Analyze open data sets with pandas DataFrames.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Analyze open data sets with pandas DataFrames\n",
    "\n",
    "In this notebook, you'll learn how to use open data from the data sets on the Data Science Experience home page in a Python notebook. You will load, clean, and explore the data with pandas DataFrames. \n",
    "\n",
    "Some familiarity with Python is recommended.\n",
    "\n",
    "The data sets for this notebook are from the World Development Indicators (WDI) data set. The WDI data set is a statistical benchmark that helps measure the progress of human development. The WDI data set draws on data from the World Bank and more than 30 partners. You'll look at seven key development variables, sorted by country and year.\n",
    "\n",
    "## Learning goals\n",
    "\n",
    "1. Access data from the Data Science Experience home page.\n",
    "1. Merge multiple data sets in pandas DataFrames.\n",
    "1. Structure data for analysis.\n",
    "1. Generate summary statistics.\n",
    "1. Create charts with custom formatting.\n",
    "1. Show relationships between all variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Import Some Modules\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib; matplotlib.style.use('ggplot')\n",
    "\n",
    "# inline plots\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Table of contents\n",
    "The main parts of this notebook are:\n",
    "1. [Load data into DataFrames](#load)\n",
    "    1. [Load the first data set](#first)\n",
    "    1. [Load another data set](#loadsecond)\n",
    "    1. [Join the DataFrames](#joindataframes)\n",
    "    1. [Add more data](#loadmore)\n",
    "    1. [Create a MultiIndex](#multiindex)\n",
    "1. [Check the data](#check)\n",
    "1. [Analyze the data](#analysis)\n",
    "    1. [Create scatter plots with matplotlib](#matplotlib)\n",
    "    1. [Create a scatter plot matrix](#matrix)\n",
    "1. [Next steps](#summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<a id=\"load\"></a>\n",
    "## 1. Load data into DataFrames\n",
    "\n",
    "To load the data, you obtain access key links and put them into the code to create pandas DataFrames. You will load data into multiple DataFrames and then merge the DataFrames and create a MultiIndex to structure the data for analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<a id=\"first\"></a>\n",
    "### A. Load the first data set\n",
    "\n",
    "The first data set that you'll load contains life expectancy data.\n",
    "\n",
    "To get your access key link and load the first data set:\n",
    "1. Find the data set and get the access key link:\n",
    "    1. On the Data Science Experience home page, search for \"life expectancy.\"\n",
    "    1. Click the card with the title [Life expectancy at birth by country in total years](https://apsportal.ibm.com/exchange/public/entry/view/f15be429051727172e0d0c226e2ce895).\n",
    "    1. Click the link button.\n",
    "    1. Hover above the link button next to the access key to display the link.\n",
    "    1. Double-click the link to select it, copy the link, and click **Close**.\n",
    "    1. Insert the access key link and run the cell to load the data:\n",
    "    1. In the cell below, replace the **LINK-TO-DATA** string in the `read_csv()` method with the link.\n",
    "    1. Run the following cell to import the pandas and numpy libraries, rename and load three columns of the data into the DataFrame, and show the first five rows of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>life</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>60.509122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>60.065366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>59.600098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2009</td>\n",
       "      <td>59.112341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2008</td>\n",
       "      <td>58.607098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2007</td>\n",
       "      <td>58.091366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2006</td>\n",
       "      <td>57.570659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2005</td>\n",
       "      <td>57.058439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2004</td>\n",
       "      <td>56.569732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2003</td>\n",
       "      <td>56.107561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2002</td>\n",
       "      <td>55.671878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2001</td>\n",
       "      <td>55.256220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2000</td>\n",
       "      <td>54.848561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1999</td>\n",
       "      <td>54.432878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1998</td>\n",
       "      <td>53.992195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1997</td>\n",
       "      <td>53.508488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1996</td>\n",
       "      <td>52.972780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1995</td>\n",
       "      <td>52.375073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1994</td>\n",
       "      <td>51.709390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1993</td>\n",
       "      <td>50.985244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  year       life\n",
       "0   Afghanistan  2012  60.509122\n",
       "1   Afghanistan  2011  60.065366\n",
       "2   Afghanistan  2010  59.600098\n",
       "3   Afghanistan  2009  59.112341\n",
       "4   Afghanistan  2008  58.607098\n",
       "5   Afghanistan  2007  58.091366\n",
       "6   Afghanistan  2006  57.570659\n",
       "7   Afghanistan  2005  57.058439\n",
       "8   Afghanistan  2004  56.569732\n",
       "9   Afghanistan  2003  56.107561\n",
       "10  Afghanistan  2002  55.671878\n",
       "11  Afghanistan  2001  55.256220\n",
       "12  Afghanistan  2000  54.848561\n",
       "13  Afghanistan  1999  54.432878\n",
       "14  Afghanistan  1998  53.992195\n",
       "15  Afghanistan  1997  53.508488\n",
       "16  Afghanistan  1996  52.972780\n",
       "17  Afghanistan  1995  52.375073\n",
       "18  Afghanistan  1994  51.709390\n",
       "19  Afghanistan  1993  50.985244"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# life expectancy at birth in years\n",
    "life = pd.read_csv(\"data/UNdata_life_expectancy_at_birth_total_years.csv\",usecols=['Country or Area','Year','Value'])\n",
    "life.columns = ['country','year','life']\n",
    "life.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<a id=\"loadsecond\"></a>\n",
    "### B. Load another data set\n",
    "\n",
    "The life expectancy figures are more meaningful if you combine them with other data sets. Get an access key link for this data set: [Total population by country](https://apsportal.ibm.com/exchange/public/entry/view/889ca053a19986a4445839358a91963e). Paste your link in the following cell, which loads three columns of the data and prints the number of countries in each data set: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>30551674.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>29824536.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>29105480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>28397812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2009</td>\n",
       "      <td>27708187.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  population\n",
       "0  Afghanistan  2013  30551674.0\n",
       "1  Afghanistan  2012  29824536.0\n",
       "2  Afghanistan  2011  29105480.0\n",
       "3  Afghanistan  2010  28397812.0\n",
       "4  Afghanistan  2009  27708187.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# population\n",
    "population = pd.read_csv(\"data/UNdata_population_total.csv\",usecols=['Country or Area', 'Year','Value'])\n",
    "population.columns = ['country', 'year','population']\n",
    "population.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nr of countries in life: 246\n",
      "Nr of countries in population: 245\n"
     ]
    }
   ],
   "source": [
    "print \"Nr of countries in life:\", np.size(np.unique(life['country']))\n",
    "print \"Nr of countries in population:\", np.size(np.unique(population['country']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "life.year.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.year.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Masking and Data Scrubbing\n",
    "Data is sometimes (ALWAYS) dirty. To make analysis easy, we have to first \"scrub\" them\n",
    "* Remove Bad entries\n",
    "* Filter/ Fill missing NAN values (or not!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>life</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11969</th>\n",
       "      <td>footnoteSeqID</td>\n",
       "      <td>Footnote</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11970</th>\n",
       "      <td>2</td>\n",
       "      <td>Derived using linearly extrapolated data</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11971</th>\n",
       "      <td>3</td>\n",
       "      <td>Derived using linearly interpolated data</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11972</th>\n",
       "      <td>4</td>\n",
       "      <td>Derived using extrapolated data assuming the s...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11973</th>\n",
       "      <td>5</td>\n",
       "      <td>Derived using interpolated data</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             country                                               year  life\n",
       "11969  footnoteSeqID                                           Footnote   NaN\n",
       "11970              2           Derived using linearly extrapolated data   NaN\n",
       "11971              3           Derived using linearly interpolated data   NaN\n",
       "11972              4  Derived using extrapolated data assuming the s...   NaN\n",
       "11973              5                    Derived using interpolated data   NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using masks and apply lambda functions!\n",
    "# A mask is just a boolean dataframe/series with dimensions of the original dataframe\n",
    "\n",
    "life[life.year.apply(lambda x: not x.isdigit())].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Remove non-numeric rows!\n",
    "life = life[life.year.apply(lambda x: x.isdigit())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Change to Float (preferred numeric format of dataframe)\n",
    "life['year'] = life['year'].astype(float)\n",
    "population['year'] = population['year'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 11969 entries, 0 to 11968\n",
      "Data columns (total 3 columns):\n",
      "country    11969 non-null object\n",
      "year       11969 non-null float64\n",
      "life       11969 non-null float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 374.0+ KB\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13121 entries, 0 to 13120\n",
      "Data columns (total 3 columns):\n",
      "country       13121 non-null object\n",
      "year          13121 non-null float64\n",
      "population    13091 non-null float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 307.6+ KB\n"
     ]
    }
   ],
   "source": [
    "life.info()\n",
    "print \"\"\n",
    "population.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Join DataFrames\n",
    "\n",
    "As you can see, the two data sets don't fit together perfectly: one contains more countries than the other. When you join two DataFrames, it's common to introduce nulls or NaNs (a value that's not a number when a number is expected) into the resulting DataFrame. \n",
    "\n",
    "Use the pandas `merge` function to make the data fit together. In the following cell, the `how='outer'` option specifies to keep the data from the `life` and `population` columns. The `on=['country','year']` option specifies which columns to merge on. To view the options for the `merge` function, see [Merge, join, and concatentate](http://pandas.pydata.org/pandas-docs/stable/merging.html). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.merge(life, population, how='outer', sort=True, on=['country','year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13124 entries, 0 to 13123\n",
      "Data columns (total 4 columns):\n",
      "country       13124 non-null object\n",
      "year          13124 non-null float64\n",
      "life          11969 non-null float64\n",
      "population    13091 non-null float64\n",
      "dtypes: float64(3), object(1)\n",
      "memory usage: 512.7+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "country          0\n",
       "year             0\n",
       "life          1155\n",
       "population      33\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check integrity of data\n",
    "df.info()\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>life</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>31.580049</td>\n",
       "      <td>8774440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>32.095976</td>\n",
       "      <td>8953544.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>32.611878</td>\n",
       "      <td>9141783.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1963.0</td>\n",
       "      <td>33.127317</td>\n",
       "      <td>9339507.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>33.641244</td>\n",
       "      <td>9547131.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country    year       life  population\n",
       "0  Afghanistan  1960.0  31.580049   8774440.0\n",
       "1  Afghanistan  1961.0  32.095976   8953544.0\n",
       "2  Afghanistan  1962.0  32.611878   9141783.0\n",
       "3  Afghanistan  1963.0  33.127317   9339507.0\n",
       "4  Afghanistan  1964.0  33.641244   9547131.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>life</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>World</th>\n",
       "      <td>1986.5</td>\n",
       "      <td>63.752502</td>\n",
       "      <td>5.008097e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low &amp; middle income</th>\n",
       "      <td>1986.5</td>\n",
       "      <td>60.697168</td>\n",
       "      <td>3.904088e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle income</th>\n",
       "      <td>1986.5</td>\n",
       "      <td>61.954126</td>\n",
       "      <td>3.418550e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper middle income</th>\n",
       "      <td>1986.5</td>\n",
       "      <td>65.769969</td>\n",
       "      <td>1.778217e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Asia &amp; Pacific (all income levels)</th>\n",
       "      <td>1986.5</td>\n",
       "      <td>66.344457</td>\n",
       "      <td>1.695653e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lower middle income</th>\n",
       "      <td>1986.5</td>\n",
       "      <td>57.737533</td>\n",
       "      <td>1.640333e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Asia &amp; Pacific (developing only)</th>\n",
       "      <td>1986.5</td>\n",
       "      <td>65.107053</td>\n",
       "      <td>1.490551e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High income</th>\n",
       "      <td>1986.5</td>\n",
       "      <td>73.603806</td>\n",
       "      <td>1.104008e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Asia</th>\n",
       "      <td>1986.5</td>\n",
       "      <td>56.502828</td>\n",
       "      <td>1.078283e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1986.5</td>\n",
       "      <td>66.247287</td>\n",
       "      <td>1.055207e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           year       life    population\n",
       "country                                                                 \n",
       "World                                    1986.5  63.752502  5.008097e+09\n",
       "Low & middle income                      1986.5  60.697168  3.904088e+09\n",
       "Middle income                            1986.5  61.954126  3.418550e+09\n",
       "Upper middle income                      1986.5  65.769969  1.778217e+09\n",
       "East Asia & Pacific (all income levels)  1986.5  66.344457  1.695653e+09\n",
       "Lower middle income                      1986.5  57.737533  1.640333e+09\n",
       "East Asia & Pacific (developing only)    1986.5  65.107053  1.490551e+09\n",
       "High income                              1986.5  73.603806  1.104008e+09\n",
       "South Asia                               1986.5  56.502828  1.078283e+09\n",
       "China                                    1986.5  66.247287  1.055207e+09"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"country\").mean().sort_values(\"population\", ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13124 entries, 0 to 13123\n",
      "Data columns (total 4 columns):\n",
      "country       13124 non-null object\n",
      "year          13124 non-null float64\n",
      "life          11969 non-null float64\n",
      "population    13091 non-null float64\n",
      "dtypes: float64(3), object(1)\n",
      "memory usage: 512.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9952 entries, 0 to 13123\n",
      "Data columns (total 4 columns):\n",
      "country       9952 non-null object\n",
      "year          9952 non-null float64\n",
      "life          8979 non-null float64\n",
      "population    9919 non-null float64\n",
      "dtypes: float64(3), object(1)\n",
      "memory usage: 388.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# We Need More Lemon Pledge!\n",
    "countries_filter = pd.read_csv(\"data/countries.csv\",usecols=['English Name'])\n",
    "countries_filter = countries_filter['English Name'].unique()\n",
    "df = df[df['country'].apply(lambda x: x in countries_filter)]\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = df.reset_index().drop(\"index\", axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### This is getting boring..... Lets PLOT something"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmeSgclkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff8feeac950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('year').mean()['life'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>life</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>San Marino</th>\n",
       "      <td>81.494746</td>\n",
       "      <td>2.348367e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liechtenstein</th>\n",
       "      <td>79.796534</td>\n",
       "      <td>2.754563e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>77.495377</td>\n",
       "      <td>2.479294e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Isle of Man</th>\n",
       "      <td>77.247561</td>\n",
       "      <td>6.739013e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>77.157326</td>\n",
       "      <td>8.490987e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>77.107230</td>\n",
       "      <td>1.171442e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bermuda</th>\n",
       "      <td>77.011644</td>\n",
       "      <td>5.729831e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>76.807119</td>\n",
       "      <td>6.688197e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>76.661077</td>\n",
       "      <td>4.226021e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>76.227148</td>\n",
       "      <td>2.656919e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>75.954468</td>\n",
       "      <td>3.831348e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>75.931324</td>\n",
       "      <td>4.734284e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>75.833318</td>\n",
       "      <td>1.629205e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>75.822701</td>\n",
       "      <td>5.594686e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    life    population\n",
       "country                               \n",
       "San Marino     81.494746  2.348367e+04\n",
       "Liechtenstein  79.796534  2.754563e+04\n",
       "Iceland        77.495377  2.479294e+05\n",
       "Isle of Man    77.247561  6.739013e+04\n",
       "Sweden         77.157326  8.490987e+06\n",
       "Japan          77.107230  1.171442e+08\n",
       "Bermuda        77.011644  5.729831e+04\n",
       "Switzerland    76.807119  6.688197e+06\n",
       "Norway         76.661077  4.226021e+06\n",
       "Canada         76.227148  2.656919e+07\n",
       "Spain          75.954468  3.831348e+07\n",
       "Israel         75.931324  4.734284e+06\n",
       "Australia      75.833318  1.629205e+07\n",
       "Italy          75.822701  5.594686e+07"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_life = df.drop(\"year\", axis = 1).groupby(\"country\").mean().sort_values(\"life\", ascending = False)\n",
    "avg_life.head(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>life</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rwanda</th>\n",
       "      <td>45.709723</td>\n",
       "      <td>6.328390e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guinea</th>\n",
       "      <td>45.239984</td>\n",
       "      <td>6.447129e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malawi</th>\n",
       "      <td>45.063332</td>\n",
       "      <td>8.470915e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>45.012043</td>\n",
       "      <td>9.415954e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chad</th>\n",
       "      <td>44.755199</td>\n",
       "      <td>6.303956e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central African Republic</th>\n",
       "      <td>44.615013</td>\n",
       "      <td>2.813273e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equatorial Guinea</th>\n",
       "      <td>44.479256</td>\n",
       "      <td>3.957122e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niger</th>\n",
       "      <td>43.852782</td>\n",
       "      <td>8.202466e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mozambique</th>\n",
       "      <td>43.275768</td>\n",
       "      <td>1.451595e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Sudan</th>\n",
       "      <td>42.480832</td>\n",
       "      <td>5.662798e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mali</th>\n",
       "      <td>42.117103</td>\n",
       "      <td>8.406656e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>41.465868</td>\n",
       "      <td>1.061080e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sierra Leone</th>\n",
       "      <td>37.945028</td>\n",
