Skip to content

Instantly share code, notes, and snippets.

@omendezmorales

omendezmorales/investigate-a-dataset-Orlando-Mendez.ipynb

Last active February 13, 2021 16:07
Show Gist options
  • Save omendezmorales/767c1502041370dc584620f444c66939 to your computer and use it in GitHub Desktop.
Save omendezmorales/767c1502041370dc584620f444c66939 to your computer and use it in GitHub Desktop.
Download ZIP
Project_3 Udacity
Display the source blob
Display the rendered blob
Raw
investigate-a-dataset-Orlando-Mendez.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Project 3: Investigating relations between education, employment and income in the Latin America region\n",
"<a id='toc'></a>\n",
"## Table of Contents\n",
"<ul>\n",
"<li><a href=\"#intro\">Introduction</a></li>\n",
"<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
"<li><a href=\"#cleaning\">Data Cleaning</a></li>\n",
"<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
"<li><a href=\"#conclusions\">Conclusions</a></li>\n",
"</ul>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='intro'></a>\n",
"## Introduction\n",
"\n",
"For this project, I have decided to use the data provided in [Gapminder World] (https://www.gapminder.org/data/https://www.gapminder.org/data/) for the countries of the [Latin American region] (https://en.wikipedia.org/wiki/Latin_America). I will concentrate in the following indicators:\n",
"* Income per person (**wealth**). GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2010 U.S. dollars.\n",
"* Years spent in school (**education**). The average number of years of school attended by all people in the age and gender group specified, including primary, secondary and tertiary education.I have chosen to use age group 25+\n",
"* Employment rates (**employability**).Percentage of (fe)male population, age group 15+, that has been employed during the given year.\n",
"\n",
"\n",
"I pretend to answer the following questions:\n",
"1. Is there a relationship between the time spent on education and the Income? of what nature?\n",
"2. Is there a relationship between the time spent on education and the Employment rate? Does more time spent at school guarantee a higher employment rate?\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='wrangling'></a>\n",
"## Data Wrangling\n",
"<a href=\"#toc\">go above</a>\n",
"\n",
"### General Properties"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I begin importing the required modules"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plot\n",
"import numpy as np\n",
"\n",
"%matplotlib inline\n",
"\n",
"latam_countries= ['Argentina','Bolivia','Brazil','Chile','Colombia','Costa Rica','Cuba','Ecuador','El Salvador','Guatemala','Honduras',\\\n",
" 'Mexico','Nicaragua','Panama','Paraguay','Peru','Puerto Rico','Dominican Republic','Uruguay', 'Venezuela']\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First,load the income dataset."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(193, 242)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 193 entries, 0 to 192\n",
"Columns: 242 entries, country to 2040\n",
"dtypes: int64(241), object(1)\n",
"memory usage: 365.0+ KB\n"
]
},
{
"data": {
"text/plain": [
"None"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>1800</th>\n",
" <th>1801</th>\n",
" <th>1802</th>\n",
" <th>1803</th>\n",
" <th>1804</th>\n",
" <th>1805</th>\n",
" <th>1806</th>\n",
" <th>1807</th>\n",
" <th>1808</th>\n",
" <th>...</th>\n",
" <th>2031</th>\n",
" <th>2032</th>\n",
" <th>2033</th>\n",
" <th>2034</th>\n",
" <th>2035</th>\n",
" <th>2036</th>\n",
" <th>2037</th>\n",
" <th>2038</th>\n",
" <th>2039</th>\n",
" <th>2040</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>603</td>\n",
" <td>603</td>\n",
" <td>603</td>\n",
" <td>603</td>\n",
" <td>603</td>\n",
" <td>603</td>\n",
" <td>603</td>\n",
" <td>603</td>\n",
" <td>603</td>\n",
" <td>...</td>\n",
" <td>2550</td>\n",
" <td>2600</td>\n",
" <td>2660</td>\n",
" <td>2710</td>\n",
" <td>2770</td>\n",
" <td>2820</td>\n",
" <td>2880</td>\n",
" <td>2940</td>\n",
" <td>3000</td>\n",
" <td>3060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>667</td>\n",
" <td>667</td>\n",
" <td>667</td>\n",
" <td>667</td>\n",
" <td>667</td>\n",
" <td>668</td>\n",
" <td>668</td>\n",
" <td>668</td>\n",
" <td>668</td>\n",
" <td>...</td>\n",
" <td>19400</td>\n",
" <td>19800</td>\n",
" <td>20200</td>\n",
" <td>20600</td>\n",
" <td>21000</td>\n",
" <td>21500</td>\n",
" <td>21900</td>\n",
" <td>22300</td>\n",
" <td>22800</td>\n",
" <td>23300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Algeria</td>\n",
" <td>715</td>\n",
" <td>716</td>\n",
" <td>717</td>\n",
" <td>718</td>\n",
" <td>719</td>\n",
" <td>720</td>\n",
" <td>721</td>\n",
" <td>722</td>\n",
" <td>723</td>\n",
" <td>...</td>\n",
" <td>14300</td>\n",
" <td>14600</td>\n",
" <td>14900</td>\n",
" <td>15200</td>\n",
" <td>15500</td>\n",
" <td>15800</td>\n",
" <td>16100</td>\n",
" <td>16500</td>\n",
" <td>16800</td>\n",
" <td>17100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Andorra</td>\n",
" <td>1200</td>\n",
" <td>1200</td>\n",
" <td>1200</td>\n",
" <td>1200</td>\n",
" <td>1210</td>\n",
" <td>1210</td>\n",
" <td>1210</td>\n",
" <td>1210</td>\n",
" <td>1220</td>\n",
" <td>...</td>\n",
" <td>73600</td>\n",
" <td>75100</td>\n",
" <td>76700</td>\n",
" <td>78300</td>\n",
" <td>79900</td>\n",
" <td>81500</td>\n",
" <td>83100</td>\n",
" <td>84800</td>\n",
" <td>86500</td>\n",
" <td>88300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Angola</td>\n",
" <td>618</td>\n",
" <td>620</td>\n",
" <td>623</td>\n",
" <td>626</td>\n",
" <td>628</td>\n",
" <td>631</td>\n",
" <td>634</td>\n",
" <td>637</td>\n",
" <td>640</td>\n",
" <td>...</td>\n",
" <td>6110</td>\n",
" <td>6230</td>\n",
" <td>6350</td>\n",
" <td>6480</td>\n",
" <td>6610</td>\n",
" <td>6750</td>\n",
" <td>6880</td>\n",
" <td>7020</td>\n",
" <td>7170</td>\n",
" <td>7310</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 242 columns</p>\n",
"</div>"
],
"text/plain": [
" country 1800 1801 1802 1803 1804 1805 1806 1807 1808 ... \\\n",
"0 Afghanistan 603 603 603 603 603 603 603 603 603 ... \n",
"1 Albania 667 667 667 667 667 668 668 668 668 ... \n",
"2 Algeria 715 716 717 718 719 720 721 722 723 ... \n",
"3 Andorra 1200 1200 1200 1200 1210 1210 1210 1210 1220 ... \n",
"4 Angola 618 620 623 626 628 631 634 637 640 ... \n",
"\n",
" 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 \n",
"0 2550 2600 2660 2710 2770 2820 2880 2940 3000 3060 \n",
"1 19400 19800 20200 20600 21000 21500 21900 22300 22800 23300 \n",
"2 14300 14600 14900 15200 15500 15800 16100 16500 16800 17100 \n",
"3 73600 75100 76700 78300 79900 81500 83100 84800 86500 88300 \n",
"4 6110 6230 6350 6480 6610 6750 6880 7020 7170 7310 \n",
"\n",
"[5 rows x 242 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"income_df = pd.read_csv('income_per_person_gdppercapita_ppp_inflation_adjusted.csv')\n",
"display(income_df.shape)\n",
"display(income_df.info())\n",
"display(income_df.head(5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, we trim the dataset(s) to get only Latin American countries."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>1800</th>\n",
" <th>1801</th>\n",
" <th>1802</th>\n",
" <th>1803</th>\n",
" <th>1804</th>\n",
" <th>1805</th>\n",
" <th>1806</th>\n",
" <th>1807</th>\n",
" <th>1808</th>\n",
" <th>...</th>\n",
" <th>2031</th>\n",
" <th>2032</th>\n",
" <th>2033</th>\n",
" <th>2034</th>\n",
" <th>2035</th>\n",
" <th>2036</th>\n",
" <th>2037</th>\n",
" <th>2038</th>\n",
" <th>2039</th>\n",
" <th>2040</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Argentina</td>\n",
" <td>1640</td>\n",
" <td>1640</td>\n",
" <td>1650</td>\n",
" <td>1650</td>\n",
" <td>1660</td>\n",
" <td>1660</td>\n",
" <td>1670</td>\n",
" <td>1680</td>\n",
" <td>1680</td>\n",
" <td>...</td>\n",
" <td>20800</td>\n",
" <td>21300</td>\n",
" <td>21700</td>\n",
" <td>22100</td>\n",
" <td>22600</td>\n",
" <td>23100</td>\n",
" <td>23500</td>\n",
" <td>24000</td>\n",
" <td>24500</td>\n",
" <td>25000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Bolivia</td>\n",
" <td>854</td>\n",
" <td>854</td>\n",
" <td>854</td>\n",
" <td>854</td>\n",
" <td>854</td>\n",
" <td>854</td>\n",
" <td>854</td>\n",
" <td>854</td>\n",
" <td>855</td>\n",
" <td>...</td>\n",
" <td>9230</td>\n",
" <td>9420</td>\n",
" <td>9620</td>\n",
" <td>9810</td>\n",
" <td>10000</td>\n",
" <td>10200</td>\n",
" <td>10400</td>\n",
" <td>10600</td>\n",
" <td>10900</td>\n",
" <td>11100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Brazil</td>\n",
" <td>1120</td>\n",
" <td>1120</td>\n",
" <td>1120</td>\n",
" <td>1120</td>\n",
" <td>1120</td>\n",
" <td>1120</td>\n",
" <td>1120</td>\n",
" <td>1120</td>\n",
" <td>1120</td>\n",
" <td>...</td>\n",
" <td>17700</td>\n",
" <td>18000</td>\n",
" <td>18400</td>\n",
" <td>18800</td>\n",
" <td>19100</td>\n",
" <td>19500</td>\n",
" <td>19900</td>\n",
" <td>20300</td>\n",
" <td>20800</td>\n",
" <td>21200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>Chile</td>\n",
" <td>744</td>\n",
" <td>744</td>\n",
" <td>744</td>\n",
" <td>744</td>\n",
" <td>744</td>\n",
" <td>744</td>\n",
" <td>744</td>\n",
" <td>744</td>\n",
" <td>744</td>\n",
" <td>...</td>\n",
" <td>30000</td>\n",
" <td>30700</td>\n",
" <td>31300</td>\n",
" <td>31900</td>\n",
" <td>32600</td>\n",
" <td>33200</td>\n",
" <td>33900</td>\n",
" <td>34600</td>\n",
" <td>35300</td>\n",
" <td>36000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>Colombia</td>\n",
" <td>937</td>\n",
" <td>932</td>\n",
" <td>927</td>\n",
" <td>923</td>\n",
" <td>918</td>\n",
" <td>913</td>\n",
" <td>908</td>\n",
" <td>904</td>\n",
" <td>899</td>\n",
" <td>...</td>\n",
" <td>18500</td>\n",
" <td>18900</td>\n",
" <td>19300</td>\n",
" <td>19700</td>\n",
" <td>20100</td>\n",
" <td>20500</td>\n",
" <td>20900</td>\n",
" <td>21300</td>\n",
" <td>21800</td>\n",
" <td>22200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 242 columns</p>\n",
"</div>"
],
"text/plain": [
" country 1800 1801 1802 1803 1804 1805 1806 1807 1808 ... \\\n",
"6 Argentina 1640 1640 1650 1650 1660 1660 1670 1680 1680 ... \n",
"20 Bolivia 854 854 854 854 854 854 854 854 855 ... \n",
"23 Brazil 1120 1120 1120 1120 1120 1120 1120 1120 1120 ... \n",
"34 Chile 744 744 744 744 744 744 744 744 744 ... \n",
"36 Colombia 937 932 927 923 918 913 908 904 899 ... \n",
"\n",
" 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 \n",
"6 20800 21300 21700 22100 22600 23100 23500 24000 24500 25000 \n",
"20 9230 9420 9620 9810 10000 10200 10400 10600 10900 11100 \n",
"23 17700 18000 18400 18800 19100 19500 19900 20300 20800 21200 \n",
"34 30000 30700 31300 31900 32600 33200 33900 34600 35300 36000 \n",
"36 18500 18900 19300 19700 20100 20500 20900 21300 21800 22200 \n",
"\n",
"[5 rows x 242 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"latam_income_df = income_df[income_df['country'].isin(latam_countries)]\n",
"display(latam_income_df.head(5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we load the employment rate datasets:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(179, 33)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 179 entries, 0 to 178\n",
"Data columns (total 33 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 country 179 non-null object \n",
" 1 1991 179 non-null float64\n",
" 2 1992 179 non-null float64\n",
" 3 1993 179 non-null float64\n",
" 4 1994 179 non-null float64\n",
" 5 1995 179 non-null float64\n",
" 6 1996 179 non-null float64\n",
" 7 1997 179 non-null float64\n",
" 8 1998 179 non-null float64\n",
" 9 1999 179 non-null float64\n",
" 10 2000 179 non-null float64\n",
" 11 2001 179 non-null float64\n",
" 12 2002 179 non-null float64\n",
" 13 2003 179 non-null float64\n",
" 14 2004 179 non-null float64\n",
" 15 2005 179 non-null float64\n",
" 16 2006 179 non-null float64\n",
" 17 2007 179 non-null float64\n",
" 18 2008 179 non-null float64\n",
" 19 2009 179 non-null float64\n",
" 20 2010 179 non-null float64\n",
" 21 2011 179 non-null float64\n",
" 22 2012 179 non-null float64\n",
" 23 2013 179 non-null float64\n",
" 24 2014 179 non-null float64\n",
" 25 2015 179 non-null float64\n",
" 26 2016 179 non-null float64\n",
" 27 2017 179 non-null float64\n",
" 28 2018 179 non-null float64\n",
" 29 2019 179 non-null float64\n",
" 30 2020 179 non-null float64\n",
" 31 2021 179 non-null float64\n",
" 32 2022 179 non-null float64\n",
"dtypes: float64(32), object(1)\n",
"memory usage: 46.3+ KB\n"
]
},
{
"data": {
"text/plain": [
"None"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#For women\n",
"employment_women_df = pd.read_csv('females_aged_15plus_employment_rate_percent.csv')\n",
"display(employment_women_df.shape)\n",
"display(employment_women_df.info())"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>1991</th>\n",
" <th>1992</th>\n",
" <th>1993</th>\n",
" <th>1994</th>\n",
" <th>1995</th>\n",
" <th>1996</th>\n",
" <th>1997</th>\n",
" <th>1998</th>\n",
" <th>1999</th>\n",
" <th>...</th>\n",
" <th>2013</th>\n",
" <th>2014</th>\n",
" <th>2015</th>\n",
" <th>2016</th>\n",
" <th>2017</th>\n",
" <th>2018</th>\n",
" <th>2019</th>\n",
" <th>2020</th>\n",
" <th>2021</th>\n",
" <th>2022</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Argentina</td>\n",
" <td>0.424</td>\n",
" <td>0.428</td>\n",
" <td>0.405</td>\n",
" <td>0.404</td>\n",
" <td>0.364</td>\n",
" <td>0.383</td>\n",
" <td>0.397</td>\n",
" <td>0.419</td>\n",
" <td>0.413</td>\n",
" <td>...</td>\n",
" <td>0.437</td>\n",
" <td>0.432</td>\n",
" <td>0.435</td>\n",
" <td>0.429</td>\n",
" <td>0.428</td>\n",
" <td>0.429</td>\n",
" <td>0.429</td>\n",
" <td>0.426</td>\n",
" <td>0.423</td>\n",
" <td>0.420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Bolivia</td>\n",
" <td>0.525</td>\n",
" <td>0.530</td>\n",
" <td>0.536</td>\n",
" <td>0.554</td>\n",
" <td>0.552</td>\n",
" <td>0.548</td>\n",
" <td>0.573</td>\n",
" <td>0.567</td>\n",
" <td>0.561</td>\n",
" <td>...</td>\n",
" <td>0.571</td>\n",
" <td>0.592</td>\n",
" <td>0.524</td>\n",
" <td>0.528</td>\n",
" <td>0.529</td>\n",
" <td>0.530</td>\n",
" <td>0.531</td>\n",
" <td>0.532</td>\n",
" <td>0.534</td>\n",
" <td>0.536</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Brazil</td>\n",
" <td>0.397</td>\n",
" <td>0.403</td>\n",
" <td>0.414</td>\n",
" <td>0.420</td>\n",
" <td>0.432</td>\n",
" <td>0.433</td>\n",
" <td>0.436</td>\n",
" <td>0.436</td>\n",
" <td>0.441</td>\n",
" <td>...</td>\n",
" <td>0.483</td>\n",
" <td>0.486</td>\n",
" <td>0.480</td>\n",
" <td>0.461</td>\n",
" <td>0.450</td>\n",
" <td>0.457</td>\n",
" <td>0.459</td>\n",
" <td>0.458</td>\n",
" <td>0.456</td>\n",
" <td>0.455</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 33 columns</p>\n",
"</div>"
],
"text/plain": [
" country 1991 1992 1993 1994 1995 1996 1997 1998 1999 \\\n",
"4 Argentina 0.424 0.428 0.405 0.404 0.364 0.383 0.397 0.419 0.413 \n",
"18 Bolivia 0.525 0.530 0.536 0.554 0.552 0.548 0.573 0.567 0.561 \n",
"21 Brazil 0.397 0.403 0.414 0.420 0.432 0.433 0.436 0.436 0.441 \n",
"\n",
" ... 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 \n",
"4 ... 0.437 0.432 0.435 0.429 0.428 0.429 0.429 0.426 0.423 0.420 \n",
"18 ... 0.571 0.592 0.524 0.528 0.529 0.530 0.531 0.532 0.534 0.536 \n",
"21 ... 0.483 0.486 0.480 0.461 0.450 0.457 0.459 0.458 0.456 0.455 \n",
"\n",
"[3 rows x 33 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"latam_women_employment_df = employment_women_df[employment_women_df['country'].isin(latam_countries)]\n",
"display(latam_women_employment_df.head(3))"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(179, 33)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 179 entries, 0 to 178\n",
"Data columns (total 33 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 country 179 non-null object \n",
" 1 1991 179 non-null float64\n",
" 2 1992 179 non-null float64\n",
" 3 1993 179 non-null float64\n",
" 4 1994 179 non-null float64\n",
" 5 1995 179 non-null float64\n",
" 6 1996 179 non-null float64\n",
" 7 1997 179 non-null float64\n",
" 8 1998 179 non-null float64\n",
" 9 1999 179 non-null float64\n",
" 10 2000 179 non-null float64\n",
" 11 2001 179 non-null float64\n",
" 12 2002 179 non-null float64\n",
" 13 2003 179 non-null float64\n",
" 14 2004 179 non-null float64\n",
" 15 2005 179 non-null float64\n",
" 16 2006 179 non-null float64\n",
" 17 2007 179 non-null float64\n",
" 18 2008 179 non-null float64\n",
" 19 2009 179 non-null float64\n",
" 20 2010 179 non-null float64\n",
" 21 2011 179 non-null float64\n",
" 22 2012 179 non-null float64\n",
" 23 2013 179 non-null float64\n",
" 24 2014 179 non-null float64\n",
" 25 2015 179 non-null float64\n",
" 26 2016 179 non-null float64\n",
" 27 2017 179 non-null float64\n",
" 28 2018 179 non-null float64\n",
" 29 2019 179 non-null float64\n",
" 30 2020 179 non-null float64\n",
" 31 2021 179 non-null float64\n",
" 32 2022 179 non-null float64\n",
"dtypes: float64(32), object(1)\n",
"memory usage: 46.3+ KB\n"
]
},
{
"data": {
"text/plain": [
"None"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#for men\n",
"employment_men_df = pd.read_csv('males_aged_15plus_employment_rate_percent.csv')\n",
"display(employment_men_df.shape)\n",
"display(employment_men_df.info())"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>1991</th>\n",
" <th>1992</th>\n",
" <th>1993</th>\n",
" <th>1994</th>\n",
" <th>1995</th>\n",
" <th>1996</th>\n",
" <th>1997</th>\n",
" <th>1998</th>\n",
" <th>1999</th>\n",
" <th>...</th>\n",
" <th>2013</th>\n",
" <th>2014</th>\n",
" <th>2015</th>\n",
" <th>2016</th>\n",
" <th>2017</th>\n",
" <th>2018</th>\n",
" <th>2019</th>\n",
" <th>2020</th>\n",
" <th>2021</th>\n",
" <th>2022</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Argentina</td>\n",
" <td>0.715</td>\n",
" <td>0.708</td>\n",
" <td>0.689</td>\n",
" <td>0.675</td>\n",
" <td>0.627</td>\n",
" <td>0.633</td>\n",
" <td>0.655</td>\n",
" <td>0.665</td>\n",
" <td>0.653</td>\n",
" <td>...</td>\n",
" <td>0.694</td>\n",
" <td>0.687</td>\n",
" <td>0.687</td>\n",
" <td>0.674</td>\n",
" <td>0.675</td>\n",
" <td>0.676</td>\n",
" <td>0.676</td>\n",
" <td>0.673</td>\n",
" <td>0.669</td>\n",
" <td>0.666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Bolivia</td>\n",
" <td>0.768</td>\n",
" <td>0.769</td>\n",
" <td>0.761</td>\n",
" <td>0.788</td>\n",
" <td>0.789</td>\n",
" <td>0.777</td>\n",
" <td>0.802</td>\n",
" <td>0.795</td>\n",
" <td>0.788</td>\n",
" <td>...</td>\n",
" <td>0.783</td>\n",
" <td>0.807</td>\n",
" <td>0.777</td>\n",
" <td>0.780</td>\n",
" <td>0.779</td>\n",
" <td>0.778</td>\n",
" <td>0.778</td>\n",
" <td>0.778</td>\n",
" <td>0.778</td>\n",
" <td>0.778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Brazil</td>\n",
" <td>0.790</td>\n",
" <td>0.780</td>\n",
" <td>0.779</td>\n",
" <td>0.777</td>\n",
" <td>0.773</td>\n",
" <td>0.766</td>\n",
" <td>0.757</td>\n",
" <td>0.746</td>\n",
" <td>0.736</td>\n",
" <td>...</td>\n",
" <td>0.718</td>\n",
" <td>0.714</td>\n",
" <td>0.700</td>\n",
" <td>0.673</td>\n",
" <td>0.659</td>\n",
" <td>0.666</td>\n",
" <td>0.667</td>\n",
" <td>0.668</td>\n",
" <td>0.668</td>\n",
" <td>0.667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 33 columns</p>\n",
"</div>"
],
"text/plain": [
" country 1991 1992 1993 1994 1995 1996 1997 1998 1999 \\\n",
"4 Argentina 0.715 0.708 0.689 0.675 0.627 0.633 0.655 0.665 0.653 \n",
"18 Bolivia 0.768 0.769 0.761 0.788 0.789 0.777 0.802 0.795 0.788 \n",
"21 Brazil 0.790 0.780 0.779 0.777 0.773 0.766 0.757 0.746 0.736 \n",
"\n",
" ... 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 \n",
"4 ... 0.694 0.687 0.687 0.674 0.675 0.676 0.676 0.673 0.669 0.666 \n",
"18 ... 0.783 0.807 0.777 0.780 0.779 0.778 0.778 0.778 0.778 0.778 \n",
"21 ... 0.718 0.714 0.700 0.673 0.659 0.666 0.667 0.668 0.668 0.667 \n",
"\n",
"[3 rows x 33 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"latam_men_employment_df = employment_men_df[employment_men_df['country'].isin(latam_countries)]\n",
"display(latam_men_employment_df.head(3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we will load the information about the number of years in school for men:"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(174, 41)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 174 entries, 0 to 173\n",
"Data columns (total 41 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 country 174 non-null object \n",
" 1 1970 174 non-null float64\n",
" 2 1971 174 non-null float64\n",
" 3 1972 174 non-null float64\n",
" 4 1973 174 non-null float64\n",
" 5 1974 174 non-null float64\n",
" 6 1975 174 non-null float64\n",
" 7 1976 174 non-null float64\n",
" 8 1977 174 non-null float64\n",
" 9 1978 174 non-null float64\n",
" 10 1979 174 non-null float64\n",
" 11 1980 174 non-null float64\n",
" 12 1981 174 non-null float64\n",
" 13 1982 174 non-null float64\n",
" 14 1983 174 non-null float64\n",
" 15 1984 174 non-null float64\n",
" 16 1985 174 non-null float64\n",
" 17 1986 174 non-null float64\n",
" 18 1987 174 non-null float64\n",
" 19 1988 174 non-null float64\n",
" 20 1989 174 non-null float64\n",
" 21 1990 174 non-null float64\n",
" 22 1991 174 non-null float64\n",
" 23 1992 174 non-null float64\n",
" 24 1993 174 non-null float64\n",
" 25 1994 174 non-null float64\n",
" 26 1995 174 non-null float64\n",
" 27 1996 174 non-null float64\n",
" 28 1997 174 non-null float64\n",
" 29 1998 174 non-null float64\n",
" 30 1999 174 non-null float64\n",
" 31 2000 174 non-null float64\n",
" 32 2001 174 non-null float64\n",
" 33 2002 174 non-null float64\n",
" 34 2003 174 non-null float64\n",
" 35 2004 174 non-null float64\n",
" 36 2005 174 non-null float64\n",
" 37 2006 174 non-null float64\n",
" 38 2007 174 non-null float64\n",
" 39 2008 174 non-null float64\n",
" 40 2009 174 non-null float64\n",
"dtypes: float64(40), object(1)\n",
"memory usage: 55.9+ KB\n"
]
},
{
"data": {
"text/plain": [
"None"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"schooling_men_25_yo_n_older_df = pd.read_csv('mean_years_in_school_men_25_years_and_older.csv')\n",
"display(schooling_men_25_yo_n_older_df.shape)\n",
"display(schooling_men_25_yo_n_older_df.info())"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>1970</th>\n",
" <th>1971</th>\n",
" <th>1972</th>\n",
" <th>1973</th>\n",
" <th>1974</th>\n",
" <th>1975</th>\n",
" <th>1976</th>\n",
" <th>1977</th>\n",
" <th>1978</th>\n",
" <th>...</th>\n",
" <th>2000</th>\n",
" <th>2001</th>\n",
" <th>2002</th>\n",
" <th>2003</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Argentina</td>\n",
" <td>5.9</td>\n",
" <td>6.0</td>\n",
" <td>6.1</td>\n",
" <td>6.2</td>\n",
" <td>6.3</td>\n",
" <td>6.4</td>\n",
" <td>6.6</td>\n",
" <td>6.7</td>\n",
" <td>6.8</td>\n",
" <td>...</td>\n",
" <td>8.9</td>\n",
" <td>9.0</td>\n",
" <td>9.1</td>\n",
" <td>9.2</td>\n",
" <td>9.3</td>\n",
" <td>9.4</td>\n",
" <td>9.5</td>\n",
" <td>9.6</td>\n",
" <td>9.7</td>\n",
" <td>9.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Bolivia</td>\n",
" <td>3.3</td>\n",
" <td>3.4</td>\n",
" <td>3.5</td>\n",
" <td>3.7</td>\n",
" <td>3.8</td>\n",
" <td>3.9</td>\n",
" <td>4.1</td>\n",
" <td>4.2</td>\n",
" <td>4.3</td>\n",
" <td>...</td>\n",
" <td>7.3</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>8.2</td>\n",
" <td>8.3</td>\n",
" <td>8.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Brazil</td>\n",
" <td>2.5</td>\n",
" <td>2.6</td>\n",
" <td>2.7</td>\n",
" <td>2.8</td>\n",
" <td>2.9</td>\n",
" <td>3.0</td>\n",
" <td>3.1</td>\n",
" <td>3.2</td>\n",
" <td>3.3</td>\n",
" <td>...</td>\n",
" <td>5.7</td>\n",
" <td>5.8</td>\n",
" <td>6.0</td>\n",
" <td>6.1</td>\n",
" <td>6.2</td>\n",
" <td>6.3</td>\n",
" <td>6.4</td>\n",
" <td>6.6</td>\n",
" <td>6.7</td>\n",
" <td>6.8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 41 columns</p>\n",
"</div>"
],
"text/plain": [
" country 1970 1971 1972 1973 1974 1975 1976 1977 1978 ... \\\n",
"5 Argentina 5.9 6.0 6.1 6.2 6.3 6.4 6.6 6.7 6.8 ... \n",
"17 Bolivia 3.3 3.4 3.5 3.7 3.8 3.9 4.1 4.2 4.3 ... \n",
"20 Brazil 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2 3.3 ... \n",
"\n",
" 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 \n",
"5 8.9 9.0 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 \n",
"17 7.3 7.4 7.5 7.6 7.8 7.9 8.0 8.2 8.3 8.4 \n",
"20 5.7 5.8 6.0 6.1 6.2 6.3 6.4 6.6 6.7 6.8 \n",
"\n",
"[3 rows x 41 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"schooling_latam_male_age_25_older_df =schooling_men_25_yo_n_older_df[schooling_men_25_yo_n_older_df['country'].isin(latam_countries)]\n",
"display(schooling_latam_male_age_25_older_df.head(3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and then for women:"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(174, 41)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 174 entries, 0 to 173\n",
"Data columns (total 41 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 country 174 non-null object \n",
" 1 1970 174 non-null float64\n",
" 2 1971 174 non-null float64\n",
" 3 1972 174 non-null float64\n",
" 4 1973 174 non-null float64\n",
" 5 1974 174 non-null float64\n",
" 6 1975 174 non-null float64\n",
" 7 1976 174 non-null float64\n",
" 8 1977 174 non-null float64\n",
" 9 1978 174 non-null float64\n",
" 10 1979 174 non-null float64\n",
" 11 1980 174 non-null float64\n",
" 12 1981 174 non-null float64\n",
" 13 1982 174 non-null float64\n",
" 14 1983 174 non-null float64\n",
" 15 1984 174 non-null float64\n",
" 16 1985 174 non-null float64\n",
" 17 1986 174 non-null float64\n",
" 18 1987 174 non-null float64\n",
" 19 1988 174 non-null float64\n",
" 20 1989 174 non-null float64\n",
" 21 1990 174 non-null float64\n",
" 22 1991 174 non-null float64\n",
" 23 1992 174 non-null float64\n",
" 24 1993 174 non-null float64\n",
" 25 1994 174 non-null float64\n",
" 26 1995 174 non-null float64\n",
" 27 1996 174 non-null float64\n",
" 28 1997 174 non-null float64\n",
" 29 1998 174 non-null float64\n",
" 30 1999 174 non-null float64\n",
" 31 2000 174 non-null float64\n",
" 32 2001 174 non-null float64\n",
" 33 2002 174 non-null float64\n",
" 34 2003 174 non-null float64\n",
" 35 2004 174 non-null float64\n",
" 36 2005 174 non-null float64\n",
" 37 2006 174 non-null float64\n",
" 38 2007 174 non-null float64\n",
" 39 2008 174 non-null float64\n",
" 40 2009 174 non-null float64\n",
"dtypes: float64(40), object(1)\n",
"memory usage: 55.9+ KB\n"
]
},
{
"data": {
"text/plain": [
"None"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"schooling_women_ages_15_older_df = pd.read_csv('mean_years_in_school_women_25_years_and_older.csv')\n",
"display(schooling_women_ages_15_older_df.shape)\n",
"display(schooling_women_ages_15_older_df.info()) "
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>1970</th>\n",
" <th>1971</th>\n",
" <th>1972</th>\n",
" <th>1973</th>\n",
" <th>1974</th>\n",
" <th>1975</th>\n",
" <th>1976</th>\n",
" <th>1977</th>\n",
" <th>1978</th>\n",
" <th>...</th>\n",
" <th>2000</th>\n",
" <th>2001</th>\n",
" <th>2002</th>\n",
" <th>2003</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Argentina</td>\n",
" <td>5.5</td>\n",
" <td>5.6</td>\n",
" <td>5.7</td>\n",
" <td>5.9</td>\n",
" <td>6.0</td>\n",
" <td>6.1</td>\n",
" <td>6.2</td>\n",
" <td>6.3</td>\n",
" <td>6.5</td>\n",
" <td>...</td>\n",
" <td>9.1</td>\n",
" <td>9.2</td>\n",
" <td>9.3</td>\n",
" <td>9.4</td>\n",
" <td>9.5</td>\n",
" <td>9.6</td>\n",
" <td>9.8</td>\n",
" <td>9.9</td>\n",
" <td>10.0</td>\n",
" <td>10.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Bolivia</td>\n",
" <td>1.7</td>\n",
" <td>1.8</td>\n",
" <td>1.9</td>\n",
" <td>2.0</td>\n",
" <td>2.1</td>\n",
" <td>2.2</td>\n",
" <td>2.3</td>\n",
" <td>2.4</td>\n",
" <td>2.5</td>\n",
" <td>...</td>\n",
" <td>5.3</td>\n",
" <td>5.5</td>\n",
" <td>5.7</td>\n",
" <td>5.8</td>\n",
" <td>6.0</td>\n",
