{
 "metadata": {
  "name": "",
  "signature": "sha256:c1f6442e4af372a89c591c78d94ee85d760eface18e42bad1c7e82541840f37a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Dataframes and exploratory plotting: Pandas and Seaborn\n",
      "\n",
      "[Tom Wallis](http://www.tomwallis.info) and [Philipp Berens](http://philippberens.wordpress.com/)\n",
      "\n",
      "The University of T\u00fcbingen\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Introduction\n",
      "\n",
      "Python is a general-purpose programming language used in a vast number of domains. Thanks to add-on packages (\"modules\"), it can be used to analyse data in a manner similar to domain-specific languages (like R). One advantage of using a language like Python over something like R is that knowing some Python will probably be useful to you even when you're not doing data analysis.\n",
      "\n",
      "Here we introduce `Pandas`, a module for handling data sets as *dataframes*, and `Seaborn`, a high-level plotting library"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "- We will be running through code examples to do basic data manipulation using Python, and you will be able to have these lecture slides for use in your own data analysis applications\n",
      "- The web is also full of tutorials, guides and answers on how to do things: if you have a problem, do a web-search for the thing you're trying to do. StackOverflow is a particularly useful website."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Importing modules into the Python workspace\n",
      "\n",
      "To use the modules that extend the functionality of Python, they must first be imported into memory."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import seaborn as sns\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# set up to have plots appear in the ipython notebook:\n",
      "%matplotlib inline  \n",
      "sns.set_style(\"white\")  # sets the style of seaborn plots"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Example: Sleep in Mammals\n",
      "\n",
      "We have compiled a few interesting datasets to use in this course. First we will look at a dataset of sleep times in mammals. \n",
      "\n",
      "In this paper (Allison & Cicchetti, 1976), the authors collected a dataset of the number of hours various species of mammal sleep, and what type of sleep (slow-wave or REM sleep). They also compiled some other metrics, including things like lifespan and gestation, as well as environmental factors like the species' risk of predation. Here's the abstract from the paper:\n",
      "\n",
      "> The interrelationships between sleep, ecological, and constitutional variables were assessed statistically for 39 mammalian species. Slow-wave sleep is negatively associated with a factor related to body size, which suggests that large amounts of this sleep phase are disadvantageous in large species. Paradoxical sleep is associated with a factor related to predatory danger, which suggests that large amounts of this sleep phase are disadvantageous in prey species.\n",
      "\n",
      "Allison, T. & Cicchetti, D.V. (1976). Sleep in mammals: ecological and constitutional correlates. *Science, 194*(4266): 732-734\n",
      "\n",
      "The dataset is provided as a tab-delimited text file. First, let's import the data into a Pandas dataframe: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# import the dataset csv file into a Pandas dataframe object:\n",
      "sleep = pd.read_csv('../Datasets/sleep.txt', sep='\\t')  # the \"\\t\" means it's a tab separated file."
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that Pandas offers lots of other read / write functions. For example, it's easy to read from an excel spreadsheet using `read_excel`. \n",
      "\n",
      "Note how we've referenced Pandas as `pd` (as we imported it, above). The dot after `pd` (as in `pd.read_csv`) tells us that we're using a function (`read_csv`) from the Pandas module. This is a nice feature of Python code, because it's clear where all of the functions you're using come from."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can see the contents of our current \"workspace\" (the stuff our IPython Notebook knows about) by typing `whos` in an otherwise empty cell and running it:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "whos  "
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Variable   Type         Data/Info\n",
        "---------------------------------\n",
        "np         module       <module 'numpy' from '/Us<...>kages/numpy/__init__.py'>\n",
        "pd         module       <module 'pandas' from '/U<...>ages/pandas/__init__.py'>\n",
        "plt        module       <module 'matplotlib.pyplo<...>es/matplotlib/pyplot.py'>\n",
        "sleep      DataFrame                       Specie<...>n\\n[62 rows x 11 columns]\n",
        "sns        module       <module 'seaborn' from '/<...>ges/seaborn/__init__.py'>\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Objects\n",
      "\n",
      "Most variables in the workspace are *objects*. An *object* has a specific meaning for programmers. \n",
      "\n",
      "If you don't know this meaning, think of an *object* like a car:\n",
      "\n",
      "- you can do things with it\n",
      "- you can store things in it\n",
      "- if you use it for something it wasn't intended for, bad things can happen\n",
      "\n",
      "Similarly, an *object* in programming usually contains *methods* (things you can do with it) and sometimes data of some sort (things stored in it). If you try to use an object in a way it's not intended to be used, it will return an error.\n",
      "\n",
      "We can explore what stuff we can do with an object in the workspace by using dot completion in the notebook."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Dots, tab completion, accessing help\n",
      "\n",
      "When using the IPython Notebook, you can check what stuff you can do to an object in the workspace by putting a fullstop after it and then hitting tab in the cell. This shows you a list of all the methods that can operate on that object.\n",
      "\n",
      "You can also hit `shift + tab` to get help on a method; hit this multiple times to get more help.\n",
      "\n",
      "Finally, be sure to look at the user interface tour of the IPython Notebook, and to check out some of the keyboard shortcuts."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sleep.mean()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "BodyWt         198.789984\n",
        "BrainWt        283.134194\n",
        "NonDreaming      8.672917\n",
        "Dreaming         1.972000\n",
        "TotalSleep      10.532759\n",
        "LifeSpan        19.877586\n",
        "Gestation      142.353448\n",
        "Predation        2.870968\n",
        "Exposure         2.419355\n",
        "Danger           2.612903\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# look at what the \"sleep\" dataframe contains:\n",
      "sleep.info()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 62 entries, 0 to 61\n",
        "Data columns (total 11 columns):\n",
        "Species        62 non-null object\n",
        "BodyWt         62 non-null float64\n",
        "BrainWt        62 non-null float64\n",
        "NonDreaming    48 non-null float64\n",
        "Dreaming       50 non-null float64\n",
        "TotalSleep     58 non-null float64\n",
        "LifeSpan       58 non-null float64\n",
        "Gestation      58 non-null float64\n",
        "Predation      62 non-null int64\n",
        "Exposure       62 non-null int64\n",
        "Danger         62 non-null int64\n",
        "dtypes: float64(7), int64(3), object(1)\n",
        "memory usage: 5.8+ KB\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# let's look at the first few lines of the sleep dataset using `head`:\n",
      "sleep.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Species</th>\n",
        "      <th>BodyWt</th>\n",
        "      <th>BrainWt</th>\n",
        "      <th>NonDreaming</th>\n",
        "      <th>Dreaming</th>\n",
        "      <th>TotalSleep</th>\n",
        "      <th>LifeSpan</th>\n",
        "      <th>Gestation</th>\n",
        "      <th>Predation</th>\n",
        "      <th>Exposure</th>\n",
        "      <th>Danger</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>        Africanelephant</td>\n",
        "      <td> 6654.000</td>\n",
        "      <td> 5712.0</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>  3.3</td>\n",
        "      <td> 38.6</td>\n",
        "      <td> 645</td>\n",
        "      <td> 3</td>\n",
        "      <td> 5</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> Africangiantpouchedrat</td>\n",
        "      <td>    1.000</td>\n",
        "      <td>    6.6</td>\n",
        "      <td> 6.3</td>\n",
        "      <td> 2.0</td>\n",
        "      <td>  8.3</td>\n",
        "      <td>  4.5</td>\n",
        "      <td>  42</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>              ArcticFox</td>\n",
        "      <td>    3.385</td>\n",
        "      <td>   44.5</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 12.5</td>\n",
        "      <td> 14.0</td>\n",
        "      <td>  60</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>   Arcticgroundsquirrel</td>\n",
        "      <td>    0.920</td>\n",
        "      <td>    5.7</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 16.5</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  25</td>\n",
        "      <td> 5</td>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>          Asianelephant</td>\n",
        "      <td> 2547.000</td>\n",
        "      <td> 4603.0</td>\n",
        "      <td> 2.1</td>\n",
        "      <td> 1.8</td>\n",
        "      <td>  3.9</td>\n",
        "      <td> 69.0</td>\n",
        "      <td> 624</td>\n",
        "      <td> 3</td>\n",
        "      <td> 5</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "                  Species    BodyWt  BrainWt  NonDreaming  Dreaming  \\\n",
        "0         Africanelephant  6654.000   5712.0          NaN       NaN   \n",
        "1  Africangiantpouchedrat     1.000      6.6          6.3       2.0   \n",
        "2               ArcticFox     3.385     44.5          NaN       NaN   \n",
        "3    Arcticgroundsquirrel     0.920      5.7          NaN       NaN   \n",
        "4           Asianelephant  2547.000   4603.0          2.1       1.8   \n",
        "\n",
        "   TotalSleep  LifeSpan  Gestation  Predation  Exposure  Danger  \n",
        "0         3.3      38.6        645          3         5       3  \n",
        "1         8.3       4.5         42          3         1       3  \n",
        "2        12.5      14.0         60          1         1       1  \n",
        "3        16.5       NaN         25          5         2       3  \n",
        "4         3.9      69.0        624          3         5       4  "
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# we can also use the \"describe\" method to summarise some information in our variables:\n",
      "sleep.describe()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>BodyWt</th>\n",
        "      <th>BrainWt</th>\n",
        "      <th>NonDreaming</th>\n",
        "      <th>Dreaming</th>\n",
        "      <th>TotalSleep</th>\n",
        "      <th>LifeSpan</th>\n",
        "      <th>Gestation</th>\n",
        "      <th>Predation</th>\n",
        "      <th>Exposure</th>\n",
        "      <th>Danger</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>   62.000000</td>\n",
        "      <td>   62.000000</td>\n",
        "      <td> 48.000000</td>\n",
        "      <td> 50.000000</td>\n",
        "      <td> 58.000000</td>\n",
        "      <td>  58.000000</td>\n",
        "      <td>  58.000000</td>\n",
        "      <td> 62.000000</td>\n",
        "      <td> 62.000000</td>\n",
        "      <td> 62.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>  198.789984</td>\n",
        "      <td>  283.134194</td>\n",
        "      <td>  8.672917</td>\n",
        "      <td>  1.972000</td>\n",
        "      <td> 10.532759</td>\n",
        "      <td>  19.877586</td>\n",
        "      <td> 142.353448</td>\n",
        "      <td>  2.870968</td>\n",
        "      <td>  2.419355</td>\n",
        "      <td>  2.612903</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>  899.158011</td>\n",
        "      <td>  930.278942</td>\n",
        "      <td>  3.666452</td>\n",
        "      <td>  1.442651</td>\n",
        "      <td>  4.606760</td>\n",
        "      <td>  18.206255</td>\n",
        "      <td> 146.805039</td>\n",
        "      <td>  1.476414</td>\n",
        "      <td>  1.604792</td>\n",
        "      <td>  1.441252</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>    0.005000</td>\n",
        "      <td>    0.140000</td>\n",
        "      <td>  2.100000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  2.600000</td>\n",
        "      <td>   2.000000</td>\n",
        "      <td>  12.000000</td>\n",
        "      <td>  1.000000</td>\n",
        "      <td>  1.000000</td>\n",
        "      <td>  1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>    0.600000</td>\n",
        "      <td>    4.250000</td>\n",
        "      <td>  6.250000</td>\n",
        "      <td>  0.900000</td>\n",
        "      <td>  8.050000</td>\n",
        "      <td>   6.625000</td>\n",
        "      <td>  35.750000</td>\n",
        "      <td>  2.000000</td>\n",
        "      <td>  1.000000</td>\n",
        "      <td>  1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>    3.342500</td>\n",
        "      <td>   17.250000</td>\n",
        "      <td>  8.350000</td>\n",
        "      <td>  1.800000</td>\n",
        "      <td> 10.450000</td>\n",
        "      <td>  15.100000</td>\n",
        "      <td>  79.000000</td>\n",
        "      <td>  3.000000</td>\n",
        "      <td>  2.000000</td>\n",
        "      <td>  2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>   48.202500</td>\n",
        "      <td>  166.000000</td>\n",
        "      <td> 11.000000</td>\n",
        "      <td>  2.550000</td>\n",
        "      <td> 13.200000</td>\n",
        "      <td>  27.750000</td>\n",
        "      <td> 207.500000</td>\n",
        "      <td>  4.000000</td>\n",
        "      <td>  4.000000</td>\n",
        "      <td>  4.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 6654.000000</td>\n",
        "      <td> 5712.000000</td>\n",
        "      <td> 17.900000</td>\n",
        "      <td>  6.600000</td>\n",
        "      <td> 19.900000</td>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 645.000000</td>\n",
        "      <td>  5.000000</td>\n",
        "      <td>  5.000000</td>\n",
        "      <td>  5.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "            BodyWt      BrainWt  NonDreaming   Dreaming  TotalSleep  \\\n",
        "count    62.000000    62.000000    48.000000  50.000000   58.000000   \n",
        "mean    198.789984   283.134194     8.672917   1.972000   10.532759   \n",
        "std     899.158011   930.278942     3.666452   1.442651    4.606760   \n",
        "min       0.005000     0.140000     2.100000   0.000000    2.600000   \n",
        "25%       0.600000     4.250000     6.250000   0.900000    8.050000   \n",
        "50%       3.342500    17.250000     8.350000   1.800000   10.450000   \n",
        "75%      48.202500   166.000000    11.000000   2.550000   13.200000   \n",
        "max    6654.000000  5712.000000    17.900000   6.600000   19.900000   \n",
        "\n",
        "         LifeSpan   Gestation  Predation   Exposure     Danger  \n",
        "count   58.000000   58.000000  62.000000  62.000000  62.000000  \n",
        "mean    19.877586  142.353448   2.870968   2.419355   2.612903  \n",
        "std     18.206255  146.805039   1.476414   1.604792   1.441252  \n",
        "min      2.000000   12.000000   1.000000   1.000000   1.000000  \n",
        "25%      6.625000   35.750000   2.000000   1.000000   1.000000  \n",
        "50%     15.100000   79.000000   3.000000   2.000000   2.000000  \n",
        "75%     27.750000  207.500000   4.000000   4.000000   4.000000  \n",
        "max    100.000000  645.000000   5.000000   5.000000   5.000000  "
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "You can see that we have some missing cases: not all variables have 62 entries.\n",
      "\n",
      "Let's keep only the complete cases, by using the `dropna` method:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sleep = sleep.dropna(axis=0)  # drop on axis 0 (i.e. rows)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sleep.info()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 42 entries, 1 to 60\n",
        "Data columns (total 11 columns):\n",
        "Species        42 non-null object\n",
        "BodyWt         42 non-null float64\n",
        "BrainWt        42 non-null float64\n",
        "NonDreaming    42 non-null float64\n",
        "Dreaming       42 non-null float64\n",
        "TotalSleep     42 non-null float64\n",
        "LifeSpan       42 non-null float64\n",
        "Gestation      42 non-null float64\n",
        "Predation      42 non-null int64\n",
        "Exposure       42 non-null int64\n",
        "Danger         42 non-null int64\n",
        "dtypes: float64(7), int64(3), object(1)\n",
        "memory usage: 3.9+ KB\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sleep.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Species</th>\n",
        "      <th>BodyWt</th>\n",
        "      <th>BrainWt</th>\n",
        "      <th>NonDreaming</th>\n",
        "      <th>Dreaming</th>\n",
        "      <th>TotalSleep</th>\n",
        "      <th>LifeSpan</th>\n",
        "      <th>Gestation</th>\n",
        "      <th>Predation</th>\n",
        "      <th>Exposure</th>\n",
        "      <th>Danger</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> Africangiantpouchedrat</td>\n",
        "      <td>    1.000</td>\n",
        "      <td>    6.6</td>\n",
        "      <td>  6.3</td>\n",
        "      <td> 2.0</td>\n",
        "      <td>  8.3</td>\n",
        "      <td>  4.5</td>\n",
        "      <td>  42</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>          Asianelephant</td>\n",
        "      <td> 2547.000</td>\n",
        "      <td> 4603.0</td>\n",
        "      <td>  2.1</td>\n",
        "      <td> 1.8</td>\n",
        "      <td>  3.9</td>\n",
        "      <td> 69.0</td>\n",
