Area_Plots_Histograms_and_Bar_Charts.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\" width = 400> </a>\n\n<h1 align=center><font size = 5>Area Plots, Histograms, and Bar Plots</font></h1>"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "## Introduction\n\nIn this lab, we will continue exploring the Matplotlib library and will learn how to create additional plots, namely area plots, histograms, and bar charts."
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "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>\n2. [Downloading and Prepping Data](#2)<br>\n3. [Visualizing Data using Matplotlib](#4) <br>\n4. [Area Plots](#6) <br>\n5. [Histograms](#8) <br>\n6. [Bar Charts](#10) <br>\n</div>\n<hr>"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "# Exploring Datasets with *pandas* and Matplotlib<a id=\"0\"></a>\n\nToolkits: The course heavily relies on [*pandas*](http://pandas.pydata.org/) and [**Numpy**](http://www.numpy.org/) for data wrangling, analysis, and visualization. The primary plotting library that we are exploring in the course is [Matplotlib](http://matplotlib.org/).\n\nData set: Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml) from United Nation's website.\n\nThe dataset contains annual data on the flows of international migrants 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. For this lesson, we will focus on the Canadian Immigration data."
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "# Downloading and Prepping Data <a id=\"2\"></a>"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Import Primary Modules. The first thing we'll do is import two key data analysis modules: *pandas* and **Numpy**."
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "outputs": [],
            "source": "import numpy as np  # useful for many scientific computing in Python\nimport pandas as pd # primary data structure library"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": "from __future__ import print_function # this line adds compatibility to python 2"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Let us download and import our primary Canadian Immigration dataset using *pandas* `read_excel()` method. But before we can do that, we need to download a module on which *pandas* requires to read in excel files. This module is **xlrd**."
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Requirement already satisfied: xlrd in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (1.2.0)\nxlrd installed!\n"
                }
            ],
            "source": "# install xlrd\n!pip install xlrd\nimport xlrd\nprint('xlrd installed!')"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Download the dataset and read it into a *pandas* dataframe."
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Data read into a pandas dataframe!\n"
                }
            ],
            "source": "df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n                           sheet_name='Canada by Citizenship',\n                           skiprows=range(20),\n                           skipfooter=2)\n    \nprint ('Data read into a pandas dataframe!')"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Let's take a look at the first five items in our dataset."
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>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 \u00d7 43 columns</p>\n</div>",
                        "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]"
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df_can.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Let's find out how many entries there are in our dataset."
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                },
                "scrolled": true
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "(195, 43)\n"
                }
            ],
            "source": "# print the dimensions of the dataframe\nprint(df_can.shape)"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to `Introduction to Matplotlib and Line Plots` lab for the rational and detailed description of the changes."
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "#### 1. Clean up the data set to remove columns that are not informative to us for visualization (eg. Type, AREA, REG)."
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>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    <tr>\n      <th>2</th>\n      <td>Algeria</td>\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>...</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>American Samoa</td>\n      <td>Oceania</td>\n      <td>Polynesia</td>\n      <td>Developing regions</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>...</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>Andorra</td>\n      <td>Europe</td>\n      <td>Southern Europe</td>\n      <td>Developed 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>...</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 \u00d7 38 columns</p>\n</div>",
                        "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   \n2         Algeria   Africa  Northern Africa  Developing regions    80    67   \n3  American Samoa  Oceania        Polynesia  Developing regions     0     1   \n4         Andorra   Europe  Southern Europe   Developed regions     0     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   \n2    71    69    63    44  ...  3616  3626  4807  3623  4005  5393  4752   \n3     0     0     0     0  ...     0     0     1     0     0     0     0   \n4     0     0     0     0  ...     0     0     1     1     0     0     0   \n\n   2011  2012  2013  \n0  2203  2635  2004  \n1   539   620   603  \n2  4325  3774  4331  \n3     0     0     0  \n4     0     1     1  \n\n[5 rows x 38 columns]"
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df_can.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)\n\n# let's view the first five elements and see how the dataframe was changed\ndf_can.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Notice how the columns Type, Coverage, AREA, REG, and DEV got removed from the dataframe."
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "#### 2. Rename some of the columns so that they make sense."
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Country</th>\n      <th>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>...</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    <tr>\n      <th>2</th>\n      <td>Algeria</td>\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>...</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>American Samoa</td>\n      <td>Oceania</td>\n      <td>Polynesia</td>\n      <td>Developing regions</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>...</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>Andorra</td>\n      <td>Europe</td>\n      <td>Southern Europe</td>\n      <td>Developed 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>...</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 \u00d7 38 columns</p>\n</div>",
                        "text/plain": "          Country Continent           Region             DevName  1980  1981  \\\n0     Afghanistan      Asia    Southern Asia  Developing regions    16    39   \n1         Albania    Europe  Southern Europe   Developed regions     1     0   \n2         Algeria    Africa  Northern Africa  Developing regions    80    67   \n3  American Samoa   Oceania        Polynesia  Developing regions     0     1   \n4         Andorra    Europe  Southern Europe   Developed regions     0     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   \n2    71    69    63    44  ...  3616  3626  4807  3623  4005  5393  4752   \n3     0     0     0     0  ...     0     0     1     0     0     0     0   \n4     0     0     0     0  ...     0     0     1     1     0     0     0   \n\n   2011  2012  2013  \n0  2203  2635  2004  \n1   539   620   603  \n2  4325  3774  4331  \n3     0     0     0  \n4     0     1     1  \n\n[5 rows x 38 columns]"
                    },
                    "execution_count": 10,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\n\n# let's view the first five elements and see how the dataframe was changed\ndf_can.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Notice how the column names now make much more sense, even to an outsider."
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "#### 3. For consistency, ensure that all column labels of type string."
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                },
                "scrolled": true
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "False"
                    },
                    "execution_count": 11,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# let's examine the types of the column labels\nall(isinstance(column, str) for column in df_can.columns)"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Notice how the above line of code returned *False* when we tested if all the column labels are of type **string**. So let's change them all to **string** type."
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "True"
                    },
                    "execution_count": 12,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df_can.columns = list(map(str, df_can.columns))\n\n# let's check the column labels types now\nall(isinstance(column, str) for column in df_can.columns)"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "#### 4. Set the country name as index - useful for quickly looking up countries using .loc method."
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>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>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    <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>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>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>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>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>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>American Samoa</th>\n      <td>Oceania</td>\n      <td>Polynesia</td>\n      <td>Developing regions</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>...</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>Andorra</th>\n      <td>Europe</td>\n      <td>Southern Europe</td>\n      <td>Developed 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>2</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 \u00d7 37 columns</p>\n</div>",
                        "text/plain": "               Continent           Region             DevName  1980  1981  \\\nCountry                                                                     \nAfghanistan         Asia    Southern Asia  Developing regions    16    39   \nAlbania           Europe  Southern Europe   Developed regions     1     0   \nAlgeria           Africa  Northern Africa  Developing regions    80    67   \nAmerican Samoa   Oceania        Polynesia  Developing regions     0     1   \nAndorra           Europe  Southern Europe   Developed regions     0     0   \n\n                1982  1983  1984  1985  1986  ...  2004  2005  2006  2007  \\\nCountry                                       ...                           \nAfghanistan       39    47    71   340   496  ...  2978  3436  3009  2652   \nAlbania            0     0     0     0     1  ...  1450  1223   856   702   \nAlgeria           71    69    63    44    69  ...  3616  3626  4807  3623   \nAmerican Samoa     0     0     0     0     0  ...     0     0     1     0   \nAndorra            0     0     0     0     2  ...     0     0     1     1   \n\n                2008  2009  2010  2011  2012  2013  \nCountry                                             \nAfghanistan     2111  1746  1758  2203  2635  2004  \nAlbania          560   716   561   539   620   603  \nAlgeria         4005  5393  4752  4325  3774  4331  \nAmerican Samoa     0     0     0     0     0     0  \nAndorra            0     0     0     0     1     1  \n\n[5 rows x 37 columns]"
                    },
                    "execution_count": 13,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df_can.set_index('Country', inplace=True)\n\n# let's view the first five elements and see how the dataframe was changed\ndf_can.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Notice how the country names now serve as indices."
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "#### 5. Add total column."
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>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    <tr>\n      <th>American Samoa</th>\n      <td>Oceania</td>\n      <td>Polynesia</td>\n      <td>Developing regions</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>...</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      <td>6</td>\n    </tr>\n    <tr>\n      <th>Andorra</th>\n      <td>Europe</td>\n      <td>Southern Europe</td>\n      <td>Developed 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>2</td>\n      <td>...</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      <td>15</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 38 columns</p>\n</div>",
                        "text/plain": "               Continent           Region             DevName  1980  1981  \\\nCountry                                                                     \nAfghanistan         Asia    Southern Asia  Developing regions    16    39   \nAlbania           Europe  Southern Europe   Developed regions     1     0   \nAlgeria           Africa  Northern Africa  Developing regions    80    67   \nAmerican Samoa   Oceania        Polynesia  Developing regions     0     1   \nAndorra           Europe  Southern Europe   Developed regions     0     0   \n\n                1982  1983  1984  1985  1986  ...  2005  2006  2007  2008  \\\nCountry                                       ...                           \nAfghanistan       39    47    71   340   496  ...  3436  3009  2652  2111   \nAlbania            0     0     0     0     1  ...  1223   856   702   560   \nAlgeria           71    69    63    44    69  ...  3626  4807  3623  4005   \nAmerican Samoa     0     0     0     0     0  ...     0     1     0     0   \nAndorra            0     0     0     0     2  ...     0     1     1     0   \n\n                2009  2010  2011  2012  2013  Total  \nCountry                                              \nAfghanistan     1746  1758  2203  2635  2004  58639  \nAlbania          716   561   539   620   603  15699  \nAlgeria         5393  4752  4325  3774  4331  69439  \nAmerican Samoa     0     0     0     0     0      6  \nAndorra            0     0     0     1     1     15  \n\n[5 rows x 38 columns]"
                    },
                    "execution_count": 14,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df_can['Total'] = df_can.sum(axis=1)\n\n# let's view the first five elements and see how the dataframe was changed\ndf_can.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Now the dataframe has an extra column that presents the total number of immigrants from each country in the dataset from 1980 - 2013. So if we print the dimension of the data, we get:"
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                },
                "scrolled": true
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "data dimensions: (195, 38)\n"
                }
            ],
            "source": "print ('data dimensions:', df_can.shape)"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "So now our dataframe has 38 columns instead of 37 columns that we had before."
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "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']"
                    },
                    "execution_count": 16,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# finally, let's create a list of years from 1980 - 2014\n# this will come in handy when we start plotting the data\nyears = list(map(str, range(1980, 2014)))\n\nyears"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "# Visualizing Data using Matplotlib<a id=\"4\"></a>"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Import `Matplotlib`."
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Matplotlib version:  3.1.0\n"
                }
            ],
            "source": "# use the inline backend to generate the plots within the browser\n%matplotlib inline \n\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\nmpl.style.use('ggplot') # optional: for ggplot-like style\n\n# check for latest version of Matplotlib\nprint ('Matplotlib version: ', mpl.__version__ ) # >= 2.0.0"
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "outputs": [],
            "source": "import matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport numpy as np"
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "pandas.core.series.Series"
                    },
                    "execution_count": 20,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "type(df_can['2013'])"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "# Area Plots<a id=\"6\"></a>"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "In the last module, we created a line plot that visualized the top 5 countries that contribued the most immigrants to Canada from 1980 to 2013. With a little modification to the code, we can visualize this plot as a cumulative plot, also knows as a **Stacked Line Plot** or **Area plot**."
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>Country</th>\n      <th>India</th>\n      <th>China</th>\n      <th>United Kingdom of Great Britain and Northern Ireland</th>\n      <th>Philippines</th>\n      <th>Pakistan</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1980</th>\n      <td>8880</td>\n      <td>5123</td>\n      <td>22045</td>\n      <td>6051</td>\n      <td>978</td>\n    </tr>\n    <tr>\n      <th>1981</th>\n      <td>8670</td>\n      <td>6682</td>\n      <td>24796</td>\n      <td>5921</td>\n      <td>972</td>\n    </tr>\n    <tr>\n      <th>1982</th>\n      <td>8147</td>\n      <td>3308</td>\n      <td>20620</td>\n      <td>5249</td>\n      <td>1201</td>\n    </tr>\n    <tr>\n      <th>1983</th>\n      <td>7338</td>\n      <td>1863</td>\n      <td>10015</td>\n      <td>4562</td>\n      <td>900</td>\n    </tr>\n    <tr>\n      <th>1984</th>\n      <td>5704</td>\n      <td>1527</td>\n      <td>10170</td>\n      <td>3801</td>\n      <td>668</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "Country  India  China  United Kingdom of Great Britain and Northern Ireland  \\\n1980      8880   5123                                              22045      \n1981      8670   6682                                              24796      \n1982      8147   3308                                              20620      \n1983      7338   1863                                              10015      \n1984      5704   1527                                              10170      \n\nCountry  Philippines  Pakistan  \n1980            6051       978  \n1981            5921       972  \n1982            5249      1201  \n1983            4562       900  \n1984            3801       668  "
                    },
                    "execution_count": 21,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df_can.sort_values(['Total'], ascending=False, axis=0, inplace=True)\n\n# get the top 5 entries\ndf_top5 = df_can.head(5)\n\n# transpose the dataframe\ndf_top5 = df_top5[years].transpose() \n\ndf_top5.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Area plots are stacked by default. And to produce a stacked area plot, each column must be either all positive or all negative values (any NaN values will defaulted to 0). To produce an unstacked plot, pass `stacked=False`. "
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1440x648 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "df_top5.plot(kind='area', \n             stacked=False,\n             figsize=(20, 9), # pass a tuple (x, y) size\n            ) \n\nplt.title('Immigration Trend of Top 5 Countries')\nplt.ylabel('Number of Immigrants')\nplt.xlabel('Years')\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "The unstacked plot has a default transparency (aplha value) at 0.5. We can modify this value by passing in the `alpha` parameter."
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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                        "text/plain": "<Figure size 1440x648 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "df_top5.plot(kind='area', \n             alpha=0.25, # 0-1, default value a= 0.5\n             stacked=False,\n             figsize=(20, 9),\n            )\n\nplt.title('Immigration Trend of Top 5 Countries')\nplt.ylabel('Number of Immigrants')\nplt.xlabel('Years')\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "### Two types of plotting\n\nAs we discussed in the video lectures, there are two styles/options of ploting with `matplotlib`. Plotting using the Artist layer and plotting using the scripting layer.\n\n**Option 1: Scripting layer (procedural method) - using matplotlib.pyplot as 'plt' **\n\nYou can use `plt` i.e. `matplotlib.pyplot` and add more elements by calling different methods procedurally; for example, `plt.title(...)` to add title or `plt.xlabel(...)` to add label to the x-axis.\n```python\n    # Option 1: This is what we have been using so far\n    df_top5.plot(kind='area', alpha=0.35, figsize=(20, 9)) \n    plt.title('Immigration trend of top 5 countries')\n    plt.ylabel('Number of immigrants')\n    plt.xlabel('Years')\n```"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "**Option 2: Artist layer (Object oriented method) - using an `Axes` instance from Matplotlib (preferred) **\n\nYou can use an `Axes` instance of your current plot and store it in a variable (eg. `ax`). You can add more elements by calling methods with a little change in syntax (by adding \"*set_*\" to the previous methods). For example, use `ax.set_title()` instead of `plt.title()` to add title,  or `ax.set_xlabel()` instead of `plt.xlabel()` to add label to the x-axis. \n\nThis option sometimes is more transparent and flexible to use for advanced plots (in particular when having multiple plots, as you will see later). \n\nIn this course, we will stick to the **scripting layer**, except for some advanced visualizations where we will need to use the **artist layer** to manipulate advanced aspects of the plots."
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "Text(0.5, 0, 'Years')"
                    },
                    "execution_count": 24,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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                        "text/plain": "<Figure size 1440x648 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "# option 2: preferred option with more flexibility\nax = df_top5.plot(kind='area', alpha=0.35, figsize=(20, 9))\n\nax.set_title('Immigration Trend of Top 5 Countries')\nax.set_ylabel('Number of Immigrants')\nax.set_xlabel('Years')"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "# Histograms<a id=\"8\"></a>\n\nA histogram is a way of representing the *frequency* distribution of numeric data set. The way it works is it partitions the x-axis into *bins*, assigns each data point in our dataset to a bin, and then counts the number of data points that have been assigned to each bin. So the y-axis is the frequency or the number of data points in each bin. Note that we can change the bin size and usually one needs to tweak it so that the distribution is displayed nicely."
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "**Question:** What is the frequency distribution of the number (population) of new immigrants from the various countries to Canada in 2013?"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Before we proceed with creating the histogram plot, let us first examine the data split into intervals. To do this, we will us **Numpy**'s `histrogram` method to get the bin ranges and frequency counts as follows:"
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "Country\nIndia                                                   33087\nChina                                                   34129\nUnited Kingdom of Great Britain and Northern Ireland     5827\nPhilippines                                             29544\nPakistan                                                12603\nName: 2013, dtype: int64"
                    },
                    "execution_count": 25,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# Let's quickly view the 2013 data\ndf_can['2013'].head()"
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "[178  11   1   2   0   0   0   0   1   2]\n[    0.   3412.9  6825.8 10238.7 13651.6 17064.5 20477.4 23890.3 27303.2\n 30716.1 34129. ]\n"
                }
            ],
            "source": "# np.histogram returns 2 values\ncount, bin_edges = np.histogram(df_can['2013'])\n\nprint(count) # frequency count\nprint(bin_edges) # bin ranges, default = 10 bins"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "By default, the `histrogram` method breaks up the dataset into 10 bins. The figure below summarizes the bin ranges and the frequency distribution of immigration in 2013. We can see that in 2013:\n* 178 countries contributed between 0 to 3412.9 immigrants \n* 11 countries contributed between 3412.9 to 6825.8 immigrants\n* 1 country contributed between 6285.8 to 10238.7 immigrants, and so on..\n\n<img src = \"https://ibm.box.com/shared/static/g54s9q97mrjok0h4272o7g09cyigei0v.jpg\" align=\"center\" width=800>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "We can easily graph this distribution by passing `kind=hist` to `plot()`."
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 576x360 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "df_can['2013'].plot(kind='hist', figsize=(8, 5))\n\nplt.title('Histogram of Immigration from 195 Countries in 2013') # add a title to the histogram\nplt.ylabel('Number of Countries') # add y-label\nplt.xlabel('Number of Immigrants') # add x-label\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "In the above plot, the x-axis represents the population range of immigrants in intervals of 3412.9. The y-axis represents the number of countries that contributed to the aforementioned population. \n\nNotice that the x-axis labels do not match with the bin size. This can be fixed by passing in a `xticks` keyword that contains the list of the bin sizes, as follows:"
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 576x360 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "# 'bin_edges' is a list of bin intervals\ncount, bin_edges = np.histogram(df_can['2013'])\n\ndf_can['2013'].plot(kind='hist', figsize=(8, 5), xticks=bin_edges)\n\nplt.title('Histogram of Immigration from 195 countries in 2013') # add a title to the histogram\nplt.ylabel('Number of Countries') # add y-label\nplt.xlabel('Number of Immigrants') # add x-label\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "*Side Note:* We could use `df_can['2013'].plot.hist()`, instead. In fact, throughout this lesson, using `some_data.plot(kind='type_plot', ...)` is equivalent to `some_data.plot.type_plot(...)`. That is, passing the type of the plot as argument or method behaves the same. \n\nSee the *pandas* documentation for more info  http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.html."
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "We can also plot multiple histograms on the same plot. For example, let's try to answer the following questions using a histogram.\n\n**Question**: What is the immigration distribution for Denmark, Norway, and Sweden for years 1980 - 2013?"
