ch12-code-listing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 12: Data processing and analysis with `pandas`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Robert Johansson\n",
    "\n",
    "Source code listings for [Numerical Python - A Practical Techniques Approach for Industry](http://www.apress.com/9781484205549) (ISBN 978-1-484205-54-9).\n",
    "\n",
    "The source code listings can be downloaded from http://www.apress.com/9781484205549"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.mpl_style', 'default')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s = pd.Series([909976, 8615246, 2872086, 2273305])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     909976\n",
       "1    8615246\n",
       "2    2872086\n",
       "3    2273305\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2, 3], dtype='int64')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 909976, 8615246, 2872086, 2273305])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s.index = [\"Stockholm\", \"London\", \"Rome\", \"Paris\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s.name = \"Population\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stockholm     909976\n",
       "London       8615246\n",
       "Rome         2872086\n",
       "Paris        2273305\n",
       "Name: Population, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s = pd.Series([909976, 8615246, 2872086, 2273305], \n",
    "              index=[\"Stockholm\", \"London\", \"Rome\", \"Paris\"], name=\"Population\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8615246"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[\"London\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "909976"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.Stockholm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Paris    2273305\n",
       "Rome     2872086\n",
       "Name: Population, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[[\"Paris\", \"Rome\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2572695.5, 3667653.25, 3399048.5005155364)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.median(), s.mean(), s.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(909976, 8615246)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.min(), s.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1932472.75, 2572695.5, 4307876.0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.quantile(q=0.25), s.quantile(q=0.5), s.quantile(q=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count          4.000000\n",
       "mean     3667653.250000\n",
       "std      3399048.500516\n",
       "min       909976.000000\n",
       "25%      1932472.750000\n",
       "50%      2572695.500000\n",
       "75%      4307876.000000\n",
       "max      8615246.000000\n",
       "Name: Population, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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4Iq1yzJ2VlpSz/F+/cWhfvt6hiFZ4ehk5d0AEA4bHEtsrHH9fH71Dchg2O4PO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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104cc5850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 4, figsize=(12, 3))\n",
    "\n",
    "s.plot(ax=axes[0], kind='line', title=\"line\")\n",
    "s.plot(ax=axes[1], kind='bar', title=\"bar\")\n",
    "s.plot(ax=axes[2], kind='box', title=\"box\")\n",
    "s.plot(ax=axes[3], kind='pie', title=\"pie\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-series-plot.pdf\")\n",
    "fig.savefig(\"ch12-series-plot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame([[909976, 8615246, 2872086, 2273305],\n",
    "                   [\"Sweden\", \"United kingdom\", \"Italy\", \"France\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>909976</td>\n",
       "      <td>8615246</td>\n",
       "      <td>2872086</td>\n",
       "      <td>2273305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>United kingdom</td>\n",
       "      <td>Italy</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0               1        2        3\n",
       "0  909976         8615246  2872086  2273305\n",
       "1  Sweden  United kingdom    Italy   France"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame([[909976, \"Sweden\"],\n",
    "                   [8615246, \"United kingdom\"], \n",
    "                   [2872086, \"Italy\"],\n",
    "                   [2273305, \"France\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>909976</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8615246</td>\n",
       "      <td>United kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2872086</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2273305</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0               1\n",
       "0   909976          Sweden\n",
       "1  8615246  United kingdom\n",
       "2  2872086           Italy\n",
       "3  2273305          France"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.index = [\"Stockholm\", \"London\", \"Rome\", \"Paris\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.columns = [\"Population\", \"State\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Population</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Stockholm</th>\n",
       "      <td>909976</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>8615246</td>\n",
       "      <td>United kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rome</th>\n",
       "      <td>2872086</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>2273305</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Population           State\n",
       "Stockholm      909976          Sweden\n",
       "London        8615246  United kingdom\n",
       "Rome          2872086           Italy\n",
       "Paris         2273305          France"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame([[909976, \"Sweden\"],\n",
    "                   [8615246, \"United kingdom\"], \n",
    "                   [2872086, \"Italy\"],\n",
    "                   [2273305, \"France\"]],\n",
    "                  index=[\"Stockholm\", \"London\", \"Rome\", \"Paris\"],\n",
    "                  columns=[\"Population\", \"State\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Population</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Stockholm</th>\n",
       "      <td>909976</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>8615246</td>\n",
       "      <td>United kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rome</th>\n",
       "      <td>2872086</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>2273305</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Population           State\n",
       "Stockholm      909976          Sweden\n",
       "London        8615246  United kingdom\n",
       "Rome          2872086           Italy\n",
       "Paris         2273305          France"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"Population\": [909976, 8615246, 2872086, 2273305],\n",
    "                   \"State\": [\"Sweden\", \"United kingdom\", \"Italy\", \"France\"]},\n",
    "                  index=[\"Stockholm\", \"London\", \"Rome\", \"Paris\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Population</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Stockholm</th>\n",
       "      <td>909976</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>8615246</td>\n",
       "      <td>United kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rome</th>\n",
       "      <td>2872086</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>2273305</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Population           State\n",
       "Stockholm      909976          Sweden\n",
       "London        8615246  United kingdom\n",
       "Rome          2872086           Italy\n",
       "Paris         2273305          France"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Stockholm', u'London', u'Rome', u'Paris'], dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Population', u'State'], dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[909976, 'Sweden'],\n",
       "       [8615246, 'United kingdom'],\n",
       "       [2872086, 'Italy'],\n",
       "       [2273305, 'France']], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stockholm     909976\n",
       "London       8615246\n",
       "Rome         2872086\n",
       "Paris        2273305\n",
       "Name: Population, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stockholm     909976\n",
       "London       8615246\n",
       "Rome         2872086\n",
       "Paris        2273305\n",
       "Name: Population, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Population\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.Population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "909976"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Population.Stockholm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexing._IXIndexer"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.ix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population    909976\n",
       "State         Sweden\n",
       "Name: Stockholm, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix[\"Stockholm\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.ix[\"Stockholm\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Population</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>2273305</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rome</th>\n",
       "      <td>2872086</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Population   State\n",
       "Paris     2273305  France\n",
       "Rome      2872086   Italy"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix[[\"Paris\", \"Rome\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Paris    2273305\n",
       "Rome     2872086\n",
       "Name: Population, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix[[\"Paris\", \"Rome\"], \"Population\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2273305"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix[\"Paris\", \"Population\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population    3667653.25\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4 entries, Stockholm to Paris\n",
      "Data columns (total 2 columns):\n",
      "Population    4 non-null int64\n",
      "State         4 non-null object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 96.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population     int64\n",
       "State         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Population</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Stockholm</th>\n",
       "      <td>909976</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>8615246</td>\n",
       "      <td>United kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rome</th>\n",
       "      <td>2872086</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>2273305</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Population           State\n",
       "Stockholm      909976          Sweden\n",
       "London        8615246  United kingdom\n",
       "Rome          2872086           Italy\n",
       "Paris         2273305          France"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rank,City,State,Official population,Date of census/estimate\r\n",
      "1,London[2], United Kingdom,\"8,615,246\",1 June 2014\r\n",
      "2,Berlin, Germany,\"3,437,916\",31 May 2014\r\n",
      "3,Madrid, Spain,\"3,165,235\",1 January 2014\r\n",
      "4,Rome, Italy,\"2,872,086\",30 September 2014\r\n"
     ]
    }
   ],
   "source": [
    "!head -n5 /home/rob/datasets/european_cities.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Larger dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop = pd.read_csv(\"european_cities.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Rank</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Date of census/estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>London[2]</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>8,615,246</td>\n",
       "      <td>1 June 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>3,437,916</td>\n",
       "      <td>31 May 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Spain</td>\n",
       "      <td>3,165,235</td>\n",
       "      <td>1 January 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Rome</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2,872,086</td>\n",
       "      <td>30 September 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>2,273,305</td>\n",
       "      <td>1 January 2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank       City            State Population Date of census/estimate\n",
       "0     1  London[2]   United Kingdom  8,615,246             1 June 2014\n",
       "1     2     Berlin          Germany  3,437,916             31 May 2014\n",
       "2     3     Madrid            Spain  3,165,235          1 January 2014\n",
       "3     4       Rome            Italy  2,872,086       30 September 2014\n",
       "4     5      Paris           France  2,273,305          1 January 2013"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop = pd.read_csv(\"european_cities.csv\", delimiter=\",\", encoding=\"utf-8\", header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 105 entries, 0 to 104\n",
      "Data columns (total 5 columns):\n",
      "Rank                       105 non-null int64\n",
      "City                       105 non-null object\n",
      "State                      105 non-null object\n",
      "Population                 105 non-null object\n",
      "Date of census/estimate    105 non-null object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 4.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_pop.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Rank</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Date of census/estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>London[2]</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>8,615,246</td>\n",
       "      <td>1 June 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>3,437,916</td>\n",
       "      <td>31 May 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Spain</td>\n",
       "      <td>3,165,235</td>\n",
       "      <td>1 January 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Rome</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2,872,086</td>\n",
       "      <td>30 September 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>2,273,305</td>\n",
       "      <td>1 January 2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank       City            State Population Date of census/estimate\n",
       "0     1  London[2]   United Kingdom  8,615,246             1 June 2014\n",
       "1     2     Berlin          Germany  3,437,916             31 May 2014\n",
       "2     3     Madrid            Spain  3,165,235          1 January 2014\n",
       "3     4       Rome            Italy  2,872,086       30 September 2014\n",
       "4     5      Paris           France  2,273,305          1 January 2013"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop[\"NumericPopulation\"] = df_pop.Population.apply(lambda x: int(x.replace(\",\", \"\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([u' United Kingdom', u' Germany', u' Spain'], dtype=object)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop[\"State\"].values[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop[\"State\"] = df_pop[\"State\"].apply(lambda x: x.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Rank</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Date of census/estimate</th>\n",
       "      <th>NumericPopulation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>London[2]</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>8,615,246</td>\n",
       "      <td>1 June 2014</td>\n",
       "      <td>8615246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>3,437,916</td>\n",
       "      <td>31 May 2014</td>\n",
       "      <td>3437916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Spain</td>\n",
       "      <td>3,165,235</td>\n",
       "      <td>1 January 2014</td>\n",
       "      <td>3165235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Rome</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2,872,086</td>\n",
       "      <td>30 September 2014</td>\n",
       "      <td>2872086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>2,273,305</td>\n",
       "      <td>1 January 2013</td>\n",
       "      <td>2273305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank       City           State Population Date of census/estimate  \\\n",
       "0     1  London[2]  United Kingdom  8,615,246             1 June 2014   \n",
       "1     2     Berlin         Germany  3,437,916             31 May 2014   \n",
       "2     3     Madrid           Spain  3,165,235          1 January 2014   \n",
       "3     4       Rome           Italy  2,872,086       30 September 2014   \n",
       "4     5      Paris          France  2,273,305          1 January 2013   \n",
       "\n",
       "   NumericPopulation  \n",
       "0            8615246  \n",
       "1            3437916  \n",
       "2            3165235  \n",
       "3            2872086  \n",
       "4            2273305  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank                        int64\n",
       "City                       object\n",
       "State                      object\n",
       "Population                 object\n",
       "Date of census/estimate    object\n",
       "NumericPopulation           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop2 = df_pop.set_index(\"City\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop2 = df_pop2.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Rank</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Date of census/estimate</th>\n",
       "      <th>NumericPopulation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</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>Aarhus</th>\n",
       "      <td>92</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>326,676</td>\n",
       "      <td>1 October 2014</td>\n",
       "      <td>326676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alicante</th>\n",
       "      <td>86</td>\n",
       "      <td>Spain</td>\n",
       "      <td>334,678</td>\n",
       "      <td>1 January 2012</td>\n",
       "      <td>334678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amsterdam</th>\n",
       "      <td>23</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>813,562</td>\n",
       "      <td>31 May 2014</td>\n",
       "      <td>813562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antwerp</th>\n",
       "      <td>59</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>510,610</td>\n",
       "      <td>1 January 2014</td>\n",
       "      <td>510610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Athens</th>\n",
       "      <td>34</td>\n",
       "      <td>Greece</td>\n",
       "      <td>664,046</td>\n",
       "      <td>24 May 2011</td>\n",
       "      <td>664046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Rank        State Population Date of census/estimate  \\\n",
       "City                                                              \n",
       "Aarhus       92      Denmark    326,676          1 October 2014   \n",
       "Alicante     86        Spain    334,678          1 January 2012   \n",
       "Amsterdam    23  Netherlands    813,562             31 May 2014   \n",
       "Antwerp      59      Belgium    510,610          1 January 2014   \n",
       "Athens       34       Greece    664,046             24 May 2011   \n",
       "\n",
       "           NumericPopulation  \n",
       "City                          \n",
       "Aarhus                326676  \n",
       "Alicante              334678  \n",
       "Amsterdam             813562  \n",
       "Antwerp               510610  \n",
       "Athens                664046  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Rank</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Date of census/estimate</th>\n",
       "      <th>NumericPopulation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</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>Aarhus</th>\n",
       "      <td>92</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>326,676</td>\n",
       "      <td>1 October 2014</td>\n",
       "      <td>326676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alicante</th>\n",
       "      <td>86</td>\n",
       "      <td>Spain</td>\n",
       "      <td>334,678</td>\n",
       "      <td>1 January 2012</td>\n",
       "      <td>334678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amsterdam</th>\n",
       "      <td>23</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>813,562</td>\n",
       "      <td>31 May 2014</td>\n",
       "      <td>813562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antwerp</th>\n",
       "      <td>59</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>510,610</td>\n",
       "      <td>1 January 2014</td>\n",
       "      <td>510610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Athens</th>\n",
       "      <td>34</td>\n",
       "      <td>Greece</td>\n",
       "      <td>664,046</td>\n",
       "      <td>24 May 2011</td>\n",
       "      <td>664046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Rank        State Population Date of census/estimate  \\\n",
       "City                                                              \n",
       "Aarhus       92      Denmark    326,676          1 October 2014   \n",
       "Alicante     86        Spain    334,678          1 January 2012   \n",
       "Amsterdam    23  Netherlands    813,562             31 May 2014   \n",
       "Antwerp      59      Belgium    510,610          1 January 2014   \n",
       "Athens       34       Greece    664,046             24 May 2011   \n",
       "\n",
       "           NumericPopulation  \n",
       "City                          \n",
       "Aarhus                326676  \n",
       "Alicante              334678  \n",
       "Amsterdam             813562  \n",
       "Antwerp               510610  \n",
       "Athens                664046  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_pop3 = df_pop.set_index([\"State\", \"City\"]).sortlevel(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Population</th>\n",
       "      <th>Date of census/estimate</th>\n",
       "      <th>NumericPopulation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <th>Vienna</th>\n",
       "      <td>7</td>\n",
       "      <td>1,794,770</td>\n",
       "      <td>1 January 2015</td>\n",
       "      <td>1794770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Belgium</th>\n",
       "      <th>Antwerp</th>\n",
       "      <td>59</td>\n",
       "      <td>510,610</td>\n",
       "      <td>1 January 2014</td>\n",
       "      <td>510610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brussels[17]</th>\n",
       "      <td>16</td>\n",
       "      <td>1,175,831</td>\n",
       "      <td>1 January 2014</td>\n",
       "      <td>1175831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Bulgaria</th>\n",
       "      <th>Plovdiv</th>\n",
       "      <td>84</td>\n",
       "      <td>341,041</td>\n",
       "      <td>31 December 2013</td>\n",
       "      <td>341041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sofia</th>\n",
       "      <td>14</td>\n",
       "      <td>1,291,895</td>\n",
       "      <td>14 December 2014</td>\n",
       "      <td>1291895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Varna</th>\n",
       "      <td>85</td>\n",
       "      <td>335,819</td>\n",
       "      <td>31 December 2013</td>\n",
       "      <td>335819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croatia</th>\n",
       "      <th>Zagreb</th>\n",
       "      <td>24</td>\n",
       "      <td>790,017</td>\n",
       "      <td>31 March 2011</td>\n",
       "      <td>790017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Rank Population Date of census/estimate  \\\n",
       "State    City                                                    \n",
       "Austria  Vienna           7  1,794,770          1 January 2015   \n",
       "Belgium  Antwerp         59    510,610          1 January 2014   \n",
       "         Brussels[17]    16  1,175,831          1 January 2014   \n",
       "Bulgaria Plovdiv         84    341,041        31 December 2013   \n",
       "         Sofia           14  1,291,895        14 December 2014   \n",
       "         Varna           85    335,819        31 December 2013   \n",
       "Croatia  Zagreb          24    790,017           31 March 2011   \n",
       "\n",
       "                       NumericPopulation  \n",
       "State    City                             \n",
       "Austria  Vienna                  1794770  \n",
       "Belgium  Antwerp                  510610  \n",
       "         Brussels[17]            1175831  \n",
       "Bulgaria Plovdiv                  341041  \n",
       "         Sofia                   1291895  \n",
       "         Varna                    335819  \n",
       "Croatia  Zagreb                   790017  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop3.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>Rank</th>\n",
       "      <th>Population</th>\n",
       "      <th>Date of census/estimate</th>\n",
       "      <th>NumericPopulation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gothenburg</th>\n",
       "      <td>53</td>\n",
       "      <td>528,014</td>\n",
       "      <td>31 March 2013</td>\n",
       "      <td>528014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malmö</th>\n",
       "      <td>102</td>\n",
       "      <td>309,105</td>\n",
       "      <td>31 March 2013</td>\n",
       "      <td>309105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stockholm</th>\n",
       "      <td>20</td>\n",
       "      <td>909,976</td>\n",
       "      <td>31 January 2014</td>\n",
       "      <td>909976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rank Population Date of census/estimate  NumericPopulation\n",
       "City                                                                  \n",
       "Gothenburg    53    528,014           31 March 2013             528014\n",
       "Malmö        102    309,105           31 March 2013             309105\n",
       "Stockholm     20    909,976         31 January 2014             909976"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop3.ix[\"Sweden\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank                                  53\n",
       "Population                       528,014\n",
       "Date of census/estimate    31 March 2013\n",
       "NumericPopulation                 528014\n",
       "Name: (Sweden, Gothenburg), dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop3.ix[(\"Sweden\", \"Gothenburg\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Rank</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Date of census/estimate</th>\n",
       "      <th>NumericPopulation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</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>Nottingham</th>\n",
       "      <td>103</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>308,735</td>\n",
       "      <td>30 June 2012</td>\n",
       "      <td>308735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wirral</th>\n",
       "      <td>97</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>320,229</td>\n",
       "      <td>30 June 2012</td>\n",
       "      <td>320229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coventry</th>\n",
       "      <td>94</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>323,132</td>\n",
       "      <td>30 June 2012</td>\n",
       "      <td>323132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wakefield</th>\n",
       "      <td>91</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>327,627</td>\n",
       "      <td>30 June 2012</td>\n",
       "      <td>327627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leicester</th>\n",
       "      <td>87</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>331,606</td>\n",
       "      <td>30 June 2012</td>\n",
       "      <td>331606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rank           State Population Date of census/estimate  \\\n",
       "City                                                                  \n",
       "Nottingham   103  United Kingdom    308,735            30 June 2012   \n",
       "Wirral        97  United Kingdom    320,229            30 June 2012   \n",
       "Coventry      94  United Kingdom    323,132            30 June 2012   \n",
       "Wakefield     91  United Kingdom    327,627            30 June 2012   \n",
       "Leicester     87  United Kingdom    331,606            30 June 2012   \n",
       "\n",
       "            NumericPopulation  \n",
       "City                           \n",
       "Nottingham             308735  \n",
       "Wirral                 320229  \n",
       "Coventry               323132  \n",
       "Wakefield              327627  \n",
       "Leicester              331606  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop.set_index(\"City\").sort([\"State\", \"NumericPopulation\"], ascending=[False, True]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "city_counts = df_pop.State.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "city_counts.name = \"# cities in top 105\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop3 = df_pop[[\"State\", \"City\", \"NumericPopulation\"]].set_index([\"State\", \"City\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop4 = df_pop3.sum(level=\"State\").sort(\"NumericPopulation\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>NumericPopulation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>16011877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>15119548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>10041639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>8764067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>6267409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                NumericPopulation\n",
       "State                            \n",
       "United Kingdom           16011877\n",
       "Germany                  15119548\n",
       "Spain                    10041639\n",
       "Italy                     8764067\n",
       "Poland                    6267409"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop5 = (df_pop.drop(\"Rank\", axis=1)\n",
    "                 .groupby(\"State\").sum()\n",
    "                 .sort(\"NumericPopulation\", ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>NumericPopulation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>16011877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>15119548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>10041639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>8764067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>6267409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                NumericPopulation\n",
       "State                            \n",
       "United Kingdom           16011877\n",
       "Germany                  15119548\n",
       "Spain                    10041639\n",
       "Italy                     8764067\n",
       "Poland                    6267409"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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NN1TPdzgcbNmyhfz8/H53BF999VW2bdvGzZs3KSsrY8eOHcD9pRw2m40FCxa4nP/tt9/y\n5ptvOttXU1tbS9FZnerX3anMTedHqQMXiBRChB6pkyU86ZtT3OUYh8OB2WymqKjI7R871dXVpKWl\nudSvUsth7nJhW1sblZWVWCwWOjo62LhxI2+99Zbb/g6UwyRfCTHyDDWPyZOsESY9PZ29e/diMBjQ\n6TxPZnQ6HYqiUFZWRkREBElJSfzmN78hNjaWadOmYTKZmDJlCvHx8f2+r69Ro0YRHx/Pgw8+OGAf\nK3PTvRrT+PjogU8KA+G0jng4aHn8Wh67EGo++ugjDh8+jKIo6HQ65+YXfXOMTqdj2bJlbN26FZ1O\nR1JSEi+//LLLOVVVVVy/fp1nnnkGwGMO6yshIYFZs2ZRUlLC5MmTiYuL83i+pxzm73wVjGvHSH/H\nRuJJvECRSVaY6l0Pq/e/V65c6fz30qVLPZ7f1dXF5MmTee6559DpdGzfvp3GxkbS09NZtWqV27jZ\n2dmqfdLpdMydO3fAvstdPiGEEAsWLOi3KkItxzz66KNs3rzZ7dd+8Ytf8Itf/MLlM7UcppY7f/7z\nn/Pzn/98EL2WHCaEGBxZLqhhd+/e5T/+4z9ob7+/HW1mZiaLFy/2up1Lly7xn//5n/zwhz8ccBve\n2tpalyUdQoiRTZYLipFEcpgQ2iPLBYXXoqKi/LKLUnp6unP7diGEEEIIIbROP/ApItg2b96M2Wym\nuLiYQ4cODbmdmpoaP/bK1a5du1i7di1nz54dthjhru+uOFqj5fFreexCSA4bumBcOwIdU+JJvFCO\n5wt5khUGdDodRqOR6OhoNm3aRHZ29pC2mK2trXXZpt2f1qxZQ3V19aDOrWts96rt8fHRI6IYsRBC\naJGWcpjkKyFED5lkhQmHw8G9e/cAiIyMxGq1UlNTg8PhYO7cuc7EY7VaaWho4MaNGyiKgtlsRq/X\ns3PnTmcB44yMDJYsWQKobwVfVVVFfX09kZGRREdHs3r1asaOHcuRI0c4deqUsx5W7yLFg1XwwSWv\nzq/MTR8RSStcdsMZLloev5bHLgRoJ4f5O18F49oR6JgST+KFcjxfyCQrTJSXl6PX61m4cCHd3d18\n/PHHlJaWotPpKC0t5fHHHyc5ORmAlpYWTCaTSzHHvLw8jEajs4DxQOrq6igvL+fQoUOkpKQwduxY\nAObMmcPcuXO5e/cuxcXFQ0pQQgghtEVymBBCa2SSFSaKi4uJibl/d+zSpUukpaUREREBwNSpU7lx\n44YzQWVmZrokp6HIzs5m3bp1TJ482WV5xoULFzhz5gwGgwG73e5TjMFqbW2F77bMDVR17+E47r2O\nOBT6I+MPbHX63j+DYPcnkMfuiscK7dFKDmttbeX4l3V++x3avXs3M2bMCOjvbH19PatXr5Z4Ek/i\nfXc81DwmW7iHAYvFQmFhIQaDAYD29na2bNmCxWJx3gVct24dSUlJWK1WbDZbv9ojAAUFBVRUVLgk\nr8LCQioqKrDb7RiNRt544w0cDgdbtmwhPz+fqKgolzZeffVVtm3bxs2bNykrK2PHjh3Or1VXV5OW\nluZxe9va2lqKznouktxXZW76iKhLcvx4+BTQGw5aHr+Wxy5buAst5TB/56tgXDsCHVPiSbxQjgey\nhfuIp9N9f1GPj4/npz/9KZs3b8bhcJCTk0NSUpLbc3ubMWMG5eXlpKSkOIsWp6ens3fvXgwGg/P7\ndDodiqJQVlZGREQESUlJ/OY3vyE2NpZp06ZhMpmYMmUK8fHxOBwOl3hVVVVcv36dZ555RnUslbnp\nXo19fHy0V+eHKq3+kd1Dy+PX8tiFAO3kMH/nq2BcOwIdU+JJvFCO5wt5kiX66erq4sCBAzz33HPo\ndDq2b9/Os88+S3q6d5Mjd6SQoxDaIk+yRKBJDhNC+NNQ85jUyRL9REVF0dTUhMViYfPmzTz00EN+\nSU5a1/v9HC3S8vi1PHYhAm0k5bBgXDsCHVPiSbxQjucLWS4o+omKiiI/Pz/Y3RBCCCG8JjlMCBEK\nZLlgmOpZy67X64mLi3MmFJvNxvvvv8/ixYsHbGPXrl18/vnn/PrXv/bb8ofedUrckaUWQmiLLBcU\nasIxj0kOE0J7ZOMLjdHpdBiNRueWuD0MBsOgEhN4V+Hen+oa2706f3x89IgoRiyEEOJ74ZrHBsph\nkrOEECCTrLDW9yGk1Wrl6NGj2Gw2l7tw+fn5ZGZmcvHiRTIyMli2bJnHdo8cOcKpU6doamoiJyfH\nWaxRrZ333nuPkydPMmnSJGw224D9LvjgklfjrMxNHxEJS8vbeIO2x6/lsQvhSTjmsYFymD9zlmzh\nLvEkXnDj+UI2vghj5eXlWCwW/vf//t/A/eKLZrO533mdnZ0sWrSIsrIyzpw5M2C7c+bMYePGjZSX\nl3PkyBGP7dy+fZvTp0/z+uuv89JLL3Hnzh0/je57ra2tzn8fP37c5aVHOZZjOQ7tYyE8GYl5zJ85\nq76+PuC/s/X19RJP4kk8N8fekneywpTFYqGoqKjfMgvov56893Hfr7krvlhfX8+ZM2cwGAycOHHC\nWazRXTtffPEFx44d46WXXnLbfl9aLkYshBbJO1lCTTjmscHkMMlZQowssoW7Bg3X/HjPnj08//zz\ng/oPavz48Vy+fBlFUWhra+P27dvD0ichhBAjj+QxIcRIJe9khbHeVep90bfC/bRp0zCZTEyZMoX4\neM934xISEpg1axYlJSVMnjyZuLi4AeNV5qZ71b/x8dFenR+qjh8Pn3XEw0HL49fy2IXwJBzz2EA5\nzJ85KxjXjkDHlHgSL5Tj+UKWC4qA0vL2t+F0YRgOWh6/lscuywXFSBLoHCaTLIkn8YIbD4aex2SS\nJQJKy5MsIbRIJlliJJEcJoT2yDtZQgghhBBCCBECZJIVxj766CPWr1/PK6+8wuHDh4fUxq5du1i7\ndi1nz571W7+MRqPf2hpJfNkGdCTQ8vi1PHYhvGGxWLDb7YM+v6amxqv2bTYbBw8e9LZbQROMa0eg\nY0o8iRfK8XwhG1+EsQULFmAwGLDb7cyfP39IbaxZs4bq6mo/98yzusZ2r84fHx89IooRCyGE8K/a\n2lrmzZs36PMNBgOLFy/2KeZgcpjkLSGETLJGgN6v1VmtVhoaGrhx4waKorBp0yYiIiI4d+4cBw4c\nQKfTsXDhQrKysjy2eeTIEU6dOkVTUxM5OTnk5uYCkJ+fT2ZmJhcvXiQjI4Nly5YB8N5773Hy5Ekm\nTZqEzWbz2HbBB5e8Gl9lbvqISFZa3figh5bHr+WxC+ErtXy0c+dOGhsbsVgsZGRksGTJEhobG9m/\nfz/r1q0DwGw2YzQaMRgMWK1Wjh49is1mc6mDpda+msHkMH/lrWBcOwIdU+JJvFCO5wuZZI1ALS0t\nmEwm9Pr7q0EVRWHfvn2UlZURFRVFaWkpM2fOJCoqSrWNOXPmMHfuXO7evUtxcbEz6XR2drJo0SIS\nEhIoKChg2bJl3L59m9OnT/P6669jt9vJz8/363haW1vhu8KOPY+Je37J5FiO5Ti0j2NjYxHCF2r5\nKC8vD6PRiNlsdp6bmppKe3s7XV1dNDc388ADD2AwGADIzs4mOzu735J2tfb9IRR+B+VYjuXYt+Oh\n5jHZXTDMWa1WbDYbCxYscHsM9ycpGzZs4MEHHwSgvb2djRs3kpKSAkB1dTVpaWkuOybV19dz5swZ\nDAYDJ06cYMeOHcD996167gD2/PuLL77g2LFjvPTSS/3O6au2tpais97VRanMTedHqZ7rnISD48e1\nu403aHv8Wh677C4ovGGxWCgqKiIm5vunQGr5CNznmyNHjqAoCt988w2zZs3ikUcecfl63+/x1H5f\ng81h/spbwbh2BDqmxJN4oRwPhp7H5EmWBiQkJDBx4kQKCgoGPRvfs2cP27Zt4+bNm5w4ccLjuePH\nj+fy5csoikJHRwe3b9/2R7eFEEJoUN97v57yUXd3N4qiOFduAPz4xz9m+/bt6HQ655J2T7zJd0II\nMVgyyQpjH330EYcPH0ZRFHQ6nXPzC53O9S5bT6LZunUrOp2OpKQkXn75ZZdzqqqquH79Os888wwA\n06ZNw2QyMWXKFOLjPd+NS0hIYNasWZSUlDB58mTi4uI8nl+Zm+7VOMfHR3t1fqjS6pOMHloev5bH\nLoS3du7c6Zw0bdiwoV8+cjgczjw3Y8YMysvLSUlJYeXKlcD9zS3GjBnDxIkTBxXPm3wHg8th/spb\nwbh2BDqmxJN4oRzPF7JcUASUFHIUQltkuaAYSSSHCaE9UoxYiBDX8wKlVml5/FoeuxBi6IJx7Qh0\nTIkn8UI5ni9kkiWEEEIIIYQQfiTLBcPYzp076e7udtYDGYqamhqvCjnabDbef//9IRdzlKUWQmiL\nLBcUas6fP8+BAwfQ6/XExsby6quv9nun2Bdq+c2XPCY5TAjtkd0FNaa7u5u///3vRERE0N3dTWTk\n0P6vrK2t9WqSZTAYhjzB6lHX2O7V+ePjo0dEMWIhhBD32e12/vSnP2EymTAYDHR3d/t1ggXq+c3X\nPDbYHCa5Swhtk0lWmPr888955JFHiIyMpK6ujpkzZ1JUVMSWLVsA1zoghw8f5tixY+j1erKzs8nJ\nyQHuPwlrbGzEYrGQkZHBkiVLgPu1thoaGrhx4waKomA2m9Hr9VitVo4ePYrNZnOpMXLkyBFOnTpF\nU1MTOTk5AxZyLPjgkldjrcxNHxGJSsu1kkDb49fy2IVwp6GhgenTpzsLBffcKPSUf2pqanA4HMyd\nO9c5eVLLP57ym695bLA5zB+5S+pkSTyJF9x4vpBJVpj661//ysyZM9Hr9fzlL39h5syZqncBP/30\nU4xGY7+t1fPy8jAajZjN5n7f09LSgslkcqk9kp2dTXZ2Nkaj0eXcOXPmMHfuXO7evUtxcfGAkyxv\ntba2wndFHUOl+rccy7E3xz1CpT+BPB5sbT6hLW1tbSQkJLj9Wt/809bWxscff0xpaSk6nY7S0lIe\nf/xxkpOTVfOPWn4LdB7z9Xeovr7ep+8fynF9fb3Ek3gSr9fxUPOYvJMVhhRF4be//S0PP/wwAF98\n8QW7du2ipKTEeWeu95OsW7ducezYMdrb23niiSd49NFHnW31Pq+H1WrFZrOxYMECt/H7fk99fT1n\nzpzBYDBw4sQJduzYodr32tpais56tySkMjedH6UOXLtECBF65J0s4c758+f57LPP+hULdpd/Ll26\nxCeffMJLL70EwL59+3jsscfIyMjwmH/c5Te1rw02j3mTwyR3CTEyyDtZGnLx4kV++MMfkpeXB8Cu\nXbs4f/48iqIA99e62+125/ljxozh2Wef5c6dO5hMJioqKpxf6+7uRlEUlydW3tqzZw/btm3j5s2b\nnDhxYsDztVqMWAghxH1Tp05l3759dHV1ERsbS2dnp2oh+/Hjx/O3v/3N+d5WQ0OD80mTp/zjTX7z\nJo8NNodJ7hJC22SSFYZOnjzpsh519uzZnDx5kqlTp7J3714MBoPL0sE9e/bw1Vdf0dXVxfz5813a\nmjFjBuXl5aSkpLBy5Urn5968gDxt2jRMJhNTpkwhPn7gu3ZavbMXTuuIh4OWx6/lsQvhTkxMDMuX\nL6eiogK9Xo/BYOCVV14B+uef+Ph4fvrTn7J582YcDgc5OTkkJSUB/fOPw+Fwfr9afnPHmzwWyBwW\njGvHSH/HRuJJvECRSVYYev75512OMzMzyczMdPls6dKlzn+/8MILqm2tWLGi32fZ2dke4/ddfrFq\n1SqP5wshhBB9TZ8+nenTp7t8ppZ/et6l6stT/nGX33pIHhNCDDd5J0sElNQYEUJb5J0sMZJIDhNC\ne4aax4b+Io4ISRaLxeV9rIHU1NR41b7NZuPgwYPedksIIcQIppZ71HKGu9zTd8e/4SA5TAgRKDLJ\n0rja2lqvzvdXMWJv/vd1++AnjaGs73beWqPl8Wt57ELb1HKGt7nHXwKdw3zNX8G4dgQ6psSTeKEc\nzxfyTpYGeFOssbGxkf3797Nu3ToAzGYzRqMRg8EgxYiFEEIMmlrOUCsU3N3dzd69e7l48SIZGRnO\n7d17b7fe+99quSc/P5/MzMx+7QQjh0n+EkK75EmWBsyZM4eNGzdSXl7OkSNHnJ/n5eWRmpqK2Wx2\nJrnU1FQQvKYvAAAgAElEQVTa29vp6uri2rVrPPDAAxgMBuD+i8fuChertS9chctuOMNFy+PX8tiF\ndqnlDHe5B6Cjo4NFixZRVlbGmTNnBmxfLfd0dna6bSccc1gwrh2BjinxJF4ox/OFPMnSgAsXLjiL\nLA7mfa3Zs2fzl7/8hW+++YacnBy/t++t1tZW+G7L3EBV95ZjOZZj/xzHxsYixGAkJiaSmJgIQHT0\nwDWm1HKPv9rxh9bWVo5/WRdSv5NyLMdy7N3xUPOY7C44wlgsFgoLC51PnwBeffVVZ5HFsrIyl0r2\nBQUFzjolPWw2G9u3b0en01FcXNwvRu/lGgO131dtbS1FZwdfgwvuL7cYCbW1jh8Pn9oOw0HL49fy\n2GV3QW1wl3t69M0Z4D73qC0LLCwspKKiArvdjtFo5I033gDUc49aO2r9Gc4c5mv+Csa1I9AxJZ7E\nC+V4MPQ8Jk+yRqCdO3c6E9eGDRu8LtZoMBgYM2YMEydOHFQ8b4sRV+amezWe8fED34kUQggRXH1z\njyfeFApOT09n7969GAwGl0LF3uYeNcOZwyR/CaFd8iRLBJTUGBFCW+RJlhhJJIcJoT1SJ0sIIYQQ\nQgghQoBMsoQIkJ4XKLVKy+PX8tiFEEMXjGtHoGNKPIkXyvF8IZOsMHb+/Hk2b95MaWkp27Ztw98r\nP2tqatx+brPZOHjwoF9jCSGE0I4VK1ZgsVjYunUrN2/eDHZ3MBqNwe6CEGKEkY0vwpTdbudPf/oT\nJpMJg8FAd3e3ywvB/lBbW8u8efP6fW4wGFi8ePGQ261rbPfq/PHx0SOimKNWd5froeXxa3nsQrgz\nceJEzGYzly9f5ne/+x0WiyXYXRq0QOawYFw7Ah1T4km8UI7nC5lkhamGhgamT5/u3C43MvL+/5VW\nq5WGhgZu3LiBoiiYzWb0ej1Wq5WamhocDgdz5851Tp7UKt3v3LmTxsZGLBYLGRkZzoKRVquVo0eP\nYrPZXLbAVWvHnYIPLnk11src9BExyRJCCOHqoYceIjk5mcbGRlJTUzl37hwHDhxAp9OxcOFCsrKy\nAMjPzyczM5OLFy+SkZHBsmXLsFqtfPbZZ1y5coX58+fz0UcfUVxczLhx41RzklqO7FFTU8O1a9d4\n/vnnVW9cSg4TQgyGLBcMU21tbSQkJLj9WktLCyaTCYvFgl6vp62tjY8//hiLxUJZWRnHjx+nubkZ\nUK90n5eXR2pqKmaz2TnBAsjOzsZsNveLqdaO+F44rSMeDloev5bHLsRAxo0bR3NzM4qisG/fPl57\n7TU2b97Mhx9+SHd3NwCdnZ0sWrSIsrIyzpw54/zelJQU5s6dy7fffsuTTz7JlStXAM85qW+O7LF/\n/366urp44YUX/L4yZKjknSyJJ/GCG88X8iQrTI0ePZqvvvrK7dcyMzNdEkdTUxNpaWlEREQAMHXq\nVG7cuEFycrLfKt37qx13Wltb4btijqFS/VuO5dib4x6h0p9AHsfGxiKEJzdv3iQ5OZn29naam5up\nqKgA7k+smpubSUlJITExkcTERACio7+vPdXzWXx8PC0tLdy7dw/wnJP65kiAq1evEhUVxdKlS/0+\nPl9yWH19vVfn++O4vr5e4kk8idfreKh5TOpkhSm73c7mzZsxmUzExsbS2dlJXFwcVqsVm83GggUL\nnOe2t7ezZcsWLBYLOp2O0tJS1q1bR1JSksdK9wUFBVRUVPRLRnD/JeHeywU9tdNbbW0tRWe9u0NY\nmZvOj1KHXmhSCBE8UidLuNOTQ7766iv++Mc/YrFYcDgcmM1mioqK+v1R0zvn9Py7J9/B/XeFW1pa\nmDBhAllZWao5yV2O7Glz8+bNbNmyhRdffJEHH3zQbb8lhwmhPUPNY/IkK0zFxMSwfPly5yTIYDDw\nyiuvAPRb5hAfH89Pf/pTNm/ejMPhICcnh6SkJKB/pXuHw+H8/hkzZlBeXk5KSgorV6702J++7XhS\nmZvu1VjHx0cPfJIQQoiw0fPO76hRo/jtb38L3M9dy5YtY+vWreh0OpKSknj55Ze9arcnf3nKSWpL\nAWNiYnjllVfYvn0769evJzk52e15ksOEEIMhT7JEQNXW1vL4448HuxtBcfz48bDaFcfftDx+LY9d\nnmSJkSTQOSwY145Ax5R4Ei+U48HQ85hsfCGEEEIIIYQQfiRPskRAaflJlhBaJE+yxEgiOUwI7ZF3\nsvzk/PnzHDhwAL1eT2xsLK+++qpft3Ltu2HEQFasWEFaWhp2u52nn37a73+sqPVn165dfP755/z6\n17/ul1BsNhvvv/++TwWJhRBCCHd68rBOpyMyMpKCggKXHQWFECIcyCSrF7vdzp/+9CdMJhMGg4Hu\n7u6g18qYOHEiZrMZRVEoLCwM2B3hNWvWUF1d7fZrBoPBpwlWXWP7kL93MMbHR4dk4Uctv5cD2h6/\nlscuhDfsdjtVVVW89tprxMbGcufOnZCbYA0lhw01L8k7WRJP4gU3ni9kktVLQ0MD06dPx2AwABAZ\n+f2PZ8uWLdjtdmw2G4qiOOt4qFWnv3TpElVVVSiKwtixY8nLywOgu7ubvXv3ulStH4y2tjaioqKc\nx2px161bx7Rp07h69SqPP/64s5Cwu+1v4f5Tqd27d3P16lUee+yxAWuEWK1Wjh49is1mc3kC1nu8\n48aNY+3ataptFHxwaVBjHqrK3PSQnGQJIYTwrCcP92zh3jPBslqtNDQ0cOPGDRRFYdOmTURERKjm\nQm9zs9r57gwlh0leEkJ7ZJLVS1tbGwkJCW6/VlRUBMAf/vAHnnnmGQBndfqysjKioqIoLS1l1qxZ\nRERE8Pbbb2M0Gp2FEnt0dHSwaNEiEhISKCgoGHCS1djYSElJCffu3XMmA7W4kZGR3L17lxdffJHo\n6Gg2bdrEvHnz+vWhtzt37vDiiy8SExPjPF9t21qA7OxssrOzMRqNzs8cDofqeIMhVIsXz549O6T6\nI+OX40AcSzFi4Q1PebilpQWTyeSs3aiWC/V6vVe52V07M2fOdLmx6S/e/g71fBbo3+Gh9lfiSbyR\nGE+KEfvB+fPn+eyzz1QnPh9++CGjR4/mySefBO7/Mb9hwwZn0cL29nY2btyIwWDg97//vctEpIfa\nEyU1RqMRi8WCyWSisLCQ5ORk1bgpKSkubf7xj3/kqaeeIj09XTWu2vkA1dXVpKWluX3Jt/f3tbW1\nqY63r6EUcvSWFH4UInTIxhfCG+fPn6euro7ly5eza9cuLl++zKpVq7h+/Xq/IsJquTAmJsar3Owp\np/Y11BwmeUmI8CUbX/jB1KlT2bdvH11dXcTGxtLZ2UlcXBxwfwnDN998w89+9jPn+QkJCUycOJGC\nggKXWa7D4eD27dvcunWLMWPG+Nyv6Oholi9fzttvv01hYaFqXIDm5mY6OjqIjY3l8uXLzuV/iqIA\n99e72+32fuePGjWKL7/8csDlgu7Ex8f7dbwjVTitIx4OWh6/lscuhDemTp3Ku+++y89//nPWrFnD\nu+++y927d92e6ykHe5ObPeXUYJN3siSexAtuPF/IJKuXmJgYli9fTkVFBXq9npiYGNatW4fBYODN\nN99kzJgxWCwWYmJiKCoqUq1Or9PpWLVqFTt37kRRFEaPHs2GDRt86ltmZibHjx93/sflLi5AXFwc\n77zzDteuXWPOnDn84Ac/ACA9PZ29e/diMBhcNvPoOf/q1as89dRTzvN7VFVVcf36decSSXf6jjcx\nMZH169ernl+Zm+7Lj2JA4+ND6yVpIYQQgxMTE8OvfvUrKisriYyMJC4ujgceeICmpqZ+G1F5ysHe\n5Ga189UMJYdJXhJCe2S54Ajj7RbxgSY1RoTQFlkuKEYSyWFCaM9Q85h+GPoihBBCCCGEEJolk6wR\nJpSfYmld311xtEbL49fy2IUQQxeMa0egY0o8iRfK8Xwhk6wwtWLFCiwWC1u3buXmzZvB7s6gdhYU\nQggxvM6fP8/mzZspLS1l27Zt+PuNAG+v9T25qri4mNraWr/2xVN/du3axdq1azl79my/r9lsNg4e\nPOj3vgghRG+y8UWYmjhxImazmcuXL/O73/0Oi8US7C4NWl1j+7C2Pz4+OiSLPobLbjjDRcvj1/LY\nReDY7Xb+9Kc/YTKZMBgMdHd399ssItB6cpWiKBQWFgbs/bw1a9ZQXV3t9msGg4HFixcPue3hymHu\nclcwrh2BjinxJF4ox/OFTLLC3EMPPURycjKNjY2kpqaqVq3Pz88nMzOTixcvkpGRwbJly7BarXz2\n2WdcuXKF+fPn89FHH1FcXMy4ceM4cuQIp06doqmpiZycHHJzcwGwWq00NDRw48YNFEXBbDY7C0MC\n1NTUcO3aNZ5//nnV5F7wwaVh/ZlU5qaH5CRLCCGGU0NDA9OnT8dgMAAQGfl9it+yZQt2ux2bzYai\nKFRUVACo5oxLly5RVVWFoiiMHTuWvLw8ALq7u9m7d69LLhmMtrY2l+K+anHXrVvHtGnTuHr1Ko8/\n/jhLliwB1Os72mw2du/ezdWrV3nssccGLENitVo5evQoNpvNZXl97/GOGzeOtWvXqrYxXDlMcpcQ\nI4tMskaAcePG0dzczIQJE9xWuY+MjKSzs5NFixaRkJBAQUGBMzGmpKTw0EMP8e233/Lkk09y5coV\nxo0bx5w5c5g7dy53796luLjYOckCaGlpwWQyuUyuAPbv34/BYOCFF14I5PD7aW1the+KPgaqGvhg\njnuvIw6F/sj4A1udvvfPINj9CeSxP+sOdXR09CszIb7X1tZGQkKC268VFRUB8Ic//MFZkkNRFLc5\nIyIigrfffhuj0UhiYqJLOx0dHW5ziZrGxkZKSkq4d++ec6KmFjcyMpK7d+/y4osvEh0dzaZNm5g3\nb16/PvR2584dXnzxRWJiYpznJycnq56fnZ1Ndna2yzJDh8OhOt5A6/s7tHv3bmbMmBHQ39n6+npW\nr14t8SSexPvueKh5TLZwD1O97+Tt2LGDJUuWEBcXp1q13t1dQKvVis1mA+4vn2hpaWHChAlkZWVR\nX1/PmTNnMBgMnDhxgh07dgA4v2fBggUu/Xnuued4+OGHWbp0KdOnT1ftd21tLUVnh3f5SmVuOj/6\nbpIVSsKpgN5w0PL4tTx2f2zh/uWXX7J7927u3r3Lm2++iaIovPXWW6xZs8ZPvRwZzp8/z2effaY6\n8fnwww8ZPXo0Tz75JHD/hpS7nGEwGPj973/v9n0ntSdKaoxGIxaLBZPJRGFhIcnJyapx++aqP/7x\njzz11FOkp6erxlU7H6C6upq0tDS3W673/r62tjbV8fY1nDnMXe4KxrUj0DElnsQL5Xgw9DwmT7LC\n3FdffcWtW7dITU1VrXI/FHv27GHbtm3cvHmTEydODHj+5MmTKSkpYcuWLSQkJDiTp/ieVv/I7qHl\n8Wt57P7wzjvvsHHjRnbt2gWAXq/n66+/DnKvQs/UqVPZt28fXV1dxMbG0tnZSVxcHHB/KeE333zD\nz372M+f5CQkJbnOGw+Hg9u3b3Lp1izFjxvjcr+joaJYvX87bb79NYWGhalyA5uZmOjo6iI2N5fLl\ny87lf4qiAPffO7Pb7f3OHzVqFF9++eWAywXdiY+P9+t4/UneyZJ4Ei+48Xwhk6ww1djYiMViYdSo\nUfz2t78F8LpqvTs971FNmzYNk8nElClTiI+Pd3tOXzExMbzyyits376d9evXqy7ZqMxN96pP3hof\nHz2s7QshAkun0zFu3DiXz+7evRuk3oSumJgYli9fTkVFBXq9npiYGNatW4fBYODNN99kzJgxWCwW\nYmJiKCoqUs0ZOp2OVatWsXPnThRFYfTo0WzYsMGnvmVmZnL8+HHnXWi1XBUXF8c777zDtWvXmDNn\njnN5aHp6Onv37sVgMLjkoJ7zr169ylNPPdVvOWlVVRXXr193LpF0p+94ExMTWb9+ver5w5XDJHcJ\nMbLIckERULW1tW6XbmiBlpeMgbbHr+Wx+2O54Pbt28nJyeH//J//Q2FhIX/+85+5c+cOq1at8lMv\nRagYzBLEYAp0DpPlghJP4gU3Hgw9j0mdLCGEECFt5cqVHD16lKtXr/Lyyy9z584dVqxYEexuCSGE\nEKrkSZYIKC0/yRJCi/zxJEuIUCE5TAjtkY0vNGbFihWkpaVhs9nIzc1lzpw5Hs/ftWsXn3/+Ob/+\n9a8HnSBsNhvvv/++T0UbhRBCiL4khwkhRjqZZIWpiRMnYjab6e7uxmg0Dpig1qxZQ3V1tVcxDAbD\nsCSnusZ2v7fZ2/j46JAs6Kjl93JA2+PX8tj94fTp08yaNct5rCgKe/bs4aWXXgpir4QvJIe51zd/\nyTtZEk/iBTeeL2SSFeZ6turtce7cOQ4cOIBOp2PhwoVkZWV5/P6qqirq6+uJjIwkOjqa1atXM3bs\nWKxWK0ePHsVms7m8hKxWb+uzzz7jypUrzJ8/n48++oji4uJ+u4H1KPjgkh9Grq4yNz0kJ1lCiKE5\ndOiQyyRLr9dz9erVIPZI+IvkMFeSv4QYOWSSFaYaGxsxm800NjY6K18risK+ffsoKysjKiqK0tJS\nZs6cSVRUlGo7dXV1lJeXc+jQIVJSUhg7diwA2dnZZGdnD6o4I0BKSgoPPfQQ3377LU8++SRXrlxR\nTVDDrbW1Fb4r6BioauCDOZ49e3ZI9UfGL8eBOPalZt+1a9e4du0a7e3t/PWvf8XhcKDT6WhtbeXm\nzZtDblcEn+Qw91pbWzn+ZZ3Lnfred+4D9TvcO7bEk3hajzfUPCYbX4SpnjtwdrudyspK8vLy0Ol0\nbNiwwVkIuL29nY0bN5KSkgJAdXU1aWlpLuvZP/zwQz788EMmT57M6tWr+9UY6budrtpdQJvNBtxf\nntHS0sKECRPc3oGsra2l6Kz7Olv+Upmbzo9S4wc+UQgx7HzZ+OLUqVOcPHmSc+fO8Y//+I/Oz6Oi\nopgzZw6PPvqov7opAkxymHuSv4QIPbLxhUZFR0cTERFBV1cXDzzwABMnTqSgoGBQs26Hw8G5c+fY\nvn27xzuFvSmKAoDdbsdut/vUd60Jp3XEw0HL49fy2H3xxBNP8MQTT/DWW2/xr//6r8HujhgGksM8\nk3eyJJ7EC248X8gkK0w1NjZisViw2WxkZWWRmpoKwLJly9i6dSs6nY6kpCRefvlll++rqqri+vXr\nPPPMM+h0OhRFoaysjIiICJKSkvjNb37jMbmlp6ezd+9eDAYDOp363TxPX6vMTfdytN4ZHx89rO0L\nIQJLJlgjj+Qw9yR/CTFyyHJBDevq6uLAgQM899xz6HQ6tm/fzrPPPkt6+vAlEKkxIoS2SJ0sMVwk\nhwkhAkGWCwqvRUVF0dTUhMViASAzM3NYk5MQQgzFnTt3OHnyJC0tLTgcDhwOB62trfzqV78KdtdE\nEEkOE0KEMn2wOyDus1gsbteH22w2Dh482O/zmpqafp8NdhelHlFRUeTn52OxWLBYLIOqJ6LWHzGw\nvrviaI2Wx6/lsfvD9u3bOXHiBP/v//0/vvnmG/76178SERER7G4JH/TOV97mrh5DyWHhJhjXjkDH\nlHgSL5Tj+UKeZIU4tWKKtbW1zJs3L2T64w2tFiMWQgzN7du32bp1KzU1NYwfP55f/vKX/O53vwt2\nt4RGSQ4TQgyGTLJCmFoxxZ07dzpfGs7IyGDJkiUAdHd3s3fvXi5evEhGRgbLli0D3G9ZC3DkyBFO\nnTpFU1MTOTk55ObmApCfn09mZma/dtT6o9aOGq0WIw6X3XCGi5bHr+Wx+8Po0aMBeOCBB7hw4QIz\nZszg1q1bQe6VGA5q+UotL6kVI1bLS1arlYaGBm7cuIHD4cBkMvH1119TXV3NunXrADCbzRiNRgwG\ng9s+BjKHBePaEeiYEk/ihXI8X8gkK4SpFVPMy8vDaDRiNptdPu/o6GDRokUkJCRQUFDgTEJq5syZ\nw9y5c7l79y7FxcXOJNTZ2em2HbX+qLUjhBD+MHXqVNra2nj00Ud59913OXfuHBMnTgx2t4QPem4U\nArS0tAx4vlpeUitG7CkvtbS0YDKZ0OvvvzExceJE2tvb6erqorm5mQkTJqhOsIQQYrBkkjWCJCYm\nkpiYCNyvPTKQCxcucObMGQwGg8v7YP5qJ1haW1vhu2KOgaoGPpjj3uuIQ6E/Mv7AVqfv/TMIdn8C\neTyYekcDWbp0qfPfmzdvprGxkSlTpvjcrgie1NRU543CwbyTpZaXsrOzWbduHZMnT3ZZQu8pL2Vm\nZjonWD2efPJJPv30U+eTr2DqncN2797NjBkzAvo7W19fz+rVqyWexJN43x0PNY/JFu4hwmKxUFhY\n6PbuWd+K9QAFBQVUVFS4JAq1ZRaFhYVUVFRgt9sxGo288cYbALz66qts27aNmzdvUlZWxo4dOzy2\no9YftXbcqa2tpeisev0Rf6jMTedH3yWoUBJOBfSGg5bHr+Wxyxbuwh1v85W78x0OB1u2bCE/P79f\nMWK1vGS1WrHZbCxYsMDl/G+//ZY333zT2b6aQOewYFw7Ah1T4km8UI4HsoX7iLBz507npGnDhg0e\nz50xYwbl5eWkpKSwcuVKj+eqFV+cNm0aJpOJKVOmEB8/9EmJt+1otRixVv/I7qHl8Wt57MPB4XDw\nt7/9TbbrHoEGWywY8FiM2FNectfuqFGjiI+P58EHHxywj4HMYfJOlsSTeMGN5wt5kiUCSgo5CqEt\n/niStXXrVjZu3Ojy2X//7/+d1157zad2RXjzdzHi3//+96xYscLjzULJYUJoz1DzmNTJEiJAwqm2\nw3DQ8vi1PHZ/6OjocDlWFOX+eytC03oXI968eTMPPfTQkCZYly5d4s033yQ9Pd2nVR3DIRjXjkDH\nlHgSL5Tj+UKWCwohhAhJ//f//l8OHz5MU1MTr776qvPzjo4OfvjDHwaxZyIU9BQj9lV6erpz+3Yh\nhPAXWS44Ap0/f54DBw6g0+mIjIykoKBgULsEBoIstRBCW3xZLtjV1UVHRwf//u//zoYNG+hJV9HR\n0c6d5sTIUldXx4EDB4iMjCQrK4v58+f71J67zZt8ITlMCO2RjS8EAHa7naqqKl577TViY2O5c+dO\nyEywetQ1tg9r++Pjo0OyGLEQwjuxsbHExsby4osvMm7cuGB3RwTA/v37KSkp8cvW/8NFcpgQYjBk\nkjXCNDQ0MH36dGeC6plg9a5yrygKmzZtIiIignPnzjmfei1cuJCsrCwA1c8vXbpEVVUViqIwduxY\n8vLyPJ7vTsEHl4bzR0BlbnpIJigtb+MN2h6/lsfuD4888ojz3/fu3SMiIiKIvRHD6cEHH+TEiRM8\n/fTT6HQ6PvnkEwCeeuopAOrr67lw4QJLly5VzTvvvfceJ0+eZNKkSdhsNmfbaufn5+eTmZnJxYsX\nycjIcBY6VhPIHCZbuEs8iRfceL6QSdYI09bWRkJCgtuv9a1yrygK+/bto6ysjKioKEpLS5k1axZ6\nvd7t5xEREbz99tsYjUaXpTru2pk5c2a/uiVCCOGNDz/8kJ/97GfO4927d3P69GkSExNZv349kyZN\nCmLvxHBYuXIlx44do6KigsWLFzNhwgTOnz/PF198QWdnJy0tLaSmpqrmr/b2dk6fPs3rr7+O3W53\nvrOldn5kZCSdnZ0sWrSIhIQECgoKBpxkCSHEYMgka4QZPXo0V65cAWDXrl1cvnyZVatWAf2r3Le3\nt9Pc3ExFRQUAnZ2dNDc3ExMT4/Zzg8FAYmJiv3ch3LVz+/ZtUlJShnu4brW2tkKvQo4w/NXAB3M8\ne/bskOqPjF+OA3Hsy7KvEydOOCdZn3zyCd9++y1vv/02169f55133qGkpGTIbYvQpNfr+clPfkJW\nVhYWiwWj0YjVauX9999HURQmTZrknEz1zTu3bt2ivb2dtLQ09Ho9o0aNct50VMt3KSkpLnktFJbX\n985h4HrnPlC/w71jSzyJp/V4Q81jsvHFCGO329m0aRMmk4kf/OAHvPvuu8ycOZOmpqZ+Ve4dDgdm\ns5mioiKX/4A8fV5YWEhhYSFjxowZ8Hx3amtrKTrrucCkrypz0/lRamhtwyuEVvmy8UVJSQmvv/46\nAIWFheTn5zvfzTKbzVgsFr/1U4QGRVHQ6/XYbDYsFgvl5eVs3LiRf/7nf2bs2LH8+c9/Ztu2bYwa\nNcpt3mlra6OyshKLxUJHRwcbN27krbfe8pinem+OMdBGGZLDhNAe2fhCABATE8OvfvUrKisriYyM\nJC4ujgceeICmpqZ+Ve51Oh3Lli1j69at6HQ6kpKSePnllz1+vmrVKnbu3ImiKIwePZoNGzaonq+m\nMtf7OibeGB8f/DuR7oTTOuLhoOXxa3nsvpgwYQL/63/9Lzo6Opg0aZLL5he937URI8e7777LlStX\nUBSF5cuXA2AwGJg3bx6xsbEcOnTIOUlyl3cSEhKYNWsWJSUlTJ48mbi4OEA93w1FIHOYvJMl8SRe\ncOP5Qp5kiYDS8va34XRhGA5aHr+Wx+7LkyybzcahQ4cAWLRoEQaDAbj/xP706dP8y7/8i9/6KcRg\nBDqHySRL4km84MaDoecxmWSJgNLyJEsILfJlkiVEqJEcJoT2DDWP6Qc+RQghhBBCCCHEYMkka4Sp\nq6vDZDJhsVg4fPiwz+0ZjUY/9EpA/11xtEbL49fy2IXwZOfOnbz55ps+tVFTU+PV+TabjYMHD/oU\nM1CCce0IdEyJJ/FCOZ4vZOOLEWb//v2UlJT4tG3ycKtrbA92F/oZHx8dkgWMhRBipOru7ubvf/87\nERERdHd3Exk5tD9JamtrmTdv3qDPNxgMLF68eEixIDA5THKSEOFP3skaYf7whz+QlpbG008/jU6n\n45NPPgHgqaeeAqC+vp4LFy6wdOlSzp07x4EDB9DpdCxcuJCsrCwA3nvvPU6ePMmkSZO4dOkS//7v\n/w6gen5+fj6ZmZlcvHiRjIwMj4UcA7H97VDIlrlCDA95J0uoqaur49SpU0RGRjJjxgxmzpxJUVER\nW8HNQ1MAACAASURBVLZsAVy3Uz98+DDHjh1Dr9eTnZ1NTk4OcP9J2KlTp3j44YfJyMhgyZIlAFit\nVhoaGrhx4waKomA2m9Hr9VitVo4ePYrNZnPZqv3IkSOcOnWKpqYmcnJyyM3NddvnQOUwyUlChA7Z\nwl0AsHLlSo4dO0ZFRQWLFy9mwoQJnD9/ni+++ILOzk5aWlpITU1FURT27dtHWVkZUVFRlJaWOgs8\nnj59mtdffx273U5+fj6A6vmRkZF0dnayaNEiEhISKCgo8DjJEkIIIQD++te/MnPmTPR6PX/5y1+Y\nOXNmv1IjPT799FOMRqNzS/YeeXl5GI1GzGZzv+9paWnBZDKh13//ZkR2djbZ2dn9lsLPmTOHuXPn\ncvfuXYqLi1UnWUIIMVgyyRph9Ho9P/nJT8jKysJisWA0GrFarbz//vsoisKkSZOck6nm5mYqKioA\n6Ozs5NatW7S3t5OWloZer2fUqFEkJCQAuD2/ubmZlJQUEhMTSUxMBCA6OjRrVA3WcFYP772OeLir\nk4fisZbH3/dnEOz+BPI4lJcui+BRFIW6ujra2+8vvfviiy9QFEX1/Ly8PD7++GPa29t54oknePTR\nRweMkZmZ6TLB8uTChQucOXMGg8GA3W4f3CACYPfu3cyYMSOgv7P19fWsXr1a4kk8iffd8VDzmCwX\nHGEURUGv12Oz2bBYLJSXl7Nx40b++Z//mbFjx/LnP/+Zbdu2MWrUKMxmM0VFRS7/8bS1tVFZWYnF\nYqGjo4ONGzfy1ltv4XA43J4Prks6ev/bHS0vFwxGbYdQouXxa3nsslxQuHPhwgVqa2vJy8sDYNeu\nXcyePZt9+/ZRUVGB3W7HaDTyxhtvuHzfnTt3MJlMzht+AAUFBVRUVLhMqKxWKzabjQULFriN3zdX\nvfrqq2zbto2bN29SVlbGjh073H5foJcLBuPaMdLrHkk8iectWS4oAHj33Xe5cuUKiqKwfPly4P5L\nvvPmzSM2NpZDhw45J0nLli1j69at6HQ6kpKSePnll0lISGDWrFmUlJQwefJk59IMnU7n9vyhqMxN\n989g/Wh8/PA/gdPqH9k9tDx+LY9dCHdOnjzp8nsxe/ZsTp48ydSpU9m7dy8Gg8Fl6eCePXv46quv\n6OrqYv78+S5tzZgxg/LyclJSUli5cqXzc7Wlh+5MmzYNk8nElClTiI/3fMMtEDmsJycF49oR6JgS\nT+KFcjxfyJMsEVBSyFEIbZEnWWIkkRwmhPZIMWIhQlw41XYYDloev5bHLoQYumBcO0Z63SOJJ/EC\nRSZZQgghhBBCCOFHslwwxK1YsYK0tDRsNhu5ubnMmTPH4/m7du3i888/59e//vWwLWmw2Wy8//77\nQyrmKEsthNAWWS6obZLDhBDhTja+GKEmTpyI2Wymu7sbo9E4YIJas2YN1dXVw9ong8EwpOTUo66x\n3Y+98Y/x8dFMiI8JdjeEEGJEkRzmG8lNQoQvmWSFia6uLpet073ZNh2gqqqK+vp6IiMjiY6OZvXq\n1YwdO1a1yr3VaqWhoYEbN26gKApmsxm9Xo/VauXo0aPYbDaXmGrtuFPwwSVffhTDojI3fdgTmZa3\n8QZtj1/LYxcCJIcN1Wv/Mo4JP5wUsHgw8rfklngSL1BkkhXiGhsbMZvNNDY2OouvDUVdXR3l5eUc\nOnSIlJQUxo4dC3iuct/S0oLJZHKpPZKdnU12djZGo9GlfU/thJNQKN4qxyPvuEeo9CeQx1KMWNsk\nh/mms7PT5Y/KQBV7lXgST+J9fyzFiEeonjt8drudyspK8vLySExM9HgXsLq6mrS0NJd14x9++CEf\nfvghkydPZvXq1fzgBz8AoL6+3lnl/sSJE84CjN4WclRrpy8tFyMWQovknSxtkxzmG8lNQgSfvJM1\nwkVHRxMREUFXVxeJiYkoigKA3W7Hbrd7/F6Hw8G5c+fYvn07UVFRLl/bs2ePs8r9iRMnhtw/f7Uj\nhBBi5JEcJoTQGplkhbjGxkYsFgs2m42srCxSU1MBSE9PZ+/evRgMBrdV7auqqrh+/TrPPPMMOp0O\nRVEoKysjIiKCpKQkfvOb3xAbG+uxyr27dtV4aqevytz0QbcbKOPjo4c9RjitIx4OWh6/lscutE1y\nmG90XS1AYJ9kjfR3bCSexAsUmWSFuHfeecft5ytXrnT+e+nSpS5f+8UvfsEvfvEL53FXVxeTJ0/m\nueeeQ6fTsX37dhobG0