        "      <td> 624</td>\n",
        "      <td> 3</td>\n",
        "      <td> 5</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>                 Baboon</td>\n",
        "      <td>   10.550</td>\n",
        "      <td>  179.5</td>\n",
        "      <td>  9.1</td>\n",
        "      <td> 0.7</td>\n",
        "      <td>  9.8</td>\n",
        "      <td> 27.0</td>\n",
        "      <td> 180</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>            Bigbrownbat</td>\n",
        "      <td>    0.023</td>\n",
        "      <td>    0.3</td>\n",
        "      <td> 15.8</td>\n",
        "      <td> 3.9</td>\n",
        "      <td> 19.7</td>\n",
        "      <td> 19.0</td>\n",
        "      <td>  35</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>         Braziliantapir</td>\n",
        "      <td>  160.000</td>\n",
        "      <td>  169.0</td>\n",
        "      <td>  5.2</td>\n",
        "      <td> 1.0</td>\n",
        "      <td>  6.2</td>\n",
        "      <td> 30.4</td>\n",
        "      <td> 392</td>\n",
        "      <td> 4</td>\n",
        "      <td> 5</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "                  Species    BodyWt  BrainWt  NonDreaming  Dreaming  \\\n",
        "1  Africangiantpouchedrat     1.000      6.6          6.3       2.0   \n",
        "4           Asianelephant  2547.000   4603.0          2.1       1.8   \n",
        "5                  Baboon    10.550    179.5          9.1       0.7   \n",
        "6             Bigbrownbat     0.023      0.3         15.8       3.9   \n",
        "7          Braziliantapir   160.000    169.0          5.2       1.0   \n",
        "\n",
        "   TotalSleep  LifeSpan  Gestation  Predation  Exposure  Danger  \n",
        "1         8.3       4.5         42          3         1       3  \n",
        "4         3.9      69.0        624          3         5       4  \n",
        "5         9.8      27.0        180          4         4       4  \n",
        "6        19.7      19.0         35          1         1       1  \n",
        "7         6.2      30.4        392          4         5       4  "
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Tidy data\n",
      "\n",
      "Most of the tools we will use throughout this course expect your data to be in a format that we will call *tidy data* (after Wickham, 2013):\n",
      "\n",
      "<img src=\"pics_for_lectures/tidy_data.png\">\n",
      "\n",
      "All of the datasets we have provided follow this format. It can also be called a \"long format\" data frame, because it tends to be longer (rows) than it is wide (columns). \n",
      "\n",
      "As an example of a *wide* (i.e. *untidy*) data frame, imagine that we measured three peoples' heights, once at time one and once at time two:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "heights = pd.DataFrame({'person': ['Jim', 'Sally', 'Meg'],\n",
      "                        't1': [120, 123, 118],\n",
      "                        't2': [152, 147, 153]})\n",
      "heights"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>person</th>\n",
        "      <th>t1</th>\n",
        "      <th>t2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>   Jim</td>\n",
        "      <td> 120</td>\n",
        "      <td> 152</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> Sally</td>\n",
        "      <td> 123</td>\n",
        "      <td> 147</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>   Meg</td>\n",
        "      <td> 118</td>\n",
        "      <td> 153</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "  person   t1   t2\n",
        "0    Jim  120  152\n",
        "1  Sally  123  147\n",
        "2    Meg  118  153"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Do you see how the columns `t1` and `t2` are mixing up what should be one variable (time) with two levels (t1 and t2)? While a data frame like this can be useful in some circumstances, in general it makes things harder. \n",
      "\n",
      "Thankfully, we can easily reshape wide dataframes into long dataframes using Pandas' `melt` function:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tidy = pd.melt(heights, id_vars='person', \n",
      "               var_name='time', value_name='height')\n",
      "tidy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>person</th>\n",
        "      <th>time</th>\n",
        "      <th>height</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>   Jim</td>\n",
        "      <td> t1</td>\n",
        "      <td> 120</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> Sally</td>\n",
        "      <td> t1</td>\n",
        "      <td> 123</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>   Meg</td>\n",
        "      <td> t1</td>\n",
        "      <td> 118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>   Jim</td>\n",
        "      <td> t2</td>\n",
        "      <td> 152</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> Sally</td>\n",
        "      <td> t2</td>\n",
        "      <td> 147</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>   Meg</td>\n",
        "      <td> t2</td>\n",
        "      <td> 153</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "  person time  height\n",
        "0    Jim   t1     120\n",
        "1  Sally   t1     123\n",
        "2    Meg   t1     118\n",
        "3    Jim   t2     152\n",
        "4  Sally   t2     147\n",
        "5    Meg   t2     153"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can find out more about how you can reshape datasets in Pandas, including aggregating values using pivot tables, in the [documentation on reshaping](http://pandas.pydata.org/pandas-docs/stable/reshaping.html)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Exploratory plots\n",
      "\n",
      "Now we have only the complete cases, let's look at two variables of interest by using the Seaborn plotting package. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# simple histogram of Total sleep:\n",
      "vec = sleep['TotalSleep']\n",
      "g = sns.distplot(vec)\n",
      "sns.despine(ax=g, offset=10);    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {
        "png": {
         "height": 283,
         "width": 389
        }
       },
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAwsAAAI3CAYAAADUacfYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAWJQAAFiUBSVIk8AAAIABJREFUeJzs3Xd8XFed///3jHpvlmzLcpFl+dhOXJMQUuyEAKEk1ISS\nLAEebNgvm5Av+/09vl+WsmTphOyyC8smZIENbLJsKhAg1CSkOD0hcbePZLnITbZ6bzOa3x8zujMj\nz0iyZ6QrjV7Px0OPm8+959z5TCDSfObcc44nEAgIAAAAAMbyup0AAAAAgJmJYgEAAABATBQLAAAA\nAGKiWAAAAAAQE8UCAAAAgJgoFgAAAADERLEAAAAAICaKBQAAAAAxUSwAAAAAiIliAQAAAEBMFAsA\nAAAAYqJYAAAAABATxQIAAACAmNITvYExJl3SLZI+KWmZpBOSfiLpNmutbxL9SyV9VdLVksol7ZV0\nu7X2wRhtqyV9TdKVknIk7ZH0b9banyX6PgAAAABES8bIwh2SviOpWdJ3JR1T8MP/fRN1NMbkSXpM\n0qckPS/p+5KKJd1vjLl5TNtVkl6RdL2kHZL+Q1JA0r3GmO8n4X0AAAAAiOAJBAJn3dkYc7GkZyU9\nZK39UMT5n0r6qKR3WWt/O07/L0j6uqSbrbU/CJ3Ll/SCpGpJ1dba5tD5pyRtkfT31tp/irjHjyT9\ntaSrrbW/O+s3AwAAACBKoiMLo9/+f2XM+c8r+K3/jRP0v0lSk6S7Rk9Ya3skfUNSroKjCDLGLFCw\nUNgbWSiE/F9J/tARAAAAQJIkWixskdRsrd0TedJae0JSfeh6TMaYGkmVkrZaa8cObzwVcX8pOMog\nSS+PvY+1tlNSo6QLjTFM2AYAAACS5Kw/XBtjsiQtktQQp8khSSXGmLI412tCx9P6W2ubJA1KWhk6\nNRg6Zsa5V6GkbEkLxs8aAAAAwGQl8k18aejYEed6Z+hYFOf6aBERr39XRN89kvolvdkYUxjZyBhz\nUcS94r0WAAAAgDOUSLGQEToOxrk+ej47gf7ZkmStHVBw1aUKSb8xxmwwxuQbY94i6X8k9UryhH4A\nAAAAJEEi+yz0h47xHg3KCh17E+gf2feLkhZL+pCk1yLO/7uC+zN8SFLfOPnGZYz5rqQNkrZZa//u\nbO4BAAAApJpEioVOBVc8ivfoT1Hoemec6+0R7WIpVHCDN0mStXZY0nXGmH+VdKmkYUl/ttbuNsY8\nGXqtk2f0DsI2SLrsLPvGc/Zr0gIAAACTM6VP1px1sWCtHTLGHFZ4paKxqhVcKSnenIS6iHZRjDEL\nFRxZsDFe92VFrIpkjElT8MP+IWtt/9j2AAAAAM5OokuNbpW00BhTG3nSGFMpqVbSi/E6WmsbFVzy\ndLMxZmxFdHno+ELEPZ8xxrwS41ZXKDg68fszzh4AAABAXIkWC/eEjt8c/cAfOn4rdP6HE/S/V1KV\npE+PnjDGFCg4P6EvdH1Ug6TzjDGXRbQtlvTPCj6S9M9n/zYAAAAAjOUJBBJ7tN4Yc5+Ck4tfVnAz\ntYsVnFPwkLX2QxHtviwpYK39SsS5AkmvKjgK8QtJByRdI2mZpFustXdGtF0iabuCqyj9TMEJ0tdK\nWijpb621ExUm472HpxScs/C0tfbys73PGMxZAAAAwFSb0jkLydjx+AZJt0qaJ+kzCi5v+iVJHxnT\n7tbQj8Na2y1ps6S7Q8ebJLVJui6yUAi1bZR0oaTHJL03dP99kq5MpFAAAAAAEFvCIwupgJEFAAAA\nzFIzfmQBAAAAQAqiWAAAAAAQE8UCAAAAgJgoFgAAAADERLEAAAAAICaKBQAAAAAxUSwAAAAAiIli\nAQAAAEBMFAsAAAAAYqJYAAAAABATxQIAAACAmCgWAAAAAMREsQAAAAAgJooFAAAAADFRLAAAAACI\niWIBAAAAQEwUCwAAAABiolgAAAAAEBPFAgAAAICYKBYAAAAAxESxAAAAACAmigUAAAAAMVEsAAAA\nAIgp3e0EACCV/fYPj6urZ8jtNJKuMD9TV739LW6nAQCYYhQLADCFunqGVLJojdtpJF37sT1upwAA\nmAY8hgQAAAAgJooFAAAAADFRLAAAAACIiWIBAAAAQEwUCwAAAABiolgAAAAAEBPFAgAAAICYKBYA\nAAAAxESxAAAAACCmpOzgbIxJl3SLpE9KWibphKSfSLrNWuubRP9SSV+VdLWkckl7Jd1urX0wRttq\nSd+Q9FZJhZIOhV7rn6y1/iS8HQAAAABK3sjCHZK+I6lZ0nclHVPww/99E3U0xuRJekzSpyQ9L+n7\nkool3W+MuXlM28WSXpL0YUnPSvqepEFJ35T0syS9FwAAAABKQrFgjLlYwRGFh6y1l1lrv2Ct3SLp\nHknXGGOumuAWn5G0UdIt1trrrbWfk7RB0m5J3zbGlEe0/bykeZL+zlr7PmvtZyVtkvSMpA8aY7Yk\n+n4AAAAABCVjZGH02/+vjDn/eUkBSTdO0P8mSU2S7ho9Ya3tUfBRo1xJ10e03RS6590RbX0KPoYk\nSReeYe4AAAAA4khGsbBFUrO1dk/kSWvtCUn1oesxGWNqJFVK2mqtDYy5/FTE/UedlORRcF5EpKrQ\nsflMEgcAAAAQX0LFgjEmS9IiSQ1xmhySVGKMKYtzvSZ0PK2/tbZJwfkIKyNOf0+ST9Ldxph1xpg8\nY8x7JX1WUqOkh8/4TQAAAACIKdGRhdLQsSPO9c7QsSjO9dEiIl7/rsi+1to/S3qnggXENkndkn6h\n4AjGJaHHlwAAAAAkQaLFQkboOBjn+uj57AT6O32NMesVnK+QLel+Sf8i6VUF5zJ8zxiTObm0AQAA\nAEwk0X0W+kPHeB/Ss0LH3gT690rOXg6PSKqQ9CZr7QujjYwx/6rgqkpfk/T3k8ocAAAAwLgSHVno\nVHB1oniPGRWFrnfGud4e0S6Wwoi+F0taKum+yEIh5LOhdh+fOGUAAAAAk5FQsWCtHZJ0WFJ1nCbV\nCq6UFG9OQl1EuyjGmIUKjizY0KlFoePeGHkMS9ovaR6PIgEAAADJkehjSJK0VdINxphaa2396Elj\nTKWkWkm/jtfRWttojGmUtNkY4xmzfOrloePoKMKJ0VuPvU/oEaVqSR2hAgYAZoVAIKDO3iGdbO1T\nU1uvunqHlJOVrvycDOXnZio/J0PlxTnKy8mY+GYAACRZMoqFeyTdIOmbxpgPWmsDxhiPpG+Frv9w\ngv73SvqipE9L+r4kGWMKQuf6Qtcl6VlJRyVdZ4z5gbX21Yh73Krgykw/SML7AYAp19zep7/sO6Vj\nzT0aGPKP29YjqbqySOtq56lyXp48Hs/0JAkAmPMSLhastU8YYx6Q9CFJLxhjnlJwfsGlkh6y1v5u\ntK0x5suSAtbayN2eb5f0QQVXM7pM0gFJ1yi48dot1trW0Ov4jDEfl/SopK3GmF9IOi7potDr7Zb0\nhUTfDwBMpZ6+Ib24u0n2cPvEjUMCkg4c79SB450qK8rWuhXzZJaWKM2bjH01AQCIL1l/aW5Q8Nv9\neQquSlQh6UuSPjKm3a2hH4e1tlvSZgWXRN0s6SZJbZKus9beOabtnyW9UcGC4UpJ/1vSAkn/JOli\na228idQA4Kohn18v7Tqhn/1xX9xCwevxqLQwW3nZ8b/Hae0c0JN/OaqHnqhXW9fAVKULAIAkyRMI\nBCZuleJCoyGXSXraWnt5km7Lv1gAuu/h38lbVKPfPHtAnT2nT6mqqsjX0gWFWlCWq3nFOUpPC36H\n4x8JqLd/WG2dA9p1oEWHm7pP65ue5tGl6xdpTXXptD+a1H5sj6679p3T+poAgJim9A9AMuYsAADi\naO0Z0TOv7Vf/oC/qfFlRti5ZV6nF8wti9kvzelSYl6nCvEwtqyxUR/egdjS0aN+hNg37RiRJPn9A\nT712VEdOduvy86qUncmvdABAcvGXBQCmyKt7T+rx3X75R8LncrPTdeE5C7RqWam8ZzAaUFyQpS0b\nFmnTynI99nKjjreE97psONapk+19evfm5SopyB7nLgAAnBlmxwHAFHjspcP62t0vRRUK84qz9cE3\nr9Sa6rIzKhQi5edm6j2X1ejCcxYo8hY9fcN65OkG5jEAAJKKYgEAkuxPLx3Wvz24TSMj4alLVRX5\net9lK5KyX4LX49H5q+fr/ZevUEFueB/KvgGfHnm6Qa2d/Qm/BgAAEsUCACTVzv0tuvPh7VHnVi4p\n0dWXViszIy2pr7WgLE8feHOtyorCjx71DwYLhpYOCgYAQOIoFgAgSU609Opb//Wy/BEjCqsWevWW\nCxZP2Z4IOVnpes+WGs0rDhcMA0N+PfJMg5rb+6bkNQEAcwfFAgAkQW//sL5294vq7ht2zl2yvlIb\nl3qnfFnT0YKhvCTHOTc45Nejzx1UT9/py7UCADBZFAsAkCD/SED/9N+v6sjJHufciqoi/d2HN07b\n/gfZmel6z+YazS/Ndc71Dfj0u+cPadjnn5YcAACph2IBABL000d36y/7TjlxaWGW/uETF077vgdZ\nmWl61+blKinMcs41d/TriVeOiA04AQBng2IBABKwva5Zjzzd4MSZ6V79wycuVFlRzji9pk5WRpqu\nurha2ZnhydQNxzr18u4mV/IBAMxuFAsAcJb6Bob1vQdfjzr3dx/epNrFJS5lFFSUn6V3XLQsai+H\nV/edUl1ju4tZAQBmI4oFADhLP/7VLjW3h5cofcsFS7R54yIXMwqrLM/XZZuic/nzq0dYUhUAcEYo\nFgDgLLy696Qee7nRiecVZevG95zrYkanW1Ndpg0ry53YPxLQH186zIRnAMCkUSwAwBnq6RvS9x/c\nFnXulg9tTMruzMl20dqFWjK/wIk7ugf1zLZjLmYEAJhNpnepDgCI47d/eFxdPbNjT4Dn631q6wqv\nLrRivld256uyO09vu3P3Xm1ZtGYas4vm9Xj05gsW6/7H6tQ/6JMk7TvUrsUVBVq5xN25FQCAmY9i\nAcCM0NUzpBIXP1RP1uETXTrUctCJC3Iz9aY3rlRmRlrM9v2vxaggplludobe+oYl+vXWA865p147\nqvmluSrKzxqnJwBgruMxJACYJP/IiJ7dfjzq3BXnL45bKMwki+cXaJOpcOJh34j+9NJh+UdGXMwK\nADDTUSwAwCTt2N+ijp5BJ15TXaqqinwXMzozbzhnQdQOz6fa+/XSLvZfAADER7EAAJPQNzCsV/ec\ndOLMDK/eeO5CFzM6c2lej668cIkyM8K/+rfVNauptdfFrAAAMxnFAgBMwou7mjTkCz+yc8HqBcrJ\nmn3TvgrzsnT5pionDkh64tUj8vl5HAkAcDqKBQCYwKn2Pu091ObEJQVZWrtinosZJWZFVbFqFhU5\ncUf3II8jAQBiolgAgHEEAgE9O2ZfgkvWVyrN63Epo8R5PB5t2bhI2Znhidnb6pt1ooXHkQAA0SgW\nAGAc9Uc6dKK1z4mXLSzU0gWFLmaUHLnZGbos4nEkSXri1UYN+3gcCQAQRrEAAHH4/SN6cdcJJ/Z6\nPLpkXaWLGSXXiqpiragKP47U2TMU9X4BAKBYAIA4dh9sU3ffsBOvq52n4oLU2sRsy8aqqInaO/a3\nsDoSAMBBsQAAMQz7RvSXfRFLpaZ7dd6qinF6zE45Wem6bNOiqHNPvXZU/pGASxkBAGYSigUAiGFn\nQ4v6BnxOvGFlhbIzZ99SqZNRs6hYyyNWR2rtHND2+mYXMwIAzBQUCwAwxuCwX6/ZU06cnZmm9bWz\nd6nUydi8YZEy0sN/El7Z06Su3iEXMwIAzAQUCwAwxvb6Zg0O+Z1406oKZWakjdNj9svPyYjakdrn\nD+jp148qEOBxJACYyygWACDCwKBP2+rCj+DkZqdrbU1qjyqMOremTBUluU7c2NSt/Uc7XcwIAOA2\nigUAiPCaPRW118D5q+crPW1u/Kr0ejy6/LwqeSL2m3t2+7GoURYAwNwyN/4CAsAk9A4Ma2dDixMX\n5GZqTXWpixlNv/LiHK2vLXfivgGfXmDvBQCYsygWACBkW12zfP7wM/oXrJmvNO/c+zX5hjXzVZCb\n4cS7D7Sy9wIAzFFz768gAMQwMOjTroZWJy7Kz5RZUuJiRu7JSE/Tlo1VUeee/At7LwDAXJTwouHG\nmHRJt0j6pKRlkk5I+omk26y1vnG6jvYvlfRVSVdLKpe0V9Lt1toHI9osk3RgEukss9Y2nuFbAABt\n398inz88V2GTqZDX6xmnR2pbtrBQNVVFaghNcG7rGtC2ulM6b9V8lzMDAEynZOwwdIeChcJWSY9I\nulTBD//rJX1gvI7GmDxJj4XaPiipUdK1ku43xpRba+8INW2X9BVJsb7WMpKuU7DIOBnjOgCMa2jY\nr537w3MV8nMyZJbOzVGFSJvXL9KRpm4NhSZ8v7LnpFZUFasoP8vlzAAA0yWhYsEYc7GChcJD1toP\nRZz/qaSPGmOustb+dpxbfEbSRkk3W2t/EOr7dUkvSPq2MeZBa22ztbZTwWJh7OunS3pRUr+ka621\ng4m8HwBz066GVg0Oh1f82WjK5+RchbHycjJ00dqFevr1Y5Ik/0hAz7x+TFdfWu1yZgCA6ZLoX8Ob\nQ8exH+Q/r+AowI0T9L9JUpOku0ZPWGt7JH1DUq6k6yfo//eSNkn6mrV27yRzBgCHzz+ibfXhfRVy\nstK1prrMxYxmlnOWl2l+acTeCye7VX+kw8WMAADTKdFiYYukZmvtnsiT1toTkupD12MyxtRIqpS0\n1Vo79vGipyLuH69/haTPSWqQ9M9nnDkASNpzsFX9g+HpVRtqy+fMvgqT4fF49KbzquSN2nvhuIZ8\nTHYGgLngrP8iGmOyJC1S8MN6LIcklRhj4n1FVxM6ntbfWtskaVDSynFS+AdJeZK+NJmJ1AAwln9k\nRK/b8KhCVkaazq1hVGGssqIcbVgZ3nuhf9Cn7Y0j4/QAAKSKRL4+G92pKN54dGfoWBTn+uhf5Hj9\nu+L1NcYUSfqEpIOSHhg/TQCIzR5uV0//sBOvXTFPmRlpLmY0c52/eoEKcjOduP7kiOoa213MCAAw\nHRIpFkZ37Ik3qXj0fHYC/eP1/YSCcxr+JcYjTAAwoUAgoNfrwqMK6WlerV8xz8WMZraMdK+2bFgU\nde4HP9/O3gsAkOISKRb6Q8fMONdH19aLt+3nZPrH6/tRSQOS/mu8BAEgnoMnutTRHf6u4tzlZcrO\nSsZq0qlrWWWhqisLnXj/0U79/vmDLmYEAJhqiRQLnQqueBTvMaOi0PXOONfbI9rFUhirrzFmsYL7\nMvwptHISAJyxbfaU889ej7SullGFydi8YVHUBPB7f79XbV0DLmYEAJhKZ10sWGuHJB2WFG/B7WoF\nV0qKNyehLqJdFGPMQgVHFmyMfu8MHR+efLYAENbU2qsTrX1OXLu4JOp5fMRXkJupC9aEd3HuG/Dp\nP3+9y8WMAABTKdH1AbdKWmiMqY08aYyplFSr4IZpMVlrGxXcsXmzMcYz5vLloeMLMbq+MXR89mwS\nBoDIuQqSolb6wcTW15arKCccP/P6MW2rOxW/AwBg1kq0WLgndPzm6Af+0PFbofM/nKD/vZKqJH16\n9IQxpkDSFyX1ha6PtVFSh7X20NmnDWCu6uge1IFj4Sccl8wv0LzinHF6YKw0r0cXLI9eNequX+zQ\nsM8fpwcAYLZKqFiw1j6h4NKl10h6wRhzm6SnJd0g6SFr7e9G2xpjvmyM+ccxt7hdwc3bvmeMedgY\nc7ukbZJWS/qstbY1xsvWSDqeSN4A5q7I3ZolRhXOVkWhV2+5YIkTH2vu1c+f3O9iRgCAqZCMbUpv\nkHSrpHmSPiOpQtKXJH1kTLtbQz8Oa223pM2S7g4db5LUJuk6a+2dY1/IGJOp4EZs8SZNA0BcfQPD\n2neozYnnFeeoqiLfxYxmt49fvUYFuRlO/ODjdTrREm8ROwDAbJTwOoGh3ZO/HvoZr13MwsRae0rS\njZN8rSElp8ABMAftbGiN2hdg48pyeTxjp0xhsorys/Sxq87Rvz+0TZI07BvRXb/coS/f+Eb+vQJA\niuCDN4A5Ydg3ol0NLU5ckJuhFVXFLmaUGt76hiVatbTEiV/bd0rP7zjhYkYAgGSiWAAwJ9Q1tmtg\nKDwBd31tubxevv1OlNfr0U3Xro/6d/mjX+1U38Cwi1kBAJKFYgFAygsEAtoeMbE5M92r1ctKXcwo\ntVRXFundm5c7cWvngP7nj7G2yQEAzDYUCwBS3pGT3WrvHnTiNdVlysxIG6cHztR1VxqVFWU78W+e\nPaCDx1mLAgBmO4oFAClve314roJH0toV89xLJkXlZmfok+9d68QjIwHd+fB2jURMKAcAzD4UCwBS\nWlvXgBpPdjvx8kVFKszLdDGj1HXx2oU6b1WFE+873K7HXj7sYkYAgERRLABIaTv2t0TF62vZhG2q\neDwefer965SZHv7T8tNH96izZ3CcXgCAmYxiAUDKGhj0yR4Ob8JWUZKjBWW5LmaU+haU5emDb1np\nxD39w/rJo7tdzAgAkAiKBQApa/fBVvn84Wfm19eyCdt0eP+bVmhReXhn7CdeOaLdB1pdzAgAcLYo\nFgCkJP9IQDsbwh9Q87LTVVNV5GJGc0dGepr+9pp1Uefu/Pl2+fwjLmUEADhbFAsAUlLD0Q719oc3\nBju3Zp7SvPzKmy7ra8t12cYqJ25s6tYjTze4mBEA4GzwlxNAyhm7CVua16Nzl5e5mNHc9NfvPkd5\n2elOfN+frJpae13MCABwpigWAKScptY+nWrvd2KztETZWenj9MBUKCnM1kevWuPEQ8N+3fWLHQoE\n2HsBAGYLigUAKWf7/uaomOVS3fP2Ny6TWVLixH/Zd0rP7TjuYkYAgDNBsQAgpXT1DunA0U4nXjK/\nQKWF2S5mNLd5vR7d/IH18nrDq1D96JGdUfNJAAAzF8UCgJSys6FFkQ+5rKud51ouCKquLNK7Ny93\n4rauQf337/e6mBEAYLIoFgCkjCGfX3sOhpdLLSnI0pL5BS5mhFHXv22V5hXnOPFvnz+ousZ2FzMC\nAEwGxQKAlGEPtWtoOLyW/zo2YZsxcrLS9an3rXXiQEC64+Ht8rP3AgDMaBQLAFJCIBCImticlZEW\nNbEW7rvw3IV647kLnPjAsU49+txBFzMCAEyEYgFASjh8oludPUNOfM7yMmWk8ytupvmb965TTlaa\nE//37/eqOWKZWwDAzMJfUgApIXJUweuR1tawCdtMVF6So796+2onHhjy64eP7HAxIwDAeCgWAMx6\nLR39Onqqx4lrqoqVn5vpYkYYz9WXVGv5oiInfnFXk17adcLFjAAA8VAsAJj1duxviYrZhG1mS0vz\n6uZr1yty7vldv9yp/kGfe0kBAGKiWAAwq/UNDEctwbmgNFfzS3NdzAiTsXJJia66uNqJWzr69T9/\n3OdiRgCAWCgWAMxquw+0yj8S3oZt/UpGFWaLj7xjtUoLs5z411sP6ODxznF6AACmG8UCgFnL7x/R\nzobwJmz5ORlaXlk0Tg/MJHk5Gfrke8N7L4yMBHTHQ9ujij8AgLsoFgDMWvVHO6Kec1+7Yp68XjZh\nm00uWVep81ZVOLFtbNcfXjjkWj4AgGgUCwBmpUAgoO314eVS09O8WlNd6mJGOBsej0efev86ZWaE\n916453d71NY14GJWAIBRFAsAZqXjLb1q6Qh/oFy9rETZmekuZoSztaAsT9ddaZy4b8CnH/9ql4sZ\nAQBGUSwAmJUiRxUkad0KJjbPZu+9rEZLFxQ48dZtx/TavlMuZgQAkCgWAMxCnT2DOni8y4mXLSxU\ncUHWOD0w06WneXXztRuizv3gF9s1OOx3KSMAgESxAGAWGrsJ27oV81zKBMm0urpUb3vjUiduau3T\nA49ZFzMCAFAsAJhVBof82nuozYnLirJVVZHvYkZIpo9dtUZF+ZlO/Mun9utwU9c4PQAAUykpswGN\nMemSbpH0SUnLJJ2Q9BNJt1lrfeN0He1fKumrkq6WVC5pr6TbrbUPxmibJumm0GutkNQs6c+Svmit\nPZ6M9wNg5tpzsFXDvhEnXl9bLo+H5VJTRUFupv763efqX/7nNUmSzx/QnQ9v17duupRlcQHABcka\nWbhD0ncU/OD+XUnHFPzwf99EHY0xeZIek/QpSc9L+r6kYkn3G2NujtHlvyR9T9JgqO12SR+T9Jwx\npiThdwJgxhoZCUQ9gpSTla6Vi4tdzAhT4fJNVVpfG360bM/BNj3+SqOLGQHA3JVwsWCMuVjBb/kf\nstZeZq39grV2i6R7JF1jjLlqglt8RtJGSbdYa6+31n5O0gZJuyV92xjjLHFijLlW0vWS7rPWXmCt\n/Xtr7bsl/T9JSyX970TfD4CZq+FYh3r6h514bU2Z0tJ4mjLVeDwe3XTNemWkh/+3/emju9XZM+hi\nVgAwNyXjr+zot/9fGXP+85ICkm6coP9Nkpok3TV6wlrbI+kbknIVLA5G3SKpK+I1R/1A0r0KPv4E\nIAUFAgFtqwuPKqR5PTq3honNqaqyPF8fePNKJ+7uG9bdv9ntYkYAMDclo1jYIqnZWrsn8qS19oSk\n+tD1mIwxNZIqJW211gbGXH4q4v4yxuRLulTSk9bajjGv1Wet/Zi19oeJvBEAM1dTa59Otfc5sVla\nopwsNmFLZddesUKLysOT1//86hHt2N88Tg8AQLIlVCwYY7IkLZLUEKfJIUklxpiyONdrQsfT+ltr\nmxSclzD61dIaSR5Ju40xbzTGPGaM6TbGNBtjfmyM4StGIIWN3YRtfS2bsKW6jPQ03XTtuqhzdz68\nI2qCOwBgaiU6slAaOnbEud4ZOhbFuT5aRMTr3xXRtzJ0PE/SM5IyJP2HpD2SPiHpWWNM4SRyBjDL\ndPUO6sCxTideMr9ApYXZLmaE6bJuRbmuOH+xEx9r7tGvnon3/RQAINkSLRYyQsd4s85Gz8f7qz6Z\n/qN980LHtym4JOvl1tr/a629TNK/KDgC8eXJJA1gdtlR36LI5xQZVZhbPvGuc5SXk+HE9z9m1dze\n72JGADB3JFosjP62zoxzPSt07E2g/2jf0XHnkzp9MvWtkvokfSBupgBmpcFhv/ZEbMJWWpitxfPZ\nhG0uKcpZGL64AAAgAElEQVTP0g3vWO3Eg0N+/eevd7mYEQDMHYkWC50KrngU7zGjotD1zjjX2yPa\nxVIY0Xf0uNNa649sZK3tU3AydaUxJl7hAWAW2nuwbcwmbPPYhG0OevtFy7R8UfhPxXM7jut1e8rF\njABgbkioWLDWDkk6LKk6TpNqBVdKijcnoS6iXRRjzEIFRxZs6FR96BivGMiQ5AvlBCAFBDdhC09s\nzslK18ol7L04F6V5Pfrb90dPdv6PX+7QsM8fpwcAIBmSsXTqVkkLjTG1kSeNMZWSaiW9GK+jtbZR\nUqOkzcaYsV8VXh46vhA6Nii4H8MFoWVUI1+rWMGVlViEG0ghB453qrsvvAnbuTVlSmcTtjlr1bJS\nvfUNS5z4WHOvHnmayc4AMJWS8Vf3ntDxm6Mf+EPHb4XOT7T3wb2SqiR9evSEMaZA0hcVnIdwryRZ\na0ck/UhSjqTvjLnHNxUchbj7rN8FgBlne114VCHN69G5y+Otwoy54mNXrYma7PzA43VMdgaAKZRw\nsWCtfULSA5KukfSCMeY2SU9LukHSQ9ba3422NcZ82Rjzj2NucbuCjxh9zxjzsDHmdknbJK2W9Flr\nbWtE228pOFLxSWPMC8aYfzLGbJX0KQVHOO5M9P0AmBmaWnvV1BbehG3lkhLlZmeM0wNzQazJzj99\nlEFlAJgqydr+9AYFHwH6uKTPKDiP4UsKFgKRblVwwrOzmpG1ttsYs1nB0YF3SXq7pL2SPm+tfTCy\ns7V2wBjzZkmfk3S9pFskHQv1/Vpo9AFIWUeOHtPuvfUTN5yFunu6FTkbYdtpm7Cx7yKC3n7RMv3p\nxcM6cDy47sUz247pnZdU6xxGngAg6ZJSLFhrfZK+HvoZr13MkQxr7SlJN07ytfol/WPoB5hTjh9v\nUpe/SHl5BW6nklRtbc3q7Q2PInT1DunA0fAiaovn56usKMeN1BDH9h07XX395SUjOnA8HH/7p8/q\