6dOn065du/Bn9OrVK1zoKv8D\niNb2rl27GvX91677itf2fc1PrYwk6ow8oG6POHQcQntwRNr/C22vW/Iuri9epr/xOGh0/HDJ7ej7\n3syw1PbNwj7Zlu1ItXNycvjggw8AwvfLulLjaKvMzEz27t0bntsRDAbp3r07+/fvr/OHvvXWWzz5\n5JOYTCbGjBnDe++9R3JyMiUlJYAS7aSkpFBSUsKMGTPIysrizjvvBGDatGmMHTuWW26p2Cta1jzq\njtXr4uENi9htPUELQzz/vHIyl1paRtqsOiECfpz/m4tzzUsQ9KPJuIyEO/8u992QSM5Doy7J3qVL\nF/Ly8sLtvLw8unTpUqcPA2Xr2ldffZWjR49y/Phx7HY777//fpWfUalU1aYWZL66YShy2bjnm3+z\n23qCtnEWFl51Z9QKR+D0T1jfuBHnqtkQ9GO66kGSH18rhUMiaSRqFI+ysjK6devG8OHDGTFiBN27\nd8dms3HjjTcyfvz4C/7ALVu2cOWVV5KamopWq2XixIls3LiRjIwMCgsLAThx4gRpaWkAtGnThvz8\n/PDrjx07Rps2DTO7OVpojLxrnr2Eu7/5N4dtZ+iS0IIFw+7kkvimn/xXX9+EELi+e4/il0bgP7oZ\ntSUDy0MfY775eVQNPCelLsR6zlz6d/FSY83jueeeAyq+7VcOceoSAWRmZvKnP/0Jl8uF0Whk7dq1\nDBw4kPj4eBYsWMCsWbNYsGABEyZMAGD8+PHccccdPPHEExQUFHDo0CG5om89OVh6kgc2fMQZj4Ne\nya14c/BtDT75rykI2k5h++gJvLtXAWC4/GZl+fQIiKBEcrFRqxnmhYWFbN68GZVKxcCBA8NRQV15\n8cUXWbBgAWq1mn79+vGvf/0Lm83GpEmTyMvL+9lQ3RdeeIF58+ah1WqZO3cuY8ZUXX9I1jxqz87i\nAh7ZuIgyn4eslu2ZO2gicVp9pM26YDy7V2P78HGE/RQqYyLmW1/C0G+iTGlKJBdAo+5h/tFHH/HU\nU08xfPhwAL7++mteeuklbrvttjp9YGMgxaN2bDj5E//3/Se4Aj6uadWVF/uPj7pZ40FHMfZPnsaz\n9WMAdF2GkHDH39Akt42wZRJJ9NGoBfM///nPbN68mYULF7Jw4UI2b97Mn/70pzp9mKRuNETe9fOC\n/Uzf+DGugI+b2vXkrwMmNAvhuBDfPLtWUjxnqCIcOhPxE/6M5eFPmrVwxHrOXPp38VLj3UMIQcuW\nFSNwUlNT66xUksiwJHcnf9y+hiCCX3Tuz1M9r4mq5UaCjmLsS36NZ9sSAHSdBpNw+1w0LTtF2DKJ\n5OKlxrTVU089xc6dO7njjjsQQvDhhx/Su3dvXnzxxaaysUZk2ur8vHtoE3/d8xUA0zOH8uBlV0ZV\nXcDzw2fYFj2FsJ8CfRzmG57BOOSXqOSeMhJJvWnUmocQgiVLlvDtt98CMGzYMG6++eY6fVhjIcXj\n5wgheH3fN7x9cCMAv+l1LXd0viLCVtWeoP2MEm1sV5bD0XUeQsLtr6Jp0THClkkksUOj1jxUKhVX\nXHEFY8eO5eWXX2bMmDHYbLY6fZikblxo3nWftYh7c/7D2wc3olGpeKHf9c1WOM7lm2fncornDFGE\nQx+P+ZY5WB75JCqFI9Zz5tK/i5caax5vvfUWb7/9NsXFxRw+fJhjx47x8MMPhzeHkjQfzngcvLH3\nGxbn7kQAyXoTz10+jhGt6r4iQFMStJ/GvvjXeHZ8CoRGUt0+