{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook covers the basics of operations on Series and DataFrames. Again, a quick check on whether you know the material.\n",
    "\n",
    "1. Can you use numpy ufuncs (e.g. `np.exp`) on DataFrames?\n",
    "2. What happens when you add two DataFrames with different indicies?\n",
    "\n",
    "If you know the answers already, you can skip to our first [application](#Application:-Economic-Timeseries). Maybe download some additional datasets from [FRED](https://research.stlouisfed.org) and play with those; try to figure out how to fix the economy. You've got about 5 minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DataFrame`s and `Series` support all the usual math operations. Additionally,\n",
    "numpy [`ufuncs`](http://docs.scipy.org/doc/numpy/reference/ufuncs.html) (e.g. `np.log`) can  be used as expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `ufuncs` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>1.560186</td>\n",
       "      <td>1.559945</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.580836</td>\n",
       "      <td>8.661761</td>\n",
       "      <td>6.011150</td>\n",
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       "          0         1         2\n",
       "0  3.745401  9.507143  7.319939\n",
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       "2  0.580836  8.661761  6.011150"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "df = pd.DataFrame(np.random.uniform(0, 10, size=(3, 3)))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "2  1.580836   9.661761  7.011150"
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     "execution_count": 3,
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     "output_type": "execute_result"
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   "source": [
    "df + 1"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>2.434182</td>\n",
       "      <td>2.433429</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.337371</td>\n",
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       "           0          1          2\n",
       "0  14.028030  90.385769  53.581513\n",
       "1  35.839198   2.434182   2.433429\n",
       "2   0.337371  75.026112  36.133926"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>2.252043</td>\n",
       "      <td>1.990602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.789521</td>\n",
       "      <td>0.444805</td>\n",
       "      <td>0.444651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.543287</td>\n",
       "      <td>2.158918</td>\n",
       "      <td>1.793616</td>\n",
       "    </tr>\n",
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       "          0         1         2\n",
       "0  1.320529  2.252043  1.990602\n",
       "1  1.789521  0.444805  0.444651\n",
       "2 -0.543287  2.158918  1.793616"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "np.log(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we don't just have NumPy arrays. We have `DataFrames` and `Series`, which have *labels*.\n",
    "Pandas automatically aligns by label when doing operations between `DataFrames` and `Series`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>A</th>\n",
       "      <th>B</th>\n",
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       "   A  B\n",
       "0  1  4\n",
       "1  2  5\n",
       "2  3  6"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})\n",
    "df2 = pd.DataFrame({'A': [2, 4, 6], 'B': [1, 4, 9]})\n",
    "\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "   A  B\n",
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   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>A</th>\n",
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       "   A   B\n",
       "0  3   5\n",
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       "2  9  15"
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df1 + df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>A</th>\n",
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       "   A  B\n",
       "2  6  9\n",
       "0  2  1\n",
       "1  4  4"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Note the index order\n",
    "df2 = pd.DataFrame({'A': [6, 2, 4], 'B': [9, 1, 4]}, index=[2, 0, 1])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>A</th>\n",
       "      <th>B</th>\n",
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       "   A   B\n",
       "0  3   5\n",
       "1  6   9\n",
       "2  9  15"
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 + df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>B</th>\n",
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       "      <th>0</th>\n",
       "      <td>-0.580878</td>\n",
       "      <td>-0.525170</td>\n",
       "      <td>-0.571380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.924083</td>\n",
       "      <td>-2.612549</td>\n",
       "      <td>0.950370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.816445</td>\n",
       "      <td>-1.523876</td>\n",
       "      <td>-0.428046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.742407</td>\n",
       "      <td>-0.703344</td>\n",
       "      <td>-2.139621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.629475</td>\n",
       "      <td>0.597720</td>\n",
       "      <td>2.559488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          A         B         C\n",
       "0 -0.580878 -0.525170 -0.571380\n",
       "1 -0.924083 -2.612549  0.950370\n",
       "2  0.816445 -1.523876 -0.428046\n",
       "3 -0.742407 -0.703344 -2.139621\n",
       "4 -0.629475  0.597720  2.559488"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Different index entirely\n",
    "df3 = pd.DataFrame(np.random.randn(5, 3), columns=['A', 'B', 'C'])\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>0</th>\n",
       "      <td>0.419122</td>\n",
       "      <td>3.474830</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.075917</td>\n",
       "      <td>2.387451</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.816445</td>\n",
       "      <td>4.476124</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B   C\n",
       "0  0.419122  3.474830 NaN\n",
       "1  1.075917  2.387451 NaN\n",
       "2  3.816445  4.476124 NaN\n",
       "3       NaN       NaN NaN\n",
       "4       NaN       NaN NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 + df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recap\n",
    "\n",
    "- `ufuncs` work just fine\n",
    "- broadcasting works fine\n",
    "- Operations **align by label** and then are performed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Application: Economic Timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from itertools import chain\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "# from pandas_datareader import data\n",
    "# from pandas.io import data\n",
    "import matplotlib.pyplot as plt\n",
    "from toolz import partitionby\n",
    "\n",
    "%matplotlib inline\n",
    "pd.options.display.max_rows = 10\n",
    "sns.set_style('ticks')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas recently split off some web-based data reading functionality into its own package `pandas-datareader`. Earlier, I used it to grab data from [FRED](http://research.stlouisfed.org).\n",