nbWvT5U3CSlmF+Zm66u1vSfg+AJAKkjWyAGCapKWlKS09tf7TTfOmRcU79kdvwraBTdhmnEGfVLJo\njWuvX7JIOtR5WPuPBhfba++VmoYWJGV0of3YnoTvAQCpgmVFAMwoQ8N+7T0YnqpUUpilxfNTayQF\nyXHxuoVKTwuPJLy0u0mDQyylCgDJRLEAYEbZfbBVQxGbsG2oLWcTNsRUkJupjabCifsHfXplb5OL\nGQFA6qFYADBj+EdGtL2+xYnZhA0T2biyQvm54VWydu5vUXvXgIsZAUBqoVgAMGPUH+lQb394E7Z1\nK+axCRvGlZHu1SXrKp14JCA9u+P4OD0AAGeCv8IAZoRAQHrdhpdLTU/z6twalsLExGoWFalyXp4T\nNzZ168jJbhczAoDUQbEAYEZo7c9QW8TjI2uqS5WdmVqrPmFqeDweXbq+MurcczuOayQQiNMDADBZ\nFAsAZoTGjmznnz0eaT3LpeIMlJfkatXS8PyW1s4B7TvU5mJGAJAaKBYAuK6la0gdA+FJqrWLi1WY\nl+liRpiNLjz39KVUh3wspQoAiaBYAOC6fY29UfHGlRVxWgLx5edkaEPE/3f6BnzaFjEPBgBw5igW\nALiqo3tQR1rCcxWWzC/QvOIcFzPCbLZxZblyssJzXV6va45aYQsAcGYoFgC4alt99De/Gw1zFXD2\nMjPSdOE5C5zY5x/RS7vZqA0AzhbFAgDX9A0MR01CLS/J0aLyfBczQipYXV2q0sLwhPm9h9rU0tHv\nYkYAMHtRLABwzY79LfKPhJe33LiyQh6PZ5wewMS8Ho8uXrcw6tyLu064lA0AzG4UCwBcMeTza1dD\nqxNnp/tVs6jIxYyQSpYuKFRVRXiU6nBTt44397iYEQDMThQLAFyx92CbBofDy1ouKRqQ18uoApLn\nonOjRxde2HlCATZqA4AzQrEAYNr5RwLaVhee2JyZ7tHCgkEXM0IqqijNVU1VeLSqqa1PB090uZgR\nAMw+FAsApt3+ox3qiVjOcmVVntL4bYQp8MZzFipyGsyLu05ohNEFAJg0/jwDmFaBQECv21NOnJ7m\n0crKPBczQiorLsjSmuoyJ27vGpQ93O5iRgAwu1AsAJhWR052q7UzvAnb6mWlysrkVxGmzgWr5ys9\nLTy88PLuJvn8Iy5mBACzB3+hAUyr12x4roJH0oaVbMKGqZWXk6F1K8L/P+vpH9bOhhYXMwKA2YNi\nAcC0OdXWp2MRy1euWFyswrwsFzPCXLHJVCgrI82JX9t3SkM+/zg9AAASxQKAafR63amoeCOjCpgm\nWZlp2rSqwokHhvzaUc/oAgBMhGIBwLTo7BlUw9FOJ66qyFd5Sa6LGWGuWVtTppysdCfeVteswSFG\nFwBgPBQLAKbFtvpmRS5YuXFlRdy2wFTISE/TeRGjC4PDfm2rbx6nBwCAYgHAlOsf9GnvwTYnLivK\n1uL5+S5mhLnqnOVlysvJcOLt9c0aGPS5mBEAzGwUCwCm3I79LfKPhMcVNpkKeSJ3ygKmSXqaV+dH\njC4M+0ZOm0sDAAijWAAwpYZ9/qhlKgtyM1RTVexiRpjrVleXqiA304l37G9V38DwOD0AYO6iWAAw\npfYeaouaRLq+tlxpXkYV4J40r1cXrJnvxD7/iF6zjC4AQCwUCwCmzMhIQNvqwhNIszLTtKa61MWM\ngCCzpERF+eHRhV0NrertZ3QBAMaiWAAwZfYf7VB3X/gD2NqaecpITxunBzA9vF6PLlizwIn9IwG9\nXsfKSAAwFsUCgCkRCER/+ErzerS2pszFjIBotYuLVZwf3kF894EW5i4AwBgUCwCmxNFTPWrp6Hfi\n1ctKlZudMU4PYHp5PR6dtzq8MpLPH9B29l0AgCgUCwCmxOsRE0Y9ktavLHcvGSCOlYtLVJgXnruw\ns6FVg8OBcXoAwNySPnGT8Rlj0iXdIumTkpZJOiHpJ5Jus9ZOuNONMaZU0lclXS2pXNJeSbdbax+M\n0fZeSX8V51bfttZ+/mzeA4Dkam7v05FTPU68vKoo6nEPYKbwej06b1WFnvzLUUnBfRf2nXA5KQCY\nQRIuFiTdoWChsFXSI5IuVfDD/3pJHxivozEmT9JjobYPSmqUdK2k+40x5dbaO8Z0WS+pSdJdMW73\nbALvAUASjZ0ounFlRZyWgPvM0hK9svekekKT8euaRtTTP6z8HB6bA4CEigVjzMUKFgoPWWs/FHH+\np5I+aoy5ylr723Fu8RlJGyXdbK39Qajv1yW9IOnbxpgHrbXNofMZklZJ+rW19quJ5A1g6nT1Dmr/\n0Q4nXlSep/mluS5mBIwvzevVeaZCT79+TJI07JceffaAPvxW43JmAOC+ROcs3Bw6fmXM+c9LCki6\ncYL+N2nMSIG1tkfSNyTlSro+ou1qBYubHQnkC2CKbatvUSDikW9GFTAbrFpWqrzs8Pdnv3q6gZWR\nAECJFwtbJDVba/dEnrTWnpBUH7oekzGmRlKlpK3W2rGzyZ6KuP+odaEjxQIwQ/UP+rT3YKsTlxZm\na8mCAhczAiYnPc2rjSZc2Pb0D+u3zx10MSMAmBnOulgwxmRJWiSpIU6TQ5JKjDHxFlavCR1P62+t\nbZI0KGllxOnRYmGVMeY5Y0y3MeakMeZuY8zCM34DAJJuV0OLfP5w7b/JlMvj8biYETB55ywvU05W\neHThkacbNDA44TodAJDSEhlZKA0dO+Jc7wwdi+JcHy0i4vXvGtN3tFj4koIFxl2S6iR9XNLLxphF\nE+QLYAoN+0a0Y3+LE+fnZGjF4hIXMwLOTHqaVxsjlvjt6h3SH1485F5CADADJFIsjC4TMRjn+uj5\n7AT6R/btU7A4ON9a+1Fr7f+z1m6W9A8KjnD826SyBjAl9h1u08CQ34nX15YrzcuoAmaXc2rKFDG4\noJ8/uV+Dw/74HQAgxSVSLIxuzZoZ5/roouq9CfR3+lpr32+tXWWt3Tum3bcUfOTpamMMS64ALhgZ\nCWhbxHKpWRlpWlNdOk4PYGbKTE/Tqsrwn8aO7kH96cXDLmYEAO5KpFjoVHDFo3iPGRWFrnfGud4e\n0S6WwnH6OkKTo7crOFJRNVF7AMnXcKxTXb1DTnxuTZkyM9JczAg4eyvne6P2WPj5k/Ua9jG6AGBu\nOutiwVo7JOmwpOo4TaoVXCkp3pyEuoh2UUITlrMk2VCcbYy5wBizNs69ckLHgcnkDiB5AoGAXq87\n5cRpXo/WrpjnYkZAYjLSPXr35uVO3No5oMdfbnQxIwBwT6JLp26VtNAYUxt50hhTKalW0ovxOlpr\nGxXcsXmzMWbsg82Xh44vhI6LJL0k6b/H3if06NEmSadC9wQwjY4196q5vd+JzdIS5WWz8y1mt3dt\nXq7ciH0XHv5zvXz+ERczAgB3JFos3BM6fnP0A3/o+K3Q+R9O0P9eBR8d+vToCWNMgaQvKjih+V5J\nstY2KPio0VpjzPURbT2SbpM0T9IPEnwvAM7C6/ZUVMwmbEgF+bmZuvrS8OjCqfZ+PfnqERczAgB3\nJFQsWGufkPSApGskvWCMuU3S05JukPSQtfZ3o22NMV82xvzjmFvcruDmbd8zxjxsjLld0jYFd2v+\nrLW2NaLtpxScFH2vMebnxph/kfSygoXG05K+mch7AXDmWjr61Xiy24mXVxapuCBrnB7A7PHuzcuV\nnRmee/PQE/XyM7oAYI5JdGRBChYGtyr47f5nJFUouBfCR8a0uzX047DWdkvaLOnu0PEmSW2SrrPW\n3jmm7UuS3iDpF5IuC7XNU3Dp1CuttcNJeC8AzsDrESsgSdJGUx6nJTD7FOVn6apLwtPqTrT26rkd\nx13MCACmX/rETcZnrfVJ+nroZ7x2MQsTa+0pSTdO8rV2S/rAmeYIIPm6+4ZUf6TdiRfOy9OCsjwX\nMwKS7z2X1eg3Ww9oyBccUXjoiXpt3rCInckBzBnJGFkAMAdtr29RIBCONzFXASmopCBbb71wqRMf\nOtGlV/aedDEjAJheFAsAztjQsF97DoanFJUUZGnpwgIXMwKmzvsvXxG1G/mDj9cpEFkpA0AKo1gA\ncMb2HGzTsC880XPDynIey0DKqijN1WWbwnt+2sPt2tXQOk4PAEgdFAsAzsjISEDb94cnNudkpWvl\nkhIXMwKm3rVX1CqyHn7wibr4jQEghVAsADgjDcc61NMXXnzs3OVlSk/jVwlS2+L5Bbpo7UIn3lbX\nrLrG9nF6AEBq4C88gEkLBALaFrFcaprXo3NrylzMCJg+H7hiZVT88J/rXcoEAKYPxQKASTvR2qtT\n7f1ObJaWKDc7w8WMgOmzYnGxNpnwql8v7DyhxqYuFzMCgKlHsQBg0rbXtUTF62vZhA1zywfeXBsV\nM7oAINVRLACYlI6eQR043unESxYUqLQw28WMgOl3zvIyrV5W6sRPv35MTa29LmYEAFOLYgHApOyo\nb46KNzCqgDnI4/Hog28Jz10YGQnoF0/tdzEjAJhaFAsAJjQw5NPeQ+GVX8qKslVVke9iRoB7zltV\noerKQid+/OVGtXUNuJgRAEwdigUAE9p9oFU+P5uwAVJwdOEDbw6PLgz7RvSrpxtczAgApg7FAoBx\n+UdGtHN/eGJzbna6ahcXu5gR4L6L11Wqcl6eE//+hYPq7htyMSMAmBoUCwDGtf9Ih3oHfE68bsU8\npXn51YG5Lc3r0bVXhFdG6h/069FnD7qYEQBMDf7iA4grEAhoW8TE5vQ0r85ZziZsgCRdft5izSsK\nrwj2m60N6h/0jdMDAGYfigUAcR1r7lFLR3ji5uplJcrOTHcxI2DmyEj36n1vWuHE3X3D+uOLh9xL\nCACmAMUCgLi21UUvl7qO5VKBKFdeuFSFeZlO/Mun9mvY53cxIwBILooFADG1dQ3ocFO3E1dXFqo4\nP8vFjICZJzszXe/ZUuPEbV2DeuKVIy5mBADJRbEAIKbtbMIGTMo7L6lWbnb48byfP1kvf8RSwwAw\nm1EsADhN/6BP9nB4E7aKkhwtjFgmEkBYfk6Grrqk2ombWvu0dftxFzMCgOShWABwmt0HWuUfCTgx\nm7AB43v35hplpof/pD78RJ1GIv4bAoDZimIBQBT/SEC7DrQ6cV5OhpYvYhM2YDzFBVm68o1Lnfhw\nU7de2dPkYkYAkBwUCwCiHDzeqd7+YSc+d3mZ0ryMKgATed/lK6L+W3nwiToFAowuAJjdKBYARNlR\n3+L8c5rXwyZswCRVlOTqTectduK6xg7t2N8yTg8AmPkoFgA4mtv7dKK114lrFxcrJ4tN2IDJuuaK\nFYqc3vPQE3XuJQMASUCxAMCxoyH6W9B1K+a5lAkwO1VVFOjidZVOvL2+RfsOt7mYEQAkhmIBgKTg\ncqn1jR1OvLAsT+UluS5mBMxOH7iiNip+4DFGFwDMXhQLACSdvlzqWkYVgLNSU1WsC9bMd+JX955U\n/ZH2cXoAwMxFsQAgznKpRS5mBMxuH36riYrv/xOjCwBmJ4oFACyXCiTZyiUl2rSqwolf3tOkhqMd\n4/QAgJmJYgGAdu5nuVQg2a4bM7rwwOOMLgCYfSgWgDmutbNfx1tYLhVItlXLSrVhZbkTv7DzhA4e\n73QxIwA4c3wiAOa4yLkKknRuDRObgWT58FuNttU1O/EDj9Xpcx+7wJVcfvuHx9XVM+TKa0+lwvxM\nXfX2t7idBpCyklIsGGPSJd0i6ZOSlkk6Ieknkm6z1vom0b9U0lclXS2pXNJeSbdbax+cRN+HJb1f\n0jJrbePZvgdgLhoa9sseDq/SUlGSo/mlLJcKJMs5y8u0bsU8Zyfn53Yc1+ETXVq6sHDac+nqGVLJ\nojXT/rpTrf3YHrdTAFJash5DukPSdyQ1S/qupGMKfvi/b6KOxpg8SY9J+pSk5yV9X1KxpPuNMTdP\n0PcaBQuFwHjtAMRW19iuYd+IEzOqACTf2JWR7nvMupQJAJy5hIsFY8zFCo4oPGStvcxa+wVr7RZJ\n90i6xhhz1QS3+IykjZJusdZeb639nKQNknZL+rYxpjxWp9BoxB2J5g/MVYFAQDsbwo8gZWWkaUVV\nsYsZAanp3JqyqEUDntt+nLkLAGaNZIwsjH77/5Ux5z+v4Df+N07Q/yZJTZLuGj1hre2R9A1JuZKu\nj9PvXyVlSHpREms8AmfoREuv2roGnHjVslJlpLPmAZBsHo9Hf/X2VVHnfvaHfS5lAwBnJhmfDLZI\narbWRj00aK09Iak+dD0mY0yNpEpJW621Yx8leiri/mP7vV3SDZL+j6RTZ505MIedNrGZ5VKBKbO2\nZp421IYHyl/a3aS6RnZ1BjDzJVQsGGOyJC2S1BCnySFJJcaYeJ9CakLH0/pba5skDUpaOeY1CyT9\nUNJj1tp7ziJtYM7rHRhWw9HwYxCL5+eruCDLxYyA1PdX72B0AcDsk+jIQmnoGG9bytFPI0Vxro8W\nEfH6d8Xoe7ukEkn/azIJAjjd3oNtGgmEB/OY2AxMvVVLS3X+6vlO/Jo9pd1jRvgAYKZJtFjICB0H\n41wfPZ+dQH+nrzHmMkl/I+lWa+2hyacJYNRIIBD1ASU/J0PLFkz/Mo7AXDR27sK9v9+rQIAF/QDM\nXIkWC/2hY2ac66PPNfTGuT6Z/r2SZIzJkfRjSa8quDwrgLPQeKJbPf3DTnzO8jJ5vawRAEyHFVXF\nunjdQifefaBV2+ubx+kBAO5KtFjoVHDFo3iPGRWFrsdbI649ol0shRF9vyZpqaQbY0yGllgRCZiU\n3QfDowpej7R6Wek4rQEk2/VvWyVPxF+s//79PkYXAMxYCRUL1tohSYclVcdpUq3gSknx5iTURbSL\nYoxZqODIwujuNdcquOP0dmPMyOiPpHeHrh8MxQDi6O4b0uETXU68rLJIeTkZ4/QAkGxLFxTqso1V\nTmwb2/XCzhMuZgQA8SVj6dStkhYaY2ojTxpjKiXVKrgPQkzW2kZJjZI2G2PGjgxcHjq+EDr+q6Qv\nx/gZLSa+G4oBxLH3UFvUdufnVLNcKuCG695mlBbx+N9//XaPfH6+7wIw86Qn4R73KLjnwTeNMR+0\n1gZCH/y/Fbr+wwn63yvpi5I+Len7krM86hcl9YWuy1r7vVidjTGbJBlJ3w0VHwBiGBkJaO/BNicu\nyM3U4vn5LmYEzF2V8/L1jouW6dHnDkqSjrf06o8vHtZVl8QbqAcAdyQ8smCtfULSA5KukfSCMeY2\nSU8rWEA8ZK393WhbY8yXjTH/OOYWtyu4edv3jDEPG2Nul7RN0mpJn7XWsq4ckASNTdETm9dUl8rj\nYaoP4JYPvdUoJyv8nd19f9qnvoHhcXoAwPRLxmNIUrAwuFXSPEmfkVQh6UuSPjKm3a2hH4e1tlvS\nZkl3h443SWqTdJ219s5JvHYg9ANgHKdNbK5mYjPgpuKCLF1zxQon7uwZ0i+e3O9iRgBwumQ8hiRr\nrU/S10M/47WLWZxYa09JuvEsX/t9Z9MPmEtiTmzOZmIz4Lb3bKnR7547pLauAUnSL59u0DsuXqay\nohyXMwOAoGSNLACYwZjYDMxM2ZnpURu1DQ379T9/tOP0AIDpRbEApDgmNgMz25vPX6wlCwqc+PGX\nD0eNBAKAmygWgBTHxGZgZktL8+rjV61x4pGA9KNf7WSjNgAzAsUCkOL2HGJiMzDTnb96vtatmOfE\n2+tb9DwbtQGYASgWgBTWOzCsQxGPMyxdWMjEZmAG8ng8+pv3rpU3YqO2H/9qlwaGfC5mBQAUC0BK\ns4faFfkkwxomNgMz1tKFhbr60vCmbC0d/Xr4iXoXMwIAigUgZQUCAe09FJ7YnJedriXzC8bpAcBt\n11+5SsX5WU78i6f260RLr4sZAZjrKBaAFHWipVcdPYNOvGpZadQjDgBmnrycDH0sYrLzsG9EP/7V\nLhczAjDXUSwAKSpyVEGSVi9jYjMwG1xx/mKZpSVO/PKeJr2696SLGQGYyygWgBQ0NOzX/qOdTryo\nPE9FEY82AJi5vF6P/tf71ipyheP/+OUOJjsDcAXFApCC6o90yOcfceLVy5jYDMwmtYtLdOWFS524\nqbVPP/vDPhczAjBXUSwAKWhPxI7NmRle1VQVuZgNgLPxsavWRE12/vUzDbKH28bpAQDJR7EApJjW\nzn6dau9z4pVLSpSexn/qwGxTkJupT71/nROPBKTvPbBNwz6/i1kBmGv4BAGkmL0Ho795XMPEZmDW\numR9pS5au9CJj5zs1oOPs/cCgOlDsQCkEP/IiGxjuxPPK85WeUmuixkBSNTfvn+d8nPCO68/9ESd\nDh7vHKcHACQPxQKQQg6d6NLAUPgRBZZLBWa/ksJs3fiec53YPxLQvz3wuvwRixgAwFShWABSyL5D\n4VEFr9ejlYtLxmkNYLa44vzF2rSqwon3H+3U/Y/VuZgRgLmCYgFIEb0Dwzrc1OXE1QsLlZ2V7mJG\nAJLF4/Ho5mvXKycrzTn34ONWOxtaXMwKwFxAsQCkiLrD7QoEwvEqHkECUkpFSe5pqyN952d/UVfv\nkItZAUh1FAtACggEAtp3OPwIUm52upbML3AxIwBT4U3nLdblm6qcuLVzQP/2wOsKRH5TAABJRLEA\npIBT7f1q6xpwYrOkRF6vx8WMAEwFj8ejv71mnRaW5TnnXtrdpN8+d9DFrACkMooFIAXsG7OrK48g\nAakrNztD/++G85QW8YXA3b/ZzXKqAKYExQIwy/n8I6pv7HDi+aW5Ki3MdjEjAFOtdnGJPvrONU48\n7BvRN37ysjp7Bl3MCkAqolgAZrmDxzs1OBzeW2HVUpZLBeaC915Wo00mvJzqybY+3XbPKxr2sf8C\ngOShWABmuci9FdK8HtWytwIwJ3i9Hv1/12/S/NLwLu27Glr1H7/cwYRnAEnDIuzALNbTP6wjJ7ud\nePmiImVlpo3TA8BEtu/Y6XYKZ+S8xQH9qUMaHVD444uH1dJ0RGZh9O+Cnbv3asuiNTHuAADxUSwA\ns1jd4XZFfn+4aikTm4FEDfqkkln0obpE0pV5nfrd84ecc68dGlFl1TItWRBeQrn/tdlVBAGYGXgM\nCZilAoGA9jWGV0HKy8lQ1fx8FzMC4JbqyiJdtHahEwck/fHFQzrV3udeUgBSAsUCMEu1dPSrvSu8\n8olZUiyvh70VgLlq48pymSXhOUtDvhH9ZusBtXYOjNMLAMZHsQDMUpE7NkvSyiU8ggTMZR6PR5ef\nV6WF88Ibtg0M+fXrrQ3qYElVAGeJYgGYhfwjAdUfCe+tUF6co7Ii9lYA5rr0NK+uuqRaFSU5zrm+\nAZ9+9UyDhgNMUwRw5igWgFnoyMlu9Q/6nNiwtwKAkKyMNL3r0uVRmzP29A3r+PAS9fYPu5gZgNmI\nYgGYhezh8MRmj0eqXVzsYjYAZprsrHS9e8tyFeVnOueGA5n6+ZP16ujmkSQAk0exAMwyg8N+HTze\n5cRL5hcoNzvDxYwAzER52Rl6z5YaFeSGfz909w3r50/W62QbqyQBmJyEH2A0xqRLukXSJyUtk3RC\n0k8k3Wat9Y3TdbR/qaSvSrpaUrmkvZJut9Y+GKPtEklfk/QmSWWS9km601r7n4m+D2C2ONzUJ/9I\neHcFw94KAOIoyM3Ue7bU6NdbD6ird0hScNLzI0836B0XRe/DAACxJGNk4Q5J35HULOm7ko4p+OH/\nvok6GmPyJD0m6VOSnpf0fUnFku43xtw8pm2VpJclXSdpa+h1MyT9yBjz70l4H8Cs0HA8vGNzZrpX\n1ZWFLmYDYKYrys/SNW9aoSxPeAlVn39Ev33ugPZFPNIIALEkVCwYYy5WcEThIWvtZdbaL1hrt0i6\nR9I1xpirJrjFZyRtlHSLtfZ6a+3nJG2QtFvSt40x5RFtb5dUIen91tq/stZ+NtT3WUk3GWPOSeS9\nALNBe49PJ9vDzxuvWFys9DSeJgQwvtzsDC3KaFRVRXjjxpGA9MQrR7R127Go0UoAiJTop4zRb/+/\nMub85xXcQPLGCfrfJKlJ0l2jJ6y1PZK+ISlX0vWSZIzxSKqU9Iq19tGItn5JD4fCC8/uLQCzx67D\n0cbOBh8AACAASURBVM8ZR27ABADj8XpGdPUl1VpRFb0gwo79LfrV/9/encdHVZ79H/9MNhJ2CGFf\nRXKzyI4KKJv71tallmpr9elifVxqW/t0fbTaxe33tNVardrNarUqaq11X3ABBQEVQcCLfQ8QtoQl\ne+b3xzmZTOJMEsgkJ5l8368Xr8N9n/tMrhlmyLnm3t5ep5WSRCSmxiYL04F8M1sZXWlmecAa/3xM\nzrmheAnAPDOr/ZXGW1GPj5mFzWymmcVKCIb7x51HHr5I6xEOh1m2sTpZ6NQ+o8bmSyIi9UlNTeGM\nEwcyoUbHPeTtOcSTb6wmb/ehgCITkZbqqJMF51w7oB+wLk6TjUA351x2nPND/eNnrjezHUAJkBvn\nZ4ecc/2dczcCVwIfAi81PHqR1sc27WPfwZp7K4RCoQAjEpHWKBQKMWV0X86aPIj0tOrbgMPF5Tz7\n9loWr9xBpYYliYivMT0LVUuw7I9zvsA/dolzviqJiHd9YR3XPgxsxhv+tBo4x8wq44cq0vrN/WBL\njbKGIIlIYwzt35UvnjKMbp3aReoqw7Bo5U6eeWut9mMQEaBxyULVws3x/jepqs+Mc74h18e79kPg\nDuBVvGFI7zrnBsUPVaR1KyuvYN5H2yLlXt3b0zXqF7yIyNHo3jmTL54yjKH9an43t3PvYZ543fhk\n3W7CYfUyiLRljdlnocg/ZsQ5X3UnE28AZEOuj3mtmf2u6u/Ouf/GW0b1Xry9GkSSzuKVOzkYNflw\n+CD1KohIYmSkp3Lm5EGs2riX+R9vp6zc66gvrwjz9kfbWLt1PzPG96db53jf34lIMmtMz0IB3opH\n8YYKdfHPF8Q5vy+qXSyd67g2wsz+iDfv4Ux/gziRpPNm1BCklJC3ZKqISKKEQiFGDslm9mm59Mmu\nuXDCtvxDPP7aat7/JI/yCo34FWlrjjpZMLNSYBMwJE6TIXgrJcWbk7A6ql0Nzrk+eD0L5pfbO+fO\nds5NifNYm/Cei7aylaRTcLCEJauqF/sa0LM9mRnKi0Uk8bp0bMf5M4cyZXQfUqIWUKgMh1ny6S7+\n+aqxeUdhgBGKSHNr7NKp84A+zrlh0ZXOub7AMGBhvAvNbDPeJOVp/j4K0Wb6xwX+sRvwAt4OzzX4\nvQkj8SZE7z7ypyDSss1fuo3yiuoxw8f07VhHaxGRxkkJhZjgejL79Fz61lqeufBQKf+Zv4FXFm7U\nvgwibURjk4WH/eOtVTf8/vE2v/7Beq5/BOgPXFtV4ZzrBPwMOOyfx8y2Ae8BE5xzs6PahoBfAb2B\nh7UikiSj6FWQsjJS6J/TPsBoRKSt6N45k/NnDOXU4weQmZFa49zarQU8+sqnLFubT6UmQIsktUaN\nZTCzN5xzTwCzgQXOubeAqcDJwBwze7GqrXPuZiBsZtG7Pd8JfAm42zk3A1gPXAQMBq4zsz1Rba/C\n68l41Dl3Md7Qo5OAE4DFeLtGiySVrbsOsHpz9Ui+UQOzSE3R3goi0jxCoRDDB3VncJ/OLFiex8oN\neyPnysormbd0O59u2seM8f3p1V1fZIgko8b2LABcBtwE9ACuB3oCNwJfrdXuJv9PhJkdAKYBf/WP\nVwN7gUvM7L5abT8BjgeeBGbg9UZ0xdtrYYaZHUYkybz5wdYa5dGD9ctYRJpfZkYasyYO4MKZx5Ld\npeaqSPn7inhq7hre/nArxaXlcR5BRFqrRs+SNLNyvKFAv6qnXczExMx2Ad9s4M9aC1x6pDGKtEaV\nleEaqyD1y+lA3+4ZbNPcQhEJSJ8eHbj41FyWrcln0cqdNVZH+mT9HtZtK+CkMX3IHagd5kWSRSJ6\nFkSkCazYsIf8fUWR8qxJA/TLV0QCl5oSYrzryaVnOob07VzjXFFJOa8v3sK/31lPwUHtAC2SDJQs\niLRQby7ZUqM8c8KAgCIREfmsTu0zOGfqEM6dOoRO7Wvur7ot/yCPv2YsXa0J0CKtnZIFkRaouLSc\n+R9vj5SPG5qtyYMi0iIN7tuZS85wTBzes8beDOUVYd5dtp2n565lT0FxgBGKSGMoWRBpgRat2EFR\nSfVEwVMmqldBRFqu9LQUJh/Xh9mn59I7u+YXG7v2HebJ11ezdHU+YfUyiLQ6ShZEWqC5UUOQMtJS\nOGls3wCjERFpmO6dM7lg5rGcPLYvaanVtxiVYa+X4fn5GzhUrM3cRFoTJQsiLcy+wmI+sl2R8uTj\n+tA+Mz3AiEREGi4lFGLssBwuOSOX/j1r7ji/eecBnnhtNZvytKybSGuhZEGkhXn7o61URvXUz5qk\nIUgi0vp07tCOz087hpPH9iUlajPJopJynn93A+8t267JzyKtgJIFkRbmzSXVG7F17dSO8bk5AUYj\nInL0Qn4vw8WnDKNbp3Y1zn20Op8X5m+gpLQioOhEpCGULIi0IBu2F7B+e0GkPGN8f1JT9TEVkdat\nR9csLj41l5FDuteo37zzAE/NXcO+A1otSaSl0l2ISAvy5gdba5RP0RAkEUkS6WkpzJo4gNNPGEhq\n1LCk/QdLeGruGjbt0DwGkZZIyYJIC1FRGebtD6tXQRrcp/NndkcVEWntcgd248KZx9Ihq3rhhtKy\nSl6Yv4GVG/YEGJmIxKJkQaSF+HhNPnsLSyLlWRP7E4ra4EhEJFn07N6ei08dVmOzyTBe7+rS1fnB\nBSYin6FkQaSFeDNqb4WUEMyY0D/AaEREmlaHzHQumDEUN6hbjfp3l23n/RU7tIGbSAuhZEGkBThc\nXMZ7y/Mi5THDcsjukhVgRCIiTS81NYVTJw1g3LCaq74tWbWTeR9vV8Ig0gIoWRBpAd79eDulZdXL\nB56qic0i0kaEQiGmjunDiaN616hfvnY3b324VQmDSMCULIi0AG9EDUHKapfG5NF9AoxGRKR5hUIh\nJo3oxfRx/WrUr9ywl3eX5SlhEAmQkgWRgO3Yc4gV66tXADl5bF8yM9ICjEhEJBijj+3BaccPIHpp\nh4/X5LNk1c7AYhJp65QsiAQsemIzaG8FEWnb3KDuzKr1/+CilTv5eI1WSRIJgpIFkQCFw2HmflCd\nLPTq3p6RQ7IDjEhEJHgjBnfn5LF9a9TN/3g7qzbuDSgikbZLyYJIgFZu2MuOPYcj5VMmDSAlRXsr\niIiMHZbD8SN71ah7c8kW7fQs0syULIgEaG6tIUizJmoIkohIleNH9GLssB6Rchh4deEm9hYWBxeU\nSBujZEEkICVlFcz/eFukPHJId/r06BBgRCIiLUsoFOKkMX1xA6s3bistr+SFdzdQXFIeYGQibYeS\nBZGALFyex+Hi6l92p0waGGA0IiItUygUYtbE/vTJbh+pKzxUyksLNlJRWRlcYCJthJIFkYBED0HK\nSEv5zGQ+ERHxpKamcNaUwXRqnx6p2777EO98tE17MIg0MSULIgHYU1DE0tW7IuXJo/vQISu9jitE\nRNq29pnpnHvSENLTqm9dVm7Yy+od6l0QaUpKFkQC8NYHW6mM+jLsVA1BEhGpV3aXLE4/oeb/lx9t\nqmT15n0BRSSS/JQsiDSzcDjMG1FDkLp3zmRsbk6AEYmItB5D+nZhyug+kXJlGO54eDEHD5cGGJVI\n8lKyINLM1m0tYMvOA5HyrIn9SdXeCiIiDTY+N4fBfTpHyrv2FXHX4x9p/oJIE1CyINLM3liyuUZ5\n1iTtrSAiciRCoRCnHj+gxoTn91fs4N/vrAswKpHkpGRBpBmVlVfy9ofVeysc278Lg3p3ruMKERGJ\nJTMjjTMnDya6Y/ah51fy6aa9wQUlkoSULIg0oyWrdnIgalyt9lYQETl6vbq3Z9yg6luZisowdz6y\nhINFZQFGJZJclCyINKO5UUOQ0lJDTB/fL8BoRERaP9c7pcaE5/x9RTz4r2UBRiSSXJQsiDSTgoMl\nLFm1M1KeNKIXXTq2CzAiEZHWLxQK8Z3Z48nplhWpe/ODrby7bHuAUYkkDyULIs3knY+2UV5RvVKH\nhiCJiCRGx6x0vvflCYSi5i/cO+dj9hYWBxeUSJJIS8SDOOfSgOuAbwGDgTzgb8DtZlbegOu7A78A\nzgNygFXAnWb2ZIy2w4CfA6cB3YCdwPPATWa2OxHPR6QpzP2gem+FTu0zmDSiV4DRiIgkl9HH9uAL\n04fy7NveikgHDpdyz5NLuekbJxIKaXlqkaOVqJ6Fe4HfAPnAXcA2vJv/f9Z3oXOuA/AacBXwHnAP\n0BV43Dl3Ta22I4HFwJeBd/2ftca/9n3nXHaCno9IQm3eUcjaLfsj5RkT+pGepo49EZFEuuzsEQzs\n3SlSXrJqJ68s3BRgRCKtX6PvVpxzU/F6FOaY2Qwz+6mZTQceBi5yzp1bz0NcD4wHrjOzS83sx8A4\nYAVwh3Muemvb3wKdgS+a2UVm9iMzOxW4ERgC3NTY5yPSFF5fvKVG+RTtrSAiknAZ6al8/5IJNTa6\n/Mtzn5C3+1CAUYm0bon4arPq2/9batX/BAgD36zn+quBHcD9VRVmdhD4NdAeuBTAOdcJOBVYYmbP\n1nqM24ES4KyjiF+kSZVXVPLmkupkYWDvThzbv2uAEYmIJK+h/btyyZkuUi4ureCeJ5dqd2eRo5SI\nZGE6kG9mK6MrzSwPb4jQ9HgXOueGAn2BeWZW+1P8VtTjA4SAH+L1LtRWCZQDHY80eJGmtnjlDvYf\nLImUTz9hkMbPiog0oS/OGoYb1C1SXr5uN6++r+FIIkejUcmCc64d0A+It7/6RqBbHXMJhvrHz1xv\nZjvwegty/XKhmf3OzB6P8TinAx3whi6JtCivLaq5t8Ksif0DjEZEJPmlpqZw/ezxpKVW3+b87T8r\n2FNQFGBUIq1TY3sWuvvH/XHOF/jHLnHOVyUR8a4vrONaAJxz7anubXiwrrYizW1PQREfRO2tcOKo\nPtpbQUSkGQzo1Ykvn54bKR8qLuf+Z5ZpOJLIEWpsspDuH0vinK+qz2zE9fGuxTmXAcwBRgLPmtlT\n8UMVaX5zl2yhMur30uknam8FEZHmcuGsYQzu0zlSXvjJDt5blhdgRCKtT2OThar+vIw456u+Qo23\nDEFDro95rb/k6nPA2cAi4LI6IxVpZuFwuMYQpB5dsxiX2zPAiERE2pb0tBSu+9I4ohZH4v5/LePA\n4dLgghJpZRqbLBTgrXgUb6hQF/98QZzz+6LaxdI51rX+cqpvAmcAC4AzzEzrokmL8sn6PTWW6zv1\n+AE1lvMTEZGmlzuwG5+fPjRS3n+ghL8+pymOIg3VqGTBzEqBTXh7HMQyBG+lpHhzElZHtavBOdcH\nr2fBatUPwtuQbRLwCnCamRUeefQiTeu1WitvnHa8hiCJiAThK2cOp1f39pHy64s3s2L9ngAjEmk9\nErF06jygj3NuWHSlc64vMAxYGO9CM9sMbAamOedqf+U60z8uiHrMHni7PR8LPA6cZ2Za2kBanENF\nZbwbNS527LAe9M7uEGBEIiJtV2a7NK69eGyNuvue/pjyisqAIhJpPRKRLDzsH2+tuuH3j7f59fWt\nUPQI0B+4tqrC34DtZ8Bh/3yVB/EShaf93Z4rGh++SOK989FWSsuq356nnzAowGhERGRcbk9mjK9e\nunrzjgP8++14K7+LSJW0xj6Amb3hnHsCmA0scM69BUwFTgbmmNmLVW2dczcDYTOL3u35TuBLwN3O\nuRnAeuAiYDBwnZnt8a+dAJyPNwdis/9YtRWZ2R2NfU4ijfVq1MTmjlnpTBndJ8BoREQE4BufH8Xi\nVTs4XFwOwD9fM6aN60fPqCFKIlJTInoWwFuJ6CagB3A90BO4EfhqrXY3+X8izOwAMA34q3+8GtgL\nXGJm90U1nRb19+9FPVb0nx8l5umIHL21W/ezdkv1NJ2ZE/qTkZ4aYEQiIgLQrXMml509IlIuKa3g\nwWeXBxiRSMvX6J4FADMrB37l/6mrXczkxMx2Ad+s59q7gbuPNkaR5vLygo01ymdM1hAkEZGW4uyp\nQ3hj8WbWbvUWW3x/xQ4WrdjBCaN6BxyZSMuUqJ4FEQEOF5fx9odbI+Xhg7oxpG+dm5CLiEgzSk0J\ncfUXxxKKWlblgX8to7i0PLigRFowJQsiCfT2h1spLq2e2Hz21MHBBSMiIjENG9CNs6cMjpR37Svi\nqTfWBBeQSAumZEEkQcLhMC8t2Bgpd8xK56Sx/QKLR0RE4rvsnJF07dguUn76zbVs330wwIhEWiYl\nCyIJsnrzPjZsr94f8JRJA2inic0iIi1Sx6x0rjhvZKRcXlHJn579JMCIRFomJQsiCfLygpo7Np8V\n1cUtIiItz6yJAxgxuHukvGTVThat3BFgRCItj5IFkQQ4WFTGO0u3RcqjjslmQK9OAUYkIiL1SUkJ\n8e0LRteY7PynZ5fX2FRTpK1TsiCSAG8u2VLjl4t6FUREWoeh/bvW+D97x57DPPPW2uACEmlhlCyI\nNFI4HOblhRsj5c4dMjhpjHZsFhFpLS47ewSd2mdEynNeX83OvYcDjEik5VCyINJIKzfsZfOOA5Hy\nqccPJD1NE5tFRFqLTu0zuPzc6p2dS8sr+ctzmuwsAkoWRBrt+fnra5TP0o7NIiKtzmknDOLYAV0j\n5QXL8/jw010BRiTSMihZEGmE3fuLeG95XqQ8wfWkb07HACMSEZGjkZoS4r8vHFOj7sFnl1FWrsnO\n0rYpWRBphJcWbKSyMhwpn3fykOCCERGRRskd2I3TTxgYKW/LP8S/31lfxxUiyU/JgshRKi2r4JWo\nic19sjswcXiv4AISEZFGu/zckXTISo+Un3jN2L2/KMCIRIKlZEHkKM1buo2Cg6WR8rknDyElJVTH\nFSIi0tJ16diOy84aHikXl1bw1/+sCDAikWApWRA5CuFwuMbE5syMVE47fmAdV4iISGtx1pTBDOnb\nOVKet3Qby9bmBxiRSHCULIgchU837mPt1oJI+ZRJA2p0W4uISOuVmprCty+oOdn5gX8tp7yiMqCI\nRIKjZEHkKNReLvW8k48JKBIREWkKo47JZtbE/pHy5h0HeH7+hgAjEgmGkgWRI7SnoIh3l22PlMfl\n5jCgV6cAIxIRkaZwxXmjyGqXFik/9sqn7CssDjAikeanZEHkCL20YCMVUculfm6aehVERJJR986Z\nXHpm9WTnopJyHnphZYARiTQ/JQsiR6C4tJwX390YKffObq/lUkVEkth5Jw+p0Xs8d8kWVm7YE2BE\nIs1LyYLIEXhj8RYOHK5eLvW8k48hVculiogkrbTUFK66cHSNuvufWVajh1kkmSlZEGmgisowz769\nNlLumJXOGScOCjAiERFpDmOOzWHauH6R8obthby8YGNg8Yg0JyULIg20YPl2duw5HCmfPXVwjYlv\nIiKSvL7+uVG0y0iNlB95aRUFB0sCjEikeShZEGmAcDjMM29W9yqkpabwOS2XKiLSZvTomsXs03Ij\n5UNFZTz84qoAIxJpHkoWRBrgk3V7WLNlf6R8yqQBdOucGWBEIiLS3M6fMZR+OR0i5dcWbWL15n0B\nRiTS9JQsiDTAM29V9yqEQnDBzKEBRiMiIkFIT0vlyvOrd3YOh+GPzyyjUpOdJYkpWRCpx6a8Qpas\n2hkpnzCyN/17ahM2EZG2aMLwnkw+rnekvHbLfl5btDnAiESalpIFkXpE9yoAXDjr2IAiERGRluCb\nXxhNRlr1LdTfX1hZY1ltkWSiZEGkDvn7injno62R8vBB3Rg5JDvAiEREJGi9urfni6dWT3Y+cLiU\nf7ykyc6SnJQsiNThqbmrKa+oHouqXgUREQHv90Gv7u0j5ZcXbGTd1v3xLxBppZQsiMSxe38Rr75f\nPQ51YO9OnDiqT4ARiYhIS9EuPZUrz6/e2bkyDA/8a7kmO0vSUbIgEsdTc9dQXlEZKX/5dEdKSijA\niEREpCU5fmQvJo3oFSmv2riXtz7cEmBEIomnZEEkhj0FRbyycFOkPKBXJ04a0zfAiEREpKUJhUJ8\n6/zjSEutvp362/MrOVRUFmBUIomVlsgHc86lAdcB3wIGA3nA34Dbzay8Add3B34BnAfkAKuAO83s\nyXquGwcsBi4ys+ca8xxE4LO9CpeoV0FERGLo26MjF846lidfXw3A/gMlPPbqp3zrC6PruVKkdUh0\nz8K9wG+AfOAuYBvezf8/67vQOdcBeA24CngPuAfoCjzunLumjut6A0+hXhJJkM/2KnRk6lj1KoiI\nSGwXnzKMHl2zIuXn529gU15hgBGJJE7CbrCdc1PxehTmmNkMM/upmU0HHgYucs6dW89DXA+MB64z\ns0vN7MfAOGAFcIdzLifGzxwLvAscA+hrX0mIp99cS1l5da/C7NMcqepVEBGRODLbpfHNLxwXKVdW\nhnngX8sJhzXZWVq/RH4bX/Xt/y216n8ChIFv1nP91cAO4P6qCjM7CPwaaA9cGt3YOXcnsAjoBcw/\n6qhFouwtLOaVBRsj5f49O3LyuH6BxSMiIq3D1NF9GDes+nvN5et2M2/ptgAjEkmMRCYL04F8M1sZ\nXWlmecAa/3xMzrmhQF9gnpnVTsPfinr8aD8A3gcmAnOPPmyRanPeWE1pdK/C6epVEBGR+oVCIa68\nYHSN3xl/eW4FRSX1TtkUadESkiw459oB/YB1cZpsBLo55+JtfTvUP37mejPbAZQAubVOnWNm083M\njjxikc/avvsgL723MVLul9ORaepVEBGRBhrQqxNfmD40Ut5bWMyjL38aYEQijZeonoXu/jHe1oUF\n/rFLnPNVSUS86wtrX2tmLzc4OpEGePjFVVREbaZz2Tkj1KsgIiJHZPbpuXTvnBkpPzdvHZ9u2htg\nRCKNk6hkId0/lsQ5X1WfGed8Q66Pd61Io9mmvbz78fZI2Q3qxtTR2q1ZRESOTPvMdK68oHrZ1HAY\nfv/EUsrKKwKMSuToJSpZKPKPGXHOt/OPhxpxfbxrRRolHA7zt+drTLXhv84bRSikXgURETlyJ43p\ny9Qx1V84bdl5gCf8fRhEWptEJQsFeCsexRtm1MU/XxDn/L6odrF0ruNakUZZtGIHK9bviZQnH9eb\nUcfEm14jIiJSv6suGEPHrPRI+ak31rBhu25lpPVJSLJgZqXAJmBInCZD8FZKijcnYXVUuxqcc33w\nehY0kVkSrqKikodeqO5VSEkJ8bVzRgYYkYiIJINunTNr7L1QURnm9098REVFZR1XibQ8iVw6dR7Q\nxzk3LLrSOdcXGAYsjHehmW0GNgPTnHO1x37M9I8LEheqiOfVRZvZuutgpHzmiYMY0KtTgBGJiEiy\nOGXSACYM7xkpr91awLNvx1s4UqRlSmSy8LB/vLXqht8/3ubXP1jP9Y8A/YFrqyqcc52AnwGH/fMi\nCXOoqIzHXqle0i4zI5VLznQBRiQiIskkFApxzRfHktUuNVL36CufsimvMMCoRI5MwpIFM3sDeAK4\nCFjgnLsdeBu4DJhjZi9WtXXO3eyc+3mth7gTb/O2u51zT/k7NC8FRgA/NLM9iCTQP15exf4D1Qtw\nXThrGN06adEtERFJnJ7d2nP5uaMi5bLySn7z2AdaHUlajUT2LICXGNwE9ACuB3oCNwJfrdXuJv9P\nhJkdAKYBf/WPVwN7gUvM7L56fm7Y/yPSIGu37ufFdzdEyjndsrhgxtA6rhARETk6Z08ZzLhhOZHy\nhu2F2qxNWo20RD6YmZUDv/L/1NUuZpJiZruAbx7Fz70FuOVIr5O2qaIyzH1PfUzU/mtcef5oMtsl\n9OMgIiICeItnXP/l8Vz7f29yqKgMgGfeWsukEb04bmiPgKMTqVuiexZEWrxX39/Emi3VC3MdP7IX\nJ47qHWBEIiKS7Hp0zeKai8ZGyuEw/O6fH3K4uCzAqETqp2RB2pT9B0p4OGqp1Iz0VK48f7Q2YBMR\nkSY3bXw/ZozvHynv2lfEg88uDzAikfopWZA25aEXVnCwqPpbnNmn5dI7u0OAEYmISFty1UVj6NGl\nejGNNxZvYd5H2wKMSKRuShakzVi+djdvLN4SKffL6cgFMzWpWUREmk/HrHS+e8mEGnX3zPmIbfkH\n41whEiwlC9ImHC4u464nPqpR998XjiE9LTXOFSIiIk1j7LAcLpx5bKRcVFLB7X9fTEmZllOVlkfJ\ngrQJf/73J+zaezhSnjmxP2Nzc+q4QkREpOlcds4IRgzuHilvzCvkgWeWBRiRSGxKFiTpvf9JHq8t\n2hwpZ3fJ5NsXjAkwIhERaevSUlP44WWT6NwhI1L32qLNvLF4cx1XiTQ/JQuS1AoOlvCHOR/XqLt+\n9ng6ZqUHFJGIiIinR9csbvjKRKIX5Lvv6WVsyisMLiiRWpQsSNIKh8Pc+9TH7D9YEqk776QhjHc9\nA4xKRESk2gTXky+dlhspl5ZV8OuHFnHgcGmAUYlUU7IgSWvuki0sWJ4XKffL6cDl540MMCIREZHP\nuuSM4Yw5tnon57zdh7j974spr6gMMCoRj5IFSUqb8gr5Y9REsZSUEN+/dCKZGWkBRiUiIvJZqSkh\nfvDVieR0y4rULVu7m/ufWUY4HA4wMhElC5KEDhaVcetDiygprV6C7uJTh5E7sFuAUYmIiMTXrVMm\nN379RDIzqpf0fmXhJv4zf32AUYkoWZAkU1kZ5nePfcj23YcidaOH9uCS012AUYmIiNRvSN8u/KDW\nhOe//PsTPvh0Z3BBSZunZEGSypNvrGbRyh2Rco8umfzwskmkpuqtLiIiLd+Jx/Xh8nOq59dVhuGO\nh5ewduv+AKOStkx3UJI0lqzayWOvfBopp6Wm8OPLj6drp3YBRiUiInJkLpx1LKdMGhApF5WU8/MH\nF7Bl54EAo5K2SsmCJIUtOw/wf49+QPQ8sKsuHI0b1D3+RSIiIi1QKBTi2ovHctzQ7Ehd4aFSbnrg\nPXbtPRxgZNIWKVmQVi9/XxE3PfAeh4rKInVnnDiIMycPDi4oERGRRkhPS+XGr5/I0P5dInW7C4r5\n3wfeY9+B4gAjk7ZGyYK0aoWHSrnpwffYXVD9H6cb2I1vXzA6wKhEREQar31mOrd8awr9e3aMUNlv\nOgAAHcRJREFU1OXtPsRNDyzgoDZtk2aiZEFareKScn7x54Vs3XUwUtcvpyM3fuNEMtJT67hSRESk\ndejSsR2//PZUekbtwbAxr5Cf/vFd9TBIs1CyIK1SWXklt/19MbZ5X6Quu0smv/j2FLp01IRmERFJ\nHj26ZvHLb0+tsWDHhu2F/OTe+ezapzkM0rSULEirU1pWwZ2PLOZD2xWp65iVzi1XTqFnt/YBRiYi\nItI0+uZ0/EzCsC3/ED/6w3y25R+s40qRxlGyIK3K4eIybvnzQhZ+Ur2XQkZ6Kj//5mQG9e4cYGQi\nIiJNa3CfztxxzcnkRA1J2r2/iB//YT7rtxUEGJkkMyUL0moUHirlf+9/j2Vrd0fq0tNS+MnlxzN8\nsJZIFRGR5Nc3pyN3XDONfjkdInX7D5bw43vnsfCTvAAjk2SlZEFahT0FRfz43vms2VK9g2VWu1Ru\n/tZkJo3oFWBkIiIizSunWxa3XzONY/pWL6taVFLBr/+2iMdfM8LRmw6JNJKSBWnx1mzZx//cM6/G\nzpWd2qfzq6tOYsyxOQFGJiIiEoyundrx66tPqrFxG8CjL3/KHQ8voaikPKDIJNkoWZAW7dX3N/Gj\nP8wnf19RpK5753bcds3J5A7sFmBkIiIiweqYlc4vrpzK2VMH16h/d9l2fljrSzaRo6VkQVqksvIK\n/jBnKfc8uZSy8spIfZ/sDtxx7TRNZhYREcGbu3f1RWO5+otjSU0JReo35hXy3d++xX/mraeyUsOS\n5OilBR2ASG3b8g/ym0c/qDE/AWDSiF7ccOkEOrbPCCgyERGRlunsKYMZ2KsTt/19EQUHvd2dS8sr\nefDZ5SxauYPvfnk82V2y6nkUkc9Sz4K0GBUVlTw9dw3f+b83ayQKoRBceobjxq+fqERBREQkjlHH\nZPO7785k9NAeNeqXrs7n2v/3Jq8v2qReBjli6lmQFmHD9gJ+/8RHrN1ac53oDlnp/OArE7XikYiI\nSAPkdMviV1dN5bl56/j7C6sor/CG8h4sKuPuJ5by0oKNXHn+aNwgLTkuDaNkQQJVeKiUOW+s5j/z\n1lNR69uOUcdk890vj6d3doc4V4uIiEhtKSkhzp9xLONye/Lbxz5gw/bCyLnVm/fzg9/P45RJA7j8\n3JF075wZYKTSGihZkEAUl5Tz3Lz1PP3mGg4X11zeLatdKpefO4qzpwwmJWqyloiIiDTc4D6d+c31\n03ni9dU88+baGguGzF2yhfkfb+fsKYO5YOZQzWeQuJQsSLMqKinn9UWbmfPGavYdKPnM+QnDe3LN\nRWPp2b19ANGJiIgkl/S0VL561ghOO34gf/3PChYsr97lubSsgn+/s44X39vA6ScM5KJZw/T7Vz5D\nyYI0ix17DvHCuxt47f1NHCr+7EYxvbq357KzRzB9fD9CIfUmiIiIJFLv7A789IoTWLp6Fw8++0mN\nPRjKyit58b2NvLJwEyeM6s2ZkwcxLrdnjaVYpe1KSLLgnEsDrgO+BQwG8oC/AbebWb1bCDrnugO/\nAM4DcoBVwJ1m9mSMtu2BnwCXAH2BDcC9ZnZfIp6LJE5JWQUfrNrJ3CVbWLRyB7F2n+/asR2zT8/l\nzMmDSU/T4lwiIiJNaVxuT+65YSZvf7SVJ19fw7b8g5FzFZVhFizPY8HyPHp2y+L0Ewcxa+IAeqm3\noU1LVM/CvXiJwjzgWeBkvJv/scDFdV3onOsAvOa3fRLYDHwReNw5l2Nm90a1TQXmAGcDL/jtzwH+\n4JwbYmb/k6DnI0eprLyCpavzeWfpNt7/ZEfc7eY7ZqXz+elDOX/GULLaqYNLRESkuaSmpnDKpIHM\nmDCA95Zt58nXV7Mxr7BGm137inj05U959OVPOaZfF6aO7sPk0X0Y2KuTRgC0MY2+S3POTcVLFOaY\n2eyo+oeArznnzjWzF+p4iOuB8cA1ZvZH/9pfAQuAO5xzT5pZvt92Nl6i8P/M7Ed+25uAl4HvO+f+\nbmafNPY5ScOFw2E27TjA0tX5LF29i0/W76GktCJu+4G9O/G5k49h5sT+ZGYoSRAREQlKakqIaeP6\ncdKYvnzw6U5eXrCJJat2UHsrhvXbCli/rYB/vPwpfbI7MGZYD44b2oPRQ7M1MboNSMTd2jX+8ZZa\n9T8BLgO+idcLEM/VwA7g/qoKMzvonPs18BhwKXB31M8qA26NalvunPtf4D3gG8D3jvqZSL32Hyhh\nzZZ9rN2yn9Vb9rNmy77ITpHxpKWGmDSiF+eddAxjhvXQNxIiIiItSEpKiONH9ub4kb3ZU1DE64s2\n8+r7m9i1r+gzbfP2HCJvzyFeWbgJgH45HRg2sBtD+3VlaL8uHNOvCx2y0pv7KUgTSkSyMB3IN7OV\n0ZVmluecW+Ofj8k5NxRv3sEcM6s9ov2tqMe/2znXDjgB+NDMCmq1XQwU1fWzpGEqK8MUHCwhf38R\newqKyNt9mK27DrAt/yBbdx2k8FDdiUGVlJQQY4/twfTx/Zh8XB/tvCwiItIKZHfJYvbpjotPzeXT\nTXtZsDyP95bnsWvv4Zjtt+UfYlv+Id76YGukLqdbFn17dKBvTkf65XSkT48O9OiSRXaXTDp3yNCX\nhq1Mo5IF/wa+H7AwTpONQK5zLtvM9sQ4P9Q/rqt9wsx2OOdKgFy/ahCQGqdthXNuS1TbNq2krIJV\nG/ZQXFpBRWWYysowFRWVlJRVUlJaTnFpBcWl5RQVl1N4uJQDh0opPFxK4aFS9hUWU15xdFvBd+3Y\njrHDchiX24PjR/amS8d2CX5mIiIi0hxSUkKMHJLNyCHZfP1zo9iwvZCFn+SxbO1ubNPeOu8V8vcV\nkb+viI/X7P7MubTUFLp3yaRLhww6ta/6k077rHQyM1Jpl5FKu/Q0OndIZ1xuT81rbAEa+y9QtVf4\n/jjnq3oAugCxkoXseq4v9K9tSNsCYJhzLsXMKuO0SXqHi8v4zm/eYmecbwASqWf39gwb0BU3sBvj\ncnMY1LuzNlETERFJMqFQiGP8IUaXngnFpeXYxn0sW+clDuu2FnCwqKxBj1VeUcmuvYfj9lRE69Oj\nA7+/YabmOAassa9+1aC0z+6uVbM+3l7iDbk+8wjaVv2spr9TbqE25hUmPFHokJVO/5yO9OvZkf49\nOzKkbxeGDeiqngMREZE2KDMjjbG5OYzNzQG8xU527Sti3db9rN9WwLb8g2zPP8T23QcprmPRk/rk\n7T5E/r4iBvTqlKjQ5Sg0NlmomvkSb0B61d3koUZcf+gI2obNrM0mCgDH9OvCkL6d2bC9sM52KSHI\nbJdG56puwA4ZdG6fQffOmWR3zSSnaxbZXbLo2a09XTpqfGFL0alTB4o2rqGkIDXoUBKqvLyMtHRN\niBMRaY1CoRC9urenV/f2TB3TN1IfDofZW1jMzr2H2VNQ7P8pYm9hMQcOlXKgqMw7Hi6lqKT8M/sx\nTRjek749OjTzs5HaGpssFABhqocK1dbFP197QnKVfVHtYumMt8FbQ9p2AQ7GOReEQO6uMzPS+P0N\ns4L40dIMRo4YzsgRw4MOQ47AWVN+FHQITSJZnxck73NL1ufl7QUrLVEoFCK7S5aWV23lGrVlrpmV\nApuAIXGaDMFbKSnePIPVUe1qcM71westML9qI1Aap20qMCCq7ZFaCrztH0VEREREhEYmC755QB/n\n3LDoSudcX2AY8VdKwsw24+3YPM05V/ub+Jn+cYHfthx4H5jgnOtYq+0JQFZV2yNlZt81s5lm9t2j\nuV5EREREJBklIll42D/eWnXD7x9v8+sfrOf6R4D+wLVVFc65TsDP8CYqP1LrZ7UjagM451w68Eu8\n4U5/OupnISIiIiIiNYTCtWeTHAXn3D+B2cAivM3UpgIn4222Njuq3c14k5Cjb/Y7AUvweiGeAdYD\nF+ENQrzOzO6LapsCvOM//uvAh8BZwBjg/5lZsg7IFBERERFpdonoWQC4DLgJ6AFcD/QEbgS+Wqvd\nTf6fCDM7AEwD/uofrwb2ApdEJwp+20q85OB3wAjgO/5zuAb4cYKei4iIiIiIkKCeBRERERERST6J\n6lkQEREREZEko2RBRERERERiUrIgIiIiIiIxKVkQEREREZGYlCyIiIiIiEhMShZERERERCQmJQsi\nIiIiIhKTkgUREREREYlJyYKIiIiIiMSkZEFERERERGJSsiAiIiIiIjEpWRARERERkZjSgg4gGTnn\n7gLGBR2HiIiIiLQJS83su03xwEoWmsY4YEbQQYiIiIiINIaShaaxNOgARERERKTNaLJ7z1A4HG6q\nxxYRERERkVZME5xFRERERCQmJQsiIiIiIhKTkgUREREREYlJyYKIiIiIiMSkZEFERERERGJSsiAi\nIiIiIjEpWRARERERkZiULIiIiIiISExKFkREREREJCYlCyIiIiIiEpOSBRERERERiSkt6ACSmXNu\nC9AvzumzzOzV5own2Tnn0oDrgG8Bg4E84G/A7WZWHmBoSc8590vgZ3FOP2FmlzRnPMnOOdcXWAXc\nZGZ3xzj/NeB7wDBgH/Ck3/ZQswaapOp6/Z1z3wD+FOfS981sSlPHl6ycc72Bm4FzgZ7AXuB1vH+H\nDbXa6jOQQA197fX+bxrOuWzg53ivfx9gA/AQ8Fszq6jVNuHvfSULTcQ51x0vUVgIvByjydrmjahN\nuBcvUZgHPAucDPwCGAtcHGBcbcFYoAS4Lca5T5o5lqTmnOsIPAN0AsIxzv8E+DXwMfB7YAzeL47J\nzrmZZlbWjOEmnfpef7zPAsDtQHGtc1ubMLSk5t+sLgL6A68CjwHDgUuBs51zk81srd9Wn4EEOpLX\nHr3/E8451wmYDzjgOeApYBpwh3/8fFTbJnnvK1loOmP842Nm9odAI2kDnHNT8RKFOWY2O6r+IeBr\nzrlzzeyFoOJrA8YAK8zsF0EHksycc4PwblTH13H+F8B7wIyqb5ycc7cANwJX4iXVchTqe/19Y4A9\nZvbT5omqzbgZ72b1+2Z2V1Wlc+4rwCPAb4Av6DPQJG6mAa+9X633f+L9BC9R+E70/aRz7lHgEufc\nOWb2YlO+9zVnoelUJQvLAo2i7bjGP95Sq/4neN/+fbN5w2k7nHOdgYHovd6knHPfBZYDo4G5cZpd\nCaQCt9bqmr4VKESfg6PWwNcf//zyZgmqbbkA2BV9swpgZo8C64EznHMh9BloCvW99mdGVev9n3iD\ngM3AfbXqn/CPk/1jk733lSw0HSULzWs6kG9mK6MrzSwPWOOfl6ah93rzuB5vnOp0vG/zYpmOlxy/\nFV1pZiV4QyLH+l3acuTqff2dc/2BbuizkFDOuRS8oRU3x2lSAmQA6egzkFANfO3TnXPpev83DTP7\nipkNNrPKWqeG+8ed/rHJ3vsahtR0xgB7gCudc5cDQ/Am3D6Cl/WVBhlcMnHOtaN6fkgsG4Fc51y2\nme1ptsDajqpkoadz7jVgEt5/WG8APzOz1YFFllyuBF43s7BzbnicNkOBnWZ2OMa5jf4xF/igCeJL\ndg15/as+CxnOuWeBqUAm3rCAG81scTPEmXT8m6Tfxzrn/1sMB9aZWalzTp+BBDqC177MOaf3fzNw\nzvUEvog3kmIT8A//VJO999Wz0AT8THwUkI33bdRc4M9AOXAT8IJzLjW4CJNOd/+4P875Av/YpRli\naYuqfkH8AO/f4AHgfeAi4H3n3Nh4F0rDmdlrZhZrQm20bPQ5aBINfP2rPgtX4X3T/RfgNeBUYJ5z\n7owmDLHN8X/X/gEIAQ/61foMNIM4r73e/03MX3lwB95rvx8408yq3tdN9t5Xz8IRcM5txBubXZd7\ngV8Cq/GWFrvAzAr969sBc4DzgKuBe5oq1jYm3T+WxDlfVZ/ZDLG0ReV431pcYWbvVFU65y7F+8bj\nr8DEYEJrc9LR5yBIIbzPws/M7J9Vlc656Xg9bX9zzh3jDwuQRvDnJzwAnAIsBqrG0+sz0MTqeO31\n/m966/BWmnJ4k8rnOefOMrOPaML3vpKFI/MM0KOeNu+b2S5irJZhZiXOue/gJQuXoGQhUYr8Y0ac\n8+38o9bXbgJmdm2c+secc1cC051zuRqO1CyK0OcgMGZ2GzGWDzazd/yVS74GzMBbflKOkr+nzp+A\ny/Funr4QtZeOPgNNqK7XXu//pmdmD1X93Tl3Lt5Sqg/jTSxvsve+koUjYGbfT8BjbHTO7cfbNEwS\nowBvjHy87rUu/vmCOOel6XyEN+lqMF5vmzStfdT9OQB9DoLyEd7N0uCA42jVnHPt8Xroz8b7P+U0\nM9sR1USfgSbSgNe+Lnr/J5iZveCcmwuc4s/VabL3vuYsNAHnXLZz7iR/ZYDa50J43UC1NyuRo+RP\nFt+EN4k8liF4KyXFG8snR8k5l+qcm+CcOz5Okyz/qPd781gN9PKHPNY2BKjAWx1MmoBzbqxzblqc\n0/osNJJzrhveHMCzgQ+Bk82s9kZf+gw0gYa89nr/J57/O/Y059xpcZpswhv+1QPvvd+7Kd77Shaa\nxvl4uwj/T4xzE/GShSXNGlHymwf0cc4Ni650zvXF2/I83kpJ0jjpeJOZX/YnvEX4ifFUoAxYGkBs\nbdE8vHW2aywV7JzLxFuLe4WZaQhG03keeNM5lx3j3Mn+Uf/3HwX/Pfw8cALe0pAzzWx3jKb6DCTY\nEbz2ev8nXgj4D/Bo7d+xvrFAJd5+F/Pw7usT/t5XstA0nsfLnv/LOZdbVelvXnU33pAY7SCZWA/7\nx1v9m9Sqm9Wq8ZMPxrxKGsXMivHe792AH9c6fQNwHN4u5oXNHVsb9Rjet0c3O+eix67+FOiEPgdN\n7Sm836u3Rlc65y4GzgHerr0XjDTYrcAUvGU4zzazg3Ha6TOQeA197fX+TzB/PsjTQA61voB2zv03\n3hfQL5hZPk343g+Fw/WtBCdHwzl3HV5icBBvl71SvInNA4DbtRV64jnn/gnMBhbhffsxFe/bjDlm\nNjvA0JKac+4YYAHef2av423IMxFvItsKYLqZ7QsuwuTjnLsCb5Wp75rZ72uduw34EbAKL5EbhfeL\nej5wqpmVNW+0ySfe6++c647Xi3ksXo/bu3irlpyDt8/OyWa2sbnjbe2cc73xhluk473utYceVbnN\nX0hEn4EEOZLXHuiA3v8J54+QWAj0B14BPsFbROcUvB6Fk6vmjjTVe1/JQhNyzp2HlwlO8KuWA783\ns8eDiyp5+as0/Bi4Am+Ttk14m+DdqV8OTcs5NwD4BXAm3lrPW/G+DfmlmR0IMrZk5G/0+Ffge7WT\nBf/81XjLMw/F+yX9DHCL/i0So67X3x/bfTPecNQ+wC68X9o/N7OdyBFzzp2P9x4O4w3LiCUMdIta\nqlyfgQQ40tde7/+m4Zzrhfc79jy8L+a24f27/Kr2l3FN8d5XsiAiIiIiIjFpzoKIiIiIiMSkZEFE\nRERERGJSsiAiIiIiIjEpWRARERERkZiULIiIiIiISExKFkREREREJCYlCyIiIiIiEpOSBRERERER\niUnJgoiIiIiIxKRkQUREREREYlKyICIiIiIiMSlZEBERERGRmJQsiIiIiIhITGlBByAiIo3jnLsZ\nuOlIrjGzBn9Z5JybBPzBzCYfYWjRjzEYWA+sM7Nhtc6FgEuArwATgGxgP7Aa+Ddwr5kdrnXNQ8DX\ngMvM7NGjjUtEROqmZEFEpPX7GPhHrbqhwBRgJ/BaIx//PSC1kY9RJRxdcM61A54DTgcOAh/ixZwD\njAemAtc552aa2fr6Hk9ERBJLyYKISCtnZv8C/hVd55y7HC9ZWGVmX2vkj2jKIav/i5coPAdcYWb7\nq0445zoD9+L1ODyF1+sgIiLNSHMWRESSUyjoABroMrzegauiEwUAMysEvgFsB8Y6544PID4RkTZN\nPQsiIm2Qc+6rwLeBsXi/C9YA/wTuMrNiv80VwF+jrqkENpnZkKi62cB/4X3r3xU4hDcs6n4ze7wB\nofT0jzGTGzMrdc79FhgOlDTgeaX5z+u//GsqgI+Au/0emFjXXAr8N95rkQKsAB40s7/UancF3uvx\nX8A+4OfASGAX8ALwSzPLqy9GEZHWRD0LIiJtiHMuxTn3GPAw3g3+u8BLQF/gVmC+c66L33wt8CjV\n8wL+ATwT9Vj34SUYU4AleJORtwHTgcecc9c1IKSleInCP5xzw2M1MLPfmtmVZrasnueWDvwHuAcY\nDLztP78TgKedc7+Kcc2f/ec1FngfeB1wwJ+cc4/E+VEX4Q376uz/vFLgKmChc25Qnc9WRKSVUbIg\nItK2XAt8GTBghJmdbWYXAUPwvh2fANwPYGbzzaxqmFDYzL5mZjcA+EOCrsLrkRhiZueY2cVmdhxw\ng/+zGpIs/C9QDswCVjrnljvn7nbOXeScyz7C53YTcCbwKnCsmZ1rZucAI4B1wE+dc6dXNXbOfQP4\nOl7PwwgzO93MzsebHP4+8BXn3Ldi/JzzgD8Bzsy+hNeD8WdgAHDXEcYsItKiKVkQEWlbvod383+F\nmW2uqjSzg3gTiQuALznnBtTzOJ2Bp4GfmtneWuce9I/1PQZmNhdvgvNqP65ReEnGHCDfOTffOXdR\nfY/jr6p0HVCEt5xqZP6DmW0CrveLN0Rd9j/+z7zczLZFtd+DN1eidvsq64FrzKzSb1+Bl4TtBD7v\nnOtTX7wiIq2FkgURkTbCTwAGAVvN7P3a5/0JxS/hDQuaXtdjmdkbfk/C01GPn+mcG4e3/wFARkPi\nMrO3zWy4/zNvx/tWvwzvRn4qMMc597C/H0M8E/ASmJVmlh/j/Fy8+QsnOedC/g19LrDXzD6JEdNK\nvInVw5xzPWudfsZPEKLblwIv4712s+p90iIirYQmOIuItB19/ePGOtpUnetV34M557LwJvt+AW+i\nb1+8m+Wj2vvAzN7Fm2OAc64DMBO4Evgc8FW8/R7uj3N5VS/GRH8idjztge5R7bPraR/22+6Kqlsb\np+1W/6ieBRFJGkoWRETajoYsp1q1+VqdKw855/oC7wDH4K0MtAh4Am8lpDeBTfX9IP8b+1xgQ/Qw\nIAAzO4Q3h+IF59wdeEOGLiF+slAV93q8pKI+Ve134c1xqMuBWuWKmK2qX99450VEWh0lCyIibUfV\nDfmQOtoc4x931vNYv/bb/gVvj4TIDbJzrhMN62H4hv84t+JNdI7nL3jJQrc62lQtWbq+IZvQOee2\n+38tPIpN6/rHqa9aCWnLET6eiEiLpTkLIiJthJltwfvGv79zbnLt8/6SqWfgfTP+TtSpWDf9J/r1\nd9Yev+8/RkNU9QB8zR92FE/Vkqor6mizCCgGTnDO9ah90nlWOOeehMik5y3AMbGWbHXOdXfOfeyc\neyVGbGfHaJ8FnIW3stMbdcQpItKqKFkQEWlbfucfH4reE8A51xFvv4FOwNNmFj1Gv2qTts5RdZvx\neg++EP3gfhLyh6hyZrxAzOxtvKSkP/C6c+642m2cc1OB+/ASmLjLkprZYbzlTDsDDzvnukc9Rjfg\n73hLqEZ/638X3u/BR5xzA6PaZ+FtvjYaOOgPiYp2gnPuB1Ht04EHgGzgkdo7UYuItGYahiQi0rbc\ng7fC0JeAVc65d4DDwDS8m90P8XYzjrYaGA+845xb5g/buQuvB+FO59yX8HoshuCtSvQa3mTnUXiT\nfTfUEc+FwIt4PRXLnHMr8fZuCOPd3Du8+RNXxVrBqZYf+z//LGCdc26Jf+00vCToPWoOd7rLfy0u\nwtvjYQne0rFTgB7+874qxs/Z6j/vr/ptTgAG4u3X8IMY7UVEWi31LIiIJKeY8wXMLGxmX8ZbxehD\nvJvl0/Bu9m8AppjZvlqXfRtvp+XhwBnOuS5m9jJwDjAfb+7CmXg35t/Au1l/24/hvLqC9PdomAJc\nhrcrcge8fRdOx+u5uBcYZ2Z/ifH8ajxHMysCTgW+j7cJ22TgJLzk4wbgNL9N5LXAS5qqNmYbh7fs\naR7wS+DEOMuwPo63OlOK//xKgFuAaTFeOxGRVi0UDh/VCnciIiJtinPuCrzhSf9nZj8MOBwRkWah\nngUREREREYlJyYKIiIiIiMSkZEFERKRhPjNPQkQk2WnOgoiIiIiIxKSeBRERERERiUnJgoiIiIiI\nxKRkQUREREREYlKyICIiIiIiMSlZEBERERGRmJQsiIiIiIhITEoWREREREQkJiULIiIiIiISk5IF\nERERERGJScmCiIiIiIjEpGRBRERERERiUrIgIiIiIiIxKVkQEREREZGY/j/gY3wRvfUaJAAAAABJ\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c6c2da0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# the jointplot function can be used to set up a subplot grid:\n",
      "g = sns.jointplot(\"BodyWt\", \"TotalSleep\", sleep); "
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {
        "png": {
         "height": 426,
         "width": 423
        }
       },
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAA04AAANUCAYAAACE7X18AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c69aac8>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "We can see that the body weight variable is extremely skewed. It ranges from near zero (0.005 kg for the Lesser Short-Tailed Shrew) up to nearly 2.5 tonnes (for the Asian Elephant):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sleep['Species'][sleep['BodyWt']==sleep['BodyWt'].min()]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "31    Lessershort-tailedshrew\n",
        "Name: Species, dtype: object"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sleep['Species'][sleep['BodyWt']==sleep['BodyWt'].max()]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "4    Asianelephant\n",
        "Name: Species, dtype: object"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sleep['BodyWt'].describe()  # use the describe method on the variable BodyWt"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "count      42.000000\n",
        "mean      100.813905\n",
        "std       402.082389\n",
        "min         0.005000\n",
        "25%         0.316250\n",
        "50%         2.250000\n",
        "75%        10.412500\n",
        "max      2547.000000\n",
        "Name: BodyWt, dtype: float64"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Indexing Pandas dataframes\n",
      "\n",
      "For the next step in our exploratory analysis, we want to access variables in our `sleep` dataframe.\n",
      "\n",
      "There are a number of ways to access subsets of a Pandas dataframe; an overview can be found on the [10 minutes to Pandas website](http://pandas.pydata.org/pandas-docs/stable/10min.html#getting). We will concentrate here on selecting single variables (columns).\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sleep['BodyWt']  # prints the column of \"bodyweight\" variable."
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "1        1.000\n",
        "4     2547.000\n",
        "5       10.550\n",
        "6        0.023\n",
        "7      160.000\n",
        "8        3.300\n",
        "9       52.160\n",
        "10       0.425\n",
        "11     465.000\n",
        "14       0.075\n",
        "15       3.000\n",
        "16       0.785\n",
        "17       0.200\n",
        "21      27.660\n",
        "22       0.120\n",
        "24      85.000\n",
        "26       0.101\n",
        "27       1.040\n",
        "28     521.000\n",
        "31       0.005\n",
        "32       0.010\n",
        "33      62.000\n",
        "36       0.023\n",
        "37       0.048\n",
        "38       1.700\n",
        "39       3.500\n",
        "41       0.480\n",
        "42      10.000\n",
        "43       1.620\n",
        "44     192.000\n",
        "45       2.500\n",
        "47       0.280\n",
        "48       4.235\n",
        "49       6.800\n",
        "50       0.750\n",
        "51       3.600\n",
        "53      55.500\n",
        "56       0.900\n",
        "57       2.000\n",
        "58       0.104\n",
        "59       4.190\n",
        "60       3.500\n",
        "Name: BodyWt, dtype: float64"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sleep['BodyWt'][0:3]  # the first three rows of the bodyweight variable"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "1       1.00\n",
        "4    2547.00\n",
        "5      10.55\n",
        "Name: BodyWt, dtype: float64"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Boolean indexing. All bodyweights of animals with less than 6 hours of sleep:\n",
      "sleep['BodyWt'][sleep['TotalSleep'] < 6]  "
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "4     2547.00\n",
        "11     465.00\n",
        "21      27.66\n",
        "28     521.00\n",
        "51       3.60\n",
        "53      55.50\n",
        "57       2.00\n",
        "Name: BodyWt, dtype: float64"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Variable transforms\n",
      "\n",
      "- Linear bodyweight is extremely skewed; this complicates data analysis\n",
      "- It makes sense to consider the log of body weight instead\n",
      "- We can create a new variable of log bodyweight."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sleep['log_BodyWt'] = np.log(sleep['BodyWt'])\n",
      "sleep['log_BodyWt'].describe()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "count    42.000000\n",
        "mean      0.803995\n",
        "std       3.069388\n",
        "min      -5.298317\n",
        "25%      -1.168641\n",
        "50%       0.804719\n",
        "75%       2.342741\n",
        "max       7.842671\n",
        "Name: log_BodyWt, dtype: float64"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sleep.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 42 entries, 1 to 60\n",
        "Data columns (total 12 columns):\n",
        "Species        42 non-null object\n",
        "BodyWt         42 non-null float64\n",
        "BrainWt        42 non-null float64\n",
