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| "name": "python", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.8" | |
| }, | |
| "kernelspec": { | |
| "name": "python", | |
| "display_name": "Pyolite", | |
| "language": "python" | |
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| "nbformat_minor": 4, | |
| "nbformat": 4, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "source": "<center>\n <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\">\n</center>\n\n# Data Visualization\n\nEstimated time needed: **30** minutes\n\n## Objectives\n\nAfter completing this lab you will be able to:\n\n* Create Data Visualization with Python\n* Use various Python libraries for visualization\n", | |
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| "cell_type": "markdown", | |
| "source": "## Introduction\n\nThe aim of these labs is to introduce you to data visualization with Python as concrete and as consistent as possible.\nSpeaking of consistency, because there is no *best* data visualization library available for Python - up to creating these labs - we have to introduce different libraries and show their benefits when we are discussing new visualization concepts. Doing so, we hope to make students well-rounded with visualization libraries and concepts so that they are able to judge and decide on the best visualization technique and tool for a given problem *and* audience.\n\nPlease make sure that you have completed the prerequisites for this course, namely [**Python Basics for Data Science**](https://www.edx.org/course/python-basics-for-data-science-2?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) and [**Analyzing Data with Python**](https://www.edx.org/course/data-analysis-with-python?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\n\n**Note**: The majority of the plots and visualizations will be generated using data stored in *pandas* dataframes. Therefore, in this lab, we provide a brief crash course on *pandas*. However, if you are interested in learning more about the *pandas* library, detailed description and explanation of how to use it and how to clean, munge, and process data stored in a *pandas* dataframe are provided in our course [**Analyzing Data with Python**](https://www.edx.org/course/data-analysis-with-python?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\n\n***\n", | |
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| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "## Table of Contents\n\n<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n\n1. [Exploring Datasets with *pandas*](#0)<br>\n\n1.1 [The Dataset: Immigration to Canada from 1980 to 2013](#2)<br>\n1.2 [*pandas* Basics](#4) <br>\n1.3 [*pandas* Intermediate: Indexing and Selection](#6) <br>\n2\\. [Visualizing Data using Matplotlib](#8) <br>\n2.1 [Matplotlib: Standard Python Visualization Library](#10) <br>\n3\\. [Line Plots](#12)\n\n</div>\n", | |
| "metadata": { | |
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| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "# Exploring Datasets with *pandas* <a id=\"0\"></a>\n\n*pandas* is an essential data analysis toolkit for Python. From their [website](http://pandas.pydata.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01):\n\n> *pandas* is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, **real world** data analysis in Python.\n\nThe course heavily relies on *pandas* for data wrangling, analysis, and visualization. We encourage you to spend some time and familiarize yourself with the *pandas* API Reference: [http://pandas.pydata.org/pandas-docs/stable/api.html](http://pandas.pydata.org/pandas-docs/stable/api.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "## The Dataset: Immigration to Canada from 1980 to 2013 <a id=\"2\"></a>\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Dataset Source: [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\n\nThe dataset contains annual data on the flows of international immigrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. The current version presents data pertaining to 45 countries.\n\nIn this lab, we will focus on the Canadian immigration data.\n\n\n\nThe Canada Immigration dataset can be fetched from <a href=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01\">here</a>.\n\n***\n", | |
| "metadata": { | |
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| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "## *pandas* Basics<a id=\"4\"></a>\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "The first thing we'll do is install **openpyxl** (formerly **xlrd**), a module that *pandas* requires to read Excel files.\n", | |
| "metadata": {} | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "import piplite\nawait piplite.install(['openpyxl==3.0.9'])", | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "execution_count": 1, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Next, we'll do is import two key data analysis modules: *pandas* and *numpy*.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "import numpy as np # useful for many scientific computing in Python\nimport pandas as pd # primary data structure library", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Let's download and import our primary Canadian Immigration dataset using *pandas*'s `read_excel()` method.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "from js import fetch\nimport io\n\nURL = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx'\nresp = await fetch(URL)\ntext = io.BytesIO((await resp.arrayBuffer()).to_py())\n\ndf_can = pd.read_excel(\n text,\n sheet_name='Canada by Citizenship',\n skiprows=range(20),\n skipfooter=2)\nprint('Data downloaded and read into a dataframe!')", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "text": "Data downloaded and read into a dataframe!\n", | |
| "output_type": "stream" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Let's view the top 5 rows of the dataset using the `head()` function.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_can.head()\n# tip: You can specify the number of rows you'd like to see as follows: df_can.head(10) ", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 4, | |
| "outputs": [ | |
| { | |
| "execution_count": 4, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": " Type Coverage OdName AREA AreaName REG \\\n0 Immigrants Foreigners Afghanistan 935 Asia 5501 \n1 Immigrants Foreigners Albania 908 Europe 925 \n2 Immigrants Foreigners Algeria 903 Africa 912 \n3 Immigrants Foreigners American Samoa 909 Oceania 957 \n4 Immigrants Foreigners Andorra 908 Europe 925 \n\n RegName DEV DevName 1980 ... 2004 2005 2006 \\\n0 Southern Asia 902 Developing regions 16 ... 2978 3436 3009 \n1 Southern Europe 901 Developed regions 1 ... 1450 1223 856 \n2 Northern Africa 902 Developing regions 80 ... 3616 3626 4807 \n3 Polynesia 902 Developing regions 0 ... 0 0 1 \n4 Southern Europe 901 Developed regions 0 ... 0 0 1 \n\n 2007 2008 2009 2010 2011 2012 2013 \n0 2652 2111 1746 1758 2203 2635 2004 \n1 702 560 716 561 539 620 603 \n2 3623 4005 5393 4752 4325 3774 4331 \n3 0 0 0 0 0 0 0 \n4 1 0 0 0 0 1 1 \n\n[5 rows x 43 columns]", | |
| "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>Type</th>\n <th>Coverage</th>\n <th>OdName</th>\n <th>AREA</th>\n <th>AreaName</th>\n <th>REG</th>\n <th>RegName</th>\n <th>DEV</th>\n <th>DevName</th>\n <th>1980</th>\n <th>...</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>2010</th>\n <th>2011</th>\n <th>2012</th>\n <th>2013</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Immigrants</td>\n <td>Foreigners</td>\n <td>Afghanistan</td>\n <td>935</td>\n <td>Asia</td>\n <td>5501</td>\n <td>Southern Asia</td>\n <td>902</td>\n <td>Developing regions</td>\n <td>16</td>\n <td>...</td>\n <td>2978</td>\n <td>3436</td>\n <td>3009</td>\n <td>2652</td>\n <td>2111</td>\n <td>1746</td>\n <td>1758</td>\n <td>2203</td>\n <td>2635</td>\n <td>2004</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Immigrants</td>\n <td>Foreigners</td>\n <td>Albania</td>\n <td>908</td>\n <td>Europe</td>\n <td>925</td>\n <td>Southern Europe</td>\n <td>901</td>\n <td>Developed regions</td>\n <td>1</td>\n <td>...</td>\n <td>1450</td>\n <td>1223</td>\n <td>856</td>\n <td>702</td>\n <td>560</td>\n <td>716</td>\n <td>561</td>\n <td>539</td>\n <td>620</td>\n <td>603</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Immigrants</td>\n <td>Foreigners</td>\n <td>Algeria</td>\n <td>903</td>\n <td>Africa</td>\n <td>912</td>\n <td>Northern Africa</td>\n <td>902</td>\n <td>Developing regions</td>\n <td>80</td>\n <td>...</td>\n <td>3616</td>\n <td>3626</td>\n <td>4807</td>\n <td>3623</td>\n <td>4005</td>\n <td>5393</td>\n <td>4752</td>\n <td>4325</td>\n <td>3774</td>\n <td>4331</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Immigrants</td>\n <td>Foreigners</td>\n <td>American Samoa</td>\n <td>909</td>\n <td>Oceania</td>\n <td>957</td>\n <td>Polynesia</td>\n <td>902</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Immigrants</td>\n <td>Foreigners</td>\n <td>Andorra</td>\n <td>908</td>\n <td>Europe</td>\n <td>925</td>\n <td>Southern Europe</td>\n <td>901</td>\n <td>Developed regions</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 43 columns</p>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "We can also view the bottom 5 rows of the dataset using the `tail()` function.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_can.tail()", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 5, | |
| "outputs": [ | |
| { | |
| "execution_count": 5, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": " Type Coverage OdName AREA AreaName REG \\\n190 Immigrants Foreigners Viet Nam 935 Asia 920 \n191 Immigrants Foreigners Western Sahara 903 Africa 912 \n192 Immigrants Foreigners Yemen 935 Asia 922 \n193 Immigrants Foreigners Zambia 903 Africa 910 \n194 Immigrants Foreigners Zimbabwe 903 Africa 910 \n\n RegName DEV DevName 1980 ... 2004 2005 2006 \\\n190 South-Eastern Asia 902 Developing regions 1191 ... 1816 1852 3153 \n191 Northern Africa 902 Developing regions 0 ... 0 0 1 \n192 Western Asia 902 Developing regions 1 ... 124 161 140 \n193 Eastern Africa 902 Developing regions 11 ... 56 91 77 \n194 Eastern Africa 902 Developing regions 72 ... 1450 615 454 \n\n 2007 2008 2009 2010 2011 2012 2013 \n190 2574 1784 2171 1942 1723 1731 2112 \n191 0 0 0 0 0 0 0 \n192 122 133 128 211 160 174 217 \n193 71 64 60 102 69 46 59 \n194 663 611 508 494 434 437 407 \n\n[5 rows x 43 columns]", | |
| "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>Type</th>\n <th>Coverage</th>\n <th>OdName</th>\n <th>AREA</th>\n <th>AreaName</th>\n <th>REG</th>\n <th>RegName</th>\n <th>DEV</th>\n <th>DevName</th>\n <th>1980</th>\n <th>...</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>2010</th>\n <th>2011</th>\n <th>2012</th>\n <th>2013</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>190</th>\n <td>Immigrants</td>\n <td>Foreigners</td>\n <td>Viet Nam</td>\n <td>935</td>\n <td>Asia</td>\n <td>920</td>\n <td>South-Eastern Asia</td>\n <td>902</td>\n <td>Developing regions</td>\n <td>1191</td>\n <td>...</td>\n <td>1816</td>\n <td>1852</td>\n <td>3153</td>\n <td>2574</td>\n <td>1784</td>\n <td>2171</td>\n <td>1942</td>\n <td>1723</td>\n <td>1731</td>\n <td>2112</td>\n </tr>\n <tr>\n <th>191</th>\n <td>Immigrants</td>\n <td>Foreigners</td>\n <td>Western Sahara</td>\n <td>903</td>\n <td>Africa</td>\n <td>912</td>\n <td>Northern Africa</td>\n <td>902</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>192</th>\n <td>Immigrants</td>\n <td>Foreigners</td>\n <td>Yemen</td>\n <td>935</td>\n <td>Asia</td>\n <td>922</td>\n <td>Western Asia</td>\n <td>902</td>\n <td>Developing regions</td>\n <td>1</td>\n <td>...</td>\n <td>124</td>\n <td>161</td>\n <td>140</td>\n <td>122</td>\n <td>133</td>\n <td>128</td>\n <td>211</td>\n <td>160</td>\n <td>174</td>\n <td>217</td>\n </tr>\n <tr>\n <th>193</th>\n <td>Immigrants</td>\n <td>Foreigners</td>\n <td>Zambia</td>\n <td>903</td>\n <td>Africa</td>\n <td>910</td>\n <td>Eastern Africa</td>\n <td>902</td>\n <td>Developing regions</td>\n <td>11</td>\n <td>...</td>\n <td>56</td>\n <td>91</td>\n <td>77</td>\n <td>71</td>\n <td>64</td>\n <td>60</td>\n <td>102</td>\n <td>69</td>\n <td>46</td>\n <td>59</td>\n </tr>\n <tr>\n <th>194</th>\n <td>Immigrants</td>\n <td>Foreigners</td>\n <td>Zimbabwe</td>\n <td>903</td>\n <td>Africa</td>\n <td>910</td>\n <td>Eastern Africa</td>\n <td>902</td>\n <td>Developing regions</td>\n <td>72</td>\n <td>...</td>\n <td>1450</td>\n <td>615</td>\n <td>454</td>\n <td>663</td>\n <td>611</td>\n <td>508</td>\n <td>494</td>\n <td>434</td>\n <td>437</td>\n <td>407</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 43 columns</p>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "When analyzing a dataset, it's always a good idea to start by getting basic information about your dataframe. We can do this by using the `info()` method.\n\nThis method can be used to get a short summary of the dataframe.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_can.info(verbose=False)", | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 195 entries, 0 to 194\nColumns: 43 entries, Type to 2013\ndtypes: int64(37), object(6)\nmemory usage: 61.0+ KB\n", | |
| "output_type": "stream" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "To get the list of column headers we can call upon the data frame's `columns` instance variable.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_can.columns", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "execution_count": 7, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "Index([ 'Type', 'Coverage', 'OdName', 'AREA', 'AreaName', 'REG',\n 'RegName', 'DEV', 'DevName', 1980, 1981, 1982,\n 1983, 1984, 1985, 1986, 1987, 1988,\n 1989, 1990, 1991, 1992, 1993, 1994,\n 1995, 1996, 1997, 1998, 1999, 2000,\n 2001, 2002, 2003, 2004, 2005, 2006,\n 2007, 2008, 2009, 2010, 2011, 2012,\n 2013],\n dtype='object')" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Similarly, to get the list of indices we use the `.index` instance variables.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_can.index", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": true, | |
| "trusted": true | |
| }, | |
| "execution_count": 8, | |
| "outputs": [ | |
| { | |
| "execution_count": 8, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "RangeIndex(start=0, stop=195, step=1)" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Note: The default type of intance variables `index` and `columns` are **NOT** `list`.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "print(type(df_can.columns))\nprint(type(df_can.index))", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "text": "<class 'pandas.core.indexes.base.Index'>\n<class 'pandas.core.indexes.range.RangeIndex'>\n", | |
| "output_type": "stream" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "To get the index and columns as lists, we can use the `tolist()` method.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_can.columns.tolist()", | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| }, | |
| "jupyter": { | |
| "outputs_hidden": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "execution_count": 10, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "['Type',\n 'Coverage',\n 'OdName',\n 'AREA',\n 'AreaName',\n 'REG',\n 'RegName',\n 'DEV',\n 'DevName',\n 1980,\n 1981,\n 1982,\n 1983,\n 1984,\n 1985,\n 1986,\n 1987,\n 1988,\n 1989,\n 1990,\n 1991,\n 1992,\n 1993,\n 1994,\n 1995,\n 1996,\n 1997,\n 1998,\n 1999,\n 2000,\n 2001,\n 2002,\n 2003,\n 2004,\n 2005,\n 2006,\n 2007,\n 2008,\n 2009,\n 2010,\n 2011,\n 2012,\n 2013]" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_can.index.tolist()", | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| }, | |
| "jupyter": { | |
| "outputs_hidden": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "execution_count": 11, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "[0,\n 1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n 8,\n 9,\n 10,\n 11,\n 12,\n 13,\n 14,\n 15,\n 16,\n 17,\n 18,\n 19,\n 20,\n 21,\n 22,\n 23,\n 24,\n 25,\n 26,\n 27,\n 28,\n 29,\n 30,\n 31,\n 32,\n 33,\n 34,\n 35,\n 36,\n 37,\n 38,\n 39,\n 40,\n 41,\n 42,\n 43,\n 44,\n 45,\n 46,\n 47,\n 48,\n 49,\n 50,\n 51,\n 52,\n 53,\n 54,\n 55,\n 56,\n 57,\n 58,\n 59,\n 60,\n 61,\n 62,\n 63,\n 64,\n 65,\n 66,\n 67,\n 68,\n 69,\n 70,\n 71,\n 72,\n 73,\n 74,\n 75,\n 76,\n 77,\n 78,\n 79,\n 80,\n 81,\n 82,\n 83,\n 84,\n 85,\n 86,\n 87,\n 88,\n 89,\n 90,\n 91,\n 92,\n 93,\n 94,\n 95,\n 96,\n 97,\n 98,\n 99,\n 100,\n 101,\n 102,\n 103,\n 104,\n 105,\n 106,\n 107,\n 108,\n 109,\n 110,\n 111,\n 112,\n 113,\n 114,\n 115,\n 116,\n 117,\n 118,\n 119,\n 120,\n 121,\n 122,\n 123,\n 124,\n 125,\n 126,\n 127,\n 128,\n 129,\n 130,\n 131,\n 132,\n 133,\n 134,\n 135,\n 136,\n 137,\n 138,\n 139,\n 140,\n 141,\n 142,\n 143,\n 144,\n 145,\n 146,\n 147,\n 148,\n 149,\n 150,\n 151,\n 152,\n 153,\n 154,\n 155,\n 156,\n 157,\n 158,\n 159,\n 160,\n 161,\n 162,\n 163,\n 164,\n 165,\n 166,\n 167,\n 168,\n 169,\n 170,\n 171,\n 172,\n 173,\n 174,\n 175,\n 176,\n 177,\n 178,\n 179,\n 180,\n 181,\n 182,\n 183,\n 184,\n 185,\n 186,\n 187,\n 188,\n 189,\n 190,\n 191,\n 192,\n 193,\n 194]" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "print(type(df_can.columns.tolist()))\nprint(type(df_can.index.tolist()))", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "text": "<class 'list'>\n<class 'list'>\n", | |
| "output_type": "stream" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "To view the dimensions of the dataframe, we use the `shape` instance variable of it.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "# size of dataframe (rows, columns)\ndf_can.shape ", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 13, | |
| "outputs": [ | |
| { | |
| "execution_count": 13, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "(195, 43)" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "**Note**: The main types stored in *pandas* objects are `float`, `int`, `bool`, `datetime64[ns]`, `datetime64[ns, tz]`, `timedelta[ns]`, `category`, and `object` (string). In addition, these dtypes have item sizes, e.g. `int64` and `int32`.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Let's clean the data set to remove a few unnecessary columns. We can use *pandas* `drop()` method as follows:\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "# in pandas axis=0 represents rows (default) and axis=1 represents columns.\ndf_can.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\ndf_can.head(2)", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "execution_count": 14, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": " OdName AreaName RegName DevName 1980 1981 \\\n0 Afghanistan Asia Southern Asia Developing regions 16 39 \n1 Albania Europe Southern Europe Developed regions 1 0 \n\n 1982 1983 1984 1985 ... 2004 2005 2006 2007 2008 2009 2010 \\\n0 39 47 71 340 ... 2978 3436 3009 2652 2111 1746 1758 \n1 0 0 0 0 ... 1450 1223 856 702 560 716 561 \n\n 2011 2012 2013 \n0 2203 2635 2004 \n1 539 620 603 \n\n[2 rows x 38 columns]", | |
| "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>OdName</th>\n <th>AreaName</th>\n <th>RegName</th>\n <th>DevName</th>\n <th>1980</th>\n <th>1981</th>\n <th>1982</th>\n <th>1983</th>\n <th>1984</th>\n <th>1985</th>\n <th>...</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>2010</th>\n <th>2011</th>\n <th>2012</th>\n <th>2013</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Afghanistan</td>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>16</td>\n <td>39</td>\n <td>39</td>\n <td>47</td>\n <td>71</td>\n <td>340</td>\n <td>...</td>\n <td>2978</td>\n <td>3436</td>\n <td>3009</td>\n <td>2652</td>\n <td>2111</td>\n <td>1746</td>\n <td>1758</td>\n <td>2203</td>\n <td>2635</td>\n <td>2004</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Albania</td>\n <td>Europe</td>\n <td>Southern Europe</td>\n <td>Developed regions</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>1450</td>\n <td>1223</td>\n <td>856</td>\n <td>702</td>\n <td>560</td>\n <td>716</td>\n <td>561</td>\n <td>539</td>\n <td>620</td>\n <td>603</td>\n </tr>\n </tbody>\n</table>\n<p>2 rows × 38 columns</p>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Let's rename the columns so that they make sense. We can use `rename()` method by passing in a dictionary of old and new names as follows:\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True)\ndf_can.columns", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "execution_count": 15, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "Index([ 'Country', 'Continent', 'Region', 'DevName', 1980,\n 1981, 1982, 1983, 1984, 1985,\n 1986, 1987, 1988, 1989, 1990,\n 1991, 1992, 1993, 1994, 1995,\n 1996, 1997, 1998, 1999, 2000,\n 2001, 2002, 2003, 2004, 2005,\n 2006, 2007, 2008, 2009, 2010,\n 2011, 2012, 2013],\n dtype='object')" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "We will also add a 'Total' column that sums up the total immigrants by country over the entire period 1980 - 2013, as follows:\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_can['Total'] = df_can.sum(axis=1)", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "text": "<ipython-input-16-8cd345a76c6e>:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n df_can['Total'] = df_can.sum(axis=1)\n", | |
| "output_type": "stream" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "We can check to see how many null objects we have in the dataset as follows:\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_can.isnull().sum()", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": true, | |
| "trusted": true | |
| }, | |
| "execution_count": 17, | |
| "outputs": [ | |
| { | |
| "execution_count": 17, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "Country 0\nContinent 0\nRegion 0\nDevName 0\n1980 0\n1981 0\n1982 0\n1983 0\n1984 0\n1985 0\n1986 0\n1987 0\n1988 0\n1989 0\n1990 0\n1991 0\n1992 0\n1993 0\n1994 0\n1995 0\n1996 0\n1997 0\n1998 0\n1999 0\n2000 0\n2001 0\n2002 0\n2003 0\n2004 0\n2005 0\n2006 0\n2007 0\n2008 0\n2009 0\n2010 0\n2011 0\n2012 0\n2013 0\nTotal 0\ndtype: int64" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Finally, let's view a quick summary of each column in our dataframe using the `describe()` method.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_can.describe()", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 18, | |
| "outputs": [ | |
| { | |
| "execution_count": 18, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": " 1980 1981 1982 1983 1984 \\\ncount 195.000000 195.000000 195.000000 195.000000 195.000000 \nmean 508.394872 566.989744 534.723077 387.435897 376.497436 \nstd 1949.588546 2152.643752 1866.997511 1204.333597 1198.246371 \nmin 0.000000 0.000000 0.000000 0.000000 0.000000 \n25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n50% 13.000000 10.000000 11.000000 12.000000 13.000000 \n75% 251.500000 295.500000 275.000000 173.000000 181.000000 \nmax 22045.000000 24796.000000 20620.000000 10015.000000 10170.000000 \n\n 1985 1986 1987 1988 1989 \\\ncount 195.000000 195.000000 195.000000 195.000000 195.000000 \nmean 358.861538 441.271795 691.133333 714.389744 843.241026 \nstd 1079.309600 1225.576630 2109.205607 2443.606788 2555.048874 \nmin 0.000000 0.000000 0.000000 0.000000 0.000000 \n25% 0.000000 0.500000 0.500000 1.000000 1.000000 \n50% 17.000000 18.000000 26.000000 34.000000 44.000000 \n75% 197.000000 254.000000 434.000000 409.000000 508.500000 \nmax 9564.000000 9470.000000 21337.000000 27359.000000 23795.000000 \n\n ... 2005 2006 2007 2008 \\\ncount ... 195.000000 195.000000 195.000000 195.000000 \nmean ... 1320.292308 1266.958974 1191.820513 1246.394872 \nstd ... 4425.957828 3926.717747 3443.542409 3694.573544 \nmin ... 0.000000 0.000000 0.000000 0.000000 \n25% ... 28.500000 25.000000 31.000000 31.000000 \n50% ... 210.000000 218.000000 198.000000 205.000000 \n75% ... 832.000000 842.000000 899.000000 934.500000 \nmax ... 42584.000000 33848.000000 28742.000000 30037.000000 \n\n 2009 2010 2011 2012 2013 \\\ncount 195.000000 195.000000 195.000000 195.000000 195.000000 \nmean 1275.733333 1420.287179 1262.533333 1313.958974 1320.702564 \nstd 3829.630424 4462.946328 4030.084313 4247.555161 4237.951988 \nmin 0.000000 0.000000 0.000000 0.000000 0.000000 \n25% 36.000000 40.500000 37.500000 42.500000 45.000000 \n50% 214.000000 211.000000 179.000000 233.000000 213.000000 \n75% 888.000000 932.000000 772.000000 783.000000 796.000000 \nmax 29622.000000 38617.000000 36765.000000 34315.000000 34129.000000 \n\n Total \ncount 195.000000 \nmean 32867.451282 \nstd 91785.498686 \nmin 1.000000 \n25% 952.000000 \n50% 5018.000000 \n75% 22239.500000 \nmax 691904.000000 \n\n[8 rows x 35 columns]", | |
| "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>1980</th>\n <th>1981</th>\n <th>1982</th>\n <th>1983</th>\n <th>1984</th>\n <th>1985</th>\n <th>1986</th>\n <th>1987</th>\n <th>1988</th>\n <th>1989</th>\n <th>...</th>\n <th>2005</th>\n <th>2006</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>Total</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>195.000000</td>\n <td>195.000000</td>\n <td>195.000000</td>\n <td>195.000000</td>\n <td>195.000000</td>\n <td>195.000000</td>\n <td>195.000000</td>\n <td>195.000000</td>\n <td>195.000000</td>\n <td>195.000000</td>\n <td>...</td>\n <td>195.000000</td>\n <td>195.000000</td>\n <td>195.000000</td>\n <td>195.000000</td>\n <td>195.000000</td>\n <td>195.000000</td>\n <td>195.000000</td>\n <td>195.000000</td>\n <td>195.000000</td>\n <td>195.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>508.394872</td>\n <td>566.989744</td>\n <td>534.723077</td>\n <td>387.435897</td>\n <td>376.497436</td>\n <td>358.861538</td>\n <td>441.271795</td>\n <td>691.133333</td>\n <td>714.389744</td>\n <td>843.241026</td>\n <td>...</td>\n <td>1320.292308</td>\n <td>1266.958974</td>\n <td>1191.820513</td>\n <td>1246.394872</td>\n <td>1275.733333</td>\n <td>1420.287179</td>\n <td>1262.533333</td>\n <td>1313.958974</td>\n <td>1320.702564</td>\n <td>32867.451282</td>\n </tr>\n <tr>\n <th>std</th>\n <td>1949.588546</td>\n <td>2152.643752</td>\n <td>1866.997511</td>\n <td>1204.333597</td>\n <td>1198.246371</td>\n <td>1079.309600</td>\n <td>1225.576630</td>\n <td>2109.205607</td>\n <td>2443.606788</td>\n <td>2555.048874</td>\n <td>...</td>\n <td>4425.957828</td>\n <td>3926.717747</td>\n <td>3443.542409</td>\n <td>3694.573544</td>\n <td>3829.630424</td>\n <td>4462.946328</td>\n <td>4030.084313</td>\n <td>4247.555161</td>\n <td>4237.951988</td>\n <td>91785.498686</td>\n </tr>\n <tr>\n <th>min</th>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>...</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>1.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.500000</td>\n <td>0.500000</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>...</td>\n <td>28.500000</td>\n <td>25.000000</td>\n <td>31.000000</td>\n <td>31.000000</td>\n <td>36.000000</td>\n <td>40.500000</td>\n <td>37.500000</td>\n <td>42.500000</td>\n <td>45.000000</td>\n <td>952.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>13.000000</td>\n <td>10.000000</td>\n <td>11.000000</td>\n <td>12.000000</td>\n <td>13.000000</td>\n <td>17.000000</td>\n <td>18.000000</td>\n <td>26.000000</td>\n <td>34.000000</td>\n <td>44.000000</td>\n <td>...</td>\n <td>210.000000</td>\n <td>218.000000</td>\n <td>198.000000</td>\n <td>205.000000</td>\n <td>214.000000</td>\n <td>211.000000</td>\n <td>179.000000</td>\n <td>233.000000</td>\n <td>213.000000</td>\n <td>5018.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>251.500000</td>\n <td>295.500000</td>\n <td>275.000000</td>\n <td>173.000000</td>\n <td>181.000000</td>\n <td>197.000000</td>\n <td>254.000000</td>\n <td>434.000000</td>\n <td>409.000000</td>\n <td>508.500000</td>\n <td>...</td>\n <td>832.000000</td>\n <td>842.000000</td>\n <td>899.000000</td>\n <td>934.500000</td>\n <td>888.000000</td>\n <td>932.000000</td>\n <td>772.000000</td>\n <td>783.000000</td>\n <td>796.000000</td>\n <td>22239.500000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>22045.000000</td>\n <td>24796.000000</td>\n <td>20620.000000</td>\n <td>10015.000000</td>\n <td>10170.000000</td>\n <td>9564.000000</td>\n <td>9470.000000</td>\n <td>21337.000000</td>\n <td>27359.000000</td>\n <td>23795.000000</td>\n <td>...</td>\n <td>42584.000000</td>\n <td>33848.000000</td>\n <td>28742.000000</td>\n <td>30037.000000</td>\n <td>29622.000000</td>\n <td>38617.000000</td>\n <td>36765.000000</td>\n <td>34315.000000</td>\n <td>34129.000000</td>\n <td>691904.000000</td>\n </tr>\n </tbody>\n</table>\n<p>8 rows × 35 columns</p>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "***\n\n## *pandas* Intermediate: Indexing and Selection (slicing)<a id=\"6\"></a>\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "### Select Column\n\n**There are two ways to filter on a column name:**\n\nMethod 1: Quick and easy, but only works if the column name does NOT have spaces or special characters.\n\n```python\n df.column_name # returns series\n```\n\nMethod 2: More robust, and can filter on multiple columns.\n\n```python\n df['column'] # returns series\n```\n\n```python\n df[['column 1', 'column 2']] # returns dataframe\n```\n\n***\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Example: Let's try filtering on the list of countries ('Country').\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_can.Country # returns a series", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": true, | |
| "trusted": true | |
| }, | |
| "execution_count": 19, | |
| "outputs": [ | |
| { | |
| "execution_count": 19, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "0 Afghanistan\n1 Albania\n2 Algeria\n3 American Samoa\n4 Andorra\n ... \n190 Viet Nam\n191 Western Sahara\n192 Yemen\n193 Zambia\n194 Zimbabwe\nName: Country, Length: 195, dtype: object" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Let's try filtering on the list of countries ('Country') and the data for years: 1980 - 1985.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_can[['Country', 1980, 1981, 1982, 1983, 1984, 1985]] # returns a dataframe\n# notice that 'Country' is string, and the years are integers. \n# for the sake of consistency, we will convert all column names to string later on.", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 20, | |
| "outputs": [ | |
| { | |
| "execution_count": 20, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": " Country 1980 1981 1982 1983 1984 1985\n0 Afghanistan 16 39 39 47 71 340\n1 Albania 1 0 0 0 0 0\n2 Algeria 80 67 71 69 63 44\n3 American Samoa 0 1 0 0 0 0\n4 Andorra 0 0 0 0 0 0\n.. ... ... ... ... ... ... ...\n190 Viet Nam 1191 1829 2162 3404 7583 5907\n191 Western Sahara 0 0 0 0 0 0\n192 Yemen 1 2 1 6 0 18\n193 Zambia 11 17 11 7 16 9\n194 Zimbabwe 72 114 102 44 32 29\n\n[195 rows x 7 columns]", | |
| "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>1980</th>\n <th>1981</th>\n <th>1982</th>\n <th>1983</th>\n <th>1984</th>\n <th>1985</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Afghanistan</td>\n <td>16</td>\n <td>39</td>\n <td>39</td>\n <td>47</td>\n <td>71</td>\n <td>340</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Albania</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Algeria</td>\n <td>80</td>\n <td>67</td>\n <td>71</td>\n <td>69</td>\n <td>63</td>\n <td>44</td>\n </tr>\n <tr>\n <th>3</th>\n <td>American Samoa</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Andorra</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>190</th>\n <td>Viet Nam</td>\n <td>1191</td>\n <td>1829</td>\n <td>2162</td>\n <td>3404</td>\n <td>7583</td>\n <td>5907</td>\n </tr>\n <tr>\n <th>191</th>\n <td>Western Sahara</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>192</th>\n <td>Yemen</td>\n <td>1</td>\n <td>2</td>\n <td>1</td>\n <td>6</td>\n <td>0</td>\n <td>18</td>\n </tr>\n <tr>\n <th>193</th>\n <td>Zambia</td>\n <td>11</td>\n <td>17</td>\n <td>11</td>\n <td>7</td>\n <td>16</td>\n <td>9</td>\n </tr>\n <tr>\n <th>194</th>\n <td>Zimbabwe</td>\n <td>72</td>\n <td>114</td>\n <td>102</td>\n <td>44</td>\n <td>32</td>\n <td>29</td>\n </tr>\n </tbody>\n</table>\n<p>195 rows × 7 columns</p>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "### Select Row\n\nThere are main 2 ways to select rows:\n\n```python\n df.loc[label] # filters by the labels of the index/column\n df.iloc[index] # filters by the positions of the index/column\n```\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Before we proceed, notice that the default index of the dataset is a numeric range from 0 to 194. This makes it very difficult to do a query by a specific country. For example to search for data on Japan, we need to know the corresponding index value.\n\nThis can be fixed very easily by setting the 'Country' column as the index using `set_index()` method.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_can.set_index('Country', inplace=True)\n# tip: The opposite of set is reset. So to reset the index, we can use df_can.reset_index()", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": true, | |
| "trusted": true | |
| }, | |
| "execution_count": 21, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_can.head(3)", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 22, | |
| "outputs": [ | |
| { | |
| "execution_count": 22, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": " Continent Region DevName 1980 1981 1982 \\\nCountry \nAfghanistan Asia Southern Asia Developing regions 16 39 39 \nAlbania Europe Southern Europe Developed regions 1 0 0 \nAlgeria Africa Northern Africa Developing regions 80 67 71 \n\n 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\nCountry ... \nAfghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \nAlbania 0 0 0 1 ... 1223 856 702 560 716 561 \nAlgeria 69 63 44 69 ... 3626 4807 3623 4005 5393 4752 \n\n 2011 2012 2013 Total \nCountry \nAfghanistan 2203 2635 2004 58639 \nAlbania 539 620 603 15699 \nAlgeria 4325 3774 4331 69439 \n\n[3 rows x 38 columns]", | |
| "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>Continent</th>\n <th>Region</th>\n <th>DevName</th>\n <th>1980</th>\n <th>1981</th>\n <th>1982</th>\n <th>1983</th>\n <th>1984</th>\n <th>1985</th>\n <th>1986</th>\n <th>...</th>\n <th>2005</th>\n <th>2006</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>Total</th>\n </tr>\n <tr>\n <th>Country</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Afghanistan</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>16</td>\n <td>39</td>\n <td>39</td>\n <td>47</td>\n <td>71</td>\n <td>340</td>\n <td>496</td>\n <td>...</td>\n <td>3436</td>\n <td>3009</td>\n <td>2652</td>\n <td>2111</td>\n <td>1746</td>\n <td>1758</td>\n <td>2203</td>\n <td>2635</td>\n <td>2004</td>\n <td>58639</td>\n </tr>\n <tr>\n <th>Albania</th>\n <td>Europe</td>\n <td>Southern Europe</td>\n <td>Developed regions</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>1223</td>\n <td>856</td>\n <td>702</td>\n <td>560</td>\n <td>716</td>\n <td>561</td>\n <td>539</td>\n <td>620</td>\n <td>603</td>\n <td>15699</td>\n </tr>\n <tr>\n <th>Algeria</th>\n <td>Africa</td>\n <td>Northern Africa</td>\n <td>Developing regions</td>\n <td>80</td>\n <td>67</td>\n <td>71</td>\n <td>69</td>\n <td>63</td>\n <td>44</td>\n <td>69</td>\n <td>...</td>\n <td>3626</td>\n <td>4807</td>\n <td>3623</td>\n <td>4005</td>\n <td>5393</td>\n <td>4752</td>\n <td>4325</td>\n <td>3774</td>\n <td>4331</td>\n <td>69439</td>\n </tr>\n </tbody>\n</table>\n<p>3 rows × 38 columns</p>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "# optional: to remove the name of the index\ndf_can.index.name = None", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 23, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Example: Let's view the number of immigrants from Japan (row 87) for the following scenarios:\n1\\. The full row data (all columns)\n2\\. For year 2013\n3\\. For years 1980 to 1985\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "# 1. the full row data (all columns)\ndf_can.loc['Japan']", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": true, | |
| "trusted": true | |
| }, | |
| "execution_count": 24, | |
| "outputs": [ | |
| { | |
| "execution_count": 24, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "Continent Asia\nRegion Eastern Asia\nDevName Developed regions\n1980 701\n1981 756\n1982 598\n1983 309\n1984 246\n1985 198\n1986 248\n1987 422\n1988 324\n1989 494\n1990 379\n1991 506\n1992 605\n1993 907\n1994 956\n1995 826\n1996 994\n1997 924\n1998 897\n1999 1083\n2000 1010\n2001 1092\n2002 806\n2003 817\n2004 973\n2005 1067\n2006 1212\n2007 1250\n2008 1284\n2009 1194\n2010 1168\n2011 1265\n2012 1214\n2013 982\nTotal 27707\nName: Japan, dtype: object" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "# alternate methods\ndf_can.iloc[87]", | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| }, | |
| "jupyter": { | |
| "outputs_hidden": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 25, | |
| "outputs": [ | |
| { | |
| "execution_count": 25, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "Continent Asia\nRegion Eastern Asia\nDevName Developed regions\n1980 701\n1981 756\n1982 598\n1983 309\n1984 246\n1985 198\n1986 248\n1987 422\n1988 324\n1989 494\n1990 379\n1991 506\n1992 605\n1993 907\n1994 956\n1995 826\n1996 994\n1997 924\n1998 897\n1999 1083\n2000 1010\n2001 1092\n2002 806\n2003 817\n2004 973\n2005 1067\n2006 1212\n2007 1250\n2008 1284\n2009 1194\n2010 1168\n2011 1265\n2012 1214\n2013 982\nTotal 27707\nName: Japan, dtype: object" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_can[df_can.index == 'Japan']", | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| }, | |
| "jupyter": { | |