    "\n",
    "```python\n",
    "gdp = data.DataReader(\"GDP\", data_source='fred', start='1929', end='2014').squeeze()\n",
    "cpi = data.DataReader(\"CPIAUCSL\", data_source='fred', start='1947-01', end='2015-05').squeeze()\n",
    "rec = data.DataReader('USREC', data_source='fred', start='1854-12-01', end='2014-08-01').squeeze()\n",
    "gdp.to_csv('data/gdp.csv', header=True)\n",
    "cpi.to_csv('data/cpi.csv', header=True)\n",
    "rec.to_csv('data/rec.csv', header=True)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATE,GDP\r\n",
      "1947-01-01,243.1\r\n",
      "1947-04-01,246.3\r\n",
      "1947-07-01,250.1\r\n",
      "1947-10-01,260.3\r\n",
      "1948-01-01,266.2\r\n",
      "1948-04-01,272.9\r\n",
      "1948-07-01,279.5\r\n",
      "1948-10-01,280.7\r\n",
      "1949-01-01,275.4\r\n"
     ]
    }
   ],
   "source": [
    "!head data/gdp.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.read_csv?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gdp = pd.read_csv('data/gdp.csv', index_col='DATE', parse_dates=['DATE']).squeeze()\n",
    "cpi = pd.read_csv('data/cpi.csv', index_col='DATE', parse_dates=['DATE']).squeeze()\n",
    "rec = pd.read_csv('data/rec.csv', index_col='DATE', parse_dates=['DATE']).squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "1947-01-01    243.1\n",
       "1947-04-01    246.3\n",
       "1947-07-01    250.1\n",
       "1947-10-01    260.3\n",
       "1948-01-01    266.2\n",
       "Name: GDP, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp.head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "1947-01-01    21.48\n",
       "1947-02-01    21.62\n",
       "1947-03-01    22.00\n",
       "1947-04-01    22.00\n",
       "1947-05-01    21.95\n",
       "Name: CPIAUCSL, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "1854-12-01    1\n",
       "1855-01-01    0\n",
       "1855-02-01    0\n",
       "1855-03-01    0\n",
       "1855-04-01    0\n",
       "Name: USREC, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table Summarization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas has a few methods for summarizing the contents of a DataFrame or Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      269.000000\n",
       "mean      5074.190335\n",
       "std       5158.153013\n",
       "min        243.100000\n",
       "25%        654.800000\n",
       "50%       2860.000000\n",
       "75%       8551.900000\n",
       "max      17078.300000\n",
       "Name: GDP, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`describe` actually combines and few different *aggregation* methods that summarise a DataFrame or Series. These include `mean`, `max`, `std`, `quantile`..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean:     5074.1903345724895\n",
      "std:      5158.153012839137\n",
      "quantile: 6132.956\n",
      "max:      17078.3\n"
     ]
    }
   ],
   "source": [
    "print('mean:    ', gdp.mean())\n",
    "print('std:     ', gdp.std())\n",
    "print('quantile:', gdp.quantile(.66))\n",
    "print('max:     ', gdp.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can also be useful to get the argmax, or the index label where the maximum occurs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2013-10-01 00:00:00')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp.idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrames and Series can make decent looking plots with a few lines of code.\n",
    "\n",
    "I wanted to incude bars for recession indicators.\n",
    "The only pandas-relavent bit is `Series.iteritems`, which you *almost* never want to use since it's relatively slow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('1854-12-01 00:00:00'), 1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(rec.iteritems())  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "groups = partitionby(lambda x: x[1] == 1, rec.iteritems())\n",
    "recessions = filter(lambda x: x[0][1] == 1, groups)\n",
    "spans = [(months[0][0], months[-1][0]) for months in recessions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_rec_bars(ax=None):\n",
    "    ax = ax or plt.gca()\n",
    "    for span in spans:\n",
    "        ax.fill_between(span, *ax.get_ylim(), color='k', alpha=.25)\n",
    "    return ax\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (12, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dr7y/3TfO+FEvXdGp5ffu8VLhzjQAAADOa33xEc1/f7uMBoPCzUat3302MHe5LEQGg7Tv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\njQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1126313c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = gdp.plot(title='GDP', linewidth=3)\n",
    "add_rec_bars(ax)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3/9gql9sfbLdaTLpsXLZunDZA6clMpwAiAQEZAIA25Pb69coHe/TxxiIVl9WG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\nREREpIUDMhERERGRln8BT/SJM4ZEwFoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1126f16a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = cpi.plot(title='CPI', linewidth=3)\n",
    "add_rec_bars(ax)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's put some of those operations to use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Execrise: Convert CPI to be base 2009\n",
    "\n",
    "CPI is the Consumer Price Index. The Index part just means that it doesn't really have any units,\n",
    "just some (arbitrary) time span that is set to 100, and every other observation is relative to that time.\n",
    "The CPI we have is based on 1982-1984.\n",
    "\n",
    "**Convert the CPI base-2009 by dividing the entire Series `cpi` by the average CPI in 2009.**\n",
    "\n",
    "Try breaking the problem into pieces:\n",
    "1. Select just the rows from 2009 (`.loc`)\n",
    "  + Timeseries (dates in the index) have speical rules for slicing. Pass in a string with the subset you want\n",
    "    + `.loc['2010-01-01']` select all observations from that day\n",
    "    + `.loc['2010-01']` selects all observations from that month\n",
    "2. calculate the average on those rows\n",
    "3. divide `cpi` by that number (assign that to `cpi09`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Your code goes here\n",
    "# Assign the result to cpi09"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "1947-01-01    0.100110\n",
       "1947-02-01    0.100762\n",
       "1947-03-01    0.102533\n",
       "1947-04-01    0.102533\n",
       "1947-05-01    0.102300\n",
       "                ...   \n",
       "2015-01-01    1.093736\n",
       "2015-02-01    1.096108\n",
       "2015-03-01    1.098690\n",
       "2015-04-01    1.099818\n",
       "2015-05-01    1.104707\n",
       "Name: CPIAUCSL, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load -r 1:5 solutions_operations.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Uhu8/8AbxvByu++QYXlm1k18v2sD+Q0kASgpjnHv6AG78zDgG9yto874mjunPv33zUvr0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\nIiIiIgJoMBYRERERATQYi4iIiIgA8Dep5oCb0i3mfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11275d668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = cpi09.plot(title='CPI-2009')\n",
    "add_rec_bars(ax=ax)\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Real GDP: Alignment\n",
    "\n",
    "Let's say we want real GDP (adjusted for inflation).\n",
    "\n",