F01qh8gaJpFIfkaNaas+ffqEFyLc\nvn07AL169Qov1tccuNjTVr5ggP8c2co/9n+L3e9Fq1IzpVM/HsocQqLOGGnzaoVnx1JsH89EOM4o\n0cb4ZzEOvlvWNiSSRqRR9vMox2AwYDAYwm2/3x9VBddYRgjB10WHeWnXl+Q6lEUlh6V34qme19Ax\nITXC1tWOYFkRtiW/wbtzGQC6rsOUaCOlfit+SiSSxqXGr3XDhw/n+eefx+l08sUXX3Dbbbdx4403\nNoVtkhDnyrsesZ3m4Y2LePS7xeQ6SuhgTuHNrFt5c/BtUSEcwu/F+dXfWPXoFXh3LkNliMd821+x\nPLwkpoQj1nPm0r+Llxojj+zsbN555x169erFP//5T8aNG8e0adOawjbJOSj1uvnHgW/54MhWAkKQ\noDPwcOYQbu/YD51aE2nzaoV3/1fYP3mawMlDCJ9A32MM5onZMSUaEkmsU6u1rTweD/v370elUpGZ\nmYle37zWQroYah7+YJDFuTt5Y983WL0u1Ki4pUMfHu02jBRDXKTNqxWBM0exf/pMeCFDTcvOmG9+\nHn23ayNsmURycdKoNY8VK1bw0EMP0amTMpv3yJEj4QhE0jR8fyqXObv+x6GyUwAMaNGOWb1Gcpml\nfgtUNhXC41A2afrqb+D3oDLEEzf6KUxXPYAqChdllEgktYg8LrvsMlasWBHeAOrw4cOMGzeOAwcO\nNImBtSEWI49Sr5uVx/ayJHcnO7/fgiGzHW3iLDzZ82qubXVpVAxaEELg2f4JjuXPErQeB8AwYDLx\nNzyDJjEDUHLK5dtlxiLSv+gm1v1r1MgjMTGxys6BnTp1IjExsU4fJqkeIQRbz+SzOPcHvig4gCfo\nByBeq+fhbsO4u8vAZrGYYW3wF+zG/snT+A5vAEB7SR/ME7PRdRgQYcskEklDUGPk8dBDD5GXl8ek\nSZMAWLRoEe3atWPUqFEATJw4sfGtrIFojzxOu+0szdvNJ7k/hIfcAmS17MAt7XtzTauu6KNENIKO\nYhyrsnFveBdEEJW5BfHX/w7jwDvknA2JpJnRqGtb3XPPPVVSJEKIKu358+df8IdarVamTZvGnj17\nUKlUzJ8/n65duzJ58mRyc3N/thnU7NmzmTdvHhqNhtdee43Ro0dXdSIKxcMfDLLh5E8szt3J+sIf\nCYQuQ5rRzM3tezOhXS/axidF2MraI4IB3BsX4lj5AsJZAmoNpqHTiLtuJmqTJdLmSSSSc9Co4tEY\n3H333QwfPpz77rsPv9+Pw+Hg+eefp0WLFsycOZM5c+ZQUlJCdnY2e/fu5Y477mDz5s0UFBRw7bXX\ncvDgQdSVvsVGk3jkO6x8mvsDn+bt4qTbDoBGpWJ4Rhduad+HK9M6oj3rG3pzz7t6D+Vg//S3BI7v\nAUDX9SrMN7+AtlVmja9t7r7VF+lfdBPr/jVqzaOhKS0t5ZtvvmHBggWKAVotFouFZcuWsX79ekAR\nlxEjRpCdnc3SpUuZMmUKOp2ODh060KVLl/ByKdGCN+DnyxOHWJy7k+9O5Yb728cnc3P73tzUrict\njOYIWlg3AmdysS97Fu8PywFQJ1+CecKf0Pe6PioK+hKJpO40uXj89NNPtGzZknvvvZedO3dyxRVX\n8Oqrr1JUVER6ejoA6enpFBUVAXD8+PEqQtG2bVsKCgp+9r7Tp0+nXTtlklliYiK9evUKf2MonyXa\n1O1WvTNZnLuT/36+ArvPgyGzHQa1lp6ngwxL78x9196MSqWq8f3K+yLtT3n7m6++wLNtCX0Kl4Hf\nw/enDRivuJWRj2Sj0psu6P2GDh0acX8asy39i+52rPmXk5PDBx98ABC+X9aVJk9bbdmyhcGDB7Nh\nwwYGDBjA//3f/5GQkMAbb7xBSUlFsTglJYXi4mJmzJhBVlYWd955JwDTpk1j3LhxVQr1zSlt5fR7\n+bxgP4tzf2BHcYXIXWZJ45b2fRjXtjsWfXQsVng2IhjEs20xjs/+SLC0EADDFbcRf8Pv0SS1irB1\nEonkQmn0PczLcbvddfqQyrRt25a2bdsyYIAyZPPWW29l27ZtZGRkUFio3JBOnDhBWpoyAa5Nmzbk\n5+eHX3/s2DHatGlTbzsaEiEEe6yFPLdjDdes/hvPbF/FjuIC4rV6buvQl/8On8qiEfcwpVO/OglH\nc1hfx5e7Detr47D9+2GCpYVo211O0q9WkfiLv9dLOJqDb42J9C+6iXX/6sN501bZ2dlcddVVLFq0\niN/97ncAXHnllWzbtq1eH5iRkcEll1zCwYMHufTSS1m7di09evSgR48eLFiwgFmzZrFgwQImTJgA\nwPjx47njjjt44oknKCgo4NChQwwcOLBeNjQUZT43K/P3sjh3J/tLT4b7+6S05tb2fRjdJpO4KJ9B\nHSgrxPHZn/Fs/i8A6oR04m94BkP/SXLorURyEXPetNWnn37K+vXreeedd+jduzfdunVjzZo1fP75\n52Rm1jyKpjp27tzJtGnT8Hq9dO7cmfnz5xMIBJg0aRJ5eXk/G6r7wgsvMG/ePLRaLXPnzmXMmDFV\nnWjCtJUQgu3FBSw+upPPj+/HHVAm8ll0Rsa368nE9n3oktiiSWxpTITPjWv9P3GufRnhcYBGj2nE\nw8Rd+zjqKCzuSySSn9MoQ3XXrVtHVlYWgwcPZvPmzezbt48bbriBa665hv3797Nx48Z6Gd2QNKV4\nLDq6g+d2rAm3s1q255b2faJqIl91CCHw7l6FfenvCZ45CoC+1zjM4/8YldvASiSS89MoNY81a9Zw\n/fXXc/jwYZ588kk2bdpEXFwc8+fPb1bC0dSMbHUpreMSuf/Swawc9SBvD7md69p2a1ThaKq8q/fw\nBqyv30DZvKkEzxxFk5GJ5eHFWO5b2GjCEes5ZelfdBPr/tWH897xZs+eDSjb0N51111s3bqV06dP\nM2TIEFJSUli+fHmTGdmcSDHEsXrUQzE1j8GXvwPHyhfw7f8SAFV8CvFjZmK88h5UMRBNSSSShqfG\nobozZ87kxRdfBODyyy9n+/btnDp1ipYtWzaJgbWhOQ3VjSb8RQdxrJwdnuSnMpgxXT0d0/CHUBsT\nImydRCJpbJpseZKdO3fSp0+fOn1QYyLF48IIFOfhWP0ini0fgQiCzohp6C+JG/kr1PEpkTZPIpE0\nEY06z6MyzVE4LgYaKu8aLCvCtngWxS8MUobeqtQYr7yHlKc3Yx7/x4gIR6znlKV/0U2s+1cfZEL7\nIiDoKMH51Ru4vn4LfC5QqZSZ4dfNlCOoJBJJnYjIqroNjUxbnRvhseP8+i1cX76BcJcByrDb+LG/\nQduqW4Stk0gkkSaqVtWVND5B+2lcG97F9c2/EPbTAOguHU78uN+ia98vwtZJJJJYQK4vEQXUNu/q\nP7Ef24ePc+a5vjhXZSPsp9G2vwLLI5+Q9PDiZikcsZ5Tlv5FN7HuX32QkUeUI4JBfAe+xLn+H/gO\nrAv367uPxjT8IXRdh8XUnBSJRNI8kDWPKEV4nbi3