       "      <td>3.700744e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.146730e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.829533e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cayman Islands</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.633122e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monaco</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.885876e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern Mariana Islands</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.631998e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turks and Caicos Islands</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.373030e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuvalu</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.459556e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               life    population\n",
       "country                                          \n",
       "Rwanda                    45.709723  6.328390e+06\n",
       "Guinea                    45.239984  6.447129e+06\n",
       "Malawi                    45.063332  8.470915e+06\n",
       "Nigeria                   45.012043  9.415954e+07\n",
       "Chad                      44.755199  6.303956e+06\n",
       "Central African Republic  44.615013  2.813273e+06\n",
       "Equatorial Guinea         44.479256  3.957122e+05\n",
       "Niger                     43.852782  8.202466e+06\n",
       "Mozambique                43.275768  1.451595e+07\n",
       "South Sudan               42.480832  5.662798e+06\n",
       "Mali                      42.117103  8.406656e+06\n",
       "Angola                    41.465868  1.061080e+07\n",
       "Sierra Leone              37.945028  3.700744e+06\n",
       "American Samoa                  NaN  4.146730e+04\n",
       "Andorra                         NaN  4.829533e+04\n",
       "Cayman Islands                  NaN  2.633122e+04\n",
       "Monaco                          NaN  2.885876e+04\n",
       "Northern Mariana Islands        NaN  3.631998e+04\n",
       "Turks and Caicos Islands        NaN  1.373030e+04\n",
       "Tuvalu                          NaN  8.459556e+03"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_life.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53.810140611225371"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_life = df[['year','life']].groupby(\"year\").mean()\n",
    "median_life.loc[1960][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA34AAAHzCAYAAAB/mM87AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+EmSJElSjTP4SZIkSVKN+//ZXPT2LiQmDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff8fee11390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Line plots are boring. How about a histogram?\n",
    "import time, sys\n",
    "from IPython.display import clear_output, display\n",
    "from pylab import text\n",
    "\n",
    "f, ax = plt.subplots(figsize = (15,8))\n",
    "\n",
    "for year in range(1960,2012):\n",
    "    ax.set_xlim([20,85])\n",
    "    ax.set_ylim([0,0.1])\n",
    "    plt.xlabel('Life Expectancy');\n",
    "    plt.ylabel('Distribution of Countries');\n",
    "    plt.title('Life Expectancy: From 1960 to 2010')\n",
    "    text(0.05, 0.9, str(year), fontsize=20,ha='center', va='center', transform=ax.transAxes)\n",
    "\n",
    "    data = df[df.year == year][\"life\"].dropna()\n",
    "    sns.distplot(data,hist=True, kde=True, rug=False, bins=40);\n",
    "    \n",
    "#     year_df = df[df.year == year]\n",
    "#     median  = median_life.loc[year][0]\n",
    "#     data_low = year_df[year_df.life <= median][\"life\"].dropna()\n",
    "#     data_high = year_df[year_df.life > median][\"life\"].dropna()\n",
    "#     sns.distplot(data_low,hist=True, kde=True, rug=False, bins=30);\n",
    "#     sns.distplot(data_high,hist=True, kde=True, rug=False, bins=30);\n",
    "    \n",
    "    time.sleep(0.0001)\n",
    "    clear_output(True)\n",
    "    display(f)\n",
    "    ax.cla()\n",
    "\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA34AAAG8CAYAAACBlneCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhBCii5PETwghhBBCCCG6uP8D18RkorpU3xkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff8fcd13150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def hist_plot(year1, year2):\n",
    "    f, ax = plt.subplots(figsize = (15,7))\n",
    "    clear_output(True)\n",
    "    ax.cla()\n",
    "    data1 = df[df.year == year1][\"life\"].dropna()\n",
    "    data2 = df[df.year == year2][\"life\"].dropna()\n",
    "    sns.distplot(data1,hist=True, kde=True, rug=False, bins=40);\n",
    "    sns.distplot(data2,hist=True, kde=True, rug=False, bins=40);\n",
    "    \n",
    "    plt.xlabel('Life Expectancy')\n",
    "    plt.ylabel('Distribution')\n",
    "    plt.title(str(year1) + \" vs \" + str(year2))\n",
    "    \n",
    "hist_plot(1960, 2012)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1960, 1970, 1980, 1990, 2000, 2010]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years = [year for year in range(int(df.year.min()),int(df.year.max())) if year%10==0]\n",
    "years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# use list comprehension to to extract decades (Uncomment to try, but might be messy)\n",
    "\n",
    "from ipywidgets import interact\n",
    "#interact(hist_plot, year1 =years, year2 =years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA34AAAG+CAYAAADMXtaJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8VfWd+P/XuftNbvZwgYQlQOCwhX2RTbAu2KqtjHjb\nsR2nzuJoW5fa6ch0nG/bn1bpY7Cj0g517K/WTlu/vR2XaouoVAgie9hJOBBJCEkIgezJvbnr+f5x\nkxhCAlnOTRDfz8eDR5KzvD+fe4734X3fz+d83oqu6wghhBBCCCGEuHaZhroDQgghhBBCCCHiSxI/\nIYQQQgghhLjGSeInhBBCCCGEENc4SfyEEEIIIYQQ4honiZ8QQgghhBBCXOMk8RNCCCGEEEKIa5xl\nqDsghBDi2qGq6ijgNLEvFmdrmnZwiLs0KFRV3QqM0DRt8lD3RQghhOiOjPgJIYQw0t8BYcDX9vs1\nR1XVuaqqShFcIYQQnyqS+AkhhDCEqqoKcB/wPrAJuEdVVdvQ9iourh/qDgghhBB9JVM9hRBCGOUm\nIAf4AdAM/BVwJ+DteqCqql8E1gAzgXrgL8AaTdMq2/ZvBVKBp4DngJ2apt3dtu9O4HFgBqAAhcB/\napr2207xs9rOvQlwA7XAB8C/app2prfHdNPvrcDytt91IF/TtBWd9o8HNgBLiI18vg08qGlac6dj\nVgL/BswFdOAg8JSmaZt6uK6oqroZmAVkaZoW7LKvEAhpmjazL/FVVb0PeAiYArQCR4EfaJr2ly6v\n95L70J9rJ4QQYmjJiJ8QQgij/AOxhO9/iSU8NXQz3VNV1b8C/gh8TCwxfIxYMvW+qqrOTocmAv/a\nFvcHbed+GXgDqAC+DKwCjgG/UVX1Hzqd+yawGPgO8DngX9r+fq9tZLK3x3T1T8Cf2n6f3/Z3Owfw\nW+A14A7gN8DX2uK2v/bbgHeAJmKJsYdY0vRnVVW/0EObAK8AGcBtnTeqqjqNWOL2677Eb0v6fgns\nAG4Bvkrsy+CNqqrmdWn7kvtA/66dEEKIISQjfkIIIQZMVdUMYkncbzVNa2nb9jvgm6qqjtI0rbzT\n4U8CuzVN+5tO57cCvyKWALaPTOUCn+8yUvUjoAj4sqZpkbZz3yM2cvjvwC9UVU0nlpQ9qmnaH9rO\n26Gq6lFiSUqSqqqWKx0DNHZ9nZqmaaqq1rT9vq/L7rHAP2ma9m5bv/KJJV4rgf/Tdsx/AEeAOzVN\nC3Xq/5G217bxkosb8xrwM+BeYolvu7uBCPC7PsYfDryuadq32gOpqlpBbHRwddvx7S66D725vnRz\n7YQQQgwtGfETQghhhL8BbMDLnbb9itj/Z/62fUPbFMGpxJ4D7KBp2h81TUvrkuRFiU0BbT93DDAB\neKs96Ws7VyeW0IxRVXUs0EIs8bhfVdUl7SNQmqYd1DTtJ5qmNfbymL7yA+916lcUOANktvV/NLHR\nudfbk7K248LERhFndRnx7Hx9fMRGUm9rS7Lb3Q38RdO0s32Jr2naWk3T7urSTHHbz9Fdtl90H4jP\ntRNCCBFnMuInhBDCCH9PLMkpUlU1s21bGaABXyc22gSQ1fbzXC9i1ndOYIDstp8V3Rx7tj2+pmmn\nVVVdTWx65Hagtu0Zud8Db2qaFtU0LXClY3rRv66q25LQzkKAuUv/f6Cq6g96iJFFbApsd35FbPGc\nvwZ+qqrqVGJJ9DN9jd+WPK4hNko7itg01XZdvxSu75JIxuPaCSGEiDMZ8RNCCDEgqqpeB0wnNlJ0\nvss/FchVVXV52+HtSUFvVvsMdfn7ciUU2p8riwJomvY+MI7Ys3b/F7iO2HTJP3UaobriMXHyE2B2\nD/8qL3Peh8ApYqOrEBvta+HiqZ9XjN/22t4HHgVeBW5t27ewh3a73oehvHZCCCH6SUb8hBBCDNTf\nE0u4VhNbVKQzB7HE5O+AfGKjgtBlOmFbspACtGqa1tpDO+3PCY7qZt8lo4GapgWITXH8k6qq3yK2\nMMn/IfYc4dbeHmOg9tdu7k9he03TdFVVf01sRG88cA+xaZ0tfYnftnjLbOCnmqb9n07bJ/SxP4N5\n7YQQQgyQjPgJIYToN1VVE4mtrvmBpmlvaJq2ucu/PwF/Blarqpqkadp54CRwR9sCK+1WAHXEVuns\nVtsCMceBL6qq2vH/r7bfb48dopW3FVh/ua1v7efqxFYSBcjszTGXedl6W7vmyxzTXf8riC1Ms1pV\nVXvnfaqqfldV1Qd7EebXbe3/CJjU9ndf47df965lF77d9vOyr2uA104IIcQQkRE/IYQQA/EVYqs4\n/vIyx/wS+FLbsS8RqzHnBbyqqj5PbIXJZ4iVZXjzCu39K/A68Kqqqr8klqR8ndiiJu2LlVQRG30c\n3xa/klitue8SKzHxAeDsxTE9aX+e8F9VVT2qadqV+tzZGmIjoO+rqvojIEjsObuH29q+LE3TSlRV\n3UbsWlZ008/exD9O7BnLb6iqWkRsuuh9xGr5VQJLVFW9ntjze93pzfUVQghxlZERPyGEEAPx98QK\nsHd9zqyzjcQSjb8DaCsBcBcwhljphv8GPgJu1DTNf7nG2pKsLxIrnfAG8Adi0zxv1zTtjbZjKoBl\nbf16EdhGrBRCNbBM07Ta3hxzmW68CBwCvk+shESvaZr2FtBeT+81YjX3rgf+VtO0db0M80rbz991\nXUilN/HbrvFdwAViC7L8ithCPA8CTxMbsfu/9PDl8ACvnRBCiCGi6PrlnpUXQgghxNVEVdU7iY16\nTtU07fhQ90cIIcSngyR+QgghxKdE23OFO4iVWFg51P0RQgjx6SHP+AkhhBBXOVVVhxF7jvEhPinN\nIIQQQvSaPOMnhBBCXP1uIVYiYQ7wBU3Tjg1td4QQQnzaDMpUT4/H85/EirvqwCNer3dvp303EXuY\nPAJs9Hq9T3o8nhXEHthv/x/bEa/X+1DcOyqEEEIIIYQQ16C4T/X0eDzLgYler3eRx+OZQmxZ70Wd\nDnkBWElsWep8j8fzWtv2fK/Xu7oPTcnDikIIIYQQQojPOqW7jYPxjN+NtNVl8nq9RR6PJ83j8SR7\nvd5Gj8czHqj1er1nADwez8a244/0p6HKykqj+izaZGVlyXUdYnIPrg5yH4ae3IOhJ/dg6Mk9uDrI\nfRh6cg+6l5WV1eO+wUj8RgAFnf4+37atse3n+U77qoEJxBK/qR6P5y0gHfih1+t9/0oNXe6Fiv6T\n6zr05B5cHeQ+DD25B0NP7sHQk3twdZD7MPTkHvTNUKzq2e3QY5d9J4EfAl5gPLDF4/Hker3e4OUC\nS9ZvPPk2ZejJPbg6yH0YenIPhp7cg6En9+DqIPdh6Mk96N5Qj/hVEhvZa5cFnO1hXzZQ6fV6K4Df\nt2372OPxVLXtK4lzX4UQQgghhBDimjMY5RzeA1YDeDyeOcQSuyYAr9dbCiR7PJ4cj8djAW4H3vN4\nPF/1eDz/3HbOCGA4scVfhBBCCCGEEEL0UdwTP6/XuwMo8Hg8O4it4PlNj8fzdY/Hs6rtkAeBV4EP\ngd97vd4TwFvAco/H8yHwR+DBK03zFEIIIYQQQgjRvUF5xs/r9a7psulQp33buLi8A20jgncMQteE\nEEIIIYQQ4po3GFM9hRBCCCGEEEIMIUn8hBBCCCGEEOIaNxTlHIQQQgghhBDimlVSUsITTzzB6tWr\nWbVqFWVlZTz77LMAjB49mm9/+9uYzWaKi4v5j//4DwCWLFnCvffeSzgcZu3atZw7dw6TycTjjz9u\nSM1CGfETQgghhBBCCIP4/X5eeOEFZs+e3bHtxRdf5J577uH555/H7XazZcsWAJ599lm+853vsGHD\nBk6fPk1rayubN2/G5XKxfv16vva1r/HSSy8Z0i9J/IQQQgghhBDCIDabjbVr15KZmdmxraKigsmT\nJwMwf/589u3bR21tLX6/n0mTJmEymfj3f/93HA4H+/fvZ9myZQDMnTuXo0ePGtIvmeophBBCCCGE\nuOZE//AyesFHhsZU5i7BdPd9lz3GbDZjNpsv2jZu3Dh27drFypUr2bt3L3V1dVRVVZGcnMzatWsp\nLy9nxYoVrF69mrq6OlJSUgAwmUwoikIoFMJqtQ6o7zLiJ4QQQgghhBBx9OCDD7J161Yee+wxdF1H\n13UAzp49y4MPPsi6det45513KCkpueTc9mMHSkb8hBBCCCGEENcc0933wRVG5waL2+3mmWeeAWDP\nnj3U1NSQlpZGTk5Ox+heXl4epaWlZGRkUFtbC0A4HAYY8GgfyIifEEIIIYQYBP5QHedaCglGWoa6\nK0IMupdffpmdO3cCsGnTJhYtWsTIkSPx+/00NjYSjUYpLi5m9OjRzJs3j/z8fAB27NjBrFmzDOmD\njPgJIYQQQghD6bpOS+g851uOc96ncd6n0Rw8B4CCQppzHMMTpzE8cRqZCRMxm2xD3GMhjKNpGhs2\nbKCqqgqLxUJ+fj73338/69ev55VXXiEvL49FixYB8I1vfIPHH38cRVFYsGABubm5jBs3joKCAh56\n6CGsVitr1qwxpF+KUXNGrwJ6ZWXlUPfhmpOVlYVc16El9+DqIPdh6Mk9GHpyD4bep+EeVLcUsbfy\nFzQHqzu2WU0JZCZMItk+khr/x9T4PkYnAoBZsTLCNYM5I+8lwZo+VN3uk0/DfbjWyT3oXlu9P6W7\nfTLiJ4QQQgghBkzXoxRd+BNHq/8XUBiVPJ9hCZMZlqCS4hiNSfnkCaNQpJXzvuOcazlGVfMRKpoK\nOO/TWJh9P1lJs3tuRAjRb5L4CSGEEEKIAQmEm9hd8XPONh/GaUlj0ehvMSxhUo/HW80OspJmkZU0\nC13X+bjuLxyo+h0flv2ESRm3MsP9Zcwm+ZgqhJHkHSWEEEIIIfrtgq+YneU/xReqYYQrj4XZD+Cw\nJPf6fEVRyE2/iYyEiew88zNO1GzifIvG4tHfxGUbHseeC/HZIqt6CiGEEEKIfjlZu5kPSp7CH6pl\nuvsurh/zz31K+jpLc4zl5vH/HzmpS6lrLeHdj5+gvHGfwT0W4rNLEj8hhBBCCNFn5Y372H/2FWzm\nRJaPfZxpw+5EUQb20dJqdrAw+59YmP1PgM6OMz/lXPMxYzosxGecJH5CCCGEEKJPGgMV7K54EbNi\nY0XOGoa7phkaPyd1KcvG/DOKAh+deYHGQIWh8YX4LJJn/IQQQgghRK+FIn62lz1PONrKolHfINUx\nOi7tuBMnMz/rH9ld8XO2nX6Wm8Z/H4clJS5tCWG0kpISnnjiCVavXs2qVasoKyvj2WefBWD06NF8\n+9vfpri4mA0bNnScc/r0aZ588klycnJ46qmnaGlpwel08sQTT5Cc3L8p1J3JiJ8QQgghhOgVXY+y\nu+LnNAXPomZ8gTEpi+LaXk7qEqYNW0VL6Dzby54jHA3GtT0hjOD3+3nhhReYPfuT0iQvvvgi99xz\nD88//zxut5stW7agqirPPfcczz33HE899RRjxoxh6tSpvPbaa8yaNYv169ezbNkyXn31VUP6JYmf\nEEIIIYTolcILb1HRtJ/hidOYMdwzKG1OG7aKsSlLqPEXs6fiv9H16KC0K0R/2Ww21q5dS2ZmZse2\niooKJk+eDMD8+fPZt+/ihYt+//vfs3r1akwmE/v372fZsmUALF68mIKCAkP6JVM9hRBCCCHEFVU2\nHeRo9eskWDNYNOqbmBTzoLSrKArzs/4eX+gCZxp346p2D1rSKT7dXt5fzY6yRkNjLh6TzH1z3Jc9\nxmw2YzZf/P4YN24cu3btYuXKlezdu5e6urqOfYFAgL1793LfffcBUFtbS0pKbFpzamoqtbW1hvRd\nRvyEEEIIIcRlNQXOsat8AybFwpLRj2C3JA1q+2aTlSWjH8FlG0HRhbcprf9oUNsXYqAefPBBtm7d\nymOPPYau6+i63rFv+/btXHfddZhM8U3NZMRPCCGEEEL0qP25vlDUx4Ls+0l3jhuSftgtSVw/5ju8\nd+rf2X/21wxPnIrTmjYkfRGfDvfNcV9xdG6wuN1unnnmGQD27NlDTU1Nx76dO3fypS99qePvjIwM\namtrcblcnD9/noyMDEP6ICN+QgghhBDiYnoEJeLHFKrnTM0mavzFjEqez7jUZUParST7CGYO/wqh\nqI+Cs78e0r4I0Rcvv/wyO3fuBGDTpk0sWvTJwkiapjFhwoSOv+fNm0d+fj4A27ZtY8GCBYb0QUb8\nhBBCCCE+hSKRCKdPn6apqYn09HQyMjJwOp0oitK3QHoER+N+Eho+whRuRtEDKHoYgKAe5XCwCjMK\nS8xpWAJnCdtHxuHV9N6EtBsoa9hJRdM+zjTuZXTy/CHtjxBdaZrGhg0bqKqqwmKxkJ+fz/3338/6\n9et55ZVXyMvLuyjxa25uJiEhoePvu+66ix/96Ec8/PDDuFwuvve97xnSL6Xz/NJPOb2ysnKo+3DN\nycrKQq7r0JJ7cHWQ+zD05B4MPbkHQy8rK4ujR49SWFhIUVERfr//ov0Oh4OMjIyORHD8+PG4XK7u\ng+k6tpZCXDXvYgmdR1csRCzp6CYbusmObrKx23eKQ60fM9c6nMUmKwBBxzh8qUsIJk4BZWgmjzUF\nzrLp43/DZk7g87k/xmZOHNT25b0w9OQedC8rKwug229/ZMRPCCGEEOIqFwgEOHnyJG+++SZlZWVA\nLMmbOXMmbreburo6amtrqampoaKigoqKCiD27NDnPvc5Jk6ceFE8q78E14V3sAbOoGPCn7yAlvQb\niVo+KRLdFDjHkbo1JFgzyJnwJPWtp0mo/wibvxhbVQkRSxq+lEX4UxfDIK3w2S7JPpJpw+7kSPUf\nOFj1OxZk/+Ogti/Ep5EkfkIIIYQQV7HCwkLy8/MJhUIoisKYMWOYNm0a48aNw2K59KNcKBSirq6O\n8vJydu3axTvvvMPp06e5/vrrcdKI68KfsPs0AFoTp9OScQsR27BL4hw89zuiepiZw7+CxewkmDiZ\nYOJkzMFzJNTvwNF0gKSajdj8H9Mw4h4w2eJ+LTqbnPkFzjTuoaR+G2NSFjHCNX1Q2xfi00YSPyGE\nEEKIq1RBQQEfffQRDoeDuXPnsmLFCnw+32XPsVqtuN1u3G43OTk5vPvuuxQWFuKvLeHeuVVYdR9B\nxziaM28l7BjTbYyq5iNUNu1nWILK6OSFF+2L2IbT5F5Fc8YtJJ/zYvdppFW8RH3W36Kbe5hWGgcm\nxcL8rH9g86nvs6/yl9ya+zQWk2PQ2hfi00ZW9RRCCCGEuMrous727dv56KOPcLlcrF69mgULFpCa\nmtqnOOnp6dx9990sm69y99RSrLqPQt8M6rL+ocekL6qHOVD1GxQUZo/4mx4Xi9HNiTSMvBd/0mys\ngXLSyn+OKWRMoeneSnfmoGZ8npbQeY5Uvz6obQvxaSOJnxBCCCHEVSQajbJ582b2799PWload999\nN+np6f2OZ9MbuWnkAZIdUTYXZ/K7red54803L1kYpt3J2s00BioZn3YDac6xlw+umGly301L2gos\noRrSyjdgCQzughvT3H+FyzackzWbqPWXDGrbQnyaSOInhBBCCHGVCIfD/PnPf6aoqIjhw4ezevVq\nkpKS+h3PFKojreIlzOEGmjM+z8Tl32DcuHGUl5fzzjvvEI1GLzq+NdzIseo3sJoSyHOv7l0jikJL\nxkqaMu/AFGkhtfy/sfqK+93nvrKYbMwd+XV0dA6f8w5au0J82kjiJ4QQQghxFQgEArz55puUlJQw\nevRoVq1ahdPp7Hc8U6i+Lemrpzn9Fnxp1+N0Orn99tsZP3485eXlbN++/aJzii78iVDUx3T3X2G3\n9C3h9KcupnHEX6PoYVIrf4W9+Wi/+95XI1zTGZ44nXMtRznXfGzQ2hWiJyUlJXz1q1/ljTfeAKCs\nrIxHHnmERx55hHXr1hGJRAD4xS9+wbe+9S2++c1v8uqrrwKxun5r1qzhoYce4l/+5V9obGw0pE+S\n+AkhhBBCDLFgMMhrr71GZWUlEydO5I477sBm6/8qmaZwA6mVL2EO19GcfhO+9Bs69imKws0330xa\nWhoHDx6kqKgIgEC4iY9r/4LTksaEtBv71W7AlUd99t+hK2aSzv0Bc/B8v19DX80YfjcAh6u9XEN1\nqsWnkN/v54UXXmD27Nkd21588UXuuecenn/+edxuN1u2bKGkpISDBw/y05/+lPXr17Np0yZqa2t5\n7bXXmDVrFuvXr2fZsmUdCeFASeInhBBCCDHE8vPzuXDhAtOmTWPlypXdlmnoLSUaILXi/8cSqqUl\n7QZ86ZcmcXa7nTvuuAO73c4HH3xAVVUVJ2vfI6IHUTO/gNnU//ZDzvE0ue/CpAdJqfodREP9jtUX\n6c7xjEqeT63/FBVNBYPSphDdsdlsrF27lszMzI5tFRUVTJ48GYD58+ezb98+EhMTCQaDHf8URcFu\nt7N//36WLVsGwOLFiykoMOa/ZynnIIQQQggxhE6cOEFRURFut5sVK1ZgMg3se/nEmk1YQufxpSyi\nJf3mHo9LTU1l5cqVvPXWW/z5nT+Sdt1+bGYXE9JWDKh9gEDSDHz+UyQ07ibpwp9ocq8acMzeyHOv\npqJxH0eq/5espDmYFBnj+CwrPOin8kzQ0JhZo21MnXX5Kdhmsxmz2XzRtnHjxrFr1y5WrlzJ3r17\nqaurw+12s3z5cr7yla8QjUa59957SUxMpLa2lpSUFCD2Pq2tNWa1XHk3CCGEEEIMkaamJj744AMs\nFgsrV6685MNiX1l9xSQ07CJsG05zxuehh1IM7XJycli8eDF6WimhqI/c1JsNq4XXnHkbIdtInI17\nsDcdMiTmlSTbs8hJXUZjoILTDTsGpU0heuPBBx9k69atPPbYY+i6jq7rVFZWsn37dn73u9/xm9/8\nhrfeeou6urq49UFG/IQQQgghhkA0GuXdd98lGAxy4403kpaWNqB4SqSV5Or/RcdEo/tuMFl7dd6s\n2Xmcsv03kbCJqkIH04frPdbu6xOTlcYRf03amZ+SVP06YXs2EVvmlc8boGnDVnG6YQfHql9jTPJ1\nA5q2Kj7dps5yXnF0brC43W6eeeYZAPbs2UNNTQ3Hjx9nypQpOByxL1smTJhASUkJGRkZ1NbW4nK5\nOH/+PBkZGYb0QUb8hBBCCCGGwL59+6isrGTChAlMnTp1wPFcF/6EOdyAL+0Gwo7sXp9X2vARutkP\ntWMpOlrMkSNHBtyXdhHbMJrcqzDpQZIH6Xm/RFsmuWk30hK6wKm6LXFvT4jeePnll9m5cycAmzZt\nYtGiRWRnZ6NpGtFolHA4zKlTp8jKymLevHnk5+cDsG3bNhYsWGBIHyTxE0IIIYQYZGfPnmX37t24\nXC5uvPHGAY+w2VqKcDYVELJn0dJpBc8rieoRjl/4MybFyo2z/hGHw8G2bduor68fUH86CyTNwp88\nH2vwLK6ajYbFvZwpw76IxeSg8MIfCUdbB6VNIdppmsajjz7Kpk2beO2113j00UdZuHAhv/71r3ng\ngQfIyMhg0aJFqKrKvHnzePjhh3n00Ue57bbbGDFiBHfddReapvHwww9z8OBBvvzlLxvSLxn7FkII\nIYQYRIFAgHfffRdd17nllls6pnn1lxJpIan6DXTMsSmeSu+fEzzTsJuWUDUT0j6HO200N9xwA++8\n8w7btm3ji1/84oD61VlT5u1YW8tIaNhFyDmegCvPsNjdcViSUTM+z7Hzb3Ci5j2mDjPutQhxJaqq\n8txzz12yfcOGDZdsu++++7jvvvsu2uZ0OnnqqacM75eM+AkhhBBCDKL8/HwaGxuZO3cuo0aNGnC8\npPNvYY400ZJxMxH7iF6fp+tRii68jYKJyZm3AZCbm0t2djalpaWUlpYOuG8dTDYaRtyDrlhJqn4N\nJdxkXOweqBmfx2Z2cfzCnwlGWuLenhBXO0n8hBBCCCEGyYkTJzh+/Dhut5vrrrtuwPHszUdwNB8m\nZB+NL3VZn86tbD5IQ6CcMSnX4bK5gVhx9+uvvx5FUfjwww+JRCID7mO7iM1Nc8atmKIBXLWbDYvb\nE6vZyeSM2whFfZyq2xr39oS42kniJ4QQQggxCAKBAPn5+VitVm699dYBl25Qwk0kVb+JrlhpHH43\n9KFmna7rFJ1/G4DJmbdftG/YsGFMnz6duro6Dh8+PKA+duVPWUjY6sbRuBdzoMrQ2N2ZkH4DFpOD\nEzXvEomG496eEFczSfyEEEIIIQZBQUEBfr+fefPmkZqaOuB4rtrNmKI+mjNuIWIb1qdzL/hOUOMv\nJitpDqmO0Zfsv+6667Db7ezevRufzzfgvnZQzDRnfgEFnaQLfwJdNy52N2zmRManLscfruNM4+64\ntiXE1U4SPyGEEEKIOGtqauLAgQO4XC5mzZo14Hjm4HkcjfsIW4fhT1nU5/OL26Zaqhm3drvf6XRy\n3XXXEQwGO5agN0owUSWQMBGb/2NsvuOGxu7OpIyVKChoNRvR45xoCnE1k8RPCCGEECLOdu7cSSQS\nYdGiRVitvSusfjmJte+jEKUl45Y+reIJ4A/VU960l2R7NsMSJvd43PTp00lPT+fYsWNUV1cPtMsX\nac64DR0TrgsbQY/vFMxE2zBGJS+gvrWMcy3H4tqWEFczSfyEEEIIIeKourqa48ePk5mZyeTJPSda\nvWVprcDRfISQfRSBxGl9Pv9U/VaieoTc9JsuWz/QbDZz/fXXA7Ei0kaOlkXsw/GnLMASuoCzIf5T\nMCdnfgEAbZDqCApRUlLCV7/6Vd544w0AysrKeOSRR3jkkUdYt25dx8JJb731Fg888ADf+ta3Ooq2\nh8NhnnrqKR566CEeeeQRKisrDemTJH5CCCGEEHGi6zoffvghAMuWLRtwoXYAV80mAJozboU+xovq\nET6u3YLF5CAnZckVjx8zZgzjx4+nsrKSkydP9qu/PWlJv4moyUFi7V9QIgY+R9iNdOd4hiWoVDUf\nob71TFzbEsLv9/PCCy8we/bsjm0vvvgi99xzD88//zxut5stW7ZQV1eH1+vlhRde4Cc/+Ql/+MMf\nCAQCbN68GZfLxfr16/na177GSy+9ZEi/JPETQgghhIiTkpISKioqyMnJYfToSxdR6ato3TFs/mKC\nzlxCCRM0Ye/xAAAgAElEQVT6fH5l0wH84VpyUpZgNTt7dc7SpUsxmUxs376dUCjU5zZ7opsTaUn7\nHKaon8TavxgWtydqx6jfO3FvS3y22Ww21q5dS2ZmZse2ioqKjhH/+fPns2/fPqqqqhgzZgw2mw2b\nzcaECRMoKipi//79LFsWK88yd+5cjh49aki/LIZEEUIIIYQQF4lEInz00UcoisLSpUsHHlDXiZb+\nAWgb7euH9kVdctNv6vU5qampzJkzh3379rF//34WLlzYr7a7409dhLNxN86GXfhTFhJpqycYD1mu\nWSTZRlLWsIMZ7rtxWtPi1pa4Omzfvt3wkeqJEyde8f1sNpsvKdcybtw4du3axcqVK9m7dy91dXVk\nZ2dz6tQpGhoasNlsHDt2jJkzZ1JXV0dKSgoAJpMJRVEIhUIDfj5YRvyEEEIIIeLg2LFj1NXVdSyS\nMlD2lmPQVEKrK4+wI7vP5zcGKjnXcoxhCZNJcYzq07nz5s3D6XRy8OBBgsFgn9vukWKhOePzKERj\nC73EkaKYUDM+T1SPcLL2vbi2JURXDz74IFu3buWxxx5D13V0XSc5OZkHHniAf/u3f+OZZ54hJyen\n23ONer5WRvyEEEIIIQwWCATYvXs3VqvVmBEyPUJizXuAiZb0m/sVorhtOuXEPoz2tbPZbMycOZNd\nu3Zx7Nixi55dGqhg4lSCzvHYfRpWXzGhhFzDYneVk7qEI9V/oLj2A6Zkfgmr2RG3tsTQW7p0qTGj\n7QZwu90888wzAOzZs4eamhoAVqxYwYoVKwB48sknGTFiBBkZGdTW1gKxhV4AQ1YDlhE/IYQQQgiD\ndS7WnpCQMOB4jqb9WELnUUYu73OxdoBwtJXS+g9xWFLJTp7brz7MmDEDi8XCgQMHOlYkNISi0JwR\ne/4usW6rcXG7YTbZmJh+M6Goj5L6/Li2JURnL7/8ckdNzE2bNrFo0SIikQiPPvoowWCQ2tpaiouL\nUVWVefPmdazwuWPHDkNqf4KM+AkhhBBCGKq9WHtiYqIxH9iiIRJrNqMrFsxjvgQ1/j6HOF2/g1DU\nz6SMWzEp/fv453A4mD59OgcPHuTEiRNMmTKlX3G6E3ZkE3TmYvMXY2ktJ9zHqah9kZt+E0UX/sSJ\nmveYmH4ziiLjIMJYmqaxYcMGqqqqsFgs5Ofnc//997N+/XpeeeUV8vLyWLRoEQDLly/nm9/8Joqi\n8PDDD2M2m7nhhhsoKCjgoYcewmq1smbNGkP6JYmfEEIIIYSB9u7dSyQSYfHixYZMz3I27MQcaaQl\ndTkp9jSgb4mfruucrN2MgonxaSsG1JdZs2Zx6NAh9u/fz+TJkw0pT9GuJW05Nn8xCfXbaBxxj2Fx\nu7JbkhidspDS+g8511LICNf0uLUlPptUVeW55567ZPuGDRsu2bZq1SpWrVp10Taz2czjjz9ueL/k\nKw4hhBBCCIM0NzdTWFhISkoKqqoOPGA0RGL9NqImB7605f0KccF/kobAGbKT55JgHdgiM8nJyUya\nNImamhpOnz49oFhdhZwTCNmzsDcfxRyqMTR2VxPSbgDgVN2WuLYjxNVEEj8hhBBCCIMcOHCAaDTK\nvHnzMJkG/jHL0XQAU6QFf/JC9F7W3euqPyUcLmfOnDlA7DlGQykKvtTrUdBx1m83NnYXGc5cUuyj\nKG8soDXcENe2hLhaSOInhBBCCGEAv9/PkSNHcLlcHYWaB0SP4qzbThQTO07MZMeWZvbtrKamOkwk\n0rvl3QPhJsob95Bsz8KdYMwzecOGDWPMmDFUVFRQVVVlSMx2Add0IpY0nI37UMLNhsbuTFEUxqfd\ngE6EkvoP49aOEFcTSfyEEEIIIQxw6NAhwuEwc+bMuaR4c1/4fVFKTgY4tesQ1vB5ii9MpeyMk5rq\nMAW7LrBjSzOb3mhg59ZmTha2UnchTDTafSJ4umEHUT3C+NQVhj6PN3dubGXQ/fv3GxYTAMWML3UZ\nih4moWGnsbG7yEldglmxcqpuq2F10oS4msniLkIIIYQQAxQMBjl06BAOh4Np06b1K8bZ8iDFRQHq\na2OlEr4wKZb4+FKWcsMXkrDZFfRwMiePV3OhOsyFc7F/AEkpJhYsc5GQ+Ml3+rquc6ouHwUzY1OX\nDPAVXmzUqFG43W6Ki4upr68nNTXVsNj+5Lkk1m7G2bATX9r16Ca7YbE7s5kTGZ28kNKG7VT7ihie\nODUu7QhxtZARPyGEEEKIATpy5AiBQIDZs2f3eSVPXdc5fsTPvo98NNRFyHRbWDinlpFJZwgkTCRr\nylhcSWZsNhPjcpOZPieBFbcmc8uXkpm7KIGs0VaaGqJs39xEQ124I25da0lsUZek2TgsyYa+XkVR\nOp71O3DggKGxMdnwpS7CFPXjaNxnbOwu2lc5PVUri7yIa5+M+AkhhBBCDEA4HObAgQNYrVZmzJjR\nt3NDOgd2+6iqCJGQaGL+0kSSU80kV70NzeBLvb7Hc+0OE1ljbIwcbSU1I0DhwVY++qCZeYsTcY+0\ncqpuGwDj+rka6JXk5uaSnJxMYWEhCxcuNKRQfTt/yiIS67aRUL8df8p1oPR/6uzlZCZMItmeRXnT\nPgLhJuyWpLi0Iz57fv7zn3PkyBEikQj33HMPkydP5umnnyYajZKens73vvc9bDYb77//Pq+99hqK\nonD77bdz2223EQ6HWbt2LefOncNkMvH444+TlZU14D4NSuLn8Xj+E7gO0IFHvF7v3k77bgKeBiLA\nRq/X+2SnfU7gKPCk1+v91WD0VQghhBCiLwoLC/H5fMydOxe7vffTEn3NEfZsb6GpIUqm28LcxQnY\n7CZMoTrszUcI2UYSck64YhxFUZigOnAmmDiwy8eeD1uYNtdMWXAnTksaI1x5A3l5PTKZTMyZM4et\nW7dy6NChjoLURtDNifiT55HQsBN78xECSbMMi91Z+yIvB6t+S2n9dtTMz8elHfHZcuDAAUpLS/nZ\nz35GQ0MD999/P3PmzOHOO+9kxYoVvPTSS7zzzjvccsst/M///A//9V//hdVq5YEHHmDZsmXs2LED\nl8vFE088wd69e3nppZf4/ve/P+B+xX2qp8fjWQ5M9Hq9i4C/B17ocsgLwF3AEuAWj8fTeYL1E0Bt\nvPsohBBCCNEfkUiEgoICzGYzs2fP7vV5F6pDbHu/maaGKOMm2li4PBGbPfaxLKF+Owo6vrRl0IcF\nWbJG21h0gwuLVeHgyV2Eoj7GpizFFKfRMoApU6bgcDg4fPgwwWDQ0Ni+1KXoKCTUbYM4Lr6Sk7IE\nk2Ll47otssiLMMSMGTM6EjWXy0VraysHDx5k8eLFACxevJiCggKKiopQVRWXy4Xdbmf69OkcPXqU\n/fv3s2zZMiC2kNLRo0cN6ddgjPjdCLwJ4PV6izweT5rH40n2er2NHo9nPFDr9XrPAHg8no1txxd6\nPJ7JwFTgz4PQRyGEEEKIPjtx4gRNTU3MmDGj11MdS4sDHN3vBwVmzHMydsIno4RKJPZcW8ScTMDV\nt2mjAOmZFpbe5OI9bRcArafnEx2mYzIbt6JnZ1arlZkzZ7J7926OHz/e56mulxO1phNw5eFoPozN\nf5JgwiTDYndmtyQxOnk+pxt2cN6n4U40oBSHuCokXtiIo/mIoTFbXXm0ZH7hsseYzWaczljdzY0b\nN7Jw4UL27t2LzWYDIDU1ldraWmpray9aGCktLY2amhrq6upISUkBYiPriqIQCoX6/PxwV4OxuMsI\n4Hynv8+3betuXzUwsu33Z4HH4t47IYQQQoh+0HWdffv2YTKZOsobXIl21M+RAj9Wm8KiFa6Lkj4A\nZ+MeTHoQf+qS/j/XZq8h5DyBLZBLdWka+3f74jqSNX36dBRF4dixY4bH9rU9n5hQF99aex2LvNTJ\nIi/CONu3b2fjxo08/PDDvTq+p/epUe/foVjc5XJfOSkAHo/nXmCn1+st8Xg8vQ5sxEOP4lJyXYee\n3IOrg9yHoSf3YOjJPfjE0aNHqaurY+7cuaiqesXjP9YaOHGsnuQUK7evHktSsu2i/Xo0TKRsF5gd\npKhfJNXS/Qjile7BjuJ3Abh+1l9xqsXJ2TN+/HkuctWUXr6yvpsyZQqFhYXouk52draBkbMIN6rY\nGjRGpplRnMMNjP2JkfpIDlb/mvKmvaQP+y4O65VXQZX3wtC74j3I+gfD27QDvXknffjhh3i9Xl55\n5RVSU1NxuVykp6fjcDgoLy9n1KhRqKrKwYMHO16Hz+dj0qRJlJSUoCgKWVlZhEIhTCYTY8eOHXDf\nByPxq+STET6ALOBsD/uy27bdBoz3eDy3A6OAgMfjKfd6vZsv21BlpWGdFjFZWVlyXYeY3IOrg9yH\noSf3YOjJPfiEruu89957AEydOvWK16WhLsz2vzRjtsCcRQ6ami/Q1HzxMY7GApKD9fhSl9JcXQ/U\nXxLnSvcgqkc5Uv4OFpODFCYydbaF6nOwbXMlZmsTdkd8JnuNHz+ewsJC8vPzWbFihaGx7Y6ZpDRo\nNBZvpCVjpaGxOxuTtIxDvlfZdfw1Jl2hHXkvDL2r+R40Nzfz9NNPs27dOnw+Hz6fj5kzZ/L73/+e\nm2++mddff528vDzcbjcHDhzgxIkTmM1m9uzZw3333cfUqVN5/fXXmTBhAtu2bWPGjBm9fq2XS4YH\nI/F7D/gh8KLH45kDVHq93iYAr9db6vF4kj0eTw5QDtwOfNXr9f60/WSPx/MDoPRKSZ8QQgghxGAp\nLy+nurqa3Nxc0tLSLntsoDXKnu0tRCMwf2kiSSndTOHUdRLqP0THhC+l/8XWz7UcxReqYXzaCiwm\nBxYXTJnh5NgBP4cL/MxbnIDShwVjeisnJ4fExESOHz/O0qVLsViM+4gZSJxO1PQWjsYCWtJviltp\nh5zUpRw+56W0fvsVEz8hLmfLli00NDTwwx/+sGPbmjVrWLduHW+//TbDhw9n5cqVWCwW7r//fr77\n3e+iKAr33nsvLpeLG264gYKCAh566CGsVitr1qwxpF9xT/y8Xu8Oj8dT4PF4dgBR4Jsej+frQIPX\n630DeBB4te3w33u93hPx7pMQQgghxEC0Fy1vL2Lek0hEZ+/2Flp9OpPzHIzI7n5xBqv/FJbgOVpd\nM4laU7s9pjdK2mr3jU/9pHbfuIk2zpYHqSoPUXkmRPYYW0+n95vJZGLKlCns27eP4uJiJk82cIEU\nk5XWpNkkNOzE5tMIJk698jn94LAkM8KVx9nmgzQGKkm2y1RO0T933HEHd9xxxyXb161bd8m25cuX\ns3z5xbU2zWYzjz/+uOH9GpRn/Lxeb9c09VCnfduAHgu/eL3eH8SpW0IIIYQQfVZfX09paSkjRoxg\nxIgRPR6n6zpHCvzU1UTIGmMld0rPNf6cDbFVOH0p/a+FFwg3UdFUQLI9m/RO9f8URWHW/AS2vtvE\nkQI/mW5LXKZ8Tp06lX379nHs2DFjEz+gNXk+CQ07cTbsi1viBzA2dTFnmw9yun4HecNXx60dIYbC\nYKzqKYQQQghxzTh48CAAs2Zdvqh4yckgZ0qCpKSZmTm/5ymWpnAj9pZCQrYRhB1j+t2v0w07iOph\nxqcuv6StxCQzU2Y4CQV1Dhf447LKZ2pqKtnZ2VRUVFBff+nziQMRto8kZM/G5juOKdxgaOzOspNm\nYzHZOd2wU2r6iWuOJH5CCCGEEL3U2tpKYWEhLpeL3NzcHo87XxWi8KAfu0Nh/tJELJaen6tzNBag\nEKU1ZWGfCrZ3VVL/IQpmxqYu7nb/uIk20oeZO6Z8xsO0adMAKCwsNDy2P3kBCjqOxv2Gx25nMTnI\nTppHS6iaGn9x3NoRYihI4ieEEEII0UvHjh0jHA4zc+ZMTKbuP0a1NEco2OlDUWDekkScCZf5uKVH\ncTbuIarYaE26/Aji5dS3llHfepqRSTNxWLpfbL59yqfJDEcK/ARao/1urye5ubnYbDaKioqIRo2N\nH0iaga5YcTbuBd34vrcbmxJLnE837IhbG0IMBUn8hBBCCCF6IRqNcujQIaxWa8fIVld6VOfAbh+h\noE7eXCfpmZdfTsHm0zCH6wkkzUI3Ofrdt9L67QCMS1162ePiPeXTYrGgqiotLS2cPn3a0Ni6yUGr\naybmcB1W/ylDY3c23DUNuzmZMw27ierhuLUjxGCTxE8IIYQQoheKi4tpbm5mypQpOBzdJ2klJwPU\nXYgwcrSVMeN7XsylnbNhNwD+lIX97ldUj3C6YQc2s4uRriuPGnae8nk2DlM+25PiY8eOGR7bnzwP\nAGfjHsNjtzMpZsakLCQQaaKq+Wjc2hFisA3Kqp5CCCGEEJ92V1rUpbkpQtGRVmx2hbw5zivGM4Xq\nsPlOELKPJjyA0gHnmo/SGm4gN+1GzKbuy0V01j7lc8umJo4faWXkKCuKybjafm63m8zMTEpKSmhp\naSExMdGw2GHHGMI2N/bmQpRIC7rZuNidjU1ZzMna9zndsIOsAUzBFZ9dP//5zzly5AiRSIR77rmH\nyZMn8/TTTxONRklPT+d73/seNpuNpqYmnnzySZxOZ0fdv3A4zNq1azl37hwmk4nHH3/8soXZe0tG\n/IQQQgghruDs2bNUVVWRk5NDauqldfZ0XefQHh/RCOTNcfaqXIKzcQ8K+oBG+yC2qAtATuqyXp+T\nmGRmdI6NluYoZ8vjM+qn6zrHjx83NrCi4E+ej0Ikrou8pDsn4LK5qWgsIBRpjVs74tp04MABSktL\n+dnPfsaPf/xjfvazn/Hyyy9z55138sILL5Cdnc0777wDwE9+8hPy8vIuOn/z5s24XC7Wr1/P1772\nNV566SVD+iWJnxBCCCHEFbSP9s2ePbvb/SUng9ReiDBylJWRo6886oYextm4j6jJSatrRr/7FYy0\nUNG0nyRbFunO8X06N3eyHRQ4WdRq+LN+qqpiNps5duyY4bFbk2ajY8bZuA/iVHJBURTGpiwmogep\naCqISxvi2jVjxgy+//3vA+ByuWhtbeXgwYMsXhxbOGjx4sUUFMT+u/rud797SeK3f/9+li2LfZEz\nd+5cjh41ZsqxTPUUQgghhLiMpqYmiouLyczMZNSoUZfsb2mKUHTYj9WmkDfX2WO9vs7szYWYIs34\nUpZAL6Zn9uRMwx6ieoic1CW9arezxCQz2aOtVJSFqD4bZnhW//vRlcPhIDc3F03TqKysJDs727DY\nujmRgGsajubDWFtPE3LmGBa7s7Epizl2/k3KGnaQk7okLm2I+DpY9SpnDH4edHTyAmaN+OvLHmM2\nm3E6Y9O9N27cyMKFC9m7dy82mw2I1bysra0FICEh4ZLz6+rqSEmJrc5rMplQFIVQKITVOrD3qIz4\nCSGEEEJcxuHDh9F1nVmzZl2SXOm6zsG9bVM85/ZuiieAs3Hgi7oAlDZsBxRyrrCaZ09yp8QWqTlZ\naPyo39SpU4F41fSbD4CjcZ/hsdsl2UeS5hhHVdszlEL01fbt29m4cSMPP/zwgOIY9d6UET8hhBBC\niB6EQiGOHj2K0+lk0qRJl+wvLQ5Sez7CiGwrWb2Z4gmYg9XY/KcIOicQsQ3rd9+aAue44DvB8MRp\nJFjT+xUjOdXM8CwL5yrD1J6PkOE27qPhqFGjSE5O5uTJkyxfvrxjtMMIIed4IpY0HM2Hac68Hd3c\n/1IYlzM2dTF1VSWcadjDxIyb49KGiJ9ZI/76iqNz8bJnzx5++9vf8uMf/xiXy4XT6SQQCGC32zl/\n/jwZGRk9npuRkdExIhgOx0qKDHS0D2TETwghhBCiR0VFRQQCAfLy8rBYLk6KWpojFB3q2xRPAGdD\nbOrZQEf7TjfEavf1d7Sv3cT2Ub8iYxcxURSFyZMnEw6HOXXK4Lp7igl/8jwUPYS9xfiyEe3GJF+H\ngiLF3EWfNDc38+KLL/L000+TnJwMwJw5c9i2bRsA27ZtY8GCBT2eP2/ePPLz8wHYsWNHjysJ95WM\n+AkhhBBCdEPXdQ4dOoTJZGLGjBmX7tvrJxKBGfOdOJy9/C49GsLRVEDEnEQgceoA+haltH47FpOD\nUW217forLdNChtvC+aow9bVhUtON+3g4adIk9uzZw8mTJ5k8ebJhcQECrhm4at/H3nyY1uS5hsZu\n57Sm4k6cxrmWozQHz+GyDY9LO+LasmXLFhoaGjrKMwCsWbOGdevW8fbbbzN8+HBWrlxJJBLhO9/5\nDs3NzVy4cIFHH32Ue++9lxtuuIGCggIeeughrFYra9asMaRfkvgJIYQQQnSjrKyMuro6pkyZcskC\nDGWngtRUhxmRbSV7TO+nYDmaD2OKttKStggUc7/7dt6n0RK6QE7qMiymgU9znDjFTk11mOKiAPOW\nGPfxMD09nczMTE6fPt0xzc0oEVsmIXs2Nl8xSqQZ3ewyLHZnY1MWca7lKGUNu5k67ItxaUNcW+64\n4w7uuOOOS7avW7fukm3PPfdctzEef/xxw/slUz2FEEIIIbpx6NAhAGbOnHnR9mAgStHhVswW+jTF\nE8DRuBcdpWNxkv4qrTdmmme7zOEWUtPNnC0P0dQYMSRmu4kTJxKNRvn4448NjQvQ6pqJQhR7c/ym\ne2Ynz0XBTHkcF5IRYjBI4ieEEEII0UV9fT2lpaWMHDkSt9t90b7jR1oJBXXU6Y7eT/GkbVGX1tOE\nnBOIWtP63bdwNMCZxj0kWDNxJxgzfVJRFHKnxEbjig1+1m/ixIkAnDhxwtC4AIGk2BRcR9Mhw2O3\ns5kTGZ44lbrWElqCF+LWjhDxJomfEEIIIUQXhw8fBi4d7auvDXP64yBJySbGTezbtEVHY6xg80BH\n+8ob9xGOtpKTsgRFMe6j3IhsK65kExWnQ/hajBv1S01Nxe12c+bMGfx+v2FxAaKWFIKOHKytpZji\nWHIhu+05yvImGfUTn16S+AkhhBBCdBIMBjl27BiJiYlMmDChY7uu6xwpiCUu0+c6MZn6UDBdj+Bo\n2k/U5CTg6v+iLgCn6z8CMLyouKIoTJziQNfh4+MBQ2NPmjQJXdfjMt0zkDQDBR178xHDY7cblTwX\nUChv3Bu3NoSIN0n8hBBCCCE6KSoqIhQKkZeXh9n8yQIsZaeC1NdGyB5jJdPdt5pathYNc6SZ1qRZ\noPR/8ZTm1vOcazlKhjOXJPvIfsfpSdYYK85EE2WngrT6o4bFjed0z9bEPHRMcZ3u6bCkMCxhEhd8\nJ/GH6uPWjhDxJImfEEIIIUSbziUcpk+f3rG984IuU2c5+xzX2TZFsHWA0zyPV32Ajm7Yoi5dmUwK\nuZPtRKNQWmzcqF9SUhIjRoygoqKClpYWw+IC6BYXQecErIFyTKFaQ2N3Nip5PqBT0VQQtzaEiCdJ\n/IQQQggh2pSVlVFfX4+qqheVcOhY0GVa3xZ0ATCFG7G1aITsWYQHMEqn6zqFle9hUiyMTh5Y8ffL\nGZVjw2KBM6VB9KhuWNz26Z7FxcWGxWz3ySIvhw2P3S47KVYrUFb3FJ9WkvgJIYQQQrTproRD+4Iu\nrmQT4yb1bkGXqK5TdN7Hy/urOVi4DYUoh4PTOVzVQkVjkEC479Mo61pPU9NSSlbSbOyW+NSsA7BY\nFLLG2Gj16VyoDhsWNzc3F4CTJ08aFrNdIHEaOmYczfGb7ployyTdOZ7qliJaQ41xa0eIeJEC7kII\nIYQQdF/CofOCLnlXWNAlqutoF/x8VNbEjrImanxhQOcrM44QiJr41/3pNEXOdByf5jCzamoGt6lp\nWHqxUIzRtfsuZ/Q4G2WngpwpCTJsRN+eZ+yJy+UiOzubiooKmpqaSEpKMiQugG52EkxUsbcUYg6c\nI2IfbljszkYlzaPWf4qPz+8kjby4tCFEvMiInxBCCCEE3Zdw6M2CLudbQvyi4Bz/8ObHrHmvjLeP\n19EaivK58cn8ZBmMdTZSY53CvfPG8uW8DG6akMKskYkEozq/3F/NYxtLOXbOd9m+RfUwZQ07cFpT\nGOmaYdyL7kFahpnEJBNny0MEg8Yv8hKPUb/WtuviaI7fdM9RbWUdis99GLc2hIgXGfETQgghxGde\ndyUcerOgy7bSRn6+p4qWUJREq4nPjU9myZhkZo5IxGpWSDq3A4LgHH4dtySkXnRuYyDCbw6e573i\ner63uYzlOcl8fY6bdOelH8/ONh8mEGliVtYqTANYFbS3FEVhzDgbRYdbqSwLkZPbt5qFPcnNzSU/\nP5+TJ08yZ84cQ2K2CyROQVes2JsP0ZJ+Eyh9KLfRS0n2kaTYR3G6dh+zMv1YzX1f6EeIoSIjfkII\nIYT4zOuuhIN2tOcFXXyhCM/tqOTZjyoJR3W+sWAEr9w1kUcWZTEv24XVrKBEA9ibjxCxpBFyjr+k\nzWS7mW8sHMF/3DqW3HQH+aWNfOOtU/yxqJZwl0VV2qd5Ts26JU5X4FKjcmwoCpwpCRoWMyEhgVGj\nRnHu3DkaGgwuuG6yEUicgiVUgyVQaWzsTkYlzycSDXE2js8TChEPkvgJIYQQ4jOtuxIOjfURSj8O\nkphkYtzEi0e7tAt+vr2xlC0ljeSmO/jPL4xj5cRUrP+PvfuOk/OqDj7+u8/0me29aIt2teq2JDc1\nS7ZxBQzkBTIJnQAJgdAhgRCSQMgbIIHwYlIIKZAYQxhCHAx2cC+yJVl91dcraau29+ntue8fo5Vl\nWWW188yuJJ/v56OPbO3suXebNGfuuefYXnnC5Artx9AJogXXgzr/U66WUg9/fXcDH72pCrsB/7Zn\niC8+3kU0mSmxTKTD9AX3UuCqpSK/xeKP/vzcHoPyKjsTY2mmJtKWxV28eDGQq3LPTJmuK4dJ2XS5\npwxzF1caSfyEEEII8Zp29ggHrTUH90ZBw8o1HoxTCV3a1Pz0wAhfeKyLwVCSt68o5et3NVBb4Dxn\nXM/ULjSK2KkxABdiMxR3txTxD29uZkN9Pm0jMb6x5SQpU9M9+SKmTtFYdDMqB+WLF1K3MPOxWXnq\n19zcjGEYORnmnvAtxjTcmXt+2rq7iWcqdNVR5KmlP9RKyrTu8yJErkniJ4QQQojXtH379gEvN3Xp\n7/T+Yd4AACAASURBVE0yOpSissZORXWmoUsokeZPn+zmx/tHKPbY+cs76nnP6vJXnfJNsyWGcMS6\nSXgXYTqKzvmYcylw2fjsxhqur/Gxtz/Md7f30zmxBYWisXBjlh/ppauqceBwKnq7EpgWzfRzu93U\n19czMjLC+Pi4JTFPU3bivhXYUpM4Yt3Wxp5eQikWVW4iZcYZDB3IyRpC5IIkfkIIIYR4zRobG6Or\nq4uamhoqKipIpzSH90VRxssNXSLJNF95qodDQ1HW1eVx3xsWsrLSe8G47lNDvmP5N1zynuyG4o82\n1dJS6mb3yU5Go8eo9K3E4yi+9A8wS4ZNsaDBQSKuGexLWhY3l909p4e5u3LY3bOlYhMgw9zFlUUS\nPyGEEEK8Zk0PbF+9ejUAx9viRCOapsUu8vJtRJMmf/F0Ly+Nxrh1YQF/dHMteS7bhYPqNJ7gXkzD\nQzxv+az25bYb/NmtC7im6jAAY5HVs4pjhbqFmTuOVpZ7NjU1YRgGx44dsyzmtISnGdNw4wofBm3N\nKeXZKguW4LGXcDK4h7Rp3ZB7IXJJEj8hhBBCvCbFYjGOHDlCfn4+TU1NRMIm7UdiuNyKxcvdxFIm\nX32mhyPDUTY3FPCJddXYZjBo3Rluw0iHiOWvhixGL+S7DJaWHyKVdvKj1iqe75qadaxsFBbbKCiy\nMdSfIha15t6cy+Wirq6OkZER67t7KhsJ31JsqcmcdfdUSrGg4HqSZoSRSFtO1hDCapL4CSGEEOI1\n6dChQ6RSKVatWoVhGBxpjWKmYdm1HtJK83+f6eXQUJSN9fl8asPMkj4AT3A3ALGCSy/zPNNwpI1Y\neoSqvBtwGG6+vbWfXd0W34mbofomJ1rDyS5rT/0AOjo6LIs5Le5bAZA59cuRmvw1APTJWAdxhZDE\nTwghhBCvOel0mtbWVhwOBytWrGBkKEVfT5KiEhsVdTb+6tle9g9GWFeXx2c21sw46VOpEM7wUZLO\nalKumqz22DHxHADXVN7KF2+pBTSfe3A/3ZPxrOLORm29A8OA7o4E2qLyyYULFwJw4sQJS+KdKeFt\nQSt7ThO/cu9S7IaL/uDenK0hhJUk8RNCCCHEa87x48cJhUIsW7YMh8PJoT0RAJatdvONLX3sG4hw\nY20en9tYi32GSR+AO7gXhUms4OIjHC4kmY7RO7UTn6OCcu8Srq3y8cn1NYQTaf5u+wBmju6unY/T\nZVBZ6yA0ZTIxZs1Mv7y8PCorKzl58iSxWMySmNO04SLhacaeGMBIjlkae5rNcFDpu4ZgYoBgvD8n\nawhhJUn8hBBCCPGaMz3CYfXq1XSfSDA1aVLX6OCHbUPs7gtzfY2Pz2+qOe+4hnPSGk9wNxpb5n5f\nFnqmdpAy4ywsuhl1avj75sYC7lhSQdtIlCeOW3wvbgbqczDTr6mpCa01nZ2dlsWcNjflnpkRIH3B\nfTlbQwirSOInhBBCiNeUgYEBBgYGaGxsxOsp4OiBGHY7nHDH2NIVZFm5hy9srsVhu7SnSfb4SeyJ\nQeK+pWibL6s9dp4q82ws2vSKP//0bS247Qb/vneIydjcdpMsr7Tj9ihOdidIp6w5cZy+55eLcs+4\nbykahSuUu8SvOi+T4PeFJPETlz9J/IQQQgjxmjJ92rdmzRqO7I+RTGh89Qb3Hxqm1GPn85tqcV5i\n0gfgPt3UJbsyz1BikOFIGxW+5ficZa94W0W+i3evKiOUMPnh3qGs1rlUylAsaHSSSsLASWtm+pWU\nlFBYWEhXVxeplLWJrLbnk3TX44h1otIhS2NP8ziKKHYvZDjcRjIdzckaQlhFEj8hhBBCvGaEQiGO\nHTtGaWkpPk8V3ScSePIU3+8YwFCKz2+updgzixEMZhJ3sJW0LZ+Ed3FWe+yYeB6AhUWbz/n2Nywu\npqnYxVMnpjg4GMlqrUu1oCFT7tnXa03ip5SiqamJZDJJb2+vJTHPFPctR6FxhY9aHntaTf5qNGkG\nQgdytoYQVpDETwghhBCvGfv378c0TVatWsXBPZmGIlvSUwSTJh+5qZIlZZ5ZxXWFj2CYUWL5a0Bd\nZMD7BWht0jmxBbvhZsF5Tg5thuIjN1WhgH/cMUAyPXeNXvIKDHz5BkP9SVJXQLlnwrccmKuxDtLd\nU1zeJPETQgghxGtCMpnk4MGDuN1uPI6FTI6nmfSk2BcM8/qWIu5oLpp1bKvKPIfCR4gkR6krWIvd\ncJ/3cYvLPNzTUkTvVIJfHMlN18pzUUpRU+fATMNQvzWnftXV1bjdbjo6OiwbFTEt7Swj5azAGTkG\npnVNac5U7G7AbS+kP9iKqa0ZcC9ELkjiJ4QQQojXhLa2NmKxGMuXraT9cAptaH4VHGN5uYcPXl85\n67hGahJnpJ2kq460syKrPU7P7ltYfO4yzzO9e3U5hW4bPz04wmAoN0nNuVQvcADQb1G5p2EYLFy4\nkHA4zODgoCUxzxT3LUfpJM5Iu+WxAZQyqM5bTTwdZCxq/amlEFaRxE8IIYQQVz2tNfv27cMwDByq\nhWRC82IqiNdj8PlNtZc2tuEs7qm9KDTRLE/7EukIvVO7yHNWUeZpuejj85w2PnBdBYm05p92Dlp+\nWnY+BUU2vD6Dwb4kaYvKTHPb3XMuyj0z3T1lmLu4nEniJ4QQQoirXldXF2NjY9TXNzN00sE4SdqI\n8oXNtRTNppnLNK1xB3ehlZ143qqs9tgz+SJpnWBh0SaUmlkiektjAddWetndF2Z7T246V55NKUV1\nnYN0CoYHrOnEWV9fj81my0nil3LVkrYV4AofAW3N8PmzVfpWYig7faHWnMQXwgqS+AkhhBDiqrdn\nzx4AHOZSAJ5PTfF7N1ayeJbNXKY5Yl3Yk6PEfSvQtvPfyZuJjoktgKKxaOOM30cpxYdvqsRuKP5l\n9yDJ9NzcMXu53NOaElOHw0FdXR1jY2NMTExYEvM0ZRDPW45hRnFEu6yNfYrD5qbCt4yJWBeR5Nzd\nuRTiUkjiJ4QQQoir2uDgIL29vZSXLiAVL+YlM0JTnZs7mwuzjj3d1CXbMs+peD+j0XaqfCvxOkov\n6X0XFLh44+IiRiIpnu6YymofM1VUYsPtVQycTGJeAeWeCd8yAFzhQ5bHnnZ6mHtQhrmLy5MkfkII\nIYS4qu3atQsAO8uIa5N2Z5Q/WFs143LK8zITuIL7SdsLSXqaswrVObEFgMbiTbN6/7csK8FuKH5+\naJS0mfu7fkopqmsdpJIwMmRNuWdOEz9PE6bhOlXumZvPj9zzE5c7SfyEEEIIcdWamJjg+PHj+Lxl\nuGxV7DaDfHRjFfmu2c/am+YOHcTQCWL514Ga/VMqU6fpnHgeh+GlNn92J4elXge3NxUyEEryQndw\n1nu5FNV1mWHu/T3WdPf0er1UVVXR399PNBq1JOZpyk7CuxRbahx7YsDa2KfkOSsocNUyGD5MKkej\nI4TIhiR+QgghhLhqTd/t8zmWM0qKpUs9XFvlsyS2eypzkhgtuCGrOAOh/URT49QXrsduOGcd563L\nSzAU/Neh0Tnp8FlSZsPlVvSfTGJadMrY1NSE1prOzk5L4p1purunM4flnjV5q0nrBEM57CAqxGxJ\n4ieEEEKIq1I4HObIkSPY7fm4nXV05EV556pyS2LbEiM4Yx2ZEkJHSVaxjo8/A0Bz8a1ZxanKd3Jz\nQwFdE3F2nQxnFWsmlFJUL3CQTGhGh6+Ack/fYjQ2XKFcjnVYA8g9P3F5ksRPCCGEEFel1tZW0uk0\nhZ7lHFJRfm9zZVbz+s70clOX7E77Iskx+oP7KHYvpNjTmPW+3rY8k4T+bI5O/U5397So3LOkpISi\noiK6urpIpaxJJqdpw03C24Qj0Y+RmrQ09rRS7yKcNh/9oX1zNldRiJmSxE8IIYQQV514PE5r634M\nw03SXc/1a/JYUOCyJrhO457ajWm4iftWZhWqY+I5NCZNWZ72TWssdnPTgjzaRqIcGrL4ntw5lJTb\ncboy3T21heWeqVSKnp4eS+KdKeFdAoAz3GZ5bABD2aj0rSCSHCWY6M/JGkLMliR+QgghhLjqHDhw\nkGQyQYFnKeNlmrsXZz+6YZoz0o4tHSSWtwoMx6zjaG3SMf4sdsNFQ+F6y/b39hWZcRA/OzRqWczz\nMQxFVa2DeEwzNmrNcPTpcs9c3PObTvxckdwkfgBVedcAMBA6kLM1hJgNSfyEEEIIcVVJp9Ps3LkX\npewM+er54KaK7Ec3nGG6qUus4Mas4gyGDxFOjlBXsA6HLbtB8mdaUubh2kov+/rDtI/m/tTv5XJP\nazpZVlVV4XK56OzstLxcMu0sI+UowxE5BtraUtJpVXnXApmmPUJcTiTxE0IIIcRVZdfOwySTEZzu\nZjavL6XAbbcstkoFcYWPkHRWkXLVZBXr+PjTQPZNXc7lbadO/X4+B6d+ZRV2HA5Ff2/SkkTNMAzq\n6+sJBoOMjY1ZsMNXSniXYOgEjmin5bEBvI4SCly1DIWPkjatufsohBUk8RNCCCHEVSOVMk+NcFCY\nDS3c1JBvaXx3cB8Kk1jBDZDFKWIsNUlfcA+FrjpKshz+fi6rqry0lLrZ1hOiezJuefwzGTZFZa2d\nWFQzYVG5Z2NjIwBdXV2WxDtT3DcH5Z6+laR1gpHISzlbQ4hLJYmfEEIIIa4ajz9xhGRqEtNTz3tu\nbbA2uNZ4pnahsRHLX51VqM6J5zF1mqbiWy0tQ52mlDp91++/5+DUr3rBqWHuvdaccDU0ZL52ubjn\nl/QsRCtHzhq8gJR7isuTJH5CCCGEuCoMDyfo7sg80V6zfg15Tpul8e3xHuzJIeJ5y9G22Q+B11pz\nYvwZbMpBY9FGC3f4SjctyKOu0MmznVMMhqy5f3c+5VV27HYsK/f0er1UVFTQ19dHImHx3pWdhHcR\n9uQwRtL6UlKAct8SDOWgXxq8iMuIJH5CCCGEuOKlUponn2gnnhzGyK/hlpX1lq/hOd3UJbvZfcOR\nowQTAywouAlnFgnkxRhK8bblpZgaftk2nrN1AGw2RWWNg0jYZHLcmnLPhoYGTNPMyViH+HR3z/BR\ny2MD2A0X5d4lTMZ7iCYncrKGEJdKEj8hhBBCXPG2bg0yOdEKwJvuvtny+MqM4wq2krYXkfAsyirW\ny01dbrNiaxd0c0MBhW4bz5yYJJE2c7pWVW2mu+dQvzXdMqfv+eVyrINzLsY6hA/mbA0hLoUkfkII\nIYS4onWeiNPf2UM8OUJpTQMNNVWWr+EKHcTQCWL514Ga/dOneCpE79Qu8p01lHkXW7jDc3PYFLc3\nFRJMmGzrDuZ0rfIqO0rBYJ819/wqKytxu905GetgOopIOStxRk+AmZsy2Jfn+ck9P3F5kMRPCCGE\nEFes4FSa1p1hxkL7ALj71tzcmXNP7UKjiGZZ5tk1+QKmTtJUfEtOmrqcy53NRQA8diy3JYcOp0FJ\nuZ2JsTTxWPani9NjHcLhMKOj1jeoiXuXoHQqk/zlQKFrAR57MYOhg2id29NWIWZCEj8hhBBCXJFS\nKc2250LE4n0kU6MsbGqmrKzM8nVsiWGcsU6SnmZMR/Gs42itOT7+FIay01hkfTnq+dQUOLm20svB\noSgnp3Lb5KWyOjMz0apTv5yWe/pyW+6plKIybyXxdJDxmPVjKYS4VJL4CSGEEOKKtH93hFjIpD+0\nB4AN69flZB331G4AogXXZxVnKHyYqXgfdQVrcdsLrNjajN25aG5O/SprrL3nV1+fadKTi3l+SXcD\npuHGFW4Di0tJp1WfLveU7p5i/tnnYhG/3/9tYB2ggU8GAoGdZ7ztDuCvgDTwSCAQ+Krf7/cCPwQq\nATfw1UAg8Ku52KsQQgghLn89HQlOdiYZiHdipCZoaWmhtLTU+oV0Ck9wN6bhIe5bkVWo9rHHAWgp\nudOKnV2S9XV55LtsPHViknevKsNhy81r/758A2+ewfBAEjOtMWzZlbN6vV4qKyvp6+sjHo/jcrks\n2imgbCS8LbhDB7Alh0k7K6yLfUqlbyWgGAgdYHn5my2PL8SlyPmJn9/vvwVoCQQC64EPAved9ZD7\ngLcBG4G7/H7/cuBNwK5AIHAL4Af+Ntf7FEIIIcSVITiVpnVXhLiZZjKS6eS5du3anKzlCh3GSIcy\nTV0Mx6zjhBMj9AX3UOJpotTbbOEOZ8ZhM7i9qZCpeJoXe0M5W0cpRWW1nVQKRkes6+6ptaa7u9uS\neGc63d0zR8PcXfZ8SjwLGYm0k0xHc7KGEDM1F6WetwP/AxAIBI4AxX6/vwDA7/c3AWOBQKAnEAiY\nwCPA7YFA4KeBQOCvT71/HdA7B/sUQgghxGUuldLseiGMNmFPrA1XcoolS5ZQUlKSk/U8UzsAiBZm\nl1geG38SjWbRPJz2TbtzUSEAj85RuedgnzWJX0NDA5Cbcs/Eqc6qrlyOdfBdgybNUPhIztYQYibm\notSzCth9xv8Pn/qzqVO/D5/xtiHg9Mtgfr9/K7AAuHcmC9XU1GS7V3EO8nmdf/I1uDzI12H+yddg\n/s3n10BrzdOP9hGaMjmUDlFtHgOluPfeeykvL7d+vcgA6WPHUYVLqWxcNes4qXSczpeew+MoZO2S\n38Buc2a1r9l+DWpqYM3ecfb2TmB6ilhQ7M1qH+dTWanZvbWN0SFtyfdLVVUVDz/8MD09PVRXV1ve\nDTU10ogz3El1RTHK7pnx+830YzM9t3J45BcE9XFqamb0lFbMkPybcGnm5I7fWS700/qKtwUCgQ1+\nv3818CO/378qEAhc8OZtX1+fFfsTZ6ipqZHP6zyTr8HlQb4O80++BvNvvr8GRw9EaT8SZ1gnGTQ7\nWBieYOnSpSSTyZzsK2/kEbzAhHsN8Szid4w/Ryw5xbKyNzE0OJLVnrL9Gtxa72Fv7wQ/2voS711j\n/Z22aaWVNgZ6E7zU1kNevi3reHV1dRw9epT9+/dbnuT7HE34dCejJ54nkTeze5yX9HXQRdgNN8eH\nXmRpofwdZpX5/vvocnWhZHguSj37yJzsTasB+s/ztlqgz+/3X+/3++sAAoHAPjIJqvUv5QkhhBDi\nitB5LE774ThRI82jqTGWJztQSnHTTTflZkEziXtqF6bNRzxv+azDaK1pH3schaK5+HUWbnB2NtTn\nk+c0ePLEJCkzN50swfqxDtPlnrkY6xA/NdYhV+WehrJT6VtBKDFIKDGUkzWEmIm5SPweA94O4Pf7\nrwP6AoFAECAQCHQCBX6/v9Hv99vJlHQ+BmwGPnvqfSqBPCC7l8iEEEIIcUUaOJnkwJ4o2qb5ZWKM\ndYXjRIOZ076ioqKcrOkKH8Qwo0Tzrwc1+wKp0egxxmOd1ORfj89p/YzBS+W0Gdy2sJCJWJqdOWzy\nUlF9aqyDRff86uvrUUrl5J5fyrUA0/BmGrzkaKxD1emxDvtzEl+Imch54hcIBLYCu0/d17sP+AO/\n3/9+v9//f0495CPAT4AtwE8DgcBLwPeACr/fvwV4GPiDU81fhBBCCPEaMj6SYve2MIYBjyTHsLnS\nFI4cwTCM3J32AZ7JTFOXWGF2a5we4VA6f01dznbXqZl+uWzy4vYYFJXYGB1OkUxkn0x5PB4qKyvp\n7+8nFotZsMMzKIOEdzG29BT2RP/FHz8LLyd+B3MSX4iZmJM7foFA4Atn/VHrGW97Dlh/1uOjwDvn\nYGtCCCGEuEyFgmle3JLp4HnAG6Y/nuR9RaP09gVZs2YNhYWFOVnXFh/EGesk4VlE2jH72YDR5AS9\nUzsocNVS4V1m4Q6zU1/kYlm5h339YQZDCSrzsms2cz4V1Q4mxtIMDyapqct+jcbGRgYGBuju7mbx\n4sUW7PBlcd9i3KF9OCPtpFzWNwzJc1bgc5QzFD6CqU0MNRdFd0K8knzXCSGEEOKyE4+ZvPhsmGRC\nk6ox2T4ZZGO1g8H2/bjd7tye9k29CGQ/wuHE+DOYOk1LyR2Wd6LM1l2LitDAE8cnc7ZGZY219/wa\nGxuBHI118LQA4Iy8ZHnsaZV5K0iaEcZjnTlbQ4gLkcRPCCGEEJeVVFLz4nNhImGTymY7P+oZptBt\n49p0B8lkkrVr1+JyuXKzuJnAHdxL2pZP3Df7UzpTpzg+/hQOw0ND4c0WbtAaG+vz8TkMHj8+STpH\nTV4Ki2243Iqh/hTagrtz5eXleL1eOjs7LYl3Jm3PI+mqwRHtAjNhaexplb5Mx9Ch0KGcxBfiYiTx\nE0IIIcRlI5nQ7NgSYnI8zYKFDgLDIyRNzfuXumg/eoTi4mJWrlyZs/XdoQMYZoxYwQ2gZj+GoHdq\nN9HUOI1Fm3DY3Bbu0Bouu8HmxgLGoyn2D0ZysoZSispqB4m4ZmIsbUm8hoYGotEow8PDF3+HS5Tw\nLkaRxhk9YXlsgApfpjvsYFgSPzE/JPETQgghxGUhHjPZ+nSI0eE01QscHPfEaB+LcWtDPlPte9Ba\ns2nTJmy27OfCnY9n8kU0imjBjbOOobWmbfQRQNFScod1m7PYLY0FADzXmbtyzwqLyz3r6+sB6O7u\ntiTemV4u92y3PDaA215Akbue4chLpHJ0qijEhUjiJ4QQQoh5FwmbvPBkiKmJNPVNTkqXGfzk4DDF\nHjv3VMbo7u6mrq7u9Dy3XLDH+3DEe0h4F2M6imcdZzhylLHoCRbkX0++q9rCHVprSbmHCp+dbd0h\n4qncNE8vr3RgGDBo4VgHyM09v6SnHlM5c5b4AVT4VmDqJKM5XEOI85HETwghhBDzKjiZ5oUng4RD\nJouWuVh+nZv7tg+QMuGjN1awa/tWlFJs3rw5p01S3KdGOGTb1OXoyK8AWFL2xqz3lEuGUmxqKCCa\nMtnVl5uZfnaHorTCztREmmgk++TS4/FQUVFBf38/iYTFp2bKTtLThD05jJEctzb2KVWn7vlJuaeY\nD5L4CSGEEGLejI+meOGpELGoZvkqN8uu9fDzw2OcGI9ze1MhrrEOxsfHWbFiBaWlsx+tcDHKjOMO\n7iNtLyThXTLrOBOxHvpD+yn3LqHMu8jCHebG5tPlnlM5W6Nyeph7v3XlnqZpcvLkSUvinSnhzW25\nZ5l3CQqbJH5iXkjiJ4QQQoh5MTyQZNszIVJJzeqbPDQvdXNiLEbgwAilXjvvXlnAiy++iMPhYN26\ndTndi3tqF4aOEy1YC1nMWGsbeQSApZf5ad+0xmI3DYUudp0ME0pk34DlXK6oe37ezHzAXCV+Dpub\nUm8z49EOEulwTtYQ4nwk8RNCCCHEnOvpSJwezn7DRh91C10k05rvbOsnreHj66o5tG8PsViMG2+8\nEa/XO6t1tNbocBB9sht9pBV9dD+6sx3d34seH0VHwuh0Es/kNrSyEy2cfVOXSHKUrsltFLhqqc5b\nNes4c21zYwEpU7O9J5iT+L48G3kFBiODKdKp7McwVFdX43A4cnLPL+0oJW0vxhk9Bjo3iXClbwUa\nzVD4SE7iC3E+9vnegBBCCCFeO1IpzcE9UXo6EtgdcOPNPsoqMqWAP9k/TOdEnLsXFdHoTvBAaysF\nBQWsXr36onH12DD6+FE48RJ6bAgmx2FiLPN76sInTa7mPOxvbyByPEV6+/3QtBTVvBTKqy7pTmHb\n6KNo0iwpfQMqi1PDubapMZ/7W4d5tnOKO5qLcrJGZbWD421xRkdSVFQ5sopls9lYsGABHR0dTE1N\nUVBQYNEuAaVIeFvwTO3AHusl5bG+mVClbwWHhh9kMHyIBQU3WB5fiPOZceLn9/trgDrgFT2UA4HA\nVqs3JYQQQoirT3Aqze4XwgSnTAqLbVy/wYsvL/O04uBghP8+PEZVnoP3rSnj17/8BaZpcvPNN2O3\nv/Lpik6loLcjk+gdP4o+fgTGRl65mGFAYQksaISiElRhMRQUAxpiUYjHIBZFx2P4rsuU3IWf70UP\ntMEz/4sGyC+EpiWo5qWo1WtR1XXn/dgS6TAnxp/GYy+moXC9dZ+0OVCZ52RpmYcDAxHGoilKPNaf\nC5RX2zneFme4P/vEDzLlnh0dHXR3d1s+1zHuXYxnageuSHtOEr9SbzN2w81g6LDlsYW4kBn9ZPv9\n/m8CHwcGgTPPvTXQlIN9CSGEEOIq0tuZYP/uCOkUNC5ysny1B5stc5oWiqf59tY+lILPbKyh46Wj\nnDx5kqamJpqbmwHQqSQc2ofe+Rx63w6IR18Onl8Iq9ehFi1FNS2FyhrIK0AZFz91s8UHcPV8h4Sn\nCfPP/wKj+wT6xFE43pb5vXUHunUH+r//A5atwnjdvXDtDSjjlbMEj489RcqMsaL8N7AZ2Sc2c21z\nYwFHR6I83zXFm5eWWB6/pMyOzQZDA0lW4Mk63vRYj66uLssTv6SnGY2BM9pOGOvnMBrKTrl3Cf2h\nViLJMbwO6z/fQpzLTF/SeQfQGAgE+nO5GSGEEEJcXdIpzcG9UbpPZEo7r9/gpabOefrtWmu+t3OA\nkUiKd1xbxgKPyf3PP4/D4eCWTZvgyD7Mnc+j92yFyKlmGKUVqLW3QPNS1KKlUF496zEP3slM4VKk\ncCPK7sic8DUtYfr5vh4fRb90EL3lMTjSinmkFcoqUbe9AbXxTpQvj7SZ4KWxR3EYHpqKb8vq8zVf\nNjbk8y+7B3muMzeJn82mKKu0M9iXIhI28fqyK4UtLCykoKCAnp4eTNPEmEGSP1Pa5ibprsMR60al\no2hb9onq2Sp9K+gPtTIYPszCopstjy/Eucw08euVpE8IIYQQl2JqIs2e7WGCk68u7Zz2bOcUW7qC\nLC3z8JsrSnn01/9LIpHgFp8N71c+hhmczDywqBS14Q7UTZugscWSeX4qHcYd3EvaXkLCt/Tcjyku\nzSSZa29B93ain/oV+sVn0D/7AfoXP0atu43OzTXEUpMsLX0jTtvsmtDMtyK3ndVVPvb0h+mbSlBT\n4Lz4O12i8ioHg30phgeSNDS7soqllKK+vp6DBw8yODhIdXW1RbvMSHhbcMa6cEaPEc+7xtLYAJV5\nK2AQhkKHJPETc2amid+/+v3++4EfA5NnvkHu+AkhhBDiTKmU5qVDMU60xdH61aWd0wZDCb63LS76\nrAAAIABJREFUYxCP3eDTG6rofOFpjh07RnV4ghUHdkF+IerWN6Bu3ASLls2odPNSeCZ3oHSKSNH6\nGY1wUAsaUe/9GPpt70M//zj66Ucwn/s1bQvzUUU2WoqtLwucS5sbC9jTH+a5ril++5oyy+NXVGWe\ndg4NpLJO/IDTiV9XV1dOEj/GnsAZac9J4lfoWoDLls9g+BBaa0teyBDiYmaa+P3xqd/PfklC7vgJ\nIYQQ4rTBviQHdkeIRjRen8E113uoqH71nbe0qfn21n6iKZNPVE5R+J1/4mFPJYbNya1GDNtHvgCr\n1qJstnOsYgGdxjO5HVO5iF1iZ0Xly0fd/Vb0nW+hZ8/9BD1P0nAojuuhr6Pf/wlUTX1u9pxja+vy\ncO5QPNc5xW+tLLU8GfHl2/DmGYwMJjFNjWFkF7+urg6lFN3d3ZbPeUy5FmAansw8P63B4s+FUgaV\nvhV0T20nmOinwFVjaXwhzmVGiV8gEFiY640IIYQQ4soVi5oc3BulvyeJUrBomYuW5W7s9nM/Yf7Z\n/kGODEfZOHGEW575Ac/VLCVc4OamlmbKX/+pnO/XFTqILT1FpHAD2nDPKoZWikNFR1Fxg2XxVdCx\nHfOrn0K96R2ou9+au6Q1R7wOGzfW5vFCd5AT43GaS2b3ebmQiio7nccSjI+mKS3Prnuoy+WiqqqK\ngYEBYrEYbreF+1UGCe8i3KED2JLDpJ0V1sU+pSIvk/gNhg5J4ifmxEy7eioyDV7uAirIdPf8VSAQ\n+HkO9yaEEEKIy5w2NZ3HExw9ECWVhOJSG9fe4KWg6NxJjzZN2p7byk97SyiLT/Lhtp8zePM9HJxM\nUVxczA133j0n+/ZOvIBGEc1i9ELP1ItMxU+ysGgzhe/5XfQ12zF/9I/oB+9H79mG8f5PoBY0Wrfp\nOXBLYwEvdAd5rnMqJ4lfeZWDzmMJhgeSWSd+kCn37O/vp7e3l0WLFlmww5clvC24QwdwRtqJ5iDx\nq/QtB2AwfIiW0jstjy/E2WZaLP83wCeBPcBPgFbgi36//89ztTEhhBBCXL601gz2JXn20SAH90RR\nKK69wcPG2/POn/S1HST49S/y7WOZuyKfzO/H99Xv8pSRD8Dtt9/+qpl9uWCPdeOI95DwLiXtnN1d\nNlOnOTT03yhsLC9/CwBq9TqMr/wdav1t0HUM8y8/g/nYg2itrdx+Tl1X48PnNHiuc4q0af2+yyrs\nKAOG+lOWxKuvz5TVdnV1WRLvTAlPC0Cm3DMH8pwV+BwVDIWPYGozJ2sIcaaZ/u36euD6QCAQm/4D\nv9//z8AO4Cu52JgQQgghLk8TYykOt8YYHUqBgvqFTpZc48btOffryXqgF/O/fohu3cF3V76XAU8Z\nb29yc+36t/Liiy8yPj7ONddcQ03N3JS7eSdeACBStGHWMbomtxFMDNBUfBt5Z5wGKV8+6gOfRt9w\nM+b9f4/+2Q9gfBR+8wOWN6fJBYfNYENdPo8fn+TwcIRrKn2Wxrc7FKVldkaGUsRjJi53dp+TyspK\nXC4X3d3dljdJMR1FpBwVOKMnQKdAWf+iRGXeck6MP8N4tINSb7Pl8YU400x/2mxA/Kw/i17C+wsh\nhBDiChecTLBnW5gtj4cYHUpRUW3nlrvyWXWT95xJn46EMH/8T5hf/ji07uB/VvvZUbaSayu9vHNt\nA4ODg+zcuROfz8eGDbNPwi6FkZrEFTpIyllJ0jO7J9qmTnF4+EEMZWd52ZvP+Rh17Y0Yf/xNqK5D\nP/EQ+gf/D52y5pQr1zY3FgCwpTOYk/jl1ZkEangg+8+HYRjU1dURDAaZmJjIOt7ZEt4WlE7iiFp/\nogiZeX4AQ+HDOYkvxJlm+tLF08BDfr//+8A4UAp8EHgqVxsTQgghxOUhETc5diROx7HjmGlNYbGN\n5avclFW+ulvnNN26A/P+f4DJMaio5vA9H+KBnnxK3HY+e3MN6VSSX//615imyV133YXLlX17/5nw\nTGxFYRIp3DDrTo2dEy8QSgyxqOQOfBcoFVUlZRh/9DXM734Vvf0ZdDiE8eHPo+boY52tFRVeCt02\ntvcE+fCNldiy7L55tooqB0daYwwNJFnQmP28wPr6eo4dO0Z3dzfFxcUW7PBlCW8L3skXcEZeIpmD\nE7kK3zIAhsJHWFb+JsvjC3GmmZ7YfQp4AfhD4J+BTwPPAZ/J0b6EEEIIMc+Sycw8vicfnuJ4Wxyv\n186atV423Zl33qRPh6Yw//lbmH/3lxCeQr3lXUx84f/xzaEiFPBHN9dQ5Lbz7LPPMjk5yfXXX09d\nXd2cfDwqHcEzuZ20LZ9Y/ppZxUibKQ4N/w+GcrCs7OJP1FVeAcZnvgor1sCBXZjf/lN0ODSrteeK\nzVCsr8tnMp7m8HDE8vj5hQYut2J4IGXJ/ceGhgYgV/f8FqKx5eyen9teSKFrAcORNtLmlXEiLK5c\nMx3nEAe+fuqXEEIIIa5i6ZSm81ic9iNxkgmN06VYsdrFuk2NDA0NnPf99O4XMB/4HgQnYeFijPd9\nArO6jm892c1ELM0HrqtgWYWXtrY2jhw5QkVFheXz1y7EM7kVQycIFt0BxvlPKy+kY+I5IskRWkru\nxusomdH7KJcb42NfQv/gO+gdz2H+zR9jfOrLqKLSWe1hLqyvy+fX7RNs6w5afs9PKUVFtYOejgST\n42mKSrK7O5efn09xcTEnT54knU5js3KMhuEk6WnEGT2OSoXQ9jzrYp9S4VvGZLyXsehxyn1LLI8v\nxLQL/qT5/f7/DQQCr/f7/e1kGnC9SiAQWJyTnQkhhBBiTpmmpvtEgvbDMWJRjd0BS65x09Tiwu5Q\n2O3nad4yNY7543+C3VvB4US9/XdQd7wZZbNx/54hDg1F2VCfz5uXFjM5OcnTTz+Nw+HgnnvusfZJ\n+gUoM453Yium4SFWeNOsYqTNJIeHf4FNOVlWdu+lrW93wAc/A7589NMPY3798xif/gtU5eU5v21l\npZd8l42tPSE+dIPGsHiAeUWVnZ6OBEMDqawTP8iUe7a2ttLf38+CBQss2OHLEt5FOKPHcUaPEc9f\nbWlsgArfctrHHmcofFgSP5FTF/tJ+7NTv38o1xsRQgghxPzQWtPXneTogRiRsIlhywxgb17iwum6\n8K0QvXc75n98F0JBWLQM432fQFXVArCtJ8iDR8aoyXfy8XVVaK159NFHSSQS3HnnnRQVFc3FhweA\ne/JFDDNKqOQOtDG7O3Ynxp8hmhpjSekb8Dgufe/KMOAdvwf5heiHfoz5rS9hfPGbqKKZnRzOJbuh\nWLsgjyeOT9I2EmVZudfS+GWVdlAwPJBk8fLs5wU2NDTQ2tpKV1eX9YmfpwV4FGckV4nfMkAxGD7M\nCv6P5fGFmHbBxC8QCOw89Z8fDgQC7zz77X6//0VgbS42JoQQ4ty01jDcj+48Br2dkIhDOg3pFKRS\nkE6h0ynQGlVaAbUNqJp6qK5DuT3zvX1xGdFaMzyQ4sj+GFMTaZQBjYuctCw//2iG0++bSqJ//u/o\nJx7KnPL99u+ibnvj6ZEFfVMJ7tvWj9Om+PymGrwOG9u3b2dgYIDFixezdOnSufgQM8wk3onnMZWL\naOHsuocm0zEOjzyE3XCxtOyNs96KUgr1pt/GtNnQD96P+ff/F+Nzf3VZNnxZX5fPE8cn2dodtDzx\nc7oMiktsjI+kSSY0Dmd2J4q1tbUYhkF3dzcbN260aJcZKVc1puHL3PPTetZNgc7HafNR7G5gNHqM\nlJnAbmTf8EaIc7lYqeebgDcD95zq6HmmIkAGjgghRI7p8VHoeAnd2Y7uOgadxyAys+YQ+qzfKa2A\nmnpUTT3qmuuhZcUVMVtMWG9iNJPwjQxlGkrUNjhYstKNL+/ipZd6eADz+38Dne1QtQDj9z+Pqm04\n/fapeJqvPtNDJGnyqfXVNBa7OXnyJDt37qSgoIDbbrvN0nlrF+MJ7sKWDhIuugVtm92LH0dHfkUs\nNcHy8t/AbS/Iek/q9W+HwT701icxf/BtjN/7o8vuZ3FVlRefw2Bbd5APXFdh+desvMrB+GiakaEk\n1QuyS3YcDgc1NTX09vYSiUTwei1MVJVBwtuMO7QfW3KY9BlzG61S4VvOeKyT0Ug7lXkrLI8vBFy8\n1PNFwAf8BnDyrLd1An+dgz0JIcRrno6E0bu2oF94Ek60vfKNFdWolddBwyJUQzN4vGBzgM0GdjvY\n7JnftYahfnRfF/T1oPu6oa8bDuxCH9iFfvS/oagUddNm1NrNUNc0p0/GxfwIBdMc3R+jvzcJQHmV\nnWXXuiksntk9K71nG+YP74NoGLX+NtQ7f/8VJ8mJtMnXnu2lL5jkbctLuK2pkFgsxqOPPgrA3Xff\nPWejGzIbTuMdfw6t7ESKZncSFE6M0Db6CB57McuyOO07k1IK3vNR9MgA7N6KfujHqN94tyWxreKw\nGdxYm8cznVMcG4vRUmptxUBFtZ2XDsFQfyrrxA+grq6O3t5eent7WbzY2hYUCe8i3KH9OCPtRHOS\n+C2jbfQRBsOHJfETOXOxUs8h4D/9fv/RQCCwb472JIQQr0naNOHofvQLT6L3boNkIlNStHw1ask1\nqMaWTLLnu4SucgVFqEXLXrlOOAidx9C7nkfv3op+7EH0Yw9mSkFv2pz5VVFt7Qcn5l0yYfLSoTgd\n7XG0hqISG8tWuSmrmFl3S51KMv79b2H+4ifgdKLe/0mMjbe/4jGm1ty3rZ/Dw1Fubsjn3avL0Vrz\n+OOPEwqFWLduHdXVc/u95Q7uw5aaIFK4Hm3Pn1WM1sH/JK2TXFv5W9iN7O+jTVN2B8bv/zHm1z6H\nfjiAWVWLse42y+JbYUN9Ps90TrG1O2h54ldUbMPhVAwPJNFaZ/3CU0NDA9u2baO7u9v6xM/TAoAz\ncozoLF9AuJBy7xIUhgxyFzk10zZKP/P7/dLVUwghckBPjaOfehi97SkYG8n8YUUNauPtqHW3oUrO\nPyB6NpQvH1asQa1Yg37nh+HAbswdz0LrTvQvHkD/4gFYdRPGW96Fqlto6dpi7mlT03UiQdvBGIm4\nxuszWLbKTfUCx4yfaOvRIczvfYNQZztU12WGkNfWv+pxD7SOsKUryLJyD59cX42hFNu3b6ejo4O6\nujpuuOEGqz+8i2zcxDv+LBqDSNHmWYUYDrfRM/UiJZ5mGgrXW7xBUPkFGB//M8yv/SH637+LLqtE\nLVpu+Tqztbrah9uu2NYT5L2ryy2tClCGorzSTl9PklDQJL8guw6v5eXluN1uuru7LUkkz2Q6ikg5\nynFET4C2ft6ew+ahxNPEWPQEyXQUxyxLkoW4kJkmfmd39SwB3gk8Ye12hBDitUNHwpnTticegngM\n3B7UprtQG26H5qVzUnapHE64bj2269Zn9rN3O3rLo9C6A7N1B+rGTag3vwNVZW2XPDE3RoaSHNob\nZWrCxGaHpde6aVrswmab+feWbj+M+Y9fg+Ak3jveROw33oNyvfrU67FjE/zXoVGq8x18cXMtTpvB\n8ePH2bFjBwUFBdxzzz0Yc3yHzRU+hD05TDT/BsxZdOHU2mTvwAMArKl6N0rlZv+qOnNP0vzOlzH/\n/q8ynT7Lq3Ky1qVy2Q1uqM3j+a4gnRNxFhZbd+IJmXLPvp4kQ/3JrBM/pRR1dXW0t7czPj5OSYm1\n3VIT3kV4J7fhiPUAr37hI1sVvuWMRo8xHGmjJgfdQ4WY6QD3Z8/+M7/f/xDwHPBPVm9KCCGuZjqZ\nQD/9MPqR/4JwEAqLUW97P2rD6875hHquKK8PtfF29IbXwaE9mP/zAHrnFvSuF1AbbkO96R2ZLqHi\nshcJpzm87+V7fHWNTpZee/FOnWcztzyGfuB7gEa96/cpfeeH6Ovre9Xj9vaH+ccdA+S7bPzZrXUU\nuO2Mjo7y2GOPYbfbuffee/F45vgEQ2u8Y0+jUUSKb5lViI6J5xmPddBQuIEy7yKLN/hKavlq1Ds+\njH7gHzG/+1WML/w1ymvt4PTZ2lCXz/NdQbZ2By1P/MqrHECU4YEUzRaMsKuvr6e9vZ3u7u6cJX7O\nSDtgfblnpW85R0YeYih8RBI/kRPZTMwsBhou+ighhBAA6HQavfVJ9C//E8ZHwONDvfW9qNfdO68J\n39mUUrDyeowV18HebZkE8IUn0dufRW2+C3Xvb6MK5m7+mpg509SceClO28EYZhqKS22sXOOhqPTS\n/rnX6TT6Z/+GfvKX4MvPdO1ceu05H9s5HuMbz53EphR/srmWmgIn8XicX/3qVySTSe655x7Kyqwt\nV54JZ6QNR6KfWN61pJ2Xvn4yHeXAUACbcnJt5W/lYIevZtz6eszBk+gnHsL8l29hfPxPL4uGS9fV\n5OG0KbZ2B3nXqnJLY7s9BgWFBqNDKVIpjd2e3cdbX585ievu7mb1amuTp6SnCY2BM3LM0rjTSr0t\nGMou9/xEzszoXwK/39/OGd3AARtQDfxbLjYlhBBXG314L+ZPvg8DJzMzz+55G+qet2bu212mlFJw\n3QaM1WvRO55DP/QT9NOPoHduQf3272WawFwGT0pFxsRoitZdEaYmTJwuxYobPNQ2zPwe3zQdDmF+\n/6/h8D6oqcf42JfOW3bYN5XgL57uJZoy+cOba1hW4cU0TX79618zOTnJ9ddfb3mTjRnRGt/YUwCE\ni2fXLOXIyC+JpSZZWf5WvI65G7CufvN30H09me67zzyCus2aLqLZ8DgMrqvxsb0nRM9knLpCa7uy\nllc7mJqMMzacoqJ6Zs2Gzic/P5/i4mJOnjxJOp3GZsuufPRM2nCTdNfjiHWhk2HL4k6zG05KPYsY\njrSRSIdx2i6PE19x9ZjtHb800B8IBI5bvB8hhLiq6EgY/V8/QG95DAwDtfke1L2/hSoune+tzZgy\nbKh1t6Fv2IR+5mH0gz9C/8u30Luex3jXR1BFc/ekWLxaKqk5eiBKR3sCgLqFTpavcuN0Xfp9NN3f\ni/l3X4Wh/kyDnw9+BuU59zy03qk4f/pED2PRFB+4roKbGzKz7bZv305XVxcNDQ2sX299M5SZcIYP\n44j3EPOtIO269LtyocQQbaO/xusoZUnZG3Kww/NThg3jdz6J+eWPZ/7uWLYaVVU7p3s4l/V1+Wzv\nCbGtO0jdNRYnflV2jh+NMzSQfeIHmbEO+/fvZ2BggNpaaz93Ce8inLFO9MRhwPqvS4VvGcORowyF\nj7CgYI6bIYmr3oz+VTh1x28MWAKsA5YB2f9kCiHEVUwf3J158rblMViwEONPvoXxno9eUUnfmZTd\njnHHWzD+/D5Ycg3sexHzz/8A84Un0fqcjZ9Fjg2cTPL0/07R0Z7Al2+w/jYfq2/yzi7pO7QX82uf\ng6F+1OvfjvHRL5436euejPOlx7tPJ31vWZZJ/tvb29m1axeFhYXcfffdc97MBQCdJm/0UTQG4dK7\nL/3dtWbfwAOYp8c3zOHMwVNUUQnGuz8CiQTmv30bnU7P+R7OdmNtHnZDsbUnaHnskjI7NhsMDyQt\niXdmuafVEp7MXU89ccjy2ACVvswMPyn3FLkwo7+R/X7/V4FngHuApcAbgef9fv8Xc7c1IYS4MulI\nCPOH92F+5yswNY560zsw/uSbqPrm+d6aJVRFNcZnvop610cgbaJ/+B3M+76CHhue7629ZsTjJru2\nhtn5fJh4XNOy3MUtd+fPeCbf2cwtj2He9xVIJlEf+izGW9+LOk/S1jUR50tPdDMeS/O7N7yc9A0P\nD/P444/jcDi49957cbvn596qe2oP9uQwsYIbSDsv/T5az9QOTgb3UO5dSn3BuhzscGbUDTej1t4C\nHS+h//dn87aPaT6njdVVXjrG4/QHE5bGttkUpRV2QlMm0YiZdbza2loMw8hJ4pdyL8A03Ojxg5bH\nBijxNGNTTobCR3ISX7y2zbTU873A0kAgcPpfdb/fXwHsAP4qFxsTQogrkd6/E/P+f4CJUahbiPE7\nn7oqZ+Epw0Dd+nr0Nddj/sffw8E9mH/+MdQ7fg9jw+0XDyBmbbAvSevOCPGYprjUxqobveQXzu4e\nk9Ya/T8PoB8JQF4+xh/8yQVnyLUPhfjSE91MxdP8/o2VvH5xMQDBYJCHHnqIVCrFG9/4RkpL5+lU\n20zgG3sCrRyESy79+zCeCrKn/9+xKSc31nxo3u+wqnd+GP3SIfSvfoq+5gZUQ247i17Mhvp8dvWF\n2dod5G0rrP0al1c5GOpPMTyQpL4pu1NWl8tFVVUV/f39xGIxa1+EUDYSnmbc4UMYyTFMi+9/2gw7\nZd7FDIYPEktN4rYXWhpfvLbNtAZj9Myk75ThU7+EEOI1TyeTmKfasBOcRL3lXRhf/NZVmfSdSZVW\nYHzqy6j3fRyUQv/gO5g/vA8dj8/31q46yaSmdUeEHVvCJBOaZde62fi6vNknfckk+l//NpP0lVdh\nfOFvLpj0nRiL8ZGf7iEYT/MHa6tOJ33xeJxf/OIXhMNhNm3aRHPz/J1seye3YktPESnaiGkvuOT3\n3zNwP/F0kGsq3k6+qzIHO7w0ypuH8f5PQDqN+S9/i07M78/VTQvysSnYloNyz4qqzFnE0IA1w9Hr\n6+vRWtPT02NJvDMlT432yIx1sF6lL/NzKKd+wmoXPPHz+/0bTv3ng36//0HgATLJXgnw28BPc7s9\nIYS4/OnRYczvfR0626G2AeNDn0UtaJzvbc0ZpRTq5jvRS67B/N430C88ge5sz4wAkMHvlhgZSrHv\nxTDRiKagyGDNWh8FRbPvVqjDocxQ9rYD0LQk07kz//wnC20jUb7ydA+RhMnH1lVxR3NmnEc6nebh\nhx9mbGyMVatWWd4+/1KodBjv+LOYhodI0aXP7Ts5tYfuyW2UeJppmcXdwFxRy1ejXncv+qlfoR+8\nHz79Z/O2l3yXjWuqfOzrDzMcTlLus67dgy/fwONVjAym0KZGGdmdttbV1bF9+3Z6enpoaWmxaJcZ\n0/f8nJF2YoVrLY0NmQYvkEn86gvnr9xYXH0uVur5wFn/f/bf6DcC37RuO0IIcWXRB/dg/uu3IBRE\nrb8N9a6Polxz3wzicqDKqzC+8A104N/QzzyC+ZefRb3vYxg3bprvrV2x0mnN0f0xTrwUBwUty10s\nXu7GsM3+SbEeGcS87y+gvyczruODn0Y5z/89+9ixCf5p5yCm1nz5DctYXZxp5KO15oknnqC3t5fm\n5mY2bdo0r6WRvvFnMcwYwbI3om2XVtqXSIfZ1f8DDGXnppoPYah5aEpzAeqt70Mf3ot+4iFir3s9\nlM9fl8/1dXns6w+zvSfIm5ZaV+aolKK8ykH3iQQTY2mKy7IZNQ2VlZU4nU66u7vRWlv6vZl2lIKr\nDGf0OGgTLP5+KfYsxG64GZQGL8JiF/ypCgQCV3eNkhBCzJI2TfTDAfQvfwI2G+o9H0Vtunve7wTN\nN+Vwot71+5gty9H/8ffo7/8N5kuHUP4PohzSDPpSBCfT7N4WJjhp4ss3WLPWS/ElDmI/m+46lkn6\npiZQd74F9fbfOW8Tl2Ta5J93DfHosQnynQafu3kBb1hRTV9fH5AZ29DW1kZVVRV33XXX/HTwPMVI\njuOZ2EraXkR0Fick+wZ+Qiw1wcqKt1HovvxOqZXLhfGBz2B+/Q8Z+/aX0V/6NsqbNy97uWlBPt/b\nMcj23pCliR9ARbWd7hMJhgZSWSd+hmFQV1fH8ePHmZycpKioyKJdAkqhilfw/9m78zCpqjPx499z\na1963/eGZhGQfRUUiYoKAmqipdFo4pho1JhJ4phJzC+ZjDETs0wWY9SYxExijKZUQNwAFQVllx2a\nrWl63/eufbnn90c1ioo2ULdX7ud5fBqrq859q7u66r73nPO+SsN6jMEaItZC7cYGFGEg034edZ7d\n+MJt/dpHUje89bbU87/dbvd/uVyuJz/tPm63+w7tw9LpdLrBS3q6UP/yG9i/A9IyY0sai7VdSjTU\nKbPmIwtHxpZ+vvMa8vgRlDu/+6mNwM+WKiWtvgi1XSFqu0LUdYeo6wrR5o8QUSVRKYmqkqgKESlR\nVYnDbCDNbiTNZiLVbiTVZiTNbiTTYaI4xYLZMLCzPVJKqspD7N/lR41CUYmZ8VNsGI3xXVSQ+3ag\n/vHnEAoibrwD5dIln3rfVl+Yn79by+GWACNSLHx/fh5ZTvMH39+/fz/bt28nKSmJJUuWYBrgpN7R\n9iaCKN7UhSDOLGFo8OzjeMd6kq1FjEv/9J/JQBMjRiOuuoHoy88invsT4t++PSBxpNqMjEm3Udrk\noysYJdGiXYP09EwjQsTaOow9P/6CLIWFhRw7doyqqiptEz9ApExENqzH7CvTPPEDyHSMp86zmyZv\nKcXJF2o+vu7c1Nu7Y1PP19q+DkSn0+mGAll1DPWxn0FrE5w/Ldbg2nnmRSTOBSI7H+X7v0I+9yTy\nvTdQf3ofyh33I8af/T6wYESltNnP7novexu81HSFCEU/2UPQblIwKgKDIjAKMBkEVkVBEeAJRilt\n8iPxf+JxBgHFKRZGpdoYk25lVKqVgiQLhjj3G52ucEhlz/t+6qvDmMyCaXNs5OSbe39gL9R31yL/\n8RgYjCh3fR8x9dNnxQ40+fjFu7V0BKJcXJzIPbOzsRg/TIYrKip4++23sVqtXH311djtp+71118M\nwQas3bsIm7MJJJzZayscDbC97ikESs8Sz/hmmfqaWHw9xoO7CW9+GzlvIWLs+QMSx5x8J4db/Gyv\n6ebSEu0SKpNZITnVQHtblFBIxWyO7yLMyf38Jk2apEWIHxDJ45AIzL6j+FIv0XRs+Og+Pz3x02ml\nt6Wef+j5Z7vb7X6kH+LR6XS6QUvu2Y76p1/GZkyWfhGx5IZPXSanixEWC+LL96KOHIv85xOov/0x\n4vrbEJctO61lsVJKKjuC7G7wUvpeIzurOwirsUTPbBAUJJnJTTCTm2gmr+drboIZh/mzZyEiqqTd\nH6HNH6HNF6HVH6auO0xZq5/ytiDH2oKsKYvd12oUnJdhZ1qOg+m5DvISzX2ypLetJcIhv2S4AAAg\nAElEQVTOzbECLqnpBqbOcWB3xPf6klIiX34W+fJz4EiIFXEZNe6U91Wl5NXD7fx1ZxMS+Or0TJaM\nTfnIc62qquL1119HURSWLl2q+SzK2XC2rkYgY83az3Cv1a6Gp/GFWxiXvowUW3HfBKghYTSScs/3\naPrOV1Cf/SPKD3+LMGg343a65hQk8LfdzWyp8Wia+AFk5phob43S0hghtyC+ix5JSUkkJiZSU1OD\nqqqaLkcWJicRSx6mQBVCDSIVbfd2J1sLMRsc+j4/naZO99LWTS6X62m3293ep9HodDrdINX9yvOo\nT/wSTEaUr38PMe2CgQ5pSFEuuhyZW4j6+M+Q7r9AdTnccg/CdOoTuxZfmHXlnbx1rJMGT/iD24uT\nLUzNcTAlx8H4TNtZL8s0KoIMh+mUVQnDUUlVZ5AjLX6OtgY40hqbYdxd7+WpnZDpMDIt18m0HAcT\ns+3YTfGdeEtVcvRQkCP7A0hgzAQLo8dbUeKcZZSRCPKZx5HvvQHpWSj//l+fWmX1aKufJ7c3cqQ1\nQJLFwP0X5TIxy/GR+7S2trJ8+XIikQiLFy8mJycnrvi0YPKVYfEdJmQdQcg+9owee7x9A8c7NpBi\nLWZCxjV9FKH2LGMmxKrovrsW+c5riEuX9nsMuYlmCpPM7K73EoioWI3aJVQZ2UYO74fmhvgTP4Ci\noiL27dtHY2Oj5q/ZkH00pmANJn85IcepL6icLSEUMuzjqO1+H0+oCac5U9Pxdeem00389gJ7XS7X\nVqDt5G/oe/x0Ot1wJlUV+eL/0bF2JSQkodz7Q8SIMQMd1pAkSs5D+cGvY8nf5reR9TUodz+ASIk1\ngg5HJdtqu3nrWCe76r2oEiwGwfyiRKblOrhiykhCXa19HqfJIChJtVKSamVRz22tvjC76r3srPOy\nu8HL6qMdrD7agVGBiVkO5hQ4mZ2fQIrtzJYKBgMqO7f4aGmMYLUJps5xkJ4Z/3JDGfCj/vEXsX2o\nRaNir9uklE/crysQ4R97Wlhb1oEE5hUmcPv0TNLsH02IOzs7WblyJX6/n8suu2xAe/V9QA2T0LwS\nicCTfhWcwSxsR6CaHfV/w6TYmVtwLwZlaBUeEtfegtyxEfnSP5EzL0Ik9v/M6+z8BJ4/0MquOi8X\nFCZoNm5yigGTWdDUENakGmdBQQH79u2jqqqqTxI/R/vbmH1HNU/8INbPr7b7fZq8pXrip9PE6X66\n1AJ/PsXtn9xYodPpdMOEDAZRn/o17NyMMb8Y9e4HNC9Ocq4RKWko9/8P8unHkJvXof70OzTf9j1e\nDaTyzvEuuoJRAMakWVk4KpkLixI+mFFLd1qo6xqYuNPsJi4rSeaykmSiquRIq5+ddV7er/Wwq97L\nrnovT2xrZEy6jTkFTubkJ5Cb+NmzFa1NEXZs9hIMSLJyjUyZZcdsiX/mRHa1oz7yE6gsi+1DvfM/\nEVbbR+4TVSVryjp4Zk8znpBKQZKZO2ZkMSnb8YnxvF4vK1euxOv1ctVVVw2OpA+wd6zHGG7FlzSX\niPX02xuEowE2Vf+eqAwxJ/+uIXlCLRKSEFffjHz2SeTyvyO+8s1+j2FOQSzx21LTrWniJxRBRpaR\nuuownm6VhMT4ZtQLCgoQQlBVVcXs2dr23AtbC1CFGbOvTNNxTzh5n9/IlAV9cgzdueV0E79St9v9\n/MdvdLlc39I4Hp1OpxsUZFcH6qMPwfEjMHYiWf/9Oxq6PQMd1rAgTGa47d+pzD2PFaVtvLtLRRXt\nJFkMXH1eCpeVJFOYPHh7IRoUwbgMO+My7Nw8OYNGT4htNR62VHdT2uzncIufv+1qpiDJzMw8J7Py\nnIxJt31QIEZKybFDQQ7tCwAwbrKVkrEWTfYNytoq1N8/CK1NiHmXIb50N8L44Ud9VJXsrPPyzN5m\njrcHsZsUbp+eyeIxKRhPsbQ0GAzy0ksv0dnZycyZM7nooos+aOcwkAyhFhzt64kaEvGmLTztx0kp\neb/+KbpD9YxJW0R+4ow+jLJviYsXxZZ7bnwTOf8KxMgzW+oar5JUC+l2I9trPURUecrXz9nKyI4l\nfs314bgTP4vFQlZWFg0NDQSDQSxa9lkVRsK2kVh8h1DCHagmbWdeEy15WAyJNHpLNe9FqDs39dbO\nIR8oAn7hcrlqgJNfccnAg8Bv+y48nU6n63+yoRb1kf+G5gbEnM/FmpAnJIKe+GniULOfFw60sr21\nGLKKKfQ1cW3FW8wbn4t58u0fSVSGgiynmaXnpbL0vFQ6AxG218aSwD0NPpaXtrG8tI1Ei4HpuQ6m\nZzkw1Ci0NkSx2gTTLnCQlqHN85Wlu1Cf+Dn4fYirb0JcdcMHJ4oN3SHePNbJW+WdtPkjAFwyMpFb\np2R+6vLUcDjMqlWraGlpYeLEicyZc+b98fqElCQ0v4SQETwZS5DK6Zf9P9b+NlWdm0mzjWJylqsP\ng+x7wmBA+eKdqL/8Puo//4jywK/6tdiUEILZBQm8erid/Y0+puR8crb4bGVkmwA/TQ0RtMhnCwsL\naWhooKamRvMZ65B9NBbfIcz+owRMMzUdWwhBpmMc1V1b6Q41kGgZ+H21uqGtt0+b8cC3gTzgnx/7\nXhh4tC+C0ul0uoEiq8pRf/tf0N2JWHpjrHqnfpU1blJK9jT4eH5/C/ubYm0Uzku3cd2ENKaZk+Cx\n5+GdXah1FbGliQOwZ0kLSVbjB0tCgxGVPQ1ettd62F7rZf9xP5lVFhKEgXZjGJktSQoqmIN2EuLs\nhaZuWIN85nFQFMRX70OZfTGhqMqW6m7eKOtgb6MPAIdJYdHoZC4flczI1E9PmKLRKK+99hr19fWM\nGTOGBQsWDJq/A4tnL2Z/GUH7GIKO029n0OavYFfD05gNTuYWfGPQt244HWLMBMSsi5Hb1iM3vom4\n6PJ+Pf6cfCevHm5na023pomfza7gTFRobY4QjUoMhvhee4WFhWzbto2qqqo+SfwAzL6jBBK1Tfwg\nts+vumsrTd4DeuKni1tv7RzWAmtdLteDbrf7R/0Uk06n0w0IWXYQ9ZEHIeBDfOlulIuvHOiQhoWj\nrX7+vqv5g+RjWo6D6yakMT7T1pNMOJHf+wXqX38HOzfF+v3d8wCicHDsJTtbFqPCrPwEZuY5KT8a\npHR3ACklFRY/b/s6iR4FjsbuW5RkYXymjVFpVrKcJrIcZtLsxl77B0pVRS7/O5G1K6lJK6Zi2R2U\nG9M4/kYlZW1BAhEVgAmZNhaWJDO3MOEjPflORVVV1q5dS2VlJUVFRSxcuHDQJH0iGsDZ8ipSGPFk\nLDvtgi6hqJdN1b9HlRHm5N2F3ZTWx5H2H3H9V5B7tiGX/w057QKEQ7v9dr2ZkGknwaywtdrD12ZI\nFA1fJ5nZJsqPBGlrjvTMAJ69rKwsTCYTVVVVGkX3oagpnagxKbbPT6pn3FKkN5mO8UBsn9+o1Ms0\nHVt37jndy13PuVyu/3C73b9yuVwTgCeIFXb5ptvt3t134el0Ol3/kKW7Uf/wU4iEEbd/B2X2xQMd\n0pBX0xXkH7tb2FzdDcD0XAc3T86g5BQzTcJqQ/n6fyJfdSNfegb15/+JuPXeIf97iIQle7b7qKsO\nY7YIps1xsCw7ha+GszjS6qe0yUdpU2xfYGVn8INEEGLN5DMcJjKdJjIdJgxCEFElUSmJqpJoJEqk\noox2fy6VFz1ESDFCOUA7AshLNDMr38llJcnk9VJo5gQpJevWrePo0aPk5uayePFiDAPQJ+7TONrW\nYoh240m9nOhpJm9RNcR7Vb/BG25ifPoychK0beQ90ERyGmLpDcgX/g/50jOIm77eb8c2KIKZ+U7W\nlXdxrC3A6DRb7w86TRnZRsqPBGluiD/xMxgMFBQUUF5eTkdHh7b9J4UgZB+Nret9jME6ItZTt0w5\nW05zFjZjKk3eg0ipIjROLHXnltNN/J4Cftnz78eA1cCOnn/P7YO4dDqdrt/I3Vtipe8RKHd9HzFF\n28pv55oWX5jn9rbwVnknqoxV6Pzy1EzOz7J/5uOEEIglNyDzi1H/8mvkn/8Xtfo44vO3IJTBk3yc\nru7OKO9v9OLpVklJMzB9rgObPXbSZjMpTM52MLmnimZElRxrC1DVEaTRE4795w3R6Amzt8H36Qcx\n5WI0RilItlKSbmdkipWRqRaKk63YTGd2giilZP369ZSWlpKZmcnSpUsxmQZPmwNjoAZb5xYipgx8\nKRed1mOkVNlS+wTNvsMUJM7m/Mwv9HGUA0NcuhT53hvId1YjL7oCUTCi3449Oz+BdeVdbKn2aJr4\npWUYUQzQ1BBmPPGPW1hYSHl5OVVVVdomfkDIFkv8zL6jmid+J/b5VXZupDNYS7K1QNPxdeeW0038\nktxu94sulysTmAxc6na7Iy6X65e9PVCn0+kGM3XLO8i//hZMZpR7foAYN3mgQxqyfOEoLx5oY9Wh\nNkJRSX6imVumZDA733lGSwXFlNkoD/wK9dGfItcsR1aXo3z1PiC374LXWE1liL3bfUSjMHKMhXGT\nP7shu1ERjE23MTb9kye4gYhKiy/WxN4gBIbqcsTTj6J0tmKaMQ/rzXdgtsTX6FpKyaZNm9i7dy9p\naWlcc8012lY/jJdUSWheiUDSnXE1nOb+vN0N/6SmazsZ9vOYnXfnsJ0tEUYTyo13oP72v1Cf/SPK\n/T/rt+W5U3McmA2CLdXd3DIlQ7NxDUZBWoaR5oYIAb+K1Rbf766oqAiAqqoqJk3SdtY3ZC9BIjD7\njuJL/ZymY0Nsn19l50aavKV64qeLy+n+FUmXy2UHbgTW9iR9JmAQfSrodDrdmVHXr0Y+9Ruw2lC+\n/aCe9J2lqCp581gHd68q54UDrSSYDdw7J5tHrhrBnIKEszoBFTkFKD/4FUycAaW7UR/8FsEDg39n\nQTQq2bfDx64tPoSA6XPtTJhq+8ykrzdWo0J+ooW8BDNZ779J2iMPkNpaQ8qy60i47Z64kz6Abdu2\nsWPHDlJSUrj22muxWk+/UmZ/sHVuxhSsxZ8wlbD99PZ+Hm55nSNta0i05HFh4beGXJP2MyUmTIUp\ns+FoKezd3m/HtRgVpuU6qOkKUdMZ1HTsjOxYgt/cEI57rKSkJJKSkqiuriYajcY93smkwUHEkosp\nUIVQtf0ZwMn7/Eo1H1t3bjndxO8xoAb4EfDTntueAVb0RVA6nU7X19S1K5H/eAyciSj3/RRRct5A\nhzQk7Wv0ct/qCn6/pQFvWOWLE9N5bNlILitJ7rUwSW+E3Ynyjf+H+Pyt0NVO0/fuRH39RaSqahS9\ntnxelU3rPFSUhUhIUrjo8gRyC+JPygBkKIj862+R/3wCbHaUb/83ypVf0GRWZ+fOnWzdupXExESu\nueYa7PbPXpLb3wzBRpytq1EVO560xaf1mKrOLexu/Cc2YwoXF92P2aBdxcnBTLn2FhAK6oqnkaq2\nyc1nmZ0fKyizpUbbljeZPXv7mhoimoxXWFhIOBymoaFBk/FOFrKPRhDF5D+u+dgOczoOUyZN3kOo\ncnC+/+mGhtNK/Nxu96NAIZDtdrv39Nz8oNvt/s8+i0yn0+n6iPrGS8jnn4LkNJTv/gxROHKgQxpy\n6rtDPLyhhv/3ZjXH24MsGJHI48tGcuOkdKy9VI08E0JRUBZdh3LfQyjJKcjlf0N99CGkp0uzY2ih\nsS7MhrXddLRFyS82ceFlCTgTtNmXKJsbUB/+LnLz2zBiDMoPf6PZ7PTevXt57733cDgcXHvttSQk\n9F9FyNOihklsfA4hI3RlfgFpdPb6kCbvQbbW/hGTYmN+0X8MqwqevRG5hYgLPge1lchtG/rtuDPz\nnCgCtvYUctKKM1HBahM0N0SQqox7vMLCQoA+qe55cluHvpDpGEdY9dERqOyT8XXnhs/8dHa5XHf0\nfH0A+CbwXZfL9UDP/y/r+arT6XRDhvrmKqT7L5CcinL/TxHZ2m7EH+584Sh/39XEN145zuZqD+el\n2/jlFUV8e24u6fa+W0onxpxP9u//CeMmw773UX/ybeSxQ312vNOlqpKDe/1se9dLNCKZNMPGlFl2\njEZt9lfJfTtQH/oOVB9HzL8ytncrVZt9VAcPHuSdd97BZrNx7bXXkpSUpMm4WnK2rsEUasCfOIuQ\nc3yv92/2HeG9qt8AknkF/06ytbDvgxxkxLIvgtGIXPkMMhL/EsnTkWAxcH6mnSOtAVp92h1TCEFm\ntolwSNLRHv8MZn5+Poqi9EniF7YWogpznyV+WfpyT50GerssW9zzdfSn/DeqzyLT6XQ6janrXkH+\n68+QlBpb3pk5dIqFDDRVStaVd3L3y8d5sbSNZKuB++bl8vDlhYw5RUGSvmBITkX51o8Ry26C9hbU\nX34fdc3yfl3SdrKAX2XzOx7KDgaxOxUuvMxJUYlFk+WXMhxGXf531N8/CKEg4ivfRLnlboRGVTYP\nHz7Mm2++icVi4ZprriE1NVWTcbVk9h7B3rmRiCmD7vSrer1/o7eUDZW/IKKGmJN3F1nOCf0Q5eAj\n0jIRFy+C1ibkhjX9dtw5BbHZ4q0aL/fMyInt82uqjz+htFgsZGdn09jYiN/vj3u8jxBGwraRGMPN\nKOEObcdG3+en00ZvDdwf6Pl6m8vlsgAJQIfb7dZmsbVOp9P1E/Xt15DPPglJKSj3PYTIzhvokIaM\nIy1+/vR+I0daA5gNghsnpvH58Wm9NgLvC0IxIJbeiBw1DvXP/xvrXbZjE8qX70XkFfVbHC2NYXZs\n9hEKSrLzTUyZacdk1miWr+JorJl9XRWkZaLc9T1EkXbXWcvKyli7di0mk4lrrrmGjAztKjFqRUQ9\nJDS9gMRAV/aNoHz2Xsn67r1srP4tEsm8gnvJS5zeT5EOTmLx9cj33oz1xZx7KcLa9xdnZuU7efL9\nRrZUd7N4TIpm42ZkGRECmuojjD0//vEKCwupq6ujurqaMWPGxD/gSUL2UVh8hzD7ywiYZmg6ts2U\nTII5l2bfYVQZQTnNyrY63cl6/dR2uVwXulyujYAHaAQ8Lpdrtcvlmtrn0el0Op0G1PWrY0UxEpNj\nSV+OvrzzdLT5I/xucx33r6nkSGuAeYUJPLZ0JF+clDEgSd/JxLjJKD9+FDHrYjh+BPUn30Zd9Wyf\nL22TquTIgQCb13sJhyQTpliZMVebpE+Gw6grnkb92f1QV4VYsAjlx49omvQdP36c1atXYzQaufrq\nq8nKytJsbM1ISWLji7FG7WmXE7F89sx8Tdf7vFf9awAuLPz2OZ/0AYjEZMTCq6GrA/nWy/1yzAyH\niVGpVvY3+vAEtZuFN5kVUtINdLRFCQbjL2xyclsHrfXHPr+IGqStDwrI6M4Nn3m5wOVyzQFeBB4A\nrgG6iC3/vB5Y43K5Frvd7vd7O4jL5foNMAeQwL+73e7tJ33vMuB/gCjwmtvt/knP7b8ALuqJ8Wdu\nt3v5GT87nU53zlM3rIlV70xI6kn69B5IvQlFVVYdbOf5A60EIiojUix8bXoWE3ppwN7fREIi4mv3\nIWfNR/3HY8iXn0Xu3ITy5W8iRozW/HgBv8qurT5aGiNYbYLpcx2kpmtz1f0Ts3xfvlfz9iJVVVW8\n9tprKIrC0qVLycnJ0XR8rdi6tmLxHSJkK8GffOFn3reqcwtbah7HoJi4qPA+Mh3j+inKwU9cfg3y\nnVdjvTAvvhLhTOzzY84pcFLWFuD9Og8LRmi3ZzQz20Rbc5Tmhgj5RfFVys3IyMBqtVJVVYWUUtN+\nh1FTBlFjUizxkypo3DcyyzGeY+1v0eQtJd2u/Xucbvjr7RPrAeAet9v9wkm3HQYecrlch4CfAQs/\nawCXy3UxMNrtdl/gcrnGAU8BF5x0l0eAK4BaYL3L5XoRyALO73lMGrAL0BM/nU53RtSNbyKf/kNP\ny4aHELnnXqGHMyGlZEuNh7/ubKLREybRYuC2aVks1KA1Q18Sk2eijH4U+eLfkBtWo/7sfsTCqxHL\nbkJo1IS8qSHMri2xpZ1ZuUYmz7JjscR/UifDYeQrzyFXvwiqiliwGPGFWxFWbZPsmpoaXnnlFQCW\nLFlCfv7gnPU2hBpxtryKqtjoyrr+M0+cy9vf4f26pzAqVuYX/Qfpdm2X7Q11wmZHLLoe+fxTyNUv\nIq67rc+PObsggX/saWFLdbe2iV+OkUP7Yvv84k38FEWhoKCAo0eP0tbWRlqahlVfhSBkH42t632M\nwToiVm3/zk5c2Gj0ljI+42pNx9adG3r71JrysaTvAz23915iCy4FVvY85iCQ4nK5EgFcLtdIoM3t\ndle73W4VeK3n/huIzSoCdAAOl8ulTV1snU53TpA7NiL/9ig4E1Du+0m/7v8aiiraA/zwrWoe3lBL\nizfM1eel8PiykVw5OmVQJ30nCLsD5Za7Uf7jp5CeiVy7AvVHd6G+8zoyfPbLP09U7dy63ks4LBk/\nxcrMCx1xJ30yEkF9dy3qD+9CvvY8pKSjfOcnKDd/XfOkr76+npdffhlVVVm8ePEHJe0HHTVEYsOH\nrRtU46kTB1Wq7G54lu11f8FkcLCg+Ht60vcpxOcWQ0o6ct2ryPbWPj9eQaKZ3AQzO+u8BCPa9ZtL\nTDZgsfa0dZCDvK2D7cRyzzLNx7YYE0iyFNDqO0pUDWk+vm74623Gz9fL90+nkVI2sOOk/2/uua2r\n52vzSd9rAkrcbncU8PbcdjuxJaC9LhjPzdUr9PUF/ec68PTfwZnx79hMy5//F2G1kfnQHzCPPp1r\nVL0bjr+HNm+IP24sZ+XeOlQJF45M498/N4ri1MHZ8LrX30FuLuoFF9P13J/xrHoW+czjKGuXk+i6\nDcfCZQjT6c8WdHeFeOu1WhrrgyQmmbhscT4Z2fEVyZDRCL63V9P57J9QG2rBZMZ57c0k3Xwnik37\npbTV1dWsWrWKaDTKTTfdxPnnx18doy/+DqRUUQ/+ARlqQOR8jvTRp15MFIx4eX3fTzneupUUewFX\nT3mIFMfgnL3sS2fyO/Dcehftv/sJtnWrSL33B30YVcxl4/z8fVsVVSELFxdqVzioaCQcKe3EbEiN\n++/Q4XDw1ltv0djYGNfr+VSPleEEoo3PkRCtJLkP/lZKumexs+pFpL2d3FS93MZw/FzuS70lforL\n5coBPu1y79nMwn3WpeOPfM/lcl1NLPG7/HQGrqurO4twdJ8lNzdX/7kOMP13cGZkWSnqb34ECMQ9\nD9DiSAYNfn7D7fcQjKi8fKidFw604o+o5CeauX16JtNynRDopK6uc6BD/IQz+h1c/nnE7M/BmuVE\n179O+x8epv3ZPyMWXY+Yd1mvbRHqa0Ls2eYnHJbkFpqYNMNOWG2nrq79rGKXahS57V3ky89BUx0Y\njYjPXYVYfB3+5DT87R3Qrm0J+ObmZpYvX04oFOKKK64gNTU17tdwX/0dOFrX4mh/n5B1BB32S075\nN+sJNfFu1a/pCtaS7ZjIBQX34O9U8HcOn7/L03GmvwM5bhpk5+Nd+xL+eZf3eUXj81Nip3Kv7ali\ntF27YksJSbGxDuyrZ4xqjXu81NRUysvLqaqqwmg88726n/V7SLHkYuw8SlPNcaSizXLzExw9ndZK\nKzdgDAzC4kz9aLh9Lmvls5Lh3l7po4AaPj1ZO5359jpiM3sfxAPUf8r38npuw+VyXQH8ALjS7XYP\nvjMQnU436MiqctRHfgLRKMpdDyDGThzokAYdVUrereji6d3NNPsiJFgM3DEliytGJ2McAks6z4RI\nSkG4bkde8flYgYv1ryOfeRz5+vOIS5cips1FpH/0xCkSlhzY5afqeAjFAJNm2CgcaT7rAhCyow25\ne2ussmJDDRiMiIuvRCy+XrNG7KfS2trKihUrCAaDLFy4UPOy9VqydO/G0f42EVMqnTk3wynK1Dd5\nD7Kx+hFCUQ+jUy9nSvZNKELfAXI6hMGAcs2XUJ94GPnSM4g7v9unxxudZiXFZmR7rYeoKjVbKp6e\nbQQR2+c3ZkL8iV9hYSG7d++mrq5O8+XPIftoTMFaTP7jhBznaTp2hn0sAgON3gN8uCtKpzs9vfXx\n06Ic0Vrgv4E/ulyuaUCd2+3u7hm/wuVyJbpcrmJiCeYS4GaXy5UE/BK4zO12t2kQg06nG+ZkQy3q\nb/8LAj7E7d9BTJ450CENOqVNPp7a2cTR1gBGRXDtuFSuOz8Np3l4n0CfMgF8/q/I5/8KBSMQUy9A\nTJ1NuyWfXdv8+DwqickGps2xk5B05j8b2VSP3LUFuWszlB8GKUFREBddHkv40vv2Kn1bWxvLly8n\nEAhwySWXMG7c4K10aQxUkdj0IqpioTPny0jDR5cYSykpa3uT3Y3PICXMyLmNktRLBijaIWzaBVA0\nCvn+e8glN/TpnmdFCObkO3n9aAelzT4mZmmzbNxsVkhJNdDeFiUUVDHHuc/2ROJXVVXVJ4mfo/0d\nzL4jmid+JoONVNtI2vxlhKJezIbBuSxfNzj1efdHt9u9yeVy7XC5XJsAFbjH5XJ9Beh0u90rgLuA\nZ3vu/i+3233E5XLdAaQDbpfLdWKoW91ut/a7cHU63ZAnW5tRf/ND6O5E3Px1lNkXD3RIg0pNZ5Cn\n9zSzpdoDwIVFCdw6JYMsZ3zV8YaaDxLARdchd21G7toCB/cSramibH+AshHLQAhK0toZM0ZgMKQh\nZcJnzvZJvw/aW6CjFVl2KJbs1VT0HFCB0RMQ0y6IJZep6X3+HDs6OlixYgV+v58FCxZosqevryjh\nDpLqnwYZpSv7S0TNmR/5fjDiYXvdn6jt3onFkMDcgnv1dg1nSQiBsuyLqL//CfJVN+KO+/v0eHMK\nEnj9aAdbqj2aJX4AmTkm2lujNDdGyCuM7/0rLy8Pg8HQJwVewtZCVGHB7Dui+dgAWc4JtPqP0uw9\npPet1J2RPk/8ANxu9/c+dtOek763gY+2d8Dtdj8JPNkPoel0uiFOdnXE9vS1tSCuvQVlweKBDmnQ\naPWF+de+Vt441oEqYWy6jX+blsl5GfEVRhjqREISYv6VMP9Kupu97Hqvk86QHa2JekoAACAASURB\nVFughcn7/0hqx2EgdqUSkxlS0iAlHZGcCooB2dEK7a3Q0QoB/0cHNxph4gzE1DmIKbMRCdqVtO9N\nV1cXy5cvx+v1ctFFFzFp0qR+O/aZEmqQpPq/Y4h66E5fQsgx9iPfb/YeZkvt4/jCrWQ6xjEn7y5s\nppQBinaYmDgDCktis35Lb+zTnqYTMu04TApbq7v56vRMzXrlZeYYObw/ttwz3sTPZDKRm5tLdXU1\nXq8Xh0PDmTNhJGwvweItxRBuJWrSsGUEsX5+pc0rafQe0BM/3Rnpl8RPp9Pp+oIM+FEfeRAaaxFX\nXItYdN1AhzQoeENRVpS28dKhNkJRSX6imVumZDA736lps+KhTEpJ5bEQpbvDRKN28otMTJhUgGna\nDcjq49De+mGC194Ch/d9dFO7MwHSs2IJYUoaJKchcvJhwjREH1Tn7E13dzfLly/H4/Ewd+5cpk4d\nxNX+pEpioxtTqB5/4iz8SXM/+JYqVQ42r+JA83JAMDHzOs5LX4qicSPsc5EQAmXpDah/+J/YrN9X\n7+uzY5kMghl5TtZXdFHeHqQkNf49eQBJKQbMlg/bOsT7flZYWEh1dTVVVVWaL4kO2sdg8ZZi9h7B\nn3xB7w84A2m2URiEmUZvqabj6oY/PfHT6XRDkoxEUJ94GCrLYlUav/CVcz6pCUdVVh/t4F/7W+kO\nRkmxGfnapHQuHZk0JHrx9RevJ8qe7X5amyKYzIIps23kFvTMHkycgZg44xOPkZEwdLSBGo0leWZt\nK/XFw+PxsHz5crq6upg9ezYzZnwy/kFDSpzNq7B4SwnZSujOiC2vBfCGWthW9yRN3oPYTWnMyb+b\nDL0/n7Ymz4b8EbEKs0tu7NMKn3MKYonflupuzRI/IQSZ2UZqKsN0dURJSonvNLaoqIiNGzf2SeIX\n6nntmn3aJ34GxUSGYywNnn34wx3YTMmajq8bvvRLaDqdbsiRUiL//igc2BU7Uf/S3ed00hdVJW8d\n6+CuVeX8eUcTkajk5snpPLFsJJePStaTvh5SlRw7HOCd1d20NkXIyjWy4MqED5O+zyCMJkR6FiIz\nd1AlfV6vl+XLl9PZ2cmMGTOYNWvWQIf06aTE2bIKe9dWwuYcOrNvAmFASsmx9ndYfez7NHkPkpcw\nnctHPqQnfX1ACIGy5AaQKvK15/v0WFNznJgUwZbqbk3HzcyJtWJpqo/EPVZaWhp2u52qqipNGsOf\nTDWlEDFlYvYfA1W7thYnZDkmAPRU99TpTo8+46fT6YYcufIZ5OZ1UDwa5c7vIs6iB9NwIKVkS7WH\nf+xppqYrhEkRLDsvhesmpJFkPTd/Jp+muzPKnu0+2lujmC2C82fZyC0wDekLBj6fj+XLl9PR0cH0\n6dO54IILBu/zkRJnyyvYO7cQMWfTkXc70mDHF25je92fafDsw6TYmJX7NYqTLxq8z2M4mDoHcguR\nW9+JVfjMzOmTw9hMClNyHGyv9VDXFSI3UZtiUhnZsfe2poYwo8fHN5MohKCwsJBDhw7R3NxMZmZm\n7w86AyHHGOwd72EKVBK2j9J07MyexK/JW0px8jxNx9YNX/qMn06nG1LUd15DvuaGjGyUe3+IsGiz\nhGio2dPg5f41lTz8bi113SEuK0ni8WUjuX16lp70nURVJUdKA2xY2017a5TcQhMLrkwgr/Dse/MN\nBieSvvb2dqZOncrcuXMH7/OREmfra9g7NxExZ9GedzuqYqe8fQOry75Hg2cf2c5JXDnqYUakzB+8\nz2OYEIqCWHIDqCry9Rf69FhzCpwAbKnRbtbPbFFITjXQ3hIlHFLjHq+4uBiAioqKuMf6uGDPrLWl\nD6p7plgLMRucNHoPaD5bqRu+9LMDnU43ZMhdW5D/fBISklC+9WNE4rm3r+FIi5+n9zSzt8EHwLzC\nBG6anE5+4uBZfjhYtDZH2LfDR3enisUqmDTDTnaeaaDDipvf72flypW0tbUxefJkLrzwwsGbLEmJ\no3U19o73iJgyac+9HU80wI6ax6n37MWoWJmZezsjki8evM9hGBLT5yKz85Gb1yGvcvVZb8mZeU4U\nAVuqPXx+vHaVLTNzTHS0xdo6nM5S7c9SWFiIEILKykrNl0qHrcVIYcLsOwxoW3FaCIVMxzhqurbj\nCTWRYOnb/qC64UFP/HQ63ZAgyw6i/ulXYDKh3PsjRGbuQIfUr6o6gzxzUi++KTkObpmcwai0c3PG\n87MEAyqle/zUVMT21RSOMDN+ihWTeegvcgkEAqxYsYKWlhYmTpzI/PmDeIZMShxta3F0bCBiyqAt\n99840rmJfU3PE1GDZDnOZ2bu7TjMfd/fUPdRQjEgrnIh//Jr5OsvIm65u0+Ok2Q1Mj7Dxv4mP23+\nCKk2bU47M3OMHDkQ2+cXb+JntVrJzs6moaEBv9+PzaZhuxvFRMg2EovvMEq4A1XjIixZjvHUdG2n\n0XtAT/x0p0VP/HQ63aAn62tQH30IohGUb/w/xIjRAx1Sv2n0hHh2bwvrK7o+6MV365QMzs/q/5YB\ng52qxlo0HNrnJxKGxGQDE6fbSE0fHh91wWCQlStX0tLSwoQJE1iwYMEgTvpUHK1repK+NI6nXcm2\n6l/T5i/HbHAyK+8rFCfNG7zxnwPEzIuQLz+H3Pgm8qrrEakZfXKcOQUJ7G/ys7W6m0VjtOnFmJx6\noq1DWJO2DsXFxdTX11NVVcXYsWN7f8AZCNnHYPEdxuw7QiBJ2xnFDwu87GdU6iWajq0bnob+5U+d\nTjesya4O1N/9GLzdiFvuOWWp/eGoIxDhyfcbufvlct4+3kVBkoUfXJzHzy8v1JO+U2hrifDuGx72\n7/QjEJw/zcb8hc5hk/SdmOlrampi/PjxXHLJJYM3aVLDJDY+h6NjAwFjCu+YM1lT+Qva/OUUJc1l\n0aifMyJ5EC9PPUcIgwFx1fUQjSBXv9hnx5mdnwDA1hqPZmMKIcjIMhLwS7o749/nV1RUBPTNPr+T\n2zpozWnOxmZMpcl7ECnj/znohr/h8Ymo0+mGJRkKov7hp9DahFh6I8qFCwc6pD7nD6u8dKiNFaVt\nBCIq2U4TX5yUzvziRBT9RPkTfF6Vw/v81FTGlnUWFJsZN9mKxTp8rmueSPqam5sHfdInIt0k1z+N\nKVjNMUMSG0INeLz7cJjSmZ57GznOSQMdou4kYvYC5Cv/Qr67FrnoekSKdvvwTsh0mhiZYmFfoxdv\nKIrDbNBm3BwTtVVhmurDJCbHN2ZGRgYOh4PKykpNZhBPFjWnEzGlYfaVgYyC0Ob5QywBznKOp6Lj\nPToCVaTYijUbWzc8DZ9PRp1ON6xIVUX+3yNQfhgx62LE0i8OdEh9KqJKXj/SztdXHePZvS1YjII7\nZ2bx6JKRLBiRpCd9HxMOxfbxvf1aFzWVsRO/eZc4mTLbPqySPr/fz/Llyz9I+i699FIUZXA+P0Ow\nkdSaxwgFKnhVqrzmO4A33MrYtEVcOepnetI3CAmDAbH4eohEkGuW99lx5hQkEFFhe612s34ftHWo\nj79HnhCCoqIiAoEAjY2NcY/3cSH7GBQZxBSo1HxsvZ+f7kwMzk8PnU53zpMv/RO5/V0YNQ7xlXsH\n7QxHvKSUbKzq4t5XynlieyOBiMqNE9N4YtlIFo9JwWQYns/7bEWjkvLDAd56tZtjh4KYrYIps+3M\nv9xJasbwWsTi9/s/KORy/vnnc+mllw7avwOz7whJNY+xP1jD0+EWykN1pNlGsbDkQaZk34RR0YsQ\nDVZizucgLRO5YQ2yq71PjjG3MLbcc1OVdm0dLNZYW4e2liihQd7W4YPlnl7tl3t+mPiVaj62bvgZ\nXp+SOp1uWFA3vvVhr767f4AwadP4d7A52urnT+83cbjFj0HAotHJ3DAxnRSNKt8NJ1JKyg53snl9\nNz6vitEE4yZZGTHagsE4OJOhePh8PlasWEFraysTJ04cvIVcpMTatQ1/4wusibTRJEOYFDszsr/E\nyJQFCKFfXx7shNGIuPLzyGeeQL65CvH5L2t+jIIkC/mJZnbVe/GHVWwmbV4XWXmxtg5N9RHyi+L7\nnCgoKEBRFCorK5kzZ44m8Z0Qso1ECiNm3xG8XKnp2DZTComWXJq9h4iqEQyK/vmh+3T6q0On0w0q\n8vA+5NN/ALsT5Zs/QiQkDnRImmv1hXl6dzNvH+8C4IKCBG6dkkFu4vBMcOMhpaShNsyRA0G6OjoR\nCowYY2H0eAsWy/BMKk5O+iZNmsTFFw/OHnci6sfc+AI7OzeyT/UCUJQ0jynZX8RqTBrg6HRnQsy7\nLFbh853XkVdeh7A7ND/G3MIE3Ptb2VHn4cIibd7Xs3NNHN4XoLEuHHfiZ7FYyMnJoba2Fp/Ph92u\nYREtxUzYWozZX4YS6UI1avu5lumYQFnwDdr8x8hwaFuVVDe86ImfTqcbNGRDDepjPwNAufv7iOz8\nAY5IW6GoyksH23jhQCuBiGREioWvTs/Sq3SewocJX4CujtgyrlFjEykcJXE4tSuOMNh4PJ6PNGcf\nrH36jL5y6mufZHOoHj8qieYspuXcRpZzwkCHpjsLwmRGLLwa+eLfkO+8Ftv3p7F5PYnfpqpuzRK/\nhCQFm13QVB9GVSWKEn9bh9raWiorKxk3bpwmMZ4QdIzF7C/D7DtKIHG6pmNnOcZT1vYGjd79euKn\n+0zD83KpTqcbcmR3F+ojD4LPg7j1HsTYiQMdkmaklGyq6uKel4/zjz0tWAwK98zO5n+vLNaTvo+R\nUlJXHWLDmm7e3+ijq1Mlr9DEgkUJXLo4f1gnfR0dHbzwwgu0tbUxZcqUQZn0STVCsOFfrKv4KetC\ntYSFwqTM67m85GE96RvixMWLwO5AvrkKGQxqPn5RsoWcBBM76jwEI9q0HhBCkJVrIhKGtuZI3OOd\n2OdXWal9EZYP2zoc1nzsTMc4BAoNnv2aj60bXvQZP51ON+BkJIz6+P9AcwNisQtl7qUDHZJmGj0h\nHt/WyK56L0YFrhmXiuv8NM1Kmg8Xqiqprw5z9GAg1pdLQF6RidHjrSQkDv+fVWtrKytWrMDn8zF7\n9mxmzZo16JK+aKCatzd+j72BWiRQYB/P5Lyv4TCnD3RoOg0Imx2x4Crka27kxjcQlyzRdnwhmFuQ\nwIulbeys93JBQYIm42blmagoC9FYFyE9yxTXWKmpqTidTiorK1FVVdMKulFTBlFjcp+0dTAbHKTa\nSmjzlxGKejEbtF+qqxse9MRPp9MNKCkl8p9/hKOliOnzEFffNNAhaSKiSlYdbOPZfS2EopIp2Xbu\nmJlNnr6P7yMiYUlVeZDyI0H8PvlBwjdmvBXnOZDwAdTX17Nq1SqCwSDz589nypQpAx3SR0g1TE39\n39nVsSG2rFOxMyX3q+QkzRzo0HQaE5ctRb65ErlmBXL+lQijtqeJcwsTebG0jc1V3ZolfmkZRgxG\naKgLM36KNa4LJkIIiouL2b9/P42NjeTk5GgSY8/ghOxjsHVtwxioIWIr0m5sIMc5kVb/URq9pRQk\n6n+bulPTEz+dTjeg5LpXkO+uhcISxG3fQgzSHmVn4nCLn8e2NlDRESTJYuAbszOZX5w46GZwBlLA\nr3L8aJDKshDhsEQxQPEoMyPHWob1cs6Pq6qq4tVXXyUSibBw4ULN9xXFq7N9AzsanqFZ9WFEMC97\nITkpX8SgxDezohucREIS4sLLY+/L29YjNF59UZJqIdNhYluNh3BUxWSI//3eYBBkZpuorwnj6Vbj\nXiFQVFTE/v37qaio0DbxA4I9iZ/Fd0TzxC/LOZH9zctp8OzVEz/dp9ITP51ON2DkgV3If/0FEpNR\n7nkAYbEMdEhx8YWjPL27mdePdCCBy0qS+MrUTBIs504i05uujijHjwSpqQyhqmC2CMaeb6VolHnY\nVun8NMeOHeP1118HYPHixZSUlAxwRB8K+CsprX2Co8EaAEos+UzIu5uSktnU1dUNcHS6viQuvxa5\n/nXk6uXIOZ/T9GKcEIK5hQmsPNjG7nofM/OdmoyblRtL/Bprw3EnfifaOlRUVHDBBRdoEt8JYXsJ\nEgWz7zDetIWajp1qG4nZ4KDBsw8ppX6hUXdKeuKn0+kGhGyoRX3yF2BQUO5+AJGaMdAhxWVvg5ff\nba6nxRchP9HM3bOymaAXbgFi+/ca68IcPxqitSlWgMHhVBg51kJBsXlY9uHrzYEDB1i3bh1Go5El\nS5ZQUFAw0CEBEI50c6z2SQ549hBBkqbYmZZ9M6kp8wc6NF0/EWkZiNkLkJvegt1bYZq2yc+JxG9T\ndbdmiV9mjhEENNaFGTXOGtdYZrOZvLw8qqur8Xq9OBza7ZeTipWwbQRm/zHN2zooQiHLMYHqrm10\nh+pJtORqNrZu+NATP51O1++kz4P66EPg88aWd5acN9AhnbVQVOUfu5t56VA7ioAbJqZx/YQ0TZYw\nDXWhoEpVeYiKsp79e0B6lpERoy1k5RgRcZZeH4qklGzatIkdO3ZgtVpZtmwZ2dnZAx0WqhqkuuEZ\n9rS/i58IdgzMSllAXvaXUPSG0OccceUXkJvXob72PMrUOZrOHo1Os5JmM7K1pptwNBuTIf6xLVaF\nlDQDba1RgkE17tUDRUVFVFdXU1lZyfjx4+OO72RBx3mY/ccwew8RSJql6djZzolUd22jwbNPT/x0\np6S/m+t0un4lo1HUJ38JjbWIy69FmXvJQId01o63B/jNxnoqO4PkJpj5zrwcRqfZBjqsAdfZHqHi\naIiaqhBqFAwGKCoxM2K0hYSkc3fZazgcZs2aNZSXl5OcnMyyZctITk4e0JhkNERL8wvsaltHuwxi\nQjDNMYGReXdiMKUMaGy6gSNy8mHqHNi5GQ7ugfHaFRxShOCCwgReOdzOvkYv03K1mfXLzjXR3hKl\nqS5CwYj4imgVFxfz3nvvUVFRoXniF3KMg5ZXsfRR4gfQ4NnHmLQrNB1bNzzoiZ9Op+tX8oX/gwO7\nYOIMxBduHehwzkpUlbx0qI1n9rQQUSWLRifzlWmZWI3n7ixfNBprx1BRFqS9NQqA3aFQPNpM4Qgz\nJvO5+7MB6O7u5pVXXqG5uZn8/HwWL16M1RrfkrR4yGiQ1qbn2du+nmYZQABjrSMZl/