CI_Workshop_Teplitskiy_Introduction.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Statistical Learning with Python\n",
      "\n",
      "Agenda:\n",
      "\n",
      "**Intro and Plugs**\n",
      "- Sociology / computational social science\n",
      "- <a href=http://www.knowledgelab.org>www.knowledgelab.org</a>\n",
      "- <a href=http://www.dssg.io>www.dssg.io</a>\n",
      "\n",
      "**If you couldn't get your environment set up!**\n",
      "- Use: https://wakari.io/ \n",
      "\n",
      "**IPython Notebook** as your IDE\n",
      "    - Advantages/disadvantages\n",
      "        - notebook: markdown, code, inline images\n",
      "        - server\n",
      "        - \"--script\"\n",
      "    - Sharing is caring: http://nbviewer.ipython.org/ \n",
      "    - Keyboard shortcuts"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=http://i.minus.com/iEdBFdHPKBG8Q.gif>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Pandas**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "    - Creating Series and DataFrames\n",
      "        - Setting column names, index, datatypes\n",
      "    - Indexing\n",
      "        - By index, by label\n",
      "    - Subsetting\n",
      "    - Missing values\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=http://img3.wikia.nocookie.net/__cb20131231081108/degrassi/images/9/93/Panda-gif.gif>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Matplotlib**\n",
      "    - scatter, plot, hist\n",
      "    - useful plot customazation\n",
      "    - plots inside of pandas\n",
      "    \n",
      "**Regression Example**\n",
      "\n",
      "**Classification Example**\n",
      "\n",
      "* 2 examples, 2 research communities\n",
      "* Understanding vs. predicting"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=http://www.totalprosports.com/wp-content/uploads/2012/11/14-nolan-ryan-high-fives-george-w-bush-gif.gif>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Pandas"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Provides a crucial 2-d data structure: the ``pandas.DataFrame``\n",
      "    - pandas.Series is 1-d analogue\n",
      "    - Like the ``R`` data frames \n",
      "    \n",
      "``numpy`` does too, BUT ``pandas``\n",
      "\n",
      "  1. can hold *heterogenous data*; each column can have its own data type,\n",
      "  2. the axes of a DataFrame are *labeled* with column names and row indices, \n",
      "\n",
      "Perfect for data-wrangling: can take subsets, apply functions, join with other DataFrames, etc."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Load car dataset\n",
      "df = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Auto.csv')\n",
      "df.head()  # print the first lines"
     ],
     "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>mpg</th>\n",
        "      <th>cylinders</th>\n",
        "      <th>displacement</th>\n",
        "      <th>horsepower</th>\n",
        "      <th>weight</th>\n",
        "      <th>acceleration</th>\n",
        "      <th>year</th>\n",
        "      <th>origin</th>\n",
        "      <th>name</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 18</td>\n",
        "      <td> 8</td>\n",
        "      <td> 307</td>\n",
        "      <td> 130</td>\n",
        "      <td> 3504</td>\n",
        "      <td> 12.0</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "      <td> chevrolet chevelle malibu</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 15</td>\n",
        "      <td> 8</td>\n",
        "      <td> 350</td>\n",
        "      <td> 165</td>\n",
        "      <td> 3693</td>\n",
        "      <td> 11.5</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "      <td>         buick skylark 320</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 18</td>\n",
        "      <td> 8</td>\n",
        "      <td> 318</td>\n",
        "      <td> 150</td>\n",
        "      <td> 3436</td>\n",
        "      <td> 11.0</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "      <td>        plymouth satellite</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 16</td>\n",
        "      <td> 8</td>\n",
        "      <td> 304</td>\n",
        "      <td> 150</td>\n",
        "      <td> 3433</td>\n",
        "      <td> 12.0</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "      <td>             amc rebel sst</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 17</td>\n",
        "      <td> 8</td>\n",
        "      <td> 302</td>\n",
        "      <td> 140</td>\n",
        "      <td> 3449</td>\n",
        "      <td> 10.5</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "      <td>               ford torino</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "   mpg  cylinders  displacement horsepower  weight  acceleration  year  \\\n",
        "0   18          8           307        130    3504          12.0    70   \n",
        "1   15          8           350        165    3693          11.5    70   \n",
        "2   18          8           318        150    3436          11.0    70   \n",
        "3   16          8           304        150    3433          12.0    70   \n",
        "4   17          8           302        140    3449          10.5    70   \n",
        "\n",
        "   origin                       name  \n",
        "0       1  chevrolet chevelle malibu  \n",
        "1       1          buick skylark 320  \n",
        "2       1         plymouth satellite  \n",
        "3       1              amc rebel sst  \n",
        "4       1                ford torino  "
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'Shape of DataFrame:', df.shape\n",
      "print '\\nColumns:', df.columns\n",
      "print '\\nIndex:', df.index[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Shape of DataFrame: (397, 9)\n",
        "\n",
        "Columns: Index([u'mpg', u'cylinders', u'displacement', u'horsepower', u'weight', u'acceleration', u'year', u'origin', u'name'], dtype='object')\n",
        "\n",
        "Index: Index([u'chevrolet chevelle malibu', u'buick skylark 320', u'plymouth satellite', u'amc rebel sst', u'ford torino', u'ford galaxie 500', u'chevrolet impala', u'plymouth fury iii', u'pontiac catalina', u'amc ambassador dpl'], dtype='object')\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Get the ``df`` nice and cozy"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.index = df.name\n",
      "del df['name']\n",
      "df.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>mpg</th>\n",
        "      <th>cylinders</th>\n",
        "      <th>displacement</th>\n",
        "      <th>horsepower</th>\n",
        "      <th>weight</th>\n",
        "      <th>acceleration</th>\n",
        "      <th>year</th>\n",
        "      <th>origin</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>chevrolet chevelle malibu</th>\n",
        "      <td> 18</td>\n",
        "      <td> 8</td>\n",
        "      <td> 307</td>\n",
        "      <td> 130</td>\n",
        "      <td> 3504</td>\n",
        "      <td> 12.0</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>buick skylark 320</th>\n",
        "      <td> 15</td>\n",
        "      <td> 8</td>\n",
        "      <td> 350</td>\n",
        "      <td> 165</td>\n",
        "      <td> 3693</td>\n",
        "      <td> 11.5</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>plymouth satellite</th>\n",
        "      <td> 18</td>\n",
        "      <td> 8</td>\n",
        "      <td> 318</td>\n",
        "      <td> 150</td>\n",
        "      <td> 3436</td>\n",
        "      <td> 11.0</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>amc rebel sst</th>\n",
        "      <td> 16</td>\n",
        "      <td> 8</td>\n",
        "      <td> 304</td>\n",
        "      <td> 150</td>\n",
        "      <td> 3433</td>\n",
        "      <td> 12.0</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ford torino</th>\n",
        "      <td> 17</td>\n",
        "      <td> 8</td>\n",
        "      <td> 302</td>\n",
        "      <td> 140</td>\n",
        "      <td> 3449</td>\n",
        "      <td> 10.5</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "                           mpg  cylinders  displacement horsepower  weight  \\\n",
        "name                                                                         \n",
        "chevrolet chevelle malibu   18          8           307        130    3504   \n",
        "buick skylark 320           15          8           350        165    3693   \n",
        "plymouth satellite          18          8           318        150    3436   \n",
        "amc rebel sst               16          8           304        150    3433   \n",
        "ford torino                 17          8           302        140    3449   \n",
        "\n",
        "                           acceleration  year  origin  \n",
        "name                                                   \n",
        "chevrolet chevelle malibu          12.0    70       1  \n",
        "buick skylark 320                  11.5    70       1  \n",