lPT2fVqlVu28/OzvbYr74vKau1444sfRBCCG2QHOab48frIH1ywOIJIfxH\n3snSgLt37/If//EftLff33Y2MzPTp+1rfSE1RoTQFnknS/hKcpgQIpjknSyhKioqivz8/GB3Qwgh\nhPCa5DAhRDjSD3yKCEUrVqzAYrFQXFxMbW1tQGPbbDYOHjwY0JgjQd/tvLVGy+PX8thFeGpqauKF\nF17AZrMBYLFYBtygoqampt9nfbdKH6pdu3axdu1azp4965f2wH99G07BuHYEOqbEk3ihHM8X8iQr\nTE2cOBGz2YyiKBQWFgZ0OY7BYPBpqUZdY7sfe+Mf4+Ojh70YsRBChBOdTkdtbS0LFy4c1Pm1tbXM\nmzdvWPqyZs0aqqurh6VtbwUyhyljprjEk1wlRPiQSVaYa2trc9nS1mq1UlNTg8PhYO7cucybNw+r\n1cpnn33GlStXmD9/Ph999BHFxcWMGzdOtcp9fn4+mZmZXLx4kYyMDJYtW+Zs/+jRo9hsNpcXh9Xa\ncafgg0vD9NMYusrc9GFPXOGyG85w0fL4tTx2EZ50Oh0ZGRmcPXuWn/3sZ8D3W6kfOHAAnU7HwoUL\nycrKAmDnzp3OnQQzMjJYsmQJAN3d3ezdu7dfLlFrx2q10tDQwI0bN3A4HJhMJiIiIlT76W0Oe++9\n9zh58iSTJk1yPqUDOHz4MMeOHUOv15OdnU1OTo5qzMDnsP9y/msk5iqJJ/FCOZ4vZJIVphobGykp\nKeHevXvk5eUB9ydcH3/8MaWlpeh0OkpLS50v6KakpPDQQw/x7bff8uSTT3LlyhXGjRunWuW+s7OT\nRYsWkZCQQEFBgTNBZWdnk52d3W+ZhVo74SZQ1cPlWI61chwbG4sIT3q9nn/6p39y1o1yOBzs27eP\nsrIyoqKiKC0tZebMmURFRZGXl4fRaMRsNru00dHR0S+XKIrSr51Zs2YRGXn/T5KWlhZMJhN6/cBv\nNHiTw27fvs3p06d5/fXXsdvtLu95ffrppxiNRuLi4vz14xsWra2tHP+yLqR+x+VYjkf68VDzmOwu\nGKaMRiMWiwWTyURhYSHJyclcunSJTz75hJdeegmAffv28dhjj/Ff//Vfzjt2BoOBlpYWJkyYQFZW\nlmqVe6PR6HxS1fvfveP3/kytnb5qa2spOjv42iWBUpmbPuzb8oZTbYfhoOXxa3nssrtgeGpqauJ/\n/s//ydq1a9m6dSuKorB27dr/3969x0VZ53sA/zzcUUFQHA2ELPBYmqSpHcIsRE2itbV197w2Ktss\nbTXW2FQUEZmBjBXqrKejrXbZTDfO8WCX88oLrrCMyaqtaSiZl7xQJ0S5XwQBYZ7zBy+eZWQG5hnm\n/nzefzEPM7/v78c8/L7zm+fyxerVqxEWFgYAaGpqQnJyMlQqFYD+c0X3zw0NDXjttdcMtqPVatHa\n2oq4uLhefcrLy0N4eLje3f3k5LDvv/8ehw8flnJkz+fU1NTg8OHDaGpqwrRp03DPPfcY/LvYO4e5\nYq5iPMZz5HgA7y6oSF5eXnjmmWfw3nvvYfXq1VCpVLh06RI6OjogCAIuXLiA+Ph4VFVVGW3DUlXu\nLdUOERE5Dm9vb4SHh+PAgQMYMmQIQkJCsGrVKoPf7HZ0dECn0/V7BMrf37/PduSQk3tGjhyJK1eu\nQKfT4caNG6irq5N+N3z4cMyfPx/t7e1IS0vDxo0bB9QvIiIuspxcZGQkiouLpZX9nDlzoFarIYoi\nYmNjERgYaPB1gtD1TZycKvd9kdNOTnyE2XGsZaSfl9VjKPVIRjclj1/JYyfn1J0jACAuLg579+6F\nIAhISEhAdnY2BEFAYGAgli9fLj1v4sSJyMrKgkql0is2bKjtvtrpGft2ubm5KC8vx7x58wDIyz3+\n/v6YOnUqUlNTERoaqndq4Pbt2/HDDz+gpaUFc+fO7bMde+YwV8xVjMd4jhxvIHi6INkUCzkSKQtP\nFyRXwhxGpDzm5jHWySKyEWeq7WANSh6/ksdOROazx9zh6nWPGI/xbIWLLCthIUciIiLDFi5cCI1G\ng7Vr16KwsNCmsVtbW/HJJ5/YNCYRKQ8XWVbUXcjRVNZMNMuWLUNMTIzV2qf+OdN5xNag5PEreexE\nhoSEhCA9PR2vv/468vPzbRrbx8cHCxYssGlMc9lj7nD1a2wYj/FshTe+sBIWcjReyLFn9XpnNtLP\ny+pFIYmIXFljYyM8PT2lx1qtFgUFBRBFETNnzsTs2bOh1Wpx+vRplJWVYe7cucjPz8fatWsxYsQI\n2TlMq9Xi0KFDaG1t1bvdvLF2DHGkHMY8ROS4uMiyIhZyNGzVvosm/w0dWU58hKzkpuRaSYCyx6/k\nsRMZcvXqVaSmpqKzsxOJiYkAuhZcBw8eREZGBgRBQEZGhnSTCZVKhbvuugs3b95EdHQ0ysrKMGLE\nCFk5DABiYmIQExPT63R3Y+0Y4kg5TG4eMoWr1z1iPMazFS6yrKT7po0xMTHIzs4G0DXp19bWSvU3\nmpubUVdXJxVyNCQgIAABAQEAuupiAV2FG29vp7a2VmonMjLSpAUWAJw9e1Yq5NjzmjFDcaurqxEe\nHg43Nzf4+vrC399fen5iYiIOHjzYbyFHV+Mo1cj52LEfd3OU/tjy8UDrIJFrCg4ORnp6OtLS0qR9\npLKyEuHh4dLZF2PHjkVFRQUASPnIz88P9fX16OzsBCAvh/XFWDuOrqGhAcWXT1n0f7a0tNSmcwTj\nMZ4jxwNgdh7jLdytpLKyEn/5y1/w2muvITc3FwcOHMC2bdvwxhtvYM2aNQbfsFWrVmHjxo16CyRD\nVetFUUR6errBdrRaLVpbWxEXF9er/by8PISHh+vdfnbFihVSIcfMzEy8/fbbRuM2NjYiJycHGo0G\nN27cQHJyMrZu3aoXo79CjoWFhVhz0ngNFGeSEx+B+4PNry1GpAS8hTsZ0p1XTp8+jf3792P16tVo\nbGzExo0bodFopCNZSUlJOHXqlHR6uo+PD+rr6zFq1ChERUXJymGG4ncz1s7tHC2HMQ8RWZ+5eYxH\nsqyEhRyNc8RixOawRVFIIiJXFhkZieLiYukUoDlz5kCtVkMURcTGxiIwMNDg67rznJwc1hc57ThS\nDmMeInJcPJJFNqXkQo7OdB6xNSh5/EoeO49kkSuxdQ6zx9zh6tfYMB7jycVixERERERERA6AR7LI\nppR8JItIiXgki1wJcxiR8vCaLAIA6Vx2Nzc3DB48WO8260RERI6MOYyIXAUXWS5GEASkpKTA29tx\nixM6UiFHWxJa6hEZEWrvbtiNkq9LUvLYieRgDtPX0NCAoUOH2iyesZjWLHrs6tf0MJ5zxxsILrJc\n0O1ngGq1Wly4cAEVFRXQ6XRIT0+Hm5ub0Qr3K1euRGRkJM6fP48JEyZIRRwvXryI3Nxc6HQ6BAUF\nSQUkS0pKsHv3bgiCgCeeeAJRUVF99s+RCjna0rrpI+zdBSIih8ccdrsqG8frHdMaRY+JXB0XWS4o\nKysLbm5uiIyMxFNPPQUAqK+vR1paml4NLmMV7pubm/Hkk0/C398fq1atQkJCAkRRxHvvvYeUlBSp\nwCMA6HQ6fPzxx8jMzISnpycyMjIwZcoUeHp62nbQTsDW30Y6Gmf55skalDx2IrmYw5TF1vMj4zGe\nrXCR5YLWrl3b61SLyMhIveQEGK9wHxAQICUhL6+uGhxNTU1627s1NTWhtrZWKj7c3NyMuro6qFQq\ni4/LFdiqOjkf87GjPDZUeJ2oL8xhjssR5hQ+5mNbPzY3j/Hugi5Go9Fg9erV8PHxkbZptVq0trYi\nLi5O77nGKtynpKQgKytL72dRFLF69WqsXr0aw4cPl9oQRRHp6elYs2aNSTthYWEh1pw0XizZla2b\nPgKP3Dva3t2wG2c6j9rSlDx23l2Q5GAOc0w58RG4P9j8Ys99cfVrehjPueMBvLsg9SAIvROAoW1y\nKtwLgoAlS5Zg8+bN0Ol0GDp0KF577TUIgoCEhARkZ2dDEAQEBgZi+fLlfbaVEx8hb0AuQmipt3cX\niIgcHnPYPznSjS+ISB4eySKbYo0RImXhkSxyJcxhRMpjbh5z6/8pREREREREZCousohspPsCSqVS\n8viVPHYiMp895g5bx2Q8xnPkeAPBa7JcjFqthiiKuHXrFqKiovDkk08afe4777yDM2fO4MUXX7TY\n6Q89LzgmIiKSi3mMiFwBF1kuRhAEpKSkwMvLC+vXr0dMTAz8/f0NPnfZsmXIy8uzcQ+BU1ebbB7T\nEfjdfb9ixw4AEfdPs3cX7EapdxYkMoej5zFbzuP2yBu2jtkz3kg/L6sXPXb1uk6M5zi4yHJBoiii\ns7MTAODh4QGtVouCggKIooiZM2di9uzZfb6+qKgIx48fR2VlJWJjY6UCjytXrkRkZCTOnz+PCRMm\nICEhAQDw2Wef4R//+AdGjx6N1tbWfvu3at/FAY6QnFFOfITVkycRuQZHzmPMYdbDPEGuhNdkuaCs\nrCxs2LABTzzxBDo6OnDw4EFoNBpkZmaiuLgYtbW1fb5+xowZSE5ORlZWFoqKiqTtzc3NePLJJ5GZ\nmYkTJ04AAOrq6vD1119jw4YNWLRoEdrb2606NnJeDQ0N9u6C3TjTOeREjoB5jKzF1a8hYjzHwSNZ\nLmjt2rXw9u76JujixYsIDw+Hu7s7AGDs2LGoqKjAsGHDjL7+7NmzOHHiBHx8fNDW1iZtDwgIQEBA\nAADAy6urZkZ1dTXCw8Ph5uYGX19fo6d0EAGOU73d1o+VPH5TCrwS3Y55TJkaGhpQfPmUVeek0tJS\nm86BjOfc8QCYncdYJ8vFaDQarF69Gj4+PgCApqYm/OEPf4BGo4EgCMjIyEBSUhICAwMBAHl5eQgP\nD9e7YHjFihV48803UV1djczMTLz99tsA9C8G7v65sbEROTk50Gg0uHHjBpKTk7F161aj/SssLMSa\nk72LSpLry4mPwP3BfRcMJdfDOlkklyPnMeYw62KeIEdkbh7jkSwXJAj/TAB+fn6YM2eOdLem2NhY\nKTF1y83NRXl5OebNmwcAGDduHNLS0jBmzBj4+fU92fn7+2Pq1KlITU1FaGgoBg8e3G//cuIjzBgV\nObuRfl727gIROQlHzmPMYdbDPEGuhEeyyKYKCwstdptdZ1NcXOxUd8WxNCWPX8lj55EsciW2zmH2\nmDtsHZPxGM+R4wHm5zHe+IKIiIiIiMiCeCSLbErJR7KIlIhHssiVMIcRKQ+PZFGf8vPz8fvf/x6v\nvvoqDhw4IG0vKCgwuY133nkHr7zyCk6ePGmNLhIRERnFPEZEzoQ3vlCIuLg46Va2c+fOlbYXFhb2\nW9Sx27Jly5CXlzfgvti6er2jaGhowNChQ+3dDbsRWuoRGRFq727YhZKvySKyFEfJY7bMYfbIG7aO\n6cjxRvp5Dbg4sqtfs+Tq8QaCiyyF6Xl26ObNm3H16lVoNBpMmDABv/zlLwEARUVFOH78OCorKxEb\nG4v4+Hij7ZWXlyMvLw9JSUkAgPT0dKSkpEi33jVk1b6LFhqNM6qydwfsZt30EfbuAhG5AHvnMdvn\nMHvkDVvHdMx4OfERA15kkXJxkaVgiYmJSElJQXp6ut72GTNmYObMmbh16xbWrl3bZ3IKCQlBU1MT\nWlpaUFtbi1GjRvW5wCLlGjp0qEMUx+VjFiMm18E8RtZkqeLI3Ww1BzMeixGTjWm1WrS2tiIuLk7a\n1rMwY7fS0lKcOHECPj4+OHLkiFTEETBc9LGwsBCCIKCyshKTJ0/GuHHjjPaBhRyVi0UmlYk3viBL\nsnceYw5TFuYtAnjjCzJTR0cHdDqd3rbt27fj+eefN3mHio6OxldffYUrV670ucAiZWtoaLB3F+zm\n9m/fiMhymMfIkdl6/mc8x8HTBRUiPz8fBw4cgE6ngyAI0kXDEydORFZWFlQqFRYvXgwAGDduHNLS\n0jBmzBj4+flBFEUIwj+/ucvNzUV5eTnmzZsHAPD19YWfnx/CwsJM6ktOfISFR+cceOOLent3gYic\nmKPkMVvmMN74wr7xRvp5Wbk35Mp4uiBZxJYtW7Bw4UL4+fV9WJ01RoiUhacLkrMwJY8xhxEpj7l5\njEeyaEAuXryIPXv24N577+13gUVERORomMeIyBp4TRYNSEREBJKSkvRqlpBhznQesTUoefxKHjuR\no3PkPGaPucPVr7FhPMazFR7JUohTp05h9+7d8PDwQFRUlNnJpLW1FXv37sWCBQss3EMiIiLDmMOI\nyNnwmiyFSE1NRWpqqt1r1hQWFsJ91Fi79oGop5F+Xiw2aUW8JossgTmMnBnzjHPjNVnUp7CwMBw5\ncgSzZs2S7rCUlJSEcePG4f/+7//wwAMP4Je//CUAoKioCMePH0dlZSViY2OlIo5arRaHDh1Ca2ur\nXk2SlStXIjIyEufPn8eECROQkJDQZ19W7btopVESyZcTH8HkR+TgmMPImTHPKBOvyVKIxYsXw9PT\nExs3bsT3338PALh16xZeeOEFvP766ygpKUF9fdcttmfMmIHk5GRkZWWhqKhIaiMmJgbp6em92m5u\nbsaTTz6JzMxMnDhxwjYDIrKQnvW7iouL9c73ttTj7m3Wat+RHxNZAnMYuQJbzLl/+tOfGM9Kj+Xi\nkSyFcHNzw6OPPoqoqChoNBq88cYb8Pf3h4+PDwDg7rvvRnV1NQICAnD27FmcOHECPj4+aGtr67ft\ngIAABAQEAAC8vFhTgpxLz3opDz/8sN7vLPW4e5K2VvuO/PjkyZMgGijmMHIFjjAn87H8x+bmMR7J\nUgidTgcAEEUR3Zfh1dbW4saNG9DpdLhy5QpGjRoFANi+fTuef/55XkdBZCG3T9xEJA9zGJFpbJ1v\nXD3eQPBIlkLs3LkTZWVl0Ol0eOaZZwAAgwcPxkcffYSffvoJM2bMwJAhQwAA48aNQ1paGsaMGWOV\nmiE58REWb5PIXCP9+M01kaNjDiNnxjyjTLy7oIKlpKToXfxrC4WFhXjggQdsGtNRFBcXO9U3MJam\n5PEreey8uyBZixJymD3mDlvHZDzGc+R4gPl5jKcLEhERERERWRCPZJFNKflIFpES8UgWuRLmMCLl\n4ZEsC0lJSTH4c19aW1vxySef9NpeUFAw4D6Y8pz3338fx44d67c/5jL170BERGSqb7/9Fmq1GhqN\nBhs2bEB7e7u9u0REZDG88YUF+Pj4YMGCBb22FxYWYvbs2VaNvWvXLoSFhSEqKqrf/jiKU1eb7N0F\nu2hoaNC7XbjSKHn85ox9pJ8Xi1eSy2pra0Nubi7WrVuHQYMGob293Wlun27LHGaPedPWMRlP2fHk\n5jpnusaZiywTGasIb6yC/ObNm3H16lVoNBpMmDBBqkRfUlKC3bt3QxAEPPHEE9Li6LPPPsM//vEP\njB49Gq2trSb1KT8/H4Ig4LHHHpO2ya1on5ubi9LSUnh4eMDLywtLly5FUFCQ0f5otVoUFBRAFEXM\nnDkTs2fPhlarxenTp1FWVoa5c+ciPz8fa9euxYgRIwz2e9W+iyaNzzVV2bsDdqbk8csbe058BBdZ\n5LIuXLiA++67D4MGDQLwz/pUxnKkVqvFhQsXUFFRAVEUkZaWhsOHDxvNPUVFRTh+/DgqKysRGxuL\n+Ph4AIZzYXl5OfLy8pCUlAQASE9PR0pKilSD63a2z2H2mDdtHZPxlBrPlXMdTxc0kbGK8MYqyCcm\nJiI4OBjp6enSAkun0+Hjjz/GunXroFarsX//fnR0dKCurg5ff/01NmzYgEWLFpl0ykR5eTn++te/\nwttbf8eUW9H+1KlT2LBhA6ZMmYJZs2YhKCjIaH8aGxtx8OBBaDQaZGZmori4GLW1tQAAlUqFmTNn\n4ubNm4iOjkZZWVn/f1Qi6pOtq9lb4zGRIY2NjfD399fbZixHdquvr0daWhrUajXc3d0BGM89M2bM\nQHJyMrKyslBUVCS1YSgXhoSEoKmpCS0tLfjpp58watQoowssIrKshoYG6We5OcXWOU0uHskykSUq\nwjc1NaG2thYbN24E0DXZ19TUoKmpCeHh4XBzc4Ovr2+vxGPIsGHDkJ2djfT0dEyePBlhYWFm9T8m\nJgZJSUkIDQ2VTm2sqakx2J/KykqEh4dLyW3s2LGoqKiQ2gcAPz8/1NfXo7OzU86fhogMcJRq9wN5\nfPLkSRDdbujQodKC6J133sGVK1ewaNGiXjmytrYWKpUKABAZGQk3N/3vho3lnrNnz+LEiRPw8fFB\nW1ub3vMN5cLo6GgcO3ZMOvJFRLbR81RDR8hZhh6bm8e4yLqNTqdDZ2cnOjo6pMWEuTo6OqDT6aSk\n4O/vj5CQEKxatUo6RQIAfH19ceXKFeh0Oty4cQN1dXX9tu3r6wsPDw+8/PLL2LZtGzIyMmT3VxRF\nlJSU4K233oKnp6e0XaVSGeyPSqXCpUuX0NHRAUEQcOHCBcTHx6OqSsmngBERkVxjx47Fzp078fOf\n/xzLli3Dzp07odPpMHr06F450hzbt2/Hm2++ierqahw5cqTf50dHR2PTpk0AgF//+tcDik1E1sNr\nspzYvHnz8MYbbwAA5s+fP6C2Jk6ciKysLKhUKixevBiCICAhIQHZ2dkQBAGBgYFYvnw5/P39MXXq\nVKSmpiI0NBSDBw82OUZYWBimTJmCvLw82YlBEATodDpkZmbC3d0dgYGBeOmll4z2x9/fH3PmzIFa\nrYYoioiNjUVgYKDRto3JiY+Q1U9XoeQbPwDKHr+5N74gclXe3t547rnnkJOTAw8PDwwePBghoC0I\ngAAAFR5JREFUISF4+umne+XIbn3lldufM27cOKSlpWHMmDHw8/Pr93W+vr7w8/Pr96wQwLY5jDe+\nYDxXj+fKuY51shSspaUFu3fvxrPPPgtBEPDWW29h/vz5iIiwXgJhjREiZWGdLHIWW7ZswcKFC/tc\nlDGHESmPuXmMR7IUzNPTE5WVldBoNAC6zne35gKLiIjI0Vy8eBF79uzBvffea9JRLyIiU/Dugk5s\noIUcPT09sXLlSmg0Gmg0GoeureUKlH6nNSWPX8ljJ9vqWTze1ELyxgrYFxQUDLgPpjzn/fffx7Fj\nx/rtj7n6609ERASSkpIwd+5ci8W0FHvMHbaOyXiM58jxBoJHspwUCzk6H93wMYodO6Ds8bvS2Fkk\n2fUYK2BfWFgo3XXWWnbt2oWwsDCpHlZf/XEUtvxftsfcYeuYjMd4cuNda2pzijzERZaTYiFHZ6X0\nOzEqefyuMXZXLhzpyowVpDdWwH7z5s24evUqNBoNJkyYINV7NJZjjBWw70t+fj4EQcBjjz0mbTPW\nH2P9z83NRWlpKTw8PODl5YWlS5ciKCjIaH+0Wi0KCgogiiJmzpyJ2bNnQ6vVGs2FhrAYMeMxnn3j\n5cQPdYo8xNMFnRQLORIRkamMFaQ3VsA+MTERwcHBSE9PlxZYxnKMsQL2fSkvL8df//pXeHvrf1Ay\n1h9j/T916hQ2bNiAKVOmYNasWQgKCjLan8bGRhw8eBAajQaZmZkoLi5GbW0tAOO5kIjIXDyS5aRY\nyJGI7KGhoQEI7ro5QPe58d01Sww9Hmi9I7IMY3O3HE1NTb1yTE1NDZqamgwWsO/LsGHDkJ2djfT0\ndEyePLnfW6cb639MTAySkpIQGhoqndpYU1NjsD+VlZUIDw+XvmQcO3YsKioqpPaB3rmQiByXKTnI\nEo/NzWNcZDkpFnIkInvoWQ/l9oKQhh6fPHnSJv2iLjqdDp2dnejo6JBdoP52HR0d0Ol00pdz/v7+\nCAkJ6ZVjfH19DRaw74uvry88PDzw8ssvY9u2bcjIyJDdX1EUUVJSgrfeeguenp7SdpVKZbA/KpUK\nly5dQkdHBwRBwIULFxAfH4+qKtc4lZdIaUzJQZZ4bG4e4yLLSbGQIxHZgysXjnQF8+bNwxtvvAEA\nmD9//oDamjhxIrKysqBSqbB48WIIgoCEhIReOcZYAXtThIWFYcqUKcjLy5P9BZ0gCNDpdMjMzIS7\nuzsCAwPx0ksvGe2Pv78/5syZA7VaDVEUERsbi8DAQKNtG8McRmRfzpKHWIyYLIaFHInodixGTNbS\n0tKC3bt349lnn4UgCHjrrbcwf/58q9Z7ZA4jUh5z8xhvfEEDdvHiRWzatAkREREs5NgHZ6rtYA1K\nHr+Sx05kLZ6enqisrIRGo4FarcZdd91l1QWWPbBOFuMxnn3jDQSPZJFNFRYW2rsLRGRjPJJFroI5\njEiZzMljXGQRERERERFZEE8XJCIiIiIisiAusoiIiIiIiCyIiywiIiIiIiIL4iKLiIiIiIjIgrjI\nIiIiIiIisiB3tVqttncnSBlOnz6NLVu2oKioCCNGjIBKpbJ3l2xmy5Yt+N///V+pvsOYMWPs2yEr\nO3v2LP793/8dFRUVuP/++wEo5/03NHalvP/vvvsu9uzZg6KiIowfPx5DhgxRzPtOrkPOPmuJ/VtO\nG4b+x6wZDwBu3bqF3/3ud3B3dze7DpmcmDU1NcjJycHf/vY3/Pjjj5g0aZJV4x06dAjvvvsutFot\nVCqV7PfQ0JxviX5ZIp4l9hc58QDL7C9yYlpif5ETT9b+IhLZQGdnp7hu3Tqxra1NbGtrE9evXy/q\ndDp7d8tmtmzZIlZVVdm7GzZz6tQp8auvvhJ37NghiqKy3v/bxy6Kynv/S0tLxXfffVfU6XSKed/J\nNciZqywxr5nbRvf/mFzmxNu7d6+Yk5Mj5ufny45nTsw//vGP4rlz58yKZU68FStWiJ2dnWJzc7O4\ndu1a2fEMzfmW6NdA4/Vk7v5iTryB7i9yYw50f5EbT87+wtMFySauXbuGO+64A15eXvDy8sLIkSNx\n7do1e3fLpkQFlaSLjIzU+8ZMSe//7WPvpqT338fHBx4eHqioqFDM+06uQc5cZYl5zdw2uv/H5JIb\nr62tDadPn8bUqVPNnsPkxNTpdLh+/TrGjRtnViy58QBg9OjR+O6773Dy5EmMHTtWdjxjc/5A+zXQ\neD2Zu7/IjWeJ/UVOTEvsL3LiAfL2F/P+4kQy3bhxA4MGDcL27dsBAIMGDUJTUxPuuOMO+3bMRnx8\nfPD2229j8ODB+M1vfoNRo0bZu0s2xfdfWe9/UVER4uPjFf++k/ORs89aYv82t43u/zG55Mbbv38/\n4uLiUF9fLzuWOTEbGxvR3t6O7Oxs3Lx5E48//jgefPBBq8UDuj5g7927Fx0dHZg7d66sWNbslyWZ\nu7/IZYn9RQ5L7C9yydlfeCSLbGLIkCFoaWlBQkICnn76aTQ3N8Pf39/e3bKZRYsWITMzE7/+9a+x\nc+dOe3fH5vj+K+f9//rrrxEcHIyQkBDFv+/kfOTss5bYv81po+f/mFxy4rW0tODcuXNmXeNibswh\nQ4Zg0KBBWLlyJVJTU/HZZ5+hvb3davGuX7+OkydPYvXq1UhNTcUXX3whO541+mVJA9lf5LDU/iKH\nJfYXOeTuLzySRTYxatQoVFRUSI+vXbvm8t/mG+Lp6Ql3d3d7d8Mmep4qoLT339hpEq7+/l++fBnf\nffcdFi5cCEB57zs5Pzn7rCX2b7lt3P4/JpeceOfOncOtW7ewadMmVFVVobOzE/fddx9Gjx5ttZge\nHh4YPnw46uvrMWzYMLNOcZMTT6fTobOzE0DXvG3uB3RTTo2z5Hxo6ql4A91f5MSz1P4iJ6Yl9hc5\n8eTuL4KopAsFyK5OnTqF3bt3AwB+9atfITIy0s49sp1Nmzahrq4Ovr6+ePHFFzFixAh7d8mqPv/8\nc5SUlKC+vh7jx4/HkiVLFPP+Gxr7H//4R9TX17v8+5+YmIjhw4fDzc0NYWFheOGFFxTzvpPrMLbP\nHj16FN7e3njggQf6fa614vX8HwsNDcWiRYusGq+bVqtFW1ub2afTyYlZXV2N9957Dy0tLXjooYfM\nOs1NTrxPP/0U58+fh06nw/Tp0xETEyMrlqE531gsS+wvcuJZYn+RE6/bQPcXOTEtsb/IiSdnf+Ei\ni4iIiIiIyIJ4TRYREREREZEFcZFFRERERERkQVxkERERERERWRAXWURERERERBbEW7gTEREREZFL\nOnv2LHbs2IHx48fjueeeM/q8lpYW5OTkSI8vX76Mjz76yOy4XGQREREREZFLunXrFp566imcP3++\nz+cNGjQI6enpAIAffvgB+/fvH1BcLrKIrOzcuXP48ssvsWTJEqSkpCAzM3NABfN6ysvLw/Tp0xEc\nHKy3vaysDHV1dZg8ebLB1129ehXFxcX4t3/7N4v0wxTWiNnfOC2ttbUVe/bswb59+7BkyRJERUXp\n/f7DDz/E2bNnIYoi5s2bh0ceeUT6nVqtRltbGzw9PQEA69evt9h+QESu5dixY9i3bx8A4NKlSxgz\nZgzc3d0xefJkPPXUUya1sXfvXsyZMwdeXl4mx/3iiy9w/PhxlJWVYceOHWb13dEYy5MDYc7fdiDO\nnTuHXbt2oampCW+++abe7y5duoQPPvgAOp0OAQEBSExMxJAhQwAAZ86cwaZNm3DHHXcAAGbMmIE5\nc+ZYrF/O8hkkMjIS3333nd62kpIS7N69G4Ig4IknnuiVz/fv34/HH398QHGZ4Yms7OzZsxg/fjwa\nGxvh6+tr0Q/Wv/rVrwxuLysrw+XLl41OcMHBwTZdYFkrZn/jtLTy8nIMGjQIEydO7PW74uJi3Lx5\nE9nZ2Whra8O6detw3333YdiwYQAAQRCwYsUKBAUF2aSvROS8oqKipA99r7zyClJSUqQPzqbat28f\nHnnkEVkLgXnz5mHevHlYuHChrFiOzFieHAhz/rYDcebMGTz++OPYtWuX3nZRFLF161b8/ve/R3Bw\nMLRaLf77v/8bL730EoCuvDN58mQsW7bMKv1yps8gPel0Onz88cfIzMyEp6cnMjIyMGXKFOlL0Kam\nJtTU1ODOO+8cUBwusois5Msvv0RBQQF++OEHhIaGIi8vDzqdDn/5y1/w7LPP9vv67777Dnl5eejs\n7IROp8OLL76Iu+66CwBw4MAB/P3vf8ePP/6I9evX4+6775Zel5+fj/3796O1tRVlZWWYOHGiNBG2\nt7fj9ddfR0tLC4KCgrBmzZpefT548CAAICIiAs8//7z0u+5K9YIgoLW1FcnJySYtGPqKeebMGXz6\n6ae48847ceXKFdy8eROpqanw8/Prt92+xtnW1oYPP/wQP/30Ezo7O/HII49I30idOXMGu3btgkql\nQnl5OYYMGYJXX33VpA8w4eHhCA8PR1lZWa/f/f3vf5e+Yfb29sZDDz2EI0eO4Gc/+5n0HNZ+JyJL\n6GuOa29vR2ZmJurr6/GHP/wB7u7uWL58uTRfHz16FEVFRbh58yba29vx6quvDvgoT3/zamlpKf7n\nf/4HQNcpWYsXL5b6o1arMW7cOJw/fx4NDQ34+c9/jpiYmAH1B+g7T6rVajz44IMoLS3F1atXERcX\nZ9JRi/7+ttYa54IFC1BZWdlr+5UrVzB06FDp/Xv44YfxX//1X9IiayA5x1U+gxjS1NSE2tpabNy4\nEQDQ3NyMuro6qFQqAEBBQQFmzZplVtt6RCKyqhUrVoiiKIp5eXnisWPHTHrN9evXxd/97ndiVVVV\nn89Tq9XipUuXem0vKioSP/jgA6OvO3PmjJiVlaW37ccffxTT09PFjo4OURRF8YMPPhAPHTok/T45\nOVm8cuWKSf03Nea3334r/va3vxV/+uknURRFcfPmzWJhYaHJbRobZ25urrhz505RFEWxra1NXLt2\nrVhaWirFXLZsmVhXVyeKoih+/PHH4o4dO2SNZcuWLeLRo0f1tq1YsUKsrq4WV65cKX744YfioUOH\nxD//+c/S77OyssQ1a9aIGo1GPH78uKx4RKRcy5YtE5uamvS29TXH9fU6URTFxsZG6ec9e/aI27Zt\n6/Wc5557TlYf+5pXGxoaxGXLlok1NTWiKIriV199Ja5fv156rVqtFj/55BNRFEWxvr5eXLJkidjQ\n0CArfl8M5Um1Wi317/r16+Jvf/tbWW0a+ttae5zXr18XX3vtNb1tR48eFf/0pz+JR44cERctWiRW\nVVWJiYmJ4o0bN0RRFMXvv/9eXLx4sZieni5u3bpVbG5uNjmWq30G+fbbb6X3XKfTiWlpaQb/Hh0d\nHeK6devEzs5Os2N145EsIitqbGyEv78/gK5vnB5++GGTXvfNN98gKirKaqeWiQa+3SotLUV1dTVe\nf/11AF3flPY8ujNr1ixs27YNDzzwAKKjoxESEjLgmABw5513Sm2pVCo0NzfLateQU6dOISkpCQDg\n5eWFmTNn4ptvvsF9990HAAgNDUVAQAAAYPr06Xj//fcHHFMQBABASEiI9G1YT8nJyXBzc8ONGzew\nfv16hIaGYuTIkQOOS0TK098c1xc/Pz+UlZXhhx9+wNWrV1FXV2eRPhmbVy9cuIB77rlHOnX6wQcf\nxJ///Ge0trbCx8cHADBp0iQAwNChQzF27FiUlZUhMjLSIv0yZvr06QAsl3fsMU43NzepvdGjR8Pb\n21vv9xEREdi2bRsEQcC+ffvw0UcfYenSpf2262qfQT7//HOUlJSgvr4eN2/exJIlS5CQkIDs7GwI\ngoDAwEAsX74cAHD8+HFMmTJF+tsOBBdZRFby5Zdf4osvvkBzczM0Gg0uX76MrVu3IiIiot/TBQVB\ngE6ns1FPu3h4eGDatGl6h+d7euyxx/Doo4+ipKQE//Ef/4Ff/OIXvS4UdSQ9J3FRFKVFkKHnmXOd\n3O3tBQUFoaamRvrg8+mnn+ottron7CFDhkjJlYssIjKXqXPc7d555x0AXdd93X333aipqbFK37rn\nVUEQen2otsacbG/2GGd33hk/fjw0Gg06OjrQ3t6OwYMH6/UL6Fr0/e1vfzOpXVf7DDJ//nzMnz9f\nb9s999wDtVrd67mW/FzDYsREVvLII49g2rRp+M1vfoPExETpH9qU67EmTZqEI0eOoKKiwqzYXl5e\naGhoAACTJ8pJkybh2LFjuHbtmrStZ8LQ6XTw9vbGv/7rvyI6OhoXL140q2+WZGyckyZNks7rbmtr\nQ1FRkd4FuJcuXUJVVRUAoKioyKRvf3sSRbFXMn344YdRUFAAoOsuhEePHsVDDz0k/b67f62trbh8\n+TIiIiJkxSQi6tbfHAd0zY/19fUA9Ofy48eP46WXXsKkSZNw+fJli/XJ2Lz6L//yLzh//jyqq6sB\ndF0TFhwcrHfU5ciRIwCA6upqXL58We8aH6CrflFHR4fF+jpQhv62lhinXHfddRfq6+tx9epVAMDh\nw4cxbdo06fc93/dvvvkG48ePN6ldfgaxDOf7qoDIiZw/fx5xcXH49ttvce+995r8OpVKhVdeeQVb\nt26FTqeDIAh4+umnTW5j4sSJ+Pzzz5GWlgZfX1+sWLFCb6IXBKHXt2sqlQovv/wy/vM//1M66vLM\nM8/gnnvuAQDs2LEDly5dgiiKGDp0KF5++WWTx2MsprFtpjI2zl/84hf48MMPkZqaCp1Oh0cffRQT\nJkyQ2h89ejR27dqF8vJyBAUFISEhwaR4J0+eRF5eHiorK3Hu3Dns27cPGRkZAIDo6Gh8//33SE5O\nhiiK+NnPfiadNtLW1oaMjAy4u7tDEAQ8++yzGD58uMnjJCLlMjQn9jXHdZszZw6ys7MxYsQIREdH\nSxfyL1iwAKtWrcLw4cMxdepUgwut9vZ2rF+/3uRbfvc1r/r5+WHp0qXYtGkTBEHAoEGD8Morr+i9\n3sPDAxqNBo2NjXjxxRel0+u6bd68GePHj9e7kZAlyck7gOG/rSXGaUx2djaqqqpQWVmJlJQUxMXF\n4dFHH4UgCFi6dCk2b96sdwv3bgcOHMDhw4fh4eGBO+64Ay+88IJJ8VzxM4g9CKKxCyWIiFzQmTNn\n8MUXX/S6qxEREZlnIPOqRqPBc889N+CjOo5OKeOkf+LpgkSkKIa+QSMiIvNxXiXqjUeyiIiIiIiI\nLIhHsoiIiIiIiCyIiywiIiIiIiIL4iKLiIiIiIjIgrjIIiIiIiIisiAusoiIiIiIiCzo/wFDD/qZ\nn/j7ZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109c71410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "city_counts.plot(kind='barh', ax=ax1)\n",
    "ax1.set_xlabel(\"# cities in top 105\")\n",
    "df_pop5.NumericPopulation.plot(kind='barh', ax=ax2)\n",
    "ax2.set_xlabel(\"Total pop. in top 105 cities\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-state-city-counts-sum.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-03', '2015-01-04',\n",
       "               '2015-01-05', '2015-01-06', '2015-01-07', '2015-01-08',\n",
       "               '2015-01-09', '2015-01-10', '2015-01-11', '2015-01-12',\n",
       "               '2015-01-13', '2015-01-14', '2015-01-15', '2015-01-16',\n",
       "               '2015-01-17', '2015-01-18', '2015-01-19', '2015-01-20',\n",
       "               '2015-01-21', '2015-01-22', '2015-01-23', '2015-01-24',\n",
       "               '2015-01-25', '2015-01-26', '2015-01-27', '2015-01-28',\n",
       "               '2015-01-29', '2015-01-30', '2015-01-31'],\n",
       "              dtype='datetime64[ns]', freq='D', tz=None)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(\"2015-1-1\", periods=31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-03', '2015-01-04',\n",
       "               '2015-01-05', '2015-01-06', '2015-01-07', '2015-01-08',\n",
       "               '2015-01-09', '2015-01-10', '2015-01-11', '2015-01-12',\n",
       "               '2015-01-13', '2015-01-14', '2015-01-15', '2015-01-16',\n",
       "               '2015-01-17', '2015-01-18', '2015-01-19', '2015-01-20',\n",
       "               '2015-01-21', '2015-01-22', '2015-01-23', '2015-01-24',\n",
       "               '2015-01-25', '2015-01-26', '2015-01-27', '2015-01-28',\n",
       "               '2015-01-29', '2015-01-30', '2015-01-31'],\n",
       "              dtype='datetime64[ns]', freq='D', tz=None)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(datetime.datetime(2015, 1, 1), periods=31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 01:00:00',\n",
       "               '2015-01-01 02:00:00', '2015-01-01 03:00:00',\n",
       "               '2015-01-01 04:00:00', '2015-01-01 05:00:00',\n",
       "               '2015-01-01 06:00:00', '2015-01-01 07:00:00',\n",
       "               '2015-01-01 08:00:00', '2015-01-01 09:00:00',\n",
       "               '2015-01-01 10:00:00', '2015-01-01 11:00:00',\n",
       "               '2015-01-01 12:00:00'],\n",
       "              dtype='datetime64[ns]', freq='H', tz=None)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(\"2015-1-1 00:00\", \"2015-1-1 12:00\", freq=\"H\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ts1 = pd.Series(np.arange(31), index=pd.date_range(\"2015-1-1\", periods=31))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01-01    0\n",
       "2015-01-02    1\n",
       "2015-01-03    2\n",
       "2015-01-04    3\n",
       "2015-01-05    4\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1[\"2015-1-3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-01-03 00:00:00', offset='D')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1.index[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2015, 1, 3)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1.index[2].year, ts1.index[2].month, ts1.index[2].day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1.index[2].nanosecond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2015, 1, 3, 0, 0)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1.index[2].to_pydatetime()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ts2 = pd.Series(np.random.rand(2), \n",
    "                index=[datetime.datetime(2015, 1, 1), datetime.datetime(2015, 2, 1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01-01    0.323774\n",
       "2015-02-01    0.636933\n",
       "dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "periods = pd.PeriodIndex([pd.Period('2015-01'), pd.Period('2015-02'), pd.Period('2015-03')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ts3 = pd.Series(np.random.rand(3), periods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01    0.023390\n",
       "2015-02    0.998680\n",
       "2015-03    0.451616\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2015-01', '2015-02', '2015-03'], dtype='int64', freq='M')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts3.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01    0.323774\n",
       "2015-02    0.636933\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2.to_period('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2015-01', '2015-02', '2015-03', '2015-04', '2015-05', '2015-06',\n",
       "             '2015-07', '2015-08', '2015-09', '2015-10', '2015-11', '2015-12'],\n",
       "            dtype='int64', freq='M')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(\"2015-1-1\", periods=12, freq=\"M\").to_period()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temperature time series example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1388530986\t4.380000\r\n",
      "1388531586\t4.250000\r\n",
      "1388532187\t4.190000\r\n",
      "1388532787\t4.060000\r\n",
      "1388533388\t4.060000\r\n"
     ]
    }
   ],
   "source": [
    "!head -n 5 temperature_outdoor_2014.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('temperature_outdoor_2014.tsv', delimiter=\"\\t\", names=[\"time\", \"outdoor\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = pd.read_csv('temperature_indoor_2014.tsv', delimiter=\"\\t\", names=[\"time\", \"indoor\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>time</th>\n",
       "      <th>outdoor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1388530986</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1388531586</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1388532187</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1388532787</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1388533388</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time  outdoor\n",
       "0  1388530986     4.38\n",
       "1  1388531586     4.25\n",
       "2  1388532187     4.19\n",
       "3  1388532787     4.06\n",
       "4  1388533388     4.06"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>time</th>\n",
       "      <th>indoor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1388530986</td>\n",
       "      <td>21.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1388531586</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1388532187</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1388532787</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1388533388</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time  indoor\n",
       "0  1388530986   21.94\n",
       "1  1388531586   22.00\n",
       "2  1388532187   22.00\n",
       "3  1388532787   22.00\n",
       "4  1388533388   22.00"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1.time = (pd.to_datetime(df1.time.values, unit=\"s\")\n",
    "              .tz_localize('UTC').tz_convert('Europe/Stockholm'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1 = df1.set_index(\"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2.time = (pd.to_datetime(df2.time.values, unit=\"s\")\n",
    "              .tz_localize('UTC').tz_convert('Europe/Stockholm'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = df2.set_index(\"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>outdoor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:03:06+01:00</th>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:13:06+01:00</th>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:23:07+01:00</th>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:33:07+01:00</th>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:43:08+01:00</th>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           outdoor\n",
       "time                              \n",
       "2014-01-01 00:03:06+01:00     4.38\n",
       "2014-01-01 00:13:06+01:00     4.25\n",
       "2014-01-01 00:23:07+01:00     4.19\n",
       "2014-01-01 00:33:07+01:00     4.06\n",