| "outputs_hidden": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 26, | |
| "outputs": [ | |
| { | |
| "execution_count": 26, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": " Continent Region DevName 1980 1981 1982 1983 \\\nJapan Asia Eastern Asia Developed regions 701 756 598 309 \n\n 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 2011 2012 \\\nJapan 246 198 248 ... 1067 1212 1250 1284 1194 1168 1265 1214 \n\n 2013 Total \nJapan 982 27707 \n\n[1 rows x 38 columns]", | |
| "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>Continent</th>\n <th>Region</th>\n <th>DevName</th>\n <th>1980</th>\n <th>1981</th>\n <th>1982</th>\n <th>1983</th>\n <th>1984</th>\n <th>1985</th>\n <th>1986</th>\n <th>...</th>\n <th>2005</th>\n <th>2006</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>Total</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Japan</th>\n <td>Asia</td>\n <td>Eastern Asia</td>\n <td>Developed regions</td>\n <td>701</td>\n <td>756</td>\n <td>598</td>\n <td>309</td>\n <td>246</td>\n <td>198</td>\n <td>248</td>\n <td>...</td>\n <td>1067</td>\n <td>1212</td>\n <td>1250</td>\n <td>1284</td>\n <td>1194</td>\n <td>1168</td>\n <td>1265</td>\n <td>1214</td>\n <td>982</td>\n <td>27707</td>\n </tr>\n </tbody>\n</table>\n<p>1 rows × 38 columns</p>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "# 2. for year 2013\ndf_can.loc['Japan', 2013]", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": true, | |
| "trusted": true | |
| }, | |
| "execution_count": 27, | |
| "outputs": [ | |
| { | |
| "execution_count": 27, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "982" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "# alternate method\n# year 2013 is the last column, with a positional index of 36\ndf_can.iloc[87, 36]", | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| }, | |
| "jupyter": { | |
| "outputs_hidden": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 28, | |
| "outputs": [ | |
| { | |
| "execution_count": 28, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "982" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "# 3. for years 1980 to 1985\ndf_can.loc['Japan', [1980, 1981, 1982, 1983, 1984, 1984]]", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 29, | |
| "outputs": [ | |
| { | |
| "execution_count": 29, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "1980 701\n1981 756\n1982 598\n1983 309\n1984 246\n1984 246\nName: Japan, dtype: object" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "# Alternative Method\ndf_can.iloc[87, [3, 4, 5, 6, 7, 8]]", | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| }, | |
| "jupyter": { | |
| "outputs_hidden": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 30, | |
| "outputs": [ | |
| { | |
| "execution_count": 30, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "1980 701\n1981 756\n1982 598\n1983 309\n1984 246\n1985 198\nName: Japan, dtype: object" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Column names that are integers (such as the years) might introduce some confusion. For example, when we are referencing the year 2013, one might confuse that when the 2013th positional index.\n\nTo avoid this ambuigity, let's convert the column names into strings: '1980' to '2013'.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_can.columns = list(map(str, df_can.columns))\n# [print (type(x)) for x in df_can.columns.values] #<-- uncomment to check type of column headers", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 31, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Since we converted the years to string, let's declare a variable that will allow us to easily call upon the full range of years:\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "# useful for plotting later on\nyears = list(map(str, range(1980, 2014)))\nyears", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 32, | |
| "outputs": [ | |
| { | |
| "execution_count": 32, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "['1980',\n '1981',\n '1982',\n '1983',\n '1984',\n '1985',\n '1986',\n '1987',\n '1988',\n '1989',\n '1990',\n '1991',\n '1992',\n '1993',\n '1994',\n '1995',\n '1996',\n '1997',\n '1998',\n '1999',\n '2000',\n '2001',\n '2002',\n '2003',\n '2004',\n '2005',\n '2006',\n '2007',\n '2008',\n '2009',\n '2010',\n '2011',\n '2012',\n '2013']" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "### Filtering based on a criteria\n\nTo filter the dataframe based on a condition, we simply pass the condition as a boolean vector.\n\nFor example, Let's filter the dataframe to show the data on Asian countries (AreaName = Asia).\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "# 1. create the condition boolean series\ncondition = df_can['Continent'] == 'Asia'\nprint(condition)", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": true, | |
| "trusted": true | |
| }, | |
| "execution_count": 33, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "text": "Afghanistan True\nAlbania False\nAlgeria False\nAmerican Samoa False\nAndorra False\n ... \nViet Nam True\nWestern Sahara False\nYemen True\nZambia False\nZimbabwe False\nName: Continent, Length: 195, dtype: bool\n", | |
| "output_type": "stream" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "# 2. pass this condition into the dataFrame\ndf_can[condition]", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 34, | |
| "outputs": [ | |
| { | |
| "execution_count": 34, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": " Continent Region \\\nAfghanistan Asia Southern Asia \nArmenia Asia Western Asia \nAzerbaijan Asia Western Asia \nBahrain Asia Western Asia \nBangladesh Asia Southern Asia \nBhutan Asia Southern Asia \nBrunei Darussalam Asia South-Eastern Asia \nCambodia Asia South-Eastern Asia \nChina Asia Eastern Asia \nChina, Hong Kong Special Administrative Region Asia Eastern Asia \nChina, Macao Special Administrative Region Asia Eastern Asia \nCyprus Asia Western Asia \nDemocratic People's Republic of Korea Asia Eastern Asia \nGeorgia Asia Western Asia \nIndia Asia Southern Asia \nIndonesia Asia South-Eastern Asia \nIran (Islamic Republic of) Asia Southern Asia \nIraq Asia Western Asia \nIsrael Asia Western Asia \nJapan Asia Eastern Asia \nJordan Asia Western Asia \nKazakhstan Asia Central Asia \nKuwait Asia Western Asia \nKyrgyzstan Asia Central Asia \nLao People's Democratic Republic Asia South-Eastern Asia \nLebanon Asia Western Asia \nMalaysia Asia South-Eastern Asia \nMaldives Asia Southern Asia \nMongolia Asia Eastern Asia \nMyanmar Asia South-Eastern Asia \nNepal Asia Southern Asia \nOman Asia Western Asia \nPakistan Asia Southern Asia \nPhilippines Asia South-Eastern Asia \nQatar Asia Western Asia \nRepublic of Korea Asia Eastern Asia \nSaudi Arabia Asia Western Asia \nSingapore Asia South-Eastern Asia \nSri Lanka Asia Southern Asia \nState of Palestine Asia Western Asia \nSyrian Arab Republic Asia Western Asia \nTajikistan Asia Central Asia \nThailand Asia South-Eastern Asia \nTurkey Asia Western Asia \nTurkmenistan Asia Central Asia \nUnited Arab Emirates Asia Western Asia \nUzbekistan Asia Central Asia \nViet Nam Asia South-Eastern Asia \nYemen Asia Western Asia \n\n DevName 1980 \\\nAfghanistan Developing regions 16 \nArmenia Developing regions 0 \nAzerbaijan Developing regions 0 \nBahrain Developing regions 0 \nBangladesh Developing regions 83 \nBhutan Developing regions 0 \nBrunei Darussalam Developing regions 79 \nCambodia Developing regions 12 \nChina Developing regions 5123 \nChina, Hong Kong Special Administrative Region Developing regions 0 \nChina, Macao Special Administrative Region Developing regions 0 \nCyprus Developing regions 132 \nDemocratic People's Republic of Korea Developing regions 1 \nGeorgia Developing regions 0 \nIndia Developing regions 8880 \nIndonesia Developing regions 186 \nIran (Islamic Republic of) Developing regions 1172 \nIraq Developing regions 262 \nIsrael Developing regions 1403 \nJapan Developed regions 701 \nJordan Developing regions 177 \nKazakhstan Developing regions 0 \nKuwait Developing regions 1 \nKyrgyzstan Developing regions 0 \nLao People's Democratic Republic Developing regions 11 \nLebanon Developing regions 1409 \nMalaysia Developing regions 786 \nMaldives Developing regions 0 \nMongolia Developing regions 0 \nMyanmar Developing regions 80 \nNepal Developing regions 1 \nOman Developing regions 0 \nPakistan Developing regions 978 \nPhilippines Developing regions 6051 \nQatar Developing regions 0 \nRepublic of Korea Developing regions 1011 \nSaudi Arabia Developing regions 0 \nSingapore Developing regions 241 \nSri Lanka Developing regions 185 \nState of Palestine Developing regions 0 \nSyrian Arab Republic Developing regions 315 \nTajikistan Developing regions 0 \nThailand Developing regions 56 \nTurkey Developing regions 481 \nTurkmenistan Developing regions 0 \nUnited Arab Emirates Developing regions 0 \nUzbekistan Developing regions 0 \nViet Nam Developing regions 1191 \nYemen Developing regions 1 \n\n 1981 1982 1983 1984 1985 \\\nAfghanistan 39 39 47 71 340 \nArmenia 0 0 0 0 0 \nAzerbaijan 0 0 0 0 0 \nBahrain 2 1 1 1 3 \nBangladesh 84 86 81 98 92 \nBhutan 0 0 0 1 0 \nBrunei Darussalam 6 8 2 2 4 \nCambodia 19 26 33 10 7 \nChina 6682 3308 1863 1527 1816 \nChina, Hong Kong Special Administrative Region 0 0 0 0 0 \nChina, Macao Special Administrative Region 0 0 0 0 0 \nCyprus 128 84 46 46 43 \nDemocratic People's Republic of Korea 1 3 1 4 3 \nGeorgia 0 0 0 0 0 \nIndia 8670 8147 7338 5704 4211 \nIndonesia 178 252 115 123 100 \nIran (Islamic Republic of) 1429 1822 1592 1977 1648 \nIraq 245 260 380 428 231 \nIsrael 1711 1334 541 446 680 \nJapan 756 598 309 246 198 \nJordan 160 155 113 102 179 \nKazakhstan 0 0 0 0 0 \nKuwait 0 8 2 1 4 \nKyrgyzstan 0 0 0 0 0 \nLao People's Democratic Republic 6 16 16 7 17 \nLebanon 1119 1159 789 1253 1683 \nMalaysia 816 813 448 384 374 \nMaldives 0 0 1 0 0 \nMongolia 0 0 0 0 0 \nMyanmar 62 46 31 41 23 \nNepal 1 6 1 2 4 \nOman 0 0 8 0 0 \nPakistan 972 1201 900 668 514 \nPhilippines 5921 5249 4562 3801 3150 \nQatar 0 0 0 0 0 \nRepublic of Korea 1456 1572 1081 847 962 \nSaudi Arabia 0 1 4 1 2 \nSingapore 301 337 169 128 139 \nSri Lanka 371 290 197 1086 845 \nState of Palestine 0 0 0 0 0 \nSyrian Arab Republic 419 409 269 264 385 \nTajikistan 0 0 0 0 0 \nThailand 53 113 65 82 66 \nTurkey 874 706 280 338 202 \nTurkmenistan 0 0 0 0 0 \nUnited Arab Emirates 2 2 1 2 0 \nUzbekistan 0 0 0 0 0 \nViet Nam 1829 2162 3404 7583 5907 \nYemen 2 1 6 0 18 \n\n 1986 ... 2005 2006 \\\nAfghanistan 496 ... 3436 3009 \nArmenia 0 ... 224 218 \nAzerbaijan 0 ... 359 236 \nBahrain 0 ... 12 12 \nBangladesh 486 ... 4171 4014 \nBhutan 0 ... 5 10 \nBrunei Darussalam 12 ... 4 5 \nCambodia 8 ... 370 529 \nChina 1960 ... 42584 33518 \nChina, Hong Kong Special Administrative Region 0 ... 729 712 \nChina, Macao Special Administrative Region 0 ... 21 32 \nCyprus 48 ... 7 9 \nDemocratic People's Republic of Korea 0 ... 14 10 \nGeorgia 0 ... 114 125 \nIndia 7150 ... 36210 33848 \nIndonesia 127 ... 632 613 \nIran (Islamic Republic of) 1794 ... 5837 7480 \nIraq 265 ... 2226 1788 \nIsrael 1212 ... 2446 2625 \nJapan 248 ... 1067 1212 \nJordan 181 ... 1940 1827 \nKazakhstan 0 ... 506 408 \nKuwait 4 ... 66 35 \nKyrgyzstan 0 ... 173 161 \nLao People's Democratic Republic 21 ... 42 74 \nLebanon 2576 ... 3709 3802 \nMalaysia 425 ... 593 580 \nMaldives 0 ... 0 0 \nMongolia 0 ... 59 64 \nMyanmar 18 ... 210 953 \nNepal 13 ... 607 540 \nOman 0 ... 14 18 \nPakistan 691 ... 14314 13127 \nPhilippines 4166 ... 18139 18400 \nQatar 1 ... 11 2 \nRepublic of Korea 1208 ... 5832 6215 \nSaudi Arabia 5 ... 198 252 \nSingapore 205 ... 392 298 \nSri Lanka 1838 ... 4930 4714 \nState of Palestine 0 ... 453 627 \nSyrian Arab Republic 493 ... 1458 1145 \nTajikistan 0 ... 85 46 \nThailand 78 ... 575 500 \nTurkey 257 ... 2065 1638 \nTurkmenistan 0 ... 40 26 \nUnited Arab Emirates 5 ... 31 42 \nUzbekistan 0 ... 330 262 \nViet Nam 2741 ... 1852 3153 \nYemen 7 ... 161 140 \n\n 2007 2008 2009 2010 \\\nAfghanistan 2652 2111 1746 1758 \nArmenia 198 205 267 252 \nAzerbaijan 203 125 165 209 \nBahrain 22 9 35 28 \nBangladesh 2897 2939 2104 4721 \nBhutan 7 36 865 1464 \nBrunei Darussalam 11 10 5 12 \nCambodia 460 354 203 200 \nChina 27642 30037 29622 30391 \nChina, Hong Kong Special Administrative Region 674 897 657 623 \nChina, Macao Special Administrative Region 16 12 21 21 \nCyprus 4 7 6 18 \nDemocratic People's Republic of Korea 7 19 11 45 \nGeorgia 132 112 128 126 \nIndia 28742 28261 29456 34235 \nIndonesia 657 661 504 712 \nIran (Islamic Republic of) 6974 6475 6580 7477 \nIraq 2406 3543 5450 5941 \nIsrael 2401 2562 2316 2755 \nJapan 1250 1284 1194 1168 \nJordan 1421 1581 1235 1831 \nKazakhstan 436 394 431 377 \nKuwait 62 53 68 67 \nKyrgyzstan 135 168 173 157 \nLao People's Democratic Republic 53 32 39 54 \nLebanon 3467 3566 3077 3432 \nMalaysia 600 658 640 802 \nMaldives 2 1 7 4 \nMongolia 82 59 118 169 \nMyanmar 1887 975 1153 556 \nNepal 511 581 561 1392 \nOman 16 10 7 14 \nPakistan 10124 8994 7217 6811 \nPhilippines 19837 24887 28573 38617 \nQatar 5 9 6 18 \nRepublic of Korea 5920 7294 5874 5537 \nSaudi Arabia 188 249 246 330 \nSingapore 690 734 366 805 \nSri Lanka 4123 4756 4547 4422 \nState of Palestine 441 481 400 654 \nSyrian Arab Republic 1056 919 917 1039 \nTajikistan 44 15 50 52 \nThailand 487 519 512 499 \nTurkey 1463 1122 1238 1492 \nTurkmenistan 37 13 20 30 \nUnited Arab Emirates 37 33 37 86 \nUzbekistan 284 215 288 289 \nViet Nam 2574 1784 2171 1942 \nYemen 122 133 128 211 \n\n 2011 2012 2013 Total \nAfghanistan 2203 2635 2004 58639 \nArmenia 236 258 207 3310 \nAzerbaijan 138 161 57 2649 \nBahrain 21 39 32 475 \nBangladesh 2694 2640 3789 65568 \nBhutan 1879 1075 487 5876 \nBrunei Darussalam 6 3 6 600 \nCambodia 196 233 288 6538 \nChina 28502 33024 34129 659962 \nChina, Hong Kong Special Administrative Region 591 728 774 9327 \nChina, Macao Special Administrative Region 13 33 29 284 \nCyprus 6 12 16 1126 \nDemocratic People's Republic of Korea 97 66 17 388 \nGeorgia 139 147 125 2068 \nIndia 27509 30933 33087 691904 \nIndonesia 390 395 387 13150 \nIran (Islamic Republic of) 7479 7534 11291 175923 \nIraq 6196 4041 4918 69789 \nIsrael 1970 2134 1945 66508 \nJapan 1265 1214 982 27707 \nJordan 1635 1206 1255 35406 \nKazakhstan 381 462 348 8490 \nKuwait 58 73 48 2025 \nKyrgyzstan 159 278 123 2353 \nLao People's Democratic Republic 22 25 15 1089 \nLebanon 3072 1614 2172 115359 \nMalaysia 409 358 204 24417 \nMaldives 3 1 1 30 \nMongolia 103 68 99 952 \nMyanmar 368 193 262 9245 \nNepal 1129 1185 1308 10222 \nOman 10 13 11 224 \nPakistan 7468 11227 12603 241600 \nPhilippines 36765 34315 29544 511391 \nQatar 3 14 6 157 \nRepublic of Korea 4588 5316 4509 142581 \nSaudi Arabia 278 286 267 3425 \nSingapore 219 146 141 14579 \nSri Lanka 3309 3338 2394 148358 \nState of Palestine 555 533 462 6512 \nSyrian Arab Republic 1005 650 1009 31485 \nTajikistan 47 34 39 503 \nThailand 396 296 400 9174 \nTurkey 1257 1068 729 31781 \nTurkmenistan 20 20 14 310 \nUnited Arab Emirates 60 54 46 836 \nUzbekistan 162 235 167 3368 \nViet Nam 1723 1731 2112 97146 \nYemen 160 174 217 2985 \n\n[49 rows x 38 columns]", | |
| "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>Continent</th>\n <th>Region</th>\n <th>DevName</th>\n <th>1980</th>\n <th>1981</th>\n <th>1982</th>\n <th>1983</th>\n <th>1984</th>\n <th>1985</th>\n <th>1986</th>\n <th>...</th>\n <th>2005</th>\n <th>2006</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>Total</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Afghanistan</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>16</td>\n <td>39</td>\n <td>39</td>\n <td>47</td>\n <td>71</td>\n <td>340</td>\n <td>496</td>\n <td>...</td>\n <td>3436</td>\n <td>3009</td>\n <td>2652</td>\n <td>2111</td>\n <td>1746</td>\n <td>1758</td>\n <td>2203</td>\n <td>2635</td>\n <td>2004</td>\n <td>58639</td>\n </tr>\n <tr>\n <th>Armenia</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>224</td>\n <td>218</td>\n <td>198</td>\n <td>205</td>\n <td>267</td>\n <td>252</td>\n <td>236</td>\n <td>258</td>\n <td>207</td>\n <td>3310</td>\n </tr>\n <tr>\n <th>Azerbaijan</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>359</td>\n <td>236</td>\n <td>203</td>\n <td>125</td>\n <td>165</td>\n <td>209</td>\n <td>138</td>\n <td>161</td>\n <td>57</td>\n <td>2649</td>\n </tr>\n <tr>\n <th>Bahrain</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>3</td>\n <td>0</td>\n <td>...</td>\n <td>12</td>\n <td>12</td>\n <td>22</td>\n <td>9</td>\n <td>35</td>\n <td>28</td>\n <td>21</td>\n <td>39</td>\n <td>32</td>\n <td>475</td>\n </tr>\n <tr>\n <th>Bangladesh</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>83</td>\n <td>84</td>\n <td>86</td>\n <td>81</td>\n <td>98</td>\n <td>92</td>\n <td>486</td>\n <td>...</td>\n <td>4171</td>\n <td>4014</td>\n <td>2897</td>\n <td>2939</td>\n <td>2104</td>\n <td>4721</td>\n <td>2694</td>\n <td>2640</td>\n <td>3789</td>\n <td>65568</td>\n </tr>\n <tr>\n <th>Bhutan</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>5</td>\n <td>10</td>\n <td>7</td>\n <td>36</td>\n <td>865</td>\n <td>1464</td>\n <td>1879</td>\n <td>1075</td>\n <td>487</td>\n <td>5876</td>\n </tr>\n <tr>\n <th>Brunei Darussalam</th>\n <td>Asia</td>\n <td>South-Eastern Asia</td>\n <td>Developing regions</td>\n <td>79</td>\n <td>6</td>\n <td>8</td>\n <td>2</td>\n <td>2</td>\n <td>4</td>\n <td>12</td>\n <td>...</td>\n <td>4</td>\n <td>5</td>\n <td>11</td>\n <td>10</td>\n <td>5</td>\n <td>12</td>\n <td>6</td>\n <td>3</td>\n <td>6</td>\n <td>600</td>\n </tr>\n <tr>\n <th>Cambodia</th>\n <td>Asia</td>\n <td>South-Eastern Asia</td>\n <td>Developing regions</td>\n <td>12</td>\n <td>19</td>\n <td>26</td>\n <td>33</td>\n <td>10</td>\n <td>7</td>\n <td>8</td>\n <td>...</td>\n <td>370</td>\n <td>529</td>\n <td>460</td>\n <td>354</td>\n <td>203</td>\n <td>200</td>\n <td>196</td>\n <td>233</td>\n <td>288</td>\n <td>6538</td>\n </tr>\n <tr>\n <th>China</th>\n <td>Asia</td>\n <td>Eastern Asia</td>\n <td>Developing regions</td>\n <td>5123</td>\n <td>6682</td>\n <td>3308</td>\n <td>1863</td>\n <td>1527</td>\n <td>1816</td>\n <td>1960</td>\n <td>...</td>\n <td>42584</td>\n <td>33518</td>\n <td>27642</td>\n <td>30037</td>\n <td>29622</td>\n <td>30391</td>\n <td>28502</td>\n <td>33024</td>\n <td>34129</td>\n <td>659962</td>\n </tr>\n <tr>\n <th>China, Hong Kong Special Administrative Region</th>\n <td>Asia</td>\n <td>Eastern Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>729</td>\n <td>712</td>\n <td>674</td>\n <td>897</td>\n <td>657</td>\n <td>623</td>\n <td>591</td>\n <td>728</td>\n <td>774</td>\n <td>9327</td>\n </tr>\n <tr>\n <th>China, Macao Special Administrative Region</th>\n <td>Asia</td>\n <td>Eastern Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>21</td>\n <td>32</td>\n <td>16</td>\n <td>12</td>\n <td>21</td>\n <td>21</td>\n <td>13</td>\n <td>33</td>\n <td>29</td>\n <td>284</td>\n </tr>\n <tr>\n <th>Cyprus</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>132</td>\n <td>128</td>\n <td>84</td>\n <td>46</td>\n <td>46</td>\n <td>43</td>\n <td>48</td>\n <td>...</td>\n <td>7</td>\n <td>9</td>\n <td>4</td>\n <td>7</td>\n <td>6</td>\n <td>18</td>\n <td>6</td>\n <td>12</td>\n <td>16</td>\n <td>1126</td>\n </tr>\n <tr>\n <th>Democratic People's Republic of Korea</th>\n <td>Asia</td>\n <td>Eastern Asia</td>\n <td>Developing regions</td>\n <td>1</td>\n <td>1</td>\n <td>3</td>\n <td>1</td>\n <td>4</td>\n <td>3</td>\n <td>0</td>\n <td>...</td>\n <td>14</td>\n <td>10</td>\n <td>7</td>\n <td>19</td>\n <td>11</td>\n <td>45</td>\n <td>97</td>\n <td>66</td>\n <td>17</td>\n <td>388</td>\n </tr>\n <tr>\n <th>Georgia</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>114</td>\n <td>125</td>\n <td>132</td>\n <td>112</td>\n <td>128</td>\n <td>126</td>\n <td>139</td>\n <td>147</td>\n <td>125</td>\n <td>2068</td>\n </tr>\n <tr>\n <th>India</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>8880</td>\n <td>8670</td>\n <td>8147</td>\n <td>7338</td>\n <td>5704</td>\n <td>4211</td>\n <td>7150</td>\n <td>...</td>\n <td>36210</td>\n <td>33848</td>\n <td>28742</td>\n <td>28261</td>\n <td>29456</td>\n <td>34235</td>\n <td>27509</td>\n <td>30933</td>\n <td>33087</td>\n <td>691904</td>\n </tr>\n <tr>\n <th>Indonesia</th>\n <td>Asia</td>\n <td>South-Eastern Asia</td>\n <td>Developing regions</td>\n <td>186</td>\n <td>178</td>\n <td>252</td>\n <td>115</td>\n <td>123</td>\n <td>100</td>\n <td>127</td>\n <td>...</td>\n <td>632</td>\n <td>613</td>\n <td>657</td>\n <td>661</td>\n <td>504</td>\n <td>712</td>\n <td>390</td>\n <td>395</td>\n <td>387</td>\n <td>13150</td>\n </tr>\n <tr>\n <th>Iran (Islamic Republic of)</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>1172</td>\n <td>1429</td>\n <td>1822</td>\n <td>1592</td>\n <td>1977</td>\n <td>1648</td>\n <td>1794</td>\n <td>...</td>\n <td>5837</td>\n <td>7480</td>\n <td>6974</td>\n <td>6475</td>\n <td>6580</td>\n <td>7477</td>\n <td>7479</td>\n <td>7534</td>\n <td>11291</td>\n <td>175923</td>\n </tr>\n <tr>\n <th>Iraq</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>262</td>\n <td>245</td>\n <td>260</td>\n <td>380</td>\n <td>428</td>\n <td>231</td>\n <td>265</td>\n <td>...</td>\n <td>2226</td>\n <td>1788</td>\n <td>2406</td>\n <td>3543</td>\n <td>5450</td>\n <td>5941</td>\n <td>6196</td>\n <td>4041</td>\n <td>4918</td>\n <td>69789</td>\n </tr>\n <tr>\n <th>Israel</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>1403</td>\n <td>1711</td>\n <td>1334</td>\n <td>541</td>\n <td>446</td>\n <td>680</td>\n <td>1212</td>\n <td>...</td>\n <td>2446</td>\n <td>2625</td>\n <td>2401</td>\n <td>2562</td>\n <td>2316</td>\n <td>2755</td>\n <td>1970</td>\n <td>2134</td>\n <td>1945</td>\n <td>66508</td>\n </tr>\n <tr>\n <th>Japan</th>\n <td>Asia</td>\n <td>Eastern Asia</td>\n <td>Developed regions</td>\n <td>701</td>\n <td>756</td>\n <td>598</td>\n <td>309</td>\n <td>246</td>\n <td>198</td>\n <td>248</td>\n <td>...</td>\n <td>1067</td>\n <td>1212</td>\n <td>1250</td>\n <td>1284</td>\n <td>1194</td>\n <td>1168</td>\n <td>1265</td>\n <td>1214</td>\n <td>982</td>\n <td>27707</td>\n </tr>\n <tr>\n <th>Jordan</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>177</td>\n <td>160</td>\n <td>155</td>\n <td>113</td>\n <td>102</td>\n <td>179</td>\n <td>181</td>\n <td>...</td>\n <td>1940</td>\n <td>1827</td>\n <td>1421</td>\n <td>1581</td>\n <td>1235</td>\n <td>1831</td>\n <td>1635</td>\n <td>1206</td>\n <td>1255</td>\n <td>35406</td>\n </tr>\n <tr>\n <th>Kazakhstan</th>\n <td>Asia</td>\n <td>Central Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>506</td>\n <td>408</td>\n <td>436</td>\n <td>394</td>\n <td>431</td>\n <td>377</td>\n <td>381</td>\n <td>462</td>\n <td>348</td>\n <td>8490</td>\n </tr>\n <tr>\n <th>Kuwait</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>1</td>\n <td>0</td>\n <td>8</td>\n <td>2</td>\n <td>1</td>\n <td>4</td>\n <td>4</td>\n <td>...</td>\n <td>66</td>\n <td>35</td>\n <td>62</td>\n <td>53</td>\n <td>68</td>\n <td>67</td>\n <td>58</td>\n <td>73</td>\n <td>48</td>\n <td>2025</td>\n </tr>\n <tr>\n <th>Kyrgyzstan</th>\n <td>Asia</td>\n <td>Central Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>173</td>\n <td>161</td>\n <td>135</td>\n <td>168</td>\n <td>173</td>\n <td>157</td>\n <td>159</td>\n <td>278</td>\n <td>123</td>\n <td>2353</td>\n </tr>\n <tr>\n <th>Lao People's Democratic Republic</th>\n <td>Asia</td>\n <td>South-Eastern Asia</td>\n <td>Developing regions</td>\n <td>11</td>\n <td>6</td>\n <td>16</td>\n <td>16</td>\n <td>7</td>\n <td>17</td>\n <td>21</td>\n <td>...</td>\n <td>42</td>\n <td>74</td>\n <td>53</td>\n <td>32</td>\n <td>39</td>\n <td>54</td>\n <td>22</td>\n <td>25</td>\n <td>15</td>\n <td>1089</td>\n </tr>\n <tr>\n <th>Lebanon</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>1409</td>\n <td>1119</td>\n <td>1159</td>\n <td>789</td>\n <td>1253</td>\n <td>1683</td>\n <td>2576</td>\n <td>...</td>\n <td>3709</td>\n <td>3802</td>\n <td>3467</td>\n <td>3566</td>\n <td>3077</td>\n <td>3432</td>\n <td>3072</td>\n <td>1614</td>\n <td>2172</td>\n <td>115359</td>\n </tr>\n <tr>\n <th>Malaysia</th>\n <td>Asia</td>\n <td>South-Eastern Asia</td>\n <td>Developing regions</td>\n <td>786</td>\n <td>816</td>\n <td>813</td>\n <td>448</td>\n <td>384</td>\n <td>374</td>\n <td>425</td>\n <td>...</td>\n <td>593</td>\n <td>580</td>\n <td>600</td>\n <td>658</td>\n <td>640</td>\n <td>802</td>\n <td>409</td>\n <td>358</td>\n <td>204</td>\n <td>24417</td>\n </tr>\n <tr>\n <th>Maldives</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>2</td>\n <td>1</td>\n <td>7</td>\n <td>4</td>\n <td>3</td>\n <td>1</td>\n <td>1</td>\n <td>30</td>\n </tr>\n <tr>\n <th>Mongolia</th>\n <td>Asia</td>\n <td>Eastern Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>59</td>\n <td>64</td>\n <td>82</td>\n <td>59</td>\n <td>118</td>\n <td>169</td>\n <td>103</td>\n <td>68</td>\n <td>99</td>\n <td>952</td>\n </tr>\n <tr>\n <th>Myanmar</th>\n <td>Asia</td>\n <td>South-Eastern Asia</td>\n <td>Developing regions</td>\n <td>80</td>\n <td>62</td>\n <td>46</td>\n <td>31</td>\n <td>41</td>\n <td>23</td>\n <td>18</td>\n <td>...</td>\n <td>210</td>\n <td>953</td>\n <td>1887</td>\n <td>975</td>\n <td>1153</td>\n <td>556</td>\n <td>368</td>\n <td>193</td>\n <td>262</td>\n <td>9245</td>\n </tr>\n <tr>\n <th>Nepal</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>1</td>\n <td>1</td>\n <td>6</td>\n <td>1</td>\n <td>2</td>\n <td>4</td>\n <td>13</td>\n <td>...</td>\n <td>607</td>\n <td>540</td>\n <td>511</td>\n <td>581</td>\n <td>561</td>\n <td>1392</td>\n <td>1129</td>\n <td>1185</td>\n <td>1308</td>\n <td>10222</td>\n </tr>\n <tr>\n <th>Oman</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>8</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>14</td>\n <td>18</td>\n <td>16</td>\n <td>10</td>\n <td>7</td>\n <td>14</td>\n <td>10</td>\n <td>13</td>\n <td>11</td>\n <td>224</td>\n </tr>\n <tr>\n <th>Pakistan</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>978</td>\n <td>972</td>\n <td>1201</td>\n <td>900</td>\n <td>668</td>\n <td>514</td>\n <td>691</td>\n <td>...</td>\n <td>14314</td>\n <td>13127</td>\n <td>10124</td>\n <td>8994</td>\n <td>7217</td>\n <td>6811</td>\n <td>7468</td>\n <td>11227</td>\n <td>12603</td>\n <td>241600</td>\n </tr>\n <tr>\n <th>Philippines</th>\n <td>Asia</td>\n <td>South-Eastern Asia</td>\n <td>Developing regions</td>\n <td>6051</td>\n <td>5921</td>\n <td>5249</td>\n <td>4562</td>\n <td>3801</td>\n <td>3150</td>\n <td>4166</td>\n <td>...</td>\n <td>18139</td>\n <td>18400</td>\n <td>19837</td>\n <td>24887</td>\n <td>28573</td>\n <td>38617</td>\n <td>36765</td>\n <td>34315</td>\n <td>29544</td>\n <td>511391</td>\n </tr>\n <tr>\n <th>Qatar</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>11</td>\n <td>2</td>\n <td>5</td>\n <td>9</td>\n <td>6</td>\n <td>18</td>\n <td>3</td>\n <td>14</td>\n <td>6</td>\n <td>157</td>\n </tr>\n <tr>\n <th>Republic of Korea</th>\n <td>Asia</td>\n <td>Eastern Asia</td>\n <td>Developing regions</td>\n <td>1011</td>\n <td>1456</td>\n <td>1572</td>\n <td>1081</td>\n <td>847</td>\n <td>962</td>\n <td>1208</td>\n <td>...</td>\n <td>5832</td>\n <td>6215</td>\n <td>5920</td>\n <td>7294</td>\n <td>5874</td>\n <td>5537</td>\n <td>4588</td>\n <td>5316</td>\n <td>4509</td>\n <td>142581</td>\n </tr>\n <tr>\n <th>Saudi Arabia</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>4</td>\n <td>1</td>\n <td>2</td>\n <td>5</td>\n <td>...</td>\n <td>198</td>\n <td>252</td>\n <td>188</td>\n <td>249</td>\n <td>246</td>\n <td>330</td>\n <td>278</td>\n <td>286</td>\n <td>267</td>\n <td>3425</td>\n </tr>\n <tr>\n <th>Singapore</th>\n <td>Asia</td>\n <td>South-Eastern Asia</td>\n <td>Developing regions</td>\n <td>241</td>\n <td>301</td>\n <td>337</td>\n <td>169</td>\n <td>128</td>\n <td>139</td>\n <td>205</td>\n <td>...</td>\n <td>392</td>\n <td>298</td>\n <td>690</td>\n <td>734</td>\n <td>366</td>\n <td>805</td>\n <td>219</td>\n <td>146</td>\n <td>141</td>\n <td>14579</td>\n </tr>\n <tr>\n <th>Sri Lanka</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>185</td>\n <td>371</td>\n <td>290</td>\n <td>197</td>\n <td>1086</td>\n <td>845</td>\n <td>1838</td>\n <td>...</td>\n <td>4930</td>\n <td>4714</td>\n <td>4123</td>\n <td>4756</td>\n <td>4547</td>\n <td>4422</td>\n <td>3309</td>\n <td>3338</td>\n <td>2394</td>\n <td>148358</td>\n </tr>\n <tr>\n <th>State of Palestine</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>453</td>\n <td>627</td>\n <td>441</td>\n <td>481</td>\n <td>400</td>\n <td>654</td>\n <td>555</td>\n <td>533</td>\n <td>462</td>\n <td>6512</td>\n </tr>\n <tr>\n <th>Syrian Arab Republic</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>315</td>\n <td>419</td>\n <td>409</td>\n <td>269</td>\n <td>264</td>\n <td>385</td>\n <td>493</td>\n <td>...</td>\n <td>1458</td>\n <td>1145</td>\n <td>1056</td>\n <td>919</td>\n <td>917</td>\n <td>1039</td>\n <td>1005</td>\n <td>650</td>\n <td>1009</td>\n <td>31485</td>\n </tr>\n <tr>\n <th>Tajikistan</th>\n <td>Asia</td>\n <td>Central Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>85</td>\n <td>46</td>\n <td>44</td>\n <td>15</td>\n <td>50</td>\n <td>52</td>\n <td>47</td>\n <td>34</td>\n <td>39</td>\n <td>503</td>\n </tr>\n <tr>\n <th>Thailand</th>\n <td>Asia</td>\n <td>South-Eastern Asia</td>\n <td>Developing regions</td>\n <td>56</td>\n <td>53</td>\n <td>113</td>\n <td>65</td>\n <td>82</td>\n <td>66</td>\n <td>78</td>\n <td>...</td>\n <td>575</td>\n <td>500</td>\n <td>487</td>\n <td>519</td>\n <td>512</td>\n <td>499</td>\n <td>396</td>\n <td>296</td>\n <td>400</td>\n <td>9174</td>\n </tr>\n <tr>\n <th>Turkey</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>481</td>\n <td>874</td>\n <td>706</td>\n <td>280</td>\n <td>338</td>\n <td>202</td>\n <td>257</td>\n <td>...</td>\n <td>2065</td>\n <td>1638</td>\n <td>1463</td>\n <td>1122</td>\n <td>1238</td>\n <td>1492</td>\n <td>1257</td>\n <td>1068</td>\n <td>729</td>\n <td>31781</td>\n </tr>\n <tr>\n <th>Turkmenistan</th>\n <td>Asia</td>\n <td>Central Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>40</td>\n <td>26</td>\n <td>37</td>\n <td>13</td>\n <td>20</td>\n <td>30</td>\n <td>20</td>\n <td>20</td>\n <td>14</td>\n <td>310</td>\n </tr>\n <tr>\n <th>United Arab Emirates</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>2</td>\n <td>2</td>\n <td>1</td>\n <td>2</td>\n <td>0</td>\n <td>5</td>\n <td>...</td>\n <td>31</td>\n <td>42</td>\n <td>37</td>\n <td>33</td>\n <td>37</td>\n <td>86</td>\n <td>60</td>\n <td>54</td>\n <td>46</td>\n <td>836</td>\n </tr>\n <tr>\n <th>Uzbekistan</th>\n <td>Asia</td>\n <td>Central Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>330</td>\n <td>262</td>\n <td>284</td>\n <td>215</td>\n <td>288</td>\n <td>289</td>\n <td>162</td>\n <td>235</td>\n <td>167</td>\n <td>3368</td>\n </tr>\n <tr>\n <th>Viet Nam</th>\n <td>Asia</td>\n <td>South-Eastern Asia</td>\n <td>Developing regions</td>\n <td>1191</td>\n <td>1829</td>\n <td>2162</td>\n <td>3404</td>\n <td>7583</td>\n <td>5907</td>\n <td>2741</td>\n <td>...</td>\n <td>1852</td>\n <td>3153</td>\n <td>2574</td>\n <td>1784</td>\n <td>2171</td>\n <td>1942</td>\n <td>1723</td>\n <td>1731</td>\n <td>2112</td>\n <td>97146</td>\n </tr>\n <tr>\n <th>Yemen</th>\n <td>Asia</td>\n <td>Western Asia</td>\n <td>Developing regions</td>\n <td>1</td>\n <td>2</td>\n <td>1</td>\n <td>6</td>\n <td>0</td>\n <td>18</td>\n <td>7</td>\n <td>...</td>\n <td>161</td>\n <td>140</td>\n <td>122</td>\n <td>133</td>\n <td>128</td>\n <td>211</td>\n <td>160</td>\n <td>174</td>\n <td>217</td>\n <td>2985</td>\n </tr>\n </tbody>\n</table>\n<p>49 rows × 38 columns</p>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "# we can pass multiple criteria in the same line.\n# let's filter for AreaNAme = Asia and RegName = Southern Asia\n\ndf_can[(df_can['Continent']=='Asia') & (df_can['Region']=='Southern Asia')]\n\n# note: When using 'and' and 'or' operators, pandas requires we use '&' and '|' instead of 'and' and 'or'\n# don't forget to enclose the two conditions in parentheses", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 35, | |
| "outputs": [ | |
| { | |
| "execution_count": 35, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": " Continent Region DevName 1980 \\\nAfghanistan Asia Southern Asia Developing regions 16 \nBangladesh Asia Southern Asia Developing regions 83 \nBhutan Asia Southern Asia Developing regions 0 \nIndia Asia Southern Asia Developing regions 8880 \nIran (Islamic Republic of) Asia Southern Asia Developing regions 1172 \nMaldives Asia Southern Asia Developing regions 0 \nNepal Asia Southern Asia Developing regions 1 \nPakistan Asia Southern Asia Developing regions 978 \nSri Lanka Asia Southern Asia Developing regions 185 \n\n 1981 1982 1983 1984 1985 1986 ... 2005 \\\nAfghanistan 39 39 47 71 340 496 ... 3436 \nBangladesh 84 86 81 98 92 486 ... 4171 \nBhutan 0 0 0 1 0 0 ... 5 \nIndia 8670 8147 7338 5704 4211 7150 ... 36210 \nIran (Islamic Republic of) 1429 1822 1592 1977 1648 1794 ... 5837 \nMaldives 0 0 1 0 0 0 ... 0 \nNepal 1 6 1 2 4 13 ... 607 \nPakistan 972 1201 900 668 514 691 ... 14314 \nSri Lanka 371 290 197 1086 845 1838 ... 4930 \n\n 2006 2007 2008 2009 2010 2011 2012 \\\nAfghanistan 3009 2652 2111 1746 1758 2203 2635 \nBangladesh 4014 2897 2939 2104 4721 2694 2640 \nBhutan 10 7 36 865 1464 1879 1075 \nIndia 33848 28742 28261 29456 34235 27509 30933 \nIran (Islamic Republic of) 7480 6974 6475 6580 7477 7479 7534 \nMaldives 0 2 1 7 4 3 1 \nNepal 540 511 581 561 1392 1129 1185 \nPakistan 13127 10124 8994 7217 6811 7468 11227 \nSri Lanka 4714 4123 4756 4547 4422 3309 3338 \n\n 2013 Total \nAfghanistan 2004 58639 \nBangladesh 3789 65568 \nBhutan 487 5876 \nIndia 33087 691904 \nIran (Islamic Republic of) 11291 175923 \nMaldives 1 30 \nNepal 1308 10222 \nPakistan 12603 241600 \nSri Lanka 2394 148358 \n\n[9 rows x 38 columns]", | |
| "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>Continent</th>\n <th>Region</th>\n <th>DevName</th>\n <th>1980</th>\n <th>1981</th>\n <th>1982</th>\n <th>1983</th>\n <th>1984</th>\n <th>1985</th>\n <th>1986</th>\n <th>...</th>\n <th>2005</th>\n <th>2006</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>Total</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Afghanistan</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>16</td>\n <td>39</td>\n <td>39</td>\n <td>47</td>\n <td>71</td>\n <td>340</td>\n <td>496</td>\n <td>...</td>\n <td>3436</td>\n <td>3009</td>\n <td>2652</td>\n <td>2111</td>\n <td>1746</td>\n <td>1758</td>\n <td>2203</td>\n <td>2635</td>\n <td>2004</td>\n <td>58639</td>\n </tr>\n <tr>\n <th>Bangladesh</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>83</td>\n <td>84</td>\n <td>86</td>\n <td>81</td>\n <td>98</td>\n <td>92</td>\n <td>486</td>\n <td>...</td>\n <td>4171</td>\n <td>4014</td>\n <td>2897</td>\n <td>2939</td>\n <td>2104</td>\n <td>4721</td>\n <td>2694</td>\n <td>2640</td>\n <td>3789</td>\n <td>65568</td>\n </tr>\n <tr>\n <th>Bhutan</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>5</td>\n <td>10</td>\n <td>7</td>\n <td>36</td>\n <td>865</td>\n <td>1464</td>\n <td>1879</td>\n <td>1075</td>\n <td>487</td>\n <td>5876</td>\n </tr>\n <tr>\n <th>India</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>8880</td>\n <td>8670</td>\n <td>8147</td>\n <td>7338</td>\n <td>5704</td>\n <td>4211</td>\n <td>7150</td>\n <td>...</td>\n <td>36210</td>\n <td>33848</td>\n <td>28742</td>\n <td>28261</td>\n <td>29456</td>\n <td>34235</td>\n <td>27509</td>\n <td>30933</td>\n <td>33087</td>\n <td>691904</td>\n </tr>\n <tr>\n <th>Iran (Islamic Republic of)</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>1172</td>\n <td>1429</td>\n <td>1822</td>\n <td>1592</td>\n <td>1977</td>\n <td>1648</td>\n <td>1794</td>\n <td>...</td>\n <td>5837</td>\n <td>7480</td>\n <td>6974</td>\n <td>6475</td>\n <td>6580</td>\n <td>7477</td>\n <td>7479</td>\n <td>7534</td>\n <td>11291</td>\n <td>175923</td>\n </tr>\n <tr>\n <th>Maldives</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>2</td>\n <td>1</td>\n <td>7</td>\n <td>4</td>\n <td>3</td>\n <td>1</td>\n <td>1</td>\n <td>30</td>\n </tr>\n <tr>\n <th>Nepal</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>1</td>\n <td>1</td>\n <td>6</td>\n <td>1</td>\n <td>2</td>\n <td>4</td>\n <td>13</td>\n <td>...</td>\n <td>607</td>\n <td>540</td>\n <td>511</td>\n <td>581</td>\n <td>561</td>\n <td>1392</td>\n <td>1129</td>\n <td>1185</td>\n <td>1308</td>\n <td>10222</td>\n </tr>\n <tr>\n <th>Pakistan</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>978</td>\n <td>972</td>\n <td>1201</td>\n <td>900</td>\n <td>668</td>\n <td>514</td>\n <td>691</td>\n <td>...</td>\n <td>14314</td>\n <td>13127</td>\n <td>10124</td>\n <td>8994</td>\n <td>7217</td>\n <td>6811</td>\n <td>7468</td>\n <td>11227</td>\n <td>12603</td>\n <td>241600</td>\n </tr>\n <tr>\n <th>Sri Lanka</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>185</td>\n <td>371</td>\n <td>290</td>\n <td>197</td>\n <td>1086</td>\n <td>845</td>\n <td>1838</td>\n <td>...</td>\n <td>4930</td>\n <td>4714</td>\n <td>4123</td>\n <td>4756</td>\n <td>4547</td>\n <td>4422</td>\n <td>3309</td>\n <td>3338</td>\n <td>2394</td>\n <td>148358</td>\n </tr>\n </tbody>\n</table>\n<p>9 rows × 38 columns</p>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Before we proceed: let's review the changes we have made to our dataframe.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "print('data dimensions:', df_can.shape)\nprint(df_can.columns)\ndf_can.head(2)", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": true, | |
| "trusted": true | |
| }, | |
| "execution_count": 36, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "text": "data dimensions: (195, 38)\nIndex(['Continent', 'Region', 'DevName', '1980', '1981', '1982', '1983',\n '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992',\n '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001',\n '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010',\n '2011', '2012', '2013', 'Total'],\n dtype='object')\n", | |