        },
        {
            "cell_type": "code",
            "execution_count": 29,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>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    <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>Denmark</th>\n      <td>272</td>\n      <td>293</td>\n      <td>299</td>\n      <td>106</td>\n      <td>93</td>\n      <td>73</td>\n      <td>93</td>\n      <td>109</td>\n      <td>129</td>\n      <td>129</td>\n      <td>...</td>\n      <td>89</td>\n      <td>62</td>\n      <td>101</td>\n      <td>97</td>\n      <td>108</td>\n      <td>81</td>\n      <td>92</td>\n      <td>93</td>\n      <td>94</td>\n      <td>81</td>\n    </tr>\n    <tr>\n      <th>Norway</th>\n      <td>116</td>\n      <td>77</td>\n      <td>106</td>\n      <td>51</td>\n      <td>31</td>\n      <td>54</td>\n      <td>56</td>\n      <td>80</td>\n      <td>73</td>\n      <td>76</td>\n      <td>...</td>\n      <td>73</td>\n      <td>57</td>\n      <td>53</td>\n      <td>73</td>\n      <td>66</td>\n      <td>75</td>\n      <td>46</td>\n      <td>49</td>\n      <td>53</td>\n      <td>59</td>\n    </tr>\n    <tr>\n      <th>Sweden</th>\n      <td>281</td>\n      <td>308</td>\n      <td>222</td>\n      <td>176</td>\n      <td>128</td>\n      <td>158</td>\n      <td>187</td>\n      <td>198</td>\n      <td>171</td>\n      <td>182</td>\n      <td>...</td>\n      <td>129</td>\n      <td>205</td>\n      <td>139</td>\n      <td>193</td>\n      <td>165</td>\n      <td>167</td>\n      <td>159</td>\n      <td>134</td>\n      <td>140</td>\n      <td>140</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows \u00d7 34 columns</p>\n</div>",
                        "text/plain": "         1980  1981  1982  1983  1984  1985  1986  1987  1988  1989  ...  \\\nCountry                                                              ...   \nDenmark   272   293   299   106    93    73    93   109   129   129  ...   \nNorway    116    77   106    51    31    54    56    80    73    76  ...   \nSweden    281   308   222   176   128   158   187   198   171   182  ...   \n\n         2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  \nCountry                                                              \nDenmark    89    62   101    97   108    81    92    93    94    81  \nNorway     73    57    53    73    66    75    46    49    53    59  \nSweden    129   205   139   193   165   167   159   134   140   140  \n\n[3 rows x 34 columns]"
                    },
                    "execution_count": 29,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# let's quickly view the data set \ndf_can.loc[['Denmark', 'Norway', 'Sweden'], years]"
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f162ab69320>"
                    },
                    "execution_count": 30,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "# generate histogram\ndf_can.loc[['Denmark', 'Norway', 'Sweden'], years].plot.hist()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "That does not look right! \n\nDon't worry, you'll often come across situations like this when creating plots. The solution often lies in how the underlying dataset is structured.\n\nInstead of plotting the population frequency distribution of the population for the 3 countries, *pandas* instead plotted the population frequency distribution for the `years`.\n\nThis can be easily fixed by first transposing the dataset, and then plotting as shown below.\n\n"
        },
        {
            "cell_type": "code",
            "execution_count": 31,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>Country</th>\n      <th>Denmark</th>\n      <th>Norway</th>\n      <th>Sweden</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1980</th>\n      <td>272</td>\n      <td>116</td>\n      <td>281</td>\n    </tr>\n    <tr>\n      <th>1981</th>\n      <td>293</td>\n      <td>77</td>\n      <td>308</td>\n    </tr>\n    <tr>\n      <th>1982</th>\n      <td>299</td>\n      <td>106</td>\n      <td>222</td>\n    </tr>\n    <tr>\n      <th>1983</th>\n      <td>106</td>\n      <td>51</td>\n      <td>176</td>\n    </tr>\n    <tr>\n      <th>1984</th>\n      <td>93</td>\n      <td>31</td>\n      <td>128</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "Country  Denmark  Norway  Sweden\n1980         272     116     281\n1981         293      77     308\n1982         299     106     222\n1983         106      51     176\n1984          93      31     128"
                    },
                    "execution_count": 31,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# transpose dataframe\ndf_t = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\ndf_t.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 32,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 720x432 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "# generate histogram\ndf_t.plot(kind='hist', figsize=(10, 6))\n\nplt.title('Histogram of Immigration from Denmark, Norway, and Sweden from 1980 - 2013')\nplt.ylabel('Number of Years')\nplt.xlabel('Number of Immigrants')\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Let us make a few modifications to improve the impact and aesthetics of the previous plot:\n* increase the bin size to 15 by passing in `bins` parameter\n* set transparency to 60% by passing in `alpha` paramemter\n* label the x-axis by passing in `x-label` paramater\n* change the colors of the plots by passing in `color` parameter"
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 720x432 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "# Let's get the x-tick values\ncount, bin_edges = np.histogram(df_t, 15)\n\n# Un-stacked Histogram\ndf_t.plot(kind ='hist', \n          figsize=(10, 6),\n          bins=15,\n          alpha=0.6,\n          xticks=bin_edges,\n          color=['coral', 'darkslateblue', 'mediumseagreen']\n         )\n\nplt.title('Histogram of Immigration from Denmark, Norway, and Sweden from 1980 - 2013')\nplt.ylabel('Number of Years')\nplt.xlabel('Number of Immigrants')\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Tip:\nFor a full listing of colors available in Matplotlib, run the following code in your python shell:\n```python\nimport matplotlib\nfor name, hex in matplotlib.colors.cnames.items():\n    print(name, hex)\n```"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "If we do no want the plots to overlap each other, we can stack them using the `stacked` paramemter. Let us also adjust the min and max x-axis labels to remove the extra gap on the edges of the plot. We can pass a tuple (min,max) using the `xlim` paramater, as show below."
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 720x432 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "count, bin_edges = np.histogram(df_t, 15)\nxmin = bin_edges[0] - 10   #  first bin value is 31.0, adding buffer of 10 for aesthetic purposes \nxmax = bin_edges[-1] + 10  #  last bin value is 308.0, adding buffer of 10 for aesthetic purposes\n\n#Stacked Histogram\ndf_t.plot(kind='hist',\n          figsize=(10, 6), \n          bins=15,\n          xticks=bin_edges,\n          color=['coral','darkslateblue','mediumseagreen'],\n          stacked=True,\n          xlim=(xmin,xmax)\n         )\n\nplt.title('Histogram of Immigration from Denmark, Norway, and Sweden from 1980 - 2013')\nplt.ylabel('Number of Years')\nplt.xlabel('Number of Immigrants') \n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "# Bar Charts (Dataframe) <a id=\"10\"></a>\n\nA bar plot is a way of representing data where the *length* of the bars represents the magnitude/size of the feature/variable. Bar graphs usually represent numerical and categorical variables grouped in intervals. \n\nTo create a bar plot, we can pass one of two arguments via `kind` parameter in `plot()`:\n\n* `kind=bar` creates a *vertical* bar plot\n* `kind=barh` creates a *horizontal* bar plot"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "**Vertical bar plot**\n\nIn vertical bar graphs, the x-axis is used for labelling, and the length of bars on the y-axis corresponds to the magnitude of the variable being measured. Vertical bar graphs are particuarly useful in analyzing time series data. One disadvantage is that they lack space for text labelling at the foot of each bar. \n\n**Let us start off by analyzing the effect of Iceland's Financial Crisis:**\n\nThe 2008 - 2011 Icelandic Financial Crisis was a major economic and political event in Iceland. Relative to the size of its economy, Iceland's systemic banking collapse was the largest experienced by any country in economic history. The crisis led to a severe economic depression in 2008 - 2011 and significant political unrest.\n\n**Question:** Let us compare the number of Icelandic immigrants (country = 'Iceland') to Canada from year 1980 to 2013. "
        },
        {
            "cell_type": "code",
            "execution_count": 35,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "1980    17\n1981    33\n1982    10\n1983     9\n1984    13\nName: Iceland, dtype: object"
                    },
                    "execution_count": 35,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# step 1: get the data\ndf_iceland = df_can.loc['Iceland', years]\ndf_iceland.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 36,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 720x432 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "# step 2: plot data\n\ndf_iceland.plot(kind='bar', figsize=(10, 6))\n\nplt.xlabel('Year') # add to x-label to the plot\nplt.ylabel('Number of immigrants') # add y-label to the plot\nplt.title('Icelandic immigrants to Canada from 1980 to 2013') # add title to the plot\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "The bar plot above shows the total number of immigrants broken down by each year. We can clearly see the impact of the financial crisis; the number of immigrants to Canada started increasing rapidly after 2008. \n\nLet us annotate this on the plot using the `annotate` method of the **scripting layer** or the **pyplot interface**. We will pass in the following parameters:\n- `s`: str, the text of annotation.\n- `xy`: Tuple specifying the (x,y) point to annotate (in this case, end point of arrow).\n- `xytext`: Tuple specifying the (x,y) point to place the text (in this case, start point of arrow).\n- `xycoords`: The coordinate system that xy is given in - 'data' uses the coordinate system of the object being annotated (default).\n- `arrowprops`: Takes a dictionary of properties to draw the arrow:\n    - `arrowstyle`: Specifies the arrow style, `'->'` is standard arrow.\n    - `connectionstyle`: Specifies the connection type. `arc3` is a straight line.\n    - `color`: Specifes color of arror.\n    - `lw`: Specifies the line width.\n\nI encourage you to read the Matplotlib documentation for more details on annotations: \nhttp://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.annotate."
        },
        {
            "cell_type": "code",