        "plymouth satellite                 11.0    70       1  \n",
        "amc rebel sst                      12.0    70       1  \n",
        "ford torino                        10.5    70       1  "
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Accessing, adding data\n",
      "You can use the dot ``.`` or bracket ``[]`` notation to access columns of the dataset. To add new columns you have to use the bracket ``[]`` notation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mpg = df.mpg  # get mpg column using '.'\n",
      "weight = df['weight']  # get weight column using brackets\n",
      "df['mpg_per_weight'] = mpg / weight # note the element-wise division\n",
      "\n",
      "print df[['mpg', 'weight', 'mpg_per_weight']].head()  # get a bunch of columns at the same time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   mpg  weight  mpg_per_weight\n",
        "0   18    3504        0.005137\n",
        "1   15    3693        0.004062\n",
        "2   18    3436        0.005239\n",
        "3   16    3433        0.004661\n",
        "4   17    3449        0.004929\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Looking at data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Pandas indexing is really smart!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# To look at all the Fords, create array of length = #rows of True and False, where True if 'ford' in string\n",
      "arr_for_indexing = ['ford' in name for name in df.index]\n",
      "df[arr_for_indexing].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>mpg</th>\n",
        "      <th>cylinders</th>\n",
        "      <th>displacement</th>\n",
        "      <th>horsepower</th>\n",
        "      <th>weight</th>\n",
        "      <th>acceleration</th>\n",
        "      <th>year</th>\n",
        "      <th>origin</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>ford torino</th>\n",
        "      <td> 17</td>\n",
        "      <td> 8</td>\n",
        "      <td> 302</td>\n",
        "      <td> 140</td>\n",
        "      <td> 3449</td>\n",
        "      <td> 10.5</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ford galaxie 500</th>\n",
        "      <td> 15</td>\n",
        "      <td> 8</td>\n",
        "      <td> 429</td>\n",
        "      <td> 198</td>\n",
        "      <td> 4341</td>\n",
        "      <td> 10.0</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ford maverick</th>\n",
        "      <td> 21</td>\n",
        "      <td> 6</td>\n",
        "      <td> 200</td>\n",
        "      <td>  85</td>\n",
        "      <td> 2587</td>\n",
        "      <td> 16.0</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ford f250</th>\n",
        "      <td> 10</td>\n",
        "      <td> 8</td>\n",
        "      <td> 360</td>\n",
        "      <td> 215</td>\n",
        "      <td> 4615</td>\n",
        "      <td> 14.0</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ford pinto</th>\n",
        "      <td> 25</td>\n",
        "      <td> 4</td>\n",
        "      <td>  98</td>\n",
        "      <td>   ?</td>\n",
        "      <td> 2046</td>\n",
        "      <td> 19.0</td>\n",
        "      <td> 71</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "                  mpg  cylinders  displacement horsepower  weight  \\\n",
        "name                                                                \n",
        "ford torino        17          8           302        140    3449   \n",
        "ford galaxie 500   15          8           429        198    4341   \n",
        "ford maverick      21          6           200         85    2587   \n",
        "ford f250          10          8           360        215    4615   \n",
        "ford pinto         25          4            98          ?    2046   \n",
        "\n",
        "                  acceleration  year  origin  \n",
        "name                                          \n",
        "ford torino               10.5    70       1  \n",
        "ford galaxie 500          10.0    70       1  \n",
        "ford maverick             16.0    70       1  \n",
        "ford f250                 14.0    70       1  \n",
        "ford pinto                19.0    71       1  "
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###But it can get confused: Indexing by \"label\" and by \"location\", ``.loc`` vs ``.iloc`` vs ``.ix``"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "``.loc`` -- by label\n",
      "\n",
      "``.iloc`` -- by location\n",
      "\n",
      "``.ix`` -- by a mix"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[0:5, ['weight', 'mpg']]  # select the first 5 rows and two columns weight and mpg"
     ],
     "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>weight</th>\n",
        "      <th>mpg</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>chevrolet chevelle malibu</th>\n",
        "      <td> 3504</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>buick skylark 320</th>\n",
        "      <td> 3693</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>plymouth satellite</th>\n",
        "      <td> 3436</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>amc rebel sst</th>\n",
        "      <td> 3433</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ford torino</th>\n",
        "      <td> 3449</td>\n",
        "      <td> 17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ford galaxie 500</th>\n",
        "      <td> 4341</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>chevrolet impala</th>\n",
        "      <td> 4354</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>plymouth fury iii</th>\n",
        "      <td> 4312</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pontiac catalina</th>\n",
        "      <td> 4425</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>amc ambassador dpl</th>\n",
        "      <td> 3850</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>dodge challenger se</th>\n",
        "      <td> 3563</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>plymouth 'cuda 340</th>\n",
        "      <td> 3609</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>chevrolet monte carlo</th>\n",
        "      <td> 3761</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>buick estate wagon (sw)</th>\n",
        "      <td> 3086</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>toyota corona mark ii</th>\n",
        "      <td> 2372</td>\n",
        "      <td> 24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>plymouth duster</th>\n",
        "      <td> 2833</td>\n",
        "      <td> 22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>amc hornet</th>\n",
        "      <td> 2774</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ford maverick</th>\n",
        "      <td> 2587</td>\n",
        "      <td> 21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>datsun pl510</th>\n",
        "      <td> 2130</td>\n",
        "      <td> 27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>volkswagen 1131 deluxe sedan</th>\n",
        "      <td> 1835</td>\n",
        "      <td> 26</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>peugeot 504</th>\n",
        "      <td> 2672</td>\n",
        "      <td> 25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>audi 100 ls</th>\n",
        "      <td> 2430</td>\n",
        "      <td> 24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>saab 99e</th>\n",
        "      <td> 2375</td>\n",
        "      <td> 25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bmw 2002</th>\n",
        "      <td> 2234</td>\n",
        "      <td> 26</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>amc gremlin</th>\n",
        "      <td> 2648</td>\n",
        "      <td> 21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ford f250</th>\n",
        "      <td> 4615</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>chevy c20</th>\n",
        "      <td> 4376</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>dodge d200</th>\n",
        "      <td> 4382</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>hi 1200d</th>\n",
        "      <td> 4732</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>datsun pl510</th>\n",
        "      <td> 2130</td>\n",
        "      <td> 27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>chevrolet cavalier</th>\n",
        "      <td> 2605</td>\n",
        "      <td> 28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>chevrolet cavalier wagon</th>\n",
        "      <td> 2640</td>\n",
        "      <td> 27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>chevrolet cavalier 2-door</th>\n",
        "      <td> 2395</td>\n",
        "      <td> 34</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pontiac j2000 se hatchback</th>\n",
        "      <td> 2575</td>\n",
        "      <td> 31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>dodge aries se</th>\n",
        "      <td> 2525</td>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pontiac phoenix</th>\n",
        "      <td> 2735</td>\n",