        "NonDreaming    42 non-null float64\n",
        "Dreaming       42 non-null float64\n",
        "TotalSleep     42 non-null float64\n",
        "LifeSpan       42 non-null float64\n",
        "Gestation      42 non-null float64\n",
        "Predation      42 non-null int64\n",
        "Exposure       42 non-null int64\n",
        "Danger         42 non-null int64\n",
        "log_BodyWt     42 non-null float64\n",
        "dtypes: float64(8), int64(3), object(1)\n",
        "memory usage: 4.3+ KB\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = sns.jointplot(\"log_BodyWt\", \"TotalSleep\", sleep)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {
        "png": {
         "height": 426,
         "width": 423
        }
       },
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAA04AAANUCAYAAACE7X18AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSahicJEmSJKnG/weI88VJeCvZxwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c726390>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we see a much nicer and more linear spread of the data. Heavier animals seem to sleep for less hours."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Grouped plots\n",
      "\n",
      "The sleep in mammals dataset contains a number of other variables. For example, the variable `Danger` is an ordinal scale variable, ranging from 1 (\"least danger from other animals\") to 5 (\"most danger from other animals\").\n",
      "\n",
      "Does the total time spent sleeping change according to danger?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = sns.violinplot(sleep['TotalSleep'], groupby=sleep['Danger'])\n",
      "sns.despine(ax=g, offset=10);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {
        "png": {
         "height": 283,
         "width": 391
        }
       },
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAw8AAAI3CAYAAADdgmeiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAWJQAAFiUBSVIk8AAAIABJREFUeJzs3XmYlPWd7/33XVtX9d4NDc2m4PZDUNGoETFGs0wWzWPO\nnDk5zzOT8+Q8M8kkM9fMlZzJmYzHzGhcIopDcAlxX3AXjIoGjCubCoqIirL82KFlX5qt91qeP6r6\n7uqmG5qmuu6qrs/rurjq/t11V9VXuynqU7/NSSQSiIiIiIiIHI/P6wJERERERCQ/KDyIiIiIiEiv\nKDyIiIiIiEivKDyIiIiIiEivKDyIiIiIiEivKDyIiIiIiEivKDyIiIiIiEivKDyIiIiIiEivKDyI\niIiIiEivKDyIiIiIiEivKDyIiIiIiEivKDyIiIiIiEivKDyIiIiIiEivBLwu4GQYYwYBvwGuBoYB\nm4AZwDRrbSztuh8DD/XwNB9Yay/t51JFRERERPJe3oYHY0wZ8C5ggFeAPwKXA1NSt9ekXT4hdXs7\n0Nzlqb7o30pFRERERAaGvA0PwHUkg8PPrbXT208aY54G/toYc5W19tXU6fOAfdbaX3tQp4iIiIjI\ngJDPcx5OBbYC93Y5PzN1OzHt3LnAZ9koSkRERERkoMrbngdr7Q97uGts6nYXgDFmJFAFrMhGXSIi\nIiIiA1XehoeujDFDgP8G3ARsAZ5K3XVe6jZkjJkNTALCwGLgemvth9muVUREREQkH+XzsCWXMeYW\nYCcwHTgAfNtaezB1d3t4+AcgBDwCvAl8A3jHGPOtLJcrIiIiIpKXBkR4ADaQXEnpJaCGZCi4IHWf\nA2wGfmitvcpae5219q9Ihgc/8JgxpsiDmkVERERE8oqTSCS8riGjjDFXk1y6dZW19tzjXDsD+BHw\nHWvtGyf4OncB5wOfWGv/Vx/LFRERERHJGwNmzkM7a+1cY8zbwDeNMadbazcc4/KPSYaH0X14qfOB\nK/rwuGMZWElORERERHKV05cH5WV4MMb4ga8BWGvf6uaSranbQcaYUqDCWruom+siqduuG8eJiIiI\niEgX+TrnwQH+BDxtjOnuv2ECECc512EOMM8YM6ib676Sul3WH0WKiIiIiAwkeRkerLVR4AWSk6N/\nlX6fMeYfgQuBudba3cAfSf53Tu5y3Q+Aq4CF1tpV2ahbRERERCSf5eWwpZR/A74K3GaMuRL4HLgA\n+DqwEfhZ6rpbgKuBvzfGnAe8BxiSwWE78LfZLVtEREREJD/lZc8DgLV2O3Ax8BDJvRx+AZwO3Alc\nbK3dmbpuP3AJ8HtgOPBzkiHjIeBCa+3mrBcvIiIiIpKHBtxSrdlijFlAcrWlhdbaKzP0tPphiIiI\niEg29Gm1pbzteRARERERkexSeBARERERkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5ReBAR\nERERkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5ReBARERER\nkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5R\neBARERERkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5ReBAR\nERERkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5ReBARERER\nkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5ReBARERERkV5R\neBARERERkV5ReBARERERkV5ReBARERERkV4JeF3AyTDGDAJ+A1wNDAM2ATOAadbaWJdrfwT8C3Am\nUA/MAm6w1jZks2YRERERkXyVtz0Pxpgy4F3gn4HPgN8DB4EpwEtdrr2OZKgAuAf4lGSQeMMYE8xS\nySIiIiIieS2fex6uAwzwc2vt9PaTxpingb82xlxlrX3VGHMqcDOwGLiivUfCGHMTcD3wU+APWa9e\nRERERCTP5G3PA3AqsBW4t8v5manbianbnwJ+YHKXoUyTgUPAT/qzSBERERGRgSJvw4O19ofW2tHW\n2niXu8ambnelbr8KJIAFXR7fArwPTEgNgRIRERERkWPI52FLnRhjhgD/DbgJ2AI8lbrrdGCXtbax\nm4dtTt2eBXzU3zWKiIiIiOSzAREejDG3AP+eau4Cvm2tPZhqDwI29PDQ9msq+rE8OQFtbW28/PLL\nrFu3jmuuuYbx48d7XZKIiIiIpOTtsKUuNgC3k1xlqQZ4xxhzQeq+INDSw+Paz4f7tzzprXnz5vH4\n44+zePFifvOb39DY2F2HkYiIiIh4YUD0PFhrZ7QfG2OuBl4BngDOBZqAUA8PLUrdaq+HHLFx40b3\nuKWlhT179nDqqad6WJGIiIiItBsoPQ8ua+1cYB4wzhhzOskN4XoaltR+/mAP90uWtba2dmq3tPTU\naSQiIiIi2ZaX4cEY4zfGfNMY880eLtkCOMBgYC1Qa4wp6ua6MUAMWNc/lcqJ6hoeurZFRERExDt5\nGR5IBoM/AU8bY7r7b5gAxIGNwDsk/zu/mn6BMSZMci+IldZaDVvKEQoPIiIiIrkrL8ODtTYKvEBy\ncvSv0u8zxvwjcCEw11q7B3iGZO/CjcaY9LkPvwbKgAezUrT0SnNzc6e2hi2JiIiI5I58njD9byR7\nE24zxlwJfA5cAHydZI/DzwCstdYYMxW4FvjYGDMHGA9cBbwLPJT90qUnXcNC1zAhIiIiIt7Jy54H\nAGvtduBikh/+zwN+QXJDuDuBi621O9OuvQ74Z5I7Tf8cGAdMA6621rZluXQ5hq5hQeFBREREJHfk\nc88D1tpdpHoYenHtvcC9/VuRnKyuYaGpqcmjSkRERESkq7zteZCBqeumcAoPIiIiIrlD4UFySkND\n54WvtMO0iIiISO5QeJCc0dbWRjQa7XRO4UFEREQkdyg8SM44cuTIUee69kSIiIiIiHcUHiRndBce\nDh8+7EElIiIiItIdhQfJGYcOHTrqnMKDiIiISO5QeJCc0V1QUHgQERERyR0KD5Izuut5OHToEIlE\nwoNqRERERKQrhQfJGQcOHOhoOMlfzWg0qknTIiIiIjlC4UFyRn19vXvs+ALdnhcRERER7yg8SM7o\nFB78Rd2eFxERERHvKDxIzti/f7977ATC3Z4XEREREe8oPEjO2LNnj3vsC5V0e15EREREvKPwIDkh\nFot16mHwhcrdY4UHERERkdyg8CA5Yd++fcTjcSA538EfKnXvU3gQERERyQ0KD5ITdu3a5R77giX4\ngsVue+fOnV6UJCIiIiJdKDxITti+fbt77CsqxZfW87Bz505isZgXZYmIiIhIGoUHyQk7duxwj32h\nUhx/0F2uNRqNsnfvXq9KExEREZEUhQfJCZ16HlK9Dr5QmXtu27ZtWa9JRERERDpTeJCcsGXLFvfY\nn1ppyVfUER62bt2a9ZpEREREpDOFB/Fcc3Nz52FLRcnw4C+qcM+lhwsRERER8YbCg3iurq6ORCIB\npOY7+PwA+MMd4WHz5s1elCYiIiIiaRQexHObNm1yj31pvQ3px1u3btWKSyIiIiIeU3gQz61bt849\n9keq3GNfoAgnEAGgtbVV8x5EREREPKbwIJ5bu3atexwIV3e6zx+p7vY6EREREck+hQfxVEtLS6f5\nDOlhASCQ1rbWZqssEREREemGwoN4at26dcTjcSC5r4PjD3a6Pz1MrFmzJqu1iYiIiEhnCg/iqc8+\n+8w99hcPPur+ZHhwgOSqTPX19dkqTURERES6UHgQT61YscI9DpQMOep+xxfAXzzIbX/++edZqUtE\nREREjqbwIJ5paWnpNBQpUFzT7XWB4o5QkR42RERERCS7FB7EM59//jnRaBRIznfwBSPdXhco6QgV\ny5cvdzeUExEREZHsUngQzyxdutQ9DpTW9nidv3gw+AIA7N69W/s9iIiIiHhE4UE8kUgk+PDDD912\nsGx4j9c6jo9gWrhIDx0iIiIikj0KD+KJzZs3s2fPnmTDF+x2paV0gdJh7nF66BARERGR7FF4EE+8\n++677nGwtBbHOfavYnp4WL16dUfwEBEREZGsUXiQrEskEixatMhtB8tHHfcxvkARgZKhbvudd97p\nl9pEREREpGcKD5J169atY+fOncmGL3DMydLpghUdIWPhwoX9UZqIiIiIHIPCg2TdggUL3ONg+Ugc\nn79XjwuWjYTU8KaNGzdq1SURERGRLFN4kKxqaWlh3rx5bjtUfkqvH+v4g53mPrz++usZrU1ERERE\njk3hQbLqvffeo6GhAQBfsAR/yZDjPKKzUNVp7vG8efNoaWnJaH0iIiIi0rOA1wX0lTGmFrgRuBoY\nAuwH3gJusNZuSrvux8BDPTzNB9baS/u5VEnz2muvucfBqtNwHOeEHh8oGYoTLCbR1siRI0d47733\n+PrXv57pMkVERESkG3kZHlLBYSkwEngDeAYYC/wN8F1jzERr7frU5RNSt7cDzV2e6osslCspmzZt\nYvXq1amWQ6hy9Ak/h+M4hKpOo2X35wC8+uqrfO1rXzvhECIiIiIiJy4vwwPJHoeRwC+ttXe1nzTG\n/BB4Evgd8P3U6fOAfdbaX2e7SOnsxRdfdI+D5SPxBcJ9ep5Q5Rha9qyCRBxrLatWrWL8+PGZKlNE\nREREepCvcx7+EtidHhwArLVPAxuBb6edPhf4LIu1STd2797daW+H0KCz+vxcvkCYYEXHROuXXnrp\npGoTERERkd7Ju54HY4wPuBVo7eGSFiBojAkCQ4EqYEWWypMevPLKK8TjcQD8xTUEItUn9XxFgwxt\nBzYD8MEHH1BXV8eoUcffbE5ERERE+i7vwoO1Ng7c0919xpixJOc+bLDWthljzkvdFTLGzAYmAWFg\nMXC9tfbDbNRc6A4cONBpWdWiQeakn9NfVE6gdBjRIzsAeP755/nlL3950s8rIiIiIj3L12FLR0n1\nSEwHHODB1On28PAPQAh4BHgT+AbwjjHmW9musxC98MILNDcn56r7iip6vaP08RQNHuseL1y4kLq6\nuow8r4iIiIh0b0CEB2OMAzwAfB34EGifC+EAm4EfWmuvstZeZ639K5LhwQ88Zowp8qDkgrFv3z5e\nffVVtx0eck7GVkYKFA8mUJIMIvF4nGeeeSYjzysiIiIi3cv78GCMCQCPAj8GNgDft9ZGAay1t1lr\nT7PWPpv+GGvtIuBpYBhwRZZLLiizZs2itTU5PcUfruq0Q3QmFA3pWGXp3XffZdOmTce4WkRERERO\nRl6HB2NMMfAy8D+BtcDXrLU7e/nwj1O3o/uhNAHq6uo6z3XIYK9Du0CkmkDZcLf96KOPkkgkMvoa\nIiIiIpKUt+HBGFMFzAO+CywHvmKt/aLLNROMMZf38BSR1G3XjeMkQx599FFisRiQWmGpZGi/vE64\n5hz3+JNPPmHZsmX98joiIiIihS4vw4MxJgzMAb4MLACutNbu7ebSOcB8Y8ygbu77SupWnzT7wfLl\nyzt9iI8MndBvu0D7wxWEqk5z24888ghtbW398loiIiIihSwvwwMwGbiU5JKr37XWHunhuj+S/G+c\nnH7SGPMD4CpgobV2VX8WWoii0SgPP/yw2w5WjsEfqerX1yyqOQd8QQC2bdvG3Llz+/X1RERERApR\n3u3zYIypBf4p1VwDXGdMt/sG3AbcAlwN/H1qz4f3AEMyOGwH/rbfCy5As2fP7lg21RcgPOScYz8g\nA3yBIsI142je9SkAzzzzDF/5ylcYPHhwv7+2iIiISKHIx56HiUAQSAB/B9zQzZ/rgSJr7X7gEuD3\nwHDg58AFwEPAhdbazdkufqDbuXMnzz7bsbhVuGYcvkA4K68dqj4DX6gMgKamJh588MHjPEJERERE\nTkTe9TxYa2dzAqHHWlsP/CL1R/pRIpHgvvvuc5dm9YUrCVWfmbXXdxwfkWEX0rBlAQBLlizh/fff\nZ+LEiVmrQURERGQgy8eeB8lRCxcuZPny5W47MuxCHCe7v2KBkhqClWPc9gMPPEBDQ0NWaxAREREZ\nqBQeJCP279/PAw884LZD1WcQiFR7Ukt46Hk4/uTG4Xv37uWRRx7xpA4RERGRgUbhQU5aIpHgD3/4\nA0eOJBe9coLFnfZeyDafP0S49gK3/eabb/Lhhx96Vo+IiIjIQKHwICdt3rx5LF261G0XD78Yxx/0\nsCIIVYwiWD7SbU+fPp3Dhw97WJGIiIhI/lN4kJOye/duHnroIbcdqjqDQMkQDyvqEK79kjt8af/+\n/dx///0kEgmPqxIRERHJXwoP0mexWIypU6e6E5J9wRLCQ8/1uKoOvkARkeEXuu1FixYxf/58DysS\nERERyW8KD9Jns2bNYvXq1amWQ2TEJTi+3Fr9N1g2otPqS/fffz/bt2/3sCIRERGR/KXwIH2yatUq\nnnvuObddVDOeQPEgDyvqWaT2fHyhUiC5edzUqVOJRqMeVyUiIiKSfxQe5IQdOnSIqVOnEo/HAfAX\n11A0eKzHVfXM8QUoHjERcABYt24dTzzxhLdFiYiIiOQhhQc5IfF4nDvvvJM9e/YA4PiCFI/4Mo7j\neFzZsfkjVZ3mY7z00kudVogSERERkeNTeJAT8tJLL7Fs2TK3HRnxZXzBYg8r6r1Q9VkESoe57Tvv\nvJNdu3Z5WJH0ViwWY9q0afziF79g7dq1XpcjIiJSsBQepNdWrlzZabhPaNBZBMuGe1jRiXEch8jw\ni3ECEQCOHDnClClTaGtr87gyOZ7Fixczf/58Nm7cyO9+9zuvyxERESlYCg/SK/X19dxxxx0d8xwi\ngwgPyZ1lWXvLFyiieOSlpM9/eOSRR7wtSo5r8+bN7nF9fb13hYiIiBQ4hQc5rmg0ypQpU9i/fz8A\njj9E8ciJOE5+/voEigcRHnqe2547dy7z5s3zsCI5nvbQCmijPxEREQ/l56c/yarHH3+clStXuu3I\niIl5M8+hJ6HqMwmWj3Tbf/jDH9i0aZOHFcmxpIeH9GMRERHJLoUHOaZ3332X2bNnu+2imnMIlg71\nsKLMcByHyLCL8IXKAGhtbWXy5MkcOXLE48qkO+m9DQoPIiIi3lF4kB5t2bKFu+++220HSofl9H4O\nJ8rxBykeNQlSu2Lv3LmT3/3ud/pwmoNisVi3xyIiIpJdCg/SrSNHjnDrrbfS3NwMgC9Umhf7OZwo\nf1E5xcMvdtvLli3j2Wef9bAi6U5ra6t7nEgktEO4iIiIRxQe5CjxeJxp06axY8eO5AnHT/HISTj+\nkLeF9ZNg+UhCg4zbfu655/jggw88rEi6Sg8P3bVFREQkOxQe5CjPPfccH374oduODL8Yf7jCw4r6\nX3jIOQRKhrjtadOm8cUXX3hYkaRraWnp1G7vERMREZHsUniQTpYuXdpp2E5okCFUMcrDirLDcXxE\nRkzESa0i1djYyOTJk2lsbPS4MgFoamo6ZltERESyQ+FBXNu2beu0e2+gZAjhIed4WFF2+QJFlIyc\nBKn9K+rq6rj77ru1r0AO6BoWFOpERES8ofAgQPLD2K233up+KHOCxclv4vN0I7i+8keqiAy7yG0v\nXryYP/7xjx5WJAANDQ2d2goPIiIi3iisT4bSrUQiwT333ENdXV3yhOOjZOQkfIEibwvzSKjyVELV\nZ7jtJ598kuXLl3tYkXTdf0P7cYiIiHhD4UGYPXs27733ntuODLsIf6TKw4q8Fx46AX/xYCAZrqZO\nncru3bs9rqowJRKJo8LC4cOHPapGRESksCk8FLjPPvuMGTNmuO1Q1RmEKk/1rqAc4Tg+ikdeihMI\nA8kPq7fddpuWCPVAU1PTUf/fDx486FE1IiIihU3hoYDt27ePO+64w91R2R8ZRLh2gsdV5Q5fIEzx\nyEuB5MZ469ev58EHH/S2qALUXVBQeBAREfGGwkOBisVi3HHHHRw4cAAAx19E8cjCmyB9PIHiwYSH\ndgSq119/nbffftvDigpPfX39Uef279/vQSUiIiKiT4oF6umnn2bVqlVuu3jkRHypPQ6ks1D1GQTL\nO/a6uO+++9i6dauHFRWWffv2HXVO4UFERMQbCg8F6KOPPuL5559320U1nXdXls4cxyEy/CJ8oTIg\nudvxlClTtMtxluzdu7dX56Qw7dixg3Xr1mk/FhGRLFF4KDD79u1j2rRpbjtQMpSiwWM9rCg/OL5A\ncv5DaljX1q1bNf8hS3bt2nXUuX379tHW1uZBNZJL5s+fz09/+lN++ctfMnXqVK/LEREpCAoPBSQe\njzNt2jQOHToEgBMIExnxZRzH8biy/OAPVxCpvcBtv/nmmyxatMjDigrDjh073GMnlHzLisfj7Nmz\nx6uSJEe888477nH6ctMiItJ/FB4KyOzZs1mxYoXbLh4xEV9qKVLpnWDlGIIVp7jte++9Vx9i+1l6\nePAXB9zj7du3e1GO5JD0oYOxWIxYLOZhNSIihUHhoUBs2rSJJ5980m0XDT6bQEmNhxXlJ8dxiNR+\nCSc1ubyhoYG77rrLXe5WMqulpYWdO3e67UBFx67nmrQuXff/aGlp8agSEZHCofBQAFpaWpg6dSrR\naBQAf7iKoppxHleVvxx/kOIRl7jtFStW8PLLL3tY0cD1xRdfuBNh/SVBAuUKD9KhqampU1uLGIiI\n9D+FhwLw7LPPdnzQcvxERlyi/RxOUqB4MEWDz3bbTzzxBHV1dR5WNDBt2rTJPfaXh/CXh9z2xo0b\nvShJckhjY+Mx2yIiknn6BDnArV+/npdeeslth4dOwF9U5mFFA0dRzTj84SoAotEo06dP1/ClDFu3\nbp17HKgsIlDZ0fOwZcsWDVMpcF17HhQeRET6n8LDABaLxfj973/vfqD1F9cQqjrN46oGDsfxERl+\nEZBcrWrVqlW89tpr3hY1wKxdu9Y9DlYV4Qv68JcGgeSKSxs2bPCqNPFYPB4/Kiw0NDR4VI2ISOFQ\neBjAXnrppY6hHakPulqWNbP84cpO+2TMmDFDqy9lSGNjY6ehSe29DoHqjhXCVq5cmfW6JDc0NjYe\ntTHckSNHPKpGRKRwKDwMUHv37uW5555z2+Ga8fhDpR5WNHAVDT7b3X26qamJGTNmeFvQALF69Wq3\n1yxQEcIX8gMQGtQRHj7//HNPahPvHT58uFfnREQkswLHvyR3GWNqgRuBq4EhwH7gLeAGa+2mLtf+\nCPgX4EygHpiVum5A9nM/8cQT7nhwX1EFoUFneVzRwOX4/ESGXUjDlgUALFq0iKuvvppx47Si1cn4\n+OOP3ePgoEjH8eCO45UrV9La2kooFEIKy8GDB3t1TkREMitvex5SwWEp8FNgJXBXqv03wIfGmDPS\nrr0OmJFq3gN8SjJIvGGMCWax7KxYu3Yt8+fPd9uR2vO1ulI/C5TUECwf6bYfeughTZ4+ScuWLXOP\nQ0OL3WN/SdCd99DS0qLehwKl8CAi4o18/kR5IzAS+KW19jvW2muttd8HfgRUA78DMMacCtwMLAYu\nstb+2lr7PeAW4FKS4WPASCQSPPTQQ247UDaCQMkQDysqHOEh50EqpK1fv54FCxZ4W1Ae2759O9u2\nbUs2/A7BwZ13Qk8PE0uXLs1maZIj9u/f36tzIiKSWfkcHv4S2G2tvSv9pLX2aWAj8C1jjEMyHPiB\nydbaWNqlk4FDwE+yVG9WfPTRR6xZsybVcggPPc/TegqJL1RCUdrwsGeeecbdmE9OzDvvvOMeh2oi\nOP7Ob1Wh2o7wsHjxYmKxGFJY9u3bd9Q5hQcRkf6Xl+HBGOMDbiXZ+9CdFiAEBIGvAglgQfoF1toW\n4H1ggjFmQGx8kEgkOk2SDlWfrknSWVY0aCyOPzn+fteuXSxcuNDjivLTu+++6x4XjTj6dzg4OIJT\nlJxAXV9fr6FLBai78KCVzkRE+l9ehgdrbdxae4+19v6u9xljxgJjgQ3W2lbgdGCXtba73YM2p24H\nxGziTz/9FGttsuH4KBo09tgPkIxz/EFC1We67eeff17fip+gDRs2sHnz5mTD5xCqLTnqGsdxKBre\ncX7evHlZqk5yxa5du446V19fT2trqwfViIgUjrwMDz1J9UhMJ7lr14Op04OAAz08pH12XUU/l5YV\nM2fOdI9DlaPxBSPHuFr6S1H1GeBLLmS2bds2Fi9e7HFF+eX11193j4uGl+ALdv82FT6lo8Pw3Xff\n1Rr/BWbnzp3ucbEv+TuSSCTYvXu3VyWJiBSEARMeUvMbHgC+DnxIcvUlSA5daunhYe3nwz3cnze2\nbNmSNnTDUa+Dhxx/iKK03oc5c+Z4WE1+aWxs7DTUKzy6vMdrA5VF+MuTQ8RaW1s1Qb2AtLS0sHfv\nXrdd4fO7xzt27PCiJBGRgjEgwoMxJgA8CvwY2AB831rbPlO1ieT8h+4UpW7zfq+H9G9rg+Uj8IWO\nHuoh2ROqPoNkBxisWrWKuro6bwvKE2+99RaNjckRhv7SIMFBPed6x3GIpIWLV155RUPECsT27dvd\n3aXLfX6qAh1bFunvmohI/8r78GCMKQZeBv4nsBb4mrV2Z9ol9fQ8LKn9fF4vDt7a2tppX4dg5Wke\nViMAvkCYQNlwt/3GG294WE1+iMVivPzyy247cnoFjuMc8zHhU8pwUsOaduzYwQcffNCvNUpu2Lp1\nq3tcGfBT6Vd4EBHJlrwOD8aYKmAe8F1gOfAVa+0XXS5bCww1xhR1fTwwBogB6/q10H62ZMkSd7y3\nEyzRvg45IlQ5xj1+++23aWtr87Ca3LdgwQJ3vLoT8hEedfxF0JyAj8iYjt6HWbNmud9Iy8C1adMm\n97jaH6AqLTyk3yciIpmXt+HBGBMG5gBfJrkM65XW2r3dXPoOyX0evtrN4ycCK621eT1sacmSJe5x\nqHL0cb+tlewIlNbiBJKT1g8fPsyqVas8rih3RaPRTssMR06rwAn07u0pcloF+JO/8xs2bOD999/v\nlxold2zYsME9HhwIMjht2NKWLVsU1EVE+lHehgeSm7xdSnLn6O9aa3taauUZkr0LNxpj0uc+/Boo\no2NVprxK9zDUAAAgAElEQVTU1tbG8uXL3XawfKSH1Ug6x3EIlo9w29oJuWdvvfWWu3qOE/QROb2y\n14/1hQOdeh+efvppzX0YwBKJRJfwEKDI56MsNWk6Go12LPUrIiIZFzj+JbnHGFML/FOquQa4zhjT\n3aW3WWutMWYqcC3wsTFmDjAeuAp4F3goCyX3m1WrVtHU1AQkhyz5QgNiv7sBI1A6jNb96wH48MMP\n+clPfqKeoS4aGxt5+umn3XbxmZU9Ls/ak+Izq2jadAhiCbZs2cKbb77Jd77znUyXKjmgrq6Ow4cP\nAxB2HDc0DAkEONyaDI2rV6/mzDPP7PE5RESk7/K152EiySVYE8DfATd08+d6UqspWWuvA/45df3P\ngXHANOBqa21e929/+OGH7nGirYGWPd0PjWnevZKDq56nefdK3Z/F+wPFNeAkP9zs2LGD7du3d/v4\nQjZz5kwOHEhuxeKLBJLDkE6Qr8hP8ZkdvRVPPfUUDQ15PRpRerByZcffsdpgyA3jtcFQt9eIiEhm\n5WXPg7V2NicYfKy19wL39k9F3nF3lJac5Pj8BEqGED2SXHveWsuIESOO86jCsXXrVl555RW3XTK+\nutdzHboqPqOS5i2HiTdFOXjwIE899RQ/+9nPMlWq5IgVK1a4x7XBoHs8LO34s88+IxaL4ff7ERGR\nzMp4eDDGDAO+BgwjOdegDnjbWtvTLs/SR7FYjI0bN3pdhhyHP1LlhocNGzbw9a9/3eOKckM8Hmf6\n9OlEo8ktWQLVYYpGlPb5+ZyAj5Lxgzi8bBcAc+fO5corr6SHIY2Sh2KxGB9//LHbHpnW21DlDxBx\nfDQl4hw+fJj169frZy8i0g8yFh6MMaeS3NX5Gtp3x+oQNcY8CfyrtbY+U69Z6LZt20ZraysATiBC\n+Vnf6/Ha8JDxhIeM1/0e3O8PV7nH6RM9C91rr73G6tWrkw0Hys6vOen5IEUjSmjeGqFtdxOJRILp\n06czbdo0gmnfSkv+sta6w9FKfL5OS7Q6jsOoUIi1Lc0ALFu2TOFBRKQfZGTOQ6q3YRHwfWA38Dhw\nK8kVkZ4muQnb3wJvpTZ1kwxI73Xwh3u/Oo1kV3p42Lhxo/YhILlD8GOPPea2i8+sIlDe00bwvec4\nDmUTatylWzdv3syzzz570s8ruSF9WepRwaKjwuaoUMd2PlqyV0Skf2Sq5+F6YBTwMPDP1trW9DuN\nMRHgAeB/AL8CbsrQ6xa09g21AHxFWmUpVzmBcHLSdCJGU1MTDQ0NlJb2fXhOvovFYtx55500Nye/\nIfaXBSk2mQu//pIgJeOqafhsHwAvvPACF110EePGjcvYa0j2xeNx3n33Xbc9pujofT9PCYXwkxwv\nu3nzZurq6hg1alT2ihQRKQCZWm3pamAD8I9dgwOAtbYJ+DGwFfibDL1mwauv7xgB5kttRia5x3Ec\nfMGOn0/6z60QzZw5kzVr1iQbDpRdOBTHn9mF3yKnVRCsSf4/j8fj/O53v3N3YZf8tHr1avbuTe4D\nWuQ4jAge3VMVdHycktb7sHDhwqzVJyJSKDL1L/YQ4GNrbY87M6WWRF1KsodCMiD9Q6gTCHtYiRyP\n4+/4+RRyePj000877SRdPLaaYOXR3yCfLMdxKLtgiLty0+7du7nnnns0ZCyPvfHGG+7xmKIwvh7m\nx5xe1PF37e2339aGgSIiGZap8LAKuMAYc7znOwtYn6HXLHjta+MD+PyZ/wAmmZMe7go1PNTX1zN1\n6lT3A3xwcJjis/pvro6/OEDZBTVue8mSJfzpT3/qt9eT/nPkyJFOQ5bGFvXc03pqqIhwKljs3buX\n5cuX93t9IiKFJFPh4SbgNOBeY0y3C2sbY/4VOA+4PUOvWfDi8XhHw6f1zHOZ4+v4q1aI34S2tbVx\n2223uYHXKfJTftHQft9tu2hEKeEx5W770Ucf5fPPP+/X15TMe/PNN92V5Qb5A9QEep6u53cczgp3\nhIs5c+b0e30iIoUkUxOmq4C5wE+BbxpjXiTZwxADRgLfBb4MfAGcb4w5P/3B1tp/y1AdIpKDHnro\noY5lWYHyC4fgC2dnj8rScwYTrW8heqCFWCzGlClTmDZtGjU1Ncd/sHguGo122khwXCRy3NA5Lhzh\ns6ZGEsDy5cvZtGkTY8aM6edKRUQKQ6Z6Hh4D2jcZOA34V+B+4CHgNySDAySDxL92+fO/M1SDSF7o\n72/bc81rr73Gn//8Z7ddMr6a0JDsrdjs+B3Kv1yLE0q+3R04cIDJkye7qz1Jblu0aJE7UTri+Djz\nGEOW2pX7A4xOmzj94osv9lt9IiKFJlNf/f3dSTxWMxj7qNOH0ES85wvFe2kTdQspPHz66afcf//9\nbrtoRCmRM7K/J4m/OED5xbUcXLwdErB+/XruvPNOrr32Wny+zK70JJkTjUY7TbAfH4kQ6OXfnwmR\nEja1tgDJAPKDH/yAU045pV/qFBEpJBkJD9baGZl4HjkxZWUdezskYketkCs5JB5rcY/Ly8uPceXA\nUVdXx2233ebO8QhUhCi74OR3ke6rUE2E0nMHc2RF8lvsxYsX89RTT/GjH/3Ik3rk+N566y127NgB\nJJdnHR/ufY/VkGCQUcEQdW2txONxnn76aa677rr+KlVEpGD0y1duxpghxpgLjTFnpdol/fE6ha6y\nsuMb3HhUQzByWSLt55P+cxuo6uvrufHGG2loaADAF/ZTPnGYu3SqVyKnVRA5rcJtP//887z++use\nViQ9aWxs7LQ7+IRICUUn2Et0UUnHZoyLFy/u2F9ERET6LKP/khtj/t4YswbYQXJPh/9I3fWiMeYF\nY4xmKGZQVVWVe5xQeMhp6T+f9J/bQNTU1MTNN9/csQO636F84jD8kexMkD6eknMGERra8Q32vffe\ny7JlyzysSLoza9Ys9u/fDyTnOoyPnPg8mZpAkNPS5j7cf//9BbnamYhIJmUsPBhjngQeILmXww4g\nfWxCLfCXwDvGmIpuHi59UF1d7R7H2xo9rESOJRGPusPKfD7fgB62FI1GmTJlCuvXd2znUn7x0H7Z\nCK6vHJ9D2UVDCVQkdyiOx+PcfvvtrF271uPKpF1dXR0vv/yy276kpJRgH4e7XVJSRvtC1hs2bOi0\n2ZyIiJy4jIQHY8yPgR8CHwDnWGtHdrnka8ArJIPFLzPxmgIjR3b8b463HPKwEjmWWNrPZvjw4fj9\nA3NPjng8zt13381HH33kniudMJii2twbtegL+ii/dBi+VG9IS0sLN954I1988YXHlUk8Hmf69OlE\no1EAhgaCnJm2a/SJKvP7Ob+443fw8ccfZ9++fSddp4hIocpUz8NPgUPA96y1q7reaa3dD/w1sBf4\nLxl6zYKXvnJIrOWQu3Ov5Jb0YHfqqad6WEn/SSQSPPbYYyxYsMA9V3xWFZExudvR6A8HqJg0DCeY\nfBs8fPgwN9xwg7ssqHhjzpw5rFqV/GfEAS4rLTvpSfYTIiWUpzbSbGhoYPr06Xq/FBHpo0yFh/HA\nAmttj1/nWGubgCWAdurJkIqKio7Jt4kY8bYGbwuSbsWaD7rHA3WpyOeff57Zs2e77fDocorPzv25\nHYGyEBWXDgN/8sPpnj17uOGGGzh0SD15Xti2bRuPP/64274gUsLgQPCknzfgOFxR1jFccNmyZbz1\n1lsn/bwiIoUoU+EhDvRmbEJF6lrJkNGjR7vHsab93hUiPYo1d/xcBmLPw6uvvsqTTz7ptkPDSiid\nMDhv9rMIVoep+HKtO0urrq6OG2+8kcZGzSPKptbWVqZMmUJra3J+ULU/wAXFmRvyNiwYYny4Y4O5\nBx54gLq6uow9v4hIochUePgUuMQYM6KnC4wxpwAXAZ9l6DUFGDt2rHsca9Rwi1yTiMc6hbpx48Z5\nWE3mLVy4sNMmcMHBEcovGpI3waFdaGgxZRcOcdvr1q3j1ltvdT/ISv979NFH2bRpEwB+4Gtl5fgz\n/Hv05ZIyKlNzjlpaWrjjjjtoaWk5zqMk37S1tXHfffcxdepUjhw54nU5IgNOpsLDfUAp8Iox5pyu\ndxpjxgIvAMXAIxl6TQHGjx/vHkcVHnJOrLne3f172LBhA2qZ1qVLl3LnnXe6Y8cDVUWUX1KL48/P\nHZvDI8sonTDYba9YsYIpU6a4E3el/yxcuJC5c+e67YklZQzKwHClroKOwzfKKtzVlzZv3sz999+v\n+Q8DzB//+EdeffVVFi5cyAMPPOB1OSIDTkb+lbfWPgM8ClwAfGqM2ZO661vGmLXA58CFJAPE490/\ni/SFMQZfauOkeMtB4tppOqekB7r0oJfvPv30U26//XZ3zXx/WZCKS4fhC+ZncGgXGVNBybiOJZCX\nLl3KXXfdRTyu0Zb9ZcOGDdxzzz1ue3SoiHFpw4sybVAgyKUlZW77rbfeYs6cOf32epJ91lr3ePPm\nzd4VIjJAZexfemvtT4C/B9YBg1KnhwBnANuA/w38d2utvuLJoEgkwplnnum2o0d2eViNdBU9stM9\nHijhwVrLrbfeSltbGwC+4gAVlw3HFxoYS9AWn1VF5MyOXcAXLlzIfffdp2+n+8GBAwc6DQ+r8Pu5\nsrS834e9nR2OdFr+9eGHH2bFihX9+pqSPel/VxX8RTIvo18TWmsfsdaOBUYAE4HLgDHW2lOttXcq\nOPSPCy+80D2OHtnhYSWSLhFr6zQPJf3nlK82bdrEjTfeSFNTEwC+sJ/Ky4bjD+fG7tGZUjKumvCY\njtV5XnvtNWbMmKEAkUHNzc3ccsst7NmT7KgOOg7fLqsk5Ov/3ivHcbi8tJyaQPL3Nh6PM3nyZLZu\n3drvry39Lz0w6O+sSOb117v0TmADsNZau6WfXkNSOoeHnXqzzBHRhp1A8mdxxhln5P18h+3bt3PD\nDTe4ExCdkI+Ky4bjL8n82HSvOY5D6XmDKRpZ6p578cUXmTVrlodVDRyxWIypU6e6u3o7wDfKKqgM\nZC+EBhyHb5VVUpwKKw0NDdx0003s369V6/JdenhoH1opIpmT0fBgjPkLY8xrJDeM2w3cmTo/yxjz\nO2NM/w1kLWBnnHGGu99DItaiJVtzRNvhjl6giy66yMNKTt7evXu5/vrrOXDgAABOwEfFpOEEykIe\nV9Z/HMeh7EtDCNUWu+eeeuqpThN75cQlEgkeeOABPvjgA/fcpJIyTgkVZb2WEr+f75RXEkit07t7\n925uuukmGhq0Z04+Sw8PGrYkknkZCw/GmN8CrwPfAsK4q6YDyU3k/gV40xgT7ubhchJ8Ph8XX3yx\n2247/IWH1QhAIhGn7fB2t53+88k3Bw8e5IYbbmD37t3JE36HiktrCVZm/8Netjk+h/KLhxKs6bw/\nQPpO2nJinnzySf785z+77fMixYyPFB/jEf1rcCDIN8sr3H+wNm7cyC