CI_Workshop_Teplitskiy_Introduction.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Statistical Learning with Python\n",
      "\n",
      "*inspired by: http://www.datarobot.com/blog/introduction-to-python-for-statistical-learning/*\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",
      "\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": 1
    },
    {
     "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": 5,
       "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": 5
    },
    {
     "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: Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "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",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 3504</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 3693</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3436</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 3433</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 3449</td>\n",
        "      <td> 17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 4341</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "   weight  mpg\n",
        "0    3504   18\n",
        "1    3693   15\n",
        "2    3436   18\n",
        "3    3433   16\n",
        "4    3449   17\n",
        "5    4341   15"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "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": 8,
       "text": [
        "73    40\n",
        "78    36\n",
        "76    34\n",
        "82    30\n",
        "75    30\n",
        "81    29\n",
        "80    29\n",
        "79    29\n",
        "70    29\n",
        "77    28\n",
        "72    28\n",
        "71    28\n",
        "74    27\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "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": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Uh oh, let's change it back!\n",
      "df.year.str[-2:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "0     70\n",
        "1     70\n",
        "2     70\n",
        "3     70\n",
        "4     70\n",
        "5     70\n",
        "6     70\n",
        "7     70\n",
        "8     70\n",
        "9     70\n",
        "10    70\n",
        "11    70\n",
        "12    70\n",
        "13    70\n",
        "14    70\n",
        "...\n",
        "382    82\n",
        "383    82\n",
        "384    82\n",
        "385    82\n",
        "386    82\n",
        "387    82\n",
        "388    82\n",
        "389    82\n",
        "390    82\n",
        "391    82\n",
        "392    82\n",
        "393    82\n",
        "394    82\n",
        "395    82\n",
        "396    82\n",
        "Name: year, Length: 397, dtype: object"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "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": 12,
       "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": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSY0y4CWpUf8fyb44jAmGbfIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x643d5d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "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": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "C:\\WinPython27\\python-2.7.5\\lib\\site-packages\\matplotlib\\axes.py:4752: UserWarning: No labeled objects found. Use label='...' kwarg on individual plots.\n",
        "  warnings.warn(\"No labeled objects found. \"\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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X5vAZ5FBnnnWvzbHufolJs/Ug33YXSbv9QWo3Uw15n9Uj4JPAHRFxSc2k1cCK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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x644fb10>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(df.year.astype(int), df.weight)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<matplotlib.collections.PathCollection at 0x648a210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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//i/HmkopBg3qz6BB/YNnNBeYP38BR44cR6QcHk8mEye+yR13dCMiQn8+yUKP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\nd0ymAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6586470>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.boxplot('weight')\n",
      "# df.boxplot('weight', 'year')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "C:\\WinPython27\\python-2.7.5\\lib\\site-packages\\pandas-0.15.2-py2.7-win32.egg\\pandas\\tools\\plotting.py:2625: FutureWarning: \n",
        "The default value for 'return_type' will change to 'axes' in a future release.\n",
        " To use the future behavior now, set return_type='axes'.\n",
        " To keep the previous behavior and silence this warning, set return_type='dict'.\n",
        "  warnings.warn(msg, FutureWarning)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "{'boxes': [<matplotlib.lines.Line2D at 0x6a12d70>],\n",
        " 'caps': [<matplotlib.lines.Line2D at 0x6a125b0>,\n",
        "  <matplotlib.lines.Line2D at 0x6a12990>],\n",
        " 'fliers': [<matplotlib.lines.Line2D at 0x6a1d4b0>,\n",
        "  <matplotlib.lines.Line2D at 0x6a1da70>],\n",
        " 'medians': [<matplotlib.lines.Line2D at 0x69f3230>],\n",
        " 'whiskers': [<matplotlib.lines.Line2D at 0x6a06f90>,\n",
        "  <matplotlib.lines.Line2D at 0x6a12190>]}"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x55bd3b0>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "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 0x69ea110>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "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": 18,
       "text": [
        "<matplotlib.rc_context at 0x6cf7790>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "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": 19,