        "      <td> 27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ford fairmont futura</th>\n",
        "      <td> 2865</td>\n",
        "      <td> 24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>volkswagen rabbit l</th>\n",
        "      <td> 1980</td>\n",
        "      <td> 36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mazda glc custom l</th>\n",
        "      <td> 2025</td>\n",
        "      <td> 37</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mazda glc custom</th>\n",
        "      <td> 1970</td>\n",
        "      <td> 31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>plymouth horizon miser</th>\n",
        "      <td> 2125</td>\n",
        "      <td> 38</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mercury lynx l</th>\n",
        "      <td> 2125</td>\n",
        "      <td> 36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>nissan stanza xe</th>\n",
        "      <td> 2160</td>\n",
        "      <td> 36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>honda accord</th>\n",
        "      <td> 2205</td>\n",
        "      <td> 36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>toyota corolla</th>\n",
        "      <td> 2245</td>\n",
        "      <td> 34</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>honda civic</th>\n",
        "      <td> 1965</td>\n",
        "      <td> 38</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>honda civic (auto)</th>\n",
        "      <td> 1965</td>\n",
        "      <td> 32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>datsun 310 gx</th>\n",
        "      <td> 1995</td>\n",
        "      <td> 38</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>buick century limited</th>\n",
        "      <td> 2945</td>\n",
        "      <td> 25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>oldsmobile cutlass ciera (diesel)</th>\n",
        "      <td> 3015</td>\n",
        "      <td> 38</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>chrysler lebaron medallion</th>\n",
        "      <td> 2585</td>\n",
        "      <td> 26</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ford granada l</th>\n",
        "      <td> 2835</td>\n",
        "      <td> 22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>toyota celica gt</th>\n",
        "      <td> 2665</td>\n",
        "      <td> 32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>dodge charger 2.2</th>\n",
        "      <td> 2370</td>\n",
        "      <td> 36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>chevrolet camaro</th>\n",
        "      <td> 2950</td>\n",
        "      <td> 27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ford mustang gl</th>\n",
        "      <td> 2790</td>\n",
        "      <td> 27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>vw pickup</th>\n",
        "      <td> 2130</td>\n",
        "      <td> 44</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>dodge rampage</th>\n",
        "      <td> 2295</td>\n",
        "      <td> 32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ford ranger</th>\n",
        "      <td> 2625</td>\n",
        "      <td> 28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>chevy s-10</th>\n",
        "      <td> 2720</td>\n",
        "      <td> 31</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>397 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "                                   weight  mpg\n",
        "name                                          \n",
        "chevrolet chevelle malibu            3504   18\n",
        "buick skylark 320                    3693   15\n",
        "plymouth satellite                   3436   18\n",
        "amc rebel sst                        3433   16\n",
        "ford torino                          3449   17\n",
        "ford galaxie 500                     4341   15\n",
        "chevrolet impala                     4354   14\n",
        "plymouth fury iii                    4312   14\n",
        "pontiac catalina                     4425   14\n",
        "amc ambassador dpl                   3850   15\n",
        "dodge challenger se                  3563   15\n",
        "plymouth 'cuda 340                   3609   14\n",
        "chevrolet monte carlo                3761   15\n",
        "buick estate wagon (sw)              3086   14\n",
        "toyota corona mark ii                2372   24\n",
        "plymouth duster                      2833   22\n",
        "amc hornet                           2774   18\n",
        "ford maverick                        2587   21\n",
        "datsun pl510                         2130   27\n",
        "volkswagen 1131 deluxe sedan         1835   26\n",
        "peugeot 504                          2672   25\n",
        "audi 100 ls                          2430   24\n",
        "saab 99e                             2375   25\n",
        "bmw 2002                             2234   26\n",
        "amc gremlin                          2648   21\n",
        "ford f250                            4615   10\n",
        "chevy c20                            4376   10\n",
        "dodge d200                           4382   11\n",
        "hi 1200d                             4732    9\n",
        "datsun pl510                         2130   27\n",
        "...                                   ...  ...\n",
        "chevrolet cavalier                   2605   28\n",
        "chevrolet cavalier wagon             2640   27\n",
        "chevrolet cavalier 2-door            2395   34\n",
        "pontiac j2000 se hatchback           2575   31\n",
        "dodge aries se                       2525   29\n",
        "pontiac phoenix                      2735   27\n",
        "ford fairmont futura                 2865   24\n",
        "volkswagen rabbit l                  1980   36\n",
        "mazda glc custom l                   2025   37\n",
        "mazda glc custom                     1970   31\n",
        "plymouth horizon miser               2125   38\n",
        "mercury lynx l                       2125   36\n",
        "nissan stanza xe                     2160   36\n",
        "honda accord                         2205   36\n",
        "toyota corolla                       2245   34\n",
        "honda civic                          1965   38\n",
        "honda civic (auto)                   1965   32\n",
        "datsun 310 gx                        1995   38\n",
        "buick century limited                2945   25\n",
        "oldsmobile cutlass ciera (diesel)    3015   38\n",
        "chrysler lebaron medallion           2585   26\n",
        "ford granada l                       2835   22\n",
        "toyota celica gt                     2665   32\n",
        "dodge charger 2.2                    2370   36\n",
        "chevrolet camaro                     2950   27\n",
        "ford mustang gl                      2790   27\n",
        "vw pickup                            2130   44\n",
        "dodge rampage                        2295   32\n",
        "ford ranger                          2625   28\n",
        "chevy s-10                           2720   31\n",
        "\n",
        "[397 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# useful function!: value_counts()\n",
      "df.year.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "1973    40\n",
        "1978    36\n",
        "1976    34\n",
        "1975    30\n",
        "1982    30\n",
        "1980    29\n",
        "1970    29\n",
        "1979    29\n",
        "1981    29\n",
        "1971    28\n",
        "1972    28\n",
        "1977    28\n",
        "1974    27\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Let's change year from \"70\" to \"1970\" "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.year.apply(lambda x: '19' + str(x)) # this spits out the Series we like\n",
      "# df.year = df.year.apply(lambda x: '19' + str(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "name\n",
        "chevrolet chevelle malibu    191970\n",
        "buick skylark 320            191970\n",
        "plymouth satellite           191970\n",
        "amc rebel sst                191970\n",
        "ford torino                  191970\n",
        "ford galaxie 500             191970\n",
        "chevrolet impala             191970\n",
        "plymouth fury iii            191970\n",
        "pontiac catalina             191970\n",
        "amc ambassador dpl           191970\n",
        "dodge challenger se          191970\n",
        "plymouth 'cuda 340           191970\n",
        "chevrolet monte carlo        191970\n",
        "buick estate wagon (sw)      191970\n",
        "toyota corona mark ii        191970\n",
        "...\n",
        "honda civic                          191982\n",
        "honda civic (auto)                   191982\n",
        "datsun 310 gx                        191982\n",
        "buick century limited                191982\n",
        "oldsmobile cutlass ciera (diesel)    191982\n",
        "chrysler lebaron medallion           191982\n",
        "ford granada l                       191982\n",