       "2014-01-01 00:43:08+01:00     4.06"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2014-01-01 00:03:06+0100', tz='Europe/Stockholm')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1p2AQ05gPGh4vbTca9ap4Lun8INSLYnhZuYzuFYyNrOBG2ewiVb5H1hKFxchx\nf/JbVjQJCo5d7JxhGeSRDH/iIYUWDgkQFFyLqFiRcZFXOy//Fnt7haPRKMTwCX2pf1NWYcHiOR52\nBrDYOJBSbLP7hWFfTjU+Pl6CX41ufQA9wzAIDvVDcKifZG2y2zmUX61HSX41igtqcPxAHmqrmxAV\nGyjGcgmuhUEhfi5dC+12XoohvO3+EdI5cRwPVnFpXzxbiui4IKd4Q57n8fG/D2BgWhz6D42Bxcxh\n1VLZ4qtMOEMgVkBybcWJteDIOQFAdJyctezrD48BAE4fK8LpY0VY/NIcnDycL9WXA2TLb2lxrTsx\nUiiUbgpVsLoYPdnPtyefuzuoXGS6qiyaGi2oKm/EHb8chZ/25SLnQjl0OgbF+TWYelN/bD5Xjcom\nK+ziKvuqvfkAAKOvbDVRQuaWdz0yBv979wiqq5okNztAzlAHALc/OAq+fgYs3HgJ00mabpZFQnIY\nYnoFKxpVt61CVABYB2sXzwvuUDYrp3mc0r3J5G+QrGXps/thyMh4HN59WWgHQHOTs3tYdmYJsjNL\n8PvlN8JgFOpFgWHg62dAU5MV763cjYAgXyxYOBY8z+OD1/eh/KpgEbj57jRVdkQAiE8KxdZvz2Dq\nTf1hCnCONZNEwajfs+DBgUFmVBBumdEHa3fkAQA+Pi4koCioaVaLy2XLynbVLnqAXOhX2iL+7DZx\nn8I6CwrFvqzitdIvNQIbmnkM4XnYxG1E0SIug3aOlyxsjaL17eKZUlX/w4YNw5mDhyRlnhNHYWVZ\n6BlAp/jtn0xPxONfZWN8YjAGRqmTbrQFnY5FdFwQouOCkCaGTDWbbbhaKGQrzM4sxo4fssFzvCqW\nK6ZXsBSvZrPapYUJAssyqvpd1ZWN+OYjQckhirXdxuHTtw9KSv7Vwlrs/DFbOmZMem/0GxKN2IQQ\nKOndP1Iev/g/1StqkZEEMXoNF1MCiQcMCPJBfW0zfP0MUuIMp8WVTqar3mM7m64oF51Oh8bGRphM\nps4eynUNz/OorKyE0ej6maIFVbAoFAqlDdhtHN56UUgFntwnAtmZguUlO1OYnKf0jwTOVYMBcKVK\nHZ9BXMUc443I5DwxJRwJKWEoL6lDWIQ/eJ5H1k+F2PSV4O6VMiBSaB+AVceCEaf+PANExgbi/t9M\ncGpTK7apWZwkSt+JL7xKEXE+rqFOVjz0Bp2kcI1N7w2GZaTPP2aXI9jBFewXv50IHx893l25G68/\ntxUA8McsG5jCAAAgAElEQVTnZsoTaB6oqmhEVUUjivOrUVPVJClXgBCDQywjZAJbmFsFXz8DRk9K\ndhqrFjwjnBfLC4V8SwL8EBAR4LTfZceYJG+SXOhYybIpKQFEzuKrXfGb+IrXRK0Yr0Tm3xwHSUEn\nr5IFS6FcDIryx5nSBnkcUiyW2KX42Uriv1gGdh4wKn7fMJMBT0zshX/tysXqeQPgo+84JcDHV4/E\n1HDJfZPnedTVmKVU8Qd3XMLVolokpIQhKTUc506VIDHF2cKkVLAOKpKVnDtVgv5DY7Bny3lJudJi\n6k39NbcrL3tirVL+h4h1kvyXp8zph9PHimAKMCJfTIlPePwv06X3339+UrpXEKoqGuDrZ4CfRiIa\nCsWRqKgolJaWorras1hdSuvgeR7BwcEICHB+NriDKlhdjK5Ya+Fa0ZPP3R1ULjJdURb7tl0AeGDe\nL0aCYRnMuGUQxk9LlbKLBYb4ujyWYRj84okJThMq5aQuMMgXDfUWKXGEktsfEOI+hPgAf8lFkNOw\nNpE2tRSlZjFTnl20jFjFz1bl6rpGmzaFC51erxPc0hh5Uk/aK6qzIFCcAD/4+0nYu+UCouOEQvQL\nFo7Fnk3nUZRXjVeXbwEAVInWrhETEnH8QB4+WX0QADBqcjIqSuuRc77caSx+JgOaGq1I7huBffv3\nub1OVDJgAJbnJZntzWl5suJJ1JBSoZUyKxIlgHFQsBgGvJ5FmckHDaJSWi++ym6AcsJ2O08ULLXC\nBQAXRcvIfb8ej09WH5QsYZmZmQDkkgEhZitSZ/fHh6fLMcrGIcDBKjQ1JRT7cqrx/pEi/HpCLw/O\nuH1gGAZBIX4ICvFDvyFCrTGrxY7X/roFl7OFmmxNjerMjizLwNJsw+XsMtTVNCHrp0LcdOdQ7N92\nEbkXy9FvSDSO7snBzJ8Phk7PonffCBzbnwtfkwFmvgATJkx0Ox6Cu6yexPVSp2PxwG8nAgwk10Ni\nuVJCYiEtFjve/sdO9B8ag6Oi62tbyxO0ha54j+0KdEW5MAyD6OjolndsZ7qiLLoiXcs2TaFQKN2M\nw7uuIDI2EKkDhcx0Rh89QhXufAaDc8yTkuj4YLff+/oZUFJQIylXQaGu05zLFidnbUiKr3Jz1yeJ\nJ0ISQwHIiR8AIDI60Hl/RVY40q6fIjmFMvaIE1f/I2MCMe8XcpH5hN5huPfx8apYGeIKFxUbpOpv\n6ux+LpMZEPetyFjncTpC5sw8AIvZBgPHgySO81O4egX5CL9dhL/7hBvu+vgis1TKnmcWX3nJ0ihb\nkczje+NiWIB07o1WBwWL4yVLmF1yFRS+synkTNwHSUIGooyRS4JYcS6H+sMUG4QGox7NihgsJb+d\nmIA9V6pxoqjO6/NvTxxdAh0vb1bH4p1/7sKP6zKxZ/MFpI1NwOCR8eg3NAZBoX7Sf2fYmF4YMjIe\n/oE+SJ/dD2NES6vezX9U2ZVW6J3jNr2ehU7Pqlz/LM3O7rHEGma3cairMUvKFSBYZFcsycDH/z6A\nJhfp+68VlmYb6mo8z4xIoVAEqILVxejJqwI9+dzdQeUi09VkcfmcsKJ+j5saPO7qAJXWW7DhdJn7\nThjgtJgdb+6CYbh74VgAwLwHRiKv2oxZ7x6X5MLAnQVLHV+lxZBRvXAyKhihvUNRFOCrmnSnjUvA\nU8/PUu1v0UgCoTVZZYAWs8U99n/TpKLKRFkjq/y3PzgKTz4/S+Vu5whx2woI8mnxOsmqEFz+Aqx2\nVJUJ7nRWUaHRi+1EBxjRJJ5foMME35MkF0TO2xRuYsf35wp9EMVV4SJoEZOUSAqWReibKEwcLytm\nnGTBEvYpb3Cu1UWsosQ6OWKEkBDixnuG42RUMMpMPlI2Qoudl2KwlAT56vHH9ASs3J0nWda6IkpX\nVQCS5YthgLxLgvzn/WKk5rXf0rXibR0vZTzllDn9AAjunZ4w965hqs8lBTXIv1yJorwqnD5e6NU4\nWoujPF5/bqsqcU1Ppas9ezoTKgvPoAoWhULpknB2DmUlddLEtyshxEIV4KsPfgIgWK1aQmuetvlC\nJd46UKDaFtMrGEl9whWdCS93LxyLgWlx8BFTsJv8jahwcJWSY6e0LFjk1fm7UDEZhsnfiKsBvjAE\n+CIrKlgVa8IwjGYmQ0/RKtzqGnXGu8AgX7l+kwvlhuzrH+Cj+T1hc0o0XtiZ67SdWJmIgjMwyiQp\nIFYHq5lnhYYZxZkIzL5jKDgAOgOLnGCTFBhlZxipD9J/k3jdk+12XrZgWR1isEyi1S0m0Ig+4YKF\n02DUoSDQDzGJoVi5Oxc5Yvzf8r35uBrgq1LmrHYOOheKxNiEYIyMD8TbB6/NBN8Vj/3fNKd6VEp8\nFZZTkuCEAYOrhTUYmBYrWZi9xjv9Ciyr/I+Qg50vGMfLePFLczBweByeFBcxEnqHIS4xBJu/Po1P\n1xzCxnWn8Npft2haw1pLdUWjZC3TqsdFMhyGRbZfohMKpadwXSlYdjvX6eb0ttKTay305HN3R0+U\nC8fxeGXpZnzw+j68tnwLThwUsrp1FVl899lJZHwpJJpQ1svxFi1l4f7fTMBNd8or2XViqvfYRHV2\nMz9/ozR9c5QLz2gVrRJfNCaMMX0iUO4nx4ERC4mW25grGK2ZqDgMnmWlNt0hFcd1SMihzAgYGR0o\n1XLSOlanZz26TngA9RrZ4IhVjgxXx8h1p7zBz98AOwNY9TqpDtiA4bHYmhINjgfOhwdKLp1Nep3U\nL1F6KsU4NEH5EdwCyXc2kq5dYd0Sxsrgomidq26y4kxkEBiGwabzlfjhqFB8uVknn7PNLlvJtCxY\nhMfGxeN4UR0O5dV4LYf2IjDYF0EhovKoYSVVKgFkwYNhBMU5zuG/o6Sla6VFC5YnKfu9uHzIQkJ0\nfBAYBjA3yYsoVosduzaeA8/z2JVxDiuWZMDuopSAJ5D6eYBQnNtisankceZ4EQKDfd1m5OwpdJVn\nT1eAysIzrgsFy2IRHkSrlm7GWy9sp5XRKZRuzpkTRaq0x2dPFqEgp6oTRyRTVd6A81lChsCouCCP\nXYiUCsiIOCFOyKZhCtl2sRJv7ZetWsT9ydGCExLmnJqXWK54dzFYGuNNHBaLY7GhcjvisC5XmqHX\nqJulCePwCtmlrSHEddyYkrBIf9z/xAQ5yR5RuBQK1fS5A/D75TOdjiWuWZ78HjyALSnR2J8QgZS5\nA1Hpa4BfjJAhirjkEZ2KZRinors8z6Oy0YqKRisqG63YcUmdLY6MY1vvaPA6FsEOcXMVoksfb9Ch\nyUePRoNO6tcm+pMRhdRq52HUs+B4OfZKStduU++rVJJW7s6TzhUAbCYdLkUHg1PsQ2K3LHZOM7sk\nwWTU4U9TE/Hq3nwpu2Fn8eDvJmH4uESn7UZFAW2jGDtXmCskLKltZQzRkFHxUsHvuQuGYfy0lFa1\no417rYvVMaipEpRlUswYAE4ezsfKZzbhyO4rAIAv3j2MxnrvF5aVWRdnzRuMI7uv4PW/blUt+lRX\nNiI+KRTghX5Itk5AiM1qT2sahXK90e0VrKtFtXj9r1tV25QpWrsbPdm3tSefuzt6olwy1p+Czcph\n1rzBmPeLkSjMrcbn7xzCpEmTOnVcdhsnZQcEgIFpsa1qh0yI7RoK1tdZZdhwRo7LUk54sksbUKmI\ntyFTYnKNkD05TRdBkuSiZQWEHJ4S5ov7n5iAh59Kh53jcdADC4aqdbmCsWpC57pfBjHxwVIjkoKl\ndFVkGae6Yep9vfvPRMQF4WhcGPTBghJEEkcQuTOMs0sgD+Dxr7Lx2Jdnsf5UKf6+w9nlUHlOPqL7\nGmmmmShsDIM98eGAQokjBgmrXVamDCwDuyIRB1HGzIpCw4Cc/R2Q3QjJeUTHxuKSvzqjpU1RT6sl\na+Ww2EBMTQnBG/vz3e/YwUTGBmr+/krIYgRJ2sK6UbrdXStz7hiK3v2EMggD0+I0E8y4v6p5D/bR\nhmVZSXEi/52ho4Vsjsl9IxASbsL4aSkoyqvGv1/ajsYGz5UsjuNx4fRV6XNy3wjMuXMoAMAXQh88\nz+PimVKERphQkFOlSjnfWG/B689tlcordCYXTl/Fge0dP+fric9hV1BZeEa3V7A+enM/AOFmQFwE\nDu263JlDolAobSDngpCCe8EjYzFsTAJSB0YhPEqwLny59ug1HYvNapdccBrrLVi1TEi7fM9jQlIL\nvZ7FnPeO43+ZV122IaGsp0NiXzwJ5lHw+2/P49+KmC3HeaMyO55T925cBB03GcTZdnSgD0LCTAiN\n8Ed2WQOWbXZ9b9UqtKr0nvLuTNWKlSdWKW/2Ve5hsamtQVJSCdKuRns8D1SbbahttrcYosMAGDwi\nHg89KU9KgkVri1I+kgVLVLSI8mS18zDoWNh5HhzHQ8fIFizHOlgswyDMpFedo6R0a/wAVskS5t5F\nkPCr0XG4XNGEnZe6hjVZhcb5BYpKsyeLCu3Zr/QV7/jGc3Q6BvLh4u8kWozu+OUoPLJoCibN7Iu0\nsQkIDPbFnk3nPWp3/X+P4JVnN+G7z05I2/R6HYaMjMeoSUmSUndOrOOnzIYYGSNY3v/9klD3L8hN\nCYprQVlJHTZ8chz7tl7Ans2enT+Fcq3o1grW9u/OSu+PH8iDzcZh1KQkpA6IdHNU16Yn+7b25HN3\nR3eVy/msEhTlVblMq63F1aJarP+voET1SpZd1kh2uZwLFR5ZQtqLV5dvwYZPjuPDN/ZJk4r4pBDE\nikkhWJYBxwMXyho9bjM51FdKna0V2+M4n3ecm9n1rKTgEcg1IluwnPt1V2hYmoyLnRnEQH29cl83\nYn/gdxMxfnqqxgm0bgVfKoqra4WCxTJ4b+MBzP/4lMt9leMh1iSrIuEDICstDCOPh+DtJciyDMIi\nA6SOye/PQ7BOAbLljPQrK1gcDDoGHM/DzgvKFnEjJGOWXQTlk5OGKL4pLilxGpfagtWyjH30LP48\nLQn/PlDgnGClk1FaemV3WOGzO/fHtt5feQ+u7lZZsHSs9Ocn9zwSj6V0953588H42fxhOHW0AKeO\nqhPmVJU3IPNIPniOlxIG5VyokL4fM6U3AHlxJDYhBEf2XMGKJRn4/ouTQh+i7CJjA8HzPEoKZCt2\nbbUZlWVy8W8tyorlFP92O4fi/GrkX66UkmusWJLhVHDZU4pyqzBoRBz6DopGVbnn9+DW0F2fwx0B\nlYVndOtCw8cOyG4Z278XlK3wqABcym4h7TGFQulQ6mvN+PZTeYXUk8KZzWYbvvvsBOYuGIbYhBDV\nqrNygrT67zvw+F+mqVZW25vL58qkDIEkFfuMWwZi23dnMXx8ojRzI5nrvJlAsQwD4iFm8yB5gmMi\nDB4Q4iIAOBcEcj0ed1kEyf6OCp9KwXJj+cpqsGHryRyEOezWu38kDmc7FwVuCSmcywu3RnIUwzDI\nbdKpanS547W9gssbOXdSvNdql5NdOFqxWFbojYe2RfBX/zuD/5uW5LSdSLdSoZxYOSFizuKQsZDU\nprKKLoJNVkGx07OM7D7okOSCVWQjlIobk741LgoplquFJBdK+kf6Y+7ACKzak4fnZ6V4nca8IyHF\npqUheaBgeYXWH8txm0ZXcQkhTtlQWzJqKcdMFKxmF9d0QkoYAGDTV1nY9FUWwiL9USmWHwCAzV+f\nVu1/891pqK81IzouGEd2X5FcLhN6hzm1TYorBwT6oLbajDMnigAAE25IxYHtl5B/uVJYPNDgwPZL\n2Lf1AmbeNghbNpxx+t7oo4el2YbvPz+J7z8/iYV/moLgUOfYUlfY7Rx8fPVITA1H3qWKlg/wgJLC\nGvj6GTRjXCkUb+iSFixLs01a2TA3WbHu/SNCthzxgZd/pRInDwkBvHc9PEZ1bFRcEKorGlFW0rmF\nEVtLT/Zt7cnn7o7uKJeCK1WIEJMGMCyDI3uuuN2f53ls/joLSanhGJgWp/lwS5/dDz6+ejQ1WCQr\nV3tRW92ErGOF0n2HKFcEvYHFiAnChJllWWkCVyMVjm2Zs1eFCQ/LyBYMmycFchzjfxSfyTjINaIz\n6HA+LMBFmnaiYTl3QSboZo2kCcQNTTNLoMiuy9U4WuB8zx08Ih4He4V7VDdKNVaoFStP5seStYJh\n0Cs+3qv+ANmCZLFzYBlZ4eF53sm6o2MYTcWKUFjbjFMl9apxKTmcV6v6LCSxUMflma0cfPSslOSC\nxEmxjMK1z8HqpWMYVd0sQLbQRUQJacoDfXQIEOt62RTteJMx8t7h0ahstCLjvHNyj86C54Hb7hNq\nfRGhO15HWnTo/VX8Tec/PAb3Pj7eq0OV1w0pceAugdfil+Zg2s8GAIBKuUobm+C074BhsRg9ubeU\nIIwoc/6BPpKyRhbFiIs0qW9XXSFYiqJig9Crd6im4gQAezafx76tFwBAc58HfzcJv19+Ixa/NEfK\nUviff+1GaVGt076usNk46PQs9HoWNmvrsykSaqub8PFbB/Duit1O96zu+BzuKKgsPKNLWbDyLlXg\nf+8dUW178/lt0vtzmSX4cV2m6vuouCDVag3xEc69WCG9p1Ao14bKsnq8v0pwHxidnoy0sYnY9u0Z\n7Np4Dv2GRLtcnTx5KB+V5Q1uJyHjpqZg0PA4vP2Pnci/XAmrxQ6D0cMMdy6orW7CO//cpfndr5+e\njtV/3wEA+ONzcoFdlmUkZcXRWuCOalEZYxjZ8uBJDJbzHgpXKKdveOSE+CPVTaCVdkyR0GajuFot\np/0Gbll7El/cO8T9IBVNFte1vVQGEadkiPAoMQeZTHv2eziiLLrrq2dhtnEw6hjYedH1TgGrSHzh\namRaIyCK0BUxOxzBqGOc4vKE/lkpyYXFxkHPMmJWQx5GHSMpT0TR4nheSutORNAkFiwuqhWyUfrq\nWTRa5ZTwOgbiOXquYRl0LP40NQl//vEiRsQFICbQfe2xawLPIzZBSMfuaMFqLyub1m+a1CcCVy5o\nW2nJ/i1Z23+37EbnY3mFy6+HPqmjJycjdWAk3lu5B4//ZRoCgnzRUNeMk4e1E5MQ5UkpH8d7KlnY\n1htY8DyP2mrh2mVYBmPSe6PgShU4jldZ3JoaLTi0U4jXZHUMODuPlP6RuHyuDFNv6o8x6b1Vffxm\nyQ0ov1qPta/txdnMYkTFBXl0vnYbB71OULDstrZljzY3WZF7sQKJKWHIu1yJ9f89ijt/NbpLWWgp\n3YsuY8H6cV2mk3Il+fQr9nHEYNDh7kfH4fG/TMPil+ZIN7KdP2Z33GA7kJ7s29qTz90d3UkuRLny\n8dVjTHpv1SqsclVVSXVlI7Z+ewa33DNcs76NkpOnjkrxWKv/vh05F8pRWlSLFUsyUF3RiJqqJnB2\nz1YyVyzJcFKuSH0lg1EHfxeTRuU5kbfexOQwUBZ41YjBctzgoCxo9UWuEfKdloIh1wZynjCQ45oU\nE29AVsYWfJrl1mLT0oPEW3VHp1d35tEkRzGZLigs8npAxCXQaucEBUu0INk4XpKDVKPKk/gjjT7q\nRQvEQdGCVSomFPAR07ADihpXNg5GPSPGYLGSEsSywhiJdQuQLY82jgfHCxYxYiXNFv93Z0sFy0NV\nk026Pjaeq5AyEHqjYAFA7zA/zB8ahRW78jyqcdbR8ICzQkVcJ91coF7dXzXOM6lPOB78nXZ207jE\nEM3kL8qxAdpWTi082Y/EvJL7l/I+9ss/TJYKGQPaborVNdoJTBiGAXhI2QoZBkgdEAW9gYVNVG52\n/HAWK5Zk4K0XtkvHzb5dWJy5/cFRAJwVOEJEdABm3jYINZVNmt9rsW/rRRzceRk6PQtbG+qBXcou\nxZvPb8Omr7IQKSp3uRcrVG7u3ek53NFQWXhGp1uwtmw4jZOHnFdX7v/NBPiaDDi44xIWvzRHVX8B\nAGJ6BaOkoAY6PQuTXl0Eb+DwWFw6W9qh4+5uZB7Jx+avT+PXS6bDP6ALrDZSris+f+eQVKfqpvlD\nMXiE4KKlXNX8cu1PeOr5WVLcEiC4m+zbdgGDRsQhLMIfnhAZE4iouCCUKhJiAHLRzF7JoRiT3hs6\nPQv/QB/4mQwICFJnu6qqECad46en4uCOSxgxPhFTbuovTaTdWUAYjbgkb6aXHM9LE1KtOliOuNvD\nWRkTXzQOIv97rUlapRg8TyxYFg+VVEKZInW8qY0ryYCw4r85JRr3eDFp8i5eS2ZK7xDsvlKtcrvz\nNehgtnEwsAz0LCP9Xh/+JATjp4T5ARCu9y8ytZ81Wr/bT4VqN0piSfLRsajjBQunMkOgUceirtkO\no6j461gGBlawrvnoWMl9U8qEaOdh0DHQMfKYjQ7WE4bRLg/gSZILR+4YGoUDeTX45nQZbh8S5fXx\n7QrvXIqtNZZMd5BFCk9JTAlXWb+VeFW814vzIF7HykWJ2bcPQUR0ACKi1bFSWspOyhAf3DJ/nNN2\nwfLOobHegsAQX4WHECNd7D/tk+PiH/3zVLzzz13oMzAKv106Q9GO6+uMYRmczyqB1WpvcbGt/Krg\ngjv1pv7Q63UuF/A84dQROTmIf4ARi16cjcO7r+CnvTlO1jlH7HYO4IFVyzbjoafSPX6OUa5/OtWC\ntWJJhkq5WvTibNz/xAQ88ewNiOkVjJAwk+QHTFY/dDoGwaF+bquX9x8Sg4SU8I4dfAfREb6tNqtd\nCnK1dHKRSHdQv15turpcrBa7qgjwgKFybSjyMB02Rqit8srSzTh9rBAAkHuxHO+u3I2zJ4oxalKy\nR30RWfz8fiHWIjpeWG385R+EFWSDUYeCnCp8/dExrP/vUXzw+j6seXknrhbKma9WLMnAeyv3oFdy\nKCbP7AsAaKhvhsGgg07PCj79Lh7ug0bEIS4xRPrsWPzXHb1DBSVPKBgrbLMpXAwbLHbYON5ZAXIT\ng0VmkkQunJusfUYfHW69d7hKwSWUiG59xILlmP7bFSRb4fl2zuDl7QR53LQUDB8nxJqwDIO4+Di3\n+xPlg1UoG0RJabbJLoI6loFBx0iJL8jv5SMmBfB1U4+JZJcTElhwaLDY8drePNU+RM56RQCUTeEi\n6CtaqUjafJYRxmO22uGjlxUss012FdSzDHSsbCV1dm9koJVbRe+lYiq0zWDxlCR8erwEedWtK+bb\nXvDgFZlc1N8N04hDIri7vzZY7Jj17nHpc0JKGB5ZPMXtODxNvjN5Vj88vChd+KAhep7X3OwBzj/u\n0NG9JPdJdR/O+06bnq4Or1BkLKwVXVsf/dNUBInFw5X3K6KwRccFweRvlI7zFevAAdrlHAipAwUl\n/dPVB13uQ/jgjX2IigvCmPTeiO4VhLoas+S+6DWKc9DrdWAYBmOn9IbBqJPqhWldJzkXyrFq6Wap\nfAcpQH+909XnJF2FTrdgAcAji6dIQe0x8cGa+6T0j8Tvl98IVseCAfDJmhb+gF3AZaGjOXOiCGeO\nF+HOX412ux9JgRoW4Q+7B1nLKBRv2LhecN19eFE6dDpWVQSUPHwnzeyLsyeLYbXYsXH9KWxcL6fQ\njksMQVSsOl7ybGkDLDYOaXHacZTk733f4+MlhWHxS3PAczxWPrsJADBkVDyyfipEdHwQPnrrAB56\ncrLkwggAM24ZJL0PDPasnsvP5g+T3u+PD8Pt/SKBM5Wat5v0WX2xZ/MF6fOEpGBcqTIL6bbFyS9x\nByutt+IXX5zGvMHOJSa0sggSSBA/sTiRz1r/coZh0G9IjOZ5xQYKkyFiwSIT95ZixLQKGmdFOsdP\nFNU2o19kICrrPYvPIr36mjxb5U+f1Q/VlYKSxyhSlbfUPs/LCUesnKykCC6CdgT56GBgGUmBIZYk\ncnx67xBsuaCd6EH5s/38g0xNa6WfONnUMWQxgpdirsw2DuEmg/BZJ+8nxF7x8NEzqDY7/F52ISGH\nnpVjusob1O6MAyJNyCyuh0EnxHINifFHVkkDjPrWTefjg33w4KhY/GtXLl69pZ/XrobtBc8J9xsf\nX72TlcRobN1UJ7NYsJLYOSHLIsMw0lylpK4Z58saMSVFLifxwO8mIjLas9hvvZ5FQKBw33GVQIZh\nGfB2Hh77EKL9pz5aPSvla7XYkXmkAKERJikJx4C0WFkxc7ge3CXLIVb2lpKU2ax28BwvWbmMRiGT\nYO7FCqkYsyd89NZ+pA6IUsW4ZWcWY+TEJDAMgz6DovDdZyfA82kIDvXDJ6LiN3hkPBJ6hyLjyyz1\nuNwkIaH0PDo9BuvJ52d5nA7T6KOHXlxhnveLkfjVH11o0d04KNFT39bzWSX48X+ZUlFWd5CbgNVq\nl+podEWoX682rZVLQ32zlGK3o/jPil04n3UVcxcMQ2i4v7SqSZCywLEMfrd0BuYuGKb6/v4nJuDe\nx8c7TYie3ngRf/rxolN/Ur0nYoFwdH9iGSz801Q8+uepSJ/VDxNuSMX8h4RMo0S5uuuRMRg/PRWR\nCqWOFCT1hnofg3R+PIBVe/JQ1iArECMnJeOex8bJdZXEZzjHK7MIqq0MxXXNThMQxwmTVt2dr7YL\nD34iRm/dvSRLiIMFy6pwFdRSEOwO/Tz07AwUBTrLkuMBy5A4nIhxXkV3hzfufmQo+3NrUFTUQgyW\n4hhyilKadDsPX4Mc82TQCcoWAMQFCRNAkj3SEzmzDOMkuwiTsKKfHCrISkgFL3xn43j4iBY0H1Hp\nIdYlhmEkZcuoYyXLWrPCgqUT3RotDvFZcb52VVt6lkFMoBGBotubwV2gUgvcPDAC/kYdPj/pQcHt\nDoLjODAMo0oY4Ymy4e7+ShLTmG0cyhosqsWOD34qxgvbc1T7R8UGeeei6ujTqMDHR9e69PLexINq\nXL9O8vBgCDt/zMbXHx4DICx0jUnv7VRqwVNI/cOiPNfFrE8cykNYpL+qHmBYhD/2brng8hgtrhbW\nYv+2i7icXYa5C4bh5/ePwB2/lBes+wyMBgB8//lJSbkCgNPHCp2Uq1GTknDViwyI3Rk6V/OMTlWw\nnnphdqtr2QQG+yI8Srv2AgDU1ZivaUHSa4nNxqmCL1siOj4IaWMTUFdjxs4fs6VU1JXlrfdZpnR9\nVtGP7aAAACAASURBVL+0A/u2OSsp3lJ+tR4nDuY5bbdZ7aipbEL6rL4YmKbtkiUXt2XB6lgMGCa7\nD958d5pLi3Vb/rnBoX4ICvGDf6APJt3YV+We8siiKUhMCZdcAwFg7l3DMGSU92m9lXA8j43nKlSp\nyg0GHeKTQqUJLlFeiAXLoJMtI8pJiHOhYQcLloZwyF2UTJ69va02O0zGmxXKBmHzeec6M44WLNky\n1LZ7LznaFGCU3EBbgsjw7cNF0vF2jkd5g7PVjDwaTAadnKpcPPfcarPk+qcXXQSJGBrEFWqSKVGp\n7OZUNaHJakemWFiViEDLojMsVnh25VaZVeMBRAVPdP8j8VPKJkhhYh89K425WZFanrgIEojbZ4hB\nVup1rKDG2+w8fESrnNGbPO0OMAyDRVMS8c3pMlzs4IKvWtz18BjcNH+Yxjdtuw6jA4R7x4HcGtz3\n2WnsvlItfdce2eVc6VcPL0rH0DEJUh8jJyTh5rvTPGrTk8LH0r4e/E+Jktd3UHSL+97/xAT5g3hS\nTpd/C2Kb98BIAMCnaw653Gfnj+cQ7LAw33dwFBrqmt2Gj7ij76Bo9BkUDR9f2dqZkBKGxS/NwcOL\n0hHb24A/PjfT6X70h7/OBCAUaS4tbl15IEuzDcX51V16AZziPZ2qYLVb8T8HSotqUVpch1dEV6Hu\nhCe+redEl78JN6Qipb/gUqRUlmw2Du+v2gPOzsFqtSMswh/xSaFI7huuqsL+/it7kPVTgcdZ1zqa\njvTr7Srn2BpaIxdyoz6y+4rkOtUaeI7H2tf2Yuu3Z7BiSYaqWObn/zkMABg3LdXV4VLSCFYnr8AL\nr1ApW440uahpQmTh7QR+3gMjcfej4xAS7mwtHzg8TqWEtQbijqV1R5MULBtJgS67gclp0UksFpBd\n6n7hQ+vUh6YJky+ldcIbzosB4pKCpZi4EzZp1Dxq0jvEqvGqlzaj0+vwiycmerSvcsIbFyco/J+d\nvIp7PzvtMsudMuGI8przUSpYRKHRMYqkFMI2pcf1o19mY83BQuzPFe6x7u44RKkjbnnCOITvmm2c\n1D+JvSKWMgaQLFg+elZy4dx7pRocLxYnZtXFiIliHxcjxLeQmlc6VihKbBLjZoxu4sk8IdLfiMfG\nxeOfu3K9TpLSVhJTwzWTC3hym9C6v54vb8Q7hwolC6HJKMjmqqIEQbtOnhyUtdBwf7AsI82RTAFG\n4X7pSRbBNv75lPK479fjERElWPojPCh7o1wwa60Fy8fXIGWKLVbMVxzpN0St8CX1iYApwIiqigY0\nNVpw6qcCbPjkOGqqtOOySLbFB347EYtfmuMy7hYQfo/7Fs6A3qBDvVju4J7HxiE2IRgGow4L/zQF\nfQZFo6Gu2SslibNzKCuuwzv/3IVPVh/Em89va/fELB0BjcHyjE53EewIbIosVt3hYvUWlmXQf2gM\nYnoJNzObjcP7r+zBpq+yYLXa8eqyzagsa8ArSzfjteVbcPZkMXIulCNc9A1P7hchWf8yvszCK0s3\nd9q5XAtsNg6vLN3c7SyaNqsdzWbvV7SqyhtU9ePeXbEbWzac9rqdFUsypHimG24WCli+tnwLtn93\nFs1mq0pZdwlZxXSY8OvaOJnzdgafOiBKcj3pCMh8UmsuQSbuJCud1c7DzgsT5RBxtVR5m3IKk/Tg\nXIliJFlNvJzUnBFTeDu6CDa3sBp8JjIIpwbIijJZPff2trvjUhWOFzmv/rZmmq4sskxqP2nBgCQc\nEQbbaLUjzKQHAzl5hUEnu+T5GnRoFC1YREFyvKdsPFeBy2Ka6VrRvcyqoWwQBSRCTATA8RBjrQSl\nW6ngAQorGCNbmnwU/6H8Gvk89SwDPQs0icp7k/g8tCrjtKR6WhxM4sTS0A4LnjP6hCI+yAcfidkW\nuysZ2RVYf6pU+p2ixN9JGZPYLpEICsVZi7RxCd5b19txzqNMjOHufPsOdrZuSaNohZxIko1P/n0A\n3312AiuWZGDd+0IZH7LIN3i4s9dEVGwgTh0twFsvbMemL7Nw4fRV/Odfu1T71NcKVuPjhwSvDK1F\nN3dYmoX/dXxSKO77tWCxCw41QS/+H71xU3xl6WZ88MY+mJuskiWMZsC+frguFSzlQ6+hzvUDtivi\niW+r1WqX0sXW1ZilG8ipowV4/a9bNI9hdQymzemPJ/82C3f+crRT/Nonqw/gyw9+alMtibbSEX69\n9bVmvCpm+OE5HjzHSy6SXZUr58uwYkkGXl2+BW/8bRt2bN/j1fGHd19x2qZVCgEAms1WfLrmIEqL\n1b7jlWX10vvkfhEYOTFZeogeO5CLwtxqxCWG4I/PzXQ7FqJYORa31TtaPjxEisFq1dEdB7GCuLNg\nNds4+BqEukocKR4ralMkJktrEuPo8qPlAnQ8M0v1nXLCraTJRUzeuMQgp7EqX11hZxlYFSu/Uh0u\nh/0i/N1bCP++Iwev7Fa4oZJ2vJgsKksfkRgsnnet8Bl1QipzOy/EPDVaBWWDB+AvWnX0LCtZkXz1\nLBpE+RHrkNboThQJ/50DoiXLrCFDcrzVxsFkEGpW2TgefnoWjVa77KLo4CIoWLDk8RDig+TSG0KS\nCxZNNg4M5FTwFRXlUt+CgiUodSaDHNPVVhiGwe8nJ2DLhUqcvlrf8gEdjCfXj9Zzh2TjJNc/Uahj\nAr1Ire4BjNMbNVNm98ecO4Z62ag3cYstx2BJ2TDdtavltswwCAz2dTrOW33r3CkhM1/uxQpkZxYr\nChg7X69VFY1SqvhHFk3B7NuHqGLisk8WY83LO2G3cdj+3VkAnqfed4z/1aLPoCgp62BLZInZdAFg\n+PhEKQPjNx8LWSvraswoKfRgEbMToDFYnnF9Klg2hV+8B0kguhtWi1AjoqayEWUldSolkvz3g0LV\ngeYmfyMYlnFpOSjOr8GVc2V4ddlmTaWU53l8//kJ5F1yjsPoyuxXxCCtWrYZ/3tfLmZ9YPtFrFiS\ngZqqax8z4AqO47Hhk+MwGHVgWAbJ/SJwbHsDODsHnuel1TdXZB0rxKmjBZKLhZ+/PCEgK29KDu64\njKK8aukhBghuCyQhxOKX5uBOMej3tvtG4O5HxyEgyAeVZQ0Ii/R361YByNejY+B3Wy1Y/LW0Rrqb\nVygSVwBAoYbFRKms+IqFa8mk3tHypN2Jdp9KiHcbGYer9OG3fZCJowXOgdhfZ5U5jRUAzIp76bQU\nFwkqNMbj6JI30kU2SHUzzg158zOzGkka3CmIBh0rpcz31bNostpla45Odt2TFBoD26IFa1CU7KZ2\nVcyY2DdCXiEflyAoskSxFupWsVJ6daOeRZNVTm5hcFigYBjZRZD8xiwjZPIbHid4JZDU8lY7Dz+D\nnKCDWEatYuFkwYLFw4+ccyuzCDoS6mfAbycl4F+78lwq9NcKmwtXY1fUN9vQZLVL/xFHBaui0Yoa\nsw0WG+cye2RraM9giajYwBYzCxOS+0bgrofHtLlPLRdchmXw2P9Nc97Zi5NVPr8AIdnEwR2XXO4/\ncUYfRMQE4A9/nYmQcBMGpMWC53hUVzYKc5gvTgIA3nxB8PBwF8fvEjf3pMkz+6KhrrnFdPH1tWZk\niJl0p8zpjxtvHaT6vqGuGW//Yyc+fusAVizJaHVcGaVzafUs55133sFzzz2H5cuX4+pVQWPPzMzE\nsmXLsGzZMmRlZbXQQscRmxgMPzFLk2Oml65OS76tK5ZkYMcP2Th2IBdNjYL7WGKqUPNr+lzBjcsU\nYMTseYPVB2rc1H71x8l48m9yEUSSVW3133dI22xWu+Aq9swmZGeW4H/vHcFHb+1XxeK0Fx3h11uY\nW41b7x0ufc6/LD8U920VlK///Gt3u/fbGjKP5OOVZzfBZuXwh7/OxB+W34gps/qB5wRXgmP7c7Hm\n5Z0AgM1fZ6H8qtqlqqaqSbppExcLlmWkWnIN9erJ/+Hdl3FkzxXEJ4XA3Ci7Iq57Xyjee9+vxzuN\nMSjEF/W1zdj5Y7ZU58Qdrlb7Wutec619v596YTaeen62y+/JIiqZEH92wnn10qZIIOFn0MFi48Dx\nwjFmhxgsLZyzCCrfC5+SUvup+krv7TpbX0WjFbPePY7dV5yzdJkVsVcso1ZQXA1RuZlzsBgRNzlv\nf2/Z1dBzDcvgIygKdgCxsYLb4t6cGqcxSvsrLFh+BtGCZSTp0IUBC7FywjY/PYsGi+xmBwhKi9K1\nLi0uwEn2SmXXxvFihj9ZzgYdA4tNsCoZdYIS56snFjSHLIJgJBdB0i7HCy6HfopjyP4mg052UdUH\niGMXYrB89Sx4yKnijW3IIujI5OQQDIoy4b0jnmVz7ChS+kciPCbAbT035T3l/s9PY9nmy4gSiwBX\nNamLP797uAjzPz6Ftw4UeKX8u0QOVGqHxkhTDJL7Rni0L8sy0vyB4HiP7ZUciqQ+4W6H6OmC1813\np6GPB8kyAODxv0zDI4vS8ce/zcLil+aolDUSHuHI4BHx+OXvJ0vWIJLG/d0Vu7HyGTkmn6STv+We\n4c6NuECK/3WzD1lw3PFDtst9eJ6XnuMPL0rHqIlJTvso52CAsDjcUur6awmNwfKMVt9RH330USxf\nvhzz58/Ht99+C57nsW7dOjz77LN49tlnsW7duk6LfxqYFocnnpUrh3eEMtAZKK1xD/xuorSqMXC4\nMJkYNSkZw8cn4rb7RiAyVlgpffTPUwFo154IjwpQWRImzejjtI+WP/HVwlppIt9WrBa7UAm9AwkV\ng589zUbWXnB2zuM06ZZmm1QMmtQq0ht0iIoLkoJ5yU17xZIMZB4pwNrX9qGhrlmyOBJX0d79nWsp\n+fkbUSPGhpibrCjOr8bujPMAgOj4YJw8LLsQVpTWI6lPuGZhSuWKYmgHV6yfnuo6Zupa3VqUgeZK\npKQWDMnA5vpWSuorma0c/Awsmu08dKIloslGMgsK+2qlQk9ICUNEtLzSyqs1LACy65+WO5ojpIsX\ntuVI24bFBKiOt9jkTHbyeWgLXWl5ImMjW6qbPC9srjwv8rbZzuPg/7P33YFxlNf2Z2a2q8uyZNkq\ntnHv3djIhWabEnoPIYSYmoQ8EtIggRAIeS8/QxLICyQxDwgBQkhCgNBMMdgyGBvjgnu3ZVWrl5V2\nZ2fm98c395tvdmdXq2Jbxjr/SLs7vd7znXvPPZxcmozbrWDd4CzoshQTBFmpguzvsCwfPKaCFRFc\n+0jBonMe0Q1OaBgJM1MEdcsR0laXA4sMTRjE7g8xuKfldUR0s4GxDo/CCJdbZuRJMyzCTg2IRVdA\nInw+oWFrOGLYXADdwjYTwTpgOhaqGlOwiEymk017D1wEnXDHnAJ8cqgJn5efGOvq8qYQ5i0ehRcD\nKXgxSfv4oKqjujXMUy5dwnUgIrq3WHcRz0WwLyEjK4Arb5qZMEUw2VhvzKR8Tno6Q2q6D16fm9c2\nib0KRXv2rmLqnCL+v/hcTRaJ9jUzO4DFl01AdUUzdm+tciy5eO73HwNgsVnWgBRbDFY4PBtZpuKd\nk5eK08ZY7/JnH1uDFa9sxaG9xyeLSNd0x6yXfiSPHg9Z+Xw+uFwuVFZWIj8/Hx6PBx6PB3l5eaiq\nOrFdrWnkfuuG8k6m7Dtwym0NtoWx/JFV+MfTTFm4++ElyM1Pt25e4X4/56JxGFKchUCKB3c/vMTq\nS5TEE1xsQtza3IFl97yNz0oP8nXe/fASfO8hNpq/64uqbtcxqWEN//foaiy752387ufv4jc/W4Ha\n6tZjkterhiN8NGva3GK+H4SbvjcPAJIzbOgiHv3ZCvz2/nfRHozfXDWissa7H3/A1LTvP7QYX7nW\nbsebVdiKO+8/x2l2PPGrlXjiVyvxyQcsbeLyG6fj8q9Pj5muvS2MFa8wNff3D75v6+lRbI5g0vkM\nhyM495LxMcsAYHs5Jiq+rmkN46n1Fb1uLJJMHvzxACkQpNhoCbaHxg86bLbbTGUQrdsBYPfR2HTV\nxZdNwI3ftUYMRUJDr++de1ndHbn+JTo+ZY2xaaYUXIvugX63YiNYFGQmso03+HdmjZFuJ5DJ4qsv\nssGG1Qcacd+K/UnP1+jzwADw2nZ7ajitnsiQblhKD1MW2XkJCI1/AUtxAgCfyzK5IHe5tqjGokfb\nVG5qUWcG4eK+U4+rUESH3/zrkmVur+6OMrBwRytYUmwN1oyCNIQ1nZNC2jcA8LsVtEc0eBUJAz06\nclPdUE2b9j21bMDltAHsHdFTF8FopHpduGteER5ZdRitxzlQe2NnLb7x8nYcNq/1jw82xp02+r3j\nMg1AgNh+dVdPYk6MyfqB7Khpwzdf3t6lbT/RiPseTiJV+ljj1h8txPlXTepWe5/x04bgth8vxKSZ\nhQCA08+M737rBH5cOtnXIcWZaG5ox2svbMJv71thc/F979XtqKlsgcslx/SMBICrl87CN783HwCw\n8PwxMan0W9Yfwcv/t77X3n+6pqO5sR0tTdY7of5oK37383fx6M9W4LEH3sMff/0hNqw5aCOL/TVY\nyaF77c0FrFy5Eueffz5aW1sRCATwzDPPAAACgQBaWlp4qsaJxPuvbcfU04s6n7APQtcN/OGXHzj+\nNqNkKHLz0+D1dW4xneh9MGrCIOzeWmVLARBrckTIsgSf392jfg2/E4w4Lrl+Kv79143HZKSk7EA9\nmhs74HYr+PqdZ2DAQEtxmX5GMQYVZCBk7sfqFbtx9lfGYvWKPTj3kvFJpb/FQ9WRJvz1D5/wz0//\nphS3/eRMRyVk+6YKbBOKXZ2aVEqSBI/XBY/XhXAowgmiSHDXvMeUxmGjYtUrAJi3eBRWv7Mbb/1j\nC//ugqsmYfiYgbbrh5aZkRW/8e6dPz8HEqSEI5of7mvAS5urcdEFsapoZ6CXR6Jr9nj3Ej/Y0I6m\n9ggmm7VElCJ2wFQFExEIUjBYMA1zehZQG1HTXDYhF884OLC9sbMWi0cxIkyBMWD1pgoTiSPClmBf\nnPpCUQDZobL0sbqgCr9bRl3Q2jEiDh0RHbVtKgozfTHLiTaVoFS3RCmQiSA68K093ITh2X6evpUM\nhqR7WV0c1R8J6Zo+l8x7ke0wXRSJZPAUQc3g9+2euiBq21TWf0qzpwoS3t1Tj1lmnVWGz4Wa1rCN\nfDMFi/Wv8rsVs5kwGVpInIATeaLPpGDVB1VBUWPHlnqvDctm9yyrGzPTGt0yKlt0eFwyVF1DhltB\nQzAC2WsNlNDx7A0XwWjMKEjH7KIM/GFtOX64IDYVqrcRjuioaQvj/8zURCLAXSH4iizx50u0spxh\ntnOINu2Jh/f31sc1nAFgPciSPPROmSjHC4nWPWJs7jFruyMiLcOHcQ7ugYlw7sXj0N6uYtqcYni8\nLqSm+2yDrF1FZ5dS9kCmip1z8Tjs3V6DzevKMGV2EcoPNWDTp4eRlunDLT9YkHAZtH35hRlYcN5o\npGX40R4M44mHWergn5et6nQZwbYw3nt1O3ZvNWM5CbjzvnPg8bqwYc3BmDTGybMLce7F41H67h6o\nYQ3FIwZg7tkj8NlqNu3KN3b26LjFg6pqcCly1xp0nyToEcH67LPPMHjwYAwZMgQVFRUIBoNYunQp\nDMPA8uXLkZ6eOCWrtLSU53ISI+7tz4OLMlFxuPGYLf9Yf971qWkTW+hCfVUEA3LTY6Y3DANTzwwk\nPJ5lR8pQWlrj+Ht+YQZ2b63Crt3WDSfefLf9eKFt+m9+fx6e/f2HaG2KHdHobH/mzmXmCxNL/EhJ\nVzBiXB4GFWRg8+bNSMuyXvrvrVgFj0/C/Pnzun381r7JXKzcHgW7Nm8G9lq/uzPqUNdSh+GjWZ3R\nob113Nhhz7ZqFI/1ILfQjQUL2frff5dtz7x58bfn0I4QKg9YxDM1S0ZrA5PZ331rFVIyFD79a//8\nELs3sFGj0xcOx9oP92P4RMsJTFx+SUkJSktL4U81EA5Zv0+eH8DI08aheMQAPHLvOxiQ74qZn9Bh\nMBK37fMKzFs8Cm2RI6hr3Y+xPvaymnZWAJ9/YI20rVmzJu7x9XhcnR7/AwcPAvBgYH4a2s8ejdWr\nSyFJ1u+hUDju9UpBTc3RowCGOi5/x+5NGDPTCvCP9f34k9e3oy4sY8XSqQCAt1avA+DnAfbu6iYA\nMi6fMDBmfgrOWkMqFJkFwZoBtDY3gh7B6z/fBMBvIyLi8fldaRnaynYD8NuW/85uNmgQ1u0KVHl5\nBUpLD5rH08Cz76zFyFQNQIrNBv7dPXUIuBXUNzYBYAG/S5bQEQrBrRkArHuytoFN89LmarywqRr3\njWkDkGILXunfjz/5BD4FKBjP1NTqmqPmel2OxxcAQqHYQJSOb2lpKX6xMwVzizPw83OHxz1fQIpj\nGLjm44/hloHx01lqUWt7CHDpcMsedKgaRqREsLfNhYHmwMqhQwcAeFHbFsaIHHbMKS3M55LR0NwK\nCVJCh8CaxlZ4XS5biiA1mG4MqshwGwgZXk6wtHAIHoU9A45WVQDwcKJVXnYYgAceRebOgkcO7AFg\n3QO1NdUA3NANoKGuFoCL12D5ZQ1hQ0KG14VDegfCoQ6cNTCCD456oMgS7hvThk8+XnNM7p+bZw3G\njS9swvJgBZaeN6fXly9+3u0djr9trkaqogOQsaWSvQPC7a1xnzf0fGVIgUuWUF1VBcDNz92mrTsB\nePnn+vo6FPslHGpXEm5Pq1qQ8Hd6HyZ63oqf6Uo6ls878XiIv4c72LXucssxv1fVHUDuadazoq/E\nTyUlJZg8uwilpaVYt76yx8sDgKLh2aivb0gYb42Z6UNL6BBmlIzFP57+DOsFZ9+rl85K+nyXlJTA\n63Pzz3c/vATL7nkbzQ3tWL16NebNm4emhiC2bN0ASZJQUlKCw/vq8PenLDMvDgN47IH3Yr7OHpiC\n5sY2bP60DNPmFMMwgJFTfRiQH8KQ4iwMKc7CO29+hC9K7cYdvRG/n3HGGfjd/e9i0FA3ho7zOv7+\n0du7UFtXgUFDPX3ienL6HA+S0U2tcf/+/SgtLcUNN9wAANB1Hffffz9+9rOfwTAMPPTQQ3jwwQfj\nzv/+++9j2rRp3Vl1l1BX04rn/veTuHbS7cEwGuuCjvUmJxqGbvA+RD0ZOVh2z9soOXdkXEncMAzA\nYC//3VursG9HDVewbv3RQlvuMyHUoeLJ//4Q3/35uQh1qHj8F+/jrgcXJZTuqyuaef6xuD+iEjNu\nymDkF2bg/dd3YMa8ofD73RgxLg9HDtTj3VdZqsXI8XmdqkztwTD+9yGm/P3XA+cmdLt746XN2LGZ\nqQbDxwzE/p1H+W/nXjwOba1h7kaY6DxEp0ze/IP5NgMNmrejXbX1qbrprhI+6pUIHe0q1LDmeD7i\nbU9quhe3/fhM2/Z9/6HFcUeLlt3zNq68aQaKRyRXJB0P9769D+uPNGPF0qlYtHwj/nPjZK4ORG9X\nNMKajguf3owFwzJx79nDerQdvYWbXt6OI00hTrD21QVx+yu7cPf8IixbdRgT8lKwtboNl4wfiDvm\nFPD5nvmsAkMyvPhdaRkiuoHZhRlYV9YEzQDmFGXgk8NNUCTg4fNG4Edv7sVXpw7C8xvZvffitRPw\no7f2YvkVY7Fo+UY8dtEo3Pkaq5uj7Vi0nFn6njsyG+/uqcc1k/Pwt83VuHjcQHxrLtuOX35wAB/t\nb+TnYlZhOtaVNeOqSbn4+xbWc2X0wAD21AZRYF5blS0hjB2Ygi1VltX26IEB7DoaxAVjBuCNnXV8\neSIuHJOD/+ysxT+/NhFpXhf21AbxrX/vwoLhmdB0A6UHm/i2i1i0fCNyAm68cN0E235dNyUPL2yq\ntq3LaX5xObIUq1i8duNkXPTMZjxw7nDc/+5++N0yCjN8qAuqqAuquHjcQLy6/Si+M7cAj398BN8t\nKcTvSlld4pmnZWHlvgZcOzkPL26uRl4qIyVNHRGkehRUt4Z52h8ATByUii+qWjEg4IZuGMjyu7C/\nng2mFGf54JIlHKxvx6iBAZQ3hTBqYACfHWlBYYYX+elerCtr5vv97bkF+P3HR/CNGfl4+rNKeBUJ\nV07Kw183VuH6qYPw141WpgEd+4IML8blpmDFnnqUDM1E6cFGDE73oKE9gkmDUvFpWTOKM324ftog\n/PKDgwmPZ29hW1UrHnz/AJ68bAwye9jUOxF+V3oYb+y0alR+d9EofPe13ZgyOBW/Pn9kp/PTNfaV\nsTl4fUctrpyYi5e/qMGN0/PxzIZKfH16Pp7dUImiTB/SfQq2VrUlPH7L15Xj71tq4k5jGAae+/3H\nuOE7ZyS1f888VoraqtZjoiR0hraWEJ741cqYdS+7521c/605tibDX2ZQvJSs4vJZ6QGkpvtQdaQJ\nYyblxzXoSBb0Hi8YmoULrp6MP/7Ph/y3a2+djRf/+Cn/fNuPFyI13edYzpE3JB3V5c248btnIDsn\nxdYP9ZKvTcOIsbn8c3swjKceWY1v/+zsmOX0xr4AzrHVU4+sQkNdMO7vfQWff/45zj479th0O+n6\n0Ucfxb59+/DAAw/g6aefhizLuOKKK/Dggw/ioYcewpVXXtmjDe4tZOWkIKJqthxTAKg43IgvNrCG\ndM8/sRYNtW0naAvtIGbcHgxzcnXHPWf1aJnX3zEH00uGxv1dkiRIsgSXS8a4KYMxc54V1MYL5hVF\nhhrW0FDbhmYzzz2RJe7h/XWcXCXC9k0VeN/sT/HZ6oNYvWIPnv5tKSdXAFOY/vDLD2y5zdEglx7a\nv0Q4bYz1IJk1bxhyB1vK67uvbrdZvX/x2RHH9EhK0ROhmyPZ5GipRXRUVzRzcnXx9VNx98NLOiVX\ndE34/O6kyZUTrrttNoaOzEn4Yrjj3rN6RK4O1LdjX10Q602bYyOJGqVo0ChxTMNdHP/c77o2FWEH\nlYJUKTIQINkk2q3shU3VaO5gjWN1g6UfUSqNS6i3oXVQzZrXJeNAQzuvIRGXPSHPSnXNN/vylFcx\nokRBvlinVdXCUgJ317L7xW8SXer1BDDVhUhCulfhFt8iKF1vl0OdGOE/O1ntk2GwYHVHTRv/uTL+\nVgAAIABJREFU3B2EnS6CboCuw+1mb6aQqdRRrVKamTKX4WcKW8AtY9kFI/HwktN4PRYpTT43SxEk\n04s0r4IBAYs0fGGSUpfpCiimEWq6Aa8iQzPAjTW85qCUJFkOgUQSeD8sob6KMDSbPQvoeqCxrSNN\nIX5tlZq1Rx5FRkjVuDGGLDGnv0cv7Jx09AbGD0rF2SOy8diasmNaP9kcYimBvJbOPG75ad648zjV\nYG2rZtctPbfIYJHu+8ONHQlzxf575UE8ufYIv+5DEZ2fCxGSJCVNrgBg+tyhmDL72JY7xHvGpqR5\n8a2fOsciyRpXnMyg40LxUrKYUTIMYyblY+H5Y3pMrkQcOdiA1St2274TydWiS8cjNd0eL1xz8ywA\nzD2RnIYH5KZCVmR8VxAhGuvs8bCiyL1eg7VlvXNvToJhGGioC2LyrMIer+tEodspgr///e9jvps8\neTImT57sMPWJAwUz2z4vtyk4Lzy51jbdR2/twiVfO/aKWrIg9QVgtus9QVdv6tzB6Zi/ZBRmlsRX\nD0ipeupRqwluJKLBG+eSKl3BCMics07jhgwiLrh6Eo427cP4MVOxd0cNVr+zG5feMA2SJKHsQD3q\nj7ZhyeUToIY1eH1uPP6L97BlXRnmLxntuD4tomPqnCJMP2Nopz2XxkzOx+hJg1B+sAEFw7Jxw7fn\n4tOP9mP1O/aH1+yFw/HOv7binX9txfhpg3HeFZMA2EdhZFnC6WeehmGjB8JnBl1XLZ2FZx9bg707\navD6i5sAANNLhmJkkna13cWoCYOQOcCqpRpclNVpf5Se1J4BwLf/vQuqbmDB8Ex8tL8R+80apc5i\nqrLGDl7TQ0Fkd+t2ehPXvrgVXxmbE5N2RoFWMKrxrBORpJ5XLSENLtka1aKgOajqvH+W2LA4ep1E\nNiblW4R84WlZeHFTNSejlLImBuJZJmmoNNdB2xiduuZRWF+uNB+b3hdFsMTeTZ2BpthjkrpkzqXT\nFOEoh9FLxjvXGCaL3bVWzRz1iwIsgpUTYNe/363w4/zhPmZnT8fD55LREFSR6XejPshq0RSHQZwU\njwJNN5hjoCwhohtQNaGvlkthjpKcPFm1U1+fno/H1pQ5ECzrKJU1svP5lXEDsbW6zeY06OLEwoPK\nFqaw6ZD48hRZgiJLmDCoG32AuomvT8/Ht17dhQ/2NeDsEdnHZB2rDzASk+JREFR1fo0PzfJh+bpy\nXDUpD+m+xGHP1MFpfIBI5wTJbnYh/uaED8xr5sqJbPBu1YEG/L+PDmPF0qnYUtmK0QMDeGNnLVyy\nhKFZfjy1vhyPXDgKj68pw13z4hOoiTMKMDG5FlfHBP5A7Pth6d3zkZkdcJi6H8cCVy2dCVmS8Lc/\nr8P2jRWYtWAYtn5WjqBZW3v9HXPw1z984thAmWIhWZaw5PKJWHTpBD4A7XYrPAUxxpxKYoZclWWN\nvZbtRa7Jd95/Dv7wsN1noGx/PV5avg4Ai5UOnWT9VwlfykbDThDtxsNhy1Dh6qWzsPD8Mdi7o+ZE\nbFYMqKaKQL2pjidkWcKs+cMTjtI4/RavGd4Tv1qJisONuPVHCx1T9e78+TkYMykf8+fPw4DcVEya\nyVKbioYPwPDRA7FgyWhc+rVp8Ac8SM/0w+tzYd7iUVi36gA2fnLItqyOdpVvx/S5Q5N+8EuShIJh\n1kt/9oLh/P/5S0ZjwXmjkS3YkjfVt8PQDZu9uduj4K4HF2Hu2SOQX5ABf4A5OdJI0Vumtf2VN83A\nmeePSWq7gO73nLjouimYv9iZgB4rUABSYQbz282R4ETh9cGGdnzzHzs4OaCgyCkoPxH9NxocrMa1\nKAUrHLXtgKWa6IZhC5Qta3frHnpyLauRI+7iJLpaDoHWd9R3KzOLXbu8JkiYhgry95pkh4JFkSep\nusH6cpkKFgBuWc6n0WKJmYjcVEvFoesgzOdxnMUGp0bD4Yj9O1F1I4QiOp75rMJRGaF9oZ9SPNYr\nzyVb5yDNDEaIjIqGD1K0guVirn9+N1Ml3bKEVGG7csyBlY6IDo+LWbG7FMuxkIwryIpdJFhEjDKj\nSK7ioGANN00thplKVoup3qR6FH7OLxrHCKm1TsvF8njD45LxowXFeHJtuaPRSm9iskmO15cxoqTp\nBv6+pQafl8f2EiopKcH/++gQNlew38RXm2X+YjaY7mJLETpd4qvx7jf24M2dtXhybTn++Gk5NpQ3\nY0dNEHVtKt7adeIDya4+Y08VctVXej8VDR9gi1UmzSjEHfeehetuY/XkuflpuOHbczHabPciInMA\nO1ey+dxzMiYpGJaFYSPtA1lkcPL8E2vxWemBLh+LUIfqaGQ2cUYBd3kWfydyRduoH+NWPscKPTK5\nOFkwe+FwfPrhfhzcU8utzgHgvCsmonB4NvKGpOPDN3fC0A18seEIJs4o6DStrDswDCOp5W5Zf4T/\nfzJ08B6Qm4q6mlb86dcf4a5fLLIpRuFwhPdpSsvwOUoZHo/9MvSawY6SoC9L0XD2gHn/9R2YOqcY\nu76owsfv70VdDUvP8Qfc/GHSXSy5fAL8KR6eQrh/F6vNuuIbM/CPpz/jKZxA4romQkTVEEj1oHDY\nsRm97QuwlAumFFBALJKlxZdPsCllK3az5s8NQRX56V6usPSyw3sPEHvfRqcIhrRY0sIdyMz0O8BM\nETQX5XIapBAVrKifeRqhwxaqZn8l6oelC2SFgun/mGlnpArZ+zOxwD8UUTnZoOD+uimsrqsjosOr\nSML5YX9nFKRhw5EWG4l6f2+97ThFdMOmsCSLaAXLaRGPrj6MlfsacNmEXHO7rN/of/pqbG4KNpS3\noF3VzXPBFkjXacD82y6kO9M0XkUkWDonzT6XjHSfRbDG5qVg9YFGVDSHMGFQCsIaS0fUDUYGiVjR\nLUHpbJrOXAgB2FQucRvEW+L0onT8zaxZA8D7hQVVLaYfGSmJXsVKETwRGJETwMXjB+LR1Yfxy8Wn\ndek9G47oCKqarYarpjXs6CpJajgNOETI3TLOst/dU+9I0FVN533LAIsojc0NdElhj0fMNN3ggyTv\nmI6g9UEV2YFjV6fWjy8HrrllNv72p0+Rbjr+Di7KxG0/XghZkW1lDgBw9lfGYkBuKlcgEzk+XnNz\n/B5jk2YW4MM3d2HNe3tx5/