            "execution_count": 37,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 720x432 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "df_iceland.plot(kind='bar', figsize=(10, 6), rot=90) # rotate the bars by 90 degrees\n\nplt.xlabel('Year')\nplt.ylabel('Number of Immigrants')\nplt.title('Icelandic Immigrants to Canada from 1980 to 2013')\n\n# Annotate arrow\nplt.annotate('',                      # s: str. Will leave it blank for no text\n             xy=(32, 70),             # Place head of the arrow at point (year 2012 , pop 70 )\n             xytext=(28, 20),         # Place base of the arrow at point (year 2008 , pop 20 )\n             xycoords='data',         # Will use the coordinate system of the object being annotated \n             arrowprops=dict(arrowstyle='->', connectionstyle='arc3', color='blue', lw=2)\n            )\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Let us also annotate a text to go over the arrow.  We will pass in the following additional parameters:\n- `rotation`: rotation angle of text in degrees (counter clockwise)\n- `va`: vertical alignment of text [\u2018center\u2019 | \u2018top\u2019 | \u2018bottom\u2019 | \u2018baseline\u2019]\n- `ha`: horizontal alignment of text [\u2018center\u2019 | \u2018right\u2019 | \u2018left\u2019]"
        },
        {
            "cell_type": "code",
            "execution_count": 38,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 720x432 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "df_iceland.plot(kind='bar', figsize=(10, 6), rot=90) \n\nplt.xlabel('Year')\nplt.ylabel('Number of Immigrants')\nplt.title('Icelandic Immigrants to Canada from 1980 to 2013')\n\n# Annotate arrow\nplt.annotate('',                      # s: str. Will leave it blank for no text\n             xy=(32, 70),             # place head of the arrow at point (year 2012 , pop 70 )\n             xytext=(28, 20),         # place base of the arrow at point (year 2008 , pop 20 )\n             xycoords='data',         # will use the coordinate system of the object being annotated \n             arrowprops=dict(arrowstyle='->', connectionstyle='arc3', color='blue', lw=2)\n            )\n\n# Annotate Text\nplt.annotate('2008 - 2011 Financial Crisis', # text to display\n             xy=(28,30),                   # start the text at at point (year 2008 , pop 30)\n             rotation=72.5,                # Based on trial and error to match the arrow\n             va='bottom',                  # Want the text to be vertically 'bottom' aligned\n             ha='left',                    # Want the text to be horizontally 'left' algned.\n            )\n\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "**Horizontal Bar Plot**\n\nSometimes it is more practical to represent the data horizontally, especially if you need more room for labelling the bars. In horizontal bar graphs, the y-axis is used for labelling, and the length of bars on the x-axis corresponds to the magnitude of the variable being measured. As you will see, there is more room on the y-axis to  label categetorical variables.\n\n\n**Question:** Using the `df_can` dataset, create a *horizontal* bar plot showing the *total* number of immigrants to Canada from the top 15 countries, for the period 1980 - 2013. Label each country with the total immigrant count."
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Step 1: Get the data."
        },
        {
            "cell_type": "code",
            "execution_count": 39,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                },
                "scrolled": true
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "Country\nRomania                                                  93585\nViet Nam                                                 97146\nJamaica                                                 106431\nFrance                                                  109091\nLebanon                                                 115359\nPoland                                                  139241\nRepublic of Korea                                       142581\nSri Lanka                                               148358\nIran (Islamic Republic of)                              175923\nUnited States of America                                241122\nPakistan                                                241600\nPhilippines                                             511391\nUnited Kingdom of Great Britain and Northern Ireland    551500\nChina                                                   659962\nIndia                                                   691904\nName: Total, dtype: int64"
                    },
                    "execution_count": 39,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# sort dataframe on 'Total' column (descending)\ndf_can.sort_values(by='Total', ascending=True, inplace=True)\n\n# get top 15 countries\ndf_top15 = df_can['Total'].tail(15)\ndf_top15"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "Step 2: Plot data. We will use a for loop to cycle through the countries and annotate the immigrant population."
        },
        {
            "cell_type": "code",
            "execution_count": 40,
            "metadata": {
                "collapsed": false,
                "deletable": true,
                "editable": true,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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SRGTJkiWi0WiK/QF0wIAB0rVrV+W1m5ubfPrpp0ZpunTpYnQsmtKxY0cZOnSo2fUl+eyaN2+e+Pn5GY0dEZEnn3xSxowZIyLm+7979+7Sq1cvERGZPXu2dO7cWdq1a6eMgVdeeUXefvtto7oU9/7966+/ip2dXZH373/961/SvXt3o3wKfv5lZWWJVqtVAnlLzAXP69atE5VKJUuWLJGcnBy5deuWdO7cWQDIe++9p6S7fv26tGjRQgCIlZWVODs7y+bNm5X1+Vd9XLlyxSj/GTNmiIuLS4nqWBrHjx8XOzs7WbBggbJs7Nix4ufnVyRt165d5ZVXXimy3FLwvGrVKrG3txe1Wi0uLi6yadOmsmtAOdKAiIjKVGxsLNRqNVq3bq0sU6lUaNOmDWJjY43SPvnkk0avW7ZsaXSP06Pw8vLC8OHDlddNmjSBra0thg4diilTpsDNza3INiKi1LM4SUlJSE5OxlNPPWW0PDw8HAsXLkROTg6sra0BAI0bNzZK4+Pjg1u3bpW6PfXq1VPyLG0+YWFhxa7Pv0e9QYMGRsv1ej3s7e0B5N2LNnnyZPj6+uKZZ55B+/bt8corr8DFxcVkniXty7i4OOj1epN9OW/ePKNlf6YvLfVBvoJjUavVIjQ0FKdPny7RtqVlY2ODkJAQ5bWPjw8A4NatW6hVqxZOnz6N5s2bQ63+Y2qWwsdKSZXmeCyp4vbHiRMnkJ2djS5duhiNgdzcXGRmZuLBgweluqe3UaNGyv89PT0BAA0bNiyyLCkpCf7+/qXaTkSQnJwMb2/vIuWWZh+YGmNffvkllixZgitXriAjIwN6vR5arbb4xhZSUcdIvtK8vxUkIiW+V7wk/VKrVi1UrVrVqC16vR537tyBu7s79Ho9oqKisHLlSty4cQNZWVnIzs5WJmDLb0fLli2N8m3dujV+//135XVaWho++eQTrF+/HomJicjJyUFWVhZsbGwstqO4z65Dhw7h6tWrcHJyMkqTlZUFLy+vYvNt3749Pv30UwDAtm3b8PTTTyM9PR3btm3D22+/jR07duDzzz9XygGKf/8+dOgQHj58CA8PD6M02dnZRbYrOIa0Wi3c3Nwe6TOroC5dumDmzJkYPnw4+vfvD2tra4wePRp79+6FlZUVAMBgMGDgwIFwcnLCzp074eDggJUrV6Jbt27YuXNnkXoWVtzYa9++PQ4ePAgA0Ol0JZpN/8yZM+jUqRP69u2L/v37l6idjzJXQqdOnXDs2DHcvXsXy5cvR8+ePbFt2zaL7a1sDJ6JiCpISb5g5QdeZa1ly5YQEVy9etVk8Ozj4wMHB4cSBxOF22Gq3oW/EKpUKhgMBuW1Wq0usl3hmT1Lkk9x8r9AmWMwGKBSqXDo0KEiX4rzg4YqVarg2LFj2LVrF7Zu3Yr//Oc/GD16NHbs2GHyQz4oKAhAXtD23HPPWayjqb4svKw8+8Ccgvsmvy8KLjO1r0pKo9EYtTH//wXbVN4TmpUm4CmsuP2R/++aNWtQq1atItuWdn8UHJf59TW1rPB4eNTtCipp/xRu07fffosRI0Zg2rRpaNWqFZycnPDdd99h6tSpJcrPUj3K+hgpSXnFCQoKwg8//AC9Xg+NxvxX65L2i6m2AH/sqylTpmDWrFmYNWsWGjZsCEdHR0RFRWH37t1G9bW0/4YNG4atW7dixowZCAgIgL29PYYOHfpIk74V7CODwYDGjRtjxYoVRdJZGv/t27fHu+++izNnzmDHjh0YP3480tPTMW/ePBw7dgx37txB+/btlXIsvX8bDAa4u7srfVNQ4R8JynIMFfT+++9j+PDhuHnzJqpUqYLMzEx8+umnqFOnDoC8yT3Xr1+PO3fuKD+aNGnSBDt37sQXX3yBhQsXKj863Lx5EzVr1lTyvnXrlvLjmCnLli1DZmYmABj9EGbO0aNH0alTJ/zrX//CF198YbTOy8sLSUlJRY6/W7dulfhH2oIcHByUH/zCwsJw9OhRzJw5E0uXLi11XhWJs20TEZWxevXqwWAwGH1Yiwj27NmDevXqGaUt/Fifffv2GZ2RKytHjx6FSqVC9erVTa7XaDTo3r07li5diuvXrxdZbzAYcP/+fbi7u6NatWpFZt7euXMnAgMDTZ6VMcfd3R0JCQlGy2JiYkq8fT6tVvvIj34KDQ2FiODGjRvw9/c3+qtdu7aSTqPRoF27dpg0aRKOHj2KqlWrmvxiCOSd0Xv66afx+eefIy0trcj6rKwsZGRkIDAwEBqNxmRfFh4nlvyZPshXcCxmZ2cjJiZGGYv5s2UX3F+F91X+F8+yeAxX3bp1ceDAAaMv5IWPFVNM9UNpjkdLeZVEw4YNYW1tjUuXLhUZU/7+/iX6AvtX8Kj7AMgbw82bN8d7772Hpk2bIiAgAJcuXTJKU5L+LctjpDBT4/VR39/efPNNpKSkYM6cOSbX37t3T8nHUr+UxM6dO/H888/jrbfeQpMmTeDv7280g7+HhwdcXV2xZ88eo+0Kv965cyfeeustvPbaa2jUqBF8fX1L/CSA4j67QkNDcf78ebi4uBQZ//lBoLn3C39/f9SsWRMzZ86EWq1Go0aNEBYWhgcPHmDu3LkICgpSrpQoyft3aGioEvAVTlOjRo0StbUsqFQqeHl5wc7ODsuXL4dGo8Hzzz8PAEhPTwcA5Ux0PisrK+X4CwwMhJubGzZt2mSUZtOmTUZX1RRWvXp1k59ppuzduxft2rVDnz59igTOANCqVSukp6cbvZfevn0bMTExxdahpAwGg9Gs3X9VPPNMRFTG6tWrh+effx4DBgzA/Pnz4ePjg9mzZ+PChQtYs2aNUdp58+bB398fjRs3xuLFi3Hs2DEsWbLEbN4iguPHjwPIe95iRkYGjh07BrVarVySuXDhQtjZ2aFx48bQarXYsWMHIiMj0bNnz2IfGTRt2jTs3bsXYWFhmDhxIsLCwuDg4IAjR44gOjoa48aNw7PPPosPPvgAY8eOhZ+fH1q3bo1NmzZh0aJFxdbblA4dOmDUqFFYvXo1GjRogBUrVuDQoUPF/opuip+fH65du4bDhw/D19cX9vb2sLW1LdG29erVwxtvvIE+ffpg2rRpaN68OR48eIDDhw/j/v37GDlyJFatWoWEhAS0bt0abm5uOHDgABISElC3bl2z+X799ddo3bo1mjZtik8++QSNGjWCRqPB3r17MX36dKxatQrBwcEYNGgQPvjgA1StWhX16tXD999/j02bNmHnzp2l7oNdu3bh+vXr0Ol0j/Ss3kmTJsHV1RU1a9bE1KlTcf/+fQwYMAAAEBISAm9vb4wbNw7Tpk3DzZs38dFHHxlt7+npCZ1Oh02bNsHf3x82NjZwdnYudT0AICIiAnPmzEFERASGDh2Ka9euYfz48QCKP5tmaiyU5ni0lFdJxlXVqlURGRmJUaNGQa/Xo3379sjOzsaJEycQGxuLyZMnl75DKsGj7gMg70zsihUrsH79egQFBeGXX37BunXrjNKUpH+dnZ3L7BgpzNfXFwCwfv16vPTSS9DpdHBycnqk97fWrVtj9OjRGDlyJC5fvoxXX30VNWvWxI0bN/D9998jJSUF3377bYn6pSSCgoLw888/Y9euXXB3d8eiRYtw7Ngxo0uiR44ciaioKPj7+yM0NBQ//fRTkT4LCgrC6tWr8cILL0Cn02Hq1KlITk5GQECAxToU99n11ltvYfbs2ejSpQsmTpwIf39/3Lx5E1u2bEGTJk3QpUsXs/0PAO3atcM333yDl156CSqVCtbW1mjdujW++eYbvPPOO0odSvL+/dxzz6F169Z44YUXMHXqVDRo0AB37tzB7t274ezsjD59+pS6/wuKjY1FTk4OkpKSkJmZiWPHjgEA6tevD41GA4PBgOjoaHTq1AkajQbr16/Hxx9/jAkTJihnkJ966ik4Ozujd+/emDBhAhwcHPDDDz9g165dyqOirKysMHLkSEyaNAkBAQFo1KgRFixYgLi4OLM/5JbG1q1b8eKLL6JXr14YMWKE8vxtlUqlXPLesGFDdOrUCe+88w4WLFgAe3t7jB49GnXq1MHLL7+s5HXlyhXcu3cP8fHxAIBTp07B2dkZvr6+cHZ2xvXr17F48WI8++yz8PT0xN27d7Fs2TLs2bPnkY6HCldB91YTEf3tlPRRVVqt1uyjqpYvXy6tW7cWGxsb8fPzs/hoDFOPBAIgNjY2SpoFCxZI3bp1xd7eXuzt7aVBgwYyc+ZMZWKg4qSmpsrYsWOlbt26otPppGrVqtKiRQuZP3++PHz4UETyJluaPHmyxUdVFZ5tumfPnsrjYETyJmQZPHiwuLm5ibOzswwbNkzGjBlj8lFVBS1YsMCovQ8fPpRu3bpJ1apVizxSyNSM14WX5+TkyKRJkyQgIECsra3Fzc1N2rZtKz///LOIiGzZskXCw8PFxcVFeexQSWbSvnnzpgwbNkzq1KkjWq1W3N3dpW3btrJ8+XJl4p/8x/B4enqKtbW12cfwFJ4kq1WrVkaPJ9m7d680atRIdDqdMgmPuclazE0Ytm7dOmnUqJFotVqpV6+e0QRbInnjPb+Mxo0bK+O/4CNKFixYILVq1RIrKyuLj6oqyNRkY/mPSdJqtdK4cWNZu3ZtkUmvCjM3Fiwdj6XJqyRjO7+dDRo0EK1WqxxHBSffKczchGEFHzlnqp8uXbokAJSZg03td1OTM+VP0pb/aLriHlVlbh+YeyxeZmamvP322+Ls7CxOTk7Sq1cvmTlzZomOW3OPqvqzx4gpEydOFC8vL1GpVEaPqrL0/mbO6tWrpX379uLs7Kw8QioiIkKZ0Kok/WJq4qnC+y85OVlefvllcXBwEFdXVxk2bJiMHj3a6L1Tr9fLqFGjxMXFRezt7aV79+4SFRVldOzFx8dL+/btxc7OTry8vGTixIkmx3JBJf3sSkpKkv79+yv7zcfHR1599VWjp0mY6n+RPya4+/LLL5Vl+TOoF97Plt6/RUTS0tJk5MiRUrNmTbG2thYPDw957rnnlEcumRvHpiZiLMzDw8PkZ3L+vsrNzZW2bdtKlSpVxMbGRho1aiRLliwpks+hQ4ekU6dO4urqKvb29tK4cWP59ttvjdIYDAaZOHGiVK9eXbRarTzxxBNF3qcfVffu3S1+txDJey996623xNnZWezs7KRLly5FvgOZyyv/s+LWrVvStWtXZWx4enpKx44djSZI+ytTiZTTDXZERGTW2bNnERISgkOHDiE0NLSyq0P0l/fbb7+hU6dOiIuLK9GZMSp73AdE9E/Hy7aJiIjoL2fOnDkIDQ2Fp6cnTp06hWHDhiE8PJxBWwXiPiAiMsbgmYiIiP5y4uPjMXXqVCQlJcHLywvPPvssoqKiKrta/yjcB0RExnjZNhEREREREZEFj8fzEoiIiIiIiIgqEYNnIiIiIiIiIgt4zzMRPTYSEhIquwr/GG5ubkhOTq7savyjsM8rFvu7YrG/Kxb7u2KxvytWRfS3t7e3yeU880xERERERERkAYNnIiIiIiIiIgsYPBMRERERERFZwOCZiIiIiIiIyAIGz0REREREREQWMHgmIiIiIiIisoDBMxEREREREZEFDJ6JiIiIiIiILGDwTERERERERGSBprIrQERUUm8vOlrZVSAiIiKicrakX5PKroJJPPNMREREREREZAGDZyIiIiIiIiILGDwTERERERERWcDgmYiIiIiIiMgCBs9EREREREREFjB4JiIiIiIiIrKAwTMRERERERGRBXzOMxGVSq9evfDtt9+WOH1sbCzWrl2LDz74AIcPH8b169fx0ksvlWMNiYiIiMgUexsN3n++IXyrOUIARK85jix9LiI6N4Ct1gq3Uh5i6s/HkJGth6OtNca+1hSB3lWw+fh1zN0YazLPRr6ueKdDCKyt1DifeB/Ra0/AIAIAGNSpLsL83ZGZk4uZa47jws1UZTu1CvhP/9a4k5qJcT8crojm/2kMnomowoSGhiI0NLSyq0FERET0jzSoUz0cvnAbk1bFQKNWwcbaClPebI4Fm8/g5NW76NioOl5rWRvLfo9Dtt6Ab34/B99qjvB1dzSZnwpA5AuNMOa7A7hxNx29wwPxTKPq2HTsGpr5V4OPiz3envs7gn2cEVULSQQAACAASURBVNG5PoYt3qts+1KYH64lp8FO+/iEpI9PTYnoLyU2NhY//vgjHB0dce3aNdSuXRsRERFQqVQ4duwYli5dCkdHR/j5+Snb/P7777h48SL69euHw4cPY/Xq1dDr9XB0dERERAScnZ0rsUVEREREf192Wg0a1HTBjDXHAQB6g0CfpUd1V3ucvHoXAHD0UjImPxmGZb/HISsnF7HX7sG7qr3ZPJ3stMjJNeDG3XQAQEz8bXRv5Y9Nx67hyUAPbDlxAwBw9kYK7HXWcHGwwd20LLg56hAW4I7vd1/Aq839zOb/V8PgmYge2aVLlxAdHY2qVati7NixOHfuHGrXro2vvvoK48aNg6enJ2bNmmVy2+DgYEyePBkqlQpbt27FmjVr0Lt3b6M0W7ZswZYtWwAAUVFR5d4eIiIior8rz6p2uJ+RjZEvNERtDyecT7yPeZtO40pSGp4M9MC+uFtoE+KFak62Jc7zfkY2rNQqBHhVwfnE+2gd4oVqVXQAADdHHW6nPlTSJqdmwtVRh7tpWRjYqS4WbjkDOxvT4aibm5vZMjUaTbHryxODZyJ6ZP7+/nB1dQUA+Pr6IikpCTqdDu7u7vDy8gIAPPXUU0oAXNDdu3fx+eef4969e9Dr9XB3dy+SpkOHDujQoUP5NoKIiIjoH8BKrYK/lxPmbozFuYQUDOxYF91b1UH02uMY1Kkeej4VgH1xt6DPNZQq3ymrj2Jgx7qwtlLjSPxt5Bry7neGSlUkrYigeYA7UtKzceFmKhrWcjGZZ3Jystny3Nzcil1fFry9vU0uZ/BMRI/M2tpa+b9arYbBUPI328WLF6Nr164IDQ1VLgEnIiIiovKRnJqJ26mZOJeQAgDYfSYRr7fyx7Lf4/DR8oMAAB8XezT3L3pCozhnbqRg5Df7AABP1HZDdVf7/y/v4f+fxb4HAHBzyjvr3KauF1oEuqOZfztoNWrY2Vhj9EuNMe2XY2XU0vLDR1URUZny9vZGUlISbt68CQDYvXu3yXQZGRlwccn7tXHHjh0VVj8iIiKif6J76VlITs1UgtvGfm64evsBqthpAeRN/vVGG3+sO3LFYl6RLzZCkHcVAFC2t7ZS4/WWdbDuyFUAwP64JHRo6AMACPZxRkamHnfTsrBk2zm8+cU2vPWf7Ziy+iiOX0p+LAJngGeeiaiMabVavPvuu4iKioKjoyOCg4Nx7dq1Ium6deuG6OhouLi4ICAgAElJSZVQWyIiIqJ/jrkbYzHmpcbQWKlxMyUDM9ccR4eG1fF8aC0AwJ6zN/Hb8etK+m8i2sHeRgONlRpPBnngo/8exNXkNPi5O+JuWhYAoNuTtdE80AMqFbD+8BUcv3wHAHDwQhKa+VfDkiFtkaXPxcw1Jyq+wWVMJfL/D+EiIvqL6zRxfWVXgYiIiOgfzU6b96zoyT/FlFsZS/o1MbuuMu955mXbREREREREVCIZ2fpyDZz/yhg8ExEREREREVnA4JmIiIiIiIjIAgbPRERERERERBYweCYiIiIiIiKygI+qIqLHRnEzL1LZqoiZLMkY+7xisb8rFvu7YrG/Kxb7+5+DZ56JiIiIiIiILGDwTERERERERGQBg2ciIiIiIiIiCxg8ExEREREREVnA4JmIiIiIiIjIAgbPRERERERERBYweCYiIiIiIiKygMEzERERERERkQUMnomIiIiIiIgsYPBMREREREREZAGDZyIiIiIiIiILGDwTERERERERWcDgmYiIiIiIiMgCBs9EREREREREFjB4JiIiIiIiIrKAwTMRERERERGRBQyeiYiIiIiIiCxg8ExERERERERkAYNnIiIiIiIiIgsYPBMRERERERFZwOCZiIiIiIiIyAJNZVeAiKik3l50tLKrQERERPSPsaRfk8quwl8KzzwTERERERERWcDgmYiIiIiIiMgCBs9EREREREREFjB4JiIiIiIiIrKAwTMRERERERGRBQyeiYiIiIiIiCzgo6qIqFgpKSlYunQpLl68CI1GA3d3dzRr1gyHDx/GBx98UCT9/Pnz0bVrV1SvXr0SaktEREREVD4YPBORWSKC6dOnIzw8HMOHDwcAXL58GYcPHza7zcCBAyuqekRERESPLXsbDd5/viF8qzlCAESvOY6mdarhuSY1cT8jCwCwZPs5HLpwGxq1CsO6NECAdxWIAPM2xeLElbtF8mzk64p3OoTA2kqN84n3Eb32BAwiAICGtVwwsGNdaKzUuJ+Rjchl+1HNSYfIFxujqoMNRAQbYq7il4OXK7AXHi8MnonIrNjYWGg0GnTs2FFZ5uvri/T0dJw6dQozZ87EtWvXULt2bUREREClUmHChAno1asX6tSpg169eqFz586IiYmBVqtFZGQknJ2dcfjwYaxevRp6vR6Ojo6IiIiAs7NzJbaUiIiIqGIN6lQPhy/cxqRVMdCoVbCxtkLTOtXw84FLWLU/3ijtc0/UBAAM/GoXqthpMfmNMEQs3A0pkEYFIPKFRhjz3QHcuJuO3uGBeKZRdWw6dg32NhoMfa4+Pl5+ELdTM1HFTgsAyDUIvt58GhdupsJWa4U5/VsjJj4ZV5PTKqgXHi+855mIzLp69Sr8/PxMrrt06RL69OmD6Oho3Lp1C+fOnSuSJisrCwEBAZg+fTpCQkKwdetWAEBwcDAmT56MadOmoWXLllizZk25toOIiIjor8ROq0GDmi7YeOwaAEBvEKRn6c2mr+nmgKOX7wAA7mdkIy0zB4HeVYzSONlpkZNrwI276QCAmPjbaB3sCQBoV98He87exO3UTCUPALibloULN1MBAA+zc3EtOQ1ujroybOnfC888E9Ej8ff3h6urK4C8s9FJSUkIDg42SqPRaNC0aVMAQO3atXHixAkAwN27d/H555/j3r170Ov1cHd3N1nGli1bsGXLFgBAVFRUeTWFiIiIqEJ5VrXD/YxsjHyhIWp7OOF84n3M23QaAPB8s1p4uqEPzifex9ebTyMtU4/4W6l4MtADv59KQLUqOgR4VUE1J1ucS7iv5Hk/IxtWahUCvKrgfOJ9tA7xQrUqeYFwdVd7WKlVmNarBexsNPjl4CVsOXHDqE4eVWxRx7MKzt5IUZa5ublVQG+UjkajqbR6MXgmIrNq1KiBAwcOmFxnbW2t/F+tVsNgMBRJY2VlBZVKpaTJzc0FACxevBhdu3ZFaGgoYmNj8eOPP5oso0OHDujQocOfbQYRERHRX4qVWgV/LyfM3RiLcwkpGNixLrq3qoM1hy5j+a7zEAHeaheEAc/URfTaE9h07DpqujlgTv9WSLr/EKev3UOuQYrkO2X1UQzsWBfWVmocib+tpMkPqsd8dwA2GjU+f7sVzlxPUc5S66ytMLZbU8z/7TQysv84A56cnFwxHVIKbm5u5V4vb29vk8t52TYRmVW/fn3k5OQoZ38B4MKFCzh9+vSfyjcjIwMuLi4AgB07dvypvIiIiIgeN8mpmbidmolzCXlneXefSYS/ZxWkpGfDIIAA+DXmKoK88+aEMYjgq81nMHjBbkxYeQQOOmsl8C3ozI0UjPxmH95bvAcnr95Fwv+nuZ2aicMXbyMrJxepD3Nw8upd1PZwBJAXWI/t1hTbTt7AnrM3K6YDHlMMnonILJVKhVGjRuHEiROIiIjAiBEj8OOPP6Jq1ap/Kt9u3bohOjoa48aNg6OjYxnVloiIiOjxcC89C8mpmajuag8AaOznhqu3H8DFwUZJ0zLYE5dvPwAA2GjUsLG2AgA84eeGXINBmdQr8sVGCPr/+5/zJwKztlLj9ZZ1sO7IVQDAvrhbqF/TBWqVCjYaNYJ9nJXtRzzfENeS07D6wKUKaPnjTSUiRc/3ExH9BXWauL6yq0BERERUJmp7OOH9rg2gsVLjZkoGZq45jkGd6qGOpxNEgFv3H2L2+pO4m5YFjyq2mNwzDCLAndRMRK87gaT7DwEAX77TGuN/OIzbqZno/3Qwmgd6QKUC1h++gp8LPHbqtSdro2Oj6hABNh69ip8PXka9GlUR3acl4m+lIj8szH88FgAs6dekwvvFksq8bJvBMxE9Nhg8ExEREf3BTpv3rOjJP8WUS/4Mno3xsm0iIiIiIqLHUEa2vtwCZyqKwTMRERERERGRBQyeiYiIiIiIiCxg8ExERERERERkAYNnIiIiIiIiIgs0lV0BIqKS+ivO+Ph3VREzWZIx9nnFYn9XLPZ3xWJ/Vyz29z8HzzwTERERERERWcDgmYiIiIiIiMgCBs9EREREREREFjB4JiIiIiIiIrKAwTMRERERERGRBQyeiYiIiIiIiCxg8ExERERERERkAYNnIiIiIiIiIgsYPBMRERERERFZwOCZiIiIiIiIyAIGz0REREREREQWMHgmIiIiIiIisoDBMxEREREREZEFDJ6JiIiIiIiILGDwTERERERERGQBg2ciIiIiIiIiCxg8ExEREREREVnA4JmIiIiIiIjIAgbPRERERERERBYweCYiIiIiIiKyQFPZFSAiKqm3Fx2t7CoQERFROVvSr0llV4HIJJ55JiIiIiIiIrKAwTMRERERERGRBQyeiYiIiIiIiCxg8ExERERERERkAYNnIiIiIiIiIgsYPBMRERERERFZwOCZiIiIiIiIyIJyD56TkpIwcuRIo2UrV67EmjVrit3u4sWLWLx4MQAgNjYW586dK3XZQ4YMQWpqarHL4+PjMWTIEFy6dAmHDx/GL7/8UupyTImNjUVUVFSZ5FUSN27cQGRkJEaPHo2bN28arcvMzMSCBQsQERGB0aNHY8yYMdiyZUuZlb1+/XpkZWWZXDdhwgQMGzYMkZGReP/994std/78+bh+/ToAYPXq1SUqe8qUKUhPTy99pR+RubG7cuVKvPnmm7h//76yrFevXqXOv2C7TR075cXcsVKcCRMm4OLFi5VWPhEREZXONxHtMP/dNvjyndb4T79WAIA3nwrAf4c9jS/faY0v32mNZv7VAACOttaY1qsFfhnTCUOerWc2z5EvNMQ3Q9sp29f2cFLWDepUF0uGtMW8AW3g7/nH8tA61bBwcDiWDGmL11vWKafWEpU9TWVXwJw6deqgTp28gyk2NhY6nQ5BQUFlWsaVK1cwc+ZMvP/++/Dz84Ofnx9CQ0PLtIyKcujQITRr1gyvv/56kXXz58+Hu7s7vvjiC6jVaqSmpmLbtm1F0hkMBqjVpf89ZcOGDWjTpg1sbGxMrn/vvfdQp04dpKWlISIiAm3btoVGYzz0DAYDBg4cqLz++eef8corr1gs+8MPPyx1fcuLo6Mj1q5dizfffLPU24oIRKTE7S6J3NxcWFlZPfL2+XV6lDFBREREf02jl+1H6sMco2U/H7iEVfvjjZZl6w345vdz8K3mCF93x2LzXLD1DHafMT5508y/Gnxc7PH23N8R7OOMiM71MWzxXqhVwJBn6+HD/x5Acmom/tO/NfbH3cLV5LSyaSBROar04HnChAnw9/dHbGwsMjIyMHDgQISEhCA2NhZr165F3759sXnzZqjVauzatQt9+/aFj48Pvv76a9y5cwcA8NZbbyE4OBgPHjzAF198gdTUVPj7+0NEzJZ748YNzJ07FxEREfD39wcA/P7777h48SL69euHuXPnwtbWFvHx8UhJScGbb76JFi1awGAwYPHixTh9+jTc3d0hImjXrh1atGiBY8eOYenSpXB0dISfn59SVlpaGr788kskJSXBxsYGAwYMQK1atbBy5UokJSUhJSUFiYmJ6N27N86fP4+jR4/CxcUFY8aMKRJkXr58GQsWLEBWVhY8PDwwaNAgxMXFYf369VCr1Thz5gzGjx+vpL958yYuXLiA9957TwmCnJyc8NJLLwHI+2Fi1apVcHZ2xuXLlzFr1izs3LkTv/76K/R6PQICAtC/f3+o1WosWLAAFy9eRHZ2Nlq0aIHXX38dGzZswN27d/HJJ5/AycnJqOzCMjMzYWNjo9SjV69e6Nq1K44fP47evXtjxYoV6NWrF/bv34/s7GxERkaiRo0aeO+99zBt2jTcuXMHOTk56Ny5Mzp06AAg74zllClTkJmZiSlTpiAoKAhxcXFwcXHB6NGjodVqjepw+PBhrF69Gnq9Ho6OjoiIiICzszNWrlyJ5ORkJCUlITk5GZ07d0bnzp0B5J0N3rFjB9zc3ODo6IjatWubbF+7du2wY8cOvPTSS3BwcDBat27dOmzfvh0A0L59e3Tp0gVJSUmYMmUK6tWrh7i4OPj6+hq1u0ePHjAYDJg/f36RNt28eROLFi1CamoqbGxs8O6778LHxwdz586Fg4MDLl++DD8/P+h0OrPtMqVwnSIjI5GQkICVK1dCr9fDw8MDgwcPhk6nM9rO1NjI3z/h4eE4cuQI9Ho9RowYAR8fn1Idq0RERFTxsnJyEXvtHryr2j/S9k8GemDLiRsAgLM3UmCvs4aLgw08qtgi4V4GbqY8BAD8HpuAJ4M8GDzTY6HSg2cg76zjlClTEBMTg1WrVmHs2LHKOnd3dzzzzDPQ6XR44YUXAABffPEFunbtiuDgYCQnJ2Py5MmYNWsWfvzxRwQHB+O1115DTExMsZcIT5s2DREREQgODjabJiUlBZ9++ikSEhIwdepUtGjRAgcPHsTt27cxY8YMpKam4v3330e7du2QnZ2Nr776CuPGjYOnpydmzZql5LNy5Ur4+flh9OjROHXqFObMmYPp06cDAG7duoXx48fj+vXr+Pe//42RI0fizTffxPTp0xETE4OwsDCjOs2ZMwd9+/ZF3bp18cMPP2DVqlXo06dPkT7Kd/36ddSqVavYs4cXLlzAzJkz4e7ujuvXr2Pv3r2YOHEiNBoNFi5ciF27diE8PBz/+te/4ODgAIPBgE8//RRXrlxB586dsX79eowfPx5OTk4m8589ezasra2RmJiIPn36KHXJyspCjRo10L17d6P0PXv2xMaNG5U+AoDBgwfDwcEB2dnZ+PDDD9G8eXM4Ohr/CpqYmIhhw4Zh4MCBiI6Oxv79+/HUU08ZpQkODsbkyZOhUqmwdetWrFmzBr179wYAJCQkYPz48Xj48CGGDx+Ojh074urVq9izZw+mTZuG3NxcjBkzxmzwrNPp0K5dO2zYsMHoCoD4+Hhs374dkydPBgB89NFHqFu3Luzt7ZGQkIBBgwahf//+AIB9+/Yp7U5KSjLbpq+//hrvvPMOvLy8cP78eSxcuFD54SIxMRFjx46FWq3GypUrTbar8I8yBRWsU2pqKlavXo2xY8dCp9Phl19+wbp16/Daa68ZbWNqbNSqVQtA3hn5qVOnYtOmTVi7di0GDhxY4mN1y5YtyrqKvA2CiIjob0mAz3o2BwCsP3IFvx69BgB4vlktPN3QB+cT7+PrzaeRlqkvVbZ92gWhZ5sAHLuUjMXbziEn1wA3Rx1upz5U0iSnZsLVUQdXp6LLg32cjfJzc3N71BZWCo1G89jV+XFWmf1d7sGzSqWyuDw/QKxduzaSkpIs5nny5Enl3lgAyMjIwMOHD3HmzBmMGjUKAPDEE0/A3t78L2UNGjTAtm3b0LhxY7OBZbNmzaBWq1G9enXlXtazZ8+iRYsWUKvVcHZ2Rr16efeAJCQkwN3dHV5eXgCAp556SvnSf/bsWeXe1fr16yMtLQ0ZGRkAgCZNmkCj0aBmzZowGAxo3LgxAKBmzZq4ffu2UX0yMjKQnp6OunXrAgDCw8ONgvSSWL16Nfbt24fU1FR89dVXAAB/f3+4u7sDAE6dOoVLly4pl0NnZ2crQfHevXuxdetW5Obm4t69e0pgbkn+Zdupqan497//jcaNG6NatWpQq9Vo0aJFieq9YcMGHDp0CACQnJyMxMTEIsGzu7s7fH19AeSNpcL9BwB3797F559/jnv37kGv1yvtBvLGjLW1NaytrVGlShXcv38fZ86cQVhYmHJJuqXL+p977jmMHj0aXbt2VZadPXsWYWFhytnasLAwnDlzBqGhoXBzc0NgYKDZ/Ey1KTMzE+fOnUN0dLSSTq//40Muf3wW1y5XV1ezZRas0/nz53H9+nXlBy29Xm+yvsWNjebNmyv1P3jwIACU+Fjt0KGDcpUBERER/TnvL92Lu2lZqGKnRdSbzXHtTjrWHbmC5bvOQwR4q10QBjxTF9FrT5Q4zyXbzuFuWhasrdQY1qUBXm9ZG//ddQEwEQOICExFBoUvQEtOTi5lyyqXm5vbY1fnx1lF9Le3t7fJ5eUePDs6OiItzfgyjLS0NKOgxdraGgCgVqthMBgs5ikimDx5cpFLcgHzwXph/fr1w4IFC7Bw4UIMGDDAZJr8euWXWfDf0ihum/wzgGq1GlZWVkr9VSoVcnNzS11WYdWrV8eVK1eU+5lfeeUVvPLKK0aTWRW8V1lEEB4ejjfeeMMon6SkJKxduxZTpkyBg4MD5s6di5wc4/tlLHFycoKfnx/Onz+PatWqwdraukT308bGxuLkyZOYNGkSbGxsMGHCBJNlF9xfarUa2dnZRdIsXrwYXbt2RWhoKGJjY/Hjjz8q6wqejVWr1Ur/l3RMAYC9vT1atWqF3377TVlW3P4vfPlzYabaZDAYYG9vb3Rmvrg8zbWrJHUSETRo0ADDhw83m97S2Cg4xguWXZp+JSIioj/vblreBK/3M7Kx5+xNBHs749TVu8r6X2Ou4tMezR4pz5xcA347fg2vtci7Qi859SGqOdkCuAcAcHPSKUF23nIoy++kZf6ZZhFVmHKfCUin06Fq1ao4efIkgLzA+fjx48VeLl2Yra0tMjP/OKgaNmyIjRs3Kq8vX74MAAgJCcGuXbsAAEePHi12FmaVSoVhw4YhISEBP/zwQ4nrEhwcjAMHDsBgMCAlJQWxsbEA8n6dSEpKUma63r17t7JNwXrFxsbC0dERdnZ2JS4zn52dHRwcHHDmzBkAwM6dOxESElLsNp6enqhduzZWrFih/DBhKqjM16BBA+zfv185056Wlobbt28jIyMDOp0OdnZ2SElJwbFjx5RtdDqd0f4xJysrC5cvX4anp6fFtBqNRjmbmpGRAXt7e9jY2ODGjRs4f/68xe3NycjIgIuLCwBgx44dFtOHhITg4MGDyM7OxsOHD3HkyBGL23Tt2hWbN29W+jskJASHDh1CVlYWMjMzcejQIbP7rWC7zbGzs4O7uzv27dsHIC/AzT8GylpgYCDOnTunjOusrCwkJCQYpSlubJhTmmOViIiI/jwbayvYaq2U/zetXQ2Xbz+Ai8MfJ1FaBnvi8u0HFvOKfLERgryrAIDx9kF/bL8/LgkdGvoAAIJ9nJGRqcfdtCycS7gPHxd7eDjbQqNWoW09b+yPu1Vm7SQqTxVyz/PQoUOxaNEiLFu2DADw2muvlSiAyte0aVNER0fj0KFD6Nu3L95++20sWrQIo0aNQm5uLkJCQjBgwAB069YNX3zxBcaMGYOQkBCL18JbW1tj9OjRGD9+PKpUqWLxLCCQdwnqyZMnMXLkSHh5eSEgIAB2dnbQarV49913ERUVBUdHRwQHB+Patbz7SF5//XV8+eWXGDVqFGxsbDBkyJASt72wIUOGKBOGubu7Y/DgwRa3GThwIL777jtERETAwcEBWq0WPXv2NJm2evXq6NGjByZNmgQRgZWVFfr164fAwED4+vpi5MiRcHd3N5r5vEOHDvjss89QtWpVkxOGzZ49G1qtFnq9HuHh4WbvGS7o6aefRmRkJPz8/DBo0CBs3rwZo0aNgre3NwICAixub063bt0QHR0NFxcXBAQEWLxNoHbt2mjZsiUiIyNRrVq1Ev3o4+TkhLCwMKxfv17Jo23btvjoo48A5E0Y5ufnZ7Lsgu3u0aOH2TLee+89LFiwQJn8rFWrVsrl3WXJyckJQ4YMwRdffKGcTe7Ro4fRpSy+vr5mx4Y5pT1WiYiI6M+paq/F+Nfzbj+zUquw/VQCDl+8jcgXG6GOpxNEgFv3H2L2+pPKNt9EtIO9jQYaKzWeDPLAR/89iKvJafBzd1TOOI95qTGq2GuhggoXb6Uq2x+8kIRm/tWwZEhbZOlzMXNN3qXgBhHM3XgKn70RBrVKhd+OX8eV25wsjB4PKuE0t6WWmZkJnU6HBw8e4KOPPsLEiRPh7OxseUMi+lM6TVxf2VUgIiL6R7PTavD+8w0x+aeYcitjSb8m5ZZ3eeA9zxXrb33P899RVFQU0tPTodfr8eqrrzJwJiIiIqJ/hIxsfbkGzkR/ZQyeH8GECRMquwpERERERERUgcp9wjAiIiIiIiKixx2DZyIiIiIiIiILGDwTERERERERWcB7nonosfG4zb75OOPMoRWPfV6x2N8Vi/1dsdjfROWDZ56JiIiIiIiILGDwTERERERERGQBg2ciIiIiIiIiCxg8ExEREREREVnA4JmIiIiIiIjIAgbPRERERERERBYweCYiIiIiIiKygMEzERERERERkQUMnomIiIiIiIgsYPBMREREREREZAGDZyIiIiIiIiILGDwTERERERERWcDgmYiIiIiIiMgCBs9EREREREREFjB4JiIiIiIiIrKAwTMRERERERGRBQyeiYiIiIiIiCxg8ExERERERERkAYNnIiIiIiIiIgsYPBMRERERERFZwOCZiIiIiIiIyAJNZVeAiKik3l50tLKrQERE9LeypF+Tyq4C0WODZ56JiIiIiIiILGDwTERERERERGQBg2ciIiIiIiIiCxg8ExEREREREVnA4JmIiIiIiIjIAgbPRERERERERBYweCZ6THXv3h2RkZEYOXIkoqOjkZWVhaSkJIwcOdJk+h9++AEnTpwAAEyYMAEXL14EAEyZMgXp6emPVIfffvsNO3bseLQGEBERERE9RvicZ6LHlFarxfTp0wEAs2fPxubNmxEWFmY2fffu3U0u//DDDx+5Dh07dnzkbYmIiP7Ovoloh4fZehgMglyDIGLRHrQJ8USv8EDUcHPAe4v24HzifQCAo601xr7WsmblMgAAIABJREFUFIHeVbD5+HXM3RhrMs/3uzZEoHcVAMCNu+mY8b/jyMzJhYNOgxHPN4JXVTvk6A2YufY4rtxOAwCMeL4hmge4IyU9G+/+H3v3H1hz3f9//HHOfpr9MtswYhg2P4b8LiGtckV0XfnR1dXv4nKRflwz4arwkfyKq4iKUOnHl0pXXKmQhCQkwwwZy49tZraZ7ezsbOec7x+7nDptOovtbLjf/mnn/X69X+/n+5Vfj/N6v1/vNza75+KBqxThGbgKREdH6/jx45Ikm82m119/XYcPH1ZISIjGjRsnb29vLViwQJ06dVL37t2djh09erSmT58us9msF198UVFRUUpNTVWDBg30+OOPy8fHR6NHj1aPHj2UlFT6l/mTTz6p+vXra+XKlfL19dXAgQM1efJkRUVFKSkpSSaTSSNHjlRMTIxsNpvee+89HThwQMXFxbr99tt16623KicnRy+//LJMJpNsNpsee+wxxcTEuH3sAACoKuPe2a68wmLH59Qz+fq/D3/QE3e0c2pnKbHp7U2HFBkWoMjwgIv298a6AzJZSiRJI26N0cAukVq5LUX33BillNN5+r8Pf9B1dWtr9J/aavy730uS1iWe1OqdqUoY1KEKrhC4tnDbNnCFs1qt2rNnjxo3bixJSk9PV79+/TR37lz5+flp+/btFe4rLS1NcXFxeumll1SrVi19+eWXjn1+fn6aPn26+vXrp7feeqvc4202m6ZPn64HH3xQH330kSRp48aNjmOnT5+ur776SpmZmdq6davat2+v2bNna/bs2YqMjLzkMQAA4EpwIitfJ8+WfVSqqNiqpBM5spTYfvf4C8FZknw8PSTZJUmNwwK051hW6TnOFqheUC0F1/aWJO0/nq3zvwrwAC4dM8/AFcpisSghIUGSFBMTo759+yo7O1vh4eGOINqsWTOdOXOmwn3WrVtX0dHRkqRevXpp7dq1GjhwoCTpxhtvdPz37bffLvf4C7eNN2vWTJmZmZKkxMREHT9+3BHiTSaT0tPT1bx5c7322msqKSlR165dyw3PGzZs0IYNGyRJM2bMqPB1AABQ7ezSi3/rJkn67Ief9fmPJyql2/g7Y9UlKlzHs/K1aP0BSdKx03m6Mbq+kk7kqFVEkOoF11JogK9yCywu+wsNDa2Uuq5lnp6ejKMbVed4E56BK9Svn3n+NS8vL8fPRqNRFovrvzgvMBgMF/18sZ/LO7fRaJTNVvrtud1u18MPP6wOHcreLjZlyhTt3r1b8+fP18CBA9W7d2+n/XFxcYqLi6tw/QAA1BRPv7VN2flFCvLz1oz7uunE2QLtP5592f3OWbNXRoM0ql9b9W4ToXWJJ7Xi2xT94/bWWji8p45lnteRjDzZ7PYK9ZeVlXXZNV3rQkNDGUc3csd4R0RElLud27YBOGRlZenw4cOSpK1btzpmoSVp27Ztjv+2aNGiwn126NBB69atU0lJ6a1maWlpMpvNOnPmjIKCghQXF6e+ffvq2LFjlXglAABUr+z8IknSOZNF3x7MUHREcKX1bbNL3ySlqWd0fUmlt3PPWbNXoxZv1exPExXk562MnMJKOx+AUsw8A3Bo2LChNm3apEWLFql+/fpOq2kXFxdr4sSJstvtevLJJyvcZ9++fZWZmalnnnlGkhQYGKiEhAQlJSVpzZo18vDwkK+vrx5//PFKvx4AAKqDj5eHjAap0GKVj5eHOjUL03tbfrqkvhIGtdfqnak6lHZOEXX8lJZjkiR1b1lPJ/73/HRtH08VFVtVYrPrTx2v0/7j2U7PRwOoHAa7vYL3dAC4qmVmZmrmzJmaM2dOmX0XVuQODAyshsp+cfvUz6r1/AAAVET94FqaNLSzJMnDaNDX+9P0wdYjuqFVPY3q10ZBft4qMJco5XSe/vX+Dkmlr7aq7eMpTw+j8s3FmvjeDh3PytfC4T01acUuZeWZNeehHvLz8ZRBBh09naf5a/fLZClRTMNgJQzqIJvdrp+z8vXvNYnKN5eG5/F/7qDYJnUV5OetnIIiLf/mJ32555fnr5c92tH9A3SV4bZt96rO27aZeQYAAAAqUUZuof6xaEuZ7dsOnda2Q6fLPebB+V+X2ebn7alT2SadyTNLkv751nflHpt8KlePLNxU7r4Zn+ypYNUAXGHmGcAVg5lnAAAqFzPPl4+ZZ/diwTAAAAAAAGowwjMAAAAAAC4QngEAAAAAcIHwDAAAAACAC6y2DeCKwaIm7sPiJ+7HmLsX4+1ejLd7Md5A1WDmGQAAAAAAFwjPAAAAAAC4QHgGAAAAAMAFwjMAAAAAAC4QngEAAAAAcIHwDAAAAACAC4RnAAAAAABcIDwDAAAAAOAC4RkAAAAAABcIzwAAAAAAuEB4BgAAAADABcIzAAAAAAAuEJ4BAAAAAHCB8AwAAAAAgAuEZwAAAAAAXCA8AwAAAADgAuEZAAAAAAAXCM8AAAAAALhAeAYAAAAAwAXCMwAAAAAALnhWdwEAUFEPL/mxuksArjnLHu1Y3SUAAFAjMPMMAAAAAIALhGcAAAAAAFwgPAMAAAAA4ALhGQAAAAAAFwjPAAAAAAC4QHgGAAAAAMAFwjMAAAAAAC7wnmfgGjFs2DA1btxYNptNDRs21OjRo+Xj43PR9vfff7+WL1/utC07O1vLli1TfHx8uccUFBRo69atuv322yu1duBqExboq4RBHVTH30d2u11rdx/Xf3akOvYP7t5Mw2+N0ZCX1imvsFgBtbz03OBOahkRpPWJJ7Xgi6SL9v3Qza10U0x92ezSf3f9rE93lvb7j9tbq2tUuMzFVs1ZnagjGXmSpM7NwzTy9tbyMBj0+Y8ntHJbSlVeOgAAVyzCM3CN8Pb21uzZsyVJ8+bN0/r16zVgwIA/1EdISMhFg7NUGp7XrVtHeAZcsNrsWrT+gI5k5KmWt4defayndh/N0vGsfIUF+qpjs1CdzjU52ltKbHp70yFFhgUoMjzgov3e1r6RwgJ99djCb2SXFOTnLUnqEhWmhiG19fCCTYpuGKwxd7TVk0u3yWiQRvdrownvfa+sPLPmP9ZT2w+f1vGs/KoeAgAArjiEZ+AaFB0drePHj0uSZs2apbNnz6q4uFh33HGH4uLinNrm5eVp5syZuvvuu9WoUSPNnDlTc+bM0YkTJ7Rw4UKVlJTIbrcrPj5eK1asUEZGhhISEhQbG6shQ4Zo1qxZKigoUElJie655x516dJFmZmZmj59ulq1aqXDhw8rJCRE48aNk7e3d3UMB+B22flFys4vkiQVWqw6kZWv0ABfHc/K199va60lXyVr0tDOjvZFxVYlnchRRJ3av9vvgE5NNOOTH2X/3+dzJoskqUfLetqw95Qk6eCpXNX29VKIv4/qBdVSWo5JGbmFkqRNSWnq0aoe4RkAgHIQnoFrjNVq1Z49e9ShQwdJ0qhRo+Tv7y+LxaIJEyaoW7duCggondnKzc3VrFmzdM899yg2NlaZmZmOftavX6877rhDN910k0pKSmSz2XTvvffqxIkTjhluq9WqsWPHys/PT3l5efrXv/6lzp1LA0F6erqefPJJjRw5UnPnztX27dvVq1cvp1o3bNigDRs2SJJmzJhR5WMDVId6QbXUvH6QDp7KVfeW4crKM+vo6fOX1FeDOn7q3SZCN7Sqp3MmixZ+maS0bJNCA3x1Jq/Q0S4rz6y6Ab6qG1h2e3TDYKc+Q0NDL+3CajhPT8+r9tpqIsbbvRhv92K83as6x5vwDFwjLBaLEhISJEkxMTHq27evJGnt2rXauXOnJCkrK0vp6ekKCAiQ1WrV1KlT9eijj6p169Zl+mvZsqVWrVqls2fPqlu3bmrQoEGZNna7XR988IGSk5NlMBiUnZ2tc+fOSZLCw8MVGRkpSWrWrJnOnDlT5vi4uLgyM+HA1cTXy0PPDemk19cdkNVm0197RmnCezsuuT8vT6MsJVaNWfKtboyur/g72yv+7e8kg6FMW7vdrrJbJbvd+XNWVtYl11OThYaGXrXXVhMx3u7FeLsX4+1e7hjviIiIcrcTnoFrxK+feb4gKSlJ+/bt0wsvvCAfHx9NnjxZxcXFkiQPDw81bdpUe/bsKTc89+zZU1FRUdq9e7emTZumkSNHKjw83KnN1q1blZeXpxkzZsjT01OjR4+WxVJ6G6mXl5ejndFodGwHrhUeRoOeG9JJG/ed0rcHMxQZHqD6wX56bcRNkkoXFVsw/CY9seRb5RQUVajPrDyztiZnSJK+PZih+Dtj/7e9UGGBtSTlSJJCA32VnV8kLw/j/7bLsf1svrkSrxIAgKsHr6oCrmEmk0m1a9eWj4+PTp06pZ9++slp/6hRo5SWlqb//Oc/ZY49ffq06tWrpzvuuEOdO3fWzz//rFq1aqmw8JdbQE0mk4KCguTp6an9+/eXO7sMXKv+eWesTmTla9X3xyRJqZnnNWzuBj04/2s9OP9rnckza/TiLS6Dc8Kg9moVESRJ2nYoQ+0j60qSYpuE6GR2gSRp++FMxcU2lCRFNwyWyVyi7PwiHUo7p4YhtVUvuJY8jQb1aROh7YdPV9UlAwBwRWPmGbiGdejQQevXr9fYsWMVERGhFi1aOO03Go166qmnNHPmTNWqVUsdO3Z07Nu2bZu2bNkiDw8PBQcHa/DgwfL391erVq0UHx+vDh06aNCgQZo5c6bGjx+vyMhINWzY0N2XCNRIba6ro7jYRjp6Ok8Lh/eUJC37+pB2Hrn4F0xvj7lZtX085elhVI9W9TTxvR06npWvpuEBjsXHVnybomf+3FF/6d5UhRarXv7vXknSjiOZ6hIVpmWj+6ioxKo5q0u32+x2Lfhiv168t6uMBoPWJZ7Uz2dYLAwAgPIY7PbfPt0EADXT7VM/q+4SgBrFz9tTT98Zq2kf766ycyx7tKPrRlcgnlF0L8bbvRhv92K83as6n3nmtm0AAK5QJktJlQZnAADwC8IzAAAAAAAuEJ4BAAAAAHCB8AwAAAAAgAuEZwAAAAAAXOBVVQCuGFfrqr81ESuHuh9jDgBAzcbMMwAAAAAALhCeAQAAAABwgfAMAAAAAIALhGcAAAAAAFwgPAMAAAAA4ALhGQAAAAAAFwjPAAAAAAC4QHgGAAAAAMAFwjMAAAAAAC4QngEAAAAAcIHwDAAAAACAC4RnAAAAAABcIDwDAAAAAOAC4RkAAAAAABcIzwAAAAAAuEB4BgAAAADABcIzAAAAAAAuEJ4BAAAAAHCB8AwAAAAAgAuEZwAAAAAAXCA8AwAAAADggmd1FwAAFfXwkh+ruwTgmrLs0Y7VXQIAADUGM88AAAAAALhAeAYAAAAAwAXCMwAAAAAALhCeAQAAAABwgfAMAAAAAIALhGcAAAAAAFwgPKPGyszMVHx8vNO2lStXavXq1b97XEpKipYuXSpJSkpK0qFDh/7wuUePHq28vLwy2zdu3Kj4+HiNHTtW8fHx2rlzpyRp06ZNys7OdtlvRdtdjpdfflljx47Vf//733L3JyQk6OWXX67SGl5//XWdPHmySs8BAAAAuBPvecZVp3nz5mrevLmk0vDs6+urVq1aXXa/Z8+e1SeffKKZM2fKz89PZrPZEbA3bdqk6667TiEhIb/bR0XbXarc3FwdPnxYCxcuLHf/yZMnZbPZlJycLLPZLF9f30qvwWazaeTIkZXeL3A1CQv0VcKgDqrj7yO73a61u4/rPztSHfsHd2+m4bfGaMhL65RXWKyAWl56bnAntYwI0vrEk1rwRVK5/Q7s3ER/7tZUESG1HcdK0s1tIzT0htI/F80Wq+Z/vk9HT593WQcAAPgF4RlXrMmTJysqKkpJSUkymUwaOXKkYmJilJSUpDVr1uiRRx7R+vXrZTQatWXLFj3yyCNq2LChFi1apLNnz0qSHnzwQUVHR+v8+fN65ZVXlJeXp6ioKNnt9jLnO3funHx9fR2B88LP27dvV0pKiubNmydvb29NmzZNq1ev1g8//CCLxaKWLVtqxIgR+v7778u0O3nypN5++22ZzWYFBgZq1KhRqlOnjtauXav169fLw8NDjRo10lNPPeVUi8Vi0ZtvvqmUlBR5eHjogQceUNu2bfXCCy/o3LlzSkhI0COPPKKYmBin47Zu3apevXrp1KlT2rVrl3r27OkYy8jISB07dkx5eXkaPXq0/vOf/+j48eO64YYbdM8990iSNm/erM8//1wlJSVq0aKFHnvsMRmNRt1///0aMGCAEhMT9cADD+j//b//p/vvv1/NmzfXnj179MEHH8hmsykgIEDPP/+8jhw5orfeeksWi0Xe3t4aNWqUIiIiKv3XCFBTWW12LVp/QEcy8lTL20OvPtZTu49m6XhWvsICfdWxWahO55oc7S0lNr296ZAiwwIUGR5w0X6TTubo+58yNeuB7k7bT+cWKuGd75RvLlHn5mF6sn87Pbl02+/WAQAAnBGecUWz2WyaPn26du/erY8++kjPPfecY194eLhuvfVW+fr6auDAgZKkV155RQMGDFB0dLSysrI0bdo0/fvf/9aHH36o6OhoDR48WLt379aGDRvKnCsyMlLBwcEaPXq02rVrp65du6pz587q3r27vvjiC0dYlKR+/fpp8ODBkqT58+frhx9+KNOupKRES5cu1bhx4xQYGKht27bpgw8+0KhRo/Tpp5/q1VdflZeXlwoKCsrU8uWXX0qS5syZo1OnTumFF17QK6+8onHjxmnmzJmaPXt2ueP13Xff6dlnn1VaWpq++OILR3iWJE9PT02ZMkVr167V7NmzNWPGDPn7+2vMmDHq37+/zp07p23btmnq1Kny9PTUm2++qS1btqh3794qKirSddddp2HDhjmdLy8vT2+88YamTJmi8PBw5eeX/oM8IiJCU6ZMkYeHh/bu3av3339fY8eOrfD/d+BKl51fpOz8IklSocWqE1n5Cg3w1fGsfP39ttZa8lWyJg3t7GhfVGxV0okcRdSp/bv9pmSUfdxEkg6czHH8fPBUjkIDarmsAwAAOCM8o8YyGAwut3ft2lWS1KxZM2VmZrrsc9++fU7P4ppMJhUWFio5OdkR3q6//nrVrl32H6hGo1ETJ05USkqK9u3bp7fffltHjx7V0KFDy7Tdv3+/Vq9eraKiIuXn5+u6665T586dndqkpaXpxIkTmjp1qqTSLwLq1KkjSWrcuLHmzZunLl26OK7x1w4ePKg//elPkqSGDRsqLCxM6enpqlWr1kWv/ciRIwoMDFRYWJjq1q2r1157Tfn5+fL395ckR32NGzdWo0aNHLXUq1dPZ8+e1cGDB3Xs2DFNmDBBUunsd2BgoGNsunfvXuachw8fVkxMjMLDwyXJcS6TyaQFCxYoIyNDkmS1WsutecOGDY4vMmbMmHHRawOuZPWCaql5/SAdPJWr7i3DlZVn1tHT56vsfP06NNbOlLJ/Xv66jgtCQ0OrrI7q5unpeVVfX03DeLsX4+1ejLd7Ved4E55RYwUEBDhmKi/Iz893BDFJ8vLyklQa3mw2m8s+7Xa7pk2bJm9v7zL7LhbWf9smKipKUVFRio2N1cKFC8uEZ4vFoiVLlmj69OkKDQ3VypUrZbFYyu2vUaNGmjZtWpntEyZM0IEDB7Rr1y59/PHHmjt3rjw8PJyu44/69ttvderUKY0ePVqSVFhYqO+//1633HKLpF/G0mAwOH6+8Nlqtcput6t379669957y/Tt5eUlo7Hi6w+uWLFCbdq0UUJCgjIzMzVlypRy28XFxSkuLq7C/QJXGl8vDz03pJNeX3dAVptNf+0ZpQnv7aiy87VvUle3d7xO/3xr20XrMFlKHNuzsrKqrJbqFhoaelVfX03DeLsX4+1ejLd7uWO8L/Y4Iatto8by9fVVnTp1tG/fPkmlwTkxMVHR0dEV7qNWrVoym82Oz7Gxsfriiy8cn1NTUyVJMTEx2rJliyTpxx9/LPdW6ezsbB09etTp2LCwMEethYWFkqTi4tIFegIDA2U2m/X99987XdOFdhEREcrLy9Phw4clSSUlJTpx4oRsNpuysrLUtm1b3XfffTKZTE7XIEmtW7d21JuWlqasrKzffWbYZrNp+/bteumll7RgwQItWLBACQkJ+vbbb39v+Jy0a9dO27dv17lz5ySV/v84c+bM7x7TsmVLJScnO+4KuPBliMlkciyatmnTpgrXAFxNPIwGPTekkzbuO6VvD2aoQUht1Q/202sjbtLbY25WWKCvFgy/SXVq+1TK+ZqGB+ipAe00ecUunf/fQmLl1QEAAMrHzDNqtMcff1xLlizRO++8I0kaPHiw6tevX+HjO3XqpLlz52rnzp165JFH9PDDD2vJkiUaO3asrFarYmJiNGLECA0ZMkSvvPKKnnnmGcXExJR7K4jVatXy5cuVk5MjLy8vBQYGavjw4ZKkPn36aPHixY6FwG655RbFx8crPDzc8Rx0ee3i4+O1bNkymUwmWa1W3XHHHWrQoIHmz58vk6l0saD+/fuXuY38tttu0+LFixUfHy8PDw+NGjXKabb4t5KTkxUSEuK0ynfr1q01b9485eTkXPS4X2vUqJHuuecevfDCC7Lb7fLw8NCjjz7q+AKhPIGBgRoxYoReeukl2e12BQYG6rnnntOgQYO0YMECffbZZ2rTpk2Fzg9cbf55Z6xOZOVr1ffHJEmpmec1bO4v6y28PeZmjXlzq2PF7ItJGNReq3em6lDauYu2CQv01fNDOmn2p4k6le385eBv6wAAAOUz2C/l/k8AqAa3T/2suksAKkWb6+po7kM36OjpPMdjGMu+PqSdR365m+O34fntMTerto+nPD2MyjcXa+J7O3Q8K18Lh/fUpBW7dCbPrEFdIjXkhmYK8fdRboFFO45k6uX/7tNTA9qpZ3QDZZ4r/VLOarNrzJJvXdax7NGO7hwWt+I2S/divN2L8XYvxtu9qvO2bcIzgCsG4Rlw5uftqafvjNW0j3dXSf+EZ1QWxtu9GG/3Yrzdi2eeAQDAH2aylFRZcAYAAM4IzwAAAAAAuEB4BgAAAADABcIzAAAAAAAuEJ4BAAAAAHCB9zwDuGJczSv/1jSsHOp+jDkAADUbM88AAAAAALhAeAYAAAAAwAXCMwAAAAAALhCeAQAAAABwgfAMAAAAAIALhGcAAAAAAFwgPAMAAAAA4ALhGQAAAAAAFwjPAAAAAAC4QHgGAAAAAMAFwjMAAAAAAC4QngEAAAAAcIHwDAAAAACAC4RnAAAAAABcIDwDAAAAAOAC4RkAAAAAABcIzwAAAAAAuEB4BgAAAADABcIzAAAAAAAuEJ4BAAAAAHDBs7oLAICKenjJj9VdAq5xyx7tWN0lAACAasLMMwAAAAAALhCeAQAAAABwgfAMAAAAAIALhGcAAAAAAFwgPAMAAAAA4ALhGQAAAAAAFwjPAAAAAAC4wHueUePdf//9Wr58eZX0vWPHDh0/flyDBw/WypUr5evrq4EDB5bbdtOmTUpJSdGjjz562ed9/fXXNWDAADVq1Mhl26SkJM2aNUv16tWTxWLR9ddfrwceeOCya/ityZMn6/7771fz5s2dtv/6utetWycfHx/17t37ss61du1arV+/Xk2bNtWNN96olJQUDR069LL6xJXtn3fGqluLcOUWWPT3NzZLkib+paMa1a0tSart66UCc7FGLd6qekG1tPgfvXXybL4k6eCpXM1bu79Mn+0j62p4XIy8PIz6Kf2c5q7ZK5vdrpvbRmjoDaW/zs0Wq+Z/vk9HT5+Xl4dRcx7sIS9PozyMBm1JTtfyb35y0wgAAICajvCMK5LNZpPRePk3TqxevVrjxo2rhIr+mJEjR/6h9jExMRo/frwsFovGjRunrl27Kjo6uoqqu7jbbrutUvpZt26dJk6cqPDwcNntdq1YsUKDBg2Sj49PpfSPK8+6xJNavTNVCYM6OLa9uOpHx88j4mJUUFTs+JyeY9KoxVsv2p9BUsLA9nrm3e91KrtAD/RuqVvbN9KXe07odG6hEt75TvnmEnVuHqYn+7fTk0u3qdhq07jl22UutsrDaNDch3po55EzOngqt0quGQAAXFkIz7hiJCUl6aOPPlJwcLBSU1P173//W7NmzdLZs2dVXFysO+64Q3FxcZJKZ6vvuOMO7d69W97e3kpISFBwcLBTf2lpafLy8lJgYGCZc12YGfXw8FCjRo301FNPOe3ftWuXVq1apZKSEgUEBGjMmDEKDg7WypUrlZmZqdzcXKWnp+uBBx7QTz/9pB9//FEhISF65pln5Onp6TTLu2fPHn3wwQey2WwKCAjQ888/f9Ex8Pb2VmRkpLKzsyVJZrNZS5cu1YkTJ2S1WjVkyBB16dJFmzZt0o4dO1RcXKzMzEz17NlTQ4YMUWZmpmbOnKk5c+ZIKv3ywGw2O2Z9t2zZomXLlqmwsFD/+Mc/FBUV5XT+X8/OZ2RkaPHixcrLy5PRaNTTTz+t+vXrO7X/73//q6+//lqS1LdvX/Xv31+LFi3S6dOnNXPmTN18880aMGCAWrdurR9++EE33HCDy18HuDrtP56tekG1Lrq/V+sGGvfu9gr3