Lab1.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Python, Pandas and Matplotlib"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###-1. Preliminary plotting stuff to get things going"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import scipy as sp\n",
      "import matplotlib.pyplot as plt\n",
      "import pandas as pd\n",
      "pd.set_option('display.width', 500)\n",
      "pd.set_option('display.max_columns', 100)\n",
      "import seaborn as sns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###0. Preliminaries about Python\n",
      "\n",
      "In working with python I always remember: a python is a duck.\n",
      "\n",
      "What I mean is, python has a certain way of doing things. For example lets call one of these ways listiness. Listiness works on lists, dictionaries, files, and a general notion of something called an iterator.\n",
      "\n",
      "But first, lets introduce the notion of a comprehension. Its a way of constructing a list"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "alist=[1,2,3,4,5]\n",
      "asquaredlist=[i*i for i in alist]\n",
      "asquaredlist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "[1, 4, 9, 16, 25]"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Python has some nifty functions like `enumerate` and `zip`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "enumerate(asquaredlist)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<enumerate at 0x1090fe3c0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Thats a strange type. But its really a duck."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[the_tuple for the_tuple in enumerate(asquaredlist)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[(0, 1), (1, 4), (2, 9), (3, 16), (4, 25)]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This next one is very usefil in combining lists together..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zip(alist, asquaredlist)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[(1, 1), (2, 4), (3, 9), (4, 16), (5, 25)]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Open files behave like lists too!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "linelengths=[len(line) for line in open(\"data/olive.csv\")]\n",
      "print linelengths"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[88, 49, 49, 48, 48, 49, 48, 48, 49, 49, 50, 50, 50, 50, 49, 49, 50, 49, 50, 49, 49, 50, 49, 50, 50, 50, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 46, 47, 47, 47, 47, 47, 47, 47, 46, 47, 47, 47, 47, 46, 46, 46, 47, 46, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 46, 47, 47, 47, 52, 51, 52, 51, 51, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 52, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 52, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 52, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 52, 53, 53, 53, 53, 53, 52, 52, 53, 53, 53, 53, 53, 53, 53, 52, 52, 53, 53, 52, 53, 53, 52, 53, 53, 52, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 46, 47, 47, 47, 46, 45, 45, 45, 44, 45, 45, 45, 45, 44, 44, 45, 46, 46, 47, 47, 46, 47, 46, 46, 47, 46, 45, 45, 46, 44, 44, 44, 45, 45, 45, 45, 53, 53, 52, 53, 52, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 54, 53, 53, 53, 53, 53, 53, 55, 55, 54, 54, 54, 55, 55, 55, 54, 55, 54, 53, 54, 54, 54, 53, 54, 54, 55, 54, 54, 54, 54, 54, 55, 54, 54, 55, 54, 54, 54, 54, 55, 54, 54, 54, 55, 54, 54, 54, 54, 54, 54, 55, 54, 54, 55, 53, 54, 54, 53, 54, 53, 54, 53, 54, 53, 53, 54, 53, 54, 54, 54, 54, 54, 54, 54, 54, 55, 54, 54, 54, 55, 54, 54, 54, 54, 56, 54, 55, 54, 55, 54, 54, 55, 54, 53, 54, 53, 53, 54, 53, 54, 54, 53, 53, 53, 54, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 51, 50, 50, 50, 50, 50, 50, 50, 51, 50, 51, 52, 50, 51, 51, 51, 51, 50, 51, 50, 50, 50, 51, 50, 50, 51, 50, 51, 51, 50, 49, 50, 50, 50, 50, 50, 50, 51, 51, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 48, 51, 50, 50, 50, 51, 51, 51, 49, 49, 49, 50, 51, 51, 49, 48, 47, 47, 50, 51, 48, 50, 48, 51, 51, 49, 49, 50, 50, 49, 49, 49, 48, 50, 47, 49, 51, 48, 49, 49, 51, 49, 49, 49, 49, 51, 51, 51, 48, 50, 49]\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And so do dictionaries. But know what you are accessing:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "adict={'one':1, 'two': 2, 'three': 3}\n",
      "print [i for i in adict], [(k,v) for k,v in adict.items()], adict.values()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['three', 'two', 'one'] [('three', 3), ('two', 2), ('one', 1)] [3, 2, 1]\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`xrange` is another duck!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mylist=[]\n",
      "for i in xrange(10):\n",
      "    mylist.append(i)\n",
      "mylist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From python 2.7 onwards you can construct dictionaries using dictionary comprehensions:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "{k:v for (k,v) in zip(alist, asquaredlist)}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "{1: 1, 2: 4, 3: 9, 4: 16, 5: 25}"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "create a dictionary with keys the integers upto and including 10, and values the cubes of these dictionaries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "thelist=range(11)\n",
      "{k:v for (k,v) in zip(thelist, [i*i*i for i in thelist])}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "{0: 0,\n",
        " 1: 1,\n",
        " 2: 8,\n",
        " 3: 27,\n",
        " 4: 64,\n",
        " 5: 125,\n",
        " 6: 216,\n",
        " 7: 343,\n",
        " 8: 512,\n",
        " 9: 729,\n",
        " 10: 1000}"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###1. The Olive Oils dataset"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some of the following text is taken from the rggobi book (http://www.ggobi.org/book/). It is an excellent book on visualization and EDA for classification, and is available freely as a pdf from Hollis for those with a Harvard Id. Even though the book uses ggobi, a lot of the same analysis can be done in Mondrian or directly in Matplotlib/Pandas (albeit not interactively)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<hr/>\n",
      "\n",
      "\"The Olive Oils data has eight explanatory variables (levels of fatty acids in the oils) and nine classes (areas of Italy). The goal of the analysis is to develop rules that reliably distinguish oils from the nine different areas. It is a problem of practical interest, because oil from some areas is more highly valued and unscrupulous suppliers sometimes make false claims about the origin of their oil. The content of the oils is a subject of study in its own right: Olive oil has high nutritional value, and some of its constituent fatty acids are considered to be more beneficial than others.\"\n",
      "\n",
      "In addition, fatty acid contents vary with climate: this information is important in deciding which varieties to grow where.\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\"Source: Forina, M., Armanino, C., Lanteri, S. & Tiscornia, E. (1983), Classification of Olive Oils from their Fatty Acid Composition, in Martens, H. and\n",
      "Russwurm Jr., H., eds, Food Research and Data Analysis, Applied Science\n",
      "Publishers, London, pp. 189\u2013214. It was brought to our attention by Glover\n",
      "& Hopke (1992).\n",
      "\n",
      "Number of rows: 572\n",
      "\n",
      "Number of variables: 10\n",
      "\n",
      "Description: This data consists of the percentage composition of fatty acids\n",
      "found in the lipid fraction of Italian olive oils. The data arises from a study\n",
      "to determine the authenticity of an olive oil.\"\n",
      "<hr/>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "Image(filename='data/Italy.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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TKIMCP1V/yshaXfI8cSIMK/tAjqGhKbQjElYbw9rJyQWpatVyEoOu9PR0ZL5w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\nd5VsbLksWBnxIcY6DCPpU4nPU+FvyxAssWOVYUsJP2B5+boTLJFHfON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       "prompt_number": 11,
       "text": [
        "<IPython.core.display.Image at 0x1023552d0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In working with pandas and matplotlib I dont always remember the syntax. A programmer is a good tool for converting Stack Overflow snippets into code. I almost always put what I am trying to do into google and go from there. \n",
      "\n",
      "That said, I found the following links very useful in understanding the Pandas mode, how things work.\n",
      "\n",
      "* http://blog.yhathq.com/posts/R-and-pandas-and-what-ive-learned-about-each.html\n",
      "* http://www.bearrelroll.com/2013/05/python-pandas-tutorial/\n",
      "* http://manishamde.github.io/blog/2013/03/07/pandas-and-python-top-10/"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###2. Loading and Cleaning"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's load the olive oil dataset into a pandas dataframe and have a look at the first 5 rows."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df=pd.read_csv(\"data/olive.csv\")\n",
      "df.head(5)"
     ],
     "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>Unnamed: 0</th>\n",
        "      <th>region</th>\n",
        "      <th>area</th>\n",
        "      <th>palmitic</th>\n",
        "      <th>palmitoleic</th>\n",
        "      <th>stearic</th>\n",
        "      <th>oleic</th>\n",
        "      <th>linoleic</th>\n",
        "      <th>linolenic</th>\n",
        "      <th>arachidic</th>\n",
        "      <th>eicosenoic</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1.North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1075</td>\n",
        "      <td> 75</td>\n",
        "      <td> 226</td>\n",
        "      <td> 7823</td>\n",
        "      <td> 672</td>\n",
        "      <td> 36</td>\n",
        "      <td> 60</td>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2.North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1088</td>\n",
        "      <td> 73</td>\n",
        "      <td> 224</td>\n",
        "      <td> 7709</td>\n",
        "      <td> 781</td>\n",
        "      <td> 31</td>\n",
        "      <td> 61</td>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3.North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  911</td>\n",
        "      <td> 54</td>\n",
        "      <td> 246</td>\n",
        "      <td> 8113</td>\n",
        "      <td> 549</td>\n",
        "      <td> 31</td>\n",
        "      <td> 63</td>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4.North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  966</td>\n",
        "      <td> 57</td>\n",
        "      <td> 240</td>\n",
        "      <td> 7952</td>\n",
        "      <td> 619</td>\n",
        "      <td> 50</td>\n",
        "      <td> 78</td>\n",
        "      <td> 35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5.North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1051</td>\n",
        "      <td> 67</td>\n",
        "      <td> 259</td>\n",
        "      <td> 7771</td>\n",
        "      <td> 672</td>\n",
        "      <td> 50</td>\n",
        "      <td> 80</td>\n",
        "      <td> 46</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "       Unnamed: 0  region  area  palmitic  palmitoleic  stearic  oleic  linoleic  linolenic  arachidic  eicosenoic\n",
        "0  1.North-Apulia       1     1      1075           75      226   7823       672         36         60          29\n",
        "1  2.North-Apulia       1     1      1088           73      224   7709       781         31         61          29\n",
        "2  3.North-Apulia       1     1       911           54      246   8113       549         31         63          29\n",
        "3  4.North-Apulia       1     1       966           57      240   7952       619         50         78          35\n",
        "4  5.North-Apulia       1     1      1051           67      259   7771       672         50         80          46"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's rename that ugly first column. \n",
      "\n",
      "*Hint*: A Google search for 'python pandas dataframe rename' points you at this <a href=\"http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.rename.html\">documentation</a>."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df.columns\n",
      "df.rename(columns={df.columns[0]:'areastring'}, inplace=True)\n",
      "df.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Index([u'Unnamed: 0', u'region', u'area', u'palmitic', u'palmitoleic', u'stearic', u'oleic', u'linoleic', u'linolenic', u'arachidic', u'eicosenoic'], dtype=object)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "Index([u'areastring', u'region', u'area', u'palmitic', u'palmitoleic', u'stearic', u'oleic', u'linoleic', u'linolenic', u'arachidic', u'eicosenoic'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's explore. Which unique regions and areas are contained in the dataset?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'regions\\t', df.region.unique()\n",
      "print 'areas\\t', df.area.unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "regions\t[1 2 3]\n",
        "areas\t[1 2 3 4 5 6 9 7 8]\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's create a *crosstab*ulation or contingency table of the factors.\n",
      "\n",
      "*Hint*: A Google search for 'python pandas cross tabulation' points you at this <a href=\"http://pandas.pydata.org/pandas-docs/stable/reshaping.html#cross-tabulations\">documentation</a>.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.crosstab(df.area, df.region)"
     ],
     "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>region</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>area</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  25</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  56</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 206</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>  36</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>   0</td>\n",
        "      <td> 65</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>   0</td>\n",
        "      <td> 33</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>   0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>   0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>   0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 51</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "region    1   2   3\n",
        "area               \n",
        "1        25   0   0\n",
        "2        56   0   0\n",
        "3       206   0   0\n",
        "4        36   0   0\n",
        "5         0  65   0\n",
        "6         0  33   0\n",
        "7         0   0  50\n",
        "8         0   0  50\n",
        "9         0   0  51"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Do we need to clean the dataset before we can explore it a little more? Let's have a look."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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>areastring</th>\n",
        "      <th>region</th>\n",
        "      <th>area</th>\n",
        "      <th>palmitic</th>\n",
        "      <th>palmitoleic</th>\n",
        "      <th>stearic</th>\n",
        "      <th>oleic</th>\n",
        "      <th>linoleic</th>\n",
        "      <th>linolenic</th>\n",
        "      <th>arachidic</th>\n",
        "      <th>eicosenoic</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1.North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1075</td>\n",
        "      <td> 75</td>\n",
        "      <td> 226</td>\n",
        "      <td> 7823</td>\n",
        "      <td> 672</td>\n",
        "      <td> 36</td>\n",
        "      <td> 60</td>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2.North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1088</td>\n",
        "      <td> 73</td>\n",
        "      <td> 224</td>\n",
        "      <td> 7709</td>\n",
        "      <td> 781</td>\n",
        "      <td> 31</td>\n",
        "      <td> 61</td>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3.North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  911</td>\n",
        "      <td> 54</td>\n",
        "      <td> 246</td>\n",
        "      <td> 8113</td>\n",
        "      <td> 549</td>\n",
        "      <td> 31</td>\n",
        "      <td> 63</td>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4.North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  966</td>\n",
        "      <td> 57</td>\n",
        "      <td> 240</td>\n",
        "      <td> 7952</td>\n",
        "      <td> 619</td>\n",
        "      <td> 50</td>\n",
        "      <td> 78</td>\n",
        "      <td> 35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5.North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1051</td>\n",
        "      <td> 67</td>\n",
        "      <td> 259</td>\n",
        "      <td> 7771</td>\n",
        "      <td> 672</td>\n",
        "      <td> 50</td>\n",
        "      <td> 80</td>\n",
        "      <td> 46</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "       areastring  region  area  palmitic  palmitoleic  stearic  oleic  linoleic  linolenic  arachidic  eicosenoic\n",
        "0  1.North-Apulia       1     1      1075           75      226   7823       672         36         60          29\n",
        "1  2.North-Apulia       1     1      1088           73      224   7709       781         31         61          29\n",
        "2  3.North-Apulia       1     1       911           54      246   8113       549         31         63          29\n",
        "3  4.North-Apulia       1     1       966           57      240   7952       619         50         78          35\n",
        "4  5.North-Apulia       1     1      1051           67      259   7771       672         50         80          46"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's get rid of the junk numbering in `df.areastring`. For single column Pandas Series we use `map`. Here's the <a href=\"http://pandas.pydata.org/pandas-docs/dev/generated/pandas.Series.map.html\">documentation</a>."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.areastring=df.areastring.map(lambda x: x.split('.')[-1])\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>areastring</th>\n",
        "      <th>region</th>\n",
        "      <th>area</th>\n",
        "      <th>palmitic</th>\n",
        "      <th>palmitoleic</th>\n",
        "      <th>stearic</th>\n",
        "      <th>oleic</th>\n",
        "      <th>linoleic</th>\n",
        "      <th>linolenic</th>\n",
        "      <th>arachidic</th>\n",
        "      <th>eicosenoic</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1075</td>\n",
        "      <td> 75</td>\n",
        "      <td> 226</td>\n",
        "      <td> 7823</td>\n",
        "      <td> 672</td>\n",
        "      <td> 36</td>\n",
        "      <td> 60</td>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1088</td>\n",
        "      <td> 73</td>\n",
        "      <td> 224</td>\n",
        "      <td> 7709</td>\n",
        "      <td> 781</td>\n",
        "      <td> 31</td>\n",
        "      <td> 61</td>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  911</td>\n",
        "      <td> 54</td>\n",
        "      <td> 246</td>\n",
        "      <td> 8113</td>\n",
        "      <td> 549</td>\n",
        "      <td> 31</td>\n",
        "      <td> 63</td>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  966</td>\n",
        "      <td> 57</td>\n",
        "      <td> 240</td>\n",
        "      <td> 7952</td>\n",
        "      <td> 619</td>\n",
        "      <td> 50</td>\n",
        "      <td> 78</td>\n",
        "      <td> 35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1051</td>\n",
        "      <td> 67</td>\n",
        "      <td> 259</td>\n",
        "      <td> 7771</td>\n",
        "      <td> 672</td>\n",
        "      <td> 50</td>\n",
        "      <td> 80</td>\n",
        "      <td> 46</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "     areastring  region  area  palmitic  palmitoleic  stearic  oleic  linoleic  linolenic  arachidic  eicosenoic\n",
        "0  North-Apulia       1     1      1075           75      226   7823       672         36         60          29\n",
        "1  North-Apulia       1     1      1088           73      224   7709       781         31         61          29\n",
        "2  North-Apulia       1     1       911           54      246   8113       549         31         63          29\n",
        "3  North-Apulia       1     1       966           57      240   7952       619         50         78          35\n",
        "4  North-Apulia       1     1      1051           67      259   7771       672         50         80          46"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To access a specific subset of columns of a dataframe, we can use list indexing."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[['palmitic','oleic']].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>palmitic</th>\n",
        "      <th>oleic</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1075</td>\n",
        "      <td> 7823</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1088</td>\n",
        "      <td> 7709</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  911</td>\n",
        "      <td> 8113</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  966</td>\n",
        "      <td> 7952</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1051</td>\n",
        "      <td> 7771</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "   palmitic  oleic\n",
        "0      1075   7823\n",
        "1      1088   7709\n",
        "2       911   8113\n",
        "3       966   7952\n",
        "4      1051   7771"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice that this returns a new object of type DataFrame."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To access the series of entries of a single column, we could do the following."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df['palmitic']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0     1075\n",
        "1     1088\n",
        "2      911\n",
        "3      966\n",
        "4     1051\n",
        "5      911\n",
        "6      922\n",
        "7     1100\n",
        "8     1082\n",
        "9     1037\n",
        "10    1051\n",
        "11    1036\n",
        "12    1074\n",
        "13     875\n",
        "14     952\n",
        "...\n",
        "557    1010\n",
        "558    1020\n",
        "559    1120\n",
        "560    1090\n",
        "561    1100\n",
        "562    1090\n",
        "563    1150\n",
        "564    1110\n",
        "565    1010\n",
        "566    1070\n",
        "567    1280\n",
        "568    1060\n",
        "569    1010\n",
        "570     990\n",
        "571     960\n",
        "Name: palmitic, Length: 572, dtype: int64\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice the difference in the syntax. In the first example where we used list indexing we got a new DataFrame. In the second example we got a series corresponding to the column. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"type of df[['palmitic']]:\\t\", type(df[['palmitic']]) \n",
      "print \"type of df['palmitic']:\\t\\t\", type(df['palmitic'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "type of df[['palmitic']]:\t<class 'pandas.core.frame.DataFrame'>\n",
        "type of df['palmitic']:\t\t<class 'pandas.core.series.Series'>\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To access the series of values of a single column more conveniently, we can do this:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.palmitic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "0     1075\n",
        "1     1088\n",
        "2      911\n",
        "3      966\n",
        "4     1051\n",
        "5      911\n",
        "6      922\n",
        "7     1100\n",
        "8     1082\n",
        "9     1037\n",
        "10    1051\n",
        "11    1036\n",
        "12    1074\n",
        "13     875\n",
        "14     952\n",
        "...\n",
        "557    1010\n",
        "558    1020\n",
        "559    1120\n",
        "560    1090\n",
        "561    1100\n",
        "562    1090\n",
        "563    1150\n",
        "564    1110\n",
        "565    1010\n",
        "566    1070\n",
        "567    1280\n",
        "568    1060\n",
        "569    1010\n",
        "570     990\n",
        "571     960\n",
        "Name: palmitic, Length: 572, dtype: int64"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get the unique areastrings of the dataframe `df`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.areastring.unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "array(['North-Apulia', 'Calabria', 'South-Apulia', 'Sicily',\n",
        "       'Inland-Sardinia', 'Coast-Sardinia', 'Umbria', 'East-Liguria',\n",
        "       'West-Liguria'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a new dataframe `dfsub` by taking the list of acids and using pandas' `apply` function to divide all values by 100.\n",
      "\n",
      "If you're not familiar with pandas' `apply` function, a Google search for 'python pandas dataframe apply' points you to the <a href=\"http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.apply.html\">documentation</a>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "acidlist=['palmitic', 'palmitoleic', 'stearic', 'oleic', 'linoleic', 'linolenic', 'arachidic', 'eicosenoic']\n",
      "\n",
      "#your code here\n",
      "\n",
      "dfsub=df[acidlist].apply(lambda x: x/100.0)\n",
      "dfsub.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>palmitic</th>\n",
        "      <th>palmitoleic</th>\n",
        "      <th>stearic</th>\n",
        "      <th>oleic</th>\n",
        "      <th>linoleic</th>\n",
        "      <th>linolenic</th>\n",
        "      <th>arachidic</th>\n",
        "      <th>eicosenoic</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 10.75</td>\n",
        "      <td> 0.75</td>\n",
        "      <td> 2.26</td>\n",
        "      <td> 78.23</td>\n",
        "      <td> 6.72</td>\n",
        "      <td> 0.36</td>\n",
        "      <td> 0.60</td>\n",
        "      <td> 0.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 10.88</td>\n",
        "      <td> 0.73</td>\n",
        "      <td> 2.24</td>\n",
        "      <td> 77.09</td>\n",
        "      <td> 7.81</td>\n",
        "      <td> 0.31</td>\n",
        "      <td> 0.61</td>\n",
        "      <td> 0.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  9.11</td>\n",
        "      <td> 0.54</td>\n",
        "      <td> 2.46</td>\n",
        "      <td> 81.13</td>\n",
        "      <td> 5.49</td>\n",
        "      <td> 0.31</td>\n",
        "      <td> 0.63</td>\n",
        "      <td> 0.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  9.66</td>\n",
        "      <td> 0.57</td>\n",
        "      <td> 2.40</td>\n",
        "      <td> 79.52</td>\n",
        "      <td> 6.19</td>\n",
        "      <td> 0.50</td>\n",
        "      <td> 0.78</td>\n",
        "      <td> 0.35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 10.51</td>\n",
        "      <td> 0.67</td>\n",
        "      <td> 2.59</td>\n",
        "      <td> 77.71</td>\n",
        "      <td> 6.72</td>\n",
        "      <td> 0.50</td>\n",
        "      <td> 0.80</td>\n",
        "      <td> 0.46</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "   palmitic  palmitoleic  stearic  oleic  linoleic  linolenic  arachidic  eicosenoic\n",
        "0     10.75         0.75     2.26  78.23      6.72       0.36       0.60        0.29\n",
        "1     10.88         0.73     2.24  77.09      7.81       0.31       0.61        0.29\n",
        "2      9.11         0.54     2.46  81.13      5.49       0.31       0.63        0.29\n",
        "3      9.66         0.57     2.40  79.52      6.19       0.50       0.78        0.35\n",
        "4     10.51         0.67     2.59  77.71      6.72       0.50       0.80        0.46"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice that we can replace part of the dataframe by this new frame. Since we need the percentages, let's do this. The `Oleic` percentages should be in the 70s and 80s if you did this right."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[acidlist]=dfsub\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>areastring</th>\n",
        "      <th>region</th>\n",
        "      <th>area</th>\n",
        "      <th>palmitic</th>\n",
        "      <th>palmitoleic</th>\n",
        "      <th>stearic</th>\n",
        "      <th>oleic</th>\n",
        "      <th>linoleic</th>\n",
        "      <th>linolenic</th>\n",
        "      <th>arachidic</th>\n",
        "      <th>eicosenoic</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10.75</td>\n",
        "      <td> 0.75</td>\n",
        "      <td> 2.26</td>\n",
        "      <td> 78.23</td>\n",
        "      <td> 6.72</td>\n",
        "      <td> 0.36</td>\n",
        "      <td> 0.60</td>\n",
        "      <td> 0.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10.88</td>\n",
        "      <td> 0.73</td>\n",
        "      <td> 2.24</td>\n",
        "      <td> 77.09</td>\n",
        "      <td> 7.81</td>\n",
        "      <td> 0.31</td>\n",
        "      <td> 0.61</td>\n",
        "      <td> 0.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  9.11</td>\n",
        "      <td> 0.54</td>\n",
        "      <td> 2.46</td>\n",
        "      <td> 81.13</td>\n",
        "      <td> 5.49</td>\n",
        "      <td> 0.31</td>\n",
        "      <td> 0.63</td>\n",
        "      <td> 0.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  9.66</td>\n",
        "      <td> 0.57</td>\n",
        "      <td> 2.40</td>\n",
        "      <td> 79.52</td>\n",
        "      <td> 6.19</td>\n",
        "      <td> 0.50</td>\n",
        "      <td> 0.78</td>\n",
        "      <td> 0.35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10.51</td>\n",
        "      <td> 0.67</td>\n",
        "      <td> 2.59</td>\n",
        "      <td> 77.71</td>\n",
        "      <td> 6.72</td>\n",
        "      <td> 0.50</td>\n",
        "      <td> 0.80</td>\n",
        "      <td> 0.46</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "     areastring  region  area  palmitic  palmitoleic  stearic  oleic  linoleic  linolenic  arachidic  eicosenoic\n",
        "0  North-Apulia       1     1     10.75         0.75     2.26  78.23      6.72       0.36       0.60        0.29\n",
        "1  North-Apulia       1     1     10.88         0.73     2.24  77.09      7.81       0.31       0.61        0.29\n",
        "2  North-Apulia       1     1      9.11         0.54     2.46  81.13      5.49       0.31       0.63        0.29\n",
        "3  North-Apulia       1     1      9.66         0.57     2.40  79.52      6.19       0.50       0.78        0.35\n",
        "4  North-Apulia       1     1     10.51         0.67     2.59  77.71      6.72       0.50       0.80        0.46"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.palmitic.mean(), df.palmitic.std(), df.palmitic.var()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "(12.317412587412583, 1.6859226405632937, 2.8423351499639087)"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.palmitic.std(), np.sqrt(df.palmitic.var())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "(1.6859226405632937, 1.6859226405632937)"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.std(df.palmitic), np.mean(df.palmitic), np.var(df.palmitic)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "(1.6844482872943494, 12.317412587412583, 2.837366032568867)"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.sqrt(np.var(df.palmitic))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "1.6844482872943494"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[acidlist].corr()"