       "text": [
        "<matplotlib.text.Text at 0x6cf7230>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ss8/WeK5QKET33Xdf1PMpKCigJ598MsoWcj6cPWLJkiXVluvatSsBoHXr1vFTYlOnTo0o\nM3fuXAJAH330UbXnWrFiBQEgvV5PBoMhqu2IYQuszLvvvksA6N///neVZf78808qKCggkUhEBw4c\niFkmFArR448/TgDo4YcfrrbNiYQpFSIqLy8nADRo0CAiIho9ejRvFzlx4gQBoLFjxxJReHidnZ1N\nrVu3jviw33rrLX7euTqCwSBlZGREeehUhpsGOv8juBQMHDiQAER5rHH07t2bRCIRtWvXjuRyedTH\no9freeHOzWtz3jESiYRyc3Opbdu29O9//5uGDRtGEyZMoDfeeIOfzz9w4ABvFF68eDHNmDGDxo8f\nT02aNCEAlJeXR3/++Sffnv79+/MKq2nTptS5c2fq378/Pfjgg1RcXFzlx3z48GGaM2cOTZo0ie68\n807S6XQEgLKysujYsWO1ulfbtm0jANS6dWtauXIlFRcX87aE8z/qjz/+OGJKMBQK0f33308A6KWX\nXoooW1BQQHl5ebVqAwc37QMgYp79fCor3speYZUJhUJUUFBAOp2O7HZ7jXVzSqUqg7bH4yGtVkuN\nGjWq3cVQeErpgw8+oEmTJtGgQYP4abScnBw6ceJElcd98skntbIpbNu2jUQiERUUFFCjRo1IJpNF\nOWhwCoqzHVUFp3xkMhkVFBRQYWEh9enTh0aOHElPPfUUzZgxg7xeb9RxPp+P74DqdDrav39/zPN/\n//33lJeXR8BfHnfnY7PZeHtSw4YNqaSkpNo2JxKmVM7RqlUrKigo4L2+NBoNbzDT6XTUpUsXIiL6\n8ssvYxr3ioqKCACtXLmy2nq2b99OAOiee+6pscz48eMv/sLOg/PsOXLkSMz9PXv25JVHjx49aMKE\nCbRo0SLat28f74o7YsSICMX03XffEQCaOHFijfVzBtPzfwqFgkaOHBml7O644w4Cqndxrg0ul4tv\n9+jRo2ssf+DAAWrVqlXMtgKgwsLCiPKcraCyF6HP56MePXpEbW/SpAk1bNiw1m3nRj0ZGRkEgNq0\naVPlKJfzogPC3odOpzOqTFlZGQGgvn371qp+biRf3aimTZs2pFKpandBMXA6nbzd48EHH6yyHDdz\n8Morr9R4zgkTJvD3IpaRnbNhfPXVV9Weh/N+q8pVORZlZWX8jETz5s3p559/jlnuzTffJIlEQmKx\nmKZNmxZzBHr48GHe8+vaa6+N6HQlI/94mwpHp06dcPLkSX7R2V133QWNRgORSISrrroKO3fuRCAQ\nwJw5c6BWq3HPPfdEHM/Nh/7555/V1rNw4UIAwG233VZlGW4BWFU2l4uhpnPb7XZIJBIYjUZ+od7Q\noUNx5ZVXQqVSAQA/L8zNRXPnSklJqbH+UCgEAGjatGmEXaV79+544YUXohaZcraHqhZi1haVSoVZ\ns2ZBrVbjm2++qbH8ihUr+EV5hYWFePDBB/Hhhx9i9+7d6NixI/bt2xcxr+3z+QAgYoGmTCbD0qVL\n0aJFCwwfPhyrVq0CAMjl8lpHF1i1ahUeffRR6HQ6bN68GaNGjcL+/fvx3HPPRZUNhUIIBAIoLCxE\nt27dsGHDBvTp0wd2uz2inM1mA1D7e8o9X4VCEXN/KBTC2bNnkZ2dXavzxUKtVuODDz6AQqGo9vnU\n5duYOnUqv5h47NixUftr+27V5f0Gwt9Hz5498eOPP6Jfv37YuXMnrrjiiqhy8+fPx9ixY2EwGLBh\nwwZMmjQpyp7pcDjQtWtX/PbbbxgzZgw2b96MgoKCWrUjUTClco5OnToBAL8y+8EHH+T3tW3bFm63\nG2vWrMHq1avxn//8hzfacdxxxx0Qi8WYOXNmhEGzMjt27MD777+PvLw89OnTp8q2cC9vPJRKTec2\nmUwwGAzVGuu5czidTgDgy1ItgjNwiqlPnz4oKSnBggULkJOTg6+++gqtWrXC/PnzI8rXJfSFyWTC\nhg0bqtxvNpvh9XqRmZlZ47m+++47AMDatWuxY8cOzJw5E/feey/atWuHDh06wO/3Y9euXXz5WEoF\nCHc2Vq1aBY1GgwEDBmD16tWQyWS1Uip79uzBkCFDIBaLsXz5crRu3RpvvvkmGjdujFdffZVvIwdn\nLE5JScHKlSvRtWtXbNq0CT169IDRaOTL5efnIy0tDVu2bIky+BIRZs6cib59+/L7OOFbVTSCdevW\nwWg0onv37tVej9FoxLffflvlfpPJBJ/PV+3zqcu3sXr1algsFtxyyy1o37591H5O2XKKpyrq8n4D\n4XcgFArhsccew//+9z/eoSVW/Zdffjl+/PFHdOvWLWYZl8sFlUqF119/He+++y7kcnmt2pBQEjVE\nslqt9MMPP9BXX31V5UKe7777jt599136+uuvqzRMXir279/PD5Xbt28fsY9zoW3UqBEBoB07dsQ8\nB7cgbeDAgRHTNaFQiD777DPewF2Te2xdpyfqwpQpUwgArVmzJub+7OxsatOmTbXnePrppwkAv3aC\nWwhanSs1B2dTqeyeabVaaezYsfzq6eLiYv55c/e0NgsWOVfsWPPtZWVl/ErnWKvKK/Pzzz/z00yx\n3jvOxXbcuHH8Ns6mVjlaQmU2btxISqWS5HI5paamUvPmzattw++//867s58/7fLNN9/wc+tGo5Hf\nbrfbCfhrHY3D4eAN1s2aNYtY6/LII48QEHbfLi8vp2AwSFu2bOGn6wwGA7+O4qWXXiIA9Pnnn0e1\n8+TJk9SoUSMSi8U1rjvhognMmzcval9paSlvr4pVDwf3ndY0hen3+/kpo507d8Ysw02HnjlzJmK7\n0Wik5cuX89Ni3NRmrHZXRSwby/n4fL4o1/QLPVcykRClMnPmTEpNTaX09HRSqVSUlZUV4VNut9vp\n1ltvJSDs9SMSiahHjx61MipeKFzIFgA0ZcqUiH2//PILr3Datm1bpYIzGo3Url073mjdo0cPGjZs\nGL9wTiKR1GqhGLdArl+/flWWCYVC9Nprr1W5CKwquA+7qjUwCoWCevToUe05Xn75ZQJAW7ZsIaKw\noVahUFCrVq3o888/p/Xr19OaNWtoxYoV9Omnn9Ls2bP5jgO3kCyWTemnn36i7OzsiA+YCzUzYcIE\n+uqrr2jt2rW0cuVKWr58Oc2bNy/CaH3w4EE+LMtNN91EEydOpKeeeor69evHr4a/5557auygcF5o\nVS1gLC8vJ7lcTnl5efy5OIVW3bz72rVr+XZUt87AZDJRy5YteQUbC84JoGfPnrxgslqtBIC6du3K\nl/N4PLytQiKR8NEAzGYzH+YF54zQ3N+dO3eOcGbgru2aa66h+fPn04YNG2jJkiU0adIkfjX+c889\nV+X1cPzyyy+8I8HNN9/MP5++ffvyTiH33ntvtc9n165dBERHEjgfznGiOttlv379CAivv5k2bRo9\n/PDD1L59e957kVv0fOjQIQJAPXr0oFWrVtG6deto9erV9Pnnn9Mnn3xCH330UUSbP/roI5JKpVXa\nUYjCMq6goIAeeOCBaq+jqKiItFptlY41yYjgSuXXX38lAPTpp58SUdir4aabbqK0tDReaYwePZqy\nsrJ4d8j9+/dTgwYNaNKkSXFt27BhwwiIXnwUCoWoadOmteqtuN1uevXVVyNi9gCgPn360MGDB2vV\nDo/HQ8OHD6dvvvmmyjKbNm0ioGbXyvP5/vvv6T//+U/M+FehUIhkMlmNbtErVqyIWnw3fPjwKo3a\nqORh43Q6qWnTplUq1+PHj1O/fv348B/Hjh3jPYNi/c5fyLl7924aMGAAqVQqvoxKpaKePXvSkiVL\najXiffvtt6lXr15Vul0ThZVdly5d+PNxLrq//vprtedev3495eTkUFFRUZVlOGeQquJ+EYXvIxcy\nhRshOJ3OKKXCsWrVKmrZsmXEKNRut9Mbb7xB7du3p9atW9PQoUPpxx9/jKpz9+7ddO2118a8/yqV\nil577bVazyTs2LGD+vfvz4cy4s7Rq1cvWrZsWY3nsVgsNHTo0CpnCzhGjBhBMpms2k7Xo48+GvOa\nCgoKaMSIEXynqXKIl6p+lZ87F+qIc7mPBddRvfnmm6u9jg4dOpBEIqlXSkXwKMVPPfUUvv76a+zc\nuZPfVlZWhry8PMydOxf9+/eHwWDA+++/j5EjR/Jlpk2bhldffRXl5eUXFEK9Nvj9fpSWliI/Pz9q\n3/bt27Fr1y7cf//9ta7/9OnTMJlMyM3NjRnl+GLo27cvfvzxR5w8efKiI+9WZtOmTbjsssuqjKzK\nEQqFIiL1hkIh7NixAzt27EBpaSkfRValUqFhw4YYMGBAlYbemjAajdiyZQt++eUXOJ1OiMViyGQy\n6PV6XH/99THnywOBAEpLS+HxeJCXl3dJ71EsnE4nfvjhB/z73/+usSzVEEaeiLBz50506NAhKhpy\nZUwmEzZu3Ij+/fvz55s9ezYyMjIwcODAmMec/9xqCxHh559/xubNm1FaWgqtVosOHTqgU6dOMaNL\n10S8n4/ZbEZJSQlatWpVZRmr1YpFixbB4XDAYDCgSZMmaNWqVcxIwF6vF9u2bcOuXbt4+5RMJkNK\nSgpatGiB22+/nX8Gdrsd+/fv5+20VbFt2za0bt26SpsLEJaNp0+fRrt27Wpz2UmB4Epl9OjROHr0\nKDZu3BixvWnTphg1ahSuuuoq3HrrrbDb7REv68aNG9G9e3ecPHkyptD/J1FaWooGDRrg6aef5lcN\nMxgMRjIguPdX586dsXXrVmzduhVAuAe0bNkynDp1CllZWfjjjz9gMBiiej/p6ekAwpr7n05KSgpG\njRqFRx99NNFNYTAYjAgEz/x41113Yd68ebjuuutQWFgIu93Ox3q68sorcejQoZjugpz76sWuV0hW\nfD4fTCYT7HY7LBYLLBYLnE4nnE4nvF4vXC4XbDYbevbsifbt22PIkCHIzMzErl27MHXqVHg8Hj55\n0fmDT7FYDIlEAolEAplMBrVaDbVazccRUigUSElJQUpKCuRyORQKBZ/8SKVSITU1FQaDAXq9nl+7\n83cgFArB4XDAZDLBZrPB7XbD4XDA6XTy/3o8HrjdbrhcLrhcLrjdbvh8Pv5vr9cLr9fLJz7jEIlE\nEIvFEIvFkMvl/P1VqVRQKBSQy+WQy+X8s+D2azQaaLVaaLVapKWlQa/XIzU1tcpEXfUFIoLD4YDV\naoXNZuP/rXxvnU4n/3+HwwG3280/E5fLBZ/PF/V+i8ViSKVS/ieTySCXyyP+5u6vTqdDdnY2DAYD\nlEolUlNTkZOTg+zs7PrhqhuDYDAIo9EIo9GI8vJy2Gw2/v5ZrVaYzWY4HA7Y7XY4nU4EAgEQEZYu\nXRq3NiUk82MwGMQXX3yBXbt2ISsrC1u3bsXmzZtx+vRprF+/Hr169YLZbI7wH1+0aBFGjhwJm83G\nvwA9evTgPzzu5dFoNNBoNEhPT0dmZib0ej0MBgNSU1Oh1Wp5oRkPwejxeCKEEacczGYzKioqUFJS\ngtLSUlgsFpSWlqKiogJ2ux02m41fkFYTCoUCY8aMwdatW9GlSxesXLkSEokESqUSUqkUEokEYrGY\nv75QKIRQKIRgMMhnfeQ+Yo/HA4/HwwvF2iASiaDX65GRkcELSYPBgMzMTKSlpSE9PR0ZGRnIycmB\nXq/nM0ty9/9SCkc6lwXQ6/XCarWivLwcFosFbrcbRqORVxbnf2ilpaU4deoUzp49W2U2yVgoFAqo\nVCr+XeMUhEKhgEwm4+87EfH3PRQKwefz8R0EThH5/X74fL5ar33IyMhAXl4esrKykJ6ejtzcXBgM\nBuh0OuTk5CAnJwdpaWlQq9XQarVITU2Ny3vu8/lgs9l4xWsymWAymVBRUQGTyQSj0Qiz2QyTyYSy\nsjKUl5fDarXCaDTC6/XWqg6RSISUlBQolUpeIajVan7RaOX7HAwGEQgE+B93Xyv/zSmm6lAoFEhN\nTUVeXh6ys7ORmZmJrKwsXhHl5OQgIyODlzfcs1cqlRdko4oFEcHtdqO8vBwVFRVwOBy8DDEajThz\n5gxKSkpw9uxZ3l5rMpmqXBdX+V5qtVqo1WpeRgwYMABFRUVxsU8nPJ3w3r170bFjR7z22mt47LHH\nYDabkZWVhY8//hiDBw/my91zzz04evQoP21mtVqh1+sxbdo0XHPNNRg4cCDfe6wJkUjE99RVKhWU\nSiXfq+HjrENxAAAgAElEQVQEBHezOQHBCWWuZ8qNCrxeL9xuN5xOZ42CWSKRICsrC2lpacjMzERm\nZiZSU1P5kUBGRga0Wi0vjLmPifu4tFpt3AzOXO/b5/PxQtpqtcLtdsNut8NkMsFsNsNms/ECxOl0\nwuVy8T0lo9FY4/3nBDLXU1cqlZDL5fz954RzLAHNKZDKbaqNoFIoFPz90+l0yMzMRH5+Pho0aICM\njAx+NKBSqfhOCTdy4N4RtVp9yYQHBxHB4/HwvXTuZ7PZeIHNCZWSkhKcOnWK75GePXu2VpGOuY4W\ndw0KhYLv1XMj2MoC2u/38/fZ4/Hwgpl7L2qqUyqVIj09HWlpacjKyorobHBCmhuFabVavl2c8uC+\nx3jca7vdjpKSEpjNZng8HthsNr6jx80QnD59GuXl5Xynj5shqY7KI1Guk8GNlirf68pK0O/3w+/3\n88+fe6er6+SoVCrk5OQgNzcXubm5yMzMREZGBj/6yszMhE6ni3jXU1NTo+6l2+1GSkoKpk2bhokT\nJ170vT2fhI2piQgrV67E6NGj0blzZzzwwAMAwiuQ+/TpgylTpqCwsBBNmzbF/PnzsXjxYrz66qv8\n8ampqSAiOJ1OdO3aFRUVFQDCoyCn04mKigpUVFTAaDTCYrHAZrPBbrfzPZfKUxuVPx6uF8mFE+GG\n19z0EffScNs4gZWSkoLU1FRoNBq+F6vT6aDX63lBZjAY+J56KEQwm72w2dyw2TywWBwwGivgdFrw\nxx8W7N17Ak6nDU6nAx6PCy6XE1arGTabFS6XHTabFR6PC16vFz6fj5/+MplMF+SNc+utt/K9ryVL\nllxwKAifz8d/lGazGRaLhZ/qsNvtEVMZXLt9Ph9//7l7z/V1RCIR/1FKpVJ+tMApYo1Gw09lcAKM\nGz1xSjoRUxtcj1osFlep+EQiEVQqFVQqFW8zrAs+nw9Wq5UXjGazGS6Xix/9Vp5K8ng8/FQq16vn\nOksAeI86bjTJ9cIrd7a4aVGuw5OSksKPWiuPXi+1QgAAjUbDT39V1zOvCpFIxHfg6oLP50NZWRlK\nSkr4jpXFYoHL5eIVLydPuPvLfZOVR1BExN9jbmpOJpPxHcbKioCbZeFGnAaDgR+V1nbkuWLFCixY\nsABms5n/7pxOJzp27Ijly5fzsjMuxN1pOQYzZsygNm3aEBAO9HZ+fvg//viDrrnmGpJKpfwq9KFD\nh0atG1CpVHEJunix7N27jz74IJx18Omnn6aHHnqIBg0aRF27ErVoQZSZSSSTEQGX9ieX6yKyydUF\nVPK5T2asViuVlJRUGRAzWagv97O+UB/u5/Hjx+n06dNRGTaTkXjKzoSMVNauXYvOnTtj8eLFaNOm\nTdT+hg0bYsuWLVi5ciXOnDmDq6++GoWFhVHllEplredphWTOnPmYMSMPo0d3xEsvvSRYvVKpGi6X\nK2YCrL8LlWOuUWJnbv8WVO75svt5cTRp0oT/O9nvZTxlZ0KUypdfflljGYlEgjvuuKPaMnK5PCmV\nSkmJCUB0VNJ4IxanxG9Iy2Aw/jbEU3bW6yjFcrmcjw6bTFitdgBawesVi5VxiWzMYDD+XsRTdtZr\npSKTyWrtCiskbrcHwIWFJLkYRCI1UyoMBqNG4ik767VSUSqVUfkgkoHwsFJ4pQJooxIyMRgMxvnE\nU3bW62W6CoUiKW0qLpcbgAphG2iRgDWrLvhFKSoSsp0XDmvnpYW189JRH9rIEU/ZmfDFjxfD1Vdf\nDYPBgLVr1ya6KRE0bdoex4/PAdBB0Hq12sH44IN+GDJkiKD1MhiM+kU8ZWe9nv5KVpuKy+UAUPcF\niBdLMKhm3l8MBqNGmE2lCiQSCb/yPZnwet0A4pu/IxbBYEqtQncwGIx/NvGUnfVaqXAxi5KNQMCH\nRBjqg0EV8/5iMBg1Ek/ZWa+VSjIqFADw+xPjUhwIqOB0spEKg8GonnjKznqtVILBYNxSC18MwaAf\ngCwBNetRWmpOQL0MBqM+EU/ZWa+VyoXm2443oVAQifHWToXZzNapMBiM6omn7Ew+iVwHAoFAUmbE\nC4X8SJxSqV2yLwaD8c8lnrIzYRL55MmT+Oabb0BE6NmzJ3JzcyP2+3w+fPDBB9i5cyeaN2+OMWPG\nRESoBZJXqYTnKxOhr/UwGtn0F4PBqJ54ys6EjFQ+++wztGzZEi+//DKmTp2K6667Dn/88Qe/v6Sk\nBFdeeSWefvppVFRUYMaMGWjRogWOHDkScR6v1wuFIhHhUGpDIvK456CsrCQB9TIYjPpEPGWn4ErF\n4/HgkUceweTJk3Ho0CEcO3YMTZs2xfjx4/kyjzzyCEKhEA4cOIBVq1bh6NGjaNy4MZ555pmIc7lc\nLqSkpAh9CbUkEetnUuF0WhNQL4PBqE/EU3YKrlROnDiB8vJy3HLLLQDC/tLNmjXDmTNnAAA2mw2f\nf/45pkyZgvz8fADhPNtjxozBsmXLYLP9ZTNwu91xy9l+MYjFUgB1T3l68aTD4TAloF4Gg1GfiKfs\nFFypNGrUCA0bNsTDDz+Mbdu2Yfbs2fjoo49w0003AQC2bNmCYDCI3r17Rxx3+eWXIxQK4cSJE/w2\nh8NxQfnY401YqQQSUHMKiERwOBwJqJvBYNQX4ik7BbdyK5VKvPfee7j99tvRqVMnAMCVV17JT3+V\nl5dDqVQiNTU14jjOSG+xWACEjeEulwtqtVrA1tcOqVR+blW98G1TKDJRVlaWlMqWwWAknnjLTsFH\nKsFgEBMnTkSbNm2wfv16zJ07F2fOnMHNN9+MUCiEtLQ0eDyeqLDMVmvYVsApF26/SqXC+vXrIRKJ\non7FxcWCXhtHeKSSmECXEokuYoqQwWD88yguLo4pE3v27BkhO+OB4COVZcuW4cSJEzhx4gSysrIA\nADk5Obj11luxdetW3rX4jz/+QIsWLfjjDhw4ALlcjlatWgEALzhTU1OTToiqVBq4XE4AmYLXLRJl\noLy8XPB6GQxG8qPT6SJkZzwQfKRy6NAh5Ofn8woFAFq3bg0g7Ercrl07pKenY8WKFRHHff7552jX\nrh3kcjkA8BkOtdrky3YolysBJCYjJVEaP0XIYDAYldFoNBGyMx4IrlTatGmDo0eP4quvvgIQDhcw\nb948SKVSXH311ZBKpRg5ciSmTZuGJUuW4NSpU5g4cSKWLVuGBx54gD8PF+JdrVbj3nvvBRFF/RI1\n/ZWSogWQGEXn82WhrKwsIXUzGIzkoLi4OKZMnDdvXoTsjAeCT3/dcccdGDJkCHr37o3c3Fw4nU4E\ng0G8/fbbKCgoAAA8//zz8Pv9GDp0KEKhEFJSUjB16lQMHz6cPw/XG9fr9UJfQo2EjeSJ8cDy+3W8\n/YnBYDDOJ96yU3ClIhaLsWjRIkyZMgUHDhxASkoK2rVrB4PBwJdRqVR48803MX78eJw6dQotWrSI\n2A+Ep8oAIDs7W9D214bww0rMepFgMAsnT/6ZkLoZDEbyE2/ZmbDAWc2aNUOzZs2qLZOfn88vgDwf\n7sbk5ORc8rZdLAUF2QASNQWlhcXC1qkwGIzYxFt21tsoxdwUT1paWoJbEk1Wlh5AoozlGlitTKkw\nGIzYxFt21lulYjKZoFarIZMlIhlW9aSmqiESORNUuwZ2O1MqDAYjNvGWnfVWqZw9ezYqXH6ykJ6e\nDqWyIkG1G2AysfhfDAYjNvGWnfVWqRiNRmRmCr+4sDbodDpIJIlakKmG2+1OUN0MBiPZibfsrLdK\npaysDOnp6YluRkxycnIgkSQqr4kcPp+35mIMBuMfSbxlZ71VKqdPn67SMyzR5ObmIhQ6m6Da5fD7\nmVJhMBixibfsrJdKJRAIoKKiIindiYHwSMXrTZxSCQQSE8ySwWAkN0LIznqpVLiAaMnoTgyEFz+G\nQh4AibBtcGH3GQwGIxIhZGe9VCpmsxlA8ioVkUiEtLRcAIkYrSjh8zFDPYPBiEYI2VkvlQoXu+b8\n0C3JhEajQ2IWQMoQDLLpLwaDEY0QsrNeKhUudHMyZzcM+4EnYqQiQyiUiFTGDAYj2RFCdtZrpRKv\nJDOXgqysdADGBNQsZUqFwWDERAjZKWhASZfLhenTp8Pv90Mul0OhUEChUEAqlUIkEuH6669H27Zt\nAQDffPMNpk2bhrKyMvTs2RNPPfUUH6rZ4QiHIUlJSRGy+XWiQYMMAIlYVS8GESWgXgaDkewIITsF\nVyrfffcdzGYzfD4f/H4//H4/ysrK4HK58N5776Ft27aYPn06Jk6ciP79++Paa6/FwoULsWLFCuze\nvRsajQZOZziuVrIrFbG4AqFQolvCYDAYYYSQnYIqlYyMDHz77bcR206dOoWrrroKffr0wYMPPoiT\nJ09i8uTJeO211zB+/HgAwLhx49C4cWPMnz8fY8aMqRdKRa1WQyo1wie4d28IIpFI6EoZDEY9QAjZ\nmVCbChHh3nvvhV6vx7x58yAWi/Hll18iLS0NjzzyCF/OYDCgb9+++N///gcAcU+HeSnQaDSQShMR\nLTgIkahemsoYDEacEUJ2JixJFwCsX78eGzZswKpVq6DVagEAW7ZswTXXXAO5XB5RtkmTJti8eTOA\n8I0RiURQKpWCt7m2pKamQiJJRJ76EEQiSQLqZTAYyY4QsjOhXdqioiJ06tQJvXv35rc5nU7odLqo\nsiqVCr5zc0k+nw9yuTypp3nS0tIgFpsTULMfYnFC+woMBiNJEUJ2Jkyp7Ny5E9u3b8eECRMiLjAj\nIyNmPhCTyYSMjAwA4RvDJZjp0KEDRCJRzF9xcbEg1xKLsGK0JqDmACSS5EtcxmAwhKO4uDhKHu7Z\nsydCdsaLhCmVWbNmoVmzZrj99tsjtjds2BCHDh2KcovdvXs3CgsLAQBerxcKhYL/OxnRaDQgSoRN\nxQmFInltTQwGIzEoFIoI2RkvEqJUzGYzFi9ejNGjR0MiiZz/v/XWW/H7779j+/bt/Lbjx49j8+bN\n6NSpEwDA