{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After reading a [great blog post by Randy Olson](http://www.randalolson.com/2014/10/29/the-reigns-and-deaths-of-the-roman-emperors/) on roman emperors, I decided to revisit the data found in this [Wikipedia page](https://en.wikipedia.org/wiki/List_of_Roman_emperors). This will allow us to do a little bit of HTML parsing using beautiful soup as well as a little data visualization. My main focus will be to reproduce Randy's plot and then draw another type of plot using the same data to gain additional insight.\n",
    "\n",
    "First, let's get the data!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To grab the data in the table, we first need to download the Wikipedia webpage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = requests.get('https://en.wikipedia.org/wiki/List_of_Roman_emperors')\n",
    "r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A 200 response means we were able to download the webpage without any hiccups. Let's now parse the HTML data using beautiful soup:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "soup = BeautifulSoup(r.text, 'html.parser')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The soup contains the table data, which we now need to extract by tags. We're trying to find the elements in the table hierarchy that goes like this:\n",
    "\n",
    ">table tr td \n",
    "\n",
    "We can use the `select` function [defined in the BS4 documentation](http://www.crummy.com/software/BeautifulSoup/bs4/doc/#css-selectors) to do that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "items = soup.select('table tr td')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many items do we have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "994"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(items)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at a single item:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<td>40 years, 7 months and 3 days</td>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items[6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next item we want is 7 items later, because there are 7 columns in the table of the webpage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<td>22 years, 5 months and 27 days</td>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items[6 + 7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All we now need to do is to filter these accordingly (take every 7th, to jump from row to row):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dates = [items[i] for i in range(len(items)) if (i + 1) % 7 == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We take only the first 86 emperors in the table (before the fall of Rome)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dates = dates[:86]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's preview them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<td>40 years, 7 months and 3 days</td>,\n",
       " <td>22 years, 5 months and 27 days</td>,\n",
       " <td>3 years, 10 months and 6 days</td>,\n",
       " <td>13 years, 8 months and 18/19 days</td>,\n",
       " <td>13 years, 7 months and 27 days</td>,\n",
       " <td>7 months and 7 days</td>,\n",
       " <td>3 months and 1 day (91 days)</td>,\n",
       " <td>8 months and 3 days</td>,\n",
       " <td>9 years, 6 months and 3 days</td>,\n",
       " <td>2 years, 2 months and 20 days</td>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's extract the text:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dates = [items.text for items in dates]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['40 years, 7 months and 3 days',\n",
       " '22 years, 5 months and 27 days',\n",
       " '3 years, 10 months and 6 days',\n",
       " '13 years, 8 months and 18/19 days',\n",
       " '13 years, 7 months and 27 days',\n",
       " '7 months and 7 days',\n",
       " '3 months and 1 day (91 days)',\n",
       " '8 months and 3 days',\n",
       " '9 years, 6 months and 3 days',\n",
       " '2 years, 2 months and 20 days']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the same way, we can extract the emperor names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "names = [items[i] for i in range(len(items)) if (i + 6) % 7 == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "names = names[:86]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's preview that also:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<td><b><a href=\"/wiki/Augustus\" title=\"Augustus\">Augustus</a></b><br/>\n",
       " <small>IMPERATOR CAESAR DIVI FILIVS <b>AVGVSTVS</b></small></td>,\n",
       " <td><b><a href=\"/wiki/Tiberius\" title=\"Tiberius\">Tiberius</a></b><br/>\n",
       " <small><b>TIBERIVS</b> IVLIVS CAESAR AVGVSTVS</small></td>,\n",
       " <td><b><a href=\"/wiki/Caligula\" title=\"Caligula\">Caligula</a></b><br/>\n",
       " <small><b>GAIVS</b> IVLIVS CAESAR AVGVSTVS GERMANICVS</small></td>,\n",
       " <td><b><a href=\"/wiki/Claudius\" title=\"Claudius\">Claudius</a></b><br/>\n",
       " <small>TIBERIVS <b>CLAVDIVS</b> CAESAR AVGVSTVS GERMANICVS</small></td>,\n",
       " <td><b><a href=\"/wiki/Nero\" title=\"Nero\">Nero</a></b><br/>\n",
       " <small><b>NERO</b> CLAVDIVS CAESAR AVGVSTVS GERMANICVS</small></td>,\n",
       " <td><b><a href=\"/wiki/Galba\" title=\"Galba\">Galba</a></b><br/>\n",
       " <small>SERVIVS SVLPICIVS <b>GALBA</b> CAESAR AVGVSTVS</small></td>,\n",
       " <td><b><a href=\"/wiki/Otho\" title=\"Otho\">Otho</a></b><br/>\n",
       " <small>MARCVS SALVIVS <b>OTHO</b> CAESAR AVGVSTVS</small></td>,\n",
       " <td><b><a href=\"/wiki/Vitellius\" title=\"Vitellius\">Vitellius</a></b><br/>\n",
       " <small>AVLVS <b>VITELLIVS</b> GERMANICVS AVGVSTVS</small></td>,\n",
       " <td><b><a href=\"/wiki/Vespasian\" title=\"Vespasian\">Vespasian</a></b><br/>\n",
       " <small>TITVS FLAVIVS CAESAR <b>VESPASIANVS</b> AVGVSTVS</small></td>,\n",
       " <td><b><a href=\"/wiki/Titus\" title=\"Titus\">Titus</a></b><br/>\n",
       " <small><b>TITVS</b> FLAVIVS CAESAR VESPASIANVS AVGVSTVS</small></td>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We still need to extract the right names from the HTML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "names = [name.find('a').text for name in names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Augustus',\n",
       " 'Tiberius',\n",
       " 'Caligula',\n",
       " 'Claudius',\n",
       " 'Nero',\n",
       " 'Galba',\n",
       " 'Otho',\n",
       " 'Vitellius',\n",
       " 'Vespasian',\n",
       " 'Titus']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The core of our work is done. Let's now move on to the visualization part."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing we will do is to build a table using pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data=list(zip(names, dates)), columns=('Emperor name', 'Years in office'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Emperor name</th>\n",
       "      <th>Years in office</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Augustus</td>\n",
       "      <td>40 years, 7 months and 3 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tiberius</td>\n",
       "      <td>22 years, 5 months and 27 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Caligula</td>\n",
       "      <td>3 years, 10 months and 6 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Claudius</td>\n",
       "      <td>13 years, 8 months and 18/19 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nero</td>\n",
       "      <td>13 years, 7 months and 27 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Galba</td>\n",
       "      <td>7 months and 7 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Otho</td>\n",
       "      <td>3 months and 1 day (91 days)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Vitellius</td>\n",
       "      <td>8 months and 3 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Vespasian</td>\n",
       "      <td>9 years, 6 months and 3 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Titus</td>\n",
       "      <td>2 years, 2 months and 20 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Domitian</td>\n",
       "      <td>15 years and 4 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Nerva</td>\n",
       "      <td>1 year, 4 months and 9 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Emperor name                    Years in office\n",
       "0      Augustus      40 years, 7 months and 3 days\n",
       "1      Tiberius     22 years, 5 months and 27 days\n",
       "2      Caligula      3 years, 10 months and 6 days\n",
       "3      Claudius  13 years, 8 months and 18/19 days\n",
       "4          Nero     13 years, 7 months and 27 days\n",
       "5         Galba                7 months and 7 days\n",
       "6          Otho       3 months and 1 day (91 days)\n",
       "7     Vitellius                8 months and 3 days\n",
       "8     Vespasian       9 years, 6 months and 3 days\n",
       "9         Titus      2 years, 2 months and 20 days\n",
       "10     Domitian                15 years and 4 days\n",