        "toyota celica gt                     191982\n",
        "dodge charger 2.2                    191982\n",
        "chevrolet camaro                     191982\n",
        "ford mustang gl                      191982\n",
        "vw pickup                            191982\n",
        "dodge rampage                        191982\n",
        "ford ranger                          191982\n",
        "chevy s-10                           191982\n",
        "Name: year, Length: 397, dtype: object"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Uh oh, let's change it back!\n",
      "df.year = df.year.str[-2:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "name\n",
        "chevrolet chevelle malibu    70\n",
        "buick skylark 320            70\n",
        "plymouth satellite           70\n",
        "amc rebel sst                70\n",
        "ford torino                  70\n",
        "ford galaxie 500             70\n",
        "chevrolet impala             70\n",
        "plymouth fury iii            70\n",
        "pontiac catalina             70\n",
        "amc ambassador dpl           70\n",
        "dodge challenger se          70\n",
        "plymouth 'cuda 340           70\n",
        "chevrolet monte carlo        70\n",
        "buick estate wagon (sw)      70\n",
        "toyota corona mark ii        70\n",
        "...\n",
        "honda civic                          82\n",
        "honda civic (auto)                   82\n",
        "datsun 310 gx                        82\n",
        "buick century limited                82\n",
        "oldsmobile cutlass ciera (diesel)    82\n",
        "chrysler lebaron medallion           82\n",
        "ford granada l                       82\n",
        "toyota celica gt                     82\n",
        "dodge charger 2.2                    82\n",
        "chevrolet camaro                     82\n",
        "ford mustang gl                      82\n",
        "vw pickup                            82\n",
        "dodge rampage                        82\n",
        "ford ranger                          82\n",
        "chevy s-10                           82\n",
        "Name: year, Length: 397, dtype: object"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Visualizing data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Most popular library: ``matplotlib``\n",
      "\n",
      "Others: \n",
      "- ``seaborn``\n",
      "- ``ggplot``\n",
      "- ``prettyplotlib``\n",
      "- ``bokeh``"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### common matplotlib plots\n",
      "- plt.hist <-- histograms\n",
      "- plt.scatter <-- scatter plot\n",
      "- plt.plot <-- most others"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "plt.hist(df.weight)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "(array([ 31.,  90.,  59.,  54.,  36.,  40.,  29.,  34.,  18.,   6.]),\n",
        " array([ 1613. ,  1965.7,  2318.4,  2671.1,  3023.8,  3376.5,  3729.2,\n",
        "         4081.9,  4434.6,  4787.3,  5140. ]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f55f50>"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### common plot features to tweak\n",
      "- plt.title('Sk00l Rox', fontsize=20)\n",
      "- plt.xlabel('')\n",
      "- plt.ylabel('')\n",
      "- plt.xlim(min, max)\n",
      "- plt.legend()"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###We can also used pandas' ``plot`` and other plotting function!!!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.weight.hist()\n",
      "plt.title('OMG THERES A TITLE!!!11', fontsize=20)\n",
      "\n",
      "# let's add decoration\n",
      "plt.xlabel('weight')\n",
      "plt.ylabel('frequency')\n",
      "plt.xlim(0, df.weight.max())\n",
      "plt.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mSSQSSKVSSCQSrjfCfvzsjSRXPY1Zgcv2KFkhzEZmtVqttQpdPp8PnU4HjUaD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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x80ce6f0>"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(df.year, df.weight)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "<matplotlib.collections.PathCollection at 0x6ce9890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Zu3YllStXzrPmoUPH8Pu7/X7s99fj0KE3tey0sIhGVq5cycqVK/VE/mkFGPAkcBDYBxwF\nMoFZfzinPLAtuD8KGBXy2mKgCVAC+CGkvQfwyt+8p+5it3xj165d8n//11mqVm0kAwbcKRkZGVp6\nr7/+uhjGTQIS3E5ITIxbKyXD448/LnCpwFCB3gLDJSmpjJadHTt2ESglsE3guMA1kpBQSkvzzjvv\nk5iYIb9/dpvtKWnfvpuW5pAhw4OpHVIFTgjUlXvuuV9L08LiQoBwr/AVkdEiUkZEKgA3Ycbt+yil\nSoac1hnYFtz/BLhJKeVUSlUAqgDrRCQFSFdKNVFm1rLewPy83rAiwcmTJ2natA3Llzfjp5+eZ9as\nY3Tu3FNL0/wqQnvlfu2kbidPnsS8XycCrYCFeDx6axEqVaoM1MFcPVsFsGkPpB44cBSvt/Hvx4FA\nY+1FXvv3H8HMPFoKKAMUYN++I1qaFhb/Vc5ngrrCjPUDPKWU2qqU2oLpYe4GEJEdwBxgB/AZMCR4\nVwIYAkwDdgG75S9m+kQzK1euJDu7KoHAd8BIsrPLsWLF52RkZORZs2PHjrjda7DbHwY+xDA6MXDg\nEK0bQFpaGnAd5kNbf+ADRPQyd/fv35e4uHXA/cBUDGMHo0YN09K88soWGMYU4CTgwe1+liuv1Js2\nm5aWCQzHfEDNBAaTnp6ppQmwYcMG7r13JGPGPMz+/fu19SwsooLzfVTI740oDfvMnTtXIFHgVoG3\nBOoLuCUrK0tL98CBA9Kr1+3Spk1HmTTpee2snnfddVcw5HM6lLRbYmIKaGmKiLz99ttSvHg1KVCg\nogwcOERbz+/3y9Ch94jd7hS73SmdOt2s/V1OnfqaGEYtge8ENohhVJfp02dqaS5fvlwMo6jAWLHb\n75HExOKye/duLc0LiXnz5smQIXfJ+PETJD09PdLmWPwNWFk984/HHntMoEEwW6QIpAk45NSpU5E2\n7X8wnX+iwDSBLwSaitOp5/y3bt0qcXFFBCYLzBXDqCovvTQlLPZ6vV7JyckJi1YgEJDx45+WEiWq\nSMmSVWXSpOe1U1o3bNhWYHbI2MQYGTx4uLatx48fl/nz58vSpUvF6/Vq6+UH48ZNEMOoJvC0xMbe\nJFWr1hOPxxNpsyz+grw4f6uS1zmyfft2wOBMIrJYQJGamkp8fHzkDPsDpUuXxuG4HJ9vHmbenAYk\nJurF0mfMeIvMzCGYkTvweEry7LNDGTZssLa9Dkf4foJKKUaNuo9Ro+4Lm2ZGRiZwZogrEEgmLW2T\nluamTZto1qwtubm1UeoYVavGs3nzGmJjYzWtDR+BQIBx4x4jN3cnUIacHOHIkStZsGAB3bt3j7R5\nFmEgepLSRDnmH+ZGzJmtXwA3ALFhdV7hoFevXiQlbcZuTwTq4HYvYOzYUVqa5hhE6MI7ibpqY/lF\n795dMYx7gU3ASgxjPL16ddHSbN++Ozk5TyHyFYHADnbuTGLMmDFhsTdc+P1+fD4vcLquskKkBJmZ\n+mMoFtGB5fzPETM/TgBzSUNX4BsgN6p6/QBFihTh0kvrAGuw2zcCmTRv3kRL89Zb+2AYk4EuwE24\nXDdz3336vf4LgVGj7mXEiE6UKtWTChXu4tVXJ9ChQwctzRMnjgNtgkd2oB0rVnyta2pYiYmJ4cor\nryU2tj/m/I23UGopV1xxRaRNswgTlvM/R6pXr445sekksB84jlJ27RW54eatt97iq69S8Pt34fd/\nS1bWM3Tp0ldLs2jRorjdBjabE0gmEMigWrW8L8Y6zYkTJ5gyZQovvvii9qKx/MJmszF27BgOHdrB\n3r2b6dVLb3qviR14HrMzcQKYQSCQEwbd8PLhh7Po2tWgZMnO1Ks3jS++WETZsmUjbZZFuDjfQYL8\n3ojSAd+NGzeKy5UskBIc/FsuiYnFwlZzN1x07NhJ4IGQ2T5HRSlDS/Ohhx4Rh2NgiOaHcsklzbQ0\nDx48KEWKlBGX62aJjb1d4uOLyubNm7U0LxTi4goJFBCIF4gVSJT+/QdE2iyLCxisMo75R/369Rk5\nciguVy2SkpoSH38T8+fPDksuf6/Xy8mTJ0/f/LQw8+G/j9mjFOAVlHJqaR46lILPF1q5qxLHjx/X\n0nz88af59debyM5+h5yc18jIGMu99z6ipZlffP/99wwdOpQRI0Zof26Atm3boVQVoCxQndjYgnTo\n0E5b18LifLCc/3kwduxofvhhPQsXPsOBAztp06bN2S86C1OnvkZ8fEFKlqxA1ar1tBcR9ex5Mw6H\nB6iImU/vDerVu1RL0+v1YI51rMNM0HoPgYBeuOvYsZP4/TVDWmpy/LheDd/8YNGiRdSq1ZgpU/Yw\nadIqSpWqxr59+7Q0r7qqJUodAIYC7YE07WI7Fhbnzfk+KuT3RpSGffKDtWvXimEkC+wSCIjNNkFq\n1WqqrfvAA4+Iw2FIbGxhqVy5jhw8eFBLb/Dg4QLdBSoJlBS4WZKTq2tpzpjxphjGJcHPfkQMo5WM\nGfOolmZ+kJRUTuCZYLgrINBbGjW6TEuzXLlaAl/9Hkaz2++Uhx56JDwGW1yUYIV9LizWrVuH338t\n8DPwPoFAJ3bsWK9dFP7JJ8eSmnqUPXu28OOP31G6dGktvWuvbYdhrAM+Ajbjdqdz3XVXaWn27dub\nUaN6Eh/fDLe7Jr1712XsWP000eEmPf0U0Dx4pICW7NqlNzidnZ0NvIaZE/ES/P7D5OTkamlaWJwv\nlvOPIKVKlcLn+wS4A/gAaE5cXEHtcYTc3Fw+/vhj3n33XXbv3q1t59VXX83TT4+iQIH2uN3V6NKl\nBM8/r1cjQClFmzYtadmyLS1atOXqq9tG3ZoJAIfDBjwFZAO/AJNJTNQrOVm6dEnMG/5SYAqwnKpV\nK/3zRRYW4eZ8HxXye+MiCvvMmzdP7PaaAjnBEMAXkphYQkszKytLihWrKOAUSBKlEuX9998Pk8Xh\nY82aNeJ2FxF4VeBtMYzSMmfO3Eib9SeuvLKjQJ3g9xkrUEMeemislmZCQrLAjpDZU49J//4Dw2Sx\nxcUIVtjnwuLw4cM4HM2Ad4CJgJ2MjBNaYZ/77ruP48dzMJ8m+iJyNX363KFt6/bt2+nRoz/XXtuD\nDz+cp6330ktvkJX1IDAA6InH8xJPPfWKtm64eeONFylZMoP4+PrExVWhfv0ijBo1QkvT48nB7Pmf\nZi+7d/+kpWlhcb5E33P2RUT9+vXJzX0Qc9FYHaArJUtW0Ar7bNq0GTNEsQqIB7aSk5OmZeePP/5I\no0aXkZ1t1sJdsmQ5U6em0b9/vzxrmp2V0BQRtrBMdQ03ZcuW5aefNrN27VqcTifNmjXTDk8lJbk4\nebIXMAizoulCLr30Zi1NEeH116fz4YeLKVasII899gAVKlTQ0rT4b2P1/CPIzz//jNN5CWbs9xlg\nGb/99quWpt1uw3SqVYHLMP+L7Vqa48Y9SXa2wizU9hxeb3Huv19vTv6wYbdiGI8DbwCzMYxhjBiR\n9yLz+Ulqaio7d+7kxx9/5NSpU9p6d901jJiYJMwCeRnExnq57bZbtTQfffRJ7r77RZYs6cK77yZT\nv34Ljh7VS+gHsGPHDqZPn86iRYu0JyJYRBdWzz+CnDx5EpvtEs70gKuSlZVGIBDIc+8/Li4eMw3F\nrGDL/6GUXi6a7dt3AvcCp1MbTCMt7VotzRYtWrBo0VyefPIlvF4fQ4c+T9euegnT8oPNmzfTsmU7\nfL5rsdnSefjhCWzevCaY6ylvFCiQCGRwugCezXZ6cV7eee65l/B4VgGVCQQgK+sAc+fO5c4778yz\n5gcffEifPoOx2Tqg1FYuv3wGCxa8H5aFjRaR55z+F5VSdqXUJqXUguBxIaXUUqXUT0qpJUqpAiHn\nPqCU2qWU2qmUahfS3kAptS34Wt4rlP+HMAvTfwSsAFKJibmHli3baf1xVapUEbNw+2nKBmes5J1S\npYpjpoc+TSaxsTFamgCtWrXi888/YMWK+VHp+AHuuGM0GRmPk509HY/nA375pQMTJjyjpfnUUy/j\n9X4CbAG2kJNzGzNnzjrbZf+IuejuzP+JiFMr75SI0K/fQLKyPiMz800yMtby1Vd7WLRokZadFtHD\nuXqF4Zip/U4HZUcBS0WkKrA8eIxSqiZwI1ATc+niFHUm9+9UoL+IVAGqKKXah+cjXLjUrFmTDz54\nk5IlB+J2V6J166PMm/eWlma/fv2IjX0TWALswW4fyA03dNPSLFKkKGYFznGY89P74HbrhZIuFI4d\nOwHU/v3Y663NkSO/aGmaYxv2kOMYAgG98Y4BA27DMG4CPkOp54mNnU+XLnm/oXq9XjIzfwPqBluc\nBAJ1SElJ0bLTIoo423QgoDSwDDMH7YJg206geHC/BLAzuP8AMDLk2sVAU8xqGD+EtN8EvPI375d/\n86EuEj799FOpUOFSKVy4rNxyy2Dt6ksjRowKlq8cInCLwONSsWLdMFkb3dx99yhxu9sLpArslbi4\nmvL22+9oaU6YMEni4moJfCwwReLiisj333+vpen3+2X8+EnSqNGVcvXV3WX79u1aeiIitWo1Ebt9\nnIBfYJMYRjHZtm2btq5F+CE/yjgCc4F6mIHk087/15DX1elj4CWgZ8hr0zCT3zfAfFI43d7ytNZf\nvF9+f08W58nTTz8tECfwULCUYzFp0KBFpM36V8jOzpZ69VoIOEUpt9x4Y0/t0pCBQECmTHlVmjVr\nL+3bd5P169eHydrwcuDAAalQoZYo5RCnM0HeeuvtSJtk8Tfkxfn/44CvUupa4LiIbFJKtf6bJwdR\nSoV1jt7YsWN/32/dunUwNm4RKXbt+hm4EzPufxR4lKNHL45hm5dffoXt2w8CDRGx89FHn7Fs2TL+\n7//+L8+aSikGDx7A4MEDwmdoPrBgwUIOHjyCSCV8vmwmTXqZG2/sTkyM/niPhR4rV65k5cqVeiL/\ndGcAngQOYs5JOwpkAm9hhn1KBM8pyZmwzyhgVMj1i4EmmKGh0LBPD6ywzwXDpEnPiMt1nYBPQESp\nF6R586sibdbf8uuvv8pvv/0WFq2SJSsJlBf4UGCGQEG56qrrw6IdzQQCAbHZEgTaCCwSeEwgQWbM\nmBFp0yz+AsK9wldERotIGRGpgBmnXyEivYFPgNPlofoC84P7nwA3KaWcSqkKQBVgnYikAOlKqSbB\nAeDeIddYhBmfz8fixYuZPXs2hw4d0tYbOnQIdepkEh9fj8TEKyhUaBLTp0dfzz8nJ4eWLf+PQoVK\nU7BgMm3bXk1url7CtF9/zQJexSxheQswhn37DuobG+Xk5OQQCGQCjTFzG+0A6rNq1arIGmYRNs53\nnv/p8M4EYI5Sqj/m8tTuACKyQyk1B/OX4gOGBO9KAEOAmYAbWCQii/VMt/grvF4vbdpcy5Ytv6BU\neUTu5PPP59O8efOzXvt3uFwuVq36nNWrV+PxeGjatCkFCxYMn9Fhon//QaxatRYYDQT44ouJDBly\nJ9Om5T1tRIkSRdm/PwP4GnACOdSqVUPLThHh+edf5u23PyIxMZ4nn3yAZs2aaWmGGzO04wK+w/w+\nNwILKV26dSTNsggn5/uokN8bUR722bFjhyxdulSOHj0aaVP+kmnTpolhXPF7iAY+kEqV6kTarH+F\npKTywURxEtxeksKFK2lpTp48RSAhmNytkiiVKKtXr9bSHDdugsTF1RVYLPCGGEYR2bp1q5ZmuPH5\nfKKUQyAt5PtsL1OmTIm0aRZ/AVZit/zl/vsfokGDtnTr9gSVK9dm8eLoe3g5ePAQWVlNOTOPvAXH\njh2OpEn/GrGxTqBQSEshYmNjtTQ/++xLbLY7gc3AjzidbVm27AstzalTZ5CZOR24CriVrKwBvPPO\nbC3N/MBudwDe349dLkViYmLkDLIIK5bzP0fWrVvH5MmzyMraTlraF2RmfsQNN/SKunwnzZs3w+1+\nD3OcPoDDMYlGjfRDCgsXLqRWreZUrFiXsWOfiLrPDfDQQ3ei1HDMFdPLUOoexo27V0tz587dBALX\nBY/s5ORcw/btejUS7HY7ZvI9E6WycTiia9Gc3W6nWLHSmMt73gOGk539lVVu8r/E+T4q5PdGlIZ9\n3nvvPUlI6BbyCCzidCZKampqpE37E+PHT5KYGLc4HIbUq3eZHDt2TEtv1apVYhjFBT4RWCuG0UQe\neuixMFlMmkmdAAAgAElEQVQbXl56abKUKlVDSpeuIa+++pq2Xv36lwv0Dy508gg0k8GDh2ppvvLK\na2IYFQVmiFKPS0JCMdmzZ4+2reHE6/UKuASGCtwgMFCglIwcOTLSpln8BVhhn/yjVq1a+HxfA3uD\nLR+SlFQgKgc+R426l8zMNFJTj/Ldd19rJSEDmD37Qzye4cB1QGM8nsm8+eYcbTtnzXqbypXrU778\npUyc+Mzpm3+e8Xq9rFmzkZSUPRw9uoc1azZq5bcByMzMwixcXxYoA/hJT8/R0hw48HZmzpzI9dcv\npVevn1m//isqVqyopRlufD4fkAu8CMwBXgEu55df9FJbWEQPVlbPc6RWrVrcffcAxo+vDygcDjvv\nvz+XM6mLoouYmJiwLcbJyckCQnO6nCA31/t3p58TCxYsYPDgMXg8M4E4HnvsdmJjY7nrrmF51nz8\n8Yl8/PFB/P5UIMDcuR2pWvVZRo/Oe/EVl8sN3ADsxpyo5sAw9Jw/wA03dNPOuZSfuFwuEhKKc+rU\ndOA24GeUWkLPnnMjbZpFmLB6/ufIsWPHePnl1xB5GpE1KNWZMWOejLRZ/wrmDe4tYAQwCXO+u57z\nnznzAzyeBzFjyo3xeCbx5psfaGkuXboaj+dOzCI2iXg8d7B0qd689K5drwQew1zLqIApdO58tZZm\nfvH5558zbNg9PProOFJTU7X1Vq/+nISEB7HbS2CzVeeRR+6mTZs2YbA0/IgIv/76a1SORUUrlvM/\nR77++mtEGgO3AzXIzX2F9evXkJ6eHmnT8h2XywD6YT4oHgQexuWK19JMTDRQ6lhIyzHi4vQKoxcr\nVhBYE9KyiuLFC/3d6efEJ5+sxExRNRZ4DqXuZsGCz7U084NXXnmdLl0GMHlyCZ544gB16jTjt99+\n09I0DIMiRYqi1CkcDhtly5YOk7Xh5dtvv6Vo0bKUKFGOggVLsGzZskibdEFgOf9zxDAMRI5xZp3b\nSSCgPZXwQqBPnx4YxluYmbqvwTBeZdCgPlqaI0cOJy7uRZQaBYzDMO7miSdGamna7Qoz5fS1wNXA\nW8G2vJORkYnZ6zcRKUVaWqaWZn4wevSjeDyfAPfj9U7j5Mm6vPfee1qa11xzI/v334LPl0Fu7gaG\nDh3J5s2bw2NwmPB4PHTo0IXU1JfJzU0nPX0OnTr14MSJE5E2LeqxnP850qZNG0QOYjqWSUAzWrRo\nFZXOPysri1mzZvHiiy/y/fffa+s1aNCAuXNnUqnScyQn38Vdd3Xi/vvv0dKsXr063323mnvvVQwf\nns5XXy2mVatWWppHjqQCkzGzh9wCPBtsyzu9e3fFMO4FNgErMYzx9OoVfYVncnI8QPHfj32+EmRm\n5v0mlZuby08/bUbkbsxwV3WU6sCGDRu0bQ0ne/fuxe9PBDoGW1rjcFTlhx9+iKRZFwbnOz0ovzei\ndKrnkiVLJC6utsBTAncJvCwOh0tyc3Mjbdr/kJmZKTVrNpK4uHYSGztI3O4i8tlnn2lppqamSmJi\nSYEYAYfYbAVk6dKlYbI4fIwY8aC43dcLZAt4xO3uoD0l1e/3y9133y9JSWWkSJGK8vrrr4fJ2vDS\nq9ft4nJdK7BN4AMxDL0aAYFAQBITiwmsCU5tzpL4+Nrav6Vwc+LECYmNTRLYH7TzmLjdRWXXrl2R\nNu1fhfzI5/9vb9Hq/N9//31JSOgcMs8/IDEx8WHLHhkuJk+eLG73dQKBoJ1LpEyZGlqa3bp1F6gk\ncFQgV6CnFChQTtvW48ePy+TJk+WFF16Q/fv3a+tlZWXJ1Vd3E6czQWJi4qVjxx6Sk5Ojpbl27Vqx\n2wsE6xnESlxcMTl+/Li2rUeOHJHZs2fLggULtG0UMT/7wIHDJTm5mlxySTNZuXKltuaCBQvEMIpI\nQkI3iYurLl279tauZZAfPPfcS+J2l5CEhBvEMMrIgw9G5xqU/MRy/vnIwYMHJT6+qMDTAjPFbh8k\ntWs3jbRZf2Ls2EfFZnsg5CZ1ROLji2pp1qjRQOCZEM1tYrMV0tI8ePCgFCxYUhyOOmK3NxTDKCib\nN2/W0jxNamqqnDx5MixaxYtXEegp4A3muWkkbdvqpbPeuHGjJCQUk4SEzhIf30Tq1btMu9paVlaW\n3HbbHVKiRBWpUaOxrFixQkvvND/++KO8/PLLsmjRoqh0/KfZsmWLvPvuu1FbGCe/sZx/PtOp002i\nVDGBy8VmS5A33pgeaZP+xNdffy2GkSywSSBdnM5b5Prre2hp9u7dR6BTyNPE61KkiF7CtB49+goU\nEBgscK9AAWnY8HItzfwgJqaYwLchN77XpEyZS7Q0L720hcCbQT2/uFzXy3PPPaelefPNt4nLdb3A\n9wLztMM+ImYSw+TkyuJ2lxCnM15eeGGylp5F/mE5/3xkzZo1EhdXISTL4XoxjALi8/kibdqfmDHj\nTUlMLC4Oh0uuuqqLdmjq1KlTUrx4RYH6AleJw5Eo33zzjZZmuXKXCIwJcarTxDBKaWnmB2XKXCLw\n+O+hPugm117bSUuzSJHyArtCPvt4ueuu+7Q0DaOgQMrvmjExd8jTTz+tpVmxYm1R6pWg5l4xjOSL\ntmcd7eTF+Vuzfc6RAwcOEAjUAU5nNWyI1+uPynn+t9zSh7S0FLzeLBYv/pCkpCQtvfj4ePbv/555\n8x7k7bd7c+DADzRt2lRLs3DhwkBoSoMKxMfHaWnmB488cjcwHrPsdF3gcx577GEtzRYtmuN0TgL8\nQAqG8SaXX573egsAdrsTs9ieic+3H5fLlWe93Nxc9u3bgcjtwZYKQDs2bdqkZadF9PCPzl8p5VJK\nrVVKbVZK7VBKjQ+2j1VKHVJKbQpuHUKueUAptUsptVMp1S6kvYFSalvwtegrA3UWChUqRFbWcsw6\nNQCzCASUtmO9UHC5XHTu3JmePXuSnJysrXf//UOJiXkMs0jIThyOe7njjlu1dcPN1KnvAC9jViid\nBNzBq6/O1NKcMeNlGjTYg91uYLeX5957b6Zz585amjabAjpg1lnqjcjXOBx5z97idDopUKA4MANz\nkdtc4BvKly+vZadF9PCPvw4RyVZKtRERj1LKAaxSSl2GudLpWRF5NvR8pVRN4EbM1UClgGVKqSrB\nx5KpQH8RWaeUWqSUai8XUDWvX3/9ldjYGuTkNMWscBSPUj7S0tKiMrlbtHPjjd05fvwXnnjiRvx+\nH7fd1lcrB09+kZnpwez1nl6D8CMZGVu1NNPT0/n++x34/QmAh6+//gYR0coTlZGRDhQGvsIstS3a\nJTyHDr2Vxx+/G7gG2ESBAtC2bVstTYvo4axhHxHxBHedmBVCfg0e/9UvtSPwnoh4RWQ/ZjasJkqp\nkkCCiKwLnjcL6KRj+L9NiRIlgFTgZ2AL8DE2myIhISGyhl3A3HHHEFJSdnPixH7Gj38Umy36opD9\n+nUnLu5uYC2wBMOYQJ8+egnZWrToQHp6T+AEkMLKlft56KGHNC11AEuBRcCXQDsyMjLyrCYiPP/8\nVGAlZj7/baSnx/HZZ59p2mkRLZz1r00pZVNKbQaOAV+IyOklo3copbYopd5QShUItiUDod2NQ5hP\nAH9sPxxsv2AoWrQoPl8q0AwYCbTF5YoPFuaw+K8yYsTdjB7dgwoVBlKt2oNMn/4s7dq1O/uF/8CR\nI4eBAZj9p0SgD4sW6eWjiY11AGdyI9ntBShXrlye9Xw+Hx5PGlAn2BJDIHApKSkp/3RZxDh16hTb\nt2/Xzmd0MXEuPf+AiNQFSgOXK6VaY4ZwKmCOgB0FnslPI6OBrVu34nK1Bfpi9rJeITvbY/3YoggR\n4emnn6NUqeqULl2D559/SVtTKcXo0SPYu3czO3eu48Ybu2trOp1uzB46mNlRF1KmTPF/uOLs9Ot3\nC253D8wY/QTc7vl07NjxbJf9LTExMVxySSPs9scxB6a/Q2Rh1BWaB/jss88oWbICzZvfQHJyBd57\n7/1Im3RhcD5Tg4CHgPv+0FYe2BbcHwWMCnltMdAEKAH8ENLeA3jlb95DHnnkkd+3L774InzzoTRY\nvXq12GyFBMoLdBFIEofD0J7q+eWXX0q1ag2lcOGycsMNfSU9PT1MFl98vPLK6+JyVRa4XeA2cbkq\nyMyZsyJt1p945ZVXgiuG6wuUlpiYwnL06FEtzR07dojbXUSglCgVJ92766/G/fnnn6V27WZiszkk\nPr6wzJkzV0svP0hPT5e4uMICq4NTUreK211YDh8+HGnT8pUvvvjif/wk4Z7nDxQBCgT33ZijSVcA\nJULOuRt4N7hfE7PStRPzyWAPoIKvrQ3eCBRmt6f937znv/HdnTfz588XKC1mKT8JLqZxitfrzbPm\nrl27JC6uiMA8gT0SG9tL2rfvGkarLy7q1r1MoKDAiODCsYLSpMkVkTbrT/z8889SrFgFcTiKi92e\nKB079hC/36+lmZxcVeB+gecEpgiUlQ8++EBL88SJE9KmzXXiciVJqVLVZMmSJVp6+cG2bdskIaF6\nyJoJkcTE5mFJb3EhkR/OvzbwXdChbwVGBNtnBY+3APOB4iHXjMYc6N0JXBXS3gDYFnztxX94z3/h\nqzp/OnfuLHDV//zIIFZ2796dZ80pU6aI231riF6m2O1ObUeQH8ya9bYkJ1eTQoXKyJAh90RdQjsR\nkWLFqgi8EPJ9Pi3JydUjbdafaNXqGrHbHwvamCGG0VRmzJihpQkOgaJi1ty9UqCsNG7cWEuzadMr\nJSbmToETAovFMIrITz/9pKV5mnAtjjx58qTYbIbAluD3uUeUitf6u7wQyYvz/8eYv4hsE5H6IlJX\nRC4VkaeD7X2Cx3VEpJOYie5PX/OkiFQWkeoi8nlI+0YRqR187c5/et9opEiRIpiFQtZhznR9AXAQ\nF5f3hUkJCQnYbAc5UyPgILGxRtSVhly+fDmDBo3kyJE3OHlyBTNnbmHkSL2FTvmBuf6gbEhLWcqU\nKRMpc/6W77//Hr+/R/AoDo+nI5s2bddUdQEfYK5JWAKUZ9euXXlW83q9rFu3Eq/3GcwAwFUodTVf\nffWVlpW7d++mevWGxMQ4KVKkLEuXLtXSO3nyJHZ7LGZFuBZAY2JiCoSlktl/neibWxelPPDAA5gF\nra/E/EObQMGChYJTQPNGly5dKF06FZerO/A4hnEV48c/HnXOf968T4MF3FsAlfF4JjF37ieRNutP\nDBzYE7d7DGbu/Q243Q8zcGBPbd2VK1fSp89ABg68Myx54qtVq4bN9lHwKBvD+IzatatrquYClwT3\nFVBHaxqyw+EgJsbAjNwCBPD7f9Ba0xIIBLjiiuv56adeiOSQmjqTzp1v5uDBg3nWFJGgnVuAJ4Hv\niI0tcDqKYPFPnO+jQn5vRGnYR8RMmla0aGVxOovJpZc2l9TUVG3NkydPSp8+feSKK/5PpkyZEgYr\nw8+YMQ+LwzEkJJwyX2rW1M9o+umnn0qjRldK3bqt5I03ZmjrBQIBmTBhkpQsWVWSk6vJs8++oD3o\n+emnn4phlBB4XpR6VOLj9ROm7dmzR0qWrCSJiXXFMErL9dffpB0GgQSBvgLpAusFkqRZs2ZamuXK\nVRcoJnCfQGux2RJl27ZtedZLSUkRl6vwH+Lz18q8efPyrOn3+6Vp0yvE5eopsFCczkFSvXqDsKTJ\nvpDASux2YeH1eqVFi3YSF9dMYmP7istVVN5/f06kzfoTR48elaJFy4rTeaso9YAYRlH5/PPPtTSX\nLVsmbncJgQ8EFolhVA7LDSDc1K3bKjggb/61KPWo3H77MG3dzMxMWbt2rXz//fdhSZUMhkCpYOw/\nTqC0dOnSJc96ubm5YrM5BL4QeFJguhhGL5k2bVqeNbOyssTpjBPYE/w+PRIXV1lWr16dZ00RM/Hg\nsGH3SpMm7eS224aFLZ33hYTl/C8w5syZIw5HRQGngFugrCQkFI60WX/JsWPHZOLEifLQQ4/Id999\np613ww23CEwO6QUulPr124TB0vBSo0ZTgRUhdr4gvXsPiLRZf6J06coChQSGC1wlShWSOXPy3pEI\nBALidicFZ7WJgE/i45vIRx99pGXnyy9PFcNIFrf7NomPry3du98S1XUCLhTy4vzznvnJQptvv/0W\nn+/0WHkMcJJTp3IJBAJRl+pgyZJlvPjiDLKzPZw8mcFzz9UiJiYmz3qxsTGYOWhOk0lMjP7P0e/3\n88MPP6CUonr16torsAcN6sXIkbeTnd0K8ON2L6Jfv+hbRLRkySc0b34FWVnv4/dncMstt9CtW97T\nUCilePnl5xk27Aq83m7ExGyiTp0krr32Wi07hw4dROPGDdiwYQPlynWmQ4cOUTfGdbFgOf8IYhbD\ndgHbMdfKPQM8GkmT/pLly5czcOD9eDzvA8WYPn0QTufDPPvs+Dxr3nPPYD76qB2ZmQLE4XaP4+GH\np2vZmZ6eTuvW1/DTT4cB4ZJLKrBixQKtGVkNG9YjEEgFElDqV+x2RZUqVbTszA9q1KjB4cO72blz\nJ4UKFQpL9s2qVSsD2dhsn+D1/kbdurdrZQo9TUpKCkeOHCUmJgafz6fVibDQ4HwfFfJ74yIK+zRv\n3lwgdJ5/roDSWjiWHwwZcpfAxBA7N2lXsxIR+e6776R37wHSvXu/sJQdHDz4bomNvSU46JkuLtfN\nctddI7U0mzT5P4FZv392u/0+GTbsHm1bLwSSkkoKLAx+9l/F4SirveJ+zJhHJS6umsDDYhhtpHXr\na7QHuwOBgMyePVseeGCMzJw5MyrXyeQ3WGGfC4uuXbuyZs1UIBvzCeBL7PaEsPSuwknBgok4HAfw\n+U63HAhLNtN69eoxa9ar2jqn2bhxOzk5aUBRALKz67FxY5qW5sGDhwgtOuP3V2L3br256fnFJ598\nwnvvzaZEieKMHTtWq9ZEbm4uaWnHMGsEABTA52vGggULaN26dZ40PR4PTz01Ea93L1Acj8fHhg31\n+Prrr/OsCTBw4HDefXcVmZkdiYt7jfnzlzBv3ttWOOlsnO/dIr83LqKefyAQkEaNWgkkC7QUpeLk\n/fffj7RZf+LMbJ/+otTosMz2yQ+qVq0tEC/wsMCDAvFSu3YDLc1ixSoKNBXYLbBRoLS0bds+TBaH\nj7FjHw3O+GkqkCyGUVyrfGdOTk5w1tB9AqMEHhUoJqNGjcqz5vHjxyU2tqCcqQUtkph4tcyfPz/P\nmocOHZLY2EJyprxqlhhGWdmyZUueNS9EsHr+FxZKKdau/YLVq1dz9OhRmjVrRunSpSNt1p8oUaIE\n27evZ+bMmWRkeOjUaTH169ePtFl/4tixdOBpYFCwpTCHD+tl9nQ47MCPQFPMIbJTuN3RV27yscee\nw6y41QPIxuNpxIgRI3jttdfypOd0OomPN8jIeBsYCqwCsunQocNZrvx7ihQpQuXKVfjxxwfw+YZh\n1h3YqFUSND09nZiYguTkJGBOIDBwOEpEZXnVaMNy/hFGKcVll12G3++P6toAxYoV4/7774+0Gf+I\nzWYHioW0lND+Tm02JzAWM3N5LJDFmVWv0UMg4AHaYzpAF/B/HDq