dVLNbBsexU\nN7CURdeh7tyM+voLGDRM/CC23POVw+1sru7WLPHLyjVxcG+Axrpw3IlfSkoKiYmJVFVVEY1GMRi0\nu2AVNaURMWdi9h8FNQSKdpWe7aY0Ei25NHkPElXDehEm3SfoiZ9Op+s36sY3kW++BDkFKF+9D6EM\nvdmfZm+Y32yq40CTnxSrgXvn5DA9T5sTl6HI51WpPBakqjxEKBhbzpmZY6R4lIXM7HNzOefHNTU1\n8fLLL+P1epkwYQILFizQ9ETyTCjBZppbXmJP5xaaZaxJ9whLEefl3EqiY8yAxKQbnETx6NhMX+lu\nZPlhxMixmo19XrqNZKuBLdUevj5TYtDgfcKZqGB3KDQ3hFGjEiWOJaQn2jrs3buXhoYG8vLy4o7v\nZEH7OBwd6zH7j8VmADWU7ZjIkeAaWnxHyHJO0HRs3dCnJ346na5fyGOHkP94DOxOlG/8AGEfeg1m\n9zR4+dV7dXQFo8wpcHLPrGwSrefe26iUkpamCMePBmmsi4AEk1kwcqyF4lHmc6odQ2/KyspYu3Yt\nkUiEiy66iClTpvR/tT0ZwdS9j4bW1ez1H6VJhgEYYS2OJXz20f0bj27IUBZdh1q6Ozbrd88PNBvX\noAguKEjg9aMdHGjyMSk7/s8DIQRZeSaOHwnS2hwhIzu+2a6ioiL27t1LRUWF9omfI5b4WbwHtU/8\nnJM40raGBs9ePfHTfcK5d8ai0+n6nexoQ338YYiqKHfej8gcWpvOVSn565YKnnivGkXAnTOzWDQ6\n+Zwrlx0JS2oqQxw/GsTTpQKQlGKgeJSZvMJzszrnp4lGo2zZsoUdO3ZgMplYsmQJI0eO7L8ApMQY\nqofOLRxrf5f9kXY8xJbgFtvHcl72l0iyFfdfPLqhaexEGDkWdm9F1lYh8go1G3puYSzx21TVrUni\nB5Cda+T4kSCNdeG4E7/8/HwMBgPHjx9n3rx5msR3QsRagKo4MHsPgVRBaLcUPsMxFkWYaPDuZ7Jm\no+qGCz3x0+l0fUqGw6hPPAydbYjrbkOMnzrQIZ0RTyjK7zbXs63GQ5rdyH9elMfY9HOrgIu3O8rx\nshDVx4NEwrFzlLxCEyNGW0hOM5xzCXBv2tvbWbNmDU1NTSQlJbF48WIyMvqhXYmMYvIfx+ItxdO9\ni13Beg6rXqKAURgZk3ghJRnLSLRo25RaN3wJIVAWfQH1D/+DXPMi4t++rdnYEzLtJFoMbKnu5o6Z\nWSgavI+kZhgxmqChNsyEqfH1sjOZTBQUFFBRUUFHR4e2hZiEQtBxHrbuHRiDtUQ03FdrVCxk2MfQ\n6D2AP9yBzTSwBaR0g4ue+Ol0uj4ln3sSjh1CzJqPuPyagQ7njFS0B3j43Vrqu8PMKEzhmzPTSDpH\nlnZKKWltjlJ+OBBbzglYrIKRYywUlZix2vq5WIuMIrsrsHiOoEQ6USJdGCKdKJFODJFOhAwhUQAB\nQsS+IpDCgGpwoBqcqAYn0uBENcb+HTUmETWlIhV7z2PiDFFKSktL2bBhA+FwmPHjxzN//nzMZu2K\nN3ycEunE5K/E4jtI2LOfo+F2jqg+mnuWczqNyYxKW8SIlAWYDXpfSd1ZmDQLcgqQ2zYgr74ZkZap\nybAGRTA738kbxzo51OxnfGb8r09FEWRmm6irDtPdqZKYHN+y85KSEioqKigvL2fatGlxx3eyUE/i\nZ/Ee1DTxg1h1z0bvARq8+xmRfKGmY+uGtnPjDEan0w0Idf1q5IY1UDACceu9Q2pmaP3xTh7d2kAo\nKvnC+FT+Y9EkmhoaBjqsPidVSX1tmGOHgnS0xZYGJqcaGDHGQm6+Ka6CCWcYCMZQIyb/Mcy+Y5j8\nx4nKIEkfvxsC1ZCAqtgBGbtFyg//rQYwhdsQqJ96KFWxEDWmEjWloppSiZpSiBrTiPb8G9H7yWMg\nEGDdunWUlZVhNpu58sorGTNG42IpUsUQasQcqMDkr8QUqCQSbuOY6udw1EdNT7EWgUKOczIlKZeQ\nkzAFRcNlZLpzj1AUxKLrkE/9BrlmBeKmOzUbe25hAm8c62RTVbcmiR/EqnvWVYdprAvHnfiNGDEC\noG8SP/toJAbM3kN40y7XdOxs5yT2ND5Hg2evnvjpPkJP/HQ6XZ+QZaXI/8/efYfHVV6JH//eO72r\nWb1L7hXb2LhgYxswNhDSEFmSbNom2SS7S7KbZBPSSdvNpvyyybJJWDakEkTYUEyJK7j3XmVLltX7\nSJre7vv7Q7aDDQRZc6UZ4ffzPDzmsXTPHI/sO3Pmfd9zHv8FOF2on3wQxWJJdUrDIoTg90d7qD3e\ni92k8tklhSwscWFU39pvnuNxQfP5KA1nIgQDQ0VSfpGJqikWsnLG6KVCJLD4T2DxH8McakDVgn/J\nz5SNIfsmBuNONKOHhMGNZvSgGV1vXpgJDUULocb9qAk/asKHmvBjiPVjiPdhiPVhjPVgira/9lJU\nNGMGCVMWcVP2xf/3DOVgzEAzumlp7WD9+vX4/X4KCgpYvXo1brd75M+DFsMQ68UY68EQ7cYQ68YY\n7cEQ7ULRwvSIGGe0MBdEjHYtjGCom2q2bSJlGYspdS/EYnSN/PEl6SrKjTcjnvkdYvsGxF33obj1\n2T44K9+B06yyo8nHh+bm6tLdM7fAiKJAZ1uMidOSG5lit9spKCigvb2dYDCI3a7fqrlQLUTtVViC\ndaixfjQdt2R6LMVYjRl0+o8jhIYiP/yRLpKFnyRJuhPe3qFmLkJD/djnUXLyUp3SsMQSGj/d3cHL\njYPkO018ZUUxxe7xUbCOVCwqOH82QkNdhFhUoKpDw9YrJ1twusamO6cS92Mb3IdtYDeGxCAACaOH\nkGMeMVslUXsVmtFDYWEhoba2ETyAijA4SBgcJHiDv4tCoCQCFwvBXgyxK381h85hDp173UttEZXC\nOSommweHJ4gIvYSI2BCqFaFaEAxtPL34QJd/VRJhVC2IkgiiJoKv+n8/ysXvE0IwSIJGLUYzgqZE\ngNDFlT1QyLJVUOSaS6lnEU6zPlvwJOlqitGIcvvbEY//ArFpHco73qdLXOPF7p4b6gc42R1kZl7y\nTV7MFpWsHAO93QkiYQ2LNbmip6qqivb29lEa5j4FS7AOS+AUoYxFusVVFIV85wwa+7fjDTeRJRs5\nSRfJwk+SJF2JWBTtv78Lg/0o930EZer46CvmjyT47tYWjneFmJxj5UvLi9/S5/nisaGCr/7MUMFn\nMitMnGahYqIl6TdKw2UMt2Ab2InVdxSFBJpiIehZRMizkIQpV5dzd8OmKAijk7jRSdz62s6FihZB\njfVhiA+ghXvobj5DaKANtyVOlhNy3AoG+lCCvSNOQVNtRFUr3cYs2kjQmfDTGesmrAUuf4/F4KbM\nOZ8C5yzynTPlyp40ZpQltyHWPYHY8jzijnei2PRZ/VpW7mZD/QDbGn26FH4wtN2ztztBZ1uM0srk\nPryrrKxk+/bt1NfX6174RexTcfEs5qC+hR8Mbfds7N9Oh/+oLPyky96672okSRpzQgjE734G5+tQ\nblqBsuptqU5pWDp8UR56uYXWwSiLSlx8ZnEBFuNbc2tMPC5oPBvh3Om/FHxTZ1kpr7ZgNI1NoWWM\ntOHsfg5zuHEoJ1MOIc8iwu65CDW5rVmjRagWosZcjp/uYs+ek4TDYZzOIhYvXoxj8mSiinJxS2l0\naFupFkJJhFEur87Bq9f9AOKKEW98gJ5oF32RFvrCFxgINCAujl0AsBkzKXFOJ8c+iRz7ZDKtpXLb\nlpQSisWCsupuxNO/RWx9CWX1O3WJOz3XTqbNyI6mQT46Pw+TDueI84pMnDwSpqM1+cIvIyODrKws\nmpqaiMVimEzJjYl4Nc2UQcxcgDnYgKJFEKp+O0zyHTMAhQ7/MaZNGB+vgGI5IQAAIABJREFUxdLo\nk4WfJEm6EVv/jNixEcqqUd7/yXHRzOVMT4hvv9zCQCTB26dm8YEbJujSVjzdxOOCC/URzp2KEI0I\nTCaFyTOtVEy0YNKx4EskEni9XiKRCIlEAk3TLv9KIkS5eogJ6hkUBGHbRMIZS4naq3WdY6W3eDzO\nuXPn2LdvH16vF5PJxKJFi7jhhhswGl/1MqqoCIMVYbCikXlFDCEE/mgXvaFz9IXq6Q3V0x9uQhPx\ny99jUExk2SrItFWQY59Ijm0idlP2uPh3JF0flBVrES89hdjwDGLlXSim5DvWGlSFpWUunjvt5XB7\ngBuLnUnHdLoMuNwq3R1xYjGR9D2usrKS/fv309TURFVVVdL5vVrUMRVTtB1z8CwR5wzd4lqMLjKt\n5fQEzxJLhDAZrq8xRNLrk4WfJEm6EPWn/9LM5RNfQDGn/9m4PS0+vr+9jbgm+PiNeaydlPnmF40z\nQghaL8Q4dTREOCQwmmDSdAuVkyyYzMkVW0IIfD4fHR0ddHZ20tHRQXd3N/F4/Orv5IaiEKun+HAa\nNbr9Btad9FDf48dmewWn8wAulwuPx0NOTg45OTlkZWVhMIzNGcPXI4Sgs7OTU6dOcebMGaLRKIqi\nMHPmTBYuXPimTR6EEAxEWuj0H6czcJLe0DmiCf/lrysYyLCWkmWruFzseSxFqIp8WZbSl2J3oixf\ng/jz/yF2bkZZfocucZeXu3nutJetjYO6FH4ABSVm6k6E6WqLUVSWXIFaVVXF/v37aWho0L3wizim\n4vBuxhw4pWvhB0NjHbzh83QFTlHk1rcrqTQ+yVcYSZKSJga9Q0PaNQ31o5/Tbc7TaNraOMiPdrZh\nUhW+tLyY+UX6vNlIJ/29cY4fCuHtTaCqUD3VQtVkC2ZLcgVfd3c3R44cobGxkWDwL503FUUhOzub\nvLw8HA4HqqqSYfIx3XaYTOMACWGgPjabU+ESLLkhSmw+fD4fXq+X7u7uKx5DVVWysrIuF4I5OTm4\nXKN/ni0YDHL69GlOnjxJX18fAA6Hg1mzZjFt2rS/OsQ5FOun3X+EzsAJugInCccHLn/NYZpAvmMG\nWfYqsm1VZFjLMKqjN99PkkaLcuvbEJueGyr+lt6GosMHNNVZVvKdJva0+AjHNaw6bLUvLDFRdyJM\nW3PyhV9ubi4Oh4Pz58+jaRqqjl2e45ZCEgY3lsAZfELTdfdDgWs2p3qepc1/SBZ+EiALP0mSkiTi\ncbSffw/6+1De9QGUaXNSndKbWn+un4f3dGAzqXz1lmKm6jQ/Kl2EQxqnjoZoaRwa4l1QbGLabCt2\n58jfoAkhOH/+PIcPH6alpQUYKoiqqqrIz88nPz+f3Nzcv5x/ERp271YcfZtR0Ag7puPPuQuXKYMF\nrxM7HA7j9Xrp6emhp6eH7u5uent76enpufx9Tz/9NDabjZycHLKzsy//6nK5sNls17wlMhqN0tPT\nQ1dXF11dXXR3d9PX14cQAlVVqa6uZtq0aZSWlr7hG71I3EfL4D6aBnbTFTzNpa6dVmMGZZ4l5Dmn\nk+eYjt2UdU25SVK6UjKyUBavQmx9CXFgB8qCZcnHVBSWlbupPd7L3hY/y8qTGIdykctjwOlW6eqI\nEY+JpM4wK4pCZWUlx44do62tjeLi4qTz+0twdWiY++BeTOEmYjo2Ysm2VWM2OGnzHUYIIbeNS7Lw\nkyQpOeKpX0HdCZi7WLfD/qPp2dN9PHqgC5fFwDdWllCVlZ7NREYikRA0nIlw9lSYRBzcGQam32Aj\nJ3fkt/poNMqpU6c4fPgwAwNDK1glJSXMmTOH8vLy130joSRCuDufxBI8RcLowTfhHUQdk9/wMRRF\nwWazYbPZKCwsvPz7mqYxMDBwuRgMBAK0tLTQ3NxMc3PzFTFUVcVut+NwOHA6nTgcDiwWC4lEgng8\nTiKRuPxfPB6nv78fr9d7RQyTyURBQQETJ05k0qRJ2GyvfyYmrkVoGdxP08AuOvzHLzdjybFPosS9\ngDzHDNyWQvkmS3rLUla/A7FtPeLFpxA33qzL3/VLhd/WxkFdCj+4tOoXobM9RlFpcqt+lwq/hoYG\nfQs/IHKx8DMHTula+KmKSoFzNhcGduANN5Jlq9AttjQ+ycJPkqQR0/a8gtj4DBSUoH7on9L6ja4Q\ngidP9PK7Iz1k2ow8tLKE0oz0P4c4XL3dcY7sDRLwa5gtCtPnWCmtMKOMcCCyEIIjR46we/duotEo\nBoOBadOmMWfOHHJyct7wOmOkDU/77zDE+4jaqhnIvw9hGNk2WlVVyczMJDMzk4kTJ1JYWEhbWxvR\naPTyaqDX68Xv9+P3+wkEAnR3d9PZ2fmmsc1mM8XFxUyYMIHc3Fxyc3PxeDx/dQtXOD7A2d4NnPNu\nunxeL9NaTqnnJkrcC3GY3/h5kaS3EiW3AGX+EsS+bXD8IMycl3TMEo+FikwLh9r9+CIJXJbkt5AW\nFJupOxEZ2u6ZZOFXXFyM2WymoaGBm2++OencXi1qq0YoJiyB0wRy1ugau8h1AxcGdtDmOyQLP0kW\nfpIkjYxoOY/49U/AakP9xBdRrOm7XVIIwa8Pd/N/J/vIdRh5aFUpBa63xvmqeExw6miIxnNRACom\nmpk8w4bJPPIifGBggA0bNtDW1obVamXhwoXMnDnzTRuaWAcP4Op+GkXECWSuIJB166h06zSbzRQU\nFFBQUPCarwkhCIVCBAIBYrEYBoPhdf+zWCzD/qBiINzCmd4XuTCwE03EMRucTM15GxUZS3FZXpuD\nJF0PlDXvRuzbhvbikxh0KPwAlpW5+dXhbnY1+7i9+o3P0w6Xy6PidKl0tSe/3dNgMFBeXk5dXR09\nPT0UFRUlnd9lqomovRpL4BSGaA8JHT9EynfOQlUMtPkOMSM3/XflSKNLFn6SJF0zEfCjPfxdiEaH\nir4Cfbe96EkIwSP7O3m+rp9Cl5mHVpUwwaHfHKZU6uqIcXRfkFBQ4HSrzL7RTlbOyG/rQgiOHTvG\njh07iMViVFVVsWLFijct+NBiuHrWYRvci6ZaGci/n6hj6ojzSIaiKNjt9jfPeRi8oQsc63qSdv8R\nAJzmPCZl30FFxlKMaTpvUJLGilJSATPnw7H9iLMnUSYmP9z85vKhwu+VxkFdCj9FUSgoMXH2ZISu\n9hiFOmz3rKuro6GhgdmzZyed36tFHNOwBE5hCZwgaF6uW1yTwcYE+xQ6AycIxvrkeePrnCz8JEm6\nJkLT0B79IXR3oKy9F2XuolSn9IaEEDxyoIvn6/opy7Dw0MoSMmzj/7YXjWqcPBym+XwURYGJ0yxM\nnGbFkMTgY5/Px8aNG2lubsZisbB69WomTZr0pqtiSiKEp/1XmMMXiJkLGCx4LwlT9ojzSAehWD/H\nup7kfP82QJBjn8Tk7DUUuuaipvG8QUkaa+rae9GO7Ud7oRbDA19POt4Eh4lpE2yc6AzSG4yRbU/+\nQ7rCEjNnTw5t90y28CsrK0NVVerr65PO62oRxzQEf8LiP0owU7/CD6DQdQOdgRO0+Q5TnbVS19jS\n+DL+3wFJkjSmxLon4Nh+mHYDyj33pzqdNySE4LFD3Tx/xkuZx8K3VpXgto7/W15ne4wje4NEwgJ3\nhoE5C2x4MpNb5Tt58iRbt24lFotRXl7OypUrcTrf/FyeGh8ko+2XGKMdhJ2zGMx9N6jjdzU1rkU5\n0/sCp3vWEdcieCzFzMm/n3znzFSnJklpSameCpNnwvGDiMazKOUTk465rNzNye4Q2y/4uGdq8qtT\nLo+Kw6XS2R4jHhcYjSP/gMxisVBcXExTU9NrmkMlSxjsQ9s9g3WosT40HVfmCl03cKjjt7T5DsnC\n7zonP7qUJGnYxLH9iHV/gOxc1I/+C4qaugHbb+b3R3t4+lQfxe6h7Z3jvejTEoITh0Ps3RogFhVM\nmWnl5tucSRV9mqaxdetWNm3ahKIo3Hrrrdx9993DK/pifWS2/BxjtIOgZxGDefeN26JPCEHTwC5e\nPPd5jnc9hUGxML/gQ9xe9S1Z9EnSm1DvrAFAe/5JXeItLnWhKkOzVvWgKAqFJSa0BHS1x5KOd2mA\n+8mTJ5OOdbXIxfuN1X9M17hOcy5uSxFdgRPEtYiusaXxRRZ+kiQNi+juQPufH4DBiPqJL6A49Wm3\nPRpqj/VQe7yXfKeJh1aN/+2dAX+CHZv9NJyJ4HCpLL3VycRpVtQRduwEiMfjvPTSSxw5coSsrCzu\nv/9+pk2bNqyGJ4ZIB5ktP8MQ7yOQuRJ/zt2j0sRlLITjA+xo/n/sanmYcHyQqTl3cefE71OVtRJV\nSd8PNiQpbUyZBVVT4PBuREtj0uE8ViNz8h2c6wvTNhhNPj+GunsCtDcnX/hVVAx1xjxx4kTSsa42\ntN1TxaJz4QdDq34JEaMzoH/e0vgxPl+pJUkaUyISGWrmEgygvPfvUcqqU53SG/rTyV5+d7SHXIeJ\nb91aqssZkVRqa4qydb2P/r4ExeUmlt3mSmqVDyAcDvP0009z7tw5ioqKuPfee3G7h1fIG8NNZLb+\nAkPChy/nTgLZt0Eaj/H4a5oH9vDiuS/Q6jtIrn0qa6r/jVl592EyvP78PkmSXktRlMurfuIFfVb9\nLs3x23pBn1U/d4aKw6nS2Ta03TMZTqeTvLw8GhsbCYfDuuR3yaXtnqZIK2qsT9fYha4bAGjzHdI1\nrjS+yMJPkqS/SgiB+O3D0HIeZdlq1KW3pTqlN7TuTB+PHeom227kW7eO7+6d8bjgyL4gB3YFEQLm\nLLBzw0JHUu3IYaiJyx//+Efa2tqorq7mnnvuwWIZ3jxDU/AcGa2PomgRBnPfTShjaVK5pEok7mNX\n83+xs+WnJLQoN+S/j1vKv4DTnJvq1CRpfJoxD0orEfu3Izpakg63sMSJ2aCwtXEQIZIr1OAv3T0T\nOm33rKysRNM0zp8/n3Ssq43Wds9sWzUWg4s232GE0HSNLY0fsvCTJOmvEi+/iNi9BSomobznY6lO\n5w2tP9fPI/u7yLQa+NaqUvKc43dOn38wwfYNPpoaorgzVJbd5qKkIvk/T09PD7W1tfT19TFnzhzW\nrFmD0Ti81UNzsI6MtsdQRIKB/PsJu/WZ2zXW2nyHean+izQN7ibbVs3qqm8xKXs1yjjdqipJ6eDy\nqp8QiBefSjqe3WTgxiInrYNRGrz6nEkrLBn6IFCP7Z7V1UO7Xs6ePZt0rKuN1nZPVVEpcM4mHO/H\nG27UNbY0fshXOkmS3pCoP4144n/A6Ub9+39FMaXnCtrOpkH+e28HbouBh24tpdA9fou+zvYY2zb6\n8A1qlFebWXqrC6c7+bNmLS0t/PGPfyQQCLB06VJuvvnmYQ8wN4Ua8bT/FhSF/sIPEnVOTzqfsaYJ\njWOdT7Kt6QdEEwFm5d3HyoqvyAHskqSXOTdBQQli9xZET2fS4ZZf3O65uWEg6VgA7gwDdudQd89E\nkts9MzMzKSwspKmpiVAopEt+l8jtntJokoWfJEmvSwx60X72b6BpqB/7HErWhFSn9LqOdgT4wY52\nzAaVr64optQzvG2L6UYIwbnTYfZuDaAl4IaFdmbOsyc1m++StrY2nn32WeLxOKtXr2bu3LnDLvqM\n4VY87Y/BxZW+mD19z3e+kUjcx9YL/8HJnmdxmHK5teJrTM25S87kkyQdKaqKcmcNaBripeRX/eYV\nOfFYDbxyfoBYIvmtiYqiUFhsIhGHro7kV/1mz56NpmmcO3cu6VhXG63tnvnOmaiKgTbfYV3jSuOH\nfNWTJOk1RCKB9vP/gP4+lHe+H2Xq7FSn9Loa+sJ855VWAB5cXsTE7PHZlCMRFxzaE+TUkTBWm8KS\nlU6Ky/VZtezu7ubZZ59F0zTuvPNOJk+ePOxrDdFOMtr+F0WLMph3H1HHVF1yGku9wXrWN3yFzsBx\nCp1zuL3qITJt5alOS5LekpT5SyG3ALFjI8Lbm1Qso6qwosKDL6qxp8WvS34FOm73nD176HWxrq4u\n6VhXG63tniaDjQn2qXjDjQR1Xk2UxgdZ+EmS9Briqceg7jjMXYSy+p2pTud1tfuifH1LM+G4xj8v\nLmB2viPVKY1IKKixY7Of1gsxMrMN3Hybi4xsfcZPeL1enn76aaLRKLfddtvlNuTDYYj1ktH6KKoW\nxJf7TiKuWbrkNFaEEJzr28Tmxm8RjPUxM/fdLC39DGbD+Px7IknjgWIwoKx5N8TjiPV/SjrerVUe\nADbU67Pd05NpwO5Q6WhLfrtnRkYGhYWFtLa24vfrU5heMrTds2qUt3vKVb/rkSz8JEm6grZ3K2LD\nM5BfjPqhB4a9JXAs9YXifG1zMwPhBB+/MY8lZek7U/Cv8fbE2bbBx4A3QUm5mUUrnFht+tyWfT4f\nf/rTnwiFQqxYseKaVvrU+AAZrY9eHNlwF2H3fF1yGiuaiLO//X850P4YRtXK8rLPMW3CPbKBiySN\nAeWmWyBrAmLrS4jB/qRilXgsTM6xcaQ9QHcg+VW6y909ddruOWnSJGCUVv2cQx+26b3ds9A1B5Dn\n/K5X8lVQkqTLREsj4lc/AasN9ZMPoljtqU7pNfzRBN/Y3EynP8bfzMxhzaTMVKc0Iq1NUXZu8ROJ\nCKbNsTJ7gU2X83wAwWCQp59+Gr/fz6JFi5g5c+awr1US/qGiL+7Fn3UboYwluuQ0VqKJIFsv/IAG\n78tkWMu4vfKb5DuH/+eXJCk5itGEcse7IBpFbHwm6Xi3VXkQwCadmrxc6u7Z2qRPd09FUcbVdk+n\nORe3pYiuwAnimr5zCKX0Jws/SZIAEEE/2sPfgWgE9UOfRikoTnVKrxFNaHz75RYa+yOsmZjBfTOz\nU53SiNSfCXNwVxBVhYU3O6iabNVtZTUSifDMM8/g9XqZO3cu8+dfw2qdFiWj7VcYY90EMpYRzFyh\nS05jJRDtZtP5h4bO87luYGX5l3GYc1KdliRdd5Slt4InE7H5BUTAl1SsJWUurEaFTfUDaDrM9PNk\nGnC6VTpbY0SjyTWNsdvtlJaW0tXVRX9/cqubVxvN7Z7FrnkkRIx231Fd40rpTxZ+kiQhNA3tf34I\n3R0oa96NMndRqlN6DU0IfrSznZPdIZaUuvjo/Ly03Ib61wghOHEoxMnDYSxWhcUrXeQW6DciIxaL\n8dxzz9Hd3c306dNZsmTJ8J8joeHp/AOmSAsh1zwC2XfAOHp+e4P1bDz/DQYjrUzKWs2Skk9jMlhT\nnZYkXZcUk3nofHgkhPhzcmf97CYDS0rddAViHOsMJp+bolBSbkbToE2HVb/R3e45Ot09SzwLAWge\n3KNrXCn9ycJPkiTEuifg2H6YNgfl7e9NdTqv67GDXexs8jE918ZnFhdgUMdPUQKQSAgO7g7SUBfB\n6VZZeqsLT2by8/kuEUKwfv162tramDhxIitWrLiGok/g7FmHJXCKqK0aX+47xlXR1zy4jy2N3yES\nH2Ru/t9yQ8H75KgGSUoxZfkd4MlCbHou6bN+t11s8rLxnD7bPYvLzaBA8/lo0rEqKysxGAycOXMG\nocOK5KuN1nZPj6UElzmfNt9hud3zOiNfGSXpOieO7EM89zhk56J+9LMoqn7FiF6eO93HM6e9FLvN\nPLisGJNhfN26YlHBnq0B2ppiZOYYWLLSid2h759h165d1NfXU1RUxO23346qDj++rX879oFdxM35\nDOS/F5T0+zvwRup617Oz+ScoisrS0n9mYvZtqU5JkiRAMVtQ7rwXopGk5/pNmWCjyG1mV7MPfySR\ndG5Wm0puvpH+vgS+geTiWSwWysvL8Xq99PT0JJ3bqwmDY1S2eyqKQol7IQkRpc13RLe4UvobX++e\nJEnSlehqQ3v0h2Ayo37iiyjO9OuOuavZx6MHusi0GvjqimKclvFTlMDQuIadm330dsXJLzKxaLkT\ns0XfW++pU6fYv38/Ho+HtWvXYjAM/zmy+I/h6n2BhMFNf8EHEONke6QQgmNdT3Go4zdYjW5Wln/5\ncrc6SZLSg7L0dsjKQbz8IqJ/5HP9FEXh1koPMU3wSuOgLrmVVAzNSm1uTH7V71LXZLndU0p3svCT\npOuUCIfQHv4uhAIo7/sESllVqlN6jdPdIX64ow2LUeErK0rIc+oz1Hys+H0JdmzyMTigUV5tZv5i\nOwajvlso29vb2bRpE2azmbvvvhubbfhD7E2hRtydtWiKmYHCD6CZMnTNbbRoQuNA+2Oc7H4apzmX\nVRVfJdNWluq0JEm6imIyodx5H8SiiBeeTCrWikoPqgIb6/VpopJXaMJkVmhpjKJpyW3RLC8vx2Qy\nUVdXNwrbPacjMGD1HQIdY3ssxbjMhbT7DhNLyO2e1wtZ+EnSdUgIgfbYj6H1AsqKtaiLV6U6pddo\nG4zyrVdaiGuCzy8toiprfKxEXTLYn2DHJj+hoGDKTCsz5tpQdD6XODg4yLp16xBCsHbtWrKysoZ9\nrSHajaf9NyA0BvPfS9xSqGtuoyWhxdjV8lPqvZvJsJaxsvwrOM25qU5LkqQ3oCxeBRPyEdvWI3q7\nRxwn02ZkfpGTBm+Ehr7kCxWDQaGo1EQkLOjuiCcVy2g0UlVVhc/no6OjI+ncXk0Y7EQcUzFGOzFG\nWnWLqygKpZ4FQ909/XKY+/VCFn6SdB0SL/4RDuyESdNRav4u1em8xkA4zje2NOOLJPjEgnzmFTlT\nndI18fbG2bnFTzQimDnXxsRp+o1ruCQajfLcc88RCoVYvnw5paWlw75WSQTwtD+GqgXx5b6dqGOS\nrrmNlmg8yNam79MyuI8J9imsKH8Q2zhZpZSk65ViNKLc9R6IxxHPP5FUrFsvNXnRadXv8nZPHZq8\nXOrueebMmaRjXS3sngeA1XdA17gl7ovbPQfkds/rhXEsHqSmpuZHwE2AAB6ora3d96qv3Qp8B0gA\nL9TW1n7z4u/PAJ4BflRbW/vTschTkq4H4th+xNO/hcwc1I//K4pxTG4DwxaJa3z7lRY6/DHunZ7N\n7dXj6419T1ecvdv8JBIwZ4H98hsLPWmaxksvvURvby+zZs1i1qxZw79YxPG0/w5jrI9A5i2E3Tfq\nnt9oiMR9/PHAt+gKnKHINY9FxZ/EoI6vrb+SdL1SblqOePFJxI6NiDvehZJbMKI48wqdZFgNvNI4\nyAfn5mJOstGXJ9OAy63S2RYjGtGSOn9dUlKC1Wrl7NmzLFu27JoabL2ZqH0iCYMLq+8w/uy1oOoz\nBshjLcZtKaLdf4RYIoTJMPyjAtL4NOorfjU1NcuBibW1tYuAjwD/edW3/CfwLmAJcHtNTc20mpoa\nB/ATYNNo5ydJ1xPR0Yr2yA/AaEL91IMo7vQqqjQh+PGuds70hFle7ua9s8fX8O2u9hh7tvrRNJi3\naHSKPoCdO3fS2NhISUkJy5YtG/6FQuDqfgZz+Dxhx3QCWeOjA2Yo1s/mxm/TOXiGioxlLC75R1n0\nSdI4oqgGlLfdD5qGWPeHEccxqgorKz34oxq7m/3J56UolFQMzfRrTXKmn8FgYOLEiYRCIVpaWpLO\n7QqKgbDrBlQtjCVwStfQJe6h7Z5tcrvndWEstnquAp4GqK2tPQVk1tTUuAFqamoqgb7a2trm2tpa\nDXjh4vdHgLVA2xjkJ0nXBREKoj38naFmLu//FEpZdapTeo3fHu5mR5OPaRNs/ONN+eNqQHtbc5S9\n2wMA3LjUQWHJ6BQmp0+f5uDBg2RmZrJ27dprG9swsAPb4H5ilkIG82pgHMy6C0R72Nz4LQYjrdxQ\n+k5uLPw71HE0bkKSpCHKvCVQVIbY/QqifeSF0arKoe2eG3Ta7llUZkbRaabfaHb3lNs9JT2MxR6v\nfODVf0u7L/7e4MVfX33Stwuoqq2tjQPxmpqaa3qgwsLx0ZxgvJHPa+ol+zMQmkbvtz9HqL0Z5z1/\nQ+a70m9I+9NH23jqZB+lmTZ+fN98Mmz6bGXR0xv9HOpO9nNwVz9Go8odby+hsNgxKo/f2trK5s2b\nsVqtfPjDH2bChAnDvlbrPYzW8wKYM7DO+RyFluE3gkkVb6CFFw5+F3+0iwUV72Vx1YfG1YcBb1Xy\nNSH1xuvPIPjBf6D325/DsvFP5Pzrd0cUo7AQbjjcx6GWASIWDxXZyd9vS8qbaDrvx2LOIjtn+I3E\nrv455Ofns2HDBhoaGpgwYQImk56vY4XEvVVYfGcpyLai6HQPL6SQfZ0VdASOkp3rwWIcndev0TJe\n/y2kSioO9/y1V+2kXtHb2uQCod4KCwvl85pievwMtGcfR+x+BabMIrimhlCa/UwPtwf47pZmXBYD\nD95cQNDbTdCb6qyu9EY/h6aGCEf2hTCZFRYus4M6QFvbgO6PHwqFeOKJJ4jH46xZs4ZYLDbsvxeG\nSAeZLT9DUQx4c+8n3hsm3TdU9IebeeXCvxOODzAz914q7HegKIq8H6WYfE1IvfH8MxBlk6C0itDW\nDbSuuAuluGJEcW6vcHCoZYD/3XaGTyzITzqv3EKNpvNwcG8r0+cM75zbG/0cqqurOXDgANu3b7+8\nAqgXq3UWbl89A2dfIph1i25xC2xz6fWf50DdC5RnLNEt7mgbz/8WRtNfK4bHYp9PG0Mre5cUAu1v\n8LUi0v3diCSNM+LwbsRzj0N2LurHPo9yDcO9x0JTf4R/39aKqig8uKyIAtf4Obt1of4vRd+iW5xk\nZo/OZ2mXmrkMDg6ycOFCKiqG/2ZJifvJaP81qogwmHsvcWvJqOSop77QebY0fodwfIAb8t/PtAlv\nS3VKkiTpQFEU1LcP7TjRnnl8xHEWFrvIdRjZ3DCAL5JIOq+8Av1m+k2fPh2A48ePJ53X1SLOWQjF\niNW3X9eZfiWeBQA0D+7VLaaUnsai8FsPvBugpqZmLtBWW1vrA6itrW0E3DU1NeU1NTVG4K6L3y9J\nkg5E6wW0R38EZjPqJx9EcblTndIVvKE433y5mWBM44FFBUzLtac6pWFrPBfh6P4QZovC4hVOPJmj\nV1Dv2rWL5uZmKioqWLBgwfAvFHE8Hb/FEPfiz1pFxHUN3T9TpCcgfylBAAAgAElEQVR4lpcbv0ss\nEeDGwr9jUvbtqU5JkiQ9zZgHlZPh8G7E+ZGdhTOoCndOziSaEKw/l/xZP9WgUFxmIhoRdLUnN9Mv\nIyODkpISWltb6evrSzq3VxMGKxHHdIyxXkzhC7rFdVsK8VhK6PAfJZoI6hZXSj+jXvjV1tbuBA7U\n1NTsZKiD56dqamo+WFNT846L3/IJ4HFgG/BEbW1tXU1NzbyampqXgQ8CD9TU1LxcU1OT/gdSJCmN\nCN8A2k++CeEQygcfQCmtTHVKV7g0tqErEOe9s3JYVp5eRelfc/5shGMHhoq+Rbc4cWeMXtF39uxZ\nDhw4QEZGBrfffvvwz7gJgavraczhC4Sdswhmrhq1HPXSFTjNKxe+R1yLsLD4E1RmLk91SpIk6UxR\nFNR3fgAArfZ/ESNcubq1KgOrUeH5Oi/xJFfp4FUz/RqTb/IyY8YMYHRW/ULu+cAoNHnxLEATcdp8\nB3WNK6WXMTnjV1tb+4WrfuvIq762FVh01fcfAG4Z/cwk6a1JxGNo//1d6O1Cufs9qDfenOqUrqAJ\nwQ93tnG2N8zKSjf3zshOdUrDdr4uwvFDISzWoaLP5Rm9oq+3t5eNGzdiMpm48847sVgsw77W1r8N\nm+8AMUsxg7nvhjRvitLpP8G2ph8iSLC45B8oHifzBSVJunbK5BkwZyEc3gMHd8G8xdccw2k2sKrS\nw/N1/exq8nFzkh8eujMMuD1DM/0iEQ1LEjP9KisrsdlsnD59msWLF2PUcV5uzFZJwpiBxXcMX87d\noNNomxL3Qo53PUXTwB7KM5bqElNKP+nfy1uSpGsihED89mE4exJl3hKUu96T6pRe41eHutnd7GdG\nnp1PLigYN50aG86E/1L0rRjdoi8SibBu3TpisRi33XYb2dnDL47NgVM4e18iYXAzUPB+3Yb9jpZ2\n31G2Nf0AgcaSkgdk0SdJ1wH1XR8EgwHtqccQ8ZHN0Ltr8tBmsOfOJN8NTFEUiivMCA1aklz1MxgM\nTJs2jXA4zLlz55LO7QqKStg1F1VEsPr1W1F0WwrIsJbR4T9GOK5/gzIpPcjCT5LeYsSGpxE7NkFZ\nNcqHPo1yDXPexsKLdV6ePtVHkdvMF28uwmQYH0XfkQO9nDgcxmIdOtPnco9e0SeE4M9//jMDAwPM\nmzeP6urhz1w0RDpwd/wBFCMDBX+LZkzvLbStgwfZ3vwjAJaWfoZC1w0pzkiSpLGg5Beh3LIWujsQ\nW14YUYxCt5n5hQ7O9IQ40xNKOqeScjOqARrPRhFp3OQlNEoz/SozliNI0Ni/Xde4UvpIr3eEkiQl\nRRzdh/jjY+DJQv3Ul1CuYWvgWDjY5ucX+zvxWAx89ZZinJb06jD6RurPhNm9tROrTWHxSifOUSz6\nAPbs2UNjYyOlpaUsWrTozS+4SElc6uAZZTDvXuLWolHMMnnNg/vY0fyfKKjcXPZZCpzp33xGkiT9\nKHfdB3YHYt0fEP7BEcW4e8rQqt+608mv+pktKiXlZoIBjY62ka1CXnKpyUtbWxu9vb1J5/ZqmimL\nqLUCc6gBNaZfA5myjMWoiokG7ysjPnsppTdZ+EnSW4RovYD2yPfBaBoq+jLT69xcozfM97a1YVAU\nHlxeTP44GdtQfybMycNh7A4ji1Y4cbpGt+hraGhg7969uN1uVq9ejTrcFVsRx9P+OwxxL4HMVUSc\nM0c1z2Rd6N/JruafYlBNLC/7PHmOaalOSZKkMaY43Sh31kAwgFj3xIhizM63U+oxs6NpkJ5gcsUa\nQMWkoQ9MG85Eko41c+bQfXg0Vv3CF1f9bIP6NWMxGxwUu+fji7bTEzqrW1wpfcjCT5LeAq7o4Pmh\nB1AqJqY6pSv0BmM89HILobjGpxcXMGXC8AbkplrDxaLPYlW4+96yUS/6+vv7Wb9+PQaDgbVr12Kz\nDfN5EgJX1zOYw42EnTMJZK0c1TyTdd67ld2tP8OoWrml7F+Z4NB3yLEkSeOHsuIumJCPePkFROe1\nj3JWFIW7p2SREPBiXfKjHVxuA7kFRvp6EvT3JTfaoaKiArvdzunTp4nHk4t1tYhzBppiHtruKTTd\n4lZmDHVTPu99RbeYUvqQhZ8kjXMilt4dPMMXxzb0BuO8f84Elpal95mzSxrqIn8507fSSUbm6G6b\njUajrFu3jmg0yqpVq8jNzR32tbb+7dh8+4lZii528EzfW3t932b2tj2C2eDglvIvkG0f/vlFSZLe\nehSTCfVdH4BEAu2px0YUY3m5G5fFwJ/PeonEky+CKnVa9bvU5CUSiXD2rL4raEK1EHHNxhDvxxw4\nrVvcXMdUHKYJNA/uIZZI/tyklF7S992BJElvSmga4rEfp20Hz4Qm+MGONur7Itxa5eFd08bHOM6G\nuggnLnbvXDwG2zuFEGzcuJG+vj5mz57NlClThn2tOXASZ++LJAyuix0803cL7dneDexv/yUWg4tb\nyr9Alq0i1SlJkpQO5i6G6qlwaDfizLVvi7QYVe6ozsAX1Xj5/MjOCr5aTp4Rl0elrTlGKJhcIXlp\npt+xY8eSzutqQc8SAOw6NmNRFJWKjGXEtQhNg3t0iyulB1n4SdI4Jp7+DWLvVqiagvLh9OrgKYTg\nkf2d7G3xMzvfzicW5I+LsQ3nz15V9I1yIxeAgwcPcu7cOQoLC1m6dPjzk4zhVjyXO3h+AM3oGcUs\nk3O65wUOdvwaq9HDivIHybSWpTolSZLShKIoqPd+GADtyf9FaNdebK2ZlIFBgWdP9yXdmERRFCon\nWRACGs8lt+rndrspKyujo6ODnp6epGJdLWHJI2KfiDl8HmO4Vbe4FZk3AwoN3pd1iymlh/R5lyhJ\n0jXRXn4R8eJTkFuI+qkvo5jTq4Pnn0728eLZfsozLHxhWRFGdXwUfccP/mVO31gUfU1NTezcuROH\nw8GaNWswGIb3mGp8AE/7r0HEGci7L607eJ7sfpYjnY9jM2ayovxLeKzFqU5JkqQ0o1RORlmwDC6c\nQ+y99vNl2XYTS8vctAxGOdQeSDqfojIzZovChfoo8XhyheSlVb9RGe1wcdi6nqt+dlM2Bc6Z9IXq\nGQi36BZXSj1Z+EnSOCSO7EP8/ufg8qA+8DUUV3qdm9vaOMivDneTbTfy1RXF2E3pP7bh6qJvNOf0\nXTI4OMhLL72EoiisXbsWh8MxrOsULYKn/dcYEoP4s9cQdU4f5UxHRgjBsc4/cqzrSeymbFZWfAm3\npSDVaUmSlKaUd/4tGE2I//sNInLtK233TB06TvDEsd6kV/0MBoWyKjOxqEh6oHtFRQUOh4PTp08T\niyXfefTVoraJxM15WPxHUXUcvF6ReQsADf2yyctbiSz8JGmcEY1n0X7xPTAZUf/hyyi56fVG+nhn\nkB/vasduUvnqLcVk202pTulNNdSNfdF3qZlLOBxm+fLlFBQM8+coNNwdT2CKtBFyL7j8aW+6EUJw\npPNxTvY8g9Ocy8ryL+M056U6LUmS0piSnYty29vA24N4/trHO1RlWVlQ7OR0T0iXVb/yaguqOvQa\nkUwhqaoq06dPJxqN6t7kBUUh6FmCgoatf5duYQudN2AxuLjQv4OEpm9HUil1ZOEnSeOI6O4YGtsQ\ni6J+9LMolenVBr9pIMJ3trYAgi8sK6I805rqlN7Uqxu5jFXRJ4Rgw4YN9PT0MHPmzMvbgIbD2fMC\nluAporZqfBPeBml4blIIjYPtv+JM74u4zIWsLP8yDnNOqtOSJGkcUNbWQNYExPo/IVoar/n6v5k5\ndK/5/dGepFf9rDaVwlITAZ9GV3tyxc/06dNRFIWjR4/qPhw97JqDZnBgG9wLWnKrk5cYVCNlGUuJ\nJHy0+fSbFSilliz8JGmcEAEf2n8+BIP9KO/5KMqcm1Kd0hX6QnEe2txMIKrxDwsLmJ0/vG2LqdRw\nJnxFI5exKPoA9uzZQ319PUVFRSxbtmzYTW9sA7uxD+wgbs5lIP9+UNJvC60mNPa1Pco57yY8lhJW\nVjyIzZSZ6rQkSRonFKsN9b1/PzTe4bcPX3Ojl8osK4tKXJztDbOv1Z90PpWThj7AbKhLrsmLy+Wi\nsrKSrq4umpubk87rCqqJkHshqhbC5jugW9hLM/3kds+3Dln4SdI4IGJRtIe/Ax0tKLe/HXXlXalO\n6QrBWIJvbmmmOxjnvbNzWFGZvt0lL6k/E+bE4TBW29CcvrFo5AJQV1fH3r17cbvdrF27dtjNXMyB\nUzi7n0MzOOgv+ADCMMzh7mNIEwn2tv6c8/1bybRWsKL8i1jTuNOoJEnpSZl1I8q8JVB/GrH1z9d8\n/f2zclDQZ9XPk2kgO9dIT2ecwf5EUrFuvPFGAPbt25dUnNcT9NyEwICtf4duA9091iKybdV0+I8R\niOrbkVRKDVn4SVKaE/E42s+/B3Unhmb1veuDqU7pCrGExr9tbaXBG+H2ag/3Ts9OdUpvqv50mJMX\ni75FYzCn75Kuri42btyIyWTi7rvvxmYbXvFmDF3A0/E4KAb6C/4WzZR+8xATWoydzT/hwsBOsm3V\n3FL+r1iMrlSnJUnSOKW856NgcyD+79eI/r5rurY0w8LNZW7OeyPsbtZj1e/iQPckV/1yc3MpKyuj\ntbWVtra2pPN6NWF0EXbNwRjrxRw8o1vciszlgOB8/1bdYkqpIws/SUpjQtPo+9E34MhemDYH5SP/\nnFaz+hKa4Ec72znSEWRBsZO/vzH9Z/XVnw5z8sjYF32BQIB169YRj8dZvXo12dnDK5AN0S4y2n8F\nIsFA/v3EraWjnOm1i2sRtjf9iFbfAXId01he9q+YDem/1VeSpPSlZGQNdfkMBRB/eOSar79vVjaq\nAo8f7UFLctUvr9CIw6nSeiFKJJzcatqorvqNwmiHUvdCTKqdc32bSOh0flBKnfR5BylJ0hWEEIjH\nf0Hw5RehcjLqJ76IYkqfDpmXBrTvaPIxPdfGZ5cUYkjjWX1CCOpO/KXoWzyGRV88HmfdunX4/X4W\nL15MZWXlsK5T44NktP0SVQvhy30HUceUUc702sUSIbZe+A86AscocM5hWem/YDKkf1MfSZLSn7Js\nNVRNQRzYgTh6bYVSsdvC8nI3FwYi7LjgSy6PiwPdNQ3OnUpu1a+wsJCioiIuXLhAV1dXUrGulrDk\nE7VVYw41YIzos6JoMtiozlpJJDFIo44FpZQasvCTpDQlnv4d4uUXMJVXo/7T11Cs6XWm6w/Henjx\nbD8VmRa+tLwYizF9bydCCE4dDXPmeBi7Q2XJSieOMSr6hBBs3ryZzs5OJk+ezLx584Z1nZIIkdH2\nSwzxfvxZtxN2zx/lTK9dJO5jS+N36Q6eocS9kCUlD2BQzalOS5KktwhFVVHf/ykwGNB+9zNEJHxN\n1983MwdVGXq9SmjJrfqVVJqx2RUaz0UIBtJ51W8JADYdi7SJWatRFSOne19E0+n8oJQa6ftOTZKu\nY9qf/4R4oRZyC5jwzZ+iOJypTukKz5/x8odjveQ7TXxtRQkOc/p1l7xECMHxgyHqT0dwuFQWr3Ri\nd45dvnv37uX06dPk5eWxatWq4W2F1WJ42n+DMdpB0HMTwYuDdNNJKNbPlsbv4A2fpyJjGTcVfxKD\nakx1WpIkvcUoRWUot78D+roRz/7+mq4tcJlZWemhZTDKtguDSeVhMChMnmFD06DuxLUVoFcrKSkh\nLy+P+vp6ent7k4p1tah9EnHTBKy+o6jx5P7Ml9hMGZR5luCPdtCmY9dQaezJwk+S0oy2bT3ij7+E\njGzUzzyEISu95p9tbRzkkf2dZFgNfH1lCZm29H2zLzTBkX0hGs9FcXmGVvps9rG77R0/fpw9e/bg\ndru56667MBqH8VwJDXdnLebwecKOGfhz7k67WX3+aBebG7/FQKSFiVm3c2PhR1AV+XIiSdLoUO66\nDybkIzY+i2iqv6Zr75uRg1HVZ9WvuMyEy6PS3BjF2zvyLZ+KojB//tAujv379yeV02uDqwQzlqKQ\nwO7VbwzDlJy1AJzqeV73OYTS2JGv1JKURrR92xG/+S9wulH/+SGUnLxUp3SFQ+0BfryrDZtJ5Wsr\nSihwpe+2Pk0THNoTpPl8FE+mgcUrnFisY3fLa2hoYMuWLVitVu655x4cjmE0OxECV/czWAPHiVor\nGMyrgTQrqPrDzWw6/0380U6m5dzDDfnvQ0mzHCVJemtRzBbU930SNA3t1/+F0IY/ViHXaeK2qgza\nfTG2nB9ILg9VYcpMGwjYuyO583mVlZVkZ2dTV1fHwEByeV0t7J5LwpiJbWAPasyrS0y3pZAi11z6\nQvX0BOt0iSmNPflqLUlpQhzajXj0h2Cxon766ygFJalO6QonuoJ895UWFBS+tLyYyqz0beCRSAgO\n7AzS2hQjM8fAolucmC1jd7trb2/npZdewmAw8La3vY3MzGEMMBcCZ89z2Ab3ErMUMlDwflDTp5kP\nQE/wLFsav0043s+c/PcyM+/dad/FVZKktwZl2hyUm26BC+cQLz51Tde+e0Y2JlXhiWM9ROLJnVHL\nKzSSmWOgsd5HX098xHEUReHGG29ECMGBAzpvn1SM+LNvQyGBo2+DbmEn59wJwOmedbrFlMaWLPwk\nKQ2IAzvRfv7vYDSi/uNXUcqqU53SFU53h3hoSwtxTfD5mwuZkWdPdUpvKB4T7N0WoKM1Rk6ukZuW\nOzGZx6448Xq9PPfccyQSCdasWUN+fv6bXyQEzt4XsQ/sIm7Op7/ww2k3oL3df5SXG/+NWCLEgqKP\nMzn7jlSnJEnSdUa57+8gIxvx7O8R9aeHfV2O3cSdkzPpCsR56mRyZ+oURWHqrKH786mjoaS2PVZX\nV5ORkcHJkyfx+5OfN/hqEedsYuYCrL7DGCLtusScYJ9Etm0ibf7DDIRbdIkpjS1Z+ElSimn7tqP9\n4ntgNKM+8HWUSdNTndIVzvaG+MaWZqIJjc8tLWJBcfoO5Y6ENXZu8dPTGSev0MiCmx0YjWNX9AUC\nAZ555hnC4TArV66koqLizS8SAkffeuz924ibcvEWfgSRZjPwmgZ2s73phwgES0oeoOLirChJkqSx\npDjdqH/3LyAE2iPfRwQDw772vpnZZNuMPHWij7bB5ObRZU8wUlrhpK87QVf7yFf9VFVl3rx5aJrG\nwYMHk8rpNRSVQPZqFATO3j/rFnbqpVW/3hd0iymNHVn4SVIKaXteQTzyfTBbUD/zDZSJ01Kd0hUa\n+sJ8fXMz4bjGZxYXsqg0fYs+vy/B9o1+BrwJSivNzF/iwDCGRV8kEuGZZ55hcHCQm266ienTh1fA\n272bcHhfJm7Kpr/oIwhjenVwPde3kV0tD2NQzCwv+xxF7rmpTkmSpOuYMnkGyp010NuF+O3Dw15x\ns5sMfGR+LnFN8PN9HUk3KFmwNBeA00mu+k2ZMgWn08nx48cJBoNJ5XS1qH0SUWsFluAZTKHzusQs\ndN2Ay1xA08BOgrE+XWJKY0cWfpKUItquLYhHfwRWG+o/fxOlKr2Gc1/oj/DVzc0Eohr/dFMBy8rd\nqU7pDfX3xdmxyU8woDFpuoVZ822oYzhMPhaL8fzzz9PT08OMGTMuz2h6M3bvyzj7NpEwZtJf9FE0\nY/o8x0IIjnY+yYH2X2ExuFhR/iC5jqmpTkuSJAnlrvcMDXbftw2xc9Owr1tc4mJugYPDHUG2JznU\nPTvHSnG5icEBjdYLsRHHMRgMzJs3j3g8PgodPhX8OUPb8p29L4EO3TgVRWVyzlo0kaBOx5VEaWzI\nwk+SUkDbsQnxy/8HNjvqv3wTpWJSqlO6QstAhK9sasIXSfCphfmsqPSkOqU31NUeY+cWP9GoYOY8\nG5Nn2Ma04UgsFmPdunW0tLRQVVXFLbfcMqzHt3m34ez9MwljBt6ij6IZ0+c51kScvW2/4FTPszjN\nedxa+VUybeWpTkuSJAkAxWBA/ehnweZAPP4LRMfwzpspisLHbszDbFB49GAXwdjwu4O+nskzrKgq\nnDkeRkuMvKiaPn06brebI0eO6D7XL24tJeyYjinchDlwUpeY5Z7FWI0e6r2biSaGv91WSj1Z+EnS\nGNO2rUf86j/B7hwq+tKskUvbYJQvb2pmIJzg72/M47bqjFSn9IaaG6Ps3RZACJi/2E55tWVMHz8W\ni/Hcc8/R3NxMZWUld9xxB6r6JrdVIXD0bsDV+wIJg5v+wo+gmYbR9XOMxBJhtjX9iMb+7WTZKllV\n8RWc5vQaKyJJkqRk56L+7acgEh467xcb3qpbgcvMu6dn4w3F+f2RnqRysDsMlFWZCQY0LtSP/Nyg\n0Whk2bJlCCF45ZVXdJ+TF8i+HYGCs3c9iOSKXQCDamZS1mriWpgzvS/pkKE0VmThJ0ljRAiBtv5P\niF//FBwu1H/5FkppVarTukKjN8wXN1zAG4rzkXm5rJmUPgXJqwkhqDsR5vCeIEajwk3LnRQUj+1M\nwUtF36WVvjVr1mAwGP76RULD2fMsDu9mEsYsvMUfJ2HOGZuEhyEcH2BL43fo8B+lwDmbFeVfxJpG\nK5GSJEmvpsxfinLz7dDUgPi/Xw/7undOy6LQZeL5Oi8NfeGkcpg4zYrRCHUnw8RiIy/YKisrKS8v\np6WlhbNnzyaV09US5lzC7nkYY11YfYd0iVmddRtWo4czPS/Is37jiCz8JGkMCE1D1D6KePKXkJGN\n+tlvo5QMo+PjGKrrCfGljU30hxN8bH4eb5uSleqUXlc8Lji4K8iZ42FsdoUlq5xkTzCOaQ6xWIxn\nn332ctF3xx13DKPoS+DurMU+sJu4OR9v8cfRTOnzHPsiHWw6/xDe8HkqMpaxtPTTGNX0ndUoSZIE\nF0c85BcjNj6DODa8eXgmg8rHb8xHE/DfezvQklhhs1hVqqZaiUYEp46ERhwHYNmyZaiqyrZt24hG\nk+s8erVA1q0IxYijbyNoIz+TeInJYGVm7r0kRJSjnbU6ZCiNBVn4SdIoE7Eo4pHvIzY+CwUlqF/8\nHkpRWarTusLxziBf2dRMMKbxwKIC/j97dx4fx10ffPwzM3tJuyutZFmXLfk+E+ewEydOnDvkINBw\nJBMI8DRQCjw8LW2h0FJaGo5COcpRCrSUq7QFMhxtwhGSmJCYxEl85vIl35esW1rtvTszv+ePWcly\nIsmWLGtl+ft+vea1u7Ozsz/tVzO73/lddyyZmjV96ZTL079N0nqkQPVMg2teEyVaeYqEa4Ll83ke\nfPBBjh07xsKFC08v6XPzVB7/T0LJFyiEmumd9Z4pNZBLR2on6w58gmS+g+U1d3J547vRtclNpoUQ\nYjy0YMjr7+fz4X7vK6i+0+sjd0lDmGvmRGnpzvLY3vgZlWHhkiAVlTqH9uXpbBt/UhWLxVi1ahWp\nVIpNmzadUZleyfVVkq68CsOOUx5/ZkL2OTd2DbHQHA7Fn6Yns39C9inOLkn8hDiLVDqJ+5X7UZuf\ngkXL0f/qc2jVM0tdrJNsPpbkE787gu26fHhtIzdO0YFcejptfv9Ygv4+b7qGNddFCIYm9xSWz+d5\n6KGHaG1tZeHChdx6662nTPo0J0us9XsE07vJlS8qztM3dSZnP9D3FE8e+hwFJ8PljX/Mirq7JnVw\nHCGEOFNa83y0u94FiTjuv/wDKnd6zTffubKWMp/OD57voC97BvPxGRqXXFGOpsELm9Jn1OTzsssu\nIxqNsm3bNnp7e8e9n+Gkq67H1cso7/kteuHM961rOpfU3wvAtrb/nvC+iWLiSeInxFmiertxP/9R\naHkZVl6F/hefRAtPrTnanj7Uz2ee9EZD+9h1s7mqeerUQg11eH+ODU8kKeQVF64s86ZrMCY3Oenv\n7+fnP/85ra2tLFq06LRq+nQ7TuzYtwhkD5KNXES84f+APrl9EUeilMtL7T9h47F/w9CCXDf3I8yv\nurbUxRJCiHHRbrzD6+93aK832It76kFMZpT7edvFNSTzLt/ceGZz+1VW+Vi0PEgmrdjx/PibfPr9\nfq655hpc153wgV6UUUay5nXoKk9Fx88mZHqHuvByZkVX0pVu4WhigqejEBNOEj8hzgJ17DDuP34Y\njh1Cu+EO9Pd+GM0/NX7wD1i3r48vPt1KwNC5/4YmVjZOraQUwHUVL29N88KmTHEQlzDzFgUnvUaq\nq6uLr3/963R0dLB8+XJuvfXWU47e6cseoerI1/Hnj5OpWE1/3T0wRZpP2m6eZ45+gx1dDxEJ1HLz\n/PupCy8vdbGEEGLcNE1Du/d9sOxieGGj16f+NLx2cRUX1pbx7JEkv9x9ZrVgi5aFqIjpHN6fp+MM\nmnwuWLCApqYmDh8+zP79E9uEMhu9lFz5EgKZfYT6N07IPi+uewsaBi+0/RhnAvoPirNHEj8hJpja\nvg33838FPV1ob/5DtLe+B02f3H5oo1FK8aMXO/nas22E/TqfurmJC+rKS12sV0mnHDY8nuTAnjzR\nCp1rXhOhps4/6eU4ePAgP/nJT4jH41x11VXcdNNNp0z6goltVB37FrqTJFFzB4mZbwBtapxuM4U+\nnjj4WY70P0dN+WJunnc/FcGGUhdLCCHOmObzob/vr6GhCbXuIdzHf3nK1xi6xofWzqIyZPD9bR20\ndI2/tk43NC5ZPaTJZ358NWqapnH99dej6zrr16+ncJpTVZzmzknUvglXDxHp+vWENPmMBhtYVH0z\nqUIHe3oenYBCirNlavwSEWIaUErhPvwz3K9+AvI5tHf9Bfptb55S/aUKjsuXNxznxy91Uxv285lb\n5rBoxtTpbzbg2OE8Tz6SoLfbYVazn6tvjhKOTH7y/OKLL/KLX/wC13W59957ueyyy0aPp3IJdz1M\nZbuF0nzEG+4jE1sLU+R/oCu9l0f3/x3dmb3MqbyK6+f8NUFftNTFEkKICaOVh9E/8HGoiKF+/G3U\nC6ceJKW6zMeHrm7EceELTx0jkRv/XHcDTT6zacWOMxjls6qqiksvvZREIsGWLac3Wunpcn0VJGte\nP6FNPpfPfAMBI8yOzgfJ2v0TUEpxNkjiJ8QEUNkM7r99DoER9rIAACAASURBVPXz/4DKKvQPfxZ9\nzQ2lLtZJ+rM2H//tEZ482M+SmhBfuHUOzZWTO+H5qdi24vmNabY+k0YpuGR1OZdeWY7fP7mJk+u6\nrF+/nieeeIJQKMSb3vQmLrroolFfozlZKo//gHDfemx/Db2z308+vHiSSnxq+3uf5HcH/4GcHefi\nurdyxaz3YeiTX4MqhBBnm1ZTh/4nfwt+H+6/fwF1eN8pX3NxfZh7VsygI2Xzz88eP6O+dSc1+Tw+\n/tq6yy+/nHA4zJYtW+juPr3RSk/XRDf5DPoiXDDzjRTcDNs7fj4BJRRngyR+Qpwh1dGK+9kPw5YN\n3sidf/cltPlLSl2skxztz/HhRw6xozPD1c1RPnVTM7GyqdHfbEC812b9owmOHMhTETO49pYoTfMC\nk15jmsvl+NWvfsXzzz9PdXU199xzDw0NozeFNPKdVB395omRO2e/HycwNUZvdZXNluP/wabWb+PT\ng1w758MsrXntlKqJFkKIiabNW4z+Rx+CfA73a59C9XSd8jXmhTVcVF/OxqNJHtw1/knJJ6rJZyAQ\n4Prrr8dxHH79619P7Nx+Z6HJ58Lqm4gG6tnX+zu606dOtsXkk8RPiDOgXtyE++kPQethtJtej/7B\nT6NVTK058F5qT/FXjxyiLVng7gtm8JdrGwn6ps6hr5Rif0uOp9YlSSVc5i8OsvbmCJHo5DftbG1t\n5Yc//CEHDhygqamJu+++m4qKUUY6VYpQ/yaqj3wNX6GDdGwt8Yb7psx0DVk7zhMH/5G9PeuoDDbx\nmvmfoD6yotTFEkKISaGtXIN21zuhrwf3a59EZdKjbm/oGh+6qpGqkMEPtnWyq3P8TTW9Jp8hspkz\nG+VzwYIFXHLJJfT29vL4449P6CifE93kU9d8rGq4D4XLs8e+ScE5vWk1xOSZOr/+hDiHKNfF/eWP\ncf/l02AXvP58b/ljNN/UqUVTSvHInj7uf/wIWdvlA1fW8/ZLZqJPoZqeRNzh6ceTbN+WwefXuOLa\nMBdcWoYxyVM1uK7Ls88+y89+9jOSySSrV6/mzjvvJBgcuSms5qSpaPshFR0/R2kG8bq3kqy5Y8oM\n4tKZ2s2j+/6OzvRuZldczk3zPk4kUFfqYgkhxKTSXnMn2vW3w9GDuF/+OCqVGHX7WJmPD61tROH1\n9+s/g/5+i5YHqYgZHD6Q5/D+3Lj3c/XVV1NfX09LSwsvvfTSuPczHK/J59IJa/JZF7mAJTNeSzLf\nzra2/5yAEoqJNDV+oQhxDlE9nbhf+jvUgz+Eqhr0v/rclOvPlyl4g7h8Y2MbIZ/O/Tc2cdOCWKmL\nNcixFTtfzHgDuHQ5NMz2c92tUWobJr/PWV9fHz/96U/ZuHEjkUiEN7/5zVx55ZWjjtzpT++j+vBX\nCaVeJh+aR0/Tn5GLjt4HcLIo5bKj8yF+d/AzZO04F9Xew1Wz/xS/ESp10YQQYtJpmob2lvegrbkR\nDrTgfvFjqP6+UV+zoi7MW1fU0JW2+cqGVhx3fDVhuq6x6qpy/AGNFzdn6BznFA+GYXD77bcTCoVY\nv3497e3t49rPsDSNRO0bB5t8Grkz3/eK2ruJheZwoG89R+ITM2WEmBiS+AkxBu5zT+Le/wHY/RJc\nvBr9b7+ENmdBqYt1ksN9Of7yNwd58mA/i2eE+PLt81hRFy51sQZ1tBV44jcJ9u7MESrTWH1NmMuu\nDhMqm9zTkVKKnTt38qMf/Yi2tjaWLFnCvffeS2Nj4ygvsgl3PUys9TvoTpJk9S30zXo3rn9qJNVZ\nO876Q1/kpY6fEPJVcsPcv2HZzNdJfz4hxHlNMwy0+z5woubvCx9F9Y4+WMpdF87g0oYwW1pTfGNj\nG+44m0FGogaXrw2jabB5Q4pEfHw1iNFolFtuuQXXdXn44YfJZieuGaXrqyAx843oKk/s+PfR7NFr\nRU/F0H2smf1+DC3A5uPfJV2Y2IFpxPgZ999/f6nLMFHuTyTO7B9VvFo0GkU+V1CpBOr7/4z65Y/B\nMNDe9j70u+5DC579WpSxxODx/XH+4cmj9GYdXr+0ig9dPYuK0NSYQzCXdXlxU5qdL2axbcWCJUFW\nXR2monLyy5dIJFi3bh2bN2/G5/Nx0003ceWVV+IbpaluRHXg3/dvhFLbcfzVxBvvIxe9eMpM1dCR\n2skThz5HX+4wDZGLuW7OR6gIjZLEnoPkfFR6EoPSkxiMj6ZpsOIyyOW8Cd63PYt28Wq0cGTE7S+f\nFeGFtjSbW1Mk8y4rG8KDF9LGEofysE44onPscIH21gKNzQF84xitOhaLef3i9++np6eHxYsXT9iF\nPSdYh0IjlNpBIHOQbPQS0Mb//Rz0RQkYUY72b6Qvc4g5sasn/CKkHAvDi0ajAJ8Y7rmp0yFJiClK\n7Xge93tfhb5uWLAU/V1/gVY7tSa8ztku39rczrp9ccJ+nQ9e3ciapqkxP5ttKw605Ni7K4tdgFi1\nwUWXlVFZNfmnn0KhwJYtW9i6dSu2bdPQ0MAtt9xCZWXliK/RnAzh7kdw9m7EjyITvYzkzNeh9Kkx\nFYbj2uzoepCdnQ8CGhfXvYUlM25HmyJ9DYUQYqrQNA3uug9CZaiHfoj7+Y+if/BTaA2zh90+HDD4\n+xub+NvHDvOr3b2U+XTeccn4RmyeNSdAKumy++UsG3+f4qobI/h8Y0+EVq9ezfHjxzlw4ABbt25l\n1apV4yrPcNJVN2IUuilLbKOi3aK//q1n1G99QdUNtCVf4FhiK7u7fs2yma+bsLKK8ZHET4gRqFwW\n9T//ifrtL7xavje8He22N6MZU6MGbcC+nixf3XCcQ/EcC6qDfGTtLOqjgVIXC8dRHN6Xp2VHlnxO\n4Q9oXLgyxNwFATR9cmvJlFLs2bOHp556imQySXl5OTfccANLly4d+QqkUoQS24h0P4zuJKG8kd6q\nOyiUzZ/Uso+mL3uE5479G33ZQ5T7Z7Bm9vupKZ86cwcKIcRUo2ka2uvfghsMon7yPdwvfBT9g59E\nmz1v2O0rggafuKmJv3nsED/d3k3Ip3H3hTXjeu9Fy4Okky5HDubZ+myKy68Kj/n7UNd1br31Vn70\nox+xYcMG6uvrmTVr1rjK8yrFKR6MQh+h1Ms43Y+SqrntDHancXnju+ne9ze81PFT6iIXUF02/Ocs\nJoc09RSjOh+r0ZVSsO0Z3H/5FOx4Hupno//Z36Nffg3aKAN+nC0jxSDvuPzwxS6++sxx+rIOty+K\n8eFrZlEZKu31HNdVHDmQZ8uGFK1HCqDBomVBVq0JM6PWN+n9zTo6Onj44Yd5/vnnsW2bVatWcfvt\nt1NfXz9iWYx8O5VtP6Q8/jSgSM24hbKL/oR4rvQJNYCrXHZ1/4pnj36DjN3LvNi1rG3+C6LB+lIX\n7aw6H89HU43EoPQkBhNDW7AMKmKw+SnUpqfQ5i9Bq6kddtsyv84Vs6M8eyTBM0eSRAI6l82rHXMc\nNE2jtsFHb5dDZ5tNwWZcg5r5/X7q6+vZuXMn+/fvp7m5mXB4gvryazq58DKCqR2E0jtxjErs0PgT\nS58eJBZq4mD8KdqTL9NUccWEDTYmx8LwRmvqKYmfGNX5dlCp9lbcb/8T6tc/hXwe7bY3ob/7Q2gz\nhv8ymAzDxWB3V4ZP/u4ozx5NMjPs5yPXzOJ1S6sxJrkmbSjXVbQeKbBlQ5ojBwq4DsxbHOSyq8LU\nNQYmfYqGjo4O1q9fz/r160kmkyxYsIDXv/71LFq0CGOEWlvd7ifc/RsqOn6Oz+4lF15GX8Mfkg8v\npaKickocC4lcO08f+QoH+tYT9EW5cvb7WVbzOgx98kdEnWzn2/loKpIYlJ7EYOJocxdBTR1sfRq1\n4XHw+WHB8C1BwgGDy2dFePpwgg2HE9RGg8wqH8d76hr1s3y0tRboaLUJBDSqZoz9gm00GiUSibBn\nzx727t1LU1PTxCV/up98+WJCiRcIpl6mEJqD668e9+4GphI6lthCW/JlmiuvxNDP/EKqHAvDGy3x\n0yZyIsgSU62traUuw7TT2NjI+fC5qlwW9eufoB79H7BtWH4J+lvfg1Y/fLv/yTQ0Bjnb5b9f6OSh\nXb0o4I7FMd5xSS1l/tL158plXQ7vz3Nwb45sRqFp0Dw/wKLlIcrKJ3+kzkOHDrFlyxaOHTsGQE1N\nDddccw1NTU0jvk5zUpT3rqc8/gyaKmD7q0nW3EE+vHxwm1IfC66yael+hJc7fo6j8jRVXMGqhj8k\n6JsafTknQ6ljICQGU4HEYOKpvTtw/+0LXl/+iy73+vKPMOjL4XiOjz12mETO4f9dUc9rFo5vVOd0\nyuH3jyXJ5xRLV4RYuCw4rhYxO3bsYN26dQSDQd74xjdSWztxF6r9mYPEjn0bpfvonfVenOD4xzdQ\nSrG17Qfs7VnHjLJFXD/3I/j0M6v5k2NheMXRyYf9Z5LET4xquh9UXrPOZ3Ef+Db0dHrz8t3zbli5\nZsoMgT8Qg+ePp/jmxjbakgUaon7+9MoGLqgdx+XGCRLvtTnQkufY4TyuC4YPmuYGmL84SDg6uf0g\nbdumpaWFrVu30tPTA0BTUxOrVq2iqalpxFhqbpayvqco730KXeVwfJWkqm4kW7HqVaOZlfJY6Ezt\nZsvx7xPPHSVoRFnZ8A6aK9eUpCylNN3PR+cCiUHpSQzODpWI4377n7wuHjNq0d/3V2hzFw277f6e\nLH//u6P0Z21ev6SKd66sHVeLm0Tc4bn1STJpRfP8ACtWlaGPYz87d+7kscceIxgM8oY3vIG6urox\n72MkwcTzVLY/gKuHiNe/g0L5+Pu5K+Xy7LF/5XD8GeojK1jb9EEMffzdU+RYGJ4kfmLcpvNBpXa+\ngPvQD2HvTjB8aLfciXbHPZMyRcNY5IKVfP43L7O5NYWuwR8srebei2oI+ia/ls8uKNpaCxzam6On\ny5uLKBzRmbsoSNPcAP7A5CXLSim6urrYvXs3u3fvJpVKoes6ixYtYuXKlcycOfLIa5qToiy+kfK+\np9DdNK4RIVV1PZmK1TBCk8lSHAtZu58X2n/Mwb7fAzC/6gYuqr37vKrlG2o6n4/OFRKD0pMYnD3K\ndVC/fAD1ywe8Qd3ueTfadbcPe/HQKYvxZz/ZypF4novqyvnw2kYqxtHHPptx2fj7FPFeh5n1Pi67\nKjyuqR527drFY489ht/v5w1veAP19RPX5zuY2EZF+88A6K+725vKaJxcZfPU4a9yPPk8TRVXcOXs\n96OPc+RQORaGJ4mfGLfpeFCplu1ewrf7JW/Fxau9OfmmQLPOofqyNj9+sYtH98ZxlOLCunLetbKW\nBdWTm5g6jqLjeIHWwwXaWr2+ewAz633MWxyktn5yB2yJx+O0tLSwa9cuent7AQgEAlxwwQVccskl\nA23bh2XkOyjve5pQYiuasnH1EOnYtWRiV51yeobJPBZc5bC/90le6rDIOylioTmsariPmvKFk/L+\nU9V0PB+dayQGpScxOPvU9m1e7V+yH231tWhvfz9a2cktbBobG9l76Ahf2XCc544mqQ37+ZvrZjGv\nauzf0XZBseWZFB3HbSpiOquviYyrq8Tu3bt59NFH8fv93HnnnTQ0TNzUU/70Pirb/hPdzZGccRvp\n2LXjnsfWdvOsP/R5OtO7mV91A5c1vHNcvyPkWBieJH5i3KbTQaX27cJ98L9h5wveihWXof/BWxmp\nKUep5B2Xh3b18tOXu8nYLs1V5bz9oipWz4pMWoLluorOdpvWw3najhawbW99OKIza46fWXMCRCap\nOadSit7eXg4dOsSePXtoa2sDwDAM5s6dy9KlS5kzZ87Ik68rRSC9h7L4UwTTewBwfFWkY1eRrbgM\ndZp9DCbjWFBKcSyxmRfbf0oi34pPD7Gi9i4WVt+MfgYT6U4X0+l8dK6SGJSexGByqJ4u3G99Hvbt\ngsoqtLvuQ7vi+sHv4YE4uEphvdTNj17qImBofODKBq6ZWzHm93NdxctbMxzalydUpnHFtREqYmM/\n77e0tPDII4/g8/m44447aG5uHvM+RmLk2ogd/z6GHSddeSXJmtePe56/vJPmdwc/Q1/2EAuqbmRl\nwzvQtbHVmMqxMDxJ/MS4nesHlVIKWl7G/c3P4eUt3srll6D/wb1oC5aWtnCvkLNd1u2L8z87uulM\n20SDBm9dUcM7r11GR3vbWX1vpRSppEtnm01nW4HuDnsw2Ssr12hsDjCr2U9FzJiU5DOdTnPkyBGO\nHDnCoUOHSKVSgDcU9uzZs1myZAkLFiwgGBy5lk6344QSzxPq34Kv0AlAPjSXdGwt+fCyMX9Zne1j\noSO1kxfaH6Answ8NnXlV13HhzDdR5h/fwAHT0bl+PpoOJAalJzGYPMq2Ub/5Gerhn0A+DwuXo9/7\nXrSmea+Kw3NHEnxpw3Gytsublldz70Uz8Y9xNGulFPt259j5QhafD1auCVPXOPYRm/fs2cMjjzyC\n67qsWrWKK6+8csTRrMdKt+NUtn4ff76NXHg58bp7YJwjdGbtOE8c/Bzx3BFmli/hqqY/JeSrPO3X\ny7EwPEn8xLidqweVymVRzz6B+t2v4Nghb+WSFV7Ct/iC0hbuFZI5h1+39PKL3b305xwChsZrF1dx\n94UziASMsxaDXNalu9MeTPYy6RPngnBEp7bBR2NzgKoZZzfZU0oRj8dpa2ujvb2d1tZWOjs7B58P\nhUI0NTXR3NzM3LlzRx+u2i0QTG0nlNhKIL0XDYXCIBdZQTq29ozmIjpbcejJHOSljp/QlnwRgNkV\nl7Oi9m4qzmD0tOnqXD0fTScSg9KTGEw+1d2Ba30Htj4Dmo52/e00vvdDtCWSJ213OJ7js08epTVR\nYHZFgPdeXsdF9WOfYqH1SJ5tz6ZxXW/QtOWXhAgEx3axsr29nd/85jfE43Fqa2u57bbbiMUm5kKi\n5mSpbPsvApl9FIKN9NfeNe4RPwtOlo2t3+Jo/ybKfNWsbf4zqstObwAZORaGJ4mfGLdz7aBSHa2o\n3z2MenodZFJe5+xVV6PdcAfawmWlLt5JutIFHtrZwyN742Rtl3BA57WLqnjd0ipiQzqIT0QMHEfR\n3+fQ2+3Q123T2+2QTrmDz/v9GjV1PmbWe0t5+Ow0K3Rdl/7+frq7u+no6KCtrY2Ojg5yudzgNrqu\n09jYSHNzM83NzcycOXP0xFM5+DMHCCVfJJh8Ed319lUINpGtWEk2chHKOPPRTyfyWFBK0Z56mV1d\nv6I9tR2A2vByLqq7hxmn+YV3PjrXzkfTkcSg9CQGpaO2b8P98beg7Rh6ZRXqDW9Hu+omNP1EUpbK\nO/zn8538Zk8fCrh2TgXvXFVLddnYmjH29di8sClDf59DIKhxwSVlzJrjH9OF2Hw+z5NPPsnOnTvx\n+/1cd911LFu2bGIu5iqbaOdDlPVvQqGTrrqOVNUNIw6QNuqulGJn1y94qeOn6JqPyxrfxbzY2lO+\nTo6F4UniJ8btXDioVD4HL27Cffq3J5pzVlahXXsb2rW3osXGP+noRHOV4uX2NOv2xXn6cD+2C9Vl\nPu5cVsUtC2OU+1+dcI01BoW8S3/cJdHn0B93iPc69Pc5uCfyPPwBjaoZBlUzvEQvVmWgTeDk77Zt\nk0gk6OnpGVy6u7vp7e3FcZyTto3FYtTV1Q0uM2fOHLm/3gA3TzDdQjC1nUBqF7qbBcDxVZKNXko2\neilOYOLmMoKJORZcZXM4/hy7u39NX/YwAHXhC1ha8zrqwhdMmSlEpqpz4Xw03UkMSk9iUFrKLqDW\nPQS/slDZDNTNQnvNnWhrbkALnOh+sLc7y79uamNPd5Yyn869F9dwx+KqMU374LqKAy05dr+cxXGg\nps7HRZeVEY6M7eJsS0sLjz/+OPl8nkWLFnHjjTeO2lViLAKp3UQ7/xfD7sP2zyRR+yYKZXPHta/j\niRd45ug3KLhpFlW/hovr7h11ugc5FoYniZ8Yt6l6UCnbhh3bUBvXo57fCLmM98TCZV7t3so1aL6x\nX3U6WzqSBR4/EOe3++J0pAoAzKoI8Kbl1Vw3twK/MXITjpFikM+5JBMuqYRDst+lP+4letn0yce0\npkFFzBhM9GIzDMIR/YySDKUUmUyGRCJBf38/8Xj8pCWRSLzqNT6fj6qqKqqrq6murqauro7a2lpC\nodMbXEUv9BJI7yWY3kkgvQdNeZ0QHV8lufBycuELKJTNG3dH81M5k2MhXejhYN9T7Ot9nHShGw2N\npoorWFJzB9Xj/II8H03V89H5RGJQehKDqaE24KPtW19CPfcE2DZEKrzfHze8Fi3q9VNzleKxvXF+\n8HwHybzL3FiQd66s5eL68jF9B6eTDi9uydDZZqMbsOSCEPOXBMc0519/fz+PPPIIx48fJxKJsGbN\nGpYsWYKun/l3pubmCHc/Sln8GTQU6corSc249bQHTxsqkWvnqSNfpj93jEignkvr76Uhcsmwn5cc\nC8OTxE+M21Q6qJTjwJ7tqE2/R23ZAKliclFTh3b5Nd6Qy7PnlrSMQ6ULDpuPpfjtvj5eaEujgJBP\n46rmCm5eUMnymWWjnviVUhTyivKyGRw62E466ZJMOKQSXsJXyL/62A2GNCpiBhWVBtFKg4qYTqTC\nwBhjB3PHcUgmkyQSicHbgSRv4L49MPrLK4TDYSorK4nFYsRiMWbMmEF1dTXRaHRMXzCakyGQ2Yc/\nvZdAZi++Qvfgc3agbjDZs4ON4x5SeizGeiw4rk1rchsHep+kLfmi19tQCzC/6joWz7iNyATXSJ4P\nptL56HwlMSg9icHUMBAH1deD+t2vUE88DOkk+ANoa270agHrvX7l/Vmb/3i+k3X74gDMiQX5g6VV\np7zwO5RSitbDBV7eliGfU5RHdOYvCtI0L3Da8/65rsumTZvYtGkTrusSi8VYvXo1ixcvnpAE0Jc5\nREXnz/HlO3B8laRj15GpWDXmwV8KToaXOn7K3p51KFzqIyu4tP5tVARP7qcvx8LwJPET41bqg0r1\ndKK2b0Nt3+pNw5D2RneksgrtsrVoq6+FeYunTBO5rnSBTUeTPHc0yUvtaWzXO76WzSzj5gWVXNUc\nPak5Z6GgyKRcMmmXdGrIknRJpxzswqvfQ9OgPKITieqEo0bxVqei0jitzt+u65JOp09K6obeJpPJ\nwVE0hxMKhYhGo0SjUSoqKohGo1RWVg4up2ymOQLdjuPPHsafOYQ/exBfrhUN7/Nz9SCFsgXkyxaQ\nL1+ME6gZ13ucidM5FpRy6c7s40j/Rg71PU3O8S5OVJfNZ17sOporryQwAf0Nz1elPh8JicFUIDGY\nGl4ZB5XNoJ7+LWrdg9DV7n1ZL7rA+62yag1aRRV7u7M8uLOHpw734yqoDBm8dnEVty2KndS3fzT5\nnMvul7McPpDHdbw++s0LAsxbFDztuf8SiQSbN29m+/btuK5LVVUVq1evZtGiRWeeACqbcM8TlPc9\nWZwvt5xM5ZWkY2tQRmRMu4pnj7Gt7b9oT72Mhs7C6tdwYe0bCRjegDlyLAxPEj8xbpN9UKlsGvbu\nOpHsHT9y4skZtWgXrkRbdTUsuRBNL/28Zo6r2N+bZUtrio1HE+zrOTFIybxYkCsaIqysiRDVDDJp\nt5jkKdLFZG+4WjsAw/CSu/KwTm1dBYoM5REvwSsP6yM271BKkc1mT6qle2Vyl0qlcId2+BtC13Ui\nkchgYjdwPxKJDCZ5gcD4hm0+iZvHlzuOP3cMf/YQ/uxhDLvvxN+BQSHUTL58Afmyhdih2VDieexG\nOhYct0BHagdHE1toTWwla3tXdINGlDmxq5kXu5ZYqGmyizstyZd86UkMSk9iMDWMFAflOLDtGdzH\nfwl7dngrNR0WF5PAlWvoMsL8ancvj+7tI1Vw8esa182r4Jo5FVxYV47vNJpw5rIuh/blObAnRz6n\n0DRobPIzf0mQWPXpJZH9/f1s2rSJnTt3DiaAq1atOuV0SadDs5OUx5+hLP4MuptBaT4y0VVkYmvH\ndPFWKUVrYhvPt/83yXwHfr2ceVXXsqDqRpbOWyXHwjAk8RPjdja/YJTrQvsx1P7dsH+3d3vsMKhi\nUhIIwpIVaBdcinbBpV4H6hLX7OUdlz1dWbZ3pNnVnuFodx6/oxHBIKoZzCoLUOP3E3R1ClnFCPkV\nhgFlYS+JKysv3oZ1yst1yiM6gaD2qkli4cSgKUMTu1cmeCM1wdQ0jXA4fFIy98rb8vKx9Ts4HZqT\nxJdrw59rxVdcjELXYG0egGuEKYTmUAg1e7fBWeMaGexsGmzWoxSJfCsdqZ20p3bQlnwJuzi4TNCI\n0hhdyayKldSHLxq1U7oYO/nBW3oSg9KTGEwNp9UKpKcLtXUDavNT3kTw4CWBSy5Eu3AV2QUX8Hhh\nBr9o6aMt6TXxiQR0LpsV4cqmKCsbwgR9o9fAOY7i2KE8+1tyJOLej45opU79LD91jX5i1aeekike\njw8mgEopDMNg7ty5LF68mLlz5+L3n8H3sZunrH8z5X1PYdi9KDQKZfPJhS8gF1mOe5rz9jlugZae\nR9jd9TA5px+A2VUX0xRey6zoZfJ9O4QkfmLcJuoLRuVzcPwoqvUwHDuEOnIADrR4Uy4MCARgzkK0\n+Uu9RG/hcrQzOdmMt6xKkc8pkkmXo9052noL9PTbpFMudk5RjkE5OoFRBhEJhjTKyvUTS1inrPzE\nuqGJ3YCBfnVDl4FErrOzk0QiQTabHfE9B5pgDldjF41GCYfDE9KGf1jKRbf78RU6MPKd+PIdGPkO\nfPlOdPfkZqOuHsQONGIHG7CDsyiEmnH8Myaln954uMqmP9dKwd/BntZn6UjtHPzSAQj7a5lVsYrZ\n0VXMKF+EfpYGlxHyg3cqkBiUnsRgahhrHFRPJ2pLMQncv/vEE8EQzoLl7Jh3Oc+F5/Fcwk93xhv9\nOmBoXNoQZmVjmKU1ZTRVBkccFVQpRVe7zYE9OTrb7MELz8GQRl2jlwTW1Pnw+Ub+ro3H4+zevZvd\nu3fT29sLgN/vZ968eSxevJimpqbxJ4HKIZjcTnnfSsWy0wAAF/5JREFUU/hzJ1pzFYKzB5PA0xmN\n23FtjiW2sK/3cTpSXo1q0KiguXINDZGLmBlegk+fmBFLz1WS+IlxG8uJTbkO9PZAVzuqqx06Wr1E\nr/UwdLbBK//XahvR5i+B+Uu821lz0MbZP+x0uK6X0OWyLtlM8TarSKcd4gmHdNqlkFNQAG3448Xb\nj67whzQqowbRiEFZuU5oMMnTCJXpJw2m4rou2WyWdDo92H8ulUoN3h+4TafTI76nz+cbMaEbeHxG\nV+RORSk0N4tuxzHsXoxCD0ahu3jbi2H3DI6yOfgSNBx/FY6/FjtYjx1sxA424viqztrIm2eq4GTo\nzx2jN3uI3uxBejOHiOeO4qoTnS3LfFXUhpcxM7yM2vJlRAK1Ja+JPl/ID97SkxiUnsRgajiTOKje\nblTLy9CyHbVn+0ndWpTPz955l7Gx7iKeDc7mmHMiiQn5dBbPCLG4poylNWUsrglROUzfQNtWdLYV\naG+1aW8tkM95v790A2JVBpVVBpXV3lROkaj+qumclFJ0d3ezZ88eWlpaiMe9LgyaplFTU0NDQ8Pg\nEo1Gx/wdqBf6CKZ2EEztwJ85gIaXpTq+agqhJm8JzvYGbxul9U+o0uGZ3Q9wsO/35J2kt2/Nz8zy\nxdRHVlAfWUFlcDbaFP3NcbaUPPEzTfPLwJWAAv7MsqxNQ567GfgM4AC/tizrU6d6zQgk8TsLBpu3\nFQrQ3wf9vRDvRcW9W/q6vSSvqx16usAZpplhJAqNc9Aam2FWc/F2Dlo4Ou5yKaVwbMjnFYW8Sz7n\njYCZzytyWUU+55LLnUjuclkXZ5iBUoZylSKNSwqHLC6+oEY4rFNd4aOhOsCcmUFiUQPd8CZFzWaz\nZDKZk5aBBC+dTg8mc5lMhtGOM5/P96ommANLNBpl4cKF9PX1nZ3kQrlobgbdSaLbKe/WSRQf92PY\ncXS7H92Oo6v88J+bXobjry4uM7EDddiBmTj+minXXNNVLjk7TtruJVPoJpFrJ5FvI5lvI5FvG+yf\nN0DXfFQGm6gqm8P8+pUECo2S6JWQ/OAtPYlB6UkMpoaJjIPq74O9O1At21F7dngXzIujux0rm8n2\n2HxaqubTUjWXo/6qk15b4YfGiiCzKkM0VgSYFQ3QWBGgPuIn6NNRrqK3x6G9tUDH8QL9cZchPS0w\nfN6UT7Eqg3DEGBxfoCys4/NpKKXo6Ohg7969tLa20t7eftI4AeFwmLq6OqqqqgZH866srCQcDp/W\nd6XmpAmmdhFMbcef2T84Ly+AQi9eOJ5d/F1RjePzfm+gBwZj4LgFutIttCVfoi310uA8uQA+PURl\ncDaxUDOxUBOVoWZiwSb8Rtl4wzXljZb4nfUGsaZpXgcssixrjWmay4DvAmuGbPLPwK3AMeBJ0zR/\nBsw8xWvEGCjXgUIB8jnIZrwlN3Cb9SYgzaQhlfSmSEgnUcX7x/NZnN4eb4ji0VTEYM4CtJo6qKnz\nBmKZWQ+zmiEaQ9M0XFfhOl57dMdROP0Ojq2wbYVd4MR920vqCoViUpdX5AteYlcoKOyC8pK407xm\nkVUuWVzSyibr5smoPFlVIK8K+P0O5UFFNOhSEXCJ+BUzDZeQ5lAoFMjlcmTbsuw5lOPlXI5sNks+\nnx9xcJSh/H4/5eXlVFZWUl5eTnl5OZFIZDDJG7gNBAKjnhzD4fDg1baTKAfNLaCpPJoqgJtHd3No\nJy1ZNJVDdzLFBG/gNo3uZtCc9OCVtpG4RhgnUEPBqMTxVeD6YsUkbwaOvxpVopOnq1xsN0PByWC7\nWQpuhryTIuckydsJck6SnJMgbyfI2H1k7F4yhV7UMH+vhka5fyb1kRVEAw1UheZQVTaXimAjuuad\nJuXHlhBCiLNBq4jByqvQVl4FFAeI6TwOxw4z+9ghZrUe4pbWp2DXAyT1IHuiTbRUNLO3oonWspm0\n5KvY1Z171X7LsakyXKoCUBXUqAoaVDf6KdfL8Ksg5P3YaY3eLofeLudVrw8EtWISGKEidCk1y1Zh\nrHBJZ7uJ97fT09NOZ9dx9u/f/6rX+nw+YrEY0Wh08DdQWVnZSbeBQIBAIIAbvZRsxUpQLkahB1/u\nCP7sUfzZI/jyx/HnhhlYzYhidzRQ4ZbhGmGiRoQ5ZQtwoxeRUorj2cMcz+yjN3eUnswBujN7T/7b\njAjl/hmDS9g/gzJfFQFfhKARIWBECBhh/PrEj31QSme9xs80zU8Chy3L+nbx8S5gtWVZ/aZpzgd+\nYFnW2uJzHwWSeInfsK8Z5a2mXI2f2vmC15cN5TVzHFgGkgblevcH1rnuiXWuC65z4r7jUMyavBOC\nY3tXg2zbu+843uNCAcd2OV5xITZ+lOPtU6EBGkp75a2O0vTBdQodiuuU7oNAkN5QjN5gDHw+lOEH\nw++1F9B9oBso3QA0cL0/FQWa0kBBPt9DLtvurfP+aNRJGZsq1oadWAYeq4F1ykWhcJWLi4ujXBwc\nnOJjVzkoXJRyi7cOGi46DjouunLRlHti0Jhx0HWdUChEVcRgeW2aQMBHwO/D7/fj9/vw+4uPfb7B\nxz5dP/F3KRcolqP4QZ0ok4OmHK/2DcdL6JQLykZTNn6fhp3PeE0pi+s0t+BtOw4KHWWU4erl3q0R\nOXnxDdxW4BgV46q1Sxd6OBJ/7kRs8P7PFaoYJwdXebFyizFzlYOr7FcttpvHUXmcV9za7qu/5Eai\nYVDmr6LMV0W5v5oyfzXlvioigTqiwXrC/pkYp/g7JfErPYlB6UkMSk9iMDWUIg7KLnitq3o6UT2d\n0NMJPV3ku7vpSOZotQO06hFay2fSGYrRG6igNxClPzD6NAoGUI2fqGYQUxoVmk4Ug4jmo0zzo59i\nzmHHzVBwEjiqH8dNYDsJ8nY/+XwCVw0/4Nwreb+n/AQCAfx+Pz6fD8MwCPh0ZpTnqAzmqQzmqKvU\nqYl4CaI3Gvip8xhbaXRr0KVculWBbjdHUuVJujnsU1z81tDxG2UYWhCfPnQJsbTmDmrDy07r75tM\nJa3xA+qBLUMedxbX9RdvO4c81wEsAGpGec05QSmF+50vec0hzzZNA58ffD7w+empXsGL898+oW9x\nUuvogdzlFRylcFA44CVoKPp6n8Wxu1+98QSWa7BsmoahG/h8BoZh4PMFMAxjcBk4kQycXAbu+3w+\ngsHg4Aln6BIMBgkGg/h8Pm9UzO7HCPc+PnxhFFAoLhNAoYHmA8ePpnTQfCg9hKv5QA+gND9KC6B0\nv3df96P0EEoPorQgSg/iDjzWQ7iGl+gpLXjWB1LZ3/s7tnf+74TsS0PH0AMYWgBDDxD0RTG0IH4j\nhE8vw6+X4ddD+IwyAno5AV+UoBEhaEQJ+ryrdkEjet618RdCCDG9aD4/1DZAbcNJv+pDQHNxUY7j\ntd5K9kOiH5L95PsP0Jcq0Jt16C24pPIuSRtSDqRcnaTykdL8ZDQf/ZqPTs0gr/nJGX5yegDdCBLU\nfAQ1nTJ0guiE0AhpOuW6zurZFbhOBY7deKLlVgFs28V28jhuFtfN4rhZHDeD42YxfDlm1Cny+TyF\nQoF8Pj+4JJNJHMcZ7CYztD6xurqat7/d+43bUF9L+9E96Hay2FUlhVa81Z3UYOsnXeWY4eaoGWgN\nVbyQTrFFWFI5JJRDvxGmr3I1OSdF3kl4rYjsJAU3jePmyDtJ0oVunGLXl5nlS6Zk4jeaUox9Otov\nzpGeO61fqcUMd8oofO7fsY8eBF33fmhrmjeohVYc0VH3atcwdO9HafGxZhTvGz5vrjrDQDOKtwOP\n/QHw+dB8fu+5IWa5ipmHUxQKbvFtT3x8g8XQNIqVfoP3T9yCrmleUXWN7mSOg71pdF3zFsOrATM0\n0AwNY2DRdXRN89ZrGoaukUm8ld7ONu853Vtn6Dp+Q8en6/h9Oj7DKL63hq7rg/eN4nrDMNB1ffC5\ngURO13V8Pp9XluLjs03Vmqi+FXiZb/HDQjv5frEGFU33BokZ8hjNKN4OXXzeer14O2QZiN25Nkjx\nzLp3s6h3tVfTqw3E1EBDQ9N0dM0YXAYeG7ofXfNh6P7i4kPX/FNqiOapdo45H0kMSk9iUHoSg6lh\nusdBOTaqYKPsAnbBxs7nyRcK5HMFCoUCblUtscowFaGRW8wopXAHuvgMWcrDPvz+U01V4WDb9knL\nK+cTrm8eX+KllALlEHELRNwC9W4B/FE049RzFSvl4rgFfMa5N3roZPyiasWrrRvQCBwf4blZxXX5\nUV4z8htNtaYPRgDmLJ6YfbkUm4i6eFVKIw/rP/DWp/G/e5KBxpYocJT3Vo2NjeTyPVxYN7DFgFM0\nM1TeJhXlOnVzzuzE6LruafWpmzz1p97ktLl4/+4jO1eb9QQZfdLy4SuOneIy+v93KZyrcZhOJAal\nJzEoPYnB1HDexkHXoSyIXhZEd9Ike9KcYhSIYaVGHsh8VLnciW4eEx+D0+9CMpWNdkFiMto+PQrc\nBWCa5kqg1bKsBIBlWQeBCtM055qm6QNeV9x+xNcIIYQQQgghhBibs17jZ1nWBtM0t5imuQHvAv//\nM03zPiBuWdb/AP8X+FFx8wcsy2oBWl75mrNdTiGEEEIIIYSYrial84xlWX/9ilUvDHluPcNM1TDM\na4QQQgghhBBCjIMMcyeEEEIIIYQQ05wkfkIIIYQQQggxzUniJ4QQQgghhBDTnCR+QgghhBBCCDHN\nSeInhBBCCCGEENOcJH5CCCGEEEIIMc1J4ieEEEIIIYQQ05wkfkIIIYQQQggxzUniJ4QQQgghhBDT\nnCR+QgghhBBCCDHNSeInhBBCCCGEENOcJH5CCCGEEEIIMc1pSqlSl2GiTJs/RAghhBBCCCHGSRtu\npW+yS3EWDfsHCiGEEEIIIcT5Tpp6CiGEEEIIIcQ0J4mfEEIIIYQQQkxzkvgJIYQQQgghxDQniZ8Q\nQgghhBBCTHOS+AkhhBBCCCHENCeJnxBCCCGEEEJMc9NpOgcxAUzT/DxwDd7/xmeBTcB/AgZwHHiH\nZVm50pVwejNNsxz4PlAHhIBPAS8gMZh0pmmWAS/jxeC3SAwmlWma1wM/AbYXV70EfB6Jw6QyTfNt\nwEcAG/g48CISg0ljmuYfAe8Ysuoy4Grgm3jzF79oWdb/LUXZzhemaUaAHwBVQBD4BNCGxGBSmaap\nA/8KXAjkgfcBKeR8NCZS4ycGmaZ5A3ChZVlrgNuArwCfBL5uWdY1wF7gXSUs4vng9cBmy7KuA0zg\nS0gMSuVvgZ7ifYlBaTxpWdb1xeVPkThMKtM0ZwB/D6wFXgfcicRgUlmW9Z2BYwAvFv+B9938Z5Zl\nXQ1UmqZ5eynLeB64D9htWdYNwF3AV5EYlMKdQKVlWVcBfwR8ETkfjZkkfmKo9cDdxft9QBi4Hnio\nuO4XwM2TX6zzh2VZD1iW9fniwybgKBKDSWea5lJgOfCr4qrrkRhMBdcjcZhMNwPrLMtKWJZ13LKs\n9yAxKKWPA58D5lmWtam4TmJw9nUBM4r3q/AuCEoMJt8iYCOAZVn7gDnI+WjMpKmnGGRZloNXbQ7e\n1ZRfA7cOqTbvABpKUbbzjWmaG4DZeFfZ10kMJt0/AX8C/GHxcVhiUBLLTdN8CKjGa14lcZhcc4Hy\nYgyqgPuRGJSEaZqXA0fwmtz2DnlKYnCWWZb1Y9M07zNNcy/ecfB64OtDNpEYTI6XgL8wTfMrwEJg\nPlAu56OxkRo/8Sqmad6Jl/j9ySue0kpQnPNSsSnDHwD/xcmfu8TgLDNN8/8Az1iWdWCETSQGk2MP\nXrJ3J14C/h1OvlgpcTj7NLyajjfhNXf7HnI+KpV34/X/fiWJwVlmmubbgcOWZS0EbsT7Xh5KYjAJ\nLMt6GK/Gbz3w58BOoDBkE4nDaZDET5zENM1bgY8Bt1uWFQeSxUEuAGYBrSUr3HnANM1Vpmk2AViW\n9TzeD92ExGBS3QHcaZrms3g/tv4OOQ4mnWVZx4pNn1WxWU8bUCVxmFTtwAbLsuxiDBLI+ahUrgc2\nAJ2caHYIEoPJcDXwCIBlWS8AZUDNkOclBpPEsqy/tSzr6uJgOlXAUTkfjY0kfmKQaZqVwBeA11mW\nNTCoxTrgzcX7bwZ+U4qynUeuBT4EYJpmHRBBYjCpLMu6x7Ksyy3LuhL4Nt6onhKDSWaa5ttM0/zL\n4v16vJFuv4fEYTI9CtxomqZeHOhFzkclYJpmI5C0LCtvWVYB2GWa5tri029CYnC27QWuADBNcw7e\nBZCdEoPJZZrmxaZpfrd4/zZgK3I+GjNNKVXqMogpwjTN9+D14WgZsvoP8X78hoBDwDuLXzziLChe\nufoO3sAuZXhN3TbjDSUtMZhkpmneDxzEu9orMZhEpmlGgR8CMSCAdyxsQ+IwqUzTfC9e03+AT+NN\n8SMxmESmaa4CPm1Z1u3Fx8uBf8O7eP+cZVkfLGX5prvidA7fxbv45MNrBdKGxGBSFadz+C7ewGtZ\n4G14fV7lfDQGkvgJIYQQQgghxDQnTT2FEEIIIYQQYpqTxE8IIYQQQgghpjlJ/IQQQgghhBBimpPE\nTwghhBBCCCGmOUn8hBBCCCGEEGKa85W6AEIIIcSpmKapgCbLso6+Yv1ngUOWZf2raZrvBz4OfM2y\nrH84zf3eB3wdOPKKp45ZlnXTmZf89Jim+ceWZf37ZL2fEEKI848kfkIIIc5ZlmV9dMjDNwMfsyzr\nO2PczTOWZd08gcUak+IE9R8BJPETQghx1kjiJ4QQ4pxlmub3gb1ABbAGWGaaZhPwWeALwG14E8B/\ny7Ksz4xx3z68Ccs/ZVnWz03TnA88A1wKfAboBS4BFgNbgLdYlpUuTrD9TaAByOFNKry5uM+/At6L\nN/HwL4EPARuA2aZp7gIuAlYB/wKEARf4gGVZ60zTnFt8/88CfwxUAx+0LOsB0zQ14J+ANwIFvCTy\n28AxYJ5lWe3F9/8i4LMs68/H8lkIIYQ490kfPyGEEOc8y7I+AmwEPmJZ1v14NWjLgRXABcBdpmm+\nboz7tPESrM+ZphnCS6zutyyrtbjJG4G7gCagEvhj0zR14H+BH1iWtRh4H/CgaZo+0zTXAu8GLgYu\nBNYWX/8u4LBlWUsty8oD3wK+YFnWUuAfgX8dUqwawLUsawXw58Cni+vfBqzGS0IvA/4UWASsA+4Z\n8vo3Aj8ey+cghBBiepAaPyGEENPR64F/tCwrB+RM0/wB8Ca8WrZXWlOsbRvqW5ZlfcmyrM2maf4S\n+AleDdvQJOxBy7K6AUzT/F/gKuAxoBb4LoBlWU+bptlZfO424FeWZSWKr7ker0bwmle89yWAKt7/\nPTB/yHM+4HvF+1uB5uL91wI/tSyrABRM01wGpIEfAR8A/tk0zYsAw7KsZ4f7wIQQQkxvkvgJIYSY\njmLAl03THGjeGcSrERzOqfr4fQNoAf7Isiw1ZH3PkPu9QFXxfcuBnaZpDjxXAczAq60bqC3Esqw0\nwJDtBrwN+IBpmlHAALQhzzmWZaUG7hefp7jvviH7ThX3/RDw76ZpzgPeAFij/J1CCCGmMUn8hBBC\nTEetwBctyxquhm+sPgt8Bfgb0zQfGJJ41QzZphovEWwF+ovNNE9imublQ19jmuaMYbaZhdc/7wrL\nsp43TXMRXtJ5Kl2v2HcdkLEsq980zV8Ad+M1K33naexLCCHENCSJnxBCiOnoQeDdpmk+jDdAyseA\nzZZl/WYsOzFN8w5gFl7i5Ac+iTcgC8BtpmnGgARebdoDwCHgqGmad1mW9VPTNGuAr+H17XsIsEzT\n/Lvia/4XbxCYQ0CkOJjMTCAF7Co+fk+xHJFTFPUh4MOmaX4N77v9KeAP8QaO+WGxDLZlWVvG8vcL\nIYSYPiTxE0IIca54wjRNe8jjd4+y7deBucB2vKaSm/Fq7YYzXB8/gJvwEqa7LctSxYRth2ma/118\n/rfAz4FleM1Iv1vc7i3Av5qm+Wm8pPNLxVrCZ03T/ALwPF7fvofx+uBF8GoL2/BG9Pw1Xi1fO16S\nuRZ4Em+6ipE8gDci6B4gC3zHsqwNxecewWtu+s1RXi+EEGKa05RSp95KCCGEEIMGppGwLOvTp9p2\nKjBNczteAruj1GURQghRGjKdgxBCCDGNFWsgj0vSJ4QQ5zdp6imEEEJMU6ZpPoY36MtdpS6LEEKI\n0pKmnkIIIYQQQggxzUlTTyGEEEIIIYSY5iTxE0IIIYQQQohpThI/IYQQQgghhJjmJPETQgghhBDi\n/7dfBzIAAAAAg/yt7/GVRTAnfgAAAHMBa0TtD6123+IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff8fee1a690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize = (15,7))\n",