     ],
     "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>palmitic</th>\n",
        "      <th>palmitoleic</th>\n",
        "      <th>stearic</th>\n",
        "      <th>oleic</th>\n",
        "      <th>linoleic</th>\n",
        "      <th>linolenic</th>\n",
        "      <th>arachidic</th>\n",
        "      <th>eicosenoic</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>palmitic</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.835605</td>\n",
        "      <td>-0.170392</td>\n",
        "      <td>-0.837335</td>\n",
        "      <td> 0.460684</td>\n",
        "      <td> 0.319327</td>\n",
        "      <td> 0.228299</td>\n",
        "      <td> 0.501952</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>palmitoleic</th>\n",
        "      <td> 0.835605</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.222185</td>\n",
        "      <td>-0.852438</td>\n",
        "      <td> 0.621627</td>\n",
        "      <td> 0.093112</td>\n",
        "      <td> 0.085481</td>\n",
        "      <td> 0.416350</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>stearic</th>\n",
        "      <td>-0.170392</td>\n",
        "      <td>-0.222185</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.113599</td>\n",
        "      <td>-0.197817</td>\n",
        "      <td> 0.018917</td>\n",
        "      <td>-0.040979</td>\n",
        "      <td> 0.140377</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>oleic</th>\n",
        "      <td>-0.837335</td>\n",
        "      <td>-0.852438</td>\n",
        "      <td> 0.113599</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.850318</td>\n",
        "      <td>-0.218171</td>\n",
        "      <td>-0.319962</td>\n",
        "      <td>-0.424146</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>linoleic</th>\n",
        "      <td> 0.460684</td>\n",
        "      <td> 0.621627</td>\n",
        "      <td>-0.197817</td>\n",
        "      <td>-0.850318</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.057439</td>\n",
        "      <td> 0.210973</td>\n",
        "      <td> 0.089045</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>linolenic</th>\n",
        "      <td> 0.319327</td>\n",
        "      <td> 0.093112</td>\n",
        "      <td> 0.018917</td>\n",
        "      <td>-0.218171</td>\n",
        "      <td>-0.057439</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.620236</td>\n",
        "      <td> 0.578319</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>arachidic</th>\n",
        "      <td> 0.228299</td>\n",
        "      <td> 0.085481</td>\n",
        "      <td>-0.040979</td>\n",
        "      <td>-0.319962</td>\n",
        "      <td> 0.210973</td>\n",
        "      <td> 0.620236</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.328663</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>eicosenoic</th>\n",
        "      <td> 0.501952</td>\n",
        "      <td> 0.416350</td>\n",
        "      <td> 0.140377</td>\n",
        "      <td>-0.424146</td>\n",
        "      <td> 0.089045</td>\n",
        "      <td> 0.578319</td>\n",
        "      <td> 0.328663</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "             palmitic  palmitoleic   stearic     oleic  linoleic  linolenic  arachidic  eicosenoic\n",
        "palmitic     1.000000     0.835605 -0.170392 -0.837335  0.460684   0.319327   0.228299    0.501952\n",
        "palmitoleic  0.835605     1.000000 -0.222185 -0.852438  0.621627   0.093112   0.085481    0.416350\n",
        "stearic     -0.170392    -0.222185  1.000000  0.113599 -0.197817   0.018917  -0.040979    0.140377\n",
        "oleic       -0.837335    -0.852438  0.113599  1.000000 -0.850318  -0.218171  -0.319962   -0.424146\n",
        "linoleic     0.460684     0.621627 -0.197817 -0.850318  1.000000  -0.057439   0.210973    0.089045\n",
        "linolenic    0.319327     0.093112  0.018917 -0.218171 -0.057439   1.000000   0.620236    0.578319\n",
        "arachidic    0.228299     0.085481 -0.040979 -0.319962  0.210973   0.620236   1.000000    0.328663\n",
        "eicosenoic   0.501952     0.416350  0.140377 -0.424146  0.089045   0.578319   0.328663    1.000000"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[acidlist].cov()"
     ],
     "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>palmitic</th>\n",
        "      <th>palmitoleic</th>\n",
        "      <th>stearic</th>\n",
        "      <th>oleic</th>\n",
        "      <th>linoleic</th>\n",
        "      <th>linolenic</th>\n",
        "      <th>arachidic</th>\n",
        "      <th>eicosenoic</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>palmitic</th>\n",
        "      <td> 2.842335</td>\n",
        "      <td> 0.739522</td>\n",
        "      <td>-0.105556</td>\n",
        "      <td> -5.728752</td>\n",
        "      <td> 1.885769</td>\n",
        "      <td> 0.069818</td>\n",
        "      <td> 0.084793</td>\n",
        "      <td> 0.119180</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>palmitoleic</th>\n",
        "      <td> 0.739522</td>\n",
        "      <td> 0.275566</td>\n",
        "      <td>-0.042857</td>\n",
        "      <td> -1.815928</td>\n",
        "      <td> 0.792300</td>\n",
        "      <td> 0.006339</td>\n",
        "      <td> 0.009886</td>\n",
        "      <td> 0.030781</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>stearic</th>\n",
        "      <td>-0.105556</td>\n",
        "      <td>-0.042857</td>\n",
        "      <td> 0.135019</td>\n",
        "      <td>  0.169392</td>\n",
        "      <td>-0.176485</td>\n",
        "      <td> 0.000901</td>\n",
        "      <td>-0.003317</td>\n",
        "      <td> 0.007264</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>oleic</th>\n",
        "      <td>-5.728752</td>\n",
        "      <td>-1.815928</td>\n",
        "      <td> 0.169392</td>\n",
        "      <td> 16.468194</td>\n",
        "      <td>-8.378221</td>\n",
        "      <td>-0.114820</td>\n",
        "      <td>-0.286050</td>\n",
        "      <td>-0.242406</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>linoleic</th>\n",
        "      <td> 1.885769</td>\n",
        "      <td> 0.792300</td>\n",
        "      <td>-0.176485</td>\n",
        "      <td> -8.378221</td>\n",
        "      <td> 5.895146</td>\n",
        "      <td>-0.018086</td>\n",
        "      <td> 0.112848</td>\n",
        "      <td> 0.030448</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>linolenic</th>\n",
        "      <td> 0.069818</td>\n",
        "      <td> 0.006339</td>\n",
        "      <td> 0.000901</td>\n",
        "      <td> -0.114820</td>\n",
        "      <td>-0.018086</td>\n",
        "      <td> 0.016819</td>\n",
        "      <td> 0.017720</td>\n",
        "      <td> 0.010563</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>arachidic</th>\n",
        "      <td> 0.084793</td>\n",
        "      <td> 0.009886</td>\n",
        "      <td>-0.003317</td>\n",
        "      <td> -0.286050</td>\n",
        "      <td> 0.112848</td>\n",
        "      <td> 0.017720</td>\n",
        "      <td> 0.048533</td>\n",
        "      <td> 0.010197</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>eicosenoic</th>\n",
        "      <td> 0.119180</td>\n",
        "      <td> 0.030781</td>\n",
        "      <td> 0.007264</td>\n",
        "      <td> -0.242406</td>\n",
        "      <td> 0.030448</td>\n",
        "      <td> 0.010563</td>\n",
        "      <td> 0.010197</td>\n",
        "      <td> 0.019834</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "             palmitic  palmitoleic   stearic      oleic  linoleic  linolenic  arachidic  eicosenoic\n",
        "palmitic     2.842335     0.739522 -0.105556  -5.728752  1.885769   0.069818   0.084793    0.119180\n",
        "palmitoleic  0.739522     0.275566 -0.042857  -1.815928  0.792300   0.006339   0.009886    0.030781\n",
        "stearic     -0.105556    -0.042857  0.135019   0.169392 -0.176485   0.000901  -0.003317    0.007264\n",
        "oleic       -5.728752    -1.815928  0.169392  16.468194 -8.378221  -0.114820  -0.286050   -0.242406\n",
        "linoleic     1.885769     0.792300 -0.176485  -8.378221  5.895146  -0.018086   0.112848    0.030448\n",
        "linolenic    0.069818     0.006339  0.000901  -0.114820 -0.018086   0.016819   0.017720    0.010563\n",
        "arachidic    0.084793     0.009886 -0.003317  -0.286050  0.112848   0.017720   0.048533    0.010197\n",
        "eicosenoic   0.119180     0.030781  0.007264  -0.242406  0.030448   0.010563   0.010197    0.019834"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[acidlist].median(), df[acidlist].mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "(palmitic       12.010\n",
        "palmitoleic     1.100\n",
        "stearic         2.230\n",
        "oleic          73.025\n",
        "linoleic       10.300\n",
        "linolenic       0.330\n",
        "arachidic       0.610\n",
        "eicosenoic      0.170\n",
        "dtype: float64,\n",
        " palmitic       12.317413\n",
        "palmitoleic     1.260944\n",
        "stearic         2.288654\n",
        "oleic          73.117483\n",
        "linoleic        9.805280\n",
        "linolenic       0.318881\n",
        "arachidic       0.580979\n",
        "eicosenoic      0.162815\n",
        "dtype: float64)"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###3. Quick Intro to Numpy and Matplotlib"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is just a quick and dirty intro. Please read the excellent tutorials <a href=\"http://nbviewer.ipython.org/urls/raw.github.com/jrjohansson/scientific-python-lectures/master/Lecture-2-Numpy.ipynb\">here</a> and <a href=\"http://nbviewer.ipython.org/urls/raw.github.com/jrjohansson/scientific-python-lectures/master/Lecture-4-Matplotlib.ipynb\">here</a>."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Lets start with numpy"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "v = np.array([1,2,3,4])\n",
      "v.shape, type(v)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "((4,), numpy.ndarray)"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "v"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "array([1, 2, 3, 4])"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "v.T, v.T.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "(array([1, 2, 3, 4]), (4,))"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for j in v:\n",
      "    print j"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1\n",
        "2\n",
        "3\n",
        "4\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M = np.array([[1, 2], [3, 4]])\n",
      "M"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "array([[1, 2],\n",
        "       [3, 4]])"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M.T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "array([[1, 3],\n",
        "       [2, 4]])"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(M), M.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "(numpy.ndarray, (2, 2))"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M32=np.array([[1,2],[3,4],[5,6]])\n",
      "M32"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "array([[1, 2],\n",
        "       [3, 4],\n",
        "       [5, 6]])"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M32.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "(3, 2)"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M23=M32.T\n",
      "M23"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "array([[1, 3, 5],\n",
        "       [2, 4, 6]])"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M23.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "(2, 3)"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.palmitic.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "array([ 10.75,  10.88,   9.11,   9.66,  10.51,   9.11,   9.22,  11.  ,\n",
        "        10.82,  10.37,  10.51,  10.36,  10.74,   8.75,   9.52,  11.55,\n",
        "         9.43,  12.78,   9.61,   9.52,  10.74,   9.95,  10.56,  10.65,\n",
        "        10.65,  13.15,  13.21,  13.59,  13.78,  12.95,  12.75,  13.36,\n",
        "        13.09,  13.4 ,  12.99,  12.21,  12.45,  12.85,  12.48,  13.56,\n",
        "        12.6 ,  12.61,  13.04,  13.44,  13.23,  12.92,  12.54,  13.12,\n",
        "        12.13,  13.59,  12.66,  12.98,  12.72,  12.78,  11.84,  13.82,\n",
        "        11.83,  12.61,  11.98,  12.25,  13.39,  11.32,  13.81,  14.09,\n",
        "        13.06,  13.72,  13.36,  14.01,  13.9 ,  14.32,  14.12,  13.66,\n",
        "        13.83,  12.83,  12.96,  12.87,  13.51,  12.41,  12.67,  12.35,\n",
        "        12.55,  14.54,  13.47,  13.64,  14.1 ,  13.84,  14.12,  14.1 ,\n",
        "        15.09,  13.17,  12.86,  12.73,  14.63,  13.99,  14.13,  13.69,\n",
        "        14.88,  13.23,  13.11,  12.86,  13.8 ,  13.94,  13.24,  12.9 ,\n",
        "        13.61,  13.87,  13.69,  13.03,  13.46,  13.69,  13.05,  13.51,\n",
        "        12.83,  14.49,  13.1 ,  13.6 ,  13.  ,  13.68,  12.07,  13.48,\n",
        "        13.34,  13.01,  12.26,  12.01,  12.97,  12.48,  13.35,  12.19,\n",
        "        13.18,  12.64,  12.01,  12.52,  12.73,  13.51,  13.36,  14.99,\n",
        "        14.25,  13.58,  13.46,  13.92,  13.11,  13.14,  14.09,  13.42,\n",
        "        13.87,  14.13,  14.3 ,  13.36,  13.72,  13.3 ,  14.12,  13.21,\n",
        "        14.01,  14.01,  14.57,  14.51,  14.38,  14.62,  15.29,  15.1 ,\n",
        "        14.37,  13.27,  14.38,  14.47,  13.55,  13.69,  14.71,  14.56,\n",
        "        13.14,  14.08,  13.97,  14.13,  15.39,  13.04,  13.41,  15.08,\n",
        "        15.15,  12.62,  13.07,  12.94,  14.6 ,  14.76,  14.82,  13.88,\n",
        "        13.67,  12.72,  13.23,  12.06,  13.83,  15.21,  13.5 ,  14.22,\n",
        "        12.98,  14.47,  13.47,  13.39,  13.88,  15.27,  14.95,  14.87,\n",
        "        13.99,  14.89,  13.39,  14.82,  14.34,  13.47,  13.4 ,  14.53,\n",
        "        13.06,  13.49,  12.54,  11.68,  13.46,  13.9 ,  12.83,  12.14,\n",
        "        14.91,  14.79,  14.45,  14.39,  13.87,  14.26,  14.51,  14.93,\n",
        "        14.19,  13.42,  13.49,  14.4 ,  14.6 ,  12.49,  13.48,  13.41,\n",
        "        13.98,  14.54,  13.34,  14.38,  13.03,  13.23,  14.17,  13.6 ,\n",
        "        14.2 ,  14.72,  13.68,  13.67,  14.03,  14.13,  12.01,  13.59,\n",
        "        15.18,  14.34,  13.67,  14.61,  13.68,  14.19,  15.14,  13.28,\n",
        "        14.69,  12.22,  16.39,  13.45,  13.39,  11.94,  11.12,  12.22,\n",
        "        11.36,   9.26,  11.05,  11.09,  12.84,  11.2 ,   9.16,   9.05,\n",
        "        12.06,  14.57,  13.27,  13.03,  14.44,  15.05,  14.29,  14.91,\n",
        "        13.93,  14.04,  12.22,  11.53,  11.69,  13.69,   9.93,   9.8 ,\n",
        "         9.67,  11.28,  11.88,  12.57,  12.62,  12.83,  12.63,  13.69,\n",
        "        13.53,  11.87,  17.32,  16.2 ,  15.43,  14.98,  13.99,  12.93,\n",
        "        14.2 ,  17.21,  17.42,  13.91,  15.17,  12.69,  15.77,  15.9 ,\n",
        "        16.21,  17.53,  16.79,  14.19,  16.93,  16.92,  16.38,  14.97,\n",
        "        14.42,  16.8 ,  14.63,  11.29,  10.42,  11.03,  11.18,  10.52,\n",
        "        11.16,  11.08,  11.29,  10.85,  11.04,  10.98,  11.35,  11.58,\n",
        "        11.33,  10.95,  12.01,  12.13,  11.08,  10.75,  10.59,  11.76,\n",
        "        11.59,  11.32,  11.07,  10.92,  11.19,  11.06,  10.47,  11.65,\n",
        "        11.58,  10.95,  11.76,  11.03,  11.12,  10.91,  10.8 ,  10.51,\n",
        "        10.96,  11.42,  10.47,  11.14,  11.4 ,  10.75,  10.92,  10.76,\n",
        "        11.78,  10.95,  11.66,  11.54,  11.77,  11.6 ,  11.22,  11.32,\n",
        "        10.96,  11.31,  11.84,  11.35,  10.84,  10.86,  11.4 ,  11.38,\n",
        "        11.59,  10.51,  10.48,  10.61,  11.05,  11.45,  10.49,  11.05,\n",
        "        10.3 ,  10.7 ,  11.03,  10.4 ,  11.  ,  11.18,  10.65,  11.31,\n",
        "        10.8 ,  10.75,  10.4 ,  11.28,  10.6 ,  11.03,  11.1 ,  10.91,\n",
        "        10.94,  11.31,  11.75,  10.76,  11.2 ,  11.52,  11.41,  10.98,\n",
        "        11.26,  10.87,  11.15,  11.78,  11.62,  10.85,  10.85,  10.9 ,\n",
        "        10.8 ,  10.9 ,  11.05,  10.6 ,  10.5 ,  11.  ,  10.65,  10.85,\n",
        "        10.8 ,  10.85,  10.75,  10.9 ,  10.95,  10.9 ,  10.95,  10.9 ,\n",
        "        10.95,  10.95,  10.8 ,  10.9 ,  11.05,  11.1 ,  10.75,  10.75,\n",
        "        10.65,  10.7 ,  10.7 ,  11.  ,  10.75,  10.5 ,  10.9 ,  10.5 ,\n",
        "        10.75,  10.98,  11.05,  11.1 ,  10.58,  11.15,  11.05,  10.72,\n",
        "        11.1 ,  11.1 ,  10.55,  11.  ,  11.05,  10.95,  11.1 ,  10.92,\n",
        "        12.9 ,  11.7 ,  11.  ,  11.2 ,  11.6 ,  12.  ,  11.4 ,  12.2 ,\n",
        "        11.8 ,  12.1 ,  12.2 ,  11.8 ,  11.6 ,  11.3 ,  10.8 ,  10.9 ,\n",
        "        10.2 ,  10.9 ,  11.2 ,  10.8 ,  11.6 ,  11.  ,  10.5 ,  10.9 ,\n",
        "        11.2 ,  11.2 ,  11.9 ,  11.7 ,  11.2 ,  11.9 ,  14.  ,  13.5 ,\n",
        "        10.9 ,  11.5 ,  12.4 ,  12.2 ,  11.8 ,  11.7 ,  11.7 ,  11.8 ,\n",
        "        12.  ,  11.4 ,  11.6 ,  11.3 ,  11.5 ,  11.1 ,  11.5 ,  11.8 ,\n",
        "        10.2 ,   6.1 ,  11.9 ,  11.1 ,  10.2 ,  10.7 ,  10.1 ,  10.6 ,\n",
        "        10.6 ,  10.3 ,  11.2 ,  10.3 ,  10.7 ,  11.4 ,  10.9 ,   9.8 ,\n",
        "         9.8 ,   9.6 ,   9.9 ,  10.6 ,  12.4 ,  10.6 ,  10.2 ,   9.7 ,\n",
        "        11.8 ,  10.6 ,   9.9 ,  10.1 ,  10.4 ,  10.4 ,  10.2 ,  10.2 ,\n",
        "        10.1 ,   9.2 ,  10.3 ,   9.6 ,  10.3 ,  10.1 ,  10.2 ,  11.2 ,\n",
        "        10.9 ,  11.  ,  10.9 ,  11.5 ,  11.1 ,  10.1 ,  10.7 ,  12.8 ,\n",
        "        10.6 ,  10.1 ,   9.9 ,   9.6 ])"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[['palmitic', 'palmitoleic']].values, df[['palmitic', 'palmitoleic']].values.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "(array([[ 10.75,   0.75],\n",
        "       [ 10.88,   0.73],\n",
        "       [  9.11,   0.54],\n",
        "       ..., \n",
        "       [ 10.1 ,   0.9 ],\n",
        "       [  9.9 ,   1.2 ],\n",
        "       [  9.6 ,   0.8 ]]),\n",
        " (572, 2))"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.zeros(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.ones(10).reshape(5,2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "array([[ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.],\n",
        "       [ 1.,  1.]])"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "myrandom=np.random.rand(5,3)\n",
      "print myrandom"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.05230491  0.91185191  0.20743992]\n",
        " [ 0.74409837  0.44205873  0.47194589]\n",
        " [ 0.42524805  0.84568696  0.7758562 ]\n",
        " [ 0.08106272  0.53397608  0.21833691]\n",
        " [ 0.85845655  0.76218189  0.07040454]]\n"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "myrandom[0], myrandom[-1], myrandom[0][0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "(array([ 0.05230491,  0.91185191,  0.20743992]),\n",
        " array([ 0.85845655,  0.76218189,  0.07040454]),\n",
        " 0.052304912419592231)"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "myrandom[:,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "array([ 0.05230491,  0.74409837,  0.42524805,  0.08106272,  0.85845655])"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "myrandom[0,:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "array([ 0.05230491,  0.91185191,  0.20743992])"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Moving on to matplotlib"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "current_palette = sns.color_palette()\n",
      "print current_palette\n",
      "sns.palplot(current_palette)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[(0.2980392156862745, 0.4470588235294118, 0.6901960784313725), (0.3333333333333333, 0.6588235294117647, 0.40784313725490196), (0.7686274509803922, 0.3058823529411765, 0.3215686274509804), (0.5058823529411764, 0.4470588235294118, 0.6980392156862745), (0.8, 0.7254901960784313, 0.4549019607843137), (0.39215686274509803, 0.7098039215686275, 0.803921568627451)]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nINEFCBJdgCDRBQgSXYAg0QUI+gPWP1yvUOeqAgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109124790>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig=plt.figure()\n",
      "plt.scatter(df.palmitic, df.linolenic, c=current_palette[0])\n",
      "axis = fig.gca() #get current axis\n",
      "axis.set_title('linolenic vs palmitic')\n",
      "axis.set_xlabel('palmitic')\n",
      "axis.set_ylabel('linolenic')\n",
      "#ax can be got with fig.gca()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "<matplotlib.text.Text at 0x109157d90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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f3W7n9cWfUFDhIEAH9981DavNxruf/IDFIREXpuf+OTPrfCENTurMvv8eQWPo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\n3kOH1eQeFc9cQveBdc+Ys44d41+vL6G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5MkUtdtR6KwZ3Af/++xXE2eM6ySfLMnf+62lq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ernu3NY0zTmzF/M29M+xdD6CoJCowDYu2kBSe26UFq0l+TMgb+2VSnMW4mrroaY1plEJrSneHcu\n0UmZWILDADiUv4b4dr0wmYNw7P2esloFozmYpA6XUXFoB+agMMKifTOdeav2YXUWsG1PKRmXjAKg\nMG+Ff39ej5v+bcro3CGF9745hDnEdxOetzKfV5+4gdR27c7Hy9ikNm/Zyg9rN9ImKZ7rrxl60rZH\n3r/vl65kS95uenTOICfr0uYI85zl79rF5H/MwxTluwzqsR/mrmvaMuKqK/xt9Pz903Nu4MvPYDiz\nM2DNeiQfERFBdXW1f/nYa36qqvL444+Tn5/P559/3pyhCdFsLh/Yn8sHNpx9TlEURo4YxseffQu/\nFngAg8mKOTIFY1nxUW0Nvi5wHieRCb5BajRN9Rd4gLDoZOpqynz/V4MIi03AGuLbrtNeSXRSx/p9\nhCVTuG8DQSFRKIqC1+30d7sDMJrMlNpcWPYW+Qs8gBaSzOYt21tEke/RrQs9unU5dcOjDMy5jIE5\nl52niM6PjT9vQwlt4182hcSRv6fwJM8QetesRT47O5u5c+cyZswY1qxZQ48ePRqsv/vuuwkKCuLL\nL788ret1Ho9Xl4MeXAwDOoDk15j0NnFsLismNNI3XKvqtuOp2OUbue7XG9VUrwfV40L1uCkv3E5M\n60yMJiuOmnKCw3xd0arL9pHYvi+aphFicKAGhVFVspvg8DhCIuKxlezx/4GgVu2hTatYtuQfQlO9\nGM1WnLX13es8LgfxrayktW3N0q0FmMJaAWCo3UunjFG6ex9b8uezc8dMWDgHon03DXpqDpHeLqNB\nLi05v1PRc25wdoPhNOvpek3T/HfXA0ybNo1169ZRU1ND37596du3LwMHDvS3f/jhh7nhhhtOuD0Z\n8a5lkvxO7qE//o2CMhW8Lm6+ui/xcfHM+fYn9h08hGK04HXbMRktREWFYcZNtctIVW0dlaX7CYtu\njepxYfA6iG2VQoe2sTww4Sa+nPctqzfupLyyFsVgwl5VTFh0EimtExh5RR+6denIS1M/5KdNeQSF\nx2N0V9CxYwcUUyjtEoJ5cNJtGI1GPv3ya9ZvL8SgaIwY1Juc/i3jNPaZaOmfz+Wr1jL3+/WomkL/\nHincNPKaButben4no+fcoAUU+aYmRb5lkvxaNsmvZdNzfnrODWRYWyFatJqaGqqrq07ZzuPxcPjw\n4VN2AxNCCJmFTogLwNR/f8Lq7RWoGOiWbOLPf7ir0ftSlq9cy7TZK6nTQok21/CXh35P66SkAEQs\nhGgJ5EheiABbvfZH1uw1YY3vSnB8Z7ZXteKr+d802vaTuatQYnsRHNcBR0Qv/j19bjNHK4RoSaTI\nCxFgBw4ewnRU1zRzUDhl5cefttc0DUf9vC4oioLTI8M+CyFOTIq8EAF2+YBsDLat/mW1Io+B/Y+f\nXlNRFFJiTaheX6V32yvolHr87HBCCHGEFHkhAiw+Po4/TbqOjiH7yAjZx0O/z6HDCWZl++vku+iT\nUEpa0F6u7W1l3M03NnO0QoiWRLrQXYAuhm4gIPm1VJJfy6bn/PScG0gXOiGEEEIcRYq8EEIIoVNS\n5IUQQgidkiIvhBBC6JSMeCeEDi1ftZYvF/2ERzXQq2MCE2+7KdAhCSECQI7khdCZsrIy3p/9E7ag\nLtSGZLJsJ3z9zXeBDksIEQBS5IXQmW15eXisrf3LpuAY9uwvDmBEQohAkSIvhM507tQJk7PQv+y2\nl9O+bWIAIxJCBIoUeSF0JjY2lgkj+xJRt5UQ+3YGZXi59qorAx2WECIA5MY7IXRoUPZlDMq+LNBh\nCCECTI7khRBCCJ2SIi+EEELolBR5IYQQQqekyAshhBA6JUVeCCGE0Ckp8kIIIYROSZEXQgghdEqK\nvBBCCKFTUuSFEEIInWr2Iq+qKvfccw9ZWVkMHjyYXbt2HdfGbreTnZ1NXl5ec4cnhBBC6EazF/nZ\ns2fjcrlYtWoVL7zwApMnT26wPjc3l4EDB7Jnzx4URWnu8IQQQgjdaPax61euXMnw4cMB6NevH7m5\nuQ3Wu1wuZs+ezdixY5s7NCEaNXveIlas342CxrWX9+LyAf2pra3l5benU1arYnDZMFmDcWsWUhND\n+cv/3InBYOCq0eNwmxNQvW6clfuIT+mOojp44u7R9L/sMr757nve/GQhmiEIs6eMdmldwGBg7col\nBEW3w+2yo9pLiG7dGaNq52+P3k7P7t35y5TnWb7pAGZLKI6y3WQNGIKGxtD+nRk+5PIT5jHjv18y\na/5aMFhIjTPyrxefQtM03vpgJjsLqwgyaowfM5ROHTJYvWYNL7zzOZohGKO7jLSMTDTFjNlbiWaN\nwahoXHt5H7Ivu+SE+3v9nQ9YvCYfDEZ6ZUQz5U+PnrDtlq3b+c+X3+NSDXRpF82dY3971n/ka5rG\n2x/OYscBG1ajyvjRQ8ns1OGstiVES9fsRb6qqoqIiAj/stFoRFVVDAbfSYWsrKzmDkmIE1rz0zq+\nWFWMKbwjAB/O+5nUlGQ++u98ClzpYDZwMH8Zbbr4it26Ijtvvj+L75YsIzJ9CGExbQAoyl+LNTqJ\n8NgU/jb1v8zp04dXPvyG1l19f/A67TbWbVmGo6qU9n1uJDQy0f+8qLbdMFtD+dOL03jjmQdYvd1G\nxiWjAXBUl7Hj0AHiUnoy8/vdJCcl0L1rl+PyOHhwP9MXbCax0zAAim3FPP/yVNokt2HN/lBMQa2w\nAS+/P4c3//4wf5v6X1p3GwGA22nnp40LiEvpgdGcRHhQWwD+/eVPZLRPITHx+GlsV65ey+JNlcR1\nGgLA1tI9TPtkJnfcdstxbZ1OJ699tABvdE8wwvJdNiK/ms9vbxhxVu/ZjM/nsKogBHOwL65Xps3l\njWcfwmSS+bjExafZP/URERFUV1f7l48u8GfKZDISGRncVKFdMEwmI4Auc4OWld+O3XsxhSfXPxDR\nnl+2baXSAYrZiMflICgs2r/aZA1hf/EB9hysID2rjf/x2DZdObB9OeGxKZhCEtm2bRPB0e38660h\nkRgMRowmi7/AA0QmpmOvLCIyMR0s0cyd+wXRrTP964PDY7GV+O5rMUaksj0/n5ys3xyXx2dfrCEi\nqbN/OTQykZ37tmMKjsIU1Nr/eJU3nKKifZhC6mMwW0MwWUOpq62gVfql/sfV0Hbk5e+gY8fU4/a3\ncPG3xLSpbxsR357cTct45P7j3/NduwqpUqMJPbK/4EgOlhSd9eejqKwGc3BSfU5qJE5nNbGxrU/y\nrHot6fN5NvScn55zg/r8zkSzX5PPzs5m/vz5AKxZs4YePXo0dwhCnLaOacl4akv9y2rNfnr16EpU\niIKmaRjNQTjtNv96r8dNXJSF+EgLjuoy/+OVxflEtfKdDfA4DtOnTx8ctkL/eo+7Dq+7DnddTYPt\nVZftJzgi3rfgrmbkyJFUlezxr3fV1aAovq+xt/YQXTqlNZrHwJz+VJfu9i/X1VbSJiGMhJhgvG6n\n//EwYw3p6el4HIfrc/a6cddVY7aGYq+qfy0U+0G6d+3U6P6uHJyDrSjfv1xbWUT3zNRG2yYlJRFu\nrKp/LVx2WseHNdr2dCREB+Nx1/mXw4zVxMXFnfX2hGjJmv1I/sYbb2Tx4sVkZ2cDMG3aNGbOnElN\nTQ2TJk06o215PF5sNsf5CDOgjvwVqsfcoGXld9kl/di2cz9rt/yComhcn5VJcuu23Hf7aP7x9izK\nazVSYsBk24AHC6lxZh6+634ee+AOeuTcSFB0e7xeFzXF+cSl9qS4fBd3jRmE12vk98N78enCRWAM\nwltTSOcOnfFo6fy8ZgaRiR1xuxw4Kg6iuqoxeOw8NG447dp1oF2UnfwNX2OyhGAryqPXb7JQqrcw\ntE8aXTO7Nfq6JiS0YUiveBat/RbFaCHWaufpN15AVVUOvv4BBaVOgkwaY8cMpq5O5a4xg3j/s2/Q\nTMF4aw/RqUMGRpOCWrsZs7E1RgMMv6Iz0VEJje7v8gGD+H7FOnK3f4eimEiNV7hn4tMneM8V7rih\nPzPnrcblVchMCmHMyDvO+vPx+9EjOfD6B+wpqSPIpHHbTZfjcHhxOE5vey3p83k29JyfnnMDX34W\ny5mVbUXTNO08xXPeuVweXb6ZF8MHFSS/lkrya9n0nJ+ec4OzK/IyGI4QQgihU1LkhRBCCJ2SIi+E\nEELolBR5IYQQQqekyAshhBA6JUVeCCGE0Ckp8kIIIYROSZEXQgghdEqKvBBCCKFTUuSFEEIInZIi\nL4QQQuiUFHkhhBBCp6TICyGEEDolRV4IIYTQKSnyQgghhE5JkRdCCCF0Soq8EEIIoVNS5IUQQgid\nkiIvhBBC6JQUeSGEEEKnpMgLIYQQOiVFXgghhNApKfJCCCGETkmRF0IIIXRKirwQQgihU1LkhRBC\nCJ2SIi+EEELoVLMWeVVVueeee8jKymLw4MHs2rWrwfq5c+dy6aWXkpWVxXvvvdecoQkhhBC6Y2rO\nnc2ePRuXy8WqVatYu3YtkydPZvbs2QC43W4effRRcnNzCQkJITs7m+uvv56EhITmDPGCUlJayruf\nzKbObSAzNZZbx4xEUZRAh6ULn301n407DmExqEz43TW0SU4+Ydup737Akp92gabSvlUwQRHJWAwe\njIoXh9eCVldBXkExqjGYNjEmpr35HAaDgZdefYs1vxwA1cPBgm2ExLanrqYMj8tOWEwK7poSvp7x\nJlFRUQ32N+Wl19i08zCoTjKSIzCHtSIsSOH+O8YQHh5xwjiffOYfbN9nQ/M6MHjsqNZYLIqDN55/\ngtjYuAZtl61cw3ert6AAI4f2o0/P7g3Wl1eU8/ZHn2N3GQi3urG7DKgYyPlNR35741UN2jqdTt74\nYCbl1R7iI63cN+F3mM3m03wnLhyapvHJ//uKXYUVhFhgws3XExcXG+iwhDgnzXokv3LlSoYPHw5A\nv379yM3N9a/btm0bGRkZREZGYjabycnJYfny5c0Z3gVF0zSmvPoRu+rSKFTbs3CLh0+/nBfosHRh\n3jffMfsnG4Xe9hS403n29Zm43e5G234+ew7f/mwnMv1KIjOGsq3Ywp7qaPa4M1iy4SAHve1ZvmEP\n4R2uJip9MKXmrtz76NO888F01hQYiEq/kqgOVxGZ3JOEtMvIHHAHUa06077P9aT3H8uIW+9vsL+p\n737A5uJwItMHE9lhOLn5NeyuiSGvth3PvDLthDk9//JUdtYkEpk+mKiO12AjjojUHIJSr+KOPzzT\noO3Pv2zjg6+3cMCTxn5PGq/PWM7BwsIGbf728ofsdPg+e5tL41mXb+OgN43p3+5m5ZrcBm3/b+qH\nbC5PolBNY31pPC+/9fGZvB0XjJmfz2XRL172OduxrSqFZ177KNAhCXHOmvVIvqqqioiI+iMRo9GI\nqqoYDAaqqqqIjIz0rwsPD8dms510eyaTkcjI4PMWb6CYTEYqKyupcIZiDfcduVtCYthTeEAX+ZpM\nRoCA5bJzXzGWsFb+5Qo1jqqqw6SlpR3XdsmqdcQkD/Ivx6f2oXTvRhLT+hIWk0xN+QEi4lP9Z1iC\nw2IoKHBgW7WJqFb1z0tIu4SD25fTvtc1BIXF4HU7MZqtBEW2bfA6rN+6l4ijn9e+N2X7fiY5cwBF\n1RoREUGNns3ZuruYsDZd/MtRienYq0oJi26NxxTTYB+bt27HGHlUrpEdWb9pE106pwPgcDgoc1iw\nhPr2Yw2LRi32AmCKSOGnjdsZlNPPv80imxdDiO/I3WiycMjmbpGf072HKjCHtAVAURTK6oJQFHeD\n3yw9CPT373zSc25Qn9+ZaNYj+YiICKqrq/3LRwo8QGRkZIN11dXVREdHN2d4F5Tw8HCCjXX+ZVX1\nEhFy5m+wOF5UmAWvx+VfDqKa+Pj4RtumJMdTV1PhX64pP0BIhO8SkrO2guCIROpqyv3rNdWL1egi\nKT4Cl6P+81xTtp+IuHYAuJ21GEwWAFz2+m0DxIRbcDvt/uXqsoOEx6YAEG5VTni5JjxYaZCTvaoE\na4jvj2bFa2/QNjkpDo+j0r/srS2hY0Zq/esRFESIuf7MhqZ6Ub2+ZY+zhlYJDTctD9UAAAzJSURB\nVC8vhAc3jCnM2jIvKYUHG9FUr3852OgkLCwsgBEJce4UTdO05trZF198wdy5c5k2bRpr1qzhmWee\nYd483ylot9tN165dWbt2LaGhoWRlZTF37lySkpJOuD2Xy4PN5miu8JvNkb9C58xfwn++WonDa6JN\nFDw1eRIhISEBju7cHckvUO+d2+1myj/eYc9hLxaDlzFX9eHqIZc32lZVVW6//wnKnGGoqget9hBx\nKd0weh14HJUooa2wFe/ArURgtIajOIv57N/PkJCQwDU330+1Fo3qcXH4wDbi2vWkrrqMOnul70i7\nsohx1/blzgl3+Pfn8Xj47Z2TqTPGo3rqUBzFxLTpSphFY9LNV9K3d49G47Tb7dxyzxO4zQl4nDXY\nbcVEJGSguWzcffNgbrj26gbtX337Y9bll6OgcUWfdoz//agG61etzWXal8uxu41o9mIUSzgGcwjd\nUkL5+18eQFEU//u3I38Xr304h0q7QmwIPHrnjaSmtjuHdygwamtr+ds/36OoSiHY4mXsdTnk9L8k\n0GE1uUB//84nPecGvvwsljM7Ad+sRV7TNO677z42b94MwLRp01i3bh01NTVMmjSJr7/+milTpqCq\nKhMnTuTee+896fb0XuSP5ObxeDCZmvXKynl1oXwRPR4PRqPxtG5mdLlcmEwmDAZDg+cd/d7U1dUR\nFBTUIL+jn1dZWem/ye7of5/O/k73/a+rq8NisWAwGPzxnIjX60VRFP/ZtMYc2bemaaiqitFoPOH7\np5fPaWioGZPJFPDP5/lyoXz/zgc95wYtoMg3tYulyOuN5NeySX4tm57z03NucHZFXgbDEUIIIXRK\nirwQQgihU1LkhRBCCJ2SIi+EEELolBR5IYQQQqekyAshhBA6JUVeCCGE0Ckp8kIIIYROSZEXQggh\ndEqKvBBCCKFTUuSFEEIInZIiL4QQQuiUFHkhhBBCp6TICyGEEDolRV4IIYTQKSnyQgghhE5JkRdC\nCCF0Soq8EEIIoVNS5IUQQgidkiIvhBBC6JQUeSGEEEKnpMgLIYQQOiVFXgghhNApKfJCCCGETkmR\nF0IIIXRKirwQQgihU81a5B0OB6NHj2bgwIGMGDGCw4cPN9qutLSUjh074nK5mjM8IYQQQleatci/\n9dZb9OzZk+XLlzNu3DieffbZ49p88803DBs2jJKSkuYMTQghhNCdZi3yK1euZPjw4QAMHz6cb7/9\n9rg2RqOR7777jujo6OYMTQghhNAd0/na8Pvvv8+rr77a4LHExEQiIiIACA8Px2azHfe8IUOGnPY+\nTCYjkZHB5xboBchkMgLoMjeQ/Fo6ya9l03N+es4N6vM7o+echzgAmDhxIhMnTmzw2OjRo6murgag\nurqaqKioc9qHwaBgsZy3FAJOz7mB5NfSSX4tm57z03NuZ6pZT9dnZ2czf/58ABYsWMDAgQObc/dC\nCCHERUXRNE1rrp05HA5uv/12Dh06hNVqZcaMGSQkJPDKK6+QkZHBdddd52+blpbG9u3bsVgszRWe\nEEIIoSvNWuSFEEII0XxkMBwhhBBCp6TICyGEEDolRV4IIYTQqRZb5EtKSmjbti07duwIdChN7vnn\nnycrK4tLLrmEjz76KNDhNClVVZkwYQI5OTkMHDiQvLy8QIfUJNauXcvgwYMByM/P9+d33333oYfb\nXo7Ob+PGjQwcOJDBgwczfPhwXYxOeXR+R8yYMYOsrKwARdS0js6vpKSEkSNHMmjQIAYOHEhBQUFg\ngztHR+e2fft2cnJyGDBgABMnTmzx3z23283YsWMZOHAg/fr1Y+7cuWf8+9Iii7zb7ebuu+8mNDQ0\n0KE0uaVLl7J69WpWrVrF0qVL2b17d6BDalKLFi2itraWH374gb/+9a88+eSTgQ7pnL300ktMmjQJ\np9MJwKOPPspzzz3H8uXL0TSNr776KsARnptj83vkkUeYOnUqS5YsYdSoUbz44osBjvDcHJsfwIYN\nG/jggw8CGFXTOTa/xx9/nLFjx7Js2TKmTJnCli1bAhzh2Ts2t6effpq//OUvrFixAqfTybx58wIc\n4bmZPn068fHxLF++nIULF3L//fczefLkM/p9aZFF/rHHHuPee+8lKSkp0KE0uUWLFtG9e3duuOEG\nrrvuOq6//vpAh9SkgoODsdlsaJqGzWbTRRfJjIwMvvjiC/9f1OvXr/ePAXH11Vc3OnxzS3JsfrNm\nzaJHjx6A7w/u4OCWPbrYsfmVlZXx5JNP8uqrr7b4I0E4Pr9Vq1axf/9+hg4dyvTp07niiisCHOHZ\nOza34OBgysrK0DSN6urqFv/7MmbMGKZMmQL4zoKazeYz/n1pcUX+ww8/JD4+nmHDhgHo4kt4tNLS\nUtatW8dnn33G22+/za233hrokJpUdnY2dXV1ZGZmcvfdd/Pggw8GOqRzNmrUKEym+hG2jv5MhoWF\nNTp8c0tybH6tWrUCfMXijTfe4A9/+EOgQmsSR+enqioTJ07k5ZdfJiwsLMCRNY1j37+CggJiYmJY\nvHgxKSkpLfpMzLG5Pfjggzz88MN06dKFkpISBg0aFMDozl1oaChhYWFUV1czZswYnn32WVRV9a8/\nnd+XFlfkp02bxuLFixk8eDAbN27k9ttvp7i4ONBhNZm4uDiGDRuGyWSiY8eOBAUFnXBK3pbopZde\nIjs7m7y8PP/7p7cphQ2G+q9VUwzffCH69NNPuffee5k/fz6xsbGBDqfJrFu3jvz8fO69915uueUW\ntm7dyqOPPhrosJpUbGys/wzhddddR25uboAjajq33XYbK1asYNu2bYwdO5bJkycHOqRztn//fq64\n4grGjRvHLbfccsa/Ly2uyC9btoylS5eyZMkSevXqxccff0xiYmKgw2oyOTk5LFy4EIDCwkJqa2t1\n9SNaW1vrn6QoOjoat9uN1+sNcFRNq3fv3ixbtgzQ5/DNn3zyCW+88QZLly4lNTU10OE0qUsuuYQt\nW7awZMkSZs2aRZcuXXj55ZcDHVaTysnJ8V+rXrZsGd26dQtwRE3HbrcTHh4OQFJSEpWVlQGO6NwU\nFxczbNgwXnrpJcaPHw+c+e+LjOJ/gRkxYgTLly/n0ksvRVVV3nzzTRRFCXRYTeaxxx7jjjvuYMCA\nAbjdbp5//vkWf033iCPv0z//+U8mTZqEy+WiS5cu3HTTTQGOrGkoioKqqjz88MO0a9eOUaNGATBo\n0CCefvrpwAbXBI79nmmapqvv3tGfzzvvvJO33nqLqKgoZsyYEeDIzt2R3N577z1uuukmgoKCsFqt\n/Pvf/w5wZOfmueeew2azMWXKFP+1+ddee42HHnrotH9fZFhbIYQQQqda3Ol6IYQQQpweKfJCCCGE\nTkmRF0IIIXRKirwQQgihU1LkhRBCCJ2SIi+EEELolBR5IcQJHT261qmsW7eOSZMmAfDuu+8ya9Ys\nAJ566inmzp17XuITQpyc9JMXQpyQwWBoMFb26Ro/fjyDBw/m9ttvPw9RCSFOlxzJC3ERWbp0KUOG\nDGHIkCFkZmYybtw4XC4XTz75JP3796dTp05kZ2cfNx/E008/zYQJE+jbty8pKSl8/PHHjB8/ns6d\nO/O73/3Ov+3Bgwfz3XffMWfOHJ566ikWLVrE+PHj+eijjwB45ZVXyMzMpGvXrjzxxBPNnr8QFxsp\n8kJcZNasWcM777zDtm3bqKur47XXXmPHjh2sXr2avLw8MjIymD59+nHP++WXX/jxxx/55JNPmDBh\nAk888QRbtmxh/fr1bN682d/uyiuvZOTIkUyZMoVhw4ahKAqKovDjjz/y1ltv8dNPP7F582bWrVvH\nhg0bmjN1IS46Mna9EBeZIUOGkJ6eDsDYsWN59913mTp1Ku+++y55eXmsXr2ajIyM4543dOhQDAYD\nKSkpJCUlkZmZCUBycjIVFRUn3aemaaxYsYLrr7/eP4HI4sWLmzgzIcSx5EheiIvM0fNve71eFEVh\n2LBhAIwZM4Ybb7yRxm7VMZvNjW4Djp/cpTFms7nBdgsLC085F7YQ4txIkRfiIrNkyRKKiopQVZX/\n/Oc/5OTkcPnll3PXXXfRuXNnFi1adMbT/x77R4HJZMLtdjd4bMCAASxYsIDa2lo8Hg+33HIL69at\nO+d8hBAnJqfrhbjIJCcnc+utt1JYWMiwYcO47bbbGDVqFL179yYuLo6rr76aPXv2APVH6Eeuqx9x\n7JH70e3Ad0ngz3/+M1FRUf7He/fuzQMPPED//v1RVZXRo0dzxRVXnPd8hbiYSRc6IS4iS5cu5cUX\nX2TBggWBDkUI0QzkdL0QF5Fjj8iFEPomR/JCCCGETsmRvBBCCKFTUuSFEEIInZIiL4QQQuiUFHkh\nhBBCp6TICyGEEDolRV4IIYTQqf8PiLl7vRZic+gAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109148e50>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist(df.palmitic)\n",
