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Created on Cognitive Class Labs
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"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>"
]
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"source": [
"## Introduction\n",
"\n",
"In this lab, we will continue exploring the Matplotlib library and will learn how to create additional plots, namely area plots, histograms, and bar charts."
]
},
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"source": [
"## Table of Contents\n",
"\n",
"<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
"\n",
"1. [Exploring Datasets with *pandas*](#0)<br>\n",
"2. [Downloading and Prepping Data](#2)<br>\n",
"3. [Visualizing Data using Matplotlib](#4) <br>\n",
"4. [Area Plots](#6) <br>\n",
"5. [Histograms](#8) <br>\n",
"6. [Bar Charts](#10) <br>\n",
"</div>\n",
"<hr>"
]
},
{
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"source": [
"# Exploring Datasets with *pandas* and Matplotlib<a id=\"0\"></a>\n",
"\n",
"Toolkits: 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",
"\n",
"Dataset: 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",
"\n",
"The 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."
]
},
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"source": [
"# Downloading and Prepping Data <a id=\"2\"></a>"
]
},
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"source": [
"Import Primary Modules. The first thing we'll do is import two key data analysis modules: *pandas* and **Numpy**."
]
},
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"source": [
"import numpy as np # useful for many scientific computing in Python\n",
"import pandas as pd # primary data structure library"
]
},
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"source": [
"Let's download and import our primary Canadian Immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
"```\n",
"!conda install -c anaconda xlrd --yes\n",
"```"
]
},
{
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"source": [
"Download the dataset and read it into a *pandas* dataframe."
]
},
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"cell_type": "code",
"execution_count": 2,
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Data downloaded and read into a 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",
" )\n",
"\n",
"print('Data downloaded and read into a dataframe!')"
]
},
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"source": [
"Let's take a look at the first five items in our dataset."
]
},
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"</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",
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" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Afghanistan</td>\n",
" <td>935</td>\n",
" <td>Asia</td>\n",
" <td>5501</td>\n",
" <td>Southern Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>...</td>\n",
" <td>2978</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Albania</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
" <td>Southern Europe</td>\n",
" <td>901</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Algeria</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>912</td>\n",
" <td>Northern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>...</td>\n",
" <td>3616</td>\n",
" <td>3626</td>\n",
" <td>4807</td>\n",
" <td>3623</td>\n",
" <td>4005</td>\n",
" <td>5393</td>\n",
" <td>4752</td>\n",
" <td>4325</td>\n",
" <td>3774</td>\n",
" <td>4331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>American Samoa</td>\n",
" <td>909</td>\n",
" <td>Oceania</td>\n",
" <td>957</td>\n",
" <td>Polynesia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Andorra</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
" <td>Southern Europe</td>\n",
" <td>901</td>\n",
" <td>Developed regions</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 43 columns</p>\n",
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" Type Coverage OdName AREA AreaName REG \\\n",
"0 Immigrants Foreigners Afghanistan 935 Asia 5501 \n",
"1 Immigrants Foreigners Albania 908 Europe 925 \n",
"2 Immigrants Foreigners Algeria 903 Africa 912 \n",
"3 Immigrants Foreigners American Samoa 909 Oceania 957 \n",
"4 Immigrants Foreigners Andorra 908 Europe 925 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"0 Southern Asia 902 Developing regions 16 ... 2978 3436 3009 \n",
"1 Southern Europe 901 Developed regions 1 ... 1450 1223 856 \n",
"2 Northern Africa 902 Developing regions 80 ... 3616 3626 4807 \n",
"3 Polynesia 902 Developing regions 0 ... 0 0 1 \n",
"4 Southern Europe 901 Developed regions 0 ... 0 0 1 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"0 2652 2111 1746 1758 2203 2635 2004 \n",
"1 702 560 716 561 539 620 603 \n",
"2 3623 4005 5393 4752 4325 3774 4331 \n",
"3 0 0 0 0 0 0 0 \n",
"4 1 0 0 0 0 1 1 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head()"
]
},
{
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"button": false,
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},
"source": [
"Let's find out how many entries there are in our dataset."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"button": false,
"collapsed": false,
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"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(195, 43)\n"
]
}
],
"source": [
"# print the dimensions of the dataframe\n",
"print(df_can.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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},
"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",
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},
"source": [
"#### 1. Clean up the dataset to remove columns that are not informative to us for visualization (eg. Type, AREA, REG)."
]
},
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>OdName</th>\n",
" <th>AreaName</th>\n",
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" <th>1985</th>\n",
" <th>...</th>\n",
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" <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",
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" <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 × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" OdName AreaName RegName DevName 1980 1981 \\\n",
"0 Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"1 Albania Europe Southern Europe Developed regions 1 0 \n",
"2 Algeria Africa Northern Africa Developing regions 80 67 \n",
"3 American Samoa Oceania Polynesia Developing regions 0 1 \n",
"4 Andorra Europe Southern Europe Developed regions 0 0 \n",
"\n",
" 1982 1983 1984 1985 ... 2004 2005 2006 2007 2008 2009 2010 \\\n",
"0 39 47 71 340 ... 2978 3436 3009 2652 2111 1746 1758 \n",
"1 0 0 0 0 ... 1450 1223 856 702 560 716 561 \n",
"2 71 69 63 44 ... 3616 3626 4807 3623 4005 5393 4752 \n",
"3 0 0 0 0 ... 0 0 1 0 0 0 0 \n",
"4 0 0 0 0 ... 0 0 1 1 0 0 0 \n",
"\n",
" 2011 2012 2013 \n",
"0 2203 2635 2004 \n",
"1 539 620 603 \n",
"2 4325 3774 4331 \n",
"3 0 0 0 \n",
"4 0 1 1 \n",
"\n",
"[5 rows x 38 columns]"
]
},
"execution_count": 5,
"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\n",
"df_can.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Notice how the columns Type, Coverage, AREA, REG, and DEV got removed from the dataframe."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### 2. Rename some of the columns so that they make sense."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"button": false,
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"outputs": [
{
"data": {
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" <thead>\n",
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" <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 × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Country Continent Region DevName 1980 1981 \\\n",
"0 Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"1 Albania Europe Southern Europe Developed regions 1 0 \n",
"2 Algeria Africa Northern Africa Developing regions 80 67 \n",
"3 American Samoa Oceania Polynesia Developing regions 0 1 \n",
"4 Andorra Europe Southern Europe Developed regions 0 0 \n",
"\n",
" 1982 1983 1984 1985 ... 2004 2005 2006 2007 2008 2009 2010 \\\n",
"0 39 47 71 340 ... 2978 3436 3009 2652 2111 1746 1758 \n",
"1 0 0 0 0 ... 1450 1223 856 702 560 716 561 \n",
"2 71 69 63 44 ... 3616 3626 4807 3623 4005 5393 4752 \n",
"3 0 0 0 0 ... 0 0 1 0 0 0 0 \n",
"4 0 0 0 0 ... 0 0 1 1 0 0 0 \n",
"\n",
" 2011 2012 2013 \n",
"0 2203 2635 2004 \n",
"1 539 620 603 \n",
"2 4325 3774 4331 \n",
"3 0 0 0 \n",
"4 0 1 1 \n",
"\n",
"[5 rows x 38 columns]"
]
},
"execution_count": 6,
"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\n",
"df_can.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Notice how the column names now make much more sense, even to an outsider."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### 3. For consistency, ensure that all column labels of type string."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# let's examine the types of the column labels\n",
"all(isinstance(column, str) for column in df_can.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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": 8,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 8,
"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\n",
"all(isinstance(column, str) for column in df_can.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### 4. Set the country name as index - useful for quickly looking up countries using .loc method."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": 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 × 37 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 \\\n",
"Country \n",
"Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 \n",
"Algeria Africa Northern Africa Developing regions 80 67 \n",
"American Samoa Oceania Polynesia Developing regions 0 1 \n",
"Andorra Europe Southern Europe Developed regions 0 0 \n",
"\n",
" 1982 1983 1984 1985 1986 ... 2004 2005 2006 2007 \\\n",
"Country ... \n",
"Afghanistan 39 47 71 340 496 ... 2978 3436 3009 2652 \n",
"Albania 0 0 0 0 1 ... 1450 1223 856 702 \n",
"Algeria 71 69 63 44 69 ... 3616 3626 4807 3623 \n",
"American Samoa 0 0 0 0 0 ... 0 0 1 0 \n",
"Andorra 0 0 0 0 2 ... 0 0 1 1 \n",
"\n",
" 2008 2009 2010 2011 2012 2013 \n",
"Country \n",
"Afghanistan 2111 1746 1758 2203 2635 2004 \n",
"Albania 560 716 561 539 620 603 \n",
"Algeria 4005 5393 4752 4325 3774 4331 \n",
"American Samoa 0 0 0 0 0 0 \n",
"Andorra 0 0 0 0 1 1 \n",
"\n",
"[5 rows x 37 columns]"
]
},
"execution_count": 9,
"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\n",
"df_can.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Notice how the country names now serve as indices."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### 5. Add total column."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": 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",
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" <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",
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" <td>1223</td>\n",
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" <td>560</td>\n",
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" </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",
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" <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",
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" <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 × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 \\\n",
"Country \n",
"Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 \n",
"Algeria Africa Northern Africa Developing regions 80 67 \n",
"American Samoa Oceania Polynesia Developing regions 0 1 \n",
"Andorra Europe Southern Europe Developed regions 0 0 \n",
"\n",
" 1982 1983 1984 1985 1986 ... 2005 2006 2007 2008 \\\n",
"Country ... \n",
"Afghanistan 39 47 71 340 496 ... 3436 3009 2652 2111 \n",
"Albania 0 0 0 0 1 ... 1223 856 702 560 \n",
"Algeria 71 69 63 44 69 ... 3626 4807 3623 4005 \n",
"American Samoa 0 0 0 0 0 ... 0 1 0 0 \n",
"Andorra 0 0 0 0 2 ... 0 1 1 0 \n",
"\n",
" 2009 2010 2011 2012 2013 Total \n",
"Country \n",
"Afghanistan 1746 1758 2203 2635 2004 58639 \n",
"Albania 716 561 539 620 603 15699 \n",
"Algeria 5393 4752 4325 3774 4331 69439 \n",
"American Samoa 0 0 0 0 0 6 \n",
"Andorra 0 0 0 1 1 15 \n",
"\n",
"[5 rows x 38 columns]"
]
},
"execution_count": 10,
"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\n",
"df_can.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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": 11,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": 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": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"So now our dataframe has 38 columns instead of 37 columns that we had before."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": 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": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# finally, let's create a list of years from 1980 - 2013\n",