" <td>6.1</td>\n",
" <td>6.3</td>\n",
" <td>6.4</td>\n",
" <td>6.6</td>\n",
" <td>6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Brazil</td>\n",
" <td>2.1</td>\n",
" <td>2.2</td>\n",
" <td>2.3</td>\n",
" <td>2.4</td>\n",
" <td>2.5</td>\n",
" <td>2.6</td>\n",
" <td>2.7</td>\n",
" <td>2.8</td>\n",
" <td>2.9</td>\n",
" <td>...</td>\n",
" <td>5.8</td>\n",
" <td>5.9</td>\n",
" <td>6.1</td>\n",
" <td>6.2</td>\n",
" <td>6.4</td>\n",
" <td>6.5</td>\n",
" <td>6.7</td>\n",
" <td>6.8</td>\n",
" <td>7.0</td>\n",
" <td>7.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 41 columns</p>\n",
"</div>"
],
"text/plain": [
" country 1970 1971 1972 1973 1974 1975 1976 1977 1978 ... \\\n",
"5 Argentina 5.5 5.6 5.7 5.9 6.0 6.1 6.2 6.3 6.5 ... \n",
"17 Bolivia 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 ... \n",
"20 Brazil 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 ... \n",
"\n",
" 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 \n",
"5 9.1 9.2 9.3 9.4 9.5 9.6 9.8 9.9 10.0 10.1 \n",
"17 5.3 5.5 5.7 5.8 6.0 6.1 6.3 6.4 6.6 6.7 \n",
"20 5.8 5.9 6.1 6.2 6.4 6.5 6.7 6.8 7.0 7.2 \n",
"\n",
"[3 rows x 41 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#selecting LatAm countries\n",
"schooling_latam_female_age_25_older_df = schooling_women_ages_15_older_df[schooling_women_ages_15_older_df['country'].isin(latam_countries)]\n",
"display(schooling_latam_female_age_25_older_df.head(3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='cleaning'></a>\n",
"### Data Cleaning\n",
"<li><a href=\"#toc\">go above</a></li>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have our datasets containing data from the Latam Region, we need to choose a common period for all of them. Looking at the available years in all datasets, I choose the period 1990-2015, *except* that for **employment rate**, the dataset contains no data prior 1991, and for **education**, there is no data available after 2009."
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"country string\n",
"1990 int64\n",
"1991 int64\n",
"1992 int64\n",
"1993 int64\n",
"1994 int64\n",
"1995 int64\n",
"1996 int64\n",
"1997 int64\n",
"1998 int64\n",
"1999 int64\n",
"2000 int64\n",
"2001 int64\n",
"2002 int64\n",
"2003 int64\n",
"2004 int64\n",
"2005 int64\n",
"2006 int64\n",
"2007 int64\n",
"2008 int64\n",
"2009 int64\n",
"2010 int64\n",
"2011 int64\n",
"2012 int64\n",
"2013 int64\n",
"2014 int64\n",
"2015 int64\n",
"dtype: object"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#slice and dice the income dataset to have only the years required\n",
"latam_income_df = pd.concat([latam_income_df['country'], latam_income_df.iloc[:,191:217]], axis=1)\n",
"\n",
"#convert country to string type\n",
"latam_income_df['country'] = latam_income_df['country'].convert_dtypes()\n",
"display(latam_income_df.dtypes)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 19 entries, 4 to 174\n",
"Data columns (total 26 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 country 19 non-null string \n",
" 1 1991 19 non-null float64\n",
" 2 1992 19 non-null float64\n",
" 3 1993 19 non-null float64\n",
" 4 1994 19 non-null float64\n",
" 5 1995 19 non-null float64\n",
" 6 1996 19 non-null float64\n",
" 7 1997 19 non-null float64\n",
" 8 1998 19 non-null float64\n",
" 9 1999 19 non-null float64\n",
" 10 2000 19 non-null float64\n",
" 11 2001 19 non-null float64\n",
" 12 2002 19 non-null float64\n",
" 13 2003 19 non-null float64\n",
" 14 2004 19 non-null float64\n",
" 15 2005 19 non-null float64\n",
" 16 2006 19 non-null float64\n",
" 17 2007 19 non-null float64\n",
" 18 2008 19 non-null float64\n",
" 19 2009 19 non-null float64\n",
" 20 2010 19 non-null float64\n",
" 21 2011 19 non-null float64\n",
" 22 2012 19 non-null float64\n",
" 23 2013 19 non-null float64\n",
" 24 2014 19 non-null float64\n",
" 25 2015 19 non-null float64\n",
"dtypes: float64(25), string(1)\n",
"memory usage: 4.0 KB\n"
]
},
{
"data": {
"text/plain": [
"None"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#drop last 7 cols\n",
"latam_men_employment_df = latam_men_employment_df.drop(['2016','2017','2018','2019','2020','2021','2022'], axis =1)\n",
"\n",
"#convert country to string type\n",
"latam_men_employment_df['country'] =latam_men_employment_df['country'].convert_dtypes()\n",
"display(latam_men_employment_df.info())"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 19 entries, 4 to 174\n",
"Data columns (total 26 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 country 19 non-null string \n",
" 1 1991 19 non-null float64\n",
" 2 1992 19 non-null float64\n",
" 3 1993 19 non-null float64\n",
" 4 1994 19 non-null float64\n",
" 5 1995 19 non-null float64\n",
" 6 1996 19 non-null float64\n",
" 7 1997 19 non-null float64\n",
" 8 1998 19 non-null float64\n",
" 9 1999 19 non-null float64\n",
" 10 2000 19 non-null float64\n",
" 11 2001 19 non-null float64\n",
" 12 2002 19 non-null float64\n",
" 13 2003 19 non-null float64\n",
" 14 2004 19 non-null float64\n",
" 15 2005 19 non-null float64\n",
" 16 2006 19 non-null float64\n",
" 17 2007 19 non-null float64\n",
" 18 2008 19 non-null float64\n",
" 19 2009 19 non-null float64\n",
" 20 2010 19 non-null float64\n",
" 21 2011 19 non-null float64\n",
" 22 2012 19 non-null float64\n",
" 23 2013 19 non-null float64\n",
" 24 2014 19 non-null float64\n",
" 25 2015 19 non-null float64\n",
"dtypes: float64(25), string(1)\n",
"memory usage: 4.0 KB\n"
]
},
{
"data": {
"text/plain": [
"None"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#drop last 7 cols\n",
"latam_women_employment_df = latam_women_employment_df.drop(['2016','2017','2018','2019','2020','2021','2022'], axis =1)\n",
"\n",
"#convert country to string type\n",
"latam_women_employment_df['country'] =latam_women_employment_df['country'].convert_dtypes()\n",
"display(latam_men_employment_df.info())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I remove year columns prior to 1990"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>1990</th>\n",
" <th>1991</th>\n",
" <th>1992</th>\n",
" <th>1993</th>\n",
" <th>1994</th>\n",
" <th>1995</th>\n",
" <th>1996</th>\n",
" <th>1997</th>\n",
" <th>1998</th>\n",
" <th>...</th>\n",
" <th>2000</th>\n",
" <th>2001</th>\n",
" <th>2002</th>\n",
" <th>2003</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Argentina</td>\n",
" <td>8.0</td>\n",
" <td>8.1</td>\n",
" <td>8.2</td>\n",
" <td>8.3</td>\n",
" <td>8.4</td>\n",
" <td>8.5</td>\n",
" <td>8.6</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>...</td>\n",
" <td>8.9</td>\n",
" <td>9.0</td>\n",
" <td>9.1</td>\n",
" <td>9.2</td>\n",
" <td>9.3</td>\n",
" <td>9.4</td>\n",
" <td>9.5</td>\n",
" <td>9.6</td>\n",
" <td>9.7</td>\n",
" <td>9.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Bolivia</td>\n",
" <td>5.9</td>\n",
" <td>6.1</td>\n",
" <td>6.2</td>\n",
" <td>6.3</td>\n",
" <td>6.5</td>\n",
" <td>6.6</td>\n",
" <td>6.7</td>\n",
" <td>6.9</td>\n",
" <td>7.0</td>\n",
" <td>...</td>\n",
" <td>7.3</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>8.2</td>\n",
" <td>8.3</td>\n",
" <td>8.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Brazil</td>\n",
" <td>4.6</td>\n",
" <td>4.7</td>\n",
" <td>4.8</td>\n",
" <td>4.9</td>\n",
" <td>5.0</td>\n",
" <td>5.1</td>\n",
" <td>5.2</td>\n",
" <td>5.4</td>\n",
" <td>5.5</td>\n",
" <td>...</td>\n",
" <td>5.7</td>\n",
" <td>5.8</td>\n",
" <td>6.0</td>\n",
" <td>6.1</td>\n",
" <td>6.2</td>\n",
" <td>6.3</td>\n",
" <td>6.4</td>\n",
" <td>6.6</td>\n",
" <td>6.7</td>\n",
" <td>6.8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" country 1990 1991 1992 1993 1994 1995 1996 1997 1998 ... \\\n",
"5 Argentina 8.0 8.1 8.2 8.3 8.4 8.5 8.6 8.6 8.7 ... \n",
"17 Bolivia 5.9 6.1 6.2 6.3 6.5 6.6 6.7 6.9 7.0 ... \n",
"20 Brazil 4.6 4.7 4.8 4.9 5.0 5.1 5.2 5.4 5.5 ... \n",
"\n",
" 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 \n",
"5 8.9 9.0 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 \n",
"17 7.3 7.4 7.5 7.6 7.8 7.9 8.0 8.2 8.3 8.4 \n",
"20 5.7 5.8 6.0 6.1 6.2 6.3 6.4 6.6 6.7 6.8 \n",
"\n",
"[3 rows x 21 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"drop_years = [str(s) for s in np.arange(1970,1990)]\n",
"schooling_latam_male_age_25_older_df = schooling_latam_male_age_25_older_df.drop(drop_years, axis =1)\n",
"display(schooling_latam_male_age_25_older_df.head(3))\n"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 19 entries, 5 to 169\n",
"Data columns (total 21 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 country 19 non-null string \n",
" 1 1990 19 non-null float64\n",
" 2 1991 19 non-null float64\n",
" 3 1992 19 non-null float64\n",
" 4 1993 19 non-null float64\n",
" 5 1994 19 non-null float64\n",
" 6 1995 19 non-null float64\n",
" 7 1996 19 non-null float64\n",
" 8 1997 19 non-null float64\n",
" 9 1998 19 non-null float64\n",
" 10 1999 19 non-null float64\n",
" 11 2000 19 non-null float64\n",
" 12 2001 19 non-null float64\n",
" 13 2002 19 non-null float64\n",
" 14 2003 19 non-null float64\n",
" 15 2004 19 non-null float64\n",
" 16 2005 19 non-null float64\n",
" 17 2006 19 non-null float64\n",
" 18 2007 19 non-null float64\n",
" 19 2008 19 non-null float64\n",
" 20 2009 19 non-null float64\n",
"dtypes: float64(20), string(1)\n",
"memory usage: 3.3 KB\n"
]
},
{
"data": {
"text/plain": [
"None"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"schooling_latam_female_age_25_older_df = schooling_latam_female_age_25_older_df.drop(drop_years, axis =1)\n",
"\n",
"#convert country to string type\n",
"schooling_latam_female_age_25_older_df['country'] = schooling_latam_female_age_25_older_df['country'].convert_dtypes()\n",
"display(schooling_latam_female_age_25_older_df.info())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We check then the datasets for duplicates and null values. First for **income**:"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"country 0\n",
"1990 0\n",
"1991 0\n",
"1992 0\n",
"1993 0\n",
"1994 0\n",
"1995 0\n",
"1996 0\n",
"1997 0\n",
"1998 0\n",
"1999 0\n",
"2000 0\n",
"2001 0\n",
"2002 0\n",
"2003 0\n",
"2004 0\n",
"2005 0\n",
"2006 0\n",
"2007 0\n",
"2008 0\n",
"2009 0\n",
"2010 0\n",
"2011 0\n",
"2012 0\n",
"2013 0\n",
"2014 0\n",
"2015 0\n",
"dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# After discussing the structure of the data and any problems that need to be\n",
"# cleaned, perform those cleaning steps in the second part of this section.\n",
"display(latam_income_df.isnull().sum())\n",
"display(latam_income_df.duplicated().sum())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then for **employment**:"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"country 0\n",
"1991 0\n",
"1992 0\n",
"1993 0\n",
"1994 0\n",
"1995 0\n",
"1996 0\n",
"1997 0\n",
"1998 0\n",
"1999 0\n",
"2000 0\n",
"2001 0\n",
"2002 0\n",
"2003 0\n",
"2004 0\n",
"2005 0\n",
"2006 0\n",
"2007 0\n",
"2008 0\n",
"2009 0\n",
"2010 0\n",
"2011 0\n",
"2012 0\n",
"2013 0\n",
"2014 0\n",
"2015 0\n",
"dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"country 0\n",
"1991 0\n",
"1992 0\n",
"1993 0\n",
"1994 0\n",
"1995 0\n",
"1996 0\n",
"1997 0\n",
"1998 0\n",
"1999 0\n",
"2000 0\n",
"2001 0\n",
"2002 0\n",
"2003 0\n",
"2004 0\n",
"2005 0\n",
"2006 0\n",
"2007 0\n",
"2008 0\n",
"2009 0\n",
"2010 0\n",
"2011 0\n",
"2012 0\n",
"2013 0\n",
"2014 0\n",
"2015 0\n",
"dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(latam_women_employment_df.isnull().sum())\n",
"display(latam_women_employment_df.duplicated().sum())\n",
"display(latam_men_employment_df.isnull().sum())\n",
"display(latam_men_employment_df.duplicated().sum())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And finally, for **education**:"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"country 0\n",
"1990 0\n",
"1991 0\n",
"1992 0\n",
"1993 0\n",
"1994 0\n",
"1995 0\n",
"1996 0\n",
"1997 0\n",
"1998 0\n",
"1999 0\n",
"2000 0\n",
"2001 0\n",
"2002 0\n",
"2003 0\n",
"2004 0\n",
"2005 0\n",
"2006 0\n",
"2007 0\n",
"2008 0\n",
"2009 0\n",
"dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"country 0\n",
"1990 0\n",
"1991 0\n",
"1992 0\n",
"1993 0\n",
"1994 0\n",
"1995 0\n",
"1996 0\n",
"1997 0\n",
"1998 0\n",
"1999 0\n",
"2000 0\n",
"2001 0\n",
"2002 0\n",
"2003 0\n",
"2004 0\n",
"2005 0\n",
"2006 0\n",
"2007 0\n",
"2008 0\n",
"2009 0\n",
"dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(schooling_latam_female_age_25_older_df.isnull().sum())\n",
"display(schooling_latam_female_age_25_older_df.duplicated().sum())\n",
"display(schooling_latam_male_age_25_older_df.isnull().sum())\n",
"display(schooling_latam_male_age_25_older_df.duplicated().sum())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='eda'></a>\n",
"## Exploratory Data Analysis\n",
"\n",
"<a href=\"#toc\">go above</a>\n",
"\n",
"Once we have our input datasets clean, and in the desired shape, I proceed to explore them.\n",
"\n",
"### Research Question 1:Is there a relationship between the time spent on education and the Income?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To address this question, we need to begin exploring the datasets.\n",
"In income, we want to see the average income per coutry for the period previouly defined. For this, we create a new column:"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>1990</th>\n",
" <th>1991</th>\n",
" <th>1992</th>\n",
" <th>1993</th>\n",
" <th>1994</th>\n",
" <th>1995</th>\n",
" <th>1996</th>\n",
" <th>1997</th>\n",
" <th>1998</th>\n",
" <th>...</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>2014</th>\n",
" <th>2015</th>\n",
" <th>avg_income_1990_2015</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Argentina</td>\n",
" <td>11400</td>\n",
" <td>12200</td>\n",
" <td>13000</td>\n",
" <td>13900</td>\n",
" <td>14500</td>\n",
" <td>14000</td>\n",
" <td>14600</td>\n",
" <td>15600</td>\n",
" <td>16000</td>\n",
" <td>...</td>\n",
" <td>18000</td>\n",
" <td>18600</td>\n",
" <td>17300</td>\n",
" <td>18900</td>\n",
" <td>19800</td>\n",
" <td>19400</td>\n",
" <td>19600</td>\n",
" <td>18900</td>\n",
" <td>19200</td>\n",
" <td>15850.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Bolivia</td>\n",
" <td>3700</td>\n",
" <td>3820</td>\n",
" <td>3800</td>\n",
" <td>3880</td>\n",
" <td>3980</td>\n",
" <td>4080</td>\n",
" <td>4170</td>\n",
" <td>4290</td>\n",
" <td>4420</td>\n",
" <td>...</td>\n",
" <td>4910</td>\n",
" <td>5130</td>\n",
" <td>5210</td>\n",
" <td>5340</td>\n",
" <td>5530</td>\n",
" <td>5720</td>\n",
" <td>6010</td>\n",
" <td>6240</td>\n",
" <td>6440</td>\n",
" <td>4711.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Brazil</td>\n",
" <td>10300</td>\n",
" <td>10300</td>\n",
" <td>10100</td>\n",
" <td>10400</td>\n",
" <td>10800</td>\n",
" <td>11100</td>\n",
" <td>11100</td>\n",
" <td>11300</td>\n",
" <td>11200</td>\n",
" <td>...</td>\n",
" <td>13300</td>\n",
" <td>13900</td>\n",
" <td>13700</td>\n",
" <td>14600</td>\n",
" <td>15100</td>\n",
" <td>15200</td>\n",
" <td>15500</td>\n",
" <td>15500</td>\n",
" <td>14800</td>\n",
" <td>12403.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>Chile</td>\n",
" <td>8970</td>\n",
" <td>9510</td>\n",
" <td>10400</td>\n",
" <td>10900</td>\n",
" <td>11300</td>\n",
" <td>12100</td>\n",
" <td>12700</td>\n",
" <td>13500</td>\n",
" <td>13900</td>\n",
" <td>...</td>\n",
" <td>18500</td>\n",
" <td>19000</td>\n",
" <td>18500</td>\n",
" <td>19400</td>\n",
" <td>20300</td>\n",
" <td>21200</td>\n",
" <td>21900</td>\n",
" <td>22000</td>\n",
" <td>22300</td>\n",
" <td>15768.46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>Colombia</td>\n",
" <td>7730</td>\n",
" <td>7730</td>\n",
" <td>7890</td>\n",
" <td>8150</td>\n",
" <td>8470</td>\n",
" <td>8750</td>\n",
" <td>8770</td>\n",
" <td>8910</td>\n",
" <td>8810</td>\n",
" <td>...</td>\n",
" <td>10400</td>\n",
" <td>10600</td>\n",
" <td>10600</td>\n",
" <td>11000</td>\n",
" <td>11700</td>\n",
" <td>12000</td>\n",
" <td>12400</td>\n",
" <td>12900</td>\n",
" <td>13100</td>\n",
" <td>9633.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>Costa Rica</td>\n",
" <td>7740</td>\n",
" <td>7720</td>\n",
" <td>8210</td>\n",
" <td>8570</td>\n",
" <td>8730</td>\n",
" <td>8870</td>\n",
" <td>8760</td>\n",
" <td>9030</td>\n",
" <td>9450</td>\n",
" <td>...</td>\n",
" <td>12300</td>\n",
" <td>12700</td>\n",
" <td>12500</td>\n",
" <td>12900</td>\n",
" <td>13300</td>\n",
" <td>13800</td>\n",
" <td>13900</td>\n",
" <td>14300</td>\n",
" <td>14600</td>\n",
" <td>10785.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>Cuba</td>\n",
" <td>6860</td>\n",
" <td>5700</td>\n",
" <td>4780</td>\n",
" <td>3670</td>\n",
" <td>3640</td>\n",
" <td>3810</td>\n",
" <td>4250</td>\n",
" <td>4400</td>\n",
" <td>4310</td>\n",
" <td>...</td>\n",
" <td>6050</td>\n",
" <td>6340</td>\n",
" <td>6480</td>\n",
" <td>6700</td>\n",
" <td>6930</td>\n",
" <td>7140</td>\n",
" <td>7380</td>\n",
" <td>7630</td>\n",
" <td>7890</td>\n",
" <td>5579.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>Dominican Republic</td>\n",
" <td>5520</td>\n",
" <td>5460</td>\n",
" <td>5960</td>\n",
" <td>6290</td>\n",
" <td>6330</td>\n",
" <td>6580</td>\n",
" <td>6850</td>\n",
" <td>7340</td>\n",
" <td>7710</td>\n",
" <td>...</td>\n",
" <td>10500</td>\n",
" <td>10700</td>\n",
" <td>10600</td>\n",
" <td>11400</td>\n",
" <td>11600</td>\n",
" <td>11800</td>\n",
" <td>12200</td>\n",
" <td>13000</td>\n",
" <td>13700</td>\n",
" <td>8951.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>Ecuador</td>\n",
" <td>7460</td>\n",
" <td>7600</td>\n",
" <td>7590</td>\n",
" <td>7560</td>\n",
" <td>7710</td>\n",
" <td>7720</td>\n",
" <td>7680</td>\n",
" <td>7850</td>\n",
" <td>7940</td>\n",
" <td>...</td>\n",
" <td>8820</td>\n",
" <td>9230</td>\n",
" <td>9130</td>\n",
" <td>9310</td>\n",
" <td>9880</td>\n",
" <td>10300</td>\n",
" <td>10600</td>\n",
" <td>10900</td>\n",
" <td>10700</td>\n",
" <td>8511.92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>El Salvador</td>\n",
" <td>4510</td>\n",
" <td>4520</td>\n",
" <td>4770</td>\n",
" <td>4980</td>\n",
" <td>5140</td>\n",
" <td>5320</td>\n",
" <td>5310</td>\n",
" <td>5420</td>\n",
" <td>5520</td>\n",
" <td>...</td>\n",
" <td>6200</td>\n",
" <td>6310</td>\n",
" <td>6150</td>\n",
" <td>6280</td>\n",
" <td>6490</td>\n",
" <td>6650</td>\n",
" <td>6760</td>\n",
" <td>6850</td>\n",
" <td>6980</td>\n",
" <td>5775.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>Guatemala</td>\n",
" <td>5100</td>\n",
" <td>5170</td>\n",
" <td>5290</td>\n",
" <td>5370</td>\n",
" <td>5460</td>\n",
" <td>5600</td>\n",
" <td>5640</td>\n",
" <td>5750</td>\n",
" <td>5900</td>\n",
" <td>...</td>\n",
" <td>6710</td>\n",
" <td>6780</td>\n",
" <td>6670</td>\n",
" <td>6710</td>\n",
" <td>6850</td>\n",
" <td>6900</td>\n",
" <td>7010</td>\n",
" <td>7150</td>\n",
" <td>7290</td>\n",
" <td>6183.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>Honduras</td>\n",
" <td>3210</td>\n",
" <td>3010</td>\n",
" <td>3110</td>\n",
" <td>3220</td>\n",
" <td>3140</td>\n",
" <td>3240</td>\n",
" <td>3200</td>\n",
" <td>3260</td>\n",
" <td>3280</td>\n",
" <td>...</td>\n",
" <td>3950</td>\n",
" <td>4030</td>\n",
" <td>3850</td>\n",
" <td>3910</td>\n",
" <td>3980</td>\n",
" <td>4070</td>\n",
" <td>4110</td>\n",
" <td>4160</td>\n",
" <td>4250</td>\n",
" <td>3557.31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>Mexico</td>\n",
" <td>13600</td>\n",
" <td>13900</td>\n",
" <td>14100</td>\n",
" <td>14100</td>\n",
" <td>14600</td>\n",
" <td>13500</td>\n",
" <td>14100</td>\n",
" <td>14900</td>\n",
" <td>15400</td>\n",
" <td>...</td>\n",
" <td>16800</td>\n",
" <td>16700</td>\n",
" <td>15600</td>\n",
" <td>16200</td>\n",
" <td>16500</td>\n",
" <td>16900</td>\n",
" <td>16900</td>\n",
" <td>17200</td>\n",
" <td>17500</td>\n",
" <td>15615.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td>Nicaragua</td>\n",
" <td>2970</td>\n",
" <td>2900</td>\n",
" <td>2850</td>\n",
" <td>2780</td>\n",
" <td>2810</td>\n",
" <td>2910</td>\n",
" <td>3040</td>\n",
" <td>3100</td>\n",
" <td>3160</td>\n",
" <td>...</td>\n",
" <td>3960</td>\n",
" <td>4040</td>\n",
" <td>3850</td>\n",
" <td>3970</td>\n",
" <td>4160</td>\n",
" <td>4380</td>\n",
" <td>4530</td>\n",
" <td>4690</td>\n",
" <td>4850</td>\n",
" <td>3586.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td>Panama</td>\n",
" <td>7860</td>\n",
" <td>8420</td>\n",
" <td>8920</td>\n",
" <td>9220</td>\n",
" <td>9290</td>\n",
" <td>9260</td>\n",
" <td>9440</td>\n",
" <td>9850</td>\n",
" <td>10400</td>\n",
" <td>...</td>\n",
" <td>14000</td>\n",
" <td>15100</td>\n",
" <td>15000</td>\n",
" <td>15600</td>\n",
" <td>17100</td>\n",
" <td>18500</td>\n",
" <td>19400</td>\n",
" <td>20000</td>\n",
" <td>20800</td>\n",
" <td>12579.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td>Paraguay</td>\n",
" <td>7930</td>\n",
" <td>8000</td>\n",
" <td>7930</td>\n",
" <td>8120</td>\n",
" <td>8350</td>\n",
" <td>8720</td>\n",
" <td>8650</td>\n",
" <td>8820</td>\n",
" <td>8640</td>\n",
" <td>...</td>\n",
" <td>8610</td>\n",
" <td>9030</td>\n",
" <td>8880</td>\n",
" <td>9740</td>\n",
" <td>10000</td>\n",
" <td>9830</td>\n",
" <td>10500</td>\n",
" <td>10900</td>\n",
" <td>11100</td>\n",
" <td>8751.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>135</th>\n",
" <td>Peru</td>\n",
" <td>5250</td>\n",
" <td>5260</td>\n",
" <td>5130</td>\n",
" <td>5300</td>\n",
" <td>5840</td>\n",
" <td>6160</td>\n",
" <td>6220</td>\n",
" <td>6500</td>\n",
" <td>6360</td>\n",
" <td>...</td>\n",
" <td>8640</td>\n",
" <td>9350</td>\n",
" <td>9380</td>\n",
" <td>10100</td>\n",
" <td>10600</td>\n",
" <td>11200</td>\n",
" <td>11700</td>\n",
" <td>11900</td>\n",
" <td>12100</td>\n",
" <td>7783.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>185</th>\n",
" <td>Uruguay</td>\n",
" <td>9840</td>\n",
" <td>10100</td>\n",
" <td>10800</td>\n",
" <td>11000</td>\n",
" <td>11800</td>\n",
" <td>11500</td>\n",
" <td>12100</td>\n",
" <td>13000</td>\n",
" <td>13500</td>\n",
" <td>...</td>\n",
" <td>14400</td>\n",
" <td>15400</td>\n",
" <td>16000</td>\n",
" <td>17200</td>\n",
" <td>18000</td>\n",
" <td>18600</td>\n",
" <td>19400</td>\n",
" <td>19900</td>\n",
" <td>19900</td>\n",
" <td>13940.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>188</th>\n",
" <td>Venezuela</td>\n",
" <td>14600</td>\n",
" <td>15700</td>\n",
" <td>16200</td>\n",
" <td>15900</td>\n",
" <td>15200</td>\n",
" <td>15500</td>\n",
" <td>15200</td>\n",
" <td>15800</td>\n",
" <td>15500</td>\n",
" <td>...</td>\n",
" <td>17600</td>\n",
" <td>18200</td>\n",
" <td>17400</td>\n",
" <td>16900</td>\n",
" <td>17300</td>\n",
" <td>18000</td>\n",
" <td>18000</td>\n",
" <td>17100</td>\n",
" <td>15600</td>\n",
" <td>15776.92</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>19 rows × 28 columns</p>\n",
"</div>"
],
"text/plain": [
" country 1990 1991 1992 1993 1994 1995 1996 \\\n",
"6 Argentina 11400 12200 13000 13900 14500 14000 14600 \n",
"20 Bolivia 3700 3820 3800 3880 3980 4080 4170 \n",
"23 Brazil 10300 10300 10100 10400 10800 11100 11100 \n",
"34 Chile 8970 9510 10400 10900 11300 12100 12700 \n",
"36 Colombia 7730 7730 7890 8150 8470 8750 8770 \n",
"40 Costa Rica 7740 7720 8210 8570 8730 8870 8760 \n",
"43 Cuba 6860 5700 4780 3670 3640 3810 4250 \n",
"49 Dominican Republic 5520 5460 5960 6290 6330 6580 6850 \n",
"50 Ecuador 7460 7600 7590 7560 7710 7720 7680 \n",
"52 El Salvador 4510 4520 4770 4980 5140 5320 5310 \n",
"68 Guatemala 5100 5170 5290 5370 5460 5600 5640 \n",
"73 Honduras 3210 3010 3110 3220 3140 3240 3200 \n",
"108 Mexico 13600 13900 14100 14100 14600 13500 14100 \n",
"122 Nicaragua 2970 2900 2850 2780 2810 2910 3040 \n",
"132 Panama 7860 8420 8920 9220 9290 9260 9440 \n",
"134 Paraguay 7930 8000 7930 8120 8350 8720 8650 \n",
"135 Peru 5250 5260 5130 5300 5840 6160 6220 \n",
"185 Uruguay 9840 10100 10800 11000 11800 11500 12100 \n",
"188 Venezuela 14600 15700 16200 15900 15200 15500 15200 \n",
"\n",
" 1997 1998 ... 2007 2008 2009 2010 2011 2012 2013 \\\n",
"6 15600 16000 ... 18000 18600 17300 18900 19800 19400 19600 \n",
"20 4290 4420 ... 4910 5130 5210 5340 5530 5720 6010 \n",
"23 11300 11200 ... 13300 13900 13700 14600 15100 15200 15500 \n",
"34 13500 13900 ... 18500 19000 18500 19400 20300 21200 21900 \n",
"36 8910 8810 ... 10400 10600 10600 11000 11700 12000 12400 \n",
"40 9030 9450 ... 12300 12700 12500 12900 13300 13800 13900 \n",
"43 4400 4310 ... 6050 6340 6480 6700 6930 7140 7380 \n",
"49 7340 7710 ... 10500 10700 10600 11400 11600 11800 12200 \n",
"50 7850 7940 ... 8820 9230 9130 9310 9880 10300 10600 \n",
"52 5420 5520 ... 6200 6310 6150 6280 6490 6650 6760 \n",
"68 5750 5900 ... 6710 6780 6670 6710 6850 6900 7010 \n",
"73 3260 3280 ... 3950 4030 3850 3910 3980 4070 4110 \n",
"108 14900 15400 ... 16800 16700 15600 16200 16500 16900 16900 \n",
"122 3100 3160 ... 3960 4040 3850 3970 4160 4380 4530 \n",
"132 9850 10400 ... 14000 15100 15000 15600 17100 18500 19400 \n",
"134 8820 8640 ... 8610 9030 8880 9740 10000 9830 10500 \n",
"135 6500 6360 ... 8640 9350 9380 10100 10600 11200 11700 \n",
"185 13000 13500 ... 14400 15400 16000 17200 18000 18600 19400 \n",
"188 15800 15500 ... 17600 18200 17400 16900 17300 18000 18000 \n",
"\n",
" 2014 2015 avg_income_1990_2015 \n",
"6 18900 19200 15850.00 \n",
"20 6240 6440 4711.54 \n",
"23 15500 14800 12403.85 \n",
"34 22000 22300 15768.46 \n",
"36 12900 13100 9633.85 \n",
"40 14300 14600 10785.77 \n",
"43 7630 7890 5579.23 \n",
"49 13000 13700 8951.15 \n",
"50 10900 10700 8511.92 \n",
"52 6850 6980 5775.77 \n",
"68 7150 7290 6183.08 \n",
"73 4160 4250 3557.31 \n",
"108 17200 17500 15615.38 \n",
"122 4690 4850 3586.54 \n",
"132 20000 20800 12579.23 \n",
"134 10900 11100 8751.15 \n",
"135 11900 12100 7783.85 \n",
"185 19900 19900 13940.00 \n",
"188 17100 15600 15776.92 \n",
"\n",
"[19 rows x 28 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"latam_income_df['avg_income_1990_2015'] = round(latam_income_df[latam_income_df.columns[1:]].mean(axis=1),2)\n",
"display(latam_income_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the following plot we can observe how income has evolved over the chosen period of time for the countries in the region"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAnEAAAHwCAYAAADJiTnYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdd1yW1f/H8ddhiyA4wQWoORARXDgStw0z1EzLhpa5Mi2zaVpZmZVZX39maWbmyEFZrtSGJpmpKW4U3AoIIohMwZtxfn/ct4QICArcoJ/n48FDuObnurjLt+ec61xKa40QQgghhKhYLMxdgBBCCCGEKD4JcUIIIYQQFZCEOCGEEEKICkhCnBBCCCFEBSQhTgghhBCiApIQJ4QQQghRAUmIE0KYhVLqXqXUCaVUilKqv7nryU0ptUkpNczcddwKpdRbSqkF5q7jdiiljiilupm7DiHKOyXzxAlRMSmlzgIjtNabzV3LrVBKbQHWaa3/r4SOtwiI1FpPKeZ+U4F7tNZPlUQduY4bBPgArlrrqyV5bCGEAGmJE0KYjztw5FZ2VEpZlXAtJUop5QH4AxoIKMPzlov7opSyNHcNQtwNJMQJcQdQSj2jlNqulJqplLqslDqjlHow1/pqSqnvlFJRpvVrcq0bqZQ6qZSKV0qtU0rVybVOK6XGmro9k5VSHyilGimldiilkpRSPyilbHJt31cpdUAplWDapmUB9Z4CGgLrTd2ptkqpOqbzx5vqGZlr+6lKqVVKqe+VUknAM8W8P/+nlIow1bxXKeVvWv4A8BbwmKmOg6blQUqpEUW5twUYCuwCFgHXdcsqpRYppb4yddmmKKX+UUq5KqVmmY4fppRqlWv7Okqpn5RSsaZzv1jYfTEt+z7XNp1Nv4sE0z14xrT8IaXUftM9iTC1SF7bx8P0ux+mlApXSsUppSYXcn8XKaXmKqU2KqVSge43qbuSUmqx6XpDlVKvK6Uic60/q5TqZfre1nRvokxfs5RStqZ13ZRSkUqpV5RSF5VS0UqpZ2/yuxHijiEhTog7R3vgGFADmAF8q5RSpnVLAXvAC6gF/A9AKdUD+AgYDNQGzgEr8xz3fqAN0AF4HZgPPAXUB1oAQ0zHagUsBEYD1YGvgXXX/sLNTWvdCAgHHtZaO5i6G1cCkUAd4FFguqm+a/oBqwBnYFkx780ewBeoBiwHflRK2WmtfwWmA4GmOnwK2L+we5ufoaYalwH3K6Vc8qwfDEwxHe8qsBPYZ/p5FfA5gFLKAlgPHATqAj2BCUqp+3Mdq8D7opRyBzYBXwA1TffggGl1qqlOZ+Ah4Hl149jEzkBT03nfUUp5FnLNTwAfAo7AjpvU/S7ggTHI98b4eSrIZIyfPV+M3dN+GO/dNa6Ak+k8zwFfKqWqFnI8Ie4YEuKEuHOc01p/o7XOAhZjDGUuSqnawIPAGK31Za11htb6L9M+TwILtdb7TEFqEtBRGbsDr5mhtU7SWh8BQoDftdantdaJGAPCtVajUcDXWut/tdZZWuvFGANKh5sVrpSqD9wLvKG1TtdaHwAWYAwZ1+zUWq/RWmdrrdOKc2O01t9rrS9prTO11p8BthjDSVHle28LuJbOGLuKf9Ba7wVOYQw4ua3WWu/VWqcDq4F0rfUS0/ED+e+etgNqaq3f11obtNangW+Ax3Mdq7D78gSwWWu9wvR7v2S6t2itg7TWh037HQJWAF3z7P+e1jpNa30QYyArKOQCrNVa/6O1zga8b1L3YGC66fMYCcwu5LhPAu9rrS9qrWOB94Cnc63PMK3P0FpvBFIo3u9WiApLQpwQd44L177RWl8xfeuAscUsXmt9OZ996mBsfbu2XwpwCWOrxjUxub5Py+dnB9P37sArpm67BKVUguncdbi5OqYak3MtO5enjogiHCdfSqlXTd12iaa6nDC2ehVVQfc2P8MwBt0408/LydOlSvHuaZ089/Qtrg+Qhd2X+hhD5A2UUu2VUltN3Z2JwBhuvCcXcn1/hYKvOW8dN6u7Tp7tC7uG6z6jpu9zf6Yuaa0zi1GnEHeMcjEIVghRqiKAakopZ611Qp51URj/wgVAKVUZY1fo+Vs8z4da6w9vYd8oU42OuYKcW546bulRetP4t9cxdukd0VpnK6UuA9e6Q0vsEX2lVCWMrUyWSqlrAcgWcFZK+ZhatIojAjijtW5cyDaF1R+BsfsxP8uBOcCDWut0pdQsihdsC6vjZnVHA/WAo6af6xdy3Guf0WsPwbiZlglx15OWOCHucFrraIzdnl8ppaoqpayVUl1Mq1cAzyqlfE1j16YD/2qtz97Cqb4BxphaeJRSqrJp8LxjEWqMwDiO6iOllJ0yPhDxHPB94XvewNK0/7UvG4xjtDKBWMBKKfUOUCXXPjGAh2n82e3qD2QBzTGO4fIFPIG/ub5ruKh2A8lKqTdMDwNYKqVaKKXaFXH/ZUAvpdRgpZSVUqq6UsrXtM4RY+tnulLKjxu7fG/Hzer+AZhk+jzWBcYVcqwVwBSlVE2lVA3gHYr/uRDijiQhToi7w9MYxw6FAReBCQCmOebeBn7C2DrSiOvHWxWZ1joYGImxdecycJLiPUU6BONg9yiM48TevYU58N7E2B157etP4DfgV+A4xq64dK7vvvvR9OclpdS+Yp4vr2HAd1rrcK31hWtfGO/Jk6qYU4CYxsj1xRgGzwBxGMcKOhVx/3CgD/AKEI/xoYZr49rGAu8rpZIxBqMfilPbbdb9PsaHWM4AmzE+mFHQXHrTgGDgEHAY4wMg00qqViEqMpnsVwghhFkppZ4HHtda532wQghRCGmJE0IIUaaUUrWV8bVrFkqpphhbClebuy4hKhp5sEEIIURZs8E4j2ADIAHjHIFfmbUiISog6U4VQgghhKiApDtVCCGEEKICkhAnhBBCCFEB3XVj4mrUqKE9PDzMXYYQQgghxE3t3bs3TmtdM791d12I8/DwIDg42NxlCCGEEELclFLqXEHrpDtVCCGEEKICkhAnhBBCCFEBSYgTQgghhKiA7roxcfnJyMggMjKS9PR0c5ciyhE7Ozvq1auHtbW1uUsRQgghbiAhDoiMjMTR0REPDw+UUuYuR5QDWmsuXbpEZGQkDRo0MHc5QgghxA2kOxVIT0+nevXqEuBEDqUU1atXl9ZZIYQQ5ZaEOBMJcCIv+UwIIYQozyTElSNr1qxBKUVYWFipn+vAgQNs3Lgx5+d169bx8ccfl/p5hRBCCFEyJMSVIytWrKBz586sWLHihnWZmZkleq68IS4gIIA333yzRM8hhBBCiNIjIa6cSElJYfv27Xz77besXLkSgKCgIPz9/QkICKB58+ZkZ2czduxYmjVrRu/evenTpw+rVq0CYO/evXTt2pU2bdpw//33Ex0dDUC3bt1444038PPzo0mTJvz9998YDAbeeecdAgMD8fX1JTAwkEWLFjFu3DgAnnnmGV588UU6depEw4YNc86RkpJCz549ad26Nd7e3qxdu9YMd0oIIYQQUIpPpyql6gNLABdAA/O11v+nlPoUeBgwAKeAZ7XWCUopDyAUOGY6xC6t9RjTsdoAi4BKwEbgJa21VkpVAwIBD+AsMFhrffl26n5v/RGORiXdziFu0LxOFd592KvQbdauXcsDDzxAkyZNqF69Onv37gVg3759hISE0KBBA1atWsXZs2c5evQoFy9exNPTk+HDh5ORkcH48eNZu3YtNWvWJDAwkMmTJ7Nw4ULA2Iq3e/duNm7cyHvvvcfmzZt5//33CQ4OZs6cOQAsWrTounqio6PZvn07YWFhBAQE8Oijj2JnZ8fq1aupUqUKcXFxdOjQgYCAABk7JoQQQphBabbEZQKvaK2bAx2AF5RSzYE/gBZa65bAcWBSrn1Oaa19TV9jci2fC4wEGpu+HjAtfxPYorVuDGwx/VwhrVixgscffxyAxx9/PKdL1c/PL2eKi+3btzNo0CAsLCxwdXWle/fuABw7doyQkBB69+6Nr68v06ZNIzIyMufYjzzyCABt2rTh7NmzRaqnf//+WFhY0Lx5c2JiYgDjtBtvvfUWLVu2pFevXpw/fz5nnRBCCCHKVqm1xGmto4Fo0/fJSqlQoK7W+vdcm+0CHi3sOEqp2kAVrfUu089LgP7AJqAf0M206WIgCHjjduq+WYtZaYiPj+fPP//k8OHDKKXIyspCKcVDDz1E5cqVb7q/1hovLy927tyZ73pbW1sALC0tizy27to+144PsGzZMmJjY9m7dy/W1tZ4eHjIFBxCCCGEmZTJmDhTV2kr4N88q4ZjDGPXNFBK7VdK/aWU8jctqwtE5tom0rQMwMUUFgEuYOy6rXBWrVrF008/zblz5zh79iwRERE0aNCAv//++7rt7r33Xn766Seys7OJiYkhKCgIgKZNmxIbG5sT4jIyMjhy5Eih53R0dCQ5OblYdSYmJlKrVi2sra3ZunUr586dK9b+QgghhCg5pR7ilFIOwE/ABK11Uq7lkzF2uS4zLYoG3LTWrYCJwHKlVJWinkcbm4t0ATWMUkoFK6WCY2Njb/FKSs+KFSsYMGDAdcsGDhx4w1OqAwcOpF69ejRv3pynnnqK1q1b4+TkhI2NDatWreKNN97Ax8cHX19fduzYUeg5u3fvztGjR3MebCiKJ598kuDgYLy9vVmyZAnNmjUr3oUKIYQQosSoa11lpXJwpayBX4DftNaf51r+DDAa6Km1vlLAvkHAq8B5YKvWuplp+RCgm9Z6tFLqmOn7aFO3a5DWumlhNbVt21YHBwdftyw0NBRPT89bvMqylZKSgoODA5cuXcLPz49//vkHV1dXc5d1x6pInw0hhBB3HqXUXq112/zWlebTqQr4FgjNE+AeAF4HuuYOcEqpmkC81jpLKdUQ4wMMp7XW8UqpJKVUB4zdsUOBL0y7rQOGAR+b/rzj57zo27cvCQkJGAwG3n77bQlwQgghhBlkXLiAlYuLWWdoKLUQB9wLPA0cVkodMC17C5gN2AJ/mC782lQiXYD3lVIZQDYwRmsdb9pvLP9NMbKJ/8bRfQz8oJR6DjgHDC7F6ykXro2DE0IIIUTZ0dnZpIeEkLx1Kylbg7gaFkaD1T9jZ8bemtJ8OnU7kF883ZjPMrTWP2EcO5ffumCgRT7LLwE9b6NMIYQQQoh8ZaemkrpzpzG4/bWNrLg4sLCgUutW1HrtVaxq1DBrfaXZEieEEEIIUaFkREUZQ1vQX1z591+0wYCFoyMO/p1x6N4dB39/LJ2dzV0mICFOCCGEEHcxnZ1N+qFDJG8NIiUoiKvHjC+OsnZ3o+qQITh07459m9Yoa2szV3ojCXFCCCGEuKtkpaSSuuMfUrYGkbJtG1mXLoGlJfatW1Prtddw6N4d24YNzF3mTUmIKycsLS3x9vZGa42lpSVz5syhU6dOBW5/9uxZ+vbtS0hICMHBwSxZsoTZs2cXuP28efOwt7dn6NChpVG+EEIIUa5lnD9vbG3bupUru3ejMzKwqFIFB39/HLp1w8G/c7npJi0qCXHlRKVKlThwwPgQ72+//cakSZP466+/irRv27Ztads23ylkcowZM6bQ9UIIIURFlH31KpmxsWRejCXz4kXT9xf/+z72IpkXY8lKTATAxsODqk89hUP3bti3alUuu0mLSkJcOZSUlETVqlUB43tLX3/9dTZt2oRSiilTpvDYY49dt31QUBAzZ85k3bp1NGzYkAMHDuBs+tdE48aN2b59O3PnzsXBwYFXX32Vb775hvnz52MwGLjnnntYunQp9vb2ZX6dQgghREH+C2cXbwxopnCWcTGWbFM4u461NVY1a2BVsyY2Hh7Yt2uHjbs7lbt0wbZB+e8mLSoJcXltehMuHC7ZY7p6w4MfF7pJWloavr6+pKenEx0dzZ9//gnAzz//zIEDBzh48CBxcXG0a9eOLl265HsMCwsL+vXrx+rVq3n22Wf5999/cXd3x8Xl+lfKPvLII4wcORKAKVOm8O233zJ+/PgSuFAhhBDi9qQfPUrU5ClcDQ29ceUN4cwPq1o1sapZy/hnrVpY1ayJpbMzyqJMXg9vVhLiyonc3ak7d+5k6NChhISEsH37doYMGYKlpSUuLi507dqVPXv20LJly3yP89hjj/H+++/z7LPPsnLlyhta7QBCQkKYMmUKCQkJpKSkcP/995fqtQkhhBA3o7OyuLTgW2LnzMHK2ZkaL47H2sX1v3BWqxaWTk53RTgrKglxed2kxawsdOzYkbi4OGJjY29p35MnTxIbG8uaNWuYMmXKDds888wzrFmzBh8fHxYtWiRvgRBCCGFWhshIot54k7S9e3G8/35cp76LlWlYkSiYxNlyKCwsjKysLKpXr46/vz+BgYFkZWURGxvLtm3b8PPzK3BfpRQDBgxg4sSJeHp6Ur169Ru2SU5Opnbt2mRkZLBs2bLSvBQhhBCiQFprEn5ezZl+/bl67Bh1PvmYurP+JwGuiKQlrpy4NiYOjB/qxYsXY2lpyYABA9i5cyc+Pj4opZgxYwaurq6cPXu2wGM99thjtGvXjkWLFuW7/oMPPqB9+/bUrFmT9u3bk5ycXApXJIQQQhQs8/JlLrzzLsl//IF927bU+eRjrOvWNXdZFYrSWpu7hjLVtm1bHRwcfN2y0NBQPM34AltRfslnQwghSl7K338T9dZbZCUkUuulF6n27LMoS0tzl1UuKaX2aq3znUdMWuKEEEIIUSay09K4+OlMLi9fjm3je3CbPx87+YfyLZMQJ4QQQohSlxZyhKjXXsNw5gzVhg2l5sSJWNjamrusCk1CnBBCCCFKjc7M5NKCBcTO+RKr6tVx+24hlTt2NHdZdwQJcUIIIYQoFYbwcOPUIfv3U6VPH1zffQdLJydzl3XHkBAnhBBCiBKltSbxp5+Imf4RWFpS59NPcXq4r7nLuuNIiBNCCCFEicmMjyf6nXdI2bwF+/btqfPRdKzr1DF3WXckmey3nLC0tMTX1xcfHx9at27Njh07SuS4I0aM4OjRowB4eHgQFxdXIscVQggh8kr56y9OB/Qj9a9t1Hr9ddy+WygBrhRJS1w5kfvdqb/99huTJk3ir7/+um6bzMxMrKyK9ytbsGBBidUohBBC5Cf7yhViZswgYWUgtk2aUOfbb7Fr2sTcZd3xpCWuHEpKSqKq6ZUjQUFB+Pv7ExAQQPPmzQHo378/bdq0wcvLi/nz5wOwbt06fH198fX1pWnTpjRo0ACAbt26kXdyYyGEEKKkXD19hjOPDCQh8AeqDR+Ox6ofJcCVEWmJy+OT3Z8QFh9WosdsVq0Zb/i9Ueg21167lZ6eTnR0NH/++WfOun379hESEpITzBYuXEi1atVIS0ujXbt2DBw4kICAAAICAgAYPHgwXbt2LdFrEEIIIfJKO3iQiNFjwMICt+++o3KH9uYu6a4iIa6cyN2dunPnToYOHUpISAgAfn5+OQEOYPbs2axevRqAiIgITpw4kfOi+xkzZlCpUiVeeOGFMr4CIYQQd5PkoCDOT3gZq5o1cVvwDTbu7uYu6a4jIS6Pm7WYlYWOHTsSFxdHbGwsAJUrV85ZFxQUxObNm9m5cyf29vZ069aN9PR0ADZv3syPP/7Itm3bzFK3EEKIu0PCTz8T/c472DVtSv35X2NVo4a5S7orSYgrh8LCwsjKysppXcstMTGRqlWrYm9vT1hYGLt27QLg3LlzvPDCC/z2229UqlSprEsWQghxF9Bac+nr+cTOmkXlTp2oO3s2lg6Vb76jKBUS4sqJa2PiwPgfyeLFi7G0tLxhuwceeIB58+bh6elJ06ZN6dChAwCLFi3i0qVL9O/fH4A6deqwcePGsrsAIYQQdzSdlUXMh9O5vHw5VR5+mDofTkPZ2Ji7rLua0lqbu4Yy1bZtW533ac3Q0FA8PT3NVJEoz+SzIYQQkH31KlGvvU7y779Tbfhwar36CspCJrgoC0qpvVrrtvmtk5Y4IYQQQhQoKymJyLEvcCU4mFpvvEH1Z58xd0nCRGK0EEIIIfKVERPDuSef4srBg9SZOVMCnMml8yms+XwfV5IMZq1DWuKEEEIIcYOrp04RPmIk2YmJuH09j8qdOpm7pHLhRHAMfy4JxaaSFamJV7GvYr5xgRLihBBCCHGdK/v2E/H88yhra9y/X4qd6Y1Bd7PsbM2uNafY/3s4rg2deGB0Cyo72Zq1JglxQgghhMiR/OefnH95ItaurtRf8A029eubuySzS0/N4PdvjxBxNB6vLnXxH9wYSyvzj0iTECeEEEIIAC7/8AMXpr6HnZcX9b+eh1W1auYuyeziIlPYNO8QKQlX6f5UM5p3rmPuknKYP0YKAC5cuMDjjz9Oo0aNaNOmDX369GH+/Pn07ds33+1HjBjB0aNHAfDw8CAuLq4syxVCCHEH0VoT++WXXHjnXSp3vhf3Rd9JgANO7InhpxnBZGVkM+CV1uUqwIG0xJULWmsGDBjAsGHDWLlyJQAHDx5k3bp1Be6zYMGCsipPCCHEHUxnZXHhgw9IWBmIU//+1P7gfZS1tbnLMqvsrGx2rjnNgT/Cqd3IiftHmX/8W36kJa4c2Lp1K9bW1owZMyZnmY+PD/7+/qSkpPDoo4/SrFkznnzySa5NztytWzfyTloM8P333+Pn54evry+jR48mKyurzK5DCCFExZKdnk7kSy+RsDKQ6qNGUfuj6Xd9gEtPyWD9Fwc58Ec4LbrWpd/LrcplgANpibvBhenTuRoaVqLHtPVshutbbxW4PiQkhDZt2uS7bv/+/Rw5coQ6depw77338s8//9C5c+d8tw0NDSUwMJB//vkHa2trxo4dy7Jlyxg6dGiJXIcQQog7R1ZCAhFjXyBt/35cJk+m2tNPmbsks4uNSGbTvMOkJl6l+9PNaH5v+eo+zUtCXDnn5+dHvXr1APD19eXs2bMFhrgtW7awd+9e2rVrBxjfx1qrVq0yq1UIIUTFkBEdTfjIkWScC6fu/z6nygMPmLskszu+5wJbl4RhW9maR15pg0uDKuYu6aYkxOVRWItZafHy8mLVqlX5rrO1/a8J19LSkszMzAKPo7Vm2LBhfPTRRyVeoxBCiDvD1RMnCB85iuyUFOovWEDl9n7mLsmssrOy2bn6FAc2R1D7HiceGOVt1gl8i0PGxJUDPXr04OrVq8yfPz9n2aFDh/j777+LdZyePXuyatUqLl68CEB8fDznzp0r0VqFEEJUXFeCgzn75FOQlYX790vv+gCXlmIwjn/bHIF3t3r0e7lVhQlwICGuXFBKsXr1ajZv3kyjRo3w8vJi0qRJuLq6Fus4zZs3Z9q0adx33320bNmS3r17Ex0dXUpVCyGEqEiS/viD8OHPYVW9Oh4rV2DXrJm5SzKr2PBkfpweTPTJRHoM9aTL402wtKxYsUhde9rxbtG2bVud96nO0NBQPD09zVSRKM/ksyGEuBNcXrmSC+9/QCVvb+rNm4tV1armLsmsju++wNalYdg5WPPAaG9cPMrv+Del1F6tddv81pVa5FRK1VdKbVVKHVVKHVFKvWRaXk0p9YdS6oTpz6qm5UopNVspdVIpdUgp1TrXsYaZtj+hlBqWa3kbpdRh0z6zlVKqtK5HCCGEqGi01sTOns2Fqe/h0KULbou+u6sDXHZWNtt/PMEfC49Sy6MKgya1K9cB7mZKs90wE3hFa90c6AC8oJRqDrwJbNFaNwa2mH4GeBBobPoaBcwFY+gD3gXaA37Au9eCn2mbkbn2k8drhBBCCEBnZhL99tvEfTUXp0cHUm/OF1hUqmTusswmLdnAutkHOLglgpbd6xEwwbdCjX/LT6k9naq1jgaiTd8nK6VCgbpAP6CbabPFQBDwhmn5Em3s392llHJWStU2bfuH1joeQCn1B/CAUioIqKK13mVavgToD2wqrWsSQgghKoLstDTOvzyRlKAgaox9nhrjx3M3d1ZdOJ3IbwtCSEvKoOcznjTrUNvcJZWIMpliRCnlAbQC/gVcTAEP4ALgYvq+LhCRa7dI07LClkfmszy/84/C2LqHm5vbrV+IEEIIUc5lXr5M5JjnSTt0CNd336HqkCHmLsls0pIN7FxzitB/onGoZssjr7WmlnvF7T7Nq9RDnFLKAfgJmKC1Tsr9LwGttVZKlfqTFVrr+cB8MD7YUNrnE0IIIcwh4/x5wkeMJOP8eer+3yyq3HefuUsyi+xszdG/z7Nr7Wky0rNocK89Dm4ZnIg8zInIm+9fVC1btsTR0bHkDlhMpRrilFLWGAPcMq31z6bFMUqp2lrraFN36UXT8vNA/Vy71zMtO89/3a/XlgeZltfLZ3shhBDirpN+7BgRI0aSffUqbgu/xb5tvg803vEunE5k28rjxIYn49xIY1ktkt2nzsCpkj+Xu7v7nRniTE+KfguEaq0/z7VqHTAM+Nj059pcy8cppVZifIgh0RT0fgOm53qY4T5gktY6XimVpJTqgLGbdijwRWldT2m7cOECEyZMYM+ePTg7O+Pi4sKsWbNo0qTJDduePXuWvn37EhISctvn7datGzNnzqRtnv/Y161bx9GjR3nzzTcL2FMIIUR5kfrvbiJfeAGLypVx/34pdvn83XHNxeR0dp66RI9mtXC0u3Nedp+WbGDn6lOE7ojG0jkNK+8LnIiNoFJ2JXr27Enr1q2xsirZ2GNtbd77V5otcfcCTwOHlVIHTMvewhjeflBKPQecAwab1m0E+gAngSvAswCmsPYBsMe03fvXHnIAxgKLgEoYH2iokA81aK0ZMGAAw4YNY+XKlQAcPHiQmJiYfENcWQgICCAgIMAs5xZCCFF0Sb/+StRrr2Pt5obbgm+wrl3woP1TsSkM/XY35xPSqGRtSYBPHZ5o70bLek4V9sGH7GzNkW3n+XfdaVIzL2NxTwyxKVFUSqlEr169aNeu3XWvsLyTlObTqduBgj4RPfPZXgMvFHCshcDCfJYHAy1uo8xyYevWrVhbWzNmzJicZT4+Pmitee2119i0aRNKKaZMmcJjjz123b7p6ek8//zzBAcHY2Vlxeeff0737t1ZtGgRa9asITU1lRMnTvDqq69iMBhYunQptra2bNy4kWrVqgGwdOlSRowYQWZmJgsXLsTPz49FixYRHBzMnDlzWL9+PaUDPQ4AACAASURBVNOmTcNgMFC9enWWLVuGi4sLQgghzCt+6ffETJ9OpVatqP/Vl1g6Oxe47b7wyzy3aA+WForZQ1rxz4k41h2MIjA4gua1qzCkvRv9fOtQpQK1zl04nchfK44RfSGKLJcokjMvUinrzg9v15TJ06kVyd8/HCcuIqVEj1mjvgP+gwtuUQsJCaFNmzY3LP/55585cOAABw8eJC4ujnbt2tGlS5frtvnyyy9RSnH48GHCwsK47777OH78eM5x9+/fT3p6Ovfccw+ffPIJ+/fv5+WXX2bJkiVMmDABgCtXrnDgwAG2bdvG8OHDb+im7dy5M7t27UIpxYIFC5gxYwafffbZ7d4WIYQQt0hrTez/ZnFp/nwcevak7mczsbCzK3D7LaExvLB8Hy5V7Fgy3A/36pUJ8KnDlL6erD0QxfJ/w3l7TQjTN4QS4FOHIe3d8CnHrXNXkoxPnR7ec4x05wjSq1/C3saeXt3ujvB2jYS4cmz79u0MGTIES0tLXFxc6Nq1K3v27KFly5bXbTN+/HgAmjVrhru7e06I6969O46Ojjg6OuLk5MTDDz8MgLe3N4cOHco5xhDT4+ddunQhKSmJhISE6+qIjIzkscceIzo6GoPBQIMGDUr1uoUQQhRMZ2QQ/fY7JK5Zg/Pgwbi+8zaqkLFeP+yJYNLqwzSvXYXvnm1HDYf/Ao6jnTVPdXDnyfZuHIpMZMXu8JzWOc/aVXjCrz79WtUts9Y5rTXnE9IIjU6mmasj9avZX7c+OyubkG1R/L1xP4lWpzFUv0ylSvb0uvfuCm/XSIjLo7AWs9Li5eXFqlWrSvy4uT/MFhYWOT9bWFiQmZmZsy7vv7Ty/jx+/HgmTpxIQEAAQUFBTJ06tcRrFUIIcXPZV64QOWECqdv+psb4cdQYO7bA1jKtNXP+PMlnfxzHv3EN5j3Vhsq2+f+1r5TCp74zPvWdmfyQJ+sOmlrn1h5h+sYw+raszRPt3fCt71xirXPXAtvhyEQOnzd+HYlKIj7VAICdtQWTH2rOU+3dUEoRfTKBTSv+JfpKKBmVE6hkV4ne/r1p27btXRferpEQVw706NGDt956i/nz5zNq1CgADh06hLOzM4GBgQwbNoz4+Hi2bdvGp59+Snp6es6+/v7+LFu2jB49enD8+HHCw8Np2rQp+/btK/L5AwMD6d69O9u3b8fJyQknJ6fr1icmJlK3rnEe5cWLF5fAFQshhCiuzPh4IkaPIf3IEVzff4+qgwcXuG1WtubddSF8vyucAa3q8snAlthYFe1Nm4521jzZ3p0n27tzKDKBFbvDWXsgih/3RtLM1ZEn2rvRz7cuTpWK3jqntSbychoh5/8LbCHnE7l8JQMAKwtFExdHenu60KKeE/fUdGDuX6d4e00Ifx2KpqchjdPnD5Jhm4Ctox29utydLW95SYgrB5RSrF69mgkTJvDJJ59gZ2eHh4cHs2bNIiUlBR8fH5RSzJgxA1dXV86ePZuz79ixY3n++efx9vbGysqKRYsWFftDbWdnR6tWrcjIyGDhwhueH2Hq1KkMGjSIqlWr0qNHD86cOXO7lyyEEKIYDBERRIwYScaFC9Sb8wWOPXoUuG16RhYTVh7g1yMXGN21IW/c3wwLi1trPWtZz5mW9ZyZ/FBz1h2IYsXucN5Ze4TpG0N5uKVx7FyrPK1zuQPbIVNYyxvYmro6cr+XKy3qOuFd14mmro7YWVted24/d2fmffMX8Wd2ccw2ActKNvTo2pMOHdtjY1Ox33laUpTxodC7R9u2bXVwcPB1y0JDQ/H09DRTRaI8k8+GEMLc0o8eJXzUaHRGBvXnzsW+dasCt028ksHIJcHsPhvP232b81znkh/DfDgykeW7w1l34DyphiyauToyoFVdEtIyclraEvIENu+6ToUGtrwO/hvGb5v+4AqXsMSGaBs3fk+qwoA27rwb4IVDAd3CdyKl1F6tdb4zN989d0EIIYSoYFJ37CBy/ItYVKmC++JF2DZqVOC20YlpDFu4mzNxqcwe0ooAnzqlUpN3PSc+qufN5Ic8WX/Q2Dr30aYwrC2NXaIPtri+hc3WqvDAlpvWmnWBv7M/dCcW2NDGqxP39euKsrRm9pYTfBV0kl1nLvG/wb609ahWKtdXkUiIE0IIIcqhxF82EDVpErYeHtRf8A3WhczPeSImmaELd5OcnsniZ/3odE+NUq/PwdaKIX5uDPFzIyohjeoONsUKbHkZDAaWfBNIZOwpnKxdeW7c01Rxrpyz/tX7m9K9WU1eDjzI4K938ny3RrzUs0mRx/rdie7eKxdCCCHKqUuLFhH16qvY+/jgvuz7QgNc8Nl4Hp23k8xsTeDoDmUS4PKq41zptgLc5fjLzP58LpEXT1G/ihfjXx95XYC7po17NTa+5M+jberx5dZTPDL3H05eTL6d0is0CXFCCCFEOaGzs4mZ8SkXP/4Ex/vuo/63C7CsUqXA7X87coEnF/xLtco2/Px8J7zqOBW4bXl18vhJvpwzl9QrSfi4dWX4hEexsik4EDrYWjHjUR++froNUQnpPDR7O4v+OUNZjvFPTs9g0+FoMrKyy+yc+ZHuVCGEEKIc0AYDUZOnkLR+PVWfeAKXyW+hLAsOM8v+Pcfba0LwrufMwmFtqe5Qsabb0Fqzfds/bNm6GcsMe7q364t/P+8iz0N3v5crrdyceWPVIaauP8qWsIvMHOSDS5WC31xxOyLir7A5NIY/wy6y6/QlMrI0y0e2p1Ojsm/5vEZCnBBCCGFmWSmpnH/xRVJ37KDmhAlUHz2q0El8Z20+wf9tOUH3pjX58snW2NtUrL/ODQYDq39aQ+ixo9im16Bv34fx7uxe7OPUcrRj4TPtWPZvONM2HOX+WduYPsCbPt61b7vGrGzN/vDLbAm7yJbQGI7HGF/J2ahmZZ69twE9m9WijXvV2z7P7ahYv/U72IULF5gwYQJ79uzB2dkZFxcXZs2aRZMmxXuDxPTp03nrrbeKtU+3bt2Ijo7Gzs4OGxsbvvnmG3x9fQHo06cPy5cvx7mQlyoLIYS4dZlxcUSMGk36sWPU/vBDnAc+UvC2Wdm8vTaEFbsjeLRNPT56xBtry4o1Mury5css+34FcXEXcUxvyMCn++DR4tZbs5RSPNXBnU6NqvNy4AHGLtvHI63rMjXAq9ivC0tOz2Db8Ti2hMUQdCyW+FQDVhYKvwbVGNy2Pr08XfCoceNYPXOReeIw/1xgWms6derEsGHDGDNmDAAHDx4kKSkJf3//Yh3LwcGBlJSUYu3TrVs3Zs6cSdu2bfnuu+9Yvnw5f/zxR7GOcacy92dDCFG6dFYWGVFRWNerZ5aXvRvOnSN8xEgyY2OpO+t/OHbrVuC2aYYsxq/Yz+bQGMZ1v4dX7mtSbl9QX5BTp07xww8/YkjPpHq6F4Oe74mLR8Fj/oorIyubL/48yZdbT+JaxY7PB/vQvmH1QvcJv/RfN+m/Z4zdpM721nRvWosezWrRpUnNYr2doqTJPHHl3NatW7G2ts4JcAA+Pj6AMeC9/vrrbNq0CaUUU6ZMyXkZ/WOPPUZSUhKZmZnMnTuXDRs2kJaWhq+vL15eXixbtoz+/fsTERFBeno6L730Us5rvQrSsWNHPv3005yfPTw8CA4OpkaNGixZsoSZM2eilKJly5YsXbqU9evXM23aNAwGA9WrV2fZsmW4FPIUlRBClAc6M5OkjRuJ+2ouhrNnsfNpSc1x46jcuXOZBaO0wyFEjB4N2dm4L15EJdP/9/OTcMXAc4uD2Rd+mff7eTG0o0eZ1FhStNbs2LGDzX9sxjLTnvq05ZGJnXB2sb/5zsVgbWnBxN5N6Na0JhMDD/D4N7sY1aUhE3s3yXl6Nitbsy/8sjG4hV7kxEVjw8c9tRwY3rkBPZu50NrNGasK0MIpIS6PrYvmc/Hc6RI9Zi33hnR/puDwFBISQps2bfJd9/PPP3PgwAEOHjxIXFwc7dq1o0uXLixfvpz777+fyZMnk5WVxZUrV/D392fOnDkcOHAgZ/+FCxdSrVo10tLSaNeuHQMHDqR69YL/VfLrr7/Sv3//G5YfOXKEadOmsWPHDmrUqEF8fDwAnTt3ZteuXSilWLBgATNmzOCzzz4r6q0RQogylTe82TZpQo3x40j46SciRo4qszCX8vd2Il96CauqVan/zTfYNiz4zQrnE4yT+IZfusKXT7QukfFeZclgMLB27VqOHDmCbXoNPBxaEzC+DZWdSu9BjNZuVdnwoj/TNoTy9V+n2XY8jmfv9WDnqUtsPXaRhCsZWFko2jesxhA/N3p61sK9evnpJi0qCXHl3Pbt2xkyZAiWlpa4uLjQtWtX9uzZQ7t27Rg+fDgZGRn0798/ZwxbXrNnz2b16tUAREREcOLEiXxD3JNPPonBYCAlJeW6EHjNn3/+yaBBg6hRwzhuoVo140zZkZGROS2DBoOBBg1K/hUvQghxu24Ib02bUnf2/+HYqxfKwoIaI0eSsHoNcV/PM4a5li2pOe4FKvv7l3iYS1y7lqjJU7C95x7qz/8a61q1CtzWkJnNs9/tJiYpnSXP+dHhJl2D5U18fDyBgYHExMRQObkBjet502dMS2wqlX78qGxrxUePeNOzWS3e+OkQr686RFV7a3o0rUVPTxf8m9Qo9pi58kZCXB6FtZiVFi8vL1atWlWsfbp06cK2bdvYsGEDzzzzDBMnTmTo0KHXbRMUFMTmzZvZuXMn9vb2dOvWjfT09HyPt2zZMtq0acNrr73G+PHj+fnnn4tUx/jx45k4cSIBAQEEBQUxderUYl2HEEKUJp2ZSdKGDcbwdu7cDeHtGmVjQ9XHBuM8oD8Ja9Zwad7XRIwaXaJhTmtN/MKFXPx0JvYdOlDvi9lYOjoWus83f5/meEwK3w5rW+EC3MmTJ1m1ahWZhmyc4lvg5eNJz2GeWJbxGxZ6NXdhi0dXIuLTaF6nCpYWFWscYWHKf4fvXaBHjx5cvXqV+fPn5yw7dOgQf//9N/7+/gQGBpKVlUVsbCzbtm3Dz8+Pc+fO4eLiwsiRIxkxYgT79u0DwNramowM44uHExMTqVq1Kvb29oSFhbFr165C61BK8cEHH7Br1y7CwsJuqPHHH3/k0qVLADndqYmJidStWxeAxYsXl8wNEUKI26QzM0lcu5bTD/Ul6o03UXZ21P1iNg1W/0yV++67LsDlpmxsqDp4MI1+3YTr+++RZXpy9Ozgx0j5669bnlBWZ2dz8eOPufjpTKr0eZD687++aYA7G5fK7C0n6OPtSk/PijPWWGvN9u3bWbZsGRaZtlSJ8aFdFx96P9u8zAPcNc72NnjXc7qjAhxIiCsXlFKsXr2azZs306hRI7y8vJg0aRKurq4MGDCAli1b4uPjQ48ePZgxYwaurq4EBQXh4+NDq1atCAwM5KWXXgJg1KhRtGzZkieffJIHHniAzMxMPD09efPNN+nQocNNa6lUqRKvvPLKdQ83gLG1cPLkyXTt2hUfHx8mTpwIwNSpUxk0aBBt2rTJ6WoVQghz0ZmZJKxZw6mHHjKGt0qV/gtvvXsXGN7yui7MffA+WfHxRIwec0thLttgIOrVV4lfvISqQ5+mzsyZWNjYFH4dWvP22hBsLC1492GvIp/L3AwGA6tWrWLz5s04WdbG/nwL/Pt70/nRxqg7LECVBzLFCDKNhCiYfDaEqBh0ZiaJv/xC3Ny5ZJwLx7ZZM2qOewGHHj2KHNwKPX5GBolr1xI3dx4Z589j16IFNca9gEPXroV2s2alpBA5bjxXdu2i1muvUm348CJ1y67Zf54JgQf4oJ8XT1eQJ1Hj4+NZuXIlsbGx1KAp6mIteg5tTtP2ruYurUKTKUaEEELckXRmJonrfyFunim8eXpSb84XxQpvqVczqWxb+F+Hytoa50cfxalfv5wwFznm+ULDXMbFi0SMGs3Vkyep88nHOPXrV6R6Eq4Y+OCXo/jWd+aJ9sV/i4E5XBv/pjW4GFphkeLEgy94U795NXOXdkeTECeEEKLCyQlvc+eSEW4Kb1/OMYa3Ij6AEH7pCu+tP0LQ8VgWDGtL96YFPyV6zXVhbt2668PcC2Nx6NYNpRRXT58hYuRIMi9fpv7cuTj4dy7ytX28KYyEtAy+f8S73I/hSk1NZcuWLezbt4/qVWtgHdEYG2XPw6/4UtOt8DF/4vZJiBNCCFFhlER4S8/I4qugU8z76xTWFoo6zna8HHiAX8Z3pl7Vok0+q6ytcR44EKeAgP/C3PNjsfPywmngI8TN/gIsLHBfvJhK3i2KfH27z8Szck8Eo7s2xLN2yb3JoKRlZ2ezb98+tmzZQnp6Ol73+BL3ryMOzvY8/KIPTjVLdhJfkT8JcUIIISqE1H93c+GD9zGcPIVtc0/qffUlDt27Fzm8aa3ZHHqR99YfIfJyGgE+dXirjyfpGVk8/MV2xi7bx49jOubM7F8U14e59cTNm0fM+x9gXb8+bgu+wca96N2hVzOzeGv1YepVrcRLPRsXeb+yFhkZyYYNG4iOjsbd3Z12LfzZ9l0ENes78NALPthXKfyhDVFyJMQJIYQo1zJiYrj4yQySNm7Eum5d4zxvvXsXa962c5dSmbruCFuPxdK4lgMrRnagY6P/5l2bOdiH0Uv38sEvR5nW37vYNRrD3CM4BTxMyrZtVGrVCqtqxRsPNv+v05y8mMJ3z7bD3qb8/fWcu+vUwcGBgQMHck+Dpvzw4R6qVLej34RWZTKJr/iP3G0hhBDlks7IIH7JUuK+/BKdmUmNsWOpPmokFnZ2RT5GmiGLuUEnmbftNDaWFkx5yJNhnTywzvNezPu9XBndpSFfbztNG/eqDGhV75ZqVtbWOPbsWez9zsSl8sXWkzzUsnaRxuaVpezsbPbu3cuWLVswGAx06tSJrl27YmNjw+8LjnAl0cAjr7eRAGcGcsfLiQsXLjBhwgT27NmDs7MzLi4uzJo1iyZNmuS7fe4X0wshxJ0mddcuLnwwDcOpUzh064bLW5OwcXMr8v5aa/44GsN7649yPiGNfr7GrlOXKgUHwNfub8r+iAQm/XyY5rWdaOpaNgPztdZMXn0YWysL3u3bvEzOWVQRERFs3LiR6OhoPDw86NOnD7VMrwkL3RHNyb0X6dC/IS4e5Xf83p1MQlw5oLVmwIABDBs2jJUrVwJw8OBBYmJiCgxxQghxJzJ2nX5C0sZNWNerR72vvsKxR/diHeNsXCpT1x8h6FgsTVwcWDmqQ5FeWWVlacGcJ1rx0OztPP/9XtaOuxfHMni35ur959lx6hLT+regViEhsyylpqayefNm9u/fj6OjI48++iheXl45XdgJF6+wLfA4dRo70+q+ijENyp1I3thQDmzduhVra2vGjBmTs8zHx4esrCz69u2bs2zcuHEsWrQo5+cZM2bg7e2Nn58fJ0+eBGD9+vW0b9+eVq1a0atXL2JiYsrsOoQQ4lZpg4FL337LqQf7kLzlT2qMG0fDX9YXK8ClGbL47Pdj3Pe/bQSfvcyUhzzZ8KL/TQNc7knvaznaMWdIK87FX+GNnw7d8mu2iupyqoFpG0Jp7ebME35Fb2ksLdnZ2ezevZsvvviCgwcP0qlTJ8aNG0eLFi1yAlxWVjZ/fHsES0tFr2ebY1HOp0G5k0lLXB4J609hiEot0WPa1KmM88ONClwfEhJCmzZtin1cJycnDh8+zJIlS5gwYQK//PILnTt3ZteuXSilWLBgATNmzOCzzz67nfKFEKJUpe7caew6PX0ah+7djV2n9esXeX+tNb8dieGDX4xdpwNa1WXSg81u2qqVkXGZk6dmEhPzC00av02dOo8C0L5hdd54oCnTN4bx7fYzjPBveFvXV5iPNoWSlJbB9Ee8zR6GIiIi2LBhAxcuXKBBgwb06dOHmjVr3rDd7vVnuHgumQdGtcCxWvloObxbSYirwIYMGZLz58svvwwYH/1+7LHHiI6OxmAw0KBBA3OWKIQQBcq4cIGYTz4hedOvWNevT715c3Hs1q1YxzgTl8q7646w7XgszVwdCRzVgfY3bXnL4nxUIKdOfUZWVjL29g0JDXuD5OQQGjd+CwsLG0b6N2Tvuct8vCkM3/rOtPUo+TcP7Dp9iR+CI3m+WyOauZpvTFlKSgqbN2/mwIED+Xad5nb+2GX2/XYOz3tr06h1+XoA424kIS6PwlrMSouXlxerVq26YbmVlRXZ2dk5P6enp1+3Pvd/YNe+Hz9+PBMnTiQgIICgoCCmTp1aOkULIcQt0gYDlxYvJm7uPMjKosb4cVQfMQILW9siH+OKIZMvt57km21nsLWy4J2+zRna0R0ry8JHCSUmHeTYsXdJTj6Ms3N7mjZ5F3v7Rpw6/Snh4QtITgnF2/tLbG1q8OkgHwK+2M4Ly/ex4UV/ajgUvb6buTYnXP1qlXixh3nmhMvOziY4OJg///wTg8HAvffeS5cuXbAt4PeQnprB5kVHcapZic6Dyu88dncTGRNXDvTo0YOrV68yf/78nGWHDhnHYhw9epSrV6+SkJDAli1brtsvMDAw58+OHTsCkJiYSN26dQFYvHhxGV2BEEIUTeqOHZzu15/Yzz6ncseONNzwCzVfeKHIAU5rza8h0fT+fBtfbj1F35a12fJqV4Z3blBogDMY4gkNnURw8ECuXo3Bq/n/aN1qGQ4OTbGwsKLxPZPwav4/kpND2LOnH4mJB6hiZ81XT7Yh4UoG45fvJzMru8DjF9e8oNOcjk1lWn9vKtkUfXLhkhIeHs78+fPZuHEjderU4fnnn6d3794FBjitNUHLwriSZOC+57ywsZM2oPJAfgvlgFKK1atXM2HCBD755BPs7Ozw8PBg1qxZDB48mBYtWtCgQQNatWp13X6XL1+mZcuW2NrasmLFCgCmTp3KoEGDqFq1Kj169ODMmTPmuCQhhLhORnQ0MR9/QvJvv2Ht5kb9r+fh0LVrsY/z7rojLNl5jmaujvwwuiN+DQrv5vyv63QmWVkpuNUfToMG47GyunH6EFfXACpXbsyhw2PYu28IzZq+T/M6g/hwgDev/niQz/84zusPNCt2zXmdik3hy60nCfCpQ9cmN445K01ZWVls3ryZnTt3UqVKFQYNGkTz5s1vOnFy6I5oTu2LpeOARtRy/6/rV2tNdPQqTp6aQWZmSonW6lKrD82aTcfSsuRaQO80qrSfvClv2rZtq4ODg69bFhoaiqenp5kqEuWZfDaEuD3aYODSosXEzZ0LWlNj9CiqDR9erK7Ta34MjuC1VYd49l4PJvfxvHnXaeIBjh1/l+TkEFPX6VQcHG4+bVNGxmVCQl4i/vI/1K37JE0aT2HymjBW7I5gwdC29GruUuzar9FaM+SbXRyNSmLzK12p5Vh2DwakpqayatUqzpw5Q7t27ejVq1eBLW+5JcRcIfDD3bg0cKLfS74o0wMYBsMlwsImExv3B85O7XByLv4DegXJyLhMVFQgzs7t8Wn5db6h+26hlNqrtW6b3zppiRNCCFEqMqKjiRg9hqvHj+PQqycub07Cpl7dWzrW0agkpqwJoWPD6jcNcAbDJU6dmklU9A/Y2rjg5TULl1p9i/yaLmvrqvj4LOTU6ZmEh39DSkoYk+6fzeHziUz84QC/jPfHrfqtveD9p33n2XU6nukDvMs0wEVFRREYGEhKSgr9+vW7oWenIFmZ2fz+7REsrS3o9UzznAAXF7eV0LA3ychI4p57JuFWfzhKlewIrarOHTga+jp79z2Or89CbG1vPTzfqWRMnBBCiBKXfvw4Zx8fQkZUFPXmfkX9OXNuOcAlpWcwdtlenO2tmT2kVYEBTussIiO/Z+eu3kRf+Bk3txF06PA7ri4PF+s9q4BpnNybtPD6P5KTj3Jo/yN83t/Y7vH8sr2kZ2QV+zriUw18uOEobd2r8ni7ok+hcrsOHTrEwoUL0VozfPjwIgc4gN3rTxMbnkyPpzxxqGpLVtYVwo69zcFDI7C2rka7dqtxdxtR4gEOjN3bvj7fkpYWQXDwo6Smnirxc1R0EuKEEEKUqCt79nDuqadBa9yXfY9j9+K9cSE3rTWv/nCQyMtpfPlEa2o65t/9l5i4nz3BAzh2/F0cHTzx8/uFxvdMwsrK4ZbPDeDi0pe2bX5EWdgQeXI4/3s4nCNRSUxdd6TYx/pwQyjJ6ZllNidcVlYWv/76Kz///DN169Zl1KhROQ++FUVkWDz7fg+nuX8dGraqSWLSQXbvCeD8+RW41X+Odm3X4Ohw+2MEC1Ot2r20br2cbG0geO9gEhP3ler5KhoJcUIIIUpM0u+/E/7cCKxq1MBjxXLsmja9rePN33aa34/G8OaDzfKdq81giONo6BsE730Uw9U4Wnj9H61afY9D5ZKbAsPR0RO/dmuoWrU9JH7Mxz038WPwGX4MjijyMXaciuOnfZGM7tqQJi6lP74rNTWVpUuXsmvXLtq3b8/QoUNxcCh6oE1PyWDzd0dxrmXPvQMbcPrMF+zdO4isrHRatVpK48ZvldkDB1UcW9C2zY9YWzuxb//TxMX9WSbnrQgkxAkhhCgRl1es4PxLE7Dz9MR92fdYF6PVJz//nr7EjN+O0cfblec6Xz9xudZZREQuZeeu3ly4sAY3t5F06PA7Li5FH/tWHNbWzvj6fIu722hqWm7ig85zmbHxH45GJd103/SMLKasDsG9uj3jy2BOuKioKObPn09ERAT9+/fnwQcfxNKy6NOYaK3Z+n0YaSkZdH3agYMhT3DmjHFcYXu/jVSr2rEUq89fpf9n7zzDo6q6NnyfKekhvRcCpBBKEiAJPUGKoiBSBRUVQbqoKCIiXVD8xAIiSHkVUESKgry8FBFMKFISOiQhPaRBes9Mppzvx8BICaElJMDc15VrMqfsvc9kMueZ9ey9lqknwe02YWHuy9lz48jK2vTQx9AQqTMRJwjCD4Ig5AiCcP66bRsFQTh99SdVEITTV7d7CYJQed2+7687bFeW0gAAIABJREFUp50gCOcEQUgUBGGJcPW/UxAEW0EQ9gqCkHD10aaursWAAQMGDNweURTJWbyYy3PnYREejueaH5HZPNhHck6Jgrc2nKKxrRmfDwq4QZgVF58kKmoA8fFzsLRsRfvQ/+HjPe2BrdM7IQhSvL2n0qrlElzMM5ge+n8s2LaJ4kpVjecti0giOa+c+f1bYSKv25xwZ86c0c9/GzVqFEFBQffcRsyhLJJP5xDUP4aEjGFUVCTTquViWrb8Crm8/ipLGBnZ06bNz9jadCY27iNSUpbWeW3bhk5dRuLWAL2v3yCK4lBRFINEUQwCfgN+v2530rV9oiiOu277cmA04HP151qb04B9oij6APuuPn9kkUqlBAUF0bJlSwIDA/nyyy9vqNbwIMyaNYu//vqrxmO2b9/OwoULa6W/6lizZg0ODg4EBQXRvHlzvv766zrp46233qp23zUbISsri8GDB9d63wYMPKmIajXZM2aQv/x7rAYPwn3pt0hMTR+oTbVGy1sbTlGmULN8eDssTeT6fckpS4g+MYQqVT6tWn1Lm6B1mJt7P+hl3BNOTn0ICf4NazMLXvP9P5bv/PK2YiIxp4zlEYn0D3Klq0/d5YTTaDTs2rWLrVu34u7uztixY3F1db3ndgovl3NkezTevVdSIf0KK6sg2ofuxMmpbx2M+t6RycwJCFiJs/MAklO+5mL8LETx3heZPC7UWYoRURQPCILgVd2+q9G0F4HuNbUhCIIL0EgUxaNXn68D+gO7gBeAblcPXQtEAB8++MjrB1NTU06fPg1ATk4OL7/8MiUlJcydO/eB2543b94dj+nXrx/9+vV74L5qYujQoSxdupT8/Hz8/PwYPHgwHvdQ5Lo2cHV1rbbEmQEDBu4dbWUlmZPfoywiAvsJ47GfNKlWrMwv9lzkeEoBXw8NxM/53/lj2dm/k5KyGGen/vj5zUMmM3/gvu4XCws/unTczp5DYwmxWcXWiAz6h3+JRPLvPDFRFJm+9RxmRjJm9G1RZ2MpKytj8+bNpKWl0aFDB3r16nVP9uk1NCotEb+vw7P7CmQmSry9Z+Dh/nqdrDx9ECQSOS38v8DY2Im0tO+pqsqjZYuvkUofXsoW0P1968K6vxfq6y/TFbgiimLCdduaCIJwShCESEEQul7d5gZkXHdMxtVtAE6iKGZf/f0y8NgkkHF0dGTlypUsXaoLFSsUCt544w1at25NmzZt+PvvvwFd5Kl///706tULLy8vli5dyldffUWbNm3o0KEDBQUFAIwYMUIvXLy8vJg9ezZt27aldevWxMXF6du6FsW6cuUKAwYMIDAwkMDAQP755x8A+vfvT7t27WjZsuUNJcIsLCz4+OOPCQwMpEOHDly5cqXG67Ozs8Pb25vsbN2f7+effyY0NJSgoCDGjh2LRqPRtzt58mRatmxJjx49yM3NBaBbt25cS9icl5eHl5eXvu309HS6deuGj49PtQI4NTWVVq1aAbpvrlOmTKFVq1YEBATw7bff3u2fyICBJx51YSGXRrxBWWQkzrNn4fD227VyQ9tz4TIrDiQzvIMnA9q467cXFUUTG/cxNjYd8fdfWK8C7hpyuTXPha/nYtkArMRdRB55EaXy38+/zdEZHE8pYPpzzWu17ur1ZGZmsnLlSjIzMxkwYAC9e/e+LwGnVpdz8K+3adR8EaZmzoSGbMPT440GJ+CuIQgC3s0+wNdnJrm5ezl1+nVUquI671ehVhCZHsncI3PpuaUnmWWZdd5nTdRXst+XgA3XPc8GPEVRzBcEoR2wTRCElnfbmCiKoiAItzXGBUEYA4wB8PT0rLGtXbt2cfny5bvt+q5wdnbm2WefvadzmjZtikajIScnh59//hlBEDh37hxxcXE8/fTTxMfHA3D+/HlOnTqFQqHA29ubzz//nFOnTjF58mTWrVvHu+++e0vb9vb2nDx5kmXLlrFo0SJWr159w/63336b8PBwtm7dikajoaxMV0rlhx9+wNbWlsrKSkJCQhg0aBB2dnaUl5fToUMHFixYwNSpU1m1ahUzZsy47bVdunQJhUJBQEAAsbGxbNy4kcOHDyOXy5kwYQLr16/ntddeo7y8nODgYL7++mvmzZvH3LlzWbp0aY2v2/Hjxzl//jxmZmaEhITQp08fgoOrTXTNypUrSU1N5fTp08hkMr3oNWDAQM1UZWSSPno0qsxM3BZ/Q6Onn66VdlPzypmy6QyB7lbMvC5yVVmZwdlz4zExcaF1q6VIJPIaWnm4SCQyXn36Mz5c70xfzx84erwfga2/Qy1rzYKdsYR62TKkXd04DqdPn+a///0vFhYWjBw58r7sU9ClZzlzejJqowwkZYPp3HfeDRHFhoyHxwiMjBy4EDOFEyeHEhT4AyYm9/c63I6cihwiMyKJTI/kWPYxFBoFZjIzOrt1RqlW1mpf98pDF3GCIMiAgYC+PocoikpAefX3E4IgJAG+QCbgft3p7le3AVwRBMFFFMXsq7Zrzu36FEVxJbASdGW3avFyHgqHDh1i0qRJADRv3pzGjRvrRdxTTz2FpaUllpaWWFlZ8fzzzwPQunVrzp49W217AwcOBKBdu3b8/vvvt+zfv38/69atA3Rz9aysrABYsmQJW7duBXQRr4SEBOzs7DAyMqJv3776Nvfu3Vttvxs3buTAgQPExcWxdOlSTExM2LdvHydOnCAkJASAyspKHB0dAZBIJAwdOhSA4cOH68ddE7169cLOzk5/nYcOHbqtiPvrr78YN24cMpnu38DWtuYajAYMGABFXBzpo8egVSrx/OE/mN3m/+teqazSMO7nE0ilAt+90hZjmS6apFaXcfbsGERRRWDAauRy61rprzaxNJHzbt+xjPvRlreCVnHy1HASyp9H0Iby6cCOtZ4TTqPRsGfPHo4fP06TJk0YPHgw5ub3HpnUalWkpC4lNXUZ6kobSuI/pv/E15BI6nbxRW3j5NQHuZEtZ8+OI/rEEIICf7yr8mq3QxRFYgpiiEyPJDIjkpj8GADcLNwY6DOQcPdwgp2DMZIa1dYl3Df1EYnrCcSJoqi3SQVBcAAKRFHUCILQFN0ChmRRFAsEQSgRBKEDcAx4DbjmeW0HXgcWXn38ozYGd68Rs7oiOTkZqVSqFzS34/q6dxKJRP9cIpGgVqtrPEcqld72mJuJiIjQF002MzOjW7duKBQKAORyud5GqanNa3PioqOjefrpp+nXrx+iKPL666/z2Wef3XEM1/qQyWT6RR/XxnDzMbd7bsCAgfun/OgxMt56C4m5OY1//gkT3/u/UV6PKIrM/OM8F6+U8uOIENxtzK5u13DhwmTKKxIJDPwBc/OmtdJfXeDrZMnbzzzD9N8seS90O03Nt/J/YbsQStNQ2YxCLq+dBAq1Nf+tvDyZmJj3KSk9i6a4G6mRAxn4fhhyo0dLwF3D1qYj7dpu4PSZkZw4OZTAgFVYW9/9F4xKdSXHso8RkR7BwYyD5FTmICAQ6BDIO23fIdw9HG9r7wZ3T6nLFCMbgCOAnyAIGYIgjLq6axg3WqkAYcDZqylHtgDjRFG85m1NAFYDiUASukUNoBNvvQRBSEAnDOtuaeVDJjc3l3HjxvHWW28hCAJdu3Zl/fr1AMTHx3Pp0iX8HjCBZk306NGD5cuXA7pvfMXFxRQXF2NjY4OZmRlxcXEcPXr0vtsPDg7m1VdfZfHixfTo0YMtW7aQk6MLpBYUFJCWlgaAVqvVz+X75Zdf6NKlC6Cb13fixAmAWxYp7N27l4KCAiorK9m2bRudO3e+7Th69erFihUr9KLTYKcaMHB7SnbtIn30aGTOTnj9uqHWBBzAxqh0tpzIYFJ3H7r5/fvFNTHpC/Ly9+PjMxM72y611l9d0b+NG4OC/Zn/zzBWxszByeEpUtO+5/A/4SQmLaKq6sE+Y66f/zZw4MD7mv9WUZFCQuJCjkc9T0XlJawkc0nY8wrt+7bGwePRLjJvadmC4HZbMDKy49Tp18jJ3VPj8VfKr7Dp4iYm7ptI11+7Mmn/JHan7ibQMZAFXRYQMTSCn577iTdbv4mPjU+DE3BQt6tTX7rN9hHVbPsNXcqR6o6PBlpVsz0f6PFgo2w4VFZWEhQUhEqlQiaT8eqrr/Lee+8BMGHCBMaPH0/r1q2RyWSsWbPmhghcbbN48WLGjBnDf/7zH6RSKcuXL6d37958//33+Pv74+fnR4cOHR6ojw8//JC2bdsyffp05s+fz9NPP41Wq0Uul/Pdd9/RuHFjzM3NOX78OPPnz8fR0ZGNGzcCMGXKFF588UVWrlxJnz59bmg3NDSUQYMGkZGRwfDhw29rpQK8+eabxMfHExAQgFwuZ/To0bdNUWLAwJNMwU8/c+XTTzFt2xaP75Yita49S/N8ZjGztl+gq4897/T4NxFuVvYWLl1ahZvbcDzcX621/uqaGX39MZZJeCGoC63dh1NWFk9K6lLS0r4nI2Md7u6v4ekxEiOje5u+cerUKXbs2IGFhQWjRo3CxcXlrs/VapXk5u4lM3MDhUVHEQQpDg7P4NjoPbZ9kYZHC2sCuz/cTAF1hampO+3abuLM2dGcO/cWfn5zcXd7GQCtqCU2P5aIjAgi0yOJLYgFdDbpYN/BhLmHEeIUglzacOZc3gnhSUuUFxwcLF5b2XiN2NhY/P3962lEBm6HhYWFflFFfWF4bxh4khFFkdyvviZ/1SosevbAbdEiJCa1l8ahuEJFn28PotGK/O/trtia6+YYFRVFc/LUcGysQwkM/E+DWshwv5SVJ5CaspQrOf9DKjW9KuZG3ZWY27t3L4cPH77n+W8VFSlkZv1KdvbvqFQFmJi44+Y6FBeXwcgk9mz+PJryIiXDZoZibvVoLGS4WzSaCs6dn0R+fgTY9OVghTUHMg+SW5mLRJAQ6BBIuHs44e7hNLNu1iCjbNcQBOGEKIrVRiTqa3WqAQMGDBhowIgqFdkzZlL8xx9YDx2K86yZCPcx9+p2aLUi7206zZUSBRvHdtQLuMrKdM6eG4+pqTutWn37WAg4AAtzH1q1WoxX+VukpiwlLW3FdZG524u5qKgoDh8+TLt27XjuuefuaJ9qtUpycv8kM3MDRUXHEAQp9vY9cXMdhq1tF33KkENbEsjPKKPPhIDHTsBdLr/MgYwDRObK8VAYEcIOTCtMaOvwNOGe3eni1gUbk8ejyJNBxBlosNR3FM6AgScVbXk5Ge9OpvzgQezfnoT9+PG1HqlYHpnEvrgc5vZrSVtP3Q1VrS7lzNnRiKKawIBVyOVWtdpnQ+AGMZf63b9izu1VPD1HYWRkpz82KSmJnTt34uPjQ58+fZBIbj+Nvbw8maysX8m+/DsqVSEmJh40azoFF5dBGBvfuEAuPaaAM3+l0zrcDa8A+zq71oeFVtRyIe+CLg1IRiRxBbr8p+4W7ni6v47cJIdg/sDBXknrpnVTW7e+MIg4AwYMGDCgR11QQPrYcSguXMD5k3nYDBlS6338k5jHl39e5PlAV17r2BjQrUQ9f+FdKiqSCQr8ETOzJndo5dHGwtyHVi2/oYnXW7o5c5dWkpH5k17MlZSIbN68GXt7ewYNGlStgNNqleTk7CEz69erUTfZdVG3ztUm6lVWqtn/Uyw2zmZ0GvRwy5XVJhWqCo5kHyEyPZIDGQfIV+QjESQEOQQxud1kurl3o4lVE71gS0vzIzHp/8jM2qCfI/c4YBBxBgwYMGAAAGVyCunjx6G+fAX3pd9i2b3Gyoj3xeViBZM2nKKpgwULB7bW32QTEz8nPz8CP9952NreflX544a5ufd1Yu470i6tJD1jHTlXWmJk1IKXXx6LyU3zEMvLk8jK2qiPupmaeNKs6QdXo24112c9vCWB8iIlg6YGI3vE0olkl2UTmRFJREYEUdlRVGmrsJRb0tmtM2HuYXR164q1SfWLbjw9x5BfcIjExIXY2YZhaupe7XGPGgYRZ8CAAQMGKN2/n6wPpiIYGeH544+YtW1T632oNFre+uUklSoNG4e3xdxYdwvKytrEpfT/4O7+Ku7ur9R6v48COjH3NZ4e44iMnIqdfTQOjufJzzfB3PxNpFILcnNvjLo52PfCze0lbGw63lV5rLQL+cQezqbtM544NWn0EK7qwdCKWs7lndMn3Y0v1CW597T0ZGjzoXRz70YbpzbI72LepCAI+DdfyLHjzxIb9xFtgtY9FraqQcQZMGDAwBOMqNWSt2w5eUuXYtKyJe7fLkF+n+Wb7sTCXXFEpxXy7Utt8HbU5SQrLDxG3MVZ2Np2xcf79uX6nhQOHkzkxIk29O07GlPT/aRdWk16xk9IJMao1UW6qFuzqbqom9Hdz2dTVqqJ+DkOGxdzQvrWnlWt0qqITI/kz9Q/qVRX1lq7GlHDhfwLFCgKkApSghyDeL/d+4R7hOPVyOu+BJipqRve3tO4eHHmY2OrGkRcA0EqldK6dWv982HDhjFt2rQ66SsiIoJFixaxY8eOOmnfgAEDjwaa0lKyPpxG2f79WL3wAs5z59RqCpHr2Xkum/8cSmFEJy+eD9SJxMrKS5w7PxFTUw9atVyCRPJk35KOHz9OVFQUnTp1Ijj4aaAvXl4TSbu0Eq1WiavLkLuOut3M4c3XbNTWyOQPbqNmlGbwW8JvbE3YSr4iH3tTexxMa7Zy75X2zu0J9wini1sXrIxrZ5GLm+tL5Obsfmxs1Sf7P6YBYWpqyunTp+t7GNWiVqv19UUNGDDweKBMTiZj4ltUXbqE08cfYzP8lTqzl5Jyy/hg8xnaeFoz/Tld3kXdStQxiKKWwICVyOW3t/c0Wg2pJanE5McQkx9DsbJYJxrMHHAwdbjh0VRmWifXUNckJSWxa9cufH196dmzp367uXkzWvh//kBtp13IJ/afbNo+0/iBbFSVVkVEegRb4rfwT9Y/SAQJYW5hDPYdTBe3LkgfgZqrgiDQvPlnHDv+HLFx02gT9NMjbasa7swNnKioKN555x3Ky8sxNjZm3759/Pbbb0RHR7N06VIA+vbty5QpU+jWrRvjx48nKiqKyspKBg8ezNy5cwHYvXs37777LmZmZvryVaArNTVy5EiSk5MxMzNj5cqVBAQEMGfOHJKSkkhOTsbT05MNG26ulGbAgIFHldJ9+8ia+iGCsTGeP/6AeWhonfVVUaVm/M8nMJZLWfZKW4xkErRaNefPv01FRQpBQWtuWIl6TbBdyL+gF21xBXF6q85EaoKNiQ15lXmotKpb+rOUW2JvZo+jqeO/j6b2OJrd+GgmN6uza75XcnNz2bRpEw4ODrddiXq/XG+jht6njZpems7vCb/ro27O5s5MCJrAAO8BOJs719pYHxampm74eE8j7uKMR95WNYi4m4iP/4TSsthabdPSwh9f35k1HnOt7NY1PvroIwYMGMDQoUPZuHEjISEhlJSUYGpa87fMBQsWYGtri0ajoUePHpw9exZfX19Gjx7N/v378fb2ZujQofrjZ8+eTZs2bdi2bRv79+/ntdde00cEY2JiOHTo0B37NGDAwKOBqNWSt/Q78pYtw6RVK938t3so33TP/YkiH/1+joScMtaNDMXFSvdZkpi0kPyCA/j6zqMABw4lba9WsJnKTPGz8WOA9wBa2LWghV0Lmlg1QSaRIYoiJVUl5FTkkFuZS25F7i2Pp3NOk1uRS5W26paxWcgt9KKucaPGvNP2nVqz7O6FiooKNmzYgFQq5aWXXqr1koqHNydQXlzFoLGtkcrvXhyqtCr+vvQ3W+K3cCT7iC7q5h7GEN8hdHbt/EhE3WrC1XUYOY+BrWoQcQ2E6uzUc+fO4eLiQkhICACNGt05DL5p0yZWrlyJWq0mOzubmJgYtFotTZo0wcdHV5dw+PDhrFy5EoBDhw7x22+6srXdu3cnPz+fkpISAPr162cQcAYMPCZoSkvJ+mAqZRERWA0YgPOc2UjqsAYzwOe7L/LH6Sw+eMaPTs1sSSxM5GLaakwKNhOjduGjyMW3FWwt7VrSxKrJbcWCIAhYGVthZWyFj41PtccAerGXW5FLTmUOeZV55FTc+Lg1cSuJRYms6LXiodqxGo2GzZs3U1xczOuvv46NTe1WEUg7f9VG7d0YJ6+7s1HTS9L5LeE3tiVuI1+Rj4u5CxODJjLAewBO5k61Or76RBAE/P0/4+ixZ6/aquvua65hfWMQcTdxp4hZQ0Amk6HVavXPFQoFACkpKSxatIioqChsbGwYMWKEft/9cLf1+QwYMNCwUSYmkvHWJKoyMnCaNRObl16q83lAqw8ms+LIUdoEJnC08lfWbriIm7SM8Q5K4pVyooUmDPRpqYuw2baoUbA9CNeLPW+b6pPb7k7dzdTIqXwQ+QHfPPUNsoewwEIURXbu3ElKSgr9+/fH09OzVttXVqj4++c4bF3NCe1Ts42q0qj4O/1vNsdv5mj2UaSClHD3cAb7DqaTa6dHPup2O0xMXPHx/oi4ix+TmbnhkUxvYxBxDRg/Pz+ys7OJiooiJCSE0tJSTE1N8fLyYtmyZWi1WjIzMzl+/DgAJSUlmJubY2VlxZUrV9i1axfdunWjefPmpKamkpSURLNmzW6Y39a1a1fWr1/PzJkziYiIwN7e/q4ifgYMGHg0KNm7l+wPpyGYmtJ4zY+YBVdbR7vWqNJUsfDAJjbEbsaiWTKpKhmthda83KwnrRT/xcjIk5FdtzLeqPqkrPVBb6/eFCmKWHBsAXOPzGVep3l1LnKPHz/OiRMn6Ny58w1TaWqLQ1sSqSip4rnxt7dRL5Vc0kfdChQFuJi78FbQW/T37v9YRd1qwtV1KDm5u0lMWoidXRimph71PaR7wiDiGgg3z4nr3bs3CxcuZOPGjUyaNInKykpMTU3566+/6Ny5M02aNKFFixb4+/vTtm1bAAIDA2nTpg3NmzfHw8ODzp11Wc9NTExYuXIlffr0wczMjK5du1JaWgrAnDlzGDlyJAEBAZiZmbF27dqHf/EGDBiodUStltxvvyV/+feYBATgvmQxcue6m4SeUpzCb/G/sSV+K+XqEkzN7BkTOInBfgOxkhkRfWIwVRIjgoPWYNyABNw1hjUfRr4in+/PfI+tiS2T202us74SExPZvXs3fn5+9OjRo9bbTz2XR9w/2bTr3RjHxjd+Kc+tyOVAxgF2pe7iWPYxfdRtiN8QOrp0fGyjbrdDlwT4U52tGjuNNm1+eqRsVUEUxfoew0MlODhYjI6OvmFbbGws/v7+9TQiAw0Zw3vDwKOIpqSEzA8+oDzyAFaDBuI8a1adzH+r0lTxV9pfbI7fTPSVaKSCFFVpS5yFcH5/43WsTI3RatWcOfsmhYVHaBO0FhubDrU+jtpCFEU+OfoJm+M380HwB7zW8rVa7yM3N5fVq1djbW3NyJEja30hg7JCxYa5xzA2l/PiRyFIZAJxBXFEZERwIP0A5/PPA+Bm4cZAn4H09+6Po5ljrY7hUSQzayNxcdPx852Lu/vw+h7ODQiCcEIUxWpD6IZInAEDBgw8RigTEkh/6y1UmVk4z56F9bBhtW4NJhcn81v8b2xP2k6Rsgh3C3eG+47jl31O2Bvb8eu4TliZ6sRJQuKnFBQcpHnzTxu0gANdVObj9h9TpCzii+gvsDGx4flmz9da+xUVFfzyyy/IZLI6WYkKcGhzAhWlVbgM1rAgej6RGZHkVOQgINDaoTVvt3mbcI9wfKx9Hun8aLWNq8uL5OTsIjHpc+zswh8ZW9Ug4gwYMGDgMaHkzz/JnvYRgpkZjdeuwaxdu1prW6lR8lfaX2yJ30L0lWhkgoynPJ9iiO8QPEwDGPL9UYwEkZ9GtsfBUidOLl/+g4yMtXi4j8DNdegdemgYSCVSPuv6GUXKImYdnoW1sTVd3bs+cLtqtZpNmzZRUlLCiBEjsLauXUs5pyKHvQeOUHTEkjMe+1gesx0zmRmdXDsR7hFOV7eu2Jna1WqfjxPX26oxsR/Sts3Pj4StahBxBgwYMPCII2o05C75lvwVKzAJDMB9yRLkTrUzMT25OJkt8VvYnrSdYmUx7hbuvNv2XV7wfgF7U3sKy6sYsuIIZQo1G8d2xNNOl0S3rCye2LiPsbIKxtu7bkoI1hXGUmOWPLWEkXtG8n7k+6x+ejUBDgH33d61laipqakMGDAAD48Hj/KIokhMQQwH0g8QkRFB4pUUhp6ehsY8F6+nLBjReAXBzsEYSY0euK8nBRMTV3x9PiY27iMyMtfj4f5qfQ/pjhhEnAEDBgw8wmiKi3Xz3w4cxHrIYJxmzkRi9GA3bqVGyd60vWyJ38KJKyeQCTK6e3ZnsO9g2ru0R3I1QlFRpeaNNVFcKqhg3chQWrjqJtGr1WWcOz8Rmcyc1q2WIJHIH/g6HzYWRhYs67mM13a9xoR9E1jXex1NrZveV1vHjh3j5MmTdOnShcDAwPseU6W6kmPZx4jMiORA+gFyKnU2aaBDIG+WfoyosWTQpOC7zgln4FZcXIbobNXEz7G3C8fUtHZTv9Q2BhH3hCGKGlSqIgRBhkxmgSA8WSuRDBh4nFAmJZE+fgKq7Gyc587FZuiL99WOKIpklWcRkx/DiSsn2JG8g2JlMR6WHkxuN5l+zfphb2p/wzkqjZYJ609yNqOI5cPb0aGpnb6t2LiPqKhIpW2bnzA2fnRTVdib2rOi1wpe3fkqY/aO4efnfr7nMlMJCQns2bOH5s2b071793segyiK7E3by/ak7RzNPopSo8RMZkZnt86Eu4fT1b0rJQla/rf9LMHPeRkE3AOiq636KUeP9SYmdlqDt1UNIu4JQRQ1VFXlU1WVhyhqrm4VkMrMkMkaIZNaIpXWbfZ2AwYM1B4VJ0+SPn4CglxO47VrMWvb5q7OE0WRzLJMfZmrmPwYYgtiKVIWASCTyOjh2YPBvoMJdQ7VR92uR6sVmbrlLBEXc1k4sDXPtPxX2KRnrCEnZyfNmk1t8AsZ7gYPSw++7/U9b+x+g7FF4ZwlAAAgAElEQVR7x7K291qsTe5uPltOTg5btmzBycmJAQMG3HNN1KSiJD499inHLx/H1dyVQT6DCPcIJ9jpX5tUUa7ij5+PYedmTvBzXvd6eQaqwcTEBV+fGcTGTSMj82c83Gt/lXJtYRBxDQSpVErr1q31z4cNG8a0adPo1q0bixYtIvimBJ07duxg5syZaLVaVCoV77zzDmPHjr2l3Wvi7ccfV3Hy5Hm++eYzjIwcARG1uhS1ugSlIhsl2UgkxshklshkjZBKzW67csnCwoKysrJavX4DTxaiKHIk6wjZ5dl0duv8SBbRrk9K//qLzPenIHdxwWP1aozc3ao97ppgu76YfGxBLMXKYgBkggwfGx96ePbQ1yb1sfHBuIYvdKIosmBnLFtPZTLlaV+Ghf5rNxUVRZOYuBB7+5409hxTuxddjzS3bc6S7ksYt3ccE/dPZFWvVZjJzWo8p7y8nA0bNtzXStRyVTnLTy9nfex6zORmzGg/g8G+g6vN4XZocwKVpSr6TgxEKmu4EaNHDReXweTk7iIx8f+wsw3HzKxxfQ+pWgwiroFQXe3U26FSqRgzZgzHjx/H3d0dpVJJamrqDcfcHHkTBGPkcivMzLz0x8hk5oAzWq3yqqAr1Z8jCNKrgk73c7+2q1qtRiYzvM0M6BBFkYOZB1l+erk+XxXobpLh7uGEu4fT0r5ltdEfAzoKf93I5XnzMGndCo/vv0d2td6mKIpklGXcEGGLyY+hpEpXC1kmkeFj7UNPz5762qQ+Nj73PPH9+8hk/nMohRGdvJj41L9lrKqq8jh//m1MTFxp4f/FY5e+IsQ5hM/DPuf9yPd5P/J9lnRfgvw2c/1uXolqZWV1V32IosiulF18Gf0lOZU5DPQZyDtt38HWxLba41PO5nHx6GWCn/PCwdPyvq/NwK0IgkBzvwUcO66rrdq2zfoGaasa7q6PIKWlpajVauzsdHNQjI2N8fPzA2D79m3Mnz8PpbISGxsrfvzxWzw9W2JsbI8gpFJcXExAQAApKSlIJBLKy8tp3rw5ycnJrF+/lZUrV6JUVtK0qScrVnyCqWkRqamZjB49nfJyBf369dOPQxRFpk6dyq5duxAEgRkzZjB06FAiIiKYOXMmNjY2xMXFER8fXy+v05NMekEFx1IK6NXCCSvT+p9UfrN4czV3ZXbH2QQ6BHIw8yCR6ZGsOreKFWdXYG9qT5h7GGHuYXR06XjHiMeTgiiK5C39jrzvvsM8PAy3r77iXHkCf59Yw4X8C8Tmx94i2Ho17vVAgu1mNkWn8/nuOJ4PdGVW3xZ6oSaKGs5feBeVuojgwC3I5Y/nvKyejXsyo8MM5h2Zx+zDs5nfZf4tXzjUajU7duwgLS2NgQMH3vVK1MTCRD49/ilRl6NoYdeCr5/6usYVsYpyFRHr47BzszDYqHWEiYkLPt4ziI37kIyMn/DweL2+h3QLBhF3EzMTMjhfVlmrbbayMOUTH/caj7m57NZHH33E0KHV51WytbWlX79+NG7cmB49etC3b1+GDXsRtbqQoCAn9u5dg1zeiJ9/3snSpb/y5Zdf6s+1srIiKCiIyMhInnrqKXbs2MEzzzyDXC5n4MCBjB49GoAZM2awceNhJkwYxfTpUxg1ahjDhj3LqlW/AiIKRTbbt+/n9OnTnDlzhry8PEJCQggLCwPg5MmTnD9/niZNai68bKD2UGm0/BVzhV+OX+JgQh4ArlYmLHoxkE7N7O9wdt1wTbwtO72MC/kXcLNwY07HOfRr1g+5VCcufWx8GNlqJEWKIp2gy4jkz9Q/+T3hd4wkRoS6hOqjdC4WLvVyHfWNqFZzee48ijZvRjK4LweHBbDlr+EkFiXeINha2usKyvtYP7hgu5m/Yq7w0e/n6Opjz5dDApFI/o20JSd/TWHhEfybf46lZYta7behMcR3CAWVBSw9vRRbE1umhEzR70tKSmLnzp3k5+cTHh5OQMCd05KUVZWx/IzOOjWXmzOzw0wG+Qy6Y/mrQ5sSUBhs1DrHxWWQzlZN+gI7u24NzlY1iLgGwr3YqQCrV6/m3Llz7N37J198sZCdOzezfPknXL5czKhRM7h8OYeqqqpqRdTQoUPZuHEjTz31FL/++isTJkwA4Pz588yYMYOioiLKysp45plnkMnMOXIkmq1bdyCVirz++pvMnv0NVVX5REbuZsCAcKqqsrCzsyY8PJyoqCgaNWpEaGioQcA9JC7lV/Br1CU2RWeQV6bE1cqEyT19CfCw4pP/xvDK6mO82aUJ7z/th4n84axGrk68ze00l+ebPX9bC8raxJrnmz3P882eR6VVcfLKSSIzIolMj2TBsQUsOLYAPxs/wtzD6ObRjVb2rZ4I21VbWUnG++9z8uLfHHzPn0izv1Ge2E1Lu5bM6TiHZ5s8W+fRyqjUAib+cpJWro34fng7jK4TDXl5+0lNW46ry4u4ug6u03E0FMYEjCFfkc/amLXYmdox0GMge/bsISYmBltbW1555RV8fHxqbEMURf6X8j++iv6KvMo8vXVqY2Jzx/5TzuZx8dhlgvsYbNS6RrdadQHHjvVukLaqQcTdxJ0iZg0FUdTg6+uIl9ezDBgQQkDAc6xb9wtTp07ivffeo1+/fkRERDBnzpxbzu3Xrx/Tp0+noKCAEydO6Je9jxgxgm3bthEYGMiaNWuIiIjQnyMIAhKJHCMjO0DA0tIfmcwSicQEtboUlaoIjaYCUdQCYG5u/hBehSeXKrWWv2KvsOFq1E0iQPfmTrzc3oNwX0ekV6Mk7ZvY8unOWFYdTOFgQh5fDw3C36XurK77EW/VIZfIae/SnvYu7fkg+ANSSlL0SU1/OP8Dq86twtbEVifo3LvR0fXxtF0Lc9L56atR7G6aRXp7GebyLF5o8gKDfAfRwu7hRLziLpcwak0Ubtam/DAiBHPjf28blZXpXIh5HwuLFvj6zn4o42kICILAtNBpFFYUsmPfDtJL0pEIErp3707Hjh2Ry2t+rycUJrDg2AJOXDlBS7uWLH5qMa0dWtd4zjUU5Soifo7Dzt2C4Ge9auFqDNwJE2NnfHxmEBv7IRkZ6/DwGFHfQ9JjEHGPGKKooaAgjWPHDtClSztkskbExyfTuLEXUqkZxcXFuLnpVqqtXbu22jYsLCwICQnhnXfeoW/fvkiluuhMaWkpLi4uqFQq1q9fr2+nc+fO/PrrrwwfPpz169cDIAhSwsN7smLFCt588x2ysy9y6NARPvlkMqmppQ/hlXi8qKy8REnpBWysQ68K5epJyy9nw/F0tpxIJ6+sClcrE97r5cuQYHdcrExvOd7MSMb8/q3p4e/E1C1neWHpYaY848ubXZreYIc9KKIociDjAMvPLNeLt3md5tG3Wd97Em/VIQgCTa2a0tSqKSNajaBYWcyhzENEpkeyL20f2xK3IZfICXUOpat7VwLsA/C19a1xhWVDRhRFTueeZuPptexN30+Vv4i/3JO5IW/S26v3QxWr6QUVvPaf45gaSVk3KhQ7i39fU41GybnzEwGRgNbfIZWaPLRxNQSSk5LxOOeBeaE5GWYZDOw7kLAWYTWeU1pVyrLTy9gQtwELIwtmdZzFQO+Bd7ROr+fgpngUZSr6TjLYqA8TF+dB5OTsvs5W9arvIQEGEddguHlOXO/evVm4cKH+uW61aR5VVfkoFCUsXryWyZM/xdTUDHNzc9asWQPAnDlzGDJkCDY2NnTv3p2UlJRq+xs6dChDhgy5Idr2ySef0L59exwcHGjfvj2lpToxtnjxYl5++WU+//xzXnjhBf3xAwYM4MiRIwQFtUEQBBYu/AwnJ3vi4uLRapWIorZBhZ0bIhpNJalpy0lLW4UoVgECVlZtsLfrgb39U5ib+6LSiOyN0UXdDiXmIZUIdG/uyMuhnoT5OuijbjXxlJ8je94N46Pfz/Lpzjj2x+Xw5YtBuFnfKvzuhWvibdmZZcTkx9SqeLsdVsZW9Gnahz5N+6DSqjidc5qI9AgiMyJZeFz3PyMTZDSzbqZPm9HCrgW+Nr6YyBqu0ChWFvPfpP+yJX4LScVJmFbBUxelvNJvBm3CHr5NmV+m5PUfjqNQadg8rhPuNjeKx/iEuZSWXiCg9YoGn9W+NikqKmLPnj3ExsZia2vL4GGDmZ84nxknZ+Bk70Qbx1vz9YmiyI7kHXx14ivyK/MZ7DuYt9u8fdf55q6RciaX+GNXCOnjhYOHwUZ9mOhs1fk6WzV2Gm3b/tIg7m+CKIr1PYaHSnBwsBgdHX3DttjYWPz9/etpRDUjilqqqnKpqspHFDXIZI0wNnZEKn2wm29dIYoaFIosVKoipFJTTEw8HukkwnX13hBFkby8vcQnzEehyMTJqR9urkMpLDxGXv5+Skt16TeUohPRl1twLKs5pdrWDAluypBgD5yt7k+MiKLI5hMZzN1+AYlE4JMXWvFCkOs9p4MQRZHIjEiWn1lOTH4M7hbujAkYU6fi7W7IKsu6Ib3GhfwL+iS2UkF6i7Dzs/GrV2EniiKnck6xJX4Lf6b9iVKjpIVJE8L/l0GXTEt8l63CxM/3oY+rTKnm5VVHuXi5lJ/fbE+I140pLrKytxAb+yGNG4/Du9kHD3189YFareaff/7h4MGDiKJIWFgYnTp1QiaTUaAo4PVdr+vmyfVei4/Nv/PhLhZc5NNjn3Iy5ySt7Foxo8MMWtq3vOf+FeUqNsw9hmkjI4ZMCzZE4eqJ7OzfiImdio/PDDw93ngofQqCcEIUxeBq9xlEXMMVcaIoUqlIR60qbvDi7WZUqmIUikxEtJgYuyCX2z6SeaPq4r1RUZFCfPw88gsOYG7ui5/vHGxs2gO6uW5/xlzmjxOnUZYdIsjxAi3t4pFJqpBKzbG17Yq9/VPY23XDyOj+V5xeyq/gvU2niU4rpG+AC/P7t8La7M6rGa+Jt2WnlxFbENtgxNvtEEWR7PLsW3KnFSoLgfoTdsXKYrYnbWdL/BaSi5OxkFvQp2kfeue5YTH9G+Tu7niuXoXc1bVOx1EdSrWGUWuiOZKcz4rh7ejZ4sayWaWlsUSfGIRVozYEBa1FInn8DZ3ExER27txJQUEB/v7+PPPMM1hb3xhFyyzL5LWdusz+Pz33E5ZGlnrr1NLIknfbvssAnwH3vRhn7w8XSIzOYfBHwYYoXD0iiiJnzo6msPAI7UN3YGZW9wv4DCLuOh4VESeKIkplNlVV+RgbO2Ns7FDfQ7pntFoVCkUGanUZMpklJiZuj1wh7Np8b2g0FaSmLiPt0n+QSIxo2vRd3N2GI5HIyStTsupgMluiM8gvr8LN2pSXQj0YEuyBvblIYeER8vL2k5f/N0rlZUDAqlEQ9vbdsbfvgbm57z2LZI1W5PvIJL7eG4+9hTGLhgTSxedWYXitrubpnNOsvbBWL97GBo6lT9M+DVK81YQoilwuv6yP1FUn7JpaN6WFbQv87fyxNKq9G6ZW1HIs+xh/pv5JlbaKAPsABvsO5hmvZ1Bs2sqV+QswDQzEffkyfRLfh4lWK/L2r6fYcTabLwYHMCT4xhxnanUpx6NeQKOpJDT0vxg/wBeJR4GioiJ2795NXFwcdnZ2PPvss3h7e9/2+PjCeEbsHoGVkRWV6koKFAUM8R3C223fxsr47hL+VkfSyRx2rzxPSN8mhPY1rPqvb5TKKxw91htzc1/atd1Q57aqQcRdx6Mi4pTKKyiVORgZ2WNi8ujmxhJFEZUqH4XyMgISTEzckMvv/8PsYVMb7w1RFMnN/ZOEhPkolFk4O/XH2/tDjI0dAbhcrOClVUe5VFBBT39HXgr1pKtP9XPdRFGkrCyG3Lz95OXto7T0HAAmJm7Y23XH3r47NjbtkUju3sI+n1nMuxtPk5hTxohOjXmtayOSSi7qRE7ehRvqanpYejAmYMwjKd5q4hZhVxBDbH4sBYqCWu/rWtRtiO8Q/Gz9dO+PbxaTv2IFFt274/blIiSmDz/iLooic7ZfYO2RNKY925xx4c1u2X/u3Hjy8vfTts0vWFtXe095LLhmnR44cABBEAgLC6Njx453VX3m5JWTjN07Fl8bX6a3n35f1uk1FOUqjv6RzIWDmTh4WDLow3ZIpQYbtSGQnf07MbEf0CZoHba2neu0L4OIu45HQcTpFi9kIZdbY2Li/kjakDej0ShQKNLRaBTI5TaYmLjcdymve0UUNWg0FUgkxkgk95YA9UHfGxUVKVyMn0tBwUEszP3w9ZuLjXWIfv81AZdbqmTtyFDaNb636ItSmUNe/t/k5e2noOAwWm0lUqkZtrZdcLDvhbPzC7d9na8vhH429wK74qO4okhCkFUAusUB3jbeOpvRVmc1+tv5I3sC7DO4Om+xMg+FRlGr7TqYOujtWlGlInv2HIp//x3rIUNwnj0LoZ7K1H27L4Ev98bzZpcmfNzH/5bPnbRLq0hMXIiP93Q8PUfVyxgfBgkJCezatYuCggJatGjB008/fYt1eifKVeWYykzv2zoVtSIxh7M4ui0ZZaWagG7uhDzfBGPTJ+N/T6VRcSrnVK1/iXK3dKeVfataaUsURUrLLtDIsnbaq4maRNyT8Y54hNDNJcvS24+Pg4ADkEpNMDNrhlKZQ1VVLhpNOSYm7lfrt9Y+Go0StaYUtboEjboC0H1ZkUhM9PVgpVKzOnt9NZoKUlKXcenSaiQSY3x8ZuDu9uoN84cuFysYtvIIeWVV9yXgAIyNHXFzHYqb61A0GoXOds3/m7y8feTm/klJ6Xn8fGfdINiuRZtuLoTubeNNM9tuRF80o6zUmTFdwpgQ3vyuVr8+jgiCgINZ3U1j0FZUkDF5MuWRB7CfOBH7tybW2//7L8cu8eXeeAa0cWP6c7cKuMLC4yQlfYGDQ288PEbWyxjrmsLCQvbs2aO3Tl999VWaNWt25xOrwVx+/59rOWklRG6IJye1BBdvK8Jf8sPOzeK+23tUKFQUcijzEBHpEfyT9Q9lqrI66WeY3zDeC34PU9mDRbsFQXgoAu6O4zBE4hpOJE6tLqOiIhWp1BQzM6+HFql62KjV5SgUGWi1VRgZOWBs7PjAcwpEUUSjqUCtLkGtLkWrVQIgkRgjkzVCJjNHo1GgVpei0ZQDulx3un2WyGQW1b7e9/re0Fmne4hPmI9SmY2z8wC8m314y5zG7OJKXlp59IEEXE1Uqas4eGYSFP9FlMaPHfklNwg2HxufGybz+9j46POqFZZX8fG2c+w8d5lQL1u+fDEQD9vHL5FufaIuLCR97DgU58/jPGsWNsOqL7H3MNh9PpsJ608S5uvAqteCkd9k1ymVORyP6odMZkFI8FZkssdrUr1Go+HQoUMcPHjwnq3T2kRRpuLIH0nEHMrCzNKIToO88Q11emy+yN+MKIokFSXpqrJkRHIm9wxaUYu9qT3h7uGEuYfh1cir9vpD5LeE3/gp5ieaWDXhs66f0dLu/q3uh4nBTr2OhirisrJSeeediURHn8XW1gEjI2OmTp3KgAED7rmtb775hjFjxmBmVrc3XgsLC8rK7u/bki4VSTYqVSFSqcnVVCT3tiJQFDWo1aX6H1HUAAIymbk+2lbd3DCtVo1GU1bDeY30tuu9vDfKy5OJj59LQeEhLCya4+c7t9p5Q9cLuHWjQmnrWXsCTqVRsS1pG6vOruJyeRZj7FX4mqi5YNQTN8cetwi22yGKIr+fzGT29gsAzO3XkoFtH5/IcH1SlZFB+pujUWVn4/blIix79qy3sRxNzue1H47T0rUR699sj5nRjcJFq1Vz6vSrlJScJST4dyws/OpppHWDWq1my5YtxMXF0aJFC5555hmsrB7unF2tViT2cBZHtiVRVakh4Cl3Qvs2wegxtE5VGhVRV6I4kHGAiPQIMssyAfC39SfcI5xu7t3wt/Ov03J6R7OP8vGhjymoLGB80HhGthrZ4KeIGETcdTREEadWK+jUqQMvv9KftydNRyIxIi0tje3btzNp0qR7bs/Ly4vo6Gjs7et25diDiLhr3JiKxBm53K5GoaDRKHXiS/OvTaqLqFlejapVH1G7HboIXrle0N0cwUtKukKLFq1qbFOtLic19Tsupf+ARGJMs6bv4eb2SrWpF7KLKxm28igFZVWsrUUBp9Ko2Jq4ldXnVpNdnk2AfQBjA8cS7NCKs6dfRqnMIST4t3teDp9eUMH7m85wPLWAZ1s583Ef/1uSvhq4eyrPXyB9/DhEZRUey5dh1q5dvY3lQlYxw1YcxcnKhM1jO2Jjfut80YTEhVy6tIoWLb7Exbl/PYyy7lCr1WzevJmLFy/y7LPP0r59+4c+hispJRz49SI5aaW4+lgTNsz3sbNOCxQFHMw4SGRGJP9k/UO5qhxjqTEdXDoQ5h5GuHs4TuZOd26oFilWFrPg6AJ2pe4iyCGIT7t+ioelx51PrCcMIu46GpqI02pV7Ny5noULvyMy8uAt0ag1a9YQHR3N0qVLAejbty9TpkyhW7dujB8/nqioKCorKxk8eDBz585lyZIlTJkyBT8/P+zt7fn777/5888/mT17NkqlkmbNmvHjjz9iYWGBl5cXL730Ert27UImk7Fy5Uo++ugjEhMT+eCDDxg3bhxlZWW88MILFBYWolKpmD9/vr5qwzURV9Mxd/saKBSZqNWlyGQWmJi461OR3Nkmrd25bXqRqC5Bo6kgKekypWUfYWcXjr19D+xsu+jtJFEUycndRULCApTKy7g4D6SZ94e3TbtQFwKuOvE2IWgCnVw76V+TyspLREUPQi63Jrjdb8jl91Y7VaMVWXUwmS//vIhKI9Lc2ZIe/o708HciyN26Vst3Pa4ok5LIW7ackp07kTk54blqJcZ3KJBel1zKr2Dg8n+QSwV+G98J12oqd+Tm/snZc+Nxc3uZ5n6f1MMo6476FnCVZVUc3ZZMzOEszBoZ0XmQNz4hj4d1KooiCUUJ+mjb2dyziIg4mjoS5qGrdRzqEvrAc9Jqg/8l/48FRxegETVMC51Gf+/+DfJvYBBx13EnETf3vxeIySqp1T5buDZi9vO3eu+iqKG8Ipll3/1AZmYpixcvveWYmkRcQUEBtra2aDQaevTowZIlSwgICLghEpeXl8fAgQPZtWsX5ubmfP755yiVSmbNmoWXlxcffvgh48ePZ/Lkyezbt4/Dhw+jUCho1aoVV65cQa1WU1FRQaNGjcjLy6NDhw4kJCQgCIJexNV0zN2iS0VSgEKZjYAEIyMHtFrFHe3OukSr1RATcwZB+Jm8/AjU6mIEQY6NdSh2dt3Iz4+goPAwFhYt8POdXWPKhayiSl5apRNw60aF0uYBBVyVpoptidtYdW4Vl8svE+AQwITAG8Xb9RQWRXHq1KvYWLcnMPA/95Wg9VJ+BbsvZLMvNofotEI0WhF7CyO6+TnS09+RLj4OWBg3bFviYXO9eBNMTbF95WVsR46slxxw18gtVTL4+38orlSxZVxHvB1vneNWUZHK8agXMDNrQnC7jfeUsqahU58CTqsViTmUxdE/rlqn3d0J7fPoWqeiKFKqKiW3IpfMskwOZR7iQMYBvU3awq4F3dy7Ee4Rjr/trQtmGgLZZdnMODyD45eP092jO7M7zcbWxPbOJz5EDKtTGyCiqKWiIg2tRolcboNE8m8ag4kTJ3Lo0CGMjIyYOHHibdvYtGkTK1euRK1Wk52dTUxMDAEBATccc/ToUWJiYujcWZfHpqqqio4dO+r39+vXD4DWrVtTVlaGpaUllpaWGBsbU1RUhLm5OdOnT+fAgQNIJBIyMzO5cuUKzs7O112LeMdj7oQgCBgZ2SGVWqBQpKNUXr6rhQd1iUQiRSo1xd//K7RaNcUlp8jL20de3n4SEhcgk1ni6zsbN9eXaxRFtSngqhNvczvOpaNrxxo/IG2sQ2juN5/YuA9JSJyPn++ce+7b086MMWHNGBPWjKKKKiLjc9kXm8OfFy6z5UQGRlIJ7Zva0tPfie7NHZ/oxRDKxESdeNu1C8HUFLs3R2H7xhvIbOv35lCqUDHix+PklChZP7p9tQJOo6nk3PmJCIKU1q2WPrYC7rnnniM0NPSh9X05pZgDG+LJvVSKm681XYf5YufaMK3T68VZTkUOeZV51T7mVuai1Cj155lITejg0oE3W79JmHsYjmaO9XgVd4eLhQurnl7FTzE/sfjkYgb+MZB5necR5h5W30O7K+pMxAmC8APQF8gRRbHV1W1zgNFA7tXDpouiuPPqvo+AUYAGeFsUxT1Xt/cGFgNSYLUoiguvbm8C/ArYASeAV0VdBfEHorqIWW0jiiKVleloNOWYmnoQENCObdvm6fd/99135OXlERwcjEwmQ6vV6vcpFDqxl5KSwqJFi4iKisLGxoYRI0bo993cV69evdiwYUO1YzE21n1ASyQS/e/XnqvVatavX09ubi4nTpxALpfj5eV1Sz93c8zdIpUaY2bWFK1WhURi1GC+uUkkMmysQ7CxDsHHexqVlRnIZI3uaE1mFeks1MLyBxNwVZoqtiZsZdW5VVypuEKgQ+BdibfrcXUdTHlFApcurcbczAd391fuaywA1mZGvBDkxgtBbqg0WqJTC9kfd4V9sTnM3n6B2dsv4Od0zXZ1JMjD5olIVXKreHsT25Fv1Gvk7RoKlYYx605w8XIpq14PrtbOF0WRixdnU1Z2kcDA1ZiautfDSOuG2wk4URQpyCpHZiTBzMoYuVHtflmsLK3iyLYkYg9nY2ZlxNOjWuId7Fivn20KtYIzuWfIqcjRi7HcitwbHq8XZ9cwl5vjYOqAg5kDAQ4B+t8dzRxxNHOkpV3Leq1FfL9IBAmvt3ydjq4dmXZwGhP3TeRF3xd5P/h9zOQN+8toXUbi1gBLgXU3bf9aFMVF128QBKEFMAxoCbgCfwmCcK3q83dALyADiBIEYbsoijHA51fb+lUQhO/RCcDldXUxtcX/s3fmYVHV+x9/nRkGGGDYd5FFZFVBxN00NEvTNE3NzEyzRbufPhEAACAASURBVM02y9ttz1Yr9fbLFrXSFrPMblqZayrkclUE3BAEFNmRfRlmYbbz+wMll0FQQUB5PY8PMpzlO3ic8z6f92cRRRGttgCDoRoray9kMkeGDRvGK6+8wrJly5gzZw4AanVdw1V/f3+++OILTCYT+fn5xMfHA1BdXY2trS0ODg4UFRWxZcsWYmJiAFAoFCiVSlxdXenfvz9z587l1KlTdO3aFZVKRX5+PsHBTRuqXVVVhbu7OzKZjNjYWLKzs69pm6tBECRIG6mebG2acnO7UMCtfqwfPTtfXcNQMC/e3h70NgO8mi7eLqRr4IuoVZmkZ7yFjY1/s3Qal0klDAh0YUCgC6+ODiezpIadqcXsPFnEit2ZfBF3GmdbS4aG1Am6wUGuKKxvnmkPALUZGZQuW0b1lq1tTrxBXV7jvJ+PsD+zjI8nRzI0xHyEJCfnKwrP/oq//1O4usTc2EW2IAaDgXXr1pGenn6ZgNuzNp3jf+fXb2spt8DWwRIbBytsHS2xtbfC1tEKGwdLbB3OfXVsXOyZTCIpe/I58Hsmeq2Rnnf60me0P5bWrWeAaQ1a/pv+X1Ymr6RUU1r/up3MDle5K+427kS4ReBu417//Xmh5iZ3a/OC5noJdgpm7ei1fHr4U7478R0Hzx5k4W0L6eHWo7WX1iAtdjWJorhbEAT/Jm5+L7BWFMVa4IwgCKeA83HuU6IoZgIIgrAWuFcQhFRgGPDguW2+AxbQDkRcra4Yvb68rj/auQR4QRD47bffmDdvHh999BFubm71+WuDBg0iICCA8PBwwsLC6NWrFwCRkZFERUURGhpK586d6+1SgCeeeIKRI0fi7e1NbGws3377LVOmTKG2tu7J6t13322yiJs6dSpjxoyhR48e9O7dm9DQ0Gva5lYjv7Kujci1CjidUcf6jPV8ffxritRF9HTreV3i7TyCIKVbt49JSJzE8eSn6NN7fbMPcO7iZkcXNzseH9KFKrWevzNK2JlaxI7UIn5NykMmFegX4MIdYe7c3d0LT4f29+R+ngvFm0Qux+Xxx3F+ZEabEW9QJ1Te+D2ZLclneW10GOOjzD+AFBb+yqnTH+LuPoouAc/c4FW2HA0JOICEzVkc/zuf7kM64RFgj6qqFlWlDnVVLaqqWgpPVaGqqsVkuDx33NJaepG4u1DgSaUSErZk1VmnIY4MmRyCs3fLNDZvCpeKt76efVkwYAEBDgG4yl1venF2NVhKLXmh9wsM8RnCK3tfYdqWacyKnMXjPR5vk61IWrSw4ZyI+/MSO3UGUA0kAC+IolghCMJnwAFRFH84t91KYMu5w4wURfGxc69PA/pRJ9gOiKLY9dzrnYEt589zJVqzOlWnK0WrLTw3dqqj51Z74FqujXoBp9ax+tGrE3DmxNuTPZ+kv1f/Zr1eNJo8DiWMx8LCnj6919+QebYGo4nE7Ap2nixmR2oRmSUq7KwsWHJ/JCO6NT1/si1Qm5FByRdfoNy6DYlcjtNDD7U58Xaej/9K55OdGcy+PZCX7jb/gFVauotjx2fj5NifyMivbpo8uCsJuOTd+fz9Yxqh/T0Z9nAYQgN2vyiK1KoNqCprUVfp6oReVS2qqnNir1KHurruq9HwT+qLrYMlgyYF0TW69axTrUHLL+m/sCp5Vb14mx05mz6efRrfuQOqddW8f/B9NmVuIsI1gvcHv4+fvd8NX0dbKmxYBrxD3Qykd4AlQIvPcBEE4QngCQBfX9+WPp1Z9PpKtNpCLCzsOwTcTUx+pYYHvtxPpVp/VQLuvHj76vhXFKuL6enWk3cGvdPs4u08crkPET2WkXR4GseTn6Jn5Kr6ti4thYVUQr8uLvTr4sIro8I4Vazk+XVHmbU6kWeGdeW54cFtvl2JNj2d0mXL6sWbyxNP4DxjepsUbwCrD2Tzyc4MJkX78O+R5hv1VlYmcDz5KezswunR44ubUsCNHj2aPn3+ES6nk4r5+6c0/Hq4EDMttEEBB3VOibWtDGtbGS6dGj5fvdirqkVbo8fNV9Fq1qk58fbRkI86xNtVYm9pzweDPyDGJ4a3D7zNpI2T+FeffzExaGKbuYff0CtMFMWi838XBOEr4M9z3+YDF3ba8zn3Gg28XgY4CoJgIYqi4ZLtzZ33S+BLqIvEXefbuGoMhho0mjykUhvk8s5t5h+/g+blQgH3w6P9iGyigDurOstzsc9xouwEUe5RvDvo3RYTbxfi6NibsNB3SUl9kfSMdwkNeatFz3cpXd0VrJs1gNd+S2bprlMkF1Tz8eSeOMjbXr6cNj2d0i+Wody6FYmNTZsXbwCbjhXyxu/JDA9zZ+F9PcxeTzU1aRw99jhWVl70jFyJhUXbrJa8Wq4k4PLSKti+6gSeAQ6MeLw7UmnzTAe4UOy1FhqDhl/S6sRbmbaMfp79WDRkEb09G25/1EHjjAwYSU/3nry27zXe3v82u3N38+bAN3GVt2xD/aZwQ0WcIAheoigWnvt2PJB87u9/AD8KgvAf6gobgoB4QACCzlWi5lNX/PCgKIqiIAixwETqKlSnA7/fuHfSdIxGNRpNNhKJFXK533XPCO2gbXKtAu7Q2UPM/3s+tcZalty+hDv97ryhIt/LawIq1Smyc77E1rYrnX2m3bBzA1jLpCyaGEGkjwNvbUxh3Of7+HJaNEEeVz+f02SqRa+vvmxO7fWgTUujdNnyf8Tb7Fk4T2/b4g1g36lS5v18hGhfJz6d0gsLM0JFo8nnyJFHkEisier5HZaWLq2w0ubnSgKuJEfJ5mXHcHS3YfTciGavRG0tzIm3xZGLO8RbM+Jp68mXd37Jj6k/8nHix0z4YwJvDXyLmM4xrbqulmwx8hMQA7gKgpAHvAnECILQkzo7NQuYBSCK4glBENYBKYABmCvWdXhFEISngG3UtRhZJYriiXOn+DewVhCEd4HDwMqWei/XitFYi1qdhSBIsbHxv6YGqx20ffIq1Ez56sBVCThRFPnx5I8sPrQYH4UPnwz9hC6OXW7Aai8nMHA+KnUmGRnvYCP3x8Vl8A09vyAITBvgT4inPU+uSWTc5/tYcn8kI7t7NbiPyVRLTU0a1cpklNXHUSpPUKNKB0xERa3ByfHabCPRYECdlERNbBw1sbHosrKQ2Nq2G/EGcDyviie+TyDA1ZaV0/sgNyNUdLpyjhydgdGkplevtTdNKxGDwcDPP/9MRkbGZQKusljNxk+PYCW3YMzTka0aMWsuLhNvXv1YErmEaI/WG+d2MyMRJDwU/hD9vfrz8t6XeXrX03x555cM8B7Q+M4tRMfEBlqmsMFk0qNWn0YUTdjYBLb5lhkdmKexa+NCAbfmsX5E+DQu4LQGLe8ceIc/Tv9BTOcY3r/tfRSWVx95ak4MhhoSkyaj1ebTO/pXbG0DW2UdhVUaZv+QxNHcSp4a2pV5dwaDqEOlulSwpVGXSQEWFg7YK7qjUHSnqHgzgiDQr+8mpNKmVdwZq6qo2bOXmthYavbswVRdjSCTYdO3L3ZDh2I/elS7EG8AZ0pVTFz2P6xlUn6dM9Bs5a/BoOLw4YeoUaXRs+d31yx42xoXCrh77rmH3r3/iUKpqmpZvygRncbIff/qhZNn61WKNgcag4Z1aev4JvmbevE2J3JOh3i7geiNen47/RsTgiYgaWGHrS0VNtwSmExG1OosRNGIjU1Ah4C7ScmrUPPAlweo1jRdwBXWFPJc3HOklKXwZOSTzIqc1eIfAE3BwsKOiB5fcihhPEePPX6uYvXq+9pdL+52Er6eImftvr2UFv3I+u2FuFjlXyDYHLFXdMfX9zEUiu7YK7pjbe1Tb0G7uAwh6fBUTp1eTEjwGw2epzbzTJ1oi4tDnZQERiNSZ2cUd9yB3dAYbAcOQmrXvm70xdVapq08iAisfrSvWQFnMuk4njyXamUyET2W3RICrlZjYOOnR1Er9Yx7LqpdC7jz4m1V8irKteUdkbdWRCaVMSl4Umsvo0PENTeiaEKjycJkqsXGxr/J0YDzc0jPc+nM1OthxowZ3HPPPUycOPG6j3UjEEURk2hCKmm7+SoXCrgfmijgDp09xAtxL6A36Vk6dClDfYfegJU2Hbm8ExERy0hKeojjx+fSs+e3LVqxajLpUNakoqxORqlMplqZjEqVjigaCLcFk609J8u8SS27k7F9hhHSuW+jld1OTv3x8ZlGXt53uLuNxMnpXFNXvR51YlK9cNOda0htFRKCy2OPoRgag3VEBIKk9QX1tVCl0fPwqnjKVTp+erw/XdwuL1AQRRMpqS9SXr6HsNAPcHMb3gorbX6uJOAMeiObvzhGRYGK0U9F4BFw5QkrbZVLxVt/r/7MiZxDL49erb20WxZRFCnPz8PZu1Orfm50iLhmRqstwGhUI5d3breVXgaDAQuLG39piKKISq+iWFOM1qDFV+GLnWXb+h2aTCL/Tczjw60n0RtNTRJwoiiyJnUNixMW42vvyydDPyHAoXkb7DYXjg7RhIW+T0rqfNLSFxAa8m6zFlrodGWUlcVRUrqL8vI9GI0qAGQyJxSK7ri4PF5vjVpbd8Ixu4I5PySxNs3AkklS7u7R+Fq6Br5IWenfpJyYT1jFM2hi/0fNnr2YlMo6m7RfP5wenoYiJgZZpyv0jGgnaPVGHv8+gdMlNaya0cdsTqYoiqRnvEtR0UYCu/wLb+/WjyA0B3q9nnXr1pkVcCaTyF8rUyjIqOTOmeH4hrfPwo38mnxm/zWbrOqsDvHWyhgNevJST5CZGM/ppHiqis4y9f2P8QwMarU1dYi4ZsbKyh2p1LZZraisrCxmzpxJaWkpbm5ufPPNN/j6+jJjxgzs7e1JSEjg7NmzfPTRR0ycOBFRFHn66af566+/6Ny5M5aWlvXH8vf3JyEhAVdXVxISEpg/fz5xcXEsWLCA06dPk5mZia+vLwsXLmTatGmoVHU32c8++4yBAwdSWFjI5MmTqa6uxmAwsGzZMgYPvr5EeFEUqdHXUKIuQWPQIJPIkElk5Cpz8bf3Ry6TX9fxm4vk/Cpe/z2ZwzmV9PZz4v37ehDcSBWl1qDl7f1vszFzI0M7D+X9295vc8L0Ury8xqNSnyI7ezl2tkF07jzjmo8liiIqVTqlpbsoLd1JVfURQMTK0gMPjzE4O9+GvSICa2tvs2Kxj78zfz59G3PWJDJnTRJPxgTywl0hZuewiqKI7kydTeqcqiB/1AnS41/G+aAHijvvxG5oDHYDByKxbb922qUYjCae+ekwh7LK+eSBKAYHma/Mzc5eRl7ed3Tu/Ah+frNu8CpbhisJOFEU+fvHNDKPlHDbpCCC+7avZtLnSStPY86OOWiNWlYMX8HATgNbe0m3HBplNWcOJ3A6MZ6so0noNGqkMhm+3SPpM+Y+7N3Mj7C7UXSIuEvZ8hKcPX7Nu0sAy0tf9OwBd39wxf00Gg09e/as/768vJyxY8cC8PTTTzN9+nSmT5/OqlWreOaZZ/jtt98AKCwsZO/evZw8eZKxY8cyceJENmzYQFpaGikpKRQVFREeHs7MmY33VE5JSWHv3r3I5XLUajV//fUX1tbWZGRkMGXKFBISEvjxxx8ZMWIEr776KkajsX7G67VgTry5yz2p0VhRU6vDwqqE7OpsAhwCsLJovbzCSrWOxdvTWHMwBxdbS5ZMiuS+Xo03bC6oKeC52Oc4WX6SuT3n8kTEE20i/60pBHZ5AbXqNOkZ72FjE4CLy+1N3tdkqqWiIp7Ssp2Ulsai1eYBoFB0JyDgGVxdhqJQdG9yhM/TwZq1T/Tnzd9P8EXcaU4UVLP0gSgcbGTnbNJEamJjUcbFoc/OAc7ZpKURlA09RvC8RTg7t171WEshiiKv/ZbM9pQiFowJZ2ykt9nt8gt+5nTmEjw97iWo6ys3RZ9KvV7Pzz//zKlTpxgzZgzR0RfnhMVvPEPK3gJ6jfQj8o7ODRylbRNfGM+zsc9iI7Phu5HfEeTUetGeW4k6mzSX04nxnE6MpzD9JKJowtbRiZABt9Eluh9+3SORWbeNcYEdIq6NIJfLOXLkSP3353PiAPbv38/69esBmDZtGi+++GL9duPGjUMikRAeHk5RUV0v5d27dzNlyhSkUine3t4MGzasSWsYO3Yscnld1Euv1/PUU09x5MgRpFIp6enpAPTp04eZM2ei1+sZN27cRcKzqZgTb562Xuh1cs5W6hAwYm9thbLWBUFWQmZlFn72/thY3lghJ4oiqloDw5b8TaVax/QB/sy7M7hJzWgPFh5k/t/zMZgMfDrsU27v3HQR1BYQBAnh4UtITJrM8eRn6NP7V2xtuza4vU5XSmlZHKWluygv34vRqEIiscbZeRD+fnNwdR2KlZXHNa/HykLKBxMiiPBxZPEvB3l33i5mSvKRHDqAqaYGwdISm/79cJ4+vc4m9fbGaFRzMH40J0++Qr9+Ta9WbS8s2Z7O2kO5PDW0KzMGmbfnS0q2c/Lkazg7DyYs7IObok9lYwLuWGwuCZuzCBvkRf97W6dtz/WyNWsrr+x5BV+FL8vvXI6nbfuMJLYXjAY9eSknOJ10kMzEeKqK6+6l7v6B9LtvMoHRffEICGyTObMdIu5SGomYtTWsrP4RNk1pF2NhYYHJVDffT6vVXvQz2wtspo8//hgPDw+OHj2KyWTC+txTx5AhQ9i9ezebNm1ixowZPP/88zz88MNNWqs58eZl54XUZEdhpRadsRZHuSVejtbIpBJ0BjlnlRYojQVkVmXjIPXGw16OpUXLFzyodQYKKrVUqPUEutny1th+hHs3nhQtiiKrU1bzn8T/4GfvxydDP8Hfwb/F19sSWFjYEhlxrmL16OP06bMemayu1cY/NulOSkp3UX3eJrXyxNNjLK6ud+DkNACp9PqfVkVRRHf6NDVxcQyMjeOHw4cRTCYqrBVIBw4h/L67sR0w4DKbVCq1ISz0Q5IOT+HU6Y8ICV5w3WtpK3yz7wyfxZ5iSl9fXrgr2Ow2FRXxJJ94Fnv7HvTo/jkSyWUeQbtDq9Wybt06MjMzzQq4jIQi9qzLICDSlZgHQ9pl1HFN6ho+jP+QKPcolg5bioNVy881vhVRV1dx5nACmYnxZB1LQqfRYCGzxLdHJH3GTqRLdB8Uzq0/kaExOkRcO2DgwIGsXbuWadOmsWbNmkZz0IYMGcKKFSuYPn06xcXFxMbG8uCDDwJ1OXGJiYncfffd/Prrrw0eo6qqCh8fHyQSCd999x1GoxGA7OxsfHx8ePzxx6mtrSUpKalREWdOvHnbeSOXKiisqkWpVWMtk9LFyQ67C2YNWlpI8HVypEorJa8mhypDIZVFbjjJLXGzt8KqBcScwWjibLWWcpUOC4kEZ1sZ62ZFNelmoDFoeGv/W2zK3MQdvnfw3m3vYStr3/lX1tbe52asTuXY8bn4+82itDSW0rJdaLV1k+4Uih4EBDyLq+tQFHbdmuXGKep0qBMSUMbGURMXhz43FwCrsDDcZs9C13cQryWqScytZo7Bh/ly81E2J6e++PhMv6Batf91r621+f1IPm9tTGFENw/eHWfellbWnOTY8SewtvYhMuJrLCza93UIoFQqWbNmDUVFRWZdgNyUcnZ8k4JXoAN3PdoNSTON07pRiKLIJ0mfsDJ5JcM6D+PDIR9ibdE2LLubAVEUKcvN5nTSITIT4ynIOAmiiJ2TM6EDb6dLdB98u0cis2pfv/MOEdcO+PTTT3nkkUdYtGhRfWHDlRg/fjy7du0iPDwcX19fBgz4Jx/ozTff5NFHH+X1118nJiamwWM8+eSTTJgwge+//56RI0fWR+ni4uJYtGgRMpkMOzs7vv/++waP0ZB4s7d0oESpI69GhQTwcpDjYmeJpIGbv4O1AgQf8pR5WFpXUKFxokKtx8lG1mxiThRFytU6iqq0GE3gameFh70V6VUWTRIl+TX5PBf7HGnlaTwd9TSP9Xis3eS/NYaDQy/CQj/gRMrzHKk8eM4mvQ1//7m4ugzFyqp5EnsNFRXU/P03NbFxqPbuxaRSIVhZYdu/Py6PzsQuJgaZ5z+20o+9jby1MYVl9XlyPXG0uTza1DVwPmVlsaSkvkS/vpvataDZnV7C/F+O0i/AmU8eiDJb4KHR5HLkyAykUttz47ScW2GlzUtZWRmrV69GpVLx4IMPEhR0cX5YUVY1m1ccx8nTltFPRmDRzsZp6U16FvxvAX+c/oNJwZN4td+rbbrFUntCU6Mkdfcuju3cRlleXc6sR5euDJgwhcDovrgHBLbLiO15OiY20DITG25lLhNvUhlucjccrBxQao0UVmrQGU042lji5VBnnTaFck05hapC7K0ckRidKVfpQOS6xVyddapBrTNia2mBt6O8flRRU66N/QX7eXH3ixhNRj4Y8gFDfIZc0zraOiUlfyEIFs1rk546VRdti41Fc+QIiCIWbm7YxcTUNd3t3x+JzZVz2X6Kz+GN35PxcpCzYlo0YV6X294VlYdISpqCT6eHCAlZcN1rbw2O5Fby4FcH8HOx5edZ/bG3vjw3U6crJSHxfvT6SqKjf8bOtv0nw+fn57NmzRoAHnzwQXx8Lh4RVnFWxfrFScispEx4MRpbh/bVXF2tV/P838+zL38fc3vOZVbErHYtKtoCoiiSn5bC8R1bST+wD4Neh2fXYLrHDCcwuh92zu2r3UzHxIYObgjnxVuxuq7Pm0xaF3lzsHJAbxDJLtOg1OrrrFNnO+ysru7yc5Y7YxANlKhLcJVbEOrpRomyljKVjgq1HkcbGe5XIeYusk6lEjo72+AolzX5A1QURb5P+Z7/JP6HAPsAPhn2CX72flf1ntoTbm53NstxRFGkJjaO0s8/R3uibhSydXg4rk8+iV1MDNbdwq8qgXhKX1+CPRTM+SGRCcv+x6ZnBhPgenG0zcmxD519ppOb9y1u7iNwdmpf1aqnS2p45Jt4XO2s+G5mH7MCzmBQcuToTGpri+gVtfqmEHAZGRmsW7cOW1tbpk2bhovLxTdfVWUtG5ceRRBg7DM9252AK9eWM3fHXFLKU1gwYAETgie09pLaNZoaJSl/7+LYzq2U5+diKZfTLWY4EcNH4u7fPotcGqNDxHVw3VxJvImiQHG1lpIaXZOs08Zwk7thMBko1ZRiIbHA29EFN4VVvZirbIKYE0WRcpWOs9VaTBdYp9KrEA4ag4Y3//cmW85s4U6/O3ln0DvtPv+tpakTb7GUfvY52pQUZL6+eLzxOoo77kDmce2VqwDRfk78NncQd328m7c3nuCbR/petk1g4HxKy2JJTX2Jfn03txtb9WyVlodXxiOVCKx+tC/uCnPjtGo5dnwONTUnieixAgeH9t8M9siRI/zxxx+4u7szdepUFIqLezJqVXr+WHoErUrPuOejcPRoX9XHucpc5uyYw1nVWf4v5v/a3ASX9oIoiuSfPMGxndtIP7AXo16PV9cQ7pr9DKEDhrSZViAtRYeIu8kRRRGdUYfGqEFr0KIx1H01iaZmP9eF4k1AoFqrP1d1asLJxhLPq7BOG0IQBLxsvTCKRs6qziKVSHG0csTbUV4v5sovFHMKK6xk/4g5tc5AfqUGjc6IrZUFnRzlWMuuzoaNL4znvYPvcabqDM/2epZHuz/aYX9cgfPireSzz6hNSUXm64vXwoU4jLkHoRkng3g7ynn2jiDe25zKztQi7gi7WBhKpXLCwz4iMekBTp3+iNCQt5rt3C1FpVrHw6sOUqXRs/aJ/vi5XC48RdHIiZT5VFTsJzxsEa6u7VsMiKLIvn372LFjBwEBAUyePLm+Ov48Bp2RzcuOUVmk5p6nInH3a1/jtFLLUpmzYw56k56v7/qanu5X36rpVkejrCZld+wFUTcbug+9i4g7Rty0UTdzdIi4m4jGBJsgCFhLrXGwckAqNG/SrJXUCnsreySChFq9kfxKNTW1BqxlUgKd7bC9Suv0SgiCQCe7ThhNRgqUBUgFKQpLBTKppEEx52JnSblKR7lKh0wqwdfZBoersE4BilRFLE5YzNasrXSy68Ty4cs7OqhfAVEUqdm1i5LPP68Tb36+eH2wEId7mle8Xcj0gf6sPZTD23+mMKir62UC3dGxN507zyA39xvc3Ubg7Nx2//00OiOPfpdAVqmab2f2oXuny1tNiKJIWvrbFBdvpmvXl/Dyuq8VVtp8mEwmtm3bxsGDB+nevTvjxo27bASgyWhi29cnKDxdxV2PdqNzWPsq3NhfsJ95cfNQWCpYNWIVXRxvHcFxvYiiSH7qCY7t3Er6wX11UbegEEbMfpaQAYNv+qibOTpEXDvlagSb3EKO3EKOpdSyRSsmjSaRs9WaeuvU+5x12hJRKokgobOiM1nVWeQp8/Cz98NGVmenmBNzFWodAsI1Wad6o57VqatZfnQ5RpORJyOf5JHuj3SU/zdAa4i381haSFgwthvTVsazcu8Z5g69vEFxYJcXKC2NJfXky23WVtUbTTz1YxJJORV8/mAvBgaa71eVX/AT+fk/4Ov7GH6+j9/gVTYvBoOBDRs2cOLECfr3789dd92FxMz/0z0/Z5B1rJQhDwQT1Pv6bPgbzebMzby671X87f1ZPnw5Hrbta/2tRV3UbRfHdmylvCAPS7kNPYbdRY9ht1bUzRwdIq4dUC/YDJp60XaZYLOwxtHKEWsL6xsi2C5dX7VGT0GVFn0zWqeNIZVI8bX3JasqixxlDgH2F4/nulDMVWn02FlZXLV1WmusZcLGCZypOkOMTwwv9n2Rzor2OcanpRFFkZqdOyn5/AtqU2+seLuQwUFujOjmwWe7TnFfr054OVw8e7fOVv3wnK36IaEhb9+wtTUFURR5ef1xdp4s5t1x3RnVw8vsdkajljNnluLo2Jeugf++watsXrRaLWvXriUrK4u77rqLgQPNR0jTDhSSvDufnnf60iPGx+w2bZXvT3zPooRFRHtEs3TYUuwt25cFfKMRRZG81GSO7dhKxsF9GA2Guqjb8rdT2wAAIABJREFUnOcI6X/bLRl1M0eHiGsjCILA1KlT+eGHHwDQ6rT4dPIhMjqSz9Z8dkXBZiW1uizatXz5cmxsbJo8TeFaMZlEcivUVGnqqk59m9k6bQyZRIafvR9nqs7Uz1mVSS+u3JNJJbjaXV3Vmt6o56z6LGWaMvRGPZ/f8Xm7aR2iTUmhYt06av7ejayTN/Ju3bA+98fS3x9B2rxW+qXizdLPD+8PP8B+9OgbKt4u5LXR4QxP+5v3N5/k0ylRl/28zlZ9hNzcVbi7jWxTtuoHW0/y38Q85g0P5qH+DVc75xf8hE5XQvdun7TrcVrV1dWsWbOGkpIS7rvvPiIiIsxuV5ZfQ9yaNLyDHBkwrnmiLxqDhpXHV/LH6T/wVfgS7hJe/6ezonOzuAgm0cTHiR/z7YlvudPvThYOXoiVtH1V0TY3JqMRVVUFqvJyaiorUFWUUVNx/ms5qooKlGUlaJTVWNnY0uOOkUTcMQI3P/Pj5W5lOkRcG8HW1pbjx4+TU5aDwcLA9i3bcfVwxSgaL7JEzQk2c8yePbvF12wwmsguU6PSGfB0sMbNrmlra24spZb1Ebns6mz8HfyxkFzbpW0STZRpyijVlCIiorBU8Nu439r8h66xRkX15k1UrvsFbXIygpUVdkMGYyguoWLtz4i1tQBIbGywCg9rFmEnmkwod+6k9PMvqD15sk2It/N0drZh9u2BfLIzg6n9fOnf5fK+UIFdnqe0dBepJ89Xq9q1wkov5qvdmaz4O5OHB/jxzB0Nz6o1GrVkZ6/AybE/Tk79buAKm5fS0lJWr16NRqPhwQcfpGtX8+9ZpzGwZcVxLOUW3PXY9U9jEEWRXbm7+Cj+IwpUBQz0HkhlbSU/pP6A3qQHQCFTEOYSRjeXbtcs7PRGPa//73U2ZW7igZAHeKnvSzd1E9+LxFlFOarKc18ryuvFWU1FGerqKri0R60gYGPvgJ2TC7ZOTrgHBNIpNJyQAbe1uykKN5IOEdeKmEQTKr0KpU6JiEjfoX359fdfufe+e9n1xy4emvoQB/YdwNvOG5VKxZNPPklycjJ6vZ4FCxZw77338uyzz+Li4sIbb7zBtm3beO+994iLi+Ptt9/Gzs6O+fPnc+rUKWbPnk1JSQlSqZRffvmFLl268OKLL7JlyxYEQeC1115j8uTJTV67zmAiq1RFrdGEr7ON2U75NxK5hRxfe1+yq7PJUebgp/C76g/LGl0NhapCdEYdCksFnraenC4+3aYFnObECSrX/UL1xo2Y1GqsgoLwePVVHMaOQepQlwgvGgzUns5Ee+JE/Z+Kn9chnpudK9jYYB0Whrx704RdWxZvFzInJpD/Juax4I8T/Pn0bVhccuO/3FZ9p5VWWseviXm8tzmV0T28eHPMlceX5ef/WB+Fa6/k5eWxZs0aJBIJM2bMwNvb2+x2oiiy8/tUqku1jJt3/b3gcqpzWBi/kL35e+nq2JVVI1bRx7MPUCe6MiozSClLqf9jTtidF3XdXLo1KOxUehXzYuexv3A/z0Q9w2M9Hrspq9j1Wi1Htm/iyPZNVJeWmBVntg6O2Do6Y+fsjEeXQGydXLBzcqr76uiErbMztg5OSJrZJbgVaDufuG2ED+M/5GT5yWY9ZqhzKP/uW5ezojfpqdHVoNQpUelVmERTfe7a9KnT+c8H/2HOg3NIT0nnySee5MC+AwC89957DBs2jFWrVlFZWUnfvn0ZPnw4CxcupE+fPgwePJhnnnmGzZs3X5YMPHXqVF566SXGjx+PVqvFZDKxfv16jhw5wtGjRyktLaVPnz4MGTIELy/z+TcXotUbOVOqwmQSCXCxwc5M49HWwFZmi4+dD7nKXPJq8uis6NykvMDz1ml1bXV9VE9hqWh0v9bCWKOietMmKtetQ3viBIK1NfZ3343j/ZOQ9+x52Y1CsLDAOiQY65BguG88cE7YZWaiTb6ysLPuFo68e/c6YefnhzI29mLx9tGH2I8a1abE23msZVJevyeM2T8kseZgDtMH+l+2jaNjb3w7zyQnd+U5W3XQjV8oEHuymBd/Pcagri78Z3Kk2XFa5zEaNWTntO8oXHp6OuvWrUOhUDBt2jScnRuuMD26M5fMwyUMvK8r3kFO13xOjUHDV8e+4tsT32IpteRfvf/FlLApyCT/fH7JpLJ6gXYevVHPqcpTnCg7US/s1qSuaVDYhbuEYyuz5ckdT5Jekc7bA99