| "output_type": "stream" | |
| }, | |
| { | |
| "execution_count": 36, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": " Continent Region DevName 1980 1981 1982 \\\nAfghanistan Asia Southern Asia Developing regions 16 39 39 \nAlbania Europe Southern Europe Developed regions 1 0 0 \n\n 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\nAfghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \nAlbania 0 0 0 1 ... 1223 856 702 560 716 561 \n\n 2011 2012 2013 Total \nAfghanistan 2203 2635 2004 58639 \nAlbania 539 620 603 15699 \n\n[2 rows x 38 columns]", | |
| "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>Continent</th>\n <th>Region</th>\n <th>DevName</th>\n <th>1980</th>\n <th>1981</th>\n <th>1982</th>\n <th>1983</th>\n <th>1984</th>\n <th>1985</th>\n <th>1986</th>\n <th>...</th>\n <th>2005</th>\n <th>2006</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>Total</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Afghanistan</th>\n <td>Asia</td>\n <td>Southern Asia</td>\n <td>Developing regions</td>\n <td>16</td>\n <td>39</td>\n <td>39</td>\n <td>47</td>\n <td>71</td>\n <td>340</td>\n <td>496</td>\n <td>...</td>\n <td>3436</td>\n <td>3009</td>\n <td>2652</td>\n <td>2111</td>\n <td>1746</td>\n <td>1758</td>\n <td>2203</td>\n <td>2635</td>\n <td>2004</td>\n <td>58639</td>\n </tr>\n <tr>\n <th>Albania</th>\n <td>Europe</td>\n <td>Southern Europe</td>\n <td>Developed regions</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>1223</td>\n <td>856</td>\n <td>702</td>\n <td>560</td>\n <td>716</td>\n <td>561</td>\n <td>539</td>\n <td>620</td>\n <td>603</td>\n <td>15699</td>\n </tr>\n </tbody>\n</table>\n<p>2 rows × 38 columns</p>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "***\n\n# Visualizing Data using Matplotlib<a id=\"8\"></a>\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "## Matplotlib: Standard Python Visualization Library<a id=\"10\"></a>\n\nThe primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01). As mentioned on their website:\n\n> Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.\n\nIf you are aspiring to create impactful visualization with python, Matplotlib is an essential tool to have at your disposal.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "### Matplotlib.Pyplot\n\nOne of the core aspects of Matplotlib is `matplotlib.pyplot`. It is Matplotlib's scripting layer which we studied in details in the videos about Matplotlib. Recall that it is a collection of command style functions that make Matplotlib work like MATLAB. Each `pyplot` function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. In this lab, we will work with the scripting layer to learn how to generate line plots. In future labs, we will get to work with the Artist layer as well to experiment first hand how it differs from the scripting layer.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Let's start by importing `matplotlib` and `matplotlib.pyplot` as follows:\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "# we are using the inline backend\n%matplotlib inline \n\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 37, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "\\*optional: check if Matplotlib is loaded.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "print('Matplotlib version: ', mpl.__version__) # >= 2.0.0", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 38, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "text": "Matplotlib version: 3.5.1\n", | |
| "output_type": "stream" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "\\*optional: apply a style to Matplotlib.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "print(plt.style.available)\nmpl.style.use(['ggplot']) # optional: for ggplot-like style", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 39, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "text": "['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']\n", | |
| "output_type": "stream" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "### Plotting in *pandas*\n\nFortunately, pandas has a built-in implementation of Matplotlib that we can use. Plotting in *pandas* is as simple as appending a `.plot()` method to a series or dataframe.\n\nDocumentation:\n\n* [Plotting with Series](http://pandas.pydata.org/pandas-docs/stable/api.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01#plotting)<br>\n* [Plotting with Dataframes](http://pandas.pydata.org/pandas-docs/stable/api.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01#api-dataframe-plotting)\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "# Line Pots (Series/Dataframe) <a id=\"12\"></a>\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "**What is a line plot and why use it?**\n\nA line chart or line plot is a type of plot which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields.\nUse line plot when you have a continuous data set. These are best suited for trend-based visualizations of data over a period of time.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "**Let's start with a case study:**\n\nIn 2010, Haiti suffered a catastrophic magnitude 7.0 earthquake. The quake caused widespread devastation and loss of life and aout three million people were affected by this natural disaster. As part of Canada's humanitarian effort, the Government of Canada stepped up its effort in accepting refugees from Haiti. We can quickly visualize this effort using a `Line` plot:\n\n**Question:** Plot a line graph of immigration from Haiti using `df.plot()`.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "First, we will extract the data series for Haiti.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "haiti = df_can.loc['Haiti', years] # passing in years 1980 - 2013 to exclude the 'total' column\nhaiti.head()", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 40, | |
| "outputs": [ | |
| { | |
| "execution_count": 40, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "1980 1666\n1981 3692\n1982 3498\n1983 2860\n1984 1418\nName: Haiti, dtype: object" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Next, we will plot a line plot by appending `.plot()` to the `haiti` dataframe.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "haiti.plot(kind=\"line\")", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 41, | |
| "outputs": [ | |
| { | |
| "execution_count": 41, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "<AxesSubplot:>" | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": "<Figure size 640x480 with 1 Axes>", | |
| "image/png": 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" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "*pandas* automatically populated the x-axis with the index values (years), and the y-axis with the column values (population). However, notice how the years were not displayed because they are of type *string*. Therefore, let's change the type of the index values to *integer* for plotting.\n\nAlso, let's label the x and y axis using `plt.title()`, `plt.ylabel()`, and `plt.xlabel()` as follows:\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "haiti.index = haiti.index.map(int) # let's change the index values of Haiti to type integer for plotting\nhaiti.plot(kind='line')\n\nplt.title('Immigration from Haiti')\nplt.ylabel('Number of immigrants')\nplt.xlabel('Years')\n\nplt.show() # need this line to show the updates made to the figure", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": true, | |
| "trusted": true | |
| }, | |
| "execution_count": 42, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": "<pyolite.display.Image at 0x40a5310>", | |
| "image/png": 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tfDaBj3bqD1/qj7rUJ77UH77UH76iuT9iY2NJT08PdzPCSgFgAFRWVlJRURHQcxpjvOdWpR71x97UH3WpT3ypP3ypP3ypP0RTwCIiIiJRRgGgiIiISJRRACgiIiISZRQAioiIiEQZBYAiIiIiUUYBoIiIiEiUUQAoIiIiEmUUAIqIiIhEGQWAIiIiIlFGAaCIiIhIlFEAKCIiIhJlFACKiIiIRBkFgCIiItIoduk3uD98C2ttuJsizRQb7gaIiIhIy+B+7u9QvBPTqSsc0j/czZFm0AigiIiI7JctLYHinc73Cz8Pc2ukuRQAioiIyP7lb/N+axd9ga2qCmNjpLkUAIqIiMj+7agJANlVBCuXha8t0mwKAEVERGS/bH6e72NNA7doCgBFRERk/3ZUB4AdugCaBm7pFACKiIjI/lVPAZvBp0Bya00Dt3AKAEVERGS/bPUIoGmXjTnyeOc5TQO3WAoARUREZP/ytztf22ZhjhoMaBq4JVMAKCIiIvtkKyqgKN95kJEFvftpGriFUwAoIiIi+1ZQXQImPgFSWmNiYzUN3MIpABQREZF989QAzGiHMQZA08AtnAJAERER2SdPAght29U82bsfpGgauKVSACgiIiL75ikB0zbL+5SJjcUcoWnglkoBoIiIiOxbfs0UcG2aBm65FACKiIjIPtVMAWf5vqBp4BZLAaCIiIjsW37dKWDYaxr4W00DtyQKAEVERKRB1u2uKQK91xQwgDm6ehp4saaBWxIFgCIiItKwogKoqgSXC9Iy6r7e+3BNA7dACgBFRESkYZ4EkPRMTExMnZdNTIymgVsgBYAiIiLSoHprAO5F08AtjwJAERERaZinBmBGVsPHaBq4xVEAKCIiIg3Lrx4BrCcBxEPTwC2PAkARERFpkPXsA7yPKWDQNHBLowBQREREGla9BnDvGoB1aBq4RVEAKCIiIvWy1nrXAO53BFDTwC2KAkARERGpX2kJlO92vt/HGkAPTQO3HAoARUREpH6eEjCtUzHxCfs/XtPALUZsuBtQn/z8fP7zn/+wZMkSysvLyc7O5rrrrqNnz56AMyT92muv8dFHH1FSUkKfPn0YO3YsHTp08J5jz549TJs2jQULFlBRUUH//v0ZO3YsaWlp3mOKi4uZOnUqCxcuxBjDsccey5VXXkmrVq1C/ZFFREQiTyMygGvzTAPbz97Hfvs55pD+QWycNEfEjQAWFxdz5513Ehsby/jx43nsscf47W9/S3JysveY2bNnM3fuXK666ir++te/kpCQwMSJE9mzZ4/3mBdeeIGFCxdy0003ce+991JQUMCkSZN8rvXEE0+wfv16JkyYwO23386PP/7I5MmTQ/ZZRUREIllNBvB+EkBq8U4DL1qgaeAIFnEB4OzZs2nbti3XXXcdvXr1Iisri/79+5OdnQ04o39z5szh/PPPZ+DAgXTt2pUbbriBgoICvvnmGwBKS0v5+OOPGT16NIcddhg9evTguuuuY8WKFaxcuRKADRs2sGTJEn7/+99z8MEH06dPH8aMGcOCBQvIz88P2+cXERGJGN4M4MaNAAI108DFOzUNHMEiLgD89ttv6dGjB48++ihjx47l1ltv5cMPP/S+npeXR2FhIYcffrj3uaSkJHr16uUN7lavXk1VVRX9+vXzHtOpUycyMzO9x6xcuZLk5GTvtDJAv379MMaQm5sb7I8pIiIS8Wy+HyOAygZuESJuDWBeXh4ffPABZ599Nueddx6rVq3iueeeIzY2lqFDh1JYWAhAamqqz/tSU1O9rxUWFhIbG+szbVzfMW3atPF5PSYmhpSUFO8xe6uoqKCiosL72BhDYmKi9/tA8pwv0OdtqdQfvtQfdalPfKk/fKk/fDW6PzzbwLXNalLfmaOHOOsAFy2A3/weExPjd1slOCIuAHS73fTs2ZNLL70UgO7du7Nu3To++OADhg4dGta2zZo1ixkzZngfd+/enYceeoh27ZowNN5Enqlvcag/fKk/6lKf+FJ/+FJ/+Npff2ws3IEbaNf7EOJrJVruj81qx6Ypk3DvLCJj+yZaDTimmS2VQIu4ADA9PZ3OnTv7PNe5c2e++uorAG8Wb1FREenp6d5jioqK6Natm/eYyspKSkpKfEYBi4qKvO9PS0tj586dPtepqqqiuLjYJ1O4tvPOO49zzjnH+9jz29C2bduorKxs8mfdF2MM2dnZbNmyxSnEGeXUH77UH3WpT3ypP3ypP3w1pj/snnLchc6a+O1uF2bz5iZdww44Dv73Htvff4uY9l2a3eZAio2NDergTUsQcQFg79692bRpk89zmzZt8v6gsrKySEtL4/vvv/cGfKWlpeTm5nL66acD0KNHD2JiYvj+++857rjjvOfYvn07OTk5AOTk5FBSUsLq1avp0aMHAMuWLcNaS69eveptW1xcHHFxcfW+Fqx/UKy1+seqFvWHL/VHXeoTX+oPX+oPX/vqD+upAZiQiE1Khib2mzlqMPZ/72EXLsB9yTWaBo4wEZcEcvbZZ/Pzzz8zc+ZMtmzZwvz58/noo48444wzAOe3luHDhzNz5ky+/fZb1q1bx1NPPUV6ejoDBw4EnKSQYcOGMW3aNJYtW8bq1at55plnyMnJ8QaAnTt3ZsCAAUyePJnc3Fx++uknpk6dyqBBg8jIyAjb5xcREYkItbaA82vtZO9+kNJG2cARKuJGAHv16sXNN9/Myy+/zBtvvEFWVhajR4/mhBNO8B4zYsQIysvLmTx5MqWlpfTp04fx48cTHx/vPWb06NEYY5g0aRKVlZXeQtC1/eEPf2DKlCncd9993kLQY8aMCdlnFRERiVT+ZADX5mQDH1ddFHq+ikJHmIgLAAGOOuoojjrqqAZfN8YwatQoRo0a1eAx8fHxjB07tk7QV1tKSgrjxo1rVltFREQOSP7UANxLTTbwF9hLlQ0cSSJuClhEREQigGcKuJHbwNVL08ARSwGgiIiI1GGbuA9wfTzTwAD22/mBaJYEiAJAERERqatWEejm8O4K8tPSZjdJAkcBoIiIiPiwVVVQsN150MwAkI7VNQDzt2Hd7uadSwJGAaCIiIj4KsoHtxtiYiE1ff/H70taW3C5oLISdhYEpn3SbAoARURExJcnASS9LcbVvFDBxMRAeqbzYHteMxsmgaIAUERERHx4dwFp7vSvR/V5vOeVsFMAKCIiIr48NQCbUwKmFm8tQQWAEUMBoIiIiPhq5i4gdbRt73xVABgxFACKiIiIj5pt4AIzAug5j6aAI4cCQBEREfEVoBqAHt7zKACMGAoARURExMtaWxOoBWgNIJmeKeBtzvkl7BQAioiISI3iXbCn3Pk+IzMw50xvC8ZAxR7YVRiYc0qzKAAUERGRGp49gFPTMXHxATmliY1zCkKDagFGCAWAIiIiUiPQ078e3kSQbYE9r/hFAaCIiIh4eTKAA5UA4lGTCLI1oOcV/ygAFBERkRo7AlwCxqNtTSKIhJ8CQBEREfGyQZ8C1hrASKAAUERERGoEuAagh8msPt92TQFHAgWAIiIiUsOTBRzoKeCM6gAwX7UAI4ECQBEREQHAlpc5dQChJmALFE9AWfsaEjYKAEVERMTh2QM4MRmTlBzQU5u4eEhNr76O1gGGmwJAERERcXgTQAK0A8jePOsKVQw67BQAioiICFCrSHOAE0A8PIklVrUAw04BoIiIiDiqRwBNoBNAPLzFoFULMNwUAIqIiIgjyCOAeEcANQUcbgoARUREBADrSc4IdAZwtZrt4BQAhpsCQBEREXF49wEO0hRwZk0AqFqA4aUAUERERLCVlVCQ7zwI9DZwHp6Rxd2lUFoSnGtIoygAFBERESjcAdYNsbHQJi0olzAJCdA61XmgaeCwUgAoIiIiNQkgGe0wriCGB1oHGBEUAIqIiEhNZm6wMoA9qtcXKhM4vBQAioiIiHd7NhOs9X/VTNv2zjcKAMNKAaCIiIhA/nbna5ADQE8msNV2cGGlAFBERERCNgVsPJnA+QoAw0kBoIiIiHiTQIJWA9DDUwtQI4BhpQBQREQkyllrvUWgQ5UEQmkxdndpcK8lDQpoAFheXs7HH3/M+++/z7Zt2uhZRESkRdhVCBV7wBhIbxvUS5lWSZDc2nmgRJCwifX3jf/4xz/Izc1l0qRJAFRWVvLnP/+Z9evXA5CUlMRdd91F9+7dA9NSERERCY4d1QkgqRmY2LjgX69tFpTscgLAzt2Cfz2pw+8RwB9++IFjjjnG+3j+/PmsX7+e//u//2PSpEmkpaXx+uuvB6SRIiIiEkSehIxgr//zUC3AsPM7ACwsLKRdu5ob5euvv6ZHjx4MGTKEzp07c8opp5CbmxuQRoqIiEjweAKxYNcA9FAtwPDzOwBMSEigtNRZvFlVVcXy5cvp37+/9/VWrVp5XxcREZEItiNECSAeGgEMO7/XAPbo0YOPPvqIQw89lG+//Zbdu3dz9NFHe1/funUrqampAWmkiIiIBE9NDcAQjQBmZmFBpWDCyO8RwIsvvpiioiJuv/12ZsyYwbHHHkuvXr28r3/99df07t07II0UERGRIPLWAAzVCKCmgMPN7xHAnj178vjjj7NixQqSk5Pp27ev97WSkhLOOOMMn+dEREQkQnlqAIZoDaB3pLF4J7a8DJPQKjTXFS+/RwCXL18OwMCBA+sEesnJyQwZMkRrAEVERCKcLSuF0mLnQaimgJNSIDHZeaBRwLDwOwC89957Wbp0aYOvL1u2jHvvvdff04uIiEgoeBJAklKcIs2h4gk2d2jjiHAI2lZwFRUVuFzaaU5ERCSihTgBxKt6vaHdsTW01xWgiWsAt2/fTl5ezVDtxo0bvVPBtZWWlvLhhx/61AkUERGRyGNDXQKmmmlbnQmsEcCwaFIAOG/ePGbMmOF9PHPmTGbOnFnvsS6Xi6uuuqp5rRMREZHg8hSBDnEA6A04tQYwLJoUAB5//PF06dIFgMcee4yzzjqLPn36+BxjjCEhIYFu3bqRlpYWsIaKiIhIEHgzgDNDellPLUC7XVPA4dCkALBz58507twZgGuvvZa+ffuSlRXi3xhEREQkYGx+iGsAeniul68p4HDwuw7g0KFDA9gMERERCQvPFGxGmALAogJsxR5MXHxorx/l/A4AATZs2MAnn3zC1q1bKSkpwVrr87oxhrvuuqtZDRQREZHgsJUVUFTgPAh1FnBya0hoBeVlTiJIdqfQXj/K+R0A/u9//+OZZ54hJiaGjh07kpKSUueYvQPCxnjttdd8Ek0AOnbsyOOPP+4952uvvcZHH31ESUkJffr0YezYsXTo0MF7/J49e5g2bRoLFiygoqKC/v37M3bsWJ81icXFxUydOpWFCxdijOHYY4/lyiuvpFUrVSMXEZEokb8drIW4eGidGtJLG2OcUcBN65xRSAWAIeV3APj666/TvXt37rjjDtq0aRPINtGlSxfuvPNO7+Pa9QRnz57N3Llzuf7668nKymL69OlMnDiRRx99lPh4Z/j4hRdeYNGiRdx0000kJSUxZcoUJk2axF/+8hfveZ544gkKCgqYMGECVVVVPPPMM0yePJlx48YF9LOIiIhEKuud/m3nBGShVh0A2h1bCcPVo5rflZrz8/M5+eSTAx78gRPwpaWlef94rmGtZc6cOZx//vkMHDiQrl27csMNN1BQUMA333wDODUIP/74Y0aPHs1hhx1Gjx49uO6661ixYgUrV64EnKnrJUuW8Pvf/56DDz6YPn36MGbMGBYsWEB+fn7AP4+IiEhE8iRghHr6t5o38US1AEPO7xHArl27Bi1Y2rJlC9dccw1xcXHk5ORw6aWXkpmZSV5eHoWFhRx++OHeY5OSkujVqxcrV65k8ODBrF69mqqqKvr16+c9plOnTmRmZrJy5UpycnJYuXIlycnJ9OzZ03tMv379MMaQm5vLMcccU2+7KioqqKio8D42xpCYmOj9PpA85wvLb2QRSP3hS/1Rl/rEl/rDl/rDl7cfdtRkAIejb0xm++pi0Hn62YSY3wHgb3/7Wx577DGOOOIIevfuHbAGHXzwwVx33XV07NiRgoICZsyYwV133cWkSZMoLCwEIDXVd51Camqq97XCwkJiY2NJTk7e5zF7j1zGxMSQkpLiPaY+s2bN8lmf2L17dx566KGg7niSnZ0dtHO3ROoPX+qPutQnvtQfvtQfvhJ376IEaN2tB6m11tKHSmmvHHYAcbsKaR+G60czvwPA2bNnk5SUxF133UXnzp3JzMyss/evMYZbb721Sec94ogjvN937drVGxB+8cUXdOoU3gWi5513Huecc473see3lW3btlFZWRnQaxljyM7OZsuWLX4l0xxo1B++1B91qU98qT98qT98efqjdP0vABTHJ1K6eXPI22Fdztr9PZs3sDmE14+NjY367Wr9DgDXrVsHQGZmJmVlZWzYsKHOMYEYzk1OTqZjx45s2bKFQw89FICioiLS09O9xxQVFdGtWzcA0tLSqKyspKSkxGcUsKioyJsFnJaWxs6dO32uU1VVRXFx8T53L4mLiyMuLq7e14L1D4q1Vv9Y1aL+8KX+qEt94kv94Uv94cu7D3BGu7D0i/WsPSzMx12xBxNb//+xEnh+B4BPP/10INvRoLKyMrZs2cIJJ5xAVlYWaWlpfP/9996Ar7S0lNzcXE4//XQAevToQUxMDN9//z3HHXccAJs2bWL79u3k5OQAkJOTQ0lJCatXr6ZHjx4ALFu2DGstvXr1CsnnEhERCSfrdkNBTQAYFq1TIT4e9uxxStJkaRo4VJpVCDoYpk2bxtFHH01mZiYFBQW89tpruFwuhgwZgjGG4cOHM3PmTDp06EBWVhavvvoq6enpDBw4EHCSQoYNG8a0adNISUkhKSmJqVOnkpOT4w0AO3fuzIABA5g8eTJXXXUVlZWVTJ06lUGDBpGRkRHOjy8iIhIS7sJ8qKwE44K0tmFpgzHG2YFkywanFqACwJAJSAC4e/duSktL6x0+zsxs2ubS+fn5/P3vf2fXrl20adOGPn36MHHiRG/SxogRIygvL2fy5MmUlpbSp08fxo8f760BCDB69GiMMUyaNInKykpvIeja/vCHPzBlyhTuu+8+byHoMWPG+PHpRUREWp7KvOo1d+kZmNgwjge1bQdbNmB35KkWYAg16yf+/vvv884777B169YGj5k+fXqTznnjjTfu83VjDKNGjWLUqFENHhMfH8/YsWPrBH21paSkqOiziIhErSpPABjqPYD3YtrWlIKR0PG7EPT777/PlClTyM7O5uKLLwbg7LPP5txzzyUtLY1u3bpx7bXXBqyhIiIiEjieEUATpiLQXpnVAeh2BYCh5HcA+O6779K/f3/Gjx/PqaeeCsCRRx7JJZdcwmOPPcbu3bvZtWtXwBoqIiIigVOVt8X5JlwJIB7V17f5CgBDye8AcOvWrRx11FGAU0QZ8NbC8yRivP/++wFoooiIiARa5bbqALBtmKeAM9s732gEMKT8DgCTkpKoqqryfh8fH8/27du9rycmJu5zVw0REREJn6pImQL21gLcga2OKyT4/A4Au3Tpwi+//OJ9nJOTwwcffEB+fj7bt2/nww8/pIO2dREREYlI3izgMI8A0iYdYmPB7YaC7fs/XgLC7wDwhBNOYP369VRUVABw4YUXsmHDBq699lquv/56Nm3a5E0OERERkchhS4uxpSXOgzCvATQuV00blAkcMn6XgTn55JM5+eSTvY/79OnDo48+ysKFC3G5XBx++OF07NgxII0UERGRAPJsAZfSBpPQKrxtAWcUMm+zagGGkF8B4J49e/jwww/p1q0bffv29T7fvn17hg8fHrDGiYiISOBZz0hbuDOAq5nM6lqASgQJGb+mgOPj43nppZfYtGlToNsjIiIiwZbvjACacK//8/AEoioFEzJ+rwE86KCD2LZtWyDbIiIiIiHgHQEMdwawR3UxaKsRwJDxOwC8+OKL+fDDD1m6dGkg2yMiIiLBtrMQAJPWNrztqGY829EpCSRk/E4Ceffdd0lJSWHixIlkZWWRlZVFfHy8zzHGGG699dZmN1JEREQCaFeR8zWlTXjb4eHZDq5gO9ZdhXHFhLc9UcDvAHDdunUAZGZm4na72bJlS51jjFEuj4iISKSxxTudbyIlAEzLgJgYqKqCwvyISU45kPkdAD799NOBbIeIiIiESnUAaFpHRgBoXDGQngnbtzolahQABp3fawBFRESkhdoVYSOA4N2RxO7YGuaGRAe/RwBr7/vbkPj4eFq3bq2pYBERkQhh95RDeZnzoHVqeBtTi2mbpVqAIeR3AHj99dc36rj4+Hj69OnDyJEj6dOnj7+XExERkUAo3uV8jYmBxKTwtqU2T03CfJWYCwW/A8Df//73zJ07lx07djBkyBCys7MB2Lx5M59//jmZmZmcfPLJbNmyhc8++4z77ruP8ePHc9hhhwWs8SIiItJExU4GsKtNGsYYrLVhblA1by1ATQGHgt8BYEFBAZWVlTzxxBMkJyf7vHbRRRdx5513smfPHq644gpGjhzJ7bffzowZMxQAioiIhFN1AkhMajoREvoBtaaAd2gEMBT8TgL54IMPGDZsWJ3gDyAlJYVTTjmFd999F4DWrVtz8skns3r1av9bKiIiIs1mqxNAXG3SwtuQvXmngPOwbnd42xIF/A4Ad+3axZ49exp8vaysjJ07d3ofp6WlRc4ws4iISLQqjtAAMK0tGBdUVsLOgnC35oDndwDYs2dP5syZ4y0IXdsvv/zCu+++S69evbzPbdiwgbZtI2PLGRERkahVawo4kpjYWEivjhM0DRx0fq8BHDNmDPfeey+33norOTk53iSQLVu2sHLlShITE7nyyisB2LNnD8uXL+e4444LTKtFRETEP7tqkkAiTmYW5G/Dbt+K6anKIcHkdwDYtWtX/va3v/Hmm2/y3XffsWrVKsDZGu70009nxIgR3hG/+Ph4Hn744cC0WERERPxmvVPAkVMD0MNkZGH5QaVgQsDvABAgIyODMWPGBKotIiIiEmzVdQAjbQoY8JaCUTHo4NNWcCIiItEkkqeAq/cA1nZwwdfoEcBnnnkGYwzXXHMNLpeLZ555Zr/vMcZw7bXXNquBIiIiEkCRmgUMmMz2qgUYIo0OAH/44QeMMbjdblwuFz/88MN+36M9gEVERCKHtbYmAExNh4qqMLdoL7VrAVqrOCKIGh0APv300/t8LCIiIhGutASqiyzHtEmFHflhbtBeMjLBGNizx5mqjsBRygOF1gCKiIhEi+rRPxISMfEJ4W1LPUxsHKRmOA92KBEkmJqVBezhdrspLS2t97WUlJRAXEJERESayxMAtm4T3nbsS9t2ULgDuz0P0z0n3K05YPkdAFZWVjJ79mzmzZvHjh07cDewb9/06dP9bpyIiIgEUHUGMCmRGwCatu2xq36CfI0ABpPfAeC//vUvPv30U3Jychg4cCBJSUmBbJeIiIgEmKcItGkdeUWgvdo6pWBUCzC4/A4Av/zyS0488USuv/76QLZHREREgsUzBRzBI4CeYtBWawCDyu8kkISEBA4++OBAtkVERESCaVfkB4CmbXvnGwWAQeV3ADh48GAWLVoUyLaIiIhIMHmngCM3APROAe9wagFKcPg9BXzZZZfxzDPP8OCDD3LyySfTtm1bXK668WSPHj2a1UAREREJDNsSpoCrt4OjvAxKdkV2W1swvwPAiooKrLUsXryYxYsXN3icsoBFREQihCcLOIJHAE18glMAemehMw2sADAo/A4A//GPf/D1118zePBgevXqpSxgERGRSOeZAk6J4CxgcLaE8wSAXXuFuzUHJL8DwO+++44zzzyTK664IoDNERERkaAp3uV8jeARQACT2R67ZqVTDDrcjTlA+Z0EkpiYSHZ2diDbIiIiIkFiKytgd4nzINKnVTNqEkEkOPwOAE855RQ+//zzBncAERERkQjiGf0zLkiK8G1aVQsw6PyeAu7cuTPffvstt912GyeddFKDWcDHHntssxooIiIiAVDs2QauNaae/68jiWmbhQWNAAaR3wHg448/7v3+xRdfbPA4ZQGLiIhEgBZQBNqrrTMCyI5t4W3HAczvAPDuu+8OZDtEREQkiGwLSQABagLA3SXY0mJMpE9Zt0B+B4B9+/YNZDtEREQkmLxTwJEfAJqEVk47i3fC9jw4SAFgoEX2IgAREREJjF0tpAagh2cUMF/rAIPB7xFAgJ9++omPP/6YvLw8SkpK6uzZZ4zhkUceaVYDRUREJABawjZwtbXNgl9yVQswSPwOAN955x1efPFF4uPj6dixIykpGp4VERGJWJ4AsHXr8LajkUymJxNYiSDB4HcA+NZbb9GnTx9uu+02bQMnIiIS4ax3BLBlTAGb00Zghp0DaW3D3ZQDkt8BYHl5OUOGDFHwJyIi0hJ41wC2jClgo8AvqPxOAjn00ENZt25dINsiIiIiweLJAm4JZWAk6PwOAMeMGcOyZct46623KC4uDmSbREREJICstbWSQFrGFLAEl99TwJmZmZx66qm8+OKLvPTSS8THx9e7FdwLL7zgd+PefPNNXn75ZYYPH84VV1wBODfxa6+9xkcffURJSQl9+vRh7NixdOjQwfu+PXv2MG3aNBYsWEBFRQX9+/dn7NixpKWleY8pLi5m6tSpLFy4EGMMxx57LFdeeSWtWrXyu70iIiIRqXw3VFY637eQKWAJLr8DwOnTpzNz5kwyMjLo2bNnwNcC5ubm8sEHH9C1a1ef52fPns3cuXO5/vrrycrKYvr06UycOJFHH32U+Ph4wAk6Fy1axE033URSUhJTpkxh0qRJ/OUvf/Ge54knnqCgoIAJEyZQVVXFM888w+TJkxk3blxAP4eIiEjYebaBi4/HJCSEty0SEfwOAD/44AOOPPJIbrnllnpH/pqjrKyMJ598kmuuuYaZM2d6n7fWMmfOHM4//3wGDhwIwA033MBVV13FN998w+DBgyktLeXjjz9m3LhxHHbYYQBcd911/PGPf2TlypXk5OSwYcMGlixZwgMPPEDPnj0BZ0r7gQce4PLLLycjIyOgn0dERCSsNP0re/E7cqusrOTII48MePAH8O9//5sjjjiCww8/3Of5vLw8CgsLfZ5PSkqiV69erFy5EoDVq1dTVVVFv379vMd06tSJzMxM7zErV64kOTnZG/wB9OvXD2MMubm5Af88IiIiYdXSikBL0Pk9AnjkkUfy448/ctpppwWyPXz++eesWbOGBx54oM5rhYWFAKSm+v4Gk5qa6n2tsLCQ2NhYkpOT93lMmza+fwliYmJISUnxHlOfiooKKioqvI+NMSQmJnq/DyTP+QJ93pZK/eFL/VGX+sSX+sNXtPeH9ZSAad0GY0zU94c0IwC88MILefzxx/n3v//NsGHDyMzMrHc0sCk7hGzfvp3nn3+eCRMmeNfzRZJZs2YxY8YM7+Pu3bvz0EMP0a5du6BdMzs7O2jnbonUH77UH3WpT3ypP3xFa3/sdEERkNgum7a1kiajtT+kGQHgjTfeCMDatWv54IMPGjxu+vTpjT7n6tWrKSoq4rbbbvM+53a7+fHHH3n33Xd5/PHHASgqKiI9Pd17TFFREd26dQMgLS2NyspKSkpKfEYBi4qKvFnAaWlp7Ny50+faVVVVFBcX+2QK7+28887jnHPO8T72/Oa0bds2Kj3ZVQFijCE7O5stW7bU2WM5Gqk/fKk/6lKf+FJ/+Ir2/qjauB6A3bFxbN68Oer7IzY2NqiDNy2B3wHgyJEjAz503K9fP/72t7/5PPePf/yDjh07MmLECNq3b09aWhrff/+9N+ArLS0lNzeX008/HYAePXoQExPD999/z3HHHQfApk2b2L59Ozk5OQDk5ORQUlLC6tWr6dGjBwDLli3DWkuvXr0abF9cXBxxcXH1vhasv0DW2qj8y9kQ9Ycv9Udd6hNf6g9fUdsftdYA1v78Udsf4n8AeNFFFwWyHQAkJiZy0EEH+TyXkJBA69atvc8PHz6cmTNn0qFDB7Kysnj11VdJT0/3ZgUnJSUxbNgwpk2bRkpKCklJSUydOpWcnBxvANi5c2cGDBjA5MmTueqqq6isrGTq1KkMGjRIGcAiInLAsUoCkb34HQCGy4gRIygvL2fy5MmUlpbSp08fxo8f77NmcPTo0RhjmDRpEpWVld5C0LX94Q9/YMqUKdx3333eQtBjxowJ9ccREREJvl3ONnAtZR9gCT5jGzn260l+OP/883G5XD7JEPtywQUX+N+6FmLbtm0+2cGBYIyhQ4cObN68WcPzqD/2pv6oS33iS/3hK9r7o2rCtbB1I65b/orJOSzq+yMuLk5rABt74Ouvvw7Aueeei8vl8j7en2gIAEVERCKapoBlL40OAPfO5m1Kdq+IiIiEh62qgpJdzgMFgFIt8Nt4iIiISOTwBH/GQHLr8LZFIoYCQBERkQOZZ/o3KQUTExPetkjEUAAoIiJyINul9X9SlwJAERGRA5lnBLC1AkCpoQBQRETkAKYi0FKfRgeAc+bMYdOmTcFsi4iIiASaikBLPRodAL7wwgusXr3a+3jUqFHMnz8/KI0SERGRANEUsNSj0QFgSkoKhYWFQWyKiIiIBJymgKUejS4E3bdvX15//XXWrl1LUlISAJ9++ikrV65s8D3GGK688srmt1JERET8YpUFLPVodAA4duxYnn/+eZYuXUpRkbOeYOnSpSxdunSf71MAKCIiEkbVI4CmdWqYGyKRpNEBYGpqKuPGjfM+HjVqFP/3f//HkCFDgtIwaTpbWgJF+ZgOXcLdFBERiRSaApZ6+F0G5tprryUnJyeQbZFmcv/zQdx3XY/7v69hrQ13c0REJBIUO7N2CgCltkaPAO5t6NCh3u83bNjAtm3bAGjXrh2dO3dudsPED+udLG375n+c3/guHINxqdSjiEi0suXlsGeP80BZwFKL3wEgwDfffMO0adPIy8vzeT4rK4vRo0dz9NFHN6tx0ni2ogKKd9U8/vAtJwgc/QdMbLN+zCJ+cb/9Kvan73D94W5MQqtwN0ckOnlG/2JjISExvG2RiOL38NCiRYuYNGkSAJdccgm33HILt9xyC5dccgkAf/vb31iyZElAGimNULjD+Robh/ndHyEmBvvlJ7ifnogtLwtv2yTqWGuxH7wJK3+A3B/D3RyR6OVd/5eKMSa8bZGI4ncA+MYbb9C1a1ceeeQRzj33XI4++miOPvpozj33XB555BEOOuggXn/99UC2VfalMN/5mpaB67iTcV0/AeLjYdlC3I/dhS3Zte/3iwRSwXbYXQqA3bY5zI0RiWIqASMN8DsAXLduHSeddBKtWtWd2mnVqhVDhw5l3bp1zWqcNJ71jACmtQXA9DsK1033Q1IKrPoJ98N3YAt2hLGFElU21fq7v21L+NohEuWsZwpY6/9kL34HgHFxcRQXFzf4enFxMXFxcf6eXpqqegTQpLf1PmV69sF164OQlgGb1uF+8Fbslo3haqFEEVsrALR5CgBFwsZTA1AjgLIXvwPAww47jDlz5tS7E8jPP//M3Llz6devX7MaJ03gHQHM8HnadDoI120PQVZHyN+G+6HbsGt/DkMDJar4jABqClgkbHZVL/9RACh78Ts99LLLLuPPf/4zd955J7169aJjx44AbNq0idzcXFJTU/nNb34TsIbKfhT4TgHXZjLb47rtQdxP3Ae/5OL+2wRc14/HHNI/xI2UaGE31goAt2/FWqsF6CLhoBqA0gC/RwCzsrL429/+xllnnUVJSQkLFixgwYIFlJSUMHz4cB555BGysrIC2VbZB9vACKCHaZOG6+b7oc/hUL4b9xP3Yhd+HsIWSrSwbjdsXl/zRHkZ7CwMW3tEopn1ZAFrGzjZS7MKxKWmpnLFFVcEqCnSLJ41gPWMAHqYVkm4/nA37imTYOEC3JMfxvzmWlwnnRmqVko0yN/mBH0xsc7C88J8JxEkNT3cLROJPtoGThqgbSIOANbamjIw6Q0HgAAmLg7X1bdgTjwTrMX+5xnc70zX1nESOJ71f9mdoH0nAKwygUXCY5cnCaR1mBsikUYB4IGgtBgqqrf6aWAKuDbjisFcdi3m7IsAsLNfcraPEwkATwaw6XgQJquD86QSQUTCQ1PA0gAFgAcCTwJIcmtMXHyj3mKMwXXuZZiLrwLAvjdTxaIlMDwjgB0PgnbZzvcaARQJOet212wRqilg2YsCwAPBfhJA9sV1yq+gU1eoqsIu+SrADZNo5MkANh0PwlQHgJoCFgmD0mKwbud7BYCyFwWABwDbyPV/DTFHD3bO862ygqV5rNsNW6ozgDseBO2qp4DzNAUsEnKe6d/EZExss3I+5QDkVwBYXl7Obbfdxvvvvx/o9og/qkcA95UBvC/mqCHONz9+hy1peHcXkf3avhX27IHYOMjKrpkC3lWELSsNb9tEoo03A1gJIFKXXwFgQkICeXl5KuwaKQqqRwD9mAIGMB06V08DV2oaWJrHmwHc2Uk2Skqu+c9n+9bwtUskGu1SAog0zO8p4AEDBvDdd98Fsi3ip5oi0P6NAAKYo6qngVUcWprBmwHc6aCaJ73TwFoHKBJKVjUAZR/8DgBHjhzJ5s2befLJJ/npp5/Iz8+nuLi4zh8JgUYUgd4fzzpAli/BlurnJn6qnQFczWS2B5QIIhJyu5xt4IwCQKmH36tC//SnPwGwYcMG5s+f3+Bx06dP9/cS0lieEcB0/6aAAUyHLs5/2pvWYZd8hRl0SoAaJ9GkdgawVzvVAhQJC28NQAWAUpffAeDIkSO1BjAC2MpK7295/q4B9DBHDcZuWudkAysAlCay7irYssF5UDsAzFIpGJGw0BSw7IPfAeBFF10UyHaIv4oKwFpn39WU5i30NUcPxr79inca2CSlBKiREhXytkBlBcTHQ/W0L4Bpl40FFYMWCTG7SwGgNCxgdQBLS0txu92BOp00lmf6NzUd42rej9N0PAg6dKnOBv46AI2TqOLNAO7iey96poB35Dkj1iISGtUjgEZZwFKPZkUMq1atYuLEiVx22WWMGTOG5cuXA7Bz504efvhhfvjhh4A0UvahmUWg9+YtCq1sYGmiejOAAVLTIS4e3G7I3xaGlolEKU0Byz74HQCuWLGCu+66iy1btnDCCSdgrfW+1qZNG0pLS/nggw8C0khpmLcETGrz1v95eItCL1+MLS0JyDklStSTAQw4o4GeKeHtmgYWCRlNAcs++B0AvvLKK3Tq1IlHH32USy65pM7rhx56KLm5uc1qnDRCQfUuIIEaAexUPQ1cWYn9TtPA0njeEcC9AkDAuyOIVS1AkZCwFXugfLfzQFnAUg+/A8BVq1YxdOhQ4uLi6s0GzsjIoLCwsDltk8Yoat4uIPVRUWhpKltZCVs2Og/qCQCNZ0s4JYKIhEbxLuerywWJyeFti0QkvwPAmJgYn2nfveXn59OqVSt/Ty+NZAuavwvI3rxFoX9YpGlgaZxtm6GqEhJaQUa7uq9XJ4JY1QIUCQ1PebCUNirZJvXyOwA8+OCD+fLLL+t9raysjE8++YS+ffv63TBpJM8uIAGaAgacEZzszs408FJNA0sjeNb/dehSbza6ydIIoEhIeYtAKwNY6ud3AHjRRRexevVqHnjgARYvXgzA2rVr+eijj7j99tvZuXMnI0eODFhDpS5rba0yMAGcAjYGc9Qg5xrfahpY9s+7A8jeGcAetaaA9zVzICKBoX2AZX+aNQJ4xx13sGXLFp5++mkAXnzxRf71r3/hdru544476Nq1a8AaKvUo2w3lZc73gRwBpPY08GLs7tKAnlsOQA1kAHu1bQ/GOPfrrsKQNUskalVnAGsfYGmI3zuBABx22GH8/e9/Z82aNWzZ4vxm3759e3r06KE1B6HgGf1LTMYkBHi9Zadu0L4TbN2I/e5rzHFDA3t+OaDsMwMYMHFxkJ7p1AHcthXapIeyeSLRR/sAy34EZCeQ7t27c/zxxzNo0CB69uyp4C9UvAkggZv+9XCmgZUNLPtnKysgb5PzoKERQKgpBaNEEJHg0xSw7EezRgArKir46KOPWLx4MXl5eQBkZWVxxBFHMGzYMOLj4wPSSKmftwh0EAJAqN4beM5rsGwRdncpJjEpKNeRFm7rJqiqgsQkZ5SvAaZdNnbF986ewSISXN4sYCWBSP38DgB37NjB/fffz6ZNm0hLSyM72/ntfu3atSxZsoR3332XO++8k7ZtA7s2TWrxZAAHsASMj87dIKsj5G3CLv0Gc+xJwbmOtGi2dgbwvkb/VQtQJGRqkkBah7chErH8DgCnTJnCtm3b+OMf/8hxxx3n89oXX3zB008/zZQpU7j11lub3UhpgGcEMMAJIB7GmOpRwNedbGAFgFIf7x7A+0n6Ui1AkdCpDgCN1gBKA/xeA/j9999z9tln1wn+AI4//niGDx/O999/36zGyb7ZAs8uIMEbZfWsA2TZQmyZsoGlLu8IYMcu+zxOtQBFQsg7AqgpYKmf3wFgYmIiqakN31hpaWkkJib6e3ppjOoRQBOkNYAAdOkOWR2gsgL73TfBu460XPvJAPbyTAHvLMSW7Q5yo0Sil7VWSSCyX34HgEOHDuWTTz6hvLy8zmtlZWXMmzePYcOGNatxsh+FIRgBVDaw7IOt2ANbq6d09xMAmqQUSK5ej7Rdo4AiQbO7xEnMApWBkQY1eg3gV1995fO4e/fuLF68mBtvvJGTTjrJmwSyZcsWPv30U1JSUjjooP2MCIjfrLsKdhY4D9KDOAJIdTbw3BlONnDZbkwrjexKtS0bwbohKblxu9G0y4aSXU4twM7dg98+kWjkGf1LSMTEqRqH1K/RAeCjjz7a4GuzZs2q81x+fj5///vfGTRoUJMa9P777/P++++zbds2ADp37swFF1zAEUccAThD26+99hofffQRJSUl9OnTh7Fjx9KhQwfvOfbs2cO0adNYsGABFRUV9O/fn7Fjx5KWluY9pri4mKlTp7Jw4UKMMRx77LFceeWVtGoV4ILKwbKzENxuMC5okxbca3Xp4fzHvW2Lkw18zInBvZ60GLbWDiCNqf9p2mVj1/6M3bYZVQsVCZJdygCW/Wt0AHj33XcHsx1eGRkZXHrppXTo0AFrLZ9++ikPP/wwDz/8MF26dGH27NnMnTuX66+/nqysLKZPn87EiRN59NFHvXUHX3jhBRYtWsRNN91EUlISU6ZMYdKkSfzlL3/xXueJJ56goKCACRMmUFVVxTPPPMPkyZMZN25cSD5ns3kSQFLTMK6YoF7Kmw089w1nGlgBoHh41/81cttHlYIRCT6t/5NGaHQA2Ldv32C2w+voo4/2eXzJJZfw/vvv8/PPP9O5c2fmzJnD+eefz8CBAwG44YYbuOqqq/jmm28YPHgwpaWlfPzxx4wbN47DDjsMgOuuu44//vGPrFy5kpycHDZs2MCSJUt44IEH6NmzJwBjxozhgQce4PLLLycjI7hTqgFR5CkCHZo6i+aoIdi5b8D3CzUNLF52f3sA782zG4iKQYsEjbcGYGtlAEvDmrUTSLC53W6++OILysvLycnJIS8vj8LCQg4//HDvMUlJSfTq1YuVK1cyePBgVq9eTVVVFf369fMe06lTJzIzM70B4MqVK0lOTvYGfwD9+vXDGENubi7HHHNMve2pqKigoqLC+9gY4810DvT2d57zNXRe6ykCnd42NFvvde3pnQbm+4WYY04I/jVr2V9/RJuI6Q9vDcDGTQG7sjpQBbBtS8j/zkQb9YevaOoPU7wLi1MDsKHPG039IfVrVgD4008/8fHHH5OXl0dJSYmTel6LMYZHHnmkyeddt24df/7zn6moqKBVq1bcfPPNdO7cmRUrVgDUKT+TmppKYWEhAIWFhcTGxpKcnLzPY9q08R0aj4mJISUlxXtMfWbNmsWMGTO8j7t3785DDz1Eu3btmvwZG8uTXLO3wopydgHJnbqQXmv9YzAVnnQGu2a8QMIP35I54qKQXHNvDfVHtApnf7jLythYPZWbfcRAYhpRkLwyzsVmgPw8srPaYWIC/zuo7hFf6g9f0dAfhbbS+f8hu+N+/3+Ihv6Q+vn9r+8777zDiy++SHx8PB07diQlJSVgjerYsSOPPPIIpaWlfPnllzz99NPce++9ATu/v8477zzOOecc72PPb07btm2jsrIyoNcyxpCdnc2WLVvqBNYAVRt+AaA0PpGyzaHZWcH2GQC8wO5v5rNp7RpMQugSZvbXH9EmEvrD/pIL1kJKa7buLseU7f8+tG43xMZBZQWbf/ge0y5w//lEQp9EEvWHr2jqj6otzt/FUhPT4P8P0dQf9YmNjQ3q4E1L4HcA+NZbb9GnTx9uu+02kpKSAtkmYmNjvb+V9OjRg1WrVjFnzhxGjBgBQFFREenp6d7ji4qK6NatG+AUoK6srKSkpMRnFLCoqMibBZyWlsbOnTt9rllVVUVxcbFPpvDe4uLiiIuLq/e1YP0FstbWe27PFLBNTQ/ZX157UA/IbA/bt+L+7htcA4eE5Lo+bWigP6JVOPvDvdF3/V+j2mGMs5Rg83ps3mbnfgow3SO+1B++oqE/PGsAbXLr/X7WaOgPqZ/fhaDLy8sZMmRIwIO/+rjdbioqKsjKyiItLc1ni7nS0lJyc3PJyckBnIAxJibG55hNmzaxfft27zE5OTmUlJSwevVq7zHLli3DWkuvXr2C/nkCosCzC0hokkBg76LQ80N2XYlQTc0A9vAkgigTWCQ4dhUBYJQEIvvg9wjgoYceyrp16wLZFgBefvllBgwYQGZmJmVlZcyfP5/ly5fz5z//GWMMw4cPZ+bMmXTo0IGsrCxeffVV0tPTvVnBSUlJDBs2jGnTppGSkkJSUhJTp04lJyfHGwB27tyZAQMGMHnyZK666ioqKyuZOnUqgwYNahkZwFCzC0gj1l0Fkjl6MPa9mfD9t9jyspBOA0tkaXIGcDXTLhsLsC00SxdEoo7KwEgj+B0AjhkzhokTJ/LWW28xbNiwgK0BLCoq4umnn6agoICkpCS6du3Kn//8Z2/m74gRIygvL2fy5MmUlpbSp08fxo8f760BCDB69GiMMUyaNInKykpvIeja/vCHPzBlyhTuu+8+byHoMWPGBOQzBJstL3O2+oGQlYHx6toL2mbBjjz4/ls4OvTTwBIhGrsH8N40AigSXAoApRH8DgAzMzM59dRTefHFF3nppZeIj4/H5ao7o/zCCy806bzXXnvtPl83xjBq1ChGjRrV4DHx8fGMHTu2TtBXW0pKSssp+ry36ulfElpBiOvxeYtCvzcL++3nGAWAUcmWl8H2rc4Df0cAVQtQJOBsZSWUVg8QaB9g2Qe/A8Dp06czc+ZMMjIy6NmzZ0jWAkq1ourp37QQ1QDcizlqiBMAfv8ttrwck5AQ8jZImG1a73xtnYpp6n8y7arLUmxzsg9Vh0wkgEp2OV+Ny9mjW6QBfgeAH3zwAUceeSS33HJLvSN/EjzWMwKYFqb1it1qTQMv+xaqE0Mkevi7/g9wMn+NgfLdzmL1YO9lLRJNPNO/ySlB3yZUWja/I7fKykqOPPJIBX/hUFidARziBBAPYwzmyOMBsMuXhKUNEmbeHUCamAEMmLi4muQlrQMUCazqDGBtAyf743f0duSRR/Ljjz8Gsi3SWJ4M4NQwZix36QGA3bopfG2QJrFuN+4PZ2OXLWz+uZozAgjeaWAlgogEmDcBpHV42yERz+8A8MILL2Tjxo38+9//ZvXq1ezcuZPi4uI6fyTwbPUIYKhLwNRm2nd0vlEA2GLYhQuw06fg/seD2J2FzTuZvxnA1bw7gCgAFAkoqwxgaSS/1wDeeOONAKxdu5YPPvigweOmT5/u7yWkIdUjgKEsAl2HJwAs3KF6gC2AtRY793XnwZ5y7AezMSNH+3eu3aWQv8154O8IoGcHENUCFAmsXU4AqCLQsj9+B4AjR45U9l64hDsJBDDJrZ0phuJdkLcZunQPW1ukEX5YDOvXOJmB1o2d91/s6ec1PYMXvKN/pGZgkv2s/5mlKWCRoNAIoDSS3wHgRRddFMh2SCNZt9unDExYte8ExT/B1o0KACOce+4MAMwp52BX/gDrVmE/fAtz3mVNPlfN+r8ufrenZjcQBYAiAeVJAlEAKPuhFN6WpngnVFU5ZTRS08PaFOMZxdE6wIhmV/0EK5dBTCzmtHNx/copom4/fhvrqRnWFNU1AP3JAPby1AIsKnCKSotIQHjXAKoItOyH3yOAM2bMaNRxF1xwgb+XkPp4EkBap2Ji/f7xBUb7Ts5XBYARzTv6d9xQTEYmNr0tdO4OG9ZgP3wbM+LSJp2v2RnA4EwdJ6VAabEzCti5m9/nEpFaqgNAoxFA2Q+/I4jXX3+9UccpAAywggiZ/sXJBLaAzVMAGKnsxnXw3ddgDObM8wGnjqPrnFG4//kg9qO3saf9GpPUhLV8zcwA9mqXDb/kwnYFgCIBs8szAqgkENm3Zm0Ftze328327dt59913+fHHHxk/fnyzGid1RUIJGK8slYKJdPbdN5xvjjgek9255oUjjnNG8Datw378Duacixt3vtLimlHoDv6vAQRnCYH9JRebtwWlk4k0n7VWSSDSaAFdA+hyucjKyuK3v/0tHTp0YOrUqYE8vUBNCZhwFoH2qF4DSPFO/9aSSVDZHXnYrz8FwHXWSJ/XjMuFOad6LeAHbzmlXRrDM/2bnolp7j6jqgUoEljlZVBZ4XyvAFD2I2hJIIcccgiLFy8O1umjl3cEMPwBoGmVWFOKRqOAEce+NwvcbjikP6bbwXVeN0cNguzOUFqMnfffxp0zABnAXtW1AK1qAYoEhicDOD5etVllv4IWAK5atUp1AoPAOwUcAWsAAW8iiNYBRha7sxA73ynQ7jqr/nW4xhWDOdsp52Q/eBNbtnv/Jw5EBrDn+p4RZI0AigRGcfVMjEb/pBH8XgP46aef1vt8SUkJP/74I19//TXDhg3zu2HSgOoi0GHdBaQW074jdsX3GgGMMPajd6BiD3Q7GPoc3uBxZuAJ2LdfhbxN2E/nYs44f9/nDUAGsJdnCnhHHraqChMT0/xzikQzrf+TJvA7AHzmmWcafK1169aMGDFCGcDB4C0CHf4pYECJIBHI7i7FfuJM6brO2veOPSYmBnP2hdjn/o59bxZ26PB9Tx0FKgMYnFHs2DhnzVL+tpqAUET8Yr1FoJUBLPvndwD41FNP1XnOGENycjKJiYnNapTUz1bsqRnij4QsYGqVglEAGDHs/96F0hLI7gQDjtvv8ebYodh3psO2LdhP38Wcfm795y3eCUUFzoNmZgCDk4hCZnvYsgG2b1UAKNJcqgEoTeD3GsB27drV+ZOZmangL5iqM4CJi3eK6EaC9jUjgNba8LZFsBV7sB/MBsCcOdIJsvbDxMRghl/ovP+9mdg95fUf6Jn+bZvlJAAFQnXQp0QQkQDQLiDSBNoKriUp8CSAZEROgk1mNhgXlO+GnYXhbk3Us1987IzSpWdijj2p0e8zx50MbbNgZyH2s/frP3cg1/95rutJBMlTIohIs2kNoDRBk6aAb7755iad3BjDI4880qT3SMNspK3/A0xcHGRmOZmcWzeGfX/iaGbdVdh3ZwJgTh+BiY1r9HtNbCxm+AXYF5/BvvsG9sQzMHHxvgcFcv2fh7cUjAJAkeayuxQASuM1KQBMSUlp1MhTYWEhmzZpTVjARVgGsFdWB2f92NZNmJzDwt2aqGUXLnAC8eTWmBPOaPL7zfGnYP/7GuRvx87/EHPycN/zV5eACegIYLsOWABNAYs0n2cNoKaApRGaFADec889+3y9sLCQN998k59//hmXy8UJJ5zQnLbJ3iJpG7haTPtO2B8WKxM4jKy12LkzADDDzvGrCKyJi8OceQH25X9i587ADjnNGeH18IwAdgrgCGBWzW4g1trIWdog0hIVKwtYGs/vLODaPIHfRx99RGVlJSeccALnn38+2dnK6gsoTxJIxI0AOokgygQOox8Wwfo1kNAKM+xsv09jhpyKnfMaFGzHLvgIc9KZQHV5iV1FYAxkB2AXEI/M9s45y3Y7oxfawF7Ef1oDKE3QrACwvsBv5MiRtG/fPlDtk1pqdgGJnDWAUFMKBu0GEjbuuW8AYE44o1klIExcPObMkdhXn8XOeR07+BRnLaEnASSzPSYhIRBN9l6PtLZQsB3yNisAFPGTdVdBSbHzQFPA0gh+BYB7B34nnngiI0eOJCsrK9Dtk9qqRwAjbg2gpxRM3mas292o0iMSOHbVT7ByGcTEYk4b0ezzmRNOx855HfK3Yb+Y5zze+IvzYiATQDzaZTsjjtu3Ynr2Cfz5RVoI90dvw/rVmN9cWzcJa39KisFTiiu5deAbJwecJgWABQUF3sCvqqqKk046ifPPP1+BXwhYa33KwESUtu0gNtbZ0aFgu1NORELG7Vn7d9xQTEZms89n4hMwZ5yPfX2qMwp4/LDgZAB7rtcuG7tymRJBJKrZij3YGc9BZSV06tb0X+Y8079JKdpWURqlSQHg//3f/1FRUUG3bt0477zzyMrKori4mOLi4gbf06NHj2Y3UoCSXU6ABRG3BtC4YqBdB9i83ikFowAwZOzGX+C7r8EYzJn73se3KcxJZ2LffQO2b8V+/WlQagB6eXYAUS1AiWZrfnaCP3CSsE44vWkF1z3bwGkZhTRSkwLAigonAFm7di2PPfZYo94zffr0prdK6vIkgKS09s3MjBRZTgBot27C9D0i3K2JGu53nbV/HHE8JrtzwM5rElphTj8X+8YLTmmY6rVFwRgBrNkNRAFgJLDuKueXOgkp+/MPNQ92FWE/fse7Q0+jeBNANP0rjdOkAPDaa68NVjtkf7wJIJE1+udh2ndyEkGUCRwylVs3Yb/6FADXWSMDfn4zdDj2vZlOcgY4O750CFyQ6b2OtxagAsBwc3/9P+wLT2AuGINrrzqQElw290fnm559YNVPzraMQ8/CNHLbT6sMYGmiJgWAQ4cODVIzZH9sQWQHgJ5EEJWCCZ1dM/8Dbjcc0h/T7eCAn9+0SsScdi521ovOE+2ym74wvTE8tQCL8rHl5QHNMpamsfM/gD17sK/8C5vVAXOoRvNDwbqrYJUTALouvgr31MedGZUP3sKMuLRxJ9nlKQKtKWBpHKVrthSeDOAIKwLtYbyZwAoAQ8HuLKTk/dkAuM66IGjXMSefDZ4RiGBM/wImuTUkJTsPtmsUMFxsxR7wjEJZN+5/Paxf6EJlwy+wuxRaJUKXHriqgz774eyakb390QigNFFACkFLCBRG3j7APjwB4Pat2MqKJu1DG81sWSn2y0+cQsjWOiN61l391YLbgq2q/lrr9U3rsHvKodvB0OfwoLXPJCZhzhmFfW0K5tABQbsO7TrAL7nONHCnrsG7jjQs90eo2AOpGc7+3qt+wv30RFy3P4zxBOgSFDZ3ufNNjz6YmBjsEcdDl+6wfg32vVmYkaP3fxIFgNJECgBbiEgtAu2VmgEJraC8DLZvhQAmJBzI7FuvYD+Y7ff7XWddEPTt01ynjcAeNSioyw9Mu2zsL7nYbVvQZnDhYX/8DgBzSH/MBVfgvv8m2Lwe978n4brhz0oMCaafnQDQHNzX+epy4RpxGe6n/oL9+G3sqb/GpKbv8xTWmwWsAFAaRwFgS1EdAEZcEehqxhgnE3j9Gti6WQFgI9iKCuyCj50H/Y/BpLQGV4yzNZox4HI5iRcuV/Vztb6PiSGtZw47DxsYkraajHbBvYCnFIxqAYaN/Wmp880hh2NS03FdPx73w3fA999i3/wP5vxGjEJJk1lrsd4A8NCaFw4/GrrnwJqV2LkzMBdfte8TFe9yzqERQGkkBYAtRaQngVCdCbx+DXbrRgyhCUxaMrvkK6e+Y1pbXNfd0aQRFmMMKR06sGvzZqdIeEunUjBhZUuLYW0uAKZPf+drt4Mxo/8P++9J2Llv4O7UDdexJ4WzmQembVugKB9iYqF7TTKXMQbXuZfhfuwu7Kdzsaefu+9fxDQFLE2kJJAWwFZW1BT5jNAkEACylAjSFHb+BwCYQadE/fSaUTHo8FqxzFlbmt3ZZzcZ17EnYc50SgzZF57Erv05XC08YHnX/3XrhYnfKwP+kP6QcxhUVmL/+/q+T6RC0NJECgBbgqJC52tMbGTv8ahSMI1md+TBj0sAMENODW9jIkG7Ds7XHXlOSQwJKeu5Fw+pm1BkzrsM+h0NFXtwP/1XbFFBiFt3gPNM//bqW+clYwyuEb8BwH7+QYMj5La8HPaUOw80AiiNpACwJaiVAGJckfsj85aCUQC4X/bzj5ys3t79aka/oll6hrOfdFUl5G8Pd2uiTk0CyIA6rxlXDK6xf3LW9RbuwP2PB7DVu0JJ89W7/q8Wk3MoHHoEVFVh3361/pOUVE//xsY6pWREGiFyowmpEekZwB6eALBgu/MbqdTLut3YBR8BYIacFubWRAbjioHM9s4DrQMMKZu/HbZsdJKMeh9W7zEmKRnXDROceo2rfsK+9MyBsfY0zOzOQmf/dIBefRo8zjsK+OUn2M0b6h6wq2b9X7CrAsiBQwFgC+DZBSRSM4A9TEqbminqbRoFbNBPS2FHHiQmY448PtytiRzV08BKBAkt+5Mz+ke3Xvvcdsy074jr6lvBuLCff4T96O0QtfAA5ln/16mrUxC9AaZ7DvQ/Bqwb+/YrdQ9QAoj4QQFgSxDpRaBry6pey7VV5Twa4k3+OPakuou+o5h3KlwBYGj96JR/MYf03++h5tAjMBdeCYB9bSp2+eKgNu1AZ/eq/7cv3lHAbz7Dbljjex5PAKgEEGkCBYAtgWcKOJIzgKuZ9p0AsJ5pDfFhS3ZhF38JaPq3Dm8pGP3yECrW2pr1f43cUcac+mvM8cOc7eImP4JV1r/fPAEg9SSA7M106Y45eggA7tkv+75YnQGsGoDSFAoAWwDrHQGM/ACQ9p4RQP2nUB/75adQWQFdumO69gx3cyKKRgDDYPN6pwZdXDz0OqRRbzHGYC6/Dnr0htJi3E9NxO4uDXJDDzy2rBTWrQYaNwIIYH59ibNWc8lXviV5vFPAEVwlQiKOAsCWoIWsAQTAMwKoUYE6rLU1078a/aurVgCoBIPQsNXTv/Q6BBMX3+j3mbh4XNfe4fxSWr1dnMr3NNHqFU7txbZZjd5px3TogjnOKcbtnv1SzQveJBBNAUvjKQCMcNZa5zd0aBFrAFUKZh/WrYINayA2DnPs0HC3JvJ4soB3l3q3tZLgqqn/N6DJ7zVpGbiuGw+xcbD0G+ybL+3/TeLVlPV/tZlzLoaYGFi2yHuOmjWAmgKWxlMAGOl2l0J5mfN9SxgB9CSB7CpytpcSL+/o35HHY5IbzraMViY+oeYe1zrAoLNVVbByGQCm7/4TQOpjujvbxQHYuTNwV5c3kv1ryvq/2kxWB8xgp3i8+83/OIMEygIWPygAjHSeBJCkZExC5GeMmlZJkFo9UqlMYC+7pxz71f8ATf/uU5b2BA6ZtT87v2AmpUCX7n6fxnXcUMxZ1dvFTXvKm1QiDbOVFbBmBdD0EUAAc/ZFTtHnlcucslJKAhE/KACMcJ4agC1i9M+jOhGkpWYC26Xf4H7rZecf6UCdc9EC2F0CbbOgd7+AnfdAo0SQ0PEGan0Ob/Ze1ObcyzEDT4CqKtz/eBC7cV0AWngA+2UV7NnjJG106NLkt5uMdpiTzgKcUUA0BSx+UAAY6VpSBnA1TykYWmAiiC3cgXvyw9i3X8XuXWqhOeed/yEAZvCpEb2dX9h59gRWABh09idP/b/GlX/ZF+NyYa4c52QS7y7B/cS92jN4H2xuzfSvvzt3mLMugPh4J5mkegRQU8DSFPqfKNIVejKAIz8BxKsFJ4LYN//j3VTdvjcTW71GqlnnzNsMK74HYzCDTmn2+Q5onlqAG9Zgy3aHuTEHLlteBqt+BPxLAKmPiYvHdf2fIasj5G/D/eRfnOtIHd4EkCau/6vNpKZjTj7b90kFgNIECgAjnC1seVPAJssJAG0LCwDtutXYBR87D3r3A2txT30cW1rSvPN+7oz+0XcApm3jyj1EK5Pd2flm3Wrct16J++V/YjesDWubDkg/L4fKSshoV5O4FQAmpQ2ucXc5gcgvubif/ZvKw+zFut2QWx18+7H+rzZzxkhISHQeJCZhYuOa2zyJIgoAI51nDWB6CxwBzNvUYuq5WWtxz3gOrMUMPAHXDX92RqN25GFf/Zf/562qwlZnRrqU/LF/XbpjLr3GCUp2l2LnzcF97x+oeug23F/Ow1bsCXcLDwie/X/NIYf7PQXZEJPVEdcNE5zyMN99jX312Rbz70BIbN4AJbuc6duDmlcM3rRugznt184Djf5JE8WGuwF7mzVrFl9//TUbN24kPj6enJwcLrvsMjp27Og9xlrLa6+9xkcffURJSQl9+vRh7NixdOhQ85vsnj17mDZtGgsWLKCiooL+/fszduxY0tLSvMcUFxczdepUFi5ciDGGY489liuvvJJWrVqF8iPvk2cXkBZRBNqjXQcwxskw3FUIbdLD3aL9+/5b+PE7iI3FnHc5plUSrjF/xP3wHdgv5mH7H4M5anDTz/vDImcdZ0pr6H9s4Nt9gDHGYE4+G3vSWbDie9yfzoUlX0Huj9jcH7Gv/hszaBjmxDMx2Z3C3dwWy5sAEqDp372Znn1wjb0J9z8fws6bA+06YE4bEZRrtTTe9X/de2Nim/9fsDnjPNiRF7SfpRy4Im4EcPny5ZxxxhlMnDiRCRMmUFVVxf33309ZWc1aktmzZzN37lyuuuoq/vrXv5KQkMDEiRPZs6dmdOCFF15g4cKF3HTTTdx7770UFBQwadIkn2s98cQTrF+/ngkTJnD77bfz448/Mnny5JB91kbxTgG3nBFAExfnTC1BiygFYysrcb/+HADmlF95M1FNr0OchdaA+8Vnaqbjm8Dtqf133MlOv0ijGJcLc0h/Yn5/O64Hp2DOvcy5p0p2YT+YjfvOa6maNAH77fyAZmtHA7trJ6xfAzR+/19/mKMGYy640rnm61OdTHiBn38AwBx8aEBO5/ll1XX8yQE5n0SPiAsA//znPzN06FC6dOlCt27duP7669m+fTurVzt7JlprmTNnDueffz4DBw6ka9eu3HDDDRQUFPDNN98AUFpayscff8zo0aM57LDD6NGjB9dddx0rVqxg5cqVAGzYsIElS5bw+9//noMPPpg+ffowZswYFixYQH5+ftg+f222qhKKCp0HLWkEEGq2hGsBpWDsZ+/Dlg2Q0hoz/EKf18yvLoauvaBkF+7nnnDW7zT2vDsLYKlzT6r2n/9MWgausy/C9cC/cP3fndD/GGc/1J+W4p78MO7bfod71ovY7VvD3dSWYcVSsBY6dcWkBnd03px+LmbocGc97b8fxa5eEdTrtQT+7gAiEmgRFwDurbTU2WQ8JcXZOSEvL4/CwkIOP7zmN9ekpCR69erlDe5Wr15NVVUV/frV1Fvr1KkTmZmZ3mNWrlxJcnIyPXvWrMHo168fxhhyc3OD/rkao6og39kr0uWCNi1rj0dTXQsw0kvB2NIS7FtOuRfzq0swSb47dJjYWFy/u8lZr7N8sTOd1dhzf/EJVFVB9xxMp66BbHZUMq4YzOEDiblhAq4HnsWcM8opOr6zEDvndaruuIotN/6Wqjf/g/15ubPThdThmf41h/i3+0dTGGMwF18F/Y6Gij24n7rfyYqPUnbHNsjf5vyb3qN3uJsjUS7i1gDW5na7ef755+nduzcHHXQQAIWFhQCkpvoGRKmpqd7XCgsLiY2NJTk5eZ/HtGnju2g2JiaGlJQU7zF7q6iooKKiZrrJGENiYqL3+0AyxlCVv6264Rm4YiL6R1WHad8JC7B1U0D6xnOOQPeze+4Mp4hqdidcJ51V7/lNxy5wwRgnI/WN56Fvf0zHg/Z5Xmutd+s315DTgnJ/1P4abUxmFpx7Gfaci7HffY39dC52+RIqfl4OPy/HvjPdyYrs0x9z2JHOn7ZZ4W52SDV0j9gfPfX/+ofk/jGxsZhrbqXq4dud7O4n7iPmjkcwKa2Dfm2fdkTA3xm7qnr930E9cCUmha0dEBn9IeEV0VHFlClTWL9+Pffdd1+4mwI4CSozZszwPu7evTsPPfQQ7doFp7RH6ZqfAIhv1572HQJXqiEUdh/Sj+1A7I48sgPY9uzs7ICdq3LrJjZ/9DYAmVf/icQuDVfkt5f+ju0rllK2cAGuF56g/aTn97mmr3z5d+Rt2YBJaEWHX1+IKyk4e/8Gsj9arC5d4JyRVOVvp2zRl5Qt+oKyxV/i3lmEXfwFdvEXAMR27kqrowbR6sjjSDjsKFwRlOwVTLXvkcqtm9i8bTO4Yuhw4qm4kpL38c7Aqrr/abb+6Qqqtm4k9tlHaDfxaUxcfMiu7xHOvzP5G9dSAqQMOIb0CPk3Xf+GRK+IDQCnTJnCokWLuPfee2nbtmb9myeLt6ioiPT0mvUrRUVFdOvWzXtMZWUlJSUlPqOARUVF3venpaWxc+dOn2tWVVVRXFzskylc23nnncc555zjfez5zWnbtm1UVlb6+1HrZYwheUceABXJbdi8uWVNm9g45z/Xik3r2bRxY7N3vzDGkJ2dzZYtWwJWUqLqX3+Dij2YPodT0KUXhfvpY3vJNfDT91SsWsHGfz1KzPm/bfjcs191vjlqEFuLdkHRroC02SMY/dHSGWPIPvUcdh12NOY31xLzyyrsD4txL1sIq1dQueEXijf8QvHsVyA2DnPwoZjDjsAccTwmgLXwIkV994j7M2dUmu45bC3aCUU793GGILjhTnjwVsp/WMzGv96Oa+yfQrYzTiT8nalcUr1OvWM3ysL8b3ok9Ec4xcbGBm3wpqWIuADQWsvUqVP5+uuvueeee8jK8p22ycrKIi0tje+//94b8JWWlpKbm8vpp58OQI8ePYiJieH777/nuOOOA2DTpk1s376dnJwcAHJycigpKWH16tX06NEDgGXLlmGtpVevXvW2LS4ujrgGRn2C8Reoakf1FHBaRov7C2oz2kFMLFTsweZvhwAVQLbWBqQv7JqV2K//5+zOceGV3nPvU2o6rsuvx/3PB7Fz38B92FH1LuS2ZaXYbz4DwAw+Lag/u0D1x4HEWuskiXQ7GNPtYGLOvghbWgw/LsX+sAj7w2LI34b9cQn2xyUw6z+4/nQ/ptch4W56UNS+R+zyJYAz/RuW+6bjQbh+f7uzVdzX/8Od2R7XeZeHtAnh+jtjS3bBpuo9knsdEjF/b/VvSPSKuCSQKVOm8NlnnzFu3DgSExMpLCyksLDQW+LFGMPw4cOZOXMm3377LevWreOpp54iPT2dgQMHAk5SyLBhw5g2bRrLli1j9erVPPPMM+Tk5HgDwM6dOzNgwAAmT55Mbm4uP/30E1OnTmXQoEFkZERGyRVvAJjewjKAARMTA+3aOw8iLBHEWov7tSlAdXmWJhRjNUcNwhw/DKwb95RHsbtL657/28+hvMzJhFamX0QwSSmYowbh+u0NuB78N677nsaM+p2T4V1Zgfu5xw/4bcus2x3Q/X/9ZfoOwFx+vdOmOa/j/uz9sLUlpKp3/yC7E6Z1y0rqkwNTxI0Avv++84/BPffc4/P8ddddx9ChQwEYMWIE5eXlTJ48mdLSUvr06cP48eOJj69ZTzJ69GiMMUyaNInKykpvIeja/vCHPzBlyhTuu+8+byHoMWPGBPXzNUVV9RQwqZERkDZZ+06wZSN268aQZBw22qIvnH+M4+Mxfow+mEuudvYI3pGHffVZzJXjfF73JH+YwadqgXUEMsZAhy6YDl2wg07Bfc8fIG8z9o3nMZf+PtzNC55Nv8CuIohPCHsGqmvwqbi3b8W+Mx370j+xnbtjuh8c1jYFmw1w/T+R5oq4APC1117b7zHGGEaNGsWoUaMaPCY+Pp6xY8fWCfpqS0lJYdy4cQ2+Hm6eEUDTAkcAAUz7jt5M4EhhKytwv/E8AOb08/zqW5NYvUvI38ZjF3yE7T8Qc+Qg5/yb18Oqn8DlwgwaFsimSxCYpBRcV/wf7sfuxs6bgx1wHKbvgHA3Kyjs8urdP3IOi4g9Y82vL8Vu/AUWf4l78kO47nwMkxzazOBQ8tT/o5dmBSQyRNwUsNTwjgC2tCLQHlnO9n02kgLAeXNg2xZITceccb7f5zE5h2LOdN7vfvFp75Z9dv6HzgH9jg56kV0JDNP3CMzQ4QC4n3/CWS94AKqp/xe+6d/ajDG4rviDd89t99THm1RovSWx5eXwyypABaAlcigAjFC2bDe2tMR5kN4yp4BN++r9myMkALQlu5z6cIAZ8RtMq8Rmnc/8+lI4qAcU78L9wpPYygrsFx8DTu0/aTnMBVdAVgco2I599dlwNyfgbGVFzRZkEbRnrElKwfX72yA2DpZ+g31vVribFBxrV0JVpbOlZ2b7cLdGBFAAGLmqR5RolYhpFd6CoX6rHgFkx1ZsgMvk+MO+Mx1Ki50tsAaf0uzzmdg4Z5eQuHhYthD3Px9y1lilpjs7H0iLYRJa4bryRjAu7BfzsIu/DHeTAmv1SicxKaUNRNiuNOagnphLrgLAvvmis772AFN7/Z/WBUukUAAYoWzhDuebtJY5+gc4bY9PcLZD80xnh4ndugk7778AuC4cg3HFBOS8puNBmJGjnQfffe08d/wwJwtaWhTT6xDMGecB1dP6u4rC3KLAsT/VbP8Wqrp7TWFOOANz3FBwu3H/62/OPtoHEPtzdQawpn8lgkTevwTiKHACQNNS1/+B8x+NZxRw68awtsX9xvNOIHrYkZhDjwjouc3JZ0OtxIFAjC5KeJhfX+qMkO0qwv2fZw6Y+mie9X9EUjZ+LcYYzGXXQYcuUJSP+9lJWPeBsZezrapyEsMAowQQiSAKACOVZwq4hWYAe7V3dliwYawFaFcug8VfgnHhuiDwZX6My4XrinHeqWWT3Tng15DQMHFxuMbcCDExsOgL7FefhLtJzWbLSmHNSgBMn8hIAKmPSWiF69rbIaEV/LQU+9Yr4W5SYGxYA+W7ITEZOu17D3GRUFIAGKFqpoBbdgBo2ndyvglTIoh1u3G/NtVpywmnYYL0D7BJb0vMPU86gaC0aOagnphzLgbAvvwvZyebFsyu/MEZ/W6XjWkX2fu+mg5daopE//c17LKFYW5R83nW/9HrkIAtPREJBAWAkarQMwXcgtcAQthLwdiv/we/5EJCImbEpWFpg7Q85qwLoHsO7C5xMrxb8FSwd/u3CB79q8117EmYk84EcHbbyd8W5hY1j2f934G61aC0XAoAI5QtOFBGAMNXCsbuKcfOmua046yRmDaqyyeNY2JinKzguHhYvhj76dxwN8lv9kdn+zciqPzL/phRY+Ggnk6JpckPO2VsWiBrbU35He0AIhFGAWCk8owAtvg1gNVTwPnbsHvKQ3ppO++/kL8d0jMxp44I6bWl5TMdOmPO/y0A9vXnwrqO1V9V+dth41qg5YwAApi4eKc+YGIyrF6BfeOFcDfJP1s3OaWhYuOg24G91Z20PAoAI5B1u6GougxCCx8BJKU1JCU732/bErLL2vIyb1FZM+JSTEJCyK4tBw4z7Bzo3Q/2lON+7u8tLjO1bOm3zjddumNatwlvY5rItMvGNcZZU2s/fAu7cEGYW9R0Nrd6+7fuB2Piwr/9nkhtCgAjUXGRs2jbGGiTFu7WNIsxpmYUMISlYOz/3nN+885sjzl2aMiuKwcW43LhunIctEqE3B+x778Z7iY1SfmS6tqULWj6tzYz4DjM6dW1GV94ouWNwlbv/6vyLxKJFABGogKnBIwrLQMTGxvmxjSfyaouBbN1c0iuZ/eUY9+b6Vx7+IUHRB9K+Ji2Wc6aNMDOfgm78Zcwt6hxrLWUeQPAljP9uzdz3uXQqy/sLsX9j4dCvpSkOazW/0kEUwAYiarX/8W0zQpzQwIkxCOAdv4HzhR6RjvM8SeH5JpyYDODT4XDB0JlJe6pj7WMpIS8zVRt2wIxsdCCAxATG4vr6lugdSpsWNPovZqt240t2IFdsQz3Z+9TNeN5Cp97EvdPS0OyNaUtzHeWvRgDPfsE/XoiTaWhkQhkq4tAx7RtR/h30A2A9qErBWMrKrBz3wCqM39jte5Gms8Yg+u3N+C+5wZYtxr739cwI34T7mbtk3f3j559MAmtwtuYZjLpbXGN/RPux+/GfvY+7l59cQ0a5qyXLtwBWzdht22GvM3YPOcr2zbDnj0+59nl+SYx2dkRqP9AzKFHBWd9pGf9X6duGM86aJEIogAwEnlHAA+MANC074gFCMH6Hfv5h07/pWU4ozYiAWJS03H95lqnLMmc17GHH4PpHrmZnfbHJQC4InT7t6YyfQdgzrkY+/Yr2JeeoerdN5wRtn2NxrpckNkesjpgsjqSaCylX30GxTux386Hb+djjQt69sYcPhBz+EDoeJCzdrmZrGf9n/b/lQilADACmT7OP9iJRx1Hy1ntsg+e/YB3FmJ3l2ISk4JyGVtZgZ07AwBz5khMXHxQriPRyxw9BLP4S+zX/8P93OO47no8IkeZrbsK+9P3gBM4HSjMORdhV/0EyxfD5vXOkzExkJldHeR18PlKRpZ3DbAxhrYdOlC+cQN29Urs0m+xS7+GDWudBJ/cH7Ezp0HbrJpgsPdhfv874t0BpAVPv8uBTQFgBDK9D8PVpx+JHTpQuDk0iRPBZBKTnGzmnYXOKGDXXkG5jv1iHuRvgzZpmBNOD8o1RMyl1zjTq5vXYz98C3PmyHA3yYe1FvvOdCjZhUlMPqDqzxlXDK5rb8cu/hLTJq06yGuHiWn8FmvGFeNMi/fsA+ddht2xDfv9N9il38KP38GOPOy8/zp1RBNawSEDMF17Qmo6Jq0tpKZDWgaktMG46l9Gb0tLYIOTLGQO1g4gEpkUAEpotO/ojABu2YgJQgBoq6pqRv/OOA8Tr7p/EhwmuTXmgiuwz/0d+/ar2GNOxGS0C3ezgOqRv1eexX4yB4A2l/yOkpiYFr2V3d5Mq8SAJneZtu0wQ4fD0OHY8jL4aSn2u6+dgLAoH5Z8iV3yJQA+vRgTA63TnGAwNd3ZtjM1w3m8sxCs29l/uaXXcpUDlgJACQnTvpOzJiZIiSD2q0+d9UApbTAnnRWUa4h4mOOHYT/7AHKX454+hZhrbw93k7AVFbinTIKFC8AYXBdfRZuRv6XkAJhFCBWT0Ar6H4Ppf4wTNK9bjV22ELZtwRYVQGG+ExQW73RqtRbu8K7Zri/EVv0/iWQKACU0POsAg5AIYt1V2DmvA2BOP7fFZzxK5DPG4PrN73H/5UZYtAC7bBHmsCPD1h5bWoL7mb/Ciu8hJhbzu5twHXNC2NpzIDDGQNeezvTvXmxlpTPKV1QARflO5Ya9v6+swJx0ZugbLtJICgAlJDyZwMEoBWO/me/UGExujTl5eMDPL1If07kbZtivsB/Oxv3KZFz3PBmWxCNbVID77/fA+jWQkIjr+vGYAyTzN1KZ2FjIyHT+AM3PGRYJPRWCltBoXzMCGMj1SNbtxv73NQDMqb/CtApOhrFIfcyvL3HWfeVt9u4+E0o2bxPuh25zgr/Wqbhu+auCPxFpFAWAEhrtsp2K+KUlzvqZQFn8hVMOIjEZM+ycwJ1XpBFMYhLmojEA2DkzsNu2hOza9pdVuB+8zVn72i4b1+0P1TtdKSJSHwWAEhImPgE8mZIB2hLOut2435nunP+UczBJKQE5r0hTmIEnQJ/DoWIP7kZuU9Zc9sfvcD8yHnYVQZfuuG57CONZZysi0ggKACV0sjoAYLcGKCvRU8Q1IRFz6q8Dc06RJjLG4Lr0GmfP3aXfYL/7OqjXc38zH/ff74Xy3dC7H65bHsCkpgf1miJy4FEAKCFj2ndyvgnACKC1Fvc71Wv/hg3HJLdu9jlF/GU6dMGcNgIA9yv/wpYHZw8f98fvYJ99BKoq4ahBuMbdE7SddUTkwKYAUEKnffUIYCBKwSxbCL/kQnwC5rRzm38+kWYy54xyskJ35GHnvh7Qc1trcc/6D/aVf4G1mKHDcV19CyYu8rahE5GWQQGghEzNCGDzAkBrLe63X3XOOfQsTOvUZrZMpPlMQitco8YCYN+bGbCSR7aqCjvtKeyc6hHvEZdiLr3G2dJMRMRPCgAldGqXgnG7/T/Pj0tgzUqIi8ecfl5AmiYSEEccD4cdCZWVuF+e3OySR7YwH/eT92HnfwDGhbn8elznXOwUKRYRaQYFgBI6bds7+2fu2eOUbvGDM/pXnfl74hla/C4RxRiD65KrITYOli+GRQv8Oo+1FvfX/8N99w3ww2KIjcP1+9twnXhGgFssItFKAaCEjImJgeppYPdfbsQ99THshrVNOoddsQxyl0NsLOaM84PQSpHmMVkdMWeOBMD96r+xZbub9H67ayd28sPYZ/8GpcVwUE9cEx7DHHl8MJorIlFKAaCElOuKP0DvflBVhf1iHu57/0DV3+/B/rS0UdNl9p3qtX9DTsOktw12c0X8Ys4aCZntoXCH955tDLvkK9x3X49d+DnExGB+dQmuOx7BdDooiK0VkWikvYAlpEz3HGJunohd87OzUH7RF7BsEe5li6BrL8wZ52GOHOSMFu6lfPkS7E9Lnc3uz7wgDK0XaRwTn4DrkqtxP/kX7IdvYY8/ZZ9BnC0txr76b+wXHztPdDwI15gbMV17hajFIhJtFABKWJjuB2N+fxs2bzP2w9nYzz+EX3Kx/3oE2zYLc9q5mCGnYhJaed+z85UpznsHDcO0bReupos0ijl8IAw4FpZ8hfvlf+K6eWK9yRt2+RLczz8BBdvBGMzp5zmZvnHxYWi1iEQLBYASViarA+bS32N/dSl23n+x8/7r1FF79V/Yt1/BnDwcc/LZsGMbZYu+AJcLc5ZG/6RlcF18Fe7li2HlMuxXn2KOG+p9zZbtxr7xAvaTOc4TWR1wXTkO06tveBorIlFFAaBEBNO6DebXl2DPOB/7xUfY99+EbVuw70zHvjcLUtOc444bimmXHda2ijSWaZuFGX4R9s3/YF+fij18ICYpGfvzctzPPQ7btjjHnXw2ZuRonxFvEZFgUgAoEcUkJGCGDseeeAYs/hL3e7Ocmn/b88DlwjX8onA3UaRJzOnnYb+YB1s3Yme+gE1IxH7wJlgLGZm4rhiHOaR/uJspIlFGAaBEJOOKgaMG4zpyEPz8A3b+B6QOGEhxdqdmF9cVCSUTF4fr0qtxP3Y39tN3a54ffArmorGYpOQwtk5EopUCQIloxhjIOQxX73607tCB4s2bw90kkSYzfY/AHD0E++18SE3HdfkNmP4Dw90sEYliCgBFRELAXDkOjjwec0h/TEqbcDdHRKKcAkARkRAw8QmYgSeEuxkiIoB2AhERERGJOgoARURERKKMAkARERGRKKMAUERERCTKKAAUERERiTIKAEVERESijAJAERERkSijAFBEREQkyigAFBEREYkyCgBFREREoowCQBEREZEoowBQREREJMooABQRERGJMrHhbsCBIDY2eN0YzHO3ROoPX+qPutQnvtQfvtQfvqK1P6L1c9dmrLU23I0QERERkdDRFHCE2r17N7fddhu7d+8Od1MigvrDl/qjLvWJL/WHL/WHL/WHKACMUNZa1qxZgwZoHeoPX+qPutQnvtQfvtQfvtQfogBQREREJMooABQRERGJMgoAI1RcXBwXXHABcXFx4W5KRFB/+FJ/1KU+8aX+8KX+8KX+EGUBi4iIiEQZjQCKiIiIRBkFgCIiIiJRRgGgiIiISJRRACgiIiISZbQZXpAsX76ct956izVr1lBQUMDNN9/MMccc4329sLCQl156iaVLl1JSUsIhhxzCmDFj6NChg88xL774IkuXLqWsrIyOHTty3nnncdxxx3mPKS4uZurUqSxcuBBjDMceeyxXXnklrVq1CunnbYxQ9cn111/Ptm3bfK596aWXcu655wb9MzZFIPpjy5YtvPjii/z0009UVlbSv39/xowZQ1pamveYlnKPhKo/Wsr9MWvWLL7++ms2btxIfHw8OTk5XHbZZXTs2NF7jLWW1157jY8++oiSkhL69OnD2LFjffpkz549TJs2jQULFlBRUUH//v0ZO3Zsi7tHQtkf0XaPfPjhh8yfP581a9awe/dunnvuOZKTk32u1RLuEWkajQAGSXl5Od26deN3v/tdndestTzyyCPk5eVxyy238PDDD9OuXTv+8pe/UFZW5j3uqaeeYtOmTdx222387W9/45hjjuGxxx5jzZo13mOeeOIJ1q9fz4QJE7j99tv58ccfmTx5ckg+Y1OFqk8ALrroIv71r395/5x55plB/3xN1dz+KCsrY+LEiRhjuPvuu/nLX/5CZWUlDz30EG6323uulnKPhKo/oGXcH8uXL+eMM85g4sSJTJgwgaqqKu6//36fvw+zZ89m7ty5XHXVVfz1r38lISGBiRMnsmfPHu8xL7zwAgsXLuSmm27i3nvvpaCggEmTJvlcqyXcI6HsD4iue6S8vJwBAwZw3nnnNXitlnCPSBNZCboLL7zQfvXVV97HGzdutBdeeKFdt26d97mqqir7u9/9zn744Yfe5y677DL76aef+pzryiuv9B6zfv16e+GFF9rc3Fzv64sXL7YXXXSR3bFjR7A+TkAEq0+stfa6666z77zzThBbH3j+9MeSJUvsRRddZEtKSrzHlJSU2Isuush+99131tqWe48Eqz+sbZn3h7XWFhUV2QsvvND+8MMP1lpr3W63veqqq+zs2bO9x5SUlNhLL73Uzp8/3/v44osvtl988YX3mA0bNtgLL7zQrlixwlrbcu+RYPWHtdF1j9S2bNkye+GFF9ri4mKf51vqPSL7phHAMKisrATwKcDpcrmIi4vjp59+8j7Xu3dvFixYQHFxMW63m88//5yKigoOPfRQAFauXElycjI9e/b0vqdfv34YY8jNzQ3RpwmMQPWJx5tvvsmYMWO49dZbeeutt6iqqgrNBwmQxvRHRUUFxhifY+Li4jDGeI85UO6RQPWHR0u8P0pLSwFISUkBIC8vj8LCQg4//HDvMUlJSfTq1YuVK1cCsHr1aqqqqujXr5/3mE6dOpGZmek9pqXeI8HqD49ouUcao6XeI7JvWgMYBh07diQzM5OXX36Zq6++mlatWvHOO++wY8cOCgsLvcf98Y9/5PHHH2fMmDHExMQQHx/PzTffTHZ2NuCsiWrTpo3PuWNiYkhJSfE5T0sQqD4BOOuss+jevTspKSmsWLGCV155hYKCAkaPHh2GT+afxvRHTk4OCQkJvPTSS1xyySVYa3n55Zdxu93eYw6UeyRQ/QEt8/5wu908//zz9O7dm4MOOgjA+5lSU1N9jk1NTfX5+cfGxtZZz7X3MS3tHglmf0B03SON0RLvEdk/BYBhEBsby80338w//vEPxowZg8vlol+/fhxxxBHYWhuzTJ8+nZKSEu68805at27NN998w2OPPcZ9993n/Qt+oAhkn5xzzjne47t27UpsbCzPPvssl156aYvZ9qgx/dGmTRtuuukm/v3vfzN37lyMMQwePJju3btjjAnzJwisQPZHS7w/pkyZwvr167nvvvvC3ZSIEOz+0D0i0UABYJj06NGDRx55hNLSUiorK2nTpg3jx4+nR48egJPN+O677zJp0iS6dOkCQLdu3fjpp5949913ufrqq0lLS2Pnzp0+562qqqK4uNgno62lCESf1Ofggw+mqqqKbdu2+WTHRbr99QdA//79efLJJ9m5cycxMTEkJydz1VVX0b59e4AD6h4JRH/UJ9LvjylTprBo0SLuvfde2rZt633e8/MrKioiPT3d+3xRURHdunXzHlNZWUlJSYnPqFdRUZH3/S3tHgl2f9TnQL5HGqOl3SPSOFoDGGZJSUm0adOGzZs3s2rVKgYOHAjgzdDaeyTH5XJ5RzxycnIoKSlh9erV3teXLVuGtZZevXqF6BMEXnP6pD5r167FGFNnCqOlaKg/amvTpg3JycksW7aMnTt3cvTRRwMH5j3SnP6oT6TeH9ZapkyZwtdff81dd91FVlaWz+tZWVmkpaXx/fffe58rLS0lNzeXnJwcwAmaY2JifI7ZtGkT27dv9x7TUu6RUPVHfQ7ke6QxWso9Ik2jEcAgKSsrY8uWLd7HeXl5rF27lpSUFDIzM/niiy9o06YNmZmZrFu3jueff56BAwfSv39/wFnzlJ2dzbPPPsvll19OSkoK33zzDUuXLuW2224DoHPnzgwYMIDJkydz1VVXUVlZydSpUxk0aBAZGRlh+dz7Eoo+WblyJT///DOHHnooiYmJrFy5khdeeIETTjjBuzA6UjS3PwDmzZtHp06daNOmDStXruT555/n7LPP9o5StKR7JBT90ZLujylTpjB//nxuvfVWEhMTvWutkpKSiI+PxxjD8OHDmTlzJh06dCArK4tXX32V9PR0b1CclJTEsGHDmDZtGikpKSQlJTF16lRycnK8AUBLuUdC1R/Rdo+As8avsLDQ+/dv3bp1JCYmkpmZSUpKSou5R6RpjN3X0In47YcffuDee++t8/xJJ53E9ddfz5w5c3j77bcpLCwkPT2dE088kQsuuIDY2JqYfPPmzbz00kusWLGCsrIysrOz+dWvfsWJJ57oPaa4uJgpU6b4FOccM2ZMRBbnDEWfrF69milTprBx40YqKirIysrixBNP5Jxzzom4tTuB6I+XXnqJTz75hOLiYrKysjjttNM4++yzfUZJW8o9Eor+aEn3x0UXXVTv89dddx1Dhw4Faor8fvjhh5SWltKnTx9+97vf+UxTegoff/75597i2PUVgo70eyRU/RGN98hrr73GjBkz9nmelnCPSNMoABQRERGJMloDKCIiIhJlFACKiIiIRBkFgCIiIiJRRgGgiIiISJRRACgiIiISZRQAioiIiEQZBYAiIiIiUUYBoIiIiEiUUQAoIgeEv/71r1x55ZXe7bBqKy0t5eqrr2b8+PG43e7QN05EJMIoABSRA8LYsWOprKzkhRdeqPPayy+/zK5du7j66qtxufTPnoiI/iUUkQNCVlYWF1xwAZ9//jnfffed9/nc3Fw++OADzj77bLp16xbUNuzZs0cjjCLSImgvYBE5YFRVVXH77bdTVlbGpEmTiI2N5Y477qCkpIRJkyaxfft2Xn31VZYtW8aePXvo0qULF1xwAUcffbT3HMXFxcycOZPvvvuOvLw8XC4XvXv35tJLL/UJIH/44Qfuvfdexo0bx/r165k3bx6FhYVMnTqVhIQEZs2axWeffcaOHTtISEigU6dOXHjhhRx++OFh6BkREV+x4W6AiEigxMTEcPXVVzNhwgTeeOMNUlNTWbNmDePHjycvL48777yTjIwMzj33XBISEvjiiy945JFH+NOf/sQxxxwDwNatW/nmm284/vjjycrKorCwkA8//JB77rmHRx99lIyMDJ9rvvHGG8TGxvKrX/2KyspKYmNjef3113nzzTcZNmwYvXr1Yvfu3axatYrVq1crABSRiKAAUEQOKAcffDBnnHEGb731FnFxcQwePJgBAwbwl7/8hczMTB544AHi4uIAOOOMM7jrrrt46aWXvAHgQQcdxN///neftYInnngif/zjH/n444+54IILfK5XUVHBgw8+SHx8vPe5RYsWccQRR3DNNdeE4BOLiDSd1gCKyAHn4osvpnXr1hhjGD16NMXFxSxbtozjjz+e3bt3s3PnTnbu3MmuXbvo378/mzdvJj8/H4C4uDhv8Od2u9m1axetWrWiY8eOrFmzps61TjrpJJ/gDyA5OZn169ezefPm4H9YERE/aARQRA44SUlJdOzYkV27dpGWlkZubi7WWqZPn8706dPrfU9RUREZGRm43W7mzJnD+++/T15enk9SR0pKSp33ZWVl1Xnuoosu4pFHHmHcuHF06dKFAQMGcOKJJ9K1a9fAfUgRkWZQACgiBzxPEPerX/2K/v3713tMdnY2ALNmzWL69OmcfPLJjBo1ipSUFIwxvPDCC9SXM7f36B9A3759efLJJ/nmm2/47rvv+Pjjj/nvf//LVVddxSmnnBLATyYi4h8FgCJywGvfvj3gJInsLwnjyy+/5NBDD+Xaa6/1eb6kpITWrVs3+popKSmcfPLJnHzyyZSVlXH33Xfz+uuvKwAUkYigNYAicsBLTU3l0EMP5cMPP6SgoKDO6zt37vR+X1+h6C+++MK7RrAxdu3a5fO4VatWtG/fnoqKiia0WkQkeDQCKCJR4Xe/+x133nknN998M6eccgpZWVkUFRWxcuVK8vPzeeSRRwA46qijmDFjBs888ww5OTmsW7eO+fPne0cRG+Omm26ib9++9OjRg5SUFFatWsVXX33FGWecEayPJyLSJAoARSQqdO7cmQcffJDXX3+dTz75hF27dpGamkq3bt0YOXKk97jzzjuPsrIyPv/8cxYsWED37t25/fbbefnllxt9rbPOOotvv/2WpUuXUlFRQbt27Rg1ahS//vWvg/HRRESaTDuBiIiIiEQZrQEUERERiTIKAEVERESijAJAERERkSijAFBEREQkyigAFBEREYkyCgBFREREoowCQBEREZEoowBQREREJMooABQRERGJMv8PfchhSyg8pcAAAAAASUVORK5CYII=" | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": "<Figure size 640x480 with 0 Axes>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "We can clearly notice how number of immigrants from Haiti spiked up from 2010 as Canada stepped up its efforts to accept refugees from Haiti. Let's annotate this spike in the plot by using the `plt.text()` method.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "haiti.plot(kind='line')\n\nplt.title('Immigration from Haiti')\nplt.ylabel('Number of Immigrants')\nplt.xlabel('Years')\n\n# annotate the 2010 Earthquake. \n# syntax: plt.text(x, y, label)\nplt.text(2000, 6000, '2010 Earthquake') # see note below\n\nplt.show() ", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 43, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": "<pyolite.display.Image at 0x47d3c68>", | |
| "image/png": 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| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": "<Figure size 640x480 with 0 Axes>" | |
| }, | |
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| } | |
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| "cell_type": "markdown", | |
| "source": "With just a few lines of code, you were able to quickly identify and visualize the spike in immigration!\n\nQuick note on x and y values in `plt.text(x, y, label)`:\n\n```\n Since the x-axis (years) is type 'integer', we specified x as a year. The y axis (number of immigrants) is type 'integer', so we can just specify the value y = 6000.\n```\n\n```python\n plt.text(2000, 6000, '2010 Earthquake') # years stored as type int\n```\n\n```\nIf the years were stored as type 'string', we would need to specify x as the index position of the year. Eg 20th index is year 2000 since it is the 20th year with a base year of 1980.\n```\n\n```python\n plt.text(20, 6000, '2010 Earthquake') # years stored as type int\n```\n\n```\nWe will cover advanced annotation methods in later modules.\n```\n", | |
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| { | |
| "cell_type": "markdown", | |
| "source": "We can easily add more countries to line plot to make meaningful comparisons immigration from different countries.\n\n**Question:** Let's compare the number of immigrants from India and China from 1980 to 2013.\n", | |
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| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Step 1: Get the data set for China and India, and display the dataframe.\n", | |
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| { | |
| "cell_type": "code", | |
| "source": "### type your answer here\ndf_CI = df_can.loc[[\"India\",\"China\"],years]\ndf_CI\n", | |
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| }, | |
| "scrolled": true, | |
| "trusted": true | |
| }, | |
| "execution_count": 44, | |
| "outputs": [ | |
| { | |
| "execution_count": 44, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": " 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... \\\nIndia 8880 8670 8147 7338 5704 4211 7150 10189 11522 10343 ... \nChina 5123 6682 3308 1863 1527 1816 1960 2643 2758 4323 ... \n\n 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 \nIndia 28235 36210 33848 28742 28261 29456 34235 27509 30933 33087 \nChina 36619 42584 33518 27642 30037 29622 30391 28502 33024 34129 \n\n[2 rows x 34 columns]", | |
| "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>1980</th>\n <th>1981</th>\n <th>1982</th>\n <th>1983</th>\n <th>1984</th>\n <th>1985</th>\n <th>1986</th>\n <th>1987</th>\n <th>1988</th>\n <th>1989</th>\n <th>...</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>2010</th>\n <th>2011</th>\n <th>2012</th>\n <th>2013</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>India</th>\n <td>8880</td>\n <td>8670</td>\n <td>8147</td>\n <td>7338</td>\n <td>5704</td>\n <td>4211</td>\n <td>7150</td>\n <td>10189</td>\n <td>11522</td>\n <td>10343</td>\n <td>...</td>\n <td>28235</td>\n <td>36210</td>\n <td>33848</td>\n <td>28742</td>\n <td>28261</td>\n <td>29456</td>\n <td>34235</td>\n <td>27509</td>\n <td>30933</td>\n <td>33087</td>\n </tr>\n <tr>\n <th>China</th>\n <td>5123</td>\n <td>6682</td>\n <td>3308</td>\n <td>1863</td>\n <td>1527</td>\n <td>1816</td>\n <td>1960</td>\n <td>2643</td>\n <td>2758</td>\n <td>4323</td>\n <td>...</td>\n <td>36619</td>\n <td>42584</td>\n <td>33518</td>\n <td>27642</td>\n <td>30037</td>\n <td>29622</td>\n <td>30391</td>\n <td>28502</td>\n <td>33024</td>\n <td>34129</td>\n </tr>\n </tbody>\n</table>\n<p>2 rows × 34 columns</p>\n</div>" | |
| }, | |
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| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "<details><summary>Click here for a sample python solution</summary>\n\n```python\n #The correct answer is:\n df_CI = df_can.loc[['India', 'China'], years]\n df_CI\n```\n\n</details>\n", | |
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| { | |
| "cell_type": "markdown", | |
| "source": "Step 2: Plot graph. We will explicitly specify line plot by passing in `kind` parameter to `plot()`.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "### type your answer here\ndf_CI.plot(kind=\"line\")\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": true, | |
| "trusted": true | |
| }, | |
| "execution_count": 45, | |
| "outputs": [ | |
| { | |
| "execution_count": 45, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "<AxesSubplot:>" | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": "<Figure size 640x480 with 1 Axes>", | |
| "image/png": 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9e7f93NSpU9m4cSPV1dWEQiE2bNhAc3Mzl156aaf7ra6u5vnnn2fq1KknDTki0necA30CInJmCwQCrFy5ckCOfdttt/VKgMjKysLr9bJs2TJWrFhBXFwcP/nJTzhy5AgVFRV2uTVr1jB37lzGjRuH0+lk6NChrFu3rkNNzD333MPatWtpampiypQpPPnkkz0+RxH5aFSjIyKDnsvlYt26dRw8eJCxY8cycuRIXn/9dfLy8jDNto/JkpIS6urqeO6553jppZe45ZZbmDt3Ln/5y1+i9vfNb36Tbdu28eyzz+JwOLj11lt7rfZJRLpHfXREpFva99GxLItAIDAg5+J0OjEMo9uva99HJ1JdXR0+n4+0tDTy8/PJzc3lgQceoLy8nP/3//5fVD8egGuuuYZRo0axYsWKTo915MgRJk+ezP/93/8xderULs9JfXRE+oaarkSkRwzDiKn+J4mJiUA40O3bt4877rgDgOPHjwNE1fAAOByOk9bWhEIhAHw+X1+croicgoKOiAwKDQ0NlJeX248PHz5MaWkpKSkpeL1eNm3aRGpqKiNGjODAgQMUFxczc+ZMZsyYAYT78YwaNYpFixaxZMkSUlJS2Lp1K6+88ordB2fv3r3s27ePf/mXfyE5OZl33nmHkpISMjMzufjiiwfkukUGOzVdiUi3nKnDy19//XVmzZrV4fmCggJWrVrF6tWreeSRR6iqqiIjI4M5c+awcOFC3G63XbasrIylS5eye/dumpqayMzMZN68efZw87/85S8UFxfz5z//maamJjIyMpgxYwYLFy7knHPOOen5qelKpG8o6IhIt5ypQefjTkFHpG9o1JWIiIjELAUdERERiVkKOiIiIhKzFHREREQkZinoiIiISMxS0BEREZGYpaAjIiIiMUtBR0RERGKWgo6IiIjELAUdERERiVkKOiIyKOzatYvCwkJycnJIT09ny5YtUdsrKyuZP38+OTk5jBw5koKCAsrKyqLKVFRUMG/ePLKzs8nMzCQvL4/Nmzd3erzm5mZmzJhBeno6paWlfXZdInJyCjoiMig0NTWRnZ1NSUlJh22WZVFUVMShQ4dYv34927Ztw+v1Mnv2bBobG+1yt956KwcPHuSJJ55gx44dXHXVVcydO7fTIHPPPfdw9tln9+k1icipKeiIyKCQl5fHnXfeyVVXXdVhW1lZGXv37uXBBx/kwgsvZMyYMSxfvpwTJ06wYcMGu9yePXv4+te/zkUXXURmZiYLFy4kKSmJ/fv3R+1v27Zt7NixgyVLlvT1ZYnIKSjoiEjPWBaEmgfmx7J65RKam5sB8Hg89nOmaeJ2u9m9e7f93NSpU9m4cSPV1dWEQiE2bNhAc3Mzl156qV2msrKShQsX8sgjjzB06NBeOT8R+eicA30CInKGs3yk/O27A3Lo6nHLwPCcuuApZGVl4fV6WbZsGStWrCAuLo6f/OQnHDlyhIqKCrvcmjVrmDt3LuPGjcPpdDJ06FDWrVvH6NGjgXAT2G233UZRURGTJ0/m8OHDPT43EekZ1eiIyKDncrlYt24dBw8eZOzYsYwcOZLXX3+dvLw8TLPtY7KkpIS6ujqee+45XnrpJW655Rbmzp3LX/7yFyAchBoaGvjWt741UJciIu2oRkdEesZwh2tWBujYvSU3N5ft27dTV1eHz+cjLS2N/Px8cnNzASgvL+exxx5j586djB8/HoCJEyfyxhtvsHbtWlasWMGrr77KW2+9hdfrjdr35z73Oa655hoefvjhXjtfETk9Cjoi0jOG0SvNRx8XiYmJQLiD8r59+7jjjjsAOH78OEBUDQ+Aw+HAaukrdN9993HnnXfa295//30KCgr46U9/ypQpU/rj9EWkHQUdERkUGhoaKC8vtx8fPnyY0tJSUlJS8Hq9bNq0idTUVEaMGMGBAwcoLi5m5syZzJgxAwj34xk1ahSLFi1iyZIlpKSksHXrVl555RWefPJJgA41OcOGDQMgMzOTc889t5+uVEQiKeiIyKCwf/9+Zs2aZT9evHgxAAUFBaxatYqKigoWL15MVVUVGRkZzJkzh4ULF9rlXS4XTz31FEuXLqWwsJCmpiYyMzNZtWoVn/3sZ/v9ekTk9BiW1UvjM0VkUCgrKyMhIWGgTyPm1NfX26O3RKT3aNSViIiIxCwFHREREYlZCjoiIiISsxR0REREJGYp6IiIiEjMUtARERGRmKWgIyIiIjFLQUdERERiloKOiIiIxCwFHREREYlZCjoiMijs2rWLwsJCcnJySE9PZ8uWLVHbKysrmT9/Pjk5OYwcOZKCggLKysqiylRUVDBv3jyys7PJzMwkLy+PzZs3R5WZMmUK6enpUT8rV67s8+sTkc4p6IjIoNDU1ER2djYlJSUdtlmWRVFREYcOHWL9+vVs27YNr9fL7NmzaWxstMvdeuutHDx4kCeeeIIdO3Zw1VVXMXfuXEpLS6P2d8cdd1BaWmr/fP3rX+/z6xORzmn1chHpEcuyCIaaB+TYDtODYRinVTYvL4+8vLxOt5WVlbF371527tzJ+PHjAVi+fDkTJ05kw4YNFBYWArBnzx4efPBBLrroIgAWLlzIo48+yv79+8nJybH3Fx8fT0ZGRk8uTUR6iYKOiPRIMNTM06VfG5BjX5vzOE7HkB7vp7k5HNQ8Ho/9nGmauN1udu/ebQedqVOnsnHjRq644gqSkpLYuHEjzc3NXHrppVH7W7lyJT/84Q8ZMWIEX/rSl7jllltwOvVxKzIQ9F+eiAx6WVlZeL1eli1bxooVK4iLi+MnP/kJR44coaKiwi63Zs0a5s6dy7hx43A6nQwdOpR169YxevRou8xNN93EpEmTSE5OZs+ePSxbtoyKigqWLl06EJcmMugp6IhIjzhMD9fmPD5gx+4NLpeLdevWsWDBAsaOHYvD4WDatGnk5eVhWZZdrqSkhLq6Op577jmGDx/O1q1bmTt3Lps2bWLChAkAfOMb37DLZ2dn43a7WbRoEcXFxVE1RiLSPxR0RKRHDMPoleajgZabm8v27dupq6vD5/ORlpZGfn4+ubm5AJSXl/PYY49F9eOZOHEib7zxBmvXrmXFihWd7veiiy4iEAjw7rvvMmbMmH67HhEJ06grEZEIiYmJpKWlUVZWxr59+8jPzwfg+PHjQLjvTiSHwxFV69Pen/70J0zTJC0tre9OWkS6pBodERkUGhoaKC8vtx8fPnyY0tJSUlJS8Hq9bNq0idTUVEaMGMGBAwcoLi5m5syZzJgxAwj34xk1ahSLFi1iyZIlpKSksHXrVl555RWefPJJIDwq6/e//z2XXXYZ8fHxvPXWWyxevJjZs2eTnJw8EJctMugZ1sm+ioiItFNWVkZCQsJAn0a3vf7668yaNavD8wUFBaxatYrVq1fzyCOPUFVVRUZGBnPmzGHhwoW43W67bFlZGUuXLmX37t00NTWRmZnJvHnzmDNnDgB//OMfueOOO/jHP/6Bz+fj/PPP58tf/jK33HLLKfvn1NfXR3VqFpHeoaAjIt1ypgadjzsFHZG+oT46IiIiErMUdERERCRmKeiIiIhIzFLQERERkZiloCMiIiIxS0FHREREYpaCjoiIiMQsBR0RERGJWQo6IiIiErMUdERERCRmKeiIyKCwa9cuCgsLycnJIT09nS1btkRtr6ysZP78+eTk5DBy5EgKCgooKyuLKlNRUcG8efPIzs4mMzOTvLw8Nm/e3OFYv/nNb8jPz+f8888nKyuLr371q316bSLSNQUdERkUmpqayM7OpqSkpMM2y7IoKiri0KFDrF+/nm3btuH1epk9ezaNjY12uVtvvZWDBw/yxBNPsGPHDq666irmzp1LaWmpXWbz5s1885vf5Nprr2X79u28+OKLXHPNNf1yjSLSkRb1FJFu6bCop2VBKDQwJ2OaYBjdfll6ejqPP/44V155JQAHDx7kkksuYefOnYwfPx6AUCjExIkTueuuuygsLAQgMzOTBx980F6tHGDcuHF873vfo7CwkEAgwJQpU7j99tu5/vrru3VOWtRTpG84B/oEROQMFwpx1u/3D8ihqy7KBYejx/tpbm4GwOPx2M+Zponb7Wb37t120Jk6dSobN27kiiuuICkpiY0bN9Lc3Myll14KwB//+EeOHj2KaZpcfvnlVFZWMnHiRL7//e9zwQUX9Pg8RaT71HQlIoNeVlYWXq+XZcuWUVNTg8/nY+XKlRw5coSKigq73Jo1a/D7/YwbNw6v18uiRYtYt26dXRNz6NAhAJYvX85//ud/8uSTT5KcnMysWbOorq4ekGsTGexUoyMiPWOa4ZqVATp2b3C5XKxbt44FCxYwduxYHA4H06ZNIy8vj8jW/ZKSEurq6njuuecYPnw4W7duZe7cuWzatIkJEyYQamnCW7BgAf/2b/8GwI9+9CMmT57Mpk2bKCoq6pXzFZHTp6AjIj1jGL3SfDTQcnNz2b59O3V1dfh8PtLS0sjPzyc3NxziysvLeeyxx6L68UycOJE33niDtWvXsmLFCjIyMgAYO3asvV+Px8PIkSP55z//2f8XJSJquhIRiZSYmEhaWhplZWXs27eP/Px8AI4fPw6E++5Ecjgcdq1Pbm4uHo+HgwcP2tv9fj+HDx/G6/X20xWISCTV6IjIoNDQ0EB5ebn9+PDhw5SWlpKSkoLX62XTpk2kpqYyYsQIDhw4QHFxMTNnzmTGjBlAuB/PqFGjWLRoEUuWLCElJYWtW7fyyiuv8OSTTwKQkJBAUVERDz74IOeeey7nnXcejzzyCABf+MIX+v+iRURBR0QGh/379zNr1iz78eLFiwEoKChg1apVVFRUsHjxYqqqqsjIyGDOnDksXLjQLu9yuXjqqadYunQphYWFNDU1kZmZyapVq/jsZz9rl/v+97+Pw+Hgm9/8JidOnOCiiy7ihRdeIDk5ud+uVUTaaB4dEemWDvPoSK/QPDoifUN9dERERCRmKeiIiIhIzFLQERERkZiloCMiIiIxS0FHREREYpaCjoiIiMQsBR0RERGJWQo6IiIiErMUdERERCRmKeiIiIhIzFLQEZFBYdeuXRQWFpKTk0N6ejpbtmyJ2l5ZWcn8+fPJyclh5MiRFBQUUFZWFlWmoqKCefPmkZ2dTWZmJnl5eWzevNne/vrrr5Oent7pzx/+8Id+uU4RiaagIyKDQlNTE9nZ2ZSUlHTYZlkWRUVFHDp0iPXr17Nt2za8Xi+zZ8+msbHRLnfrrbdy8OBBnnjiCXbs2MFVV13F3LlzKS0tBWDq1KmUlpZG/RQWFnL++eczefLk/rpUEYmg1ctFpEcsywJf88Ac3O3BMIzTKpqXl0deXl6n28rKyti7dy87d+5k/PjxACxfvpyJEyeyYcMGCgsLAdizZw8PPvggF110EQALFy7k0UcfZf/+/eTk5OB2u8nIyLD36/f7+dWvfsXXv/710z5PEeldCjoi0jO+ZppvnjUgh/Y8ugE8Q3q8n+bmcFDzeDz2c6Zp4na72b17tx10pk6dysaNG7niiitISkpi48aNNDc3c+mll3a631/96ld8+OGHfOUrX+nxOYrIR6OmKxEZ9LKysvB6vSxbtoyamhp8Ph8rV67kyJEjVFRU2OXWrFmD3+9n3LhxeL1eFi1axLp16xg9enSn+/3FL37BjBkzOPfcc/vrUkSkHdXoiEjPuD3hmpUBOnZvcLlcrFu3jgULFjB27FgcDgfTpk0jLy8v3DTXoqSkhLq6Op577jmGDx/O1q1bmTt3Lps2bWLChAlR+zxy5Ajbt29n9erVvXKOIvLRKOiISI8YhtErzUcDLTc3l+3bt1NXV4fP5yMtLY38/Hxyc3MBKC8v57HHHovqxzNx4kTeeOMN1q5dy4oVK6L299RTTzF8+HDy8/P7/VpEpI2arkREIiQmJpKWlkZZWRn79u2zg8rx48eBcN+dSA6HI6rWB8IdtJ966im+/OUv43K5+ufERaRTqtERkUGhoaGB8vJy+/Hhw4cpLS0lJSUFr9fLpk2bSE1NZcSIERw4cIDi4mJmzpzJjBkzgHA/nlGjRrFo0SKWLFlCSkoKW7du5ZVXXuHJJ5+MOtarr77K4cOH7U7MIjJwDKv9VxERkZMoKysjISFhoE+j215//XVmzeo4OqygoIBVq1axevVqHnnkEaqqqsjIyGDOnDksXLgQt9ttly0rK2Pp0qXs3r2bpqYmMjMzmTdvHnPmzIna5y233MK7777L//3f/532+dXX13fZqVlEPjoFHRHpljM16HzcKeiI9A310REREZGYpaAjIiIiMUtBR0RERGKWgo6IiIjELAUdERERiVkKOiIiIhKzFHREREQkZinoiIiISMxS0BEREZGYpaAjIiIiMUtBR0QGhV27dlFYWEhOTg7p6els2bIlantlZSXz588nJyeHkSNHUlBQQFlZWVSZiooK5s2bR3Z2NpmZmeTl5bF58+aoMgcPHuSrX/0q48ePZ/To0Xz+85/ntdde6/PrE5HOKeiIyKDQ1NREdnY2JSUlHbZZlkVRURGHDh1i/fr1bNu2Da/Xy+zZs2lsbLTL3XrrrRw8eJAnnniCHTt2cNVVVzF37lxKS0vtMtdffz2BQIDnn3+e3/72t2RnZ1NYWEhFRUW/XKeIRNOiniLSLe0X9bQsi+bAwHyMeJwGhmF0+3Xp6ek8/vjjXHnllUC4FuaSSy5h586djB8/HoBQKMTEiRO56667KCwsBCAzM5MHH3wwarXycePG8b3vfY/CwkI++OADLrjgAjZt2sQnP/lJABoaGhg9ejT/+7//y/Tp07s8Jy3qKdI3nAN9AiJyZmsOWHzpyT8PyLFfuD6bIa7uB532mpubAfB4PPZzpmnidrvZvXu3HXSmTp3Kxo0bueKKK0hKSmLjxo00Nzdz6aWXAjB8+HDGjBnDs88+S05ODh6Ph5/97GekpaWRm5vb4/MUke5T05WIDHpZWVl4vV6WLVtGTU0NPp+PlStXcuTIkagmpzVr1uD3+xk3bhxer5dFixaxbt06uybGMAyee+45SktLGT16NOeddx4/+clPeOaZZ0hOTh6gqxMZ3FSjIyI94nEavHB99oAduze4XC7WrVvHggULGDt2LA6Hg2nTppGXl0dk635JSQl1dXU899xzDB8+nK1btzJ37lw2bdrEhAkTsCyL//qv/yItLY1NmzYxdOhQfv7zn1NYWMhLL71ERkZGr5yviJw+BR0R6RHDMHql+Wig5ebmsn37durq6vD5fKSlpZGfn283OZWXl/PYY49F9eOZOHEib7zxBmvXrmXFihW8+uqrvPTSS/zjH/+w+zE9+OCDvPLKKzzzzDPcdtttA3Z9IoOVmq5ERCIkJiaSlpZGWVkZ+/btIz8/H4Djx48D4b47kRwOh13r01qmfQdp0zQJhUJ9feoi0gnV6IjIoNDQ0EB5ebn9+PDhw5SWlpKSkoLX62XTpk2kpqYyYsQIDhw4QHFxMTNnzmTGjBlAuB/PqFGjWLRoEUuWLCElJYWtW7fyyiuv8OSTTwJw8cUXk5yczPz58/n2t7/N0KFDeeKJJzh8+DCf/exnB+S6RQY7DS8XkW5pP7z8TPH6668za9asDs8XFBSwatUqVq9ezSOPPEJVVRUZGRnMmTOHhQsX4na77bJlZWUsXbqU3bt309TURGZmJvPmzYsabr5v3z7uu+8+9u/fb3dcXrRoEXl5eSc9Pw0vF+kbCjoi0i1natD5uFPQEekb6qMjIiIiMUtBR0RERGKWgo6IiIjELAUdERERiVkKOiIiIhKzFHREREQkZinoiIiISMxS0BEREZGYpaAjIiIiMUtBR0RERGKWgo6IDAq7du2isLCQnJwc0tPT2bJlS9T2yspK5s+fT05ODiNHjqSgoICysrKoMhUVFcybN4/s7GwyMzPJy8tj8+bNUWX++Mc/Mnv2bMaMGcO4ceP49re/TUNDQ59fn4h0TkFHRAaFpqYmsrOzKSkp6bDNsiyKioo4dOgQ69evZ9u2bXi9XmbPnk1jY6Nd7tZbb+XgwYM88cQT7Nixg6uuuoq5c+dSWloKwPvvv8/s2bMZNWoUv/rVr3j66af561//ym233dZv1yki0ZwDfQIicmazLItgcGCO7XCAYRinVTYvL6/LFcTLysrYu3cvO3fuZPz48QAsX76ciRMnsmHDBgoLCwHYs2cPDz74IBdddBEACxcu5NFHH2X//v3k5OTw0ksv4XQ6eeCBBzBN097PZz7zGcrKyrRop8gAUNARkR4JBmHT08cG5NhfuDYNZy98ijU3NwPg8Xjs50zTxO12s3v3bjvoTJ06lY0bN3LFFVeQlJTExo0baW5u5tJLLwXA5/PhdrvtkAMwZMgQAN58800FHZEBoKYrERn0srKy8Hq9LFu2jJqaGnw+HytXruTIkSNUVFTY5dasWYPf72fcuHF4vV4WLVrEunXr7ADzqU99isrKSh5++GF8Ph81NTXce++9AFH7EZH+oxodEekRhyNcszJQx+4NLpeLdevWsWDBAsaOHYvD4WDatGnk5eVhWZZdrqSkhLq6Op577jmGDx/O1q1bmTt3Lps2bWLChAmMHz+eVatWsXjxYpYtW4bD4eCmm27irLPOOu0mNhHpXQo6ItIjhmH0SvPRQMvNzWX79u3U1dXh8/lIS0sjPz+f3NxcAMrLy3nsscei+vFMnDiRN954g7Vr17JixQoArrnmGq655hoqKyuJi4vDMAx+8pOfkJmZOVCXJjKoqelKRCRCYmIiaWlplJWVsW/fPvLz8wE4fvw4QFT/GwCHwxFV69MqPT2d+Ph4Nm7cyJAhQ5g+fXrfn7yIdBAD38NERE6toaGB8vJy+/Hhw4cpLS0lJSUFr9fLpk2bSE1NZcSIERw4cIDi4mJmzpzJjBkzgHA/nlGjRrFo0SKWLFlCSkoKW7du5ZVXXuHJJ5+09/vYY48xdepUhg0bxo4dO7jnnnsoLi4mKSmp369ZRMCwOvsqIiLShbKyMhISEgb6NLrt9ddfZ9asWR2eLygoYNWqVaxevZpHHnmEqqoqMjIymDNnDgsXLsTtdttly8rKWLp0Kbt376apqYnMzEzmzZvHnDlz7DLf/OY3+e1vf0tjYyNjxozpsL0r9fX1GpUl0gcUdESkW87UoPNxp6Aj0jfUR0dERERiloKOiIiIxCwFHREREYlZCjoiIiISsxR0REREJGYp6IiIiEjMUtARERGRmKWgIyIiIjFLQUdERERiloKOiIiIxCwFHREZFHbt2kVhYSE5OTmkp6ezZcuWqO2VlZXMnz+fnJwcRo4cSUFBAWVlZVFlysvLKSoq4oILLmD06NHcdNNNVFZWRpWprq7mlltuYfTo0YwZM4YFCxbQ0NDQ59cnIp1T0BGRQaGpqYns7GxKSko6bLMsi6KiIg4dOsT69evZtm0bXq+X2bNn09jYCEBjYyNz5szBMAyef/55XnzxRXw+HzfccAOhUMje1ze+8Q3+9re/8b//+7/8/Oc/Z9euXSxatKjfrlNEomlRTxHplvaLelqWRSAQGJBzcTqdGIbR7delp6fz+OOPc+WVVwJw8OBBLrnkEnbu3Mn48eMBCIVCTJw4kbvuuovCwkK2b9/OV77yFf7xj3/Y119XV0dWVhbPPvss06dP5+9//zuf+tSneOmll5g8eTIAL7/8Ml/5ylfYv38/Z599dpfnpEU9RfqGc6BPQETObIFAgJUrVw7IsW+77TZcLleP99Pc3AyAx+OxnzNNE7fbze7duyksLMTn82EYBm632y7j8XgwTZPdu3czffp03nrrLZKSkuyQAzBt2jRM02Tv3r1cddVVPT5XEekeNV2JyKCXlZWF1+tl2bJl1NTU4PP5WLlyJUeOHKGiogKAKVOmEBcXx9KlS2lqaqKxsZElS5YQDAbtMpWVlaSlpUXt2+l0kpKS0qEvj4j0D9XoiEiPOJ1ObrvttgE7dm9wuVysW7eOBQsWMHbsWBwOB9OmTSMvL4/W1v20tDTWrFnD7bffzurVqzFNk1mzZjFp0iRMU98ZRT6uFHREpEcMw+iV5qOBlpuby/bt26mrq8Pn85GWlkZ+fj65ubl2mRkzZrBnzx4++OADnE4nSUlJZGdnc/XVVwPhvj/Hjh2L2m8gEKC6upr09PT+vBwRaaGvISIiERITE0lLS6OsrIx9+/aRn5/foUxqaipJSUm8+uqrHDt2zC5z8cUXU1tby/79++2yr776KqFQiClTpvTbNYhIG9XoiMig0NDQQHl5uf348OHDlJaWkpKSgtfrZdOmTaSmpjJixAgOHDhAcXExM2fOZMaMGfZrnnrqKbKyskhNTeWtt96iuLiYm2++mTFjxgAwduxYLr/8chYuXMjy5cvx+/3ceeedzJo166QjrkSk72h4uYh0S/vh5WeK119/nVmzZnV4vqCggFWrVrF69WoeeeQRqqqqyMjIYM6cOSxcuDBqlNXSpUt5+umnqamp4bzzzqOoqIhbbrklaoh7dXU1d955J7/+9a8xTZPPf/7zLFu2jPj4+JOen4aXi/QNBR0R6ZYzNeh83CnoiPQN9dERERGRmKWgIyIiIjFLQUdERERiloKOiIiIxCwFHREREYlZCjoiIiISsxR0REREJGYp6IiIiEjMUtARERGRmKWgIyIiIjFLQUdEBoVdu3ZRWFhITk4O6enpbNmyJWp7ZWUl8+fPJycnh5EjR1JQUEBZWVlUmfLycoqKirjgggsYPXo0N910E5WVlVFl/vu//5srr7ySkSNH2ot9isjAUdARkUGhqamJ7OxsSkpKOmyzLIuioiIOHTrE+vXr2bZtG16vl9mzZ9PY2AhAY2Mjc+bMwTAMnn/+eV588UV8Ph833HADoVDI3pfP5+MLX/gCRUVF/XZtItI150CfgIic4SwLLN/AHNtwQ8TK4SeTl5dHXl5ep9vKysrYu3cvO3fuZPz48QAsX76ciRMnsmHDBgoLC3nzzTd59913efnll+1FTR9++GGysrJ49dVXmT59OgB33HEHAE8//XRPr05EeoGCjoj0jOUj5W/fHZBDV49bBoanx/tpbm4GwONp25dpmrjdbnbv3k1hYSE+nw/DMHC73XYZj8eDaZrs3r3bDjoi8vGipisRGfSysrLwer0sW7aMmpoafD4fK1eu5MiRI1RUVAAwZcoU4uLiWLp0KU1NTTQ2NrJkyRKCwaBdRkQ+flSjIyI9Y7jDNSsDdOze4HK5WLduHQsWLGDs2LE4HA6mTZtGXl4elmUBkJaWxpo1a7j99ttZvXo1pmkya9YsJk2ahGnqO6PIx5WCjoj0jGH0SvPRQMvNzWX79u3U1dXh8/lIS0sjPz+f3Nxcu8yMGTPYs2cPH3zwAU6nk6SkJLKzs7n66qsH7sRF5KT0NUREJEJiYiJpaWmUlZWxb98+8vPzO5RJTU0lKSmJV199lWPHjnVaRkQ+HlSjIyKDQkNDA+Xl5fbjw4cPU1paSkpKCl6vl02bNpGamsqIESM4cOAAxcXFzJw5kxkzZtiveeqpp8jKyiI1NZW33nqL4uJibr755qj5ct577z2qq6t57733CAaDlJaWAjBq1Cji4+P774JFBFDQEZFBYv/+/cyaNct+vHjxYgAKCgpYtWoVFRUVLF68mKqqKjIyMpgzZw4LFy6M2sfbb7/NvffeS01NDeeddx4LFizglltuiSrzwAMP8Mwzz9iPW4e0b9iwgcsuu6yvLk9EumBYrT3tREROQ1lZmT2PjPSe+vp6Ro8ePdCnIRJz1EdHREREYpaCjoiIiMQsBR0RERGJWQo6IiIiErMUdERERCRmKeiIiIhIzFLQERERkZiloCMiIiIxS0FHREREYpaCjoiIiMQsBR0RGRR27dpFYWEhOTk5pKens2XLlqjtlZWVzJ8/n5ycHEaOHElBQQFlZWVRZcrLyykqKuKCCy5g9OjR3HTTTVRWVtrbDx8+zIIFC7j44os5//zzmTp1Kg888AA+n69frlFEOlLQEZFBoampiezsbEpKSjpssyyLoqIiDh06xPr169m2bRter5fZs2fT2NgIQGNjI3PmzMEwDJ5//nlefPFFfD4fN9xwA6FQCAgv+hkKhVi+fDk7d+5k6dKl/OxnP2PZsmX9eq0i0kaLeopIt7Rf1NOyLIKh5gE5F4fpwTCMbr8uPT2dxx9/nCuvvBKAgwcPcskll7Bz507Gjx8PQCgUYuLEidx1110UFhayfft2vvKVr/CPf/zDvv66ujqysrJ49tlnmT59eqfHevjhh3n88cd56623TnpOWtRTpG84B/oEROTMFgw183Tp1wbk2NfmPI7TMaTH+2luDgc1j8djP2eaJm63m927d1NYWIjP58MwDNxut13G4/Fgmia7d+/uMujU19eTkpLS43MUkY9GTVciMuhlZWXh9XpZtmwZNTU1+Hw+Vq5cyZEjR6ioqABgypQpxMXFsXTpUpqammhsbGTJkiUEg0G7THtlZWWsWbOGr371q/15OSISQTU6ItIjDtPDtTmPD9ixe4PL5WLdunUsWLCAsWPH4nA4mDZtGnl5ebS27qelpbFmzRpuv/12Vq9ejWmazJo1i0mTJmGaHb8zHj16lGuvvZYvfOEL3HDDDb1yniLSfQo6ItIjhmH0SvPRQMvNzWX79u3U1dXh8/lIS0sjPz+f3Nxcu8yMGTPYs2cPH3zwAU6nk6SkJLKzs7n66quj9vX+++8za9Yspk6dyg9+8IN+vhIRiaSmKxGRCImJiaSlpVFWVsa+ffvIz8/vUCY1NZWkpCReffVVjh07FlXm6NGjXH311eTm5rJy5cpOa3tEpP+oRkdEBoWGhgbKy8vtx4cPH6a0tJSUlBS8Xi+bNm0iNTWVESNGcODAAYqLi5k5cyYzZsywX/PUU0+RlZVFamoqb731FsXFxdx8882MGTMGaAs5Xq+XJUuWcOzYMfu1GRkZ/XexImJT0BGRQWH//v3MmjXLfrx48WIACgoKWLVqFRUVFSxevJiqqioyMjKYM2cOCxcujNrH22+/zb333ktNTQ3nnXceCxYs4JZbbrG3v/LKK5SXl1NeXh7V5AVETSwoIv1H8+iISLe0n0dHeofm0RHpG2o8FhERkZiloCMiIiIxS0FHREREYpaCjoiIiMQsBR0RERGJWQo6IiIiErMUdERERCRmKeiIiIhIzFLQERERkZiloCMiIiIxS0FHRAaFXbt2UVhYSE5ODunp6WzZsiVqe2VlJfPnzycnJ4eRI0dSUFBAWVlZVJny8nKKioq44IILGD16NDfddFOHNaxuuOEGLrzwQs477zwmTpzIvHnzeP/99/v8+kSkcwo6IjIoNDU1kZ2dTUlJSYdtlmVRVFTEoUOHWL9+Pdu2bcPr9TJ79mwaGxsBaGxsZM6cORiGwfPPP8+LL76Iz+fjhhtuIBQK2fu67LLLWL16Nb/73e9Yu3Yt77zzDjfeeGO/XaeIRNOiniLSLR0W9bQsiPhD369MEwyj2y9LT0/n8ccf58orrwTg4MGDXHLJJezcuZPx48cDEAqFmDhxInfddReFhYVs376dr3zlK/zjH/+wr7+uro6srCyeffZZpk+f3umxfvWrX1FUVMR7772Hy+Xq8py0qKdI33AO9AmIyBkuFOKs3+8fkENXXZQLDkeP99Pc3AyAx+OxnzNNE7fbze7duyksLMTn82EYBm632y7j8XgwTZPdu3d3GnSqq6t5/vnnmTp16klDjoj0HTVdicigl5WVhdfrZdmyZdTU1ODz+Vi5ciVHjhyhoqICgClTphAXF8fSpUtpamqisbGRJUuWEAwG7TKt7rnnHjIzMxk3bhzvvfce69evH4jLEhHUdCUi3RSLTVcA+/fvZ8GCBfz5z3/G4XAwbdo0TNPEsiyefvppALZv387tt9/O4cOHMU2TWbNm8fe//50LL7yQ5cuX2/v64IMPqKmp4d1332XFihUkJiby5JNPYpzkXNV0JdI31HQlIj1jGL3SfDTQcnNz2b59O3V1dfh8PtLS0sjPzyc3N9cuM2PGDPbs2cMHH3yA0+kkKSmJ7Oxsrr766qh9paamkpqayic+8QnGjh3L5MmTeeutt5g6dWo/X5WIqOlKRCRCYmIiaWlplJWVsW/fPvLz8zuUSU1NJSkpiVdffZVjx451WqZV64gsn8/XZ+csIl1TjY6IDAoNDQ2Ul5fbjw8fPkxpaSkpKSl4vV42bdpEamoqI0aM4MCBAxQXFzNz5kxmzJhhv+app54iKyuL1NRU3nrrLYqLi7n55psZM2YMAHv37mXfvn38y7/8C8nJybzzzjuUlJSQmZnJxRdf3O/XLCIKOiIySOzfv59Zs2bZjxcvXgxAQUEBq1atoqKigsWLF1NVVUVGRgZz5sxh4cKFUft4++23uffee6mpqeG8885jwYIF3HLLLfb2oUOH8n//9388+OCDNDU1kZGRwYwZM1i4cGHUiC4R6T/qjCwi3dKhM7L0CnVGFukb6qMjIiIiMUtBR0RERGKWgo6IiIjELAUdERERiVkKOiIiIhKzFHREREQkZinoiIiISMxS0BEREZGYpaAjIiIiMUtBR0RERGKWgo6IDAq7du2isLCQnJwc0tPT2bJlS9T2yspK5s+fT05ODiNHjqSgoICysrKoMuXl5RQVFXHBBRcwevRobrrpJiorKzs9XnNzMzNmzCA9PZ3S0tI+uy4ROTkFHREZFJqamsjOzqakpKTDNsuyKCoq4tChQ6xfv55t27bh9XqZPXs2jY2NADQ2NjJnzhwMw+D555/nxRdfxOfzccMNNxAKhTrs85577uHss8/u8+sSkZPT6uUi0iOWZYGveWAO7vZgGMZpFc3LyyMvL6/TbWVlZezdu5edO3cyfvx4AJYvX87EiRPZsGEDhYWFvPnmm7z77ru8/PLL9qKmDz/8MFlZWbz66qtMnz7d3t+2bdvYsWMHa9euZdu2bT28SBHpCQUdEekZXzPNN88akEN7Ht0AniE93k9zczioeTwe+znTNHG73ezevZvCwkJ8Ph+GYeB2u9uO7/Fgmia7d++2g05lZSULFy7kZz/7GUOHDu3xuYlIz6jpSkQGvaysLLxeL8uWLaOmpgafz8fKlSs5cuQIFRUVAEyZMoW4uDiWLl1KU1MTjY2NLFmyhGAwaJexLIvbbruNoqIiJk+ePIBXJCKtVKMjIj3j9oRrVgbo2L3B5XKxbt06FixYwNixY3E4HEybNo28vLxw0xyQlpbGmjVruP3221m9ejWmaTJr1iwmTZqEaYa/M65Zs4aGhga+9a1v9cp5iUjPKeiISI8YhtErzUcDLTc3l+3bt1NXV4fP5yMtLY38/Hxyc3PtMjNmzGDPnj188MEHOJ1OkpKSyM7O5uqrrwbg1Vdf5a233sLr9Ubt+3Of+xzXXHMNDz/8cH9ekoigoCMiEiUxMREId1Det28fd9xxR4cyqampQDjYHDt2jPz8fADuu+8+7rzzTrvc+++/T0FBAT/96U+ZMmVKP5y9iLSnoCMig0JDQwPl5eX248OHD1NaWkpKSgper5dNmzaRmprKiBEjOHDgAMXFxcycOZMZM2bYr3nqqafIysoiNTWVt956i+LiYm6++WbGjBkD0KEmZ9iwYQBkZmZy7rnn9sNVikh7CjoiMijs37+fWbPaRoctXrwYgIKCAlatWkVFRQWLFy+mqqqKjIwM5syZw8KFC6P28fbbb3PvvfdSU1PDeeedx4IFC7jlllv69TpEpHsMq7WnnYjIaSgrK7PnkZHeU19fz+jRowf6NERijoaXi4iISMxS0BEREZGYpaAjIiIiMUtBR0RERGKWgo6IiIjELAUdERERiVkKOiIiIhKzFHREREQkZinoiIiISMxS0BEREZGYpaAjIoPCrl27KCwsJCcnh/T0dLZs2RK1vbKykvnz55OTk8PIkSMpKCigrKwsqkx5eTlFRUVccMEFjB49mptuuonKysqoMlOmTCE9PT3qZ+XKlX1+fSLSOQUdERkUmpqayM7OpqSkpMM2y7IoKiri0KFDrF+/nm3btuH1epk9ezaNjY0ANDY2MmfOHAzD4Pnnn+fFF1/E5/Nxww03EAqFovZ3xx13UFpaav98/etf75drFJGOtHq5iPSIZVk0BwZmbWCP08AwjNMqm5eXR15eXqfbysrK2Lt3Lzt37mT8+PEALF++nIkTJ7JhwwYKCwt58803effdd3n55ZftRU0ffvhhsrKyePXVV5k+fbq9v/j4eDIyMnp4dSLSGxR0RKRHmgMWX3ryzwNy7Beuz2aI6/SCzsk0NzcD4PF47OdM08TtdrN7924KCwvx+XwYhoHb7bbLeDweTNNk9+7dUUFn5cqV/PCHP2TEiBF86Utf4pZbbsHp1MetyEBQ05WIDHpZWVl4vV6WLVtGTU0NPp+PlStXcuTIESoqKoBw35u4uDiWLl1KU1MTjY2NLFmyhGAwaJcBuOmmm/jpT3/KCy+8wFe/+lV+9KMfcffddw/UpYkMevqKISI94nEavHB99oAduze4XC7WrVvHggULGDt2LA6Hg2nTppGXl4dlhZvl0tLSWLNmDbfffjurV6/GNE1mzZrFpEmTMM2274zf+MY37PvZ2dm43W4WLVpEcXFxVI2RiPQPBR0R6RHDMHql+Wig5ebmsn37durq6vD5fKSlpZGfn09ubq5dZsaMGezZs4cPPvgAp9NJUlIS2dnZXH311V3u96KLLiIQCPDuu+8yZsyYfrgSEYmkpisRkQiJiYmkpaVRVlbGvn37yM/P71AmNTWVpKQkXn31VY4dO9ZpmVZ/+tOfME2TtLS0vjxtEemCanREZFBoaGigvLzcfnz48GFKS0tJSUnB6/WyadMmUlNTGTFiBAcOHKC4uJiZM2cyY8YM+zVPPfUUWVlZpKam8tZbb1FcXMzNN99s19Ts2bOH3//+91x22WXEx8fz1ltvsXjxYmbPnk1ycnJ/X7KIoKAjIoPE/v37mTVrlv148eLFABQUFLBq1SoqKipYvHgxVVVVZGRkMGfOHBYuXBi1j7fffpt7772XmpoazjvvPBYsWMAtt9xib/d4PPzyl79k+fLl+Hw+zj//fG6++eaoMiLSvwyrtaediMhpKCsrs+eRkd5TX1/P6NGjB/o0RGKO+uiIiIhIzFLQERERkZiloCMiIiIxS0FHREREYpaCjoiIiMQsBR0RERGJWQo6IiIiErMUdERERCRmKeiIiIhIzFLQERERkZiloCMig8KuXbsoLCwkJyeH9PR0tmzZErW9srKS+fPnk5OTw8iRIykoKKCsrCyqTHl5OUVFRVxwwQWMHj2am266icrKyg7H+s1vfkN+fj7nn38+WVlZfPWrX+3TaxORrinoiMig0NTURHZ2NiUlJR22WZZFUVERhw4dYv369Wzbtg2v18vs2bNpbGwEoLGxkTlz5mAYBs8//zwvvvgiPp+PG264gVAoZO9r8+bNfPOb3+Taa69l+/btvPjii1xzzTX9dp0iEk2LeopIt7Rf1NOyLILBgTkXhwMMw+j269LT03n88ce58sorATh48CCXXHIJO3fuZPz48QCEQiEmTpzIXXfdRWFhIdu3b+crX/kK//jHP+zrr6urIysri2effZbp06cTCASYMmUKt99+O9dff323zkmLeor0DedAn4CInNmCQdj09LEBOfYXrk3D2QufYs3NzQB4PB77OdM0cbvd7N69m8LCQnw+H4Zh4Ha77TIejwfTNNm9ezfTp0/nj3/8I0ePHsU0TS6//HIqKyuZOHEi3//+97ngggt6fqIi0m1quhKRQS8rKwuv18uyZcuoqanB5/OxcuVKjhw5QkVFBQBTpkwhLi6OpUuX0tTURGNjI0uWLCEYDNplDh06BMDy5cv5z//8T5588kmSk5OZNWsW1dXVA3Z9IoOZanREpEccjnDNykAduze4XC7WrVvHggULGDt2LA6Hg2nTppGXl0dr635aWhpr1qzh9ttvZ/Xq1ZimyaxZs5g0aRKmGf7O2NpXZ8GCBfzbv/0bAD/60Y+YPHkymzZtoqioqHdOWEROm4KOiPSIYRi90nw00HJzc9m+fTt1dXX4fD7S0tLIz88nNzfXLjNjxgz27NnDBx98gNPpJCkpiezsbK6++moAMjIyABg7dqz9Go/Hw8iRI/nnP//Zr9cjImFquhIRiZCYmEhaWhplZWXs27eP/Pz8DmVSU1NJSkri1Vdf5dixY3aZ3NxcPB4PBw8etMv6/X4OHz6M1+vtt2sQkTYx8D1MROTUGhoaKC8vtx8fPnyY0tJSUlJS8Hq9bNq0idTUVEaMGMGBAwcoLi5m5syZzJgxw37NU089RVZWFqmpqbz11lsUFxdz8803M2bMGAASEhIoKiriwQcf5Nxzz+W8887jkUceAeALX/hC/16wiAAKOiIySOzfv59Zs2bZjxcvXgxAQUEBq1atoqKigsWLF1NVVUVGRgZz5sxh4cKFUft4++23uffee6mpqeG8885jwYIF3HLLLVFlvv/97+NwOPjmN7/JiRMnuOiii3jhhRdITk7u82sUkY40j46IdEv7eXSkd2geHZG+oT46IiIiErPUdAVUV1cTCAR6fb9nnXUWVVVVvb5fiab3uX+0vs9+v79P/nsZ7Px+P1VVVfp97id6n/tPX7zXTqeTlJSU0yvbq0c+QwUCAfx+f6/us3Va+kAggFoH+47e5/7R/n3We937LMuyA6R+n/uWPjf6z8fhvVbTlYiIiMQsBR0RERGJWQo6IiIiErMUdERERCRmKeiIiIhIzFLQERERkZiloCMiIiIxS0FHRAaFN954g6KiIi666CJGjBjBr371q6jtVVVVLFiwgIsuuohPfOITXH/99ZSVlUWVeeedd/j6179OTk4O48aN4+abb46aCO13v/sdI0aM6PRn3759/XGZItKOgo6IDApNTU1MmDCBZcuWddhmWRY33ngjhw8fZu3atfz6179mxIgRXHvttTQ1Ndmvv+666zAMg2effZZf/vKX+P1+vva1rxEKhQC4+OKL+cMf/hD1c91113H++eeTm5vbr9crImGaGVlEeiRyRt/+5nQ67ZlXT+Xyyy/n8ssv73RbWVkZv//973n55ZcZN24cACUlJUyePJlf/vKXXHfddezZs4d3332XX//61/aipg899BATJkzgtddeY9q0abjdbtLT0+39+v1+fv3rX/Pv//7vp32eItK7FHREpEcCgQA//vGPB+TY3/jGN3C5XD3ej8/nA8Dj8djPmaaJ2+3mzTff5LrrrqO5uRnDMHC73XYZj8eDaZrs2bOHadOmddjvSy+9RHV1NQUFBT0+RxH5aNR0JSKD3pgxYxgxYgT3338/NTU1+Hw+HnnkEY4ePUplZSUAU6ZMIS4ujmXLlnH8+HGamppYunQpwWCQioqKTvf79NNP85nPfIZzzz23Py9HRCKoRkdEesTpdPKNb3xjwI7dG1wuF2vWrOHb3/422dnZOBwOPv3pT3P55ZfbCxGmpqby6KOPcuedd7J27VpM0+SLX/wiOTk5mGbH74xHjhxhx44d/OQnP+mVcxT52LNCmMF6zEAtDn9N+DZYQ8h5IXDegJ2Wgo6I9IhhGL3SfDTQJk2axG9+8xvq6urw+/2kpqby+c9/nkmTJtllpk+fzu9+9zs+/PBDHA4HSUlJTJ48mZEjR3bY3zPPPENKSgqf+9zn+vMyRPqGZWGETmAGanAEanEEajD94futz5mBWgxCHV86xAUJCjoiIh8LiYmJQLiD8v79+/nOd77Toczw4cMBeO211zh27BhXXHFF1HbLsnj22WeZPXt2TIRAGQSsQDisRIWXllqZQA2mvxbTaj71bjAJORMJOpMIOZMJuZKJT70QfP1wDV1Q0BGRQaGxsZHy8nL78eHDh/nTn/5ESkoKI0aMYPPmzaSmpjJixAj++te/snjxYvLz85k+fbr9mmeeeYYxY8aQmprK3r17Wbx4MXPnzmXMmDFRx3rttdc4fPgw1113Xb9dn0iXrBBmsKEttNhNSxGhJthwWrsKmXEEXcmEnEkEncmEnMkEW++7kgk54sFw2OUNwyAx9Rw4erSvru6UFHREZFDYv38/X/7yl+3Hd999NwBf/vKXeeihh6isrOTuu+/m2LFjpKenM3v2bBYsWBC1j4MHD9odlr1eL7fddhv/8R//0eFYTz/9NBdffHGHACTSF4zQic5rYvyRTUrBU+7HMlx2TUw4xCQRdCVHPJcEpvuU+/m4MazWnnaDWFVVFX6/v1f3aRgG55xzDkePHkVvcd/R+9w/It/n2tpau3lHek9dXR1JSUn6fe4HZ9TnhhXADNS1Cy81UbUzZujEqXeDQciZaNfE2EEmItBYZhz08nxPffVeu1wuzjrrrNMqqxodERGRgWBZGMGGts69rU1JEbUzZrABg1MHhJA5NCq0tNbAtNXOJEQ1KQ0mCjoiIiJ9wAg1h8OLv5POvYFaHIFaDOvUs4pbhrNjk1JUqEnCMj2n3M9gpaAjIiLSXVYwukmpw3DrmtNvUnIkdKiJiQw0lmNYrzcpDSYKOiIiIpEsCyPUGFET0xZeWpuWzGD9aTYpDWkXXpIJuto3KelPcV/SuysiIoNLyIfVdARX49uYgeq2WXwjamdOq0kJRycjkyKallxJWOaQfrggORkFHRERiR1WEDNQ364/TLtOvqEmgkDyKXYVbG1S6qKTb8gxDAwtGflxp6AjIiJnBsvCCDW1NSV12sm3vtNlCDpwDCHgSOq0JiYcaBLVpBQj9K8oIiIfDyF/pzUxkU1LhnXqOc/CyxC0NR+1nzfGcqVwtncUVWfCPDrSYwo6IiLS91pXtvZ30rm39TbYeFq7CjniO4xMCrkim5TiT9qkZGgE06CioCMiIj0TtbJ1ZH+YyNqZutNqUgoZ7nYjk9o3LSWBqYVS5fQp6IjIoPDGG2/w4x//mNLSUioqKnjsscfIz8+3t1dVVbFs2TJ27txJbW0tn/zkJ1m6dCmjR4+2y7zzzjssXbqUN998E5/Px2c+8xnuvffeqKnoDx48yL333suePXvw+/1ccMEFfOc73+Gyyy7r1+vtVSG/vWZSVOfeyOHW1qmXp25b2Tq5Q9NSa5ixzCGaM0Z6lYKOiAwKTU1NTJgwgWuvvZabbropaptlWdx44424XC7Wrl1LfHw8P/3pT7n22mvZsWMHcXFxNDU1cd111zFhwgSeffZZAJYvX87XvvY1Nm/ejGmGm0qKiooYNWoUzz77LEOGDGHNmjUUFRXxu9/9jvT09H6/7lOyV7buvCYm3KR0uitbD+u0JiboTGpZ2TpBo5Sk3ynoiEjPWBacRgfRPmG4Tvvb/+WXX87ll1/e6baysjJ+//vf8/LLLzNu3DgASkpKmDx5Mr/85S+57rrr2LNnD++++y6//vWvSUhIAOChhx5iwoQJvPbaa0ybNo0PP/yQ8vJyfvCDHzBhwgQA7rrrLn72s5/x17/+dUCCjhE80WFkUngW38gmpdNd2TpyZFJnTUpn3srWEvt6FHR++ctf8otf/IIrr7ySr33ta0D4m9Gzzz7Ltm3baGxsZPz48dx0002cc8459ut8Ph/r16/nd7/7HX6/n9zcXG666SaSk5PtMg0NDaxdu5a9e/diGAb/7//9P/793/+dIUPaJl86duwYq1ev5s9//jNDhgxh+vTpXHfddTgcg3PhMpEBYflJL/v+gBy6cvTdYPT8j6vPF2528Xja1gsyTRO3282bb77JddddR3NzM4Zh4Ha3Hc/j8WCaJnv27GHatGmkpKTwiU98gueee46cnBzcbjc///nPSUtLY9KkST0+zw4iVraOnvQuYlHIUPOpd9OysnWHdZQiOvn2xcrWIv3hIwedt99+m9/85jeMHDky6vmNGzeydetWvvnNb5Kens4zzzzDsmXL+OEPf2h/QPzsZz/j97//PQsXLiQuLo7HHnuMH/zgByxdutTez8qVK6murqa4uJhgMMj//M//8Oijj/Ktb30LgFAoxP33309ycjL33nsv1dXVPPzwwzgcDq677rqPelkiMgiNGTOGESNGcP/99/PAAw8QFxfH6tWrOXr0KJWVlQBMmTKFuLg4li1bxp133ollWdx3330Eg0EqKiqA8Giep59+mq9//euMHTsW0zRJS0vjySefjPoid1qsEEawMXpkUrsZfLu/snVXnXwH78rWEvs+UtA5ceIEq1at4uabb+aFF16wn7csiy1btvClL32JqVOnAnDrrbcyd+5c9uzZw2WXXUZTUxMvv/wy3/rWt5g4cSIA8+bN4z//8z/5+9//ztixY3nvvffYt28f999/P5/4xCcAuPHGG7n//vu54YYbGD58OPv37+e9997je9/7HsnJyWRmZlJQUMCTTz7JnDlzcDrVKifSLwxXuGZlgI7dG1wuF2vWrOHb3/422dnZOBwOPv3pT3P55Zfb86ykpqby6KOPcuedd7J27VpM0+SLX/wiOTk5dv8cy7L47ne/S1paGhs2bGDIkCH84he/oKioiC1btpCRkdF2UCsEVhDDCoZn8/V9QML7vyZYeZzhTZWY/prTbFLqYmXriPlj1KQkg9lHSgNr1qzhwgsvZNKkSVFBp7Kykpqamqgq2ri4OMaMGcPf//53LrvsMsrKyggGg+Tk5NhlRowYQVpamh10/v73vzNs2DA75ADk5ORgGAZvv/02//Iv/8Lf//53zj///KhvSZMnT2bNmjW8++67jBo1qsN5+/1+/P62vgSGYTB06FD7fm9q3Z/ma+hbep/7x0nfZ8PoleajgTZp0iR+85vfUFdXh9/vJzU1lc9//vNRn2fTp0/nd7/7HR9++CEOh4OkpCQmT55s12y/9tpr/Pa3v+Uvf/kzCfFxGFaQSfd+j1d3vsJzT6/ntm/8ezjcEAwHnQiOwIcMafg9FtBat2KvbO1qCzGhdusqnWpla/2X0ZE+N/rPx+G97nbQef311ykvL+f+++/vsK2mpgaApKSkqOeTkpLsbTU1NTidToYNG3bSMomJiVHbHQ4H8fHxUWXaVwW3Hre1THsbNmzgueeesx+PGjWKBx54IGpoaG87++yz+2zf0kbvc/84++yzaWpqwuU68+cxcTgcnV5HamoqEO6gvH//fv7rv/6rQ7mM9DQIBdj56g6OHTvGzM9eijNQja8h3ITl9r2PsznOLm8aFlawGSN0IvpghhleZsB0gns4ZuZs8KRiDEkFz3Bwp2CYqp3uK/rc6D8D+V5367+gY8eO8fjjj1NcXBzVIe9MMWvWLD7/+c/bj1sTZlVVFYHAqVeq7Q7DMDj77LN5//33NcV4H9L73D8i32efzxdVM3qmaGxspLy83H5cXl7OH/7wB1JSUhgxYgSbN28mNTWVEeeew18P/IXFS+4m/18/y7RPTiLYVIFhBXnq2Q2M/cT5pA1P5q0/lFK89IfcfONXGHNeKgQauTh3PMlJCcz/zt18e/5NeIbE8fNnfsnh946Q99nPEXSlAA4swxHuExMx1NrvdFLp/gRnZ7T8PjcFgKr+f6MGAX1u9J++eq+dTudpV1J0K+iUlZVRW1vLHXfcYT8XCoU4cOAAv/rVr3jooYcAqK2tJSUlxS5TW1tLZmYmAMnJyQQCARobG6NqdWpra+0amuTkZOrq6qKOHQwGaWhoiCrz9ttvR5Wpra21t3XG5XJ1+U20r37ZLcvSf0j9QO9z/ziT3+P9+/fz5S9/2X58993hfkVzrvkiK1fcw7EjB7nn7sVUHfuQjLPS+PKsmSy89euYgbbPooNl5dy3fBU1tXWcN+JcvvXNm/iPrxcRMp1YhoPkjFR+/sR6Hlj+A75UeCuBQICxY8eydu06Lpj0L6fsNtz6/ur3uX/ofe4/A/ledyvo5OTksGLFiqjnfvzjH3PuuefyxS9+kYyMDJKTkyktLbWDTVNTE2+//Taf+9znABg9ejQOh4PS0lI++clPAnDkyBGOHTvG2LFjARg7diyNjY2UlZXZs5L+6U9/wrIsxowZY5d54YUXqK2ttZus/vjHPzJ06FC8Xu9HfDtE5IxlWUB0B1/DCgIBDCvIp6eMouLg7s5fGzrB3KLZzC2a3fKEade6hFpuLcPBXcVLuLN4aUttTLhG2Gr5aZV74cX84hdP9d11iki3dCvoDB06lPPPPz/qOY/HQ0JCgv38lVdeyQsvvMA555xDeno6Tz/9NCkpKfYorLi4OC6//HLWr19PfHw8cXFxrF27lrFjx9pBx+v1MnnyZB599FHmzp1LIBBg7dq1XHrppQwfPhyA3NxcvF4vDz/8MNdffz01NTU8/fTT/Ou//mtM9B8QkXYiRykRGWYCEc+fzjdGww4x9i2tj50dmpRE5MzW673cvvjFL9Lc3Myjjz5KU1MT48eP56677orq01NUVIRhGPzgBz8gEAjYEwZGuu2223jssce455577AkDb7zxRnu7aZr813/9F2vWrKG4uBiPx8P06dMpKCjo7UsSkb5mWUBbLUxnNTLtRyl1qSXAtIWXyFsnYGriO5FBxLDUQElVVVWvd640DINzzjmHo0ePqg24D+l97h+R73NtbW2HUZEnZbU07liBdgGmNdAEMKwQp18b4+wYYLro4HsmqaurIykpSb/P/UCfG/2nr95rl8vVN52RRUQ66LJJKaJp6bRDTHi4dfvw0lYbY6g2RkS6RUFHRLrWsrK1I1hLqOpdhlaX0xA4F9Pnx4gINafFaOngSzjItO8nAw6FGBHpdQo6IoNYd1a2DgHxwAfxszFDnvZ7igguzphqUhKRM5uCjkisal3Z2h8dZOyVrf01mNbpr2ztiEvnRCiOoDMlvH5SVG2MOviKyMeTgo7ImcgKYQYb20JLJytbO4L1p7Ure2XrqDWUole2Nkwn55xzDvVHjxKqrcVyxvfxBYqI9A4FHZGPISPUjNlFTUxroPnIK1u7ole51srWIhLLFHRE+psVDDcptYYXf63dT6a1dsZsv/hjZ7uJWtk6KSq8hJzJBF1JWObJV7YWEYl1CjoivcmyMIKNbc1JnTQtmcF6jNMYbh0yh7SFFmdSu6alZELOxJb+MXI63njjDX784x9TWlpKRUUFjz32GPn5+fb2qqoqli1bxs6dO6mtreWTn/wkS5cutZehAXjnnXdYunQpb775Jj6fj8985jPce++9UfN5lJaWsmzZMvbv349pmlx11VV8//vfj1rbT0T6j4KOSDcYoeZwU1L7/jARTUvheWNOzsJB0BVZAxNdExNyJmOZ7Uc2SU80NTUxYcIErr322g4zsVuWxY033ojL5WLt2rXEx8fz05/+lGuvvZYdO3YQFxdHU1MT1113HRMmTODZZ58FYPny5Xzta19j8+bNmKbJ+++/z7XXXsu//du/ce+999LQ0MD3v/99FixYwOrVqwfiskUGPQUdkVZWEDNQHz0yqV3Tkhk6furdYBByxHdZExN0JmE5hsXMcGvLsgiexuitvuAwPBin2TR3+eWXc/nll3e6raysjN///ve8/PLLjBs3DoCSkhImT57ML3/5S6677jr27NnDu+++y69//WsSEhIAeOihh5gwYQKvvfYa06ZN47e//S1Op5P77rsP0zTt/Xz2s5+lvLycUaNG9cJVi0h3KOjI4GBZGKEme1h1dE1M5Jwxp9Ok5Inu3OtMblc7k9gyi+/gELSaef7A3AE59jUXrMZpDOnxfnw+HxBepLiVaZq43W7efPNNrrvuOpqbmzEMI2rdPo/Hg2ma7Nmzh2nTpuHz+XC5XHbIARgyJHx+b775poKOyAAYPJ/GEttCvqjmpOimpdYmpVOvZ2bhaAksSe3CS1utjOXo+R9W+XgZM2YMI0aM4P777+eBBx4gLi6O1atXc/ToUSorKwGYMmUKcXFxLFu2jDvvvBPLsrjvvvsIBoNUVFQAcNlll3H33Xfz4x//mK9//es0NTVx3333Adj7EZH+paAjH39WCDNYb9fEtHX0Dd8Gyus4K9BwWrsKRjYpRTUthUNNyBEfM01K/cVheLjmgoHpf+Iweqcfk8vlYs2aNXz7298mOzsbh8PBpz/9aS6//HJ7IcLU1FQeffRR7rzzTtauXYtpmnzxi18kJyfHrsEZN24cDz30EHfffTf3338/DoeDG2+8kbPOOiuqlkdE+o+Cjgwsy8IIHe960ju7SSl0yl2FDHdUaOlsxNJgalLqL4Zh9Erz0UCbNGkSv/nNb6irq8Pv95OamsrnP/95Jk2aZJeZPn06v/vd7/jwww9xOBwkJSUxefJkRo4caZeZNWsWs2bNoqqqiri4OAzD4Kc//Snnn3/+QFyWyKCnT33pWyF/hxqY9v1kTMt3yt1YmHaTUmR4CblSGH72J6ioCRAyPJozRnosMTERCHdQ3r9/P9/5znc6lBk+fDgAr732GseOHeOKK67oUKZ1yPnTTz+Nx+Nh2rRpfXjWItIVBR356FpWtu6qJsYRqMEMNp7WrkKOYdGdeyNm8Q03KSV02qRkGAZG/DlY9UfBOnVHYhm8GhsbKS8vtx8fPnyYP/3pT6SkpDBixAg2b95MamoqI0aM4K9//SuLFy8mPz+f6dOn26955plnGDNmDKmpqezdu5fFixczd+5cxowZY5dZt24dF198MXFxcbz66qssXbqUu+66i6SkpH69XhEJU9CRzlkWRuhEdGix549prZ2pPa0mJctwtYUXV8fOvUFnEpiufrgoGcz279/Pl7/8Zfvx3XffDcCXv/xlHnroISorK7n77rs5duwY6enpzJ49mwULFkTt4+DBg9x///3U1NTg9Xq57bbb+I//+I+oMn/4wx9YsWIFTU1NfOITn+CBBx5g9uzZfX59ItI5w7L0Nbiqqgq//9QjcrrDMAzOOeccjh49ysfyLbYCEU1I7ZuWajD9tae5srVJyJnYcTHIiBFLljm0z5qUPvbvc4yIfJ9ra2vt5h3pPXV1dSQlJen3uR/oc6P/9NV77XK5omYkPxnV6MSiqJWtI2tiIoZbB09vlFLIjOt0Bl97lJIzQcsQiIjIx5aCzhnICJ3A9Nd2MoNvZJPS6a5sndyhJia6SUkrW4uIyKkFg0Hq6uqorq6mpqaG6upqqqurmTJlStSacf1NQefjxgpErGwdXRNjj1I63ZWtnYkRNTCRi0GGO/laZpxGKYmIyGmzLIvjx49HBZnWYFNbW0so1LHfZqI7TkFn0LBXtm63BEFEPxkz2HCayxAM7TAyKbp2Ritbi4jIRxMIBKitrY0KM9XV1dRUV9Ps63pKEBMDjxGHw5WC0zUclyOR4Y1N/XjmHSno9AGHrwr3iXcINgdJqH43HGACtae/srXh7Ni5t12o0crWIiLSE5Zl0dDQQHXFUWref5/qY8eorq2lpuk49f7ASb9yeywHbjMe052G052Ky5GIy5mIwxxmL7RrhIIM8x0jLX5gWw4UdPqAu+kfJBzbjAW0ny82vLJ1QoeamMiOvuGVrdWkJCIiH40V8ENtDdRV4//wGNVVlVRX11DT0ED1iWZqAhY1hgO/2XXNv9OCoZYLlzMRw52B6UmzA40ZMcu8QYg4xwkShgRISGggIdVNQno88alJOJxp9qirgaKg0wcC7gya48YxJOlcGnxuAlG1M4NrZWsREekdlmVBY304wNR+iFVXbYeZUHU19fW1VDcdp8YfpMZwUOOJo8Y9jAZ3+6/cLmjJN4ZlMTQEQ8yhOF3DMYZkYLjOwuVIwmEOsWtnWsXFGyQmOUlIMklIdJCQ5GBYgonD8fH9cq6/uH3AH/cJ6oaNYdg559CkeRpEROQkLF8z1FZDXU04wLSEF2qrsepqoObD8La6GpotqPbEUeMZFg4ynjiqPcOodQ8l6EqDLibgdgPDnEPwuJJwuIeDmQZmCi5HAkYn/TnjhpnhMJPkaAk0JvEJDhzOj2+g6YqCjoiISC+zQkFoqGupfaluqX2ptgONZd+vhuPRnXWDGNS7h7YEmnCoqU4ZSU3GBRx3dd0/0zQMEobEMXRoEu4hKTjMFEKBBKxgAg6z84V3h8YZ4TATGWgSHTjPwEDTFQUdERkUVq1axdatW3n77bcZMmQIF198MXfddVfUOlWWZbFixQp+8YtfUFdXx8UXX8z9998fNTT2xIkT3HPPPWzcuBGfz8dnPvMZ7rvvvqhZWqurq/ne977Hb37zG0zT5Morr+See+5h2LBh/XrN0vusE8cjAkt1uPal9sO2+63NSfU10MlQa3s/wHGHKxxkUs6lZmgC1XGJ1HjiqDOdhOg6aMTFDSMhIYmhQ5JwOZIwrASC/niCvmEYrWsCBsM/JoAJQ4YaUWGm9b7TFTuBpisKOiIyKLzxxhsUFRUxefJkAoEAJSUlXHfddezYsYO4uDgA/ud//oe1a9fy0EMPcd5557F8+XKuv/56tm/fzpAh4W/ES5YsYdu2bTz66KMkJiby3e9+l5tuuomNGzfax5o/fz4VFRU89dRTBAIB/vM//5Pbb7+dRx55ZECuXU7OCgbDwaQlwFh2M1Lr/baaGJpPPY+ZzTAIJCRRm5xOTUIKNUOGUe3wUINBjS9Ic7DriV2dTidJScnExycxxJ2M05EIoQQCvnh8x1v+dJ+AyHG8hgGeIa2BxoyqqXG5+zDQWBZGMIjT58fh8+Hw+XD6fDhaHtN4HIYN7bvjn4LWumKQrnUVI/Q+949YXOvqgw8+YNKkSTz//PN88pOfxLIsLrroIm6++WZuueUWILz+1OTJk/nv//5vvvjFL1JXV8ekSZN4+OGH+fznPw/A22+/zfTp09m0aRNTpkzhH//4B5/5zGfYsmULubm5AGzfvp0bbriBt956i7PPPrvT89FaV73Lsiw43mh31rVa+7m0BBZPcxMnKt4Ph5iGOujO++0ZCknJkJhi3zYOS6TGPZQaw0l1IET1CR81DQ3U19ef9N8yISGB5ORkhsUl43En4TDCTU3Nx4dyvLHr17k94UCT2NLUFA41Jm63efrX0Q1GINghwETeN09Se8W553A04yytdSUiZyjLwggNzB9lyzQ+8lQMdXV1ACQnJwNw+PBhKisr+dSnPmWXSUxM5MILL2Tv3r188Ytf5I9//CN+v59Pf/rTdpkxY8YwYsQI9u7dy5QpU9i7dy9JSUl2yAH49Kc/jWma/OEPf2DmzJkf6XwlzPL7WzrmVrf1fampbtd01PIT6PoLbId6GdOExOSW8JKCkZgMSS33k1IgMQX/sHhqQgY1Tcc7zArsP1bV5bHcbjcpKSkkJyUTF5eM25WISQJBfwJNDQaNDSGaayB6GeXwf1Mut0FiRFNTfEug8Xh6N9AYwWBLbYy/JcC03G/2nTrItAg6nQTdbgJuF0G3m6DHTdDtJvX886CmplfPtzsUdESkR4yQxTmlfx6QYx/Nycb6CMNaQ6EQ3//+95k6dSrjx48HoLKyEqDDt8S0tDR7W1VVFW63m6Sk6KEtZ511FlVVVfZ+UlNTo7Y7nU6Sk5Pt/Ug0y7KgoT46vLT2famtaevIW1cTHl7dHUOHhWtdkobb4cVISiF55ChqLKMt3MQnYJgOQqEQ9fX1Ues11RyupLr6bzQ0dL0YsmEYJCYmhgNNcjJxQ5PD88+EEmg+7qGhPkRjXYia2qgrJzLQ2M1NEf1o3B6jwxDvjyIcZCKblqLvmydpRmsVDjKuljDj7nAfs2P4MgwDhg5V0BER6U933XUXf/vb39iwYcNAn0pMs4dN13ascbEiRiBRVwPBU88ab3M4I5qO2mpcSErGaHmOpBRITMZwdxylZBgGZnIylX/9Kx9++CHVh/9iB5uamhqCJ/mjP2TIEFJSUkhJSSEpKZm4IUk4HYlYwXga66G+NkhdRYjaqAqQtmtzurDnn4nsR+MZ0rNAY4RCdi1MVLNSS42M43SCjMMRrolpDTCetvtBlxvL0TfNYn1NQUdEesQyDY7mZA/Ysbvru9/9Lr/97W954YUXOPfcc+3n09PTgXCtTUZGhv38sWPHyM4OX99ZZ52Fz+ejtrY2qlanqqrKrglKT0/ngw8+iDpmIBCgpqbGPsaZrMOw6Xadda2WWpjOhk2fUnxCu6aj4XagscNMcgrExZ9WKAgGg9RFNC9FNjcdP368y9eZpklycrJdO5OSnMIQTyIOM4nmEy4aaoPU1wapfCcUMbAqupnM4ew80AwZ+hEDTSjUsVkp4r4jcOqgGHI42pqVWgJNIOK+5YjN9REVdESkZwzjIzUf9TfLsiguLuZXv/oV//u//8v5558ftf38888nPT2d1157jYkTJwJQX1/PH/7wB7761a8CMGnSJFwuF6+99hpXXXUVEO6M/M9//pMpU6YAMGXKFGpra/njH//IpEmTAHj99dcJhUJceOGF/XW53WadaLLDS7j2pfOaGOpqwTp1fw2byx1dw2Lfj66JITEZw+nq/nl/hNW0Ww0bNswONK2hZqgniVBwGI0NFvW1QeprQ5QfDRIKgj1mO4LDQUtn4MhmJwdD47oZaEIhHH5/uwDT0sTU7Du9IGOaHZqVgp62PjOxGmRORUFHRAaFu+66i1/+8pesXbuW+Ph4u79MQkICQ4cOxTAMbrrpJlauXMno0aPt4eUZGRn867/+KxDunHzttddy9913k5ycTEJCAsXFxUyZMsUOOllZWcyYMYPvfOc7lJSUEAgE+O53v8sXv/jFLkdc9RUrEID62ujmok5m3/0ow6aJT7RrXNqai5Jb+sK0jUZiaFyv9DHpcjXtmhqam5u7fF1r/6jIMJOSksJ53k/w3uEa6moD4UBTFeLowSDhFp6OtT2mA+IT2gcak7hh5uldn2WdvI+M33+SmXPCwkGmYx+Z1seWw9Q6iZ1Q0BGRQWH9+vUAzJ49O+r5H/7whxQUFAAwb948mpqauP3226mrq2Pq1Kn8/Oc/t+fQgfA8OqZp8h//8R80NzfbEwZGWrVqFcXFxRQUFNgTBi5durRXriNq2HTth1FzvkTNwFtX06Nh00ZELYzdD6a1BiYhCaMPagcsy6KxsTEqxLTeP91h2q2BpjXUOB3DaKgLUV8Xrp2pejdI2Z+DvBl4r9P9mCbEJ4TDTHxEs9OwYSbGyZpKLaulRiZ6tJIdbk4nyBhGxGil6GaloNtNyOFQkPkINI8OmkfnTKb3uX/E4jw6Hze1NTUkhfykuhwcO/iPjk1HtRHzwJxk2HQH7YdNR3bcte+3NCsN6Z9J3fx+f6f9Zmpqak76WWwP024XaJKSkggGHOGambpQS5NTkPq6YJdvlWnCsIS2hSnjWwNNvInZWaCxLEx/IKpZqX0/mVNFEMswOqmJabsfcsZekOmrz2jNozPAThwP0Vgfwu08wfHGEE53uB23N6pvReTMYVlWeBmAYACCwU5uI+4feY/gYys47QHoccM6nfOlrTYmuWXYdCJGJ8N++1qnw7Rbbk93mHb75qa4uDh8zW19Z+prg1QeDlJf14Tf1/kfUcOAYfGmPaFeeJI9J5/IGkFl5fttf3wtCzMQwHH8RFuzUnNEE5Pfj3GKP9SWYRB0tY5Y6hhmQk5nzAWZM4GCTh+oPOpn/57jQNt/zKYZnieh9cdt35pRj+37npbHrt6ZQ0FEeo8VCnUdWFpvQy33u/Mt1uHEMTyVYHxSW8ddu/ZluN2M1NWw6YHQ3Nzcab+Z7gzTjqyhSUpKwuFw0Nwcor42RENtkOqKIIf/HqS+rg5fcxfvpwHDhkUHmoREB8MSTBwOoy3I+Pw4/cdxvFNO4rEPooZinzLIQLvRStF9ZEKuMz/IWJaFRYiQFcSyguFbwrchK9DyXIgQAXt752VbyhPCGjoRg+EDdk0KOn3AdBjEJ5oEAwYnTgSxQuEvdc0nLJpPdL/qrmM4ah+YzKiA5G4JSOYZMBJG5OMiXPsShEAQQiepeQkGTrpYY6ccDjCd4VuHIzz2OOrWAYmpOH4SHvL+cWuKDQaD1NXV9XyYduTopqHhZjKfL2TXzhx5J8jfao9TXxc86Wdl3DCzQ6fg+AQTF6GIZiUfzgYfjg8ililo9562X2LVArtGJnK0kt1PxuXqEGRCVgjLChCyAoSCzfYf+sgQ0BYKQi23AUJEhAO7XOvjQESA6BgqIgOFZbULIYSiXt952dbnAm3HsAJ2ud52wvg3JiQX9Pp+T5eCTh845zxI95qcffbZvP9+BYEABHwGfl+4ad3vi/4J+C18PqvlcajlvmXPn+VvedzNGSlwOIkKQ50FpajtLbVIamaTWGFZVngodFe1Lu3v041wYZgdw0pnAcbhaFtR+mS7czYP6H93vTlMuzXMJCYmYrY0m/n9FnU1fupqffz9/RM01NXR0ODnxIkAGEEwQuFbgmAEsYYG8Qy1GDosxNB4GBJnMXRIkKFOP45QMwR8EPBh+Hw0HvXR+J6/5Q99+I99yL7X8tgIETRDhEwImBa4HPhCAYJGiKBpETKstldZQSxfkFBzdA1FVKhoqfXo1u/MGcw0HBg4MA0nhmFiGi33cbTcd2C03kY8ZxpOUodlDui5K+j0gfKa19h7dB389TQKu1p+4lqfMDAwwreWE4JxmMFhEIzDCA3DCMRhtD4OxrXcDoVgHASGQnAoBIdgYIY/vwMWJ5osoBvfQI0AOJsxHCcwnM3gDN8ajmYM5wlMZzM4fZjOZozWW5cPw+HDMMxwh7zWW1ofGxiEP/BaP/TbHhvR193JY/s+Rsu+WreZvN0QT2NDYyfbw8fofP+c3vl0uA7T3rPRxbl2ep698jjiHDFbvlhGnF93rqvduZ7Ov8HHiWVZXQSXTgJMd+Z8wYgKKB2DS0SAMU5zWPFHvsbQKb6Rt35rj/gGfxq1BIGgn6bjjTQ1NdB0vJHjJxo5caKJE77jBEMBDMMCw8IwLRhqYcRZJIywMB3gcjtxuhw4XQ4cTgOH08R0AFiErCDHCdAYCnLoeJBgY5DQe+Fzt1rnnzFaQoEbSGv5OYlmoC7yQdejyKE73ZBCp9hXD53qj7/REgDCz0eEhtZwQMTrop5ztpQ1w/dpd4yWcu2PEXl8e38nKd++rMFH/12P7Iw8UBR0+kRPEr6F1fJ6ywiCs5mQs7qbuzAgNAQjMCwiDEXejw5LUY8tJ1hO8Dux/MO6dSUWIXAcx3I0YTmawNHYxf16rNbHziYwm8DsQXXpsY/+UumGP4WDz4UptxBwnQ125IvU8cOw6w/ILl7Z+ksX0UkUrJZlgazwIqKRL7QiduAk4lOtdYMZ3mhE/IRPrPPHkS83AlhWgKi/ivaccZ3/12F1eD76cccmqfArPmg4yq/f+ynWny0sK9Tl/nuVu+UnEYacqmyLqCnzAkSubtDRafxtDH8FcmBaLbct/3Ng0vaMA9Nq2WI4MczwH33DdGE4XBimE8PhAocrOjDYf9CdmJgRzztJSR5OfV0j2EEj8o9/yx/8lpqLyFDReXhpPaaJgeNj98VgsFPQ6QOfSLmcTwy/nLPPzmhpaw+2fGSFWj67W8OM1fKhF+78BUQ8br+968fdKRs+euuxWm4JYFGDFaohGLQI+k38foOgzyTgNwj4HQR8JkG/ScDfeusg6HcQ9JsE/Q5CwXDqJzgsHJq6yXAEcDgDmK4AptOPw+XHdPoxW2+dfgyXH4fTh+HyYzqaMVx+DDPAsPg4GhsbsKxQ+H1uvW299sj3qFceh9/Dzt/nyPf11P+mH+Xf+/QeR/6+tb0fPWPZ/4tcjPBUe/3IXU2MdrcfeQf2mXDSs+1scz+2SlhYhKxurPcUIywsggQIGtB+GYUokf+crf9WISDAKWoiO3/s+NBBKGR1sR3otNYUWqMZnF6tade10adXixp5rJPV8EY/7qxG2KTt6rr3Xp1e7XPEexVxrqZhqDNyLDKM8C+Jw3TiMJ1YVuxPux0KWvhb+xo1W3Y/I58vZPcxCj+Ovm0dEmoFnQSCzm5XJ5smDBnixHSG+mQ0WzhInsbIgk6ej2w6iB6dcDrNDBG3BDp5rqvOhZ10ZiTU7hy72ZH2jBWdlNrXP0X/+xvt7nWxzWi/ravXd/7oZK/zOBIYkTCFoUOHcvz4CXurBQT8fpp9PgKBAD5/M36/H7/fRzAQiLhMK2q3hmHhcDhxuRw4nU6cTgeOlh/TCP+hbB+6Q8EQwZBFMBgiFLLsH7Ba9h/x0/LYNCwMw8I0QhiE70cF/6h43PL/RtRXEXqaKNuCfocNXev9PrfShSY+T3bKtQN2fAUd6RWmw8BtgssTIhQfMbKgi/4F4T/yAYLBIP5AIPzja7nvDz8OBIP4A0GCAT+BYJBAMEAwFCQYDBIMtbT7G0FOtHZebO3QGGjpo3E8iNXawbFdZ0fLCGKYQTBDGJHbjJY+BUaQcE3IYPk0NNr6AWCGmxEsAyNkYQQtzKCFIwT4A7iGgdNPyx8Rq+3vq9XyN7flcdTfXQswTDDN8LfgyPumCaYjPNeL6bC/nbZ9s474lh31jbuttxRGxP3We2dY80HA7SaLrxAKhHjnn+/w4YcfRgzTNgi3MXV0qmHa7YVCFo31IXtCvdYRT40NITsoOFp+WnkcIVI8foa7/KR4wj/D3X6GOkMdasJCptkyYsnd6XIFXa231P3a6Y/+GCDtrDSqqioJWaGWWluwaz/tWuHQSWrCT78W/rRrjLv92ojzINTusRV9HRHX9ZHet9O6jvbHanmv40d1+m/eXxR0BsjpzVVwslqCQERnxYjyXdQStH3Db+m42GnNQeRzgYjagK7Pqf1zvS6qz0Xv+sjfIS0DcGBYZviWzjsPOgwHpunEYTpwmE5MM6IT4Uk6HNp9Alr7CNj9BTr2OejQibDdfk3DgWEZGE3HMesbMBrqMerqMevqMOvqMGpqMGprMGprT3u16YavB0ho/bdu7aBrnmzUkbMl1JxZwaMvWJYVDurBIIFAIOq2oqKCl19+udPXdTVMOyUlJWp5ikihkNUSZILhJRBqg9TVBmmsbws07bnNECnuliDj8dv34xwhu/tS68KRQfcwGjpZBdtyfrQa7LZmHnrQXHn6x0qLPwd//ZCP1TD+WKTOyDHqcO0u/lS5AfMg+AO+iLkKokPCYPHRhyW2/8Pt7FDWYTpIiE+iqfGEHQrajuGIChWtr7UsEyvgIBg0CfkdBAMt/Y4CRls/JJ+J3+9omRbAJNBs4veZYDnsdvBTae202drroP2kkY7Omtk84WY1t+vkzWyWZUHz8Yj1jmraFmhsv95RT1abTkrBaJlpN+n8TGotE872QmraaQ+b/rhYtWoVW7du5e2332bIkCFcfPHF3HXXXYwZM8YuY1kWK1as4Be/+AV1dXVcfPHF3H///YwePdouc+LECe655x42btyIz+ez17pqnY7esix+9KMfsW3bNv7yl7/gcrl444037IBzsj+sw4YNIyMjg7i4uC6HabdnhSwaG9uWPWioDlBfF6ShwSJkdZ4YXJGBxr4NEOcMYjmMiLljhhFwu6mOmOHX0npL0sqygCBGyI9h+TFCPrBa77fcWgGs+BOcfnf33qeg0wf8wePU+z5aetWwxG6+X/241pUVCvdD8vvb90OKvA116IPk81k9nDTSwmX5cIVO4Ao04mqux3WiBndzHS5/Y/gn0GDfd0c8NlsDtWFAQlLbLLuJKZCcErGEQFuwYcjQDv9mhmEQf8451B89ilFbi+F09c6b2o/eeOMNioqKmDx5MoFAgJKSEq677jp27NhBXFx4fof/+Z//Ye3atTz00EP26uXXX38927dvt2tOlixZwrZt23j00UcZNmwY3/ve97jxxhv5+c9/boeZmpoaPvOZzzBhwgR++ctfRq2ubRgGDke434zD4bDvu91ubrrppi5/ny3LoqkhRMOHzTRWh1fcrmuwqG80CHYaaAycRogUT4AUd0QNzZAAnjgHIXuJgngCbhdNHjf1WjgyNlghsALtAkdrEAlgWL6IcBK+xfJhhFq2RT4f9Xq/vd1+/jTqxkPG5RD/uX648M4p6PSBcxMu5PIh53JWWgYfflB9ivCiYYlnCsM0cHsM3B4g/vRfFwqFCNY34v+wFn9NPc21jfgbTuBv8uM/EQyHogD4Q078hgefaxh+Vzx+5zCCziGAgd/w4Hd4wJEEHuA019R0mBZuN7g8DlyezieNdLfUIrncJm7LwBUEh8OKud/FJ598MurxQw89xKRJk/jjH//IJz/5SSzLYs2aNXzrW9/iX//1X+0yF154If/3f//HzJkzqamp4amnnqKkpISsrCwAiouLueaaa3jzzTeZNGkSAN/4xjdwOBxs3rwZwzBISEiwA43ZRTOew+EI93M4fhxfZQMNH/rCtTQNFrWNBrXHHQSiAo1BaxuPwwiR4g609J8JkDQsREKCydB4JyGPi4B7mN3EVO90Uh9j/7ZnDCsYHRLahZBw4Gj/fGvg8GG0Dyn2vlq2tezDGIDRexYGluHGMl1guLBMF5YR/hky5PQW3+wrCjp9YKgrhTj3cM5JPgfj+MdrKnfpPZbf39ZcZK8w3UnTUW01ZsCPh3BGOWVGal1tOmk4wcRUAknp+BPOwj8shcDQFHyeBPzOYfjNofhDjpOOZguGDI6fgOMnQnRn0siOa7OZJCX/k0DwBMPTg3jcoahpaAx/M9iP+/GPqNvzkY9XVxeeii45OZlQKER5eTmVlZVMmTKF2tpau+9MdnY2u3bt4lOf+hR79+4lEAgwdepUIHytY8eO5ZxzzuFvf/sbn/70p+1AYxgGcXFx9i2APQ9QqKUDbNAKz20Ysmj84AR/2X6CmuYP8Vstc/+0+4h2GBbJbj/JQ4IkDbNISDBISHIwJNGF5RlGwNO2cKSfkw7WllZRzS/hENGx+SWiNsQOE5HbfRDqGEI67GsABjdYOFpCR0QIaRdEWrdjOLFMd9T2cGhxYxlOex+W4QL79W4s0wl0XhNoGAZx55wD6qMj8vFghULQ2NASYDrp+xIRXmjqegXmTkWuNh2xWCOJKRgRzUgMS7BXm3bQ1VibU11HbzezBak4UgvAkLgQQ4dEhCbfCRK+d134uPTr1DOYDz8LntNr+4/sCOz3+/nud7/LhRdeSFpaGlVVVRw8eBCAoUOHcuLECft1qampfPDBB3g8Hurr63G5XJx//vk4HA67diYjI4Pa2lqGeMILbbYGGTMYDAeb480EW1eisMJNTYGQgRUxSqzJ76DqRLgjr2lYJHrCYSYxwSAh0UF8igtPsgs87g5/UPpwkt+B06H5pa3WomOtR3gbnQSN6OaXtu2Bd4KkBZrD++rX39qWy4sICVEBohshpGPg6Pg8Z1A/ur6ioCODgtXc3GVnXau27Xnqa1rWPDpNTmdbQElMDq8w3RpeWp4jObzqtOH6KJHlo/mozWyWFV5jrS38hNdeC/hhiCeeD47VMmTocVxug5ZRuVgD+DlqtdaQRPzhD4VCnY5qiuwIfN999/H3v/+dtWvX2itst+5jyJAhxMfH2zUzHk+41ig5OZmhQ4ZgGAYehxOsEEYgEF7xOhTCDAQwm07YISZgGZxoDlfpVx/veiSSwwz/DIuDf/kXD5njzqWpufrj202mpfnFrq2I6LfR1t/D364PSEsQ6aw2pLN+I/3U/NL+LbYw7SARHThOI4i0BpYOQaSlNqQ1pBguMM78Vc7PJAo6csayQkFoqMfXVEeo7B9YNeFamNYaF6uuGmrCzUqc6HqF5U7FJ0aEl4jalqQUjMTklvvDIW5YTPVlMQwDpwucLqNlaWeH/fw556Rx9Kif2lo/w+Lb/nBb1jB4+NnwlAktVTrhEBR+HGr3OPqnk0neuqPRCU2tCyta4dmxrchZq0OAgYUjPIerYVHywP289tpr/PznPyczM9MONGPHjgXCo6qGDRmKYYUwLItjlZVMnDAB5/ETnJ2UjM/no76yiviEJPwtgaay6gOGJWXwYXN0J+3IPjWmCQ6H0TIS37Dvt/7+BHCRlBRHStpQThyt6V6Tt2W1hIXowEAntSEdO5i29f3ovO9Ia/NLy34HYMLJcLNJdJNLVOCIaFrhNGpDMN2kpZ9L5Qc1WLTVqGDE/uSug5GCjnys2MOmayL6vbQ0I1Fb0+mw6YrT3Xknw6bbal/aamJITDojRxUNFMMwwDPkI0990iEAhdoehyJm6bWscLOahQEW7Ya2t05bb3b5RdmyLJbdt5iXX36Zx9c+wzkZo/CdANMAH0FSk87hrLSzeOW3rzD2/CwMoKGhnt/v28dX5lxPvc9k1NhcXE4XW3bu5nNXXAlAeflBjhz9J7m5FwGRgcbA4zEwDEhKNlqaR8JrWBm0XmBrY5+F6a9h6If7CDZ7GFZfHa796KzJpl1tif1cPze/WBjhABHZZNJprUcXtSER2zutDYkIKb3d/GIYBsawcwjVOdSHchBQ0JF+YQUCUF/bSWCpbms6au374utGjwPDwEwaTighsUN4ie4L0/mwafl4CIU6b2oKhbquPQgP03bidDgxHS0TMprhhSCN1tqi1uY1y+L7dxfz4paNPPyjNcQNG8axYxUYBiTGxzNkqAcDi6Ibinj40Yc5f6SX887z8t8rf0RGejqfu2IaDrOJ1BSTgtmzWLHiHs5Jc5IQP4zvLy1hyoWT+MwnR2IYxzCweO/IUWpraqk68leCwQB/2/cKAKNGehk2LK7T63H4q4hv2Ir1AXRe4vRYmJ10JP3otSGW4WxpfmnXJKPmFzlDGJbiLFVVVfj9vTc+IRCyCFpwdsbZHH3//YgpxDt/qyP/BaJKdPF8dBnrNMqcej9d/RpEl2m3zbLgeBNWfS3U17bc1kFD+DZUVwMNdVBfh9VFx10r4oMyak1qz5Bw81FCIgxLgsQkiE/Aik/CaHneik/CGBZP+tlnU1lZ1fb+dnW9H+EaT1Wm2/+mp7Of0/p376pMxO9DN6+l6zLhPjDDhw/ngw8/JN3RTEpyUucvPIlQKIQVDBIKBQm13LY+btWyBFPLrYVhhGcFDv8YbbdGuKbEsFrWV2qpFTGslvtG62+TZdd0nDPm4k7P60cPfI9rZ3/evtYHH/opTzz9S+rqGviXi3N54J7b+cSo8+3yJ5qbWXLfj9iw+Tc0+3zM+PQneeCe20k/K9Uuc9t37uGZF/6vw7Ge//lPuPSTF0eclYFlha+gpvoDnB/uBdcQmoMGAcNJ0HARMJ1t9y0XQZwEcBKg9b7LfhzASaiLtfV673ej8/10/7Ori332wX46u5b4hAQa6uvtbV3/d9rFuXWx/+7up9c+8/v637SL/USX6WQ/hsGnx53LxaldfwZ/FC6Xy56k81QUdOj9oPPrf9TwP2++32v7ExlIJiE8ZhCPGWSIGeCmyRmcnZaCaViYtCzoSDiYmIaFgxAmIUzC942WcmZEeGkLM9GhZiBYEWHDssI9UEIYhFrvt9yGt4WbyCLLE/Gc3YTWet8yWtZ3PnXTywfHavnLH7peB6oZi2ZC4R8r4j4WzVbbfV/E/WZCg2YJV/n4+lLuuXwtJ2nAgo6argahyLZ8A1pCePT3h+iFkDv/5Wx7vnXBxZb/s2+NttWe7ftRR253L7omvIvi7daVNjBNg5BldbH9dPbR+Qn0ZD9dXmtX19fFa9vNTxxVxkEIjxnAYwZwG+FbjxHAbQZwG0GGOMLPu40AQ8zw8+Htwahyka93tzzvadnmbikf6VD8bBKG9m0qsetoWgJFKOJ+9PPhMBEOFS1zz1jRwSMyyNASWoJGyy0QMsAyuqqba9X570XXvyTthSIGkmP/92Vg2Pf9DosaTwC3YWIELBwhA4cV/jEMgyEYDGkNTN14+4OGRci0CBoWQROCZstj0yLU8jjyfsiAoMOy39Zu/3fQzf/Ou9hlu/8OOn/BR/7vyTCIHzaMxsZGrPbH7eKEuv5vvYtz6+IcenMf7Z9v/9nY2YbuX2vP9mMYBlM+kc5AToKgGh36tunq/Yr3u6wvPN0/aJ2XCbN8zRj2sOmaqAnsovq99GTYdIeOu23P9/ew6fb6cwmI02KPfmk3v8dHmmSs45TsUcN2B+C7eiBk8G78bOKTM1oCBxG3hn1rGK0dg42WxUfNcHOTRUsQaf0Ajfrzb9+3WsKxZRpYRvgnGGqdfwZCLQvUh0Jd/5sbRuejnExzgKqOTqGuro6kpKQOv8/BYNs8R9FzIYU6PNfZpJEfVcdJI7tYm8196rXZPm4+dp8bMayv3mvV6Awwp2ngMgyGuh0McZrd/se1QsFwX5cuZtm1Wjr0UleN0dNh0xGddQ17zpeUmBs2jRU6vXVbTmd+jw4zp7YFEqxAlzVgfXZpGO0mDovsSBrxfLvtGG6COGg6EaChyUd94wlq649TXdfEhzUNNDT58AcN+yfQMuLp8sv/P3t/Hq9HXd/948/PbNe+nJPkZA9JiIgFQiCJcBe94dbe0hYrt5YKhapVg1r0LrU/pZpgWRQBQUBAKDeQVr6ICLJE7wpaUHo3WmuNKBSVGEnInnNOzjnXdZ1rm+3z+2Pmmms511lztiTzzGMe1yyfa2auyXVmXtd7NejSdFRFQVNVNEVFUwSaomAogrbOF0lL/EFdwNAgZqTwxIxTEzKWVznYdWTDjpppFTTB6ywVNONFVQVqTBCNje99QdHItsUip7I3G0PbjAyZV+qtRyL+Ot3LVAsJmWxCoTNNSCm9Wi5N7QL6fMvLQF3M5Po9kTOebtOG4dV08eu7iGEsMaSzCG0W/ZcHpdftoYJj1LRaT1Qo0sLpV0mX8sMULKvV/pjJ0uvjLzImlZbaH20zZvzaH8OUXg/OQ0rK5TIDAwP09/cH08DAAXK53AiZTRrJWIy52RTzEgkWpFJ0qhpaOkNHMjmK96S9kKFB0LiuZ63wrDONgmZ4C6iiCtQWQTPtbSeOEpqKRo6D5qKR7YVQ8zo3mHf8Gn8TtSipGoEQarYitfRmMwS6rgTzqhp+B0KGZxY99Y4d5Pb/wv3ZVnqrZexDB+tupHGmTZPKtBcsmU5EQwr1pKdNB+6Xds3jRrCGjNYlt11J9klwv8iC10NqzONrxccaK5W2s4aMpy9MoxDx03Wnu/iYbdvkcrkmMeMJmoGm7tmt6KpKZzLJvHiCedEYXZEI82Ix5sbiRNsI49dUBTGKkKk1wZJS1gWNXRM2Xm2c4WgUNI3CJhQ000Nz0cjx1a85UjebY0PZlpRL4/th0uhma7YetXezGRGFaocTuq2OE0KhMwXIA3uRP/wubZ1K0VjQ48grUtfR1hJDMo1QWx6UQ9wvJsLtR1QOtXenjFBkrG71aBc7Mv2tAOvul6HN49q7X/xGcopBOjuXgXzZL7M+igiZguJj04mUkmKx2CRiavOFQmHEG3cmFmNePO6JGV/IzIvFSUciKC0CQgKOoVM1DGzDwI0YpLq66C2VMC0TKxZtsiLVBI1ba1LpuIGlZjgCQaO0CBolFDRHK0eXm81r6hq62Y59QqEzBSjLl6D+2Z+QnN9FSbiIeBQRiyKiOkKlyf1SFxyHEHKvtz5vInItDepmgful2cLRLCJoYw0ZT4O60dwvwyGEILtwIVVxbAUVWpY1RMjUlkcKnI9oGvNiMebF4p6Qicf9+Rh6g3CWgKPrOIZBxdCxIwaOYeAY3jpH15sz0IQgtXAh5v792H0WliUbrDMjCxqoBQM3W2qUUNCE+IRutpCpJBQ6U0AsWyC18jXgtbpLpexPk0SrpWLYLrbDlmT3K6G2Kbler4Q6/e6X4wnXdSkUCk2CpvY6ODh8Z3QFQWcsytwmMeNZa5K6gRB+swFdxzZqYsZgsCZifEEzalxP0aWQdxjMORTyLv9eeo2+wxXeeJqDMozLsTnLKRQ0IVPLRN1sQgi65s1n9+4DmFV3VrvZjqZsttlKKHSmAFdJ4GgdqEYMyxHDWEO0YSwfdRHSPnbk6He/HG9Uq9WhcTP9/QzkckHn7HYkdD1wL82LxwJR0xmNoSkKjq7hGEYgZhzD4HDNKqPr3h11FKSUVMqSQt6hkHMYzLnBvD1c8+hhs5xCQRNy9KBqCtGYQiQ6vu9so5tteOvRDGezNa4L3Wyh0JkKqqnVmOnTWbhwIT1hnYbjAsdxyOfzdatMXx8DfX30DwxQqlSGfZ8qBHN8V9O8eLO7KRKL4Rg6ttHsVqqJmbEImRpSejdWT8S4FHK+sMm7WNbwWU6JlEIqo5LOqCw7YS62m8eRBVKZ0NIXcnzS6GZLjON9s8XNNqxQOobdbKHQCQkZI01p2of7yB3uZaC/n76BHAODhabqzK2kDSMQMYFlJpkknckgI5EhgiZnGMhxCJlGqhW3WdD488PdIIWAeNITNKm0QjqjksqoJJJK8EvQK/qV5sCBIrnc0Xnju+uuu3jmmWfYsWMH0WiUdevWsXHjRlatWhWMkVJy66238sgjj5DP51m3bh033ngjK1euDMZUKhWuv/56tmzZgmmanHfeeXzxi18Mipft2bOHO+64gx/96Ef09PQwf/583vOe9/DXf/3XGMbMFdcMmVkmK5utWRzNIjdbw/bZ5mYLhU5ISAu2bZPr7yfX00uu7zD9/f305XL0FQpURggE1hWlySIzJ5mkM50m25FFiyeaXEyOoWOrKn1HcJ5m1fXEjO9qKuQ9YWNWhxdciaRCMuOLmbQvaFIK6nFg2v7JT37CBz7wAdasWYNt29x0001ceumlvPDCC8TjXr/we+65h82bN3PHHXewdOlSbrnlFi677DJ++MMfEo1GAbj22mt5/vnnue+++0in02zatIkNGzawZcsWAHbs2IHrutx8880sX76cV199lU9/+tOUSiX+/u//fsY+f8jRy2Rks82km+2Nv6czf8m4dzFpjKsFxPe//32+//3v09PTA8CSJUu46KKLOOOMMwDv19Bjjz3G888/T7FY5OSTT2bDhg0sXLgw2Idpmjz00EP8+Mc/xrIsTj/9dDZs2EA2mw3GDA4OsnnzZrZt24YQgrPOOosPfvCDwY0GoLe3l/vvv59XXnmFaDTKueeey6WXXorampI9Bia7BQSEJcani4leZ+k4VHI5cj09DPR5cTN9eU/MDJTLw9Y2FkAmEvViZuIJ5qTTdGQyZDs6SGQyOL51xjEM5AS+i+2wzPaCZqSbTzyhkMrUrDQqqYxCMqWiahMTNI3XOZfLkU6nJ/pxZg2HDx9m9erVPPHEE5x99tlIKTnzzDP56Ec/ysc+9jHAa8uwZs0abr/9di688ELy+TyrV6/m7rvv5p3v9Dqe79ixg3PPPZdvf/vbrF27tu2x7r33Xh566CH+/d//fdjzGa4FRMjkE96fR2Yy3GyNnHJ6Bye+aea6l4/LotPZ2cmll17KwoULkVLyr//6r3zpS1/iS1/6EkuXLmXLli0888wzfPzjH6erq4tvfvOb3HDDDdx2222ByfZrX/saP//5z/nbv/1b4vE4Dz74IF/+8pf5/Oc/HxznzjvvpL+/n6uvvhrHcbjnnnu47777uPLKKwEvW+XGG28km83yhS98gf7+fu6++25UVeXSSy8dz0cKOVaREtW0cIpF8ocPB/Eyffk8hwcLXj2YEQKBI6rKvHgisMp0ZLJ0dHaQ7uxExOOekNGahUzpCE/ZsqSf4eQ0CZtKefibQywuPDHTKGjSKtoEBc1EkFJSHSW9fKqIqBM3kefzXh2V2o+s3bt3093dzVve8pZgTDqd5owzzmDbtm1ceOGFvPTSS1iWxVvf+tZgzKpVq1i8eDHbtm0bVujk8/mmH3MhIbOZSXOzVR3y5X4WL3GBmUugGZfQWbduXdPyn//5n/P973+f3/72tyxZsoTvfve7vOc972H9+vUAfOITn+Dyyy/nP//zPznnnHMolUr84Ac/4Morr+TUU08F4IorruCTn/wk27dv56STTmLv3r384he/4MYbb+TEE08E4EMf+hA33ngj73vf++js7OSXv/wle/fu5XOf+xzZbJbly5dz8cUX8/Wvf533vve9aMO0ObAsq8lyI4QgFosF85NJbX+zyU95TOELGc2ycHfvwd71Ov39ffTVxEyxSG+pRG6EatQKgo54jDnJFJ2ZNB3ZDjo6O0nPnUMkk/Eam7b8/zUmVU/0f9a2ZIN1xgkCg8ul4cVCNFYXNOkGYaPp0/P9Gun7XHUkF39z+7ScRyvfvPgkohMQda7rcs0117B+/XpOPvlkALq7uwGG/EqcO3dusK2npwfDMMhkMk1j5s2bF1i6W9m5cyf/+I//yOc+97lRzyu8b0wPx/t1lq4DluVV67cssEyw68vSMv31DduDqbZ96DqnWsCuFnDMIq5ZQTOrzHVcCmevIXrJ50c/sSliwjE6ruvy7//+71SrVU466SS6u7sZGBhg9erVwZh4PM6qVavYvn0755xzDq+99hqO43DaaacFYxYvXszcuXMDobN9+3YSiUQgcgBOO+00hBDs2LGDN7/5zWzfvp1ly5Y1/UJas2YNDzzwAHv27GHFihVtz/mpp57iW9/6VrC8YsUKbr755jGbvybCggULpmzfxzRSQqUC5QqUSlAuU87l6enupqevj958jp5SiZ5SicPlMvYIvcEShsHcTIZ5HZ3MmzePufO7mLdoMZ3zu9B0fco+gmW5DPRV6T9cpe+w99p/uEohP7ybNJ7Q6JgToWNOhE7/tWNOhEhkdmQ5LViwgFKphN5w3ZwZ6KJeQ9d0dH38vxQ//elPs337dr7zne8En6X2A0nX9abPpyheF3Zd1wPXuN7yvRFCoCjKkPUHDhzgL/7iL3jXu97FX/7lX454ToZhBPeL8L4xPczkdZaOgzSrSNNEWv6rWUWaVaitq1aRllkf52/31jUsN4xnuG2N+x/Bmn0kqP7Uil0ozui1HrfQ2b17N5s2bcKyLKLRKJ/61KdYsmQJr776KsCQXzqZTIaBgQEABgYG0DSNRCIx4phW/7+qqiSTyaYxrWbg2nFrY9rx7ne/O/CrQ13N9/T0YA9bNGRiCCFYsGABBw8eDH3A7ZASxbZRqyaqaaKZ3ivlKvlcjr58nt5SkZ6yJ2Z6yyUGR4ijUhWFjmSSzkyWbGcH2TlzyM6ZQ0dnZ1NsVw0H6OntnZSP4jiSwcAy47mc8jmH0uDwAsCIiLplJpgUDKP20JZABUmFviOJWJ4kGr/Ppmk2WUYVKfnmxSfNyHkp0sayxverfNOmTXz/+9/nySefZN68ecFn6ezsBGD//v3BPHiWnlNOOQXLsujs7MQ0TXp7e5vudd3d3cyZM6fpuhw8eJCLLrqItWvXctNNN40aB2iaJgcPHgzvG9NA7ft8YP++QBy0t1w0WjRaLRyWJxrshveZZrAsa8uWBXbD9tryFImNcaNqoOugR/xXw3s1IqDpoOs4qqSqmFRFhbIoUmYQS7VxVHA0geu/Sk0jGptHIr6QRGIRqeQyEolFvHHVGyf9O61p2tTE6AAsWrSIW265hVKpxE9+8hO++tWvct111437JGeC1l9qjUzVTUVKeXzesGpCxrQahIw3r1arVIolDpeKnlWmXKKnXKa3VOJwpTximnYyFqMjmyXb0UG2s5OOzk7e+MY3UqlUhjVDT9b1dx3JYKEhKNgXNcVBd7iG2+iGIN0QFJz0BU0k0t4KMdu/K+3OTwgxIffRdCOl5Oqrr+bZZ5/l8ccfZ9myZU3bly1bRldXF1u3bg1c64VCgRdffJH3v//9AKxevRpd19m6dSsXXHAB4AUj79u3ryk+58CBA/zZn/0Zq1ev5vbbb0cZY6mA2vWdrPuGlBKJZyAN1jUtD79dNnypveX6gKYe87VjUF8pg/007KP1OP4xpOOCbSItG2lZnlCoLduWJyhsC2nXt3vr/e21ZdtG2jbYFm5t3qmPk7bjLfvb/suxvbQi/F57ELiqpb+u5qCWorZMffww64L3IJCoXrsbYiBARgRE8BrfBpdLIBUVNBWp6qBpSFVHaponQlQNqWlIVUME8yqoOqgqUq0vS1X1XO6q17rHbyCHVP19q4q3P6X2PoXG2BlXSqrOIGXrMEWrj5LVR9E8jCut4Fxrn01BJap1ENM7iGmdxPQOImra+zy1D+eAzMObCxorY9aM3d/GLXQ0TQtMUCtXruR3v/sd3/3ud7nwwgsByOVydHR0BONzuRzLly8HvKA/27YpFotNVp1cLhdYaLLZbBAkWMNxHAYHB5vG7Nixo2lMLpcLts00JcuhUHVxYmW6C6Z/0/K2eX9AMrhZ1NY1LddudsFyw3uDeTlkG63bh9yQGo4/4ntbblCNN7naspQorouwLYRlo1gOim2h2DbCclBsG9t1GDRNctUqedMkXzXJWSYFs4rZ0r1aogMZ0DMoqkoiHiOWSJKIJ4gn4sTjCWLxGKrqfWUtoFtCtwUH9lTI5XLD3NTlCDf5+vbGm7QrJdWqpFpxg9TKatWl2pC23fp/pah+EbGoIGIo6BEwIgqKCt21sYPAIMi97ffR+LBo/m7Itv9HzctyxP9PWre3/W4MfxyASKSXcqXCH5+g0yXqVrKmW9cw97GhV254Gv8vRhwzlv00LNxy/ef4/ne/zc13/R9yjsGLv92LBJKpFBHf6venl/0ld3zlKyS7lrBoyVL+z123MWdeFyeffR67BiogDS5495/xuWuupaLFSSSS3P7F6zj19DPoXHkKO/sr9Bw6yP/+4KXMX7SYv/zrq3jxtf3B9Z4zd/hfoAcOF7nr2YMoYjuuK4f9P228lm3/NsdwbY4eas6QoVbZISiA4U/HA64/jSlhWALmOHYeAxb701ixgMNtt1SEwcrTMuPY1+RyxHV0XNfFsiy6urrIZrO8/PLLgbAplUrs2LGDd7zjHYAnjFRV5eWXX+bss88GPDNxb28vJ53kmb5POukkisUir732WlCk67/+67+QUgaFvU466SSefPJJcrlcYD5+6aWXiMViLFkyg8n6Pv9vV557f3oI+N1Mn8oMUrtBGUCyvlpj7N+6oj/1NC604+AEz3ESsf1puFM8JigAcM78DAlzlpjdx8GT3/w6AB//yz9vWv+Zz9/MH134pwBc/JeXUyyVuPnaTQwW8px2xjq+dM9mVM3A9jPLrvj0JhCCTX/zcSzTZP05b+WTm67Dcb3t//Hjrezd/Tp7d7/Ou99+TtOx/vWl5h9ojTgulK2Zi3eaLIQfLxf89veVl6jbL4J1dbtJXdEJUVsWwX5EbUNtuWZIESKwuwQWXSH8fQh/F15mngje6C1rmorrukhR314/Ag3LNC2L2lrRci5N5zb0XBtOuXEvwx6n9tmC9zaMa7d92OMI77o60sR2S5hOEcstYTlFXGn5Y+vyWCAw1BiGmsRQE0S0JIYaQ6A0nUf9c4sRroO3dc2SLDMpwcdVR+eRRx5hzZo1zJ07l0qlwtatW9myZQubNm1i9erVPP3002zZsiVIL3/00UfZvXt3U3r5/fffz4svvsgVV1xBPB5n8+bNAHzhC18IjvPFL36RXC7H5Zdfjm3b3HvvvaxcubIpvfzTn/40nZ2dXHbZZQwMDHD33Xfztre9bULp5ZNdR+e53w1w/88OoQgl+LXe9GUO/mBo/uOtzTDy9qFf7pH/MIMvOzT/EUgvvsK78dQnpTbfcByJxHFd7GBysF0Xy3WRwdjmr1Lt5qNrGpqmoeu1Vx1d0xBK/SZVO6nG5cZzbvqjEs1jotEY1UrFP74Yeh3w4mg8K7jE8Qto2Xb9569oGu1XBNXrvWK8CqCKl4g12vUe9f+s+WhDbxCNt9uG69D03ajvtP1xRJsbY/M+Gz9H2+O1fM5MJkM+n+ekpMOcjuZfZ4JhECONGcua4TcMO3akMaPtJzB3+VMwD6JpuXX70PVimPWt62rjBg4dwPz//o9/nNq5NThHZIsogIa/01YRUVsefl39u9+yD1VF6AaoGsLw4jWE6sVqBMuagdBr2w2EaiAMHfxltFqch+Htq2G5dXt9WUMo0xN0fyzX0ZFSUrb76S/vpK+8k77KTvrLO6k6hSFjFaGSiSylI7aCzuhyOmIryUSWoCqTV0t4qq71eOrojEvo3HvvvfzXf/0X/f39xONxTjjhBC688MIg06pWMPC5556jVCpx8skn8+EPf5hFixYF+6gVDPzRj36EbdvDFgx88MEHmwoGfuhDH2oKKu3p6eGBBx7glVdeIRKJcO6553LZZZeFBQMbz8FxvJgY0wqCfYP5qoniNv96dKVkoFLxY2ZK9JZK9PjL+RH6NQkhyGQyZLNZOjo6mqZYLDalKZyN19l1XcpFl3yuOY5msODgDmOAUFVI+vVnUg3VgmPx2VXCfKaZzoKBUkqQbnsBEUztto/0noZtbqvgaHnfDJE/eIDkg7d6C5rmB4Ua9eBQzagLA3+5JjwCsTDMshhle6P4EBNsPXI0MRvuz5NF2eqnr7yT/spO/3UXFTs3ZJxAJRNdQmd0hSdsYsvJRJaiKlOXeQpHodA5VjlahY5wXF+81DKXrKZ5ZZio/rJt+UHAZborFXoqfiBwcRDHHd50Ho1Gm0RMTdhkMpkJCcyJIqWkXJJBU0rbMug+OEgh5wybyKCokEy1ChqFeEIJBU0bvKDOeiaKsCzmZjP07t9PLpognc2MLChaxYTrtizPTrHRhBD+pDTMj7RutG0jr8/n82RSSRYuXcbBQ4eO+gfwbOZoFTpla6AuaHxrTXtRo5CJLqEjupzO2Ao6YivJRpagKtMfwDQbhE7Y62o247rN2UotYka1h4+TcFyXnkqFQ9UK3dWqL2aKHC4MUqoOb51RFCUQMK0WmnZp2lOJlJJKWTY1pvTEjUNzNYByw/lD0u+4nfSbVKYyKomEglCOHkEjpfTST2tiw/TTU9ssyxG3Ww1praafrWK2394gbGgjeA/VZj78KaguHLJ9SpiQcJi42GicplsAC01DGJHjwqISMjoVO9cgaHbRX95J2e4fMk4gSEeWeILGFzaZ6DK0GRA1s5VQ6EwBqmmiV6qg6RiFQS+7RQgvpbDxVUpU20a1LBTLQrVsT8xYliduRqntI6Wk4Docsky6q1V6yyV6i0UOFwoMFEbupp1IJIZYZrLZLOl0esypsJOFlF5mU2Mfp5q4sYcxtAlRFzSLlmRBKZFMK17H7UkSNE1iw2wQAW2W5YjbmyuQypblpu2NQmWEIojTiua5NpRIFFdVIRLxamwcg2IjJGQmqNg5+su7gniavvIuyvbQAloCQSqyyBc1K+iMrSAbXYamRGbgrI8eQqEzBUTyg2T37oPXdjHnCPdVkyoOEkdKbFdiuQ6W42DaDrbjoEqXLimZY0R5g2bgpLM4fkqyputohoEeiWBEI0SiUSLRGIqmNosuIcBxkQO5+jIMFWftBJtg6L5qUwvVittG0HhN49ohkCQTknTSJR23ScYckjGHmOGg2CbCtujQU/QdOoDc74kEt6XoV2sRsKCY13DbG4XKLBMb9YJerfMNsRgjbK/Ni1G21+NAvJiNRvOzyOUQx0BTz5CQmaBqF3xBsyuIrSlZ7dKyBenIogb30wo6osvQlOm1rB8LhEJnCtDKZS9lcRL8kTWpoCHQhCCi4kXQTiR+zHZhsORN40D6MRbS8Yt2Of5kD13Gsf11ftEux8a1a5OFZjvEHIt5tt3wXu99BK/eeq+4lzVszEZNgkxOfeMxMGGx0VJxdIJiIyQk5Oiiag82BQn3lXdSstrdsQQpYyGdseV+BtQKstET0NWZEzW1opWu6+I4TjA/0Wm6PQWNhEJnCrASccRhz+wohcA2DBxDx4kYlBSF3rIfLzM4SF8ux0B/P4V8Hte2UYVAEQqqIlCFQBUKihBEdZ2OdIpsIkYmFidp6CQNg7iuojgOwu9v0hRn4U9BtdFa2fJaNdGa28S2miuO2jURY9Wri86WgD2/OqjQNISq+68aQtO91zbbUDUUzas4Wh+jN4xtXW7Yr1+pFE3zAoAaLVcMtXYhQArFf223vdEqNswYKZCWBMdEVqxR98Uo+wssbNDWyhYSEjIxpJSBCKhYBfqKr9Nffp3+8m4Gynspmf3e37MEpOK9ukli6hySxgIS2nwS2nzi2hyEq+EWXOycy0HXZL/76hEJi/FOtc9Sm3dHSEwZL2eddVZQO28mCIXOFFAq5env2YXruvT2HKJSKFAdHMQqDuJUK2jSRXVdktIh47qo0kWTLprrYigCQ4CGRHNdVNdBODZiGDfKeGpdThauquOqOo7wJlfRcVQDV/Hmpeq9CsNAi+poUQMtFsGI6+hxA6Vm9fAFBpovNGr1OrT2wgXFE31BTRO/BkkiFqNULDbVJPFqAjFkXWN9E9FYH6Vx3LAf3M8UOoqRNVcj4xdN7D9ItlqlGIuiRqKBubGx6stwhWtku/Vt1g0dR3txFgq2o4LJtgpM5kNd13XK5XLwcB+PCPA+izvK778l/jQUL6TYBQ7409GDoihjnoQQqKrKnDlHGsRxZIRCZwrY9fx3Wbb1GWB8BbTHRaObQ/MbsLVZFqMW6vLeUyv85QidkqlSrGgMVlQKZZVCUaVkar6Q0byg0QaicRHUn6llOaXSKpre/mE0mXJBCEFi4UJyk5m62Cp8hhNETeLIf/Wr47YbO9Z9DivYGOe+2l2v2v4m8j+QLxADFF1FcSa3Ce5EaPqEYshMixATQza3rpPDrG9cmArBVvveuq5LtVqlWCxSLBbH9ACeDVN7EVAXNMcjiuJ1s1dUFVVRRxQBw80Pt05RlGHnp3oSE0gQaIzvmylCoTMFGAsWsy/ZiRKJIowIWiyGFo9jxJNEkkm0aLylUJcnTMQYhAs1S8gR+jttu9Zxu7G4nkO5NMwDUIdoTPjF9TxBk/ZTuPVhBM1RS+Ca8jhqbThHItjaWMOy6TS5gRw2EqfWHDe4OA1yomUdLYuNlX3bjg3G1Rdq2qy2JP3P17zcsp26x3Vof7fW/Y50nFHGQyBWmo/bcB5N5zt0Gbx6Xj/4wQ84HhirJWAkEdD4kB+vCFBVlY6ODgqFQtN2iU3R7qFoHWLQPkjB3E/Z6QUhEUKCIj1tKyRxo5OO2FI6YsvpjJ9AZ3w5MSMdZgrOQkKhMwUs+IMLUN7xJyxevHjGC1I5TjtB41IqDv9LKxIVQUG9etdtBcMIA2JnK9PhEkgmkwwMDHhNfavR4LiNr2NdN9bxrfNHwubNm/nBD37Arl27iEQinH766fz1X/910Juvdqx/+Id/4KmnnqJQKHD66aezcePGpk7n1WqV2267je9///uYpsl/+2//jc9+9rNN5vm/+Zu/Yfv27fT19ZFOp3nzm9/MlVdeOfZKrhDE5yl+vJ4iGl/9bUIE8XxD1reua9lP49jm/SjBuKb9+K7j1m2qUIa3KKgKQlFQFe9V8V+ForTP3kQglZFi4Pyu4WOKWRsmIxQBikLH3Azb9/6cw5Vd9FV2cbj8GgXrAN6vAbyED917ietzm+rUdESXE9FSR/qVDJkmwsrIHL2VkRtxHEmx4A4prlcsusOaJIyIGOJuSmUUjMjRI2jGep0bYwWOhqmdO6Cda6A2P51/xm9729vG/MCeSpp6hvnzI6372Mc+xh/90R9x6qmn4jgOX/nKV/jtb3/Ld77zHeLxOEII7r//fu6//35uuukmlixZwle+8hW2b9/Od7/73aBg5jXXXMMLL7zAzTffTDqd5tprr0VRFB5//PHgeA8++CBr165l3rx5HDp0iC9+8YsAPPXkk159oNo5NpxrPpejw4jQNW8efb29XkzeaBa44JXRXaWMcVzLMcccw3YM4Pr/JJ6Y8kSZBkIdOYGgnWCbgAgbNV4OAUpdsA03bjbFsM2Gysih0OHoEjquO4ygGRw+ME43hGedaYmjiUSbBc3RIgRaH/yKolCtVocVAcdrrMBIfvvx+v9VVSWRSFCtVjnxxBOHBBe6ztiFB2McN5Z1qtq8bjwcPnyY1atX88QTT3D22WcjpeTMM8/kox/9KB/72McAyOfzrFmzhttvv50LL7yQfD7P6tWrufvuu3nnO98JwI4dOzj33HP59re/zdq1a9se6/vf/z4f+tCH2LlzJ3rN7ddCPp8nk8nM/tYEreIKCe5wwf5jFVfeuGG3NblU3VH26eI4JtK1cF0bpOM1K0ZBQfVfFVSmr23NdCNh4uKKcQi6MVjVEILOxYs5MFiYMaETuq6mgHK5zODgIJZl0dPTM6GgQsdxqVYcKmWbasWhWnWoVhxM00VKF3C9CADpIvE7iAsXVZMoikRRJULx1knLpWdQ4uweXgTURMPxxmSIgFbf/3QFBo4lzmGyaG3qmUrVzfa2LXnmidykHWs8/NGfZtAmeBfL5/MAQUPh3bt3093dzVve8pZgTDqd5owzzmDbtm1ceOGFvPTSS1iWxVvf+tZgzKpVq1i8eDHbtm1rK3T6+/t58sknWbdu3bAi56gieCB6zORdw3ZNcpXdTXVq8tV9SFzf/1cfG9M6vHia6Ao64ys4edk68ocrXjZlk5gaXohNSLDhJykMEWxj29eoyQptrosXK1c7/izAsmFu54wdPhQ6U8Bvf/tbXnjhhZk5eHVyd9f60J6NImDevHkMDAyMe98TySAIOTZwXZdrrrmG9evXc/LJJwPQ3d0NMORX4ty5c4NtPT09GIZBJpNpGjNv3jx6enqa1t1www384z/+I+VymTPPPJOvfe1rU/Vxjgsc12Sgupf+8mv0lXfRX9lJrrLXEzUtRLVMQ5dur11CTM8G24UQJKJzyCsHkKI5kPyowxczkyGaWseNuzRH21cwYrGZvEKh0JkKIpEIyWQSXddxXTd4sIKClALpKriOwHEEruMHx6EgUEAoCARCKChCQY8oRCIq0ZhGJKoSjakYhhKIjaPJKjAVHK1diI8lVNWzrMzUsSfCxo0befXVV3nqqacm94Qa+Ku/+isuueQS9u3bx2233caVV17JQw89NOv/pmYDjmuRq+7xBI3fpdsTNUMbGUfUdNAiwRM3y4lpHcfPdRa10geNeYqzh9o9mjC9/Nhi+QlvoCN9Irhx9u0dCDpuO8M0G1dUSKZU0rUsJz+OJpaY/UIjJEQIMWH30UywadMmnnvuOZ588kkWLVoUrO/q6gI8q838+fOD9b29vZxyyimAZ7kxTZNcLtdk1enp6RliCers7KSzs5MTTzyRVatWsX79erZt28a6deum8uMddTiuTa66NxA0/eWd5Kp7cGU7UZNqEjSdsZXHl6gJmRBH0e3p6GHPLpNf/7ICDDatVxRIphuCgn1BE08oiEnquB0SEtIeKSVXX301zz77LI8//nhTyjjAsmXL6OrqYuvWrZx66qkAFAoFXnzxRd7//vcDsHr1anRdZ+vWrVxwwQWAF4y8b9++YQORa8cGMM2ZqGU+e3ClTa6yr6FLd03UDC1AaajJBvfTcjqiK4jrc0JREzJuQqEzBaSzXpp21/wkmmEGGU/xpIISCpqQkBlh48aNPP3002zevJlkMhnE3aRSKWKxGEIINmzYwJ133snKlStZunQpt9xyC/Pnz+f8888HvODkSy65hOuuu45sNksqleLqq69m7dq1gdD5+c9/zi9/+UvWr19PNptl165d3HLLLSxfvnxEMXSs4UqHfHWfFyjsW2sGKntw5dAMV0NN0BH1BU1sJZ3R5cT1uaGoCZkUQqEzBXQt0Jn/R0YYOxISMot46KGHALjoooua1t92221cfPHFAFxxxRWUSiWuuuoq8vk869ev5+GHHw5q6ABB3ZyPfOQjVKtVzjvvvKBODkAsFuO73/0ut956K+Vyma6uLs477zyuvPJKIpHINHzS6ccTNfsb3E+7GKi8jtNG1OhKvLn4XmwFCX1eKGpCpoywjg5HVx2dkGbC6zw9tKaXp9PpmT6lY46jpY6OK10K1f30VXb5GVA7GajsxpFD3XK6EgtSumsZUAm9a8ZFTXjfmD5mQ8HA0KITEhISEtIWV7oMmgfoK+8MUrr7y7vaihpNiTZZaTqjK0gaXQhx9FRaDzk2CYVOSEhISAhSuhTMQ/SVX6PfL8DXX9mF7Q4tzuWJmhMaMqBWkDLmh6ImZFYSCp2QkJCQ4wwpXQbNQ/T51YRrwsZ2K0PGqsKgw8966vQzoJLGQpRQ1IQcJYRCJyQkJOQYRkrJoNlNf2Vnk6ix3PKQsaowyEZPaMh+WkEqEoqakKObUOiEhISEHCNIKSlaPU0p3f3lXVhuachYVehkA/fTcjpiK0hHFqGIY7fZZcjxSSh0QkJCQo5CaqKmv7wrKMDXX9mF6RSHjFWETja61I+nWUlnbDnpyOJQ1IQcF4RCJyQkJGSWI6WkZB1ucD954sZ0BoeMVYRGNrqsngEVXUEmuhhFhLf7kOOT8JsfEhISMouQUlI0DwfZTzX3U9UpDBmrCJVMZFlTAb50ZAmqEt7aQ0JqhH8NISEhITOElBJXOjiySqF6iF/s+j/ktr9OyRwYMlagkokuCaw0nbEVZCJLUBV9+k88JOQoIhQ6ISEhIdOE69rYsorjmthuFUeaQUPLotXDgcFfAiBQyESXBIKmI7aCbGQJqmLM5OmHhByVhEInJCTkuOCuu+7imWeeYceOHUSjUdatW8fGjRtZtWpVMEZKya233sojjzxCPp9n3bp13HjjjaxcuTIYU6lUuP7669myZQumaQa9rlrL0buuTbFS4N3v+lN+/etX+dZ3vsYb37SSoQhUoRPTOli78AO8Yel67ME4qggtNSEhk0FYHCEkJOS44Cc/+Qkf+MAH+M53vsM3vvENLMvi0ksvpVSqp17fc889bN68mZtuuonvfOc7xONxLrvsMiqVeiG9a6+9ln/5l3/hvvvu44knnuDgwYNs2PBhTKdE2eqnUD3EQGU3A9XdfP4L1zKnqwMAKR08UWNgqCni+lxSxiI6oieQiS4hE13MG+b8TxZm3oQWWm5CQiaN0KITEhJyREgpsW17Ro6tadqYG0R+/etfb1q+4447WL16NS+99BJnn302UkoeeOABrrzySs4//3wAvvKVr7BmzRq+973vceGFF5LP53n00Ue5484vs/asU7HdKtfd9Gn+5B1/zo/+43lOP+OUYP//9q8/4d+3/oy77v0yF/zrnxLX59ERPSFskxASMs2EQickJOSIsG2be++9d0aO/Vd/9Vfo+sRcPPl8HoBsNgvA7t276e7u5i1veUswJplKcPqa1fzHT3/M2//wbH78sx9hWRZrzlpF2e4DYPnKJSxcNJ+Xf/Eb1q87C02J0Ndb4PpNX2bz5s10pjsB0BQjFDkhITNAKHRCQkKOO1zX5ZprrmH9+vWcfPLJABzqPghAqiPCoHkI2zVxpUW2M8nB7v2YbpGenh50XacjMwdVMdBEBFUx6Jq3gEK/SdLoQkrJVZ/6OO973/s4/fTT2bNnz0x+1JCQ455Q6ISEhBwRmqbxV3/1VzN27Inw2Y2f5Tev/oZHH38oEDWDpid0KnY/plOvGCyEgiJUYtocYloWIQSZ6JKm/TW6zzZv3szg4CD/+3//7wmdW0hIyOQSCp2QkJAjQggxYffRdOBKpymd+9q/v4Ef/Mv/45++cRfZeUbQMmHO3DkA5PurLFvUiaZ41ppcX5FTTjmFmJ5hwfzFmKZJLpcjk8kEx+jp6Qmyrn70ox+xbds2VqxY0XQef/zHf8y73/1uvvKVr0zTJw8JCYFQ6ISEhBxDuNLFcavYrokjq9huFVdagBc0/cXr7uAH//L/2Pz1O1m27AQ0xUAVETQlwqlvWEZXVxfbfvIK69e8FYBCocCLL77I+9//fgBWr16Nruts3bqVCy64AIAdO3awb98+1q5dC8DnP/95rrrqquCcDh06xKWXXsq9997LGWecMZ2XIyQkhFDohISEHKV4osb0hI1fhM+RFiCHjFWEzheu/TL/vOVfuP/Be1nYeRJmXsUEUqkIeiwGwIYNG7jzzjtZuXIlS5cu5ZZbbmH+/PlBFlY6neaSSy7huuuuI5vNkkqluPrqq1m7dm0gdBYvXtx07EQiAcAJJ5zAokWLpu6ChISEtCUUOiEhIbMeKd0mK83IokYLrDSaYqAqERSh8o2HHwfgkvf+RdP42267jYsvvhiAK664glKpxFVXXUU+n2f9+vU8/PDDRKPRYPy1116Loih85CMfoVqtBgUDQ0JCZidCSjn0TnGc0dPTg2VZk7pPIQQLFy7kwIEDhJd46giv8/TQeJ1zuRzpdHrKjtUsajyLzeiixhM0mi9qjkby+TyZTCb8Pk8D4X1j+piqa63r+pBq5MMRWnRCQkJmjLqo8YOFxyVqDBQR3sJCQkJGJrxLTAHFQYdcv4trFSkUbQxDYEQEijK2Cq4hIcci0o+psaUfVzOqqDH8zKcImjBQlPB2FRISMn7CO8cU0HvI5qWflYFi03pNh0hEwYgIf/LmIw3zjcuqxpjL24eEzCakdHGkFVhpalabdqJGoPrxNJGgCF8oakJCQiaL8G4yBRgRwZx5Gq6jUiyamKYECbYFtuVSHBzbfhSFNoKoVRQ1CCdDIEKrUcg0I6Vscj2NRdSoim+tEV5MTSjoQ0JCpopQ6EwBC5cYLFoaCQKwXNfFMiVmVVKtSsyqi1ltv2xWXapVieuA60KlLKmUnTEfWzd8QRQVGIYvkKLCd58p/npBJOptU9XwARMydhzXxnLKVGyaivCNTdR4MTWhqAkJCZlOQqEzDQhRs8RAcozvse268GkvkBrFkcQyvQeNZfrzBYDRBZKq4QkgwxdEEV8gRYe61IyIQNdF+KA6TnClTa66j/7yTvoruyjs3ktP4Xecnr0cEV3QNNYTNfXMp1DUhISEzBZCoTNL0TSBpgniibF1O3ZdT+BUKxLT9EVQRWKakmrFXzYlZsX11lUl0gXHhrLtUi6OfgwAIWiKMYo0iKDhYo7CIOzZjysd8tV99JV30V9+jb7KLgYqu4Oqwo0IoaIrsSBI2KtTE4qakJCQ2UkodI4RFMWzyESiAKPXEZFSYlt4oqjSzoXWajVysW2QEqoVT1CBO6ZzaxeE3T7eKAzCng48UbOf/vJO+iq76C/vZKCy23dBNaMrcTpiy+mMrWDlwjMRlSxOOUIqkmmz55CQkJDZRyh0jlOEEOgG6IZKYoz+NMdpdqeNFGNUsyBNaRB21Ht13bDg13C40qVQPUBfZacnbMo7Gai83lbUaEqUzuiKQNh0RFeSNOYhhOIV/VrgFwys5Gbgk4SEhIRMjFDohIwZVRXE4oJYfGzuNCl9d1p17AJpYkHYuSDr7HgOwpbSpWAepM8XNP0VT9TYbnXIWE2J0hE9gY7YSjqjnrBJGvMRYmz/tyEhISFHC6HQCZkyGoOwx0pjEHZ7geQGsUeNQdi17eMNwh5LjNFsDML2RM0h3/200w8Yfh3brQwZqykRstETfGvNCjpjK0gZC447UXPXXXfxzDPPsGPHDqLRKOvWrWPjxo2sWrUqGCOl5NZbb+WRRx4hn8+zbt06brzxRlauXBmMqVQqXH/99WzZsgXTNINeV43l6M866yz27t3bdPzPfvazfOITn5j6DxoSEtJEKHRCZhXjDcKWEjqyXezZfZBqxfEDrtsHYVf9+aMtCFtKl0Gzu8n91F/Z1VbUqMKgI7acjmjN/bSCVGQhynEmatrxk5/8hA984AOsWbMG27a56aabuPTSS3nhhReIx+MA3HPPPWzevJk77rgj6F5+2WWX8cMf/jBo7Hnttdfy/PPPc99995FOp9m0aRMbNmxgy5YtTcf71Kc+xWWXXRYsJ5NjzbkMCQmZTEKhE3JUoyiCeEIjnVWRcvSHeRCEPca0/SMJwtZ10WQVGksQNkDR6vbETGCteR3LLQ3Zvyp0stETAitNZ3QFqcii6Rc1UkKb7KxpQeieCh0DX//615uW77jjDlavXs1LL73E2WefjZSSBx54gCuvvJLzzz8fgK985SusWbOG733ve1x44YXk83keffRR7r77bt7ylrcAcPvtt3Puueeybds21q5dG+w/mUzS1dU1SR80JCRkooRCJ+S4oikIOzW294wlCLtJIPlB2JYlsSw5bBC2RCL1w7ixPbix3cjYbtzYHqRaHnre6CTVpWT05XTEVjA3sYI5ycWo6iz4E5YWXa9dMyOH7l55HQhjQu/N5/MAZLNZAHbv3k13d3cgYADS6TRnnHEG27Zt48ILL+Sll17Csize+ta3BmNWrVrF4sWLhwidr371q9xxxx0sXryYd7/73Vx++eVo2iz4/woJOc4I/+qmgHw+T29vL4VCgVwuh6qqaJo25FXTNBQldCnMdiYjCLtacSlUeylYuxh0d1EWu6lqryOVoZYaXA2lsgilvAylsgylvBRRWYiDSh/QB/wOgMHmIOymwo9KsL7RpXYsBmFPBNd1ueaaa1i/fj0nn3wyAN3d3QBNsTYAc+fODbb19PRgGAaZTHN6/bx58+jp6QmWP/ShD3HaaaeRzWb52c9+xk033cShQ4e49tprp/BThYSEtCMUOlPA7t27+cEPfjCmsUKIYUXQWNa1bms3drj3hSJr6rBEHzl20efupN/eSZ+9E5NB0JvHKUIlE1lGNrKclLachDgB3VmEXVWbCj9W/XpHNYvRdARhRyICbSxB2EL3LCszgdBHH9OGjRs38uqrr/LUU09N8gl5fPSjHw3mf+/3fg/DMPi7v/s7PvvZzxKJjCM6PyQk5IgJhc4UEI1GWbhwIeBlaDiOg23b2LaN4zg4Tv2BJKXEsiwsa/pjHBRFGbOwGklITWT8bMpgOhKklJTtfvrKr9Ff3hUEDFedwpCxnqhZ6tWpia6gI7aSTGQJ6gQ6dTdVwq66QRD2sHWNjjgIWyGVrgIWC5bZGLqLEN52oYAidITiL8/y/9tNmzbx3HPP8eSTT7Jo0aJgfS2epqenh/nz5wfre3t7OeWUUwDPcmOaJrlcrsmq09PTM8QS1MgZZ5yBbdvs2bOnKcsrJCRk6gmFzhSwatUq3vCGNwRNPaVsLmgnpRwiftq9jnXdeMa7bj2Q1nVdr+HoDIis4SxT47VoaZrGoUOHKBQKYxqvKMoRPYjLVr9Xp6YhA6rq5IeME6hkokuaCvBlIktRlYlZIFqZcCXskdL2RwzCdijkPDdbdq6kEh8+ILsmdjwBhC+ABEJ4xSA9ceQvC2CaxJGUkquvvppnn32Wxx9/nGXLljVtX7ZsGV1dXWzdupVTTz0VgEKhwIsvvsj73/9+AFavXo2u62zdupULLrgAgB07drBv376m+JxWXnnlFRRFYe7cuVP06UJCQoYjFDozQM1dNROBia7rTqmQGm6bbdtN59Fq2ZouhBBjdv8phomMDuBE+nD0Piy1B6ddTA2CuDKflLaElL6UbOQEstFlGFp0iMiaKY44CNuEeCxD96E+ovESRkR4yVYuuBKkK6npeSk9UYHb6EwbuXq1otTFUU0A1cSQJ5hEg8VoYsJo48aNPP3002zevJlkMhnE3aRSKWKxGEIINmzYwJ133snKlSuD9PL58+cHWVjpdJpLLrmE6667jmw2SyqV4uqrr2bt2rWB0PnZz37Giy++yO///u+TTCbZtm0b1157Le95z3uCwOeQkJDpIxQ6xxmKoqAoCro+OZaFsSKlxHXdJvddOzE0nFgaabyqqpRKpRHHN55HO+GlGBZaqoieLqKnSmipImp0qKVLSrAHY9iFBFYhjp1PYA3Gwa2JmL3+9KMh7625CmfCXTgRYdAYhC2EYOHCDMlMiVyuSjwx1IokpfRFTk0Aee6y2jrXF0PeOkmDcdGfly1hRsOLo5pVqG4x8sRP