"# this will come in handy when we start plotting the data\n",
"years = list(map(str, range(1980, 2014)))\n",
"\n",
"years"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Visualizing Data using Matplotlib<a id=\"4\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Import `Matplotlib` and **Numpy**."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matplotlib version: 3.0.3\n"
]
}
],
"source": [
"# use the inline backend to generate the plots within the browser\n",
"%matplotlib inline \n",
"\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"\n",
"mpl.style.use('ggplot') # optional: for ggplot-like style\n",
"\n",
"# check for latest version of Matplotlib\n",
"print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Area Plots<a id=\"6\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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": 14,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": 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 \\\n",
"1980 8880 5123 22045 \n",
"1981 8670 6682 24796 \n",
"1982 8147 3308 20620 \n",
"1983 7338 1863 10015 \n",
"1984 5704 1527 10170 \n",
"\n",
"Country Philippines Pakistan \n",
"1980 6051 978 \n",
"1981 5921 972 \n",
"1982 5249 1201 \n",
"1983 4562 900 \n",
"1984 3801 668 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.sort_values(['Total'], ascending=False, axis=0, inplace=True)\n",
"\n",
"# get the top 5 entries\n",
"df_top5 = df_can.head()\n",
"\n",
"# transpose the dataframe\n",
"df_top5 = df_top5[years].transpose() \n",
"\n",
"df_top5.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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": 15,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
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58+aRk5PTUadFRERERERE5KApKSXSiPXr1zNkyJD9li9YsIAvv/ySRYsWUV5ezvjx4xk1alST/eXk5PDBBx/w0ksv8eyzz/Loo48ybdo0MjIy+PnPfw7AK6+8wubNm3nttddwuVxkZWXx1ltvce211/Lxxx9z4oknKiElIiIiIiIinZ7mlBI5AMuWLeOiiy7Cbrdz+OGHM2rUKFavXt3kdhdccAEAQ4cOpbCwsMF2EydOxG63A3DZZZfxxhtvAPDqq6+Sl5fXCkcgIiIiIiIi0rGUlBJpxPHHH8/atWv3W25ZVr3tHQ5HrXV+v7/WepfLBYDdbiccDje43/T09PjP/fr14/DDD+eTTz7hv//9L2PGjGnRMYiIiIiIiIgkIyWlRBpxxhlnEAgEePnll+PLVq1aRffu3Zk7dy7hcJiysjKWLl3KsGHD6NevHxs2bMDv91NZWcknn3zS5D4yMjKoqqpqtM3UqVP51a9+xYUXXhivoBIRERERERHpzJSUEmmEMYYXXniB//znP5x22mmMHj2aGTNmcNFFF/GDH/yA3Nxc8vLyuOeee+jVqxf9+vXjwgsvZOzYsdx0000MHjy4yX3k5uby/vvvk5uby9KlS+ttM27cOKqrq7nsssta+xBFREREREREOoRpaBjSIcIqKiqqtcDj8dQaOiXSkRwOB6FQiNWrV/P73/+et99+u6NDkiTWEe9fPXv2pLS0tF33KdISukYl2ekalWSna1SSna7R5NS3b18A01Q73X1PJMnNnDmT2bNnM3PmzI4ORURERERERKTVaPieSJK76aabWLZsGSNGjOjoUERERERERERajZJSIiIiIiIiIiLS7pSUEhERERERERGRdqeklIiIiIiIiIiItDslpUREREREREREpN0pKZWEBg4c2KL2BQUFXHXVVQAsXLhQd2kTERERERERkaTn6OgAkl1kxSdQUdZ6HfY4DNsPz2i9/uoYN24c48aNa7P+RURERETk0LCnKsDuYCV9Ujo6EhHpqpSUakpFGXirO2TXBQUFPPbYY/To0YP169czdOhQ/vKXv2CMYcmSJdx3333k5OQwZMiQ+DavvfYaa9as4aGHHmLhwoU8+eSTBAIBevTowcyZMzn88MM75FhERERERKTziFgWy3ZUscdXzZWDsklNsXd0SCLSBWn4XpJbt24d999/Px999BHbtm1j+fLl+Hw+br/9dl566SXefvttiouL6912xIgRzJs3j4ULFzJp0iSefvrpdo5eREREREQ6ox3uAHt9IdYXV/FVibejwxGRLkqVUklu2LBh9O3bF4BBgwZRWFhIeno6AwYM4JhjjgHg0ksv5e9///t+2+7atYsbbriB4uJiAoEAAwYMaNfYRURERESk87Esi69KPFR4Q2S67HxRVM0pfTM7OiwR6YJUKZXknE5n/Ge73U4oFALAGNPktvfeey8/+clPWLx4MQ8//DB+v7/N4hQRERERka5hT1WQ0uogBjgs3Umh2084YnV0WCLSBSkp1Qkdd9xxbN++na1btwLwzjvv1NuusrKSPn36APD666+3V3giIiIiItKJfVXiZa8vTJbLTrc0B95ghM0Vvo4OS0S6ICWlOqHU1FT+9Kc/cdVVV3HRRRdx5JFH1tvu1ltv5frrr+fiiy8mJyennaMUEREREZHOpswTZNe+AJGIhcthI9PlwGGHFTurOjo0EemCjGUd0mWYVlFRUa0FHo+H9PT0+OPIik+id+BrLT0Ow/bDM1qvP+nSHA5HfMimSFPqvn+1h549e1JaWtqu+xRpCV2jkux0jUqy+XRbJat2V+Oy28hw2khPT+ebXRWEwnD32f2aNY2ISHvS+2hyis2N3eQbhiY6b4ISSCIiIiIiciio9IfZ7vYTClvkpH03qCbbaWfrXj+7qwIckeXqwAhFpKvR8D0RERERERHhmxIPbl+YNGftj4mZLjtg+KKoumMCE5EuS0kpERERERGRQ5wnGGZzuQ9/OEJmSu2PiQ6bIdtl46sSbwdFJyJdlZJSIiIiIiIih7gNpT4q/RFS7abeeaOyU+2Ue0K4fZrvVERaj5JSIiIiIiIih7BAOMKmMh/eUJgsl73eNtkuO6GIxZo9GsInIq1HSSkREREREZFD2KYyH25fiBRb/VVSAE67jUynjdWaV0pEWpGSUkmouLiYG264gdNOO41zzjmHadOm8fe//52rrrqq3va33XYbGzZsaOcoRURERESkswtHLNaXeqkKhumWWn+VVI1uqQ52VwXxBcPtFJ2IdHWOjg4g2X2yrZIyT7DV+jssPYUzjspucL1lWUyfPp0pU6bwzDPPALBu3ToWLVrU4DaPPvpoq8UnIiIiIiKHji0Vfty+EHZjsDVQJVUjy2VjZ2U0iXXSEZntFKGIdGWqlGpCmSeIJxhptX9NJbg+/fRTUlJSalVFDR48mJEjR+LxeLj22ms566yzuOmmm7AsC4DJkyezevVqAAYOHMgf//hHxo4dy8SJEykpKQFg4cKFTJw4kXHjxnHZZZfFl4uIiIiIyKEpYll8XeLB7Q/TzdX0R8M0h41Uh2GFhvCJSCtRUirJrF+/niFDhtS7bt26ddx///189NFHbNu2jeXLl+/XxuPxcMopp/Dhhx8yatQoXn75ZQBGjBjBvHnzWLhwIZMmTeLpp59u0+MQEREREZHktqMywN5YlZTD3vRHQ2MM3VIdFLr9hCNWO0QoIl2dhu91IsOGDaNv374ADBo0iMLCQkaMGFGrjdPpJDc3F4AhQ4bw8ccfA7Br1y5uuOEGiouLCQQCDBgwoH2DFxERERGRpGFZFl8Xe6nwhujexFxSibJddoqrgmzb6+OYnLQ2jFBEDgWqlEoyxx9/PGvXrq13ndPpjP9st9sJhUL7tXE4HPE7ZiS2uffee/nJT37C4sWLefjhh/H7/W0QvYiIiIiIdAZ7qoKUVAcASGlGlVSNDKcNuw1W7NQQPhE5eEpKJZkzzjiDQCAQH3YHsGrVKj7//POD6reyspI+ffoA8Prrrx9UXyIiIiIi0rl9XeKlwhcm29X8KikAmzF0T3WwscwXn+NWRORAKSmVZIwxvPDCC/znP//htNNOY/To0cyYMYPevXsfVL+33nor119/PRdffDE5OTmtFK2IiIiIiHQ2ZZ4gRfsCWJaFy9Hyj4RZLjuV/hDF1a13l3IROTSZQzy7bRUVFdVa4PF4SE9Pjz/+ZFtlk3fMa4nD0lM446jsVutPujaHw1HvME2R+tR9/2oPPXv2pLS0tF33KdISukYl2ekalY7w6bZKVu2uxmW3keFsPCmVnp6Ox+OptSwUsVi3x8voo7M5//gebRmqSJP0PpqcYvNhm6baaaLzJiiBJCIiIiIiXcU+f5jtsbvnZaQd2MAZh82Q5bLxZbFHSSkROSgaviciIiIiInKI+KbEi9sXJvUAhu0l6uayU+oJUelTVb+IHDglpURERERERA4B3mCEzRU+/OEwmU0M22tKlstOKGKxdo/uwiciB05JKRERERERkUPA+lIvbn+YVLsNY5qc6qVRLkd0PqpVuz1NNxYRaYCSUiIiIiIiIl1cIBxhU5kPbzBMlsveKn12S7Wza18AfyjcKv2JyKFHSSkREREREZEu7tsyH5X+ECk2c9BVUjWynHYCIYv1Jd5W6U9EDj3tmpTKy8uz5+Xl/TcvL+/d2OOj8/Lylubl5W3My8t7LS8vzxlb7oo93hRb/72EPn4bW74+Ly/vvITl58eWbcrLy7urPY+rNRUWFjJmzJhay2bMmMGzzz7b6HarV6/m3nvvBaCgoIDly5e3eN8jR46kvLy80eVr1qxh1KhRrFu3joULFzJz5swW76c+BQUFXHXVVa3SV3Ns2rSJ3Nxcxo0bx9atW2utq66u5q677uK0005j3LhxnH/++bz88suttu/nn38er7f+X9yTJ0/mzDPPJDc3l7PPPpvZs2c32M9tt93Ghg0bAHjyySebte9p06bhdrtbHvQBaujanTFjBscee2ytW7cOHDiwxf0nHnd9r5220tBrpTGTJ09m9erVHbZ/EREROXSFIxbry7zsC4Tplto6VVIA6Sk2nA7DyiLNKyUiB8bRzvu7GfgayI49fhh4PD8//9W8vLxngenAM7H/K/Lz84/Ly8u7PNbusry8vBOBy4FBQF/gw7y8vONjfT0F5AI7gOV5eXlz8/PzvzrYgIsK/Xg91sF2E5eWbujb39Vq/dU46aSTOOmkkwD47LPPyMjIYPjw4a26j6+++orrrruOZ555hsGDBzN48GDGjRvXqvtoL++//z7nnXcet912237rbrvtNgYMGMAnn3yCzWajrKyMV199db924XAYu73lv9RfeOEFLr30UtLS0updP3PmTE466SQqKio4/fTTmTx5Mk6nc799P/roo/HHf/nLX/jVr37V5L7nzJnT4njbSk5ODs899xz33HNPi7e1LAvLspp93M0RCoVwOA78LbEmJptNBagiIiKSXLbu9bPXG8JuDLZWqpICMMbQPdXBdneAcMTCbmu9vkXk0NBuSam8vLwjgQnAQ8Bv8vLyDDAG+J9Yk78BvyealJoU+xngDWBmrP0k4NX8/Hw/sCUvL28TMCLWblN+fv7m2L5ejbU96KSU12MRCrZeUsp7kPMATp48mZNPPpmCggLcbjczZsxg5MiRFBQU8Oyzz/LQQw8xZ84c7HY7b775Jg8++CDHHXccd911Fzt37gTg/vvvZ/jw4ZSXl3PjjTdSVlbGsGHDsKyGj3Pjxo3ccsstPPnkk5x88skAvPbaa6xZs4aHHnqIW265haysLFavXk1JSQn33HMPEydOJBKJcM899/D555/Tv39/LMvisssuY+LEiSxZsoT77ruPnJwchgwZEt9XRUUFt956K9u3byc1NZU//elPnHjiicyYMYPt27dTXFzM5s2bue+++/jiiy9YsmQJffr04aWXXiIlJaVW3OvWreOuu+7C5/Nx1FFHMWPGDFauXMkLL7yA3W7n888/54033oi337p1K6tWreKpp56KJxcOO+wwbrzxRiBa0fXYY4/Ru3dvvvzySz766CPefPNNZs2aRSAQ4OSTT+YPf/gDdrudu+66i9WrV+Pz+ZgwYQK33XYbL774Inv27GHKlCn06NGj1r7r8ng8pKenxxNfAwcO5LrrruPf//43v/vd7/jTn/7Evffey/z58/H5fOTm5vL973+fmTNn8tOf/pSioiL8fj/Tp0/nyiuvBKIVNu+99x7V1dVceeWVjBgxghUrVtCnTx9mzZq1X6Js4cKFPPnkkwQCAXr06MHMmTM5/PDDmTFjBjt37mT79u3s3LmTn/3sZ0yfPh2AJ554gjfeeIO+ffty2GGHMXTo0HqP7/LLLyc/P59f/OIX9OjRo9a65557jtdeew2AqVOncu2111JYWMiVV17JaaedxsqVKxk0aFCt477zzjsJh8Pcfvvt+x3T1q1bueeeeygrKyMtLY1HHnmE4447jltuuYXu3buzbt06hgwZQmZmZoPHVZ+6Mc2aNYtvv/2WRx99lEAgwFFHHcXjjz9ORkZGre3quzZqnp8pU6awaNEiQqEQzz33HMcdd1yLXqsiIiIiiSKWxdclHtz+MDmtWCVVI8tlp7gqyHa3j6N71P+lq4hIQ9rzK/0/A3cAkdjjw4C9+fn5odjjHUC/2M/9gEKA2Hp3rH18eZ1tGlreJYVCIebPn8/999/PY489Vmtd//79mTZtGtdeey2LFi1i5MiR/O53v+Paa69lwYIFPP/88/EPwI8//jgjRoxg4cKFjBs3Lp60qs9Pf/pTHnzwQUaMGNFgmz179vDOO+/wt7/9jT/84Q8ALFiwgB07drB48WIeffRRVq5cCYDP5+P222/npZde4u2336a4uDjez4wZMxg8eDAffvghd911FzfffHN83bZt25g9ezazZs3il7/8JaeddhqLFy8mNTWVxYsX7xfTLbfcwj333MOHH37ICSecwGOPPca5554bP0d1k0IbNmzgxBNPbLTaZdWqVdx555189NFHbNy4kblz5/LOO++waNEi7HY7b731FgB33nkn7733Hh9++CGff/45X331FdOnT6d37968/vrrDSakbrrpJsaOHcvnC+jTAAAgAElEQVRZZ53Fb37zm3hSyuPx8P3vf59333231vNw9913k5qayqJFi+LDKWfMmMH777/PggULmDVrVr1DvbZs2cLVV1/NkiVLyM7OZsGCBfu1GTFiBPPmzWPhwoVMmjSJp59+Or5u06ZNvPzyy8yfP5/HHnuMYDDImjVrmDt3LgsXLuSFF15odLhaRkYGl19+OS+++GKt5WvWrCE/P593332XefPm8Y9//IN169YB8O233zJ58mQWLlzI448/vt9xN3RMd9xxBw888ADvv/8+9957L7/97W/j+9u8eTOvvfYa9913X4PH1ZjEmNLT03niiSd47bXX+OCDDzjppJP461//ut829V0bNXJycvjggw+YNm1afOhjS16rIiIiIol2VAao8IawAQ5763/8y3TasNtgxQ4N4RORlmuXSqm8vLyJQHF+fv7KvLy8c2KL66vttJpY19Dy+t5d6y0lyMvLuw64DiA/P5+ePXvWWr9nz55aQ3hsNhs2W+tVJdhsptEhQjXr6sZgt9txOBwYY7jwwgtxOBycfPLJ7NixA4fDgd1ux5ho39GYbfE+Pv74YzZu3Bjvr6qqCp/Px9KlS5k1axYOh4Pzzz+f7t27x/eTyBjDWWedxauvvsrYsWPjSRK73R7fj81mY/z48TidTk488URKSkpwOBysWLGCH//4xzidTvr27cvpp5+O3W5n69atHHXUURx/fHT05ZQpU5gzZw4Oh4Ply5fz4osv4nA4OOecc/j1r3+Nx+PBZrNx7rnnkpaWxpAhQ4hEIuTm5mKM4cQTT2Tnzp21Yq+srKSyspIzzzwTiFbc/OxnP6v3HCWe65rzCNFkwLx58ygtLWXNmjXY7XZOPvlkjjnmGCBaObV27VomTJgARJNtvXr1wuFwsGDBAubMmUMoFKK4uJhvv/2WoUOHYoyp9zzXnOtnnnmGYcOGUVpaysSJExk9ejT9+/fHbrczadKk+Pmv209ify+99FI8IVNUVMT27dvp1atXfBu73c6AAQMYNmwYAMOGDdvv/AEUFxfzi1/8gj179hAMBhkwYED8/OXm5pKRkUFGRgaHH344FRUVLF++nPHjx5OVlQXAeeed1+B5ttlsXHfddZx77rnxSrSaa2b8+PFkZ0dH+U6YMIHly5dz3nnnceSRRzJy5MhafdX03dAx+f1+Vq5cyc9//vP4NoFAIH4ckyZNwuVyxeOq77j69u273/NUcx4TY1q1ahUbN27koosuAiAYDHLqqafGX7s1z1dj10bi6/v999/H4XA0+7Xqcrn2e09raw6Ho933KdISukYl2ekalbZkWRYFu/fgtRz06ZGK09HySimbzUZ6enqjbXp1M2yvjnDYYYe12iTqIs2l99HOrb2G750O/DgvL288kEp0Tqk/A93z8vIcsWqoI4GiWPsdQH9gR15engPoBpQnLK+RuE1Dy2vJz8//K1BTumAlTrQM4Pf7a80TFIlEiERaLykViRhCoVCD67Ozs3G73bXalJeX069fP0KhEJZlYbfb4+tDoRChUIhwOIxlWYRCoVjMkXibSCTCP//5z/2GZlmWVasdROcqqhufZVk88MAD3HXXXdx+++386U9/iret2T4SieBwOOLbNhSLZVm19pEYY+I2iW1qtolEIqSkpMSXOxwOwuHvbj8bDAZrxV5zvhL31VBcNY499li+/PJLAoEANpuNX/7yl/zyl79k4MCB8fOclpYW3y4cDjNlypRalTcQrb55+umnmT9/Pt27d+eWW27B4/HEY6jvPNc9P927d2fIkCEsX76cI444ApfLVet4GjqXBQUF/Pvf/2bu3LmkpaUxefLk/fYdDodxOp3xbYwx+50/iFZhXXfddYwbNy4+dLHm/CU+FzabDb/fX+t5rHle6zvPNcszMjKYNGlSvFqq5hzXvX5rron09PT9+kp8Luo7pkAgQHZ2NgsXLtxvu0gkgsvlqrWv+o6rvtdEzXlMjCkUCnHmmWfWqiirWV6zTVPXRn2v7+a+Vv1+P3Xf09paz549232fIi2ha1SSna5RaUt7qgJs3u3G7wsRsocJBVreR3p6Oh5P43OQuAix3e3n62276ZWZ0mhbkdam99HkVPeL/Ya0y/C9/Pz83+bn5x+Zn5//PaITlf8rPz//CmAJMDnW7Grgn7Gf58YeE1v/r/z8fCu2/PLY3fmOBgYCy4DlwMDY3fycsX3MbYdDa3UZGRn06tWLjz/+GIjOr7RkyZJGh83V10dVVVX88dlnn81LL70Uf1wzFGrUqFHxoWb/+te/2Lt3b4N92mw2nnrqKTZv3swjjzzS7FiGDx/O/PnziUQilJSU8NlnnwFw3HHHsX379vid79555534NolxFRQUkJOTE6+8aYns7Gy6devG0qVLAXjzzTcZNWpUo9scffTRDB06lIcffjie8PL5fA3O4XPGGWfw7rvvxt8EKyoq2LFjB/v27SMtLY3s7GxKSkpYsmRJfJvMzMxaz09DvF4v69at43vf+16TbVNSUuLDzPbt20e3bt1IS0tj06ZNfPHFF01u35DKykr69OkDwOuvv95k+1GjRvH+++/j9Xqpqqpi0aJFTW5z/fXX8/e//z1+vkeNGsUHH3yA1+vF4/Hw/vvv71cdVSPxuBuSlZVF//79mTdvHhBNKH355ZdNxnUgTj31VJYvX86WLVuA6HP47bff1mrT2LXRkJa8VkVERERqfFXspcIXIsvVth/7slx2IsB/dzX9N66ISKL2vvteXXcCr+bl5T0I/BeomVzmRWBObCLzcqJJJvLz87/My8vLJzqBeQi4MT8/PwyQl5d3E/ABYAdm5efnt82nznbwxBNPcPfdd/O///u/APzmN79pVmKiRm5uLtdffz0ffPABDz74IA888AB33303Y8eOJRQKMXLkSB5++GF+/etfc+ONN3LeeecxatQo+vVrfBoul8vFrFmzuPTSSzn88MMbvHtcogkTJvDJJ58wZswYjjnmGE4++WSys7PjE5hfddVV5OTkMGLECL755pv48f7mN79h7NixpKam8uc//7nZx17Xn//85/hE5wMGDNhvDq76PProozzwwAOcfvrpdO/endTU1AbvEHf88cdzxx13MHXqVCzLwuFw8NBDD3HqqacyePBgRo8ezYABA2rdCfGKK67gyiuvpFevXvXOK3XTTTeRmppKIBDgsssua3Ci8ERXXHEFY8eOZciQIcyYMYM5c+YwduxYjjnmGE455ZQmt2/IrbfeyvXXX0+fPn045ZRTKCwsbLT9kCFDuPDCCxk3bly9Q+3qk5OTw/nnn8/zzz8f72PKlCnxIZFTp05l8ODB9e478bjvvPPOBvcxc+ZMfvvb3/LEE08QCoWYNGkSgwYNajK2ljrssMN4/PHHufHGGwkEol9F3nHHHRx77LHxNoMGDWrw2mhIS1+rIiIiIuXeEEX7AliWhcvRtkkph82Q7bLzZbGH8wb2aHoDEZEYc4jfxckqKqo9yq/mjmc1igr9eD2td47S0g19+7tarb/OoLq6moyMDMrLy5k4cSLvvPMOvXr16uiwOoXEIZEiTan7/tUeVC4tyU7XqCQ7XaPSVj7dXsmqXdWk2m2kOw88KdWc4XsAJdVBdu0LcvfZ/chydXTtgxxK9D6anGLD95qcZE7vFk041BJIbeHqq6/G7XYTDAa5+eablZASEREREWlD+/xhtu/1E45YpKe1zw3Xs112Ct0B1u7xcNqA7HbZp4h0fkpKSZurb4iaiIiIiIi0jW9KvLh9YVLbeNheIpfDRobTxqpd1UpKiUiztd+7lIiIiIiIiLQpbzDC5gofgXCEzIMYtncgurnsFO0L4g+Fm24sIoKSUiIiItLKKspCFBUewH3HRUTkoG0o8+L2h3HaDcY0OZ1Lq8py2QmEImws87XrfkWk81JSSkRERFpNKGRRuifEzm0B/D59Uy4i0p6C4WhCyBsIk+2yt/v+01NsOB2GlTur233fItI5KSklIiIiraaiLITPF8FTHaFkj+4eKiLSnjaV+6j0hXB0QJUUgDGGbqkOtsYmWRcRaYqSUkmof//+5ObmMmbMGK677jq8Xi+FhYWMGTOm3vaPPPII//nPfwCYPHkyq1evBmDatGm43e4DimH27Nm8/vrrB3YAIiJySAqFLPaWhQkFLVyphlIlpURE2k04YrG+1Ms+f5juqe1fJVUj22nHEwyzw61h3CLSNN19rwkbN26kqqqq1frLzMxk4MCBjbZJTU1l0aJFANx0003Mnj2b8ePHN9j+9ttvr3f5nDlzDjjOq6666oC3FRGRQ1NFaQi/L4LdBi6XDU+1RSgYwZGi78BERNra1r1+3L4wdhvYOqBKqkaG04YxsKJoH0f1cHVYHCLSOeivxCZUVVXh9/tb7V9LE1wjRoxg69atAITDYW6//XZGjx7N1KlT8Xq9ANxyyy28++67+207cuRIysvLKSws5KyzzuLmm29m7NixXHvttfFtR44cyUMPPcSECROYMGECW7ZsAWDGjBk8++yzQLT6qqbNGWecwdKlS+PxPPDAA4wfP56xY8fGk2B79uzhkksuiVd71bQXEZGuKxSy2FserZJyphpSXIZwKEJ5maqlRETaWsSy+LrEw15fiG4dMJdUIrstOoRvQ5kPy9IQPhFpnJJSSSwUCrFkyRJOOOEEALZs2cLVV1/NkiVLyM7OZsGCBc3u69tvv+XKK6/kww8/JCsri7/97W/xdZmZmcyfP59rrrmG++67r8FY5s+fz/33389jjz0GwCuvvEJWVhYLFixg/vz5/OMf/2D79u28/fbbnH322SxatIhFixYxaNCggzgLIiLSGZTHqqRs9uicIg4H2B2G4l1KSomItLWdlQEqvCFsgMPe8R/xsl123L4QpR79DhCRxnX8O5bsx+fzkZubywUXXEC/fv2YOnUqEJ1ravDgwQAMHTqUwsLCZvfZt29fhg8fDsAll1zCsmXL4usuuuii+P8rV66sd/ua4YNDhw5lx44dAPz73//mjTfeIDc3l4kTJ1JRUcGWLVsYNmwY+fn5zJgxg6+//prMzMwWngEREelMQsFolVQwaOF0RYeMGGNwpRqqKiNENNmtiEibsSyLr0u8VHhDHXLHvfpkOe1ELFi1S3fhk7YVDAYpLS1VVV4npjmlklDinFKJXK7vxmTb7XZ8Pl+z+6x7943Exw39nMjpdMb3Gwp9943Hgw8+yDnnnLNf+zfffJPFixdz88038/Of/5wpU6Y0O1YREelcaqqk7I7av0ecToOnOkKlO0z3HvqTQ0SkLRRXBymuCgIGpyM5ag5S7IYsp421e6rJPa57R4cjXdiOHTsoKSkhLS2NgQMHkpqa2tEhSQslx7uWtLmdO3eyYsUKAP75z3/Gq6YA5s6dG///1FNPbXafZ599NrNnzyYYDALRIYIej4cdO3bQs2dPrrjiCi6//HLWrl3bikciIiLJJBS02FsRm0vKWfuLjRSnwWaguCjYQdGJiHR9X5d42esLke1Kro922akOSqtDVAU0hE/ajtvtxu12s2HDBj777DN2796tqqlORl9bHiIGDhzI66+/zl133cXRRx/N1VdfHV8XCASYOHEikUiEp556qtl9/s///A+FhYWcf/75WJZFTk4Os2bNoqCggGeffRaHw0FGRgZPPPFEWxySiIgkgfLSEH7v/lVS8N0Qvr0VYSzLarAaV0REDkyFN8TOygBhy8KVJFVSNbJddna6A6zb42VU/6yODke6IJ/Ph9frxeVy0bt3b8rKyli2bBlHH320qqY6EXOIZxGtoqKiWgs8Hg/p6enxxxs3bmzxHfMak5mZycCBA1utv+YoLCzk6quv5l//+td+60aOHMl7771HTk5Ou8YkzeNwOGoNlxRpTN33r/bQs2dPSktL23WfkjxCQYvNG3xUV0VISzf1Jp183gj73GFOHpVBRmb7z3Wia1SSna5RORifbq9k9a5qXHYb6c62SUqlp6fj8XhavJ1lWawv83FEppOfj+jTBpHJoW737t188803OJ1ObDYblmVRXV2N2+0mJyeHH/zgB/Tu3VtfinWQvn37AjR58lUp1YT2TiCJiIh0FmWlIfw+C7u9kTkJXQYwFO8OcvRxyTEBr4hIV1DlD7N9r59g2KJHWnJVSUH090I3l52d+wIEwxFSkuCugNK1uN1u/H4/3bt3x+fzYYwhMzOTtLQ0SktLVTXVSeid4RDQv3//equkAJYuXaoqKRERabFg0MJdHiIU+u6Oe/Wx2QwpLigvUdWniEhr+qbUi9sXJi3Jhu0lynLZ8QcjbCpr/g2aRJojEolQWVmJMQabrfZrwG6306tXL9LT09mwYQMFBQWaayqJJe87mIiIiCSt8pJYlVQ9c0nV5XLZ8HksAv5IO0UnItK1+UIRNpf78IcjZCbZBOeJMlJspNgNy4tabzoUEYCqqioCgcB+CakaNVVTffr0Yd++fSxbtox169a16A720j6S9x1MREREklIwYLG3pkrK2fQ8DU6nIRKxKC1WtZSISGvYUOrF7Q/jstc/n1+yMMbQPdXO1go/EVWpSCvau3cvXq+XlJSURtslVk1t3LhRVVNJSEkpERERaZHy0hABv4WjGVVSAHaHIcVpKNkdbIfoRES6tmA4woYyH55AmGxX8s/Vl+WyUxUIs6My0NGhSBfidrsJh8M4nc4m29ZUTfXu3VtVU0lISSkRERFptmAgEquSipDSjCqpGk6Xjeoqi1BIQ/hERA7GpnIflb4QjiSvkqqR6bRjA1bu3NfRoUgXEQwGqa6uxpiWvQY011RyUlIqCfXv35/c3FzGjBnDddddh9frbbR9fXcI3L17N9dee22D27jdbl566aWDDVVERA4xZSXhWJVUy/4QdLoM4VCEvWUawicicqDCEYv1pV72+cN0T03+KikAu83QLdXB+lKfPvxLq3C73QQCAez2lr8G6s41tXTpUlVNdTBHRweQ7Jz71mIPuVutv7CjG4GsIY22SU1NZdGiRQDcdNNNzJ49m+uvv75F++nTpw/PP/98g+srKyuZPXs211xzTYv6FRGRQ1cwEMFdESIUjJCW0bLvtRyO6DC+4l0hevZuutReRET2t22vH7cvjN0Gtk5QJVUj22Wn0O2n3BvisPTG5wASaYrb7cbr9ZKZmXnAfdRUTVVXV7NhwwZKSkr4wQ9+QJ8+fTpFBWJXokqpJthDbmwRX6v9a2mCa8SIEWzduhWAn/70p5x//vmMHj2av//97/u1LS8v58ILL+TDDz+ksLCQMWPGALB+/XomTJhAbm4uY8eOZfPmzfzf//0f27ZtIzc3lwceeIDq6mry8vI477zzOPfcc/nggw8AKCws5Oyzz+b2229n9OjRTJ06tcnKLRER6ZrKSsL4fRaOlJYPGTHG4HIZ9rkjWBF9Uy4i0lKWZfF1iRe3L0S3TjCXVKIsl52IBf/dVd3RoUgnZ1kWbrcbY8wBVUolSqyaqqqqYtmyZaxdu1ZVU+1MlVJJLBQKsWTJEs455xwAZsyYQY8ePfB6vUyYMIHx48eTk5MDQElJCT/5yU+44447OOussygsLIz3M2fOHKZPn84ll1xCIBAgHA5z9913s379+nhFVigU4sUXXyQrKyue3Bo3bhwAW7Zs4amnnuKRRx7h+uuvZ8GCBVx66aXtezJERKRDBWJVUuFwhLT0A/tOy+kyeD0R9lVGyO7euT5QiYh0tB2VAcq90RtGOOydq7YgxW7IctlYt8fD2GO7d3Q40ol5PJ5WTxrZ7XYOP/xwqqur2bhxI6WlpaqaakdKSiUhn89Hbm4uACNHjmTq1KkAzJo1i/feew+AoqIitmzZQk5ODqFQiMsuu4yHHnqIH/3oR/v1d+qpp/Lkk0+ya9cuLrjgAo455pj92liWxR//+EeWLl2KMYbdu3dTUlICROe4Gjx4MABDhw6tlfASEZFDQ3lJGL//wKqkaqSkGIyBPbsCZHdPa+UIRUS6rpoqqQpv56uSqpGd6mDPviCeQJh0Z+c8Bul4brcbn89HSkrrDgOtqZpKS0ujrKyMZcuWcfTRRzNw4EDS0vQ3S1tSUioJJc4pVaOgoICPP/6YefPmkZaWxuTJk/H7/UA0sztkyBA++uijepNSF198MSeffDKLFy/miiuu4JFHHuGoo46q1eatt96irKyM9957j5SUFEaOHBnv3+VyxdvZ7XaVM4qIHGIC/poqKYu0tAP/xtDYDE6XYW9ZGMuy9O2jiEgzFVcHKa4KAgano3NVSdXIdtrZEQmwrtjDiCOzOjoc6aTcbjehUIisrLa5hlQ11f465zvaIWjfvn1069aNtLQ0Nm3axBdffBFfZ4zhscce49tvv2XmzJn7bbtt2zaOOuoopk+fTm5uLl9//TUZGRlUVVXV6r9nz56kpKTw6aefsmPHjnY5LhERSX5lJaHoXFIODvoPMqfL4PdZ+DyRVopORKTr+7rEy15fiCxn5/345nIY0lNs/LdI80rJgQmHw+zbtw84+L9HGtPQXFOaW7ltqFKqkzjnnHOYM2cOY8eO5ZhjjuGUU06ptd5ut/P0009zzTXXkJmZybnnnhtfN3fuXN566y0cDge9evXi17/+NT169GD48OGMGTOG0aNHc+ONN3L11VdzwQUXMGjQII477rj2PkQREUlCAX+Eyr1hwmGL9NSD/zDkdEb/iCzeHeKoYzV8Q0SkKRXeEDsrA0Qsi9SUzpuUMsbQLdXOzn0BgmGLFLuqTqRlKisrCQQC2Gzt8zqor2rqhBNO4IgjjlDVVCsylnVI3wHHKioqqrXA4/GQnp4ef+zct7bFd8xrTNjRjUDWkFbrT7o2h8NBKBTq6DCkk6j7/tUeevbsSWlpabvuU9rXrh0BSnaHsNkgxdk6f4DtLQ+R4rRxyqiMVumvMbpGJdnpGpWmFGyvZNWualx2G+kdUCmVnp6Ox+Nplb6qAmE2lvmYfkpvvn+45umRltm6dSsbNmwgPT0dh+O7+prWvEYbEg6HKSsrw7IszTXVTH379gVo8o9HVUo1QQkkERE5VPkTqqRcrVAlVcPpsuGpjhAMREjpxENRRETaWpU/zLa9foJhix5pnf/9Mj3FRorNsHxnlZJS0mJud7RYJDEh1V5UNdV2Ov87m4iIiLSJsuLYXFIprfvHltNliIQtyopVCSoi0phvSr24fWHSOunk5nXZYkP4tlT4iBzaI3akhfx+f5tXQzWl7lxTy5cvZ82aNZpr6iB1jXc3ERERaVV+X4R97miVlLOVhu3VsNshJcVQvCfYqv2KiHQlvlCEzeU+/OEIma6u87Et22WnKhBmV2Wgo0ORTmTv3r34/f4OqZKqq6ZqKiMjg02bNvHZZ59RVFTEIT410gHrOu9urUQXkoh0Vnr/ktYUv+NeKyekIPpNozPVRnWlRSSs61ZEpD4bSr24/WGcNtOlhgdlOqM3uVhRVNVES5HvuN1u/H4/Lpero0MB6q+aWrt2reYDPgBKStVhs9l0IYlIpxMKhdrtTiTS9dWqkmrloXs1nE5DKBRhb4V+54qIJApHLKoCYTaU+agOhOmW2rXuVGq3Gbq57Kwv1ZAnaZ5IJILb7cZms2G3J9frIbFqasOGDaxYsQK/39/RYXUqHV/7lmRSU1Px+Xz4/f4u9Y2EdE4ul0tvatIky7Kw2WykpqZ2dCjSRcSrpNooIQXgSAG73bCnKEhOz5Q224+ISHuzLItgxCIQtgiGLQLhSMLPtR/7w5H48mAkQiBkEYpYRCwLbyiC0961qqRqZKc62FHpp8IbpEeafgdI46qrqwkGg0k7KqCmasrhcLBz5078fj8//OEPycho+7sMdwVKStVhjNGtHSVp6DbRItLe/L7v7riX3op33KsrOoTPULk3gmVZXfJDl4h0XuFILLEUihCIxBJIoUitZFNiQimQmFwKW4Qti3AsuRSKQChiEQxHtw9Fovuwxd729n/3i74nOu02eqQlV1VIa8ly2YhEYPWuas45pntHhyNJbu/evXi93qT/AjY1NZVevXpRXFxMQUEBw4cPp3t3Xd9NUVJKRERE4kqLQwT8FiltWCVVw+k0uD1hqiojZHXrmh+8RJKZ2xei0B1gZ2WAI7s5GdQrvaND6nAV3hD/3urGE4gQsSzCVjRBVZOkCoaj/0csC4PBJCSWDFBTx2EAYzPYiCafbDZwOQyZNjspdrAf4kPunXYbmS4bq3d7lJSSJrndbkKhEOnpyf8elZKSQp8+feKJqVNPPZXevXt3dFhJTUkpERERAcDnjc4lFQlbuNqwSqpGitNgMBTvCiopJdIOLMtiry9ModtPoTvAXl8Ity/EPn+YZTssjshMISf90B1KFbEslu2oYldlgOpgBLuJVnUaAzYMDhtkumyk2AyOLjb5eEfIdjkorg7iDYZJS9HvAKlfMBikqqoq9lrsHK85u91O7969KSkpYenSpQwbNoz+/ft3mvjbm5JSIiIiArTPXFKJbDaDK9VQURbSED6RNmJZFuXeaEVUodtPpT+M2xciELYwQKrDxhFZKXxT6uOf31Rw9cmHYztEX4sby3zsqQoQjFj0y3Z2dDhJwUQimHDb3JAi22VnZ2WAr4o9nNovq032IZ1fZWUlgUAg6SY4b4rNZqNXr16Ul5ezcuVK/H4/xx57rG5MVA8lpURERCReJWVFLFLS2u8PJqfLsK8ygt8XIbWLzp0i0t4sy6LUE4pXRO3zh9jrCxOKRBNRaU4b3VPttRLB/bOdrC/18FWxl8G9k3+ITGvzBMOs3e1hrzfUZedxOhC9t6/j8L272PK9YVR1b90hSKkOQ5rDxhdF1UpKSYP27t2Lz+frFEP36jLGkJOTg9vtZs2aNXi9Xk488UQcDqVhEulsiIiICGXFIfy+SLtVSdVIcRqsCJTsDtH/aH0QFDlQEcuipDoYr4iqCoRx1ySiDGQ47KSnNTz8pVuqnUyvnXnflHz0IQEAACAASURBVHNMjov0Q2w41RdF1ZR7g7gcBoddlQwAJhKme+kOrPJiBpQWs2vw6VT0Oab1+jeGbql2dlQGCEUsHLZDs0JPGmZZFm63G6DTJnKMMXTv3h2Hw8GGDRvw+/2cdNJJOJ2qxqzROZ9ZERERaTX/P3t3HiNpfh72/ft7z7qr755jZ3eHNyWT3JCMJFhQbMiOTMtA7CRAxTESJYEDAjaQ5J8EgYEAChwHSIAAgoEkAhwDsQUEEBqCKZEiTXLFXVLLva/ZY+6Z7jn67q7jrar3Pn75o6p3Z2d6Zvquo38fYLCz1XU8VVP9Hs/7PM8v8DM67ZQsE+TyJ3tSoOsCy+4NWL9w0T7R11aUUZdmkk035p4TsuJEuHFKy09JpUQTUDJ18tbeEixCCJ6pWFzd8vmLWw7/wVenjjn64bHajrjbCgkSyUzhdCXjnqTU2kSLApZnPkeptc7ZD36B5XfYeP5rII4mcVe2dTa6MYuNgC/NqBXQlc/yfZ8wDMeivb9UKqHrOnfv3iUMQ775zW+OZPXXcVBJKUVRFEU55bb7VVKWNZiDPsvW8N2MOM4wTVWhoChPkmSS9U7E/XZv1TwvSmkFCVKCEFC2NXIHrCiwDY2zJZM3lzt881yRZ6rjnyhOMsk7q10aXkLV1sbi5PeoVOvLJHFCZ2aaZmGS+e0lZq6+je13WP7yr5Pphx+KX7Q0DF3wzmpXJaWURziOg+/7mOZ4LMCQz+eZm5tjY2ODN954g29/+9tUKpVBhzVw6shPURRFUU4x38vo9qukTrp1b4dlCdJU0tg6nmG6ijLq4lRyrxXy6r0237tS52eLDm8td1h2QpwgpWxrzJVMZosmOeNwlT6zJRNdE3zvap0kk0f0DobXxxse226MrkksQ50a7dDSmHJrnbZRQBgGCMHG7OdYmX6e4tJVnr/0EmboHf51hGAip7NYD8jk+H/flP1ptVrEcYxtj0+C3LIs5ufncRyH1157je3t7UGHNHBqy6soiqIop1j9kyqpwcWgG2AYgs31eHBBKMqQidKMO82AV+60+d7VOi8tOryz3GWl3RtcPmHrzJdMZorGoRNRD9KE4ELVYtmJeHO5c2TPO4xaQcK1LY9OmFLNqQaSB5Wb64goopWrfub21sQZ7p79CubGMhff/Qn5bvPwr2XrdKKUtU506OdSxkeapnQ6HYR4/Cy8UWUYBvPz84RhyOuvv87KygryFCdlVVJKURRFUU4p38vodlKyjIFVSUFvlo2VE3TbkuwUVGYoyuOEScZiI+AXSw7/5nIvEfXuape1dkQ36q0KN18ymS6Yx1rVU7J0pgoGL950aPrjmSyWUvL2cpdGkFKwNLQxO+k9rGp9hThJ8fKPtha5xUmWLnyNrO3w/Nv/lnJ99VCvVbZ0BIJ3V91DPY8yXjqdDnEco2njmbLQNI25uTmEELz99tssLi6e2sTUeP4LK4qiKIryVJ/MkrIHfzJm2RpJInGaqoVPOV28KOVm3eflRYfvXanz8pLD+2su690YP86YzuvM9RNR5gmuCneubBGlGT+41hzLE6XbjZD1bkSaZpQsNdz8QXocUmxt4pglhL77ZxNaBRYvfAM/kVx490WmV67DAb8nuiYo2xrXtvzDhK2MmVarhed5Y9W69zAhBNPT09i2zQcffMDVq1dJ03TQYZ04VaeqKIqiKKeQ72W4nZRM9lrnBs00QdNgYzVmcno8BpoqytN8uO6ydMtls9WhG2VogKEJpvM6xgkmoHZjaILzVYsrWz7Xtny+Ojc+q0T5ccaldZemnzCRUwmph1Uaa4g4olV48gqMqWFy55mvcW7jJvMfvortdVj7/DeRB6hsqeZ0ltsRLT9hIq9OUZXekHMp5dgMOX8cIQSTk5Pous6VK1cIgoCvfe1rY/++H6QqpRRFURTlFNreGOyKew8TQmDbAqeVjWVVhqI8rBumXNn0WGkFxKlkttCriJoqGANPSO2YzOkUTY3vX2sQJNmgwzkyl9Zcml6MpWsnWn02Kqr1ZcJMEuzSuvcwqWmsnPkSG9UzTNx4n+c+/kv0ONz3a5ZtnSzr/dsoShiGeN7hB+mPkkqlwtTUFIuLi7z99tsEQTDokE6M2goriqIoyinjuRluN0UOSZXUDssWxGGG2xmfk19FeZyPN73eynk5ncm8gT6Ec1OEEDxTtWj4KS/ddgYdzpFY70QsNgP8JKNiD99nPmhm6FFob+OYZcRev5NCsD39LPfnvkD+/k0uvv8XWH53X69r6RolW+PDDZWUUnpVUlEUoT+mfXRcFQoFZmZmWF1d5Y033qDb3d/v0ag6kdrIWq2WA/4SsPuv+ScLCwu/X6vV/hXw14Cdvdx/ubCwcKlWqwngnwO/C3j929/rP9d/AfxP/fv/s4WFhX/dv/1bwL8C8sCPgP9uYWFBXWpVFEVRlIf0VtyTmAMcbr4b0xKAYGs9plQ5XQeiyunSDlOWmgFBkjFvG/hDPEw8Z2icKRm8eq/NC2cLnKuM7nyXNJO8s9ql6SeUbX3sVvQ6CpXGKsQxrfL8vh/brsyyZNo8u3aNi+/8mPvf+Ot4lZm9v7ZtsNGNCeKUnKn2AaeZ4zgEQUCpVBp0KCcul8sxPz/P1tYWr732Gt/+9reZmnpyK+2oO6nLAyHw2wsLC98AXgC+U6vVfqP/s/9hYWHhhf6fS/3b/jbwxf6f7wJ/CFCr1aaA3wd+Hfg14Pdrtdpk/zF/2L/vzuO+c/xvS1EURVFGi+emuN2ULJMDXXFvN5omsGyob6th58p4u7zRq5IqWNpIJEbmSiaaEHzvaoN0hFfIvLzpse3GCHrJNuVR1foyvtSIcgdLBvj5CosXvk7seTz39o+pbt7d82Mrtk6cSq6ogeenmpQSx3EQQpy6SqkdpmkyPz+P53m8/vrrrK+vDzqkY3UiW+OFhQW5sLCwU3tm9v88aY/2d4E/6j/uDWCiVqudBf4W8OLCwkJjYWGhCbxIL8F1FqgsLCy83q+O+iPg7x3bG1IURVGUEbVTJTUss6QeZtkagS8Jg9O3+oxyOjhBwp1WQJiMzqpvmhBcqFrca4W8s9IZdDgH0g4SrvRbJifyo/G5nzTL75DrtmhZpUMlS2Mzx9KFr+NicP79l5m7e3lPK/PlDEHOELy3qlr4TrNut0sURSORsD9Ouq4zNzdHlmW8+eab3LlzZ2xnbp7YJYJarabXarVLwCa9xNKb/R/9r7Va7cNarfYHtVptpx74PHD/gYcv92970u3Lu9yuKIqiKEqf101xuxlyCKukdli2QGaSrQ1VLaWMp483PVpBQsEarUqdst2bffXjmw5OMFq/n1JK3l7p0goSipaOdspPdh+nWl+BKMTJTxz6uTLd4N75X6GRn2Dmyhs8c/1NRPrk740QgmrOYLkdjnRFnnI4juPg+z6WZQ06lIHTNI2ZmRkMw+D999/n+vXrZNn4zd08sfU2FxYWUuCFWq02AXyvVqv9FeCfAOuABfwL4H8E/imw255CHuD2R9Rqte/Sa/NjYWGBmZm99zkrykkzDEN9R5Whpr6jo0NKSXOrAxIqExqmObwnxIEX0HWO5rulvqPKMGm4EVuhi2bazJV712I1TaNQKAw4sr35gp3jw9U2L90P+K9/47mRqWS4udWllfY+99ny6M7EOlZSMtvZJDTziFIV84F/WyHEgZen3z7/FdLGCvN3LlNMQ1a//tdJ7fxj7z+nmTQ2XJoyx1dmygd6TWW03b17F8MwmJiY2PM2ZpS2owdRKBRotVpcv34dwzD41re+deDfyWF0YkmpHQsLC61arfZz4DsLCwv/R//msFar/b/Af9///2XgwgMPewZY7d/+1x+6/ef925/Z5f67vf6/oJcAA5Db29sHfSuKcuxmZmZQ31FlmKnv6OhwuykbaxFBkCE0jXh45yojREajHrO+tolxyOSZ+o4qw+SVO21WGy62LvC8XotqoVAYqaXP5/Iaby1t81emdL44/fjkwrAIk4yXrzdZaUdM5LRPPnfls3JuC5wG21qRJPlsRZNpmsSH2GlsVubxNIMLd29ypuNw/2t/jaBY3fW+mpTINObFy8vM6LMHfk1lNCVJwtraGmEY4vt7ny02atvRg7Btm1KpxKVLl6jX67zwwgvY9nAn2c+dO7en+53IZdJarTbbr5CiVqvlgb8JXOvPgqK/2t7fAz7uP+T7wO/VajXRH4juLCwsrAE/AX6nVqtN9gec/w7wk/7POrVa7Tf6z/V7wJ+dxHtTFEVRlGEnpaS+mRAEWX+Fu+Fm2oI0zWiogefKGGn4CfedkCSTFEZkltRupvI6eVPjT680CJPhbyO5tO7SDBJMHUx9eCtEB61aX0aGIe385NPvfADd0jRLz3wNmnWef+fHlJq7D27W+i18i41wbOfnKI/nOA5xHJ/aAedPUywWmZmZ4f79+7z55ptjk4g7qS3zWeDlWq32IfA2vZlSfw78f7Va7SPgI2AG+Gf9+/8IWARuAf8P8I8BFhYWGsD/0n+Ot4F/2r8N4B8B/7L/mNvAvz2B96UoiqIoQ89zM7xu7+TRMIY/KWUYoOuCzXWVlFLGx8cbHs0goTjiS90LIXimYrPtxfx8yRl0OE+02Y25VQ9wo5SqPdqf+7GSkmp9ha5uk1rHV3kR2EUWn/0GYRjz7Ls/ZWrt1q4D0Cu2TjtMWO9GxxaLMpx25kkNewXQIOVyOebn59na2uL111/HcYZ7O7wX4pRnoOXq6q5dfooyFFTbiTLs1Hd0+EkpubcY0awnmJYYiaQUQLeTEkfwa79VRNMOHrP6jirDoO7F/Phmi20vZq742Tkgo9p2staOaAQJ/81vnGW+NHwDidNM8uObLe40A/KWRt5QVVKPU2hv8/zlV7ivV2hX5x/5+WHb9x6mZSnn1m9QDTs0vvRN1i9+HcSn/z5JJvl4w+ffe77M3/ny1JG9rjLcpJRcunSJra0tKpXKvh47qtvRw0jTlM3NTXK5HN/61reYm5sbdEiP6LfvPfUgTm2dFUVRFGWMed0Mzx2dKqkdliWIY0nbUfNflNH30YaHEySURrht72FzJRMQfO9qYyhXSru27bPlRkiJSkg9RbW+QhZFdArH07r3sEzTWT77FbZKs0xde4dnr7yGlnya9DI0QcXWuLK195lCyujzfZ8gCAYdxsjQdZ35+XniOObNN9/k/v37I9vyqrbQiqIoijKmpJRsbyaEQYY5fIUMT2RaAk3A5uoQT2RXlD3YcmNW2lFvltQQr3q5X7omuFC1WGoEXFrrDjqcz+iGKR9veDhBymRhfBKBx0FkGZXGKh09jzzJHYUQbM5eZGXmIsU7V7l46S8ww08rXSo5nYaX4ASqjfu0cByHIAgwjBNfi21kaZrG7OwsQgjeffddbt++PZKJqfHZMyqKoiiK8hnuTpWUBGPEKgWEENg5QauRjuQBlqLs+GjDo+knlMdwplHF1pnI6/zwRotuOBxVjVJK3lnt0gwS8qaOtscl5U+rYnsLPfJp2eWBvH6rOs+dc1/F2Fzl4js/Jt/tjQuu2DpJJvlwwx1IXMrJcxyHKIrI54d/Vc9hIoRgenqaXC7HBx98wOXLl0nT4dge79VoHaEqiqIoirInOyvuhUGGOaLzQk1LEIWfth8qyqjZ6EasdSIyKcmNWGJ4r86XLbw444c3mkORQL7vRCw7IVGSUbbH8zM/StXtZdIopluYGFgMXmGCxQtfJ+10eP7tH1PZXsbSNUqWxgerKil1GmRZRqfTQQiB2G8iWUpEcrqrqoUQTExMUK1WuXbtGpcvXx50SPuittSKoiiKMobczuhWSe2wbAEINtdP98GmMpqklJ9WSY3RLKmHmbrG+YrJB2suS83BzoOJ0ox3V7s0/USttrcHIk2oNNdpG0UwzKc/4BhFVp6lZ7+Olwqeee9nzCxfo2LrrHdjgni0qj6U/et0OkRRtP+EFFBZWWT6/VcRiWr1LJVKVCoVbty4QbPZHGgs+7lIMZpHqYqiKIoyRJJYcn8pYvVexNZGjNNM8NyMJJYDqRx4cJbUMa7ufew0TWDa0NhSB5rK6Nnoxmx0Y6SU5MZoltRupvMGliH43pUGUTq4ysYP1z0afoKuCawRTcafpHJrAxGHA2vde1iqm9w9/6s0rTJzH73KF1c/JkpSrm+rgefjrtVq4Xketr2/gxbDdyltrZKtr5Bfv3dM0Y2WcrmMpmm8//77ZNnJb4+DJOPqpscPb+w9KaamiCmKoijKIXU76SeJKABd77We6YbAMgWmrWHZAssSmFbv76Yljm01vG4nw/f6sYz4iZlta7idjCjMsFQrjjIiPlMldQoqdoQQPFu1ub7t88qdNn/j8yffClb3Ym7WfbphymxRneLsRbW+TBInuBMTT1+z/YRITWP1zBeJGss8c+tdzj5j8t59i2+cLQ06NOUYOY6DlBLL2sewfSmpLt8mCwNSw6R45wbe+YtwyufICSGYnJxka2uL5eVlnn322WN/TSklG92Y242A+05IJ0pxo70nxNQWW1EURVEOyXMzoiijXO3NQkgSSRxBHGV4EiADKZH0E1ZmLyG1k6DaSVJZltb7ry3Q9YMdVD04S2o/x3bDyrIEnaxX+XXuwhi8IeVUWO/GbHQjMgn2iCeG9ypvaswVDX6+1ObrZwrMFk/u9zWTkreWuzT8hJKlH6gF6LTRkphSa5OGUUToQ5Y4FYLt6QuEVp6z9btsXElIvlzGqFYHHZlyDKIowvO8p9/xITmnjt1u0jJs9EKZ8tp9rHaDqDp9DFGOllwuRz6f58MPP+Ts2bOY5vG05/pxxmIzYLER0AoSWn5CJsHSBdOFvaeaVFJKURRFUQ5BSonnZggh0LTeyadpCnbb/2dZRppAFEMc92Y+SSmQSASg6wLDAsPoJageTFZ9+neB9oSE1U6VlGT0q6QAdKP3vrfWY5WUUkbCTpVUK0ip2KcrOTJfsmj4Pn96tcE//Nb8ia18d2M7YMuNyTJJIT/6272TUGmuIuKIVn5u0KE8Vqc8Q2gUiVyf5T/9Nzz3d34XMTM/6LCUI7az6p5h7D01IbKU6vIicRTjVmcwLYuCplFcuk70wl89xmhHw87g87W1Na5du8bXvva1I3vurF8VdasesNIO6YQpXpwhBJRtjZzRS3Kn2d7HV6iklKIoiqIcQhRK4kgCT9/5apqGZoG5S24lyzKSGOIY4jDD7fbLnjOQopeoMs1+S2C/msqyP62ssqzez3eqpOwxyt9YtobblSRJNrJD25XTY7UTs9ntrbhn7+MkaxzomuBC1eJWPeDDdZcXTqDlyo1SPtpwaQYJU7khq/gZYtXtFaIkw89XhqZ1bzdmLk9Ln+RKY5sLf/7HaP/ZP0YMeCi7crRarRa+71Mq7X17UdxYQfddGnahV4KuaXjlSUpb62hRSDbKAzWPiGmaVCoVbt68ycWLF/f1+e7Gi1OWGiG3mwFOkND0017LpS6YLRqHqlA9XXtKRVEURTliXjcjjjIM/XDJEk3TsGx2HUyeZRlx3BuoHoYSt9tPgPX/Y5ii90cXIHq3j0OV1A7LFridlFY9YWZ+jLJtytjpVUn1EiTVUzoDrWLrVG2dH15v8cXpPMVjXnnw3VWXpp+QM8Sht8OnhREFFNtbbJklhDbciTxNQFmHjyY+z+8s/5Dso3fR/53fGHRYyhGRUtJutxFCoO+xjVSLQsob9/HTjKj8aaLFLVcpOdsUlhfpfu6rxxXySKlUKriuy6VLl/jN3/zNfSeOMilZ6/RmRT1YFaV9UhV1NOkkteVWFEVRlEPw3IwkAfMYL8ppmoZtaxRKGuWKRqWq9/5M6BTLAl3vza9y3Qyvm2Hlji+WQTCMXhvf5ppahU8ZbsvtiC03BsAyhvtk/7gIIThfteiECT+5ebxLki87IfedED/JqNjqWvteVRqrEEe08ic/kP4gyiKhYZTYmjgHv3wRGQaDDkk5Iq7rEobhvh5TXVmCMMAplD8z1Dw1LMJCmeL92zCAVeeGkaZpTExMsL6+zubm5p4ft1OB+oNrTV5abHFpzWWzG5NJyVzRYLZoftKmdyRxHtkzKYqiKMop8+k8KQY2WFfXNeycRrGkU65olCoa+phVCwghsG1Bx8mQ+5hRoCgn6ZNZUn5K5Zirg4adpWucq1i8s+Jyp3U8CYQ4lZ9USU2cghUOj1K1vkyQCcL8aKxoVxYJEskHE18At0P21i8GHZJyRBzHwff9Pa+6Z3Ud8o1NOkIntfOP/NwtT6AHHrnGxlGHOrIKhQKGYXDp0iXSNH3s/TIpWXZCfrHk8P1rDV672+FOM6AVpFRtwVzJpJo7XJve44zXUauiKIqinKDAlyTJ3uZJKYdj2YI4lnTa6uqnMpzuOxF1LwYk1hi1zx7UdMHA0gXfu9wgTo/+9/ajDZdtL0aA+rz3wQxc8p0GjlkamVUKDQFVkXJJTJNOzcI7r5J1nEGHpRwBx3FIkgTb3kO5uZRUl2+TRiHdQmXXuwS5IolhUVy6fsSRji4hBJOTkziOw9LS0iM/70YpH667fP9ag5cWHT5Y71VFSWC2aDBTMI698ldtwRVFURTlgDw3IwrV8O2TYJoCIWBjLRp0KIryiKxfJdX0E6pq2DYAmhA8U7VY78a8dq9zpM/d9BNubPt0w5TJvPq896NaX4FodFr3dsxoEU1pcXPyIkQR8pcvDjok5ZCSJKHT6SCE2FOCtFBfx+y2ccw8crcljgGEwC1PYDe3MTz3iCMeXbZtUywWuXz5Mr7vk2aS+07Iz5ccfnCtwWv3OtxphjhBStXW+1VR+oklrtVRtKIoiqIckNfNSNPdV9NTjpbQBFZO0Kr3VntRlGFyrxXS8GOEEJhj1j57GEVLZ7ag87NFp19FdniZlLy90qXpJ5RsbWSqfYZFtb6MJwziXHHQoexLiZQ8KS9mZ5Cz83D5PbK6atEaZe12mziO9/Q7LJKEyuodwjjBL5afeF+vVEUiKdy7eVShjoWJiQm8MOYnb3zA9681eHnR4cN1l003RtCbFTVTNAZSear2moqiKIpyAFkm8b3BzpM6bSxLEAaSwFMtfMrwyKTk481+ldQpXXHvSebLFkkm+bOrDbIjSCjfbgRsdCMSKSmYqkpqP2zPwfbatEaodW+HEDCnRaxlNncqFwCQL/1IXaQYYY7j4Hke+fyjs6EeVl6/iwh8nFwRtCdvZzPdwC9WKazdRTxhhtJpkUnYijSueDmW9Vlu3lvj7vo2TpAwkdOZK5pUjmlW1F6pPaeiKIqiHEDgZ715UqN1XD/SLKv3YW+uq1X4lOFxpxnS8BI0VSW1K0MTXKha3Nj2ubzhHeq5vDjlgzWPpp+q4eYHUK2vIKMQpzA56FAOpCoSLCQ/TeeQc2dh6QZy5d6gw1IOQEqJ4/TmghnGk1fONHyX0tYqXgZxYW/D+d3yBFoYkttYOXSso8pLBUuewVstm8ttkzU3I0XDliH5+i2mC/qx7rMKnfqe76v2nIqiKIpyAF43IwolllqF/MRousC0oL6lklLKcEgzyeVNr786kTqsfpyqrVO2dX5wvYkXH7xy4f01l2YQY+tgqATg/khJtb6CKyxSKzfoaA5ECJjXIu5ledZKZ8AwkC/9AJmp6tlREwQBvu8//Y5SUl1eJAtD2sXdh5vvJrJyRHae4t3rcMqq6Zqxxodti3ccm9tdgesHmKHDvGwzY8TkSlVS1yFqbR5PAFIys3Kdi1d/ueeHqK25oiiKohyA52ZkmUQ3VanUSbJsDd/LiCN1EqIM3lIzpOkn6EIlSZ5ECMH5ikU7SHjxVutAz7HWibjTDPGjjLKqktq3vNvE9F1aVmXkWvceNCFidCQ/TWZh/hys3UfevjrosJR9chyHIAieWiWVcxrY7QZt3SKz9rBC3w4hcCsTWJ0W1ilaqdFLBZc7Jhteigy6TCQOc7pH1cjQtd7vvTAshGkRLN9Epkd7kU9PIp698SZz967gJHvfJ6q9p6IoiqLsU5b250mh5kmdNMsWZKmkvqmqpZTB2qmSagYJFVUl9VS2oXGmbPLWcpf7TrivxyaZ5J3+cPPKCa4INU6q273WvXZhtFbde5jWny11Ky2ylZ+GfBH58o+QidonjBLHcYii6MnzpLKMysoicRTj7qNKaodfqJAJQeHu9UNEOjqkhNueQZikTGYdpvWInA7ioTkTQgiMfIUsDvDX7x7Z6+fcFp/7+BcUt1dY00rcm3h2z49Ve1BFURRF2SfPy0jVPKmB0HUwTcHmxtGs5KUoB7XYDGj6saqS2ofZoomuCb53pU6S7b2l5vKmx3Z/hSh7ACtDjTyZUW2s0NFzyP1UmwypKdHb/v8smYG5s1DfIvv43QFHpexVlmW0222EEE9MMJc2VzC8Lo6d7+3890lqGl5pgvzmClocHSbkkbAVaTQiDTPxyT3l4xK6gWYXiLbukgaHm/UHMLF1l4uXX4FOm6XcHPXqmacOpH+Q2qoriqIoyj55bkYYSsbg2H7kCCGwchrdtiRLT9ecCGV4pJnkyqaPE6ZUVSvZnmmiN/R8pR3x5v32nh7jBAlXNz3aYcZEXg3xO4hiu44e+rSs8qBDORJ6v1rqSlqilatCdQJeeREZBoMOTdmDbrdLFEVPTEhpUUh5/R5+mhEWDv69dcsTiCQhv3rnwM8xCpIMlnyTOI6pir0l4PR8CSkz/OUbB17FUmQp5xYvce72+3hRyq2JZ/FLk/uuZlVJKUVRFEXZJ6+bgZQY6or9QFiWIE0yWk3VrqEMxq3GTpWUUFVS+1SydKYLBi/ecmj6T654lFLy9kqXpp9SsASaats7kGp9mSyK6Ix4696DpkVEiuDn8RTMngW3TfbWK4MOS9mDVquF7/tYlvXY+1RW70AY4ORLvQn3B5SYNmG+SPHerbEeeH7HN3BjSSl1P5kd9TRCaBj5CnF7i6TT3PdrmoHLxSuvMLF2my1pszT1PJn1hHbMBDjz3AAAIABJREFUJ1B7UUVRFEXZhySRhEGGVCdHA2OYoOuCjRXVwqecvCSTXOlX7kzmVZXUQZwtW0SZ5PvXmk+8Qr/UDFnrRMSZpGSpz/ogRJZSaazRNgpgPj4JMGoMAbNazKW0gmsWYGoG3vklWXdvFXjK4DiOQ5qmj01KmW6bQn2DLjpprnDo13NLkxheF7u1fejnGkadRLAW6ojYp2jsbxEYYeUQuom/fA2Z7X1l1FJznc9//AvM1jZ3rWk2Jp9B6AevZFVJKUVRFEXZB9/LSGLJHi9EKceg18InaDvZgUvOFeWgbtZ9WkGCrqEqdw7I0ATPVCyubflc29p9WfggyXh/zaXpJ0zk1CnLQZVam2hRMDatew+aFhGRFLySTMLMPEQB8pcvDjos5QmiKMJ1XTRN273FS0qq92+TRiGd0v6Hm+/GL5RIdYPi0vgNPJcSbrkmQZxSJXhkqPnTCCHQC2XSoEu4vbKHF8yYXb7Gs9ffIPIDbpefoVuZOfTiE2oLryiKoij74HUzolDyhKpz5QRYliCKMrrt/V0VVJTDiFPJ1U2fTpgy8bRJssoTTeR0CqbGn11r4MeP/h5fWnNp+AmmJjBVi+SBVevLJHFCtzg+rXs7LCGZFjFvJVVC3YbZM/Dxe2T1rUGHpjxGu90miiK0xwzBLtQ3sLptHDOHNMyjeVEhcMuT2PUN9HD3JPioWg112okgn7pY+sESQ5phoVkFgvVF0ujxK6Pqcchz199g9v5VmpnO7cnniXOlg4b+2RiO5FkURVEU5ZTw3N7Jk67mSQ2UaQkEgs011cKnnJydKilDqPlGhyX6Q8+bfspLi63P/GyjG3G7EeBFKVWV/DswLY0ptzZoG4VDtdYMs1ktwpcar8cVmJwBmSFf/qGqoh1SrVaLIAjI5XKP/EykCZW1O4RxjF882so+t1SFLKVw//aRPu8ghRnc9Q2SKKQsDjdjU8+XkElMsLb755PvNvj8x7+gsL3GilZmZeq5I20HVkfUiqIoirJHSdybJ6UMnqYJ7JygWU/UyYdyIuI04+qWTydKqapZUkfCNjTOlAxeu9dhpd27Qp9mkndWXFp+QiWnH7ot5DQrN9cRUUgrdzRtUMPIFpIpkfBaMkWiGTB3DhavI1fvDTo05SFSShzHAUDXH92GltfuIXwPJ18E7Wi3sZlhEhQrFJaXIBuP47glzyRIMsrS3/Nw88cRmo6eLxM1VkncB+ayScnkxhLPX3kV2e1wuzBPszqPEEebRlJJKUVRFEXZI8/tzZMS6nx0KFi2IAgkgUoUKifg+nZAq99Opqqkjs5cyUQTgj+92iDNJFe3fLbcCCkgpypSD6VaXyFOUrx8ddChHKtZLaItDd6JyzAxCbqBfOnPkWOSfBgXnucRhuGuiWY98ChureJJiPNH0xL2MLc8gR765LfXjuX5T1Iz1tgMdbTIJ68fzfdcswsIIfCXryOlREsTzt9+j7OLH+BGGbcmniMsThzLhQK1pVcURVGUPfLcjChS86SGhWkJZAbb64crW1eUp4nSjGvbPt04U+1kR0wTgmcnLO61Qn6x5HB508MJUybV53woehxSbG3imCXELlUp4yQvMiZEwi+SaVI0OHMOVu4hb18bdGjKAxzHIQgCDOPRVtLq8iIyDGjnj28gf2gXSEybwtJofy8yCbdcgyiJqIpw38PNH6c39LxK4raQG3e4ePkvqW7cYZMcd6aeJ7Mebbk8KioppSiKoih7IKXE7aYIQFdDd4eCrgssG7Y3VVJKOV7Xtvx+lZRace84lCydqbzBL+60afgxeUNXn/MhVRpriDga69a9B81pES1p8nFSglIFCgXkyz9CJmr/MCwcxyGO40fmSdlOg5xTp6ObZPbxJT4Qgm55EttpYrid43udY3Y/MOgmgkLiYR5wuPnjCMOimMQUrryKaDW4Y8+wOXn+2BPb6qhaURRFUfYgjiVxJDmiC1LKEbFsDd/NiCPVpqEcjzDJuLEd4EZqxb1jISVW0OWr4TrV+gqy26Fsq1OUw6rWlwkzSZA/HUmpAiklkfCzeAaJ6M2Wqm+SffzuoENTgDRN6XQ6CCE+2/6VZVSXbxNHMd3i8beZesUKGVC8e+PYX+s4eKngvq8j44CykR7tk0vJ+e17fKmxQpQmXLYruOXpE5nrN57LMCiKoijKEfO6GXEkj3r2pnJIliXotiWN7YT5c6qvUjl617Z7K+5ZhlBDt4+ClOS8NoVOvf+ngRH5EMc8Fwb47SmWpn8X1Gd9YGboUejU2TTLCO10JPiE6FVLLaUFbqYFvlwAWZmAX76I/OoLCNsedIinWrvdJooitIe+j6WtFQyvy7aVhxNoM5W6jl+qkl+/T/tL30Du0ko4rKSE255BkKRMSP/I2vYAjCTi+bWblFyHLaNAszBBlgboSYRuHP+x1ej8KyiKoijKAHluRhxn5AvqRGmY6AYYhmBzPVZJKeXIBUnGjW0fN06ZLajD5oMQWUbObVF8IAmlxSHEEXEqcc08XaOIVyqQswMubN5mYn2J1tnPDTr0kVVprEIU0SrPDTqUE1UmJUfKT+MZvqTfg7kzcPsa2TuvoP/m3xx0eKea4zj4vk8+n//kNi2OKK/fJ0gzwvLxzZJ6mFueoNhukl+/h/fM6GxntiONZqRhJR2Osmi36Ld5fvUmehxyNz9FoziJBmSRi+/VKZbPHPsFGbV3VRRFUQ5k5V5EGvnopyAPIKXEczME4pGrfMpgCSGwcr1qqSyTaIdcFllRHnR1y6cVpFiaqpLaKy1NyHebFDp1ip1t8t0WIokhCgmlhmPmcY0ybrlEnCt8ppInkhXc9iZzV96gPXuBzDAH+E5GV7W+jC81olzpVHWcCwHzWsTdLMdSluNzNsjJGXjrFbJv/Dpa6eQSH8pnOY5DlmWfGXJeWVmCwMfJlU60MjK2ckS5AsW7N/DOXxyJqswkg0XfJIpjZkTMkcySkJLZ1hrntu4RSbhVOUdgF6D/7Lphk0QeSexjWoXDv94TqKSUoiiKsm9hkNFxUppbDr/6TX3sT9aiUJJEEoQcdCjKLixbw3NTnGbC5LQ6iVWOhh9n3Kz7eKpK6on0OKTQaVDo1im269iug0gTiEICYdIw8rhmFW+iRGLnP7O/eGTPIQTrcxf53L0PmF38gI0vfftE38s4sPwuuW6LNas09vvm3VRFgoXkxWiG7+aWYXYebl5BvvoX8Lf+w0GHdyoFQYDneZ/5Pppuh0J9g47QSPLHm/DYTbc8wdT2Gla7SVSdOvHX3687voGbSEqpi2Ec/vdaSxOe3VhkorNNS7O4Vz1L+tBFAKEZICJ8t45h5hDi+C7Kqj2soiiKsm8dJyUMMtxOhNu2KVXHe9CS52ZEUaZW3RtSpgmaBhursUpKKUfm6paH46fYuqqSelBvXlGjXwlVx/I6kCbIKMTXc2wbOVyzjFepkBnWk5NQuwjsEs3qGaZuf0Dz/JeIiqdjUPdRqdaXIYpwqmcHHcpAfFotlWc1szlvgJw9Ax+9Q/bv/hba1MygQzx1HMchDENMs79/lpLq8m3SKKRTHkxCyC+WyRobFJeuEb3wVwcSw151EsFaqCMij6KRcdgqqVzocXH1BlbosmJPsFma7h1EPUQIgW7kSCKPMGiTy08c6nWfRCWlFEVRlH2RUtJ2MtJEohsa66sRX6jmn/7AEea5GUkCheKgI1F2I4TAtgVOK0NKqRIIyqF5ccrNeoCXnPIqqf7KeMX+LKhCp44ZuJDEZHGCp+domnlcewK/WulliB9w0N/EzelnqXS2OXv1de5+63dGor1mKEhJtb6CKwwSu3CqWvceNCFi1rD4aTzNf6WvwuQM1DeRL/8Q+R/9ntpHnLCdpFSpVAIg39jE6jg0zRzSHNAMCKHhlicoba+jRSGZNZyD8KWEW55JGKdMERx6uPlke5sLG4ukacqt0jzdXPmJ21dN09F0k9BrYloldP149oeneC+rKIqiHETgS8IgQ9Mhbxo0tsOxTgR8Mk9KMLbvcRxYtqDdSnE7GaXKeFfuKcfvyqaPEyTkTluVlMw+WRmv2K5T6DbQowDiiCTJ8Iw822YeNz9DMFVGHNNqWalusjXzHGc3Filvr9CZfeZYXmfc5DwHy++wZZVP1/f2IVp/Jb5baZGt1GRWBzl3Dm5fQ67dQ5x7btAhnhpZluE4Dpqmoes6Ik2ori4RxTFedbBVa25pgnKrTmFlie7Frww0lsdZC3XasSCXulj6wX+nhcw4t3WX2eY6XWGwNPEMibm3RJxuWL1qKb9BoXQ8iyeopJSiKIqyL+1WShhIDEPDsnXarYxOO6Mypi18YSBJYgmoeVLDzLQEINhaj1VSSjkUN0q5Vffx44zZ4uk4VJ7YukelvkKh20CLo97KeBl0jDyuWcQtzhHliwjt09+t4055NCrzTDprnLn8Kt3f+o+Rx3SFfpxU68vIMKQ9qZJ4UyJmHZufxdP8fX0dJiZhexP50p8j/8E/+syAfeX4dLtd4jhGyt4xVHn9Pprv0coV4JiS2nuVmhZBsUzx3i26z31p1xa2QQqz3iypJAqZEAkH3eqaccjzazcpem02zSKr5dl9bU+F0NAMiyjoYNlVjD0ms/ZjuD55RVEUZajJTNJpp0gpMS2BbWsIIdhYiQYd2rFxuxlRmGEYapc5zDRNYNlQ304GHYoy4i5vejhhSs7Uxr/aRErm7l/h3O33sDaWaYWS+3qF69XnuX7mV1iZ/RytibPExcpnElInQgjWZj+H2W0xfe/Kyb72KOq37nV1m3RIW5FOkt6vlrqclWhlem9I85lzsHIPuXRj0OGdGo7j4Ps+tm2jhz7FzRW8TBIXhmMlRLc0gR545Bqbgw7lEUueSZBklKWPfsCVhUuuw5fvfUTOa7NUmGaleuZACX5NMxFCELjbnyQYj5I6wlYURVH2zHUz4vDTnZGmCeycoFlPj2UnNQw8NyNNYVBjD5S9s2yt316aDjoUZUR1w5TFRtA7EbDG/DBZSs7c+5iZ5Rs0U4Mbs19kbeZ52tV5klxxKBJyXr5KqzzL7I33MAJv0OEMtUKnjhF4tE55696DpkVMIgUvx9O9G0oVyOV71VKJuoBxEhzHIUkSLMuiuryIDAOcIVq8IMgXSQyTwtL1QYfyGc1YYzPU0SKfvJ7t/wmkZL6+wudXrpAkCTcq52gVJw88n08IgWbYJIlPHLkHeo4nGfO9raIoinKUOk5KEGQ8eBHWsgVR2GvhGzcyk/iemic1KixbIDPJ1oY62VAO5uNNDydIyev6eP/Oy4xzSx8wtXqLujRZmbyAGNL2uM3p55BJzJlrbw46lKFWra+QRRGdwuSgQxkahpDMaTGX0grdrF/5OH8O6ptkV94fdHhjL45jut0uQghynSa51jYdzSCzcoMO7VNC4JYnyTW3MPyjT7YcRCbhlmsQxTFVEe57uLmeJlxcvc7Zrbu0hM31iQsEduHQcQmhI4SB79aR8miP+VVSSlEURdmTLJV0+4mnB1vZLLs3CHh9efxa+HxfksbjWQE2jnRdYFqCbZWUUg6gHaYsNQPCJKNkj29CSmQZ52+/x8TGElvkWJt45tgGlh+F2LTZnrpAZeUWhebwtdgMA5FlVBqrdPT84FYzG1LTIiKSgl8mvWSdKJSgXIVXfooMwwFHN97a7TZRFKELQXV5kSSK6Bargw7rEV6pipSSwr1bgw4FgPuBQTcR5FMXc5/DzfU04Yv3L1Np11m2q9yZOEdmmE9/4B4IIdANG5klBF7rSJ5zh0pKKYqiKHvS7WTEUYZ4qK9diPFt4fPclCiUHNH+XDkBtq3huZIkHr/KPeV4Xd7oV0lZ4ztLSmQpz9x6m+rmPTZEgY3J4U5I7dieOEekW5y9/CoiU7/bDyu2t9Ajn5Y9HHN6hoklJNMi5q2kSiD7p75zZ6HtkL37y8EGN+ZarRZBEDDTbWK4HRwrD8bwVWRmuoFfqlBYvYNIB9v+76WC+76OjAMqxj5jkZLn1m9h+11ul+bZKs8c+fB2oWkIzSAMWqTJ0V2MVkkpRVEUZU86TkoYSizr0cSTlRNEUUbHGa+TBa+bkWYS0xzPE9RxZNqCNM1oqIHnyj44QcKdVr9Kyhr+JM1BiDTh2RtvUt5aZk0vszVxbmSSb1LTWJ+7iN3aZHL15qDDGTrV7WXSKKZbmBh0KENpVovwpM4bcW+WkbBzMDUDb/4lmdsZcHTjSUqJ4ziINGFic5kgzQiGZLj5btzSBFoYkttcGVgMUsJtzyBIMirS33fb3tn6fSqdBsv5STr58oHnRz2NbtggJb7XOLKL0SoppSiKojxVkki6nRQk6Pqjuw7LEmhj1sKXpZLAl2qe1IgxjF4b3+a6Skope/fxpkcrSCmM6XBzLY157vobFOtrLBtV6tUzI7dd6xQm6RQnmbv6Jnqs2q52iDSh0lynbRRRZb27s4VkSiS8mkwRy/73fmYeohD5y58NNrgx5fs+YRhS3lqDwMfJl468aucoRXae2M5RvHO9lx0agO1IoxlpWIlLbp/XRiba28zXV9g2CmwXp44tIQWftvElkUsS+0fynMP7zVAURVGGRredEscS7TFV10II7Lyg2RifFj7fy0iSbJ/XqZRB22kn7bYzsmw8vovK8Wr5CXebIVE6nlVSehzy/NXXyDfXuW9N0qrOj1xCCgAhWJ+5iAgD5m68O+hohka5tYGIQ9W69xSzWkRHGrwX9z4nYZowMwcfvU3W3B5wdOPHcRyCVpOp1hYuGknu8IO2j1V/4LnVaWF2nRN/+SSDRd8kjmMqIt7XY/OBy7Mbi3SFwXJl7kSSf0IzQIj+0PPDH2uppJSiKIryVO1Wb7aS9YT5qbbda+Frtwbbj39UPDcjCiWm/fT7KsPFsgRxLGk74/FdVI7XRxserSChZI7fYbERBTx/9VVsZ5t71gxOeXY0E1J9kZWnPnmOybtXyHUagw5nKFTryyRxgqta954oLzKqIuHnyTTpzjn01CzIDPnyj8bmgtqwaDabaBvLmGlMu1gZdDh74hXLZEJQvHPjxF/7jm/gJpJi6mJoe99GG0nMxdXrJGnKUuUM8oRWUe1VS+VIk5AwOHwSb/z2voqiKMqRiiOJ52YIAdoTrr6YOy18K/u7wjOsPLdXafPgSoPKaOh9F2FzdTy+i8rxafgJ952QJJMUxqxKygw9nr/6Kma7yZ3cLJ3y9EgnpHZsTT1DLDTOXn5tYG02w0JLYsqtTRyzOBID6wdtVotoSpOPkhJA7zObOwu3riLXlgcc3fhI05Tu6jLFbpu2mTvWFSEzoIVNXeY47NZAajpeaYL85gpacnLHD51EsBbqiMinaOx9NquQGc+v3cCIAhbLZ0hO+CqqpulouknoNUnTw41MUEfaiqIoyhO1nZQ4kjzteHenha/VSJEj3jaVJpLAz461J185PjstfK0xaidVjsfHGx7NIKFojtcJvRV0ef7qqxidJncKc3ilqbFISAFkmsHm7EXy26tUN+4OOpyBqjRXIY5o5UajEmXQiqSURMJL8TSfHKZUp0DXkS/9AKlWdjwS7VaTeG0ZLQrwjni4uQR8DFYpc1nM8oa4wAfaGd6Sc3wk5gg43LbcLU0g4pj86p0jifdppIRbnkkYp1QJ9jXc/PzmXUquw73CNL49mPZI3bCQMiP0D1e5eiL1XbVaLQf8JWD3X/NPFhYWfr9Wq10E/hiYAt4D/vOFhYWoVqvZwB8B3wLqwH+ysLBwp/9c/wT4h0AK/LcLCws/6d/+HeCfAzrwLxcWFv63k3hviqIo467jpCSxJF98+o7StgWOl9F2UqqTw7fs7155XkaSSPZRQa0MGdMSdJyUbkcNPFd2V/fiT6uk8uNzndb2Ozx39TWE12WxeJawUB10SEeuVZphMrfG/JXX6MycJzulA76r2ytESYqfr6j5h3sgBMxpEYtpgZtpgS8bHkLTkPPnYOUucukG4vNfGXSYI8+58hGJ2yG0Czz1iuYeJAha5GiKPC1yBMIgkhqplJgyxsx8hGZymzLbWo5flXXmcA/0O5FYNmGhSPHuLdwLXzj2i5NroU47Ftipi6Xv/bWmWxvMtNbZMEs0CxMDu4gqhIZmWERBB8uuYhywWuuk9sAh8NsLCwvfAF4AvlOr1X4D+N+BP1hYWPgi0KSXbKL/3+bCwsIXgD/o349arfYrwN8HfhX4DvB/12o1vVar6cD/Bfxt4FeA/7R/X0VRFOUQwiAj8LM9r0BnWgKhwdqIt/B5bkYUPHmGljLcLFsAgnu3u6SpqpZSHvXRhocTJGM13Dzntnj+yi9hjBNSAAjB2uzn0L0Os0sfDjqagTCigGJ7C8csI7Tx+Q4ftzIpeVJ+Gs982v1ZrkIuj3z5h8hUzSI8DOl2ad27g4hCKJUO9hxAG4t7VPlAzPOGuMDHYo4lWaGdacgkoBI7nE0azGYdJgiZxeO5rI6XCd7kDJfFLNEBUx1uaQLD62C3jncAfpT1ZkklUUhF7P0CWtFv88zmEm1hslqZHXhVv6aZCCEI3O0DV6efyGXshYUFCXT7/2v2/0jgt4F/0L/9XwP/M/CHwN/t/x3gT4D/s1arif7tf7ywsBACS7Va7Rbwa/373VpYWFgEqNVqf9y/75Xje1eKoijjr+2kRIFEN/e2w9tpm3L6LXxiREuNvG6vhF9X86RGlqYJ7Bzcv+fRaGSUqjqlskaxrGFZ6t/1tNtyY1baEUkmmRyTAef5ToPnrr9BGvgslc4T5w92QjgqglyJZvUMU7c+oHn+S0RH3CY07CqNfutecW7QoYwUIWBei7ib5biT5bioBwghetVSd26RXX4f/evfHnSYTxWnGZoQ6EN2nBV8+A6e6xKaOcx91CqF6DTJ0epXQ0VCJ8wEZCmWjLCzmEkiPikm2uWpLVKeyxo0KHBDq7Ct5flVuc0s3r7eg18ok+oGxaXrhJOz+3rsfix6Jn4iKUsffY9VUmYccnH1BmEGdybPwhAkpIUQaIZNEgfEkYtl73/fc2K9Ff1qpneBL9CraroNtBYWFnbSgsvA+f7fzwP3ARYWFpJareYA0/3b33jgaR98zP2Hbv/1Y3gbiqIop4aUkk4rJU0l+dzeDyxGvYUviSVhkO16wKOMlnJVRxMGTsun7aQIDfJ5jVJZo1TRKZY18nltZJOnysHdbgQ0/YSSPfgD+qNQdLZ49sZbxEHIUvUZErs46JBOxObUs1TbW5y58jr3vvXvD7xi4CRV68sEmSDMl9Tuap+qIsFC8tNohu/mlnvV4IUSslyFV36C/MrXENbwLr17s+7z7kqXVELF1qnYOuX+n52/5w3txOfIya11nPt3iZMEYeefeN8UQRubpsjRJI8nTGKpkUiJIWOsLGRKhtjI3vHYHt+KAKbxKKchK1R5QzvLF3D4gmxgsseZYULglicp1zfQw4DUzu3tcfvQjDU2Qx0j6pI3MvbyBkWW8rnV64g4YrFynnSI2paF0BFCx3frmFYBIfZ3sefEzhYWFhZS4IVarTYBfA/46i5326n32u1fRT7h9t3e9a61Y7Va7bvAd/sxMTMz85TIFWVwDMNQ31FlYLqdGF13yOVTisXddxeaplEofHa4Yj4v8b2AdsPk81+cPolQj1SjHmKaQCGjUBi9pJryWZqmke//O8ZxitdNcZqSVkOSy///7N15kKRbWt/373mX3Jfael/u7Z57Z5g7DDBjjGVJgMOWDYSCQJLlRDYCHAJhhUCEIvAS0h8YIZmww7YQIUvYIIgAhRCRlhQGIcLMMDPg2WeYe2e5S+9b7Vvu27ucc/xHZvfte291d3Z3ZeWbWc8noqOrs7KqTlVnZb7v8z7P70Ch5LK4mKK04FMq+3hz0jUjHs1YS/POAC+VZrk0/RPPg55Hn0a+tsmZW18h0JrV5UuoTI7knKpMmO9TO/0+Tu3c4kS3Tvfk+Wmv6Ej4gw6lQZut7AKpI5gzV0rh+/P1qDqrDWs6Tz1d5rw3jBywL1wmvPINste+Tu4/+r4pr/C9jLV8+V6D1/c1zdilH2l2BhpsjLbgO4pCxiPvu+TTHgtZn3LWp5zxKGd8ylmPUsYnPYEucGsM/S//Mb1+nyidpZj2cR/asdla6OFRI0OdDE3SRCgGRuHamJSJKNqILNEwqtyBp6tGqXd8vSyW99Fg1+a5qhapuXm+WdVYUYOxPl24uILbqrGwc4/+B75t3B/DWLSF1Z6DsSEraYPvjPE7bC0XNm6RD3rcLJwkzuafM9L98DkqQxT2iII2+eIKyhl/44AjP9quVquNSqXyR8CfAhYqlYo36pY6D2yM7rYGXADWKpWKB5SB2kO33/fwxzzq9nd//V8BfmX0T7u3N9lZUSGex8rKCvIYFdOyvRHRqEV4HvR64YH3yeVy9HrvbYt2XM3GWpvzl8zMdaFsrUe0GiHpLPR6UqCYde9+jPrp4R9jLEE/otPqs7E6zGLN5oYdVIXScNQvlVZzs2OZeNtOJ2Kn0SIKYnq96efHPOp5dByl2gYrN75CO4y5U76AcX2IZjvT72nt5FcoumssfOUP2fuzfwnrzv/FhJX1G0TdLnulZeIj+P/2fZ9ozh5XRRuBdvi3rTw/mnn7tNEWy7T/8PfoXPomnHxyRkJjY/n8vTa36wPqg5iFtEvpwQYNCmstQazp9CLqsUVbi2XYpWEsZFxFIeWS8RwKaZdyxqWYervDqpge/vtZxwHtravou7fZG4QYL4XRmkDbBwHldTIEyiO0CmsNvgnwbciKDXl3QsSzPCu7joM+YPfEZdrkbZ8Nt8ynnRO8TJP32Rrewf0r79DN5kndvErv3EvgHN7x4L2+y37PIxW0wDNEY3zDJ2sblOpbrKVKNDJFSOROkQqUS6+7j+tlsWr85+Kj2n3vBBCNClJZ4M8xDC//FPCXGe7A96PA74w+5HdH//786P2frFartlKp/C7wW5VK5R8CZ4GXgS8xLKHbRR4SAAAgAElEQVS+PNrNb51hGPr9rCohhBBPyRpLu6kx1uI9Q/5OOq1o9AzNhmZhabZOEHrdUZ6UKwWpeeY4imxekc0PR1Wj0NLvGzptg9qISGcV+YJLcTTml8s7ODNWYBUHW2sFNAaa3Ix3xZV3Vzl361V6keVO+SI2weNGE6UUWycu8+La6yzfe4u9Sx+e9oomrlxbp4dLlMnL6N4zckY78V3XeXa1zwl3VHRbOQU33sJ+7pPwn/7AdBc50o8Mn77bYq0Z0Ak1y1nvPcUjpRQZ3yXjv7d/RhvDILZ0Q0Mr0Gx2hhcazWgyLus75FMOGc+lnHYpHVCwyvuPHge0QYC9+g1azSYNP88mWW6qEh1SRChCq4a75Nk+ZROQ5aGIhCN4AGdUzCWzz64t8KazyJ4aZk0t8PiuqW5hgez2PbJ7W/RPnj2UtfS1YnXgYaMBJU8zzg+g2G1wZu8udSfDdmEl0WPKrpcmDrv0ezXSufHzuI7qTOEM8BujXCkHqFar1d+rVCpvAr9dqVT+AfAa8Guj+/8a8M9HQeY1hkUmqtXqG5VKpcowwDwGfnI0FkilUvkp4A8AF/j1arX6xhF9b0IIMXe6XUMU2md+3fNTCseBrbVwpopSYWgIA8mTOm6UUqTSivvn9Do2DAZQ243Z34nxU4pc3nlQoMoXXfwxw/9FslhrWW+FaGPJpWbnuendFrdvc+b21+jGcHfhItY/3luFdnNlmqUVVq59hebZ9xGln30cMunSvRbpbpONVFE6OZ/TkorYIs0no2V+0N0CQPkp7PJJ+PqXMd/+Z3EWpxtD0BjE/PHtFjudkFBbVnLeU/+/u45DPgX5A3YajbWhGxk6gaHe06yPOoi0BU8NPybnO2T9d3ZX3c+uSrmKna++zkbd455zjqaO2PWW8IzFNyFZG7JE9NiA8qOggJO2QzEO2HTLfNY5yweoc8k2cB/RNRVkcsR+itztK4dSlLIWbvQ8BpGhbPuoMX4Y6bDPi5vX6VuHe+VTh9qxNQlKqQeFKS81fqehetZt++aE3dg4cMpPiESQ8T0xLRurIdsbEak0eI/JHnjc2EmnpYki+I7vzM9Mh0mzHnPvVog1lnQ22S/8YjzPMxoFwyJGMLCEgcWY4fFgNvdwWLpLJitjfrOiMYj5d1fr7HUjThaSkZHztI/R5Y3rnLr3Bm2tuFe+CHOW9fOs/GjAS3dfo33h/ax/y3dPezkTc3L1TZZvf4MrC5cxTwiTPizzOL5337ZJsWd9fiZzmwVnOEdltYbrb8LLr+D+xR+e2tq22iGfudtmrxdhLSxkjy5FyFpLqC2dcDiGp82wswpr0RZSnqLgu2RNgL1zk0G/T6BCTNTHd1wyyh5pAepR43sHMcCOKtJ2cpxWfV6xe5QJDrxvoVWjXN9l5898D/FzjnPuBg5vdXwI2iy58RPv7+qY9997HTfocbV8njB1+IHrk2CtJY56KOXx9//e34ExHgmze4lICCHERBht6bSHL+yPK0g9SSqt6PUMrRka4et2DFFkyR7Ncb6YAUopMllFJjs60IotQd/Q7Rq2N2PSGUU+71IoOxSKLrmCM/bWzuLorbdCWoOYzASCfifOWk6sX+XE2hWa2mFt4QIkaPelaYv8DHtLFzi5eo36xQ/SWzg57SUdPmsp76/TVSl0KiNNvYdgWUVsmTR/FC3xF9K7ACjXxZ48A9ffxGyu4Zw5+gD9m7UBX15rs9+L8V1FMXO0sdZKKdKeOjAU3VrLIDK0w5iV22+wXF9no3QWLw7QxO8IHE8iBzht25TiAZtumYY6ywdUnRdt4z27p3XzZUr1HfJ3r9N85aPP/DVjA7f6w+Lusop4Yp3GWl7YukFq0OVG8dTMFKTgfrdUhjgc/2JLsh8xQgghjlynbYhC89wj635K4TqwuXZwSHrSWGvpd4fftyN5UuIAwx2ohh1SC4suxbLCWqjXhh12197oc/X1AfduB9T24uEoqEiU9VZIoC3F9Iz9jlvLqXtvcGL1CnXtsrp4UQpSB9hbOEPo+px547Ng5+/3L9ut4/e7NFIl6c48JJ6ynHRCvqrLdMxDzwsLS+C42E/+Hkc5WWSt5WtbXb6w2manG5H1HIrpZO2zprCc7O7y76/+Ce/vrDNI5zGegzV6pgqlORVxyezjmZCv2RW+rM7S5p2j0NZ16RUWyG7dQ8VP7m56lLt9j25syesu3hjTA2f2Vym1a6xlF+lkkhO4Py7HcVHu+DmHs3HpWgghxJFpNTVhYEk/50UZpRTpjKJZNxhjEz/CFwbDsGvG2JFFCBiG4efywCgsPQwt3Y6m3dQoFZHJvR2WvrTi4UkO1VT1Is1edziCNFMn9NZy5s7XWdy6xb5Nsbl4HuUm6yQ1KazjsnXiEhc3r7C4cYP6ufdPe0mHqry3jg0DWksXnnxnMbZlFbJjfD4bL/I9qX0AlONgT5+FtdvY29dRlyf/WNLG8oW1NrdqA2q9mMWMi5+grk4njljcvcvS9m38focoithOlakXT6KjLsZqlDNb5QUHyxnboqMHbDpl6uocH1Q1Ltjmg+6dbmGBfKtOdmuV3vlLT/012rFiI3BRYZ+8Z3hSl9RCa49T++vseTn28kuJDjZ/HOcpHgvJeZQLIYSYuji2dNvDTIXD2H0ulXaIIkuzPv1t15+k1zWEkZFd98QzUUqRTjuUyi4LSy75oiIKLPs7MbevB9y5eXBehTg6662QbqhJzdJ4pTWcu/Uai1u32CXD5oIUpJ6knV+ik1vg5JtfxI3m6PfOGsq1ddpu5vjutDghKWVZVhFfjMsM7EPHAMUypLPDbik92eOYQWz45K0m1/b61HoxS9nkFKTS/TZnbn+ND7z2MU7d/CpRu809t8y1lZfZWzwHjoOJQxTgJHx071EKhFwyezgm4lV7gq+os3QZdqNG6QxhJkv+7rVhWvlTGIab+wSRpsSTw82zgy4Xt2/RUR5rpZOJDzY/LMfjuxRCCDGWTlMTRfbQXgP9FLgObK0nf4Sv1zXEEcixvjgMnjcc8ysvuqRSip3NmEDG+aZqWJQylGZkdE8ZzYXrf0J5+w7bKsv24jkpSI1DKbZOXMIJ+py88eq0V3No8q193KBP4yl2tBLjW3EietblS1HpwW1KKTh1Fna3MG9+dWJfuzWI+fiNBncbA7qh4UTew5v2BTJrKdS3eOHK53jf1z7BwuoVmqHhRu4Mt068TLt8Etxh0cYajTHxzDeau1jO2iandYN1m+GznGOVEhboFhfxOy1SrfpTfc7NwKUVKdK6S/oJF0S8OOLSxlVirbldOo11Z6vr7HnMxquyEEKII9FqaqLQHlph5t0jfEn1IE+KGRvrETMhk1UYbdm4l/zi7LyKtGG7E6EUiQ/hBVA65sK1L1HcXWXLKbC7cA6lkr/upAhSOfYXzrB45w3S7ac7iUyq8v4aJgxp5xamvZS5lFGGRRXzmXiJyL59HKDyhWHH1Kf/ABsefufddifk4zebrLdCIg1LWXeqxyFOHLG0dZOXv/4JLl75POntdXZshisLl1lbfpEgX37P+rQOh0WpOTl+KhJwSe9hTMxXOMlr6jT7uUWM45C7c3XszxMauNP3iMOAknp8HpWyhhc3r+GFA24VTxP7x+sKqby6CSGEACAKDb2uAXu47depjEMcWZr1Zw+InLRgYIkiyZMSk+G4wx38djZitJbH2DRsdiL6kWEWJvccHfHCtS9Q2N9gwyuzt3BGiuXPYHfpAjEOZ9783FOP3CSNMppSbZOWmx22IIuJOOGEtKzHa9G7utFOnoZWA/Pq5w/1692uD/jUrSZb7RClLAtTLEil+m1O3/k6H3jtY5y++RpRs8WqW+bqiZfYXTqPSWcfuTYdB1hrZy5P6nFcLOdtk5O6yV2T5zPORd5avExmdxNnzOLkrZ5PP7YUbR/3Cbmq53buUug2uZdbpp/OHca3MFOkKCWEEAKAVtMQBZbD7hb2/eFI/OZadLif+BD1OoYwMHgpeVkUk5HNOYShYXcrub8H82y9GdIYxJSOeFv1p+XGIS9c+TzZ2jarqUVq5VNSkHpGxvXYXnmR3O465Z27017Ocyk0dnDCAY106cl3Fs8spwwlFfOpeJmHrx+oTBYWluELf4Tpdp7761hr+cZ2l8/da7PTjcn4ilJ6CgUdayk0trl45fO89LVPsHjvCs1AcyN7ilsnX6b10Ijeoz+FxehobjvNSwy4ZPaIjOYz+Zf4YvYyau3eEz+uHjnsBC5e2CPrPn50f7mxzUpji22/QD23MDcdZ09Djr6FEEIA0G5o4tiSSh/ui6FSinRW0Wokd4Sv1zVoPSygCTEJnq9IpRXrd6Mj3V5cgLGWjXaIUuBPO6flMdwo4MW3Pkemscu91BLN4om5PMk7So3iCXqZAqfe+DyOnt2CcHl/jTiK6ebL017K3DvphNStz+tx4Z3vOHEKgj72c594rs+vjeULqx2+utllrxdTTjvk/KMtljs6YmnrFi99/ZNcfOtzZLbX2LEZrpZfZG3lEkF+ceznHqNDjNXYOX6q8jBcsA0WbI/bmRN8qe6zOXAe2YBpLNzseYRRRMkJHhtunu+3OLdzh5by2SidOJYFKZCilBBCCCAYGAYDA2oyV7oe7MJXS94InzWWfk/ypMTkZXMO/Z6eid0o58luN6IX6SfseTRdXtjn0pufwW/tcze9Qru4Is9Hh0Eptk5cxuu2WLn9jWmv5pk4OqbY2Kbl5Z7YtSKeXx5NXsV8Ilrm4etoyk/B8kn4+pcxa3ewg95TX2AIYsOnbje5utej1o9ZyjikjnCHvVS/w+k73+ADr36M0zdfJW41WfXKXF0ZjujpTO6pn3e0DrEmRqlkd6E+LwUs0Oc8ddKDDl9b7/ONts9B+5esDVw6kSJnuviPGdvzo4BLG9cIjeVO+Qw48/0zfJz5GfwUQgjxzFoNTTCweP5kToJ8H1wXNtcjFleSdVDd71viyB7Xi1PiCKXSCtdVrN4OWViSQ7Cjst4KafRjCgkdz/UHHc69+RncTos72ZP0C4uJLqDNmn6mQL18iuUbX6V+9mWi3GztXlesb6LCgEbmxLSXciwoBaeckNs6x3Wd4wNe7+13Lp+A+j7231WxSyuofBFbXkQVy1BagOIClMrDAta7tAPNH99pstWO6EealZyHcxQHHtZSaO6wtH2bQn0LG4Y03Sz72dP0c+8NLX9aOg6x1uIek51B+5k8r9SvkNszfDX1rewHPh8qaU6kDEpBXytWBx4mGlB0NTzi2VwZzeWNq6go5FbpHNpL1rHxUZMjIiGEOOastbSaGqMt6cxkDpDu78J3f4TPeULg41HqdQ1haOUCtJg4pRTZvEOroel3Ndn88TiInyZrLeutEGMhe8QjMuNI9ducv/knxJ0Wt3OnGORlZ7VJ2Fm+SKm9x5m3vsC9j/65mRqRKe+vE8WaXrYsxcojUkSTQfPxaJn3u70HDxfletiXPgjtJjT2sXs7YA3WWPA8yBchm0OVRsWp4gIUy+z6RT69B3t9jbaG5Zw38U5IR0cs7K6ytH2bVK9FHEbspErUymeJR6Hlz7sCYzRGRzyq8DKXlGK/sMz7G3fonTnP7cEiXwh93ldyeDkfc6PnMYgMZdt/9NietVzcvkWm3+ZW7gRBOnu030MCSVFKCCGOuX7PEgYWlJ3oluPptEOvq2nUYpYS1C3V72r0BAtyQjwsnVF027B2L+LlDyavSDJvmgNNK9CJrUGcufMN6LS4lT9LOGMdPLMk9lLsrrzA6a3bFGqbdJbPTntJY3GjgHxjh32/gDomnShJoNQwW2rVZLhrMrzoDt5+n+vCwtI77m+thTCAThv2d7E7m4DFGsvd9DJfyr1AZCwv6zapQoFBrkSQLRLkSgSZPBzisVdq0GFp+zYLu/dwgj5947CaXqC1sgijbpzDejqMwy7GalSCLjQehf38IqebW5zdu4c9m2Ffx1xtpNkZpHAdhR+3edyeGifrmyy2dtlIl2nlJCcOpCglhBDHXrupCQeG1IRHW7zRCN/WepSYopQxln7PSp6UODKOo8jkFHvbES++L4Wf0JGyebHeCmkOYjIJDDh3dESuvU/dyUhB6gjUyqdYaGxy+vXPcvM7/xJ2BvJbSrVNVBTSyC09+c7iUJVVzBaWj4cr/Hhm7bGFbaUUpDPDPyPWwps6z9cGGTqB5kx/Hy/ukq5vUdTh8KDIT2FTKcJcmcGoSHX/7yiVHb+jz1ryrV2Wt25RaGxjw4Cmk2U/c4p+fmEixzdxNCAKe1hjcI5Zq3ns+jSzZZZrG2yeeR8rHhRszOYgg6vgrPvo7rFit8GZvbvUnQzbhZWZ6tqcJClKCSHEMWaNpd3UGMvE8qTuezDCVzcYbXHc6b8Q93uGODbH7iqfmK5s1qHXidnejDj/Qnray5lra62QMDYsFJJ30pRv7qKikHZ+edpLORasctg6eZkX199gafUK+y98aNpLeqJybY1AWwbZ0nEakEoEZ9Qtdcdk2TQpzrrh2B+rLXxFl7ke59h3fUq5iG7hPN0Hd4hJhz0ygy7psE+2v0suXqds4mEIZyqFSWUIcmUGuSJBdthZNciV0P7brxmOjinvrbK8dWs0ohezkypSK505tBG9gxgTEwYtjIlxHPdYXtTbLa6wuN1gobFDfekMGWW5lOpj7aMvcqbDPi9uXqdvHe6VT4GTvIsl0yJFKSGEOMa6XUMUHl3Idzo9PBlv1GKWTkz/JLHXMYQDyzCT9PgdVInpcD1FOqvYWI04eyGVqIy1edKLNPu9COWoRJ40FRs76Ciily/BATs4icPXzS3QLCxz4uqf0DxzmTiV3CwXL+yTa+2zkyqi5OR1KhZVxCYp/jBa5kfczbE+JrSKz8aLrOoMDeuySIT/7v8+1xsVmkrvuFnFEZmgQ3rQJdMPyHQ2KcV3cDGQSoPnE2dyBLkSsZ+h2NgejuhpWM0s0FpeGha1mNwRjbWWoN8cZkkphZqBjsNJ6KZy9P0sJ3buUV88/aDj6VEvNa6Oubx+FRtH3C6fxxzzYPN3k6KUEEIcY8Nd9wypI2rW8PzhCfnmepSMolTXYKzFPcItmYUAyOZcGjVNfS9m+eT0fxfm0XorpBtqUkks+llLobFNx8uiXB9MNO0VHRvbKy9SvPMap658ifVv+e5pL+c90r0mi7v3KO+tQRjQKJ6c9pKOLWe0E981XWBPe6y48WPv37UufxwtsmXS9KzLiop4mqcf6/n0vUX6+cWHbrS4cUh60CETdsl0BmSaLQomop0psZc9RT9XOrLCZRS00XGAtfbYje29g1LsFZe5UFsnO+jQzz5mBNtaXti6QWrQ5UbxFGEq8+j7HlNSlBJCiGPKaEunbbBwZEWZd+zCN+URPq0tg74ZtrcnsItCzDffB9+D1TshSycmvxPTcbTeCulEhhO55F3Jz/RaeGGftl+Y9lKOncjPsLd0jpOr16i98CH65ZVpLwlHR5T311ncuUumU8eGAS0nw372NGGmIH28U7SkIrZI84l4mR90tx95v33j8+l4kR2TIraKZRUdThe6Umg/Tc9P0+PgUd+jenzcz5EyRh/vgtRILbfA2foGK7v3WL346HHgM/trlNo1VrOLdDKSH3gQuTQshBDHVKdtiEJz5BmL6bRDHBnqtcdfcZy0ftcQx0c3uijEw5RSZPMOnbah05bZrcMWacN2Z9il4CZw9KnQ2IYgoJMpPfnO4tDtLZ4jdH3OvP4ZsFP6/bOWXHufszdf5QOv/gFnrn8FVdtlkyxXFi6zunKZfnFJCtZT5o6ypV7XRVrm4AL3qs7wyWiZTZ0Ga1ly4rk7tpAcqfcyjkutsMRifRtXH9ztutDe59T+Gntejr38kgSbP0LyXqWFEEIcieHonj2y0b377o/wba1Nd1yl1zWEwf08KSGOXjqjUArW7owfoCvGs9mO6EeaBOyncKBic5s+LnE6uZlG88w6LtsnLpGpb7O4efNIv7YXDljeuM5LX/8kL77+aUpr12mEcDNziusnXqa2eA4zCqkWybCsImLr8EfR4jtutxau6DyfjRfYNilSGErO/F1kuJ8jpY95jtRB9vJLOEazVHtv5lh20OXi1k06ymOtdFKCzR/jmcb3KpVKFtDValWOooQQYgbFsaXb0SgF7hFvlf5ghK853RG+XtdgLXiSJyWmRClFLudQ39cEA006Iwf6h2WtFdAYaJayyfuZunFItl1jN5WXwsMUtfJLdLJlTr75RVonLr5jV7NDZw2F5i6LO3cp1LdQYUDPuuykS7SWLmJTw6sj8mhIJk9ZTjghr+oy/4mpkXcMxsKrusTVOE/N+pSISTt22kudiHCUI8Vxz5E6wCCVpZMusLK7xu7KhQedUF4ccWnjKrHW3F44j3UlNelxxjoSr1Qq/1ulUvmO0dt/HqgBjUql8v2TXJwQQojJaDc1UWindtHm/ghfbX863VJxfD9PaipfXogHMlmF0ZaNVQm6PizaWDbaw5+nf8RF93EUGjsQx7RSkic1VUqxdeIyzqDHiRuvTeRL+IMuJ1ff4v1f/TgX3/oc2c077GuP64Xz3Dr5Ms2F0w8KUiLZVlTIwDp8Nl4ksopPx4u8GReoWZ+FOS5IxVGfeJQjpRwprBxkt7hMOuhS6NYBUNbw4uY1vHDAreJp4kkWvOfEuI+sHwJ+dvT2zwJ/FWgCvwj82wmsSwghxAS1m5oosmRz06nK3B/h216PWTl59Afkvc4wTyqJm3KJ48VxFZmsYmcj5uJli5vUebMZstuN6IXJHd0rNLbRUUR/sSSdMVMWpHPsL5xh5c7r1C98gKCw+OQPegJlNKXaJgu7d8k3dyEM6agUtcwC7dLC8AUQ6YqaNSllWVYRX4jLGBSbJkXHeCw7T7fD3iwxOiYctNEmkhypx2hmS0SOx4mde3QKS5zbuUuh2+ROdol+Ojft5c2EcYtSuWq12qtUKsvA5Wq1+q8BKpXKC5NbmhBCiEkIQ0OvO8w8mNYBhlKKzGiET+ujPxG/nyeVkTgXkQDZnENtL2Z3K+L0OemaeF7rrZDGIKaQSl6X1HCMa4e2n0e5yRstPI52ly6w0NrlzBuf5853fN8zBxFnuk0Wdu+ysL+GEwyIopidVIla+SyxZETNhRNOxFs6zzWdw8Oy4hzSDnsJZK0hGAxzpJRyJEfqMaxy2C8uc6q1y6m9VVYaW2z7Ber5RQk2H9O4RalrlUrlh4CXgI8DVCqVFaA/qYUJIYSYjHbTEAaGaXdhp9IO3W5MbS/ixKmjPRHvdQ2Ko8/TEuIgnq9IpRXrdyNOnfXl5PU5WGtZa4UYC1k/eSdR2W4DNxzQ9svTXooYMa7HzokXOLt9k9LuPVonx7/m7sQR5f11FnfvkunUsUFAy81SyyzTXVh4UHiU3+j5kFGGy04PAyw6etrLmRhr7ShHKgQsjiM5Uk+yn1/iVHOHMzt3aXkZNkonpCD1FMY9JfmbwC8BIfBjo9u+B/jYJBYlhBBiMqy1tBoarZna6N59ng+uq9jeiI+0KBWFljAwcpYgEiWbc2g1NM26ZmFJcjueVWOgaQc6seM0hcYOhAGdxdK0lyIeUi+eZLGxxek3Pkdn+RzmcaHE1pJr77O4e49SbQMVDhho2EiVaC6ex6QzgLzEzKvyHBej7tPxgDjsY0wsweZjCr0U9fwC2aDHneXTIJ1lT2Xco57VarX6px++oVqt/otKpfKJCaxJCCESKQwNvY6hvDi7c/XBwBIMpju6d99whM+h1TjaEb5eVxNH0wt5F+IgqbTCdRWrt0MpSj2HtVZAK4jJ+Mn8BS82tumpFDqVlqJFkijF5snLXL73dVbufIOd933kPXfxwj4Le2ss7N4j1Wthwoi6l6OWPcUgW0bJi4qYA0ZHBIPWcGxPcqSeyt2lC2AtcoD59MYe3wMOuqTzJrB0eMsRQojk2t6Iqe9FnL2YYuXkbF45ajc1wcDi+8k4yEhlFN2OprYbceL00XRL9bqGKLSkJU9KJIhSimx+2C3V62pyebnK+izWWyGBtixkkvfz88IBmU6DbT8nJ3oJ1M8UqZdPsnz9NepnXibKFcAaio0dFnbvUqhvo8KAHi476TLN5UXwh69b8r8p5sH9HCmjY5Tj4ki3z9NRSkb2ntG4Ran3/HQrlUoJMIe7HCGESKZB39Btazptw80rAdmcQ74wWy/W1lpaTY3RlnQmGS+angeep9jaiI+kKGWtpdcZju5JnpRImnRG0W3D2t2I978yW88vSdANNbVejMP0O0EPUmjuQBzRzi1PeyniEXaWX6DU3ufsW5+jf/I8C7ureEGPOIzY9wvUCicIs4VEPr6EeB7W2uFOe3GEtRb3cSOsQhyyxz7aKpXKKmCBbKVSufeudy8D/3JSCxNCiCSp7cUEA0ux5NJpGa69MeDDH83hJaTjaBz9niUMLCiLUskoyCilSGcd2k1DHBs8b7LrCkNLFFmGL21CJIvjKDI5xf52RPRSCj+Ju8cl2HorpBNq/CPezXNchcY2cawZZIrSWZNQsZdid+Uip7fuku/WaTtpaukynfIijLJ15P9OzKM46hNHfYyVHClx9J5UAv2rDJ97fx/44Ydut8B2tVq9OqmFCSFEUoShod3UGGPJZB1Kiw6Nfc3NqwPe/0oGldRE3XdpNTTBwJBKJWu9qbSi29bU9ybfLdXvDEf3nISetAqRzTr0OjFbGxEXXkxPezkzZb0V0g0NJwvJu8KvjKHQ3KXp5x7syCaSab98htDx6aXyxOmsdEWJuWd0RBi0MTpCOZ485sWRe+yrdrVa/WOASqWyUq1We0ezJCGESJb63jCHyRtdOPI8RbHssLsVUyhFnLt4dDvHPStrLJ2WxlrwEhYA/GCEb33yRale1xBFlmxuol9GiGfmeop0VrG5NnxucWak6D1toTZsd0IcBU4CT6iynRpOFNBOlae9FPEkStEunRi+OeWlCDFpb3cZcLAAACAASURBVOdIReC4OBLSLaZg3EtJcaVS+Qng24DCw++oVqs/cuirEkKIhIhjS7Oh0bElm3/78DSdccjmLXdvBuSLDguLybsy/7BuxxCGNpH5iw9G+FqTHeGz1tLrGpRCDrpEomVzLo2aZn835sQpGaMYx2Y7oh8Z3IQW8YqNbWwY0ClIUUoIkQzDHKkWOg7BgiNdnGJKxj0q/03gbwNt4Oa7/gghxNxq1GKCgcH13hucmy84OI7i+psDgkGy931oNe+P7k17JQdLpRU6NtT34ol9jWAwypOykiclks33wfdg/U6IlcfrWNZaAc1AU0ons+BcaGzTc1IYP6FPwkKIY2eYIzXAWI2SYHMxReM++r4HuFStVhuTXIwQQiSJ0ZbGviaOLNnce6++K6UoLzjU9zXX3hzwyrdmcROYVaS1pdMeFs3cCQeJPyvPA89XbK1HExvh63UNYWgSN74oxLsppcjmh92DnbahWJKr14+jjWWzFaEAP4G7avpBj3SvRc3PS1aLECIR9P0cKROhlORIieka95X7HiBpm0KIY6XZ0ASBwXHUI1+sHVdRWnBp1jR3bwaJ7GrotAxRYEjoVAswGuHLOLRbljiaTNdZr2PQMUijgpgF6YxCKVi7E057KYm3043oRTqxJ1WFxg5EEe1UcdpLEUIIrDWEgybGRKAkR0pM37idUr8J/E6lUvklYPvhd1Sr1U8e+qqEEGLKrLXU92LC4OAuqYf5KUW+qNhcjSiUXE6eTlYGTLupCUNLOjPtlTxeerQL3/5ezKkzh1s5ssbS7xkU7x3DFCKJlFLkcsNOzGCgSWekW+pR1lshzYGmmE7m73ahuU2oLWG2IMHZQoipkhwpkUTjFqV+avT3L7zrdgtcPrzlCCFEMrSbhmAwDAYfp4iRzTnEkeHW1YBc3qFQTMaLfBxZuh0NgJvAsZaHuaMRvu316NCLUoOBJY6TGfQuxKNksopux7CxGnHp5WQ8pySNtZb1VoixloyXvEwUZTSF5i71VA4l3QhCiCl7O0fK4DjJe84Ux9NYj8RqtXpp0gsRQoiksNZS2xsGnKfG7C5SSlEsOdRrhmtvDPjwR3P4qelXQNotTRRaEl6PAt4e4eu0hyN8h5n91OsYgsDg+tP/PxFiXI6ryGQVOxsxFy/bRGbWTVt9oGkHOrEdSLn2PioKaflL016KEOKYkxwpkVQzcJoihBBHq9c19HsG1NN1FylnGHze6xpuXh1gzPTzpVoNTRRZUpnZOPBIpxVaG/Z3D3cXvl7XYPRwVzMhZkk25xBGht2taNpLSaS1ZkAriMmkknlIW2xsY8OQbrY07aUIIY4xaw1hv4nRESA5UiJZxuqUqlQqJeDngO8GVuDtC1LVavXiRFYmhBBTUtvVBAPzTIHYrqcoLTjsbccUSiHnX5jeHhFhOCquMTs5Sq4HvqfY3og4dfZwRviM5EmJGeb5ilRKsXY34tRZXx7D77LeCgm1ZSGhmVuFxg5dNyM7LAghpsZaSzBoofVw4wzJkRJJM26J9J8CHwV+HlgC/hbDHfl+cULrEkKIqRj0zYMMJs97tqtI6bRDNq+4dzOkXjvcjp+n0W4YwsDgzlBkwLtH+A7DoGeGeVJyUVDMqGzOYdDTNOt62ktJlE6oqffjxBacU/0OqX6blp+b9lKEEMdYHPXQ0QBrNEpypEQCjXuI/p8B/3m1Wv0dQI/+/kHghye2MiGEmIJhlpTFe87soXzBwfUU198c0O8d/YmktZZWUxPHkEpAttXTSKUVOj68Eb5u1xAOLL4ch4kZlUorXE+xejuc9lISZb0V0gk1aS+Zz3GF5jaEIe10cdpLEUIcU1qHhEEHYyJwJEdKJNO4RSkHaI7e7lQqlQVgE3hpIqsSQogpCANDu6kxxuI/Z1FKqeEYXxRabrwVoOOjzZcKBpZgYMbePTBJXA98fzjCdxh6HYOxEnIuZpdSimzOodXQ9LrSLXXfeiukFxmK6WSOohQb2wRWEWUL016KEOIYsubtHCklOVIiwcZ9ZH6NYZ4UwKeBfwL8MnBtEosSQohpqO/rUZfU4Xw+x1GUF12adc2dmwHWHl1hqtXUBMHzF9em4eERvug5R/i0tgz6BoWaueKcEA9LjzYrWLsrgecAQWzY6YQoLE4Cf7cdHZNv7dPx8/LcI4Q4csMcqSZaR4BCSY6USLBxi1J/HbgzevungT6wAPzIBNYkhBBHLo4tzbpGx4dbyPF9RaGk2FyL2Nk6mnwpa+2w4ys+vALbUbs/wld7zhG+fs+gJU9KzAHHUWRyiv3tiCg8nLy1WbbZHnZJeQm98p9v7UEc0Urlp70UIcQxFEc9dByMcqSkICWS7YkJG5VKxQX+a+B/AqhWq7vAj092WUIIcbQa+/GDUPDDvqqdyTpEoeH2tYB8XlEoTTbcqN8zhIEFZVEzWo1xPfBTiq3159uFr9cxBIElNb1NEIU4NNmsQ68Ts7URceHF4/2gXm+FtALNcjaZJ1uFxjYmDOktlKa9FCHEMaPjYY6U1hFKcqTEDHji2Uq1WtXATwLSLy6EmEtGWxo1TRRZUunDf+FWSlEsO1gLV98ICCfc5dBqGIKBmbmA84cppUinHbqd5xvh63UN1j77TopCJInrKdLZYeelMUebU5ck2lg228PDUs9N4O+2tRQb23S8LLgz2q4qhJhJ1miCwTBHylGSIyVmw7iP0t8A/sYkFyKEENPSbGiCwOA4k8sdUkpRXnAY9Aw3rwQTO6E0xtJu6WEhxp/tA5FUZrQL386zjfDF8f08KSHmRzbnEgzsoe1OOYt2uhG9SOMm9Op/ut/GC3q0PRndE0IcnWGOVAujI1CSIyVmx7gzJN8B/K1KpfLfA6vAg7OparX6XZNYmBBCHAVrLLW9mDCwZHOTPcFxveGOfPs7Eet3FedfTB96EazbMUShnYtWbdcdjvBtb0ScPvf0I3z9riGOLXKRUMwT3wffg7U7ISsnj+dYxnorpD7QlCbQ2XoYCo1tCAPa5TPTXooQ4hiJwi46HgxzpKRLU8yQcYtSvzr6I4QQc6XdMoQDi1KHnyV1kFTaIVewrN6OKBQ9FlcON1+q3dQEA0M6Dcx4j9D9Xfi6bUMUGvzU01WXet1htlYmO6EFCjEFSimyeYd2y9BpG4ql43Ul3FrLeivEWkvGm2w+37MqNrcZWIc4nZvxZ2EhxKzQcTgsSulYcqTEzBnr1bxarf7GpBcihBBHzdphl1QwMKQyR/d1c3mHKNRcf2vAN380Sy5/OCeVWls6rWH+kjsnGUqptKLTsuzvxJw+/3TdUr3O6GeRxMwZIZ5DOqPotGH1Tsgr33K8qq61fkw7iEnq+ZYTR2RbNfb8vJwUCiGOhHlXjpSSFnExY8YqSlUqlb/2iHcFwBrwhWq1Gjzm4y8AvwmcBgzwK9Vq9ZcqlcrPAX8d2B3d9e9Wq9XfH33M3wF+DNDAT1er1T8Y3f69wC8BLvDPqtXq/zy6/RLw28AS8Crww9VqNRzn+xNCHE+9jqHfM6COtnChlKK04FLf01x/M+BDH8niec9/8tJpaaLQ4MzReZDnqeEufJvRUxWlosgSBJMNlBdiWpRS5HIOjX1NMNCkM8enW2q9FdIMNDk/md9zobmDiiPamYVpL0UIcQxYawkfzpFykvncKMTjjHsW9iPALwM/B/z46O9fBv4m8FvArUql8u2P+fgY+JlqtfpB4E8BP1mpVF4Zve8Xq9Xqt43+3C9IvQL8FeBDwPcC/7RSqbiVSsUF/gnwfcArwH/50Of5X0af62WgzrCgJYQQj1Tb0wR9g//0cUXPzXEU5UWXTktz50aAtc8ffN5qGsLQ4s/ZTvHpjENvNMI3rl7HEEcWyfgU8yqTVRht2bh3vDZHXm+FRLEl7yez+l5obKOjiF62PO2lCCGOgSjsEscDrDE4TjJHmoV4knGLUm8A/121Wr1YrVb/dLVavQj8DPAacJ5hgeofP+qDq9XqZrVafXX0dht4Czj3mK/3A8BvV6vVoFqt3gZuMAxb/w7gRrVavTXqgvpt4AcqlYoC/mPgX40+/jeAvzDm9yaEOIYGfUO3o7GAN6VRN89X5IuK7fWInY3n20krjiy9jgbmb1wtlVZobdl7il34el09LNBNoeAoxFFwXEUmq9jejNF6Mrt5Jk0n0NT68ZFlAD41ayk2d+h4Odn1SggxcToOiMIuxsQoVwpSYnaNe+byXwH/x7tu+2Xgh6rVqgX+V4adS09UqVReBD4CfHF0009VKpWvVyqVX69UKouj284x3OXvvrXRbY+6fRloVKvV+F23CyHEgR5kSaWme2KTzbmkM4pb1we0m89emGq3NFFombN6FPD2CN/25ngdIdZael3JkxLzL5tziCLD7tbx6JZab4d0Qk3qEMadJyHTbeAGfVp+ftpLEULMuWGOVOvtHKkkFuqFGNO4JdVt4PuB33notj8P7IzezgBPPCKqVCoF4F8Df7tarbYqlcovA38fsKO//3fgr3HwllGWg4to9jH3P2gNPwH8BEC1WmVlZeVJyxZiajzPk8foBAQDjY4apFIuheL0ryxls5a97YC7N+E//K7FZ8qH2d9u4jiKUvloD0wcxyGXy03865g4otOOKRUXSaUf//MZDDS+1yCTicjlZEvk4+6oHqPTEoYD9jYVH/qW5bk/Kfnizjba8TlTSuMk8Htd2ruDZzRBeQnfH/+5Ryn1VPcX4qjJYzRZrLX0u02wGsdxcaVLCpTClYD3mTXuI/ingf+7Uqm8zrBT6QLwzcB/MXr/f8BjxvcAKpWKz7Ag9S+q1eq/AahWq9sPvf9Xgd8b/XNt9DXuOw9sjN4+6PY9YKFSqXijbqmH7/8O1Wr1V4BfGf3T7u3tPW7ZE2WNpds15PMOap6SicWhWVlZYZqP0Xm1vRFRr0Uox9LrJeMFLJO31PdCvvDZDT744QzOUzwnhIFhdycgCA1u/2i/n1wuR6/Xm/jXMVgGQcyVN7c5e+HxM3mNWkyzEWKspdc7Hh0k4tGO6jE6La5raDQ0N69vs7A0vycmQWy4vVUjDCIG/WRuYnBq+x5trRg4Hioa/7nH932ip7i/EEdNHqPJEkd9wqCHNhrX9dEmmc+JR8l1HPk5JIzW4/9/jHX2Uq1WPwa8D/g/GeZI/V/A5dHtVKvVj1Wr1b/3qI8fZT79GvBWtVr9hw/dfuahu/1F4PXR278L/JVKpZIe7ar3MvAl4MvAy5VK5VKlUkkxDEP/3dEI4aeAvzz6+B/lnV1diWONZWMt4t7NgK0NeZIX4qjEsaVZ1+jY4icoKNd17+/IF7N6++mCz+8HnE8pGutIeJ7C9xU7Y4zw9bqGKDKkJE9KHAOptML1FKu353vD4Y12SC8yeAm9iOdGAdlOnbaXn/uONSHE9FhrCIMO2sQoJdl1Yj6MfUmtWq3uAf/8Gb/OnwF+GPhGpVL56ui2v8tw97xvYzhqdwf4b0Zf641KpVIF3mS4c99PVqtVDVCpVH4K+APABX69Wq2+Mfp8/wPw25VK5R8wLJz92jOudeKMsWysRjTrMe2modkIKS+65PLyxCLEpDX2Y8LA4HrJC8pNpRX5vGLtbkSh5LJ84smt8tZa2k2NjgzZ/BxXpRjuwtftGMLQkEod/L1aa+l1DEopHGnjFseAUopszqHV0PS6em6PJdZbIa1As5xN5vdXaO5AFNHKLU17KUKIORaFPYyOUCDHOWJuqEddja9UKv9vtVr93tHbn+YRGU3VavW7Jre8ibMbGwdO+U2MMcPtm5uNmDCwpDPQ2DcUyy4f+kj2qUZ2xPyT8b3DZbTl1rWATkuTyanEFaVgWFRpNTQWxYc/mn3iCeagb7hzI6DfM+SmUJQ6ytEoHVv2d2MufyDDuYsHt0EFA8OtawGD/vyenIunM+/jezA8tqjtak6c8Xn/K5lpL+fQaWP5N2/us9EKOVlIZq7NuRt/Qn7tJldOftNT77wno1Ei6eQxmgzWaPq9feIowHH9RB7HTouM7yWP1oaf/x//Wzg4//sdHtcp9ZsPvf3PnndR4r0FqezopLhYhmY9Zms95OyF9LSXKcTcatY1wcCgnGQWpIDRc4JLfV9z7Y0B3/yRHN5jxgxbDU0wSNYo4qS4o134draiRxaleqNOKk923RPHiOMosjnF/nZE9FIK/xGdhLNquxPRi0xyL9xZQ7G5Q8vPPXVBSgghxhWG3WGXlHISexwrxLN4ZFGqWq3+1kNv/8bRLGd+GWNZvxfRfldBCiCVdsjkLHdvRiwue2RzckAjxGGzxlLbjwnD4e9fkjmOorzgUq9pbl0PePmD6QMPPu6P7hljSWeS/T0dlnTGGRaeAkMq/d4T727XoGNIy47s4pjJZB16Xc3WRsSFF+frAtdaK6DZjylmkllsy3YaOGFAyy9NeylCiDlldEwc9THG4LjJ7BgV4lmNnSlVqVS+E/gIUHj49mq1+guHvah5Y4xl/W5Eu/n2CfG7TzDzBYf6nubGW4GM8QkxAe2WIRhYlJO8LKmDeL6iWBoGexdKDmfPv7czqN8dBpwrLEol82TtsKXTik7Lsrsdv6dbarhFskExG//HQhwm11OkM7C5NuwknJfjCGst660QC2S8ZF60Kza2sGFAZ7E87aUIIeZUGHYwehhuLsc4Yt6MdRZTqVT+MfCvgO8CPvjQn2+a3NLmg9HDglRrVJDKZA8eG3IcRbHsDMf4ZDc+IQ6VtZbaXkwwmK0d2TJZl2xWced6QKsRv+f9raYh6Bv89PE5OHE9RSp98C58g74lji2o8XcuFGKeZHMuwWCYvTYvav2YbqhJ8jlYsbFDX6UwqfnqUBNCJIOOQ3Q0wGJwZERYzKFxO6V+CPjmarV6tKngM85oy/q9kFZDE0WPLkjd92CM70bI4pIrY3xCHJJex9DvGVDgzljWUL7oEEWGa28M+PC350iPRtaMsbRbGgN43mx9T88rnXbodt87wtfrGoLAHLufhxD3eT74PqzdCVk56c3F1fS1VkhzoMkltEvKC/ukuw22/Nxc/LyFEMlirSUKOxgT46hkPg8K8bzGPXJfBYJJLmTeGG1ZuzssSIVjFKTuyxccrLHceCvAGLnaL8RhqO3pmeuSuk8pRWnBIQgsN94cYPTweaHbMUShxTmGJ0GptMLo4Qjfw3odg9Hgz+D/sxCHQSlFNufQ7Rg67fnYhWi9FRIaSy6VzOe6QmMH4oh2ujjtpQgh5pCOA3QcYrEoR4pSYj6N2yn1Y8CvViqVfwlsP/yOarX6/x36qmac1pb1uyHt1rAglR2zIAXvHeM7KEdGCDG+fs/Q7Wisnd2OItcdBZ/va+7dDnjhfWnajWGhLZ2GMXZanSsPj/Ddz5UyxtLvSZ6UEOmMotOG1dshr3xrdtrLeS7tQFPvxzgk9/e62Ngmig1BtnDMnomFEJP2oEtKRyhn7ChoIWbOuI/ufw/4PoaZUv2HbrfAxcNe1Cy7X5BqtTRx+HQFqftSaYdMVsb4hDgM9QdZUrN9uuCnFPmiYv1uRC7vPuiCcGe00Pa80plhN8j9Eb5B3wzzpI7nj0OIB5RS5PIOjZomGGjSmdk9hlhvhXQCTcpL5vO3MoZ8a5eGn5cOBiHEoYuj3qhLSuE6coAj5te4RalfAL6/Wq3+4SQXM+u0tqzdGXZIxU8xsneQfFF24xPieYWBGeYumeFudrMum3OIIs2tawOKJZfj/LSQSivaLcvuVsy5F1L0OoYwsPhyIVEkjY4pvf4JbCpL74VvRecmv0NbJqPotg0b9yIuvX92iyVrrYBeZDhZSOYvdq69jxMFtP3FaS9FCDFnrDVEYRdrNY7rT3s5QkzUuCXXLiBjeo9xmAUpkN34hDgM9X1NMLBzUZCCYQdEseRijaLVNPjHeKMn11WkUoqdreHzY69rMGZ+/q/F/Mhs38Rt13Bvv0npi/8P2VuvoqLJxnQ6riKTVWxvxmg9m/mUQWzY7UYoRWKz8wrNbWwQ0s2Vpr0UIcScicIuRsconu+cUohZMO6lp58F/lGlUvl5YOfhd1Sr1flI0nwOOh6GmncOqSB13/0xvnsyxifEU4sjS7Meo7Uhm56flmfHUSwuO2htZ24nwcOWzjh024ZBX0uelEimOCKzcYXI+rRf+A5ye7fIXPki6a0b9C99G8Gpl2BCIxnZnENtP2ZnM+LMDOZTrrdCepHBT3BLaLGxQ9dNY4/zFQIhxKEzRhOFPYzVOI50SYn5N+6R0K8DfwNYB6LRn3j097GmY8vqnZB2UxPFh1eQui9fdDDGcuOK7MYnxNNo1GLCwOK683eFSTkKzz/eBSkY7cJnLau3InRsj1veu5gBma3rqEGXXu4UeCl6p7+JxrmPEPUCcl/7FKXXfh+/vgH28F/fPX+4IcD6vQg7gc8/aeutkFagKSX0ooI/6JLqtWj7uWkvRQgxZ6Kgg9UxSjlzdwwrxEHG7ZS6NNFVzKg4Ho7sddqaeAIFKXhojK8mu/EJMS6jLfWaJoos2Zy8mM+r+yN8tf2Y1P/P3p3HyJZfh33//u5We+/db3+zkENqKELiiBKtSI5sx5AiWwqUwPCNkUSyFS+xY0ExoDiJk9hCmMQIHECAYQMG5MgR5QXhDSybjkyKkkVJ1EIORQ1nyJl5s7y9t9f9eqn1Vt3t98sfVf2m581b6vXrqrpdfT5Az/S7XdV1urvq1r3nnnN+BYUnxQoiR1QaU9p8m5gCaWXuXs5UF6u0L34Cp7ND5e41qntfILnwYbrPfuLY502VyhbN/YzGfsbcQj7nMj1Iqg2b7RgLcHJaEVqrb0GS0KqtTDoUIcQUybKENOlijMay5bxPnA5DHaEEQXBr1IGcNIcTUtmIElIHZDU+IZ5MYz8j7mksa/qqpMT7HbTwuR44juwbRX4UN96GXpewcvmD+yGlSKvLNCqLFPbWKN+8wsz2bXrPfJzexY8dWzuY5ylsR7F6PT5RSamtdkI30aPqbDwW1cY2sTbExZoUaQohjoUxhiRqoXWKUnJMI06PoY5QfN+fBX4aeAmoHv5aEAQ/NIK4cu3+hFShOPoT30rNYm8n4+pbEd/+CVmNT4iHMdqwt5sSx1IldRp4BUW7CVrL31rkh4p7FO+8S6yKZOXZhyctlEW0eJl47hylu9coXvkqhc13j23elFKqXy3VyAg7GeXKyTjJWWtG1Hspszlt3VNZSqV5lz23Ihc+hBDHJssisjQGY1ByoU2cIsNeNvt/ARv4V0B3dOHkX5oa1m4cSkiNsELqMMtSzEgbnxCP1Wxoop5BKRl6fRrYtmJu0ULy9CJPShtvQdSjU312qP2QsV3Cs99Gr3eR8t13Kb/2mxSW36X7/Eskc+fgKfZlhaKi04K1Wwkf+Vj+T3KMMWw0YzBQyOlJWaW1i0oSWu7ipEMRQkyJfpVUm0wnKCuf+z4hRmXYpNT3AotBEJzqweZp0h9q3hlzQuqArMYnxKMZY9jfSYl6mkJx0tGIcXFl6LvIESsKKWxdI1JldOnJWrs+OG/qDsmFDxM++xK6PHO0eCxFqazY3UpIPuzhevl+veyEKZ04y/XCBdX6FjqO6Mwc7W8ihBD3S5MuWZagAEuSUuKUGfbI5HeBF0cZSN69LyGVGYrlyayGUKlZZIPV+IysxifE+3Tamm5XowA7p8NxhRDTrbh+BeKIsHr2aMcJB/Omnv0UncpZnJtXmP3av6Z04xuoJDpaTCWLLIM7G/m/trjejKn3Mio5rZLCGGr1LTp2CVxZql0I8fSM0SRxB52loE7O/D8hjsuwz/q/AHze9/2Xga3DXwiC4NPHHVTepIlh9UZMu52htaFYmtzJ7uE2vk1p4xPifQ6qpI5pRrAQQjwRq9emsH2DnlVBFytPV+yjLKLFZ4jnzh+aN3WV7vOfIFr50BPNm7IdRaEIm6sJFy57uZ5Lud6MSbWhXMpnjF6vjdvrsO1VJh2KEGJKJHGIzhKUUlh5XuFBiBEZ9ln/vwOXgDPAC4c+PjyiuHIjOZyQygzF4uR3FP02PovbV2O6YTbpcITIhW6o6bQ1xoDjTP51KoQ4fUprb2Li+OhVUg9wMG+qfuElkk6P8qtfYubVL+Dubz7R9ymWbaKovxBEXjWjjHovRZHfmYC1+hbEEe1ibdKhCCGmgNYZaRyidYaypEpKnE7DPvP/HPCRIAie7AjohEvi91r2jGaiFVL3O7wa38c/UULl+KqnEONwUCXlefJaEEKMnxU28XZu0XNqUDz+KhpdrNK+NJg3tX2N6u7nn2jelOv2i6t27qQsLeez7Wy9EdGKMopOfvfj1fo2kbFICk9ZCSeEENBv29MJyprMaBgh8mDYLMt1IP+DCI7R+xJSBgo5KyO/fzU+IU6zONK0mhlag+Pm67UqhDgdymtv3KuSGpkPzJt6i9mvfa4/byqNH3NXhesqwnZ+K6zXWzHdVFMr5HOelJUllFu7tNyKnDwKIZ6azhLSpIvRGqXyud8TYhyGrZT6p8C/8X3/H/DBmVJfOvaoJiyJNas3EzrtfoVU3hJSBw5W47t1NWZOVuMTp9jeTkbUM5KQEkJMhN3Zx91dpevMQqE0+gc8mDc1e+6J5k25rqIbarQ2uZsr1Us1dztJrlv3Ko27qDSm5S1POhQhxBSIo/ZguLmd2/2eEOMwbFLqrw/+/3fv226A548vnMmLY83ajfjebJq8JqQOSBufOO3SxNCsp2SZplTIT4utEOL0KK2+gUlSuvMjrJJ6AON4hOdepNdrUd6+SvnVL1FYeZfu899FMvfBWBwXMg3tpmZmLl8XsjaaMWGs8ez8HsfU6tvoOCack3lSQoink6URWRph0Ni2LFwlTrehklJBEDw36kDyII41qzdiwoOEVDG/B0YH7m/jOyer8YlTZn8vJYoMtq3kKpMQYuyc1g7u/gahNwdecSIx6GLtvnlTmyQXXyB89hPo0nvzpvrVpIb9vTR3Sam1ZkwzSlkq53TQo9m34gAAIABJREFUrzFU61u0nTLY+ZzJJYQ4GYwx/SopnWJJ254QQ8+UmnpxdCghxclISB04WI3vlqzGJ06ZLDPU9zLSxOAVTs5rVggxPUqrb6DjlO4oZ0kN4/55UzfeZPbl98+bsiyF4yga+/lagS/VhjvtGEspHDufh6bFsIkTd2l6xz/EXghxumRpD531ZwIrS5JSQjzycpTv+79Dv0XvoYIg+IFjjWgC4kj3h5q3NZyQCqn7SRufOI0a+xlxT2Op/M4gEUJML6exjdPYIizMo9ycVCo/dN7US0Qrz+N6im7HYIzJzX7zTjumG/f35XlVrW9BFNGeOz/pUIQQJ5gxhiRqo3UCKqeVoUKM2eNeCf/XWKKYoCgazJDq9Fv2iicwIQXSxidOH6MN+7spcWwolU/m61YIcYIZQ3n1W/0qqcWz5G0vdHjeVGX7Xcqv/gaFlXeInv9jNOMCcWwo5KTCdL0RU49S5nK66h5ArbFFF5u0UMrd31oIcXKkSUiWJWAUVk4rQ4UYt0cmpYIg+My4ApmUg6HmcHITUgdkNT5xmjQbmqhnUFIlJYSYALe+id3cpV1cRLn5nTGkizVal17Cad+luvk2Nb5G49wfpbGXsnJu8hewtDGst2KMBs/J5wmancaUWnvc9SryfiOEODKjNUncwZgMJbPphLgnn+/+YxR2NEqdzJa9B6nULDJtuPZWhNGP7LwU4sQyxrC/kxL1NF5h0tEIIU4dYyitvkGWZETVlUlH83hKkdZW6C0+Q3njCk7cYX83HzMod8OUTpxh5fiItNLYhjSl6VUnHYoQ4gRL4jY6SwFZnEeIw3J8CDAmZroGJB+08dUHbXxCTKNOW9PtahRgS+mzEGLMvL017PYeYWkJ5Zycq9292Qto26Wy/TadVj6SUuvNmEYvo+Lmt7q7Vt8mSxK6h1YyFEKIJ6F1SpJ0MTpDWTJLSojDTv3ZnFecvl+BrMYnpt3e3X6VlCtVUkKIcTOa0tobZIkmPglVUodZNt3l5yk1Nkh29tA5qKhea0YkWlNyc3qB0Biq9S1abgVl5zdxJoTItyQaVElZtlRJCXGfh2ZkfN//6qHPf3Y84YjjIm18Ylp1Q004WJjAyen8ESHE9PJ2bmN1GoSl5ROZpIirK1iWwd5epdOIJxpLs5fS6GVYOW5lKXXq2HGPlluedChCiBMqy2LSpAdoLOvkvW8IMWqPOqP7iO/7xcHnPzOOYMTxkTY+Ma32dlJ6PT1VbbdCiBNCa0prb5Kmhri6POlojkYp0vkzqDii/upbEw1lrRnTilKKOb7AUK1vQRzRLkrrnhDiyRlj+lVSOkUpSUgJ8SCPamj9HPCO7/s3gZLv+19+0I2CIPiBUQQmnp6sxiemTRRp2s0Mo8FxJCklhBivwt0bWGGTdmnlRFZJHTDlWYhCum9dw3zfR1GF4uPvNALrzZhuajhTye/vslbfIlQumVdE3nWEEE8qSyOyNAZjTvT7hhCj9NBLU0EQ/CTwnwE/D6TALzzkQ+SYtPGJabK/kxH1DE5eZ48IIaaXziitXyHNLJLK4qSjeXqVGXqmiP7a70zk4buJZidMsCC3rXtO3KPYrtN2K7mNUQiRX8aYwYp7CUjbnhAP9cjR/0EQ/