F+jnrWKrTaeyCyRJu4+e0bAbo/TlnhM6cDLH0e7gqRyFBvxyXnOxVZLkaTTIw2iU3f5HrwQAAFytCM+4ohw5ckRz5sxReHi4JGnUqFHy9/eXxWLRhAkT1K1bNwUEBKioqEgtWrTQX//6V7377rv66quvdPfddzv1dejQITVt2rTc83z66ad69dVX5eXlpYKCgjL7o6OjNW3aNBkMBn311VdavXq141bq06dPa9KkSTp58qSeffZZxcfH67777tPs2bO1e/dude3a1dFPXl6e3njjDU2ZMkXh4eHKz8//3evPz89Xenq6WrduLUlatWqV2rZtq1GjRqmgoEATJ05Uu3btnMbKx8dHEyZM0PXXX6+AgIDf7d9sNuuFF17QgQMH9NprrzlCdnnmzZunu+66S127dpW78WPGAAAgAElEQVTFYpH9Nynj6NGj+vrrrzVt2jRJ0sSJE9W6dWuNGDFCiYmJmjRpkuOLi+bNm+vgwYNlwvOGDRu0YcMGSdKMGTN+t3Zcvdo2DlFOQZHSsk2ObfWDa2nB8J4yFZXo7a8Paf+JHKdjzpks8jAa1KJBkH5KP6eeMQ0UFuRbpu9+HRprZ0qm47PRIL36WE9FhNTWml0/61Ca86xzaGhoJV/dLzw9Pau0fzhjvN2L8XYvxtu9GG/3qs7xJjzjihIVFeUIzlLpDPHOnTslSVlZWUpPT1dAQIA8PT3VqVMnSVKzZs20d+/eMn3l5OSUO+ssSY0bN9a8efPUpUsXp7B7QXZ2tl5++WXl5OSopKTEqaaOHTvK09NTjRs3ls1mU4cOHRx9njlzxqmfw4cPKyYmxnG8v79/ufUkJydr7NixSktL01133eWYRd+7d69++OEHrVmzRpJksViUlZUlSYqNjXWE5a5du+rgwYPq0qVLuf1f0LNnT0lS69atZTKZyv3iQJIKCwuVnZ3tGBtvb+8ybQ4ePKiuXbvK19fXUUNycnK5X1gEBQU5ZtN/LS4uznE3Aa5dN7eJ0KakNMfn7Pwi3Tdvo84XFiuqfqAmD+2sEa9vlslS4nTc9FU/auRtreXlYdQPR8/IanP+gqd9k7q6veN1+udb2xzbbHZp1OKtqu3jqUlDO6tJmL9+PvPLl1oXfn9VhdDQ0CrtH84Yb/divN2L8XYvxtu93DHeERER5W4nPOOK8utnYpOSkrRv3z698MIL8vHx0eTJk1VcXPpMpIeHhwwGgyTJaDTKarWW6cvb21smk6nMdkmaMGGCDhw4oF27dunjjz/W3LlznfYvXbpUAwYMUOfOnZWUlKQPP/zQsc/T09Nx3l/XYTAYyq2jIi4885yWlqbnn39eXbt2VWRkpOx2u+Lj48v8Bj9y5EiZPgwGgzw8PGSz2RzbLozXxVyo/bd+O8t8qW0usFgs5QZwwGgw6Mbo+nr8zV+eby622lRcWPrr+EhGntJyTGpYt7Z+Sj/ndGzyqVzFv/2dJOn6ZqGOxcckqWl4gJ4a0E7PfrBT5wvL/j4oKCpR4s9n1aV5uFN4BgAA1y5eVYUrlslkUu3ateXj46NTp07pp5/+2Kq4jRo1UkZGRpntNptNWVlZatu2re677z6ZTCaZzeYy5w4JCZEkffPNN5d8DS1btlRycrIyM0tvG3V123ZERITuuusu/ec//5EktW/fXp9//rkjqB47dszRdt++fcrPz5fFYtHOnTvVqlUrBQUFKS8vT+fPn1dxcbF2797t1P+2baUzcAcPHpSfn5/8/PzKrcPPz09169bVjh07JJWG8KKiIqc2MTEx2rlzp4qKimQ2m7Vz507FxMSU2196eroaN278u9eOa9P1zUJ14my+ss7/8nswyM9bxv99r1M/uJYahtRWRk7pF2EJg9qrVUSQo50keXkYNfSG5vrvD8clSWGBvnp+SCfN/jTR8Uz0hfa1fUq//PL2NOr6pqXnBgAAkJh5xhWsQ4cOWr9+vcaOHauIiAi1aNHiDx0fExOjd955R3a73WmG1Wazaf78+Y5Z6f79+6t27dpOxw4ZMkRz585VSEiIWrRo4Qi/f1RgYKBGjBihl156SXa7XYGBgXruued+95jbbrtNa9asUWZmpgYPHqy33npLY8eOlSSFhYVp/PjxkqRWrVpp/vz5ysjIUM+ePR2voLr77rsdK13/dsba399fzz77rGPBsN/z+OOPa9GiRVq5cqU8PDz0z3/+U/Xq1XPsb9asmfr06aOJEydKKl0w7GLPmCclJenee+/93fPh6jb+zx0U26Sugvy89e6TfbX8m5/05Z4T6t2mgTbtT3Nq265xiB7o01JWm11Wm13z1u7TeXPp7HHT8ABl55d+kTOkRzN1a1lPBoP02a6flZh6VpL0t14tFFDLW4//qY0kyWqza8ySbxXi76Oxg9rLaDDIaDBo84E0ff/Tpf3eBgAAVx+D/Y/cWwlcZZYtW6ZOnTopNja2ukupVJX5Tuqqlpubq3nz5v3uKuMX3D71MzdUhCuVn7ennr4zVtM+3u268SVa9mjHKuubZ+bci/F2L8bbvRhv92K83as6n3nmtm1c0/785z/LYrFUdxnXtKysLMdK5cDlMFlKqjQ4AwCAaxu3beOaFhwcrM6dO1d3GZWuT58+6tOnT3WXUSG/fZc0AAAAUBMx8wwAAAAAgAuEZwAAAAAAXCA8AwAAAADgAs88A7hiVOVKx3DGyqEAAADOmHkGAAAAAMAFwjMAAAAAAC4QngEAAAAAcIHwDAAAAACAC4RnAAAAAABcIDwDAAAAAOAC4RkAAAAAABcIzwAAAAAAuEB4BgAAAADABcIzAAAAAAAuVDg8//zzz1VZBwAAAAAANZZnRRv+3//9n0JCQnTTTTfppptuUp06daqyLgAAAAAAaowKh+dFixZp9+7d2rJliz788EO1atVKvXr1Urdu3eTj41OVNQIAAAAAUK0qHJ49PDzUpUsXdenSRSaTSd99951Wr16tN998U127dlVcXJyio6OrslYAAAAAAKrFH14wzGw2a8eOHdq2bZvOnj2rG264QfXr19f8+fP15ptvVkWNAAAAAABUqwrPPO/evVubN2/Wjz/+qOjoaPXt21fPPPOMvL29JUn9+vXTP/7xDz322GNVViwAAAAAANWhwuH5vffeU+/evfXggw+Wu1iYv7+/HnroocqsDQAAAACAGqFC4dlms6lp06b605/+JC8vr4u2u+WWWyqtMAAAAAAAaooKPfNsNBq1d+9eGQyGqq4HAAAAAIAap8ILhvXv318rV65USUlJVdYDAAAAAECNU+Fnnr/44gvl5ubqs88+U2BgoNO+1157rdILAwAAAACgpqhweB4zZkxV1gEALj285MfqLgE12LJHO1Z3CQAA4CpW4fB87tw59ejRo8z27du3V2pBAAAAAADUNBV+5vn1118vd/sbb7xRacUAAAAAAFATuZx5Pn36tKTS11VlZmbKbrc77fP29q666gAAAAAAqAFchucnnnjC8fNvn3sODg7WkCFDKr8qAAAAAABqEJfhecWKFZKkSZMmacqUKVVeEAAAAAAANU2Fn3kmOAMAAAAArlUVXm07MzNTH3zwgVJTU2U2m5328Z5noOZZtWqVtm7dKqPRKIPBoBEjRqhFixZl2q1YsUIxMTGKjY112r5gwQJ16tRJ3bt3v6w6Jk+erPvvv1/Nmze/rH4AAACA6lTh8PzKK6+oXr16euCBB+Tj41OVNQG4TIcPH9YPP/ygmTNnysvLS3l5eSopKSnTzmazadiwYdVQIa4k/7wzVt1ahCu3wKK/v7HZad/g7s00/NYYDXlpnfIKi+VhNOjpAbGKahAoD6NRG/ae1IpvU8r0+fSAWLWMCJIkncou0EufJspcbFVskxBNHtpZGbkmSdK3BzP03pYjkqQ/d2uqP3W8Tna7dCwzT3NW71Wx1VbFVw8AAFCqwuH55MmTmjp1qozGCt/pDaCa5OTkKCAgQF5eXpKkwMBAx77Ro0fr5ptvVmJiovr166c9e/ZUeIbZbDZr1qxZKigoUElJie655x516dJFmZmZmj59ulq1aqXDhw8rJCRE48aNc1qN32azaeHChQoNDdU999yjxYsXKyUlRRaLRd27d9fQoUMrfyBQKdYlntTqnalKGNTBaXtYoK86NgvV6f8FXUnq1bqBvDyNGvnGFvl4GrXoH721aX+aTp8rdDr2jXUHZLKUfqEz4tYYDewSqZXbSkP2/uPZen7FLqf2dQN8dFeXSA1//RtZSmz6190d1adNhNbvPVkVlwwAAFBGhZNwTEyMUlNTq7AUAJWlffv2Onv2rJ588km9+eabOnDggNN+Ly8vTZ06VTfeeOMf6tfLy0tjx47VzJkzNWnSJL3zzjuO19elp6erX79+mjt3rvz8/LR9+3bHcVarVfPmzVNERITuueceSdJf//pXzZgxQy+99JIOHDign3/++TKvGlVl//FsnS8sLrP977e11pKvkmX/1Ta7XfL18pDRYJC3l4dKrDaZisre9XAhOEuSj6eH5NRL+TyMBvl4lvbt4+mhs/lml8cAAABUlgrPPIeFhWnatGnq2rWrgoODnfZx2ydQs/j6+mrmzJlKTk5WUlKS/v3vf+tvf/ub+vTpI0m64YYbLqlfu92uDz74QMnJyTIYDMrOzta5c+ckSeHh4YqMjJQkNWvWTGfOnHEct3jxYvXo0UN/+ctfHNu2bdumr776SlarVTk5OTp58qSaNGnidL4NGzZow4YNkqQZM2ZcUs2oGt1bhisrz6yjp887bd+SnK4ererpg6dvka+Xh15fd0DnzWWDtyTF3xmrLlHhOp6Vr0Xrf/mCJ6ZRHb024iadPW/W4g3J+vlMvs6eL9JH249q+ZN9VVRs1e6jWdp9NMupv9DQ0Mq/UDfy9PS84q/hSsJ4uxfj7V6Mt3sx3u5VneNd4fBcVFSkTp06yWq16uzZs1VZE4BKYDQa1aZNG7Vp00aNGzfWpk2bHOH5Utct2Lp1q/Ly8jRjxgx5enpq9OjRslgskuS4RfzCuS9sl6SWLVsqKSlJAwYMkLe3tzIzM7VmzRpNnz5d/v7+WrBggYqLywasuLg4xcXFXVKtqDo+nkb9tWeUJry3o8y+VhHBstnsuvflr+Tv66U5D/XQj8eylJFbWKbtnDV7ZTRIo/q1Ve82EVqXeFJH0vN0/7yNMhdb1SUqTJOGdNYjCzfJ39dTPVrW04Pzv1a+uVjPDr5efds11MZ9pxz9ZWVllTnHlSQ0NPSKv4YrCePtXoy3ezHe7sV4u5c7xjsiIqLc7RUOz6NGjaq0YgBUrbS0NBkMBjVo0ECSlJqaqrCwsMvu12QyKSgoSJ6entq/f7/T7PLv6du3r5KTkzV37lwlJCTIZDLJ19dXfn5+ys3N1Z49e9SmTZvLrg/u0SCktuoH++m1ETdJKn32ecHwm/TEkm91c9sI7Uo5I6vNrnMmiw6cyFHLiOByw7Mk2ezSN0lpGtKjmdYlnnS6nXvnkTN6/E8GBdbyUvvIusrILdQ5U+mXMt8ezFDrRnWcwjMAAEBVqnB4Pn369EX31atXr1KKAVA5zGazli5dqoKCAnl4eKh+/foaMWLEH+5n0aJFeuuttyRJdevW1TPPPKOZM2dq/PjxioyMVMOGDSvc14ABA2QymTR//nw98cQTioyMVHx8vMLDw9WqVas/XBuqT2rmeQ2bu8Hx+e0xN2vMm1uVV1isM3mF6hBZV1/tOyUfLw9FNwzWJ98fkyQlDGqv1TtTdSjtnCLq+Cktp3Shse4t6+nE2QJJUp3aPsopKJIktYoIktFgUF5hsTLPmRXTKFg+nkYVldjUITJUh9PPufnKAQDAtcxgv7Dajwu/91zzihUrKq0gALiY26d+Vt0lXJPG/7mDYpvUVZCft3IKirT8m5/05Z4Tjv2/Ds++Xh6KH9heTcL8JZWu1P3Rd0clSQuH99SkFbuUlWfWnId6yM/HUwYZdPR0nuav3S+TpUQDOzfRgM5NZLXZVVRs1aL1yTpwMkeSdH/vFurdOkJWm11HMs7p5f/uc3pV1bJHO7pxVCoft/25F+PtXoy3ezHe7sV4u1d13rZd4fD8W7m5ufrwww8VExOjnj17XlZxAFARhOcrl5+3p56+M1bTPt5dZecgPOOPYLzdi/F2L8bbvRhv96rO8HzJL20ODg7WQw89pPfff/+SiwIAXBtMlpIqDc4AAABV7ZLDs1S6KFFRUVFl1QIAAAAAQI1U4QXDnn/+eRkMBsfnoqIinThxQoMHD66SwgAAAAAAqCkqHJ779u3r9NnX11dNmjRxvAoHAAAAAICrVYXDc58+faqwDAAAAAAAaq4Kh+eSkhKtWrVKmzdvVk5OjurUqaNevXrpL3/5izw9K9wNAFyyK3015SsJK4cCAAA4q3Dqfffdd5WSkqLhw4crLCxMZ86c0ccffyyTyaSHHnqoCksEAAAAAKB6VTg8b9++XbNnz1ZAQICk0ndfNW3aVAkJCYRnAAAAAMBVrcKvqrLb7VVZBwAAAAAANVaFZ5579OihmTNnavDgwY5n4T7++GN17969KusDAAAAAKDaVTg833ffffr444+1ZMkS5eTkKCQkRDfeeKPuvvvuqqwPAAAAAIBq5zI8Hzx4ULt27dJ9992nYcOGadiwYY597777ro4ePaqWLVtWaZEAAAAAAFQnl888f/LJJ2rdunW5+9q2batVq1ZVelEAAAAAANQkLsNzamqqOnToUO6+du3a6dixY5VeFAAAAAAANYnL8FxYWKiSkpJy91mtVhUWFlZ6UQAAAAAA1CQuw3PDhg2VmJhY7r7ExEQ1bNiw0osCAAAAAKAmcRme+/fvr0WLFun777+XzWaTJNlsNn3//fdavHix+vfvX+VFAgAAAABQnVyutt2zZ0/l5uZqwYIFKi4uVmBgoPLy8uTt7a0hQ4aoZ8+e7qgTAAAAAIBqU6H3PA8YMEB9+/bV4cOHlZ+fL39/f7Vs2VJ+fn5VXR8AAAAAANWuQuFZkvz8/C666jYAAAAAAFczl888AwAAAABwrSM8AwAAAADgAuEZAAAAAAAXCM8AAAAAALhAeAYAAAAAwAXCMwAAAAAALlT4VVUAUN0eXvJjdZeAGmLZox2ruwQAAHCNYeYZAAAAAAAXCM8AAAAAALhAeAYAAAAAwAXCMwAAAAAALhCeAQAAAABwgfAMAAAAAIALhGcAAAAAAFzgPc/AHzRs2DA1btxYNptNYWFhGjNmjGrXrl2p51i5cqV8fX01cOBAp+2ZmZmaOXOm5syZo5SUFH3zzTd65JFHLutcycnJWrx4sTw8PDRt2jR5e3s79t1///1avny5JGn37t1666239Pzzzys0NPSyzoma6Z93xqpbi3DlFlj09zc2O+0b3L2Zht8aoyEvrVNeYbGubxqqR26JlqeHQSVWuxZvSFZi6tkyfcYPjFVs47oqKCqWJL20eq+Ons6Tn4+nnrmrg8KDasnDaNBH3x3VusSTLusAAACoLoRn4A/y9vbW7NmzJUmvvvqqvvzyS/3lL39xex3NmzdX8+bNL7ufLVu26M4779TNN9980Tb79u3TsmXL9K9//avCwdlqtcrDw+Oy64P7rEs8qdU7U5UwqIPT9rBAX3VsFqrTuSbHtnOFFj3//3YqO79ITcL89eK93fS3V74qt9/FXyVra3KG07aBnZvoeFa+Jq3YpSA/by0Z1Vsb951Sic1+0ToAAACqE+EZuAwtW7bU8ePHHZ9Xr16t7777TsXFxeratauGDh2qzMxMvfjii4qKilJqaqoaNGigxx9/XD4+Pho9erSmT5+uwMBApaSkaPny5Zo8ebIk6eeff9aUKVN09uxZDRw4UHFxcU7nTkpK0po1azR+/HiZzWYtXbpUKSkpMhgMGjx4sLp37+7Uft++fVq+fLmsVquaN2+u4cOHa/Pmzfruu++UmJioffv26YknnihzjcnJyXrjjTc0YcIE1a9fX5J05swZvfbaa8rLy1NgYKBGjRql0NBQLViwQP7+/kpNTVXTpk01dOhQLV26VCdOnJDVatWQIUPUpUsXZWZm6tVXX1VRUZEk6ZFHHlGrVq0q838NLsH+49mqF1SrzPa/39ZaS75K1qShnR3bUjLyHD//fCZf3p5GeXkYVWy1Vehcdkm1vEv/CvL19tD5wmJZbfbfrQMAAKA6EZ6BS2Sz2bR//3717dtXkpSYmKj09HS9+OKLstvtmjVrlg4cOKDQ0FClpaVp5MiRio6O1sKFC/Xll1+WuSX7t44fP65p06bJbDbrmWee0fXXX3/Rth999JH8/Pw0Z84cSVJ+fr7TfovFooULF+q5555TRESEXn31Va1bt079+/fXwYMH1alTpzJhW5JKSko0a9YsTZ48WQ0bNnRsX7JkiXr16qU+ffpo48aNWrp0qcaNGydJSk9P13PPPSej0aj3339fbdu21ahRo1RQUKCJEyeqXbt2CgoK0rPPPitvb2+lp6frlVde0YwZM8qcf8OGDdqwYYMklbsfVa97y3Bl5Zl19PT5i7bpGVNfKRl5Fw3OD93cSn+7qYX2HMvS0o2HVGy1afXOVE0Z1kXvP3WL/Hw89eLHP8r+B+q6Gh8d8PT0vCqvq6ZivN2L8XYvxtu9GG/3qs7xJjwDf5DFYlFCQoLOnDmjZs2aKTY2VlJpeN67d68jRJrNZmVkZCg0NFR169ZVdHS0JKlXr15au3aty/DcuXNneXt7y9vbW23atNGRI0cUGRlZbtt9+/bpqaeecnz29/d32p+Wlqbw8HBFRERIknr37q0vv/xS/fv3/90aPDw81KpVK23cuFEPP/ywY/tPP/2ksWPHOq7nvffec+zr3r27jMbStQj37t2rH374QWvWrHGMXVZWlkJCQrRkyRKlpqbKaDQqPT293PPHxcWVmXGH+/h4GvXXnlGa8N6Oi7ZpEuavR/tGa+L75bdZtvGQsvOL5OVh1JP922noDc303pYj6tQ8TCkZ5zRu+XZF1PHT9L910/5F2TJZSipUW1ZW1iVdU00WGhp6VV5XTcV4uxfj7V6Mt3sx3u7ljvG+8G/m3yI8A3/QhWeeTSaTZsyYoS+++EJ33HGHJOmuu+7Srbfe6tQ+MzNTBoPBaduFz0ajUXZ76XxbcXFxuW0u9vm3XO2/FAaDQU8//bSmTp2qVatWVejZbl9fX8fPdrtd8fHxZf4AWrlypYKCgjR79mzZ7Xb97W9/q/TacfkahNRW/WA/vTbiJkmlzz4vGH6TnljyrXIKihQa4Kvnh3TS7E8TlZ5jKreP7PzSW/OLrTatSzyhwd2bSZJua3+dVn57RJKUlmNSRq5J14XW1qG0c264MgAAgD+OV1UBl8jPz08PP/yw1qxZo5KSErVv315ff/21zGazJCk7O1vnzpUGgaysLB0+fFiStHXrVscsdHh4uI4ePSpJ2r59u1P/O3fulMVi0fnz55WUlPS7i4PFxsbqiy++cHz+7W3bERERyszMVEZG6aJNmzdvVuvWrSt0nT4+Pho/fry2bt2qjRs3Sip91nvbtm1lrue32rdvr88//9zxBcGxY8ckSSaTSXXq1JHRaNTmzZtls1XsOVm4V2rmeQ2bu0EPzv9aD87/WmfyzBq9eItyCopU28dTU//aRcs2HtKBkzlOxyUMaq9WEUGSpBB/H8f2G1rVV+qZ0tu/z5wrVIempbdcBdf2VqO6/hcN4AAAADUBM8/AZWjatKmaNGmibdu2qVevXjp16pT+9a9/SSqdgR0zZoyMRqMaNmyoTZs2adGiRapfv75uu+02SdLgwYP1+uuv65NPPlFUVJRT31FRUZoxY4aysrJ09913KyQkRJmZmeXWcffdd+vNN99UfHy8jEajBg8erG7dujn2e3t7a9SoUZo7d65jwbDfzpD/Hn9/f02cOFGTJk1SQECAHn74Yb322mtavXq1Y8Gw8gwePFhvvfWW4xbvsLAwjR8/XrfffrvmzJmj7du3q02bNvLx8Sn3eLjX+D93UGyTugry89a7T/bV8m9+0pd7TpTbdmCXSEXU8dO9N0Xp3ptKf+1OeG+Hzpksahoe4JhxfuauDgqq7S2DDEo5nad5n+2TJL235SeNHdher//9Jhlk0JKNB5VXWPyH6wAAAHAXg/3ClBCAKvHrdzPj8tw+9bPqLgEu+Hl76uk7YzXt491Vep5lj3as0v6rA8/MuRfj7V6Mt3sx3u7FeLtXdT7zzG3bAIBKY7KUVHlwBgAAqA6EZ6CKhYeHM+sMAAAAXOEIzwAAAAAAuEB4BgAAAP4/e/ceF1WB93H8OzAgIjoIiASGhKighZdETbG0KLPsZq1lW5vW1lNe2kopu7tbJmpqWVo+pm65u26XtX2y+2JikVqWouH9DuaFyygj14GZ8/xBzi6LeqxkRuHz/os5c86Z3/nai90v5zIAYILyDAAAAACACb6qCsA5ozE+YflsxZNDAQAA6uLMMwAAAAAAJijPAAAAAACYoDwDAAAAAGCC8gwAAAAAgAnKMwAAAAAAJijPAAAAAACYoDwDAAAAAGCC8gwAAAAAgAnKMwAAAAAAJijPAAAAAACYoDwDAAAAAGCC8gwAAAAAgAnKMwAAAAAAJijPAAAAAACYoDwDAAAAAGCC8gwAAAAAgAnKMwAAAAAAJijPAAAAAACYoDwDAAAAAGCC8gwAAAAAgAnKMwAAAAAAJqy+HgAATteoBet9PQJ8aNE9PXw9AgAAaMI48wwAAAAAgAnKMwAAAAAAJijPAAAAAACYoDwDAAAAAGCC8gwAAAAAgAnKMwAAAAAAJijPACRJt956q9LT0zV+/HjNnDlTVVVVp1z/zjvvPCOfW1BQoPHjx5+RfQEAAAANhe95BiBJCgwM1PTp0yVJs2fP1r/+9S8NHTrUx1OhoT1yXbL6dIzU0TKn/mfel5Kk3w3spEs6tZVhGDpa5tSLH2yQvbRKVj+L/nDtReoYbZNhSK99tkkb99nr7fOxG7urY7RNLpehbQeO6uWPfpDLbXje73SeTS/d3V8vLF2n7C2HTjoHAADA2YTyDKCexMRE5