      "plt.xlabel(\"Palmitic\")\n",
      "plt.title(\"Histogram of Palmitic Acid content in Olive Oils Sample\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OfM0KAAALEdAAAFiIgAYAwEIENAAAFiKgAQCwEAENAICFCGgAACxEQAMAYCEC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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1090f4bd0>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are many many more kinds of plots."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A more object oriented approach sees us using the `subplots` function to set both figure and axis."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes=plt.subplots(figsize=(10,10), nrows=2, ncols=2)\n",
      "axes[0][0].plot(df.palmitic, df.linolenic)\n",
      "axes[0][1].plot(df.palmitic, df.linolenic, '.')\n",
      "axes[1][0].scatter(df.palmitic, df.linolenic, c=current_palette[1], alpha=0.5)\n",
      "axes[1][1].hist(df.palmitic)\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DZJVKxXPPPRd03Zw5Q7lCGzZs4NChQ1c/MhHWqpq68ceefQODY994Cp2r7VIuJ8Qago4F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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109159550>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Lets use the above method to make scatterplots of the acids in the list `yacids` against the acids in the list `xacids`. As the names suggest, plot the acids in `yacids` along the y axis and the acids in `xacids` along the x axis. Label the axes with the respective acid name. Set it up as a grid with 3 rows and 2 columns."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "new_palette = sns.color_palette(\"Set3\")\n",
      "sns.palplot(new_palette)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLkDQX3UUVuc85BAJAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1091c21d0>"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xacids=['oleic','linolenic','eicosenoic']\n",
      "yacids=['stearic','arachidic']\n",
      "\n",
      "#your code here\n",
      "fig, axes=plt.subplots(figsize=(10,10), nrows=len(xacids), ncols=len(yacids))\n",
      "k=0\n",
      "for i, xacid in enumerate(xacids):\n",
      "    for j, yacid in enumerate(yacids):\n",
      "        axes[i][j].scatter(df[xacid],df[yacid], c=new_palette[k])\n",
      "        axes[i][j].set_xlabel(xacid)\n",
      "        axes[i][j].set_ylabel(yacid)\n",
      "        k = k+1\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SBiKVTKC1tZ41q/fg6amg/LoBs8mNBQv9cXGxjnfYvDmXlpY4nJ1dHrhuFouF\nlSt/y3NLRrBmzT58fNy4cqWK2JincXV1IzQ0gdLS07S31VBZeR1f367ZuOVGZ8+eIDpGS1RUHACX\nLlWRm5tNVdURnllsHXtsMBj50x//SEhIMHq9hPHj54utqYWb9P6FPIWHqrGxgXc+/xfHHNtIL7/A\nirR1j7pIgiB8z1gsFrZv/5AFCwN48qlA/ANKOH4861EXC4D29np8fTtWdYiN9aekpLCTedTh5+dm\nO5ZJpRQWVgBw/PglnJ1VHDvWQp8+fYmIiGPcuJcxmeKJjZmJRqOwBcgAoaFuVFZWPGCtrHbsWENY\nuCN9+3rz7LMpjBsXQ0R4EH369Of8+VM0Nmby1NOBLHqmHydPrqWlpfnumd6H69evERnpZzsOC/Oh\nvLwQb++OTUxycvKZNi2Ip54OYsHCAL7++sNuKYvweBNBstCl0vftJGjKOFy9vfCLCKXCRUZ5edmj\nLtZ9sVgsfLR+Fb/d8Cm/W/cx+7MPPuoiCcIDMZvNvPzyy4waNYrk5GSuXLlidz4tLY1hw4YxfPhw\nPvzw8Q0aGhrqGRjtYhuLGxvbh+bma4+2UN/w9u7P+fPltuODB64RHh5zhytu5uMTwtmzHe2qi6uS\ni3nF/OKtrzh4oI3GhgHMnfMjJBIJDQ117Nr9AUMTapDJD3HtWhFFRTW2a0+dqu5UL/adqNQtqFSO\n1NY2AeDk5EBxiZbm5kYOHlzJ1GnWFZ0kEgmz50Rx5syxLrnvjcLC4jlwoOOzfeTIVSIjB1Na2jH0\n5Pr1euLigwHruOnwCCWNjQ3dUh7h8SWGWwhdyiKVIJN2/PZycFbT0traNXlbLLS3t3fbep832rT7\na6RDwglytT6GPJxzkri6KLEqifDYSk9PR6/Xc/jwYXJycnjjjTdIT0+3nX/99dfJzc1FrVYzcOBA\nFi5ciEZzb2vZ9iQqlZra2nbbscViQafr2i2Zv5v3jh1rUDu3otOaCQoaTURE3G3Tx8cncvhwI3kX\nLmEwmgkKHNfpNiUubjiHDzdw/nweFRUlmE1y2ttbGTwknNYWBxITJ9i2YM7O2cbSpUORSCT4+3vQ\n2qpl964W3Nzq0OkshIfP7LKtuHU6C/PmJbB5cw4SiYSLF6uZPOknpKf/jYbGatratKhU1nsVF9fh\n4RHfJff9lsViYefOz2lpOY/BaCAjIweLWYlObyJ6YBGtbe385MfLGZoQQmVlLadOFTJoUAgAdXVa\nIiPUN+ULbZuBAAAgAElEQVS3e/c6nJSN6HUW/P0TiY4ecqtbC72UCJKFLpUYGcemYyfoM2wQJoMR\n7YVCBjw3+Z6vt1gsZBzcR21jA0OiYgkfYB3TfO7iedKPHwAXFdLGVpZMmk2AX0B3VQOABl0bTq7B\ntmN1oD8lZSUiSBYeW4cOHWLKlCkAJCYmcvz4cbvzCoWChoYG23rhd1tPVy6XodH0xHGcSpydY/n6\n67P4+ztz+lQ906e/csuyftvbfL/12LXrK6ZOc8fLKwiAjV9lEhsbd8fxrVOnzr6ve90uj/VfvMvi\nxSkAGI0mVq1ahauLO1IpmE3tdu+jm5uKMWMmEBIy4IHLcKOY6Il8tWEHYeH+XCpoYMrkZVy6lMPQ\nBF/k8gDefnsdQYHetLbpUKvCefHFm1d6epD3I2PPJiZMVOHvb/3O2bLlKDk5+bzxxgx8fd3JyDjF\n/PmJ+Pt7APDZZ/tpbdVSXdWExRyKl5f9lt17920lZYIKv2++azZtOoxEEour69239pbLZRw/fojS\nsnwcHDSkTpzdpetTPywP+v+jp5DfsCvvvRLDLYQuFRUeyZzIYZCTh2PuZV5f9CIy2b1/OD/e8BmX\nPRQYhkWw6XIux0/nArAt9zCBKaNobG6i0UHC71b+g9q6WrtrTSYTyzeu5S8bV/P++hXU1Nbe6hb3\nrJ+nHw2lHcsltRRcY0D/0AfK83b0ej1FRdfQarXdkr8gADQ1Ndl9wctkMszmjp0/33jjDYYOHUpM\nTAwzZ868p2Cgpxo3dhojEl/GxXkaCxb8B25u7t1yH6OxES+vjklv4RFulJeX3+GKu2tra+PatWsY\nDHfeuKimpoby8nKc1R3vodFooqHhMvOe8GLeEz60tZWTkXEBAL3ewNGjdQQH93+g8t1OVFQ8U6b8\nOyrlZCZPfg1XVy+MxkYuXSpHqVQwcmQUr/xoOq+/Pgezub7L2zutts4WAAMMHBiIs9oJX1/re9/W\nprM7P3JkJO3tOkYlRaJWu2M2mykpKaG+vv6b9NV2Y7/j4rwpKSm66b5VVVVUVNiP687M3I5KfZ65\nc30ZOdLIF18+vsOXvs9ET7LQ5SJDw4kMDe/0dQaDgUqpgX5e1kbMb1A0xw6fIiF+MCaFjHP7DhCR\nNAJHlRKLxcIn2zbw82c7dkVas/lLTIPD8Pzm/Kdf25/vrORRY2jK2M61olwwGnliyBicnbt+/cyL\nl/LZcCwTx76+6HMymBqVwJDYQV1+H0FwdXWlubljstS369YCFBcX8/7771NUVIRKpWLx4sVs2LCB\n+fPn3zY/o9FEY2P7bc8/enLc3HxoadHfNsW3PWT3Ww+DQUVNTZOtF/JiXj2Jie73nd+pU0dobj5G\nyAANX22sIzZmLn37Bt+UbseONfTp24Ja7UDuqUvMnBWDTCbj0KELLF06zva+vvxKMn/76xlqqsux\nWGRMm/oKzc26m/LrSi4unnz++V8ZPMSZ69cv4uKspLGxnVmzEgHrZiLPLB7M/v37GDlyvN21D/J+\nWMwulJfXERBg/Q65cKGElhYtFRV1+Pl5oFI5cv16RyB9/nwx48fHcOJ4KR4eUSxf/v8YNtydK1e0\nNDT6oZBrqKhosAXKp09XEx/nYyubxWJh69ZPCQs3I5VK2LnTyOzZL1nHgjdeZnyydSiHh4czzupC\nGhraHrve5Af9/9FTaDRKHBw6H/KKIFnoMSQSiV2vFgAW6zhCldZMg1SCo0ppS6tzkts9Em6yGHG7\nw/l7odfrOXv+LBpXDaEDQpk9cWoX1OzOtucept+Eb9YpjQglY98RESQL3SIpKYktW7bw5JNPkp2d\nTVxcx9hZrVaLTCbD0dERqVSKj48PDQ1iItPdjBs3i+1fr0HtXIZWa6ZfUPIDLSVWU3OMBQutY3Xj\n44P55OOtnDqtxFltorVVTkrKQq5evcTQBClRUVEAREf34Xe/3U5cfChnTtcREtKGm5szBoORjV8d\nxslJgk7vxOik+Tg4OHSqPJWV5Zw8uQWlUorB4MLEiU/f1Ka2t7ezd9/nqFUmWlplyGQuPPuDCFQq\nJ4YODea9P23C188dg8GIQiH/pp7NyGRd27s/Zsw0du78nOaWYxiNBurq2gkLm8gf/rCFQYP60tqi\n5djRfPz8vdG2yzCaLHz99TXcNNGUl5/ghRfjbeXLzMzH02MWezL2olRd/2ZM8ihcXDqeruTkZDFl\nqhf+/tYgOiKimazMnYwZMwWj0b5sBoP5sQuQBREkCz2IXC4n1MmN6qJS3Pr6U56Ty+zo4QC8PH8x\nb/31d3Y9X3K9ya7RkRtMdzx/N83NTfzly1W4J0TTXlaK1+ljPD9vYRfW8NYsMvtRT+b7HDslCHcz\nd+5cdu/eTVKS9UfZ8uXLWbt2LS0tLSxbtoznnnuOUaNG4eTkRGhoKEuWLHm0BX4MSCQSpk59tsvy\nc3S0P66qusbP/2MaMpkMo9HEx//6F/7+0Qwe0hFgqtVOxMSMYNrUxYwa2cbmzf9kVJKeI0fyWLhw\nLEqlIxaLhRXLP7NtgW2xWDh0aDft7Q2EhQ0lOHgAra2tHD68A4BRo6agUqnIObqOpUuHAlBT08Te\nPWmkpMyzK+Pu3av4wXNhyOXWMv7utztQqaybhchkMsYnx3D61FU+/ngXM2cOp6mpjRMnLmMwNACj\nbvl3OHv2OBUVhfj69icu7t52qZVIJEyZ8sxNr0dGxlNatpspU4LIu1BFa2sIiYkT7dLs2fOZLUAG\n8Pd3obqqjsmTF932fs3Ndfj6dgzH8PR0QautBiAqMpm0jVsYlRTIxbwqnJyi7qkOQs8igmShR1k0\n8wlOns6l+EwRk0dNxs/Xutalk5MTb//oZ/xz0zq0jnLkBhNzE8fbXbt42lw+Sl+Lzklxy/N3k7Zn\nB0FTrY8pNb4+FGnP89ZHfyTcM4Dnn1jYbb0APjIVLXX1qD3c0ba04mEWQbLQPSQSCf/4xz/sXgsP\n7xga9dOf/pSf/vSnD7tYwnfU1ipsq0AUF9fg7eNim9chl8uQyioIDJxJeno6ixcPRiKRsG3bOSIi\nZgDW93jWrB+Sn38BiaQCpdIRrVaPg4MclbpjjPPWrZ8yfYYPPj6+ZGTsITe3mqLigzz/vHX1hk8/\n/QcJQ58kuF/HakJeXq4guXlNZzc3k21ilFwuw9fXld27L5KaGonFYuFiXjM1NY5IZTWcP19E375e\nLF6cTNrGC7f8G+zdt4W+fatJTgkgL6+QAweqGPMAO/QNGBCFt3cAp3Iv0KfPcGJi7Cd9WywWPD0H\ncPhwHqNGhWCxWMjcX8q0afNuk6PV4MGj2bhxLfPnx6PV6tmx4yIxMXMBCA+Pwtc3gBMnThAQMIzo\n6D73XX7h0RFBstDjDIkfzJD4wTe97uLiyhuLf3jb61xcXPnZA4xBNkuxW75O7e6KozoCvYc7X+/b\nxfSUe1+lozOemT2fzRnbqS4oxc1ByRPzF3fLfQRB6PlmzFjGpvSvkMsNODp6U15eZ3fexcWJbdtW\n4u0t4d1306mrbWHkyGjOn0+jonIIcbFjkUgkREZGs3//56xatRdtuw6pVIq7hytpaX9hzJjF+Pu3\n4+NjHTowcWIEv/7PVfz6v+bbAvLnnx/Cu+/8GQ9PNSkTrL2ger0BnfbmsKG5xf7YSemBWjWKL9af\nxmAwMXToIpKT3fh87f8yefLQ2173rbbWK0RHRwIQFeXHuXPn7+tv+V2urhqGDh150+tnzuRQUXkY\ndzcFe/eUUXjFxKXLF4ge6M+hQx+gVEYzcuSkW+bp5eWNh3sS77yznvBwH+rqjHh61ODv3xcAjebW\n9xQeH90aJJtMJpYtW0ZBQQESiYQPP/yQ6Oho2/ktW7bw9ttvI5fLWbp0KS+++GJ3Fkd4zFzIz+PM\npTwCvHwZOyKpS/LMK7jI6YIL+Hv6MG7kaLtzI6Li2XT0OAHDB2EyGrly4hQJM6cik8upvXT9Njk+\nOIlEwuzUad2WvyAIjw+5XE5q6tO24+zsvaxYnoGHpwstLe2MGRNNY0MuL78yifT0I0ydmoCjowKA\nrVtO0dgYh0bjhslkQiprZtGi2aSn5zB/vrUNNZlMrFq1CS8v+/kfTkrYtCmbyMi+REf3w2g0ET8o\nkMBALz74YAd+fv7U18mZMuUFu+ssFguNDWZ+97sNNDa20N5uZFB8AhWVxaSkLEar1bJv/xc4OZpw\n04SwYvlpXDXQ3Cxh6JBb99RabljS2mK+ZbIHZrFYqKg8zKJF1t7zyVOi+d//3clbb01BqbSOe9m2\n9Qxpaddwc1NgMjmTkvKEbUgfQGnZSd58c5rtSeOaNfuJihJzSnqLbg2St27dilQq5eDBg2RmZvKr\nX/3KtnC9wWDg9ddf5/jx46hUKpKSkpg1axY+Pj7dWSShB9Nqtazc8iXtMgkN5ddxCulLn2FxnK2o\nonjTlyye/aRdepPJxKpNX9CICbnByMLUmXjeYQ3jQ8ezOdJYju+wCM5VVFGc/gU/fu452/nIsAiQ\nSDiQdZQTV/IYvnAeMrmc+qJShnzTMwCg0+lYteVLWqXgaDDxgxnzUavVtnMrN39Bm0xy07nvutd0\ngiD0HK2trRw/noWjo4rExLEPZSLW00+/Svqmd5g1OxG12om3/3cDSaOt68dLpVJbgAzQL9iNoqKr\nxMUNprW1hYAAd1pbdXbr/8pkMtRqGTU1rhQUlDFggB9ffXWYJ58cQ1hYALt2nUQul3HoUB6LF4/H\nwUFBVWUbAwY8e8vVfXbu/JxFz/TDyysWg8HIurVZPPuDQVy7VsW2bZ9hNDaw+NlwcnIKkMmbaGoM\nYOyY5zh3Lpfz508ilzvg5eVtl6eHRzSHD19ixIhgjh4tQq3u/GpJ90Kr1eLh3vH3k0qluDjLbAEy\nQHFJKS++OAmFQk59fQtfb1tnN05ZpZTYfQ7UagmWG6N84bHVreskz549m48++giAa9eu4e7eMdEg\nLy+P0NBQNBoNCoWC0aNHk5WV1Z3F6bWqa2pYs+lL1mz6kuqamrtf0EOcPn+GVZs38NX2TZhMJj5N\nX4fjyDg8kwbTf95kKiut6066+vlwTdd0U8OzZstXmAaH4pk0GNdxCXy6bcMd75dbfBnfmIiOPPU3\n5xkZGs6yJxfzo1kLaD56jrpDuYQ0Wxg9rOOR2afp65GPiMEzaTCqMYP5eNO6jnOb1qMYGXvLc9+1\n/B7TCYLQMzQ1NZCR8QHTZ8gZNryBjWn/91CCoT59gljw9H/xwQfn+NtfzwKuXL58HYvFgre3hry8\nYlvakycv0dBg/Q5wcXHlypV6TCYTRUVVtrKWlNSiUHgzc8YS1q49xe9/v4GxY6MJC7OO001NHcz7\n7x8kOTkOBwcFFouFwqvtt13+0mypsQXhCoUcjZv1x35wsA86XR6tbeWsXZtFVFQgU6cOpbb2LJs3\nr8TP/yJPzFdzMX89hYX5dnkmJaUil41m/boGpJJRN02y6ypKpZKSEoNtVaXy8npkMm/OnevY8tvZ\nWWWb0Ofu7oyjo/0YEYPBldpa67KKJpOJ2lqpWMWiF+n2MckymYwlS5aQlpbGhg0dQUxTU5Pddqcu\nLi40NjbeNp+eu7PTveuOnWvq6+v5584NBE6wDh341+6N/MdTz+Lh4XGXKx/Mg9bl4NEcMiov45MY\nRX1bOx9+uRK9kwKXbxojqUyGw3eWUZJKrPf6buPTJjPh/M0W1RKJBL1Sgaur020bKPkNq0hIJRLk\nchkSieSmeowZOZwxI4ffMh+doxRnhcJWToNSbrte52B/Tv+dc9+ldZCi/m46p1unu1e9bVek3lIP\noffIzt7O80sTkEqlODk5kJqq5dy5U3h7+3D8+EZcNdDUKCEx8Um8vX07lfe+/WlIKAVAIgli3Dj7\nHfk8Pb15+aW3ANi27bcoVRbefXcjSicHyspqGDkqioKCMlxc1Eilp8nJkZCYOJFnF/+a99//I0ql\nmV/98gvi4gbj4ODP2LHWSXCD4idSVLSXgwfzMBpNPPXUaIqLa5g6ZSG5Jxs5fqyQdq2FlOTnuXw5\nn717P6JfsBtVVa1ED5zLkCEj0Onsd2fUaq1rUpvNZurqG6itbeHf/m0G3t7W7/ufvj6d3/12A5GR\nTwEwZ04saz8/TEhIhF2dQ0LCCAkJ69Tf8X5MmPA8a9ZsROkkQSr15JlnXuP48f3kXShEpzdjNNg/\n4Wtvt/9hNHHik+zJ2IhEWoW2XUrqxKXdXmbh4XkoE/dWrFjBH/7wBxITE8nLy0OpVKLRaOwWtW9u\nbrbraRbuzY7MffRNSbI1UH2TR7Ejcx+L5j7xiEt2Z8evXMRnqHV8uqNKSbWrEy1XS/BiiC1NXVEJ\nZrOZhqJSBvn2uSn4dTBZ7JZ8UxjvvA7l+JhBbDlxGp/BMTQUlRLvHXBfv/gVRpP9scFkd+67XxgO\nBvu0trLfkO7GPAVB6Hm+2144OSkwGvWcOLmJJc9b15u2WCysWrmR6dNfuec8T53KJj7eQHi4NY/z\n58s5d+4kMTFDbpm+rq6VGUmxPPXUmG+uL+TLL4/w6qtT8PBwYfv2E5SU7KW5uYWJE+fwo1d+j1ar\nJTNzI1KpGWdna7Da3NyERFrAv71mXQu+sbGV995LJyDAE4VcTnLyXLv7rl33G375y5m29vbPf05j\nyJARYFGzYkUGgYHelJRUU1nZyK5dJykvr2PevFH8/W9b7IaESCQSu532Ll4soaz8Mjt3rmLIkClo\nNP3u+W/XWRaLhaysrZhMDUilrowbNwtXVzemTbUPbIcNS7b9u6DgLGtWZ+Dr60hpmY5hCU/ZpZVI\nJKSk9OzvW+H+dWuQvHr1akpLS/nFL36BUqlEKu14DBEZGcmlS5eor69HrVaTlZXFm2++edu8ev7O\nTnfXLTvXmKXo29tx+mY8q769HczSbv9bPWhd9O0Guw+fsV2LRaHg5LadOKhU6Nvb8VG74JJ7lfig\nfgyKib/pXk+mTOefm9ejdZQhN5iZM2zsHcsTOWAgaicXjuXmEh8YxKARgzB+E5x2ph7zx05l1e50\ndA4yFHoTC8ZNsV0/f+xUVu1KR+d487nveuKGdE+PnfxA71lv2hUJekc97md3J+HOtFotBQUXcHf3\nJDCw+4KpW4mPH8+6detZsGAQer2RzZuuMWfOLJqbT9jSSCQSnF3ukMktVFeXMjHV2vNssVgovFJC\nVtZZ5HJHjEYjUVExthUnAJqaJLahEaWlNRiNJixmZwICPFmxIoNnnhmPQiHn1Kkijh7bx/BhyWz7\n+iOWLo1BoZBz7lwJR4/tw03jT0RExxNHjUbNgAF+zJ07ik8+ucipU0fp1y8Ud3drGl9ftd2ENV9f\na0VdNQ48/XQqra1aJkyI5+LFErRaA5MmWYP8sLAAtmw5yrPPJqNQyFm/7gATJw5i69ajDBwYxOXL\nFfzsZ5OsPzBWrcHP77UH3tlUr9dz/vwpqqqqCAwM5sKF0+h0WszmZhY/G4K3dxC1tc18vW01U6f+\nAL1eT37+eWQyBXl5p2lv1zJ6dDLBwSGEh8cSFhZDW1sbgwd39CoXF1+joaGOiIhoHG9c3FroNbq1\nFZ8/fz5Llixh3LhxGAwG/vrXv5KWlmZbuP69995j8uTJmM1mXnjhBfz9/buzOL3S5PETeW/1P3GI\nCwMJ6E9f4vlnb79MWk8xfeQ4VmRsw2PwQJrLKwlzcKPQQ0LUmI4hDrWHcpk3bdZt83B11fCzOywJ\ndyuBfQIJ7BN413Qmk4l129JothhQWWQsnD4XxTfDI3x9fHjzmVvf19fHhzfvoUz3mk4QBKu6uloO\nHVpB6qRgSktz2bNHw4QJt98yu6v5+vozSLqIdWv3I5EomD37J8jlchobJZhMJttmH02NnRtq07//\nQLKzj5KQEMjbb6/niSeSGFBWi4vrMVxclKSl7WHWrB/bdsqbMmUxBw8cpK29BWdnJf36+eDjK+Hr\nr48SHOxrGz87aFA/Ll26RnNzE0GBFtvrMTF9OHf2MrExIzh8ZCcREdb1e0tKqvHwcGHHjlzc3U0M\njC4lN/c4KmUCsbHDKStrRavV4+TkgMVioeiadTdGtcqX4uJagoKsk6aPHMln4cKxAOTmXmFUUhRn\nz15j+fIMnBwVNDa2MWfuCP747kaOHbvE//yPdfMPiUTC3LlRHM05wrhxqff9PjU3N7Jz1z+YPj0U\nuaKCPXs38+KLk6ioaCMnu4yDB7XMnTsST08X1Opimpsbycj4F0mjfcnIOM2cOYlUVraTnv47oqKS\nmTz5GSQSid3E6oyML+gf0kJ4hIYdO/cxdswS3N1vP2lceHxJLI/JNEy93tgrepeg63vJzGYzp86e\nAmBQ7CC7X/vdpSvq0tLSwpq09ZSZ23Fwceb6uTzinpqF2t2N+pIyghtMzLrDttC5Z09RUHSVkL5B\nDBs09LbpbuXUudPkXyskNjyUkcOG31SPTzZ8hnlIGE5qNQadDu2hM4yKHUz+tUL69wlk+OCEO+Z7\npzTdoTf1wELvqMf3oSf5YbbLO3euYtEzQbankRm7LxAcvAA3twcbpncvn7kjR3ah0xVitoCzOpzh\nw1Ns51paWsjMXIfa2Uxrq4yU5EUolUry8k5RUnoYR0cpep0bqakLbpv/iRNZbNu2ip++nsrZs0XU\n17fg7u6MTmdgxoxh/PGPOxk6ZBrDh08AIDt7D80t2Sxc2LGM5V/+nI63jxvPPDMesPZKv/enA3h4\nOGI01hMR2ZexY2OwWCz8/v9lsGzZbygszOfSpf3U1RVz/fp1fH3dqapuYNGicURHW3vq1609z8SJ\nP6KxsZHPPvsN/gEqqirbmDnzNXx9/dm5czXl1y/g6qqgpqYOrVaPi7MT3t4aWlq11Nc1s3DReCIj\nrSsE6fUGtm8/QU52Pk5KBT/72TxUKicAzp0rZv++q3j7+DEgJJng4DBOn86muiYXhVyCxeLH+PFz\n7vh+7tixhkXP9EEqlbJx42HmzBlh+048cuQiFdfrGJ4YQZ8+nnzwwS7q6rT88pczSE/Ptkt76OAF\n8i5eZ/iwlwgI6NgIpLa2lrKyL21rR1ssFtasKWbqlB/YlaM3tWXQO+pxP21y72/FvwekUilD4m89\nfq0ns1jM1KikhI+2jv/qkxBH4YbthIdFEO/jz7iJt18befv+DC7KtXgNC+fItRLKM7Yz+w4B9Xft\nyNpDnrQdr2HhZBSVUrptM5NG2/dcNEpNeH/Tc6BwdOR0eRG6QC/b/cp2b2Nuqv0OUDuz9nBB0obX\nsHCyi0op272N1JHjaGlpxsfH1+7HS1NTI+3t7fj4+IqZ0IJwDxwc7FcNcHNX0tbW9sBB8t1cuHCK\nfsHVxMVZg6KjR4u4fPkioaHWzS6cnZ2ZPt26xr/JZGJ/5mb0uiaaWwr50Y+swfT16w0cOrSDpKQp\nt7zH0KFjyck5gLu7M9ev1/HSS9a2rLm5jd27T+HgoKe4eA+VlTU4OZm5cuUSQ4baj+sIGeCPq6uK\ntLQcQkJ82f71GRKG9WfixFgAcnLyWb8+iwsXSsDiTnl5CSEhEZSWXWb+BB/8/a3DKtLTj3D+fDHB\nwb6o1U7IZNZ+NKVSSVjYcCQSEw4KGZ99/jvq6+p58+ezCAuztoX/93/baGvT8u//PtvWc/2b36zD\nx6djkr6Dg4LsI/m89PIUsrPz+eO7aUybPpzmJi0VlTX8+CfjAFi1aitK5QJM5lMsWDAQgMLCKk6c\nOMDQoWNu+34pFBJbWyuXy+zaXQ8PZ+rqmjl54jJHDl9kzJhwrl6tRCqV3pTWy9sVS14xra32q1m0\ntbXi7uFkO5ZIJDg6dH/HlPBoiHdW6HYWi4Ud+zNYvXkDh0/k2F6vra3F0afjEZXC0ZH+A0L5wcz5\njEu8fYBssVg4dOU8XmH9AXAPDiSv9t43+zhfVdJxbb++nKkquymNRG+0P1Y62q7xCA6koKHy1vmG\nh9jyPVZ8mT/v2sDKC0f4/coPaGmxNrbrtqXxt/2bWX72AH9c8SEGg+GmvARBsOfrG82BA5cB0OkM\nnDjegL9/wF2uenBlZZeIi+tYJ33YsCAKC2+9A9ymzR8yfbqSZ38QTHycHydOWMvr7++GVnfn5Tnn\nzFnC+3//mqiojuFgLi4qrl6tYMGCMUyfMQS5Ig9fv2qemB9CdXU9bW3WCXDHjhXg5+eOp6c7hw+V\nIJFARKS3LUAGSEyM4Pjxy/z61wv4n/+dxsWLGzh37hQGfZ0tQAaIigrExUVJSUm1dSJelQSz2cy6\nde+SNFpCyIBG3NwL+cMfnubv77/A2rWZXLxYTF1dMxpXNUajyRYgA8TG9GPzphzbMmubNmUT0McD\nuVyGSuWIs4srRsMYLl02sHDhONt1QUEqzp49TmJikO21kBAf6uut7XVbWxvl5WWYTPaTnoOC4tm5\n8xxgXYZu06ZsAAwGIwcOXODcuSJOn9EybfpQYmODCQ8PYNeuXMLDA9i37wxgnQP1xfoD6LQaQkJC\n7fLv06cvR3Pq0eut7XZm5mX8/WPu+N4Kjy/Rkyx0u5Vp62iP7ItzVBTHrhZTv3cX01Mm0adPX3RZ\nOyB8AAD1JWXEet99XPrKtHW03TDsr6Km0jYu8K5uGGAkucWAo9kjk1m7awe4u2JpaMZT7Wqf4FaD\nlG54rdWoIyppGADmyFC+3LWF8UNGUKy00C/a2vNvCNeRtmsbT02/8yNEQfi+GzhwMPn5ctatO4vF\nLGfmzB89lKcwfn4hnDuXT0yM9ZH7yZPFBAff/OSuubmJASFSnJ2tj6eTRg9k8+Ychg4NpaqqCYX8\nzj3eAQF9SEn5Mdk5nzB2rDXoam3V4uTkQL9+vmzenMOzzyazaVM2KSmBhIUFsGPHCQryy3B2UVJX\n14KLi4YJE/oTFxcMWDh3roiYGOuwiRMnLjN37khbG/nU00N4//3Pqa+TUlHhjJ+fNVC+eLGU+voW\nGhtayc4p5N9fe5+vvvqQmFgV5eW1bErP4Te/XQyAo6OCefNG8dma/Sgc5IwfF0dFZR1Go8m2DGJr\nm0oGKrYAACAASURBVI55T4xi3boDnPv/7J1nYFTXtbafqZpR7733groQHYkmmujFgDtusa+TXJeb\n5KY6+RL7pthxiis4GNN7R3QhQCCBEBJIIIR673Wk0WjK+X4MjBAd1+Do+SPNnL337HNmZs86a6/1\nroIKRo0K44c/nMX27afR6fTY2oYSGBhEWZkzvb19ptCLqqoeYmPiyDm3l0mTjV77ysoWbGzcyMlJ\np09zCU9PS/bua2fc2Gewv15IKihoGCUlYj7+6Bg1NeXU1ZeSm1tKb6+GmBhfzMwsCfCPRaXqx9xc\nQWioFxkZpTQ22FJe3srx9F10dnUTHjaC6dMfv+03RSwWk5r6Ctu27kIk1uHtlUhwcCRDfD8ZMpKH\n+MapM/Th5WBcgO39vCnJvACATCZjecocdp44AlIpQXbOTEgef//x9GpsXZ2oulSI17Bwqi4VIrE0\n51rJNUJDQu/bP94zgJyCIpwjQmgtLiPB2/+2Nv4+fvz8iR/Q06PCwsKSjKxTpj7NV0uJcva+rU/c\nTeM2FZUMspnFEglaiYimlmYsbvGe9+mHPMlDDPEghIREEhLy7RokkZHxnDzVwJUrhQiCgFIRxKhR\nt3sOZTI5vb2Dv8tXrjSj111GpbIiNnYCaWmfolSKaWjox85OikIhQSRyYfz4VBob6ygtPYnCzJG/\n/e0onp6u5Jy7yvMvjEan03O5sBJBECi6Us2ECVFYW5uTmprINk0mCxaM4dNPD+LubkNXdy8AUVF+\nHD2az7Fjl/D0tKezo4cJE6NMc9NqdXh7O/Lss9G8/YcjuLqJkEpF6LR6kpKH4e/vSlOTE7W1lYwe\nY0l0dMT16+HLgQO5zJxpdAC0tnZTXt7EJ5++ioWFgoThgfzpj9sQi8HW1hJbO0uqq5vR6w2EhnhS\ndKWa6dPjOXmiiIiI0aSmGmN5J05cxKaNn2Nvr0PdJxAaMgM3Nw8aGiLYuPEiMpkIndaJ5OSxZGT8\nncWPGW9U4uMF1q7ZxfSbZNwCA8MJDAynubmZ+oZtNDU1Y25uhlrdD4I1SUmpHDy4HguLGvr6DISG\nzCQi4sFzSMzMzJgyZfH9Gw7xyDNkJA/xjSPSGwY/Ngxsj7m7uvPKoqdu7XJv9Ab8YqNprqym8Pgp\nOhoa8Y+KxFypZPfRAxS01iISiQi0dGTR9NvVMZJHjcO9rIRL5wtIiokiKjzijkkJIpEIy+t6Tjf6\nXDxfwPSAYMJDw+867sXzBUzzD2R3ea1Jx7mluIzh7j5ERURyeMsqrFKMJW0b8i8zLSjs4c5/iCGG\n+FYZN3bmfdsoFAr6+jw5d64cPz97Dh8uIyx0Oj09rbi4eHL23CaWL4+nt7ePnTuzWbbMGFpQWtrI\n2bPpNDReYPlyYwJyY2MHJ0/As88+xmcr30ZupuInPzEmuM2alchf/ryd555PISenlIaGdj7++ADF\nxfVMmhRFcXEtOTnX8PV1prKyiZoaA9ZWcmamhrNh/QmmTY/Hxsac/ftzWLYsCYVCTkhoFOPGzuWT\nT35JWLgNcrmUtWuu8thjr3Lo0A5mz3E0naeFhYL09IskJgbT0NDO2bPFpKYOx8JCQXu7iqNH87Gx\nVaLR6Ono6MHL25GDB3J5/Q2j7nJubgl/+MMBXn/9Y5Q3FYySSqXMmvX8bYlisbFjgYEERWMculFy\nLTv7Ko2NHXR0Dv6NuYGTkxO5uVYMizTH3d2WfXuvMWPGM4hEIqZNe/y29llZ6XR3txAUFIevb8Bd\n3+s7tcvKSufChRN0dbURGBjD8uWv3PsDM8QjgeStt95667uexIOg1xvQaHT3b/hvjEJhlBD7us/D\nYDAMKqrxbaBQyNBoNKzYvJ6sq5eoKC8jPDDkjtuffZ1dXK0sQ2quoOH8JaaExuHq/HAVqW6mv7Ob\ny2XXsPFwoaOhCaWFBd5aCXbWNuTqOnCLG4a1jyctuj6E+lY8b8pMvoGDnT3hwaH4eBljGh/kPbnR\nx9bG9q5hHQ529gT7B+Lq4sow30AunzyDrq6JaDt3xg4fhUwmI9TdlyuZWejqmhjlGUzssGjA+D5u\n3L+DU5cvcKXoCuEBwQ8WPsI399n6tvk+nYdE8v1P+fhPXZd1Oh0ikei29c7PL4yOdiuuFetBsMbd\no55p0zzo6CxGqRDw9nGguLgOPz8X7O2NN+D29pacySrFz0+Gr6/RGLW0VHA+t4K6unP892vJdHb2\nEBlpzIkQiUQUXa1Dp9VyraSOyZNjkUrFyOViJk2KISjInb4+LRkZhbi62lJV1crixT/nYn4XZWXl\nODhI2b07m5dfnoGZmYz09IucOnWW9rZS3vyfiURF+bJu3QVmz/ohjY111NUdo6qqwRS2cehQLv0a\nLUpzBRYWZohEIqKi/ThyJI/i4loiI33JPHWF519IYdr0BIqLa2lo6GDsWKNjwc3NnrpaC0JD75xs\nfr/3QyQSkZV1hurqCvz9XRk5MhSdVk1lZQ/u7rdrZwcGRtHUqKC8TERc3LS7JnqmpX3BmLEwerQj\nRVdzaG0V43Q9/O/m9/vmdsXF52lpEZGTc5hRo/XMmhWOmZkOibSD/fsOM3LkpP/I78e/I192TR7y\nJD/irN21hfL+LhCBr8yaJ+cs+tZe+70vPkM5OhZLmYy2zk7W7d7KE3d4/Sljkgmvq6W0spyIpFlU\n19ewae8OArx8SLiHKkd65gma2luJj4gm0G8gJGLSmCRC62opunYVbwdffL28CQsJY+u+nTjEDyRZ\n2Hq5U3mumJFf0/lW11az9ug+dJYKJL19LBw9meCbkjpuHNcopTTX1KG0tMBeacnC0ZMGtXN2cuK5\n+UtvG//zHRvRxwRiaWmUnftk61peXfrs1zT7IYYY4qvS39/Pvn2f4u4OKpUWG5sYEhImDGrj5xeA\nn18Amze/xfwFxmMxMb789rcb6entpbu7F6XSjIAAowHW1dVD0ZVLqNWWJCWFXH8dLZWVVfziF1MQ\niURoNLpBFTotzGUkJUfR3tFDcLA7ly9X4uxsS09PHxYWCvz9XcnJuUZUlB+qbh1isZjo6OFUVObQ\n2tKLRCzm44/S8PZxIiDAjbFjA0hNHW6KI3711XFs23oYQehm+XPjqa9vY/v207S3q/D2diQg0J3I\nSB8sLZVcvFhBZuZlSkrq+H//70nS0nIYMzbcFOOcmprI2exi0/VpaGi/rbTzg3DtWgElpYdpbanH\nzt4CjUZMdvZV/PxcGDM2mM2bioA7h+vZ2ztSeDkNvf487R16/HwnEhIyEHrS39+PjW2HycieODGY\njRsvEBwcyZ49n+Lqpkfdq0cuDxnULnlCEBvWn8fGthdPT+NzSUmR7Nx5BkcnLY+Iwu4Q92DISH6E\nyTx3hg5ve3zdjXfoXfWNnMo+zdgRo7+V1++SirC6XmDD3MaGFn3pXdt6uHvg4e4xSLots6yKuqMH\nmD3pdlmkf21bT1+wB1aBwWzLPctkVRfxkTG3jXczseFRbL94Htdo4/Woyb3EWHuPQT8uX4WtJw7j\nOXVAemj3seO8eZPxu/XEIdPxAODCgSN4TRl7W7sbdHS0YzAYTAkn7WhxthyQnVPJbusyxH8wX9fn\neIiHo6urg1OndqBQQmlpDa+/PspUZnn37ouoVAmmsKwbnDt3Alu7Aa9VVtZVnn12En5+rgBs3ZrJ\nxx+n4e5uj0qlZumyGDZuPMmf/riV1jYVSqU5Mqkzu3dn09+vIzbWn5UrD2FtbUdTo4rJU4IA0PRp\nOXo0n5qaVl5+eTobN57EykpJQ307zs7WnDp1GbncEpFIxPHj23nyyWCOHMknLNyL4uJaxo4Nx8HB\nmtKSuuvqE0Yj2WAQEIvF6K47D93c7Jk/f7RJRcPd3Z5//esIMqmM4mu1/OpXizl06AKCIGBlZU57\n+2DZNDMzGdu3n0YqlVBV2czo0f/10O9DSclRxox1p6XFksTEYADa21UcPnyBlJQ4brVHS0uvUF6e\nDQhU19Tx5ptjTN+ftWvTBxnJIpHotv6CAMeP7+DxJwJMyYQH0i7Rqx6spiFcv1639r2h5nE/amur\nKCg4ikwuwt4ulJiYr8utM8TXwfd/P/B7ilar5UDGUaqLimmpqgbA2s2F+pbbpcm+KaS6wYuFWKu/\nS8sBCltqBqTU/L0partduk2r1VIjqLFyNm49usdFcvbanSWXbibAz5/R9l60njrPudWbUKtUZKrq\nefeLj9HpvvpWkUE+OPRBJ5PccnzwPafseqnSW9uBsVjJB6f283H2If657jMEQUCiHbyoPsj1HOL7\nTXp6OqNHG296i4qK8PPzIzMz8zue1aNHYWEeH374NseO7XuofoIgcOToZzzxpC+LF/uzYEEge/ee\nJS0th5KSOry9rSgoyL+tX0dHE0FBbmRmXkYQBAoLKk0GMsCCBaORySTMnj2C6dMTyM0t4w9/eIqf\n/HQhs2cn0tXZxew5gcyZM5KUlFhWf36Uygo9E5LfYNSop1m1KouCggqysopoaGijtqaZ/Pxyli1L\nwt3dHm8fR+zsrdBq9XR3OSKXy5FItOzdm8PkyTHMnj2Cl1+ewcaNJ1CrNRw/Xsh//3gl584Vo1Zr\n+PjjA1haWhMSMpYPP0xDr9fT0NDGli2ncHW1Q6XSoNe5MGPGb4iKSkKpNCM5OZK1a4+TmBhEzrlr\nlJc3YDAYWL8+g4LCGsLCPBkxIgSlMhBvb9+Hfg8tLMXU1LSYSnID2NlZolb3k5FRgoPDgNHb0FBL\nS2s6S5b6sWSpP8HBZlRXNyMIAqdPX6GqqgK1eiAPRSaT0aNyoqysCYPBwP79l/HxHo5I1G8ykAGC\ngp2oqRZTWtqIwWAgLe0yvj4jUPc6c/Wq8Ubj4MFc9HoDXZ1W972pValUXL6yncef8GfxYj8srYq4\nciXvoa/NEN8cQ0byI4jBYOC9NZ/it3gGwyaOp62ugcayCurPXyRhWMw9+5ZVlvPXjf/iL9tWs2LL\n2ts0Jh+GOYljqThykuqcfKoOZrBg/P1Lid62ZNxhO0okEt1ZYu0BGJMwklmxo/FKjCVo/Cg8oiOw\nT05kx6G9X27Am7DSi+hXG3VJdVotllrhnsf7e3vRabVY3NLu9LkzqAPd8B4eg1dcFPLh4RxIP8yc\nUclUHTpB9fl8Kg+fZGbct7MjMMS/L6+//jqffvopAGFhYaSlpfHjH//4O57Vo8XBg1spLd3GT346\nnNi4Lj5f/c4D9+3oaCc01MqU79HTo0YukzJpUjQdHT3s33cWpbKQ3NyTg/oFBcXS2yPG3d2ePXvO\n0tSkZv++86bju3bl0HN9qyg3t5SFCwd04ceMCcfG1oLISE8aGtpJSzvPr369hGeXx7Fm7Z9pbTvE\n738/l4qKFnz93FiwYAzjkyIpLq5nz56zWFkp0Wh02Ntb09xkx9SpywCwtvZGhDHxjut/JWIxf/7z\nNqJjAvnr+89jZibno4/28/LL0+hW1aDT6ZBIBD76KI3CwirGjAnj44/383//l8aUKc8gFouxs/Xh\nypV6bGwsmD9/FD//37U4OFiz4tMDvPP2ZkTASy9NISPjMl98Uc7s2S9+mbeRtlYR4eFeZGRcMj2X\nnV1KaYkCS4sJgzywFy9mMWPGQHL1nDnDycy8wpo16fj6OvPjH0/jwMEPUKm6TG1SUpZSVurNpo0d\neHvNJjg4kp4egYsXK0xtDh++wrx5L1Je5sOmjR14eRrbTZmylOoqf9555yyHD9Wg7g3h1Vd/c99z\nKiq6yOTJA6GEI0f6UVt75UtdnyG+GYbCLR5BKisrEAd5mTyVwSOHk7tuO09MmYWfj99d+wmCwIYT\nB/BOMcZtaXp72bhvB4/PXvil5pEQHUOgTzAqVTeWlve/awaIcfMj73IxTmFBtBSXEeVye6KFVCrF\nX25Ne009Nh6u5G3fi5u5DWt2bmbx9DmYXT/vu9HY0oyF80A2dm9nF0VXLtHdr2FCbOI9r9G9WD5/\nGev2bqdL0GIuiHlh/kB29JnzZxFJpVzdvAcPfz+qy8rx9fVFOHuFF+cPzqJuam3BKmrgvJU21nQV\nN+B3XXbuYa7nEN9vNBoNw4YNyI2FhoZ+pV0Rg8HAK6+8wsWLFzEzM2PlypUEBAxk8Z87d4433ngD\nQRDw8PDgiy++QC6Xf6Vz+DZobKwjJ2c7VtbQ2SEwevRSHByMa0BNTRY//dksAIKC3ImNaaG5uRkn\nJ6f7jmtpaUVT44DHsay0gQXXDdqEhCBKSxuYNDmUjRsLgIFQLF/fAAoKOjibnY9BcMDKSo6rmxU7\nd54BQGEmx9VtJOvXldDS2oOtbS3x8cb3oaNDRZ9aw7vv7sDMTIaXlyPbtp1m8eKxxMc3U1FRy/bt\nzXR3q/nRj1IRiURMn57A558fQavVsnnTaRwcfdBpbVm2zKgclJGxC0GoprS0iTVr0qmqakan1eHl\n7YRELOWZZ5IBiIry5dq1WjQaLXqdBHt7B5QKJS+9NBB3vX37aV55ZQQbNx5gaspjxMeP5/PP/0z6\nsUuo1f2IRDbIzWS8/c4zg/q89NJU/u+dfcZd0AOrsLXTou4V8PNLfiCd4ZSU5ezftxlVj5j33j2K\np6cvNjaBPPXU7XHIdnYuVFc34u1t/AzU1rZyIO0iv/7NQtzdjeFtzz0Xz8YN+0lJGSgXHhMzYtA4\nSnM17e0aU9iLCDFKpfK2djf63un5e+Hk5EZZWRFOTsaKhN3dasRixX16DfFtMmQkP4KYm5uju15p\nCYzGb5R/0KCY3Rt0d3exctdm1AoJhi4VBvuBohhm5uZ0CffX6FWpulmxcxNqhQRpv44FoyYRF2PU\nzBSJRFhZWd+zf/aFHNKv5iFIJdjrJcyITaTwfBHT7iKlBvDEnEVk5WSTsf0wHsPCcA4JRKfV8v76\nz/jps/eW1omOiOLYjjVYTh6LuquLa1nnSXhiPgDrT6bzrJkZ7q4PX6lLKpXy9NzbtTFPnTvDWXUz\nTmOiCU+MoOHwKf78P7+5q5E7JmEUnx7bhXeS0fNRm32BJbHGH9gHuZ5D/OcQEhLCT3/6U5588kkE\nQWDjxo0EBwd/6fF27txJf38/p0+fJjs7mzfeeIOdO3cCxnXkxRdfZNu2bfj7+7NixQrKy8sJCQn5\nuk7nGyPn/A6efsZoaAmCwOrPt5Ca+jIAslvCncwt5Oh0D6ZNLpPJsLKOZfOm8zg4KKhv6Bl0/Ebm\nv1Qy8F3XaDRkZGxHLNHRUK9CELqor68lLm4JcXHG3IT6+jYu5tsQH2/Mx1i79q8UF59Ao9Fy9Wo1\nfn6u6A0GXn55BgB9ff3s3XsOHx8nJkyIwsfHmff/unPQGhMd7Ye9vRW9vRdpburF3q6D7OyjKJU2\nhEeoCQuL5J23r3D5ciUJ8UFMnhKDjY2FqSLdDSQSCW+/fYSoyCiys/dTUdnK7t3GinkzZiSg1xmV\nlHpUXaxZ80caGqp4/IkRREUlAnD5chV5eWW3jGksKS6TSTl2bDOPPxFgiuteu/YwQUHD7usUMDc3\nZ8aMZwDjzd6GDf+guuYw+RdPMDxhOo2NV9DpBRLipzF8+FjS0tZgYZGNQimhp6ePV/4rxeRFB2NR\nkIaGWrZufZ/WtkacnQJISJhMYeEppDIBX594pBIRSUmRXLpUwblz16iobOCzz97GxdWZ1pZ2XFwc\nARljx87G0vLh120fHz+OZ+RTV5eHpYWc8nIDc+b84KHHGeKbY8hIfgRxcXHFrddAU0k5Fo72NGfl\n8XLqY3dsuzZtJ/aTRiAWixEEgbObduA3yiiaru3rw0K4v8TYmv07TGMAbDl8xGQk34+enh4Ol+Tj\nM2EUAH2qHgqKr7LoASrMjUwYQX5NGeYhxh8WqUyG3tWWzs4ObGxs79rPwsKC5ybN4S+rP0al78fa\n2wO9TodEKsV73AhO5mTz2Mx5DzT/GxgMBjal7aRT24elSMaSmfOQSo1fn4Lqchr7u2isq0Mw6LFx\nsaelpYWjZ0/RoVVjjhQRcOFaITKlknB3XxbHj+d45jkQiZgTkYCv1+0e9SGG+Oyzz/jVr37F0qVL\nkclkjB8/nhUrVnzp8TIzM5k2zWiYjRgxgpycHNOx4uJiHBwceO+99ygoKGDmzJmPhIEMYGU58L9I\nJMLKesDgkpv5sm9fDjNnJtDV1cORIxX84CWjskRvby8VFVdwcXFHqbQZNKZK1U15eQl+vhHY2Iyh\nt7cHtTqb/PwaoqM9qahopKG+nZycciSSgVLSe/d9zLPPRrB27XGWPT4cZ2dbLl2q4KOP9vODH0xH\nq9WxY8cZ6uvlqFRqZHIp7h5iRowI5fjxS7xz3QP7xRfHaGnpxNHRBoVCjiAI5OdX8MwzkwCwd7Dm\n4MFcpk6NQ6VSk5dXzsiRwXh4WBMZaUlu7kX8/PvZvKmQd9+bS2lpPSGhniYFCxsbY5Kwt7cT6ekX\nmTAhiubmTvLzy5gxI4Fx43zR6/V88IEZM2cmoNMZ+OjD/fT2avj445Notf3892uT2LNHRNT1nbH6\n+ja0Wj2VlU1cvVpDSIgnxcW1mJnJ2LUzG4XCD7W6GTOzgevl4WFOd3cX1taDr/+9WLXqHcaNd2D0\n6MkYDAbe/csXvPb6HCQSMV98sYak8S8yffqTHDy0kukzvFEqzdDp9Lz//h5+9KOZyGRS/v63I8TE\nejBxYjgGg4EvvjjG/v3v8T8/mY1cLuPw4ZOo1TZs3ZqFk5Mly5dPwWAwsHr1Mbo6L5Mw3J+kJH8M\nBgOfrVxBauqPkMlkaLVarl0rwt3dGR8f3/ueS3LSXPr7+9FqtcTGWjzwNRji2+GBjOSmpiacnZ2v\n10qvIzDw9kz9Ib5dnpm3hMtFl2ltbyN+0TOYm5vfsV2/VIzldeNWJBKhkJvReCwLvUyMlU7ECwtu\nF1S/Fa1UPEiDWWv2YNq9AI2NDSjcnU2PFZYWdPXfXrjjZq6WFJNXVIinswtivWFQVr+uW41Cobxn\nf4C00+mELpqJwsKCfnUfFw+nEzt9CupuFQ4K47XKzj1HY1sD0aHh+HjeXTgeBuTZLCwt6O/rY8W2\ndbz82NPG+RYXEbZwJkorS7R9fZzZsI0d3VpECaFYWFqg7esjfdU6xj+5BMX1x4dPZ/LK0mfuex5g\n3A04eOIYEomEmROnPhLb30N8Pdjb2/PBBx98beN1dXVhbT3g8ZJIJAMFb1paOH36NB988AEBAQGk\npqaSkJDAhAkT7jqeVCoxFX/4LuntlZrOQ6fTo+mTm+b10ouvs2vXRv7vnXRsbFz4nzffRSKRUF1d\nwdmzGxk12p3SspP09/szIdkYllFUVEBJyX7iE1y5ciULC/M4RoxIxtNzNhcv5vD3v6WBqIm5c4eT\ncbyU4cMnYWOjpKOjg5AQBWlp51EqZDg7G2/mIyN9SU+/yJ49ZxGJRDz3XAq7dmXR3n6E0DAvrl5t\nIje3l6eemmg6pyeeSGb//hxSUxNpa+vm1MnL/P4PT1xXYhAQi0WEhnqye3c21dUtVFY04uvrREtL\nFxER3kRFGTh7toiZqf6cOnWZtrZu5s8fzbFj+XR09FBX14q7uwOxsQF88MFe2tu7KS1pwMrSmnHj\njLsVEomEyZOjKS9vJDDQnZBQLyIjvVm7toKXXopDJBIREuLBuXPFdHb2IpdL8fFxxtXVjrKyevbu\nzaO+ToejozVKpQNh4XqaGjtob+/Gzs6oCFJbo2HMaBfTGj9gxN/9c2Vm1sno0cYCI2KxmNlzEo03\nASGezJ8fRtaZCyQnp2Bl6YxarUWpNEMqleDo4Mm+vb2AHkdHPyZODDSNMWZMOCUldZSWNhAW5sWU\nKaHs2N5MW7sFCxcOM7UbNy6ca9dqTSESYrGY5AnuNDdX4+Xlw67d/yA52YOWlnNcumRNauqTD/AJ\n/u6/Q3fjQd6PR4Eb5/HQ/e7X4O9//zurVq3iwoULNDU1kZqaymuvvcZLL730pV5wiK+Pu4Uq3IyF\nQYRWo0FmZoYgCDhb2vDm0uce6nUsBfGgMcz6dGg0Gj7dvJ5OnQ6FAZ5MXYhCcXsslbu7B31ZR6np\n19Je34Cuvx8fw91jrs6cP0tmWxUuw8PIra1HrtFSdfAEymAfNC3txDl63jcmGaANLS4WxrtyuVIB\nAtRfKUZW1cy0J55n24Hd1DqYYRfpz7aiAuKq60geNe6u4w2SZ1MoaJYMKFG4+vuivO7KkikUOLm6\n0i0VBrW393RHcdPjJtmDZSZ2dXXy/rY1+ExLwqDT8Ze1n/I/T76ETDakD/d9JjY2lgsXLtyxQJBI\nJPrSCbfW1tZ0d3ebHt9chMjBwYHAwECT93jatGnk5OTc00j+d2HGjOVs3LAGCwsDqh4Js1KXDzo+\nZ84SYMmg586fP8CSpcYQNR8fZ7ZtzcNgmIlYLKbo6nEWLTKqJXh7O/P5qnSqqgqxsNDR0wOWllqW\nP2eswvfkU85s3nyIgIBgzM3NKSttJjzCiZqa1kGvp9MZmD17IGZVLBbz3PMpbN9+mscfT+Yff99D\nR4fKZDw2N3dx9Gg+p05dwcPdHi9vJ/74x23ExweSn1/F44+PxcxMRm+vBldXW8rLG2hp7uKppyYi\nFovx83PFYBAID/eipKSezFNXSUmJZeLEaPbtO8eqVUfQafXI5VKcXWzR6wXMFJYozDzQ6/WmQkZN\nTZ2Ehxs9v2q1hra2XgIDQqmpacPW1pKQEE+OHs2jtqaVp697uZ99dgpv/2Er1ta+/P73/4tEImHn\nzj+RkhKOIISxc2cWrS19WFp5MHr0kofOv+jt1Q2aY2NjB8HBRknQ+vpO7O19AZg2bTG7d3+BTFaP\nuk9g3LjH8fY25qTs2rUanU5vMp6amjpQ9/ZjY2N0omg0WkQiGbY2Lre066Snpx9LywGjsa6undKS\nbezZ28jPfjYDiURCQACcOXON6uoqvLy8H+r8hvj34b5G8ieffMLZs2cB8PX1JTc3l8TExCEj+RHh\n6TmLWb1rEy1iA5J+Hc9OX/DQYzw5e9FtY/xj3WqkIyKxlcnQa3Ws3LHhjoUvFAoFo7yCON5U7baY\n4AAAIABJREFURuQkYxnWxotXuFpSTEjg7bGVpy5foEVqoPlYM35x0TRL9fxk4fNUVVViH2Jv0hS+\nH1LdYDk1e5GMx/xi8U7yQSQSUdzdgmes8QfLKTSQvFPnSebuRvKtcmw3PzYTBi/w9goL2ptacL7p\nOXX3YN3QB5V3S8s4is+0JMRiMWK5HKfkRNIzM0hJnvxA/Yd4NLlw4QLw4FqrD8qYMWPYs2cPixYt\nIisri6ioAdksf39/VCoVpaWlBAQEcPLkSZ5//vl7jqfT6e9Y0v3bR0ZKyoBh3N8P/ffZsYLB11ap\nlNDS0oWZmRndXa3AwI6pQehh8WO+SKUSBEHgvff2DOrb2dHJ1m2rEAQxnV322NpaolSasWtXFiEh\nHmRlXcXf35UNG46jUmlob+tmxMhgU5yuQiHH0lLJv/51hHHjItDrDRw7mk94mBdLlyWZDLKVKw8i\nCEpGjljC2jW7cXSCV14xGuuzZiXy+99vYtHigXXMztaC2lqjsW5j68gvf7GRJ54ci42NBQa9AQ8P\nB55/YSparY6VKw9SXtaKt7c7K1acZ9w4Nxobuzl58hpOTjbknLtGYWEVlwu7iY+fSuapalpbC7C1\ntSDzVBHx8YPX82GRXkyYEMGaNR9iaWmFTm9UkhCJRMybN4qtW4pJTn7BeP1u+gzdWpb6TkRGzuTd\nd3czY0YcdXXtHDtajI2NJWVlLZRckzFjRpipf3Ly4DySG88nJqby6Sf/YOw4V1pbuygsqKG9Q4qz\nSyutrSoyT7Uwe/ar6HQ6Pvnkn4wb50Jraxf5+eUoFHI2b84kJSWW5qYuGptaWf5cMrt3Zw+qkuro\naEF9fTPW1vdPEv135UHej0cBGxslcvnDRxjft4dOpxu0vSuXy7/V8sdDfDVkMhnPL3ziax+jWwJO\n172ZEpmUHtndPQHtXZ0EjRnwoLhEhZGXU3ibkdzd3UVFSwMjlhoN+Qv7D2EhltLU1Eifpu82wf67\n0dXVSZCdCzn705G52NNb18QY3zC8vX3u7rG4T2Wk2SOS2XDoIGJHGwytXcwbPpBRPSsxiQ0HDyJ2\nskHf0sn8xCQOnj5O7r6D2Lg401Zbj6q1jTObtuPo7YWupZ3Hx019oHNBJBo0N8EgIL5+Dj09Paza\nvZk+uRhzAV6YuwiZ9M5hN0M8Wvz2t78FuOvn9de//vWXGnfevHkcPnyYMWOMCg2rVq1iw4YNqFQq\nXnjhBT777DOWLVuGIAiMGTOG6dOnf7kTeASwsPQlP7+a6Ggvenr6qKkRkZho3KVqaOilvLwBPz9X\n2tq6qa9rNXkSRSIRzk52dHb2YmNjzvHjhZhb9LN0qRs6nZ4VKyo5cKCdV18dRUCAK++/v585c+IJ\nCnJn3brjLF8+GYlEwrZtmRw6eB5XVzs2bzqLq+tUrK3t2LN7C0uWhvLzXyxm1aojgzyWbm4u+Hgv\nxNXVjZiYMRxL/4vpmFwuw8JcYYov1mp15JwvobdHw7jxEfz85zMoKWlgw/oy2ts7eePNFEquFxF5\n//1dREf78/LLM+nr6+cffz+NqnsEbq62vPC8DZWV5RQVHWDJkiTs7Mypqamgq8sZK8t4SktKeeyx\nVI5nbECt1qBUmpGfX46dnSUqlRq1+hpPPjWZ9et6aWvrxtranLNnSxAEly/93o0aNYmwsDjy8s4T\nGDiBN97wpLKyHGsrJTNmuD3QGObm5ixY8Cbl5aVo+jqYNGkJTk5ONDY2oFb3Mn++L2KxGLlczsIF\nb1BeXkqfup3YmImAAXt7R9TqHkpKDvPSD5IBo3rK0aN5TJoUg06n58SJBmbO+Paq4A7x9XNfI3nu\n3LlMnDiRxx57DEEQ2L59O7Nnz/425jbEvzGyWzyh0v67y1L5enhxuqoWOx9PADpq64lzun2BPJqZ\nwfDFc02GQeyMFI7/9WPWup9B4WDHjnUZ/HDe49ja2t31tXIL8kkrOo9lgDeSLgtacgtxSxrBZamO\nwtUf89qTLyKRSAizc6WitAJ7fx+aC64ywufeigEBfv78wvcHdHV1YmVlPehG8U7HispLsQhwQgT4\nxUVTcy6PJRGjsbe3w8bG9oFvNGcmT+Gvm1fhlTIevU5H24lzLH/KmP38+e7NWCXFYyMxerc+3r6J\nHy4eKmP9fcDCwgKRSMTJkyepq6tj2bJlSCQStm7diqen55ceVyQS8dFHHw167ma1jAkTJpCdnf2l\nx3+USBw+gfz8LHbsqEIsUgxSFYiKHsfF/BwuXarEzEyGQqEYFJqi11tz6KAGg6Gd2joVr79uTEyW\nSiXMmxfE2WxbNm2sQyqFkSMep6z0HC0tRSxcOMbkaZw/fzT//eMVuLja0tVpYMIEc/q1ekaOdCQi\nwpgIZ2trQV9fPwqF0VFVU11P0ZVVPPnkzxCLxfSpB0LP9Ho96j4zNmzIoKuzBwFYtiwJuVxKWtp5\nYmL8CQx0JSy8DSfHabS2Xqa+vp2ysgYc7K1ISYkFQKGQMyXFB51WYpLJCwoKobr6MJmZhQQEuKJW\n93P5ShYTJszD29s41zmzX2bXzh1UVGQRFu5EX58OuVyKRGJg9eqjREf78f9+twF/fy/iEwLQ6soo\nKSkkMPDBksBvxdbWjuSbdtR8ff3v0frOSCQSAm9x1ri4uN6xnZeXD/n5+4kYZkFnh4bWVm/Gj59N\nTU0Rzc1dODlZExbmxcEDeRQUVKPR6AkMGG9K8B7i0UQkPEBx8S1btnDixAlTdvXcufdXJvi66e/X\nfS/c/fDob1sAdHa1sHL/TnqlIuQaPUuSpqHqUbEv9zR6mQSLfgMvLXjCFD+8JW0XV7ubARHuIjPc\nHZzxcHEjMmJAHzPt2CFqfGxNsbvavj6yN+9i7FNG5Q5BENCfKeDZeUtumw9ATW0Nf/r8IwJmTMDe\n3ehNuLD/MLEzjEVONL29uJa2kDrZ6B3Lu5RHbXMtccOicHP2Mo1x8UoB4UEh+N6ip3yvYze3OZ55\nAp1BR21rMyqFmPb6RsZHxLJs3p0VSO5Hb28vh08eQyqRMjV5smnRfXfbapzHJpja1Wfm8JP5T3+p\n1/h34fvyHfmyW3u3MnLkSI4fP26K9+/v72fs2LGmELjvmu/zunz48EbMzNro7zegVAbR1laGjY2B\nbhXExc7Bzc14s3Lo0EYeW+JsMn6Li+uorwsnIiLaNNalS2fJytrOE08Ox8vLqN2r1epISzuPi4sN\nZmZyYmL86evr5/ChPAICXbl6tRZBEDh06AITJkQhEkFsbAAuLjYcOqgnOXkWNTUVXLy4D0tLEW3t\nBtzdDYhEWpKShpkqxalUajIzrzB1ahwAH390FEdHH2pqu5BKu2lva8LG1owf/nCWyUGRkVGIg/08\nXF0HvLLvv/9f/PRnqWRkFCAIRu1hVbcXzzwzuMR0WVkRWVmr+dGPp5me27LlFIsWjWXz5pMsvikc\nZN26QqamDJb0fNg1QBAE0tO3I5F0oFYLxMWl4ux8u6H7VTl0aCMLFzkilxt3UI8dvYyLy3wkEjEr\nVvycsHAX2tu7aWhoIzTUC2trcywtrPD1XYKLy5f3mn/X/KevyXftkZubS1xcHBkZGTg7O7Nw4UDB\niRMnTjB+/O0C3kM8elTVVJF29iSIRMT6BpMYm3D/ToC3lxe/e+mHtLZ2I5VKEQSBP6z7BJ8pxgVQ\np9Wybu82li8wVntaNH0OgiBQXlnOmsxDEBDGxaoydn94mF+88joAKUmTeG/NpyjjQhFJJDRlnMUv\nfuCHRiQSIUju7IHNzMniVHM5MU8uoOZyEZ2NTfjFRiNTDHha5Eolao3G9DgmMoYkG6MHqLNTTeb5\nLE42luE8LJSConPE11QxaUzSwPhN5TgNC7nt2A1On89i27mTWLg74xsTSfP+I9jZO5A4JYmyS1f4\n37+9Q7CXL0tnzDMZPTcnn9wNc3Nz5kxNve15uXaw8oeZbqiM9feNtra2QUl6arWazs7O73BG/zlM\nmXLnm3EwxopnZKSh6e8lNHQUn63cwoKFIXR0qDl5oovZswdivXt7e2lsrMXXdwT79tUydaoeS0sF\ne/acZdmyJDIzr5g8xwqFnG5VL0VFtcyfb1ybGurbmTkzYVDYhU7XCICnpy+enkYjtaDgHC6uVwkK\ncueLL44xfXo8BoOBjRtPEBnpS1dXLydOFKLV9WJrp8I/wJHuriQiIuLYsuVDVq48xMKFY6ipaSE9\nvYaQ4DOYm0/E2tqo0KHTSzl16jKJicE4O9siCAK///1OBEGgtraSK1dysLJyIDw8FiurwaFxNzSR\nb3jEb6D8GupmZGTsZnySFFfXQARB4F//WsfsWa9/7cWYWlqrkMsHbhq8fWypKK/hzJlNvPSDCQQH\ne7BjxxlefHEqNjYWCILAJ58cwMam5ZE2kv/TuauR/NFHH7FixQp+85s7F0VIT0//Rif2n4parSbt\n+GEApidPQalUIggCB48fobtXxfiE0V/bF667u4svThzAZ5IxPjHj4hXMi5QMC73/9ldFZSUr03ah\nlomR9+mYlTAWrAbiYaUyGdl1Zch3beGJOYvIL7xEUXkJZwovEP+M0aPqFhxIYV09R04dZ/LYZCQS\nCW889RLncnMwGPS0BUdw6NwZXIMDkcikNFy8wkTfO4dFZJVdwT3JKGbvFxvNxSPHUbW20V5agTBh\nHCKRiKrjZ3hh4py7nlN2WREe44cb5xYdwfmMs0wi6bbx3aIjOH88y3TMNIfSK4itzAkaYbzRkFlZ\n4D/aOJ5PQgwXOzoQJYbzwebVLJmSyhdH9qBVypD26Vg8ZjKBfveWobuVp6bP4197t6ExE2NugOdn\nzX+o/kP8+/PSSy8RHx9PamoqBoOB3bt38+abb37X0/peIQjCXRMk9Xo9YrF40G+gIAjs2Pkhjz3m\nj42NA1u37CQ+fj7ZWXUolZ7Mnh1jkmlTqVQcOfIJzy6PQxAEVq4sIfOUnOJre/nVrx5DJpPS0aFi\n44ZzPP+C0fHU0tzH8y8MlFh+/IlkNm06yXPPpQCQlpaDj8+0QfOsri6npeU05RXNBAW58fTTE1mz\nJh2JRMxrr82lsbGD3761k4gIR159NZWOjh727j2LRCIlIiKORYteIS8vh89WZtPRUcEvfjkFMzMZ\nX3zxGWPHPIutrT3jxy2gqmo/06cb1zeRSMSIEYHk5WUjkM/SZaHU1raz4tNfEx7uTEeHCltbSzo7\ne+jtNTonGho6bnq+l+7ury4rZhDacXX1M80pIMCSzs6Oe4blfRn0OoHz50uIjzca48fTL+Hu7out\nLQQH31AEEZv0p0UiEdZWlvj5DUnmPsrc1Ui+IVj/2GOP8fLLL39rE/p3QqfT0draiqOj4329fV8H\narWav2xYidf1stF/2bCSN5Y8x4rt65FEBiB1d2Flxl6eGDUZn5uKTwiCwJa0XTT2qRBr9SycMB0X\nZ+e7vYyJ8xfzcB4+4PFwjQoj/1zRAxnJa4+k4TpxtOnxx+u20tevwWdkPGAsGmLh5EinjwN/+9dH\n6AM9cBoejKH22qBx5Eol1+qquBFZJhaLGZGQyD/X/wtZQihRQXM4s2YzEV5+TIlNJCYiijtyS4yv\nqFeDd30v8154nQNn0kEk4ulx0+59XW71Uovufky4U0yxRIxYP/D8rXHHYrHYmOSolLL5+EE8Uwa2\nHXceS+fNhzSSbWxsee3x567///3YEhtiMG+88QZJSUlkZGQgEonYtm0b0dHR9+84xAORnX0Etfoy\nZmZimppkpKY+j0QiQavVsmfvp7i46FGrDdjZxREfZ1yXy8tLGTXKGltbozG0aHEM69efIWXKgOb8\n2bNHUakuIaDBTNGLSGSs2Ll8eSy7dqkZP+453nt3J3K5FCenaBITR7B+3VEkEhEuLmMpKKgnMdFo\n+PX0aLCyUrJ9+2l6etSUlTURGSmmpPQwwUEpBASEc+7cAczMWjBXmvHJJwfo7OhFpVLz9jvG8CtH\nR2ssLCxYft3QtrOzJCzMi7wLAzcHMTEJ6HT9xMZ5oFQad+CefjqeDesPkZKyhMTE8eTlHR0kh9bS\nogWhkGWPhwLg4WHHiJE+JCUNY9OmkzTUtyMACCL+9MdjhIUlc/BAFyJRC4JgwfTpXz08TNMnQavV\nIZMZzZmG+l7CQr/+qqUeHkF0d9exe3c2Wq0OGxs7AgKCSU9vY8P6DKyslVy6VMGsWYkDuv568weS\nLB3i35f7Bmj885///I80kguvXmZHzkmkLvboGttYmJhMaNA3W33qQMYRvFLGI7n+ZfdKGc/GnVso\nbm/AtlSEXKGgrbONw+dO8/xNRvKOQ/to9XXA1iEIQRD4LG0r//vUy/fdbvJwdSOn9jJKa+PWWF9P\nD06yB/tC66S3GIDWFoTHjCRjzSYcPNzQ63R4hAZTVVRMVUEeU1OMBrVHWDBFmdmEjhlBb2cXHQ1N\nBDoPrjbX0FCPytkKDxujWPvY5cvoP3Pp7gYyEGTnQkVlDXY+nnTWNjDcM4CZk4wKEs/Mvfu26c0E\n2jhTXlGNva8XHTX1+Fnam44F27pQdtMx/5uOmeZg60JNxWWayitx9vNBo+6jqaQc50A/WmvqEEvE\n9Kl6qLl6DXtXZxxvvp6yb/4mbIhHhz179jBr1ixWr16NSCTC0dH4acnLyyM/P5+nnnrqO57ho09r\naysy2TVmphpvOlQqNfv27mTSpAWkp2/lqaeCTeEBu3fno1LFYmlpZfIS38zNj9vaWpFKi4mKdqSo\nqAaRSMbnnx/hueemGtsJImJiRhATM2LQGO7uz5j+P3VqH1u35CGRiMjNrSA4xAql0oy6unZ+/esB\nXeG1a44QEBBOfX0Rv/7NPNPz69cdp6W1mw8/PIa7uwMtLWICAgY7P7RaA/39Taz+4o84OthgZqZA\nrVYSbRj8G1BdU86xY6vR9BuYOfMFPv98J46OBnp7wdNjPHV1l265FkaHQEdHL//78wH5tb++t4cR\nIyZ/7epYEyYsYvXnK3F0MqDuFXBzG/uNOLWSk+eyYsVb2Nj0o+rR4Oc7HpFIwMfXkaXLjLuKo0aF\n8KtfriM8wgdBsCI2Zkjk4FHnvkayl5cXEydOZMSIEaY4SpFI9KUliB4V0i5k4TPZWNGHSNifnvmN\nG8kiQOAmuS8Eamqr8UwIwzP8+p16WDDXtuwf1K9R3YWlw8B2E462D1TmMyggCK/iQq6eykYil2PZ\n0cvsx++ui3oq6zRbso4gUiiouVrMzPhhSOVydFotXc2t2Lo4EzU5idbqWmxdnWmprmHYhHGEjE4k\nZ28aw+fMxD0kiOKsdTRXVNJWW09CRBSLpw1eSO70I4Qg0NzSwvoju+nDQG99M88tWGbKrJ49aRpn\nzmdTdraIaGc3xk2fdM9zvxOzJ00j6/xZSs8WEePsyrjpA5nTs+5x7OY2lz64zKVjJzAzV2Jrb4/8\nag3q6jbKyotxCAskZ/sexjz3OPkHj6Lt60OmUKDX6jDXPlhxkSH+M8jJyWHWrFmkp6ff8WZ3yEj+\n6jQ21hEUPHCza4z5NWoKS6W6QfGzvr42FBTko9F0ExgYQXZWD56e3djZWbBpUz4x0UZjsKamkuPH\nDxCfYKC6uoW5c41xxVs2n6SiopFNmzKZmvKaaVydTkdOTia1tVU4ODgRE5OIra09Y8catY8FQcDf\nv5ri4h3MmBFJv0Y76PNgbiEiL+809g7yQc/rDQYiIrxpa9UT4D+fsWM8qa2tYsvmHSxYGEVlZTNb\nt2YydWoMBoOMadPCANi27TybNvbx/AtxKJVy/vTHg0yfMYyYGG8EQWDVqk3MnPEjpFKp6fXkciVp\naRlMmxZGdXUbZ7MrmDo1Fnu7wSWWnZytUKm6H6r89K2oVCry8rKwsbEnMtKYiKhQKJg799VB+Rnf\nBAUFOUyfEURcnPFafP75eQ4cuExI6ED4o4ODDVHRo5iQ/Cy2tsbww6HdvUeb+xrJI0eOHPT4AcQw\nANBqtSxfvpzKyko0Gg2//OUvmTVrlun4X//6Vz777DOTxMwnn3wySIrou8Zwi6fU8DV4+i5fvcqJ\n/Bz6evuZnTQFh1sKY0xLnsJf1q3AI8VonBdtT6NTqybOa0DyyczcHF9Pr0H95HrQ63RIrqse6Dq6\nHlhT+LHpc+nr60Or7cfK6u5bVGq1mq15p0i4rmHsXx9P2j8+xTcmkprLV3H29+H46vUkP70MVVsH\nF/YcZMp/GQ1umZkZvtHDaKmspvrKVRy9vHAPCaKl4CpzRk66rVKfi4srNs0qetraMbezpfpENo+P\nnMTqA9tRxoZSf+YcPuPi+Sw3nbhiF+ZcV6sYFT+CUfGDvTMPy8j4REbGJz70sRuInO2YtHBAA7nl\n1Hl+uOApBEFg866teC9dgFgsJmrKBArTTyLt7iPAyY0X5y37SvMe4vvFDZ3kzz///LudyPcYP79A\nTp46gp+fUQnhypV6U6U2Cwt3SksbCQgwGkCnTpUTEqJm0mQ/ss4cwM8vmpMnelCr24mPfxJ7ewdy\nzmegVF7jqafdefcvu/jD2wPliBcuGssf/7iV116bw/ZtuXh6+qLVatm16x/Y2fcweowPAQEi9u1b\nQ4D/bFNVOJFIhIeHN2Zmy9i86SSVlQa6u3uxsjJHr9fT3i6iu6uAHpV60PNNTR309Wl5/vkUNqzP\nxs3NEw8PbxSKx1nx6V56egv57W+XsnnTSZ5dPsU0z/nz43jrN0c4kGZAp+vCyzuEmBhv01xGjnSh\nqqqCgIAgU5/AwHDq663ZsD4bOzsXnnnmD2zZfIyLl5rQaLSYmcnQaLTkXahkasqXD4NobW3hTNZq\nFi6MoKHhGvv25TNz5oDk5TdpIAM0N19j4qSBazFunAcX8ytoaGg3GeiNjR2IsPvG5zLEt8d9jeS3\n3npr0GODwUB5efl9B163bh1OTk6sWbOG9vZ2YmJiBhnJubm5rFmzhtjY2Ief9beArUFCX08PCgsL\n1N0q7ISvJudUUl7KtoIzuCfGIhEEPty9kTcWP4u5+UCym0Kh4H+eeJED1xP37OwdCUydTOHxU8Sn\nGg2v2txLjPT258CxQ0QEh+Hl6cWyGfP4cMsX9CqliPt1TB+W+FBbWgqF4o4lpW+mrq4W98gw02M7\nN1dsXJxBJMLOzQW5QoFbcBBnVm9kXEg0LlEJg+7s1d0qcvcexNnbi/gnjYl79h5uHMrIIiw49LbX\ns7O2pTD7Atr+foZ7BOLr7YtGIaUhN5+41KnGLWgvD7IOHefSuk+QymUEWzsz/w4qEPfi6rVr/PKj\nv6GXirG0t8NKL8JMIkXuZI9Mo+PxyanIZXI+3bWR2u52utrasXdzRd3ajpOrK9q2Tmo7W3Dw9qS5\nogqDTs+1smtY2tvR190D6j7e2boKbXsXV0uLcWyporWuDgtra2xcnNBpNMwYOwGFQkFbexur9m2j\nXyFFptGzbNIMFGYKPtm5gRZ9Hz0dnbhY2PLSvCW4u7o/1HkO8Wiyd+9efve739HS0mJyUIhEIsrK\nyr7jmT1a5OWdobn5PGZmIlQqS6ZPfwqlUklY6Fw2bzqOmZkEsdiNUaOMScwjRkzi5Kl95OaWoOkT\nkIjlTJpkXKcmTAxh48aLTJ40OAyxs6OAadOGARAb60d9fRtubkZPdW1tK+PHD6OpqRtbW2NexMmT\naSxZGsLp00VERhp3xBYtimH9uuMmI7m/v5+PPvoJfn5WdHb1UFfXyFtvXcPN1Za+Pi1anRKDXoRB\nULN+fQbm5mZUVDTh6GDF3LkjqKtrx8bWkebmRrKztwAqKipLePvtJxCJRAQGuVNf3467u3GeDQ3t\nWFn3MWbMFGQyGQcPrqe/X2uSPaut6cbH5/ZQMzc3T5MkHsDkyXPRaDrYvTsbhUKORqNl1uzhNDTU\n4+b25daunJwDPP10vHHegUpaW0upra3Gw8Pr/p1vQhAE9u3/HKilo70dTb8ORwdXfH0nUFNThJVV\nDxqNgLNzAtHRA05Cg0FqMvoBSksaUamMSiW/fWs9UpmErs4evLzd2LvvEgguLFz4nxeq+n3jvpbf\nP/7xD37xi1/Q09NjWqTDwsIoLCy8Z79FixaZZOMMBsNtgtrnz5/n7bffpqGhgZkzZ/Kzn/3s3hOV\nSkzJSd8Gbzz3PGt2bKW9X42L3Jwnnl3+lWKp8q4V4J5ovCEQiUQ4j47lcvElJiUl39JSyVOLjRV6\n3lr9Ke119SCCvANH6FOpcNTAuQgR9iGBXCw4zYQ2fyaPG8/vfvjfd91uqqysZMfJYyAWMzIkgpHx\nDybzdjMhIf40ZuzBLdAo2N7b2UV7fT3+8dFETjQmtDSV/3/2zjuwiirt/5/bS25y03vvJIEACT20\ngPReBMXKinXV3XV3f+92d993X7e4765bLKuiqyIKShGI1EBCCBBCCCkkkJDe681Ncnv5/XHxhkgv\nKmg+/yQzc+bMmZk7Z54553m+Ty1u3l6422XMmZrOnzb+B5/xo9B2dKJpbmXZL37MiW07B9Urlksv\nua/7D2fRFexO3BiHe0tNcRkdnU0orXa0Esmgc5SqXfGJj0GpdqOxroGSs6eZNHb8IIPiajz7pzdw\nDQlg1LxZzrIFu/YwerJjxHhz1m7EQiFBc6fS9vk+Zl6U7KQgYy8aUz+znlmHQCCgIGMv2O3Ougoy\n9jLq/hXO8t27rIyeP5tTn+9j5JyZzvWbs3bz63XP8OonO/CZOWHQeplITNDcqQRfdMzN2Y7yg67j\nhUCar/MZ+Sr4tp3HrfL888/z97//nYSEhKHRqZtEo+nGYDzJffc7NNk7OrRkZ+1iypQFhIREkJSU\nAFw6LT75gssDwKGsVwdtE4suvRci8cC65Ssm8b+//4wxY6LQG/RUnGskKSmKQ5V9zJ3rUKGxWM0I\nhWKn0eWs56K63//gT/zyVwucZT788BDtbT1MnDSM5uZurFYre/eUMm1aHKtWO/rhzs5eXnzxQ07m\n19PW5sq8eUvZufNVHnl0OBs3ZjFuXLTztzR5ciL//MdOwiN8kckk9PToSEoMx2KxIJH348LMAAAg\nAElEQVRImDZtOe++8zqxcXK6uw2IhLF4fmkG9ErI5RJWrkxzLhcX12LQ37zrgUg0uD93cZGg6zcO\nKnMi/xBabQMClEybtuSy7+zs7J0sXOiNl5fjXZaRkc+kScP49xubWfu96XhdcF3csSOfnp541GqH\nBN6UKUv4w0svMG16PFqtHolERFOzBYW8h9+8eD8bNzqCa++7z+GfbDKZ2bZ1A+np9930OQ/xzXNN\nI/kvf/kLhYWF/OIXv+Cll17i0KFDlJeXX7NiFxeHP1Jvby8rV67k97///aDt9913H8888wyurq4s\nXbqUXbt2MX/+/MtV9Y0gFAp5ePm91y54nchEInpNJsQXUnzrujR4BlxdGiZY4crRyipS5g9M35/4\n5DOiRzpGKwJGJZFzJJ+Zkx2d4+Veolqtlld3bydkuiNwLqO4DIVcTnJi0g2138XFhYXxyWzdvB2R\nXEZLxXnkEinBwwb8tH0jwjj1+X5UsSPw9vLixbVPcv/Pf0TCjGmMnOPw4e1uakHfo0WhdqOnqYUo\n98EdrlarZVdWJlH3Dsw6eMZGUnLuLGvnLualt1+nrqiE0BFJWEwmOpuaiB7nUNRwDw3mfHEVVY0N\nFHe2gABiXDxYt3oNV0LqoUasVA42vC8a3TdJRdhwSEBJFYrB5RQKVF4DU2tShcOwu3j5cvVKFfJB\n600XDCqTWDRovVEsApEQl4vWyZQKx/qbpKOzkz1ZB3FRKFk0e85Qivk7HA8PjzuqX7wbaWioIylp\nQNXG29sNs7nhhuqwWf2oqmojMtKXs2dbEAguHQ212/ydZc6dayU+fhqxsekIBAKSRzgGMFJTB/x0\nx4+bwZZP30Ik7qW2tpXOzj7y8qrx9howLF2U1kFGtJ+fBw31nSgUMhYtGofBYGLXrgJGpwy8S7y8\nXHFXezBs2KOkpjr0emUyhzHp4iInPj6YvXsLmDVrNHq9EblCwunTtfzgBwsxmaxs3dJAaqqjL5PJ\nZCxb9jw9PRpiohU3pNQQGTmGjIwDzJuXgE5nICeni+XLLp+E6XqIihrLrl37mT8/Eb3eyOHDnSxf\nNpBl73DOLoYP7yc6OgStVscnm99i0aLHL6nHatPi5TUw6h0fH0x9fQf+ASq8vAbcFBMTfWhsaHAa\nyTabjXHj4klOjkChkCGTSWhrLcXDU4nBYMLVVeEccQdHmnCZzHzT5zvEncE1jWRfX18iIyNJTk6m\nuLiYRx55hEmTJl1X5fX19SxbtoxnnnmG1asHKww8//zzuLk5/JPmz5/PqVOnrvoysFisd7UD/OzJ\ns/jnpvUQFYJZZ8BHYyRyfOxlz6mmroadx7Oxi4T0NzQP2ib8koFksdmddXwhBVfd2YJCKGH1rEWc\nOVuGd8pAVjvf4cPIySskPPj65cZMJhNarZapE6YzbWI6dQ11vG3ZRIu+j6qC00SOdkSH15wuQVNV\nS4mrJ8qD2YwbPYZI/xD8ox0do81mI9w3EN+aLjSGehK8/EifMsPZ/p4eDa9s3YAkIYqmc5UExjo6\n/uZTpcwZOwO1mzd/+OEvOV5wgsKsk+j7+gkOH+h0W86c4+jho0TOnkroFMc0WXtLGzt272PKhDQu\nh6lTg1UkxGa1IryQ3tnY3++8nhKdGbFQhM1qxWwwDCpn0unoae9wrjPp9WBn0PLl6jXqBq8X6830\n9OiRGCyD1kv0ZsQiu9Pf3G63Y+jTIZGLL/ndXI8EXGtrK//ev5WwGWk09+t48Z+v8KOHnrijRii/\nLVJ2t5pxLysrC3DM2j333HMsWbLEmbRHIBAMJXO6AcLDIykoyCY83OFfXFfXiUp1YxnZ0tOXc/Lk\nYY4fb8THO4ypUydcUmb69GXXLHMxbm7upKWtZfv29Xz++WnAQnp6Mk1N1WRmfkJ6+gpMJiVabT9u\nbg7juqmpE7CTlDSQfGTGjGSOHDlDTIzDcG9o6MBqVeHi4oLNZmPr1tewWpux2Wz09xuIjg5AIhGx\nfdtRThfV8POf30tGRgGbN7WhVntd1rD8wlC8ESIiYhGJJGz8MA+BUMLSJd+/pY/yiIhYxOIr12ez\nthAd7XhnuLkp8fA0XrYehdyX2tp2wsIc8VDFxTXMmJFMRkYx9XWdhIQ6Bm5OnWojZfSATaJQKGhp\nseDurgIcaigSiScV56pYuFBGW5uWHk2fM7NhX58eq1XJEHc310xLnZ6ezq9+9St0Oh3bt2/nt7/9\nLWlpaZw/f/6qFbe2tjJt2jReffVVpk+fPmhbT08PI0aM4MyZMyiVSu69916+973vMWfOnCvU9u1I\nf+rqKqOisgKLWUBw8OX9qIxGI3/+5F2nskZnbT0NJWUkz59Fb3sn9buzCJw2HveQQDQNzQS261k+\nxzHq+sG2TeQ2VOAfE0lfZxeG+haeWfYAO5rL8YtzdB6G/n58KtpYcp2+u1nHj5BVfQaJhxpbUxuP\nzVvBm/u2OTPrFR/IQtejxQ7YDUYm3O9wsWktOsOiyJEE+Qfy1taNaO0WVAIhjy1efUmAYM6Jo9S2\nNFFTU0XwijnUFZXS1diMVCHHZrNhbu3kpad+gkKhwGQy8cqHb2P2c8fSr0PZpUPo6YrBYKStpwu5\ntwcJU9MGj+CeOMeqBUsve35mcz8/+NP/0Gcx46J2JdjFHZlYikDtgsRk5aG5S5FIpLy55UOqu9ro\naGvBOyAAk0ZLcHg49OopralEHeRPR0Mjbj7eWExmlG4q1Cbw9/HD6iLjbEkJvWYDnkGBaJpbcRVK\niBme5DyGWu2OTqfjPzs2o5cInOulUhlvbfmQ2t4udFotEd7+rFt2/yVC+W5ucrbv+Zymji6SImNI\nvoxc3nvbN8G4gWn7jtp6ZqmCSUy4sVmFr5IhI9nBtGnTrvrxcqckc7pb+uVz54qprc1FIgWb1Zv0\n9OXObd/0b27fvn8hlfWwdOkEp9GXeaCMoKCVeHh48Oprv8TH20qPto/eXj2urgp+fpG02oYNR+nV\ndmO2WHFRyuju7ic4eAozZiwjO3s36TNsiEQCMjJOYjabKStrJyDADW9vFxYsGINKpeDDD7MYnrSO\ngICAKzXzshQX59HSehKxWIBIGMyUKTcWE3I5bvZ+7N37JvevGQj+//jj08xIf+6yZTMzP0WrLaOj\nsx0Q4OUZRFLSXOrrzyIUdWAy2omISCMmZnDf2NRUz+nTu1AoBeh1CmbNWsMHH/wds6Wazs4evv/9\n+Rw/fg65XEp1dTvz5/3E6YN+t/JNPx+3i5vtk69pJJeUlPD222/zl7/8hZUrV7J//35efPFFfvjD\nH15tN55//nk2b95MXNzAdPy6devo7+9n3bp1bNy4kb/+9a/IZDJmzpzJb37zm6vWd7d0xlfjen5s\nFRXn2NFZiU/EgHZw465DhPj446P2YNaUdHLycqltbSbML4C0sRPZcWA3FZpWThcVMuOp7zlfrnlb\ndhDs4U1PYwuKiGCEMikuXX08t+axQV/gRqOR4pIiDHo9la2NdAnMCMxWZgxPZfvpo4SnO2YO7HY7\nDZ/uRToqFqlSQV1RKUKxiP7uHrprG5jzo6cRCAQY+vvpbmzGv1XHQysG/LHsdjs1NdXY7DYiI6IQ\nCAT85m8v4TY+Gd+IMDrrGmipqsFut5M0fTLa9g502l6EQiHSsnrMagXNjY14JMYgd3FBJJHQ39LG\nY6kzOXziKP0pUWha2+isayB6rMMFo/5kEctjU4iLvrxyyu3qAI7kHyPf0o1nhCP6ue5gLj9YeL/T\n7eilT94hePKAMkb7kZP8aNntk/F6d9uHGIeFo3RX015WwRilL5PHThxU5stGcntNHbNdQ4aM5K+A\nWzWSL6a1tRU/Pz/6+/tpamoiJibm2jt9TXxX+uWvkuzsVxFL+li0aECZ5/TpGizmqYSFhQNQU3Oe\n/Qdew8UFVCo5ZrOV6dOHU1vTQ3GJibVrY5yZ3gA2ftjCPfesYv36P/OTn45xPvN2u52PNnbR29eM\ni0sHY8fGUl/fTmNjF/FxDxJxUUKjkpKTNLdUoHLxYcKEwZKaBQU5VFefISBQx6JFjlibs2dbqK8L\nZeTIq4+gX4ubvR81NeepqNzBmDF+nDvXhVCYRGrK1GvveAscPbqPkaN6CQnx4rPPjg+6hxUVTeh1\n4wgMjLxKDXc+3/Tzcbv4yozkL+ju7sbD4/amebwRviudcU+Phn9mbid0oiO4zqQ3oCqtY9X8y4+E\nHj15nBNWDW6B/hx8dwMz1w1kMKrMO0lwQjwWgxH/eg0zJk65RKOytbWVV7Z9gPfoRLqbWmgqryB2\nwhgC42I4vy8bsauLM4seQOfB45ypOY86JAA3H2/Ckx2uHLWFxVQcP4ncTYXVYsE7OAiJ1c6ipHGM\nSXakZP3nh+sxhnijae9Ae64GDxcVWpWU0fNnOevP37IDuYcau82Oi4c7bt6e5G3ZyaRVy1EH+GK1\nWDi4/gOSZkzFYjRRX1rGM+lL0JsMZOub8YmMoOlsJc0VlZgNBuIVnjy79soRxtfbAezO2k/OqRMI\nJWLUEgUefj64SuUsm7WAfTkHyTl1Ak2/lr6+fkQyKdZ+HSFh4YyJS0IqlbHzRDbuUWHEThhL7sdb\nMHb30FHfgMrXm/6Obh5Zfh9LZy9AIBCwJ/sAR0sLMeoNjB8+ikUz5zpfcq998DbVHS3IbPCLp19A\nLpdjtVr5w/b3CJ00YIRrjxTyzLLBvtitra28sW8LYTPSMOn09B45xQ8ffHzI3eIr4HYZyX//+995\n5513OHXqFDU1NcyZM4cf/vCHPPHEE7ehlbfO3dwv22w2urq6CAsLQCy+1IXp62L37g0EB3cjk0uY\nMCEei8XKW2+eYtmyHyIUCjl/voxuzSFsNh3x8UFERgZQWlrLm/8+wVNPvYhCoSAz8zUefsSh/LBr\nVymBAXOprCwkLLyLmppm0tNH4OXlxkcfFZIy+kFaW+sxW/Lw91fg4aFi+7bzLF36Q2cSjuPH9xMc\n0kJycghNTd0c2N/L3LkOSbuDB7cxOsVEXV0rqakDvtCurko+2tjMzJnXl7zpStxKH6DX66mtrcbf\nP+C2p6W+HPv3f8DsOR4oFFKqqlpobdUwffoIrFYr775byP33/T/6++9uv+Tvep98zT0KCwtZvXo1\n/f395ObmMm3aNDZt2kRKSsq1dv3OYjQaOXYyD4VMzpjRqTdkhKjV7kz0j+TIgSPYpSLURnhk9SNX\nLF/V1IDH2HhObN9FcGI8HfUNeIcEY7Na6WpqcYyoqlzormy6rIj7tsP7iF/skFTzCQ3BpNPTWlVD\nYFwM6rgIOo8XYRvj8JPVNDRj7tAQkzaOjoYmp4EMEDZyOGdyjjJ16QJ02l7OHjmO3FVFVvlpxiSP\n5sDhg0hTh2HX67G1tTNm7Wrsdju5H28ZfP5SBZGoOCfRO32dQ0YkoA5wTFmdzT3O1IdWI7kgWSdX\nuaDp7WHSuIkce+9N9u/LQiSV4BkUgI+LGw/MXs6tsnHXFvJaaoiaMQG1rw99nV2cOVlIfNp4fvD7\nXyAK9CE6fTyWsrPExcfh7u9LX7eG8ycKONR4jvDk4Yx78F76ujRs+f3LzHjsYRrLzzJ66QJn2f/8\n699o+ntxUShpD3Qjatkc+jU9ZB7OpXOLhrXL7+fPb/4D+ZhEhgeMp69bww/++Gte+/UfEQqF2C3W\nwY22Xfrt6+fnxzPz7uXQ0RyUCgWzH1h3RxnIQ1zKG2+8QV5eHgDh4eEUFBQwduzYO8ZIvltpaKih\nsHAL0TGuFBXpiIm5h5CQhG+kLbNn309OzufU1Z3h2NEj+PlFMH/+087Zvprak6xalcD27ceIjHS4\nQyQmhpE2uR8vLy8MBgNdXQb+8fddWCx2XF0TGDc2gvNVnyOVKrBYbJSW1nH0aAVjxzyEp6cXnp5e\nlJcLKC4qwWo1MG/e04Oy1Ol01SQnO2bfAgM9ULrUObcJhM1ERSUglQp56829DB8RDkBtTTv+/jee\nxOl2olAoiI//eu6jyWTifFUpp08HXfA/trF/32lKS+vo6dEhFgVcUPW6u43k7zrXNJKfffZZtmzZ\nwpo1awgJCeH111/nqaeecnbc32UuJ7nW39/P/328Ht+0VEw6Lcc2vM2za753XcbIF/VNnzCZ6RMm\nO5dPFhVSUFUONhtzxk0m5CJdyABPb4pq6lH7+hA7YSznTxTQVF5Be3UtaQ84/Nb0PVq8ZFeQ1PqS\nooJc5YLZ6Ah46K9txkUiI/+zz5EqFbSXnEUd7E+ijzc2m53mivMExDim51qrqnH386XpXCVmg5ER\ns9KpOVVEU30jAD19vchjfKg6VUTSdIc/s0AgwC86grLDuUSmjKK6oBC1yc6MWVNprz7lbJNEJqOv\nW4PKwx2b1eY0kAFcvb3oq+mm7Fw5Z+qrGbNsASKRiNNbdvHkfWvx8LhU0/NGaTb0InVRovZ1BHqo\nvDwRSSTIlErs3mrkri6o/XypP3MWd3+HMa/ycEckkSCSSPAIdAQJqTzdCYqPxd3fl4aywWVDkxJo\ntxvRGCz4BDim013c1chVKtrtjvvRZjMyIsDPuY8yNICOjg58fHxICQiltOwc7mHBtJ4sYdmIy095\nenl6sXz+4lu+JkN8PVgsFqTSgcxvUql0SJHkNlBcvIdHHh0Y6Pn4o8xvzEh2JKaYB8wbtN5qtXLg\nwCY0mko+/rgGy5c+hC0Wx9+srC08+9x4JBLH63zP7hK2bn0Li7WZjg4zTz7pSLY0depwPni/gBEj\nHOcdH59MfHzyZdtksQ7+yLZedGiLxbGturqVe1dNJijIEejW2NjF5xkN7N33FhKJAFdVDKmp374A\n09zcPRhNDdTV1rJyxXBi44IA+MlP1vPyX9Y61Uj27yukpKSEkJDrD5If4s7jmkayTqcjIWGg87jn\nnnv48Y9//JU26k4nMzeb3Jpy7BIR7iZ4evUjzq/w7Qc+J3TOVIQiEUq1G902OwWFBaSMuvLIe0dn\nJ2/v2oxRIUFstLBk7FQSYuMRCASUnSsns/kc/hMco7bv7s/g+YWrnaPC6ZOm0rRtE3VdGgCixjgi\nays376LreBE2sRAvm5hlKx+47LHj/UM5VlpOYGI8ZqORtupa7P16mg4eJVykpDNlGGNDHJ1AkUDA\nsCkTOb03k5SFc6k4doK6kjNI5XIkCjn9PT10NbbQ2dBAb2cXYqmUzq4uco4e4VjBCZpz9hOcnORM\nxwyg9vEmd+MWBEIhftGRWEPN5BTkYe5swRoVjkgsRu3qRvfBPHp83BFr+qnMzCE6PQ273U7z4TzS\nJ8/mzx+/w+jVS+jr6uJ8SRnqyBBe3baR/3rwKXwvZHW8aSxWrObBowFfLBt1ehRuroPWfbnMxZj6\ndZfdZujXIVBb0Wg1XNxaR7kLmRQNg6O1zTq9MxnN6gWLKT93lpKycyydvggvr+vTMh3izmbJkiWk\np6ezatUq7HY7W7ZsYdGiRdfecYirolAOHrSQy29sRkWr1XD8eCYSiZzJk2cPGoW9Xezd+yFLl/mg\nUk3HarXyyt8+IzOziPHjYzl+rBYvT0dwrkRicxrIAIFBrvgH2EhOnsn27ccG1dnX30p1dSUREdeQ\nHw0aS0bGUaZOjaS4uAm5fCCmw9NjOAczy+js1DB5cuLAcQM9aGnN4pe/dLgGFhTUUlpaQGLi6Fu+\nFncKJ09mExvXTXx8NBDN5s05hIR6o1DI8PJyGyTXFxHpx6mCc0NG8l2O6MUvp9T7Ep9++inDhw9n\n+/btPPnkk2zYsIGysjIeeeSRr6eFF7BabRiNlq/1mJdDo+lmS+lxwqaMxz0sGIG/F+ePniAp1pGN\n7vTZMwhCBqJZrVYrnv1mPN09WL/tI7KKCykvLycpdphzBHf9jk14zxiPR3gI6qhQ8nNySLvwtb83\nNwuXlIGPFLmvF8bKeiIuBHQAjIhPxKLp5Wz1ecw2K+3HC1m38F7mjJ9KWsIoUhKTLxnJNpvN9PRo\niI+ORdjdy4m9B2guLGF6YgovPPwkacNT6Ovvo91FhEzpGIVur60nICYKd38/ynOO0V5bR/jI4Wha\n2jD09ZO2egVWi5ngYfFEpY7CLzIcdYAfu/ZlMP6xBwgYFsvpvQepLSrF1cuDroYm6orP4B8ZTsK0\nNBQqFUq1G1n79xPkH0Dr8ULc+s1MCI3j3vlLSEsYxYzUiYSoPDh75Bg0djAiJIrNh/fS2a8lOnU0\nFXknGT33HnwjwvEfPoyThw4xLunKWR3lckendrXfllIg4nR5KS0NjUhkUspzjiJTKuhqasZU14zR\nZKKrpQ2FyoWa0yXIlArOHTuBVC6np72DzvompHIZTQVFBAoUVFSeReHmRk1hETKlknNH82gqLcNf\n5UGXxE7DmXLkLi6czy/E0t3D/FETCQ4IQmkXcfDwQaRKJZUnThIiUDB13CTneXh7eRMUEDooi+Pd\nxvXcj7sBuVyCSHTrI74zZ87E1dWVgoICtFotq1at4umnn74NLbw93Cn98o1SUVFBQIAdpVKGwWCi\n8FQ/0dHXZ8x1dXWSc+Qd1qyJJDTMyqZNnxMfP/a2uy41Nh5n5CjHLJRQKKS6pgeTMYnqaiHBwZOI\niXEYqD09/fT21eDvr8Zut7PhgxwWLEhBJBKSn19BXFwwYrGI1tZu+vp6EInaaGw04e/v0Aq+3Iyo\nj48/UmkYeXlteHiMYnjSGOe2gIAwrFZfamv7qaurIj7e0catWwqYOi0Wb2+3C+XUHDtaS2TkjQUG\n38l9QFlZDtOmBTmXFQopra0afH3V7NxRgrtaRlCQF3a7nff+k8XixeuA2/8B9XVyJ9+PG+Fm++Rr\njiS/+uqrPPzww5SWlqJWq4mJiWHDhg031chvAy2trSgC/ZzLMqWCfsvAqOD0MRN452AGYdMnYrVY\n6DxSwJgHHucX//wjw+9fiotQiL6vnw8++4SHljjcISwS8aBOyiIdeKhcpHLaL6THBtA2teDvd6lS\nw/zpsxjX0UFbextR9067quh71vEjHKwsRuDmQmd5JS/c+wi/febS2YExo1LJfO91lHOmIBKL0dY1\n0llbj1dYCAGxUQgEEBQfS097By2FpYgkYnraOghOGEgz7RUShH+0Q8nCRa1mxKzpNJSUU5lXgFAk\nwl+uoqO3z1nebrfjFuCL55RUbOeqMNV1UFZdidViYczIFLTaHjYd+hxBgDeaugYaMJCwcgFROh1H\nP9mGu//AvREIBFgktx48lZw4gt+GRVBWXoamR4POLEDb2k1PRyfjR6ZSUV+DLMAXu82OX1QEZ7Jz\nUapd0XZ0oFCpsGh6UZU3sHaRYwagtbWVvBPHUUaGcWD7fsaNGMmv/udvvPzZBiQiAUkTx9JSWYWx\nt49FyeMZP9rxgpoyYRLD44eRk5vDwvFziYoaGqH4tiMQCIiKisLPzw+73Y7VamX9+vWsXbv2m27a\nXc3Mmas4mLkdO9WIhC4sW/YEOt31GQH5+Xud6ZFdXZXMnRdIcfEpkpNvb5yOTjfYgLVaFEy7Z9Yl\n5UaOnMDJk2aqq+rQ6S10dRvIyMjHZLIQGurDH/+wCw8PGbFxfsyf71C62PRxMRZLKjt3vomXtwmj\n0Y67+6hBahA+Pr74+Nxz2bYFBoYQGLiKqqpyPtroGK2Wy5PRaAZ8lzs6tMhkl8bB3N3I6evTo1I5\nBo5On66hpLiWbduOIRQZefW1z4nPLKJHq2dY/HTc3Nzu+oC37zrXtCCMRiNHjhyhr68Pq9WKWq3m\n2LFj19rtW0t4WDi6T7MgzOEXrKlvYrjvgLZkUEAQa6fN51DeUaQCIS/c/xhvfroBYYC305dQrnKh\ny2Zy7uMhEGPU6ZApldhsNhTGAQewRffM5e8b3qLdW4XVYCJa4sawyQNG6MV4e3vj7e191fbbbDYO\nVRYTNdPhFxwxZhT/+/Yb/PnZn10S2CeRSHjh/sf47MBurHYbv3zoKaob6ijcd4z84pPI1K5krn8f\npbuaXq2WnvYOAuOiOZ9/iugLbh9nc4/jHxXurFMkFBI9dhQmvYG+Y0X8eO06Xnz1ZYr2H0Ll4U57\nTR3DZzo66ub6ekIS4xCq1bzzWQZb87Npra5lyve/h1AoRKPpxi0smBPbM1C4qlCq1ZzJPoJAIMBu\nt2MxGvHRX5d4yxXp6+uj/Gw5drud+Lh4XF3duGfGLN74+D2YNQWTVEJ/ZwPamnqCE+IcPt39ffiO\nTMLF3R29VouoppXH1qx11tfZ1cmY1DF0dXfzs5/+DFdXN6xWKx2NLfgNj8NusxM2IomwEUk0Hj8z\nqD0eHp4snH/3Tbf39/dTW1dLcFDQZQNIh7g8Dz30EEePHqWzs5OEhAQKCwtZuHDhTRvJNpuNp59+\nmqKiImQyGW+99dZlP7Yef/xxvLy8eOmll271FO5IBAIB06cvAS5OgX5zI2UOI9Z2exp2EePGLeOd\n9Rvw8hag7bETF3epgfwFKSlTUKsVbN36Lj//+QKUSoc72+bNuaSl3U9X91Fmzx7sf3zo0FbuXxPp\nLLtzZzFa7cgbej4jI+OJjBx4H+Xk7GLzpkIkUgHdXSoWLHj0Rk75jmfatCV8tPFNfHzNdHR0ExLi\nyq9+vQqAv/51Gz//+UpcXR0zeXv3nqGzsxOx+O6d2RviKkZyTk4OVquVdevW8dZbbznXm81mnnzy\nSSoqKr6WBt5pyOVyHpg8m53Z2SAWEan2Zlr65EFlAv0DuX+hQ1VBp9OhcRFj0QyMNtvtdkTmgQ75\ngUUr2bDjUzQ2ExKLnccXD0joCIVCfvDg42g03UgkUqfu7s2i1+uxygf8pgQCAe5R4ew9fIgVFwK6\nistKOFlxBpENls9aMCgRR1BAEGljJmD4GHpdpHiHBOPu74vVauXz/3uV0FHD6WpspuXMOeJCInDt\n1lDZ1IJXSDCG3j4ay8+RsnAuZoORkVYXlEolv37yR/xnx2Yaz9XhEx2CUu3opE0GA+4B/hRk7GXc\nysUIRSLa3//IoeZgt9PV0IS+p5fosaPxCHR8qFiMRjwDAwiIdbz467KO0tfXi4qBL4kAACAASURB\nVEo1kG70ejl9pphPTmbjGR9Fc0UVlmMHWJ12D6OTkmkTW5B3dtFeU0dIciL1xWV0NTZx/ng+KrU7\n2vYOms5WEp6ciEZoJe9UPgq5nM+Kj6ETC9B3awgZkYhubx4zI5NQyuWovD1w9/OhqqAQN29vvIID\n8ZXf/R1s6dkytp7KwSUqBN3+PNLDhjEhZdy1dxyC7Oxszp07x7PPPstzzzkSI/zud7+76fq2bduG\nyWQiNzeX48eP88ILL7Bt27ZBZd544w1KSkqYNm3arTT9W0tq6izef+9dHnhwNHq9iYyMBpYtXXbb\nj+Pl5c2iRc9js9muO1izpqYApXIgADAhIZi+Xnc6OgKpqGgjJsaXI0eq8PRMoqvrPHY7ZGUV4+np\nSlSUO+3tbbf0EZuWNh+73Y7dbv/KAkx1Oh2nT+fh5uZBYuLlAw91Oh0nThympaWJpKTRVyx3tfou\nt14kErFkyZPYbDYOHtzIrFkOl5WKiiZ6NP1YLAMfSzExnjQ3NxIScufomg9x41zRSN63bx/Z2dk0\nNzcPSvQhkUh48sknv5bG3amEh4bz/dDw6yorFouxWywExkZz6vN9SBVy+uqaeXHtQCYgkUjkdL24\nErdL89HFxYX+6kZsExyybqd278dsMHKyT4/0wG7CAoLYW1+G/7hEWs5X8ZPX/0RgUDC6plbum70Y\nk8lEn17H0vGT+evmD5wjxiKRiLErF3H6011EBobwg0eecLZ5X+Z+3vjdy6jDg5j60H0IBAJajhWw\nfI4jO59KpeKZ+xwjDjsz93Aq8wjNjQ3IvBz7SxUKhBcCY+wI6KirR9Pazqh5s6grLnUayADRY1Mw\n9PU7l1XR4WzftZ1Vy1dfkOO5fg4U5xM7axoA3qEhnN6byaHyQlJHjMJusdFWVcOIe6YP2q7y8SZ5\nVjqn92aSunCOc9uhg8eQCEWETp1A0b6DjFpwIbtkaAhZh44jRkDyooHyxzZvQ93Sw6JVD1/SrruN\nfUV5hE2/kNgkNITsQ8eGjOTrJDAwEKlUyrBhwygqKuK+++6jtrb2pus7cuSIM7PpuHHjyM/PH7Q9\nNzeXvLw8nnjiCcrLy2+p7d9WPD29mDRpLR9/dBCxWMbSJc9+pYoj11u3VqvFTS2hqqqFyEiHn3B2\n9lkWLbyXqKgYCguPkX+ikaioScSNjGXf/jY++ugwy5dPoKWlm82bTrB27cpbbq9AIPjKpCW7ujrJ\nzX2XJUvjaWlpJiOjkHnzBveR3d2dHDr0JivvHU5rq5SsQx9RW3uKefMeuaS+7u5OcnLeZemyeFpb\nm8nIcJTr7u7kyJF3Wbwknra2gfVfIBQKcXcPobS0nvr6Vnx91fzohaV8npFP6pgYoqICOJHXyuzZ\n0VwmfnuIu4grWg2//e1vAfjv//5vlEolzzzzDAsXLqSgoIDHH780t/sQl0cqlRIj96DJaiNhahpt\nJwp5fOXDeN+i+sDlgi2ut/yv1z3Hf73yEhaFlMjUUQQnxGG326murqMkaz9+c6dwet9B+ru6mbhm\nJVUnCzH5uvObt15h/PJFuHp7kZu9n3uSU2nt0aJQOwI1OusaSXv8ISqOHOO1LR/i5+7JqtmLuCd9\nJvekzyT/dAEnjxaD3caaifdc1vBfkD6b+XY7/9y6gQ6pndrTJRj6+pztd/XyoKe1nZpTRUSOTqan\ntc0pDwfQWH4OuUrlrK++tAxLTBQvv/c6Lzz4BBKJ5JJjXvGaiQe/nEQSCXaxEKFQSKK7H0caKwdt\nF0slfJGaR/Sl49jFQuwXrr/oS37S7b0avFSDR29C/AN5esXlpyq/yP9zt2gc20WCLy0PSZhdL0FB\nQbz00kvMmDGDn/70pwBoNJqbrk+r1eLmNpAWXiQSOUcqm5ub+d3vfsfWrVv5+OOPr6s+sVh0kbvC\n3YlY7PgAv5HzUKsVhIauuXbBrxU5vr6+nD/fTElJLQaDCQG++Po6+sapU6cPKi0W61i7dqZjNtFd\nxfR0HVarHrXa/Zto/EXtuvL9OHhwnzNpiru7ip6eSrTaDkJCQgaVeWzdeGcZrVZHc3MnPT1thIaG\nXVLfI4+mXFT2PBpNK4WFB53H8fAYWB92UcB8evps/vKX/yI42M6cOQ5/9FWrp/DHP3yCr28sU6fe\ni5ub6yXSfXcbN/N83Il8cR43vN+1CuzcuZM//elPbNmyBaVSSUFBAcuWLWPFihU3dcDvIqvnL+VM\n+Rk629p5dPkaRKKb/7F1dnXy9s7NGJUSJEYri8dOZVhM3BXL1zfWs+FgBma5GInBwn3T5hIWHMo/\nf/G/vPz6K/jHRXNy5x7kKiVWs4Xu2hqa95hISp9Cw5mznD9RgJuvD5EpI0maPpms9zbiFxlO+LhR\nWGvaEJ4+z5neTswSIZ5BATSWncVitaJVyXGLDeTvm97lvx5xpKtOTR5NavK1I8gFAgFYrERPSqWz\nvhFNazsH3ngH3+godD1ajH19qBRKjm7aipufL+fzTiKSSrFZLLj5eHHuaD5GnQ6bxYLVYkEsleIx\ncyK7MvewZPaC677WARIXulvbcPPzpV/Tg6GvDx8Ph4GxfPZCmt59A21rO25+PvR1a7AYTVitVjQt\nrQD0tLWj9vVBp+nBBxkKmZzutg7sdtC0tuF+oV6bVEx1xTncEqNw9fVB19ODL5cPvPxk92eUa9sQ\nCASESt14cPGtj/x81QQr1LS1tOPm7+PQ7LZf/4fKd53169eza9cuxo4dy/Lly/noo4947bXXbro+\nNzc3ent7ncsXT+V/8skndHR0MG/ePFpaWtDpdAwbNoyHHrp96dOH+OoQi8X4+Y6lvT0PXz9Pyst7\nWb78ygNaYvHgEV83NwUGw50dZCYSDx4cUKlkl7T50jIKxOJeDAbDZeoTXLa+K63/MhGR0SgVjYPW\nuamVpKYuuKbM3hB3B9c0km02G1OnTmXNmjUsX76c0NBQrNa7+8vomyAhPuG2pHf8aN9O/GelOR/g\n7Qeyr2okf5K9l6B70gaWD+zlhfsfQyQSsWjmXF799FOSF8xGdkE2rLDfQFdXB1X5hbRUVuEfE+XU\nXhaKRAQNiyMoPpay7FyErp6sW/UoZefKyWgswzchln3/fpcJKxbj4uFOSWY2CKxotT03PDoxJ3US\nH2dm4hITjlIi4b5p8+jX99Oj8ibEy4fKpnqsw0I5vTeT6LEpBMY6OqTSQzmMvZDJDqC9pg6z0YhY\nKsVsubF5r/sXrSDj4D6O5+zGaDCQfiFF9Bc8+8gTZBzcR0t1Kd0NDcjsFvql0FZTh76zm8qtexiR\nPBJ/pSuLljvcTDIO7UOu8OToxm14johHLJOSPCsdTUsr4qIqzKo2/JUqFi2/75L2nC4tot5NRNgo\nR6IQbUs7h3IPM23i5EvK3kmsnLuYPdkHaKwtxUfuwpIVd9oI3J3L8uXL2bt3L+BI7PTss8/eUn2T\nJk1ix44drFy5kmPHjjFixAjntovr/89//kN5efk1DWSLxXrXR+9/m9Lujh07nba2UWi1WhYu8EYg\nEFzxvAIDktmzJ4vZs4eh1xs5cqST5cv8vvHrcLX7ERw0is8/z2Tu3AQMBhOHs9tZtixoUNngoFF8\ntn0PixYnYzCYOJJzBpPJi9SU4EvqDAkeRUbGAebNc9SXdaiVZctCL/iaX7r+y/uLhB6cOXOMtMmJ\neHioOHSwiKZGK35+Yc4R5G/6et4q36bn4ytJS61UKnn55Zc5cOAA//jHP3jllVdwdb3xIKghbg8W\nyeAMeRbJ1aeuvyyBprOa2bjjUwDmTZmBr9TFaSBbzGaMdgvpax8EwC8ynMMfbiZx2oBRrm3vICp1\nFKMXzCbzjXfpf//fPLfmMVo62sj+bD9RKSNReTrcKIbPmErm2+9TVlnB+JQx3AgxkdE8plTxj83v\n0oOVrdW1jImI47HVDv+z8i0f4O7rw8R7l7Hzr/8iICYKoViMTKng5K7dzPjeQ1hMJqoKTpO6aC6n\nPtnB5MjEG3ZTmTf9HuZNv7wM0hfbv+ClTW8TP3W8c7njcD5rF68aXH6ao7xSJEY3IhzJBak+XWsn\nK6bPJjAwiCtRVVeL58hw57Kbvw8tJ85e97l8k8ye8s2mq71b0ev11NXVERoaelvqW7p0Kfv27WPS\nJIe+9jvvvMPGjRvp6+tj3bp1g8reLe48QwxGJpPhcx0JlKKihlFTI2XjxhMIBVKWLvn+HZ/NMTIy\nDpFIwsaNeQiQsHjxpW3+oszrr+2lta2RqMhk5s9fdtlzi4iIRSgUX1Sfw788IiL2wnGOD1r/ZaxW\nDT/9f8vZt+8UBoMZsVjMjBkP3vHXcYjr55pG8oYNG1i/fj1btmzB09OTlpYWPvzww6+jbd95HC/I\nWgICApwRx2pEg+Ti5IaryxYpzTYsZjNiiQRdj5aW1hZCZzpGll/ZuoGUqHjO1zfiERJEd1MLoUkD\nGZR8wkNReXpwYnsGfhFh9HZ1o+vRIhKLHRJrVguKCSP4bF8GS2YvID4imvVFhwcdX+aipKKx9rqM\nZJ1Ox7ufbcIgFSI12RDa7Zg8XYlLSsDd35e64jO8/eF/WDBjDt2NzZzf0YpRr8MjwB+BSIjdZsOo\n0+Hm68O2P/4Ns8GAX2gou//xBu4B/uR21JL9p9/wu6deGBTBrdPp+MeG9dT2dqHr0RLlG8gD85by\n6cHdGKRCBH0GhAKwusiRmmw8NG/pJRHger2emvPn0djM6Ht7EQAiG7zywVvYLBbsbkpEOpPzf/oM\nlLyRjVtECL0t7ajFUj7qNyA12VgxbQ5bs/aglwjQNLeicHNFrlDgaROT+/7HuIcGYzYY8PT0JEXu\ng9FoBO5uf7EhLk97ezvh4eH4+vqiUDjusUAgoKqq6qbqEwgEl7hrxMZeqrv+8MN3f8DoENcmPDyK\n8PC7S289LCySsLDI6yhzfQIDV6ovNDSC0NCIq+5rsThm22fNcsy2Hj58hoKTh0hIGH5dxx7izkdg\n/yIK6A7HZLJ8K4b74fqmLSqqKvn4WCbKqBD0jW1MCY5l8tgJWK1WNuz4lB67GbHZypo5iy8r2dPS\n2sKeY1lYLDZ6enroMeuor6/DMz6KYWkTkSrk2Gw2XAqqkMtl1PR2YtT206cQEjnFMZ1vNhjY/a+3\nSEqfgndoMM3nKhCKJUSljuJUxl48AgNQeXrg09LHg4tWYLfbeemNV/CcPhal2o3Sg9l4BgaQYJaz\n8CI3hSvxz43v4Do1BeGFYKJTH21DFuBD0vTJaNs7yXrvQ0dQnsWKxWxm5pOPIpHLsdvtFOzcQ3Bi\nPOfzT9FaVYNILGLu9x9HIpdTsGsPI+fMdNbbnZnHD9Z8z3lP/rz+38gmJDu3F+zag7m1k7GPrEYk\nFnNy1x5Gzp6BSCzGZrPRlXmcH655bFDb/7XxHVymjEYkFlOwaw/JF8rb7XZO7thN6qK5V/y/IGMv\nybPSneXz3vmI1IdWOpcLdu0lZcFscj/8hHH3LnGuP/Dvd0melY6uso7vL1xOWOil04F3G9/1qb0v\nU15eTkZGBn19fYSFhWG1Wjl48CDvv//+bWjlrfNd65fvZIbO4+unt7eX//u/5/H2lqBQSomLC2b6\n9BHs2tnDiguB13fDeVyNu+l+XI2vzN1iiG+G3SdzCZvhmBIlPJTDmblMHjvhinJxH2dso7a/G2x2\nhnn4U9heT/jMNKRAV8YBxJ5uTF+yDqvFQv6OzxmzaB5Wi4Wy8lKmTJjM2glTUCqV7D+SxWdbd+Li\n5YGuR8vc555g32tvo+/tQyQQIZRLqC8tw83bi/Kco3iFBnOusY3VcxcjkUj42RPP84dX/0qBpg3P\ngADUzVoWrFpCS2sLZ86VER8dR2BAIJs/3051XxfYbKSGxJA+cQoGqRD1Bak3hxayDbPegMVkIm/b\nTub/8BmEQiHlOUdprqhCIneI4AsEAsQyGb0dnUy8dyk2q5UDb7+H8ILkm8xF6ZSQEwqFGGWDp8J0\nIgGKi7bLVSpEdhB9sb9S6fzfsf+lUbJGmQi3C2WkF5UXCATIXVWX/V/h5vhfqlAMKi/z9Ry87OJw\nh3EL8Bu03j82Gp+IMIgIY/Ohffz4oe9dz09riLuIH/3oR+j1eioqKpgyZQrZ2dksXrz4m27WEEMM\nAXz44cs88ugkUlKiaWrqYuuWXNzclIjFLd9004a4TQwZyXcANpuNT3Z/hsZkwF0qZ8WcRZfIZNmv\nIl9y8Ohh2vxVBAQ5RMt3vf8xE9YMqB7Ezk2n9FAO4DD8woYnkrNxMy2VNUSMHM7WM8fJLD3J95c+\nwMRRYzjaXkPImJFOgyxkeCKGvn6S0qdQeTyfpPQpAOh6tNQWlRI9ZyYf7vyUh5euRiAQ8LNnfoTN\nZnMkTRGJOHoyj6ymCjyHRXO84CCdp8ux+nkgUykZNmUS+UVlRNbVIDZaBvkMh/kHY+nrZ/e/3mLq\ng6udfl7xaROoPlWEzWp1Gr/ajg6SZzkkjoQiESNnz6T1fDWBcTEYdfpB9YqNgwNPpebBxzXp9Rg7\nNc51ZoNh0HaJ6dLAVZHRfMXyJv3AF/jF/xv7dQCXlu/WDlrubGgkc/0H2KzWQf7h5ouitYdk1b6d\nnD17lsrKSp577jnWrl3Lyy+/zBNPPPFNN2uIIb7zmEwmPD0NpKQ4gsYDAz3x8FRhNJowGG5ObmyI\nO48hI/kO4N2tH2EdGY1C5YKmt493tmwkVO1BfWMz7kEBGPr78bBcPojGbrez6/ABRCF+1JWdRa5y\nwcXXG0NvH8oL+sWGvv5B2rxtNbXotX1MeXAV/lER2KxW8nd8zo7D+3l40UoaTpfQp+/HarE6XDI8\n1LSer+bkzt1Ejh7ISKRUu2G1WJDIZPQJHJmGMg7to6C5BrtIgL9AzmMr1pB7vpSgqWMB6OruJmbF\nPJRqNwz9/RTtzSRxWhplFWdZM2sx7+3ZikkmRmq0cv/M+WzZl0FS+hT6e3pw9fYEwGq2YLPZ2Pf6\nO3gGBdDZ1IShTzfIaO7r7KIyv4DKEwXYgT3/fJPA8DD8FG6smTl/0DV8bMlK/vLBu3RYDfRrtPi5\nqFm5ZDV79udikkvwMAmo33UQoVqFxGjl/hnz+DIPzF7CuxmfYpJLEHf3kfXW+6gD/dC1deKrcqfh\ncB72Pj1qg+2S/91NAup2ZSJSuyI2Wnhi/kr2HMilzdiP3mJGoXIhcfoUBALI/ywDoR3MPX3EpDtm\nGrprG0j2DbyJX94Qdzp+fn4IBALi4+MpKiri4YcfpqVlaJRqiCHuBIymwYpJvb16/v1GNipVKL29\nWlxd3a6w5xB3C0NG8h1Ap92Mv8qRblrhqqJFYOF798xnX85B6o+VoJbIWHGFzGt7sg4QOT8dV29H\ncpLa0yXUFBZj0ukJGZ6AADCXVCGxW+hsaELb2o6muY2gYXH4RzmCEoQiER4B/hQUF1FcUcaYh+51\nKl6UHjpMT0s70x6+H5vVQlnOMTyDAhGJReh7+5DIZBh1elwFEurq6yg2dROW7siu1tfVzU/+8t/o\nsSFpCcPD3w+hWOw03uUuLpgNRmqOnWRW2jx8vL15Yc3gCPtGg5ZhyeM5vecAFqMJmUpJ8f5DBMXH\nMnK2QzHhwNvv4xUSzJ5X32LMkvn0d/fQ163hnscfpWjfQVQeaiJSRtFWXM549yAC/AIGHcPH25uf\nPfrMJdc2KT7puu+hl6cXLzzg0CT9nw1vMHymI5W3zWrFmFvE4yseuO66AJKGJfJ/n/4Hn7RUSg8d\nxsXd4Xc+ZvF8ig9k4SuU4dPUi7GxhGS/YBbNnnlD9Q9xd5CYmMizzz7LU089xZo1a2hqaroQqDnE\nEFfHbrdz9OhBdDoto0dPxtPz1hJYDTEYnU5Ha2sPn2zOYcHCsRQUVCESCXn2uZnYbDbef/9N7r/v\nhW+6mUPcIkNG8h2A8EsZeYRmx/I9adMvV3wQnb09uA6Ldy77RUdg1OmJGZ9Ke00dRZlZhEZFI5VK\nMOafwV0ipT8oAKvZPGhKv6upmZSH76U855jTQAbwCQ8jf8fniCRiBCIh2vYOKo/nYzWZaCgqJTl1\nDJw8y31LV5NzPBf38IHMRypPD1p6NUjkUo5s/ITZTz+GobdvUPsN/TrsPt7858AOHp65kODA4C9d\nG8cIdfLsGXQ2NJG7aQtypRKJTEptUSluvt70azS019aRPHsmZYePkjR9MmEjHCodQcPiMPbrEAgE\n+I0YRv7hfNLGTLji9cwrOMH5pnpCfP1JG+sw9t/btIGyhmoifAN5/IG1V70fZrOZyopznK+rxtXT\nkzFL5mMWCcgrOEHm8RzsCEgbPRaZREJlYx0hvgFMHjdx4Pin8qlsqCXEN8B57hajCXC89MoO53I+\nrwDvsBASAsOJCYygsqGGzJzDpKfd2XrJQ9w4r732GkePHiUhIYHf/va3HDhwYEhdaIhrYrfb2b79\nDRYtDsTbW83HH7/P8KSV+PtfWWJyiBvD3d0dvc5MSKgPhw4VU3W+hanTHAMrQqEQd7WFu0QXYYir\nIHrxxRdf/KYbcT1YrTaMxqvLnd3pyOWOTGNfPg+1RM6xnMPodDq6ispZmJKGr/e1dS4BTAYjZY01\nyNVudDY0UnUkj/CUZGRKBW01tUSNHkVQUjzuIUHgpUatMZKXdwyllztnjxxHIIDzx09i0RsJS06i\nr1uD1WJxjvaW7s1EIBLiGRTI+RMFJExNIyg+Br/oSNS+3iyMGsnsKekIhULcXFQcPpaLOszREdeV\nlCGVyxi/fBHuAf6ceOcjJscO50TecQx6HfnbM4gel0JkykjU0WGcyj7MuKRRg84vUO3Fjh3bMJvN\nNJSW0dfWQfLcmUSljsJsMHBy5x7mPfckcRPGUnY4F4lMhtrXx5mmuuTAISJGj0R8IU20vr6Z8QkD\nLiMX35MdB3ZTLNIhS4yipq+butPFbN+XgT4miIhpE+iR2MnYtIn0CVc2Rv/877/jO2EUI2ZOQ6Z0\nZAWUGiyc7KxHPSKOwIkpHMzNpstVgnJErPM4iTHx7MrcQ5GgD3lSNDV93UhaNLRUVWMVizh37ATn\n8wuxmEzMWPcQoSMSydy3l261FNfRCVRqOqk5dZr4yEvlvO4mrvSM3G3I5RJEt8FPXCgUEhbmSKUb\nFxfH3Llz8fX1veV6bxff5n75buPi86ivryUgsIHoaH8EAgHDhwewf38hUVHJ16jlm+duuR8WiwWT\n+Sxz544iOjqQMWNjyck5w7BhjoGiwlNtJCSk3fHncS3ulvtxLW62Tx4aSb4DSIwbRlxUDJ2dHXhN\n9UYsvv7bkpo8itpdtWRu2kb4+BRcvTwp3rab8Y+uRtveSfjIgYxaKm9P9pRuZeID9+IZFIDNZiNr\n/QfYAENfL/k7diNTKtC0tFKZlYtNJMIrPARzbT0Fu3ZjMhhJnD5gIKqDAvjDP/5GbGQMQaGhhPsE\nsnj4ODIPn6ChvZWm1mZmPeVQXPAND8UrJoIHlq+m9G+/p7eji+TZ6bTX1NMkFhMYF0OP8VKJmfiY\nOF6O+AnV1edpVgWyyWojMM4RoOgTFurMtCeRyxk2eSIlB7MpO5xL1cnCC4a+AG17Bz5hIbSXVTA6\n8Mq6l+c0rfgmpQLgHhxAdVUe7UILo6Md+3iHhlDnUnLV+9EqMDM6JupC+WDcvDxx8/FHb9bhFxnu\naKtCgV9slPM4VVV5AJztbsEnceD4TVX1/NfSx+joaMdms/H/Xn+ZtPtXOP2uJz28mtJDOQQD6iB/\nKmryr9q2IYYY4ruBI2h6cBzL3ZQcJj8/h4aGM/x/9u48vqrqXPz/5wyZh5OBzAOZSIAQQhhCCBAI\nKKAMMqo4K1Kq9mqrtcPv3va29lb787a13ra2KhWHKk4IyiAyEyADBEiYQxgCISHzdDKeaX//OHAw\nCZNIOCfheb9evvDsvfbezzorWXnOPmuv1dFhIS1tJj4+frfkugcO7Ka2toiODgujR8/A19efwsI8\n6uqOd9p24MBuioqyKCs/zbvvtjFr1mj8/Lw4ceI8K7/YS0urmqQh1572VDg+SZIdhFarJSgo+IaO\nrWlrZvQD86ydYGw0LY1NOOcdI9FJR1nuXsLHWBOv4zm76RcVgV+YdUyuWq0m8Y6JtDY2Un++kmFT\nJ6NSqcj5+As0Tk6k3WcdVxufNoqt735IYuY4jufuIT7NujBIUfZuUh6Yx/HcPZicTJypPkVKi57/\nmP8In325gs2GzklvR3s7zc16zFqNbTxxUEw0hRu24BMcyPlzZVd8bwYMSGD1vl24+3Z+EMJyYYn0\n2tIyzh48jL6mFpVahaunN0MyM/Dq50dJwUGOfrmeHz/8AxIHDr7yG2m2dH5tsWAxdf703PV1V133\nq5QL57FcOrdi6TI7xsV9ls7XNxoM7MzLJj4mjuDgENxdXDF1GGyr9JlNJlAs3c8jhLitRUZGsXLl\nOqKiWtDp3Fmx4gCDBs2yd1jXZf/+XJycD7Pg3v4oisK//vUO0+9+DqcL3wb2lEOH8/H0PMqkyVEo\nisI777xL/8ixeHsXMfmOztvcPQ7z1NOjUJSRvP/+FlatyiU8zB+DwYe0tP/AycnJNr+w6N1k3qg+\nwETnpZbd/H1RgMfvf4R7BqXSml1IwSdf4hMchLu3N21NelvZ6jNnaaioJHnKJNs50u6bg775UhmV\nSoXWyQlDWztqtZqdyz/n4OZthA9KwD8iDBd3d4Jjo3D28mRLgfWuaN6Jw1jMZorz8jEZjRTn5aOu\nbeIvXy1H8fXsFH9bk54T+QVExQ+4aj0VrQbvgABO7S2wnbP0yDEOb9uBvq6exInjCIiKZMDoVNy8\nPNjyzgds/tcHnN5/gIV3zrp6ggyMjh5I+f6DmIxGKg4cYXh4LEn+YRzP2Y3JaOTU3gKiXbsv3PJt\nSf5hHM/O61R+dMwgLM2tnNizz7r0t76Z0znW96Xi4FGGh8deuP4gyvdZr3+u4BBVtdUUBTjx/v7t\nZOXtYnLKaPasWkNzfQPtzS3s/PAzqk6dpaO1japDRaRF9+6hFkKIm0OlDy+oDgAAIABJREFUUjF7\n9lNs2azm4+U1DB50L+HhUfYO67qUnz/GyJHWIUYqlYrx40MpKbmxFSa/i8qKIlJHR9mum5ERyokT\nezptmzAhjBMn9pCeHmfbNm7cYEJCfMnJ6eAHi3/T48m8uLXkTrKDaW9v543P3qfFVYPGaGZqUioj\nkoZd9RhftQulRcWEJgzAZDTSUFlFW7D1U+zghEEMThjE1uwsCix63Ly92Ln8c3xCgmhrbCI0fgBO\nzs50NLfg5u0FQEdrKw0V1ZgMBrTOzlSeKqG2tIzQhDi8A/rR2thE0uSJtutrnZ1w1+lw1+loOGd9\n+t4/LIzkzDSO5+wm64NPMLS14unpTf/JYzm4eTv62jq8/P1obWzE1dODlrp6BgZbE9D6+jq25e7C\n29OTSeMm2pL3IK07ZS7OnC8+SUNFFTXnygiKi6a5roHEidZhIGPvm8fWZf8m46H70ThpqTp9hr1r\n1rP/xFEmTZx01fdx3KgxRJ4Lo/DIISbGjyA6KgbGQl5+Hhs+W8+k1HTG351x1XM8ufDRy5aPCA5l\ny85tKFmFPDdlAVonLYVHDjFhwHBioq1Loo4dmUZkWRgFhw9y+vQ5Rl24k+8+Yii7t+Xyk7mPkL87\nj7zPVxEQ3Z9xDy5AsVg4t2IDP3z4YQbExvX6VZGEEDeHWq0mI2OavcP4zswmNUajCacL05aWnWsi\nNLTnZ+Ywm9UYDEacna1JbllZE2q1W6dt5841dttWXl5He5vC/PmP2+byF31Hjz24ZzQaeeyxx/jL\nX/7CP//5T4KDg0lISLDtX716NQ8//DDLli1DURSGDx9+1fPdLg+IfPDVZ7iPS8EvJhJdTCR7cnIY\nP2T4VceTxfaPZtXqldTV1FJdcpaY4UMJNzkRHxNnKxMd0R/96bMUlZ1hzH1zCUsYQGBsNHlffEn0\n8GQObsnCxd2dloZGinbl4R8eQl15BdVnStFoNHj4+VJaeIjqs+fw9Pej/OhxXL08OZVfgIevD74h\n1qEiLdV1jO4fz6Fjh3GJCqVfZDhRw5I4e/AoWlcXIpMGExjdn5zPVtJc34i+ppbEzAxMzW08NvFu\nGpsaefObL3BNG0Kl1kzuho2kJqVYHz5JGMw/3v47PiFBOLm54hMYQM2ZUvzCQgmMirTV9fT+QsIH\nJ6B1csLD14emmlpULe1kjh53zTbReetIiB2Ar4+vrUx7h4HzLQ1UtuppqW8gOqL/Vds5PDScCWPG\n0T/yUjmdt45hQ5JJSRqGr68v3l7ehAQGERQU3KltL15//5liPCJDURSF9uYWmssryNmbR3uwLyNn\n3UXIgDjUGg0qtRpdfTt3TbYOlbkdfkd6g5v14J6ju1365d6gr9QjIWEIH370FQZDMwcKy2huDmfQ\noJRrH/g9hYUN4NNP12I0tnDwQBlNTWFMmjSPTz5eg9HU2mnbRx99RUdHIwcOnObwoTK8vVMYOHAY\nW7eu4vTp/TQ3t9G/fxTQ+9ujr/xcOdyDex9++CEBAQF88MEH1NfXM2zYMGbOnAlYE+jnn3+e/Px8\n3N3dGTt2LLNmzXKop7btpV2t4PmthT/UOk9aWpqvOim5u7s7T06by8YDu7G4aPAtqWX6rPndysWG\nR3HQ+dIcq26eHkQPH4Zao8HDxQXvExV8uWMTAVGRGDs6SJo0gfDBCTTX1VN/vgKf0GBS58ykpvQc\nLXX1HFu9gaFhMTT4WB8ka29pwbupHU9PL56YuYB313xOh7OGAwX7GfPgfIpz8tn50WdEDk3E2dWt\n0+pxtHXg6enFqrUriJw8FpVKhYevD5UR/SgpOUV0dKx1iWZ3d5IvjJ3W19RRVlRM6eFjRA9Pxt3b\ny/r/KUPZt24jXv5+oCi0NjQQ5XNj4731+iY+ydtMZKZ1mrb9RSfxLNzHqOSrf6i7mtNnS/hw29eo\n/XVY6puYmzqRwfEDOxeq01Ocl4++phYvfz+O7dnDnc88SYBKTcH6TQyfMY368xUUbdtFWHwcv3zz\n/3gg4w76h8XccFxCCGFvrq6uPPLwzzlx4izBQe54enpe+6CbdN25c39MdXU1IcGXrjtv3k+6bbt3\nwQtUVVXh5tpK0pAAPD09WbnyDR54MBYPD38KCo6TnW0iPX3yLYld9JweS5IXLFjA/PnWRM1isXSa\nseHo0aPExcWh01m/Xh83bhxZWVm28pcNVKvp9QPhtReWlr5aPSJ9fCmtb0Ct0dDc0ICqroGwsMBr\nPpk8Lm0U4y48UHeR2WympKQEnU6Hv78/Xl6u1G8+RuCFWRZqS8uoKyun8lQJnt5eFJw+xqRFDxEU\nE01VyVmKsvM4tjOHyKGJDJ0yiX0ffMbJtZtwj4pA1djCE/fMpfBEMZbaBg78+3PGJCbzk6d+hEZj\nbav/+qF1gY5n/voq5ceKCRsUT2B0f86fOImxpZU9X64jKDYKfXklMxJT6NfPGxcXJ8zfrqtWQ0dH\nCzU15QQHBxMcG217L7z6+eHq4Ya/jy/HVq3HOSIY/7AQgmKiKCk4xOg5M0ClYvv7y5kybvxl3/dr\ntUnh4b34Dr2UwAYkxHK6sJg7dGOv2h5Xsy4/i/5TLw3b+CYrhzGjOt8pKdHXYmpvYMy9FxYlsVhQ\nqzU4u7mSmJnBoS1ZnD9WzNQfXVp85ZX3l/HI5Lu4K7P3dszX8zvSG2ivsoy8EOLqVCqVXW6aXe66\nV9oWFBRke93R0UFYmAUPD1cAhg2LYNWqE0Dv7YuFVY8lyR4e1hXk9Ho9CxYs4Pe//71tX1NTky1B\nBvDy8qKxsbGnQulVFs6awy9ffRm9jzs+4WEYTWaqqqsJ+o4dRktLCy+/8ybq2HCMjXoqDhwmIj0V\n9/5h5L/3Ma5eXnS4aAkbGI9fWAh+oSHkf7WOoBjrdGeBUZGc2luAv0lNkKIl952PiB6Xiqm9A/ey\nGn7+w6d57YN3cEkbRoRGQ5jZTM7ylRjVkDpoCMOGJNHR0cFHq1eir6pBazIRP8a6NHVIXCy1Z8sY\nPHEc+ppazE0tTBiVBsBdY8bzz41rCBufSkdrKxW78lkxqAGXOh+Ma8/grLo0ObvFYsHF04uUyDga\nWppxSR+GSqXiaFY24x+61zZV2oRHFnLyyBnGjEr9zu3RPzyS9bu3Wu9KA236ZgLdPL7zeb7NrFVf\n9TWAytUFLz8f22vf0BB2ffw54x+6DxcPd5qqa/AJ63x33D2gH2uOFRDo58+I5KuPYxdCCHFzaLVa\