       "11        Nerva        1 year, 4 months and 9 days"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86, 2)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to drop emperors from the dataframe for which there are no data about the years in office:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = df[~ df['Years in office'].str.contains('Unknown')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85, 2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To replicate the plot in Randy Olson's blog, we need to convert the text containing the years in office to a number. We can do so using regular expressions:\n",
    "\n",
    "- we'll try matching the years\n",
    "- if that doesn't work, we match months\n",
    "- if that fails, we match days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p_years = re.compile('(\\d+) year[s]?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p_months = re.compile('(\\d+) month[s]?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p_days = re.compile('(\\d+) day[s]?')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now build a function that returns a timestamp for every emperor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def to_timestamp(s):\n",
    "    m = p_years.search(s)\n",
    "    if m:\n",
    "        return pd.Timedelta(value=int(m.groups()[0]) * 365, unit='D')\n",
    "    else:\n",
    "        m = p_months.search(s)\n",
    "        if m:\n",
    "            return pd.Timedelta(value=int(m.groups()[0]) * 30.5, unit='D')\n",
    "        else:\n",
    "            m = p_days.search(s)\n",
    "            return pd.Timedelta(value=int(m.groups()[0]), unit='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['Reign duration'] = [to_timestamp(s) for s in df['Years in office']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Emperor name</th>\n",
       "      <th>Years in office</th>\n",
       "      <th>Reign duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Augustus</td>\n",
       "      <td>40 years, 7 months and 3 days</td>\n",
       "      <td>14600 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tiberius</td>\n",
       "      <td>22 years, 5 months and 27 days</td>\n",
       "      <td>8030 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Caligula</td>\n",
       "      <td>3 years, 10 months and 6 days</td>\n",
       "      <td>1095 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Claudius</td>\n",
       "      <td>13 years, 8 months and 18/19 days</td>\n",
       "      <td>4745 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nero</td>\n",
       "      <td>13 years, 7 months and 27 days</td>\n",
       "      <td>4745 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Emperor name                    Years in office  Reign duration\n",
       "0     Augustus      40 years, 7 months and 3 days      14600 days\n",
       "1     Tiberius     22 years, 5 months and 27 days       8030 days\n",
       "2     Caligula      3 years, 10 months and 6 days       1095 days\n",
       "3     Claudius  13 years, 8 months and 18/19 days       4745 days\n",
       "4         Nero     13 years, 7 months and 27 days       4745 days"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now replicate the plot Randall Olson did:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Reign duration (years)'] = [duration.days / 365.25 for duration in df['Reign duration']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.style.use('bmh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8e82550>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mZZ1lb2n4hIwQQgghhBBCIsEcMkIIIYS0DF7/iWWYQ0aGC+aQEUIIIYQQQohB\nmENWQJ1lb7F0lr3F0ln2Flpn2VssnWVvoXWWvcXSWc5vCR3Xcj3E0ln2FlrHHLLWx7Sss+wtDZ+Q\nEUIIIYQQQkgkmENGCCGEkJbB6z+xDHPIyHDBHDJCCCGEEEIIMQhzyAqos+wtls6yt1g6y95C6yx7\ni6Wz7C20zrK3WDrL+S2h41quh1g6y95C65hD1vqYlnWWvaXhEzJCCCGEEEIIiQRzyAghhBDSMnj9\nJ5ZhDhkZLphDRgghhBBCCCEGYQ5ZAXWWvcXSWfYWS2fZW2idZW+xdJa9hdZZ9hZLZzm/JXRcy/UQ\nS2fZW2gdc8haH9OyzrK3NHxCRgghhBBCCCGRYA4ZIYQQQloGr//EMswhI8MFc8gIIYQQQgghxCDM\nISugzrK3WDrL3mLpLHsLrbPsLZbOsrfQOsveYuks57eEjmu5HmLpLHsLrWMOWetjWtZZ9paGT8gI\nIYQQQgghJBLMISOEEEJIy+D1n4Skt3cM+voq03A6OhSdnWuaKo85ZGS4qJdDNna4zRBCCCGEENIM\nfX2SOfjo7Izjh5AQMIesgDrL3mLpLHuLpbPsLbTOsrdYOsveQusse4uls5zfEjqu5XqIpbPsLY8u\nZL4Uc8iKqbPsLQ1zyAghhBBCCCEkEswhI4QQQkjL4PWfhCT08cQcMjJcMIeMEEIIIWQIhF5MghBC\nyjCHrIA6y95i6Sx7i6Wz7C20zrK3WDrL3kLrLHuLpbOc3xI6bqiyyotJzJixMPm3fdAArRVxW6Gz\n7C2PjjlkWXQPe0zLOsve0jCHjBBCCCGEEEIiwRwyQgghhLSMolz/i7IdIx3mkA3GsjcyQL0cMj4h\nI4QQQgghhJBIMIesgDrL3mLpLHuLpbPsLbTOsrdYOsveQusse4uls5zfEjpuEbYhtM6ytzw65pBl\n0T3sMS3rLHtLwydkhBBCCCGEEBIJ5pARQgghpGUU5fpflO0Y6TCHbDCWvZEBmENGCCGEEEIIIQZh\nDlkBdZa9xdJZ9hZLZ9lbaJ1lb7F0lr2F1ln2FktnOb8ldNwibENonWVveXTMIcuie9hjWtZZ9paG\nT8gIIYQQQgghJBLmcsh6e8cMevN9R4eis3NNS30SG7D+CSGkWBQlv6Uo2zHSYQ7ZYCx7IwPUyyEb\nO9xmGtHXJ5kHVWdnHD9keGH9E0IIIYSQ0YTpHLKQ83StzyEdTfNlY8zTt76tPE5aq7PsLZbOsrfQ\nOsveYunh3hnOAAAgAElEQVSK0K776oqwDaF1lr3l0TGHLIvuYY9pWWfZWxrmkBFCCCGEEEJIJMzl\nkHEe7OiG9U8IIcWiKO16UbZjpMMcssFY9kYG4HvICCGEEEIIIcQgzCEroM6ytzw61n9rdZa9hdZZ\n9hZLZ9lbaJ1lb7F0RWjXfXVF2IbQOsve8uiYQ5ZF97DHtKyz7C1NwwGZiHSIyG9F5E8i0iMiJyXf\nbygit4nIoyJyq4hMyBWZEEIIIYQQQkY5DXPIRGQigImqukhE1gOwAMAhAI4B8DdVPV9ETgewoaqe\nUf33zCEjeWD9E0JIsShKu16U7RjpMIdsMJa9kQGGlEOmqstVdVHy/1cB/BlAB9yg7IpEdgWAQ8PY\nJYQQQgghhJDRQa4cMhHpBDAVwH0ANlPVZwE3aAOwqX9J3UF1RZhDOprmyzKHzIbOsrfQOsveYuks\newuts+wtlq4I7bqvrgjbEFpn2VseHXPIsuge9piWdZa9pRnrK0ymK14H4GRVfVVEquc6Zs59vO66\n6zB37lwAnQDWBrBB8ptpFYa7uroAAD09dwMY3/97oBs9Pa+hq2vvTH2pVEJPT0/F5+rfp6n1+4H4\nPXV/PxLK89kfeT6HKK+jY1/09Ql6eu7GkiUPA9gPHR2Kvr67qraP9T+c5Q1X/ccuz3d/WK8v1v/o\nq//lywUTJ+4LALjxxofR0zMGBx7Yhc7ONSOm/oH9kn+7ASxCreu/9fofuD6VqX99sng85f1s8fzP\nezw12h8D5S1K/s0uz3r9Vw/EnN810esrdnllYrT/PT09WLFiBQBg2bJl2H333TF9+nRk4fUeMhEZ\nC+AmAL9W1YuS7/4MYJqqPpvkmd2pqttX/y1zyAjgX6+sf0IIGaAIbWIRtgEoznaMdJhDNhjL3sgA\nId5D9iMAj5QHYwnzAByd/P9fAdzYtENCCCGEEEIIGYX4LHu/D4CjALxfRB4QkYUi8iEA5wH4gIg8\nCmA6gHP9w3YH1VU/RmxWUxSdZW+O7qC6IuwTHiet1Vn2Fktn2VtonWVveXRFaBOZQ2ZDZ9lbHh1z\nyLLoHvaYlnWWvaUZ20igqvcAaKvx6/1zRSOEEEIIIYQQ0o9XDtlQYA4ZAYoxR5sQQoabIrSJRdgG\noDjbMdJhDtlgLHsjA9TLIWv4hIyQRvT2jkFfX+Xx1dGh6OxcU6iYsRhN20oIaQ62E4QQMnLJ9R6y\ncHQH1RVhDulIni/b1yeYMaMdM2YsTP5tH9QxqKTbK2493UDMgbj1Ylqur0a6vNvqG9fitrZKZ9lb\nLJ1lb6F1lr3l0RWhTWQOmQ2dZW95dMwhy6J72GNa1ln2libSgIwQQgghhBBCCHPIyJDxqbPQ9T+a\njpPRtK2EkEqK0CZa9paHomzHSIc5ZIOx7I0MEOI9ZIQQQgghhBBCAsMcsgLqLM9bjqWzXF95dDwn\nmtNZ9hZLZ9lbaJ1lb3l0RWgTmUNmQ2fZWx4dc8iy6B72mJZ1lr2l4RMyQgghhBBCCIkEc8jIkGEO\nWWsZTdtKCKmkCG2iZW95KMp2jHSYQzYYy97IAMwhI4QQQgghhBCDMIesgDrL85Zj6SzXVx4dz4nm\ndJa9xdJZ9hZaZ9lbHl0R2kTmkNnQWfaWR8ccsiy6hz2mZZ1lb2n4hIwQQgghhBBCIsEcMjJkmEPW\nHL29Y9DXVzmVuKND0dm5puK7ImwrIaQ5itAmWvaWh6Jsx0iHOWSDseyNDFAvh2zscJshhDj6+iSz\nAe3sjOOHEEIIIYQMP8whK6DO8rzlWDrL9eXoDqqzfJyMpnPCss6yt9A6y97y6IrQJjKHzIbOsrc8\nOuaQZdE97DEt6yx7S8McMkIIIYQQQgiJBHPIyJBhDllzjKZtJYQ0RxHaCcve8lCU7RjpMIdsMJa9\nkQH4HjJCCCGEEEIIMQhzyAqoszxvOZbOcn05uoPqLB8no+mcsKyz7C20zrK3PLoitInMIbOhC1VW\nb+8YlEptmDNnPkqlNpRKbejtHZOpSeuqNXl0lXQPeRvylJVHZ/kcs3xshtZZ9pZmbC41IYQQQggh\nSK8WPB6AmzJXvVpw5YrCTpe1orCvjpAiwhwyMmSYQ9Yco2lbCSHNUYR2wrK3PBRlO0JShOs/zzEy\nXDCHjBBCCCGEEEIMwhyyAuosz1uOpbNcX47uoDrLx8loOics6yx7C62z7C2PrghtInPIbOgsH5uh\ndZavw63QWT7/i3CsM4eMBKO3dwz6+gQ9PWMAtAEAOjoUnZ1r4hqLCPcJIc3Bc4dYhccmIWSkwByy\nUUiM+ddFmUMeEp4TpAjw+GwtRWgnYnkrwnXCOqPp+m+5/i17IwMwh4wQQgghhBBCDMIcsgLqYs2D\nH01zyK3Pv+c5ET+mdd1oyquxXA95dMwha1YXNqblY91XZ/nYDK2zfB1uhc7y+V+EY71VOWR8QkYI\nIYQQQgghkYg0IJsWVNfV1RVEUxSdb1kh92+e8mLoYu2TWPuO50T8mNZ1sY7hImxrLF2MNrEYx0nY\nmJaPdV+d5WMztM7ydbgVOsvnfxGO9fD15eAqi4QQQgghpJ/yCpVpuEIlIa2DOWQF1DGHbDDWcwN4\nTrRWZ9lbLF0xcoNGV/0zh6xZXdiYlo91X10jTV+fYMaMdsyYsTD5t33QAK2Sbi9vRbj+W69/y+e/\nxWM9tq4Mc8gIIYQQQgghJBLMISugjjlkg7GeG8BzorU6y95i6YqRGzS66p85ZM3qwsa0fKz76opw\nXffVWb4Ot0Jn+fy3fKzHziHjEzJCCCGEEEIIiUThc8h6e8egVGrDnDnzUSq1oVRqQ2/v4M321fnE\njK1jDtlgrOcGjOQcspFw7lg+X2PpipEbNLrqnzlkzerCxmxUXnWbWK89zBPXcj/Bss7ydbgVOsvn\nv+VjPXYOWeFXWSwnpgLjAbQDAObNexmdnc3pCCGV8NwhhJABqttEtoeEkEaMmhyyoswNZg5Zczrr\nuQFFyCGzfO5YPl9j6Vj/I0/HHLJmdWFjFqE/UYTruq9uNNWrY9qwx7Sss+wtDXPICCGEEEIIISQS\nhc8hixvT9nxZ5pA1H9Nybohv3GLkhhRjDnkR9gnrv/U65pA1qwsbswj9iSJc1311o6leHd3DHtOy\nzrK3NHxCRgghhBBCCCGRYA5ZS2Pani9rfV695TnklnNDfOMWIzekGHPIi7BPWP+t1zGHrFld2JhF\n6E8U4bruqxtN9eqYNuwxLesse0vDJ2SEEEIIIYQQEgnmkLU0pu35stbn1VueQ245N8Q3bjFyQ4ox\nh7wI+4T133odc8ia1YWNWYT+RBGu67660VSvju5hj2lZZ9lbGj4hI4QQQgghhJBIMIespTFtz5e1\nPq/e8hxyy7khvnGLkRtSjDnkRdgnrP/W65hD1qwubMwi9CeKcF331Y2menVMG/aYlnWWvaXhEzJC\nCCGEEEIIiUTDAZmIXCoiz4rIQ6nvNhSR20TkURG5VUQm5AvbHVTHHLLmyrI+r97yHHLLuSG+cYuR\nG1KMOeRF2Ces/9brmEPWrC5szJHcn+jtHYNSqQ1z5sxHqdSGUqkNvb31uoLdXjEt60ZDvVbSPewx\nLesse0sz1kNzGYBLAPw49d0ZAH6jqueLyOkAzky+I4QQQgghBunrE8yY0Q5gPIB2AMC8eS+jszOm\nK0JIwydkqloC8Peqrw8BcEXy/ysAHJov7LSgOuaQNVeW9Xn1lueQW84N8Y1bjNyQYswhL8I+Yf23\nXsccsmZ1YWMWoz8xzausIuhGV70Cls9/tv+1aTaHbFNVfRYAVHU5gE2bLIcQQgghhBBCRi2hFvXQ\nfPLuoDrmkDVXlvV59ZbnkFvODfGNW4zckGLMIS/CPmH9t17HHLJmdWFjFqM/0e1VVhF0o6teAcvn\nP9v/2vjkkGXxrIhspqrPishEAM/VEl533XWYO3cugE4AawPYIPnNNAADhsuP9np67oab2zwt0XWj\np+c1dHXtnakvlUro6emp+JxdHuqWB+yX/Luorr/qHVzr9wPxe+r+vhXl1dsf7vdjAHy4f384dm1p\neQP7txtuH0+rsX1+9e9bnu/+jVX/1Q2n2/41dfZv2V/t+srjz+fzcB9Pof3l2R8xzteY5bH+7ddX\nnvPfpz3x8RO6vvK216H8+V7/feur+f5EmPY6/PHUXddfdX01vj5ll1eU63/o4ylGf8LHT+z2ukjt\nf09PD1asWAEAWLZsGXbffXdMnz4dWYhq44dbItIJ4JequmPy+TwAL6rqecmiHhuqauaiHnfccYfu\nuuuuKJXakkTSAebNexldXaurNtBP50usuJaJsY9D10Os4yQkRTk2rfsjrYX131qK0E7E8malP2Gl\nHoA41+KiXP95jpGhsnDhQkyfPl2yfuez7P3PAMwHsK2ILBORYwCcC+ADIvIogOnJZ0IIIYQQQggh\nOfBZZfFIVd1CVddW1a1U9TJV/buq7q+q26nqB1X1pXxhu4Pqqh8j2okZR+dblvV59ZbnkIeuV+aQ\ntVZn+XyNpWP9jzwdc8ia1YWNWYz+RLdXWUXQja56BSyf/2z/axNqUQ9CCCGEEEIIITmJNCCbFlQ3\nkNBoLabtdy5YfzeL5feQhK5XvoestTrL52ssHet/5On4HrJmdWFjFqM/Mc2rrCLoRle9ApbPf7b/\ntRmbS02i0Ns7Bn19lTmAHR2Kzs41kRwRQgghhBBCQhDpCVl3UF3Rc8j6+gQzZrQnPwsxY0b7oAFa\nMzGtz6u3PIfccm6Ib9xi5IYUYw55EfYJ67/1OuaQNasLG3Mk9ycG6PYqqwi60VWvgOXzn+1/bZhD\nRgghhBBCCCGRYA5ZS2PanhtsfV695TnklnNDfOMWIzekGHPIi7BPWP+t1zGHrFld2JjF6E9M8yqr\nCLrRVa+A5fOf7X9t+ISMEEIIIYQQQiLBHLKWxrQ9N9j6vPoQut7eMSiV2jBnznyUSm0oldrQ21vv\nsPeLaTk3xDduMXJDijGHvAj7hPXfeh1zyJrVhY1ZjP5Et1dZRdCNrnoFLJ//bP9rw1UWSaEpL4gC\njAfQDgCYN+9ldHbGdEUIIYQQQoiDOWQtjWl7brD1efVhdWFjWs4N8Y1bjNyQYswhL8I+Yf23Xscc\nsmZ1YWNav54wh6yS0VWvgOXzn+1/bZhDRgghhBBCCCGRYA5ZS2PanhtsfV59WF3YmJZzQ3zjFiM3\npBhzyIuwT1j/rdcxh6xZXdiY1q8nzCGrZHTVK2D5/Gf7Xxs+ISOEEEIIIYSQSERa1GNaUB1zyFpX\nVivKYw5Z83GZQ9aczvoccov7pLd3DPr6BMB+KN/o6+hQdHauqfEX04J5C62zXA95dMwha1YXNqb1\n6wlzyCoZXfUK1POXt1233iYWof0vw1UWCSGEDGJghdIBuEIpIYSMXNiu24U5ZC2NaXtusPV59cwh\na17HHKL4Ma3rLB+boXWW6yGPjjlkzerCxizCOWH7eh1WN7rqFQjZJ7LeJhah/S/DHDJCCCGEEEII\niQTfQ9bSmMwhs6MLG9Nybohv3GLkhhRjDrnlfWL93BlN9c8csmZ1YWMW4Zywfb0Oqxtd9QqE7BNZ\nbxOL0P6XYQ4ZIYSQUc1AovsA9RcwKT4x9gnrgRAyWmEOWUtjMofMji5sTMu5Ib5xi5EbUow55Jb3\nifVzJ8S2lhPdZ8xYmPzbPmhg0CpveXTDmVfTqn1Sz9tAzIG49WL6x60dM39Z/uVZPidsX6/D6kZX\nvQLMIYsfM4+uDHPICCGEEEIIISQSzCFraUzmkNnRhY1pOTfEN24xckOKMYfc8j6xfu7E2FbL7b+v\nrijXCeaQNaub5lVWEXSjq14B5pDFj5lHV4ZPyAghhBBCCCEkEswha2lM5pDZ0YWNaTk3xDcuc8hG\nt87ysRlaV4Rj2NEdTFeU6wRzyJrVdXuVVQTd6KpXgDlk8WPm0ZXhKouEEOJJeRW4np4xANoAZK8C\nV63jSnF+pFfZ474jhAwF3/aajCyKep2INCCbFlTHHLLWldWK8phD1nxc5pA1pwtVVnkVOODD/d/N\nm/cyOjvr67I0rfCXR2fx2BzYb0DIfVeEY9gxLZiuKNcJ5pA1q5vmVdZI1vm21yFjprHdTvhobOZf\n5b1OWNyGLJhDRgghhBBCCCGRYA5ZS2Myh8yOLmxMy7khvnGZQ9a8zvrxVIQcshjHuuX6cnQH0xXl\nOsEcsmZ13V5lFUMXI6b1dsJHMxLyr7qHPSbfQ0YIIYQQQgghBYPvIWtpTOaQ2dGFjWk5N8Q3LnPI\nmtdZP55Gag5Zq8orwjHsmBZMV5TrBHPImtVN8yqrGLoYMa23Ez6akZB/NW3YY7Yqh4yrLBJCCCGE\nEEKCk14VsUy91Ykb6YoKc8haGtP2fFnr8+otzyG3nBviG5c5ZM3rrB9PzCGLH7MVOuaQNRs3ZFn+\n5dk+nrq9yiqGLkZM6+2EjyaMt/KqiO5nIWbMaB808Mqjq6R7SN4s6Mowh4wQQgghhBBCIsEcspbG\ntD1f1vq8estzyC3nhvjGZQ5Z8zrrxxNzyOLHbIWOOWTNxg1Zln95to+naV5lFUMXI6b1dsJHY7sN\n89WNlBwyPiEjhBBCCCGEkEiM2Byy3t4xKJXaUCq1Yc6c+SiV2tDbW29zhh4zjeW5wUXJDbA8h9xi\nbkjec6KRt3J55bKGco7lP1+ZQ9ZqnfVtZQ5ZFt3BdEW5ToTIlwrZ1jWjq7cNrWs7/bwVQxcjpvV2\nwkdjuw3z1Y2UHLIRu8piOfnPMR5AO+bNexmdnRFNERKR0OfEQHmuLABNl8fzlRBilZBtXWjYdhIy\nOihEDlkRcoiYQ9ZqXdiYo2ledRHm31uvL+aQtbY85pC1Pqbt4ylszCKcE9b3ieXrfzHaCR+N7TbM\nV8ccMkIIIYQQQgghdRmxOWT5dWFjWp4bXJTcgNFU/5bnVRdh/r31+mIOWWvLYw5Z62PaPp7CxizC\nOWF9n1i+/hejnfDR2G7DfHUjJYeMT8gIIYQQQgghJBKRFvWYFkEXNqbFucG9vWOSt5rvh/LAvKND\n0dm5JndZob3F14WNOZrmVRdh/r3F+ho4X4HyOVv/fK3vL295tnN+wpZnMYesmfq3fP7bPneGHrOV\nOuaQtVo3vDGr+2LD2a77+PPX2Lx25tWNlByyEbvKIhlM5WpMDq7GRIhNQp+vPP9HFqyv5uG+I5ap\nPj7ZrhMfmEPWpK4Ic4Ot5waMpvq3PK+6CPPvi1BfgO1cU8v5MtZzyIpw/lveVtvXJuaQtV4XI6a/\nLs6541fWaGrrmENGCCGEEEIIIaMUvoesSZ3FHLJ8Gn+d5bnB/rqwMUfTvGrreRUjNYesGZ3l9xVa\nzpexmEPWTFzL57/lbbV9bWIOWet1MWL66+KcO35ljaa2jjlkxCyVyaSOxsmkxDqsVzLcjLZjrnp7\nLW1r6LoYbXVLCBmgCOe/7za0eluH9IRMRD4kIn8RkcdE5HT/v+yOoAsbczTkkJWTSWfMWJj82z7o\nYGxF3NbowsYcyfOqR0q9ModsMCM1h2zgmBs47uofc/5xhzOHrLd3DEqlNsyZMx+lUhtKpTb09g6+\njFafY6G21eL5PzLakxgx/XXMIWu1LkZMf91IziEbCed/qG3w1VVfJ7KuEVk0PSATkTEAvgPgAAA7\nADhCRN7h99eLPKOE1IWN2dPTM2RdudJKpTbceOPDnhU3/NtaDJ29+m8mruV9YnkfF6O+wvqzvq31\ndHnbzlDbWr4gf+ELiz07H/b2XdyYsXT2vLXu+u97vNvbJ63TWfYWqy227C2sbri3ofo60fiGnGMo\nUxbfDWCxqj4JACJyNYBDAPyl8Z++5BkipC5MWeVHlj09r6BUagNQ/5HlihUrapZVuZzpSlx6abvH\ncqbDt63F0oWNWa9em9EVYZ9Y3sfFqK+w/qxvaz1d3rZzJG9r63SWvYXW2fMW+vqfnk5V7p/Un05l\nb5+0TmfPW/76Cu1v+K85sXT2t8ExlAHZ2wA8lfrcBzdIKzQDjejauPpq15jynRCEkFaRvnAvWzbG\n88JNCBlNVA7wXP+EfRO7sL5INZEW9eiNoBu+mOkO1P33P+XZgQrpL2RZRdGFKatct+V6Beonf8ap\nf19djJj+umXLlg1Z12x91dKl/sLLWwhd5YX7Gc8Ld+3yLG9raN1o2tb8uhgxY+lixLSuixEzli5G\nTOu6+poitJ0jZxscoqq5/qD/D0X2AnCOqn4o+XwGAFXV89K6b37zm/rggw/2f955550BAFOnTm0Y\nY9GiRcF0IcuyrrPsLZbOsrdYOsveQusse4uls+wttM6yt1g6y95C6yx7i6Wz7C20zrK3WDrL3kLr\nYnoDgOox0CmnnJKZVDaUAVkbgEcBTAfwDIA/ADhCVf/cVIGEEEIIIYQQMspoesqiqq4Wkc8BuA1u\ntcZLORgjhBBCCCGEEH+afkJGCCGEEEIIIWRoDOnF0ISQ0Yk4toztgxBCCCFkpGPqCZmIjAfwD1Vd\nIyLbAngHgF+r6puRrZFhQES2BtCnqitFZBqAnQD8WFVfakaX0m8IYEtVfailG9Bk3Fj+hoqI9Kjq\njnV+v1G9v1fVF8O7ioOIfKze71X1/+r8rQAYp6qvDSH27ar6SrK40q4Avq6qi5opLzQicjiAWxJ/\n/w3n76uqurDJ8joBPK2qb4hIF9z5f6WqvhzKcxEJVQ++57WI7NpA11T9hyD0NsTeVhF5G4BJSKWh\nqOpdrYxpmbx9hGH05X1NFJF9ACxS1ddE5BNw5+tF5Xf/ttAj++FVDNc+GfYBWb3Op4gsAPBeABsC\nuAfAHwG8oapHNRnL+4AWkbcA2Db5+GitHZ343wbAOuXvqhs+Edk52Q4AuFtVH0QVeRoMEdkbQCcq\nG9sfp35/MoDLALwCYC6AXQCcoaq3ZW1DKETk7QD+M8PbB5ssbxGA3ZPybgZwI4AdVPWgvDoR6QYw\nI/G1AMBzAO5R1f/MiBus4fONm8dfSJrplInIGADrVXd4ReQKAN9R1T/W+LulABRA1opCqqpTEt0v\nE10mqjoj0V3SQHdS3m1IflfzXBSRuvWhqt9KyrisvkyPrYr5YwCfA7AKbkGkjQFcUC4vDyLykKru\nlLQT5wL4JoAzVXWvvGUNhTrHSdlfF4CvArgAwBdVdc8m4ywCsAeArQDcAuAmANuo6kcytA07qs10\n3hrdRGkUN0aHMVQ95Div76xTjKrq+3PGfQXZ578k5bXnaEuCbkPIbU28AcBzPnUjIucB+DiARwCs\nTsUsb2vD/Van7JrtZkqT2T8RkZ4GcXdK/t6rzvLg25fwKMfrmuO7j32Pu0T7EICd4dqGy+H6drNU\ndb+UphX7zqsfnrff5NEm5u7Hhr6ZXau8HPvkfLi29R9w16adAPyHql7pE39Y3kOW1fkUkazOp6jq\n6yJyHID/p6rnJydWdXnrADgOwA6oHBgdWyWdA2DnZIB0Clwl/xjAfmlRckG8Au6lAQJgSxH514wL\n96cBnAygA8AiAHsBuBfA+1OakwEcD6B8R/xKEfmBql5S5e0XAHZPBjU/gGswfgagevDxEwBbJ/H6\nG9tkO8ocq6oXicgBcAfMJwH8BG7BlQpE5K2Jv05UnhjVHcZtAPwvgHeich9PScmuA3ApgCtT3jLx\njLtGVVeJyEcBXKKql4jIAxnF+egmqOrLSZ39WFW/lDRwWfgeJz8B8DlVXZF8ngTgR6o6vYm4XjrP\neii/huISANsDeAuANgCvZVxsz1bVa5NO2f5wnbI5APasKu9nAGbD1esfAbSLyEWqekFKtieAo0Tk\nSQCvoepCq6qTM7Y7i2946u731AHw3gag/rm4vk8sVT0mjzcAOyX1fySA2wGcDrd9gwZkyYXvHAxc\nzMr7uXwMlM+9jwD4vqreKCLnNFFOWefVRiRan31c9vdhAD9Q1V+JyFeb9Qd3/r8p7sngJap6cVY7\nUaujCqD6yYFvW9wNj+uYZ1yvmEl5awOYicH18ZUqXaN686qHpKyaHQvf81pV3+ejyxHX51z0aktC\nb0PebRWRCXDHevnG7e8AfEVVV+RoN8scCmA7VV1Zw5tXG5by5ttuNuqfDLpBUgPf9r8c06fT69uX\naFSe1zXHdx/nrNtVqqoicgjcjc9Lk75xmrz7zqeN9eqHw7PflMT1aRO9+rE52mGvAZ5neb775IOq\nelpy3PUC+FiyjV4DMqhqy38APJD8+2kAX07+/1CWDsB7ANwHdzcDAHoydNcC+B8ATwD4V7gKuyhD\ntzD594sAjkt/V6VbANeglT9vC2BBhq4HrlO8KPn8DgD/V6V5CMD41OfxNba17O2/AHw+vZ+qdH9O\nDoZ6+/eh5N+LAHy0VlnJ9/MBnAdgFtxFfiaAmRm6EtwrDR6CO3nPgbtgDNoGz2OgYVwAvwdwBICH\nAUxOvns4o6yGuqSuNk+OjT1qHXM5j5PPAPgLXKfpeACPATi4mbg5dA3rIdHdD+DtcOdQG4BjAPxv\nnXPxfwEcWee4Kx/jR8E9cVmr2l/iZ9CP7zHR6h+fbaiq/7rnYmBvf4K7APwcwLS03wztXwAcCGBT\nuCdpGwPYOPX7mwF8F6493BBuQP5g3nJSOq82IsdxchOA7wNYAmADAGsP0d8fAByenBNTku+y2olH\nAaztURe+bbHvdaxh3DzHHFwn8ecAToPr+JwC4JS89eZbD1X1+lG4m24Tylq4O+E1f4Z4XtSLu1G9\nn5xxvLYBrkNV82cI2/kLAF8GMCX5+RKSvgSApUkd/d6zrF/DPcUK1TZ5tZuJpmH/JPRPvWMkpfHq\nS/iW5+HJ69jMc+7ADdLPBLAYwES4NR8G9Ydz+mzYxsK/H+7Vb0q+92kTvfqx8G+Hy+3GAXAPSHbI\n8udTXo598nDy71wAH0r78PkZlidkAMaKyOZwF4ov1NH9O9wBeL2q/klEpgC4M0P3dlU9XEQOUdUr\nkjs6d2foXhGRMwF8AsC+yeP3tTJ0a6nqo+UPqvqYiGTp/qmq/xQRiMjaqvoXEdmuSiOofFq0GtmP\np5gyuxYAACAASURBVN8UkSPgBpQHl31k6B6GOxmfyfhdmQUichuAyQDOFJH1AdR6Ffk4VT29Tlll\n1lXVO0RE1D2CPid5bPvFlOZGETkBwPUA+u/Oafb0Bp+4x8Ddmfuaqi4Vkclwd0ia0X0FwK0ASqr6\nx+RYWlwjrtdxoqrfF5E/wR2TLwDYRVWXNxnXV+dTD2V/j4tIm6quBnBZckfwzCrZX0Xk+wA+AOC8\n5O571uI+ayXnwKFwd+feFBGtivdk8qRtG1W9LLlDv15GWUGQ/NMzGm5DQs1zUUQurudJ60yTbMBc\nAMvgzu/fichWAF6toV2hqr+uU9YsuJsEl6jq30VkCwBnNFFOGd82AvDbx7MAfAjAN9RNA90cbiDS\nrL/jAHwWwPmquiQ5/6/K0C2Bq8fMJwcpfNti3+uYT1zfmADQoaofqlNWmUb15lsPwMATtg8DuFZV\nV4j0X8a+WSeGIjVjpAnqxV2AOtO94AY2vvhuw8ENdDVzQxuwtarOTH3+cvmOu+Z/QvY6gEUicgcq\nr8PNtk2+7SZQp38iQ5gq2YB6x0gZ375E3fJyXHN8j808587HARwJ9+RoeXKNqHhKKbWnhZb97VT1\nlU8b69sP9+1fA35tom8/1rcdLtfFQQB+kmxLVv34lOe7T24Skb/APW39bNIn+mcdj5VGfIVDxKvz\nqaq/g7srUP68BEBWo1LO73pJRN4FYDnciL+a8gF9XK0DOuF+EZmLgceKRyH7UXWfiGwA4AYAt4vI\n3wFUz5e9DMDvReT65POhcHddqvFtMDYB8IiI/AGVjW2683kcgKkAlqh7rLpxUn4WN4nIQap6c43f\nl1mZnGCLxb1v7q8Y3Nn+dPLv2anvFC63I3dcVX0EqfpW1aVwd3xz61T1WrgnqeXPS+DuGGfhdZyI\nyCfhtvVTcNMabhaRYzSVI+gbN4c/n3oAgNfF5UEuSqZgPIPsgZZvp+z7cI/cHwRwl7jpmdW5QV+C\nm6e/HdxxvxbcObRPRnkhyDU9Ax7bkFDvXFzQpNe6qOqFAC4sfxaRp1C7I3uniFwA1/lLtwHlvL92\nuCepSAZjgNvmvOWU8W0jAL99vAmS9jQ5twB3p7Ypf6raA+DfUp+XAvhaRnm+HVXfttj3JopP3Dwd\nxvkismOy3fVoVG++9VAuK7NjoU1MRcxBvbh5Byo18d0GzT8V2Zd/iEiXqpbP233gtrmfpPN4FNxT\n4K8kdTZRVf9QVda85CcUvu0mUKd/ojmnSuagYafXty/hUV6UKbCJdrmI/AJuzQLA3QS+vkrmOy20\nTMM2Nkc/3Ld/Dfi1ib79WN922HeA17C88j4RkfVEZL1a+0RVz0j6XyvUvav5NQCHZO+SwVhbZfFO\nZIz2tSohVlzezS/gOsWXwXVQv6iq32sy7toATgTQlXx1N9w80ZqjeRHZD+7R9i2q+kbV73bDQKf0\nblXNnLvs6W2/rO+TA6Ss2beGZtAqS8ldq/FwJ8WbqHG3SkT2gJuOsAHc9NB2uEUH7mtyOxrGlYGE\n1+rtqM5xaagTt8hClmZQHkyObbgBwAmq+lzy+d1wuRhT88bNoauuhwlwTwbuq9JNAvAs3HS1/0h0\n31XVJ6p0WYNlqOqy7K2u+Nuxqroq9XkR3Lzshaq6S/LdQ9V35cTQilfV29CC8teBGyx0wdVvCcAc\nVf1nle6srL9X1a9nlJl1J051YEGBP2Pg7uw6ALYE8ISqVjy9b1ROSufVRtQi4zgp38Ut+5sMt3DS\nDk36W4zsc2fbKt2/ZvlT1St8tqNZQscVkUfgpiMvhauTilzNlK5uvfnWQ6q8jTDQsRgHoF0Hzwjw\n8e87sAgaN1Vew4UpQiGeCx2IyFS4vPUJcPXxIoCj0zf3RGQOXAfy/aq6vbgFB25T1T1avR3V1Go3\nffonTcarW2eNjhHfvoRveR5+fVfj9F6NV0SOB3AC3JTHrcXlk39PK3PWc+HTxvr2w3PGbdgm5unH\nesYcg4EB3kvJAO9t2sQCICKyI1x+3EZw5+vzAD6lqn+q0n0q6+81tQhf3TjDMSDL0fncLfVxHbgn\nBqtU9bQm46Yfmb8F7g7+q6o6IaVpg+sceq3kmJx45c7WPRl3l8tlbobKxOplVZpcDUYDT79MfVwH\nwLvhcuCGcgLtmrVtGbp3YPCCEz9rMubGqY/rwOWJbKSqX8yrE5GZVZqPwi2VPeiuhs9xUsfzW9ID\nct+4efz5ICInq+pFHt/5do4HTYkEKhcSEJE/qOq7RWShqu4qbmnYezM6ir6rZ24L97RuEirPnaaO\nY59tSHQ1z0XJOU1SRK6BSyIuP20/EsAGqnp4lS49tWwduOkyf9IAd+STGwWfVtUThlqWRyyvfVz1\nN7sC+DdV/XQtTYOYm6U+ls//Cap6do0/aVSe742gYDd58rT/yc2WQVR38pvwULMehtqxqCrLe2AR\nKq5kLEwBNzCqdQc/COKxMl6Vvh1A5jT/VLv6QOqG14OqunOVznfhp4/BPSXaFK79r3Uz1msRmdD4\n1pnPMeLbl/Atz8N71kAnVVT/zbPLGuj625LkuvluuFzCcv1XvGpGWjAt1LcfPpR+U424Xv3YHOMJ\nrwGeT3kiMh/AF1T1zuTzNLhXy+xdVVZ68b514PL/F6rqYVleqhmuKYs3pf7f3/msFqlq9fSge8Q9\nCgcAiOcS1KnP/Y/Mk7t0h8CtjJjWrBaRSdUd6yySzsfhGJg3fpmIXKuqX01pPg+XoPssBvLHFK6B\nTrN76v/9DUZGzIar56nqwVV/syWAb9fZjoZL9wP4pohMhFtJ8eeq+nBGOf8N4INwi5vcCpc8WYJb\nLSx3XFX9W9WffFsy8qV8dKr6i6rYVyXeBuFznCS/y1zdE0D/iesbt5Eu70AALhfloqrvjq7+Tqve\nG1bulGWESL8Tax24qRF/rtJcIy4fbYPkbt6xAH6YUZbvilfXAvheUkbdVTs98dkGoP65mHea5LtU\n9Z2pz3eKe8JRgapWTJ8RtwrVLbUKFZEPY/CqspkdJFX9g7gp2E2X49lGAP77OO1voYhkLuft409V\nn636s2+IyP2onDrt3VGFZ1sMz+uYZ1zfmP0DLxHZFJXtziBy1FvdeoB7rUDa33QAC1G5uq8ve5YH\nFkncv4ubXt3KuO9Ut4rpUXCLXpwBN/24pQMyNFgZT0Q+oapXVvdlJEltqerDvJnc3NVE81ZkT7m6\nDK7PcSGA98FN88qarn4+3CJUdc9PuBtmK+D2V938S5/+SQ5866zhMeLbl/AtrxHamimwK9W9axGA\ne0qJqj6BNjEttFEb26gfnhW7Xr8p+X3DNjFHP9arHUZlKkb/AA+DUwN8yhtfHowlXruTG9AVqOrn\nq7ZhAwBXZ3jLZFgGZL6dVKl8ad4YALvBPdIv0/ScZFVVADeIy3upTnhfAnfQzUOqg1E9wIObdrGz\nJtOPRORcuOVe00sHnwy3YmN1g1Dtx7fB+A6Af4HrrO4Ol7+0LerTB9dADkI8lu5P/L0vGZDNAvD9\n5E7ez9ODT7g5xFPh7gB8Ulw+0uXNxpXKx/5jku0ddIz66qrYBtl5hhU0OE5+ApdzcQDcvOOj0KDz\n6Rs3Q+c1EBC3MMCRACYnx2+ZdrhpMHWp1SlT1YrkYxH5BtygO635hoh8AC6/YDu4acO3Z4TxXcBg\nlarOaeTZF59tSHQ1z0XNP/VmoYjspcmU0mTf+iydvDbcuTEIEfkegHFwna25AA6DW2mw/Pv0U9Vy\nu1k9aGlYTkrn1UYAfvu4qvM5Bm4aV9ZAxtdf+uZW+fxfu1oHz45qjhtBvjd5GsbN02EUkRlwiwFs\nAfe+wklw7U71U+269eZbD4m/hh0L8X8Pke/AwjeuzxRor4Upko5kw+mU4v/+xkYLHZQ7cT59mYvh\ncoY2FZGvwZ0PWU+BfRd+etZjMAb4LyIDNNc/qYVXnXkeI959hLydaBm+KbC/Eze1fd3kOvtvAH7Z\n4G/q4tPGevTDB9Gg3wT43zRIk9mPzXHT22uA51neEhE5GwN5vp+AGzc04jW4WUheDNcTsmpqdVLT\nK9Wsgpsz3393SVW/nCeIVM7XLZ+UWSuePJH8jEH9hvJpuBF0uYy14RZZSPMU3N2lRt7yNBh1V8+T\nypcXlufN1ppueDLcHaH7kkHXOwAMyltJ4i4HcLG4x/GnwTXw6QHZP5InjKvEJUwuh+ssNBs33blb\nBZdYPCujrIY6Gfwofzncu54GkeM4abi6Zypu+cloZtxG/nIMBObDLeCxCSr3yytwy4JXx/XulFUx\nDhkDhmQAljUIS+O7gMEvReTfMHjVzoqBpTQ/tTFzG5oc4NdiN7hFGMpTlLcC8KgkU0V14GWoD2Cg\n/tvgXoGQeR4C2FvdC30fUtUvi8g34e4gl3lr6v+rAPwGqQVjcpRTxruNyCBrH6fb1FUAfgWXA9ys\nv+9WlbcU7uZQNV4d1SHUf63rWMO4OWP+D9zg6jequouIvA+uQ1BNo3rzrYcssjoWvu8hyhpY/PcQ\n4vq8w813YYr/h2Q6JdxNtleS8qunU3q9vxENFjpQ1e8n/zbsy6jqT5PjZjrc9eTQGgMq34Wf7heR\nn8MtSpZuX6tXivRdRKb89436J15TJZFvMZE0WceIb1/CqzzJ8W62gJwB1//tgXvlzs1w59hQ8Glj\n6/bDy+ToNwF+bWKefmwa35veNR9UeJR3LNxrKsrnyt1IzYwqI5Uzm8bAPRG8xiMmgOF7MbRX51g9\nV6oR/7n86RFy+aQctOJJuXEUkXGq+npGvPKBsgLAn0Tk9uTzB5DcXUh1dJcA6BaRX6Gy0at+2ubb\nYPisnpe+A78KwFWqek9GWYDf0v0Qke3hLi4zAfwN7j04p1TJHkjuJv0o8fAyMu5o+8bN8di/ri65\n67mDeixUkeB1nMBjdU+fKQQ+/sRzOdukcXsSwHuSC9g2qvobEVkXwLpwHYw0Xp2yqvhtcJ3+ryS/\nqzVvveytveqz74pX5cTf9FQDxeDlrL2mNtbbhiqGcvGuxvfOcnpO+SoAy7X2IkLl1ddeF7eK4t/g\nBnAAAPXPnapbTgqvNgLw28c5bqT5+vtE9VMYyV6sxrej6lX/OW7y+MTNc8y9qap/E5ExIjJGVe8U\nkaypPDXrTdwTqvVV9dQaMSrw7Fj4vLg2z8DCN27DKdCqejHcQLDMk8lAthrf6ZReL9VObmJ+K/V5\nGTKmvonHy9dF5Ceq+kmkVsJMfZfmZLgbISfBDd7fj4G2NE073Gp3H0xbxuCl+7sAHC0uz7HmIjIJ\nPv0Tr6mSvnXmc4z49iV8y0OEKbCqugbuGpeVBtAsDdtY3344/PtNgF+b6NWPFf+b3l4DPJ/yVPXv\nAE4S90L3Napa3a8qk57ZtArAk6raV0M7iOGasli3kyoi71fV30qNFWgy7uD45qR5zdcVkffALU2/\nHoCtkjt+n1HVcm5N+UBZgMplR7tT/y9v47Lk5y3JD5A9ePRtMD4J19H5HNzqeVti8PLoG6jHgg4J\nPkv3A26QdTWAA1S11rSWzyT//a6I3Aq3MlGtOxo140qNefWpON/Ko0s6Cb8CsGOWLuPvfOd1/0Bc\njsbZcMsMr4fsKUZvw+CnN+lcOR9/uZazldSKTAC2hntK8T24TlA/OTrH6fir4Ka7rErKWD+J+T9w\nF+CfwDVmRyGjAy2eCxjkuBD4Tm2suQ1VcYe8jLeItKubwpLZUJef8onIeFV9DW6VpjRrJ53orDvC\nNyXnzgVwFxQFMFfcqzXqDYyr29PMcjL+1LeNADz2cXID63BNppQl59DVqnpAk/6uh3uym+aGjO+8\nOqo+9Z/zJk/DuDmPuZdEZD0AdwH4qYg8h8rcvTI1603dTIY8r6Pw6VjkeQ/RYrgbdmMBN4CusS99\n4jacAi01FpvB4BsyvtMpvd7fKP5Pg26Eu8v+G9S+qVQ9JbUN7gl8Bar6x+S/r6L2627yXOcO9NQB\nfv0Tr6mSOeqs4TGSnAefwuABb9aiWT7H3LBPgU3O13Mw0JcoH0u5F35L0bCNTY6zD2Pwvqt+qDC3\nesCUeH4uI27dNjGJ+UH1WFzP56Z3gtcAz/Mm+h5wfeJy32cF3PvhKvLtdKiri+rwrLJ4h1Yt1Zn+\nTkS+rKpfkuwVaFQbrGKVXAhKmqx4IiKnqer5VSPkdIHVK979Hu6O9TwdWM3mYVV9V47NLJd1uLp3\nTGV+5zuoyBlzoaruWvVd/8pMdf5uP2Qs3Z+cHD9R1SNr/N02qrpYKnM5+jcBwIuqWj2Vs2ZcEfmM\nupcufylLrwNPML10ifYKuIbzj1naRJN5fKTKy73iobjFGT4O4BEMXGhVB6/G19BfzrgNV2RKvtsW\nwKkY3NiW80wyFxZI6fqnD0r2il9Z3+VZ8epdGJz4++MqzTlwjX7dqY2J1mfF04arikmDaZIicpOq\nfiQ1+JRUiP6LqIj8WlUPFPfesfRdufLFNvO1BFVe11H38tK6yx+r6h0+5TSIl9lGVGnq7mMRWaSp\nV0Mk39Vtn7L8JXWwPdwTiP9IydsBnKWVi6l4kxyfX0Llqwq+olV5XlnnU7P4HHMp7Xi4qUDlzt4E\nAD+t9lf1N4PqTdxqh2+De8KczpVu6uXG4nKMjwTwR1W9O+l8Tss4XzMXutLsJy4+cd8JN33sXlW9\nStwU6FmaWihHRNKzOfoXm6nuS4h74vFxuA7xFUimU2Zcw8fBPf3uSa59mwPYUVVvq9I9Do+nQVnn\nROp3ZwI4C26Gw+sYaEvegHs6d2ai+7aq/rvUWAAq45qTuShVVv9K3NTMbVT1smSQup66mQ25EZGL\n4F4eXXeqpG+decacD+A+uOl+/QNsbf7VEyfBPTV5EG6wshWAK1X1vVU6rxVFJXk9TLKfvwo3QPqi\nqu6Z0vwFrp1bgNSgPeu8z3EjIP03mdcAEbkZrr2p3ndfrtJl9TsHfeeLiJTg9lut68w71D31zyxf\nPVYFb7Y8caunnqiqdyefu+BejVVOQQiy2mVLB2RJAzAO7o3W0zDQsLTDXSjekdKOAXCYqnrPt0z9\n7XYAfqWqb08+H6yqvxTP98GIyO9VdU+psbys5HgbeqODNMfg4xpVnVUrdnIylxd06EJlLtP6cI9V\n+ztsktzBr9Xhru7MisjdAKZnnRwicqmqHpdostgYbmBwTJ64IrKJqr5Qo8xcJI3Z2+HuEL+GjE5A\n6vjYB24Q8PPk8+EAHlHV2VVl+nTcHwWwk9Z5h52vv0TntYpV9TEsbkWmhRnlPQj35Ky6kV+Q/D5r\nQJGSVbzrbT5cPs/Vyd8cAddo7Z3xt9Xbv0BVd6v67ktw7cQ74ebLHwh3o+WwKl1Wx6DCW6JLdwTX\npHTV++QWDKwqlt4n30xp6u63ViD5Zw4MqZy8bUTyNw33sbjpah8tD9LETa29PtUm+vr7KICPweUK\npV9+/Arc3c/yxTJvR/V2uKdP5VcVHAU3sNi/Slf3JkqeuD7HnC++9SYeNzxFpKSqXRkdjKEso/04\n3NTAegPI4HGryl8bwK2qOi3jd+/AwHTKO7IGU+L5/kYRuUf1/7P33fGSFNX35+ySlyjJr0iSICJZ\nEJAoiopKVMICIlFEwhJVBAEjoiCsoAgIK0gOAoIESUsUFhbYXVAk7Iog+EMUYUEEgfv749x+U1NT\n3V09b15A3v185vPedFdXV/d0V91w7rlWG4mk4I53WUXxdZLHFsZXyf4PmdlkZtYCI3kJBH/cEQEp\nlZmNi9odDeUDvd/MlqegbZeE15WjnwRtu3W0t/1mTZ6RHMOgv88cE7XZmF+qoFinj4WM/PMZOamK\nNb1qDEHbSkdAk7WEiVqiUV/rAvgIgAMhko5C5oXm+VWDtk3mxHMgPSdJrkfydDP7EuvrcpY9m206\nVm5/3rbDgdgf47NMBhqyuDf0o70HWngKJe8liJ2nT8zsLZJfRUYCHGswn2Z2lf/N9YY8RfIjAIwK\nS49DO3teLXyM5GaQorAYyRAHPS8ULi3GlpvUW0ySVeduQuhwvvcVJmyGf+Mw+AyUME+a2R7+/wYo\nEaoie9Z5SX4WYuH5L8m3IG/nXYk+fxJvC8XaI1oxHCrV/mzvdx8A6xeTK8VElDI2c+iAp0PwmUqD\nLGd8LrksVrcyj5GpEu5n+bBBQAv7eP8YgDt9W5swn8Dg81ANnwfcmF8ULSW5mzFmMZ4ij1Ws8r4x\nszBo0H4LALeaeycpGMn6ZhbCsTcCcDPasfp9XVKsT1WOomJMlf2glUPSdI4A8u7xEQDuIHmr97UB\nBK8tJGt8ZnY5gMtJrm9myRIWLgVhTG7Jgv8zs+8E379LMkUSsjaAnUiWOVGanLf2mWugMKZ+t0L6\nfjfLgKyZ2fr+NwfGk1uHqJboquF5syDQkSQJfVxy4JS/Reve9tVvRAQrRD5xxjgA3yBZWnzdzA5n\nRRmDwBm0IOSQrltzakmpXLYGsDo838bMnqFIu+LxAxm6Uc5zVyJtv1mTZwTArygY/9UoQVE0fOaG\nAgJ7C8kfQfNfeA2pSFAdLDR3DQCAa0l+wqLobyCzQekas6A9J/0ltOdGA83mxEpyPfO6mlYP9856\nNt0YGwVFxMs4Fwq51X+vC6D7tT3EFbGG93U/kO+4KZPBgizub2YnZ7T7AYDnoUhFaATU0ndH/TQt\n5roQpFR+HJoYfwdgXIYiF/axKpQ0eBxaTIRvQJ7jiaakwKZGBSi4yqtusC4P1fy61sz+m+ygR8IM\nWKC3qy0MTZIAFi97KKlw8Ham8PHaAH5oiUKaLIl4BueNI59ZsAsqqrWutTzJC0BMZe+P2tXCWEle\nBhkVN6F9Eu2AP+aMj+R9ZrZm6LUq8daMguAoRcL29WbWkX/DZnC/7JpGVRJ5oIrk3+PN7E9Ru6LQ\n9GSIGncm5MFdAZEwD9p4C4BNYy9moq/TIXKAUlaxuvtW4mULmnUUt2wM40uMaZmq/Wb2RG5fQZ+V\n72qife49XgitGjV3WxeRcJKHmNkJJE9EWiE/OGg7GqJCr81JIPljiIyocAZ+HsCHLSLAYEaB5tzz\n5jxzvRaS74Wi7UWk43ZonYvzb0ZDRco73ruKvgmvQ2RmX4/2nQmVxagjugITpBXxNmZAoFlCNmNm\nbY5gdgmnZElRbXYZDSo5R7KMQWIumQDl5NwG6U7Xpd7HYH69DXLY/Q3ApNiQDdoV0Z4xft6Oe0Ly\nODP7WtU2ZkIl634zNoPT7wvgewD+FfRpiWvNetY5BBDYkjWlYy3xtlmw0ByhkAjnQoZR0lng7Za0\njOL0TeZib58k14v6q8xx8zY3ZhhvWetu7vruz3AhfY4bM4sdN+nzDJJBllXDg/lQpJQ3+kUoGfMN\ntkL420APaeFlHwt5Eg5KHJ9zHaXwMSqy9j0Ae0IKJyCc8QQov+G/3kdTo2Iy5FFeAIpA3AvgdTPb\nqYEHNeyvMp8v0b705WBJYWjrJBMAK/IvGIV+4+9lUjO2WthF0HY3KHn2FujebQjgmMRvkaO458Jk\ns8bni+fHocTbv0ER0V2tBafdEvK2/9S/T4IWMgPwVTO7NOov9x2rVQaYwRTWREj+DMqd2AFi9HwZ\nqnO0W9QuF9qYpQhShZuXhaLCSVax3PvW4FpTMJa2d4QleabBybPyTZv2U/WuJvouvcfMxOjnjo/k\nVmZ2BRNMft7uzGhsdTkJIdJiDFqe7FFQpCcJWWJUoDk2XuvO621qn7mgba2REmxfBZ3vYwH5vAGK\npoW1dHYys00T/VwJYP9cwzw4LuUsynLuedt4LZgFwFSryQ9kBIGOjOdSQh9mwCkrztmvnELWOLxc\nuSvKGKxGL2NQsr7OCs2D20MpDDckjMU9IUbdVSC9ZG4ob+nnUbtDfVybQsV8dwdwviWc6qm1mhHk\njflQycrfjO1w+iUAvOD/zw/gLxYgJ0hOh5wqtY6fbp51DhIEtsF4shwBJL8PObxDgqVDzOzIoM0M\nyLkyzRIGAhtCwv2YnDmxj1zPzFLkekW73By3mwBsY/V50sdD9Rp/nbre/kiZ46ZMBqsOWVYND8uH\nIv0MMuqmQg/9ygAeAjAfyX0KLwPJE8xszeC4q0j2Ma+wPAztw2mDsQDV8LEfQhPckuaUmFQx5eP9\nM847bZpUSjP7tyshPzORlTzo+8Z4nzkh9yKfbyF/CcN8vsUS7euYJ4EGhaGhgrlrWTr/YpFIKWv7\nnlAYc8aWA7so+p9A8lroeTQAXzPRF8dSSwfc4PfNHd8XIAWxjMXqq9AzWchsEBPX3NCi22aQNXjH\ncmpR5TCFFYtXLYFB8Pv9nMqxmdfMOmqpIRPaiDTjaUpqWcXq7hub53s9QNFEFzW19gPwQNQmi02K\nYoAqHEWzQ8/la4FBkctKVUjVuxpL1T0+GIImpvKiDK1C01njM7Mr/O+ZdW1dpqMEdu1/G90XZhZo\nrjuvSxMmu5htbxYk2PZIngUp2w8jyOdDC460sJmFitsvSR5Ycs4FoBIvk6JrCHM+suoQpQyvxNj7\niCxIFkyjhBNZRG1LIdAsZzudl2QKCZBbNzS3uHluNCin+Hp2+QkT69+10O89J8QGuGfUpkBM3Io0\n/LhodzwFe38JcrYcZao3GY5/HyjK9j4K4VLIPJDjOJRKqGTub1bMwSTPgHJQr/Hvm/n1hvI4RIiS\nI7XPekIGHAKbu276tmwGTTP7RnDcCyQ/jfa6gE8BeKjCOGkKCQfy5sSTIKf+b3zfFJIbJvp6b8px\nlZCXAUxzR1R4zhittDe0Vr1J8lUkAhquY3wfwHtMpFwrQqiqyrXIzO6nUF9ZMlgGWVYNDyqMezCA\nJUz4zuWgCMLVUdNnoMKLD/txK0Kel69Ci08R9h1D8n1mNt3bLQ03YlxS1MFjoAl1QYies02svAji\nZwEsHz7EpkTrfSDv0DgfQyM4pQ7hupBXqfAMjy6al/WTkDCfL4xMduTzueS8HE0KQ1flX5yBdqUs\n/t7N2F43M6NT01Kwiyr5MBSJBHRfU/lXtUqUP7PHohNOFy+AueN73tv+B8C3qFD87MH+2czsA5Cn\nZgAAIABJREFUqeD7Hb6A/TPsswuDIUcZmMsiuEqJVObesSKSQnIN68TMFxDeN9zp8RxkqMbXVBDk\nlNUXrKSqT7Svgkk2wegDMsCOge6NQcW12zyBOYqsy8+gaMeF0HO8K4L3sEE/hdTlSnWMMXWPLRPz\n33R8bqzvYO1e3nPN7DNR08qchKjPLaDIOCCIebzmAPkFmkvP2+SZSxgpbWx7iUPWsepI0j9I7gzl\nQQBCjJRFhnJq21XWIWIDT7qZHQvgWNYQWbhU1XDLyqcLJLduaG5R7V9B6/0nEUSDEu1yHF5Z5Sfc\nINkeQg1MhNAU2wX7GzM7uwF2Q6J5IedD9biOhWpyFTIzYfTW1e9s+putY2Z7BWO91p1bobwC1Ua7\nBTWpA8h41plZ05IlEFjIUdK6qE724zUQzf/Iy1kvjs9l0Bzt6/lrftycaNcngNY7cS0S74Q1z10E\nMudiM3uKbOMTSzl663LcCvk1Otfd1DlzHHO/hBzcR/j3RyF4cIzKyHLclMlgGWRZNTygC54MMbgA\nKh53CdrrjgEyfB4uvpjZH1ypmx79mAdBD9Z06MVYEjJMiuNCFrV5oElyN0ixSXl1iyKIU9hZBNFS\nHgU3WMLtTbwK8DEdDnmEHib5PghaB3RGluJz/zj4fzyA8czM5/Nj6l6OJoWhS0ksulAYc8Z2sT9z\n81PJvbujpMAilbu4FoDzfNMBJNctPEkNFfcJ0IR8IpQHtRvSz3ru+G6Cosov+/c5IYdD8Y4sEDY2\ns/2CrwsH/zc1GHKUgatJftoqmMJc6ggMDoGgj3WRlELu87GdAc0XL0Pe5TZhfSQ1WxFgCUwSXvTV\nzI72v1meSjN7GcChJOdwY7tDmF++Y5SZ/Yli/fovgDPcUXRkw34KySWcyY1WgyJNWgrtnt445295\nCDmxqJmtRMHvtjCz2Hm3aGGMeT8vUJDf+LoKY3Fu//5y3Mb3x+//OJLrJQyDrALNNY6A7GeuoZEC\nAL8nuaKpEHtKdociqUUO3l0oqVtlZreyvdD8XGg5AuGOoalmdmLqeJfGnnQTkUVdHcdSA9/MPut/\nc5EAWVH0BmtULnFGrcPLzLb2f49xw2I+yAiKZRdIOdy7RDkuHHN1Tols6m4TDOxFyKgPYbxzk5zb\n2iNClfU7u/jNnqHSJUJW1FjpvcI/teLP+qLQHAAory6upZVV0xL5RFLxGFKRlByyqUJyHQHnAbiJ\nLYjjblCuWygz/FOHLNkcwIlUWkVp7iKQPRfXkesVcjdE7lSZ4+bv32xQSo1BuVwp1nBC92tpM/sO\nycUhoqdQl13IzC52JxlMqVEpYzHXcZMWMxvwDxTe3Qaa3AEVj/1Eot19/veBYNuURLuLoIV7I//8\nDErInh2qiRK2nR2CN60KYPZEX++CSDhmQB7rBSquY0lo0pkXUrpPgCZgQC//Loljdobqm6X6mxN6\nebu9r89CE9vRqU/UdhP/u03qk+j7Ukjpvx9izzoUKuZa7Cf00BbflwWwRsn9Lf10ed2VYwvabQrB\nY4+HiAfK+psKKbXF90LZKL5f7X9nQN6jGcFnetTXZP87Ld7WzfigHKrSbdAEu1eizd4QFXi4bRRE\nntL0fm8EYAsoGhdunwlBo16FjPGZAF5KHH86lLA8EHPLUlCZgdS+e6DIWTifPNTleab5/Zvi3xeF\n8jTidrNDuRLf8HfzKAjyE7db2/v8i39fFcpPDNts7n+/mPoE7W6DFs5zIS/7/tHzm9VPdO71Aezm\n/y8MLVZd3WNIWbgLmqdP9s9PEn3dCkX4Kn8vyJh5b/B9ifCYYPtKEAz0Sf9Mhoo7N3r/g+03Qsrk\nyVCUaTxEXR63WxeqQxj+tj8L9hNCgeQ+e6OgdeSb/n1xKD8m9Z6+CEGfpvrz1XEdmefcC8pXfsK/\nLwflw4RtJmX2NS5nm2//ARTxugZCKVyFaP1E/ju2GLRObFh8urgPJ/nfqyBjou2TaD8peCdXghiQ\npyfaXQ7lPh3jba8EcE383iSO69jm2xeFjIbPAlikm9+8y+dkcwii9wq0Hr4FkWR009d6AMb4/ztD\n9QY73hNIdxjv7/YD/n9XuoT3tx00P5wNOdhmQCWYAMHmi3PW6i+Qs3yWjHMeHHwOhRw110dtstdN\n+PxXvO+QXnR3SdtPoZVK88l+/v6zQrrBeX4Pf1HSrnYu9nflPCi6+By0ni2Y6GsGFHFkzdg+DcEv\nJ0Jry18gyGbc7lQodeCP/n0BdNoRE6GI4P3+fR2IJTnua9ucbWWfwYqQLQSvms1WMuMjiXavewjV\nvO0ySIdqd4XCuwX+/U7oof4vFJWAH79LdNyqFCb5HN//I8ggKR78Mg9qTJxwKxRyN8gz/ziAfQH8\nmuTu0MMGCNs+J5QvFPe5OfRCzAZgaZKrQYxCMQPkwhAUMw5FbwLgWUvgiUtkIzSLkHwZmugWgyKV\nv/NrLM5vFDZ3Jf/+eMl5Q09wRyIuhJ1uKqVjI/lTKAH5TquHXYQyP4ACatFG22zNPHivuefmMZL7\n+fjmjht5ZPMii7D5CXmFAWyP5IcgA6iQgwBcQXJHtKCoH4KUljZcvTUoLeHnWgOtYrl3WuRdsvwc\nnMrcO5bAKIPzFKQETaGNsDwIRDGG4lpvN89XCiQLJol8mMl4SHkq8qKmUBC4cOwFbPbflig2H3zd\nFa08w0Mg5bmP4MQalgFhQDgDRXxnhRbHZH2ljHu8JoAVzVenCpnLzCZFfaU8rkdB+Qg3Q8/SxgD2\nSbQ7HcDBZnYLAJDcGIqqpurklb7/JJeFFN4toXfvIMijuiRk/MZSCan2ufO3UO5zjvwUXmwWgk2+\n7NvWitqdCeWctiW7l0VGg/GkYFz7wgvNe5vHPAoSyp0kT0EnK3L8Hn4Ret5D2TWxDdBa+X6rhkHV\nvmMkj4NgfH9A63k0yPhpAqdsGuUrokFHohUN6oDEWTr6dV3ULM4dHI107uC2Pr6J0PtwMsnDrJPQ\nqREJE2vIa1y+ixIYL5tDJU+FdLRVoXnsF9D93yg67p9oUZvHY86ujxbIEQDWMo+K+X26EXL8DiUE\ntjZnPZA6WGjx/BTMg/Gz1ghiHGwLcxfngt7fFIFFzlxMy2NirMtxK+THAD5a6KZuT/wWnVHmtc1r\nx/k1veCRtVAOht7nZUjeCTkpY4p/QGi2SzK2JWWwDLLcGh5HQw/K4iTPgxSAXePOzOxVKDqVgjiF\nRlW4YM0BMd/cD4cZQS/9a9DkeUSgCMQh0Jg4YXZExAlm9lcAa5PcJLiua8zsJqTlGGjBm+jX9CCV\n4xbLedCC91nIEPkigL8H48wSaw6peh5SOqrkQZKrm1lMRhD20yQRt1Z8UvlCxYv7KIDjKYKRi6Eo\nUen4XI6F4Je3AH0si1+PGzGPoXIcNDEdAClPm0C/WSzzAPgdyX9Cv+8lZvb/Eu0OBHAJyWd8bO+G\nFA0AgC8iH4meu9+a2c0l13ojxaJVWVqCIrzZFi1DfQLJSyyCjzGPGr8u965wEiwCTdDF2D8KRVaK\nMTSFNmZBICh2x2XRyq35MslNzWzfoFkWTBL5MJNRZvZkjrGImknePEcWIlQozYcguSakfCyJdoUs\nXuCzCXGQd48fgp7bZ8vG5vK8L5qFQ+7zqWNMOcgfhiJRgNhEY4gRIG/7LcFxE5nO1ax7/08CcLiZ\nFe/LWwDOJrkyFJHscHJlGKlNiFNyFAYA+LuZ/SbeyIDIqoG8ZmavF9dAEYnEClBRtiF0Cva9hyTH\nQpGspalk/kLmQcv4jSWnjmPOO7YVqg27LEPLWvkyq5lg/31CchzkeQ/lJlOJm9vgynq8rjOiWrfO\nAs7ZBCcuR6LcoAgll4Qpl7wGqIbxZkElA3nDnRVbQgXYz2SCUbXGSZ1dHy2QUdH88Q94mkFDZyzQ\nWwhsE+KfSlion/NNkm+RnM/SzIONnA/szF08HdIZUpIzF99J8s+QbnKZBbD0SCpz3AKZGQUKpiOd\ndlJbO84EKd0IclISgj/2lZ5iZh3iOhkUg8zyEhhhZjeQvB/yuBCCNTwfHNfI+2Fmbd5LV6guDPan\ncntSkkWc4H3ejJZCWSX/NbMXo0U7ZfEv6BPTOJ+4byVZLOJJqvoq8XuwCzq9ZAf4/lrmSbYq1K8O\n4F6ST6A9+T9Fc12biOsL3ATopfmF9/91C5I3fVLZEe0V4sMBFrlyS0JG9FlU1PUCyDh7NHHMBSQn\nomXAt7EssgFDZaBgvYyS/Axv9y2IpGMVaFK7leTTZvbxuD8q4bvIL2ibCIJ2uc9dYcyFxkbK07cT\ngFXN85uoPJsH0aqxB9YwhTEz965wEpD8HRRJeda/t7F2Fs+PZdQWcamM8gayCYAPFB43kmdDTHXh\nGHMZIO8iubLV15d6yo0K88Vgf8iZ0Cd1kzxVUH1x84LVgecO0HsTR73PA3AYoghKQpoQ4lRFqwtP\n6zwA/kCxmIULaOxx3Rda1Fcg+VcImlLmePkPpPjMAWBZkstaZzH56VTx7JDmfXrYgJqA74Ce3eT7\nD+WsdfyeZjaN5FKJseUYqdnEKcgvNvsAlbN0Fdrvcy7zayi3sqbQfMZ7eBdkUC+EdifKTAhSmZJ/\nQ46+qjqOOe9YpWHXhaGVG+W7DErkD+VSBJEtX8P+xDT7Hqx57mCpQRFJLglTLnkNoIjM3JABeh7J\n5+COPjM7zf/m5t/NdGP0CwA2oJAmsybalTqpi7UDMgDacimpqEwHKQqA60hej5ZDbnsIMhseux6U\nKvAKRYyzBgRnbfv96q6VzYhunvRj2iKVKbFMBk1UMA8G78QfYwcX0+yeu0D69N5m9hrJDaD3IbXG\n1s7FpvI/H4b0tiOo0iAXmlnMoDwDFTlubCFu7qMo8i+G7vW2EAw7lp9AEOJFSH4PXjvO+0qxPALA\nuhTarnA+PwOhALdACyEHaK7LLrM1WBGyNrFqKsiN0IIOzQrdqEK68X6E8gq6g8jlEic0kYfdsBhN\nMfMdAC1gsRTK97MkPwP98O/ycZR5GavkGigpskwpy2GenARNSFW0sLHkJOLubmbjSX4SuudfgF7g\nmE3nDtZAZXwyOw4ikVkdIh45CkFieiTF7zgLFHHqg8qhnaFyMloGWR9DZZOJNpLnIHjBPxBBDAJ5\nP1rsfmswgN02lQaevmf8fAXhxOyQwh1KHVNYU7jH4sGCCghL3levhZnQxuB7TpQXEOR4CbQW68V9\nWzZMMnASzQJgN4pIqApmsg+0ECzh13kjOmF3dZP8VdH1zQ0xhY6BInmxQZaMoCQkmxCn5h5nkzm4\n8rWmmX3cDcBR5uVDEm13h6Kli0Hz2FrQnLZx1HR3AN9C6z7c7tvC8RvJa9xpWHZv5q8Y+pyJbTmO\ngGziFFQoDImxvIZWcXgALTg6RZpyKDqdcR2FZqEI4R7Q/d0bWjfaCs2zhgra5+An0Ypk5kiRn1Ul\nOVCuHMMOqDG0mBnl87nvg1DpnXCemhdpZTqHav1qkmMiI2C8dRbjjQ2KHZAm/8glYcoir3GJYbzz\nIWIfZD5UcnvoXu9uZn+j0lt+lDhnlZO6kItJ/goqRzSH/10TiWfRzA4j+Tm0INmnm9nlUbNKOGWD\n9T87CsWMSCWbw0JzmAdvJ/lNM7vYz3EINBe0Mbia2VjXrb5DcjvISCrru3Yu9j4nAZhE1Uv7MZTX\nd27Upoo0CWhHLPw/tCCvf0fiXTSz86h6v0XtuK2sVTvusET/BuWwLQ7XJ81sCkT2d761ag4vAOk0\nLyT6SMpgFYZOUUEuaGafjNrF0KHtoaTilMWdc97w5RgFPVAXm1kHHK2mn/MgKuQzou17A9jYzMZ2\nMba5IPhQsXheD+A7FkEs3At+O/TjnwxN8N/KVKxS580quuxtC+bJPSAvwwlm9hwzKpsn+noXBEkt\nPA63QdcRLmpTzWwVqur8RDO7PHUuZlSwpyA2m0GL08egkPoFZnZlYmzJ+j3hguEe6m9YZ226Yv+H\nzGwyW0XJ48HFsJSvQMnEC0PQs4tjj563OxoZRZBzheS+AM6zdsrwsWb2M/9e5JssASm6N/j3TaGE\n9W2Cvu41s7Wounhru5fsYcusSp8Y2ykQ/DF8/x83j3SzxQyVhDaaw0uYySoYzA/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HAAAg\nAElEQVRO8g4zW5+dEKIynHYuLeu/qeTOB6maV88iXZ9j0ISOcYdqxli83zq91dv5372j7TugPY8g\nV67wT9di3dHtf9e9g4eglYhbxzzzNOShbRO25weMhibHVDi9yL9aHiIzuNYSHlyU1++JITU/gyb2\nTfx8L0Nw0rWCY/pw/R7h2hKa4GLJpRZvUkC2VnI8VS6veTTtMZL7QZ60uaM2C0BRzCegCX5Jkm14\nbJczIeWvsv6VmZ3t72vh0e/wuFt9cctQbvV7fAH0vGwPFZRcw/sqKOtr6YWh/MG4Vk0fXNMC73GN\nLOMQiViIkneZ5Huhd6aPRQ3yCDYiKDIVol4fwHIW5C0mmtbmN7FZzuSeEAvnK37scVAu7MnRMZV1\nI5Gfu2ol/8fS92yZ2RtkXbfZsi2UD/F6Yl/jItNIr1kda7epLtr3oLyNmQA2NLOnSe6PdM3KX0Ns\nt+enLsLMHic52hQ9n0Alv4foks2h9+BiCtpzEZSQH6cOzAHlB8f5wymI+VtmdgSC/Av3iIdRnHWs\nHgI9S3j/TcQvqUK993mktA5OOS9EpZ8sKRAc93XXNV70ueoVaA1oEwaQdUiJmw2CVIVRuKycqjqh\noI+LQTXlVkfr/ZkXMoCLdilCrC9ZmhArpzbrwQC+hPYadIUYvF5l20ZB7DrYFyM5Acr7LqJw20KG\neyjfRWYtNcuD/2fVemN+rmFO3lqT338cgG+QrISFWl6d1FOQQJZVjDNH5jSzm1w/eBLKO5+MdpRY\nTv3G4j7k5Mu9P6GLFG2PC77eTSGWzgJwXdkxmXJ18P8cEGtkXN6pVAbLIDsEqh31BPSgLA3gK/7i\nng15CNuU2RopLUYYyRegRXw/SPlfHPLADKW87ErR5qhWFgAAll83KktylN6BEMtIxGU7S9goKLR8\nf6JpmHj+BpQ0HkMCboMSoReAQtr3QspDqmZSZf2eQNY2sV494Nf0QskiD99vAK7wxffr0b7jfQF8\nCfotjrI0c0+TArI5MgE1Sb0u46DF+gBIQd8E/p4GcjcEdyiKbx8HeU5j2FVW/SuqcOfZENsdASxO\n5aTdFjXNSSQHxE4J6HpDWR3tCkGtAWJm+7EdJpmqVZMjVfPPKSXbJ0BKc5GQvrNva8S0mVACZ0Wn\nEgjkObyakD8Q7UnXb6LeqAL024ZG6rMm4p46WZXkS36OOf3/YhyhkrJqtG/O4LiUEpUrD0G1y1Lw\noqZFpgEZDT+GnD+ASE86qOopuN8yEOxreYhM4WQI8tdBzmSqb5UqvAu0nJlTypyZrlj9EMAP3dHz\nTWgOiJ0Tv4KiRp+EIqQ7IaFoudxLci+LYJdoVwhzSJj+TnKLYt6hIHapvOlcQ6tJzu4KAJaiEwO4\nxHlfW8Mh697/M1SZmVByCz7XySchYon3op0deSYU4SykCSFWbW1WM/uSf9/MElDJ4P+yXN6i79io\nOIdKRynm720Sz0J2LbVMh1dt8WiXHEMLyCO6yf7963RnthOIpI7fIvpe54xpKjkO3rB+4zMQmipV\n8upK6De6EQkyDwb5zSknW3ytaCf/OJnl5B+1Yp05exdA0cEsGSyWxWt8wi7yI/4UvKAnsSR3IBaS\ny0J1EeJihEtCNMPxeYscoFehonTDQaagk8nogbLGVP7IdWY2kyqsvAZUr6z0mCppoPT2RFhCxVxI\npESHSbhvQPemg43N2uvNjAGwA8mxZvaZ8NQmpsY9oLyhH7oXJDWG3MX2vxR7VfGyL4wo4sN2Vr5R\nkAKcxOib2Q0k74eU/LKoY5MCsjmS46nK9aR9vPCGm9mrAA5gmpglNwJ5AoBPmNcfoiKbF0BEFaHk\nFLeE1dTHCaRy8WYzBsi6MWVRUEeysJlNCL7/kuSBwfjmBPAfMzOSy0AG1+8SToocJRDIYPeyZvmV\nEwDcQ5VJAJQ7dWbciO1EIaOh6PjFYZOck1lmtDK3XRcyP4BHqGK14fO+BZoXmQa0tn0TLRKoG5BW\nVKYB2NMdQTNIrg0p4HPSCwyHjf23L3MofQGav/aF1tn3IuFMoHJntvfPmxCkPpZlzWxbklu6Q/B8\ntBMfhLIjgLOoUg/vgVIT4kjKOZBRVkXC9GXo2T3F9z8FefrbJHfuZ2aUjypGvAwE4SuURUOnQfa6\nv6/FWjIGnZJrBFSKiWDjbIplsDT6ZDW52pHc44ZzqjbrpKjtXZDektxWGBMkvwMZ/r8C+ogs/i/q\nfzQEPVwBQJVBnuu0BzIcXpZXPBrIZxTNiczWFvwmuYLlMWiuC70DFwC4B9Vz6UAgy2odvCaiuZTD\nPJa5LMorjKRgdJwLgl0bxNb+aqqxz5c3ALiBLfKPr1CES33kH4W4YTm3mb3U2VuHLAdFS7NksEg9\n6pK1s+pakbwagnu0eRjpcA8z29y/N6IgHwphJ5PRBZAB8mjUriiWvD4Uhv8RFE3plmVxMpQ30Kb0\nWoNaXw3PF75030IUrbAGNOtBn7NBeWg7QgbLZRAV/VVBmwcgtpwToXoZDzNicQraLo2aukHebido\nUVwDMmo/D1GhXhK0CRXnoubeGdaCHVwNveQPUcQQ90OG6DLQJF/mxcsqIFsnzEzqZQ0zqpn9h3I/\n7QTBs75NQZHfbWaTor4moFMsocykCGySDGvMiPKyujxC2K6WYIGZDJADIX7uCWgtzGOh3JOCues+\nyKifD4pa3g+RBOwS9TPJzD7MVn2jMVD+ZEGy000dolzyhzWg5w4QqUeHQ4mddSNHQ0ni+/r+d1l3\ncOlBFVYUh2U/iky7AWWWLkVSNZ5DIcjilwtDmorG/RSq8/ijoG1M1nAPpFAYgK9aUBPK980KQZsu\nspJSLMFzdxs0J/8NyqEtg+huhXbY5ePR/mwSJlfKUXbPGhhal0BRvh0RRPksIoGhiHNWtBrFyn+T\n5SCl/1jIO3++mZ0ctdsG7e9N1w4hivzqc+hc53KiznFftbVZ2YJKngvdtxAq+XOLCIyYICsr2XYl\nlINaugayGUNhqnZeFklGoq8sRtEGa2JlwW+Sp1sGg6YbsgUkdRUAv4X0vocT17AktAbOCjlj5oOc\n2o/HbXshVMrGRDN7zHWKM6Hn9EkAuwZGZdH+uxAjadI5QUX9vwe9U8UzsjhUXP0bsa7ADPIPdyJ9\nGXKy3As9w+PDudP7KqK99L9/g2yWOhiujh8kgyycZOaAFof7zdnYSD4LUU4mrXbzOmaMWPmic4Q0\nnstBkbSnomaLA/jbQD1Y3QpLmIx8X1Fo7lgocfV89qNAcROlt9dSN26qCvoxUMRzFrQ8n0Udok9A\nE8onoET5iwCcbGZLJfraCILK3mlmx1HMdAdamrFvCvQSxgt8B4MOyRWg55dQ8nJufZ3i+IfN2UBJ\nfgNKfN7Fla07E79NFqNog/OvBcGF5oc8VfMB+KE5RChoV8mMamZfIHkqPKfOzD5Ah4eWvaMZYzvL\n+ytq9uwEYHRikdoYUZQXIv/oqjyCL96VBggbMED2WnyBPBnychrkXT7AWjm5hYG1H+S5+0GJklGp\nBJIcBzmIsqL3fkw2ky3JxdCCsz1jCeYxnwt3hLzVMyBSnzIo59tO2LDItB+zMhRhKeqJPQ897w9F\n7UrzQ0l+GYIcFTChlyFHzKlRH3dChWALZtcHIW/23AAmWMCgRvL95o69mmveE3KarQwpRXNDxEin\nJdoWsMvdIIfLeGiO/2nQ5vdmlqwfRnJnMzuX5MGp/ebO3aB9rqFVrMOFg3RWSEFeJ9HfARaVnAj2\n/xR65+6kIOufgOaw6y0BWXejZm1oXrw39XzkClUz6