C7wu77ve0EQfGZMMYljdH8b37mL3qRDEuJI0sTQ2E/JMk25KG/sQjyQMVi9FlpWCTt2ha1rqG6LTvncVFzttlyHbmmJ5Ou/hvrEp7Bqs2N9/I1WTCfOcOz8JnuqjW1IE5rlpUmHIoQ4gdKkS5YmoJTMkhLiEYZajzIIgn/i+/6fAH4cuACsA/8sCIIvjTI4cTzutfFdkzY+cXLt76bEkcHO8QmMEJNW3Hyb0s3X6Dz7XcTnX5h0ONMjSyltvEWiHdLq4lS0cbkktEsLtPfKzH/5i/Aj/lgff60R0Y41i6X8HpNU61ukaUavWJ2Kv7kQYnyM0SRxB60zlCSkhHikoSZL+r7/l4DPAneAXwY2gX/h+/5fHmFs4hhVahZZJm184mTKMkN9PyNNjQw4F+JhjKFw9xa0GlRe+xLe1vVJRzQ1infeRXU7hOUzU9PGZZNhWYrG4kfhzVfRO3fG9thJZthqJyjAsfM55FxpTbVxl5ZbmYrKOCHEeCVxiM4SlFJYVj73c0LkxVCVUsB/B/xgEASvHWzwff+zwL8E/vEoAhPH63Ab39ZGwllp4xMnSLupiXsaS+V39ogQk2aHDaywQWfmEm64R+Ub/w7zyR8iWX520qGdaCqNKW68TWJc0sr81FTMKAWOSqmXL4FSmH/3/2H+0780ln3snXZMmPT36XlVau9hJREtd7xtjUKIk0/r7F6VlGW7kw5HiNwbNm27CLx537a3gYXjDUeMUr+Nz+LmtZhumE06HCGG1g01UWRwC5OORIj88nZuQRwRVZZon/84iVOi+sqv4e6sTjq0E624+Q6qF9KpnJ26pLhDSqRKmDMX4NY1zK2rY3nc9WZMo5cyU8hvBVK1vo2JI9olSUoJIZ5MErcxOkVZ1tS9bwgxCsMmpX4X+Dnf98sAvu9XgP8T+P1RBSZGQ9r4xEnUDTUAdk7bPISYOKMp7K4SWyWMVwTLpnXhO0isItVXvoizuz7pCE8klfQobr5LrIpk5elLTjgqJTMOYfUceAXMb/wKJhvtRSttDBvNGAN4Tn736bXGFqHloV2pLBdCDE9nCWnSw2iNUvlNvAuRJ8MeDfxV4DuAhu/7W0Ad+E7gvxpVYGI07m/jEyLv0tQQ9TRyoUmIh3OaO6heh6gw+95VWcumffE7SHGovfKrOPvjmxk0LYobb0PUJZzCKinoV0qBoa7n4ex5uLuJvvLaY+/3pKJUc6cV88Z2yJdvNukkWa5b99wopNBp0HQrU/l3F0KMhjGGOGoPZknZsv8QYkjDrr63Cfwx3/cvAueBjSAI1kYamRiZg9X4bl6LmZXV+ETOdUNNlplcn8AIMWne7m1MHBPPLLxv5pGxHFoXv5Pa6jeo/uEXaH33j5DNrUwszpNExV2KW1eJVImsVJuaWVKH2Upjk9Ews1ys1DDVWfjtL2A+8u0o72j90klmqPdSdsOUvW7CbpjSijLiTNOMMtLMYFuKmWJ+q6Sq9W1IEtoVea0IIYans5gsjcCAcuT8SohhDTvoHIBBIkqSUVOgUrPY28m49lbEt3+ihJIzfpFT3VATR4Yjnh8JMf10hre7RuxUUQ8YvGZsl9all6itfoPaH36e9vf8KOnM0gQCPVlK61eg1yOceX6qr3a7KqVrSv1/nDkH195Cf/33sL/vP3jsfTPdT0DtdVP2wpTdbkqjlxJnhnaU0cs0NgoDOBZUXItSOf8zVqqNLZJME5WqU5mMFEIcv3tVUoNZUkKI4T1RUkpMD1mNT5wU3Y7GGIMjV5yEeCB3fxMV94gKZx56G2O7tC5+gpnVV6h+/fO0PvWjZFVZq+RhrKhDYfsGkV1BF6c7MeGolI6ukGgbt1DEzC/By7+N/o7vwarW7t1OG0MzytgL+0mo3TCl3kuJUk0n1oRJhqUUKHAUFByL+ZKb+wTU/ZTOqDbusu9W5MRSCDG0LO2hsxgAZckxqxBPQpJSp5i08Ym809oQ9QxM9SmhEE+nsHsbnaQk8wuPfKUYxxtUTL1C7ev/ltb3/ChZZX5scZ4kpbU3MVGPcPbDJy6p8qQcUgyKhp5hydqH5TOYd9+k9du/Qf37frhfBTX4iFNNJ9F04mzwXFNYSlF04EzVwZ6CJE65tYtKYpqeJG2FEMMxRhNHbTKdyXBzIY7gsUkp3/ct4I8DvxsEQTzyiMRYVWoW+zsZ194etPFN+cG3OFl6XU2aGtTJP88RYiRUGuPub9LzZlD24w+EtVOgdXHQyvf1z/cTU1O4qtzTsLpNvLs36Tk1dKE89Slx26RkaK5ni2yQsmtc9s7OEa326L2+TtMq9ReaMGApg2tbLJcdnCldDbVW38LEMZ2ZmUmHIoQ4IdI47A83x2BNQXJeiHF77KsmCAINfE4SUtPJshSVmkV9L6PT0pMOR4j36XYMcaRxpKZTiAfy9tYgiYkKc0PfR7tFWhc/gem0qf7h57G6zRFGePKU1t7ExDFhdTpX3IsN7GjFdW3zjczhd7TLdQ1vpHP8ZjLPa2mNNW+Baljn/JWvsFSyOFt1OVtzWal6zJemNyEF/SHnHbsIrow1EEI8ntEZSRKidYay3EmHI8SJNOxRxZd93//ekUYiJsYrKIyGnbvppEMR4n26oUZrcN3pOzEU4jh4O7fJUkM6qHYK0y6325vc7e3TTSOMMQ+8n/ZKNC+9BK0GtT/8Vaxee5xh55bdqePt3KbnzEChPOlwjk1o4Fpm83upy2+lHn+QeXwzc1nNPHraAxRlVWKOlLNWzBknpTA7y4WdW8zvb046/LHxum28boumOz1/eyHEaCVxp18lpdRUXsgQYhyGrT+4BXzB9/3PAavAvaPcIAj+zigCE+NjWQrXg/puCh+SJc5EPhhj6IYaBfImL8QDqCjEad6l683cG6q6H7VoxB12enU8y6Xilqi5FapuiYLlve+1pL0yrYuf6LfyvfIFWp/8EfQUJWKOorT2BiZO6c6fnXQoTy0zsGUsNo3FrraIsbCwKaAooTgDeFb/cE6RYeEBDoYEgP3aCgv765y98hWuLf4ZzCloSak2tiGOadUevmiAEEIc0DolSbporbFsqZIS4qiGTUqVgH89+PziiGIRE+QVLLqhQWuDZUkCQExe1DOkqQH14EoPIU67wu5tiGN6MxcAiLOEbtpDYai5FaIsYj9qshc1KVguVbdMzS1Tdct4Vn9VtKxQ7Sem1l6l+sqv0vrkn8J4pQn/ZJNht3Zx99YJvVkonMzfgTHQQLGhLbaMTc8oUmNTVhYrKMr39qfv368aMgAsKmTU+xuVYmvpOZ7ZeJO59XfZv/TRMf4kk1GtbxEZRVKa7hUXhRDHI4naaJ2glC0XUIV4CkMlpYIg+MmneRDf9y8BvwScBTTw80EQ/H3f9xeAzwLPAjcBPwiCfd/3FfD3gT8NhMBfCILglcH3+vPA/zz41v9bEASfGWz/JPCL9BNonwf+myAI5Gx2SK4LYdvQamhm52XVCDF53VCTxBp7imeXCPE0vJ1VUmOjizUU0EjaxDrBGRwcF50iRSDTmjiL2Ysa7EUNCpZLxS0z41WoOGUKxRrti99Jbe1VageJKbc46R9v7MqDKqne4smrkooMbBqbDW3RMooIi4KxmVEWNWXoX2t61CGRATQ25feSUkC7PEe7PMvK239A49zzaGd6KwGsLKXS2mHPrcjJpRDisbI0Jk16GGOwh1hoRAjxcEOf7fm+/6Lv+3/b9/1/OPj3R33f/44h754CPxMEwYvA9wJ/3ff9jwH/A/AbQRC8APzG4N8Afwp4YfDxV4B/NHjMBeBngT8CfAr4Wd/3D9az/keD2x7c74eH/dnEezN7draSCUciRF837K+853qSWxbifnbYwO7sExXmUEqhjaaVhGRGY1vvv95kWxYlt8iMV6XilMiMYS9qcLO1wdXmbW61N9lWsHvuY1g7G9Re/TVUEk3oJ5sMp7mNU79D15sD92S0sWsDW9ri1czhy6nHG6nDvnEp4HERlwuWYvZeQurxzL0WvkOUYmvpWaxeh6Wb3zr+HyJHys0dVJLQ9CqTDkUIkXPGGJK4jdYplpKElBBPa6iklO/7fxb4MnAB+InB5hrwc8PcPwiCzYNKpyAIWsCVwff6MeAzg5t9BviPB5//GPBLQRCYIAi+Csz5vn8O+A+BXw+CYC8Ign3g14EfHnxtJgiCrwyqo37p0PcSQ1CDuVKNfRl2Libv3jwppWRpXSEewNvpt+5Fpf51mXYSkmQJlrIeWeVhWzblwwkqnbHbq3Oztc5bcZM3Fi+xe3cN79UvotJTsuiuMZRW30DHKd1a/quk2kbxdmbzO5nLNzKX1cwF47KoPC4riyUF3hHang0ZChf0+/e5vUKV+swKi9dew+mFx/Vj5E6tvoWOI8LSzKRDEULkXJZGZGmMMebeTEchxNENe7b3aeAHgyD4qzAYPACvAd/5pA/o+/6zwEvAy8CZIAg2oZ+4AlYGN7tAf6D6gbXBtkdtX3vAdvEEvIJFr2tIUz3pUMQplyaGJDaP7jYR4rQyBm/3NokqYgaDyZtJh1gnuGrYUZEHCarSvQRVqlPu6Igr5Rrv7Kyy/Qefo763TjrlySm3sYXTvEtYXES53uPvMAGJgVVt8XLq8vupyzvapWs8KnhcVg7nLEVVmaeag2ToLyxh8cHWzbsLlzFJyso7X3+KR8gxY6jVt2g7JZjiFkUhxNO7VyWVpShr+PdcIcTDDftKWqGfhIL3ThMNT3jK6Pt+FfiXwN8IgqDp+/7Dbvqg4ypzhO0PiuGv0G/zIwgCyuXTvdLQYY6dEXUjLFNjaelkDnmdNo7jsLS0NOkwxm5vJ8J1mqiKplyWN/w8syxL9qNjZje2KaQRrcoCnufRSyNik2BbFq5ztNeLbVl4g5Px1KsQYbB216h/69e5e+HbmJ1bYaa2xExtAcfJZ+LmYR75HDWG8tvvYDToxQu4OUpIGAN7RrGWKbaNomcUGouyZbGMoqAUxzr6yABG4aoZtHtfK7/rUl++zPLGVcKPfYp4ZuEYH3jyvE6Dkk7YLc7iuuN/DiilJvK4QgxLnqPviaM2xmQoW+HYcoyaG0phS3fFiTXsK+kPgR+n3xZ34M8BXxv2gXzfd+knpP55EAS/PNi85fv+uSAINgcteNuD7WvApUN3vwhsDLb/8fu2/9Zg+8UH3P4DgiD4eeDnB/80YTi9pehPyhhDlmVcfWcHy5WkVB4sLS2xs7Mz6TDGbmsjodGIKZYgDOUNJs/K5TKyHx2v8urbxJ0O4eIzkCTsdut0kwgbi0w/faWrQkFlGQebSzu32EkT1loXwb5KuVilWl2kVl2gWpnHPgEH5I96jrp7a2S7m7SdeVIDJJOfqxga2NQ2G8YiNBAZixIWs8qigsEaXBPUI6gktUjAeCQP+D1szZxhZm+d2Vd+k1vf88Mcb0ZsskqbN0k6bfZnl8gm8BxwXfeBv3Mh8kKeo2C0Jkk6pHGXLI1Rtnss77nieNjW8RwDieOTZcP/PYY9mvxp4Nd83/+LQMX3/S8CHwF+aJg7D1bT+wXgShAEh+dQ/RvgzwP/x+D/nzu0/ad83/9/6A81bwwSV18E/u6h4eY/BPytIAj2fN9v+b7/vfTbAn8C+AdD/mxiQCmFV1A0G9njbyzECHU7/TYSWXlPiPtojbe3RmJXwCuQGU07DdFGUzjmCqZ2ZQFlDBf2bjNvu6yee4Fer0MnbLC9c4tyaYZadYFadYFKee5EJKjex2hKq2+QpZpobuWpWt+eVmZgy1hsGotdbRGjsHCoYLGiwFVPXJx+RLo/V+oBNejacri7eJlzd69T3d2kvXR+DPGMR62xRc/YpIXyRJ8HQoj8MUaTxCFpEqKzFK0zlGXLKp1CHKOhjiCDIHjL9/1vA34U+BX6c51+JQiC9pCP8/30K62+5fv+q4Nt/yP9ZFQwSHbdBv7s4GufB/40cBUIgZ8cxLHn+/7/CvzB4HafDoJgb/D5XwN+ESgBXxh8iCfkeopOS5PEGteThIAYvyw19HpypUOIB3Ebd1Bxl15hGYBW0iHOEqzhF9N9Iq3qIsoYVnZXuaAsti6+iEGRZgndXpt2Z7+foCrWqNUW7yWorBMw+NXbXcPu1GkVl1ETSKgZAw0UG9piy9j0jCI1NmVlsYKiPLZE1KGYBivwKeNi1AerIvZnVljY3+DMm1+h/e//J6BO/nGClSaUmnvsuBU5yRRC3GOMJo27JEnnvWSUUli2K/sKIY6ZMmb4Ax7f9y8A54GNIAjWRxbV+Jjf/LVrk44hV9LEsLeT8pGPFzlz7mTNDZlGp7F9r93KuHUtIokNpfLJP+GZdtK+N16Vd7+Ku/4ueysfB8thtbNFI25RtAsjPUiebW6zXF+nvXyZrQvfdq91yxhDmkb0opBMpzi2S6lUY6a2xOzMMuUcrGT2wOeo1sx+84uYRoP68osoe3xJtMjAprHZ0BYto4ix8IxNRVnMYLAmeq6jsKkSs0VmNR94i1pnj8sbV9h46U9Sv/DhMcd3/Gb2Nrh45StcLy7TrU5mVpa0Rom8O03PUWMMaRKSxCE6SzAmAyypjso5ad/LnyzTfPpn/1t48Pzv9xnq0qDv+5eBfw58L7APzPu+/zLwnwdBcOspYhU5YztgO4rd7VSSUmIiuh1NHBm8wqQjESJn0gRvf4OeO4OyXbppjyiLUaiRHyg3ZlZQxrB09zbaUtw991FQajD8tojrFvsrEiURYdig3d5j++4tXvjQd+ciMXW/ws5NrE6DZnllLAkpbWDHWGwYi7vaIjYKlE0Fm2XAs8ZfFfVgBtDYVMl4cFKqVZ6nU5ph5a2XaZ59Bm2f7OHH1foWWZIQzs9K654Qp1g/GdUliTvoLLlXGaUsqYwSYtSGLUP4DP1h53NBEKwA8/Rb6D4zqsDEZCil8DxFu6l5kio6IY5LN+w/9xxHqqSEOMzbX4c4IirOAtBIOsQ6xh3TktT12TPszZxlZusWS3eu9vvPDum/fxSpVRepVZeI4y7Xbr5KmuXs6rrOKK5dIc0USWVx5A+XGfhq5vJK5rKauWBclpTHZWWxpAyeytd77UEL30MpxdbSczhhm8Wbb4wvsFEwhlp9i7ZTHmu1nBAiPw6SUb1wl6jbIE16GKOxbFda9YQYk2HP+j4J/M0gCDoAg1lS//1gu5gyrqeII0Mkc33EmGlt6HVNf/UvIcT7FHZuo1NNWpon1RmdJEQbgz3G+U17s2fZnznD7J3rLG5f/0Bi6oBlWdSqi4SdOrdX38jVRY7C9nWsbouwNJ4qqdvGpmEsPDwuK4dzlqKiTG73coYMhQP64RF2i1XqM8ssXn0VJ+6OMbrjVQwb2FGXlluedChCiDHrJ6N69MJdet06adIdDDF3JBklxJgNm5T6KvCp+7Z9N/CV4w1H5IHrKowx7N2VVfjEePW6hiw1Q3QeC3G6qLiH09giKsyibLs/4Fyn2GrM1R1KsTt7jnp1mbnNayzcvfHQxJTjuFTKc9zdvc3O7tp443yYLKW0/hZJZpGMYX5QZOCGtlE4LCuDnbOqqAcxaEBhUXrk7bYXL6OSmOV3XxlPYCNQq29BHNEqzU46FCHEmLyXjNoj6tZJ4x5Gp6AcbMfFsqRSX4hxe2jNv+/7nz70z2vA533f/7f0V967RH91vH8x2vDEJFg2OK5idyfh/GWZKyXGpxtqoljjnOwRJUIcO2/3NiQxUfUCxhiaSYfUpBStCQxfU4qd+QsoDPMbVzHKZn/5mQfetFAok2YRt9Zep1yeoVKe7Ml/cesqqtsmrJxHjaHC7Lq26RrFEhYqFzOjhpEBBosKmocvYpC4RXbnz7N0+wp7z3w7UXVufCEek2p9i65yyLyiXAsRYsoZY9BZTBJ3yNKYTCf9ynzLlkSUEBP2qFfgpUMfReCXgQhYGfz/Xw22iylzMFeq0za5arkQ068banTWr9YTQrynsLtKqm2yUo0w6xFnMcqMfsD5QynF3fmLNMvzLKy/zdzu6kNupqiU5zBGc/3mK6RpPOZAD8WSxhQ33iYxLmll9FVSbaNY0zaecaicgAqpwwwae4hDvJ35i6RGceatl8cQ1fGyk4hSe5+WW5E2HSGmXJbGRN19euF+PymVJShl9+dGSUJKiIl7aKVUEAQ/Oc5ARL64nqIbZoQdTaUqwz/F6Blj6IX9OWZygiDEe6xuC7u1S8ebQSlFM24TZcnYBpw/lFJsL1xGGc3i6lsYZdFYuPCAm1nMVBepN7e5ufo6H3r2pYm8xot33kV1O3Qql8by+Fe1TYzFGWWRj5X1nkSGwuuH/YhfVWY77Cxe5uzWDSp7d+gsnB1bhE9rbmcVkoRmefQJSiHEZGRZQhK1ydIIrdN+u7llY8vCBkLkytBHtL7vl4EPA9XD24Mg+P3jDkpMnusqDIrd7VSSUmIs4siQJOYEtbgIMR6F3dsQx8RzCyQ6pZP2MDDWAecPpRRbi8+idm6ytPom2rJozZ37wM1s26VamWd3d41aZZ4zK8+NN8wkorj5DjEFsvLcyFu19rRiW1uUcCiesCopeG8FPmVcjHr06ol7s2eYr29w9s2vcO37fwxUvqsOnLjLuZvfora3QTeDXnFGWveEmDI6S4jjDlnSGySjQFmWrLIpRE4NlZTyff8ngH8IxMDhZVYMcHkEcYkJs2yF68Lebsrl5ycws0ScOt1QkyQa28n3CY0QY2UM3s5tEuWhCxWacZNEJzh5ajdQijtLz3Du7nVWbr0OStGa/WDFTMErkxRibq+/SaUyR7UyP7YQixtvQ69LWL088iopY+Ad7RAbm+UTmu0w9Bc6sSiR8eiklFEW20vPcGnzHebu3KR+7vlxhPjkjGZh6yYra1dQ3ZAtt8bO0hk5SRViiugsJYk791bSM2gsZcvrXIicG7ZS6u8BfyYIgl8fZTAiXzxP0e0YtDZY1gk9shYnRjfUpImhVAZZfk+IPruzj9VtERb6Q6RbcYdUZxTtnF0sUBZ3lp/n3PY1Vm69jn7WojOz8oGbVcqz1JsR1258g4999Ptx3dH/HCruUti6SqyKZKXZke9d7hiLhlHUlIN7Aquk+gygsamQ0XzsrZuVRcJilZUrL9NcuYy2J9xaep9ip875G69RbO3SzizWa5dIijJLSohpofVBMqqHzhIwBqUsLFsWbBLiJBj2UmsM/NYI4xA55HqKNDW0m3rSoYhToNvRKJQMnBTikMLOLUwUEZcW6KRdYp2g1AQHnD+CURabyx+iaxc5e/NbzOxvUOrUKXRbeFEHJ+lhZymz5Xl6vTY3V7+FMaN/f/FW34CoR6d6buS/t8z0Z0ll2Myd8FZkQ4ZiyKShUtxZfg6n02Qzub0fAAAgAElEQVTh9pXRBvYErCzh7K1v8fzrv42zf5dVe46bC8+Rlqq5fA0JIZ6M1hlRr0m3s0sStdFpDFhYjofKWXJcCPFww75a/zbwc77v/y9BEOyMMiCRH46rUMDOdsLMnJS9itFJEkMcGymQEuIwo/F2V0mcMhSKNDrbxHkYcP4IxrLYXPkQ57evsnz7TXBdQL332lYKULQti839dTpb17lQnsFYDsZ2MbYz+NwB2773+b3/Dz7nQdsse/D932NFIe7mVdpWCV2sjnwXc9vYtI3FDA72ia2S6jNoLDzQCqzH/yzdYo1GbYmld1+hfuEFUm+yCzTX9jY5d+ubOGGbPQrcmb2AKRTlbUaIKWB0RhKHJEmI0SlGa7D6ySghxMkz7JHtO8Cngf/a9/2DbQowQRBItmJKWZbC9aCxl006FDHlup1+617O5+MKMVZuYxsVdYm8ReIsoZv2MJh8DDh/BG3ZrJ15gUIconSGpTWW6X+oQ5+7WnOrXacYZcxhoRh8HY3SGcpolMkABZbV/1CDz5UFVj/BhVLvbbffn8RSWYrpdQmrz4y8MiY2cFPbKBxmT3hCCg7mSiksiuj3jRN9uO2lZ5i5+QrLV19h82PfN9oAH8KNQs7e+ha1vU16Scat8hm6lVmpjBJiChijSeKQNA7ROkFrPRhg7sprXIgTbNik1D8Ffgn4LAx5ZCKmgudZ/Vk/qcaRAdRiRLqhJo4NxdKkIxEiP7yd25g4Jp5ZpJm0iXWCo/KdkLpHWUSF6iNvYoyhm7R5Xdl8bP55XMt90I0wOkNlKUqnqCxDmWTwf93/0BmYDJVqVKJRJsYyPZTRYDRhdRFdqIy8Qua6tgmNYglrSlYRzQCDRXnopFTsFtmdO8fSrTfZfebjxJWZ0YZ4mNEs3rnOytrbmF6XO94Muwsr4DhSHSXECWKMfu9DH/rcZGRphM5StM4GM6MkGSXENBg2KbUI/J0gCKbhKEs8AddTtFuGViNjflGSUmI0umF/roxty3NMCACyFG9vncitYRybVi9EG00hbwPOn4JSiqpboRG1uNHc4MOzl7HuP7lQqj8XxHYwcKRUj+u6qOTRK8g9rY5RrGob1zhUhmh1OykMGpsyKbtD32dn4SLzzS3OvvUytz/5gyOM7j2l9j7nb7xGob1HK7PZqF0iLVXG8thCiMczxtxLLvG+hFP2/iTUYDuYwX3MYPbgwTuAhVJKklFCTJlhk1L/N/Dj9KulxCniuP02vp2tlPnFB1zFFuIpZZkh6mmZJyXEId7+BiQRvcJZ2kmXOEtQ5HPA+dOwlUXVK7MfN7kT3uV85YMr9p0E72qbGIszyuJoqbO8ylC4/R9pyKdeZrvsLFzizJ2blPe3COfPjCw6K01YWbvCwtYN0ijhdnGB5twiKuctrkJMA2P6iaL3qpmyh1Q4aYzujwIxg9uDvnf/9+1cDIPZgIPtSg1ez7IQjhDTbNik1KeAn/J9/38Ctg5/IQiCHzj2qERuKKXwCopmXeZKidHohZosNf3xMEIIoN+6p5OMdH6eZm+HRCd41nQOcPUsl5JdYD3cpuKWmfUe3faXN/tGsa0tijgUp2CW1GGGDAsPZRyMSoe+3+7sOebrm5x98ytc/74f+8AA+qcPzDCzt8HZW6/jdFvsqhJbCxcxbkGubwhxzLROiXo9kiR+r9JJHySgDAbTb7W+Vwl1kFA6+A6HkkwH/7UslLJyu5qsEGK8hk1K/ePBhziFHFcRtjVJonFduUohjlc31MSRwZ3O820hnphKItz6HXreDDEZ3TRi2q8Sl5wiaZJxo7XOx+aex7NPRmWuMfBO5hAbmwtTeF5l6LdWW5TJaA5/P8tie+lZLt55m9mtmzTOPndsMblRyLmb36S6f4deorlZOUuvLIPMhRgFYzRRt47OYrKsn5g+SDHd+1wp1KDCSVkO0/5+JYQ4fkMlpYIg+MyoAxH55XoKrQ313ZTls5I5EMerG2q0AduREwohALzdVUhiepUzNOI2yUkacH5E/flSZepxixutdV6YvYx1ApbjvGMs6kZRUw7ulFVJ9fXbbGwqT5SUAmhUF1ksVFm58jLN5UsYe9jroA+mtGbhzjVW1t/G9HrccWfYWVxB2TLIXIhRSaI2WZqAAtuZnpmGQoh8GeoIwff9//JhXwuC4J8cXzgijxwHbFuxsy1JKXG8jDZ0u2aworucVggB/aRUliniYo12eIfMaDxn+ve9lrKouRUaSZvNTn++VJ73C5mBq9omNQ5z+Q3zqRk0iiM8/5TizvKzPLf6Ogurb7H77MePHEOptcf5m69RaO3T1DabM5dIi6NfUVGI0yxLY5I4ROsU1y2gzTQm3oUQeTDsZasfv+/fZ4EPAb8HSFJqyimlcAuKdlNPOhQxZXo9Q5YaObEQYsDqtXGadwkLs3SygwHn+a8YOi6u5VC0Cmx071Lxysx5tUmH9FCrxqZtLGaVjT2VVVJ9B3Ol0AqecGXBsDRLs7rA8ruvUL/wApn7ZJUWdhqzsnqF+a0bJEnGreICrdpSrpOVQkwDYzRx1ETrFMuy+685SUoJIUZk2Pa9P3H/tkH11IvHHpHIJddVtFuaqKcpFE/PCZIYrf48KS2te0IMeLu3IY7pzczRiDskOqNoT3+V1GElp0CapNxorvPi3HMUc9gyEhu4oW3AYXaKE1LQT0qBQlHE0H3i+28tPUvt1jdYvvoqd178I0M+qGF2d52zt1/HDtvsWmW25s/IIHMhxqTftheDAWVPd/u4EGLynia78IvAXzymOETOuZ7CaMPezvCr7wjxON1Qk2XIkHMhAIyhsLNKqlxC1yPKYsCcuqqQg/lSqcm40d5Am/xV6V7XNqFRzGOdgiRJf/Vdm/KR7h17JfZmzzJ/83XcsPXY23u9Ns+8/RUuvPt14k6Xq+VzbC5cwjxhlZUQ4miyNBq07WWop5wFJ4QQwxh2ptT9yasy8F8A9WOPSOSSbfdX4du9m3LuomQQxNMzxtDtaBQyT0oIADusY4UNQm+WVtoh1gmeOp0nBJayqLplmkmHtc42lypncrOf6BhY1Taucag+YTvbSWXIsCmTsnuk+99duMRcc5szb3+NtZf+5ANvo7RmcfNdltffwUQRm94su4vLMshciDEyRhP37mvbE0KIERv2aDflvdU/D6wDf/l4wxF5pZTC9RSdlsaY03flXhy/ODakiYEpb30RYljezm2II8K5OdrxPsZo7FMw4PxhXMuhbBe4E+5QdcssFGYmHRIAV7VDhMUZZfHBQ6NplaFw+z/uEd7+M8dlZ/EiZzauU3ruO+nOLb3v6+XmDudvfhOvXaepbTZmLpMVy5KMEmLM4qhNliUAKEva9oQQ4zFsUuq5+/7dCYJg57iDEfnmuopemNENNeWKvFGJp9PtaJLEYNly2iEERlPYvU1slWihSXSKUjK/r2gXSHTKzdY6JbtAacLzpfaNYktblHEonaKE+r1h58YBdbQ2/t3ZcyzUNzn75u9z49/7j0Ap7CTizOoV5rZvkiQpt0pLtKqLcuFLiAnI0oh00LZn2e6kwxFCnCLDDjq/NepARP65nsKYfgufJKXE0+