233EJzc7NnNcnJSV8hTT0P\nIpmXkfBgjPlL4Nckhyp9Dyjrcsl/BZYCk4B/zMRrSmeTJk1yj9sOfaGhSx6LNuyGeHJCcU1NTadJ\n7fmkqamJm266qWMzLQcqvlxLcFDhdCI6fh8Vl9QSqEqGpUQiwV133cWyZcs8riz/vPDCCzz//PNu\n+/RQEZcUlx7jEdlxSqiIy0s75risXLmS22+/3V0UQPJL+s9Ne7WIZF6meh5+ATQD37TWvmqt7fSV\njU2um/Zd4DDwwwy9pqSZMGECJSXJ3VgTbY3Emw94XFFhazvU0ftz2WWX5d2maZD8B3jy5MmsW7fO\nPVd24VBCQ737ltgrTsBHxaXD8Jcl53fEYjFuv/121qxZ43Fl+WPOnDnMmDHDbZ8SDPG1soqc+bsx\nNhxhYlqQ+eijj/jP//xP7fORh9IDg8KDSOZlKjxcACw41uRoa2098A5weoZeU9IEg0EuueQSt916\nqM7DagpbIhEnenib277ssss8rKZv4vE4d911F5988ol7rnTCYMIjvf+W2Cu+kJ+KScPxRZKTelta\nWrj55ps7emWkR3Pnzu20WdewYJBvllfiy5Hg0O684hK+FClx20uWLGHq1KkKEHmmfTU4SK7qpZ54\nkczKVHjw076szLEVAQNrTccckv4hte1Qnd4wPRI9sotEarO+wYMHc9ZZZ3lc0YmbMWMGixYtctvF\nY6uIjKnwsKLc4I8EqLhsGE4o+dZ5+PBhfvOb32iFnmOYO3cu999/v9seGgjy7bJKAjkWHNpdWFzC\nueGO3rX33ntPASLPpE94TyQSncKEiJy8TIWHNcAlxpjKni4wxlQDFwG2p2vk5FxwwQWUlSWnmyTa\nGok17fO4osLUdrBjrfgrrrjC3QE8X8ydO5eXXnrJbYdHl1Ns8nuZ2UwKlIaouHQY+JMffvfs2cPN\nN9+sDyjdePnllzsFhyGBIN8tz85eDn3lOA4TS0o5p0uAuOOOOzQHIg+0tbUd9Xfx0KFDHlUjMjBl\n6h38MaAaeM4YU9P1TmPMYOApoCJ1K/0gGAx27n04qA2Psi0Rj9KWNmTpiiuu8LCaE7d06VIefPBB\ntx2qLaZ0wuCcGZeeK4JVYcq/PNRdU27Dhg3ccccdWtklzfPPP8/DDz/stmsCAa7K8eDQznEcLi0p\n5Zxwx8IAS5YsYfLkyRpDn+O6CwoKDyKZlal38QfoWKZ1szHmw9T5ScaYN4D1wHeA94B7M/Sa0o2v\nfvWr7nFy6JLWuM6mtsPbIZH8ADlq1ChGjx7tbUEnYPPmzUydOtVdFz1QVUT5RUMVHHpQNLSE0gkd\n35UsW7aMxx57zMOKckMikeCpp57iiSeecM8NDQS5urwqL4JDu2SAKOO8tGVkly1bpl6mHFdfX9+r\ncyLSdxl5J7fWRoFrgFuBNqB9y+PTgG8CIeD3wLestfraph+NHz+ewYMHA5CItRI9ssvjigpL1yFL\n+fLB+8CBA9xyyy3uhyJfcYCKibU4gfz5sOeFyOhyImd2jNZ8+eWXeeONNzysyFvxeJwHH3yQmTNn\nuueGB4NcVZEfPQ5dOY7DJcWlXJA2ifrTTz/l+uuvd3dal9zS3fyjffs0hFckkzL2bm6tbbPWXg8M\nBS4D/h+Sy7J+HRhsrf2FtVZf1/Qzn8/XuffhYI8LYEmGxaMtRI/sdNv5MmQpGo1y++23u5vAOQGH\niom1+Iq0tkFvlIyrJjSs48Plfffdx+rVqz2syBuxWIx77rmHOXPmuOdGBUN8p7yKoJN/waGd4zhc\nXFLKxWnLuFprue666/SNdg7au3dvr86JSN9l/B3dWttirV1irZ1lrX3WWrvAWtuY6deRnl155ZXu\ncdvh7STiWiUkG9oO1dG+6NjYsWOpra31tqBeevzxx1m5cqXbLrtoKIHygb97dKY4jkP5hUPwl4eA\njjBWSB8sW1tbmTJlCm+//bZ77rRQEd8qz91VlU7UBcUlTCrp2P908+bNXHvttezapd7dXLJz586j\nzulnJJJZffpq0RjzQ3q3NGu3rLXP9PWxcnyjR4/mlFNOYevWrZCI0XZ4O6GKU7wua8BrO9ix3n96\ngMtl77zzDrNnz3bbxWdXU1RbcoxHSHecgI+KibXUz/+CRFuc/fv3c8cdd/Db3/4Wv9/vdXn9qrGx\nkVtvvZUVK1a450xRmMtLy3NuH4eTdU6kmJDjsPDIIRLAjh07uPbaa7n55ps55RS9x+aCHTt2HHVu\n+/btHlQiMnD1dVzCkyfxmglA4aEfOY7DFVdcwZNPJn9MbQe3Kjz0s3hbI7GmZNe4z+fLi43hdu7c\nye9//3u3HaotpvisHldbluPwFwcpv3goBxcnP7x8/vnnPPfcc/zwhz/0uLL+c/DgQW666aZOu5Cf\nGy5mYklp3sz3OVFnhSOEHIe3Dx8kRnI8/f/5P/+H3/zmNxhjvC6v4HW3aeMXX3xBIpEYsL+TItnW\n1/DwxPEv6ZF2LsuCyy+/3A0P0SM7ScRacfwhj6sauNrSdvSeMGEClZW5/SE8Go0yderUThOky740\nRP+4nqTQkGKKz66mcXVy0uasWbM4//zzGT9+vMeVZd7evXu54YYbOn1Yu7i4lPMjxQP+92h0UZjv\nOj5eP3yAtkSCw4cP8x//8R/8+7//O+eff77X5RWs1tbWTj0PAX+IaKyV5uZmdu/ezdChQz2sTmTg\n6FN4sNb+fxmu46QYY4YDq4EbrLV3d7nvx8BDPTz0A2vtpf1dnxeGDRvGmWeemfpGMEHboW2EqsZ4\nXdaAlT5kKX3Ceq6aOXMm1qb2a3Sg/OKh+EIDe3hNthSfVUnbnkba9jYTj8eZOnUq06dPp6Rk4AwH\n2759O9dff707yR7gKyVljEtb1nSgGx4K8b3yKl49VE9LIkFzczM33XQTv/rVr5g0aZLX5RWkzZs3\nu0tNh4vKCAWLOZRacXDjxo0KDyIZkr9LYKQYY0qBF4Eyuu/VmJC6vR24scufnkLFgNB5z4cvPKxk\nYIu3NhBrTk6ODQQCTJw40eOKjm3jxo3MmjXLbZecXU2wKuxhRQOL4ziUXTgUJ5h8e927d++A2v9h\n06ZNXHvttW5w8AHfKKsoqODQriYY5JqKakpSy9BGo1GmTJnCm2++6XFlhWn9+vXucUmkmtJItdtO\nH1onIifnpNdiNMZcDLRYa1eknXOAnwF/DVQBK4G7rLUfnOzrdXntU0kGhwuOcdl5wD5r7a8z+dr5\nYNKkSTzyyCMARBt2kYi14fiDHlc18KTvKD1hwgRKS0uPcbW3otEod999d8dGcIPCnfYpkMzwRwKU\nnV/DoQ+T33q+/vrrXH755UyYMOE4j8xta9as4cYbb6ShoQEAP/Ct8kpGhQp3da6qQIBrKqp59WA9\nB+Mx4vE499xzD01NTVxzzTVel1dQVq1a5R6XFQ8iFCqBPUffJyInp889D8aY04wxy4H3gX/ucvfj\nJHeSvhw4B/i/gXdTQ4gywhjzv4DPgHOBece49NzUdQVnyJAhnH766alWgrYjR69CIScvPTxcemlu\nj4J75ZVX2LhxY7Lhcyi7oGbAj0/3Smh4Saf9H/7whz/Q1tbmYUUnp31ztPbgEHIcrq6oKujg0K7M\n7+eaymoG+Tu+j3vooYeYOXMmiYSm+WVDIpHotOR0eelQykuGuO21a9fS0tLiRWkiA06fwoMxphhY\nAJwP7ADWpN33V8D/SDX/ExgL/BdgFzDdZG45il8Am4Cv0sPqT8aYkSR7PlZ0d38hSP8w23Zo2zGu\nlL6IR5uJNXassnTJJZd4XFHP6uvree6559x2ydlVBEo1ib6/OI5D2YTB7vClHTt28PLLL3tcVd8s\nW7aMm266iebmZgDCjsP3KqqoDer3p13E5+N7FVUMDXT07j711FM8/vjjChBZUFdX524G5/cFKYlU\nEQpGiBSVA9DW1tYpXIhI3/W15+GfgJHA08Bp1tppaff9S+r2T9baa621a621rwD/HSji6F6Kvvop\ncL619n2gp69Oz0vdhowxs40xu40xh4wxr6WGWw146eEh2rCTRCLuYTUDT/RIx+ZDY8eOzelVlh5/\n/HF3dSV/WZDI6blb60DhCwcoGdsx7nrmzJns27fPw4pO3NKlS7n11lvdXpMSn49rKqoZHNAQyK6K\nfD6uqqhiRFqoeuGFF3j00UcVIPrZsmXL3OOKsmE4qV3NK8uHu+c/+uijrNclMhD1NTx8HzgI/JO1\ntrX9pDFmEDCJ5MTlB9MfYK1dDFjgL/r4mp1Ya9+01h7v3bg9PPwDEAIeAd4EvgG8Y4z5ViZqyWWj\nRo2ipqYm2YhHiTXm1weXXBc90rGb6UUXXeRhJce2ZcsW5s3rGN1Xeu5gHJ+GK2VDeEw5/rLkB+3m\n5mZmzpzpcUW9t2TJEm677Tai0eQu9WU+P9dUVFMZOOnpcgNW0HH4dnklp4Q6AsTs2bN5+OGHFSD6\n0ZIlS9zjqrTAUFU+wj1evHixfgYiGdDX8HAW8LG19lCX81embqMkhzV1tRoY0c35/uIAm4EfWmuv\nstZeZ639K5LhwQ88ZowZ0AN2HcfhS1/6ktuONuw6xtVyIhKJBNGGjvCQ/v851zz77LPuP5qhocWE\nhhTeyjhecXwOpecMdttvvvlmpyVOc9XSpUuZMmWKGxzKfX7+r4oqygb4jtmZEHAc/qKsktFp80Fe\neeUV9UD0k927d7NmTfvoaYfqipHufeWlQwmk9jjau3dvxxLVItJnfQ0PlbhrGHTSvjbocmttQzf3\nO/Q8xCjjrLW3WWtPs9Y+2+X8IpJDroYBV2SrHq90Cg9p35TLyYk115OIJTveKisrGTMmN/fR2LJl\nC++9957bLj67+hhXS38IDokQqE4uhxuNRjstlZuLPvnkE26//XZisRgAFT4/36uoolTBodf8jsM3\nyyoYkxYgZs+ezTPPPONhVQPT/Pnz3ePKslqCgY6lp32Oj+qKUW777bffzmptIgNRX8PDPqC73Va+\nnrqd3819AGfSfejwwsep29FeFpEN5513nruiTvIDb/6u+JJL2idKQ3KJVp8vN7dNSZ+kG6otJlg5\noDvbcpLjOJScXeW258+fz8GDBz2sqGerVq3it7/9rTvHoUzBoc98jsM3yio69UA899xzvPDCCx5W\nNbDE43Heeustt11TfdpR1wypPt09XrRokTvxX0T6pq+fdpYDXzLGuDMujTFjgfEk5zvM7foAY8w5\nwLjUY7PCGDPBGHN5D3dHUrcD/l2ktLSUU0891W3HmvZ7WM3AEU0LD+PGjfOwkp4dPHiQBQsWuO1i\n7engmeDgCIGK5PCJ1tZWXn/9dY8rOlpdXR233HKLu6RlSWoFoRIFhz5rDxCj0iZRz5gxo9McJOm7\nZcuWsXNnskfd7w916mVoV1ZSQ7ioDIDGxsZOPRUicuL6Gh6eBkqBGcaYWmPMMOAPqfvWWWvfS7/Y\nGFNCx27OL/bxNftiDjA/NZG7q6+kbpd1c9+Ac/bZZ7vH0SZNmj5ZiUSCWNr/x/T/v7nk7bffdr9B\nDlQWuUNnJPscx+m0wtWf//xnd7O+XFBfX8+NN97IkSNHAIg4Pq7WHIeM8DsOf1FeybC0Faruuece\nPrWQjo4AACAASURBVP30Uw+rGhjSe1aHVp+O33f0ZH7HcRg22HR6TC793RPJN30ND8+SXLXoGmAb\n8AXwNZITpX/WfpExZoQx5rckJ0pfAiwFsjng848k/xsnp580xvwAuApYaK0tiG0n0z/cpg+3kb5J\ntDWSiCY7rYqLiznllFM8rqh76d+wRcaUa0M4jxWNKMUJJd929+7dmzPrzre0tHDzzTe7E7kDwHfK\nK6n0a1WlTAk4Dt8qr6QqFcZisRiTJ09m69atHleWv9asWcOKFe3bODnU1vS8jdSQ6tPx+5Lhbdu2\nbZ3mgYnIienTvwzW2oQx5hrgP4D/FxgCfAL8h7V2YdqlY4Bfp44XAf+1F8urZtItwNXA3xtjzgPe\nAwzJ4LAd+Nss1uKps846yz2ONuzi4Krne/W4osHjCA8Z3+vXad69kpa9vc9j+f78AKeffjr+HPx2\ndtOmTWzevDnZ8DuEhpd6Wo+A43coGlFK86bkQnXz58/n3HPP9bSmRCLBvffey/r164HkihbfLK+k\nJqh9HDKtyOfju+VVzD64n8Z4nMbGRiZPnsy0adMoLtYKaCfq2Wc71kKpqRpNONTze5zfH6S2xrBt\n1+dAcu7JpEmTcvK9WyTX9XmGp7W2xVp7vbV2tLW22Fo7yVrbdRDnWuDfga9Za6+01vbXYPtE6k/X\nGveT7PH4PTAc+DlwAckhVBdaazf3Uz05p7a2lqA+DPSL9PkkueT99993j4tqS/AFc3NCd6EJjypz\njz/44AN3RSOv/PnPf+40/n5SSRmnhDSpvr+U+v18t7yS9o+s27Zt46677tISrifok08+Yfny9imU\nDiOH/v/svXmcFOW1//+p3nv2fWOGWZihGPZlZN83jSiaCChLjKKSKKARcQERUdTcm8R7TbwmN3K9\nidGfRhOMJooREiXefEWQfS8YGGaYhdm33peq3x8181Q1zj7dXdXdz/v14mU9Vd1dx+qequc855zP\n6d0Jz0othqYjramiooLWPlAoAySgMWmO4+oA/CSQ5+g4z5sA3uzmWDOARzr+RSxarRY5OTm4fPmy\n0qaEHWp1Hr755huybciMVtASihxdohGMUQvB6UVbWxsuXryIESNGKGLL5cuXsWvXLjIebjRhpMnc\nwzso/iBZp8ecmDh8bhEjUAcOHMBf//pXLF26VGHLQgOv14v//d//JeO0pAKYTXG9vk+vMyIrtRiV\ntacAAG+99RZmzJgBs5n+5imU/kATWiOIoUOHEufBlDkRxsRhvbyj/5jSRvUrTShUP7/98j7wjhYA\nUGW9Q2trKy5evEjGnhYHkP3tkL71XBNsXDOi2EREd9H/gR73/3GGYWBMj4Kjoh0AcOTIEUWcB7fb\njf/8z/8kTeBStDrMjKF1McGi0GRGnceD0w4bAODNN9/ExIkTkZ2d3cs7KXv27EFZWRkAQKPRIidj\nXJ/fOyRtJGobL8LtcaCpqQl/+MMfcO+9EZPBTKH4hQE5DyzLXkQXaUJ9heO44b2/iuJvMjIyyLbg\ntitoSegjuG1kOzMzU0FLuubcuXM+Y0ZLU5bUhD7NTJyHs2eV0Wx49913SU2MFsCCuHjoqOMQVKZE\nx6DG7UKj1wOXy4VXXnkF//7v/07z8HugsbERb731FhkPSRsNo6Hv9SJarR65WRNQWnEAgKi8NHfu\nXNU2+aRQ1MhAZxTDABQO4h9FAZKTJcVanjoPA0bgvaSztFarRXx8vMIWfZvz588rbQKlB/RJUprE\nhQsXgl73UFlZ6dOobEp0LOKpslLQ0TIM5sbGkQcxx3HYu3evojapGUEQ8Oqrr8JuF59fZmMchqT1\nv8dOamIB4qLTAIgpUK+88gqJwFEolN4Z6NPi2y0cKapH7jwIHlsPr6T0BO+RHK/ExERVdpaWpyzF\nTU6HsRulpejipC7TbejxwB7XRumgMWvB271wOBy4evUq8vLyuv0cf/O73/2O6Nxn6PQYRescFCNZ\np8eEqGgcsVkBAO+88w7mzJlD1Ze6YN++fThy5AgZF+RMgUbT/ygNwzAoyJmCE9wnEAQely9fxvvv\nv49Vq1b501wKJWwZqFTrFT/bQQkCPpEHT9g31g4Yguzaya+pmpBrx+viqXKOGtHFGeGyi058RUVF\n0JyH06dP4+DBg2Q8PSaW1jkozDhzNM477LDyPFpaWvDBBx9gzZo1SpulKq5evYrXX3+djDNTRiA+\nJn3AnxdlisfQzPEorxYVm9577z2MGzcOo0YFrqaOQgkXgrZkyrKsiWXZfJZlNwbrnBRf5CtZAk9D\ntANFfu3UuDrY2tqKlhaxmBtaBpoomo6iRrRxBrIdzEZhf/rTn8h2odGEFB2VcFYaHcPghigpOvjx\nxx+T1ByK2MTwpz/9KZxOJwAxXWlo1vhBf25W6giSvsTzPH72s5+htbV10J9LoYQ7fnMeWJbdyLLs\nBZZl7SzLeq//B8AK4BKAV/x1Tkr/MBplK9C8stryIY3s2vlcU5VQU1NDtnUxerqqrFJ0MdKkXf6d\nBZLq6mqftI9JUVTCVy0UGU2I7yiUtlqt2L9/v7IGqQRBEPDaa6+R4n6G0WB43ixoNYNfFGEYDYpy\nZ0CnFR35xsZG/PznP1e89wqFonb84jywLHsXgF9ALIY2QmxS2jljYWT/zgD4tT/OSek/8okujTwM\nHPm1U6PzUF9fT7Y1Zhp1UCuaKMl5kH9ngeTTTz8l20P1BlokrSIYhsEokxTJ3LNnj4LWqIe//vWv\nPs3c8oeUINqc6LfPNxqiUTh0OhkfP34cb77ZZdsoCoXSgb8iDz/q+O9jAGIBrIco5ZoLIB7AcgC1\nAKoB0LQlhfDpMC3wyhkS6siunRq7djc0NJBtbZT67KOIaGXpZMFwHgRBwFdffUXGI83qS7mLdIYb\nTaQQ8cqVK6iurlbUHqU5fPgw3njjDTJOSxqG9OQiv58nKT7bp0P1n//8Z+zbt8/v56FQwgV/OQ/j\nAJzlOO4/OY6zAjgAMdIwj+O4do7jdgP4LoBFAO7x0zkp/cTtdksDRn0KQSGD7NqpUd5PnrPLGKle\nvFrRyL6btra2gJ+vsrISdXV1AAA9w2CI3tDLOyjBxqDRIMsgfS+HDx9W0BplKSsrw09/+lOiChYT\nlYyC7MkBS8PMyRiLxLghZPzaa6/hxIkTATkXhRLq+GsGGQ1A3pXqPMTIA2n7yHHc1wCOArjfT+ek\n9BO588BQ52HgyK6dj0OmEuQTUY2Bfs+qRcsAGnEi5HK54HAEVgHt2LFjZHuI3gAtrYVRJUP1Uiqk\n/DuLJGpra7Fjxw5SNG7QR2NE/twBybL2FYZhMDx3JqJMYkqU1+vFiy++iEuXLgXsnBRKqOKvmUUL\nRAcCAMBxnANiitL1mmdlAMaAogg+q+QBvAmHO4zKIw8Wi4Vsawz0e1YrDMP4OHfy7y0QyBWdrric\nOGzt+nyHrRa83lBLjyt0vFF2TwmmCpdaaGlpwfbt29HU1AQA0Gr0KC6YB4M+8L1ItFo9igvmknPZ\n7Xbs2LEj4tPHwgWPx4MPPvgAf/nLX2hR/CDxl/NwFMAMlmXl3ZDOApjMsqx89pILwOWnc1L6iVz6\nj2HopHLAyFQ+1CinKF/BZnQ08qBmGK30/QQ68lBVVRXQz6f4B6MsIlRfX0/kSSOB9vZ2bN++nUzW\nGUaDEflzEG1OCJoNRkM0igvmQ9uhwNTS0oJnnnmGpPxRQpe33noLv/3tb7Fr1y7s3r1baXNCGn/N\nLH4LsVD6AMuyd3Ts+whAIoD/Zlm2iGXZzQBKIDoVFAUg2v8AGJ1JQUtCG41OSitobm5W0JKu8ZmE\namlqipphdNL3E2hHtLa2NqCfT/EPGpnzIAhC0JS4lMZms+HZZ59FWVlZxx4xjSg+NiPotkSbE1Gc\nPxeajkW2uro6bNu2jURDKKGJvIbl6NGjCloS+vhFp4/juPdYlp0DUXXpLgC7IToUmwDcB2AtJOnW\nl/xxTkr/kU90qfMwcOTXTu6QqQWf2hbqPKgbjfT9BLp+Rv75a5JSENVN6mJJdAxKomO6PEaPB+d4\nmcuJZq+YvqTGuip/Y7Va8eyzz+LixYtkX+HQqUhOGKqYTXExaWDz5+B82X4IAo+amhps3boVL774\nIpKTkxWzizJw5KlKnYX4lIHht5wGjuMeAjAFwH93jO0AZgN4EwAHYC+AmzmO+5u/zknpH/KJrnz1\nnNI/GK107drb21X3cJfXYTAa6jyoGfn3E+gcXPnna0B/F2pG/mAO99xsi8WC7du3g+M4sq8gezLS\nkoYpaJVIYlwWhufOQufaZ1VVFbZu3YrGxkZlDaMMCPnfUrj/XQUav3YI4jjum+vG1QDule9jWTae\n4zja/10B5DmbjC7wxWfhCsMwYHRmCB4xzaS+vh5ZWVkKWyXhc1OkijrqRuY8BLr4XquVIg2/b+p7\nKsxEc3SPK+XXc9hqwVG7lX6+nz5f/r2FG62trXj22Wd9FI3yh9yAjJThClrlS3JCDti8Wbhw5f8g\nQEB1dTWefPJJvPDCC8jICH5KFWXgUOfBf/irw3QZy7Iv9+F1v4cYhaAoQGVlJdnWGmMVtCT00Rik\nyYLaClF9wrHUdwgZBEEI6OcnJSX1/iKK6gjXFJnGxkZs2bLFx3EoyJ6MzFRWQau6JjlhKIbnzSZK\ne7W1tXjqqadw9epVhS2j9Ad5loDaMgZCDX+lLeUCSO3pBSzLxgEYCyB4sgkUH+RycxoDdR4Gg9z5\nUpvzEOhJKMWPyJy7QH9vqak93qIpKkSr1SI2Nvzu1Z2r9/LJ97CcqaqKOFyPGIGQHIjGxkY89dRT\nPnUaFHXjcklin5GkYhYIBpS2xLLs1wBuuG73GpZlV/fyVgbA8YGckzI43G43rl27RsbylXNK/5E7\nX2pzHiihSaCdhyFDpO65Y81RmBodmElpbwXB9PN7/vxKlxN72sT6tKFDhwaso7JSXLp0CTt27CA1\neAwYFOXOQEpinrKG9YGk+GyMLJiPc2X7wfMetLW14emnn8bTTz+NcePG9f4BFEWx2WxkO9DS2OHO\nQCMPGwB4IXaR7nziCT384wHYABwDsG4Q9lIGSEVFBUlnYfRRYDR+LXeJODTGOLItSQuqg3CbbIQ1\nMn9BowlsT47i4mKyXUtD9qrlmuy7kX9n4cCJEyewZcsW4jhoGC3Y/Dkh4Th0Eh+bgVHDFkDX0Qei\ns5Hcl19+qbBllJ5wuVw+qUoWi4VG6QfBgGaQHMcdBmDoHLMsywN4h+O47/vLMIp/KS0tJdtaU6KC\nloQH8mtYVlYGj8cDnY46ZBT1Ip+I1nvccPI8jAF2WCj9p9otpVaEk/Owf/9+/OIXvyDCAFqtAcX5\ncxEXk6awZf0nNjoVo4sW4+ylz+Fy2+DxePCzn/0MTU1NuP3225U2j9IF7e3tPmOv1wubzYbo6GiF\nLApt/PXkWAvgN376LEoA8HEezNR5GCwanRGMPgqAuKKhpsI5nxVsurCiboIYeYiPj8ewYaL8JQ+g\n3EVzftWG1evFNY+4OqrRaMIiFUYQBHzwwQd4+eWXieNg0JsxunBRSDoOnUSZEjCm6EaYjfFk3xtv\nvIFdu3bRHgIqpKuGrmrs0xQq+KtJ3O86t1mWzYHY3yEdgBNALYAvOY6jvd0VhEYe/I/WlAiPW8yh\nvHjxIvLz8xW2SMTXeaDeg7qRvp9AOw8AMHPmTKJuc9npwHATlWxWE5dlDt3o0aORmBja92qv14vX\nX38de/bsIfvMpniMLJgPoyH0V3yNhmiMLlqM82X70W4V5Y//8pe/oL6+Ho899hiMRtpPSS101R28\nqanJpxaM0nf89rRiWTaeZdl3AVwB8BaAnwN4FcD7AKpYlv0Dy7KhfScMUex2Oy5fvkzGOjOVbPQH\n8ut49uxZBS3xRT4Jpb6Dygli5AEQnYdOrrpdsFCtc9UgCALOO6SCTvl3FYrY7Xa89NJLPo5DXHQa\nxhQuDgvHoRO9zoiRwxYgKT6H7Dtw4AC2bduG1lba0kotNDQ09GkfpW/4JfLAsqwJwD8ATATQCuBT\niE6EFkABgMUAVgAoZFl2GsdxtFoviJw/f56EUTXGeDBaQy/voPQFbVQK2VaT8yBvKtWyv7KHV/oS\nxSYiurjvjqX1XBNs3LdDwfTzB/b5wWgGlpGRgbFjx+LkyZMQAJx12DA5QKpLlP5R43ajucOZM5lM\nmD17tsIWDZympiY8//zzPj0cUhJyUTh0OjSa8Gt6p9XowObNwpXqo6ipPw9AfO5u3rwZO3bsoKvb\nKkDeJLenfZS+4a+lrh9DdBw+AZDLcdwqjuO2chz3JMdxywEM7Tg2EaJSEyWIyCe2OtmElzI4tKZE\noEPzu6ampsuwqBKEc0facCYYkQcAuOWWW8j2OYcdbhqeUgWnZFGHefPmhWwhZ3l5OTZv3uzjOGSl\njURR7sywdBw6YRgN8oeUIG9ICdl37do1PP744zhz5oyCllEA8Rndl32UvuGvp9VKAI0AVnIc13b9\nQY7jWmWv6a0XBMXPnD59mmxrqfPgNxiNFlpZ6pJaHhDUeQhNgqXWNXnyZKSliYWqTkHAOdmklaIM\njR63TwH7kiVLFLRm4Bw7dgxPPPEE6uvrO/YwKMiegrysiREjIZ2VOgJs3hxoGPE+3N7ejm3btmH/\n/v3KGhbhdNWPifZoGjj+eloVAfiU4zhLdy/gOM7Csuz/AVjop3NS+oDD4cD58+fJWBcduuoWakQX\nlQavTcybPHHiBGbNmqWwRb6T0PgZmTCkRgXkPNHFSf1K46Gf/22a/lEBb7uYxRks50Gr1eKOO+7A\nr3/9awDACZsNI01R0EXI5E6NHLVZyfa0adOQm5uroDUDY9++fXjttdfg7Ui90mh0YPNmIzEuS2HL\ngk9yQg4MhkU4f3k/3B4HPB4PXn75ZdTW1mLFihUR40ipBbfb3aWjUFFRAUEQ6PcxAPwVefAA6MsM\nJTCzGEq3nD17lsjjaYxx0OhMClsUXsidsePH1dE8XT4JFXiakqJqZIqOwYwYLVq0CMnJyQAAu8Dj\njJ1GH5SiweNGmSzqcOeddypoTf8RBAFvv/02fvnLXxLHwaCPwpiiGyPScegkNioFY4pu8pFyffvt\nt/Ff//Vf5JlMCQ6VlZXkmsdG62Eyis9Im82G2tpaJU0LWfzlPJwAMJtl2aHdvYBl2VyIEq4n/XRO\nSh84ceIE2dZFpytoSXiijUoGOsLTtbW1uHbtmsIWAXq9XhpQMR1VI3fuDIbgCRno9XosX76cjI/Z\nrXBQbfqgIwgCDlqlgP3UqVNJL45QwO1245VXXsF7771H9kWZEjGm6CZE035CMBljMKboRsTHZJB9\ne/fuxc6dO2GzUYc9WFy8eJFsp6dGIz0lqstjlL7jL+fhNwDMAD5jWXbK9QdZlp0K4DMAJgC7/HRO\nSh+Qr4bTlCX/wzAa6KJTyfjYsWMKWiMin4QKdEKoagSv9P34OH1B4MYbb0RWlrgy7BIEHJOlzlCC\nQ6XbhaqOjtIajQZ33323whb1HZvNhp07d+Lzzz8n+xJiszCmaDGMBppk0IlOZ0BxwTykJkp9gI4e\nPYqnn366y8ZlFP/DcRzZzkiLQUaaJEYgT+um9J0BOQ8sy/Isy/6+c8xx3NsA/gCABfAVy7LlLMv+\ns+NfBYCvAAwH8D7HcW/6w3BK7zQ3N8v6OzDQRaX2+HrKwJBHdNTgPMgbEwlemrakZuTfT7AbSul0\nOp/J6hmHDa1emk4RLHhBwNfWdjJetGgRcnJyeniHemhubsaWLVt87ndpSYUYUTAXWm1wneBQQKPR\nonDodGSnjyH7SktL8fjjj6O6ulpByyIDuZjJkPQYZKXHkLFcUIbSd/ypDbgGomRrBYAcALM6/mUD\nKO84ttKP56P0gvzGro1KAUNv6gFBJwtJnzhxQvF8VpNJqmsRPDTyoFYEQQAUdB4AYPr06SguLgYg\nll98be1W84LiZ8467KSvg9lsxurVoSFEWFtbiyeffNKn8WhOxlgMy5kCDRMcueFQhGEYDM0ch2E5\nUwCIBbpdXUuKf6mvryfF0lotg4zUaAzJiEVnjXRZWRlt5jcA/PaXznEcz3HcLzmOyweQC2AagOkQ\n+z7kdxyjy6BBRO480HqHwKExxILRmQGIoXx5iFQJfJ0H+ienWry+9Q5KSOwyDIMHHniAjMtdTlTK\nincpgcHB8zhikxy15cuXIzFR/TUCFRUVeOKJJ2T6+AyG5UxBTsZYqljTR9KTizAiX5JybWlpwdat\nW1Uj9R1uHD16lGxnZ8RCp9PAaNCS1CVBEFQjdhJKBGSZgOO4qxzHHeQ47muO464G4hyUnuF53ucP\nQi9bHaf4F4ZhfKIPSt+IYmKkkKzgphXTaoV3SVEh+XcWbIqKirBgwQIy/sraDp42jgsoh20WODuu\ncXp6Om677TaFLeqdsrIybNmyhTTDZBgNRuTPRnpykcKWhR5J8dkYOWwBtBoxG8BqteLZZ5/FyZNU\nT8bfHDp0iGzn5UjKV/nZ8V2+htI3aIwxTCkvL0dLSwsAgNEaoDElKGxReKOPUU/dg4/z4KJpS2pF\n7tjFxsYqaAlw9913w2wWo2ctXi/O0MZxAaPR48Y5h52M77vvvqAqbQ2E0tJSPP3002hrE3vAajQ6\njCyYj6T40KjRUCNxMWkYXbQY+g75dKfTieeee85npZwyOGw2m89i3rDcBNm2FOk7fPgwXC5XUG0L\ndQbTlWgRy7Kf9/6yb8Nx3PxBnJfSB65PWaIh5cCilaWFXbx4ERaLRbHVZPlElHfSyINakX83SjsP\nSUlJuOuuu/Db3/4WAHDYZkWh0Qyzhq4v+RNBEPCVpR2dcZ3x48dj6tSpitrUG1euXMEzzzwDi0VM\ns9Jq9Bg5bAFio1MUtiz0iTYnYnThYpy59He43Da4XC68+OKL2LFjB8aMGdP7B1B65ODBg8QpSE0y\nIzFeSulNTTYjPtaI1nYnbDYbjhw5gmnTpillasgxmCdDOoC5A/xHCTA+zgNNWQo4Gq0BWpPYTZjn\neZ/+GsEmKUnqakydB/XCO6TvRv6dKcWtt96KIUOGAADcgoBvbLR42t+UuZyo8YgdxbVaLR544AFV\nL+xUVVX5OA46rQGjChdSx8GPmE1xGF24CAa9KG/rcrmwc+dOxWvnwoH9+/eTbXaY7z2WYRiMkO2T\nv5bSO4OJPBwA8Do6ZQP6jt+TaVmWzQJwDsB2juN+0cXxuwE8CqAIQDOA9zteG5bC5i6XC2fPniVj\n2t8hOOhi0uF1iPnAJ06cwIwZMxSxQz4R9dqp9KZa4R3Sd6MG50Gv1+O+++7D888/DwA477BjpMmM\nFB1VafMHHkHAQZk065IlSzB0aLd9VRWnubkZ27dvJ+mvnRGHmKhkhS0LP0zGWIwuXIRTF/fC7bHD\nbrfjueeew89+9jPi0FP6R0NDg0/KUnHRt3+3xUXJOHhcLP4/dOgQWltbER8f/63XUb7NYJyHS2ro\n2cCybAyADwDEogvHhGXZLQBehNgF+5cAxkJ0JKayLDuX4zh3EM0NChzHkVCdxhADjZ427AkGuug0\nOBvOAQBOnTqlmB3JycnQarXwer0QnF4IHh6MjqafqA2vVbr1pKWpw8EvKSnBxIkTSd7111YLlsQl\nqHp1PFQ4bbehvaNpY2xsLFauVK9yucPhwAsvvIC6ujoAgIbRorhgHnUcAojJGItRhQtw+uI+eLxO\ntLe3EweCTmj7z969e8F3/L0NzYpFXMy3pbCTE83ITItGTZ0VHo8Hn3/+Ob773e8G29SQJKRnFCzL\n5gL4J4DJPRx/HmKTuhKO47ZyHHcLgJ0QpWTXBcvWYCJXbKBRh+ChNScDHTrnlZWVRJUk6HZotT6T\nUa8t7PzjsEDuPGRmZipoiQTDMLj//vuh6ah1qHa7cNVNCwkHi4PnccwuBbpXr16tqMJWTwiCgFdf\nfRUXLlzo2MNgeN4sxMXQZ0mgiTIloHjYPCLjWlNTg3/7t3+D10vTT/uDx+PBZ599RsZji7v/7Y4Z\nITXP/fTTT4nDQemZkHUeWJb9MYBTAMYA6K5wex0ALYCXOI6T//W9BKANwP0BNVIh5Kve2ih6ww8W\njEYrOhAdKCm7J5+Metqp86BGvLLvJSsrS0FLfMnJycGNN95IxgepdOugOWqzwt1xDYcMGeJzfdXG\n3/72N3z55ZdknD+kBEnx2QpaFFnERqWgKFdKeT19+jTeeecdBS0KPQ4cOEAW76LMOhTmda82OaIw\nCUaD5KwdPnw4KDaGOiHrPAB4BEAZgNkA3urmNbMhpjLtl+/kOM4J4GsA41iWVVbmxM+43W7ZihGg\ni07t4dUUf6OLkq73+fPnFbMjPz+fbHtbadMvtcE7PKSY3Wg0Ij1dXU0cV65cSaRbm71eXHI6FLYo\ndLF4vTgrk7695557oNMNJmM4cJSXl2PXrl1knJ5ciMxUVkGLIpPkhKHIyRhLxu+//76iIhyhhCAI\n+POf/0zG40amQavtfqqr12l9og8fffRRQO0LFwbqPDwP4M+9viqwrAMwnuO4r9F90fYwALUcx3Ul\nWn6l47/DA2CbYpSVlcHtFlc0GX00NDpTL++g+BOtLCdYSeehoKCAbLtbqPOgNtwyhy4/P1+R7tI9\nkZiY6NO47KjNSqMPA+So3YrORAiWZTFlyhRF7ekOnufx6quvkucHAOi0384TB4CKmhP46vjbqKjp\nekJLjw/++NVrJ2E0SKltr732GpxOei/vjTNnzuDixYsAAK2WwbgeUpY6mTAqDZ1lXSdPnkRpaWkg\nTQwLBuQ8cBy3g+M4RZ0HjuP2cRzX29MsGUBLN8daO/4bVpVI8gmrzkyL24KNziyp5pSVlcHhUGbF\ntrCwkGx7mpwQ6MRPVXgapd+F/LtSE7fddhuio6MBAK28F6U0+tBvLF4vOFlDuDVr1qi2+Pyzzz6T\nyYOKNjJMKCcnhD5JcdnQasUGgjU1NXjvvfcUtkj97N69m2yPLEpGdFTvanFxsUawBdKz+4MPP+wz\n9QAAIABJREFUPgiIbeFEuN8Z9AC6c9U794fV0rxcG1pLlTGCDqM1QGOMAyCu5Cm1gpGZmUnkPwUP\nD08rLXpVEy6Z8zB69GgFLememJgYH+WRE3YrdUL7yUm7jUgAjho1CuPGjVPUnu5wOp149913yTiW\npruqAq1Wj7ysCWT80UcfEelcyrcpKyvzqVkoGdt3IYqScVI/rP/3//4fqqur/WpbuKHOxEv/YQdg\n6OZYZzw2rHo9lJWVkW2tKbGHV1IChdaUCN7ZBkDszqrE5JBhGIwePZoUPrrrbdAndJ2CQAkuvJuH\np0lyHkaOHKmgNT2zZMkS7N69G3a7Hc1eL666XRhqoL+jvuDgeZyXRR2WLVum6qhDc3MzAMCgN2PU\nsAXQaLpPpRuaOQ5DM7t3hOhx/x0XBAHXGi7Aam+Gy+XCBx98gLVr13b73kjmj3/8I9kuyk9EUkLf\n14bTU6KRlx2HK5Vt4Hkef/rTn/Dwww8HwsywINwjD83oPi2pc39rN8dDDrfbjaqqKjLWmsIqIytk\nkF/3K1euKGbHhAnSipXzWldlPxQlcNfZSEeagoICJCaq18mPiYnxUQY6Zae/o77COezwdHzReXl5\nmDRpksIWdY0gCNizZw8ZD0kb3aPjQAkuDMMgO10qnt67d69PXQpFpKqqCv/617/IeMr4/stfT5kg\nqd598cUXaGho8Itt4Ui4Ow8XAKSzLNvVUlk+AC+Ai8E1KXBcvXqVaBRr9NFgNOEeWFInGqM6nIcb\nbriB6PV7Gh2o//ASrOe67j1hPddEjwfpuLNGCnaqtXhWzq233kpWzKvcLrR5adfy3hAEAedkUYfb\nbrtNtVGHS5cukUUnjUaHtORhCltEuZ6k+GwY9WL9kdVqpXKiXbB7926SVpmXHYf01Oh+f8aQjBhk\npYtF6h6Px0e1ieJLuDsP/wexz8Ns+U6WZU0ApgI4w3Fc2KQtVVRUkG0NjToohlbmPFRUVCiWJx4f\nH4/i4mJFzk3pGoEXQs55SEtL81k1l0+KKV1T7XajjReleKOjozFz5kyFLeqeQ4cOke3k+Bxo6aKT\n6mAYBilJkvz2N998o6A16qOhoQFffPEFGcsjCP2BYRhMmSBFLD777DO0toZNcopfCXfn4R2I0YUd\nLMvKax+2AogF8LoiVgWIuro6sq0xqLN7aSTA6ExAR4dQu90Oq1U5/3Tu3LmKnZvybbztLsArOpM5\nOTk+krpq5qabbiLbpU4HLZzuhVKn5GDNnTsXJpN6dTnkfYESYtXTrJDiS0KsNKntlCKliHz00Ufw\neMSIaFZ6DLIzB96+Kz8nHqnJYo8bp9OJTz75xC82hhthvcTAcRzHsuzPATwJ4BjLsh8DGAXgZgD/\nArCrp/eHGvX19WRbo4tS0JLIhmEYaPRR4F3tAMTvJSZGGWdu5syZ2LVrF1wuUW3JkNH17yK6OAnR\nxUldHqPH/Xe8+UupJmnhwoWqTWW5nkmTJiE2Nhbt7e2w8jzqPG6k67vToohsvIKAMpck8jdv3jwF\nremd8vJysh0d1f1vmKIsMTIZ8IqKCni9XtX1h1ECq9WKzz77jIwnD6DWQQ7DMLhhXCb2fH4ZAPDJ\nJ5/ge9/7nqoXAJQgXCIPAkgJoi8cx20BsKHj+MMARgL4DwBLOI4Lq6ojH+dBT50HJZFff/n