    "\n",
    "for year in years:\n",
    "    data = df[df.year == year][\"life\"].dropna()\n",
    "    sns.distplot(data,hist=False, kde=True, rug=False, bins=40, label = str(year));\n",
    "    \n",
    "plt.xlabel('Life Expectancy')\n",
    "plt.ylabel('Distribution')\n",
    "plt.title(\"Across the years\", fontsize = 18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">life</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1960.0</th>\n",
       "      <th>1961.0</th>\n",
       "      <th>1962.0</th>\n",
       "      <th>1963.0</th>\n",
       "      <th>1964.0</th>\n",
       "      <th>1965.0</th>\n",
       "      <th>1966.0</th>\n",
       "      <th>1967.0</th>\n",
       "      <th>1968.0</th>\n",
       "      <th>1969.0</th>\n",
       "      <th>...</th>\n",
       "      <th>2004.0</th>\n",
       "      <th>2005.0</th>\n",
       "      <th>2006.0</th>\n",
       "      <th>2007.0</th>\n",
       "      <th>2008.0</th>\n",
       "      <th>2009.0</th>\n",
       "      <th>2010.0</th>\n",
       "      <th>2011.0</th>\n",
       "      <th>2012.0</th>\n",
       "      <th>2013.0</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>31.580049</td>\n",
       "      <td>32.095976</td>\n",
       "      <td>32.611878</td>\n",
       "      <td>33.127317</td>\n",
       "      <td>33.641244</td>\n",
       "      <td>34.153683</td>\n",
       "      <td>34.663122</td>\n",
       "      <td>35.170073</td>\n",
       "      <td>35.672024</td>\n",
       "      <td>36.169976</td>\n",
       "      <td>...</td>\n",
       "      <td>24018682.0</td>\n",
       "      <td>24860855.0</td>\n",
       "      <td>25631282.0</td>\n",
       "      <td>26349243.0</td>\n",
       "      <td>27032197.0</td>\n",
       "      <td>27708187.0</td>\n",
       "      <td>28397812.0</td>\n",
       "      <td>29105480.0</td>\n",
       "      <td>29824536.0</td>\n",
       "      <td>30551674.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>62.254366</td>\n",
       "      <td>63.273463</td>\n",
       "      <td>64.162854</td>\n",
       "      <td>64.887098</td>\n",
       "      <td>65.438195</td>\n",
       "      <td>65.827390</td>\n",
       "      <td>66.089317</td>\n",
       "      <td>66.287220</td>\n",
       "      <td>66.474951</td>\n",
       "      <td>66.681610</td>\n",
       "      <td>...</td>\n",
       "      <td>3014579.0</td>\n",
       "      <td>2992724.0</td>\n",
       "      <td>2968028.0</td>\n",
       "      <td>2940880.0</td>\n",
       "      <td>2912559.0</td>\n",
       "      <td>2884303.0</td>\n",
       "      <td>2856673.0</td>\n",
       "      <td>2829337.0</td>\n",
       "      <td>2801681.0</td>\n",
       "      <td>2773620.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>46.135122</td>\n",
       "      <td>46.590317</td>\n",
       "      <td>47.045000</td>\n",
       "      <td>47.496195</td>\n",
       "      <td>47.941902</td>\n",
       "      <td>48.376098</td>\n",
       "      <td>48.790805</td>\n",
       "      <td>49.187512</td>\n",
       "      <td>49.572707</td>\n",
       "      <td>49.955902</td>\n",
       "      <td>...</td>\n",
       "      <td>33461345.0</td>\n",
       "      <td>33960903.0</td>\n",
       "      <td>34507214.0</td>\n",
       "      <td>35097043.0</td>\n",
       "      <td>35725377.0</td>\n",
       "      <td>36383302.0</td>\n",
       "      <td>37062820.0</td>\n",
       "      <td>37762962.0</td>\n",
       "      <td>38481705.0</td>\n",
       "      <td>39208194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>59262.0</td>\n",
       "      <td>59117.0</td>\n",
       "      <td>58652.0</td>\n",
       "      <td>57919.0</td>\n",
       "      <td>57053.0</td>\n",
       "      <td>56245.0</td>\n",
       "      <td>55636.0</td>\n",
       "      <td>55274.0</td>\n",
       "      <td>55128.0</td>\n",
       "      <td>55165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>79060.0</td>\n",
       "      <td>81223.0</td>\n",
       "      <td>81877.0</td>\n",
       "      <td>81292.0</td>\n",
       "      <td>79969.0</td>\n",
       "      <td>78659.0</td>\n",
       "      <td>77907.0</td>\n",
       "      <td>77865.0</td>\n",
       "      <td>78360.0</td>\n",
       "      <td>79218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>32.984829</td>\n",
       "      <td>33.386220</td>\n",
       "      <td>33.787585</td>\n",
       "      <td>34.188463</td>\n",
       "      <td>34.590341</td>\n",
       "      <td>34.992220</td>\n",
       "      <td>35.395098</td>\n",
       "      <td>35.799976</td>\n",
       "      <td>36.205854</td>\n",
       "      <td>36.610244</td>\n",
       "      <td>...</td>\n",
       "      <td>15976715.0</td>\n",
       "      <td>16544376.0</td>\n",
       "      <td>17122409.0</td>\n",
       "      <td>17712824.0</td>\n",
       "      <td>18314441.0</td>\n",
       "      <td>18926650.0</td>\n",
       "      <td>19549124.0</td>\n",
       "      <td>20180490.0</td>\n",
       "      <td>20820525.0</td>\n",
       "      <td>21471618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>61.782732</td>\n",
       "      <td>62.195415</td>\n",
       "      <td>62.598537</td>\n",
       "      <td>62.992585</td>\n",
       "      <td>63.378585</td>\n",
       "      <td>63.756073</td>\n",
       "      <td>64.125561</td>\n",
       "      <td>64.488073</td>\n",
       "      <td>64.842122</td>\n",
       "      <td>65.189195</td>\n",
       "      <td>...</td>\n",
       "      <td>81718.0</td>\n",
       "      <td>82565.0</td>\n",
       "      <td>83467.0</td>\n",
       "      <td>84397.0</td>\n",
       "      <td>85349.0</td>\n",
       "      <td>86300.0</td>\n",
       "      <td>87233.0</td>\n",
       "      <td>88152.0</td>\n",
       "      <td>89069.0</td>\n",
       "      <td>89985.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>65.215537</td>\n",
       "      <td>65.338512</td>\n",
       "      <td>65.432610</td>\n",
       "      <td>65.509390</td>\n",
       "      <td>65.582415</td>\n",
       "      <td>65.668683</td>\n",
       "      <td>65.782244</td>\n",
       "      <td>65.930024</td>\n",
       "      <td>66.113488</td>\n",
       "      <td>66.335098</td>\n",
       "      <td>...</td>\n",
       "      <td>38308779.0</td>\n",
       "      <td>38647854.0</td>\n",
       "      <td>38988923.0</td>\n",
       "      <td>39331357.0</td>\n",
       "      <td>39676083.0</td>\n",
       "      <td>40023641.0</td>\n",
       "      <td>40374224.0</td>\n",
       "      <td>40728738.0</td>\n",
       "      <td>41086927.0</td>\n",
       "      <td>41446246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>65.863463</td>\n",
       "      <td>66.284390</td>\n",
       "      <td>66.709854</td>\n",
       "      <td>67.137854</td>\n",
       "      <td>67.565415</td>\n",
       "      <td>67.991537</td>\n",
       "      <td>68.414732</td>\n",
       "      <td>68.830488</td>\n",
       "      <td>69.229268</td>\n",
       "      <td>69.600049</td>\n",
       "      <td>...</td>\n",
       "      <td>3025652.0</td>\n",
       "      <td>3014917.0</td>\n",
       "      <td>3002911.0</td>\n",
       "      <td>2989882.0</td>\n",
       "      <td>2977488.0</td>\n",
       "      <td>2968154.0</td>\n",
       "      <td>2963496.0</td>\n",
       "      <td>2964120.0</td>\n",
       "      <td>2969081.0</td>\n",
       "      <td>2976566.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>65.569366</td>\n",
       "      <td>65.988024</td>\n",
       "      <td>66.365537</td>\n",
       "      <td>66.713976</td>\n",
       "      <td>67.044293</td>\n",
       "      <td>67.369756</td>\n",
       "      <td>67.699000</td>\n",
       "      <td>68.034683</td>\n",
       "      <td>68.377146</td>\n",
       "      <td>68.728415</td>\n",
       "      <td>...</td>\n",
       "      <td>98742.0</td>\n",
       "      <td>100031.0</td>\n",
       "      <td>100830.0</td>\n",
       "      <td>101219.0</td>\n",
       "      <td>101344.0</td>\n",
       "      <td>101418.0</td>\n",
       "      <td>101597.0</td>\n",
       "      <td>101932.0</td>\n",
       "      <td>102384.0</td>\n",
       "      <td>102911.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          life                                              \\\n",
       "year                    1960.0     1961.0     1962.0     1963.0     1964.0   \n",
       "country                                                                      \n",
       "Afghanistan          31.580049  32.095976  32.611878  33.127317  33.641244   \n",
       "Albania              62.254366  63.273463  64.162854  64.887098  65.438195   \n",
       "Algeria              46.135122  46.590317  47.045000  47.496195  47.941902   \n",
       "American Samoa             NaN        NaN        NaN        NaN        NaN   \n",
       "Andorra                    NaN        NaN        NaN        NaN        NaN   \n",
       "Angola               32.984829  33.386220  33.787585  34.188463  34.590341   \n",
       "Antigua and Barbuda  61.782732  62.195415  62.598537  62.992585  63.378585   \n",
       "Argentina            65.215537  65.338512  65.432610  65.509390  65.582415   \n",
       "Armenia              65.863463  66.284390  66.709854  67.137854  67.565415   \n",
       "Aruba                65.569366  65.988024  66.365537  66.713976  67.044293   \n",
       "\n",
       "                                                                            \\\n",
       "year                    1965.0     1966.0     1967.0     1968.0     1969.0   \n",
       "country                                                                      \n",
       "Afghanistan          34.153683  34.663122  35.170073  35.672024  36.169976   \n",
       "Albania              65.827390  66.089317  66.287220  66.474951  66.681610   \n",
       "Algeria              48.376098  48.790805  49.187512  49.572707  49.955902   \n",
       "American Samoa             NaN        NaN        NaN        NaN        NaN   \n",
       "Andorra                    NaN        NaN        NaN        NaN        NaN   \n",
       "Angola               34.992220  35.395098  35.799976  36.205854  36.610244   \n",
       "Antigua and Barbuda  63.756073  64.125561  64.488073  64.842122  65.189195   \n",
       "Argentina            65.668683  65.782244  65.930024  66.113488  66.335098   \n",
       "Armenia              67.991537  68.414732  68.830488  69.229268  69.600049   \n",
       "Aruba                67.369756  67.699000  68.034683  68.377146  68.728415   \n",
       "\n",
       "                        ...      population                          \\\n",
       "year                    ...          2004.0      2005.0      2006.0   \n",
       "country                 ...                                           \n",
       "Afghanistan             ...      24018682.0  24860855.0  25631282.0   \n",
       "Albania                 ...       3014579.0   2992724.0   2968028.0   \n",
       "Algeria                 ...      33461345.0  33960903.0  34507214.0   \n",
       "American Samoa          ...         59262.0     59117.0     58652.0   \n",
       "Andorra                 ...         79060.0     81223.0     81877.0   \n",
       "Angola                  ...      15976715.0  16544376.0  17122409.0   \n",
       "Antigua and Barbuda     ...         81718.0     82565.0     83467.0   \n",
       "Argentina               ...      38308779.0  38647854.0  38988923.0   \n",
       "Armenia                 ...       3025652.0   3014917.0   3002911.0   \n",
       "Aruba                   ...         98742.0    100031.0    100830.0   \n",
       "\n",
       "                                                                     \\\n",
       "year                     2007.0      2008.0      2009.0      2010.0   \n",
       "country                                                               \n",
       "Afghanistan          26349243.0  27032197.0  27708187.0  28397812.0   \n",
       "Albania               2940880.0   2912559.0   2884303.0   2856673.0   \n",
       "Algeria              35097043.0  35725377.0  36383302.0  37062820.0   \n",
       "American Samoa          57919.0     57053.0     56245.0     55636.0   \n",
       "Andorra                 81292.0     79969.0     78659.0     77907.0   \n",
       "Angola               17712824.0  18314441.0  18926650.0  19549124.0   \n",
       "Antigua and Barbuda     84397.0     85349.0     86300.0     87233.0   \n",
       "Argentina            39331357.0  39676083.0  40023641.0  40374224.0   \n",
       "Armenia               2989882.0   2977488.0   2968154.0   2963496.0   \n",
       "Aruba                  101219.0    101344.0    101418.0    101597.0   \n",
       "\n",
       "                                                         \n",
       "year                     2011.0      2012.0      2013.0  \n",
       "country                                                  \n",
       "Afghanistan          29105480.0  29824536.0  30551674.0  \n",
       "Albania               2829337.0   2801681.0   2773620.0  \n",
       "Algeria              37762962.0  38481705.0  39208194.0  \n",
       "American Samoa          55274.0     55128.0     55165.0  \n",
       "Andorra                 77865.0     78360.0     79218.0  \n",
       "Angola               20180490.0  20820525.0  21471618.0  \n",
       "Antigua and Barbuda     88152.0     89069.0     89985.0  \n",
       "Argentina            40728738.0  41086927.0  41446246.0  \n",
       "Armenia               2964120.0   2969081.0   2976566.0  \n",
       "Aruba                  101932.0    102384.0    102911.0  \n",
       "\n",
       "[10 rows x 108 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stacked = df.set_index([\"country\", \"year\"]).unstack(\"year\")\n",
    "df_stacked.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>1960.0</th>\n",
       "      <th>1961.0</th>\n",
       "      <th>1962.0</th>\n",
       "      <th>1963.0</th>\n",
       "      <th>1964.0</th>\n",
       "      <th>1965.0</th>\n",
       "      <th>1966.0</th>\n",
       "      <th>1967.0</th>\n",
       "      <th>1968.0</th>\n",
       "      <th>1969.0</th>\n",
       "      <th>...</th>\n",
       "      <th>2004.0</th>\n",
       "      <th>2005.0</th>\n",
       "      <th>2006.0</th>\n",
       "      <th>2007.0</th>\n",
       "      <th>2008.0</th>\n",
       "      <th>2009.0</th>\n",
       "      <th>2010.0</th>\n",
       "      <th>2011.0</th>\n",
       "      <th>2012.0</th>\n",
       "      <th>2013.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.573634</td>\n",
       "      <td>2.567146</td>\n",
       "      <td>2.558195</td>\n",
       "      <td>2.544707</td>\n",
       "      <td>2.528732</td>\n",
       "      <td>...</td>\n",
       "      <td>2.136854</td>\n",
       "      <td>2.209878</td>\n",
       "      <td>2.314439</td>\n",
       "      <td>2.419488</td>\n",
       "      <td>2.499537</td>\n",
       "      <td>2.542610</td>\n",
       "      <td>2.541659</td>\n",
       "      <td>2.494707</td>\n",
       "      <td>2.417756</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.573024</td>\n",
       "      <td>2.815854</td>\n",
       "      <td>2.124366</td>\n",
       "      <td>1.587854</td>\n",
       "      <td>1.243415</td>\n",
       "      <td>...</td>\n",
       "      <td>2.045610</td>\n",
       "      <td>1.817317</td>\n",
       "      <td>1.556317</td>\n",
       "      <td>1.306902</td>\n",
       "      <td>1.102951</td>\n",
       "      <td>0.962878</td>\n",
       "      <td>0.889122</td>\n",
       "      <td>0.872683</td>\n",
       "      <td>0.885610</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.240976</td>\n",
       "      <td>2.200488</td>\n",
       "      <td>2.142512</td>\n",
       "      <td>2.076512</td>\n",
       "      <td>2.014000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.930927</td>\n",
       "      <td>0.908415</td>\n",
       "      <td>0.887927</td>\n",
       "      <td>0.865780</td>\n",
       "      <td>0.840829</td>\n",
       "      <td>0.811049</td>\n",
       "      <td>0.778000</td>\n",
       "      <td>0.743317</td>\n",
       "      <td>0.711122</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.007390</td>\n",
       "      <td>2.008878</td>\n",
       "      <td>2.012390</td>\n",
       "      <td>2.017390</td>\n",
       "      <td>2.019902</td>\n",
       "      <td>...</td>\n",
       "      <td>3.564512</td>\n",
       "      <td>3.334024</td>\n",
       "      <td>3.033902</td>\n",
       "      <td>2.724000</td>\n",
       "      <td>2.454220</td>\n",
       "      <td>2.247512</td>\n",
       "      <td>2.115366</td>\n",
       "      <td>2.052268</td>\n",
       "      <td>2.028268</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.973341</td>\n",
       "      <td>1.930146</td>\n",
       "      <td>1.889537</td>\n",
       "      <td>1.849537</td>\n",
       "      <td>1.810610</td>\n",
       "      <td>...</td>\n",
       "      <td>1.036024</td>\n",
       "      <td>1.024561</td>\n",
       "      <td>1.010024</td>\n",
       "      <td>0.991390</td>\n",
       "      <td>0.967659</td>\n",
       "      <td>0.940366</td>\n",
       "      <td>0.912049</td>\n",
       "      <td>0.884268</td>\n",
       "      <td>0.860537</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.453146</td>\n",
       "      <td>0.443732</td>\n",
       "      <td>0.497415</td>\n",
       "      <td>0.604098</td>\n",
       "      <td>0.752683</td>\n",
       "      <td>...</td>\n",
       "      <td>1.023610</td>\n",
       "      <td>1.006000</td>\n",
       "      <td>0.987537</td>\n",
       "      <td>0.968122</td>\n",
       "      <td>0.948244</td>\n",
       "      <td>0.929366</td>\n",
       "      <td>0.911902</td>\n",
       "      <td>0.897390</td>\n",
       "      <td>0.885780</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.128073</td>\n",
       "      <td>2.130341</td>\n",
       "      <td>2.120634</td>\n",
       "      <td>2.091415</td>\n",
       "      <td>2.034634</td>\n",
       "      <td>...</td>\n",
       "      <td>2.207829</td>\n",
       "      <td>2.012024</td>\n",
       "      <td>1.794171</td>\n",
       "      <td>1.562390</td>\n",
       "      <td>1.332902</td>\n",
       "      <td>1.118122</td>\n",
       "      <td>0.931098</td>\n",
       "      <td>0.782366</td>\n",
       "      <td>0.672585</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.800390</td>\n",
       "      <td>1.710976</td>\n",
       "      <td>1.669146</td>\n",
       "      <td>1.663171</td>\n",
       "      <td>1.684122</td>\n",
       "      <td>...</td>\n",
       "      <td>0.415463</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>0.592805</td>\n",
       "      <td>0.661000</td>\n",
       "      <td>0.705659</td>\n",
       "      <td>0.725293</td>\n",
       "      <td>0.723951</td>\n",
       "      <td>0.704683</td>\n",
       "      <td>0.680512</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year                 1960.0  1961.0  1962.0  1963.0  1964.0    1965.0  \\\n",
       "country                                                                 \n",
       "Afghanistan             NaN     NaN     NaN     NaN     NaN  2.573634   \n",
       "Albania                 NaN     NaN     NaN     NaN     NaN  3.573024   \n",
       "Algeria                 NaN     NaN     NaN     NaN     NaN  2.240976   \n",
       "American Samoa          NaN     NaN     NaN     NaN     NaN       NaN   \n",
       "Andorra                 NaN     NaN     NaN     NaN     NaN       NaN   \n",
       "Angola                  NaN     NaN     NaN     NaN     NaN  2.007390   \n",
       "Antigua and Barbuda     NaN     NaN     NaN     NaN     NaN  1.973341   \n",
       "Argentina               NaN     NaN     NaN     NaN     NaN  0.453146   \n",
       "Armenia                 NaN     NaN     NaN     NaN     NaN  2.128073   \n",
       "Aruba                   NaN     NaN     NaN     NaN     NaN  1.800390   \n",
       "\n",
       "year                   1966.0    1967.0    1968.0    1969.0   ...      2004.0  \\\n",
       "country                                                       ...               \n",
       "Afghanistan          2.567146  2.558195  2.544707  2.528732   ...    2.136854   \n",
       "Albania              2.815854  2.124366  1.587854  1.243415   ...    2.045610   \n",
       "Algeria              2.200488  2.142512  2.076512  2.014000   ...    0.930927   \n",
       "American Samoa            NaN       NaN       NaN       NaN   ...         NaN   \n",
       "Andorra                   NaN       NaN       NaN       NaN   ...         NaN   \n",
       "Angola               2.008878  2.012390  2.017390  2.019902   ...    3.564512   \n",
       "Antigua and Barbuda  1.930146  1.889537  1.849537  1.810610   ...    1.036024   \n",
       "Argentina            0.443732  0.497415  0.604098  0.752683   ...    1.023610   \n",
       "Armenia              2.130341  2.120634  2.091415  2.034634   ...    2.207829   \n",
       "Aruba                1.710976  1.669146  1.663171  1.684122   ...    0.415463   \n",
       "\n",
       "year                   2005.0    2006.0    2007.0    2008.0    2009.0  \\\n",
       "country                                                                 \n",
       "Afghanistan          2.209878  2.314439  2.419488  2.499537  2.542610   \n",
       "Albania              1.817317  1.556317  1.306902  1.102951  0.962878   \n",
       "Algeria              0.908415  0.887927  0.865780  0.840829  0.811049   \n",
       "American Samoa            NaN       NaN       NaN       NaN       NaN   \n",
       "Andorra                   NaN       NaN       NaN       NaN       NaN   \n",
       "Angola               3.334024  3.033902  2.724000  2.454220  2.247512   \n",
       "Antigua and Barbuda  1.024561  1.010024  0.991390  0.967659  0.940366   \n",
       "Argentina            1.006000  0.987537  0.968122  0.948244  0.929366   \n",
       "Armenia              2.012024  1.794171  1.562390  1.332902  1.118122   \n",
       "Aruba                0.507463  0.592805  0.661000  0.705659  0.725293   \n",
       "\n",
       "year                   2010.0    2011.0    2012.0  2013.0  \n",
       "country                                                    \n",