    "\\begin{equation}\n",
    "    rGDP_t = \\frac{GDP_t}{CPI_t}\n",
    "\\end{equation}\n",
    "\n",
    "Problem: our CPI is monthly but GDP is quarterly. Also the two Series have different start and end points. If you didn't have automatic label alignment, you'd have to jump through hoops to select the correct subset of each series.\n",
    "\n",
    "\n",
    "```python\n",
    "# this is boolean indexing, we'll see more later\n",
    "gdp / cpi09[(cpi.index.month % 3 == 1) & (cpi09.index.year <= 2014)]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... but that's unneccesary. The operations will automatically align for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "1947-01-01    2428.336614\n",
       "1947-02-01            NaN\n",
       "1947-03-01            NaN\n",
       "1947-04-01    2402.148973\n",
       "1947-05-01            NaN\n",
       "                 ...     \n",
       "2015-01-01            NaN\n",
       "2015-02-01            NaN\n",
       "2015-03-01            NaN\n",
       "2015-04-01            NaN\n",
       "2015-05-01            NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgdp = (gdp / cpi09)\n",
    "rgdp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `NaN`s are missing value indicators. `NaN`s can crop up for many reasons, but in this case it's because the labels didn't overlap perfectly.\n",
    "\n",
    "Many pandas methods, e.g. aggregations like `sum` or `mean`, will ignore missing values.\n",
    "Pandas provides methods for detetcing missing data `.isnull`, filling missing data `.fillna`,\n",
    "or you can dropping it, which is what I'll do here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "1947-01-01     2428.336614\n",
       "1947-04-01     2402.148973\n",
       "1947-07-01     2413.973150\n",
       "1947-10-01     2437.851712\n",
       "1948-01-01     2412.040298\n",
       "                  ...     \n",
       "2013-01-01    15298.871240\n",
       "2013-04-01    15391.990004\n",
       "2013-07-01    15538.670381\n",
       "2013-10-01    15666.322137\n",
       "2014-01-01    15553.401461\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgdp.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas methods are non-mutating by default. This means that even though I called `.dropna()` above, `rgdp` still has missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "1947-01-01    2428.336614\n",
       "1947-02-01            NaN\n",
       "1947-03-01            NaN\n",
       "1947-04-01    2402.148973\n",
       "1947-05-01            NaN\n",
       "                 ...     \n",
       "2015-01-01            NaN\n",
       "2015-02-01            NaN\n",
       "2015-03-01            NaN\n",
       "2015-04-01            NaN\n",
       "2015-05-01            NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgdp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To capture the change, assign a name to the result. In this case, I just the same name `rgdp`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "1947-01-01    2428.336614\n",
       "1947-04-01    2402.148973\n",
       "1947-07-01    2413.973150\n",
       "1947-10-01    2437.851712\n",
       "1948-01-01    2412.040298\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgdp = rgdp.dropna()\n",
    "rgdp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZwWOAR0RUbCnuwSMiIsrCAK9EZIBXXy0yeCzRJCIqvuwxCQzwiIiIGOCVSCbAExm83CYr\numFO+zEREZUbVWWJJhERkRMDvBKRC41amcFzzME7fHwIa7/5OB57/vCMHBsRUblgiSYREVE2Bngl\nIufg1deMzOA1H+pFMq3jzcN9M3JsRETlIq05SzSZwSMiImKAVyJyDl6dvQdPhWmKssyewYS4TYqL\nESKiqeCgcyIiomwM8ErANE07eKsK+eBxu6Dppn2muW8oCYABHhHRVDHAIyIiysYArwRUzYBhmPB6\nXPC4XQgFPQCAuDULr5cZPCKiosjuosn3VCIiIgZ4JSADt4BPBHYVAS+AzD48WaLJ2XhERFPjbLKi\n6SY03Rjj1kREROWPAV4JyAAvGBABXsj6fzypQTdM9A+zRJOIqBhULbssk2WaRER0smOAVwJ2gOdz\nA3Bk8BIqBiNJGNYMPAZ4RERT4yzRBFimSURE5JnpAyhHyZQ4gxz0Wxm8oAjw4knNLs8ERDmRqunw\netzTf5BERGXAWaIJTH8Gr/lQLzZva8W5pzfg3e+YZ7/vExERzRR+EpWAvQfPL/fgif/HkiowmH3b\neFJDdSUDPCKiyZBdNEW3YgPJaa6M+N2WA3h9bzee2nYMP/qdG+85dx7+98ffgaqQb1qPg4iISGKJ\nZgnYJZr+7CYr8aSalcFz3paIiCZOjp+RAdV0Z/CiVnfkBQ2VSKs6nnm9DS++0T6tx0BEROTEAK8E\nRgZ4VgYvoaFviAEeEVGxyAyeDPBS0xzgxZPiPfwb17wTV168FACQYIdkIiKaQQzwisAwDaS1tP11\nboAXGiODJxcHREQ0cbkBXnKam6w43+9l5+TpDjKJiIicGOAVwQ+2/hT/8NgtiKsieJMLjEBOF814\nUrOHnPut65jBIyKaPNlFMzxDJZoyW1cR8MDvFe/ruY1fiIiIphMDvCI4MnAMkXQMnZFuAHnm4AUz\nTVb6rABvUWNl1m2JiGhidEMMNlcUoNLqVjydYxJM08zK4MkTd8zgERHRTGKAVwSaLj7gh1MxAM45\neNlNVqJxFf3DSSgKsLAxnHVbIiKaGNXKlHk9bgSs99vpzOCl0joME/B53XC7XfB7rRJNZvCIiGgG\nMcArAs0QQVokFQWQbw+e+H9bdwSGCdRU+u1yIgZ4RESTIwMpv9dll8RPZ4AXt96/ZSMtO4PHAI+I\niGYQA7wiUGWAlxYBnhx0rrkT+MvBZ+EVCTwMRFIAgLqaoB38McAjIpoc1RqR4PO6HeWR0/eeGpf7\n76z3c3sPHks0iYhoBjHAKwIZ4A2nIgAyQdvOwa342esPYN/QnqzbNzgDPHbRJCpLh48P4Z/v3Yqj\nHcMzfShlS3bQ9HlnpkQzd781m6wQEdFswABvikzThG6ID/PcPXhRTSzsEkYs63vqqgPM4BGVuRd2\nHcfO/T14YdfxmT6UsiUDKZ/HWaI5nRk8q0TTL8o02GSFiIhmAwZ4UyT33wEj9+AldBHYpfUU3C7F\nvp0zg8c5eETlSS7y+TdeOs4SzRnN4OWWaDKDR0REM2hSAZ5hGLj11luxdu1arFu3Dq2trVnXb9my\nBWvWrMHatWvx0EMPZV3X19eH973vfThy5Mjkj3ocpmnCNM2S3b+TmifAk2eQo6r4Oq4l7E6aAFDP\nPXhEZS9tBR+xhDrDR1K+Uo4STb9/+rNndgYvt8kKM3hERDSDJhXgbd68Gaqq4oEHHsCNN96IDRs2\n2NepqooNGzbg/vvvx8aNG/Hggw+ir6/Pvu7WW29FMBgsztGP4jv3vYKv3/U8DKP0QZ5mZD7IszN4\nJqKq2JMXTyfsWXgAUFcdtBcEDPCIypPcH8a/8dJJz3CJphxynrsHL60xwCMiopkzqQBv+/btuPji\niwEAy5cvR3Nzs33doUOHsHjxYoTDYXi9XqxatQrbtm0DANxxxx343Oc+h4aGhiIcen6maeK1t7qw\nt2UAkXi6ZI8jyRl4ADCclnvwdMCt2cFfXM3O4GU1WUnx7D5ROZLZJWbwSmemm6zYYxL8zOAREdHs\nMakALxqNorKy0v7a7XbDMAz7unA4bF8XCoUQiUTwyCOPoLa2FhdddBEAlKyEMpnWIe96OgI81cgs\n3iKpKFRNQ1rV4fJmHjuuJhCyAjxFAWrZZIWo7KmqeE+UrfSp+NLWa+yfoTEJ7KJJRESzkWf8m4xU\nWVmJWCzTGdIwDLhcIlYMh8NZ18ViMVRVVWHjxo1QFAVbt27F3r17cfPNN+Oee+5BfX39qI9z1113\n4e67757QsTkXU5FY6RdWzhJNwzQwYD13f0VmkRFXE6iyFgBzwn543C6WaBKVOZldYpOV0pnxDF5O\nF02fYw6eaZpQFGXU7yUiIiqVSQV4K1euxNNPP40rrrgCO3fuxLJly+zrli5dipaWFgwNDSEYDGLb\ntm247rrrcPnll9u3WbduHf7lX/5lzOAOANavX4/169dnXdbW1oZLL7101O9xBkzTksHTsxdvvdEh\nAIAvqEE+elxNYF5QLADqa8T+Q2bwiMpbigFeyckAz+t17MFLzVwXTZdLgdfjgqoZSGuGndEjIiKa\nTpMK8C677DK8+OKLWLt2LQDg9ttvx6ZNmxCPx/GZz3wGN998M6677joYhoE1a9agsbGxqAc9Fudi\najg2DXvwjOzFW19MzL7z+FVHgJe0M3YywJNnmxMpHYZhwuXimV6icqJajTZiLNEsGdmpdKZKNGXF\niHx/l8eiagZSaZ0BHhERzYhJBXiKouC2227LumzJkiX2v1evXo3Vq1eP+v0bN26czMMWZLozeLkB\n3kBcBHhuf/YevIpKK8CrFgGey6Ug4HMjmdaRTGtZTViI6MSXsvaHqZoBVdPh9XCxX2wyg9eDQ3jm\naC8URQR9umFmzR4tFXlCUWbwANFoJZpQRaOVUMkPgYiIaISyG3TuzOBNT5OVnAAvIUYjKI4mK4Zp\nYOXZdTjzlDm4ZMUC+3KWaRKVr7Sj0QbLNEtDlsE2J5/B/Tv+B36/CKqnK4sn37tzM3gARyUQEdHM\nKbsAzxkszUSJ5nBKBHimJ5V1+bwGP7775Uuw7JRa+zIZ4HHxR1R+VMcCn2WapSG7aKpmCiZM+APi\n8ukaU5AZdJ6pwOCoBCIimmnlF+A5u2jOQJOVSEp00TRc2QFeXE2M+F520iQqX7JEEyjdSRzTNNE/\nnCzZ2JnZLq3pgGLAhHj+Pp+4fLo6aeY2WQEcoxIY4BER0QwpuwAv7tyDNy1jEnIzeFEAgO5KAgAq\nfWITRr4AL2i11maAR1R+1KwSzdK8F21+tRXX3PYktu7uKMn9z3ZpVQR4ks8v/p+cthLNPE1WZAZP\n5fs6ERHNjLIL8Ka/yYpYxJm6+FDvGBwAAKiIAwDmhUUH0fwBHjN4ROXINE27wyMAxBKl+Rt/dU8n\nAGD3wd6S3P9sl1YNwJUJpL1WBm86smeGYSJhjWSQXZGB7Fl4REREM6HsArzpHpOg6uIMrpkWmz+i\n6Rjg0qBDg9flQV3FHHFc0xDgvXm4D//vhr+i+dDJudgjmi2cwR2QyfQU2+F20bW3oy9Wkvuf7dKq\nDsUR4Hm84nWfjgyeLAMN+t1ZY27sEk2VAR4REc2MsgvwcjN4pd6bkrb24JlpURukeNN2B83qQBUq\nvGIsQiydJ8ALFLfJyuNvvIL+RX/E47tfKcr9EdHkqDmL+1Jk8KLxNLr7RaVAZ+/JG+DBlQmmvVav\nk+nYg5fZf5c94oZNVoiIaKaVXYDn3Osih82WUkoTj6doIoOneNJQvKLBSnUgbAd405HB60i2QvFo\naE8eLcr9EdHk5GZvSrEH73D7kP3v7oE4dGN6G60k0xp+8JvXsX1v97Q+rlNayy7RdHtkBk9c9uTL\nR/Gl725B39DI99+pyjciAXCMSWAGj4iIZkjZBXi5wdJwiffhpVRx/y4jAAUKFI8G2AFe1bQGeAlN\nNHaJadGi3B8RTU5azS7RLEUXzcPHMwGeppvoHSx+EDOW19/qxtOvt+Gnf9hd9PsutDNoStWhODJ4\nbq/4HjkH76+vtKKlM4LmQ31FP8Z8HTQBwG/tx2OJJhERzZSyC/ByF1KREu/Dkxk8NzwI+SoAAK6g\nCLBq/GFUeEVmbzoCvKQmAsuUES/K/Y0nnlTxqyfeQnsPA0oip9wh14XOwdt9sBdvHi4sGDnkCPCA\n6S/T7BkU7zNt3VEc64oU7X5fae7ANbc9iUefOTjubVVVz8ngiQAvmdZhmiZareMajKbyfv9UyAYr\no2XwWKJJREQzpewCPBks1YTFnrhSd9KUe/DcihvVgTCATIBXFQgj5BVB35hz8Ip0dj9tiEWMqkzP\nmfzndhzHg5v343dbDkzL4xGdKHLL8wrJ4Gm6gdv++2Xceu9WxBLjB4Qyg7eoSbzvTHejld7BpP3v\nl5uzxzS0dUfQ1T+5E00vN4vOoI+/eATGOGWnootmJoOnuMXrnkxr6BlM2J8Hg5FSBHijZfDYZIWI\niGZW2QV4ciHVVCsCq1LPwkvLDJ7LjSp/JQBg4SniuppAFSp801eiqZkimDXc0xPgyQVczzSXhhHN\ndiNLNMd/H4olVKTSOtKagdfe6hrztilVR1t3FC4FeNc5cwEAndMd4Dn2tb3kmMPXPRDHDT94Ft/8\n8YuTut99rf3W/SSwt6V/zNumcrpoutzidU+l9ays4kAkOeJ7pyqzBy+7yYrP67KPgYiIaCaUXYAn\n25E3zREBXqn34MkMnkfxIOwTAV53vAdAbpOVkQuMYgZ4mm7AUKxFpFtDNFn6oKtnQDzGwHDxF09E\nJzKZwfN6xFtsIRk8Z9bupeaxB5e3dAzDMEwsaAzjlLlVAGYig5d5jzlwbND++tGnDyKt6ujqj0+4\n0Ug0nsaxrkzJ9zPb28a8vapll2jKfyfTOlo7MwFeKTJ4cRng5WbwvNyDR0REM6usAjzn4NmGOSKw\nKnmJpp3B8yBsZfB0a/h5td/RZCU9slypmAFeJJ4G3Jn7OT4w9pnvYugZjAOKgX4GeERZZGAzxyoV\nL2QPnvM22/d2jRkcyfLM0xZUY159CADQ2Ts9e2+lPiugWzJfBJivNHdgYDiJv7zSYt9monvf9rUO\nAMiU2L+wsx2abox6+1Ruiab172RKyw7wSrAHL2l91gRz9+BxTAIREc2wsgrw5HDboN+N6kprD16J\nm6yodgbPjbA/lHVdTaBqzCYrFfYcvKmXkQ7H0lBczgCv9MPOO5PHEFj1V8QrD4oz6UQz5JntbXhq\nW+tMH4ZNlmjKQKWQDF7cMSsvkdKx60DPqLeVAd7SBdWYWyfedzr6YiWf+ynpuoF+Kyv2kfcuBSCy\njr9/9lDWkPehiQZ4LSLAe//KhVjUFEYknsb2ffnHMJimOWLQuamIf6fSOlq7hu3LS5nBG7EHj4PO\niYhohpVVgOfc9B6u8AEofYmmDPC8bg+q/OGs66ZzDt5wLDuD1xUZnPJ9jkXXDUQrDkFxmXBX9aN/\nuPgLqGI71N+Cn+94CMk85bJUGm8c7MFX73wW//STF0sWfGi6gf96YDvufGCHPfh7pskumjWV1gme\nAk7iRHNu49zXlssZ4IUrvAgFPEikNPE+MA0GIikYhomasB/vOXceXC4Fuw/14U9bjwAA6qvF8x6K\nTux49h4VlQdnnlKL969cCAB4dpQyTdUKJF3uzO+VDPASaS1rD96gdbzFNNoePDZZISKimVZWAZ48\nSx70e1EVEgFeyTN4htVF0+Wxm6wAgFtxIeSrQFAGeNrIuU7FDPAGI0ko7syZ877YwJTvcyzdgzG4\nqsWZdcWXRP/Q7A+a/rD3L/jT/i3488FnZ/pQyl5nXwz//vNX8c0fb8WBY4PYdaAXsRLMggPEzDRN\nF39bL+w6XpLHmChZXlldKd6HEilt3AAjbu3BO31hNQDg1T2deYeX64aJIx0iO7V0QTUURcHc+kwW\nbzrIBiv11QGEK3x4x2l1MAwTybSOFWc04Ny3NQAQ70uFMgwT+60SzWWnzMH7rADvlTc7875Hykyh\nHG4OACbE7Y53R5FI6aiu9CEU8EA3TEQL6Ew6EXI7wKgZvFlSommaJh56aj+e3zk7/jaIiKj0yirA\nszN4gUwGr9R78GQGz+f2ZJVoVvnDcCkueFxu+D1+mKZpz6mTihng9UWz51D1J4ZGuWVxbGt9Uwx1\nhxXglaAAP8lXAAAgAElEQVRLXbElrCzq8y2vzvCRlLd4UsVNdz2Pl3Z3wO9zw2cteKMl+lvsc7Tr\nf26WLGJTVolm0O9B0O+GaWZKyEcjA+CzltRhXl0IQ9G0ndFyOt4dQVrV0TgnaL/PyTLN6ZqFJ1/z\numpxAuvd58yzr/vsZcvsEvnBCWTw2rojiCU11FcHUF8TRFNtBd5+ai1SaX3EGAYgE0Q7AzzDCvDa\nrddhcVOVXSY7kWCzEJkM3syPSTBNERz/6om3RswkfGl3B375p7fwXw/uYCk9EdFJoqwCPFkGVeH3\nIBwSZTOlHpOgWRk8j8trd9EEYM/EAzDqPjyvxwWPW4Gmm1P+4O2PZg8bH04Vb/BwPts738h84U2h\nd3B6O/hNRlITi81jQ+1oGRy7Ox/ll0xreGZ725glh/taBjAYSWFBQwj33nwpFjWJv4tSnWxxtus/\n1DaE9p7oGLeeHvLv2etx2SV8scQ4AZ6VYaoIePGud4iAKV+ZprM8U5pn78ObnhJV+Zo31IgA7z3n\nzke4wocLz56Ls5fWocbKXE5kD95ea//dslNr7ctkFi/f6yADPJcnk+XUkf0+uqipEjVh8f5b7EYr\nyfH24OVk8MYL8CdD0w08sfUIbvjBM/jafz2HBzfvx7/e94r92hiGid88udc+nt2H+op+DERENPuU\nWYCXOaM6XXvwZIDnc2eXaFYHqux/y314sZxOmoqi2IuDQpowjGUwnh1gRdXSLXINw8DhyD4AgGko\nUBSgc7j0XTunKpbMnMFnFm9ynnq1Fd//9ev4w7OHRr2NLLNbeWYT6qqDCAdlNr00J1v6csqDZ0Mp\nWkrVAZeGbvMAguLPf9x9ePL6UNBrz7Z7qbljRGn34XarPHN+JsCzM3h5SjRN08TDTx/G0Z7iZW/k\nSIQ6K8CbUxXAL751Ob7xhQsAwJE1Kzyokg1Wzjxljn3ZWUtEsNfaOTzi9naA5yhN18zs13jx3CrU\nVE78WAoRHy2Dl6fJyuHjQ/i7f30a2w4VN8j704tHcM/Db+BI+zCqQj7UVQfQ3hvD77YcACBKllsc\n3URfH2e+IhERlYeyCvCcTVYqK3xQFHFWXB+jzfZUyQDP68mMSQByM3iln4U3nLCCR1MR96eXLsDb\n33cYSTMOIxmEVxWLsa7I7A/weiOZ1+T5o6/CMEr3e1Gueq1gqq179N8v2er+jMXidyNs7YctWYmm\nlU0657Q6ALOjTDOtGvA0tuK1+JPQ54ixAeOdxJFjEkIBD848pRZVIR+6++PoHsjO/Ld1iwX7KfMy\nJ5Hm1Yu5nx15SjTbuqN48KmDeHF/8YILGeDJZiqAyFa6XeL9J1OiOZEMXqbBirSgoRIuRWQmVS37\n71V2KnXuPdbN7Oe4eG7YHlUxUOQAT+7BG7XJiiODd+DYIHTDRNdQcd9zDlnZ3E++/3T8/Nb/hRuv\nXgUAeOipA2jtHMZvnhQn4i595yIAwDYGeEREJ4WyCvAyGTwv3C4FIeuDt9ib6510U3yI+9weBDx+\neF0iYKvJk8ErZSfNSEoEeCGXOKufRukGnb/athMAoA80odovHq8vUdqmLsUgz+6buhsDySG8dLR5\nho/oxCPLCHsG8/9+yb1AQCYTU1khy6VLVKJpHcul5y9GuMKL1s4IWjpGZnymk6rqgFc8X8Un/jbH\nm4Vnl2gGvXC5FCxoECeMcjuDyq+baivsy+bWjp7Bk3Mq4+nidZGUWdN6K4OXSwZ4hZZoxhIqjnVF\n4HG7cNrCTGbS53WjqTYEwzDR3pt9UkFmyJxjElQjJ4PXFB43m9jVH8dX/vMZbHltYmM2EuOUaKYd\nZfeyPDmtFbeTpwz2LzirCV6PG+ecVo/LLlgMTTfwzR9vxfGeKObWVeD6q5YjXOFFR29sVpQwExFR\naZVVgJf7gSszB6VsHS7PGPvdPiiKYmfxqv0TC/CmWqIZtQK8umC9OC5XAoZZ/AyVaZp45bgI8IyB\nJswNi6xJJF3api7FoFsNGNxD4mz2j576k539ocLYAd5A/r1eXf1xDEVTqDijGc+2bwGATMOjEp1o\nkcFGU10F3v2O+QBmvkwzper20G3TI95/xi/RFL+fldaJKRnAdTkCPNM00T0wMsCrqwnC41YwEEnZ\ne8MkGWSlVBRtVIUM8EcL8GomGODtbx2AaYrB7V6PO+u6hdYezrau7MDEHgTvGHTuDPCqK32orvSP\nG+A9+sxBHGwbwmMvHCnoWCW7yUpOgOdz7MGTr7c8uVGEflo20zRxzHpNFjZmKka+8NGzURXy2dnT\ntZctg8/rxopljQCYxSMiOhmUVYBnN1mx9kRUTUMnTTuD57GCShng5S3RLF0GT5Z/1oeqYWoeQDER\nTRe/4ULLYBt6Yn2A6ocRrcGp9WLRENNL29SlGGQL9etXfwwAoIba8KeXRt9LRiPJWW1iNMHIEwj7\nWgagBGIwa9rwyFtPYDgVRVhm8EpcollfHcQl5y0AIAK86Rr6nU9aNQArs2S6xUK70BLNiqB4T2i0\nArhuRzAdiatIpHQE/R6EgpnSQLdLsQO+zpyMn7NMMlWEGFs3TDsrWFsVyHsbZxfNQubP7T7UCwBY\nduqcEdctsoKXY93Z7zF2yaYjg5fW0/B5xMfaoibxfTVjlIsmUhq2vHYMAHDk+FDBnS8Nw0RaNeBS\nMiWZksul2McgRzmUIoPXP5xEIqUhXOG1x3EAQFXIh2s/djYAYEFDyJ4n+M63NwEAXmOAR0RU9soq\nwBstg1fKWXiZAE8stuorxAKlMVRn3ybkGyPACxQnwEtoVtvycBimKhY0fbHiDzvf3iHKGrWBBvg8\nbixtFM0gNFd8Vrfg1g0dcBkwTWDF4tPR6J8Pxa1jRyfLNCdCZvAME3lnH+5vHYDiF7/npmliR3sz\nKoNyD17xM3iGYdoZvNrqAM45rQ5VIR/ae2PonKaOkvmkNd0OPDRFvB7jZfDkaytLyxvnjMzgObN3\niqJkfb9stJK7D2/YMaogqU49qz8YScIwTFRX+uxsVS6vx4VQ0AujgPlz0YSKP209CgC48Oy5I66X\nXViPdWYHeHYwpjgDPBV+n3hPXWwFeHOsIDTfmITndrTZ7726YeJQW2HvmXJ7XdDvGfFzAEbuw5MB\nXjEzeG2O7F3uMXzg/EX452svxLf/97vhdouP+RXLGqEoQPOhvqKM5iEiotmrrAK83K5mpc4cAIBh\nBXh+j1jEfmHFp/GlC7+AZfWn2bcpdQYvmdagW/vLqgIVcBtiQdMxVPyW2L0x0QjBiIfRMCeIhpAI\naBVfEv3DxW1iUEzxtLW4M9wIBbxYOV+c4e6IjSzlax/uhKqXdrzGiSrmWKzn24e3zxHgAcC29l2o\nCpUukz4UTUE3TIQrfPB73XC7XThlriiPnq6h3/mkVR2KIoIp1doPO96gdzlGQWbmmmrF+0aPo8mK\n3H8ngz+neaN00nRmrhJF2IfXO055plToqIRHnzmIWELFuafX49zTG0Zcv7ApfwZPlmiauQGeX3ys\nLbZ+D2QGL7fJimmadmBZWyVuIzt5jkdOPAjmNFiRckclyA6yaa14ZbLy9VjYWDniOkVRcMHZc+2g\nHxBZ1WWL50DTDew60FOUYyAiotmprAK8RHK0PXilW6wb1tylgFc8ZlNlAy459cKsM6p2gJceuSCW\nHdimEuBFYirgtp67NwAvrFKtoeI3Phm05uuZqh/1NUHUWhlLeFN5MzqzRX9ULHoVww2XS8EZTaJs\nKe2KZO3DO9Tfgv/vidtw3/b/GfP+/vpKC/5n8/7SHfAslRXg5ezDUzUdh9qGoPgzl+/qfAt+q4qv\nFJn0TLOPTKlgoxUY5TYnmU6iRFMEeGkzCcAYM4NnmqajxNzK4Mk9eAMjM3iNc0YGV3Pr8wd4zj3I\nSTU7uNjXruL1wxM7MSM7qdZXjx3gFdJJcyCSxB+fE2XS6654e97byBLN491R6I5yT1n+6AzwDNNA\nwMqeyQye3IM3FE1lBVcHjg3i8PEhhCu8WHvZMgCZTp7jkaWWuSMSJHsfnirel50/g2KNw5OdbGUp\naiHOZ5kmEdFJobwCPHvTu1ggTccevEwGL/+ZXKD0GbzhWAqKFeBVeAMIusRCryda/ABvKGl1J1R9\naKipQG1AdLxTfCn0Ds/cgno8gzFxbArE6z23UuwdVPxxu9U4AOzvPQwA6IqOfYb7Z3/cjY1P7Clp\nA5/ZyJmFys3gHWkfhqYbCFVlApmUlkJXSnQnLMUcPDlwu84RbDRZHSW7xgjwjnVF8M/3bs07X60Y\nnCWaAACvOuYevFRah24YCCzbju+/9BOYpmkPEe8dTNijXuTIhMbakRk8mVHLnQvobC6SzMngPbU7\ngWfeTGIoXnjpZp+cgVedf/+dVMgsvN89dQDJtI53ntWEMx0Dzp1CQS9qq/xIa0bWSQV7mHfOcPMP\nX7QIF5+3wL4/n9eNioAHmp5dLvqElb279J2Lcc5pojlVoRk81VGimc9oJZoAkCrSPrxjXaNn8Eaz\nyhHgzeQeVSIiKq2yCvDkGXC5ry1cwtIwyYBYGAV8/lFvU+EVC6ExA7wpdNEciqWzMnghj/jA74sX\nv7OlDPBMzY+GOUF43B54EYSimGgfKH5JaLHIAM8tA7ywKAVTAnEcbM0s6loGOgBkMn75qJoBbfEr\n8L/jBUTiszdrWWyqpmc6FyK7dBDILI69FWJBv3zuWQCA/YN7AQDRRAkyeHmCjaYCMniPv3gEO/f3\n4MlXWop+TIBVouno7qh4UlnZz1yxpAq4dCjV3djevhtHBo7B63GjtiqQtc9wrBLNOmuvmWyAIjlL\nJJ0ZPN0w7ZLN7qHsIMk0zVEDgN5xRiRI441K6B6I2yWSo2XvJNklUgY1gDPAszoZe8TjXbRiLr6+\n7nx4PZmPt9xh59F42p6XeMW7T8WChkqEgl70DSXtEtSx2Bm80QI8OSpBNWCaZtYMyLRanMBKjkiY\nSAbvtAXVqLSeZ+7vCRERlY+yCvBGNFmpyB6TsGt/D+5+aGfeWVGTYZgGoJgwTcDnyd9sAChwTMKU\nMnhpO4MX9ARQ5RMf+Ha2rYiGkrJE02dnGEJu8Xidkdkb4A1Zg+A9isi0VvpC8LuCUNw69rZ32rfb\n0y6yTX2R0WdFReNpuML9cAVj6IvN7Ly1qTIMM6vsbSxyj5iUm8GT8+9Ut3jtPnzGagDAGz17AJiI\nxNWiZw36hkcGG4Vk8I5ac/K6StSIJa1mZ/AUb3rMLH0soULxZALAV4/vAOAYlWBlruRzkmWoTrWj\nBXiOLLNzD57z3z3D2QHea4fT+OETw+iPjmycVPgevLFLNB995iA03cAl5y3AkvnVeW8jySDmmGNU\ngmiyYtrjTyq94rVK6yNPJGQarYhjeX5XO9KqjvPe1oD5DZVwuRQsWyzKzQvJ4qn2HrxxMniqhkRK\ng6ZnXutiZPBiCRX9wyn4PC405An2R6MoChbPHRksz2Zyny0RERWurAK8zKDzkWMS4kkVd/zqNTz5\ncgtu+MEzRZmTperWp7zpss/Y5pMJ8EaeMS1GBm84lsrK4NUExWIpohb3AzypJpHS01BMF6B70GDt\nA6r2iccrRUlosQwnxKLU68q0E2+qFGVZh3s77Mt6EqI0U8Po2ZbBaAKK22p/njix5+jd8uMX8eXv\nP13QAsoe1O1NAjBH7MHb1zIAuFWkjSR8bi+Wzz0LdRVzMJgcQrAmBsMwi969zw42HBk8u/vkKLP6\nTNPE0XaR3S5VI5a0ZmTNZ1O86TEHnceTmv03DACvHBOzJuVzkZm7noHRM3gyiBmIZBbEqmZkZQ6d\nGbx4KvPv3pwA7622NDQdaOsbI8AbZw/eeCWaew6L/W4ffu+SMe8HABZZZYhtjkYrqipOsAEm3IoL\nQatSIqWNDPAyjVbEe7BsMvLe5fPt2yw7RQR4hezDy2TwRmuyIt7XU2l9RGlyqggZPPk6zG+ohNs1\nsovnWGSw3No5+wO84z1R/O1tT+Ke3+2a6UOhk8QLu45jz5HZe7KaqFBlFeCNNSbhkWcOYjiWRsDn\nRjyp4Y6Nr+Huh3bae1smQzOsBZnhyioHylXhE4ux0u3BS0Nxi4VY0BNEfahGPJ5W3MWrbLACzQ9A\nsc8c11aIxxtMFn8sQ7FEkjLAyyzIFtWIluxRfQADw0l0DQxDd4sFtCz7yqcvmskiRJInbplTPKni\nzcN9aO2MFFSWFkuocNd2ILjiGbgbj6F7IGFn5IaiKXT0xeALicV8Q6gOLsWF8+efCwDw1YsFdbH3\nLGZGJGSCjdrqADxuBYORFJJ5Olr0DCTsvYSdffGS7EUSJZqO4MiTRjwxRgYvqdpZeAA4HulE23BH\nVsOYaEJFLKnB73PbnUnf6HwLX3zsmzjQdwRejwvVlT4YholhK2s2HMsOrpKjZvAcw8I1E73W1/HU\nyPdHe+7gFEo0Vc1Aa9cwFAVYMr9qzPsBHJ00c0s0rdfY5/bB5xZ/2+k8HXCdwaZpmnjzsFjAnXNa\nZpzNmaeIPXvODN5wLJ13dp0ckzBak5VMBk8f0VyoGBm8yTRYkWQGr/UEyODtOdwHwzCxr8DmN0RT\nMRBJ4o6Nr+H7v9k+04dCNGVlE+DpholkWoeiAAFfdolmz2ACf3hWdGq77f95N/7Pp86F1+PCky+3\n4IVd7ZN+TM2wPuVNF7xjlmiOvgdPLhAGo5MPFIYde/AqvAE0VYkz0SmzuAGeLPnU0+J1lQu8pkqx\nSIpqs7dcMZYSr70cZwEA8+Q+PKvRylO799rXmUphAV40T2fUqRhr71OxOeeldfWP/7sSTahQKsSi\n0Fs1iERKswMlWZ45f754S2kMiezo+QtEgGeERRlssWfhZYacZzJ4bpeChhpx8iF3nyAAHGnP7E1N\nq/qYTUAmSwQfzgxeCvExpozHEirgzr7+lWM7sko0nfvvZJfex/dvQU+8Hzus+ZSyTFOWrg5Fs4OL\n7Axe5vgG44YdyHQN6ZC3iqWyfxd1x37AcZus2AHeyKC+rTsCTTcxty5kdw0di12i2R21/z5SjtfY\n5/baf9tjBnjRFI73RDEYSaEm7MeChkyDkjMWixNVB9sGoWoG9rX044vffx6Pbht5/PaYhHH24KXS\nOoZz9oAXYw/eZBqsSItPoAyeDGTzjWQhKraegQRMU+ztZhMiOtGVTYCXtDJgAZ8HLqtkJRwSC4d4\nUkMyrePCs+firCV1+Mh7l+CjFy0FkGk7Phkyg2eOl8HziEAooSbFvj2H0xZUw+9zY3+raNk9Gc49\neAFvAI3hKpiGAl1J513sTJa9/y7tQ3Wlz17EzK8Ri/mEMfq+tZkWS4lFfMCTaYZjd9IMxHGwbRCv\nHT6U+QaXPuJnJQ3GM88zVsQMnqrpuOEHz+DTtzyOG77/DP7jl9uw5bXWot1/Lucg8EL2osWTKhQr\nCPGGxIJLlgy+dVScYZ9TL056NIZE0H92wxlwu9zQvEOASy9qwyPTNEdt+GEHRnn24cn9d1IpyjTT\nqgEoOSWaY2bwNCgeay+ZT+whfLVtp6NEM5E15BwA0loazd37AADRtLgudx+e3P/m94r3p9EyeECm\nTLNjMJN5jOfcRu6HqgqNPuRcqrbm4OXbgyeD7KXj7L2T5oT9CAU8iCVUOyBXNcPOkvo8zgze6CWa\ng5EUmg+J7N3ZS+uyxtlUVviwsLESqmbgxTfa8S///QpSaR39sTwZvHHHJIjXO6XqWQ1WgKkPOx9M\nDGFX/3ZAMewREhMh5wMe64rM+kWsDPDiSS2rAypRKcj3Ft0wx+x6THQiKJsAL3f/HSCCPZ8VeLkU\nYN2HM53a5BD0qWQU7BJNUxkzwHO5XAh4/DBhIqlmL3YqK3y4/MJTAAAPP31gUscxFE0BLtlkxY+a\ncBDQrCHDRWy0Mig7aKp+u8EKACywAjzNHYeqjdyzMxvE01aA53UGeCKD5wrEsftgr91BU0qq+YOR\nwVgmILAHqBfBrgO9ONI+jFRax+H2Ibywqx13PrBjxAKxWJyBzVgNSaRYQgWsRiCGV3yvPLO+96jI\n4AUqxbHKDJ7H7bGbX8CtFTWDF0uoSKV1BP3uEVmgxjECvCPtw9axib/ZziI3WtF1A7phZDVZcXnT\n0HQjqwupUzyRmWV5/vxzEfD4cWTwGDxB8fvlzODJva9v9uyHap3AkTM27QDPCnxlqeY8a0beaHvw\ngEyjlc4BzXGb7JMchTZYAYCacHZjEyf5MyikPBMQzUEW5Qw8T2WVaHrhc4+ewZsTzgw7t8szl9aN\nuJ3ch/efv3ndLifWDWQ1SQEKGHTuc+zByy3RnGIG79G3nsRx/1Z45h/CwqaJZ/DmhP0IBb2IJtQR\nw99nG+dw+9w9v0++fBR/LVEXXDo5ORtUnWwjkKj8lE2Al7DKn3JLZuQ+vA+cvxinzM0sJsJFmJGn\nOko0xzubPVYnzY+/7zS4XQpe2Hl8Uh0+h2MJKC4THsUDr9uLqpAPZtpa0CSKNyrBHpGg+rIWeA0h\nsShSfEn0D8/OBUPCanBT4RsZ4Cn+ON442APTl52BdAZyTpFUZqERV4v3fLe+IcqFr1p9Or5/wyWY\nW1cB0wQ6SzSwO7tEs7AAT3Z6NFxpwJ1Gz4CY0bb/mAjwdLe4z8bKzOJZNr9Q3CoiRRyVkCkVHBls\njJ3BE38T550hfv5dRc7gpTXR/MORHILbZwVio5wVjjmyo9WBMFbOOwcAcCgqyoZ7BxP2z6vJyurt\n6Hgz8/2qlcGrzs3gidd7vhXgObN28bQI3uaExMeA3IfXMeDI4OUEgYU2WAGAUMADj1tBIqVZHS8z\nZLXCkgWFZfCAkZ000zklmjKDl7fJir0HL4nmQ70AYM++c1pm7cMzTOD0hdX2CcPcfXiqFfCNV6KZ\nVnUMx+UAeyvom+IevJ6YyJZ7GltRX+sb59YjKYpil2kem8VlmqqmZ/1tOss040kVP/rdLtz10E4u\nxKlonCc8hmKzcy1jmiZ6WUJKBSibAE+OGcgtmTn39HrUVvnx+cvPzLq8UmbwplD2kdVkxT32SzlW\ngNc4pwKXrFgAwwR+/+yhEdePZzgpFney/LAq5IepliLAs0o0NX9Wa+7aoNi7onhT6B2cncPO5aIv\n5MvsGwr7KxH0BERpnEeFK5i90B+K59/3IV9vAEhMIsAzTANP7H8anZFu+zLdMPHKm2Kf2vtXLcIZ\ni+fYC9rcM9fF0jnBDF40kQlCAFHa2jMQx5EOkXWcVx/CQEoEejKDB2QCPLhHZjOmondo9GBDZvBy\nZ+ElUxrae2NwuxScbw19LnaJpqplZ+8AAF7xvOOjdNKMOTJ4IV8FLly0AgDwevsuexbeW1ajicY5\nFTBNEzvam+3vl+8rchaeDH5lg5MFDSLAS6mZPZ4yeFtcL94ze4Z1RJMGIkkTsjHjiABPDpavGXv/\nHSACiXyNVkzTzGTw5hUe4MlZeG3W/rO0akBRnE1WZAYvX4AnjrelM4LeoSTCFV47yHFafno9XIo4\nQXDr378L4aAVNKr5M3gFNVmxTiI2WZnXqWbw+qLiPV3xqnjl+OuTug/ZaKWla/bum27vjcHZ3Ne5\nn7a9NwbTBEwTdka2WP74/CG7yyqVr92HekfMJh04ATJ4z2xvw9995y94+vVjM30oNMuVT4CXzO6g\nKX3186vw3//0v+yyJqnS+uAea/jweDIlmi54vZMP8ADgqtVvAwD8tXkXDnYXPsLBNE07o1ThE4/h\n9bjgNsSCpitSvNEFgylrMaD60Oh4PX0eH9ymH4rLxPHBfqi6ip++9hs8fXhr0R57qlLWoi/kzyxM\nFUVxlGnGoATEQl/RrUVpIv/C31mWmVtyW4idHXtw/47/wf07HrIv23OkD8OxNJqaFOi+AcTVRKbd\nf39pGgy0T7DJiijRzGSgXIE4egYT2Gvtvzvz1DnoiYnFltyDB2R+9xW3NqJl/FT0DsoOmiODjbl2\nBi/7ebV2RWCaojnFIqu8rdglms7Mkhy+bXpSAMxRRyXEHXvwKrwBrJh7NrxuL/b1HUZ9nQgWZNar\nsTaIjmg3umK99vePtgdPBlZzwn74PICJzB4wub/ulAbxntkb0e3s3YJaNxRFlHQ6R2j0Wa95QwEl\nmkCmk6azTLNvKIlIPI1whRf1BQSKkvx5ye6PaS2nyYoV4KXyBnjiOFRN3P6sJXX2Xm2n+Q2V+OHX\nVuO/vvp+zAkHRs26qQUOOneWaMrPoKk2WRlKZioNHt/31Kh7hceyOM9cQae0quNg28x2RZb77yTn\niS5n9YHMyBbnMSP46e+bcdf/7CzafdLs83JzB26550X86om3si6XY1QAYDhPc6jZYPdB8fu+9Y2O\ncW5JJ7uyCfASdgZv5J4IT57sWmVQLAaKsQdPNFkZu0Qz5Bs7wDtlXhXOe3s1XG97Cbc/++OCjyGe\n1GAomYWh5HeJM/bdRQzwMkPO/Zhfn733I6iIr9sHevHYvs3466Hn8es3Hi3aY0+VPKsfDmTPD2uy\nOmm6KgeguHVU+kLwGuK5DI8y4875M8xXDjae3rgIiPb3HbazKS/v7gBcGuKnbME3Nm/AFx75Kl7U\nfgHvqc3oLiD4mqi0qqNvKAGXS4HLpaB/ODXq/jApltCyhnErgRh6BhJ2g5VTFwWQ0tMIeYMI+TKv\nc9AK8OBWi9pkpX+Mdv2ZPXjZP0PZ3GPJ/GrMtQeiF7lE05FZCvtCIvBQxH6x0Uo0o44umhXeIALe\nABaGxRiP8BxxuazIaaytsLN3Z9afBsCxB686ew+e7GBZFfLB7xEBTdIqzUxY2bm6sAshv4K0Buxr\nF481b44HFT5xe2dVrZ3BK6BEE8gEVs4M3mHHz8DZ5GQ8py+sgUsRJ0MGI6msMQl+tw8+z+hjEvxe\nd1a2zTkeIdcp86oQsk4A2gFezl3KMQmjlmjmy+BZIy+mWqIZU8Xvqwd+HI90YqejVLdQmVl4+TN4\nv3lyL77yn8/i5ebsReTh40P49Dc24fEXDk/4MSdKzvqTv0POEk1ngLc7J8B7/IXDWP+9p/Gtn76E\ne2a+goYAACAASURBVB7ehU0vHIZW4DikbitL2NUfHzXbTiOZpon7HnsTf3hu4hVIM2H7PlE9cySn\n4ZazRDN3xMxsIStO9rb0s0yTxjSpAM8wDNx6661Yu3Yt1q1bh9bW7E5/W7ZswZo1a7B27Vo89JDI\nUqiqiptuuglXX301Pv3pT2PLli1TP3qHRDL/HrzRyBLNqewJytqDN0aTFSCzyI2N0Vb/fRfWQXGZ\niGiFnzl1dtC0F9IAQm4RpPTGincW1rkHTzZtkCq9Yn/j0cgRPLznCXFsqSiGk7Njj4dqWPubgtkL\nU5nBc88RJTnzw03wKOJ3YziRP7OT0DJn+fJlC8bT0isWJLF0HB2Rbpimia27O+CqHISGFHxuL7xu\nL1JmHJ7GNrQPTG0GlKYbuPuhnXh+RyYz3NUfh2kCjXOCdjZmvI6y0WQ6q5W/4s/O4NU1iEWUszwT\nACo8cg9ecZus2B0082TwRMbKhUg8nbVQO2qVBp46rwp1NUF43CK4zTcvb7KcQ859bi+qA2IxrXjT\noy4a4445eBVWU5oGax9joDKz0PB5XKip9Nv77y465QIAjj14o2Twqit9kOe+ZKMVuQevwudCfZUI\nSA52iuObW+NGhV+xbucYiG43WSks81aTp0RTBtmnFthgRZpTFcD5b58LTTex5bXWrFmD4zVZcR4L\nAJyzdOT+u3yCo+zBk2WWoaAXw8kI3urJbpCVlcGzSzQrsr53MgzTQMoQP9vzat4FANi076kJ3489\nC68zfydNWaIoF8LSC7uOI5nWsXV36bMHbVZ2Ue6VzS7RzGT3jnYM269xWtWx8Ym3cLRjGNv3duOJ\nrUdx76O7semFIwU9pjxpBJwYcwJni67+OB595iB+vmlPVsZ/tnrriPjMyv3MOxFKNDutkxtD0XTW\niQ6iXJMK8DZv3gxVVfHAAw/gxhtvxIYNG+zrVFXFhg0bcP/992Pjxo148MEH0dfXh8ceewy1tbX4\n9a9/jZ/97Gf4zne+U7QnATj24BUc4BUvgwfTlTdL6DReiSYANDVYKzCXjt7hwkYODMdS9t6doGME\nQNgnPsCLuQdPdtGE5rebWEg1AbEPb0/iJbuzHwAcG574QuCV5g585pZN+Ppdz+PhLQdwvGdq4xc0\n3bAHl1cGshemclSCp0q