3Emxi\nYmIwffp0MrZfoiFYpXA3O4jKkk6nU/2kUo5Op8O0adPI+DJtVtUtVW4XXB2RmdTUVAwfru4+pBaL\nhWwb6XNDtWi1emg1Ys8Cnudht9P0QQDYt28fuRZJCSYUDB18yjZbkIS4GHFxpK2tDfv37x/0Z4Yb\nYRF54DjuTQBv9nD8VwB+FTyLlKGxsZFsM3qzgpZQGNlDWP69KMFtt91Gbn7OKgu8o5KhNYfFn35I\nIXfcZs2apWqVpa6YPn069u7dCwC46nZiGgaeGhDOVMqiDtOnT1d1dEkQBJ9GllRlSd1oNDp4eXHN\n0263KxbRVgs8z/ukFU0ak+6XvzeNhsHE0enY//VVAMDHH3+MG2+8UdV/y8EmXCIPFMBnJYLR0pQC\nJWE0UldLpVeICgsLpV4CAmC70KyoPZGIp90FZ6W0wrt06VIFrRkYo0ePhl4v/q5bvF5YvV6FLVIn\nVW6pIePEiRMVtKR3GIZBVJS00OHx0maSakUQBHi8kmPa2f09kjl+/DiuXbsGADAZtSgu8l9j3FFs\nCvQ6cYpcXl6O8+fP++2zwwHqPIQR8hUkKtOqLPLrr7TzAADLly8n247yNnhtVG4zmNg4yWGbOHEi\nCgsLFbRmYBiNRh/1rmo3nWhej433ornDqdLpdKpuANhJcrI04XK4LD28kqIkbo8DgiBKsZvNZh+n\nL1LpjIQCwMiiFOh1/oucmYw6jCiU6kzkdRWUMElbooj4RB6o86AojFa6/nKnTikmTZoElmXBcRzA\nAzauCbET0pQ2KyLwtDh9og6rV69W0JrBMWrUKJw8eRIAUO/xoEhhe9RGg0dyyouKikKiyDIvLw9X\nr4rpGacu/K3P78tOH9Njl+Trqag5gcraU/Tz/fD5ubm5ff7ccMVqtfrIDI8Zker3c4wZkYpT58VG\ncV999RUefPBBGI1dtQ2LPGjkIYyQd5101HcdYnPUnUHr2T/CUXeGHg/gcY9Fks11qqC4lGEYrFmz\nhowd5e3wtChvV7gjCAIsp6QupSUlJaovoO0JecSk3hNWehN+QX5NQiW6NHbs2N5fRFEV48b13SkJ\nVw4ePEjmPGnJUUhJ8n+dZ0ZqNBLjRWfBbrfjyJEjfj9HqEKdhzBCp5NHG6gOu5IIsuvfmSeuNOPH\nj0dJSQkZW041UL3+AOOqscLdKEaeNBoN1q5dq7BFg2PYMKn7cJPXQ38/19EkizzIr5WamThxIi0E\nDTHk9/FI5euvvybb7LDASAwzDIPhBdJnHzx4MCDnCUVobksYYTAYpOgDfagri+z6GwzqKV5fu3Yt\njh49Cp7n4W50wHnVAtNQqpoTCHg37xN1uPnmm5GTk6OgRYMnKSkJZrMZdrsdbkGAQxBgphNPQpus\niHzIkCEKWtJ30tLSMHHiRLKqmpkyAvnZ/p+cDs0c1680Hvr5EtV1Z3Gl+igAMWWJZdmA2REKeDwe\nHD9+nIyH5SUE7FyFuQk4eKwGAHD06FEIgkCdbVDnIayQr3AbU7pOjTCljYIpbVS3n0GP++e4o+4M\nvDYxdUktkQdA7Gq8dOlSfPjhhwAAy+kGGNKjoDFSiUZ/YzvXBN4uTibj4uKwatUqhS0aPAzDIDMz\nE5cviw2U2rwemDXqcY6VRBAEtPGS85CZObhmVcFkyZIlxHm41ngBmakjYDJGtgyoWvB4XKisPU3G\nS5YsifjJ66VLl0iNZ2yMAUnxgastSk+NhsmohcPpRUtLCyoqKmjNCWjaUlhhNks5fwKV3FMUgZeu\nv9qKJletWoXUVLG4THD5ro5T/IO7yQH7ZamvwwMPPIDY2PCI8KSlSYX2Vp5X0BJ14RYEuDsijgaD\nAXFxcQpb1HdKSkowYsQIAIAg8LhSTXO71cLV2pNEQjc9PR0LFy5U2CLlkcum5mTGBtSZYhgG2ZnS\nvZtKtopQ5yGMSEqScvN4t01BSyjy65+SkqKgJd/GbDbjoYceImNnpQXOKirR6C8ED4/2I1LB/IQJ\nEzBnzhwFLfIv8fFSB1cHdR4IdkG6FgkJCSG1OswwDO655x4ybmq9ioaWcuUMogAA2ix1qJGJn3z/\n+99XVSRbKUpLS8l2Rmrg+11kpElRuEuXLgX8fKEAdR7CiM7VZAAQ3Mr3Fohk5Ndfbc4DIK40zp8/\nn4zbT9SDd9DeD/7AcqYRXqtYe2Q2m7Fhw4aQmkj2hnxF3SFQ56ETuSMVSlGHTkaNGoVFixaR8eWr\nh+B0WRW0KLLxeF0orfiKjCdOnIjZs2craJF6qKysJNupyYHvd5EmU3LqlDWOdKjzEEbIJ6k08qAs\nao48dPLAAw8Q2wQXj7ajdVQ9Z5A4r1nhKGsj43Xr1vmk+YQD8vRIN/29EDyyaxGqDbzuu+8+ck/w\neJ3grvwfeJ52Eg82giCgtPwr0rQvKioq7BYhBkNNTQ3ZTgxgvQM5R4J0jurq6oCfLxSgzkMYkZGR\nQba9ztYeXkkJJLzHCcEr9lDQ6/U+HVzVRExMDB555BEydtfZYS+lv5uB4rV70H5USleaMmUKFixY\noKBFgUGuHualzgNBHrdTk8Jaf4iOjsbmzZuh0YhTA4utAWVV39BFhSBTWXsaTW3S6vqGDRt8Mgsi\nGZvNBqtVjIhptQyizIHX/YmNlv6eW1pa4PVSh5o6D2GEXFfc62hW0JLIRn7t8/PzodWqV8lo/Pjx\nuOOOO8jYerYR7iblO2KHGgIvoP1wLQSXmLqSnJyMhx9+OCxXCuU51/QRKiF3pHx77oQWo0aN8ql/\nqG0sxYET/x8qak50+fqKmhP46vjb9Lifjtc3l+HqNem1S5cuxaxZs7p8byTS1iZFdqPM+qDcY7Va\nDUwdioQ8z6O9vT3g51Q71HkII3Jzc8lDS3DbwHtoB2El8Nol5yEUGkWtWbNG0g0XgLZvasE76bSw\nP1jPNfk0g3vsscdCMu+dMnDkU5hQdxpvv/32bxX52xwtClkTObS2X0NpxQEyHjt2LO69914FLVIf\nFosk7mEKosS4ySgtCHRGPiIZ6jyEEXq9Hnl5eWRMow/K4HU0ke3CwkIFLekbOp0OmzdvRkyMqCjB\n2z1oO1JLUxX6iLPGCvtFaWK1cuVKjBkzRkGLAgv9XfQOH+IqVAzD4JFHHsGoUVJPm6bWq2huo/ne\ngcLltuFc2X4IHSIEOTk52LJlS0hHsQKB0yktiup1wXMe9DppuuxyUSl8+qsMM0aMGEFkzDyWWuhj\nMnp5B8WfCAIPj1XKe+/UTlc7GRkZePTRR7Fz504AYv2D7VwTokeqs15DLXgsLh9Z1kmTJmHFihUK\nWhR45BPj0F5f9y8a2dUIh5xovV6PrVu34qmnniIKM1zZP1E8bD7iY9LJ63rrvEyP9348OSEXZ0r3\ngefFypmkpCQ8++yzZEGHIuHxSNVFWm3w7kBaLXUe5NDIQ5gxadIksu2x1PTwSkog8NoagY4HQGpq\nKnJychS2qO9MnjwZy5cvJ2PbhRY4q2n/h+7g3TzaDl6D4BEn06mpqdi0aRMpNg1X5A9OXYin5/gT\n+TxGPsEJZeLi4rBz506iGMYLXpy7/AXarPUKWxY+2BytOHvp7/B0iGzExsbi+eefR3p6ei/vjEx8\nFi+CeP+Rn4pGX6nzEHaMHj2aFDTyrnbwVKc7qHgs18j2pEmTQi73efXq1Zg4cSIZtx+tg6eNrrJc\njyAIaD9aB2+72M/BYDBg69atEVHnIHcetDT2QNDK/tblqRWhTnJyMnbu3InExEQAAM97cO7S52i3\n0s70g8XuaMWZ0r/D7RHrpcxmM3bs2IHc3FyFLVMv8ol7UO8+spOFelqiP6DOQ5hhMpl88q3d7TRH\nNVgIggC3Rbre8ihQqKDVarF582Yi+yt4BLR+XQPeFfppGP7Edr4ZrhrJMV+/fn1I1Lf4A/nEmEYe\nJHRh6jwAQFZWFl588UUkJCQAALy8G2cv/QPtNAIxYOyOVpwu/TvcHrGhqMlkwo4dOzB8+HCFLaN0\nBUMXSnygzkMYMnXqVLLtbi1X0JLIgne0gHeKMnImkwnjxnWf56pmYmNj8fTTT8NkEhvj8DYP2g7V\nQuBpqBYAnFUW2DhJjGDp0qU+3brDHYdDkvLVU+eBoJdNLuTXKFzIycnBCy+8QKJrogPxOU1hGgA2\nRytOl+4jjoPRaMT27dsxcuRIhS1TPz6RB4VuPzRtiToPYcnMmTOJQoPX0Qyvs62Xd1D8gav1Ctme\nPn26TyfeUCMvLw+bNm0iY3eDHZZTNE3B3eJEm6wR3Pjx47F27VoFLQo+drudbNPIg4TckQpH5wEQ\n5cBfeuklxMfHA5AiEG2Wul7eSenE5mjBmdJ9JFWpM+IQzgpt/kQNNQ80bYk6D2FJbGwsJk+eTMbu\nFhp9CDSCwMPdWkHG4bASPW3aNKxZs4aMHWVtsF+O3A7UXocHbQdrAK+46pSVlYUnn3xS1U0AA4F8\nYmygzgNB7jzIHaxwIzc31yeFiec9OHv5c7RaahW2TP1Y7S0dEQdfx2H06NEKWxY6+NRcBVFtSSdT\nW3K73UE7r1qhzkOYIp+8ulrKIPA0Zz2QuNuqIHjFm1pKSkrYPAxWrFjh093UcqoBrjqbghYpg+Dl\n0fb1NfB28e8oOjoa27Zti0gpRbnzQCMPErrrIg/hnNrQGYHwKaK+/Dla26/18s7IxWpvxpnSffB0\nNG81m8147rnnfHppUHpH7pgHtc+DXpou22yR9wy8Huo8hCmTJk1CcrKo0S94nXC3XVXYovBFEAS4\nGjkyXrhwYdisRnc2iyoqKhJ3CEDboVp42iNHgUlUVqqHp0V86Gs0Gjz55JMhJcPrT2jNQ9doGAad\nf/WCIIRd0fT15OTk4KWXXkJSUhIAgOe9OFf2BXUgukB0HCQ51qioKDz//PO0xmEAtLVJadhmU/Ba\nlcnPJbchUqHOQ5ii0+lwyy23kLGz8UJYr4QpidfeSLp56/V63HzzzQpb5F+MRiOefvppyRn18BGl\nwGTjmuGskvpdPPDAA5gwYYKCFimLfFJMpVp9kcu1RkIjqezs7C4dCFoDIWGzt3TpOIRKA1G1UVsr\npcfFROuDdt6YKAPZrqujv2/qPIQxN910E4xGIwCAd7bCa6U/+EDgbLxAtufOnUtC+eFEcnIytm3b\nJv2erB60HboW9gpMjkoLbOclZaWbb77ZxymPRGiTuO7RRZjzAABDhgzBT37yEx8H4uzlz6mMKzr6\nOFz6tuPAsqzCloUulZWVZDsx3hS088rP1dlxPZKhzkMYExMTg0WLFpGxo+EsjT74Ga+jBZ72KjK+\n7bbbFLQmsBQWFl6nwOSA5UR92P6m3M0OtF+nrLRu3ToFLVIH8u7JGuo7+CBPVgyXLtN9obMPhLwG\n4uylz2G1NSlsmXI4nBacufQPnwZwzz33HHUcBoHX68WFC9JiXVpKVNDOLT8Xx3Fh+9zrK9R5CHNu\nv/12SbbV1gCPlSpi+BNH/RmyPXny5LDvDDp9+nTcfffdZOwob4f9UvgpMHltHrR9fQ3oiKxkZ2dH\npLJSV3i9Urra+82NOGy1dPm6w1YLXm+ojajj8kZSkSbnmJ2djRdeeMFHxvXM5X/A7gi/+0NvuNw2\nnL30d7jcYmGtyWTCs88+S1OVBsnZs2dJzVVstAFxMcagnTspwQSTUbz/t7a24tKlS0E7txqhzkOY\nk56e7hN9cNadjniP2V947E3wyDp4y2VNw5lly5b5qHlZTzfCec3awztCC8HDo+1gDXinOEmOjY3F\nM888E5HKSl0RaZPi/iDP4pI7WZHC0KFD8fzzzyM6OhoA4PE4cfbS52QSHQl4vC6cvfQFHC7RqdTp\ndNi2bRtVVfID//znP8l2QW5CUM/NMAwKhkrnlNsSiVDnIQK48847odeLhUVeR7PPhJcycJx1p8n2\nzJkzkZ+fr6A1wYNhGGzYsAHFxcVkX/vhWnjaQj/HWxAEtB2pg6dV/H/RarXYsmULsrKyFLZMPdDo\nS/fI12Ui9ToVFBTg2WefJfVRTrcV5y5/AY839O8PvcHzXnBlX8LWIaDRqcw2btw4hS0Lfdrb2/HF\nF1+Q8YhhSUG3gZWdc9++fWHdz6U3qPMQASQnJ/soADnqTtK+D4PE3V5DUsA0Gg1WrVqlsEXBRa/X\nY+vWrUhNTQUACB4hLBSYbOeb4aqRoigPPvgg7fx6HZ1pkACwIiEZJdFdR2RKomOwLiU9oo57IXkP\n8usUaRQXF+Opp56CRiNOMaz2Zly48i8IQvhGrQRBwOXKQ2i1SFK1GzduxNSpUxW0KnzYvXs3ESFI\nTTZjSEbwI8F52fFIiBOdYqvVio8++ijoNqgF6jxECMuXLyehZN5lgaupVGGLQhdB4OGoPUHGixYt\nikjN/4SEBDzzzDMwmUQVCt7mQduh2pBVYHJWWWDjJGWlpUuX4sYbb1TQInXSGcUEAA9C87sOFF5Z\n6EF+nSKRkpISbNy4kYxb2qtxpfqoghYFlpr6c6hrkvLgV69ejYULFypoUfhQWVnpM1GfPC4TjAJK\nbxoNgxvGZ5Lxn/70p4iVbaXOQ4QQHx+PlStXkrGj4Sx4j6OHd1C6w9VUCt7VDkCU3ouUWoeuyM/P\nv06ByQ7r6UYFLRoYnhYn2mTKShMmTMDatWsVtEi9dC5CAIArRB3FQCAIAlwy50F+nSKVhQsXYsWK\nFWRcU38edU2XFbQoMLS01+BK9TEynj9/Pu68804FLQof3G43Xn75ZaJelpUe45M+FGxGD09BapIZ\ngNjz5j/+4z8isr6JOg8RxJIlS5CdnS0OeA8cdaeUNSgE4T0OOOrPkvFdd92FhITgFm6pjWnTpvk4\nUPbLrbCXh04HTt7pRevBa4BXnPhlZWXhiSeeiNic9d6QT4qdYZyG0l88ADqvhsFggMFg6OnlEcPq\n1at9UncuXz0Iq725h3eEFk6XFReu/AvoiMKNGDECGzZsUGRlPNwQBAH/8z//g9JSMVNCq2GwYEau\notdWo2GwcFYeEUc4c+YM3nrrLcXsUQrqPEQQOp0O9913Hxm7W67AQxv59AvHtRMA7wYgTjIjvWFY\nJytWrMCMGTPI2HKiHu5m9Ue2BF5A2+Fa8HZxVctsNmPbtm1UWakHOqU4AcBOlZcIdlkdWVxcnIKW\nqAuNRoNNmzaR1E5e8IK78iW83tDvgyEIPC6U/4s0gUtMTMTWrVsjPmXNX3z00UfYs2cPGc+anB3U\n3g7dkZUeg2kTJRGN3bt3429/+5uCFgUf6jxEGCUlJZg2bRoZ22uO0OLpPuK2XIO7rYKMf/jDH9KH\nRAcMw+DHP/4x8vLyxB080HbwGninuicI1rONcNdLihmbN2+OyPqV/tBZJA8AFnrvIFi8kiMlv0YU\n0SnfsmULqY9yONtxpfqIwlYNnsraM6STtkajwRNPPEEa5VEGx549e/DGG2+Q8fCCREwck66gRb5M\nmZCFgqHSQsqvfvUr/OMf/1DQouBCnYcIZN26dTCbxZw93tUOZyOnsEXqR+C9cNRIxX5z5szBxIkT\nFbRIfZhMJmzdulUqzHd40faNeguonVUW2EulBlarVq3C5MmTFbQoNJBPjNsjMNe3O9pljhR1Hr5N\nTk4OfvSjH5FxbeNFNLVWKmjR4LDYGnH12kkyXrVqFUaPHq2gReHDhx9+iF//+tdknJUeg+/MLVBV\nKphGw2DJgmEkEiIIAn7xi1/g008/Vdiy4ECdhwgkJSXFJ0fd2XAOXmfo5KgrgaP+DHi3KOEZHR3t\nk/5FkcjMzMTjjz9ObvLuBges55sUturbeCwutB+TCqRvuOEGWuDYRzIzJbWRZuo8EJplaTi0L0jX\nzJ8/3ye98XLloZDs/8ALPEorvkZnnUNxcTGWLVumrFFhgNfrxa5du3wiDplp0fjed4qg06lvumrQ\na3HHzcORmiwuxgqCgF/96lf4/e9/H/bNeNX3bVCCwpIlS1BYWCgOBB726m/C/sc+UDz2Jrhk0Zl7\n7rmHhqZ7YNKkST7KXvYLLarqQC14eVFS1iP+3jMyMrBp0yaiSU/pmdzcXLLd4vWAp/cNAECzR3Ie\nSPoexQeGYfDQQw+RuhmX24aKmuMKW9V/quvOkUZwBoMBjz76KBVYGCTt7e3YuXMn/vKXv5B9Wekx\nuOPm4TAa1NszJcqkx/IlI5Auq8X44x//iJ/85Cew2cK3szp9WkYoWq0WDz/8MGlk5LU3wdV0QWGr\n1IfAe2Gv/oaMx44di8WLFytoUWhw5513+qR1tR+pg9eujvoHy6lGeDu6Yev1ejz11FO0QLofxMTE\nICUlBYCoLtQUBoWvg0UQBDTInIehQ4cqaI26iYuLw7p168j4WsPFkFJfcrpsqKyVlApXr17tE42j\n9J/S0lI8+uijOHJEqoMZnp+IZUtYVTsOnZhNOqy4dYRPDcSBAwewadMmlJeXK2hZ4IgY54Fl2Z0s\ny/Ld/HtXafuUID8/30eD21F3Gl5nu4IWqQ9nw1nwHSldJpMJGzdupCvUfaBTYSU5ORkAILh5tB1W\nvv7BUdkOxxUpRW/dunUYNmyYghaFJizLku1at1tBS9RBK++FvUO2NiYmBkOGDFHYInUza9YsjB8/\nvmMkoKwydCLfFTXHwPOio5ibm4vbbrtNYYtCF57n8eGHH+Lxxx9HbW0t2T95fAZuWTgMehWmKnWH\nQa/FbYuLMGF0GtlXVVWFTZs24ZNPPgmZ33dfCZ1vZvCMA+AEsKOLf39UxiTlWb58OQoKCsQBSV+i\n8ouAmK7kbPBNV8rIyFDQotAiPj4ejz/+OHG2PI0O2C4ot8LotbphOd5AxrNnz6YdpAfIyJEjyXaN\nO/Ry1v3NNZkDVVxcTBcYeoFhGKxbt46k+rRZ69DSXq2wVb1jtTejvrmMjOX/D5T+0dDQgB07duCN\nN94gDeAMeg2WLirErMk5qiqO7isaDYP503Nx87wC6LTiPcDlcuG///u/8cILL6C5OXQibL2h/niQ\n/xgL4AzHcc8rbYia0Ol0eOSRR7Bp0yZ4vV547Y1wNnIwpRQrbZqiCLwH9qpD6CyIGz16NL7zne8o\na1QIMmrUKKxatQpvv/02AMB2vhmGtCjok0xBtUMQBLQdrYPgER3jjIwMrF+/PiQfUGpgzJgxZLvK\n7QIvCNBE8LW86nKSbaq40zdycnJw00034ZNPPgEAlFcfR0Jslqr/JuX1GVOmTMHYsWMVtCY0EQQB\nn3/+OXbt2gWrVaqFS0+NwpL5w5AYH9xnQyAoLkpGanIUPvn8EhqaRCnwQ4cOYf369XjwwQcxa9Ys\nhS0cPBGxPMKybByAoQBO9vbaSKSgoMCnwNVZdwZeR4uCFimPo+4UeJeYwmU2m/HII4+frLphAAAg\nAElEQVTQ1cQBsmzZMowaNYqM2w7XgncHN7plu9ACT6PYtE6j0WDz5s2IilK+2VCokpeXh6SkJACA\nUxBQ64nc1CWvIKBSFn0pKSlR0JrQ4s4774TRaAQA2BzNqpZutdga0dxWBUCMnHz/+99X2KLQo76+\nHs8//zxeeeUVH8fhhnEZWLm0OCwch05SksxYfftITBglpTG1t7fjpz/9KV566aWQj0JEymyoc3mA\nOg/dsGzZMgwfPrxjJMBWdShim8d5LLVwNZWS8f3330/TlQaBVqvFpk2bpP4PNg+sZxuDdn5PqxM2\nmVzsypUrfXL2Kf2HYRjccMMNZHzF6ezh1eFNtdsFd0c+c3p6Om0y2A8SExOxZMkSMq6qO63a3PDK\n2tNke9asWT6qY5Se4Xkee/bswfr163H48GGyPyHOiDuXjsDsKTnQasNvOqrTaTB/Ri6W3TwcsdEG\nsv/AgQN48MEHsW/fPtX+3nsj/L6trul0HtJYlt3Hsmwzy7JNLMv+kWXZ4T2+M0LQarV49NFHYTCI\nP3De2QpH/ele3hV+CF4XbDJ1pRtuuAGLFi1S0KLwIC0tDT/84Q/J2FHWBldd4GXsBF5A+9G6zuwz\nsCyL5cuXB/y8kYC8U/0llyNiJVsvOh1ke+rUqapOu1Ejt99+O/R6PQBxdb/NUtvLO4KP3dmGptar\nZEzvIX2nvLwcTz31FH7961/DbreT/eNHpeHuO0YhOyNWQeuCQ252PH6wfBRGsylkn9VqxS9/+Uts\n27YN1dXqr/e5nkhzHjYDaAHwGwAHAdwB4CDLsuOUMkxNZGdn49577yVjV+MFeKx1Pbwj/LDXHIXg\nEW9wcXFx2LhxI50M+Im5c+diypQpZNx+rJ7UIAQK28UWeFrFlBKDwYAf//jHtMDRT4wfPx4JCQkA\nABvPozoCC6fdAo8rMudh7ty5yhkToiQmJmLBggVkXFN/XkFruqamXhLOKCkpoX08+oDL5cLbb7+N\nH//4xzh37hzZnxhvwp1LR2DBjFzo9ZFzLzYadLhxTj6WLWERH2sk+0+ePIkNGzbg/fffhzuElOsi\nxXnwALgCYCHHccs5jnuK47jvAFgDIB7A/yppnJpYsmSJjz6/reoQhBDsADoQXK0VcLdJq0sbNmyg\nzeD8CMMwWL9+PWJjxZUm3u4JaPdpT7sLNk76/DVr1iA7Oztg54s0tFotZs+eTcbnHfYeXh2eXHI6\n0NndIScnh8r+DpClS5eS7aa2SjicFgWt8cXjdaGu6RIZ33777QpaExqcOnUKDz/8MN577z2ipKRh\nGEyZkBkx0YbuyB0Shx8sG4WSsRnoXJd0u91466238Oijj+L8efU5z10REc4Dx3EbOI4r4Djuy+v2\nvwPgSwATaPqSCMMwePjhh8kET/DYYa85prBVgYd322CvOUrGixYt8knLoPiHxMRE3HfffWRsL22F\nu8X/+fKCIMByol7sYgagsLDQZ4JC8Q/ylL4ylxNWb+TUSQmCgNOyNIxFixbRKOUAycnJwYQJE8i4\ntvGigtb40tB8hfR1yMnJoQpLPWCxWPDqq69i69atqKqqIvsz06Px/TtGYuYN2dCFUO+GQKHXazFn\nag5Wf3ekT2fq8vJyPPHEE/jNb36j+u7U9FsEOmfGeUoaoSaSk5OxYcMGMna3VcDVWqGgRYFFEMQC\ncfBiyDAjIwP333+/wlaFL/Pnz/d5AFtO1Pu9aMxZaYG7QVJX2rhxI01XCgB5eXlEmlQAcDaCog/X\nPG7SXdtoNNLaqEEil8Kua7oEXgX9hgRBwLUGyZG5+eabqYPYDQcOHMD69euxd+9ess+g12LBzFys\nXFqMlCSqbnc96SnRWHX7SMydmkMa4gmCgI8//hgbNmzw6bitNsLeeWBZVsuy7ESWZW/o5iXmjv86\nujkekUyfPt0nD9VecxS8W92e8EBxNV6A11YPQOqMTGU8A0dn+pJOJ7aZ8TQ74bzqv87mgoeH9Yyk\n5rR06VKpESLF79xyyy1k+4zDBhev/KQvGBy3SVKTc+fORUxMjILWhD6TJ08m8r9ujwPNrVW9vCPw\nWO1NsDlESU2DwUBrWrrAYrHg5ZdfxksvvYSmJilNtCgvEfeuGI3xI9Oow9UDGg2DSWMzcM/y0cjP\niSf76+vrsWPHDrz66quqjEKEvfMAQA+xOPpvLMv6/P+yLMsAmA7ADeB4F++NaNatW4f09HRxwLvF\n+ocwU1TxOlp8VKWWL1+O4uLIbpAXDLKysnxyhy1nmvzW+8F2oRm8Q0yfSUxM9OlhQvE/U6dORWZm\nJgDAJQgREX1o8LhxtaNAnGEYfPe731XYotBHq9X6LFjVyeSylUJe6zBjxgzqIF7HsWPHsH79euzf\nv5/sizLrcOvCYVi6uBAxMnlSSs/ExRrx3ZuKcPP8ApiMUv/mvXv3YuPGjTh9Wl3ql2HvPHAc5wDw\nMYBEAE9dd/gxAKMBvMNxXFuwbVM7UVFR2LRpE2mO5rXVw9V0QWGr/IfAe2GrOgh0hMeLiopw1113\nKWxV5LBixQqy0ig4vbCXDr4xodfuge1SKxn/4Ac/oFGkAKPVarFs2TIyPmm3wq2ClJNAckQWdZgx\nYwaGDBmioDXhw8KFC8l2c1s1XG7lHFGe96Kh+QoZ07Q0CY/Hg9/97nfYvn27T7ShuDAZ964Yg+EF\nSQpaF7owDNNxDUdjeIEk1lJXV4enn34a7777LrwqqSsLe+ehg8cA1AN4gWXZvSzL/pxl2S8A/BTA\nGQCbFLVOxYwcOdJnYuCoOw2vo7WHd4QOjrrT4J2iz2g0GvHYY4+RVBpK4DGbzT5dWm2lLeAdnh7e\n0Tu2802AV4yODRs2DPPmzRvU51H6xrx585CamgoAcAgCTtnVF2b3F7VuF8pdUpH/ihUrFLQmvMjK\nypJFfgWfyXuwaW6rgqdDaTAtLQ2jRo1SzBY10dzcjK1bt2L37t1kn9mkw9JFhd9aNacMjCizHrcs\nGIYl8wtgMoq1ejzP45133sH27dvR1qb8WndEOA8cx10GUALgTYiRho0AhgL4OYDpHMeFdp/wALNy\n5UpJglDgYas+BCHEVxY9Vt8oytq1a+nqoQLMmzdP0kz3CrBdGHj0wWNxwVEu1U7ce++9JGpGCSx6\nvd4nPeyE3QZHGNY+CIKAQzZJRnT27NnIz89X0KLwQ566VN98WTE76pqkc8+fP5/eSwCUlpZi06ZN\nPn0bcrPj8INlo1GUT2XN/QnDMBhRmIy77xiN7ExJ2vbkyZPYtGkTysvLFbQuQpwHAOA47irHcfdy\nHJfFcZyR47hhHMc9wXGc/yo1wxSdTodNmzaRLqC8owXO+jMKWzVwBK8btupDZDxx4kQfpQ9K8NBq\ntbj77rvJ2H6lDd4BRh9ssjWA8ePHY9w42vsxmMyfP5/00XALAo7Y1KPV7y/KXU7UdDRy0mq1WL16\ntcIWhR8zZswgzxqrvRk2++DTGfuL2+NAS5tUsE0jmOKkdcuWLWhoaAAAMAwwa3I27vjOcERH6RW2\nLnyJjTFg+RIWUydmkX21tbV48sknFe0JETHOA2VwDB06FD/4wQ/I2NlwHh5bYw/vUC/22uMQOpSj\nYmNj8fDDD1M1CAUpKSlBYWGhOOAF2AcQffBa3HBelSardFIXfK53BM867GjyDC4NTU14BQFfW6Xf\n2OLFi5GVldXDOygDISYmBpMnTybjOgWiDw3NVyBATH8cMWJExH/PR48exXPPPQeHQxSlNBq0+N5N\nwzF5fCZ9dgYBjYbBjJIhWLq4kEi6Wq1WbN++XbFCauo8UPrMrbfe6qPPb68+BIFXR/FOX3G318Dd\ncoWMH3zwQSQnJytnEAUMw2DVqlVkbC9vA+/q3+/KJiu2Hj9+PEaMGOE3+yh9Z+rUqeQeIQA4YG0P\nG4W203Yb2jrud9HR0dRBDSDy1KWG5rKgp8len7IUyZSVleEnP/kJXC6x/iMmWo+VtxUjTyYrSgkO\nRXmJuGtpMcwmsa7EbrfjhRdeQGVlZdBtoc4Dpc9oNBo88sgjMJvF1hi8ywJHnbrkw3pC8LpgrzlM\nxrNmzcKsWbMUtIjSSUlJiU/tg+NK3wvCeKcXjgop+5AWsCoHwzC4//77SX54lduFMpf/O4gHG4vX\n66OwtHLlSsTH08lToJgwYQISEhIAAC63Ha3t14J2bpu9BVa7qCCk0+ki+hnR3t6OnTt3kohDbIwB\nd946AsmJ5l7eSQkUaSlRuPPWESRVzGq1YufOnUHvBUGdB0q/SEtLw3333UfGrqYL8FjrFbSo79iv\nHYfgEW+CCQkJ+NGPfqSwRZROGIbx6ftgv9wKge/birX9ShvASwpLnR2PKcqQn5/vU0N0wNoe8o3j\nDljb4elIY8nJycGSJUsUtii80el0mDNnDhnL+y0EGnma1JQpUyK6t8Pvf/971NeLz3eDXoPv3TQc\nCXEmha2iJCea8d0bi6DTilP46upq/OEPfwiqDdR5oPSbxYsXY+LEiWRsrzms+vQld3s13K2SOsH6\n9esRFxenoEWU65k9ezYSE0XFDt7hheuatZd3iOo38ijF7bffTnNwVcCaNWvIyrGV53HY1vt3qVYq\nXE6f6MlDDz1EJZ2DgDx1qbH1KjyewEeweIFHPU1ZAgCUl5fjs88+I+O87HikJH074vDV4Sq8/Po3\n+Opw1x3B6fHAHE9PjcaCmblk/OGHH6KmpqbLzwgE9A5I6TcMw2Djxo3YsGEDrFarmL5Ufwbm9LG9\nv1kBBK8b9pqjZDx37lxMnTpVQYsoXaHX67Fo0SK8//77AMSIgjGr51U/V60NvF0syo2Li8OMGTMC\nbield2JiYnDffffh5ZdfBgCccdhQaDQhTR9aqixugce/LJJzOn/+fBrZChL5+fkYNmwYLl26BEHg\nUd98BZmpbEDP2dJWBXdHdDopKQmTJk0K6PnUzN///nefeqULZc14+fVvun39gaPVOHC0GtMmZmF6\niSR7frW6Tfbfb8uhn+bqfd5PP39gny8IAr744guf+sFAQiMPlAGRkpKCe++9l4xdjRfgtauzXYaj\n7hQEj9ipND4+Hg888IDCFlG648YbbySRA3edHV6bu8fXy/s6LFiwgEg8UpRnzpw5GD9+PACxePr/\nLG3gQ6x4+rDVCktHylVsbCzWrl2rsEWRhbyrc21jacCL72sbS8n2vHnzoNVqA3o+NfPVV18pbQKl\nnwTzO6POA2XALF68GGPGjOkYCbDVHFZd8ziPtR6uZilf9oc//CFNV1IxaWlpPv0ZnJXd9wrgXV64\naqV0GPlEg6I8DMPgoYcegsFgAAA0ej04GUKdp+vcbpx2SPbef//9tEg6yMyZM4f8fmyOZljsgZMH\nd7qsaG6TVnYXL14csHOpHa/XS2odBktOVpzPf68nPtZIP99Pn3/tWvCEBWjaEmXAMAyDDRs2YOPG\njXC5XOAdLXA1XoQxJbCh5b4iCDzsNUfIePLkyZg5c6aCFlH6wrx583D8+HEAwP/f3p2HSVXd+R9/\nVzdNN3uzCgiyCB4Qg+yiuIASEUUkEfVxQcb8gvuoE8fEPcYYzSSTuBB5YiaZ0awmJnGSGJPRqIn7\njlFQD5soOw0NvXfXdn9/3Opbt5pqKOjuutVVn9fz+NQ9996q+kp3V93vPed8T+PmWroflX7l0qat\ndZDIVceMGcPw4cOzFaJkaMiQIVx00UU8+uijALxTX8vIrqWU5/icgZjj8I/aKprvc0+aNEkLhQWg\nZ8+enHjiiTz//PMA7Ni1ll5HDOiQ93J7Hdyf+MSJEwt6bYf6+nqvl6ekSxH/etmUQ55LdsK0w1OG\n6bR0wcLxh/S6en1XPO5w/4/dKpJNTU1EIpGs9MCr50HaZOjQoVx44YVeu7FiNfFIbtxdDO9eQzzs\nDmvp1q0bV111lSbTdgIzZ8707jbGqsNEa8Npz2vamuyV8FdmkdyyaNEibxHAGPCPTjB8aWV9HXti\nbhGI0tJSrrnmGn12BOSMM87wtnft2Ug0mv7zoC0cJ87OyuSQJf97FqIePXp4Jdkj0Th1DfsfPirB\n2VudLCRQXl6etaG7Sh6kzRYtWpS86+vEaNj+z2ADAuLhOhorPvTaF198MQMGdMwdK2lf3bt3Z/Lk\nyV47vG3fSj3xSJxIRYPXPuGEE7ISmxy84uJirrvuOm/8+I5ohA8bGw7wrODsjkZY2ZD8nbv00ksZ\nPHhwgBEVtnHjxnlrwMSdWIesOF1ZtZlwxP2dLC8vL/iCGkVFRYwaNcprf7yuMsBoZH8+Xp8cyjd6\n9Oisva+SB2mzLl26cPXVV3vtaM1mIrXZG3uXTsOO98Bx7xyOHDmSBQsWBBqPHBz/l3fTtn17siI7\n65tHGDB69GgGDRqUrdDkEIwaNYrzzjvPa79ZV0N1LBpgROnFHYe/11R7w5XGjx+vNR0CFgqFUn4G\n23fZdp84vW2X9bbnzZunwguklql9+/3tNDbl3t9roaurj7By1U6v7S9v3NGUPEi7OOaYY1I+bBq3\nrwxs8nSkdjvRmuTEt6uvvrqgq2Z0RtOnT/eGiUT3NBKPpK4jEt5Zn3Ku5L7zzz+fESPcuuRR3OFL\nHV0952D9s6Ge3YmkpmvXrik9JhKc2bNn06NHDwAam2rYW9N6ycqDVdewh+raHYB7x73Qhyw1mz17\ndnKtlvoIf3v505z7ey1kjuPwzIsbvaRu0KBBWe2BV/Ig7eayyy7zPuDj4VrCWVwVtJnjxGnckRw2\nddpppzF+fNsmNEn29enThyOPPNJtOKQMUXIch/DOZNu/YKHkrpKSEq6//nqKityvnW2RCB/l0PCl\nPdEo79Qn59FcdNFFDBs2LMCIpFlZWRlz58712tsq7H7OPjjbfb0Oxx9/vIa3JpSWlqaMKLDrK1td\nyEyyy3EcXnjtMzZ8ttfbd+2112Z18UolD9JuysvLOf/88712U8Vq4llYFdQvvOcT4k3uoivdunXj\n0ksvzer7S/tpXiMAILKr0duO10e9heG6devGUUcdlfXY5NCMHTuWL3zhC177jfpaamPBr04fdxx3\nIneiPXbsWBYtWhRoTJJqwYIFXm/k3pqt1DdWtfk1I9EmKio/8dpnn312m18znxx//PEpJWtfX7mN\n197Zoh6IADmOw4tvbE4ZrnTOOeekzBPMBiUP0q7OPvtshgwZAoATj9C068MDPKP9OLEwTRWrvPbi\nxYvp169f1t5f2teECRO87cjuBt92MpEYP358Vu+2SNtdeOGFHH64W5ow4ji8lAPDl1Y31rMz6laU\n6dKlC9dff72GK+WYwYMHM2PGDK/t7zE4VDt2ryWemBs3evRojj766Da/Zr656qqrmDZtmtd+9Z2t\nPPvSRmLx3FrTqRBEY3Gefn4Db7+fnFM6a9aslAV7s0XJg7SrkpKS1JWnK9cTD7e+0Fd7atq9Bifm\nlvEbNGgQ55xzTlbeVzrGuHHjkvMeqsLEI+6XVaQymTzoy77zKS0t5brrrvN+tpsiYdY1NR7gWR2n\nOhbjrbrkZ5R/bobkFn/PwM7K9W0q2xp34mzftcZrL1y4UOV40+jSpQtf+9rXUnqCP/h4F7/5k6Wm\nlTLa0v6qqpt4/A8f8fH6ZOWrGTNmcOONNwZyo0PJg7S7mTNn+u4aOyklUztKPNpI0+7kF8GSJUso\nLW3byo8SrJ49e3LEEUd47WiVOwQuuid5oTlu3LisxyVtd/TRR6dU0Hm1roaGAO5kOo7Dy7XVNNeR\nGTFiBIsXL856HJKZiRMneoldPB5jh29thoNVuXcT4cSaROXl5Zx88sntEmM+Kisr484770wpirJ1\nRy0/+/1q1n+6dz/PlPawZkMlP/v9anbsShYKmT9/PrfeemtglcGUPEi7C4VCLFmyxGtHqj4l1g7j\nU/enaddHKaVZ9UWQH7xJ00B0bxNOLE60Opz2uHQuS5Ys8SanNjkOb9TVZD2G9U2NbI64v09FRUVc\nd911KtOZw0KhUErvw/YKe8hV/bbt+tjbnj9/vn7uB1BSUsINN9zA0qVLvaIHDY1R/vf/1vLXv39C\nU1ilXNtbQ2OUp55bz5/+tp6msHt906VLF5YtW8ZVV10V6NBKJQ/SISZMmMDUqVO9dmPF6g57r3ik\nnrBv4aAlS5Z4H27SuTWvTAzu0KVoTcRb32HIkCH07NkzoMikrbp3784111zjtdc0NbIlnL1hEI3x\nOK/6EpYFCxZo8n0nMHv2bHr16gVAU6SOyqqDrwBUW7+bmroKwL0Ymz9/frvGmK9CoRCLFy/m3nvv\nTZlPuHrNLh59YhXrNu4JMLr84TgOdn0ljz2xCusbpjRw4EC+/e1v58QQO11hSYfx9z5Ea7Z0WO9D\n066PIXH3yRijuv95pHllWYBYTZhYTfLiUuPSO79p06Yxa9Ysr/1ybTWxLE2efrOulsbEew0YMICL\nL744K+8rbVNaWsq8efO8tr8HIVP+Uq8nnngiffv2bZfYCsWECRNYvnx5Sg9/bV2EPzyzjj88s1Zz\nIdqgqrqJJ/+6lqeeW09dQ8Tbf9ppp/HQQw9hjAkwuiQlD9JhjjzySI4//niv3XQIH/IHEo82Et6b\nLLV30UUXBZ6RS/tJmfNQE04ZsuQ/Jp3XsmXL6N69OwBV8RjvN+y7onh72xmJ8HFTsoLX5Zdf7sUg\nue/MM8/0epera3dQ35j5uPtItJFdezd6bZVnPTS9e/fmpptu4qtf/Sp9+vTx9q/buJf/+c0HvPHe\nNqIxVWTKVCQa59V3tvDoEx/wyabkjdZ+/fpxxx13cMMNN+RUT7uSB+lQ5513nrcdqf6MWDtXXgrv\nXuP1OowZMybrtY6lY/Xp08cbokDMIVqVTB6GDx8eUFTSnvr3788ll1zitd/t4LUf4o7DK3XVXnv6\n9OkpNzkk9w0cOJDjjjvOa2/ftTbj5+7cvd6bJzFmzBgNVWujk046iRUrVvD5z3/e2xeJxnn5zc38\n9LerUhYyk