nWls7D0cwdMhsQ31Bjz64V1payty5c3nmmWe4//77bdt1Oh16/aXZE/R6Pb6+vpc7hY3JZO71DyVd\nvDt2tXqYTCaMPl4MuvD1vhIXzXurv2TJt5Y1Liouora+jqr6GoqbalCARP9Qpk+aaivz3qrPCLpj\nLGqNhiNZu6CfH+WnTuMbEkxURjqt+44ROX0Kh7buwC/UOiWc1tm5Uyzu3l401pUx0sUbtwfn4+xm\n/ZRcW1JK4aFDNKtVuF1IRNUaDarQAAzJA/hs/z7qG5rZsGcnQVPGkTo0jrwVX3U6t/XOqBpdYAD1\nJ0poaTFisbTh6xPIExnT2Z6fjavJgk98DJEX7rJaovtT+sUGDn25HsXdhfpz5cT4BXNP5t006fUs\nXfMpLS5qivfsJWbkpQVELGYzRoPlsu/7tdrERxfAILU3BVuyUTlr8G1XuHPh49/rZ9HTqKa9uQVX\nTw+M7e24t3ePreF0KT4uTrZyFSdOkX7/PI5n52GxKIycdRff/H0pbfpm3Lw8MXZ0oFjMmNUu5B86\nTFxUwhWu7tiu53ekN9Dp3HB2lueihbgdaDQanJ0T2L6tiJhYP3bsKCVt9AP2DkvcBD3Wi1dWVjJl\nyhTeeOMNMjM7L688cOBAiouLqa+vx8PDg6ysLF588cWeCqVXaW9vR3PhKxu4MAG806Wvbt9b+THV\n/m649/PjcF4hQ6dOwl2no/hECX9Z+gYe/fzwdXHHpAJnjYbTBQcIionGPzwUgJP5+zErFlQmE236\nZrBYaG3S4+7thYefH8fz9hA7PIXSw0fx6ueHob6JdpPRliADeAT4UVVbg0uX5bQNrdbEJiQlkS1r\ntqEeEGFLVANjotj570/QhQTTXFWD2my2zsRx7jzRag/c3C59vR4UFMS90+dw9uwZSkoKbNvVajUh\n4eG8MGUWH61dgSnOh9iQcJydnenn788vHn0Ko9GIefaj/PTPvyNp/gzUGg2VW3N54OElN9wmsyZP\nY6aiYDabv9NCL1fy2Jz7+WTtShrMHbijYfG3PgBd9PyjP+Cfm78k+9MvcHZzo/TQEbwD+jF4gvXh\nw+ozZ4lMGsy+dRvQBQZgMZsJTxxEzakzhAfe2PhrIYQQNyY9/S4qKsopPl7B8JQJhISE2jskcRP0\nWJL88ssv09jYyEsvvcRLL70EwOLFi2lpaWHx4sX8+c9/ZurUqVgsFhYtWkRISEhPhdKreHp64lzb\njMloROvkRM2pEob2s/6yVVZWUO6uJjzBOmvF6AWzObh5O0PvmIh/XBQFxScYNiaJ+iY91ZtzcT6k\npaW+kehvrboXPngg+R98yv/+5L/456cfoFja2fnvTwgbnEBjVQ2VJ09haG0jZEAcxo4OVB0G0oak\n8GleNuGjU1AUhcKv1lMWEMjxvftwO3kcr4B+NNfVc3EksaIouDg5U3b2HHXnz+MbEkxTdQ0pM6fh\nodPR1tRMR+4BAo6eZ1hQCCn3XH5oQHh4BKZNq7HERaPWaKg6cpzxkbH8/dP36Dd5DM5OWo6WlmPY\nupG7M+8EwMnJCScnJ17/+Utsz87CYrHw2KNPfe+5K1Uq1U1JkMGa7C+cOe+qZZKHJDO9qoKDtWWA\nCj9Pb47n7kFfU4NKpaZNr6etuRlDSxtOajVOrq6c2p5NxpARjEtNvylxCiGEuH7+/gFkZ6/AZNJw\n5IgRT89Ehg+/w95hie+hx5Lk119/nddff/2K+2fMmMGMGTN66vK92rMLn+Cz9V9hwMKIkAgyxliT\nntbWVpw8Lt1xValUaC48IFR66CjBA6wzG7h6e6Ht50OaZyibjp6iuuQsARemSCvdf4Cn5z7MkRPH\naff1IHFMBsa13+Du7kGrSxMzf/osx3bkcP74CUoPHuXZ+Q8TGx3DHJORrJ35FBw9yJC502msrCYl\neibBsdZr1paWUZSTx/mTp+g4fpb0mEGcO1dE4oRxVJw8jb66Bo8L49DdvD2pd3Nixp1XX7ZTrVbz\n3H2P8/nGNRhVMD4ylqGDhvBNyWE0F2YA8YkI5UxuYbdjNRoNk8ZndtvelclkYtnnn1Dd3o67ouLB\nGfNw7jLsxF5mTJrKxd+Qvy1fRlu4QmJmBmq1moqTpzmwYQtOGi0Pj5vGnRfuMPf2YQpCCNFbbdu2\nkkceHYirq/VvyJYtR6muTiYgIMDOkYkbJYPmHJCLiwsP3bOg2/b+/aMwb1uPqX84WmdnTu7MQ1Wn\np3LHHqpPn2H4I5dmBzlTegajixaNrxdOx0qpOleFYrYwJXYwFpXCR9kb0UWEsn/tBgakjeJcdj4+\nHl4019aDSoVGq2VY0lDShltnzMg7tJ8KOvCKjuDU3gI0Tk4kTrw077B/RBhtaxrIdA1m6MIpvPi3\nVxj1kLUOIXExHM/O61QXTZehGlfi4eHBo7Pvs722WCzQfmkaO0VROHj4EB3TOnC5sFzzd/HGR+9j\nSo7H280Vk8HA2ys+5JmFj3/n8/S0x2bOZ/Hvf8mms8vQujijCwxkylOLAFi9M5fzVWU8suC+a5xF\nCCFET1GrjbYEGaB/f1/KyyolSe7FJEnuRdRqNT958ElWbliLwWLm/uRxDIyzLkV86NhhVm3ZhXNI\nAOePFRM1egTBsdaZKsq25PCLeY/azvOHj95m5PxZttcF6zfh5+tDW0UNZUXHGZI5no7WNvKWLWf3\n7jzazAaaowOJDrFOedNQWcWZgoOc2ltA7EjrQ3Un8vfh5OIMajUuLi4Ylc5P9jq5upLz8QqikpMw\nnK9hZnLaDb8HI4Kj2LJ+E34R4VSePMWgmVP45OsveWT2vd/5fPWKGf8L4621zs40O+iKop6eXqjM\nFsY/fB9bln7AxEcvPRQSPy6NjV+sRrPqC2ZkXv3uvLi9WSwWnn76aQ4cOICLiwtLly4lNjbWtn/5\n8uW8/vrraLVakpKSeOONN645/aQQwqpfvzgKCooZNiwCgNyc80yYcI+doxLfhyTJvYyzszP3zZjT\nbfuQgYnExwygurqKz6ob8b+QIAOYtJ3/yJmduszhqkC0qw9NQU6cczJxaOsOvPx8ibsjgxWn9nM6\ndw8zfvETW3GfoEDyDhzBzc2NssPH8ArwxyugHxlPPER+dgGjho0AfRun9xcSnZJMXfl5vPv5068D\nHhgwin5jAm7orq+trrEJFJga8PL3I2zgANQaDR2nKrqVMxgMfLFhDQaLmeHxgxkyMLFbGa258x1t\njdFxp+3RGM1sfvs9AvpHUHmyhJB4a3LTVFNLv4gwjtVXIQOYxNWsWrUKg8FAdnY2eXl5vPDCC6xa\ntQqAtrY2fvWrX3Ho0CFcXV154IEHWLNmjW0RKCG+D4vFwpo1/8LHtx1Dh4K//3BSUrqvgvpdzrd2\n7TvofNowGCz4+Q5n+PDxVy3j65PCiBEZVzjj1c/v65vCiOEZly2jUpfR3tZGY2Mr587VkJo6gJaW\nDg4dPktjo8LMmT/s9HC6vSmKwpo17+Cta8FkAh9dMiNGTLB3WA5JkuQ+xNnZmbb2dgy1jbQ0NOLh\no8NsNFF1soT//fw9VGYLmYOG4dFhwWw0oXHS0trYiH+rhQcem8dP/vBr4mdPxdPXh+ozpZzcu5/4\ntFGEJQzg8MZtDJliHeNbtG0XyXNnEBwbTenBI5jNZiKGDKL08FEMZWUoisILj/6A377zN1rqG/Hw\n8yGwfwQj1b6EhYV/73qGhoZh2VqDLmkwKpWKupJSkgPDOpVRFIXXPlxK4B1j0Do7s67gMAaTieFD\nkjuVe2DSNJau/4oOVyfUzW0sGHvn947vu6qpqaHoZBFxUXGdJqjvavKosazZu5O41JFUnDjJ+eIT\nF6aR62DolElU7cq/hVGL3mjXrl1MmzYNgNGjR5Off+lnxtXVlZycHFxdrd+smEyma/5hv10WeeoN\nHL0e69Z9wvwF4Xh7u1tfrz2AoozCx8enU7nrrcf69Z8xd14YOt2F8607gKKkdjrf119/2qnM118f\nwmJJveaUs5c7//r1hzCbR+Hn59epzJy5Ifj4DABg1apc7r1vLNHR1hmGvvwylzNnzpGds4K5cxZ3\nv4idfLPhC2bPCcbX17qC4MaNRzAYRlx2WIij/1xdrxtd4Om7L2QtHNZ7qz5lVdlhPMalsP/TL6nb\ntpvjn60lbuoEgsePJGhiKuuO5vPwXbNRdh+hOacQv5PV/PKpHwPgGx2Bp6+1gwnoH4HFZMYvNATf\n0GDKDhyhedd+mrL20lBVbRvKEZE0mKqSs+z5ch3e/fzxHpnE3z96h9joOP747H/SX+1OQJtCsuLJ\nhLQbX6Xu27RaLT+4ewGtO/bTnFPIYKMLE8d0voNQXl4GUcG2uZ9DhiWy/+SxbueKjori9z98lp/d\nvZD/fPgp4mPibkqM1yu/cB9v7VzLAR0sy99MVl72Fcv6+vnSr38E/uGhJE4cj29YKCq1miGTJ3Aq\nfz+pkbFXPFYIsC7k5O19aYl7jUZjHeeP9UHgi38k//rXv9LS0sIdd8iT+eLmsCittgQZIDbOh/Pn\ny2/4fCZzsy2BBRgwwI/z58s6lTFbWrqU8e1W5nrPHx/v3+1Yk7kZH59Ly2YPHRpFc3N7p9eVlQ24\nuFzfMzi3itHYZEuQARIS+lFWdtaOETkuuZPcR1RWVlLuqtimhxu7+GHMOYdo9fbGN/TS9HruUeGc\nr6jg8bkLu51D1eVhOncf6x/TU/sKCQ8O4am5D/F/77+Fq6+uU7nGqmpCEwZw9tBRAIztbezZl0/q\niFE8fd+j9ITAgAB+uODhK+53d3fH1HJppgdFUVCZlSuW/z7DP76PrKJCIiZYVwL0HOlDzrZcMkZf\nfgo3nac3bp6XVvuLHpbEuv/7J+XHThDq34+ZD/5AZrcQV+Xt7d1pIaeLi/p8+/XPfvYzTpw4wYoV\nK655vttlkafewNHr4ewUwOnT1URHWz+I7d9fzdj04G7xXm89XFyCOHWqipgY62q0+/ZVkT4mpNNx\nXcvs3VvFmLSQ63qPXF2DOx27Z08FY9JCOx3r6hrM8ePniY+3/o3dtesow4ZdGuq4bdtBoqKC6Gj3\ncKh2cXcL4fjxCuLjrd9c7s6rYOTIaTe04FZvcaMLPEmS3Ee0trag/db0cMdz9tBRXkVtRQXOJTEE\nRvUHoLyoGFPy5eekjvcK4MDOXEIT4jhz4DAttfVs+Oc7BERFMjF5JABn2hpxC+zH6f2F+IWFcmjr\nDhQUAiLDCYy2XqP6bCn5B/aROmJUD9f6ynx9/YjBjbOHjuER4Ef9viM8NfP+ax94q3X9Ckhz5S93\nxo5O5/UP3kbtpCUmJZmyomKiRyRz/shxZqTenLv0om8bO3Ysq1evZsGCBeTm5jJ06NBO+5csWYKr\nqysrV66UB/bETTV69GS2b/+KvfnH6ehQiI+f/r3G6aaOyiQrazX79hbT0WEhPv6ubue7XBl3d/cr\nnLGzUSMnXvPYUSMnsmPHWjZt3EZDQw2gJTdnGwkDA9DrW6ioaCFt9BQmTbr6vPi32ogRGWTtWMuB\nwmIMBoW4uDvx9PSyd1gOSaUoypVvrzkQg8HUJz7JQPdPZIqisGH7ZuqbmxgxeCgDbuArf4vFwqvv\nvkHQnWM5tbeAgKhIfEOCURSFre/8m6DYKEwGI4rByC9mPXzZsUcWi4X//uurOCVEEhwXy9mCQ5iq\n6xgUEskj99yLSqXiR6//D0l3TeLg5iwMbW00lFegqFTMfOFHnf6oqnMO8+ANzDZxs5WcKaG2rpbE\nQYm2sZbfZu9PyV+sX01ZgBs+4SHoq2vxPll52en/AL7ZupENZ4+i0WowGU20NjTgGxpCQ2UVltoG\n/v5fL6HVXt8fAEdl7/a4WRx1WWpFUWyzWwAsW7aMvXv30tzczMiRIxk5ciQZGZceTnruueeYPXv2\nFc/Xl/vl3kbq4VikHo7lRvtkSZJvoSv9sL35yfsoybF4+PpQvu8gUyIGkdLlAbPrYTAYWLlhLfvO\nHGfI/EvzHBTtzMXFzQ2V0cRgV1/mTu3+tLrJZOL9Lz+l3tzB2eITJA4YSEZKKgMHJADw/orlVBhb\nOXGymA6LmcmLH6VoVx5e/n64enrQUFlJ7MjhAFQeOsb0iEQGxQ/8znW41RyhA9iWu5OzVecJ9vFn\nSsakK5b7evM3rDtRiC6wHy31jQyfPgV9TR0Wi5mTewsY4hPAotkP3sLIbz5HaI+bwVGT5JutL/fL\nvY3Uw36am/XU1tYSFhZuW5m1N9bjcvpSPWS4hQNTFIUV61ZT26wn3D+EsSOt8wS3tbVR5awQdeGB\nudDhSeRlF95Qknxxejj95x9iMZtRa6xf5Tu3G3ks9U50Oh3e3rrLHvvBV5+hjEgg0M2VgIxRVG7K\nZuCABBRF4Zd//B3Bk8YQEh5KSGYahRu2YGzvwGQwEDbIOk9za1MTez5ZRVxYJGmRcb0iQXYUE9Ou\nbxqkSeMm8v7qzwiOi6HuXDn712/CJygAjVZLXWkZVWrHWClQCCFuF3v2bMFkPkJkpBdfr68lbfSD\nBARceZYi0bvI7Ba3yNLPP+RkgDuGYfHkmxtYt3UjYF0cQ7F0fmBO9T1v7t8/7R4qNuyiJDufks07\nmTxwGBERkVdMkAGaVWacLyyqoVKpaHexJthbd27HEOCFf3iorWxowgDqys+jfGvBkIjEQXi5u/Ps\nvIcZN2rM94pfXJ6Liwuv/ORXnNlbwPmTp4lKTiI6JZnIpETS75/HocLuy3MLIYToGRaLBb2+kJkz\nk0hOjuKxx4aTn7/G3mGJm0juJN8ilUoH/XXWJNUvKoKTu/YD1sQnxsmb2jOl6MJCKM/Zz7xhl5/d\n4Hp5enry88eeor29HRcXl+t6AEdjMHd60t3JYE3c65sacfHwoE3fjJuXdcqYc4ePonZyQl9Ty/YP\nPib93jmUF53AvcOaNOv1TXy+cS0WjYrEiFjb0tbi+4vq35/hgZHsaGnBO8Dftt3d2wtdQD87RiaE\nELcXg8GAl9elZVpVKhWurnLvsS+RJPkmK68o58PNazE4a3DuMPHQnTMJCQpBZem8kpvyrZXeHpo1\nn70F+zh38Cx3ZdxNUGDgTYnlcg+qXcmCSXfz30v/gmtQP5pqatGYLLz677eYOmw0Rw9VcnRHNs5u\nbrTVNeDVYiRochq+YaEY2trZ+s4H+AQFkR4Rx/mKCl798E2GPTAXrVrNrmPFsA9JlG+iBbPmceCt\nP5H7+ZeMvd/61PSR7TtJCAq9xpFCCCFuFldXV0rPWTAYjDg7O1FUVIGr6/dfMEs4DkmSb7KPt3xN\n8B2X7gQv37yW5x94ktSIAezbdxDPyFAajp5gztDOQxJGDBvOCIbf6nBt1u7czKiHF6B1sn4q3r9+\nE0GTx7BpSx4PjZ/C5r05gIrUiWN5e9OXDAyzJmTObq6EDkxgSOZ4zhQc5nfv/o3+6aNsd6QDBw7g\nSO4hSZJvIg93DwIiIug3ZCBZH3yCxWLGq93Mf/3va+j1HfYOTwghbhvT717CZ5+uwMnJjKdXOGPG\nZNo7JHETSZJ8k5mcO897a3axJp13jp1Ieks9p86U0H/KvKuOD75IURQ2ZG2mXt/EyMRk4qJ7bkW1\ndpUFD6dLXxt5+/vR0dyC2UVLVGQUiyKjAFi+egVaXef5FM1GIwABCbHU1VTRXF9v22cxm1FbFPIL\n93GitISY0EhSh4/ssXrcDmpra3APDcInOJCMh+8DwLLvSKdFIYQQQvQ8FxcXpk59wN5hiB4iSfJN\n5mq02GaWMJtMuBouDasIDQ0lNDT0uqdSeevTD1CGxuAxMIQV+/byDN3+AAAgAElEQVQwpaX5hma9\nuB79XDypbWjE3ceavNdXVBKVMrRT/AAKCv2HJrJv3QaCYqKoPlOKX5h1cZKTObuJGpZM+fFiirJ3\n467zxnzyHIkRMexsrcB/VAI5JaWc37SOe+64u0fqcTsICgrGuLUKBlun59NX1xKv87/GUUII0bOa\nmhrIz9+Jl5cvI0emX/V5mG+XjY8fzI4dG6mqKmP06LGMGZNxxeMuHrt+/SoKC/PQarVEREQRGzsY\ni8WEl5cPo0aNQ69vJD9/JyqVhuaWJs6eOcnFGW9dXV1JShrFwIFJ7N27C09PHaGhEXz11YfodP4s\nXPiDbjcdLsbr6alj1Khxtrrp9Y3s2bOj23bRN8itp5vsyXvup21nIXW79tORfYAnZ3df/vl6tLe3\nU+2i4OHnC1yYGq748M0MtZN502biWVROzY697P335/RTu9G2s7Bb/FPSM2k8VMzQOzJxcnOl5eRZ\nPBraqN+xlxSPQPQlpSSkj6ZfRBjqolL+c9F/cEpfi3+MdTU+v6gIiuore6wetwOtVstDGdNo2J5P\nXfZ++p2tY+40+dAhhLCfysrz5OYtY85cN5KGVrJmzb+uWjYn11p2SFIlb739M1xdj/GT54cSHVPK\np5/944rHVlVVsGLF/09r6z6GD/dm3Lh+vPDTYaSNacFo2kvysBo++/xvZGcvI31sB1VVWzB07CM4\nuJHQ0CYSEkw8/0IyQ5Or+feHv2b2HFfc3Q+wY+ff+cUvR7Pg3kD+9refdrpmdXUV2dnLmD3HlWEp\ntaxevdS2fdeud761/e2b82YKhyF3km8yd3d3fnjvw9/7PCqVCou5811cenDdF5VKxYOz5l+zXD9/\nf56Zfi8792UTpNHyo//v97i4uNj2nzh9ivw9BQzy9GbKD5+7/KfqXrF8jWOLioziPy4MgQHk7oUQ\nwq4KCzfxwIPW52rCw/0ZnNhAWdk5wsK6P8hWULiZBx5IQaVSERHhT1SUH/fdn4FKpSIysh8JCY2c\nO3eW8PDIbsfuL9iERWkmfkAYALNnp104TwCDB0dgMpkYNdKNgEBv9uwpJiTUD7jUR14q348ZM4ZR\nXl7H7j1FvPjiXFQqFaGh/twzO5Hs7B2kp4+3XnP/Rh58KOXCfl9Shjdw5sxpjh3b1W17SckpoqJi\neuQ9FreeJMkOysXFhWitF/WlZehCgynL3cfcod9varibxd/Pn0fmWZec7jp0JC46hrjozh3EiLAY\n8g8eJTAxgZpjJ0gJjbpVoQohhLgF1OrOH9RdXTUYDYbLltWoO3+wd3bWdntt6DBe4VgVTlqN7Xrf\nPs7FxQmj0YSLqzMmk/lCmc5xdS9vRqtRd9ve0dH+rbp1P07fZEKjUXXb3tRuumzconeS4RYO7OF7\nFjBG44934RkWjb2LgXHx9g7pO2lqamTNxq9x0miYHTMMr/2nmR6RyJ3j5OlfIYToSwYMSOfLLw8C\n0NTUSm5OI/37R1+x7KpV1rLNzW0cL6pk9erdttf5e5queDc2Pj4ds8WZsnO11NU1dzpu9+5igoJ8\n2LypjAMHakhOjqamuom6umbbf98uv3LlHuLiQkhOjubDD7fZtn+8fDcTJky2XTMhIZ0vvjgAQEtL\nO1nba4iJibvs9tjYAd/nbRQORqUoPfgd/k1kMJgcfu1wk8n6CVKtVmMymXB27rxMcF9ZAx2uXZfK\nqire+mYF4ZljaKmrR3PkDE/d/xhgnbXDYDB0GqZhL32lTaQejkWnc8PZue9/Udcb+uVr6Us/c2Df\nepSXl3L4cA5OTm6MH383Go3mimXPnz/HoUPZaLWuDBiQTE7OeurqKkhKGsWUKbNpbr78XeiLx65b\n9zGnTh1CAcLDowgOisLDww1nZzcyMqZTVXWeQ4dy0OtbMBrbKT130jbUz8PDk/j4YSQkDOPo0d1o\ntS74+oaQnbMalcqZRU/8rNvf74qKMg4ezEardSEjY7qtbhe3azQuTJhwabsjtMfN0JfqcSN9siTJ\nN8l7qz7hrKmFtvZ2as6WEjloIB76Dn608HGcLkyt1ld+2ODadXnvy09QjU60fRV1/uhx7otOQd/W\nwqr8HSgebmj1rTw2dTYhQSG3LO6u+kqbSD0ciyTJvUdf+pkDqYejkHo4lhvtk/t+L34LZOXsRB8d\nSFSwdaW8uvLz6Ktr0Q0fwuffrGbhjLl2jvDWU1Qq1N8aq6VxdsZoMrFmXzb97xxv2/7ZtvU8e9/j\n9ghRCCGEEOKKenxMcl5eHpmZ3cegvvbaawwZMoTMzEwyMzM5fvx4T4fSYyrrqtEFX1pK2jckGH1d\nPU6urrRbzFc5sncqLStl6afLeefT5ej1TZctMyF5FGd3WMd+dbS2YTl+ltiYWCxdPslZnORzmhBC\nCCEcT48mya+++iqLFy+mo6P7Urn79u3jgw8+YOvWrWzdupX4+N71UNq3jRwyjPL8Qtvr4rx8wgYO\noPbUGWKDw+wY2c1Xdr6M97M30D5sAM1JMbz26Xu0t7d3KxfdP5pH0+/EOf84AcWVPP/wD1CpVLi3\nmzEbrWO32/TN+Kqdux0rhBBCCGFvPZokx8XF8cUXX3C5Yc979+7l5ZdfZvz48fzhD3/oyTB6XHT/\naKZGDaE5u4CqTTloSqtxOlPJYJMrGamOMW3bzbJtTw6RGWkAqDUagiekkrMn97Jlw0PDuW/6HO6Z\nculhhiXzHsSUexh9TiFeR8/x8KwFtyx2IYQQQojr1aPfdc+dO5eSkpLL7lu4cCHPPPMMXl5ezJkz\nh7Vr1zJ9+vQrnkur1dgGkDuiCemjmZA++qpltNrOT732Rl7uLtQZTWguDJMwNLcQGOB/3XXS6dx4\nYdGingzxO+kLbQJSD0dzsR5CCCF6L7vNk/zcc8/h5+eHk5MT06dPZ//+/fYKRXwHC6bPonLzLhqr\nqqk9U4pTcSljUq/+4UAIIYQQorexy1NTjY2NDB06lCNHjuDu7s6WLVtYdI27iyaTuU9MQQK9fyqV\nFx78AUXFh3ELdCN61FSamrqPSe4t+kqbSD0ci6NOAWexWHj66ac5cOAALi4uLF26lNjYWNv+1atX\n87vf/Q6tVssTTzzBk08+acdohRDCvm5JL35xrtzly5fT3NzM4sWL+cMf/kBmZiYuLi7ccccdTJs2\n7VaEIm4CrVZLepp1XHJvT2aEuJ2sWrUKg8FAdnY2eXl5vPDCC6xatQoAo9HI888/T35+Pu7u7owd\nO5ZZs2YRGBh4jbMKcfN9/PE/qKw8hIuLCoNRTXBQLBMn3kdgYDCffvpPzlccRLEYUBSF5pY21Go3\nZt/zHyQmJtvOkZ+fxeHDWykuPkxEZAANDc2oVL74+fkQFhpEWXml9d+ySqCV8vJy/Pw8sSguhIak\n4OcPhg4LKSl3EWTH+fyF/fR4khwVFUV2djZgHYd80cKFCzu9FkII0bN27dpluyExevRo8vPzbfuO\nHj1KXFwcOp0OgHHjxpGVlcX8+fPtEqu4fX300Zv496tmwsRUkpKiUBSFD97fwq7s9zGbfPHzryZt\nzHCGD49DURT+/e+tVFY2kJO7jIiIl/D29qagIIfGpl2gqmfJD6eRkhKLoij8+U8rSRqqw9dXy9Dk\n/hiNZhKHRGKxWHByGkRycgyKovDaa1/ywLQpeHm58d57y7nzjmccYpVYcWs53veBQgghekRTUxPe\n3t621xqNBovFglqtpqmpyZYgA3h5edHY2HjV8zn6A9XXo689LNoX6tHQcIroGHeSkqIA67fRo9MS\nOHmyggOFJwgN82f48LhL+0YncPLkeUpLqzlwII/p02ehbz6Dm5sGjUZNSkqsrezMWaP59wdbGDkq\nnlmzRvPVV3md/rWVmzmKrVsPcM89aYwdG0JNzTkGDx7yneoBfaM9oO/U47uy24N7Qgghbi1vb2/0\ner3t9cUEGUCn03Xap9fr8fX1veUxCtHapmA0mOjoMNq2nTlTRXs7GAzqbvtKS2toamoF1AwYkACA\nYnHGaDDR2tJBe7vBVvbkyfO2u8rt7YZu/1504kQF8fFhF87fREBAUA/XWjgilXK5SYwdkMFg6vXj\nX/vKQ0nQd+oi9XAsfakejvjg3hdffMHq1atZtmwZubm5/O53v2Pt2rWAdUxyYmIieXl5eHh4kJ6e\nzurVqwkJufJYTOmXHUdfqkdzczN/+tOzeHmpGTKkP1VVjdTVdTAgfhJDk8bx9tJf4OFhITGxPy3N\n7Zw9W0V9fQshoUk89OALgPXnedWqv1NTc5yWljYyJgyhtraJ7F3HCA8PJGloFAcPlJA0NIrCgtMM\nTgzl8OGzpAyLobZOz4HCSu66K4UmfQdO2gTS0+/6zvWAvtEe0DfqcSN9siTJt1Bf+WGDvlMXqYdj\n6Uv1cMQkWVEU2+wWAMuWLWPv3r22B6rXrFnDSy+9hMViYdGiRTz11FNXPZ/0y46jL9ajsrISrVaD\nRqPF09MTrfbS79TFfe3tHZhMJoKCgnB1de12vubmZsxmEyUlJfTvH4VWq8XV1ZX29vZu/zY2NmI2\nmwgMtJ6rpaUFFxeXTte9kXr0Zn2pHpIkO7i+8sMGfacuUg/H0pfq4YhJ8s0m/bLjkHo4FqmHY7nR\nPlnGJAshhBBCCNGFJMlCCCGEEEJ0IUmyEEIIIYQQXUiSLIQQQgghRBeSJAshhBBCCNGFJMlCCCGE\nEEJ0IUmyEEIIIYQQXUiSLIQQQgghRBeSJAshhBBCCNGFJMlCCCGEEEJ0IUmyEEIIIYQQXUiSLIQQ\nQgghRBeSJAshhBBCCNGFJMlCCCGEEEJ0IUmyEEIIIYQQXUiSLIQQQgghRBeSJAshhBBCCNGFJMlC\nCCGEEEJ0IUmyEEIIIYQQXfR4kpyXl0dmZma37atXryY1NZX09HSWLl3a02EIIcRtra2tjXnz5pGR\nkcH06dOpqanpVua1114jLS2NtLQ0XnrpJTtEKYQQjqNHk+RXX32VxYsX09HR0Wm70Wjk+eefZ+PG\njWzfvp233nqLqqqqngxFCCFua//4xz9ITk4mKyuLRx55hP/5n//ptP/UqVN89NFH5OTkkJuby4YN\nGzh48KCdohVCCPvr0SQ5Li6OL774AkVROm0/evQocXFx6HQ6nJycGDduHFlZWT0ZihBC3NZ27drF\ntGnTAJg2bRqbNm3qtD8yMpJvvvkGlUoFWG9muLm53fI4hRDCUWh78uRz586lpKSk2/ampiZ0Op3t\ntZeXF42NjVc9l1arQafr3R22VqsB6PX1gL5TF6mHY+lr9bCXf/3rX/zlL3/ptC0oKAhvb2/g8n2u\nVqvFz88PRVF48cUXGT58OHFxcVe9jvTLjkPq4VikHo7lRvvkHk2Sr0Sn06HX622v9Xo9vr6+Vz1G\nrVbh7GyXcG+6vlIP6Dt1kXo4lr5SD3tZtGgRixYt6rRt3rx5tn5Xr9fj4+PT7bj29naeeOIJdDod\nb7zxxjWvI/2y45F6OBapR+9ml9ktBg4cSHFxMfX19RgMBrKyshgzZow9QhFCiNvC2LFjWbduHQBf\nf/01GRkZnfYrisI999zDsGHD+Mc//mEbdiGEELerW/LR4GJnu3z5cpqbm1m8eDF//vOfmTp1KhaL\nhUWLFhESEnIrQhFCiNvSU089xaOPPsr48eNxcXHho48+AqwzWsTFxWE2m8nKysJoNPL1118D8Mor\nr5CWlmbPsIUQwm5USten6oQQQgghhLjNyWIiQgghhBBCdCFJshBCCCGEEF1IkiyEEEIIIUQXkiQL\nIYQQQgjRhcMmya+88grp6emMGjWK9957jxMnTjBu3DgyMjJ4+umnu63i56i61mP//v2EhYWRmZlJ\nZmYmn376qb1DvKb33nvPFm9aWhpubm7s3bu317XH5epRUFDQ69rDYrHwxBNP2N7/oqKiXvn7cbl6\n9MbfD4PBwCOPPEJ6ejoTJkygsLCwV7bH9ZB+2XFIv+xYpF92LDetX1Yc0NatW5WZM2cqiqIozc3N\nyq9//Wtl1qxZyvbt2xVFUZQf/vCHysqVK+0Z4nW5XD2WLl2q/OlPf7JzZDfumWeeUd5+++1e2R7f\ndrEevbE9vv76a+Xee+9VFEVRNm7cqMydO7dXtkfXesybN69Xtsff/vY3ZcmSJYqiKEpRUZGSkpLS\nK9vjWqRfdlzSL9uf9MuO5Wb1yw55J3nDhg0kJSUxe/ZsZs6cyaxZs9i7d69t8vu77rqLTZs22TnK\na7tSPdauXcuECRN48sknaW5utneY1y0/P58jR47w5JNP9sr2uCg/P5/Dhw/z5JNPkp+f3+vaw83N\njcbGRhRFobGxEWdn517ZHl3r4eTk1Ct/P44cOcK0adMAiI+Pp6ysjC1btvS69rgW6Zcdk/TLjkH6\nZcdys/plh0ySq6ur2bt3L59//jn//Oc/eeCBBzrdFvf09KSxsdGOEV6fy9Vj9OjR/PGPf2T79u3E\nxMTw29/+1t5hXreXX36Z//7v/wbole1x0csvv8xvfvMbgF7ZHmPHjqW9vZ2BAweyZMkSnn322V7Z\nHperR2pqaq9rj2HDhrFmzRoAcnNzqa6uprW11ba/t7THtUi/7JikX3YM0i87lpvVLztkktyvXz+m\nTJmCVqslPj4eV1fXTpXR6/X4+PjYMcLr07Uebm5u3H333aSkpAAwe/Zs9u/fb+cor09DQwPHjx9n\nwoQJAKjVl350ekt7QPd6zJkzp9e1x6uvvsrYsWMpKiqioKCARx55BKPRaNvfW9qjaz0effRR7rrr\nrl7XHk888QTe3t6MHz+eVatWkZCQgJ+fn21/b2mPa5F+2fFIv+w4pF92LDerX3bIJHncuHGsX78e\ngPLyclpbW5k8eTLbt28H4Ouvv7bdMndkXevR0tLC9OnT2bNnDwCbN29m5MiR9gzxumVlZTF58mTb\n65SUlF7XHtC9HtOmTet17dHS0oK3tzcAvr6+mEymXtkeXethNBqZOXNmr2uP3bt3M2nSJHbs2MH8\n+fMJDg4mPT2917XHtUi/7HikX3Yc0i87lpvVLzvsstQ///nP2bp1KxaLhVdeeYWoqCgWL16MwWBg\n8ODBvP3226hUKnuHeU1d6xEYGMgzzzyDk5MTISEhvPXWW3h6eto7zGv64x//iLOzM88++ywAxcXF\nvbI9utajsLCw17VHQ0MDjz/+ODU1NRiNRn784x8zYsSIXtcel6vHoEGDel171NXVcd9999HS0oKr\nqytvv/02Foul17XH9ZB+2bFIv+w4pF92LDerX3bYJFkIIYQQQgh7ccjhFkIIIYQQQtiTJMlCCCGE\nEEJ0IUmyEEIIIYQQXUiSLIQQQgghRBeSJAshhBBCCNGFJMlCCCGEEEJ0IUmyuC2UlJQQHR191TJv\nvvkmb7755i2KSAghbm/SLwtHp7V3AEI4iiVLltg7BCGEEN8i/bKwJ7mTLPqkl19+mcTERIYOHcpP\nf/pTzGazbV9lZSVz5sxh5MiRpKamsnnzZgB+85vf8Nvf/haAjz76iMTERIYMGcLjjz+OyWSySz2E\nEKKvkH5Z9DaSJIs+Z926daxevZp9+/axf/9+Tpw4wfr16237n3vuOZ544gny8/P58ssvWbJkCc3N\nzahUKlQqFWVlZTz//PNs3LiRQ4cOYTabWbdunR1rJIQQvZv0y6I3kuEWos/ZunUrDzzwAC4uLgA8\n8cQTvPvuu7Y12jdt2kRRURG//vWvATCZTJw8eRIARVHIzc1l7NixhIaGAvD+++/boRZCCNF3SL8s\neiNJkkWfY7FYUBSl0+tvf61nsVjYunUrPj4+AJSVlRESEsKqVasAcHJy6nS+6upqVCoV/fr1uwXR\nCyFE3yP9suiNZLiF6HMmTZrE8uXLaW9vx2Qy8e6775KZmWnroCdNmsTf//53AA4fPkxycjKtra22\n/aNGjSIvL4/KykoAfvzjH/PVV1/ZpzJCCNEHSL8seiNJkkWfM336dGbMmMHIkSMZMmQIUVFRzJw5\n0/a13l//+ldyc3NJTk5m4cKFfPjhh3h6etrGvoWEhPD6668zdepUkpKS8PT05PHHH7dzrYQQoveS\nfln0Rirl299/CCGEEEIIIeROshBCCCGEEF1JkiyEEEIIIUQXkiQLIYQQQgjRhSTJQgghhBBCdCFJ\nshBCCCGEEF1IkiyEEEIIIUQXkiQLIYQQQgjRhSTJQgghhBBCdCFJshBCCCGEEF1IkiyEEEIIIUQX\nkiQLIYQQQgjRRY8nyVVVVURERHD8+PFO21evXk1qairp6eksXbq0p8MQQggB5OXlkZmZ2W378uXL\nSUtLY9y4cTz11FMoimKH6IQQwnH0aJJsNBpZsmQJHh4e3bY///zzbNy4ke3bt/PWW29RVVXVk6EI\nIcRt79VXX2Xx4sV0dHR02t7W1savfvUrtm3bxs6dO2lsbGTNmjV2ilIIIRxDjybJL774Ik899RQh\nISGdth89epS4uDh0Oh1OTk6MGzeOrKysngxFCCFue3FxcXzxxRfd7hK7urqSk5ODq6srACaTCTc3\nN3uEKIQQDkPbUyd+9913CQgIYMqUKbzyyiudOuWmpiZ0Op3ttZeXF42NjVc9n8WiYDKZeyrc66LV\nagDsGocjxOAocUgMjhWHxNA5DrVaZdcYLmfu3LmUlJR0265SqQgICADgr3/9Ky0tLdxxxx3XPJ+9\n+2VHaG9HiMFR4pAYHCsOR4jBUeK40T65x5LkZcuWoVKp2LRpEwUFBTz66KN89dVXBAYGotPp0Ov1\ntrJ6vR5fX9+rns9kMtPY2NZT4V4Xnc56Z8WecThCDI4Sh8TgWHFIDJ3jcHbuse61R1gsFn72s59x\n4sQJVqxYcV3H2LtfdoT2doQYHCUOicGx4nCEGBwljhvtk3usF9++fbvt/zMzM3nzzTcJDAwEYODA\ngRQXF1NfX4+HhwdZWVm8+OKLPRWKEEKIa1iyZAmurq6sXLkSlcrx7oILIcStdstudSiKwvLly2lu\nbmbx4sX8+c9/ZurUqVgsFhYtWtRt3LIQQoiecTEJvtgnjxw5knfeeYeMjAwmTZoEwHPPPcfs2bPt\nGaYQQtjVLUmSt27dCkBCQoJt24wZM5gxY8atuLwQQogLoqKiyM7OBmDhwoW27WazfcctCiGEo5HF\nRIQQQgghhOhCkmQhhBBCCCG6kCRZCCGEEEKILiRJFkIIIYQQogtJkoUQQgghhOhCkmQhhBBCCCG6\nkCRZCCGEEEKILiRJFkIIIYQQogtJkoXowxoa6iktPYvFYrF3KEIIIUSvcsuWpRZC3Frrv/6G2upW\nvLx8WLN6PY88+iAeHh72DksIIYToFSRJFqIP+erL1TTUt2AwdNDQ0MRdU+cDENV/AOvWfs2Ce+fb\nOUIhhBCid5AkWYg+YuuW7Xi5hxIbFQpA3p4s6upr8PPth0ajQaWS0VVCCCHE9ZK/mkL0EecrqggM\nDLW9HpQwlNLS0wCcPHmE6Jj+9gpNCCGE6HXkTrIQfURIcCCVVWUEBYYBcOr0EVo76jlalEN0bH9G\njBhh5wiFEOLKDu7OoXL/boxqDWPvfQhvnY+9QxK3OUmShegjMidN4IP3P6Ws8CQWs5lBiQNITZ1n\n77CEEOKaDu3JxXnD58z312ExKSx77X+Y9Z+v4OTkZO/QxG1MkmQh+pBZ98y0dwhCCPGdVRTmM99f\nB4BarWKs1syZ06eIi0+wc2TidiZjkoUQQghhVyatEybzpfncKzpM6Hz97BiREHInWQghhBB2lnHv\nw7z7p5cYaWmjxmSheWgawwIC7B3W99bW1saOFctRmUzEjZ1A9AC5M96bSJIshBBCCLtyd3dn7n++\nTHl5GXGenvj4+N7QeTo6OqioqMDd3QdnZ+ebHOV3YzKZWPu/v+UxP1e0GjXffPgmysLFxCQMsmtc\n4vrJcAshhLiN5OXlkZmZ2W376tWrSU1NJT09naVLl9ohMnG7U6vVhIdH3HCCfKxgL2t/8RyN//f7\n/8feeQdGUaZ//LOzLbubzW56I5WQEEIChCZFOtJ7URQVFdudnqee1/R+3tnOcuedd+fZDlFBkY4U\nEQHpPRAIhJCQTnpI3yTb5/fHxg1IhFADOp+/MjvvvO93Ziezzzzv8z4P2175HdnpaddY4eWRcTyN\nEQorCrnL1BoTaCRn744O1SRxeUieZAkJCYmfCW+++SaLFy/G09PzvM9tNhvPPPMMKSkpaLVaBg0a\nxOTJkwkICOggpRISl0/+16u4K9BlYPcAlm5YSUxC0g3X8fojcwl3mKm3WFHg5DeD+gHgdIo4ZLIb\noiEnI52CLWtROB0QGceQKbNuyLjnYjab2bLgXbxsTVh0Xgy652G0Wu0N13E1SJ5kCQkJiZ8JMTEx\nrFq1ClEUz/s8IyODmJgYDAYDSqWSwYMHs3Pnzg5SKSFxZXg47edtqx32H2l5/XjnuSeZH+7PL/ok\n8ftBffDx8GBTTgGlDSY+Lm9g8Iw5112DyWQi/8v/MV1hY7LKSbeMQ6Rs23zdx/0h337wDneK9UxW\ni0yz1LD1o3/dcA1Xi+RJlpC4gVgsFlauWA2iHKdoY9r0Keh0uo6WJfEzYfr06eTn51/weX19PQaD\nwb2t1+upq6u7ZH8KhRyDQXMtJV4WCoUc4Gev4WbR0dEa7J0iqKspweCh4myzBTE86qJaLBYLarX6\nmmqQV5YQE93DvT08OpwF6PCZMI25vftc8/HaIj87nb4ecvd2jMGTk0W5N/x7MZrrUXq5zlchFzA0\n1HbYvfH9vXnZx11jHW4cDgcPP/wwWVlZyGQy3n//fRISEtz7//GPf7BgwQL8W1avfvDBB8TGxl4v\nORISNwUrlq0kIX4QSqUKh8PB8mUrmffAfR0tS+JnjsFgoKGhwb3d0NCAt/eVxYVKSHQU0558ms1f\nLsZeWY4iIogpd81ts13uqVMcWvAuRkszNR46Bjz6JBGdY66JBpPSg9pmM0aNBwDHyyuZ+uT8G1rx\nNDQikmMWO+Et2zVmC6qIG58ppFmtAVrT+pk9bj2H0HUzkgZKQasAACAASURBVNevX48gCOzevZsd\nO3bw/PPPs2bNGvf+I0eOsGjRInr16nW9JPwkefvttyksLOLxx58kKCioo+VItNDQUM/KFWuQC2oc\nTiuTp4zHx8f3gnaiqECpdK24lsvlyGXX36sgIXEpunbtyunTp6mpqUGn07Fz506ee+65Sx5ntzuo\nq2u+AQrb5nuv1M9dw82i43ppsNlsbF38MSpTDXajHyPvfgC5vG3P4Ji773NrqK83t9nm4KcLmOPr\nCbhi8z//+COMv/3zNdH6zLsLeWXuFHr5GGi02ajw68SQmG439HtRKnVw+zhW7tmM0umkxj+UyeOm\n3fB7o+fsB1j0yXsYLCaadF70mDO/w+5Pg0GDSnX5Ju91M5KnTJnCxIkTAcjPz7/AK3H48GFee+01\nysrKmDBhAr///e+vl5SfDI898gvuGD2VqMH9+Ptb/+LuubOkl4ybhNUr19K750gEQUAURdau+Zp5\nD957QTu704ooishaFm/YHJYbLVVCwn3/LVmyBJPJxMMPP8zbb7/NmDFjcDqdPPTQQwQHB3ewSomO\nor6ulsM7t2H0D6DXbYM6Wg4b3/8ns8R6NEolpqp8vlrwLhMe+dUV96exmfneQAbQ2q3XQKULlUrF\nC8s2dvhLS5+RYzBMn9qhGgJDQpn0x1c6/FpcDdc1JlkulzNv3jxWr17NihUrzts3Z84cfvnLX6LX\n65k2bRobNmxgwoQJPy60g2PfvtcAHRNv9f577zFi+CTiYhMBuG/uL3n3/VdZ8uXCG64FOj727GbT\noFR6IAiudbAymQylQt2mrjvvnMyyL9cgk6kQRRvTpo+5Jvpvpmvxc9dwro6bkcjISPbu3Qu4nsPf\nM3HiRLdjQ+LnS3lpCYffe4sZ/nrKTppZe+Qgk3/xdIdqMtSdRePrmqr3VKvQVpVfVX/1Rn8s9gbU\nCgXNNhsm461ftETi+nDdF+598sknvPHGG/Tv35+MjAw0GteP11NPPYWXlxcAEyZMIDU19aJG8s+d\nwjNn6JnY1b0tCAIe6lsrlcpPGRGb20MsiiIitjbbBQUF8qtfP3qD1UlISPwcKT1TyOFFH+JpaaRO\n48nt85/Cx8/vosekrl/JnCAjMpmMCC9P4spyKSkpJiQk9AapvpBGQXnedrNc+SMt28fYR59i9aIF\nqEw1WL0CcDQ2sv3Pz2AVFASMGE/PwcOuqn+Jnw7XzUhetGgRRUVF/OEPf0Cj0SAIgnuKr66ujqSk\nJE6ePIlWq+W7777joYceumh/HR37Bh0b8/Xoo0/y0ouvMe/+J5HLFezbv53wiOAOje+Bn2b825Vo\nmDBxHKtWrnXHJE+cNPaG6rqZrsXPXcP3Oq4k/k1C4lpy+PP/MddbBagQRZHFn77PpGdfuOgxclrD\ncQA8BAGb9erCEU4c2k/Z0YPYBQWDZ8/FU+/VZrv0lAOUph7AJpMzePZc9F6ujCtdp9/N4i/+R7Bo\no0RQkXTfY1elR6VSMfahxwHYtnIJY2oK8Q40ArBh0yrqEntiMBivagyJnwbX7Sk+c+ZM5s2bx9Ch\nQ7HZbLzzzjusXr3aHf/2+uuvM3z4cNRqNaNGjWLs2LHXS8pPAqPRyONPzOdvb7yCWqMjKjqMZ559\ntqNlSbRgMBh54EEpS4WEhMTNg97aDOgBl+Hrab30y2PMsDvY8Pn7TAj0xmSxckjjzYyIyCvWkJ5y\nAPk3y5jpZ8TpFPn47y8z9YW/olAoLmgnbFza2u7tl5n6wusoFAqiu3Yj6i9/p7GxkWSdDpvNxvGj\nR/AyGImIir5ibQCVWSfxNnq4t+M9FOzfs4uQkFC6JiSiVF6d11ri1ua6GckajYalS5f+6P45c+ac\nFw8ncWm6dInly+WfAR3vKZPoOGw2Gxu/3ojDIdI9sRtdunTpaEkSEhI3IbUaPU6niCDIsDkcmDwv\n7R2N6hKH6oGnWL57G3KtJ9MmTz/Ps3y5lB49xEw/17iCIGOQwk5BXi6du8Reop3jvHYymQxPT08a\n6uvY/PbLjFJDhcXGpqgExtz/yBXrq28wUYBAhNHltT5UXIqj5EvCOwXx1Ro54557Ucpl/zNGmg9s\nJ2tWf0Vzkw2naCcxMZ6kHj0ufZCExDXG6XSy4H+f0L/PaFQqNYcPHcRut3PbbckdLU1CQuImY+Rj\nv+bzj9/D09JEo86L0fN/2a7jQiMiCY144IrHra2uZtfC/6K3NpFdXEJtdIg7b3CF1UGI8cIc3Ha5\nEofFibxlAXSF1UFwGyEPe1Z8wTx/PYIgIxyw5B6nrKyUoKAry8bSKbYrJ1L3klZWidXhwCmKDI8M\nJVjvyQNOJytWLmHMffOvqG+JWx/JSG4HW7duxccQgW90IACHDu8kKjoK/Y/EVUlIXC+Kis4QGhyD\nSuXKr5yY0I+T6QckI1lCQuICPPVeTHrqdzd83J0L/s29OpBpPXB6RfFqygmmRYVSaXdQm9CPHv4X\nZpMYcue9LHzrJW6TmV3tuvWlR0DABe0UTgeC0OrZ9lEqaGxogCs0kofMvpcNBbnchoVyi5XKmmqC\n9a70cHJBoCj7FJs/+RDvyCj6DBt9RWNI3LpIRnI7qKmpJ75LN/d2WGg0BQUFdO+e2IGqJG5Fqqqq\nWLN6HXLBA6fTwphxowgNbf+qcQ8PDc3Nje5tURRxOOzXQ6qEhMQtgiiKlJWV4enpiWvpXftoaKjH\nbLbg5+d3RSEVoihSUVGBTqfD09OTlJRDlJaWojXVIvN0eYsFQUZCly7Y58wn3MtAL98LiywBaLVa\npj7/CkePHiEgIJBe4RFttgvvN5jtaz5jmL8Rm8PBbqeK6dGd3fvr6urYt28v3br1Oq/U+o+h1WqZ\n/sJrnCksoJNGQ/77/8DhdHm03zl8ghldIolsLiPvYA7fVZQzYnbbVfwkfppIRnI78PLSUVNThbe3\n65+7pDSPvgPGdLAqiVuR9es20r/PHe4fpG83beGBNoqO/BgBAQEICgt5+Vn4ePtxPH0/M2dPvV5y\nJSQkbnKam5vZ8LeXSHI2UeUQsfUazJBpsy953NbPF6LPOIJeLmOHxpupv/nTBYvpLjXu+r+9RA9H\nI7l2kfVp6YyP7oSfxoMV6VkMHdwXf70eURRp9NAReYkFdmazmfV/e4kku4liu0h+8iCGTL/rgnax\nST3JlgssP7gXp0LBxIfucVff++Lff0N5dD89g/zZ+NG/kA8cxaz5v7jkucjlcre+cc+9yPIVnyN3\nOgkICibSy+VVjtLrOJqT0e7rI/HTQDKS28Ho0aNZuWIlBUVOEJ107xGHl9el31AlJH6IXFCd57FR\nCKrL7mPmrBlkZmZSXV3NvffPQauV8mVLSPxc2f7lp8zzVqOQu9Ifbj6yi5pho/D29vnRY/JysonM\nOUbvUFfYQ0+bjfUrvmD0XRfP0NPU1MS2T95H02ziVOEZnooORKfy4z/7D3NfYiz9O4UA0Cs4kMc3\nbGXq4MHUa/QMbYehumPZIu43KlHKXXmctx3dw9kho/BrI69zSXYm8ppK7DI55cVniIxxLe6zpuzm\nFy2hZ307BfP3nZugHWOfi06nY2zLQsDNr/3xvH22q1jAeClyT53k9MbVKEQn6tjuDJ447bqNJdF+\nJCO5HchkMmbOmnnT5GCVuHVxOC04HHbkcgVOpxOLrem8/SaTicbGRgICAi46/RkXF3e9pUpISNwC\nKG1WFHLBvR2ilFNbW3NRI7m6oozuGrV7W6NUgvnSv2tbPvgHCVXFlDSYGOflyYasXGZ378rpyhpm\nJrQWu9Iolfjr9Qz705vtPg+51YJS3hoqEqxSUFtddYGRfHDLNyScOkRnL0/AztJF7xP4+1fRaDQY\n1ec7HbzVl++EOBe/waP4eNmn6JxWGuUqOs+5eD2HK8VkMpG75CPuDHKFqGSe2MsRvRfJQ0del/Ek\n2o9w6SYSAHl5uSz5YjnLlq7EYrF0tByJWxSn08G27RvZs3cr323fgN3emqR/0zffsmr5RnbvOMoH\n7/2P5mbpZUxCQuLiBCQmk1pdD7hihPc7FISFtR3P+z3xPZLZ3GBFFEUAdp+tJSK5/yXHqjyVjk6l\nZFLXGARBIK/GNe5j/Xrw5fEMd387cgtRRsa0+xzsdjuBSb1Jqapzn8c+q0B4ZBQOh+O8tvV5p1sM\nZBe9lCKF+XkAnKhrpK7ZDEB1UzMnG6/ut7qxroZ+wf7cmRhPcpA/ptqaq+rve+z289eR5J3OpL+m\n9QUhzqCnKifzmowlcXVInuR2kJ2dzYF9afRMGoDNZmXhgk95+NGH3HFQEhLtRanQMmpkazz7kdSd\nANTUVFNV2Ujv5MEAdInpxoYNXzNz5gwANmz4mpoqE07RQffEOJKTpWwWEhISkDRgMEedDtZlpmGT\nKxj19K8uGVus1Wrp94vfsvSrZSgRCRo3htiknpccy1sh0NXftTanR1AAe0rPskqmJ0fWTKTekxe3\n7kanUqFVyOkxcsIl+8vPzuLE5x9htDZT4+GJNvE2CsuKsMjlaOOj2PnSc6hFJ1WBYUx+4jcIggBe\nRhpKy9G3eIlzrA7ig12ZLX67aDWvPXYvfqKds4KSPyxafUkNF8OSlkJ3H1cWq54+XuQcOwjjp1xx\nf4W52aQt/hCjpYlatZae9z1Op8gogsMiyGyyEdYSxVlrsaII+PGZAIkbh2Qkt4OjqS4DGUCpVBEZ\n0Z3s7NPExXW9xJESEudjd5xf3tXudG3X1FRjMLROK8rlChBdEz179+zBQ+lPrx69ADh6bB9hYWH4\nt5FGSUJC4udHz0FDMYx3Va1tbzhgQFAwYx996rLG0ficH/pgDA1nxGNPE5h2lKD1i5iV6PpNFEWR\nZe2I3z25YhFzA/R8XxXw89MnmfDci5SWFHPq7RcRLWZEQSDS2syONcsYPv0uhs+8m1Xv/h1DSTFW\nQY7PsIkYjd9n0hAYMGYsQm01kZ7eV1UEBUC8xPblcnz5Z8z19wRcnvDPl31Kp9/+GT8/P3JvH8uy\nXd+iEp3UBYUz6ZzFl0d2bKXmxBGsCCRPn0NgcMhVKpFoL5KR3A5E0Ykoiu5/OLO5CQ8P6SaVuHzG\njR/NurUbkQtqHE4ro0cPBSA8PJJN32wjIrwzMpmM3PxTREaGAVBWVkFczG3uPqKiunL69GnJSJaQ\nkLhmFOTlkHviOHE9kwkJC2+zjVf/oRw9toce3noOVtfjP/gOALol9mDlN2vxabbgpVbyRWkdg37d\nduESURRJ3b+Hxvp65E0mMLQWFtG2hJ/lnc6iydTAPT0SADhSUkZaRjrgykQx5Ve/bbPvjR/8ixn2\nGrQqJfWVNaz737tMeOTJK7sggDZ5AEeO7KSXt56U6nr0/a8uRlhnt3Ku2aWztYaD9Bs9DkaPu+CY\ntAN78d69kREtHu3F7/2dUX98FQ8PjwvaSlx7JCO5HYy+YyRLPl9Bj6RB1NVVY7ZWERExqqNlSdyC\nBAYGMv/heRd8rlAouGvODDZ9swVBkBMR2Yk+ffsA4OPrQ+XZMvz9ggAoLDzN8FGXjh+UkJCQaA8H\nNn+Nft8Wxnt7su/ITspHTqbX4GEXtBs0cRqnIqJZnplO5xE96dvVVT9AJpMx/TcvcGTPVswmE0Pn\nDserjWp5oiiy5p03GGWpxlut4oPSUir1avw9tTTbbJjcs2lORkS3xlUnhwSxv9p2yfPQV5eh9XN5\nab3UKnRVZZd/Mc5h4LjJZIVHsTzjONHDe9A/PuGq+qv38sVib0CtUGC22WkwXpi544eUn0hliE9r\n4bJBHiI5pzNJSJSq/t4IJCO5HRiN3sx7cC5ZWel0iggmPHxwR0uS+Ani7e3DXXMuzG86bNhQVq9a\nQ+GZLJyina7xnQkMDOoAhRISErcSZcVnOLToQ/TmRuo0eobM/xXebRTzqN+/gzH+LqN2WICRZXu+\ngzaMZICuiT3o2oaBJggCIyZOBn485CMz4wQDTOWE+bq8x0/3jOe1rCK6RemxePkz9l5X+efwqBjy\ntq5xV75rtFoJiLl0eKNZqT5vu6C4iO1/+Q0WmUDQyAn0GDT0kn38kNiERGITrk3hsLGPPsWqzz5E\nbarD4unrTjV3MUSNFnNjOR5Kl7lWaLETFCA9/28UkpHcTjQaDUOG3A5IKeAkbjzTpksFQyQkJNom\nPeUAVSdTccgVxA0bS9o3a1GITqrzc3gk1BsMRkRRZPGn7zPpmecvOF7xg2hbhXh10beiKLJzzTLs\nVZWog0IZNGEqMpkMm8WK9pwF74IgI65XH0bMe/S84zuFR7AjaSBfpe7BU5CR7+XP1Jlz3PszUlMo\nObwfqyDn9tn3tlQahNgpd/Hl0o8JEW2k19QzxEdPrwCXF3bDplXUJ/a8rBoHoiiy66sVWCvLUAeG\nMHjSdHfYZX1dLXtXLEEhOgjrO4i4ljUjF0OlUjFu/hPtHh9g+Ky5vPd/vyHe3kiN1Y7ithH0CAy8\nrD4krhzJSJaQkJCQkLhFSU85gHzjMib7GXA4nbzxhyf5bb8kFHI5X9ZUQKjLayuTyfC0tu3gEWMT\nySs4QZReR0atCWXClYVz5efl0mQycfjrrxnfVI6fxoPSU0Vsrqnijnvn0y2pJ6s3rOReux21QsHq\nshoS5t/vPt5ms3E6MwOD0ZuhM+dgnTwDq9VCb0+9u03GkRSc65cww991vgv/9hJTX3gNhUJBdHwC\nPd9+l7q6Osr/9yG97K0p2+JUAiVFRXh1cxnJdXW1FOblEREd/aOG88aP32d0bSH+Wg1lp0v4+pNK\novsNwkOjJfXT95gXoEcQZGxfs4jTcoEu3a99CETuyeP01sjp5RWEShBYnJmGzWZDqVRe87EkLkQy\nkiUkbmEaGxtZuXINSrkap2hj/IQxFy0iICEh8dOiNPUAM/1cRp5cEJgc1YnCugaifYw4nQ73onO7\nw4lJ17YxOOLOe0nZ+R0phXkEJsdze/+Bl61j4//eJbYkCy+5nNKsPHS9uwMQrNOiLHLlMZbL5Uz+\n7Z9Z99VyRKuVxMkPEhTaCQBTQz2b/v4yw5QOKqx2Tsb0YPS9D6FSnV8QpPjIfmb6t57vYIWN/Nwc\nYmJdBZZkMhlGoxFdWCSFacWE610VSY/Z4LaISADS9u+hYeNyunvIOdrsxG/KHLr17nfBOWnLCvD3\ncR3vKZdTsnMz/Uuy2F9czqiwYATB5VUeFmBk+cG918VILjy8n1n+rfHdw5ROsrNOEX+NQkAkLo5U\nTERC4jpw6tQpVqxYzfKlK6i9Rgno22LlyjX07D6UpO6D6NF9KGtWrb9uY0nc+jidTh577DEGDhzI\n8OHDycnJOW//6tWr6du3L/369eP999/vIJUSl4NdUOJ0toZHlDU24tWSQ3h0dCRvZhaxut7Gl3gy\n6kem+utqayg9vA/FmVzy9++k0WS6LA05WZkklGbT29+XLj5GnumbxOacAvd+q7zVH3c6LRVnXhaK\nM7mc3LPd/fnuFV/wgL8nnX2MDAjyI+j0UcrLyy88X7kCh9Pp3q60OtAbLjT+B46dxKHwBFY3OPiy\nUSRi9gPodDrXNfpuAxOCfIgwGpgc7E3RlrafmxZZa2jIltwCnumTSIyPkcGdgig95xrZHA6c18mz\n65ArsDtaz7fCam9zUaTE9UHyJEtIXGNOnz7N0SOZJCbchiiKfPnFSuY9OPe6pOwRULqL2shkMgTh\n6sqwSvy0WbNmDVarlb1793LgwAGeffZZ1qxZ497/zDPPkJqaik6no1u3bsyZMwdDGwaIxM3D4Nn3\nsPDvrzBQYaPS5uCQTIO2vomKZjN7BR0PvPXuBd7YH7Ljo39xrw5kHmocTjOfL/gPk5/6fbs11NfW\nIK9v4IGd+9AoFMT5GnGoNWSerSbdLsOj/3A2f/Y/mkURr6w0ZnfyB+Tk5qVx6Ltv6TviDhQOu9sz\nC6AX4Ltli/E1eJEwdDShLV7g22ffy8K//YWBgo2zNjuVXXuTeM5C5r3ffkNVfi664Ehi+t5GltWC\nXK0mIjbe3UYlthqdAErn+VX9vqfTHZNZs3YJ3VQy8posyAWXXzHS28C2/EIsMjl+ahV7RBWT5s91\nH1eYm01+yh5kajX9x02/5PW/GENmzeWTv73EQJmFKpuD8tgexHcKu+RxVquV9Z99iWixENX3dsKi\noq9Yw88ZyUiWkLjGHE87QWKCK6+xTCYjIb4fJ04cp0+fvhc97uzZs2z+dityuZKwsCAGDByI0+l0\nVZn6ERxOy3k5vB1O64+2lZDYs2cPY8e6ik7079+flJSU8/YrlUpqa2sRBOG8+0ri5sVT78WUF16j\n6mwxsUZvkjy8KCsrpampkemR0Rd9fjQ2NmKxmPFqbkDm6VrgJhcE9E0Nbbbz9vY5754QRZHq6mr0\nPn6szsnnwyljUMrlLEk7SYrGj7pZjxLQ3ETTV4uZEuiN2Wbnk5IinCF+CIKMaC9Ptqcdoe+IOwjr\nO5AdXy1mqL8Rs83Ourxifu/ng7KxkbUfv4PzgScJi4xGp9Mx9fnXKMjLJchgJCkgwK1ny5ef0qcw\ng0F6LadzT7Iht5BfJydgrrWz6I0/M+OPLyMIAubQKMqr8gnUaig2NWEL69Lm9Uno0x9T1wRKis6Q\nXF/L9o3LGRZgxOF04giNwnf+rzCbzUyPjHI7K3KzTlH2+fsM1HsgF2SsePO4e9wrQavVMu35VynI\nyyVA70ViOxbtORwOvnrjRe4zKlErFGz49Cj2ex4jqkvcFWn4OSMZyRIS1xiZIMNms6JUurwHNXVV\nBId1arNtTU01u3buRqFUkJ9bxJDBE5HJZKQe28/ePe/g6xuIzdZMn369OHOmEJVKxejRo90P5ClT\nJ7J61TpUCg8copXxE+64YecpcetRX1+Pl1drzlW5XH7ei9izzz5L79690el0zJgx47y2baFQyDEY\nNNdV86XGB372GgACA105fO12BwbDpb2GGz/9GOHIPjxlMrLLKsDf9V2LoojFU+8+n3Pb7Tb6M+eF\nv6BQKDCbzSx96f+Ibarl9W27+e/4kShbnktzkrpx4Jvt9O2fzLr//IPJga7Fgx5KBYPDO5FbU0uM\nrzelDSZqG50YDBr6DRlEht6DtXt3k1dawv1do939TQ7y5qt939G9R2ueYl/fC+N/VXmZRBpdMcRd\njHo6a9XucQc566isOENsXFdm/+optq1eyYGSM+jjI5g1ZfqPXieDQUNoqKtwU4a/N2v37sQpVzDn\n1w+g1WovaJ+37ztMpcVkWX2oM1vQNJspLyuga3y3S34nF8PHJ6ndbTNOnGCYYEGtcH2HEwK9WXtw\nBz37XLr0+PXgZvgf+V7DZR93jXX8ZNm6ZSs11fU4nHb69e9NlDR1IfEjjB8/joULPiUirBumpgaQ\nNRITM/yCdpWVlaxeuZ6Bt43BbG7i0P40t/eupqaasXe4cibX1VXzzdffMGnCHMzmZhZ8tJD5jzyI\nIAgYjd488OB97oePlJ5Q4mJ4eXnR0NDqJTzXQC4sLOQ///kPBQUFaLVa5s6dy4oVK5g5c2ZHyZW4\nTuTmnCYg7QB9gl3FLDqp5Pzr9BmifH0w6bwY+ytXueq83Ozz2iVbLWxaspgJ985j06cLuEcnQ+nl\nR7ReS4PFwvflpZ1OkWa7HQAHsvNmJWotFrZnZBHo6UmD2YI5IJh1r/wJm8GHiY/+kvhevclIP4Hp\nk/+49TqdIukHDiA7W4FVb8QzKBjr6VPY5HKSZ91NZIzLE+z4wczHubG89WYLqsYmACwWCzUFeaga\n66lyOLFare0KiYjv2Yv4nhdP9Zadns7Tid1QtRhl23MLaWi4vBjvq0Wt8aDhnHMXRRGnTFqCdiVI\nRnI72Ld3Lw6rlvg415vgd1u2MOeeoDbfIiUk1Go1jzw2n7y8XLTaaIKD2y5hvnPnLgbeNgaZTIZG\no2PQoJFk52QQ2yUBlbL1gX38xBEmTZjT0k5LTOdeHD16lOTk5Bt1ShI/EQYNGsS6deuYNWsW+/fv\nJymp1TtlNpuRy+Wo1WoEQSAgIIDa2tqL9me3Ozr0xexmeDm8GTRcro780/l017QW3oj0MRIcGsft\n5+QrrqtrJu90PvFqJZtO52F1OBgc0Ynm6lrq6pqxNzS6Pb3/nHgHT67fzK8H9MFb48F/D6Qy7Q+v\nUFfXTI9xM1j0zqtM9tZQ2mxmS34RA8NCsNgddPHzRhSdDFfYsdSVsPxvbzLxl88S0qkzXxk7oaot\nwUel4uNTuTzWNRofhR1LQxmf7dnGw8mJgI0v//02Hn94FY1Gg+G24ezY+TXJXlp2FJWRXVNLTbOZ\nisYmjlecpYfNdV5f/fOvJFadobjeRGcvT5a99QaTnvjNNfke1Eq120AGCDPoOdNouaH3h69fKHv9\nI9DWFOHvoearWgvD7/5Vh92jN8P/iMGgQaW6fJNXMpLbQXFJOfFdbnNvR4TFkZubQ/fuUgoWibYR\nBIHOnWMAyMjI4NCBVOSCAp1ezZSpk9uO9RRFMk6lYrU2U3Ami76WwajVHjh/sKhEFEUEKVZU4gqY\nNm0amzdvZtCgQQAsXLiQJUuWYDKZePjhh7n//vsZOHAgHh4exMTEMG/evI4VLHFd6JrUiy0bV3K3\nTosowv7qOsInT7ygXWxCEgvefYune3dHp1Ky8Gg6IXePAEDwD+ZQSgZ9Q4MQRZFQP3/+ml+Jqaaa\nZ1//F126xALg4+fHyN+/wr6D+ygpLiI4J5+BYaHoVEqWncjE7nA939QKBbraavfYk3/5DCeOHiG7\nvhbfoi/w0Wrc7YK0rdP2ySqRwvw84uK7kTxsFKbkHqQcP44t1sTAA9+RVlaJt4cKVWgEkdGdAajO\nTEcd5MOkrjEcK6vgbMYJnE4nMpnsknH4l2onV6s5XFxG75brsj2/kM7t/WKuIZMef5qC7HSOV1cx\nJrEvGk3HhgPdqkhGcjvwUCtpajKh1bqq+pRXFtOr75AOViVxK9DU1MS+PUfo39f1w1JdU8nWLVsZ\nNXoUw4YN5cMPFuJtCMBqs4DMwjO/eRKLxcyM2eNYWQCpRgAAIABJREFUvWoNdhso1Q52793IoAFj\nMZubyMk/xvBRD3TwmUncishkMt57773zPouNjXX//fTTT/P000/faFkSNxidTocQm8hnB7ajkQsU\ne/kzL+nCMIL87NPMje+MZ0tKuQd6JrCsIBcAa0UJoiiy7lQ2NqeTfkF+3Pm7l4iMirrAY6jT6Rgw\nfBQ7v/uWhOgId3+zu8fxzt7WxaNN55SVlslkJPbqDcCixR+e11+DtXWBcq7FQdfgYPd2aFg4oWHh\nVFebWLhrKxFWGzlNZrxHDHWHVHgJkBDgCiHpHuDHqlM57Pm/X2ORCXgNuYN+o8dfcC0cDgdr//0m\nflWlWBDQ3z6a/ndMuKBdSNcETCk7WJ+Zg8Vux+DtR3hk1AXtbgRJvfsAHT/LcSsjGcntYPyE8Sz6\n7HNkqHA67UTHhOPr69vRsiRuAYqLiwgOjHBv+3j7k5GZDUBNTQ3hYdH0TBrgKn+692sADC05MGfN\nbo0FraurZdeu3ajVKh56aN4Vr5SWkJD46XF4+xYas09gE+T0mjwHHz+XAWi329n6xUIUDbWIvoEY\nAoOpz0ijrL6epOYabu/mmu2qMVt4/49PE9spFNE3kBGz5yIIAjJBBj/wmOYeP8rWd9/ixKED3NW/\nNVznUHHZefHubeHrF3BBfw1qLV8VVVKv0aOJ78HW//4dq0yg38y5+Pq7Fsx5+PixJC0DT5WSBouV\ndFMza0uqMMsEvIeOx2j0vmCs75Z9zuNBenQq12/1N0d2sjw3Cx+tBsGjNVTym9N5/HZgb3QtBvS3\nuzZR3bs/Pj6+OBwOvvvyU4TaKrLOFPFkkB7PENe13bRjI8vSjuCr1eLbow89Bw0FYPjMu/kg5QCG\nxjoanSKRQ4fh4yPZC7cq19VIdjgcPPzww2RlZSGTyXj//fdJSGhdnbpu3TpefvllFAoFDz74IPPn\nz7+ecq4YQRC4f969eHl5IJPJpLcyiXYTHBzCnl2phId3xul00tjYgEbnypeclnaCnkkDAJfXpHt8\nP44fT6Nv39bKT98vrDIYjEyceOF0qISExM+bo3t24L/3G0Z6eyGKIp/++3Um/ul1FAoFGz94hxn2\nWrQqJdsO78RLp2V0oB/76sqI8W01LL091ASfzcJoayDk7Bm+WdjI+Icep1tiD1Zu/IrZZgt6lYr3\njmcxNyKYMKGZrlEhfHLkOPf36k5Ns5mDRaUMV148g0C3xB6s2OiFX0t/n5dWM+yJ31JdVoqX00HE\n0V309HGdx2f/eZ1xz/8VlUpF8OCRhB/fS6JRz5GaBnz6jqD/6HEXDY2QNda7DV+AzmoFpryT+Om0\nVJtMHK6opneAD6VN5h+0k1NeWoKPjy8bP/w3Uy1n8VSrWGk6i6e61djtolUR0lBOoi6AQ9vWkq72\nIKFPf9JTDjDGoCY5qjuiKLL4RAqWydNRq9VI3HpcV3fU+vXrEQSB3bt388orr/D888+799lsNp55\n5hk2b97Mjh07