LMK1/h8ETh5SOvVxGAfcjumqB9CmdYmsgRKJ\npNkjax4XEQHrCdzfvoNrwwJlr3BAbWmFadj9GLPuQh2fHGELJRLJxYCseUQBOTk5+PJ3UPb+QxT/\n6XKca19FOEvQtutHwl1vkfLMNuJGPhaVwhHrOWXpX3QT6/7VBxl5NGOCbjveH5ZjX/J3rGKv0qlS\no+8znrjhD6HtMEDWMyQSSUSQNY9mhggG8B1cj3vzh3h2rVQm9QEqYwLGrLswDZuGJqV+ew9LJBIJ\nyJpHTOA/vkcRjG2LCZYVhft1nQZj6H8bhssnyk2YJJKLACFEVGQUIlLzCAQCXH755dx4440AFBcX\nM2rUKC699FJGjx6N1WoN/+zs2bPp2rUrmZmZfP7555Ewt9EIlBXi/OpvFL80nJKXhuNa9ybBsiI0\nLToRN/bXpPxuK0kzlrM10ClmhSPWc8rSv+imMfwTQmC3eynIL2PPrlN8+00+K5f/yH8W7ubvr23h\ns6WHGvwzG4OIRB5z586le/fu2Gw2QNkvfdSoUcycOZM5c+aQnZ1NdnY2e/fu5cMPP2Tv3r0UFBRw\n7bXXcvDgQdRRvHe28Djw7F6Fe8tHyrwMEQRAFZeE4fKbMfafjLb9FVHxzUMikZwbt9tPqdWN1eqh\ntEQ5Wq1upa/Eg98fPO9r4+JdTWhp3WnymsexY8e45557+O1vf8vLL7/M8uXLyczMZP369aSnp1NY\nWMiIESPYv38/s2fPRq1WM2vWLACuu+46nn32WbKysqo60cxrHsLrxHtgPZ5dK/D+sFzZExxAo0Pf\nfRTG/pPRd78WldYQWUMlEkmt8Hj8lFo9oYcba6lyLG+73YFqX280aUlKMpCUZMSSbCQpyYAlyUhy\nspFEiwGttmm+IEdVzePxxx/npZdeoqysLNxXVFREeno6AOnp6RQVKTn/48ePVxGKtm3bUlBQcM73\nnT59Ou3aKYXkxMREevXqxdChQ4GK0LMp28JZQv9EK97dq/lm/Zfg9zIoQ4kmtohL0V92NVdPfRJ1\nfIry+u82R9Re2ZZt2a5o+3xBeva4glKrh/Vff4PD7qVt616UlrrZsXMzXm+Adm17AZB3bBdAlbZG\no6JP7wFYkowcP74Hs1nHsOHDSEoysmfvFvR6wdChg8Kf53RDvy6N719OTg4ffPCBYm+7+g28adLI\n47PPPmPVqlX87W9/Y926dfz1r39l+fLlJCcnU1JSEv65lJQUiouLmTFjBllZWdx5550ATJs2jXHj\nxjFx4sSqTjSDyEMIQaDwAN49q/HsXoU/d2uV57Xt+qHvMQZD35suePZ3Tk5O+A8h1ohl30D611zx\neAKUlSqRgtXqobS0ImootXpwufyAIgTlolAZrVaNJcmAxWIgKdmIxaJEDpYkA5YkA3FxuqhIPUdN\n5LFhwwaWLVvGypUrcbvdlJWVcdddd4XTVRkZGZw4cYK0tDQA2rRpQ35+fvj1x44do02bNk1pcrWI\ngB/fT9/j3b0Kz+7VBM8crXhSa0B/6XD0Pceg7zEGTWJGxOyUSC42vN5AWAiqE4fzodGosCQZ8QXM\nXH5FOhaLEUuygSSLIhBx8dEhDo1JxOZ5rF+/nr/85S8sX76cmTNnkpqayqxZs8jOzsZqtYYL5nfc\ncQebNm0KF8x//PHHn120pow8gm4b3n3/w7tnNd59axHOipFhqvhU9D1GY+g5Fv2lw1EZ4pvEJonk\nYkMRh5+LgrWW4qDVqkm0GEK1BoMiDkkV0UP8