3nxL1/Dd1ARVkjhhRnYd2q/bznZrTaOX/JKEiShECqF8HWMDxeF9pa\nLbOjpXfPh6LIvd7u5XjhlFCw5p41AgBs5AoA709EUuojP30HK17ZhkfufQehjp7ZhlceacLyR1bx\nDueGbuCRe9+JUVyiUVpaiu0by+EPuHHtrbNx8VePfQFyT+HzWwSJW4KaWPMeq1/ymiOyiW4Tntdr\n3rSJCEt+YSbOvmgcAOBoZQtef3ETJ1cA0B7seSPICdMLbPVZw0cPxN0PL0FGtt3CfNqc4k7J1cLz\nmZJ0xtkjIHexh8fxrjvqDVCTVQpYxVhkonlc99UF8aM39/L0rHrznJmtkxwDmBNxLAwjNl2v9AAL\nZjsiGtxmo1iABUxtYQ2Llm/EvjpGMkOapWC5ZZm/lFxmYccZxVYKr0h6om2RE/W4qmtohEeREQxT\nLZf1GwX/pN5QsChaoJN9OGCly6WYCpbXJUPh/aIUm+08HR+PItm2/RBvastqlZwaKMfAYZLodEQn\n5WWlmY4Vcghi6Rqi8/PJ4SZOMkSCS+fH75bx1BVjccZQ65zQZD6hBks3LNUr4FH4/wAbKCCrdq+Z\nduk2FayQoGC1mBbiZLeuGwZKzPXSthHRcjrnkiQhO+Dm01wzOc9cDkAH8yWzwS6dc6rN6kpNZG/j\nmsl5aO7QbGYUyeAPa4/gque38s+GYeD6v21DXZuKfXVBGxkXiT37y75XZJZG+PiaMry9qw5PrSuP\ncegTERZqE9lyrBTcZFKZ6bzFu/7FryktEQD++UVNTI+144WT8X1zPNDXjksWqVHCcyy65oowdU4x\nisx+l4svm9D1QV5zFbMXsjIbNazhkXvfwbJ73uYD6CIe+ek7ePGPn2LZPW9zcgUAy5cx0y9DZy9V\nn9kiJiXVw4kVWdEDTB2VZcnWo/VkwilBsKJrcKafUQwAcHviC3gii04GmqZj2T1v442XNmPfzho8\n/4dP0FgXRHUle2j+9QnWF6m2ujXRYrBtbRDlhxoxZCizxxyQe/xy5LsLcXQhHPVSOGx2Hf/WvWZx\nbBL3iSxLuOOeszod3aTeZs8+voZ/R6M5Q4qPnSukOAKTX5iBs74yttN5yACkN4tc+ypG5vh5gM+J\nh0MQsuFICzZWtPDR5jqTYFk1QsdhY5OA0yVLZg6qSZ4ofU7XDa4iEY/uUHVHBYsC40xhgMIWiEVd\n/pTaJ05y9ogs/p1HkdAeYesWj/dos/8bzd9qppKJFuMRwdSCalTos0eROTnwu2UcNe3KqeeWZtYO\niesclsXOaXOHBr9LNslX4vvZ6TirUaQy0RKcUhfpGzo/W6varHMhSfxYkrrmUWQUZvpszx6uYAmK\nFWCdN69Lxgih1rE1rGGouf9kbuKWZSgS2x4iWONy7YMQugHkmMouXRt0rSQiqFS3JSqENPWts1kz\ncGofQMQtun/X8YRLlvDDBcV4dkMlTyVOBm9GETLahY8PNeL2V3bhdaG5dDTBomujQ9Xx4PsH8PqO\nWvzl80q8tKUGzao9/dIAMG0IS+sOm+TYUrCs5RHZipcyS8sSp6HrmM6VLAEzC+xqQ0Q38MdPy/G7\n0sM43NCBA/XtOFDfjic+OYJ+9IPQWW15PEycUZDQVdkJdN1mZPlx98NLkJJhrfuJX61Ea3MHXnth\nEyrLGrFnW7Vt3uklQ3H3w0t4WiIARCK6rfdVSqoXe3fU2OrZr1o6EwDbz/a2cLfNcUIdEdbbSz3+\nAxanBMECgJnzLcOGMy+IDYi/fucZuODqSfjm9+YhOyeFO8CJ2LezBgf31KKxLoidWyrxl8fX4NOP\n9qM9GMZvTIvLHZsr8cpfPufz/O1PjMVXlzOiJdbsRKO5sR0t9eaDPIEjX1+DeN2/+29WuBgORdDR\nrmJgfhpKFo1MSrUR83qTNfYQ3REXnDcat/xgATKy/Zg6pzi5je8GJEnilqE5ecnVyE2YzgKdaOk+\nGfSV3O9k4RGCbaqhcXw2UvBo3mucYPWxGixD4AYNQRV/2VCJM09jxEY1+xqJRJICwac/Y9b/HRHN\naiYrWySB3jdkVgBY+y5JsW09o9PlACvgC6Smwa3ICEdYqqAtIDeP46WmQUSLmeseithH/KmOKJ1S\nBEl1UyRu4uAXXsxB1Qo6JYnVAI0ZGODHhe27Dp9bQUQ3kkpLW/LURixavhFjcwO2fSZSl0gtcCIh\nvEbOXE7J0AxbPVyHSUgTvbxpuy2CxY6BWwgYRAWrJaTxY+gV1kXkZoP5LqAUzBSeImjAWqTE5wMY\nISrMYE540VtK9WLk2Mj2h/0dZLpMUsoZnccTSbAAoCjLh2sm52HZR4cSEpREoPP9qkmsVEG2pWXS\nNU3TLlt1mE9Dfc2mzWCuEdWm26aqGfyZFDYHUKIJlm5Yqli8zc9JcVt9qw6S4s1mIoKlSBKv+7vc\nTHGlbd9S1Yo7/r0Tt/5rJ1bua8Ar247iWGBTRYvtHJxs75vjhb52XHx+93GzLne5FAwdZbkL3/Td\ns3D7T87kn5/87w+xe2sVnn9iLV593t66g+rNL7hqMkaOZyq7qmo2kudyK7ZeYQCrNQOs7CdSzJLB\n0coWvPnyFgDA4794D2tX7sPv7n8XkSiS1drcgWX3vI1l97wd4zTeGzglarAAYMGS0TEn0C/kOQ8c\nlMbNCGRFgqEb2LejBnlD0pGa7oOu6TbiRKipbIlxmwOAr317Lp77/ceORCm6Fmv31iq89sImfvEB\nbKTgZEHR8GyEQxGMnzoYq1fsgWEYeOyB95Ca7kVrc8jWT6G3M1POPH8MQu0qtm4oxwyTbN1894Le\nXUkcfPeBc+FKchSpp93j+zpUs84EYEFDSKfapFhjBgLdARQY8RRBqpfowbWyan8Dxual8MaxPQVt\n6+qDjfjrxircMnsIVu5rQNi02W4yjTA0HdxafYOZ8tOu6o4mF5QKS/uf7bea0jpxESIJIsn4aH+j\nuV5G9EIaU8uYY50OtyKjztw2Sg9rDmlQpCgFSzd4HRIZQ5AyItaD+YTrnRwUNd1AuteFlpDGA9Am\nM8W6I2KlCHbGrwwh7Yq4JKluH+xjtUkVzSEsXr4RT142BsOiUnUTkQYiWhToAizIzfC5UNEcxoCU\n+DUvdL5o3wMeSwEDWMoZKUUAI7CD0722eVyKBFlj09N1Tnb5m8xmuLph8ONXbr7siaCFdQPLLhgJ\nVTfQ1BHh7py0HwCrR3rg3OEYnO7hpIPWTwSQpk0qZfMY49IJA/HJoSb8a2sNrpzUdUdVulfCDs8Y\n2j+DD/R0PmBJAzyqrtuW7VFkrtaKboLR6pgjzJ+2m+SX0v4o60NM79Ki0gl1XTTJOHbn64dv7sX/\nnD8CUwcff0OtfpwckGUJV9xo2Vd6fS54fS6cdeFYfPAf1lbnwmsm4z9/2wwAmLVgGEaMzUV1hWUq\n4/Yo2LOtGsG2MOqPttna3ezcUhl33cnWaWoRnat6lNV03hUTbdP89v53ccN35uIvj9tbBqVl+PDB\n6ztw8fW9W5JzyihYBFISbvvxQhuhESErMnRdxyvPfY71qw9g/eoDtiZs8fD9hxbz//MGp9sY/vcf\nWswlUjJLILz2ArPuPlrZgiEj3Ljj3rNw5oWdp52daMxawFTB+UtG46a75mFIMRvVf+ReZv7QaqZ/\nXHCNZd2faKS4uznOSy6fiLsfXnJMjEkSwe1Wjss6+1rutxMueHozr4VRZCv1yjJmcEjfMr9SNaZu\n1AkkBWABZ12bartmkj0WD31wEC9urO58wiQgutuRUxulB4U1Ax6XzNOVWLoc1bmw74KqJgTacowi\ncsVE9hzyCbVKkhRLsizHQgO/eM8+WNTc2gaPwswXvApbzgVPb8Zja8rwlw3s5XW01XJuC3iUGIJF\ntVeUIkjb9/qOWn6utgsqCaUaajqpXBbJ+ddW9ozrUDWTYOn4vCLWwU2EeIXQcSCCTkroF1VtMAA8\n+P4BRENNEIT+8oPY6V2ypcwVZPjiNoM91MDIjMc0p6DzQgMKt8wegtxUD+4zm2KHNQMp3iiyKqSG\n3jA9H4ClYC0cztKZdcNSY84Yyr6jdEJV05EVcCM31YOROQE8cK7lckoYEHBjTnEGirP82Gr24qJ1\n0LmlbVD7QP6tLEn4/oIi/H1LDQ42tHc+QxSI2FCfN1mSkG2qQRZBYn8bhGAu+r5av57VZmeY172q\nGfw6pv5wdK8QmYsIJCxhDZb5d/EoVvPSGqYUXva96ONk9f+LXV53Vb54aAlF8OuPrHpwsR7xZHjf\nnAj0HxcLdCwmmT2qUtK8GD3Rci2cv3g0Bhdl8TIOwBI0muqD+NufPoWIb35vnu2zU6+3oSOZghZx\nGCzRNB2/uW8F/vL4GrQ2W0oUWcFfd9vpWHAeq4OPJleSxDLY9myvRqij57X7Ik4pgnXXLxZh8WXM\ndSk13Rc3OJZliY8wpab78NFbu/hvRcOzcftPzsSd95+D/3rgXHznPta9+Rv/VQJJljD37BG46Lop\nAFiaW96QdHz3gXMhyRJ3yxOVMNEevrE+CH+qjECKJ2ll5EQi+j1QEKdw8lRoQtgPq5GtIovmArFq\nlKrpCGu6bcQ2L9WDujZ7DZZusD5UpNJ0Gb3EfUWTi2dNsmKlEFmNYgEWCEUbMbSGrBTBgFu21CDu\nFGdaaCsyt4Q3jFgXQXIbq2tXY5qWagarwQpHGOGj7fvPDqsu5VWhRsXnkm1OeYZhpf+RkkXbVTI0\nk59Psflwi9nuImLus1uReQ0YoSOiI+BmjYuJnCYDzWCkm5QDbswhmAwQRuYwJevfW+O32qhoZkF4\nUaYPs00DCkWWsKeu88B+gKmC0jU8wiwuV2QJb3xjMq4wex2VDMvEXNOwhI6dR6i9I5AySKmGotPm\n9VNZkELHma6bZFL6BgnNdA+aJiPRChZfXh9QsADWAPgbM/Lx6w8PdVmlIdLhNe+j2jYV9VGDNBYh\nsuY7vdheB0s/0XWkagY/PlzBipCCpfNprEEAA6GIjohu4JJnN/PlSrAGFIkoN3dY9wzArov2qBYP\n9Pf8MQM4Q+vt87W/rh3v7annSrOTo2k/+tEZXC4Zdz+8BLf/5ExIkoQF543GDd+Z6zhtfmEmsgYE\ncHh/fcxvWWYLnHFTB+OuXyzCORePi5mG1Knf3mcXOwzD4H1jaypbeDscAPjiM1a3OLgoEzPnWWVC\nw0azdPmbvjcP3//lEvj8bqRl+HC0KrFHQlfR96P4XoTikpNSHESCJZKrW364AFctnYWUNC88Xhdc\nbgVeH8uDJTOKuWePwCiz/4AkSfjat+ZygiEW+T3/xCdYds/b+Pdz9rTDWaf3fddAgpMalWqmxkwz\na6CuvXV2p/MQ+lqOc1/ByXJcjjQxxVI0D6CARByV/clb+3D7v3byUdywZmBQmgf17fYaLJpH7Jlz\nIo6F5tBegQdXZq8jgKk4Ed3gdS+ElpDGlQOxhsln/u8TgmkxDTB6nWeZdV/it3lmraLb62M26xqz\nUqftKxkaa6qS7lUQcNsVLJdiNaG1DDmAKybm4urJefx8io6HX5ipbZpZX+VWJE6ICCkeBX5Xki6C\nAjTdMEkgI2ViYAvAZo6QbxILSstLhI6IxgkNGXd0hh/ML8I/vzYRg9O9uHJiLic/rFGx/RUaiOpb\nRSlhYvxKvbdEExGAkWQyvHArMq6YmIs0r4JbZw/BBWOs+odksGQUq1+gc5rqsad9nugaLBHnjR6A\nLL8bL2ys6nxiAWS9TuTj1e1WZohIfgB7/SI9Xy4zjT+mTJ0GQDw2uq0Gi/rDuWSr1pIIlgTgofcP\n4Pq/bUNrKMLrEqO3g473y1vYIABPEZQk3hyZTgnNk+JRYkwydMPAd1+zYpKu4q2dtfj6S9vwqdkf\njHrjJVuD9dS6cmzqRIn+suJkeQ8fD8Q7FjPnDUNufvw684a6oGNJDQB89Y45OPsr4+Kadzg5yGoR\nHWX76xEORXDFN1gK46G9djOcLId2PZdePxVX3zzL1tu0pakjRlnrKU6ZGqyuQJYlx9qpRL0DkoFo\nnFFZZtUC3PyD+aitbsUrf/m8WyYIJwwO7+hbf7QQACOXybjr9ePLg6mD07CurBmKbFl2qw71EXvr\nggiqOhrMmoeWjggy/W7sNdWE6D5Lf1pXgSu6UaPRW9CN2L5BB0yFIBTReWqR3yRI0c1wW8OW6YFh\nGPjWnEI0dUSsQFuw/25qjyDFozjWLHmEmihaFgXpEd2AxyXxlCYK0kTzBUK7qmNwumxTlFyyxIke\nmSZIkHCLWVtE58IrvPxq2qhmxYDPLcOtSGgJMdv6c0dm482ddRiY4oHfrcQdgb/8uS145qpxfH8I\nms72gwJWGml3IkS06KIsu0WxLMUaEHSoOt9PlyxhTlEGPjnchESQJIkfx5tnD8GWyhZz/thpiSC5\nJHutk24ANa3seNHxJfJjI9XCsujYXz4xF51hUJoHEwdZjrMjcvzAbovEk/JHx7gvpAgSJEnC9+YV\n4fZXduL0ogyMGphc/0IiQeI5Jkt1Il/hKOUJEBz9zM9iXZUEdj3rhgG3YKvP+peJdu0scTjgUbDN\nbKb+2JpYAytOsHQyawnbvndKEeRpwg77HIro2FET5Mpasnhndx121LRxA569dYxYWTWPiQn3rqNt\nKMr04aUtNdhf34EpJ7heq6FdRZa/v1fYyYzomvT8zhyWJeC0sbnYt6MGjfVBhEMR/OXxj7n6NHRk\nDgIpHgTbwhiYn4avf+cMlB9qwOAiy1F63uJRKD/UAFmRY6zqL7x6Mv7z0mZsXleGyWbqY09xSilY\nXYFo6fjVO+YA6L4tJmHEuFwME5xYsnICuO3HC5GRFcBpY3Jx98NLsHbtxwmW0LcQrzdLIpUwkXFB\nf46zM/r6caHrgNQUVoNlH0F2ulZWmE1VX9xcjZX7GtCh6ghHdGg6eGPWaHTlWPRW0gtTk+zffXKI\nBeUhTedpYH63glDEqt+YWZCOgFtGSyjC65o+OdyEecMyceHYHJwxNBPfnltgKVhmKpJXYcfvybXl\ntnVGDHtAqRvW/dTeEeIBl5fbojtDdPgjuGSJkyfanzxBiaP1iAQrP80DCaweRZEkuGVmV+9VrEC0\nLazB77FSFm37o7O0QXJzE6fQDHv/oUYz9YtSTj1CZMqvNXPa4iwf/LJhbpP9xLVH7Jb5N88ejJ+d\nPQxdAbdFd3jOUe8wlyzhH9dPxLfmFMRM87F57RDBolo2XTcwvSANUwZ3vTXHX64ejzmCunjh2By8\ncsMknvo1wKx/4AF13xGwAAADUty4fc4Q/PqjQ3FVRV/U+/c1U7Eanm0Ra6p5o2WENR2yZJHYiYNS\nY8wpPv2c1UCrmo6AR2EETWOmL+0qS+/tMI1qQhF2XaqmyQX1lgOs82pZssfWgjV12NsoyLLEU1at\n7bL2Mbq+K+RAGJPBq9uO2mzu6Rn1zIYK27oB52fsd17dzdW3iK7jrV11eCVBSu6xxtXPb0VZY++7\nviVCX38PH09091iQT8EtP1zQ5XllWcJF17Lym/qjbbyWqqNd5XVXF1w9CQDzQACAIcVZtnh09oLh\nuOyG6Y7LHzOZ1caSE3ZvoJ9gOeDIwQabl39efhpu+eEC3gW7u1hy+URcfuMMFAxlqT7+gCduY7iT\nAVmCvNqPUxcUrJGRhUuWeADxb9NaWDdYqp8Y+F8whqUxzSnKwPljBiAr4EJdu8pGjxWpW93bf/nB\ngV4fnW9oj2BPLVPXMk2idN7oAZDARr+pBiTgZgoWBT/rjzQjqOrQDXZMxuel4LbTrYA7xaPgonED\n4XdZNU8dJgHQdYPXtJEbYXTqZEQ3BLVP4qTDo8RPyaNi+0FpnhiCRfMrErBi6VQUZVqfOHWTAAAg\nAElEQVTPJs1BwWpXGbkMR6wUQZqGEyxVi5si+LyZEkZBpHhtREwFi75pi7LXFVPzKLBtV63g06uw\nADhaOVM1A6+YBhyhiI6CDB/mDetazzwibU51K6RgKbKEdJ+Lv9x1w0CxeTwp2CYXRp9bRobPhYGp\nHgzN8uPX54/s0vY4QZYkrqSuWDqV99fK8rts9vJ9CQuHZ2Folo/XOUaDDvevPzyIRcs3cmK+0Eyd\nBYCASXApNVPVDFOBslIFo5WikC6ZvzHC1BHREdIMZPhcCKqsxlI1U1ZDmm72vdOhajrSvS6emlqQ\n4bMt14A1mEHPpNZQVA2WZD0rrfRDnc9P/Ir+0rXT1RTP6Ntvutl7i45LMo2naQrdAB4rPYwnogaA\notHcEcG2qL6fnx1p7rXnc/tJ1MqmHwySzFrcpGd2zSV7wXmjMWv+MC5y/OvZDbbfL7mepfkWj8jB\nLT9cwL0Wuoorb2JphmGzvrin6CdYDsgcEECjKaFPnlUIWZG7fEEkwjW3sLqkgIOF9MmU5zt5ViH+\n6xeLujTP9DOKuQlINE6mfT+e6MvHpV3V+AtTtBinljQ55si5YQDXvbgNr2yt4Wlf9NIflO5BQYYP\n2X436oMqsxwXFKxrJlvpgZ0di4/2N/J0st7CoQZrpHSWMNrsc8s8hQgAH/0OOjQ0dMkSfvOVUTbS\nQhBrsBraI6hsCdsUhi1m4W1089RQRIdhpi8asmJTsKpb7alIBCKINDpPcCsSqPOWkwJNi4m2afeZ\no/myZBE0qlkBgGBYYyYXwnYsWs56XRHBothOrF/RdPu6WkMar28CWAB9+ys7uZJI39Hf9JQAJ4Mz\nCuzpTCWmQ18yQaUTiKAoDgTrUzPdMJp8dag6Bqaye4FUltPM2gCfS8afLhuDxy4a1a3tSRZ/vGwM\n5hZnxKh6fQWSJOE7ZxRi5b4GbKmMLTanGoz39jK30p1H2Ts6GBauG/OcBsOa2Z/OsNU2iuYU9Lwq\nHsGOOxmyhCI6OlQNmX6XSfTNlE63zBsP6wa7zwJuhdcDxtrGGzEK1gQzjZMGjxQZQjq1fVpKQwQs\nJ1NRmesKojMI6BqcU2RXz4D4z1ibMpfErfP0ZxW46/U9aGxX8ao50HbP2/v4gEh3ERbSNI8n+vJ7\n+HjjeB+LmfOGIW9I/BTCNOG9mp7p77a7c/GIHAzMT8O2DYkHD5JFP8FyQGNdEOWHGuDxKsesjkiW\nJWTnntwKkCRJXXY79Ac83ASkHxZq28L4pYP1dF9GVUsIFz+7hb/ouGMgLFVifB67xinwEet+aBpd\nZ6rJgIAbdUHVtDq3mhVHpwZ1huPx2g1HKGVITBGUEdYMx5HVROUS0Y5zBIqD6feIbtis0NtVHeXN\nIeiG6XbGTSokTjKJlAw3e0aRwpLiUWyj2hKkTm3U2bKtOq12VYfXxQr/ZRnYX8+IqEeR8dkRtqyg\navbB0qxatWg4hYqabtjcGQeleZDqtde27atrx/3v7uef6dpqM4NrnvJoLufsEUzpGGM2Md5wpHvF\n+jxFMIGCFU2wyppCPEDlNu0uGTfNzMfUwWnICriReYxrSoZls8Aj2pijLyHD58KdZxRi2apDCIbt\nAxXRATUNIlCPtImDUvkzpTkUYcqTpsOjsFoqWWLqkBr1vKL7tUNlgyW6SSDSovrBMQXL4OfWq8h8\ncETcvjBXhax7VdVYGwSyaadpZUniDodEmqKbGouXkkgUuwI6dJR+SvtOrovJkBXajGQHsGiJK/c1\n4H8/OcK/dzIr6ApoIMVpIKur+HBfAzSd9ZZrbE/OojsU0XnPtH4cf3z/ocWYOKMAqeleXHAVSwn0\nxnm3dAejxg/C1n6Cdexw3W2nA2BkQDlGL6Nvfn8+5pw5Iub7UznP91Te940VLfjogLMdeV89LhSY\nUH0Nja4ahmHrC8W+Y59FoxhdmEaRJWQH3KgPRqDpjCRwRUx4H3epBquHA/XVLeGY7ygQaTPVGwOW\nTXSKOfotKkOkGDkF4wT6TZxCloAxA1PM/9kvqtlEmIKseiEg0A1rO8Q0vp1Hg5Al8D5DRDaijTgA\nYGJe5wM+vC+TbiAY1uBRZLOPmbX13iii6HOzPmHxHESdAhvNMLg7I8BS25wMOwBW6zYuN8WqZTKA\njrZWrtSQ4pRqtsmgALm6Nfb8JgNanlOK4O1mCqgc9dpI91qujR6BoF0zedBx699H905fVbAIc4oz\nMDk/FX9aZw9yyPgkGmQeomqsflMC0BZm7p6qbpj94QyutnIVyPy7fQ8j6R0RzUZmKN2Q1kkDKnSt\ne10yv79zAm6uTlPgrwqtKMKajmy/W2hFwdYhwVKGKF2PG2loFgmjW0dMdewKopu3U9aBU3pu3Ges\neWzSfMm1XaF10X1CpkY9DalI6e4pwQpHdDy88iA2lDfjyr9+gbvf2Jtwejou//vxEVz7wtYerftk\nx4mMSSRZwuLLJuC2H5+JsVMGY+n35yMjKzljnGRQOCwL1RXNiPQCge8nWA7IzWcpJU3daH6YLDKy\n/HA7BDn9ODVBL86TCZTGo+rRBMuahuIASnERAxiuYBksQBcVrOg6olUHGmw9ZhIh2cyvC5/ehI/2\nN8T9vTkUO1LbzlPf9Bibbb9bQVjTbQrWTTMHA0hOhaNjM6coA4ok8b48T61nhejkEEjB0X0r9tvm\nt3ptWc+VXUeDNsMQ2mYiWBNMUiVJwHljcvDq1ycl3EZ7iqDOP8sSMCqHveS8URGU22QbzXH6YFU5\nEFmyaSe0hjWuKIgYPTCAsKYjw++yna/yDoUTIVIgaH6XLOHqSbl44tLRCfc1HihgdDK5ENchIjvg\nxo4altJ2nPuhx0DsZdZXcdvpBdhwpAXrTTtxw2D1dKkJ3plhjaXk5aZ60GIqWOEIU1lDER1+t4IO\nVbd6Wpn3Uci8XUMRHYoscWc/PydYdtdH+l2RJWSZKdDpPgXrzVrJz8tbuNJs2bTryAq4UGuSDFHB\niq7B4iqVMMBET5RQNxUssX5KnP99M90yorNjp+kGOjTY+uzRwIhFTI24arRtnfyZw44j3Z9O901X\nQANYr26r7WTKxCBHR0pVPpykaUZ3B2b6cWyQ6WDD3hNQP9ctnx3pZMrO0feftCcArhPYGPdUzvM9\nlfc9kStUXz0u++vZAEQkimCJaoXYNBiw1Bi3LPE6Lc1MfRuQYhIsYdQZAP619Sgeev8ggqrepWPR\nGdEKawZ2mXUcTqAwgNLrAMs9kNUfseeEmJonAbzHDACe1paSxDMlw0wRS/MqkGWJ7z+BCFaHScYp\nVYe2T2xmTH8ZATHwzFXjMDzbx4MIIljjolQrf5ztpJoNUpWy/S60hCKWGiNJmJSfyqeRJWCoaZvu\n6sRU4V9bj8Z8FxHUiqFZPrSFNUcFa1ROAKpmINvvsp3LLL+LbxuZhBDncckSvjlrCK+B6iosBSr2\nN79gAS9C1Qw8tHg4vjlzMHJTPfjbdd0rwu4J6N751Xkj8OSlY477+ruCFI+C788vwm9KD6MlFIFm\n1hpm+WOvAXIvVTUdumHw9FePqVi5Fat9QmNHhD+nqGfbgDw2CNIRYW6Y9IwKeCx3T8CqlSTF2S1L\nyDa3h9RRgD0XA2arhTBXpQxuK+6S7ep8dO1WbIqgZfhD2xz9bOgMlvOoswIW0YG739iDH7y5B9Xp\nI/CL96x0dSJ61GqiXdVi2laIWLR8I1bsruODanSf0CBiT1ME24VMiZ6AjjMNfJw7MjvR5Pz+Icv9\nUxl9NSbpTXzw+g6sW7UfYYeB1mTRT7Di4NpbZ3e5vqgf/eguks3/Pl4IRXQcqE+s4NJ7khQVMUWQ\nXn1iYTQAnrs+MicgKFgsaCGTCyogpxdgb5tWiEgUKND+RZMQAGb9EQVgbEJZsDonkAWzrxPV4I+X\njcFl41njU5fCRtHf2mVvmKjqzNGsTahNkSU2eg4IBMskT5l+Nycdg9O9ePKysZwE0X4n68TlcVlE\nCgAGpnpQF1T56L4sSZxcuGVWx0KEqLMRaydLed2wiEzAraA1pDmqF6/vqEXYTJ2sC6pcQZpdmMEV\nLHJMo7owp9S+roBS7HY6kHNSy6LrnFRdx6zCDFxtGrZkB05cD5/cVA+GD+g906ZjhSmD01AyNBO/\n//gIIqbxTaYDwfK5ZOSlepiCZVhNlb0msfKadVXpXgWyxGr1Am72fJFgpZy1q0zBIkJBihVdL1zB\nIgVTlrj9vUux/s8OuOFWJLgViastbWGN/+53W882wxAHqJxTBBVJSE0OW+mHXQEZcUS7ERI03cC2\n6jbsrAnaUpzZuti09Hypagl3ek8vW3VYaDZvkkOHpvPdAW3fwNSeuTq3he3HcL0wMJYIfalJdz+O\nDa5aOhMAsOrt3XjiVyu7vZx+BhEHQ4qzuuyQ1xvoq/U2xwO9te/76oJ4cm3P5d3jCVeCxPQTcU28\nuKkKt/5rp+NvT62vwH+vPAgZ9maq1PNKfG3Ry4heqrmmkxprAmvl/9tSBE3nLidVryvHoqyp85SP\nZIqVxZFSauYaVDVOmqiZq0uSYpp/UqAd6CQdeFi236Z+iKO8l05gxCsS1UR4fF4K3LIUEwBSiiCR\nDTHVbm4xc9BLEezEgc57htEoPm3WnKIMToQBdj75Npt/SL3rrObCKeVGtH2P6AYqmkOO9TcACxZn\nFzGHKaqJaThaxec/zVT4bp7FlIqeEiwim/FU5xVLpzoqWCcaJ+O75aaZg7GnNoiVe+vhUqyaJxHt\nEXYvcgVLUFtVzarlU2TL4MNv1ksGPArKKmsgSzBrCq3z9ulhFnCTMYWYDguw64gUX0bwzNrIoApd\nZ/dGm8oGBlpCEa6++d0yJwkR3RBMguwDVaSUSJJF+mi+ripYhEiUEkagQQ6PIuHwEXvt216zXQRT\n+JiiVduF5ybtF1m2O/XE6wrINbKnA29tYY2fk2sm56Gxk+XR/dMX7uUTjZPxWdIVFA0fgIFmqZAa\n1tDc2L1yoX6C1Y8vHd7ZXe+YdtSP5OH0Au+IsEbA7+6uwwf7GmKmtZlc6PZRS+pZk5/GesZIkhRl\ncgFkB1zMpt2sweopmto7fwFTDUIiiAKLJowk00i5OLJNhGX6EMsa/K2bpvD9TgQyO2CE0yI91LdJ\n1VmwSMdZNwy4FJkH81xBEvpyAfaGwgAjANOHMFUnWaoxamAA6V6FW7nnmqPHnGDJEg88B6YwEk3H\npzs1Fy6BOO6uDUIz4psztKsaBgvXFQB4ZYOfC5qNVKOeEixCXpIj6HfNK8J35hb2yjpPNfhcMn6w\noBh/WlcBlyzZnBbz0zwYMcCP1hDrtdYW1qDpBidhlGYsmorQtVkXVFHWFELALaNdk5DmddkcAgHg\nK+NyAADjTLdfGkih67m8OYQZBWl46oqxqGgOoSXEjG9q21TIMlt/W1hHus8FzQBPEVQkyTII0o2Y\ntD3LyMKqW6XnDqlt3U1Toxqu2mCYDxbRdgDs3iCBvC2sYdHyjagxa5U6zKbL4u2TKE3PUrDYOsua\nQua291zBKszwxihtXUVbWMOUwWl4+spxuHFGPtyy86BeNLpqMNKPkxNTZhcBAEZPHIQ//fqjbi2j\nn2D1MZwKua3x0Fv73sdNshyRaJNP9DVB6SgXPbMZFz6zmVv75pjBCr2UrMCf1WEpEnsZKhJz6KLf\nALN3k7l83WBBR7rPhXaVmUTEK8SfefrcpLe3JY6pQjQiuuEYKNC22kw7hLTG9CiFyCVLjstJ5CDo\nBE1natCwbHvPLNV0QiO79qYOzUZEqCbMHfXZCV3dph8sKMbz107gyldq1L7LkjWySwoapSXJUesa\nkt452VSE/TpvNGtIHW1jDwAjBvht1wsFihta/DwQXmv2prIUtd55QCTLG88bPQAlXWxmfCxwop8j\n3cXY3BRcNDYHimwplCVDM1HfHkGaV0FQ1ZHqVRAyTSXIgIEaAlsNtKUYo5SAW0HEHeDEy61I3A2Q\nVNvoa52un4imQ5IkFGb6sNk0/HErEppDEWT6XPC6WIogPSdILalsCfNUP2YswbblQH0H3IoV5NPA\niCxJ/N6qbGH3VLgLpkj0THLLEnfa3F7dxhu903bQuj5vYseC6jVXm+627REdbkXGhWNz+HyJUoxp\nCymzYaSZlhrpYQ1TUNWRl+ZBS4gRwNVx3HfjoayxA/et2IcXNlUhxaNgSIYXssQGiH705l78+sOD\njvPxGqx+BeukfZZ0BROmD8EN356Lsy8aBwBoD3bd3KSfYPXjS4eeuhSdaJCVb1/BBU9vxs6atpjv\nh2SwQDna/Uo32Kis1yUjZNbH0IuYXvayJMWkCMqShCy/C7VtYdtI8oyCNAw3yYZTWkhbWLONZq7Y\nw/riiDbfiXD+/23iTlIiKOjQBdKk6QYP5qnGyOOylBIiZXcvKO62kQDZ1ldGueupZj0JAGT4XWhs\nV+EWVDNO9BRre5bOGoyfnDk07rr2d1JnR3CZ9WUTB6XghWvHx6h3siThr+YxJHOAEaaJRPT92BrW\nOnVVdAnpXBQwOylYQVVDa1jjKZhE6haNyuYDLTdMY32niPj1FsFKRGD70bu4ftog3HPmMKSZ18Kg\nNA9CER2p5j3oVWT4XEyxouvFAGxprIoMrsAS/G4ZDe0qr+dxKTKvK6T7mu5zr3Ctf7ekEPefOzxm\nOwNuBU0dESiyxHtqEUGjgQZZYsTEo0jcvU+WmOLrVWSuMoWFjAAaOHhzJ6vLTFYFqmtTETGX73PL\naDafn0FVt7l60vLFW5W+W2umSrarrMkyHd+AW+bpk04gYw7aDyKVPU0RrG0LI93rwhFTEfvzuq71\nLPq0rBlrDzfjiNCbDgAKM33YXtPGG1nHA6mH+6P68PXjywVFkZE7OB2BFA/yhqRjz7bqLi+jn2D1\nMXzZc1sTobf2vbcCqBOBLZUtuPZFe4+NZI5LWNN7JXXhla01eGTVIW6dTYTIKef+CbN5JLc9pgab\numWz3WFaeRPBoveZIlvKkG5Y6VwDAm4cbVNtNUiabmCmaVSwbv16/Pitvdha1cp/v+Glbbj37X38\nc725relx+iY5YU9dLNGgbdaiCZYZaO0yaxNEQwOackDA3W0jAQq4okeHVcFZL9PnQlDV4VIEBSvK\nwU4zDFw1KQ9nnpbluJ4/XDIav1xyGgAkDJRESJKEnBQPV7KstClrmkZT4SS1QJGAN74xGQCr/QqG\ntbj1VARZkrjyQOtyO7gRVjSz65T2OSfFjTdvmoIJ6kF+DY3LS8EfLhnNr7FwEmlAneFPl4+xjeSf\nDDiZ3y1uRcak/FR+TVCQnyrUEtLAB/1Gjp+8MbRY12gaymT6mWqem8IIlluWLCVYIXdQ9ldUpy8Y\nk4MZ5jMJAHeFTPUygkUDEoDlIKpIEi6dMBD3nzPcdCGV0dgRgW4YKMz0odWsAYu2kdcNS/VZOJwp\noaqm4zuv7kJZJ9bi1764FWsPN8MlS7wPGOFIUwc36HEiWNGNnokU0vENqrqtgXA06C6j91JLVJPl\nnqAw01L3o9tCdDpvhqWgiwNzgU6cXqNrsH66Yh8+OdQETTfw0PsHHM16vqw4mZ8l3cHoiYOw9sOu\nk+l+gtWPLx1OJn713p56rBTqmSjQbe5iAe9dr+/G917f0+PteWJtOd7ZXc/7oBxsYC9wUnGoHgEA\nTxWMLsrWdMPmBOh1yfxlrdsULJjT2+tkjraFbSRZ08Hf/LrB+sx8fKgJj646jKoWVvtAygUA7pzX\nlRc5BWMiXt9Ry/cHYC9mzbBIxbyhLNjxcEJjV7u6C82wN+4liNbl6T6raS6RC0pFor5TnfHtETkB\nXp+SbDolgciTXxjVv9w04zhqKrDiyL1bkfE/549AiocFeaRgZQfsJJhMUBTZCmxTotQyMbVJxA8X\nFOMX5w6HS5YgCaYbkiRhRI5lyf7J4dhz3VUMzfL3Wi1XP5IHkWy61hMRLAJPETSvCwCYaLppUl0U\nXXceReIKlpcTMzYP1UQ5pQFTfV/ArXA3Qt7UWxhouP30AgTcMhraI5zAdUR0pLgVtJkDD+EIU4rI\nzEETFCyqwapoDmHX0SCe/qyi02NGz1O6f+YUZSAnxY3bTy/A36+fiK9OHWSlCEKCbA4Tib2wAKZA\nkVIIsGd2bordDZOOzZmnZQkKFvtL6Yk9JVihiGFL9Zw/vGvpt2Kd1fljrEESUt47Aw3QzC1mxjqV\nLSGsOtDY5Xd2P04eDCrIQHNDe5dbA/QTrD6GUyG3NR56a99PJgXr1x8dwv98eJArHzTKGhKi42SO\ny57aduyutWyjwxE9qYJdwrUvbOXKjwhy2SM1pTjLFzMNKVeqWSQe1pj9MS8yd8k8MLDVYAmNhkUF\nq6ZVtY02a4aBlzYzeX72zJl8nrd31+HnDika3A64hy9yGi0WM1o03eDmEUQOKFAKR/SkmxwnQlgz\nbM572X4XUj0KIppFTETLcgr0yQBAERSsZPDf552GRy8c2aVtJNJDQa0sSRg1kJGY882aqeiUvKmD\n03iKls8slo82/yDV0SUEqFRT53XJeOumKbjzjELcOD2fpwxeP3UQAOCckdmcSJWUlMQdaCGDj1MN\nX4Z3y+lFGbjvnGF8gIGTeMkaWKDge/Eo1tfI6ifFnl8uWeLXGdVFpQnXHU/9FYgZIJj4JNg+Imds\n4IN9RwE5pQjSYAZte0dER8DDais95qCU3yWjrKkDisTmp7Q6Mtf5aD8jP2WN1sBSPLSFNZui9vNz\nh+GFayeYpFThdZ0AAAmYWciIQ01rmFvLA6xRcIpX4c+7a6cMiiGzj69hipamG1wtazNVIiJYooqm\nG0aXiUlHhKl/T185FkDXCRu9H9yKhCmDLTMicoSM99woKSlBu6rxmrLXtrMBuGrK9ujSVpzc+DI8\nS7qCQrP58L6dseZpy+55O+58/QSrH186UHDe00aExwOKWbej8yCACqh7tu33vL0PN/9zR6fTLVq+\nEXtqg6gLqqhoDvFibwK9jP5vvX2kdEDAja9NG2SbRtUMeBRGsJglshVMUyqGrQbLXBbVCACigmWt\nSyRKtEz6an+9mSIjAVVmATgFT4OTMFNIBM1ggRK9wA0w0kKBEREIIjiq4AjWE3xyqIlfB+leBek+\nF9J9ii1FUAz63jDrMoj40fFKlmBOG5KOCYKjWDKI7hEkC/UtFKjQiLCY2UdBq88t2+pjCCmC8yCt\ng2otfC7ZVCEkXDd1EN64aQqA+D2los01AOageE4nDUX70XfhUWSUDM3kBEu8XuiZQQRrlEm2yYzH\nAPDtuYX459cmIsVjV4LpuffRgUbceUYhll8x1uY+CFjPl0S3+JB0NgAlCfNRFgBdjmPNLICAW0aG\nzwXdsPbDq8gIRXS+zX43a5oc1tjATnNHBCkeBc1m89N5CcxTGszeihGzxpW3ZIhSxxVZQsSwSChh\nxIAALjFTKQFgX107UtwKT7FL8Sh4fmMVbn/Faufxn521fJ0hofbKY5p/AOzdtmj5Rtz56i68s6sO\nV/z1i/gH1AEdER0+t4whGT7cMntIjEtqMvNfOCYHb3xjiu37aKXcCU5W7tRmoqf9vfrRdyFJEgqG\nZeHfz31u+77sQH3C+foJVh/DqZbbKqK39p0edE5uP//8osZWv3OiQaOBtM30VxyV685x2VLVyuuo\n4oHIzlHThlfVjRhntCNmL6kGMx2QfmeOeex/3hhT1+FRZIQijDBR8O1WJK5g0SkRrYeZK56lYKma\nYVOwxGPx6afrAMSm4tUHI6gxX3Rjc1lgNVWwSu8OVE23kUPdMFDVEuapQ0SwiPS9v7cePRTN8NDi\n4Xjh2vH82HoUNrrsku31IHRsdYOl/ABW4ERtno9lTYAk2YmuLEmcUFW3skB0pBngiumOFDjSMYwO\nZgJCyheZDFxvEnlRoRXh5DhZWlqKLZV95z7vC/gyvVucUgTpOqNriP6mehiJ0g2rdxWRct4s3GWp\n0CkeBUWZPuiw3PcAiygdbHA2hvnGjHxcPJ6lnB1uDHFF+bIJuQCs+4BULsWhztBnKljpPhdUzYBb\nYdO0q5pJrDSkehX+7AkmsCpvCLJntqazzIJdDo2x2XZYzwoJEhoaWNDYHmHunBeOyeHHOazpGJET\nwIqlU5HqUaAZjHhFI6IbvPaqNaQhL9XDiQg9z3ceDfJj2hV0RHT+3PC55KQzNcoaO/CXDZWcoEUj\nxe38TCKUlpaiLaThtKjaWlKwaCDoVMCX6VmSLCbNZK02KsuYeqxFdLz053UJ5+k2wdqxYwd+8pOf\n4LnnnuPfbdmyBffddx/uu+8+bN26NcHc/ejHsQMRK6eRrT9+Wo7nPo91jOsNdKcvB40YUqBKDSC7\nYwUrpo0lo97QOkjxWbm3ATWt9jRBIlazCu1pVbJkpXrYFCyXhBBXsMzgRbFICqV7GYa1flUzuNpA\naXei+iCSKfrPyemXCOWQDB9fRzwkQz7CZkrepopWcx77725KB1Ks1J+eqqazCjOQk+JBObc4Bw+0\nAPCidCJY7apm64nzx8vGYKqZ9tKZkURvgEbFFQmYUZCOP18+BrPMFCOnPlG0TRTgRG8j1XQpksRJ\nGKVsTYyjshVlxqatApZrWT++fIhWsMSauwyfC7efPoQrUPwZK9zzWX43Viydyq8xagFw1aQ8Pk1B\nhg/fnlsASZLw9JVj8duLRgGArReXiGunDEKB+eypC6r8Xk332VPPPIIK7YkarAl4WD8vMsaQJDYI\nF1R13rA41aPw50G7quP5jVX4x5ZYhzNSpVTdnnIcDUWW+PNUkqyWIcGwBr9bxp0lhbhxRr75rfVc\nThHeN4A9Y0TVdJ46vqcuiJwUN++FKPbcemXbUXM/NLzssA9OYG0ZBEKawCpexH921OKvG6u48VI0\nAtxMJ/7B+mBfg41QTh2chiqTOK7YXZfUdvTj5MS4KYMxYlwudm6phK7peO4PHwMArl46K+483X4D\nq6qKSy+9lH/WdR0vv/wyfvrTn+KnP/0pXn755ZMiRauv4VTLbRXRW/se0eMTLHD+80EAACAASURB\nVADYWNECw3DufdRdVLeEcfGzW7o8H/Go6Ma8YtCc7HER92aSGYy2hOLnt28zrdeJ6JxebJGoaab6\nQ69TGrWjz5LE+sAAAinUWVPPcESHLFkpgh7BRZA7Ywn7rGq6TcEC7Gllmm71Tpph1mA5pWMsW3UY\ngLVdidL1iBxOi6NyVTaHmIIljHTS8qgOLTUqwPC5ZHQtWaVzSGApijSKHp0iGFR1m134sGw/JEnC\nny4fgx8tLO7lrbHjNxeOxCKzzoVs9ouz/EjxKFixdKqjUQcFLzRyH+2CRtfMocYOKxCVgJevn4jZ\nplInYsXSqVwpE3EqP0fj4ct0TKj2h571Ec1S32VJwqUTcvkghCKovdHwuOwDF7nCoIBLlnDROJYi\nNyTDx8mQaKgTDwUZXn6tWzVdZiNwYfCI1CwK7iO6gYb2iJV2bKZdd5iW9LrBpqXnaTCs4dkNlXhm\nQ2XMNtD7TY0wBWtBnHRCWZL481SWgJwBrIYyaKpmgJilYB3E6OefaJQTilgKlqoZtt9oGaLy/EVV\nK/68rnPDDoDtc0BQm5JNEaR3T7uqOSpY68qYHX0885qSkhL844sa23cpHgWV5vUwOb9nGRMnE75M\nz5KuIGtACjasOYT3XtuO2qpWTD+jGIXD46ecd5tgTZo0Camp1ohiVVUV8vPz4fF44PF4kJeXh6qq\nY6MU9OPkwaGGdsfeRb2JjRUttgc/qSSJUgfe2FmHxU9t6tF6yxo7uDHEmkNMNk72Yf/69qOobgnz\nUVV6wYUi3VewRFCw8Xl5S9xpjpojb1wtEQLixna71flA08aYtooCf3GbIxojWCHNMIvOreCFUlnI\nUUrs7SKOsHKCJVsda+rbVeSlsfUTyXFKSyGQ25duAFurWh3VKiJhotJ3tC2Md/fUYffRIL7+9+2o\nbVNtI51hjTX4JTJIgZOuA9dNycP95wzrtUpnIpiKzAISl0BWAeb2RZg4KCVm/qFZ/rgj7b2F8YNS\nedCYSBAU0z0peCHXxtwolcvJoEaSpJhi+n6c2qBnlVj/GG2/T2TdyhKIvUhFBatkaCZ3F+yN7bMa\ngNOgAvtL6bVlTR2chFF62p5a9lzjLQU0g5OBVI+VFkkNjKl9hhMloHsyqGqQJQlfm5bvmE77eXkz\n3tpF6ovEt68trHMljQY3vhDS61OimjaL7UW217TZ3r+XTrBquZwawYuGNouWb0R5gnS7oKpzxc/n\n7gLBMqeraVNjyCEA3gokXpN7AFgwLBNfnToIl4wfiMWjslF6sBE7j1K7jjjuGP340qBkETOD2rKe\nmbnMmh/bC09Er+WQtLa2IhAI4JlnnsEzzzyDQCCAlpb4wV0/nPFly229+Z878ciqQ0lN2919/9Gb\ne/GpYL0cTpAiSFh9gFmjL19XjkXLN3Zrvd/8xw7ct4K52NHLLNkeO/+fvfMOcKO69v93ZjSSVtre\nvLtu697Lmg4GQgADoQcbY4pJwI+EhLwkpL28H+UlkEIK6eE9nuERSogxzQklmDhAWDr2uvde1uvt\nTV0z8/vjzr1zRxpptc27lu/nn11Jo9HM0WjuPfec8z2/e/8wVm46xhwrGrCixxzVdHxyqBNNgWha\nu0Q1na1U8hE5WuCcTkq6rt7+++QdEeo80NQ+mm7Fp5JYKYIaSxl0KxKicRKRYhEsxYpgRbkUQb5W\njok6eK16CXroXRGNHdsnn3wKIHU9DmCtkBqGgbtf2eXoZNJjp99XqV/FTc9uwc/fOYi/m6kenZG4\nbaUzHEvu37T0lEqMyHPjC6dW4ZRR+ThjTD7mVvV/ksZSnyAhpulMIY0JuMBAvkeBIsExUnS8MdJ4\nlnzGjaVwSB6fkhBB5K/XTFN/nKitrcXiOSN63vAkItvGlhdvmcVSl6NxHUU+Z4Ee+pv5yIxQ8Li5\nNOb7LhpnU83rDwfbw+z+Ra/pxMbnhV4Xu0fSCBbtrWZFsCxngjo0uR4ifOHm1f8coPe2YIyoCI4p\n8mLVrXOStqONhAFyz21pIUIVgagVwRpT6MX86gI8eIk1oUx0UhIXBaPc41KftZDC99yjgiR0EZaq\nIm5vSm5sTwnGrMbimaQIGgYR1VhjNhB+d1+7o4O1eM4ILD2lMmVqdW1tLRoDUUwp8+ErZ43Ct86z\nMgRUWeq12MaJTLbdSzJFUWTcdve5WLzsdHzjhwvgz0tfijFgDlZubi6CwSBuvPFGLFmyBIFAAPn5\n6eVw+S+ptrZWPM7Sx3HdyGj7TZs2ZbQ/p8ebtm6zGgHSyfg6K0K16Im1WPmP99njfU1kUHluY2Of\nzw8gE/1f1x7E/n172blm8n4A2HKggdUJHTlKUjzoTXr9pi34f2/swaMfHWHv/+e/arHxaJdtf1f8\n3wa8uYsUJQdjOv62tQk/+uc+NLa0wi0bbDBzOp6dR8hASp2ozVst1cFIVzvbJwAcOrCPnJ95wKFQ\nCEfqSYQ6EjegwEAoGoPHRbqoxONRtLcSR8WtyOgMkoGTDrotra1o7yTnEtN1bFxfh9raWuYsbN93\nCHw4qKmNONCJ04lzxianjdHvYNue/QCAPa3BpPN/78OPzc8m2/p0K1Xtnb1kIN55uMkWwYpoBllx\nliwb3lxTAZcssf3fd9F4/Oxzk/r9++k0V3ep49rV0cYeA8DRow24c0wnVtw0i0Urh/L3fqT+aMrX\nVUVmjxNdQerAnlFEFgT4CNbhXVv6fDybNm1C45FDQ2aP4fi4P/fX4fh4/ScfsmjLseYWdLa32V7/\ncCNRt3NagKD7o07P+k8/GtDjA4D6I2SVmy5Orf/kI9vrgUCAOVL7d+8EYNUgNjda9Ui03qirmTxH\no0pq3Lpn6bqOX/3tQ/z0rf3seNZtJBGlYFRHOBhIebyFCdFhOiY1dgaYU1dbW4vPeutZfWVtbS02\nrPuUvefdd619j8z3QIKBQMSq5+3euwHfmRTAl88cifZQHHkuexRr6zbyXVEhjK3bdzoer26QdMO6\njz9EbW0tSxFM93041WLu27UtafsP3n8Pp4/KRyTF/jZt2oSuiIYSn8pe/93VpC7PJ2vYtnO34+f3\n9NgwDLzx9tD/nnp7fx1Ox3M8HxeX+nGgfis+/NCaT6ZCMvpRiLJlyxasW7cOt9xyC3Rdx/333497\n770XhmHgwQcfxAMPPJDyvWvWrMG8efP6+tGCE4QFy+twxuh8PHDJhEHZv6YbuOzx9bhhzgjcdloV\nAODH/9yHt/e248eXTsCpZth/wfI6fPWsUazz/OemluC17S3I8yjoimhYvaym15/NR75+deUkfPNv\nu/DU4hksnW3R05ugSMBfbprl+N5rZ5Th5S1NMAAsmFSM1btacd3MMrywuQn3XTgOP1yzD+eMLcD9\nF5NVw+ue2ph0rAuW1+Gmmgo8U2dPx51c6oNuGLh0SgmrI0jkybVH8XRdA26ZV4Gn1jXg2+eNYXVM\n54wtwHsHOnDhxCKs2d2G20+rwmOf1LPnK/LcmFbux1t72lBTlYs9LSGE4jrmVObi08NdKPOrqKnK\nw+pdrfj8zDKs2tIEzbDsfsrIPDQHYjjQHkaeR8Evr5iE6qIcdk4XTSrGu3vbWM+RkfkeHOmM4PFF\n03Dbym2ozHPjaFcUZ47Jt63AAsDCWeV4flMjk1g/a2wBfnCxPZT/+/cP4a9bmzG93I+tjQH2l6fA\n68Kcylz8a5/VcLPMr+KrZ4/CD97chzf6cM1kyhee24r6zgjGFnpxsD2M88YX4p297fjPC6rx47f2\n49LJJbj7vDEASPH2b9871KdreCBYsLwOl00pwTfPHZP02t6WEMZzqls/f+cAWxAAgO9fMBY/eesA\n/u30Kvzvx/VYNKscDd1RvLuvHauX1SBiNqruC1FNx7GuKEanEMEQZAcLltch161gVkUuPjjYwX4H\n9Hdx97lj8PC75L6W+BvZ2RTEXat24I3b5ybJlztx2WN10Izk/SQeD0D6sz1d14DXbpuLu/+2E7+5\najL7jAXL61Bd5EWpX8Wnh7vwwILxuHf1Xtx19ij8/v3DuHJaKf62rRluRUJNVR4+OtTJ9nfbaZV4\n/JOjqKnKQ1MgisMdEXhcMkbmu7G3NYwVN83E45/U44zRBfjhmn2YWJIDRZbwu6unOB7v2sOd+Onb\nB9ARjmNUgQejCjzsnrrq1tnMwUukOxLH558iTvv3PjMWD719AIVeF567eRY+/+RGdEc1FPtcaA3G\nmb3+sasVL25uRFQzcJCrv/zG/NH4de0h3HnmSDzy4RH86spJmDEiOROgIxzHjX/ezFo0HGoP497V\ne/HE9dNTfh8H28L4j9d3ozkYw5zKXGw42o3fXT0ZU8qSU6v3t4Xw4Jr9WL5wmuO+Fj+zCX+8diqL\ndNZ3RvCF57biymmlKPapuMnsydcbln98BM9tbByy+7eg/6xbtw4XXnhh0vN9jmC9/PLLWLlyJdau\nXYtHH30Usixj4cKFeOCBB/Dggw9i0aJF/TpgQfaQLk2tv9CC479ssFb8ElUE6RoCTUnjj2bhrPI+\nfW7iugRVmIvrOpNt7QjH0RpKXX8mSVZExqrBonVJZIfvHbBSH/mcdR7qXPHd7QNRDYU5rrQpJHTi\nGeJEKgBiH/q+zrBZO0Uld83VwLhu2OzsdsmIaQbXrNFKEcxRFZYSRtNWdMP6jERZdnoMvGY8rROj\np0P7NjmdX4zVYJG/p3JpaDubg1iwvI5dN9SpchLN6AjHsa/VXuvlcck4e2zhoDpXAPDwFZOw/Lpp\n7BqhqTpmpiB2t1gpksOhr3aqZbrxCZLGidCIFU3VcsmSTRWwP0qIbkUWztVJQndUSypEokIq3VEN\n44q8SYssADcmZJhm25vOB1QJ1SVL+O3VU5I+ozMSx6eHSRSfRtkS+8vxDYJptDdgjgMlfpU16ZVg\n/QafXHsUb+xsZaIOgaiWdH/lKcxxocRnRbH4c0zXE8rNvfbpYeKQ/fHaKbb3zavKwziuOb1uGDjU\nHmZ9yCh0rF5rpnPz6YaBqMbEmlqDMVvdrFftWab9YHsY+V4F9100DudUE6EPpxRBgKSKRhPlYrlj\n7wzHWYsAgNTwrl5Wg1K/2usUwS0N3Xji03q8tYdEXoNC9TTrcPW8iTPXXHMNrrnmGttzc+bMwZw5\nyTm+gsypra3NOoWWTB2svpw7vamdMjIPt67YgnOqCxHTSVPGCDeZB4AO09nh8+zpQGAYRsaDLGDl\nilPeNaMcUc3AF1duwednOkeNePhBjx5jhFNeoiTapTMcx3df241HzMGsxKeiJRizORvBmIZ8j8ux\nYfGa3a1oCcZQlEN+/jSHnf41YDkun5gDJ03to+kWmm4gruuQJVI87GG1DLTGxmAF3DncpIHuxwAn\ncqHpjmk8k0t9CETjONgegW4Y0A3g40/XAcixKVQlQp1TOuHgJwxrzfOhjmypX4VfVbCXE83wqTIu\nnlSMVVubcerofBzqaMLFk4rx5q7W4yb9XexTUexTmTiIn3NcAWA3d7y9uW4Hi942WL5oUjH+sauV\n1ZbRybBLkXDDnBG4bEpJv44nG++j/SXbbZL4K6AKoZpu4H+uc45IFPdSCMZw+JxEaOS8J82D1qC1\n+NYdJf9TJ4ouPKiKVWdFx6qzxhZixcZGFOe4WCoxAOwze0oRVc0WdEWIzHowpqPUn/pgVEW21Uu1\ntLYCIPVh6e4tvBoirW8qNcWQ6PF/5/yxtrTuUr+KCLcQR6GLfNSR4u/r33plJ1qDcTx38ywc6YzY\nBG88ioyWYCzt+P3DNSS9fX51IWr3k3Hal8LBcqdxsNb86z141XxHGXevS7Z9n+kIREkt74qNx2zZ\nF42BKKrd6RekhgvZfi8ZKESjYcGgM5j9eOiN2SVLONoVxYajXYhpOvI8LuZ8UYGEqeUkJYC/4esO\nk/BMWPbCNtvjVVtJPw86GL64uYm99vcdLXjETE3k4Xs90WOMMJGL1Af08aFO7G0NYfMxEn35zHiy\nKhfiin3bQnEUeJ0jWA+9fQDLP64HXXALxS0hC48iQZUl277I8ViroT6V9GqhEauw2ZASsCb7kbjO\nJs1eTlLXJnJhNsDUDDj2afnpZRPw26unkKafCd8Tk33nVg1p3yXLiSPw0anp5jVQlU+27QjH4XPL\nLHo3ptCLYEzH2WOJTeea0ruV5qppW5qI5GBQaRbRUvEPp5XomSP8qPQM7ern+qOZCRptMyOGO03l\nLfq9q1wES1XkJHVBgSAdV08vTYrk0kl8OgGWEXlu3Dc1taCCE+lU5gDg6Rtm4JklM9JGjSiXTiYL\nCeeOKwKQHMHqCMeZQ0ej/yUJTZIBW7CfKb8e7ggj3+NCMKql7YOlKpLNqdANsrNUUR7rM8l21OHh\nF0WogyVJkm3xLNc8Nh9nQ1WR0BWJw+uS2f2VP56GrijazUhdeyiOghzLwaLfRarsDgqNitOFwFTn\npioSU7pNJBBPrWZKasEyuwdf++RG/OH9Q0l9FRPHXMGJj