       "Afghanistan          2.541659  2.494707  2.417756     NaN  \n",
       "Albania              0.889122  0.872683  0.885610     NaN  \n",
       "Algeria              0.778000  0.743317  0.711122     NaN  \n",
       "American Samoa            NaN       NaN       NaN     NaN  \n",
       "Andorra                   NaN       NaN       NaN     NaN  \n",
       "Angola               2.115366  2.052268  2.028268     NaN  \n",
       "Antigua and Barbuda  0.912049  0.884268  0.860537     NaN  \n",
       "Argentina            0.911902  0.897390  0.885780     NaN  \n",
       "Armenia              0.931098  0.782366  0.672585     NaN  \n",
       "Aruba                0.723951  0.704683  0.680512     NaN  \n",
       "\n",
       "[10 rows x 54 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff_df = df_stacked['life'].diff(periods = 5 ,axis = 1)\n",
    "dff_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Ukraine                     21\n",
       "Belarus                     21\n",
       "Botswana                    21\n",
       "Lithuania                   20\n",
       "Latvia                      20\n",
       "Zambia                      19\n",
       "Zimbabwe                    18\n",
       "Russian Federation          17\n",
       "Central African Republic    17\n",
       "Iraq                        16\n",
       "Kenya                       15\n",
       "Estonia                     15\n",
       "Uganda                      14\n",
       "Swaziland                   14\n",
       "Bulgaria                    14\n",
       "Azerbaijan                  13\n",
       "South Africa                13\n",
       "Lesotho                     13\n",
       "Moldova                     12\n",
       "Cameroon                    12\n",
       "Armenia                     12\n",
       "Hungary                     12\n",
       "Sierra Leone                12\n",
       "Romania                     11\n",
       "Namibia                     11\n",
       "Kazakhstan                  11\n",
       "Jamaica                     11\n",
       "Togo                        10\n",
       "Gabon                       10\n",
       "Rwanda                      10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decline_lexp = (dff_df < 0).sum(axis = 1).sort_values(ascending = False)\n",
    "decline_lexp.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA24AAAG8CAYAAACmOPZqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9391fd950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_country(country):\n",
    "    dff_df.loc[country].plot(figsize = (15,7), label = country)\n",
    "\n",
    "for country in decline_lexp.head(7).index:\n",
    "    plot_country(country)\n",
    "    \n",
    "plt.title(\"Countries with significant decline in Life expectency growth\")\n",
    "plt.legend()\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<a id=\"loadmore\"></a>\n",
    "### D. Add more data \n",
    "\n",
    "Add more data about development to the DataFrame. For each data set, you'll include the country and year columns, plus a column that contains the key development variable for that data set. For the employment data set, you'll derive a column from three original columns.\n",
    "\n",
    "For each of these data sets, get an access key link for the data and paste the link into the appropriate section of the following cell:\n",
    "* [Population below national poverty line, total, percentage](https://apsportal.ibm.com/exchange/public/entry/view/3a42de810e682d5a83e76fda9fea2bbe)\n",
    "* [Primary school completion rate % of relevant age group by country](https://apsportal.ibm.com/exchange/public/entry/view/97fc6c420265012ae9b5e9439b9ee293)\n",
    "* [Total employment, by economic activity (Thousands)](https://apsportal.ibm.com/exchange/public/entry/view/21c789ae4cfadf52f1bb14b5d4a0b99c)\n",
    "* [Births attended by skilled health staff (% of total) by country](https://apsportal.ibm.com/exchange/public/entry/view/87160cc8374805cb3b07dff4b4f9223e)\n",
    "* [Measles immunization % children 12-23 months by country](https://apsportal.ibm.com/exchange/public/entry/view/752e36ea889eca360e989baa785c2a5e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def clean_year(df):\n",
    "    if df.year.dtype not in (np.int64, np.float64):\n",
    "        df = df[df.year.apply(lambda x: x.isdigit())]\n",
    "    df['year'] = df['year'].astype(float)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 837 entries, 0 to 836\n",
      "Data columns (total 3 columns):\n",
      "country    623 non-null object\n",
      "year       623 non-null float64\n",
      "poverty    623 non-null float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 19.7+ KB\n",
      "None\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5078 entries, 0 to 5077\n",
      "Data columns (total 3 columns):\n",
      "country    5078 non-null object\n",
      "year       5078 non-null float64\n",
      "school     5078 non-null float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 158.7+ KB\n",
      "None\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2909 entries, 0 to 99985\n",
      "Data columns (total 3 columns):\n",
      "country       2909 non-null object\n",
      "year          2909 non-null float64\n",
      "employment    2909 non-null float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 90.9+ KB\n",
      "None\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1468 entries, 0 to 1467\n",
      "Data columns (total 3 columns):\n",
      "country    1468 non-null object\n",
      "year       1468 non-null float64\n",
      "births     1467 non-null float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 34.5+ KB\n",
      "None\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6725 entries, 0 to 6724\n",
      "Data columns (total 3 columns):\n",
      "country    6725 non-null object\n",
      "year       6725 non-null float64\n",
      "measles    6725 non-null float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 157.7+ KB\n",
      "None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/justin/Envs/data_sci/lib/python2.7/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# Population below national poverty line, total, percentage\n",
    "poverty = pd.read_csv(\"data/UNdata_Export_20151013_112943678-poverty_clean.csv\",usecols=['Country or Area', 'Year','Value'])\n",
    "poverty.columns = ['country', 'year','poverty']\n",
    "\n",
    "# Primary school completion rate % of relevant age group by country\n",
    "school = pd.read_csv(\"data/UNdata_Primary_completion_rate_total (%_relevant_age_group.csv\",usecols=['Country or Area', 'Year','Value'])\n",
    "school.columns = ['country', 'year','school']\n",
    "\n",
    "# Total employment, by economic activity (Thousands)\n",
    "employmentin = pd.read_csv(\"data/Total employment, by economic activity .csv\",usecols=['Country or Area', 'Year','Value','Sex','Subclassification'])\n",
    "employment = employmentin.loc[(employmentin.Sex=='Total men and women')&\n",
    "                              (employmentin.Subclassification=='Total.')]\n",
    "employment = employment.drop('Sex', 1)\n",
    "employment = employment.drop('Subclassification', 1)\n",
    "employment.columns = ['country', 'year','employment']\n",
    "\n",
    "# Births attended by skilled health staff (% of total) by country\n",
    "births = pd.read_csv(\"data/Births attended by skilled health staff (% of total).csv\",usecols=['Country or Area', 'Year','Value'])\n",
    "births.columns = ['country', 'year','births']\n",
    "\n",
    "# Measles immunization % children 12-23 months by country\n",
    "measles = pd.read_csv(\"data/UNdata_Immunization_measles_%_children_12to23_months.csv\",usecols=['Country or Area', 'Year','Value'])\n",
    "measles.columns = ['country', 'year','measles']\n",
    "\n",
    "\n",
    "merge_lists = [poverty, school, employment, births, measles]\n",
    "merge_lists = map(clean_year, merge_lists)\n",
    "\n",
    "for data in merge_lists:\n",
    "    print data.info() \n",
    "    print \"\"  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>life</th>\n",
       "      <th>population</th>\n",
       "      <th>poverty</th>\n",
       "      <th>school</th>\n",
       "      <th>employment</th>\n",
       "      <th>births</th>\n",
       "      <th>measles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>76.290537</td>\n",
       "      <td>2968028.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>935.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>76.464854</td>\n",
       "      <td>2940880.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>76.632317</td>\n",
       "      <td>2912559.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>76.801878</td>\n",
       "      <td>2884303.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.3</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>76.978512</td>\n",
       "      <td>2856673.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>77.163220</td>\n",
       "      <td>2829337.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>77.350463</td>\n",
       "      <td>2801681.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2773620.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>46.135122</td>\n",
       "      <td>11277757.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>46.590317</td>\n",
       "      <td>11560650.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country    year       life  population  poverty  school  employment  \\\n",
       "100  Albania  2006.0  76.290537   2968028.0      NaN     NaN       935.0   \n",
       "101  Albania  2007.0  76.464854   2940880.0      NaN     NaN         NaN   \n",
       "102  Albania  2008.0  76.632317   2912559.0     12.4     NaN         NaN   \n",
       "103  Albania  2009.0  76.801878   2884303.0      NaN     NaN         NaN   \n",
       "104  Albania  2010.0  76.978512   2856673.0      NaN     NaN         NaN   \n",
       "105  Albania  2011.0  77.163220   2829337.0      NaN     NaN         NaN   \n",
       "106  Albania  2012.0  77.350463   2801681.0     14.3     NaN         NaN   \n",
       "107  Albania  2013.0        NaN   2773620.0      NaN     NaN         NaN   \n",
       "108  Algeria  1960.0  46.135122  11277757.0      NaN     NaN         NaN   \n",
       "109  Algeria  1961.0  46.590317  11560650.0      NaN     NaN         NaN   \n",
       "\n",
       "     births  measles  \n",
       "100    99.0     95.0  \n",
       "101     NaN     97.0  \n",
       "102     NaN     98.0  \n",
       "103    99.3     97.0  \n",
       "104     NaN     99.0  \n",
       "105     NaN     99.0  \n",
       "106     NaN     98.0  \n",
       "107     NaN     99.0  \n",
       "108     NaN      NaN  \n",
       "109     NaN      NaN  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "for data in merge_lists:\n",
    "    df2 = df2.merge(data, how='left', sort=True, on=['country','year'])\n",
    "    \n",
    "df2[100:110]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>life</th>\n",
       "      <th>population</th>\n",
       "      <th>poverty</th>\n",
       "      <th>school</th>\n",
       "      <th>employment</th>\n",
       "      <th>births</th>\n",
       "      <th>measles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>31.580049</td>\n",
       "      <td>8774440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>32.095976</td>\n",
       "      <td>8953544.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>32.611878</td>\n",
       "      <td>9141783.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1963.0</td>\n",
       "      <td>33.127317</td>\n",
       "      <td>9339507.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>33.641244</td>\n",
       "      <td>9547131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country    year       life  population  poverty  school  employment  \\\n",
       "0  Afghanistan  1960.0  31.580049   8774440.0      NaN     NaN         NaN   \n",
       "1  Afghanistan  1961.0  32.095976   8953544.0      NaN     NaN         NaN   \n",
       "2  Afghanistan  1962.0  32.611878   9141783.0      NaN     NaN         NaN   \n",
       "3  Afghanistan  1963.0  33.127317   9339507.0      NaN     NaN         NaN   \n",
       "4  Afghanistan  1964.0  33.641244   9547131.0      NaN     NaN         NaN   \n",
       "\n",
       "   births  measles  \n",
       "0     NaN      NaN  \n",
       "1     NaN      NaN  \n",
       "2     NaN      NaN  \n",
       "3     NaN      NaN  \n",
       "4     NaN      NaN  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize Employment Column\n",
    "df2['employment'] = (df2['employment'] * 1000)/ df2['population']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>life</th>\n",
       "      <th>population</th>\n",
       "      <th>poverty</th>\n",
       "      <th>school</th>\n",
       "      <th>employment</th>\n",
       "      <th>births</th>\n",
       "      <th>measles</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1986.500000</td>\n",
       "      <td>66.247287</td>\n",
       "      <td>1.055207e+09</td>\n",
       "      <td>5.300000</td>\n",
       "      <td>98.367526</td>\n",
       "      <td>0.443804</td>\n",
       "      <td>95.172727</td>\n",
       "      <td>87.645161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1986.500000</td>\n",
       "      <td>56.111806</td>\n",
       "      <td>8.224687e+08</td>\n",
       "      <td>33.550000</td>\n",
       "      <td>73.182838</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.580000</td>\n",
       "      <td>56.482759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>1986.500000</td>\n",
       "      <td>74.271993</td>\n",
       "      <td>2.460156e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.460065</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.100000</td>\n",
       "      <td>91.617647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>1988.393443</td>\n",
       "      <td>61.209935</td>\n",
       "      <td>1.728327e+08</td>\n",
       "      <td>16.242105</td>\n",
       "      <td>87.232905</td>\n",
       "      <td>0.415135</td>\n",
       "      <td>60.028000</td>\n",
       "      <td>67.026316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russian Federation</th>\n",
       "      <td>1987.952381</td>\n",
       "      <td>67.269271</td>\n",
       "      <td>1.404060e+08</td>\n",
       "      <td>15.884615</td>\n",
       "      <td>92.921962</td>\n",
       "      <td>0.461047</td>\n",
       "      <td>99.258065</td>\n",
       "      <td>94.482759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           year       life    population    poverty  \\\n",
       "country                                                               \n",
       "China               1986.500000  66.247287  1.055207e+09   5.300000   \n",
       "India               1986.500000  56.111806  8.224687e+08  33.550000   \n",
       "United States       1986.500000  74.271993  2.460156e+08        NaN   \n",
       "Indonesia           1988.393443  61.209935  1.728327e+08  16.242105   \n",
       "Russian Federation  1987.952381  67.269271  1.404060e+08  15.884615   \n",
       "\n",
       "                       school  employment     births    measles  \n",
       "country                                                          \n",
       "China               98.367526    0.443804  95.172727  87.645161  \n",
       "India               73.182838         NaN  43.580000  56.482759  \n",
       "United States       98.460065         NaN  99.100000  91.617647  \n",
       "Indonesia           87.232905    0.415135  60.028000  67.026316  \n",
       "Russian Federation  92.921962    0.461047  99.258065  94.482759  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_df = df2.groupby('country').mean().sort_values(\"population\",ascending = False)\n",
    "avg_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>life</th>\n",
       "      <th>population</th>\n",
       "      <th>poverty</th>\n",
       "      <th>school</th>\n",
       "      <th>employment</th>\n",
       "      <th>births</th>\n",
       "      <th>measles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10174.000000</td>\n",
       "      <td>9200.000000</td>\n",
       "      <td>1.014100e+04</td>\n",
       "      <td>570.000000</td>\n",
       "      <td>3665.000000</td>\n",
       "      <td>2395.000000</td>\n",
       "      <td>1291.000000</td>\n",
       "      <td>5333.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1986.723216</td>\n",
       "      <td>63.173605</td>\n",
       "      <td>2.566048e+07</td>\n",
       "      <td>30.746929</td>\n",
       "      <td>77.700596</td>\n",
       "      <td>0.387395</td>\n",
       "      <td>83.867777</td>\n",
       "      <td>76.692293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.507562</td>\n",
       "      <td>11.630020</td>\n",
       "      <td>1.040334e+08</td>\n",
       "      <td>17.937522</td>\n",
       "      <td>27.586450</td>\n",
       "      <td>0.108826</td>\n",
       "      <td>24.182225</td>\n",
       "      <td>22.523123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1960.000000</td>\n",
       "      <td>19.504927</td>\n",
       "      <td>5.563000e+03</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>1.522030</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1973.000000</td>\n",
       "      <td>54.659305</td>\n",
       "      <td>6.419000e+05</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>59.485630</td>\n",
       "      <td>0.326853</td>\n",
       "      <td>72.700000</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1987.000000</td>\n",
       "      <td>66.434780</td>\n",
       "      <td>4.325000e+06</td>\n",
       "      <td>25.850000</td>\n",
       "      <td>89.318480</td>\n",
       "      <td>0.392152</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>72.039470</td>\n",
       "      <td>1.259322e+07</td>\n",
       "      <td>44.575000</td>\n",
       "      <td>98.092740</td>\n",
       "      <td>0.466632</td>\n",
       "      <td>99.700000</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2013.000000</td>\n",
       "      <td>83.323215</td>\n",
       "      <td>1.357380e+09</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>193.263340</td>\n",
       "      <td>0.729367</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               year         life    population     poverty       school  \\\n",
       "count  10174.000000  9200.000000  1.014100e+04  570.000000  3665.000000   \n",
       "mean    1986.723216    63.173605  2.566048e+07   30.746929    77.700596   \n",
       "std       15.507562    11.630020  1.040334e+08   17.937522    27.586450   \n",
       "min     1960.000000    19.504927  5.563000e+03    1.700000     1.522030   \n",
       "25%     1973.000000    54.659305  6.419000e+05   16.900000    59.485630   \n",
       "50%     1987.000000    66.434780  4.325000e+06   25.850000    89.318480   \n",
       "75%     2000.000000    72.039470  1.259322e+07   44.575000    98.092740   \n",
       "max     2013.000000    83.323215  1.357380e+09   96.000000   193.263340   \n",
       "\n",
       "        employment       births      measles  \n",
       "count  2395.000000  1291.000000  5333.000000  \n",
       "mean      0.387395    83.867777    76.692293  \n",
       "std       0.108826    24.182225    22.523123  \n",
       "min       0.000027     5.000000     1.000000  \n",
       "25%       0.326853    72.700000    65.000000  \n",
       "50%       0.392152    98.000000    85.000000  \n",
       "75%       0.466632    99.700000    94.000000  \n",
       "max       0.729367   100.000000    99.000000  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Sierra Leone', u'Angola', u'South Sudan', u'Niger', u'Mozambique',\n",
       "       u'Mali', u'Timor-Leste', u'Rwanda', u'Central African Republic',\n",
       "       u'Ethiopia', u'Equatorial Guinea', u'Afghanistan', u'Eritrea',\n",
       "       u'Somalia', u'Malawi', u'Chad', u'Nigeria', u'Guinea', u'Liberia',\n",
       "       u'Burundi', u'Zambia', u'Guinea-Bissau', u'Uganda', u'Burkina Faso',\n",
       "       u'Bhutan', u'Lesotho', u'Swaziland', u'Madagascar', u'Tanzania',\n",
       "       u'Benin', u'Cameroon', u'Nepal', u'Haiti', u'Togo', u'Cambodia',\n",
       "       u'Comoros', u'Zimbabwe', u'Ghana', u'Botswana', u'Senegal'],\n",
       "      dtype='object', name=u'country')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract bottom 30 countries by life expectency \n",
    "low_lexp_countries = df2.groupby(\"country\").median().drop('year', axis =1)\\\n",
    "                     .sort_values(\"life\", ascending = True).head(40).index\n",
    "    \n",
    "low_lexp_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtoAAALICAYAAABB8FejAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff8f5e95450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2_low_avg = df2[df2.country.apply(lambda x:x in list(low_lexp_countries))].groupby('country').mean()\n",
    "df2_low_avg = df2_low_avg[df2_low_avg.school < df2_low_avg.school.quantile(0.99)]\n",
    "scatter_matrix(df2_low_avg.drop(['year', 'population'], axis =1),figsize=(12, 12), diagonal='kde');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# from pandas.tools.plotting import scatter_matrix\n",
    "# # Remove Outliers\n",
    "# avg_df_trim = avg_df[avg_df.school < avg_df.school.quantile(0.99)]\n",
    "\n",
    "# scatter_matrix(avg_df_trim.drop(['year', 'population'], axis =1),figsize=(12, 12), diagonal='kde');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7wAAAMdCAYAAAC4A3p8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff8fcba4ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plott Scater \n",
    "matplotlib.style.use('seaborn-ticks')\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "avg_df_trim.plot.scatter(x='school', y='life', s=avg_df['population']*0.00001,\n",
    "                         grid = None, color = \"orange\",\n",
    "                         figsize=(16,12), edgecolors='black', lw= .5, ax=ax)\n",
    "\n",
    "\n",
    "# Annotating Top Population\n",
    "label_threshold = avg_df_trim[\"population\"].quantile(0.95)\n",
    "for i, txt in enumerate(avg_df_trim.index):\n",
    "    if avg_df_trim.loc[txt].population > label_threshold:\n",
    "        ax.annotate(txt, (avg_df_trim.school[i],avg_df_trim.life[i]), size = 20, color = \"Black\")\n",
    "    \n",
    "plt.grid(False)\n",
    "plt.suptitle('Life Expendency vs Education', fontsize=20)\n",
    "plt.xlabel('Primary Education Completion', fontsize=18)\n",
    "plt.ylabel('Life Expectency', fontsize=16)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}