3WLkLzfMP/TQQwBcdNFFTfu67bbbuPjiiwG44oorKJVKXHXVVeTzedavX8/DDz8c1NABr46Ooih85CMfoVqtBgUDa0QiEbZs2cJtt92GaZosXbqUyy+/nI985CNjvvYhISGTh5CtfpXjkJ6ensl137guCrBgyZK2rquQycN7ALd3EdaouQpr4qdY7WegsouB6uvkrT0U7D2Ycqj7CSnQZQe6PRfNmota7UBUszg2Y7ZyNboKZxJFUcYc0D7cujlz5jA4OEhnZycdHR2+O8oTULX51uXafDtq/19uTQz5QihY9oWS2yCOJvqn1OhOa28xat4+E7FG+XyeTCYz6vc55MgZy30jZHKYqmut6/qIcXGNhBadKSBaGKRj5+vw2i4yhoEZj2HF41ix6JiLm4VMDlW70BBPs4v+yk5K1uE2IwXpyEI6ol7xvY7YCjqiy9CUaJuxY6fmKjxS999ELGCt8VimaWKaQ5t5jpe3ve1t43LDjSSARl1WBAogtNr62t+PAAmy9iq99dKfr1uOaFgvW2o9jnzTDVxmDVYjpSaO2rrUwr/tkJDZSCh0pgCtUvFux4NF4hSJ9/UDIIXAiscC4WPG4zjG2Cu7hoxM1R6kv1JvkdBX3knJ6m0zUpAyFvgtEpbTGVtJNroMXY1N+jnNlKsQaHIVjhQ7NVYhpus6g4ODRKPR4PN4VhYZ/FJrnK/Rbt10MdSy5M/TIpr8V+80vW3Crc/T8C4/erppDQjfdSZaBNDMB2GHhBzvhEJnChic30V5TifzI1EKe/ehl0oYxRKK62IUvXnwrAqOqmLVhE8ijhWP4YbVU0fFdIr0lXcyUNnFtu4D7O//NUWrp+3YlLHAa5PgZ0B1RJdPiaiZbSiKgmFMrGpwK43m51wuRzqdHnZsO9EzkiCayLaRxraey8y5JoQnhIQ33ySUAIS3vSaGDh/u44fP/4JkMo6ULkZEJWLoRKKaNxkauqGP6noMxVNISDPhE3WKcHUd5ncx6DrejVZK1KqJUSqhl8rea7mC6jiohUGihXqfALvB3WUmYlixmPeT8DjFdIr0l3cFVpq+8k6KVnfbsUljfr2hpW+xMdT4NJ/x8c1osTlTyVQJq9Ztw21vORuk519rWMMwC1A1i7y+5xdH8Ok9FEVF01RUVUPXx1dI9EgC4EORFYKUCGkinBKKW0Y4ZRS3jExWgCMLAzgSQqEzXQiBE41QjkYod3Z461wXvVzxLD6++NGqJprpTQzkAO9+aMWivrvLE0B2NHJMurwsp+wLmnoBvkHzUNuxCb2LztgKTpi/Gt2aSzZ6AoaamOYzDplNzLTIGo948rLQZJChFotVWLb4VCRQKVewbBvHdnAcG8d1kNJB4r/K5uVG1eS6DqbpACbloW3TppSJVnQ/0vFhlfdJRjoIt4zilOuixS2jOKVgveKWAiFTfy0h2jQ9dsXbIfk/Z+CDeIRCZyZRFKxEHCsRp1adRdgORrmEXiwH1h/VtjHKFYxyhYQfR+sqih/vEw9eXWP640COBE/UvN7QpXsnBfNg27EJfW6D+8mz1ES0ZJg9ETJraIwHmtj7Xd590dvbfp9dt7mYY72xrLdcKTtUKzaVikW1alGtOkjXwcVuK4yGX7YRiosQDl6QkgN441zp71P64svxXhupxXRVq9UJX4eJXbvxt9LJZDJUKpVjt5VOYF2pi5C6KCkNXV8TMk4ZRR7Z/59ExVXjSDWGVOJEojNbKDMUOrMMqalUUymqKb+qm5SoluW5u4qe8NHLXrxPZLBIZLBey9/RtSbhY8VjSHX0qrnTgeVUGKi83pQBVTAP0C7zJa7PpTPqdenuDETNGKvchYQcgyiKIBoTRGMw9krYcoggqi83V8KuVl1aNMuYkFKi6RJdd9EMF1Vz0HWJqrkoqoOiuiiKg1AcX0C5uHLsAfAjBcwfN610FIGhWBiKjaGYaJjowkSlguJWmqwsrVYXMYb+dyPhKlGkEsP1BYurxnAVT8C4Sgypxv3X5vWIepJN7ccoBw5MxuWeEKHQme0IgWMYOIZBJev31pESrVL1LT4ljGIZrVJBtWxiuTyxnFcTRgJ2JIKVqLu8rGhkyuN9bLdCf2V30CKhv7KTfHU4UTOnHlPjBwtHteEDXUNCQkbHq4TtVcOeSCXsanU4C1KzQBJC4NgCx1ZgjG4yRQXD8PqnRaKChCEwEgIjqmAYXmuTxuayhu6VGWjlSFrpOI5DNBoll8uNq2zDxFvpSHRVEtclUd0lrrvEdJeYLtF0F8Pw5mP+Nm+MtxzVj8xS7UiB6WiYro4pDWxpYGFgE8UhgqNEcYQnaKQax1XjoCVAjaHpxpS00pluQqFzNCIEdiyKHYvCnE5vleOil8tBvI9eLKFZFnq1il6tNqe4x/wUd18AOYYx4Xgf260yUNkdCJq+8k4K1f1eEGYLMa0jEDResPByolqmzV5DQkKmm8ZK2GNBuhLTahZE1Yr0G8z6gqix8WzVq4btOlApSyrlMVobRE0YiaDBbKRh3psiRCKCeMwTSKo68v1swi5v6SLcMthFpFVEWoNgFRFOEZySb1UpobgVVFlBkxVUTHSqKOLIiodWLEHZUij7ryVLoWIplCxB2VSG3WY5jSUSajhA0Z/Gz3ha6WiaxqmnnsrixYuP6PMfCaHQOUaQqoKZTGAmE8FXV6m5vEo1AVRCcVwMf55eL+DHVdWG2j6eAGqX4m67Jjlf1HguqF3kq/uQbYLPolq2qU5NR3Q5MT07hVcgJCRkOhGKIBIRRCJjGy+lxLHBNF3MylCXWrvmspblZa3Vlj1GF0iqBpFGq1CjQDIgGnVQrf1UC/uJqGV0pdI2jmWIO8itjPMitVwDlMDd46pxpBL1X5vdQ/XX+jiEipQS4boYto3qOMQdh/QktdIZy/jG/8t2rXSGI5PJHD1C56mnnuKnP/0p+/btwzAMTjrpJP7iL/6CRYsWBWOklDz22GM8//zzFItFTj75ZDZs2OD56HxM0+Shhx7ixz/+MZZlcfrpp7Nhw4amhneDg4Ns3ryZbdu2IYTgrLPO4oMf/GBTz5ne3l7uv/9+XnnlFaLRKOeeey6XXnop6iyJS5lpXF2nmtGpZnxXkJSophnE+tRS3BXHIdqS4l7RBQejOQ5qvXTL/Ry295Kr7h1G1GSarDSd0RXE9I7p+pghIWPirrvu4plnnmHHjh1Eo1HWrVvHxo0bWbVqVTBGSsmtt97KI488Qj6fZ926ddx4442sXLkyGFOpVLj++uvZsmULpmkGva5ay9E/99xz3HHHHfz6178mEolw9tlns3nz5mn7vLMNIQSaDpquEh9jcuTwQdguZtVBmhWkXQLbEyWqLGOoFSJqhYjmz2v+slrGkFUiThmt7MAu6JzgZ3FFZNQ4lfr6+nYpJm49h7olZSaeca2tdMbjLjzppJOm/XwbGVevqxtuuIFzzjmHE088Ecdx+MY3vsGePXu47bbbAgHy9NNP8/TTT/Pxj3+crq4uvvnNb7J7925uu+22oHjZ/fffz89//nM+/vGPE4/HefDBB1EUhc9//vPBsb74xS/S39/PRz7yERzH4Z577uHEE0/kyiuvBDy/6Kc//Wmy2Szve9/76O/v5+677+btb387l1566bguwqT3uuIo6qXiuijlAoO51xgo/Y7D1d30yH0cphe3jak1KhLM0ZfRET+RbOpEOmIriWkdM+avPWqu81HOeAoGzlYuu+wy3vWud7FmzRps2+amm27i1Vdf5YUXXiAe92otffWrX+Xuu+/mjjvuCLqX/+Y3v+GHP/xhcI/7zGc+w/PPP8/tt99OOp1m06ZNKIrCli1bgmP98z//M1dddRV/93d/xznnnIPjOPzmN7/hXe9617Dnd9z2unKtEbKC6tlBzUG3JYRbRYzSxmPEw0qFqh2l6kSp2lFMJ0rVjjWtqzoxzJblqh1BoqLrosli1GxBUvx1AiMqMAwFVTs+K2HPhl5XR9TUM5/Ps2HDBq699lp+7/d+DyklH/3oR3nnO98Z/EGXSiUuv/xyrrjiCs455xxKpRIf/vCHufLKKzn77LMB2LdvH5/85Cf5whe+wEknncTevXv527/9W2688UZOPPFEAH7xi19w4403cu+999LZ2cmLL77ITTfdxH333RdYgr7//e/z9a9/nQcffBCtjeulNSpfCEEsFqOnp2fMJrixIoRgwYIFHDx4cFbdsBzXJlfd48XU+MHCueoeXDnUHBwVCeaJRSxw5rHAmc98OZ8kqaC+ay3F3YrHg3gfV5/elhaz9TofazRe54GBgSahI6XEOcJ01ImiisiEHx6HDx9m9erVPPHEE5x99tlIKTnzzDP56Ec/ysc+9jHAu8etWbOG22+/nQsvvJB8Ps/q1au5++67eec73wnAjh07OPfcc/n2t7/N2rVrsW2bs846i0996lP8+Z//+ZjPJ5/Pk81mj87vs3QRbmWEVOa6G2jINnlk915XGMhG908bS0rN0lJzBaEl6Fq4jEOHDmFbtXgiP8aoKSC7fSD2RFBU/NgiZZhYI1HfHhUYxpGVK5gtTNU9WtO06WnqWSp51V+SySQA3d3dDAwMsHr16mBMPB5n1apVbN++nXPOOYfXXnsNx3E47bTTgjGLFy9m7ty5bN++nZNOOont27eTSCQCkQNw2mmnIYRgx44dvPnNb2b79u0sW7asyd21Zs0aHnjgAfbs2cOKFSuGnO9TTz3Ft771rWB5xYoV3HzzzWO+WBNhwYIFU7bv0XBcm8PFXRzKvcqh/Ha6C7+lt/AajhxqvYrqKean30hX6g3MT7+R+emTSEW7vD80KaFS8QoY5nL+ax7FcYakuBOJQDYDmYz/moZp6PM0k9f5eGLBggWUSqWm3l22U+Gxly+fkfO55LR/QlMnVnG1dv+aO3cuuq6za9cuuru7Oe+884LPN2fOHM4880xefPFFLrroIn71q19hWRZve9vbgjFvetObWLJkCb/4xS84++yzefnllzl48CC6rnP++efT3d3NqaeeyjXXXMOb3vSmYc/HMIzgezxT32fpmmCVwB70Am7tIljFlvmh27BLjNYkdWQU0BNeto+WQGgJfzkJWhyhJ9ts8yZNmfhjbCLX2XUl1apDpeRQLttUyo4/2ZT910rJoVJxKJe87Y4jcR0olyTl0tiCsIWASFQlGlOJxTSiMZVoTCMWV4lGVaJxzd/mrY/GVDRtltb0YWbv0RP+hriuyz/90z/xxje+kWXLlgEwMDAAeIFHjWQymWDbwMAAmqaRSCRGHNNqGldVlWQy2TSmUeQ0Hrc2ppV3v/vdwa8wqJsRjwWLjittcpV9QVXhvvIuBiq7cduIGkNNBDE1tVYJCX1u/deDC8UBlyJtivdl0t7kp7jrpRJ60Qtu1soVRLUKh7q9yceORII+XlYijhWNTlqKe2jRmR4ar7Npmk2WUdud/tolNSzbQrrjj1dwXZdNmzaxfv16Vq1ahWVZ7N+/H4COjo6mzzdnzhwOHjyIZVkcOHAAwzCIx+NNY+bOnRuMee211wD40pe+xDXXXMPSpUu57777+F//63/xb//2b3R0tI9fM02TgwcPHvn3WbqeW6cWTNumQFxzvZUGl1Cb+8W4Di2MliDaRitLc/xKYwCuVMZQ6d0FTH8CoORP42ey7htChVjSm+qoNNY6qgVhV9sGXXuB2V52mtsUhC0lgYgaqH/oEWkNwg7mo+2tR5o+9Vajo9qi8+CDD7Jnzx6uv/76ie5i2tF1fdgu0lP1kJyKpoKudMhX9zeldOcqu9taanQlHgQI1wrwJfR5bb/c4z1PKxrx6vL4LS2E63oFDWsp7qUSmmmhVato1So0pbhH68UNE/EjSnGvnXsodKaedtdYFRH+9E33z8DZeMeeCBs3buTVV1/lqaeemuQzIqix8td//ddccMEFANx2222sW7eO//t//y/ve9/7hn1vU5sI16qnK7crBtd2fclzIR2BdUUi2mQBxYbJDmpejzgCJ8EM/P1O131D1SCuKRMOwm6foda8LCU4NpRsl9IYs8aFYPQYoyZ3m0BpU9NoLMzkPXpC38oHH3yQn//851x33XXMmTMnWF+zsORyuaZfLblcjuXLlwdjbNumWCw2WXVyuVzw/mw2Sz6fbzqm4zgMDg42jdmxY0fTmFwu13QexwKudClU9zdUFN7JQGU3jhyq8HUl5nfnXhHUq0kaXdPm55VKuxR3u0n4GKUyiuP4vb3qFcaGpLjH47h6WP3gaEAIgSZmrmHfeNm0aRPPPfccTz75ZFPGaFdXF+BZeOfPnx+s7+3t5ZRTTgFg3rx5mKZJLpdrslz39PQEvy5r+znppJP8h7ckoqucsGwp+/e+jnBKntUFCdIFXJAuWuUQHQP/iP26yVyrMAnWFb0pVmUsQsWzrhggZq8L5HhgopWwWws9jiSQHNv7elYrXq2jQpuM2naMNwhbmwWdicb1JJFSsnnzZn76059y7bXXBn/QNbq6ushms7z88suBsCmVSuzYsYN3vOMdAKxcuRJVVXn55ZeDYOT9+/fT29sbpKCddNJJFItFXnvttSCt87/+67+QUgapoCeddBJPPvlk0w3npZdeIhaLsWTJkglejpnFlS6D5oGgQ3d/ZRf95V1tRY2mROloaJPQGV1O0piPmGU3KFfXqGbSQ1Pc/aKGI6W424beJHyseAw5W/vKhMx6pJRcffXVPPvsszz++OOBy73GsmXL6OrqYuvWrZx66qkAFAoFXnzxRd7/vveBdFh96pvQdZ0f/b8fcMEfvwOky+9+9xr79u1j/elvQDV7OePkhUQMg52/2cbvr14MSCzLZs+e3Sxd8EeoVl/b8xNuGc3v9Vb7aeJZV4artTKCeFFioMyCJ0zItNBYCZuxVsK2/eKONUFUaVhumK+v96wxluW51oqDoxzAR1HhTacJTjx5Yp9tMhiX0HnwwQfZunUrV111FbFYLIiFicfjGIaBEII//uM/5sknn2ThwoV0dXXx6KOP0tHRwfr164Oxb3vb23jooYdIJpPE43E2b97MSSedFAidJUuWsGbNGu677z4uv/xybNtm8+bN/P7v/z6dnV7lg9NPP50lS5Zw9913c9lllzEwMMCjjz7K+eefP6x7ajYhpUvBPBhkP/VXdtFf2YXtDs1e0ZRIIGpqBfhSs1DUjAkhcCIRypEI5Y6st8510f14n1qNH61a9dxeZo5YQxd3OxrFTNQsP3Gc2NFjSQiZWTZu3MjTTz/N5gfuJxmP0HNgD+CSSiWJRw2QLpd/8DLu/ModrFo6h2VLFnLz7fcwv2sOF/yP09CqB+iMwqV/9idcd/0X6EwKUskEG6/7MuvOOI31p78B3ArphMH7L303t3zlPhYvnMeSxQv56v0PA4I/ueAPvXgUFKQQgApCIFGwDcHAog8zZ/5SuvuLuCLmx64chX/nIbMeVRPEtHFWwjZrla9bizsObTTbWAnb8yrMXGjBuNLL3/ve97Zdf8UVV3DeeecB9YKBzz33HKVSiZNPPpkPf/jDTSbiWsHAH/3oR9i2PWzBwAcffLCpYOCHPvShpoKBPT09PPDAA7zyyitEIhHOPfdcLrvssnEXU5rqOjqu6zBoHvIrCu+iv/wa/ZXXsdtU2VSF0eB+8npAJY2FKMfZzU44Tr2ood/QVG0TMO4qCko2w6Cm+ZafGM40p7gfD8yqOjpSUnP3gIsI3D8S0Xa9i8Bl/sr1bXf3lZs/xyUXvdPfteRLd/wf/r9HnyafH+TN607n5uuv4sQVvvVHKJSrFtfecAdPf+dZqqbJef/9Ldz0hc8xr2u+L0oULNvhizfdyhNPPkWlUuGMM87guuuu441vfOOwH+u4raMzA4T1t6aeeiVsycKF88kXeo/OOjrHCpMtdEpWH72l7ZhqD3t6X6a/vAvLHdrxThUG2egyP/NpJZ3R5aQii447UTNWFNOqCx//VXGH+pWdQPTEsRIxzFgcqYXVso+ESRc6fuzKaGKlJlKaxx3pLUsgfUHiCZPmZRmsV4aMm0oBHQqd6SMUOtPHbCgYGEZ7TgGv537MS4e+2bROFTrZ6AlNGVDpyCIUET6Ax4pr6FSMTFMXd71qMk83KB7Yj1704n1U2yaWLxDLF4L3WpGIl+E1BSnuxzVtrStyGIFSEy41gXOEN70mUTK8WKkvC+qiJSQk5HghFDpTwJzYKjpjJ7J0zilE5Hw6ost9URNe7knF7+LOwoXkNcX7teB6XdyNYj3LSzPNehf3/gGgTYp7PI4TObIU96MWKRHSHKGqrbfe6Zdkin0U9bNRI3MQNZFzRIzXuiIaBMxx+H8VEhIybsIn7xQwbyDGH7x6KtkTVjKAgIyEbBkZTx4TJb1nNYqClUhgNZQuUGw/xd0XP3qpjNo2xV1pEj5WraXF0YJ0EG5lDLVW2iyPoSO0BAxAqGcgWluGtFhNxmxdmWJ3UEhISEgodKaAvb/ZwSv//htS/28bKavkTXaRpLTR02nIdkKmE5HpqM9nO6G2nEiFgmgScTWNajpNtRZX0pjiHtT4KaM47tAUd10P+nhZ8ThWLIZUp9D1ISXIhiaHNVESVLn1rS3tise1ydgb16FR26Qv+7VV1BipjgUMDNrYVhe20QlNAib8voaEhMxOQqEzBfzK6OLeN17UdlvMrvjCp0TqcJHUwRJJaxcp+1ekrKK3za2SimqkYgapVJx4OuUJoWwnItMJ2Q7IzIFkKIgmRLsUdynRy5Xmqs6VKpploQ20SXH3Y33MeAw7Gh36oJfu8NaTwMpSQnEqQ60uY7CujISrRNrXWhmm0WGtmBxi+Gw1IQSZhQupHjiAzOVAmVhF4pCQkJDpJhQ6U8CclSt4szNAWSr0FSrkTYei6eBKKGtRylqUbjrHvD/NtUl2l0ntK5KyDpK0XvOEkl0mpUHKEKSiOulElFQqTiqTJNWZRevwrEUkUogw8HZkhPA7sceC7jnCttFLBSLFfrTyAHolh+KUUW2TWMEkOmiCqCKFiVRtpGKDqIKsosihpQPGg0RpqWg7lqJx3jjCAPeQkJCQgFDoTAHrFidZvyTVlFLnSknRdClUHQqmQ6HqkK96r03rKjaFsumtsySmFNiKxoCRYsAYQ8nLgj/thbg9QMraT9Iuk8IkpbikNEhHVFJRnVQyRiqVIJVNkerMkO7IEjPUY89KJN32sSu1gNva+jYBuEI21O4xhg+9FYDwM6abDo2BVGI4WnxYa0q79VIcp4HRISEhIZNMKHSmCUUIUhGVVGR8v7arthuIoNqUrzoUKhaFfIlCsUKhYpKvuhRsKLgKRXSkEJS0GCUtxqF2O674Uy+A5c/0orkOSbdKSlikVOlZjCIa6bjhCaN0glQ6STqqBZ8nZaioE2z0Ni5cq01WUBnX0Yj3HwrWtwoa4VaPsMmh0kac+MtKFFwDxVLRTIFaBa0CAgOkgaBuSbMjRj3YORbHioUp7iEhISFTTSh0ZjkRTSGiKcyNjz37x3ElRcuzHuVLVQoDeQoDBQqDZfLFiieSTJeCLShIlYIwGFRjmKqOragMKHEGajuz/akI9DhA3p+aieOQ0lzSuuLFF8UjpBJRb97wBFE6opIyFDK6RUarEhUVVFluSGtusaa0pjnLoZWRAdxuGEtTYFcYLYJlZKtKbf24rSu1FPegn5eX4q5VvYmWFPdaltdxneIeEhISMkWEQucYRFUEaV9YLE4bsGB0l5e0Lar9/eQP91Poz1PIecKoUDI9d5rlUnAEBXQKepyqEYWohhpTSGsmKbVKWjNJa/XXlFMlXTVJO1XSlkm6UiWpmhyJ8UeiNMSpeHEp0WQnxerQ5oetFhimq45RY4q7X7hT2HZzllex1JTinsBr9OgqitfHK3GUpriHhISEzDJCoXM8I13PreO7eYxEiUy0iljoojgCxRUIx0VxK81WFqeEoL11ZayUHI2CbZC3I+SdCHnboGB7r3k7QsHxtzWsyzsGJUcnYahNVqKubBLNNYN1TRYkf4ogmEk7idQ0qukU1bQvOqVENS1f+NTaWngtLSKDg0QGW1Lc4zHMhCd8vBT3MOB4vNx1110888wz7Nixg2g0yrp169i4cSOrVq0KxkgpufXWW3nkkUfI5/OsW7eOG2+8kZUrVwZjKpUK119/PVu2bME0Tc477zy++MUvBuXof/zjH/Nnf/Znbc/hn//5n1mzZs2Ufs6QkJBmQqFzLCDtUYrDDV807shiV8QQ60rt1RURpClwKzZu0cQdrOIOFJEDBezDA1QHBikUByhUbQpajIKWoKDHg2lQi1PQJQVdoaDrFDSFkm4AUDRdiqbLwUG/P9n+4qjnqiv1GKmaGKoLIaUukvwpbagkpjL2SAiciIETMag0pLhr5UpTP68gxT1nEct5LsOmFPe4n+Iea5PiHtLET37yEz7wgQ+wZs0abNvmpptu4tJLL+WFF14gHo8DcM8997B582buuOMOli5dyi233MJll13GD3/4w6Ch8LXXXsvzzz/PfffdRzqdZtOmTWzYsIEtW7YAsG7dOl588cWmY99yyy1s3bqV008/fXo/dEhISCh0Zg1S+taVEcRKmywhxSkh5JE1JJVCbxunMnwas19ETjEm1DdI86cEMN9xoDAAA32Q60cO9EGuDwb6kLnXoNdbT34ABxjUYp4Y0uIU9ESLMKotJyhEkt6rGsUWKpYr6Svb9JXHbokSQMJQmqxEjUKo1WpUGxfRJhhgLAR2PIYdj9VXBV3c624v1bLQKxX0SgX6+gFwg/T4enFDx5imLu5SItyZaYwolbFXVv7617/etHzHHXewevVqXnrpJc4++2yklDzwwANceeWVnH/++QB85StfYc2aNXzve9/jwgsvJJ/P8+ijj3L33Xfzlre8BYDbb7+dc889l23btrF27VoMw6Crqys4jmVZfO973+ODH/zgsZfRGBJyFBAKnSlAuFVUJ4/MDWIM7kE4pVGsLRXfujLxvkGedWUstVZa1isxUGYuBkSoKmTneBMM616SroOSz9GR66NjoB+ZOwwD/ZDrI1IpUTm0A7r7IDfgN4z03wdUVKNJGA3qcfJ6wluOZynEshQiSQb1OAUlSgGdklSQwKDpMmi6HGDsYtJQRUPwdbMICoRSy7qEoaC0eQhKVcVMJTFTyWCdYlkNgc71Lu6RYolIsRSMczS1SfiY8RhSm/w/eeFKFr78yqTvdywcOO0UpDox8ZDPexaybDYLwO7du+nu7g4EDEA6neaMM85g27ZtXHjhhbz00ktYlsVb3/rWYMyqVatYvHhxIHRa+f73v09/fz8XX3zxhM4zJCTkyAiFzhQQzf+MVO//xXkdMuN8b926MnJWUKuAkUrkmO7KLBTVa4+R7YQT6oJICMG8hnpF0nWgkPctRH3IgT7iuX7iA310+ZYicnugdwDc4YWlLRTfShSnYCQoJOdSSM+lkOhkMJamYKQ8l5sSoSA1L1DbkjgSTEdyuGRzuDR265EiaIk9am9JqrvcYqTmJzFUxXN5VauBu0svltErFVTbQc0XiDZ0cbcNI4j1MePHb4q767pcc801rF+/npNPPhmA7u5ugCDWpsbcuXODbT09PRiGQSbT/Jc9b948enp62h7r0Ucf5bzzzmPRokWT/TFCQkLGQCh0pgBXTeAqMRQjhSWNBoHSKFjapzPPpHXlWEAoqtczLNMBnDiihYhCvsFN1t8kjrRcP9mBPrL5Xih1w8DOUY8thaCcnkuhYyGFbBeDyTkUEh0UImkKRpKCFqUgDC+l35RBXaSy7eJKgmUKox4qIKK2iz1KkjLSZIVLBzadrsUcu0qna5FxTJJVs7mLezSK1ZDlZUci43J5SUVw4LRTxn7Sk4icYAzVxo0befXVV3nqqacm+Yya2b9/Py+88AL/8A//MKXHCQkJGZ5Q6EwB1dQazPQZLFy4kB7f0hAyu2gSRMtGEUSD+cBNFsQQBfFEvkDK9yMch3iuh3iuh/mjnUAqEzRxtTOdDKa7KCQ7GYx3UIik/PiiCAWLpoKR+YYq2q6EqiOplmx6x2Q98kS0AqQ0yKqSjCLJaCYZ1SSjDpBRIa0LknGDeCJCPBUjmk2QSkZHuJhiwu6jmWDTpk0899xzPPnkk01WllpcTU9PD/Pn1/8He3t7OeUUT8jNmzcP0zTJ5XJNVp2enp4hliCAb37zm3R0dPCOd7xjqj5OSEjIKIRCJyRkBISiQrrDm1g5giByPUHkCx8ZuMkaAqx9gYTjQCHnTXt3oQFZfxpCstbtvsPvcN8JnZ2Q7qCU7KQQz1Awkgw6ot5SpKWSdr3liEvFdnGBnA05WzB8VFRjpcheAGLqq6R0hY+sSbOICKoQKIpAFaAKgap4FcC99d46L1Z4doggKSVXX301zz77LI8//jjLli1r2r5s2TK6urrYunUrp556KgCFQoEXX3yR97///QCsXr0aXdfZunUrF1xwAQA7duxg3759Q+JzpJQ89thjXHTRRehhLaSQkBkjFDohIZOAUBRIZ71p6YqRBVGx0OQmayeOPEFke+JpMA97dw0pBBDzpy6AZMoTQZlOXxB51qJa13vPetWJraheVewWIVTvu2YzWLYYLNsUqjZ5S1KwJQ6CsgNlx6VsS8rW2APnmwSR4omfYUWSv71dYPaRsnHjRp5++mk2b95MMpkM4m5SqRSxWAwhBBs2bODOO+9k5cqVQXr5/PnzgyysdDrNJZdcwnXXXUc2myWVSnH11Vezdu3aIUJn69at7N69m0svvXTSP0tISMjYCYVOSMg0IhTFc1ulMmMQRIPgZ5fVRVBjPJH/6tgwWPCmfa83CaJWcaQkUmSynWQyHYhMJ2Q7IDOnSRyRmYvwaxYBSNvGLpRRHcGhAz2IGKQNcKQ/UZ+3AUcKHAhcto6UOI4cR96aZwVqFUbN83VhpPrCaDTr0UMPPQTARRdd1LT+tttuCzKirrjiCkqlEldddRX5fJ7169fz8MMPBzV0wKujoygKH/nIR6hWq0HBwFYeffRR1q1b11SQMCQkZPoRMgwgoaenB8s6slo0rQghmrqXh0wNx/t1llI2WIh8N9nAYW8+1yCGcn1gj6OadSJVtwplOiA7h8yy5eSFRn7xcjKdcxFCICQI1/WmFlnlSnAQ2ELBFsKfFzgSXFd6AiiY93q0uUf4f+iJoRFEUoMwqs3PFtdaPp8nk8kc19/n6eJ4v29MJ1N1rXVdbxsX147QohMSchQjhPDieJJpWLJ8eAtRTRCNFENUsxLZlje2WID9uwP5MlDb2Yc/hawUvfWKCqoKqgaqilBUhFAQikAIBU1R0RSl3oBDghQKUhNIRQmmWpaXlNITSFLiur41qGm+vUgKrEeuxOFIrEctIqmNMBqL9SgkJGT2EAqdkJDjgCZBtPiEkQVRadDPMjuM9LPNyPUTrZQoH9oPul8RW7rgOt5kmd77GeouA0BRvMBuRUU0zCuKEgglqetI1duueqaYMX8+V0rfIjRUJDlubbv/2jBf+8y2BHuc1Z094VMXRHVh5AuiWWw9Cgk5ngiFTkhISIAQwnNbJVKweFlTYca5vvlZ5HKQSnkFFx3byyJrfLVb1kkXXNeLO8IaubuaULxq2Y2WIk0DTfeqOvvrREuRQ0UIlHGmuDdajxpFUCCS/NdmkTTUejQelIaMNLVpHvqLFr/sz7G8GsEaLJP0i0VGNREKpJCQIyAUOiEhIeNGCOGLjpG7qEsphxdEjuOLogZBJF2k7QmiEferKL67TGsWRA1utHaCqPUzqAJUBIyjGXyr9ahuMfLdacOIpNp7XQfsNgJpf97kyz/rAfY3rdf8hrRefzWlbUuRxv5rqYhKciob0oaEHGWEQickJGTKGL8g8kSQsC1PBNk2wrGRjucik64DtbGuGbjMht1vO0Gkqr4oGpsgauXIrUf+fIswKkZVTpsfp+wI+opVClUHy5XYrqS/bNM/joa04DekbWw+26adSGtj2ogaWo9Cjj1CoRMSEjLjNAsio2mbBL9DuoviugjHAcvyLEGui6wJIMebr62rucyOSBCpGmjNwdYT/XyjWY+ibpQb/uf8IEPFdV2qjmyuij1MQcjGbUXTq3FUNF2KpsvBwbGHZutKu5YitWWlrRUpEVqPQmY5odAJCQmZ/QiBVFWkqoKuQzQKrotwGwRQS4q7lBIcByldb3Lrr00uNHcigqhFDDVZilQv1ugILSNCCKKaIKopzEuMvbKy40oGG9uGDFMp25vcYLvtSixX0le26RuH9UjgW49Gcae1WpEi2vHXTDZkZgiFTkhIyNGJoiAVkDUTiZSB5ScQP0IZUt8H/BR3xUtxdwVIV4Jrg90mjsix2wiiMZzbSIKoZimaBEHUiqoIMlGNTHTst3cpJRVbtrQMGbmlSKHqULRcJDBougyaLgfGkdhvqGJ0d1rL9oSuhNajkHETCp2QkJBjAyE8y4+iIAEXGoRPXQAJ6U8O4DiBJ0kqCtLQcJVYvbZPgwjx3GPO0CDqIxFEQgmyyWRfH84zPyW/bDmu0BoqVXdCLD6lsTNCCGK6IKYrdDF265HtW4/y1RYxNKS1SLNAciSYjuRw2ebwOK1HSWP4gOxmV1t9XWg9Or4JhU5ISMixS+DyaljXYPWpi58GS1BtGI1FDQUoildDaBQdMMQ11lYY+YJIup67zALyA8jnvk2u3U4No20vsyHLscS0BhNriiAb1ciO03pUtt1mq9EILrXaVLY961HBdCmYLhTGaT1qCcpe0JFHdSpBGn+rFSlhKFPScy1k+gmFTkhIyHHBXXfdxTPPPMOOHTuIRqOsW7eOjRs3surEEwPxg+Nwy2238/VvPur1ulq7lluuu46Vy5cDIIWgbFlc+8UvsuU736FqmkGvq1o5eqEo/G7nHr7whS/wn//5n1iWxZve9CY+/elPc8455wTnM0QQSYE4/z3EqmXKB/fVG76Wi2Ca0HMQeg4OccQ1LTcKokaLULZlOT69gqgRIQRxXSWuq8xPjv19ljP22KO8vzzYaD0q2RwuNViPdhdGPJ4iIGE0utPaxyG1WpEMNbQezTbCXleEva6OZsLrPD00XudcLkc6nZ7pUxo3l112Ge9617tYs2YNtm1z00038eqrr/LCCy8Qj8cB+OpXv8rdd9/NHbffztKlS7n1llv49W9+w789+z1ihmfK+fTf/z3PvfACd958M+lUis9cdz2KqvDtJ54MrD9veetbWbFiBZ/97GeJRqM88MADPPbYY/z4xz+mq6ur7fkN1+tKVqtBiw6Z6w8avTY3eO2DUnHsF0M3GqxCHYjsnKDDvfAbvZLtgHjyqE43l1JSstwhsUeDpovUY+w/nKNQtVu2u1Rsd8LHjKgjZa41C6WahSl+DFuPZkOvq1DoEAqdo5nwOk8PIwkdKSWY1Zk5MSMy4Qfx4cOHWb16NU888QRnn302UkrOPPNMPvrRj/Kxj30M8MTHmjVruP3227nwXe+iMDDAaWecwT233867/vAPEVLy29/9jnP+8A/57mOPse6MMzjc18+bznozT33zm5x19tlIRWGwWOSNJ5/MN77xDf77f//vbc/nSJt6SrPa1MRVNnS4b+xtRmlw7DvV9GHdZMK3FJHtPOoE0Wj3Dcvx3GNjjTmqLY+zi0iAIghcaO2EUJDeXxNOUY2UoaKPs57TTDAbhE7ougoJCTkyzCruJ947I4dW7n4MItEJvTefzwOQzWYB2L17N93d3bzlLW8JxqTTac444wy2bdvGhRdeyC9feQXLsjjnbW/DjsXAlax40++xeNFi/vOXv2TtGWfS2ZFl1cqVPPHEE6w5+WQihsEj//Q15s6Zw5o3/R7CdpCq0hToPBkIIwLzFngTDN/PrCaIco0d74eKI0qDXoPXw93eRLObrOmR1SqIhoshSqSOCkGkqwqdMYXO2NgfkW6j9ahFDI2UwVaxvQKSeX/ceIhqyogutXaxR3F98jP9Zjuh0AkJCTnucF2Xa665hvXr13PyyScD0N3tPcxbfyXOnTs32NbT04NhGGQyGW+jIpCKyryueRzq78eORUFKHn3kET50+eWsXLMGRVGYO2cO33zwQeYk4oH1S4qWDu7TZJEcsyCyzLoVKNfnN3j13GYy1yCIioVxCCLNjyHqqMcNBTFEnZ67LDMHkkeHIGpEEYKk4bXfWJga+/tMx21jJXJHtCIN+tajiu252bqLY89cUwUkR3GptWspcjRYj4YjFDohISFHhhHxLCszdOyJsHHjRl599VWeeuqpyT0f4VXt2fj3f8/cefN46qmniEYifOORR/iLj32MZ5/ewoK5c/0Ud+lVeXa8X/HG4CBzD3ZDbx8xAWYshh2NTLrlZ8wfRTfGLohy/W3dZLIWP5Trg8GCl302FkGk+un1rW6yTIdnIaoFVSdS42rfMRsxVIU5cYU58bGn9btSUjLdEd1p+TYWJdPx2o7kKg65yvisRzFNaSOE2qf61wRUTJsd1qNQ6ISEhBwRQogJu49mgk2bNvHcc8/x5JNPsmjRomB9LUi4p6eH+fPnB+t7e3s55ZRTAM/aY5omuVyubtXx31OzBG3dupXnnnuOX/3qV6RS3k/701av5l//7d94dMvTfOITn2ib4g6glyuwZy9Zf7+uomDFYliJGGY8jhmP4er6jImfdgjdgLnzvYmRBJEF+THEEA3mvSy0vh5vYjRBlG2bet8ojkimj3pB1IgiBMmISjIyPutR1XaHuM/yw7jUavODppfWX7ZdyrZLd3Hs8aya4sUe/clpRf7sjYnxf9BJIhQ6ISEhxwVSSq6++mqeffZZHn/8cZYtW9a0fdmyZXR1dbF161ZOPfVUAAqFAi+++CLvf//7AVi9ejW6rrN161YuuOACAHbs2MG+fftYu3YtAOVyGQCl5cGqKAquL2iaWlp4J4eZSNCfTNEhBNXuHvRSGcV1iRSLRIr1jCpH0zDjMaxEHDMex4rFkNrEenBNJ0LXYU6XNzGCILItyA3AwGHPKpTr87PMDjdkmfVDIecLol5vYiRBpEK6o8kqlFtyAq6qB9lmZDsgmRlZEEnp71l6NZC8akv+etdfLxEN894Yt+G9rj9GNozx9yWb39e0rmn/LecQjGl3zPr74kg6au8zJBguIilb3uu3UvH36UjJoCkoWCp5S5A3VfKW4k8qeUvzXk2VvK2RszTylobpKtguDFQcqrldwCkjfT2mlFDohISEHBds3LiRp59+ms2bN5NMJoO4m1QqRSwWQwjBhg0buPPOO1m5ciVLly7llltuYf78+Zx//vmAF5x8ySWXcN1115HNZkmlUlx99dWsXbs2EDrr1q0jk8nwN3/zN/zN3/wN0WiURx55hD179vD2t799+BMUgkomiVwwn75UFFwHrVpFL1bQy2WMUhWtYqLaNrF8gVi+XgfGNlSsmI4V1bBiKk60JnxaH45umwdh/aHZ/uHb/LAc8t4hD3z/Ydn2ffXxoz7wE9KbFnqSRdABMguc4I23HNxiBbdQRhbKuIVKfRqs+q8msmh67sH+Xm/yj55v93+gCJSEhpLUURMaSsp/TWqoSQ01qaIkdZS4OitcMtNFClgIoABRfxqFiqOSsyPk7AiphWuZSaETppcTppcfzYTXeQyM8dfeSL8SBTBv3hx6errpK6qkM1lqv5mD2/2Q6z+OZTn8dtGy3LRdtiwPezzJwuWn0Y47bvk8l1z0Lm+UlHzp9nt4+BtPkM8XePP6M7jp+o2cuLJu/alUK1z7hdt4+jvfo2qa/I+3/jdu+vxn6Jo3NzjWL176FTd9+R5++fKvsWybN75hJf+///1h3n7eOcNep8LAIU4Y/Fbbc6wPVcHtQDhzEO4chDMXIYdW3ZM4oPQj1cNI5TBSPQyiMLwZ5RhGOhK3aOMMWv6rjVuwcIo27qC/PGjhlsYRsyJASXjiR0nq/qsWiCMlofuCSEeoqvcXJIT3RoTX+gMBKE3rpfDX+WNAtLxX8ePAamMa36u0jBENY5Q2+299X+u5DfPe2vsazrE2tvm86p+jY+EbOViMh3V0ZpJQ6EyQFlNsezNu66/EsT1k271vyK9E/3gd2SwD/X1I6TaMGd4UO9wv3KHvbfyl2vpet83+2/w6bvs5m39JN5udJ/fXcbuGlkfK68mLSGXnjz4wZFyMRejIpgeS/2BxIwi3E8XpRDidCLcDIY2h7xUWrppDanlcrYCjFUC1GfqAG+nh2+4hq/hjRnsQtj5AG9/buP/6+CM/5tCHr0QghMLcefPo7T3s/yUJpONCoYDM52Egh5vPQS6HzA9AbgCZy/kus3wbsToMigLpbHN1at9NJmpFGTOdkM4glNnvfpwIYR2dYxS1so9IeQeulSReyCHl2H2oQ35VDzEvD33IEpiXmx/ITcdsKx5GerC3nlcb0/UswT0AR1+d3tlF6wPUe2g0PPCEgqKoOK5ECg0pVIaaB0TL7Ajbh1mWTatHGj/Mtrbv9eabb7Ft9jWm47ZZFq37H8f7G66TFYvT2/X3zF+wkIOHuv1n6dAH96hIiWqaGKUyerHkvZbLCKmj2nPBnhsMtXXdj/WJYdXifY6DFgZCCERqIfZgrPnhGwd8DV+70q1XXDoO5Aeaq1W3Bljn+rwxruttG+iD11ttjI0nVBNEXhzRsEHVqSxCPTYF0VQSCp0p4PW+f+Hf+5/HQEFHYAgFAxHMN72iYAgRzOtCtH3f0ewPbv01NvovvfZm1iHv85d1I4JpWQS/9Bp/+TWZf1t/6TX+Gmx535h/4U7UvNyw/8b3tfnc7d431mM2f+7hTdqjPUBrv8p6DhzAzuVwIqG0nHSEhlTjCC0OSmTsVoMh+xE4kQjlSIRyR9Zb57rolSp6yRc+pRJapYpmWWgDOWIDOcB7+NrRKGbCEz5mPIYdjY5NYB0nCFWFjjnexFAhVEM6DhQGhqbaN4mjfk8QSbeehr/7d6MIokxQi0gM18ssHQqiRkKhMwVUhYELVHCpAMgG3+8E712a0NGFjq4YDa8RdMVAE4a3rET8+Qi6EkVTavMRNBFFU6PoSgRVGC0PwuYH91ge+MO+r83DdyppfAAfsy7CkJAjRVGw4jGseIySv0o4DnqpjFEqBa+qZaNXKuiVChzuBxpS3OMxzEQcKx7DmWUp7rMRoaqQneNNjCCIXAfyOV8E9SOH62WWG/AFUb83MZKFSEAqM7Q6dWMvs0yHJ4i0Y18GHPufcAY4Yf5FLJz7h2TnJDlwaA+WU8J2K1huBdupYLllf7nsL1ew3bL/6k2WU8Z2y0j/62tLC1talN3SKEcfHUWoaErMF0MxNCWKrka9VyVW36ZGgzHeaxRNjaHXlkUUTYkgxLFv6g4JOdaQqoqZSmKm6sHMimk1CZ8hKe5eWRscTfOETzzu1fiJxY+KFPfZiFDUes+wE0YRRIX8KHWIGlxm+QFv4rWRBVEy7R9/ToNVqMFaVLMQHcWC6Og981mMphjo6hzmJhdiFSITtjRIKXGk6QufRjHkvzp1cVRb3yyk/LGON8aRJgCudDCdQUxnHM39Rvy8NZHkiyK1WRwFgkqN1kVSsNwssFQl/EqGhMwUrqFTMTJUshlvhZR+intN+JTQyxVU20bNF4g2prhHjKCooRfvE/WCcUMmBaGo9WrRnDi6IBophmigzyve6LpePaJCDvbsHF0QtXGTNfUyS3fMSkE0+84oJEAIgSYiaEqEqJY54v250mmwFjWII3++tr4upFqtTXXR1GRt8sVW5YjPEBShNYsj39qk+/Ot1iZdjVFSF1EYrKApkWbrU2htCgk5MoTAjkaxo1HKczq8da7r1fUploOYH8000areFO8fAEAKgRWLelYf3/rjRIzQ5TXFNAmiZaMIosF83U0WuMj6hwoix6kLor0jCCLwXGaNvcyycyiv/31YcuLUfOAxEAqd4whFqBhqAkM98lLcrdYmz4rUIKBarU1OObA0DbU2lXGkl97vSnv81qa9w20QaEqkQSRF21ubfHfcUOtTs8AKrU0hIXjxPokEVqJ+H1FsG71UFz56sYTqOBilMkapHIxzVaVJ+Fi1lhYh045Q/GrR6Q5g5QiCyPUE0Wi9zHKtgmhXIILKtgV/FgqdkKOMabU2OY3xS0OtTUJ1KFXzbaxNMhBWk0GrtUlXG8XRUGuTtxxrH+sUWptCjiFcTaOaTlFN+42XpEQ1LV/41DK9yiiOS7QwSLRQ/yFj63pDoLPn8pJhxtCsQdRqAaWzsHTFyIKoWGibah9ds57qNJ5zK6HQCZkVTNTa1K4YVc3aNJyLrm5tKg8JEG9245X92KYjsDYNf+ajWJsaRVOMkWKgQmtTyKxDCJyIgRMxqNRS3KVEK1cahE9DinvOIpbzmjIEKe5BP68wxf1oQCiK57ZKZZoEkRCC+MKF5A4cmLFzC++OIcccjdamycCVNrZbHSKcWl10tXX1WKeGwPBZYm2qrQ8CwFutT4G1KXyohEwyQmDHY9htU9zrbi/Vsuop7n21FHfhp7jXg50dI0xxDxkbodAJCRkFRWgYqjaJsU3VQPi0DwhvtD61lCNoyMCbXmtTjNShLLYpWGychxazEXil9AXehBDevFCat82Sh9Fdd93FM888w44dO4hGo6xbt46NGzeyatWqYIyUkltvvZVHHnmEfD7PunXruPHGG1m5cmUwplKpcP3117NlyxZM0+S8887ji1/8YlM5+pdffpkbbriBX/7ylyiKwgUXXMA111xDInHk36FjibYp7paFUWyX4l4iUqyX16inuNcEUJjiHtKesNcVYa+ro5nj/To3WpuGBHs3BYSXW6xPjcKpLqzGUtHyjI6/Ym7HgnGc5fACqC6ShtlWW980ZmLC6bLLLuNd73oXa9aswbZtbrrpJl599VVeeOEF4vE4AF/96le5++67ueOOO4Lu5b/5zW/44Q9/SDTqtWz+zGc+w/PPP8/tt99OOp1m06ZNKIrCli1bADh48CBvf/vb+ZM/+RM2bNjA4OAg11xzDV1dXdx///3Dnl8+nyeTyRzX3+e21FLca8Kn6Le0aDO0luIeBDsPk+J+vN83ppPZ0OsqFDqEQudoJrzOk8dw1iZPEFVJpAwO9x8i5b6BbLYTKV0kEtd1qDheTzaJG6xnSvqhiSZBFNEEQqjtxdEo1qa+w/2cfvrpPPHEE5x99tlIKTnzzDP56Ec/ysc+9jHAEx9r1qzh9ttv58ILLySfz7N69Wruvvtu3vnOdwKwY8cOzj33XL797W+zdu1aHn74YW655RZefPFFFP8h++tf/5o/+IM/YOvWraxYsaLtJwuFzjhwXXQ/3kcvlTCKXop7K1IIrGjUK2roCyA7EkEoSnidp4nZIHRC11VISAhQi23yXFXtti1cuJAD6gFyuRxxvd7rqmK7vO+J7dN5qgH/8C6diDYxC8/eHq8ugRIrMVDZw97d++nu7ubMs97EoNmNQKBFFVaffhr/8dMfc/4F/4NtL/4HlmVx9u+vx3arCAQrVy5n8eLFbNu2jbVr12KaJrquByIHCKxBP/3pT4cVOiHjQFGwEnGsRDxYJWy7KdYnSHEvlzHKZRL0AX5Li0Qc8oNEXAczTHE/5gmFzhSwf/9+XnnlFebMmYPjOESjUSKRCLFYjEgkQjQaJRqNouv6rIlfCAk5GkkYXURUEViSQPoWJc+qFMy3WJtc1+bmG+7kjLWnseqk5bjSorvnIACZzlhTrFPHnBQHu/dTsnrZe/A1T8TEiuSrxYYxaXYf+C395V2sXreSnp5ubr/rJt73l5dSKVf5/Bc+D8C+A69TtgZa3HbevOVUKJoWFSuBK20EYbzJeJAjprh7AiiI9ykMQmGQTv+9tRT3erBzLExxP4YIhc4U0Nvby69//etRxymKEoie1qmdMKpNmqaFAilk1hBRBd+8+KQZO/ZE/hY+85nP8Npv9/DEk4+TjixASpe4th+AuDaHuD4nEEeq0EGAriT8hrigCqPFTechcVn5hmV84UubuOWLd3P7LfegKAqXfeBPmTO3E4cqZbuv7TkdLh/kxf33gm8cU4TekDXnBYQ3VQwPil2OXI7guM2kGynFvVwmK8HqPYxWqQyT4h7x3V1+inssTHE/WgmFzhSwYMECfv/3fx9N0zh8+DDlcplKpUKlUqFarVIul3FdF9d1KZVKlErja9SpqmpbQTScMGoUSCEhk40QgugE3UczwaZNm3juued48sknWbJ4WbB+4YKlAOT6yyxdXC+C2d+X55RTTiEVmc+yRW/AMi2opshmvDFSSvoP51m64EQykSVIJJdc9H4uvugv6O7uIRaPAJKHNj/GyhNWEVFTba1NilBRhI4bZNJZVB2LqlPvJzVRBCIQTPVaTC2FLNXYMMUvY/VyBH69J0UcpfcSP8XdScTJLlxI74EDYNtNLS30UhnNstArVfRKtZ7iLkSL1SdMcT9aOEq/rbObrq4u5s+fP2wAlpQS27YD8dNuqgmixtdKpYLrujiOQ7FYpFgsDnMG7dE0rUkgDSeIWi1LoUAKORaQUnL11Vfz7LPP8vjjj7Ns2bKm7cuWLaOrq4utW7dy6qmnAlAoFHjxxRd5//vfD8Dq1avRdZ2tW7dywQUXAPC73/2Offv2sW7delTFaNrnkoVeOvmjjz5KJBLhD972xySM9pXElUSG9y76R7rmz2PPvp0NmXRDq4EP25+upfxALZNOIrH8wHLoP+JrWbc2DRVNtfl6zaaRi1+qYmatTVJVMZNJzGRLinst3scXQO1T3NUm4WPGY8jwfjnrCP9HpoCDBZNfHipxuh0lbtukI82+XiEEuq6j6zqpVGrM+5VSYlnWmAVS4/qauBocHGRwcHy1VnRdH7MwahRIaujjDplFbNy4kaeffprNmzeTTCbp7u4GIJVKEYvFEEKwYcMG7rzzTlauXBmkl8+fP5/zzz8fgHQ6zSWXXMJ1111HNpsllUpx9dVXs3btWtauXRsc6x//8R9Zt24d8Xicf/u3f+Pzn/88GzduJJMZvV2KqmhEtOSk1W2y3epQMeSWWwpbjqX4ZaW9tekIE1bbWZuaaji1szY1LU++tcnVdSoZnUrGD7pvTXEvlf0u7s7QLu6GgZmo1/YJu7jPPKHQmQJePlTinv84CP/hBTemIipL0waL0wZLMxGWpA2WZAzmJXSUcfySEUJgGAaGYZBOp0d/g4+UhTsS9QAAX7ZJREFUEtM0hxVGI4mmmriyLGtCAmm84igajTZlq4SETBYPPfQQABdddFHT+ttuu42LL74YgCuuuIJSqcRVV11FPp9n/fr1PPzww0HWFMC1116Loih85CMfoVqtBgUDG3nxxRe59dZbKZVKnHjiidx8881DjjsdCCE84aBGgewR78+VdmAxGtpGZZT+dA3WplrJgqmwNqlCH8FFFwssS/uqXZSLppdpGIirhoa/jdamxi7unY1d3CuB8DFKJa+Du+lN9OcAL97Hq+pc6+flpbiHLq/pI6yjw+TX0fmPvQWe/e0AB4sO+3PDl/g3VOGJn3SExRmDpWmDJZkIi1I6ujrzD3sp5RAhNJIwahxzJBiGMaogaoxLOuGEExgYGDj+gi2nkcZaGLlcblxCO2RsHG91dKR0sV2z2YLkltv2nhvSn24Ea9Nk0mhtqrdVGc7aFCMmY2TsOGnTIFlViFckmjP0/9FVlKYO7mYifsymuId1dI5RzlqS4uylaRYuXMiuPfvYm6uyJ1dlb970plyV/QUT05Hs7K+ys79ZGCgC5id1lqQjLM00W4ISxvS5g4QQgbgYD67rUq1Wh3WjDTeZfsEv0zQxTZN8Pj+u4w7nXhspUDsSOQ6zUUJCZgFCKJNqbXJcu7l9ShtrU01I2bKCFhEUBvvbuPHaW5vK9gSsTQJSWpqFciHz5UIWykV0yfnork5ksEhksB5nWVZMBvQyBcNkMOJSjqooWmRorFOrtSlkVEKhM8VENIWVnVFWdjaLBceVHBq02JOvsjdXF0B78yYly+VAweJAweI/9zXvryOmea4vX/x4IsigMzZ7Us4VRSEWixGLxchms2N+n+u6Y7YaNY6rCaSauMrlcmM+phAiEEDjyWAzDGPWXO+QkBAvtklVUkQYPe5xNCtDo7VpaEB4syVpSH+6prYqZYpuie3yVbbzqndsKZjDXBbIhSxwF7JALmQOc4m5BrGqwcIqUPBk1mF6OSh2ckgc5ICyn8P04gqv4rhnbarHJrWzNnkiqV05gsa4Ju9VEcduTGUodGYIVREsShssShuctaS+XkpJf8XxrUAme/O+JShn0le26fenlw81p6THdYXFvgBakol4MUEZg4VJA1U5Oh7IiqIQj8eDvkNjQQhBV1cXO3fuHHcGm2VZSCmDceOh0do1nkDtsEhkSMjsp9HaFJtCa1PJKfOqW0Hah4hWXVJVlbQZIWvFiLtR5jKPuXIep8rV4IKNTbc4xEFxgANiP4fkAXJOH2X7yD9za2xTs2iqxTY1LrcpR+ALJ1XMrh+CodCZZQgh6IxpdMY0Vi9ozroomg578yb78mbdFZarcnDQomS5/PZwhd8ebn5gawosTBksSdeDoGuWoKg283FAk4GqqiQSiXEJJADbtkcNxm7nerNtGykl5XKZcrk8rmMqijKhDLZQIIWEHL2Mx9oEMADkLaspy8soltBcjUVyMYvk4mCsrULRgMGIQ06vMqAXKYtS23IEdoMFqrbsSk8lOdLCmdRMurq16fdK/5Pl8f95ZDs9AkKhcxSRMFTeODfGG+fGmtZbjufq2pOvsi9nsqfBDWY6kj05kz25oQ3v5sU1lmQiLMn4rrC0N5+JHh9fC03T0DSNRGJ8abzD1UAazeXmOA6u605YII1VFDW63fRjNMAxJORYx9V1qhmdakOKu1o1m7K89HIFzZFkSpApqSwmDsS9FPdabZ90DCsWGzbFvdXaFMQxtZQjaHTbBVYpp9IS2+T90PZim0pYbomyDWVrYHou2jAcH0+0YxxdVViWjbAsG2la70pJb9Fmb95zgzVagvJVh56STU/J5sUDzYUHUxG1bRzQeNPhj1U0TSOZTJJsKDA2FlprII01FmmyqmiPJ1A7LBIZEjLLEAInGqEcjQxJca/18xqS4j7QmOIerRc3TMSDFPfxWptGwottqjaJIdutsnThiZhHXuB7woR3s2MYRQi6kjpdSZ0zFzVvy1fsIAtsT67qiyCT7qJFoerw654yv+5ptjrU0uGb4oD8aTakw892jqRI5Hgz2KrV6qRV0a5NnZ2duK7LsmXLAleaoigoihLMh+61kJBppKGLe+0nkLAdjHIJvVh3e6m2jVGuYJQrJA5744akuMfjuMaRWYC92CavoGMsWCeYk1zIgcKBI9r3kRAKnSmg56DFjl9X6ZzjIkWVaEwQjSnE4t6rEZlYI8LJJB3V+L2oxu91Nce1VG2XfQ0CaG/eZF/OZN+QdPi6PG9Mh2+NA0pOYzr8sUhjkcjxCiTTNMcljGpCarQq2ul0eliXWKPgaSeChls3038PISHHClJTqaZSVFMNXdxr8T7FWlVnv6VFS4q7o2tNwudY6eI+bqHzq1/9im9/+9vs3LmT/v5+PvWpT/HmN7852C6l5LHHHuP555+nWCxy8skns2HDBhYuXBiMMU2Thx56iB//+MdYlsXpp5/Ohg0bmlKRBwcH2bx5M9u2bUMIwVlnncUHP/jBppouvb293H///bzyyitEo1HOPfdcLr300hlvPVDY00tvd4ze7vZpzooC0ZhCNC6IxRSiccV/rS9HIgIxA9lSY0mHb40DGjEdPqp6cUC+AKrVBppN6fDHIrW0+UgkMilVtKvVKpqm0dvbSyKRIBKJBC41KSWu6wbvdxxnQuc7nBgaSSyF36GQkFEQAscwcAyDStZvQSIlWqXqW3y8fl5apYJq2cRy+eYu7pEIVqLez8s6Cru4j1voVKtVli9fztve9jZuvfXWIdu3bNnCM888w8c//nG6urr45je/yQ033MBtt92GYXgN7772ta/x85//nL/9278lHo/z4IMP8uUvf5nPf/7zwX7uvPNO+vv7ufrqq3Ech3vuuYf77ruPK6+8EvBqrtx4441ks1m+8IUv0N/fz913342qqlx66aUTvR6TwrzCbzj9v/6DcrSTSqSTSrSTSmwulWgnppbEdaFUdCkVAdo/FISgwRKk1IWRPx+LK0SiAmWaxFBjOjwjpMPvy1d9EeSnw1cc+iulIenwMU0JgqAbhdDRlA5/LNIokBr7Mo1WGVlKGQie2mvj/Ejbau8/EoE0XitSKJBCjmuEwI5FsWNRmNPprXJc9HI5iPfRiyWvi3u1il6tEve7uEshsGKxINbHjMdwDGNWi59xC50zzjiDM844o+02KSXf/e53ec973sP69esB+MQnPsHll1/Of/7nf3LOOedQKpX4wQ9+wJVXXhl0CL7iiiv45Cc/yfbt2znppJPYu3cvv/jFL7jxxhs58cQTAfjQhz7EjTfeyPve9z46Ozv55S9/yd69e/nc5z5HNptl+fLlXHzxxXz961/nve99b9tgylrPphpCCGKxWDA/WSQXzSFxYhxjYB/VfT+Ggb5gmyM0qpGsL4A6vNf4XCqphd6rlqYiYkgpKJck5ZJD/+FhHgACotFWMeRZh2JxQdRfp6pT9wUUQjAnrjAnrnP6wuZtRdMZkgq/N2dyYNCkbI+eDr800yiCIm3T4Wv/b+GDa2oZ7TrXxMN4+5SNVSC1m6+933GcMYmkzZs384Mf/IBdu3YRiURYs2YNn/zkJznxxBObzv2uu+7iscceI5/Ps27dOm644QZOPPHEYPvDDz/M008/zcsvv8zg4CC/+tWvhjTr7O/v53Of+xz/8i//gqIo/PEf/zHXX3/9qBl+4fd5egivcxs0FSuVxEolg3gfxXd5eVYfz+2lOA5GqYRRKkGvF/Djqqrn6vKbmVrxOK7uPYNnw7We1Bid7u5uBgYGWL16dbAuHo+zatUqtm/fzjnnnMNrr72G4zicdtppwZjFixczd+7cQOhs376dRCIRiByA0047DSEEO3bs4M1vfjPbt29n2bJlTe6uNWvW8MADD7Bnzx5WrFgx5PyeeuopvvWtbwXLK1as4Oabbx5zv4wxs3AhvO0Pg0W3UsE5tA/7wF7sA3uwD/jzB/di7/sZtNykXaFQNTKeCIp1YnYuo5pdQjXZRVnPUiZGydKQEiplSaXsMNA3/I0+GlNJpnQSSY1EUieR0kgm9WA+kdTR9akJJl7VZp1pu+wZKPH64RI7+4rsPFzi9b4Su/qKVCw3SIf/9z3N71uQjrC8M8HyOXFW+K/LOxNIKVmwYMGUnH9IMwsWLKBUKs142nqj6Gk3tW53HIef//znvPe97+WUU07BcRzuvvtuPvKRj/DEE08EP3j+6Z/+ia997Wtcf/31LFq0iHvvvZfLLruMb33rW0QiERRFobe3l7POOouzzz6b22+/nVKphGEYgeVIURQ+8YlP0N3dzWOPPYZt21x55ZV85jOf4R/+4R+G/UyGYQTf4/D7PD2E13mcSAmlkpfNNZCDXA7yeRTHIVooEC00pFbFYpDNeFMuP6PXelKFzsDAAMCQXzeZTCbYNjAw0LZ2SeuYVtO4qqokk8mmMa3tBWrHrY1p5d3vfjfvfOc7g+Wawuzp6cG2J6G0ZANCCBYsWMDBgwe9X59GHE44yZsaUB0H+nqQPQeh5wCy+yBqzwFiPQeJde+H/O/g0H8O2b9EYBppyh1LqMxdQTW7hEqyi0qkg4qSpOwaVCoC14VK2aFSdujtHv58daMeMB3EDTW4yaJxBV2fPEUeB96Uhjelo7A8CnQG6fBBW4xcNZjPVx0O5qsczFf5ya6+pn1lohqLUnpDKrxXD6grTIefNBq/z6ZpNllGPavKzJyXqhKIi9F4/PHHmyxFq1atYt26dbz++uusX78ex3F45JFH+NjHPsY73vEOXNflhhtu4G1vexsvvPAC559/Pq7r8ud//ucA/OxnPwOgWCw2Hf+1117jhRde4OGHHw5u7p/61Kf4xCc+wcc//nEWLFjQ1r2Wz+d56aWXWLp0KYVCAU0L49imiiH355DxoQjozHqT66JXKujFhsKG1SqUy9504CAsWsjBBV2Teq01TQuberajlt7bjqn6stdM88OiKDB3PmLufHjT6TTe1qSUUMhBz0FkzwHo9sVQz0FE9wEihRyRQzk49Er7YxsG1vyVVOatpNK5lEpqgWcl0lJUnAjliqRSdnFssEyJZToURmgTpWkE7jBPBDXHDEVjAt2YePyDAOYlNOYlNM5c2CyEG9Pha0HQe3ImPUWLXMUmV7FHTYev1QYK0+EnTrvvsuPAM0+Mvb/YZPJHf5phPCV/hPj/t/fm0ZaddZ3359njGe881pSiEkIgDDFJIYvGl4hNh7bpxoEEJQzKC0prt672FbQrqCgLCG0SstDQQLdhEcFmkJCmFdOKRtfL1L5ChARCksp8q+48nnFPz/P+sfcZ7zl3qjvX86m11x7OPnvv89Q9e3/PbxT1ZIVa24/R0VEymQxPP/00c3NzvPrVr6a/P65TMjg4yNVXX81jjz3GTTfd1GIpqlXizmQypNPpuoD6/ve/Tz6f58orr6yP1+nTpzEMgwceeIBXvepVHa9tdnaWv/u7v6uvtxeJ3EgPtloNJC2QNsa692fN+giBn07jpxuFbEUYYVcqiYurQmpocE/HeluFTs3Csry8XL9R1NZPnjxZ3ycMQ0qlUotVZ3l5uf7+vr6+VZ2royiiWCy27HP27NmWfWrNHDfTSHI/I4SAnj7o6UNcesWq11W1DLPTdfHDTCKIZqdgfhbh+zjP/hDn2R+yKu/GMGBgGDU8RjR8gurgiVgIpZM4ocCkUpZUK5JqWREEijCE4oqkuCK7XrNhUrcIdRJC6czW0uu7pcP7kcJzenjg8XP1ekATG06HT1pj9DbmOh3+4kBKye/93u9x+vRprrgi/m7NzMQmz/ZfiUNDQ8zOzq7K5nTduEBnNpttsUCXy2WGh4cZGRlpEUZ9fX2Uy2V6eno6uths2yabzbZU0b6QIpGbbTWii0Rqtgtlmfj5HH4+V09kYPKQ1NEZGRmhr6+PBx98sC5syuUyZ8+e5V/9q38FwKlTpzBNkwcffJCXvexlAJw/f565uTkuvzx261x++eWUSiWeeOIJTp06BcBDDz1UNzfX9rnnnntYXl6uu6y+973vkU6nOXbsGBcDIpWB48+B48+hXTaoMISFmUT81NxiiQiamwLfh7lpxNw01sPfJQe01PnN98LIOGJ4DIbHCEeP4vWOU82MUBHpODYoEUKVcmwZ8j2FjKBUlJSK3cWQSNLr02nRlFrfKoxSqY2l17uWwcnRPD2yt+XXQi0dfiJxfXVPh28tpKfT4TePacaWlb0691Y4c+YMjzzyCF/60pe294LaqFmQaiLJsqx6PFA7lmXx9re/nbGxMZ599lkqlcqmW41caJHIrfRh0wJJs9/Z9F9otVplamqqvj4zM8NTTz1FLpdjaGiIn/zJn+See+5hfHyckZERPvvZz9Lf31/PwspkMrzqVa/i7rvvJpfLkclkuOuuu7j88svrQufYsWNcddVVfPzjH+cd73gHYRhy11138fKXv5yBgTgV7iUveQnHjh3jj//4j7nppptYWlris5/9LNdff/2eB0ruB4RlwcgRGDmyWgQpBcsLLSIodo8ly8VC7DIrLKMe/yEAJiRdVAA3DcNjMDKWCKFxxPExooExqulBqp5oEkGSakXVrUNeVaEkVEqSyjrp9W5KtGWTtQmjlMC0OouP5nT4l3ZJh6+5wTadDl+zAul0eCB+mB+kZ93NN9/MV7/6Ve655x6OHGmUDB8ZGQFiF9Lo6Gh9+9zcHFdeeeWGjz8yMsL8/HzLtjAMWVpaqp9jLYQQ2LaNZVlbqqK9lT5stSKRNWv7ZuhURXutViO1+V7XO9NcPGz69vT444/z+7//+/X1u+++G4BXvvKV/Oqv/iqve93r8DyPj3/845TLZa644grOnDlTr6ED8Na3vhUhBLfddhthGNYLBjbza7/2a/zJn/wJf/AHf1AvGPi2t72t/rphGPz2b/82//2//3fe85734Lour3zlK3nDG96w6UG42BBCQN8g9A0iLl99A1flUmz5qbnEZqca1qDFOfAqMPEkTDxJzYaiiGNs0qZJenAEhscQw+PxfGQMnjMOQ2Moy6Faja1BlYpM5k3WoYrEq6iWjLJuYghiMdTTW8G0olaLUKZRibo5vX6t7vDlIIqDoNvigKbWSYcfy8W9wNrdYIelO/xhQSnFe97zHu677z6+8IUvcOLEiZbXT5w4wcjICF/72tfqpS8KhQIPPPAAb3nLWzZ8nmuuuYbl5WW+973v1TNQv/71ryOl7FqaYztorqK91SKRm2k1spEq2mth2/amhFHttc2WMNBohNKRWMzOzrZkkWwHzQXWDtMQqyCAuenOImhuGsJ1xrF3IBE/sQhqWc7mEUKgpMLzGlagmmusIY4UlYpEdfeOtWA7oi56GjFDoqXukLVGRlkQKSaLjTpAcVB0vOxF3f9vhzMWR5OeYM0CqNc1D5wbbL2CgQeB//yf/zP33nsvd911V0vpinw+X3cn3Xnnndx5553ccccdHD9+nD/8wz/k4Ycf5v77769XZZ+ZmWFmZobvfe97vOtd7+Kee+4hm81y9OjRemzim970JmZnZ7nlllsIw5D/9J/+Ey95yUu48847u17fysoKvb29B+a+URNIm+3BVgsC3yqO42y4OW3z680C6bDen/cjOzXWtm1vOOtKCx22X+ioB7+NvO/PSQ8MUTVtyGQhk4NsHjJZRDYP2Vw8ZXLxNuPgm3GVlLA03yp+ml1i5XVM4ulsYgmK3WIMj9ddY/QPIppuVEopfE9RrSjSqT7On5uLhVGThahSkcgNpj1bNqvacbQLI8tuDaJu7g4/kbi/agJo2et+4rxjcKypK3wtDmg/d4c/DELn6NGjHbfffvvtdUuwUopbb72Vz3zmM6ysrHD69Gk+8IEPtAij2267jdtvv33N49SqujcXDHzf+963ZsHAgyZ0toqUsmObkW6912pzz/Mu6LztIqi/vx+l1JpxSa7rHrgfJfsNLXT2CdstdPy/+0uiz/0JtgxXxcd0JZ1tCJ9sDpHMG9vyjW010ZTNgps+MF9EVSrAzGSbCEqWlxbWfrNlwdBok/iJLUFiZJzxF13F1Nz8qi+RUorAVy0xQu0xQ5VynF6/EUyLNVPrUxkDJ0mvX/Fa44BqqfEzxYBuX7iWdPi6GyyOM3L2OB3+MAid/c7FInS2ipRyXctRJ9eb7/sXdN5m0bPRIG3HcQ7MfXmn0UJnn7DdQudv/vlp/vj7FQQKB4VDhKMiHBniSh8n8nFCDyfwcMIqbhTgyHjqtOzKAKfLPg4S13VwUimsbAYyOUS7GMrk4211C1IinPZR0LbyvDaXWCNlnvkZ1lQjQkD/YIsIYng8jg0aHosF4hoEQWvMULMQqlmIAn9jXxPDoHPgdDouxGg4MOsHnCs0MsJq6fCh7HwOQ8BI1u4YB7Rb6fBa6Ow8WujsDFEUrRJInufhOA4zMzNdRdOFPBNqPeM2m8F2GAXSfhA6ByhX4uDgp3uACgqBh8DDAGHHqUsmsEP6wlARThTGYqji4xQDHBniSB83mkqWa8LJxxES1zBwLAPXNnFsE8excV0bJ+XiplycdAo3ncbJZnBzWZxcFieXw7Wtbc02Eq4LR0/A0ROrs8RkBAtzTS6xJhE0OxUHRy/MwcIc6pEHG++rLWTzDZfY8HgjW2xkHHoHsG2B3WuS7+0uGsJQtQifTlllvqfihq1FSbkI6zVsPZFO8bxMhtRRAzct8AzJYhgy4wVMVHwmCrEQKgWSqWLAVLFzOnynOKBBnQ6v0QBxXaFMJlMv8Agbe/hGUbSlDLYwDFFK1dc3g2EY6wqkToHatm3r7/saaIsO22/RiaTCixR9g8M8OzmFF0r8UOFFEj9S+KHEixR+JPHCeO5HKt4vUvFy22vN+3rJPrXj7BWWDGNLFRJXSByhcI3YBeNYBq7VLp4cXMeJXzMFjmngWiLe3zRwTIHb9JpjCdxku2N2LzI4mkkx/dB3kTPn6+Kn7hJbWVr7QzgODI01ssSaUuYZHEZYG1elUaTwKq0xQu3CqFrZ4P9XU8NW0wXfUBRVxEIYMOUFPFPyOFf16RaPXUuHP9rjcLwpHX4s52BtQaBqi87Ooy06u8dOBiOHYbjpDLZakcit0l5Fe6OB2rtRRVtbdA4ppiHImgZDOZcg5+zoDUspRSBVi5BqEUzJcstrYezr9qs+vufj+QG+H+IFUUOESYUvwVcCDxMfE98wCYzGgz80LEIsWirOKCBMpjoS8JJp6zimaBFBNaGUT6cgyuCYz8UdfB7OaEM4ZSKf/tIcvYVZ8suz5JZnyCzNkFqaxlmeR/g+nH8Gzj/TkioPxJUNB4ZaCifWUuYZGYsLNjZhmoJMzmQtT5mUCq/aFjOUZJVVmsRQa3p9/YLox6Efh+eTBQtMG5QNVSEpqJA5P2TK9ylGkun5gCfmqy12peZ0+KO1DvE9cWB0eocau2o0FxOWZZHL5cjl1naZtxOG4aYz2CqVSr2y9oVU0d5sq5GDViTyYF3tAWFmMuCxH3j09oUofBxX4KbiyXGNeNk1sOwLb10vRM0iAjl2Pl4jCgL8QgG/WMIrlvDLJbxSGa/s4Vcq+J6HXw2o+gF+EMZTKGOBJUx8w8Y3bDzTri+vWjdtfMPCMxyipmy0mnhjlR1jPfNwCjgeT73E0yVgyojh6iJj1XnGKvE0Wlmor7syiOOD5mdQD38XoCWQuJzKsZwfodA7TLl3hHL/CF7/KP7ACCLfh2MZLZYpN7F0OabATRlkcgZ9polrGi1uwFp6fbNrrJOFSEqIAiAAB4NBHAZxeJ7RKsAioaiIiOUooqAiSoWI+ULEMxT4qoooIQlQDGUsjh2SdHiN5qBRKxC52SKRNQvSRoVRTUhdaBXtjQZpp9PpPbcAa9cV2++6evIxj4e+U1l3P8MAJxE9sRAycF2xelsqbpZpHOAKvEqpuO1EqQDlIpSKUC7GmVjlIpRKUC5AqYhKXqNUICqX8T0PT1j4phOLIiMRQsl6LI5iYVRbbggoB9+w8O00vp3Cs1PxcRJh5RlWbK3CwFcCWYsQUop+v8BoZb5FCI1VFhirzNETrv3LqWI6TKcGmUoPMpUeYCo1yHQ6Xp91+5Bt5QRMEbez6OzGEy3uvtiyZeAicKWBrQRmGE8iBHyB9BWRx4ZrDflKUkJSUhElopZlLEVfj82l4z0MOJJrBmFssA/L2HoDV81qtOtq97jY6+isVUV7rQy2WhXtzXLNNdfwile8QruuDhOj4xapl2dx3TyzM0t41bj1gVeVeJ7Cr0rCEKQkdluUN+abdVyB68biJxZDybLbEEQ1q5HVpTXCXiGEANeNp4GhxvZ13mcCtpRkq+W6OKJUQJVKUC4iykWyQlGcnkKVi1BajPcrFGLx5K0vOGsoIBQmvlkTU3YsjDI9+Jk8/tAAi6kTTKeyRKaNHXjYfplUtUi6skK2tES+uECuvEQ68jlZmuRkaXUju1AYzKX6mUzEz2QigGJhNMCS6ay+uC3iIMhikhVGMjfJYpAT8XIGAwcDR8TzftHhlqCAZQiXFEUkhasVthkiRRxYLQQYpsAyY/eibRqYIv4/FyL+P9aCSKPZP2xHFe3NBGmvVT9qN9BCZwfI5EyyeYvx8QEmJ72OKjYM44J3XlXW5zUxFK8rPC/e5nvx+30vXi6s0T28hmlRtwp1thrVXGixtWg/P4iEYTTS4mvbanMh6Bsfp9Lll5kKg7hQYbsVqVRqsS6pchFRKmCXS9jlItlSAfxC7BUrnN/ahVsW2E7c0h0FYQS+h6Vk3ULE4uq3hfk+vIFRqn2jlPuGKfWNsJIfYTk/RNHJxvFTLcHtSfB6EqBeD16vr0sKUUQofToV8bERZDASERQLoZogyiTb0sLAEoJeLFwiUiKJ51HJJIEAAsAjQhIXVJRAhIp3E/FETRyRCCViQWSsWm6IJWPVcrJOsm/bskaj2X5qafOu69abaW/kPTXr2V6hhc4eYVkCyxJksusHgCqp8H3VYhVqEUTVmiCK51LGZWfKoVy3GDE0Gmg24oearUbGqviig+RCE5YNPX3xVNu2gfet72orNkRSk6WpLqqUgjCMp01iFZawCktkn36EwVUvWpDrgd5+GBhGDI3C2HE4dgLGjyHS2a4P+kiqugiqB6h3EEyrgtjDiGIY4hgpSssVLrUNAkPVRY4ADEQsQAATEUeLdbqO5D0RIFFIlcxRhECEjEVS17KKG0MI0SKWGsvJesfl+HN0Wo5FVKuFqvn4Go1m/6KFzgFAGLVgZmCdgOM4OA38moWoySrktW+rKoKgW4ZPd2xHdIwlagRdG3WxZFoH80GwVVcbJK0wOrnaaiKpyYpEqSaaNuhqC8O4ivTSAjz9+Co5oKBmzourbed6oLcP+ocR+V7cbA43k0vakDS3JsmtWUBydXr56n2VikV2GCmCUBFGikjG3epjQSTqf71xSalEKXQZ1YYVSNWNRrEogkgpokQoqeTc7dfSqLyw0zEYiehps0qtFkfJOqtFliFgqRJytlhkPFiksFzBMWgqwbB+mQWNRtMZLXR2gFoQ1+Dgqt/jO44QAtsG2zbJbiB4X0aqbiGK44c6WY0aYkkpCPykUnAB1uosDrHXxm2KIVrLauQ4AnGArEXdWMvVth51V1uzSEqCtdXKIszPxv3EVpZi4eRVIfDjgK8asTkvPs78TOPYzefpdHLHrbcgiato5xHZbL39SGH8KDKSqKPPQblOHE1vmGAYiVtJYJpxmr3bIcwokoogsSIFkSKKFFGsXjCIRYBBYklJxkzU1E6XkRQCDEMgjPhyRGxeisWAUPW3SgUfu/OP+ev/fR9PPPE4KTfFVVdfw6+/67c4eepSlEpcbUrxsY98mC99/rMUCyu86Eeu4d2/+z6OXXIyFlUK/ucX/gdf/cqXefTh71MulfiLr32HfE9P3VIFij/9xEf55v97P2cfeRjbtvnLrz+w7v/9+SWP2/5pBnh2zf3ayyzUa00116cyRaMEQ3O2X1Oge004uWscxzIO5g8VjaYZLXR2gCeeeIK/+Zu/AeJOu9lslkwmQy6XI5PJkM1mV02Os30BqJvBMOPO3unMBlxoSe+odqtQbdnzZCKU4uUoBBlBpayobCTgWoDjiK6xRO3bTPPw3YC36mqTUQjT52HiKZh8FjU9CfPTiShaXr+rPIDvxdPSfH1Tc22hpdrG//s3wW+2PAmUYbQIH8xk3rTNMExcw8A1TbANMCwQAkXcIT6IFL6MSxHUrEFCNcSPQWwFMkRDDCkVF2pcrbcbUq4mhv7xW/+HN930Vl7y4pcgZcStt32IX3nbW/i7v7ufbDaDEII777yTz3/6Uy3dy3/znb/Y0r08Z4S85l++itf8y1dxyy23cLzXoafXTcQSKBSuEfHvXvtvmbz2Gr74+c8zlLGRxN8hpWhZVsQiK+sYXDaQIhIGpWrQUki0uS5o9zIL248haBFILYLJEq2ZgE1Zg26bmGq2TLntWYRWQ3BtZ7V1jaaGTi9n+9PLn/neg5jnJ1nxPAq+R8H3KQQ+Rd+n4PsEcvUNyrbtVYKokzA6SL1QwjDOMPM8tSrrLBZDDatRLeB6M1gWuCmDXN5FmGEihlprFdWCsW374jX5K6WguBI3VJ2dgqSp6oarRxsGpNIYbgoJFG/8JXpGR5OnuozdpVut6hqrkIZAap4Lg9Aw8YVJgEGAwJeCQMWuK4HANi1MYWDSJIaEiAXROqdeWJjnx155NZ/65Oc5ffpHQcArr7uWt/3iL/GOd7wTw4BicYXTL72a2267nZ/6qde1/A194xvf4IYbbuAHP/hB18DMz33uc7z3ve/l4YcfXnco1kovj6utr46lqhX4rMVbrY6/Wl2BvbbuNcVrNVdr96PYJbgXWIm7rpNwat/WvL7aKtXJetUQWynL4MSxI0xPTV2U6eW7ia6MfEi54sgR+tb4seVLSSkMKfgey9UqK141FkO+T6FSpbi8wnnfpxT4q1wMlmV1tAjVpppA2g+CyLIE1jqVgmtIqerB1b63OpaobjWqNnpKhSGERUmpuH4KuTBoS8fvbjVy3IMVcL0eQgjI90K+F3HpFateV9UKzMW9w1oaqs4mDVWlhHIJWYtsDwOoCxtBKAQf+8p9u/eBmnjjT/8syk0TCAtfmITCaPHLCRoWIROwDYElBAaCYqkAQG9vH0rBs888w+zsLKdP/wu8avwFNo0cL3rhVXzrm//Ej7/ytRiGiF1kBniJOA8CSRiquutsJ753piHIGCaZXejDq5QilLSIqU7B6+0ta1q3te3bLqba2tnUCCWEUlIOYD23+IXzCLYhWmKgVlmqOrj0msVURwtVB9egawldd2oP0UJnBwgyaQpjo+Qdh+ryMkYQYoQBZhAilMIxDBzHod9xINc9kEYqRSWKKAYBK16VlWq1bh0q+D6FmVkm/AkKvo/X9ovaNM01BVFtcl13X3z5DEOQSgtSadhQwHWg6jFFmUwfU5MLeNWobilqDsYOg7hw3qYDrlsCq5ssRc1iyTWw7L0fvwtBpNJw7Dlw7DmrG6qGISzMwtwUvV6FpbM/jEWT40KQDOwWMsu2ix5vBTtqFG+UQhAIm8C04kKRhk1gWPGEINEvSCn5g1vey4t+5BoGLjtFQfmcn4vLCAwP9WGKECEMFILBoSHm5maT96l6OFTgxwvlosQyGn9XsRCKBVHgx+4p35dxDFIikvbDd64bQghsE2xz5yutQ1KbpVkwtYigdmtULKa8DmKqW5mF1ixCRdhkrgpkEm+4C25AAayKpWpy23V2DTYJpq6uwYY4c+uuQGNLPe0OK1ro7ABBJkOYzZIfH2ex2VynFEJKjCDEDEOMIEjmq9eNMMQQgqxlkbUsRtPpNc8ZSkkxDCh4fquFyPcoLCwyPz3NU37sPouazIfNgmitOKJUKrVvbs5CxLV/bAdEj2B8vId0rrRGF2K1fq2imovNi4MmagHXxY3ULDKJrUJNlqKWIo5NgdfOPq9Z1I6wrLjX1+gRcuPjFCYnEcvLiJ6eeLyjCCvweeebb0pMbEFjWs+dZRhg2U2T1bLcbZyUUrGVScr4Zq5UHAwmJYaMcCOJm6wjKxBEKCkJlYjdYIbFe2+5hScfe4Q7P/VZFOAhqCQybz4uAgREmEoSyhATBd4CllKYQiAMEzuKe7eZKkIgUYmzTCbvj4AgiP8my8XWv6NmMWSIeLlakYReQDbjE0WxhehiQIjYquJakN+NNjZSEUjoHxrm2fNTVMNo3TILqxowt1uv2veNVH3/mq5SkLgZoziPY4cxBS0iqD2Db6Ouwc7WrdVtbYx9fF/TQmc3EQJlmkSmSYS79r5KxWKnLoJCzDBoWTfCEDMIMKTEMgz6HJc+x4V1eqVUwjC2CnkeK75Xjx0qFIoU5hfippCBTzkI6l4AwzDWdZdlMhnS6fS+e5Cbmwy4rsUMeW3xRa2B1/E2GcXP80pJUtlgzaJaheuGy2x1raKaWNrPAddCiFiQWBZOOrPqdSWj2OpTE0BBkwgKQyARKH4EfrX94HGavN0khJqWxSZVgAAcwFaSm2++ma9/42vc8z/+jBNHx4iiEF8qqkN9AJTnphkZHiLCIBIGcwsLXPa8FzDnNL5XQimWk+XAX8Es+9gyxASUMFGGiRQWdlgBpTAjDyVMpBHfcpvFUDJaFJYjfvBAka9zFgDHVqQzglTWIp0xSKUNUhmDdFqQStb3WwX0g4BpCCxT0J9xqGZtlNrZx2AoW4WSl4ip5qKea1mvuroGEzHV7mKs3bMjBZVQUtklg6vV5Aast64xDVzL4NUvCHjVsV3