3CwhhnZuCqQqYPVl3MPx4lCEnWmvC4F\nMd1AnkfB0+sa8NDb+9kE28nZYEp2vfWwUuAUUXn43YOsoSKPvddToky7dbNtLZ7CJqaAFbWj5yNJ\nkm3AovjdStoUQdZ7i1s9dLtkqKaselW+h6Wm0cG9O6ohz+OCqshYe6SLqSdRJ4pOcsIxnU2a6aTB\n65JZ5Eg3iM3p/hPluSWQSbdbkdlkQ1UkeEcSmVQa2YnEdaiKBFkC64cU05ydQwDYYaoPTi71QZHI\n90XlzCeV5rAmvTNG+DGt3IdqM70lbH6eepzz8egEgjpYTh8/utCLP91y6vE8rCQSG1SngvZT+8HF\n47BwVnmSwpqTTHtfyMb7aH/JVpusXlaDr549GomxJXZv6cHR6Y1dvn3eGHzn/LFptyn2qSjzuzFj\nhJ/170sFVSxlPbsS5OcBsN56VJAp10xRc8kSkxY3DGuBiY4Pf93ajDwPGQ/TZZG4ZIn9fjXdQH5B\nAdtnJlAFQH6xK1X0Ls/8TtpDcTz0uYkASNSoIxxHVb6bNYvnFxjdnHfYFIiyezJgLcysPZKsEEkp\n9av42tmjAABTynyoqcpN2UzY7UodwZo4Y06SIAjFq2be8BggGQjjiq3zqKnKPaFSBLP1XjLQCAdL\nMGjQCfxg9ocIxXQU5ljRKo9LQkwzkOtR0BKMYc3uNnYc9C+/opn4mvNnaLh95da0x7F0HilujaS4\nOfMwGXHuOfrxVqNh8sSFE4vw+/cP4+t/3cm2pc4FXY2TJTAp34lczYuqSI6O4/jiHNtxhMzP7Ixo\ncJtOTSimo74zgkBUgypLiMRJOmB3hDi0NL2wPRxHOKYjx2WlsEnm8VNnhEWwVJk5P4ZhQOMcrMQU\nQf6omYMlSzbHCiD2ViQJiiSlTBuMcCuST5oNOf+5p43VhZX5iYNV4HXhPDMa6HbJ+M1VU1jkiko8\nD5Qjnil0FZ7m/bsHMRp8PLhiaikum1KCkQVe3HHGSO67JefVH2l2wcmNU1P1s8cW4IzRyUqjfWXB\n5BJWx9MTMypy8X9pxBcmlebgksn2JqU0q0GRJfxp8XSsuGkm6xk3soDci/goF32/ASsazMug7zVr\nSNM5mXxPvZhusLGoPcP+ld+/gDic7+23xIBSLZTQ+/3mYwHUVOXh1S/OQSCqYc3uNhatB6wFLcC+\nQHugLczSEHl+8tYBLFheh+e4Wmz+M+kCVb7XhYc+NynlubgVMn9w0n5rD8fTRLCUXtVgReI6i7ot\nnFUOr6qICFYWcmKP1llIotTriQxfr5MJfTn3UExHEedguRUitkBFDACr5w6L5hhWPyc6GU+MevC0\nBmM4lKbxYZ7HihTRCeK8kam7ukcTUhfJMZL/oxpxZOhxOUX/lpsd7/kIFh0j200lKIAMwE62p4Nu\n4jF3huPwqTLcLntPD59bQThOnCEDpE6GDjQlPhXBmM7SDBVJYk4AdazphMDjkm1NgOO6wYqd+RXW\n3141GV89axR7bEWwZOzZR3qq+VQFqkKOU5ElKKYTSG3IO/X8yurVpqLilmNWRJA6MeOKcjCrIhdP\nLZ5hs1d1kRdfPmNkkh2PB9R2uVzfFwA4b5x9knei3DfmVOXZ1AatVCLy+Om6gWlOf6LY43iS7TbZ\ncLQ76bn/ung8xhSlFzoZKru4FdlWA3btzDJU5Vn37so8D4q4179tqobS30xcN9h92DAMBKNWlsGS\nuaQHHL39p4sMF3Ipd3HNQFsHcejSjWGU1ctqcMEE4uT99LKJ7PmeHKwFk8h7+EgSX0MVjOloD8UQ\nieusvjOm6Xj/QAcml6auU9rl0B8xFNNs/QzTQRtEd0a0pP6YazdvT+lguRUJLcHMovgAWUjtimi4\n57PVuOOMkUxu/kQh2+8lA8UJ52AtWF6HV7c3D/VhCNIQiGp45IPDbCUtphP1nT0tqZvD9pVwXEdx\njsrC67JEGrPyKUssSkUb8oKo/dH3A87pgxReppty4cQiLJ4zAs8umQnVLGrm9zerInWzWRpp4T+T\nTxH0uqxID1XxA5KLq21587oBj0tGazCG/7poPF6/bS4Mw2o4zEObc1o2IJ9B6pEUM4Klse38bgWR\nuNXwUpUlFimbVUFqmjY3EIdFlq30EOoY2VIENSK7HdftESw+yDm13G8rQqbjripLiHMTBppyqEjk\nMYv+aQaL7pHzs+xUZqbR5HkU3HpKJcYVeVldFk1fozL7lEevm4ZTRuUPaiQ2FbSOjdrAo8h4dslM\nfOu8ZEn0ExEauTqSZgFDIMiEmSOSZbeHK7+5ajK+f0G1rXbqzjNHoSyXOFRO/snIAi9eN+XJATLO\n0obwumFlCnRH4shzK3j1i3PwmCk3ns7B4l+L6wZLDezuoa6J5/Xb5mIGN+alipjRzzplVHKP1PFc\nylworuP6Zzbjp2/tZ+PH798/bG5nd7CuNJszA7DVZ1ECUZ2lJmaCqkhMEIsnGJdQ4HXej8+t9Cq7\noaEriq5IHHmmw/bWnjb87J0DGb9fcGJwQjlYdNL5m9pDWLC8DguW1+H5jckh4ROZbMht3XKsGy9t\nacLhDqJqFNUM/O69Q7jzpR1p39ebc4/Eddz18g5E4vYUQUkig8Stp1SybWk0iKXmGVaqVyYOllOq\nhG6QyAZNw0uUhucjRzVVdmeL+kV0GwnE+VMk8v5gTMef15Pr2uOyBqrEI6QpBoZhQDOAohwXdANQ\nXSSik6PKjrn3NI88ENHgd1upCe0hEsFSTbU/Wj9AFRnzvVa9FD29t/fanU8+1Y9XwgLAHEefSvLc\n47rB1PEyqb1xuyTkl5LvNaoRYY1oXGerjtG4DlUmKR7UMfElyPjSpspdEQ3jir34n+um4SyzWXF+\nitXJoYTajjrsumGgxK8mqXCdqPcNerkUm4X7y8xedv3lRLXHYJLtNinrozDKUNhlWrkf5bnupN57\n9P7JC/P84vJJ+LlZr8TfJ/l7clw32AJYV0SD36yTpTWcmU70wnEd/lwSudrpEA1KReL9O52oxu+v\nnpIUgf/shCJcP3sEVi+rwe2nVSFkLpjybUpmV5J7YKJa4OemlrB9HOJ6awHkfhmKaSl7eTnhVmTH\ncbywYmTKCJZPlXGgLez4WioOtYdTOn6GYWB3cxChmIZGs/nycCLb7yUDxQnlYDlJJD/6cT2bNAmG\nB3RV7Z439gIgA0Ggn9/R69ubWa8NgKyu7WwOkgHBrdhuhDHNwILJxcxBoOlr/DZUvrzbzNtP52BN\nMxsS8nnZMY3UGBEFJoM5bC9sarR9Vo4qJ9UXURlxK8WPpCx6XKT2qcSnsjS/Mod8cwqNYGlmNIjW\nRdHapxxVcVQjpO7G0a4oCr0u5mA1B2PQDavOh0aR/G6FRJ7MUTMS15OcvcVzSEqKLFlpKdS5YxLu\nLhnRuKU8KElATgqRCx56Xh5Fxus7SFE4jVLFdIOkCEpARDNYkTJNraTRN0qQy3NPHCzdQxCh6gkr\ngiXh/ovGYVzx8JTxTawlyRR6T59fTZzclebvRyDoLXefOwYrb05u6j6c2dHk7MTw7TVmV+ZiTlVy\nul44rqPU78a0cp9Zo0ne0xKMsYk7FYg43JlZhDiuk/YZy06rwou39N2W6Wq+Jpf5bPf7By8Zj2+c\nO4aNOzmqbKtHopbwumS2GMZD+2KdNbYAu5rtfQuDUQ1el9wr8Ry3S2Ky+THdQN2RLuiGgY4EBUMe\nqtzYEz/65z72f2sojlGmuuHV08sws8KP//ukHguW1+GSx9bjKy/vwNV/2oib/7Il42MXDC9OMAfL\nOUf1Kz1ERk4khltuq6YbrGYoU/iUrFK/inBMz6gOK925/6r2kE3qnN4vgzEtSS2PCizceRapm6E3\na0vkgmukG7Ea576xs8XmrL+6vRkLltexNDb+FOK6zlTuInGdRVdpd3q6AlniU5E41tDjsKUIGsTx\nCMV0TCnzOdavXTCBSPnSFTu+z5duGNjWSAZrvubJqbaM7vNwRxiFOS6EzLREgNQxWA2CLUcrYtY6\nASTHn6rt0bQcKhQhSxLLmaenTVNXfCpJEfSZA6giS/CaE4B0AzKN4vACDzHOiVJksBosj4s4vG6u\ndol3sPjiaV4R6q6zR2FqWfoUo+oiLzvP4wWvoHVOdWHKicJQ3jdWL6vBt85Lr6yWigozHZN+xx0Z\nFtb3xHC7jw4Hst0mHpecMsKQjqG0i5PqLACsq0/f9uC+C8fhlnkkmn/fReOhmO0uAGDdkS6mNNiX\nFiltXQGcMiqPSar3hd44NKePLrA1NPapiq2e6bCZPhznhJN4clQFq5fV4KwxBQjGNNtCaHdUY7bI\nFLcis/TIaFzH917fjbojXdjf0JTy+irMcUGRkFKBkPIOl/GhKhI77+kj/Mhzu/Csg0gHkH4BuC8Y\nhoFIXO9z3Ve230sGimHpYDV0RRwbV64zpTivmEpyblfdOhtAz30pBL1nwfI6fHyoA7+pPYSFT2/q\n1XsDXO72N+aPRiiuoa4+uQC5t/B9o97aSxobdoXj8LutSI2mG4hpxPmhSGUtRAAAIABJREFUEra0\nPovepHTDYNt3mhM6TTfwy38dZI0IAZKKyr/P5sRpRPqWRk86w5qtRodu6+Q4xJwcLN1gha5bjgVY\nPxA+VYQ6RjQyRMUpNFOR79oZZbh2RhkbGD0uCd1RUqzbxg1Y9HP3toZR6HUhEteRZw5Cd509ik3q\n6ZH73QpagzEWiQtENfYajVZSu8kSGfA8Ltm28giQHijhmI4cVUEwRtQIVTMFMt14TM+XX+2Ncs0q\nFYl8D1FNh9el2Bof+z12dadVW5tR6HVBloAKTrXqqullPSr0/eLySXg0RcPSwULO8ltbRZ4Hq5fV\nsMdTynxDeDQCwfFl0exyx+fTZS4AwPxxhaxXnE+VEYxqtsW44oReWVT+PBX0/p/vURDVJcfG773h\nmhlluD7FufUEFS8akZDyScfcVLhdMvxuxZbaGIhqPfbzSqQ9FGcLPdQB7opoCMYlFHqdF9gkSUKu\nx5W2Ef1Lm+3R+cml1r3Op8pscRYgY94bt8/FJFPQ44VNjQNWp2oYBi55bD2ufGIDrnpiw4DsU+DM\nsBy+l67YimfXJ3vyVGxgayOZrOeoCn5y6QRHZ+xEZTjltt7zxl6WhpcJhkHELHiZ2EKvmnGH857O\nnb9YqRPw4cFOFmECyA2RRrAkSUJlnpvJkPO1WHQwOmbmN1Onw2lFMTGlj/7vMh2qIp+K9nCM5cDb\ntpUAKUGeIlUEiw6GF08q5rZNfh+NyPB54poB3H56Fe7k1Pe8Lpk1dF17xFoR5T+XKkidMboAr982\nF1dNL0tqdqnKkk0GOBSzlJ2++xkSuThzDEndkCUJyxdOw5OLLXliur8Crwsx3YBPldlg6eYaUvYG\n4kzRCJbEeqHlqMRJpX2V8jwutlL3/MZjkCVip9dum9vrnks5qmK71o4HvMpYOobTfaOvnDO2AD+4\nePyA7Csb7DHQCJs4M5R2WTirHA9eknzNTyrNfKHB65JZHTDtETW6wN5/K93EH7DqeT0uGXHIGTVJ\nTsc51YVYdnrflFdLfSrJCtFJs3qaJh3XjR6FhkIxHV9btZMtqnZHNPjdvYvEdUc1Nl6yNHzDQNzl\nZTVvTnSE41h3JHXk8ZEPjwAAW1CiafX0vUe4NM5zxxVCkiT84ZqpWDSrHI99Uo8vrtyKO1/a7ihc\n1Ru++rI94yvShyiWuJdkxrB0sADgT2a/Gp6RBR7cdfYojC+xbj4lflX0DxhEqKR3Jry3vwMLn95k\nc7C8LqJIN7YHqdxM4CNYtL6qO6rho4NWIeymhm4yeTdvxG7FagAY0XRIIGF/KnGbKIDh5GDFuegY\nJaYZzDnwuxW0h+K2SCq9JnXuPYZh/wy+Y7xuWI5IJadiZ4tgGXYhDcvBIlG5xEFRkiRWc2STredE\nQGi0i0qdA2BNi+eaaYAFXiIiokgSWw2kZvK7FbxwyyxcZcqfyxLZvihHxezKXEwv9zPHkaaa0pRB\nRZLgSlcNzc7D+p/WY/FCFnRfMc0wlQWtCBaN0P11axMe/bgeukF6hfV3hfZ4Mbsy94RSR+sP9188\nnq28CwQnA3keF05P6NP1h2um4CpOGa8nJImkmnVHNfzg4vFYvaym1wtWFI9LRkQzhjRy7vco6I5q\niMYN5HtcLNskZGY9pIOO45ph4P439+Jge5iNZ5kyv7qQLTzGuLG/I00fLMpDb6dWApxW7sPNNaRf\n5uplNWxRErAElmqqcvHiLbPwXa6Z9e2nV7HP3dMSwuJnNuMP7x9GXzmaEM380T/3DYrCs2AYO1hO\nNHXHUOZ34+vnjMaKG2cCIMWF2eRgDafc1iXcCksm0JWVQFTD0nkVWL2sBvleUh8VT5FrztPTufP3\nVj4KM5crAKZOB51Au1325rRulwzdIFGpQq+lPkijo60OvSycUgT5CJZfVdBu9pCisLovw3KM2M3f\n/PvBwQ4oktUTiipK8WpYtl5Z5mUejRuQuHONG6Q5ZOLY085JzX50yHJCY5rBUg9oGga/MkhT70rM\nyW6uW0FMMyDLEovgzK7MxYUTSU1YnscFp4XFUQVe/PqqycxxqzH7qiRGnnqCbnHuuEK49QhJY+HS\nRXTDyvnPMVUDqa0LvS7mWFN8vVCUGmoq8jx4+MrJPW43nO4bwwFhj2SETZwZbnaZVOrrdUNxn6og\nENUco/KjCjw420Ecwgm6GDaUC1C5bgXdkTiCMc0WMWoOxODK0PN7c1crPjjQgX/ta+91HVrt/na8\ntp2IKdEIVldEg6HrvVIjTGRbYzBlbzHa9sTvVpDrcdm+R1mS8NxNM/Elrhfjqq1NAIDG7ij+sas1\n42PoisQRiGps7AZIFtC/r9rZq3MZbr+ZRDTdYErjQ8mwc7B+XXuQ/Z+oDvjJ4U6E46S4vYhO/szV\nDqfO24L+EdF0U50oMz4wI0ndpvw3QG4YgaiWtkjz3X3tjo5NRziOZc9vY4/5mz5N2wPAoidO8BGs\naNxMS1MkhGI6fG7r8n9pM7lhVeR78Mt/HcDHvEPiWIOlswm+20X253e7UOB1QZUl1luKKvzx7+f3\n45Il1liY9mHiFYl4B0vnIlheVeb6PpHmxImrlvRmXupTsas5xN6v6QbciozfXzOFpZLwjg51FBVZ\nwt++MAfjzeNSJAmTy3xQZQklPhXf+0w1ew/97FRf8+plNeyaoL2PDMO5aDkRmmLpkiVEdYkpBcrM\nwTJYUbTXRZohU7neAlPCv42LMIqaTYFAkE3QrACn2+n/fH4q7r1wXNr3jzHV7Gjkv5fZ0wOK362g\nNRSHIkk256grovWYIkjHk8PtJGOjJRhj55Yp/CfQMfZIZwSuAbBJqhRzWvKQKlggSRKum1WO1ctq\n8MoX5gAgWSk3/2ULfvbOAXz3tV0ZfT7NmplU6sPqZTVM/bU3Pbz6Q0NXBI9/Uj/on7N7mETkht1M\ng64cALDV7tAi/bkJPYXcisz6B2UDA53b+ue6Bix8amOv3qNxkZbeqNd8epjkH6/a2sRW4FSF9GLq\nTpMD/sCaffjLhmNJ536kI2Ir/LRFsDQDsyty8cbtc5EOtyKxGqyw6RSpptPlFMnQDQNv7GzFq9us\n69AxRVA3mKNAB6V8U1DBY4pVSLA7WFHNQENXBDHdQIlPRYlPZal5UU1nKQN876umAAnn015ZZD8k\nBY46jjHNcFy5pI4ElbHt5hQTXbKEyaU+prDEO1h0lc4lkwGOpuIpMvC9z4zFc2mkkCNpVJT4yBVA\nonsZ1UGZmyiyBMnlRo5Ltp2zbhisKJmKbdBz7gjFEYkTB4w2qDyRIliZInLi7Qh7JCNs4kw22IX1\nu3KIPKlKzzLldJz1MNGhofOw6PgT0y1l3JH5HoRiWo8LckvMFDzaLP5wRwSlvVR+vf30KpbZQR2e\nV7Y1Y87I9FHAb547Bp40DqBHkVCZ73F+zRwbL5tS0uPx0bnVF57byp5bn6GIGJ0njysiY2FvBUAo\n6X4zUU1HQ5ezIMfSFVvxlw3H+h1ZeuyTeuxoCqR8naaVXsRF6oaCYeVgLfnzZgDAn64nBfJ8lGJf\nG6kFKnIo+s71uNJO4E9m3j/QwaTIM4Xa8khnpFcO1uVTrZsD322dRrHSkUkvs+ZADK/vaMGC5XXE\nSfGrSVGb75w/xuZ0uRWZpQhG4zoUyXK6nFaT4lxNFlVWoqs7/CoPX3D70SGibhmO62YaooRwjESZ\nNMNyUo91R7F0xVZE4jpJgzBTOmj9UG5C7xIATH1xfEkOSxGMsAiWeV6a7liULEsSvj5/NL51/hiM\nzPcwJ0QzLOeE9rqyO1h2R0jlpNTdipxW6GFDGnlhuspKFyV1o+eiZQBoMoVIFIn0DqHFwPSt3G2C\n3VA/O5GszBXmkBqstlCM1e1l8pkCgUBwokD7P/VWuCcRT8Ii2FCReIvO8yjoimosLT8VN9dUYHxx\nDutvCVitIDLlcHuEiV/x85IFk9M7PzNH+BHRDEfnQdOJ+FYqB4yOSc0OmTxOnDrKSjV8wexXxtei\nU5oCUdscjs4Z6KLpYIg2vbCpEUtXbHXMKuttNDEVKzYcw993tKR8nQZkUlWm3LZyKxq7o/2Sqs+E\nYeNgGVyaT2W+B1PKfLZw6bHuGM5N6P5NyfUobHX+RKe/ua2bGrptqwc07aw3UFuG43rKlC8n3ue6\nrvMSq3zhen1nBM9vJAqR/9jVyn6EXREt6dwTfYZJpT7sN0U3aKPfRC6eVGJzulQu0tMSjEExI1ik\nuzu5/L98ppXb/OQ6Iq6yrdFaHaG9pPg6Ml5Ig7L5GHkPlVv3umSiWGie412mek9LMIZcj9lbSiJ1\nSFFNZ9Ek3sGqMSO2NBoGgO07ErdEM1KNh5dPLUVlngcFXheeXHsUT9c12PqJ0HRE/lyo00WfsiJY\nPQ+6Y4tSX2+J6oSGgYxqsHaZ4X6+5op/3ByMsQGHyhurioRfXTEJV88og24YWF/fjdNG5+OzE4r6\nXAA+nBnuOfHHG2GPZIRNnMkmu/TXL7JqsAbgYPoBHWp/deUk/PKKSchRZXRF4hmNF4GohoPtEatX\nY0VuD++w8/ed1sQ9ECMZKZdMLgYOb077vlFmuv386uR5Kh2zU409NGJYmqHIz48vncj+p61S7l29\nF794xxLZ+NPao7jp2S24b/UeAGSOTedCE02Vyr46WOl+M3ScP+LQ3HpkgQdL5o5gNWf9IVVtnWEY\nePCf+wGQRfXENjUAiWzW1Xfh+6/vxtdWDV4f3WHhYNHO1TxelzUx3tMSxK/ePcgUxBLJcytMZjTb\nONgWxuWPr+95Q5NvvbILS1dYoeO+SNgHohp8qoxD7c79yFJBezd99/yxNqeKb8769b/uxKMf18Mw\nDPzsnQMsKrTWQd6UOjb0GDxcR3ZeZAJAyh+sW7FELkjUiaS86YaVKjaOi7bR62hWRS6byEe5qBaF\nptkBwB2nVwGwbrAumVy7XpdsSxGk1HdGWLSK9tKKxnXkuGhDXeu8glyTZOqoRVgaombayTlFkKcw\nx4V39rXjybVHEee2p/VW/PdMHSo6GKi9yMt3qqWjJKYI6kb6viaUP10/A3+5cSaTk6VOJ3/OM0fk\nmsdqOoOShBkVuWbjY7LNvJF5+I8Lqns+CYFAIDgB6au8Oh0/PVy2wlDyh2umYNnpVZgxIhezKnLh\ndZFF9Exqdo91R/HBwQ6MLfJi+cJptgXL3hKKkbnQt84bm7Tgm4gkSfh/n61Gezh5DKQZLel4cvF0\nzE8RRHBiZoWfOZErbiKib6tNwYtAVMMzdQ0AgC3HAli1pQmXPLYewZiOU0bmMedkMCJY7WamDJWs\nD8d1/M+HRPUwGNVQ4lOxuyXkGOnb1xpiZRGpoA7Ri2bdfCK8L/GeuegfcMiQ0nQDm48FcKAtnPTa\nQDEsHCyeX14xCQBJUwqZE8iPDpIUrAnFzpPoXI+CrkhmvZaGO4m5rR8c7ECsl7VQfNQqA/E+GIZh\nC6nftWoHgjEdHeF4RhGslmAMx7qiOGtsARbOKsdFXB8nwFphAYCpZiNRelw0dTAS15POnSr70W10\n3eCkznVWAwXA1gmeJ0e1urLTIlkaoaF/3Q437ZiuI246eGHzx8mnrMY0nd24ac+SJXOJ6uLB9jCa\ngzHiYBn2XlYAqROin22ARIo0w3Jk3IqMZ5bMwD0XVrNz53t3RU058nCMiFukShHk4eVleeeQOlHN\nAWtQUBMGpN5EsDYcTZ0LnrhfPcMIlsclo9in4h+7SXPpiWabBv6cE1NbnHbLX4fZRjbUkQwkwh7J\nCJs4kw12oVkYch9DTzQdfrikCE4q9eH62ZaKsVeV0RXRMmrrMcNsa/HylqY+paQt55rJB2M6s0Um\n18moAo9NvdfajwavK70zU5Hn6VXt28NXTMZDnyORLL50JqrpeHDNPozM9+D7F4xFKKbjDx8cZsfB\nO1W+Hhysr7y0nZUW8KSzBV1kfWcvmb8d6Qjjhc1NiOsGuqNa2ibaX3pxO256dkvSojTPjqbeC1i8\nvact6Tm6YJuJ095XhtzB4nNczxidj1lmODdHVRAyJ/lPmD2xClM03czmGizaE+lQe2ovuzUYw4Ll\ndcwJO49bBaFOwr40/az+vP4YPv/UppSv93T5LfnzZtyyYgsKPK6kBocAMI7rgUXTQDvMm1C6DvOn\nj84HAJZrGzcb6gLA9qYgGrmVjrwU/Sn8bgUdpoR6fWcEqiyxyBWN3iRO/AEgGNUR0UjONBXJ4FME\neSdlTlUeVi+rwXnj7AWVXlVmQiFXT7f6mjR2x9h747rBBkUqne5WJJT53Sjzu1nDRM2wIj7hmAav\nS0bUjES1heI99i0pTOFgUTxKamdV7cWqZiYOE7156oaBeSPzcHUaFUieG00HlvYM4ScBdGJAb5ZD\nPUEQCASC4wXNfuhreSldaPMkpHEPF2ifr0yOizoEd5zRt0bHeZw0fCjDz6SU+d043BFJqj8KmpGw\nweQJU7ugJRDD2iNdkCQw4Swq+hSMajaRJ3cPF8zulhCOOqT6peONna2YU5mL8lwyXw9zvTgDUc0m\nOkLttLMpiKNcaQt1CBOhc1y6j/1tqee1vKz9k+sakl6n2gR9XZTIhCF3sDY1WCve919sdTQnDWp1\nLHramvinkldWZQmb0qycn0jQ3NYDbSF8cKCDKcakmzDSC5PWXmm6gZ1NQXx4sAOBKLm4v/Tidhal\nemDNPltBJO+80Qv+6RtmMMGATCerm491OxahlnArFrtbyA/iYAf5zO1mrZMsAe+8W2tzuGmTvyYz\nuvLegQ72Y31rTxs+4Gq+vnrWKDxsRj95/G7So4pXRvQnRLCcRA9oTyq3eR0C1o/bMAxSg5XCLvRG\nSlME47qBSxPUgeh7Y5rOokdel4zHFk7jIlkSAjEdEgwzRZDYKaIZ8LhkxDQrDbEn52c0t5IXjutJ\n3yk/HEwu9dn6dWQawZpTmcucoHTEuEhcYY6Kr549qsf3AGQhAABrHcB/bfQY15v3AX4l8JklM9hK\nX7aSTXUkA4GwRzLCJs5kg11kFrnv22TxupnlWDSrfNg6WJbgUs/HNcXMklk4q7xPn8UvNoZiVjuW\nTK4TGglMXDgORLVBScfjoU7UnS9tBwD873XTkKMqKPC6UG86SYGE9jRqmpVZOt9xGvdT2YK+5/Kp\npSz1jjoygaiGQFSzlYw0m8GBu1btwK1cacumhm78ua4B1/xpg23/r21vBgA8sID4Cne8sD3l8V/X\nw/dP+4cN5rU+pA6Wbhi45429AICXls5OUjILJ+RNpioHen1HC8s9zRZ+XXsI97+5l4WbY2nkr6kj\nctvKbezx3a/sxH2r92JPSxDFPheunl6Gzz+1CaGYhnf3tdua05Vw9VJbTKGG8ly3rf/TkY4w6hzq\npACg2oxQhbmbEc9IM6pFV1gAUlsGAI98eAQASRd7qd6DhVwkjZ4Xf+7bTGnOiyYV405OnKI81+1Y\nzOpTSQRrViV5bX9bGF6XxF4D0v/AVEWy5NBZ5IUIcDjdeKqLvDjbLHKlKYREkdAeYVOYg2XYnuMd\nIVUmTXPdMlH+03SDDaBel4yIZqDAbKSbSQ0WpSMcTwqL82ml+V4XfnqZ5ZC4lcxWR39++STcPK8y\n/UZAr9JdncjjIlgzK/zsfwD42FR05IOSZX43y1UXCASCbOO5DWTxqa9zxdNG5+PfzhjJFqqGmX+F\nV7aRiXUmk+FvnzcWf14yo8+fxS8SB2O9i2BJkoTp5X5sbLAv+AejzqrFgwGt26ZjIp/it/VYt+18\n0jmsdEE+0guVvXVHyPg7tsjLat8PmYvpoRhxsHK5uRAt/0lEAvDE2qPsXCi/f59EtqpNMa1U4/rS\nU3qeh/jS9I4bKIbUwXrEDAP+6JIJSRdfjqqgw/R8RxV4cPX0MjZJTmR+dWZdyk8EaG5r4kpUKKaz\n9LpEaLdxeqHEdIMJM7SG4jhtVD7aQ+S9VIhCdcn45FAn3tvfjpWbGs3XYrj7Fath3e2nVbGauD9+\ncATfe3234+fT6XJE0x07rVeYioJ0haXA68LWxgCTcqc/+K1dLsR0Awfbw1iwvI6d16vbW1BTlYd8\nj2IrSMzkhuV3K4hpBipyPexYaS0P7fmR6iYzpzIXqmzVAsY1A499fASdDg4K5dHrpuHuc8ew/dIe\nV4kOEE3p4KVbE6NQtDDT71Wh6wZ0w0C5aUuvSprt+sxU2p5WLmnaHxWK4Y9n9bIaXDix2PF99DyA\ngeuNEtOMHtNOnaArkyyCZet2T/7ONPPvh7pI+3iTDXUkA4mwRzLCJs5kg10OdZAIRX8VUteavSyH\nm9LqreaEOZN6GUWWUJqmzqcn+HHun3va2Pwk0+tkcpkvqaQjENN6rHcaCF794hx8/4KxWHXrbMfX\na/d32Bwufu7DC11F4jpThXaSMU9lizW72+BRJBR6XWjstpeDdITjcJt9USm/fe+Q4372JwhPtAZj\ntrRLOvbXObSFURUJizKIXu4zPyOarTLtq7Y2Y1JpDk4za214clwyPjnUAY8i4fFF0/HVs0elXEn4\nwilVjrU/JzI0pY2uHhzrjmLJnzcjFNPw3MZj+NtWS0GFijjoBpFHP9xhvzhLfCrr60DFQP6xqxX3\nrt6DH/xjH6uB6YpoGFvkxeI5JM2rIs+DWRW5mFLmQ2caERH6G+2KOK/25HtdeGkp+cG/vHQ2JJAU\nP6q6F9cN9j9AehwAJHWROh919V22G1RM03vVz4jfltZ27WsNm6+Rn0GipL2qEAn1EItg6VixsRGv\n72hJW2xLbdAV0aAbBsJxK7JHB4gct8Kei2jOoXiq7JTjUkyRC8DvtpobxzSDy6FOHxWaWZGLl5fO\nxqmj8m3HmAl0wBmoXu+fHu7s06rRbacStUY/k5GX2EFRx/5Gs9HkUDbKFAgEguPJXRmmWfdEYuRl\nuECzbF41U8QGk8SRg5Y1ZMqEkhwcarfXLe1oDNqExAYLVZFxwYRi1qwZAJ5dMhOPL7KEO/jUOV5h\nkY9UPbXuKH5dS5yfUCwzB6SxO4q39rQhohnI97oQNxeF6TXVEojZ0hMTSRRH47nhz5uxZrddqOKO\n06uYZgOFioHRedGDl4xHItRRo3OQSCZKcH1kyGuwvnP+WMfnZQnY2xrO6OR9bjkplHiiQnNb6ZdP\nc2cfevsAABKBWv5xPX73vlUE+BfTIQFIJ/APE8Ku44pz0MgcLCvtkmZqrTKdteUfH8GBtjA+M94u\nFVqco6ZtFEzrvAB7ahYPX/d0z4XVAMgqBc1ZPoWr+XnTTF9sDMTwmQmkDqzUp7KoD0Dqd9LlD1Po\nag3vYJ0zlkQ8add0VZYwtcyHy6aU2NT2FEmCSyEOlkeRWDrfy1uaMhZz0A1TRt3cnJ6vT5WTHMTE\nXVLHLxYJIaKRGwdNa6SSr/R402SQMnxuhfUn60vecTpln94wvjinT4WlU8t9mFsQYypEiizh384Y\niW/MH43xxTnwqZaMfz+UeU9IsqGOZCAR9khG2MSZbLDLldNK8eyNM/u9n3svHDcARzPw0GyTjceh\n1j7V0JTpdTIi180WtF/a3IgFy+vwyvZmm1Lv8aTEr2JUASk9mDnCb1NW5LOAaK17TNPx3MZG9nzI\nQeLcyRb//lcin/79C6qhyBJyVBk7moLY0RTEGaPz0RaKs/kLlZXn+fo5o7F6WQ2unGYJgvFfxc+4\nHl8A6ZebmxAVpCIeNAJ7+ujk7Dban4ufzgzU3CaRIZ+GVOQ5R562NhIpRr5hbSpyVAUtwRhzRrIB\nWqfy0ha71n9Tt7140jAMWziV7z91y7wKfPHUSnhdMlrNMG26Hzl1zEYX2KVNi3wulk/r1J2bd76O\ndPT8HcyuzMNlU0rwwwXjsfLmWXjttrk2x4by1p425Hlc+Pvtc/HUDTNsznZnOJ5RBItG7BRZwl1n\nj8K54wrhNX/kuZzIxW+vnoL51YW2m8mOpiBUWUIopsGrKiztcn51YUrBFR5e2ZJO/GnBJ9/T45oZ\nJIKYKi2jMSIjGtcR0w0WxaM3BFrblWm/MnrcfVHZ0/rQUy2R1ctq8MAlE/oUYcpRFVxVGWXfeySu\nY1q5H5+bWoqRBR68fOsc5jiKCJZAIDhZkCTJVkvdV84eW4DrqgavL1Bf2dVMokgrbpo16J9Fx+EF\naSIq6agu8mJvawjtoRirMQeA206rGpDj6yuv3TYXD1852TY2lvlVTCrNQUWeG7ph4BfvHMD9bxJd\nhEsnl2B2RS5+8a+D+N+PjuDnCQ5OIq1BMtei0SOfquDrf92JEblulOW60R62HKyiHJVlNVGowAqN\nxv7mqskwYJ9zehQJT99A6ut8bgUfHOyw9dMKOfQb+9/rptoeU60CALjQFHLb08soJc9vag+mfG3I\nHaxU/YuuNGWtf3LZhB73QSeNTp72iQbNbU10qGndSTuXP3vzXzYnNWimE+0XbpmFW+ZVYsncCpuN\nf/c+Cfum803cCd9JcY7Kfjx8pPAX7xzAvtYQgjGN/Sgyzd3+5rljUJVP+j64ZImFf59cPN223dt7\n2yBLpJaJOtDFOS7ioGTgYNFiyPrOCK6aXoZ7LxzHVsPcLhmqLLEwuapIzIkCiK1dsoRwXIffLbMQ\nens4nlH91/62MAth87LsAHDZ1BL2/3qHPOJEXIqEYFRjgh9UDIMG8TJ1fmjqQJ8crAFc5emrnPD8\n+fPZ90UFWez7zUzxMNvIhjqSgUTYIxlhE2eEXSwUWcKXPnfWUB9GEjTzIz9FO5bBQDMMm7R6ptcJ\nbSd0/TObbc/T9PyhwilrRZIk/OGaqWjoiuKOF7Zj9a5WfGrW4d193hiW3rdyUyPe3NXK5pfpbEHP\nk9bQf+f8sfAoEtpDcVuKoN+tOEayJEnC6mU1rGcqjQZOKfNBkSU29yrihLs2Hu2CYRgIx7Ukn4JG\n7JwWoftTqwcAf9vahFe3t6R8fcAdrI0bN+K+++7Dfffdh82bN6fdNp2MJr0URhX03ChOliRMK/c5\nFuOdqHRHNbaC8tkJRSzyccyU/5xVkcuKCHmoE8Cr1nm4C46muUm23sT0AAAgAElEQVSSBFWWmBR7\nOngFOr6J3updrVi9swUel8xkvZ0iXJnws8sn4v6LxqEiz2Orx3L6UbjNnhhO/asSOd9Md2wLWsfN\nR0VfvW0um4zzecsUWoOVoyosUtcSjMLvsK0TI8xeEIos4bdXTcazN87E6mU1KPO7mV3nVPascOd1\nyYjpBvv+883vl65G6Rle+r2pW0tkIKPo/dkX/b4SV6oATlpWRLAEAoEgK7h2Zjke4pRtB5vrZpbh\n+tkjMppj9MT3L6jG6mU1/T+oIWB6ud/2+NLH1mNHk31h0zAMWxSJRrBoOcrsylx4XDL2t4WSnB/a\nINkpU40u1v+HKa62oylISkPM/fMNi7/96m5c8th6hON60mdIkgSVK/HgeWsPKUd5YXNj0muZwJfq\nODGgDpau61i5ciXuuece3HPPPVi5cmXaCXe6RnDTy/02Ge6e8LqUjIvxhjO1tbX4de1B1HdGmGra\n/GqrJuqpOtIwTZEticrPjC/EBROK8MMF45lwA4/TpNqtSIjpRtJK/w1zkvsY5XPO2p6WIBYsr0On\nGUlzKTIMw1IIPKWPqzRlfjeMw8Qhf3zRdPzg4vH4n89PxTM32Fc4RuZ74FFktARjGSkK0R8p39xu\nQokPf/3CnKRt6QoPTR28dHIJNh8LIK4byHHJzMFqCsQyimDleRTmtMmShKnlflsaB60hG58gruEE\n7c1BJWTpsdKvtt2h27oT/en5kGkaYibcdloVlvUhZYLP/XY7nAuN8DV2p25gnY1kQx3JQCLskYyw\niTPCLnaGoz28Lhk1I49fq40vnTkK44pzbHOnvtrlggk9L2IPNTRSN7fKLhrxiysm4bXb5tpEMr62\naidefes9fG3VDgSjWlIWlVMWk8cloykQS9IHoJw1NrUSeH2nNZbHdYPN+xIbN08syUHYIUUQIGIe\ndG7AN1emQYq39rQlvacn0ukSUAY03trQ0IDKykq43cSzHDFiBHuut7hdMq6dmXmjuBxVzooIVlNE\nwmv7SMiRRqFmmL1+LplcjDd2Eo9bkSQWrSrwuvDVs0cDAMYWepMuMFqX9f0LxuKCCSQqduljZMXh\nsimleGNnK55cPB1LV2xFZV7ySsII7rkH/7kfAJiE55Zj3ZhS5oMsSXjuppksPN5fnH5wl0wuJs6R\nKcrh7sXqUmJ6W6rUVID0uHrxllnwuGT8fSf5LjrCcdYUuzkQw9yq9A7WtTPKUOB12eqwEqGH31OH\n9/NLo9geJt9BoVcFEGKOcW9rjfrjYA1kiiCtO+sPEQdlD2qOPa0hJpAiEAgEAkFv6Y8wxagCD75p\ntmwZ7tAenyU+Fd86bwxTMKbzhcKE1MzDISJgcc2TGzPaf7p5x4u3zGJ18akoznExHQHqwCU6crtb\nQo4RLIAImdHyjzyPC986bwz+8+97AADnjytMKovJhD+abaZeuGUWdm1xtsOAOljd3d3w+Xx44okn\nAAA+nw9dXV19crB6C2lMPLwdrPf2t6PM78ZkMzLlxF51FIBWrF5Wg5ZADOeOK2SOBB81Wcs1/T1/\nvDWRrMz34Ctn2SVbi3LUpBA1nStPH+HH6mU1LDrhJJWZqNQCAEdMKfjdzSFcPpWo8fXXueopx/lb\n5xHFSXph94beNLeNaYatGR5g9RmhHOlIXwh8p/kdPPyvNAWQEk1NTP3jfmzhNFTle3DZ42SV6D8/\nW41gTMOu5qC5D3LziGV4fjMrcvvcN+5o19BHhfhrxCmtgAbZLu5jgfKJiqgjsSPskYywiTPCLnaE\nPZzpjV2eWjwDeR7luPS+Ggho+lwkbuCSySVJr+d6XPjxpROYUzJ5ylSgfn/SdvdfZKlQ3npKJcrM\nzKF0c4fEuRbP6mU1WLC8DsU+FYosMaXDVARjmmOph9tlpQiGYhqbO9CepZv70J6g2CzxyEtz/AOa\nIpibm4tgMIgbb7wRS5YsQSAQQH7+8Snsy3Epw17k4gf/2JckNZlImd+Nq02BjxK/aooykK+pM6Lh\nj9dMsW3/6hfnYGaFcwPm3iCbhYVOOcdOhaX0Qg/HdUwu8ye9fjwYXZhZ77Nzxhb0KprBp7XScDSV\ndKdcPrUUmZDYk4znR5dOwOOLpqVNNxxd6LWlcfrdCsr8bmww5WplSXIMiaeixKfivouSe0NkgpOo\nxFDx0tLZ+NKZyb1fSswbOi9FKxAIBAJBbzk7TepaOkbkuU8Y5wogitMAULu/PeU2vKhERziOM0bn\nY/WyGlsK5KRSK3hwU00FFpjOWjCDdLp07G4JoSWY7Fx9+cyR+NP105meQ3so7hjB8igywnEiEhbV\nDFaqQdWr/9mHFMGIZuAms+9mKgbUwaqoqMDRo0fZ44aGBlRUpD4APqe1tra2X49bGuuxddfeAdvf\nYDwGrHqoVNtv23cIowu9ttdpeHXDgWZMLCX1Q+N8Gu6bGmAOUW+PBwAqPFpG21MFn3kF1gW+/YjV\n8K/Mrw6IfR555JFeHf9HH7yf0fb3XzweSv2WjI9H5x7TEH93s3Vdf+mMkfA1bstof//vwnH43NQS\nx9e3rP0Iowq8mFrmx+KR4ZT7c8r9NlqIGqQsS6yYdDCv30vKI1g6OtTn9w/UY/pc3ccf4MP330t6\nvbooB6uX1QyL3/vxfPzII48Mq+MZ6sfCHv27v55Mj+n/w+V4hvqxsIf1+HufGYsVN83M+t/PuCCJ\nTBX7XCm337aRlJWU+FSsqf0Q3e2kXOXLZ47ErWNCqPJqLGKV+P4JWj0AMDG03h4fYBfGoq9/fmY5\nKvM9mBbdB8BysBLfr0WC+ODTOnRGNOR7FKz96AMAJEDQ2B1FXDd6bb+Dh+t7rMWXjL7KvqVgw4YN\neP755wEAixYtwuzZsx23W7NmDebNmzdgn/vs+gYEYzpuH+JeA+mgSivpFGX+/S+fYOHpE3HeeHvE\n5em6BpxbXYCxRT0LImR6LJ+bWoJvzM8sR3jB8jrce+E4PLBmX9Jrr35xzoCo7dTW1mYUhr/7bzux\n+VhgUJR5FiyvgyIBr99O9v3+gXb815v7cO3MMry0mdR+HW9FoNraWvxwu9/22bph4NLH1mPhrHI8\nv6lxSI5rKMj0GjnZEHaxI+yRjLCJM8IudoQ9nMl2u3xt1Q7MqshNKT7XHYnj809twsSSHOxuCWHB\npGJ8+/yxGe8/GtehyFKvW6gsXbEFF00sxtOmwFuqec7SFVtQU5WHXLeCf0s4h++9thvnVBfg9+8f\nRnWRF49eNw0Lltfh6ulluHxaCe54YTteWjo7I/Eyyo/W7MPZ1URgbt26dbjwwguTthlwmfY5c+bg\ngQcewAMPPJDSuRoMGrujWLHh2HH7vN6wYHldxgIcOfmFyPUkf8k311QMmHMFkMK8rzikWKWDDxED\nVvrcQDhXQOY5zpNKfUlFlwMJv+LQbUaHNpkpeV9Ko3w5WMyfPx8/vtTeD47Wb8U0A3ecMfKkqTnK\n5gGuPwi72BH2SEbYxBlhFzvCHs5ku11+d/WUtMreuR4XHrpsInabTXlX72rt1f7dLrlP/SmfXDwD\nS0/pWcfB65LRHo471rT73QpTFubn4nMqc1nz457KdxLZ1xZ21CfgGbxZ6nGG5H624NXtzbhkckm/\n1NIGEqq8dtRskptO0AAg/a9640X3lXSFeU68bvaLOmN0PiaU5OCGuRVQZQmHehB7GAy+fObItDeC\n/sKHommj4Okj/NjdEsKWYwFcN/jN5JM4dVQ+Xr9tbtLzq7Y2nRSRK4FAIBAIBEMHL5U/uTS1WNtg\n8MVTK/F/nx5N+brHJaMjRQ2W322pjFN5dTqnpY8/MJWxM+Vgexjd0fTtcQY8gjVUjDaL2n9TewjX\nPZWZdOTxgH6pX3pxOwDicOmGkVLyuqUj0KNXPBTQlYcHLpmAL5xaBa+5GlE9gFG1xJzbVEhS78PM\nfaUy34OXls7GstOJQ/fRwd79CAcCahenc55T2X+BkxOJTK+Rkw1hFzvCHskImzgj7GJH2MMZYRce\nA7+6ctJx/cQlcyvSLiaTCFbMUfJdliTsbiaRN8XM/qHzqZ6CHqko86uYXp5+/pU1Dhbfcfqa6f3v\nsTNQBDllwzvPHImoZuCbf9uJW5/b4rh9WJdOKPWZkwG/W2GrIjf2oBpzvCn2DUzfMYFAIBAIBIJ0\nFHhdyFWMASsNGSi8LjmliuDrO1qwtZGoICdmt/W2lyglENUcy3ls++7Tnoch/Op+H+2Vkp+8tR/b\nGvsmUR2KkgjWgknFrHHytsYg6yDNYxgGooZ8XFIEhyPDIce5wqHRMuW/r52K62dn3vx6oEhnl750\nID+RGQ7XyHBE2MWOsEcywibOCLvYEfZwRtiF8N/XTsWj1w9BnUQPaIaBYMy50TDPjxLq2QESxeqN\n26AbBsJxvcfoV9Y4WADw5OLpWHpKJTrDmWvur6/vQmc4fR7lW3va8NjH9b06lkhcx5rdrQjGNEwu\n9aVUW2kKRBHVdCxYXodAVIMsgTUWFhx/rppWmlIwYnxJzrBbtZk5Ymh6kAkEAoFAIDi5KPGrKPWn\nXogeKprMoEW7w3yeNhQGgHHFyWUtDywYjxkVmc+lQqYj11P0a3jNFvtJRZ4Hows8eGV7M1MM6Ynv\nvrYb//vxEcfX/rT2KKIaiUAFe9nE+PFP6/HQ2wfw73/diZ3NQfb8k4uns//3tARx07NbmIN3oC0M\nFcO7WfJgMhxynBfOHoHv9EJ69HiQyi5P3zDDcTUmmxkO18hwRNjFjrBHMsImzgi72BH2cEbYxWI4\n2iIvTbregsnpVZZzVAWbGwJYvbOFPffWnlbb3J0nGNOY+mA6ssrBAoB8U767o4eoFI9TVMIwDDxT\n14BXt5GGuvM49RQnNjd025y6VVua2P8+LoxYwtXMbG8iX15TgHjeBzsicGfdNyIYLMpz3cjJ4Ecu\nEAgEAoFAkK14zXn2eeMKk17rKdJEU/1+8a+D7LmfvHUAf3z/sOP2waiWkVZC1si0U3LM/Mv6zogp\n3d4zTpLutEbqkQ9JdKunPlZ3v7ILsypycftpVWjsjmLeyDx8ergLZX4Vv7jCUltRFRlTynzY0RTE\nb2oPAQBq97UDAJ7bcAyVRekduWxG5Dg7I+xiIWzhjLCLHWGPZIRNnBF2sSPs4Yywi8VwtAWNKOWn\n6JG67PQqjEuhep3jIu8tz7WLhsVTqH0HY7otcJKKrIuXjC8hBvzRP/dn/B4n53bpCrvKXyCqYfXO\nFty6Ygte396MBcvrkt5jwMDD7x7Ej9/aj1EFRDa+KRBLaor7yyvs8pYrNzUCAI50RnBJD6FMgUAg\nEAgEAoFAQLj99Crc89nqlK9fP3sEThud7/haYQ6Zowei9kBKTHMOrAQyjGBlnYPlVmR4elAR0XQD\nD729H3taSIreS5ub0B6yq/pRv/XpG2bgBxePR3dEwwcHOnC0K4rXd7TACUWScLCdNN59mUsRTEzj\ncisyTje/6Esnl9he6z68M/0JZjHDMa93OCDsYiFs4Yywix1hj2SETZwRdrEj7OGMsIvFcLRFZZ4H\n540v6tN7qQo5bTpM2dcWdhTBIzVYJ2EECwC+f0F6kYKOcBxrdrfhzpd2sOe2cjLstH7r77fPRXmu\nG15VxkeHOvGe2emZ1k4l0hSIoirfk9Ex/nDBeACAS5HwwwXjcdfZowAAxWr6VESBQCAQCAQCgUAw\nMMyvLsSZY5IjXFuOJbdo+uRQp+PziWSlgzXZrL1aX99le35HUwALltfhhj9vZs+dOorUPEmmCv6z\n6xtw+8qtGF3gYYVxnhTS3FpCfmZ7KI5iM9R4hhmhevgK527XdN8fHuzAmWMKcNrofMwc4ceF5w+/\n3NbjxXDM6x0OCLtYCFs4I+xiR9gjGWETZ4Rd7Ah7OCPsYpGNtvjsxCJIDvVCfgfluQ8PdqIt1LOQ\nXlY6WFSp77uv7bY9/9HBTttjlyzhymllAKzQ4DN1DeiMaDYVQo/LWYGESrc3B6LmY50pmWiGgTdu\nn4uZFbkpj1OWSANigIQ3H75ycmYnKBAIBAKBQCAQCPqN1yUjHLMyyKaW+VDgdSXVZQHAxZOKcftp\nVT3uMysdLCcvFAA8LhkLZ5Wzx/997VScMSYfsypy0RUhDlVUI1GpzoiVi5mquWzQNPyNz1qCGDub\ngvjm/NH4r4vHpzwOyt9vr8EXTrV/ScMxt/V4cTKfezqEXSyELZwRdrEj7JGMsIkzwi52hD2cEXax\nyEZbeF2yrd9tXDdQmONCdzQ5UtURjjNp93RkpYOVis0N3ZAAPLZwGn562QSMKfJCliRMLMnB+2Z9\nFeXiSZaanzdBNOP5m2eh2OdCWyiGoBn5ev7mWSj1qeiMaBhd6IU7hVMmEAgEAoFAIBAIhgdul2zr\nZRvTDRR6XeiOaEnbvrmrFce6oknPJ5L1XkCUk1n86FAnFFnC6EIv5o20itle296MDUe78ae1R9lz\nb+5qZf/7OTnGxbPLke91odTnRiiuozkYQ1W+B/leF5qDRIkwfdwqPdmY25opJ/O5p0PYxULYwhlh\nFzvCHskImzgj7GJH2MMZYReLbLRFcY7LNnePawbyPApCMWfhuYo8d4/7zHoH60BbmP0/sSQHZ48t\nSNpmvtn5+Zm6Bsd98KHAsWajslK/ioBZq0X7XNF9z0hTdyUQCAQCgUAgEAiGB363gq6oPUXQpyqI\nOTQbVmUJlyS0WHIiax2sR66dYnsc03TsbglhRG6y1/nNc8fYHv/7OaNt75clCW/cPhcAoBvE2Hke\n8mV0hOIoMJUDU3m6vSEbc1sz5WQ+93QIu1gIWzgj7GJH2CMZYRNnhF3sCHs4I+xikY228LpkGAYQ\niZN5fEzT4XMrSc2GdcNATDegKj3nqmWtg1WZR/pRtZppexuOdgMAikyFQR6+XmrhrHJcMa0UE0p8\ntm0kScI3zx2Dc6pJtMvvVtAdiaM5GGMRLL5ATiAQCAQCgUAgEAxvJElCrlthNVcx3cD+thCe29ho\n2y6uGVBlqUcROyCLHSy3KUyR5yHOT2N3lPW8cuKq6aUAgHkjU29z2ZQSVo+V63GhO6rhjx8cxq5m\n0nh4R4oGxL0hG3NbM+VkPvd0CLtYCFs4I+xiR9gjGWETZ4Rd7Ah7OCPsYpGttsj1KNjdQubxcd3A\n+vrupG0yjV4BWexguWQJxTkuJnLRHopjfHFOyu19KnGcTknjYPHkuhU8u/4YAKvOq8yfHB0TCAQC\ngUAgEAgEwxe3IuHe1XuhGwbimoH51cmaDVFNT9m6KZGsdbAAABKwtzUEADjQHkaFmTboxOI5I/D7\nq6dkFPYD7MIXLy6dDQB4bNF0vGz+31eyMbc1U07mc0+HsIuFsIUzwi52hD2SETZxRtjFjrCHM8Iu\nFtlqC9mc/4djOuK6gcVzRiDfo2BbY4BtE9NEBAsA0BqM478/PAIACEa1pH5WPH63gsllvpSvJ+87\nxv73mPv1umT4OEl3gUAgEAgEAoFAMLxxycRx6ozEocgS3IqMzoiGr/91J9smpulwCweLUF3kBQBE\nNB3FPteA7dc/SI5Utua2ZsLJfO7pEHaxELZwRtjFjrBHMsImzgi72BH2cEbYxSJbbbHd1FHoDGtw\nyZKjIxXVDKiySBHE7adV4bRRpKFwKKYjRx04p+jyqaUDti+BQCAQCAQCgUAwtHSE41AVZ6XAuBC5\nIDz2ST1WbiISi+G4njZFsLcosoQ7zhg5YPujZGtuayaczOeeDmEXC2ELZ4Rd7Ah7JCNs4oywix1h\nD2eEXSyy1Rafn1kGADjWHUVXRHN0pOK6AUUWDha+f0E1+z8c0+FVB/Z0+TosgUAgEAgEAoFAcOLx\n5TNH4cwx+fjte4cAAMU5ycrgMbMPVib0qShp27ZtePLJJzF9+nTccsst7PmNGzfi+eefBwBcf/31\nmDlzZl92P2BMKM7BqAKiHDjQESwACA1CY+FszW3NhJP53NMh7GIhbOGMsIsdYY9khE2cEXaxI+zh\njLCLRTbbYuNRq/eVkx+l6QZcg5kiGIvFcO2119qe03UdK1euxD333IN77rkHK1euhGEYfdn9gKG6\nJMQ0cgyhmDagNVgAMKcys55ZAoFAIBAIBAKBYPgSiZPeuTfOHeFYgxXTdaY22BN9crBmz56N3Nxc\n23MNDQ2orKyE2+2G2+3GiBEj0NDQ0JfdDxhel4xwXIemG4hpBjwZep2ZYmDgHchszW3NhJP53NMh\n7GIhbOGMsIsdYY9khE2cEXaxI+zhjLCLRTbb4stnjgKAlD1x43rmKoJpUwQ3btyIVatW2Z5bunQp\nxo4dm7Rtd3c3fD4fnnjiCQCAz+dDV1cXKisrMzqQwSBHVRCOaSQ9UJUzbiKcKWeNLcR3zx/QXQoE\nAoFAIBAIBILjzP62EAAgnKIEaHNDAOvquzLaV1oHa/bs2Zg9e3ZGO8rNzUUwGMSyZctgGAaWL1+O\n/Pz8jN47WHgUCTHdQEc4jlBMH/D9e10yLppUPKD7zObc1p44mc89HcIuFsIWzgi72BH2SEbYxBlh\nFzvCHs4Iu1hksy2unVmOV7e34MaaCtvzmm6gJRjD86YyeSb0WfUhsb6qoqICR48eZY8bGhpQUVGR\n+DYbfJixtrZ2wB+/99578Lhkpggy2J8nHovH4rF4LB6Lx+KxeCwei8fi8Yn3eEyhFzeOCmPDJx8C\nAF64ZRa8soFfv/IRbv7LFswbmQevbNjenwrJ6IMSxcsvv4z169ejvb0d06dPxx133AEA2LBhA1MR\nXLRoUdro15o1azBv3rzefnSvueGZTVAVGce6o1i9rGbQP6+/1NbWZvXqQDpO5nNPh7CLhbCFM8Iu\ndoQ9khE2cUbYxY6whzPCLhYnky0icf3/t3fnwVHf9/3Hn3toJVbXSlrd94nQgTgEkYnsnyNjBwdP\nhiQ/Ozhk4iZj1+PU+bUdp03b1IObJpNO3DYdp516HE/sDrEdU4854gZsKwbMYcAYsBBgLkkICYEQ\nuu9j9/cH3RWLVkK2JRZ99/WYySRa7YrP55V9f/f72e/n8/nyjQ01rFucxEuHWvj2/17Z+s7S8eVP\nhw8f5p577pnwWutn+QfXrFnDmjVrJjxeVlZGWVnZZ/mTsyYsxMLy9Cj2NHQGuikiIiIiIjIHhFhM\nDI+5Gfzf3QVHXe5p3/LpM13Bmgm36grWE5s+IS9uHiEWM//vi+mz/u+JiIiIiMjcd9+LR3CEWekc\nHOX/libgmGfloYWJ3t9PdgVrZu+8exsKtZjpH3FN+87LIiIiIiIiAJ2DowD0j4zN7n2w5pJRl5vu\nwVFCZvgeWLNlOgvnjCqY+z4V5TJOWfinXHwpj4mUiX/KxZfy8E+5jAvmLLoGRjXA8jjd1s/HLb2E\nWAzfVRERERERmQXHL/dNe4Bl+DVY9714BIBHliazbvHU28aLiIiIiIgAPLf3AttPXWXUdW249MO7\nMrivIM77+6Bdg+UxV6YIioiIiIhI4CVF2ryDK4C9DV3Tel3wDLDmyCYXwTy3NZj7PhXlMk5Z+Kdc\nfCmPiZSJf8rFl/LwT7mMC/Ys2gdGpvU8ww+wIkMtAFqDJSIiIiIi0xYfbvP5+Yd3ZUzrdUGzBuup\nuzL48nVzJkVERERERCaz41wHP9/R4P35jW+XEhVm9f4c9Guwprvrh4iIiIiIyJHmHp+fw22Wab0u\naAZYc2WTi2Ce2xrMfZ+KchmnLPxTLr6Ux0TKxD/l4kt5+KdcxgVbFlV5MT4/W3QfrGt+XJUFQGAm\nQoqIiIiIyFxUmhTB/8l2fOrXGX4N1vt1Hfz0vQaeXJHGV4viZ/3fExERERER46g+084vdp3nnUcX\n+zw+2Ros64RHDGZ/47X96qd7SU9ERERERMRjZX4sK/Njp/18w08RvNw7vf3qbxfBNrf1esHc96ko\nl3HKwj/l4kt5TKRM/FMuvpSHf8plnLKYHsMPsH50dyYA0aGGv1gnIiIiIiIBZvg1WAAdAyM4wqyY\nTJomKCIiIiIin1/QrsECiJkXEugmiIiIiIhIEDD8FMG5JpjntgZz36eiXMYpC/+Uiy/lMZEy8U+5\n+FIe/imXccpiejTAEhERERERmSFBsQZLRERERERkJk22BktXsERERERERGaIBli3mWCe2xrMfZ+K\nchmnLPxTLr6Ux0TKxD/l4kt5+KdcximL6dEAS0REREREZIZoDZaIiIiIiMinpDVYIiIiIiIis0wD\nrNtMMM9tDea+T0W5jFMW/ikXX8pjImXin3LxpTz8Uy7jlMX0WD/Li1544QVaWlpwuVx8//vfJzEx\nEYCamhreeOMNAB566CFKSkpmrqUiIiIiIiK3uc+1Bqu2tpYPPviAxx57DJfLxfr163n66acB+NnP\nfsYzzzyDyWTy+1qtwRIRERERkblqVtZghYWFYbVeuwh26dIlkpOTsdls2Gw2EhMTuXTp0uf58yIi\nIiIiInPKlAOsmpoa/vEf/9HnP+fPn/f+fseOHdx3330A9Pb2Yrfbefnll3n55Zex2+309PTMbusN\nKJjntgZz36eiXMYpC/+Uiy/lMZEy8U+5+FIe/imXccpiej7zFMFDhw5x+fJlVq9eDcDFixfZvHkz\njz76KG63mxdffJFvfOMbJCUl+X39H//4x8/eahERERERkQDzN0XwM21yUVdXx4kTJ/jOd77jfSwp\nKYmWlhbvz5cuXZp0cDVZY0REREREROayz3QF68knnyQuLg6z2Ux6ejrf+973APj444+9uwg++OCD\nLFy4cGZbKyIiIiIichv7XLsIioiIiIiIyDjdaFhERERERGSGaIAlIiIiIiIyQzTACpC+vr5ANyFg\ngrnvU3G5XIFuwm1DWfin2vGlPCZS7finXHypdvxTLuOUxedjeeaZZ54JdCOCidvt5t/+7d+oq6sj\nNzeX0NDQQDfplgnmvt/MH/7wBxoaGsjIyMBisQS6OQGlLCZS7fhSHv6pdvxTLuNUO/4pl3HKYmbo\nCtYt5HK5MJlMhIeHMzAwwPHjxwPdpFsmmPs+HTU1NdTW1r5ytzcAABv7SURBVHLq1KlANyXglIUv\n1Y4v5TE51Y5/yuUa1Y5/ymWcspg5GmDdIi6XC7PZzMjICKOjo2RlZdHe3k5TU1OgmzbrgrnvU3G7\n3bjdboaHh0lISKCwsJDGxkba29u9vw8WysI/1Y4v5TGRasc/5eJLteOfchmnLGaWBliz6MSJE+za\ntQsAs9nM2NgYISEhZGVlYbfbCQkJoaGhgd7e3gC3dOYFc9+nMjw8zLlz5wAwmUwA2Gw27Ha7d41A\nQ0ODz++NSln4p9rxpTwmUu34p1x8qXb8Uy7jlMXs0RqsWdLa2sorr7xCZ2cn8+bNIzExEYCxsTE+\n+ugjvvzlL9Pd3c3WrVvp6uqipKQkwC2eOcHc96lcvXqVf/7nf6auro7U1FQcDgculwuXy0VDQwP3\n3nsvQ0ND7Nixg87OTvLz8wPd5FmjLPxT7fhSHhOpdvxTLr5UO/4pl3HKYnZZA90Ao7JYLDzwwAO4\nXC5qamqYP38+oaGhmM1m7HY7//Iv/8K8efNwOp3ExsYGurkzKpj7PhWXy8U999yD2Wzm0KFDZGZm\nehdc9/b2snnzZrq6umhrazP87j3Kwj/Vji/lMZFqxz/l4ku1459yGacsZpeuYM2Qd955hx07dtDe\n3k5OTg52u52kpCSsVivNzc10dHSQlZWFy+VicHCQ8PBwHn74YcrKyigoKMBsnruzNYO571O5ePEi\nH374IS6Xi9jYWO+BymazUVdXh9vtJikpCYCenh7OnTvHypUrWb58OfPnz8dutwe4BzNHWfin2vGl\nPCZS7finXHypdvxTLuOUxa2lAdYMOHv2LO+//z5r1qxh7969XL16ldTUVEJCQggJCcHtdnPu3Dly\nc3Ox2WwkJyeTn5+P2WwmJCRkTr9pg7nvU2lsbOQ//uM/SEpKorq6mrCwMJKTkwkLCyM0NJShoSFO\nnTrFwoULAYiLi2PFihU4nU4cDoehPvyVhX+qHV/KYyLVjn/KxZdqxz/lMk5Z3HoaYM2AgwcPYrfb\nqaioICEhgZMnT2K324mLi8NqtRIXF8eZM2fYuHEjbW1tPvNY5/pC22Du+1QOHz5Mamoqq1evJjk5\nmZqaGqKionA4HISEhBATE0NzczM7d+6kra2NBQsWYDKZvFukGomy8E+140t5TKTa8U+5+FLt+Kdc\nximLW08DrM9g06ZNnD17FpPJRFxcHHa7nUOHDtHX18exY8ewWq1cvXrV+wY9evQo+/bto7y8nK9/\n/esBbv3nE8x9n8rp06dpbm5m3rx5hIaGMjAwwMGDB0lOTubw4cP09/czNjZGbm4uAJcvX+btt9/G\n7XZTVVVFVFQUYIwDmbLwT7XjS3lMpNrxT7n4Uu34p1zGKYvA0wDrU+js7OT5559ndHSUzMxMdu7c\nSXp6OvHx8fT09NDU1MQ3v/lNFi9ezKuvvsqiRYuw2+2MjIzwpS99idLS0kB34TML5r5PZXh4mNde\ne40dO3bgdrs5cOAARUVFxMXF0dbWRm1tLRUVFSxcuJCNGzdSWVmJxWKhrq6OBQsW8LWvfY2oqCjc\nbvec//BXFv6pdnwpj4lUO/4pF1+qHf+UyzhlcfvQLoKfgsPhYO3atd6Fs52dnbhcLqxWK4WFhdTW\n1tLU1ERMTAwZGRnYbDYAMjIyAtnsGRHMfZ+K1WolLy+Pb3/72wBs3ryZS5cukZWVRUFBARcvXvTm\nkZ6eTnd3N06nk8WLF3v/hufmfnOdsvBPteNLeUyk2vFPufhS7finXMYpi9uHBlifkudNC9d2MSos\nLMRsNpORkUFVVRV79uzh7Nmz3HvvvURGRgawpTMvmPvuj9vtxmw2U1FRAcDo6CidnZ2Eh4djtVop\nKiri7NmzvPjiizQ1NTF//nxiYmIm/B0jfPgri6mpdnwpj3GqHf+Ui3+qHf+UyzhlcXvQAOsz8Hwj\nlp6eTnJyMnDtw2DRokUUFRVhtVoNd1D3COa+3+j6KSdjY2NYrVZiY2Nxu93ex7/61a/S1tbG4OAg\naWlpgWjmLaEsbk6140t5XKPa8U+5TE61459yGacsAk9rsD4Dz4H/6NGj9PX1sWnTJmJiYnA6nVgs\nljk713s689SN2vfPy2w2MzIyQn19PdnZ2bz77rve3azsdrt3HYBR1gJMRVn4p9rxpTwmUu34F0y5\n6HPYP+UyTlnMDbqCdZ0b52pP9Sa+dOkSf/zjHykqKqKqqor58+ffqmbOihv7PtW8daP1fSrTfU+4\n3W6Gh4fZv38/hw8fZsGCBWRlZfk8x2QyBcVBLdiyCObjxo1aWlro6ekhNzcXi8Vy0+cbPQ+P6a4D\nCvbamUww5DI4OEhISIhP3QTzseRG1+cQrLl4vkTQudrcoCtYQHV1Nc3NzWRlZTEwMMDFixdxOBxT\nHqwjIiKw2+2sXbuWhISEW9ja2WEymRgcHKS6upq8vLyg6vtkrj+Q1dfXExMTM2kuJpOJ/v5+Tp48\nyWOPPUZ5eTkmk8lQ36h67iHT0NCAw+GY9PlGzuJ6njxMJhPDw8M0NjZ63yOT9dXItfPhhx/yy1/+\nkoGBAVJSUrxbY0/FyHnAxBOihoYGwsLCsFr9f7cZTLVzfS59fX3exfb+BEMuhw8fpr6+nszMTI4d\nO0ZiYmLQfw57jrFjY2MAvPvuu2RnZ085KDdyLp7Pm6GhIQ4ePEh6enrQv0duZyb39ROag4zn4PzJ\nJ5+wYcMG1qxZw1tvvYXNZqOsrIxVq1b5/SA00q5EHqdOneLNN98kJSWFb33rW1itVr+Fa8S+T6Wz\ns5PXXnuN1tZWnnrqKcLDw6eVy/Un30bz93//96xbt44FCxb4PcExehY39rmmpoZNmzbhdruprKxk\n5cqVfuvEqLXj6VdfXx9jY2Ps2bOHqKgoli5dyrx58276umDQ1NTEtm3bqKur48/+7M8mXS9k9Nq5\n0ZUrV3j11VcBqKqqmnSL6GDIZWBggD//8z8nPj6ee+65h7vuumvSgXgw1c71/umf/omVK1dSXl7u\n9/fBkMvu3buprq6mtLSUr3/965PWQTBkcbsLyimCLpcLuDav2+VyUVhYSH5+Pps3b2b9+vX09/ez\nceNG6uvryc/Pn3BCNZfftP4uMTc0NPD++++TmJjII488MuXr53Lfb+bGA1JTUxPvvfceISEhrF+/\nfsrXTveS/VxxfR/cbjd79+4lLCyM8vJy7rnnHrq6ugD/N+40WhY3ur7PW7duZd++fTzzzDN0d3fz\nr//6r9x1113YbLYJfTdaDh6efoWHhwOQnp7OiRMnSEhIoKCg4KavM7rz58/z8ssvc+eddxIVFTXl\nlT0j186N/XnvvffYtWsXDz74IMPDw+zcuZPo6Gi/20UbLZfrz0E8Pv74Y+Li4oiJiaGqqmrK18/1\n/k/mxlzGxsZ46623cLvdrFmzhjvvvHPSQef1rzMCf++R0dFR6urqePzxx0lJSZny9UbKYq4Kuv8H\nPIMLs9lMT08Pvb29AKxZs4bu7m56e3txOBwkJCRw6NChALd2ZnkGimazma6uLq5cuQJASkoKhYWF\n2Gw22tvbvc8NFjceyPr7+wGIiooiNjbWe0keppfLXD6w3TgAHx4exmQyYbPZ2LZtG42NjXR0dHhP\nEj3ZTWYuZ3E9Tz/Hxsbo6+vjnXfeAaCyspLe3l66u7tJSEigsLCQ3/3ud4Fs6i1xYx7vvvuu93el\npaWEh4fT1NTE2NiY97lGP6Z4+un57/r6emB8/ZDNZuPYsWM+U+GmysQotePh6c/o6CgAycnJtLW1\nUVJSwpIlS4iLi+PYsWOA8XPxnIO0tbVx8OBBAL7whS/w85//nIGBAU6ePAlcmzoJxq8d8D036+7u\nprOzE4vFwvLlyzl9+jSHDx/mxIkTjIyMADf/7Jnr/J2nDg8Pc+zYMex2u/d5nvMVo+cxFwXFGiyX\ny8XVq1d93pSvvPIKb731Fs3NzYSGhpKRkcHo6Cg7d+6koqKCQ4cOERUVRX5+/pw+oLtcLo4ePUp8\nfDwWiwWXy8WmTZt49dVXOX36NPHx8d65uZ5CzsjIMNTUi5vx9PXs2bO88MILnDx5ktTUVJxOJ1ar\n1Tv16WZz4uey9vZ2wsLCvNMNamtr+fWvf01DQwM5OTnk5uZis9lobW1l9+7djI6OUlZWZtg8buRZ\ng2axWLBarfzqV78iJyeH9PR0hoeH+fDDDykvLycvL48tW7awfPlyQkNDA93sWeMvj/z8fGJjYwGY\nN28eJ06cYNeuXVy8eJG8vLxpbXoxl3lqwWQy0d7ezs9+9jNWrFhBcnIycXFxHDp0iB/84AdcvnyZ\n6upqYmJiiIiIMGwNeabxeRw5coTXX3+dq1evkpiYSHp6Oi0tLZw+fZqSkhJaW1vp6uqiuLjYkJl4\n8vCcCL/55pu89dZbpKSkkJ2dzejoKBaLhZCQEH7/+9/T29vL7t27KS0tJSQkJMCtnx1ut5uLFy8S\nFRXlXVP3+uuvs2nTJj755BMsFguFhYVkZ2fT2dnJnj17GB0dZdGiRXP6vOxmPO8Rz3lqU1MToaGh\npKamMjg4yN69eykrK2PLli1s376dxYsXT7mGUQLD8AOsffv28Zvf/Aaz2ezdvOHy5cucPXuWv/iL\nv+Do0aN88MEH3HHHHRQXF/Nf//Vf1NTUEBUVxerVq+f8m/bgwYM8++yzLF682PsN4fDwME888QRX\nrlzh/fff584778ThcNDa2kpLSwvJyclTrp2Y67q6umhoaMDpdHo/9DZs2MDhw4dZu3Ytw8PD1NTU\nkJWVRXx8PJcvX+b8+fOkpaURFhYW6ObPuH379vHss8+ydOlSoqKiqKmp4cCBA6xZs4bm5mY++ugj\nli1bRmpqKhkZGTQ2NhIaGsqCBQsMtw7Co62tjS1btpCamuqthV/+8peMjIyQlZVFeHg47777LpWV\nlRQWFvLaa6+RmJhIRkYGlZWVhquf6ebxxS9+EYDo6Gi6urpwuVzcf//9hssD/B9Hfvvb3xIeHk5q\nair9/f0cOXKEJUuWEBERQXV1NXV1dXz88ceYzWbOnz9PWVmZoTZqAGhtbWXbtm2YTCbi4+OBa1/g\nbNmyhXXr1lFbW8uOHTuorKwkJyeHX//611y+fJlTp06xcuVK4uLiAtyDmVVdXc327dtpbm5m/vz5\n3vWKhw4d4vvf/z75+fnAtSsWJpOJ9PR0+vv7GRkZ4Wtf+5rPF8NGc/z4cV566SXuvvtuzGYzZ86c\n4fjx4/zoRz8iOjqampoa4uLiSE1NJTU1la6uLrKysm66ucNcs2/fPg4cOODdIMjfeer+/fupqKig\nuLiYkydPsnfvXsbGxvjud7/rnZ4ttxfDDrBGRkZ4/vnnOXfuHOvWrfNZFNnS0sKbb75JXV0dAN/9\n7nex2WxYrVYKCgpYsmQJd91115z91qinp4ef/OQnpKamEhMTQ0dHB1arlfj4eDIyMsjOzmbr1q2E\nhYVx6tQpzGYz2dnZREVFkZeXR0xMTKC7MGv6+/t5/vnn2blzJ4sWLfJuWnHhwgXq6upYs2YNeXl5\n7N69G4fDQUpKChaLhYSEhJvOeZ5r3njjDXJycggLC+P48eNERESQkpJCTEwMS5cu5cKFC7S3t3P0\n6FFyc3NxOp2YzWYSEhI4efKkdycvI7Lb7XzwwQcAJCUlYbFYiIiIYNu2bdx9991kZmaya9cuhoeH\nycnJ8V7xjIyMNOSVmunksXPnTsbGxsjOzgYgLS2N0tLSOf8llT+THUeamprYvXs3K1asYMGCBWza\ntImUlBRSUlKwWq20t7fz5JNPcvnyZcbGxgx3tebChQv86le/Ii8vjxUrVngfr6+v59ixY0RGRnL4\n8GHuvfdeUlJSmDdvHhaLhXPnzvHjH//YcIOr2tpa9uzZw0MPPcSRI0doamoiNjaWvr4+3n77bVat\nWuV9bn9/v7dWcnNzWbBggSFrp7e3l2effZacnBzy8vK4cOECLS0tFBQUcPHiRWpra6msrCQ+Pp4P\nPvgAm83mXZdXUFBAVlaWYWpmaGiIp59+mo6ODpYvX8727dtpb28nJyeHjo4O714AAH/yJ39CWFgY\nFouFhQsXsmjRIpYtW2bI94hRGPYaa0hICA6Hg/z8fLKysujo6PBeXvY85nK5ePzxx9mxYwevvPIK\nbrebgoICv4ts55LIyEjsdjsbN27k/fffJzw8nK6uLs6fP8/Y2BiNjY0ArFq1iry8PGpqanC73Tgc\nDqKjowPc+tllt9vJzc31Lqres2cPAA888AA2m439+/djMpmIioqiu7sbuLZoPy8vL5DNnnEjIyO8\n/fbb/Pu//zsHDx4kMzPT+x4JCwujo6ODxsZG1q5dS3FxMVu2bPG+tqurC7PZ7J0XbjSe6RlVVVXU\n1tZy9epV3G43paWlOJ1O/ud//geA1NRU7zrNkpISww3APT5NHgcOHPC+zshTeCY7jnzlK19haGiI\njz76yPvF1X//938DUF5ejsvlYv369TQ2NvLAAw8Esguzoq6ujjvvvJOvfOUrAN7ttYuKiuju7mbv\n3r2sX7+eiIgINm/eDFzLrL29nePHjwes3bOlrq6OtLQ0kpOTeeihh7BYLJw4cYKkpCRyc3PZvHkz\n3d3d/OY3v/FZu2nk2omIiGBwcJBf/OIXVFdX88ADD3Do0CE6OzvJzs4mJSWFnTt3AteOPREREd7X\nTrXBxVwUGhpKeno62dnZlJaW8uijj9LZ2emdQZOTk+Nznvrb3/4WuDYN2chT0I3CsFewAPLz83nz\nzTe5cOECf/jDH0hNTSU3NxeTycT8+fNpbW3l97//PU6nk3Xr1hnmWxGAnJwczp07h8ViITU1FZvN\nRktLC+np6YSGhrJhwwZ27drFsmXLWLt2raH6fjMmk4no6GiKioqorq6mv7+f3NxcHA4HL774Iq2t\nrVy5coX77rvPsNMzLBYLOTk5fPLJJ4yNjREWFsbY2Bj9/f2kpaXR1tbG66+/zqlTp4iLi2P16tVE\nRUUxMDBAfX09S5cuxel0Brobs8JTC7GxsTQ0NNDW1kZ6ejohISHk5OSwadMm6uvrWbRoEQ8++GCA\nWzv7lId/kx1H4uLi2LhxI11dXTidTlauXEl0dDRWqxWn08nSpUupqqqaszMkbmb79u10dHTwzjvv\n0NzcTGdnJ+np6cTFxXH58mUOHDjAJ598QlVVlXe2RFpaGjExMURGRga49TPLYrHQ1NREZmYm0dHR\n3vvlFRcXk5GRwfHjx3nvvfdIS0vjW9/6VqCbe8sUFBTQ0tLC+fPnSUxM5NixY7jdbkpKSoiMjGT3\n7t1s376d9PR0Vq5cGejmzqri4mJee+01CgsLSUpKoqenh3PnzrFw4ULy8/O5cuWKz3mqzB2GHmCF\nhIQQGhrKvn37+Ku/+ivvmhGXy0V4eDglJSUsW7aMhQsXBrqpMy4iIoK+vj7a29t5+OGHmT9/PrW1\ntfT09FBUVMTChQuD9s7ejY2N1NfXc/XqVTo7O6mrq8NsNlNeXk5XVxejo6P84Ac/MOzgyiM2Npar\nV68yPDzMunXryMvL4+jRo5hMJkpKSoiLiyMrK4t7772XqKgo3G43ISEhZGRkTOsmsnOZZ01NQkIC\n7733HtnZ2URHR2O328nOzmbZsmVkZmYGupm3jPKYaLLjyKJFi4iJiSElJYWKigqio6O9+Rl9loDD\n4aCsrIysrCwyMjLIzc3lueeeo6qqioyMDIqLi0lJSWH16tXExMR4c4mPjzfc4MqjpaWFoaEhUlNT\nSUxMZOPGjRQUFJCcnExZWRnLly+npKQk0M28pSIiIhgYGGBoaIg77riDDz/8kP3795OTk0NhYSHF\nxcVUVlayePHiQDd11oWEhDAyMsLu3bupqKggISGBN954g8WLF+N0Og19nmp0xr0O/b/uuOMOoqOj\nvdPixsbGfC6/G3HTAsA7YBgcHOTMmTMAFBYW0t/fz9DQEMnJyYY/SZ5MSUkJH3/8MaGhofzt3/4t\njzzyCFeuXKG+vp5Vq1Zx5MgR732ejMxqtbJs2TI6Ozvp7+8nMjKStLQ0zpw5Q39/P8uWLWPJkiXA\nxB3BjM5zjHA6nTidTk6dOuX9XVZWls+0lWCgPCbydxxpa2vjzJkzlJeXs2DBAoAJ9x00OofDgcPh\nIDs7m6SkJMrLyxkYGACu3SstJycHMMb9rG4mNjaWzMxMjh49yunTp+nt7cXpdPqsmwnGNTRms5kl\nS5bQ19fH4OAgTz31FMXFxd7bX0RERBj+C87r3X///fT09FBXV0d3dzdFRUXeHVnBuOepRmfoK1ge\nkZGR/O53v+NLX/qSIRegTyYsLIzR0VGqq6tZsWIFSUlJFBQUGG4e86dlsVgYGBjw7vrmcDgoLCzE\n6XRit9uJjY0lOTk5KD74oqKi6Ojo4ODBgyxZsoTMzExyc3MnfLgF0+DKY3h4mA0bNnD+/HkqKysN\ntwD/01IeviY7jiQmJvo8LxhrB+Do0aO88MILOBwOvvCFL0wYTAVLLklJSbjdbnbv3s22bduoqKgI\nuitW/njOT95++23uvvtuVqxY4bMxSrCJjIzkH/7hH2hqaqK8vJy0tLRAN0k+p6A40164cKFhF+Tf\nzNKlS7FYLN4bFQbLh9rNjI6OEhER4fMtqud/V1RUBLh1t1ZFRQXbt2+nv7+fsLAw7HZ7UHy7fDMu\nl4usrCzWrVtn2PUyn4bymMjfcUTH2mtaW1t5+OGHvduQB7Py8nKKiooICwsL+uPq9crLy7FarRNu\ncB+MlixZwp/+6Z/O6R2sxZfJHQy3CBe5wcDAgCHvzSMit46OIzenk2cRCUYaYElQM9pNPj8PXbUS\n+Wx0HPFPuYhIsNIAS0REREREZIbo62oREREREZEZogGWiIiIiIjIDNEAS0REREREZIZogCUiIiIi\nIjJDNMASEZE5oaGhgSNHjvj93cWLF9m4ceMtbpGIiMhEGmCJiMicMNUAKyUlhYceeugWt0hERGQi\nbdMuIiK3ve3bt7Nt2zYGBwdJTEyktLSUBx98kOHhYX7605/S39+P0+nkb/7mb7yveeaZZygsLGTv\n3r1885vfpLq6mtTUVB577DEA6urq2LBhAy6Xi4iICB5//HGioqIC1UURETEIa6AbICIicjOrVq0i\nLCyMuro6vve973kft9ls/OQnP+HEiRNs3brV5zUmk4nExETuv/9+qqur+eEPf8hf/uVf8thjjzE6\nOsp//ud/8nd/93fExMSwf/9+XnnlFZ544olb3TURETEYDbBERGTOm2wyRmZmJo2NjWRkZBAREcHQ\n0BAAzc3NtLW18dxzzwHgcrmw2Wy3rL0iImJcGmCJiEjQsVgsJCQksH79+kA3RUREDEabXIiIyJxg\ns9no6uoCrl1x+jxSUlIYGRnh4MGD3se0JFlERGaCrmCJiMicUFpayubNm3n66aeZN28eTz31FKGh\nocC19VYmk+mmf8PzHLPZzF//9V/z0ksvsXXrVkwmE1/84hdZtWrVrPZBRESMT7sIioiIiIiIzBBN\nERQREREREZkhGmCJiIiIiIjMEA2wREREREREZogGWCIiIiIiIjNEAywREREREZEZogGWiIiIiIjI\nDNEAS0REREREZIZogCUiIiIiIjJD/j9DHLvNVwvAagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a392510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(12, 4))\n",
    "df1.plot(ax=ax)\n",
    "df2.plot(ax=ax)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-timeseries-temperature-2014.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# select january data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 49548 entries, 2014-01-01 00:03:06+01:00 to 2014-12-30 23:56:35+01:00\n",
      "Data columns (total 1 columns):\n",
      "outdoor    49548 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 774.2 KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_jan = df1[(df1.index > \"2014-1-1\") & (df1.index < \"2014-2-1\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, ..., False, False, False], dtype=bool)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.index < \"2014-2-1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 4452 entries, 2014-01-01 00:03:06+01:00 to 2014-01-31 23:56:58+01:00\n",
      "Data columns (total 1 columns):\n",
      "outdoor    4452 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 69.6 KB\n"
     ]
    }
   ],
   "source": [
    "df1_jan.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2_jan = df2[\"2014-1-1\":\"2014-1-31\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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j/z23V8w3U3wKzz2xAb74izlyQhpzbhiGMfzLzJttLpq+KHrdLi+tHi/lZxop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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a360050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(12, 4))\n",
    "\n",
    "df1_jan.plot(ax=ax)\n",
    "df2_jan.plot(ax=ax)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-timeseries-selected-month.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# group by month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1_month = df1.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_month[\"month\"] = df1_month.time.apply(lambda x: x.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>time</th>\n",
       "      <th>outdoor</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-01 00:03:06+01:00</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01 00:13:06+01:00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01 00:23:07+01:00</td>\n",
       "      <td>4.19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-01 00:33:07+01:00</td>\n",
       "      <td>4.06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-01 00:43:08+01:00</td>\n",
       "      <td>4.06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        time  outdoor  month\n",
       "0  2014-01-01 00:03:06+01:00     4.38      1\n",
       "1  2014-01-01 00:13:06+01:00     4.25      1\n",
       "2  2014-01-01 00:23:07+01:00     4.19      1\n",
       "3  2014-01-01 00:33:07+01:00     4.06      1\n",
       "4  2014-01-01 00:43:08+01:00     4.06      1"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_month.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_month = df1_month.groupby(\"month\").aggregate(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2_month = df2.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2_month[\"month\"] = df2_month.time.apply(lambda x: x.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2_month = df2_month.groupby(\"month\").aggregate(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_month = df1_month.join(df2_month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>outdoor</th>\n",
       "      <th>indoor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.776646</td>\n",
       "      <td>19.862590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.231613</td>\n",
       "      <td>20.231507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.615437</td>\n",
       "      <td>19.597748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        outdoor     indoor\n",
       "month                     \n",
       "1     -1.776646  19.862590\n",
       "2      2.231613  20.231507\n",
       "3      4.615437  19.597748"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_month = pd.concat([df.to_period(\"M\").groupby(level=0).mean() for df in [df1, df2]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>outdoor</th>\n",
       "      <th>indoor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01</th>\n",
       "      <td>-1.776646</td>\n",
       "      <td>19.862590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02</th>\n",
       "      <td>2.231613</td>\n",
       "      <td>20.231507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03</th>\n",
       "      <td>4.615437</td>\n",
       "      <td>19.597748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          outdoor     indoor\n",
       "time                        \n",
       "2014-01 -1.776646  19.862590\n",
       "2014-02  2.231613  20.231507\n",
       "2014-03  4.615437  19.597748"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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1NTWOPI71BslGh4UGC3N+JBsdNNhpkGysH4nc4NZ+CrFl4T0MWMX64Q0Rz8H6/e9/r4kT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\nAAAAiJH/B8dLamQrY8/4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109c71150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "df_month.plot(kind='bar', ax=axes[0])\n",
    "df_month.plot(kind='box', ax=axes[1])\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-grouped-by-month.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>outdoor</th>\n",
       "      <th>indoor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01</th>\n",
       "      <td>-1.776646</td>\n",
       "      <td>19.862590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02</th>\n",
       "      <td>2.231613</td>\n",
       "      <td>20.231507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03</th>\n",
       "      <td>4.615437</td>\n",
       "      <td>19.597748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04</th>\n",
       "      <td>8.105193</td>\n",
       "      <td>22.149754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05</th>\n",
       "      <td>12.261396</td>\n",
       "      <td>26.332160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06</th>\n",
       "      <td>15.586955</td>\n",
       "      <td>28.687491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07</th>\n",
       "      <td>20.780314</td>\n",
       "      <td>30.605333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08</th>\n",
       "      <td>16.494823</td>\n",
       "      <td>28.099068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09</th>\n",
       "      <td>12.823905</td>\n",
       "      <td>26.950366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10</th>\n",
       "      <td>9.352000</td>\n",
       "      <td>23.379460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11</th>\n",
       "      <td>4.992142</td>\n",
       "      <td>20.610365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12</th>\n",
       "      <td>-0.058940</td>\n",
       "      <td>16.465674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           outdoor     indoor\n",
       "time                         \n",
       "2014-01  -1.776646  19.862590\n",
       "2014-02   2.231613  20.231507\n",
       "2014-03   4.615437  19.597748\n",
       "2014-04   8.105193  22.149754\n",
       "2014-05  12.261396  26.332160\n",
       "2014-06  15.586955  28.687491\n",
       "2014-07  20.780314  30.605333\n",
       "2014-08  16.494823  28.099068\n",
       "2014-09  12.823905  26.950366\n",
       "2014-10   9.352000  23.379460\n",
       "2014-11   4.992142  20.610365\n",
       "2014-12  -0.058940  16.465674"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1_hour = df1.resample(\"H\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1_hour.columns = [\"outdoor (hourly avg.)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1_day = df1.resample(\"D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_day.columns = [\"outdoor (daily avg.)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_week = df1.resample(\"7D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_week.columns = [\"outdoor (weekly avg.)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_month = df1.resample(\"M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_month.columns = [\"outdoor (monthly avg.)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_diff = (df1.resample(\"D\").outdoor - df2.resample(\"D\").indoor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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CgUDQDdQ5XG1m5PN6G/TedfFUf89DSx1euQVPlqKq1DfIhAX4f+5Vn9+z6Ttr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sSFgmB3mZ3WW+s59HgP2z7Qgd5hXJImqKAkUyRDc8zLKTLhKOHhacaOjSH2juNsdDD1QpxcJEYm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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a095950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 6))\n",
    "\n",
    "df1_hour.plot(ax=ax1, alpha=0.25)\n",
    "df1_day.plot(ax=ax1)\n",
    "df1_week.plot(ax=ax1)\n",
    "df1_month.plot(ax=ax1)\n",
    "\n",
    "df_diff.plot(ax=ax2)\n",
    "ax2.set_title(\"temperature difference between outdoor and indoor\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-timeseries-resampled.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fill_methods = [None, 'ffill', 'bfill']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>None</th>\n",
       "      <th>ffill</th>\n",
       "      <th>bfill</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:00:00+01:00</th>\n",
       "      <td>4.38</td>\n",
       "      <td>4.38</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:05:00+01:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.38</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:10:00+01:00</th>\n",
       "      <td>4.25</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:15:00+01:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:20:00+01:00</th>\n",
       "      <td>4.19</td>\n",
       "      <td>4.19</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            NaN  ffill  bfill\n",
       "time                                         \n",
       "2014-01-01 00:00:00+01:00  4.38   4.38   4.38\n",
       "2014-01-01 00:05:00+01:00   NaN   4.38   4.25\n",
       "2014-01-01 00:10:00+01:00  4.25   4.25   4.25\n",
       "2014-01-01 00:15:00+01:00   NaN   4.25   4.19\n",
       "2014-01-01 00:20:00+01:00  4.19   4.19   4.19"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1.resample(\"5min\", fill_method=fm).rename(columns={\"outdoor\": fm})\n",
    "           for fm in fill_methods], axis=1).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selected day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_dec25 = df1[(df1.index < \"2014-9-1\") & (df1.index >= \"2014-8-1\")].resample(\"D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1_dec25 = df1.ix[\"2014-12-25\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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>outdoor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:01:45+01:00</th>\n",
       "      <td>-4.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:11:45+01:00</th>\n",
       "      <td>-4.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:21:46+01:00</th>\n",
       "      <td>-5.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:31:47+01:00</th>\n",
       "      <td>-5.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:41:47+01:00</th>\n",
       "      <td>-5.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           outdoor\n",
       "time                              \n",
       "2014-12-25 00:01:45+01:00    -4.88\n",
       "2014-12-25 00:11:45+01:00    -4.94\n",
       "2014-12-25 00:21:46+01:00    -5.06\n",
       "2014-12-25 00:31:47+01:00    -5.06\n",
       "2014-12-25 00:41:47+01:00    -5.12"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_dec25.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2_dec25 = df2.ix[\"2014-12-25\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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>indoor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:01:45+01:00</th>\n",
       "      <td>16.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:11:45+01:00</th>\n",
       "      <td>16.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:21:46+01:00</th>\n",
       "      <td>16.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:31:47+01:00</th>\n",
       "      <td>16.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:41:47+01:00</th>\n",
       "      <td>16.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           indoor\n",
       "time                             \n",
       "2014-12-25 00:01:45+01:00   16.31\n",
       "2014-12-25 00:11:45+01:00   16.25\n",
       "2014-12-25 00:21:46+01:00   16.31\n",
       "2014-12-25 00:31:47+01:00   16.31\n",
       "2014-12-25 00:41:47+01:00   16.25"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_dec25.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>outdoor</th>\n",
       "      <td>144</td>\n",
       "      <td>-7.788819</td>\n",
       "      <td>1.456085</td>\n",
       "      <td>-10.06</td>\n",
       "      <td>-9.075</td>\n",
       "      <td>-7.75</td>\n",
       "      <td>-6.8625</td>\n",
       "      <td>-4.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count      mean       std    min    25%   50%     75%   max\n",
       "outdoor    144 -7.788819  1.456085 -10.06 -9.075 -7.75 -6.8625 -4.88"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_dec25.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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ZT3ybh4l+7rh9ZpCth0NEggzUIyxbv379+pEdzmmFhYUICAiwxUsT2Q2twhlX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c6c7410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(12, 4))\n",
    "\n",
    "df1_dec25.plot(ax=ax)\n",
    "\n",
    "fig.savefig(\"ch12-timeseries-selected-month.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-01-01 00:03:06+01:00', '2014-01-01 00:13:06+01:00',\n",
       "               '2014-01-01 00:23:07+01:00', '2014-01-01 00:33:07+01:00',\n",
       "               '2014-01-01 00:43:08+01:00', '2014-01-01 00:53:08+01:00',\n",
       "               '2014-01-01 01:03:09+01:00', '2014-01-01 01:13:09+01:00',\n",
       "               '2014-01-01 01:23:10+01:00', '2014-01-01 01:33:26+01:00', \n",
       "               ...\n",
       "               '2014-12-30 22:26:30+01:00', '2014-12-30 22:36:31+01:00',\n",
       "               '2014-12-30 22:46:31+01:00', '2014-12-30 22:56:32+01:00',\n",
       "               '2014-12-30 23:06:32+01:00', '2014-12-30 23:16:33+01:00',\n",