305jsPaT/bw6BMf8No7W4nG3N7QUCjEWWedxYoVK1JKFOcKJQ/SocaOHZtyQR9uh9rc\nzZxYmKY9yVWszz//fPU65KGhQ4d62zFfd3jzeiLS+Z155pneELUY8HoHTp5e09RARdSd3FlSUsLl\nl1/eYe8lHcdfrauicgOx+IEn7DqOw47dyUTD/xpy6Hr37s11113HfffdlzKcdE+VOwTnd39Zw+49\nubOafK6o2F3PE3+2/PHZdVTXpBYC+e53v8uVV15Jjx49AoywdUoepMP5ex/CVRvbbdXp8N6NkPjC\nGD58eMqdKMkfgwcP9rbjjdG0+6VzKy4u5sorr/TaG8JNbI+0/7jpcDzOW3V1Xvvcc8/V71En9bnP\nfc67gRCLR9i959MDPqeqdjuNid7vHj16cOKJJ3ZojIXmmGOO4cEHH0wZigiwcVMVj/12Fc+/8ikN\njarKVN8Q4dkXN/Kz369m09bkjZJevXpxzTXX8L3vfS9n5ja0RsmDdLhjjjkmWTXHiafMUThUjuPQ\nVJm8g7Ro0SJVWMpTgwYNSjYSQ2hLS0vp3bt3MAFJh5gwYQInnXSS136trqbdV55+r6GOhsSQlf79\n+3Puuee26+tL9hQVFXHGGWd47Z2V6/dz9r7nzJkzh7Kysg6JrZAVFxezcOFCHnnkEebNm+eNBnAc\nWLl6J//96/d594PtBblCdTQW5833tvGTx9/n/Y8raP54KyoqYsGCBfzwhz/kjDPO6BSr2+tqSzpc\ny9rc4cp1h1ybu1m0ditOYoGfXr16ccopp7Tp9SR3Na8F0HKfhqjln3/5l3/x6u1XRKNsCLdPLyVA\nXSzGB77J2EuXLtXFYyc3Z86c5MTpup00NLU+3C0aC1O5d5PX9o/Rl/ZXXl7OtddeywMPPMDnPvc5\nb39jU4wXXtvEY0+sZsNne9v9BkEucuc1VPLob1bx0pubCUeS1z9Tpkxh+fLlXHHFFZ3qhpiSB8mK\nk046ifLycgCcaAPRmq1ter0m38qi8+bN02rSeay15EHyz6BBg1i4cKHXfruulng7XVy801BH8zTs\nMWPG6IZDHujbt2/KekK79rTeq71772fEEwuJjh49mtGjR3d4fOL+W3/rW9/i1ltvTZmntqeq0Z0P\n8fQadlXm73yInbvq+c1Tlj8+u56qmuTNkGHDhvH1r3+du+66q1NWDlTyIFlRUlKSUps7vJ8P+QOJ\nh2uJ1e0E3O4+LfCT35qTTj/VZc9fixcv9iYJVsVj2Ka2X1hUxaLYxuTrXHrppRrmmCfmzJnjbVfs\n+aTVO9m79mxM+xzpeKFQiOOPP56HH36Yyy67LGU+xKdbqvnp71bx/Kuf0tiUP/Mh6hsiPPvSRn7+\n5Go2b0v2iPXs2ZNly5axfPlypk2b1ml70PXpKVlz+umne38o0brtxCOHVs897KvRPWXKlNQx8ZJ3\n0iUK6RIKyQ89e/ZMmYuwsr6uzb0PK+vrmhcmZ+LEiUyaNKlNrye5Y/r06XTr1g2AxqaatGs+RKKN\nVNXuANwLWf/cGsmekpISvvjFL/LII48wf/58L4F3HFi5aic/efwD3v+oolMPZYrHHVau3sF//7r5\n/8Xd3zwX5Ec/+hELFy6kS5cuwQbaRkoeJGsGDRrEscce67XDew9cHaMlx3FSkoe5c+e2R2iSw9KN\nA/UvSiT55+yzz/Z+7rXxOGuaGg/5tapjUdb6nn/xxRd32rt9sq+ysjKmT5/utXfv/WyfcyqrNkEi\nfRw/fjz9+/fPVniSRnl5OVdffTUPPvggEydO9PY3NkV59qWN/OoPH7FjV91+XiE3bd1Ryy+e/JDn\nX/mMpnByrZrmeQ3Lli1LrlvUySl5kKzyX+xH9rbexdyaaN1OnKg7/KB37945uXiKtK/S0lK6du2a\nsi9fPoAlvbKyMhYtWuS132tD78M/G+pTeh2OPvrodohQcskJJ5zgbbuJQqrKqs1pz5VgjRw5knvu\nuYebb745ZQTBtp11/OLJD3nh1c8IRzpuwcj20tgU5W+JpGfn7uSIiiFDhnDHHYobqdgAAB3vSURB\nVHdw11135d2ipkoeJKuOP/54b7xjPFJHPE0X8/5EqpNfDLNnz/Yqs0h+a9n70LNnz4AikWw566yz\nvLkP1fEYnx5C5aWGeJw1vrkOF1xwQbvFJ7lj8uTJ3jCQ+sa9NIWTd61j8ShVNdu9ttYDyi2hUIhZ\ns2bx8MMPc8EFF3g/R8eBd1ft4NHf5PYq1Ws/qeTRJ1bxz48qvH1du3blkksu4eGHH2bGjBl52dOp\n5EGyqmvXrsycOdNrh6v3vUvUGseJE61O3kHSuNXC4Z9gB0oeCkH37t1TiiG833Dwc6Q+bKxPqbDk\nLxkp+aN79+4cc8wxXnuvr5pfde1Or8rSsGHDtChgjiorK/MuuP1zkmrqwjz517X89e+f5NSE6vrG\nCE/9bT1/fHY9dfURb//06dNZsWIFF1xwQV7f3FTyIFnnv+iPVG/KeOhStHYHTtz9Ix04cGDOr8Ao\n7adl8tCyLflpwYIF3p3IHdEIFdHIAZ6RFHccPmxI9josWrQoL+8Aimvy5MnedlXNjuR27fa050hu\nGjp0KHfffTc33nhjSo/z6jW7eOy3q/h0S3WA0bk2fLaXx55Yhd1Q6e3r27cvN998M3fccQeHHXZY\ngNFlh5IHybpjjz3Wu3PsROqJNe7J6HmRmmSvw4knnqgLgQLSMllorq4i+a1///6ceOKJXvujhszL\ntm4MN3mrSffr149Zs2a1e3ySO/zFOKpqt3s3paprd6Q9R3JXKBRi9uzZrFixgpNPPtnbX1sX4bd/\ntrz05uZAVqiOxuK88OpnPPnXtdQ3JHtB5s6dy4oVK5g1a1bBXJcoeZCsKykpSZnoHK3ZdsDnOI6T\ncp4mvRWWlosAKnkoHP6hS+uaGghneNHwkW+uw+mnn97pSyPK/o0cOdK7yRCJNtIUriUWi1Jbn7w7\nPGHChKDCk0PQp08fbrrpJm6++eaUXog339vGb/5kqa0LZy2W6pomHv/DR7y7KpmM9uvXjzvvvJPr\nr7++4IbSKnmQQPhL60VrD5w8xBr34MTcCZN9+vThqKOO6rDYJPe0TBa0onjhGD9+PCNGjAAgCmzI\nYOJ0TSzGloh7YVFUVMTpp5/ekSFKDiguLmbcuHFeu6augtqG3TSXaD3iiCMK7gIvX8yaNYvly5en\nzIXYuqOWn//+Q7Zsr9nPM9vHp1uq+dnvP2THruS8qxkzZvDQQw+lXMsUEiUPEojJkydTXFwMuIlB\nPLL/4Qj+XoepU6dqddgC07JUq5KHwhEKhVJKPK9tPPDQpbW+VaknTZrEwIEDOyQ2yS3+m0q1DZXU\n1e9Oe0w6n379+vGNb3yDpUuXet//dQ0RnnjK8tG63Qd49qF7/+MKfve09SZrFxcXs2zZMm6//faC\nXm9IV2ASiB49eqR0IUfrtu/n7NTjhZrpF7KWQ040BKWwnHLKKd4Fw7ZohNpY67XfHcdhnW9RuDlz\n5nR4fJIbxowZ423X1VdS21CZ9ph0TkVFRSxevJi7777bW+snFnd4+vkNvPnegUcwHAzHcXjlrc08\n++JGb5Xovn37ct9997Fw4cKCmdvQmoJJHowxXYwx/2aM+dAYU2+MWW+Mud0Yo6uQgPi7IKN1Fa2e\n58QixBrcSdWhUEiT3gqQP1koLi4u+A/uQtO3b9+Uz4tPwq2vOL0nFmNvIrkoKytLKQ0t+W3UqFHe\ndn3jXuobqrz2yJEjA4hIOsKxxx7L/fffn7Lw2ktvbuaVt7cc9MKz6TiOw99f28TrK5MJyejRo7n/\n/vsZP358m18/HxRM8gA8DHwPqAAeALYAdwO/CjKoQuZPAqJ1O1v9o4/W76J53OqoUaO0unAB8g9T\n05C1wuQvkvBJU+vzHvyJxdSpUykrK+vQuCR3DBw40JsfFY2FaWhKJg/N82YkPxx22GF85zvfSVm7\n5fV3t/Lq21va9LrNiYN/YvSUKVO477776N+/f5teO58UxLewMeYEYBnwhLX2FGvtrdbak4GfAuca\nY84KNsLCdOSRR3of9E60gXikLu150bqd3vbEiROzEpvkFiUPMnPmTO9nvz0aoaGVqksbfYmFqrIV\nllAoxOGHH+61nUSp3vLyck2WzkM9e/bkrrvuYurUqd6+11du490P9j8Men/eWLktJXE44YQTuP32\n27W2UAuF8i18TeLxGy3234J7S/vL2Q1HwB1+4p/3EKtPP3TJv18rxBYm/zAlJQ+FqU+fPinVdDan\nqbpUH4+xO5ac2Oi/qJDCMHTo0Iz2SX7o2rUrt912G9OmTfP2vfDaJtZtzGz9KL+P1u3mFV/PxaxZ\ns/jqV7+a1ytFH6pC+RY+Gaiw1n7o32mt3QasTRyXAPjHD8Z8k9uaOfEYsca9Xtt/8SCFw588aL5D\n4fInA5si+9Z43xxO7hs/fjw9evTISlySOwYPHrzPvkJY8beQlZSU8LWvfS3leuIvL2xg957MF5Xc\nuaueZ/6x0WtPmjSJG2+80asKKanyPnkwxpQChwPrWzllI9DXGKPBbAEwxnjb0fp9y625iYM732Ho\n0KEpC8VI4ejXr5+3rTHshWvKlCne9pZweJ95Ult8CYX/XCkc6cryDho0KIBIJJvKysq4/fbbvUQx\nHInz5+fXE40deFHJSDTGU88lzx02bBi33HKLehz2I++TB6D5qmNvK8ebZ1QVbsHeAI0dO9a7kxxv\nqsKJR1OOxxqSCYU/0ZDCMnfuXA4//HDKysq45JJLgg5HAjJ69GivN6HBiVPVomTrNl/yoKpshWnA\ngAH77NNE18LQu3dvbrvtNm9doIrdDbz2zoEnUL/4xmb2VLmFFrp168Ztt92mOQ4HUAjJQ3Pq2Fp5\njub9up0ZgO7du6eUW/MPUXLbyXGLWuSncJWWlvLDH/6QJ554gs9//vNBhyMBKSoqSpkntS2aTBZq\nYjFqE5Ooy8rKGD16dNbjk+D17dt3n33+nkvJb6NGjWLp0qVe++1/7tjv8KXtFXW8tzpZlOXyyy9n\n2LBhHRpjPiiE5KH5t6ZrK8ebl6pNX+pHOpz/S37f5GFv2vNEpDD5k4cdkUhyO5rcHjdunBYSLFDl\n5eUZ7ZP8tWDBAu9zIu44vPDqZ2nPcxyH51/91GtPmTKF0047LSsxdnaFkDxU4Q6ab21YUp/E8apW\njksH8y/sE29M/hiceIx4U43X1iI/IuIfvljhSxgqfImEhjgWrnTz4vr00ajkQlJUVMQVV1zhVeb7\ndEs1m7fV7HPeJ5uq2LbDvW/cpUsXrrjiChXkyFDeJw/W2jDwKTCqlVNG4VZiam1OhHQwf1IQa0r+\nGOJN1TRPlh48eLDGIIoIRx55pHdRsCcWI5Ko5e9PJDTEsXCVlJR46wc10xoPhWfUqFGceuqpXvv1\nd7fuc87rK5P75s+fr5K+ByHvk4eEl4Ahxpix/p3GmKHAWOD1QKISoGXyUO1VUImFq739Wh1URMCd\nz+BfCGxPNIbjON76DuAmGFK4/MlDKBTSjacCdcEFF6T0PvjnPmyvqEvpdTj33HMDibGzKpTk4aeJ\nx3uNMSGAxON9if0/CiQqAdwJbt6HezyKE3WrHviHLGkCk4g08w913B2LUBuPE0ncdOjVq5cmyBY4\nf/JQWlqqhSUL1ODBg5kxY4bX/sAmF5z94OPk9kknnaSKXAepIP6irLXPAb8GzgVeM8Z8G/gHsAR4\nwlr7dJDxFbpQKJSSHMTDNSmPoORBRJL8vZWV0SiVvl6HESNGaNxygSstLU27LYVn3rx53vbH6ypx\nHIdYLM6aDZVpz5HMFETykLAEuBMYAFwPDALuAFQ0Pgf4hyHEEklDzNfz4D8uIoXNfzOhKhajKppM\nHvyln6Uw+Rf3UtWtwjZ58mRvwnxdfYTtFXVs3l5LY5O7RszAgQNTVqaWzBTMX5W1Ngrck/hPcox/\nolI8XIfjOMQjdWmPi0hh899MqIpF6VlUnPaYFCZ/wqDkobAVFxczbdo0nnvuOQA2fFZFJJJcXPK4\n447TsLZDoH8xyQnNS8oDOJE6nFgYEqtNl5WVpS2/JyKFaciQId7QpJp4nCrfsCXdaBD/xWBxcfF+\nzpRCMHXqVG9787YaNvnKtvqPSeaUPEhOGDRokLcdD9fhROq99mGHHaYxzCLiKSkpSZkUXR1P3kkc\nPHhwECFJDvEnD7qrLP6FJbfuqKFit3t9EQqFNGTpEOmvSnKCv+chHqlLGbLkTyxERCD1c6E+Hve2\nBw4cGEQ4kkOUPIhfv379vM+FeBwShdkYPnw4PXr0CDCyzkt/VZIT+vbt633IO7EwsXAyedDFgIi0\n5P9cSFwL0KtXL8rKyoIJSHKGv6davdYCMHr06Iz2SWaUPEhOKC4upm/fvl47Hq71tlWzXURaSve5\noFrtAtC1a1dv2195SQrXEUccsc8+VWY7dEoeJGf4v/j9w5Z0QSAiLaX7XNCNBgFYtGiR1+OwYMGC\ngKORXJCukIKKKxw61TCTnOH/4nciDWn3i4gAlJeXZ7RPCs+ECRP41a9+RTQa9Wr8S2FLN3dS8ykP\nnZIHyRn+L34nHva2/cOZRESAtBeFulCUZpoIK34DBgzYZ5/mUx46DVuSnOH/4ndikbT7RURAyYOI\nZK5lr2QoFNL6UW2g5EFyRsoXv5Os264/cBFpqWfPnhntExHp1q1bymrjPXv21AKCbaDkQXJGuruG\nPXr0SPmDFxEBtyxrJvtEREKhUMpQtu7duwcYTeen5EFyRrq7hroYEJF0ysrK9lkATOPcRaQ1/hK+\npaWlAUbS+Sl5kJyRLlHQMAQRSScUCu1z91B3E0WkNVr/o/0oeZCcoTHMInIwunXrltJW8iAirfHP\nn1RxhbZR8iA5I12ioGEIItKasrKy/bZFRJotXbqUHj160KtXLy655JKgw+nUNBNVckbLu4ig5EFE\nWqfkQUQyNWHCBB5//PGgw8gL6nmQnNGlS5eUMYmQPqEQEYF9Jz0qeRAR6XhKHiSnaAKkiGTKP+mx\nqKhIddtFRLJAyYPklJY9DbqTKCKt8a8Bo/VgRESyQ8mD5BQNQxCRTI0dO9bbHj58eICRiIgUDt2q\nkZyiCZAikqlFixbR0NDA7t27WbJkSdDhiIgUBCUPklNaJgtaBVJEWtOtWze+9KUvBR2GiEhB0bAl\nySktkwUlDyIiIiK5Q8mD5JSWS8a3LN0qIiIiIsFR8iA5pWWyoORBREREJHcoeZCcouRBREREJHcp\neZCc0nLYUsu2iIiIiARHyYPklJYLPSl5EBEREckdSh4kp7RMFrRqrIiIiEjuUPIgOUXJg4iIiEju\nUvIgOaW4uDilreRBREREJHcoeZCc0jJZUPIgIiIikjuUPEhOadnz0LItIiIiIsFR8iA5RcmDiIiI\nSO5S8iA5paioaL9tEREREQmOrswkZ4VCoaBDEBEREREfJQ+SU/w9DUoeRERERHKLkgfJKeXl5d52\n165dA4xERERERFpS8iA5Zdq0aYwdO5bS0lIuuuiioMMRERERER8V0ZecUlxczPe///2gwxARERGR\nNAoieTDGbAIOb+XwGdbaZ7IZj4iIiIhIZ5T3yYMxph9u4vA68Nc0p6zLbkQiIiIiIp1T3icPwMTE\n4y+ttT8INBIRERERkU6sECZMNycP7wcahYiIiIhIJ6fkQUREREREMlIow5Z2A5cbY5YCo4BtwM+A\ne6214SCDExERERHpLPK658EYUwRMAPoD1wPPAz8GosCdwJ+NMcXBRSgiIiIi0nl0yp4HY8xG4IgD\nnPYw8E1gDVAJfMFaW514finwBLAAuBpY3lGxioiIiIjki06ZPAC/BwYc4Jw3rLU7gcktD1hrm4wx\n1+EmDxei5EFERERE5IA6ZfJgrf1KO7zGRmPMXmBk2yNqN6GgAxARERERaU2nTB4yZYzpD4wDPrXW\nbm5xLASUAXsP8eXfa/EoIiIiIpLX8jp5ABYB/4U7LOn6Fsem4iYPbx/KC1trb2hbaCIiIiIinUte\nV1sCngIagcuMMUc17zTG9AYeBBzcidUiIiIiInIAIcdxgo6hQxlj/hU3UagFfg2EcSdKDwe+ba29\nNcDwREREREQ6jbxPHgCMMQuAm4ApiV0fAA9Zax8PLioRERERkc6lIJIHERERERFpu3yf8yAiIiIi\nIu1EyYOIiIiIiGREyYOIiIiIiGREyYOIiIiIiGREyYOIiIiIiGREyYOIiIiIiGREyYOIiIiIiGRE\nyYOIiIiIiGREyYOIiIiIiGREyYOIiIiIiGREyYOIiIiIiGREyYOIiIiIiGSkS9ABiMsY8wAwKeg4\nRERERKRgvGetveFgnqDkIXdMAk4JOggRERERkdYoecgd7wUdgIiIiIgUlIO+/gw5jtMRgYiIiIiI\nSJ7RhGkREREREcmIkgcREREREcmIkgcREREREcmIkgcREREREcmIkgcREREREcmIkgcREREREcmI\nkgcREREREcmIkgcREREREcmIkgcREREREcmIkgcREREREcmIkgcREREREclIl6ADEGmNMWYo8BFw\np7X2waDjkWAYYwYDdwFnAYOASuBvuL8XnwQYmgTIGNMf+Dru78UQ4BPgUeD71tpYgKFJDjHG/Cfw\nFWC2tfbFoOORYBhjvgnc1srhX1trL8xmPJ2dkgfJScaYnsDvgV6AE3A4EpBE4vAmMAx4BvglMA64\nCJhvjJlprV0XYIgSAGNML+BlwAB/BH4LnAT8R+JxYXDRSa4wxswAbkDfIQLHAk3AfWmOrcpyLJ2e\nkgfJOcaYEbiJw+SgY5HA3YWbOHzFWvtA805jzMXAz4DvAecEE5oE6BbcxOE6a+0PmncaY34BXGiM\nOdNa+3Rg0UngjDFdgf9Gw7PFNRFYba29O+hA8oH+qCSnGGNuAD4APgc8H3A4ErwvADv9iQOAtfYX\nwAbg9ECikqCNAD4DVrTY/+vE48zshiM56DZgDO4QRylgxpjewBHA+0HHki/U8yC55nrcsctX4N5Z\nPDXYcCQoxpgi4FtAuJVTmoCuxpgSa20ke5FJ0Ky1F7dyaFzicUe2YpHcY4yZCNyM+/nRF5gbbEQS\nsImJRyUP7UTJg+Say4G/WWsdY8y4A54tectaGwceSncs8bsxDlivxEGMMYOAxcA3gE+BnwcbkQTF\nGFMM/ARYgzu+/bvBRiQ5oDl5GGSMeRaYhjsP5jngNmvtmsAi66Q0bElyirX2WWutJrdJqxI9Ej8A\nQsCPAg5HApaoorId93diL3C6tbYq2KgkQP+OO1/uy7qxIAnNycO/435GPAK8AZwLvGGMOTaowDor\nJQ8i0mkYY0K4H/ynAm8BD+z/GVIA1gPfBp4EBgIvG2NUbKEAGWOOwi2y8LC19o2Aw5HcEQU2AnOt\ntedZa2+21s4HLgH64E6sl4OgYUsi0ikYY7oA/wUsxb1gPMdaGw02KgmatfbR5m1jzFm4pVt/ilt0\nQQpE4sbCT3B7oW4JOBzJIdbaa1vZ/0tjzOXAycaYozR8KXPqeRCRnGeM6Q78ATdxWAPMsdZuDzYq\nyTXW2j/jjmOeYIw5Muh4JKuuAWYBV1lr69McD2U5HukcViYeRwYZRGejngcRyWnGmL7AX4AZwLvA\nGdbaXcFGJUFJTIidA2CtTVeG87PEY3/cHiopDIsTj08bY9IdfyGxf6S19rN0J0j+SXxeHAsUW2vf\nSnNKt8RjY/ai6vyUPIhIzjLGlAFP4SYOfwcWWmtrAw1KghYC/gRUG2OGJKpy+R0LxHFLPkvh+B/S\nrw00HzgOeBR33Lsm0xeWEtzJ0dXGmIH+z4vEULcTgAjwXkDxdUpKHkQkl90LHA+8Csy31jYFHI8E\nzFobNcb8DrgIuAn4j+ZjxpirgKnAn6y1FQGFKAGw1j6Wbr8xph+J5MFa+2J2o5KgWWsbjTFPAefg\nrv1xr+/wjcAxwGPW2uog4uuslDyISE4yxgzGHccM8DFwS5rhCA7wbSUVBeerwMnAfcaY2cAq3PKc\np+KuPH5FcKGJSI65EbeH4Z7E58X7uDcZTgFWA18JLrTOScmD5DIn8Z8Uppm4Xc4O8KVWznGA+3FX\nm5YCYa3daoyZDtwNLABOA7bg/i7cY63dE2R8klP0PVLgrLUbjDHTcD8v5uEmDZuB/wS+aa2tCTK+\nzijkOPqbEhERERGRA1OpVhERERERyYiSBxERERERyYiSBxERERERyYiSBxERERERyYiSBxERERER\nyYiSBxERERERyYiSBxERERERyYiSBxERERERyYiSBxERERERyYiSBxERERERyYiSBxERERERyYiS\nBxERERERyYiSBxERERERyUiXoAMQEZHOxxgzG3g+zaEYUA9sBp4DHrbW2iyGJiIiHSjkOE7QMYiI\nSCfjSx5qgf/1HeoC9AEmAUOAJuAKa+1Psx2jiIi0P/U8iIhIW+yy1l6a7oAx5gpgOfBjY8wma+0L\n2Q1NRETam+Y8iIhIh7DWPgLchnuj6nsBhyMiIu1APQ8iItKRvg9cD0wyxsy01r4OYIwZBtwIzAOO\nwP0+2gr8FbjHWru1+QWMMXcBdwJnJ877GjARiAB/B+601n7Q8o2NMQuAm4BjgTjuHIxbgB8Cp1pr\ni1qcPww32TkTGAzsAp4BvmGt3dji3DjwDnBz4vWGAxuAU6y1FQf9ryQi0kmo50FERDqMtTYG/CXR\nPA3AGHM08B5uUhEGngZeBAYCVwKvGmN6pnm5LwNPAr0Sr7kHOAd4yRgzwn+iMebfgD8CM4E3Eq9/\nBvA6brLitDh/MrASuAKoSzx3O7AUeNcYMy1NPEMT8dQA/wfsUeIgIvlOPQ8iItLRmqstHZV4/C7Q\nD7jeWru8+SRjzEDgVeBIYCHwyxavcw5wpbX2R4nzS3CTiFNxk45bEvvHAd/BTS4+b619N7H/cOBv\ngMGXPBhjugK/TcR0rbV2he/YEuBR4DfGGGOtjfjiGQL8zlp7XuLc0MH+w4iIdDbqeRARkY62N/E4\nIPG4Efeie7n/pMRd++bKTcPTvM7LzYlD4vwI8F+J5tG+864CinGHG73rO38Lbu9FS18ERgFP+hOH\nxHN+BvwOGAmc2+J5DvCQ71yVLxSRvKeeBxER6WhdE48OgLX2mpYnGGOGAJNxS7z6n+P3Rpp9OxKP\nPXz75ibe68mWJ1trXzHG7AAG+XbPSTz+PX34PAMsBk4BHm9x7J+tPEdEJC8peRARkY7WL/FY2bzD\nGDMJuBqYAYwBuicONd+9TzcEaG+afdHEo78n/QgAa+2mVuLZSGry0NzL8ZAx5qF9T/cMa9GOW2ur\n93O+iEjeUfIgIiIdrbk34X0AY8zXgPt8+34LfIjbs3AqcEcrr5PpsKCSxPuEWhlK1DIxKU48Pkuy\nJyOdDw8xHhGRvKHkQUREOowxphtuQuAAzxljRgH3AruBM6y177Q4f2E7vO1m3DkMI3B7GVpqOZ9i\nW+LxMWtty0naIiLiownTIiLSka4FyoGV1tqVwHTcO//PpEkcikiUc6Vt30/PJ95jQcsDxpgpuFWS\n/P6ReDwr3YsZY241xrxljPl/bYhJRCQvKHkQEZF2Z4wpMsZ8CbgHdzG36xKHPks8nmiM6es7vwz4\nAe7ibwBlbXj7H+AuCneXMab59TDGDCBZncnvcdw1HS40xlzd4v/jNOB23Mncb7UhJhGRvKBhSyIi\n0hYDjTE/97WLgb648xwGAY3AZdbaVxPH38Rdy+EEYK0x5pXEc07AnavwCO5CbS17BzJmrX3fGPN1\n4JvAW8aYf+Au/DYn8ViP7/vPWttgjDkfeAr4gTHmBmA17irTx+EOubrRWvv+ocYkIpIv1PMgIiKH\nonmycHfgQt9/i3EvuLcA/wmMs9b+uvlJ1to4cDbu+gh7gNNxext+l3j8euK1z0gMY2p+r9YmJ6fd\nb639ViKed3ETk1NwF5Q7AXdV66oW57+M27vwY6AUdzXqYbirX8+11j5wgH8PEZGCEHIcFYsQEZH8\nYYwZgztsaWMiWfEf6wfsAl631p4QRHwiIp2Zeh5ERCTffBlYB3zLv9MY0wX4fqK5zwJyIiJyYOp5\nEBGRvGKMGQ28jVvlaQ3u/IWuwDTgMOBF3KFI0VZfRERE0lLyICIieccYMwL4N2Ae7tyFCLAW+CWw\nvOVwJhERyYySBxERERERyYjmPIiIiIiISEaUPIiIiIiISEaUPIiIiIiISEaUPIiIiIiISEaUPIiI\niIiISEaUPIiIiIiISEaUPIiIiIiISEaUPIiIiIiISEaUPIiIiIiISEaUPIiIiIiISEaUPIiIiIiI\nSEaUPIiIiIiISEaUPIiIiIiISEb+PxB/2wNW74XHAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e71d0f0>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Out of interest, let's find which animals are in the highest and lowest danger categories:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sleep['Species'][sleep['Danger']==1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "6              Bigbrownbat\n",
        "8                      Cat\n",
        "9               Chimpanzee\n",
        "14     EasternAmericanmole\n",
        "24                Grayseal\n",
        "32          Littlebrownbat\n",
        "33                     Man\n",
        "38        NAmericanopossum\n",
        "39    Nine-bandedarmadillo\n",
        "48                  Redfox\n",
        "60            Wateropossum\n",
        "Name: Species, dtype: object"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sleep['Species'][sleep['Danger']==5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "11       Cow\n",
        "21      Goat\n",
        "28     Horse\n",
        "45    Rabbit\n",
        "53     Sheep\n",
        "Name: Species, dtype: object"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Faceted subplots\n",
      "\n",
      "Seaborn allows us to easily visualise 2D plots across various groups by *faceting* plots in rows and columns according to categorical variables.\n",
      "\n",
      "For example, we could ask: Does the relationship between total sleep time and bodyweight depend on danger level?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = sns.FacetGrid(sleep, col=\"Danger\", col_wrap=3)\n",
      "g.map(plt.scatter, 'log_BodyWt', 'TotalSleep');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {
        "png": {
         "height": 427,
         "width": 641
        }
       },
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAABQMAAANWCAYAAABK1JokAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/C+X+YLDLgg8pQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e7357f0>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Go forth and explore!\n",
      "\n",
      "Seaborn and Pandas allow you to do very powerful data manipulation and exploratory plots in relatively few lines of code. In this lecture we've provided a taste of what you can do. We encourage you to use this code, to check out more examples on the [Seaborn website](http://stanford.edu/~mwaskom/software/seaborn/tutorial.html), and to get in and play around with our data or your own.\n",
      "\n",
      "Don't be dismayed that errors are frequent. Learning to use these packages will be a lot of trial and error / hacking around / internet searchs. The rewards for your research will be worthwhile."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import HTML\n",
      "\n",
      "\n",
      "def css_styling():\n",
      "    styles = open(\"../custom_style.css\", \"r\").read()\n",
      "    return HTML(styles)\n",
      "css_styling()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<style>\n",
        "    @font-face {\n",
        "        font-family: \"Computer Modern\";\n",
        "        src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunss.otf');\n",
        "    }\n",
        "    @font-face {\n",
        "        font-family: \"Computer Modern\";\n",
        "        font-weight: bold;\n",
        "        src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunsx.otf');\n",
        "    }\n",
        "    @font-face {\n",
        "        font-family: \"Computer Modern\";\n",
        "        font-style: oblique;\n",
        "        src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunsi.otf');\n",
        "    }\n",
        "    @font-face {\n",
        "        font-family: \"Computer Modern\";\n",
        "        font-weight: bold;\n",
        "        font-style: oblique;\n",
        "        src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunso.otf');\n",
        "    }\n",
        "    div.cell{\n",
        "        width:850px;\n",
        "        font-size: 11pt;\n",
        "        margin-left:16% !important;\n",
        "        margin-right:auto;\n",
        "    }\n",
        "    h1 {\n",
        "        font-family: Minion Pro, serif;\n",
        "    }\n",
        "    h2 {\n",
        "        font-family: Minion Pro, serif;\n",
        "    }\n",
        "    h3 {\n",
        "        font-family: Minion Pro, serif;\n",
        "    }\n",
        "    h4{\n",
        "        margin-top:12px;\n",
        "        margin-bottom: 3px;\n",
        "       }\n",
        "    div.text_cell_render{\n",
        "        font-family: Optima, sans-serif;\n",
        "        line-height: 145%;\n",
        "        font-size: 12pt;\n",
        "        width:850px;\n",
        "        margin-left:auto;\n",
        "        margin-right:auto;\n",
        "    }\n",
        "    .CodeMirror{\n",
        "            font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
        "    }\n",
        "    .prompt{\n",
        "        display: None;\n",
        "    }\n",
        "    .text_cell_render h5 {\n",
        "        font-weight: 300;\n",
        "        font-size: 12pt;\n",
        "        color: #4057A1;\n",
        "        font-style: italic;\n",
        "        margin-bottom: .5em;\n",
        "        margin-top: 0.5em;\n",
        "        display: block;\n",
        "    }\n",
        "\n",
        "    .warning{\n",
        "        color: rgb( 240, 20, 20 )\n",
        "        }\n",
        "</style>\n",
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
        "                           extensions: [\"AMSmath.js\"]\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
        "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
        "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
        "                }\n",
        "        });\n",
        "</script>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.HTML at 0x105e0c908>"
       ]
      }
     ],
     "prompt_number": 1
    }
   ],
   "metadata": {}
  }
 ]
}