eXlSZI+/PBDrVixQpJ0+eWX69prr62zbmVlpaZNm6aysjLV1NTotttuU0pKigoKCjRlyhR17txZ27dvV1hYmB599FEFBgZq9+7deu211xQYGKjExESvHx/+7fMN+/XB2r1Kv6G7Z9l7q3brraztkqQbUuJ0x6UdNfvjXA3pGStJun/eV7IFB2ry7b017o1sGf+1zy9yf9TUf+ZIkibe1F1DepyvD7+v/e/JzyLdc0Wivt9VaDoHAADA2YTLtgHU4XK5lJOTo9jYWO3evVsrVqzQ5MmTNXnyZC1fvlx79uyps35AQIAmTJigqVOn6tlnn9Vbb70lw6itUwcPHtTVV5yCNYgAACAASURBVF+tmTNnKjg4WGvWrJEkzZ07V6NGjdLkyZO9fnyoKzfPrmMV1XWWlTtrPD8HBfrrp39OxUaEaP3eYklSSblTpZXV6hRtq7fPtTv/XYy3HShRRKvmntc3pMQpe+shHS2ve1vAieYAAAA4m3DmGYAkyel0Kj09XZKUlJSkyy+/XJ9//rl69+6toKAgSVLv3r21ZcsWXXDBBZ7tDMPQkiVLtGXLFlksFtntdpWUlEiSIiMjFRcXJ0mKj49XYWGhysvLVVZWpi5dukiSLr30UuXk5JxwpszMTGVmZkqSMjIyGuS4cWIjB3VW2kUxKquq0aOLa//osfuwQ5d0aqus3ANqYwtSx/NsatOqubYdKDnhPvz9LLriohi9/tkmSVJ4y2bqlxilxxavUafo5J89U0RExC8/oHOA1Wpt9Md4NiFv7yJv7yJv7yJv7/Jl3pRnAJLq3vN83PEzyKeSnZ0th8OhjIwMWa1WjRkzRk6nU1LtWenj/Pz85HQ6ZRiGLBbLac2UlpamtLS0n3EUOFP+vGKb/rxim27t30HXp7TX4pU79FnOfsVGhOjV3/dXQUmFNucfqXMv838bN+RC5ebZlZt/RJJ0/1VdtWD5Vp1ik1MqKir6ZRueIyIiIhr9MZ5NyNu7yNu7yNu7yNu7vJF3dHT0CZdz2TaAk0pKStLatWtVVVWlyspKrV27VklJSXXWKS8vl81mk9VqVW5urgoLC0+yt1otWrRQcHCwtm7dKkn66quvGmx+/Horcg8oNfE8SZLbMDTvX1s0en62Jr3zvUKCAvSjveyE2/320o6ytQjUvM83e5Z1Os+mx4f10JvjBmlA0nkaN+RCXdK5rVeOAwAA4NfizDOAk4qPj9fAgQP1xBNPSKp9YNh/XrItSampqZo6daomTpyouLg4xcTEmO539OjRngeGdevWrUFmxy8XHRasA/ZySVLfTm2VX1wqSWpm9ZMsFlVVu9Tzggi53G7lFdW+l35DN32wdq+2HSjR1d3PV6/4NnrsL2vqPEzsrldXeH4ef32yvtlRoNXbDnvtuAAAAH4Ni3E612UCwFlg8HMf+XqERmfiTd2V3D5ctuBAHSmr0uKVO9Q7oY3ahYfIbRgqKKnQ7I9/UPGxKrW1Ndfk3/aWYUjFjkrN/HCjCkoqJElz703Vs29/p0JHpT5+cogOH61QxU8PHvt66yH99auddT73eHk+/lVVJ5rjs5z8OtssuqeHFxLxHS778y7y9i7y9i7y9i7y9i5fXrbNmWcAaMIy3q//sLb/Lq3HHS6p0O/nrqy3PDjQqh/t5Sp0VEqSrpn8iennzvhgo+kcAAAAZxPueQYA/CrlzhpN/sc6X48BAADQoCjPAAAAAACYoDwDAAAAAGCC8gwAAAAAgAkeGAbgnNHYn7Z8NuHJoQAAAHVx5hkAAAAAABOUZwAAAAAATFCeAQAAAAAwQXkGAAAAAMAE5RkAAAAAABOUZwAAAAAATFCeAQAAAAAwQXkGAAAAAMAE5RkAAAAAABOUZwAAAAAATFCeAQAAAAAwQXkGAAAAAMAE5RkAAAAAABOUZwAAAAAATFCeAQAAAAAwQXkGAAAAAMAE5RkAAAAAABOUZwAAAAAATFCeAQAAAAAwQXkGAAAAAMAE5RkAAAAAABNWXw8AAKdr1IL1vh4Bp2HRPT18PQIAAMAZx5lnAAAAAABMUJ4BAAAAADBBeQYAAAAAwATlGQAAAAAAE5RnAAAAAABMUJ4BAAAAADBBeQYAAAAAwATf8ww0AXfeeacWL158Wuu+8847CgoK0vXXX9/AU+HneOS6ZPXpGKmjZU79z7wvJUkDkqJ052WddH5EiB5c8LV2HCyRJLW1Ndf8By7T/uJSSdLWH49q9se59fb58NBkdYq2SZJ+tJfpxf/boMpql5Lbh2nS8F46dLRckvT11kP661c7JUk39o7TkB6xslikT9bl6f1v9zb0oQMAAJwVKM8AcA74fMN+fbB2r9Jv6O5ZtrewVH9693s9eM1F9dY/eKRco+dnn3Kf8z7frHJnjSTpviuTdH1KnN5ZtUuSlJtn1zNvf1dn/fZtQjSkR6weXJCtapehF27vrW92FuiAvfzXHh4AAMBZj/IMNFEOh0P/+7//q+LiYknSXXfdpcTEREnSvn379Mc//lHFxcW6/vrrlZaWpsrKSk2bNk1lZWWqqanRbbfdppSUFBUUFGjKlCnq3Lmztm/frrCwMD366KMKDAzU3r17NX/+fFVVValt27Z64IEHFBISokmTJikhIUGbNm1SeXm57r//fiUlJfkyjrNebp5dbW3N6yzLLyr9Vfs8XpwlqZnVX5JxyvVjI0K05ccjqqpxS5I25hWrf+covbt696+aAwAA4FxAeQaaqEWLFmno0KFKTExUUVGRJk+erFmzZkmS8vLyNHnyZFVWVuqxxx5Tz549ZbPZNGHCBAUHB8vhcOjJJ59Ur169JEkHDx7UH/7wB91///2aOXOm1qxZo0svvVSvvvqq7r77bnXp0kVvv/223nvvPY0cOVKS5Ha7NWXKFK1bt07vvfeenn766XozZmZmKjMzU5KUkZHhnWAaiajQ5ppzb6rKq2r05optys0/csL1xl+XrJSESOUVlep//7XZszypXWu9dt8AFR+r1PzMLdpXWKq9haUaOaizWjYPkLPapZSESO04UFJvnxEREQ12XI2Z1WolOy8ib+8ib+8ib+8ib+/yZd6UZ6CJ+uGHH7R//37P6/LyclVUVEiSevXqpcDAQAUGBqpr167auXOnevbsqSVLlmjLli2yWCyy2+0qKaktTpGRkYqLi5MkxcfHq7CwUOXl5SorK1OXLl0kSZdddpmnnEtS7969PesXFBSccMa0tDSlpaWd8WNv7OylVbpj9hc6VlGthKhWmjS8l+57/cs6Z5qPm7Fso/ws0uirL9RlXaP1+Yb92nnQoTtnf6HKapdSEtro2d/00t1zs5RfVKp3Vu3WlN/2UaWzRnsOO+Ryu+vts6ioyBuH2ehERESQnReRt3eRt3eRt3eRt3d5I+/o6OgTLqc8A02UYRiaPHmyAgMD671nsVjqvc7OzpbD4VBGRoasVqvGjBkjp9MpSQoICPCs6+fn51l+Kse38fPzk/sEBQy/XLXLreqK2kx3HnLowJFyxYS38DxQ7L+5DWnlpgP6zSXx+nzD/jole+3OQo0dYlGr5gFyVFTrs5x8fZaTL0kaNaizCh2VDX9AAAAAZwG+qgpoopKTk/Xpp596Xu/du9fz89q1a+V0OnXs2DFt2rRJHTp0UHl5uWw2m6xWq3Jzc1VYWHjK/QcHByskJERbtmyRJH355Zfc1+wltuBA+f3094+o0OaKCWuhQ0dqH+qVfkM3df7pCdvRrYM92/Tt1Fb5xWWSpNYtmnmWd462yc9ikaOi2rNvSWrTKkj9E6OUtenHBj8eAACAswFnnoEmwOl06v777/e8Hjp0qEaNGqUFCxZowoQJcrlcSkpK0n333SdJSkhIUEZGhoqKinTzzTcrLCxMqampmjp1qiZOnKi4uDjFxMSYfu6YMWM8DwyLjIzU6NGjG+wYG7uJN3VXcvtw2YID9Zc/XK7FK3foWIVTo6/uKltwoJ67LUW7Djv05N++1UWxYfrdwE5yuQ253IZmf/yDjlXWlt8LIlvKXloli6QJN3RTcDOrLLJo92GHXvnp66wGJEVpaK/2crkNVVW7NGXpes8cz/zmYrVsHiCX29Crn+SqtLL+peAAAACNkcUwjFM/XhUAzhKDn/vI1yOc04IDrXr4umRN/se6Bv2cRff0aND9N1bcM+dd5O1d5O1d5O1d5O1dvrznmcu2AaCJKHfWNHhxBgAAaKwozwAAAAAAmKA8AwAAAABggvIMAAAAAIAJyjMAAAAAACb4qioA5wye4uw9PDkUAACgLs48AwAAAABggvIMAAAAAIAJyjMAAAAAACYozwAAAAAAmKA8AwAAAABggvIMAAAAAIAJyjMAAAAAACYozwAAAAAAmKA8AwAAAABggvIMAAAAAIAJyjMAAAAAACYozwAAAAAAmKA8AwAAAABggvIMAAAAAIAJyjMAAAAAACYozwAAAAAAmKA8AwAAAABggvIMAAAAAIAJyjMAAAAAACYozwAAAAAAmKA8AwAAAABgwurrAQDgdI1asN7XI+A/LLqnh69HAAAA8BrOPAMAAAAAYILyDAAAAACACcozAAAAAAAmKM8AAAAAAJigPAMAAAAAYILyDAAAAACACb6qCoAk6dZbb1VsbKzndXp6uiIjI304EQAAAHD2oDwDkCQFBgZq+vTpJ33f5XLJ39/fixM1XY9cl6w+HSN1tMyp/5n3pSSpZVCAnri5h9ragnW4pFyT/7FOpZU1svpZ9IdrL1LHaJsMQ3rts03auM9eb59tQ5vriWE91DIoUDsPlWjaP3NU4zYUEmTVI9d103mtg1Vd49aMZRu0r7D0pHMAAAA0VZRnACeVlZWldevWyel0qqqqSo899pimTZumsrIy1dTU6LbbblNKSooKCgo0ZcoUde7cWdu3b1dYWJgeffRRBQYG6tChQ5o/f74cDof8/Pz08MMPKyoqSh988IFWr16t6upq9e7dW8OHD/f14Z41Pt+wXx+s3av0G7p7lg3v30Hr9xTrnVXfani/Drq1f4IWLN+qIT1rrxa4f95XsgUHavLtvTXujWwZ/7XP31+RqKXf7NHKTQf14DUX6uoe5+vD7/N0W/8E7Trs0J/e/V7nh7fQmCEXauJfvjnpHAAAAE0V9zwDkCQ5nU6lp6crPT29zhno7du3a+zYsXr22WcVEBCgCRMmaOrUqXr22Wf11ltvyTBqa9rBgwd19dVXa+bMmQoODtaaNWskSbNnz9bgwYM1ffp0Pffcc2rdurU2bNiggwcP6oUXXtC0adO0e/dubd682SfHfTbKzbPrWEV1nWWXdG6rzI37JUmZG/frks5tJUmxESFav7dYklRS7lRpZbU6Rdvq7bNbXIS+2nxIkvSvDft1Seeo2u3btFTOniJJUn5xmdramiu0ReBJ5wAAAGiqOPMMQNLJL9tOTk5WSEiIJMkwDC1ZskRbtmyRxWKR3W5XSUmJJCkyMlJxcXGSpPj4eBUWFqqiokJ2u129e/f2fIYkbdiwQRs3btSjjz4qSaqsrNShQ4fUpUuXOp+dmZmpzMxMSVJGRsaZP+hzSOsWzWQvrZIk2UurFBrcTJK0+7BDl3Rqq6zcA2pjC1LH82xq06q5th0o8WzbqnmAyiqr5f7pDx1FxyoV0TJIkrTnsEP9E6O0Kf+IOkfb1Da0uSJaBulomdN0poiIiDN9mE2a1WolUy8ib+8ib+8ib+8ib+/yZd6UZwCn1KxZM8/P2dnZcjgcysjIkNVq1ZgxY+R01pasgIAAz3p+fn5yOp2es9IncuONN+rKK6885WenpaUpLS3tVx5B4/ZZzn7FRoTo1d/3V0FJhTbnH5HLXTd3i8VSbzvjpwu73/56lx4Y3EVz703VnoJj2nnI4SnZZoqKin79AcAjIiKCTL2IvL2LvL2LvL2LvL3LG3lHR0efcDnlGcBpKy8vl81mk9VqVW5urgoLC0+5fnBwsMLDw/Xtt9+qd+/eqq6ultvtVrdu3fT2229rwIABCgoKkt1ul7+/v2y2+pcbo9aRsiqFhdSefQ4Laaaj5bVnod2GoXn/2uJZb9bIfvrRXlZn25Jyp1oEBcjPYpHbMBTRMkjFx2q3L3fWaMayjZ513xw3SIeOVHjhiAAAAM4t3PMM4LSlpqZq165dmjhxorKzsxUTE2O6zdixY/XJJ59owoQJeuqpp3T06FF169ZNqampevLJJzV+/HjNmDFDFRUUtlNZs+2w0pLbSZLSkttp9bbDkqRmVj81C6h9CnrPCyLkcruVV1T7tOz0G7qp80/3P2/YW6wBXWrvc76y27+3b9HMKqtf7ZnpIT3OV26eXeXOGu8dGAAAwDnCYpzqukoAOIsMfu4jX4/gFRNv6q7k9uGyBQfqSFmVFq/coVXbDunJm3sqslVzFTgqNPm9dTpWWa22tuaa/NveMgyp2FGpmR9uVEFJ7R8i5t6bqmff/k6FjkpFhTbXE8N6qmXzAO085NC0f+ao2uVWUkyo0m/oLrdhaF9RqWYt26DSypqTzvFZTr5nzkX39PBJPo0Vl/15F3l7F3l7F3l7F3l7ly8v26Y8AzhnNJXyfCYEB1r18HXJmvyPdQ32GZTnM4v/8+Vd5O1d5O1d5O1d5O1dvizPXLYNAI1QubOmQYszAABAU0N5BgAAAADABOUZAAAAAAATlGcAAAAAAEzwPc8Azhk8oMp7ePgJAABAXZx5BgAAAADABOUZAAAAAAATlGcAAAAAAExQngEAAAAAMEF5BgAAAADABOUZAAAAAAATlGcAAAAAAExQngEAAAAAMEF5BgAAAADABOUZAAAAAAATlGcAAAAAAExQngEAAAAAMEF5BgAAAADABOUZAAAAAAATlGcAAAAAAExQngEAAAAAMEF5BgAAAADABOUZAAAAAAATlGcAAAAAAExQngEAAAAAMEF5BgAAAADAhNXXAwDA6Rq1YL2vR2jyFt3Tw9cjAAAA+ARnngEAAAAAMEF5BgAAAADABOUZAAAAAAATlGcAAAAAAExQngEAAAAAMEF5BgAAAADABOUZAAAAAAATfM8z0ATdeeedWrx4cYPse9euXVq5cqXuvvvuBtl/Y/XIdcnq0zFSR8uc+p95X0qSWgYF6Imbe6itLViHS8o1+R/rVFpZI0m6ILKlHrz2IrVoZpXbMDTuja9V7XKfcN+jB3fVVd3b6capn0mSLunUVr8b2EmGYcjlNvT655u1Kf/ISecAAAAA5RnAGdahQwd16NDB12Occz7fsF8frN2r9Bu6e5YN799B6/cU651V32p4vw66tX+CFizfKj+LRY/e2F3T/y9Huw8fU8vmAXK5T1ycO55nU4ugur/q1+8p0urthyXVlvAnb+6p37+28qRzAAAAgPIMNFmVlZWaNm2aysrKVFNTo9tuu00pKSkqKCjQCy+8oMTERO3YsUPt27fXwIED9e6776qkpEQPPvigEhIStHPnTv35z3+W0+lUYGCgRo8erejoaG3atEnLli3TxIkTVVlZqYULF2rXrl2yWCy65ZZb1LdvX82fP1+7du2S0+lU3759NXz4cF/H4XO5eXa1tTWvs+ySzm316FtrJEmZG/dr2u/6asHyrbq4Q4T2FBzT7sPHJEnHKqpPuE8/i3RvWpIy3l+v/olRnuWV1S7Pz0EB/jJM5gAAAADlGWiyAgICNGHCBAUHB8vhcOjJJ59Ur169JEmHDh3SI488onbt2unxxx9Xdna2/vSnP+m7777T0qVL9eijjyo6Olp//OMf5e/vr40bN+pvf/ubJkyYUOcz3nvvPQUHB2vGjBmSpNLSUknSiBEjFBISIrfbrT/96U/at2+f2rdvX2/GzMxMZWZmSpIyMjIaMo6zUusWzWQvrZIk2UurFBrcTJLULqyFDMPQ5Nt7yxYcqJWbDujd1bvrbX99SpxWbz/s2cd/6te5re6+PFGhLQL19JK1pz1TRETELzwamLFareTrReTtXeTtXeTtXeTtXb7Mm/IMNFGGYWjJkiXasmWLLBaL7Ha7SkpKJEmRkZGKjY2VJJ1//vm66KKLZLFYFBsbq8LCQklSeXm55syZo0OHDkmSXC5Xvc/44Ycf9NBDD3leh4SESJJWrVql5cuXy+Vy6ciRI9q/f/8Jy3NaWprS0tLO7IE3Av5+frrw/DCNW5CtqmqXMu7sqx0HS5Szt9izTlhIMw1IOk/pP525/m+rth3Wqm2HdWFsmO4a2FkT//rNaX12UVHRGTkG1BcREUG+XkTe3kXe3kXe3kXe3uWNvKOjo0+4nKdtA01Udna2HA6HMjIyNH36dIWGhsrpdEqqPSt9nMVi8by2WCxy/3Rv7dtvv62uXbtqxowZeuyxx1RdfeJLhy0WS53XBQUFWrZsmZ5++mm9+OKL6tmz50m3beqOlFUpLKT2bHNYSDMdLa89g1x4rEIb84rlqKhWVY1ba3cWKOE8W51tE6Jsig4L1qKxA/XmuEFqFuCvRWMG1vuM3Dy7zmsdrFbNA+q9BwAAgH+jPANNVHl5uWw2m6xWq3Jzcz1nlH/O9mFhYZKkrKysE66TnJysTz/91PO6tLRU5eXlCgoKUnBwsI4ePaqcnJxffAyN3Zpth5WW3E6SlJbcTqu31T7k6/tdhbogspWaWf3kZ7EoOTZceYW1l8Sn39BNnaNt+nZngUbMWq67Xlmhu15Zoapql0bNyZIkRbcO9nxGQlQrWf395DjJfdMAAACoxWXbQBPjcrkUEBCg1NRUTZ06VRMnTlRcXJxiYmJ+1n5uuOEGzZkzRx999JG6du16wnVuvvlmvfHGGxo/frz8/Px0yy23qE+fPoqLi9P48eMVGRmpzp07n4nDOudNvKm7ktuHyxYcqL/84XItXrlDb6/apSdv7qmru5+vAkeFJr+3TpJUWlmjpd/s0Su/T5VhSN/uLNC3Owsk1T49+0T3OP+n1KQopSW3U43Lraoat15Yuu6Uc3yWk99wBw4AAHCOsBiGYZivBqCx2Lt3r+bNm6cpU6b4epSfbfBzH/l6hLNacKBVD1+XrMn/WGe+8i+06J4eDbbvpo575ryLvL2LvL2LvL2LvL3Ll/c8c+YZaEI+//xzffLJJxo5cqSvR0EDKHfWNGhxBgAAaMooz0ATctVVV+mqq67y9RgAAADAOYcHhgEAAAAAYILyDAAAAACACcozAAAAAAAmuOcZwDmDJz17D08OBQAAqIszzwAAAAAAmKA8AwAAAABggvIMAAAAAIAJyjMAAAAAACYozwAAAAAAmKA8AwAAAABggvIMAAAAAIAJyjMAAAAAACYozwAAAAAAmKA8AwAAAABggvIMAAAAAIAJyjMAAAAAACYozwAAAAAAmKA8AwAAAABggvIMAAAAAIAJyjMAAAAAACYozwAAAAAAmKA8AwAAAABggvIMAAAAAIAJyjMAAAAAACYozwAAAAAAmLD6egAAOF2jFqz39QiN3qJ7evh6BAAAgLMSZ54BAAAAADBBeQYAAAAAwATlGQAAAAAAE5RnAAAAAABMUJ4BAAAAADBBeQYAAAAAwATlGWiEJk2apJycnDrLPvroI73xxhuy2+2aMWPGKbcvKChQdnb2Sd8bPny4PvnkE8+yBQsWKCsr61fPDQAAAJyt+J5noBHq37+/Vq1ape7du3uWrVq1SnfccYfCwsI0fvz4U25fWFio7OxspaamnvB9m82mjz/+WFdeeaWsVn6NnI4be8dpSI9YWSzSJ+vy9P63e/XEsB5qF95CktQiKEBlldUaPb/+Hy0mj0hRYrvW2pRn1zNvf1fv/dGDu+qq7u1049TPPMuS24fp/qu6yOrvp5Jyp9LfWtNwBwcAANAE8P96gUaob9+++vvf/67q6moFBASooKBAdrtdiYmJKigo0NSpUzVjxgy53W799a9/1ebNm1VdXa3Bgwfryiuv1N/+9jft379f6enpuuyyyzR06NA6+2/VqpU6d+6srKwspaWl1XkvMzNTy5cvV01Njdq2batx48apWbNmmjNnjgIDA3XgwAEVFhZq9OjRysrK0o4dO5SQkKAxY8Z4MyKvat8mREN6xOrBBdmqdhl64fbe+mZngV5Yut6zzn1pSSqrqj7h9u+u3q1mAf66tmdsvfc6nmdTi6C6v8pbNLNq7JAL9eTfvlWho1K24MAze0AAAABNEJdtA41Qy5YtlZCQ4Ll0e9WqVerXr58sFkud9b744gsFBwdrypQpmjJlipYvX66CggLdfvvtSkpK0vTp0+sV5+NuvPFGffjhh3K73XWW9+nTR1OmTNH06dPVrl07ffHFF573ysrK9Mwzz+iuu+7S1KlTNXToUM2YMUN5eXnau3fvmQ3hLBIbEaItPx5RVY1bbsPQxrxi9e8cVWedS7ucpxWbDpxw+5y9xapw1tRb7meR7k1L0oLlW+ssH3RhjL7eekiFjkpJUkm58wwdCQAAQNPFmWegkerfv7++/vprpaSk6Ouvv9YDDzxQb50NGzYoLy9Pa9bUXtJbXl6ugwcPntal2JGRkUpISKh3b3R+fr7+/ve/q6ysTJWVlerWrZvnvYsvvlgWi0WxsbGy2WyKja09k3r++eeroKBAcXFxdfaVmZmpzMxMSVJGRsbPOv6zyd7CUo0c1FktmwfIWe1SSkKkdhwo8bx/YWyYjpRV6YC9/Gft9/qUOK3eflj20qo6y9uFt5C/n0XT7uyr4GZW/fPbPcrc+ONp7TMiIkKSZLVaPT/DO8jcu8jbu8jbu8jbu8jbu3yZN+UZaKR69+6tt956S7t375bT6VR8fHy9dQzD0KhRo+rcGy1JmzZtOq3PuOmmmzRz5kwlJSV5ls2ZM0fp6emKi4tTVlZWnX0FBARIkiwWi+fn46//+wy2JKWlpdW7LPxclF9UqndW7daU3/ZRpbNGew475PqP4x3UNVpZJznrfDJhIc00IOm8E97L7O9nUcfzbHrsL9+omdVPL43qry37j+pHe5npfouKiiTVlujjP8M7yNy7yNu7yNu7yNu7yNu7vJF3dHT0CZdz2TbQSAUFBalLly567bXX1L9//xOu0717d33++eeqqam9JPjAgQOqrKxU8+bNVVFRYfoZMTExiomJ0ffff+9ZVllZqdatW6umpkZfffXVmTmYRuCznHyNfSNbE95ao2MV1frxp7PMfhaL+idGaeWmgz9rfwlRNkWHBWvR2IF6c9wgNQvw16IxAyVJhY5KfberUFXVLjkqqvVDnl3xbVue6UMCAABoUijPQCOWmpqqffv2nbQ8X3755WrXrp0ee+wxjR8/XvPnz5fb7VZsbKz8/f2Vnp6uDz/88JSfMWzYMNntds/rW2+96hH7qgAAGd5JREFUVU888YSef/55xcTEnNHjOZcdf2hXm1ZB6p8YpaxNtZdR94yPUH5xqYqOVXrWDW/ZTBl39Dnl/r7dWaARs5brrldW6K5XVqiq2qVRc7IkSau3H9aFsWHys1jUzOqnxJhQ5RWVNsyBAQAANBEWwzAMXw8BAKdj8HMf+XqEX2zGXZeoZfMAudyG5n2+WTl7iyVJ469P1tb9R/XRujzPuh3Ps2nkoM568m/ferZtF95CzQOtclQ4NWvZRn2/u+7lSv98bHCdr6q65ZJ4XdWtnQxD+nR97VdjnY5F9/SQxCVovkDm3kXe3kXe3kXe3kXe3uXLy7a55xkAvGD8m6tPuHzGBxvrLUuKCdWy7/aabvuf/rM4S9J7q3frvdW7f96QAAAAOCnKMwCcZT74bp+vRwAAAMB/4Z5nAAAAAABMUJ4BAAAAADBBeQYAAAAAwAT3PAM4Zxx/EjQAAADgbZx5BgAAAADABOUZAAAAAAATlGcAAAAAAExQngEAAAAAMEF5BgAAAADABOUZAAAAAAATlGcAAAAAAExQngEAAAAAMEF5BgAAAADABOUZAAAAAAATlGcAAAAAAExQngEAAAAAMEF5BgAAAADABOUZAAAAAAATlGcAAAAAAExQngEAAAAAMEF5BgAAAADABOUZAAAAAAATlGcAAAAAAExQngEAAAAAMEF5BgAAAADAhNXXAwDA6Rq1YL2vR2h0Ft3Tw9cjAAAAnBM48wwAAAAAgAnKMwAAAAAAJijPAAAAAACYoDwDAAAAAGCC8gwAAAAAgAnKMwAAAAAAJijPAAAAAACY4HuegUbu1ltvVWxsrNxut9q0aaNx48apRYsWPpnlqaee0vPPP++Tz/aFG3vHaUiPWFks0ifr8vT+t3v1u4GddEmntjIMQ0fLnHrxgw2yl1bV2/bjJ6/R3gKHJKnAUalJb38nSeoeF67fpyXJzyJVOF2a8cEGHThSruT2YZo0vJcOHS2XJH299ZD++tVO7x0sAABAI0d5Bhq5wMBATZ8+XZL06quv6rPPPtOwYcN8MktTKs7t24RoSI9YPbggW9UuQy/c3lvf7CzQe6t2662s7ZKkG1LidMelHTX749x62ztrXBo9P7ve8nHXXKhJ73yv/KJSDb24vUYMSNCMDzZKknLz7Hrmp5INAACAM4vyDDQhnTp1Ul5eniTJMAz95S9/UU5OjiTp5ptvVr9+/bRp0ya98847stls2rdvn3r37q3Y2Fh9/PHHcjqdSk9PV1RUlL777jstXbpUNTU1atmypcaNG6fQ0FC98847KioqUkFBgYqKinTNNdfommuukSTdeeedWrx4sSorKzVt2jSVlZWppqZGt912m1JSUnyWS0OIjQjRlh+PqKrGLUnamFes/p2j9O7q3Z51ggL9ZRg/b7+GpODA2l/dLYKssh+rf9YaAAAAZx7lGWgi3G63cnNzdfnll0uSvvnmG+3du1fTp0+Xw+HQ448/rqSkJEnSvn37NGvWLIWEhGjs2LG64oorNGXKFH388cf69NNPNXLkSCUmJmry5MmyWCxavny5PvjgA/3ud7+TJB04cEDPPvusKioq9NBDD+mqq66S1frvXzcBAQGaMGGCgoOD5XA49OSTT6pXr16yWCx1Zs7MzFRmZqYkKSMjwxsxnTF7C0s1clBntWweIGe1SykJkdpxoESSNHJQZ6VdFKOyqho9unjNCbcPtPrplXv6y+U29PaqXVq97bAk6aVlG/X8iBRV1bhUXlWjhxau8myT1K61XrtvgIqPVWp+5hbtKyw1nTMiIuKEy61W60nfQ8Mgc+8ib+8ib+8ib+8ib+/yZd6UZ6CRO362uLCwUPHx8UpOTpYkbd26Vf3795efn59CQ0PVpUsX7dq1S82bN1eHDh3UunVrSVJUVJRnm9jYWOXm1l5ibLfb9dJLL+nIkSOqqalRZGSk5zN79uypgIAABQQEyGazqaSkROHh4Z73DcPQkiVLtGXLFlksFtntdpWUlCg0NLTO7GlpaUpLS2vQfBpKflGp3lm1W1N+20eVzhrtOeyQy117FvrPK7bpzyu26db+HXR9SnstXrmj3vZ3vPyF7KVVigptrql39tXegmM6eKRcN/WN11NL1mrbgaO65ZJ43XdVkl768AftPOjQnbO/UGW1SykJbfTsb3rp7rlZpnMWFRWdcHlERMRJ30PDIHPvIm/vIm/vIm/vIm/v8kbe0dHRJ1zO07aBRu74Pc9z585VTU2NPv30U9NtAgICPD9bLBbPa4vFIvdPBXDhwoW6+uqrNWPGDN13332qrq72bPOfZ5n9/Pzkcrnq7D87O1sOh0MZGRmaPn26QkND5XQ6f9Vxno0+y8nX2DeyNeGtNTpWUa0f7eV13l+Re0CpieedcNvjDxE7dLRCG/cVq0NUK9mCAxUf2VLbDhyVJK3cdEBd2tX+kaPcWaPK6tqc1+4slL+/Ra2aB5xw3wAAAPj5KM9AExEcHKxRo0Zp2bJlqqmpUVJSklavXi232y2Hw6EtW7YoISHhtPdXXl6usLAwSdLKlSt/1izl5eWy2WyyWq3Kzc1VYWHhz9r+XGELDpQktWkVpP6JUcra9KOiw4I97/ft1Fb5xbWXVoe3bKaMO/pIkkKCrArwr/313Kp5gLq2C1NeYamOVVSrRVCAYsJqn5beM76N8otqt2/doplnv52jbfKzWOSo+PcfNAAAAPDrcNk20IRccMEFat++vVatWqUBAwZo+/btSk9PlyTdcccdCg0N1Y8//nha+/rNb36jmTNnKiwsTB07dlRBQcFpz5GamqqpU6dq4sSJiouLU0xMzC86nrPdM7+5WC2bB8jlNvTqJ7kqrazRw0OT1S48RG7DUEFJhWZ//IMkKSwkSC537dPDYiNa6sFrL5RhSBaL9PaqXcr7qSS/9OFGPf2bnjIM6VhFtWYu2yBJGpAUpaG92svlNlRV7dKUpet9c9AAAACNlMUwfu6zXgHANwY/95GvR2gw1/dqrwJHhdZsP/0/QpwJi+7pccLl3L/lfWTuXeTtXeTtXeTtXeTtXb6855kzzwBwFvjgu32+HgEAAACnwD3PAAAAAACYoDwDAAAAAGCC8gwAAAAAgAnKMwAAAAAAJnhgGIBzxsmeDA0AAAA0NM48AwAAAABggvIMAAAAAIAJyjMAAAAAACYozwAAAAAAmKA8AwAAAABggvIMAAAAAIAJyjMAAAAAACYozwAAAAAAmKA8AwAAAABggvIMAAAAAIAJi2EYhq+HAAAAAADgbMaZZwDnhIkTJ/p6hCaFvL2PzL2LvL2LvL2LvL2LvL3Ll3lTngEAAAAAMEF5BgAAAADAhP+kSZMm+XoIADgd8fHxvh6hSSFv7yNz7yJv7yJv7yJv7yJv7/JV3jwwDAAAAAAAE1y2DQAAAACACcozAAAAAAAmrL4eAADM5OTkaNGiRXK73briiit04403+nqks9bcuXO1bt062Ww2zZgxQ5JUWlqqWbNmqbCwUG3atNHDDz+skJAQSdL777+vL774Qn5+fho1apS6d+8uSdq9e7fmzJkjp9OpHj16aNSoUbJYLKqurtarr76q3bt3q2XLlnrooYcUGRkpScrKytLSpUslScOGDdPAgQO9H4CXFRUVac6cOTp69KgsFovS0tJ0zTXXkHkDcTqdevbZZ1VTUyOXy6W+fftq+PDh5N3A3G63Jk6cqLCwME2cOJG8G9iYMWMUFBQkPz8/+fv7KyMjg8wbUFlZmV5//XXl5+fLYrHogQceUHR0NHk3gAMHDmjWrFme1wUFBRo+fLguu+yycydvAwDOYi6Xyxg7dqxx6NAho7q62pgwYYKRn5/v67HOWps2bTJ27dplPPLII55lixcvNt5//33DMAzj/fffNxYvXmwYhmHk5+cbEyZMMJxOp3H48GFj7NixhsvlMgzDMCZOnGhs27bNcLvdxuTJk41169YZhmEYn376qTFv3jzDMAwjOzvbmDlzpmEYhnHs2DFjzJgxxrFjx+r83NjZ7XZj165dhmEYRnl5ufHggw8a+fn5ZN5A3G63UVFRYRiGYVRXVxuPP/64sW3bNvJuYMuWLTNeeuklY8qUKYZh8DuloY0ePdooKSmps4zMG84rr7xiZGZmGoZR+3ultLSUvL3A5XIZv//9742CgoJzKm8u2wZwVtu5c6eioqLUtm1bWa1W9evXT2vXrvX1WGetLl26eP5ae9zatWt12WWXSZIuu+wyT35r165Vv379FBAQoMjISEVFRWnnzp06cuSIKioq1KlTJ1ksFl166aWebb777jvPX2r79u2r3NxcGYahnJwcJScnKyQkRCEhIUpOTlZOTo73DtxHWrdu7XniZ/PmzRUTEyO73U7mDcRisSgoKEiS5HK55HK5ZLFYyLsBFRcXa926dbriiis8y8jb+8i8YZSXl2vLli26/PLLJUlWq1UtWrQgby/44YcfFBUVpTZt2pxTeXPZNoCzmt1uV3h4uOd1eHi4duzY4cOJzj0lJSVq3bq1pNqy53A4JNVm27FjR896YWFhstvt8vf3r5e53W73bHP8PX9/fwUHB+vYsWP1/p2O76spKSgo0J49e5SQkEDmDcjtduuxxx7ToUOHNHjwYHXs2JG8G9Cf//xn3XHHHaqoqPAsI++GN3nyZEnSlVdeqbS0NDJvIAUFBWrVqpXmzp2rffv2KT4+XiNHjiRvL/j666/Vv39/SefW7xTKM4CzmnGCb9OzWCw+mKTxOVG2p1p+svdO9u/RlP6dKisrNWPGDI0cOVLBwcEnXY/Mfz0/Pz9Nnz5dZWVlevHFF5WXl3fSdcn71/n+++9ls9kUHx+vTZs2ma5P3mfGc889p7CwMJWUlOj5559XdHT0Sdcl81/H5XJpz549uvvuu9WxY0ctWrRI//znP0+6PnmfGTU1Nfr+++91++23n3K9szFvLtsGcFYLDw9XcXGx53VxcbHnr5M4PTabTUeOHJEkHTlyRK1atZJUP1u73a6wsLATZh4WFlZvG5fLpfLycoWEhCgsLKzevprKv1NNTY1mzJihAQMGqE+fPpLI3BtatGihLl26KCcnh7wbyLZt2/Tdd99pzJgxeumll5Sbm6vZs2eTdwM7no3NZlNKSop27txJ5g0kPDxc4eHhnrObffv21Z49e8i7ga1fv14XXHCBQkNDJZ1b/5tJeQZwVuvQoYMOHjyogoIC1dTUaNWqVerVq5evxzqn9OrVSytXrpQkrVy5UikpKZ7lq1atUnV1tQoKCnTw4EElJCSodevWat68ubZv3y7DMPTll196Mr/44ouVlZUlSVqzZo26du0qi8Wi7t27a8OGDSotLVVpaak2bNjgeSJmY2YYhl5//XXFxMRo6NChnuVk3jAcDofKysok1T55+4cfflBMTAx5N5Dbb79dr7/+uubMmaOHHnpIF154oR588EHybkCVlZWeS+QrKyu1ceNGxcbGknkDCQ0NVXh4uA4cOCCp9j7cdu3akXcD+89LtqVz638zLcapznsDwFlg3bp1evPNN+V2uzVo0CANGzbM1yOdtV566SVt3rxZx44dk81m0/Dhw5WSkqJZs2apqKhIEREReuSRRzwPFVu6dKlWrFghPz8/jRw5Uj169JAk7dq1S3PnzpXT6VT37t119913y2KxyOl06tVXX9WePXsUEhKihx56SG3btpUkffHFF3r//fcl1X4FxKBBg3wTghdt3bpVzzzzjGJjYz2Xf40YMUIdO3Yk8wawb98+zZkzR263W4Zh6JJLLtEtt9yiY8eOkXcD27Rpk5YtW6aJEyeSdwM6fPiwXnzxRUm1Z81SU1M1bNgwMm9Ae/fu1euvv66amhpFRkZq9OjRMgyDvBtIVVWVHnjgAb366que25zOpf++Kc8AAAAAAJjgsm0AAAAAAExQngEAAAAAMEF5BgAAAADABOUZAAAAAAATlGcAAAAAAExQngEAAH6FOXPm6O9//7tPPtswDM2dO1ejRo3S448/7pMZjvvqq6/0/PPP+3QGAGhIlGcAANCojBkzRvfee68qKys9y5YvX65Jkyb5bqgGsnXrVm3cuFGvvfaapkyZUu/9rKwsPf30016ZZcCAAXrqqae88ln/bdOmTbr//vt98tkAmg7KMwAAaHRcLpc+/vhjX4/xs7nd7p+1fmFhodq0aaOgoKAGmsg7XC6Xr0cAAFNWXw8AAABwpl1//fX6v//7Pw0ePFgtWrSo815BQYHGjh2rJUuWyN/fX5I0adIkDRgwQFdccYWysrK0fPlydejQQVlZWQoJCdG4ceN08OBBvf3226qurtYdd9yhgQMHevbpcDj03HPPaceOHbrgggs0duxYtWnTRpL0448/auHChdq9e7datWqlW2+9Vf369ZNUe8l3YGCgioqKtHnzZqWnpys5ObnOvHa7XfPnz9fWrVsVEhKiG264QWlpafriiy+0YMEC1dTU6M4779R1112n4cOHnzKXMWPGaPDgwfryyy91+PBh9evXTyNGjNDcuXO1detWdezYUQ8//LBCQkI8OT3wwAN65513VFlZqREjRig+Pl6vv/66ioqKNGDAAN1zzz2S5MntueeekyRt2LBBCxcu1NGjRzVgwADl5+fr0ksvrZfxypUrNXjwYA0cOFDz5s3Tvn37ZLFY1K1bN91zzz2ef7//nL2wsFDdu3fXmDFj5Ha79cILL3hykKSXX35Zdrtdb7zxhg4ePKjAwEClpqbqrrvu+iX/OQGAJM48AwCARig+Pl5du3bVsmXLftH2O3bsUPv27bVw4UKlpqbqpZde0s6dOzV79myNGzdOCxcurHNZeHZ2tm6++WYtWLBAcXFxmj17tiSpsrJSzz//vFJTU/XGG2/oD3/4gxYsWKD8/Pw6295000168803lZiYWG+Wl19+WeHh4Zo3b57Gjx+vJUuW6IcfftDll1+ue++9V506ddLixYtNi/Nx33zzjZ566im9/PLL+v777zVlyhSNGDFCCxYskNvt1ieffFIvi5dfflkPPfSQ3nzzTS1dulRPP/20Zs6cqdWrV2vz5s31PsPhcGjmzJm6/fbbtXDhQkVHR2v79u319tu2bVu98cYbGjZsmCTppptu0rx58zRr1iwVFxfr3XffrbPN6tWr9cQTT2jOnDnKy8tTVlaWgoKC9MQTT6h169ZavHixFi9erLCwMC1atEjXXHON3nzzTb3yyiu65JJLTisfADgZyjMAAGiUhg8frk8++UQOh+NnbxsZGalBgwbJz89P/fr1U3FxsW655RYFBASoW7duslqtOnTokGf9nj17qkuXLgoICNCIESO0fft2FRUVad26dWrTpo0GDRokf39/xcfHq0+fPlqzZo1n25SUFCUmJsrPz0+BgYF15igqKtLWrVv129/+VoGBgYqLi9MVV1yhL7/88hfncvXVVys0NFRhYWFKTExUQkKCLrjgAgUEBKh3797as2dPnfVvueUWBQYGqlu3bmrWrJlSU1Nls9k82//3+pK0fv16tWvXTn369JG/v7+GDBmi0NDQOuu0bt1aQ4YMkb+/vwIDAxUVFaXk5GQFBASoVatWuvbaa+sV8yFDhigsLEwhISG6+OKLtXfv3pMe5/F/I4fDoaCgIHXq9P/t3L9LclEYB/CvQqF109RbGEaB0BtEU04hOjQ3amttFlRWEC39CeEQNfVDbLCioZaGlgLdXHIrrIYiiKi0H5Za13vfKdF6e816Xyr5fqZzzz33nnOui8+9zzm/PvzMiIgApm0TERFRmWpqaoLNZsPGxgYsFktJ1+r1+lz5OaDND/4qKysLvjybTKZcWaPRQBAEJBIJXFxc4ODgAH19fbnz2WwWTqfzj9e+lEgkIAgCtFptrk4URRwdHZU0n3wv5/byOJPJlNQ+/znkjzt/XiqVCkajsaCNKIoFxzc3N/D7/djb20M6nYYsyxAEoaDNy98gHo+/Oc/+/n6srq5idHQU9fX1cLlcsNlsb7YnIiqGwTMRERGVrZ6eHkxMTKC7uztX97y5ViaTQVVVFQDg+vr6U/1cXV3lyul0GslkEgaDASaTCW1tbX/d8VqlUr15zmAwIJlMIpVK5QLoy8vLV4Hod1NbW1sQ2CqK8tdAFwCCwSAAYGpqCjU1NYhEIlhcXHxXf396hg0NDRgZGYEsy4hEIvD5fFhYWPjxm6sR0ddh2jYRERGVLbPZjM7OzoJ1vDqdDkajEeFwGLIsY3t7G+fn55/qZ3d3F/v7+5AkCSsrK2hpaYEoirDZbDg7O0MoFIIkSZAkCYeHhzg9PX3XfUVRRGtrK4LBIB4fH3F8fIydnR04HI5Pjfd/6+jowMnJCSKRCLLZLLa2toq+oEilUtBoNKiurkY8Hi9pvbper8fd3R0eHh5ydaFQCLe3t1Cr1bmXJGo1//oS0cfxyzMRERGVNZfLhXA4XFDn8XgwPz+P5eVldHV1fXo9rN1ux9raGmKxGKxWK4aHhwEAWq0Wk5OTCAQCCAQCUBQFzc3NJe367PV6MTc3B4/HA0EQ4Ha7X+3I/d3odDqMjY3B7/djdnYWDocDVqsVFRUVb17jdrsxMzOD3t5emM1mOJ1ObG5uvqs/i8UCu92OwcFByLIMn8+HaDSKpaUlZDIZ1NXVwev1vlpTTkRUCpWiKMpXD4KIiIiIypcsyxgYGMDQ0BDa29u/ejhERB/C3BUiIiIi+uei0Sju7+/x9PSE9fV1KIrCHa+J6Edj2jYRERER/XOxWAzT09OQJAmNjY0YHx9n2jQR/WhM2yYiIiIiIiIqgmnbREREREREREUweCYiIiIiIiIqgsEzERERERERUREMnomIiIiIiIiKYPBMREREREREVMRvu1TbgX8cE1AAAAAASUVORK5CYII=\n",
                        "text/plain": "<Figure size 864x864 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "# generate plot\ndf_top15.plot(kind='barh', figsize=(12, 12), color='steelblue')\nplt.xlabel('Number of Immigrants')\nplt.title('Top 15 Conuntries Contributing to the Immigration to Canada between 1980 - 2013')\n\n# annotate value labels to each country\nfor index, value in enumerate(df_top15): \n    label = format(int(value), ',') # format int with commas\n    \n    # place text at the end of bar (subtracting 47000 from x, and 0.1 from y to make it fit within the bar)\n    plt.annotate(label, xy=(value - 47000, index - 0.10), color='white')\n    \nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "### Thank you for completing this lab!\n\nThis notebook was created by [Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan) with contributions from [Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani), [Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic), and [Susan Li](https://www.linkedin.com/in/susanli), and revised by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lesson interesting. Feel free to contact me if you have any questions!"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "This notebook is part of the free course on **Cognitive Class** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this free self-paced course, online by clicking [here](https://cocl.us/DV0101EN_Lab2)."
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "deletable": true,
                "editable": true
            },
            "source": "<hr>\nCopyright &copy; 2017 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python",
            "language": "python",
            "name": "conda-env-python-py"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.6.7"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 4
}