mfND4a153W0Wn1XBk2yYSNq5Ho6zGt3sk4UPuwM7JGVsn53NfnW5qcabX65HJWvf+1/Y+eW9CDKa6KQNKnRKNQQPU5XI5WDmgsFRgK7NFQOC2PrfxXPZz/PTTT4waNeqiY2zfvp0//viDxYsXA3WJwDk5OYSFhfHVV18xZMgQPv74YwIDAy/aT6lUkp+fz/jxdR8i5/st7d27lylTpiCVSvHw8OD222/n0KFDjB079orvRVVrIKtMhUQQ6OJmVz+eqq1gb2WPl+hFYU0hBTUFdLLr1ODT76XWqbuNOy5ylzY781STfILKdeuo/vPPuqhbcDAer7+Gw5gxSO2vLklasLDAOjgY62Azwu5ESr2wq1z3CxXfr67bSSYDvR5Lf/82Ld4uZEQ3TwZ1dWHJ9jTuifDCxUxuZJcuz1NatuuCatUba6smZlcwZ00iYV4KVkzr3ejEkfz8n9DpSune7dMbtMLmJSkpiY0bN+Lp6cnUqVOxs2v4912QUcn/1p+mS083et55bQVFoiiyK2cXHx76kEJVIfd0uYfno5/HzcatSfvLpDLCXMIIc/ln/N55YXdhxO7H1B/RmXQACAhYSa1YOmxpu8mlbSqXijf/yF4MmDgF7+BbZ3SlKIocPnyYnTt3MmPGDNzcmnYttQSNfgILgvAs8A2gBL4GooCXRFHc3sJraxXOR8yuhwttUqVOicFkoFhdjNxCjruNOwpLRYO5bWPHjmX+/PnExcVRVlZW/7ooivz666+EhIRcts/x48dxcXGhoKDgutd+Jao0enLK1VhKJQS42mDZDIPnWwJna2eMJiPF6mIsJBZ42npeto0569RS2rqWsDmMNSqq//yzLuqWklIXdRs1Cqf7J2EdGdms9sxFwm78OOBiYVebloZ1eFi7EG/nEQSBBWO6cfcne1i8PY2F912eNC+VWhMW9iGJiZM5deoDQkPfvWHryyhSMvPbQ3jaW/PtI30bHUVXH4VzGoCT0+VTKdoyoiiyZ88edu3aRWBgIPfffz9WVg1bo6qqWrZ9nYy9izXDpodd07WeXZ3NwoML2VewjyCnIL657Rt6e5odQXlVXCjsJlA3Kktv0nO68jQpZSmcqTrDSP+RdHPtdt3nait0iLc6VCoVGzdu5OTJk/j7+2Np2br3jaZ8Es8URfETQRBGAE7ANGA1cFOKuOulWF1Mmaas3ia1ldmisFRgZ2l3Udi+IWbOnImjoyM9evQgLi6u/vURI0bw6aef8umnnyIIAocPHyYqKors7GyWLFnC4cOHGTVqFOPGjaNfv38sFoVCgY+PD7/99hvjxo2jtrYWo9HI4MGDWbFiBdOnT6e8vJzdu3ezaNGiBtdVVlNLQaUGuaUF/i42l+UXtTVc5a4YTAbKNGVYSCxwldf12dIZdRSpi9q0dSqKItpzUbeqTZsQrzPqdj1cJOzaKUEeCqYP9GfVvjM82NePHj6XN811dIj+x1Z1v/uG2KoFlRoeXhWPpYWE1Y/2a1IFdXuNwplMJrZs2cKhQ4eIiIhg7NixlzXxvWh7o4m/Vp5ApzYw5umeWMmv7qFBrVfz9fGv+fbEt1hJrfh3n3/zQOgD11zw1BRkEhmhzqGEOoe22Dlag/Pi7dDG9WiV1fj3jGbAhCl4B99c77MppKen8/vvv6PVarnrrrvo37+/2V6GN5KmXNHnH39GAatFUTwh3IzZmc2EpdQSRytHFJYKbGQ2V23N+fj48Mwzz1z2+uuvv85zzz1HREQEJpOJgIAANm7cyKOPPsrixYvx9vZm5cqVzJgxg0OHDl207+rVq5k1axZvvPEGMpmMX375hfHjx7N//34iz0VzPvroIzw9L49YiaJIUXUtxUot9tYyOjvbXDFfp60gCAKetp4YRANFqiIkggSjydimrVNjTQ3Vf/5Jxbp11KakIsjl2I+6G6f778c6IuKmTIq+UTw7PIjfjxTw5h/J/Hf2QCRmruE6W/XcbNV+m7GwaDlxX6HSMW3lQWpqDaybNYDOzo3P/WyvUTi9Xs/69etJTU1l0KBB3HHHHY3e+A7+cYb89ErumB6Gq0/T7W1RFNmZs5MPD33IWdVZxnQZw/O9n69/iOug6eg0ag5v20TCnxtuefGm0+nYvn07CQkJuLu7M23aNLP3y9ZAEC+tJLl0A0H4BugEBACRgBSIE0UxuuWX1/z07t1bTEhIuOi11NRUwsJurZBwUxBFkfxKDeUqHc42lnRykrc7IWESTeRU56DSqwCwt7THw9ajydbpjbg2RFGkfOVKSr5YVhd1Cw3FafL92N9zD1LF1QsJvV7P4cOHSU5OxtPTk+DgYPz9/a8Y+bgV+CUhl3/99xhLJkUyIdp8o9iqqiQSEifj7X0/YaHvtcg61DoDD351kJTCalbP7Es/M+1PzJGTs5KMU+/Tq9danBz7tMjampszZ87w119/UVBQwIgRIxgwYEDj+xwtYfOy44QP9mbo1KYLhqyqLBbGL+R/Bf8j2CmYV/u9Si+PXtez/FuSS8VbQM9oBkx8EK+gy1N5bgXy8/NZv349ZWVlDBgwgGHDht3wYgZBEBJFUTSbB3DFT/VzEbc3ADcgUxRFtSAILsAjzb/MDtoSJpNITrmaaq0ed4U1Hvat0wPuejk/nqtYU4xCVmdrtyVMKhUFr76GcutW7IbfgesTT2Ddo8c1/a7Pi7c9e/agVCpxc3MjKSmJ+Ph4LC0tCQwMJDg4mKCgoCsmk9+sTOjlw4/xOSzccpK7unmgMFNV7eDQC1/fmeTkfI27+924ON/WrGvQG03M+SGJY3mVLH8ouskCzmjUkJW9AiengW1ewImiSFZWFnFxcWRnZ6NQKJg0aRLdujWeH1ZVombHt6m4+SoYfH9Qk86n1qv56nhd1am11JqX+r7E5JDJLWqd3ox0iLeLMRqN7Nmzh7///huFQsH06dMJCGh7zYaveJWLoigKgrBZFMUeF7xWBpRdYbcO2jkGo4msMjVqnYFOjnKzFX3tCalEipdt461TbjS67Gzynnqa2tOncX/xRZwfmXHN4i0pKYm9e/eiVCrx9fVl/PjxBAQEYDAYOHPmDGlpaaSnp5OamgrU2fbBwcEEBwfj4eHRLgX61SKRCLw1thv3fr6PpTszeHV0uNntugTMq2sCnPoS/fttaTZbVWcwMW/dEf5OL+HDCT24q1vT7Zj8/B/R68sICLg81aKtYE683X333fTq1atJkQuDzsjWL5MRBBj5RHcsGhmRJ4oif2X/xaKERZxVnWVs4FjmRc/rsE6vEp1GzeGtf9aJtxolAVG9GTBxCl5db03xBlBWVsaGDRvIy8ujR48ejBo1Crlc3viOrUBTHlWSBEHoI4riocY37aC9ozMYOVOqRmc04edig4O87VVs3gzU7N5N/vx/IQgCvl9/hW0DsyKvhDnxdt999+Hv718vymQyWb1YE0WRs2fP1gu6Xbt2sWvXLhwcHOq38ff3b/W+Ry1JhI8jk3t35pt9WUzu05mu7pcLNKnUmvCwj0hIvJ+MUwsJC33/us9bU2tg9upE9p4q5ZVRoUzu49vkfY1GdZuOwomiyJkzZ4iLiyMnJweFQsGoUaOIioq6qmtp98/plObWMHpuBPauV75hZlZlsvDgQg4UHiDEKYSPhnxElPvl49U6aJgO8XY5oiiSlJTE1q1bkUqlTJw4ke7du7f2sq5IU0RcP2CqIAjZgIq6QgdRFEXzA+46aLdo9EaySlWYRJEurrY3dAbqrYIoipSt+JKSTz7BKjQUn0+XYulzdYO8myLezCEIAl5eXnh5eRETE4NSqSQ9PZ309HSOHDnCoUOHkMlk9bZrcHDwTWm7zh8Rwqbjhby1MYXvZ/Y1+ztzcIjC1/dRcnK+ws3tLlxdYq75fCXKWh75Np7UQiWLJkYwqffV9TvLOxeF6xLw7DWvoSVoLvEGkLKvgNR9hUTf7Yd/j4YjaWq9mhXHVvB9yvfIpXJe7vsy94fc32GdXgWVRWc5vmsbx3ZsRVujpEuvPvSf8MAtLd4Aampq2LhxI2lpaQQEBDBu3DgcHC6vZG9rNOXKH9Hiq+ig1ampNZB9rolvoJsd1o1YGR1cPcYaFYWvvIJy+3bsR4/G6913kFxFiP5S8ebn59ck8dYQCoWC6OhooqOj0ev1nDlzpl7UnTxZN7WkU6dOBAcHExISctPYrq52Vjx/ZzBvbUxhe0oRIxqwNbsEzKO8bDcpKfPp2+cPrK3Nj4W6EmdKVUxfFU+JspavH+7N0FD3q9rfaFSTnf0lzk6DcHS8/v5mzUFzijeAklwlu9em4xPqRN8x5gfbi6LI9uztLDq0iCJ1EfcG3su86Hm4yJuWU3irYzToOZ1wkGM7t5F97DCCIKFLdF/6j78fz67tt31Qc5GWlsbvv/9ObW0tI0aMoF+/fq3eOqSpNCriRFHMBhAEwR3omEJ7E1Kl1pFToTnXxNcWS4v2cfG2J3RZWeQ+9RS6zDO4//vfOM+Y3mRBpNfrSUxMZO/evdTU1NSLt+ZMsjVnu54XdLGxscTGxmJvb18v6Nq77Tqtvx9r43N5588Ubg92M/vQIpVa0aPH58QfGsfx5KeI7vUTEknT80OP5lYy89tDmESRHx/vR5Tv1Y+MymtDuXCiKJKZmUlcXBy5ubn14q1Xr17XXPlcq9azdcVxrG1l3Dmzm9nWL5lVmbx/8H0OFh4k1DmUxbcvpqd7z+t9O7cElWcLObZrGyfidqCuqkTh4sbASVPpPvROFC4duYO1tbVs27aNpKQkPDw8mD59Oh4eHq29rKuiKRMbxgJLAG+gGPADUoGbpxV1G0AQBJ5//nmWLFkCwOLFi6mpqWHBggUsX74cGxsbHn744WY/b+m5Jr62lhb4tYMmvu0RZVwcBf96EUEqxXfVSmz792/SfubE24QJE1q8QupC2/X2229HqVSSkZFBeno6R48eJSEhAZlMRpcuXQgJCSEoKAjFNbRCaU0spBIWjO3GlK8OsOLvTJ4dbr4S0sYmgPCwjzie/CQZGQsJCVnQpOPHpRXz5JoknG0t+X5mX7q4Xb0tXReFW9HqUbhLxZu9vT2jR48mKirqutrWiCaRHd+mUlNey7gXemFjf3H+rVqvZvmx5axOWY3cQs6r/V5lUvAkpJIOl+BKGA16Th06yLGdW8k5fgRBIqFLr75EDB+Bf2QvJB2/PwByc3PZsGED5eXlDBo0iKFDh7bLNkxNWfE7QH9ghyiKUYIgDAUeatll3XpYWVmxfv16Xn75ZVxdL35Cmj179nUfXxRFRFGsDxHXNfHVUqysxd5ahq+zjdmn4A6uHdFkomzFCkqWfnou/+1TLH06NbrfpeLN39//hoi3hlAoFPTq1YtevXqh1+vJysoiPT2dtLQ00tLSAPD29iYkJITg4GA8PT3bhe06INCF0RFefBF3ignRnfBxMt9w1919BL6dHyUndyUODr3w9LzyfOH1SXm8+N9jBHso+PaRPrjbX5uBkZe/Br2+vNWicC0l3s5z+K8cso6VctukILwC/8k9EkWRbdnbWHRoEcXqYsZ3Hc+zvZ7tsE4boeJsAcd3bf8n6ubqxsD7z0XdnDuibucxGo3s3r2b3bt3Y29vz4wZM/D392/SviaTSGWRmpIcJSXZSopzqhn+SDj2Lq1XudqU/4l6URTLBEGQCIIgEUUxVhCE/2vxld1iWFhY8MQTT/Dxxx/z3nsXNxldsGABdnZ2zJ8/n1OnTjF79mxKSkqQSqX88ssveHh4cO+991JRUYFer+fdd9/l3nvvJSsrq97fT0xMZPPmzXzwwQccOnSI6ho1w+4ew2tvvEknRzlbtmzh+eefx9bWlkGDBpGZmcmff/550bkBunfvzp9//om/vz/jxo0jNzcXrVbLs88+yxNPPNEav7o2ibFGReHLL6H8awf2Y8bg9fZbjea/tTXxZg6ZTEZQUBBBQUGMGjWKoqKiekF3qe0aHBxMQEBAm7ZdXx0Vxq7UYt7blMqyhxruXx4Y+C+qqo+SevIV7OxCsbO7PI9IFEVW7M7kgy0nGRjowopp0WZ70TWFf3LhbrvhUbiWFm8A+WkVHPjtNF2j3YkY9k9hT2blOev07EHCnMNYcvuSDuv0CtRF3Q5wbMdWcpKPIkgkBEb3JeKOkfhFRnVE3S6htLSU9evXU1BQQEREBKNGjcLa2vxDljnBVppbg77WCICFTIJrZzt0GsONfAuX0ZT/kZWCINgBu4E1giAUU1elelNy9v33qU092azHtAoLxfOVVxrdbu7cuURERPDiiy82uM3UqVN56aWXGD9+PFqtFpPJhKWlJRs2bMDe3p7S0lL69+/P2LF10YKMjAy+++47+p+z8N5+5x1qkFOp0jL3ofGU5WTgKg9h1qxZ7N69m4CAAKZMmdKk97Vq1SqcnZ3RaDT06dOHCRMm4OLS8bRcm3mGvKefRpeVhcfLL+H08MONRqZSU1PZtGlTvXibOHFik58OWwtBEPD09MTT05MhQ4ZQU1NDRkYGaWlpl9mu50VdW7NdvR3lzB0ayOLt6ew7VcqgruYjFhKJjB7dlxJ/aCzHk+fSp/cGLCz+sUhNJpF3NqXwzb4sxkR6s3hSBFYW134D/ScK9/Q1H+NaqKioYP369S0m3gBUlbVsW3kCB3cbhk4LRRAEVHoVy48u54eUH5DL5LzW7zUmBk/ssE4boOJsAcd3biM5bgea6ioUrm4Muv8hug0d3hF1a4DDhw+zefNmpFLpZc2nTSaRyrNqSnKqKc5R1gm33BoMlwi20AFeuPspcPNV4ORpg6QNpB815X/mvYAWmAdMBRyAt1tyUbcq9vb2PPzwwyxdutRsY0GlUkl+fj7jx48HqH+C0Ov1vPLKK+zevRuJREJ+fj5FRUUA+Pn51Qs4rd7IFyt/YO0P3yARTRQXnSU1NRVRFOnSpUt9xGfKlCl8+eWXja536dKlbNiwAajLL8jIyLjlRZwyNrYu/00mw3flSmz792t0n4MHD7Jlyxa8vb3bhXhrCDs7O6KiooiKisJgMJCVlVXfk+5C2/W8oPPy8moTtutjg7uwLiGPN/84wZZnByNr4IPZysqD7t0+IenwNFJPvkz3bksRBIFag5Hn1x1l07FCZg4K4LXRYdeVmtBaUbizZ8/yww8/YDAYWkS8ARiNJrZ9nYxea+De53ois5Ky5cwWFh9aTLGmmPuC7uPZXs/ibO3crOe9GTDo9Zw6tJ/jO7eSk3zsn6jb8Lvxi+jZEXVrAIPBwNatW0lISCAgIIB77x2HSW3ByQOFlGSfE2x5Fwg2SwmuPgrCBnrh7tu2BJs5mlKdemHU7bsWXEuboCkRs5bkueeeo1evXjzySNMnm61Zs4aSkhISExORyWT4+/uj1WoBsLW1BaBKo2f/kRRWLl/K//YfxMfTjRkzZtRv1xAWFhaYTKb6789vHxcXx44dO9i/fz82NjbExMQ0eqybGdFkonTZMko//Qzr8HB8Pl2KrNOV899EUWTXrl3s2bOHkJAQJk6c2Katx6vBwsKCrl270rVrV0aNGkVxcXG9oIuLiyMuLg6FQkG3bt0YOHAg9vb2rbZWa5mU1+8J5/HvE/h+fzaP3tawfe3k1J/AwPmcPv0ReQ7ROLhNZdb3iezPLOOVUaE8PrjLdQvTvLwf6qJwXW5cLtyZM2dYu3YtVlZWzJw5E3f3q2uF0lQObDhN4akq7pwZToX8LC9uX0j82XjCnMP4z9D/EOkW2SLnbc9UFOZzbGddhalGWY29mzuDJk+je8xw7Jxv7YfmxlAqlaxbt47c3FyCfHpgXeTLz28eaViw+Slw8rRtV/nhDYo4QRCUgGjuR9Q1+229T92bGGdnZ+6//35WrlzJzJkzL/qZQqHAx8eH3377jXHjxlFbW4vRaKSqqgp3d3dkMhmxsbFkZ2dftN/ZKi3FSi16rQpHhQJvdxeKiorYsmULMTExhISEkJmZSVZWFv7+/vz888/1+/r7+/Pnn38CkJSUxJkzZwCoqqrCyckJGxsbTp48yYEDB1r4N9N2MdbUUPDvl6jZuRP7sWPwevttJA3kWdTvYzSyceNGjhw5QnR0NKNGjUIqvTmfpAVBwMPDAw8Pj8ts1/j4eA4dOkR0dDS33XZbq4m54WHu3B7sxv/9lc7YSG/cFA23EvHzfYKqqiTSTy3kx60mDmV58vHkSMZHXV3TZnMYjWqyc77C2Xkwjg4N5+g1J8nJyWzYsAFnZ2ceeuihFmtwevpwMUd25BI62IM/hB9Ys3ENNjIbXu//OhOCJnRYpxdg0Os5Ff8/ju3cRu6J81G3fkQOH4lfRBRCO+lh1prk5uay7ud1qNUaXGu7U5nghLsfhA/0wq3eEm1fgs0cDYo4URTbVvLKLcQLL7zAZ599ZvZnq1evZtasWbzxxhvIZDJ++eUXpk6dypgxY+jRowe9e/cmNDQUAIPJhN5oolipxcnGku63D+DXXlGEhobSuXNnBg0aBIBcLueLL75g5MiR2Nra0qfPP6N9JkyYwPfff0+3bt3o168fwcF1Cd0jR45k+fLlhIWFERISUm/Z3mrUZmaSN/cpdDk5eLzyCk7THmo0EqPT6Vi3bh2nTp0iJiaG22+/vU3YijeKC23XiooK9uzZQ0JCAomJia0m5gRB4M0x4Yz4v918tPUkiyY1HBESBAG5+5uU5x3lbp/Pefi2H7k97PoFHFwQhbtBuXAHDhxg69at+Pr6MmXKlBabD1lZpGbnd6lYeZn4QDqPopQ2HNiBAAAgAElEQVSz9dapk/XV98+7WSkvyOf4rgujbh7c9sDDdIsZjp1Th8XcVOLjE9i6ZTOC0Qr78gh8AjvTd6Y/3kE337UmiKK5YBsIgnDFK0YUxfIWWVEL07t3bzEhIeGi11JTUwkLC2ulFbUMWr2R7DI1OoMJb0drnG0trygUampqsLOzQxRF5s6dS1BQEPPmzbuBK26bXOnaUO7aVZf/ZmlJp//7P2z79W30eCqVih9//JGCggJGjx5N795towt/a3NezB05cgRBEFpNzC3cksqKvzPZ8OTABpvzHs6pYOa3h/Cxy+W5qMU4OfYmque3CML1RZKMRjX7/nc7CkU3onp+e13HagxRFNmxYwf79u0jNDSUCf/P3pvHR1me+//vZ9Zkkkky2XeyACGBBBJWlV3UIIsgioILVlo9emzt19Nje9pja0/tclrbelzq+lPR0qqtAi5glX0RBcISSFgTQvZ9Mpl9e+7fH5PEBAIJkA2Y9+uV18w88yz3TGbm+Tyf67qva8mSfgvlu11e/vabr2hpNvPemN8xLD6en03+GdlR2f1yvCuNDtdt4+dUFB9GoVSSPn4yOXPyGZY9zu+6XQR2m5P33/mIstrjqJ0GRkVN4boFGcSPCBvsoV0WkiQVCCG6PVlcKCeuAF84tbszvwC674/iZ9Ax2V1UNNtRSBJpUb3rgfr666+zatUqXC4Xubm5PPzwwwMw0isT2eGg8S8v0/TaawSMHu3Lf4vvuSWT0Wjk3XffpbW1lbvuuqvDMfUDBoOBhQsXMm3atC7OXF5eHlOnTh2wHobfnz2CNfurePrjItY8esM5oZbNx+p4dPV+YkICeOH+u9G4Qjh67CeUlj5Hevp/XNaxKyvfxe1uJq2f68J5vV4+/vhjDh06xIQJE7j11lv7pcWQ2+tmX80+dv+1HE1tBDtyPuCJmd/n9uG3D5nQqdfjobRgD2eOHCI8Lp7otOFEp6ShCej/ul/N1ZW+XLdtm3CYWwmN9rtul4rb5aVg40k2796AS2kiRjuCBcvmkphx9b+P53XiLnvHkvQmMB+oF0KMaVv2B2AB4AJKgO8IIVokSUrB1wXieNvmXwsh/q1tm/HA20AgsB54XAgh2pzC94EUoAxYKoQw9jSuq9mJ8xXwdVJvdqDTqBgWrkPtb6F1WZz92TBv3kLdb36Du7KS0NtvJ/bnT/WY/wZQU1PD6tWr8Xg8LF++nOTk5P4c9hXP2c7cQIq5tQeq+OH7B/nfJdncNfHb/9MHeyv4rzWHyYoL4c0HJnbkzRUf/Qk1Nf9gbM4bREbOuqRjejxWvto9s99dOKfTyQcffEBJSQmzZ89m2rRpfRrKNzqM7KzaydaKrewvO8T1RUuJNw/Hmneah+5fQljA0HBE2pvAH9nyJTZTCyqNFo/L6XtSkgiPTyQmbTgxqcOJSUsnOjW9T4Sdx+Xi5N7dFG7cQGXxERRKJcMnTCF7Tj7Dxoz1u24XidvlpWh7Fbu/PESD5jAovcyedgtTb+w5KnIlcSEnrlcirq311vS2h1uFEJ/2YpvpgAV4p5OIuxnYLITwSJL0vwBCiB+3ibhP29c7az97gB8A3+ATcc8LITZIkvR7oFkI8TtJkn4CGIQQP+5pXFeriPN4ZSqMdswON+FBGuLDAlFcQ3lW/UX7Z8NVXk7dr3+DZds2NMPTif3vp3pVPgSgtLSU9957j4CAAO69995+m/l3NWI0Gtm5cycHDhwYMDEnhGDpq7spbbCy+UczCQlQ8dKWUzz7xQmmjYjk5XvHE9zJ3fZ6HewruBOHo4pJE9cRGJh00cc8c+ZVTpX8ngnj/0FoaF5fvpwOLBYLf/vb36ipqWHBggXk5V3+cYQQlLSUsLVyK9srt3Oo4RCykBnhzmZm8X2oXFqmLR9O9vWDf9Hi9XgoKfiGwo2fd2oCP5GcG/NJGZeHzWSirvQUdaUnfbenS7Aa27KGLlPYNVVVcHjTvyjavtnnusXEkj37FsbMnENQ2NWXp9XftIu3/f86Q7OnHGvoKfTBIdxz3/Irrvdpb7gsESdJ0u+AicDqtkXLgL1CiB5rcfQgzhYDdwgh7jnfepIkxQFbhBCj2h4vA2YKIR6WJOl42/2atvW2CiEyehrT1SjiHG4vZU1W3F5BfGjP+W9+ek9xcTFRGzfS9Mb/h6RSEfnYY4Tfdy9SL/OHDh8+zJo1a4iMjOSee+4ZsLDg1cZAi7miahMLXtjJfVOGIYB3dp9h0bh4fn/HWDTduNs22xn27ruNwMBkxuf9A6Xy/LNbz2YgXLjm5mb++te/0trayp133klGRo8/lefF7XWzt24v2yq2sa1yG1WWKgAywzOZkTSDjPrJnPrYjC5Ey9x/yyYqeXDnyLXU1vhct05N4LNn39yrJvAWY3ObsDtF3elT1JeewtJZ2MUl+IRdm7iLTk1DE+hr3+ZxuTjZlutWefRb1y1nzlySx+T4XbdLwO30cmR7FQe+OIPN7EQkV9DkOkN6ejp33HFHv03MGWwuNSeunVuBcUIIuW1nq4ADwOUWVHsQXzi0nVRJkg4ArcB/CyF2AAlAZad1KtuWAcQIIWra7tcCV5/87gUmm4sKox2FQiItsnf5b356RgiBbDbjqa+n8S8vEzJvHtFPPok6pvcu2u7du/nXv/7V7zP/rgUMBgMLFizoyJkrKChg//795ObmMm3atD4Xc6PjQ1k+OZlVu33leh6ensaP80edtxyBTjeMrMxnKTz8MCdO/g+Zo37d7XrdUVX117ZcuMf7ZOxnU11dzerVq5FlmRUrVpCUdPFOYbOjmR2VO9hWuY2vqr/C6raiVWqZEjeFB8c8yIzEGUQGRLHrg5Mc3lZFQoaBW743msBgTc877we8Hjcl+77h0Ma2JvDtrtuc/ItqAh9sCCd4/CTSx38bnjtb2FUUFXJ051bfk23CLjwhicqjR3BYzITGxDJt+QOMnnGj33W7RDqLN7vZTUxGAA71Keoba5k6dSqzZ8/ul7zOK4HenvHDgPbZqJf9aylJ0s8AD9+6ezVAcluP1vHAWkmSRp93B2fRliN3XktRkqSHgIeAqyYXqXMDe51GxbAI3Xkrzfu5OGSnE3dNDbLFgiRJJK9a1auZpx3byzIbN27kq6++IjMzk9tvv/2qKeI72ISFhXURc/v372f//v3k5eX1uZj7j5syKK5uZcHYeL5zQ8/9a6Oi5jAs+WHOlL9KWOh44uJu73Ebj8fKmfI3iAifTmhobl8MuwslJSW8//77BAYGcu+99xIVFdWr7YQQnGw5yfbK7Wyt2EphQyECQXRgNHNT5zIzcSaT4iYRqPJdmNhaXaz78wFqTpkYNyeJ6xanD0qF+4FoAt+dsLO2GLsIu4YzpSSPGUvOnHySR/tdt0vF7fRyZFsVB770ibekrHCSJmrYtGs9TpvznPZZ1yK9EXG/BQ5IkrQF30zV6cBPLvWAkiQ9gG/Cw42iLZYrhHACzrb7BZIklQAjgSqgcwGmxLZlAHWSJMV1CqfWn++YQojXgNfAF0691LH3J0qlkuzsbDweD5mZmaxatQqdTtftuv78t/5ByDKehgY8jY1IkoQ6NhaVQkFQVlav9+HxePj4448pLCxk4sSJzJ0795q9QuxPOou5nTt3doi58ePHc/PNN/eJaDYEafjo0Rsuapu0tCcwtR7k2PGnCNZnoQ++8Azkyqr2unB9PyO1sLCQtWvXEhUVxT333NNjuRaX18Xe2r1sq9zGtoptVFurARgdMZpHxj7CjKQZZIZnnpOqUXvaxOevHsFpdXPTyixGTozt89dyIXxN4L+hcOOGjibwaXmTyJlzy0W5bpdDUJiBtLyJpOVN7HllPz1ia3Vx9KtqDm2q6BBvE25Nocp4kk82fExYWBj333+/P7+Y3rXd+rskSVvx5cUB/FgIUXspB5MkKR94EpghhLB1Wh6Fb5KCV5KkNGAEUCqEaJYkqVWSpCn4JjbcD7zQttnHwArgd2236y5lTEOFwMBADh48CPia3L/yyis88cQT56xnd3s505b/lhAWSERw73Nv/HRPe+jUXVODcLtRhoahio1BoVZDQ0Ov99PfM//8nEtYWBjz589n6tSp7Nixg71799Lc3Mzdd989KO6nQqFizOj/Y8/ehRw+/CiTJq5Dpeo+J8zjsVJe/nq/uHBfffUVX3zxBSkpKdx9990dfZbPpsnexPbK7R1hUrvHToAygClxU/hezveYnjidaN35T5TFu6rZ9vfjBIVquf3J8UQlDVz+m78J/NWFkAWVx40U7ajm9KEGZK8gKSucifNSiRoWxGeffcaBAwcYPnw4S5Ys8aentHGhtltnT11qz02LlyQpXgix/0I7liTp78BMIFKSpErgF8B/AVrgy7aTW3spkenA/0iS5AZk4N86FRN+lG9LjGxo+wOfePtAkqSVwBlgaY+v9gph2rRpFBYW8sknn/DMM8/gcrmIiIjgL6+/hUsTwst/+h3mxhrKz5RRXl7OD3/4Q37wA9+V/KJFi6ioqMDhcPD444/z0EMPAb4q+Y888gjr168nLi6O3/zmNzz55JOUl5fz3HPPsXDhQsrKyrjvvvuwWn3tcl988UWuv/76QXsfBoLOoVOFVos6NRVlW7/Zi8FisbB69Wpqa2tZuHBhn8z889N72p25xMRE1q1bx9///neWLVs2KEJOq41izJjnOXDgHoqP/pjsMS91K+Z9LpyR1D7MhZNlmS+//JLdu3eTlZXF7bff3qWJvRCCE8YTPretchuHGw77wqS6aBakLWBG0gwmxU4iQNVD2ziPzM4PTnJkexWJowzc8t0xBAQPzHtdfqSQb9a817UJ/I35DBub628CfwVia3VxbHcNRTuraW2wow1SkT0rkdFT4zHEBtHa2spbb71FVVUV06dPZ+bMmf7oRicu1LFhS9vdAGACcAhfODUH2CeEuG5ARtjH9DQ7dccHJ2issPTpMSOTgpm2dOQF1wkODsZiseDxeFiyZAn5+fncfffdhIX56ir98cWXKTxczM+f+R2rXvwDmzZ+yZYtWzCbzWRkZFBbW4taraa5uZnw8HDsdjsTJ05k27ZtREREIEkS69evZ+7cuSxevBir1cpnn31GcXExK1as4ODBg9hsNhQKBQEBAZw8eZJly5Zx9nt1tXB26FQVHY0yPPyc3JXezFxubm7m3XffxWw2s3Tp0o7WZH4GhwMHDrBu3TrS0tIGTcgBnCl/g1Onfsvw4f/FsOTvdnnO47Hw1e6ZhITkMG7sm31yPI/Hw9q1azly5AiTJk0iPz8fhUKB0+tkb+1etlb4yoDUWH3zwcZEjGFG0gxmJM5gVPioXrvGVpOTf712hJoSE7k3JTNlUdqA5L95XC52/H0V+9evQx8RRc6NtzBm1k3+JvBXIEIWVB4zUrSzitMHG5FlQfyIMEZPjydtXBQqtU+Mnzlzhg8++AC3282iRYvIuojUlquJS5qdKoSY1bbxR0CeEOJw2+MxwNP9MM5rGrvdzrhx4wCfE7dy5UqOHz/O0qVLKa+qxul0kZKSQlpUEEqFxLx589BqtWi1WqKjo6mrqyMxMZHnn3+eNWvWAL4GwCdPniQiIgKNRkN+fj4A2dnZaLVa1Go12dnZlJWVAeB2u3nsscc4ePAgSqWSEydODMp70Z+cEzoNC0MV0xY6vQT6Yuafn74lN9cXmhxsRy45aSUm035KSn5PiD4Hg+HbRHhfj1QjqSl9kwvncDj44IMPKC0tZc6cOWTkZbCuZF2XMGmgKpApcVN4OOdhpidOJ0rXu0kOnaktNfH5q4dx2jzcvHI0IyYOTFGA+rJS1r/wLE2V5Yy7ZR7T7/kOam3PRbb9DC2sJifHdtdQvLOa1kYHAUFqcmYnktXmurXjcDjYt28fmzdvJiwsjBUrVvjz385DbyY2ZLQLOAAhxBFJkq7como90JNj1l90zolr598fe4y7H3yEaXPmcrrwG/70v7/pmMCg1X6bC6dUKvF4PGzdupWNGzeye/dudDodM2fOxOFwAKBWqzuutBUKRcf2CoUCj8cDwJ///GdiYmI4dOgQsiyfN4/mSqVr6DQAdWriJYVO2+k88+++++4jMtKfhzNUGApCTpIksjL/lz17F3Gk6AdMmvgJWm0UHo+F8oo3iIiYQWjouMs+jtlsZvXq1dTV1RGaF8pfTH/h8D98P9kxuhgWpi9kRuIMJsZO7DFMeiGKd1az7b3jBIdpWfLj8UQm9n/+myx72ffJGna9/1cC9Xpu/69fkjpufL8f10/fIWRBxbFmindUc/qQz3VLGBnGlNvSSRsXhVLtc3GFEFRVVVFQUMCRI0dwu92MHDmSxYsX+/PfLkBvRFyhJElvAH9te3wPUNh/Q/ID0GJz0dBkJDo2jrTIIP70wd973MZkMmEwGNDpdBw7doyvv/76oo5pMplITExEoVCwatUqvF7vpQ5/SNHdrFNlW4j5kvYnBAcOHODTTz/t9cw/PwNPbm4ukiSxdu3aQRNyKpWenOy/sHff7Rwpepzcce9868L1QS7c7uO7+dfaf+F1eNkdvZs6Yx3Zkdk8Nu4xX+FdQ8ZlT67xemR2vH+Coh3VJGWFc/PK0QQE9f/72NpQz4aX/kTl0SOMmHQ9c7737+hC/MWyrxTOcd2C1eTcmMToqfGExXxbecFut3P48GEKCgqoq6vriBCNHz+ehISECxxhcBBCILe6cFVZcFVZ0N8Qj0I3eCWkeiPivgM8ArT/4mwHXu63EV3jCCGobXXQYHby+H/+lCcf+Q6/NRiYPXs2p0+fvuC2+fn5vPLKK2RmZpKRkcGUKVMu6tiPPvooS5Ys4Z133iE/P5+gy3CphgrC7cZVUYFss6EMC0MdE9PrbgvdUV9fz/r16ykrKyM1NZW77rrrqnMsrybaUxTahdzdd9+NRjOwBWiDgzMYlfErio/+JydPPkNd/adERMwkNGTsJe/zUM0h/vbp39BWaXEr3TjHOvn30f/OtMRpRAb2nSNsNTn5/NUj1JaayLslmcm3pZ+34HFfIYSgePtmNr/1KiDIf/T/kTV9tn+m9xWAkAUVR5sp2llNWbvrlhHGlEXppI3t6rpVVlZ2uG4ej4e4uDjmz5/fke4zFDhbsLkrzbiqLMgWt28FCQJGhKFNGbyLi171Tr2aGMpttzxemfJmGxanh4ggDXH++m+XhWyz4SqvQHi9qBPiUYVdfPPt9s+Gw+Fg27ZtfP3112i1WubMmUNeXp5/ltQVwsGDB1m7di1paWmDIuQAjh37b6qqfY76hAkfXZKIK2os4s0v30R1SkWANwBdio4HFj1ATFjf56bVlprY8OphXHYPs+/PZMSE/s9/s5tb2fj6S5z4ZhcJo7KY++9PEBo9sHXn/FwcQgiMtTZKD9RTvKsGc5PPdcu8Lo6sbly3wsJCCgoKqK+vR6PRdLhu8fHxg/gqfK/D2+rCXWnBVWXG3SbcOgs2VbQOTUIwmoRg1Il61HFBKDT9PyP6cttu+RkA7K62+m+yINEQSHjQ0LgSuVLxGI24q6uRVCq0aakoLiOnorCwkC+++AKLxUJeXh433njjVeFSXkuc7cgtW7ZswIXciBFPYbWVotXGXLSAK2oq4vVdr+MudhPliEIdpmbpoqWMSBnRL2Mt2lHF9vdOEGzQsvAHE4hICO6X43Sm7NB+Pn/5OeytrUxdtoKJC2/3lwwZong9MtUnWyg73EhZYSOtjb7c64QMA9ct7tl1i4+PZ8GCBYwZM2ZQXDchBF6TC3eVz1k7n2ALGGkYcMF2sfhF3BCgxeai0mhHqZBIjwpCp/H/Wy4VIcu4a2vxNjejCA5Gk5iIpLq099PtdmOxWPjss8+Ij4/n7rvvJjExsecN/QxJBlvIKZVa8nJX97xiJ4qainh136s0FTWR3pqOUq1kztw5XDfxun5xgd1OLzv/cZLindUkZ4Vz0wDkv7ldTnasfpsDn39CeEISi3/8C2JS0/v1mH4uHrvFxZkjTZQVNlFR3ITL4UWpVpA4ykDuzcNIyY4g2PBtakl3rtvYsWMH3HXrVrBVWpCt3Qi2RD3qhOAhK9i646LObpIkGYAWca3FYPsJIQS1JgcNFidBGhXJ/v6nl0Xn/DdVZCSqmJhLyqORZRmz2YzVasXr9TJ//nx/6PQqYdy4cUiSxJo1awZFyPX281jUWMTLB1+m9HgpY5vHEu4NJyc3h/yb8s/bju9yEEJQsr+BXf88icXoJO+WYUy+La3f89/qSk+x/sU/0lxVQd7chUxdvgK1xh+FGAoIIWiusVJW2MiZw03UlpoQAnShGoZPiCElO4LEUeGotcou21RUVFBQUEBRUdGAu24+weZsC4laOkRbZ8GmjtERMCrc57BdYYKtOy7UseHnwAdCiGOSJGmBz4GxgEeSpOVCiI0DNcirkS75b8Fa4kID/PlvbciyjNPpRKPRoFT27svly38rR8gymqQklJfQCF0Igd1up7W1FVmW0el06PX6a77B8tXG2LG+UOZgCbkLUdRYxMuHXuZA6QEmNE9gkn0SsfGxLJi3oN9m6hlrrWx/7wSVx4xEJAZz88rRxA2/+PzRi0GWvexd9yFf/WM1upBQlvzsV6Tk9G3rMT8Xj9cjU32ihdOHGzlz+NswaVSyngm3ppCSE0lUkh6pk7j3eDzU1dVRXl7OgQMHOly3cePGkZeX12+uW4+CTQHq6KtLsHXHhZy4u4Bftd1f0XYbha8x/SrAL+IuEbvLw5kmW1v+m47woKFxAhkKuN1umpubO8qbqNVqAgICOooTd+dkeJqbcdfUIKnUaNNSUFzCbFG3243JZMLlcqFWqwkPD0ej0VBTU3PZr8nP0GOoCbkjjUd4+dDLfFX+FeNax3FTy00EBgQyZ8EccnNz+8UFdjk8FGwo4+DGClQaJdPuGsmY6fH93n3BVF/Lhpf+RNWxYkZeN405332UwOCB67nqpyt2c3uYtJHyo82428KkSR1h0kiCDT4HzePxUF1TTXV1NTU1NVRXV1NfX48sywDEx8ezcOFCRo8e3aeuW/eCzYxs9dU49Qm2IJ9gS/QJNk1cEJL66hJs3XEhEefqFDa9BXhPCOEFjkqS5E/aukT8+W/nx2azYTKZkCQJg8GAx+PB4XBgNpsxm80dLcECAgLQaDRIcNn5b51Dp5IkERoaik6n85czuAboLOT+9re/sXz58gEXcu3ibXvFdjKcGSxuXozslJkwYQKzZ88ekNDpqOtiuW7xcHQh/fvahRAUbdvElrdfBSTmPvYfZE6d6f+uDQJ2s4viXdWUFTZSe7oVBASFahgxMYbU7EgSRhlAkqmvr+foqcJuBVtgYCBxcXFcf/31xMXFER8fj8Fg6JPxCSFwlphwlrRcQLBFXHOCrTsudMZztrXYqgNmAT/q9Fzf/7Jc5QghqDE5aLQ4CdKqSA7vmv+mVCrJzs7G4/GQmZnJqlWr+uUHfCgihKC1tRWr1drhgrWHUfV6PV6vF6fTicPhwG63Y7PZAFB7vagdDgIjI9FcZP7b+UKnvQ3f+rk6aBdya9euHVAhd7jhMC8fepkdVTtIEAksMy/D1eQiLiGOefPm9VsIqnPoNDIpmJu/O4a49P6vcWVrNfHlay9yau9uErPGMPfRJwiJ8rdRGgzKDjey+Z2j2M1uopL1TJyXSmJmKLLWRk1NDYdOH+fzr2rOK9ji4+OJi4sjLCyszwW4EALnCSOtG8txVZi/FWyZER0h0WtZsHXHhUTcD4F/4guh/lkIcRpAkqRbgQMDMLarhs75b5HBWmK7yX/r3Hbrnnvu4ZVXXuGJJ57oed8eD6pLnH05FPB6vRiNRlwuFzqdjtDQ0HN+GJRKJTqdDp1OhxACR2srNqMRt1KJOygImyyjbmxEq9USEBBw3rBrO+cLnfq5NulOyKnVaiotlZxoPoHD6+izY8lCZsPpDeyo2kGEKoIVihVYS62oAlXkL8xn3Lhx/RY63be+jEObKlBrlUy/eySjpyf0+8QFgNMHC/jXy8/hsJiZfu+DjJ93m790yCDgdnrZ9c+THNlRSVC8l6ybdLTaq9lfWcDn+7sKtvj4+H4XbJ05W7wpw7SELR5OUF60X7D1wHnP/kKIr4FR3SxfD6zvz0FdTVxK/tu0adMoLCzkk08+4ZlnnsHlchEREcHq1auJiYnh6aefpqSkhNLSUpKTk/ntb3/Lfffdh9VqBeDFF1/k+uuvR5ZlHnvsMTZv3kxSUhJqtZoHH3yQO+64g5SUFPbt20dkZCT79u3jRz/6EVu3bmXPnj08/vjjOBwOAgMDeeutt8jIyGD69Ok8//zzHWUapk6dyksvvdRxArwUXC4XRqMRr9dLaGhor2qveY1GqKkhSK1Gk5SMV6XE4XDgcDiwWCxYLJaOsKtWq0Wr1XacFDuHThUKhT906gfwnUDCU8NJn5rOqR2n+PmLP2dXzC5aPC39crxQTSjfjfguzqNOLFYLEydOZNasWf0WOj1VUM+uf57C2uIk8/o4pixK7/fQKYDb6WDbX9/i0BefEZk0jCU//R+ihqX2+3H9fIvb7aauro7jh09z6Otj2DwteGNtNMqCM1+dK9ji4+O7vZDuL4QQONrEm7uzeBsfg6TyVwPoDReanXq2DSSARmBnuyt3NbLl7deoP1PaJ/vyeAVOj0xowjDmfu/fepX/5vF42LBhA/n5+UydOpWvv/4aSZJ44403+P3vf88f//hHAIqLi9m5cyeBgYHYbDa+/PJLAgICOHnyJMuWLWPfvn189NFHlJWVUVxcTH19PZmZmTz44IMXPP6oUaPYsWMHKpWKjRs38tOf/pQPP/yQlStX8vbbb/Pcc89x4sQJHA7HZQk4m81GS0sLCoWCyMjIHp0wIcu4a2rwGo1d8t8U+CY/XCjs2j4pwmaz+UOn1zhCCCrNlRQ1F1HcWExxUzHFzcWYXWYAUqJTyKvP40bljWTelMno6NEEqy+v0LCRA5MAACAASURBVK0QAofdgd1qx9Jq4fj+41QdryIxMZF777m330KnzTW+0GnVcV/oNP+hMcSmDUx7oNqSk6x/8Y8YqysZP28RU+++H5Xf7e5X2gVb50kHDQ0NHQ6bAjXxSfGkjkjuyGEbSMHWmW7F2+3DCcrzi7eL5UKqorvpQinAzyRJeloI8V7/DOkqQIDLK+P2yigkiTCdukcBZ7fbO1yuadOmsXLlSo4fP85dd91FTU0NLpeL1NRvr2IXLlxIYFsXArfbzWOPPcbBgwdRKpWcOHECgJ07d3LnnXeiUCiIjY1l1qxZPQ7dZDKxYsUKTp48iSRJuN2+6dp33nknv/rVr/jDH/7Am2++yQMPPHAp70yX/DeNRoPBYOhRTMluN+7ycmS7HVVkFKqY6G5/eM4Ou7pcrg6Xzul0+kOn1xhCCCrMFT6h1nSuYFMr1Iw0jCQ/JZ+siCyyIrIYETaCo0VHWbNmDfY9dkYuH3nez4ssy9hsNsxmMxaLpWMCTvtf+zKLxdJxIgXQ6XTcdtttjB07tt9Cp3s/K6NwUwXqACUzlo0ka9rAhE5lr5c9a//B7g//ji7MwJ1P/ZrkMZd+seene7oTbPX19bTPRdTpdERFxBAupeEyahgxOoWb7hlHwCBXQhBC4DhupHXjGdyVFpRhWgy3j0CXF+0Xb5fIhcKpv+xuuSRJ4fjKi1yVIm7WAw9d1vbutvw36wXy37qjc05cO9///vd54oknWLhwIVu3buXpp5/ueK5z6PHPf/4zMTExHDp0CFmWe9WQXaVSdZxYHI5vc36eeuopZs2axZo1aygrK2PmzJmA70fhpptuYt26dXzwwQcUFBT0eIyz6Zz/FhQUREhISI9XgV6rDXfFxdd/kySpI5waEhKCLMsoFAp/6HSIIITgdOtpylvL+3S/NreNY83HKGoq4mjTUczuCws2tfLcbgQ5OTmAb9bq6tWrycnJOUektYfuO4uzdgIDA9Hr9ej1eqKiotDr9QQHB3csi46O7peip0IITu2rZ9c/T2I1uci8IY7rFqUTqB+YE3dLbQ3rX/ojNSeOMeqGGdz44CMEBPd/u65rAaPRyMmTJztE29mCLT4+npEjR3bksFUWWtj1z1MEqBTccl/GgPS9vRDniDeDX7z1FRedES+EaJb8Z8LzUmtyYHd5STLoMFzmVY/JZOoo8Llq1aoLrpeYmIhCoWDVqlUdNdZuuOEGVq1axYoVK2hoaGDr1q0sX74cgJSUFAoKCpg7dy4ffvhht8d8++23uxznu9/9LgsWLGDatGkXPZW8c/5bWFhYj/k/Qgi8RqOv/ptajTbl0uq/gU/Q+UOng4/b66agvoBtFdvYVrmNCnNFvxxHrVCTYchgburcDsE2PGx4t4LtfHQWcmfOnAF8J8t2MRYdHX2OOGt/PBgTjZqqLex4/wRVx1uIStaT/3D2gIVOhRAc3vwFW1e9jkKl5NYf/CeZN8wYkGNfCxw96nOG2yd/nS3YOodEba0utrx7lLLDTSSOMnDjiswurbAGmm7F25I28ebvTtQnXPSvjSRJswBjP4zlqiAuNIDIYA2BfVD/7emnn+bOO+/EYDAwe/ZsTp/uPhXx0UcfZcmSJbzzzjvk5+d3uHRLlixh06ZNZGVlkZSURF5eHqFtTtYvfvELVq5cyVNPPdXhtgE8+eSTrFixgmeeeYZ58+Z1Oc748eMJCQnhO9/5zkW9jovOf/N6ffXfzsp/u1qRhUx5azkN9gYiAiOIDowmSB10VbiGLY4WdlTtYFvlNnZV7cLitqBRaJgUN4kVWSsYHTm6T1+nRqEhJSTlogTb+cjJySElJQVZlgdNnPWE0+5h32enKdxc6QudLs8ga2r8gIROAWymFr547QVK9n1D8pgcbnnk/xESGTUgx77akWWZrVu3sn37dhISEli8eDERERHn/b6cPtTAlr8ew2X3MvXOEeTMSuzSWWEgEULgONZM66Zyv3jrZ6TztUGVJOkwvskMnQkHqoH7hRDH+nls/cKECRPEvn37uiw7evQomZmZgzSi/sVisRAcHExTUxOTJk1i165dxMbGXtK+qqurmTlzJseOHetVLs/F5r/JdjueZiOyqQUhy6iiolBFd5//NlD09WdDFjJnWs90ydM62nwUq9vaZb1AVSBRgVFEBkYSrYsmShdFVGDUObfB6uAhJfaEEJSaStlWuY1tFds42HAQWchEBkYyPXE6MxJnMCVuCjr1tVEDsb9w2j0Ubq7g0KYKnHYPWdfHMWVxOoHBA5fzVLp/L/965f9w2qxMW7aCvLkLkfz9hfsEu93ORx99xMmTJ8nNzeXWW29Fre7+wsTl8LDrHycp3lVDZFIwc76TRUT84ISxO8TbxnLcVRaU4QGEzEryi7fLRJKkAiHEhO6eu9Cl5fyzHgugSQhh7W5lP0OT+fPn09LSgsvl4qmnnrpkAffOO+/ws5/9jD/96U+9EnC9zX8TXi9ekwlvsxHZYQdJgTI0BFV4OIorvNixLGTKWsu6CLZjzcc6BJtWqSXDkMH8tPmMjhhNbFAszY5mGmwN1NvrabQ1Um+v52jzUbZVbsPusZ9zjLPF3tm3AyH23F43++r2sb1yO1srtlJpqQQgMzyTh3IeYkbiDLIislBI/h/xy6WLeLN5SMmJZNL8VKKSB65tldvhYOu7b1C48XOiklO487+fITI5ZcCOf7VTX1/Pe++9R0tLC/PmzWPChAnn/e7Wlpr48q1iWhvt5N2SzKQFaSgHIcesO/Hmd94GhvM6cVcr15oTNxj0Jv/N57o1I5tMCFlGoQ1AGW5AGRaGNITy13r72ehOsB1tOorN01bmpE2wZUZkMjpiNFkRWaSFpaFW9D7sZ3VbqbfV02BroMHecO6tvYF6W/15xV5kYGQXF6+L2Gtb3luxZ3QYfWHSim18Vf0VFrcFrVLL5LjJzEicwfTE6cQGXdoFg59zOVu8pY6NZOK8gRVvADUnj7P+xWdpqatlwvzF3HDXfajO4xD5uXiKi4tZs2YNGo2Gu+66i+Tk5G7X83pl9n1WRsGGMoINAcz5ThbxI8IGeLQ+3PU2Wj4uwXmqxee8zU5Cl+sXb33JpTpxfvxcNBfKf/vWdWtGdjjaXLdQVOEGpMDAfnOKhBA4vU5kce5Mwp5weV0crD94znKBr95YZ4eti2ALz2Bh+sKOxPqLFWzdEaQOIjU0ldTQCxdMPVvsNdobfY/bBN/x5uPssO3oGG9nApQB5w3dRgREUNxczPbK7RxqOIQsZKICo7gl5RZmJs1kctxkAlWBl/Ua/XRlqIg3r8fDN2ve5+uP3ic4PIKlP/8NSVnZAzqGqxlZltmyZQs7duwgMTGRpUuXEhIS0u26xlorG98qpv6MmVFTYpl210g0gQN/KpedXsybyzHvrEJSKwhbkEbQlLhrRrw5bVbqSkuIHzlqUGsg+kWcnz7hfPlvQgiE3YHH2IzXZAJZRhEQgDourl9dN6/sxeq2YnaZMbvNeGXvJe2n0d7IDzf88LzPdyfY0sPSUSkG76t1MWKvO1ev3l5Po72R483H2WnfeU6+XmZ4Jg/nPMyMpBlkhmf6w6T9gNPmpnBL5aCLNwBjTRXrX/wjtadOkDVtFrMf/De0up67q/jpHXa7nQ8//JBTp06Rl5fHrbfe2u0kGiEERdur2PXPUyg1CvIfGkN63sD3nxVCYD/ciOmzUrwmF7rxMYTmp6AcoFI2g0G7YKs7fYq60lPUlZ6kpbYGgGW/+gPxIwcvkucXcX4uCyEEXq+3I++uPf8NWcbT3Pyt66Zoc90M/ee6ubwuzC4zFrcFq9uKEAKFpECv0ROsDr4kYeUOcPPqnFe7fS4iMGLQBdvlEKQOIig0iJTQlAuuZ3PbOgRekj6JmKDBrTl1NeO0uTm02SfeXPbBFW9CCAo3fs7Wd99ApVIz/4c/IeO6qQM+jquZuro63nvvPUwmE/Pnz2fChG4jZlhbnGx+9xjlRU0kZ4Uz+/5MgsL6vtZgT3QOnarjgghfnol2WPeO4ZWKw2qh/nRJm1g7Rd3pUx2CDUAfGUVM6nBGz5hDTNpwIpOGDeJo/SLOz0UghECWZVwuF263u+OvveBpWFgYAYC7unpAXDchBHaPvcNtc3qcAGiUGsIDwtFr9OhUl9cbVavSMi5hXF8N+YpEp9YxTD2MYSGD+2N1NXO2eEsbF8WEeSlEJQ28eAOwthj54tXnKd2/l2E5udzyyOPowyMHZSxXK0VFRaxduxatVssDDzxwTv6bkAVVJ4wU7aym9EADkkJi2l0jyZ6ZMOAz0mWnl9bN5Vh2VCFpFITdlk7Q5LhBK2HSV1yMYItNG0502nB0IQNTf7G3+EXcEEGpVJKdnY3H4yEzM5NVq1b1S0Ps3tLusHUWa50FG/i6Pmi1WtQqFWqnE6qrcfaz6+aVvVjcFiwuS5cwaZA6iLCgMPQaPVrlwF+h+vFzKThtbg5tquDQ5sohId68Hg8lBd+w8fWXcDsczHrgIXJvme8vHdKHyLLM5s2b2blzZ7f5b7ZWF8d211C8sxpTgx2tTkX2jETGzEwgLHpgzwkdodNPS/G2toVO56agHMBSNn2F095CxdH9NFcaqSs9RX3pKVrqvhVsIVHRxKQOZ8zMm4hJTR+Sgq07/CJuiNC57dY999zDK6+8whNPPNHjdh6P57KLkF6UYFOr0Wg0qFQqJCHwNDXhra1F9np9rlt8PMrQ0D513drDpGaXGZvHhhACpaQkWBPcESpVKobOjFY/fnpiKIg3r8dDU2W5L8+nxOdCNJ4pw+N2EZWSxrzv/4iIxO5nR/q5NGw2Gx9++CElJSWMHz+euXPnolKpELKg8oSRou3VnD7UgOwVxI8IY+L8VNLzolCpB/73zV1vo2XdKZwlJtTxQYTfc+WFTh2Oak4d/oiirduoKbTgdfnex4AQBYakcFImTSExYyJJIyejCx2c2b2Xi1/EDUGmTZtGYWEhVquV73//+xw5cgS3283TTz/Nbbfdxttvv81HH32ExWLB6/Xyy1/+kmeffZZPP/0UgMcee4wJEyact0m91+s9b0gUuhdsnWvDCa8XT2Mj3qYmhNeLUq9HFRXVZ67bQIRJ/fgZDBxWt2+2abt4y41i4rwUIhP7V7x1CLZOYaOGM6fxut0AaAIDiU5NZ+zNtxKbPoIRk69HqfKXDulLOue/LViwgPHjx2NrdVG4+wxFO6tpbbCjDVKRPTORrKnxhMcNzuQR2emhdVM5lp3VSBrlFRU6FUKmtbWQ2uovOP71dqoP2LDW6ZCUgvjR0aRNykJraMQpH8di3Y0QO6g0v0XtoTBC9KPR68egD8kmRD+GgIDEK+Ic4xdxZ9HySQmu6r6tZ6yJDyJsQXqv1vV4PGzYsIH8/Hx+/etfM3v2bN58801aWlqYNGkSc+bMAWD//v0UFhYSHh7O1q1be7VvIQRWq5XW1taOZT0Jti7be70+562zeIuORhHYN2Ul3LKbFkcLRqcRt9eNhIROrcMQZCBYE+wPk/q5Immps1F2uJGyw43UnDQhy6JfxVtXwXaSutJTNJSXnSPYxt08j5i04cSkjcAQG+cPmfYjR44cYd26db78txUPINmC+fy1I11ct8kLUknLHRzXDdoungsbaPnsNHKrC92EtlmnQzx06vFYaTbupLFxCxUntlNbCMYToXhdSvRR0Vx31xzG3XjHOU6bLDuxWI7Taj6CufUwZnMR5RVvIoTve6JSdRZ2Y9qEXdKQE3Z+ETdEsNvtjBvnS6CfNm0aK1eu5Prrr+fjjz/m2WefBcDhcFBeXg7ATTfdRHh4eK/337mDglarRa/XX1CwdeZc8RaCKjqqT8SbEAKr24rRacTsNCMQBKmDiNZFo1fr/WFSP1ccslempsREWWEjZYebaKnz1eMLjw9i3M3JjJwYQ0RC37RFkmUvDWfKfDk+beUPugo2HTGp6Yy7Zb5PsKUO9wu2s5BlGavVSmBgYJ/3x5VlmU2bNrFr1y7i4xIYHXcD216vpLXRQUCQmuxZiYyeGo8hdnBLtrjrrLSsK8FZakKdEEzEvZlok4du6NRur6KxaTNNjZtprP+G5pIAmo9FYK0NR6FUMGLyFMbetIDEzDHnFV0KhZaQkBxCQnIgwbesi7Br++sq7EIJ0Y8ZUsLOL+LOoreOWV/TOSeuHSEEH374IRkZGV2Wf/PNNx1N7sHnpnUOhzocji7r2+12WlpaAAgNDUWn610oskO8NTYh5L4Vb2e7bkqFkvDAcAxaA1qV33Hzc2XhsLopL26irLCJ8qImnDYPCpVEwkgD2TMTScmOICSybxxrp81K2aH9lBbsofRgAQ6zz1lvF2y5+QuISU0nJm04YTF+wdYZWZZpamqiurqampoaqqurqa2txeVyAb7fYb1ej16vJzg4uOP+2Y97I/ZsNhsf/vNDSkpLiNGl4TmUwMEDtSSMDGPybWmkjRs8160d2emhdWM5ll3VSFolYYvSCZo09EKnQnhpbS2ksXETjY2bsViPY2/S0nIymabjI/A4vITHJzDx/rlkTZ9NoP7SBGgXYdeGT9idwGw+Qqv58DnCLi/3bxgMk/vkdV4KfhE3hLnlllt44YUXeOGFF5AkiQMHDpCbm3vOesOGDaO4uBin04ndbmfTpk1MnToVWZYxmUzY7XbUajVhYWHnbaLcmXPEW0gIqqjLF28drpvDiNn1resWo4tBr9H7i8YOcVwOD27npRVNPh8qtQJNoGrIhSh6g7HWSllhky9MWmJCyIJAvZrUsZGk5ESSlBmOJqBvfmJbamsoKdhD6f49VB49guz1EhCsJzV3Aqlj84gdPtIv2M6is2BrF201NTW43e2uiorY2FjGjh1LqN6Aw+HAYjFjsVqxWs3U19VjtVm7XCC3ExAQQHCQnqCgIIKD9QQHBRMcFExQUDDBwcG4nW4+2/ApNoeV4NYRaMzJjJodS9YQcN2gLXR6qIGW9b7QadDEWELyU1AGDZ08SI/HSnPzTp9wa9qC292M7FHhrM6moeh6msuNKNVqRk6+gZwb80nIHN0vvyM+YZdNSEg2CSwDugo7vX50nx/zYvCLuCHMU089xQ9/+ENycnKQZZnU1NSOyQudSUpKYunSpYwZM4bU1FRyc3Nxu93U19cjy3LHVWRPH3DfhIW2sGkfije3102L0++6XUk4bW4ays3Ul5tpKDfTcMaMqeHcnqx9gVKtIChUQ1CoFl2o1nc/TIuuY5nvVqsbXLHn9crUnGoPkzZiqve9HxEJweTdnExKTiQxKSF94mLIXi/VJ462Cbe9NFdVABCekMT4eYtIGz+J+BGjUAyhPsODiSzLNDY2drhr3Qm2uLg4cnNzCdNHovHocRqVNJZbqdlkpszWHr3Qt/3FEgBoEQiFG1nhwqtwIStdyAonss1Fa4uLFkUTsrIGWeECqWsfcoVXw4iQqUyaP4a0cVEo1YMvsIUQOI4bad14BnelZciFTn1hUp/bZjR+gxAuVKoQNJ4pmIr1lO07jctuJzw+kpn330HW9FmX7LpdDp2F3WAjCSF6XusqYsKECWLfvn1dlvW2yfmVQOf2V0qlEoPB0KV/abfb9IN4E0JgcVs6XDfw1XIzBBiuKNftavpsnI8ugu2M77a1k2ALDtcSnRxCVLKegOC+vVJ3O73YTE6sJlfHrdXkxO041/FrF3u6EC1BYZ0EXpiWoBAtujANgcEaFH0YCvK4ZapOGCk73Eh5UTMuuy9MmphhICU7kmHZEYRE9F2Y9PTBAkoL9nD6YAEOixmFUkli5hjSx08iLW8SYbFxfXKsK5meBJtarSY2Npa4uDjC9JGo3cE4jSqaKqw0lJtx2jwAKJQSEQnBRCXrCY8PQqm69N8kIQQutwO704bdZcPtdjBu4mjiU6P65DVfLmeLN6VBS8jsZHTjYwY1dOoLkx6ioXEzjY2bsFpPAKDTpRKmn0Hr6ShO7T5B7akTPtdtylRybryFhFH947oNVSRJKhBCdNvOw+/EXUW43W6MRiMejwedTkdISMgFJy