qGmjg2lI82pkSIqeI0d1C9kJ43TzPtkOiUoi0zdA7mSzXiFjda63x07hlsNZn3H2Pg3cwhNjbLSnF6qqTA0J/rZVMi4/FzoR74PSyb7cVnuLB1lZm7t1HK4uzt17G6HXasMtvzlzGuJ9VRQkyAMZqo10TrRNr2hBBj98iklO/7v8mjj7pMEAQPHg4gpo5tK1wP9ndSLj0rJ0vi6XRDg6XAsqUaRIjCzi1MHNOYqxDHdRRKTgoGLGVRcys04g5r7S0uV89N5HezZSz2jaKmbNxTVCXVpwGNRfXISSmAem2ZhfoGF77526hKjW4Ka9XzRKWaPN+FmKA4aqGzBLCkbU8IMXaPq5T6Zw/ZfgH4aTjiUizixHI9RadtMNqgLDmAFEeTJoY40qeozkCIR9AZ3t4akVOlaRKiLMG1TueA84dxLIeyU2Sru0fVK7NYmBvr42cGrmqb1DjMnbIqqQMG3W/hexpKcWf5ec5vXWXHqrG/uIyybamOEmKC0rRHGnelbU8IMTGPPOoNguAXDv/b9/1F4G/RH3D+WeDTowtN5JHrKrodTbutqc3IlRRxNN1Qk6YGKZISAtz9TYgj2t4ynbSLAWy5Uv0BRdsj1Sk3W5uUrCJltzi2x14zFi1jMads7FNXJdXXnyvlglbwFKsOhqUZrj77XcCRZqYLIY5Rf7W9FjqT1faEEJMz1KVY3/dngL8J/BTwK8B3BUFwbZSBiXxyvf6b1e52KkkpcWTdUBNHhuL4zimFyK3C7m10krJX9kjiDrYMOH8gpRQVt0wzbnGjtcZH557DGUPyLjFwXTuAw8wpTUj1ZUABRQFDb9LBCCGOQT8hlYBS0rYnhJiYx82UKgF/A/gZ4LeAPxoEwRtjiEvklGUN5krtpjz7YZkrJY6mG2oUBtuRAyBxuqk0xt3fpOvWaGVdUp3JgPNHsJSi6lZoJi1W23d4tnZ+5Ff2r2ub0CgWsVCnsG3vgCEDwKZCKkkpIU68NOmRJl20ybAsadsTQkzO4yqlbgA28PeArwNnfN8/c/gGQRB8aUSxiZxyPYtuaNCZwbKlzFc8GZ0Zel2NNG4IAd7eGiQxe94MsY5RypL2icdwLJuyXeJutE/VLbNcmh/ZY4UGVrWNaxyqT9GyNi0MGTYl0kkHIoR4Kkbr/nBznWIpadsTQkzW45JSPfrTPP/aQ75ugOePNSKRe66n6LQyGo2M+QUZxiueTLeryTKDHP8IAd7ObbLUsFeyiJMEV8k+dRgF2yM1Gbc7m1ScImW3NJLHuaodelicVRancbj5B2UovP6vQvbhU0b+qKeFMYY4aqKzWNr2hBC58LhB58+OKQ5xgrguKEuxu51KUko8sf48KXCkUlyccioKcZp3abhVulkEBmxbTg6GoZSi4pSox22utdZ5cQTzpepGcUdblIxDUaqkgEMr8BkbVDbpcMQxcKyUs+UGZ8tNYm1zs7lEM5bFtadZlkakSQ9ttLTtCSFyQTIK4okppfA8RWNfCvjFk+t2+q2fTkmuyIrTrbB7G+KY7dIsse5iWzLg/EkopZhxyzSSFrfbmzxXu3BsLSjGwDuZQ2JslpRCqqT63psrVSKjPeFoxNMo2AnnKnWWSy1KTkTBTqm6Eecr+9xoLHOztUyUScJi2hidDdr2EpS07QkhckKSUuJIXE8RdjRponFcOZESwzGmP09KKeRASJx63s4qsbZoYsi0lgHnR2BbNhWnzE6vTtWtsHJM86W2jcW+UVSUjXeqV9y7nwY0FlVJSp1QJSfifKXOUqlFyY5xLE2mFa24SCsuMuN1+fbFdS5U61xtrLDeXiAzcpw3Dfpte/3V9hSWtO0JIXJDklLiSFxPoZuG+n7K0oo36XDECRH1DGlqQE7yxClnhw3szj5bXoXEpCiUJGqPqGB7JDrldnuTslOg6j5d65E28K62SY3DWamS+oB+C58kUE+aqtvjQnWf+UKHkhNjWxlJ5tBOChyeJdWMy7STIgvFDt+1fJMLlX2uNs5wt1tDZk6dbFnaO9S2J6eAQoj8kD2SOBLHActW7GxlLK1MOhpxUnQ7mjjW2LZcdRWnm7fTb93bKdaIsghP5no8lYpTohG3uD6YL+U+xQnXqrFoG4tZZWNLAv0DDBkWbr9oSnblOWeY9bpcqO4z44WU3RgLQ5w6dNOHX1DUxmKnW8O1ElbKTRZLbVbbC1yvr9BKRrOogBgtfa9tL5VVXoUQuSNJKXEkB3OlWk0ZdCqG1w01WQLl6qQjEWKCjMHbvU0Lj64yoJB5Uk9JKUXNq9CI2txqbfChmUtHOulKDNzQDgaHWUlIPUQGFFAUMESTDkY8kGGx2OZ8pU7V61F2IsAQZR6pHr5lK9EuW+EMZSfi+ZltzpYb3Ggsc6u1JPOmTpD32vb6s2AtadsTQuSMJKXEkbmeot3SxJHGK8gJlXg0YwzdUOZJCeG0drG6bbbcArFOcOSt+FjYyqbqltmNGlS7Zc6Wl574e9zQNqFRzGOhpG3vgQwaAJsKqSSlckWhWS61OV/dp+xElJ0YMHRTj8wcNRGhCNMi3bTA7GDe1PnKPtcaK6x3FtAybyr3srRHlvQwJkNJVa4QIofkSFgcmespjDbs76ScuSBzpcSjJbEhSQwyn0Wcdt7OLdIool6qYkyC68hV6+Pi2S5FXWC1s0XFKVPzhp8vFRq4rW1s41CzZD/1cAbIsCkha/Dmg600K+UG5yoNSk5E2Y7RRhEmLprj2b8YFPW4TCspMl/s8MmVm5wP61xvrMi8qRw7aNvLdIpSjlwUFELkkiSlxJHZNjiO4u62JKXE43VDTRIbLFsOiMQppjXe3hrbVpFEGZScyB27slMkTVKut9f42NzzuAxXGXBVO0RYrCgLSZ4/mkGj8Pq/JnkKT4xjpZwtNzhbaVKyI4pOQmYU7aSAGdHAr2wwb8qzEs6V6yyXWtxuLnK9uUI7KY7kMcXRGGOIe83BansKS9rEhRA5JUkpcWRKKVxP0WlrjDFy9UU8UjfsV0oVZUaqOMXcxh2IQradIrFOKFiS0D9uSimqboVG1OJGc50Xlz702PvUjeKOtigah5JUST1Wf9h5EYwNSmZLjlvBTjhXqbNSalG0Yzw7QRuLVlxkXFnCWLvcCWcpOxEfntviXPX/Z+/OYmTL1sSu/9faQ+zYMeY8nXNquFXVt2+3+/a9bvdtG7ptgSWMhGSQUMo8ABII84DFC0gIXowwD/CAEA+AZMCCJ1opJISFkIyEu91tm3Z3+2J331t1q+rMY86RGbHnvQYeIs5Q1XXqTJEZkZnrJ2Wdk1GZEStOROy99re+71sn3D5Z5d5oicq4ErF5oFWBViXWGKTvzjWO48wvF5Ry3kkQCopTTZEbmrErQXFeLs8mfUjcznvOFRYe3ierarK4jTDKrVyfEU9I2mHMoBrycLTLWrSEfMnCibXwpfao8NgUApcl9WoWjQA8mmiSWQ/nymj6JZutE5abIyKvJpSK2shJhtIsFgZf6DfVyPhTyw/ZbA+4dbrG46SPcdszzsyzsj1dI95hN1LHcZzz4I5SzjsJQoG1guNDxdYNF5RyvplSlrIYNzl3nCtL1YSDxzz0IiprCIQ7BZ+lUAbEfsSjdI9ClVxrrRF8w8XZvpUcW0kbj9DtuPeaDBaLpOWCUuegHRRstQcsNFKafoUnNZX2Gc0sGPVVFsFJ2WJUKRajjF9ZvcPjVp9bp6scFe25GONV8qxsT9UIKd3ih+M4c8/NiJ134nkCP4DjA8XWjcash+PMqTwzaG2Rbl7qXGHh4BGqLBh4EdZaPN+dgs9a048IjM9BMaDQFe+114n95zXExsJN46Gsz7rLknoj4xI+d94/O5Z+I2OzdUI3zIiDComlUj65ms9SLG19DvIuoVex2R6w0hxyf7TM7eEKqes3dW5UnY/L9qxBevP5XnEcx3mRmxE77yxsCNLEur5SzkvlmaEqLaG7fnGusMbhfY6Voox8pMvIOTcNP6QnJKMq4cvTB1xvrbHQ6CKE4KGVjKykKzw895q8kXFQKnDNzqfOshQlbLZOaIcFsV8isBQ6RJmLkZFe6ZDdNKAdFON+U61xSd/90RK1cZceZ8kYTV0mrmzPcZwLxR2tnHcWBIIs1aSJod25GBMm53zl6bgZvu+794dzNYmqwDvZ5cALqdFE0kVoz5MnJL2wQ1Jn3Bk9ItcFS80VbpsQi0fPZUi9BQ00ELaBFeWsB3PhCQwrzRGb7RNivyT2K8CSqxBtL+K5U5DUTdI6ot9I+aXl+2y1B9w8WeNJ2juz3QGvsnHZ3inauLI9x3EuFheUct5ZEAqwgsN95YJSzp9gjKUs3FK6c7WFR/dJ65LUayMQLqt0Bsa78sWUuuJRdsCDWpFFWyx4DZe59hYsk80riFG4oNTb8oRhIz5muXFE0y+JvQqDIKsDDBd/TmURDMo23qTf1J9Zu83jtM+t0zWOixZubjA947K9Ctxue47jXDAuKOW8MykFQQgnRwq+41b/na8qcoNSFuEW7JwrrHH0gAfaUgQC35VUzIwQgshvoIXPHVUTpg9pNDch7M56aBeQBfQkKDWY9WAuKMsnC7usximBKDEWkrpxKbOInvabangVW60Bq80Rd0fL3DldIVNu7viujFHUZYIxNbhzjOM4F8y5HLW2t7f/FvAvAfs7Ozu/OLntPwX+HeBg8mP/yc7Ozv81+X//MfBvM84N//d3dnb+zuT2vwT8N4AH/I87Ozv/xeT2D4DfBBaBHwP/+s7OTnUez80ZCxuSPLMYY5Gum7Xzgjy1VKXB9XR2riqZjzDDQ44nDWd9efGzHy66fb9P5ge0iyeM0lvEZouoseIy2N6QxSBwGRlvqxvm9MOUyKs4LRtchayhUofsZgGdoOCT/hM24xNuna7wIHH9pt7W0932tKlBuLI9x3EunvM6av3PwF/6htv/652dnV+efD0NSH0P+CvAL0x+57/b3t72tre3PeC/Bf5F4HvAvzb5WYD/cnJfHwMDxgEt5xwFAWhlGZ2aWQ/FmTN5ZjBm3HvMca6ixtF9BlVB7vlIlzI4cyk+xyKibTVNv40QHml2nyS7jzFq1sO7UCwagQfGva/fnOV6+5g4qMhUyFUISD0nGNVNdtMevtR8f+UBP1q/xUbrBOH6u70xVWeT3fYs0mVJOY5zAZ3LLGJnZ+d3gOPX/PG/DPzmzs5OubOzcwe4Cfzq5Ovmzs7O7UkW1G8Cf3l7e1sA/xzwv01+/38B/uWpPgHnlZ4GHA736hmPxJkn1lryzCDAZSA4V5O1BAf3OECipEfgLhhmygIPZQuDpW9yhBD4fgvPiynKfYbJLZTOZz3MC2MclBJI4lkP5cLpNzK6jRyBRV/RbhoWyaBscZC1WYxS/szaLf706h26YTbroV0YxiiqMsEYhRAuC9dxnItp1mfBv7a9vf1vAH8I/Ac7OzsDYAv4vRd+5uHkNoAHX7v9R8AScLKzs6O+4eedcyImfaVOB26V2XmuLCxKWXBNhJ0ryksHpNkpuQwQwjU4n7VTQkaEdHXxlQmQ5zWQ0qeuhwxHX9JqXicM++71eiWDxeLRwpDMejAXiOVae0Dsl+QqOL+6hTmlrM9+1iXyKq53jllujvh8sMG90TLGXvF/nG/xtGzPaAXCc2V7juNcWLMMSv33wN9gvHD5N4D/Cvi3+Ob8Zcs3n7JftqXXS6+At7e3/yrwVwF2dnaIY7e6Ny1a1aQjRa+3QBC41Zpp8H2f5eXlWQ/jrR3sFXhySLsNUdO9Jy4jKaU7jn6LxpNPua9qiqBN5IV47qLh/AmBJyUGeCw6CGvpUyG/HnASPjJcoFIjkuwOMZu0mhsIV3L5rYSx+DTHdfzOa+mHCf2oREqJlSECkJ47R1Y02S8i+mHKD1cfsNrK+PzkGqlqznpoc6kqEqxRSCnwvDP+/E2Oo44zt9x79EKbWVBqZ2dn7+nft7e3/wfg/5x8+xC4/sKPXgMeT/7+TbcfAv3t7W1/ki314s9/0+P+TeBvTr61WeZShKfFGEtRKO7c3GdxxU1Op2F5eZnDw8NZD+OtPXlckSQVzViQZe5EcRnFcYw7jr6ENfDoC46txAgBCLRxfffOmycl2hj2RUSGZEFnCGt52Svhe220zknTB9RVSqt1HU+6Zt4vI6gRBNRVfbXaIr01y0Zvn4bMSasAYzXS8zBaz3pgc+Mob9LwPLbiA/rBkE+PN3mYLGHdG+wZoxV5doLRFQgfzvjc8vQ46jjzyr1H54/Wr/96zOwqcXt7e+OFb/8V4CeTv/9t4K9sb283JrvqfQz8PvAHwMfb29sfbG9vh4ybof/tnZ0dC/wW8K9Ofv/fBP6P83gOzlf5AUgpONx3JXzOC/2khHAp5c6VFJzuM8hTCunhuR33ZkoheCJaeEbTtt/e+3DcZyrG89qU9THD0U1q5UrTXs4gkAjbmPVALoSlKKEdFIDAWHdceJlShzxJ+/jS8Ctrd/nllbvEfjnrYc0Fay1V6cr2HMe5PM4lU2p7e/t/Bf4CsLy9vf0Q+OvAX9je3v5lxqV2d4F/F2BnZ+en29vbO8CngAL+vZ2dHT25n78G/B3AA/7Wzs7OTycP8R8Bv7m9vf2fA/8f8D+dx/NyvkoIQdgQDE/dap8DqrbUlf2WYlrHudz8g7scq4oq7NIQs27heLXtiZgSyapJXjvXwvNCpOxTqyHD0U1azS0ajWXXZ+prLONzvkcThQsafLvnvaQy5TLKX8UiOCraRF7Jh91DlqKUT483eZwucJXT8lSVouoSi3FZnI7jXArC2it9xWh/6/++NesxXCp5ZkhHhl/99RZB6FZu3tVFLt8bnmju3y7R2hI13XvhsnLley+hFeL3/3dulwVJo0PkuyySWVHS54/o4xvFunnz96q1Fq1TjKmJGqvEzU237frXeLTQ5FTypd0THGC5OeKT/hMiryJT0bPbXfneq0kMi80ET1juDFf4YrBOoa9eQMbomjw7RqsK6QXnFiR3pVHOvHPv0fmjteE/++v/IbzGKoK7UnSmKggExliOj1wJ31WXZ4aqsgRXb87oOISDxxzXBbnwCaTLiJilRyJGIVgwxVv9/ricr43nxeTlHqP0NkrnUx7lxWbRSFzg9dsILNfax8R+Nd5xz3kjBslh3mFURXzU3+PPbtxkLT7lKqVjW2spJ7vtCem5rE3HcS4NF5RypsrzwfMFR66v1JWXpwYBeJ47zDhXj9m7TaIUyo/cbjAzlOJzJCJapqTx0tbmr8fzGgRBl7oel/OV1QlXPNv8GYtG4INx7/WXWW6OaPkl1gosrpfU2xFkqsFe2qMT5Pxo/RbfW3xEKK/GnLOuUrSqAIN0fQodx7lE3OzBmSohBGEoSIbGTdavMK0sRZtvJVIAACAASURBVOFSaJ2rSdQlo5MnFMLD91yZ1ywY4EBE3JZdjLEsmOn0OpLCJwh6WKNIktvkxS7WumPdOCgFkuashzKXBMb1kpoibSX7eZesDvju4hN+bf0my9GIy5w1ZXRNXaUYoxAu+9ZxnEvGzZadqQtCwejUUBaGqOlWcq6iPDdobREu7O2cg1wbGlIg56SUwT+8x0ldUnsNfLeafa4UggPRZF80qZB41rBsMvwpXqwKIfGDDlrnZPkjlC5ox1vIK91w2GCxeLQwpLMezNxZjUfEQYG2AuvWg6dEkNRNchWy3Ez4tY2bfHmyxq3TVZS5XJc3z8v2aoQ4v7K9yFcsxwUrcUngweNRg90kRrmMSMdxpuxyHbWduRAEAmstxweazRvuguwqylNDVVpC12LEOWOZ0vyDwxFLoc8PFlpz0WMj371FZQwmjOZiPFdBiWRPxByJiBpBaDRLNqVlFZ4Q71i49yeN+0zFaO1TlocYndNqvUfgt6b8SBfHuK9U9OofvGIkhq32gKZXkdbupDht2nrsZV26Yc4vLD5iORrx6fE1BuXl+Sw+L9sbN8U/S01fsdwqWIkL2mFN01cEniXyNe/1LUdZxIPTFo9HMWntMrYcx5kOF5Rypk564AeCo8OazRtXeeX46sqzcfmm77ugpHO27mUVeW346Sin7Xt80p1t+ZDIR5wmh1RIAs8d/85ais+eaDIQDTSCpqlZNCVNzmcnM88LkbJHrUYMR1/Siq/RCJeuZDDSYhAE4wqqq/f0X2otHtL0S4yVLkvqzAiGVUyuQlbjEb3Gl3w+WOfucAVtL/Y8RL9Qtie9swkCxUHNclyyEhe0wppmoAikxVqojeC0DBhVDTyh6Eclq3HOJ0sBD4ctHo1aHGYN3IfecZx34YJSztQ97SuVjizW2is5Ob/KjLEUuUW4CYpzxipjeJxXNKxkIRD844OUbuCz3pzd6q3Zu0le11R+i9A1OD8TFhgSsiebDAkxWFqmpmsKGjPoKSOER+D30CohSe+hdUGzuYEUF/ti+M1pBCHChlhRzXowc0EKw+azLCmXRXbWauPzJO3Ra2R8f/kBy1HCZ4NNhlU866G9FWstVXGK0TVyqmV7llagWI5Llp8GonxN6BmMhUpLTmufrweaauNxkDWRwtJrVHx35YQPF0bspU0enLbYTZrU5qod9xzHmQYXlHLORBAK8kyTpYZW252grpIityhl3aKZc+YeZhWF0izIgHYo+TIr+Ad7Q/6FrT7tYAbHHWtJD+5SG4sML+ZF0DwzwLGI2BNNcnystXRMSdcWM5/MCCHwgw5KF+TFE5TOacfX8byrE4iwk+w0SYzGBaUA1uNTmn6FNhLrTornRHBatsjrkM32gIUo5WeDTe4PlzAXJFPNWoOqc1SVoXUNgHjnsj1LOxz3iFqOC+JAEQcKXxosgkp5nJQBrzN5M1YwKBoMipBWoLjWTbnWTTnMGjwctng8apFUrrTPcZzXN+t5nHNJjftKCY72lQtKXTF5Zqgqg+/mI84Z0tbyICsRWtAJPYSAD5oNPs8KfndvyD+/2Tv/TKXkiGE+Qkvf7bo3RQrBoYjYF01KPKQ1dE1O11Zzd4npexFaeNT1KcOkJG5eJwy6VyRj2AIGjxjNyawHM3Oe0Gy2Toi8iuQCZElpY5GCS/NerUzAbtpnIUr54cpdlqMRnw025zpjzRiNqjKUyjFaYYwGBPKtzyeWzguBqOazQJTFWEulPdI65O1XEQVpHZDWAYHULEQVK62CjxeHPBq1eDSMOcwiF5B1HOeV3KzZORPSEwQhDI4UNz50jT2vkjwzGA1R5CYhztnZzWsyZegIn6fXUKGUfNhs8EVW8PuHCX9upXOuO/Lljz9HqYoq6ONisu+uQrInmhyJaNyjy6hnzcvn+ejiyQAZ9FBqRJLeJm5uEjVWEFdgO9Jxs3PXSw1go3X6rJfUvKcOV8ry4EghJWz2fRrBfI/3dVkEx0Wbhldxo3PEYpTy6fEmj5LFuQqUaF2jqhSlSqxRGGMQQiC94C2ChJZOWLPcGpfmxf44EOV9JRD1J0vz3lVtPPazJgJLr1Hy3aUTPlgYsZc0eThs8WTkSvscx3k5F5RyzkwYCrLUYoxFyvk5+Ttnx1pLkY33ubosq63O/LHWci8r0cqyle2ydvczPFNjkVghWPZiPjvuc+9Jzfe9AiskSDn+U0islMBXb0NOtmr/ym3j+0N8y21yfLtBkBw/QGuL35pts/WLLsNjT8QMRAOFIDKKVZMRn1Pz8mkQQuL7XbTOSLL7KJXRiq8h5eUOVz7bgc9IkNPe8/Di8IRmo3VCw6vnPktKacvDY40xBqUUdw8MGwsB3eblCaKWOuRJ6rMYpfzK2h1WmiN+NtggV7NbNLXWolWJqjO0qjBGgbUg5VsEoyzdRj3JiCppvhCIshZKJUnOIBD1zSMRnJQRJ6Ulflbal3CcP921r8XIlfY5jvM1LijlnJkgFKSpIRkaun23OnIVVKWlri1iBs2GnavjqFIkecZ3nnzO9dEDSkJGfhthDQJLX5esm4Q/1jFdlfGeShDYyRdgDdin71OLsJPbsAg7ee+KyX/EC1/wte8FJXCC5dQaSmvQXgPhGpy/MQuMCNiTMaeEGCA2FSumpMHFDG4IIfD9FkIHlNUh2hS04xv4/uXZqv7rLBoBSCIM2ayHMzNPy/aU9ZjnLCltLI8GGqU1650a3xPsnSoeHRmyTshqV55rtulZskiOig6RV/Jh94ClKOGnx1s8Sfuc52s07hdVoOpsUqJXgwUhvTfsG2XpPQtEFUSBphUohJgEomqPaqZZeoKsDsjqAF9qFqOK5Y2Cj5eGPBrFPBq2OEhdaZ/jOGMuKOWcGT8Y7792uF+7oNQVkWeGujZ4vrsod86I0Tx+eJvmwR43hg/Ya19j0L0GX9vpLLSgLfw/rPIX/ZwF+fqBUmsMWIPAgLFgNcLY8W3WUuuaROckuqDQNcpqMAqJxDR7uKPd6zPAQDTYEzHZpHl525T0bIl/SYLbnhciZY+6HnGa3KTVvEYjXLyk2aQGi0XSurJBKV8q1lunRF7NaI6zpIy1PB5oisqw1qmJwvH7cXMBjhPN8aikqAK2FnwC//K8Vwvd4EkWsBQl/Orabe4MV/h8sEGpzzZ7xxg9bl7+LBilJ2sbPtJ73TmTpRdVrEx2zWv4zwNRWChqj8rO3xlIfaW0r+KTpVM+6I/YS+JxaV/SpNLzN27Hcc6PC0o5Z0bKcV+pk+OLU3LhvJs8M6ja0oxhnleHnYspONmlvvtHnFQxvVpxf/WHqOCbS+WEgPeBL6zk76uIvxjkNF/zLSmelvcBTyNMtVEkdU6icgpTUlmFFhb8AF808KWPEMIFpF6T/lrzcibNyzu2upT/hkJ4BEEPrRKS9C5aZzSjDaS8fNMwi8ajiZr1QGZks3VC06sm/XPm8zxorWX3RJOWhpVWRfxCGzAhYKkjiALD/qh6Vs7Xji7PYpOxkoO8Q8sv+ai/x2KU8OnRFvt5l2m/ZkbX1FWGUsVb9YsSzwJRBUtxSTQpzZNYDIJCeRemV9O4tK/BSRkSB4qtTspWN2WQN7g/bPF4FDN6zR0AHce5XC7fbMiZK2Eox4EKZfBd9syll6cGgUC68iVnimSZEd/7pwRHD/hD2ydpdGkudHlVqzpfwIfAF9bn91SD3/BLvDeY6yqjSVVGUufkqqA2CmU1AoEnJZFsXNJsl7NTITkQTQ5ERI3Et5oFk9Gy9dztpDdtQgj8oIPSBXmxi9I5rfg6vnfZepAZBMG4JvOKfTwCqViPh4Rz3EvKWsv+0DDKDYtxTSfieXnyC1qR4JoPu0PFg0PDci9kuS0v0TFPkKqIIg1Zikb8aOMWt05WuXmyTmXe7fLIWovRFXWVoVX5Qr8ogef5hL7Fl+Nd8HxpCCZ/+t4Lf5/cHvmahq/GGVEXMBD1zb5a2tePan6wfsTHi6c8HrV4NGyx70r7HOdKcUEp50wFoSAZWUanmoWly37JcbXVtaWq7JW7CHHOkDFEu1/SfPgptsw59he51b6OLyJed3PHSMB7WO7YgH+qNT/w1Dddfz2jrSadZERlqkAZRW0UIJBCEnkuEPU2cjz2RJNjEaEQNIxi2WS0LlDz8mnxvQgtPOp6yHB0k1Z8jTDoX5r31dMd+IQNsKKe9XDO1VZ7QNMv5zpL6jg1nGSaTlTTb9pvDEg9FfiwtQCHI83haUleBmwu+PhvEt2fc9pK9vMunaDgu4tPWIoSPj3e4qho8/LX0OIJiy81njD4UuNLg4dG2hxMhkeNFyt8YQg8CHxBIA1SGiQgpUWKp/dj8ITFm5SZWyuwWLDjzofaCtLaQ89had67UsbjMPOelfZ9vHjK+/0R+2mTB8MWT0axK+1znCvABaWcM+UH4zK+wz3FwpLbbeMyy9Nx6d4V2PXcOQf+8ID4zo/xkgGV8Um7H/Jl0KM0PqtIeIN+Qz0BG8BnOqIncr7jfTUQYqwhVTlJnZF+JRAFngtEvTULJATsySanNNCMm5cvm5LogjYvnxZPBsigh6oTkuQOzeY6zWgdcQkOoHYSaJTEaE5nPJrzE8qa1eaQQKq5zZI6yQyHI0MrUCzF3x6QekoKWO0KmoHhYFLOt7kQEDcu/nv1OcGobpKrkOV4xK+t3+TW6Rq5Cp8FnHwx+XPyvcAihUEKi8BgTYXRCqxBCoMnLb6E55OicZCJSSNyY+V4qw0rqLUkMwJtx9sEXEUvlvY1fc1GO2OzkzEowme79g1daZ/jXFouKOWcKSEEYUMwPLl6q+FXTZ4ZqsoSXbZKFOdciaogvv9HhAd3MbVm2Fyj7iyjkTzUHj4+sXjzBtirQC7gD3REh4xlqclUQVJnZJPSvGoSiJJAQ4auDPUdJPg8lG0SAoyFjinp2pLgkjQvnwYhJH7QQeucLH+EUjmt1nU8Gb76l+eaBQwerSsVlBpnSVXUxmceL5yTwrB3qol8xUrbIF5V//w1naYgDGDvVHH/0LLSC1hsXaZyPlDWYzft0Q1zvrf4mFw9P2JZ+8LzFIwbiys4yeA0s9TaEniSVigIfd/twvrWBLnyyZU/Lu1rVPzy+jEfLQ55Moq5PegyKBqzHqTjOFPmglLOmfMDQZaMd2ULAneSvqzybJz54L32LjKO8wJraOzdpvngJ4giI/c6ZItbiCBEAI+NJLeC/htmST0lBFy3hluq4PfKIZ/YIdLWlHpcXiSRNGTgAlFTUOBxU/ZQVtAxBV1bXsrm5dMghMD3Y7T2qepj9KigHd8gCDpTf6xQVny4cJ9HozVG1fTv/0VPS/iuioZXsxrPb5ZUVhken2hCqVjr6DcOSD3V8OHaAuyPFPsnhrwK2eh7eG95f/NJMKxihlXz2fcvstaSVZZBakjL8a6s7YZiIYbQF7h08elRxuMwH+/a1w0rPl4acq2X8jt31xkU8/c5cxzn7bmglHPmglBgjOXkSLGyfnUmqVeJ1payMPO4OOxcAF5yTOvOj/FGR9RakrTfxzQ7z1bgjYV7xsPi0XnDLCl