kV0UqOncrxy+wxrsz5sQW0+yhm7K/R8h+iX\nmRAkLDZS7oKcBBdC89xYiCX3I1G726C5eBJac7GZWUeuXuY1lDq82BBOmWto1YynccFvNmDQdMN8\nLOTc/9ZAzq/MgEqSfAgq/fFfiqH2EOi9WB3A0Wa2gfcVGjulZRRIngjNMQeb1x6lapEeD+DfZnZg\nOAaSj0IOoAkW5WWT/Jrrj0U5i50gh/zXIRKunurNg2KQdZzUk7XN7FP+PauuFcnHzGy5kn2Pm9my\n/n9WJG0ohcKXbwYpMx+D6FQvMLMro3ZXQ1GMTaEH4VXIu9gt7X2W0jsQUvc7k3wE8sjEC8Y/fH9R\nIX5X8wLKLKkQ33BclXWDmFGviNXQzNegSfQ8aBFfzY+5CYqeXejfU0r0FEghastvMrM9urjUbEk5\nP4ptbFE7F4ZAWDMopHj+qgkqmrw3sTHjC8W+CDzCkGfutahdV1FeZpRHoAgE/mHRxMj2SG94DY0j\nvL0WV5r3ghhT9zJFX8uiwYUSCMjAfrd5BCpoE0fvq+oQFfW0wnIWhfJ6OJS/9G3f/iSkEM4G4Gwz\nO9a3Lw8pCGMhY+MiAIea2ZL9uzNDI5FCNRvkaW6LGrK9yHSbIpvo7y5IAbvFv28MrWOxoppTqmIe\nALCoSHqwPy5Fcop5fhC9Rliwb1GIiOQ9ZrYZyRUBrGtmZ0Z9Lm1RwfvUNt9+IOR5LqB88wH4cTjf\nUbUZi8LgbVEIknub2WlUvmQsZp3R8VxDKyvKR0UqVoOMhRiu2sjp4YbsURCSgRBb7bctKgSfK0xQ\niA+GsAYqGbRbCjLAi4jQHZAT9c9RuzACXVDQ7+BrUpIYrvgbG1DeX6XDy9sk4ZThu9VE2IzoJjyu\nr+B3+FumdKt4m69/n4Hm2aWgKNBZZvZX319ZTqcb44Pk31EBlTShBvr0HioSdY+ZFYW0u6l5+xjk\nFIzX8NEAHoltCDJJRBb3+TD0Xp8P4BQfdyp6mxrri1BJldrSB4NF6hFLnKydS291H9O45T3RnuTc\nTeL0oAibMRkBYln8FIDjzexfFMztsH4MYR9I6S2U4dsh9sDhIC+a2bUV+9eAFrMbqQLXF6IzgRxA\n38KYMgJSOPnxvoCX1Q06G+31ij4A4MCoj/tQLrNAE++vISah/SEGyTUgampQeUCpBPvs5OQqyfFU\nRZsqmVGLsbE6p66IHlbdm3AMr6FFeVwls1rgmTezR1lOThDKXFAuDHy860A01f+EooW/giCLo0ju\nYmbXBec4mz0kw6HyYQ9GJ3zoE0Gb3PzLgyE48NVujL0P5Tk6BbFBXwQq0e+TEDnDcWxF749G+l2r\nIn/YFq3aYwDwT1d+R0OU8Mf69kd8vJ81Ry+QrGNgHbZiGUyrblylYEYpGVMYY37sRKZzjmrzQ8sM\nsUBqyRoC+SUUWSgYER+FjOk4P/AydOYQXYrO3FBYBNk2QYRj51MpCZN51M0cVRMKg7zLQIp3+F8k\nV4IMrVTOx+kUAuBISJGdG8rri+WYxLbWIKVojg+cHgUhUipl4TAoclA4JBeEjIWuDDIAd5FcOaUb\nDbBcTUU+lkIFVNINr9oolyvDcST95777Jggy+GvIiVQL7fd3JV7/YtkiUr5PdWdpm0HWwNBqQnQT\njvVNaK4tUA25DJrnQIbcNVBUrC267vIW9B4V+d7JnKuG8m60oJI7Ig2VfMv12heg4ETImNmRV0vy\nJotqnUXbLGVgmVhPLTimjPyjMN7DZ+I0CJEzBcBt/v6mcsh+Bs11U72flSGSp/lI7mM1RbMHxSBj\nfbJ2ipI3JQcCuJwKG3bAPYJ23SROD5Y0YTKCiZjiOShq8BiUW/FYtyc3s9eoxONfmdnfu+0nVyKP\n1VwsZzwDgFuo4pC/RsIwMrMHoWTpr1P0qGMBzEryWqhC/OlBX4cG/88BYZLLPBQrQ/jhTdAyKEIW\nvhWtBYc9E51Jy1mREpLXQJP1tyFWn+3N7F++ex1IuYmlSXJylTRJ6gXqmVEBRWQuB7AIRbf9eUhh\nAQBYK6fvIuuEUywU/J/KWymUv1SR0fuowqthlLfD6GO6PEKoBJwCsYzNB3mhNzOzuylY6gVwY9n7\n2hi9JcO5FFJcz0WCKcolvKaO/MtCTNGVm4Pv0yEvfjH2VASKVkJ6wnT0/piSMVaSP0SOpvG+7U1X\nQgvZxs91CwWruhD5TrphLa4YJJlWG8h0kt+ElDhA+Q6pfK0cJrM6aULWsJCZXUxFQmEqHfBmcMwK\nkGI6H9vJjuZFoKxG56kty2HNGA9DORgqOxBKrqF1k6/Xt8GdyVTucZu4sVBbzLfE6XEU2p0e/4Dg\nfYXM9G3dyvoAdiU5AxmF4XsoV6IFlXytrBFr2A5z5jEz24qKqm4D4Aw3kC6CjLO2/NMGDi8AeMX1\nzhBOmVqHcw2tJkQ3qbEVcN9cBs2dfbzjIPKtYnufHmaC5K3g13Y+RJpyPtIEUbnjfBNaR69jCyo5\nkWQIlTwKWutGQ0WhHwb6IqF985z/lmMALOTvbGh8Lhac9g/uUG0j0iG5M/TbFHI8OiUJFTWzomZv\nIU9SKIdYnoFzFvg5V4Seg69Cem2lQQbrUUG0qg8Uai8+60EJ6D/tR38fhUgY9ofyV+L9F0DQnXj7\nnpByOCjX3aN7dzTkrXjUv78HmcUso34IKVXPQxGBfwL4O0QQMuTX6WO8JfG5ueaYUZCntLb4HrwY\nc2L74wiKiCb231/1fYDuReMCsjX9jYYirWdDpA3fBfDBmmNmh6jjV0VUnDVoswKkjO8H4AMlbaYi\nKD4JKeyPBt9TRUYXRFBkNDGug9FiWzwIwOyJdksGn8UQFWNHUHAXnYWfH4i+T4bqmhTfl0c/Cjk3\nfYbi8UT7boAm+rZPsP8tKCK1bLBteqKfTSHF8G+QcrojFJ1JnXNLKL+j+H4P5KmeDi9eCkVMZi35\n/R5LbB/j57wKUh5OhVg3B/Rd6/UHQeFgyEnxA2QWny/pbwFIGbjfP+MRFMYN2tUWfc041yJQFOYW\nKHf1BMgg/z2EPAnbTvT3tChuvw6AW6NnZAJkREwIPj8B8JGS85cWkIVIRQAp7T+JPxnX9lRi29KZ\n21IFlDvef2QW80Ve8eVzoLn6GEgPuB+KSrYVQm/w2y6Z+gzC+/BQZrsbIJjtLP7ZFcANwf6seSzY\nNwqaT55P3S9oPS0+f46+fzFquxRkWD7vnysALJXo8wH/O9X/zgrg7kS7Sf73NihytVDVtWTcu88N\nwO+2vV/rYf3sZ3ZoLrwEIsT4JoDFojazIJrToPVg7uD7OH+fXoPWmRn+mQJgv6DdYtB6NBGtOexW\nyKG0WNAuXsMmobWGbRuN5ajUJ+dZL7Yh0DfKPoOWQ5YIMQ9Ysja7SJwerkLlh6wOLQhFnk5jEg4q\nyXkzCB45w7e9D1J6rjOzE3s78qEVku8Kvo6C4DE/sYhN0NteAd2XVN0guNe38IYRirL+GxW49P4K\nBzAPkplJvQ7hiT3V50T3tkOs0xO5MqToT0SLynpPa3k+s/NWMq+vNufP2/Xh09mJt4+/95QMxyMm\nz0LzVBgNTlLpxuOJ9oX5j0U0+DUzO8z3bwVFoNaDvJUXAviFRTUOSd4MeUQvs5roPTPIH0h+H4Ks\n7Gdm//Z2Y6DI5N8sQfwR9L8AtFZsbxE8ZbgLa5hWh2A821Ttt87yE2AFWUPQZg3IOFoJguUsDBkf\nU6N261pEHV0x1slm9iG2k3QV2zY3s6vYZT4nyb+Y2RLRtlTuzWTzfNQgyvdDtKcKzAspqh+Mjp0C\nsWW2FfO1Vl5tKmXhSkukLDCdBxdeb+NSPkwUhrdELl8vheTpEDFLJVSSNWyHDeaxAj2zAWTgX2Rm\nldEn9oMoLeonN9cwm+gm87w9YdCkiJd2gBBnL0BItsutIbNr0F8IlbzQ0lDJpn3ubxEbaUm7cA77\ng0UlZ0rWsI9BhuAEaycwyiUIuwgKdlzom7aHjO0vQMXTKwnPBtQgKwkxD1qyNhskTg9XYQ1VdYN+\nHoAWiuej7QtD3vR+T0bdChsyY2X2OQOtZN43ICfAt82so0gfVfNmFch705GIPRQSGynRviRhQ0af\nlUm9UdujAWwMGWTXQMb8HWb2+ejeAu1hfosXH++vlMqaASFP4rgnzGwZ/z8L2uiTYpjz96RFrGne\nrjC0QyO7uI45zGzWoG1PyXBIxgywgO7dEontlQZZSfsOohqfO7aEfv9NIA/85VaDay/pv9aIpnLF\nvgchEwpK8iUgqOaRKSN5RFpC8iQzO5DpEhR98xNr8kNRDXOzbp9hP/csUN07orz8xA+hiPyrkCK9\nCoCDzOzcRNvashxMM8otZGbPs5wVj1CO3SzePsvQ8jlnKyjHKLzPMyEF865oHG1zMwUhnRIYl9lO\nj44LkKNpc+uEcOcefzS8MLyZLU/yPRCtd1wYvqdC8g+Qc6yIbiShkqwp7xG0K53HSP4ZwL8ghfhm\nRGkKFlGoB33WEY7lFI/ONrTYgOgmR9gDBk2StwKYBzLCLkM0b8SO1sw+30LAhBnuQj+c2W50L4V2\n4zOu9VfXR9eOYJYQhFFQ/K+gRUx2J5RX9h+oflqlYTvQBlnBireHtZK1+82K904SZtYryeinlGGp\nat9gCKuZsbryBDY8/0Yl5+2gvc/oK1v5qOkni1G0QX+NPFVU/tWqEARjVSrqfK6ZbVp1XElflVTW\nJM+D6pCk8lY2NrOx/j3LoxV512eBPJSNmJoS15DFANlLiZTLudBuMPYtZhSlbyFFNPhUM1seJcJ+\nRqByjWj/PiekkAHA46Yi4v9zwpK6dy5mZt9p2N+HzGxy3fzEDCazJufNGNdaEPzvb/59F7TqBh0T\nK25sUUZvDXmWDwZwmyWYgplRloNig/tSsY0q2Hts1fOeOE9TQysrykflQK+C9nIh08wsVTA7Z5yj\noTyhsZAOcId5uaAu+uoJ2qaL8yYd8BbVjWMG22Gi77Z5zJ2rxZwZOg39lOni1xkG2Q2QIR3mce4U\nr4e5hlbqfAwis02lFzqcG7PhvevbhRJH61AIS4qvW8MSNE3WsMT+BaAyFI30sNoxDbBBlhViHpFO\nYcN6JRn9VUGeGlOLDqWQXAbA0yaCko2hBfAcEwvlJmZ2M0tgOpaA53iftYnYmWPLUj7qDDf2o4Bs\nybgaearYisxOhnI2Z0Ih+rZinkH7ZSBI8g7WCeOppLJmRpFRb5fl0Yqf5+H6fLuXPoaEnt9FP0+h\nMxr8rV4r4tE5s4zod5KwHdZSyBiIyGdBM2tKsBH2vTAAWIKIiQ2KvvZCSN4P4ONm9k+SG0Lr+v4Q\nLfQHYoOBrTIZvwBwqZldxwRldIPzV0KgG/aVa2g1ifL1u5izG+E7ogVtXA/A+8yhv90Ie4S26fLc\ngw6VzBhTlsPL22YVj64ztNgQAtvgWrJgocNZqDq0D5rZKxQBxxqQ3hAb7lnF1zPOl72GsYQgzKJ0\nD7bQQ22Sa8wOKMuiqar4FWyFmA+EGNlORZdQmXeQPArgeIoO9GLIOKss0lojq7LFcBgKUcJ4Ndji\nE/Ve6AxFx5CaywCsSVGHnw4l254PLV4bQVCFVI6VQYnu8Xm3g/KpJkL342SSh5nZpV1cRjHuz0Bw\nkBfZTqlayCfM7KtuuP0ZSnq9DS1IXC6jaJaY2aj6Vm1yH1Uv8Aw//8tQYn+fUJCX7SHFYWUogrtD\n4tyVVNZu/H6E7Zjv31onxDiXkjt81glRAr+EEuOzSticATK33yMhJ8sKAK6HvOB3QM9xIzGzxbsZ\nQz/lIGhu3xEJI3oIxjPkYgE8iKr3NQ6KCl8IJZY3FpLHQIQ5o/SVb0CKV19uiOUxmfVSRgdRsO0B\nnG6qM3WZR2FiuYqqMfkqgH18no8hh9llOUzla76Hdgh0Y2PMZWuqxlCdoVU5X/tatKiZ3elOv1/7\n9vVJLmNmT+QOiOTTAP4C5XcfamYzSc7ojzHmcjHJ0wDMT3IvCG1zRs0x/RYGUEkIkjgrdN/Wi9ot\nD13zoma2EslVILr57w7EuCwoT5Eh/3AjIYRT9kH6mM8o+n7IUTs/2nWUmZDu060MFYNmL+VUaO1e\nFWJ5/gUER40RAg9BucnJ4usNpMka9tng/zcA/D9LQ+7XDP6fA4reVubct4n1mJml7gMpVV9Cgj1t\n5JO8X0sC+BrEtvQIxLa0/FCPa4Cu9S6ICng7CALzOSTYg9Bi9ToMwP7+fykLXcZ5pwBYJPi+MIT7\n76avH/jv9AC08CwMFTqM2xXMO78A8KliHIl2lYyig/S7LAVgleD7lyAWtkchr/EqAGZUHL8clBPy\nB4jBaDq6YJSCGMlS7Kl7QxGBgbj2RgyQDfqdBnnapvj3/4Oi393292F/b3YsPoP0bGwy1M/ncPpA\ni+93oSjlMUiwITbo62CIeW7pYNv7IAP+oKhtLZNZD6/xIThjqc91G4b7Ku7LaP9/Lqggebj/75BS\ndBjEwrxR+Inangk5z5aGHBmPIGBLa3gtD/rfrb3f+Urm4Yf9b3K+BnA1gJUTx60M4KqGYzoJMvqu\n9nd5TDfzZUnfm0LOx+MhpMVgvBMPQgbCA8G2qYl2t/o8FrbLYmgchGtYEoK2/h3AcxCiY4lgfyNG\nUaiAeq/H1/EZ6vvW8BoKve4oKM2pb1vU7haIcOR6/01+AxHjdHve0jUMwLz+912pT2b/2WzMQ/4j\njHwaPTirQ4r+m0M9lgG6vlpaUG93D+ShegiurMQTN4DvA5g/+L4AgO+W9Dct+j4q3tbwOiqVD9+e\nZbgNwW+wRtXH27zui+eawXFV9MOlVNYNx5ZNyd3D+3Fv9P2U4P8OOuMG/Ra0x5OhRGoCeKTLvn4J\nwZpOh7yMp0L5bUP6LAXjWw9Onw/lXvz47aYsZF7njwA8ATnQ5u5Bfw9Atb7i7QujXWk9BzJmvgtg\npYr+tgUwj/9/JBTFWaOLcR0BOSqu9DEWqQ/LoqQkC4CPQMbFLsUn2p9dlgNCDzD4Ph/EpNrNPa40\ntIJ2lfN1PE9ExzZeS3w++Ki/009DEZTtevFcDfYnmOsKhXsM0gbZvf43fLazdILh8kGmoQVBFuf1\nZ+kmyNDbuZ/nXh8iQSnmiI7yDcP5A+kUh0OO3nejRA9Du7NmY8j59PAAjelq/zsD7VT7M5AuHRPq\nS2tCbM/Zzv0h/xFGPrUPRG29kv+Vjy/Cn85otyLkeRrr35eG4GNhm46IGUpqPxFI5U4AACAASURB\nVEGK1PVQ3ZNdAVwL4Lgur2GX1Kekba3hNgS/wS0Vn5u9zYI+0dwK4E9Q8n1HjZ+gz8n+d1q8rcsx\nDlpUBiKhKNv3RD/6PQ2Crezr9/BeKA+ym74eATBqqJ+divFNhRTMVSGFdl8E9ar+Vz4QC+erkPL8\nUvCZCeClLvorjQ6E+/y8M+vOi1ZtpPUhR8Zn0KUTCKo5tjWCOnUQYU+HgQdBC++C2MZO9k9p3TAo\n2rcrpKTu1834GlxHtmOsar5Goq5esK90Dskc46wQZOo8AM93cXz8XPTruezi/If6fDcdguX9Ho5s\nidpdC5E1FIbb5wFc2+U5e+oE8mf7JrSQLatATLFxuyxDC5mR2QbjOxo9qFfrxy4Dr+sJGTwHIHBu\nD+Bz8m4IFbCBf18C5brT6pDe9mdIN+l4nobig3Z96QYIEvz+3OMHrQ7ZiDQTNqhX8r8inmQ7BorA\nFPTJZl1Qo1JMXGuZs+BRTG/3WZA0G+L+o0TsfwE4zxrg/oM+Q/bLOaDI0P3Wmei+S+p4a0jdOtRC\n0QFvDz2rY6Dc0G9EbWqprIerDAZ5hT+H81oJJXPG8ZcB+Io5+clwk4BE4CgAfzWzM4cr0cpwkl4T\nMdFrLZE8FnKOnM8e1V+qOW9WEj4zy3KQXA7KV40JcbpigaPqKr5oZm+SnAt6FztqlbKCarvXJEwV\nY53T3oYspcwgJqPqop4ORVNfgKIQO5vZn7s431TIAbQKhCD4BYDtzGyjLsd/KwSnPc1aDJUdzIbM\nJ/XqNdFNzxg0va81oWf9GigS/kEz+3Q3Y+uVcAjKaFG1FkslZ80muWju2jygpB4j0i85HErwP8Qa\n1it5u4plJtlmMtmcB+Amtoq07gbBYUI5CbrPsPZE7JV9X+Piy2a2fzTW+dEqEhhKWGOsz3CD4EdD\nJiS/amY/9P+3tYC0guT3Y2PLlEx/AoATfMLsIPWAyA3mgjxt34EiXF8coEvotfSUvILkcmb2mCes\nh/KGP3f/jBXQDJkPwB9I3o32OnqVBYEHUWaSPBzyVG9I1WaateaYEek9EdNfndRhUwDHuQHUlOyn\nG6lNwmd7WY5vWXVZjglQROBECNa3G/p3HSsAWIoqkVFI2zzMEqrtoF1PSZjKpBfGGMVqGxqypbTy\nvRI3wCrZoc1sOoCPOwncKDOb2Y9TvmFm5qRMp7gTaI/ao8plLjObFBF0pUgdinmtjtSrluimobzu\n1yu8q+5ht/KWmb3hRuXJZnYyVcd2QIXtrJezQffyZTObz7c9ApWb+ay1ymgdNMDDqiJjMkiX6RDX\n+z4HwbQ/AEUsa2UkQjYiw0pIbgEldQOKTFydaLNg8LWPycbMjorabQYZOgBwg5ldH+3vefHlRD+z\nQjCHymhQYbiZ2af6e87+SOh5j73w7+SoBtsZILsuMk/yTDPbg+TtJU0WhCBTuzXoM1lHzMxu6maM\nvRaS74YWpnvN7HaSS0DRxbdVNPjtLh79+RQUHXuMYvBd2QaY7ZjkLRAl/iS0Owy2CNpkl+Wg04iz\nvd5gVzWcygwti2oaNYjyfRQyLIF+zBMDIb62ngAph89B+bx/tC6p1jPOV1WkuwP54g6Cz6EzCvlt\nNBSPaF0HGesbQtc7pds1neS1ENvpJR7t/zxEPLFZ1O4HkKPuVYigZH4oD2ntRJ9ZkdnM8fWkXq33\ndQ/kkD4CKkQ+IxUNHEihrNgtAaxjZl/3bcO6jJajsLaE1rrVodzwraAI6VtZfYwYZCMyXMQns7Wg\n6Bag0PR9ZnZ4xrGNF2T2uPiyH3cVWovQKAhWc3ExqVQcl2W4DbSEEKYYztQU3sQGVNYj0hKSN1kX\nxZpHZESqxA3hDhnoCAlrClt30V/PINANDK1LoCLF/aXabiwk57L+U96D5BTIo3+jQ1c/CkEC+xM5\n6pmQvA7Ai1CEsTCOYUE5iQZ99dQJ1ARO2QsIbJdj7He9Wu9nRShH/PdmdgHJpSG453Hdjq1bSekc\nbJXRGgs9z+dggMtosSbFhOT5ADYA8DvIULwZyh1tZCyOGGQjMmzEcd+rFd4EquDpAzEOOsL1joKg\nIfuE+OuS8Pcrkae157j/SPl4A8CTlqiR063hNtDSywgZyb8DeAqq3XIPtFD0SbcK2f+KsHeFodeC\niBI+AEEpCeC12AM9VJIBRRmRQRC2ipsWkMelAfxpoCIk0bkXRQumPclUe7DbvtYC8Eco+vAdCLL7\nQzO7u4u+sgytnChfr8UV9l9AzIpLUPWZ9jazr3TZ331mtqYbZqub2VvsR95SF+evhEoOdhSmG8mB\nU+YYWrmR2XeSsL1+W6HXbWRm61YcswCEkNp+IJ2YrOEGoPLuRkHG4YVm9jTJ6dYwr3Ukh2xEhpvM\nD6AoOlqmsIUeszcgb9V2YQML8tHC8HfUT89x/w2MjOOD/0sNtyGQIm8lLKgMJPJWSK4HsUW9QhXN\nXAPAeDN70pu8G4JQjIW8lb+F6oU9PAjXMayFPSwMDbHX7Qx55j4MMdQNWKJzU8l8F0dkgCWGa7lj\nqyvlvomQ3A5iRJsIzSMnkzzMzC7tpj8zu9f/fRmCpPVHFoLyL+sMrWP6eZ5u5ERoXvgNAJjZFJIb\nVh9SKf8iOTdU0Po8ks+hBRMdMCmDSqIFAS/kLpIrm9m0Hpyzp06gGE5JzwuL4ZRlhhY6c8PXREZk\nNmNcjWChmX3m5OgPhIQ5+29ADIpbVh1g4lc43T8DJlbDDWAiclkB0nVuJPk8gHnYgNADGImQjcgw\nEpJjIRriW6AJZUMAh5tZihSjm/6TkLte4P5J3mFm6ycmyK4nxuEubMBk5QvaWEgx+5aZnTKIQx12\n4tGK1SAv26pUPs8vzeyTXfSVyqkZcPa8/shwH987RdijXNmac0yBEAfP+feFIdhco8jMQECgm8Ap\nexnlyxzbPWa2dgQj7w8T3xiIOIIAdoIcnueZ2T96Nuj0eSuhkiQfgko3zALlQU2HjONi7WzMFBid\nvyMfqYs+suCUbwcIbJ0wM0f/nSx1KSYkPwTpO9sBeNrMPpLT70iEbESGjTheeSJai97XSrDX34cg\nKv/y7wtAbJRHBm1S4e8ki5GZFXUj+jP29f1vJVPk/5jhVstkxU4q658AuHzQRzr85FXPMXiD5DxQ\njcFuo1qvkJwNwBR/N56FCu0OC2nyLo7IwAnJg4Ovo6CI9jODcOpRkfHyD3THirguKiDQ3YiZ3Zpj\naPU6ypcpTzn8zVwBHAdFlhoJyZ9CBA93BptjxuGBlP+a2T9IjiI5ysxuIXlSsH8xyDk1IOLG0RUk\njwbQbUrAey2PcKuWUdQlNzLbSOpgoTmSMNBPIjkZwIAYZHRmZ4cFpiJzQw7jjFJMRsNTTMram9lk\nAJNJHgbllmXJiEE2IsNG2CIz+E1iWyibWUC/bmYvkPw0gCODNqnw90Di/d9Vtd/M/ul/swy3t4lU\n0pmzGZX1O00ecNjDWQDugwq1Tuqyr10hBXc/AIdAXubPVx0wyNIYijIiAyLhnPMGBCG+bBDOex3J\n6yFDClDdwmu76KfnEOgGhtYRUF3LtigfRCwyUPJlAOMhg+WvEGHAvl308yiA4z0KfzF0zwacxjyQ\nOqjkjADm3hMZACdQLpxySCCwDWChOX2lcvQH0lYonAz3DeA5+ivHo2WQFSkmteVp3BlwW+5JRiCL\nIzLkQnIOqE7VLVBl+MLzOS+A68xshah9bdHnkvMcaGYnVbXpVgLcNaEK8y/4//MD+Isl2HY8src4\n2pN/uyoOPBTCGiYrNqCyfieJQ2jeXcBV2M/C0CMyIsNdXEFe37/ebmb9ipL3CgKdC6eMoZ3ufOqa\nRn0ohOSSEG34DgDmhAzkC8zs0QE+byVUkuTTAH5cdryZle6rOOeE4GvhBDqjKcy0KZxyqCCwdbDQ\nhn2FaKHi3h1vZn/qdnwNzz8vdG/7U4euV2MpkExxNN6g5+AJAEdYj0rMjBhkIzLkQnIcRLDxHsgT\nWDz8L0GT6ClR+69BXvew6PNvzAsaV5znL2aWpH7ulZA8A6Jgvca/bwZgKzPbO2r3HSiyMR2a8AFN\nQslCgyPyvyXsAaMYyQvMbCxVtDMF9RjSmnFvByjKO0FInmRmB0awmz7pL0yq4rzLAlg0gsqB5PoA\nnjWzJ7roM4ZA/wbAWTne6pL+sgwtkj+CcmXDKN80M/tqN+fNHNvCAPZCJ2Pf7j3oe3UoOr+KmQ0I\nvLkEKplq9yyAU1ECQTWzbw3A8LKE5AuogFOmIntdQmA3ANA1BJZDzKDZCyG5JqTTzQPdk38B2N3h\nf8NOKBbwlSDnQk/YQUcgiyMy5GJm4wGMJ7m/ZRQyNLPjfOL5uG/6jkVFn0uk3zkHGbKOme1VfDGz\na0mmDMXtACxjZq8PwpgGRDhCZ94feZDk6v2EDh3mf4cTPDGUtwMU5Z0gv/K/x1e26r2cBCBVQ/JF\n37d5Yl+pDBAEOgtOaWaHRVG+0/sb5cuQKwHcDkEj36xpWyskZwGwGRQh+xhkDBzT334rJBcq+ax1\nUfy5SnpozDaCUw4hBLbfDJokdzazc6Nc0z7pJlLZUM4C8BUzu93Hsz5koPWL1GWgxMzehPK2+3RW\nDy5MADATIjlbHcDXLbNG2kiEbESGlbCmhod7JW40s4920fdgRMiuhxbRc33TTgA2tIg9j+RlUO20\nAWXqGixxGF6/mKzeCUJyFjN7g+TDAN4PQR5eQQsCM6RRrRH53xWS49z5Vbmth+e718zWKtnXmN1x\noCDQVXDKgYjyNRjXg2bWb7ILqmDwWACfhvJULwRwpZkNOOW9n78SKskBYFylioffjk5WxEY5k03h\nlEMFga2DhWb2sbeZnUaRn3TIQEcqU88BG9Y+HWopopIkPwlgbwDfBPCr3GsYMchGZNgIM4slkrwJ\nwDZm9mKij6q6HHOa2YBGhSlyj6Mhyn5AHqtvmZN6BO3WhDygD2GQCo0OhgzE4vq/JMUCQ3KZ1P4u\nYVxbQuUi3gM958MiR68MIlfI2/1Zf7tJSrkZyPeV5GNmtlzJvsfNbNmBOG+O5BpaJK+GSq9Mi9qt\nDOD7ZtYoytdwjN8FcFcBf+9HPzdD9Q0vM9VtGjJJQSVJviteH3twnl4Zs43glIMNgc2FhQ5nYYtE\nZBe0DHaD7sl/zCwZsRuOQnKqma1CcjyAiWZ2eZM5dgSyOCLDSXKLJb4MYBrJGxCE5c3sABti9kJf\nWMZlND0bwHEApqGVQ/a2Eo7QmXcjBLozvCrkBABbx0rjMJACIrcNxJBXRI3HAsguljki/ROqvuOO\nAJZmey2veQD0VBGO5D6Se5nZGdF49oQiF0MpuXDKRVPvlZlNI7nUQAwsIhL4BsnXAPwXXTpabIjz\nkuugkr02xlyuJvnp/hqzaA6nHGwIbM8ZNAcyd7FEToi+hxG6t1vEaDLJ3wFYGsDhVEmbbP1uJEI2\nIsNGmFkskeQXU9vNbDBrq7RJ08T5KjjP20XYIyard5I0hcBk9nmnma3Xr4ENoBQJ53XbRmRgxCFj\nSwM4Fu11mGYCmGpmbwzQeReFag6+jpYBtiaUb7q1JWpMDpbkwimHc5RvuMtQQiXdqB0DoU+6Nma7\niSAPBQS2DhbasK+ewD3fieIR0dUATDezf1FFthczs6lZx48YZCMyXISiW10NmrzfVjA+kh8ys8nM\npL0l+WPoGn+D9mt9W1Cfey7fAWZ24lCP5e0kTSEwmX2eBGBhAFeg/Vn6TelBgygk/wjgM2Y23b8v\nDeAaM/vA0I5sRAZDKArugoXsYTO7eSjHA+QbWiQvAHBzSZRvUzPbfgDH2FGDM7VtuMpwgkp2K7lw\nyuEEgU3BQhse3xO4Z4PzDTWZSM+E5Iap7WaWVYtsBLI4IsNJjslpRHI5yNu7Itqr0r9vYIZVL+bU\nrGZ2q4f8YWZ/rzik8LqtE3YD1RIZ9mJmbzoUasQgayY9ZxQDsCAEiwgdF4agwPoQy0EAJpKcDhmi\nS0IJzyMyiOKe++MALIJBzDU0s1ugGpPDSXLhlAcCuJzkTkhE+QZiYFRdzjEAFqJqVYZ1ORcbiHMO\nhAwlVJLtxY0LeREq6JsdEW4ApxxSCGwdLLSh9ArumStj/G8q3eTtFjE6LPh/DgAfhuaNrHdhJEI2\nIsNW3Ls01sz2jbbfAeGMT4Qmut0AjDKzowZ/lG3jOgbAflA+FSEY38kDoIAPCyF5IkR1fxHac/ne\nFlG+oZB3KukJVT+qKPD+iHlR9xEZPCH5OIDNzeyPtY3/x6UpnHIwo3xsWJdzRDqF5N0A1oBytAFg\nZYhAaz6I3TiLhrzB+YYEAjsQsNAA7vk6BPcEhogkiuSBZnbSYJ/3/7d351GSltUdx7+/GUAGcEBk\nSVgEBAEBQVmUQU+QxLgEFBeQgICJisYlEkxcCQFF1LgF2TwHJci+hUVRQRTZBdmHGcSFbUQEFUEG\nhn345Y/nrZmanu6mume6nqrq3+ecPt31vlVdt5mmq+773OfepUXSusDhtt/R0f2TkEUvaZbb9wJ2\nB+6ilDsMHQx9g+1thvyhu8H2Nt2PeEFMH6NcoXq/7buaYy+mlKdd2CrtG7Dl+eGuerv2JvJeNkEd\nxdYCvsHCfQuXAwfY/v3SfJ7xkrTvcMfdNs4iJl6v7zWsoRfLKVvU4VzOWJykc4CDbN/a3N4M+Bzw\nCeCcpV2SV6sEdhDKQkejLowqmkiSRPm7slkn90/JYlQnaWPKVZ49gQcoKy7yyLPGnmw2T/5G0kco\nVxFX6kqwI9uH8gf1gdYB23dK2hu4iIWlfQOzPD/Kv0+MYGknY43jKQNF925u79Mce8OIj+iu9ivH\ny1NKam4EkpB11/WSzmDxvYbn1Auprl4sp2zbh3Jd1UD628atZAzA9i8kbdq8Jk/E81UpgZ2oi5+S\n3sLC0T2X2v7+RDxPJ6FUet5xURkS3Xof12rw0XHFUFbIojqVgZ9XAO+1fXtz7M6R9oRJ2g64DVgF\nOJRShvBl29d0KeThYppte4tOz0l69TAbgBc71suasp8vAGvZflNzFXKG7eMqhzapDLcJu9sbs8dC\n0irA6bbfWDuWyUSLdkVtsSeunXWMQ/PvZOBh2wfUjqcfNRceHqSU8EFpP78a5WLVlSOVFy7B8/Vs\nCexYSfoS5SLaKc2hPYHrbQ+3R26iY+mrFTIt2gH8GeDusbynS0IW1Ul6K2Uz6quBCyl/RL9te4Oq\ngY2BRpkoP9y5To/1MkkXUFZiDnSZTr8McJOHDMGMidWUrRxLWVkGeCfwgV4tHZW0LDDb9ia1Y4no\nNW1v6h6zfVbVYPqUpGnAh1hYxn0VcAxlTuYKth+doOft2USrU5JuAV5u+9nm9lTK6/qWE/R8rbl7\ni50CptmeNJV8k+YHjd5l+zzgPEkrArtSlvXXkPRN4NzWBlwtOtR0uO9Tsz3+VpLmDnNctHWClDQD\n2AFYfcg+sunAmFvUVraa7TMlfRrA9jOS5j/Xg2Kpew/lzcbRze2rm2M9QYvO5ptC6Y56Zr2IJqem\ne997gc1ZtDttz/yuBFAG8kKZExfjYPtxysDhoUOHASYkGWuet+dKYMdpFRYOjV95Ip/I9nDbN/qK\npFmMnFS602Q2CVn0jKYz0KnAqU27392BT1L2YAHMAO6hDD38OT1UX+zO530sR9nvtgyL7iObC+y2\ntOOaYPNUBh8aQNL2lDa/0UW276Z02epVX237+hlK6+nf1QpmEjsJ+CVlb+HngHdRSr+jh3gcswhj\nUb04GqePfBG4qWnaJcpesk+N/pBJb5el8U1Sshh9o1k6b7V53RL4AWUa/a2jPrAHSVrP9hxJ0ylX\nUPruaqikbYAjKCUasynDiXdzh1PpY+lo5tf8D+WCBZTynH9vErUIYOHIBUm32N6yKR29wvb2z/ng\niD7Sq6Nxel3TFXAdyoWz1j67a4fugYuJMaV2ABGdsj3f9oW2300ZqHw7ZeDsRyqHNh6rN8vctwCz\nJM1sEpy+4TIMe0dKCeYHgM2TjFVxGmUI9Iuaj/ObYz1B0vaSrpP0qKSnJM0fobw3JlZrptBfJG1B\nKUVao2I8ERNlmu2LKYsOc2wfAuxcOaae57JC80Pb99n+XvORZKxDkh6RNLf5eGKsr3UpWYy+ojJg\ndmfKKtn6lBWac2vGNE7/C3zI9hWwYAj28ZSVv77QbP49HTjD9h2145nEVrTd3kHvO5J6qTvbUZSm\nPWdROo/tC2xcNaLJ6dimFPw/KQn8SsBBdUOKmBC9OBqnX9woaTvbGbswRu374ZrVxl0piwcdScli\n9A1JJ1LK435IaZs9u3JI49YqHxpyrN+6LK5HaSe8B/AspcvfmbZ/WzWwSaIpdwX4DGV+3+mU/Xx7\nUBqufKZWbO0kXW9721apXHNssd//mFiSNnAztH60Y1HXkGZPi7H99W7F0q96cTROv5D0S2AjYA4w\njzE2pohFjeW1LglZ9I1mXtm85mb7L27rD8b0xR/VmyQdDkyjlJa13kQ/AZwMYLvjYYK9oNlEfRDw\nrjE0OIklIOkeyu/OcM1t3CvzWyRdDrwO+DZwP3Af8E+2t6oa2CQzwqiNG2z3Van0oJN08Gjn0/Qj\nJlJzoXUxtud0O5Z+I+ntbTenUCpCdrQ9Y4SHLCIli9E3bA/SnsfWm9GhL76voLzJ7skZUkMNWSWb\nD3yibkSTh+11a8fQoX0oIx0+AhwArAu8o2pEk4ikTSmt7lce8oZhOm0d6KI3JOEavx4fjdMvPm97\nn/YDkk6i/B2P0b257etngLspZYsdyQpZRIyLpJ8Dy1L2Bp1h+87KIU1KzRDU/YH1bH9Q0kbAS2xf\nUDm06AGSdgXeCryFsnes5RFK6ffPqgQWw5J0xGjnbX+0W7H0G0l/YpTROLYvqxFXPxm6kt50t55l\ne7OKYU0KScgiKpC0JvAFYC3bb5K0GTDD9nGVQ+uYpE1s/6p2HJOdpNOAWcBetreQtAJwVe09WqMM\nywQgexK6S9IM21fXjiNGJ+ndo523fUK3Yuk3gzQap9skfZqyH3ka8BgLk9mngGNtf7pWbP1C0gbA\nv1Iazi2oQOx0ZTYJWUQFki6gdFU80PZWkpYBbrL9ssqhdWwQkspB0NY0Y8HmYUk323555biG3YvQ\nkj0J3SXpy8DngceBCylvWA+wfXLVwCImQNOReU/gK8BnbR9VOaS+IOmLSb7GR9JM4DjKBdJnW8c7\nXZkdpD05Ef1kNdtn0vxPa/sZyh6sfvId4EfAWs3tXwP/Vi2ayespScvTrEY1V+meqhsSUMpZ12nm\nAC34oAwezf7l7nu97bnALpS9DRsBH68aUcRSJul5zV7Jk4EP07+jcWo5UNLekg4CkLSupFfWDqpP\nPGH7CNuX2L6s9dHpg5OQRdQxT9ILWfgmenvg4bohjdkgJJWD4HOUFY91JJ0AXAL0whXOw4HhhmLO\nbc5Fdy3bfN4ZOMt2v/29iRhVMxrnamBryqrYdrYPtX1v5dD6ydHADGCv5vajzbF4bt+QdLCkGZK2\nbn10+uBcpYyo42OUDfYbSroKWB3YrW5IYzYISWVfa4ZPzgR2B3ag1P1/3PYfqwZWrGl71tCDtmdJ\nWr/74Ux65zczhh4HPihpdcqojYhBsTdlNM7+wEfLn0egD0fjVPQq21tLugnA9kOSlqsdVJ94GaUb\n5d+ysGSx467Z2UMWUUmzb2wTyovFr2w/XTmkMWmu/BxJGdY9myaptH1L1cAmGUmzbW9RO46hJP3G\n9ktGOHe77Y26HdNkJ2lV4GHb85vmL9Nt3187rlhckzDvx+INAt5TK6YYfE335B2A65rEbHXgotpN\novqBpNuBzWyPa8tAVsgiukjSdsA9tu+3/YykbSgzmeZIOsT2g5VD7JjtGyXtSB8nlQPiZkmvsH1T\n7UCGuF7Sfra/1X5Q0vuAGyrFNNltCqzfXAxqObFWMDGq7wJXAD8hpeDRPa09d2tIOoxSufOfdUPq\nG7OBVYBxVahkhSyiiyTdCLzO9oOS/gY4ndIm9eXAS233fNlie1LZ3N6XJqkE+iqp7GeSlmmS+lsp\nSfEdlHKdVnlOx7XrExTfmpQX9qdYmIBtCywHvC0rM93VDHfdELiZhW/wnblWvakXOqXG5NQMk/87\nymvJxbZvqxxSX5B0KaV77XXAk63jaXsf0YMkzbS9VfP10cCfbB/S3O6LF+BBSCoHQWuAp6QNhztv\n+45uxzQcSTtRyloBbrX905rxTFaSbqOU0+RFvw9I+jzwM9s/rB1LDL6mU++/ULqvzgKOaxp1RYea\niqHFdNppMSWLEd01tbWyQbkC9f62c/3y/+PUtlWwPShDI88GzpZ0c8W4JhtB7yReI7F9CaXzY9Q1\nG/gr4L7agURH9gc+I+lJ4GnSmCIm1gmU37MrgDcBLyVjbMbE9mVNZch2zaFrx9Jgq1/eAEYMitOA\nyyQ9QOl2dgWApI3onw6Fg5BUDoLVJX1spJO2v97NYKLnrQb8QtK1jKOcJrrL9vNrxxCTyma2XwYg\n6Tjg2srx9B1J76QMIr+UcgHlSEkft/1/nTw+b54iusj2YZIuBv6a0rmoVT40hVL21w8GIakcBFOB\nlWhWyiKewyG1A4ixkfQC4CXA8q1jti+vF1EMsAUNuZq9yTVj6VcHAtu1VsWaDpU/ATpKyLKHLCLG\nrJk51koq5zXHNgZWsn1j1eAmidYestpxRP9YknKa6K6mG+n+wDqURizbA1fb7mimUcRYSJpPaQoF\n5SLfNOAxUirbMUmzWquMze0pwMz2Y6PJCllEjJnta4Y59usasUxiuYQZHVvScprouv0pyfM1tndq\nOt99oXJMMaBsT60dwwC4UNKPKFVEUPbYX9Dpg7NCFhHRhyStmhED0SlJM4G/H1pO0+r6Gr1F0nW2\nt2saJb3K9pOSbrW9ee3YImJ4kt4OvKa5eYXtczt9bFbIIiL6UJKxGKMpQ0oU/0zZuxq96XeSVgHO\nA34s6SHKrMeI6FG2zwHOgVKyKOldtk/p5LFZIYuIiBhwkr5CGVraXk4zp3MPTAAABhBJREFUy/Yn\n6kUVnWjmG60MXGj7qdrxRMRCkqYDHwbWBr4H/Li5/R+UPWS7dvR9kpBFREQMviUpp4nukDTd9lxJ\nqw53PivjEb1F0neBh4