RSIOURN5nE35xfn1r3/NpEmTeOedd+jQoQMfffQR\nAN27d2fSpEl0794drVbLm2++GfEL6t21Ett/pofbmrQu6HuOxdDzOrTt+6NSayJonUQSG/j9wSpi\ncLY4OJ2+al9fHjlc7OLQmMgZ5hdI0H6GsnfvRd9DSUc1xeq10ZpXrg2x7BtI/85HIBCkrMxbVRRK\nQqOWStzY7TWLgxI5VBWFpFDtId7cMOIQ69cvaiOPaERtTiXp0WWRNkMiadYIIbDbvEq9werGGprr\nUD73wVbmobp7llqtiEOVgnSyMSQOBswJehk5RBgZeUgkkgtGCIHb5a+Y/FZFHJRoIhCo/taSmKjH\nEhKGcoEon/eQkKBHrZbi0NjIyEMikTQ45SOWzjdL2uutfiJcXJxOGaGUVDEJLinZGB7iqtFE70oR\nEikeUUEs511j2Tdo3v75/UHKSqsWpSsiiZqL0nq9hjMl++h3eRZJ5bOkK82W1uujf/BIc75+kUaK\nh0QSo/xMHM5aQqM2RWlL5aghLA7KudGk5dtv/Qwd2q2JPIpehD8IvoDy8ATAF0T4AuANPULnwhtA\nZTGi7pUeaZNrRNY8JJIoRAiBw+6jrMyDrcyLrcwTPi8tU9Zcstu81b5H5aK0pZJAlNcdzA00Yima\nEIEgOHxg9yLsHrB5EU4fuHzg9iNcPnD6we1DuPxKvyvUX952+5XXVBaGGuo/lVENaov+5esa0csK\nZM1DIokhhBA4HD7KSn8uDGGxsHkJBqv/p1epqLpshsVQJZIwx2hRWgih3MitbkSpO3wUZR6wexVB\nKBcGuxdsHuVo94LDC43xdVqtAp0a9FrQq0GnQaXXgF4DOo3ynEGDSqdBdVmLRjCg4ZHiEQXEct41\nln2Dc/sXCASx2byUlaeSSj2UlaeUQuc1jVQCpSCdkKgnMdHws2NTjVhqiusnhIAyD6LYBcUuhNUN\npW6E1V1xXuqGktCx1KN8468r8TowG1Al6NngOMyQTn3AqAWTDlWcFow6MGlRxenApFOei9OhMlV6\nzqQDQ0gY9BpUTbRKblMixUMiaWDK5ziUFLs5fKiEgDe3QiSsbmw2b7VzHEBZsttiqSwKBhIT9SRU\nEoimWra7MRDByoLgRBS7ECUhcSgXiRIX4owLSlwXlPYBlBt3kglVkgEsRlRJRkgwoEowQIIelVkP\nZr3SNofaCQaI16GqNApMm5ODLoa/3NQHWfOQSOqAEIKyMi8lxS5Kit2UlLgrzovd1W72A5CQoCcx\nlEoK1x0sBhItyn4OBkN0jlQS/iCUuBCnnYgzTjjjQpwJnZ92Is64EKcdUHyBgmDWo0oxQbIJVbKx\nqjBYjEpf6JwkIyqj/F5cG2TNQyJpBIQQ2Gxeis+4KD5TSRxKlGN1qaW4OB3JKUaSU4xVluxOtCgR\nRLTNcRBBUSEKpxxwyok47VDap8uFwQlWd+1rBgl6VMkmSDGhCj3Kz0ku74uDZCMqg7xVNTfkFYkC\nYrku0Bx8czl9FBe7FZEodlURC5/v/BFEfLyO5FQTycnGsFCkpJhISjZiDH3zbQ7+1YTw+BEnHXDS\nUUkcys8VkeC085yRwgbHYa6M71zRoUIRgNQ4VKkmaBEXOo+DVBOqUJsUU1QIQuXrFxRB/EEP3oAL\nX8CNL+DGGzr6gm68fpdyDPX5gx78QS+BoA9/0Bc69+IP+ggIpV35+fLn2iX14uYev4uw5zXT/K+e\nRFJPPB4/dpsyQslu81Ja6gkJhSIY7mqW