wu3a7tj07s2ZthEDaNtK2We/vtGYlqrnHYgGUWIXarEZCKdKWRdqyGOnwcGomUopS4CdB1UkwdRDHERWSOKKi77Pie/UAa8MwumaWNU/7URBB8usyqVmU713/12XsQutQq6g5NT8RSzU3hpdkprG8vrXIsmmpS+S0pOo3W422p0nsdiIME1yTTppe1apaNouf5mWlGssdUKa1SvzElahtMDo3IVVK8Z73/A7/+3//NV/4whe45NQpIL4JWsCVL34BIyMjPPLQd/jxV7yUSCoWlld4+MHv8fNvejNZ28CPFIFUKCEIkn5li06eMN0oo2+rCEdF2DIgUrGgy5SnMJQEBFIYKMNCCgtpmChhIQ0LOyyTKZeppgaQho0fCPxlWF7ufgew8UkZHik7Iu2qWAhlLVI9Lqn+NOneNLZzkZiG9imWIbB2Mb4qkN2sUs2B6FuIpWrZLykTIZvjqxShryh1cANecaQCWuhoLgRlGESuQ+Q6rJk7phQiirq6y8xELBlBiBlFmELQ47j0OOtYnyA2x9YyzPxGhllhdo7Fc+d5Jnmt5Aexkd8wSKfTa2aY1QSRsY9t+JYtsGyT7KYCrmVrLFGLQEqCsb240F78rJeUNlKzqL1JbJcWII4r9jzgWhgGGE7cIqON2CWWVJXuJIRkFL8ehZ0LLBoGqoMV6Mx7f597v/xl7rrrLnK5HDMzcY2hfD5fF95vf/vb+chHPsKpU6fq6eVjY6Pc8O/+DalU/D2Ymp5mamqG4sy5eP3px1hIZRkYHSff00sgTCampllZXuKxuSVCqfjriSVMQ/CcEyfoy2VxhKwLIlNGIKuEaoEfn/88pl+lVPKphg5VI0vVHaCaGkjm/fX1yHQJcAikQ8EDPGCleSDi1vZWWCEVrpCSJVKiQtr0SVkRqZQilRakczZ2LoPIZevFI9crIKnZnwhRs6RAbhfcgFLFZSEacVOdXHmKF5wcB7W5DunbiU4vZ/vTy+EQdMdVqkssUdDkNkuE0SZyUZVSlIJgVcp9s0gqJK6zShgihFglgNpT8E+dOkWhUNhX1owLJe4urLpmnbVbjbYSD7zahda9SaxtG22VkTfeCHC7UVHU2QoUBLH46cKxf3Fdx+2333ILN77hRhKTGLfeeiuf+cxnWFlZ4fTp03zgAx/g0ksvre9/2223cfvtt68+zu238zOvv4EgUvzm//Mb/M97/nzVPnf8yaf5kdMva9lmJA+npZVlzhYtXnxyjGxUYihjYciwYwFJVSwSlqpUK5JKVVANTCqRQ1WlqBo5qlaeqttPaG+smaIZebjVRdLeAilvgVR1gVRYICWqpEyPlC1xUgZGvZhkYy5q4qj2WjoTW/P2MQf+/nyA2A/p5VrooIXOhSKiqMVdZgZBW3xRQyhtRoqEUjZZiDxWEgHUIogSwRQpRTqdXtdllslk9rWFaKtEYRcLUYdtvq823RXBtCCbtTEtySXPDRgc6o1bGNRSqo2kQnGtN9QeiU4lZSMGaFVcUOISW4u14oIuIAspSuI0gvqv3rg4YtiUWn1+bonb/mm5vu6YgiN5h2O9Dsd6HI71uBzvdTjS4+CYa/8Nx73aPMLlItWlEpUVn2oxoFKWVD1BNbCoSoeKShMYqQ19BkMGuNXFWAh5i6SrNVG0WBdHrr8cV6dOZ5qsQ9nVYiiTQ2Qb7Ufq4sndnaSHi+n+vNfsB6GjXVeaC6YeYO1uJMA66hBLFKwOuo7iAOv+VIr+1Po34koYtAqgikdhucBy4DORCKWC71MKAlIbFETmLqXXbgemJchspUlse9ZZe3xRU5PYleX4x8D4iVqBx+43LWEkfZ2asopqtQFFsm6I7U+1FoYRp753cLfWXWJBB2tQGFD/oFEIrHaJKcNcHRdkJ7FBZvcsMYgDYNOGSbrNG1Qz/fuRQlZtXnFJnqmS5OmFMn6keGrJ46klr/UzAqM5OxY/vbH4OdrjcLzHJefGf7Nxr7YU9kgKe2SItdITolDFlqGKpJpUMa+s+FRLIdVKLIy80EIaNpXMCJXMSNdjCRnh+kuxRagmgkoLpOcXSHlnSVUXcf0lDBV1/usxLchkE0GUbwiiZjGUtCRpFk3a1aZZC23RQVt09iVSroolqgujtgw0sYnxjZSk5Hd3nRUDnxUvfs1M2nd0c5vV1i3r8P5eqDeJ9RT57ADnz88izCK9vb1ImbQwkLUiyWpdg0knVgufJutQm2Bih6xFqpai3pwl1iyG1nCJxR/CaKTHdxBCQqwvQJsrI0+cO8900Wdi2efZFY+JZZ+JFZ+JFY+S3z2IvS9l1gXQsR6H470uR3schjJrC7H1kJGiWk2EUEVSLUsqFUW1LGORVJZUqxu0FCqFK0tx3FB1kVRljlRpJp5XY2tRylvElJu8JztuIo6yrSKpg6tNZHOMPOdSZoplVDq9711tBxlt0dFoumEYRI5D5KwdYC2A8eFhZiYmMPygxUq0KgMtDDGFQY/r0rOe9Qnwo6jFdVZYXKIwPcOM3+o+i0yD1DqZZgdVENWaxDqOwdh4BmU4LC+bpNKdH9xKqVXCR0oSQaRqXSOQyX7xe0BFqi1Xo/sNsVaDpmEhanOhJcJoMy40UUtnNzv/HzVcYl3igpSMm6sGfgdjkEA11wiqC6HYTdbpIWsagvG8w3je4TSNSHelFEvViIlE/Dy74jOx7DGx4jNfDlmqRixVKzw003oRKctI3F+JK6zX5XiPw1je2VBhOcMUZLImmTVCfqSMLYJ14ZMIoRZhVJEoKfDMHJ6ZY9k9Ap27Z+CYYZxRpiqkZJFUsEzaW8Qtz5IqzpAqnMcqLEKlFP8R1Xq1Lc41xqvDcWvbJmsbhGh1tWVzDYtRZn+42jQXhrbosP0WHaf0CNnF+3GyA5QDE2lkUGYGaWaQRraxbGZQRhqE/jWxVTb1a6GlNlG7u6ytYGOHfmRrUQpa44ZWmtPxE4tRYBiIDlai9mk/CqLmcd6uYOR6U0sVPySVpEkYNQumxr6bv+5Gd/PVlqLVgmmrn6NrXFCtevRamLFLbGVpifyj36P/uc9n2Umhhsagt3/D11UOIs6t+HXrz7OJAJos+F17V5kCxutxQG5DCPW4pO3tj2Wr1amqCaFKs0WoSRjJDXZ/sO2korqrSFkBacMjRZlUFAujVHUBq7yMKMdB3HFgdwHKRUS5FLc/uRDqrrZ8qyBay9VW28+6OFxt2qJzSDGDeezq06jq06xdzzhGGulE9DQJIDOLNDotx3OE/q/bNC21idb+nxGRbLEKdS7YmLT1ALK2Q9Z2GFsnySWUshFQXfUprBQp+j4TTc1ffSCyLFLrVKu2D3hMghCxyAA2VBhRKdVqHepgKaqLpkYx8rbu5mvEFSUWoIYI6uxCaw+4FkLEafJdU+Wj1ligZiEURY1peRH1F59jofkAbgqGRmF4HDEyBsNjiOFxGB6DgeG4cnVCxjZ57mCa5w62/m0HkWKqWBNADUvQuRWPaqgSl5gPFFveN5ixON7kBjvWG8cB9aY61ynaCI06Vd1FlFJxzalKudk61O42k0m4VZydGFcarlVEygKNB6BpQXqkVmzRIJURpDMmR44OUi7NkVJlbL+EKMcCSNUz3BpzlYij5m31eK7CcjzVrr/TZ+r0Qd1UPVi7q6utWSTV5gcgq22/oS06bL9FxwgWsL3z9OdsCovnEWEJIcsYUTwJWYqXZXX9g3VBCrfNMpRtEkmx5aixnEGaWTAO9oOxE3seC6UURhS1Vqpuz0ALEmvRJq1E1TBsaeXRnnFWVQppWwjXJbNGLJHjrH4Ab5adsOjsJHVrUZObrC6GWoRRo3fVZqlZgdYMuF7HhdZcPXplepL81/8aZ3kBb+JpWJhb2xpkGDA4koifsVgMDY/BSLLsrh3EL5VivhzGQiex/kwsezy74rNc7W5SyTkGR5MMsOY4oJGsjbmLtZmCoNU11slCFPexWh/DJBZBSdXpdFKFOpUW9arUbqrRvqWW1UazACoVY6tRUykAyqVYJDULpJqrbaus62rLJ5lu+8PVth8sOlrosIfByCpCRBUMWcaISoionCyX4+WohCFryzWRVEZsNje4djphJ+6zmmUo22ZJyrYumxmUcGCXvxibYc+FzmaQMhY/7e6ypirWdcvRJj6KTCpYryrWmGyryIjIslCOg71GxtlaguigCZ3N0OxCaxZGzdYhJZtcatsccF0TTCuFFXp7ejl+4giTk5PIwIe5GZidQs1OJvMpmJmEuek4HmgtevriPmWJCKoLopFxyPWs+cAreI04oJob7NyKz3Qx6Hr36ZYOP553cK29KekQJhll1bJssxApotBkZdlLMgjXRxiQSrUKoXSbMHJT6xfiVFJCpdwkhhIrUqkhjmr1kmi3Lnlb/3EM7ImrTQudfcKByrpSEiGrGxNGsoyIYuuR2GJ3XoXZ5DLLtsUbJW61ulWpJpbc+K6wCxwoobNRNtD8VfgBRhBgSbmp2kRBFNUFULElqNqjIiWhaRIlVqJ0kwjK5XKcPHmScrlMpVKht7dLBOlFwFoB162ZaGrd8Jxm5ueW+cEDJpYl6tWruzaJdcDxlrCXpmBuCmbjSc3Egohyce2TpdKx+6vJCtRwiQ11dY14oeR8oeEGezYRQudX/JZ2AM20p8M3u8Fq6fC7TfN9IwxXu8ZiQdQQRl51g/cWkYihtJFYghKLUGIVSmcEqZSBscUedioMEuFTahVJ5TaR1MH9tm7m4HpsydWWR2SyHDl6VAudveZACZ2toBRCeQ0xlFiGjGgdS5La2hdDYaDMdKv7rKswqomj9JbE0b4a571gneavwvcRfoAZRVibHJ9yUsG6GPgtzV8HXvRC+oeGUEmwijAMjGQyTbO+HMfg7F9r4G7QyYXWLeB6dnaJH3xncw/+bk1i05RIl2dIlWZwV6awlqYxF6ZjUdSUldQR04pdYiMNC1BdBA2NIjrUKIqkYqYUNOKAVvxEBK2dDt+bMtvigOL5habDr8dm7xtSqjhYuilGqF0YVSsbt/a5iRhKZUSTdahpPW1gbmPD1q6utpo42mFXW/Y1P433s7+ohc5ecuiFzlZQClTQEEPNwqg53igqYyQxRyIqY6h1zOndTodAGakWy1C3QOzmzDVhWAd7nHeRtZq/Cs9H+D5GGGJLyVqS84nhAfL9/fV1RfwgiJQiUhKp4mWpkk7KQsSiqEkQNYsiLYhiVlZWyGZ66O0Z4tlnp5OCjWs3id0slg0pK6QnnCPvz5Apz5AuT+MWZrCXpzGXZhDr/fLvG2yIoLpLbDzelm0tTaiUYrka1WsBPbvicy6JA5ovdz9PyhJxHFBTOvyxntgNtpF0+PXYifuzknEl8k5CqLZercgNx4TZjmjpVN9uIUqnDSx75783dVdbk0iqu9U26GrLve7nqf7bN2qhs5doobONqDARPaUm91m7SGqzKl1IULbhYjg9BMptC75uFknZFqvSYQzK3lY6NH81w5Ae16U0v8D5lENPbx8CtSm3GcTWC6lkIooUUioksSBSCJQADKPFSlQTRRcqiP7oj/6Iv/qrv+Ls2bOkUimuvfZazpw5w2WXXdb00RW33norf/Znf8bKygrXXnstH/zgBzmVdDoH+PSnP829997Lgw8+SLFY5Ac/+EGLK+/ZZ5/ljjvu4Otf/zqzs7OMjo7yMz/zM/zar/3amnFQzQUDN3LfaGkS66m2CtdJxeum1zfkRlOSVHWBbGWGbHWavD9LtjoTW4cK05jBOunYmWzDHZZMYmQ8FkR9A3Hl6oRO6fDnknT4qMtH75YOf7THIWNv3Bq2V/fnWnp9u2us2XVWKW88vd6yaRJBiWuszUJk23v3Q0KFAaJcYvTIEWZKFZ1erjkkCAtp9YDVs06v7SZU1MF9FluK2gOxGyKpgkBhSA+qs2xGusRB2e2B2Gtnru33oOxtRcQF7kLLglRtk6BnfJyVyUmi5WWiTJLCnETxChUvC2qFcRr53kJRF0WGAEMY6954YiuRRIZRXRRFiYVICVrcZu2iqGYlaudb3/oWb33rW7nqqqsIw5BbbrmFN77xjfz93/89mUwGgI9+9KPcdddd3HHHHfXu5TfddBP3338/qaQVSaVS4brrruO6667jgx/84KrznD17FiklH/rQhzh58iSPPPII73rXuyiXy/zu7/7ulv5LOmEYSQ2ZLsUbm6k1ifWrba0/6s1ia1YjA88eYj49xDwvaD8IdlAkU5khW5kmU54hU5/PkPKX41/3T59FPX228bba3LJRg6OIkXGMkTFSw+NcNjLGZcNjcHy03sKhng5fywbbw3T47aY5vb63v/M+tf+rtWKGqhVZr1BQDCTFle4q1jRpET6dhJHj7owYEpaN6O3H7OmD0gXWLLqQ69AWHW3ROZAoiZAVTFlhuC/NwswziKjUFoi9OkB7y0HZwloViN1sKWrPVpNGBmWkDo04WjPrKnFzrktdFLXOG6KoIYg2PGrCRiHqrrKa60xCIooagqjddba4uMhLXvISvvjFL/Kyl70MpRRXX301v/zLv8w73/lOILayXHXVVXz4wx/mda97Xcupv/GNb3DDDTessuh04r/+1//K3XffzTe/+c2u+2zWorOTbKVJrBl5ZCozdeHTLILS1TmMNUxKCkGYHyTqH0UOxpYgc2wcazyejGwOpRRz25AOf6zH5Xify0suO8HM9NSBvT8HQXNGmazHEFWahNGG0+sNusYM1YSR6wrEFtyG+yHrSlt0NAcTYaDMLJGVQ/SO45dzG6qMLKTXZilqxBx1zVxTIUKFmNEKRCsbvsQ4KLs5ELuRudYtk22rQdl7igoYeeL39uTUi2O/hTAcLFGTR2uMnVLIMKyLosXzcROAtGVRXVnh6YkJZmZmeOm11+J5HoZhkM1mueqqq/j2t7+9SuhshpWVFfr6+rb8/t1m601iB/Crz6u7y1ZqVqNyiFicxVqaJl1qFUGZ8jSW9LELc9iFOXjm+63HBypOnmpuBNUzykjfGEODY1w1NIYxNo515TiBoZgLQmYqPueKQV0ITRcDir7kkbkKj8y1WhQc80mO5O24IWrNFdbrcGQP0+E3g20LbNsk39PdZbeqYWuHmKFa495ySVIuAV1s8UKAm+4SPJ1YiFLp9dPr9wItdDQXD0KgzBSRmQJ7cGPvUQqh/A2608otQdkCiYiKGNE6qb7Np0OgapWym9xn7cUf2wXUxdpGJEinQdiJdQhaKwPGViOhkjo2xNWNDSGQUvL+D93CS6+5hmtfELtoygtxTeKTQ0NkowgZxKJoqL+fqXPn8FdWkjii2Erke3FX8SAIiKKoq8vsySef5JOf/CS/8zu/s1vDsqsIo+aOAVjr77APpS4jCqkLoWpVslyRRItLqJlJjPkpzKUp7OU4QDpdmsYNCjh+AWehAAuPrzpqaDhU0iOIzAjZ9AhHcqNc1TdKNDBKcGyMogWLKmI+DJj1A2YqAVPlAD+SG+oO39wWI79H6fBbxbQE2bxJNt/9umsNW2uusWYLUT1+qBpnlFXLimo5gvnuFjQ3JVpdYxkTGZYw9lBtaKGzA8yXH2ei8P/xVHmIatnHFA6mkcIyXCzhxvNkMpuWDd3WYf8hBEq4KMNF2l2c6p2QQWvMUXNRyI6FIEsY0otdNzLef81upu2nM1It8Uadij+2WJK2Myhb2Myc+v3tOdYWzo1oLqHZekOvb2/qCSGU4rfPnOHhxx7jS5/7HIEQCKVa+kEJ4saaJgIzCYLOtvUgyyTzdBBiVapEShLV9JaIRevUzDQ3velN/Ot//a+58cYb1xREFwNCiKS3qUlrgtZYMrUiI4W3XCI4P4mcmkTNTMH8FObCFPbSFHZpHkv65EsT5EsTq98vDKqpQcrpEUrpUcqZEcrpEYrpEabTgyxZJgUzYllELMqQ+TCgKhVTxYCpYsA/nS+1HG+v0uF3kk01bG1v0tpWmVopkniviGbLkIqWufyFezc++sm6AyxWn+aHc38J65SraMcQJmZdCKVWiyERL9ttAkmLqH2IYSONXqS1icJ6KsSIKh2EUaeYo5oVqZoEZVdBVjHDhfXPkyCF0xZj1K3GUQZl5VDRQOd6GkKAuPA2EztK7SEkBGduvpm/+du/5Z577uHIiRNALE6GThwH4HyxyMCJE3HMkJLMzM9z5fNfQCBEU3B1YyiEAMsQWG0ia2p6mje95S289Oqr+cj73ocRBC2p9+1xRNVCgfL0DFGlQjkISGcymObBsiBsN4YpSA/kSA88F1743FWvqzBoVI+emSSankROTyHmpjAWpjFCn0xllkxlliG+v+r9ntNLKR2Ln3JmhFJ6hLnMKOfTg8yZDktELBGypEJKSJarEcsdusM7hmA8G7u9TvQ6XDLgcqLf3bZ0+L3GMATpTGyp6fZzT6lG9/oWa1BZMXYkA+xdMLJ++u0AfanjPG/wJ3FSBivFRULpEUmPUHmE0musyyqh9FBJgKxUEVKVCWR5269poyLKMtrFkhZRu4awkFYerPwmMtbioOwN1ThqEkwCGdc8Cn3McGlDp4qegiFhUcq9HtMdi+OkhAEY8TJG4kJr3Q5iXwRlK6V4z3vew3333ccXvvAFTiQip8aJEycYGRnha1/7Gi984QsBWCmUeOC73+Utv/ALkI4zzVQyRam4cF7gugSuC1LGNUekZHJykhvf/GZefOWVfPiDt9TTqmuus05I4FSpAj98rN78tRSEVJTEA0LTILIscByMdAozk8XJ50jncvuy4/1uICwbxo7C2NFVEVpKSlhehNnJpHXGVGN5dgpKBVx/GddfZmD5sVXHjqw0leww5fQIBXeExcwYE9kxJlP9zNjpWASpkBUifKl4uuDxdMHjm+cbxzCAPtNi2LEZTduMZ22O5l2O9zn0Zi2cVOz228sU8O1CiM5ZgHEwch+Tk1roHCqGMs9lOHv5hiLNlVJIFcbip0kIhYkQiprXlde63iyaDoGI6mSt0iJqHWpB2WaWiI1lIMTiyFstjJrjjVrS/WuVsqN6YLZQQZIltaGL7CCI4nUl2pabXttucXTmzBnuvfde7rrrLnK5HDMzMwDk83nS6TRCCN7+9rfzkY98hFOnTtXTy0dHR7n++uvrx5mZmWFmZoannnoKgB8++ijZbJajR4/S39/P5OQkN7z5zRw7dozf+f3fZ7ZaqZt/RoaGUTJKBFEcS1RLvw+kohKFpE0LyzDoc1P0rS5CHFPx42l+kWoYUgwCKjLCA3whkt5mNsJ1MbNZrGyWVE8e64B3vN8MwjCgfxD6BxGXv7D1NSEYzWWZeuifkTOTcf+wWi+x2bh6tBlWyC0/Q275GUaS912bzJVhEfYO4+dHKeZGeTZ7jKfcESasXiZFikUlWSYiQLEQhSxUQh6pVGhuTZ/FoE9Y9GLRb1gMOxajaYf+tJk0ETUaFa/b2oHsx4Df/YxOL+fwppfvlojaSVpFVENINYuhntwAfjVs2U+LqO1FAGOjA8yce5zFQkBvTw8g4wd1MocIoWRju6o5Z7Z60g6iCLN1e9s+a4mjo0ePdtx+++2384Y3vAFoFAz8zGc+w8rKCqdPn+YDH/gAl156aX3/2267jdtvv73rcT73uc/xG7/xGx3Pde7cua7XV08vHxll6tlnCIpFwlKZqFyOq1b7AVYU4ShICUHGsrCNjWcHxc1fA8pRRFVJAiEITRPp2OC6GOk0djaL05PHdA533aj17s8q8OPGqTNtDVVnk4aq4drVo1XvANHgGDP9l/BM/hjPuMM8a/RwXjrMBJLSGuWRHQR9WPSJZMKkT1jkMOvWQNsRdfHTEEJxC5DWvmgGpsWeWov2Q3q5FjocXqGzk+yciIrfvy9E1CYsUe37HDYRteXu5auET7s4kh3E0QX833cUR+2CyGyxJO2XB/qm6+goBVFEWCoRFItE5QpUPfB9rCjClhIXQcY0yVhWV5dZJwIZUQpDqjJ2mwWmgbQslOtipFKY2QxOLo+RTsVFWA4YF3J/VjKCxYXYDVa3BE3WG6tSWcdqnslSGD3J+eFTTPQeZSI1xLNkmfBtZj3Z9aeBCfRi0dskfmoWIWuN/1vDpG4dqokfJ9XWJDaxFO1E4cD9IHQO191Ys2sIITCFjWnYQG5bj72WiGq2KMWv+6SyNkvL863bNyCids+dV7MopS4+EVUXE622nTVvd0qxShDVlyNWiaO6aKJpPTn9xi4SJcx1xFGba20/1DoSAiwLq7cXq0vBwhBYAZalJCyX8QtFwlIJVa2C52OGIZaUuArShkHGskhbFrZh0ud0CIT2Q/CLsFIEYtdfJQxb3GahZYLjgOtiZjLYuRxmJo00zX0jKi8EYZgwOAyDw4grXtzymlIKioWmWKDJxCqUiKDlBSiXyD/5fZ735Pd5XtuxfTvN+SOXMzF0inM9RzjnDjBBhnO+SSBhgZCFWrPlpr/xfsdi0LYYMCz6sMhLk2xgYksDGUGlJKmUWB9BLHzamsS2W4qcZJu5xS7su80BumNqLhY2I6I2+2thMyLqQixRWkRdAEIQu6jMCxNHNUG0ynIUtYojFEKFWxBHTYKok1ttHwVlC8PAzuWwc92/Tx5QVYqgWsUrFAiLJaJKBTwPIwiwwghHKVLCIGOZ5GwHyzBIWxbp9kdJEEFQhmIZZuL0U6lU3W3m19xmtgWOi5lJY2azGOkUyrZRBzTbTAgB+R7I9yBOtcsYUJ4Xd5CfTVLlm+YszOIEFU4+/V1OPv3dlvdFCGbTA0yMXMrE4HM4lxvjnNPHhExTlAYLfsiCv9qd1uuaHMk5jKZtRlI2g5ZFv2GTkQLfo6XCdZBUuK61BGF5fcuqZdNqIepgNUqlDPzBDadX7AjadYV2XR1k9tM4ry+imoTUAXHn1USUZbhkM72EvuKI/UoG+4cRxA9vIQxEMgeBIOlInuTBxMv7sHaMSvKn1rISdXSzbfXvrEtQdrK8srLMUKrEwMhxZhcrREYGZbj7w3rURtyc0qNaKCZuszLK8xB+gBmGOErhAhnTJGvZ5NZoZtoJX0oqMsIHAtNsyzbLYKTTSMdGWtaWxON+um/UUFEEC7NN4qfNJeatbn6sgGU7x7nMCBMDx5nov4SJ7CjnrB7mVLdI9tbu8EeTnmBH8jaDjk3kq45NYn2v0RNtw01iE577/F6e/xJDu640moPO3rrz1hZR9fWOIspDJQnta1qikgLP/f0vohpupW7OeiKoJpjE7oioWtuIREhsyHpUF0cd4ovag7JXiSOFUEkhtQ4Za5Y/Te/CnxOdh4H6dYi2Yo/tLUTi6tn15aQG0k6Lo7g5ZQo3lYLhoa77RcCc7/N0sRi7zcolVKUaxxGFIVYkcYmDq7O2TY/j4pgmjmHgNMf+RBIq1XhaWKpvVkolcUQKXxhElhlnm6VSGJlM3UIUWTbK3D/xWJ0Qptno+L6qn6qCwlKTG6whgPpmJulbfoIrl5+AJxvvqZgO5zIjnMsMM5EfZ6L3OBOZYabMHNXQ4PGFKo8vtIonU8BYPu4JdrzXjdtjDDuc7Em1dIdXShEGqt7/zKvKuGFsc5NYr7EtnbG4oNi7C0RbdNAWnYOMHuftIZJhHETeTUQpj2zeZWFxlh51OX19AyilYiuTUsT/ZGMbqmV559lnIqqdji60VmtRcXGao97fYuMhgwKG9NY5aJdTIVBGqmuz2U4tRKSZgT12W/q+T7lcplIsJm6zMlQ9jCBMss1Ukm1mk3ccco6NuQlBFynV4jZzcjmqKIx0OrYQ2bGFKLKtAxdgrarlpE5QW5bYzCQszNFsfgmFwVR6kInMCBOZEc5lR5nIH+FcapDqGtXSBzNWvRr08R4n6RHm0rdOd3ghBGNjY0xNbW8DVZ11tUm00Dm46HHeHbacdQV1waOSOBqFQql4Tu21ZBvtgukiElGrsq5k0KHYY6lzcchaDSS52r2xUaRw2yxD7X3Wsqua0W5bG5FNEAQBpVKJcqmEVywiKxVUtVp3m9kydptlLYuc7dDjOKQ3WT/IrxVpNOIijbVsMyOdQto2kW0hLWvLrrPdRIUBzM+2FE6su8TmpsD34/2Aebc3Fj81EZQZYSI3ypLd3UKddYy4IWrSE+x40hx1JGtjGkJnXWk0msNP/FA3Nxrluyl2R0QpFFFr94ttu183BM9CaYZvzd5LeiKPDEVc7qBDYLlpuFh2L5YxujqwXFjYSGwVYUu/XuyxVTCtbkwrUBjKg9DDDBc3Pv7CbrIMdW8hUmsvoswMSlxYjR7btunr61u3G3wYhiyUSjxbKlEuFgnLZVSlGtckCkJcFFYkyZgmecdJrEQOtmHiCAMH4v/nIIyn4uq0JalUnH4vROI2cyDlYqTSSNuKRZFlIW0LZeyN60xYNowegdEjq76C7dWjh2emGJ6d5KrZKTj/PSgVACha6dgFlhlhIjPKRDZ2iU2nBij5dOwObxuCI0ln+J94AVyzwT7KO4EWOhqN5sBymERUqHwWq0+zuHWjTAudAstbakaZWUx7AMtwsDCxhcBWYAuFoxSWkjhEODLAVgGO9HGVhx152DJuIyJUgBkuY7K88XHFbMQbtbnPWpvRNnqwKSO1aZFgWRa9vb30dki9b7YyBEFAuVxmtlSiVCzilcqxlagt2yxtGuRtty6IsraNIQRpIA0QRhBWoFwBlladM1QqcZsZSLtWpDHVIoYiy0Za5q65ztaqHg2gykWYnSI/M8UVs5NcMTuFmn0Gzv0fWJzHFybn00NMZEZjIZSNLUHnM8ME2Dy95PH0kkf/8gNc89of2ZXP1AktdDQajaYDuy2iolSa/+uSd5HvTTM3P0XQqfhmh8y8LQWWXyAGZiKcbCxhYwsLS5hYGNgIbEgmia0kjgpxVIiNil+XJaxgLtlXYAuBhUEq6Ra/arww2oKyuwdi161HRnpDQdmWZdHT07OuOzaKIsrlMueLRcrlMuViKQ6ursZWIjMp0pgSRsNCZMfzlBUX9bMgDqyOvLi44/JKx3P5QGDUijQ6CNetZ5lFTbFEaodrE4lMDi65DHHJZateU4FPam6aUzNTPKceF/QoTPwD0dwMs1a+7v56/gufC2iho9FoNBcNnUSUa2UZyR9hfHScvNx6PEMkwySovLpO5t0FiCgipCwT7EAijUEsemwh6qLJqgsioy6OrPrrRtNyvI+VHMMyUphmBsvKYxg5hJVvCCOO4BSDWDy1BGV3ruFjmib5fJ58Pr/m9dcE0WKpxESpRKlUwiuXiMoVDN/HaMo2a3aZ5R2HvO1gGrHbzJEyjp/xfSgUO55LErvNQstE2TakkiyzJIYontsN19k2ImwHxo/D+PFVvwUMGXFkcYEjiQAaeuFVzW2+dh0tdDQazUXBH/3RH/FXf/VXnD17llQqxbXXXsuZM2e47LLGr9Var6s/+7M/Y2VlhWuvvZYPfvCDnDp1qr7Ppz/9ae69914efPBBisUiP/jBD1a5R37hF36B73//+8zPz9Pb28srXvEKbr75ZsbGxnb8c5qGhYmFY2a3/dgNEdVWobxNRK0qvrkpEaXwifA76bwLjI0yIBZJGNiTiWBqE1GWsLAMJ3b7mSksI41pZDDNLKaVxTTzGGYey+rBsHoxrD5MM4dpxI/TjQoiKSXlcplSqcR8IohKpVIcS+R5GH6ALSWOgpxt1wVRj+M2XGeAqxRuLY6o3L1DeAiNIo2uG2eZ2VbiNmu4z7YjwLqlevTzX4I7Pg6Tkxd0zAtBCx2NRnNR8K1vfYu3vvWtXHXVVYRhyC233MIb3/hG/v7v/55MJgPARz/6Ue666y7uuOOOevfym266ifvvv59UKgVApVLhuuuu47rrruODH/xgx3O9/OUv5z/+x//I6Ogok5OTvO997+OXfumX+PKXv7xrn3cnODgiqkpU38dvVCwHPBReUsuoheb1LRTyNSCJdTKxhJ249ZzExZfCMlOYRhbTjEWTZWQwzRSW62KnXIaGU4wZ/ZjGWGu1cmy8apxpNlMq8WQiiCoLS8ia2yyJI4oFkVu3DsXCyME2TSzAiiKIotht1gUFhIaIs80cJ7EQ2U0WosR9tocB1ptFp5ej08sPMnqcd4e10suVUkRqazVfLhRTuFuuczM/P8+LX/xivvjFL/Kyl70MpRRXX301v/zLv8w73/lOIE75vuqqq/jwhz/M6173upb3f+Mb3+CGG27oaNFp56//+q9529vexpNPPondJdV50009NRumVUTFRTd7+3NMz54njKqxSIqqRFExmUpEshxvk5W6YAqVT6hCQhUSqIgQuStl8ETihusYWF7LzBMuSprIUBAFgtCXBL7Cr0bIKliewPINnMgga7jkrBQ5y60Lo1wijDbT/FVC4jZzmmKIrJZYIunYjB47zuTMtE4v12g0B5NIeXzx4Xfsybl/9vn/DUuktvTelZU4ELSWpvzMM88wMzPDK17xivo+PT09/MiP/Ajf/va3VwmdjbK4uMg999zDtdde21XkaHaWdkuUEILx/nHM6vCFPXyVQkYlZLiEDJeIwhVkuEwYFWKxFJWIonIilqqJqPIJVUSAIkQRKNlYbltvdGOTBHKTgeUCcJNpTR1uYCgLpAWRRSpMk4oypMMsGTJkyZIlQ9bIkBUZcmaKnJEmZToYgFPLNqt0d5tVJqfhkuMbv/ZtRgsdjUZz0SGl5Pd+7/c4ffo0V1xxBQAzM3E37vZfiUNDQ/XXNsP73/9+PvnJT1KpVLj66qv51Kc+deEXrtlfCIFh5TCsHHBsY+9RClQQF3hsLvbYUvwx3iZr1qWoRCS9WAglYqgujFCEShEgk9fUKhEVIAgg2S4JVBSXSQBAIoUPpg8mlFlhI3LKUlYsg1S2Pk8kUdO2HFmyPDr3bUa00NFoNAcVU7j87PP/256deyucOXOGRx55hC996UvbfEUN/v2///f83M/9HOfOneP222/n13/917n77rv3X3NTze4iBAgHaThIu3/j75MBhiyTispkkkrZRlRasxDkWpWyo7ogknVx1CyifGHhGxYBFr4wCITRKpiUIlARVRVSkEsEcppQ+sStWJscegpGzZcysuUBu3C00NFoNBeEEGLL7qO94Oabb+arX/0q99xzD0eOHKlvHxmJb8Wzs7OMjo7Wt8/NzXHllVdu+jwDAwMMDAxw6aWXctlll3H69Gm+/e1vc+211174h9BcfBg20ugFq3fjsdIqamshUmsV0qiWbUdl3LpIKiFkFdESmZ30Y+vm4RMGmC6YLlI4KDNDYKTwRQrfcPANh76xq1hdV3r30EJHo9FcFCileM973sN9993HF77wBU6cONHy+okTJxgZGeFrX/saL3xhXCW2UCjwwAMP8Ja3vOWCzw1x40qNZtcQJsrKE1n5TYgjiZCVRtsQ2eRW62hJircLJIbyIfQxgeafPqI8TCm3uvLybqGFjkajuSg4c+YM9957L3fddRe5XK4ed5PP50mn0wghePvb385HPvIRTp06VU8vHx0d5frrr68fZ2ZmhpmZGZ566ikAfvjDH5LNZjl69Cj9/f185zvf4bvf/S6nT5+mr6+Pp556ij/8wz/k5MmTXHPNNXvx0TWajSMMlJkl2kwJAaUQstrWeLYRb5TvfzFsb2LzptBCR6PRXBTcfffdALz+9a9v2X777bfzhje8AYBf+ZVfoVwu8+53v5uVlRVOnz7Npz/96XoNHYA//dM/5fbbb6+v/8zP/EzLcdLpNF/5yle49dZbqVQqjIyMcN111/Hrv/7ruO7WYoo0mn2NECgzTWSmwR5se0nQO7S3BQN1HR10HZ2DjB7n3WGtOjqa7UHX0dk99H1j99ipsd5MHZ3daZGq0Wg0Go1GswdooaPRaDQajebQooWORqPRaDSaQ4sWOhqNRqPRaA4tWuhoNJpNoYM3dwY9rhrNznDg08vvu+8+/tf/+l8sLS1xySWX8La3vY3LLrtsry9Lozm0WJZFqVQik8nodgbbgFKKcrmMZR3427FGsy850N+sb3zjG9x999284x3v4LnPfS5/+Zd/yfvf/37uuOMOenvXbNeq0Wi2SDabxfM8CoXCXl/KocF1XV1jR6PZIQ600PmLv/gLfuInfoIf//EfB+Ad73gH3/nOd7j//vv5qZ/6qb29OI3mEKMfzBqN5qBwYIVOGIY88cQTLYLGMAxe9KIX8eijj3Z8TxAELYUBhRCk0+n68nZSO5427e8sepx3Bz3Ou4Me591Bj/PusR/G+sAKnZWVFaSU9PX1tWzv6+vj/PnzHd/zpS99iT//8z+vrz/nOc/hQx/60IarK26FsbGxHTu2poEe591Bj/PuoMd5d9DjvHvs5VgfWKGzFX76p3+a1772tfX1msKcnZ0lDMNtPZcQgrGxMaampnQ2xQ6ix3l30OO8O+hx3h30OO8eOzXWlmVt2EhxYIVOT08PhmGwtLTUsn1paWmVlaeGbdvYtt3xtZ36Y1dK6S/SLqDHeXfQ47w76HHeHfQ47x57OdYHVuhYlsWpU6d46KGHeOlLXwqAlJKHHnqI17zmNZs+1k6hU0Z3Bz3Ou4Me591Bj/PuoMd599jusd7M8Q70//JrX/ta7rzzTk6dOsVll13GV77yFTzP47rrrtvUcfr7+3fmAmFH4380DfQ47w56nHcHPc67gx7n3WMvx/pAV0Z++ctfzpvf/GY+//nP8+53v5unnnqKM2fOdHVd7SaVSoXf+q3folKp7PWlHGr0OO8Oepx3Bz3Ou4Me591jP4z1gbboALzmNa/ZtKtqN1BK8eSTT2r/7w6jx3l30OO8O+hx3h30OO8e+2GsD7RFR6PRaDQajWYttNDRaDQajUZzaNFCZ4ewbZvXv/71XdPZNduDHufdQY/z7qDHeXfQ47x77IexFko7KTUajUaj0RxStEVHo9FoNBrNoUULHY1Go9FoNIcWLXQ0Go1Go9EcWrTQ0Wg0Go1Gc2jRQmeHufHGG/nHf/xHAGZmZrjxxht56qmn9vaiNBqNRqO5SDjwlZF3kjvvvJNSqcS73/3ubTne0NAQn/jEJ8jn89tyPI1Go9FoNGujhc4uYhjGvujDpdFoNBrNxYIWOhvkve99LydOnMBxHP72b/8Wy7J49atfzY033ljfZ3Jyko997GOcPXuWkZERfvEXf7HlGDMzM/yH//Af+C//5b9w8uRJpJR8/OMf56GHHmJpaYmhoSGuv/56fvInf3K3P55Go9FoNIcSLXQ2wT/8wz/w2te+lg984AM8+uijfPSjH+WKK67gxS9+MVJKbr31Vvr6+nj/+99PuVzmU5/61JrHk1IyODjIb/zGb5DP53nkkUf4xCc+QV9fHy9/+ct36VNpNBqNRnN40cHIm+CSSy7hhhtuYHx8nFe+8pWcOnWKBx98EIAHH3yQ8+fP86u/+qucPHmSF7zgBfz8z//8msezLIsbb7yRSy+9lJGREX7sx36M6667jm9+85u78XE0Go1Gozn0aIvOJjhx4kTLen9/P8vLywCcO3eOwcFBBgYG6q9ffvnl6x7zvvvu4/7772dubg7f9wnDkJMnT27rdWs0Go1Gc7Gihc4msKzVw3UhrcK+/vWv86d/+qe85S1v4fLLLyedTvPlL3+Zxx577EIuU6PRaDQaTYIWOtvE0aNHmZ+fZ3Fxkf7+fgAeffTRNd/zyCOP8LznPY/rr7++vm16enpHr1Oj0Wg0mosJHaOzTbzoRS9ifHycO++8k6eeeoqHH36Yz372s2u+Z2xsjMcff5x//ud/5vz583z2s5/l7Nmzu3TFGo1Go9EcfrTQ2SYMw+A3f/M38X2fM2fO8LGPfYyf+7mfW/M9r371q/nRH/1R7rjjDm6++WaKxWKLdUej0Wg0Gs2FIdSFBJloNBqNRqPR7GO0RUej0Wg0Gs2hRQsdjUaj0Wg0hxYtdDQajUaj0RxatNDRaDQajUZzaNFCR6PRaDQazaFFCx2NRqPRaDSHFi10NBqNRqPRHFq00NFoNBqNRnNo0UJHo9FoNBrNoUULHY1Go9FoNIcWLXQ0Go1Go9EcWv5/YxmgmxVif+MAAAAASUVORK5CYII=" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "<details><summary>Click here for a sample python solution</summary>\n\n```python\n #The correct answer is:\n df_CI.plot(kind='line')\n```\n\n</details>\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "That doesn't look right...\n\nRecall that *pandas* plots the indices on the x-axis and the columns as individual lines on the y-axis. Since `df_CI` is a dataframe with the `country` as the index and `years` as the columns, we must first transpose the dataframe using `transpose()` method to swap the row and columns.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "df_CI = df_CI.transpose()\ndf_CI.head()", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 46, | |
| "outputs": [ | |
| { | |
| "execution_count": 46, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": " India China\n1980 8880 5123\n1981 8670 6682\n1982 8147 3308\n1983 7338 1863\n1984 5704 1527", | |
| "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>India</th>\n <th>China</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1980</th>\n <td>8880</td>\n <td>5123</td>\n </tr>\n <tr>\n <th>1981</th>\n <td>8670</td>\n <td>6682</td>\n </tr>\n <tr>\n <th>1982</th>\n <td>8147</td>\n <td>3308</td>\n </tr>\n <tr>\n <th>1983</th>\n <td>7338</td>\n <td>1863</td>\n </tr>\n <tr>\n <th>1984</th>\n <td>5704</td>\n <td>1527</td>\n </tr>\n </tbody>\n</table>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "*pandas* will auomatically graph the two countries on the same graph. Go ahead and plot the new transposed dataframe. Make sure to add a title to the plot and label the axes.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "### type your answer here\n\ndf_CI.plot(kind=\"line\")\nplt.title(\"immigrants\")\nplt.xlabel(\"Year\")\nplt.ylabel(\"Number of Immigrants\")\n\n\n\n\n\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 64, | |
| "outputs": [ | |
| { | |
| "execution_count": 64, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "Text(0, 0.5, 'Number of Immigrants')" | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": "<Figure size 640x480 with 1 Axes>", | |
| "image/png": 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7Hi0g7xrmj7+3BbuhOVRc3V6hiU5EkighhBAnlYIKO2kFVSiOFpUDWJQiMcyc2pMpPdEUkkQJIYQ4qThX5SVH+BHqV3fjjqRwsyu4rNATTSFJlBBCiJOKsx5qWNyxvaASa4rM0woliRLHJ0mUEEKIk4bD0KzPqkmiuh6bRB0diZLpPHF8HXYD4k8++YT333+fCy64wLXRoNaaDz74gG+//ZaysjL69u3LjBkziIuLc72vurqad999l5UrV2Kz2Rg8eDAzZswgLCzMdUxpaSnz5s0jNTUVpRSjR4/m+uuvx8/v6OaOeXl5vP7662zduhU/Pz8mTJjA1VdfjZeXV1vdAiGEEB62r6CSkioHAd4WUqL8j3k9IcwXBRRWOiiosBPu32F/THZa3bp148033+S8886r9/WVK1fy+9//nm3bthEaGtrG0TVPhxyJ2rNnD19//TUJCQl1nv/0009ZsmQJM2fO5Omnn8bX15ennnqK6uqjqyjeeecdUlNTueOOO3jssccoKChgzpw5dc7z0ksvcfDgQWbPns19993H9u3bmTt3rut1wzB45plnsNvtPPnkk8yaNYsffviBBQsWtO4nLoQQolWtq5nKGxwbgNWijnndz2ohLtgHkOJyd+Xk5DB79mxOPfVUkpKSGDFiBNOmTeOnn35q0vtHjBjB+vXr233bm6bocElUZWUl//znP/nzn/9MYODRoVatNYsXL+bSSy9l5MiRJCQkcPPNN1NQUMCaNWsAKC8v57vvvmPatGkMHDiQ5ORkbrrpJnbu3MmuXbsAyMjIYMOGDdxwww307t2bvn37Mn36dFauXEl+fj4AGzduJCMjg1tuuYXExESGDh3K1KlTWbZsGXa7ve1vihBCCI9wJlFD44IaPMbZfFOKy5vv4MGDnH/++fz888/Mnj2bb775hv/+97+MHTuWBx98sEnn8PHxITo6GqWOTXI7mg6XRL3xxhsMHTqUU045pc7zOTk5FBYW1nk+ICCAXr16uRKkffv24XA4GDRokOuYbt26ERUV5Tpm165dBAYG0rNnT9cxgwYNQinFnj17XMf06NGjzhTgkCFDqKio4ODBgw3GbrPZKC8vd/2pqKhwvaaU8uif1jhnZ/8j90Tuh9wPuR+N/SmrNth1xPy+PKxrUIP3JCmipi6qsKpd/306owceeACAL7/8kgsvvJCePXvSp08f/vznP/P555+7jsvPz+ePf/wjPXv25LTTTuOrr75yvbZy5Uq6detGUVERAAsWLKBfv3788MMPTJgwgd69e3PNNdeQnZ3tes+GDRu48sorGThwIH379uWyyy5j8+bNTYq5Jfe/Q032/vzzz6SlpfHMM88c81phYSHAMfOjoaGhrtcKCwuxWq11RrDqO+a3Q4ReXl4EBQXVOaZ2AlX7us5j6rNo0SI+/PBD1+OkpCSeffbZJu0E7Y7Y2NhWOW9nJvekLrkfdcn9qOtkux/f7szB0JAUGcDg3j3qPSY2NpbhZVb+syGXg6WOOjW3bamiogJvb2/XY601Vfa27aLusBn4Wq1NTioKCgr4/vvveeCBB+qtZYqKinJ9/Pe//52HH36YRx99lDfffJObb76ZdevWER4ejtVqpibe3t54e3tjtVqpqKhg7ty5/Otf/8JisXDTTTfx5JNP8tprrwHmLNaVV17JkCFD0Frz6quv8oc//IFVq1YRFNTwqKOPj0+L/o07TBKVl5fH22+/zezZs/Hx8WnvcNxyySWXMHnyZNdj5xdebm6uR6cBlVLExsaSlZWF1rI1Acg9+S25H3XJ/ajrZL0f327NBGBQFz8OHz5c57Xa9yRUm3W26UfK2J9xCB+vtp+0qa6uxmazuR5X2g2mLtjV5nEsmJqCn7Vpn//u3bvRWpOUlFQn9vr8/ve/56KLLgLgnnvu4fXXX2fNmjWcccYZrp+XNpsNm82G3W7HZrPxzDPPkJiYCMC0adN48cUXXdcZM2ZMnfP/9a9/5dNPP+XHH3/knHPOaTCO6urqY74WAKxWa5MGQDpMErVv3z6Kioq49957Xc8ZhsH27dtZunQpL774IgBFRUWEh4e7jikqKnLd1LCwMOx2O2VlZXVGo4qKilwjS2FhYRQXF9e5tsPhoLS0tM4xzqm92udwvtYQZ9Zcn9b4RqW1Pqm+ATaF3JO65H7UJfejrpPpfmitj271EhfQ4OettSbC34tgHwsl1Qb7C6roFelX77GiruZ8LfXr18/1cUBAAMHBweTl5TV4vL+/v+tnPUBMTEyd43Nzc3nuuedYuXIlR44cweFwUFFRwaFDhzwa9291mCRq0KBB/O1vf6vz3KuvvkrXrl2ZMmUKMTExhIWFsXnzZteNLC8vZ8+ePZx77rkAJCcn4+XlxebNm11ZaWZmJnl5eaSkpACQkpJCWVkZ+/btIzk5GYAtW7agtaZXr16uYz7++GOKiopcQ5KbNm3C39+f+Pj4Vr8XQgghPOtgUTVHyu34eCkGRAc0eqxSisRwPzZnl5NeWNkhkihfL8WCqSltek1vqzcW3fRZlKSkJJRSxwxC1Hvu3ww4KKUwDKNZx9dOfm6//XYKCgp4/PHHiY+Px8fHh9/97nfHHRFrqQ6TRPn7+9OjR905al9fX4KDg13PX3DBBXz88cfExcURHR3N/PnzCQ8PZ+TIkYCZzZ555pm8++67BAUFERAQwLx580hJSXElUfHx8QwZMoS5c+cyc+ZM7HY78+bNY+zYsURERAAwePBg4uPjefnll7nmmmsoLCxk/vz5TJo0qcGRJiGEEB3XusOlAAyIDsC3CdNTieG+bM4uJ62DrNBTSuFnbduCc29vCzZb068ZHh7OxIkTefvtt/njH/9IQEDdZLX2wISnrVmzhqeffpqzzjoLgEOHDrlW3LemDpNENcWUKVOoqqpi7ty5lJeX07dvXx544IE6NVTTpk1DKcWcOXOw2+2uZpu13Xrrrbz55ps8/vjjKGU225w+fbrrdYvFwn333ccbb7zB7Nmz8fX1ZcKECUydOrXNPlchhBCe49rqpZ4u5fVJls7lbnnqqae4+OKLufDCC7nrrrvo168fDoeDH3/8kXfffZfly5e3ynWTkpL46KOPGDx4MCUlJTz55JN1Gmi3lg6dRD366KN1HiulmDp1aqPJjI+PDzNmzDgmcaotKCiI2267rdFrd+nShfvvv79Z8QohhOh4quwGW3NqWhvUs19efRLDju6hp7Xu1G0H2lJCQgJLly7lpZde4vHHHycnJ4eIiAhOOeWUelfee8qcOXO45557OO+884iLi+O+++7jiSeeaLXrOSl9slQVtqPc3FyPzssqpYiLi+Pw4cMnTVHo8cg9qUvuR11yP+o62e5H6qFSHv8hgy4BVl6/uGe9CdFv74nNYa6Gc2h44+KedAls21KO4uLidu/Y7e3t3eo1Re2tofvs7e3dpNV5Ha7ZphBCCOFJzlV5zgabTeHtZSE+tGY0Sqb0RAMkiRJCCHFCc2310sR6KKck55ReBykuFx2PJFFCCCFOWNml1WSWVGNRcEpM460NfisxXJIo0ThJooQQQpywnKNQfaP8CfTxatZ7k5wr9AplOk/UT5IoIYQQJ6z1h92byoOjI1FZJTYqbA03ghQnL0mihBBCnJBsDs2mrHIAhsU1vAltQ8L8rIT7W9HA/sK2n9JrrIO3aDlP3F9JooQQQpyQduZVUGE3CPX1IjnC161zHC0ub9spvYCAAEpKSiSRaiWGYVBSUnJMV/Xm6tDNNoUQQgh3rcs0t3oZEheIxc1mmYnhvqw7XEZ6G49EWa1WAgMDKS0tbdPr1ubj40N1dXW7Xb+1BQYGYrW2LA2SJEoIIcQJaf3h5m31Uh9ncXl79IqyWq3t1nDzZGvI6i6ZzhNCCHHCKaiws6+mNcGQJm71Uh9ncfn+wioMSSbEb0gSJYQQ4oTjHIXqGeFLmJ/7ky7dgn3wtigq7ZqskhN7CxTRfJJECSGEOOG4Whu4sSqvNi+LoodrM2LpFyXqkiRKCCHECcVhaI/UQzkl1UzppUvncvEbkkQJIYQ4oewrqKSkyoG/1UKfKP8Wny9Jtn8RDZAkSgghxAllfc1WL4PjArBa3GttUFtSWPut0BMdmyRRQgghTijrnFN5LayHckqoGYnKK7dTUuXwyDnFiUGSKCGEECeM0moHO/MqABjagtYGtQX5eBEdaK7wk82IRW2SRAkhhDhhbMoqw9AQH+JDdJC3x86bWNN0U4rLRW2SRAkhhOj0qh0Gh0uqWbG/BPDcKJRTYpgUl4tjybYvQgghOiytNeU2gyPldo5U2DlSbjM/LreTV24jv8Jeb62SJ1ob1JbsHImS6TxRiyRRQgghjqugws5H245QbdfMHBGDt1fLV701Zkt2Of9ek012mY1Ku9Gk9/h4KaICrKRE+XNKrIdHomqKyw8UVmM3tEdW/YnOT5IoIYQQDSq3Ofhkez6fbs+n0m7uHTcwJoDTE1t3Y9z3N+Wyv+jo1Fmgj4Uof28iA6xEBFiJCrASGeBNpL+VyAArUQHeBPpYUKp1kpuYIG/8rBYq7QaHiqtJqJneEyc3SaKEEEIcw+bQfLWnkAWb8yiqmSoL9rFQUm3w3b6iVk2iskqq2ZpTgQKem5RAjzBf/KytV8Kry0pwzJlNwdDR6IuurvcYi1IkhvmyI6+CtIJKSaIEIEmUEEKIWrTWrNhfwn825pJVam642zXYmz8M6UJSuB83fLaPjVllHCm3ERngudVvtX2fVgTA4NgAUjzQcfx49OZUOJhG6cE0LFFxqFPPqPe4pHAziUovqIKkVg9LdAKSRAkhhADM9gDvrM9lT75ZPB3m58WVg6I4p1eYqwaofxd/tuVW8ENaMZcNiPR4DIbWfJ9WDMCZyaEeP3+90ncfvf77r2Hp1Q/VJfaYw5x1UWmFskJPmCSJEkKIk1x6QSXvbsgltWa7FD+rhUv6RzClbwT+3nWn0c5IDmVbbgXf7Svi0v4RHq9B2pZTQXapDX+rhTHdgz167obotF0AqIBAdHkZxpsvYLn7GZSXV53jkly9omSFnjBJnyghhDhJ5ZbZeHFlJrcvTic1swwvBRemhDF3SjJXDoo6JoECOK1HMD5eiozianYf8Xwy8d0+cypvXEIwvq1YB+Wk7XY4sA+AqAeeBT9/2LsDvWThMccmhPmigMJKB4UV9laPTXR8kkQJIcRJpspu8I8f9nDDp3v5Pq0YjZkcvXJRMn8aGUuYX8OTFIE+Xq4RImfC4ymVdoOfD5jNMttsKu/QfrDbICAQ3yGjsVxzAwD68/nofTvrHOpntRAX7APIlJ4wSRIlhBAnmf9tyuM/aw5gMzQDYwL423kJ3DO+mytBOJ6zahKcn/YXU+1oWg+npvjlQAmVdoPYIG/6dWn9gnIAXVMPpZJSUEqhxpyBGjkeDAPjzRfQlRV1jk9y1kXly5SekCRKCCFOKubqO7Nw+4aRMTx5Vnd6RzYvYRkUE0Ckv5XSaoM1GaUei805snVmcmir9Xs6Rk09FIm9AcxE6pobISIKcg6jF7xR53ApLhe1SRIlhBAnkf2FVeSU2fC1WjirZ5hbyYqXRXFGzWiUp6b0cstsbM4uB2BiUus28qyt9kiUkwoMwjL9L6AUesXX6HW/uF5LCpPicnGUJFFCCHESWV0zcjQqIaJFhdtnJJuJzrrDZRR4oMj6+7QiNOYoV0xQ06YVW0pXVkDmQQBUzUiUk+ozCHXuJQAY776MLjwCHB2Jyiiu9uhUpuicJIkSQoiTyOpDZhJ1eq+oFp0nPsSXPlF+GBqWp7dsNEprzfe1pvLazIG9oA0Ij0KFRRzzsrr4GuiRDGUlGG/9A20YRAVYCfKxYGg4WFTddrGKDkmSKCGEOEkcKbex+0glChjfs2VJFBxNeL7bW4zW2u3z7MirILPEhp9VcWob9YaCo1N5JPaq93Vl9cYy407w9oFtG9DffY5SytUvKk2m9E56kkQJIcRJYk3NKFRKlD+RgS2fMhuXEIK3RbG/qIp9Be4XWjvrqsb2CK63N1WrSTu2Huq3VFx31O+nA6A/ehedkX60uLwFn7M4MUgSJYQQJwlXPVR8kEfOF+Tjxeju5rm+dbPAvMpusGJ/G/eGquHqVP6beqjfUhPPh0EjwG7DeGMOicFmHy0pLheSRAkhxEmgwmawKctc/TY63nNTZmcmmYnPj+nF2BzNn9JblVFKuc0gOtDKgOgAj8V1PLqkCI7kmA8S6p/Oc1JKYbnuFggOhUP7SVz3FWC2OWjJNKbo/CSJEkKIk8CGw2XYDE1skDfdQz23+m1IXCDh/lZKqhyszWx+zyjnVN7EpFAsbdUbCo5uOhwbjwoIPO7hKiQcy7RbAYj/7gO80JRVG+SVy/YvJzNJooQQ4iSw+pA5ZTYqPsijjSy9LIozavo6Nbdn1JFyGxuzzE2P234qr6Ye6jhTebWpwSNRE8/HWzvoVpELnLjF5cbXn1Lwr2fRhqO9Q2mQ1prcMlu7xiBJlBBCnOAchmbNITNZ8VQ9VG3Oxpuph0oprGz6yMwPacUYGvp38W/yljOe4lqZl9T0JApAXT4dYuNJLM4ATszicl1UgPHBPEq/XIjeta29w6lXdmk1j3x3kHuX7aesuv0SPUmihBDiBLczr4KSKgdBPhb6d/F83VGPUF96R/rh0GZtVFNorets89KWtNau6bzGVubVR/n6YplxJ4llWQCk7Tng8fjam177s9k/C2DXlvYN5jcMrVmyq4Bbv0xnY1Y5JdUOdh9pv9FASaKEEOIEt6pmVd6IrkF4WVqn7uiMpOZtA7P7SCUZxdX4eClOS2i73lCAWVBeUgReVohPavbbVUJPkgb3ByD9SAU657CnI2xXevXyox/v3NyOkdSVVVLNQ98e5LU12VTaDfp38ecfFyQxJO74NW2tRZIoIYQ4wXm6tUF9xieGYLUo0gqqmlQn5Ey2Tu0eTIC3V6vFVR9nPRTxiShvb7fOkXT2GQBk+YVT/vXnngqt3encLNi38+jjfTvRtvbtzG5ozRc787n1yzS2ZJfj66WYOSKap87pQdeQtp0G/i1JooQQ4gSWUVxFZkk1VotiaNfW+409xNeLkd3MJO14o1E2h8FP+81pv7aeygNqTeU1rx6qtvAAX8KtBlpZ2L8zHW2cGPvo6dU/AqD6DcYSFgm2aqjpp9UeMourefDrA7y+Nocqh2ZgTAD/uDCJyX0i2nY1ZwMkiRJCiBOYcxRqUExAq4/4nFWTEC1PK8ZuNNw/afWhUkqrDSIDrAyKabveUE46vSYpSGxePdRvJUaZSWmaCoK9O1oaVoeg1/wEgBo9Ad9ThpnP7Wz7uiiHofl0ez63LU5jW24FflbFn0fG8MRZ3dt8EUJjJIkSQogTWFtM5TkN7RpIqJ8XRVUOUhvpGfXdXnOk6oyk0Far0WqINhywfy/QspEogN5RZgK4LTQZnfpzi2NrbzojHQ7tB6sVNexU/AYON59v4+LyjOIq7v/6APPW5VDt0JwSG8BLFyZxQUp4hxh9qs2tJCovL48dO+pm3enp6bz88sv8/e9/Z/Xq1R4JTgghhPuKKu3syK0AcE21tSarRTEx0ewZ9X0DU3oFFXbWHTbbLZyRHNLqMR3jcAZUVYKvP8R2a9GphtYUNG+I6I193a+dfkrPOZXHwBGogCB8B5lJFHt3oG2t34/JYWg+3naE279MZ2deBf5WCzeNiuXxM7sTE9RxRp9qcyuJmjdvHgsXLnQ9Liws5LHHHmPVqlVs376dOXPmsGrVKo8FKYQQovnWHipFAz0jfOkS6F4BdXM5a5zWHCqluOrY/j3L04swNPSJ8iM+xLdNYqrNuV8eib1QlpZNb/aJ8ifA20KpdyB7HP7tWjvUUlrro/VQo04HwNo9EYLDzLooZ1+tVnKgqIp7v9rPO+tzsRmaoXGB/HNyEpN6hzXYHFanrsRY+W2rxnU8biVRe/fuZdCgQa7HP/74I9XV1Tz//PO89tprDBo0iM8/P3FWKwghRGfkbG0wqlvbtRBIDPcjOdwXuwE//aZnlNkbynzO2RKhzTmLyhMb3y+vKbwsyrW8fn1EH/S6X1p8znazb6fZ+sHXH3XKSMDcM1D1GQC0bquDtYdK+cvidHYfqSTQ28ItY2J55Iz4RhN/fTgD461/oN/6Bzp1ZavFdjxuJVGlpaWEhh79D5Camkr//v2JjY3FYrEwatQoDh065LEghRBCNE+V3WDD4dbrUt4Y52jUt7+Z0ksrqGJ/YRXeFsX4hHaYyqPWdi/NbLLZkOE1Kx7XRfRBp/7caTckdo1CDR2N8j06QqhSzAGT1qyLen9THnZDMyQukJcmJ3F2z4ZHnwB0ZQXGq89AVQX0GQRDRrdabMfjVhIVEhJCbq65b1BZWRm7d+9m8ODBrtcNw8Do5HPDQgjRmW3OLqfKoYkKsJIU3rbTZqcnhuClYG9+JfsLj26L4kyqRsUHEeTbtr2hALPf0aF080ELV+Y5Oeui9gbHU1RcBgf2euS8bUk7HEdX5Y2aUOc11Weg+cHe7Wi75+uiDhRWsTe/Ei8Fd46NIyqg8WlnrTX63Zfh8EEIjcDyp7tQXm3/teTkVhI1aNAglixZwhdffMHLL7+M1ppRo0a5Xs/IyCAyMtJjQQohhGie2qvyPLnhcFOE+lkZ8ZueUTaHdm0Jc1Z79IYCOLAPHA4IDoWIKI+cMjLAm6RwX7SysCEipV2nlty2c5PZwT0oGPoNrvta1x4QFALVrVMX9X2a+fUxolsQIX7W4x6vv/vSTPi8vLDccA8qJNzjMTWHW0nU1VdfTXx8PO+99x6bNm3iD3/4A9HR0QDYbDZ++eUXBg4c6NFAhRBCNI2hNaszSgAYFd/GW6rUONPVM6oIh6FJzTQLzcP9vNptm46jmw6neDSxHFq7LqoTTunpVTVTeSPGoax1ExmlFKSYP8893S/KYWh+SKupkWtCYq33bEcvfNOM6/LrUb36ezQedxw/7atHWFgYTzzxBOXl5fj4+GCtddO11jz00ENERXkmyxdCCNE8e45UUlDpwN9qYWC0f7vEMLxrECG+XhRUOlh/uMw1IjWhHXpDubiKylvWH+q3hncN4uNt+WyI6IOxfQGWQ+lu7cnXHrStGr3eLIhXI0+v9xjVZyB63UqzLurCKzx27U3Z5eRX2An2sTDiON30dXEhxtxnweFAjRyPOusij8XREm6NRH344YccOHCAgICAOgkUgI+PD15eXixdutQjAQohhGge51TesK6BeHu1T09lby/FhJqeUZ9uz2ftITOmdtnmpcbRonLPJlF9u5itDoq9A9kbHI9O7USr9DanQkW5Ob3Zq1+9h6iakSj2bEfb7R67tDOxHp8Y0ujXqXY4MP79PBTmQ1x31P/d3OZT1A1x63/XwoULOXDgQIOvHzx4sE4fKSGEEG2nLbuUN8aZMG3KLsehoWeEHwlhbd8bCkCXl0J2zarxBM8mUVaLYnCs2b3cuUqvszBWLwcwR3csDaQEXXuY9VLVVbB/j0euW25z8OtBc8r5eO0u9Kf/gZ2bwdcPy433ofzaZ3S1Pq3yK0ppaekxI1RCCCFaX1ZJNfuLqrAoc5qpPSWF+5JYK2lqt4JygPSaH/5dYlHBnm+vMKzmXq+P7AuHD6IzGx5o6Ch0RTlsWgscbbBZH2WxQO+aflEeanWw8kAJ1Q5NfIgPvSP9Go5xw6/oJR+ZcUy7FRXX3SPX95QmZzrbtm1j27ZtrserVq0iKyvrmOPKyspYuXIlPXr08EyEQgghmmxNzbTZgOgAgtuhjUBtSinOiNK8VQhWpRmf2D69oeBop3JP10M5Daup6dkd3J0SawAh61aiunbsn4N6/a9mN/LYeOie3Oixqs8g9Ppfzaab51/e4ms7twU6Iym04Y7kOZkY8140r3/277CMHNfi63pak5OorVu38uGHH7oer169usE98uLj45k+fXrLoxNCCNEsHWEqT1dXmYXIPy7jjLQ01g64lkHlhwi2eqY3k1sxOUeiWimJigrwJiHUl/1FVWyISGF86i8w+cpWuZan6DVHt3k5Xo2RShmIBtizA223H7OKrzmyS6vZklOBAiYk1Z9Y66oqjFf/atZr9eqHuuw6t6/Xmpp8F6ZMmcJ5552H1pqZM2cyc+ZMRo+u2yVUKYWPjw8+Ph1zo0AhhDiRlVY52JJTDsCoNthw+Ld05gH0j8vQv3wP5WYyF2Sx8NjWt8Fug+2jYOCwNo8LgPSakSgPdSqvz7CugewvqmJdZF/Gb5+Pzs5ExXRtteu1hC4pgm0bgMan8ly6JUBAkPnvemAvJPdx+9rOtgaDYgPq3dpFa43+778gIx2CQ7H8+Z4WJW2tqclR1U6OXn75ZUJCQvD1bZ8CQSGEEMdKzSzF0JAQ6ktscNv8Mqurq9CpK9E/LoU924++ENEFNf5c1Glno5csRH+/GL16OaodkihdcMRc2aUs0KPxaauWGNY1kEXb89nQpT/GdoVatxLlgamv1qDX/gyGAQm9mpToKYvF7Be14Vf0zi0oN5MorbWrweaZDRSUuxJxi8VMoMI6bvNut1K7Ll26eDoOIYQQLbS6ph5qZBtM5elD+9E/fYX+5TsoN/fow2KBU0ZhOX0SDBiCstTUZI2aYCZR635FX1NVZ2+2NuFsstmtB8q34SLmlurXJQA/q4Uiux9pQV3pmbrSI/VDrUE7V+WNnnCcI49SfQaYhd67tsD5l7l13R15FRwuseFnVYzpfmwjWJ22Gz3/3+b1Lv0/VJ9Bbl2nrbiVRGmt+eabb/juu+/IycmhtLT0mGOUUsyfP7/FAQohhDg+m0OzLrN1NxzW1VXotSvQPy6DvTuOvhAZXTPqdFb9owY9+0JkNBzJQW9ag2rjAmFXUXkrTuWB2RtrcGwAqzJKWRfZj577v0HnZaOiYlr1us2lj+Sao4ZKNevfQqUMMuuidm9DOxxu7Vn3/T5zKm9sj2D8ves2CNAlxRiv/RXsdhg6BnXuJc0+f1tzK4n6z3/+wxdffEFiYiLjx48nMLB9WvgLIYQwbc0pp9xmEO7n1eiScXdpux3j+QeOjupYLDBkNJbxk6D/kIZ7DGH+Uq1GT0AvXmiOgLR1EuWMObFXq19rWNdAM4nqNoTf7/8GvW5lh0sGnAXlpAxs3lRZfAIEBJojjwf2QTObllY7DFbsr9nm5TdTedpwYLwxB/JzIborlutu6zANNRvjVhK1fPlyRo8ezR133OHpeIQQQrjBuVfeyPggLK3ww0d/tchMoAICUZMuRY09CxUW0eT3q1FmEsXmVHRZKSqwbQrftWG4ekSpxNZfHTgsLgjIZrdPF0qt/gSlroSOlkStOroqrzmUxcvsF7VxNXrX5mZ3fl+dUUqZzSAqwMrAmIC6MX2+ALatBx8fs6FmQOcYnHGr2WZ1dTWnnHKKp2MRQgjhBq310dYG3Ty/4bDOyUR/bpZnqKv+hOWC3zcrgQJQ3XpAfCI47G3b0TvnMFSUgbeP2Xm7lUUHeRMf4oOBYmNECuzbic7Pa/XrNpXOPAAZaeBlRQ0f2+z3qxZsRuzsDTUxKbROoq+3rkd/UfP19YdZqPjEZp+7vbiVRA0cOJA9ezzT+l0IIUTLpBVUkVtux9dLcUpswPHf0Axaa4z/vGq2KOg/BDV6otvnchYx69U/eii649M1rQ3okdxmy+SH1zTeXJcwyoxhfcfZS8917wcOQwU2P+F2FXrvMeuimqqwws66w2bN3hnJR3tDacPAWDjPPPeE87CMOaPZMbUnt5KoGTNmsHv3bj7++GNKSko8HZMQQohmcK7KGxIXiK/Vs7t56V9/gO0bwdsHyzU3tqhORY2smT7ataXtRmdcmw63XaNP5xYwG4KTMFAdZi89rbUriVIjx7t3ku6J4B9oNsE8uK/Jb1ueXoyhISXSj/iQWqszN6yCQ/vBPwB16f+5F1M7cistv/3229Fas2DBAhYsWICPjw+WeooK33nnnRYHKIQQonGt1aVclxSjP3gTADV5Kio6rkXnU5FdoHd/c3XXmp9Qk1q/VuhoUXnrdCqvz4Bof3y9FAUOK/uDYknasx1dVIAKDW+zGOqVvhtys8DHFzVk9PGPr4dZF9UfNq1B79rS5G10XL2hau2fqLXG+HKBed4zJqMC2nevR3e4lUSNHj26U1TNCyHEiS6v3Mbe/EoUMMLDXcr1h29BaTF0S/DYCjM1eiJ69zZzlV4rJ1HabjNXkUGzi6BbwtvLwimxAaw5VMa6nuNI2rgQvf4X1MQL2iyG+rhGoYaMPm6/rMziakq8Sqhvwk/1GYjetMasi2rC10VaQSVpBVVYLYpxCbW2edm81vz38fVDnf275nwqHYZbSdSsWbM8HQcAX331FV999RW5ubmAuQff5ZdfztChQwEza/3ggw/49ttvKSsro2/fvsyYMYO4uKO/HVVXV/Puu++ycuVKbDYbgwcPZsaMGYSFhbmOKS0tZd68eaSmpqKUYvTo0Vx//fX4+R39osrLy+P1119n69at+Pn5MWHCBK6++mq83OiLIYQQrWVNzShUnyh/wvw8V/Ojt29Er/wWlMLyh1keqydSw8ei/zcXDuxDHz6IiuvukfPW69ABs5YrIAi6tGwUrbmGdQ1izaEy1ncZwGUsRKeuhHZMorThQK/5CTBXStZ7jNZsy61g0bYjrDlUhmIvd4/vxmk96qZSrn30dm9DG46jTVUb4CwoH9ktyLUpttYa44uaUaiJ56OC229z6pbw7OR5C0VERHD11Vfz17/+lWeeeYaBAwfy3HPPcfDgQQA+/fRTlixZwsyZM3n66afx9fXlqaeeorq62nWOd955h9TUVO644w4ee+wxCgoKmDNnTp3rvPTSSxw8eJDZs2dz3333sX37dubOnet63TAMnnnmGex2O08++SSzZs3ihx9+YMGCBW1zI4QQoomcU3mjPTiVp23VZjE5oCacj+rZ12PnVkEhMMDc+kWvWu6x89bH2WSTxN5tPnsyLM4sLt/hCKDM6gc7t5j71bWXnVugqMBMKAcMqfOSw9D8crCEe7/azwNfH2DNIbMAXAN//zmTrTX7Mbp0Twb/AHPV48H0Ri/rMDTL02t6Q9UqKGf7RkjbBT4+qHMvbtnn1o5alEQdOXKEtWvX8uOPP7J8+fJj/jTXiBEjGDZsGHFxcXTt2pWrrroKPz8/du/ejdaaxYsXc+mllzJy5EgSEhK4+eabKSgoYM2aNQCUl5fz3XffMW3aNAYOHEhycjI33XQTO3fuZNcu8z9TRkYGGzZs4IYbbqB379707duX6dOns3LlSvLz8wHYuHEjGRkZ3HLLLSQmJjJ06FCmTp3KsmXLsNvtLbllQgjhMeU2B5uyazYc9mQS9eUHkJMJoRGoS/7gsfM6OfsT6dU/orX2+Pld0p1F5W03lecUG+xD12AfDA2beo8DbaA3rHL7fFqbic7aQ6WUVTd9VZzr/c5RqOFjUVZz099qh8FXewq5+Ys0/vrjIXbmVeJtUUzqFca/LkpmYq8obIbm6eUZHCyqcp1LeXlBr/7meXc13upg/eEyCisdhPh6Mbzr0a9RVy3U+EmokHauFWsBt8Znq6ureeWVV1i1alWj/wEmTGj6njy/ZRgGv/zyC1VVVaSkpJCTk0NhYWGd/lQBAQH06tWLXbt2cdppp7Fv3z4cDgeDBh3da6dbt25ERUWxa9cuUlJS2LVrF4GBgfTs2dN1zKBBg1BKsWfPHkaNGsWuXbvo0aNHnSnAIUOG8MYbb3Dw4EGSkpLqjdlms2Gz2VyPlVL4+/u7PvYU57mkLu0ouSd1yf2o60S9HzvzKrEbmtggb+JDfZv8+TV2P/Sh/eilHwNgufpPWFqhKaZl6Bgcvn6Qm4VK3+32ZrbHo9OPrsw73r1pja+R4d0CydxRzfoeIzh1+zeQuhJ1+iS3zrVkVwGvrckGwKIgOdyPgTEBDIwJoH90AEE+DU+paZvNtUJQjZ5IWbXBkt0FfL4jn8JKMyEL9LFwYUo4F/aJINzfilKKJyZ3Z+Z/V7Mjt4LHvj/Ic5MSiQwwEzBLn0EYm9fCri2NjiT9kGaOQk1IDMHbyxy30bu2wK6tYLViOe+yTv3/0q0k6n//+x+rV6/myiuvJCUlhccee4xZs2YRFhbG4sWLKSgocLtu6sCBAzz44IPYbDb8/Py46667iI+PZ+fOnQCEhtZtFR8aGkphYSEAhYWFWK3WY7ah+e0xISF15169vLwICgqqc0ztBKr2dZ3H1GfRokV8+OGHrsdJSUk8++yzrbZhc2xsbKuctzOTe1KX3I+6TrT7kXdgPwCD4sPp2rVrs9//2/uhDYOcOQ/icNjxG306URe23g+4I6eeQfkPS/Dfspbw0yZ6/PxGRTmHDpulILGjx+EVEdWk93nya+ScgT58vqOAjZYuZg3Rjo3EBAViaWb9T3p+GW+tN38GRgX6kFdWzZ78SvbkV/LJ9nwU0CcmmGHxYQzrEcbQ+DBC/Lxd76/4dTl55WXkxybznU7kk0/2Um4zk6eYYF+uHtGDKafEEehzbErwzyuG88f3UzlQUMEzK7L491XDCPSxUjV2AjkfvgW7txEbHV3vPnollTZWZZhxXzG6F3ExZm1VzstP4AACz51CRP+BzboXHY1bSdSvv/7KxIkTufjii119oiIiIhg4cCCnnHIKjz32GMuWLWPmzJnNPnfXrl15/vnnKS8v59dff+WVV17hsccecyfMNnfJJZcwefJk12PnN5/c3FyPTgMqpYiNjSUrK6t1h8I7Ebkndcn9qOtEvR+bDpiLcOL8NIcPH27y+xq6H8byJRjbN4GvP7bLriMrK8vjMbuudcoo+GEJpd8vpeLCK93azLYxetcWMAwIjyKnygbHuT+t8TXS1dvAx0uRW+HgQNIwEtLWcXjZZ1hOO6vJ57AbmgeWpVNlNxgcG8hjZ3WnoMLOluxyNmeXszW7nEMl1ezILmFHdgnvpx5EAYnhvgyKCWRgTAChX/3A4r5TWRE7FEdqBgAJYb5c2j+S8YkhWC2K4iO5FNdzPyqL85l9elfuXpbOrpxS/vJBKg+d0R2vwDDw9UeXlXB47a+oHsnHxL5sdwHVDoMeob6EOEo4fLgUvXcHjg2rwcuLygkXNOvrti1ZrdYmDYC4lUQVFxfTq5e5kaOPjw8AlZWVrtdHjx7NRx995FYSZbVaXb8JJCcns3fvXhYvXsyUKVMAKCoqIjz86PxpUVERiYmJAISFhWG32ykrK6szGlVUVOQaWQoLC6O4uPaXCjgcDkpLS+sc89uO7EVFRa7XGuLt7Y23t3e9r7XGN26t9Qn1A8ET5J7UJfejrhPtfqQVmN93E8N83fq8at8PXZiP8aHZ209dfA2ER7Xuveo3GIJCoKQQvX0jDBjq0dMb+2qKypN6N+vz8OTXiLdFMSgmgNTMMtannE5C2jqM1J9RY89s8jkWbM5l95FKgnws3HZqLAqI8LdyemIIpyeaI1pHym1szalgS3Y5W3LKOVRcTVpBFWkFVXy2Ix/8x4NZWcIpMQFc0j+CoXGBrl/0G/t8tdbEBHnz0MR4Hvz6AOsPl/Hyr5ncOiYOeveDLeswdm7C0v3YMpfvalblOQvKtdY4nCvyxpwBEV06/f9HtwrLQ0NDXSNQvr6+BAYGkpmZ6Xq9oqKizoq5ljAMA5vNRnR0NGFhYWzevNn1Wnl5OXv27CElxexEm5ycjJeXV51jMjMzycvLcx2TkpJCWVkZ+/Yd7bS6ZcsWtNauxDAlJYUDBw64EieATZs24e/vT3x8vEc+LyGEaIkqu8GhYvP7bGK473GOPj694A1ztVVCL9SZF7b4fMejrFbUiHHmtVtjlZ6zHqoNm2zWZ5hzCxj/mp8d29ajy8ua9N6deRUs3HIEgBtGxrrqkX4rMsCb0xNDuGl0LP+6KJm3Lu3FXad15bzeYcRbq7EadsYW7eJvkxJ44uweDOsa1Oxp2t6R/twzvhsWBd/tK+b9TXmoFLP+WO/ceszxh0uq2Z5bgUWZ9VAAev9eszeUsqAuuLxZ1++o3BqJ6tWrFzt27HA9Hj58OJ9//jnh4eForfnyyy9dSUtzvP/++wwZMoSoqCgqKytZsWIF27Zt48EHH0QpxQUXXMDHH39MXFwc0dHRzJ8/n/DwcEaOHAmYheZnnnkm7777LkFBQQQEBDBv3jxSUlJc8cTHxzNkyBDmzp3LzJkzsdvtzJs3j7FjxxIRYW6oOXjwYOLj43n55Ze55pprKCwsZP78+UyaNKnBkSYhhGhLB4qqMDSE+noR4d+yHk560xr02hVgsWD5v1nH7fvjKWr06egfFqPX/4KuvhHl0/Jk0MnZ3qC9k6jhXYN4nRy2FxmUd00mIHMfetMa1JiJjb6vwmbw95WZGNpMQsYnNr2OKsLfyrh4f05LXYT+9mO01limXI0lyr9Fn8uIbkHcOCqWV1Zl8cGWI0QlD+BsgN1b0YaBqrVzibND+eDYQFfy51qRN/p0VHTza/g6Irf+511wwQX88ssv2Gw2vL29mTp1Krt27eLll18GICYmhuuvv77Z5y0qKuKVV16hoKCAgIAAEhISePDBB10r8qZMmUJVVRVz586lvLycvn378sADD7imFAGmTZuGUoo5c+Zgt9tdzTZru/XWW3nzzTd5/PHHXc02p0+f7nrdYrFw33338cYbbzB79mx8fX2ZMGECU6dOded2CSGEx6UVmEvOE8ObviqvPrqyAuO/rwGgzp6C6tHzOO/woOS+EBkNR3Jg0xqoGZlqKV1SZJ5TKUjo5ZFzuisu2Ie4YG8Ol9jYMuhsRmX+G71uJRwniXprXQ6HS2xEBlj508iYZl1TZx3CeGMO7DfLUizjz0VNutTdT6GOc3uFkVduY8HmI7yWBmExpzAiexNk7od4c0rP0Jrv99X0hkqqGYXKSIf1v4JSqAt+75FYOgK3kqi+ffvSt+/R5mtRUVH8/e9/58CBA1gsFrp16+ZWZ+8bb7yx0deVUkydOrXRZMbHx4cZM2YckzjVFhQUxG233dbotbp06cL999/feMBCCNFO0mvqoZLCG9++43j0Z+9Dfi5ERqN+d5UnQmsyZbGgRp2OXvIhxqof8fJQEuWcyiOmGyogsPFj28CwuEC+LClkXXgKowC2rENXVqD86h8ZWpNRyrI9hQDcfmpco+0LatNao3/+Bj3/daiqhIAgLP93M2r4WM98IjWuGhRFbpmd7/YVMafvlTxenk/Kzi2omiRqe04FOWU2/K0WxnQ3V+TpxQsBUMPGtm6X+jbW7Jqoqqoq/va3v/HTTz/VPZHFQmJiIj169JCtUYQQopU5R6KSWlAPpffvQX/zOQCWa2487n5qrUGNruknuGUtuqzUI+d0TeW1Q5PN+gyraTK5rtiC7hILtmr05tR6jy2qtPPPVeaKtSl9wzkltmlJoC4rxZj7LPqdf5oJVJ9BWB55yeMJFJgDGrNGxzI0LpAqZeWpQdM5vDvN9fp3NVN5pyUE42u1oA9nmNPFgJp8hcfjaU/NTqJ8fX3ZvHkzVVVVxz9YCCGEx2mtSS+smc4Lcy+J0g47jndeBm2gRo5HDRruyRCbTHVLgG4JYLeb01weoNNrVle3cz2U06CYALwtirxyOxlDzzafrOdz1VrzyqosiiodJIT6cu2QpvUY1Du3YDx2K6SuBC8v1KXTsNzxOKqJvbHcYbUo7hnfleQAKPYJ4gnv4RRV2KiyG/y831x4dmaS2V9RL14IWsOQ0a7RqhOFW6vz+vbt69pGRQghRNvKKbNRbjOwWhTxoe4lUaWfLYADeyEgEDW14fKHtuAcjfLEKj2ttbknG2an8o7A12phYEwAAOu7DgZAb1qN8cV8dPputGEA8O2+IlZllGK1wF9Oi8PHq/Ef0dpux/j4XYw5D0JBHkTHYbn3OSznX9YmiwMCvL2YfXYiXSoLOOwXyVNf7+PH9GIq7AbRgd70i/ZH5xxGrzb/XS2TT7y6YreSqOnTp7Njxw7mz5/PkSNHPB2TEEKIRuyrmcrrEeqD1dL8onJ9JIei/9QUk192HSq0ffcuc+6lx64t6IIW/kzJy4bSYvCyugqdOwJXq4MKf4iNh+pq9KfvYzx1J8Zd08icN5fXV5mtgq45pctxa910TibGs/eil3wIWqNOOxvLQy+2+RRmZLAfD5X9TJCtnJ0lmn+tNhu0npEcgkUp9NKPzKanA4ej2rnIvzW4VVh+991343A4WLRoEYsWLcLLy6vepf/vvPNOiwMUQghRV0uLyo1vPkdXVkDv/qhx53gyNLeoyGhzQ9s929BrfkSde4nb53JN5cUnojpQS5phXQN5MxW25VZQdefT+G1ahd6SCts24igp5sXKBCp9LfQv3MfkL97B2D8MNWiE+XnUWn2ptUav/A79v39DVQUEBGL5wyxXz6320L1nD+7//m0eHfJnbJgjYGckhaKP5KJXfgeA5cITqxbKya0kavTo0Z16w0AhhOjMWlxUftBsNmwZd06d3j7tSY0+Hb1nG3rVj9CCJIr0jjWV59Qt2IeYIG+yS21sLvVi9OmT4PRJaLuNj37ayc5MK/6Oam7dsQCvygL0nq3oRe9BWARq4HCzZi2hN/rDt1xF2qQMxPLHv6AiWmd/1qZSfQbS75P/cPveRczpfTlDYgOJC/bB+O9H4LBD31NQvfq1a4ytxa0kyt3NhYUQQrRc7R5RzaW1Nnv2UFPU3UGo4ePMpfkH9qIPZ6Dimr87hLbb0Du3mA86SFG5k1KKYXGBLNldyPrMMkbHm0v/9xU7mH/Y/FH8p3EJxF7wHHpLqrl6b8dGKMxHr/gaveLroyezWFC/uxrVRrVPx5XYG3x8ODVjNW/84Q8EJ3RDFx5xxXwi1kI5tazNrRBCiDZVWu0gp8wGQFKYG9N5JYVmzZBS0IH69ajgEOg/FDavRa9ejppyTbPer9N2Y7zzEhzabyYZvfu3UqTuG9bVTKJSM8vQWlPt0LzwcyYODad2D+aMpBCUCkVNvAAmXoC2VcOurejNa82kKicTusRimXEnKrlPe386LsrqDT37wfaNhO/fiiU5AWPZIrDbzGnalIHtHWKrcSuJWr688RUUSim8vb2JjIwkKSlJtkoRQggP2V8zCtUlwEqQrxujEBn7AbDGxYOvX4faAFaNnmAmDKuWo393dZPKRnRVFfqz99FffwragKBg1NU3oqLj2iDi5jklNhCrRZFTZuNQcTVLdheSUVxNuL+Vm0bHHvP5Km8fGDAUNWAoXDkTXVwIQSEdZgq2NpUy0NxIeudm9PCx6B+XAuYo1Ilc/uNWEvWvf/2ryccGBARw8cUXM2XKFHcuJYQQopa0QrOoPNHNonKdaSZR3gm9sHksKs9Qg0ehfXwhN8vsOn6cuia9YxPGuy+bxwNq1ATUlTNQwaFtEW6z+VktDIj2Z2NWOe9syGV1htlc9NYxsYQ0ISFWIWGtHKH7VMpANKB3bYGvPoHqavPfr/+Qdo6sdbmVRD333HO88sorBAcHM2nSJGJjYwE4fPgwy5Yto6ysjD/+8Y8UFhaydOlS3n//ffz9/Tn33HM9GrwQQpxsWlxUnuFMonp2vCTKzx81ZAx69XL0quUNFofr8lL0h2+jf/rKfCIsEsu1N6EGj2zDaN0zrGsgG7PKXQnUBSlhro7mnVpSCnj7QEkR+pvPAHNF3ok8CgVu9on68ssvCQ0N5aGHHmLUqFH06NGDHj16MHr0aB566CFCQkL47rvvGDVqFLNnzyYlJYVly5Z5OnYhhDjptDSJ0pkHAPBO7Jg9e9Ros2eUXvMT2uE45nW9/leMh292JVBq4vlYHn+lUyRQQJ2EqVuID9cNjW7HaDxHeXuDs07LYYfuSXBK5/g3aQm3kqg1a9YwcmT9N0cpxYgRI1i1apV5AYuF0aNHk5WV5X6UQgghcBiaA4XOJKr503naMMCZRCX09GhsHtN/KAQFQ3Eh7NzkeloXF2C89izGv56GonyI7orl7qfNPf/8A9ov3mbqHuJDYpgv3hbFX8bG4WvtePVN7lJ9Brk+tlx4YtdCObk1nWcYBpmZmQ2+fujQoTrFilarFR8fH3cuJYQQosah4mpshsbPaiEmyI0FO0dyzM1prd5Yu3WHnFzPB9lCympFDT8NvXwp+tfl0G8I+pfv0QvegPJSc+XdpEtQk69E+bi/+XJ7UUrx9Dk9qLQbRAacWIuu1Ckj0J//D+ITYeiY9g6nTbiVRI0YMYJly5YRGxvLmWee6UqQqqur+fbbb/n6668ZO/boztG7du1y1U0JIYRwT1pNp/LEMF8s7vyWfyjd/DuuO8qr43a4UaMnmknU+l/QxQWwdb35Qo9kLNNuQfXooKNoTRTo40WgTwfo7+RhKqEXltkvQESXDrmCsDW49b/o+uuvJzs7m7feeov33nuPsLAwAAoLC7Hb7fTq1Yvrr78eMBMrHx8fLrzwQo8FLYQQJ6P0whbWQx0yp/JUtx4ei6lV9OwLEV0gP9dMoKzeZnPJc6agrB03+RN0+gS3udz6agwKCuKJJ55g9erVbNy4kdxcc0h48ODBDB48mJEjR2KpyUJ9fHy44YYbPBexEEKcpPYVuF8PBZiNKAHVLdFDEbUOZbGgzrgA/dE7kDIAyx9uRsV2a++whDiG2ym9UorRo0czevRoT8YjhBCiAUc3HnZzJKpmuxc60HYvDVGTLkWNHA/hUSfN1JDofGRcVAghOoGCCjuFlQ4sChLC3Ngzz2Yztw0BVHwnSKKUgsgTY/m/OHE1KYmaNWsWFouFv//971itVmbNmnXcpYtKKf75z396JEghhDjZOYvK44J93FsWn50BDgf4B0J4lIejE+Lk1KQkqn///iilXHVOzsdCCCHaRnpLm2zWFJXTrYd8/xbCQ5o8EtXYYyGEEK3L1ak8zN2i8nQAVNeOP5UnRGch1XpCCNEJHN14uIUjUZ2gHkqIzqJFheV5eXlkZ2dTVlZWp0O5k6zcE0KIlquyGxwqrgZasPGws72BjEQJ4TFuJVF5eXm8+uqrbNmypdHjFixY4FZQQgghjjpQVIWhIcTXiwj/5n/b1hXl5pYvAB290aYQnYhbSdQrr7zCrl27uPjii+nduzcBAZ1n80chhOhsaheVu1UUXrPpMKERqKAQD0YmxMnNrSRq165dTJkyhSuuuMLT8QghhPiNNFeTTfeKynXNVF5naLIpRGfiVmF5ZGQkgYGBno5FCCFEPZwr8xLdaLIJ1NruRabyhPAkt5Koiy66iO+++46qqipPxyOEEKIWrbUHNh52jkQleigqIQS4OZ13zjnnYBgGt956K2PGjCEyMtLViLO2yZMntzhAIYQ4meWU2Si3GVgtivhQN7Z70VpGooRoJW4lUQcOHOCzzz6jsLCQpUuXNnicJFFCCNEyzqm8HqE+WC1uFJWXFEJpMSgFcZJECeFJbiVR//73vykvL2fmzJmyOk8IIVqRs6g80c2icjJqpvK6xKF83aypEkLUy60kKj09nSuuuIKzzz7b0/EIIYSoJa3Fe+Y566FkFEoIT3OrsDw6OtrTcQghhKhHS4vKj9ZDJXooIiGEk1tJ1BVXXMGyZcvIy8vzdDxCCCFqlFU7yC61Ae5vPKylqFyIVuPWdN62bdsICAjg9ttvZ9CgQfWuzlNKcf3113skSCGEOBk5R6GiAqwE+Xo1+/3aMI52K5eRKCE8zq0katmyZa6P161b1+BxkkQJIYT7WtqpnLxsqK4CqzdEx3kwMiEEuJlEycbCQgjR+lpaVE5mTVF5XDzKq/kjWUKIxrlVEyWEEKL1pbd0ZV6Gsx5K9swTojW4NRJVm2EYlJeX1/taUFBQS08vhBAnJYeh2e9amefmdJ6rHkqSKCFag1tJlN1u59NPP+X777/nyJEjGIZR73Ey7SeEEO45VFKNzdD4WS3EBHm7dQ6dkQ7ISJQQrcXtjuXLly8nJSWFkSNHSsdyIYTwMOdUXmKYLxbV/O1etM0GOZnmA0mihGgVbiVRv/76K6effjqzZs3ydDxCCNEh6MpyjFeeRsUnoi6/vs0Ls4+uzHOzqDw7AxwO8A+E8CgPRiaEcHIrifL19aV3796ejkUIIToMnboSdmxC79gEBUdgxh0oq3vTau44ujLP3SabznqoHig3RrKEEMfn1uq80047rdH+UEII0dnp9b8e/Tj1Z4x/PYO2VbfZ9Y9uPOzudi/pAKiuMpUnRGtxK4m69tprCQgI4K9//SurVq1iz5497Nu375g/QgjRGemqKti+AQB1+XXg7QOb12K89Di6sqLVr19YYaew0oECEsLc3Xi4ZiQqXpIoIVqLW9N5NpsNrTXr169n/fr1DR4nq/OEEJ3StvVQXQ2R0ahzL0ElpmD88wnYsQnjxUew3PowKqD1Wrik1bQ26Brig5/VzXZ+h6RHlBCtza0k6tVXX2X16tWcdtpp9OrVS1bnCSFOKHrDKgDUkNFmPVGfgVjufALjxUdh7w6MObOx3P44KjikVa6fll8zlefuKFRFORzJMR9IEiVEq3Eridq4cSPnnXce1113nYfDEUKI9qUdDvSm1QCooWNcz6ukFCx3P4XxwsNwYB/G8/djueNxVFikx2NIK2zpdi81U3lhEajAYA9FJYT4LbfGif39/YmNjfV0LEII0f72bofSEggMhl7967yk4pOw3PMMhEXC4YMYz92Pdo74eFBLNx7WNUXlSFG5EK3KrSTqrLPO4ueff26wU7kQQnRWen3NVN4pI+rtDaVi481Eqkss5GZhPHcfOuuQx65f7TA4VGyuAnR7JKqmqFxJUbkQrcqt6bz4+HjWrl3Lvffey4QJE4iMjMRiOTYfGz16dIsDFEKItqK1Rm8wWxuoIWMaPE51icVy9zMYLzwEWRnm1N5fHkfFJ7Y4hgOF1RgaQny9iPB3b3tTXVNULiNRQrQut/6Hvvjii66P33vvvQaPk9V5QohO5dB+yMs2WxoMGNrooSo8EsvdT2O8+AgcTMP424NYbn8UldiyRsS1+0O50yRTa320R5SMRAnRqtxKoh555BFPxyGEEO3OOQpF/yEo3+PXI6mQMCx3PoXx0mOwb6e5au/WR1C9+x/3vQ1xFZW7uTKP4kKzpkspiO3udhxCiONzK4nq39/9bxBCCNFR6Q01q/IGj2rye1RgEJa/PIbx8lOwczPGiw9jmfUgqn/jI1kNcbY3cLeo3Nkfii5xKF83EzEhRJO42cVNCCFOLDo/F/bvAaWalUQBKL8ALLc+DAOHQ3U1xj+fQO/a2vwYtGZ/C9sbuOqhuvVw6/1CiKZr8kjUvHnzmnVipRTXX399swMSQoj24GywSc9+qJCwZr9f+fhimfUAxmvPwsbVGF9/glfKgGadI6fMRpnNwGpRdAtxd2Wes1N5onvvF0I0WZOTqGXLljX75JJECSE6i9pdyt2lrN5YLr4GY+Nq2LIOXVGO8m/6jg5pBeYoVPdQH7y9ml9UDkdHopSMRAnR6pqcRMlKOyHEiUqXl8KuLUDLkigAuiVCTDfIPoTetAY1ekKT35pe0MKpPMM42q1cRqKEaHVSEyWEOOnpzangcEBcd1RM1xadSymFGn6aed61Pzfrvfta2KmcvGyorgKrN0THuXcOIUSTSRIlhBDraxpsDm24wWZzqBFmEsWWVHRleZPfl15TVO7uxsNk1hSVx8XX221dCOFZkkQJIdqFsfJb9L6d7R0G2mZDb1kHNN6lvFniEyG6K9ht6I1rmvSWsmoH2aU2oAV75mVIUbkQbUmSKCFEm9N7d6Df+gfG3x9GFxW0bzA7NkFVBYRFQEJPj5xSKeUajdKpTZvSc45CRQVYCfZ1cxTJVQ8lReVCtAVJooQQbU7v2W5+UFmBXtTw1lFtEotrr7zRqHr2AHWXsy6KLevQlRXHPb6lReUAOiPdvLaMRAnRJpr0HWPx4sVkZma2dixCiJOETjs6jadXfotO390+cRgGeqOzS7mHN0zvnmQWd9uq0ZuOP6XX0qJybbNB9iHzgYxECdEmmpREvfPOO+zbt8/1eOrUqaxYsaLVghJCnODSapKm2HjQGmP+6+bGuW0exy4oKgD/AOg7yKOnrrNKr5EpvdJqB9/sLWRdZhlgbjzsluwMMAzwD4TwKPfOIYRoliYlUUFBQRQWFrZyKEKIk4EuKoD8XFAKy6wHwNcP9u5Ar1re9rFsrGmwOXA4yurt8fO7VultTq0zpVdhM/gxvZinlmcw7aM9/PPXLPIr7HhbFH2j/N26lrOonG49UMq9Rp1CiOZpUrPN/v37s3DhQtLT0wkIMLvvLl++nF27djX4Htn2RQhRL+dUXlx3VGw86vzL0Z/8B/3R2+gho1F+7iUR7tDra7Z6aWmDzYZ0T4YusZCbRdXGtazvOoSf0otZc6iUasfRkbeEUF/GJQYzITGEyAA3k7lM58q8BE9ELoRogiYlUTNmzODtt99m06ZNFBUVAbBp0yY2bdrU6PskiRJC/JaumcpTSSnm3+dejP75G8jNQi/5CHXJtW0TR1YGZGWAlxU1cHirXMOhYf3QC1hxoJRVu8Oo2HPI9VpcsDfjE0IYlxBCgrt9oWo5OhIlSZQQbaVJSVRoaCi33Xab6/HUqVO55ZZbGDduXKsFJoQ4Mem0mhFsZxLl7YPl99Mx/vU0+qtF6HFno7rEtn4czg2H+wxCBQR67rxaszm7nBX7S1h5oJiS6r5Q8+lE+XsxLjGU8Qkh9Izw9ey0W017AxmJEqLtNHnvvNpuvPFGUlJSPB0LixYtYvXq1Rw6dAgfHx9SUlK49tpr6dr16DYMWms++OADvv32W8rKyujbty8zZswgLu7oFgfV1dW8++67rFy5EpvNxuDBg5kxYwZhYWGuY0pLS5k3bx6pqakopRg9ejTXX389fn5HV8bk5eXx+uuvs3XrVvz8/JgwYQJXX301XtIJWAi3aMOA9LojUYA5ndZvMGzfiLFwHl43PdD6sTg3HB7quam8jOIq/rUqi605R+ufQv28GHtoLeP2r6Tv1KlYh/X22PWcdEU5HMkxH0gSJUSbcaspysSJE4mOjgYgIyOD9evXs379ejIyMloUzLZt25g0aRJPPfUUs2fPxuFw8OSTT1JZWek65tNPP2XJkiXMnDmTp59+Gl9fX5566imqq6tdx7zzzjukpqZyxx138Nhjj1FQUMCcOXPqXOull17i4MGDzJ49m/vuu4/t27czd+5c1+uGYfDMM89gt9t58sknmTVrFj/88INsxCxES2Qfgopy8PGp88NeKYVl6kywWGD9r+jtG1s1DF1UADXd0j3R2sDm0CzYnMdtX6azNacCXy/FOT1Defys7rx1SS/+1K2afsX7UetaaVWzs8lmWAQqMLh1riGEOIbbneXWrFnDLbfcwp133slf//pX/vrXv3LnnXdyyy23sHbtWrfO+eCDDzJx4kS6d+9OYmIis2bNIi8vz9VeQWvN4sWLufTSSxk5ciQJCQncfPPNFBQUsGaN2YelvLyc7777jmnTpjFw4ECSk5O56aab2Llzp6sQPiMjgw0bNnDDDTfQu3dv+vbty/Tp01m5ciX5+fkAbNy4kYyMDG655RYSExMZOnQoU6dOZdmyZdjtdndvmxAnNb2vZiqvR69j9nZT3XqgJl4AgLHgDbTD0XpxbFwNWkNib1R4ZIvOtT23nL8sSeP9TXnYDc2wuED+OTmJm8fEMTg2EC+LqrVKby26qrLxE7pBH0o3P+gqo1BCtCW3kqh169a5Rnauuuoq7r77bu6++26uuuoqAP72t7+xYcOGFgdXXm5u3BkUFARATk4OhYWFnHLKKa5jAgIC6NWrlytB2rdvHw6Hg0GDjvZ86datG1FRUa5jdu3aRWBgID17Ht3iYdCgQSil2LNnj+uYHj161JkCHDJkCBUVFRw8eLDFn5sQJ6WalXkquf5yAPW7qyAoGA7tR/+4tNXCcE3ltWBVXlm1g9dWZ3H/Vwc4WFRNqK8Xd57WlYfPiCcmyKfuwT16QlQMVFfDZvd+yWzUoZp6qHhJooRoS27VRH300UckJCTw2GOP1akhGjFiBOeddx4PP/wwCxcuZMiQIW4HZhgGb7/9Nn369KFHD7P7rrNXVWhoaJ1jQ0NDXa8VFhZitVoJDAxs9JiQkJA6r3t5edXph1VYWFgngap93YZ6ZtlsNmw2m+uxUgp/f3/Xx57iPJf0gjlK7kldHfV+uFbmJfepNzYVFAJTrsX476voT/4Lo043n2uh2vdDV5ZDzXShZegYt+7RrwdLeG212dsJ4KyeoUwfFtPgnndKKfSIceilH6FTf8Yycrybn0kDDh1tb9CUz6ejfn20J7kndcn9aBq3kqgDBw5w1VVX1UmgnPz8/Jg4cSL/+9//WhTYm2++ycGDB3n88cdbdJ62tGjRIj788EPX46SkJJ599lm6dOnSKteLjW39FUydjdyTujrS/TCqKjlUM+0UM3oc1ui4eo/TU6eR/fPX2NL34P/1J4TfdK/HYoiNjaV8xTccsduwdu1O7PDRzfohkVtaxXPf7OKH3bkAdA/z5/5z+zAyIeK4760+72Kyl34Em1OJCQ/HUs/3T3dorck8fAANdBk8Ap+4+u9rfTrS10dHIfekLrkfjXMrifL29qa0tLTB10tLS/H2dr/775tvvsm6det47LHHiIw8Wq/gHBkqKioiPDzc9XxRURGJiYmuY+x2O2VlZXVGo4qKilzvDwsLo7i4uM41HQ4HpaWldY5xTu3VPkftOH7rkksuYfLkya7Hzm/Oubm5Hq2jUkoRGxtLVlZW+2yV0QHJPamrI94PvWc7OBwQEkaOXaMOH27wWOPy6fC3Byhd/BEVI09HxSe26Nq174f9e3Oa0DFwOFlZWU16v6E1y3YX8s76HMptBl4KLh0QyRUDo/C1VnG4kc/FSQeGQlQMOi+bw19/gcVZJ9VCuqgAo7gIlCLPx7/R++rUEb8+2pvck7pO9vthtVqbNADiVhI1cOBAFi9ezJAhQ45pdbB7926WLFlSp26pqbTWzJs3j9WrV/Poo4+6VgA6RUdHExYWxubNm11JU3l5OXv27OHcc88FIDk5GS8vLzZv3syYMWMAyMzMJC8vzxVrSkoKZWVl7Nu3j+TkZAC2bNmC1ppevXq5jvn4448pKipyTeNt2rQJf39/4uPj643f29u7weSxNb4ItdYn5Rd3Y+Se1NWR7odRsxrO2R+qsbhUn4EwfCykrsTxv39jufNJj0wrGDabazNgNWRMk+7NgSKzbcH2XLNtQe9IP24eHUtizUbBzbm/avhp6GUfo9euQA8f68ZncCydkW5+0CUOvH2aFU9H+vroKOSe1CX3o3FuJVHXXnstDz74IA899BC9evVy9XHKzMxkz549hIaGcs011zT7vG+++SYrVqzgnnvuwd/f31V7FBAQgI+PD0opLrjgAj7++GPi4uKIjo5m/vz5hIeHM3LkSNexZ555Ju+++y5BQUEEBAQwb948UlJSXElUfHw8Q4YMYe7cucycORO73c68efMYO3YsERHmsPzgwYOJj4/n5Zdf5pprrqGwsJD58+czadKkFo2yCXHSqmmyWac/VCMsl1+PsWkt7NwM63+BYS1POvTurVBeBsGh0LNP48dqzcKtR1iwOQ+7AX5WC38YEsX5vcPxsriX0KkRNUnUpjXoqiqUrwc6ldfUQyFF5UK0ObeSqOjoaP72t7+xaNEiNmzYwMqVKwHo0qULF1xwARdffPExxd9N8dVXXwHw6KOP1nn+pptuYuLEiQBMmTKFqqoq5s6dS3l5OX379uWBBx7Ax+foaphp06ahlGLOnDnY7XZXs83abr31Vt58800ef/xxV7PN6dOnu163WCzcd999vPHGG8yePRtfX18mTJjA1KlTm/15CSGOdipvahKlomJQky5Bf7EA44N5WAYOR/m0LOnQ6381z33KSJSl8aa5m7LL+e/GPABGdgvkzyNj6RLYwl+gEnpBZLTZGHNLqjna1lI1dWZK2hsI0ebcSqLAXKl23XXXeTAU+OCDD457jFKKqVOnNprM+Pj4MGPGjGMSp9qCgoLqbGVTny5dunD//fcfNyYhRON0SRHkZZsPEpvesVuddxn652/hSA76q09Qk93/JUZrXatL+ZjjHr9iv1k3eWZyKLeOifXIdKJSqmY0ahE69WeUB5IoLe0NhGg3bjfbFEKIJnM22YyNb9Y+dcrXD3XZNAD0kg/R+Xluh2DbtxPyc8HH19xiphF2Q/PLQXPxzMSkEM+2KBlu7jmqN65GV1W16FzaMI52K5eRKCHanCRRQohWp9ObN5VXmxp1OvTqD9VV6I/ecTuGil+Wmx8MGHrcacEt2eWUVDkI8fViYHSA29esV2LNlF51lTml1xIZ6eZ5rN7QQMsIIUTrkSRKCNHqXNu9uJNEKYXlypmgFHr1cvSebW7FUPGrmUQ1pUv5zwfMqbxTuwe7XUTeEKUUarjZ3kCn/uz2eXR+Lsa/njYfpAw4ZhsdIUTrkyRKCNGqtGGAcySqge1ejkcl9ESNOwcAY/4b5jmbE0NuFra0XaAsqFNGNnqsw9D8WjOVN7ZH62zm69xLT29ag65u/pSeLszHmDPbLFCPjsNy/e0ejlAI0RRuF5YLIUST5Bw22wpYvaFbotunURdfi167AvbvwfjLNRAUCsEhEBSCCgr+zWPzb9cf56q8lAHH3UZmc3Y5xTVTeYNiPDyV55TYu+4qvWa0b9AlRRgvPGTe18hos4dW2PE7pgshPK/ZSVRVVRUPP/wwZ511lqvBpRBCNMTZ2oCEniir+7+3qZAw1KXT0P991UzKyssgJ9O8xm+v2dA52nkqzxWHUqjhY9FffYJe+zOqiUmULivF+PvDcPgghEWaCVRE62wrJYQ4vmZ/R/P19SUnJ0c2JRRCNE0z+0M1xjLxfPTI8VBcACXFUFqMLjX/dv7RpSVQUnT0uUqz07jyD3DVIjWkLabynNTw08wkqmZK73jF7rqiHOMfj8LBNAgJw3LnE6gusq+ZEO3JrV8LhwwZwsaNGznnnHM8HY8Q4gTjGonyQBIFoAKDIDAIahajHe/XOW2zocpLiE1IJLuopNEtLNpkKs8pKQUiuphtF7asg2GnNniorqrEeOlxMyENCsZyxxOo2Pq3nxJCtB23Cssvu+wyDh8+zD//+U927NhBfn4+paWlx/wRQpzctK3aHDnBMyNR7lDe3qiwSCwBQcc9duWBEgDGdA9qtak8V1w1U3rQ+Co9XV2F8cpTsGcb+Adiuf1xVDfpCSVER+DWSNSdd94JQEZGBitWrGjwuAULFrgXlRDixHAwDRx2s7g7Kqa9o2mUw9D8ctBMok7r0Xjxuaeo4aehv/4UvbH+KT1tt2G89ixs3wi+/lhuewSV0LNNYhNCHJ9bSdRll10mNVFCiOOqPZXX0b9ntOlUnlNyH4iIgvw82Loeam1Hox0OjNf/BpvXgo8PllseQvXs2zZxCSGaxK0k6oorrvB0HEKIE9E+zxWVt7a2nMpzUkqhhp2G/uZTc5VeTRKlDQd63t9h3S9gtWK56UFUn4FtEpMQouk80myzvLwco5nN74QQJ76WbPfSltpjKs/paOPN1WhbNdow0O++jF79I3h5YbnhPtSAoW0akxCiadxu2rJ3717mz5/P9u3bsdvtzJ49m4EDB1JcXMxrr73GhRdeyIABAzwZqxCiE9GlxWZDSICk3u0bzHFsyTGn8oLbcirPKSkFwqOgIA+2rENv24D++VtQFiwz7kQNHtW28QghmsytkaidO3fy8MMPk5WVxfjx4+ssGQ4JCaG8vJyvv/7aY0EKITqhtN3m39FdUYGt23OppX7eb45CndqGU3lOymJxrdIz3n0Z/cNiUAo1/TbUiHFtGosQonncSqL+97//0a1bN1544QWuuuqqY14fMGAAe/bsaXFwQojOy1lU7u5+eW2lPafynFxNQEvNbunq2puwjDmjXWIRQjSdW0nU3r17mThxIt7e3vWuuImIiKCwsLClsQkhOjHXyrzEjp1EtetUnlNyH6jpPq6u/BOW0ye1TxxCiGZxqybKy8ur0a6/+fn5+Pn5uR2UEKJz01pDeucYiXJO5Y2Jb/upPCdlsWC580kozJc2BkJ0Im6NRPXu3Ztff/213tcqKyv54Ycf6N+/f4sCE0J0YrlZUFoCVivEJ7V3NA0y98qrmcpLaJ+pPCcVGS0JlBCdjFtJ1BVXXMG+fft45plnWL9+PQDp6el8++233HfffRQXF3PZZZd5NFAhROfhmsrrnozy9m7fYBqxNaecovaeyhNCdFpuTef17t2b+++/n9dff51XXnkFgPfeew+AmJgY7r//fhISZG8nIU5aaZ2jP9SKWlN51naayhNCdF5u94kaOHAg//jHP0hLSyMrKwutNTExMSQnJ3f47R2EEK2r9nYvHVVHmsoTQnRObidRTklJSSQlddyaByFE29J2GxzYB3TskSjXVJ6PRabyhBBucTuJstlsfPvtt6xfv56cnBwAoqOjGTp0KGeeeSY+Pj4eC1II0YlkpIPdBoHBEB3X3tE06OeavfJGdw+WqTwhhFvcSqKOHDnCk08+SWZmJmFhYcTGmv1N0tPT2bBhA0uXLuWhhx4iMjLSo8EKITq+o1N5vTvs1H7tBpvjZCpPCOEmt5KoN998k9zcXP7yl78wZsyYOq/98ssvvPLKK7z55pvcc889HglSCNGJ7KspKu/ATTa35pRTVClTeUKIlnEridq8eTMXXnjhMQkUwKmnnkpaWhpLlixpcXBCiM5Hd4ImmzKVJ4TwBLf6RPn7+xMaGtrg62FhYfj7+7sdlBCic9JlpZB1yHzQQUei6u6V17E3RhZCdGxuJVETJ07khx9+oKqq6pjXKisr+f777znzzDNbHJwQopPZv9v8u0ssKrhj1hrVnso7JTawvcMRQnRiTZrOW7VqVZ3HSUlJrF+/nttvv50JEya4CsuzsrJYvnw5QUFB9OjRw/PRCiE6NL2v4zfZXClTeUIID2lSEvXCCy80+NqiRYuOeS4/P59//OMfjB071v3IhBCdTu2VeR2Rw9CslKk8IYSHNCmJeuSRR1o7DiFEJ6e1rrXdS592jqZ+MpUnhPCkJiVR/fv3b+04hBCd3ZEcKCkCLy/okdze0dTr5wPFgEzlCSE8w63CciGE+C3XVF58Esq74+1Y4DA0vxyQqTwhhOe4ve3Ljh07+O6778jJyaGsrMwcyq9FKcXzzz/f4gCFEJ1EWscuKt+QUUhhpYMgmcoTQniIW0nUF198wXvvvYePjw9du3YlKCjI03EJITqZo0XlHTOJ+nqnucfnGJnKE0J4iFtJ1GeffUbfvn259957CQiQLROEONlpux327wU65kiUw9B8vysXkKk8IYTnuFUTVVVVxbhx4ySBEkKYMveDrRr8AyGma3tHc4xtOeXkl1fLVJ4QwqPcSqIGDBjAgQMHPB2LEKKTcjbZJKk3ytLx1qvIXnlCiNbg1ne76dOns2XLFj777DNKS0s9HZMQoh04DM2+/MpjFok0iXPT4Q62X56hNR9tPcLS3QUAjOvRMbeiEUJ0Tm7VREVFRXH22Wfz3nvv8d///hcfHx8s9fz2+c4777Q4QCFE2/h8Zz5vrctl6qBIrj6lS7Pe69ruJbnjJFElVQ7+8Usmaw6VAXDhgFiGdpWpPCGE57iVRC1YsICPP/6YiIgIevbsKbVRQpwAft5vTnl9uj2fySnhhPg17duDriiHrAzzQQfZ7mVXXgXPrzhETpkdb4ti5sgYrhvfj6ysLPdG2oQQoh5uJVFff/01w4YN4+677653BEoI0bmUVjnYk18JQKVd89mOAq4d0sTRqPTdoDVERqNCwlsxyuPTWrN4VyHz1mVjNyA2yJt7x3ejZ6Q/SkktlBDCs9xKoux2O8OGDZMESogTxObscgwN3haFzdB8uauAi/tHEOTjddz36g7SZLPc5uDlX7NcReRjugdx65g4ApvwOQghhDvcSqKGDRvG9u3bOeecczwdjxCiHWzIMuuGzj70C1uDEzkQFMvn/3iDK/JWg48v+PiYf3ubfyvXxz7onVvMk7RjEpVeUMmzPx0is8SGl4JpQ6P5Xd9wGX0SQrQqt5Ko3//+97z44ou88cYbnHnmmURFRdU7KiWdzIXoHNbvywF8GJK3nX75e3lhwDV83mUkk/d+Q4Cj6pjj66sqUj37tnqc9flmbyFz12RT7dBEBli5Z1w3+nbxb5dYhBAnF7eSqNtvvx2A9PR0vv766waPW7BggVtBCSHaTuaypWQ7EvEyHAwaNRjfM85lwQ/5HCoPYOk1T3J5tA2qq8BWha6urvm4GlwfV0FkNCT3adO4q+wGr63J5rt9RQAMjQvkjrFxTS6IF0KIlnLru81ll10mw+RCnACM7xez4ef10CeRFGs5AZdchVKKK4Z48/eVh/ks28LkMf3x9zZHmjvK//qM4iqe+ymT/YVVWBRcNSiKywdGYpHvS0KINuRWEnXFFVd4Og4hRBszflyKfv81Ng64FoAhAxJdvxyNTwjhf5vyyCq1sXR3AZf0j2zPUOtYsb+Yf/6aRaXdINTPi7tO6ypbuQgh2oUsrxPiJGT89BX6vX/hQLG5S38AhnY9WsPoZVH8fqCZOH2yPZ8qu9Eucf7W13sKeX5FJpV2gwHR/rx4QZIkUEKIduPWSNSHH37YpOMuv/xyd04vhGhFxs/fot97BYB9Z15FmWEl0NtCrwi/OsdNTAplweYj5JTZ+GpPIRf1jWiPcF2q7Ab/2ZgLwOQ+4UwfFo2X7IMnhGhHbiVRCxcubNJxkkQJ0bEYv3yPfucl0Bp1xoVsHHg2bMpjUGzAMQmJ1aK4bEAEr67O5uNt+UzqHYaPV/sNXi/dXUhhpYPoQCvXDZUESgjR/tze9uW3DMMgLy+PpUuXsn37dh544IEWByeE8Bxj1XL0W/8wE6iJ56Ou+hMbvzkAwJAGpsTOSg7lgy1HOFJu59u9RZyf0j4dySvtBh9tOwLA7wdG4e0lCZQQov157NdKi8VCdHQ0//d//0dcXBzz5s3z1KmFEC1krPkJ/ebfQRuo8eeirvozlXbNzrwKAIbE1Z9EeXtZuKymqPzDrUewOdpn37kluwooqnQQE+TNmcmh7RKDEEL8VquMzffr14/169e3xqmFEM2k165AvzHHTKDGnYO69iaUxcLWnHLsBsQEeRMX7NPg+8/uGUq4nxd55Xa+Tytqw8hNlXaDRdvyAbhiYCRWmcYTQnQQrZJE7d27V/pICdEBGKkrMV7/GxgGauxZqD/MQtXsLrDhsLnVS0NTeU6+VourxcGHW49gN9p2NGrxzgKKqhzEBnkzMUlGoYQQHYdbNVHLly+v9/mysjK2b9/O6tWrOfPMM1sUmBCiZSp++QHj38+ZCdSYM1DTbnYlUADra5KowXEBxz3Xeb3D+GjrEbJLbfyYXtxmU2oVNoNF22UUSgjRMbmVRP3rX/9q8LXg4GCmTJkiK/OEaEfGxtXkvfoMOByoURNQ19+Ksni5Xs8rt5FRXI0CTok5fp8lX6uFi/tF8M6GXBZuyWNCYkibrI77clcBxVUO4oJlFEoI0fG4lUS9/PLLxzynlCIwMBB/f9n4U4j2pDenYrz6DNjtqJHjUdNvr5NAAWysGYXqFelHsK9Xfac5xnkpYXy87QiZJTZW7C9mQisnNeU2B5/UrMi7YmCUtDQQQnQ4biVRXbp08XQcQggP0FvXY/zrabDb8T/tTKr/71awHFv6uCGrHDh+PVRtAd5e/K5vBP/dlMfCrUcYnxjSqnvVfbmzgJJqg67BPkxIDGm16wghhLtk2xchThB6+0aMV54Cuw01dAyR9zyN8jp2lMnQmo1ZNUXlDbQ2aMiFfcIJ9LZwsKiaXw6WeCTu+pTbHHxSUws1dVCkjEIJITqkJo9E3XXXXc06sVKK559/vtkBCSGaT+/cgvHyE2CrhsGjsPz5HpS1/v/e6QVVFFU68LMq+kQ1b/o90MeLyX3DWbD5CB9sPsKp3YNbZTTqix0FlFYbxIf4MD5BRqGEEB1Tk5OooKCgJrUtKCwsJDMzs0VBCXEi0Vqjf1qG/uZz1LizUWdPqbNKrsXn37UV45+PQ3U1DBqB5c/3oqzeDR6/oWYUakB0gFudvy/qE8Fn2wtIL6xiTUYpo7sHux17fcqqHXyywzkKJbVQQoiOq8lJ1KOPPtro64WFhXzyySfs3r0bi8XC+PHjWxqbEJ2ettnQ/5uL/ukr8/HCt9CbU7FcfzsqIqrl59+zHeOlx6GqEvoPxXLjfSjvhhMoOFpU3typPKdgXy8u7BPOh1uPsGDLEUbFN+0XrKb6fGcBZTWjUKf18GyCJoQQnuRWYXltzuTp22+/xW63M378eC699FJiY2M9EZ8QnZYuOGKukkvbBUqhTj0TvXYF7NiE8ditWP5wE2rEOPfPv28nxj8ehaoK6DcYy6wHUN4Ndx4HqHYYbMttfKuXpvhd33A+35HP3vxKUjPLGNEtyO1z1VZa7eCzmlqoK2UUSgjRwbmdRNWXPF122WXExMR4Mj4hOiW9exvGa3+F4kIICMIy8y7UwGHo8y/HeGMO7N+DMfc51KY1qKv+jPI/fsPLOudP343x4qNQWQF9BmGZNRvl43vc923LqaDaoYnwt9I9pPGEqzGhflbOTwnnk+35LNicx/CugR4ZjfpsRz5lNoMeoT6cliCjUEKIjq3ZSdRvk6fTTz+dyy67jOjo6NaIT4hORWuN/mEJesHr4HBAtwQsNz2Aio4DQMV2w3Lfc+jP56OXfIj+5Xv07m1Y/vgXVK/+TbvGgb0Yf38YKsqgd38sN89G+R4/gYJaW73EBbQ46bm4XwSLdxWw60glG7PKWzSyBVBa5eDzHQUAXHlKVKu2TxBCCE9ochJVUFDgSp4cDgcTJkzg0ksvleRJiBraVo3+76von78FQI0Yh7ruVpSvX53jlNWKuuRa9MBhGG++AHnZGM89gLrw96gLpza4qg5AH0zDeOFhKC+Dnn2x3Powyq/pK+ycReWDm9EfqiHh/lbO7RXGFzsLWLA5j8GxLUvMPt2RT7nNIDHMl1M9XKwuhBCtoclJ1C233ILNZiMxMZFLLrmE6OhoSktLKS0tbfA9ycnJHglSiI5O5+eZ9U/pu0FZUJf9H+rcSxpNKlTv/lge/gf6f/9G//o9+osF6K3rsfzxDlRM12OvcWg/xgsPQVkJJKVgue1RlF/TpwELK+2kFVQBzWuy2ZhL+0ewdHch23IreOjbg9wwMob40KaNitVWUnsUapCMQgkhOocmJ1E2mw2A9PR0/v73vzfpPQsWLHAvKiE6Eb1rC8Zrz0JJEQQGY/nTXaj+Q5v0XhUQiPrjXzBOGYH+z78gbRfGE7ejps5AjTvHlYTpzAMYc2ZDaTEk9MJy+6PNrqPaVNOlPDHMlzD/Fq8pASAywJsbRsbw77XZbM4u57bFaVzaP5LLB0Tia216G4dPtudTYTdICvdldHfPFKkLIURra/J30htvvLE14xCi09Fao7//Ev3Bm2b9U3yiWf/UpfkrUy0jx6N79sWY9yLs3Ix+92X0prVY/u9mKCs2E6iSIuiRjOUvj6MCmp9obGhha4OGnNMrjFNiA5i7JpvUzDI+2HKEH9OL+fPIGIZ1PX6cxZV2vtgpo1BCiM6nyUnUxIkTWzEM07Zt2/jss89IS0ujoKCAu+66i1GjRrle11rzwQcf8O2331JWVkbfvn2ZMWMGcXFxrmOqq6t59913WblyJTabjcGDBzNjxgzCwsJcx5SWljJv3jxSU1NRSjF69Giuv/56/PyO1q7k5eXx+uuvs3XrVvz8/JgwYQJXX301XvVsoyFOPrq6Cv2fV9G/fAeAGnU66v9uPqb+qTlURBcsdzyB/voT9KL/wIZfMdJ2Aspc5RefaCZQgc1PoLTWrnooTydRADFBPjw0MZ5fD5by+tpsskptPPZ9Bqf1COaPw6OJDGi4d9Un2/OptBskh/syOl5GoYQQnUeH2juvqqqKxMRE/vjHP9b7+qeffsqSJUuYOXMmTz/9NL6+vjz11FNUV1e7jnnnnXdITU3ljjvu4LHHHqOgoIA5c+bUOc9LL73EwYMHmT17Nvfddx/bt29n7ty5rtcNw+CZZ57Bbrfz5JNPMmvWLH744QeZnhQA6PxcjOfuNxMoZUH9/nrUjDtblEA5KYsFy6RLsTzwN4jrDkUFUJRvrvK74wlUkHtboGQUV3Ok3I63RdG/S/O2emkqpRSn9gjm5YuSmNI3HIuCnw+UMOvzND7fkY/D0Me8p6jSzpe7jq7I82TTTiGEaG0dKokaOnQoV155ZZ3RJyetNYsXL+bSSy9l5MiRJCQkcPPNN1NQUMCaNWsAKC8v57vvvmPatGkMHDiQ5ORkbrrpJnbu3MmuXbsAyMjIYMOGDdxwww307t2bvn37Mn36dFauXEl+vtnkb+PGjWRkZHDLLbeQmJjI0KFDmTp1KsuWLcNut7fdDREdji7Mx3jqTti/B4KCsdz+KJbjFJC7Q/VIxjL7BdSkS2HYqWYCFRzq9vmcU3n9ov2bVavkjgBvL6YPj2HOeYmkRPpRYTd4IzWHu5els/tIRZ1jzVEoTc8IP0Z5qGGnEEK0lQ6VRDUmJyeHwsJCTjnlFNdzAQEB9OrVy5Ug7du3D4fDwaBBg1zHdOvWjaioKNcxu3btIjAwkJ49e7qOGTRoEEop9uzZ4zqmR48edaYAhwwZQkVFBQcPHmzNT1N0cPqz982ptdh4LA++gOo/pNWupXx8sVx+HV433o8KCWvRuTY6p/I8tCqvKZIj/Hh2UgI3jooh0MfC3vwq7l66n9dWZ1Fa7aCw0s6XNbVQV8solBCiE/LMEp02UFhYCEBoaN3fxkNDQ12vFRYWYrVaCQwMbPSYkJC6UyJeXl4EBQXVOaZ2AlX7us5j6mOz2VyrGMGc3vD393d97CnOc8kPnaPa4p7oQ/vRK74BwGvaLW4VkLeV2vfDbmi2ZJsjQEPjPNNZvKm8lOL8lAjGdA/h7XU5fJ9WxJLdhfxysISkcD+qHJqUSD9GdPPs/nu/Jf9n6pL7cSy5J3XJ/WiaTpNEdQaLFi3iww8/dD1OSkri2WefpUuXLq1yPdmf8FiteU9y//0cDm3gf+oZRJ1+Vqtdx5NiY2NZn1FIhd0gzN+bsQOS2mX1WxzwXHJ31uzP59lvdrE/v5z1NVOMN03sQ9eukW0Sh/yfqUvux7HkntQl96NxnSaJco4MFRUVER4e7nq+qKiIxMRE1zF2u52ysrI6o1FFRUWu94eFhVFcXFzn3A6Hg9LS0jrHOKf2ap+jdhz1ueSSS5g8ebLrsTODz83N9WgtlVKK2NhYsrKy0PrYYt2TUWvfE2PHZozVP4HFQvWFV3D48GGPX8OTat+PbzZnAzAo2p/srKx2jSveB16Y1J1F2/L5YEseg2ICSPKravX7Kf9n6pL7cSy5J3Wd7PfDarU2aQCk0yRR0dHRhIWFsXnzZlfSVF5ezp49ezj33HMBs0O6l5cXmzdvZsyYMQBkZmaSl5dHSkoKACkpKZSVlbFv3z5XR/UtW7agtaZXr16uYz7++GOKiopc03ibNm3C39+f+Pj4BmP09vbG27v+pdyt8UWotT4pv7gb0xr3RBsGxsJ5AKjTz4OYbp3mvmut6+yX1xHitloUvx8YyaX9I1zPtVVc8n+mLrkfx5J7Upfcj8Z1qCSqsrKSrFq/Kefk5JCenk5QUBBRUVFccMEFfPzxx8TFxREdHc38+fMJDw9n5MiRgFlofuaZZ/Luu+8SFBREQEAA8+bNIyUlxZVExcfHM2TIEObOncvMmTOx2+3MmzePsWPHEhFhflMfPHgw8fHxvPzyy1xzzTUUFhYyf/58Jk2a1GCSJE5ces1P5mo8X3/URVe2dzjNUlrlYE9+JeCZ/fI8ycsitRZCiM6tQyVRe/fu5bHHHnM9fvfddwGYMGECs2bNYsqUKVRVVTF37lzKy8vp27cvDzzwAD4+Pq73TJs2DaUUc+bMwW63u5pt1nbrrbfy5ptv8vjjj7uabU6fPt31usVi4b777uONN95g9uzZ+Pr6MmHCBKZOndrKd0B0NNpmQy96DwB13qUtXiXX1jZll2Fo6BbiQ5dA+QVACCE8SWkZp2t1ubm5dVbttZRSiri4OA4fPizDrDVa654YX32CXjgPwiKwPDkX5dv8zXXbg/N+PPTJOpbuLuTCPuH8aURMe4fVbuT/TF1yP44l96Suk/1+eHt7N6kmqtP0iRKiremyUvSXHwCgplzTaRKo2lz1ULHN26xYCCHE8XWo6TzRNMaWdVRmRaKVNzo8CiV1Wq1CL/4AykuhWwJq7JntHU6zZRRWkFVqw0vBwBhJooQQwtMkieqEjIXzyD2033ygFIRGQFQ0KioGIqMhsubjqBgIj0JZ5Z+5uXReNvq7LwCwXHYdytL5Np5enW5uY9Qnyp8A784XvxBCdHTy07UTUnHxeFks2LMPQXU1FB6BwiPoPdtdx7hmsJUFwiOOJlYJvVFDRqMiW6cB6IlCL/oP2O3QbzAMHNbe4bhl1X4ziRoc17FW5QkhxIlCkqhOyOuG+4iLiyMzMxNdXAhHctB5OZCXDUey0UdyIC8HjuSArRry8yA/D717G/zyPXr+v6FHT9TQ0aghY8zpKmnt76LTd6NXLwdqRqE64b1xGJq1B8x96dpyvzwhhDiZSBLViSmlICQMQsJQSSnHvK61NjfLzatJrHIOo7ethz074MBe9IG96E/fhy6xqKFjzISqZ59OOXXlKVprjA/fBkCNmYhK6Nn4GzqovfmVFFfaCfS20DvSr73DEUKIE5IkUScwpRSEhkNoOKpnX/PJyVPRxYXoTWvQG1bB1vWQm4X+6hP0V59AcChq8Cgzoeo/GOXt0+g1Tjib18LOzWD1Rl18bXtH4zbnvnSDYgOlqaUQQrQSSaJOQiokDDXuHBh3DrqyArauR2/4Fb1pDZQUoVd8jV7xNfj6wcBhqCFjUMNORfl0viX+zaEdjqOjUGddhIqMbt+AWuDoVi8ylSeEEK1FkqiTnPLzh+FjUcPHou122LXFTKjWrzIL1lNXolNXoheGoc69GDXhPJTfiblcXq/8Fg4fhMBg1AWXt3c4bquwGezMKwckiRJCiNYkSZRwUVYr9B+C6j8EfdWfYf8e9PpV6F+/h/xc9Idvo5d8hDr7ItSZk1EBQe0dssfoqkqzPgxQk6/o1J/bzweKsRsQH+ZPXJD0EBNCiNYiSZSol1IKEnujEnujL7oSvWo5evFCyMlEf/o++qtPUGdciDr7d6jg0PYOt8X0159AUb5ZZD/xgvYOx21aaz7dbrY2uHRwN5RSJ+WWDUII0RYkiRLHpaxW1GlnoU+diF77s7kVSuYB9OKF6G8+M6f4zr0EFRbR3qG6RRcXoJcuAkBd8geUtfOO3mzIKudAUTX+VgsXnxJHaUFee4ckhBAnLEmiRJMpixdq1OnoEeNgwyqMLz8wWyV8/Sn6+8Wo8eegJl3W6Rp56s/+B1UVkJSCGjGuvcNpEeco1Nm9Qgn286a0neMRQogTmSRRotmUxQLDTsUydAxsWYfx5QLYuwP9/WL0j8tQp56JOv8yVHTX9g71uPThDPRPXwFgubxzNtZ0OlBYxfrDZVgUXNSnc44KCiFEZyJJlHCbUgoGDccycBjs3GyOTO3YZLZI+Plb1JgJqKtvMFcAdlDGx++AYcDgUaiUge0dTot8tsMchRodH0Rs8EnW30sIIdqBJFGixZRS0PcUvPqegt6zHWPxQti8Fv3L9+jcLCy3PoLy73htEfSurbBhFVgsWC67rr3DaZHCSjs/pBUDMKWvjEIJIURbsLR3AOLEonr1w+vWh7Hc/QwEBMKe7RgvPoIu71jVOdowMD58CwA1/lxUXHw7R9QyS3cVYjM0vSP96Nul4478CSHEiUSSKNEqVMoALHc8CQFBsG8nxgsPo8tK2jssF736R0jbBb7+qIuuau9wWqTaYbB4l7nZ8JS+EZ26rksIIToTSaJEq1EJPbHc9RQEhcD+PRhzZqNLits7LHRVFfrjdwFQF1yOCg1v54haZnlaMUVVDroEWBnbI7i9wxFCiJOGJFEnkaySakqqHG16TdU9yUykgkPhYBrGnAfRxYVtGsNv6a8WQUEeREajzpnSrrG0lNaaT2sKyif3DZfNhoUQog1JEnWS+GJnPn/+bB93L0vH5jDa9NqqWwKWu5+G0HA4tB/jbw+iiwraNAYnXXAEvfQjM67LrkN5d+5VbOsPl3GwqBo/q4Vzeoa1dzhCCHFSkSTqBKe1ZsHmPF5fmwPA4RIb3+4ravM4VFx3LHc9DWGRcPggxvMPoAuOtHkcetG7UF0FvfqhRpzW5tf3tE93mMnoOb1CCfTxaudohBDi5CJJ1AnM0Jp563J4f5O59UffKHPV1odbjmBztP1+aiq2mzkiFdEFsg9hPH8/+khum11fp+9G//I9AJYrZnT6Auz9hVVscDXX7Nx1XUII0RlJEnWCchiaf/6axWc1IxUzhkfz+FndCffzIrfczvdpbT8aBaCi48xEKjIacrPMRCovu9Wvq7XGWPCGGcOYM1BJvVv9mq3taHPNYGKCOve0pBBCdEaSRJ2AbA6D51cc4rt9RVgU3HZqHBf1jcDXauGS/pEALNxyBLvR9qNRACoqBss9z0CXWDiSY07t5Rxu3Yum/gx7toOPL+qSP7TutdpAYYWd5c7mmv1kFEoIIdqDJFEnmAqbwZM/ZPDLwVKsFsU947txZnKo6/XzeocR5udFTpmNH9ppNApARXQxG3LGdIP8XDORyjrUKtfStmqMD982rzvpUlREVKtcpy0t2V2AzdCkRPq5pmmFEEK0LUmiTiClVQ4e+e4AG7LK8bMqHpoYz6nd6/YNMkejzG1B2nM0CkCFR5pTe3HdofCIuWrv8EGPX0d/8xkcyYHwKNSkSz1+/rZWZTdYsqsQgCn9pLmmEEK0F0miThD5FXYe+OYAO/MqCfKx8PhZPRgSF1jvsef1DifU14usUhs/prdv80sVGm72keqWAEX55ojUof0eO78uKkB/udC81qV/QPn6euzc7WV5+tHmmr9NkoUQQrQdSaJOANml1dz/1X72F1YR7m/l6XMS6NPIFI+f1cLF/czRqA+25OFox9EoABUShuXOp6B7EpQUmYnUnu0eObf+9L9QVQFJKahREzxyzvaktXYVlF/UN0KaawohRDuSJKqTO1BUxX1fHSCr1EZMkDfPnNODhLDjj7acnxJOiK8Xh0vafzQKQAWHYLnzSUhKgbISjBceQm/4tUXn1Af2oVd8DYDlij+iLJ3/y93ZXNPfauHsnqHHf4MQQohW0/l/qpzEdudV8MDXB8ivsNM91IdnzulBXHDTlrr7e1uY4hqNOtLuo1EAKjDYTKQGjQBbNca//oqxfKlb59JaY3zwJmiNGjke1aufh6NtH9JcUwghOg5Jojqp1AMFzP7mACVVDnpH+vH0OQlEBng36xwXpIQR7GMhs+T/27v7qKjKfQ/g3z3vDMMwIAIiCCqCHY9hVy2rlWZmZsJJbmDlzeUSX+6t1r2uupZmWalpqXHrdHpZncLUrpRlmuYxblraOaipR0vFUkRRRDAUmRlmYIZ52fcPctcOVBxnmBn5ftZiKXue2Tz764P+fHj2s1tQeir4s1EAIGh1UDz+LITb7wZEL8T/fRveDashildZ5B3YDRw9BKjUEB6YHJjOdrLfbq6Zzc01iYiCjkVUGNpT3Yj/WnsAzW4vbkzQY8GoFBi1Vz8roVcr8acQm40CAEGphDD5PyFkPwQAEDetgbjqTYiejj08WXS74P30g9Zz3TMeQrf4gPW1M11cCzUshZtrEhGFAhZRYcbqcKOwtAYtHi9uSTZg3shk6NW+/1gnOzMGkRoFqq0t2FnV6MeeXhtBEKC4fyKERx4DBAXE0i3wvrUIotNxxfeK3/wNqKsFomMgjH2gE3obeOZmN7Zf3Fyzf2yQe0NERACLqLBj1Knw5O1JyPljD8wZngyN8tr+CPVqJf7U/9c79bxX+2OzAFOMuBeKx+YAag1w6J/wFj4HsfHSm4SKjRaIm9YAAITxj0DQ6TurqwG1+VgD3F4RmXE69O/OzTWJiEIBi6gwdEtKFJ4fe4Pfbm/PzoxBpFqBKksLdoXQbNRFwqBhUDy5EIiMAirL4X1lNsRzZ9tt691YDDTbgV59INx2Vyf3NDBkm2tyFoqIKGSwiCIYNErk9G9dqLzmUH3IzUYBgJB+AxSzlwCx3YG6GnhfeRpi1XFZG9ep4xC3t97Np5gwDYLi+rh77duTVlidHsRHqjCMm2sSEYUMFlEEAMjJjIVercApixO7T9uC3Z12CT2SoXhmKZCcBljN8C6dC/HH76XXze+/Dohe4KZhEDL/GLR++pMoitjwU+uC8uxMbq5JRBRKWEQRAMCgVUq3za8JwbVRFwmmbq0PLs4cCDib4X1jAbzfbYP30D/h2L8LUKqgyJsS7G76zfe1dlRbWzfXHJ3OzTWJiEIJiyiS/Kl/LCJUClQ2OLGnOjRnowBA0EdCMfNFCEPvADweiEWvwVv0P62v3Z0DIb5HkHvoH80uL4oPngcA3JMefU13YRIRkf+xiCJJlFaJcRdnow6dv/oNLjuRoFZDmPbfEEbf33rA1ghFdAwU4x4Mbsf8xOb04IVvqnCs3gGdSoEcLignIgo5LKJI5v7+MdCpBJxocGLvmdCdjQIAQaFofSbeg1OB2DjEPD4Hgj4y2N26Zg3NbszdWoWj5x0waBRYMCoF3SOvbjd6IiIKPBZRJGPUqXBfRuts1MeH6kN6Nuoixd33Q7X0A+hvHxXsrlyzOpsLz2w5hVNmJ2J0SiwenYrMOO4LRUQUilhEURvjb4iFVing+AUH9tXYg92dLqPa4sScLadQ2+hCfKQaL9+TilSTNtjdIiKiS2ARRW1E61QYK81GhfbaqOvF8QsOPLOlCvVNbiQbNXjlnl7oEcXn4xERhTIWUdSu3BtioVEKOFbvwPe1nI0KpMN1TXhuaxWsTg/6xurw8uhe6KbnGigiolDHIoraZYpQYWw/EwDORgXS/hobXvzmNJpcXgyIj8BLd6fAqFMFu1tERNQBLKLoknL/0A0apYCj5x0hf6deOCo9ZcWib6vR4hExJCkSL4xM4V5QRERhhEUUXVJMhApjfpmNWvqPGuw4ZQ1uh64jX1WY8WppDdxe4I7UKDwzIhlaFb8diYjCCf/WpsualNUdNycb4PKKWFpag3WHw2Pbg1D2+U/1eGv3WYgAxqSb8MRtSVDxmXhERGGHRRRdllalwJw7ekrP1Vv5wzm8s+dneLwspK6WKIpYfeAcPth/DgDwr3+IxaM3J/ChwkREYYorWOmKlAoB04ckINGgRtG+OvxfhRnn7C48dUcS1/BcgSiKaHJ5YWvxYMNPF/C3cjMAYNKg7sgb0C24nSMiomvCIoo6LKd/LLpHqlG4owb7a+2Yu6UK8+5M7nK341eZnThra4GtxYtGpwe2ll8+nK3FUmOLB/YWDxpbvLC3ePD7Sbt/H5og7QpPREThi0UUXZVhKVFYPLoXFm6vRmWDE0+VnMK8kcnoHaMLdtcCyun24h+nrCg5ZsaxesdVv1+jFBAbocK/ZXXH8DRjAHpIRESdjUUUXbV+3SKwbEwqFmyrRrW1BXO+qsLsO5LwL0mGYHfN76otTpQcM+ObSgvsLV4AgEohoHeMFgaNElEaJQxaBQwa5S8fChi0vxzXKGHQth7TKLn8kIjoesMiinySYNBgyZhUvPL3Mzj0cxMWbq/GfwxNlLZECGcuj4jd1Y348pgZZT83SccTDGqMSTdhVN9omLghJhFRl8d/CchnBo0SL4xMwVu7a7Gt0oq395zFWVsLJg3qDoUQfnec1dlc+KrCjC3HzTA7PAAAhQAM6WnA2H4mDOoRGZbXRUREgcEiiq6JWilg5q09kBilwUcHz2Pdjxfws82Fmbf2CIvNIz1eEd/X2lFyrAH7auzSIvAYnRKj0024J92E7pFda+E8ERF1DIsoumaCIOChgXFIiFTjzd212FHViPomN54d0TPoz4HzeEVYnR5YHG6YHa2/WpweWBwemB1uHDzbhDq7S2p/Y6Ie9/Yz4ZbkKG6ASUREl8UiivxmZJ9oxEWq8PLfz+DI+WbM3HwSWYl6pHfToW+sDn1idH6dnfJ4RdTaWnDK7MQZqwuuw42ouWD5tVhyeNDo9OBK24IaNArc1ScaY/qZkGzU+q1/RER0fWMRRX41MCESS+5JxcLt1fjZ5sK2Siu2VbY+c08hAClGLfp20yE9Vof0bjqkmbRXLKxEUcT5JjdOmZ2oMjtxyuLEKbMT1ZYWuDqwc7oAwKhVIlqnRLROhWidEqZffk00aHBLsiEsfvRIREShhUUU+V1KtBZvjOuNQ2ebUHGhGccvOFBR70CDw9NaAFmc+OaEBUBrYdUrWou+vxRVfWN1aPF4UWVunWE6ZXaiyuJEk8vb7tfSKgX0MmnRK1qL1HgTlG4HjFolTL8pmKI0Sj5ahYiI/I5FFAWETqXA0GQDhia37h0liiIuNLtR8UtBdfyCAxUXHLA4PDhpduKk2Ymvfyms2qMUgJ5GDVJNWvQyaZEarUWqSYt4gxoKQYAgCOjRowdqa2v5gGQiIuoULKKoUwiCgG56Nbrp1bglOQpAa2FV3+z+taiqd6CywQG1UoFUk1b2kRSlgVrJ2SQiIgodLKIoaARBQJxejTi9GsNSooLdHSIioqvC1bREREREPmARRUREROQDFlFEREREPmARRUREROQDFlFEREREPmARRUREROQDbnFwBSUlJfjiiy9gNpuRmpqKgoICpKenB7tbREREFGScibqMnTt3YtWqVcjLy8OSJUuQmpqKRYsWwWK59M7aRERE1DWwiLqMTZs2YdSoURg5ciSSk5Mxffp0aDQabNu2LdhdIyIioiDjj/Muwe1248SJExg/frx0TKFQYODAgSgvL2/3PS6XCy6XS/pcEARERERIv/eXi+fy5znDHTORYx5yzEOOebTFTOSYR8ewiLoEq9UKr9cLk8kkO24ymVBTU9Pue9avX4+1a9dKn/fu3RtLlixB9+7dA9LHxMTEgJw3nDETOeYhxzzkmEdbzESOeVweiyg/ys3NRXZ2tvT5xQr+3LlzcLvdfvs6giAgMTERZ8+ehSiKfjtvOGMmcsxDjnnIMY+2mIlcV89DpVJ1aAKERdQlGI1GKBQKmM1m2XGz2dxmduoitVoNtVrd7muBGISiKHbJwX05zESOecgxDznm0RYzkWMel8eF5ZegUqnQp08flJWVSce8Xi/KysqQkZERxJ4RERFRKOBM1GVkZ2fjrbfeQp8+fZCeno7NmzfD6XTizjvvvKrzqFSBiTlQ5w1nzESOecgxDznm0RYzkeuqeXT0ugWR83SXVVJSgo0bN8JsNiMtLQ1TpkxBv379gt0tIiIiCjL+OO8K7r33Xrz99tsoLi7G4sWLQ6KAam5uxuzZs9Hc3BzsroQMZiLHPOSYhxzzaIuZyDGPjmERFYZEUURlZSUX+/0GM5FjHnLMQ455tMVM5JhHx7CIIiIiIvIBiygiIiIiH7CICkNqtRp5eXmX3JOqK2ImcsxDjnnIMY+2mIkc8+gY3p1HRERE5APORBERERH5gEUUERERkQ9YRBERERH5gEUUERERkQ+65kNxQsCPP/6IjRs3orKyEg0NDZg1axZuvvlm6XWz2YzVq1fj4MGDsNvtuOGGG1BQUIAePXrI2nz44Yc4ePAgHA4HkpKSkJubi2HDhkltbDYbli9fjn379kEQBNxyyy2YMmUKdDpdp17vlXRWHo8//jjOnTsn+9oTJ07E+PHjA36NV8sfmZw9exYffvghjhw5ArfbjaysLBQUFMBkMkltutIY6Uge4TJG1q9fjz179uDMmTPQaDTIyMjAI488gqSkJKmNKIr45JNP8PXXX8Nut6N///6YNm2aLJOWlhasWrUKO3fuhMvlQlZWFqZNmxZ2Y6Qz8wiHMeKvPLZu3YrS0lJUVlaiubkZH3zwASIjI2VfKxzGR6BwJipInE4n0tLSMHXq1DaviaKIZcuWoa6uDk899RSWLl2K7t27Y+HChXA4HFK7N998EzU1NZg9ezZeffVV3HzzzXjttddQWVkptXnjjTdw+vRpPPfcc5gzZw5++uknvPvuu51yjVejs/IAgAkTJuCvf/2r9HHvvfcG/Pp8ca2ZOBwOLFq0CIIg4IUXXsDChQvhdruxZMkSeL1e6VxdZYx0NA8gPMbIjz/+iDFjxmDRokV47rnn4PF48NJLL8m+JzZs2IAvv/wS06dPx+LFi6HVarFo0SK0tLRIbVauXIl9+/bhySefxPz589HQ0IDCwkLZ1wqHMdKZeQChP0b8lYfT6cSgQYOQm5t7ya8VDuMjYEQKuvz8fHH37t3S52fOnBHz8/PFqqoq6ZjH4xGnTp0qbt26VTr2yCOPiN9++63sXFOmTJHanD59WszPzxcrKiqk17///ntxwoQJYn19faAu55oFKg9RFMXHHntM3LRpUwB7Hxi+ZPLDDz+IEyZMEO12u9TGbreLEyZMEA8cOCCKYtcaIx3JQxTDd4xYLBYxPz9fPHz4sCiKouj1esXp06eLGzZskNrY7XZx4sSJYmlpqfT5Qw89JO7atUtqU11dLebn54tHjx4VRTF8x0ig8hDF8BwjvuTxW2VlZWJ+fr5os9lkx8N1fPgLZ6JCkNvtBgDZJmcKhQJqtRpHjhyRjmVmZmLnzp2w2Wzwer3YsWMHXC4XBgwYAAAoLy9HZGQk+vbtK71n4MCBEAQBFRUVnXQ1185feVz0+eefo6CgAE8//TQ2btwIj8fTORfiRx3JxOVyQRAEWRu1Wg1BEKQ2XWmMdCSPi8JxjDQ1NQEADAYDAKCurg5msxk33nij1Eav1yM9PR3l5eUAgBMnTsDj8WDgwIFSm549eyIuLk5qE65jJFB5XBRuY8SXPDoiXMeHv3BNVAhKSkpCXFwciouLMWPGDOh0OmzatAn19fUwm81SuyeeeAKvv/46CgoKoFQqodFoMGvWLCQmJgJoXSNiNBpl51YqlTAYDLLzhDp/5QEAY8eORe/evWEwGHD06FF89NFHaGhowOTJk4NwZb7rSCYZGRnQarVYvXo1Hn74YYiiiOLiYni9XqlNVxojHckDCM8x4vV6sWLFCmRmZqJXr14AIF1TdHS0rG10dLTsz1+lUrVZ4/L7NuE2RgKZBxB+Y8TXPDoiHMeHP7GICkEqlQqzZs3CO++8g4KCAigUCgwcOBA33XST7Inaa9asgd1ux7x58xAVFYW9e/fitddew4IFC6RvlOuBP/PIzs6W2qempkKlUuG9997DxIkTw+rxBh3JxGg04sknn8T777+PL7/8EoIg4Pbbb0fv3r0hCEKQr8C//JlHOI6RoqIinD59GgsWLAh2V0JCoPMItzHC8RE4LKJCVJ8+fbBs2TI0NTXB7XbDaDRi7ty56NOnD4DWu4xKSkpQWFiIlJQUAEBaWhqOHDmCkpISzJgxAyaTCVarVXZej8cDm80mu9MkHPgjj/b069cPHo8H586dk921Eg6ulAkAZGVl4S9/+QusViuUSiUiIyMxffp0JCQkAECXGiPAlfNoT6iPkaKiIuzfvx/z589Ht27dpOMX//wsFgtiYmKk4xaLBWlpaVIbt9sNu90um32xWCzS+8NtjAQ6j/aE8hi5ljw6ItzGh79xTVSI0+v1MBqNqK2txfHjxzF06FAAkO6e+P2MgkKhkP7nnZGRAbvdjhMnTkivl5WVQRRFpKend9IV+Ne15NGekydPQhCENtPR4eRSmfyW0WhEZGQkysrKYLVaMWTIEABda4z81qXyaE+ojhFRFFFUVIQ9e/bg+eefR3x8vOz1+Ph4mEwmHDp0SDrW1NSEiooKZGRkAGgtPJVKpaxNTU0Nzp8/L7UJlzHSWXm0JxTHiD/y6IhwGR+BwpmoIHE4HDh79qz0eV1dHU6ePAmDwYC4uDjs2rULRqMRcXFxqKqqwooVKzB06FBkZWUBaF0DkpiYiPfeew+TJk2CwWDA3r17cfDgQcyePRsAkJycjEGDBuHdd9/F9OnT4Xa7sXz5ctx2222IjY0NynVfSmfkUV5ejmPHjmHAgAGIiIhAeXk5Vq5ciTvuuENabBlKrjUTANi2bRt69uwJo9GI8vJyrFixAuPGjZP+t9yVxghw5TzCaYwUFRWhtLQUTz/9NCIiIqT1J3q9HhqNBoIg4L777sO6devQo0cPxMfH4+OPP0ZMTIxUWOr1etx1111YtWoVDAYD9Ho9li9fjoyMDOkf0nAZI52VR7iMEX/kAbSueTKbzdL3XlVVFSIiIhAXFweDwRA24yNQBPFy/02ngDl8+DDmz5/f5viIESPw+OOPY/Pmzfjiiy9gNpsRExOD4cOHIy8vDyrVr3VvbW0tVq9ejaNHj8LhcCAxMRE5OTkYPny41MZms6GoqEi2CVpBQUHIbYLWGXmcOHECRUVFOHPmDFwuF+Lj4zF8+HBkZ2eH5DoGf2SyevVqbN++HTabDfHx8Rg9ejTGjRsnm7HrSmPkSnmE0xiZMGFCu8cfe+wx3HnnnQB+3Uxx69ataGpqQv/+/TF16lTZj5wubi65Y8cOaQPS9jbbDPUx0ll5hMsY8Vcen3zyCdauXXvZ84TD+AgUFlFEREREPuCaKCIiIiIfsIgiIiIi8gGLKCIiIiIfsIgiIiIi8gGLKCIiIiIfsIgiIiIi8gGLKCIiIiIfsIgiIiIi8gGLKCLqkhYvXowpU6ZIj8P4raamJsyYMQNz586F1+vt/M4RUVhgEUVEXdK0adPgdruxcuXKNq8VFxejsbERM2bMgELBvyaJqH3824GIuqT4+Hjk5eVhx44dOHDggHS8oqICW7Zswbhx45CWlhbQPrS0tHCmiyiM8dl5RNRleTwezJkzBw6HA4WFhVCpVHjmmWdgt9tRWFiI8+fP4+OPP0ZZWRlaWlqQkpKCvLw8DBkyRDqHzWbDunXrcODAAdTV1UGhUCAzMxMTJ06UFWEXH6A8c+ZMnD59Gtu2bYPZbMby5csRGRkZhKsnomvFmSgi6rKUSiVmzJiBuro6fPbZZygpKUFlZSWmTZuGuro6PPvsszhz5gzGjx+PSZMmQavVYtmyZdizZ490jp9//hl79+7F4MGDMXnyZOTk5KCqqgovvvgiLly40OZrfvbZZ9i/fz9ycnLw8MMPQ6VSdeYlE5EfcSaKiLq85cuXY8uWLVCr1Rg8eDBmzpyJhQsXwmKx4OWXX4ZarQYAiKKI559/HlarFX/+858BAC6XC0qlUrZ2qq6uDk888QRyc3ORl5cH4NeZqISEBBQWFkKj0XT+hRKRX3Emioi6vIceeghRUVEQBAGTJ0+GzWZDWVkZbr31VjQ3N8NqtcJqtaKxsRFZWVmora2VZpnUarVUQHm9XjQ2NkKn0yEpKQmVlZVtvtaIESNYQBFdJziPTERdnl6vR1JSEhobG2EymVBRUQFRFLFmzRqsWbOm3fdYLBbExsbC6/Vi8+bN+Oqrr1BXVydbKG4wGNq8Lz4+PmDXQUSdi0UUEdHvXCyEcnJykJWV1W6bxMREAMD69euxZs0ajBw5Eg8++CAMBgMEQcDKlSvR3moJzkIRXT9YRBER/U5CQgKA1oXnN95442XbfvfddxgwYAAeffRR2XG73Y6oqKiA9ZGIgo9rooiIfic6OhoDBgzA1q1b0dDQ0OZ1q9Uq/b69zTh37drV7p15RHR94UwUEVE7pk6dinnz5mHWrFkYNWoU4uPjYbFYUF5ejgsXLmDZsmUAgMGDB2Pt2rV4++23kZGRgaqqKpSWlkqzWUR0/WIRRUTUjuTkZLzyyiv49NNPsX37djQ2NiI6OhppaWl44IEHpHa5ublwOBzYsWMHdu7cid69e2POnDkoLi4OYu+JqDNwnygiIiIiH3BNFBEREZEPWEQRERER+YBFFBEREZEPWEQRERER+YBFFBEREZEPWEQRERER+YBFFBEREZEPWEQRERER+YBFFBEREZEPWEQRERER+YBFFBEREZEPWEQRERER+YBFFBEREZEP/h8eHyT0MRzwEgAAAABJRU5ErkJggg==" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "<details><summary>Click here for a sample python solution</summary>\n\n```python\n #The correct answer is:\n df_CI.index = df_CI.index.map(int) # let's change the index values of df_CI to type integer for plotting\n df_CI.plot(kind='line')\n\n plt.title('Immigrants from China and India')\n plt.ylabel('Number of Immigrants')\n plt.xlabel('Years')\n\n plt.show()\n```\n\n</details>\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
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| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "<br>From the above plot, we can observe that the China and India have very similar immigration trends through the years.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
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| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "*Note*: How come we didn't need to transpose Haiti's dataframe before plotting (like we did for df_CI)?\n\nThat's because `haiti` is a series as opposed to a dataframe, and has the years as its indices as shown below.\n\n```python\nprint(type(haiti))\nprint(haiti.head(5))\n```\n\n> class 'pandas.core.series.Series' <br>\n> 1980 1666 <br>\n> 1981 3692 <br>\n> 1982 3498 <br>\n> 1983 2860 <br>\n> 1984 1418 <br>\n> Name: Haiti, dtype: int64 <br>\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
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| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "Line plot is a handy tool to display several dependent variables against one independent variable. However, it is recommended that no more than 5-10 lines on a single graph; any more than that and it becomes difficult to interpret.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": "**Question:** Compare the trend of top 5 countries that contributed the most to immigration to Canada.\n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": "### type your answer here\n\ndf_can.sort_values(by='Total',ascending=False,axis=0,inplace=True)\nf=df_can.head(5)\nf[years].transpose()\n\n\n\nf.plot(kind=\"line\",figsize=(14,8))\n\n \n \n \n \n \n \n \n \n \n \n", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "trusted": true | |
| }, | |
| "execution_count": 66, | |
| "outputs": [ | |
| { | |
| "execution_count": 66, | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "<AxesSubplot:>" | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": "<Figure size 1400x800 with 1 Axes>", | |
| "image/png": 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| "source": "\n\n #The correct answer is: \n #Step 1: Get the dataset. Recall that we created a Total column that calculates cumulative immigration by country. \n #We will sort on this column to get our top 5 countries using pandas sort_values() method.\n \n inplace = True # paramemter saves the changes to the original df_can dataframe\n df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n\n # get the top 5 entries\n df_top5 = df_can.head(5)\n\n # transpose the dataframe\n df_top5 = df_top5[years].transpose() \n\n print(df_top5)\n\n\n #Step 2: Plot the dataframe. To make the plot more readeable, we will change the size using the `figsize` parameter.\n df_top5.index = df_top5.index.map(int) # let's change the index values of df_top5 to type integer for plotting\n df_top5.plot(kind='line', figsize=(14, 8)) # pass a tuple (x, y) size\n\n\n\n plt.title('Immigration Trend of Top 5 Countries')\n plt.ylabel('Number of Immigrants')\n plt.xlabel('Years')\n\n\n plt.show()\n\n```\n\n\n", | |
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| "source": "### Other Plots\n\nCongratulations! you have learned how to wrangle data with python and create a line plot with Matplotlib. There are many other plotting styles available other than the default Line plot, all of which can be accessed by passing `kind` keyword to `plot()`. The full list of available plots are as follows:\n\n* `bar` for vertical bar plots\n* `barh` for horizontal bar plots\n* `hist` for histogram\n* `box` for boxplot\n* `kde` or `density` for density plots\n* `area` for area plots\n* `pie` for pie plots\n* `scatter` for scatter plots\n* `hexbin` for hexbin plot\n", | |
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| "source": "### Thank you for completing this lab!\n\n## Author\n\n<a href=\"https://www.linkedin.com/in/aklson/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01\">Alex Aklson</a>\n\n### Other Contributors\n\n[Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01),\n[Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01),\n[Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01),\n[Weiqing Wang](https://www.linkedin.com/in/weiqing-wang-641640133/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01)\n\n## Change Log\n\n| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n| ----------------- | ------- | ------------- | ---------------------------------- |\n| 2021-05-29 | 2.4 | Weiqing Wang | Fixed typos and code smells. |\n| 2021-01-20 | 2.3 | Lakshmi Holla | Changed TOC cell markdown |\n| 2020-11-20 | 2.2 | Lakshmi Holla | Changed IBM box URL |\n| 2020-11-03 | 2.1 | Lakshmi Holla | Changed URL and info method |\n| 2020-08-27 | 2.0 | Lavanya | Moved Lab to course repo in GitLab |\n\n## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n", | |
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