       "               '2014-12-30 23:26:33+01:00', '2014-12-30 23:36:34+01:00',\n",
       "               '2014-12-30 23:46:35+01:00', '2014-12-30 23:56:35+01:00'],\n",
       "              dtype='datetime64[ns]', name=u'time', length=49548, freq=None, tz='Europe/Stockholm')"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Seaborn statistical visualization library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('temperature_outdoor_2014.tsv', delimiter=\"\\t\", names=[\"time\", \"outdoor\"])\n",
    "df1.time = pd.to_datetime(df1.time.values, unit=\"s\").tz_localize('UTC').tz_convert('Europe/Stockholm')\n",
    "df1 = df1.set_index(\"time\").resample(\"10min\")\n",
    "df2 = pd.read_csv('temperature_indoor_2014.tsv', delimiter=\"\\t\", names=[\"time\", \"indoor\"])\n",
    "df2.time = pd.to_datetime(df2.time.values, unit=\"s\").tz_localize('UTC').tz_convert('Europe/Stockholm')\n",
    "df2 = df2.set_index(\"time\").resample(\"10min\")\n",
    "df_temp = pd.concat([df1, df2], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8k+Kd1G3iq1at4sknn+SWW27BarXy1FNPERUVxS9+8QssFgvR0dGsWrXqtAIWBOE7LT2t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\nJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c381c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n",
    "df_temp.resample(\"D\").plot(y=[\"outdoor\", \"indoor\"], ax=ax)\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-seaborn-plot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#sns.kdeplot(df_temp[\"outdoor\"].dropna().values, shade=True, cumulative=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2v//Zsu+tdgsAFUUlBPL8mJjYjk1+wI/t2JiGOeWlhYXXMa/YhjH8/xMgELrw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\nERdRMIuIiLjI/w/OPC/gW5FksAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bda0c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_temp.to_period(\"M\")[\"outdoor\"][\"2014-04\"].dropna().values, bins=50);\n",
    "sns.distplot(df_temp.to_period(\"M\")[\"indoor\"][\"2014-04\"].dropna().values, bins=50);\n",
    "\n",
    "plt.savefig(\"ch12-seaborn-distplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4M5WLv+c978GZM2fwgz/4g0iSgc6h0+lgcXFxxyZHEDtJLpjsDmmJerFGlGg0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\niIgoVBiYiIgoVBiYiIgoVBiYiIgoVP4CenfrZwvhtZkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a432a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"white\"):\n",
    "    sns.jointplot(df_temp.resample(\"H\")[\"outdoor\"].values,\n",
    "                  df_temp.resample(\"H\")[\"indoor\"].values, kind=\"hex\");\n",
    "    \n",
    "plt.savefig(\"ch12-seaborn-jointplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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t2tG7TTe6tuqo9m7tv5/t5uTlqLXcRykrK2Pz5q+QyWS88MLLDbqL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\nEMKFJHiFEEIIF5LgFUIIIVzo/wHQtPYMFnlxmQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109e6ba50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df_temp.resample(\"H\")[\"outdoor\"].dropna().values,\n",
    "            df_temp.resample(\"H\")[\"indoor\"].dropna().values, shade=False);\n",
    "\n",
    "plt.savefig(\"ch12-seaborn-kdeplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9Hfr+2oqCu8ulLnFZeac96PlTM+SCDDfecitWriyHThdZd1SXfpQUERFdVnBw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\nP2wxfNZikanqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11066c110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))\n",
    "\n",
    "sns.boxplot(df_temp.dropna(), ax=ax1, palette=\"pastel\")\n",
    "sns.violinplot(df_temp.dropna(), ax=ax2, palette=\"pastel\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-seaborn-boxplot-violinplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FU48oigDHacluVpafsSQoroaQFwrmZPWePXsGAODbMFB2vNbeLzs3n8uZkrDE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\nhyAKAkpKViA9PfBmXYpJ5NnZ2Thy5AgKCwtx9uxZjBnTdze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kMfh2oZGqIw0REUWmqKjQ\n1SqJ2Kmo75YzzsiJiCiYoqKivGO9PqqfMwMXsRnMd3O+lKsJiYgo8vhuN5O66BgTORERUZD1LDom\nbb5hIiciIgoy3/okUj++jdhEzufiREQklVA+sY3YbMZqbkREJBVBEP2OpRCx2YwL3IiISCpCV9ez\ne8dSiNhE7tsnloiIKJicXV3P3GMmckkwkRMRkVQcju4y4E6nXdJrRXAiZ211IiKShsPRnbztdkc/\nZwYuYhO5IDCRExGRNHxn5L5JXQpM5EREREHW8xm5s58zAyd7G1O5uFzSLj4gIqLI5XK5oFKrIYrS\n5xvOyImIiAaxiJ2RM5ETEZFUNBoNxK48I3VJ8IidkftuP+NWNCIiCiZ2PwsBJnIiIpKK0Wj0O5ZC\nxCbynpjIiYgoeMxmi9+xFCI2kfestc6660REFDzR0Va/YykwkYMNVIiIKLh6JvJoSa8VsYncdxUh\nEzkREQUTZ+QhIPV2ACIiily+z8UtFs7IJaHVRuwWeiIikpjJZPKOjUZTP2cGLoITuW7gk4iIiB5A\nVFSUz5j7yCXhu1mfiIgomFQqtd+xFCI2kfPWOhERhYMITuSckRMR0eAXsdNSrlonIiIpSV1j3SNi\nEzm3jhMRkZRWrXo+JNeJ2EROREQkpfj4xJBcJ2KfkRMREYUDJnIiIqJBLGITucHgrrQzfPgIeQMh\nIiIKgEoUxUHXjLu+viUor3P9+jXExg6BxSJtr1giIqJAJSb6r9ke0YvdUlOHyx0CERFRQCL21joR\nEVE4YCInIiIaxJjIiYiIBjEmciIiokGMiZyIiGgQYyInIiIaxJjIiYiIBjEmciIiokEspAVhWlpa\nsHbtWrS2tsLhcKCsrAwTJ07E2bNnsWXLFmg0GkyfPh1r1qwJZVhERESDVkhn5H/+858xbdo07Nq1\nC1u3bsXrr78OANiwYQO2b9+OPXv24Ny5czh//nwowyIiIhq0QjojX7lyJfR6PQDA6XQiKioKNpsN\nDocDaWlpAID8/HxUVFQgKysrlKERERENSpIl8n379uHdd9/tcWzr1q0YP3486uvr8corr+DVV1+F\nzWbr0bTEbDajpqZGqrCIiIjCimSJvKioCEVFRb2OX7hwAaWlpfjFL36BnJwc2Gw2tLa2er9vs9lg\ntVr7fe2+OsAQERFFmpA+I//mm2/w4osvYvv27ZgxYwYAwGKxQKfToaamBqIoory8HDk5OaEMi4iI\naNAKaT/y1atX48KFC0hOTgYAWK1W7NixA5WVldiyZQtcLhfy8/Pxs5/9LFQhERERDWohTeREREQU\nXCwIQ0RENIgxkRMREQ1iTORERESDGBM5ERHRIBbRibyyshIlJSVyh+HlcDiwdu1aFBcXo6ioCIcP\nH5Y7JLhcLqxbtw7Lli3D8uXLcfHiRblD8mpsbMSjjz6KK1euyB0KAGDRokUoKSlBSUkJfvnLX8od\nDgBg586dWLp0KZ566ikcOHBA7nAAAAcOHPD+Oz399NOYMGECbDabrDEJguD9PS8uLsbly5dljQcA\n7HY7XnnlFSxduhQrVqxAVVWVrPH4vl9evXrV+2+1ceNGyLlm+t738U8//RSlpaWKiOf8+fMoLi5G\nSUkJVq1ahcbGRkmuGdISrUry9ttv48MPP4TZbJY7FK+PPvoIQ4YMwW9+8xvcuXMHCxcuxOzZs2WN\n6ciRI1Cr1dizZw9OnDiB3/72t/j9738va0yA+0PPa6+9BqPRKHcoAIDOzk4AwK5du2SOpNvx48dx\n5swZ7N27F21tbXjnnXfkDgmA+wPPokWLAACvv/46ioqKelR3lMO///1vtLe3Y8+ePaioqMDvfvc7\nvPnmm7LGtG/fPhgMBuzduxdXrlxBaWkp9u/fL0ss975fbt26FT//+c+Rm5uLDRs24B//+AfmzJkj\ne1ybN29GeXk5xo4dG/JY/MWzZcsWrF+/HpmZmXjvvffw9ttvo6ysLOjXjdgZeXp6Ot566y1ZP0ne\nq6CgAD/96U8BuGcIGo1G5oiAOXPmeJvb1NbWIiYmRuaI3H79619j2bJlSExMlDsUAEBVVRXa29ux\natUqPPvss6isrJQ7JJSXl2PMmDFYvXo1nn/+edk/FN7riy++wMWLF/1WgAw1g8GAlpYWiKKIlpYW\n6HQ6uUPCN998g5kzZwIAMjIycPPmTdnuXNz7fvn1118jNzcXADBz5kxUVFQoIq7s7GxZ7xDcG88b\nb7yBzMxMAN39RaQQsYn8scceU0Si9GUymWA2m2Gz2fDiiy/ipZdekjskAIBGo0FZWRk2b96MBQsW\nyB0O9u/fjyFDhiA/Px8AFPFhzGg0YtWqVfjTn/6ETZs24eWXX4YgCLLG1NTUhC+//BJvvvmmNyYl\n2blzJ1544QW5wwDgTgB2ux0FBQV47bXXsGLFCrlDQlZWFo4cOQIAOHv2LJqamtDW1iZLLPe+X/r+\nzZlMJrS0tMgRVq+45s+fL0scHvfG45lonD59Grt378bKlSsluW7EJnKlunHjBp599lksXLgQjz/+\nuNzheG3btg2HDh3C+vXr0dHRIWss+/fvR0VFBUpKSlBVVYWysjI0NDTIGtOIESPw5JNPesexsbGo\nr6+XNaa4uDjk5+dDq9UiIyMDUVFRaGpqkjUmj7t376K6uhp5eXlyhwIAeOedd5CdnY1Dhw7hgw8+\nQFlZGex2u6wxPfXUU7BYLFi+fDn+/ve/e3+vlECt7k4dra2tA/bHiGQff/wxNm7ciD/+8Y+Ii4uT\n5BpM5ArS0NCAH//4x1i7di0WL14sdzgAgPfffx87d+4E4L79qFKpevwRy+Evf/kLdu3ahV27diEz\nMxO/+tWvkJCQIGtM+/fvx7Zt2wDAewtU7tv+kydPxrFjx7wxtbe3S/ZG8l2dPHkSU6dOlTsMr/b2\ndu9zTavVCofDIfsdlXPnzmHq1Kn461//innz5iExMdHbBlpuWVlZOHHiBADg6NGj7I/Rhw8++AC7\nd+/Grl27kJqaKtl1Inaxm4dKpZI7BK8//OEPaGlpwY4dO7Bjxw4A7pmCVM9V7kdBQQHKysqwYsUK\nOJ1OvPrqq4p5M1GSJUuWYN26dSguLgbgXgwk9weeWbNm4eTJk1iyZAkEQcCGDRsU8/teXV2N4cOH\nyx2G16pVq7Bu3TosX74cTqcTpaWlMBgMssaUkZGBl156CTt37oRer8fmzZtljQfofr8sKyvD+vXr\n4XA4MGrUKBQUFCgiLs9Y7t9zlUoFQRCwZcsWJCcnY82aNQCAvLw8SR4nsdY6ERHRIMZb60RERIMY\nEzkREdEgxkROREQ0iDGRExERDWJM5ERERIMYEzkREdEgxkROREQ0iP0/R3RASt+kpfoAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11042f510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=df_temp.dropna().index.month, y=df_temp.dropna().outdoor, color=\"skyblue\");\n",
    "\n",
    "plt.savefig(\"ch12-seaborn-violinplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_temp[\"month\"] = df_temp.index.month\n",
    "df_temp[\"hour\"] = df_temp.index.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "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>outdoor</th>\n",
       "      <th>indoor</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:00:00+01:00</th>\n",
       "      <td>4.38</td>\n",
       "      <td>21.94</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:10:00+01:00</th>\n",
       "      <td>4.25</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:20:00+01:00</th>\n",
       "      <td>4.19</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:30:00+01:00</th>\n",
       "      <td>4.06</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:40:00+01:00</th>\n",
       "      <td>4.06</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           outdoor  indoor  month  hour\n",
       "time                                                   \n",
       "2014-01-01 00:00:00+01:00     4.38   21.94      1     0\n",
       "2014-01-01 00:10:00+01:00     4.25   22.00      1     0\n",
       "2014-01-01 00:20:00+01:00     4.19   22.00      1     0\n",
       "2014-01-01 00:30:00+01:00     4.06   22.00      1     0\n",
       "2014-01-01 00:40:00+01:00     4.06   22.00      1     0"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "table = pd.pivot_table(df_temp, values='outdoor', index=['month'], columns=['hour'], aggfunc=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>hour</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</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>1</th>\n",
       "      <td>-1.692312</td>\n",
       "      <td>-1.750162</td>\n",
       "      <td>-1.826649</td>\n",
       "      <td>-1.879086</td>\n",
       "      <td>-1.922527</td>\n",
       "      <td>-1.968065</td>\n",
       "      <td>-2.020914</td>\n",
       "      <td>-2.035806</td>\n",
       "      <td>-2.101774</td>\n",
       "      <td>-2.001022</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.457849</td>\n",
       "      <td>-1.696935</td>\n",
       "      <td>-1.814194</td>\n",
       "      <td>-1.812258</td>\n",
       "      <td>-1.853297</td>\n",
       "      <td>-1.898432</td>\n",
       "      <td>-1.839730</td>\n",
       "      <td>-1.806486</td>\n",
       "      <td>-1.854462</td>\n",
       "      <td>-1.890811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.613690</td>\n",
       "      <td>1.521190</td>\n",
       "      <td>1.479405</td>\n",
       "      <td>1.464371</td>\n",
       "      <td>1.506407</td>\n",
       "      <td>1.485595</td>\n",
       "      <td>1.499167</td>\n",
       "      <td>1.516946</td>\n",
       "      <td>1.669226</td>\n",
       "      <td>2.067725</td>\n",
       "      <td>...</td>\n",
       "      <td>3.573593</td>\n",
       "      <td>3.360741</td>\n",
       "      <td>2.939390</td>\n",
       "      <td>2.501607</td>\n",
       "      <td>2.357425</td>\n",
       "      <td>2.236190</td>\n",
       "      <td>2.204458</td>\n",
       "      <td>2.137619</td>\n",
       "      <td>2.024671</td>\n",
       "      <td>1.896190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.192366</td>\n",
       "      <td>2.866774</td>\n",
       "      <td>2.628000</td>\n",
       "      <td>2.524140</td>\n",
       "      <td>2.384140</td>\n",
       "      <td>2.235538</td>\n",
       "      <td>2.243387</td>\n",
       "      <td>2.622258</td>\n",
       "      <td>3.419301</td>\n",
       "      <td>4.466290</td>\n",
       "      <td>...</td>\n",
       "      <td>7.790323</td>\n",
       "      <td>7.930914</td>\n",
       "      <td>7.595892</td>\n",
       "      <td>6.770914</td>\n",
       "      <td>5.731508</td>\n",
       "      <td>4.983784</td>\n",
       "      <td>4.437419</td>\n",
       "      <td>4.022312</td>\n",
       "      <td>3.657903</td>\n",
       "      <td>3.407258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.832738</td>\n",
       "      <td>5.336012</td>\n",
       "      <td>4.926667</td>\n",
       "      <td>4.597059</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>4.109769</td>\n",
       "      <td>4.123699</td>\n",
       "      <td>4.741437</td>\n",
       "      <td>5.878035</td>\n",
       "      <td>7.272299</td>\n",
       "      <td>...</td>\n",
       "      <td>12.175556</td>\n",
       "      <td>12.500059</td>\n",
       "      <td>12.494483</td>\n",
       "      <td>12.361156</td>\n",
       "      <td>11.989240</td>\n",
       "      <td>10.454881</td>\n",
       "      <td>8.857619</td>\n",
       "      <td>7.712619</td>\n",
       "      <td>6.974762</td>\n",
       "      <td>6.293512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.792204</td>\n",
       "      <td>9.369351</td>\n",
       "      <td>9.009839</td>\n",
       "      <td>8.670914</td>\n",
       "      <td>8.463387</td>\n",
       "      <td>8.446919</td>\n",
       "      <td>8.772324</td>\n",
       "      <td>9.562742</td>\n",
       "      <td>10.723622</td>\n",
       "      <td>12.047717</td>\n",
       "      <td>...</td>\n",
       "      <td>15.542581</td>\n",
       "      <td>15.744624</td>\n",
       "      <td>15.784839</td>\n",
       "      <td>15.799570</td>\n",
       "      <td>17.009892</td>\n",
       "      <td>15.685161</td>\n",
       "      <td>13.632796</td>\n",
       "      <td>12.216290</td>\n",
       "      <td>11.291237</td>\n",
       "      <td>10.622849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13.209556</td>\n",
       "      <td>12.792889</td>\n",
       "      <td>12.382889</td>\n",
       "      <td>11.967889</td>\n",
       "      <td>11.735778</td>\n",
       "      <td>11.886667</td>\n",
       "      <td>12.503778</td>\n",
       "      <td>13.338167</td>\n",
       "      <td>14.343444</td>\n",
       "      <td>15.665475</td>\n",
       "      <td>...</td>\n",
       "      <td>18.630556</td>\n",
       "      <td>18.866292</td>\n",
       "      <td>18.680611</td>\n",
       "      <td>18.529832</td>\n",
       "      <td>20.057877</td>\n",
       "      <td>18.853389</td>\n",
       "      <td>16.969777</td>\n",
       "      <td>15.675111</td>\n",
       "      <td>14.658778</td>\n",
       "      <td>13.898167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17.956344</td>\n",
       "      <td>17.348641</td>\n",
       "      <td>16.793152</td>\n",
       "      <td>16.309892</td>\n",
       "      <td>16.001559</td>\n",
       "      <td>15.986774</td>\n",
       "      <td>16.506613</td>\n",
       "      <td>17.478226</td>\n",
       "      <td>18.850054</td>\n",
       "      <td>20.533763</td>\n",
       "      <td>...</td>\n",
       "      <td>24.598441</td>\n",
       "      <td>25.030000</td>\n",
       "      <td>24.869194</td>\n",
       "      <td>24.764409</td>\n",
       "      <td>26.155161</td>\n",
       "      <td>24.896505</td>\n",
       "      <td>22.550269</td>\n",
       "      <td>20.882649</td>\n",
       "      <td>19.699022</td>\n",
       "      <td>18.822634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.498205</td>\n",
       "      <td>13.960128</td>\n",
       "      <td>13.555128</td>\n",
       "      <td>12.995641</td>\n",
       "      <td>12.651410</td>\n",
       "      <td>12.485974</td>\n",
       "      <td>12.680130</td>\n",
       "      <td>13.403506</td>\n",
       "      <td>14.578780</td>\n",
       "      <td>16.170833</td>\n",
       "      <td>...</td>\n",
       "      <td>20.473810</td>\n",
       "      <td>20.292381</td>\n",
       "      <td>20.328795</td>\n",
       "      <td>19.642436</td>\n",
       "      <td>19.373846</td>\n",
       "      <td>18.713462</td>\n",
       "      <td>17.034872</td>\n",
       "      <td>15.843590</td>\n",
       "      <td>15.146154</td>\n",
       "      <td>14.596667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11.133000</td>\n",
       "      <td>10.725667</td>\n",
       "      <td>10.362444</td>\n",
       "      <td>9.976833</td>\n",
       "      <td>9.729333</td>\n",
       "      <td>9.503944</td>\n",
       "      <td>9.357500</td>\n",
       "      <td>9.689778</td>\n",
       "      <td>10.600778</td>\n",
       "      <td>11.829106</td>\n",
       "      <td>...</td>\n",
       "      <td>16.336983</td>\n",
       "      <td>16.828268</td>\n",
       "      <td>17.031056</td>\n",
       "      <td>16.786983</td>\n",
       "      <td>15.853556</td>\n",
       "      <td>14.534637</td>\n",
       "      <td>13.350444</td>\n",
       "      <td>12.545278</td>\n",
       "      <td>11.954190</td>\n",
       "      <td>11.399056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.602011</td>\n",
       "      <td>8.490598</td>\n",
       "      <td>8.382486</td>\n",
       "      <td>8.257097</td>\n",
       "      <td>8.166774</td>\n",
       "      <td>8.140054</td>\n",
       "      <td>8.140161</td>\n",
       "      <td>8.148333</td>\n",
       "      <td>8.410914</td>\n",
       "      <td>9.054946</td>\n",
       "      <td>...</td>\n",
       "      <td>11.330323</td>\n",
       "      <td>11.189194</td>\n",
       "      <td>10.836865</td>\n",
       "      <td>10.361568</td>\n",
       "      <td>9.781022</td>\n",
       "      <td>9.373441</td>\n",
       "      <td>9.134570</td>\n",
       "      <td>8.956505</td>\n",
       "      <td>8.820270</td>\n",
       "      <td>8.623297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.847111</td>\n",
       "      <td>4.765922</td>\n",
       "      <td>4.815642</td>\n",
       "      <td>4.773240</td>\n",
       "      <td>4.809611</td>\n",
       "      <td>4.785833</td>\n",
       "      <td>4.741222</td>\n",
       "      <td>4.739778</td>\n",
       "      <td>4.794500</td>\n",
       "      <td>4.965389</td>\n",
       "      <td>...</td>\n",
       "      <td>5.526034</td>\n",
       "      <td>5.342753</td>\n",
       "      <td>5.081250</td>\n",
       "      <td>5.056629</td>\n",
       "      <td>4.959106</td>\n",
       "      <td>4.868111</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>4.774389</td>\n",
       "      <td>4.720722</td>\n",
       "      <td>4.699722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.366369</td>\n",
       "      <td>-0.390556</td>\n",
       "      <td>-0.447374</td>\n",
       "      <td>-0.370111</td>\n",
       "      <td>-0.353128</td>\n",
       "      <td>-0.319832</td>\n",
       "      <td>-0.358667</td>\n",
       "      <td>-0.410278</td>\n",
       "      <td>-0.483167</td>\n",
       "      <td>-0.344667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.738944</td>\n",
       "      <td>0.367056</td>\n",
       "      <td>0.152167</td>\n",
       "      <td>-0.106111</td>\n",
       "      <td>-0.182500</td>\n",
       "      <td>-0.244167</td>\n",
       "      <td>-0.290000</td>\n",
       "      <td>-0.305333</td>\n",
       "      <td>-0.302778</td>\n",
       "      <td>-0.325642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "hour          0          1          2          3          4          5   \\\n",
       "month                                                                     \n",
       "1      -1.692312  -1.750162  -1.826649  -1.879086  -1.922527  -1.968065   \n",
       "2       1.613690   1.521190   1.479405   1.464371   1.506407   1.485595   \n",
       "3       3.192366   2.866774   2.628000   2.524140   2.384140   2.235538   \n",
       "4       5.832738   5.336012   4.926667   4.597059   4.380000   4.109769   \n",
       "5       9.792204   9.369351   9.009839   8.670914   8.463387   8.446919   \n",
       "6      13.209556  12.792889  12.382889  11.967889  11.735778  11.886667   \n",
       "7      17.956344  17.348641  16.793152  16.309892  16.001559  15.986774   \n",
       "8      14.498205  13.960128  13.555128  12.995641  12.651410  12.485974   \n",
       "9      11.133000  10.725667  10.362444   9.976833   9.729333   9.503944   \n",
       "10      8.602011   8.490598   8.382486   8.257097   8.166774   8.140054   \n",
       "11      4.847111   4.765922   4.815642   4.773240   4.809611   4.785833   \n",
       "12     -0.366369  -0.390556  -0.447374  -0.370111  -0.353128  -0.319832   \n",
       "\n",
       "hour          6          7          8          9     ...             14  \\\n",
       "month                                                ...                  \n",
       "1      -2.020914  -2.035806  -2.101774  -2.001022    ...      -1.457849   \n",
       "2       1.499167   1.516946   1.669226   2.067725    ...       3.573593   \n",
       "3       2.243387   2.622258   3.419301   4.466290    ...       7.790323   \n",
       "4       4.123699   4.741437   5.878035   7.272299    ...      12.175556   \n",
       "5       8.772324   9.562742  10.723622  12.047717    ...      15.542581   \n",
       "6      12.503778  13.338167  14.343444  15.665475    ...      18.630556   \n",
       "7      16.506613  17.478226  18.850054  20.533763    ...      24.598441   \n",
       "8      12.680130  13.403506  14.578780  16.170833    ...      20.473810   \n",
       "9       9.357500   9.689778  10.600778  11.829106    ...      16.336983   \n",
       "10      8.140161   8.148333   8.410914   9.054946    ...      11.330323   \n",
       "11      4.741222   4.739778   4.794500   4.965389    ...       5.526034   \n",
       "12     -0.358667  -0.410278  -0.483167  -0.344667    ...       0.738944   \n",
       "\n",
       "hour          15         16         17         18         19         20  \\\n",
       "month                                                                     \n",
       "1      -1.696935  -1.814194  -1.812258  -1.853297  -1.898432  -1.839730   \n",
       "2       3.360741   2.939390   2.501607   2.357425   2.236190   2.204458   \n",
       "3       7.930914   7.595892   6.770914   5.731508   4.983784   4.437419   \n",
       "4      12.500059  12.494483  12.361156  11.989240  10.454881   8.857619   \n",
       "5      15.744624  15.784839  15.799570  17.009892  15.685161  13.632796   \n",
       "6      18.866292  18.680611  18.529832  20.057877  18.853389  16.969777   \n",
       "7      25.030000  24.869194  24.764409  26.155161  24.896505  22.550269   \n",
       "8      20.292381  20.328795  19.642436  19.373846  18.713462  17.034872   \n",
       "9      16.828268  17.031056  16.786983  15.853556  14.534637  13.350444   \n",
       "10     11.189194  10.836865  10.361568   9.781022   9.373441   9.134570   \n",
       "11      5.342753   5.081250   5.056629   4.959106   4.868111   4.833333   \n",
       "12      0.367056   0.152167  -0.106111  -0.182500  -0.244167  -0.290000   \n",
       "\n",
       "hour          21         22         23  \n",
       "month                                   \n",
       "1      -1.806486  -1.854462  -1.890811  \n",
       "2       2.137619   2.024671   1.896190  \n",
       "3       4.022312   3.657903   3.407258  \n",
       "4       7.712619   6.974762   6.293512  \n",
       "5      12.216290  11.291237  10.622849  \n",
       "6      15.675111  14.658778  13.898167  \n",
       "7      20.882649  19.699022  18.822634  \n",
       "8      15.843590  15.146154  14.596667  \n",
       "9      12.545278  11.954190  11.399056  \n",
       "10      8.956505   8.820270   8.623297  \n",
       "11      4.774389   4.720722   4.699722  \n",
       "12     -0.305333  -0.302778  -0.325642  \n",
       "\n",
       "[12 rows x 24 columns]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/v6Hv35tvvqnRo0cHFePTTz9V37596/eb3/3ud0HFOX78uKZMmaJx48Zp7Nix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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a2b3090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n",
    "sns.heatmap(table, ax=ax);\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-seaborn-heatmap.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%reload_ext version_information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/json": {
       "Software versions": [
        {
         "module": "Python",
         "version": "2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]"
        },
        {
         "module": "IPython",
         "version": "3.2.1"
        },
        {
         "module": "OS",
         "version": "Darwin 14.1.0 x86_64 i386 64bit"
        },
        {
         "module": "numpy",
         "version": "1.9.2"
        },
        {
         "module": "matplotlib",
         "version": "1.4.3"
        },
        {
         "module": "pandas",
         "version": "0.16.2"
        },
        {
         "module": "seaborn",
         "version": "0.6.0"
        }
       ]
      },
      "text/html": [
       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]</td></tr><tr><td>IPython</td><td>3.2.1</td></tr><tr><td>OS</td><td>Darwin 14.1.0 x86_64 i386 64bit</td></tr><tr><td>numpy</td><td>1.9.2</td></tr><tr><td>matplotlib</td><td>1.4.3</td></tr><tr><td>pandas</td><td>0.16.2</td></tr><tr><td>seaborn</td><td>0.6.0</td></tr></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{|l|l|}\\hline\n",
       "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
       "Python & 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)] \\\\ \\hline\n",
       "IPython & 3.2.1 \\\\ \\hline\n",
       "OS & Darwin 14.1.0 x86\\_64 i386 64bit \\\\ \\hline\n",
       "numpy & 1.9.2 \\\\ \\hline\n",
       "matplotlib & 1.4.3 \\\\ \\hline\n",
       "pandas & 0.16.2 \\\\ \\hline\n",
       "seaborn & 0.6.0 \\\\ \\hline\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Software versions\n",
       "Python 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]\n",
       "IPython 3.2.1\n",
       "OS Darwin 14.1.0 x86_64 i386 64bit\n",
       "numpy 1.9.2\n",
       "matplotlib 1.4.3\n",
       "pandas 0.16.2\n",
       "seaborn 0.6.0\n",
       "Mon Aug 03 23:34:30 2015 JST"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%version_information numpy, matplotlib, pandas, seaborn"
   ]
  }
 ],
 "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": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}