70h3jrcN0cRtyyz3WLCIzAEGDwzy4dAjisbhoqfGKtXbh1Fmz68ACihunJvD6O6GQ9UcP0BA7C7DSXw4PN5MLW6sTa4hN23wo9J83VViqKm3F1I/b6g8AQDem5UaTkRJI4ytBnYVJjbbUvt23/HiqPFvnCpPoQ0nInkDZ+Miljc/19SjtRU1PDxx9/TE2Nr0hru2DLzc3FoI9E5dHjalLSUGGlusjMaZsFsHQItvTx0b7PdbKeiPjgIeGO9SdCCBzHmmndVO4Tb+EBGJaMQJcXPWiN6j0eM03NO32TEpq24nY3I0lKQkMnMGL4T4mMnE11UR0bn38Ja8s3hCckMWvF98icPpvA4MEpgj2U8Yu4q4DOpUMUCgXh4eEEBAScf32P59vZprJ82eLtfK5bTJA/120wcVh9DltDuZn6M2Yayltpbfx20os+IoDoZD1ZN8QR1XZiGwzB1h2aABWaABVhMReuUO92erGanB0unt3sBvouuiBJEtHDQogepu+7MOnxo5Ts30NJwR6M1ZUARCQmM37+YtLzJhE3MgOFf1Z2F1wuF9u2beOrr75Cp9Mxe/pNBErh3Qs2lURE/LUn2DrTId42luOuGnzxZrdXtE1K2IKx5RuEcKNShRIRMYPIyNlEhE9HrQ7FbjGz5c3XOLpjC1HDUpn3+JMXnGHqxy/irng8Hk9H83mtVktYWBjKC+TJyDYbrjPlCK/nssSbV/ZidVsxu32dFLyy1++6DRGsJid7PjnN0V3VtGdLdAi2qfFEJeuJTg7p89DoYKDWKgmL1g14O6KLwWGxUHaogJKCPZQdLMBhtaBQqkganc24m2/1hUljYgd7mEOW0tJSPvnkE4xGIxnpo6E8gcMfOIAaFCqJyIRgho+P9n2uh4Vcdmj0SkYIgeNom/PWLt7uGIEud2DFmxBeTKYDNDZuprFpM1brSQB0unSSklYQGXEjoaF5KBTfSpCSgm/48rUXsZtbue6OZUxevNRfcLoX+EVcHyOEQPShEwAgIXV7JWKz2TCZTEDvSod4W1txVVQiqVVoU9IvWry1t7+yuC1Y3VaEECgkBXqNvuPP77oNHi6HhwNflnPwy3Jkr2DMjERScyL7JZfNz4Vprq6idP8eSgv2UHmsCCHLBOpDSJ8wmbS8iQzLyUM7iL2RrwRsNhtffPEFBw8eJCzUwCjDNJp2SQQbBNPuGklceug1Ldg6MxTEmy9MuoPGxk00NW3D7TYiSSrCwiYSH38XkRGz0OlSztnOYbGw5e1XKd6xhajkFBb/5GliUgen1NeViF/E9TG1tlqa7c19vl+tUkuAKoBAVSBahRaX1YXD4UCtVmMwGHqsW+RpbsZdXY0iMBDNsGFIvahz1Jv2V4GqQL9wG2Rkr0zxrhr2fHoae6uL9LxorlucRmiUXyQMFF6Ph+rjxZTs30tpwR6MNVUARCYNY+LCJaTlTSJuxEh/mLQXCCE4cuQIn3/+OXa7nWERWdiPhtOqUTFl0TDGzk5CpfG/j3A+8TYSXW7UgIg330S6Q1RWvktd/fq22aRhREbMJDJyNuHh01Crzz97tKRgD1++/iI2UwtTltzNlNvv8rtvF4lfxPUxerUeldTHVb+RcXqcWN1WrHYrQZ4gJCTcKjdygIzJbSJABBCgDDinw4EQAk99PZ6GBhR6PZrExAs2p+8Ik7YJN6/sSxzXqXW+HDe13l8WZIgghKCssJHda0ow1tqIGx7KrY9kE5saOthDuyZwWCycPriP0v17OX1wH06rFaVKRWJWNrn580nLm0RodMxgD/OKoqWlhc8++4yTJ08SFhRJkCkLe00gWdMSmDQ/FV3I0MjZHGw6xNvGM7irrSgjBla8eb0O6uo/pbLyXczmIyiVQcTHLyUmZj6hIbldwqTd4bBY2LLqNYq3byYyOYXFT/6cmLTh/T7uqxG/iOtjgjXBBGsuvqVOWVkZ8+fP58iRIx3Lnn76aYKDg/nRj36ELMuYzWasDisKpQJJJ+EVPsFlcpo6ttEoNQSqAn2unTIAVX0LcksLSoMBdXx8t+HW7sKkSklJsCYYvUZPkDoIVQ9fSj8DS93pVr766BTVJ1sIi9Ex99+ySR0b6U8A7keEEBhrqjqK7lYdK/aFSUNCGT7hOtLHT2JYzjg0gX4H9GKRZZm9e/eyadMmvF6ZCG8GUkk0SaMjuX5JOhHxfdOm7EpHCIGjuJnWTZ3E250j0Y2LRlL2/3ffbq+gsmo11dX/wONpQacbTsbIXxIbuwiVqnf/o9IDe/ny1RewmlqYcvtdTFlyt999uwz69cwsSdKbwHygXggxpm1ZOPA+kAKUAUuFEEbJd/b5P+BWwAY8IITY37bNCuC/23b7jBBiVdvy8cDbQCCwHnhcXIWF7zqXDtFoNISHh3cpHeKW3Tg8DuweOw6PA6vbSqvDRIxRoHNBa4gKd4ggwNHkE3jKAJxe5wXDpDpV71pz+RlYTA02vl5byqmCegL1amYszyDzhjiUg1Qu4GrH6/FQdayY0v3fUFKwh5ZaX2mLqOQUJt12J2l5E4kbPtLfKeEyqKur45NPPqGyspJgRRSaulSiYiO44QcjSMrqfX/oq5nBFG9CyDQ376Cy8q80Nm1BkhRERt5EYuK9GMKm9Po84bBa2LrqDYq2bSQiMZlFfvetT+hve+Vt4EXgnU7LfgJsEkL8TpKkn7Q9/jEwFxjR9jcZeBmY3Cb6fgFMwFc7oECSpI+FEMa2db4HfINPxOUDG/r5NfU7QghcLhdOpxNJkpgxYwajR49m//79LF++nMOHDzN//nzuuOMOAAwhBiwWC0GqIB577DE2b9pEQmQkKoWCZSuWM3vpPK4bPZn3v3wfQ4SBIweP8OwvnmXVulWcKDzBr//r17hdbnSBOt566y1iM2KZPn06zz//POPGjQNg6tSpvPTSS4wdO3YwgvX8pgAAIABJREFU35prFofFzd71pzmyrQqFUmLCvBRyb0rus3pl1zpOmxVjdRXNNVUYa6q63Pc4nShVKpLGjGX8rYtIy5tISFT0YA/5isfj8bB9+3Z27tyJAhX6lgwMmkSmLE9n1HVxfVq0+UrG0+yg+f3juM60ohpA8eZ2m6ip+ZDKqr9it59Bo4kkJeVREuKXERBwcUWnTx/YxxevvYC1xcjkxUuZsmQZql60gPTTM/16BhBCbJckKeWsxbcBM9vurwK24hNxtwHvtDlpX0uSFCZJUlzbul8KIZoBJEn6EsiXJGkrECKE+Lpt+TvAIi5TxG3YsIHa2trL2cU5xMbGMnfu3PM+3y7a2nv9uVyujmVarRaFQoFKpaKgoACABx54oNv9fPTRR5wuLWX/2nXU1deRt2gR3wuLIjkkGbVCTbohneCQYKoDqtEqtWSEZ5A4IZH5u+ajUqnYuHEjP/3pT/nwww9ZuXIlb7/9Ns899xwnTpzA4XD4Bdwg4HF5KdxSScHnZ3A7PGReH8ekBWkEhfnzEi8Wj9uNqa4WY00VzdWVGGuqMdb4bm2mlo71JElBaHQMhrh4kjLHkDg6m2HZ49AE9E1nBj9w5swZPl73MU3NTQQ4ogmxDWf8TcP9FyZnYTtYj3HNKYC2Om8x/S7ezOajVFa9S23tOmTZQWjoeNJSf0h0dD4KxcXlJDptVra+8wZHtnxJROL/z96bx7dxnoe6zwz2hSBBcF9AUlxF7YtlrdYWWV7kOHJsN/ZJbGdp6zZO2qa9PTnHdbcsp2l8cu89bnqa3lwnzWlP5CTel3iLLUuWF1m7SGojRQLcF5AAiB2Y+c4fgCBSu22RoqR5fprfDAYfBwNSwDzzft/3vm7u/Iu/oqS2forO/PrkSnxaioUQ/ZntAeDUyN9yoHtCu57Mvgvt7znH/hmPEIJUKpWNtsXjcfx+P6qqkkqlsFgsmEwmbDYbOTk56PV6vvCFL1z0uDvffputa9YgA1U33MD69esnPW+QDeQYc3CanehlPTpZRyAQ4MEHH+TEiRNIkpQtXXPPPffwne98hx/+8Ic8+eST5xVHjalBqILjuwf44PmThMbiVM9zsXyrNjboYghVZXzUl46m9fcx1teT3Q4MDSLE6WLm1tw8nKVlzFq8DGdpGfllFThLy8ktLtGiBFNELBbjjTfeYO/evehUM7mBucxf3MyNn63F7tRuTE6hxlP4n+8gsm8IY5WD/N9rRJ9//gTul0IqmaT/xFG8LQfxthzCP9CHs7Scgsoq8ivK0dkHiIg3iST3IctmSoo/S0XFl8jJaf5Er9d1YC+v/esThEdHWfa5e1hx9/3a52oKuKK3PEIIIUnSlI9hkyTpD4A/AHC73Rdse6GI2afhTGlT1fTFRJZlzGYz1dXVjI+PU1x8ejab3++ntjadL8dmO116R6/XZ39eVVUSiQRKMIgSDEJFBcZZNcimyV+IE38mFjudtf+xxx5j/fr1PPvss3R1dbFu3ToArFYrmzZt4vnnn+dXv/pVNgqoMfV0HxnlvWfaGekOUejOYeNDzVQ0Oq/0ac0olFSSsf4+Rnu78fV2M9rbk176e0jF49l2epMJZ2k5RbPqaFq9FmdpOc7SMpyl5ZhtmhBPJ21tbbz4wstEo2EskXLqShdy09eaKHRrpZQmkugex7ftKMpojJyNbhwb3J8o+qaqCoMn2/G2HMLbcpC+o22kkgkkSaa4to6ahUvw9XXRuvN1UrFU9udM9oUUuusQ7mqSlV5cFeCqdF/y5yUdffv/aXn7dfLLK/nsd39IaV3jxz5/jUvjSkjcoCRJpUKI/kx36VBmfy9QOaFdRWZfL6e7X0/t357ZX3GO9mchhPhX4F8Bli5dOi0THxRFmSRtipJO1SHLMkajEZPJhMlkQqfTZQeGlpaW8tZbb7FhwwZGR0d59dVX+ZM/+RN+9rOfTTp2dXU1e/fu5d577+WFF14gmUyS8HpZuWwZ//vVV/naf/7PDA4Osn37du6///5JP3Prrbfy9NNPZ48VCAQoL08HMH/+859Pep2vfe1r3HHHHaxZswanU5OIqWakJ8T7z7TjbRslx2Vm01ebqb/CBaqvNIlohNHenoyodePr7WG0txv/YD9CPR1VyykoxFVeScXsOeSXV2RkrRx7vkuboHOFCQaDPPf0i5z0nECXtFGpX8GGLy2hep72t5mIUAXj7/QQfMODzmGk8A/nY6q+9HRBQgh83R68rWlp62lrIR4JA1Dgrmb+Z26hcu4CKpvnklA8dHX9M8aR1ylWVeymNVjFWhIBByPdXnw9XlrefoNk/PQNvz3fRUFlFa4Kd3pd6cZV4Z40zKDr4D5e+8n/IDw6yg133s3Ku+9Hb9TSwkwlV0LiXgAeBP4hs35+wv5HJEnaRnpiQyAjeq8B35ck6ZRF3Az8FyHEqCRJQUmSlpOe2PAA8MR0vpFzEQ6HCYfDpFLpOxtJkjAajdhsNkwmE3q9/rxfXL/4xS/4+te/zre+9S0A/uZv/iYbiZvI7//+73PnnXeyYMECNq1Zg81iQZeTw+89/DA7Wltpbm6msrKSxYsXk5ubmz3WV7/6VR577LFstA3gL//yL3nwwQf57ne/y+233z7pdZYsWYLD4eDLX/7y5fjVaJwDoQp6jo3RsqOXkweGMVn0rPx8HfPXVVw3tR6FEEQC/kmSdkraQqO+bDtZpyOvpIyCyioalq/GVV5BfnklzrJybbzaDGX3e/t4/c3fklIUchO1bNi0lrlrK7TZ1GeQCsQZe+oY8ZMBLPMLcG6tR7Zc+PIshCAwNIi35QDelkN0tx7Kju3MKy6lYcVq3HMXUNk8D1te+vIZjw/TcfI79Pf/Br3eQWXlV6govx+L5eweKqGqBEeG8fV4Gen24Ov2MNLtpaftFVLJRLado7CYgko3Or2BE7vfI7+sgs9+54eU1mvRt+lAmsqMHJIk/ZJ0FK0AGCQ9y/Q54FeAG/CQTjEymkkx8k+kZ5hGgC8LIfZkjvMV4L9mDvs9IcTPMvuXcjrFyG+Bb1wsxcjSpUvFnj17Ju07cuQIs2fP/rRvF4BQKEQsFstG2gwGw5TcbQpVJdnXh3JGDrhQKITdbsfn87Fs2TJ27dpFScknq8vY19fHunXrOHr06KSUJtcTl/P/xkRioSRH3u+ndWcvgaEoZpuB5tVlLLrZjdl27Y4biYVCjHi7GPZ2MuztYqTbw1hvD7FwKNvGYLaQX1aRlbT8zDqvuBTdJVQa0bjyDHWP8ZttzzMU7sKQzGHZ3PXc9Ln5mC4iJtcj0ZYRRp8+AYpK3mfrsC4pOu81IzQ2SnfLwWy0LTic7siyOfNxz12Ae8583HMXnDV7WlHidHc/SZfnf6KqCSorHqC6+pELVlM4H6qqEBgazIhdRvB6vIyPDDNv42ZW3ftFLfp2mZEkaa8QYum5npvq2an3neepjedoK4Cvn+c4TwJPnmP/HmDupznHy43dbsdun9qxNkJRSHR3o4ZC6IuK0BcWZj/0W7Zswe/3k0gkeOyxxz6xwP3iF7/g0Ucf5Uc/+tF1K3CXGyEEg51BWnb00r5nCCWlUlqbyw2311C7uBC94dopJaQqCmP9vQx7MrLm7WLY08W4bzjbxmzPSUfVVqzGVV6ZEbZKclxa0uKrFf9ghLee3UNLz3uouhg1RXPZet9tOPK1BMhnoiYUAi+dJLx7AEOFnfwvNGEoODuiLIRg3yvPc+h3rzHam57jZ7bZqZwzn6V33IV77gLyyyrO+ZkRQjA09ArtHT8gFuulsGATdXXfPmcN00tFlnU4S8pwlpRRf8OKT3wcjcuDdlt0lSGSSRIeD2osjqGsDH3+5GSY27dvvyyv88ADD/DAAw9clmNd7yRiKY7vHqRlRy++nhAGs47Zq0qZe1M5rvKrf3B9JOBn2JOOrp2SNV+vFyUz01nW6cgvq6C8qZnCqhoK3dUUVFVjd2pjoq4V/IMRPnr5JPvbdhOxeTFbbNx115domK0VMj8Xid4Qo9uOkhqJkrOuAsdnqpD0Z98sK6kkr//kCdp2vEXF7LnMXb8J95z5FFbXXLQObzB4iOMnvksgsBe7vYlFC/8X+fkrp+otaVwhNIm7ilDjcRJdXQhFwVjlRpejzeqayfh6Q7Ts6OXYhwMkYwquCjtr72+kYVnxVZkLK5VMMtrbfUZ0rXNSjjVbnpMCdzWL5t2RljV3NfnllVpqgWsU/2CEPa90cWRvJ8HcYyTtQebMnssdd27BbP50KTGuRYQqCO3qI/BqJ7LNQMFX52KuO/eksWhonBf/+/fpbjvMynv/E8vv+sIl3fTE4gN0dDzOwMCzGAwumpq+T1np3UjStRPp1zjN1XcluU5RIhGSHg9IEqbqGmSrNpB7JqIkVdr3DdG6o5f+jgA6vUzd0iLm3lROcY3jqog8xSORSZMLTq0Dg6fzrOkMBgoqq6hZtDQra4Xuaqy5eVf47DWmg7GBMHt+28Xx3QMkbMOEitrRG3R8fsvnmTdv3pU+vRmJMp5g9NfHiR8fw9zswvn5enTnGf/qH+jnmR/8HcGhAW77xl8we/W6ix9fieL1/pQuz08QQqHK/YdUV/8Rer12s38to0ncVYASDJLo7kYyGDBWVZ2VA07jyhMYjtC6s48j7/UTCyXJLbSw8vN1zF5Ritk+86JQQgjC/jFGMznWTonaaG83obHRbDtZp8dZWkZhVQ1NK2+iICNszpIyZJ12Z3+9cUreTuweRDIoyA3dBAJeqiqr2Lp1K3l5msSfi+jRUcZ+fRyRUMj7XB22G0vOe0PXe+wIz//wOwjg7se+R0XTnAseWwjB4OCLtHf8gHh8gKLCW6mr+8tzzjjVuPbQJG4GoyaTpIaGUMbGkC0WjFVVSNrsvBmDqqh0HfbRuqMXb9sokixRs6CAuWvKqWhyzoj8bqqqEBwaOiuqNtrXQzwczrYzWtIzQt3zFpJfXpmdaJBXXKLJmkZa3l7p4sRHg+j0MpXLjRwb/pDIeJiNGzeyatUqbQLUORBJlcBvOwm914ehxEb+fY0Yim3nbX901zu8+j//HxwFhWz9z3+Ds/TCRYgCgf0cP/E9gsH95OTMYU7z/43Tuexyvw2NGYxmBDOA9evX8+1vf5vNmzcD6dmnP/re9zna2sr/+OvH0Ltc6IuKkK7wxfShhx5iy5Yt3H333Vf0PK4USkplbCCCrzfEcPc4HXuHCI3FseWZuGFLDc2rymZE6aBoaJzWt9/gyLvvMNrbPSmnkzU3D1d5JU0r156WtYoKbZKBxjmZJG8GmfkbyglaOtm95wNcLhf3338fZWVlV/o0ZyTJwTCjvzxKciCCfVUZubfUIJ0n96MQgg+f/RW7nvpflDfN4c6/eBRLzvnTf8RifbR3/JDBwRcwGouYPfsHlJbchSRpIn29oUncDOC+++5j27Zt3LxpE8roKKnhYZ56+jf8t8cew1Rfj6zl3JlWhBCoiiCVVElEU7zxZCu+3hBj/RFUNZ2GUNZLlDc4WXNvA9XzXcgzIHnpYGcHB157maO73iGViFPa0MTCW7aQX16RlrWySsxTnP5G49rgTHlb+Bk3lYutvPzaCwwMDLBkyRI2b96M8Tr4bhJCgCIQKfX0klTT+5Lq5P0pFVKC1GiM4NvdyGYdri/PwdKYf97jK6kkb/zrj2l9501mr1nPzX/4zfNOBEqlwni8/4rX+1NAUF39darcf4hef/7onsa1jSZxM4DPf/7z/NWjjzLe2opBkvD6/QyMjpLKyWHV2rXE43Fqa2v52c9+ht1up7q6mgcffJAXX3yRZDLJr3/9a5qamgiHw3zjG9+gpaWFZDLJ3/7t33LnnXfyta99jVMJjnt7e3nkkUdYu3Ytjz/+OC+99BIAjzzyCEuXLuWhhx5i7969fOtb3yIUClFQUMDPf/5zSktLJ53z3//93/Piiy8SjUZZuXIlP/nJT67KSI6qCpSkSiqpkEqopBIKqaSKyMhaPJKirz1CQbmd6nkFuCrsuMrt5BVZZoS4pZJJTnzwLvtff5n+40fRm0w0r1nPgptvo6h61pU+PY2riGRCoefoGCd2D9C+dygtb5vcLNhYSdvxQ/zbf2zDaDTyhS98gaampit9upeNaJuP8R09iJhylpCJlICUevGDnANTg5P8exrQ5ZxfdGOhEC/86Pt0tx5ixd33s+Lu+86T701lYOBZOjr+O/HEIMVFW6it/Usslgt3t2pc+2gSdwbHj3+H8dCRy3rMHPtsGhoeO2u/EAI1FMI+OsaS5mZe37WLrV/8Is/+0z9x8+bNfO973+PNN9/EZrPxgx/8gB/96Ef89V//NQAFBQXs27ePf/7nf+bxxx/npz/9Kd/73vfYsGEDTz75JH6/n2XLlvGZz3yGn/70pwB4PB5uueUWHnroITo7O895rslkkm984xs8//zzFBYW8tRTT/Hoo4/y5JOTcy0/8sgj2XP50pe+xEsvvcQdd9xxyb8ToQpUVZxnnX5ekgBJQpKYvI0EmX2SdGpbyrQ5Yx/pfQBqSpyWtcxamfAFLUkSOoOMyapHb9ShN8jYQyYe/P6iS35f00VwZJhDb77K4bdeIxLw4ywtY90Dv8+cdRu1wu4al0xoLE7X4RE8h0foPjqGklQxmHUs3ORm0SY3ipTg+Ree5vjx49TW1vK5z32OnGsktZGaUAi8fJLwhwPoCyzoi6xIeglJLyMZZCSdDAY5/fjU/gkLE/dn2kuG9H7ZICM7jBe8sfUPDvDsP/wt/sEBbn3kz2lesz59XmqCSKSTUOhodhkPHSGRGMbhWMDceU+Ql7tkun5NGjMcTeKuEGokQnJgEDUSRjIauf+LX+Tpt97i7ocfZtu2bWzdupXnnnuOVatWAZBIJFix4nR27LvuugtI1zd95plnAHj99dd54YUXePzxxwGIxWJ4vV5mz55NLBbjnnvu4YknnqCqquq8Enfs2DFaWlrYtGkTAIqinBWFA3j77bf5x3/8RyKRCKOjozQ1zWbzpltRlfOImSIQKtnHFyv3JkkSAmCKysLp9DJ6o4zZZkBvlNEZZHR6+awv3ZkwOeEUQgi6Ww9x4LWXad/zAUIIZi2+gUU3307V/EVI2sByjYsgVMFw9zidh0boOjTCSHe63JmjwMyc1WVUzyugrCEPnV7m+PHjPP/888RiMW655RaWLVt2zUxeSPSMM/rUMVLDUayrCkgu9aIzpzAaizCZitDrp/ZGqO/4EZ774XdRlRQ3f/MebKUnaG19iVD4KOFwB0KkE2VLkhGbrY78/NW4XGspLrpdG/emMQlN4s7gXBGzy4kaj5MaHEQJBpH0egylpeicTraWlfHnjz7Kvn37iEQiLF68mE2bNvHLX/7ynMcxZdKM6HQ6UqkUkL7IP/300zQ2nl14+OGHH+auu+7iM5/5DAB6vR5VPR2FisVi2WPMmTOH999//7zvIRaL8cd//Mfs3P4eRfmlfO8fvoN/eBz/YGRyQwlkWUKSJWRZQtZL6GU5/VgnZZ879Xx6nRanUzIlhID0v9Pb2fXkfUKQaXtqW2TapLdPiZvOoEOeQXJ2MRLRCK073uLAay8z2tuNOcfB0i1bWbDpVnKLPllZNY3rh2RcoefoKF2HRuhq8REJJJAkKJmVy4qttVTNc5Ffast+5pLJJK++/Fs++ugjioqKeOCBByguLr7C7+LyIFTB+I4egq97kGwQ2rSLE7p/Qz2SmNROp7NhNBZiMhVjyqyNpkJMxtPrjyN7ihIjHD5BKHSM4x/sYv9vTmCwp6jZ4mEgsgc6wGQqwW5vwuVah93WiN3ehNVagyzPvBRFGjMHTeKmiXS6kGGUsTEkWUrXPHW5sjNO7XY769ev5ytf+Qr33Xcfy5cv5+tf/zrt7e3U1dURDofp7e2loaHhvK+xefNmnnjiCZ544gkkSWL//v0sWrSIH//4x4yPj/Ptb38727aqqoq2tjbi8TjRaJTf/e53rF69msbGRoaHh3n//fdZsWIFyWSS48eP09zcjKqoxEIJ+jtHUFWBSbYTCAR5+dUXuGvrXeQVWycJWba781OQ7RpNP/pUx7ra8PV4OfD6y7S+8xbJWJTiWfXc8sd/RsOK1RiMV34WrMbMZXw0hqfFR9ehEXqOpbtJjWYdlc0uaua7cM91YbGfPVarr6+PZ599luHhYZYvX87GjRsxXCPVNlL+OL5tbSS7QkTLj9LT8ASSHkpL7qGk+LOoaoJ4YohEfJB4Yph4fJBEfJjg+CHiI0OoauysY+p01mz0zmQswmhKbxsNLmLx/kx36DEikU6EUBk64KJ/dxGOcoll988jv/g+7PYm7PZGDAYtx57Gx0eTuClGKAqpkRFSPh8IgT4/H31R4Tnzvd13331s3bqVbdu2UVhYyM9//nPuu+8+4vE4AN/97ncvKHGPPfYYf/qnf8r8+fNRVZWamhpeeuklHn/8cQwGAwsXLgTSUbmHH36Ye++9l7lz51JTU8OiRelxX0ajkd/85jd885vfJBAIkEwkefgPvk6xw00iqhCPpshzOvnKQ19hw20rKSkp4cbly9AbdVdlKamZhqootO/5gAOvvUx36yF0BgONK9awcPPtlNadHWHV0IB0hGnIM07X4RG6Dp/RTbqmjOr5BZTVpbtJz0UqleKdd97h3XffxW6388UvfpG6urrpfAtTyuiHBwm/7EMoCoNz/h21YYT6im9TUvzZS4qmCSFQlBDx+CDx+FBG9tLreDy9HRw/nJG9aPbnLGY3dnsjha5baH15gP7dbTStuonNf/RnWik6jcuCdLGxSdcaS5cuFadmap7iyJEjzJ49+7K+jlDVbLoQoSjocnPRFxXN+GoLSlIlHk2RiKZIxFMg0t2bRrMek1WP0aybEbMyp4up+L9xLvyDAxx9dzsHf/cqId8IOQWFLNh0G/M23IzVkTvlr69x9aEkVbqPjHLy4DCewz4iwUw3aW0u1fMLqJ5XgLPEetFoeF9fH8899xxDQ0MsXLiQzZs3Y7Fc/WX9VDXOYPerhF4ZwuJpIprbQWpDB2VNW3E4Fp3uPo7F6G8/hj3fRW5RCbpPkVD9lOwlEiMYjYXo9XZi4RAv/ui/4W05yPLP38fKe+6/Kmfya1w5JEnaK4RYeq7ntNDJZUYIgRIIkBocRCSTyHY7xuJi5Bn6pSiEIBlXSERTxKMplGSmNqZexmo3YrTqMZh02pfOZSYaGqe75SCeQwfwHN5PYGgQgKr5i9j45YeZteQGZFmrlKAxmVRSwds6Ssf+IboOjpCIKRgtetxz8qmeV0DVHNcll3lLpVLs2LGDnTt3YrPZuP/++y8Y6b9aiEQ89PVtw9e6h8IDX8AcbUBdOoL79jswWVzZdmP9vRx4/RVat79JPJKuXiLJMnnFJThLyyct+WXl2Jz5F/0elCQJvT4nW680MDTAM//wd/gH+rnlj/+MOWs3Tt0b17gu0STuMpPs60uXyTKbMZSXo5uByVVVVaQjbRlxO5UTzWDSYXGaMFr06A2aQFxOUskk/ceP4Dl8AM+h/QycbAchMFosVM6Zz5LbP0fNwqXklZw9E1jj+iaVUPC0+ujYN0zXoRGScQWTTU/t4iJqlxRR0eg8bzfp+ZgYfVuwYAG33HLLVR19U9UUPt/b9PT+B6Mjuyjo/BxlJx9Bdsi4HpyHuTov007h5L49HHjtJTyH9iPr9DQsX0XTqrXEQuOM9fcy1tfLWH8v3sMHJ1U7MZjMaakry4hdaRnOsgqcpeWYrNazzqn/xDGe++F3UFMp7n7076mcM3/afh8a1w+axF1m9M58ZJsNXW7ujIteqYpKJJggOp5ECHFddZOqqprJJTc9fxMhBL5uT1bauo+0kIrHkWSZ4lkNzNuwlZzCBmS5mKAvQcfBKG3ve7A6+rHlmbDmGrHlmrCdWmf2aeMOrw+ScQVPi4+OfUN0tfhIxRXMdgP1NxRTu7iQ8kYnuk/weU2lUuzcuZOdO3ditVq57777zjmb/WohHh+kt+9X9PVtIx4fwJacTX3rE8hDNqyLi8j7bC2yWU8kGKDl7Tc4+MYrBIeHsOe7WHnvf2L+xluw5TnPeWyhqoyPjjDW18dof09a8Pr7GGg/xrH3d05Kf2TNzSO/rAJnaRnO0nJknZ53f/lv2PLzuevbf0t+WcV0/Uo0rjO0K0IGIcRlucDLVguydWbd0Z4pbyarAUuO4ZrrJlUVhVQyiZJMoiQTKKlk9rGqKEiShKzXo9Pr0en02W15wmNZp5uc3uRjEBobxXv4AF2H9uM5dIBIYAwAS04RjsLFyPoq4tEi/D49fh9AAqRu7E4TjgIL+SVmomGFwa4gEX+cVPLsTPEGk26S4FnzTJNkz5prxJZn0mTvKiQRS+E5nBY3T4uPVFLFkmOg8caStLjV532qG63+/n6ee+45BgcHmT9/PrfccgvWc0SQZjLpVEIJ/P499PT+b0ZG3kAIhfy81dQm/g51lwVkGef9dVjnFzLQfpwDr7/M0fd2oCSTVDbPY+2XvkrtkhsvOvZNkmUcBUU4Coqomr9w0nOpRILA0ACjfT2M9fcx1t/LaF8v7Xs+JBoMAFDWMJs7/6+/0sa0akwp2sQGoLOzk5ycHFyua6sI+LnkzZZrRG+8OrtK0zVNlbSkpdJylpW2VAJVmSw9Or0encGQXvSG9GSTVAo1lUJR0usz//+fEj1J1hGOxRju70Me92N3FZCTX4Dd5cLiyAMhERgK0rH3AN6WAwx1tREN9GcOYkHWu5ENVegMbnT6XHJcZnKLLDgKLFjsKRA+EtEhwmP9jPZ68fV4ScSi5JdVUOCuptBdTV6JG0tuKUh2osEkYX+cSCBBOBDPLInzyp7epCPHaaJ5dRlzbirHcJX+za91EtEUnYdG6Ng3hLdtFCWpYnUYqV1USO3iIkrr8z51TkNFUdi5cyc7duzAarWyZcuWaSubJYRCONxOPDGMqkRRlCiqGkNRIijytVGkAAAgAElEQVRKDEWNpvdn9qmZfWe2U9Vodi2EAoBen0dZ6ecpzf894r+NE23xYZqVi2NrDe1tuznw2ksMdJzAYLbQfNMGFt58GwWVVVP+nmOhEOO+YfLLKz/VJAkNjVNcaGKDJnGkk1v29PRkE95e7QhVkIilSMQUEOkLusly9XSXqqqCmlLSa2XycmYFB0mnQ6fTIWcWSZ6wLUkINV3IXkmp2aS/MOEwQkVkFwFCBVSEqhIaHqL15d+QDAfPOEMJJBuIKKAAOmRDBTZnHa6K2RTV1JBXZMNsV1CSI0QD/fh6u/H1ePB1e4mOnz6e2Z6Dq8JNQWUVJpsNX4+XYU8XweHBbBuTzUahuyYtd1XV6e3KKgxmM0IIEjGFSCBO2J8Wu3AgLXvD3nH6TvixOIwsvtmtydwMIR5JZsRtGG+bDzUlsOUambW4iLrFRZTU5l62ZNQDAwM899xzDAwMMG/ePG699dYpjb6lUiGCwYP4A/sIBPYSCOxHUUIX/BlZtqDTpZf0thldZp+ss6CTLcg6c7qNbEans2I2V1BYuJlkZ5SxXx1DCScxrXRx1P8hh99+neh4kPyyChZuvp3mmzaec8yahsbVgiZxEziXxF0rRMcT7H/Dy+HtPaSSKvVLi1l6WzX5pbYrfWpnoaoKgcF0d4Svp5vR3h5Ge7sZ7evJzhSDdDQtt6iEvJJS8krKyCsuSa9LSnEUFJ11p6sqKkOecTytPrytw/SfaEGJt6EmO9IVJE5dGPRmZL0FncGK3mBFZ7SiN1oxGK0YTFb0JhsGsw2DyYIkQSoZJBUPkIwHSUT9JKJ+rDm5VM1bSEVzPam4H19vWtJGerz4uj2E/WPZ8zJaLLgqqyjICJuroooCdxXW3LxzRn/jkTAjXg/D3i5GvJ0Me7oY9naRjGVyUEkSecUlZ8ldblHxpPJbfe1+Pnqpk56jY1hyDCzeXKXJ3DQihCA0FmfIE2SoK8hgZ5D+jgCqIrA7TdQuSk9OKKlxXNYSbxOjbxaLhS1btkxJqpxYrA9/YG9a2Pz7MnWnVUDCbm8kN3cxublLsJgr0iJ2hrDJsukT9X6IlErg9S5CO3oRORJH5I9oOfg2ALVLb2Th5ttxz11wTfWsaFy/aBI3gWtR4iLBBAfe8HL4nR6UpEr9DWl5c5ZceXlLJuKM9fUy2tuN75So9XYz1t+LkikXBmBz5uMqryC/vDK9lKYHCdtdroum2giNxfC2jeJtHaXn6CjR8X7URBsidQwlFcJotdO4YjUmq5VYKEQsNE4sPJ7eDqcfpzIJlc+FJMuYbXbM9hzMdnt2Oxoax9ftZdw3nG2rN5lwlWdErdJNQYUbV2UVOa6CT31BEapKcGQoI3SdjGTEbmygLxtaNJjMFLirKHTXUFhVQ9X8hThLy8+SuUU3VzH3pnIMJk3mLiexcPK0sHWNM9QVJBJMz3CUdRIFFXbK6vOoXVxEcfXlFbdTTIy+zZ07l9tuu+2yRN9UNUUofJSAf29W3OLxASBducDhWEBu7hLycpeQm7som2bjcpMcCOPbdoTUQJTu1DE+7HkJY46N+Rs3M/8zt+AoKJqS19XQuFJoEjeBa0niZpK8RUPjjPakI2m+jKiN9nYTGB7KCoYkyeQWFZOfkTXXKWErr8Bsu/RULKmkQn97AG+rD2/bKKN9YYQaRie3I5QjRIN9yDodNYtuYM7aDdQsuuGi2dFTiURW6NLLKdkLnd7OPk5vGy3WTFTNTYE7HV3LLSya9kL0yXgMX7eXYe8EufN0Egunu7GcZRXULlnGrMU3IOvK2Puql+4jmsydi1QqRV9fH11dXXR1ddHT00NeXh5ut5uqqiqqqqpwOBzptgmFkZ4Qg51BBrvS4hYYPp2t31lipajKQVG1g+JqB64K25Sm7lEUhXfffZd33nnnskTfUqlxAoH9WWELBg+iKOn6yCZTyWlhy1uC3daELE/t+K9kOEbvr/ehO5okrsT4aPgVRIWehZu30LB8tVYBQeOaRZO4CVwLEhcJprtNW66wvAWHhzjy7naOvLsdX483u19nMJBfWp6Nqrkq0mtnSRl649n1Gi+GEILAUBRPq4/utlF6j42RSqpIOoXc/AGS0VZGe9sQqkrxrDqab9pI06qbrutZYenf2SAn933EyX276W49jKqkMNls1CxcSn75HAa68ug7EUvL3KYq5q69/mTuTGnzer2kMhHi4uJiKioq8Pv9dHd3k0ikI2pmvQ2TkocI2NDFHegUC/Y8M8XVDoqqcyiqdlBU5cBkmb5B7YODgzz33HP09/czZ84cbrvtNmy2j/d9kEqFGRt7D9/oTgL+PYTCxwEByOTYZ5ObtzgrbmZz2ZS8jzNJJuJ4Du6nf/thigZLselz8USPEG1IMO/WWyiede2UBtPQOB+axE3gapa4s+RtWTFLb51eeYuFQhz/8F2O7NxOz5EWAMqbmpm1eBmuCjeu8kocRUWfutpAIpqi59hYppvUx7gvPenEUWjGVRoiNn6IvqMfEY+EsTvzmb1mPc03bZiW2WdXI4lohK5D+zm5dzcn931EdDyIJMsUVjWgqtUEx0qx5RVd8zJ3IWkrcBVSUlBOgaMUh6UANa4jGkyko23eADElSNIYQLGMkzIGSIm01FmtNqqrq7LRuuLiYuRpiMaqqko4HGb//v1s374ds9nMli1baG5uvqSfF0IQjXYx4tuOb2Q7Y/7dCJFAp7NlxrItJi93CQ7HgkuqL3q5SMZjdB3Yx7EP3qXvQBvz7KuptDUSM0TRr8mjcsMidHot6qZx/aBJ3ASuRomLBBPsf91Dyzu9KKnpl7dUMknn/o84snM7J/ftRkmlcJZV0LxmPbNXryW3qOSyvE44EKfz4Agn9w/Re8yPqgoMJh3ljU6K3ILw2GFO7t3BWH8feqOJ+mUraL5pA+55C7QSVR8DVVUYaD9OR0boRrxdABjMLlRRjSW3gaW3r2T+evdVKXPJhEJ0PEEslCQciNHb20vvQDdDvj7GwsOomRQVJnIwJPLQRXLQx3ORxdliYDDpyC+zZaJs6W7R3EILSDAyMoLX68Xj8eDxeAgE0vnBTCYTlZWV2e7XsrIy9B8j1cQpORsfHycUCjE+Pj5pObUvFAplU+RcavRNUWL4/R+mxc23nWg0HUG3WusocK3FVbCevNwlyPLHj5h/GpKxGCf3f8TxD3Zxcv9HKPEEswtX0pyzAlnW4djgxrG2EuljVqbQ0LgW0CRuAleTxCViKfa/7uXAG16UlErDshKW3lZNXvHUT5cXqkrvsTaO7NzOsQ92Eg+Hsebm0bRqLc1r1lNUU3tZZn4FhqOcPDDMyf3DDHQGQEBukYVZCwopq7cSHG7l6Ltv0d12GICK5rnMuWkj9Teu0tIGXCbS3a5pofO2HEJVUiCZMFhmUb/sRlbf+xlyXHmf6NhCFURDyUz6k4l57hJEAnHikXSuPqFmErmqAiHO2M6sVfXUvnO0zzyXUhTiBEgY/SSNAZLGIEhqOtVOyoaFfHKMheTbirA7bFjsRsz2dPJrs91w+rE9/fjjjGHz+/2TpG5kZAQAvV5PeXk5VVXpaJ3Var1kOZuI1WolJycHu91OTk5OdikqKqK6uvq85xWN9uDzbWfEt52xsfdR1RiybMbpXEGBax0u11oslsqP/bf9tCSiETr2fcSJD3bReWAvqUQca24e8+ZvoCrSiDSqYmpw4ryzFr1rZiVQ19CYTjSJm8DVIHGqKjiyq48PX+wkGkxQt7SIG++YNS3y5uvp5si7b3Pk3e0Eh4fQm0zUL1tJ8+p1uOctRNZ9usiMEILRvnBa3A4MM9KdHnxfUGmnZkEBRVUQ8XvoOriPE7vfIxWPk1dSSvNNG2hes4HcouLL8TY1zkMiGsFz6AAt7+zCc2gPSjIMSOSW1DJv3WqaVq0mt6jkgnKWTkqcEbVgIlubdyJmmwFbnhGT1YAkp5MsS7KEJJFZZ7YlUEihiDhJkUARCVJqejulxkkqcZJqgqQSI6nESaSiqCKd/NiZW0BFaSXV1dXMqqshLz9nSmaDno9wOJyVOq/XS39//wXl7NRypqTZ7XbsdvslR/NUNYE/sBefbzs+3zuEwycAsJjduArWUeBaR17ejeh05sv6fi+FeCRMx97dHP9gF10H96Ikk9jynNTfuJKGxauwdVmIfDiAbDeSd8csLPM+/axuDY2rHU3iJjDTJc7b6mPX0+2M9oUpmZXLqnvqKKmZ2gH6obFRjr23g7adbzPU2YEkyVQtWETz6nXU3rAco/nT3QULVTDoCdJ5YJiO/cMEhqIgQUm1nYLKGHr9EKM9J+g9foTw2CiQTnDbuHwNzWs3UtbQpH2RXwGEqtK6Yx8fvfg2Y/2tCCUTWTJVIOmbkPX1SPLk/xtmu2FySTCHcVItWGuuEZvDhKRLD8YfHh4mEolkl3A4POlxJBI5b/kzg8GA1WrNLjabDbvdjtvtzka8ZhLxeJyenh4SiURW0Gw228fqaj3/sQfx+d5hxLed0dFdKEoISTLizFuWFTeLpfqKfI5ioRAdez/k+Afv4jm0HyWVwp7vouHGVdQvX0VZfROxllH8L3WghpLYV5ThuLkKWSsdp6EBaBI3iZkqcb7eEO893Y63bRRHgZkVW+uoXVw4ZV+6iViU9t3v07bzbbyHDyKESvGseprXrKNx5U3nLQp9qaiKSl97gJP70xG3sD+ORBxnSRCzdYTouJfhrnZSiXR+NkdhEWUNsylvbKascTYF7iptnNsMYuBkgN0vHGSsbz8h3yFioUEkWUdJ7Xzqlq2h/sblOFx2dOcZs6SqKoODg9kJBR6P56wKKWcK2YUeW61WjJ9gpvO1hKrG6ev7Nb19TxEKtQHp1B8uV1ranM6V6PXTn24oGY/Re7QNb+shulsOMniyAyFUcgoKabhxFQ3LV1Fa14gky6R8Ucae7yB+fAxDuR3n1jqMFVOTX05D42pFk7gJzDSJCwfi7H6xkyO7+jBa9Cy5tZr56yrQGaZmAO9It4ePXnia4x/uIhWP4ygspnnNOppWr8NV/unGxaSSCj1Hxug4MEzngWGi48NIoh+L3Ucq3kNoNF1bVJJliqprKW+cTVljM2WNTeTkF1yOt6cxDQghGPZ00rbzbY7ueofw2ChGi5WG5atpXrOOitlzEXBeacvPz6e6uprq6mpKSkqw2WxYLJZpmdF5LaAocfr6f4XH8y/E4wM4cuZTWHQLBa512GwN0x5tU1JJ+tuP091yCG/LQfqOH0VVUsg6HaX1jVTOWcCsRUspqTt9biKlMv5OD8G3u5F0Erk3V2FbUTat3d0aGlcLmsRNYKZIXDKhcPDNbva95kFJqsxdW84Nt9dgtk/N1PmhrpN88Mw2Tnz4HgaTmdlr1tG8ZgNljbM/1Zd+2B+n5+goJw8O0nWgjUS0B0Q/qP2kEunxbiabjbL6JsoamylvnE1JbQMG8/SPx9G4/KiqQnfLYdp2vpW9MZDNVpIOJzFbHqrZMknaqqqqyM29fvP3fRrOlLfc3KXMqvkTnM4V0ypuqqow3NWJtzUtbb1HWknGYyBJFFXPwj13Ae65Cyhvaj7nUIxYhx//c+2khqNY5hWQt2UWulzTtJ2/hsbVxoUkTht0MM0IVXB89wAfPH+S0FicmgUFrLyrbsomLQy0H+f9Z7Zxcu9ujBYry+/6PRbfdieWHMcnOl4snMTTMkD73qP0HTtOeLQXVRlCKMOki8FDbnEJ5Y3L0l2jDU24KtzTXsVAY2pRVZWBgYHTkbZgnHh1M/rxANZoEN1wH7ahXvIr3MxdMp+mVSu1aOsnJC1vT+Hp+hfiiUHycm+gefYPp03e0pORevC2HKS75RDdrYey1UDyyyqYs24j7jkLqJgzD4v9/F2hSihB4JVOIvuG0OWbcT00B0tT/pSfv4bGtYwWiZtGeo+Pses37Qx7xyl057Dq7jrKGz7d2LPzvtaxI3zwzDa6DuzFbLOz+LY7WXTrHR+rvBVAYGSUY++14Dl8lGHvSWLj/QhllHQmd9AbLbgqa6hoaqC8qZmyhtmfejydxsxDCMHAwACdnZ3Z7tF4pt7sxEhbdXU1DoeDSMDPsfd3cmTndvrbj4Ek4Z4zn9lr1lO/bKWWHuYSOJe81dR8c1rkLTgyhPfwwey4tlBmwlFOQWE20uaeMx97vuuixxKqILJnEP9vOxFxhZybKsjZUIls1Ma8amhcClp36gSuhMT5ByO890w7nQdHsDtNLP9cLQ03FJ81/iOVTBILjRMPh4hOqN+ZiEYxWa2Z4uuZIuyZYuwTM5cLIehpO8wHz2zD23IIS46DJVu2svDm2y960RRCEBweYqC9nc6DR+g/0U5guBslEci20Rkd5Ba5Kauvo3rhbEpm1eIoLNZmjl6jCCHo6+ujra2NtrY2xsbGgHNL24UY6+9Nl2fbuR3/YD96g5HapTcye806qhcs1rLvn4GixOnr24bH85PT8jbrT3DmLT/rsyaEIBmPkYzFSCXiJONxUvE4yURmHY+l9018Lh6b8Pzp5049Hw0FGR8ZBsDiyMU9Z35W3HKLSy758y6EIH7CT/BNDwnvOMZqB86tdRiKp3+yhYbG1YwmcROYaokTQpCMRYmFQwSGxjj4u3Y6D/QgyXHK6szkl+hIxMKTiqpHM0XVU5nIxsfBYDJjttuRdXqioSCJSASdwUhZQyOVc+Zjy3OmhW+C/JmsNoIjQwx1djDY2UHfsXZ8PZ2kEqeKd0tIunwsjjIKq2ZRPa+RhhXzcLi0CNtMRwiBooRJJEZQlCh2eyOSdOld2UIIent7aW1tpa2tjUAggCzL1NTU0NzcTH19/UWl7ULH7j9xjCPvvs3R93YSGw9iMJkpqa2ntL6R0vomSusbr9tI7lnylrcsHXnLyFsqkcDX42XY28WIt5NhTxfD3i6iwcDFDz4BWafHYDJhMJnQm0wYjJm1yYTeaMJosVJa10Dl3AUUVFZ97Js0IQSxI6ME3/KS7AmhyzXh2OTGukS74dPQ+CRoEjeBqZa43z35Lxx47aXzPq8zGDJSdSqadlquLPYcTDb7pMdmux2D2UIiGs1KXywcykhgkMGT7fQebSM6HkTW6zHbc0AI4uEQSqYm5AWR9EhyAbKuCLurgvLGeupumI27uRiTVYuQzASEEKRS4yQSI+klOXJ6OzFCIuGb9FhVT6fusNubqZ31LVyudee9gKqqSk9PTzbiFgwGkWWZ2tpampubaWxsvOw515RUiq6D+/Ac2k//iaMMdZ1EVTJjKouKs0JXWt9IUfWsazpapygx+vq20eX5CYnEELm5N1Cc92USficj3i6GPZ0Me7sY6+9FqOlExnqjiYJKNwXuGpylZRjNloyImbMyNlHSDGYzeqMJvdGI7jLkpTsXQhVEW32Mv+Ul2R9Gl28mZ10FtsXFWrksjWsOkVJJeIMYqxxIuqn9/61J3ASmWuI+eHY7B97cRzyio9BdyIINdRTVFGXFzGD89LOwhKrSvucDPnjmKYY6O3AUFrHszruZs24TekP6YhcLJxjp9jPYNYyv14d/wEdgeIxIYBxEDEm2k+OqoGpuLZXNhZQ3OrE6ru+8W1cCIQSxWB/h8HGiUQ/xxJmCNkIy6UNVE+f4aRmDwYnJWIBx0uLCaCxAUeN4Pf8f0ZiXXMciZtX+OfnOFUBa3Lq7u2ltbeXIkSOMj4+j0+kmiZvFMn2ljpKJOEMnO+g/cZT+E8foO3GU0KgPSN/4FNfUZaN1ZQ1N5Liu/kkSihLD0/nvHDvwbwQHIqhhN0qwBH/fKPFIONsut6iYAncNhVXVFLqrKXDXkFdSMmPyKApVED08TPCtblKDEfQFFnLWV2JdWDjlFzcNjelCKIJkX4hYh594u594VxBSKoV/tABT1SfrnbhUNImbwFRL3J7fdtG+Z5CVn6/D3XzxQb8fB1VVOPHhe3zwzFOMeLvILS5l4c1bKaxeSmA4zlh/hLGBMGMDESLB0xd9nV4mr9iCs8SGs8SKszRd0NtRoNUjnC6EEMQTg4RDJwiHTxAKHyccTm8ryukLtiTpMBhck2TsvIvBiSRd+EKuqkn6+n9NV9ePiccHsJgX4fevo60tRigUQqfTUV9fT3NzMw0NDZhnUOqXcd8I/SeO0nfiGP0njjF48gRKMgmAPd91WurqmyiaVXtZbpCmCqGq+IcGGPF0Mdh1gp7juxjp7iYeOP33M1osaVlzV1NYlZa1gsqqGTsJRCiCyIEhxrd3kxqOoi+y4NjgxjK/UMv3pnHVI4QgNRgh1u4n3uEn3hlAxNK9BYYSK6bavOwim6b2hkqTuAlMtcQFhoaJR0LojQZ0ej2yTo+s0yHr9eh0uvRjve5j3UWnEkkOvP479r7yNCFfPyZbEfaCVSTis0jGT//9jBZ9VtKcJVbyS2w4S63kuCzI2pfqtJFIjBAKnyAcOj5J2FKpYLaNweDCbqvHZm/AZqvHbmvAaq3BYHB+rDFsF0NRFDweD21tB/H5nqaoeB9GY5xEvImSkj+kuXkzJtPMlZ+JKKkkw12dGak7Sn/7MQKDAwDIOh2FVbMoqWsgv7SMvJIy8kpKcRQWZ6PT04EQKqHAAENdRxjqamek28todz9j/T6URPoCgCQw5SZwFNuobFxFZeNNFLqrcRQWXRVjxkRKJbJ/iODb3SijMQwlNnI2VmKZU6DJm8ZVixACZTSWjrR1BIh3+FFD6ZtGncuMOSttuejs09trpUncBKZa4v7jr/6RgRM7LrG1DpBBkgE5E1WR4dQaGUmSEWoEoY4jyS70luXkFMwhvyzndGQtI25Wh/GquAicC1VNEo12E4l2Eol0Eo/1o4oUoCKEAkIghIJg4raKEGqmjQpCRaCc3hYKgnRbUJFlEzrZgqwzo9NZT2/LFnQ6C7LOktlOPy/L5jP2Z9rJZmTZQDLpT8taRthOyVoyOZp9X3p9LnZbAzZ7PTZbQ1rcbPUYjZc3Sjv5d6ni9Xo5dOgQR48eJRKJYDAYaGhooKmpBov1PXp7nySVGqeo6DZm1fwpNlvtlJ3PVBL2j9HffjzbDTt4sp1ENJJ9XpJkcgoKyCsuJa+kNC13xSXZtcF0/sijECqxWE+mSztAMuknmfKTSgZIpvwk4gECgz4C/UHGB6OEhhQiPh3J0Glp1JlSWFxxzPlxLK4YtkKJoqom6uq/gdO5fEp/N5cbkVIJ7xlgfHsPij+OodyOY4Mb8+x8Td40rkqUYJx4RyAbbVP86cmFco4Rc91padM7r2wPhSZxE5hqiTvwxj66204iVCW9CAU1u62e3j/pORWhpia1UTPPC1VBlnVUL1xD06qV5JflYLJcnTmahRAkEkOEIyeJRDqJRrqy27FYd0a20uh0dmTZwCmRTQuuhCTpkDLiK0m69HPZx6dleNI2EkgyqppAVaIoahRFiaKqMRQlgqp+/FnBkqRHiNMTR3Q6eyailo6u2W0NGVmbuvq3Z+Lz+Th06BAHDx7E7/djNBppbGykubmZ2traSbVGk8kAXu9P6e75OYoSo7RkKzU138RiqZiWc50qhBBEx4P4B/rwD/QzNtBPYLA/vT3YT2w8OKm93ZlPbnEpuUUFWJwyRkcM2TqMMHmJJY+jqukZ28mojpjPRHTUTNRnIj5qJTpmQCjpv60kg73QQm6pA2dZIa7KUgoqq3AUlGMw5mHQ52EwOJDlqyPqORGRVAjtHmD8nR7UYAKjO4ecjW7MDc6r9qZR4/pEjSSJnwxkom1+UkPpz7dk0WOelYspI276QsuM+r89oyROkqRG4KkJu2YBfw3kAb8PDGf2/1chxCuZn/kvwFdJlwT4phDitcz+W4D/l3RI66dCiH+42OvPlLJbl0Iy6ScS9RCL9QECifOJSkZiyEiNlI7mSRnpObuNhCybkGQjOtmELBuRpMsXxUulxolEOs9eop0oyukoiSybsFprsFpqsFqrsVpnpR9bazAY8i7LuVwKQqgZoYuiKDEUNZKWPSWGqmb2KREUNYqqxLISaNA7ssJmMpVekQ99NBqltbWVgwcP0t3dDcCsWbNYuHAhTU1NFy0Sn0iM0OX5Cb29/44QgrKye6mp/jomU/F0nP60Exn3M+jdx6D3IL7ekwQGBwiNjBP1C1KRyd2uRqsee34ukWCUWPD0/1tbnpMCdzWFVafGr9WQX15xzc2gVeMK4Q/7Gd/RgxpKYqxx4NjoxlSbN6MucBoa50ONJIl3BYmfDBA/6SfZHwYBklHGWJ2bjbYZSm0zOpo8oyRu0ounDaMXuBH4MhASQjx+Rptm4JfAMqAMeBNoyDx9HNgE9AAfAfcJIdou9JozSeLSkakRolEPkWgX0aiXaMRDJOohGvVMGkM1HciyMS13UkbudCZkKb1Pzsje+baz4hbtJJEYmXBUCbO5Apu1BktG0GwZWTOZSi7r+K/rBUVR6Ojo4ODBgxw9ehRFUSgsLGTBggXMnz//E+Vxi8X66er6MX39v0aSdFSUf5GqqocxGq/eskiJxAih0DFCoaOZ5Rih8AmESE/6kSQ9NmstdnsTdnsjJv0slIiT8GicwOAA/oF+AsOD2J2uzESD9OxQa+703WBcCdRYitD7fYR29qJGUpjq8nBscGOapdW81ZjZnE/a0EuY3A5MmWibsSLnqkp7M5Nrp24EOoQQngvc2d0JbBNCxIFOSZLaSQsdQLsQ4iSAJEnbMm0vKHHTjRAq8fhAWswinoywedLCFvVMikxJkg6zqRyLtQqHYwFWSxUWaxVmczkSMoKJY70mj/tCZLpiUc/YVs/eLxSESKGq8cySyG4rahyReXzmtqJESCb9Z+3/P+y9eZAcWX6Y9+VZWXf1fZ9A454BMPdeM7vcg7ukl7siuRQpiRRJL5eSSEU4QmGbjnCEIySFQ3Y4FCGFLFKUKJmyKZGSaIm05F1eEj17zbUzgzlwow80Gt3os+7K+/mPl11V3WhgLvQF5BeR8TJfZlVXA/VLm2MAACAASURBVF2VX/3ee79fGDpoWpJUaoKurs9E0TQZWUtao2ja4RtCOogsLS1x4cIF3nrrLWq1GslkkieffJKzZ88yODj4kaIjljXAiRN/l7GxbzA98w+5Of8vWLj9u4yM/AJjo19H1+9dE3M/2fwitPkFol67Ecna5S1fJhJmH5nMcUY7PxFJ2wlSqQlUNU6rs4m7WKP+6hK115cRto91vIPsD43uevqEmJgPS1j3cGbKONNFnOkS3tKmtKkkRrMycjxZwBzJohiHR9o+CPstcT+NjLJt8quKovwc8Brwt4QQG8AQ8FLbNbeiPoD5bf3P7vRDFEX5BvANgNHR0Qfzyu/B2tqLrK1/W8pafQ7bvrklx5eimCSTI6SSY3QUniWZGpOylpSyJueBxcRIqtVqc57bnTt3UFWVY8eOce7cOY4ePYr+gBO3JpOjnD71vzE29svMTP8DZmf/Ebdu/Z+MjX6DkZGfQ9P2J92F71epN2ajoflZ6tFcynp9hiCoNq9T1QTp9DG6uj5DJnNcClv6+KGOKO4moe1Tv7BC7dUlvFtV0BSSZ7rJfmoIc/hginvMo8t9pW0sS+5zYyQm81LaDlGk7aOwb8OpiqKYwG3gtBDijqIofcAq8r/k7wADQohfVBTlHwEvCSH+r+hxvwV8M3qaLwohvh71/yzwrBDiV+/3c3d7OPX69f+F+Vv/shlFSyY3JW2UZHIcy+p/z9xeMY82nudx5coVLly4wPXr1xFCMDQ0xNmzZzlz5swDr55wPyqVd7kx/fdZW/tzDKOLjo7n0PUsup7F0HNoUavr2S37up5F09IfaLg8DD1s+1ZTztoXwDjunbYrFSxraEvEV86rnMSyBuIh+vdACIE7V6b2yhKNt1cRXojRnyL1dD+pc71o6fiLZMzB4L2kLTFZeCSk7aAOp34JeF0IcQdgswVQFOWfApu1qxaAkbbHDUd93Kd/35iY+G84cuS/iyf+xnwghBDMz89z4cIF3nnnHRzHIZvN8olPfIKzZ8/S09OzL68rmz3NubO/RbH0A2Znf51q9SK+X8H3y/eoItGOgq5n0LUsupGTrZ5FbxO9MHSaQ6GNxvyWFb+G0UEqNUFn5ycjYZsklRonmRxD0w5OUuLDQlBxqb++TO21JfyVBoqpkTrfS/rpfozhTPyZFbPvBGUXZ7aEM1PCnS1vlbbx3CMZaXsv9lPifoa2oVRFUQaEEIvR4V8A3on2/xD4V4qi/H3kwoYp4BVAAaYURZlAyttPA39pj177PYnnf8V8EMIw5PLly7z44ossLS1hGAYnT57k7NmzTExMoKoH44OqkH+Sc2f/2Za+MHQioWttnl8maNv3/QpB277j3KFauyb7gwqKopNKjpNOH6e354tbZM0wOvbpt314EKHAvrZB/ZUlGpfWIRSYYzk6fnKY5GM9u55pPibmXmwm13VmSjgzZdzZEv6arPusmCrmaCRtR/IHciGCEIJi3SNj6Rj7WF5uXyROUZQ0clXpL7d1/6+KopxDevfs5jkhxLuKovwb5IIFH/gVESUUUxTlV4E/QqYY+edCiHf37JeIifkIhGHIxYsXefHFF1leXqazs5Mvf/nLnDlz5tBUUFDVBKaZwDQ/XB1TOZVDxMOfu4C/blN7bYn6D+4QlFzUtEHmk4Okn+rH6D2YZbxiHm5EKPDu1HGjSJszUyasyGi+mtIxx/Oknx0gMZHHGEwfiLq7QghWKg6za3Xm1mrMrdWZjdq5tRpl2+c//s1PcmZo/1Zux8l+Y2L2kCAIeOedd3jxxRdZW1uju7ub559/ntOnT6NpcVQk5sMj/JDGu2vUXl3CuVEEwDrWQeqpfpInOw9cJCPm4Ub4Ie5CNZK2Ms5sGWHL6RJa3sQcz5OYyJOYyKH3pPYtT1sQChZLDW6u1Zuy1hK1Og2vLQm9qjDckWSsK814V4rRzhRfPjtIX253p3cc1DlxMTGPDL7v89Zbb/Htb3+bjY0N+vr6+NrXvsbJkycPzJBpzOHEW6pRe3WJ+hvLhHUfrZAg99lRUk/1oRfiuYMxe0PoBrhzUtbcmRLufAXhhQDoPUlSj3VjTuRIjOfROhJ7OgfTC0IWNhpNOZtdq0XSVmN+vYEbhM1rTU1ltCvFWGeKjx/pZrw71ZS2wUJyX4dOdyKWuJiYXcT3fd544w2+853vUCqVGBgY4Kd/+qc5duxYLG8xH4rQ9qN6jxs414r4qw2ZGuR0F+mn+2VFhQOcfT7m8CMCgXenhnurgnerijtfwbtTgxBQwBjMkH6mX0bbxnNo2d3Px2h7AfPr9R2HPheKDYKwNeqYNDTGulJM9Wb53Kk+xjqlpI11p+nPWWiH6P0TS1xMzC7geR4/+MEP+O53v0ulUmF4eJgf/dEfZWpqKl4FGPOBEEGIO1/BvlbEuV7EnS9DCIqhkpjMk/nYAMk4NUjMLrG5AMG9VcGdj4TtdrUZZVOSOuZwhuyJERLjOcyxHKq1O2pRc/zmfLR2WZtbq7FYtmmfHZa1dMa70jw+nOfHzg4y1tWKqPVk9zYSuJvEEhcT8wBxXZfXXnuN7373u9RqNcbGxvjqV7/K5OTkQ/OhEbO7CCHwVxs414rY1zZwpksIJ5ARjqEM2RdGZKHusVw8zy3mgRNU3EjYKri3qni3KoT1KPWPrmIORVG2kSzmcBaty3pgn222F7BcdlgsNVgs2Vsia7NrdVarzpbru9ImY10pnpvsYqwrHYlaivGuNIWU8Uh85sYSFxPzALBtm1dffZXvf//71Ot1JiYmeOGFFxgfH9/vlxZzCAiqLs6Nooy2XSsSlOTNSuu0SJ3tITFVwDpSQE3F0baYB0fo+JGoVZviFhQjUVLA6EtjneqSwjaSxehLfehVow03YLHUYKlks1iyWSrbW49LNmu1u3NP9ucsxrpSfPZEL6ORoG3KWtbam/eDE4bcsl1uNlxu2tHWcJmzHX7j1DiTqf3LKBBLXEzMR6DRaPDyyy/z0ksvYds2R48e5YUXXmBkZOS9HxzzyCK8EGe2hH29iHNtA+92DQDF0rGO5El8ZgRrqoDeldznVxrzsLAZ4XXnyrhzFZybZfzlukzqhfzCYI5mMT8xiDmcxRjKoJr3XzHvByE1N6Dm+FQdnzvllpDJttEUtmLdu+vxHSmD/nySgbzF2ZECAzmL/rzFQD5Jfz7BUCFF8j1ew4MgFIIlx2sK2lzD4abtMh9J26Lj0Z7Hw1QUhi2TsaSJK8J7Pu9eEEvcA8YvObhzZVKP70+G/Zi9oVqt8vLLL/PKK6/gOA7Hjx/n+eefZ2ho6L0fHPPIEdp+c06RM13EmSmDH4KmyKSmXxjDmurAGMrEixJiHgjCC3EXKq0VozfLhDU5LFq3NEr9SZynunE6E7i5BA1FRDLmUL9Wo/qOT83xqTkBVcen7vpUnSDq86m5PrZ3b4Hpzpj05y2GO1I8Pd4ZyVmbpOWsPRE0AD8UrHs+tzdFbVPSbJe5hsst28Vtm1CnAAMJg1HL5BMdGUYtk7FkglHLZNQy6U8YqAdkqDaWuAdM+U/mqL9+ByWhkTweF91+WPB9n/n5eW7cuMGNGzdYXJTFRU6dOsXzzz9Pf3//Pr/CmIOC8ELcxSpeNKfIvVXBX2k0z+t9KTLP9pOY6iAxkY+rJsQ8EIKqK4Vtrow7W2b1VpmFMOQWIYsplduWyoIVMt9wWa17MLsh0+rvgKJAxtRJJ3TSCU22ps5QwSST0EgldDJR3+b5TEKnN5tgsJCkN5cgoe/u37UXClY9jxXXjza5v+r6rHit4xXXZ93z2Z4Rt0PXGEmanMpYfKkn3xS00aTJsGWSOCTZA2KJe8AUvnwEb6HK+u9cpuevPY45mNnvlxTzIRBCsLy8zI0bN5ienmZubg7P81BVleHhYT796U9z+vTpfatpGnMwEKHAX6k3J4G78xVZ7zGQtww1a2CO5Eid75XzioYy8by2PUIIQcMLKNY9uTVcSnWPUsOj2JB9pYZLse5RdwN6sgkGoihRK2JkkU8evAnyIhR4yzVuX11n+vo6MwtlbtYcFghZQLCghpTbUmpQhwFDzi373GQno10phgpJcpZBymxJ2Ka0JQ1tX35nIQRFP2DR8bjteCw7kYhtk7VV12fDD3Z8jpSm0mPo9Jg6E8kEz+TTdJs6PaZBn6lHopYgt8uSuVfEFRt2gaDksPy/vwlAz6+cQ88fjjJKjzrlcpnp6enmVq1WAeju7mZycpIjR44wNjaGZcUJVB9FhBAERWersC1UEa68mSgJDXM401y1Z4xk0XLmgROAw0wYCq7cqXBtuUqp7kZytilkLSkrNjxKdW9LEtftGJpCPmlSSBlYhspqxWW5YhNuuyVahtoc/hvIWwwULDmPK9cSvc70R/9/9oOQuhdguwENT251Vx7XGh5LN0vM3Cozu1KTwiYCGm2PV4GhnMVYT5rxnnS0AEAuAhjtTGEZ+ystQgjWvIBFx21KmmxdFu3NY5fG9v8AIKup9JgGPabeFLJNUevZPI7OpR/Cyjf3q9gQS9wu4d6usvIbb6F3WfT8tcdRE3HQ86Dhui6zs7NMT09z48YNVlZWAEilUk1pm5ycJJ/fv7p4MftH6Aa40VyizWHRsBpNztYUjMGMlLZhuXJP707G89keMJvS9tL0Gi9Nr/HyzPpdE+RTpkYhaZBPmRSSBoWU3PJJk/zmcdIgnzIoRNJWSBk7Rpv8IGSl6rBYslkstq2eLMvJ+kvRJP1gm2iYukp/JHWDeUuWYVLkisxGJGV2JGUNT/a1H9tegBe8971YB4Y0jdGMlLWJkTyTY3nGuzMMFZKY+5RyRgjBquezYEsR2xS0RcdjIVoYsOR6ONv+3XQF+kyDwYTJgGUwmJDbQMJkIGHQlzDoNnSSB6xKwl4TS1wbe1k71b6yzupvv4s11UHXz51G0eIP+P0kDENu377dlLb5+XnCMETTNMbGxpri1tfXF1dTeAQRocBbqDYrIThzZTksqoDek2pF2UayGP3pOEfbLhCGgstLLWl7ZbYlbaOdKZ6b7OS5yS7ODOXpSElJ22txCULB2qbota3AbK7KLDe4U3JQFEiacmiy2bbvR62lKhiNgETNR6+4GBsOphdioZDUNTK9SbIDWfrGc4ye6MHcg+oH9/zdhWDedrlas7lWd7hWs7lWt7ledyhtG940FYWBhMFAwmDQMlv7kaQNJgy6TR0tjlS/J7HEtbGXEgdQfXmR4r+/Tvq5AQpfORIPrewhYRiysrLC7Owss7OzzMzMYNs2AP39/U1pGx0dxTDieUqPIv66LRPqXi/i3Cg2k5oaA2mZm+1oB+Zodtcy0D/qhKHg0lKZl6bXeTmKtJUad0vbs5NdDBUOf7qV5hzKmxXcm3en+dB7k5ijOczRLInRHHrv/hSGt4OQ6YYTyVpL2KYbzpZoWq+pM5WymEpbHE0lGGmTtW5Dj+93D4j7SVz8ybTLZJ4dwF+zqb54C73TIvv88H6/pIeW7dI2NzdHvV4HIJfLceLECY4cOcLExASZzMFacCKEgEDE0Z1dJmz4UVLdDezrRYI1KfVazsQ62YU1VSBxpLAntR4fRdql7aXpNV7ZJm0/fLrvoZK2oObJOZQ3y1FbkdU3kDkBzdGsLAw/msMcyaIm9/aWXPJ8rtUdrtZtrtecSNhsbjZcNmcTKsBY0uRoyuLTnVmm0hbHUlLaCkasEPtN/D+wB+S/OE6wYVP65gxah0Xqse79fkkPBfeTtnw+z9TUFOPj44yPj9PR0bHPr/ZuwrqHfaOIfWUD59oGQclFy5loXUn0Lgu9O4neaaFHx3E06IMj/BD3ZqU5ROreqoAAxdRk3dGPD2JNdaD3JOOowS5gewFXliq8OrvOS9PrvDKzRtmW0c6xrhRfPN3Pc0c6eXaii8FDKm1CCMK6j7/WIFiz8dcaeKsNvFtV/NVo6YECRn+a1LmeZqRN79rbOZSrrs+blTpvlGu8WW7wdrXOsus3zydUhclkgsezKX6ir4OplMWxtMVkMoH1iM9JO8jEd4U9QFEVOn/qGCslh/Xfu4KWN0mM5vb7ZR06tkvb7OwsjYb8kCwUCgde2ppzrq6sY18r4t4sS6GwNCkSvSmCooO/1sC+sk742tYJ3GrG2CJ1encSvSuJ1mmhpg7m0EXoBoQ1r7kFdZ+w6hHW5bHwQxRDRTG1La1qaCimitJst16jGvIcurLl9xZC4C/Xm8XinemSXD2qgDmSJfuZEawpOUT6YcsHxexMqe7x7mKJi7fLXLxd5t3bZa6vVJuLAMa6UnzpzMChlDYhBGHFw19rRJu9pRX21vlgWt7EGMyQerJPVkEYzu5pPsCaH/BWtcGb5TpvVOq8Ua4zb8uSVipwLC2jasfTSaZSCaZSFqNJM56f9l7U1+H2G7D4Jtx+U7Y/+x+g68i+vaRY4vYIxdDo+rlTLP/jC6z99kV6f+UcemecquJ+hGHI8vJyM8q2XdqOHz/O+Pg4Y2NjB1LaAIKyi311Q867urYh51xtFjL/zAjW8U7M4eyOi15CJ2jeKIL16Iax2sCZLlF/c5n27JWKpaN3R4K3KXqdCdBVKTmqAor8QoGCzObZPFbkJ7sSCZECqApK1G5erygKoeNHQuYT1CIZa5My2dcSNXGvjO4qqCkDRVcRXojwAnntB52iq7BF9IQXNleQ6l0WqSd6sY7KIdK9Hqp6WBFCcLtkR6JWagrbQrGV8KI3m+D0YI7Pnerl9GCecyOFAy9tIhQEJecuQQui/S1/yypoHRZ6p0VqpLf1xapL9il7mM7DCwWXaw3eiITtzXKdKzW7ORw6Ypmcz6X4haFuzudSPJ5Jkn5IcqTtKjsJW/Fm63zHBAw9CeHO+er2inhhwx7jrdRZ/scX0DIGvX/97EOb+FMIQRAEeJ7X3FzX3XK807Z5TaVS4ebNm1ukbTPKdpClTfghzmxZStuVDZn4FZn01ZrqwDrWQWKqAy390f7fhRfib0ipa910GvjrNsGGDXtczk9JaKhpAzVtoKX05r6aNtDa9tWUjpY2UCz9rqEkIQT4gtANWmLnbmu9EOGG97wGBczxHNbRjvhL0gPAD0KmV2tbZO3iYrm5YlRRYKIrzanBHKcH85wazHFqIEdP9uDnxgzdgOr1DeavrbN6u0zfok3Gbbsfasrdke/oWOtI7EskVwjBTMOVQ6JRhO2dagM7inZ2GhrnsinO51Kcy6Y4l0vRYz6c95gHSm0NFt9oydrtC1DaJmyD52DgHAyeh4HHIbl396B4dWob+y1xAM50kZXfeofEWI7uXzxzaCezO47DtWvXuHjxIqurq3dJ2of52zIMA8MwSCaTjIyMNMWtUCjswm/wYPDXGjLadnUD50ZRyoSmkBjLkTgmxc0YSO/ZcKcIQoINB3/DRoQCQiEjXKFACEAIuYWROIWyT4jWdQgZmaDtehGCaqqoGQM11SZoKQPFOJx/w48qm9UMyg2fiu1Rtn3KtkfF9tmouVxeqnDxdonLSxUcX34jMHWVE/1ZTg3kOD2Y49RgjhP9OdIHNAemE4YsteUru+14LBTrLKzXWbBdlghZMxVE2/uyT1E5appMZS2OF9JMZSymUha95t5OV2gEIfNRMfbNOp+XqjYXKnWKUSqPpKpyNpvkbC7F+UjcRq04ufR78n6FbfC8lLaBs5Dc3/tPLHFtHASJA6i9sczG710h9UQvHV87dmjeeI7jcPXqVS5evMi1a9fwfZ90Os3IyAimaTYlzDCMu47vtW1ep+sHb16XCAXC9gntgND25X4jaBY0t69utFY4dlpYkbQljuTjBM8xDxwhBLYXNjP6N9yAuutTsX3KDSlh5UjKKrbXlLRKm6RtttsT1raTs3ROD+absnZ6MM9kTxrjgMwjbARS0G5H2f83Ja09+/+q59/1uLQn6HNC+gKFwVSCoc4UI30ZcgmduYbMf3a9Lldp1tqqPeR1jaPR3LGptMVUKsGxtMWI9eHmkQVCyGLsDYc522W+sSlsLjdthzvu1tduqQpHU1YzwnY+l+JYykKPk0vfn8CDpbdh/mW4+RIs/ABK863znZNRdO3cgRG2nYhTjBxA0ud7CdYalP/0JnqnRe5zY/v9ku6JbdtcuXKFixcvcv36dYIgIJPJ8MQTT3Dq1ClGR0cPdHJcIQTBuk1QcQntIJIyv7XfaNvfJmubJZV2QjFUEkcKZD8xhHWsA737YM/5iXn/CCEo1j3Wai6uHxIKQSgEQSiQwU1BGAoCIaObQXNfEISt86GgrV9uXiAiAfOjNmzL6O/T8MJm6aW667ekLep7v2QTOrmkQdbSyVo6fTmLqV6drCX7WucMcm1tPmnQk03sXeRYCCpByFpUqHwt2ta9YEtfe1v2754vUNA1BhIG/ZrGydCguxjStdigtxbQ5wmGB3L0HOvEeqLzPd+rQggWHU/mR6vbzXxpf7Ze5neX1pvXba7o3BS79hWdlSBoyVkkaHO2w82Gy4Lj4rc5tAoMWgajVoLPdOYYTZqMRTU+Ry2THlNHPWBfcA8kjQ249ZoUtvmXpbR5MmMB+REYfgqe+aUDLWwflDgSt48IIdj4t1epv75Mx08dI/1E336/pCaNRqMpbjdu3CAIArLZLKdOneLUqVOMjIwceHHzblVpvLtK45211lL/7WgKqqWhWjqKpd+9n7xHv6WjFRKHdij8USQIBRt1l9Wqw2olaqsOK9uOV6sOa1UX/z6RqgeFpiqkDA3rPhn921sruia1me3f1EgZ2hZZyyUNMqaOegCiNKuuz+vlGrdstyVmns+62xKydS/Au8d9KKEqdBk6XYZOp6HTaWh0mTrdhs6gJbP+9+s6XXds1GtFGpfX8e/Im7ZWSGCd6MQ60UliMo9qPpjJ/BueL6N1NZurdZtrUX61edu977qcLmOz+PpWQRtNmgwlTIwD8P91qBAC1qdh/hWYfwluvgwrl+Q5RYP+x2DkWRh9Fkaeg/zQ/r7ej0AciTugKIpCx49PERQdNn7/Glo+gXVk/74Z1Ot1Ll++zMWLF5meniYMQ/L5PM888wynTp1iaGjoYItbIHBmS9jvrtF4d5Wg5IKqkDiSJ/OJQZmXKRIw1dJRk1pr9WbMrmN7QVSc3KUUFSm3P0Bk6f3gBbIkkpQxKWYrFbm/XnPuKm4OYGoq3RmT7myCvpzF6cEc3ZkE3ZkEXRmThK6hqQqqAqqqoCoKmrLtWJXvZ9mvoKpE/dF1itI8NnW1KWOGpjw0f3+hEFyvO7xaqjW3Gw1nyzUdupSwTkNnPJngiVyqJWhmS9a6DI0uQyel7fz+DCou9pUN7Ct3sK9tULMD+V6fyJF+cgLrROeu5f7rMHSezus8nU9v6W9EVQ6u1Wxu1B1yusZo0pSiZpnxitCPiu/A4oXW0Oj8K1BblucSeRh5Gs78BIw8I1eNJg5WQvfdIo7EHQDCusfyr18gqHj0/o2zGL2pPfvZtVqNy5cv8+677zIzM4MQgkKh0Iy4DQ0NHeibjPBD7OtFGu+sYl9aI6z5oKtYxzpInu4iebLzoV0BvB8IIag6PsW6R6nhNaVs81j2uVG/F8maPHZ2GALbLSxDbYpYdyZBT9bccrwpbd2ZBDnr4M3FPAw0gpA3K3VeLdV4pVTjB6UaG9Gk+05D4+l8mqdyaZ7Jp5lMJejQ9fc9h0sIQVj15GrrdRt/3cbfaO0HRSmHatbEOt5B8kQniaOFOCH2w0R9vU3YXoaF1yGIvhR0jMvo2uizMtrWcxIOcIDhoxIvbGjjIEocyBqOy//4TRRTo/dvnEXL7F7Zn2q1yqVLl7h48SKzs7MIIejo6OD06dOcOnWKgYGBA31TCx0f+8oGjXfXsC+vI5wAJaFhnewkebob63jHAxs6eVTwgpDVqsOdssNy2eZOxWGlbMvjymbrsFF37zshPmloFFIG+aTcCimDQtKUfe370fmkqfEg/9J0VaUzY5I2tQP9N3wYWXY8XtmMspVrvF1pNIdBp1IJKW15KW1Hku89py60/Zakbdhb9oMN564cg2rWQO9Monck0PvSWMf3dtV3zC4jBCy9Bdf+GK79Cdx6Fbkk3pDz10afk8I28gxk+/f71e4pscS1cVAlDsC5WWblN9/GHEzT80uPPfCEkaVSie985zu8/vrrBEFAV1dXM+LW399/oD8Mg5qHfWmdxrur2Nc2wBeoaUNG2053kThSiOen7YAXhKxUpIDdKdssVyJJi/bvlB1WKjZrNZftHwWqAt0ZOcTYm03Qm0vQlU5QSBnkkgaFpEEhZUaiJvusPUxyGrN7BEJwtWY3pe2VUo2bUcZ/S1U4l001he3JXJou894RMPd2FfdmZWskbcOWia/bUBIaeqeF1mmhR4l0tU7Z6h2JPU2gG7NH2CWY/vNI3P4Uqkuyf/A8TH0BJl6AoSfAeLQXjcVz4g4JidEcXT99nLXfucT6712h8y+dfCC19drlTQjBuXPneOaZZ+jr6zvY4lZyaFxco/HOKs5MCUI5WTnz7ADJ092Y47k9rT14ULG9gJvrdaZXakyvVplZqTGzWmN2rc5q1bnr+nY5GypYnBsp0JdL0Ju1trRdmQRa/O/7UFP2A+aiPGQ3o5WU03WH18s1KlGKjW5D55l8ml8Y6uaZfJrHsknM9zF0FboBpW/OUPv+ouzQFPQOKWbmcOYuYVOS8bD2Q48QsHIlkrY/hpvfh9CXc9qO/pAUt6Ofg0zvfr/SQ0MscQeM5Jlu8j8yQek/zVD61iyFH5n40M9VKpX49re/zRtvvIEQgvPnz/PJT37ywFY7AJmXrfHOKtXvLODerACg9ybJvjBC8nQXxlDmkfygD0PB7VKDmVUpaFLYasysVrm10dgSRevJJpjsTvPZE70MFKxmJG2zjeXs0cEOQm45LUHbTHOxmfpiM3HsJjldZcxK8NW+Dp6OIm1jHyKBrDNdYv3fXSVYt8l8fJDM88NoOTP+0vUo4tZg5tutUmGo6gAAIABJREFUYdLNxLp9Z+Djf1OK2/AzoMU68mGI/9UOIJlPDuGv2VRfvIXeZZF5duADPX5T3l5//XWAQyFvAPb1IqVvzeDdqqL3Jsn98BjJ0917utBjvynWXW5EkbSZ1SrTzf3aloUBaVNjoifN+ZEOfvz8MJM9aSa7M4x3p8ha8UKOhxkhBL4ATwh8Iaj4wZZEsXMNt5ntf8nxtqS9MBWFkSitxflcqpnmYixaRVkwPtotIXQDyt+apfq922idFj3feIzE5OHPxRXzAVmflsJ29Y9g9jtyQYKRhiOfgef/Fhz9/KFO+XGQiCXuAKIoCoUvHyHYsCn+wXX0QgLreOd7Pq5YLDaHTUHK26c+9akDXbIK5JyZ0rdmca5uoBUSdHztGKnzvQ/dt3bbC7hTtlks2SyWGiyWbJZKdrO9tVFnI6pJCaCrCqOdKSa603xqqpuJ7gwT3WmO9KT3NBlrzP0RQlAPQ8p+QNnfbAMqfkBp234lCGkEoRSwUDRFbPuxG7b1C4G35fjer0UBBhMGI5bJpzoyjFqJpqCNJk36TGPXksY6M1H0bc0m/bEB8l+aiBcYPSrU1+H263D9z2TEbe267O+agqe/DlOfh7GPg37wa+oeNmKJO6AomkLnXzrJym9cYO13LtP9i6dJjOd3vPawypu/blP+41nqb66gpnTyPzpB5rnBQ1mHs+76TSGTUrZN0so26zX3rsflLJ2BfJL+vMWZoTxHetJMdMttpDN1YMocPcw4kYBV/FCKViRezS0I2sRsh2uCgOA91ofpCuR0jaymkdJUDEVBVxXZKgpJVUU3FMxt/a3rQFcUTFVFV2idVxVSmiqja1aCIcsgscepFkI3oPxHUfStw6L7lx7b13yXMbuIEFC8KUtZLb0Fi2/J/fIteV63YPxT8Mwvw9TnZFmrmF0llrgDjJrQ6P7506z807dZ/a136PzLJ0meaEXkisVic84bwBNPPMEnP/nJAy9vQdWl8l/mqb60iKIqZD89QvaFYdTk3v05CiFw/BC7rQblTq0shRTtN0shyXa95jYlrdTw7voZnWmT/pzFQN7i/GiBgbxFfz4ZtRb9OevAFg/fbcK2KJOISlMF0CpphewLo2tDAQGCoO18KKJjWuWtPCGobopWsE3GtktYIFv7fVRmyGoqOV1rbv0Jg2Npi6yukde1u85v35Lqw5PUtx1ntsTGv7uGv9qQ0bcvTqAm4ujbQ0HgwerVlqgtvSU3uyTPK6qMtI0+BwOPQ//jMgWI+ehMfzkIxClGDgFB1WX1X7yLt1il42vH8SbMQylvoRtQ/fYClRdvIdyA9NP95D47ipZ/cCH2IBQslhrMrdWZXasxt1Znbq3GzfUGFVtWCNgUtA9aVUlRaJZGsgxNSlreakrZQN6iP9eStMOUbkMIQS0I2fADip5P0Qta+37ARtTam0OBQuCF3DXk5+0wNNh+7EXX7V3aX0iqKjl9Z8nKapGE7XA+q2vkNJWsrsV1K7cRugHlP56j+t0FOQXiJ4/F0bfDjFOFO+9IWVu8INvlS63kuroFfadlKav+x2Xett5TsbDtEXGeuDZ2W+I2NjZoNBpYlkUikcCyLDTto9/MQ9tn5l+8xqsLb3HVWALl8MibCEJqry5R/tObhFUP63QX+R8e/9ALFrwg5NZGQ0raao259XpT2m6tN3CDliKYuspoZ4rRzlQzuez7qVG5pY32E4ekRJcXCpZdjzuuR9ELWhLmBRR9n42oryVoASXfv+9cK0tVyOsayWgosH04sH1oTw75tQ0FtvUb0X77UKGm0CxhpSkKCrLVFFCJSlYRHSsKKm3X01bSqu1x7YKW07W4JuUDxpkrs/Fvr8ro23MD5L80jvqIRpQPJdXlKLr2VmtIdH0aNpfAJDsiUYuia/2PQ9fRePXoPhLnidtDXnrpJV5++eUtfYZhNIWuXe7e736j0eA73/kOb6y8AToc9wb4+DMfY/hHTx9oqRBC0Hh7lfIfz+GvNjDHc+R/9hSJsdx7PnYz99nsak22UVRtdq3G7aK9pWpAytQY60pzvC/L50/1Md6VZqwrxXhXmv6cdSAKgT8I3DBk2fW540hBW3I8ll2fpehY9svC4vciq6kUDJ0OXaNgaAxaJgVdo8PQyUd98py+pT8Zz8175BFeQOmPouhbPkH3189gHT3YK94facIQNma2zl1beguqd1rXFEalpD3+F2WUbeBxyA3JYYeYQ0EscQ+Yp556iomJCWzbxrZtHMe5a7/RaFAsFpv9QfDeRcA1TePJJ5/kEx//BOI/r1D73hLF4DqFrxw9kKs47RtFSt+M0oX0pej6q6fgSJ6VmsvqfJHVisNaTRYmlwXKtxYtL9a3zjHLWTrj3WnOjXTw1XMpxrrSjHelGO1K0ZM53Cs1/VBwx/VY3EHOlqPjO67H+g7F4jUFek2DPtNgJGnyVD5Nn2nQnzDoiQqKFwyNgi5l7P3WroyJaWdL9O3ZfvI/MhFH3w4SviOHP5ciWVt8Sw6PulV5XtGg5wQc+aHWkGj/GRl1iznUxMOpBwDf9+8rfWEY8thjj5HPy9WpQgjKfzRH5c/nST7WTedfPL4vJac2i6Gv19ymfDUWKgxfWKd/zaWsK/zHPHwLn5WqQ8XZOUKUTehRMXKTrnSC7qxJb9ZirKsla4XU7tWS3U3cMGTJkYK26HjcdjxuO67ct2XfsuvdNUdMU6DPNOg1DfoTetRKWetLGPSZOv0Jg05DRzvEAhtzsBFeQOlP5qh+W0bfOn5iCmsqvvHvK42iFLT26NrKZVn5AGQ+tv4zkahF0bWek2BY+/u6Yz408XDqAUfXdTKZDJlM5n1drygK+S+Oo6YNSv9pmtWGT9fPnvzI34y9IGSj5rJed1mvRm1t67ZRd1mrynaj5jXnn/Wj8HUSfAGDKvBbus/3Cyr5bIKT2RTPZ6SkdWcScsu2jnd7AUAgBLdsFzcUzXlTm3OvNudWKdGcLI1ojlX7XKyo3R7ts4OQJXdTxlxut8naQiRqK+7d4prWVAYTBoMJkxMZi4Fovz9h0G/q9CUMugw9nkwfs684N6Po20qD9DNR9M2Kbxl7QhhAeQE2ZmFjDopzUaTtbbm/SbpXStrU51vz1zonYY/TzMTsH3Ek7pBT+8EdNn7/KsZghu6fP42WuTtiVXV8FjYaLBTrLBRtbhcbrFYcKWZtolax7z2XKp806EybdKQMulMmk5rGqNAYcEO6GiGFxQaKAurTfXR9dgwruz+Rs3XP51K1waWazcVqg4tVmyu1Bo0PuhT1HmhtE+p3Sk2R1zUGEkYkZgYDCZNBq20/YZDVD8+q1ZhHD+GFlP50juqLt9ByCTp+Mo6+PXCEgNqqFLKN2aht2y/dakXWQKbz6Bi/e8FBtm+ffoGYvSSOxO0h1e/dpv72qnyTCkAIRBjtR60QYut+85xAhESPbdtXFPS8iRYVit5s9U6LxOkuQuUob/7+VVb/4StUnuxh0fZYKNosFBssbNQpb5MzQ1PoziToSJl0ZUxGOlJ0pk0paWmTrrRJR8qkM2lQ8ASpkotYaeDdqeHdqeMvNGhmN1VA70qSeLqP7A+Noj/AdCH3wwsF1+t2m6xJcVt0WnPpOg2N05kkPzfYzfG0RVJTZW4xZHRORLnHNnOMbc09tjU/2U7XZPRWRG3QMhgwDdKxoMUcYIQfEtY8gppH2LYFNY+w7hPWPNyFKsG6HUffPipOtSVnm7LW3J8Dr7b1+lQ3dIzB4BNw+sflfmFMtvkR0OJyejF3E787HzAysinkYgNVjtspiiKPFUBRUFTZtp9HAdSt16FIWbGdgNpGAxYqmFc30Lelhy9JtcArB6z/l3lqGmRzJqc6EjwzUmCgK8VQIclgIclwR5KeTGLLik0RCoKi05K0uTLeUg1vpYHnh0SpHdE6LYzeFMmTneh9aYy+FEZPEmUXh0OFEKy4PhdrMqomZa3B1ZqDF0WRDUXhWDrBJwoZTmWSnMpYnEon6TH1Q73gIebwIEJB2PDlVvcIGz6i3tqX/bIVoUDRVRRdiVq5sb3PUFE0FYyoX4v6tl0PyOeueoT1+wha1UO491hEpYCa1FHTBnohQcdXj2Idi6NvW3BrUF+TEbT6OtRX247Xtm61FWhsbH28kZbRtI5xmHihTdLG5SrRxPubThMT086+DacqijILVIAA8IUQTymK0gn8HjAOzAI/JYTYUOSd+B8APwLUgZ8XQrwePc9fBf7H6Gn/rhDit+/3c/djONUPQkoNj2LDo1j3KDVceVzfPPYo1t228/K4bPtbUmlsciSd4EzaYsoyGVM1eoVCwRVYFQ+16rFFWxTQ8oktETytkCCseXh36nh3avjLdYTbmlqv5RPofSkpaZGs6b2pXc/EvuH5XKvZXKs7XK3ZTXFrT5kxkDA4mbYiWUtyMm1xNGXFucBiHgjCDyPZ8lritXlc3ypprWMfcZ+pCABKQkNN6bIqiaaCHyL8EOGFiCBEeALhhxCE8AA+khVDRU0bzU1L6VuP0wZqykDNGPJ1pYwDucp91xFCRsjuvCPFq74GtU0ZW9167Dd2fg5Vh1TX3Vt+OBK1cdmmuuLUHTEfioM8nPoZIcRq2/GvAX8mhPh7iqL8WnT83wNfAqai7Vng14FnI+n7n4CnkB99P1AU5Q+FENu+Au0dv/PyHN98e4liw5VCVvfuuSpzk5ylU0iZ5JMGhZTBcEeSQsqgkDQppAzySYPBQpKhQpKBgkXiPkN23kqdld96h7DikvnYIIqpEmw4+Os29tUNwkqrfqeaNTD60qSf7o+kTQrbbg6fCCG47Xhcq9tcqzlcq9tcjcStXdaSqsKJdJIvduc4mUlyKp3kZMaiw9jvP9mYg44QAuGGLfHaLmHbo2Rt54V3n1oSCpGISfHRMgZGT1IKUFJvypC6uZ9sbcr7zLMnZA0yKXh+iPBFS/i2bG39grskLS48fw+cqizUfutVuPWabGsrW69J5CDVKYc3M/3Qd6Z1vClo6bZ9Kx/LWcy+cdDuiF8BPh3t/zbw50iJ+wrwL4UMG76kKEpBUZSB6No/EUKsAyiK8ifAF4F/vbcvu0XDDai7Pr1Zi2O9WfJtMlZIGeSSBoWkQSFlUkjKY+0BfgM2elL0/co5Vv/5O1S/f5vOnzpO/gvjzfPCC/CLDmpKfhvfLbxQMNtwtsjatbrN9bpDra2iQkHXmEpZ/HB3jqmUxVTaYiqVYMQy49WZMfcldAP8O3W8xRreUg13sYa/Wies+9y3Ir2mSNmKREvrtDCSmUjCIklrF7HoWsXUdj1apSgKREOqMR+RMIS165GwRdK2/C5ysjGy7ufRz8PwUzB4HrIDUtb0vZnXGxPzINhPiRPAHyuKIoB/IoT4TaBPCLEYnV8CNpfeDAHzbY+9FfXdq3/f+PqnJvn6pyb38yWgZU16fvlxVn/7XdZ/9zJh3SPzsUEAFEPD6Hlw9e4CIeTQZ7XBtXorsjbbcJtz1gAGEwZTKYufGUhLWUtZTKUTdBvxvLWY+yNCQbBhy3makbB5S3X8tUZz6FExVYz+NNbxTrSM0YyWqUk9ipK1HRuHo3xazAeksQELP2hF2G69BnZRnkvkYfhJOPHfwvDTMPSkFLaYmEPOfkrcJ4UQC4qi9AJ/oijK5faTQggRCd5HRlGUbwDfABgdHX0QT3ngUS2dnl88w9q/ukzxD24QVD1ynxv9SDcvIQQ3bZc3K3XeKNd5s1znrWqDehRZ0xQYtxJMpRN8sTsfRdVkZC0Tr9qMeR+Etr9V1halsDUn5EeroY2+FKlzPRj9aYyBNFqH9WjO6XpUCQOZN619WHT1SnRSkcXZT31FCtvw09B9LM6dFvNQsm8SJ4RYiNplRVH+PfAMcEdRlAEhxGI0XLocXb4AjLQ9fDjqW6A1/LrZ/+c7/KzfBH4T5MKGB/ubHFwUQ6Prr5xi4/++RuXPbhLWPQpfPvK+b3arrh8JW403yw3eqNSapZ8SqsKZTJKf6e/kfC7FmWySyWQCM/6gjHmfBBUXZ6a0RdiCotM8r1g6xkCa9FN9TVnT+1LxfK9HhcCD0vzW/GmbKTpWrrRKSqW6pKg9/jXZDj4B1nvXZ46JeRjYF4lTFCUNqEKISrT/BeBvA38I/FXg70XtH0QP+UPgVxVF+V3kwoZSJHp/BPzPiqJsroX/AvA/7OGvcuBRNIWOn5xCTetUX1wgrPt0fu3YXXNuan7AW9WGjLBFkbZ5Wy6CUIBjaYsvdOU5n0txLpfiZNqKhS3mAyMCgX11ndqrd7Avr8mEfCroPSnMsRzGc+lWdC1nxsOeDzNCyGLs2/OnbeZUKy+05q+BXAWaH5ErPc/+TBRle0pWKIj/TmL2gNC2cWdncaencW5M40zfoO/Xfg2jb/+SLu9XJK4P+PfRB7QO/CshxLcURXkV+DeKovzXwBzwU9H1/y8yvch1ZIqRXwAQQqwrivJ3gFej6/725iKHmBaKolD4kUm0tEHpm7MsNjyWvzLBBdtuCtvVmt2s3zlsGZzPpvn5oW7OZ1M8nk3Gw6ExHwl/rUHttTvUfnCHsOyiZgwynxom9Vg3Rn86nsj/sOJUYH2mLZI22xK14k3w7a3XZ/qlpI1+LMqp1pbwNjsI2kFbixfzMBKUyzg3bjRlzb1xA2d6Gu/WLfnlA0BRMEZG8FdW91Xi4rJbjwBuGPJmuc73ilW+fXOd11wHR5PfXDsNjXNZGV07H7U9ZpwZPOajI7yQxrur1F5dwrlRAgWs452kn+rDOtn5vtNuxBxw6uuwMSNlbX26bZuB2vLWaxN56BiNEtyOtbVjMuGtkdyP3yDmEUQIgb+8LAUtiqq5N6ZxZqYJVlqZzxTTxBwfxzwySWLyCIkjk5hHjmCOj6Mm9mYl80HOExezCzht0va9YpXXSrVm7dBTaYu/qKU58eoaTwzkefwnT6DGN9OYB4h7u0r9tTvU3lhGNHy0TovcF8ZIPdm3Z2XZYh4gQshcau1y1i5rmytAN8kNQccEHPthOdTZOSGPO8YgGVeBiNlbhO/jzs/jzszI6NqNaZzpadzpacJqtXmdms2SmJwk86nnpahNTpKYnMQYHkbRDu5IVCxxDwHvJW1/ebCLjxcyPFfI0Bkly63485S+OUs5NUP+v5qM5x7FfCRC26d+YYXaq0t4t6qgKSTPdJN+uo/EZCFeOXrQ8Rqy6HppXg5zNkVtRkbZ3NbNDkWVc9M6J+HMT0hJ65yUW8d4HE2L2RfCRgN3dlYOf07faLbu7BzCa9XU1nt7MY9Mkv+xH5PRtSNHMCcn0Xt6DuV9MJa4Q4gThrwRSdv3t0nb6YzFX4mk7dk2adtO5vlhgrJL9bu3UbMmuU+P7HhdTMy9EELgzpWpvbJE4+1VhBdi9KfIf3mS1LneXU0mHfMBEELmUCvNQ3FetqVbUtY297dXLVANKWSdEzD+iZakdU5KgdPNfflVYmKCYhFnerotqiZb7/bt1nw1VcUYGSYxeYTMCy9gbg6DTk6iZbP7+ws8YGKJOwS0S9v3Nqq8Vq5hhwIFOPU+pW07iqKQ/9FJgqpH+VuzaFmT9JP7Nzkz5vAQVFzqry9Te20Jf6WBktBIne8l/XQ/xnDmUH6bPdQEPlSX2gRtu6zNg1fb+hg9CYURWd+z//FoP9oKI3JIVD24Q0gxDzciDPGXlnCmZ1pRtRs3cGZmCNbWmtcpiQTmxATJs2fJ//hfkFG1iUnM8bE9m6+238QS94D5Z7dW+H+WiwRCEAgIEYRRGwia+6GQ1Q5CIIzaQIi7zgfIhQm+kKk+TmeS/NxgdyRt6Y9US1RRFTq/dozVmsfG719FTRskT8RZzGO2IkJBUHRwF6o03lymcWkdQoE5nqPjhRGSj3fHudt2gzCURdgri1C5I9vqnR2Ol0AEWx+b7JQy1nUUJj/TkrTNNi7GHnMACEolOQQ6MyNTd8zMynZuDmG3Vi6ruZycr/bpF0hMHmkOgxqDgwd6vtpeEEvcA0YFDEUhoSpoioICaIqCqoCGbFVFQUO2qgIqClrUbj0v9y1V5Xw29ZGlbScUXaXrZ0+y8ptvs/47l+j+pcdIjMaJMh9VmhUT2qoleEs1hCMlQU0bZD45SPqpfozeB1e+7ZEiDKC2eh8pi46rd+6WM5CClh2AbB/0noRs/9YoWn4YzPTe/14xMTsQOg7ezZs47ZI2O4s7M0OwsdG6UNMwhodIjE+Qfu45zIlxzIlJEkcm0bq64gj/PYhTjMQAcohs+TcuIBo+PX/97AOtrxqzM5sRLn+ljrfaIKz7su5nxkDLmLLNmigJ7d4fYF5Dzm3akjB1VrblRXkzT3Zs2woIqwPf78FrdOBV0nhFA281JCj5zaeWFRNSzeS7Rn8aczBz+HO6CSGrAfg2BK5sfSfaov3Aaevb7N/pemeHfvs+5xywSzvLWapL5knLtm3N40jaMn1xgfaYA4cQIhr+nN4aUZudxVtYaM1VA7SebhJj45gTEzJ1x8Q45vgE5vAQihnPtdyJ+6UYiSUupom/1mD51y+g6Cq9f/0sWpwO4iMjhCCsefirDfyVBv5qA29zf60Bwft4/2mgJQWq6aKpVVTW0YIlVHcezZ1HpYimbKApRRQ9QNnMw5UbAK9BUGnglRJ4tRxeowvPG8ATo8Dm/2+ArtzCUGYx1FkM/TZGqoiWBiUlpW9T/lA0KUCh19b6bce+lJbm/g7XtD9ehHLulaLK51Y1OcynRH3t5xRV1r/c8ZwSzeFSdhatYJtc8QA+97SEFCo9AboFmilb/T36rXwkZW2SlumLFwvEHBrCep3GO+/QuHChubXnVlNTKSlo422yFgmblsns3ws/pMQS10YscffHXaiy8k/eQutI0PvXzqIm4xH390PoBlLU2mTNX23grTQQdiu6haagd1no3Sn0niRGl4WeaqAbq6j2TcKVBYKVFcL1EkG5QVgPCcIcoSgQUCAUHQRKJ2GYQw7eb0NVokiegZLQ8dcahGW3dTptyKhaj4HREWDkbIxkGcXbkCsYNze7CI3i1r7GhvxGrRmyBJJmyFWMmh61RlvfDudUXQpN++MVVYpcGMh2y37wPs+J1rEI36dQbbb3uzbRdt22x2tmXFA95pFACIE7O7tF2JwrVyGQ0WRjbJTUuXNYjz1OYmoKc3wcvfdwpus4qMQS10Ysce+NfW2D1f/jXczRLD2/+BiKEd+s2hF+SP3tVdy5clPagpKz5Rotn0DvSaJ3GugZB93cwFAX0bwZlEp7eocFGZVqJ93TlsV+bGv5ofwwaAYiFIQNn7DqElQ82Va9LcehHaB3WVuGQ7VsHO2JiYm5N0GlQuOtt5rCZr95gaBUAkBNp0mefRzr7FmS0aZ3PAIJnIWQuRKryzIdT3VZViOprsBTvyinOuwiccWGmA+ENdVB508dY/1fX2H9dy/T+ZdPxslagaDqUntpkepLi4RVD8XS0LtMEgMh+qSDrq+gM4fuXkOtzsDGPNzaVnYIRQ6lFUZg6Ek49dVt6R1GIfHeww2KqqClDbS0gRFnhomJifkQiCDAuXGjJWwXLuBcvyGlRVFIHD1C5vOfawpb4siRh2c1qBByfup2Kast7yxrfmPn5zn62V2XuPsRS1zMjqTO9hJUPEr/cZriH1yn8NWjj0543LO3vKG9hQ0qFxPUF7pAaFjp62S6/5SE8+coa1VYa3usbsloWX5Elh0qjEaCNixlLTsYz32KiYnZc0QQ4M7dxL50EefyZex336Vx4S3CmswhqOXzWOfOkv3Sl6Lh0ccObmJcIeSiLrcGbgWcqoyUbbb32m8U2z7bV+Rc2e0oqlxklO6FTI9McJ3plSMkmd5Wf7oX0t1yWsg+EktczD3JfnKIsOJS+f9uoWVNcp8b2++X9OFx6/f+plW90/atawWcMkIo2OFTVIOv4ITnULBJm/+ZTP4VjA7kGzr3V7YKWn5UvqkfFdmNiYk5kISOg3P1Gvali9iXLuFcuox99SqiXpcXGAaJqaPkfuzLpM6dI3n2LMbY2IP9oh6GMnq1KVteA7x61DZkAuotfVHr1tv2t0maW2vt77TCeye0hBzdMDNg5aR89ZzYWcoyvVLgDlGi61jiYu5L7ovjBBWX8p/eRM2aZJ4d2O+XtBW3JpOdVpZk1vrK0g7Hd8Ap7fx4q9B6Iw88TmgNUN84SfXmEH7VREsr5J7Ok/nYBGr+83v7u8XExOwawvcJikWC9XXCeh3z6NFDuXIyKBaxL1/GvnRZRtkuXcaZnm4uPFAzGawTJyj85E9gnTiJdeokicnJD5bOw2vsPMTY/oW4thrJWiRnvv3ez7sdVQcjLevvGhaYWZkmKdXZmmpiRttO+4no+va+fY6U7TaxxMXcF0VR6PiJKcKaR/E/XEfLGCRPd+/+D26Xs8riPSTtDjjlux+rJVp5tnpPyoz1mzm2toTCe5pDm37Rpvq9RWqvLCFsH2MkS+eXB0me6UbR4oUdMTEHGZnKp05Q3CBYWSG4M49/5xbB6hLByjL+xjpBsURQqhBUG/hVl9AO73oeozOBNd6PdeIE1hMfI/HUC+h9fQdiKokQAn9xEfvSpUjYLuFcuiRrhkbofX1YJ06Q+dxnm8JmDA2hbF9JLYSMaL3XPLDN1q3s/KIS+dbnac9xKVFGEoxUtCWlVBnJbf2pVl/zfOqhF67dIF6dGvO+CN2A1X/6Nu5ijZ6vnyExnv9wT+RU27LSt0naZvmgzW2nD412OdtMgJrpayVC3TxOdrzvIU1nrkz1uws03pE5jpJnusl8YojEWFy1IibmICCCAH9pCffGZdzLb+LeuIK/uEhQquCXawRVh6Du3Xt0TRXoZohmhWiWgp4x0TIWWi6D1pFH7+hAMS2c69exZ5ewl128aiu+oaVUrOEOrKlJEo+fx3rmh/j/27vT4DjS+77j33+fmMFgcBMEQC6PJfc+uLuUvNJKiqocybIqyVqVqKIcshI55bhG/2WkAAAZ0ElEQVQsl6UkTpVyVeUoV46Kj6SiWHbiVdlVluUkPrJ54ehWFMVaacldkntpl1juQQAkAQIEMMBgzn7y4nkGM7i4PAYYDPD/VHV1z9Mzg24+nMEPT/fzPNFdD2zJDf7J0hLlyUnKly7Z9UR9u/T66yu9RBEhOnKEjnvvoePYEeIjw3Qc6CNImTXDAm0wTFCtbKP7wcB+f9YuLa5cctzofrBB21qmtpwOMdJAQ9ytqy6Vmf7iWaq5Mvt+7iHC/Q1T+9xOOAs6Ng5jjY+79ttLn034i9hUE5ZfvMri9yYpXcwhHT6d7x4m895hgh79UlJqu5lymfLEBKXzL1H60VlKb5ynfHGc0uUZyrPLqwKaeIYgXSXoMPjpAD8TE2TT+N1d+D29+P0DBIND+IP78YcO4g0cQDoH7L1ON9KpqLRE9cJzFE//HwovnKHw+lsUJhYozgkk9vtHAujYnyY+MkLHfffTcfL9xI99EC+9+XRnxhiqMzM2nE26kDY5QXlinPLEBOVLl0lyi6tf5HuEvWnC7oioN6Rj0KOjp0ycWcSrzNnxHM36FsUVYeeqmVrqA3f3bhzW0gPa8WoH0hDXQEPcDUgS+5dafsZOwJ2fscvSVSqzeaZO/xhiqgwOf5Gg9IZ9Tjm//n2Cjo2nDlob0poUzt7xtPJlFn94maXvT1KdLxEMpMg8MUL60SG8uH1uZFWqHdXm0Cy9eo7S+RcoXxijND5B6fI1ynPFVZNoiJ8QdVWJekOioR7C0RGiw0eJ7nqA4NgJpO+Qnfliuy5zGoOZeYviqW9ROPNDCq++RuGtKYpTZZKKu1QphrgvIL5jgI5jR0nKCeWpGSpX5ynPLlK+VsCsmaHFCxLCzqpd0lWCziphulJ/3JHY2zk6uu2ybgq9hqWjZ930ejpF2+6gIa7Bjg1xxjWDz70N8+N2INi5izD/tr1HAdl81PtVI+NHa0bJ32hfYFvOVgW0mXpoW762+V93UYZS+CDT1/4+frjMvge+iZftsr0y104ltJ1fshtIChXKV+wE7qWLOZbPTmPKCfGxHjJPjNBxd5+Of6fUbbL3oy1RmZqiMjVNZeoKlYk3qVwcozI5TmV6mtL0PJWF1YNae6ELan0x4VAv0YFRoqPHie5+CP/oQ0jv4R1/uc6Ulimf+78UTn+PwkvnKF4YpzC5SCVvv1f8jiphBsKsT9gT21a1gS7CwT7CoQH83n77PRln60EtztpelLXtqFN7vO9xGuIatCzEJVV7eXHuohup/2J9e+6iDW7lpdWvCVJubLH99vFm80+unaOyWrLbSWX9cTQS315iSPfbEJbus83pK4/7V5el+1e+VIsX5ph+6kWikQwDf+dBvKh1LVlJsUplKk/5yhLly3nKU3kqV5aoztenm5LIJ/WQvd8tGt78kodSyloXzqZrIW3Kbl+5QvnyJJWZWUyhtO714icEqYQgZYj6UoT7+4kOHiS68zjh3Q/jH3oA6bljV97MXr30JpJK4WUH2mq4CrUz6YwN2+nSOZh8rh7MaiFtYWL9uDbpfjvO2MBxO+rz2jHH0n239xeYMZtPQh5nbM+iW5z/MT7aQ99fvYfZL7/C7Jd/RP8n70P8rf1r0ZSrlKeWKV9ZonIlb1vZrixRvdZwg27gEe5LER/tIRhK2ymn9qXxe2JtdVMKF84WF6lM1wLZ9LqgVp6eojJ9tT6uWAMJhSBlCOMiqVRCcKhK0FEl6O8h2D9KcOBOgiP34o3ehwwet99reyzI+MOHW30Iao/QENdsZ38fnvnPtpUrO2K/wA69pz6Kfy2gdY/aZvKtJFKflHwLpB8cIHnyTub+5HWmvvA8fja2QS70kGDtIlDbDj3E95BQkMCrlzc811QNlWkX1C7nqUzlqcws1++b8YVwMEV0R5bwZJpwf5pgqJOgr0PDmtqTjDEkudz6cDY9TXnl8TSV6WnM8vophKQjIuzpJOj0SXVVCHrKBN4iQVwmSFVtq1pPBm/4GDJwHPqP1Ze+oxClW3DWSu1tGuKa7b2/CI//vL0/zN/9/7yZx0cwVcPy2WmquRKmkmAqCZQTTDXBlI19nNziZXsPgv4U4f40qYcHCV3rWtDfoeO3qT3FGEN1bs52Dnj7bUpvvU3p7bcoT0yuhDVTWD/AqqRShAN9BD2dpEYzBMc7CaICgbdAwFUCM0WQSvBD9xn1Y+i/E/ofciGtIbB19m/zWSulrmf3p4ztlt1hMxpsg64nRul6YvS6zzGJC3OVBFMxK2FvJfTVyl34AwgG04SDKdtap9QeYIyxLWcXL9ZD2kpge5skt3qInmB4mGh0lNR9dxN0PUCQNgRRkSDIEZgZguokfv5tSF5veJW42zYOQe9Dbn0Ieg/b7czQLd9moZTaXhri1LYQT5DIhxZ2gFBqJzBJYgevdeGsfLEe0koXL66+D833CUdGiEaG6P7guwj7YqKuhKhjiTCYxctPwty37FySACW3pPtdODsBvT9VD2o9h+xtHToWmFK7goY4pdSOY5KEJJ8nWVoiWaqtlxrKNnlcKiJhiBdFSBQjcYxEERJHeHGMhJEtiyP7nDi2z4sivDhyz62XSRRCuUxSKJAsFzDLebe9jKmVFZZJlpcbtgskhWXMcuN2wb0uT/XqDKbU0HM6DAlHhoiGeug8ej9hFqJUgSieJ0wmkaXnbIckgKJbUr22Na33CBz5wJrWtDvs9EdKqV1PQ5xSasuYSoXqtWtUZmaoXJ2hOnOVytUZKjNue24Os5Snml+qB7Z8fsNekZuRdBqvM42f7kTiGFOpYIpFTKlEUirZ7WLR9tbeSr6Pl0ohqQ68uAMvDpE4wAt9/JQgmQgvCPDvyRBlSkTRApE/TWAmEe+thhPyIBqG7EHo+bGGTlF3uO0DGtKUUoCGOKXUTTLlMpXZWSpXr1J14awyc5WqC2eN29Vr1zYMTxJF+AP9+D09+OlOwsF9eIc78Trdkk7Xt69bllo/ufdGx2wMVCokxRKmXA92plSyZaUSpmTLbPArYQp5zPIiIhU8qSBeGY8iQhGPZbwkjySLeJUFvGoOKbk5KQvjG89gUhN0NPRUf8z2Vu85WA9r2ZFdOXaaUqr5NMQppdYx1Srl8XGKY2MUz5+neH6M4tgYlcuX65NwryHpNEF/P0F/P+Edd5B65BGCgX78/n6C/oH69sAAXiTI3FuQn72Jo6rd8HUN8thl/YFDuWBDVDkP5WUo5xG39kv1svp6TVnJlSXlDX5AAz+2YznWpjnqOwIdj6yfn3LtEnfpCPxKqabQEKf2hmrZjt3Xyl53xtiQUFyAwjwUFqA4X98uLbnZNirXmZGjvPHgzbXn1GbrqFYAY3sarrT6NAwmnT0AQWR7Q05OUjh/nlJjYLtwYdVwFeHICNHxY6Qfe3TDUBb09+Ol14wTVpiH2Tdg9gLMvgDjb8C5C3DtDTt7yXYLOiBMQZh265SdIDzK2InAa2VR5+rnbTZnZZja/nNQSqkGGuKa7c3/Z39pjZyAwXt232WRcgFyk7BQWybsulpyU3PVpu3qX/14qwYCLRdg8TLkGpaNHi9fs89f+UXe2fCLPG2Pb+WX+5rtKL3ml797bVJ2QawxlNW25zcuf6ep0Bpdd/7bcP38uUFsZ+KoPR/s+Y99A5O7TGXZozgfUpwP7HopRemaR1KuX+4M+ruJjx6m9+MfI77nfuLjx4mO3omf2WBgamNsS9rsBTj/ZzaczV6oL/mZ1c/P7LeDwt7549B32G53DgJNbJUSb01dNQS2PTZrgFJq99MQ12wv/Hc4/SW7HXTA0P0wfAJGHtn5wa6UXx3MVtYN2/mr618Xd9shC/Kz66cWqwlSm8zP2rcm/Ll1mIalqdVhLHcJFq/Ydc6tC3Prf5YX2MDQNWQHLT38hG1pMckml8+WbNDKXVm9r7S0+flsJs7WJ7COs/Y4Bu5qmOA6u/lk11FnPah5/g1dcjNJYjsE5HJUc4skuQWquZx9PDdH8eIF27o2dp5koT7GmN/VQbwvRffBhDizRBxdJe5axo8mgVeg/KfwWjdMHYQXD9ZvqC8uNLSuvWFbEleIbe3rOwz3/kUb0mpL7+Gtn6FEKaX2GDFb3WNrhzl58qQ5derU1v2AJIHZ12HyDFw649ZnoeR+gfox7H/ABbsTdr3v3q0NdkkCy7Orw0/uUj2YzU/Y7Y0CUaoPsqP2ZuvsyAbbw/Wecklif6kvzdhWmPxVt56Bpas25NXKao9LufU/czNeaC8Pdu1fvWT22xkyuobsOtXXvMum1bILdI3hbxnKS/Z4agEsztp/h5to7THGYAoFG7oWF0kWFmwQW8xRXcjV17kc1cUcyYJb5xap5hZIcoski4vX7XXpdXcTHz9GfPw48bFjxMeOEx8/RtDXt/qJSWJD8/w4zL1t5/ydH3dzALv5f4vzNiD33LEmoB1x60O2NVAppVTTiMhpY8zJDfdpiNsGSWJbLiafb26wWxXO3uFy4kaX8ToHNwlmDeutvu+nUlwT9Nx2aQky+1xAc0szw1kTmFKJ6uKiDVm10JVbG7Ia97mgVntNLgeVd7i86nn4XV14bqlt+5kMXjaL35XBy3ThZ7vwMl14XRn8bBYvk8Hv7sbv7UWadRN9MWdbVPfAdHJKKbVTaIhr0JIQt5FasLt0xoW7s3YpLtj9fmwvxdYuwRYWGsKZa1HbLJylehvCT0MLVWZo9WNtNbmupFikPDFhp0AaH6c8PkF5fNxtj6+bAmkjXmfnqvDldWXwu7J2nenCy7p9mS4byLpcMMtm8TMZJJ1uXghTSinVdq4X4vRP6iZb+NrXWH7+DH531raEdHfjdXfjd/fg97jHmYwd22rgmF0e/Cv2xUlibw5vbLF74X/Ug12qtx7GBu6uh7GVS4r77b6wo3X/AG3EVKtUrlyhdNGGsvKEC2jucWV6etXzJY4JR0cJD4ySfuQE/sBAPZDVWr+y2Xogy2QQX2+mV0optTU0xDVZ4YUXufYHf3D9Eed9Hz/bEPJcuPO7e+rlPe/GP/Ih/IezeEEZr38YL9t3w4ObKkjyeSrT01SmpihPTVGemLRhbfwipfEJypcuQblhLDDPI9g/RHTgIJ3vfz/hgVGiAwcI3RIMDOi/vVJKqR1DL6dukaRUIpmfp9q4zNW256jOz9v9c/NUFxZWnpMsLFz/jUXwUqmNR7C/kVHu3WO/O4ufzSLhDu0pex3J0pINZ9PTlKemqExNr4S1xnWyuLjutX5fH+GBA0QHRglHDxAePFAPasPDbfnvoZRSavfSy6kt4EUR3uAgweDgTb3OVKtUFxbWBcBVk4AvLZHkV08MXp660jAH5Y3PPeml064lsGelZbC+ZN2l4NWXg/3ubqSj46bv1TJJ4qY3stMerZ3uqD4FUpEkv7xhMKtMTZEsLa17b4ljgsFBgn37iO++m873vY9gn/33D/fts+uREbxOHeZCKaXU7qAhbocR3yfo7YXe3tt6H5MkJPnlNcGvHvqquYV6S2BDWCyOja1sr7rUuPY4owhv5b6/HiQK7XyTtYBWKmJK5VVzVJrrvN+mPyeOCVwI2zCcuX1eNqsdAJRSSu0pGuJ2KfE8/EznxiPt3wBjDCafr1/q3ehScMPlYFMoInGM15XBiyIkipE4RqIQL47t4yjapCxyj93+MMJLp+wcmxrOlFJKqQ1piFMbEhHE3UcXDg+3+nCUUkoptca2d7UTkYMi8m0ReVlEXhKRz7ryfy4iEyJyxi0fbXjNPxKRMRF5VUR+oqH8I65sTEQ+v93nopRSSinVKq1oiasA/8AY85yIdAGnReTrbt+vGWP+feOTReQ+4BPA/cAI8A0Rucvt/gLwIWAceFZEnjbGvLwtZ6GUUkop1ULbHuKMMZeAS247JyKvAKPXecmTwFeMMUXgDREZA97t9o0ZYy4AiMhX3HM1xCmllFJq12vpyKUichh4BPiBK/oFETknIk+JSK175ihwseFl465ss3KllFJKqV2vZSFORDLAHwKfM8YsAL8B3AmcwLbU/UoTf9bPisgpETk1vWYqJaWUUkqpdtSSECciITbA/Z4x5o8AjDFXjDFVY0wC/Bfql0wngIMNLz/gyjYrX8cY81vGmJPGmJODNzn4rlJKKaXUTtSK3qkC/DbwijHmVxvKG8ex+Bjwott+GviEiMQicgQ4DvwQeBY4LiJHRCTCdn54ejvOQSmllFKq1VrRO/UJ4JPACyJyxpX9Y+CvicgJwABvAn8XwBjzkoj8N2yHhQrwGWNMFUBEfgH4KuADTxljXtrOE1FKKaWUahUxxrT6GLbVyZMnzalTp1p9GEoppZRS70hEThtjTm60r6W9U5VSSiml1K3REKeUUkop1YY0xCmllFJKtSENcUoppZRSbUhDnFJKKaVUG9IQp5RSSinVhjTEKaWUUkq1IQ1xSimllFJtSEOcUkoppVQb2nMzNojINPDWFv+YAeDqFv8MdfO0XnYerZOdSetl59E62Xm2q04OGWMGN9qx50LcdhCRU5tNkaFaR+tl59E62Zm0XnYerZOdZyfUiV5OVUoppZRqQxrilFJKKaXakIa4rfFbrT4AtSGtl51H62Rn0nrZebROdp6W14neE6eUUkop1Ya0JU4ppZRSqg1piLtBIvKUiEyJyIsNZQ+LyPdF5AUR+V8iknXlkYh8yZWfFZEPNrzmMVc+JiL/UUSkBaezKzSxTr4jIq+KyBm37GvB6ewKInJQRL4tIi+LyEsi8llX3iciXxeR827d68rFfQ7GROSciDza8F6fcs8/LyKfatU57QZNrpdqw2fl6VadU7u7hTq5x323FUXkl9a810fcd9iYiHy+FeezWzS5Xt50v3POiMipLTlgY4wuN7AAHwAeBV5sKHsW+HNu+9PAv3LbnwG+5Lb3AacBzz3+IfA4IMCfAj/Z6nNr16WJdfId4GSrz2c3LMAw8Kjb7gJeA+4D/h3weVf+eeDfuu2Pus+BuM/FD1x5H3DBrXvddm+rz69dl2bVi9u32Orz2Q3LLdTJPuBdwC8Dv9TwPj7wOnAUiICzwH2tPr92XZpVL27fm8DAVh6vtsTdIGPMd4HZNcV3Ad91218H/rLbvg/4lnvdFDAHnBSRYSBrjHnG2Br+XeCntvrYd6tm1Mk2HOaeYoy5ZIx5zm3ngFeAUeBJ4Hfc036H+v/7J4HfNdYzQI/7nPwE8HVjzKwx5hq2Lj+yjaeyqzSxXlST3GydGGOmjDHPAuU1b/VuYMwYc8EYUwK+4t5D3YIm1su20BB3e16i/mH5OHDQbZ8F/pKIBCJyBHjM7RsFxhteP+7KVPPcbJ3UfMk1ef8zvcTdHCJyGHgE+AEwZIy55HZdBobc9ihwseFltc/EZuXqNt1mvQB0iMgpEXlGRPSP0Ca4wTrZjH5Wtsht1guAAb4mIqdF5Ge34hg1xN2eTwM/LyKnsc2uJVf+FPaDdAr4deDPgGpLjnDvuZU6+RvGmAeB97vlk9t6xLuQiGSAPwQ+Z4xZaNznWqG1W3wLNKleDhk7Sv1fB35dRO5s/pHuHfpZ2ZmaVC/vM8Y8Cvwk8BkR+UCzj1ND3G0wxvzIGPNhY8xjwO9j70vAGFMxxvw9Y8wJY8yTQA/2uvoEcKDhLQ64MtUkt1AnGGMm3DoHfBl7eULdIhEJsV9+v2eM+SNXfKV2Oc6tp1z5BKtbRGufic3K1S1qUr00fl4uYO8nfWTLD36Xusk62Yx+VpqsSfXS+FmZAv6YLfjdoiHuNtR6MYqIB/xT4IvucVpEOt32h4CKMeZl1xS7ICKPu0t2Pw38z9Yc/e50s3XiLq8OuPIQ+AvAixu+uXpH7v/1bwOvGGN+tWHX00Cth+mnqP+/fxr4adcb8nFg3n1Ovgp8WER6XS+wD7sydQuaVS+uPmL3ngPAE8DL23ISu8wt1MlmngWOi8gREYmAT7j3ULegWfUiIp0i0lXbxn6HNf93y1b2mthNC7ZV5xL25sVx4GeAz2Jbc14D/g31wZMPA69ib4j8BvbyQ+19TrqKfB34T7XX6NKaOgE6sT1Vz2Hvp/sPgN/qc2vXBXgf9jLDOeCMWz4K9APfBM67f/8+93wBvuA+Dy/Q0EsYe2l8zC1/u9Xn1s5Ls+oFeK97fNatf6bV59auyy3UyX73PbeA7Zg1ju0oh3vda66+/kmrz62dl2bVC7a38Fm3vLRV9aIzNiillFJKtSG9nKqUUkop1YY0xCmllFJKtSENcUoppZRSbUhDnFJKKaVUG9IQp5RSSinVhjTEKaWUUkq1IQ1xSim1jUTEb/UxKKV2Bw1xSim1CRH5lyLyuYbHvywinxWRfygiz4rIORH5Fw37/8RNdv1S44TXIrIoIr8iImeB92zzaSildikNcUoptbmnsNPj1aZy+wRwGTiOnQfxBPBYw8TWnzZ23t6TwC+KSL8r7wR+YIx52Bjzve08AaXU7hW0+gCUUmqnMsa8KSIzIvIIMAQ8D7wLOw/i8+5pGWyo+y42uH3MlR905TNAFTuhtlJKNY2GOKWUur7/Cvwt7ByJTwE/DvxrY8xvNj5JRD4I/HngPcaYvIh8B+hwuwvGmOp2HbBSam/Qy6lKKXV9fwx8BNsC91W3fFpEMgAiMioi+4Bu4JoLcPcAj7fqgJVSe4O2xCml1HUYY0oi8m1gzrWmfU1E7gW+LyIAi8DfBP438HMi8grwKvBMq45ZKbU3iDGm1ceglFI7luvQ8BzwcWPM+VYfj1JK1ejlVKWU2oSI3AeMAd/UAKeU2mm0JU4ppZRSqg1pS5xSSimlVBvSEKeUUkop1YY0xCmllFJKtSENcUoppZRSbUhDnFJKKaVUG9IQp5RSSinVhv4/pvw5k2vpgnwAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#display(latam_income_df.iloc[1:,1:-1])\n",