8Ts4AL5bK3yEzpWcWq/n2+4zneH9mz8jGZ17GwbZB9A4mUFkvnudlp12C\njVfdiYagWNh0R6cWpO/c34MnX27Bfz/WbC/m5FnAeXWhMRuSOEWS8azGIa5AHL2DCXQPJBD0e6D4\nxe92Q2V2dqTCzuBpRc3gjZVNUhTF3ifa7VgYyjO2S+ZXw+1SHGWw4rmbpoknXz6KllEyG4UQpYOZ\nwKPaLwO81KijEuJJzTHLUjyfxgrxOroCmd/DhjkVSGkp7Ok5AAUK3rVwBRRFQVJLQTd01FT64VLE\naAJNN+xGAVUVXgS8MoNnQtNNpDXRXdjvBRqqxPuXTHTMm+NGyC8uc+7DywTVEyzRdJQ7HTkuXttC\nRyQ4Xf4u0XH4zy+3IKU6A+mxxyQAmUxQ0O8puLmL/DzJDcpkmWtl0Itf7noY39ryA7zRmSn3yh50\nLt4Pi5HBi6sJQDFhah584NT3IuDxo7l7H15s3TahkTi1VQGEAh5EHWMnJFUzcLRDBDfOoe9Aplvu\nsWkIfmQGb+Uy8R6dL4Pn97mz9uFte6sLsaSGU+dV4Z/+7gJctfp0AMAfnj1YUBavz7HAPxHmBM4W\nstxf0w37JNBsFU2o9vt731ASuvV7YZpmTgZv9gV4aVXP+h2V1TNE+UwqwNu+fTsuvvhiAMDy5cvR\n3JzZ7H/o0CEsXrwY4XAYXq8Xq1atwrZt2/ChD30IX/7ylwGIDKDbPXZJ40Ql5B68UTa956rwe6Ao\norSz0PKNXLKNvmK68u7nyHq8AgI8eRYeAPa1dRV0DENZGbxM8FITEAuY4VRxNtGntDSSWgqmoaAu\nHB7RNbSpUnTShEuHqbuhD4rgZH/XxOe4bd3dgURKx1tH+/Hzx/fg/2x4Ck+8dHTSx+5sYOEckwBk\nMnhyH8v8qiZ7L090lABPNVKOf0/8Q8C5h+aVI2/hZ38Qfz/hBvEhuax+KVyKC3UVImjuj08tSJeL\ntL6hpH0WXJ4FnFtfeIAXS4vv8brEQtoVzNx+2Slz0BuX+++ysyOZLprFDfD6BmWAlz+b1FRnPS8r\nmDVNE0dzskdz60QmWr4eLzd34u6HduH/Prp70seVVg0ojsCjysrgwZMeddh51NGhVJZzN1j7GDV3\n5ixtU20Fmrv3QTM0nF53KqoCYfu9JaYm4Ha77EBmYDiVncGzGi4mVNPOygV9igiGqzJ/z5UBBZUB\n14gMnmGYdoajrsA9eDV59uA5y2QnatWZjairDqCjNyYWko5AWv7dprVRMnjW63LWklq4x3m/lmTJ\nvzMoM0wRHCuKuL4zIrJdLx/bbt/Gl2fQeWMR9uANW+8dpubDafMacOnSiwAA//XSfbju9zfhey/e\ni7YCTqopimLPw8vNVLV2DtufiUc7hu0Zs85uuQOR1Jh/y5qhQzMmPzLHNE17D97ytzVAUUR2TS7G\nZYB38fIFADJlms9YjScufeciXHjOPPzth8/CoqZK9A4l8dyO8fe2y9JmAFM6yXOy6XCchG2f4gnZ\nUtt7tN8udzcM0+78HUuo9h5dYHYGeLLyRppqgPdKcwfueXhXwWtg3TDx4F/34cCx4m39odKZVIAX\njUZRWZnZg+V2u2EYhn1dOJzpThYKhRCJRFBRUYFQKIRoNIobbrgBX/nKV8Z9nLvuugvLli3L+u/S\nSy/Ne1u5B6/QEk2XS5lyo5WUtZBwYfxgtSYgPkxlE4p8ounMQu5gZ/eot3MajqYzM/AcJZp1FSLg\nimnFOQs55JiBN79u5OylsxYutP/94aUfwlzfYgDArraJ79WQGbvPfvAMrLDKc948NPlOabGElml6\n4c4J8Kw9eNL8cJNd6pVvxl1a1aEjk4WRpZ8TEVUzH4BKaBh7jvQDMBFVxM/8jPqlAIC6kAjwkkZs\nSvtB9rdm3ozfPCJeR7lAml8fcgRC4wR41gmIplCj6NjqVgG3+BB8+6m16M7TYAVwdtEsbolmn2PI\n+c6ON7G19fWshhN24GqV4fQMJBBLaqiu9Nlz0eZaz12OopB7jqbSWVN1ZPC8bg+qrb99UaI5WgbP\nWaJpZfAqRaCcNDMLzYY5Qezs2AMAWGGNUgjJk0ey0YqVXevsiyGR0uFxKwj6PY49eKa9/04Gcc4A\nb94cd9Z1sop9KJqCppsIV/jsEsTx1IR99vcC4n26oy8Gjzsz124i3G4XLrvgFPtrJd+YhFEyeHLP\n5fK3NeS9Pp/MHrzMqkr+u8LvgculIGa97q937LYz5PL1SaQ0MV5EyTSmSWmY9N6ZziHxt+zSfagJ\n+/H5cz+Oz5zzMSypWYSUlsKrbTvxH8/dg6Q2/v6hzD687M+Ig22ZE0qGYdrNVo5a3XKl0TJcpmni\nG3+5HTf/5fZJP8++oSSSaR1VIR9qqwKYEw7AMEWGLZHSMBBJwetx4f2rxOdO88E+ROJpvPZWF1wK\ncMkKcbnLpeBT7xdZvEefOTju8fQ5ArzWDmbwCuVs2NU+A2WDm19twU9/vxtGAeWhe45kryV6rM7f\nuTMhnWXls0VnXwxKIIqKM96AEohOKcAzTRM/fuQNPLH1KHbsK2y9+eqbnfjVn/fivscmvu+Xpt+k\nArzKykrEHDPCDMOAyyXuKhwOZ10Xi8VQXS3O1HZ0dOCaa67BJz7xCXzkIx8Z93HWr1+Pffv2Zf33\n1FP59xtkBp3nHzwLAM1d+/B/X/uNXcpiN1qZdIAnHtOljP8ynl4rFiUH+o+OehvZCQ8AWnoKC2ic\ne/CcTVYaw9UwTSBpxqFP4UyqlCnPHLn/DgAW18wHAJxasxB/e8EVOHfhEgBA6+DESzTlGcAPv3cJ\nPmF9OA9GJz9QPJpQ7b06fk/23Kg5gWp7YQiIAE+Om8gX4EXimdcbAHRz4nu3knrm5+ytjAAwUDkn\nibSRQkOozh47UeMIDJxlhhNhmmZWgLfH2nsgA5i5dSE02c1Gxg7wkpo4hrA/lJkhGBDfc+apteix\nujqO2IOX1UUzXZSN4f3DSUQTKjxuFyqCbnzvxXtx50s/wzc334EDfWK/TVNO+aW992teld3cw87g\n9cWg6wZetUZV9A8lJ72XJJ1TOpgp0UznPZlkGKY4QSXHJFivV0OFmMk2lM4suJtqK9Ay2AYAONM6\nESAb2sRyRiXI51td6YeiKFl78Jz77wCgttJlj0aYWyOCmtwSTXssxQQ6X2ZKNMViqaVj2OpiGoZ3\nnH3Lo7nswsWZUmGZwfN4xxyTAACfWn06rr/qXHz0oqUFP1a+OXhJx/47AIhaJz4GEkM4av1sZImm\nLKcKBbzweFyQcfFkt3y29orPBb87CEVR4HV7sebsD+M/Lr8F93zs33BK9QJ0xXrxwO4/jntfch9e\nbrnloeMioJM/H1kBsDenXHO0Ms2klkLL0HG0Dh23g9+JkuWZMgiVHUh7BjLzIOfWVeDtp9bC63Hh\nSMcQ/vzSUWi6ieVva7D3ogLA+1YuRG2VX+zLG2ch6xx0zQxe4bICvGnO4PUMJPCj3+3CH58/jL0t\n4wc8MiiSf9uyqkV20JRVB7Mxg9fRF4O7oQ1mTTu8je1o7YwgOsmqmINtg/YJjaMdhf2uHzg2AMUf\nQ2v3zHbYpcJM6hN25cqVeO655wAAO3fuxLJly+zrli5dipaWFgwNDSGdTmPbtm0477zz0Nvbi2uv\nvRY33XQTPvWpTxXn6B0KyeD9ce9fsPnQ82ju2gcACMlZeJP8A0mrhWfwlsxZDLfiQuvQ8VHPrjoz\neB2DhaXAnXvwAp7Mh1ptVQhQrbPnRWh0MpRydtAcGeCdVnsKvr36/2fvu8Msqeq036q6dfO9nXOc\n1MPkwCSGjICACRVZxUXZZd1VVz9RNygCiny75hVMKwqu4i6SJElmmBlgYIbJeXpmerp7OqfbfXOq\nW3W+P06dU1U39e2eYR/k8/c8Pg7dN1TfW1Xn9573/b3vl3HHpbdAEiVcvHgRACCqBSijUer7RFOI\nxBW4HBIqfA6TScPsb7bRhMJZzmyAR6MSavm/67zVHOAl8nxP0bgh9wQAlcx8cyBN6E3VLtmhCRmc\nv86P8zfSBmZhldF8MoAHOZWT51ZqDU/EKMDVG2I2q8KaJM0RgiLTnxVzk8yoGhTo81xOD+p99DMT\nnXQub2FrBcaiOoOXM4NHz0tJVqEyMHMGtfvYKL70H1sB0DDqifgk37Q5NXka39j0ffxq94Oo1Wee\n2Gdnnr9jZQC8OI70BPhmj6oRBCOz21RIKxog5DNZSWHb/sGc3K54KgMCDYKkQhREHq3AmNCJeAAV\nfnreVpe7MBChILTJ3wAA8OimLLGsqIRuE8ADAIecy+C5dJZOEg2ZZlOllcFjEk0WS5HPtbRQZefg\nsWOaO4OA8+yqrXDj3HNohqEgGTl47NpOFZhFK/M6cPXGOTMClvlm8AyDFRsIIRYQs2eISnsZg8cA\ng89Dj82uL0+zncMbmqLrglfOvQdXuyvxuXWfgiiIeP7EFnSOF3c05AxeFlDr1hm8C1dS+ePxPgbw\n9BxGnRkvBPDM69hkYnaNIItvaK6lahHGfo4HDYDXUOWFXZawsK0ChAAPbzoBgOaImku2SfjghdRt\n9vEtXUXf1wzwpiKpd2STX6gIIdi0sy/HOIQQgie2dmHz7pmPS5RaZlD3v83gPbb5BDIqvZ66+ouf\nb0pGwwl9o2LDUnr/ZLOdTKrJNj7eid/9SCAOUZ9195XT48veeCm1tpuMkpj52HR1eLAXzhWvI1G9\nb9Z981/qf69mBfCuuOIK2O12fPzjH8d3v/tdfP3rX8czzzyDRx55BLIs42tf+xpuvvlmfPzjH8d1\n112H2tpa/PKXv0QkEsHPf/5z3HjjjbjxxhuRSp09Ctxg8AoDvLhCb96RFL0ZMYnmbBm8pN5IiML0\nAM9hs6O1vAmEEHRPns77mEjKuDGOR0IlyQ0og0ebHDOD11DlBlF0R71ZLrLmKuagyWpxbQe8dvq7\nhY0NEFQZsCnY3VX6wjI0Tj+DphqvHpRMG6PgGcglzBJNZ5ZEEzDm8Go91ZAlGW47fUw+IB7OZvCE\nmYEVQgiIRF93STXdGFm31gm46U2ayTMBWIDBdA6XhYo1Z6sW1kK2iegfjSAY0V9P0PDrQ7/Gf3Xe\nB9jSRRm8mMnG32s3M3gxtNb54HJIPP6BmYOwYtJhm12Xcc9SpkkIwa+fPIQ779uBYCSFpfOq8M83\nrsGwHhi/sGourl30XkiCiE2nXofkMizPhydieFq38O5oqeCvySWagRh2HB6xvN9szQKUjNXd0a8z\neD4/wUQoiVt/sQ3//fwxPvcQN82IumQnZxedshM+hxeKlkFTPb1XVVZSSaBbdvENAJZ/yZ009ZlE\nZmZSpoMLK4Onywztxizae1e6cNVKF5oqbZbfMYkmd9As0WAFAJfCBiMpEEJ4AzZnhg6a2cXMViQb\n/TvMJivKLIyPClW+HLyk/tl5nTLSqmKYbQHYywCezuCxe7jfzQAe/UxnO4c3FtZBuytXJg8Acytb\n8aFzrgQBwX/uegDpIjEubXmcNFVV48zvNRvbAbCZJYLjusHK5Wtb+fPylXkdm0qWPj+smuaAGIPH\nAZ7Oxo9PxbmDZmMNXWuWzaOKgVRahcMu4bxlDTmvfdV57XA5bDjYNWFRNFjeXzNMNpgBUKEYiXdi\n7To2inse3oe7/7DP8vPe4TB+86cj+Okjpc9azaRUVbOsHf+bDF4glMBLbxn9xclpshtPDQaRzmho\nrvVinr7JxPIV2YZea50PgkD7QvVt+LzOpIYnYhAc9HjZ+tY5S5mmeb3LjovIV4QQ9E1RUCi6o2ds\nfveXevtrVgBPEATceeedeOihh/DQQw9hzpw5eP/734/rr78eAHDppZfisccew+OPP44bbrgBAHDb\nbbdh27Zt+P3vf8//53DkNtuzLWZeUIzBYw0722H06YvubMOXFV2iKQmlzf0tqKKyxROB/JbN5p1P\nVUhhpIR8rmDU7KJpALzGGi+IQj/fswHwgqYMvIbq/M2FuQRBQJlMG/1tncdLfp/Bcfo+jTX0Pfwe\nXS4RTZUEePNVLKlwEJzN4AHGHF6jj7ICLjv9HPNJvaLxtIXBI0JmRsc1EY1QFzzVhiV1VH7aNXka\nxwN0VnGhnmsGGEY5ZyLRZLuVS+ZUoaOVApvX9g8goxKU1SYQU+LIaBk4KicQiSt81k9VNYuJAg3i\nZgDPjQYvY/ASuHh1MwKJKahEQ7nTD3vWZ8w2HkQb/Q7Cs9z5O943hadf74ZNEnDT+xbj/372fNRW\nuDESpQCvtbwJNyy/FnN1ObTsosc7Eojh2/fvQCSuYM2iOmxc0chf02www+bvmGkLY6xmWjQmgc3g\nySjXgfqcVic+9p4FIKBsw/1PU3OdmGn+ziNbwRMDy9de2YRv/t0G2Dx0cW/y13MgaDB4ehaezuD1\njeoATwdZZhfNeEqXaDoMgFfjl7Ckxfju3FkSzQA3WCldosmu32A0hZv/7WW8vJM2Y3MaZs/gAcDa\nRXXYuLwBLfX0bzfHJMwmm7JQ5cvBM0s0GXvnkV2QJRldk70IJkI5M4oslmc6Bo8QUlTCHIhRwFTt\nLfz5fXTJNWjy12M4MoY7t96NRw8/gwMjR3PAXqWfBrlH4mku1eofiyKd0VBX6UZHawW8LhlTkRRO\nDYQwHIjBaZdw0SrK7BWKEbAwePHS1p7X9w/iOlO+HjNYaa7VJZrluRJNttHIAB4AbFjSkLcH8Lhk\nXHVeOwDg+Td78x5DSF9jyrx2zjCf/jNy0mSbJ4e7JyybU6/vp+YyGVXLCY8/GzU2lYCqf24AvZeW\nAiT7RyP49VOHzsh187HNJ5FRNc5GT8fgsXiExXOqDNlvFoNXWebkveFs16q3q0YCMe5WnUQYAJnV\nHN7geBT9oxG4nTaIAv1vs9IqEErg/qcPW8yxRifjvIcW5BQGx/8S0fBOr3dh0HlhsJXiAI8uytxk\nZbYSTZ3Bk0pg8ACgQ5ffdQV68/7ePIMn2JSSaPNQNJXXRdPntsOm0RvY4NRE3ufOpIIJg8Grz4pI\nKFTtFbQRODqcn7HMV+ym0awDPNkmwuuSoRHM2oHRItGUcgHeOdUUaC2uWQAA8BYBeOGYYmHwIKpI\npkuXoA5M0puxpDkwv6odALB/+AhGo+Nw2BxoLTPAR7mZwZulRJMxeAtbK7B4Dp3p2rybOs15qo3z\ny1nDZJr0ff7z8YO48ZsvcBlWLKEA3OXRjQZdotkxX8Z1ly3gxh8MJJvLZTNm8ID8kmhVI9My6czp\ncsPSBnz0sgXcCZG5GLJjYjNvqpCE0y4hkVIxMBZFW70P//zX51ocFN1OGeVeB5SMhvGpBCr9Dqxf\nQgPGJ0KlNR4aIUikjYYmOwePMXihVASfumYxbvub9QDA5/1iWSHn5mKRE4oYwZpFdRjUHRKb9BB0\nwJjBi2dl4THZUpnHCvCsLpqFlwBPAYlmzQwkmrJNRKXfCUJoc+51ybhsTQuWFAkaL6UkScTXP70O\nc5vpZ2s2WZlJXMB0lW8GzyzRZGCm3FmGpbWUkd87fIQzeKyYRJMZ3RTKwntsRxwPbosVBHnhpD5/\nVlZe8JjtkozPr/sUHJIdJwM9ePTIs/i3V3+KOzb/yPK6giBg+XwKjrbo7pOndAZkfnM5BEHAwja6\nKfTU65T9XtBSgfoqD+yyxOdgs8sM8Epl8J569RQyKsGvnjyEvZ1jJoBH14FaUzM+xCWaulqkrYLL\nbpnpSr66Smd9tx0YzGtaxRw0K/1OtDXQ8+rtnsNLpDL40YN7+GbPmRQ7VkKAbQeG9H8T/m/AmMs9\nm8UY1bZ6P6rLXVA1UtJ69btnj+Lp17px+71vzkoOGQgl8OIO2lt85ROrYZNEDI5HixqSMYOVRe2V\nqClnrLB1Bq/S74Rfv17fSTJNTSMYDYUg2Og6qhAFkNM43jc1Y6bxLX0zc93iejTWeKFphMuiAboB\n+eSrp/DwJmNz/mR/ENA3ymFLc5b9L/XOrXcNwCvFZCWbwWO7qrOVaKYZgyeWBvBYQ38i0J13ATcv\njLClcboE2jwYSVnkXeZiWXgDZwHgjUUoUPDYvHCW6FS6rIkylqHMRMkAhdH+jMEDDHvz2co0w/EU\nBJF+3rKUe36saVqOu6/+Jt6/kDq0epy0mcgXgRAxzTwC0AFe6TJNNltpF1yYU9EKAQKXNnZUtVvO\nJavJyswBnpJR0TMUgiAAC1rLsXgObapP6TM2xG2cFxn3KCBQqc1EMIGXd/ZB1Qg3ZYmZbPy9djeX\naI7GJqBqKp449gIA4Mr5F+UcB5cO6yCGMeZT4ST+uPkk7rxvB264/Tl84rbnsP9EYROEiVD+GTDG\n4LFj8jvouRNJxThDV+a14/abN+S9PzAXUQBYv6SBy8FK2VlOZwgefiOGX74UQShOF1kawG0yWdGB\nelhnwc9dVAeHXcLYVALhGHXW5Aye3bp5wubwmEPpYJjGpzT5TQCPxSSk88dGsJ11B5No5nHRzFcu\nXaKZVCijWyx3sFh9/dNr8dmPLMdPvnoJ/ufbV+PLejN2NoqBOfMM3lkFeHlm8CwMng6qPXY3zm2k\nrqZ7hw7BbrOuCYZEEzmvx0rTCPomMhgJqpiM5jZshBDEM7qEvbKy6HEvqJqDn77v27jlvL/D+zre\nA4/sQvdUH04ErK7GjNV6Ycdpi2PmvGbKYC1so+/zuh4xcE57BSRR4MBrIA+LZ5ZolsLgDY5H+UaU\nRoDv/X4XJsNJ2G0ivxbNEk3uAKyvEXZZwqeuWYT3bmjjrsv5qrHGiyVzq5BMqxbQw4ox1JV+pxEh\n8TYyePGkgm/+aju27hnAk6+eyrGeH59K4MCJ8ZJfz9wvvL6fmv10D4YsYdjZm8bdgyFs3Tswm8Pn\nxcYqGqo9fD5/ujm8aELBnk563x4Yi+Lb9+2Y8Wz241u6oGQ0nLesAfNbytHe6AchxvqWXYQYbNfi\nuZU5DB4LOa/wvTMBXiCUREayMrA1tRpSaRU9Jc7QsWLyzA1LG9DWQM/13mHjczt4kvYGO4+O8l61\nqz/I1ylBJOib+EsG3zu93jUArxSTlaTOyHCAd4YzeIo+e2ErUaLZ4K2Fx+5GMBlGIJ47BxA1LYyC\nTZlWF00IQTBiYvBs1sauykV3X0cjZ34hliINyq62CspGCa4odh0rLdePAbymWgPgZRs1zLSiSXrj\nlgU7l7VlV6O/noMrnx6GnskTgRCMW8O+BVFFMlU6gzcapg2PS/LAJTstjXpH1TzLY30OLwQIdD5u\naubSmu7BEDIqQXOtD26njEXtlcaxCxoiAv1OqtwV0IQMRN8kRifjeGF7L5edDuu7s1GTjNBr96DM\n6YfT5kA0HcMzJ17BRHwSTf56bGhenXMcjJXSBPp5Mgbv7of24bfPHsXuY6N8g6ZY3iEDXNkM0nCU\nNkHM+IXlzoVTEaxdXA+fW8atN63jYC+7mH0+QBe86nLrwl+oMirBU7tiGJpSoRFgPEzPA+qiaczg\n+ex0njSSjkHVVEiiwGd8ugaC9P5jy406AYAadzbAYwYruQxeLIvBY8WMirhEs8AMXnaJosBBXjiu\nGDN4JTB4W3u2Y+fAfgDUYfV958/BnMayafNCsyuUDOPbW+7Gm3178v6eseylxCTMpvLO4CnGDB6X\naNrdWN2wDABwYPQYVJKB3WTmYpisCDmvl/26ADAWyr2nTEVS0PRYkjp/YQaPVbmrDBtbz8WnV12H\ny+fR3NpXTr1hecyqjlrUVroxNhnHvhNjvDme10xfnzF4zFF2oS7zbq1jDFcuALJINEtg8LbuoQDj\nsjUt2Li8gd8LGmu8nG1nzfhwII7JcBI2SbCch9dePB9f+NhKSNNsHFyxjs4PvvxWrqqEGaxUq5+t\nhAAAIABJREFUlblM84nhs+L6m13RhII77t2OY72T/J785FbDFCelqPjaL7bhtnvfLClUPqWoGJ6I\nQRQFOO0STvQFMRKIcSDLGNBsBu8H/70bP/qfPegenD2zx2ciq71o0kH3dHN4bx0eRkbVML+5DLWV\nbhzvm8J3f7fLkkNXrJSMihf17/DjV1DmfIF+zp4sINMcmoghFE2j3OdAQ5UHfo8ddpuIWIKOJrD5\nywq/g/cc7ySAZ5ZnsqrTx01nItOcCifReXoSsk3EqoU1mKMDPAYSA6EE78PGJuN8k6NrIMjXNQAY\nnCzs9B5XEphKnH22+C81s3rXADzO4BUAeBrRuESTLcpePoM3W4mmDvDE0gCeIAjoKDCHRwixLIxC\nCQxeIpVBOqNBsDGTFWvjVeenu69n40IL6YHpDWXFd47NxeSGoiuKXcdGpnk0bSLM+WysuJNmxPo9\nZVStpMU3kqQ3RRbQPV35XfS9M8jdUQzF6fHZBF3qKapIzIDBC8TpzdKnm9HMr2znv1tYbbVvl0QJ\nfocXggCEU9EZMYWAVZ4JUMahXb+Zi54QVGTQ5KvHRW1UMiiVU2kUk70ARpxCtkSTuo/S3fJHjzwL\nAPjo4mt4XIq5nDqDl4ECgCAcTyOaUHDg5DhEUcCXP7EaP/rSRRAEYNfR0YISGwYwzCHbGU3FeCwA\nAQKPZ2AMXigVwafftxi/v/Nqzl7mq/pqCpDcThuWza/mALIYg6dpBM/ujaNvwljwYknanChZEk1R\nFOG3M1aRLpyMITk1ENQz8PTPNnsGT5dojmcBvGYTwONB5/p9zee2wyYZQMpw0aT/nUwTxPLM4OUr\nJtMMRlJ8TqtQsDyr7sk+/GLnA/jx9vtmbZPP6rXenTg8dhzPn9yS9/cMzDlsdlNMwtsg0Szgohk1\nAbxqTyXaypqQyqRwdPykRabJZnrYd5CPwTODvtE8AG8kEIdgo3+vzzH9HLS5Lpt7PgBge/8exNPG\neS2KApcuPvtGD3c5ZQYUbG6XFWP0CkUsAEDELNGcZv6bEIKte6k89LI1Lfjyx1dzA55m0yaf1yXD\naZeQVujnUlfpKTms3lznL2+Ey2FD5+mpnGNnkRaVficq/U54XDIicSUnH+1MK6NquOPeN3G8bwq1\nFS585/MXQBIFbDs4xCXyT716ynD/LUFW2T8agUaAphoP1i+hXf9r+wax7QBlXj9xJQVB5k3jyXCS\nS2G7B2c/pz/EGVUPN76ZjsFjc4FXbmjHXX9/Hsq8duw9PoYnXy3ucsrqWO8kUmkV7Q1+Pi+5oIUB\nvPwmOke7DXmmIAgQBMHC4k3pM3gWBu8dlIU3HIhxB01Wvgp6r3t+ey+/NgDaSz36ygm8uON0TtzP\nzqMjIITmgbqdRk/AohIOZ2UO7zw6Ak0jODUQtIynjEWDBf0H7tpyD2557ltIKLOPt/pLnXm9KwCe\nqmpIKypEATmzD6zMA+bZM3izdfXLcIBXmkQTMBr6riyAl1YVzggCgCArGA7EkCwiW2CSRVHf/XfK\nVtOalkra8EaUM5eZMGlQc2XpszNlTj88shuCLYNDvYPTmpGMT8WhZDR9+N8AY/mcNGMJBZ//wWvY\ndHj67y6WMmIJSjpul844Ife1Q0m9obPRhUyQNCQKhFfnq2Aior8HvanOqzRCm5kJj7nMMs3xaYxW\nQtEUfv/8Mb4xcOI0XbQ7Wo3dfgZ0RJ8uValdgHMbKfMgVoxjy54+BKMp3tgywG2VaNK/nTFmiqqg\nyVePjS3n5j0umyjp7AoBRBXRuIK9naNQNYLFcypx2ZoWdLRWYMncKigZjZudZBeTUFWbAMZ4LACN\naKh2V3AGh828hXUwNV0jyGIT1i+ph2wTOTNQDOBtPpxE10gGDhmYX08/q5gue0xnVAiCweABBqvI\nzIrm67vNXQNBxJKGi2bODJ7HAHgJJYlAYgqyaLNkDXr5DB49XlEUUGFi8dj1I4kC7DaAAIgmmESz\n+BLAAGD/aBQZVYPPLcNpNza0JmKTOTmbz5ygWaWqpmLv0JnNFh0aPQYAGAgN5d3MMSSaMp+vLeYc\nOdNyyBIEAchoBouVz2TFqxvdLKyZpx/vsMVoxaePA7AZvHx7NalpGLzhQJxfgzMFeA2+Wiyp7UBK\nTWNb3y7L7y5f1wqbJGDX0VGk0iqqy104He3GXVvvRlKLcKDVUOXhcnlmapEP4M0kJuFY7yRGAnFU\nlTmxdF41nA4b7rh5A967oQ0fvWwBf5y5GQdQ0Ml5unI6bNwkhhn+8GM1bWAIgsBZvFJGJWZSx3om\ncbI/iEq/E9/5xwuwZG4VLlzVBE0jePr1U5gMJ/HY5hP88aXEDrBjbKv387/vyVe7MBKIo8LnwKXn\ntsDlsCEYSfFZM3NcS+8ZhLoPjxubso3VuQzetgODuP/pw9zEIxxLY/8Jurm3cVkDGmu8+NxHVwCg\nETil1AFdQrhigSHJnd9i3FPz1T5d7rrUNP/L5vCGJ2KIxNMQRQF+j/0dKdE0M3jt5XTW1OlLoanG\ng/7RCP7nhU7+2N/86TAeeO4Yfvbofnzl7lfR2TuJtKJi55ERPLON9p4bljZgc/ebeG7kEUDMcIB3\n6BT9bNnG8K6joxgJxBBLZuBwGvcoVUgUnFMfCA8jkUliNFq6xPgvdfbrXQHwWOPvdNgKSvCSqhng\nzW4G70i8C6dThnafATK5RAYPABboRisnswAeOybWpIiyAkIKO5UBMByOdAMRt83aHM6rpYYXKXJm\nbkdpVUEGaRBNQFtN6QBPEAQegJ6RQ5wBKFTmiARzlXtzZ/B6h8MIRtMYmpqewYul9fMjj4NmvvLq\nM3hEUHMkIxEd4LlkFwRCG7hIqnQXsGiaLnxVbgrcztEbwjkVLTnzVwAFyUBpUQkv7+zDI5tO4Cv3\nvIYte/q5Hbh5F34JA3h+CvCW1HZgfmU7PLIHoiOBtBQCQLBiYwT2hbswPBWkWV8mIxB2nIzBA4CP\nLL46L3vHigMXPex85xG6kDNDEwC4SM/dek2f98mufDlsLCKBOaECBsCLpEprWs5b2oBbb1qHf/jw\ncgB0B18QqJQl3/B6NKnh4Ok0RAH4yDoPWqvp9c/cJq1B5/QeU+YwZKOAAfBODYQQSxgzeDkAT5do\njscnMaAbrDT46iyftTvLRZP9DawYgwcYMk0CQBIBeZq9KQYAu3X5jnn+rnuyD//4zG34wbZfcvAV\niE9he99u/pi3Bq2W7TOpjJrBsXG6ox9TEnkNO8wzeIbJytlrygRByJmbY//vdck8XJ5dE8zRNJFJ\n5WXwis3gsfgFgDJ42YB2aDwG6Awe22SZSV02h7J4r3Rvs/y8wufEecsMc6f5zWXY1L0Nh0aPY9fg\nAS7TXNhu3EdaC2ToAdZRg2AyDE0rLLvbosszL1ndzDdiqstd+MLHVvJrhBVrxgEjImE2xWSaW3b3\nW9weOYOnbyC16XN4Z9tJk92X1y+tR60+W/jhi6nR10s7TuNXTxxCIqXC5aDnTymxA+wY2xr8WLWw\nhrOPAGUtJUk0mBr9WjYDvNmayWRUDaNTdGyhvsrDgfegDkpTioqfPrIfT756Cr99hhpxbT80DFUj\nWDG/mt+bViyogSjQz6bYpjYrNpu4Up+5PDTaiaORPbDLEkYC8RxVlqpqPOR+zSLDCIxtGjBZZ7nX\ngWg6CrdLl6a/gwCeOSJheT3NGZ6IB3DLJ1ZDFIAnXu3Cke4Ann2jB0+/Rt2mq8uc6B4M4Z9/+jo+\necfzuOs3b6F3OAy7LGH9kno8e3wTjgU64aqIcPB/sIsCvBuvXgSbJKLz9CR2d9L12us1rfFyKu+5\nqagKV1YEzoKD+1+KSmX/8GIn7cNmUO8KgPfwy3S3a1F7YfmgOdMsapIyAaUFncfVJF6N7MHWsNG8\nKDOUaALA/CrK2HRP9XEGEDAG02s9VRAFEUTMAILGd1XyFZ1JIyACCzq3MnjzG2pBiAAipc9oV5uZ\nQyBj5zt0pVazHsYsuKIYCRQHmgN6RIJ5/g4wTFbMM3gM7KTV6QFeXAd4DltpsRwu9jgpkyMXZE20\nR3ZCgr5BkCxdxhFX6XGzGZq28mbcetEXcMt5f5f38eUWgFccSLKbbVpR8R8P7sVwIAa7LPGFHaDD\n5ZJEIPnojXdxzQKIooiV9Uvon1wxBve8TuyPvgqpLADFNYpgJEWt/E0mK4DhmNngq8X5rWuKHhsz\nABKkDIKRFF8w1i02AN7G5Y0QRQH7T4znzFsqGRXBaAqiKKDcZ4AXw2Cllv+MSTTDyeKNEQMHoijg\nvGUN8OiMvmwTUe51QCOGdba5jvSnQQDMq7OhsdIGjw6CmOxRyZhn8Og9hjF4oaSR72WXJYxOxjE2\naTAzHrsV4DlsdpQ5/VA1FYdHqaOZef7O/Bw2gwdMD/AAOn9XaEOMv7bO4J3SM/XM4Hr/yBEQEOwd\nPoxNpyhoeOHkVqhEwyLdkXb/8JFZxxacCPRY5ukGQrnMrnkGTxIliIIIlWjIaKXPxU5Xjqy5OQbE\nPC6bZQYPMM7zZCYJh2ysCzOdwUtngGDcCowGJ4MQBMAuOmakGmG1vmUVPHY3eqb60T1pZa+u1s1W\nADp/Nx6