746L05GSaiQl1URyiomUcDRhiorUkvAHFSE46UAU2ZXj\nSQdUPre6a/dmFgOqlvGoWsRB6Kg5E4d26NAKsUg2NavicCDow+234/LZcPvteANO3H47Hr8TT/kx\n4MDtd+D1K0eP34EnoDx/aEchX3p0YYFoCuL1yU3yOfVF1jwkUUsgEMRu92Ev84SFwVZJJJSjB6+3\n+vqDXq8mJdVESoopJBIVYmEyNe/vV8LhVUThhB1O2BCFduVx0o4ocsAZZ81pJK0aWsYpwtAyo1h0\nXAAAHshJREFUHlXLkDiEBELVMl6JGiIUKfiDPpxeK05fKU6fFZfPhstXhstvw332ub8sJBRKvzfg\nalBbdGojOo3y0GtM6EPnOrWx4lxjQqc2oNMY0Kj1aNU6NGodWrUerVqPRqUL9YXaaqWtnOsxaRNI\niWuaTe9kzUMSMwgh8HgCOOxeRRjsXhx2L47wuXK02724nNVv+FOOVqsmIVGPOUFPQoIyYikllGJK\nSTU12PLdjcE5xaHSkVJP9W+gVkELE6o0M6r0eFRp8VDpXJVmVlJITTTfw+t3YfcW4/CW4PSFBMFr\nxRESBqe3NCwSyrm1XgKgQo1Jl4BRm4BRZ8agicegDT00cRi1ZgzaOAzhY3z4Z4yhn9NrTOg0RrRq\nA2pV84mqIo0UjyggGvLmtcHj8WMrU2785RPdNn2/gUsu6VVFHGoaqVSOSgXxZkUQyoWh6lEZ1mow\naCImDjVdOxEIKrWG4zbE8TJEgQ1RUKa0ayMOeg2qVgnQyowqIwFVKzOq9HJxMCs1h0ZKIwkh+Orr\ntfTufxkObwkOX4lyDD3s3qpth7ekTqkftUpLnC6ROF0ScXoLJp0FkzYBoy6BOF0iRm0CJl1CuM+k\nTcCkS8SoS8Cgia/XtY+V/73GoMnFIz8/n6lTp3Ly5ElUKhUPPPAAjz32GMXFxUyePJnc3NzwboJJ\nSUkAzJ49m3nz5qHRaHjttdcYPXp0U5stqYZAIKjc/MNpIyWNZCurOX2Ud6wYl6NqylGnU2NO0BNv\n1mOO1xGfoBzL++JD53FxuqiYIS0cXkUMykWhQBEKygWiuiGreg2qDDO0CglDRtUjKaYGFUYhBN6A\nE5vnDHZvMTbPaezeM0r7rD67p5jc3VZau4y1fn+t2kC8PhmzPjkkBknE6SzEh45xOku4z6SzEK+z\nYNCam21keDHT5DWPwsJCCgsL6du3L3a7nSuuuIJPP/2U+fPn06JFC2bOnMmcOXMoKSmpso/55s2b\nw/uYHzx4ELW64tuUrHk0DsGgwOlURCEsAnZvleKzzebF6ah+gb1yytNHlSOEhARlYx+zOSQSZn1U\nrsYq7F7EsTLEsdLQsQyRr5zXWJBuEYeqdQKqNonKsfy8VUKDpZSEELh8ZZR5TlHmOYXNc4oy9yls\nntPhts1zGpv3DL5A7aMDncaIWZ9CvD45JApnneuSw+14fTJ6TcOKnaR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Mxac490iatpKQKD1edYRfasajIObOnUubNm2YOHEiarWakydPApo4/LwIGtDE9sfGxqJU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\ntVv77o7gXOLQ/GZx1lzw+SrURBoSiDYlEWVMJNqYRJSx+Xki4YY41J2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