0M896IkJOTibmZIQKgOB0tuDY\nsWNnufLvUUqxbNnH3HzzADZubEipUuV4662FFC+e93TWlStXJi7OR0ZGCSANiEMpB3Xq1DnbpRc9\n0TWf8CLk/ffnkJhYDKczlsaN20ZtsQyAtLQ0UlJSTofnog6nMwDcB6zGTEA7kthYPU2HQ2HOwHIC\nvwGvE6srmg/ExCQAjTHz8CQCb2r1qH0+H36/lzM1lxQxMZW0qoOB+RS5YsUnpKWlsGPHWho0aKCl\n5/P5yM7OxcxplAW8h9frJzc3V0v3YsBy/hFky5Yt9Ot3B6dOfUYgkMWmTY3o2FE/F024EREGD76b\nokVLUb78JTRocHlYEmeJCPv372fXrl0EAgFtvczMAHAVZphiONCBjAy/pqoNmIx5A3geswKXvq3h\nxufzAddh9vy3A3atXnpMTAytW7fH6RwE7MUs4L6YNm3ahMPcsLF3714CgUTgBsycRu2IianG999/\nf5YrLSznfx6ICMuXL2fmzJls366bhRFWrVqFSCfMbNcx+Hzj2LDhq7A4wnDy1ltvMWvW13i9h8jJ\nOcH27XW47bbhWpq5ublcfXU3atRoQt26bWnYsJV2VbTy5Ytj9vrnYNad/YpKlUpqabpcbv634mhZ\nXK54Lc38QCQbeBDzT7oCcLN2Afd5897immu8FC7chho1JrFkycdhWT8QTooVK0ZubgpnqsSmkpu7\nm5Il9f7fLwrOd4Q4vzeidLZPIBCQHj1ulbi4GhIX11Pc7mIya9bbWppz5syR+PhmAr7gTIV1kpRU\nPEwWh48BA+4QeDZknv827Tz548aNF7e7g0C2gF+cztulb99BWpo7duwQp7OAQAGBghIbW0A7//w9\n94wUuERgs8CXolRRmTs3+ipaJSWVCpmT7xNoKJMnT460Wf8KEyc+K253shhGVzGMcjJixIORNulf\nh3Dn87c4w9dff80nn6wiM3MjmZlvk5W1ggEDBgcft/NG586dqVcvifj4y3C7b8ftvoZp0yaH0erw\nULVqeVyulZi1XEGpLyhfvpyW5vr128jKuglzENVGbm4vNmzYqqVZo0YNdu/eylNPjebpp0ezZ892\n7dW4W7f+iFm++kZgGDZbVTZt2qalmR/07dsdM/TRGbiUmJi9WukdLiQuu6wJDoeP3NxliKRz5ZUt\nI23ShcE/3Rkwpw2sxazktQMYH2wvBCwFfsKsHFEg5JoHgF2YlbzahbSfruS1C3jhH97zX7hPnj+z\nZ8+WhISu/5OO1ulM0M4g6PV65YMPPpCpU6fK1q1bw2RteMnKypLGjduIYdSQ+PjmUqhQKdm5c6eW\n5ujRj4jL1T3YSw1ITMx9csMNfbU0c3Nz5eab+4vd7hS73Sl9+w7UXj1auXIDgW9D/t9flxtuuEVL\nMz8oWLCUwCKBtwUWict1w0XR88/IyJCkpBICnwb/f76SuLgicvz48Uib9q9CPlTyygbaiEhd4FKg\njVLqMsxC7UtFpCqwPHiMUqomZhepJua8synqzDK7qUB/EakCVFFKtde4Z/3rNGjQAL//S2ADICj1\nMiVLlqFAgQJaug6Hg65duzJo0CBq164dFlvDjdPppEqVSni9R/B691KkSDGtoh4AY8bcT+3ax4mP\nv4SEhPqUKfM5L700UUvz8ccnMn/+z/j9qfj9J5g790cmTnxWS/OyyxoTE/MC4AXScLneoFWrxlqa\n+UFOjgeoC/QEOuD3FycrKyvCVv01KSkprFixQqvS2Gn27dtHIJAEXBNsaYnDUVW76M6+ffto1eoa\nSpasylVXdeXo0aPatkYd53qXAAxgPWa5oJ0E6/YCJYCdcqbXPzLkmsWYq2NKAj+EtN8EvPI375Of\nN0gt5s37SOLiCondHisVK9YOWz3T/CAnJ0fS0tLCojVt2jQxjKYCpwQC4nDcK9dc011b1+fzybp1\n62TNmjWSlZWlrdesWXuBuQKfBbd3pXXr67U0X3ttmthshYKrXWPFbk+StWvXatsabvr0GRgcQ9kk\nMFsMo4j88MMPWpqBQEBmznxTevToL/ffPyYsefLN4jiFJSnpcnG7i8ojjzyhpffLL79IbGySwN5g\nzz9F3O6iWjV8MzIypESJimKzTRTYIQ7HA1K5cp2oy7kVCvmRzx9z+sBm4BTwVLDt15DX1elj4CWg\nZ8hr04CumCGfpSHtLYEFf/N++f09aREIBCQzMzOsmrt375ZvvvkmbM56/PhJEhPjFofDkLp1W0hK\nSoqW3p8HfLdLcnK1sNgaTjp2vEmgpEBLgRYCJaV79z5amrVrXxYMp6QGb35PSb9+g8NkcfjIzs6W\n3r1vl0KFykuFCnW0E7CJiIwa9bAYxqUCU8XpvE3Klashp06dyrNebm6uxMUVEljzu6M2jGTZvHmz\nlp0vvTRV3O7ikpjYWQyjlIwd+6SW3qpVqyQxsWHI7z0gcXEVtG+m+UlenP9ZB3xFJCBm2Kc0cLlS\nqs0fXhfOVCD/z6OUwjCMsOkNHz6SWrWa0b79HZQrV52NGzdq6S1ZsoRx46bg9f6Ez3eK7dub0qPH\n7VqaNWpUwu3+HDAHt222hVSuXElLMz+w2RTmg+h64DuguPYKX/PnrTCHueIBdbqTElWsXr2aDz/8\niNzcppw4EcNDD03A6/XmWS8QCPDMM0/j8SwGBpGb+zqpqWX59NNP86yZmpqKz6eAjUBf4GXs9jrs\n2aO3YnrYsEGsX7+cadN6sHr1pzzyyANaeoZh4PP9grm2oy8wHp8vTSs1eDRyzukdRCRNKbUQsxd/\nTClVQkRSlFIlgePB0w4DZUIuK405AfdwcD+0/fDfvdfYsWN/32/durVWxr9oZtmyZbzxxnyys38k\nO7sgMIfOnXvx8895j1euWfNNcBaN+XX7fPexfr3eWEKfPr0ZPXo8ZnbLQojs4b773tXSzA++/XY9\ncAy4ArM/soo1a/RWeo4YMYgBA24hJ6cWEMDl2sTgwZ/rGxtm+vYdgsczCzMLp59Nm65g9uzZ9O7d\nO096gUCAQMAPnMkMKlKAnJycPNtYtGhRRALAO8DtwBdkZKymShW9sZ7c3Fw+/ngh69dvo3btKlSr\nVg23251nvVq1auHzZWB2IroAb+J0uqIq79bKlStZuXKlnsg/PRZgrhUvENx3Y66ZvwJ4imBsH3Ow\nd0JwvyZmiMiJudJkD6CCr60FmmB2oxYB7f/mPfP18SiaeOmll8TlGhTyeJkrStm08pGPGzcumNnx\n9NqBDyQpqbSWnU8+OV5iYroJPCMwVmCiNGyoX3IxEAjIDz/8INu2bQtLPNXlKi5mNs/T3+cIiYsr\nqaU5f/58cTqLCbws8IS4XIW0ipjnFy5XYjA0ZX52h+NemTBhgpZmx449xOXqLPD/7Z13eFTV1off\nPTNJZiaNFCAQehcREFARkK6ACoiKoFIUrIAVVLBc8XptcK2IHRULqICCFBH0o0mHACJNihAI0kIJ\n6eu7tGAAACAASURBVDOZ9f2xTyR6QXB2YgY57/PMk5k9c35ZU846++yzynJR6g2JiSkvaWlpQetl\nZGSIw+EWGCJwsUBPCQu7wKhCbCAQkC5drhOPp5PAB+J2XyuXXNLeKMprypQpovshF+5DuQIxsmrV\nqqA1SxpKYNmnAvB/Sqm1lvOeLiLfA88DlyulfgbaW48RkY3o9MqNwDfAIMswgEHoawBbgW0iMjuY\ng9U/ifPPPx+ncy5wyBr5nKpVzyuGFo6HgIvQl1vuwu83i/rYti0Vn28tOsV/NfAiqak7jTRzc3Np\n1+5qmja9nObNr6Fx45bGJSMcjjD0/KKQ5tZY8Dz55Evk57+DLhnxKHl5D/Laa8XXg6C4uOiilrhc\nz6FLT2wnPPwLWrRoYaQ5YcJ7NGuWgdd7LUlJL/P1159TsWLFoPV8Ph+BgAImAHWBNfh8uzl+/HjQ\nmjt37uT77xeSkzMNuIXc3C9Yt24na9euDVpT19fycqIAQhgQEdJ1t4LhdKGe60WkiYg0FpGGIjLa\nGj8sIh1FpI6IXCEiR4ts86yI1BKReiLybZHx1SJygfXcvSX3ls4e2rVrx+DBNxERUYfo6AYkJIxg\n6tRPjTTdbjdhYZcBzwE3AJ/g8ZiVI8jNzQCqApejd9q+FBSYNcp44YUXWbIEsrN3kJW1lU2bGnPv\nvY8YacbGutHv+zi6wuNzxMWZrdPqdfMTn59IlFVILLSYNOkDGjZcgdPpITy8ES+8MILLLjNLdhox\nYiQpKRlkZz/HoUNXctNNt3HsWPDNcXRCZAGwAvgIWAd4+fnnn4PWPHLkCPn5gnbQAA5ycxV79+4N\nWrN79+4otQ+9qLEEuBunM582bdoErRmK2Bm+pcwLL/yb7dvXs2jRp6SmbjEuRXvzzTcTHT0Hp3Me\nkIHXO4THHx9maGUYsAbtUMsAHxMIBL/2CzB9+lx8vl6WtiIQuJnvvltkpNmwYSP0Wn8CuptXOE2a\nXGikOWhQP7zeIehcxkl4vc8xcOBNRpolQWRkJDVqVMPrLUPZspWpWtVsfToQCPDGG2PIzp4J9MXn\ne4WMjLrMnDkzaM3Dhw+jv+/CYAEvUNeoppPT6cThCHCi58BwlDpiVHk1OTmZTp3aolOTrgQmcvPN\n1xEVFXo1nYz4q+tEJX0jhNf8N27cKC1bdpYqVRrIzTffXmyhmcVNamqqDBp0v/TseYt88YV5HZpu\n3a4RGFxkLX2mREdXNtKsW7eJQFcBn+jOTkMkNraKkeby5cvF60201v0flcjIRElJSTHSDAQC8sYb\nb0vDhpdJs2YdZNasWUZ6JcU119wkTuclAlcLdJWIiHhZs2ZN0Ho+n0+cznDR/ZD19x4Z2VPGjx8f\ntObhw4etfInXBAoEFgh45c033zTSjIiIEegpcInAdeJ2x8nOnTuD1ty0aZNERCQIXCQQL9BSwsOj\nZf/+/UFrljSURJz/330LVed/4MABiYurKEqNEVgjERH9pHXrLqVt1t/C4MH3Czwm8B+BRwU+kuTk\n84w0+/e/U6CuQHWBegI1pUWLK4xtXbdundx33zB54IGH5KeffjLWKwlycnKkT5/bJSoqUcqWrSYf\nfBC8Qy3E5YoSuEBggsCTAmXk8cfNCpz16nWLlTj2f+JwjJYyZSoY5YwcPnxYXC6v6LalDoEYcbvP\nkxkzZhjZ+dFHn4jHkyCxsW3F40mUV1553Uhv1qxZolS0wLsC+wVGi1IxRgfTksZ2/iXI5MmTJTr6\n6iKzX5+4XF7JyMgobdNKnC+//NKasd0u8IRAjPTseaOR5o4dOyQ2NklcruvF4egjXm9CSGbOlgQD\nBgwWj6erQJrACvF6k+X777830tT9e7uKrmhaQ+Ay6dWrl5FmXl6eDBv2mFxwQSvp0qVnsWS0N23a\nQiDaOvBHSXR0+WI5g961a5fMmTNHtm/fbqw1Y8YMgToCQwW6WROfpH+c87fbOJ4hbrebgoJd6G5J\ne4CmiBQQFmYWTXI2MHfu90Af4C1r5CJ++MGspWP16tXZsGEVn3zyCT6fn+uvf4x69eqZmnpWMGPG\nN+TkzAIqAhXJzr6bmTO/pX379kFrOhxhBAJl0LUWtwBdKVvW7PpReHg4o0f/h9GjjWR+w+/3s3Xr\ndmAycAWwE5/vEvbs2UP9+vWNtKtUqUKVKlWKw0wqVKiAUvsRSUcneU1AqSzKli1bLPqhgu38z5D6\n9euTm/sL8A7QCnie+Phk3G53KVtW8ixdugqd7FJIZQ4ePGism5yczCOPmEX4nI3ExJThwIGt6Mgp\ncDo3k5Bg5vyU8gMvoVNzygJ9Qu4C5aFDh8jPL0A7foBqhIc3Z/PmzcbOvzjx+Xw4nQn4/ePQMTFd\ncTorkpubW9qmFSt2tM8ZsmrVKrzeDsCN6CTmMRw9ut8o9O1soXr1KsB/gfnAJuA+vN5//kGvpEhK\nKgPcZN06U1DwFbVr1zLSjIyMQc/6QVed3RhSGakAiYmJuFygc0UB0vD7V1KnTh0jXRFh5syZPP/8\n80yZMqVw+diI8PAIYB+wDEjH5QovFt1Qwnb+Z0hMTAywm8KGJrAPkQKjNPKzhccee4SwsFxgAHAF\nLtd2HnkkNFM1MjMzmTFjBjNnziQrK6u0zTkpy5atRpdNWINOiK/NO++8b6RZUJAPXINuYN8NkeJp\nOHP06FGWLl3Kjh07jLVcLheffjoOl6srDkd9HI7zGDJkAA0aNDDSHTr0UXr1GsYTT6TTv/9/6Nfv\nTiNHXb16dbKzU4F6wN1AbfLzM0hOTj7NlmcZf/UiQUnfCNELvj6fT5o37yBebyeBkeL11pannjKr\nHng2MX/+fGnRorM0atRaXnrpNQkEAqVt0v+QlpYm0dHlrQuKMRIbmxSS4Xn6ouzzVuBAjsDFUrNm\nbSNNpdwCcwSeE3hb4E7p18+soumSJUskJqa8xMRcJG53WRk27DEjvUAgIO3aXS0REdcKjBWH4wEp\nX766HD16NGjNvXv3SkREmSKlLTLF6002ivSaNGmSQJTAz5ZmioBHli1bFrRmSYMd7VOypKSkSPXq\nF0hUVEW57LJORj/as42CggJZs2aNLF68uNhLWhcXTZu2Eqhs1Y25RKCitGhhXoOouHE6ywhsLRI5\n9py0adPRSNPhiBG4ReCwwFKBeBk8eLCRZrly1QSmWjYeEre7qixYsCBovfT0dCskNf+39+52X2YU\n6rlhwwaJiqpd5LMUiY1tLgsXLgxa88MPP7TCZqXILblYymSXFME4f3vZ5ww5cOAAbdp05pdfKpKZ\n2YTFizO46qobStusvwWfz0enTj1o1eo6unS5l1q1GrJz587SNut/+Omnzda9kcATAKxdu7G0zDkl\nTZs2AT6xHmXjdE6ib9/eRpp16tQGNgBVgBsID/dyxRVXnGarU5Ofn8+BA6lAN2skgdzcS4wqSSql\n8PvzgcLscCE397BRzZyaNWsSHR1AqdfQNa0+wuFIpWHDhkFrdujQAaV2oEuUAaxAqSM0adIkaM1Q\nxHb+Z8jcuXPJzAyga9zcQCDgYMmSxUZFqQBmzJhBUlJN3O5oOnToZlzcrCR48823WLw4h6yszWRk\nrOLAgYEMGBB6a/5KhQMvossaXwW8QCgGtE2a9AGVK3+G11sXt7s6113XkFtvvdVI86uvPqFcuYPE\nxFTF7c5m4MCedO3a1dBSN7pOI+hS2QuM+k3k5eXhcHjQ382HwACUOmRUJz8iIoLhw+9F5HGgHDCY\ne+65jdjY2NNtekqOHDmCUmHApeh1/8tRSsjMzAxaMxQJvT0jRPn5558RqYaOdVdAd0QSKCgo+PMN\n/4QNGzbQq9cAsrMnAY1YtOgJevToy8KFs4rH6GJiw4at5OR0obB4VkFBN7ZsGV+6Rp2E2NhIqwhd\nIceJj48pNXtORZUqVVi9ehHTpk0jMTGRrl27GldyrVevHjt3bmTLli3ExcVRtWrVYrA0F3gQeBrd\nfqO80UXPhIQEIiIgJ+ci4DugLG63wyi/4+jRo9x33wjgdaAfMJ1///sm+vXrR82awTUc2rRpE4FA\nODqqrwmwhEBgL7t37zaqahpq2DP/M6R+/foo5UE7foAIHA4nLlfwx88FCxYgcg3QBiiDz/dfFi+e\nSyAQKAaLi49mzS7A650CZAOCy/UxjRqFXrP5+PgyaGf1IjAaGE5iYkLpGnUStmzZQv36zRg69EP6\n9HmIzp2vtSpemuHxeGjcuHGxOP7w8HDq1GmAXqLZDOQDu4zyMsLCwpgwYTxe73hiY/fh8XzG/fff\nSePGjYPW/Oqrr9CRU7cCTnTEU3XeeeedoDUPHDiALo39AzqvZzGQ948L67ad/xly2WWXodsa/Bsd\n734tyck1jBJp4uPjcTq3oH9oAJuJjIwrhnr+xUuvXr1wOn9Bt3dIpqDgTR588M7SNut/SEyMBaKB\npej2E5GULRv86T/o6pYDB95FZGQCsbHlefHFl43t7NdvMOnpw8jI+IGsrI0sWZLFe++9Z6x76NAh\nvv32W1auXFlMse5edBuOPcD/ERERabzMec013fn557V8/vlDrF79fzz77JNGetohH0UvS4GuPLvX\nqFKoLuOdiF72An1wiS6WA3QoEVpeJoRJSUnB46mLTnJ6AqhJWtp2o6y/a6+9lnr1FJGRVxAW9gAe\nT2fGjjV3LsXN2LFvkJ9/GbAemIfIfxkx4llj3YyMDCZOnMhHH33E/v37T7/BafD5nMAj6IZy5wMP\nW2PBc/vtd/P++5+SnX0nGRnXM2zYE4wbN85Ic8eO7Yh0sR6FkZ3dkc2btxlprlq1ipo1G9Cr1yja\ntevNddf1NTqD9Pl8bNiwEl2KoT7QkUDgPJYtW2ZkJ+iZ9e7du9m9e7fxQapbt8IL0pcAd6CbGBXQ\np0+foDX1Uu5udEnnNHSPiMx/XJLXmYReVgbmoUMJfgLutcZHoqcEa6xblyLbjEB37NoMXFFkvCna\ng2wFXj3F/yvpqKigmDJlikRHX1kk9CtPXC6PHD9+3Eg3Ly9PPvzwQxk9enTIxhHfeee9ols4Fr73\n9ZKcXM9I88CBA1KpUh2JirpSIiOvl7i4irJlyxYjzZYtLxeIFXhYYJhAGenQ4WojTbe7gsCnRd77\nw1K7diMjzQ4duovLNUJ0KeujEhnZVD7++GMjzZo1GwlMFMgUyJTIyIvk888/D1ovEAiIUjECr1jv\n+2eBOHnllVeM7HzrrXfF660gXu8tEhVVX/r0ud04Z6Rv34HW995MIEE6dbrGSG/06NECNQVaCZQX\n6CiQKFOmTDHSLUkoiTh/IAlobN2PQleNOg94EnjwJK8v7OMbBlQDtnGij+8K4GLr/kn7+Iaq809P\nT5eEhEricIwWWCJud2+5/PLupW3WSZk4caLEx1cSj6e8dOnSXXJzc430Pv/8c/F66wvsFciTiIi+\nctNNtxlpDhkyVMLChvzmVB2O/0qnTtcZabZq1Vl0rfhCRz1KOnbsZqSpnf/CIppvSO3aFxpp7t27\nV+rUuVC83koSHh4rAwcONnaA4eGRons3uwW8olRHee6554LWy8vLE6Wcouvu6/fudPaWt99+O2jN\n3NxciYiIEvg/K39gsURG1pQlS5YErVnI3LlzZeTIkTJ9+nRjrVWrVomuYvuqwE7RfaujjPoXlzTB\nOP/TLvuIyD4RWWvdz0SvexRe8j9ZL7/uwEQR8YnITsv5X6KUqgBEi8gK63Ufoa/OnBXEx8ezbNk8\nOnRYQp0699GnT1m++sqs5WJJMG/ePG68cSCHD19HTs7jfPPNStq163L6Df+Enj17MnRob1yuGjid\n0bRqdYy33nrJSHP37n34fOFAZ6ADgcBh0tJMe6Q60SeqhVRBxGzZp3fvrsAQ9InvEmAkjzwy2Eiz\nQoUKbNy4kvXrF5CauoX33nsdpczaYoaFxQItgSzgJ0TWGQUjhIWFEROTiH7PADm43euNLiYfPXoU\nHRx3A/Au0AufL9y4N+7Bgwd5443xjB//FW+8MZ49e/YY6TmdTlyuSOBL9Ge6GKfTEXKBGKb8pV+H\nUqoacCG62lFL4B6lVD9gFTBUdC/fitbzhexBHyx81v1C0jhxEDkrqFWrFnPmfFnaZvwpo0aNRjdu\nf8UaacGyZW2NNJVSVKtWmbi4CuTmZlOjRlWjNnkA1aqVA8ahoym8wF0kJ5sl0fTpcw0pKY+TnV0Z\nCOD1Pknfvo8ZaY4b9yZHjvRm1qzWOBwOHnrobgYOHGikCdrB1KhRw1inEL//OPAo+jJedRyOm43C\nkJVSTJz4Addf3wOXqyUFBRvp2rWVUeJYbGwsfr8PWAQ0Bo6Qn1+b8PDwoDULCgpo2/Yqtm5tic83\njtTUr2nRoiNbtqwJuu5WVlYWXm9VMjLmWyOCx1Pr3I3zV0pFoa/+3CcimUqpN9GhL6ADgV8EzPcK\nYOTIkb/db9u2LW3bti0O2XMCh0MB8UVG4tB9bYNnzpw53HPPv8jOngyU55NP7sDtfpzXXhsVtOav\nvx4BnkXPAgHeZv9+s8Lxd9wxkGPHMnj11ZtRSjFs2GD69+9rpLl48WLmzp2H3387TmcGY8eO47bb\nBhZTHH3xUbZsMnv2LEP3nC3A40mhYsXbjDS7dOnCxo2rWLlyJRUqVKBFixZGZyhHjx4lIiKKvLzC\n0M44vN6Lyc/PD1pz27Zt7Np1AJ/vJUBRUNCUY8dmsHbtWi699NKgNBs1aoTHc4jMzBcJBLrhdH5M\nuXKRQecNlATz5883yrYGzqy2D3r9/lvg/lM8Xw1Yb90fDgwv8txs9KX4JGBTkfEbgbdOolUyi2LF\nwE8//STNm18uycnnSe/eA0Kyts/ixYut9cpPrBovl0j79lcaad59930Co4qse6+VSpXqG2m2bt1J\nYHQRzUlSuXIDI82S4OKLO1qfZeG69yMyePADpW3W/zBv3jyJjEyU6OgbJCqqibRu3UV8Pl9pm/U7\n/H6/xMUlC3z2W+BARES8bN26NWjNnTt3ittdViBbCjvsRUbWktWrVxvZum3bNmnRopOULVtd2rfv\nJnv27DHSK2koiTV/pQ/144CNIvJKkfEKRV7WAx3FA/A10FspFa6Uqg7UBlaIyD4gQyl1iaXZF5j6\nl49WpcTBgwdp1epyli/vQVra53z5pdC1q1k9FtCZw1279qZp0/b861//MY4lbtGiBa+99hzh4Q/g\ncFzNBRcE+Oabr4w0ExPL4HJtLzKy3Sh9HsDpdAHPoE8Y3wIG43KFXijdsWMZ6JIemoKCqhw+nHHq\nDUqJtm3b8tJLz9C48RE6d67DZ5+NM1rzLwmUUvh8uegFgnigGfn5eUbd8KpUqUKnTh3xeq8C3sTj\n6UGTJrWMEsdA1wyaP386a9f+wLffTvnnlXOGM4r2aYXOQlpLkbBO9AXbH4F1aCdevsg2j6Iv9G4G\nOhUZLwz13Aa8dor/V/KHySCYNGmS1cM3W2C3QK5xD9+9e/dKbGySOByjBL4Vr7edDBgwyMjOrVu3\nSlhYrBX1UUaUSjCeqW7fvl2czhiBGwQeEIiUN99800hzwIBBVihdWYF4gZbSqlUXI82SYOTIZ8Tr\nvVRgo8BS8XqrybRp00rbrP/h1VdfF6+3hsBr4nINlvLlq8uhQ4dK26zfkZqaKhAhsNbah/YJJMkL\nL7xgpJuTkyNt23aQhIRK0rx5y2LpCTxr1iyJjIwXj6eclCmTZFQl9O8Au6RzyTFz5kwJC6shulZ8\neYEq4nCEGYVRvvPOO+L13lRk6SNdwsI8RmF//fv3F6gq8KsVR/6oOJ1xQeuJiPzrXyMFkgUqWPHP\ncXL++Rcbab7yyqsCFQVWCWwSaCB33GFWgrgk8Pv98tBDj0liYjWpUKGOvPPOe6Vt0kkpU6aiwPrf\nfkseT295/fXXS9us37Fz507RdfKlyK2tUUhqIBCQ88+/yJpA9BAoL5Uq1ZOCgoKgNffv3y9udxmB\nFlIY7x8VlSiZmZlBa5Y0wTh/O8P3DNGnrAfRqQr7gGcQiTCKVHA6neiaKYXkoZTZV5KWthfdHjAJ\nHYl7DwUFZr1H58z5HmiEznrcBoxk69ZfjDQXLFiFjhNoiq6c+CrLlv1opFkSOJ1ORo36DwcP/sLe\nvVu4/fZiiWkodvLzc4ETdYz8/oSQ6zlbuXJlYmKi0BU9AVJwOlPo2bNn0Jpr165lw4YfgWboxYn6\n7Nmzh+nTpwetmZKSQl6eoCPRZwJtyMrys22bWRZ2qGE7/zNk9uzZwGVoRwXQBxE/u3btClqze/fu\nREWtxOUaDkzE6+3GkCH3GEVUdOnSGZiLjqwFmE9CQlLQegDR0TFAR3QcPUB768AVPPHxMTgcO4uM\n/EKZMqFXgfNsoVev3ng8t6Cjrj8lPPxzrr766lK26vc4HA7efPNFlLoHHd57KXfd1d8oikb3lYgA\nLkbHpFwJONi+ffufbfanZGRkIJIEPATUBZ5GJJycnJygNUOSv3qqUNI3QnTZ59VXXxVIEjhqna6u\nEAiXnJwcI909e/bIgAGDpHPnnjJmzBvGmZ4rVqwQpWIF6gp0EoiSK6+81khz/Pjx4nBUEThPoLpA\nU7nyyp5Gmtu3b5eYmPLidN4pSj0gXm9CyJa3KAl27dolH374oUyaNMn4NySiM3JvueVOSUysITVr\nXhiSa9R+v18SEioJTBI4JPCjeDxl5eeffw5aU2fjlreWOAuXkuoalWJYs2aNhIVVEt0VbavAAXG5\nykhqamrQmiUNQSz7hFY4QAgzZMgQRo9+kz176gANgOUMGNAft9t9uk3/lOTkZMaNG1ssNgJ8++0c\n9OznF/QyTR8WLTJLTKtUqRJKZaOv8ZdHqVupVq2SkWZ0dDRer5ecnHk4HC5cLgdxcXFGmmcLy5cv\np2PHroh0RKm9VKkyihUr5hk1NVmwYAFffDEFpTqTm7uVhx9+ioULvzGKpCluDh48SGZmNvApcD9Q\nBYejPuvXr6d27dpBaeoz0CzrFoUuQX3EKNKpUaNG1K+fzLp1ldB5Mkdo06YNlStXPt2mZxX2ss8Z\n4nA42LnzJ5599n5uuaUKX3zxAePGBV8zvJD09HRGjHiCfv3u5LPPPjfWy8w8jsgM4G7gbWA9+fnB\nZ3oCfP75lxQU9AMmoK919OLLL2caaT799Aukp1+Fz7cFn28DmZmPcO+9jxpplhSBQIDt27ezZ8+e\nwrNTI2677QEyM28mK8tJZmZdtm2L4+233zbSvPXWIWRnf0pW1sdkZi5h/foAEyZMMLa1OImLiyM/\n3w9UR9fIv4OsrFVGYcNhYWG4XF6gA7pzW2dcLpeR5v79+9m6dRs6E3kPMJPly1eduxm+NnqWcf/9\n93Po0CEqVKhw+g1OQ0ZGBhde2JJ9+9rg8zViypSn2Lr1F554YnjQmps2bQL6o7saAXxEfn7w/UwB\n1q1LQde2eQQoD4zk0CGzuu6pqfvw+aoAPQEfgUCzYqjtU/wcOXKEdu2uZuvWXQQCeXTqdDmTJ39k\nNLPcuXMb8Cu6NHga+flf8NNPVYzsTE/fh77oCeAgL6+Jcc2c4iYjIwMoQOd2KOAWYByHDx8OWrNG\njRpER7s4cqQpcAC4ALd7Aw0aNAhac8uWLbhcddFdvADaAvHs3LnTSDfUsGf+f4H33x9PXFx56tW7\nhIoVa7Fu3Tojva+++ooDB8ri8y0DniU7ux7PPPOM0ewyNXU3urlFIccQMSsalpGRA9yFTt8YCHxE\nIGB2wbdWrYro1nudgF7AGKpUSTTSBFi/fj0PPvgwQ4c+wsaN5s3bBw0axsaNDcnOTiU3N5U5c/bz\n6quvG2kWFDjRZ1ED0AeA/mRlmc0qmzW7FKXaow/OtXE4PqRly5ZGmsVNQUEBIvnAQWvED/zKL78E\nHznm8Xj47rvplC//LQ7HGOLipjBr1hQSEoLv4JacnMzx4+uBVGtkE9nZ/6wWjmA7/zNm06ZNDBny\nMHl5K8jO3svBg8/QqVMPQ0edSl7eOiAcnTf3f+Tl+Yw0y5ZNRFcjHIZe9rmaE5E/wVG1amWg6Hq0\n1/hax44de9GhnrehK328w86dB4w0V65cSfPm7Xn5ZQ8vvRTOxRe3Yc2aNUaaixevwOdriW5f8RI5\nOZ1YuHDFabb6c2JjY4CiRcciqV7drMhbTEw0UAtIAd5GJGDUZa6QlJQUxowZw6RJk4wKxYFutu5w\nhKNn0v8GrsTpzDHq4Qs63PPgwcMEAi6OHs1i6dLlRnpHjhzB5YpCn0l1AFrjdIYbdQcLSf7qFeKS\nvhGi0T4TJ06U6Ojrf5egEh4eLenp6UFr3njjjQIXCviLRBC5jRJUrr/+eoG2VkJWosBlolRU0Hoi\nOqLC5YoVGC8wWxyO2vLUU/8x0qxTp4mcaBQiAlMlJqaakWaXLj0F3iii+bL06NHHSDM6uoLoRiEj\nBO4WiJYmTS4x0nziiX+L01lRIE4gScLComXdunVGmh5PGYEDv713l+t+GTVqlJHmRx99Ih5PeXG7\n75LIyObStu1V4vf7jTRbt+4iSl0scLNAF0lIqGyUkXvo0CErcewFgf0Cbwt4ZfPmzUFrLlq0SByO\nWqIb2MwW2CEOR1njZkMlCXaSV8lRrVo1CgpWcWJJZYXxhSUdqdCQE/HzjYB8o5l/UlISsBpdS+8y\nIBOn0yzio2nTpsyePYXzznuNKlXu5+mnB/LEE2YXZ8uXTwSeQtd1nwAMIibGrEx0RkYWetmjkCRr\nLHjy8gLo9oAbgXSgB0ePZhtppqXtQ6lG6MZ4X+NwRHHo0CEjzcjIWGDHb4/Dw3cY/TZFhLvuGkJO\nzlxyc98kK2sRq1YdYObM4C/0iwjZ2dko5UP3hDqC3+8zqmelK1tGAQ8D5dDfVVW+/vrroDWVUogc\nAsaia1o+jUh+yPXWNuWf9W5KkObNmzNwYE+83guIje2C13sVEyeON0p2GjRoEEp9BaxEZ/o+QqVK\ndY008/N96Ov49YGr0L1Hgy+ZC5Cdnc2DDz7B7t0ujhypzahRr7BhwwYjzZ49uxMRkYRu6PY5xFi4\nEQAAHaBJREFUYWHJXHttdyPNcuWigAfRDUh+AIZRvny0kabTWYBePlsLzAem4vWaLXlNmzYLv/9l\ndOuLi8jLG8yMGbONNIcPvwd9/aQ/0JawsFXcdNNNQev5/X5yco6ji/TGAPXx+WI5ePDgabY8Nenp\n6axbt4ZAoBs6A70Jfn9dFi9eHLSmzrDP4MSkLBs4aHRB3ul04nTGAcfRy30eHI7wYon0CiVs5/8X\nuPbaK4mMdJCZOY86derStKlZ85FLL72U227rDbQG4ggLe59vv51spLlv369AN3TFzIHAJAIBs6/5\n9dfHsnlzEpmZSzl+/GsyMp7kttseMNIcPPguunRpAPwfsIALL3TzwgtPGWn++usx9LWTu9Hdt662\nxoJHX9i+hRN5ExeTm2s288/OzkW3sS5kI7t27TjVy8+It976CH3QXwb8SGZmgO+//z5ovbCwMNzu\neMvOAJBLXt4io4uebrcbn68AWI7+bbrIylqLzxf8NammTZuilBNdf/IxoA1KFdCxY0cjO/3+A+hq\n9KuAaRQUZBvlYYQitvM/Q1JTU7n66p4cPPgmBQX7Wb++PVdc0cNIc/ny5bz77gR0WOaL+Hzl6NLl\nOiNNnYgSju6QNQrYhdNp9jWnpKwnP/8yCrt2irRiy5afjTSXLVtmzXYTgFhSUjYwbVrwp+oAFSuW\nw+GojC40uxaHozwVK5Y10gwEBF0rSaE/1174fGafZ1iYALcCQ4HewDwSE81KcGzfvhP4D3o55RcK\nCmKYMmVK0Ho+n88qZ3AVOtb9AyCClJSUoDWPHDmCDj74El0351WgGj/+GHxNp4oVKzJo0B2Ehx9D\nqYWEh6dzww3djUIyT0QfPYeu4/UwoEIudNYU2/mfIcuWLcPhuAydPRtLQcFTbN26ySgCYOzYsehu\nmCOtvzNJTd1h3H4PJqKrbKeh2yaY9Qg4fvwo+mByyNJ6CZ/PTPPhh/+F3x+Dzhr+Cr+/EkOHPmmk\nOWrUk8TGjsXrvQmvtzdxce/zzDOPG2nWr18L+AzdDc0HfMYVV5iFUF5wwYXo8NZcoCpud1Uuusis\n/rzObO1l3Y8FrjRaptDbZgOjgTLoqJdO7N27N2hNHeev+H3rb4fRAQVgzJjRfPHF6/z73534+OPn\nmTjxfaP6WAsWLACqoCdlMcC9QDQrV640sjPUsJ3/GRIfH4/INk6ETe4CCoxOBfV65S7gPOBy4FLA\nb/TD3b17N3oZaSZ6ZvUVBQVmuXy1atVBr9FWRjuWzSQkmJVi2LVrH/qg1wqdTPMihw+bJY7VrFmT\nTZtSeOWVdrz6akc2bUoxbrc4e/YUEhO/wuGogcNRmYYNsxgz5pXTb/gnPPPMcByOj4A5wPuUKXOI\nfv36nW6zP6Vq1drAF9ajDByOmVx3XfBnkWFhYYSFeTmxPFWAw7GFdu3aBa2Znp6OPnvqBkwHHgC2\nGlcfzczM5NtvFzB16ny++Wa+dYYRPPHx8cB+oDD3Ih04apQ7EJL81fCgkr4RoqGeBQUF0rFjN4mM\nbCFhYQ+I11tFXn55jJHm2LFjBbwCzwrMEmgpDkeskebQocMEHioS7pgm4eFljDRXr14tXm+iwLsC\nkyUysoGMHv2ykeZVV10n8K8ido6XevXMegSUFMeOHZMPPvhAPvvsM8nPzzfWu+SSDgLPWe87R1yu\n5vLuu+8aabZs2dEKHT1fIEGczjhZunSpkea7746TiIgkcTjukYiIltK8eQej1pDbt28XCBd4VKCz\nwJ0CF0jfvn2D1iwoKJDzzmtqvfcyAnGSnFxL8vLygtbMzMwUh6OMQD2B+wSqSlhYvFEIdklDSTRz\nQU/35qHz+38C7rXG49G1g39GT2HKFNlmBHrKsBm4osh4YSevrcCrp/h/f8NHFRx+v18mTJggo0aN\nkgULFhjrde/eXeDaIg7wkIDLKJZ6yZIl4naXF1gmcFBcrl5y3XXB71yFLFq0SFq3vlqaNm1fLNVH\nt2zZIl5vgsD9Ao9JREScLFq0yNjO4iYtLU2qVj1PoqMbS1RUbWnR4nLjKpxOZxmrWmTh9/6ctGnT\nwUjT7Y4R2CWwUmC7uFwPGnfIEhF57bXXpGPHy+WWW24x6lonInL06FFRKkJO9NsNCDSWGTNmBK35\n448/im6w1EfgW4FBAlEyc+ZMI1tXr14tHk+cKBUpUVFlZfv27UZ6JU1JOf8koLF1Pwp9Rek89NXE\nh63xR4Dnrfv10XFxYejG7tsAZT23ArjYuj8L6HyS//d3fFYhQYcOHQSuKuIE9gmEGSfSTJz4mSQm\nVhGPJ1auueYm4522pHjvvfekXLmKEh9fXkaOHFna5pyUbt1uFJfrUdGJePni8VwjTz/9rJGmThp7\nyvrOMwWaStWqNY00k5JqCswX8AkUiNd7hYwbN85Ic8yYN8TrrSJKPSZebxe58MJWRjNqEZEGDS4W\naC0wWeBO8XjKGnXIevfddwUS5ESiZECghjz88MNBa+bm5krt2o3E5eovMErCw3tK06atz72Z//9s\noK8kdrRm9eXlxAFis5yY9T9S5PWzgeZABWBTkfHewFsn0S/hjyl00D0CogQeFvhcoIEo5TWeVZ8N\nTJs2zVryut2a/Xvlv//9b2mb9T/UqHGhdYAOt+ztJtdf399IU8/8EwTKiW4/mCwdOnQ00vzggw8E\nYgScAm6Jj082OkMJBALidkcXOUMJSFRUK5k8eXLQmtnZ2eJyeaxlyW4CgyQyspZR74Fly5ZZyz35\nlp0FApVk4sSJQWsuXbpUIiIqi86QbyOQIGFhCUZ9B0qaYJz/X7rgq5SqBlyIDtQtLyL7raf2cyK1\nsiI6NqyQPUDyScbTrPFzlv79+1OuXDwwGXgEl+sgjz76kNEF35IkEAiQn2+WMFbIAw88ga7p/g7w\nMvAaTz31arFoFycOh6ArUaajT2I3o5TZZ9C8+YXoENdP0BUuj3D33XcZaQ4Z8ig6JDUHWM3hw9m8\n//77Qev5/X7y8nKAqtaIIje3olF02/Hjx3E6PejSy9PQGbS1rQvBwdGsWTN0TuQ16AveN6NUBl27\ndg1ac//+/eTlHUYnX84HFuDzZZGVZZYtHmqcsfNXSkUBU4D7ROR3YRmFR57iMmrkyJG/3XT69j+T\nmJgY2rVrj8uVTVhYEi5XPp07B5+cUpI89dSzuN1ReDxRdOp0rXFt8+PHs4Gi7fuqk5trfmA5fPgw\nEyZMYOLEicVSiCsQUMC/0CueFdAF84Lv2wzwyy97gc/REV63APezcqVZuGNWVjo6sS8MvfLa3wol\nDo6wsDCczmh0stxeYDp+/0wiIoIvwZGYmEgggGXncWAGWVkLqFatWtCaa9asoaDAgS7C9jlQFZF4\n5syZE7SmrgZbHb1qDXA+EE9qauopt/m7mT9//u/8ZDCcUQygUioM7fg/FpGp1vB+pVSSiOxTSlVA\nF9MGPaMv2vKmEnrGn2bdLzqedrL/F+ybOdv45ptvmDFjJX7/ViAKn28GPXvewq+/hlaj6MmTJzNq\n1Mf4fFuBRBYsGMhddz3IJ58E38zG6cxHlzNujK4Y+oDxjDo1NZVmzS4jJ6cxECAy8jFSUn4wykqN\njy/Djh2r0WG4AMspV84szPXo0WPoGXohWaSlmYUn6gNSCnpFNgCswuPx/Pkmf0J+fj5+/zG0478A\nKEt4eCujPraHDh1CKT86TuQZoCpeb0N27dpF48bB5TnomkgBdHZvOPosbSIHDgRfIXb58uXojO61\n6N/nIuAIy5cvp1u3bkHrFidt27albdu2vz1+6qm/nh1/2pm/0msQ44CNIlI0wPlrdCERrL9Ti4z3\nVkqFK6WqA7WBFSKyD8hQSl1iafYtss05yY4dOygouAw9qwToxP79vxDQ06OQ4bvvFpGdfTt6lS6C\nvLzhzJ+/yEjT4YhElyC+HH1JKBKlzGrmPPTQkxw+fCuZmdPIzJxOenovRoz4t5Gmdib/Qi+pXAV8\njemqXE5OJvqS1/vostYfsHnzJiPNCy+sj1766Isu6reOd999N2i98PBwwsOj0TWSmgLZ5OcvM2pi\nFBUVhYgPPUPPAdbjcBw3KkDXokULnE4BuqOX0W5EqaNceeWVQWs2adIEcAPt0O6rB6Cs8X8OZ7Ls\n0xLoA7RTSq2xbp2B54HLlVI/A+2tx4jIRvTi20bgG2CQtSwEMAh4Dx3quU1EzKpZneU0btwYh2MW\nenYFSo2jVq1GIVc9sHLlJCIiVnFiZW+VVT00eJo3b4iO+n0V7QR3U6eOWY/U3bv3UVDQ7LfHfv9F\npKb+aqR57Fg2MB59kLoJeIKDB83qBSnlQpd2mIf+7vvjcplVNF21ainXX3810dFzqVgxnR9++IYL\nL7wwaD2/329d35mMjuTeCMSycOHCoDW9Xi+PPvoYkZGtUeoxIiM7cvHFNWjdunXQmjExMXz99QRc\nrh9wOB7E6fyGTz55y6jf7vDhw3E6A2jXNxBogNsdSY8eZuVcQo6/eoW4pG+cQ9E+IiLPPDNKwsOj\nJTKyiiQl1ZBNmzaVtkn/Q0ZGhpQvX1WUiheIlbCwMrJs2TIjzQMHDkhiYrI4HFXF4aguXm8Z43rp\nTz75H/F6OwgcEzgqXm9ree650Uaad9/9gLjdPazY9P3i9V4o7733vpFm+/ZXCrQS2CzwvUCsTJo0\nyUizuMnJyRFwWNEzhaHIN8ptt91mrD1z5kx5+OFHZNy4cUZJY4W89da74nYnSmRkG3G7y8mzz5p9\n5yIiBw8elPPPbyLR0VWkadNL5fjx48aaJQl/R6hnSd9C3fkvW7ZMJk+eXGxJH4FAQBYuXCjjx4+X\ntLS0YtEsbubMmSMeTyWBpQI7xO3uKPfeG3wcdSFpaWly3333yd133y0bN2401vP5fNK37+3idIaL\n0xkuAwYMMs6ZyM7Olm7deovTGS4ul1uGDh1hHIqbl5cnXbr0kIiIRImKSpYxY8wyxUuK2NhkgTct\nx/+LKBUv8+bNM9JcsmSJREQkCsSKUrEyZszrRnrp6enidscWCUlNE48nUXbs2GGke7ZhO/8S5o47\n7pXIyGoSE9NdvN5EmTx5ipFeIBCQ/v3vEq+3ssTGtpOoqLLyww8/FJO1xcegQfcLjCoyA1wrlSrV\nN9I8ePCgVKlST6KiLpeoqGskPj5Ztm7dWiz2+ny+YplRiujv6Pnn/yvVqzeSevUuLpYZut/vl0cf\nHSnVqzeSBg1ayOzZs4vB0uLnp59+kpiYCuJ0JorD4ZFnnjHLGPb7/VaOQx+BFIHRAl5Zs2ZN0Jrr\n16+X6Oi6RX6bIrGxLYolA/9swnb+JcjixYslMrKGQIb1I1stXm8Zo5nlrFmzJDLyfIHjlubXkpRk\nlulZEjz22L/E5bq7yA72pZx//qVGmvfeO0zCwgb9pulwvCCdO19vbGtGRoZMnTpVpk2bViyn6qNH\nvyxeb0OBJQLfiMdTUebMmWOk+dBDj4vX21J0286vxOstJytWrDC29fHHH5fatRtL8+aXFcuZlIg+\nkO7cubNYssTXrl0r4C6SjSsCzeWWW24JWjMzM1Oio8uJbrcoAkskMjJR9u3bZ2zv2YTt/EuQk/fw\njTHq4TtmzBhxu+8qopkvSjlCLo18//79Uq5cNYmI6C8Ox8Pi8STK3LlzjTS7d79Z4MMi732+NGjQ\n0kgzLS1NKlasJdHRHSQ6up1UrlxX9u/fb6SpM3znF7FzjNxwQ/DOSkQkKamWwPoimk/KsGHDjTSv\nv763QJLovsh3iMMRLdu2bTPSLG42bNggECZwpEg27nkycOBAI90FCxZIbGx58XjKS2RkvFGtoLOV\nYJx/aIWVhDCNGzfG71+Arm8H8CGJieWIiws+5rtRo0b4/VMpjPaBd6lUqW7IRfuUK1eOn35awbPP\nNmTkyGiWLv3OqFMSQMeOLfF63wQOAzl4PC/RoYNZnfyHHnqSAweu5/jx7zh+/P/Yt+9KRoww6w52\n8GA6ULR14X5+/nmLkabb7UH3RtC4XIeIigo+Jh9gypTp6Kic+4C3CQQu57HHHjPSLG7q1atHWFgk\nujfAWOAGYA9Dhw410m3RogUDBvShfv069O3b2/i3ec7wV48WJX0jRGf+IiIff/ypREREi9udIBUq\n1JT169cb6c2ePVvCwiqIrkpYRaCilC1btXiMDXEKCgpk0KAHfrs4e801NxlXy2zevJPAjCIz6inS\ntm03I80KFWpZNV6eFxghUEa6dLnWSHPChIni8VQUeFGczgckLq6i7Nmzx0hTL6fsLfLe75Krr77a\nSLMkSElJkcjIOFEqRpxOt4wf/7GRXiAQkPPPv1jgUuvidAepVKleyJ09lzTYyz4lT15enuzbt69Y\nflwnln2OCGwXyArJZZ+SJD8/X3Jzc4tFa8SIJ8XjudIKy8wUr/dyGTnyGSPNAQMGS3h4R4EhAveI\n291I3njjLWNbv/vuO7n99iEybNhwSU1NNdarVauRQFuB1QITBLwya9YsY92SoKCgQPbt21csvRFS\nUlKsyVNhmWifQAWZOnVqMVh69mA7/7OM+fPni9dbTXQpZxEYJzVqNCxts85a8vLypEePm8Tl8ojL\n5ZYbbuhv7GCOHz8u7dt3FZfLKy6XW+64496QPDgfP35cGja8VJzOBPF4Ksgbb7xR2ib9LXz99dei\nq6MGipz11JWxY8eWtml/K8E4/8I6+yGDUkpCzaaS5Mknn+H550cREVEetzufefNmcv7555e2WWc1\nmZmZKKWMWmz+kYyMDMLCwozq5dgUP8ePH6dMmcoEAv3RPXenotQr7Nq10SjL92xDKYWI/KXCI7bz\nDwEOHDhAeno6NWrUMKqaeDZy7Ngx/H4/8fHxIVvK2ia0mTNnDl273kR+vh+Xy8Enn7xJr169Tr/h\nPwjb+ducNRQUFNCv351MmvQZSjlp2bI106d/VqyzdZtzBxEhIyODmJiYc3ISEYzzD62YwnMQEWHp\n0qVMmzaNtLSTVrj+R/LKK2OYOnUbPt8+8vMPsWRJFEOHhlZoos3Zg1KK2NjYc9LxB4vt/EsREaFX\nr1u5/PJ+9Ov3LnXrNmbevHmlbdbfwsKFK8nOHoAuZx1GXt5dLF68qrTNsrE5Z7CdfykyY8YMvvlm\nLVlZP5KRMYOsrE/p3XtAaZv1t1CnTlXCw+dTWCba6ZxPjRpVStMkG5tzijPq5GVTMuzatQu//1Kg\nMIKkHYcO7SYQCIRclm9x8/jjjzB9env27r0Upbx4PDsZM2Z+aZtlY3POYDv/UqRZs2Y4HC8Aw4Gq\nKDWWunWb/OMdP0BsbCxr1y5m4cKF+P1+WrVqRUxMTGmbZWNzznDaaB+l1Pvo/nUHROQCa2wkcBsn\nip48KiLfWM+NAAag+9/dKyJzrPGmwIfo/mizROS+U/y/cyra55VXXueRR4bjcHgoX74s338/nZo1\na55+QxsbGxuLEgn1VEpdBmQCHxVx/k8Cx0XkpT+8tj4wAbgI3fD1O6C2iIhSagUwRERWKKVmAa/J\nSdo4nmvOHyAnJ4djx45Rrly5c2LWb2NjU7yUSKiniOjW9Sf5fycZ6w5MFBGfiOwEtgGXKKUqANEi\nssJ63UfobtM2gMfjISkpyXb8NjY2fxsm3uYepdQ6pdQ4pVQZa6wisKfIa/agzwD+OJ5mjdvY2NjY\nlALBXvB9E/i3df9p4EV0m/tiYeTIkb/db9u2LW3bti0uaRsbG5uznvnz5zN//nwjjTMq76CUqgZM\nL1zzP9VzSqnhACLyvPXcbOBJYBcwT0TOs8ZvBNqIyF0n0Tvn1vxtbGxsTPjbyjtYa/iF9ADWW/e/\nBnorpcKVUtWB2sAKEdkHZCilLlE6/7ovMDWY/21jY2NjY85pl32UUhOBNkCiUmo3eibfVinVGJ2e\n+QtwJ4CIbFRKfQFsBPzAoCLT+EHoUE8POtTzfyJ9bGxsbGz+HuyqnjY2NjZnOXZVTxsbGxubM8J2\n/jY2NjbnILbzt7GxsTkHsZ2/jY2NzTmI7fxtbGxszkFs529jY2NzDmI7fxsbG5tzENv529jY2JyD\n2M7fxsbG5hzEdv42NjY25yC287exsbE5B7Gdv42Njc05iO38bWxsbM5BbOdvY2Njcw5iO38bGxub\nc5DTOn+l1PtKqf1KqfVFxuKVUnOVUj8rpeYUaeCOUmqEUmqrUmqzUuqKIuNNlVLrredeLf63YmNj\nY2NzppzJzP8DoPMfxoYDc0WkDvC99RilVH2gF1Df2uYNq20j6KbvA0WkNlBbKfVHzbMG08bJfxe2\nncWLbWfxcjbYeTbYGCyndf4isgg48ofhbsB46/544Brrfndgooj4RGQnsA24xOr5Gy0iK6zXfVRk\nm7OOs+UHYdtZvNh2Fi9ng51ng43BEuyaf3kR2W/d3w+Ut+5XBPYUed0eIPkk42nWuI2NjY1NKWB8\nwddquGs33bWxsbE5mxCR096AasD6Io83A0nW/QrAZuv+cGB4kdfNBi4BkoBNRcZvBN46xf/ahj6Y\n2Df7Zt/sm307s9u2M/HlRW8uguNroD/wgvV3apHxCUqpl9DLOrWBFSIiSqkMpdQlwAqgL/DayYRF\npFaQNtnY2NjYnCGndf5KqYlAGyBRKbUb+BfwPPCFUmogsBO4AUBENiqlvgA2An5gkLUsBDAI+BDw\nALNEZHbxvhUbGxsbmzNFnfDNNjY2NjbnCqWa4auUKqOUmqyU2qSU2qiUaq6UulgptVIptcb6e1Ep\n21jXsqXwdkwpde+fJbqFkJ33KaVGW5/vOqXUl0qp2BCz8d4izw9VSgWUUvGlZePp7FRK3WN9nj8p\npV4IQTvvC7V9yLJ1hFJqg5XoOUEpFRFq+9Cf2Bky+9Cf2VnkuTPbj/7qRYLivKFzBAZY911ALDAf\n6GSNdQHmlaaNf7DXAfwKVAZGAQ9b448Az5e2faew83LAYY0/Hyp2FrXRelwZHSDwCxBf2vad4rNs\nB8wFwqznypa2fSexs0qo7UPogJEdQIT1+HP0tcKQ2of+xM6OobQPncpO6/4Z70elNvO3jp6Xicj7\nACLiF5Fj6B9w4ZG1DDonIFToiL6qvptTJ7qFAh2B7SKyW0TmikjAGl8OVCpFu4rym43W45eAh0vR\nnlNR9Du/G3hORHwAInKwVC37PYV2phJ6+1AG4AO8SikX4AX2Enr70MnsTBOR70JsHzqpndZzZ7wf\nleayT3XgoFLqA6VUilLqXaWUFx0u+qJSKhUYDYwoRRv/SG9gonX/VIluoUBvYMJJxgcAs/5mW07F\nbzYqpboDe0Tkx9I16aQU/c5rA62VUsuUUvOVUs1K0a4/UtTOkNqHROQw8CKQinb6R0VkLiG2D53C\nzu/+8LJS34dOZedf3o9K8dSlGfrodZH1+BXgaeA7oIc11hNdQ6jU7CxibzhwEOtUHzjyh+cPl7aN\nJ7OzyPhjwJTStu+PNqJnLcuBGOu5X4CE0rbxFN/5euBV6/5FwI7StvEUdobUPgTUREcAJqCXd78C\n+oTaPnQKO28u8nxI7EOnsLMfsOyv7EelOfPfgz5KrbQeTwaaABeLyFdFxi4uDeNOQhdgtZw41d+v\nlEoCsGoXHSg1y37PH+1EKXULcCVwc2kZ9QeK2lgTvYa5Tin1C/qUerVSqlwp2lfIHz/LPcCXANbv\nNqCUSigt44rwRztDbR9qBiwRkXQR8aM/w0uBfSG2D53MzhYQcvvQyey8hb+4H5Wa8xeRfcBupVQd\na6gjsAHYqpRqY421B34uDftOwo2cOK2GE4lu8PtEt9Lmd3Za1VMfArqLSG6pWfV7frNRRNaLSHkR\nqS4i1dEOtomIlLYjgP/9zqeif5NYv9twEUkvDcP+wB/t3BZi+9BmoLlSyqOUUuh9fSMwndDah05q\nZwjuQyezc4qIJP2V/ahU4/yVUo2A99CnrduBW9HrqmOBCCAHnSi2ptSMBJRSkcAuoLqIHLfG4oEv\n0NEVO4EbRORoqRnJKe3civ58D1svWyoig0rJxJPa+IfndwDNRK9rlhqn+CzDgPeBxkA+MFRE5pea\nkZzSzmaE3j70MNrBB4AU4DYgmtDbh/5o5+3oSWnI7ENw8s9TrEAE6/nT7kd2kpeNjY3NOYjdxtHG\nxsbmHMR2/jY2NjbnILbzt7GxsTkHsZ2/jY2NzTmI7fxtbGxszkFs529jY2NzDmI7fxsbG5tzENv5\n29jY2JyD/D+oUSp0YmQ9kQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6d8ad10>"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.boxplot('weight')\n",
      "# df.boxplot('weight', 'year')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "{'boxes': [<matplotlib.lines.Line2D at 0x7a02950>],\n",
        " 'caps': [<matplotlib.lines.Line2D at 0x7a02190>,\n",
        "  <matplotlib.lines.Line2D at 0x7a02570>],\n",
        " 'fliers': [<matplotlib.lines.Line2D at 0x7a09130>,\n",
        "  <matplotlib.lines.Line2D at 0x7a096f0>],\n",
        " 'medians': [<matplotlib.lines.Line2D at 0x7a02d30>],\n",
        " 'whiskers': [<matplotlib.lines.Line2D at 0x79f9c10>,\n",
        "  <matplotlib.lines.Line2D at 0x79f9df0>]}"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x6f14630>"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas.tools.plotting import scatter_matrix\n",
      "_ = scatter_matrix(df[['mpg', 'cylinders', 'displacement']], figsize=(14, 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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48GPKy8tb500RoguTgkcIITqA8HALsABYw2OPPaZzmq4tLm47Bw+GcOBAEJs2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\nUGen+AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7586370>"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Regression next. But first..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.xkcd()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "<matplotlib.rc_context at 0x7ddb650>"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=http://replygif.net/i/209.gif>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.weight.hist()\n",
      "plt.title('WOT, THERES AN XKCD STYLE???', fontsize=18)\n",
      "plt.xlabel('weight')\n",
      "plt.ylabel('freq.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "<matplotlib.text.Text at 0x8f1df90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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EIqE///yTiIi3w4eL2GvXrh0B4Ge9Dz30EAGgW2+9NcRvsWvXLgJA7dq1q1GbPB4PXXfd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       "text": [
        "<matplotlib.figure.Figure at 0x8effa90>"
       ]
      }
     ],
     "prompt_number": 86
    }
   ],
   "metadata": {}
  }
 ]
}