lzf9kIogr17ehvsqN965vQ1xJ4IH9f0TKFoRsEzERTOTcI80STUIIgqn8a5iSUbFNl+pd\nmhVOnq/MDF5j1ewBXkdrBVrqfAhGU9h11GCMzC6agCFDnUkWnqqRad0FT+gSQnMe59ymMqxcUINk\nWsUbB4dgkwTc/EGqrJgpgyfbJJy31MgCvEDfOGPS1558AG+WLOXoZByaRlBd7oJdllBf5YEoABNT\ncSgZFTsODfPxmadf78Zbh4f5d37+igYMhUdACIHXJWNuczkyKsHRaebJogkFJ/unIIkClsytgqZp\nuGf7/fjd/kfR3Eav++w5vM7TU4glFDTVeCw9DJPjszihsjIRX3zuDrwa+iMAIBR95wA8M4O3rO4c\n+rPoOM5pq8RHL1sAQoDvPbALv3qSZnF+8fqV+M+vvQfXX94BmyQimVYxt6kMf33VOfjZP10Kv0fG\niM6wVdfQNWzPsTEMT8TgctiwdF4Vls2rAiHU0AYAnKblSZDTGMwTu2GW5efzmng3FCEEKeXsrTHT\n1SOvnMCDLx3Hgy92Tv9gU/3ZA7yugSBe2NELSRTwtx9YUvBxKRPAi6Xj0IjGG7pSGLwkoc9Pasbr\nsCYinzNjofLaPWj01UHRMugNGu5VnFV0ePl8FmzpogAvGEnRRlKg4CWbQfF7nBAytLnrnZg9Vc5m\n+GgG3swW1hbTHN5wFsAbCcSw66gxm2cweMZ7aERDRBgBxIwlk4UBPCWDaefwEgp9Hgswn64YUBZE\nNcfZi82veOxunn/IJKClVEqjz28oNzYjVjYs4Rb/2WWEnad5IGuhGgnQ31+2poW7FM5vLrMYD1SV\nufAPN7QCoopGXx3KXVSeuKF1JQDA1tQFUmXM4AkOakseSSQhSCoECNzM57yWc3FNx2X40oabi7J3\ngIldljI4dCqAWEJBc63X4pZa5nVgZUcNVI3gzUPWZj5gar7MkksWkWABeE5jBq9Q7ejfi0/98Rbs\n6N+b9/eFZJqEEBzuo/eLZa20aWcyRi7RNLloyhI9R8pNUQkAtfmfqzdcB7smCko0AaDWTc+V/SNH\nAABNfmsUhZGDZ5wfbE7OLktwmpgkp8lUZTp5pvkxvTpDYAZ4XZPGefLAgT+iLziITadeBwDcuOIj\nmFfZhrSq4MDI0WnfJ18dGrUuZv2hXOdDs0RTEIS3hcVjrFtaXya4RNNkssI2Pdi9I6EkLQwec9F0\n8NfKM4OX9bNsmeZwaEp/r9mBG7sk48K2dQCAzd1Ws5Wl86q4FLOjpQJjcQPgLZ5ThV/fegWWzqvG\njv59eOb4Jjx17AX+eCbTDEZSSKYzOWqUqUQo7z1619FRRBMK5jaWcSe/fDUQGkZcSVjMlUrJYi1U\ngiBwFu8Nk5tm9oxp2yycNJ99oxuf+95mPLG18CzZiT4KPhgzyurDl8zn/37/BXOxZhG9pw2VkDfG\n+gQW78BkmlVlTr7xzaToPcMhROJpnB4JwyaJcNolTEVSszIxy56Zl20iaivd0Ahdj1gkD5uPu/uh\nfTjYNQ6bJGDSeRi3PH8nfn/gcRBCsFzPMzzUVdz1+1DXBDRCzZtcDhu6Jnu5HL+8ln6H2Xl4rM9Y\ns8i6OWYwePTacvvTSChJTCp04zAce+fM4A2ZAN6CqjlwSHZE0zFE0zF84spz0N7gp0ZMGsFfXd6B\ny9a0wmm34carF+G3d1yJ39x2Je75yiX4qysWoqHag4nEFFeh+cvoevUnPYdyydwqSJKIdbrChl0b\nsmxsOgm2NGdqzRX9MwN4yUwKX3vpO/jv/Y+X/JwHXzyOT9z23FmXbxcq9j4v7zw9I1PIP2uAp2kE\n9z5+EIQAH7hwLrc2zldmBo+AIKEkZ+SiqRB6IajQoBK68Gb0n7EmrtRis1Zdk738Zxzg2d3wOvT5\nLptS9AQKRo3Qbbctv/GBndDX6hqZ3uSkUA3obkkScRaNochXjMET3ZEcBu8nD+/Ht+9/C9sP0UWW\n7QaZm/4HDz6Jx/oegK2hx7IAsXk0jQDFImBUjfDvySWXBvA40yepOZR4MkNvdD6nCzZBz1FMlwbw\n0hkCTaR/QykueIA57DzFB/ALFQPQH79iIb73hQuwsqMGH75kPsKpKF7qeg37h4+AEIKknS5ei2s7\n+HNX1C2CQEQIAoHL5sIFeiMoOKgteViXpjolF2d9HDY7blr1McytbJ3273DbDQaPDYOb5ZmsLl7F\nZJpW6262wFRnGXwwBq8hr0QzWhD87xk6BI1o6Jw4lff3hQDe4JSGYFyD1ymgrZZe9zwvLsViEswS\nzWwGz7iemQQtEk8XlGgCxhze8Qm6+OYyeMxFM1eiWea1Osc6ZeOW7yrioMmKgdeUnvXIPn9CCJ8j\n7qiai1QmhW9u/hFiSgILq+dhflU71jevAgDuplmswqkonj+xBUnTUD4DeCt0OdJAOPcexpQJDNg5\n3gajlexwcrNEM6pkMXj6fTiRSVlm8Iyg8+kZPPatjAYNgKcRgokYBRosH3M2dfncCwAAr/fttHxG\ngiDga59ei899dDkWzfVxM6DsGbqxGG2+ByOjfA6veyiMP7zYib+560Xc/ss3uUSzuYze+3/93C78\n/Xc25ayzrPkvll3XOzWAr754F+7b8xCPSgDOTKIJAKsWUvB0tJeubUpGRSSehiQKPDeSMXinR8Il\n54yxIO0/vHTcsiHJajKcxEQwAZfDljOKsGphDZbPr0ZDlQd/dcVCVPqdcNolROLpoiqjSDyNyTDd\nUGCOwCs7avD5jy7Hv964ljvXtjcaEs1jPZMghIJMDmRLcOvMLqYaMbNi7N+HTk1g/4kx2CQB3/y7\nDVizqA7RhAKNACs7atEd6gUAPHN8E5489iKWL6AA72BX8c3oAyfp79n83b7hI/x3ktvk+GgqFsnA\nQDMrdk7FdJbR4aGfs6IpgKC+Y4LOo/E04pkoBFGDz+6BW3ahjkUVRScg20R89ZPnoqbChavOa8cN\n7z3H8vwyr8PCgAPAcMRgr50eeq9hbqosH3Pt4qw1WjL7RKTyMnjmGdxA4p0P8A4PnUL3VB/+1PmK\nxYCqWB06NQElo2Hn0dn31aUWIYRvoiVSKl4ymeBNV3/WAG/r3n50np5Cuc/BXaIKVbZMKJqmNLQo\nCkilc2etsittssxPEfpvVd/9sM8Y4LUDyAZ4umzU7uESM8GWRs9Q/t1PAAhGknlDzs3lkelr9U3O\nPguvX3+uW5r5otqiL/KCM8btlAG9QdRvwg++eByaRjCo/56FnB8YOYqnO1/mzzfLJcyGI+k8zRKr\npAIILOS8RImmXZIBAgiihmjSeM+UoupOkIDX4YZNd+VkEtDpKpYiEGT6emUlNmn8cXKqqMmKklEx\nqYzCsfw13HfoPhyP78EnP1KLo6nX8Pk/3Yr79vwB//7az3Dbpu/jDX1GakmtYWLglJ24uuNSNPsb\ncdd7vooLdMklZfCi/KbtsuUCkFLKZWLwWOUsHqCD37JNxJHuADdVAYzIgqpCEQkmBs8uyXDZnFCJ\nZpEtmqsvSGVCZsBlrkJRCUcH6UK4tMUOUQdOHqcu0UwSEEKomYBoNVlhUluzo+0884xRgaBzAKj1\n0sVWI/Qe1eRrsPyeyRMVVeFNO2MhzPJMwArqpnPQBABPFghkM3iB+BSCyTC8dg/+5YLPoszh4wCT\n5Umua6as8J6hgxY5er566OBT+K99j+C3+x4FQJnyrsleiIKIK+dfDIAarWQX+3sZsOMM3tk0WjGF\nk2uEgJH6bqdJoilnSTSVJOw6wLNJAo/vKTaDx35WX06fNxoy1qRokkAV6H2mzDV7gNda3oQ55S1I\nKMkcZrWt3o9rNs7BhKkpywZ4bEd+ODKKFp3B+9UTB/HgS8eRUQk6T0/ytazFT9UbxweHMRKIY4sO\n6AAaKr372ChEUcAlqwsDvM6JLhBCcHKimzeokijkRKXMtFrrfPA4bRifSmBsKs43kCr8Tg6IyrwO\nNFZ7kEyrOFnAuCO72CZbIpXBQy8fz/n9SX3+bkFLeU5kiCAI+LfPnY97v/4eeF2UkW4oIVeObQK3\n1vn4awqCgKs3zsGiOYZSpL3eD0EABsYi2K+DpCVzqwwgW2AzWdMIeofDOW6M5uMyA27G5j266QQ0\nQu/zZV4Hbvn4Kr7xdNGqJu4IDAB/OPQURoVOSKKArv5g0Vmj/Wz+Tgd4e4cP8d/FBArY2ecMULVP\n73AYLoeEJXPpZlkyk8ITR1+AZreCWslpWsttyjtmBs/soMkMtngWrb4Gtjf4cf83rsA/XreipDga\nNr8OAKLd+ncu09nUuko3NxuqLndZsoEFOc3jFMxlMVnKk4PJzHbeKbW/lwImImh45tD2kp4T1I2K\nCkVyTFcnJrrzKlLy1WQ4yTcgAMqyZkrccPqzBXjpjIb/0od2/+b9i6dllswMHkABlSAI3N1suuHF\nNDF+z8BeRmfyZNvMAF6DPrs0HjO05oZE08MlOH4/DYVmYdPZFYwWDjlnVeGgDNBIqHBmyXTFpEHM\nKGImVeb0w22jTpqDpsD1iWCSyx97h8PYuncA41MJSKKA2kqaFfizt37HHy/YFASjxu6+2XCkGMBL\nKYSzKY4STVYEQYAI+p2GYsb7RGJpC6C2M4CnlAbwIkmNmyT4S3TBM7toFmPwRifjEMtHITrjODTW\nid8f+CPu2PwjvNC1FWlVwbK6c+B3eHFyshd9IQpuFtd0WF7jptXX4T+uvh2t5U2cNRIcCQxPxHJm\njWZa5hk8gM6/npNnZtbtlLFmUR0IyZJPsQw2k8mHJSLBW215HT6Hl8pdgFRNRb9uWBIuIONk72MG\nmdGEglOj9Fxa2mqcS7IE2CTqtKio9N4k6ACPRXMwCe5gxGhqWFQCAAi2/EHngGG0AgACBDRmyXkF\nQeDxCsxJk6kZmrPmWS0zeCUAvGwZJ2usT05S9m5+ZRv8Th8+s+YGALTpWNtIHfEafXVo8TcgpiRw\nZPwECpVGNOwZok3alp7t6J48jaPjJ6ARDQuq5mCB7jzcHx62bHYRQiwmK+b/f7sYPAbC7DY6u1lQ\nopkxJJo+t93EehuvlV2MwWOmPWMmo5VgnJgiEs6MvTqvlbrdvmkywzEXi+QAgMkseSUDeGlVQWU1\n/XlGJaivclNGSsqAgMAtu1Dt0a9vmd4fX9jRy19r654BqBrB2kV1FsfX7GIbMWPxAOqrKPhqa/BP\nm3c3XYmigEW64dSxnkkjAy/rWFboJh6lBI4TQricUhCodX32PN6JfuZsXJH9dF5mxp27UpYA8NqK\nKJgAakLXUOVBRiUcbC+ZU8XlsYXMZLbuHcAXf7gF375vR05UTz4Gr0EHexM6aL58LVV4lHkd+L+f\n3Yi/v3YZ1i2rxkR8EpIo4W9X/xUA4Hf7H0bzvCQ0Ahzpyd+vTARpRpvLIWFBazmmEiH0TPVzH4SR\n2AhcDhEToSQPLmfs3YoFNZBt9Jp8YP8f8YdDT2HLwGbL6xObscYKNuUdM4M3MhHn8swaPTqnTl/z\nRkxOldPNVJvLDPAyorHOeVwy5jQZaxOTac5vLrP00cz4LZ01i1ZIoqlpBD95eB+uv/VZdM4gt+/t\nrp4Jg8l8qfOtkp7DZvPNGwmlVlxJ4M4tP8Z3X/9FSY9nEvFF7ZVoqvFiIpjAmwdLA4d/tgAvmcog\nnlCwfH41Llk9/YB2LsCbWdi5GeClCL3omT24fQYzeABQo8/UTJgWUkOi6eEW2B1z6f9vyrJzZhWK\npguGnPP38unvdQZa6IkoXZSqPKXJCrOLyTTHEsYNhVHOLK/rvqdog1df5YEoAr9463cIJcOo1j8r\nwaYgkVKRTGdACLEYjhSLh0tlwEGwUyqNwQPADVTCSeN9InFDEuuyOWGX2LxNaYtAKKlAEAlEIsNe\nItg0m6xMRZIFmeaRQByC3khd0LYOF7WvR4OvFhe1rccP33sbbr/kS/jZ++7CJ5Z9CD6HF6salqLC\nVTi0ngM8ewJDgSjiGfo5zHb+hzFTNgc9/rWL6wrGFzCXs+OmmyezYzbPgLGIhCpTRAIrw2glt2kZ\niowio7PvwTy/BwwgY2bwth0YhqoBrdUSytzGrVMQBJPRCqELnmB10WzSr4Gh8ChvclvrfEYYdrEZ\nPK8B8Go9VXnPnew5vPnN5fjxly/G5z6y3PI48wyey17KDF4Wg1dO7zNdgV76ProaYV3zStx52Vdw\n2yVfssxjrtVZvGIyzZ6pfu6QSUDwX/sexcERKs9cVncOypx+eO2enPDcjEbBhCRK/D3ttrMfds5k\nlWnFAHiM1StosqIYEk1msAIAdtl4rexK6UtMtV+Eyy4gqRCE9TiLUIwYEQn22c+fAcB5LasBALuH\nDuVlOsdM61Iqk+KbBoC1YausUbFyAZWB//Srl+LClU0chHrtblTq9xfBThvtvpEIOnssAYP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waPsaalNpMJjQGw4sPw2cVkmo2NMn76T5egbsVJyB178cIhY/d9KBCBIKchQJiVGU6+YsYlgiNh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\niIiIiIiIAuL/A/6tGCwN9kMTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114dc6940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = gdp.pct_change().plot(figsize=(15, 5), label='GDP', legend=True, linewidth=2)\n",
    "rgdp.pct_change().plot(ax=ax, label='real GDP', legend=True, linewidth=2)\n",
    "add_rec_bars(ax)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recap\n",
    "\n",
    "DataFrames allow you to rapidly explore data, iterating on ideas.\n",
    "\n",
    "- `ufunc`s work as expected\n",
    "- operations between pandas objects automatically align on labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}