7XZDpVLxfycjWq0WwWAibCpuKBSqBNTLYDCSGZVKFSE740VCJt8/+eQTeDwe3HXXXVH7\n2rdvjxtvvBH9+/fH888/D6VSiYkTJyInJ4cv73Q6eZe4Y8eOCdr22qJQKBAKJWIUxZQKg/FPp7i4\nOOb0f2XZGS8SMlJZv3497rjjjpiJYkQiEVauXIk777wTY8aMwYgRI3D99ddj48aNfLwaIW7MxSKV\nShEKJSKwox0ajTYB9TIYjGRHCNmZkJHKBx98UK2ftFarxVtvvYU33ngDoVAIUmlkMzkPhmQmPFJJ\nRF4TDxSK5HW1ZjAYiUMI2ZkQpVLbrGNisRhicfRgKhQKxdyeTMhksgSNVJzQaJJ7FMdgMBKDELIz\nuSVzFdQHpRKe/kpEtGALDIbkTF7GYDASC1MqVUBESa9UxGIxiIIJqNkNjYYZ6hkMRjRCyM7klszV\nkMyr6QEkUOnZYTAkb54ZBoORWOItO+utUmE5Q2IjkZSfy+XCYDAY0cRbdtZLpSISiRBK8kQliVJ6\nSuWZc6mMGQwGIxIhZGe9VCoymQx+fyI8q2pPWKkIP0Unlf4VI43BYDAqI4TsrJdKhYthk8yEQolK\nlmWNa/5pBoNRfxFCdjKlEifCSkX4vCbBYBkyMzMFr5fBYCQ/TKlUARcYLZkJBoMQi4VXKl7vWTRo\n0EDwehkMRvIjhOysl0pFrVbzaTGTlUAgkICRSgCBgJvPoslgMBiVEUJ21kulIpfL+SyQyUpYqQgd\nBecM0tJykn5hKIPBSAxCyM6ES59AoPpQJrFuQP2Z/hJaqZxFenrt4qoxGIx/Hn/76a+FCxciLS0N\nFoslat+qVavQqlUrKBQKNGnSBJ999hm/T61Ww+v1JvValcSMVMqRlZUlcJ0MBqO+IITsTJhSWbp0\nKYYPH45JkyYhLS0yAOJnn32G2267Dddccw2+/PJL9O3bF0OHDsWKFSsAgA+bn8x2FZfLBYlE6LS+\ndqSmMnsKg8GIjRCyMyGh7/fv34///Oc/mDp1Kp555pmIfaFQCE888QQeeOABzJo1CwBw2223wWQy\n4cUXX8Qdd9wRkUqYS9yVbNjtdojFQgt4I3Jz2cJHBoMRGyFkZ0JGKi+//DIGDhwYpVAAYN++fTh1\n6hTGjh0bsb1///7YuXMnfD4flMpwEiqPxyNIey8Eh8MBkUhohWdHejobqTAYjNgIITsFVyonTpzA\np59+CqvVikaNGqFRo0Z4+OGHYTQaAQC7du2CWq1G8+bNI47Ly8sDEB4BcOkwnU6nsI2vA2FjmLAZ\nGMViO9LSmFJhMBixEUJ2Cj79tWjRIgSDQdjtdowaNQpnz57FggULcPz4cXz11VcIBoOQy+VRIU44\nN1kiishVn6zY7XYQCRsuRSazQ6tl3l8MBiM2QshOwUcqf/75J7p27YrNmzfj2WefxcyZM/H+++9j\n3bp1KCsrQ1ZWFiwWS5SrcVlZGYCwoYnTtg6HA0VFRRCJRDF/xcXFQl8ej9FoRCCQLmidEomTN8Qx\nGIx/LsXFxVHycOrUqRGyM14IrlTkcnlUsMXLL78cRITy8nK0aNECALB79+6I47Zu3YomTZpArVbz\n2tbhcCStod7hcCAQEC5X/OWXAytWPII777xTsDoZDEb9obK8/FsplebNm2Pv3r0RC3D27NkDtVqN\nxo0bo3Xr1mjevDnmzJnD5yQpKyvDhx9+iK5duwKI9GDg/k42wkpFGIUnlwMrVwI339yORShmMBgx\ncblcEbIzXghuUxkyZAgmTpyI4cOH47nnnsORI0fw1FNPYdiwYfzUzYsvvoghQ4bg1KlTKCwsxPz5\n82G323lvMU5w2mw2jBkzBmPGjBH6Mmrk7FkjQqGGgtQ1bBjQuDFgtVpx/fU9YbXaodFooVAoodGk\nwGBIg0ajgl6vRYMGGWjQoAEyMjKg1WqRlZWF3NxcaLVaSCTCB8BkMBiXnuLi4pjT/ydOnAAQlp3x\nQnClkpmZidWrV2PcuHG44oorkJqaivvuuw8vvvgiX2bQoEFo3LgxnnvuOXz55Zfo1asXJk+ejIYN\nw0LaYDAAAMxms9DNrzVGow2AMKOG0aPD/7777kwcO1YAt/tZAHYAHgBOABYAbgA2SCQVUCoPQSo1\nArAhFCqDx3MGgYAbOl02MjJykZmZCZ1Oi5ycDBgMWuj1Wmi1WqSkpECj0UClUkGj0UChUKBLly74\n+eefccUVVwhyrQwG48IRQnYmZPFjt27dsGfPHjidTiiVypg95MLCQqxZsybm8RqNBlKpFBUVFfFu\n6gVz5kwZAGFCpjRtGv733XfnwO1eBqBqAR8MArEdPwKwWM7AYinB0aPlCCulCgB2iMV2yGSlkEhc\nkEjsEIk8EIkcAMJ5GZI5XA6DwfgLIWRnQpQKB+eJUFfEYjFyc3Nx5syZS9yiS4fVagOgE6QuhSL8\nr81mBND4As8iBdDw3C+SUAioKq+PRtOcX1DFYDCSGyFkZ8KjFF8o+fn5OHXqVKKbUSVlZWcBZAtS\nl/Rc1yAYDEDofgKRJ2mdJRgMRjTxlp31VqlkZ2ejvLw80c2ICRHBaDyJWL3+eMDNHoZCfgitVAKB\n5HXrZjAY0cRbdtZbpaLT6WC1WhPdjJhYrVaIxXIAwixE5JQKUQiAsB5cgUDyunUzGIxo4i07661S\n0Wg0sNvtiW5GTCoqKiCXCxctmEv0GAoFIfQjDYUCzBWZwahHxFt21lulksx56m02G8RiYYz00Qj9\nSImlL2Yw6hHxlp31VhqkpqbC6/UmZa56m80GkeifES2YKMSUCoNRj4i37Ky30oBbVZ+MU2BGoxFE\nwk1/BYOV/yfsmpGGDRsypcJg1CPiLTvrrTTgVoYm4wJIoSMUcwGdxWIpgEC1ZS8l48cDf/zxh2D1\nMRiMiyfesjOhix8vhvT0sNA2mUwJbkk0LpcLgYBwIej9/vACSIlEhlAoAEAR9zpTUoAXXgj//eOP\nP8Lv97M4YgxGPSDesrPeKhUhom1eKA6HA36/cDYVrxfQaMJKxe/3C1LnDTcASiVw6tQpdOt2C1Sq\ndhcdR0wul0Mul0OpVPIpDpRKJaRSKUQiESQSSUR+CL/fD6m03r7CDEZCiLfsrLdfZFpaGgDAYrEk\nuCXRmM12EOkFq49LNy2TKeHxuAGkxb3Oc2lvsGnTJsjlvWC1Lq2098LiiBF5AfhA5EEo5EYg4EAg\n4AZREEShc+twKtUSCDClwmDUkXjLTsG/SCKC2WyGVCqFz+eD3++HRCKB0+lE48aRcavMZjMOHTqE\n/Px8FBQUROzLzMwEgKRcVW+1OgHkC1YfNziRSuUAhPGGyzoXK/PPP/+E2930vL0XFkesLkilagQj\nPRQYDEYtiLfsFNxQ/9tvvyE9PR06nQ6ZmZlo0KABsrOz0bx5cxw7dgxAWPG88847aNy4MTp37oxG\njRrh/vvvh7eSNNLpwutA4pkX4EIxmYQLew8AnGegTCacUjnX2cGZMxUIBoXzdOOQSJTwcEM0BoNR\na+ItOwUfqXCjkWeeeQa33HILpFIpgsEgGjdujPz8cO9+/vz5GDt2LJ5//nmMHDkSO3bswH333YeM\njAw+74riXGjeZBQsDocLQoVoAf5SKkKOVLjILGazHUIqUA6xWJ6Ua5QYjGQn3rJT8JEKF3Pm5ptv\nRtu2baHT6XDFFVfwCoWIMGXKFDz00EOYPHkyGjRogL59+2LixImYOXMmb1ySSqVQKBRxzbV8odjt\nDgDCBVnk3g25XAGhlAqXtdjhcAMQPvaXRJKSlM+ewUh24i07L4lSGTFiBIYNG1arsmfPngUATJky\nBWlpaWjTpg3y8/Px1ltvAQhPj504cQKjRo2KOO6WW26BxWLB0aNHAQAikQgGgyEpXYrDBrD4G8s5\nuDVMYa8OYbzhOKViMgl7rRwikSJiOpTBYNSOeMvOS6JU9u/fjx07dtSqLKdUDh48iGXLluHQoUMY\nN24cxo4di8OHD+PgwYMAgH/9618Rx2WdswxXzgOg1WqT0qbicNgBCOdSzGUGDS9qMgpSJ5eXy+Fw\nAriwZGsXg0ikZEqFwbhA4ik7L4lNZefOnbUuq1Kp0KhRI6xZswatWrUCADz//PP48MMP8cUXX6BN\nmzYAolPUcvPncrk84lzJuE7F7RZ2+otLD6zVahDOSR9/uKSdXq8HgPCZH0Wi5A0oymAkO/GUnXUa\nqRw7dgx79uzBgQMHcObMmQvqKd5www34/fffeYUChIdjeXl5OHv2LO/uVlZWFnEcl/6yadO/3FcV\nivAUiNfrjVgUx/2Ki4vr3L5LQVipCNd756ZG09K0CK8JiT+cUhF6VPYXKqZUGIwqKC4ujikTuYWP\nnOyMB7UeqYRCIXTo0CEquYter8dll12Gyy67DDk5OdDr9bwLcG3x+/04evQoBg4ciH/9619QKBT4\n7rvv0KhRI77Mxo0bkZmZicsuu+yvxkulSbkALhj0A5AJVh+3hiktTQNAGOM15/3l9SbGUE+UwpQK\ng1FHAucCBXKyMx7UeqTi9Xrhcrmwbds2/Pbbb9i5cydWr16NYcOGYd++fbBYLDh9+jTWrVuH5cuX\nV3mePXv24Nlnn+Wnt4gIL7zwAkwmEwYOHAitVotevXrhnXfe4RXY4cOH8c4776B3794QiUR/NV4s\nRigUSro4U+FkWcIpOi4uXEFBFiSSUkHqVJ/zmE6cUmGGegajrnByl5Od8aDWkk+hUEChUMBiseCa\na67ht/fu3RubN2/G0KFDazU6CQQCeOWVV7B582ZcffXV2LVrFzZt2oTJkyfza1heeeUV3HTTTWjZ\nsiU6dOiA77//HmlpaXiBi2B4DrFYDCICAP7fZCAc1FE4RccZ6nU6HWSykxBioTlnqPd6nRByTQ5H\nKMSmvxiMqiguLq52+r+y7LzU1HqkIhaLMWTIEDz++OMoLf2rN2w0GnH8+PGoECtV0bFjR+zbtw9N\nmjTB999/j4yMDKxbtw5Tpkzhy7Ro0QK//vorHnnkEWRnZ2PKlCl8uJbKVB61JBNhpSL8SCUrKwty\nuTBha7gZx2AwACGn+jgCAQ2cTmGcEhiMvxvxlJ11knxFRUW4/vrr0bhxY/Tu3RtZWVlYu3YtCgoK\n0L1791qfp1WrVpg/f361ZbRaLZ555plqyxBRkioWgpDrSiuPVEQiYQJscjOOoZAfiYhLGgiwxY8M\nxoUST9lZJ8mXn5+PPXv24JVXXoHFYsH27dvRp08frFu3LiHG8lAolJRKJTysFK5d3EglMzMTRMKM\nVDilEo4cLLxNKxjUwmZjSoXBuBDiKTvr3J3W6/V45JFHsGHDBuzatQtdunRJ2Nx2KJTM+dGFaxfn\n/aXT6RAMWqsvfIngbnvYKSERzyAFNhuzqTAYF0I8ZWedznrkyBHcfvvtePLJJ/mw45s2bUL37t0T\nEtjR7/dDJhN+Pj/ZqDxS8XoTkQogEUpFxZQKg3GBxFN21kkajB07FmazGXPnzsWMGTMAAK+//jps\nNhvWr18flwZWh8/ni1hhn1zEx10vFoEAYDIBGo3mXL3/BAO2AWVlyRf3jcGoD8RTdtZJqezcuRMP\nPPAApkyZgmnTpsHj8UClUuGqq67CL7/8EpcGVkeyjlTEYgkAYRNImc1hjw6tNh1A/IVtpNuycAr0\nL9LOBbNkMBh1JWlGKmq1GkajEaNHj4ZEIsGCBQsAhN2Kw8EMhSVZRypisRRAfFarVgXnAaZWpwKI\nv12FW4ybiGsNk4WyspIE1Mtg1H/iKTvr5LL1yCOPoKioCNnZ2XjsscfwyiuvQKfT4dChQ3VyKb5U\neDweKJXCBzOsCalUiUDAAyFXmnNLh7KycnD6dAmANnGtz+8HFApAIpGdW5ejiGt90eSjouK0wHUy\nGH8P4ik766RUHn30UTidTj61r8fjwfDhw/HSSy+hZcuWcWlgdSTrSEWpTIHH4wKgF6xOLiRbRoYB\nQPynhbxeQKMJKxW/3x/3+qJJh9frgsvlglot/Ip+BqM+kzQjFYlEgsmTJ/O5T8rKytC+fXs+14nQ\nJODLCJYAACAASURBVKtSUatTYbFYAeQJVieXqCscqTj+OWY4Zz+ZTAmPxw3hE3WJIJfrYbFYmFJh\nMOpI0iiVRYsWYf369WjatCmeffbZuDSoLiRrLzUlRQOhQtBzcCMVg0EYmwo3OJFK5RAqhfH5SKWp\nsNuFvc8Mxt+BeMrOWhvqiQgjR47E0aNHYbfbEx7AMRAIwOfznXOjTS6UShUAYdftcEolKysNQkx/\nncuZBpkscUpFJGKhWhiMuhJv2VlrpSISidC6dWv07t0b06dPT3h4FK6HqtUmIkFU9YQflrBrRbjp\nqJQUNaTS+C8K5JRKIkcqIpGaBZVkMOpIvGVnnRc/vvPOO/jzzz8vSeVEhP/973/45ptvovbZ7XYs\nWLAAs2fP5rM+VobLr5yamnpJ2nIpSU8XZrRQGS5SjkqlgkQS/xTLnBKTyxVIlFIhCttUGAxG7Ym3\n7KyTTcXr9cJut6Nt27YYMGAAJBIJPw3Wr18/9OzZs06Vz549Gw899BC6dOmCHj168NvXrVuHESNG\nwGazQaFQYNy4cXj33XcxatQovgyXwEun09WpTiFQq5UAhA0hwnXYU1JSIJW6EO/8VZwpI5yeNP5K\nLBahkIbZVBiMOhJv2VmnkYrRaESfPn3QtWtXGI1GlJWVoby8HOXl5XXuMR45cgTjx49HXl6kh1RJ\nSQkGDRqEbt264ezZsygrK8PTTz+NBx54AKdOneLLcfWlpQntdVQzWq0aQgtaTqmo1WqIxfGfEuIW\nW4YXvRrjXl8svN4clJSwBZAMRl2It+ys00hl4sSJl6TSQCCAYcOGoXPnzmjdujX27t3L71uwYAEU\nCgU+/PBDfnHOpEmTMHPmTMydO5fPZmY+J9X0euHWgtSW1FQ1hLapuM/psJSUFIjF8R8lcUpMqxXe\nfsQRCLA89QxGXYm37Kx2pPLRRx9h7dq1/P83btyI/fv3X3Slr776Kvbu3Yv3338/yuC/YcMG3HHH\nHRGrPaVSKTp27BhRN9dDzc7Ovuj2XGoyM/WQSIQNdsgplfB9i7/nGed0FV4Xk5gpKKIUWK3MUM9g\n1IV4y85qlconn3zCx/cCwnmPFy5ceFEV7tu3D0VFRZg2bRqaNm0atf+PP/5Aw4YNo7anp6ejrKyM\n/3/FuXjvmZmZF9WeeGAwGCCXC2tA5kwLYY+O+At5brYzLU0DIFFuvek4fboiQXUzGPWTeMvOapXK\nDTfcgCVLluD111+H2+2GXC6/qHUBFosFd955J/Ly8nDttddi7969MBqNcLvdKC8P5wHRaDRwu6Pt\nEU6nM2KxjtFohEqlStIV9WpIJMJOy3C3TK1Wgyj+dXM5XAoKsiCRlMa9vthkoLQ0MfYcBqO+Em/Z\nWa1N5eGHH8bWrVvxxBNP4Mknn4RYLMa2bduwceNGyGQy+Hw+PoRyXl4e5s6di0aNGlV5vuXLl+O3\n334DAHTq1CliX05ODnbu3ImsrKyYLsS///47unTpwv/fYrEgPT0dANCsWTMcO3Ys6piioiLeBiMk\nWq0WYnH8Q6VUpvL0F1H8p784Q71Op4NMdvK8UPhCoYbDUb+mv1wuFywWC2w2GxwOB1wuF8xmMxwO\nB86ePQu32w2LxYHTpytQVmaEw+GCXC7FqFGDMHjw4KSMIMFIPoqLi/H8889HbT9+/HiE7IwH1SqV\ntLQ0rFq1CmfPnsWePXvwxhtv4NixYxg+fDgAQCwWQywWw+12o6KiosaolyNHjsSgQYPgdrvh9/vh\n9XpRXFyM/fv3Y+HChWjdujWuv/56zJw5MyLdZWlpKXbt2oXx48fz5yovL+dvjNGYXL1VjUYDkUhY\nYccNIDUaDYLB+NfNjVSysrIgl5cjAYk/ASjhjbfvdAx27doFq9UKv98Pl8sFj8cDt9sNh8MBp9MJ\nq9UOm80Fm82FsjITzGYLyspKUV5+Cj6fG3K5HlJpKkSiFIhEKSBKQyikhdebjUAgBUR6AM0ApANI\nAWDH3r1z8N5787Fr12bBr5fx9yEzMzNCdsaDWnl/5ebmIjc3F/v27YPH47koLzCtVhuxklOv10Oj\n0eDyyy8HAAwePBjFxcUoKipCUVER7HY77rnnHmg0Gtxyyy38cSaTCenp6QgGg0m3AM5gMIDILGid\nnBOUWq1GICCcS7FOp4NIlKj7r4fFInz2x8LCQgCATncziNQgUiIUUiEQSEEgoEEwqAGQiXDqAwPC\nwTazEQ4wmg63u+7RKJzOjvj99+sv2TUw/nlIpVJoNBpedsatnroUfuyxxzBs2LBL2gCJRAKJRML/\nv3nz5nj//ffx2GOPYcaMGXx2yYULF0a4wLndbhgMhogFmMmCRqNBKCTs9BcX4FGpVCIYjP8aGW6k\nkpmZCaLyuNcXmzTY7cIrtN9++w3t2/eC1SpkCu0UeL3MfZpRO4qLi6uc+udkZ7yok1JRq9WXfE53\n4sSJUQvYRo0ahVtuuQVfffUVFAoF+vXrFxVSwOl0IiUl5ZK25VKRmZkJn09YryTOpiGVShEKxd/A\nwQ0OdTodgsH4R0WOTWIEbUpKiiBTjJEo4fcnJnIB4+9FvGVnnZRKPMjOzo7pL11QUID/+7//q/K4\nioqKhKQwrg0pKSnw+4V1sw2dSxMfHrnFX6lUHql4vYkaqSRG0CoUCoRCQttyZAgGE5EMjfF3I96y\ns05hWpIFIoLZbEZGRkaimxITuVx+zslAOMHDzQAKFT06EABMJi4icwiJWVWfGEEbnmIU2jNBKkhn\ngfH3RgjZWS+VisPhABElZYRiDoVC2JXmichEYDaHlZhWmw5AeIN5ogStXC5HMCh0ZGZR0tkOGfUP\nIWRnvVQqyRz3i0OtFiatL8c572tBBQ/nAaZWC5NtMprECFqhphgjSWz+IsbfAyFkZ8JtKheCyRTu\nFSerTQUANJo0VFQIJ2il555kIBCASCSBELK29NxC+qysHJw+XQKgTfwrBaDRAMOGAa1bi6DTXVzY\noAsh0QnqGIwLRQjZWS+VCpdkJhmzPnLo9XqcOCHcokyZLPxvIBCARCJDIBD/OrkUxhkZBgiZlGz+\nfGDAACDce787av+FrFr3eDywWs2w2y3wep3w+z0IBv0Ihf66kSKRCCHOI0Jw2NQX4+IRQnbWS6XC\nxQYLJ4hKTgoKGmDPnuhwM/GCG6n4/X6IRMI8Vi6IZThSsXBTfd26hf8dO3YCzpz5f/a+PMyJKnv7\nrVT2pJPeN0AEGlBR2REdZEQRAVFx+bEIDjIoCoKIDiqu7SgiuKMioqLojBvK4orDuAOD+ikyLCI6\ngCy9d9JJOnsq5/uj+lYSuptON+QWSN7nyRNIqqtu3dw6557tPdVwOuuPQtW6AbFCRTPkwkVtw0sD\nQFDFxRgDpS2kNI4YPGTncalUjuWujwzt2uUB4FerwiwVSZKg0fD5WZmlkp3NN6ayfTtw7rnAqlWr\n8PvvOgAP4GhUrbcEplQkSYIgaLi4GGOIcPtd0/jjgofsPC4D9ceDUikoyIYg8MuIYkolGAxCo+HD\n3MyUSn5+Jni6vz7+WH6/4YYpEMXLAIwBMAxATwC5SFVQO95SEQTej04IGo2O8zXT+KMhrVSaASMR\nNBgMKo+keRQWFsBk4tfqNjFQz2dHy0gkLRYztFp+le2smt9isUCr5VdkGh+3EgTx8AcfdYSg1R57\nbR7SOL7AQ3Yel/Z0KCTXCByLvVQY8vLyoNXyc3+xqZAFHp8dLSOxNJlMEEU/l+QAIJGRWav1ghdR\nsdUqvwcCAWi1Js50/xGIIv/H1e/3w+l0HvWkh3hoNFqIoh5arR5mcwZuuul6zJ49M2U91E9k8JCd\nx6VS8TY0SD+We0vI7L38gtdquL9Yn3rZYvBxE+7MQjKZTNBo+NG0sOVWX18PrdbK7X5lBKDX809M\nycsrQjRqhFZrP6pJDzIIQBTRaATRqNybye8/iIUL5+Odd4bi55//H+/b/cODh+w8bpWKTqc7pi2V\nrKwsrvT3bI3I2V983IJMqZjNZmg0/Gha4huSCQI/uhSWMOPz+aDR8Bbw6ri/iET4/VshU/kfbQgA\nxIYXW7PZCAQegMMxIgXXS4OH7FRNqezduxdr166FxWLBkCFD0L59+4TviQgrVqzA2rVrkZOTg5tv\nvhkdOnQAIO/Gj+V4CsDo7/n5+xnpqMfjgSBYuVyTCXeLxQKNhl9MJb53jCDwuy5bcoFAAIJw+IZ0\nRx9B6HT8lUpOThF8vjKkRqk0B3Xu9UQAD9mpSqB+0aJFKCkpwd13342ZM2eic+fOeOONN5Tvg8Eg\nhg8fjquvvhplZWX44IMP0LVrV3z66acA5Ie6pS6TakNmsuW3i2asC/X19ZBdEKlHvMUA8LvXeKUC\n8FMqbHMn+6V5C70g9Hr+G6mCgkIAlZyvqs69ngjgITtVUSqBQACLFy9GVVUVamtrMWvWLNx1113K\n94899hg2btyITZs2Ye3atdi+fTvGjx+Pm2++GUR0XCgVk8nEVakUFsrvNTU1iEb5sDez4ke5Opcf\neSaLqZjNZhCpo1R4uRhjqIfFwscCjUdWFt90cRnq3OuJAB6yUxX31+23357w/4EDB+K5555T/v/a\na6/hpptuUtq2iqKI2267Da+++qrSFzzZQBMRNQQA/aitrUVNTQ38fj8CgQAcDgdcLhe8Xi8CgQAi\nkQjC4Qii0cSqNlHUQKsVodfrodPpoNPpYDKZFN+kyWSC1WqFxWJBZmYmevToAYPBwJUenaWdV1ZW\nIhjM53JNZqnwFu4sQC6b8fyi5czFKKdl8lYqXlit/JvS5edngz8DtTr3eiKgNbKzrVAtpiJJEtau\nXYvPP/8cr7zyitKQq6ysDLt27cLIkSMTjj/llFMgCAIEQWjVxNx446148cVF0GrNMBhyIYo5kDNS\njIhGsxGJZCISMUOSjIhGtQB0SDTg5AwVQIIouqDRhKHRhCGKfghCGIIQhEYThCDUA6iHXu9HVdVm\n6HQ6RKP8en2wILLX60UkwjemYjQaQcRPgbLr8m6WFZ/9RcR7J+1Gdjb/FFubzQyAdyM0de71RMAf\nWqmsW7cOl156KaLRKLp27Yq5c+cCkN03ABp1g9RqtbBarRAEAeFwGDpdy7UY0WgUb7zxDxD9D+Hw\nyUof97ZCktBibQIrQtRq+fb6YJaK1+tHNMonMym+XoRne934BAGe12WWit/vBxFv96sHNht/l5Cs\nVHg3YFPnXk8EJCs7jwSqVdQPHz4cDocDb775Jurq6jBq1CgQkVLwxOgEGCRJgsfjgUajgSRJEEW5\notlgMCgWzKGv0tJS+P0uAMXc7iu+rS+PXvEMRUXy+8GDNZDrBVKP+IB5JMJP8MS7v9SyVCSJN0O2\nA4WF/PsH2e0ZEEW+rbHVutc/EkpLSxvJw3A4nCA7UwVV61TsdjvGjRsHjUaDsWPHYseOHejSpQs0\nGg327t2LAQMGKMfu3LkTgEw/Hj8x0mFMB41GA6PRBq+3DgCfOENiW19+jIN5DRmfe/eWAeCT488s\nP7m9Lj8XCQvU827ry9jCvV4vJIlP4W1mJjB1KtCnzwj07cu/2FcubD3IlT1Ao3EhJ+fY5fU7XiGK\nIhelwt1SkSQJK1asSFAGXbt2BSBn1RiNRgwePBirV69O+LtVq1Y11CXIPS00Dex+LfW3kLsS8qts\n590rnoEJPIfDCV6WCvsJtVotV6uM0cHwbuvL5tjj8SAc5mOp/OMfwIIFwNixZ6KkpITLNePBqHB4\nwmCoSWkP9RMVGo0mQXamCtwtFafTiWuuuQb/+c9/cNddd8HtduOOO+5At27d0LNnTwDA9ddfj2uu\nuQb9+vXDuHHj8MEHH+DBBx/EtGnTlPMkq1Ree201qqv5KRW1Wl6wOhWfj1+dSryrj2f8iCkz3tdl\n3F9OpwfRaOoDySUlwMUXy2t89uy52Lr1lyPm1hJFLXQ6I3Q6PXQ6A/R6AywWK6xWCzIz7bDbrSgs\nzMa55w7ElVdeyZ0KBwB0unIUMX9uGm1CaWkpSktLm/zuD6dUcnNz8fLLL+OGG27Ak08+CQDo1KkT\n3nvvPeVmx48fj8rKStx777247bbbIIoiJk+ejPnz57f6eiYT3wI5tRo5MUICh6MK6rj6+IP3dVkH\nVofDA6BDyq/HEiA3bdqEl19eBa/3ERwptxYQgZzNFWp4BQHUQw7GuyDXGzmwe3c9rrySPxUOAAhC\nXZpM8jiGKjGVCRMm4LLLLsOPP/4IrVaLs846K8HPJwgCZs+ejb/85S/49ddf0aFDB7Rr1y7hHMkK\nbd6Feew25EZOfHrFm0zyKxKJwOOpBsBnl6eWVabWdVmGXUVFDeS+LalFToMXc926fyMQuBzAFa08\nQ1PcWsmBZfbxpsIBgGg07f5KJVK94VUtUG+1WjF48ODDHpOTk4OcnMbxARasTwZ2O9+YSrxS0WhE\nLgFO5ilwu93Qai0Ihfj0+lDLKlPrusxScbk8AFIfU2EJCRqNgGiUb4MutahwACASqUNWVjr7KxVo\njexsK47LJl0siyEZtG+fD57cRWo0cmLC7uDBgzAY2h3+4KOIxMZg/JpWHdrWlxcYFU51dTV4ECyy\nZmQmk4l7sFwtKhyAEAhUIy+PJ4HliYPWyM624rhUKlqtFpEkO0J17doBWu2BFI8oBlbLwBo58QDb\n1Lndbmg0/FIx4xWoKPLbSavV1pfFrWpqygEUpvx61dXye1FREQwGvqSOalHhAP6GZIJ06+RUoDWy\ns604LpWK0WhEIJBc8LC4uBhGY1mKRxQDUyperxeiyKeuIH4HTcTPF80sFbmHCz9PqhrWoMEg/7aS\nJMHlqgSPglp3g9fWarVCo+EXFwTUo8IB/NDrj93me8c7WiM724rjUqno9XqlLWZLyMnJgSjWpnhE\nMTAmW549N5hSKS8vRzic+h00AxPucvyIn1I5tK0vD+Q3JNR5PJ6Ga6b+fplSycrKgiDwZQpWiwoH\ncDXUlqWRCrRGdrYVx6VSaY22lQN+/DowMlZpeffOx4Rn7q+qqmoEAnzSiQF1WhgDjdv68kC84jYa\n+dD+xAfLifjGVNSiwgGqkZ3Nbw2faEhbKs3AYDA00I+3DLvdjmiUX/ZXYstZPkWILFBfXV3X0C+c\nDxID9fwsFTXa+rKyiaqqKmg0fIQe61djtVq5KxW1qHAAN2y2NEVLqtAa2dlWHJdKxWQywe9PrsrX\nZDKBiF9FMKPykMfHx/0Vq59wQC6K4wPm6pOVCr/AqhptfZk16PF4APBxzzD3l81mQyTCb2MEqEeF\nA9QhKyutVFKF1sjOtuK4VCpWq7WhbW7L4O0TZjVbTqeTm9XA3EG86icY1HJ/qdHWl9UC1dTUIBLh\nw61WVydT0mRmZiIUcoInQalaVDiAA3l56RqVVKE1srOtOG6ViiRJSfkGrVYrwmF+1N3MUqmtrYUk\n8RE+TMj6/QHw7EjIlJkcP+J3XTXa+rJAfVlZGQIBPjEVSQLKyuSYil5vAsAv4YSBPwVPHQoK0kol\nVWiN7GwrjkulYrPJ7gePp+U0S5PJBEkKAuCz22KC1ufzcaNHZ8kBvNvcsqZVHo8HgsCvqZIabX1Z\nTKWsrAaSxC9tu6whGz4vrz2Ag9yuqxYVDlAPuz3dSjhVaI3sbCuOS6XC2mH6fC1X+mo0Guh0JvCi\nmoivU+HV1jcWuPZDJhfkAxZnkM1pfoJAjba+bI6dTn4xFSBWAJmfXwCezBBqUeFotalvd3siozWy\ns604LpWKoSFSm6wJp9MZAfDJYGnYCMDn8yMS4SPgY3UbfsistXzA0mxramoQjfLbvavR1pf9rvX1\nfBU3o2rJysoEwK9WRS0qHIPB0STfXxpHB62VnW2Bakpl7969+Nvf/obLLrsMc+fOxcGDiaZ9IBDA\nggUL0KdPH5xzzjl48803ld4plgapkqy21euNkOm+Uw8maMvKasErEyvmcqsHwM8NxbLOKisrEQzy\nqy1Qo60vUyoORx1kynk+YF218/OzATi4XVctKhyNxg+TiZ/SPtHQWtnZFqiiVD744AOcfvrp+Pjj\nj2G327Fs2TL07dsXTqdcpBgIBDB48GDMmzcPF1xwAXr06IFrr70Wc+fOBRCbGK83uawuo9ECXu4v\ntsnat68CQAGXa7IU22CQXxozEHMJ8XT1Aeq09WVxq/p6L3i6+tizb7OZwWtjBKhDhQMAghCEXs8v\nk/BEQ2tlZ1ugCvX9k08+iZtuugnz5s2DVqvFnj170LlzZ3z00UeYOHEinnvuOfz888/YvHmz0kJ1\n0KBBmDp1Ku655x6lgU9dXXLuAKORH30329E6nS4AfLJYmFKJRELgGahnlorX60c0ym93qUZb31hy\nQAA8FTd79mWlwi81/lAqHF496gUhCKOR3/yeaGit7GwLVLFUPv/8cyxYsADahpLsqqoqAFAa87z7\n7ruYNGlSQk/usWPHQhRF/PTTT8rEMMumJcjmNJ9dXkzgucEroMuUSjjMN6WY1W4cPFgDgJ8fPLGt\nL1+lUl/PtxaIlRTY7RkQRX6p8WpQ4cjwNDTWSyMVaK3sbAtUD9Tv378fU6ZMQadOnTBkyBCEQiF8\n9913OPfccxOOMxqNKC4uRllZmbLokk2L0+v50XfHArr8hA+jS5GkMAB+le2s5cXevWXgwdrLkNjW\nl88cM1ef7GLkZ5UxS8VisUCr5adU1KDCAQAif9pSSSFaKzvbAlWVyqpVq9CzZ08Eg0GsXbtW4aWJ\nRqOw2xtTNZhMJrjdbmQ3SJWampqkrmM287NUYi4hFwA+dBNMqUSjEnh6NNmG0uFwgqelwrutLxDb\nufNWKsxSsVqtXBt1qUGFIyOkZCilcfTRWtnZFqiiVCRJwqxZs3DFFVdg/Pjx+Omnn9CtWzcA8sOj\n1+vhcDTOdHE4HNBqtdDr9bDZbHA4HNiyZQsEQWjyVVpaCoApFT4pxUzg+f1u8FcqYfBUKqxORc46\n4xe85t3WF4gvMPWCZ9o2y/w0mUzQaPgF6tWgwgGAaDRtqRwNlJaWNpKHvXv3TpCdqYIqgfolS5bg\nmWeewcqVK3H55ZcnfCcIAk466ST8/PPPCZ9XVFSgrKwM3bt3ByBXhrrdbqVC9HAwGPTg4f4yGmUB\nL0kSQiEfeKb3AqxQjX8nRIejCgC/lGLebX2BeBdjBDxdjIz7z2g0QhD4sQWrQYUDyEolnVKcGjCX\nF5OdqYIqlsrrr7+OCRMmNFIoDKNGjcKKFSsS2l4uX74cWq0WPXr0ACD7BpNVKmYzn+LH+Gp6rdYM\nQA2uCz7XNJnkVyQSgcdTDaCIy3UB/m19AUBsyKrlbQ3G91QRBH694tWgwgEASfIqaa9pHF0wRcJk\nZ6qgiqXy+++/Q6/XY/LkyXA6nfD7/cjNzcX999+Pbt26Ydq0aViyZAlGjhyJm266CZs2bcKCBQsw\nc+ZMJdaSm5uL6upq5OTktEglodfrAIRTfl9Mqfj9foiiCeHUXxKATDwoKqUEUQCprytgmV9utxta\nrQWh0B+3rS8Qm18iPvPLEK9UeKXFy9eT33lS4ej1wKhRF8Fq5Wvh/xFRWlqquP8PBZOdqYIqlsrE\niRMRDofhdDphtVrRqVMnOJ1O/PrrrwCAbt26YdOmTfB6vRg9ejSWLl2KefPmYeHChco5CgoKkp4Y\njUYDHrThBQ21jrKg5fdgsBoCuaUvn4ICFtc4ePAgDIZ2XK4JqNPWF4hVmMvJEPweGxZTkbs/8rdU\neFLhLF4MvPvuW+lAfYrRGtnZFqhiqTz66KMtHtOzZ09s2LAB4XAYWq22EQV3ZmZm0gU8vEjxWDV9\neXk5RJFfim04LO/yNBototEIeARWWZDe7XZDo+HXVOnQtr68rMFE8FMq8W19eaXFA/ypcDp1AqZM\nkZ/VJUuWAACKi4uRmZmJ3NxcZGZmIj8/Hzodv3jWHxWtkZ1tgSpKpTVobhEVFBSgqqoKkUhEKaJs\nDrJSSX2sgaXY1tXVgYgfPxQTrFqtvqGqPvXZSfHBciJ+ZJJqtPVVEyxQz7tXPG8qHBZe/f7773Hb\nbfMAjIRO9zEEoQ7RaA0kyQW/vwqiqIVeb4bJZEN2dh5sNjuysuzIz89Gfn4WMjMtMJvNyM7Ohslk\ngsFggMFgQEZGBkwmE/R6PUwmE4xGIywWC4xGI0RRhNlshiRJDV6NPzZaIzvbgmNeqTSHwsJCSJIE\nh8OB/PzDCxdeSiVWdV0PIn5VwcxFIru/+Gzd4y2GcJhPsBxQp60voE7cCogpFd4dTHlT4XTuLL+/\n994ahEKTIEnz0LjrLSESCSAS8cPnq0NtbTUANwAX5AZmdQC80GorodfvgCgGIAhBAAEIggdAAERB\nEAUQjfoRiXghSQGlsyX/hmTqoDWysy04bpUKo3Sprq5ucWIkKQoeLosY7b0PksS/6tpkssLn84JH\nmi0T7lVV1QgE+FkMarT1BeSe7aIY72Lko1Ti3V88LZVEKpzUW92svuv33ysgSQObOUqAXHhqgswA\n3rnJoyIR+dU6CCeMUmmN7GwLjltbL6tBqrkYN/hhEIlI4CEEWKDe6XQiFOLXEpXt6GSKfz61DCxQ\nX11dByJ+96pGW18g5mIURR2AVkusNoNZoUajEZLEr06FNxUOs4xcrnou1zuR0RrZ2RYct0qlNcRo\noVAYPILXbBe9d28ZIhF+Ao/R+FgsGQBSx+kTjxhVigO8+sYA6rX1ZRaDrFT4ZQewHbder4ckhbhd\nlzcVTiweyY/e6ERFqkklj3ulkoy29Xi84MHXxNxfBw5Ug2eFOatjkvP7+ZAOsuwgnlQpgHptfZnF\nIHcR5UeXwtLFRVFUfP88wJsKh7nbePeriQdrAvhHR2tkZ1tw3CoVxg/kbxzNawReC5UJWlmJ8Xsw\nGDecbNby6Q7IaDz8fr50+2q19Y3PsAP4WQwMvP39vKlwYg3f+HYvZRAEzQmjVFojO9uC41aptKYt\npjx5qU+LjM/+4qlUWB1Thw4FAKq4XDNGsMiXxkOttr6hBj2i0/FVKmrFjnlT4bB6x1CI73pipXGJ\nyQAAIABJREFUEAQREq9OZCoj1S2Fj1ulwqgc6utbdvfwWqjMhPd6feCpVFhMJT8/C3JaZeoR67fB\n12JQq60vUyq8LZX4XvG8oAYVDrN8w+EgeLIiM4iivmGD9MdHa2RnW3DcKhWj0QidTpcUMVo4zIe+\nmwU35QZd/Pz9TKnYbGYIAp9ahli7WT5WIINabX1ZTEVu+MZfqUiSBEHg87iqQYUT617Kl2qfQRSN\nJ4xSaY3sbAuOW6UCyGac19uyEOXVEyLGUuwCT6VSWyu/5+TkwGhMXfOdeLB7lXup8POBq9XWlylu\nnq2pgURLhZdSOZQKhwcSW2Lzp77XaPQNcuLEQLKysy04rpWKyWRKyi8YDPrAY6EmmvD8/MIsM9Bu\nt0MUU0dpHQ8mBOROiPwsBrXa+rI5ljvn1XK7LmMpikQiEAQ+BZdqUOEk9qvhX5Ot0RhTFrg+FpGs\n7GwLjgmlIklSQu+UeITD4WazMlj74ZYgK5XU+9+ZoJX5t/iZ8GVl8nthYSFEsYLLNRPvlZ8CVaut\nL9vUZWRYAfCjS4m5GQMNrqjUQw0qHLYhk9cTf9JIQTixLJVkZWdboLpSYa2EH3vssYTPf/vtN4we\nPVohg5s7d24jc00Uk8vYkJVK6v3+LIgsm/D8du8VDXqkqKgI0Wg5l2smuiv4KRW12vqymGZmJr8C\nUyCRLZhXOwU1qHCYRSYXePKPqWg0GSkLXB+LSFZ2tgWqcn99+OGHGDNmDPx+f0IPhbKyMpxzzjko\nLi7GW2+9hZqaGjzwwAMoKyvD8uXLleO0Wm2zFg5DNBpFOMwnmBwz4fnutphSKSwsRDDIR6nE7jUM\nnveqVltfxhSemcmvwBSIz7LzQaPhY6moQYUT66ypjvsLSF2M4VhEMrKzzedOyVmTxObNmzFr1iy8\n9NJLCabY/PnzYbFYsHHjxoaOd0C7du1wxRVXoLS0FJ06dQKQnAknt/Y1IRJJvVGW2MiJ39QGg4DD\nIRc/RqMByIHk1AogJtx536tabX1ZgWmHDvkQxUrwKmlge61AIABB4GP9JlLh8KMbioG/A4UoQ+nh\nfiLgD+v+uvfeezF//nyEQiHFUiEifPjhh7jhhhsUhQIAI0eOhE6nw5dffql8lszEeDwe6HRqENTx\nndqqKrnqOjOzCEDqrZWYUuEr3NVq6xufDKHTpYbeoimwWAOvDEZAPSqcGPhXfEajaaVytKB6TCUS\nicDtdit0zE6nE3v37kXv3r0TjtPpdMjPz8fvv/+ufJaMX7C+vh6iqEbPa76UD0zoWa128CqABFhR\nHr9lpFZbX2ap5OfnQ69PXSvWQxGvVASBT+xKLSocNRGNGhAI8GOBVhupjKmorlQcDpmrqqCBN57d\nqF7feFem0WgSOJCSmRiHwwFR5MOiy/ih5HoCvpQP5Q3GSVERH0slEWr1oeC3fOMtFUHgp7RjxZ78\n0tTVosKJgR97AEM0ajqhUopPCKWSlyeT1mVnZ0Oj0aCmJrGILxqNoqamBsXFMR+vRqNR6CsEQWj0\nKi0tbWDi5PNgsLgX754bQKwAMj8/BzzqKBLX4x+fiI8tx7y8PBDxs1Tis7+I+FjcalDhqL2eIhFL\nyuo21EBpaWmTMpG1S46XnUcbqisVlhvOmDNFUURJSQl++OGHhOO2bt0Kr9eLAQMGKJ8JgtDixDid\nTi6d64DYgyEXqfG1VBipZHFxLoDUV9Wze5VbGPO7V7WED8v+stvtkCR+MRVmqfj9fhDxCdSrQYXD\nNmTyeuK7IZOvn9FA8//HRvwm/A+rVOwNhFnxPDSjR4/GG2+8oeSNS5KEhQsXIjc3Fz169GjV+Ssr\nKxEK8emhHusOyP/BYEKvuDgXGk3qlQq7V95CQC3hE2+pBIPqWCqSxCfhRA0qHFZ3qIaVLyMb5eV8\n2kaoCR4tFFRVKq+//jqmT58OAJgxY4ZSgzJ79myEQiH07NkT9913Hy688EK88cYbmD9/PrTaWKaR\nJEmKOUdEjV6lpaXYvXs/gsF2XO6HJVOo0XODWe5msxlabep9w2r1F1GrrW8kIqdtywyvUfCqqmcd\nEb1eLySJT7GnGlQ4bP3yboIWgw1OJx+KIx4oLS1tUiYydpJ42Xm0oapS0Wq16NSpE6ZNm4b+/fsj\nv6HqqrCwENu3b8ewYcPw/vvvw2Aw4PPPP8d1112X8PfhcBg63eEL4MrKasGjyRBwKD06X8ZTVgxs\ntVqh1aa+OI8lysgWA7/2umq19QXkYL0gCMjIyAGvZmhMqXg8HoTDfKwGNahw2Po1GCwA1Iht2OBw\n8HNrqo1kZGdboWrx4/jx4zF+/Pgmv8vJycHzzz9/2L8PBoMJlfhNoaKiGjx6bAMASx7R6QzgrVSY\n99Bms0EUU+8bZsXHJpMVPp8XvBR3fFtfmXafX3YSywAzm21wOl0AOqT8moz7y+n0cIsNqkGF43AA\nXboAGRmZcDrrAJzE5boxFKKyspLzNdVDMrKzrVA9pnIkCIVCTaYex6O8vBxAEZfxsN2WxZIBnlQe\ngPxQAnL/aY3GmfLrMQWq1xsB8MvvV7OtL5M5+fmFAPgQd7Je8Q4Hv/iGGlQ4bFNkt/NriZ0IW0MM\n6cRAMrKzrTiulUoyJpzHUwcgi8t4mF/YYODvF2YKTU58SL0Zz4qPZQXK72FUq60vALgapjU3Nxu8\nCkxZ47eKihrwsrjVoMJhbk05C1SNZlk21Nfzqz9SG6l0fx3XSiUQCCipyM1BXih2LuNhtSKZmZng\nWdUOJMZUiFJvJbGdpRy45meVqdXWF4gpUpmpmE9Ql1kqcrorH0tFDSqc2Hoyg2drgRhs8Pv/OIH6\nlpCM7Gwrjmul4vf7GzrxNY1gMIhgsB4AH/pulnZaUJAHgF/aKRBTKhkZGZCk1FsO7F6zsvi6K9Rq\n6wvELJXsbBt4WINArAtjdXU1eMWt1KDCidHg8CnebYwMRKN0wvB/tSQ7jwTHtVJpSdtWVVXBYMgF\nr9tk6zEvLxO8hA4DizUYDAZEo6l3H7Biyw4dCgBUpfx6DGq19QViSiU/n58l2r69/F5TUw6AT71V\nIvg8OywJol27XPBWKuedB+zYISAc9iIjI7XWIBHB7XZj37592LFjR8r6xLeEVFoqqmZ/HQmICD6f\nDxZL8zQSdXV1EEV+2UGsADErywqecQYg3i2kbfCFpxZMuOfnZ4Gnq0+ttr5AzEqyWMzQamuQonYU\nCgwGOb1XkiS4XJUA1KCh5wOW+JGZaYUg1CNFxd5N4oUXgG7dYv9nNR2SJEGSJIRCIQQCgYR3j8cD\nr9cLj8eD+vp6BAIB+P1+eL1euN31KCurhcPhRl2dG5WVlfB43PB6XfD5XAAE6HSZEEUr/P69WLdu\nLc477zxu95uM7DwSHLdKJRgMgogOa8LJjY34dQdkJnxRUT70+v3g2Z2UXUu2VFJ/YaZUbDYzBMHL\nTQio1dYXiCVimEwmiKI/5UqFNcvyeDzQak0Ih/k8rpIUi6vIhZ6pj6uwDZnJZIJW61Asbx5g93po\ntbkgaCAIIkTRAFE0QKPRQ6MxQRD00GgyAJhBlIFoNANERkiSCZGIBZGIFcBpkGO5NgAFDe92yHGx\nDITD8rUslmsSmNd5IBnZeSQ4bpUK4ww7XFqc7B/l10uF7aJtNht0OhdXpcIEnE6n42KpsKSEnJwc\nGI3/BS+CV7Xa+gIxhWaxWKDV+pCidhQKWDylvLwcRmMxN0EbiciCVqPRNnRiTL1SYe7UoqIiGAw/\nclUqN90ErF2Lhr5OJhDJDxOR/IpGkbLxhEJ5DfEyfkhGdh4JjtuYCguoHc4H6na7QcSvyVBZmfxe\nVFQErbaM23WBGNGi7P5KPX1JPBW8KPLzC6vV1heIKRWz2QyNJvVWEuvAWFVVBY0mP+XXY1CDCic+\nm1Cj4btZ+Ppr+T0SiUCj4df4DQDC4Qy4XHzjKsnIziPBcatUWIDLZmteaQQCARDxazLEChDz8/O5\n0qMD8m4KYJTWqWcNZgq0sLAQosinEBBo3NaXJ5g1ZrFYoNGknkokq6G8ShYC/DZHalDhMKWSlZXF\ntV8NALByDUmSGhjGeSK3gUqKH5KRnUeC41apsD4smZnNB+J5kvABiVXt0Wjqq9rjwWIaqSKJOxQV\nDXqkqKgI0Si/pmBqtfUFYkpFzppJPYtAUQMRRE1NDSIRPmnxQCIVDq8Mu3hCVCK+sTLGdRYIBCCK\nfKj+467e0GGTH5KRnUeC41apOBukS05O8w9bfX19Q9CMD5jAy83NRSjEd/fBgdE6AUypFBYWIhjk\np1TUausLxJITZLdB6l00LFBfVlaGQIBf5pcaVDhsbuXiXXWUitfrhVbLpylZ3NVRX8+XQDMZ2Xkk\nOKYD9b/88gvq6urQs2fPRjnVLLjFets3Bb/fj0iEn/vL75eDnBaLBeGwnBFVWlqqfP/AA6XN/u2R\ngimVtjTeacs4g0HZMsvKykI0GoC8o03tXLNxsqHydpMwS0XeTTcvCI7W7842kmVlNZCko69Umhun\nGlQ48YSokUhijCHVzxFLggoEAtBo2raG2z5GC3elkozsPCLQMYj9+/fTJZdcQpCbVdNJJ51EH374\nYcIxjz76KAEgl8vV7Hnmzr2bgAcolseR+teBA/K1TSY7EZFyD/IrddcVRfm6kUiEAKFVf9vWcf78\ns/y3ubknE/C/lM/toeO02c7k+tt26iSPYefOnZSR0fWoz+ehr2XL5HNdffV1BLyQ8vlkn2/dKn9X\nXHwKAdu4zK0oEkUiRF6vl0TRQED0qM9nc68+feRrbN68mWy2M47qXLb8+oLOPHNw0rLxaCAZ2Xkk\nOObcX5FIBJdeeil27dqFL774Anv37sXIkSNx5ZVXYt++fcpxrCvk4Qp4fL4geLVDZShv8ATl5PBp\nDMYQo9aIcunuBsTcfVarHby5zgBwbesLJPKrSVLqXTQsjir73PlZ3GpQ4UiSnPxhNpuh15vAs7CV\nMcCHQiEIQmro4JuHFV4v3yzGZGTnkeCYUyoff/wxfvrpJ7z//vs477zz0LFjRzz77LPIz8/H4sWL\nlePq6+sbitCaz9YIhfhRdzMwQZuZyYcZmSHGLBvllsHCFGhRUREAfnEVBp5tfYHEYHIkwk+pOBx1\n4Nk3Ri0qHJZRmJfXHsBBbtdlPWv8fj94b0IBM/x+vu6vZGTnkeCYUypr167F4MGD0S2ON0EURQwd\nOhTfffed8pnP54PZfPjMrnCYT+FWPFjPjcLCAq7XZetDbhPK555ZAaR6JID82voCsQC20WiEJKVe\n2LIwYn29FwC/ALJaVDisBjA/vwAAv3RxJkY8Hg+I+BVLyzA2dNjkh2Rk55HgmAvU79ixA927d2/0\neX5+PjZs2KD83+12t5hnLUlR8Nab7IEsLDx8EEynkwOxNpvcMjYvD8jJkXdNWVlyHw2LRV7wRqP8\nb5tNfs/IkD8zGOR3vT4mgMxmM8LhxEUajcruBUmSBaPPJ7/8/lhNwr///W94vV6EQiEEg/Ju1emU\nj/F45KC8wyEHVJ1OWaGw4GpxcS6AmqM4i8khIyMHTqcDrRW4RqNM1