CrKaOA1qDs093fdsezWZOQRUREDKhmHMWatq8acvw1wH29Plh8spH0fdu7\nSLoLMIs277HtF1cKLSKG0d5dUdJU4D7gRbafGMv3Sf14RETE4DocmDvM8Yebc9FDbO/SfN7A9oub\nz62PJGMRvad9htt84HdjTcYgTT0iIiIG2Zq2Zw09aHuWpPW7H06MRtKoswUz5zGi52wlqXXRS8C0\n5vaYZrglIYuIiBhcq4xyblrXoohOfW2UcwYyGDqihyytGW5JyCIiIgbX9ZL2s/2t9oOS3gfcUCmm\nGIHtnWrHEBHdl6YeERERA0rSmsC5wFMsTMC2BZYD3mb7/lqxxcgk7TvccdsndjuWiJh4ScgiIiIG\nnKSdgC2am7fa/mnNeGJ0ko5su7k8pZ32jbZ3qxRSREygJGQRERERPUzSKsDptt9YO5aIWPrS9j4i\nIiKit80DNqgdRERMjDT1iIiIiOghks6ndFUEmApsBpxZL6KImEgpWYyIiIjoIZJ2ZGFC9gwwx/a9\nFUOKiAmUhCwiIiKiB0h6hJKIacgpA08CdwAH2r6427FFxMRJQhYRERHR4yRNpXTKPMX2Fs91/4jo\nH2nqEREREdHjbM+3PRM48jnvHBF9JStkERERERERlWSFLCIiIiIiopIkZBEREREREZUkIYuIiIiI\niKgkCVlEREREREQlScgiImLMJN0t6TFJcyU90nw+onZcERER/WaZ2gFERERfMrCz7UtqB9JO0hTb\nz47h/lNtz5/ImCIiIkaTFbKIiBgvDXtQerekKyV9XdJDkm6XNKM5/ltJ90vat+3+x0v6pqSLmpW2\nSyS9qO38ps25P0u6TdLuQx57jKQfSHoEeK2k6ZJOlPRHSXdJOnCE2B4ADh4m/oMlnSHphCaeWZK2\nbjv/yeZnmitptqS3LsHPvpykr0qaI+m+5md53vj+OSIioh8lIYuIiInwSuBmYFXgNOB0YFtgQ2Af\n4ChJK7Tdfy/gs8ALgZnAKQDNfS4CTgZWA/4ROEbSpm2P3RM41PbzgauAo4DnA+sDrwX2lfTPbfd/\nFXA7sAZw2Ajxvxk4FVgZOB84uu3c7cCrbU9vYj5Z0prj/Nn/G9gI2LL5vDbwXyPEFBERAygJWURE\njNd5kh5sVoIelPTetnN32T7RtoEzgHWAz9p+2vaPgacoCUjLD2xfZftp4EBge0lrA7u0fy/bM4Gz\ngd3bHvtd29c0Xz8N7AF8yvZjtucAX6MkQi332j7G9rO2nxzhZ7vS9o+a+E+iJEwA2D7b9h+ar88C\nfkNJwsbzs+8HHGD7YdvzgC9REsyIiJgksocsIiLGa9dR9pD9oe3rxwFsPzDk2Eptt+9pfWF7nqSH\ngLWA9SjJ2YPNaQFTgROHeyxlFW0Z4Ldtx+ZQVp6Gu/9I7m/7+jFg+db+tKbk8ADKChzAis3ztnT0\ns0taHVgBuEFaUP05hRFKQSMiYjAlIYuIiPFamonDugu+qbQS8ALg95Tk6VLbbxjlsW77+gHKKtl6\nwC+bY+sB945w/zFp9rYdC+xk++rm2E2M77/FA5Rkb3Pb9403poiI6G8pWYyIiG54roTlHyTtIGk5\n4FDgGtv3At8HNpa0t6RlJC0raVtJmwz3TZoOi2cCh0laSdJ6lNWsk5ZS/CsCzwIPSJrS7E3bosPH\nDo3VwLeAw5vVMiStLen1SxhrRET0kSRkERExXuc3nQZbH2ePct+hq1JDb58KHAL8GXgFsDeA7UeB\n11Oaefy++fgSMFonwo9SVp7uBC4HTrZ9fEc/0cjcxHMbZU/aNZSyxs2BKzt57Ai3P0VpEnKNpL9Q\nGphsvISxRkREH1G5QBcREVGHpOOBe2ynu2BEREw6WSGLiIiIiIioJAlZRETUllKNiIiYtFKyGBER\nERERUUlWyCIiIiIiIipJQhYREREREVFJErKIiIiIiIhKkpBFRERERERUkoQsIiIiIiKikiRkERER\nERERlfw/elw146FTyyYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8e76ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "df.plot(kind='bar', ax=ax, x='Emperor name', y='Reign duration (years)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also do other things with the data, for instance plotting the reign distribution histogram of these 85 emperors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000009109DA0>]], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GiSMdIOlMYDgiBiUt3s2h0Wzn8uXL2fzkq0yaMQuAfSZPZcohc5k+J1u5v/Bo1rFXdXtw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\nWUV4QjczqwhP6GZmFeEJ3cysIv4/d7lmAWCgcxgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8e767f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='Reign duration (years)', bins=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this plot, it looks like it was difficult to reign a long time over the Roman empire and that it was a lot more common to have a very short reign.\n",
    "\n",
    "Another look at this with cumulative distributions gives us this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000A9DC940>]], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zwO9ExOnAWcC7JL0MWAWsj4hTgQ3A1dU2dg09cX0wcRaJs0icRTE1B/SI2B0R\no/n3+8g+IPpE4GJgTb7aGmBFsw7SzMxqa6iGLunFQD+wEeiOiDHIBn2gq9o2rqEnrg8mziJxFomz\nKKbmh0SXSHousBa4KiL2SYqKVSrbAKxdu5Z77riHo45bCMDhR8/jmBN64ZweIP2JVfpBjm8fZcux\nY0B31ce3bN7I+PZdB6wPQP7B0vWuP1W7k/qvtT/3X73d6M9zrvc/3Z/nXOh/dNNeFq1YDsDw8DAA\nAwMDB7WHh4cZGhoCoKenh66uLgYHB2lEXQO6pCPIBvNPR8S6fPGYpO6IGJO0ENhTbdve3l5e8pJf\nm3Tflb+R55/ST9+Sk7l5/Y6qj/ctOZP5D+2Ycvsi6zez//Htoy3vv9b+2tX/+PZRWHbyrHz+U7Wb\n0X953bgd/TfSbnb/lcs64fn3L+2daJcG8mrtgYGBA9ojIyM0qt6SyyeBuyNiddmyW4DL8+8vA9ZV\nbmRmZq1Tz2mLrwbeBLxW0p2SRiRdAFwHnC9pKzAIXFtte9fQE9cHE2eROIvEWRRTs+QSEf8KHD7J\nw8tm9nDMzGy6fC+XFvI5tomzSJxF4iyK8b1czMw6hO/l0kKuDybOInEWibMoxjN0M7MO4Rp6C7k+\nmDiLxFkkzqIYz9DNzDqEa+gt5Ppg4iwSZ5E4i2I8Qzcz6xCuobeQ64OJs0icReIsivEM3cysQ7iG\n3kKuDybOInEWibMoxjN0M7MO4Rp6C7k+mDiLxFkkzqIYz9DNzDqEa+gt5Ppg4iwSZ5E4i2I8Qzcz\n6xD1fGLRJySNSfpO2bLjJN0maaukWyUtmGx719AT1wcTZ5E4i8RZFFPPDP1G4HUVy1YB6yPiVGAD\ncPVMH5iZmTWm5oAeEcPATysWXwysyb9fA6yYbHvX0BPXBxNnkTiLxFkUM90aeldEjAFExG6ga+YO\nyczMpqPmh0TXKSZ7YPXq1dyz80mOOm4hAIcfPY9jTuiFc3qAVDMr/WYe3z7KlmPHgO6qj2/ZvJHx\n7bsOWB+AZSc3tP5U7Wb1X14fbFX/tfbXrv5L28zG5z/V/prR/2M7t7Hw7JVt638mnv90X0+V7d3f\nXJuND23qvxnPf3TTXhatWA7A8PAwAAMDAwe1h4eHGRoaAqCnp4euri4GBwdpxHQH9DFJ3RExJmkh\nsGeyFc8991x27T9j0h1V/ok1/5R++paczM3rd1R9vG/Jmcx/aMeU2xdZv9P6r7W/dvVf+cJvdf+N\nbO/+W9fVwl3uAAAFGElEQVT/MSf0HrCsE55//9LeiXZpIK/WHhgYOKA9MjJCo+otuSj/KrkFuDz/\n/jJg3WQbuoaeuD6YOIvEWSTOoph6TlscAv4NeKmk+yS9DbgWOF/SVmAwb5uZWRvVc5bLGyPihIg4\nKiJ6IuLGiPhpRCyLiFMjYnlEPDTZ9j4PPfE5tomzSJxF4iyK8ZWiZmYdwvdyaSHXBxNnkTiLxFkU\n4xm6mVmH8P3QW8j1wcRZJM4icRbFeIZuZtYhXENvIdcHE2eROIvEWRTjGbqZWYdwDb2FXB9MnEXi\nLBJnUYxn6GZmHcI19BZyfTBxFomzSJxFMZ6hm5l1CNfQW8j1wcRZJM4icRbFeIZuZtYhXENvIdcH\nE2eROIvEWRTjGbqZWYcoNKBLukDS9yX9QNL7q63jGnri+mDiLBJnkTiLYqY9oEs6DPhr4HXA6cCl\nkl5Wud62bdumf3Qd5rGdzqLEWSTOInEWyXQmw0Vm6EuB/4yIeyPiaeDzwMWVKz366KMFuugszz7u\nLEqcReIsEmeR3HXXXQ1vU2RAfxHwo7L2j/NlZmbWBkc0u4Pdu3fz399+8Dj/c8cfw/gTzzS7+1nl\nyZ/ubvchzBrOInEWibMoRhExvQ2lM4EPRsQFeXsVEBFxXfl673znO6O87LJo0aI5eyrj6OjonH3u\nlZxF4iySuZzF6OjoAWWWefPmcf3116uRfRQZ0A8HtgKDwC5gE3BpRHxvWjs0M7NCpl1yiYhnJf0W\ncBtZLf4THszNzNpn2jN0MzObXZp2pWg9Fx11MkmfkDQm6Ttly46TdJukrZJulbSgncfYCpJOlLRB\n0nclbZH0nnz5XMziKEnflnRnnsUH8uVzLosSSYdJGpF0S96ek1lI+qGku/LXxqZ8WcNZNGVAr/ei\now53I9nzL7cKWB8RpwIbgKtbflSt9wzwOxFxOnAW8K78tTDnsoiIJ4HXRMQZQD/wi5KWMgezKHMV\ncHdZe65msR84LyLOiIil+bKGs2jWDL2ui446WUQMAz+tWHwxsCb/fg2woqUH1QYRsTsiRvPv9wHf\nA05kDmYBEBGP5d8eRfYeVjBHs5B0InAhcEPZ4jmZBSAOHo8bzqJZA7ovOqquKyLGIBvogK42H09L\nSXox2cx0I9A9F7PISwx3AruBr0TEHczRLICPAu8j+6VWMlezCOArku6Q9PZ8WcNZNP3CIpvSnHlH\nWtJzgbXAVRGxT1Llc58TWUTEfuAMSfOBL0k6nYOfe8dnIemXgLGIGJV03hSrdnwWuVdHxC5JLwRu\nk7SVabwumjVDvx/oKWufmC+b68YkdQNIWgjsafPxtISkI8gG809HxLp88ZzMoiQixoGvAxcwN7N4\nNXCRpHuAzwGvlfRpYPcczIKI2JX/uxf4B7KydcOvi2YN6HcAvZJOknQk8OvALU3qazZT/lVyC3B5\n/v1lwLrKDTrUJ4G7I2J12bI5l4Wk40tnKkg6Gjif7D2FOZdFRFwTET0R8RKy8WFDRLwF+EfmWBaS\njsn/gkXSPGA5sIVpvC6adh66pAuA1aSLjq5tSkezlKQh4DzgBcAY8AGy37xfBH4WuBe4JCIeatcx\ntoKkVwPfIHuBRv51DdmVxTcxt7LoI3tz67D86wsR8aeSns8cy6KcpHOB342Ii+ZiFpJOBr5E9n/j\nCOCzEXHtdLLwhUVmZh3CH0FnZtYhPKCbmXUID+hmZh3CA7qZWYfwgG5m1iE8oJuZdQgP6GZmHcID\nuplZh/j/r8zyKUg7XDwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa7154a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='Reign duration (years)', cumulative='True', bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final visualization I would like to offer is the cumulative reigned years of the Roman emperors. This will allow us to identify the periods of turmoil and those of peace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cumsum = df['Reign duration (years)'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
       "       52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,\n",
       "       69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumsum.index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xaa9c6d8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8pk6dSmd5HbtpPnCDqn4WMp/EZOB/VXVyp3YschewB7geOE1VS0VkAPC2qo5r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\n6RCyUP1YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaada518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(cumsum, cumsum.index.values)\n",
    "plt.ylabel('number of emperors that have reigned over the empire')\n",
    "plt.xlabel('duration of empire (years)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Judging from this curve, it appears that one can distinguish periods of turmoil (steep slope) and peace (horizontal slope). In particular:\n",
    "\n",
    "- the beginning of the empire and the [Pax Romana](https://en.wikipedia.org/wiki/Pax_Romana) (from Augustus to Marcus Aurelius)\n",
    "- the crisis around the year 300 ([third century crisis](https://en.wikipedia.org/wiki/Crisis_of_the_Third_Century)) \n",
    "- a new stability from 300 to 650 years reigned, also called [the Dominate](https://en.wikipedia.org/wiki/Dominate)\n",
    "- [fall of the western Roman empire](https://en.wikipedia.org/wiki/Fall_of_the_Western_Roman_Empire)\n",
    "\n",
    "Let's annotate the previous plot with this information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x5c6c240>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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taL8DvgHeMM4dRORJoJ8x5hrbJxOpDuwHGuffZUxEgoH5OIrk/cCtxpiTRffV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\n9v333/Pzzz/TpEkTpk6dSnp6OuD7y5Oenk6HDh1o3rw58+fPJzY2ttQ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V74IaocNKDtzd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      "text/plain": [
       "<matplotlib.figure.Figure at 0x5c50940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(cumsum, cumsum.index.values)\n",
    "plt.ylabel('number of emperors that have reigned over the empire')\n",
    "plt.xlabel('duration of the Roman empire (years)')\n",
    "plt.text(20, 17, 'Pax Romana', fontsize=20)\n",
    "plt.text(230, 35, 'Third century crisis', fontsize=12)\n",
    "plt.text(450, 50, 'Renewed stability: the Dominate', fontsize=20)\n",
    "plt.text(600, 85, 'Fall of the western empire', fontsize=12)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}