+PDDD6moqLiecq6KrKxM/u//XuL119/C3rJq90bjdDrZuHETK1espqqqqkM0SFwe\nnp6eKFR2Nm5awc7dm9i4aSl33DEacDlUbLbWacPaumq3F7m0tJR//uNdPnjvM975x3/Jy8vtEP0/\nBzIzM1m3dh2pqakdLUVC4rI5m36MJG9X+jaZTEZfuZ3CwnwAvKrK0apcleAqG5voHejygnYL8GNb\nbqG7j5TiUhqtVgaEhVDe2ER1uut/QRAEpj/3J7bH9mNFYBeMfr6EGV1jJQT44RBFNmTlcqysEu/w\nSKKjYy6pV+6h4evTuaw8mUlxVTWmFR8zOusAJWu+oKdP63ncpnRS0PLcGzxpOo3j72a5b2ccU+/n\ntjvGXzJ22Dsmjsy6Vs/21twCEgL96BUcSE19A8dKyliZnklxYyPpFa2/p1tLzxLZch6eVaXu0BAv\ntYr0irPudlty8t0vGn19DZSkHQGg/Hgqyeecx0CVSG521iWvi8TNyXX1JE+ZMsXt/crPz8fbu/XN\nNSMjg5iYGHc1p8GDB7Nz586bMt1QamoqWzfvZuwdczE1NvCnF/7Mq6+9dEOnvEVRZOGCT+meMIgg\nf0/WrPqa8RNHEXxOLJbEzUd9fR1Ou4pxY1z3tdnczIYNXzN16pTzUsU1Nplw0kBs7DAAvli8lLF3\n3OnuZ8WyL3nud1Ks6LVm3969lJWaiI3pQ1FxHhtLv2Hc+LEdLUtCot3YVWpsJoc700SxzUlsy/R+\nucXKl8cz0CmVHCkpY1q3Lijlcgweasw2G0vSTuKpUmFQq2mw2tCqlAyLCicjrdWok8vlDJ0wBYCN\nWSfOG7vZ6Ie1UxgmQUGPKXe26S0VRZENH/4bz/Iz1Jkt+DTWMjXRVf3O6RRZm5mNtlMgIRoVVrvD\nnRliZ3E5ui8+okguwxwWw9h5jxLfs/0ZfXoPG82epibSs05QVHmWeB8j8f6ul4Quvt5MS3DF4svS\nM9l0OoejJWU02uw4gLOVFYSFR9Asby01bbLaqDA1kltdi83pJETvSbmpiUhvA802G2JLfman2gNL\nkx21wmVeFVntBPtfugDIjyGKIhsX/BdtaT5WmZyA4ePcoR0S15/rHpMsl8uZN28eq1evZsWKFe7P\n6+vrzyt3qtfrqaur+9F+OjJp/bffbGbi+HsB0Ht6MfC2URw8uJsxY8bcMA2ZmacIDorBU+fKQ3lb\nv1EcOniQe++764ZpOJebKTn4zayhvLwQf9/WFxkPDw0CMgwGDVarnIKCAhpNNmwOGx5q0d3PD8tL\nq5Sai57nrXAtbkYNBQXFJHYbDECn0CiOHN15TfRfaeJ6CYnLZdhd9/Pp318mzlpDjRPk/Ya5Y3Qd\noshdLQbpqOgI3tp1kCFRYRTVN6BTqZjRvbUC2+bsfNadysZLrcJ4Tpnnc+k8cRZ//dcb+MgcVAlK\nxv3mRXcVt/q6Wr56798ITgchyYOI6dYdgB2rlzK+qRwffz2g56uTNTRYrOjVKgRB5s7UM7ZLFH87\nfILB4SEUmJrwUqtR11Qgl8kIb25gz8a1DB4/5bKuzaDxU2D8FE4cO0rohkXuz88tf51bU8cDyYn4\ntmTM+CrjNOs/+ZCY0BA8EpL567pl+OCgyNTEy0P6u4/LqKxibXYBCf4+pAka5j5+t+v7mD2Xz/7+\nMnHms9Q5RcTk2+lxTlXAy2XX2pWMrivC38/12//1plXUJiS1GYctce25IQv3PvnkE9544w369+9P\nRkaGK5+hwXBekH9DQ8N5nmYJiRvJmtXrKC+rRRQd9O2fRO/e1yYHcUREJBvWbSc83JUEqLTsDKGd\nXF6FV15+nRnTHkSjcXkg0k8eZdu27QwfPoy6uiqsVgsqlRqHw05V9c0biiQhIdFxeHh4MOOPryCK\nFrRaLWZzaxEJz6Z6wOVV1qiUeOs0KAWBrr4+rMjMYVKL53ZRajrJIQFEeRtQCAJf5xUyro2xcg7t\nY358FP4t5Zz3HdxLVJc4LBYLW99+hZEe0Gy3k5+VgXD3I0R37Ya9qhIfj1YPc3yAL3nVtSQFB7Ak\nLQNFi71aUFuPEBJG+DMvUrl/L9XLP+ah5CQEQca23ELSd2+/bCPZPWb3RFatVXF/y/nuKakg2mhA\nJsiQy3AbyACJgf5EmGvpaVWzYfcx+hp0BHlqqWhsZkV2ATNjIjDb7CzPLuSZ5ARsTidCg5mTqYdR\n6TwJi4hg1h9exuk0o9FosFjEK9L8PZbKcvzP0ZegUVCYn4+xp2Qv3Qiuq5G8aNEiioqK+MMf/oBG\no3Gtlm15a+zatSunT5+mpqYGnU7Hzp07ee655360r45MWn/H2NF8vXE548bOxGSqZ+/+Lbz62ks3\nVE9gYDilZd/h6xuITuvJ/oNbmDBp9M8+Ofi10LB3zx4cNi8SWxaV7tu9C1/fQLy9fa6JhlF3DGPX\nzp3I5Qp8/Qz07jOKurpmrFbRbSADhASHcTwtheTk/gwddjvfbFqFn18gVdUV9Ovf66Jj/JS+jxup\nISIilKzs48TGJFJUnIefv+Ga6L/SxPUSEleCTCZzexbN5tb7t6KhwV3tzuF0UtFoxqjxoKTJQszo\nSbyZnYXcaae28ixDIzuhUSpJrziLn2hrcxxNST7+fq5UqKGeWuRncgA4eSwVZdkZyvx88FKrKC0t\noWr3NqK7dsMjJJzik8WEerqedUdLK5ALMgrq6rk9ohPrM7NZdyqbIE8dcXofjEZvSooKmdYl2u3x\nHR4dzncpJ6/4+sjlcqb87i98tXoZ2CyEBgWRVV1Dz+BAAnQ6tuTkM6pzJABHSsoZ28WVsm1CVCfe\n2ZtClHcMjdY6si0iy/WhlJ09y92xke54Ze/aek598QF9/b1JsTgJmHIPA0a40sRaLFf3PPEMi6Tw\n2BnC9S3Xz+wkuXNHZF7+eXJdn+IzZ85k3rx5DB06FJvNxjvvvMPq1avdievffvttxowZg9Pp5KGH\nHrpp42t79eqFTqdl/drP0el1vPzKn294Ci6ZTMYDD93P/v27KT/bzLQZE6Q0dNeIsrIK4mJai8VE\nRXbj9OnT9OvXv2V/KSdPZhAb24VOncIuu//w8HDumRt+weeJid1IO55CUqIrl+XuvZt56OG5AAwf\nMYyg4ECyT2fTf0B3knr0uIIzk7gUAwYOJDMzk6zMFDqFdaJXr4EdLUlC4rL53YShJAf6UttsoVBr\nYGhcFxSiE4VGx7ITmWiVCppsdroY9cT5+RAHbC88xeDnXsTPz4/FD0xjfKzL8OrsY+Tt/UfaHMci\nOz+MyNpS6riuvp66ZjOHi8tQK+TUmS00ZWYAMHjCFLbUVSM/k0tNsxk/lZrJcS4j1OZwEOKlZ1JX\n10K5LcdOwxt/oqmsgjKdQCeD3t3Ow7vt37uMIymc2bIepeikVGOgdN82Ijw15DY0cudr/yEiOhoA\nlUrF6Dtdz9dFOzYxI7m7+3zf2HuYBv9Qas0WvORyt/FrdziJ9DbQ2cdIZx8jB7ftQ1Gcj6fZQlpN\ntXvhXnZVDXe1hK50Bt5b9AFnt6/HJpNTptBAxlE0gkCB2hOPxnqCFDJK7TDjL28RGOSyezKPHaHg\n27UonA5k0V0ZPuseAAaMmcDWmmoOF2RhE+SET5uLXu/VrvtC4uq5rkayRqNh6dKlP7p/4sSJt0xa\nq9jYOF78ywsIgtBhnjJBEBg3zvVS0dBg6RANP0WMRi+qayrx8Xbl5CwqzmHEaFdqtpRDKWRnFRPT\nOYF9u9MICSt0Vyu7WiZOmsjSpUtZv3EJdpuV8RPH4N+SFxQgPj6e+Pj4y+ozNTWVrMwcnKKDoUNv\nJySk7ZLYEq3ExcURFxd36YYSEjchz00ZyUtD+hPs5fLwLkhJI6GxmoQgf9ar5GTVmvCz2ym32Bgf\n3fqS76cUaGiow8/PD73h/Kl7T28fNi9egOChZejUWShaFqEFj5rI+q+X000tcMzixOO2EWxevIDU\nkyfp7eXJjATX/1F2VQ3/O9OaCWLU3a3Fj775+D0OFGRgVMrZUNOE2GTls9QTZDU082ivboTpleAV\nyp+/243V7sRX68GStAwM/VsXq53Jy+XUnm2YnSKeWWnMDvUDBF7Zvpm/DBmAILg85y/87nEMkTE4\nVB488PzLaLUub6zK2RqSAqCVC2SercYik+OsqqFkXwqeKhUnK6t4aaRrzcLO/DPM75VAhJcSvJQs\nrqnkuzOlhGo9qP9BRIW/vZnJWtfLwsaMDO5Kcj3HP0o55g4hcTpFXv+/Z/nFh19QX19H2apFzA7y\nBgQK8tM4sNmH/qNdQS8j77r3su4JiWuHNB/YDux2O39+8SUC/cOxWM0YvD14+OEbn9P5ww8+oqHe\ngkrpQXnlGf78lz+5H14SV87wESNYuWIlefkncTgddI2PJqBlocXJk6fp3XMYAN3ik0k5su2aGckA\nd95556UbtZO0tBPkZZfTPd6lb8O6b5lzzww8PT2v2RgSEhI3F8EqhdtABhgQHsInqcd5M2g4OB1M\n6hyOt1pNWbOFPUVlJAT4YXc42Y+GaeGRACi79+ZsfTF+Gg+KGhppbDDxSEMxTVU2Pn/rJSY89Xvs\ndhtJtw2iJi6eUxnp+KhUNK//kimB3pwtyeH2hNYXzRhfb/SlrSWmnU4nZ84U4u3tw9gHHyc3J5uS\n2hrCMtNJLjxJZ4Mnnx3LoJOnlppmM1qlgruTunGkuJQ6iwdJgQEczXF5pnMzMyhf8iGzAr1pttn4\ntPgMzmBfBEFGpNHgDtGQCwJdDHoejA6k2Wbjjcfv5YkFS1EoFJTWN1DZ2IS/TktZQyM1jY38X39X\nuxczTcwfORiNUsmStJN8l1PIhK6dKTU1MiQyjNpmMxqlgkEB3qTdNo760DDUu7dRUXeGAI0HpaZG\nlC0zzTnVNQyO6ITZZsdstyOjddGgIMgw2syAq7JhsqbVSx/hqeNgYd61v1kkLhvJwmoHb//9H0ye\nMBddS2aJfQe2k519mpiYLjdMQ1ZWJnJBz9g7XJ73xsYG/vH2P3jutz8exy3RPmQyGTNntZ16UJAJ\nP2h79WE2drud/Pw8IiOjruglp6ioiB3bdyMICvz8jYwePQqAEycyiIvt7W6XEN+H48ePM2DAgMvS\n9n05bGR2pk6bgkbTcdkqJCQkLk5xk5naZlesMbhifqfHu9KbFdXW0WSz0clLT0FtPRYPLSuUPtg1\nSsY/eC/ylrRxMf0G8lFL1oqcOhNvDnc9M7QqJX2rK/n6d4/T2ejFIZtAJ5WMcJmTtMpqxoa6nAm3\nR4ZxuKSccbGu0IbSehPaMFdIRX19PQt/9SC3+xvJbDaTZoPb/LzRCVBfU0fnBFeohY9ayUcpx4j3\n96XOYuX02Woe6p2EV8uiv9SjrrR0Obu2MivQ5fnWKJUMjuhETk0tXXy9KWkwua+LKIrk19S5282O\nCmXX9q0MHzUGjUrJsbIKmm12dEolXfx83e3mJSdSWNdAnJ8Pc5K68dy2/TQn9iXfP5yPUo4R5+dD\nvcVKbqOFKb/si8FgJCY2jl3rVmGtKOWM2cLciE4ARBgNfJZ6glg/H/RqFUXn5G0WRZE6s8tI7hQZ\nRXqTnbCWhF8VzWbUoVeeNk7i2iEZye3A6ZS5DWSAyPDOpJ84cUON5JPpJ4iMaE3UrtPpcThu2PA/\nW/wDjJSUFBASEkF5RTEGb+2lD7oIu3fvYvt3+4iKjGXFsvX06NWVcePaWkfeNlarla/Xb2bQAFcu\n39KyM+zatZuJE0ejVMhpbm5yLwYsLSuiV5/LW+CxfOkKYmP64eGhweGws/TL5cx74L7L6kNCQuLG\n8fqaLTw/dST9Q4OoM1s4XFLO5PgY1p3KxuoUmd29KwADw0P519EMRs1/AnB5ZLO+WYNKFDlbXMgf\nencDYE3GafdiP4BGiwWaGzEJIob6Bu7u1wuAwUF+vLFrP938/bA6HByvqKSoyeUFzhHURMWHsO2f\nr7L74EH+OqCnO/+xV1YenTUKuvh6U1XT6m12iCLzeiW6222UybC2/Mg5nSIJA1wL4Zwy4Tx9JofI\nrmYr6WIDOVXVvLp9L+FGL/Jr6jGoW1Npmmw2PL1cVqghJIyzjbXolEqOllVwX8/WSsCNVjsBnhr3\nuM12O6PmP4Fj8QKm+qrc+Y+XnMxm+wf/RK9SoYlPYsjkGYDL+P36o3fRVxZS3mQmKSSIkVGuMJco\no4F/7k2hs48Rk9WGWu0ax8fHF4/RU1m6fSNq0UlTSCTjJk2/qvtC4tpwY1ef3aIEBflRUJjj3k49\ntp/Bt99+QzUMGjyE1NS97u2CghyCgq8896JE+xgzdgwGH4GsnAOoNWamTJl8Vf3t2LaPaVPupWeP\n/kyacBdHj1zeiu2iokJCQ1pfloKDwigvqwRcFfy2freetOMpHDi4g+ycjEtWpfohToeAh4frwS2X\nK5ChusQREhISHYlSqeTphSvwnjwL5eCRvDV2GNO7xTKpawyeKuX5bVueB/X1deR9/gF3qWxMV9sx\nNjdwvLySdaeyCdBp+e+RE5gsVk6drWZH/hnm905iUtcYt7caYG9hMZPiYpjUNYYZCXEYPbQETpmN\n19ip+IeEcpejhmlqB+GC0234AkR6e7EmPZP1mTkY1Cq2FJbQbLNRYrae1y7a28ixirPUmy18Wl7P\nwGmzAegzeRaLy2ppsFjJrq0nO7QLc954l2F/eotgLy+eG9yfe3t257nB/ai32lztqmr4traZvv1c\nC7Rjxk0jNDCYUZ0jCPby5NPUE+52X6adxOEUabBY+TztJBq1mm+XLqahuMhtIJssVrBbuV8vMF1t\nJ/zwDo7u2QG4ZiYnPPIEk978N33mPUysT2s9iDCjF0pBxqjOEfh7ajHcNsS9L2nQEDR9b8farRcD\np8+57Ge3xPVB8iS3g3kPzOM///4PGadSsViaGTJsAL6+fjdUg7+/P/0H9OSrdYtQqzWoPWQ88eQT\nN1TDz5V+/ftds740mvPjg7Way4sX9vcPYE91qntWwWIxo1C63nWtNgcTJ8ymoaEOhUKJ1WqmtLSc\nqKj2e5NtDut5205n26mgJCQkbg6KC/I59fE7TPX34kxDIysLS3ksyRUfXNbQSJPVhkapoLbZTJ3D\ntWAt68Qxbte35i7Or6qlm68Pk7rGcKiolJMlZRw0eiHIZAToWmfPRHD3V9JgYmB4qHvf2JgITHs3\nkxwcxIc5p6iLi8ZPpyVQr+NwcTm9QwMRRZGV6Vk80icJX62WhcfSUY0axSadHhsGDhXl0LdTMKIo\nsiorl96PPstOcxNjBw9zh335+vsz/Lcvs33vLox+fkxM7uvWEOJtcBvaHkoFgUYj79k8COwcyxMv\ntM6I9RlxB0WdY/n25HEOHT3N2MAAPjlyHH+thmC9jnJTIwW1dYQbPEmvPMvs6lyWleZzsElPvyA/\nMs9Wc3tE6yLIBG89yzPTEQcOOc+4jUtIZNUiJ/d4uTzfKdX16EZN5l/FxfSb9TAzWox2URRZ9far\nzBCaMajVLPvnIZJ/8Vv8A4N+1Fg+15v+U+RmOT/JSG4nTzz5RIfngb19yBAmThrToRp+7pjNZg4f\nTkGn09GjR88fbWexWEhJOeRu9/0/+5kzOTQ01KHXGzCbm8nNy7ys8fV6L6JjQth3YDNKhRq7s4n7\n57lWPickdOXIoWPExbrSxaUe282cey5vym7M2JGsX7cJpcIDu93M8JE3dsZEQkLi8khdv5L8Uxk4\nKn0wWa2U1TXw6r5GjGoV9U5YeOS4a4GaqRFVSAQAwWGRZDWaCfDUARDtY6R7kCuzTt9OwRTXNzCi\ns6utTqWksLaecKMXXiol/9qfQoTRQObZapKDAon2NQKQW13LwPBQBEHGo70SWHsqmynxXbgrqRt/\n/HYHB4uKKTM1MjEuBv+WcR/smcALyxfRI9CPirIKnLHRrjARh4O+QX6cXLqQCB8D32adZPIvnnHl\ne3Y42LLgP/icLaVMkGOurqLvKFf4mTYy9rxrc7a5ma4Fp2jMOcGXFaXc9dTv3Ps6RUTSKSISW10N\nZ7asQS7IyKuto6bZzPHySnQqFVlnqxjSEl88tXMYT2/awX5fI6UNjQztHOlOUXey4ixlVfns+tNT\n/D975x0YVZX98c/UZCY9k95774QACdJ7kaKIBRt23V3XdZuru+u6a9lVV39rW1cRFAtSBKVKEITQ\nCQmBVBJI721mksxk+u+PFydEStRVQDffv/Iy99537n3v3Xvuued8j8bRiczbH8DNLRaFQoEscRQv\nfL4RpURMj8qPe2+58zwK2aqKcrJ1HXh4C77R2Qoxnzz1WyI8PdA4OjHqtvsJCBGex5GdW9Hu34Uc\nK2rvIOY/9Ohlp6T9IWG1Wtn8xku4t9ZhRIxL9lTGzrpyLGgjSvKPCO3t7XyyIQ+xWEJwcBCjMkcN\nX+l7htVqZcvmrVgtVhRKOdNnzLyqPlCbzcaWzVvR6w3IHaTMnz/PHpzy36KnR8v7731MakoO7S09\nrC76gJ//4p5vVO7W2wTOy7TUUWzdvg5HRyU6fR8pyenfqm8ikYic8TnkjM/BarUiEonYtm07JoMJ\nuaOUoFBvSisOYbGYmTNvGs7OLsM3fA78/f255947sFqtV9VzHcEIRnBhFB7Yx1+zR6MccK1YX1xO\niLsrWUEBvLD/CA+NHcwe+mx+MQCBwSHUjJrI+iN7ccCGRjxUFTjXgmcDPm1oI6zPSHFrB49NGAwE\n/uOuPFIDfOnSG/BWOAxxx8hvV2MSnaW8rZ0Hx6QT5OZKUUsb/SYzn5SeRioWE+zqTJa/Nwvio3EQ\nixgTPEhZWdjUynxPB8I93Onoa+etJx4lMjCQyoZ6sk29aIxGxBYLndvXs/5UIR6ODshik3nik/fx\nlUup6dZwT3oScT7Cqe8bRw7xjxvn4ubgANEJREZEIlJ3YfPwgoRMgrtakOv7CTfokItAJhYzNSIU\nnckMwCuHC3h26nh7IOErhwvoEstxkUk53dHJo6mD7B7vf/QOcU//ndbWVrxLj7J0dAoA6n4Dezau\nZfJ1NwJQdHAfHUX51Le2knVOEpjDDc38MjnOfv3BmpUE/PZJOjo6kOz/nCX+Qp80/Wq+2LCGKUtu\n/mYvy48A+zatY4GxC3d/YcOw51AuremZ+F4kVfoPjREl+UcCg8HAJ+s/I2fcbEQiEVVnShBLTpCe\nfnFr5g+BdWvXExaShrOTCzpdLx9/tJabbrnxsspwKaxbu57QoBScnV3R6/tYs2Ytt9xy0/fSdu7O\nLxifPQexWIybqwdmk4mSkhISExPPK5czbjYSiQQ3Vw9MRhPPPfM8/n7BnDlbyZLr7rIr7scKdg97\nX5vNxocffITJKFhRwiMDmTJlMmKxmPXrNxDom4Crqzt6vY7yiiMsu/W/nzBHFOQRjODHAX8Pd7uC\nDJDg7UVjj8CiEOAydJPs7zgYY5AzfzG2eYuw2Wzs/2wDpWXHSHB35lhbF2Ud3Vxrs9Gt72dbq5qf\nv/k+IpGI7geGzvWhHm44LFhGgpcv5Ts20WubH+iAAAAgAElEQVQw4iSX8WbxaZYnRRPi7oomNIBX\nDxfwx0nj8HR0YE1VDb/OGY1IJGL32VpUA4p1RoAvqwpOcXt6Emp9P/mNzdydKZyK7a6u4/6ocFxF\nfewz9+IgkzI/IgSbzcbb+UVkKR1IDfDl8Q9X8Lvxo3F1dKBbpye3qoYOfT8ibJisNp6ekIVIJOJA\nbSPHt5/kF9mZdDd3sd01gOyHf8exQwdoffdV4v296TWasNrgZGsbs2IiMFutdgUZYG5MBAcSsslZ\nsJjOR+4aMi6mpjoAmmpriFEMjrm7owNmjRCsWHzsME57PmOipys2HyX/PFBCtMoTLycFBrN5SHtO\nA1RxzQ11Q9pzc3TAolUP+478mGBSd+J+zjjHKOVU1tdeMSV5ZCX8kaCy8jSR4cn2HX5UZCJnz1x+\nHkWj0YbzANOHUumMyXTlfYbOhdFgw9lZyEakUDhhMQ19xY1GI1qt5rs1LhINUR4VSmd0uvPdXmww\nxHqtdHImNjqN0ZmTWDB/GRs2rqKltZGCE/sRiS189ME6Pnh/DVVVVRe8bW5uLhGh6YweNYmxWVPp\nbNPT0FAPgEFnwdXVfaC/SqyWkU/6pwqbzTZ8oRH8z0ERFceZrkFFaU9NHWOChCxup7u6MQ/4Ifeb\nzNTbhp6qiQbmtAkLl9A143rWeoRjWXQHEx57mr/Vq1lhcuQX//kAiUSCWCymTN1jb89gNlPe0cX4\nk/tQbXsfhacX+5PGsDkoHolcTrinOxKxGE+FI94uzqwtLufdE6UsS02wr2NTIkKpVWsB8HFSUmaw\n8rd6NU9Wt+Pm6GgvJ7JhV1A1BqPd4iwSiZgQFgwDy1C8l7u9nIdSQY/RSKKPF0fqm7kuMcbeXk5o\nIO06nVBO4YDtTNnAnG3DYrWilMuJ91ZRrVZjtFjZXF6FRCyion2QjWNPXRPZEyYhFotp7enFNMDE\nYTRbaNUKm5TohEQO6wcV3jJ1D6poIbFI88njZHq62vuxMD6aT8sq2VJxhm6DaUh7WhdPACKiYzmi\nH6S1Klf3oIr6aSVC8opJoEw9SJV3WGcmKi7hiskzYkn+kUClUlFaXIi/vxAsYDQaEUsuv4JqtQzd\n4Vqt5ouUvDKwfC3Q7NzAs127dlFf247C0QlNTxt3Lr8NufybszdkZmawZ9c+MkdNwGw2UVp+lLnX\nPnReOWdnBXv37WDihFmYzSY+37mR+XOFpCEKhZLYuCh8/OXIHX3RdtsIG/Cjy9u7hxOFJ5BKZaRn\npBEaKvig9fb0Eejrbm8/MCCUurp6goKCsXxt/L9+PYIfN/bs2cPjjz/OwYMHKS8vZ86cObz//vvf\na0KbEfy4ccsjj/HOU4/hWnaWXosV9xmL2dHWgMxgI+HG5fxj93Z8RDZakHLXC29ctJ2kUVkkjRoM\nUk549qXzy9x4N4+teo0IDzcqOjuxWaxsraiiz2jm2JcHyQjwRSGTcbC6HhdjP04yGb1GEzKbjaXJ\n8aw8fpI6tRZ/FyFgWW8ysbu2Ab3ckTaRlJ+//i7Ozs7U19ZQ9X9/YWPpaWQSCd0DfMIAVpsNk8WC\nbMAQUavRku4vcAqr+wcDj202G629OvbXNuCpVPDl2VpuSRNSUfebzHT0DbbZ3t4GCBz0rg5yTra0\nIZdIcJHJkUskzI+LYj5R/Hb/caLbu+mzWIm78R57AL9/RCSfllfhKJViMJsJjBToYZ2dnYm85T7W\nbPsEB2zIU8eTc81kQT4HBUadxR5oWN9vxBwRi9nZiZDwWNY2N6LU9aB3dmfm7fcB4OTkRNSt97Nm\n6wYcsCFLyWH8xKnDvyQ/IqSPn8QBrYby08UYEBFz873f2m3w+8SIkvwjgb9/AM4uRRSe2I+jozOd\n3Y3cufzy89eOyxnN3j07UXn6oe1pZ8y4y+8XfSlk52Tx5W5Bvq7uVrLHZwLQ2dlJe0svWZmTAGGT\nsWXzVhZft+gbtx0SEsKU6SLyjx1BJILld912wWQgfb164uNSOXDwC2zYmD71Whoaa3Bz86CltQFv\nbxVyuZz6+gaSEybS2FiLg4MjMVGpnK2uICM9i/37DmEcayQ6OprQsBBq6yoJDREm3sozxSxcLARw\n5lwzht27Pkfl6Y9a007W2NT/cgRHcDXhV7/6FatXrwaENOXbt29n2bJl5Ofnf6f2rFYrDz74ICdP\nnsTBwYG3336byMhB9pNjx47x6KOPYrPZCAwM5L333vtWG8kRXBnkLFhCa3E+XjI5Y+YvsTNB2Gw2\nQXFrbyEwKIz6ynLqjh/GKpUzYckt3yhRUI9Ww/4NHyG1Wjn9+WfMig5HZzKR4KOiRq1h2YDi6VIk\nZ0pECH4uzohscGNyvF2Gxz7fy8sHj1HU3IZYLKKltw83RweKW9uJik/g+pfeGnLP4NAwqrKm4Hjq\nCC5SMcVyZz5q7CJSakXt7MHrjWoy5TbUFhtlShUWdR+uPTpOa3W8dayIJD8vtpZX8cvs0XgNsHO8\nf6KEj06WEuTqwp6zdThKJRxraKa6W03ngFXZ0N+Pi4PcnhSlpaePffVNHG3t4IxVytInniEyXnCv\na21t5bVf3Y8DVpxjkpEbDfiILZy1SUlcfIu9L+ExcYTH/OG8cZ10wzLefeGvJBi60VhsmNLGc+P1\nw7sGhkfHEv7L89v7KSFnzgKYs+BKiwGMKMk/KsyZOxup1IpOp0Mmc7oi9CgxMTHU19XR3NxMZHQw\ncXFxw1e6jIiOjiYiIoKOjg5UKpVdie3s7MDDY5BXWi6XY7V++/aDg4MJDg6+ZBmFQoHSyZmcbGGH\nX1xynLaOWizFfRiMfVgtMiwmJ7q79Lz/4RtcM34Gak0XZeUnmTB+BgBpKeM4UXiI6OhoRo0axZrK\nj9j02SHMZiPzrp2Dm5tgWY6MjCQsLAyDoQcvLy90uhFL8k8JBoOBpKQk+3VcXBxm83d/xps2bcJo\nNHLw4EGOHDnCo48+yqZNmwBBmbn33nvZsGEDERERvPXWW1RXVxMb+9M6zv2+0dPTQ2NjPW5uviiV\n/12yoeHQ2tqCTtdHaGi43fWrrDAf42fvEy8RoZRJ2fL8X7juiWcQi8Vsf+ffTBtIl1x7opYtVbU8\nlJGIWWdl5fN/YfHjT18ysNlgMLDzxb9yp48LYrGIcmyk+Hnj7aSkQdPDsYZme9mMAF82lFSQ4OON\nm2LQp1QkEpHg68W4kECmRYSxobSCR7JjMFosaPT9HHF0pfJ0BYFBwUilUg4eyMPH15/JN97KOomc\ns1Wn+cXvf43RaKC0tITJiUk4ODjy+efbCAoK5e6MDGpqqunq7GSydC1B/VreLSwmwt3driADjA0O\noLZbg4+Tkgx/H/KbWgl2cyXd3xd9dRMAUdHRJLdW0NLTR6/RSLiHG2HpWbg98Eumq7zs64lOp2Pj\n7x7k96OTkYjFbD9dQP+Ua3Ebnc00lUqIG6o6jYuLG46OrhccW7lczvWPPUVnZydhCgVOTk7f/cUY\nwQ+GESX5G0Kv11NUlI+7uzuh52S+u9x4++0VNDU1c+edd+PvHzB8he8ZmzZ+iqdbKOkpiXR1t7Nh\nwydcf/11l12O/v5+iorycXV1JTx8KO2PRCLB13doSs+wsHD2fLGfoMAwRCIRNTUVhA5Q+/w3KC4u\nobOzk5iYRPsCOXPWDN5f/SE2iwyT2UhYRABLbxbcMt5b9SGjRwvHbcFB4Xy5dzvhYYKFWCqVYTAK\nR4A2m4329lYAzp49i8XkwMJrb8NqtbL/4DaSkpJwcHCw99ff339AohEl+aeE2NhYfve733Hrrbdi\ns9lYs2YNMTExw1e8CA4cOMCsWQJd1pgxY4ZYpE+fPo1KpeKf//wnxcXFzJ07d0RBHgYnD+9Hs2MD\n0RI4aLQRduNyohJTfpB7bX/nDUJqS3GTSNggdmLBb/+MXC6nMm83tsZGvAP9qdL2IO3WUldbQ1h4\nBMqmany8BNeGUFdnQpTCnCGViBlHP3V1NZfkUS8tKmSOkxixWDDIhLi74j2geAa5uQxhs1iRf5L5\ncZF4KBTsOlPNlPBg3BQKrFYb9RotmRY/mnv7iPHytPsNL0iIoWTfEVw+foMveg2cqW/ghphwWvp0\n/L64gpuTYxmnVPDMdTOZkJpMslLG4S1rOF5Zxe0J0bTk6Xjm5W7mhwXgK5Wwav9RZkQE87Oxo9hf\n28Dus7VMiRDc1g7VN+EgkaC3WPBUKgh2c8HPxQmr1YY4KAwQeI3ffeMFJgT54a5w5M1jJwheuOy8\noLHcLZ9yU2wYkoGNyuzocF7Y8zmT5y1Cp9Ox9fm/kC0ycNpipS0mjWm3LL/g+IpEIry8Lm/OhRF8\nO4woyd8AanU3H32wntSUHJobWjl08Dg33rT0ssvx8M8fYeb0xcRE5vDKy2+y7PYlJCQkDV/xe0SP\ntp/oCEEB9fTwprq27LLeH86hWEvOpqWxgwN5x1h22y2XrCOXy1l8/bXk5u5GIpYSHOzH6KzRl6wz\nHNZ+vA4XpT/u7ireW/URS5YuRKVSIRaLue32ZVgsFsRi8RCLv+RrVEty+aDFxdvbj1OnjuPt5cfB\nQ7txdhUWo8LCItJSBNolsVhMUuJYiopOkJU15r+SfwRXP1asWMEf//hHbrrpJmQyGRMmTOCtt94a\nvuJFoNVqcXUdtGxJJBI73V9HRwcHDx7ktddeIzIyknnz5pGZmcnkyZMv2p5UKrHzx18JSAf8Oa+U\nDO17tnGdrwcAccCG3E8Zlf39f5cVpaUoTh6m1WKhUywmWNzN4a3rmX/HXTTXVvGb1CS7Imu01ODg\nKDwXq2xoxr2vgsEAuixWYgN8Ljl2/kG+dPQb8R9wCZWKhgYGN/XqWFVSyfG6Bu5MjScjQFAmY73G\n8+BnOxkXEkBpWye/vmY0/i4uJPh48cWZGt4pOImXUolMJCbR25M4L0/K2yv5Y84oJGIxCYAIG7He\nKnydnZgQHsSSEGHdiQPE6i4SfL3s5dxEkOjtSaavN8tSBXeIOG8Vz+07TK9vIJp+AydaWglycUEp\nl7G3ug6/iCg+6zGhc3Jhwa8ewcVVwcnCw0wO8WdssJAkJUblwT8P72PR7bcO6XdoVCjtJ/MIdRey\n6ZktVswSYcy/XLOSO1UKpBLBMnyosoje3i4CAwO5FMoKj1P+2SdIrBYcYhOZefOtlyz/TXClv4+r\nSQ6p9LtRwX4jJbmtrQ0fHx90Oh1NTU1ERV05S+qVQO7OL7gmZy4ikQg3Vw/6q/TU1tYQGhp22WT4\n5JN1TLhmtt2KfdON9/LWf/7JSy+/eNlkgPMDw74eyHcxaDRq8vL2o1QqmTRp0n9FMbZz565BKjY3\nT8xmM5WVp4mOvrSFzdvbm5tv/u6bG5vNxkcfrsHQb0Ot7sLfL4yQEMEKMz57Nu+sWInJZCQlNYV5\n8y7Mz+zk4kBXVxuenj5oNN3U1p0he9wUbDYbn+/cSHxcCiWlhWSNnkBtQxEAYrEIs9mEVCoseFpt\nN5F+56ck/4oqztAPVqsFG/2IRY6IRRIQGbn19mXfG2f0CC4PPD09ee2117639lxdXenpGYwcP5cP\nW6VSERUVZbcez5o1i/z8/Esqyf/rkH1t/vv69TeBwWAgd+1H2EwmRs+cg98FlKmaM5VIbTbmJQhz\nXGVnNzvKSgCIjI1HbBl8pr4uTsgHlOOQWfN57c1XcLOaUEvk9DsoONutoc1oQpOciY/P+fPIufD2\n9WNlTQMmkxE3R0cKmltRKRUk+HhR1NJGu9lK2qSZVL75hj0YD0AulZDg68VdmamYLVY2lJ5m6QDv\nb4CLM8FursR4eVLe1klrXx8AEpHIbpkF8HN2QtNvwNfZCcevxX4YLRZe2H8Um83G9Kgwmnp6SfHz\nxtVxqP+8t5OS+X99nn27d+HS2sjCgfGr7laz2SJn/tMvDCnfXF9LtutgkJhMIsHBdv4znTh5Kv/4\n4D2sDU2oFAo+rqxl9PL72LziTTpqq5G6DsrrLZeg7e6+pJKs1WqpWv02i33cARHVpcc4sN2TnNlz\nL1pnBJcHwyrJ//rXv1i5ciWFhYW0tbUxb948HnnkEe67777LId9VAZFoqDXQ0VFJ/znRtpcDHR2d\nhJzzkYnFYuRyx0vU+GGQlp7I8cI8ggOjaGqpJiU1ftg67e3tbNywhbFZ09Hpelm54l2W333Hd/ep\ntg3l8VUonNANBF78kMjdmUt4SBquru40NtWh1w/eUyQSYTZJuHbuHVRVlfLqK68OSRtuMpkwGPpZ\nuGgBu3J3UVZZjZPSgezxmWzZ/iEmk5Gw8AAcFQrcXT0pLNrHTbdcD8Ds2bNY+c57REWkodP1ojd2\nEBc34Tz5tm3dQVhIKm6uHuh0veQfP8CEawS/aL1ex+bNW1i48OoIhhjBpZGenk5hYeEFN5NfZR37\nLsjJyWHz5s0sWbKEw4cPk5Iy6BoQERFBb28vZ86cITIykry8PO6+++5Ltmc2W65o9s8rnQVV6x1I\nt64ND0cHGnt1GANjv5UsZrOZjc88we0eDsgkYjb8/SmS7v81foFDXcGMBis556SAjlZ5sFcnQaPR\n45+RzeHtaxnr5YbFauWoSMkiD180Gj215aeZFRpIpJszJd1aqhLG0BIVi5u7B3FBwZeUtbe3h+KC\nY9yeEEWv0Uif0USijxejA/042tDM+JBAaro1TG4o56S7Mx8VlfFITiYikYjPK8+S5Cu4EUglYswD\n76vFamV/bSN3ZiQDEOej4uSpdgBcHB3s7hEWq5V1xRU8NkFI21ze3sm44AC8nJQ0aXupVWt4bOI4\ndEYTf/wij6cmC2wvxxqbmRcbiZeTktaePgo61Vyn0WPotwxJVBLu4Y6ix0ZdXTOurm72tWjcpNms\n3biWRzJTEItF7DpbS+g1cy84Tve9+G/y9u6huLWJ6Kh0wg5/QbSrM9s7mtitkTMl2A+r1cY+o5T5\nfiGXHOuTBUWkOwx+6+HOTuSXlaPJnnLROt8EV/r7uJrkcHNTIJd/e+eJYWu8+eabHD16FICwsDAK\nCgrIysr6n1KS09JTOHLoEGkp4zCZjNTUlTB91oV9jH4o3Hrr7fzh93/i9lt/hkwmZ9/+naQPTDSX\nE2lpaYSHh9He3szEqXMRi4dX1PftzSN77ExEIhEuLm4EBsRSVlZGQsJ34z4clZlB3t48MtKuwWw2\ncbqygLsn3/md2vo26O3tI9BPCJjz9wti89aPCQkORy534MDBXaSlCkT10dGJQ9JNf7FrN7XVzTg6\nOtGr6+D2O261+xMDzJkzx/53Q0M9arWaayYP0tM5Ojpyz73LqaioQKn0IiRkItu2bsNoNA2hitNo\newkLEqyA3d2dBAdH2NtVKJSYTd8hUnEEVwSFhYWAYOn9PrFo0SJyc3PtFHIrV67ko48+ore3l3vu\nuYcVK1Zw8803Y7PZyMnJYfbs2d/r/X9qmHvvLzi8bQOmjjbEIYFMm/vtNqEn848yz9FqpwG7PsCT\ntV9sw++2e4eUi0lM5mTediYrBWWjqbcP/6TxACRmjqEE+Ky0AItExuw7brKfGEmqSohUCRbeRA9X\nKipOkrBwySVlMhgMbH7pacL7uuk1WdjQ3c39aYIxZEX+SYwWK9EqDz44Wcbpzi7yG1sIdHPBbLXy\nfN5RHGUSWnt7eXr6JGAgvkKnY3N5FZp+Az5OSqQSQSFs7u0jr66BTp2eRp0eRdIoik43oLNBzB0P\n8Ju3X8VFJkOpUFLQ3IrBbOFgXSPPTBeMBEq5jDvSk3mutY/UKH9u+dvLPPn3J/HCSodIwl/W7QAg\nIjaBkzvW263drX062itrqH7hjzSI5CTdei9h0bG4uroy66l/8tyLf0MhEhE0fjrTr118wXHat3Et\ntvw8QsRQ060hOlE45Z0dEcJrVY30OnljkUiZcdtSZF9ze/k6gsLCKdWZCBa8N+jQ9+MQcGkr/wgu\nD4ZVks1m8xAKILlc/j+XjSsqKgqJRExZ6XGkMgnL77r9sh9bKxQK/vDEb3nu2eeQiKWMGTea6667\n/rLK8BXc3NwJCRECxb7pzvBcq7FYJMZm++6Lf1iYEHxXUlyARCLijuW3XpCK7ftGSEgwdQ1nCAmK\nRCwW4+vrQ0PzCSQSKQ2NteRkTxssPNBdtbqbliY1Y7IEi4DJZOS5Z/5OVFQcFqsJVzcnerVGbDYL\naRmJJCenEBR0PnuGVColMTFRyDD11jukp0xCoVCSt/cgxnFGMjNTiIwIpbqqipCQKLy9/dm5a6M9\nKLC1rRFvH097e7k7c2lt7cZmtZCalkBK6gh13NWEv/zlLwAXPW3505/+9J3aFYlEvPHGUK7ccwMB\nJ0+ezJEjR75T2/+LEIlEzBrwHf0uVjKZgwM68+CpgNVqwyY6f331CwikfsJs1h34Ahk2dMGRzJ41\nz/57YuYY3KZOOk+OrzsKmBn+9O7Lj1dzm5MIuZs3ANuwsrK+HQ8HB4LcXLk/S8jyOjUylGf2HqLX\naMRN4cjZzm7CPNxQyKS09PSy5mQpTnI5vUYTrnIH5scJSuT/HS7gPycrcJJJOVZTx7/mTLW/588e\nKiB01BgMIgkpKenM2rgLgNy//Z4ZKiFGo7y9c4i81d1q/Dz8EPf10NvVwSs7vrCPw6EdW9BXnMIg\nEiNLGcu6shPIbFYKa2pR9PdxutFIt74f48erCHviWQACg0N58OUL+/0f+nwL+vJTaAwGLGcr8JRJ\n0IvFSPuHPntvlSfzf/6r857HxeDpqUI+bQEff7kDB5uVPv8w5lx7+QPiR3A+htUsFi5cyJQpU1i6\ndCk2m41PPvmEa6+99nLIdlUhPDyCtDQhIOBKHRl4e3vz9orXr6gM3wU547PZujmXMaOn0t+vo6au\nmGkXscRXVlZyqqgYK1ZmzZqJs7PzBcuFhoaSkiL4uF2uscgcncme3Xs4Wbwfi9XC+AmjyRpYMOrq\nGjhdWUxMdBK1tVWIpcLC19nZiYf7oEVAJpPj5xtGWso1WK1Wtm5fy/y5QqrXwoL9BAYG4umpuqgM\njY0N+HiHo1AIC0Z6ajZFJw6TmZnCmLFZtLRut8uXNTaVwpNfIhFLcfd0YfZ0gdXg6JGj2MzOpKcI\nQZ+FRQcICAwcibK+iuDkJFA85uXl0dTUxM0334xEImH9+vUEBf33rCwjuDqQlJbBJ3t2oOjV4iaX\n8XGnjum3/vKCZUdPmwXTZl3wt66ODvZv24erSkVy1gS70uk8egKHjn7BKHcXDnb14D314mt37qcb\nKD1yAInJhDzC2/7/MCdH6oMSMbu7oWqqH1LHW6mwK7+rCk4R76Mi2Veo+9X/Ad46UcrG0gp6jBac\nXVy5a8DqGuuq5ERzG+kBQlBehLOCxXITYOKDt/4P1ePP4uDggNuYiazZthZHk4lOo5nXjhRy16hk\nznZpqNP08rN4F8BE0ZFdbOxVI5VI6NH2kVFXQry74GP8wcljKMdOwmw0YDh5kj9MGouDVIreZOKp\nvGMXHZevULhvD6EFe0lwd6EPMx8ajSweMC6sOVnGF2dquSY0iCMNTdSIvj0VYPqEKTDhv3OvGMH3\nj2GV5L///e+sW7eOffv2IZPJePjhh1m4cOHlkO2qgc1mY8P6DRgMVrBZiU+MISMj40qLdUXQ3d3F\npo1bkMscsWFi9pxZqFQXV+oA/Pz8WLBoNgcPHEDu4EBGZjqr312DSCRBoRBz/Q3XD/BKVnH8aAnJ\nSWOxWCy89+6H3H3PHRdMZvDM08+hVLhjMhkJCFRxy7JLs1t8hfLycrq7u0lLS/tGRPpfx+QpFw5i\nuv+B+3l/9WpWrd5JXHwsv/zlLwAICQll1869hIRECH08U4anp6A0i8ViVJ6DCnREeAIVFacZN27c\nee1v2vgpWk0/BmM/fb16oiIGifr7+nrZs/tLYmKjmTRp0rB9aGxsJiZyMLNWVEQSFRVleHld843H\n4WpEcfEpThQUIxJJEEusLL3xhh/tqdevf/1rAPvc6+gouDXdd999jB8//kqKNoLvESKRiMWP/IHj\nB/PQ9WiZPXHqt56XmuprKXnrZRb7utFeZuCzo0dZ8PPfADB25lxqomLZUnqS2AWjCAwJvWAb7z3z\nJ8brO7k+yIcDdY08fyCf3+RkCgHFdc3c7OiAqk/Bb1va0ZtMKGQyNPp+OnSDBoqxwQGYBlJWFza1\nMi0yFIVMhrbfwJluNcsnjiW3qpY0/8E5LzPQn40lFaQH+KLtNwwJ3MuU26itPktMXDwmk5FIT3cy\nVe6o3FxQ9/bx72Mn6DeauD1j0K9erVETWLCfLD9vDjS10mU2g7sLNpsNdX01s9wdcXd0oNfTDYeB\n00eFTEaUx4W5jM9F5+kSJg8o3BUdXfaEIwA3psTzfN5RTFYLCd5edFov7V4xgh8PLqokFxQUkJGR\nwd69e/Hx8eH66weP9vft28eECecHDv1UkZubi593HB4egjJYUJhHZGSEPaHD/xI+3bSV0RnTEIlE\n2Gw2Nn+6jTuWn09VY7PZ2LZ1GzqdAUdHOXPnzeHaBQvo6dHyyfrtZGYIyqZa3cnL/3yZoOBQzp49\ny7zZgrIrkUiIj8mkrKyU1NS0IW2/8fobZI+dibeXQDd04OAXVFVVEhUVfUnZ1368DieFLx7uKt5d\n9SFLb1yEh4fnJet8UxQWFiKyOXHdwruoqTvNoUOHGDduHDKZjGkzJvDRB+/i4KCgu7uDm2980F5P\n26Ox/11Xf4ZJU8+npdu7dx/uriFERwj9PXL0S8rLTxIeHkPegR3I5Y4Y9W5s33KQgGCPYVMWu7u7\n0tXdjqeH98B9qxg/Mf37GIYrhv7+fo4cKmJslhCo2NvXw7at25g3f94wNa9udHV1DQnS0+v1aDSa\nS9QYwY8NIpGIzJzvvp6e2vEZS/0FGjo/pYL4lnqam5vsPPphkVGERV6akcq5voqMgZO58aFBFHdp\n2GBV0NLVzXhfgYYNIN1HxauHC3BxkFPdpeGJyYMb+vKOLrQGAzVqDcFuLuysqkE8YNHO8vVCIhaT\n5OvF6Y5O/FyE9tT6fj6vrKZB20t1t7dDsd0AACAASURBVJqfjR00PtUYLMQM8N137stlbqigXF8T\n5M+ak2U8kj2aRm0P5R2dBLgKJ46dOj2Lw0OEfgT68Z9jJ+jS91Pdrcbf2Zm9NfXIxGLa+4YGeqtt\nw2+mrUpn+tpbcJLL8XdxprStkyA3QWnW9htI9FUxK1pQnPN6RuI/fiq4qJL8xhtv8NZbb/HnP//5\ngn5xe/bs+UEFu5qg1fYRGD1oLQ3wD6e+vv5/UkmWiOX290EkEiEWD1p5Dx48SFnJGSQSGWery5kz\n80bCgt3o6+vlySf/SkRYLM3NdYzKmGivU3Qqn7FZs3B1dae5sQuLxYxEIryW2l41oZEh58mgUffa\nFWSA+LgUjhw5fEklubm5CTEuhIUKZcaPm03uzt3csPS7+3UfOniQ4/mnkEhkVFSWEBocTV+fDn2/\njqPH9lNRVovFYqK45ARREYnIZHK61Go+z12LSuWH1WrA1U1GfsFerFYL0TGh+Pn5n3efzo5OYs/p\nW1xcKlXV+XRqRCiUDuSMFY5gE+JHcfT47osqyWfPnqGkpJSIiHDKSss5W12K1WIhKiaUgIBLc3he\n7Whra8Xba7APzk4u1NUbr6BE3w/uu+8+Ro0axbx587BarXz22Wd2K/MIRgCc52Ushm/NfiL+WiNS\nqYypD/2GU4XH8d7xof3/ZpuN31wj8ECbLVb+8EUeowP9URuNVDt74dmrR2vtp8do4c5Rg3PWq4cL\n2FxehdFioa23j5ZeHS4OcvbV1PP6tTPtHM9P7j3M6KhI9CIxzjkz7EYMfUcLhA5aoNv6dLx2vBiN\nwUi/rpdGTQ9OcjmduqGsU95OSubHRbGnug6Lxcq0qDChf2IxLx7MJ9LTjdq+ftzGTWbXv1/CKBKT\nfcOtuF/AeCISi1l7qhw/F2f0ZjNHmtrI7+jGWSqlXelChKcP2xrb6JI7kXbb/d9q/Edw9eKiSvJX\nhPVLly7lgQceuGwCXY3w9HSjs7MVlUrY1dY3niUr+38z6ttiNdi5VW02GxarARDcMM5WNjE2Swhe\n0+sNuLgIobpOTs74+4YxetREdLpeDh/Zi59vAEajERHg6ipsNnKyp/DxuhVMnjgHjUZNp6aOBRHn\n+2h5ernT0tqIn6+gFJWUFjJzzqVdBfR6HUrlYNpPq9VCxenTfPThekRiK4sWLRjCODEctFoNJwoq\nmDxR8PHr7zcyY9pgZPvO3E2MHiVsBqQSBaGhUXh7+ZKRPpYduWu5bskcnJ1dvhENnq+vNy1tjfj5\nCP2tqalg9uzZeHt78/57a7HZbOh0fSiVTkjEFw4oPXTwIM2NWmKiR1FddRp3Dw+uXzL1G/f3aoev\nrx/tHfuIjBjwU9d24+RyZQn0vw88+uijTJw4kb179yISidiwYQOpI0GWIzgHYi9fNuw+wnUJMXTr\n9Gyurudn52SIa21uovp0GVEJKXh5e1+wjS6fYMraO4n3VlHQ3EpvSBTH9u/D1cOTXJuSW4wmFDIp\nTX16Kjq6iPXy5NPySq6NjWRsUAAGs4n/HD/FL8eNQiQS8UV9C1ur6pgbFcLaskrSA3zJCQnEZrPx\nt7yjLE0KxsXBkbY+nV1BBghTeTLmyZeQSCSIRCLKik/Sp1XTqeunvL2TOG8Vp1rb0ZvM/G7cKHRG\nI6/nn+SW1ASsNvjP8SKK2rtI9fYkv7kNz4GMgM4yGUFeg/zHs2MiaItIZOw9P8fr1Ankn68jS+WG\nzWrj3ZefYc7jz5zn5ifu6+HOUSmYLVZMFgtqfT93ZiQjEok41KGBeUuJSkodlsliBD8uDHvG8Oqr\nr14OOa5qTJkyhU51DadKDnLi1F5GZcbj4jK8D9NPEYuvW0BB0W5OFu+nqGQvixbPB6C5uRlf30Gr\nr/VrloyvrpVKZ7RaNbu/3ErRyWOcOVuO2WwCwNFRgaurBzp9H65u7khEDvT29trbKCgoYNPGT0lL\nS+XAoe1s3baWjZtW4+7pQHh4BJdCWFgEdfXlGI2CdfGTTauZM+NGUhJziIsaw8dr1l207lf3PTfq\n/8SJE4SGDlpKnJ1chtRxOuc6MiKOtrYmADRaNSajjS2f7uGtN1dRUV7BcMgZP55+QyuFRXnkF+4h\nLiEU768WO7GR7TvWc7qymNxdn9KnV1+wjarKOmJjUhGJRISHxdJQ1zrsfX9McHBwIGf8aE6c2svJ\n4gM0NpUwe/aFg5x+DNi8eTMA7777LqWlpXh5eaFSqThx4gTvvffeFZZuBFcTDPU1ZAcHsLm8ivym\nVhZHBNHQUAfA8S9zafz3P8jK38WZV5/h1NFDF2xj+ZN/50hYMk/XdFAYmYaHTkva4W04bXwHhbcv\nO0JTWaeKwDs5gx6DQaBz0xsYHxqEVCKmtL2Lm5Lj7Zv+qcF+FNQ38PGpMs60d9o5nkUiEUsSY/jQ\nKYiNPlHUmgWLNIDRbKHBIkIqlSISifjs9Zdw37SKlANb6db302Mwsrm8CpPZgouDoIiWtXdxU1Is\nYrEYqURMZoAf+89U8/GpMo7UNqA1CetOrJcnudWDQYfl6h6C0zJxdHSkpSifLJWbXb4cuZXqqsrz\nxsg1PJoqTS9SiZjyji6mR4UN8it7udFQeGxEQf4JYtjAveDgYKZMmcKYMWPswSMikWhYCiKTycTy\n5cupra3FYDDwxBNPMH/+fPvvL730EitWrLAv9m+++eYQKqKrDYsWL7wqCLGvNFxd3bhh6fX09HQQ\nGBiEySRMEmFh4Rw6sIEA/+CBcu7s2r2JsJAY6hqqcHYWrMVqdRf+/kGMyRKsrBnpY1m7YQVZoyZw\nprqcpMQMwgayCnZ2tLLynffw9PSira2RyIhUFHIfWpv6sFpg7pwbMBoNHMnPxWKxXJKWTywWs/zu\n29mxfQcWi42gwEAcHYXnKZPJwXbhTyE3dxcWoyPREVk0t9SzdctW5s6bi6+vL7s+X09p6QnMZjMd\nXW2MzhyPTCbHYrHQ0TmohB47vp/ssYIPdmHhIW64fpDZ4+CBXcTGxQ477nPnXTjzksUsYs7sQd7T\no/m7Llju6xbrnp5e3l+9Fpt1kHrux464+DjGjBV8q3/s32h+fj7z589nz549FzxtuO22266AVCO4\nGlFzphLv6EA7m8SOympUA7RyXfu/YImf4DowU6lg7Zc7SM46PzAYYOFtQuKY7W+9iryzlcMaCUaL\nhZbTVfhHxOAkk6IMCKNC547So4+y4/mYLVakEjEqpYI6tcbua9xvMpMe4Mu82Eg2lVVisliQDczP\ntdo+FP1nkXc5oErO4E95eYQ6K6ju07PseYGesKqinPT2auJ9BMadDF8vatQaFDIZZ9UaVAMWYi8n\nBXUaLYEDWfLqNVoeGjvK3qeXjxaxUS/GIFLivmgZH5ecQI4NWUoO468R5mSL3BFT/6B8TQYzfqrz\nmX5GT5nBvh4tp86U0yh2IrWnl2A3wVhmMJuxul7+5F4j+OExrJI8duzYIdc2m+0bNfzBBx/g7e3N\n6tWr7WwC5yrJBQUFrF69mvT0H0fA0D9ffJGamkYM+j5+//jvhrVc/hDYtWsnq1a+j5OTC0ajjpWr\nVl52GUpKStiw7jOUChf6DT2MGpWKvt+AzWYjLSOBdZ+8hUwqJyjYnwd/djft7W3M9rqGvH15nCze\nT2NTPSmJg5O0XO5AamoKo8fFgbQXH2/hmLC/X09zSwMzpi8CoKm5nrz9uYzJmkBTUwOdnd18vG4F\nZrOZzIxsSkqKSUm59DG0XC7n2gWCe8S7K98f8pvFYrpgndbmTtJThaAaf79gjheeBYQkD+2dzTS1\n1OOkdKG3V8tb7zxPTs5EbDYzGaMSB6jYzKSmR1NWcRQQ4fC1tKlSyfnMHV9h37482ts6cHNzYeq0\nqRdUliRiGaeK89FqNTg6KhGLL/xJB4f6UV1TQXhYLPUNZzGbLaSnCC4qhQX7CQgIHJal5FKorj5L\nSXEJYGPCxIm4urp957ZGMMiTvGrVqisryAguCZvNxhcb1qFrbcLRL5SsqTMvuwyh0bH8dsdnOMpk\nqPsNxHt5Url5AzXu7lj79cAgHZnsHH763h4t+z9Zg8RqJTJnEk2V5ehbmyg8mMfvkiJQKRW09fbR\nV1lNQ0UpMomE0NZGGoNjkau8yFpyK89u+ogZgT609unYfaaGfTX1OMllnOlS8+wMwRAyMyqMF/cf\nZVpUGB19eso6OnkkWwhQrtV0UBPqz8TwEGw2G3/86+8R9/fTpNFyd2Ikf993BJlEjEIq4YExg0F9\nrx0p4FhDM939/XxZ34JWLMNDKqZTbxgyNh5KBZN/9cTgP2acb2yYeMMyVr3wFKnmbrrMNlojksj7\n+5M4imzETJ/HhHP4qCcsEGJYuro6+ey3D2Aw9OPm4EB+Uyt+i0bcoH6KGFZJfvLJJ4dcW61Wqqur\nh214yZIldkYMq9V6XrKH48eP88wzz9DS0sLcuXP5/e9//y3Evrx44YXncXcJ4r67b8ZiMfOvl//F\nn//yOO7uly9wr6amhq2bd/HbR59GIpFy6PCX3HnnHaxcueqyyQCw7uNPmDRhHt7efnR0tJL7xWfc\ntPQejEYj69evYOmSe5BIpJwqPsqZqjPExQs+olOnCf6vBoOBN157m8DAMAAqq0oIDw8hICCQ+fP9\nee/d1YhQ0NhYS2bGoJ9xgH8wQYGhhIVG0d3dSVJiGuNzpmOxWPjgozcJj75wVqSNn2yiR9uPzWYh\nISkWNzdXuru6yB4/hr17Pkcmc8Jo6mPK1Av7NH8945l1YJEpLy9HLldw/z2/QCqVcSx/P7m7NnLz\nLZfOZvXM08/R19czsNExUF1z+oLltm7ZisLBl7josajVnaxft54lN5zfdl3dGbJGTyM5KZOurg5y\nd2+4YHsTJ06kpLiYqqqj1NbWMmPaYFtRkcmUl5eSk/PdKOBqa2s5sO846WnjsVqtfLB6LXfedav9\n5GkE3x1btmzhqaeeoqOjw26gEIlEnD179gpLNgKA7e/8m2maenwUjlQ3nmFPVyeTl9x8WWWobm1l\nTlwU0yLDMFks/HFXHn+U9OFkMPFiUxP1bo4EOztRpe1DFCUwBRmNRj5/8Snu8HZGIhbz4nNPcHNC\nNP5OCjKjBa7fOTGRFLe209zTy6/HZyERi/m0rJLe/P0smZRNbWUtzFyAKSUdX5kc1Z9/xSM5o5GI\nxXxSWsGm0kqWJMdRr+kh1d+HQFcXevqN3JwymGk11M2Vk81tgMCOkeMkZ3Z6HCaLhb/sPsCfJmcj\nl0pZe6qMAzUN5IQFcaZLTV23hsUJMcR5q4jwcKdmxo24hYbR+cJT1Ko1hLq7caZLTav78FnrHB0d\nuf7xp2lra8UNKPjtgzyWlYJELGbL5+vZB0MUZYC6s1UsiQ7FRS5HZzKTGejH+o6flgvbCAQMqyS/\n8sorPP744/T19dkn6fj4eEpKSi5Zz8lJOHbp6elhyZIlPP3000N+v+mmm3jooYdwcXFh0aJFbN26\nlblzL3ykDCCVSuzuDpcbjfWtzF6+DACJRMqsGYt5++03+Otfn7psMjz88IM8eN+f7MwP48ZOoqDw\n4GUfEzc3Fd4D1l4vL19CBlIfNzfXMz5npl2+5KQsKivz7cffX0GrNSGTObB33w6kUhlSqRSVt9Te\nj4d/eT8mk4menh4+en8LgYECr2dPj8a+0TpdWcLSJYLLgkQiYfbMxdTXVzNpUvaQe+3cuQtP9zBi\nIoWAy0NHdqNUuhASFMGXu/NYfvdNODs7X5CH+SukZcRy4sQhkpNGU15RREJiGG5uChoazjJtynyk\nUsEHbXTmeE4UHbng8+ju7uazT7cjEomRyxw4USSkebdarYSHR12wTk9PP2HBgh+fu7uKmjrbBctF\nRETj7yckl/D09CImOv6i70R2zmiyc0bz+ee5dHW12fmaa+srmTMv+zu/SyXFp0hPE7h7xWIxqcnZ\nnD5dwjXXXH4+X+lAet8rNVd8XY7/Fg8//DD/+te/SEhI+EZBniO4vFA2nsHHWzhyD3dx4sTZ8ssv\nRO0Zpo0TlF+ZRMLNqQlsqTjD0uR4Hh2dzPNNWsK8ffGMj2LSJMFYUVx4nDlKsZ2XOMZFib+T8M2E\ne7hR0NQCQGVHF7elJ9nLLYiPprBZUAZDXZwoqKsi6ebbWb96JXedU25xQiy/2XeM6qpmeqzwUFAA\nfkoFcomEfTX1LEoQXCtrujW4OzrY73VtfPSQftRqeohWeXBDcjyvHymgq78fL6WCaC+VPcX0F119\nZCYk4uzszAPPv8ba1/5Bb3UtypAI7n380W80hCKRCF9fPz5d+xHL4iLs/ZgXE8ELu7aepySHRcVQ\ntNnMHFcXXB0dqO/twznywhzUI/hxY1gl+cUXX+TEiRM8/vjjPPvss3z55ZeUl3+ziaC+vp7Fixfz\n0EMPceONNw757eGHH8bVVZhc5s6dS2Fh4SWV5CsJnb7XzugAQuR8SPD5qYN/SAQFBqDRdBM0oDRa\nLBb6+nqHqfX9w2od6pZgMgnXSqUTrQPBaUI5KzbOpyGqra0lOiqZwIDBIL8zNUOzHclkMjw9PUnL\niOL48T2IRFLq66u4Jkdw1+nv1w2hilOruzhaUIi6y4TcwcY9996OWCymo62LsJBBt5jYmBRamhvw\n8FAxbswstm7+nFtvvwkArVbLnt1f4uzszJSpk+0KSW1tA05ObhzLP4CXyof6OqGPY8eO5XRpx5D+\nfn1sQEjrvuLtDxiXNRuRSISxX45UKiMmWsjeuGPnOl75v1XYbFbiE8OYPl1g87BZhyaUtVi+nmBW\ngM1m+dr18PycQUGBrFzxMb4+QZjNJjq7m7nF47t/e2KxCLPZZN8waHvUBIQMb8EZwfDw8PC4aufF\nEYBRJPna9eVPXtNrNmOz2exzVodOT1VnN5+VV6GQSAnNuIbpy+4cUsfZ1ZUugwn/gfhivWno/NIs\nlrNRbaBe7kKnTm9XSM0WK4pzgtNMA/c0Wa106vR2vmKzxYpFKuXeF9/AZrOxc/XbyBtr6NYb6O/t\nY2PpaaRiMXqjiRqpgo5uPfvqm5kXG2Vnu+jS9xPh4W5vL9DVxe53fbzXwEa1AaNIgig1iwP//icy\nrEijE7nnD38GhLiEPes+xFZzGqNITPTc64iMT7zkWHr6+tJxSk+Iu6CbWKxWDLbzN6fu7h5oEjN5\nZfdWlGIxnR4+3Dtz5Dv9KWJYJdnHx4eIiAhSU1M5deoUd9xxx7DJCgBaW1uZMWMGr7/+OpMnD81S\nptFoSElJobS0FKVSye7du7nrrrsu2Z7ZbLliwTj3P3Afq99/jVmzrqO7u5PdX27h32++flnlee31\nt7nj9uVIJBI8PFRs276eRYvn/6Ay6PV6ZDLZEFeZyVOv4cDBXGJjUjhdVUxnVyOtbU1oNWp279mC\nTCrD3UPFri82s3DR7PPk8/T0pak5364kd3W3o1QqL9iPpOR0kpIFS7TNZiN3Zy5lFZVkZCbz8bq3\nmTJ5Plqtmi/3buPeu4UMUz09ap74w18JC4uksqocuczXHkxYVHSU/n49ak0XSqUzDkobGo2erq5O\n1q7ZxLgx09Hp+njpxTdYfvcdiEQiNN39pKeOJnqAi/944V40Gj2jR4/nw/d/hdzBES+VDztzN3Hf\nA3fT2NhuTykMcPZsFf6+UfbrhPg0Ptn0Lp4eXlSeKcFLFcCodOF7OnhoFydPlOHi4kZBwTG6u3Qk\nJqRTeaaMhobKi4xRPMcL8ggNjqGppZqY2Ihh34ljR0+yeOHt9muNtpt9+w6TnZ19XtnDhw9TW92I\n1Wph7LhMwiPO98WfNHkqK995j5jIdPT9OjS9TYSF5VyR7/VqCa51c1Mglw87vV4Ue/fuBYRTu1/8\n4hcsXLgQqVRqV4b+l5I5Xc3wnjiT3N2fkaiQk99nImTRN8v8+X1iwWNP8fdnH+eWlHgatb1sKqvk\nX3MFKs6S9k6qtBpyP1iJf2wCSZkCx3FMfCJbfMIwt57FXSblWIca98pqkny9KWxuxeDqxeTHnmYy\n8MrP7sBsteKhcGRV8WlclEo+OFGKViYn5Y6HAAgMCmbj1rVYbEK5D06U4hMpBCSLRCJm3naPXd6t\n/3mFiJYq3GVSdhokLPvtkyiVSvxLT/HcX3/PranxNGp72FRaybK0RExWC1vKz9BjNjFa28sJnYm0\n2+4nafRYtFoNhS8+yQI/D0BMZfkxDuf6Mnb6TA7v3EZ6dRFhA8GE69e8jf/vnkapvHjK6GsmTuHV\n9R9gowVvpZL3y89ywwtvnleup0eLoiSfn6cK7oRVml7yv8wlc9L0//p5juDqwrCzuLOzM3v27CE5\nOZlPP/2UzMxMWlpahm34mWeeQaPR8NRTT/HUU4Jbwj333ENfXx/33HMPzz33HJMnT8bBwYFp06Yx\na9bVS9eUnp6Ov78/b7zxCmFhofz7zdeviByr3n2Hu++6lZbmVp77x4skJSX/IPcxm828u3I1jg5u\nGAx6gkJ9mDFD+PgzMjIIDw+jtbWRnImLsdlklJWV0qnuYNbM63B2dkWrVbPkujvYtuMjssaMGdK2\ns7MLaemxfLz+P4jFEkJCArhz+Z3nC/E1iEQiZsycYb8eM+Ysq99bhdVqYca0Rfb/5xccZNqU63F0\nVBATlcmadW8TGR6L0WSkqbmOW24SSN7PVldgMAuW4D179jI+W7D0urq6E+gfw8p33sFT5UVXd8cQ\nOaznWHhf+r9/svPzzZRX7eeGmxaxP+8YRYVn6O3tZtKUHGJiYtDr+2lvbyU8TDhetFjMdKvbKSk/\niFajZf5cQVk1mYxotVpmzRR8q5sa2xiVkU1DYw0JcSlo1J0XHJfklBRCQkNobq4ne8IMZDKnC5Y7\nF1KZmNbWRqrOlAupsVVeJKWGn1fuZFERbc06khIE5XnP7i9QeanOC8pzdHTknnuXU1d3BmdnP3x8\nJp7X1gi+Hc5N4tTQ0MCpU6eG/P6/lMzpakb6hCmYxo2htrKS1MCwK5JgKiE5De//W8X7q9/C4unO\nnyYNylDXrWGKpI5YkTvFuaXsb21m/NyFAMy77//ZO++AqO5sj3+mwjDA0HtvUiwgKtix9xJLojGJ\naabsJpvdbDbv7du8bDb73ia7b3ezKbtJdtOMsffeFVFQESxYAKUISC8zwMAw/f1xcZBgxCQqbjKf\nv7jO7/e759473jn33HO+5yVKiq5Qrm1lhGEVg5VSyjQtjAgJxGjoioi/+MEXHN6/l5z6GuJCB5JU\nfpFolTMX1C1crRHerMmkEoJdXWlo03G5Qc2QID+uuKrQarXdggYAM555kZLiIspbW5ibMMAmmxYb\nPwCfv69g+acf0eboyCuj3NAaDKh1ep4ZOoi/lDVyefx84qNjcHMTOgwW5V9imLIrsh3t6kz+lQKY\nNAVteYnNQQZIkosov1pKbPyto8kvvP8pR48c4kJtLY+/9OZNneriwnyGOnW5T1EqZ86UXAG7k/yD\no1cn+b333uPTTz/lL3/5C5999hmxsbE9ivluxrvvvsu77777jZ8vXryYxYsXfytj+xIvLy8efHBB\nlz5tHzFz1iwuXbpEWFhPp+Z22LtnL40NzZjMJoYNG2wrrLuR3bt2M2jAGBQK4eZw8VJutzanra2t\nZGVl4uXlSW7OeRobmiktLUYmq6VD146Do4Kc3Ey0bRrefecDXFyVLHxwARqNGoXCiezsXGbPeASF\nwomc3AzKysoICwu7qb1qdRNarZbAwCBbuktlZSU7dxygvc2Kt08Qx7L2ExwcLgjQg03aTS6XExne\nj5EjJlBeXkxkRJfUWkR4PwquXNcUFtlu4larlZzTx5k6eS4KhZLKil2s3/gFiYOGUV5egsK5+6u3\nCRMnMrSlhS2b9jIseTz1DbX0ix7ImlWriYqKRqvVUlEh/JCoXN04ey6b1JSJJMQlcq3yKrv3bmRw\n0nAaG+ro16/rocfJyRlnZ1di+wnSbKKvt8S6AQcHR7y8PHFycsJ4c5GObgwenMTKFRuZPnUhRqOR\nLduXM2tuz6YtRcWl9IvsesiJ65fM+fPnGTmyZ66xVColKUnIi+zrKO4PgfT0dNvftbW1+Pr60tbW\nRlVVFdHRt26/bufe4uXlhZeXV5997w0GAxmffsCQdjUao5lN6iaeGRQHgM5kop+H4DT3d3Ph8oVc\n6HSSASI6O3mu27ACL6UrXkonrFYrddquY8nYtAZRbibBIiuVzS1Exwuv1fq7u1J4PhdmPkBEbAIG\nLy9a21pRyKSo9Xok1RUU/PHXVEscGPTY84RERHbt9xvaZHt4ePDCr/6Lutpacv/8GjMihLeAtdo2\nYkelkTi0e9AlKCyCS7v0tnSQ+nYdiiihZkas8kBTWYObg1BzUqg3kxB4e51FR4/teT/svt9ILrUZ\nCOxsl9Cg60AeYE8x+yHSq5Pcv39/3nnnHdRqNRs33rxy/oeOWt3En95+h2FDx3LqRBF1DVd59T9+\ndc/teHzpk4wdPZW4mFH813++zjPPPfGtoslZmZlIRO4M6tTEPXE8ncCggB6NUQwGk81BBvDxDqS2\nthZ//wD++PbbiKwK4mIH8cVnm7h27SrLnvolwYFxHD9xiPnzhMhoVVU5B9N3ERk+gPKKUl77zeuM\nT5tFWXkR4WExtvWHJI/h1Kksm5NcXFzMubN5AFgBvU6Es1LFzu17eezxJTg5ObFt2zbyLxUwccIs\nWls1XLiQy559qwkKDqOtvbnbsdTVV2K1WlEolFwuumgrNNTrO7jenG7kyOHs2LaP1GETuVpWRP+E\nJBQKIQKRNmY6GUf3onJ1Z/SoyeRdyLKtvWvnbprVepRKFZcvX6FZ005UZByZWQfx8w1l0ABBLeKQ\nfgfeXn64uLji4upCQpzgTAYFhnH23Ek0mkbadVrO5GUTHib8aCkUCvIL8oiLHUh5RQlWUXdpo+sU\nFhZyLCMbX58QGpuOMDAxzuasfhPZJ3OYPnUhIpEIuVzOhLQ5nD17hiFDhnYbp3RSoG1rtTVKqa4p\nZ/DQ+1fL/IfIe++9x+eff86ZOMjfKwAAIABJREFUM2eor69n1qxZ/OIXv+DZZ5/ta9Ps3CccXvMl\njyitOKgEbd9tZiNfVNTjJpdRbRLxdsYJ3BWONOv0WNy94b230YslDJ73ML6d7ejNFitrzuejlMlo\nNRjAPwyAirKreOedYHiQEBzaqFF327el8+Hd19eP8olzaD1xCKvVQlVVLWEiA7oWEy5WK+fWf0nI\nf/yu12NZ9c5biEoK0BiMWFqb0XfokInFtOoNyEN7/tb5+PpyJn4In2TsQyERU6vy5ufzHwQgbf4i\ntn30Lq61FRjEEnwmzb1jkX4vLy/Kxs1kfcY+ZFYzWv8wZsyef0fWtnN/0auTfPbsWRYtWkRbWxtZ\nWVmkpaWxbt06kpOTe5v6g+HvH3zIogeX2QqTzp6Tc/bsGRIT753G81tv/Q9TJj3AwAFDAIgIj+FP\nb/83X361/LbXqK6uIza6S/c6LCSW4qIiEpMGdxsXHhFKSWkBEeFClPlKcR5LRgiSYdpWI4seFBzh\niIh+rFv/GQAXLuaSlNSlfxwQEEJEWDTh4TGEh8dgsZiIiozDWemCprnrRmu1WrFaBNWU4uJivlqx\nDn/fYNTqRnx8/Bg2VMi9DAuNYtfO3SxYOJ9jR7P41cv/IzQBARQKZzZs/BR//xDcPVzIOrEHR0cX\nOjq0TJ0+npKyHORyGVExgRw/eQAHuSMGo5bHHhcUS/z8/FAopRxK30ZHhw4HByXRUfE2+0CEj48/\nACaTkdzcHAwGAy0aIwMHpHTaF01m1kHb8R7J2GM7xsGJIzh3MZ2gwGDcPbrfpMViCfUNtZjNZny8\n/dm89SsCA0JQa5qoy8/hSskZwkJD+OUvX77pNT1xPNfWCjw8LIbsnIO9OsnXj+t69NxoNCCT9rwV\nTJ4ymS+Xf4XI6ojJbCAg0Os7v8Gw8934+OOPyc4W1FDCwsI4ffo0w4YNszvJdmxIOtpwuOH/b5SL\nEx1LXiAiIoo/PzqX10cMRS6V0GYw8Kej2cwbIAQKvvr4r0z+zVvI5XL8HWXMHRBnW2OzRngor6u6\nxgClg+3ffZROHLtWy/AAbzIbW3AcMpaDWzbg7O7BsAlTcJsvpL79af4MpsfH4yCV0m4w8v753juL\nbvzoPca2VJMQL0Sc/37iNBMjQnDtlJJcf5NCda1Wi/XCaWKcHWk3GgkTGziXlUniyFGIxWJm/+QX\nt3UOmzVqctIPonRzJ2Xs+NtSkkkeNwnG2dMrfuj06iS/+OKLbNq0iSVLlhAcHMxHH33E888/b7tx\n/xiQSuU2BxkE6bNrFRX31EkuLCxkSGJXTq5UKsPV9ds9FSuVTrS0aGzzqqqvMiCpZwHQ4MGD0Xdk\nceFSFhaLiekzJ9ryspydu0edr29HR8VzpTjfViR3o2QbgFwu3OiETnzb8fT0wU3lzuH0XZRVFCJC\nzMns48yYuhgfH38KL5+3RXNBkN4rr7gGgErlZnOQAby9/ZBI5AxOHINa3ci2XStwV3khkUJiYlK3\nCOm4cVaMRiMymYzdu/fQ1qqjsvoaAxNGkthfSCdJz9hDcXEBISERpB/ZQ1NTHWazmfyCM9TW1aDV\nDKS4tAhfn4Ab7JN06/h34022qPgS8+bNw8/PnxMnTnCp4Az9ogdy4dJpnJWujBohOLnZp47i4uxC\nUmIqRqOB/Qc38utfC/rh58+fp+DSZSwWM+MnjrOl/Ui/1jzkuuLHrRg/IY1VKzeQOnQi7e1tXC2/\nwPjJjwOC87xv7z5amttwcJTx6GNLbN0Mr6e72Ll3mEymbhKFcrncfh3sdMOjX38uZe0h3s1FSBcz\nSZgWLKggxbk6I++UI1TK5YS7d9UTDJaYuFZRTkRkFC3u3nQYtTjKpGj1Bto9hZSFfgMSSd+zkQed\nhXuxk4uK0v6pbLZa8RwSSNvOdSzwcaOuqINtebk89prQidfLQW5z3J3kMlSy3iURWwrOk9CvSzVq\nYmQomeWVTIuJ5HRTC/6TJ/eYc/nieTTXypiQEIObowPrLxZizTpK4k1Swr6Juppqcv7+Rxb6qmi4\n0sHWvFzm/uzV255v54dNr7+o7e3txMd3iX9PmjSJV1555a4adb8RFxfNubxsBg0chsViITPrAK/+\n58/vqQ2vv/4G//zoCx55+HlEIhFnz2XfVGLtVkyZOpnVq9Zg0IuwWsz0iw//xi5rw0eMYHhPsQPy\n888xauRE3FQetLRouJR/Fm8fbywWE5rmKnbuXoeDg4KLF8/w6JKfANDcrOb8xVwAqqrLSRs7nbq6\nKkpLC0EkYsG8p3F38yQ4MI7c08fx8fFHLJZy9uxJggLDEIvF5Bfk2Zxud3dXck9nkTx4hKB6cWAr\nw4aO4fjJdNrbtMye8Sjubp60alt4/bU3+MPb/2Oz/3qKwfLlK4gKTyI00JPgoBZyco/ZnN5BA4Zy\n/EQ6DY21DB8+jv0H1/Gvz/+Ih7sH8+cusymMbNu+hpDgSMRiMRcu5uLU6dS3alsoKy8hM+sgJrMJ\ns9mERtOMn58/qamp+Pld5dKlPK5V5jNjald74X4xCWzbvhqz2Yyuo52ISCHiU1hYSMHFcuLjhOPd\ntHE7jz62CCcnJ5yc5Wg0jbi5eaJta0Uq670jpkrlxmNLF5OZeQwnhcKm5AGwZfNWvD2iCPL3pL1d\ny6qVa3jk0XvbHMFOF3PnzmX8+PE89NBDwrXftInZs2f3tVl27iMGj53ASYOeouJ8DBIxqc8/YyuG\na/qatFtTe4ft7yKtjqZ//Y1yuQyNkxsfVVzFVyKiygIzf/M8IBTuxz/xM9bs2IAMKx5jRjNxjKBW\ntfdfH7DIX2h5HaB0IqGmgvLyckJCQmj72n51pptLWN5Ih9wRndFok5grUmsocg+g3eyI+6hRDB6a\n2mNOu05HWngw3kohiLN4QBx/Kbt6O6fNxpmdm1js745IJMLfWUliXSVXS0sI64OuunbuP3p1kj09\nPTl79qxte+XKlXh4eNxVo+435sydy8YNG9m9ZzVGs4Flzz6Ks7PzPbUhIiKS1OGJvPfBmyiVrrR3\nNPPxxz2laW6FSCTi4SXfr1gyIT6Rz5e/j7ubB01NDSTEJ/L4k4t6jPty+VqKivMxFhioqq5g0cKn\ncHMTHK8TJ9MZP04QZz+SsQd3N8FRd3FxQ2/oIDPrIG3tgjb10cz9SCVSHBVOBAYK0Y2xaWPZsH4r\nZ86epF3XhkatZsqkubi5ebB77+au9Zxd8XD3velxNNY3MySxa5zoBn3TS/lnGDliHCqVBydOHiE0\nOI45M59gx841tmixRCIlefAI1m/4gsDAYBoaq4nuF8GlwuMU5F/ikYef69ItbahFq221rR8WFkZY\nWBiuLq7U1lXi6yPkBV64dIaEhCSCgyMpuJzL1GnCq7yLF/OJjxPSOkQiEQmxwzifl0dKaipzH5jD\n/n37aSgtxknpyKLFD93WdVQqlUye3LOFbrvWgHukcF6cnJyxmO1Ry77k7bffZsOGDWRkZCCTyXjp\npZeYO3du7xPt/KhImTQd1QIhJ/bGAsLQeY/w/oblhDk7UdzahtY7gN2V9WiMJrQtWpZ1SpitOHuR\nRxMF1Qer1cpXqz5j1i//G4CgsHCCXhBqcCwWCwfXrgBNI6VFV7CG+9juc216PReWf4qHqzPNVjH/\nm34cTydHmnQdeIf1Xmy69I0/8vKiGcS5uaDR6bFG9OOl19+65Rx3D09kX3uD15sW8tcRWbu/+ZOJ\nROjN3y4AZeeHS69O8j/+8Q+WLl3KxYsXUalUREdHs3Llynth233F/AXz+1yDde4D81namUfbZwoC\nYgu/+NlvbZu79qy1/W02m9m3bx96vQGNppEJaUJ+WmbWQdzcuhwvlUrQeVap3Kmrr+62vNlsZuSI\nCdTUXCM/Pw86uzy2t2lpadWyZctWykrLeOmF1wCoqa1ErW7E01OoLFYquz+8NDbW3/QwOjo6um3X\n1Fxj7/7NGAx6LHRwpViMWCzlWmUpC+Y9DsCwoWM4lL6T8WkzMJtNZJ86SkhIOBaLhZEjplDXWMzC\nB+fxz382cDI7g9SUsZjNJo5lHWDajHE9bBg1ehTbtm6n8lwJFrOJwcmxeHp5oFZreOLJR3BwEHIB\npRIxBoMeuVzYblLXET9A0Jm+Lo13p76b5ttsYmLn3iASiYiMjMTX1xer1YrZbOazzz7jySef7GvT\n7PwbMGbqTJg6E41Gw2g3Ic1Or9dz9uRxJmZ31U1c73oHwnfO2dDRYy2AnR+/x6yOetwcHaj3c+X/\ncs7z6tCB1GrbOdPYzEshekQ6A3J3JZ6+bgwNDqBe28YXLb3fR1a9+388lhDD8JBALBYrbx09SXNz\nMyqV6hvnxPUfwMYdSvwNRhQyKRtrNAxf8vztnh4A4idOZ8vn7zHXzx2t3sBxRzfmf4P6hp0fH706\nyXq9nszMTLRaLWazGZVKxYkTJ+6FbfcV//Hqf+Km8qa9vY2Y2DAeffSx3ifdYZYv/4KiyxU4OTmh\naWngj3+89VP23aClpZ7lK/5OQEAINTXXEEmEm5/VauWzT78gaWAaCoUTdTX72LNvPdFRA6hrqGL/\nwa0onVwwmow0NtYxZdIDKJXOlJcXs2PnWmJjB1F4+TzxsYMAodNRSGgEkRFdEnW792wgOnwYDlJP\n/v7hW4SGRtLe3oaDg4K4WEGxIzI8hp2719MvZgBVVeXoDd1VIZqbNbS2tqJwknA4fRfBQeFUVpdj\nxcqUSYJTn194jtQRCQQFBfPnP71vm+vnF8SRjL1kZh2kpUVNk7oBDw8vHB0VHDq8k4BAobrc08OL\nqmuNrF3/KUaDkciofoi/oRBk9pxZPf4tvLM2Tq/XU1NTTdq4NFavWouPVxgdHe04OlmJiro7N/Hh\nI4dy5PB+fLyDaWisYvCQ/ndlP3Zuj8cee4zjx4/T2NhIfHw8Z8+eZdasWd/ZSbZYLPzkJz8hLy8P\nBwcHPvnkEyIjI3uMe+aZZ/D09OStt+79PcbOncfNrat+xcHBgX4DB5F1YCtzOtMUKrXtmC0WJGIx\nBpMZrYv7TddxaajCzUsIRHg7KQgNDWOdWyiVHTUsju4qBp4aGcr2giJhnLOSAE3DTde7EV3+OYZ3\nttcWi0XMj49m2+YNPPr4NzcaE4vFPPDqb9m9fRPmDh1J86cQEBzyjeNvRlBYOLJnX2HdkQPInJx5\nYOZcewt4Oza+0Uk+duwYZrOZZcuW8cknn9j+3Wg08txzz3HlypV7YuD9wBu//R3j0+baWkLv3ruR\n0tISwu9hzlJpaQn1tVoWPfg0ANcqy/j9m7/nv1//73tmA4C+w8ojDz+HRCLFbDazcvVHABQXFxHg\nF22Tdhs5fDLnLx0lMTkCNw8J7a0Sgjvl13btXs/J7CM4Oirw8w3ExUWFs7MLLi5u+PkJqQcB/sGs\n2/ApAf7BKBRKDh3eQVKikJOWn5/HrJkP2eTc9h3YSlnZFUJDoykovICjoxOFl8/j4+2Hv1+XLuau\nnbtQN3bg7OyGRCJH4SymorKQhoZ65s7q6pTl6xNIdXU1QUHBODlJKCouICoylvKKEgzGNpxdHWhu\nNRAaEsH4cUIr0tq6Ko5l7gLA2UWJi4sJBwcFVqsVjaaR4JDQb3WeL1++TEb6Cfx8QqhryCQldSh+\nfr44ODjg5KRky5atWMwW4hPiiI3tqXX9XYmOjiY0NJTa2hq8vYfj2FlZbqdvyMjI4PLly7z44ov8\n7Gc/A7A1Z/oubNmyBYPBQFZWFidPnuSXv/wlW7Zs6Tbm448/5sKFC6SlpX0f038UHNi+hYsrPiJU\n5UJBo4ZZb/wfCQN7V5e5k1gsFv7x8nOEmnRozWbc06Yz9aFHbjnHzc0d/wVLWXtgOzKrFa8ZC1lT\nVYGiTUu70o3JT/7kpvP0XysMtiqUTFm8lKulJVSteA+/zuYdHUYT52rqEIlEtOoNVOt7jyRrzGZM\nZgtSSacefksrUf16v7dJpVImPCDIvqkbG1n75msodW2oZQrSnn4R19uQffMNCGTy4qW9jrPz4+Mb\nneT9+/eTkZFBdXU1v/1t1+t1mUzGc889d0+Mu1/o6DDZHGSAxIHD2LVzBz994Wf3zIbt27aSlDja\nth0UGMrhIzd/JXY38fT0tSkoSCQSvLx8bX+bbijOsFqtSCQSQkJCOZV92iY9ZzabMRgNTJ40F6lU\nxtFj+5HLHfDzDcTby4+16/9FbL/+GIx6Ro4aSoOmCH2NniZ1A35+QYAQDb7uIAMkJw1n4+YvSYhP\n4szZ4zz26Av4+wVR31DDyewjnD59WuhopzGROEiQqQsPi+ZySTZPPfUEZ86cZe/ubbi7e2E2m1Gr\na3npF0K71Z+88BM2btzA1p3LCQ8L4e0/CZG1o0czaNV05ev6+gSgbdPx5fK15F/KIzIigZEjJmA2\nm9m5ax35ly4xanTX9euN41k5DE8RcpLDw/uRnXOQJ55KxGKx8M+PPiV12GRkMjl5Z3Iwm82MGDHk\n213IWyCXywn+ltEYO3eHgIAA5HI5cXFx5OXlsXjxYsrKyr7zepmZmbbupikpKeTk5HT7PCsri+zs\nbJ599lkKCgp6XU8qldhSffoCaadyQ1/ZcHHFR7yeNhyRSITFYuU/f/sKIw5l3lMb3nv1ZZ4NdMfb\nWbg/rkjfhXHeA3h5ed1y3tDRwxk6evgtx3yd+AWL2LJ6OREiM1eQkPj4c6hUCgYlJrAjdzAt53Nw\nFYtYn3eJ36UNRy6VYrFY+cPJs71eo6Gz5/HWxtVMj4mgVtvOkfJKXhuVirPz7V/b3e98yCIHMyK5\nI1arhbUrPuah37zxrY7xTtHX3837xYb7xY7rNnzred/0we9+Jwh///73v8fJyYmf/vSnzJo1i9On\nT/PMM898Nyv/TTGZOmhtbcbFRciNKiktZOSo23d47gSjRo8m52QBfr5CZLSlRYPZfBvt1e4wHfrW\nbtsaTT0b1m3CbDHT0tKMu5snKpU7J08dZO48Icrq4qK0Sc9VVpWRNCjVJqk3etQkVqz8BynDxlJS\nms/kqeNJTU1FJpPR2trCnj37kYgkNNTXcObsSQYNHEpVVRma5ibcVEIBaXFJAR36NprUtQwcOBj/\nTmfa28uPyMg4WppEaLUdlJYWMaC/IAcnkUjBKji5DfV1JCePJMBPkB86mrkPvV5vK86cP39Bj/OQ\nmJjIV8s3EhoivKrWalsJ9A8jOXEMujYDw1PHde5Hwojh42nX1X6r8/xN0m4VFeUEBkTZJPAS4odQ\nkH/yeznJra0t7Nq1F7FIQkREKMlDfjwa6Pc7gYGBvPXWW0yYMIFXXxVkqTQaTS+zvpmWlhZcXbtk\nHCUSCRaLBbFYTHV1NW+++SabN29m7dq1t1jFznUi3F1tr+bFYhFRHt+cP3u3EDfV4x0VZNtO9vPi\n7JnTTJzUUzLt+9J/aApRAwZRU1PDzM4HuOvMfOpZWloWCYpY//VL5J1qRGKxiCAfLyoqKnBxcemW\n+nEjTh1t/HpMKrnVNST4eBLt5UFFeRlxvbSRvhGlTovIUXibKRKJULb31Fa2Y+fb0GtO8o4dO/jT\nn/7Epk2bcHJy4vTp08ybN48FC3o6Dj9U/vint/nFS78kPDwWfYcOq6iDxMRH76kNgwcPYc/u/Wzf\nsQYHRwWlVwt4529/uac2ADz3/DJWLF+FVOJAu66ViPBY4voNx2q1cjRzJ0g01NRX89DiB2zdjSZN\nnsSXy1egbTXS0FBDTPQg23oWiwWpTERF1Tn6D4qx5doajUZWfbWeUSOmIxaL0bWDWtPAyVMZOLuq\n2LZ9Nf7+wRgNBjo6dAzon8K4sVPZuWtdN3sVjkq8vf3w9vbDZDazcdNy/PyCqKmtZMYsIVJ7Ovcc\nM6d3vZ5MiBvMF198RkJCf1JTU3Fz65mf5+LiStKQONIztiMWS6i4VsbiB7saPJjNJptj29KqocPY\nwp49u79xva+jUMpukHZrQdIp7ebkpESna7eNE4q5vntxndlsZsWXaxg9YgZisZji4kvU1u3CarES\nHR1FTL9+vS9i567x2WefsXPnToYNG8b8+fNZs2YNH3744Xdez9XVldbWrgfd6w4ywIYNG2hoaGD6\n9OnU1NTQ3t5OXFwcjz32zfUXJpO5T9uQ93UxdYm6xdaYx2KxUtTUfM9t0bt4UK9tw7tTyzi3pp4R\nkXF31Q53d190OjM6Xfd9qFQqXF1VWP2DbedFqzdQqW6m8Z03KTRB28AU0hYuucmivjQ2VJASJEhx\nbqluYojK51sdh0amsO3XarWikTr22Xejr7+b94sN94sdKpUCubz3PgJfp9cZFouFsWPHsmTJEubP\nn09ISAjmH5k8ikQi4b0P/tbnF/q/fvPrPrfB09OTsIgQxIi5fKWVIcmCaLtIJCI2JhkXV0hJTbUp\nM4DQ1rujw8TI4RNp1bawfuOnKBwVuLt7cvjITp597imCg7vn7JaWlhAWGm/7AR+cNJyDh3cwPCWN\niooSxqXNwLsz1eP4yXQcOpuV9IsZQMaxfQxISOZSwTn8/LoafripPOjfP5l+Mf3RaluprrqMLj4O\nsVREXV21ravenn2bmDhhNh7unqxfu43Zc6fg6+vX41ykpqYyZYoQMf7bOx9xLOsAErEEnV7H+k2f\nM2HcbNraWsg8vo/JE+bj6enNF5+t4aHFc/H397/lefbz9+Vo+n7c3LzQaBoYMkwoTPT29kYqN1BU\nnI+7myeXCnN4aNEDt38Bb8BgMFBZeY2QwBjbeY6MjGfHrjVMn/og+ZcKuXatkvETxn+n9e18f+bP\nn8++ffsAobHTiy+++L3WGzlyJNu3b2fhwoWcOHGCgQMH2j67cf3ly5dTUFBwSwfZDiQ8+hxvduYk\nFzZpmPO7P99zG558/Q+dOclVQk7yuBl4et461eJuM+rJF/jqi3/gqteRd62SXyfF23KN08+fpD5t\nsq0h0nVGz5rH3uUNKKrK6BCJCZ7z8LeWWh395Aus/eojlLo2NDInxj790zt2THZ+nPTqJDs5OfHn\nP/+ZgwcP8v777/Puu+/i4uJyL2y7r9i4cT17du3Dgpl3333vnuskAzQ3N/Pw4sVIJTImTBrH0qVP\n3HMb1q3dQG21hobGWjw9vDmcvptJE4XmBqfPZOHsrOT8uWKMJi1LH38UqVTKoYPpjEydikgkwsPd\ni2lTFtCgLqbdUMPLr7xISUkJublnCQkOYnBnu/PW1lYaGmptucdms4nz53Opra3C0VHBvv2bCQuN\nxmg0UlR0iceXCj/u4eHRFJcUUF1dgYeHF/X1NcT2G4jZbCYz6wAzZwg6wm3trZzPK0DTpMdR7syq\nNR8TFzeI+voaEgel4uMtOMXDUyZx+PBRFi1a2ONcWCwWtm/fia69gzZtC6nDpqFQKDEYDJzI2YOL\nmxmr2ETSoJH4+grO+qQJc1m3dgMv/fzWzk7xlXJmzujSn845fdj297z5D1BSUoRareHxJ5Z8Y3Gd\nxWJh//796HQdDBmSTFBQkO3fV3y5EpHVkZYWNUqlivDwfp3n2YyriwdisZioiDhO5R6+6dpfp7Gx\nkT17TiCXyxk9Os3WzMDO90On09kaNNwJHnjgAfbv38/IkSMB+Pzzz1m9ejVarZZly5Z1G2uv8O+d\nibPmMv8RQXu+rwIXYrGYF/72z3sWQDl3IpPakisE9ksgIXnoTcd4eHkx6xWh+57xkw+QGptsn/nK\nxDSrm3o4ySKRiKmPf7926+6enjz0utA8qq+jp3Z+GPTqJK9cuZLPPvuMTZs24eHhQU1NDatWrboX\ntt03LF/+BY317fz8Z2+i07Xz6iu/5r0P3unWdvluYzQa+Y9f/Rcvv/Q/KBRO7Nm7ifffe48Xf3bv\nigcBck7lMG7sTCZPmkNZWRHbd64lJCScqqoKVCoPWy6uXt/Btq3bGDI0GZPJ1O0HVyqVk5Q0mP79\nB3DgwEGMOjnR4cO4VlnKnj17mTp1Cm5uKnLPHKetTYuDgwOncjOZMX0h0VHxlJUXs33HWsJChcp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OLAYYzsjICnDhvLzp0r8fHxxtvbBwcHB5KThTSUzz75ggEJo1Aqnck4ksHoscNI7cx18PT0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5QrZ5rr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\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e/NtrxAyfifqqCsOOdUvoP2k+Mpkcu83KkU0ryJgqqiyYGuvZu/bDO8pJBnj4ntlYreKF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HjJ+3jbz2Vv/zx4U1zeGBN4Z1zeGBN4Z1zeGBNc/c/kbk2SL8ZoNGIymTzbJpOJwMDAS56jUEj4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\nRERk4z+w7sCywuhUdgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109186610>"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###4. Numpy, and some stats\n",
      "\n",
      "(From Shalizi). In the real world we only always deal with samples from populations. We assume that whatever it is that gave us our data is well-modeled by some random process. In effect, each time we make a measurement, we ask the process to spit out numbers according to some distribution; we sample the distribution. If we think of sampling as closing our eyes and picking a data point out of a box, we call the set of all data-points in the box the population. (That last isn\u2019t as silly as it sounds; think of opinion polls.) In either case, we assume that the true distribution is the unknown one which lies behind our data. If we want to learn about that distribution from the data, then we need to know something about the kind of data it is likely to give us. That is to say, we need to know the sampling distribution, the distribution of data values given that the true distribution takes a certain functional form, with certain parameter values, and we sample from it in a specified way.\n",
      "\n",
      "In the next two sections, $X_1, X_2 . . .X_n . . .$ are independent, identically-distributed\n",
      "(IID) random variables. X has the finite mean \u03bc. The average of the first n of\n",
      "them is\n",
      "\n",
      "$$ S_n \\equiv \\frac{1}{n} \\sum_{i=1}^{n} X_i $$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### The Law of Large Numbers\n",
      "(From Shalizi)\n",
      "The means of IID sequences converge to the mean of the variable (if E|X| < $\\infty$):\n",
      "\n",
      "$$S_n \\rightarrow \\mu$$ \n",
      "\n",
      "as \n",
      "\n",
      "$$n \\rightarrow \\infty$$\n",
      "\n",
      "\n",
      "That is, the sample mean approaches the population mean.\n",
      "\n",
      "The obvious application of the law of large numbers is to take our favorite event A from any probability distribution we like, with random variable Y , and then apply an indicator function I(A) to Y , a function which gives 1 when its input is in in A and 0 otherwise. Now we have a Bernoulli random variable with parameter p = P(A). So if we take a long sequence of random trials, then with probability one the frequency of successes will converge to the true probability .\n",
      "\n",
      "If you simulate many samples of a population, then the laws of large numbers\n",
      "tell us the the empirical frequencies of your simulations will approach the actual\n",
      "sampling distribution. This is called \u201cMonte Carlo simulation\u201d, or even \u201cthe\n",
      "Monte Carlo method\u201d. (Several other things are also called Monte Carlo, but\n",
      "they\u2019re actually related.) By the same procedure, you can get the sampling\n",
      "distribution for an arbitrary statistic of your data.\n",
      "\n",
      "#### Central Limit Theorem\n",
      "\n",
      "(Again, Shalizi).\n",
      "Assume that $X$ has a finite population variance $\\sigma^2$. Then as $n \\rightarrow \\infty$, the $S_n$ converge to a Gaussian random variable with mean $\\mu$ and variance $\\sigma^2$.\n",
      "\n",
      "More prosaicly: the CLT tells us that, if only we can arrange to be dealing\n",
      "with IID sequences, in the long run we know what the distribution must be.\n",
      "Even better, it\u2019s always the same distribution; still better, it\u2019s one which is\n",
      "remarkably easy to deal with, and for which we have a huge amount of theory.\n",
      "Manipulate your problem so that the CLT applies, and, at least asymptotically,\n",
      "you\u2019ve got it made.\n",
      "\n",
      "It\u2019s generally true that, whatever measure of variability we pick, its value in a sample will tend to be smaller than its population value. (We have already mentioned that this is true of the variance.) It\u2019s not hard to see why this should be so; consider, as an extreme case, the limit where our sample consists of a single piece of data, and so there is no variation in the sample. More generally, picking out a subset from the population, which is what sampling does, is unlikely to give an exactly representative sample, and naturally the higher probability events will be the ones which tend to show up in the sample.\n",
      "\n",
      "#### Sampling and De-Moivre's formula: \n",
      "\n",
      "The standard deviation of the sampling distribution of the mean is given as:\n",
      "\n",
      "$$\\sigma_{sample} = \\frac{\\sigma}{\\sqrt{N_{sample}}}$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def throw_a_coin(size):\n",
      "    return np.random.randint(2, size=size)\n",
      "\n",
      "def make_throws(numsamples, numthrows):\n",
      "    start=np.zeros((numsamples,numthrows), dtype=int)\n",
      "    for i in range(numsamples):\n",
      "        start[i,:]=throw_a_coin(numthrows)\n",
      "    return np.mean(start, axis=1)\n",
      "\n",
      "def mean_std(means):\n",
      "    return np.mean(means), np.std(means)\n",
      "\n",
      "make_throws(20,10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "array([ 0.8,  0.6,  0.2,  0.6,  0.4,  0.5,  0.8,  0.4,  0.7,  0.5,  0.6,\n",
        "        0.6,  0.3,  0.3,  0.5,  0.3,  0.6,  0.4,  0.4,  0.4])"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      ">as a function of the size of the samples"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sampsize=range(1000)\n",
      "mss = [mean_std(make_throws(100,i)) for i in sampsize]\n",
      "plt.plot(sampsize, [e[1] for e in mss])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1093b37d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AzooJZgAAJLi4BGub1SKLRfITrAEAOKu4BGuLxaIku1U+v6G124+ovtEbj2IAANAtxCVY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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109d98fd0>"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(sampsize, [e[0] for e in mss])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1093a0d90>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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88891hsEP+oY4J5KOVY12DNxe+YKhqGu+sTnERaSs6HmKclg1Cs+8tU6Z1y1e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\nBQ4AgIUocAAALPRv4zVHSL5uNJ8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a19d210>"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      ">As a function of the number of samples"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numsamps=range(1000)\n",
      "mss2=[mean_std(make_throws(i,100)) for i in numsamps]\n",
      "plt.plot(numsamps, [e[1] for e in mss2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1093be690>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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msTmMp1/fhG7Zbmzfr1/K1uYUewDAio36FfESpdC37j6FrbtPoeSivgDMx1uc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\nKESBAwAQhf4FPdMHXlhZYvQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a18cc50>"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(numsamps, [e[0] for e in mss2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10939d510>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wkh3Ha2Nj2xt2HMVvXqrET54ux1Ovf6YP8xyvST3bpqEphJ/NXo63lxstbdm7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\nBf0PCA1urJbui44AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109d98510>"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###5. Pandas Data Munging, indexing, plotting"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The first concept we deal with here is pandas `groupby`. The idea is to group a dataframe by the values of a particular factor variable. The documentation can be found <a href=\"http://pandas.pydata.org/pandas-docs/dev/groupby.html\">here</a>."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "region_groupby = df.groupby('region')\n",
      "print type(region_groupby)\n",
      "region_groupby.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.groupby.DataFrameGroupBy'>\n"
       ]
      },
      {
       "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></th>\n",
        "      <th>areastring</th>\n",
        "      <th>region</th>\n",
        "      <th>area</th>\n",
        "      <th>palmitic</th>\n",
        "      <th>palmitoleic</th>\n",
        "      <th>stearic</th>\n",
        "      <th>oleic</th>\n",
        "      <th>linoleic</th>\n",
        "      <th>linolenic</th>\n",
        "      <th>arachidic</th>\n",
        "      <th>eicosenoic</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>region</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
        "      <th>0  </th>\n",
        "      <td>    North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10.75</td>\n",
        "      <td> 0.75</td>\n",
        "      <td> 2.26</td>\n",
        "      <td> 78.23</td>\n",
        "      <td>  6.72</td>\n",
        "      <td> 0.36</td>\n",
        "      <td> 0.60</td>\n",
        "      <td> 0.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1  </th>\n",
        "      <td>    North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10.88</td>\n",
        "      <td> 0.73</td>\n",
        "      <td> 2.24</td>\n",
        "      <td> 77.09</td>\n",
        "      <td>  7.81</td>\n",
        "      <td> 0.31</td>\n",
        "      <td> 0.61</td>\n",
        "      <td> 0.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2  </th>\n",
        "      <td>    North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  9.11</td>\n",
        "      <td> 0.54</td>\n",
        "      <td> 2.46</td>\n",
        "      <td> 81.13</td>\n",
        "      <td>  5.49</td>\n",
        "      <td> 0.31</td>\n",
        "      <td> 0.63</td>\n",
        "      <td> 0.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3  </th>\n",
        "      <td>    North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  9.66</td>\n",
        "      <td> 0.57</td>\n",
        "      <td> 2.40</td>\n",
        "      <td> 79.52</td>\n",
        "      <td>  6.19</td>\n",
        "      <td> 0.50</td>\n",
        "      <td> 0.78</td>\n",
        "      <td> 0.35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4  </th>\n",
        "      <td>    North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10.51</td>\n",
        "      <td> 0.67</td>\n",
        "      <td> 2.59</td>\n",
        "      <td> 77.71</td>\n",
        "      <td>  6.72</td>\n",
        "      <td> 0.50</td>\n",
        "      <td> 0.80</td>\n",
        "      <td> 0.46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">2</th>\n",
        "      <th>323</th>\n",
        "      <td> Inland-Sardinia</td>\n",
        "      <td> 2</td>\n",
        "      <td> 5</td>\n",
        "      <td> 11.29</td>\n",
        "      <td> 1.20</td>\n",
        "      <td> 2.22</td>\n",
        "      <td> 72.72</td>\n",
        "      <td> 11.12</td>\n",
        "      <td> 0.43</td>\n",
        "      <td> 0.98</td>\n",
        "      <td> 0.02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>324</th>\n",
        "      <td> Inland-Sardinia</td>\n",
        "      <td> 2</td>\n",
        "      <td> 5</td>\n",
        "      <td> 10.42</td>\n",
        "      <td> 1.35</td>\n",
        "      <td> 2.10</td>\n",
        "      <td> 73.76</td>\n",
        "      <td> 11.16</td>\n",
        "      <td> 0.35</td>\n",
        "      <td> 0.90</td>\n",
        "      <td> 0.03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>325</th>\n",
        "      <td> Inland-Sardinia</td>\n",
        "      <td> 2</td>\n",
        "      <td> 5</td>\n",
        "      <td> 11.03</td>\n",
        "      <td> 0.96</td>\n",
        "      <td> 2.10</td>\n",
        "      <td> 73.80</td>\n",
        "      <td> 10.85</td>\n",
        "      <td> 0.32</td>\n",
        "      <td> 0.94</td>\n",
        "      <td> 0.03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>326</th>\n",
        "      <td> Inland-Sardinia</td>\n",
        "      <td> 2</td>\n",
        "      <td> 5</td>\n",
        "      <td> 11.18</td>\n",
        "      <td> 0.97</td>\n",
        "      <td> 2.21</td>\n",
        "      <td> 72.79</td>\n",
        "      <td> 11.54</td>\n",
        "      <td> 0.35</td>\n",
        "      <td> 0.94</td>\n",
        "      <td> 0.02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>327</th>\n",
        "      <td> Inland-Sardinia</td>\n",
        "      <td> 2</td>\n",
        "      <td> 5</td>\n",
        "      <td> 10.52</td>\n",
        "      <td> 0.95</td>\n",
        "      <td> 2.15</td>\n",
        "      <td> 73.88</td>\n",
        "      <td> 11.26</td>\n",
        "      <td> 0.31</td>\n",
        "      <td> 0.92</td>\n",
        "      <td> 0.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">3</th>\n",
        "      <th>421</th>\n",
        "      <td>          Umbria</td>\n",
        "      <td> 3</td>\n",
        "      <td> 9</td>\n",
        "      <td> 10.85</td>\n",
        "      <td> 0.70</td>\n",
        "      <td> 1.80</td>\n",
        "      <td> 79.55</td>\n",
        "      <td>  6.05</td>\n",
        "      <td> 0.20</td>\n",
        "      <td> 0.50</td>\n",
        "      <td> 0.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>422</th>\n",
        "      <td>          Umbria</td>\n",
        "      <td> 3</td>\n",
        "      <td> 9</td>\n",
        "      <td> 10.85</td>\n",
        "      <td> 0.70</td>\n",
        "      <td> 1.85</td>\n",
        "      <td> 79.55</td>\n",
        "      <td>  6.00</td>\n",
        "      <td> 0.25</td>\n",
        "      <td> 0.55</td>\n",
        "      <td> 0.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>423</th>\n",
        "      <td>          Umbria</td>\n",
        "      <td> 3</td>\n",
        "      <td> 9</td>\n",
        "      <td> 10.90</td>\n",
        "      <td> 0.60</td>\n",
        "      <td> 1.90</td>\n",
        "      <td> 79.50</td>\n",
        "      <td>  6.00</td>\n",
        "      <td> 0.28</td>\n",
        "      <td> 0.47</td>\n",
        "      <td> 0.02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>424</th>\n",
        "      <td>          Umbria</td>\n",
        "      <td> 3</td>\n",
        "      <td> 9</td>\n",
        "      <td> 10.80</td>\n",
        "      <td> 0.65</td>\n",
        "      <td> 1.89</td>\n",
        "      <td> 79.60</td>\n",
        "      <td>  6.02</td>\n",
        "      <td> 0.35</td>\n",
        "      <td> 0.20</td>\n",
        "      <td> 0.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>425</th>\n",
        "      <td>          Umbria</td>\n",
        "      <td> 3</td>\n",
        "      <td> 9</td>\n",
        "      <td> 10.90</td>\n",
        "      <td> 0.60</td>\n",
        "      <td> 1.95</td>\n",
        "      <td> 79.55</td>\n",
        "      <td>  6.00</td>\n",
        "      <td> 0.28</td>\n",
        "      <td> 0.42</td>\n",
        "      <td> 0.02</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "                 areastring  region  area  palmitic  palmitoleic  stearic  oleic  linoleic  linolenic  arachidic  eicosenoic\n",
        "region                                                                                                                      \n",
        "1      0       North-Apulia       1     1     10.75         0.75     2.26  78.23      6.72       0.36       0.60        0.29\n",
        "       1       North-Apulia       1     1     10.88         0.73     2.24  77.09      7.81       0.31       0.61        0.29\n",
        "       2       North-Apulia       1     1      9.11         0.54     2.46  81.13      5.49       0.31       0.63        0.29\n",
        "       3       North-Apulia       1     1      9.66         0.57     2.40  79.52      6.19       0.50       0.78        0.35\n",
        "       4       North-Apulia       1     1     10.51         0.67     2.59  77.71      6.72       0.50       0.80        0.46\n",
        "2      323  Inland-Sardinia       2     5     11.29         1.20     2.22  72.72     11.12       0.43       0.98        0.02\n",
        "       324  Inland-Sardinia       2     5     10.42         1.35     2.10  73.76     11.16       0.35       0.90        0.03\n",
        "       325  Inland-Sardinia       2     5     11.03         0.96     2.10  73.80     10.85       0.32       0.94        0.03\n",
        "       326  Inland-Sardinia       2     5     11.18         0.97     2.21  72.79     11.54       0.35       0.94        0.02\n",
        "       327  Inland-Sardinia       2     5     10.52         0.95     2.15  73.88     11.26       0.31       0.92        0.01\n",
        "3      421           Umbria       3     9     10.85         0.70     1.80  79.55      6.05       0.20       0.50        0.01\n",
        "       422           Umbria       3     9     10.85         0.70     1.85  79.55      6.00       0.25       0.55        0.01\n",
        "       423           Umbria       3     9     10.90         0.60     1.90  79.50      6.00       0.28       0.47        0.02\n",
        "       424           Umbria       3     9     10.80         0.65     1.89  79.60      6.02       0.35       0.20        0.01\n",
        "       425           Umbria       3     9     10.90         0.60     1.95  79.55      6.00       0.28       0.42        0.02"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The function `groupby` gives you a dictionary-like object, with the keys being the values of the factor, and the values being the corresponding subsets of the dataframe."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for key, value in region_groupby:\n",
      "    print \"( key, type(value) ) = (\", key, \",\", type(value), \")\"\n",
      "    v=value\n",
      "\n",
      "v.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "( key, type(value) ) = ( 1 , <class 'pandas.core.frame.DataFrame'> )\n",
        "( key, type(value) ) = ( 2 , <class 'pandas.core.frame.DataFrame'> )\n",
        "( key, type(value) ) = ( 3 , <class 'pandas.core.frame.DataFrame'> )\n"
       ]
      },
      {
       "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>areastring</th>\n",
        "      <th>region</th>\n",
        "      <th>area</th>\n",
        "      <th>palmitic</th>\n",
        "      <th>palmitoleic</th>\n",
        "      <th>stearic</th>\n",
        "      <th>oleic</th>\n",
        "      <th>linoleic</th>\n",
        "      <th>linolenic</th>\n",
        "      <th>arachidic</th>\n",
        "      <th>eicosenoic</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>421</th>\n",
        "      <td> Umbria</td>\n",
        "      <td> 3</td>\n",
        "      <td> 9</td>\n",
        "      <td> 10.85</td>\n",
        "      <td> 0.70</td>\n",
        "      <td> 1.80</td>\n",
        "      <td> 79.55</td>\n",
        "      <td> 6.05</td>\n",
        "      <td> 0.20</td>\n",
        "      <td> 0.50</td>\n",
        "      <td> 0.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>422</th>\n",
        "      <td> Umbria</td>\n",
        "      <td> 3</td>\n",
        "      <td> 9</td>\n",
        "      <td> 10.85</td>\n",
        "      <td> 0.70</td>\n",
        "      <td> 1.85</td>\n",
        "      <td> 79.55</td>\n",
        "      <td> 6.00</td>\n",
        "      <td> 0.25</td>\n",
        "      <td> 0.55</td>\n",
        "      <td> 0.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>423</th>\n",
        "      <td> Umbria</td>\n",
        "      <td> 3</td>\n",
        "      <td> 9</td>\n",
        "      <td> 10.90</td>\n",
        "      <td> 0.60</td>\n",
        "      <td> 1.90</td>\n",
        "      <td> 79.50</td>\n",
        "      <td> 6.00</td>\n",
        "      <td> 0.28</td>\n",
        "      <td> 0.47</td>\n",
        "      <td> 0.02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>424</th>\n",
        "      <td> Umbria</td>\n",
        "      <td> 3</td>\n",
        "      <td> 9</td>\n",
        "      <td> 10.80</td>\n",
        "      <td> 0.65</td>\n",
        "      <td> 1.89</td>\n",
        "      <td> 79.60</td>\n",
        "      <td> 6.02</td>\n",
        "      <td> 0.35</td>\n",
        "      <td> 0.20</td>\n",
        "      <td> 0.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>425</th>\n",
        "      <td> Umbria</td>\n",
        "      <td> 3</td>\n",
        "      <td> 9</td>\n",
        "      <td> 10.90</td>\n",
        "      <td> 0.60</td>\n",
        "      <td> 1.95</td>\n",
        "      <td> 79.55</td>\n",
        "      <td> 6.00</td>\n",
        "      <td> 0.28</td>\n",
        "      <td> 0.42</td>\n",
        "      <td> 0.02</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "    areastring  region  area  palmitic  palmitoleic  stearic  oleic  linoleic  linolenic  arachidic  eicosenoic\n",
        "421     Umbria       3     9     10.85         0.70     1.80  79.55      6.05       0.20       0.50        0.01\n",
        "422     Umbria       3     9     10.85         0.70     1.85  79.55      6.00       0.25       0.55        0.01\n",
        "423     Umbria       3     9     10.90         0.60     1.90  79.50      6.00       0.28       0.47        0.02\n",
        "424     Umbria       3     9     10.80         0.65     1.89  79.60      6.02       0.35       0.20        0.01\n",
        "425     Umbria       3     9     10.90         0.60     1.95  79.55      6.00       0.28       0.42        0.02"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The `groupby` function also acts like an object that can be **mapped**. After the mapping is complete, the rows are put together (**reduced**) into a larger dataframe. For example, using the `describe` function. The documentation of the `describe` function can be found <a href=\"http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.describe.html\">here</a>."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfrd=region_groupby.describe()\n",
      "print type(dfrd)\n",
      "dfrd.head(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n"
       ]
      },
      {
       "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></th>\n",
        "      <th>region</th>\n",
        "      <th>area</th>\n",
        "      <th>palmitic</th>\n",
        "      <th>palmitoleic</th>\n",
        "      <th>stearic</th>\n",
        "      <th>oleic</th>\n",
        "      <th>linoleic</th>\n",
        "      <th>linolenic</th>\n",
        "      <th>arachidic</th>\n",
        "      <th>eicosenoic</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>region</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"8\" valign=\"top\">1</th>\n",
        "      <th>count</th>\n",
        "      <td> 323</td>\n",
        "      <td> 323.000000</td>\n",
        "      <td> 323.000000</td>\n",
        "      <td> 323.000000</td>\n",
        "      <td> 323.000000</td>\n",
        "      <td> 323.000000</td>\n",
        "      <td> 323.000000</td>\n",
        "      <td> 323.000000</td>\n",
        "      <td> 323.000000</td>\n",
        "      <td> 323.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>   1</td>\n",
        "      <td>   2.783282</td>\n",
        "      <td>  13.322879</td>\n",
        "      <td>   1.548019</td>\n",
        "      <td>   2.287740</td>\n",
        "      <td>  71.000093</td>\n",
        "      <td>  10.334985</td>\n",
        "      <td>   0.380650</td>\n",
        "      <td>   0.631176</td>\n",
        "      <td>   0.273220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>   0</td>\n",
        "      <td>   0.741054</td>\n",
        "      <td>   1.529349</td>\n",
        "      <td>   0.507237</td>\n",
        "      <td>   0.398709</td>\n",
        "      <td>   3.451431</td>\n",
        "      <td>   2.106730</td>\n",
        "      <td>   0.079727</td>\n",
        "      <td>   0.111644</td>\n",
        "      <td>   0.083915</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>   1</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   8.750000</td>\n",
        "      <td>   0.350000</td>\n",
        "      <td>   1.520000</td>\n",
        "      <td>  63.000000</td>\n",
        "      <td>   4.480000</td>\n",
        "      <td>   0.200000</td>\n",
        "      <td>   0.320000</td>\n",
        "      <td>   0.100000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>   1</td>\n",
        "      <td>   2.500000</td>\n",
        "      <td>  12.680000</td>\n",
        "      <td>   1.215000</td>\n",
        "      <td>   2.015000</td>\n",
        "      <td>  68.830000</td>\n",
        "      <td>   8.555000</td>\n",
        "      <td>   0.320000</td>\n",
        "      <td>   0.560000</td>\n",
        "      <td>   0.220000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>   1</td>\n",
        "      <td>   3.000000</td>\n",
        "      <td>  13.460000</td>\n",
        "      <td>   1.630000</td>\n",
        "      <td>   2.230000</td>\n",
        "      <td>  70.300000</td>\n",
        "      <td>  10.900000</td>\n",
        "      <td>   0.370000</td>\n",
        "      <td>   0.620000</td>\n",
        "      <td>   0.270000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>   1</td>\n",
        "      <td>   3.000000</td>\n",
        "      <td>  14.190000</td>\n",
        "      <td>   1.850000</td>\n",
        "      <td>   2.495000</td>\n",
        "      <td>  72.835000</td>\n",
        "      <td>  12.025000</td>\n",
        "      <td>   0.440000</td>\n",
        "      <td>   0.690000</td>\n",
        "      <td>   0.320000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>   1</td>\n",
        "      <td>   4.000000</td>\n",
        "      <td>  17.530000</td>\n",
        "      <td>   2.800000</td>\n",
        "      <td>   3.750000</td>\n",
        "      <td>  81.130000</td>\n",
        "      <td>  14.620000</td>\n",
        "      <td>   0.740000</td>\n",
        "      <td>   1.020000</td>\n",
        "      <td>   0.580000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"8\" valign=\"top\">2</th>\n",
        "      <th>count</th>\n",
        "      <td>  98</td>\n",
        "      <td>  98.000000</td>\n",
        "      <td>  98.000000</td>\n",
        "      <td>  98.000000</td>\n",
        "      <td>  98.000000</td>\n",
        "      <td>  98.000000</td>\n",
        "      <td>  98.000000</td>\n",
        "      <td>  98.000000</td>\n",
        "      <td>  98.000000</td>\n",
        "      <td>  98.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>   2</td>\n",
        "      <td>   5.336735</td>\n",
        "      <td>  11.113469</td>\n",
        "      <td>   0.967449</td>\n",
        "      <td>   2.261837</td>\n",
        "      <td>  72.680204</td>\n",
        "      <td>  11.965306</td>\n",
        "      <td>   0.270918</td>\n",
        "      <td>   0.731735</td>\n",
        "      <td>   0.019388</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>   0</td>\n",
        "      <td>   0.475023</td>\n",
        "      <td>   0.404111</td>\n",
        "      <td>   0.138514</td>\n",
        "      <td>   0.176363</td>\n",
        "      <td>   1.418783</td>\n",
        "      <td>   1.072336</td>\n",
        "      <td>   0.053844</td>\n",
        "      <td>   0.118826</td>\n",
        "      <td>   0.007436</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>   2</td>\n",
        "      <td>   5.000000</td>\n",
        "      <td>  10.300000</td>\n",
        "      <td>   0.350000</td>\n",
        "      <td>   1.990000</td>\n",
        "      <td>  68.820000</td>\n",
        "      <td>  10.570000</td>\n",
        "      <td>   0.150000</td>\n",
        "      <td>   0.450000</td>\n",
        "      <td>   0.010000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>   2</td>\n",
        "      <td>   5.000000</td>\n",
        "      <td>  10.852500</td>\n",
        "      <td>   0.882500</td>\n",
        "      <td>   2.120000</td>\n",
        "      <td>  71.372500</td>\n",
        "      <td>  11.122500</td>\n",
        "      <td>   0.230000</td>\n",
        "      <td>   0.660000</td>\n",
        "      <td>   0.010000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>   2</td>\n",
        "      <td>   5.000000</td>\n",
        "      <td>  11.075000</td>\n",
        "      <td>   0.960000</td>\n",
        "      <td>   2.220000</td>\n",
        "      <td>  73.255000</td>\n",
        "      <td>  11.465000</td>\n",
        "      <td>   0.270000</td>\n",
        "      <td>   0.720000</td>\n",
        "      <td>   0.020000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>   2</td>\n",
        "      <td>   6.000000</td>\n",
        "      <td>  11.372500</td>\n",
        "      <td>   1.040000</td>\n",
        "      <td>   2.395000</td>\n",
        "      <td>  73.810000</td>\n",
        "      <td>  13.065000</td>\n",
        "      <td>   0.300000</td>\n",
        "      <td>   0.810000</td>\n",
        "      <td>   0.020000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>   2</td>\n",
        "      <td>   6.000000</td>\n",
        "      <td>  12.130000</td>\n",
        "      <td>   1.350000</td>\n",
        "      <td>   2.720000</td>\n",
        "      <td>  74.390000</td>\n",
        "      <td>  14.700000</td>\n",
        "      <td>   0.430000</td>\n",
        "      <td>   1.050000</td>\n",
        "      <td>   0.030000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">3</th>\n",
        "      <th>count</th>\n",
        "      <td> 151</td>\n",
        "      <td> 151.000000</td>\n",
        "      <td> 151.000000</td>\n",
        "      <td> 151.000000</td>\n",
        "      <td> 151.000000</td>\n",
        "      <td> 151.000000</td>\n",
        "      <td> 151.000000</td>\n",
        "      <td> 151.000000</td>\n",
        "      <td> 151.000000</td>\n",
        "      <td> 151.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>   3</td>\n",
        "      <td>   8.006623</td>\n",
        "      <td>  10.948013</td>\n",
        "      <td>   0.837351</td>\n",
        "      <td>   2.308013</td>\n",
        "      <td>  77.930530</td>\n",
        "      <td>   7.270331</td>\n",
        "      <td>   0.217881</td>\n",
        "      <td>   0.375762</td>\n",
        "      <td>   0.019735</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>   0</td>\n",
        "      <td>   0.820542</td>\n",
        "      <td>   0.825635</td>\n",
        "      <td>   0.264388</td>\n",
        "      <td>   0.389560</td>\n",
        "      <td>   1.648155</td>\n",
        "      <td>   1.431226</td>\n",
        "      <td>   0.168865</td>\n",
        "      <td>   0.293586</td>\n",
        "      <td>   0.007298</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>   3</td>\n",
        "      <td>   7.000000</td>\n",
        "      <td>   6.100000</td>\n",
        "      <td>   0.150000</td>\n",
        "      <td>   1.700000</td>\n",
        "      <td>  73.400000</td>\n",
        "      <td>   5.100000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.010000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "              region        area    palmitic  palmitoleic     stearic       oleic    linoleic   linolenic   arachidic  eicosenoic\n",
        "region                                                                                                                           \n",
        "1      count     323  323.000000  323.000000   323.000000  323.000000  323.000000  323.000000  323.000000  323.000000  323.000000\n",
        "       mean        1    2.783282   13.322879     1.548019    2.287740   71.000093   10.334985    0.380650    0.631176    0.273220\n",
        "       std         0    0.741054    1.529349     0.507237    0.398709    3.451431    2.106730    0.079727    0.111644    0.083915\n",
        "       min         1    1.000000    8.750000     0.350000    1.520000   63.000000    4.480000    0.200000    0.320000    0.100000\n",
        "       25%         1    2.500000   12.680000     1.215000    2.015000   68.830000    8.555000    0.320000    0.560000    0.220000\n",
        "       50%         1    3.000000   13.460000     1.630000    2.230000   70.300000   10.900000    0.370000    0.620000    0.270000\n",
        "       75%         1    3.000000   14.190000     1.850000    2.495000   72.835000   12.025000    0.440000    0.690000    0.320000\n",
        "       max         1    4.000000   17.530000     2.800000    3.750000   81.130000   14.620000    0.740000    1.020000    0.580000\n",
        "2      count      98   98.000000   98.000000    98.000000   98.000000   98.000000   98.000000   98.000000   98.000000   98.000000\n",
        "       mean        2    5.336735   11.113469     0.967449    2.261837   72.680204   11.965306    0.270918    0.731735    0.019388\n",
        "       std         0    0.475023    0.404111     0.138514    0.176363    1.418783    1.072336    0.053844    0.118826    0.007436\n",
        "       min         2    5.000000   10.300000     0.350000    1.990000   68.820000   10.570000    0.150000    0.450000    0.010000\n",
        "       25%         2    5.000000   10.852500     0.882500    2.120000   71.372500   11.122500    0.230000    0.660000    0.010000\n",
        "       50%         2    5.000000   11.075000     0.960000    2.220000   73.255000   11.465000    0.270000    0.720000    0.020000\n",
        "       75%         2    6.000000   11.372500     1.040000    2.395000   73.810000   13.065000    0.300000    0.810000    0.020000\n",
        "       max         2    6.000000   12.130000     1.350000    2.720000   74.390000   14.700000    0.430000    1.050000    0.030000\n",
        "3      count     151  151.000000  151.000000   151.000000  151.000000  151.000000  151.000000  151.000000  151.000000  151.000000\n",
        "       mean        3    8.006623   10.948013     0.837351    2.308013   77.930530    7.270331    0.217881    0.375762    0.019735\n",
        "       std         0    0.820542    0.825635     0.264388    0.389560    1.648155    1.431226    0.168865    0.293586    0.007298\n",
        "       min         3    7.000000    6.100000     0.150000    1.700000   73.400000    5.100000    0.000000    0.000000    0.010000"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "One might let pandas take care of concatenating the series obtained by running `std` on each dataframe back into a dataframe for us. Notice that the output dataframe is automatically indexed by region for us!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfbystd=df.groupby('region').std()\n",
      "dfbystd.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>area</th>\n",
        "      <th>palmitic</th>\n",
        "      <th>palmitoleic</th>\n",
        "      <th>stearic</th>\n",
        "      <th>oleic</th>\n",
        "      <th>linoleic</th>\n",
        "      <th>linolenic</th>\n",
        "      <th>arachidic</th>\n",
        "      <th>eicosenoic</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>region</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.741054</td>\n",
        "      <td> 1.529349</td>\n",
        "      <td> 0.507237</td>\n",
        "      <td> 0.398709</td>\n",
        "      <td> 3.451431</td>\n",
        "      <td> 2.106730</td>\n",
        "      <td> 0.079727</td>\n",
        "      <td> 0.111644</td>\n",
        "      <td> 0.083915</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.475023</td>\n",
        "      <td> 0.404111</td>\n",
        "      <td> 0.138514</td>\n",
        "      <td> 0.176363</td>\n",
        "      <td> 1.418783</td>\n",
        "      <td> 1.072336</td>\n",
        "      <td> 0.053844</td>\n",
        "      <td> 0.118826</td>\n",
        "      <td> 0.007436</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.820542</td>\n",
        "      <td> 0.825635</td>\n",
        "      <td> 0.264388</td>\n",
        "      <td> 0.389560</td>\n",
        "      <td> 1.648155</td>\n",
        "      <td> 1.431226</td>\n",
        "      <td> 0.168865</td>\n",
        "      <td> 0.293586</td>\n",
        "      <td> 0.007298</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "            area  palmitic  palmitoleic   stearic     oleic  linoleic  linolenic  arachidic  eicosenoic\n",
        "region                                                                                                 \n",
        "1       0.741054  1.529349     0.507237  0.398709  3.451431  2.106730   0.079727   0.111644    0.083915\n",
        "2       0.475023  0.404111     0.138514  0.176363  1.418783  1.072336   0.053844   0.118826    0.007436\n",
        "3       0.820542  0.825635     0.264388  0.389560  1.648155  1.431226   0.168865   0.293586    0.007298"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or one can use `aggregate` to pass an arbitrary function of to the sub-dataframe. The function is applied columnwise."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfbymean=region_groupby.aggregate(np.mean)\n",
      "dfbymean.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>area</th>\n",
        "      <th>palmitic</th>\n",
        "      <th>palmitoleic</th>\n",
        "      <th>stearic</th>\n",
        "      <th>oleic</th>\n",
        "      <th>linoleic</th>\n",
        "      <th>linolenic</th>\n",
        "      <th>arachidic</th>\n",
        "      <th>eicosenoic</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>region</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2.783282</td>\n",
        "      <td> 13.322879</td>\n",
        "      <td> 1.548019</td>\n",
        "      <td> 2.287740</td>\n",
        "      <td> 71.000093</td>\n",
        "      <td> 10.334985</td>\n",
        "      <td> 0.380650</td>\n",
        "      <td> 0.631176</td>\n",
        "      <td> 0.273220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 5.336735</td>\n",
        "      <td> 11.113469</td>\n",
        "      <td> 0.967449</td>\n",
        "      <td> 2.261837</td>\n",
        "      <td> 72.680204</td>\n",
        "      <td> 11.965306</td>\n",
        "      <td> 0.270918</td>\n",
        "      <td> 0.731735</td>\n",
        "      <td> 0.019388</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 8.006623</td>\n",
        "      <td> 10.948013</td>\n",
        "      <td> 0.837351</td>\n",
        "      <td> 2.308013</td>\n",
        "      <td> 77.930530</td>\n",
        "      <td>  7.270331</td>\n",
        "      <td> 0.217881</td>\n",
        "      <td> 0.375762</td>\n",
        "      <td> 0.019735</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "            area   palmitic  palmitoleic   stearic      oleic   linoleic  linolenic  arachidic  eicosenoic\n",
        "region                                                                                                    \n",
        "1       2.783282  13.322879     1.548019  2.287740  71.000093  10.334985   0.380650   0.631176    0.273220\n",
        "2       5.336735  11.113469     0.967449  2.261837  72.680204  11.965306   0.270918   0.731735    0.019388\n",
        "3       8.006623  10.948013     0.837351  2.308013  77.930530   7.270331   0.217881   0.375762    0.019735"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or one can use `apply` to pass an arbitrary function to the sub-dataframe. This one takes the dataframe as argument."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "region_groupby.apply(lambda f: f.mean())"
     ],
     "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>region</th>\n",
        "      <th>area</th>\n",
        "      <th>palmitic</th>\n",
        "      <th>palmitoleic</th>\n",
        "      <th>stearic</th>\n",
        "      <th>oleic</th>\n",
        "      <th>linoleic</th>\n",
        "      <th>linolenic</th>\n",
        "      <th>arachidic</th>\n",
        "      <th>eicosenoic</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>region</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1</td>\n",
        "      <td> 2.783282</td>\n",
        "      <td> 13.322879</td>\n",
        "      <td> 1.548019</td>\n",
        "      <td> 2.287740</td>\n",
        "      <td> 71.000093</td>\n",
        "      <td> 10.334985</td>\n",
        "      <td> 0.380650</td>\n",
        "      <td> 0.631176</td>\n",
        "      <td> 0.273220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 2</td>\n",
        "      <td> 5.336735</td>\n",
        "      <td> 11.113469</td>\n",
        "      <td> 0.967449</td>\n",
        "      <td> 2.261837</td>\n",
        "      <td> 72.680204</td>\n",
        "      <td> 11.965306</td>\n",
        "      <td> 0.270918</td>\n",
        "      <td> 0.731735</td>\n",
        "      <td> 0.019388</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 3</td>\n",
        "      <td> 8.006623</td>\n",
        "      <td> 10.948013</td>\n",
        "      <td> 0.837351</td>\n",
        "      <td> 2.308013</td>\n",
        "      <td> 77.930530</td>\n",
        "      <td>  7.270331</td>\n",
        "      <td> 0.217881</td>\n",
        "      <td> 0.375762</td>\n",
        "      <td> 0.019735</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "        region      area   palmitic  palmitoleic   stearic      oleic   linoleic  linolenic  arachidic  eicosenoic\n",
        "region                                                                                                            \n",
        "1            1  2.783282  13.322879     1.548019  2.287740  71.000093  10.334985   0.380650   0.631176    0.273220\n",
        "2            2  5.336735  11.113469     0.967449  2.261837  72.680204  11.965306   0.270918   0.731735    0.019388\n",
        "3            3  8.006623  10.948013     0.837351  2.308013  77.930530   7.270331   0.217881   0.375762    0.019735"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's rename the columns in `dfbymean` and `dfbystd`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "renamedict_std={k:k+\"_std\" for k in acidlist}\n",
      "renamedict_mean={k:k+\"_mean\" for k in acidlist}\n",
      "dfbystd.rename(inplace=True, columns=renamedict_std)\n",
      "dfbymean.rename(inplace=True, columns=renamedict_mean) \n",
      "dfbystd.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>area</th>\n",
        "      <th>palmitic_std</th>\n",
        "      <th>palmitoleic_std</th>\n",
        "      <th>stearic_std</th>\n",
        "      <th>oleic_std</th>\n",
        "      <th>linoleic_std</th>\n",
        "      <th>linolenic_std</th>\n",
        "      <th>arachidic_std</th>\n",
        "      <th>eicosenoic_std</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>region</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.741054</td>\n",
        "      <td> 1.529349</td>\n",
        "      <td> 0.507237</td>\n",
        "      <td> 0.398709</td>\n",
        "      <td> 3.451431</td>\n",
        "      <td> 2.106730</td>\n",
        "      <td> 0.079727</td>\n",
        "      <td> 0.111644</td>\n",
        "      <td> 0.083915</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.475023</td>\n",
        "      <td> 0.404111</td>\n",
        "      <td> 0.138514</td>\n",
        "      <td> 0.176363</td>\n",
        "      <td> 1.418783</td>\n",
        "      <td> 1.072336</td>\n",
        "      <td> 0.053844</td>\n",
        "      <td> 0.118826</td>\n",
        "      <td> 0.007436</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.820542</td>\n",
        "      <td> 0.825635</td>\n",
        "      <td> 0.264388</td>\n",
        "      <td> 0.389560</td>\n",
        "      <td> 1.648155</td>\n",
        "      <td> 1.431226</td>\n",
        "      <td> 0.168865</td>\n",
        "      <td> 0.293586</td>\n",
        "      <td> 0.007298</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "            area  palmitic_std  palmitoleic_std  stearic_std  oleic_std  linoleic_std  linolenic_std  arachidic_std  eicosenoic_std\n",
        "region                                                                                                                             \n",
        "1       0.741054      1.529349         0.507237     0.398709   3.451431      2.106730       0.079727       0.111644        0.083915\n",
        "2       0.475023      0.404111         0.138514     0.176363   1.418783      1.072336       0.053844       0.118826        0.007436\n",
        "3       0.820542      0.825635         0.264388     0.389560   1.648155      1.431226       0.168865       0.293586        0.007298"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pandas can do general merges. When we do that along an index, it's called a `join` (<a href=\"http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.join.html\">documentation</a>). Here we make two sub-dataframes and join them on the common region index."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfpalmiticmean = dfbymean[['palmitic_mean']] \n",
      "dfpalmiticstd = dfbystd[['palmitic_std']] \n",
      "\n",
      "newdfbyregion=dfpalmiticmean.join(dfpalmiticstd)\n",
      "newdfbyregion.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>palmitic_mean</th>\n",
        "      <th>palmitic_std</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>region</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 13.322879</td>\n",
        "      <td> 1.529349</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 11.113469</td>\n",
        "      <td> 0.404111</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 10.948013</td>\n",
        "      <td> 0.825635</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "        palmitic_mean  palmitic_std\n",
        "region                             \n",
        "1           13.322879      1.529349\n",
        "2           11.113469      0.404111\n",
        "3           10.948013      0.825635"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rkeys=[1,2,3]\n",
      "rvals=['South','Sardinia','North']\n",
      "rmap={e[0]:e[1] for e in zip(rkeys,rvals)}\n",
      "rmap"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "{1: 'South', 2: 'Sardinia', 3: 'North'}"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's get a dataframe with just the acids."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mdf2=df.groupby('region').aggregate(np.mean)\n",
      "mdf2=mdf2[acidlist]\n",
      "mdf2.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>palmitic</th>\n",
        "      <th>palmitoleic</th>\n",
        "      <th>stearic</th>\n",
        "      <th>oleic</th>\n",
        "      <th>linoleic</th>\n",
        "      <th>linolenic</th>\n",
        "      <th>arachidic</th>\n",
        "      <th>eicosenoic</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>region</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 13.322879</td>\n",
        "      <td> 1.548019</td>\n",
        "      <td> 2.287740</td>\n",
        "      <td> 71.000093</td>\n",
        "      <td> 10.334985</td>\n",
        "      <td> 0.380650</td>\n",
        "      <td> 0.631176</td>\n",
        "      <td> 0.273220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 11.113469</td>\n",
        "      <td> 0.967449</td>\n",
        "      <td> 2.261837</td>\n",
        "      <td> 72.680204</td>\n",
        "      <td> 11.965306</td>\n",
        "      <td> 0.270918</td>\n",
        "      <td> 0.731735</td>\n",
        "      <td> 0.019388</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 10.948013</td>\n",
        "      <td> 0.837351</td>\n",
        "      <td> 2.308013</td>\n",
        "      <td> 77.930530</td>\n",
        "      <td>  7.270331</td>\n",
        "      <td> 0.217881</td>\n",
        "      <td> 0.375762</td>\n",
        "      <td> 0.019735</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "         palmitic  palmitoleic   stearic      oleic   linoleic  linolenic  arachidic  eicosenoic\n",
        "region                                                                                          \n",
        "1       13.322879     1.548019  2.287740  71.000093  10.334985   0.380650   0.631176    0.273220\n",
        "2       11.113469     0.967449  2.261837  72.680204  11.965306   0.270918   0.731735    0.019388\n",
        "3       10.948013     0.837351  2.308013  77.930530   7.270331   0.217881   0.375762    0.019735"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's make a bar plot of the relative mean percentages of the acids. In pandas this is as simple as:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax=mdf2.plot(kind='barh', stacked=True)\n",
      "ax.set_yticklabels(rvals)\n",
      "ax.set_xlim([0,100])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "(0, 100)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3BA3ckauq5crlu9+OlBQdq9Nb4fbIoigTAIq1w0d36fYOCaoWZfd3KV4T99tR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\nmii+Y7Hc1DAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109379650>"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.palmitic.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 73,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1095c12d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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85b9qPQJAkQR6mOrs3JUbv/7znNFyVq1HGdCev+7IxHP+o9ZjABRHoIexM1rOSvOEd9V6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\ndMgpHMqDhbgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1095c6cd0>"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can make a mask!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mask=(df.eicosenoic < 0.05)\n",
      "mask"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "0     False\n",
        "1     False\n",
        "2     False\n",
        "3     False\n",
        "4     False\n",
        "5     False\n",
        "6     False\n",
        "7     False\n",
        "8     False\n",
        "9     False\n",
        "10    False\n",
        "11    False\n",
        "12    False\n",
        "13    False\n",
        "14    False\n",
        "...\n",
        "557    True\n",
        "558    True\n",
        "559    True\n",
        "560    True\n",
        "561    True\n",
        "562    True\n",
        "563    True\n",
        "564    True\n",
        "565    True\n",
        "566    True\n",
        "567    True\n",
        "568    True\n",
        "569    True\n",
        "570    True\n",
        "571    True\n",
        "Name: eicosenoic, Length: 572, dtype: bool"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The first gives a count, the second is a shortcut to get a probability!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.sum(mask), np.mean(mask)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "(249, 0.43531468531468531)"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pandas supports conditional indexing: <a href=\"http://pandas.pydata.org/pandas-docs/dev/indexing.html#boolean-indexing\">documentation</a>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loweico=df[df.eicosenoic < 0.02]\n",
      "pd.crosstab(loweico.area, loweico.region)"
     ],
     "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>region</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>area</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 19</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> 11</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>  0</td>\n",
        "      <td> 17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>  0</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>  0</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "region   2   3\n",
        "area          \n",
        "5       19   0\n",
        "6       11   0\n",
        "7        0  17\n",
        "8        0  11\n",
        "9        0  14"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas.tools.plotting import scatter_matrix\n",
      "scatter_matrix(df[['linoleic','arachidic','eicosenoic']], alpha=0.3, figsize=(10, 10), diagonal='kde');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jKIrEyEjzvRCLNdD1NqLRKDt28EtjVnA4mkuhc3NRXnvNga5nufdeFUnyc++9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\nPJ4APp+Ls2evACJud4Jk0oHVWlpRNFaWZXI5B3q9ndnZGUQ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x7PaqMTc7G+eFFyqYTDHM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Wt4TsH/7hH7j77rtpbGzk7rvv5rHHHgMgFouxbt061q9fT2VlJZ/73OfyFZIQQgghRFHIW0L2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       "text": [
        "<matplotlib.figure.Figure at 0x10a8ec110>"
       ]
      }
     ],
     "prompt_number": 77
    }
   ],
   "metadata": {}
  }
 ]
}