FhYKFPLZ2fL8242y+9sjs1m\neY5NpthvIf+9EXv2/AaTKTb3hxYnE8ncUdFoFKGQPN/19bE5D4WgfO71xubZ55PfPR7gpIbmh717\nn45IJBsHD8bWVypxtKhwRFFe0zabnMmWkyOv64wMee1nZMhzazbLrzPPlP9u2bJnEA4XKt/JY/JC\nr9dDq00UWdGonBzDXuGwPK/x//b75bl3u4GqKjmDkH1WVRXj/dLr9cjKygVfjkcdl2LleMyfPx9r\n165N2fmPOaWi1+sRZZV8cQiFQglNZVwuV4t51v/3f6NRXGxDba38kLrdsUXm9cq+Y59PfsjZAmT/\njkblMJogyPEKUZRfBoP80mpj/zeZZGFkswHDhsnXHjXqQgDAww8/jJycHOTk5KCoCCgokB+sQxMv\nQqEQHA4HPB4P6urqUFdXB6/XC6/Xi2AwiPJyH375xY36+nrU19fD7/cjGAwmEN1FIhFIkgQiSji3\nRqOBKIoQRRE6nQ5msxlmsxlGoxFGoxFvv22AxWKBxWKBXq+HwfAisrPt6NTJDpPJBJvNhuzsbGRl\nZcFqtTaK2Tz11ELMmVOPujpZ2TgcsmvM4ZAr4OvqYoKyvl6ecyZUAwH5nc1//Nyz29BoYjEjm80G\ns9kMm82G7t37wOeLwmiUFXFenvwbmEzy/DLlHK8o7PZYVlU0GkV9fT0cDgfcbjf8fj/q6+vh9XqV\nd4dDJgr0+Xzw+Xx4/XU/QqEQfD6f8hsEg0GEw+GEeRcEARqNBhqNBnq9Xplfk8kEg8EAvV4PvV6v\n/BYWiwV2uwXt2lmRkZGBjIwMRCKZcDiysHTpk4owDYVkxVJWJs+vyyULxvJy+XOvV55jl0te50xZ\n+f2xtc4EMBsnEUEQBIiivNlhG43OnU9Cp06ywmTKk22E2JyyeWbvOTmx9/g1TkSor6+Hy+WC2+1W\n3uPndtUqr/J/tsbZb+Lz+RAKhRqtb41GA61Wq7x0Op2ifNi/Y/NrR0FBAU47LRtGoxE2mw2FhYUo\nKCgAoMewYcOwd+8wBIPyOq6okOexslL+d22tPMdOZ2wuA4FYVmDnzp2V55EpNvaKRmVZIoqy7NDp\n5JfJpENeXtvbUkuShNraWtTW1qK6uhput1uZP5fLBafTifr6eoUAMxKJIBqNpqxGBQAEOlQCqYyJ\nEyeivr4eq1evTvh83Lhx8Pv9WLNmDQBg6NCh8Pv92LBhAyZNmgSLxQKz2Qyr1Qqr1YqcnBzk5eUh\nKysL2dnZsNlsyMjIaBCahpQEswOBgCKMAoGAohycTidqampQUVGByspK1NXVobKyEjU1NfB4PHC7\n3UlTYIuiCKvVqggno9EIvV4Po9EIrVYLURSh0WiU+4tGo4hGowrjajgcVh7iQCCAQCCgCMVkIAgC\nsrKykJubqwjJ7Oxs5OXlITMzEzk5OcjNzUVhYSGysrKQmZkJu92uzP+hO80jAREhEokgGAzC5XKh\nuroadXV18Pv9qK2tVZTFoQ9aZWUlDhw4gPLyckRawQppMBhgMpkUQcV+A4PBAJ1Op8w7ESnzLlsq\nIWWDwBRROBxGKBRqtAFoDrm5uWjXrh3y8/MbNihFyM7Oht1uR2FhIQoLC5GZmQmz2YyMjAzYbLaU\nrPNQKAS3260oXofDAYfDgZqaGjgcDtTW1sLpdMLhcKCqqgrV1dVwuVyora1FMEmyNEEQYLFYYDQa\nFYUgB/D1CeubWYORSER5sXmN/zdTTIeDwWCAzWZDu3btUFBQgLy8POTn5yuKqLCwELm5ucjMzERW\nVlbC83e0Co6JCH6/H9XV1aipqUF9fb0iQ2pra1FWVoaKigqUl5fj4MGDytxLUvMMGmwuMzIyYDab\nFRlht9uxcePGozLuRtc81pTK448/jkceeQTl5eWKAJIkCR06dMCMGTNw1113AZBzwj/77DNEo1GU\nl5crgsOfBLOhIAjKTt1kMsFoNCq7GiYgWBCLCQgmlNnOlFkFwWBQobxuSTCLooj8/HxkZmYiLy8P\neXl5sNlsiiWQm5uLjIwMRRizh4k9XBkZGSnrgcB237L7SxbSLpcLfr8fHo8HDocDTqcTbrdbESBe\nrxc+n0/ZKdXW1rY4/0wgs506U4ps/plwbkpAMwUSP6ZkBJXBYFDmz263Iy8vD+3bt0dxcTFyc3OR\nlZUFm80Gk8mkbEqYZcHWiMz3dXRdqUSEQCAAn8+nKB2ZOt2tCGwmVCoqKnDgwAFlR1peXg6fr+UA\nL9tosXswGAzKrp5ZsPECOhwOK/McCAQUwczWRUvX1Gq1yMnJQWZmJvLz8xM2G0xIZ2RkIDMzExkZ\nGcq4mPJgz2Mq5trj8aCiogJOpxOBQABut1vZ6DEPwcGDB1FdXa1s+rzelt1/8ZYo22Qwayl+ruOV\nYDgcRjgcVn5/tqYPt8kxmUwoLCxEUVERioqKkJeXh9zcXBQUFCgbO7vdnrDWbTZbk3N5wQUX4IIL\nLlDk6dHEMadU9uzZgy5duuDZZ5/F9OnTQUR46KGHcN999+HHH39E7969D/v3kiTB6/WipqYGNTU1\nqK2tRV1dHdxuNzwej7JzYdaE3+9PeHjYLpK54ARBaHANyO4jtmiYxmcCy2KxwGazwWq1KrtYu92O\nrKwsRZBlZ2cf1Z36sYhQKKQ8lE6nE3V1dYqrg/WeYMorvj8Fm3829025krRarWItMEVstVoVVwYT\nYMx6Yko6VWysaiMUCsHlcimC0el0wufzKdZvvCspEAgorlS2q2ebJSA2xzqdTpnnQzdbBoMBdrtd\n2fBYLBbFao23XnlRBfFAKBRCVVUVKioqlI1VXV0dfD6foniZPGHzGwwGFWuJvZiLka1jnU4HnU6n\nbBjjFQHzsjCLMzs7W7FKk7U8V69ejeXLl8PpdCrPHXN/SZKEm266KaFY82jimFMqgByHuOeeezBw\n4EBIkoTvvvsOs2fPxhNPPKH20NJII4000jgMjkmlAgDffvstli9fDiLCxIkT8ac//UntIaWRRhpp\npNECjhmlEgqFcM8992Dw4MEYNWpUo+89Hg/efvttfPfdd4hEIliyZIni1qioqMC8efOwY8cOnH76\n6Zg7dy4KC2P9vIkIH374IZYuXYpIJIJJkyZhzJgxrTbTnU4nli1bhp07d6Jbt26YMmVKQy6/jGg0\nijfffBP/+Mc/oNVqMW3aNIwYMSLBZC0vL8fDDz+MHTt24IwzzsDcuXMbsk9iY/3ggw+wdOlSSJKE\nyZMn4//+7/9aFXA9cOAAli1bhgMHDqB3796YNGmSkpf+xRdf4J133oFWq0UwGGyg+xaQk5ODBQsW\nKOf45Zdf8PDDD+PAgQMYPHgwbr311oT+C5Ik4ZVXXsGKFStgNpsxc+ZMnH/++UmPMRqN4sMPP8SG\nDRuQl5eHq666CieffHLCMQ6HA4888gh++OEHlJSU4K677kLHjh0Tjvnyyy/x9NNPw+fzYcyYMZg0\naVKCi7G+vh6PPfYY1q9fj/bt2+POO+/EKaeckvQ4g8EgVqxYga1bt6JDhw4YN25cAmfS/PnzcfDg\nQRARgsEgBEGA3+/H1VdfjZEjRwKQf9MVK1bg1VdfhUajwdSpU3HJJZck/KZVVVWYN28etm3bhtNO\nOw1z585FcXHr+b727duH6dOn46qrrsK1116rfJ7M2ox/jpJZm9deey3GjBnTpmSAH374Abfffjvu\nvvtuZd0cK2vz3nvvRV1dndJKWBAE+Hw+TJ06FX/+85+V+Xzttdfw1ltvwWAwYMaMGbjwwgsTznPw\n4EE89NBD2LVrF3r37o077rgDeXl5CfO5cuVKLFu2DAAwZcoUXH755UnNZyAQwO233w4AiutYFEV4\nPB6Ulpaie/fu+Ne//oVVq1YlzCcAFBcX48EHH1TOtWPHDsyfPx9lZWUYMmQIZs+enVBtH4lE8NJL\nL2HlypXIyMjALbfcgnPPPbf5waWE/KWVqKyspEGDBhEA+tvf/qZ8vn//frr//vtp/vz5dNlll9GI\nESPooosuojvvvJMikQgREf3www+UkZFBJSUlNH36dOrWrRtlZmbSr7/+SkRE0WiUpk6dSgDoyiuv\npAkTJpBOp6PJkye3aozfffcdFRUVUV5eHg0fPpwyMjKouLiYHA4HEcmcW5dddhmJokgTJkygK6+8\nkjQaDd19993KOb7//nuyWq3UtWtXZaxZWVn022+/KWO97rrrCABdddVVylivu+66pMe5evVqslqt\ndPLJJ9OFF15Ier2e+vXrR+FwmIiIXn/9dQJAgwYNoosuuohGjBhBw4cPp6efflo5x3vvvUdarZb6\n9etH06ZNo8LCQiopKaG6ujoiIgqFQnTBBReQXq+nSZMm0aWXXkoAaOHChUmPc8SIEaTRaKh///50\n0kknkVarpX//+9/K97t27aK8vDxq3749TZs2jXr27Ekmk4l++OEH5Zh77rmHANDFF19M1157LZlM\nJho1ahRFo1EiIiovL6eOHTtSbm4u3XjjjXT22WeTKIoJ1zkcfD4f9ejRgwwGAw0cOJDy8/PJZrPR\nL7/8ohwzbNgwysrKogsuuEBZn1deeSV99913REQkSRKNGTOGNBoNjRs3jsaMGUOiKNJtt92mnOOn\nn34iu91OnTt3punTp9Opp55KNpuNfmbkakkiHA7TwIEDCQBdf/31yueHrs2rrrqKNBoN3XXXXcox\n33//PWVkZLR6bU6ZMqVVYyQicjqd1L59ewJAjz76qPL5kaxNp9NJRM2vzQULFiQ9vv79+1NeXp7y\nmw4bNozGjh1L//3vf5V5Hj58OOl0Orrmmmvo8ssvJ0EQ6MEHH1TOsWHDBjKbzXTKKafQ9OnTqXPn\nzpSXl0e///67Mp8TJ04kQRBo7NixNG7cOBJFkWbMmJHUGCORCGVlZVHHjh1p6NChyjgnT55MBxoI\nCJcuXUoAaPDgwQnz+fzzzyvneeONN0gURRo4cCDdeOONlJeXR6eeeip5PB4iIgoEAjRo0CAyGo00\nefJkuvjiiwkALVq0qNmxHRNKZc6cOdS1a1cqLi6m2bNnK59v2bKFANC2bdua/LtoNEq9evWiCy+8\nkPx+PxHJi6p37940adIkIiL617/+RQBo9erVyt99+umnBEBZJMlg8ODBNG7cOGWyd+/eTQBo6dKl\nRES0fPly0mq1tGHDBuVvXnnlFdLpdFRWVkbRaJTOOOMMuuiii5SxBoNB6tWrl6Lg1q5dSwDo/fff\nV87x8ccfH3YODp2PoqIimjNnjqJE2L1++eWXRET01ltvkSAIFAqFmjyH2+2m7OxsuuGGG0iSJCIi\ncjgclJeXR/PnzyciokWLFpHJZKKffvpJ+bunnnqKLBaL8nC3hIcfflj5e0mSaPz48TR8+HDl+6FD\nh1K/fv3I7XYrx7CHh0gm/wNAL730kvI3P/zwAwGgzz//nIiIJkyYQCUlJVRdXa3Mz9WIYXL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       "text": [
        "<matplotlib.figure.Figure at 0x6d1fd30>"
       ]
      }
     ],
     "prompt_number": 19
    }
   ],
   "metadata": {}
  }
 ]
}