"#display(latam_income_df.columns[1:-1].values)\n",
"for c in range(0,latam_income_df['country'].count()):\n",
" ax = latam_income_df.iloc[c,1:-1].plot(figsize = (10,8), title= 'Income for Latin American region', \\\n",
" xlabel='year', ylabel='US dollars')\n",
"ax.legend(latam_income_df['country']); #legend= latam_income_df['country'].values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and we plot it, per country:"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"latam_income_df.columns[1:] #x\n",
"latam_income_df.iloc[0][:].values[1:]#y\n",
"plot.bar(latam_income_df['country'].values , latam_income_df['avg_income_1990_2015'].values)\n",
"plot.title('Average Yearly Income for period: {} - {}'.format(latam_income_df.columns[1],\\\n",
" latam_income_df.columns[-2] ))\n",
"plot.xlabel('Country')\n",
"plot.xticks(rotation=90)\n",
"plot.ylabel('Income in USD');\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='income_plot'></a>\n",
"The plot above begins to reveal relevant information about income in the region; however, I would like to have the bars sorted for a better understanding:"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"ax.plot([1, 2, 3])\n",
"ax.legend(['A simple line']);"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"latam_income_df.sort_values(by='avg_income_1990_2015', ascending=False, inplace=True)\n",
"\n",
"ax = latam_income_df.plot(kind= 'bar', x='country' , y = 'avg_income_1990_2015',legend=False, \\\n",
" title = 'Average Yearly Income for period: {} - {}'.format(latam_income_df.columns[1],\\\n",
" latam_income_df.columns[-2] ))\n",
"ax.set_ylabel('Income in USD');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By looking at the average income distribution, we notice a gap: there are no countries listed with an average income between ~11,000 and 12,000 dollars:"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = latam_income_df['avg_income_1990_2015'].hist() #.plot(, title = 'Distribution of income');\n",
"ax.set_title('Distribution of income')\n",
"ax.set_xlabel('US dollars / year');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The average yearly income for the region is (in US dollars):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mean_income_LatAm = int(latam_income_df['avg_income_1990_2015'].describe()[1])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9776"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(mean_income_LatAm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, above the average we have:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>avg_income_1990_2015</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Argentina</td>\n",
" <td>15850.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>188</th>\n",
" <td>Venezuela</td>\n",
" <td>15776.92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>Chile</td>\n",
" <td>15768.46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>Mexico</td>\n",
" <td>15615.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>185</th>\n",
" <td>Uruguay</td>\n",
" <td>13940.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td>Panama</td>\n",
" <td>12579.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Brazil</td>\n",
" <td>12403.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>Costa Rica</td>\n",
" <td>10785.77</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" country avg_income_1990_2015\n",
"6 Argentina 15850.00\n",
"188 Venezuela 15776.92\n",
"34 Chile 15768.46\n",
"108 Mexico 15615.38\n",
"185 Uruguay 13940.00\n",
"132 Panama 12579.23\n",
"23 Brazil 12403.85\n",
"40 Costa Rica 10785.77"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"latam_income_df[['country','avg_income_1990_2015']].query('avg_income_1990_2015 > {}'.format(mean_income_LatAm))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we add an average country-wise for the education dataframes:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>1990</th>\n",
" <th>1991</th>\n",
" <th>1992</th>\n",
" <th>1993</th>\n",
" <th>1994</th>\n",
" <th>1995</th>\n",
" <th>1996</th>\n",
" <th>1997</th>\n",
" <th>1998</th>\n",
" <th>...</th>\n",
" <th>2001</th>\n",
" <th>2002</th>\n",
" <th>2003</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>avg_years_1990_2009</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Argentina</td>\n",
" <td>8.0</td>\n",
" <td>8.1</td>\n",
" <td>8.2</td>\n",
" <td>8.3</td>\n",
" <td>8.4</td>\n",
" <td>8.5</td>\n",
" <td>8.6</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>...</td>\n",
" <td>9.0</td>\n",
" <td>9.1</td>\n",
" <td>9.2</td>\n",
" <td>9.3</td>\n",
" <td>9.4</td>\n",
" <td>9.5</td>\n",
" <td>9.6</td>\n",
" <td>9.7</td>\n",
" <td>9.8</td>\n",
" <td>8.89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Bolivia</td>\n",
" <td>5.9</td>\n",
" <td>6.1</td>\n",
" <td>6.2</td>\n",
" <td>6.3</td>\n",
" <td>6.5</td>\n",
" <td>6.6</td>\n",
" <td>6.7</td>\n",
" <td>6.9</td>\n",
" <td>7.0</td>\n",
" <td>...</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>8.2</td>\n",
" <td>8.3</td>\n",
" <td>8.4</td>\n",
" <td>7.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Brazil</td>\n",
" <td>4.6</td>\n",
" <td>4.7</td>\n",
" <td>4.8</td>\n",
" <td>4.9</td>\n",
" <td>5.0</td>\n",
" <td>5.1</td>\n",
" <td>5.2</td>\n",
" <td>5.4</td>\n",
" <td>5.5</td>\n",
" <td>...</td>\n",
" <td>5.8</td>\n",
" <td>6.0</td>\n",
" <td>6.1</td>\n",
" <td>6.2</td>\n",
" <td>6.3</td>\n",
" <td>6.4</td>\n",
" <td>6.6</td>\n",
" <td>6.7</td>\n",
" <td>6.8</td>\n",
" <td>5.67</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" country 1990 1991 1992 1993 1994 1995 1996 1997 1998 ... \\\n",
"5 Argentina 8.0 8.1 8.2 8.3 8.4 8.5 8.6 8.6 8.7 ... \n",
"17 Bolivia 5.9 6.1 6.2 6.3 6.5 6.6 6.7 6.9 7.0 ... \n",
"20 Brazil 4.6 4.7 4.8 4.9 5.0 5.1 5.2 5.4 5.5 ... \n",
"\n",
" 2001 2002 2003 2004 2005 2006 2007 2008 2009 avg_years_1990_2009 \n",
"5 9.0 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 8.89 \n",
"17 7.4 7.5 7.6 7.8 7.9 8.0 8.2 8.3 8.4 7.18 \n",
"20 5.8 6.0 6.1 6.2 6.3 6.4 6.6 6.7 6.8 5.67 \n",
"\n",
"[3 rows x 22 columns]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"schooling_latam_male_age_25_older_df['avg_years_1990_2009'] = round(schooling_latam_male_age_25_older_df[schooling_latam_male_age_25_older_df.columns[1:]].mean(axis=1),2)\n",
"schooling_latam_male_age_25_older_df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>1990</th>\n",
" <th>1991</th>\n",
" <th>1992</th>\n",
" <th>1993</th>\n",
" <th>1994</th>\n",
" <th>1995</th>\n",
" <th>1996</th>\n",
" <th>1997</th>\n",
" <th>1998</th>\n",
" <th>...</th>\n",
" <th>2001</th>\n",
" <th>2002</th>\n",
" <th>2003</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>avg_years_1990_2009</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Argentina</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>8.4</td>\n",
" <td>8.5</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>8.8</td>\n",
" <td>...</td>\n",
" <td>9.2</td>\n",
" <td>9.3</td>\n",
" <td>9.4</td>\n",
" <td>9.5</td>\n",
" <td>9.6</td>\n",
" <td>9.8</td>\n",
" <td>9.9</td>\n",
" <td>10.0</td>\n",
" <td>10.1</td>\n",
" <td>9.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Bolivia</td>\n",
" <td>3.9</td>\n",
" <td>4.1</td>\n",
" <td>4.2</td>\n",
" <td>4.3</td>\n",
" <td>4.5</td>\n",
" <td>4.6</td>\n",
" <td>4.8</td>\n",
" <td>4.9</td>\n",
" <td>5.1</td>\n",
" <td>...</td>\n",
" <td>5.5</td>\n",
" <td>5.7</td>\n",
" <td>5.8</td>\n",
" <td>6.0</td>\n",
" <td>6.1</td>\n",
" <td>6.3</td>\n",
" <td>6.4</td>\n",
" <td>6.6</td>\n",
" <td>6.7</td>\n",
" <td>5.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Brazil</td>\n",
" <td>4.4</td>\n",
" <td>4.5</td>\n",
" <td>4.6</td>\n",
" <td>4.8</td>\n",
" <td>4.9</td>\n",
" <td>5.1</td>\n",
" <td>5.2</td>\n",
" <td>5.3</td>\n",
" <td>5.5</td>\n",
" <td>...</td>\n",
" <td>5.9</td>\n",
" <td>6.1</td>\n",
" <td>6.2</td>\n",
" <td>6.4</td>\n",
" <td>6.5</td>\n",
" <td>6.7</td>\n",
" <td>6.8</td>\n",
" <td>7.0</td>\n",
" <td>7.2</td>\n",
" <td>5.72</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" country 1990 1991 1992 1993 1994 1995 1996 1997 1998 ... \\\n",
"5 Argentina 7.9 8.0 8.1 8.3 8.4 8.5 8.6 8.7 8.8 ... \n",
"17 Bolivia 3.9 4.1 4.2 4.3 4.5 4.6 4.8 4.9 5.1 ... \n",
"20 Brazil 4.4 4.5 4.6 4.8 4.9 5.1 5.2 5.3 5.5 ... \n",
"\n",
" 2001 2002 2003 2004 2005 2006 2007 2008 2009 avg_years_1990_2009 \n",
"5 9.2 9.3 9.4 9.5 9.6 9.8 9.9 10.0 10.1 9.01 \n",
"17 5.5 5.7 5.8 6.0 6.1 6.3 6.4 6.6 6.7 5.30 \n",
"20 5.9 6.1 6.2 6.4 6.5 6.7 6.8 7.0 7.2 5.72 \n",
"\n",
"[3 rows x 22 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"schooling_latam_female_age_25_older_df['avg_years_1990_2009'] = round(schooling_latam_female_age_25_older_df[schooling_latam_female_age_25_older_df.columns[1:]].mean(axis=1),2)\n",
"schooling_latam_female_age_25_older_df.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will need to cope with the fact that both employment and education data are dissagregated by gender.\n",
"To achieve this, I decided to merge the female and male datasets of these two variables, so that we have a national average, just like with income."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"#merging female and male average years of schooling\n",
"schooling_combined = pd.DataFrame( schooling_latam_female_age_25_older_df['country'])\n",
"schooling_combined.insert(1,'avg_years_women', schooling_latam_female_age_25_older_df['avg_years_1990_2009'].values, True)\n",
"schooling_combined.insert(2,'avg_years_men', schooling_latam_male_age_25_older_df['avg_years_1990_2009'].values, True)\n",
"schooling_combined.insert(3,'nal_avg_years_schooling', round(schooling_combined[schooling_combined.columns[1:]].mean(axis=1),2), True)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = schooling_combined[['avg_years_men','avg_years_women']].plot(kind='barh',\\\n",
" title= 'Average years in school by gender', \\\n",
" ylabel='number years', xlabel='country', figsize = (10,8))\n",
"ax.set_yticklabels(schooling_combined['country']);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An interesting insight of the graph above, is that *in Uruguay, Panama, Costa Rica, Brazil, and Argentina women spend more time in school than men*. In all other countries seems to occur the opposite"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='education_plot'></a>\n",
"If we aggregate both female and male results, and plot these below to get an impression on years spent on education per country, we will see:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"schooling_combined.sort_values(by='nal_avg_years_schooling', ascending=False, inplace=True)\n",
"\n",
"ax = schooling_combined.plot(kind= 'bar', x='country' , y = 'nal_avg_years_schooling', legend=False, \\\n",
" title = 'Average years of study for period: {} - {}'.format(schooling_latam_female_age_25_older_df.columns[1],\\\n",
" schooling_latam_female_age_25_older_df.columns[-2] ))\n",
"ax.set_ylabel('years');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the top five countries investing more time in education"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>nal_avg_years_schooling</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>Cuba</td>\n",
" <td>9.28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>Chile</td>\n",
" <td>9.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Argentina</td>\n",
" <td>8.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166</th>\n",
" <td>Uruguay</td>\n",
" <td>8.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>Panama</td>\n",
" <td>8.41</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" country nal_avg_years_schooling\n",
"39 Cuba 9.28\n",
"30 Chile 9.08\n",
"5 Argentina 8.95\n",
"166 Uruguay 8.72\n",
"118 Panama 8.41"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"schooling_combined[['country','nal_avg_years_schooling']].head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='top_3_education'></a>\n",
"we observe three of them (all South American) also appear regarding income:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Argentina', 'Chile', 'Uruguay'], dtype=object)"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.intersect1d(latam_income_df['country'].head(5).values,schooling_combined['country'].head(5).values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At the other end of the the spectrum, these are the bottom six schooled countries:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>nal_avg_years_schooling</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>El Salvador</td>\n",
" <td>5.82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Colombia</td>\n",
" <td>5.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Brazil</td>\n",
" <td>5.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>Nicaragua</td>\n",
" <td>4.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>Honduras</td>\n",
" <td>4.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>Guatemala</td>\n",
" <td>3.57</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" country nal_avg_years_schooling\n",
"47 El Salvador 5.82\n",
"32 Colombia 5.76\n",
"20 Brazil 5.70\n",
"110 Nicaragua 4.77\n",
"67 Honduras 4.76\n",
"62 Guatemala 3.57"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"schooling_combined[['country','nal_avg_years_schooling']].tail(6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='bottom_4_education'></a>\n",
"and four of these countries (curiously, all Central American) have the least average income per capita:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['El Salvador', 'Guatemala', 'Honduras', 'Nicaragua'], dtype=object)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.intersect1d(latam_income_df['country'].tail(6).values,schooling_combined['country'].tail(6).values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, it looks like the more time spent on education, the more likely a country will have a higher average income per capita. There are some outliers (e.g. Mexico), ranked 10th in education, but 4th in income."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Research Question 2: Is there a relationship between the time spent educating and the Employment rate?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similar to education, we need to aggregate the dataframes for female and male employment rate:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>1991</th>\n",
" <th>1992</th>\n",
" <th>1993</th>\n",
" <th>1994</th>\n",
" <th>1995</th>\n",
" <th>1996</th>\n",
" <th>1997</th>\n",
" <th>1998</th>\n",
" <th>1999</th>\n",
" <th>...</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>2014</th>\n",
" <th>2015</th>\n",
" <th>avg_empl_1990_2015</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Argentina</td>\n",
" <td>0.424</td>\n",
" <td>0.428</td>\n",
" <td>0.405</td>\n",
" <td>0.404</td>\n",
" <td>0.364</td>\n",
" <td>0.383</td>\n",
" <td>0.397</td>\n",
" <td>0.419</td>\n",
" <td>0.413</td>\n",
" <td>...</td>\n",
" <td>0.436</td>\n",
" <td>0.434</td>\n",
" <td>0.440</td>\n",
" <td>0.421</td>\n",
" <td>0.440</td>\n",
" <td>0.439</td>\n",
" <td>0.437</td>\n",
" <td>0.432</td>\n",
" <td>0.435</td>\n",
" <td>0.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Bolivia</td>\n",
" <td>0.525</td>\n",
" <td>0.530</td>\n",
" <td>0.536</td>\n",
" <td>0.554</td>\n",
" <td>0.552</td>\n",
" <td>0.548</td>\n",
" <td>0.573</td>\n",
" <td>0.567</td>\n",
" <td>0.561</td>\n",
" <td>...</td>\n",
" <td>0.578</td>\n",
" <td>0.601</td>\n",
" <td>0.602</td>\n",
" <td>0.601</td>\n",
" <td>0.600</td>\n",
" <td>0.563</td>\n",
" <td>0.571</td>\n",
" <td>0.592</td>\n",
" <td>0.524</td>\n",
" <td>0.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Brazil</td>\n",
" <td>0.397</td>\n",
" <td>0.403</td>\n",
" <td>0.414</td>\n",
" <td>0.420</td>\n",
" <td>0.432</td>\n",
" <td>0.433</td>\n",
" <td>0.436</td>\n",
" <td>0.436</td>\n",
" <td>0.441</td>\n",
" <td>...</td>\n",
" <td>0.496</td>\n",
" <td>0.501</td>\n",
" <td>0.498</td>\n",
" <td>0.492</td>\n",
" <td>0.483</td>\n",
" <td>0.483</td>\n",
" <td>0.483</td>\n",
" <td>0.486</td>\n",
" <td>0.480</td>\n",
" <td>0.46</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" country 1991 1992 1993 1994 1995 1996 1997 1998 1999 \\\n",
"4 Argentina 0.424 0.428 0.405 0.404 0.364 0.383 0.397 0.419 0.413 \n",
"18 Bolivia 0.525 0.530 0.536 0.554 0.552 0.548 0.573 0.567 0.561 \n",
"21 Brazil 0.397 0.403 0.414 0.420 0.432 0.433 0.436 0.436 0.441 \n",
"\n",
" ... 2007 2008 2009 2010 2011 2012 2013 2014 2015 \\\n",
"4 ... 0.436 0.434 0.440 0.421 0.440 0.439 0.437 0.432 0.435 \n",
"18 ... 0.578 0.601 0.602 0.601 0.600 0.563 0.571 0.592 0.524 \n",
"21 ... 0.496 0.501 0.498 0.492 0.483 0.483 0.483 0.486 0.480 \n",
"\n",
" avg_empl_1990_2015 \n",
"4 0.42 \n",
"18 0.57 \n",
"21 0.46 \n",
"\n",
"[3 rows x 27 columns]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"latam_women_employment_df['avg_empl_1990_2015'] = round(latam_women_employment_df[latam_women_employment_df.columns[1:]].mean(axis=1),2)\n",
"latam_women_employment_df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>1991</th>\n",
" <th>1992</th>\n",
" <th>1993</th>\n",
" <th>1994</th>\n",
" <th>1995</th>\n",
" <th>1996</th>\n",
" <th>1997</th>\n",
" <th>1998</th>\n",
" <th>1999</th>\n",
" <th>...</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>2014</th>\n",
" <th>2015</th>\n",
" <th>avg_empl_1990_2015</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Argentina</td>\n",
" <td>0.715</td>\n",
" <td>0.708</td>\n",
" <td>0.689</td>\n",
" <td>0.675</td>\n",
" <td>0.627</td>\n",
" <td>0.633</td>\n",
" <td>0.655</td>\n",
" <td>0.665</td>\n",
" <td>0.653</td>\n",
" <td>...</td>\n",
" <td>0.701</td>\n",
" <td>0.697</td>\n",
" <td>0.688</td>\n",
" <td>0.691</td>\n",
" <td>0.698</td>\n",
" <td>0.696</td>\n",
" <td>0.694</td>\n",
" <td>0.687</td>\n",
" <td>0.687</td>\n",
" <td>0.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Bolivia</td>\n",
" <td>0.768</td>\n",
" <td>0.769</td>\n",
" <td>0.761</td>\n",
" <td>0.788</td>\n",
" <td>0.789</td>\n",
" <td>0.777</td>\n",
" <td>0.802</td>\n",
" <td>0.795</td>\n",
" <td>0.788</td>\n",
" <td>...</td>\n",
" <td>0.787</td>\n",
" <td>0.804</td>\n",
" <td>0.799</td>\n",
" <td>0.803</td>\n",
" <td>0.806</td>\n",
" <td>0.788</td>\n",
" <td>0.783</td>\n",
" <td>0.807</td>\n",
" <td>0.777</td>\n",
" <td>0.79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Brazil</td>\n",
" <td>0.790</td>\n",
" <td>0.780</td>\n",
" <td>0.779</td>\n",
" <td>0.777</td>\n",
" <td>0.773</td>\n",
" <td>0.766</td>\n",
" <td>0.757</td>\n",
" <td>0.746</td>\n",
" <td>0.736</td>\n",
" <td>...</td>\n",
" <td>0.739</td>\n",
" <td>0.742</td>\n",
" <td>0.737</td>\n",
" <td>0.736</td>\n",
" <td>0.730</td>\n",
" <td>0.718</td>\n",
" <td>0.718</td>\n",
" <td>0.714</td>\n",
" <td>0.700</td>\n",
" <td>0.74</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" country 1991 1992 1993 1994 1995 1996 1997 1998 1999 \\\n",
"4 Argentina 0.715 0.708 0.689 0.675 0.627 0.633 0.655 0.665 0.653 \n",
"18 Bolivia 0.768 0.769 0.761 0.788 0.789 0.777 0.802 0.795 0.788 \n",
"21 Brazil 0.790 0.780 0.779 0.777 0.773 0.766 0.757 0.746 0.736 \n",
"\n",
" ... 2007 2008 2009 2010 2011 2012 2013 2014 2015 \\\n",
"4 ... 0.701 0.697 0.688 0.691 0.698 0.696 0.694 0.687 0.687 \n",
"18 ... 0.787 0.804 0.799 0.803 0.806 0.788 0.783 0.807 0.777 \n",
"21 ... 0.739 0.742 0.737 0.736 0.730 0.718 0.718 0.714 0.700 \n",
"\n",
" avg_empl_1990_2015 \n",
"4 0.67 \n",
"18 0.79 \n",
"21 0.74 \n",
"\n",
"[3 rows x 27 columns]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"latam_men_employment_df['avg_empl_1990_2015'] = round(latam_men_employment_df[latam_men_employment_df.columns[1:]].mean(axis=1),2)\n",
"latam_men_employment_df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>nal_empl_rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Argentina</td>\n",
" <td>0.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Bolivia</td>\n",
" <td>0.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Brazil</td>\n",
" <td>0.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Chile</td>\n",
" <td>0.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>Colombia</td>\n",
" <td>0.56</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" country nal_empl_rate\n",
"4 Argentina 0.55\n",
"18 Bolivia 0.68\n",
"21 Brazil 0.60\n",
"32 Chile 0.53\n",
"34 Colombia 0.56"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#aggregate female and male employment rates\n",
"employment_combined = pd.DataFrame( latam_women_employment_df['country'])\n",
"employment_combined.insert(1,'avg_empl_women', latam_women_employment_df['avg_empl_1990_2015'].values, True)\n",
"employment_combined.insert(2,'avg_empl_men', latam_men_employment_df['avg_empl_1990_2015'].values, True)\n",
"employment_combined.insert(3,'nal_empl_rate', round(employment_combined[employment_combined.columns[1:]].mean(axis=1),2), True)\n",
"employment_combined[['country','nal_empl_rate']].head() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='employment_plot'></a>\n",
"and we obtain this plot:"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#plot the sorted employment rate per country\n",
"employment_combined.sort_values(by='nal_empl_rate', ascending=False, inplace=True)\n",
"ax = employment_combined.plot(kind= 'bar', x='country' , y = 'nal_empl_rate', legend=False, \\\n",
" title = 'Average employment rate for period: {} - {}'.format(latam_women_employment_df.columns[1],\\\n",
" latam_women_employment_df.columns[-2] ))\n",
"ax.set_ylabel(\"employment %\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The top and bottom five countries for this variable are, respectively:"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>nal_empl_rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>125</th>\n",
" <td>Peru</td>\n",
" <td>0.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Bolivia</td>\n",
" <td>0.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td>Paraguay</td>\n",
" <td>0.66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Ecuador</td>\n",
" <td>0.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>Guatemala</td>\n",
" <td>0.62</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" country nal_empl_rate\n",
"125 Peru 0.68\n",
"18 Bolivia 0.68\n",
"124 Paraguay 0.66\n",
"47 Ecuador 0.62\n",
"64 Guatemala 0.62"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>nal_empl_rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>Costa Rica</td>\n",
" <td>0.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>Colombia</td>\n",
" <td>0.56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Argentina</td>\n",
" <td>0.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Chile</td>\n",
" <td>0.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>Cuba</td>\n",
" <td>0.51</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" country nal_empl_rate\n",
"38 Costa Rica 0.57\n",
"34 Colombia 0.56\n",
"4 Argentina 0.55\n",
"32 Chile 0.53\n",
"41 Cuba 0.51"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(employment_combined[['country','nal_empl_rate']].head(5), employment_combined[['country','nal_empl_rate']].tail(5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We observe three Andean countries (i.e., Peru, Bolivia, and Ecuador) in the top five of the list for employment."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I was expecting to find a similar relationship between education and income. However, I find no common countries in the top five between education and employment rate:"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([], dtype=object)"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.intersect1d(employment_combined['country'].head(5).values, schooling_combined['country'].head(5).values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='inverse_relation'></a>\n",
"What I find odd (based on my previous assumption that the higher the number of years spent in school, the higher the employment rate),\n",
"is that three of the top five countries spending more years at school, are among those having the lowest employment rates(!):"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Argentina', 'Chile', 'Cuba'], dtype=object)"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.intersect1d(employment_combined['country'].tail(5).values, schooling_combined['country'].head(5).values)\n",
"#display(employment_combined['country'].tail(5).values, schooling_combined['country'].head(5).values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Out of the bottom five, the only country with few years in schooling **and** a low employment rate is: "
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Colombia'], dtype=object)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.intersect1d(employment_combined['country'].tail(5).values, schooling_combined['country'].tail(5).values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='conclusions'></a>\n",
"## Conclusions\n",
"\n",
"<a href=\"#toc\">go above</a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Having explored the datasets available for this project, I think they provided me a sufficient frame to answer the proposed questions. Nonetheless, the size/reach for one of the variables could have been better, namely, for education we only had data available until 2009, missing six more years of information compared to the other variables (income and employment rate).\n",
"\n",
"I admit I had to spent some time thinking what to do with employment rate and average years of school (which were separated by gender), in order to relate these to national income. This exercise helped me to realize the importance of getting familiar with data, and try to extract meaning from them.\n",
"\n",
"The main findings of the analysis based on the research questions are:\n",
"\n",
"* <a href=\"#education_plot\">Education</a> and <a href=\"#income_plot\">income</a> seem to have a positive correlation, <a href=\"#top_3_education\">Three</a> *of the top five nations which have invested more years in school, are also in the top five earners in the region. Likewise, <a href=\"#bottom_4_education\">four</a> of the last six countries spending less years in school, are too in the bottom six regarding income (these countries are all in Central America)*.\n",
"\n",
"* *Education and <a href='#employment_plot'>employment</a> seem to have an (odd) <a href='#inverse_relation'>inversely proportional</a> relationship, at least in the lower end*.\n",
"It would be interesting to analyze (all the) other factors behind that behavior, which are beyond the scope of this project and the data available."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally (barring the natural time constraints imposed to deliver this project), further analysis could be done in the direction of analyzing trends in other regions of the world (e.g. Sub-saharan Africa, or the Far East). Another improvement point would be to add statistical tests to the data used, however the author does not regard himself confident at the moment of submission to address this aspect of the analysis."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
@omendezmorales
Copy link
Author

Initial commit

@omendezmorales
Copy link
Author

ADDED PREVIEW OF MY NOTEBOOK TO SUBMIT

@omendezmorales
Copy link
Author

removed template

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment