home work 2- 2013 election prediction

gistfile1.txt

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   "cells": [
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Homework 2: Desperately Seeking Silver"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Due Thursday, Oct 3, 11:59 PM"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<center>\n",
      "<img src=\"http://www.scribewise.com/Portals/202647/images/photo.jpg\">\n",
      "</center>\n",
      "<br>\n",
      "\n",
      "In HW1, we explored how to make predictions (with uncertainties) about upcoming elections based on the Real Clear Politics poll. This assignment also focuses on election prediction, but we are going to implement and evaluate a number of more sophisticated forecasting techniques. \n",
      "\n",
      "We are going to focus on the 2012 Presidential election. Analysts like Nate Silver, Drew Linzer, and Sam Wang developed highly accurate models that correctly forecasted most or all of the election outcomes in each of the 50 states. We will explore how hard it is to recreate similarly successful models. The goals of this assignment are:\n",
      "\n",
      "1. To practice data manipulation with Pandas\n",
      "1. To develop intuition about the interplay of **precision**, **accuracy**, and **bias** when making predictions\n",
      "1. To better understand how election forecasts are constructed\n",
      "\n",
      "The data for our analysis will come from demographic and polling data. We will simulate building our model on October 2, 2012 -- approximately one month before the election. \n",
      "\n",
      "### Instructions\n",
      "\n",
      "The questions in this assignment are numbered. The questions are also usually italicised, to help you find them in the flow of this notebook. At some points you will be asked to write functions to carry out certain tasks. Its worth reading a little ahead to see how the function whose body you will fill in will be used.\n",
      "\n",
      "**This is a long homework. Please do not wait until the last minute to start it!**\n",
      "\n",
      "The data for this homework can be found at [this link](https://www.dropbox.com/s/vng5x10b837ahnc/hw2_data.zip). Download it to the same folder where you are running this notebook, and uncompress it. You should find the following files there:\n",
      "\n",
      "1. us-states.json\n",
      "2. electoral_votes.csv\n",
      "3. predictwise.csv\n",
      "4. g12.csv\n",
      "5. g08.csv\n",
      "6. 2008results.csv\n",
      "7. nat.csv\n",
      "8. p04.csv\n",
      "9. 2012results.csv\n",
      "10. cleaned-state_data2012.csv"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Setup and Plotting code"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "from collections import defaultdict\n",
      "import json\n",
      "\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import pandas as pd\n",
      "\n",
      "from matplotlib import rcParams\n",
      "import matplotlib.cm as cm\n",
      "import matplotlib as mpl\n",
      "\n",
      "#colorbrewer2 Dark2 qualitative color table\n",
      "dark2_colors = [(0.10588235294117647, 0.6196078431372549, 0.4666666666666667),\n",
      "                (0.8509803921568627, 0.37254901960784315, 0.00784313725490196),\n",
      "                (0.4588235294117647, 0.4392156862745098, 0.7019607843137254),\n",
      "                (0.9058823529411765, 0.1607843137254902, 0.5411764705882353),\n",
      "                (0.4, 0.6509803921568628, 0.11764705882352941),\n",
      "                (0.9019607843137255, 0.6705882352941176, 0.00784313725490196),\n",
      "                (0.6509803921568628, 0.4627450980392157, 0.11372549019607843)]\n",
      "\n",
      "rcParams['figure.figsize'] = (10, 6)\n",
      "rcParams['figure.dpi'] = 150\n",
      "rcParams['axes.color_cycle'] = dark2_colors\n",
      "rcParams['lines.linewidth'] = 2\n",
      "rcParams['axes.facecolor'] = 'white'\n",
      "rcParams['font.size'] = 14\n",
      "rcParams['patch.edgecolor'] = 'white'\n",
      "rcParams['patch.facecolor'] = dark2_colors[0]\n",
      "rcParams['font.family'] = 'StixGeneral'\n",
      "\n",
      "\n",
      "def remove_border(axes=None, top=False, right=False, left=True, bottom=True):\n",
      "    \"\"\"\n",
      "    Minimize chartjunk by stripping out unnecesasry plot borders and axis ticks\n",
      "    \n",
      "    The top/right/left/bottom keywords toggle whether the corresponding plot border is drawn\n",
      "    \"\"\"\n",
      "    ax = axes or plt.gca()\n",
      "    ax.spines['top'].set_visible(top)\n",
      "    ax.spines['right'].set_visible(right)\n",
      "    ax.spines['left'].set_visible(left)\n",
      "    ax.spines['bottom'].set_visible(bottom)\n",
      "    \n",
      "    #turn off all ticks\n",
      "    ax.yaxis.set_ticks_position('none')\n",
      "    ax.xaxis.set_ticks_position('none')\n",
      "    \n",
      "    #now re-enable visibles\n",
      "    if top:\n",
      "        ax.xaxis.tick_top()\n",
      "    if bottom:\n",
      "        ax.xaxis.tick_bottom()\n",
      "    if left:\n",
      "        ax.yaxis.tick_left()\n",
      "    if right:\n",
      "        ax.yaxis.tick_right()\n",
      "        \n",
      "pd.set_option('display.width', 500)\n",
      "pd.set_option('display.max_columns', 100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "C:\\Users\\Manu\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\pandas\\io\\excel.py:626: UserWarning: Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.\n",
        "  .format(openpyxl_compat.start_ver, openpyxl_compat.stop_ver))\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#this mapping between states and abbreviations will come in handy later\n",
      "states_abbrev = {\n",
      "        'AK': 'Alaska',\n",
      "        'AL': 'Alabama',\n",
      "        'AR': 'Arkansas',\n",
      "        'AS': 'American Samoa',\n",
      "        'AZ': 'Arizona',\n",
      "        'CA': 'California',\n",
      "        'CO': 'Colorado',\n",
      "        'CT': 'Connecticut',\n",
      "        'DC': 'District of Columbia',\n",
      "        'DE': 'Delaware',\n",
      "        'FL': 'Florida',\n",
      "        'GA': 'Georgia',\n",
      "        'GU': 'Guam',\n",
      "        'HI': 'Hawaii',\n",
      "        'IA': 'Iowa',\n",
      "        'ID': 'Idaho',\n",
      "        'IL': 'Illinois',\n",
      "        'IN': 'Indiana',\n",
      "        'KS': 'Kansas',\n",
      "        'KY': 'Kentucky',\n",
      "        'LA': 'Louisiana',\n",
      "        'MA': 'Massachusetts',\n",
      "        'MD': 'Maryland',\n",
      "        'ME': 'Maine',\n",
      "        'MI': 'Michigan',\n",
      "        'MN': 'Minnesota',\n",
      "        'MO': 'Missouri',\n",
      "        'MP': 'Northern Mariana Islands',\n",
      "        'MS': 'Mississippi',\n",
      "        'MT': 'Montana',\n",
      "        'NA': 'National',\n",
      "        'NC': 'North Carolina',\n",
      "        'ND': 'North Dakota',\n",
      "        'NE': 'Nebraska',\n",
      "        'NH': 'New Hampshire',\n",
      "        'NJ': 'New Jersey',\n",
      "        'NM': 'New Mexico',\n",
      "        'NV': 'Nevada',\n",
      "        'NY': 'New York',\n",
      "        'OH': 'Ohio',\n",
      "        'OK': 'Oklahoma',\n",
      "        'OR': 'Oregon',\n",
      "        'PA': 'Pennsylvania',\n",
      "        'PR': 'Puerto Rico',\n",
      "        'RI': 'Rhode Island',\n",
      "        'SC': 'South Carolina',\n",
      "        'SD': 'South Dakota',\n",
      "        'TN': 'Tennessee',\n",
      "        'TX': 'Texas',\n",
      "        'UT': 'Utah',\n",
      "        'VA': 'Virginia',\n",
      "        'VI': 'Virgin Islands',\n",
      "        'VT': 'Vermont',\n",
      "        'WA': 'Washington',\n",
      "        'WI': 'Wisconsin',\n",
      "        'WV': 'West Virginia',\n",
      "        'WY': 'Wyoming'\n",
      "}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is some code to plot [State Chloropleth](http://en.wikipedia.org/wiki/Choropleth_map) maps in matplotlib. `make_map` is the function you will use."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#adapted from  https://github.com/dataiap/dataiap/blob/master/resources/util/map_util.py\n",
      "\n",
      "#load in state geometry\n",
      "state2poly = defaultdict(list)\n",
      "\n",
      "data = json.load(file(\"us-states.json\"))\n",
      "for f in data['features']:\n",
      "    state = states_abbrev[f['id']]\n",
      "    geo = f['geometry']\n",
      "    if geo['type'] == 'Polygon':\n",
      "        for coords in geo['coordinates']:\n",
      "            state2poly[state].append(coords)\n",
      "    elif geo['type'] == 'MultiPolygon':\n",
      "        for polygon in geo['coordinates']:\n",
      "            state2poly[state].extend(polygon)\n",
      "\n",
      "            \n",
      "def draw_state(plot, stateid, **kwargs):\n",
      "    \"\"\"\n",
      "    draw_state(plot, stateid, color=..., **kwargs)\n",
      "    \n",
      "    Automatically draws a filled shape representing the state in\n",
      "    subplot.\n",
      "    The color keyword argument specifies the fill color.  It accepts keyword\n",
      "    arguments that plot() accepts\n",
      "    \"\"\"\n",
      "    for polygon in state2poly[stateid]:\n",
      "        xs, ys = zip(*polygon)\n",
      "        plot.fill(xs, ys, **kwargs)\n",
      "\n",
      "        \n",
      "def make_map(states, label):\n",
      "    \"\"\"\n",
      "    Draw a cloropleth map, that maps data onto the United States\n",
      "    \n",
      "    Inputs\n",
      "    -------\n",
      "    states : Column of a DataFrame\n",
      "        The value for each state, to display on a map\n",
      "    label : str\n",
      "        Label of the color bar\n",
      "\n",
      "    Returns\n",
      "    --------\n",
      "    The map\n",
      "    \"\"\"\n",
      "    fig = plt.figure(figsize=(12, 9))\n",
      "    ax = plt.gca()\n",
      "\n",
      "    if states.max() < 2: # colormap for election probabilities \n",
      "        cmap = cm.RdBu\n",
      "        vmin, vmax = 0, 1\n",
      "    else:  # colormap for electoral votes\n",
      "        cmap = cm.binary\n",
      "        vmin, vmax = 0, states.max()\n",
      "    norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)\n",
      "    \n",
      "    skip = set(['National', 'District of Columbia', 'Guam', 'Puerto Rico',\n",
      "                'Virgin Islands', 'American Samoa', 'Northern Mariana Islands'])\n",
      "    for state in states_abbrev.values():\n",
      "        if state in skip:\n",
      "            continue\n",
      "        color = cmap(norm(states.ix[state]))\n",
      "        draw_state(ax, state, color = color, ec='k')\n",
      "\n",
      "    #add an inset colorbar\n",
      "    ax1 = fig.add_axes([0.45, 0.70, 0.4, 0.02])    \n",
      "    cb1=mpl.colorbar.ColorbarBase(ax1, cmap=cmap,\n",
      "                                  norm=norm,\n",
      "                                  orientation='horizontal')\n",
      "    ax1.set_title(label)\n",
      "    remove_border(ax, left=False, bottom=False)\n",
      "    ax.set_xticks([])\n",
      "    ax.set_yticks([])\n",
      "    ax.set_xlim(-180, -60)\n",
      "    ax.set_ylim(15, 75)\n",
      "    return ax"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Today: the day we make the prediction"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We are pretending to build our model 1 month before the election\n",
      "import datetime\n",
      "today = datetime.datetime(2012, 10, 2)\n",
      "today"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "datetime.datetime(2012, 10, 2, 0, 0)"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Background: The Electoral College\n",
      "\n",
      "US Presidential elections revolve around the <a href=\"http://en.wikipedia.org/wiki/Electoral_College_(United_States)\"> Electoral College </a>. In this system, each state receives a number of Electoral College votes depending on it's population -- there are 538 votes in total. In most states, all of the electoral college votes are awarded to the presidential candidate who recieves the most votes in that state. A candidate needs 269 votes to be elected President. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Thus, to calculate the total number of votes a candidate gets in the election, we add the electoral college votes in the states that he or she wins. (This is not entirely true, with Nebraska and Maine splitting their electoral college votes, but, for the purposes of this homework, we shall assume that the winner of the most votes in Maine and Nebraska gets ALL the electoral college votes there.) "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is the electoral vote breakdown by state:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*As a matter of convention, we will index all our dataframes by the state name*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd C:\\Users\\Manu\\Desktop\\data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "C:\\Users\\Manu\\Desktop\\data\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "electoral_votes = pd.read_csv(\"electoral_votes.csv\").set_index('State')\n",
      "electoral_votes.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>Votes</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>State</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td> 55</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Texas</th>\n",
        "      <td> 38</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New York</th>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Florida</th>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Illinois</th>\n",
        "      <td> 20</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "            Votes\n",
        "State            \n",
        "California     55\n",
        "Texas          38\n",
        "New York       29\n",
        "Florida        29\n",
        "Illinois       20"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To illustrate the use of `make_map` we plot the Electoral College"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "make_map(electoral_votes.Votes, \"Electoral Votes\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Hj9OrVy9atGgBPF4ZYOfOnSxYsIDKlSvj7OxMixYtSEhIYPr06Vy9epU///yT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\nRERERERERERERERERERERERERFTX/R+i+TNt5KZ0KgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8244e48>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Question 1: Simulating elections"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### The PredictWise Baseline"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will start by examining a successful forecast that [PredictWise](http://www.predictwise.com/results/2012/president) made on October 2, 2012. This will give us a point of comparison for our own forecast models.\n",
      "\n",
      "PredictWise aggregated polling data and, for each state, estimated the probability that the Obama or Romney would win. Here are those estimated probabilities:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predictwise = pd.read_csv('predictwise.csv').set_index('States')\n",
      "predictwise.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>Obama</th>\n",
        "      <th>Romney</th>\n",
        "      <th>Votes</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>States</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Alabama</th>\n",
        "      <td> 0.000</td>\n",
        "      <td> 1.000</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alaska</th>\n",
        "      <td> 0.000</td>\n",
        "      <td> 1.000</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arizona</th>\n",
        "      <td> 0.062</td>\n",
        "      <td> 0.938</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arkansas</th>\n",
        "      <td> 0.000</td>\n",
        "      <td> 1.000</td>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td> 1.000</td>\n",
        "      <td> 0.000</td>\n",
        "      <td> 55</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "            Obama  Romney  Votes\n",
        "States                          \n",
        "Alabama     0.000   1.000      9\n",
        "Alaska      0.000   1.000      3\n",
        "Arizona     0.062   0.938     11\n",
        "Arkansas    0.000   1.000      6\n",
        "California  1.000   0.000     55"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**1.1** Each row is the probability predicted by Predictwise that Romney or Obama would win a state. The votes column lists the number of electoral college votes in that state. *Use `make_map` to plot a map of the probability that Obama wins each state, according to this prediction*."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "make_map(predictwise.Obama, \"P(Obama): PredictWise\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1d3d9e8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VIiUlhV69enn29+nTB7vd7umucSNr166lVq1abNu2jREjRjBmzJgb3qsQt0qS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\nZ6/xGMnZjR5ftiezNA97/ae4AxVcqLocsIe1lAhvkccd3p54MfjE3M0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0tIwhWBlDak0D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\n8JS5i5Ou+CZfbljH9OnT49rfk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       "text": [
        "<matplotlib.figure.Figure at 0x8eddf98>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Later on in this homework we will explore some approaches to estimating probabilities like these and quatifying our uncertainty about them. But for the time being, we will focus on how to make a prediction assuming these probabilities are known.\n",
      "\n",
      "Even when we assume the win probabilities in each state are known, there is still uncertainty left in the election. We will use simulations from a simple probabilistic model to characterize this uncertainty. From these simulations, we will be able to make a prediction about the expected outcome of the election, and make a statement about how sure we are about it.\n",
      "\n",
      "**1.2** We will assume that the outcome in each state is the result of an independent coin flip whose probability of coming up Obama is given by a Dataframe of state-wise win probabilities. *Write a function that uses this **predictive model** to simulate the outcome of the election given a Dataframe of probabilities*."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Function\n",
      "--------\n",
      "simulate_election\n",
      "\n",
      "Inputs\n",
      "------\n",
      "model : DataFrame\n",
      "    A DataFrame summarizing an election forecast. The dataframe has 51 rows -- one for each state and DC\n",
      "    It has the following columns:\n",
      "       Obama : Forecasted probability that Obama wins the state\n",
      "       Votes : Electoral votes for the state\n",
      "    The DataFrame is indexed by state (i.e., model.index is an array of state names)\n",
      "    \n",
      "n_sim : int\n",
      "   Number of simulations to run\n",
      "   \n",
      "Returns\n",
      "-------\n",
      "results : Numpy array with n_sim elements\n",
      "   Each element stores the number of electoral college votes Obama wins in each simulation.   \n",
      "\"\"\"\n",
      "\n",
      "#Your code here\n",
      "def simulate_election(model, nsim):\n",
      "    sim = np.random.uniform(size =(51, nsim))\n",
      "    greter = (sim < model.Obama.reshape(-1,1)).astype(float) * model.Votes.reshape(-1,1)\n",
      "    return greter.sum(axis =0)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following cells takes the necessary DataFrame for the Predictwise data, and runs 10000 simulations. We use the results to compute the probability, according to this predictive model, that Obama wins the election (i.e., the probability that he receives 269 or more electoral college votes)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result = simulate_election(predictwise, 10000)\n",
      "result"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "array([ 314.,  303.,  313., ...,  316.,  302.,  284.])"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#compute the probability of an Obama win, given this simulation\n",
      "#Your code here\n",
      "(result>=269).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "0.99509999999999998"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**1.3** **Now, write a function called `plot_simulation` to visualize the simulation**. This function should:\n",
      "\n",
      "* Build a histogram from the result of simulate_election\n",
      "* Overplot the \"victory threshold\" of 269 votes as a vertical black line (hint: use axvline)\n",
      "* Overplot the result (Obama winning 332 votes) as a vertical red line\n",
      "* Compute the number of votes at the 5th and 95th quantiles, and display the difference (this is an estimate of the outcome's uncertainty)\n",
      "* Display the probability of an Obama victory    \n",
      "    "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Function\n",
      "--------\n",
      "plot_simulation\n",
      "\n",
      "Inputs\n",
      "------\n",
      "simulation: Numpy array with n_sim (see simulate_election) elements\n",
      "   Each element stores the number of electoral college votes Obama wins in each simulation.\n",
      "    \n",
      "Returns\n",
      "-------\n",
      "Nothing \n",
      "\"\"\"\n",
      "\n",
      "#your code here\n",
      "\n",
      "def plot_simulation(simulation):\n",
      "    plt.hist(simulation, bins = np.arange(200, 538, 1), normed = True, label = \"simulations\")\n",
      "    plt.axvline(269,0, .5, label = \"Victory Threshold\", c ='black')\n",
      "    plt.axvline(332,0, .5, label = \" Actual Outcome\", c = 'red')\n",
      "    five_p = np.percentile(simulation, 5.)\n",
      "    ninety_p = np.percentile(simulation, 95.)\n",
      "    spr = int(ninety_p - five_p)\n",
      "    victory = (simulation>=269).mean() * 100\n",
      "    plt.title(\"Chances of Obama Victory: %0.2f%%, Spread: %d votes\" %(victory, spr))\n",
      "    plt.xlabel(\"Obama electoral College Votes\")\n",
      "    plt.ylabel(\"Probability\")\n",
      "    plt.legend(loc = \"upper left\", frameon = False)\n",
      "    remove_border()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pg = np.percentile(result, 5.0)\n",
      "print pg\n",
      "pk = np.percentile(result, 95.0)\n",
      "pk"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "287.0\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "347.0"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_simulation(result)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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06VJeeeUVfv/9d6pWrUr9+vVp1aoVRYoU4c033+Sll14iJSWF0qVL06BBAxo2\nbMjFF198vh8NkXxnwi9f+x4PaHJjHpZEJHpGjhw58nz3UVA75ERqFLBEyWOPPcagQYOidv9VEcmf\nqs0a7Hu8u/vYPCyJSPR4ItBBWk2ukueOHDnCgQMHFMyJiIjkUPZT74vkEu9cd1u3biUCtc0iIiKF\nlmroJM/s2rWLzz//nLvuuov27dvndXFERERcSzV0kmeWLVuW10UQEREpEFRDJyIiIuJyCuhERERE\nXE4BnYiIiIjLKaATERERcTkFdCIiIiIup4BORERExOUU0ImIiIi4nAK6Qmjjxo30798/r4sBwIoV\nK+jTpw81a9bMNu+5c+eYOHEiN9xwA127diUhIYEbb7yR999/PwolFRERyb80sXAhNH36dN555x1e\neOEFSpQoEfb2R48eJTY2llKlSp13Wdq2bcvSpUtJSkrKMt/Jkyfp2LEjKSkpLFq0iIsuugiAzZs3\n07FjR5YuXcqbb74Z1nMnJSVRvXr1HJddREQkv1ANXSFz5swZfvjhB44ePcqHH36Yo3307duXI0eO\nRKQ8Ho8npKDqqaeeYuXKlXzwwQe+YA6gXr16zJ49m7feeos33ngj5Oc9c+YMjz76aI7KLCIikt8o\noCtk5s+fz3PPPUfjxo2ZMmVK2Nu//fbbvPPOO1iWlQulC27fvn3MnDmTG264IWjwd/3111O7dm1G\njx5NampqSPvs3bs3mzdvjnRRRURE8oQCukJm8eLFdO7cmccff5w1a9awZs2aDHksy2Lq1KkMHz6c\ngQMH0q5dOzZu3MiJEydYsmQJAC+88AITJ05k69attGzZ0tcHbt++fQwfPpyYmBi+++473/7GjBnD\nuHHjGDt2LJ06dWLv3r0hl3nZsmWkpqbSqlWrTPO0bt2a/fv38/PPP7N8+XLKlStH9+7dAdNn8M47\n7yQmxnzc//Of/7B582aOHDnCwIEDWbhwIQAnTpwgMTGR0aNHc//993P//fdz/Phx33O888479O7d\nm6FDh3LttdcyduxYX2D78ccfc8cddzB06FBeeeUV6tWrR7ly5XjvvffYtm0b//znPylfvjw333wz\nJ0+e9O1z/fr19O3bl65du1K/fn1efvnlkI+LiIhIQWdFFOT+XxRs3rzZGjVqlGVZlnXy5EmrbNmy\n1kMPPZQh35AhQ6xJkyb5llu3bm21adPGsizLWrZsmeXxeKykpCTf+uHDh1s1a9b0Le/YscPyeDzW\nt99+a1n4gB3FAAAgAElEQVSWZX3yySdW0aJFfetvu+02q0ePHr7lWbNmWR6PJ9Nyjx071vJ4PNb0\n6dMzzTN48GDL4/FY8+fPtyzLsq677jqre/fuvvVvvfWW33OMGDHCqlGjhm85NTXVuu6666x169ZZ\nlmVZx48ft4oXL249++yzlmVZ1owZM6wWLVr48v/xxx9WmTJlrGeeecayLMs6c+aMVbduXatx48a+\nfQwePNgqV66cNWHCBCstLc06cOCAVbp0aWvGjBmWZVnW0aNHrc6dO/v2+dFHH1kej8datGhRpq9T\npKCr+tYg359IYRGJwEeDIgqRt956i759+wJQsmRJHnjgAWbOnMkrr7xC2bJlAdi/fz8TJ07k2LFj\nvu1mzJjBwYMHM92vx+PJsgn2yiuvZPjw4b7lkiVLsmPHjpDL7fF4ALJ8jrS0NL883m0C95GZTz75\nBIAmTZoAUKpUKT799FNq1aoFQGJiIk8++aQvf6VKlXjkkUeYNGkSzz77LKVLl6Zy5crUrFnTt4/4\n+HjGjRtHQkICHo+HihUr0rBhQzZu3AjAlClTOHToEEOGDAHg7NmztG3bln379oVwVERERNIpoAtF\nFPuL5ZZz586xZMkStmzZ4ks7fvw4p0+fZvbs2fTr1w+AVatWUaZMGeLi4nz5GjRocF7PXbNmTYYM\nGcL777/PgQMH2LdvX7YBVuD2AAcOHMg0jzfgrFGjRo7KuGLFCqpUqeKXdvPNNwMmyN27dy8XXHCB\n3/qrrrqKc+fOsXHjRlq1apUh4CxWrFiG5ylatKivGffnn3+mXbt2jBkzJkdlFhER8VIfukJiwYIF\njBo1igULFvj+vvnmG6688kqmTp3qy5ecnMzBgwc5e/ZsxJ77wIEDtGzZkvLly9OvXz9q1KgR1qCK\n+Ph4ihYtysqVKzPNs2bNGipWrOirHQsnYATzujObOiU2NhaA3bt3+6VXqFABwC/4DYX3tZ86dYrt\n27dnWH/u3Lmw9iciIpLfA7pL8roABcXcuXPp1KlThvQHH3yQLVu2sHTpUgDq169PWloa06ZN88u3\ncOFC0tLSgjZ/ejweX5MnkGGk6bBhw0hOTqZDhw5B12enYsWKPPLII/zv//4vO3fuzLB+zZo1/Prr\nrwwePNgXfHk8Hr/nCXzOwGbiBg0asHr1an755Re/fJ988gkVKlSgVq1afP/9937r9u7dS6lSpWjU\nqJFvn9lx5qlTpw6ff/65XxNrSkoKEydOzHY/IiIiTtEO6KoCbwCPAnOAhpnkqwG8B3wUZF1PYDgw\nAhgd+SIWPL/88gupqalBa5K8Qd6kSZMAaNiwITfffDNPP/00zz33HF988QWJiYkcO3aMmJgYypUr\nB8CmTZvYunUrf/31FzVr1mTPnj2sWLGCgwcPMnv2bABfjdfevXvZtWsXf/zxB1u3bmXNmjXs37/f\n10yakpIC4BcUBho/fjxt2rThnnvu8Wt6TUpKolu3btx3331+d7+oUaMG3377LXv37mXz5s0sWrTI\nr0zlypVj//79HDt2jHXr1vHAAw9Qvnx5OnTowBtvvMGiRYt4+OGHqVOnDgCjR4/mhx9+4McffwRM\nQPvBBx8wbNgwX9NqSkqKX5DofT3Jycm+tJSUFF96r169OH36NB06dGDhwoV8/fXX3Hvvvb7AV0RE\nJD/yAGuBG+3l+sB2IDZI3suA14DvAtJvB35wLM8FegTZPq8GquQ7P/30k/W3v/3Nql27tvXFF1/4\nrTt8+LA1bNgwy+PxWDExMdaQIUOslJQU6+DBg1aXLl2skiVLWpdffnmG0aUdOnSwKlSoYI0fP96y\nLDPC8/bbb7cuuOACq3379tavv/5qNWnSxHrllVesP//80/rmm2+sKlWqWBUrVrRGjRplzZ071ypT\npozVt29f66effrLatm1rxcTEWOPHj7cOHz6c6Ws5d+6cNXHiRCs+Pt5KSEiw7rzzTuumm26y3n77\n7Qx5t27dal111VXWhRdeaD388MPWggULrFtvvdWaM2eOlZqaau3Zs8eqVauWVbt2bWvJkiWWZVnW\nmjVrrGuuucYqUaKE1bx5c+v777/32+f7779vtW7d2ho4cKDVp08fa8qUKb51CxYssMqWLWs1bNjQ\n+uGHH6xdu3ZZjz76qBUTE2P179/f2rdvn/XJJ59YZcuWterWresbAfzxxx9bderUsUqUKGG1aNHC\nly5SWGmUqxRG5xVd2cLraHR+bgI+BUoDKXbaf4GhwMdB8icCNwDXOtJ+ABYD3l7k/7S3bxSwbaSO\nj4iIRFG1WYN9j3d3H5uHJRGJHk+4Hb+DiGaTaxtMjVyKI20L0D7E7YsCzQDn9P5bMc22FSJRQBER\nERE3imZAVwk4HpB2DKgW4vblgDh7G6+j9v9Q9yEiIiJS4EQzoEsBkgPSwnl+b82ecx/e7aPZdCwi\nIiKSr0RzYuG9QNuAtLLAzhC3P4QJ5soEbA+wJzBzYmKi73F8fDzx8fEhPo2IiIiIu0QzoFsGDA5I\nqwvMDnF7C1gO1Hak1QM2ARluIeAM6EREREQKsmg2ua4CkoB29nI9oCTwOWbUauBI1WBlexPo7Fju\nBLwV2WKKiIiIuEs0a+gszDxywzFz0F0D/B04BdwCrAM22HmvA27DDHa4ExP0JQPzgOqYAPA0JkCc\nELVXICIiIpIPFdTBBJqHTkTEhTQPnRRGbpuHTkRERERygQI6EREREZdTQCciIiLicgrogkhOS83r\nIuSLMoiIiIg7RHOUq2vExcT6dczNC5HuDLxnzx4aN27Ml19+SdOmTSO6b68TJ04wc+ZMvvjiC9q3\nb8/gwTk7hpMmTeLtt99m7dq1ES6hiIhIwaQaukKiVKlStGrVijJlymSf+Tyeo0ePHqxevZpz586F\nvF1SUpLfcs2aNWnWrFmkiyciIlJgKaArJEqXLs3ChQu54oorcvV5SpUqRbly5ULOb1kW3bt390u7\n7bbbmDZtWqSLJiIiUmApoCtk0tLS8roIfkaPHs3y5cszpKemqg+hiIhIqBTQFUBvv/02L7/8MhMm\nTOCSSy5h1apVTJ8+nZYtW/Luu+8CsGbNGnr27EmHDh346quvaN68OaVLl6Zv376cPHmSp556iurV\nq1O3bl02bdoEwLp167jiiito187cvW3Hjh08+uijxMTE8Pvvv2dano0bN/LYY48xffp0/vGPfzBl\nyhQAdu3axapVqwAYOHAgc+bMYdu2bQwcOJBq1ar57WP16tX07NmTESNG0LFjRx5++GGOHTsGwMqV\nK+nWrRsPPPAA8+fPp06dOlx88cW8//77vu23b9/O008/zcyZM7npppvo379/hI62iIhI3lNAV8Cc\nOXOGQYMG8fTTTzNgwACmTp1KTEwMbdq04aeffvLla9KkCWlpaaxZs4aTJ0+yevVq5s2bx2uvvcYz\nzzxDYmIi27dvp2LFijz//PMAXH311bRp0wbvhNY1a9bk3nvvzbZM999/P5deeik9e/Zk6NChPPHE\nE+zatYtLL72Uu+++G4CXXnqJbt26Ub58eYoXL87+/ft922/YsIHOnTvz/PPPM3LkSBYuXMimTZu4\n5ZZbsCyLFi1acOjQIVasWIHH4+G3337j3nvv5YknnvDtIzExkeuvv54ePXrw2Wefcckll0TkeIuI\niOQHCugKmOTkZA4dOsTkyZMB6Ny5M3Xq1KFhw4Z++WJjY6lWrRqlS5fmzjvvJCYmhvj4eABatGhB\nqVKliI2N5brrruPXX3/1befxeAj3tmo9evSgU6dOAJQsWZK0tLQMAyG8ypYtS61atfzSxo0bR7Nm\nzahYsSIARYoUYejQoaxevZovv/ySmJgYKlSowOWXX05CQgJFihTh73//O0eOHPEFhufOnWPSpEmc\nOHGCEiVK8NBDD4X1GkRERPIzBXQFTKlSpRg5ciRPPPEEnTp1Ys+ePZQtWzakbYsVK5YhrWjRohw/\nfvy8ytSnTx9KlSrFyy+/zKeffgqE15dv7dq1XHDBBX5pV111FQA///yzL80ZaBYtWhSAs2fPAjBs\n2DB+/vln6tevz4IFC7j44otz9mJERETyIQV0BdCQIUOYP38+GzZs4Morr+THH388r/0F1siFew/h\nKVOm8OSTT9KnTx9fE2s4YmNj2bVrl19ahQoVAIiLiwtpHw0bNmTdunU0btyYhIQEnnrqqbDLISIi\nkl8poCtgDhw4wIYNG+jSpQubNm3iyiuv5OWXX47Y/j0ej98I1OxGo+7evZsnnniCXr16Ubx48Qw1\nc6EEh61atWLjxo1+NYV79+4FoHXr1iHt6+uvv6Z69eosWrSICRMmMHHiRI4ePZrtc4uIiLiBAroC\n5tSpU0ydOhWACy+8kISEBKpUqUJycjKA34S/gcGYN9jy5vXmcdbQ1axZk19++YXNmzeza9cu5s6d\nC5gRr17JycmkpKQAsH//ftLS0vjpp584e/Ys8+bNA8ydKw4fPuybs27z5s388ssvWJble37vPgYN\nGoTH4+H111/3Pcd7773Hrbfe6gvoUlJS/IJF7+v0vsaZM2dy8uRJAB588EFKly5NqVKlQjuoIiIi\n+Zxu/RVEclpqxG+9lZMyxMXE5mjbadOmUaRIERo0aMCmTZsYM2YM48ePB+CDDz6gefPmpKSksGTJ\nEvbt28e8efPo1KkTc+bMAWDu3Lm0aNGC5ORkFi9ezL59+3j33Xf5n//5Hx5//HGWLl1K06ZNueWW\nW+jfvz+bN29m06ZNNG/enOnTp/PHH3+wZMkSOnToQOvWrUlISGDChAmsWLGCyZMn89FHHzFq1Cga\nNmzIDTfcwNVXX81NN93E888/T2pqKh999BEej4cXX3yRvn37csUVV7B8+XKeeuopkpKSqFixImfO\nnGH+/PkArFq1ihUrVnDy5EkWLVpEs2bNmD59Oh6Ph6lTp5KYmMi+ffvo0KED9913H1u3buWjjz4i\nNjZnx1dERCS/Ca8zlHtY4Y7EFBGRvOe8j3Ze/7AWiRZPuJ3Tg1CTq4iIiIjLKaATERERcTkFdCIi\nIiIup4BORERExOUU0ImIiIi4nAI6EREREZdTQCciIiLicgroRERERFxOAZ2IiIiIyymgExEREXE5\nBXQiIiIiLqeATkRERMTlFNCJiIiIuJwCOhERERGXU0AnIiIi4nIK6ERERERcTgGdiIiIiMspoBMR\nERFxOQV0IiIiIi6ngE5ERETE5RTQiYiIiLicAjoRERERl1NAJyIiIuJyCuhEREREXE4BnYiIiIjL\nKaATERERcTkFdCIiIiIup4BORERExOWKRPn5qgLPAuuBVsB4YGOQfD2BSoAHU8Zhdrr38UHgMuAE\nMDp3iywiIiKSv0UzoPMAnwGDgK+Bb4FFQG0g1ZHvdqAb0MZengv0AGYCfYDjwOv2umXAUuCHXC67\niIiISL4VzSbXG4H6wHJ7eROQDNwRkO8ZYLFj+ROgn/34CuAix7ojQNlIF1RERETETaIZ0LUBtgMp\njrQtQHvHclGgGbDZkbYVaAhUwAR3T2KCw6sx5V+Se0UWERERyf+i2eRaCdNc6nQMqOZYLgfE2ele\nR+3/1TBNtcMwQdwa4Hr8m2tFRERECp1o1tClYJpYs3p+b+1dcpA8HvuvEmZgRS3gG6BkZIspIiIi\n4i7RrKHbC7QNSCsL7HQsH8IEc2UC8gDsAQYApYAhwIeYwRCDgBGBT5aYmOh7HB8fT3x8/HkUXURE\nRCT/imZAtwwYHJBWF5jtWLYwgyZqO9LqYQZQHMD0t1topycBkzDNrhk4AzoRERGRgiyaTa6rMEFY\nO3u5Hqa59HNgDNDITn8T6OzYrhPwlv34F+BKx7oSmL50IiIiIoVWNGvoLMwcc8Mx05dcA/wdOAXc\nAqwDNgDzgOqYIO80JgicYO9jNPAq8AJmcuHSwNCovQIRERGRfMiT1wXIJZZlWXldBhERCVO1Wek9\nc3Z3H5uHJRGJHo/Hc97xmO7lKiIiIuJyCuhEREREXE4BnYiIiIjLKaATERERcTkFdCIiIiIup4BO\nRERExOUU0ImIiIi4nAI6EREREZdTQCciIiLicgroRERERFxOAZ2IiIiIyymgExEREXE5BXQiIiIi\nLqeATkRERMTlFNCJiIiIuJwCOhERERGXU0AnIiIi4nIK6ERERERcTgGdiIiIiMspoBMRERFxOQV0\nIiIiIi4XTkBXJNdKISIiIiI5Fk5AtwBollsFEREREZGcCafW7QOgCfAwcACYD6zPjUKJiIiISOjC\nCejet//PAMoDk4CrgbnAO8D2yBZNREREREIRTpPrZcAFwOPAt0AH4BNgKXAf8LadR0RERESiKJwa\nusXApUASMBF4Fzhjr1sBPIAJ8K6OZAFFREREJGvhBHQngC7A15msvwyocN4lEhEREZGwhNPkehsZ\ng7mLgcr24xeABpEolIiIiIiELpyA7uEgaQeAyfZjC/jrvEskIiIiImEJpcn1UeAeoDpwU8C6CkDp\nSBdKREREREIXSkA3FUjFBHOLAI9j3UnMiFcRERERySOhDoqYgZmW5GyQdRdFrjgiIiIiEq7sAroa\nwB+YQK42ZhCEUyxwF9Ar4iUTERERkZBkF9CtAF7BzDvXAXgpk3wK6ERERETySHYBXVtgn/34A/vx\ne471MQQf/SoiIiIiUZJdQJfkeLwXE9Q5pWHuDiEiIiIieSSrgK4iUD+b7T3AHUD/iJVIRERERMKS\nVUB3EfANsAczaXAwMUAVFNCJiIiI5JmsArotwBOYeeiycl/kiiMiIiIi4cru1l/ZBXOgiYVFRERE\n8lR2gyJaA5uBw8D1QK2A9bFAJ+DOyBdNREREREKRXUD3LmYeuslAPfvxQcf6WOCS3CmaiIiIiIQi\nu4CuIXDafjwP2AV8EZAnIdKFEhEREZHQZRfQnXY8PowJ5i4HymAGTZwEPs6domXJA/wDuAxYAyzP\ngzKIiIiI5AvZBXROdYC5QGN7ORV4DRgEJIe4j6rAs8B6oBUwHtgYJF9PoBImcCsCDHOsKw38C1gC\nvBxG+UVEREQKpOxGuTrNwfSfa4OZo64KsA5IDHF7D/AZJhibCowFFmL64TndDnQDRgEjMYFkD0d5\nPwbWomBOREREBAgvoGuA6S+3EjiGCe7eBc6FuP2NmDtPLLeXN2Fq9u4IyPcMsNix/AnQz358D6Zm\nb3gY5RYREREp0MIJ6D4AKgdJD3WUaxtgO5DiSNsCtHcsFwWaYaZK8dqKGZxREeiOuafsOODfwJeY\nZlwRERGRQiurPnTXYAInrxjgO0zNmjPtRIjPVQk4HpB2DKjmWC4HxNnpXkft/9WAq4FpmH54YJpv\n3wQ6hlgGERERkQInq4DuV8wo14+y2cfXIT5XChkHTwTWEHpr75KD5PEAFwLfO9ZNBz7HvA5nzZ8U\nEB6PBwDLyux2wiJSINjfdd4alLflEHGprAK6U5jBCQezyBMLtAV2h/Bce+28TmWBnY7lQ5hgrkxA\nHoA9wH7gAse63ZiAryzwp3PHiYmJvsfx8fHEx8eHUEQRERER98lu2hJnMFcWeMD+73Gk3YsZ8Zqd\nZcDggLS6wGzHsoUZNFHbkVYP08y7H/gRM+rVqzhmLjy/YA78AzoRERGRgiycQRFvYu7teiNQEzPB\ncFv8+9llZRWQBLSzl+sBJTFNpmOARo7n6ezYrhPwlv14GmZCYa/rgBlhvAYRERGRAieciYW/xARP\n9TAjTlcAJYCJIW5vYeaYG46ZvuQa4O+Ypt1bMHPabcDcYqw6Jsg7jQkCJ9j7WA7MxPSd24YZKDEw\njNcgIiIiUuCEE9DVBe7C1Kj1wNTuxWFqzHqFuI/twIP24zcc6c0C8mU1afDrIT6XiIiISKEQTkD3\nGebuDr8Cr2Du63oVsCAXyiUiIiIiIQonoPsO04fO62qgPGZkqoiIiIjkkXAGRRTB3IJrBbAec+eI\ny3KjUCIiIgVdclpqlssi4Qinhm4SZtqSD4DfMLfpGovpC/dp5IsmIiJScMXFxFJtVvpsXru7j83D\n0ojbhRPQ/RO4AXMPVa+XMP3pFNCJiIiI5JFwmly3YZpaA52LUFlEREREJAeyqqGrgZm41+tLYBaw\nxJEWCzSJfLFEREREJFTZNbm+ipns13tndA/QPSDPlEgXSkRERERCl1VAtxO4EzNdiYiIiIjkU9n1\noQsM5u4DlgKbgUWYW3aJiIiISB4KZ5Trk8DTmGlLkoBiwGNATdTsKiIiIpJnwgnoWgBX4D+q9VVg\nZERLJCIiIiJhCWfakhUEn6KkWITKIiIiIiI5EE4NXXWgPbAaKAnUAXqEuQ8RERERibBwauhewvSh\nOwHsx9TYlQL65EK5RERERCRE4dSutcQMgkgGqmGmNTmQC2USERERkTCEU0M3G9PMuhf4ifRg7oII\nl0lEREREwhBOQNcNSMkkXURERETySDhNrs8DVwVJt4A3IlMcEREREQlXKAFdfeBmYCrwG7Dbsc4D\nPJQL5RIRERGREGUX0DUHvgfi7OUkoA2mH53XmFwol4iIiIiEKLs+dInAE8BFmJGty4FnA/KcjXip\nRERERCRk2QV0R4DpwDFMrVwvTGDnpImFRURERPJQdgHdXwHL54B9AWn/jFxxRERERCRc2dWu3Y2Z\ne86DGc3qsZeX2uvjgEbAO7lVQBERERHJWnYB3V/AHiDVkZYUsH1gE6yIiIiIRFF2Ad0jwJfZ5Lk5\nQmURERERkRzIrg9ddsEcwFeRKIiIiIiI5Ew4t/4SERERkXxIAZ2IiIiIyymgExEREXE5BXQiIiIi\nLqeATkRERMTlFNCJiEhIktNSs1wWkbyj+7CKiEhI4mJiqTZrsG95d/exeVgaEXFSDZ2IiIiIyymg\nExEREXE5BXQiIiIiLqeATkRcTR31RUQ0KEJEXE4d9UVEVEMnIiIi4noK6ERERERcTgGdiIiIiMsp\noBMRERFxOQV0IiIiIi4X7VGuVYFngfVAK2A8sDFIvp5AJcCDKeOwIHluBAbb/0VEREQKrWgGdB7g\nM2AQ8DXwLbAIqA04J466HegGtLGX5wI9gJmOPBcDI4Dk3C2yiIiISP4XzSbXG4H6wHJ7eRMmILsj\nIN8zwGLH8idAP8eyB+gNzLEfi4iIiBRq0Qzo2gDbgRRH2hagvWO5KNAM2OxI2wo0BCrYyz2B2QH7\nERERESm0ohnQVQKOB6QdA6o5lssBcXa611H7fzXgGuBPYEculVFERETEdaLZhy6FjH3eAgNKb61b\ncpA8pYF4YFQoT5aYmOh7HB8fT3x8fGilFBEREXGZaAZ0e4G2AWllgZ2O5UOYYK5MQB6A6sBQYIi9\nHGv/ncLU3P3q3LEzoBMREREpyKLZ5LoMuDwgrS7pgyQALHu5tiOtHmYAxTtAcaCE/fcIZqRsSQKC\nOREREZHCJJoB3SogCWhnL9fDBGOfA2OARnb6m0Bnx3adgLeC7M+DRrmKiIiIRLXJ1cLMMTccM33J\nNcDfMU2mtwDrgA3APEzz6hjgNCYInJDJ/qxcL7WIiIhIPhftO0VsBx60H7/hSG8WkO/lEPY1x/4T\nERERKdR0L1cRERERl1NAJyIiIuJyCuhEREREXE4BnYiIiIjLKaATERERcTkFdCIiIiIup4BORERE\nxOUU0ImIiIi4nAI6EREREZdTQCci4lLJaalZLuf29iKSf0T71l8iIhIhcTGxVJs12Le8u/vYqG4v\nIvmHauhEREREXE4BnYiIiIjLKaATERERcTkFdCIiIiIup4BORERExOUU0ImIiIi4nAI6EREREZdT\nQCciIiLicgroRERERFxOAZ2IiIiIyymgExEREXE5BXQiIhIRyWmpWS6LSO4pktcFEBGRgiEuJpZq\nswb7lnd3H5uHpREpXFRDJyIiIuJyCuhEREREXE4BnYiIiIjLKaATERERcTkFdCIiIiIup4BORERE\nxOUU0ImIiIi4nAI6EREREZdTQCciIiLicgroRERERFxOAZ2IiIiIyymgExEREXE5BXQiIiIiLqeA\nTkRERMTlFNCJiLhEclpqnm4vIvlXkbwugIiIhCYuJpZqswb7lnd3HxvV7Qub5LRU4mJiM10WyU8U\n0ImIiAShAFjcRE2uIiIiIi6ngE5ERETE5RTQiYhESeCgBA1SEJFIiXYfuqrAs8B6oBUwHtgYJF9P\noBLgwZRxmJ1eHHgV+AdwGngReCN3iywiEhnqkyUiuSWaNXQe4DPgX8BUYCywEAgcMnQ70A0YBYwE\n6gA97HUDgaXAdcA84HWgTW4XXERERCQ/i2ZAdyNQH1huL28CkoE7AvI9Ayx2LH8C9LMf78cEcr8B\nA4AkFNCJiIhIIRfNgK4NsB1IcaRtAdo7losCzYDNjrStQEOgAjA9YJ/7gd8jXlIRERERF4lmQFcJ\nOB6Qdgyo5lguB8TZ6V5H7f/OfGD605UFPo1gGUVEJJ/SoBKRzEVzUEQKponVKTCg9NbeJQfJ4wnI\n+wim2fV0REonIiL5Wn4bVKI7SUh+Es2Abi/QNiCtLLDTsXwIE8yVCcgDsMeR1ggT/H2R2ZMlJib6\nHsfHxxMfHx9mcUVERDKX3wJMKdyiGdAtAwYHpNUFZjuWLcygidqOtHqYARQH7OUqwA3AREeeIvj3\nzfML6EREREQKsmj2oVuFGZXazl6uB5QEPgfGYGrdAN4EOju26wS8ZT8ug5mTbom9fUNgCKY/nYiI\niEihFM0aOgszx9xwzPQl1wB/B04BtwDrgA2YaUmqY4K805ggcAIm+PwUMwddL8d+3wf+isorEJGw\nqZ+RiEjui/adIrYDD9qPnXd4aBaQ7+Ug21pAfOSLJCK5Sf2MRERyn+7lKiIiIuJyCuhERCJE86T5\n0/EQiZ5oN7mKiBRYwZqXC3Nzs5rbRaJHNXQiIiIiLqeATkRERMTlFNCJiIiIuJwCOhERERGXU0An\nIiIi4nIK6ERERERcTgGdiIgUSJoHTwoTzUMnUsjo3qpSWATOg7ej2/N+6/XZl4JEAZ1IIaPJXqWw\n0j5u+okAABDQSURBVGdfCjI1uYpInlKzmIjI+VMNnYjkqfxca6LmaRFxCwV0IiKZyOtgUwGkiIRK\nAZ2IhMVttVbhlDe/vZa8DihFxD0U0IlIWNwWZIRT3uzyui2YFZHCQwGdiEiI3BbMikjhoVGuIiL5\nhEb4ikhOqYZORCSfyG81gGpSFnEPBXQiIhJUfgswA6lPo0g6BXQiIuJK+T3gFIkm9aETERERcTkF\ndCIieUSDIEQkUtTkKiKSR9RkKCKRoho6EcmSapFEDH0XJD9TDZ2IZEm1SO6hUZ+5S98Fyc8U0ImI\nFBAKOEQKLzW5ikiBkl2zWOB6NaNFj469SO5RDZ2IFCjZ1VKpFivv6NiL5B7V0InkM6rF8KfjISKS\nPdXQieQzqsXwp+MhIpI91dCJSL6iGjgRkfCphk4knwt3Kgq3T12hGrnIcdt7LyI5p4BOJJ8LN8BR\nQBSeghz0hPtZCPdYnO+xC/fHiYhkTgGdiMvl9UWvINUIFvbgN9o/HvyOfS7sXyLL7d/1gk4BnUgu\ny+2TYF5f9PL6+UUkOvRdz98U0InkMp0ERaJDNUZSmCmgE5GI0kVV8sr5/njSZ1fcTAGdSCEX6Sbh\n3K6R1EVXckt+r02Pdh82fdfcRQGdSCEX7YtYfg8YRfKraH/29V1zFwV0IhJVukhIfqEaKClIFNCJ\nnCcN5Rdxp2h3Dyjok4JL3lJAJ3Kegl0U3NyHTBcRkZwJ/O4Enht2dHs+y+0jPahD3+XCRQGdiMvk\ndq2CmkRFcia7746+u5Kboh3QVQWeBdYDrYDxwMYg+XoClQAPpozDQlwn4jr6Fe1eeu9EJL+IZkDn\nAT4DBgFfA98Ci4DaQKoj3+1AN6CNvTwX6AHMzGadCOC+fiua5sO9VCMiuel873Wb3blN54aCJZoB\n3Y1AfWC5vbwJSAbuAD525HsGWOxY/gQYignaslonEbJ8+XLi4+Pzuhghy67fSrj3owzs5xLuSXD5\n8uWhFj0q3BZ05Lfj5yY6ducnvx2/823CjXb/XrddO/KZeNLjoxyJiUgxQtMG2A6kONK2AO0dy0WB\nZsBmR9pWoCFQMYt1FXKhvIVWuCe15LTULJcjLXD/3pOW9y/c7QNlt7/s1md3/HL7+LhdfruouomO\n3fnR8Ts/On7nJf58dxDNGrpKwPGAtGNAtf/f3pkHWVFdYfw3M46IIKAIAqKoaIIEt4ALqIDBqDFu\nlEtSakWMWKVBE1xKCTFaUStucUWNJhW3KEHLHUXRGI2JEk3UuAUlGkaNCiouaMQV8sd3u/pOv+6e\nN87CDPP9qrre6763+93+ut+7551zbt9ofR2gPmxPeD+8blpSNhh4p9VaappFa3uAmvKANffzWtuD\n19Lz62weM2OMMR2f9jTovkAh1pishzDx3n2eU+fLkrKaFreuHelseQytnZPW1HrW4GkpNqCMMaaS\ntu6LWtoXNLc9zhlsP6YD/8xsmwNcHq3XAJ+iwQ8J2wHLkYevqKx/5rgvASu8ePHixYsXL146wXIN\nLaQ9PXQPAlm3y9dpfBIrUFLgZtG2YWgAxaKSsrcyx920pY01xhhjjDGV1ADPAruE9WHAm8CawJnA\nFmH7geiRJgmzgBOqKDPGGGOM6ZK0d+7ZJsCpwOMoXDoDeAL4B/BL4NZQ70SgD7AM6IU8eyuqKDPG\nGGOM6XJ0qsEEpl3YCDgIhbHvBt5eqa0xxhjTGdgI9x2mmYwDnkaPQJkLbJApr0X5euOibeujwRdH\nAdeiZ9d1Vcr0Owh4FNg4s4/1SynSbyfgdGAqcD3KD02wfinbAI8A7wH3A33D9jKNrJ8o0q7sO23t\nUor0S3DfUU6Zfu47yinSrkv3G/3RiY0AdgcakDgxU4AlwNiwXoPCuruG9c3RA4674tjlMv3Go39W\ngzL7WL+UIv1qgZdJH6MzjlRX65eyOkqt6A70AOYBybQceRrVYv0SirTrR/F32tqllN17Ce47iinT\nbzzuO8oo0q4WPZGjy/Yb3wfWitYnoVy6hJ2APYGFpF/KbwMf03hE74vA/m3Wyo5LmX7zgVNy9rF+\nKUX6rYs06hm2b4XyQsH6xayHftwSzkb/Tss0sn4iT7szKP9OW7uUonsvwX1HOWX6ue8op0i7Vu83\n2nPqr9ZgFvBhtL4YeCW87wuMQc+2i6lmyrGuQpF+o5GrdyPgZvQFnRLqWL+UIv3eQf+mrkMDdY4F\nfh7qWL+UxcBn4X039EN3EeUajUGdbFfXL0+7Cyj/TfS9l5Kn34Vh3X1H0xTpNwb3HU1RpF2r9xud\nzaDL8k3givB+KuocslQz5VhXJdFvFOoUpgEHAIcAFwPbY/3KiO+/A9GjeN4AHgDuCdutXyV7o5Hu\nu6K8kDyN3kcaDaDxdH/QtfXbG3gMaTcipzy+J33vVZKnn/uO6snqNxL3HdWSd++1ar/RmQ26HujZ\ndTOAI4EbSK1gSEfwVjPlWFck1q8ncucm8+E+iVy/eyHtrF8liX6XhPUBwB/Rv/xr0BcVfP/lMRvN\n+PIwSgQuusdqsH5ZZgP7kWoXk70nrV0lWf0m476jOWT164H7jmrJ++62ar/RmcU9Ebkov0QG3VMo\nd2QZMAS4D7gRWb69M/v2AV5vt5Z2TGL9FqEvZsxrwDro4c/Wr5JEv+Xo4dj3oLyIg4DzgN8hN7r1\ny6cBOALlkbxNsUbWr5IGUu3ikYbxPQn+7SuigVS/6bjvaC4NpPotx31Hc2gg1W5D3G8AMuCGRuv1\nmfI4sXUMla7Ll5GAXZWsflsit3ms411oFo7RWL8sWf22Q3kSCXUoZDgS69cUr1L+HbV+xbxK6k3K\n+020duXE+iW476ieV4HhuO/4KryK+w1Ao7gORXHnYWio72GZOgtJnyVUNOVY97ZuaAdlEvn6PQhM\nDHVWR4nV62H9skyiUr/jgHeBgaFOd/Tvfi2sX8w6KI8kYRya9g8qNVqENLJ+oky7SRT/Jlo7UaZf\njPuOfMr0ewj3HWUUadcHPZeuy/Ybe6C48vJo+RLYNFMv/pcFmnLsGuBH4XVkG7ezo1Km32AUZpgG\nXArsFu1n/USZfhOAmcDxaARTPBrJ+olRyFD7MwoNHh6VlWlk/fK1q6Hp30RrJ8ruvRj3HfmU6ee+\no5wy7dxvGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDFmZTEc6L+y\nG1HCamii745GW7drMJoaKOFrQL82/DxjjDHGdFB2Bu5A8wNejiaA3iMqn4gmJR9buWuHYCBwE3rY\na0ei2nZNRg8TnRnqL0UP/t23if32Bz4AfhDWj0IPD27r6zQIuBW18ffABlHZDsAzwCxg7TZuhzHG\nGGMCE0nnBUzYCE0t88NoWwMd16ADGE/LDbqjW6EdWcZT3q6LgX+hKZAS1gfmA/tUcfyHSA06aL/r\n1AtNR3RZTtkdQM8qjlGHjFljTDtSu7IbYIxpdXoAvw3LE9H2BuAcYAZp+G5Fu7as/dkdTRTenkxA\nU/wcR+PJt19H0/hkJ4TPI3td2us6LQWuAg4G1oy2D0RzSX5UxTFOB3Zs/aYZY8pYbWU3wBjT6uyG\nJoSem1M2B80ZeBCpF2Y0cCXQN2z7Rdi+P7A1modwDJrwfQ3gZ8jzdyvyBG4C7Ik8f99Fk3Pvg4yQ\nseH9i8i4mow8h1l2Br4DDEUTfB8K/C+n3nDkueoHbIwMj0XIK3QCCk1uBzwJnBfasw4wHc032QCc\nhLxQm4fPOh55nqagOVAXAMcAI4IGvdCf34HAj3PalGUysIR8/R8K7QZpMwEZUbsgXZ+u4vj1wMmh\nXeOAs4HbQtlO6PoPQtfj4VD3MWRg9gv7XA/8uuD4lwFTgUPQnwKQ5tdHderI17EHyi1cG2l+LvJS\nHoMmHR+J5rJ8AeUFHoI8nWfQOMRrjDHGdHlORnlQm+WUdQtlM8L6QmT41CKj7Atgv1D2BppYGmAe\nsHd4vxfwLurIAf4A/Ckcuw54DeVcATwKHBDVOzanTT2BG6L1Z0mNyvGkoc1a4Oao3mzg2vD+LGTc\nEdr1OdCdytDoSciDlHAT8BvkNZuKjNGNkaHRC010v26o+yYy8rLtyvIc8HhBWcIQFJJNvHV7Im9e\nr7D+II1DrvGk8ScjAxuk7YdIwxrgv8ioIrTh/PD+e6T6jArnNbSkfXcDT0Xrd2XKi3QEOA24Oiqb\nSertm4HyCgEuQn8IQHobY1qAPXTGrHqUheeSNIs47DcbGXlzgAeQV+p25FF7HhkAvYE+of5HKGl/\nflhfACwDPg3r/0H5en9D3phXgGHIa5QcI2YvYAAyVEBeqvqcetshb2BSb3E4jzoUykw8PPORV25Z\nzjGOQJ6khKvDuR6NPIcLowXkUXoHebXqqG5AwGrIYCrjEKRtcq3mhPf7ogEJZRyOruPOyHibh0bF\nvoc0rgv1HkbXNdnnGaRRHbrOg4GXCz7jUmTUjQnHyBqoZTrG99YA5AGODfkPw+tC4AL0x2Fm8eka\nY6rBBp0xqx4vhNcNgH9nytYPry8W7Ps8CjuCDLRzgetIjac8VmTKlqMQHMjwOwO4Exl6eXm7GyKD\n4ZyC4ycMCe3O1huIwnmxIfsh+QymcW7YK8h4TLxwWWO4W/i8K1AIuJr8twXIiCljMKknLW7LoCqO\nvyHyvH2W2V6L9JmIPJdrkXrKNkTh4gVh/ZdNfMY9yNibgq5hVvMiHbOPVhmCQuJ51/ZSYAvgEeBX\nwLQm2mSMKcGDIoxZ9bgPeBvlpGWZAHxC49BlTDdk1HVHYb8ZyLNTRplHcA4K1/0FGUN5dZegEGbM\nVgX1xtD4j+hmwMfII7ZLtH0N5B3K0oBytxK6hf0X59QdjEZ2nkbzRtrORPmI3yqps5DKkHg3ZPQ2\nxRIan2sNMoyWo9zIfZEH7UbkJc3bB/I1jrkceWsHIYMtpoF8HRfltHUE8pgm9ENG+ACUb7gXcCTK\nmzTGfEVs0Bmz6vEJ6iiPALaMtvdHXpDjUD5YQl30uj0y4oajTrceGSeboHBpHZVeqprMttqw3hcN\nqqhHBuLw6Bgxc4FtkCdvEDKE9oiOlTAvHOdK5H0cFc7xA+QBvBTYFYV3fwq8hbxqa4fj9EdGymHR\ncXcJ++Wdx/YoN211FEJeN2p/2W/nLOAWlFMWG221KDS8MwqrrkeaF7ce8tjdEdazOtdG63eigQs7\nII/ruchwqkHG5+loEMQbpJ7SO5G+u4fPmk7TEZqrUE7l7TllZTp+hIy2GnRtGsL5DkX3wKno/psc\nzvNe5AWu5pEoxhhjTJdjR2QgXIE629vRqM+YY5EH7Uz07LTkcRPdgL8ij8vZaNDBAmQgXoJCmmOR\nYXUPSvAfAWyLvF3XIwPoFjSA4krgJ8jIGJfT1gNQiO+9UDcJ312FPD9Ju8eiQQdLUSJ+77C9H8oF\nXIoGaAwJ2+tRTt4DpIM4TgnHPQkZQ/XIkLyNxp7Nfshb2QCcGM7p7+Gcr860K0styjF7Crg/1L8M\n5QEmjEaG1jRkRH8jbN8NaTYTeQkPRoM8LkRGcm/kYf0AeU/HR595L/KmfYI8di+g0OjqSNd3gZeA\nAwvaneV8KkPDCXk6goy2t0JZz3Bej6F7Zi5pruPVyPidhEbcrlFlm4wxxhhjVll2RB7AhFoUft16\n5TTHGNOeOORqjDGrBiegsHLyu94ThVefW2ktMsYYY4wxzWJbFNp8Ew1oOYv0uXbGGGOMMcYYY4wx\nxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDEx/wd4ktGjXWhh/QAAAABJRU5ErkJg\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9154e10>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Lets plot the result of the Predictwise simulation. Your plot should look something like this:\n",
      "\n",
      "<img src=\"http://i.imgur.com/uCOFXHp.png\">"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Evaluating and Validating our Forecast\n",
      "\n",
      "The point of creating a probabilistic predictive model is to simultaneously make a forecast and give an estimate of how certain we are about it. \n",
      "\n",
      "However, in order to trust our prediction or our reported level of uncertainty, the model needs to be *correct*. We say a model is *correct* if it honestly accounts for all of the mechanisms of variation in the system we're forecasting.\n",
      "\n",
      "In this section, we **evaluate** our prediction to get a sense of how useful it is, and we **validate** the predictive model by comparing it to real data."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**1.4** Suppose that we believe the model is correct. Under this assumption, we can **evaluate** our prediction by characterizing its **accuracy** and **precision** (see [here](http://celebrating200years.noaa.gov/magazine/tct/accuracy_vs_precision_556.jpg) for an illustration of these ideas). *What does the above plot reveal about the **accuracy** and **precision** of the PredictWise model?*"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We simulated the probability of predictwise function and plotted a histogram to visualize the predictive distribution.We can evaluate the prediction using Precision and accuracy. [precision and accuracy](http://chemistry.about.com/od/unitsconversions/fl/What-Is-the-Difference-Between-Accuracy-and-Precision.htm) is explained clearly.\n",
      "\n",
      "<b> Accuracy:</b> The histogram is approximately centered around on the actual outcome. So from this outcome, the model seems accurate. We want see more elections, to see whether expectation of the predictive distribution consistently matches the true outcomes.\n",
      "    \n",
      "<b> Precision: </b> Precision is all about how spread is our data. The spread in our histogram is 60 votes, which is relatively high. So to reduce the spread of likely outcomes, we have to add more information to the model."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**1.5** Unfortunately, we can never be *absolutely sure* that a model is correct, just as we can never be absolutely sure that the sun will rise tomorrow. But we can test a model by making predictions assuming that it is true and comparing it to real events -- this constitutes a hypothesis test. After testing a large number of predictions, if we find no evidence that says the model is wrong, we can have some degree of confidence that the model is right (the same reason we're still quite confident about the sun being here tomorrow). We call this process **model checking**, and use it to **validate** our model.\n",
      "\n",
      "*Describe how the graph provides one way of checking whether the prediction model is correct. How many predictions have we checked in this case? How could we increase our confidence in the model's correctness?*"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*Your Answer Here*\n",
      "In this case we checked only one prediction, as there is only one outcome. Histogram shows the predictive  distribution of election outcomes, assuming the model is true. \n",
      "\n",
      "The graph assumes that the model is correct(hypothesis test). we would want to test more outcomes against predictions that were made in the same way. We could also break the election down into state-by-state outcomes, and test the prediction for each state against that state's outcome."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Gallup Party Affiliation Poll"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we will try to **estimate** our own win probabilities to plug into our predictive model.\n",
      "\n",
      "We will start with a simple forecast model. We will try to predict the outcome of the election based the estimated proportion of people in each state who identify with one one political party or the other.\n",
      "\n",
      "Gallup measures the political leaning of each state, based on asking random people which party they identify or affiliate with. [Here's the data](http://www.gallup.com/poll/156437/heavily-democratic-states-concentrated-east.aspx#2) they collected from January-June of 2012:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gallup_2012=pd.read_csv(\"g12.csv\").set_index('State')\n",
      "\n",
      "gallup_2012[\"Unknown\"] = 100 - gallup_2012.Democrat - gallup_2012.Republican\n",
      "gallup_2012.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>Democrat</th>\n",
        "      <th>Republican</th>\n",
        "      <th>Dem_Adv</th>\n",
        "      <th>N</th>\n",
        "      <th>Unknown</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>State</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>Alabama</th>\n",
        "      <td> 36.0</td>\n",
        "      <td> 49.6</td>\n",
        "      <td>-13.6</td>\n",
        "      <td>  3197</td>\n",
        "      <td> 14.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alaska</th>\n",
        "      <td> 35.9</td>\n",
        "      <td> 44.3</td>\n",
        "      <td> -8.4</td>\n",
        "      <td>   402</td>\n",
        "      <td> 19.8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arizona</th>\n",
        "      <td> 39.8</td>\n",
        "      <td> 47.3</td>\n",
        "      <td> -7.5</td>\n",
        "      <td>  4325</td>\n",
        "      <td> 12.9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arkansas</th>\n",
        "      <td> 41.5</td>\n",
        "      <td> 40.8</td>\n",
        "      <td>  0.7</td>\n",
        "      <td>  2071</td>\n",
        "      <td> 17.7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td> 48.3</td>\n",
        "      <td> 34.6</td>\n",
        "      <td> 13.7</td>\n",
        "      <td> 16197</td>\n",
        "      <td> 17.1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "            Democrat  Republican  Dem_Adv      N  Unknown\n",
        "State                                                    \n",
        "Alabama         36.0        49.6    -13.6   3197     14.4\n",
        "Alaska          35.9        44.3     -8.4    402     19.8\n",
        "Arizona         39.8        47.3     -7.5   4325     12.9\n",
        "Arkansas        41.5        40.8      0.7   2071     17.7\n",
        "California      48.3        34.6     13.7  16197     17.1"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Each row lists a state, the percent of surveyed individuals who identify as Democrat/Republican, the percent whose identification is unknown or who haven't made an affiliation yet, the margin between Democrats and Republicans (`Dem_Adv`: the percentage identifying as Democrats minus the percentage identifying as Republicans), and the number `N` of people surveyed.\n",
      "\n",
      "**1.6** This survey can be used to predict the outcome of each State's election. The simplest forecast model assigns 100% probability that the state will vote for the majority party.  *Implement this simple forecast*."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      " \"\"\"\n",
      "Function\n",
      "--------\n",
      "simple_gallup_model\n",
      "\n",
      "A simple forecast that predicts an Obama (Democratic) victory with\n",
      "0 or 100% probability, depending on whether a state\n",
      "leans Republican or Democrat.\n",
      "\n",
      "Inputs\n",
      "------\n",
      "gallup : DataFrame\n",
      "    The Gallup dataframe above\n",
      "\n",
      "Returns\n",
      "-------\n",
      "model : DataFrame\n",
      "    A dataframe with the following column\n",
      "     * Obama: probability that the state votes for Obama. All values should be 0 or 1\n",
      "    model.index should be set to gallup.index (that is, it should be indexed by state name)\n",
      "    \n",
      "Examples\n",
      "---------\n",
      ">>> simple_gallup_model(gallup_2012).ix['Florida']\n",
      "Obama    1\n",
      "Name: Florida, dtype: float64\n",
      ">>> simple_gallup_model(gallup_2012).ix['Arizona']\n",
      "Obama    0\n",
      "Name: Arizona, dtype: float64\n",
      "\"\"\"\n",
      "\n",
      "#your code here\n",
      "def simple_gallup_model(gallup):\n",
      "    return pd.DataFrame(dict(Obama= (gallup.Dem_Adv > 0).astype(float)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, we run the simulation with this model, and plot it."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = simple_gallup_model(gallup_2012)\n",
      "model = model.join(electoral_votes)\n",
      "prediction = simulate_election(model, 10000)\n",
      "plot_simulation(prediction)\n",
      "plt.show()\n",
      "make_map(model.Obama, \"P(Obama): Simple Model\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QqpKce+65XHXVVZV2v1BtnJYPDebrM27Z3MWQpC1KohwGLNs9qkrx448/smzZ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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x902fda0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x902ff60>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jhx9+YPLkydm2qkJma/HZs2c91qlUKoKDg/Hy8nJ3BUhJSclye7PZjKIonDx5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\nAAAAAAAAJrt/Aufjl3VjC9pSAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x915cbe0>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**1.7** Attempt to **validate** the predictive model using the above simulation histogram. *Does the evidence contradict the predictive model?*"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*Your answer here*\n",
      "The model does not predict the red line (correct result). Reject the model. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Adding Polling Uncertainty to the Predictive Model\n",
      "\n",
      "The model above is brittle -- it includes no accounting for uncertainty, and thus makes predictions with 100% confidence. This is clearly wrong -- there are numerous sources of uncertainty in estimating election outcomes from a poll of affiliations. \n",
      "\n",
      "The most obvious source of error in the Gallup data is the finite sample size -- Gallup did not poll *everybody* in America, and thus the party affilitions are subject to sampling errors. How much uncertainty does this introduce?\n",
      "\n",
      "On their [webpage](http://www.gallup.com/poll/156437/heavily-democratic-states-concentrated-east.aspx#2) discussing these data, Gallup notes that the sampling error for the states is between 3 and 6%, with it being 3% for most states. (The calculation of the sampling error itself is an exercise in statistics. Its fun to think of how you could arrive at the sampling error if it was not given to you. One way to do it would be to assume this was a two-choice situation and use binomial sampling error for the non-unknown answers, and further model the error for those who answered 'Unknown'.)\n",
      "\n",
      "**1.8** Use Gallup's estimate of 3% to build a Gallup model with some uncertainty. Assume that the `Dem_Adv` column represents the mean of a Gaussian, whose standard deviation is 3%. Build the model in the function `uncertain_gallup_model`. *Return a forecast where the probability of an Obama victory is given by the probability that a sample from the `Dem_Adv` Gaussian is positive.*\n",
      "\n",
      "\n",
      "**Hint**\n",
      "The probability that a sample from a Gaussian with mean $\\mu$ and standard deviation $\\sigma$ exceeds a threhold $z$ can be found using the the Cumulative Distribution Function of a Gaussian:\n",
      "\n",
      "$$\n",
      "CDF(z) = \\frac1{2}\\left(1 + {\\rm erf}\\left(\\frac{z - \\mu}{\\sqrt{2 \\sigma^2}}\\right)\\right) \n",
      "$$\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Function\n",
      "--------\n",
      "uncertain_gallup_model\n",
      "\n",
      "A forecast that predicts an Obama (Democratic) victory if the random variable drawn\n",
      "from a Gaussian with mean Dem_Adv and standard deviation 3% is >0\n",
      "\n",
      "Inputs\n",
      "------\n",
      "gallup : DataFrame\n",
      "    The Gallup dataframe above\n",
      "\n",
      "Returns\n",
      "-------\n",
      "model : DataFrame\n",
      "    A dataframe with the following column\n",
      "     * Obama: probability that the state votes for Obama.\n",
      "    model.index should be set to gallup.index (that is, it should be indexed by state name)\n",
      "\"\"\"\n",
      "# your code here\n",
      "from scipy.special import erf\n",
      "def uncertain_gallup_model(gallup):\n",
      "    sigma = 3\n",
      "    prob =  .5 * (1 + erf(gallup.Dem_Adv / np.sqrt(2 * sigma**2)))\n",
      "    return pd.DataFrame(dict(Obama=prob), index=gallup.index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We construct the model by estimating the probabilities:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = uncertain_gallup_model(gallup_2012)\n",
      "model = model.join(electoral_votes)\n",
      "#model"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Once again, we plot a map of these probabilities, run the simulation, and display the results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "make_map(model.Obama, \"P(Obama): Gallup + Uncertainty\")\n",
      "plt.show()\n",
      "prediction = simulate_election(model, 10000)\n",
      "plot_simulation(prediction)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Fjz/+6F5WVRWXy+XxPYLUbg8NGjRIdx86nS7D81BV9bZ93crLy8vdwrx161b3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\nhIQWm1k9HJsQBwWqA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       "text": [
        "<matplotlib.figure.Figure at 0x9178d30>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zzz6Lx+Nh0aJFPP/88/z+++9Uq1aNhg0b0qpVK4oUKcJrr73Gc889R3p6OmXK\nlOGCCy6gUaNGnH322af70hARkXxg+PDhw093H4Wxk0+0RjJLjNx3330MGDAgZvezFRERyS88UeiQ\nrWZcydf279/P7t27FeiJiIicotxvOyCSB7xz+W3YsIEo1GCLiIgUWqrZk3xp69atzJ8/n5tvvpl2\n7drldXFEREQKLPXZExEREcmn1GdPREREREJSsCciIiISx+I92Mv9/l8iIiIicSzWwV414BWgFzAT\naJRDvh7AEGAo8JQr3QOMAX4HdgB3BWzXHsh0/V0RrYKLiIiIFESxHKDhAVKBAcAXQENgAVAXyHDl\nuwF4DGjtLM8GPgemAv/GBnrLgBTgbaAs4L07/URgivM4HVgdpBwaoCEiIiIFQkEboNEeG+AtcZbX\nAWnAjQH5HgMWupbnAX2dx8ucP4BPsEGi9yTUBRoDVYGfCR7oiYiIiBQqsQz2WgMbsTVuXr8C7knU\nigLNgPWutA3Y5t6K2Fo9r85Ab+Cos9wUKAHMBbZig0sRERGRQi2WwV5l4FBA2kGgumu5PJDkpHsd\ncP5781UEXgBmYQPIRCf9HWzAVxvbXPyBc0yJwNq1a3n44YfzuhgALF26lN69e1O7du1c8548eZJx\n48Zx1VVX0bVrV1JSUmjfvj1vvfVWDEoqIiKSf8Xydmnp2GZbt8Bg01vrlxYkj7e59k9gMPAVMA3b\nrDvNlX8bcDPwE7b/36unVepCZvLkybz++us888wzlChRIuLtDxw4QGJiIqVLlz7tsrRp04ZFixax\nZcuWkPmOHDlCx44dSU9PZ8GCBfztb38DYP369XTs2JFFixbx2muvRXTsLVu2ULNmzVMuu4iISH4R\ny5q9HdjBFG7lgO2u5b3YQK9sQB4C8h0HPgT+B7gkyLGOYQd1lAuyjmHDhvn+lixZEm75497x48f5\n5ptvOHDgAO+8884p7aNPnz7s378/KuXxeDxhBVyPPPIIy5cv5+233/YFegANGjRgxowZTJs2jVde\neSXs4x4/fpxevXqdUpmlYEvLzAj6WESkIIvlaNxWwGdAGVfab8Ag4F1X2mfA/wJjneWu2BG8waZp\n6Q2UAkYFWTcR+BQbFLppNG4O3njjDUqVKsXw4cNJSkri+++/j2j7WbNmceedd7Jp06ao1YrNmDGD\nu+++m8zMzKDrd+7cSY0aNbjqqqv49NNPg+apX78+hw4dYtu2bSQmJgbN49a9e3cWLVrEpk2bTqvs\nUjBVnz7PuASUAAAgAElEQVQQgG13BftYERGJrYI2Gvc7YAvQ1lluAJQE5gMjsSNpAV7DDr7w6kRW\nM217oIbz2IOdR8+7rp+zT7B99epjp3aRMC1cuJDOnTtz//33k5qaSmpqarY8xhgmTZrEkCFD6N+/\nP23btmXt2rUcPnzYF2w988wzjBs3jg0bNtCyZUtfn7udO3cyZMgQEhIS+Prrr337GzlyJKNHj2bU\nqFF06tSJHTt2hF3mxYsXk5GRQatWrXLMc9lll7Fr1y5++OEHlixZQvny5bnrLjtF49q1a7nppptI\nSLBvhZ9++on169ezf/9++vfvz8cffwzA4cOHGTZsGE899RS33347t99+O4cOZXVBff3113nggQcY\nPHgwl19+OaNGjcL7o+L999/nxhtvZPDgwTz//PM0aNCA8uXL8+abb/Lbb7/xr3/9iwoVKnDNNddw\n5MgR3z5Xr15Nnz596Nq1Kw0bNmTs2LGIiIjkd+cBM4D7nf9NnfRUoIsr36PYAPBxYDRZNZAzsE29\no4GHgHpOugdbi7cfeBYYiB3sEYyJKjjzfzGwfv16M2LECGOMMUeOHDHlypUzd999d7Z8gwYNMuPH\nj/ctX3bZZaZ169bGGGMWL15sPB6P2bJli2/9kCFDTO3atX3LmzZtMh6Px3z11VfGGGPmzZtnihYt\n6lt//fXXm+7du/uWp0+fbjweT47lHjVqlPF4PGby5Mk55hk4cKDxeDzmvffeM8YYc8UVV5i77rrL\nt37atGl+xxg6dKipVauWbzkjI8NcccUVZtWqVcYYYw4dOmSKFy9uHn/8cWOMMVOmTDEtWrTw5f/j\njz9M2bJlzWOPPWaMMeb48eOmfv365qKLLvLtY+DAgaZ8+fLmhRdeMJmZmWb37t2mTJkyZsqUKcYY\nYw4cOGA6d+7s2+e7775rPB6PWbBgQY7PU6Kj2rQBptq0AXldDBERY0x0miJjOUAD7NQrdzqP3Z2o\nmgXky6kK484c0g1w7SmXSpg2bRp9+vQBoGTJktxxxx1MnTqV559/nnLlbNfHXbt2MW7cOA4ezBos\nPWXKFPbs2ZPjfj0eD6FeqxdeeCFDhgzxLZcsWTKi5lNv7XaoY3ibgL15AmvEc6shnzdvHgBNmjQB\noHTp0nz44YfUqVMHsH1AH3roIV/+ypUrc++99zJ+/Hgef/xxypQpQ5UqVahdu7ZvH8nJyYwePZqU\nlBQ8Hg+VKlWiUaNGrF27FoCJEyeyd+9eBg0aBMCJEydo06YNO3fuDOOsiIiIZIl1sBd/4qD/38mT\nJ/n000/59ddffWmHDh3i2LFjzJgxg7597ZzW3333HWXLliUpKcmX74ILLjitY9euXZtBgwbx1ltv\nsXv3bnbu3Jlr8BW4PcDu3btzzOMNRmvVqnVKZVy6dClVq1b1S7vmmmsAGwDv2LGDs846y2/9xRdf\nzMmTJ1m7di2tWrXKFowWK1Ys23GKFi3qaxr+4YcfaNu2LSNHjjylMouIiHjF+t64kg/NnTuXESNG\nMHfuXN/fl19+yYUXXsikSZN8+dLS0tizZw8nTpyI2rF3795Ny5YtqVChAn379qVWrVoha+kCJScn\nU7RoUZYvX55jntTUVCpVquSrVYu0r2taWlqO0794B3xs27bNL71ixYoAfoFxOLzP/ejRo2zcuDHb\n+pMnT0a0PxEREQV7wuzZs+nUqVO29DvvvJNff/2VRYsWAdCwYUMyMzN59VX/qQs//vhjMjMzgzap\nejwev5G0GRn+01k8+eSTpKWl0aFDh6Drc1OpUiXuvfde/vd//5fNmzdnW5+amsrPP//MwIEDfYGZ\nx+PxO07gMQObni+44AJWrFjBjz/+6Jdv3rx5VKxYkTp16rBs2TK/dTt27KB06dI0btzYt8/cuPPU\nq1eP+fPn+zXbpqenM27cuFz3IyIi4qZgr5D78ccfycjICFoD5Q0Ax48fD0CjRo245pprePTRR3ni\niSf45JNPGDZsGAcPHiQhIYHy5e2YmHXr1rFhwwb++usvateuzfbt21m6dCl79uxhxowZAL6ash07\ndrB161b++OMPNmzYQGpqKrt27fI1vaan23m2c5p6BWDMmDG0bt2aW2+91a85d8uWLXTr1o1///vf\nfncFqVWrFl999RU7duxg/fr1LFiwwK9M5cuXZ9euXRw8eJBVq1Zxxx13UKFCBTp06MArr7zCggUL\nuOeee6hXz44Peuqpp/jmm2/49ttvARvsvv322zz55JO+5tr09HS/ANL7fNLSsuYPT09P96X37NmT\nY8eO0aFDBz7++GO++OILbrvtNl9QLCIiIjnLqwE1+c73339v/v73v5u6deuaTz75xG/dvn37zJNP\nPmk8Ho9JSEgwgwYNMunp6WbPnj2mS5cupmTJkua8887LNgq2Q4cOpmLFimbMmDHGGDsS9YYbbjBn\nnXWWadeunfn5559NkyZNzPPPP2/+/PNP8+WXX5qqVauaSpUqmREjRpjZs2ebsmXLmj59+pjvv//e\ntGnTxiQkJJgxY8aYffv25fhcTp48acaNG2eSk5NNSkqKuemmm8zVV19tZs2alS3vhg0bzMUXX2xK\nlSpl7rnnHjN37lxz3XXXmZkzZ5qMjAyzfft2U6dOHVO3bl3z6aefGmOMSU1NNZdeeqkpUaKEad68\nuVm2bJnfPt966y1z2WWXmf79+5vevXubiRMn+tbNnTvXlCtXzjRq1Mh88803ZuvWraZXr14mISHB\nPPzww2bnzp1m3rx5ply5cqZ+/fq+kcrvv/++qVevnilRooRp0aKFL13OLI3GFZH8JBqBTywnVc4v\nonXuRCQOaVJlEclPCtqkyiIiIiISYwr2REREROKYgj0RERGROKZgT0RERCSOKdgTERERiWMK9kRE\nRETimII9ERERkTimYE9EREQkjinYExEREYljCvZERERE4piCPREREZE4pmAvQmmZGXldhHxRBhER\nESkYiuR1AQqapIRE343S80q0b9C+fft2LrroIj777DOaNm0a1X17HT58mKlTp/LJJ5/Qrl07Bg48\ntXM4fvx4Zs2axcqVK6NcQhERkfikmj2hdOnStGrVirJly57RY3Tv3p0VK1Zw8uTJsLfbsmWL33Lt\n2rVp1qxZtIsnIiIStxTsCWXKlOHjjz/m/PPPP6PHKV26NOXLlw87vzGGu+66yy/t+uuv59VXX412\n0UREROKWgj3xyczMzOsi+HnqqadYsmRJtvSMDPVZFBERCZeCvUJm1qxZjB07lhdeeIFzzjmH7777\njsmTJ9OyZUveeOMNAFJTU+nRowcdOnTg888/p3nz5pQpU4Y+ffpw5MgRHnnkEWrWrEn9+vVZt24d\nAKtWreL888+nbdu2AGzatIlevXqRkJDA77//nmN51q5dy3333cfkyZP55z//ycSJEwHYunUr3333\nHQD9+/dn5syZ/Pbbb/Tv35/q1av77WPFihX06NGDoUOH0rFjR+655x4OHjwIwPLly+nWrRt33HEH\n7733HvXq1ePss8/mrbfe8m2/ceNGHn30UaZOncrVV1/Nww8/HKWzLSIikvcU7BUix48fZ8CAATz6\n6KP069ePSZMmkZCQQOvWrfn+++99+Zo0aUJmZiapqakcOXKEFStWMGfOHF566SUee+wxhg0bxsaN\nG6lUqRJPP/00AJdccgmtW7fG4/EAtm/dbbfdlmuZbr/9dmrUqEGPHj0YPHgwDz74IFu3bqVGjRrc\ncsstADz33HN069aNChUqULx4cXbt2uXbfs2aNXTu3Jmnn36a4cOH8/HHH7Nu3TquvfZajDG0aNGC\nvXv3snTpUjweD7/88gu33XYbDz74oG8fw4YN48orr6R79+589NFHnHPOOVE53yIiIvmBgr1CJC0t\njb179zJhwgQAOnfuTL169WjUqJFfvsTERKpXr06ZMmW46aabSEhIIDk5GYAWLVpQunRpEhMTueKK\nK/j5559923k8HowxEZWpe/fudOrUCYCSJUuSmZmZbVCGV7ly5ahTp45f2ujRo2nWrBmVKlUCoEiR\nIgwePJgVK1bw2WefkZCQQMWKFTnvvPNISUmhSJEi/OMf/2D//v2+oPHkyZOMHz+ew4cPU6JECe6+\n++6InoOIiEh+pmCvECldujTDhw/nwQcfpFOnTmzfvp1y5cqFtW2xYsWypRUtWpRDhw6dVpl69+5N\n6dKlGTt2LB9++CEQWd/BlStXctZZZ/mlXXzxxQD88MMPvjR3EFq0aFEATpw4AcCTTz7JDz/8QMOG\nDZk7dy5nn332qT0ZERGRfEjBXiEzaNAg3nvvPdasWcOFF17It99+e1r7C6zJ8zbjhmvixIk89NBD\n9O7d29dsG4nExES2bt3ql1axYkUAkpKSwtpHo0aNWLVqFRdddBEpKSk88sgjEZdDREQkv1KwV4js\n3r2bNWvW0KVLF9atW8eFF17I2LFjo7Z/j8fjN1I2t1Gz27Zt48EHH6Rnz54UL148W41eOIFjq1at\nWLt2rV8N444dOwC47LLLwtrXF198Qc2aNVmwYAEvvPAC48aN48CBA7keW0REpCBQsFeIHD16lEmT\nJgFQqlQpUlJSqFq1KmlpaQB+kx0HBmreQMyb15vHXbNXu3ZtfvzxR9avX8/WrVuZPXs2YEfmeqWl\npZGeng7Arl27yMzM5Pvvv+fEiRPMmTMHsHf02Ldvn29OvvXr1/Pjjz9ijPEd37uPAQMG4PF4ePnl\nl33HePPNN7nuuut8wV56erpfIOl9nt7nOHXqVI4cOQLAnXfeSZkyZShdunR4J1VERETyHXM6Tmak\nn9b20XCqZdi0aZNJTEw0Dz30kJk0aZLp0aOH2b17txk5cqTxeDymXbt25scffzSpqammWbNmpnjx\n4ubdd981f/31l5kwYYLxeDzm6quvNmvWrDGrVq0yTZs2NcWKFTOvv/66yczMNHv27DFXXnmlKVmy\npOnSpYtZunSpufzyy83EiRPNkSNHzIsvvmgSEhJM8+bNzbJly0xmZqa5+eabTYkSJcwVV1xh1qxZ\nYy655BLToEED89NPP5kjR46Ypk2bmurVq5uZM2ea1NRU0759e5OQkGBGjBhhDh48aIwxZuXKlSY5\nOdn06NHDPP744+aRRx4xx48fN8YYs3z5cnPuueeaChUqmPnz55udO3ealJQUk5CQYB577DFz9OhR\nk5ycbFq3bm0mTJhg+vbtaz7//POoXSspeKpNG2CqTRuQ18UQETHGRDjqMQeRdbCKD9E6dyISh7z3\nvo72PahFRE6FJ9LO8EEUiUZBIlANeBxYDbQCxgBrg+TrAVTGBqNFgCeddA8wGrjNSX8cmB7GdiIi\nIiKFUiz77HmAj4APgEnAKOBjIDEg3w1AN2AEMByoB3R31v3L2ce5wIPAq0CJMLYTERERKZRiGey1\nBxoCS5zldUAacGNAvseAha7leUBf5/Ey5w/gEyCDrKboUNuJiIiIFEqxDPZaAxuBdFfar0A713JR\noBmw3pW2AWgEVATcN1ntDPQGjoaxnYiIiEihFMtgrzIQeLuFg4D7rvblgSQn3cs74Zk3X0XgBWAW\nNoBMDHM7ERERkUInlsFeOrbZNtTxvbV+aUHyeJtr/wQGA7eS1U8vnO1ERERECp1YjsbdAbQJSCsH\nbHYt78UGbGUD8gBsd6UdBz4E/gdoAkwLczsAhg0b5nucnJxMcnJyWE9AREREpKCJZbC3GBgYkFYf\nmOFaNtgBHHVdaQ2wgzl2B9nnXuCE8zjs7dzBnoiIiEg8i2Uz7nfAFqCts9wAKAnMB0YCjZ3017CD\nL7w6YWvuwI7oreE89gBXuNaF2k5ERESkUIplzZ7B9rEbgp2C5VLgH9jRtNcCq4A1wBygJjYAPIYN\nEF9w9nE7NqB7Dds8+wRZNXehthMREREplArj4AXdLk1EcqTbpYlIfhKN26XFshlXRERERGJMwZ6I\niIhIHFOwJyIiIhLHFOyJiIiIxDEFeyIiIiJxTMGeiIiISBxTsCciIiISxxTsiYiIiMQxBXsiIiIi\ncUzBnoiIiEgcU7AnIiIiEscU7ImIiIjEMQV7IiIiInFMwZ6IiIhIHFOwJyIiIhLHFOyJiIiIxDEF\neyIiIiJxTMGeiIiISBxTsCciIiISxxTsiYiIiMQxBXsiIiIicUzBnoiIiEgcU7AnIiIiEscU7ImI\niIjEMQV7IiIiInFMwZ6IiIhIHFOwJyIiIhLHFOyJiIiIxDEFeyIikqu0zIygj0Uk/yuS1wUQEZH8\nLykhkerTBwKw7a5ReVwaEYlEvNfsVcvrAoiIiIjkpVgHe9WAV4BewEygUQ75egBDgKHAU6704sBE\n4E9gK3B/wHbtgUzX3xXRKriIiIhIQRTLZlwP8BEwAPgC+ApYANQF3B1AbgC6Aa2d5dlAd2Aq0B9Y\nBLwE3AO8DPwEfOPkTQGaOY/TgdVn5qmIiIiIFAyxrNlrDzQEljjL64A04MaAfI8BC13L84C+zuNd\nwBzgF6AfsIWsoLAu0BioCvyMAj0RERGRmAZ7rYGN2Bo3r1+Bdq7lotiaufWutA3Y5t6KwOSAfe4C\nfnceNwVKAHOxTbzto1VwERERkYIqlsFeZeBQQNpBoLpruTyQ5KR7HXD+u/OB7b9XDvjQWX4HG/DV\nBlKBD5xjioiIiBRasQz20rHNtqGO7631SwuSxxOQ915sU+6xgPRtwM3ATmz/PxEREZFCK5YDNHYA\nbQLSygGbXct7sYFe2YA8ANtdaY2xgeEnORzrGPC5a1s/w4YN8z1OTk4mOTk5VLlFRERECqxYBnuL\ngYEBafWBGa5lgx3AUdeV1gA7mGO3s1wVuAoY58pTBP++gACJ+Pf983EHeyIiIiLxLJbNuN9hR8+2\ndZYbACWB+cBIbG0dwGtAZ9d2nYBpzuOywJPAp872jYBB2P57/Zw0sH316mOndhEREREptGJZs2ew\nfeiGYKdguRT4B3AUuBZYBazBTq1SExsAHsMGiC9gA9MPsRMl93Tt9y3gCHANNhCchB3gcTPZa/tE\nRERECpVY3xt3I3Cn8/gVV3qzgHxjg2xrgOQQ+772lEslIiIiEqfi/d64IiIiIoWagj0RERGROKZg\nT0RERCSOKdgTERERiWMK9kRERETimII9ERERkTimYE9EREQkjkUS7MV6Tj4REREROU2RBHtzyT75\nsYiI5IG0zIygj0VEAkVSW/c20AS4B9gNvAesPhOFEhGR0JISEqk+fSAA2+4alcelEZH8LJJg7y3n\n/xSgAjAeuASYDbyOvRWaiIiEkJaZQVJCYrbHhYnOgUhsRdKMey5wFnA/8BXQAZgHLAL+Dcxy8oiI\nSA68NXLVpw8stEGOzoFIbEVSs7cQqAFsAcYBbwDHnXVLgTuwwd8l0SygiIiIiJy6SIK9w0AX4Isc\n1p8LVDztEomIiIhI1ETSjHs92QO9s4EqzuNngAuiUSgRERERiY5Igr17gqTtBiY4jw3w12mXSERE\nRESiJpxm3F7ArUBN4OqAdRWBMtEulIiIiIhERzjB3iQgAxvoLQA8rnVHsCNzRURERCQfCneAxhTs\n1Congqz7W/SKIyIiIiLRlFuwVwv4Axvk1cUOyHBLBG4Geka9ZCIiIiJy2nIL9pYCz2Pn1esAPJdD\nPgV7IiIiIvlQbsFeG2Cn8/ht5/GbrvUJBB+lKyIiIiL5QG7B3hbX4x3YgM8tE3vXDBERERHJh0IF\ne5WAhrls7wFuBB6OWolEREREJGpCBXt/A74EtmMnTA4mAaiKgj0RERGRfClUsPcr8CB2nr1Q/h29\n4oiIiIhINOV2u7TcAj3QpMoiIiIi+VZuAzQuA9YD+4ArgToB6xOBTsBN0S+aiIjEWlpmBkkJidke\ni0jBlVuw9wZ2nr0JQAPn8R7X+kTgnDNTNBERibWkhESqTx8IwLa7RuVxaUQkGnIL9hoBx5zHc4Ct\nwCcBeVKiXSgRERERiY7c+uwdcz3ehw30zgOaAGc56e+fgXKJiIQlLTMj6ON4P7aISLhyq9lzqwfM\nBi5yljOAl4ABQFqY+6gGPA6sBloBY4C1QfL1ACpj5/ErAjzppBcHXgT+iQ1EnwVeCWM7EYlTedns\nqCZPESkIcqvZc5uJ7a/XGjsHX1VgFTAszO09wEfAB9hRvqOAj7H9/txuALoBI4Dh2CCzu7OuP7AI\nuALbrPyyU57cthMREREplCIJ9i7A9s9bDhzEBn5vACfD3L499o4cS5zlddgawRsD8j0GLHQtzwP6\nOo93YYO8X4B+2Nu5tQ5jOxEREZFCKZJg722gSpD0cEfjtgY2AumutF+Bdq7lokAz7HQvXhuwA0Uq\nApMD9rkL+D2M7UREREQKpVB99i4FRruWE4CvsTVy7rTDYR6rMnAoIO0gUN21XB5IctK9Djj/qwN/\nutKLA+WADyPcTkRERKTQCBXs/YwdBPFuLvv4IsxjpZN9IEdgzaK31i8tSB5PQN57sU25x8gaGRzO\ndiJnhMdjX2rG5HQraZFCxHk/oPeDSJ4LFewdxQ542BMiTyLQBtgWxrF2OHndygGbXct7sQFb2YA8\nANtdaY2xgeEnEW4HwLBhw3yPk5OTSU5Ozr30IiIiIgVQblOvuAO9csAdzn+PK+027Mjc3CwGBgak\n1QdmuJYNdgBHXVdaA2zT8W5nuSpwFTDOladIGNv5uIM9ERERkXgWyQCN17D3ym0P1MZOrtwG/359\noXyHHT3b1lluAJQE5gMjsbV13uN0dm3XCZjmPC6LnTvvU2f7RsAgoFgu24mIiIgUSpFMqvwZMAUb\nZFUClgIl8K9hC8Vg58Ibgp2C5VLgH9jm4muxc/atwU6tUhMbAB7DBogvYAPTD7Fz7PV07fct4K8Q\n24mIiIgUWpEEe/WBm7E1cd2xwVcS9m4WPUNs57YRuNN57L7zRbOAfGODbGuA5Fz2H2w7ERERkUIr\nkmDvI+xdL34GnscOjrgYmHsGyiUiUqClZWaQlJCY7bGISKxFEux9je2z53UJUAE7ElZEJKjCGvTo\nvrkikl9EMkCjCPb2Y0uB1dg7apx7JgolIvHDG/RUnz6w0AR6IiL5SSTB3nhgBPa+tFOxAypGYQdd\niIhIPpeWmRH0sYjEt0iacf+Fnd/uP66057D99z6MZqFERCT61LQsUjhFUrP3G7b5NtDJKJVFRERE\nRKIsVM1eLeycdl6fAdOxExp7JQJNol8sEREJlNMAl8D0wjQQRkRyl1sz7ovYiY69d7L2AHcF5JkY\n7UKJiAQqrKN63XJqhnWnB64TEQkV7G0GbsJOuSIikqfU30xE5NTk1mcvMND7N7AIWA8swN7mTERE\nRETyqUhG4z4EPIqdX28LUAy4D6iNmnJFRERE8qVIgr0WwPn4j759ERge1RKJiBRC6pMoImdKJMHe\nUoJPs1IsSmURESm01CdRRM6USIK9mkA7YAVQEqgHdI9wHyIiIiISQ5FMqvwcts/eYWAXtqavNND7\nDJRLRAoo3YZLRCR/iaRWriV2QEYaUB07NcvuM1AmESnACsKcb/mlf5z65olILERSszcD23S7A/ie\nrEDvrCiXSUQKMXfN4JmqJfQGpNWnD8zTYMtdjliLxXkWkfwhkmCvG5CeQ7qISFTkl0As3uk8ixQe\nkTTjPg1cHCTdAK9EpzgiIiIiEk3hBHsNgWuAScAvwDbXOg9w9xkol4iIiIhEQW7BXnNgGZDkLG8B\nWmP77XmNPAPlEhEREZEoyK3P3jDgQeBv2BG4S4DHA/KciHqpRERERCQqcgv29gOTgYPY2rye2KDP\nTZMqi0i+pBGnIiK5B3t/BSyfBHYGpP0resUREYkejTgVEcm9Vu4W7Nx6HuyoW4+zvMhZnwQ0Bl4/\nUwUUERERkVOXW7D3F7AdcLd/bAnYPrBZV0RERETyidyCvXuBz3LJc02UyiIiIiIiUZZbn73cAj2A\nz6NREBERERGJvkhulyYiIiIiBYyCPREREZE4VpCDvXPCyFPtjJdCREREJB+LdbBXDXgF6AXMBBrl\nkK8HMAQYCjwVsK4W8CbwbpDt2gOZrr8rTrvEIlLoaXJmESnIYnn3Cw/wETAA+AL4ClgA1MV/apcb\ngG7Ye/ACzAa6A1Od5UxgH1AjyDFSgGbO43RgdfSKLyKFlXdyZoBtd43K49KIiEQmljV77YGG2Pvr\nAqwD0oAbA/I9Bix0Lc8D+rqWfwf2YoNHt7rYCZ6rAj+jQE9EREQkpsFea2AjtsbN61egnWu5KLZm\nbr0rbQO2ubdiLvtvCpQA5gJbscGliIiISKEWy2CvMnAoIO0g/nfgKI+9BdtBV9oB539ud+p4Bxvw\n1QZSgQ+cY4qIiIgUWrHss5eObbZ1Cww2vbV+aUHyBDbb5mQbcDPwE7b/36uBGYYNG+Z7nJycTHJy\ncpi7FhERESlYYhns7QDaBKSVAza7lvdiA72yAXnA3qM3XMewd/YoF2ylO9gTEcnP0jIzSEpIzOti\niEgBFstm3MXAeQFp9ckasAFgnOW6rrQG2MEcuyM8XiL+ff9ERAoc70hg72hgEZFIxTLY+w7YArR1\nlhsAJYH5wEjsSFqA14DOru06AdMC9hWs3P2cfYLtq1cfO7WLiIiISKEVy2Zcg+1DNwQ7BculwD+A\no8C1wCpgDTAHqIkNAI9hA8QXXPu5ArgeO2DjJmywmA5cAzwJTMIO8LgZ/5G/IiIiIoVOLIM9sFOv\n3Ok8fsWV3iwg39gQ+/gauDhI+rWnXiwRERGR+FSQ740rIlGkW4KJiMSnWNfsiUg+pVuCiYjEJ9Xs\niYiIiMQxBXsiIiIicUzBnoiIiEgcU7AnIiIiEscU7ImIiIjEMQV7IhI38nLKmMI6XU3g8y6s50Ek\nP9PUKyISN9zTx0Bsp5DJy2PnpcL6vEUKEtXsiYiIiMQxBXsiIhIRNdWKFCwK9kRE4tiZCMy8Tbfu\n5lsRyb8U7ImIxBl3gKfATEQU7IlInlOzYHQpwBMRNwV7IpLnFJyIiJw5CvZERCKgWshTo/MmkncU\n7AtZNDMAABdCSURBVImIROBUaiEjDXTiKTDynqukhMS8LopIoaVgT0TkDIs0QFSztohEk4I9ERER\nkTimYE9EREQkjinYEylk3P3B4qlvmIiIBFckrwsgIrHlvnG9blovIhL/VLMnIiIiEscU7ImISL6m\nrgcip0fNuCIikq+p64HI6VHNnogUaqopEpF4p2BPRAq1nCYwVhAoIvFCwZ6ISBC6i4WIxAsFeyIF\nQKQd1APznE4tlTrHi4gUbAV5gMY5wK68LoRILETaQd2dP9xtonVsERHJX2Id7FUDHgdWA62AMcDa\nIPl6AJUBD7aMT7rW1QKeBqoDV0awnYiIiEihE8tgzwN8BAwAvgC+AhYAdQF329ANQDegtbM8G+gO\nTHWWM4F9QI2A/ee2nYiIiEihE8s+e+2BhsASZ3kdkAbcGJDvMWCha3ke0Ne1/DuwFxs8RrKdiOQj\n6v8nIhIbsQz2WgMbgXRX2q9AO9dyUaAZsN6VtgFoBFQMse9T3U5E8ohGu4qIxEYsg73KwKGAtIPY\nvnde5YEkJ93rgPPfnS/QqW4nIiIiEtdiGeylY5ttQx3fW+uXFiRPYLNtNLYTERERiWuxHKCxA2gT\nkFYO2Oxa3osN2MoG5AHYHmLfEW03bNgw3+Pk5GSSk5ND7FpERESk4IplsLcYCOycUx+Y4Vo22AEc\ndV1pDbCDOXaH2HdE27mDPRHJLi0zg6SExLwuhoiIREEsm3G/A7YAbZ3lBkBJYD4wEmjspL8GdHZt\n1wmYFrCvYOUOZzsRCYMGT0he0N1aRM6MWNbsGexceEOwU7BcCvwDOApcC6wC1gBzgJrYAPAYNkB8\nwbWfK4DrsQMvbsIGi2lhbCciIvmY7tYicmbE+g4aG4E7ncevuNKbBeQbG2IfXwMX57Au1HYicprc\nzbtq6hURKRgK8r1xRSTGVPMiIlLwxLLPnoiIiIjEmII9EYkZdbrPn3RdROKbgj0RiRmN8s2fdF1E\n4puCPRE5JZomQ0SkYFCwJyKnxF0bpFG58UlBvEh8ULAnIqdNQUF8UvOuSHxQsCcip60gBAUKSEWk\nsFKwJyKFQkEISEVEzgQFeyIiIiJxTMGeiORbanqNH7qWInlHwZ6I5Ftqeo0fupYieUfBnoiIiEgc\nU7AnIgWOmgRFRMKnYE9EChw1CUowuquLSHAK9kREJM9EMyjTXV1EglOwJyIieUa1tCJnnoI9ERER\nkTimYE9EREQkjinYExEREYljCvZERKTA0IhbkcgVyesCiIiIhMs7oANg212j8rg0IgWDavZERERE\n4piCPRER+f/t3XuUFOWZx/HvzAiIoCDKRUDxAisi5rIQDKAwKKuswaBHYvaEnIUs6EnWuOvtqKvR\nuGoSQxJzQVndXcUkhmA2UUSFaDSgu4kxG3XjZUGMMmpUvAu6YsJt/3jest8pqqp7umdqut/6fc6Z\nM1OXrq6nuuftp5/3rSoRCZiSPREREZGAKdkTERERCZiSPREREZGAKdkTaTC69IQUgd7bIp1HyZ5I\ng0m72bs+HCUktd4zN+1Lkb4sSRHpOnsigajm+mNbd2xvlzCKhCLt/0HX6ZMiCr2yN6y7d0CkHqRV\nMGqtnoiESNU/CU3elb1hwMXAY8BEYCHwZMJ6pwNDgCZsHy+pcNl04B5veg7w407ad5GGpWqGSOX0\n/yKhyTPZawJWABcA9wL3A3cBowD/q9MsYC4w2U3fAswHbiizDOAUYLz7exuWVIqISE40NECk/uTZ\njTsdOAxY46bXAluBk2LrnQ+s8qaXA2dVsGwUcAQwFHgCJXoiIrnzq2L1Ql2xUnR5JnuTgWexiltk\nPXCMN90Tq8yt8+Y9DRwODCyzbBzQG7gNeAFLLkVEpOA0NlWKLs9kbwiwOTZvEzDcmx4A9HDzI2+7\n3yMzlg0DlmEJ30HA74Bb3XOKiIiIFFaeyd42rNs26/mjqt/WhHW2Zyxr8ub9EZgNbMTG+ImIiIgU\nVp4naLwEHBWb1x9o86bfwJK5frF1AJ7PWPZibLtbsLNy+5Pgsssu++Dv1tZWWltby+y6SD78we2V\nDHTXYHgRESknz2RvNRAfMHEocJM3vRM7gWOUN280djLHxoxlryY8Xwvtx/d9wE/2RLpDWlLX0Us+\n6BIRIiJSTp7duL8BngOmuenRwB7AncCV2Jm0AP8OnOg97gTgxgqWneO2CTZW71Ds0i4idSftlmci\nIiKdLc/K3k5sDN2l2CVYJgAzgfeAGcAjwOPAfwAjsARwC5YgXu22kbasCTgOu8DyddhJHLNpf+av\niIiISOHkfQeNZ4F57u/F3vzxsfW+mbGNtGUzqtwnkS4RH0+n8XUi9Uv/nxKyvJM9kcKIX1x2w9yv\ndOPeiEgWjX+VkOU5Zk+kodV6c3Rd2FVERLqDkj2RCumkCpH6otugiVRG3bgidUpjiESyxYdKqPtV\nJJkqeyJ1St2+Il1LlUEpCiV7IiJSSPpCJUWhZE9EREQkYEr2REREUtR6Fr5IPdAJGiIiIil0/T0J\ngSp7It1M1QKRxqcKoNQzVfZEupkqByKNT//HUs9U2RMREekgVe+kkaiyJ1IjXfxYpHh0QWdpJKrs\nidQo3uiLiIjUEyV7UlgaUC0iIkWgblwprK4aUK1B2iIiUk9U2RMREREJmJI9ERERkYAp2RMREREJ\nmJI9ERERkYAp2RMREalDumKAdBYleyIiIhXIO+GKrhgwfMmFunC71ETJnkgV9C1bpHj85KtSqs5J\nPVCyJ1KFahp9ESkGP6lTdU7qgZI9kRh9ExeRWlTyZVDtjORJd9AQiYnfWUN3xBCRzrB1x/YPqntd\ndQcfkSSq7ImIiOSgoxU/kc6iZE9ERKRONPJ4YHVN1y8lexK0eIOjBkhEiiLv5Esno9QvjdmT4KSN\niwGNwROR4tC4QImosicNJe2batqlDqrZrohIo1JbJknyruwNAy4GHgMmAguBJxPWOx0YAjRh+3hJ\nJyyTAKR9U631G6y+AYtId/F7I6pZv5azfP3HdnQ/pHHkmew1ASuAC4B7gfuBu4BRgP9VZBYwF5js\npm8B5gM31LBMAqSGSURCUEmCVm54Slc+tzS+PLtxpwOHAWvc9FpgK3BSbL3zgVXe9HLgrBqXFd6a\nNWu6/DmqGQxcywDiSrpr84i7HinuYlHc4fPbu2ri7qyTNbrzpLcivd4xrbVuIM9kbzLwLLDNm7ce\nOMab7gmMB9Z5854GDgcGVrls387Z/caWxz9JNWdipSVsndWAFLVxUNzForiLpZq4azlTNm1MdN5n\n3d63+pep+xW41lo3kGc37hBgc2zeJmC4Nz0A6OHmR952v0dWuWw48HrVey2Z0rpSqxkH4ncnbJj7\nlbLPISIinddGprXbtXb1pm23o3+3NDW3Kwx09HOiks+lSvYjD539fHkme9uwbltfvLIYVf22Jqyz\nvcplTR3bzfx09I1Uyz9JV+13NSdMVLJfumWZiEhlajkpI2s7lWyzkqQpa7vl/vYTunL7W07a+pXu\na5pakshK9rXRXAT8T2zeSmCxN90E/Ak72SIyAdiBVQarWTYo9px/AHbqRz/60Y9+9KMf/TTAz03U\nKM/K3mognqYeSvsgdmIncIzy5o3GTubYWOWyV2PPObLjuy4iIiIi5TQBjwPT3PRo4GVgD+BK4Ag3\n/1PYZVkiy4Bza1wmIiIiUkh5j2c7GLgU+C3WzboIeBj4HfBV4Fa33nlAf2ALsBdWEdxZ4zIRERGR\nwqnbkxdEOsGBwKlYV/5dwGvdujciIrU7ELVrUnBTgd9jl3i5G9i/zHywW7gtBj4PfB+7Nl+jyYoP\n7Mzk1W69SOhxnwr8Gjgo9piQ4z4KuBy7mPjN2JjYSAhxfxT4FfAW8AtgHzc/K7aQ4w69XUuLOxJq\nu5YVd8jtWlrcobdrkfj7OfR2rWqDsKDHAscDbdgbZmDKfLDK5sPY3T3A7vDxLNBIF3VLi9t3BvAG\nMMVNhx53K/atd2jsMSHH3Qw8Q+mSQ1MJ633eExvq0RvoAzwIRNdkSIqtmbDjDr1dy3q9IyG2a1lx\ntxJuu5YWdzN2BY1Q2zWf/35Oiy2Udq0mfwPs6U3Pw8bufTplPsBfAe/R/qzkp4BTumonu0Ba3JGj\ngBOADZQaxdDjXgt8KeExIce9LxZbXzf/w9hYWAgj7sHYB0LkKuzbflZsocZ9Bdnv/1DjvtybDrVd\ny4o75HYtLe7Q27VI/P3c6e1anrdL62rLgHe86VeA54BbUuZDZbdwq3dpcYOVwSdh1zP0hRz3RKzM\nfyDwU6yBPMOtE3Lcr2Pf9n6AnZx0JnCJWyeEuF8B/uz+7oV9OHyH7NgmYY1naHFfTfb/faiv97fd\ndMjtWlrckwi7XUuLO/R2DXZ9PzdhsaW1XVW1ayEle3F/CVxXZn4lt3BrNH58Z2EfiHEhxz0e+xC8\nEJgNzAG+CxxJ2HGDXX5oNPAScB+wys0PKe4TsbP5p2PjVJJiexuLbQjtb6EIjR33Q1jcYxOWh9qu\nJcVdhHYtHvc4itGuJb3eobdrSe/nwezadtXUroWa7PXBrtv3vTLzK7mFWyOJ4lsEnAb8iNK3JSid\nfR1y3H2xknZ0P+RHsLL/TCzmEOOO3s9DgHuxb4g3YY0khPV634HdKecBbLB22mvaRHhxn0Qpbl/I\n7Vo87gUUo12Lx92HYrRrSe/zkNu1pM9psFvAdmq71mgHplLnYeXeHWXmvwT0i63TH3ixS/eu60Tx\nbcfeRI9i43i2ACOAe7Bu7ZDj3og1jL4XgAHYRbxDjHsHdnHyVdg4l1OBbwA3YF0focXdBszHxvO8\nRnpsIcftn6EZervWRinuiyhOu9ZGKe4dFKdda6MU9wGE3a6lfU6fjsXoC7Vdq9ppwCHedI+M+RPZ\ntQz8DPamajRpcUf8gcyTCDfuD2HdHX78d2J3Uwn59Z6AjXuJtGBl/3GEFbfvebLfyyHHHVWzQm/X\nfH7ckVDbNd/zwBiK0a75nqd47Vr0fs6KLcS4O2we8Fmsf380dpr23Iz5kHwLt9557XAnmUd6fJEN\nlK7fk3brulDiXg2c7NbpiQ1cH0zYcZ8NvAns59bpjVU69iSMuAdg43kiU7HbLMKusW3EYgs97nmE\n265lxe0LrV3LinsN4bZraXH3x667F2q7Fhcle0mxhdSu1WQG1o+9w/vZjnVvJM0f6R53MDYO4O/d\n73E57nNnSIt7ZGw9/xswhB33cKxb50LgGuA473Ehx30ssBQ4BzuTzT87q9HjHo81dvdj/9Of85Zl\nxRZi3E2U/78PMe4kobVrWXGH3K5lxR1yuxbnv59DbtdERERERERERERERERERERERERERERERERE\nRERERERERERERES60xhgUHfvRIbdsBvB15uu3q/h2G2kIn8BDOzC5xMREZEGdDRwO3afycXYTcZn\neMtPxm7OPWXXh9aF/YCfYBchrSeV7tcC7AKyS936m7ELI88q87hTgE3A37rpz2MXV+7q12kocCu2\njz8E9veWfRx4DFgG7N3F+yEiIiIVOJnS/SUjB2K3Ifo7b14b9ZvsAbRSe7L3hU7Yj7hWsvfru8D/\nYre6igwD1gKfrGD7aygle5Df67QXduuqaxOW3Q70rWAbLViiKyI5au7uHRCRXPUB/s39POzNbwO+\nDiyi1CW4M9c9y9/x2I3k83Qsdjuos2l/g/cXsVsfNVWwjfjrktfrtBm4EfgMsIc3fz/s3pzvVrCN\ny4HJnb9rIpJlt+7eARHJ1XHYTcfvTli2Erv35KmUqjcTgeuBfdy8f3bzTwE+gt3PchIwF9gduBir\nGN6KVRAPBk7AKoafwG7e/kksQZni/n4KS7wWYBXHuKOBvwYOwW4A/1ng/xLWG4NVvAYCB2FJyUas\nmnQu1t05AXgE+IbbnwHARdg9R9uA87Hq1WHuuc7BKlZnYPedXQ98ERjrjsFe2Jfm/YB/SNinuAXA\nGyQf/zVuv8GOzbFYgjUNO66/r2D7PYAL3H5NBa4CbnPLjsJe/6HY6/GAW/chLPkc6B5zM/AvKdu/\nFjgLmIN9YQA75jd767SQfBz7YGMZ98aO+UKsuvlF7Mb247B7oq7DxiHOwSqkV9C+21hEREQyXICN\nuxqVsKyXW7bITW/AkqJmLGHbBpzklr2E3bwc4EHgRPf3TOBN7EMe4MfAL922W4AXsDFeAL8GZnvr\nnZmwT32BH3nTj1NKOFspdZc2Az/11rsD+L77+2tY4ofbr61Ab3btbj0fqzxFfgL8K1ZtOwtLVA/C\nkpC9gO3Avm7dl7EEML5fcU8Av01ZFhmBdfNGVb4TsCrgXm56Ne27cf2bp1+AJd9gx/Yd7Bg2AX/E\nEi7cPnzL/f1pSsdnvIvrkIz9uwt41Ju+M7Y87TgCfBlY4i1bSqlKuAgbxwjwHezLAtjxFpEaqLIn\nUixZXX7RsA6/K/EOLAFcCdyHVbOWY5W4J7HkoB/Q363/LnYCwVo3vR7YAvzJTT+LjQ/8DVbFeQ4Y\njVWbom34ZgJDsCQGrLrVI2G9CVgVMVrvFRdHC9Y9GlWG1mLVvC0J25iPVaAiS1ysX8Aqjhu8H7BK\n1OtYNayFyk5O2A1LprLMwY5t9FqtdH/Pwk6OyPI57HU8GkvsHsTO3n0LO8Ytbr0HsNc1esxj2DFq\nwV7n4cAzKc9xDZbwTXLbiCevWcfRf28NwSrHfpL/jvu9Abga+1KxND1cEamEkj2RYlnnfu8PPB1b\nNsz9firlsU9iXZlgydtC4AeUEqskO2PLdmDdemBJ4RXACiwJTBpDfACWTHw9ZfuREW6/4+vth3UR\n+knuOyQbTvuxaM9hiWVUvYsnyr3c812HdStXMt5uPZbgZBlOqQLn78vQCrZ/AFax+3NsfjN2fE7G\nKp57UqqwHYB1Qa93018t8xyrsETwDOw1jB/ztOMYvzzMCKybPem1vQY4AvgV8E3gwjL7JCIZdIKG\nSLHcA7yGjYGLOxZ4n/bdob5eWMLXG+tKXIRVhLJkVRJXYl2A/4klSknrvoF1i/o+nLLeJNp/gR0F\nvIdV0qZ583fHqkpxbdhYsUgv9/hXEtYdjp2B+mU6dkbwUmz84zEZ62xg1272XlhCXM4btI+1CUua\ndmBjMWdhlbdbsOpq0mMg+Rj7FmNV3qFYMudrI/k4bkzY17FYpTUyEEvQh2DjG2cCp2HjNEWkSkr2\nRIrlfexDdD7wIW/+IKx6cjY2/izS4v0+EkvwxmAfyD2wxOVgrAu2hV2rW02xec1ueh/sBI8eWPI4\nxtuG727go1gFcCiWJM3wthV50G3neqxqOd7FuAmrHF4DTMe6jP8JeBWrxu3ttjMIS2Dmetud5h6X\nFMeR2Fi4nli39L7e/me1q8uAn2Fj2PyErhnrbj4a66odTGkc3mCs0ne7m44f52ZvegV2EsXHsUrt\nQiypasIS08uxEzJeolRhXYEd3+Pdc11E+V6fG7ExnMsTlmUdx3exhK4Je23aXLyHYO+BS7H33wIX\n58+x6nEll3URERERz2QsebgO+yBejp2d6jsTq7xdiV0bLrpkRi/gv7BKzVXYCRDrseTxe1g36RQs\n6VqFnWwwFvgYViW7GUuOfoadzHE98I9YAjI1YV9nY92Gb7l1oy7BG7GKUbTfU7ATIDZjJwX0c/MH\nYmMPN2Mni4xw83tgYwDvo3RCyZfcds/HEqUeWJJ5G+0rogOxKmcbcJ6L6b9dzEti+xXXjI1pexT4\nhVv/WmzcYWQiloRdiCXYh7v5x2HHbClWXfwMdsLJt7EEuh9Wmd2EVV1bvef8OVaFex+r9K3Dult7\nYsf1TeAPwKdS9jvuW+za3RxJOo5gCd2rbllfF9dD2HvmbkpjK5dgifE87Mzg3SvcJxEREZFCmoxV\nDiPNWJfuR7pnd0QkT+rGFREJ37lYV3XU5vfFumyf6LY9EhEREZFO8zGsu/Rl7OSar1G6bp+IiIiI\niIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiEho/h9SSa9oi61I1wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x95f8278>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**1.9**  *Attempt to **validate** the above model using the histogram. Does the predictive distribution appear to be consistent with the real data? Comment on the accuracy and precision of the prediction.*"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*Your answers here* :\n",
      "Predictive distribution is consistent with actual data. The accuracy is not very good as the centre of the distribution falls far from observed outcome.The precision is marginally worse than the predict wise model."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Biases\n",
      "\n",
      "While accounting for uncertainty is one important part of making predictions, we also want to avoid systematic errors. We call systematic over- or under-estimation of an unknown quantity **bias**. In the case of this forecast, our predictions would be biased if the estimates from this poll *systematically* over- or under-estimate vote proportions on election day. There are several reasons this might happen:\n",
      "\n",
      "1. **Gallup is wrong**. The poll may systematically over- or under-estimate party affiliation. This could happen if the people who answer Gallup phone interviews might not be a representative sample of people who actually vote, Gallup's methodology is flawed, or if people lie during a Gallup poll.\n",
      "1. **Our assumption about party affiliation is wrong**. Party affiliation may systematically over- or under-estimate vote proportions. This could happen if people identify with one party, but strongly prefer the candidate from the other party, or if undecided voters do not end up splitting evenly between Democrats and Republicans on election day.\n",
      "1. **Our assumption about equilibrium is wrong**. This poll was released in August, with more than two months left for the elections. If there is a trend in the way people change their affiliations during this time period (for example, because one candidate is much worse at televised debates), an estimate in August could systematically miss the true value in November.\n",
      "\n",
      "One way to account for bias is to calibrate our model by estimating the bias and adjusting for it. Before we do this, let's explore how sensitive our prediction is to bias."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**1.10** *Implement a `biased_gallup` forecast, which assumes the vote share for the Democrat on election day will be equal to `Dem_Adv` shifted by a fixed negative amount.* We will call this shift the \"bias\", so a bias of 1% means that the expected vote share on election day is `Dem_Adv`-1.\n",
      "\n",
      "**Hint**  You can do this by wrapping the `uncertain_gallup_model` in a function that modifies its inputs."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Function\n",
      "--------\n",
      "biased_gallup_poll\n",
      "\n",
      "Subtracts a fixed amount from Dem_Adv, beofore computing the uncertain_gallup_model.\n",
      "This simulates correcting a hypothetical bias towards Democrats\n",
      "in the original Gallup data.\n",
      "\n",
      "Inputs\n",
      "-------\n",
      "gallup : DataFrame\n",
      "    The Gallup party affiliation data frame above\n",
      "bias : float\n",
      "    The amount by which to shift each prediction\n",
      "    \n",
      "Examples\n",
      "--------\n",
      ">>> model = biased_gallup(gallup, 1.)\n",
      ">>> model.ix['Flordia']\n",
      ">>> .460172\n",
      "\"\"\"\n",
      "#your code here\n",
      "def biased_gallup_poll(gallup, bias):\n",
      "    copy_gall = gallup.copy() # I am making a copy of gall up function\n",
      "    copy_gall.Dem_Adv -= bias\n",
      "    return uncertain_gallup_model(copy_gall)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**1.11** *Simulate elections assuming a bias of 1% and 5%, and plot histograms for each one.*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "model = biased_gallup_poll(gallup_2012, 1)\n",
      "model = model.join(electoral_votes)\n",
      "prediction = simulate_election(model, 10000)\n",
      "plot_simulation(prediction)\n",
      "plt.show()\n",
      "\n",
      "model = biased_gallup_poll(gallup_2012, 5)\n",
      "model = model.join(electoral_votes)\n",
      "prediction = simulate_election(model, 10000)\n",
      "plot_simulation(prediction)\n",
      "plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VaNiwIa1bt6ZIkSK88cYbTJw4kZSUFMqUKcOVV15Jo0aNuPTSSy/20gjK5B9W\nMqhpx7AeM5JM/mElgM6hiGQwatSoURe7j8LYySdUI5klTB577DGGDBkStvvZSvCqzx3K/l7j8roY\nBVb1uUMBdA5FJANXCDpkqxlX8rXjx49z5MgRBXoiIiI5lP1tB0TygHsuv127dhGCGmwREZFCSzV7\nki/t27ePpUuXcuedd9KhQ4e8Lo6IiEiBpZo9yZdWr16d10UQERGJCKrZExEREYlgCvZEREREIpiC\nPREREZEIpmBPREREJIIp2BMRERGJYAr2RERERCKYgj0RERGRCKZgT7xs27aNgQMH5nUxAFi3bh1P\nPPEEtWrVyjZvUlISU6ZM4cYbb6RHjx7Ex8fTsWNH3n333TCUVEREJP/SpMriZebMmbz11lu8+OKL\nFC9ePOjtT5w4QXR0NKVLl77osrRr145Vq1aRmJiYZb4zZ87QpUsXUlJSWLZsGZdccgkAO3bsoEuX\nLqxatYo33ngjqGMnJiZSo0aNHJddREQkv1DNnnicP3+er7/+mhMnTvD+++/naB/9+/fn+PHjISmP\ny+UKKOB66qmnWL9+Pe+9954n0ANo0KAB8+bNY86cObz++usBH/f8+fM8+uijOSqziIhIfqNgTzwW\nL17Mc889x1VXXcW0adOC3v7NN9/krbfewrKsXCidf4cOHWL27NnceOONfgPDG264gbp16zJmzBhS\nU1MD2me/fv3YsWNHqIsqIiKSJxTsicfy5cvp1q0bjz/+OBs3bmTjxo0Z8liWxfTp0xkxYgSDBw+m\nffv2bNu2jdOnT7NixQoAXnzxRaZMmcKuXbto1aqVp8/doUOHGDFiBFFRUaxdu9azv7FjxzJ+/HjG\njRtH165dOXjwYMBlXr16NampqbRu3TrTPG3atOHw4cN8//33rFmzhvLly9OrVy/A9FG84447iIoy\nb4Uff/yRHTt2cPz4cQYPHswnn3wCwOnTp0lISGDMmDHcf//93H///Zw6dcpzjLfeeot+/foxfPhw\nrrvuOsaNG+cJej/44ANuv/12hg8fzssvv0yDBg0oX74877zzDr/88gt///vfqVChAjfffDNnzpzx\n7HPLli3079+fHj160LBhQyZNmhTweRERESnMrJCC3P8Lgx07dlijR4+2LMuyzpw5Y5UrV8566KGH\nMuQbNmyYNXXqVM9ymzZtrLZt21qWZVmrV6+2XC6XlZiY6Fk/YsQIq1atWp7lPXv2WC6Xy/ryyy8t\ny7KsDz/tOLNlAAAgAElEQVT80IqNjfWsv/XWW63evXt7lufOnWu5XK5Myz1u3DjL5XJZM2fOzDTP\n0KFDLZfLZS1evNiyLMu6/vrrrV69ennWz5kzx+sYI0eOtGrWrOlZTk1Nta6//npr8+bNlmVZ1qlT\np6xixYpZzz77rGVZljVr1iyrZcuWnvy//fabVbZsWeuZZ56xLMuyzp8/b9WvX9+66qqrPPsYOnSo\nVb58eWvy5MlWWlqadeTIEatMmTLWrFmzLMuyrBMnTljdunXz7HPhwoWWy+Wyli1blunzzEvV5gzJ\n6yIUaNXmDNE5FBG/QhH4aICGADBnzhz69+8PQIkSJXjggQeYPXs2L7/8MuXKlQPg8OHDTJkyhZMn\nT3q2mzVrFkePHs10vy6XK8tm3SZNmjBixAjPcokSJdizZ0/A5Xa5XABZHiMtLc0rj3sb331k5sMP\nPwSgadOmAJQuXZqPPvqIOnXqAJCQkMA///lPT/7KlSvzyCOPMHXqVJ599lnKlClDlSpVqFWrlmcf\ncXFxjB8/nvj4eFwuF5UqVaJRo0Zs27YNgGnTpnHs2DGGDRsGwIULF2jXrh2HDh0K4KyIiIikU7B3\nscLYPy23JCUlsWLFCnbu3OlJO3XqFOfOnWPevHkMGDAAgG+//ZayZcsSExPjyXfllVde1LFr1arF\nsGHDePfddzly5AiHDh3KNvjy3R7gyJEjmeZxB6M1a9bMURnXrVtH1apVvdJuvvlmwATABw8epGTJ\nkl7rr776apKSkti2bRutW7fOEIwWLVo0w3FiY2M9TcPff/897du3Z+zYsTkqs4iIiJv67AlLlixh\n9OjRLFmyxPP3xRdf0KRJE6ZPn+7Jl5yczNGjR7lw4ULIjn3kyBFatWpFhQoVGDBgADVr1gxqgEdc\nXByxsbGsX78+0zwbN26kUqVKnlq1YIJJMM87s+lfoqOjAdi/f79XesWKFQG8AuNAuJ/72bNn2b17\nd4b1SUlJQe1PREQk0oO9anldgIJgwYIFdO3aNUP6gw8+yM6dO1m1ahUADRs2JC0tjRkzZnjl++ST\nT0hLS/PbpOpyuTzNqECGEbHPP/88ycnJdOrUye/67FSqVIlHHnmE//znP+zduzfD+o0bN/LTTz8x\ndOhQT2Dmcrm8juN7TN+m5yuvvJINGzbwww8/eOX78MMPqVixInXq1OGrr77yWnfw4EFKly5N48aN\nPfvMjjNPvXr1WLp0qVezbUpKClOmTMl2PyIiIk7hDvaqAa8DjwLzgUaZ5OsDjABGAmMc6cWAacDv\nwD7gcZ/tOgJpjr/rQ1XwSPXDDz+QmprqtwbKHQBOnToVgEaNGnHzzTfz9NNP89xzz/Hpp5+SkJDA\nyZMniYqKonz58gBs376dXbt28eeff1KrVi0OHDjAunXrOHr0KPPmzQPw1JQdPHiQffv28dtvv7Fr\n1y42btzI4cOHPU2vKSkpAF4Bo68JEybQtm1b7rnnHq/m3MTERHr27Ml9993ndVeQmjVr8uWXX3Lw\n4EF27NjBsmXLvMpUvnx5Dh8+zMmTJ9m8eTMPPPAAFSpUoFOnTrz++ussW7aMhx9+mHr16gEwZswY\nvv76a7755hvABLvvvfcezz//vKe5NiUlxSuAdD+f5ORkT1pKSoonvW/fvpw7d45OnTrxySefsHLl\nSu69915PUCwiIpIfuYBNmIAMoCGwG4j2yXcb8LVjeQHQ2378PHAXcCUwGRPQtXXknQZcY/81yaQc\neTWgJt/57rvvrL/+9a9W3bp1rU8//dRr3R9//GE9//zzlsvlsqKioqxhw4ZZKSkp1tGjR63u3btb\nJUqUsGrXrp1hFGynTp2sihUrWhMmTLAsy4xEve2226ySJUtaHTp0sH766SeradOm1ssvv2z9/vvv\n1hdffGFVrVrVqlSpkjV69GhrwYIFVtmyZa3+/ftb3333ndWuXTsrKirKmjBhgvXHH39k+lySkpKs\nKVOmWHFxcVZ8fLx1xx13WDfddJP15ptvZsi7a9cu6+qrr7ZKlSplPfzww9aSJUusW265xZo/f76V\nmppqHThwwKpTp45Vt25da8WKFZZlWdbGjRuta6+91ipevLjVokUL66uvvvLa57vvvmu1adPGGjx4\nsPXEE09Y06ZN86xbsmSJVa5cOatRo0bW119/be3bt8969NFHraioKGvgwIHWoUOHrA8//NAqV66c\nVb9+fc9I5Q8++MCqV6+eVbx4catly5ae9PxII0kvjkbjikhmchZyeQuu89LFuQn4CCgDpNhp/wOG\nAx848n0NLAfcPdP/budpjKnxm+nIuwcT4E0A6gJzgXHA50BmnZtCde5ExFZ97lD29xqX18UosKrP\nHQqgcygiGbiC7WjuRzibcdtiavJSHGk7gQ6O5VigOeC8fcEuTHNvRbwDPYDDwK/242ZAcWAJpom3\nIyIiIiKFXDiDvcrAKZ+0k0B1x3J5IMZOdzth/3fmA9N/rxymthDgfUzAVwvYCPzbPqaIiIhIoRXO\nYC8FSPZJ8z2+u9Yv2U8e32rMR4BBwDmf9P3AncAhTP8/ERERkUIrnJMqHwTa+aSVA/Y6lo9hAr2y\nPnkADjjSGmMCw08zOdY5TL+9cv5WJiQkeB7HxcURFxeXVblFRERECqxwBnurgaE+afWBeY5lC1iD\nGWzh1gDYDrjn1KgK3Ag4JxwrgndfQDCjfHfghzPYExEREYlk4WzG/RZIBNrbyw2AEsBSzMjbxnb6\nG0A3x3ZdgTn247KY6VdW2Ns3AoZh+u8NstPA9NWrDyzLhechIiIiUmCEs2bPwvShG4GZY+9a4G/A\nWaAzsBnYCiwCamACwHOYAHEyJjD9CDNRcl/Hft8FzgA3YwLB6ZgBHneSsbZPREREpFAJZ7AHZuqV\nB+3HrzvSm/vkm+RnWwuIy2LfnXNcKhEREZEIFen3xhUREREp1BTsiYiIiEQwBXsiIiIiEUzBXpCS\n01Lzugj5ogwiIiJSMIR7gEaBFxMV7blpeV4J9c3SDxw4wFVXXcVnn31Gs2bNQrpvt9OnTzN79mw+\n/fRTOnTowNChOTuHU6dO5c0332TTpk0hLqGIiEhkUs2eULp0aVq3bk3ZsmWzz3wRx+jduzcbNmwg\nKSkp4O0SExO9lmvVqkXz5r6Dt0VERCQzCvaEMmXK8Mknn3DFFVfk6nFKly5N+fLlA85vWRa9evXy\nSrv11luZMWNGqIsmIiISsRTsiUdaWlpeF8HLmDFjWLNmTYb01FT1WRQREQmUgr1C5s0332TSpElM\nnjyZyy67jG+//ZaZM2fSqlUr3n77bQA2btxInz596NSpE59//jktWrSgTJky9O/fnzNnzvDUU09R\no0YN6tevz/bt2wHYvHkzV1xxBe3bm7vh7dmzh0cffZSoqCh+/fXXTMuzbds2HnvsMWbOnMldd93F\ntGnTANi3bx/ffvstAIMHD2b+/Pn88ssvDB48mOrVq3vtY8OGDfTp04eRI0fSpUsXHn74YU6ePAnA\n+vXr6dmzJw888ACLFy+mXr16XHrppbz77rue7Xfv3s3TTz/N7Nmzuemmmxg4cGCIzraIiEjeU7BX\niJw/f54hQ4bw9NNPM2jQIKZPn05UVBRt27blu+++8+Rr2rQpaWlpbNy4kTNnzrBhwwYWLVrEq6++\nyjPPPENCQgK7d++mUqVKvPDCCwBcc801tG3bFpfLBZi+dffee2+2Zbr//vu5/PLL6dOnD8OHD+fJ\nJ59k3759XH755dx9990ATJw4kZ49e1KhQgWKFSvG4cOHPdtv3bqVbt268cILLzBq1Cg++eQTtm/f\nTufOnbEsi5YtW3Ls2DHWrVuHy+Xi559/5t577+XJJ5/07CMhIYEbbriB3r178/HHH3PZZZeF5HyL\niIjkBwr2CpHk5GSOHTvGa6+9BkC3bt2oV68ejRo18soXHR1N9erVKVOmDHfccQdRUVHExcUB0LJl\nS0qXLk10dDTXX389P/30k2c7l8uFZVlBlal379507doVgBIlSpCWlpZhUIZbuXLlqFOnjlfa+PHj\nad68OZUqVQKgSJEiDB8+nA0bNvDZZ58RFRVFxYoVqV27NvHx8RQpUoS//e1vHD9+3BM0JiUlMXXq\nVE6fPk3x4sV56KGHgnoOIiIi+ZmCvUKkdOnSjBo1iieffJKuXbty4MABypUrF9C2RYsWzZAWGxvL\nqVOnLqpMTzzxBKVLl2bSpEl89NFHQHB9Bzdt2kTJkiW90q6++moAvv/+e0+aMwiNjY0F4MKFCwA8\n//zzfP/99zRs2JAlS5Zw6aWX5uzJiIiI5EMK9gqZYcOGsXjxYrZu3UqTJk345ptvLmp/vjV57mbc\nQE2bNo1//vOfPPHEE55m22BER0ezb98+r7SKFSsCEBMTE9A+GjVqxObNm7nqqquIj4/nqaeeCroc\nIiIi+ZWCvULkyJEjbN26le7du7N9+3aaNGnCpEmTQrZ/l8vlNVI2u1Gz+/fv58knn6Rv374UK1Ys\nQ41eIIFj69at2bZtm1cN48GDBwFo06ZNQPtauXIlNWrUYNmyZUyePJkpU6Zw4sSJbI8tIiJSECjY\nK0TOnj3L9OnTAShVqhTx8fFUrVqV5ORkAK/Jjn0DNXcg5s7rzuOs2atVqxY//PADO3bsYN++fSxY\nsAAwI3PdkpOTSUlJAeDw4cOkpaXx3XffceHCBRYtWgSYO3r88ccfnjn5duzYwQ8//IBlWZ7ju/cx\nZMgQXC4X//rXvzzHeOedd7jllls8wV5KSopXIOl+nu7nOHv2bM6cOQPAgw8+SJkyZShdunRgJ1VE\nRCSf0+3SgpSclhry25XlpAwxUdE52nbGjBkUKVKEK6+8ku3btzN27FgmTJgAwHvvvUeLFi1ISUlh\nxYoVHDp0iEWLFtG1a1fmz58PwIIFC2jZsiXJycksX76cQ4cO8fbbb/OPf/yDxx9/nFWrVtGsWTM6\nd+7MwIED2bFjB9u3b6dFixbMnDmT3377jRUrVtCpUyfatGlDfHw8kydPZt26dbz22mssXLiQ0aNH\n06hRI2688UauueYabrrpJl544QVSU1NZuHAhLpeLl156if79+3PFFVewZs0annrqKRITE6lUqRLn\nz59n8eLFAHz77besW7eOM2fOsGzZMpo3b87MmTNxuVxMnz6dhIQEDh06RKdOnbjvvvvYtWsXCxcu\nJDo6Z+dXREQkvwmug1VksIIdMSoiWas+d2ie/wgqyNz329Y5FBFfrmA7w/uhZlwRERGRCKZgT0RE\nRCSCKdgTERERiWAK9kREREQimII9ERERkQimYE9EREQkginYExEREYlgCvZEREREIpiCPREREZEI\npmBPRCJWclqq1/+CegwRkYuhYE9EIlZMVDTV5w7N8b2k88sxREQuhoI9ERERkQimYE9EREQkginY\nExEREYlgCvZEREREIpiCPREJG41cFREJPwV7IhI2wYxcdQaECg5FRHKuSF4XQETEH3dgCLC/17g8\nLo2ISMGlmj0RERGRCKZgT0RERCSCKdgTERERiWAK9kREREQimII9ERERkQimYE9EREQkginYExER\nEYlgCvZEREREIpiCPREREZEIpmBPREREJIKF+3Zp1YBngS1Aa2ACsM1Pvj5AZcCFKePzdnox4BXg\nLuAc8BLwegDbiYiIiBRK4Qz2XMDHwBBgJfAlsAyoCzjvcn4b0BNoay8vAHoDs4HBwCrgVeBh4F/A\nj8DX2WwnIiIiUiiFsxm3I9AQWGMvbweSgdt98j0DLHcsfwgMsB8fBhYBPwODgETSg7usthMREREp\nlMIZ7LUFdgMpjrSdQAfHcizQHNjhSNsFNAIqAjN99nkY+DWA7UREREQKpXAGe5WBUz5pJ4HqjuXy\nQIyd7nbC/u/MB6b/XjngoyC3ExERESk0whnspWCabbM6vrvWL9lPHpdP3kcwTbnngtxOREREpNAI\n5wCNg0A7n7RywF7H8jFMwFbWJw/AAUdaY0yA92mQ2wGQkJDgeRwXF0dcXFz2pRcREREpgMIZ7K0G\nhvqk1QfmOZYtzACOuo60BpjBHEfs5arAjcAUR54iAWzn4Qz2RERERCJZOJtxv8WMnm1vLzcASgBL\ngbGY2jqAN4Buju26AnPsx2Uxc+etsLdvBAwDimaznYjkU8lpqX4fi4hIaISzZs/CzIU3AjMFy7XA\n34CzQGdgM7AVM7VKDUwAeA4TIE7GBKYfAdcDfR37fRf4M4vtRCQfi4mKpvpcU+m/v9e4PC6NiEjk\nCfcdNHYDD9qPnXe+aO6Tb5KfbS0gLpv9+9tOREREpNDSvXFFREREIpiCPREREZEIpmBPREREJIIp\n2BMRERGJYAr2RERERCKYgj0RERGRCKZgT0RERCSCKdgTERERiWAK9kREREQimII9ERERkQimYE9E\nREQkginYExEREYlgCvZEREREIpiCPREREZEIpmBPRMRHclqq1/9gtgl2OxGR3KZgT0TER0xUNNXn\nDiUmKjrobYLdTkQktynYExEREYlgCvZEJN/LSbOqv+0vZh8iIgWVgj0Ryfdy0qzqb3s1sYpIYaRg\nT0RERCSCKdgTERERiWDBBHtFcq0UIiIiIpIrggn2lgDNc6sgIiIiIhJ6wdTWvQc0BR4GjgCLgS25\nUSgRERERCY1ggr137f+zgArAVOAaYAHwFrA7tEUTERERkYsVTDPuX4CSwOPAl0An4ENgFXAf8Kad\nR0RERETyiWBq9pYDlwOJwBTgbeC8vW4d8AAm+LsmlAUUERERkZwLJtg7DXQHVmay/i9AxYsukYiI\niIiETDDNuLeSMdC7FKhiP34RuDIUhRIRERGR0Agm2HvYT9oR4DX7sQX8edElEhEREZGQCaQZ91Hg\nHqAGcJPPuopAmVAXSkRERERCI5BgbzqQign0lgEux7ozmJG5IiKSR5LTUomJis7wWEQEAh+gMQsz\ntcoFP+suCV1xREQkWDFR0VSfOxSA/b3G5XFpRCS/yS7Yqwn8hgny6mIGZDhFA3cCfUNeMhGRMFKN\nmIhEquyCvXXAy5h59ToBEzPJp2BPRAo01Y6JSKTKLthrBxyyH79nP37HsT4K/6N0RURERCQfyC7Y\nS3Q8PogJ+JzSMHfNEBEREZF8KKtgrxLQMJvtXcDtwMCQlUhEJEzUT09ECoOsgr1LgC+AA5gJk/2J\nAqqiYE9ECiD10xORwiCrYG8n8CRmnr2s3Be64oiIiIhIKGV3u7TsAj3QpMoiIiIi+VZ2AzTaADuA\nP4AbgDo+66OBrsAdoS+aiEQq9ZUTEQmf7IK9tzHz7L0GNLAfH3WsjwYuy52iiUikUl85EZHwyS7Y\nawScsx8vAvYBn/rkiQ91oUQk59y1Zqo9ExERyL7P3jnH4z8wgV5toClQ0k7/IBfKFYhAahSr5Xop\nRPIZd62ZAj0JpeS0VL+PRST/yy7Yc6oHfA/8P2ATcAKYDMQEsY9qwOvAo8B8TM2hP32AEcBIYIzP\nupqYu3gs9LNdR8xEz+6/64Mom4iIZML9I0I/JEQKnuyacZ3mY/rrtQV+BmIx98tNAJ4NYHsX8DEw\nBFiJGcW7DKgLOH8m3gb0tI8DsADoDcy2l9MwtYyX+zlGPNDcfpwCbAmgXCIiIiIRK5iavSsxwdR6\n4CQm8HsbSApw+46YO3KssZe3A8mYO3A4PQMsdyx/CAxwLP8KHMMEj051gcaYSZ5/QoGeSLbUNBc4\nnSsRKaiCCfbeA6r4SQ90NG5bYDemxs1tJ9DBsRyLqZnb4UjbhWnurZjN/psBxYElmIEkHQMsl0ih\n5Wyac1Mg45+aMUWkoMoq2LsWWO34awis9Un7EvhLgMeqDJzySTsJVHcsl8f0ATzpSDth/3fm8+d9\nTMBXC9gI/Ns+pogEQAM7REQiU1Z99n7CjMb1NxDCaWWAx0rBNNs6+Qab7lq/ZD95fJttM7MfuBP4\nEdP/b0aA24lEPJfLvI0sK7PbXYvkY/b1i65fkaBkFeydxQyUOJpFnmigHSbAys5BO69TOWCvY/kY\nJtAr65MH4EAAx3A7B3zu2NZLQkKC53FcXBxxcXFB7FpEJGvOOQ5DPd9hMPMo5mY5RKTgyG40rjPQ\nKwc8YP93OdLuxQyKyM5qYKhPWn1gnmPZwgzgqOtIa4AZzHEkgGM4RePd98/DGeyJiIRaZncICUXw\n5d53IHce0Z1KRASCG6DxBuZeuR0x/eJqY2rqxge4/bdAItDeXm4AlACWAmMxI2ndx+nm2K4rMCeA\ncg+y9wmmr159zNQuIiL5ggZ5iEheCGaevc+AWZiAqhKwDjP6dUqA21uYPnQjMIM9rgX+hmku7gxs\nBrZibstWAxMAnsMEiJMd+7keuBUzYOMOTLCYAtwMPA9MxwzwuBPvkb8iEgA1/YmIRJZggr36mABq\nKWaS4yjMyNm7gL4B7mM38KD9+HVHenOffJOy2Mda4Go/6Z0DLIOIZEFNf/mDgm4RCZVggr2PgXGY\nUbovY+6TezVmXjsREQkhBd0iEirBBHtrMX323K4BKmBG0IqI5LrCWttVmJ6riIReMAM0imBuW7YO\ncyuy9wh8QmURkYsWyACHSLwDiL87nYiIBCqYYG8qMBr4GZiNGVAxDjPoQkQkX1BgJCLiLZhm3L8D\nNwL/daRNxPTf+yiUhRKRyKEmSBGRvBVMzd4vmOZbX0khKouIRCDVtImI5K2savZqYua0c/sMmAus\ncKRFA01DXywRERERCYXsmnFfwUx07L7rtAvo5ZNnWqgLJSIiIiKhkVWwtxdzh4q14SmKiIiIiIRa\ndn32fAO9+4BVwA7MfWd11wqRQigSpzcREYlUwYzG/SfwNGZ+vUSgKPAYUAs15YoUKrq7Q8HjHhWt\n0dEihU8wo3FbAlcAQzD3tX0FM8de5Vwol4iIhJA7QFegJ1L4BBPsrcP/NCtFQ1QWEREREQmxYJpx\nawAdgA1ACaAe0DvIfYiIiIhIGAVTszcR02fvNHAYU9NXGngiF8olIpIrNLhERAqbYGrlWmEGZCQD\n1TFTsxzJhTKJSAGXnwcBaHCJiBQ2wdTszcM03R4EviM90CsZ4jKJSAGnW6SJiOQfwQR7PYGUTNJF\nREREJB8Kphn3BeBqP+kWZioWEREREclnAgn2GgI3A9OBn4H9jnUu4KFcKJeIiIiIhEB2wV4L4Csg\nxl5OBNpi+u25jc2FcomIiIhICGTXZy8BeBK4BDMCdw3wrE+eCyEvlYiIiIiERHbB3nFgJnASU5vX\nFxP0OWlSZREREZF8Krtg70+f5STgkE/a30NXHBEREREJpexq5e7GzK3nwoy6ddnLq+z1MUBj4K3c\nKqCIiD/OiZvz8yTOIiJ5Lbtg70/gAOC8v1Ciz/a+zboiIrlOd8IQEQlMdsHeI8Bn2eS5OURlERER\nEZEQy67PXnaBHsDnoSiIiORvyWmp2WfKI/m5bCIieS2Y26WJSCGWn+93m5/LJiKS1xTsiYiIiEQw\nBXsiIvmEmqNFJDco2BMRySeCaY5WYCgigVKwJyJSAKmfoogESsGeiIiISARTsCciUog4m3/VFCxS\nOCjYE5GQUOAQnLw6X87mX91iTqRwULAnIiGhPmT+ZRbUOW/3ll8pgBeJDAr2RERyUUEOggty2UUk\nnYI9ERERkQimYE9EREQkginYExEREYlgCvZEREREIpiCPREREZEIpmBPREREJIIV5GDvsrwugIiI\niEh+VyTMx6sGPAtsAVoDE4BtfvL1ASoDLkwZn3esqwm8AFQHbghiOxEREZFCJ5zBngv4GBgCrAS+\nBJYBdQHnNO23AT2BtvbyAqA3MNteTgP+AC732X9224mISB5KTkv13KLN+VhEclc4m3E7Ag2BNfby\ndiAZuN0n3zPAcsfyh8AAx/KvwDFM8BjMdiIikod0X16RvBHOYK8tsBtIcaTtBDo4lmOB5sAOR9ou\noBFQMYt953Q7ERERkYgWzmCvMnDKJ+0kpu+dW3kgxk53O2H/d+bzldPtRERERCJaOPvspWCabZ18\ng013rV+ynzy+zbY53i4hIcHzOC4ujri4uCx2LSIiIlJwhTPYOwi080krB+x1LB/DBGxlffIAHMhi\n30Ft5wz2RERERCJZOJtxVwO1fdLqkz5gA8Cyl+s60hpgBnMcyWLfOd1OREREJKKFM9j7FkgE2tvL\nDYASwFJgLNDYTn8D6ObYriswx2df/sodyHYiEoTktNTsM4mISL4WzmZcCzMX3gjMFCzXAn8DzgKd\ngc3AVmARUAMTAJ7DBIiTHfu5HrgVM/DiDkywmBzAdiISJPdUGQD7e43L49KIiEhOhPsOGruBB+3H\nrzvSm/vkm5TFPtYCV2eyLqvtRCSfy28T7ea38oiI5ERBvjeuiEQY56S7+UF+K4+ISE4o2BMRERGJ\nYAr2REQkT7kHAmlAkEjuULAnIiIhkdOgzd1crv6RIrlDwZ6IiISEgjaR/EnBnoiIiEgEU7AnIiJe\ngmmGVT87kfxPwZ6IiHhxTqYdaF5NTyOSfynYExEREYlgCvZERIKkpsusOc+PzpVI3gv37dJERAo8\n3TM4azo/IvmLavZERCKIatJExJeCPZF8SncVkJzQgAkR8aVgTySf0gS1IiISCgr2RERERCKYgj0R\nERGRCKZgT0RE1DdUJIIp2BMpBPRFLtnRwA6RyKVgT6QQCOb2V1J46EeASOGgYE9EpJBSbZ5I4aBg\nTyRCqdZGRERAwZ5IxFKtjeQW/ZAQKVgU7ImISFD0Q0KkYFGwJxIBVNMiIiKZUbAnEgFU0yIiIplR\nsCciIiISwRTsieRzziZaNdeKiEiwiuR1AUQka84Jkff3GpfHpRERkYJGNXsiIiIiEUzBnkg+Ekwz\nrZp0RUQkEAr2RPKRYEbVXswIXI3cFREpPBTsiYiIiEQwBXsiIiIiEUzBnoiIiEgEU7AnIiIiEsEU\n7ImIiIhEMAV7IrnMPUVKZnfC0BQqIiKSmxTsieQy9xQpMVHRGdJ800VEREJNwZ6IiBRoqikXyZru\njU1MyiAAABHlSURBVCsiIgWa7h8tkjXV7ImIiIhEMAV7IiKSL6g5ViR3qBlXRETyBTXHiuSOSK/Z\nq5bXBRARERHJS+Gu2asGPAtsAVoDE4BtfvL1ASoDLkwZnw9wXUfgc8fyP4D3QlR2ERERkQInnMGe\nC/gYGAKsBL4ElgF1AWfnjNuAnkBbe3kB0BuYnc06gHiguf04BRNUioiIiBRa4WzG7Qg0BNbYy9uB\nZOB2n3zPAMsdyx8CAwJYVxdoDFQFfkKBnoiIiEhYg722wG5MjZvbTqCDYzkWUzO3w5G2C2gEVMpm\nXTOgOLAE2IcJLkVEREQKtXAGe5WBUz5pJ4HqjuXyQIyd7nbC/n9FFuuqAe9jAr5awEbg3/YxRURE\nRAqtcAZ7KZhm26yO7671S/aTJzWLdS5H2n7gTuAQpo+fiIiISKEVzgEaB4F2PmnlgL2O5WOYYK6s\nTx6AX7NYd8Bnv+cwo3LL4UdCQoLncVxcHHFxcdkUXURERKRgCmewtxoY6pNWH5jnWLYwAzjqOtIa\nYAZzHMpi3RE/x4vGu3+fhzPYExEREYlk4WzG/RZIBNrbyw2AEsBSYCxmJC3AG0A3x3ZdgTkBrBtk\n7xNMX736mKldRERERAqtcNbsWZg+dCMwU7BcC/wNOAt0BjYDW4FFQA1MAHgOEyBOtveR2ToXcDNm\nguXpmEEcd+I98ldERESk0An3HTR2Aw/aj193pDf3yTcpi31ktq5zDsskIiIiErEi/d64IvlGclpq\n9plEJGB6T4kERsGeSJjEREVTfe5Qqs/1HackIr6cgZxvUOd+H8VERYe7WCIFkoI9kTym2gmRjJw/\njhTUiVwcBXsieUw1fiIikpsU7ImIiIhEMAV7IiIiIhFMwZ6IiIhIBFOwJyIiIhLBFOyJkPU0D7mx\nXU5p5K6IiAQr3HfQEMmX3CNiAfb3Gpfr2+VUuI8ncrGS01I1dYpIHlPNnoiI5BpNLSSS9xTsiYiI\niEQwBXsiIiIiEUzBnoiIhJ0GG4mEj4I9kRzI7otKX2QiWVNfPpHwUbAnkgPOUbFuzgBPX2QiIpJf\nKNgTCREFeCIikh8p2BMRERGJYAr2RERERCKYgj0RERGRCKZgT0RERCSCKdgTERERiWAK9kQCpLnz\nRESkIFKwJ4VaMAGcplYRKfic73n9gJPCQsGeFGr+JkcWkcjl/NEWExWd18URCQsFeyJZ0C9/kbyX\n2ftQ70+RwCjYE8mCmm5F8l5mNfCqmRcJjII9ERERkQimYE9EREQkginYExEREYlgCvZEREREItj/\nb+/Og6Qo7zCOf3eXBREE5b6UeJB4YOKBByCXEjVeaHkkpZZCAEujJBgIeCRa8UQToxHPpBSNSNCK\nF0YUjYpGQzQxxiscHizGCw0iYERFIH88b2d6e3tmZ6/Z3ZnnUzW12/329LzT8+67v3mPfh3sWcmp\nbQafZ/iZFQffU89M2jR3BswKLT6D751xM+qcbmatg/+WzcQte9Yi+Ru5mTUX1z9WbBzsWYvku9yb\nWVPLFtS5/rFi42DPioq/kZtZvhzUWanwmD0rKh6jY2ZmVp1b9szMrGi0lHV0o9dzD4O1BA72zMys\naGRbz7q2dXQbOyiLXs/dw9YSONizouBvz2bWENmCxDQeG2ytjYM9a7XSZs/lc6yZWUN4Yoe1Ng72\nrNWqyzfxuhxrZpaUz1hAf6m0lsqzcc3MzGqRbaa/7wBgrYFb9qxo+Vu2mTWU6xErBg72rEWprWKt\nS8Xrrlsza6jaxgPHOTC0lqrQ3bh9gfOBl4HBwJXAaynHnQb0AspQHn/WCGnWzDZs2vj/wczZfo8q\n1mzdIe4yMbOWqrb6KZ86MP67WWMpZLBXBswDpgN/Ap4CHgIGAPGvQ2OAU4GhYfsuYDxwSwPSrAXw\nmBczK2WuA625FLIbdzSwC7AwbC8GNgBHJ46bBjwc274fmNzANLN6WbhwYXNnwVoJlxWri9rKS7ZZ\nvmm/u/u46I1s6AkKGewNBd4CvortWwYcGNtuCwwClsT2vQ7sBnSvZ1q3xsl+aSr1isX/wC1fLitW\nF48/+UTO9OS9/NLu6+dVOkrGyIaeoJDBXi9gbWLfGqBfbLsLUBn2Rz4JP3eqZ1r8/FZHaRVOPhVL\nbfeeKqWA0cxKV7a6rqKsPHUCmetGawqFDPa+Qt22uV4/avXbkHLMxnqmldUtmy1bY97AM59ugvo+\nr7aZsPVdmsjMrDWp610B6rMaUFPW5WnH17dOru//r0I/zxrmPOCfiX3zgRti22XAF2iyRWRfYBNq\nGaxPWo/Ea74BbPbDDz/88MMPP/xoBY/baKBCzsZ9Ekh+XfkG1d/EZjSBY0Bs385oMscH9Uz7MPGa\nO9U962ZmZmZWmzLgFWBU2N4ZeB/YErgE2D3sPx7dliUyF5jSwDQzMzOzklTo8Ww7ABcAz6Nu1pnA\nC8DfgcuAe8NxU4GtgfVAJ9QiuLmBaWZmZmZmZjV0QS3QZmaNyXWLWT2MAF5Ct3hZAGwb9vdFE0FO\nB25H998jjzQrbtnKC8AzaILPJqrfv9HlpTTtCTwLrAYeA7qG/a5bLE228gKuWyxdOZrbMCJsu27J\nogd60wOBQ4Aq9EcG6ioeHX7fBd3cuRx1Y6el+Q6VxS9Xedkbrau8V3hEM7pdXkpTWzTMpD3QAVgE\nXBrSXLdYUq7y4rrFsjkTWAUMJ3t5cN0CfA/YKrY9Fo3dGw18RvWZx0uBY4Fv50iz4patvADcAfyE\n6rO7weWlVPVE/8AjM4CLyF0eXFZKV7byAq5bLN0BwGHAchTsNXrdUsibKje1ucC62PZK4G20TNty\n0pdpG5IjzYpbWnlZgb4ddUEzuZeG4yrDMfks+WfFZyXwZfi9Hfpnfg25y4PrltKVVl6uxnWLpeuK\n6ov5YbuMJohbiinYS9oLuBHdcHlNIu0TtIxaWlpyCTcrDXsBN6HVWA4HegOnhN8vC8fks+SfFa8j\n0Z0ERqMxMmnlwXWLRY4EnkPlZSCuWyzdZPTlMa4njRy3FGuw1wHdt28m+gNLW6atjPyWcLPiF5WX\na2P7NgOzgbOBk8M+l5fS9iBapedpVDY24LrFsnsQOJpMeYm4brHIROBOMi3BkUaPW4q1ME0FJqEL\n9h7QOZG+NfAuuqlztjQrHVF52ZSS9gAqE+DyYprIMx7oBnyE6xbLrYpMeemaSHPdYhOBF9F48fVA\nf+BR4DR0r+A41y0JE4EdY9vDqdk8/iZwAjA4R5qVhmR5qUyk9yKzpvMQXF5M3iZ3eXDdYnFvU3MR\nA9ctlhRN0MhVf7huQTMqT0ZLse2M7lczFniZ6su0fYCmxWdbwq19oTJszWosNcvLVPRNPGr1vhQt\nxQcuL6WqCxp/FRmBlniEmuXBdYtlKy+DgAm4brHsomAvrTy4bgkORf3Ym2KPjcBOaJm224AfhJ97\nx56XK82KV7byMgn94SwEzgWOSjzP5aX0DEIV7VOofIyLpblusaS08lKGAkDXLZZLFOyB6xYzMzMz\nMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzM7OmsivQo7kzkUMbYL/mzkSKps5XP2C72PbX\nge5N+HpmZmbWCg1Da3PeAtwAzEc3mo4cgxbnHl7zqS1Cb+BudBPSliTffE1AN92dE45fi27sPaaW\n5x0LrAFOCduno5uDN/Xn1Ae4F+XxDmDbWNr+aJWiucA2TZwPMzMzy8MxwCdUv+v614D3gO/H9lXR\ncoM9gJE0PNg7oxHykTSS3Pn6NfAvoGdsX19gMTVXVUizkEywB4X7nDoBq4HrU9IeADrmcY4KFOia\nWQGV136ImRWRDsBvw+OF2P4q4ApgJpkuwc0FzVnhHQJMKfBrHoSW0DobWBnb/y5a+qgsj3MkP5dC\nfU5rgVuBE4EtY/t7o2XAPs3jHBcBQxs/a2aWS5vmzoCZFdTBaKH2BSlp84GrgRPItN4MBm4GuoZ9\nPw/7jwX2QGuADgFOBbYAzkcthveiFsQdgMNQi+HhwArUerUZtUYdBSxFgdcE1OKYNAz4DrAj0BY4\nGfhvynG7ohav7sD2KCj5ALUmTUHdnfsC/wB+EfLTBTgPuAsFvNNQ69Uu4bV+jFqszkTrbC8DzgIG\nhmvQCX1p7g38MCVPSROAVaRf/4Uh36BrcxAKsEah6/pSHuevBKaHfI0AZgD3hbQD0OffB30eT4dj\nn0PBZ/fwnNnAjVnOfz0wGTgJfWEAXfPZsWMqSL+OHdBYxm3QNb8StW6eBWyFys04YAkah3gSaiG9\nmOrdxmZmZpbDdDTuakBKWruQNjNsL0dBUTkK2L4Cjg5p76EF3wEWoUXeAY4APkb/5AF+DzwRzl0B\n/BuN8QL4C3Bc7LhJKXnqCNwZ236FTMA5kkx3aTnwh9hxDwK3h98vR4EfIV8bgPbU7G6dhlqeIncD\nv0GtbZNRoLo9CkI6ARuBbuHY91EAmMxX0qvA81nSIv1RN2/UyncYagXsFLafpHo3bnzx9Oko+AZd\n23XoGpYB76CAi5CHq8Lv3yVzfQaF97Vjjvw9BLwY2/5jIj3bdQS4EJgVS5tDppVwJhrHCHAN+rIA\nut5m1gBu2TMrLbm6/KJhHfGuxAdRADgfeBy1Zt2PWuJeQ8FBZ2DrcPynaALB4rC9DFgPfBG230Lj\nA/+KWnFWADuj1qboHHFHAL1QEANq3apMOW5f1IoYHbcyvI8K1D0atQwtRq1561POMR61QEVmhfd6\nBmpxXB57gFqi/oNawyrIb3JCGxRM5XISurbRZzU//D4GTY7IZRz6HIehwG4Rmr27Gl3jinDc0+hz\njZ7zMrpGFehz7ge8meU1rkMB35BwjmTwmus6xstWL9RyHA/y14Wfy4FfoS8Vc7K/XTPLh4M9s9Ky\nJPzcFng9kdY3/Fya5bmvoa5MUPB2JfA7MoFVms2JtE2oWw8UFF4MzENBYNoY4u1QMHFFlvNH+od8\nJ4/rjboI40HuOtL1o/pYtBUosIxa75KBcrvwejehbuV8xtstQwFOLv3ItMDF89Inj/Nvh1rsvkzs\nL0fX5xjU4rkVmRa27VAX9LKwfVktr/EwCgTPRJ9h8ppnu47J28P0R93saZ/tdcDuwLPAL4FzasmT\nmeXgCRpmpeVR4CM0Bi7pIOBzqneHxrVDAV971JU4E7UI5ZKrJXE+6gL8MwqU0o5dhbpF476V5bgh\nVP8COwD4DLWkjYrt3wK1KiVVobFikXbh+StTju2HZqBeSN1mBM9B4x8PzHHMcmp2s7dDAXFtVlH9\nvZahoGkTGos5BrW83YVaV9OeA+nXOO4G1MrbBwVzcVWkX8cPUvI6ELW0RrqjAL0XGt94BDARjdM0\ns3pysGdWWj5H/0THA9+M7e+BWk/ORuPPIhWxn/uhAG9X9A+5EgUuO6Au2Apqtm6VJfaVh+2uaIJH\nJQoed42dI24BsCdqAeyDgqRDY+eKLArnuRm1Wg4K73ENajm8DhiNuozPBT5ErXHbhPP0QAHMqbHz\njgrPS3sf+6GxcG1Rt3S3WP5z1atzgXvQGLZ4QFeOupuHoa7anmTG4fVELX0PhO3kdS6Pbc9Dkyj2\nRy21V6KgqgwFphehCRnvkWlhnYeu7yHhtc6j9l6fW9EYzvtT0nJdx09RQFeGPpuq8H53RGXgAlT+\nJoT3+QhqPc7nti5mZmYWMxQFDzehf8T3o9mpcZNQy9sl6N5w0S0z2gHPoJaaGWgCxDIUPF6LukmH\no6DrYTTZYCCwD2olm42Co3vQZI6bgR+hAGRESl6PQ92Gq8OxUZfgrajFKMr3cDQBYi2aFNA57O+O\nxh6uRZNF+of9lWgM4ONkJpT8NJx3GgqUKlGQeR/VW0S7o1bOKmBqeE9/C+95ViJfSeVoTNuLwGPh\n+OvRuMPIYBSEnYMC7N3C/oPRNZuDWhdPRBNOrkYBdGfUMrsGtbqOjL3mI6gV7nPU0rcEdbe2Rdf1\nY+AN4Pgs+U66iprdzZG06wgK6D4MaR3D+3oOlZkFZMZWzkKB8Vg0M3iLPPNkZmZmVpKGopbDSDnq\n0t2jebJjZoXkblwzs+I3BXVVR3V+R9Rl+2qz5cjMzMzMGs0+qLv0fTS55nIy9+0zMzMzMzMzMzMz\nMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMrNv8DuphgcPgLpE8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbffb6d8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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aNGnShLZt2xIfH8/rr7/Oc889R2pqKuXLl+eCCy6gadOmVKtW7UyfGiIiUgiM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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x952ce10>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that even a small bias can have a dramatic effect on the predictions. Pundits made a big fuss about bias during the last election, and for good reason -- it's an important effect, and the models are clearly sensitive to it. Forecastors like Nate Silver would have had an easier time convincing a wide audience about their methodology if bias wasn't an issue.\n",
      "\n",
      "Furthermore, because of the nature of the electoral college, biases get blown up large. For example, suppose you mis-predict the party Florida elects. We've possibly done this as a nation in the past :-). Thats 29 votes right there. So, the penalty for even one misprediction is high."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Estimating the size of the bias from the 2008 election\n",
      "\n",
      "While bias can lead to serious inaccuracy in our predictions, it is fairly easy to correct *if* we are able to estimate the size of the bias and adjust for it. This is one form of **calibration**.\n",
      "\n",
      "One approach to calibrating a model is to use historical data to estimate the bias of a prediction model. We can use our same prediction model on historical data and compare our historical predictions to what actually occurred and see if, on average, the predictions missed the truth by a certain amount. Under some assumptions (discussed in a question below), we can use the estimate of the bias to adjust our current forecast.\n",
      "\n",
      "In this case, we can use data from the 2008 election. (The Gallup data from 2008 are from the whole of 2008, including after the election):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gallup_08 = pd.read_csv(\"g08.csv\").set_index('State')\n",
      "results_08 = pd.read_csv('2008results.csv').set_index('State')\n",
      "\n",
      "prediction_08 = gallup_08[['Dem_Adv']]\n",
      "prediction_08['Dem_Win']=results_08[\"Obama Pct\"] - results_08[\"McCain Pct\"]\n",
      "prediction_08.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>Dem_Adv</th>\n",
        "      <th>Dem_Win</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>State</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Alabama</th>\n",
        "      <td> -0.8</td>\n",
        "      <td>-21.58</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alaska</th>\n",
        "      <td>-10.6</td>\n",
        "      <td>-21.53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arizona</th>\n",
        "      <td> -0.4</td>\n",
        "      <td> -8.52</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arkansas</th>\n",
        "      <td> 12.5</td>\n",
        "      <td>-19.86</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td> 19.4</td>\n",
        "      <td> 24.06</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "            Dem_Adv  Dem_Win\n",
        "State                       \n",
        "Alabama        -0.8   -21.58\n",
        "Alaska        -10.6   -21.53\n",
        "Arizona        -0.4    -8.52\n",
        "Arkansas       12.5   -19.86\n",
        "California     19.4    24.06"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**1.12** *Make a scatter plot using the `prediction_08` dataframe of the democratic advantage in the 2008 Gallup poll (X axis) compared to the democratic win percentage -- the difference between Obama and McCain's vote percentage -- in the election (Y Axis). Overplot a linear fit to these data.*\n",
      "\n",
      "**Hint**\n",
      "The `np.polyfit` function can compute linear fits, as can `sklearn.linear_model.LinearModel`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "plt.plot(prediction_08.Dem_Adv, prediction_08.Dem_Win,'o')\n",
      "pol_fit = np.polyfit(prediction_08.Dem_Adv, prediction_08.Dem_Win, 1)\n",
      "\n",
      "x = np.linspace(-40,80, 10)\n",
      "y = np.polyval(pol_fit, x)\n",
      "plt.plot(x,y)\n",
      "plt.xlabel(\"2008 Gallup Democarat Advantage\")\n",
      "plt.ylabel(\"2008 Election Democrat win\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<matplotlib.text.Text at 0xd5fae48>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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B/Yp7SbjsDqxRrepcZ200h00k9NRnq5DHq7RdByz0TzkiIsHlr60uquuxiuzV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\n1Im91TWlAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc15f2e8>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice that a lot of states in which Gallup reported a Democratic affiliation, the results were strongly in the opposite direction. Why might that be? You can read more about the reasons for this [here](http://www.gallup.com/poll/114016/state-states-political-party-affiliation.aspx#1)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A quick look at the graph will show you a number of states where Gallup showed a Democratic advantage, but where the elections were lost by the democrats. Use Pandas to list these states."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "adv = prediction_08[(prediction_08.Dem_Adv > 0) & (prediction_08.Dem_Win < 0)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We compute the average difference between the Democrat advantages in the election and Gallup poll"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print (prediction_08.Dem_Adv - prediction_08.Dem_Win).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "8.06803921569\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*your answer here*:\n",
      " Bias is around 8 %. There might be various reasons for this Bias. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**1.13** * **Calibrate** your forecast of the 2012 election using the estimated bias from 2008. Validate the resulting model against the real 2012 outcome. Did the calibration help or hurt your prediction?*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "#your code here\n",
      "model = biased_gallup_poll(gallup_2012, 8.06)\n",
      "model = model.join(electoral_votes)\n",
      "prediction = simulate_election(model, 10000)\n",
      "plot_simulation(prediction)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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yy3zxxRcceeSRRFFE9+7dWbFiBVOnTiUWi/Hvf/+bU045hQsvvDD/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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbb0b860>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**1.14** *Finally, given that we know the actual outcome of the 2012 race, and what you saw from the 2008 race would you trust the results of the an election forecast based on the 2012 Gallup party affiliation poll?*"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*Your answer here* <br>\n",
      "The 8% calibration completly destroyed our prediction of 2012 polls. Our model assumed that bias in 2008 is same as 2012. Gallup might have changed thier methodology for polling in 2012. It is better to calibrate the bias on statewise."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Question 2: Logistic Considerations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the previous forecast, we used the strategy of taking some side-information about an election (the partisan affiliation poll) and relating that to the predicted outcome of the election. We tied these two quantities together using a very simplistic assumption, namely that the vote outcome is deterministically related to estimated partisan affiliation.\n",
      "\n",
      "In this section, we use a more sophisticated approach to link side information -- usually called **features** or **predictors** -- to our prediction. This approach has several advantages, including the fact that we may use multiple features to perform our predictions. Such data may include demographic data, exit poll data, and data from previous elections.\n",
      "\n",
      "First, we'll construct a new feature called PVI, and use it and the Gallup poll to build predictions. Then, we'll use **logistic regression** to estimate win probabilities, and use these probabilities to build a prediction."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### The Partisan Voting Index\n",
      "\n",
      "The Partisan Voting Index (PVI) is defined as the excessive swing towards a party in the previous election in a given state. In other words:\n",
      "\n",
      "$$\n",
      "PVI_{2008} (state) = \n",
      "Democratic.Percent_{2004} ( state ) - Republican.Percent_{2004} ( state) - \\\\ \n",
      "                \\Big ( Democratic.Percent_{2004} (national) - Republican.Percent_{2004} (national) \\Big )\n",
      "$$\n",
      "\n",
      "To calculate it, let us first load the national percent results for republicans and democrats in the last 3 elections and convert it to the usual `democratic - republican` format."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "national_results=pd.read_csv(\"nat.csv\")\n",
      "national_results.set_index('Year',inplace=True)\n",
      "print national_results"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "      Dem  Rep\n",
        "Year          \n",
        "2004   48   51\n",
        "2008   53   46\n",
        "2012   51   47\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let us also load in data about the 2004 elections from `p04.csv` which gets the results in the above form for the 2004 election for each state."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "polls04=pd.read_csv(\"p04.csv\")\n",
      "polls04.State=polls04.State.replace(states_abbrev)\n",
      "polls04.set_index(\"State\", inplace=True);\n",
      "polls04.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>Dem</th>\n",
        "      <th>Rep</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>State</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Alabama</th>\n",
        "      <td> 37</td>\n",
        "      <td> 63</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alaska</th>\n",
        "      <td> 34</td>\n",
        "      <td> 62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arizona</th>\n",
        "      <td> 44</td>\n",
        "      <td> 55</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arkansas</th>\n",
        "      <td> 45</td>\n",
        "      <td> 54</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td> 54</td>\n",
        "      <td> 45</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "            Dem  Rep\n",
        "State               \n",
        "Alabama      37   63\n",
        "Alaska       34   62\n",
        "Arizona      44   55\n",
        "Arkansas     45   54\n",
        "California   54   45"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pvi08 = polls04.Dem - polls04.Rep - (national_results.xs(2004)['Dem'] - national_results.xs(2004)['Rep'])\n",
      "pvi08.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "State\n",
        "Alabama      -23\n",
        "Alaska       -25\n",
        "Arizona       -8\n",
        "Arkansas      -6\n",
        "California    12\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**2.1** *Build a new DataFrame called `e2008`.* The dataframe `e2008` must have the following columns:\n",
      "\n",
      "* a column named pvi with the contents of the partisan vote index `pvi08`\n",
      "* a column named `Dem_Adv` which has the Democratic advantage from the frame `prediction_08` of the last question **with the mean subtracted out**\n",
      "* a column named `obama_win` which has a 1 for each state Obama won in 2008, and 0 otherwise\n",
      "* a column named `Dem_Win` which has the 2008 election Obama percentage  minus McCain percentage, also from the frame  `prediction_08`\n",
      "* **The DataFrame should be indexed and sorted by State**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We construct a similar frame for 2012, obtaining `pvi` using the 2008 Obama win data which we already have. There is no `obama_win` column since, well, our job is to predict it!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Your code here\n",
      "e2008 = pd.DataFrame(dict(pvi = pvi08, Dem_Adv =prediction_08.Dem_Adv - prediction_08.Dem_Adv.mean(), Dem_Win = prediction_08.Dem_Win))\n",
      "e2008['Obama_Win'] = (prediction_08.Dem_Win>0) * 1\n",
      "e2008 = e2008.sort_index()\n",
      "e2008.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>Dem_Adv</th>\n",
        "      <th>Dem_Win</th>\n",
        "      <th>pvi</th>\n",
        "      <th>Obama_Win</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>State</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Alabama</th>\n",
        "      <td>-13.154902</td>\n",
        "      <td>-21.58</td>\n",
        "      <td>-23</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alaska</th>\n",
        "      <td>-22.954902</td>\n",
        "      <td>-21.53</td>\n",
        "      <td>-25</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arizona</th>\n",
        "      <td>-12.754902</td>\n",
        "      <td> -8.52</td>\n",
        "      <td> -8</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arkansas</th>\n",
        "      <td>  0.145098</td>\n",
        "      <td>-19.86</td>\n",
        "      <td> -6</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td>  7.045098</td>\n",
        "      <td> 24.06</td>\n",
        "      <td> 12</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "              Dem_Adv  Dem_Win  pvi  Obama_Win\n",
        "State                                         \n",
        "Alabama    -13.154902   -21.58  -23          0\n",
        "Alaska     -22.954902   -21.53  -25          0\n",
        "Arizona    -12.754902    -8.52   -8          0\n",
        "Arkansas     0.145098   -19.86   -6          0\n",
        "California   7.045098    24.06   12          1"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pvi12= e2008.Dem_Win - (national_results.xs(2008)['Dem'] - national_results.xs(2008)['Rep'])\n",
      "e2012 = pd.DataFrame(dict(pvi = pvi12, Dem_Adv = gallup_2012.Dem_Adv - gallup_2012.Dem_Adv.mean()))\n",
      "e2012 = e2012.sort_index()\n",
      "e2012.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>Dem_Adv</th>\n",
        "      <th>pvi</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>State</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Alabama</th>\n",
        "      <td>-14.684314</td>\n",
        "      <td>-28.58</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alaska</th>\n",
        "      <td> -9.484314</td>\n",
        "      <td>-28.53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arizona</th>\n",
        "      <td> -8.584314</td>\n",
        "      <td>-15.52</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arkansas</th>\n",
        "      <td> -0.384314</td>\n",
        "      <td>-26.86</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td> 12.615686</td>\n",
        "      <td> 17.06</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "              Dem_Adv    pvi\n",
        "State                       \n",
        "Alabama    -14.684314 -28.58\n",
        "Alaska      -9.484314 -28.53\n",
        "Arizona     -8.584314 -15.52\n",
        "Arkansas    -0.384314 -26.86\n",
        "California  12.615686  17.06"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We load in the actual 2012 results so that we can compare our results to the predictions."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results2012 = pd.read_csv(\"2012results.csv\")\n",
      "results2012.set_index(\"State\", inplace=True)\n",
      "results2012 = results2012.sort_index()\n",
      "results2012.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>Winner</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>State</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Alabama</th>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alaska</th>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arizona</th>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arkansas</th>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "            Winner\n",
        "State             \n",
        "Alabama          0\n",
        "Alaska           0\n",
        "Arizona          0\n",
        "Arkansas         0\n",
        "California       1"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Exploratory Data Analysis"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**2.2** Lets do a little exploratory data analysis. *Plot a scatter plot of the two PVi's against each other. What are your findings? Is the partisan vote index relatively stable from election to election?*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "plt.plot(e2008.pvi, e2012.pvi, 'o', label = 'Data')\n",
      "fit_values = np.polyfit(e2008.pvi, e2012.pvi, 1)\n",
      "x = np.linspace(-40, 80, 10)\n",
      "y = np.polyval(fit_values, x)\n",
      "plt.plot(x, x, '--k', alpha=.3, label='x=y')\n",
      "plt.plot(x, y, label='Linear fit')\n",
      "plt.xlabel(\"2004 PVI\")\n",
      "plt.ylabel(\"2008 PVI\")\n",
      "plt.legend(loc='upper left', frameon = False)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "<matplotlib.legend.Legend at 0xbb43cc0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BAuAWYFVNFyciIn8xDIPtCb9x/Is5ROxYjgmDwC43ET52MZaA+q4uT8SjmV1d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\niYiISPsokZOAW18Fk8zt//x1ShdfhbtmL6n98si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       "text": [
        "<matplotlib.figure.Figure at 0xbb31e80>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**2.3** Lets do a bit more exploratory data analysis. *Using a scatter plot, plot `Dem_Adv` against `pvi` in both 2008 and 2012. Use colors red and blue depending upon `obama_win` for the 2008 data points.  Plot the 2012 data using gray color. Is there the possibility of making a linear separation (line of separation) between the red and the blue points on the graph?*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Your code here\n",
      "plt.ylabel(\"pvi\")\n",
      "plt.xlabel(\"GallUp Democrat Advantage(from mean)\")\n",
      "color = [\"Red\", \"Blue\"]\n",
      "\n",
      "ax = plt.gca()\n",
      "for idx in [0,1]:\n",
      "    colors = color[idx]\n",
      "    new = e2008.Obama_Win == idx\n",
      "    j = \"2008 Mc Cain states\" if idx == 0 else \"2008 Obama States\"\n",
      "    ax.scatter(e2008[new]['Dem_Adv'], e2008[new]['pvi'], c = colors, s= 60, label = j)\n",
      "\n",
      "ax.scatter(e2012['Dem_Adv'], e2012['pvi'], c ='gray', s= 60, marker = 's', alpha =.3, label = '2012 states')\n",
      "plt.legend(frameon = False, loc ='upper left', scatterpoints = 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "<matplotlib.legend.Legend at 0x9732da0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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yy6ZPnz5myZIlbs85ceKEGThwoBk/frwZPXq0GTFihElLS3PVJycnm1GjRpkB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\n5qiJ8sEHExdYupTyuXNZt2pVl8q8dm32Y9H5gHO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       "text": [
        "<matplotlib.figure.Figure at 0x97329e8>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### The Logistic Regression"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Logistic regression is a probabilistic model that links observed binary data to a set of features.\n",
      "\n",
      "Suppose that we have a set of binary (that is, taking the values 0 or 1) observations $Y_1,\\cdots,Y_n$, and for each observation $Y_i$ we have a vector of features $X_i$. The logistic regression model assumes that there is some set of **weights**, **coefficients**, or **parameters** $\\beta$, one for each feature, so that the data were generated by flipping a weighted coin whose probability of giving a 1 is given by the following equation:\n",
      "\n",
      "$$\n",
      "P(Y_i = 1) = \\mathrm{logistic}(\\sum \\beta_i X_i),\n",
      "$$\n",
      "\n",
      "where\n",
      "\n",
      "$$\n",
      "\\mathrm{logistic}(x) = \\frac{e^x}{1+e^x}.\n",
      "$$\n",
      "\n",
      "When we *fit* a logistic regression model, we determine values for each $\\beta$ that allows the model to best fit the *training data* we have observed (the 2008 election). Once we do this, we can use these coefficients to make predictions about data we have not yet observed (the 2012 election).\n",
      "\n",
      "Sometimes this estimation procedure will overfit the training data yielding predictions that are difficult to generalize to unobserved data. Usually, this occurs when the magnitudes of the components of $\\beta$ become too large. To prevent this, we can use a technique called *regularization* to make the procedure prefer parameter vectors that have smaller magnitude. We can adjust the strength of this regularization to reduce the error in our predictions.\n",
      "\n",
      "We now write some code as technology for doing logistic regression. By the time you start doing this homework, you will have learnt the basics of logistic regression, but not all the mechanisms of cross-validation of data sets. Thus we provide here the code for you to do the logistic regression, and the accompanying cross-validation."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We first build the features from the 2008 data frame, returning `y`, the vector of labels, and `X` the feature-sample matrix where the columns are the features in order from the list `featurelist`, and each row is a data \"point\"."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import LogisticRegression\n",
      "\n",
      "def prepare_features(frame2008, featureslist):\n",
      "    y= frame2008.Obama_Win.values\n",
      "    X = frame2008[featureslist].values\n",
      "    if len(X.shape) == 1:\n",
      "        X = X.reshape(-1, 1)\n",
      "    return y, X"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We use the above function to get the label vector and feature-sample matrix for feeding to scikit-learn. We then use the usual scikit-learn incantation `fit` to fit a logistic regression model with regularization parameter `C`. The parameter `C` is a hyperparameter of the model, and is used to penalize too high values of the parameter co-efficients in the loss function that is minimized to perform the logistic regression. We build a new dataframe with the usual `Obama` column, that holds the probabilities used to make the prediction. Finally we return a tuple of the dataframe and the classifier instance, in that order."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def fit_logistic(frame2008, frame2012, featureslist, reg=0.0001):\n",
      "    y, X = prepare_features(frame2008, featureslist)\n",
      "    clf2 = LogisticRegression(C=reg)\n",
      "    clf2.fit(X, y)\n",
      "    X_new = frame2012[featureslist]\n",
      "    obama_probs = clf2.predict_proba(X_new)[:, 1]\n",
      "    df = pd.DataFrame(index=frame2012.index)\n",
      "    df['Obama'] = obama_probs\n",
      "    return df, clf2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We are not done yet. In order to estimate `C`, we perform a grid search over many `C` to find the best `C` that minimizes the loss function. For each point on that grid, we carry out a `n_folds`-fold cross-validation. What does this mean?\n",
      "\n",
      "Suppose `n_folds=10`. Then we will repeat the fit 10 times, each time randomly choosing 50/10 ~ 5 states out as a test set, and using the remaining 45/46 as the training set. We use the average score on the test set to score each particular choice of `C`, and choose the one with the best performance."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.grid_search import GridSearchCV\n",
      "\n",
      "def cv_optimize(frame2008, featureslist, n_folds=10, num_p=100):\n",
      "    y, X = prepare_features(frame2008, featureslist)\n",
      "    clf = LogisticRegression()\n",
      "    parameters = {\"C\": np.logspace(-4, 3, num=num_p)}\n",
      "    gs = GridSearchCV(clf, param_grid=parameters, cv=n_folds)\n",
      "    gs.fit(X, y)\n",
      "    return gs.best_params_, gs.best_score_\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally we write the function that we use to make our fits. It takes both the 2008 and 2012 frame as arguments, as well as the featurelist, and the number of cross-validation folds to do. It uses the above defined `logistic_score` to find the best-fit `C`, and then uses this value to return the tuple of result dataframe and classifier described above. This is the function you will be using."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def cv_and_fit(frame2008, frame2012, featureslist, n_folds=5):\n",
      "    bp, bs = cv_optimize(frame2008, featureslist, n_folds=n_folds)\n",
      "    predict, clf = fit_logistic(frame2008, frame2012, featureslist, reg=bp['C'])\n",
      "    return predict, clf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**2.4** *Carry out a logistic fit using the `cv_and_fit` function developed above. As your featurelist use the features we have: `Dem_Adv` and `pvi`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "res, clf = cv_and_fit(e2008, e2012, ['Dem_Adv', 'pvi'])\n",
      "predict2012_logistic = res.join(electoral_votes)\n",
      "predict2012_logistic.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>Obama</th>\n",
        "      <th>Votes</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>State</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Alabama</th>\n",
        "      <td> 0.004367</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alaska</th>\n",
        "      <td> 0.008462</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arizona</th>\n",
        "      <td> 0.068250</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arkansas</th>\n",
        "      <td> 0.033851</td>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td> 0.994326</td>\n",
        "      <td> 55</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "               Obama  Votes\n",
        "State                      \n",
        "Alabama     0.004367      9\n",
        "Alaska      0.008462      3\n",
        "Arizona     0.068250     11\n",
        "Arkansas    0.033851      6\n",
        "California  0.994326     55"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**2.5** *As before, plot a histogram and map of the simulation results, and interpret the results in terms of accuracy and precision.*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#code to make the histogram\n",
      "#your code here\n",
      "prediction = simulate_election(predict2012_logistic, 10000)\n",
      "plot_simulation(prediction)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Pm4SEBNOkSRPz7bff+kcgJycnmxUrVvi33bFjh+ncubMpXbq0Of/8883YsWP9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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x955fd30>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#code to make the map\n",
      "#your code here\n",
      "make_map(predict2012_logistic.Obama, \"P(Obama): Logistic\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xdc91f60>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dyoABAwgMzDlduBAFTdqs5rMPPxiNRylKab7SqUoIcctYsmQJU6dORdM0vv32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\nFq5jkJZY57EhZVNEiCLCFDkVxSku1m7cgNfrbXQ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RxpOPP8JJJ53UQlchROyRZFUI0W6E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\nzn/kLwy6/PcsX7GCtLQ00tPTe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       "text": [
        "<matplotlib.figure.Figure at 0xc88c9b0>"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "The precision has worsened with respect to the predictwise (and even the gallup) model. The accuracy has improved with respect to the gallup model, but is not as good as in the predictwise model."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Classifier Decision boundary"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "One nice way to visualize a 2-dimensional logistic regression is to plot the probability as a function of each dimension. This shows the **decision boundary** -- the set of parameter values where the logistic fit yields P=0.5, and shifts between a preference for Obama or McCain/Romney.\n",
      "\n",
      "The function below draws such a figure (it is adapted from the scikit-learn website), and overplots the data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib.colors import ListedColormap\n",
      "def points_plot(e2008, e2012, clf):\n",
      "    \"\"\"\n",
      "    e2008: The e2008 data\n",
      "    e2012: The e2012 data\n",
      "    clf: classifier\n",
      "    \"\"\"\n",
      "    Xtrain = e2008[['Dem_Adv', 'pvi']].values\n",
      "    Xtest = e2012[['Dem_Adv', 'pvi']].values\n",
      "    ytrain = e2008['Obama_Win'].values == 1\n",
      "    \n",
      "    X=np.concatenate((Xtrain, Xtest))\n",
      "    \n",
      "    # evenly sampled points\n",
      "    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
      "    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
      "    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 50),\n",
      "                         np.linspace(y_min, y_max, 50))\n",
      "    plt.xlim(xx.min(), xx.max())\n",
      "    plt.ylim(yy.min(), yy.max())\n",
      "\n",
      "    #plot background colors\n",
      "    ax = plt.gca()\n",
      "    Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
      "    Z = Z.reshape(xx.shape)\n",
      "    cs = ax.contourf(xx, yy, Z, cmap='RdBu', alpha=.5)\n",
      "    cs2 = ax.contour(xx, yy, Z, cmap='RdBu', alpha=.5)\n",
      "    plt.clabel(cs2, fmt = '%2.1f', colors = 'k', fontsize=14)\n",
      "    \n",
      "    # Plot the 2008 points\n",
      "    ax.plot(Xtrain[ytrain == 0, 0], Xtrain[ytrain == 0, 1], 'ro', label='2008 McCain')\n",
      "    ax.plot(Xtrain[ytrain == 1, 0], Xtrain[ytrain == 1, 1], 'bo', label='2008 Obama')\n",
      "        \n",
      "    # and the 2012 points\n",
      "    ax.scatter(Xtest[:, 0], Xtest[:, 1], c='k', marker=\"s\", s=50, facecolors=\"k\", alpha=.5, label='2012')\n",
      "    plt.legend(loc='upper left', scatterpoints=1, numpoints=1)\n",
      "\n",
      "    return ax"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**2.6** *Plot your results on the classification space boundary plot. How sharp is the classification boundary, and how does this translate into accuracy and precision of the results?*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "points_plot(e2008, e2012, clf)\n",
      "plt.xlabel(\"Dem_adv from mean\")\n",
      "plt.ylabel(\"PVI\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "<matplotlib.text.Text at 0xdbdaf28>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CCCGE0CAJ0oQQQgghNEiCNCGEEEIIDZIgTQghhBBCgyRIE0IIIYTQIAnShBBC\nCCE06JI7FsoSFtHt4eFC9JQlLKK/hyCEEOIicckFactf/Xd/D0EIIYQQ4oxkulMIIYQQQoMkSBNC\nCCGE0CAJ0oQQQgghNEiCNCGEEEIIDZIgTQghhBBCgyRIE0IIIYTQIAnShBBCCCE0SII0IYQQQggN\nkiBNCCGEEEKDJEgTQgghhNAgCdKEEEIIITRIgjQhhBBCCA2SIE0IIYQQQoMkSBNCCCGE0CAJ0oQQ\nQgghNEiCNCGEEEIIDZIgTQghhBBCgyRIE0IIIYTQIAnShBBCCCE0SII0IYQQQggNkiBNCCGEEEKD\nJEgTQgghhNAgLQdpOuBzYErr6wTgBeBXwN+Bkf00LiGEEEKIXqflIO3XwBjAByjARuB/gb8C/x+w\nCdB39cYjx7Opravvo2EKIYQQQgSeVoO0yUAWUN36+logFdje+vow0Az8tKs37zuWy8btaaR9e4SS\nsgq8Pl8vD1cIIYQQIrC0GKTFAP8DfNT6WgGuQg3aPO2uOwb8qKsbXPuDsej0eo7mlrLtqwN8sedb\nsnMLaWpu7s1xCyGEEEIEjKG/B9CFB4CVp7TFAVWntFUBiV3dYNyYoYxITcbpLCIrO5/0w7k4S6tR\nDmQydpADh91GRJgFRVF6YfhCCCGEED2ntSDtLuAfQNMp7S2o05vtnTYLaDIaSRmcxOBBiYwZPYSM\nzHy+2HuUb07k8c2JPJLjInHEWbHHWTGZjAH8CEIIIYQQPafFIG1tu9dmYCvqlOfBU66NBLK7usn7\nL6/2f5064UpGTLiSuNgYxoxOITunkNzcIg4eLyCruBJFl8Goy+wk2m1ERoRJdk0IIYQQvSbjYBoZ\nB9PO6lqtRyRZwFzULNonQHi7vgxgKfDuKe/x/WOP87Q39Xq9lJS4yM4pZOfeo9C6sSA5LopEu434\n2BiMRq3Fr0II0XdGJYTgaXJ32WcwBXMgX3bQCxEIv509CbqJxy6USGQ3kAP8ELV22nAgBLUMxznT\n6XTEx1uJj7cyZvQQcpyFZDsLOHisgKziCnQ6HWMGO0iw2wgPlbVrQohLj6fJzdq/vNRl36J7F/bx\naIS4NF0oQZoPmAk8gVqKYxLwE6Dr/807ByEhQaQOT2bY0IGMHV1OVnYBX6Yd45vjeXxzPI/B9mgS\n7bHExUZj0HdZlk0IIYQQIuC0HqQlt/s6E5jX+vULgX6QTqfDYbfhsNsYO3oI2TkF/Pu/35FR6CKj\n0IVOr2dFgTk8AAAgAElEQVR8SgKJjlgsIcGBfrwQQgghRAdaD9L6RWhoCKNGppA6PJmCwlKysgv4\n+psM0o46STvqZPiAWBxxVmzWKPQ6LZaaE0IIIcSFToK009Dr9SQlxpOUGM/YMUPJzMrn810HOOIs\n4YizBL3BwLjBDuzxVsIsIbJ2TQghhBABI0HaWYoID2X82GGMTB1EXn4xOc5C0r7LJu2oE446GWyP\nxhFnJS42GpNR6q4JIYQQomcuxtTPGUtwBEplVQ1OZxGf7jpAi0c9sUrRKYwcGE9CvI3oqHDJrgkh\nLkhSgkOIvnG6EhwXYwTRZ0Fam5aWFoqKy8lxFvFF2jF/3bXLYiNIdMRij7NiNEjSUgghhBAdXQx1\n0jRNr9eT4IglwRHL+LFDceYWkZ1TwHdH88kuqUKnz2TcYAeJjjhCLbIzVAghhBBnJpm0XuL1eikq\nLiczK5/d+47724cn2Uh0xGGNiUQnU6FCCCHEJU0yaf2gfd21MaOHkJmVz2e7DnAkt5QjuaUMsIVj\nj7XiiLMSHGzu7+EKIYQQQmMuyiCtucWLUa+d+mXhYRbGjRnKiNRknM4isrLzST+ci7O0Gg5mMjzJ\nhiPeRqw1Cp3UXRNCCCEEF+l0573v7ic+PAirxUSY2aC5HZY+n4+yskpycgvZsfswPq8XgMSYUBxx\nNhLsNoKDJLsmhBBCXOwuyenOwqoGCqsaiAw2Yg01ERNiwqCR7JqiKNhsUdhsUYwZPYS8vBKyc/LZ\ndyCHvPJaOJzNiIGxJMbHEh0dIWvXhBBCiEvQxfjb3/dtfiXOCjfOinoOFFQDoCjgiAjGajFhMek1\nmV1zuarIyilgx+5D+LxqGY+BtnAc8ep0qNkkRXKFEEKIi8klVyftWEkNAF6fj5KaRpyuer7IdoEa\n9xAdYsIaaiIqxIRBp71vQWNjE85cde3at0fyALVI7qjL7CTabURGhGkuyBRCCCHEubtkg7T26ps8\nrdk1N4cKT2bXEiODsVrMhJj0fT3OM/L5fJSUusjKKmDn3qP+IrnJcVEkOWKJj4vBoNfeuIUQQghx\ndiRIa8fr9VFU00COy81X2S5/uzXURIzFRFSwCb0Gs2v19Q1k5xSQ4yzk4PECAHR6PWMHO7DHWYkI\ns0h2TQghhLjASJDWjdpGD9muej47Voq3dQ2YTqeQEB5EjMVEiAbXrnm9XgoKS8nIzGNPeqa/PTku\nEkeclfg4q6xdE0IIIS4QEqSdQYvXR2FVA86Ker7OqfC3R4eo2bXoEKNmdoa2V11ThzO3iNzcIn92\nTVEURgyMwx5nxRodIXXXhBBCCA2TIO0c1DZ6yK1wk1vZce2avbXuWqgG6655vV6KS1zkOAvZteco\nvta1a4kxodhjrSTE2wgJCernUQpx4RqVEIKnyd1ln8EUzIH8+j4ekRDiYiFB2nnwen0U1zaSe8rO\n0KgQI1aLmRiLNteuNTQ0kptXTE5uId8cPHmG6YiBcSQ6YomJitBckCmE1g23Kaz9y0td9i26dyFH\nSn19PCIhxMXikixm21M6nYI9PAh7eBCjHRHkVtbjdLk5UFhNRX0zGeUKCRFB2ELNBBu1s8MyKMjM\nkJQBpAxOYsK4VLJyCvj8i4McyinmUE4xl8VGkuiwYY+zYjTIj18IIYTQqovyt7TX60NRCFjGKNik\nZ2hsGCnWUEbaw8l21fFVdoU6LVrhxhZqxhZqIiLYqJnTARRFISoqnKiocEamDiLHWUhWdj7fHc0n\nu6QSnT6TcSkJJNpthFpC+nu4QgghhDjFRRmkNXu9KIBep6NtRjIQAZtOp2CPCMIeEcRIezg5rno+\nP15GaW0jpbWN6HUKSZHBxFhMBGkou2Y2mxg6ZCApg5MYPaqczKx8du87zr6jTvYddTIkIQZHnI1Y\nW5Rk14QQQgiNuCh/I8eFmiiubcLTenC5XlHQKQQ0uxYeZGS0I4LhcWHkVzaQ46pnf14l2a56sl31\n2ELNWC0mIkO0k13T6XQ47DYcdhtjRqWQmZXPZ18c5Hh+Ocfzy9HpdIwZ5CDBYSM8VOquCSGEEP3p\nogzSIoMMhJr0NHi8FFQ30uLz0eLz9Up2zajXcVlMCAOjgxmTEI6zws2OE52za9ZQE2aDdrJr4eGh\njBs7jFEjUygoLCXHWchX+0/wzYk8vjmRR4ojhgS7jXhbNHo51UCIC9ahtHR2fZyOp9mIwdjMVdPH\nMmLC2P4elhDiLFyMqRJfU1PTyRc+H40tPho8XoprT7ar2TUloNm1Ns0tXgqq1OzavtxKf3tcmBlr\nqJmIIO2V8QCorasnO7uAf//3O1o8HgD0ej3jhySSYLdhCQnu5xEK0T8u1BIch9LS2fj6YcqLn/G3\nxcQ9yI3zUiVQE0IjLrkSHO2DtPY8Xp8/u9a2Yb59di3QgZPP56PS3UyOq54dJ8rajt4kIthITGuh\nXJNBe8VmPZ4WCgpLycrO73CqwfAkGwn2WGwxkVIkV4gLwLrfvcGxb5/r1D507CLueuz2fhiREOJU\nUoKjlUGnEGrSMyQmmKYWH+7W7Jp/7ZquNbtGYAI2RVGICjERFWJiRHw4eZVuclz1pOdXUeVuJstV\nR3xYENZQE2EaKpJrMOgZkBTPgKR4xo4eSlZ2Pp9/cZAjuaUcyS0lyRpGQrwNR7yNILOpv4crhOiG\np7nrI+I8TZfUf/qFuGBdkn9TFUXBbFAwG3Qd1655fbTg65XsmsmgY5DVQnJMCKMd4eRUuNmZUUZh\ndQOF1Q1EBhuxhZo1dwRVZGQY48cNZ+TIweTmFpOVnc83h5zkltWgHMxiVLKdRLuNyIgwzQSZQgiV\nwdjcdbvJ08cjEUKcj4vxt2q3052nfZPPR6NHza6V1LVbuxbg7Fp7Dc0t5Fa4cVbU811BxyOoYiza\nyq618fl8lJVVkpWdz3+/PuI/guqy2AjscVbssTEEBZn7eZRCCOhuTdoSbpw3QtakCaERsibtHDW3\neGnweCmsaeqTtWten4+SmkZyXPV82e4IqshgY+sB79pcu+Z2N5CVXYAzt4gDx/LVRkUhdUAs9tgY\nbNYo9LJ2TYh+dSgtnV1bvsXTZMBg8nDV9WMkQBNCQy69IK2+FvRGNS3VA16fj0aPGrCV1J2cNujN\n7Jq7qYXcynpyK9z+7BoKxIep2bXwIINm6q618Xq9lJZVkOMs4r9fH8bnVaNMvcHAuMEO7PFWwkMt\n/TxKIYQQQnsuvSAt9yBKSCQ+UwiYgkHpWTbH5/P5d4a2z67pWst49FZ2ray2idxKde1a287Q8CAD\n0RYTVosZswaza01NzeTlF5PjLCTtu2x/+2B7tFp3LTYGg9RdE0IIIYBLMUjLOww+b+tLBcKsYAoJ\naHbN7fFS2ppdU1CPjOqt7Fqjp4X8ygacFfV8k1dF20Pjw4KwaWxnaHuVlTU4c4v4dNeBjnXXhiaS\naI8lJDion0cohBBC9K9LLkhrzv5WDdJ8XnzGk4vYeyO75vZ4KWqXXdMrCkovZdd8Ph8V9c3kVNTz\nn3Z11/w7Qy1GDBpcA9bS0kJBYRmZWXkn664pCiMGxpJkjyM6KlyTQaYQlzI5qUCIvnHpBWk537V7\n5QNfCz5jUK9m1xo8XhqavZTW9112zVnhxunquDM0MSKYmFATIUa9JgOfispqMrPy2f7lIXyt9eku\ni40ksXUq1GTquq6TEKLvyEkFQvSdSztI87f6+iS71uz10dDspai2b7JrbTtDs8vr2Z3t8rdHt55o\noNXsWmNjE9k5hWTn5PPdUXVnqKJTGDkwHke8leioCM1tkBDiUiEnFQjRd+TEAVDTTIoe0KO0tPiz\na776SqivxJ9dM1vU7Np5PULBpFcw6XVYWovkNni8lNU3Q+sB74HOrukUhfjwIOLDgxjlCMdZUc+n\nR0tx1Tfhqm9CKQNHRDBWiwmLSTvZNbPZxLChAxmSksToUeXkOAvZtfcYB7IKOZBVyABrOI54Gwl2\nG2bJrglxRoGcnpSTCoTQhjP9jYsBys9wTTTgOsM12qIooBhQPM0ds2s1pVBTihIShc8cAsbg854K\n1esULCY9IUYdYWa9P7vWdqqBvvVw90Bm10LNBkbEhzM8NozimkacFWrdtfxKN/mVbmIsJqwW9Zgq\nvU4bwZpOp8Nht+Gw2xg3Zii5eerO0PTDuTjLqlEOZqqnGjhiiQwP1UyQKYSWnJyePJn9Ki9+EOC8\nAjU5qUAIbThTLYS7gK/OcM0C4OvADCcglj+x+J6zu1JR1ClOnR7F24Li9YDeAM1uaKiB+grQ6U/+\nOQ+KoqDXqUdQRQYZsBj1mPU6aptb8Pp8eH0+/7Ro2/U9pSgKYUEGEiODGe0IxxZqJttVT11TC+V1\nTRRUN4DPh8mgw6ihI6iMRgMxMZEMSk5gyCA7YRYzzoJySipqOOEsor6mBoCQ4CA54F2Idja8tpW8\nzD93aHPXTaO+9nUm/ODcg7RgC+Se+Afuumn+tpi4JVz3s1HYHPE9Hq8Q4qR/v7cOYEVXfWfKpD0L\n/F+gpZt+IxAPdF68cKFRdKDoUDyejtm16hK12xKlrl3rYXYtxKQn2KgjtI+yaxaTgdT4MIbGhlJU\n3UCOq56vcyrUTQcVbmyhZqyhJiKDjZpZA6YoCrG2aGJt0YwZNYTsnAJynIUcPF5AZlEFOr2e8SkJ\nJNhthFpC+nu4QvS7QE9PtmXfdm1ZJCcVCNGPzvQ3+ACwkdMHaTMCOqL+1mHtmhqw+QxmfHUVUFeh\nBnNhVjBZ1KzbeT3i5Nq1ULO+485Q38kD3hWFgK1d0+sUEiKDSYgMZpQjnBxXPZ8fL6O0tpHS2kb0\nOoWkKHXtmtmgnWKzISFBjEgdxPBhlzF6VClZ2QV8tf8EaUedpB11MizJRqI9FltMpGTXxCWrN6Yn\nR0yQkhtC9Lcz/fb/CfDhGa6ZDnwcmOEEhK9652bM8XZ0xgAtOO9uZ6glujW7FtTjMh4+n4/mFrXu\nWvEpO0N1rRm2QK/Ham7xUlClZtf25VaqjRo/ggqgqrqWrOwCPtt1AG+L+v8PSdYwHHFWHHE2goPl\ngHfRf/qjvlhvHqQ+KiEET5O7yz6DKZgD+fU9ur8Ql7qelOBIBQ4HekBnMAVYCyQDX6KuecsFEoDH\ngG+BK4E/AQe7eL8v95lHUXQ6LCPGEJSQgCEsPDAj8/kAnz+71nYSuhIUpgZr5hDQ9Xz3U0vrEVT+\nnaF0POAdAhuw+Xw+KtzNOF317DjR+QiqmBATQUbtZNfaNDd7yM0rIjM7n28OOv3tw5JsOOKsxFqj\n0MsRVKIP9Wd9sd46SH24TWHtX17qsm/RvQs5Uurrsk8IcXZ6UoLjHeA+4D8BHlN3YoE7gF+gBmUv\nAa8B16FOuz4MbAN2AJuBIXQ1FWsOxddYS+2Bb6g98A3BySkEJSRissWi9OSXtqIA6maDtqlQfF41\nVGuoARR17ZrZAgZzQHaGhpr0/uyap7X4q95fcy1wGw2iQ0xEh5hIjQ8jr7KB3NYjqKobPGSX1xMb\naibG0rp2TSM7Q41GA4OSE0m+LIGxo4fidBaxY/chjuaWcjS3FJ1ez9jBDhxxVsLDLLIzVPS6XR+n\nd9hhCVBe/Ay7tizq9SBNpieFuPicKUg7AlwNPAjsAd4AnKd7Qw/9CPgNUIO6Hm458CJwLWpWb3vr\ndYeBZuCnwPun3iR44CC8jY00V5Tjq6vAnXUCd9YJdHo9ljHjCXIkog8O7tlI29au+XSnrF1zQZ0L\nJTi8tUhuSI92hpoN6s7Q0Na6awXVjbT4fLS0W7sWyI0GZoOewVYLg2JCGJMQQW6Fm+3HSympbaSk\nthGdTlFPNbCYCDFpI0ulKAo2axQ2axSjR6dQUFBKjrOQPemZ7D+Wy/5juSTHReGItxIfGyN110Sv\n6ev6YjIVKcTF7Uz/5XgIyGn9+nuomSw7sAFYDwT6vwDvnPK6GDUovArIAtqvgj2GGtR1CtIAdGYz\n5ngHPm88nupKPK5yvB43Nfv3Urt/L8FDUwlyJGKMjkbpyYLzLrNrLWp2zV0NKCihrWvXepBdM+gU\nQk16hsQE09iiTod2yK4FuEhu++zaiPgwCqoayK1ws9dZgbOiHmdFPTEWE7ZQM1Eayq6ZjEYuG+jg\nsoEOxo8bjtNZiDOviEPHC8kqrkBRTjDysniSHLFERoRJdk0EVF/XF/M0uU87FSmEuLCdKUjLaff1\nV61/zMAs1EzXf4C/A5/3yujgctRM2jCg6pS+KiDxTDdQdDqMkdEYIqLwNrjxuMrx1VdSf+ww9ccO\nY3YkYbY7MNsd6IOCejba7rJrteVAecCya0EGhaBTs2utZTx6I7tm1OsYGB3CwOgQRjvCya1089mx\nUsrrmiiva1J3hkYGYw3V1s7Q8DALo0amMCJ1EOPHutRTDfYc9Z9qMCg+iiRHHPGx0bJ2TQTEVdPH\nUl78YKcF/FddP6YfRyWEuFCdaw7ehlrgdiGQBBSi1knrDRZgNOr6tDWo05vtdZv++uPb7/m/vmr0\nCCaPHqkWlQ0OQZ8Qgs9jx1NVQXNFOY0FuTQW5KKAml2zOzDGWC/I7Jq72UtJXe9l1wAigo1EBBtJ\njQujoMpNjkvNrmW76smuqCc+zIzVYiY8yKCZLJVOp8Meb8Ueb2XsmKFkZxeQnVPgr7um1+u5fGgS\nifZY2RkqeiQQ9cW6m8KMCtFjMpkprjw5gRERBFaLgk5voKS66yyeEEJbMg6mkXEw7ayuPdNv0cuB\nfajTjfegZtC8wP8PPA/sP/9hntGTwAtAKfAoMBsY167/IyC7dVzt+co2nTpr2jWfz4e3vo7mShfU\nV+Fr3dZotieq2TVHQs+zaycfBvjAq54Z6t8ZGoDsWnserxqsFdY0+st49EZ2rb3K+iayT9kZGhls\nVA94DzFhMmivfpnX6yW/oJTMrDz2pGeqjYrCiAGx2OOsWGMi0UvdtUuKVtZ3dbeb0mpRuHrKFNZ/\ntN3f9rMfX80fn3qG7026grK6jrssA7XzUivfFyEuVj3Z3bkOdXpzBGpAtAx4ld4/q/Mu4C3UAA1g\nJ/DIKdcMA17vyUMURUFvCUVvCcXn8eCpqqS5spzGwjwaC/NQ9iuEDBtBkCMRQ1RUzwKctuyavvvs\nGqExarBmMPUouxZm1mMxBdPYWsajpK65V7NrkSEmxoWYGBEfTm5FPdmuer4rqKbS3UyGUkd8WBDW\n1rprWsquJSXGkZQYx5jRQ8jMymf7lwc5lFPMoZxi9AYD41MSsMdZCQuVUw0uBX2xvutCDHhOPybt\njVeIi8mZgrTxqDsql6IWte2LgjjzADfqaQbDgTjUmmnZwA9R178NB0KATYF6qGIwYIyxYoiOUbNr\nFS5wV1F35CB1Rw4SlDgQc0Ii5rh4dCZTDx92ytq1tuxabZnaHRzRLrt2ftkcnaIQbNR3WLtWWNPU\nq2vXTAYdg22hJFstjEmIwOmqZ2dmOUXVDRRVNxDRml2zWkyaOjM0KjKcCePDGTViMHn5JeQ4C9h3\nIIe9R3LgSA5DE60k2mOJtUbJqQaiR2ShvxDiXJwpSPs98A9gGnA76k7Kul4cz/Wo2bv2834+1KzZ\nf4AnUEtxTEI9DaHr/yXtgfbZNa+nGU+FC0+Fi4a8HBryclAUhZDhIwmyJ/RCdq3lZN01dxX+7Jo5\nBPTnl11TFAWjXsGo12Ex6fsku6ZTFGyhZmyhZkY5wsmrbMDpqic9v4oqdzNZ5XXEhwdhs5gINWsn\nu2Y2mxg8KJFByQlcPi6VnNxCPt15gGN5ZRzLKyMpJgxHvJUEeyxB5h4G6uKCERtuJCJInYY8VX9n\nv1p8Cg//9kFSU4dR1dCxz2AKRjJdQlzYzhSkbQS+Qc1qgVq3bDJQ0Evj2dLuWV2Z1/rPF3rp+R3o\nDEZMtjiM1lhaaqrxVLqgoYa6wweoO3wAc8IAghwOzPEOdOYeLjhXFFAM6tmdp2bXakEJac2uGS+s\n7Fr7umujHOH+7FphVQOFVQ1EhRixWsxEW0wYNFLGQ1EUIiPDiIwMY2TqIHLzisnMymf/wRxyy2tQ\nDmUz8rJ4Eh2xREkZj4uet8XD4cNHO6wFa9Pf2a8PPlY31i9KHtbF+jMJ0IS40J0pSHsS9cSBf6Lu\ntlyFejTTvb08Lk1RFAVDeASG8Ai8zU14KivwVLpozHfSmO9Us2tDUzHbEwJXd+3U7Fo9UN+aXQuz\nqlOheuMFk11T2mXXRtjVtWtOl5sDhdVU1DejlEFihFrGI8TUO4U/z4fBYCD5sgQuG+hg7JihZGbl\n89+vDvvLeCTHRZHosGGPtWLQUPkRIYQQF74z/TasAF5u/boKtfTGe6dcY6BjkdmLms5oOpldq6vF\nU1GuZteOHqLu6CG17lq8Xa27FpBTDU7NrpmhRt1PoYREtq5dCwalZ9m1YKO+y+yarvUIqkBm14KN\neobGhpFiDWWkI5yc8np2Z7vIrXSTW+nGajFh1ViRXEVRsMZEYo2JZPSoFLJzWst4HCsgq7gCnT6D\ncYMdJDpiCbXIRoNLSaAPVDeYgrvM0EUEwYFDx3oyVCHEBeZMQVrtKa+bgKJT2m4D3gzYiC4QiqJg\nCA3DEBqmrl2rrMBTWdG57logTzXQ61BaWtRdocYgfPWVUF9Jh+ya4fzXShn1ug7ZNbfHS2ldM97W\nI6h0gV67plOwhwdhDw/yT4V+eqyUsromylqL5CZEBGO1mAjWyBFUAMFBZlKHJTNsyEDGjCwjMzuf\nr/afYN+xXPYdy2VoglqTLc4aLdm1C8j5BEd7vvym9UD1k+d1lhc/CHDegVp3a9xGJYSQNGgoi+4d\n2qlP1p8JcXE6029aF+qaNAV1Ab8CDAWOtvYbUQvORvbWAM+Db8/vf499sIOgkADVODvbB3dXdy1h\nAEGOBMx2R893hp582MkD3o0n18MpIZHqAe/GoPPOrp18hI9mr3oEVVFNk39rr15RUPyHvAc209Xi\n9VFQ5SbbVU+as9LfbrWYiLGYiAoxoddIdq296upaMrML+GzXAbwtLYBa5mPMIAf2eCuR4aGydu0C\n1V3dMoCy7CD2732uU/vlExex4k+/BDpuLrgQS3AIIXrX6eqknem3hhPYAbR0029ALXSbfL6D6wW+\nV666HkVRSL5yLAkpiUTbY/r8F6Rad62CZlc5iq8JUAMaS+oozAmJGMIjAjcmn8+fXcOnrilDUSDM\ndnLtWg95fWqw1tDspbRerWzeG9m19qrczeRWuPnseCleb2vxXwUSWg94t5j0mgt8PB4P+QWlOHOL\n+Gr/CX/7ZbER2OOs2OOssjP0AnO6IK3weDgH0p/p1B5pvY2JV4cCgSsqK4S4OPUkSJsGfHKGa6YC\nW899WL3G9+5t91KTm+nPZMWOSMExOAH7IAfmPj72x+fznVy75q72Z6OCBg4iKCERc2wciiFAC+W7\ny65Zolp3hgafd5Hck4/on+xaYXUDuRX1fJVd4W+PCjl5qoGW6q61qa2tx5lbRE5uIQePtW6IVhRS\nB8TikFMNLhiny349+fBb7Pv6z53aY+Ju4vLvWwEJ0oQQp9eTIO1C5Ev7w1qaG5soKyihLL8YT2Ux\noC6ST75qPAkpiUTF9bDG2XnwNjXhqSjHV+vyT4kpOh2ho8dhdiRgsIQG7mFtwZrBjL8GsaJrXbtm\nAX3PA0Ovz0dDs7oztH12Ta/TqXseCHzAVtvoIbfCTV6lm4OF1eozFbCHB2ELNWsyu+b1eikprcCZ\nW8R/vz6MrzUraDAamDA0SequXcAOpaXz8ZuHKSo4mU0Ltixg2LhKbPYoQII0IcTpXZJBmv+F10e1\nq5LSvCJq87L8mZ+4kUNIGJJI/GV2TEF9+wvS5/XSUlNFs6scmk+uQQlOHoLZ4cBsi73wsmstPtwe\nL8W1HbNrOkVRA7YAB05er4+S2kacFW6+yCr3x6FtJxpode1aY2NTh1MNQP3ejLwsnqSEOFm7piFn\nu2uz5kQ6f1mzmZaWIPT6BpJS9P4ADfo3SAv0zlMhROD15OzOC56iU4iwRhFhjaJp+CB/dq344HGK\nDx5Xs2v/Mw7H4IQ+W7um6HQYIqIwRETR0uBWs2t1lbizjuPOOo6i02EZOZYghwNDWHgPH9Z6BBV6\n/5mhPoMZX10F1FUEJLumKAomg4KpXZHcBo+XsvpmWny9UyRXp1OIDw8iPjyI0fZwcirq+feREsrr\nmiiva0KnU0iKVHeGBhm1s8Oy/akGY0cPJeOUumuD7dEkOmKJs0Vj0Gtn3JeaQ2npZ71rc+KV47h8\n31etrwKYDe+hc/kMQghtuhh/CyxfeO30Ljv0BgNhURHEJtkJjorBg4mm6gpcuUU4049RUVGPz+cj\nJMyCvo9KJ+gMRgxh4egjY8BgxtvkgZYmmkqKqM84Tou7AUWnQx8c0rMyHtAasOlQvF4Ub4satOn0\n0FgH9ZWtYbwCOsN5Z9d0ioJJryPYoCPMbMBs0FHb1ILX56PF5+tw+mugAjajXoc11MwVA6IYEB1C\niMlAfqWbKnczhdUNNDSrmymCDDrNZKkURSEkJIgERyzjRw/CFh1GVl4p5VV15BSWcTirAL3Xg9Fo\nxGwyambcl4oNr20lL7PjWjN33TTqa19nwg86BjhZ36bzr3fTyc/yUpRbjdHUiCXsZI3E7026grJ+\n2LR5Lp9BCNF//v3eOoAVXfVd9Jm0rig6hcjYGCJjY2hKHUR5YSll+cWUHDpOyaHj6HU6Uq6+gqQh\nSYRGhfXRmPQYI6MxRkbjbWigubItu3YCd9YJdHo9oWMux+xICFCR3HYHvPuzay6oc6EEhZ084L0H\n2TWzQcHcLrtWUN1IS2uw1j671nZ9T+l1ComRwSRGBjPaEU62q57tx0sprW2ktLYRnU4hMSIIq8Ws\nrbprwUGMSB3EsKEDyS8oJSs7nz3pmew/nsf+43kkx0XiiFNrr5mMPd+pK87M09z199nT1PHvw6G0\ndLa8dRRX8Qf+NpP+QW6ckcrEK8cB/VfD7Gw/gxBCuy75v62mIDP25ETiB/4/9t48PLKzPPP+veec\n2s06wQoAACAASURBVDeVal+0L71vXsFgg/FCMGGSfEnINwmbE5Zr4MLhAycBHCbJTELIOgMBMsQE\nSCBMIBuBhDi2wca73e3e90VLlUprqapUUu3L+f6oUklqSe3uVkktt8/vurjaek/VOaeaVtVT9/s8\n9x1iZjrJZHSczOgQZ378Emd+/BJtN+0g3NeGJ+xF2qAJQsloxOAPoVb9lGdmKCfjVMt50of2Iw7t\nr5nkBoLoXO7mmOSKhWKtEfCenwVErXfNYAbFeNXqmiIJrHqZPpeJQqU2GToxV1yIoGpMhTZPXXOY\ndOwJOdjutzE6kyeazHEgkiSSzBFJ5vBYDbitelpMOqRNolLJskx7m5/2Nj/79m4lEh0nGh3nxLlR\nBidSiOMD7OmupRrYbZZrfbvXNYqutPK6fmm4yrP/cYSx2FKftPHRP+Pvv/MAtt55teraeJ9d7mvQ\n0NDYvGweOaF5rLrdeSmEEBgtJlwBD85QCFUxUpxJkIpNMnL8PNPTs5TLZYwWEzr9xqgZQkjIJhNK\nSyuS2U61UoVygdL0FLnhQcqpGdRSCcloRFqrwlLfCkVICLWKqJZr256lHOTStZ9Vtb4VenWFoRAC\nRRIYFYkWo4JZJzNXqFClNilaVef9khcev1ZkSdBi0tHeamZ7wIbLYmAomSVTKBOfKzKWziNELQh+\nswS8Q613zetppac7TFe7D5vFQDQWZzyR5uzwGPlsBkmSMJuMm6bI3ChOvnyE733tUV760UkOP3sI\nkwU8QX9Tr2GyQPT835HLvLWx5vJ9nHt+YeeSa730o5Mk429d9nxH62PcfOeupt7TlXK5r0FDQ+Pa\nom13XiFGi5n2rd2EetuZHptiMjpO/MwA8TMDHAPab9lNsCeIO+xB3oDmbiEEstmMbG5fMMlNJiiM\njVAYG4FDYO7dgiEQRO/2INZyTyuqa5WaupZLAwJhba1thyqGV5W6Zjfq2BnUsdVnJTaTZziR5WA0\nxdB0lqFEFr/NiNuix25UNk0PmCRJBAMeggEPu3f1Mjg4yuNPH+VcbJpzsWkUncINfW2EAx6Mxo31\nALwWbFQz/Py5nn3kAcpFBUVf5g0/tXvZNTazWnW5r0FDQ2Pzsjk+iZrLEguOppxQVZlNpomPTjAb\nudBwv1cUmb4330ywJ4Rtg3rXFt9TNZelnEyg5mZQ68WNkGWsO/diCARRrE2aNFNVQF3muyZM9oXe\nNWntxWq5bpI7mi405gvWYzJ0HlVVSeZKDCeyPHU+Tt37mBaTDrdVj8usR9mEJrnlcoWR2AQDgzEO\nHh+qLQrBzk4/4aAXp8O2aYrMZvPw7/8tZ4/+IjX/bAUoA/fSv+cf+cBD797w+1koGhd80ly+j/Nf\n3rddK4Y0NDQui9e0T1qzKZdKJMbjxEcnKUzFGuvBPVsJ9oTwdwVQdBsrUKqVCuWZFOVUAsoLzuim\nrl4MwVAt1aBZil/Dd60eQwU0S11buIS6RF2bR17HCKpCuUI0mWM4keXY6IJJbrAe8L4ZTXJVVSWR\nmGFgKMZTL5xqJGx0+ZyEgx78Xhe6ZvntbRL+9ONfYGKkHfiDRasP4QtHePDPH7gm93Ty5SM8+8hR\nTa3S0NC4KrQibZ3IzmaIj04yM3iWcrmWICDLEv133kK4L4zFsfGeSZVcjnIqgZpJLlPXjMEQsqVJ\nDeeXVNcsoDc1T10rVRmd3Rh1raqqTM0VGJ7O8txQomEZ4jTPm+TqNmUEVS5fYHh4jMHhWCOCSkgS\nu7r8BH0enC3XRl1rdqD47/zq58jO/dOydbPt5/m9v/6kFmCuoaHxqkMr0taZaqVKairBVGyc7Fik\nsd5+006CvSE8bd4N6V1bjFqtUE7PUE4mlqQamHu3YAyF1z4ZuuRiK6trWF1gMIOsb4q6li/X1LXJ\nzMaoa5limUgiRzSV4+RFEVQuix6bYfP0rs1TrVYZG4szNDzK8wfPNdY7PPZGwLtpA3vXLhVO/vob\nbuXv//7y3fBPvnyEv/vf/06xsIf5bU64AwBf24d48M/ef8nrreb8r7nya2hoXEte04kDG4EkS7T6\n3bT63WRnu5gaGWdm8ByRA8eJHDiOTifTf+ethHpDmO0bY52w2HetkqunGmRTZM+fIXv+DIZgG4ZA\nEGMwhGRY44f2xb5r1XqxNheHORAmR83GQ2eGqywMhRCYdAKTTsKql8iXq4zNFqlUVSqsT6qBRa+w\nzW9ji9fKrqCdaD2CanQmz+hMnhaTjlazHpdFj17ZHOqaJEmEQl5CIS97924hEh0nEh3n2OkRhqfS\ncHyArW0eAn4PXlfLhn95mGdqLMlfffEU46OXNwAw70dWLPzLotWH6n/eQThofsVrOs0yWz2VJWv7\nnz9cz97UXPk1NDQ2H1qR1mTMNgsd23qo9HUwPRYnPjpBYSrGiUef48Sj0H7zLkK9oQ31XZNNJmRT\nGLUSoJxKUkpOUxiNUhiNMntQYN6yHWMojNKyxtD5+clQeX4ytLLgu5abqR2zueoRVLqrVtd0soRO\nlrDoZQpllXy5wmSmtDAZ2mR1TZIEAbuRgN3IrqCdkWSOSLLWu5bKlRhIZPBZDbgsehybyHfNYjax\nbUsXW/s72benFvD+kxdOcTo6xenoFLIss6cnSNDv2XDftcj5ColFzfYA0xN/xrOPPLBicbSSH1mt\nL+0zmCx/y9t/5nWveM1qtbJMZXv56fgSI9pXug8NDQ2NjUQr0tYJWVHwtvnxhH1k0t3EYxOkh84T\n2X+MyP5juPq7CHQHCfWGMFlfWQVoBkKW0bncKK0uqtkMpeQ05NJkTp8gc/oExrbO2qCBz4+kX2Po\nvBAgFFDVReqaAWbjQBxhdiz0rl2l75pUV9eMimikGixW16S6jUcz1TWTTqbPa6XXY2F3yEE0mePp\nC3EmZgtMzBawG3W4LXrcVv2m6V0TQuD1tOL1tLJ7Vx+x2CTDkTEOHB3k4NkoB89G6Qu5CAd9eN1O\n5GZtg1+CasW44vpqbviruecrulNs2VvLz7zcEPOfe9udyKL22KGjz5CYuPz70NDQ0NhItHeidUYI\ngdVhw+qwUe7rJDE+xdTIBNNnB5k+O8hxoONa+K5ZrMgWK9VSiXIqQTmZIB8dIh8dquVKbtuJMRBs\nsrpWWVDXskB2Xl1z1yOork5dE0Kgk8Uida1KrlxlKlOiWo+gkpqsrgkh8FgNeKwGdgbsjKRq6trh\nkRnS+RKDiQwBey2CymrYPJOhep2Ors4QXZ0hbti7leHI+BLftTaXjaDfQzjgwWC48kL94sZ9hxF+\n4b43A1BRBf/yH0/U/rs0u+Lz8/nEiuur+ZE5Wit4Au4rukdZqPzRn9RUvLFYy8rX2wQ+ZxoaGhpa\nkbaBKDoFb1sAT9hPZqabqdgE6eHzDL90lOGXjqLTyfS9uTYZulG9a5JOh97jQ+f2UJmdrdl45GfJ\nnDxG5uQxjOGOmrrmDzRXXSuXasWazgCzU7XD5pa679pa1TUZ46LM0PFF6posBKLJ6ppekeh2W+hy\nmdkZsDOcyPLMwELvWqu5pqw5zTqUDVCpLhe73cqunb1s29pV910b4eDxYaLTs7x0cpCdXQFC/iub\nDC0Xc3zhS19haixJ5HwFGSNzcxPYndP8j//xm43HqRSo9ZQtttL4NKjFi08JwBvetod0/BOMjy5s\nkZos76etd21fauzOaUrF91Au/W1jzeX7OG/4qd1rOq+GhoZGM9CKtGuAEAJrix1ri51yfyeJ+d61\n+CgnH3uek49B5627Cfe30RpwIW3AB7sQEordgWJ3UC0Wa+paKkl+ZJj8yDBCCCzbd2EIhlHs9rWr\na0IG5Ia6puqMqNkUZFMsqGsWUK4u7koIgV4W6OV6sVaqki9XmcqWakUi66Ouua0G3FYD2wN2Ioks\nkWSOE2NpEtnipvVdUxSZzo4gHe0Bdu3sY3AwxtMvnebYwCjHBkaveDJ0aizJmcNOcpmHG2uy+ACp\nRI4HPvIhAMbO/V+OH3kr8Blq6XQV4Kcwmr634jm337iHNgd86fM/R6ViRJbztPXKeALOpa9Fb2pc\n42KMxuVtBRabDBxBVH+OfNFId5eHPW/RfM40NDQ2B1qRdo1RdDq87QE8bX6ys721ydChcwy9eJSh\nF4/i3d5LqC9MsDuEzrAxmaGSXo/e60fn8VGZm6WcnIb8LHMnjjJ34iimjm4M4bbmmOTW1bV5ZW2p\nujZVV9csoDeuSV0z6+X6ZKhMrm6Su57qmkkns8Vno89jZbvfRjSZ44WhBLFUjlgqh9Osw2XZXKkG\nQgg8bicet5OdO3oZjowRiY5x7EysNhl6YpCtbR5Cfg8et3PVAYnI+cqSAg1gbu5hvvOdB/iv7bXi\np6gWqdln3LHkcYr+H1e9v5tfv5cbDr5Y/2llD8JL+6CtfMxik7n1Fg/xjMoDH3nPZfe2aWhoaKw3\nWpG2SRBCYLFbsWzvpdzbQXx0kqmRcSZPnmfy5HmOShK9b7qJcH/bhkVQCSFQbHYUm51qsUA5maA6\nlyA3PEBueKBmkrtjTy2CyrbGe7osdc1T911bg7qmCPT1rdBCef3VNUkShFpMhFpM7ArVbDxGkjmO\nj6VJZktcEBlCDhNuqx6LfvP8OprNRrZt7WLrlk727U0yHBnnqRdPcToyyenIJO1uO6GAh3tu6EAR\nC/1bDiO4HP4Vm/GLxYX/397wtj1MT3xiWZzSpbYZL6WSKXoTqxVhFz/fYYRbb7lpyTFJVoCV+940\nNDQ0rhWb51NBo4Gi1+HvDOFrDzIznWQyOk5mdIgzT7zEmSdeInzjDoLdQbztvg2LoJL0BvS+AKrH\nVzfJnUYtZZk9epDZowcxdfZiCAbRe31Ia40iWlVdm4RZEGZn3XfNdNU2HrK0SF0z1LZDx9dZXbPo\nFbb6bPR7rewM1nrXnh9MMJLKMZLK4bLo8VgNOE06JGlzbIUungzds6uP6Eitd+3IqSiReJqpsRHO\nnTiE3QgGReC2CEZi51c8l16/UARdTfj31ahkKz1/q0cQz1yslmkFmoaGxuZjc3wSNJcNTxzYCPKZ\nLJMjE6QGzlCp1P3AZIneO24i0B3E4XZseI9TJZ+rBbxnU6iVmkmokKRa71ogiGJv4j0tLtbmk9BF\nXV3TX726tphKVV2qrsG6qGuLyRTKDCez/OjMFJVq7XXJkqCtpaauGZRrYzZ7KVRVZWoqyYXBEcYG\nBnnuxQMAtJpl2p0SLz/3EomJPUua8U2W9/MbD70OW++17/XSoqM0NDQ2E1os1HVEpVwhOVkLeM+N\nRxvrvp39BLuD+LsCGEwbF/sDoFarNXUtlYBiprFubO/CEAg2x3etcTF1acFWR1ictcnQNahrC5dQ\nKdUzQ8fnio3M0Jq6JtYlM7RSVRmdyTGcyHEgkmys+2w1mw+7UaHDolLMzq34fL3ZSiS78b1tlswk\nf/rlrzMwOouqCpwmicnxGNWZGby27YxP5hpN/v/z9z+pxTJpaGhoXIQWC3UdISsy7qAPd9BHPtND\nfHSS6bEpJo6fZeL4WYQQdN22l3BfGKevdUPUNSFJ6Fqc6FqcVAv5WqpBKkk+Mkg+MrjguxYMozjW\nqK4t6V0rNwLe1UwSMsnacMH8ZKh8df+8F0+GLvZdiy/qXZMlqXYrNKdgkyVBm9NMm9PMzoCN4WSO\nJ89NNUxyW0w6ipYSP/ju3yGqyz28PvLB9wP2Nd/HlWKzGPE6dLhtTtK5KoV8GQiBP8Stt9yE1+2k\nO+zB73Ks2Dd28uUjfP8bp5ie0GKZNDQ0NC5GK9JexRgtZsJ9nQR72klPp4jHJpiLDTHw7CEGnj2E\nf1c/4b42/F2BjetdMxjR+wLovL6VfdfauzCGwuh9/ib0rkkgpEax1lDX0pO1w5bWurpmbE7vWt13\nbWKuuBBB1VDWmqeutZj1tJj1bPPZiKZyDCeyHI3NMFAsMtfag6GcRc6lkAqzm0YKlyRBi0VGNUvY\nrDpSmQqzhSqHj4zBkTF06Tijx6eQJQsGQ6Whlj37H0eWFGigxTJpaGhozKMVadcBkiTR4mmlxdNK\ncVsP8dgE8dgE48fOMn7sbKN3LdQbxtZ6+caka2El37VSMrGgrkkS1p17MARDKNZmToYuVtcSkEkg\njLZasWawgHR1PV5CCAyKwLDIJDdfV9cq66Su6RWJHreFbpeZXUE75yOjvKhCQbGAzYLeXkXKpZBz\nqTVfq1kIITAbBGaDxHt/9g3sj2R49tGf8PRjKYpzC7YcE6Mfo1qtrBr3pMUyaWhoaGhF2nWH3qAn\n2N2GvzNEairB1Mg42bFIYzI0uGcrwd4w/k7/Bk6Gzvuueamk05SS06jFDLNHDzF79BCm7r6auub2\nNMF37WJ1rVLrKcvPAgJhratrimFN6ppFL2NepK6Nr6O6Nh9BZfUZebESI6+zkdc7mCpIYGwFYytH\nJgsoluKGT4bqzdb6VuvKx7YY3fzLFxIU57685NhM/H/zL99+N9ZV+ie1WCYNDQ0NrUi7bpEkiVaf\nm1afm3ymm6nYJKmBM4weOc3okdMNdS3YG8LeusYEgctECAnF0YLiaKGaz9eKtUyS3MA5cgPnkBQF\n6659GIIhZJNprRerqWuqtFRdm5sGphEmez2CytxUdS2WLlBR1SXqWjMHDSS1grmYwlRMYZON5PV2\nRksGErkyibnZmi+bo5YZatavz2TooWde5D+/8wKlooJOX+atv/Q69r3x1uUPrLeflVZRyyplPcZ2\nG/rxDy8p4qyOdzM3I/OXv/N/tUECDQ2N1zRakfYawGgx09bfSainndTUNFOxiUXqGtdGXTMaMQRC\nqBU/5XSKUmKaajlP+tB+xKH9mPq3YQwE0bnciLXEYs0HvK+kruXSNFtd63OZKFTU5b1rzY6gAnSV\nPLpcnj4kXt/2JiaqFg5EkkSTOaLJHB6rHrfVQItJt2o6wJVy6JkX+eafvcTEyJ821iZHapmcKxZq\ngG4VVay/3cIvv/cengi9wIGn3kO1YiCTnaBSDDE69JeNx13NIIFms6GhoXE9sFn6jpvJdW3B0Szy\nmWxDXSuXax5nsizRc/uNBHtCG+67pqoq1VyWUnIasjOodS80QyCMIRDA4A8im5dnL17lxQC1oa5R\nN9lohrq2mHLdxmN0ttCw8ViLuiYVMmTm0ises1jtVA0W0vkSkWSOH5+dolr3XbMbFVwWPW6LAb2y\nNpuOz3308xx74U+Xre9+/YP81hd+fcXnLBR2f9xYM9s+yJ7bsgTaPaiqSqZQ5ZZb7+Dzn/0es6Pf\nWnaO/j0P8IGH3n3Z97nVI/jCl76y4rEHPvIhLfpJQ0Nj06BZcGgsY5m6NjJBdjzC2Sf3c/bJ/fh3\nbSHcF96wyVAhBLLZgmy2oJbLlGeSlJIJCmMjFMZGADD3bcUYDG2gumYBRX/V6poiCawGmT69iXy5\npq5NZq5eXasaLJgMlpWP1f+0G3XsDOjY6rUSm8kzlMgSnUoxk8qSnhF4zAp+i0yrUapZjVymv1q7\nuUoxO4cornyfxcLq/0bmFbZHv/sgFATjk4P07DASaPcAtdduNcrcees2vuN5idnR5eeYTVfIZHNY\nzGvcBtfQ0NB4FaEVaa9xJFmi1e+h1e8hn+lu+K6NHzvD+LEzyLJE/5230Nbfhtm+coHQbISioHN5\nUFrdVHPZWqpBbobsudNkz53GEGzDGAxhCASRDGs07r24d61azwxt9K456uqaaU29ayadqNt4SOTL\nVcZmFyKo1qN3TZElOlrNtDtNjBrzfP17TzFW0jP/ZU1PBTmX4oO/9A5QWl7xfMXsHF/6q68yPhFd\n8bjecOlG/31vvJV9b7wVP2m+9FdfXfVxRlN1xfW5Qpp/feIA/WE3QZ8br7sVZROmMWhoaGg0E61I\n02iw2HctNZVgMjpGbjzKqcdf4NTjL9B5627C/W20BlxIa1GyLpMl6lolQDmVopScpjAapTAardk9\nbN2BMRRGcbSs3SQXAfJK6tpM7ZjVVdsKXYO6ppMldItMcmvqWmmZuiYXs2RfYWvz8l6WoNUkY8+O\nYxUyeb2dvN7OVF4Gk4sXYjkUs4LHasBhVF7x77Bnp5FM+kNkZue3Ep9Cb/gLEpNePvfRz68+RHCZ\nvPWXXsfkyG8u2RrV6T/Ajhs8lGSZsyNxzo7EkWSZPT1BwgEvNmuTtsE1NDQ0NhlakaaxjMWTodnZ\nbiajY6SHzjP04lGGXjyKd3svob4wwe4QOsPaMzMvByEr6FxulFYX1cxcrXctlyZz6jiZU8cxtnVi\nCIaaE0G1irrGXLx2uKGumeEqi1VJCEw6GeOiydDF6lq+UOKRx59kamKcQn5pA/wHfu3+Vbc9L3lN\ntYK5kMRUSGKTjeT0DiQhmJwtMDlbwGHS4bbocVn06OSVX1dti3KKCyd+gexcgeysh2LhHxi5ACMX\nXnmI4JXY98ZbuXDiLP/+zXdSLGwHKpSK72bw8L/xyx8L4OnqJBId58VD5zl0Nsqhs1G2tHkIB7x4\n3M6mDUhoaGhobAa0Ik3jkphtFjq391Lu6yA+OsnUyASTJ88zefI8RyRB9xtuINgT3LgIKiGQrTZk\nq41qqUg5maCcSpCPDpGPDjXUNUMgiM65xntapq5VasMGsFRdM5hBvjp1TQiBThbL1LWxnMAbCOAN\nBJhLzxKfHCc5HW8MVKyFxZOh79jiYFpyMJzIciQ2w0yuxMB0hqDdiNtqwKKXl/0dBto9BNrhmR9G\nmUt9bcmxiZE/5tHvPrgmNe388QTFwncvOu8d/OifHuS3vvB6OtoD7N3dz8BQjB89c5wz0SnORKdo\n99gJ+T2E/B6gSVmxGhoaGteQV1uRFgIeAo4Crwf+GDhxTe/oNYKi0+HvCOFrCzIznWQyOk5mdIjz\nT7/M+adfxru9l0BXkEB3AKNlY5q7Jd0ik9zFEVR1dc0QascYCKL3B5CNxrVdTAgQSi2782J1bQ6E\nua6u6ZqjrqUpkUvF0ducWO02rHYb4Y5OpqemaOZQtl4WdLssdLnM7AzaGUpkeXZgmthMnthMvj4V\nqsdjXl4cVior/51eaojglcxvDz36IhdORIDfBcrAvcAdy85rs1nYs6uf7Vu7iUTHGRgc4cipKJGp\nNOLEIMWbO3nXu95Fq2O54rhShqiGhobGZuTVVKQJ4PvAbwGPAz8B/h3oAyrX8L5eUwhJNCKoCtu6\nmR6bIj462VDXjgIdt+4m0B3EHfYgrzVB4HLuaXEEValEOZWgnEpSiEUoxCIIqPmuBUPoWl3NmQy9\nWF3LAtm6umZz1QPedVetrklUGRu6gCTJOFxunB4fit6ELxhE1ZsoV6vN9V2rpxp4rAZ2+G1Ekjke\nOz3JdKbIdKZIQleiZPUh55JIlSIAspxf8VyXGiKoTZKuHAR/6NGaVUd2drGK9lD9zztWPK9Op9DT\nHaa7K8TuXX0MDMZ49sBZvv/iAAB9IRdtQR9et3NRH6VWoGloaLw6eDU1cNwD/Cu1d/j5d+szwKeB\nf1r0OM0nbYNRqyqzyRnio5PMRi5QrW/J6XQyW+9+PaG+MEbzGpWsK70nVaWSmaupa7n0gu9asA1D\nMIgxEFr7ZOjCxQC1rq4tnFOYWxYmQ8WVFYbRaJSvf/3rS9ZMFitOj5/b7rwb2WQFauqb1IihWvnX\n+XL81QCeeOwZ/vbhJykWdOgNJd71a29my603MpzIMjiRpFqqFWcuk4LPInP+5SP8n/91jNGRP2mc\nzxv+Dd7ziVuvartzNQ82+AzecP6yzzuXyTI4OMrjTx+lUql7ACoK+/pCBH1urBZt0EBDQ2PzcL34\npL0BGGChQAM4C7yFpUWaxgYjJIHd1YLd1UJ5axeJ8Tjx0UkKUzGO/cczHH9E0H3bXkJ94Q3tXVOs\nNhSrbanvWn0ydFYIzFu2YwyGUZzOJvauVWpToTojajYF2VTtmM1dU9eUqx+0yGXmyGXOY+V23A4X\n+XKVqUyJaj2CSlrFd+1y/NWeeOwZ/uChnxAZ+lzjWHToUzz0B3DnPW9kZ8DOcCLLE+emSMzBuTkV\nqX0P93xQ4cC/f4JKSYfeUObed179dGdplVB1s22I93ziZy77vFaLmV07e9m2tZPoyASDQzEOHh/m\nwKlhODVMX9BFwO/G52lFp7ya3gI1NDRea7yalLT/A+wGblu09i3ABvzMojVNSdsEqKpKZmaWyeg4\n6ciFhpLl3d5HsCdIoDuIYZVw7fW8p2pmjtJF6pox3IEhFMbgDyDpmjStqqp1G4/qReqaE9VgBp3p\nkluhMzMzpNMrq192ux2Hw4GqqpSqKon0HIm5ha1HgQqVMlTKWKy2y7LruP+dv8+zT35u2fob7/wU\nX/vOQ42fS5UqozN5oskcByLJxrqrPhXqNOtRrjLg/WrSDC4HVVVJzcwyHBnniWePN9Q1IUns6goQ\n9LtxOmwbmrChoaGhMc/1oqSVgdJFayvuIX3l8R82/vvG7j5u6u5bx9vSWAkhBNYWO9YWO8X+TuKx\niXrv2jkmT57jiKipa8HeMK3+azEZWu9dSybIjwyTHxlGCIFl+26MoRCK3bHWi9VsPJAXqWsG1GwS\nssnacZunZuMhLy8MHQ4HDsel70EIgV4WlOZm+JdvfosWtwenx0dVrk02qqrKTXv3YNCbX7F3rVhY\nuTgt5Je+RejqJrkdrWZ2h+xEkjmeODvV6F0TAoIOE26LfsXJ0EuxkkeaN/wb3PvO1132OVZCCIGz\nxY6zxc7O7T2MjceJRMZ4/uA5jl6IcfRCjA6PnYDPQ8DnwmTc2C8PGhoary0unHiZCydevqzHvpq+\nOn4aeCewd9HaD4Eh4MOL1jQlbZOiVlXSiRRTsQnmRgYXqWu9BHtCBLqCGMwbr65VZtM137XCXGPd\n1NlT813z+hDN2hJbTV2zOOuToZdW11bj4v41s81Bq8eHztbCjTfcgGyyIgtRG0hYpXftcpW0lahU\nVcbTeSLJHC8OJRrrTrOuobAplzmsceiZF3n0uy9QLChr3j59JTKZHJGRcSKRMY6didUWhWBbPY/1\nLgAAIABJREFUm4dQwIvb1aL5rmloaKw7l1LSXk3vQK8H/pOlo2EXgE8Bi8fBtCLtVUCxUGR6dJJ4\nbIJyagKoFQ9dt+0l2BOi1d+6IakGi6kWC5SSCdS5BOqiLTHrzj0YAkEU28pTiVfFfLG2KOAdIdV7\n11ZW11ZjpSEDqNmm/PK734et1U08WxOh5yOohGCJurbQk/aHjee3dX6S3/6DN3PnPW+87HvJFMpE\nUzlGkjmOj9W2a4WAkMOEx6rHrN984r2qqkzFkwxHxnnqxVOo9fSHdo+dcMBL0O9G36xtcA0NDY2L\nuF6KNEHNH+0B4Alga/3PbmCxJbtWpL2KmFfX4qMTzEYX1DXP1h4C3UGCPcEN811buKcq5fQM5eQ0\nlBbsGkydPRgCQfQ+P9ImUtdWK9IAOjp28p3vHCGfV1D0JX7xfbdzx91vAECuT4aKurr2xGPP8M2v\n/oRCXsFgLPPu97/pigo0WJgkraoq8WyFSLrEYKLWLyfp9KDocVsMOM26TalSFYslItFxBodiHDlV\nyykVklSLoAp6sVs3Jr9WQ0PjtcP1UqRBrSD778BLwC3AXwAXb+xqRdqrlIa6NjpJOTkO1P6Bdt22\nl3B/24ZNhi6mms9RSiVQM6nl6lowjGK1Nu9iq6pr871rKxeGqxVp586N8fzzrUQiCz1e3d2f5rN/\n9BZ2vuHm+SssCXiHtfmu5abHePivl95LWdKR1zvouOF2pko1RUqWBKF675pJv/mC0lVVZWIywcDg\nCM8dONtY7wm0EvS78Xtc6HSbTxXU0NB49XE9FWmXg1akvcpRVZXZZJp4bGKJ75pvZz/h/jYCXQGU\nDf6AVKtVKrMzlJIJKGYa6+aefgyhMHq3Z20muUsutpq61lpX14xL1LXVirRvf/ssAwPfWbZ+770P\n8YMf/A6Fikq+XGVirtg4drG6dqWsVKTNc//97yMptzCcyPJyNNVYdy+aDJWvcjJ0PZmbyzYiqCrl\nmgOQkAQ7OwMEfW6cTvumVAU1NDReHVwv050arxGEENhbHdhbHZT6O5mKTRCPTTBx/CwTx88iyxL9\nd95CuK8NywqxP+tyT5KE4nCiOJw1dS2ZQM0kyV44S/bCWQyBMIZgEEMg1KQIqkUB73V1Tc0kIJNA\nGG0LAe+ygt1u5/777192mkcf/QoDA8tPn8vVpi6NilgS8D6aLlBRVSp137VmqWvzKJKgvdVMe6uZ\n3SEHkWSWJ87FiWeKxDNFJEkQchjxWA2YdJtHXbNazeze2ceObd2Mj08zHB3juZfPcWxglGMDo3R4\nHISDXoI+t6auaWhoNBXtHUVjU6Mz6Al2t+HvDJGaSjAVHSc7HuHU4y9w6vEX6LhlF+G+Nlwh94YN\nGkhGE4ZACLXir5vkTlMYG6EwNlKLoOrbWougcrmbE0ElFoo11EptmzI/CwiExYnDbMZhDy/rXXM4\nVi50TKalKWqKJLDqZfpcpiXqWrneQC83pkKbU6wBOEw6dpkcbPfbGZvJE0lmeWk4STSZI5rM1SOq\n9DhMm6d3TZZlQiEvoZCXvbu3EB0ZZygyxrHTIwxPzSBJF+q9az5sVi3VQENDY+1snq+rzeN3P3T3\n2671PWg0GSEEJqsZd9CLIxBEVYwUZ5IkRyaIHjtHfCpNqVDCaDaiM+g35p4kCdlkRnG6kExWqhUV\nygVK03Fyw4OUkymqpSKy0bR2k1xRK9YQEkKtIqplkBQo5SCXrv1crdb61uoRVE6nwv79D5NM3tU4\nTXf3p/jt376bvr4OfvjDp/nYx77G1772DN/97o9xOhW2bemkkJmlkplBqpYoFotUykXKpQLlYgEZ\ntdEbt1LBVs7NcfDQ4RVfwo037ENnti1Zk4TAbtLR5jSzPWCj1aJnOJkjUygzNVcknS9RrYJBkTbV\nVqhOp+B2tdDdGaKnw4/FpCc6Gmc8kebs8BjFXA5ZljCbDJpJroaGxiV57B8eBvi9lY5pSprGqw6z\nzULHth7Kve1Mj04xFRtn+uwg02cHOQ603bSDQHcIb5t3Q3rXhBDIFiuyxVqPoEpRSk1TGI9RGI8x\ne+hArXctEETv8SLWEjp/KXUtl2ZeXVMNFu57W20y88tffohcTsZkqvDhD9/Dfffdzg9/+DSf+MTj\nXLjw2capBwY+DcCuXZ18o97jJoTA1tKK0+NDNtu4Yd8+ZFlfU9ckmhbwDmA36tgVdLDVZyOWyjOU\nyHB4ZIZ0vsxgIoPfbsRj0WM1KJum8JEkCb/fjd/vZvfO3kbv2pmRKc6MTNHuthMKeAj5Pej1mo2H\nhobGlbE53umaizY48BpjPoIqPjpJevg8lUp9m06W6H3TTYR6wthdTfQ4u8x7quaylFMJ1OxMw3tL\nyHLddy3UvMnQ+YD3iyZD/+PpU3zxqy+SLxkxGst85CN3c999twPw9rf/Do899tllp7p53wf5+3/8\n5IqDCDqDkf/3Xe+hZLCvOBkqhLjsMPfLe1kqiWyRoUSOZy7Eqc+P4DTr8VjXFkG1npRKZaIj4wwM\nxjh8MgLUBg12dwcJBbw4bJZNU2RqaGhce7TBAY3rmsURVJX+TpIT08RHJ8lNRDnz45c48+OXCO3b\nTlt/G952H5K8/r1rQghkswXZbEGtBCmnU5STCdRyjtkjB5k9crCmrgVDNXWtyb1rP/zRy3z8f57i\nwvCfNB42cOFToKrc9/Y7yOdX/tUfPxVl/49/vOKxUiGPUi3ROd+7VqoykVnUuyYJJL0ZY6t5xSKk\numzllV6WwGUx4LIY2OG3EUlmiSRqJrnJ7NoiqNYTnU6huytMV2eI3Tv7uDA4wrMHznLkfIwj52N0\n+ZwE/W4CXpemrmloaFySzfGu1lw0JU0DgNxclvjoJKmBM5TLtWZ5V38XwZ4QwZ4gpmvQ3F3J5Sin\npilUVQrFmvWFZLUheYMIXwBhNGG3WmiRiq9wpktz37se5rGnP79s/d63PMi/fe8h3v7//DGPPf6H\ny46/lZvZcbsJ85vetOJ577//ftra2ho/l6squVKVsdnCqupaM6lWVcZn80QSOV5YIYKq1axHtwFF\n+JWSyeQYHB4lGh3nxLlRoPZ3s6PTT9DnprXVsWkGJDQ0NDYWTUnTeE1isppp6+8k2N1GYiLOVHSs\n0bt2DOi4ZTfBniCesHdD1DUA2WRCNoWZsbbyg4f/EpdQsci18kZFkLU6eNMv/QqOdt+a1LVCceXh\niVxWwMwYH/mVHRx45r0k83/TONbDO/kop3mqsH3Jc8bOnWN2/36M5TJffPJJfvrBB7n9vvuA2mSo\nzSBj1ZvIl2uToZMXq2tCNK13TZIEQYeJoMPErpCdaHIhgiqZLSFEBr/NiMuix27cPL1rFouJndt7\n2L61iz27p4lExnn2wBmOD45xfHCMNreNgNdN0OfGbF6jhYuGhsZ1g1akaVz3yIqMJ+TDHfSSmelm\nKjZBevg8wy8dZfilo+h0MlvecivBnhBm+wb5rskKcVUirqpYquCWVJyyimUuRfnIflKjrRiDIQyB\nIJLhykPnDfqVlTiTvoQoFbjv7hu5q+cLzJ64mTwWjGT4KKd5O3M8pijcum8fAGcOHMDyk5/w1fFa\nAgSRCJ8eGwNoFGpQn77VCUw6CZtBbqhrlapKBXVd1DWLXmGrz0a/18qukJ1IMsdzA9OMpfOMpfM4\nTDpcZj2tFj0GZXOoa5IkEQx4CAY87NnTTzQ6znBkjCOnokTjs3BykC1hDwG/G6/bibKWIRMNDY1X\nPdfjO4BmwaGxIkII9EYDTq8Ld2cnOouDYkWiOJdm8kKUC/tPks2XawWH3byuvmtZo4VDB/YDghKC\nGVUQrwpKqqCvqws5MUFhdITsudOoqoqk0yEZjcyoBiZn88wUqiv+T9WZMIoKTodg/+FvkZy5t3HN\n7vYH+czH9tLXE0ZUq/j8DnKH/5NvzhzjXYzST5GPul2Ub7yB6FSc4eFhTv/DP/D1WGzJvd+VTPLV\nRII3//Ivr/jaJCEwKBKtJgWLXsGgCDKlKtW6Ue5imlGwCSGwGhRCDhN7Qw78diNGnUwkkSWZKzGa\nzlMsV5Gl2n1tFnVNpyi4XC10d4XY2huitcXC8Eic+EyGodE4pwZH0VHFZDRoAe8aGtcxmgWHhsZF\nKDod3vYAnjY/mXQP8dgE6aHzRA4cJ3LgOIoi03/nLQR7QlhbmpjPeQnKCCZVAdv3oRsdqAW859Jk\nTh0nc+o4xnAHKbuXbz/yCGWxcgH5vl/7AC12HffddQMAX/rGx8gXdBgNJT7yvhsa6wjB7XffCULi\noW98GzWbJ1UssPWn3sKOG/aQKamMTs8ws8q9yrncK76eeXXNqIhGqsHYbLGhrkl1k9xmqmtGnUyv\nx0qP28LuYE1de/pCnInZAhOzBVpMtd41t0WPskl614QQuFwtuFwt7NrRS2x0kuHIGPuPDPDymQgv\nn4mwrd1LOOjF1dqi9a5paLyG0Io0jdc0QgisDhtWh41KfyeJ8ThTsQkKUzFOPvY8Jx+D0L7thHpD\n+Dr8yMr6i89CCBSrDcVqo1oqUU5OU04lyY8MU9bF6UtOkLE6mJb1ZCR5WdLAPPfdtagoW4Xb73oT\nt9/1JiLpEn//rb/B7nFRKuSxGIz0+Vs47Fi5QK2YTFf0enSyQCdLWPQyhXKVXLnKVKZEtR5BJTW5\nd00IgdtqwG01sDNgJ5rKMZzIcjQ2QypXYmA6s2knQzs7gnR2BNm7ZwsDgzGeePY4pyKTnIpM1iKo\nAh78PjcGbTJUQ+O6RyvSNDTqyIqCJ+wnNhfn4JHzkMmRLRYIJibpPuRFUWS23nUrob42TNbLL1LW\ngqTToff60Xl8VOZmUQu1gG/rXAorULLYScg6krKe6hoLjXyhyMDIGIOxcdxOO0GPi+1vu4tPzqT5\nXGSk8bhPdXdzz4c/fHWvRwhMOnlJZuj4InVNroe7N1Nd0ysSPW4L3S4zOwN2hqYzPDeUIJbKEUvl\nNu1kqMNuZd+eLezY1s1wZIzBoRhH6xFUQlxge4ePgM+Nu9WxYZFoGhoaG8vm+PrYXDQLDo2r5vDp\n45z8wT/zp4l4Y+3/sztp2X4z4foHuAA6X7+XcH8brf7Wqyom8r4ws/mVm/ttRj3GiZEVj005vPzt\nX34Rl1BxSyoGqdbjVUUwa2vh7nffT0eg9YrvJ5Iu8Y2/fnjZusmgZ2t7Jwe++0/IhTwVg4G73vcr\n3P5zvwB6CyhrV3MqVZVCuUq+XGUqWwJYF3VtMZlCmUgyx0gqx4mxmvmuEBCw1yZDbZso1WCearXK\n5GSCocgYz+4/g1rv7wu7rAS8bgI+N1bLxnx50NDQaB6aBYeGxmVy+Lmn+MKiAg3gf6WTPDAd4c6f\nfzdT0XHSw+cZfP4wg88fxrezn3BfmEB38IoiqIwTI6xqtLBaI1idMoIJVTBRUXFUwS2qOGQVx2yS\n0kvPMBMOYgwG0Xt9a4ugAnKFInvueBPvePtd9QiqKqrOALNTwBTC3IKqt4De2MgMvVJkSWDWy5h0\nElaDTL5UZXxufdU1i0Fhm9/GFq+VXfXetecGpxmdyTM6k6fFpMNjNdBq0aFsEpVqcQTV3t39REcm\nGI6OcfhEhJHpOTg1xNZ2L+GAF7dL613T0Lge0Io0jese0+6tZCvlFY+ZZYXc0dONn3Xl0oqP05VL\njd61Ul8H8dFJpkbGmTh+lonjZ5Flib4330yoN4zNaVvxHM1HMKPCjCpjqKq4hYqQZXKD58gNnkNI\nUj2CKohiu3Qslt1q4X2/9oFVjyGKIGRAbqQaqDoDajYF2RQgwOYBgxnkq1PXhBDoZYF+Ue9aQ11b\np941SRL47Ub8diO7gnZGkjkiySzHRtOkciVEHEIOEx6rHrN+87xdGo0G+nrb6e1p48a92xiKjPHE\ns8c5HZnkdGSSDo+dcMBL0O9BtwH5tRoaGuvD9fhVS9vu1FiCuqOXv/r8yv8mPvjrDyBOnG/8/I2v\nfZkvnDu97HG/3reV9/7q0j6sarXKzFSSyZExsmORxnpw7zaC3UF8nX50TW7uvtQ2KYBVJ6OcO0E5\nlYBStrFu6uiuRVB5fUjNsnNQ1aXqWh1hdqIazKAzrTrUcPmXUClV1Ya6Nm/gUVPXxPqkGqgqk7MF\nhhNZnh9cSDVwW/W4LQacZt2mVKmKxRKR6DiDQzGOnIoCICSJvT1BwkEftmuQsKGhofHKaNudGhqX\nyd7b7uDB6fiSnrRPtLrYc9sdyx4rSRJOnwunz0V2tot4bIKZoXOMHj7F6OFTSJKg5403EugO4vQ5\nm1JMXHKbdB5nKzpnK9V8jlIqgZpJkRseIDc8gJAkLNt2YQgGUeyOtd2TEMvVNcWAmk1CNlk7bvOA\nfu3q2ov/+QiPffGLyPkCBb2eXe/7IDfc/daGSa4QNE9dEwvq2o6AnUgiy+Nnp4jPFYnPFZElQZvT\nhNti2DQmuQB6vY7enjZ6usPs2tHLwFCM5w6c5dC5EQ6dG2FL2EM46MXjatEGDTQ0XiVoRZqGxiL2\nbt0JwK8/9xRKuURZ0bHntjsa66thtllo39pNta+D1FSC+OgkmdFhzj11gHNPgXd7bz0zNITeuHJk\nU7ORjCYM/hBqNUBldoZSMoFazDB34ghzJ45gbOvEGAqj9/nXrq4JaUnAe0NdS0/WDlucqPqrU9ee\n/uEPefwTn+APL1xorH1qaJBWo0LXG+9aiKBqKGvNU9esBoXtATv9XiujM3mGElkORlMMTWcZSmTx\n24y4N1kElRCi0bu2a0cvg0OjPP7MMc6MTHFmZIp2t52Az03Q78ZkvPI0Cw0NjY1jc7yrNBdtu1Nj\nCVey3dlMCrk806OTxMemqKQmgFoPVO8dNxHub8Peeuk+sfWgWihQTiUozyQR1Pr0hCRh2bEbYzD0\nir1rV8R8saYYYH6jUkiL1LXL+474O29/O5997LFl6w/dey+f+f4PyNd71+KLJkObra7No6oqyVyJ\n4USWp87HmQ9QsBt1uCw1Kw/DBnjpXSmlUpnoyDgDgzEOn6xvzQvB1jYPQZ8bj9uJrKlrGhrXBG27\nU0PjGmAwGQn2tBPoaiOdSDEZHWcuNsjZJ/dz9sn9hPZtJ9gTwtfhY//hozz1g0fQlUqUdDrueMdP\n8fqbL21EezVIBgN6XwCd18cjTx3l4X8bplhU0Ctnuf92K++4740YQiEMTZgMXV1dqxWswtJaV9eM\nl1TXlHx+xXU5l0OWBBa9jFm34Ls2MVdcN3VNCEGrueaptt1vqw8a5DgSmyGdLzE4ncVrM+C26HGY\nNk/vmk6n0N0VpqszxJ5d/QxHx/jJC6cagwayorC3N0jQ59F61zQ0NhFakaahsc4ISeBwO3G4neS3\ndDI1MkFq4AyxQyeJHTrJUCpOZvQCf55MNp7zybFaIbMehRrAYwfO85lvZRga/3ZjbTjx34BnuHu7\nHyHLtcnQYAjFssZYrPneNVVa2ruWSUAmgTDZa8Wa3gzS8sKwbFy5C29x6oEQAoNSy+acL9bm1bWK\nujTgff7xa8WgyPR4rHS7LewOOYgms/zkfJzJ2QKTswVkSRBuMeGy6DHpNoe6JoTA43Hi8TjZvbOP\nkdgEw5ExXj42xMunI7x8OkJv0EXQ78bnaUWnaB8RGhrXks3xNa+5aNudGku4EguOjaJSrpCcnCY+\nOsGjj/8z35iZXvaY37phN7/5e59el+v/4n9/jCcOfXXZ+pt3vZdvfqB/yWSouae/Nhnq9qxdXZun\nMRlaQdXNF2ECYa2ra4qhoa7N96R9dnFPWnc39/z5n3P7ffdd4hIqxYraUNcWT4ZKde+1ZveRFctV\nRmdq6trBaKqx7rHqcVkMtJh0yNLme9tNpWYZjo7x42eOU6lUgNpgzK7uAEGfmxaHbdP03GloXG9o\n250ar2lyR0+v+m3klWPC1wdZkXEHvbiDXvbvfxxWKNIKUylSk0kcnpbGB+TVJBU8v//gsq3UQmnl\n4YWiasHU1Usll6OcnEbNpsheOEv2wlkkRcG6ax+GQBDZvMYtsdXUtblpYHqJujZfiD305S8j53JU\nTCbu+fCHL1mg1S6xXF2LpQtUVHWZutbMCKpOl4WOVnMj4P3J83Gm5opMzRWRJEG47rtm3CTqGkBL\ni42WFhs7tvUwNh5nODLGCwfPceR8jCPnY3T5WggHvQS8bpRN2HOnoXG9ohVpGhrXmIph5Qm71GyG\nJ7/2r/h29hPsCRLoCjKbL/K1v/zSio//1f/2kWX2HM/vP8gzf/U3/PH4RGPtk2MT5MzLLUUAjPpa\nASibTMimMGolQHkmRSk5TbWcJ31oPxwCc+8WDIHg2tU1IQBxUe9apaZ65dLMq2u33/MWbn/b267a\nd22+d63PZaKwSF1r9K6tQ8B7i1lPS713bSydZziR40AkSSSZJZLM4rPVAuAdm2gyVFFk2sI+2sI+\n9u3ZwnBkjOHoGCfOjjI4kUKSL7CvL0RbwIfZ/IpmMBoaGmvkevxK9Lsfuvtt1/oeNDQuH5OJv4sO\nc29uYYvx404X3TffgcXkZC4aZfzMEBdePE5e1jM0PAA6edkH+76bb8FSyCxZ+/ZXvs4fnR9Ysnb3\nXIbHnBI5w2lScwu/K53+j/Ib/zVET8jVWBOShGwyozhbkcw21ApQLlBKxMlHhsidOw16PbLJ3AQb\nD7EwbKBWEdUySAoUs5BL135W1draVUZQCSFQJIFRkWgxKpgVmblihapaM7GtqirQ3LxQSRI4TDra\nW81s89twmvUMJ7LMFSpMzRVIF8pUqioGRdpUW6F6vQ6Px0lPV5jOsBeLScfI6DRj8RlOD41SKuQR\nQmAyGjTfNQ2NNfDYPzwM8HsrHdOUNA2Na8ylvNmq1SrpeJL46CRzI4OMHzvH3KFBjL5WZJcFqcWE\nuMQHpK60cszVFnOFn3+Xh6/+2/vJF/UY9UXe/9Md3Htz/4qPF0Igmy3IZgtqJUh5Jkk5laBazjN7\n6ABzhw5g6t+GMRRG1+pau0nuZahrqt4Civ6q1TVFElgNMn16E/mySr5cYTJTWjd1DcBh0rEn5GCb\nz8ZIKsdQIsvR2AwzuRIDiQw+qwHXJpsMlSSJUMhLKORl185eBgdj/Pi5EwuTobLMnt4QQZ8bm9W8\naVRBDY3rAa1I09DYBOzdunNFw1xJkmjxumjxuihu7WZSkREvvUB+IgETCYQkMHX5kV2WFc9bWkHd\nKgFpIbj35v5Vi7JLIWQZXasbxemiurh37ewpsmdPYQi1YwyFMfgDSPo1Gvde3LtWrQ0aLPSuOeq9\na6YVJ0Mv7xICk05gVESjd21sdiHgfb1617rdFrpcC71rT1+IMzFbYKI+GdpWnwzdVL1rDhv79m5l\nx/YeRmKTRKJjHDg6yMEzEQ6eidDtdxLye/D7XNpkqIZGE9g8v/3NQ9vu1Ggah08f55Hv/yMn9j/H\nwSMHwWTC7/Zek3uRFQXbrn4OnDkJBgOoEhSLlJJzlGIJ+m7Yg9u09Fdatlr42pnz3D1X2wZ9GvgN\nZwt9d9xGRUiUKxVarFdnsSGEQNLpUOwOZIcLVchU80Uq6QSFWJTs2drUrNDpkAzGJkRQCZBkRLW2\nFSoqJdRqBQpztZD3+e1SSb4qdU0IgSzVBg2cJgWrXsagSGRKVar1YYOLH79WhBCY9QoBh5Ebwi0E\nW4yYdQqjM3lSuRJj6TzFcrVxX5tFpZJlGafTTmdHkB1bO3A7rQyNxEmks0TGpzk9NIZCFaPBgL7J\n+bUaGtcb2nanhsZVcPj0cU7+4J/5wqIczwena//9SjFR64okwGYEmxE1b4V0DpFOo1MEY8+8ROCN\ntzQeOu+z9lv/9gip9BwnMxnuf+fP8TN3v5mp1AzHBoYIe9x1h/6rLwCEoqB3e9G5PFTmZiknpyE/\ny9zJY8ydPIYx3IEhGGqiuqaAqi5R15ir/X+zoK6Z4Sp7pSQhMOlkjIsmQzdCXetyWehsNbM7VJ8M\nPTfVUNccJh1uix6XRY9O3jw9YA67ld07+9i5vTYZOjg0ygsHzzXUta3tXsIBL25Xy6bZwtXQeLVw\nPf7GaD5pGk3hG1/7Ml84t9xD7df7tvLeX/3wNbij1T3fquUKVoOB3NHT6FNDjfX5gi2Tz/ODZ1/E\nZDDwM298HQC5QpGvfP+HfPhnfxq9rvZ9TVVVhBBUKlXkNRYC1WKBcipJOZVEUI9sEgLzlu0YAiF0\nTucl++muCFUFtVJPNVjwXcPqAoMZ5KvvXZunqqoU6ia5k5mFXr/16F2bp1iuEk3lGK73rkHtZQQd\nJjwWPWb98gGSlaiUy2Tn0thaWpt6f6uRTs8xMBjjx88ep1rv8evw2AkHvAT8bvRrHTLR0LiO0HzS\nNDSuAl155aZ7ZZX1S3Eyn+LF738PfbFIUa9n70+/g7033wqAPeAjPTax6nMXG+6u5vkms+D5Vmzp\nBECfGmLsmZcAmAp4yOTz3LZzW+M5w+MTuOx2coVCo0irVlXGE9OcHI6SmstwY38v3UH/Fb9eAElv\nQO/1o/P4aupaKgG5NJnTJ8icPoEh1I4hEMDU3nnZxVoiNUNri2P5gUupa3MgzHV1Tbd+6ppUj6Bq\ntrrW47bQ7TKzM2BnKJHlucFpYqkcsVSOjlYz4RbTJc9x5sh+cnOzKDo9BqMJR6sbb7ijKfe3Gna7\nlb17trB9ezeRyDiDQzGOnIoyPJVGHB/gZ998EybTpcPdK+UyuewsVrtzXe9VQ2MzoxVpGq8priR9\noKSs/G2/vMr6ahw+fZyT//kD/mJ8rLH2gdOneaRvK60eLx/4nYd4eJUAeKiHwF/RFWvMF2vVapXD\nTz6OWqn8/+y9eXBciX3n93lH3wf6vnEfvEnMcDQzGkmja2YkWZLLWqV2XevsqrI+17uxEttVqSTl\nxFuupHa3pLjWiV2xFTuuSuz4Wh+SbY3O0YyGnIPDmzgIEkcDaDSORjeuPtDHe/njNRoACYAk0Gg2\nyPepYg3x+noP6GF/8f39ft8fhpYJCGo9dcnFNCaDXI2d0JhaWCC5mOZYa5Sh+BTffu8cS+IJAAAg\nAElEQVQDPvfCc/sWalCNvXA4kR1OlFJJW/C+lGE9MUklk0Io5rH0ndrzOa7eGmJuIYUoirhdLXjd\nLXS1te70YoAA0sZkaNVdywG5Ze02h6+64N2w7941gyRgkERsRon1skK+rLCQLaFUQ3LFQ8hdCzhM\nBBwmTocdxNN5vn97HkVVWcxp2XZe6/1l5JmJUQY/eIePfu5L+COtLC8uEB8ZxBuKIkoP58IdBKPB\nQE93K91dMc6c7mVsfJqrQ9PcSecRhAJnIjsIbmBs8ArrhTwGowlJNuB0+/AGo4d6rjo6zYgu0nSe\nKnKVMn+wiyD66v/6W6inempfn/vpf8avf+MP+NoWcfVrHi/nXto5CHY3rl18i9/Z8hwA38jn+NJ0\nHPyHP4RQqlRIi3Y6g5rQSr79PsuFAgsi2Cxm3I7NwQGPw0HE68VokIn4vIwn57gznaArEqqVQg+C\naDBg9Acx+AIo2bWaSMqPDADsKNbGJ6f5zo/e5ouvfZJTfT3MLaS4MTRCWySMtJfQ2NFdM8Hqgnaz\n1bU5GbrP3LWt7prDKJEvK8wesrtmNcqcCDnoC9hrz/v60Nx9Ym29kGd04Crhtk78EU3QWuwOxodv\ncvzZF5Dk7SVuRVEOLe9MEARCQS+hoJcXPnQaSZL4zoXb3JxZrt1nQ7DNTY8zOniVD33iC7h8QdaW\n0yTGb+PyBRHFwxeWOjrNhC7SdHSq5NYL9zla6UCYLxWLdIXDlIvlWn7Zo7Bb2dRc7dU5bERRJL22\nyqdOnaNo1aI67k6OMzt2k3PRMMm33yf40nOIoojTZkWtOmuyJGEyGjDKcl161LYiCAKS3QFAsbDl\nhqpYA02wra8XeffKdaLhIKf6NAEd8Hm5NjDE8/1naHFqz7GnwNjmrlVq+0LV3JI2Fbrhrplsmru2\nz+vZcNe2Lng/THdta/DtZ08EAbaJtek7t8ln1zh27kO1+80nJnG4PJTWCxhNWu+eqqqsLqVJjN3B\n4XLj9Phwur0cFlJ1Q8VnPnKsdmxDsJXW8yjxAYKxDlw+7ZqMZiuJ8dt0n36u9n3b7J0sI0n6x5jO\nk4v+7tbR2QOPPwD+AP/8q7+CMHB3X8+xW9m0IIo4D3JyD0mhVCTs8mCqNmuXymVuJxOEwz2EO3oh\nm2D2wiXN7fjIh2ofhCNTCQySRCzgr6tA24sNwWY0a+7acHya9NIyn64OOwCMTU7jdDiobBG5oiiy\nls1x6/YdlldWeeb0CQK+HYRG1V0TyqVqSK6y6a6tLiBY3agmKxgs+x40EAUBq0HCsqV37bDdtQ02\nxJqiKPzHNydRTDYkT7h2e2ZhFoPJxNY0kdTMFNPjdwhEWnF6fEyODGJ3eeg4tncJup5sCLbx4Zt8\n8/00sVMvcnNmmTORFtJzCWxOF6X1ApIkV12/CqtLi8xNj2N3urE6WnD79l+S19FpVnSRpqPziFwb\nvsW1i29hKJcoyQb6H+Cu9b/0Mr++uszXZpO8BXwXGBUESsUi6YX5Qz9fh9lC1ONlKDFFxO1hJDmD\nQZI429aByWCo9a4BzF64BEByZYXpFjv9vd30xiL3PedOS9s34j7qQbGguSXDY0nM5Tyt5Sz5kQEs\nfaeYnpnFYjZRLm/2Fq6uZYknZggH/Cwtr/Anf/P3/Bef/wytu/XSbYTkIm1x10youQzkMtrtDv9m\n79o+EAQBoyRgbKC7tkGpWKTLrtLde4YFYDFXJLe6QiI5i9fpwN7iqt23XC6TX1vFHQhhc7RgstpY\nySwC1KXE/bAoisJ8YpLn+vv4yGdfrrlrM3cn8BmMqKpSO5epu4MsL84R6z6JyWIlPnITURRp8Tye\nDEMdncNCF2k6Oo/AfrLT+o+fRm0L8+pvf522bJY/VFUtMmJtlZ+/M8y1dy4e+nmfa+9iKZvl7twM\nIZebl/pOMJyYwmY20+r1I1VLhcvWMOm1NTIrCfoMBloSc9C5fRJwt6XtQH2FWrnMUjZLW9BPuShi\nNMPC9ctMXL+CrbUDv3czTsJqMXOytxtRFGmPRZhfTHN7dIzWh+mlq7lr5e3u2oomoAWbuzoZWkd3\nraQwu7bprkmCoGX11sldEyWJxYUFXvv8T3LerX2frl+e5GqhgKn7OIu5Im6ThChJWO0Olha1Pr1K\nuUx6bgZfOEalXK71rTWCckkTktHOXkBz17IrS1xZNjMcL2BcAVaWORm0MTZ0lb6zz+MJRBAEAUEQ\nSE6O0uIJNFRY6ugcNrpI09F5BK5dfGubQAP4WjrFVy++taeb9tKLH+Fa77f4369e2Xb8G/kcv/q3\nf8sv/Pe/setjrZJci9d4VH48HOfPLs5RLJswyuv89EtBnuuKUK5UGF+Yoy8cRRJFVFVlMrVAqVLG\nabFy7PgLGGSZdVWtxXiAlrv21rde3ybQAP797Bz/3d+/XleRJosSc+kMrz73DKC5a4OjM2TzBXrV\nIoU7gyiKgu34GSRJqvXSqarK3EKK5/vPPNoLbnPXNMGmyibUbAayGW24YOtk6D7Y6q7ZtrhrqVxJ\nG3CAarAwB3LXCvkckVgrFosVgFKpxNCtm3z6/HE+/NEXMJnNfHtwFkGosFqs4HR7uPDtv6H71DOY\nLFY6jp1uqEADTVgup1M8/6nNjTEz8TFKhQKfefUjtPUc4zsXbvPOjWGSS1k+Emnf1qMGD+hN1NE5\ngugiTeepwirJ/MJXf2XH26SHmPDbb3aaFjK7vONtwuz8nv1uBxFoX/tWnun0/1M7Nr34b4A4Hzve\nzheefZ5ypcJqIc8/XHmf1XyeV87047LZMMgy5UoFWZLuy11TU4s7vp5cfPT8uL3IrReI+rzYLVoO\nmKIo3BydwO920xGIUCyAwSTUJkPnLC6uDw5jNpn4zCc+yvGeLmCfQkcQ71nwvpO7ZgODed/umiQK\n2IwSVsNmOXRurbi54L3auybsw11zOFtobe/g1vWrRFvbGB68hSRLPPPc85jM2sDAa8f8jI7c5ltD\nN7AF24i//xbHTSb6P/IpoLGlToBiIY8vFMVg1M6vXCoSHxnAH24l3NYJaO7a8NUlXOVubs0sYTSX\nKGRXEHNZLDa7LtB0njh0kabzVLFbGCyA6ezxXQXchpt1kOy0Bz32UTLcHoY/uzi3TaABTKd/lz+/\n+C/52HGthClLEg7Jwk+ef5GbUxO8OXQTRVFp9wfwOZycbu2olUI3xNq62bHzddQ5Rd5htdIRDnJj\ndJyOcJDB8TgVReH5433YqkKjtK79NI1m8KymEFKz3Jqa4fwv/BxQB6HxUO7aRu/a/v45FQQBk6zt\n5rRXYzxmVtapVPeFbl1BtXH/h+HZ518kvZhiZGiQUDjKRz/xaYZu3cBqm6e79xjjd+9we/AWn3/h\nNMdPnea311a4/O7b2CKauN0pd+0wsdqdBKJtjA/fwBeKMXl3CNlgpOfMMxiMm8G30c5ekvExXnqu\nC3uLiz/9s79nPjFHqE8bkNDLnTpPEs0o0n4L+Dk0t/8bwNY60E8BLwJpoBX4VaC+v77rPLXsJeA2\n3Kz+l17m1xdTfG1LyfNhs9Me9Ni9Mtz2E2hbLO+c6L5evv/D12oy8ULPMV7oOcbEwhyTqXkCTldN\noN17Hb+WWuDrmU1H7d+aLXSG20ncnSbYHkI21OefludPHCO1vMLgRBxvi5OXzpzkzvQMS2tZOsPB\n2odxIacgijJn2rsZHJ9i5uoH2FojDwzJfSR2dde00q9g81R71w7mrtmNEr1eC+sV9cDumsfr48WP\nau+vUqnEneEhTpw+C0A2u4bZYqGtQ3OpPv/8Kd4rp3nOK/DB4t4huYdF37nnWF3KMDU6jC8U49yH\nP8H48E0sNjuhti5EUcTmaCHS0c3Q5XdwB0KEzatETnfy8udf5XvvbjrSuwXl6ugcJZpNpP0ckAA+\nBXwR+PfAMPAnwHnga0AfoAD/Afif2C7idHQOlY2+s69efAu5XKIsGx46O+0gj90PRnl9x+Mmubjn\n4zr8QTr8wV1v33odUrFItqwQCHXiWi5x6a++jyxLHPvU80R7W7E6rPu/gCq+Ficvn9P6y0rlMtfu\njHKup0trFl9ME/Z6tpW52v0hcrkKsHdI7r7ZcNdU8R53LQ3ZNILZsbng/QDumlkWtq2gOqi7ZjAY\n+Cc//TMU17X3RVtHJ4mpydrtiqJgtzuQDTKfPaENG2zNXQMwSSKSqAX4HhYOl5uT5z8MQLlcYiY+\nSlvPido1ipLEsX5tJ+17P/gHREmm68RZREnitZf6EARhW1DuqbCT5GyKgN+NocF9djo6B6XZPOFf\nBH5/y9c/AgaBX0YTank0IQfwYeCbQBTY+qmjL1jXOZKop3r2dtIeMadtsyftd2vHYp5/w69/0VIr\nd9YLpaKQdzuZHBolN1cdKhDA19tBoK+dtt42rIvJvZ/kESiWylQUhb97+x28LU4+fu4MRoPMhZuD\nlCsVznZ3btukUG1zorS8DEYLpnAEo89f5wXvSi0oV0PQetdMVpD3765tvoRKsaKSr7prG2jumvDI\nvWuVSoXRkdtk11Ypl0vk83ncHi9n+nce/vj24CwTiznWywoBuwmf3UiLxYDYgNLi1klTpVJhJbPI\n3VtXSc/P0v/SJ/bcRfqX/3iJ+NgEoiTR3x0hFglit+2971RHp5EcpQXrv3/P13PAxq96HwH+jy23\n3QG8wFngg8M/NR2dTXbLSntQhtpefWeSu77lGU2Ixfnzi/+S9bIRk1zkn70UqLtAAxAlEWtriLf+\n+i9gvQQrBcSlJdR33gHghZdf5lhPkGh3FLt75562R2FjIfwnnz3H0MQkV0buYrdaUFF58dTxWnDv\nBhshueVMhtJKnNydYURZxna6H3M4gmSzHeyEHuSuWZyoBguYrCDWp3dtL3ftYcSaJEn0nTgJwOLC\nAkaTEYdz9/fga8eDDM6u8MadFPNr68yvreM0G/DZjPjsRgyHGHgsyTKFXJbE+F1AJT2XxOUPcuLZ\nF7A5XXv2oX3qhV6GXAbevXKHKyNTXBmZ4nirn1gkiM/raojI1NHZL80m0u6lD/hvq38PAlvH45aq\n/42hizSdBrJbVtrYVJzCtct7Zqjt1Xf28//z/1j3c/3Y8fZDEWV7YjKA34DiscHaOqwWKBdLDH3/\nXYa+D5H+E0S6o4Q6Dt67FnS7CLq1D+lCsYTFpPVP7fahLfljqMYlSpk0SrnA6rUPWL0Glu4+zOEI\nRn8AQTpAKW9jBdW23rUKKkB+hU13zQayad/umryld22ru1brXXvEkFyv3//A+0iiwJlIC8eDDqaX\n8sTTOa5NL7NSKDGezhJ2mvHZTNhNh7Nf02y10XXyLEqlQvep/trxBw0KeL0uPvpSP2dP9zA2nuCH\nF24xPLXA8NQC7X4n0XCAcNCHyVjfwRcdnXrQzCLtJ4E/AGaqX5fZPiSw8Wub/muQTkPZLSvty+/+\nmP+cy913/EEZak8skggtFnCaOf75T7P4xnssT9xh5toQM9eGkCSRvk8+T2tfK1bnwZwsQRBqAm3j\n6x3vJ8kYPD5ktxcln6e8lEbNLZEfHSE/OoIoSdjPPospEkWyHLAk9jDu2kbvmrg/Ybiru1YNyX1U\nd+1hMEginV4bHR4rp8NOJtI5LowtMrNcYGa5gMtiwGc34rHW310TBOG+/LaHvS6n007/uWOcPNnF\n5OQs4xMJrg9NEV9YQRBGOdkeJBry4/G06O6aTtPQSJHWClzZ4/a/Y7PfLAqcAf6XLbcnga1e/MZe\nk8S9T/T73//H2t/Pd/XyXFfvPk5XR2dndstKs1R2Xpj+oAy1Dawm8wMjQI4kgoDV04LteBdKbztL\nC2nmp2fJz05V3bV36XjhLNHeVrwRb0OyrgRBQLJakaxW1EqY8soS5cwiSrnAytVLcPUS1t7jmCNR\nDF7fwXrXHsZds3u03DXZWBd3bb2suWvz2f27aw++LAGf3YTPbuJU2MlkOsfUUp5bMyss5UsIQpaQ\n04zPZsRhkpsmFsNoMNDT3Up3V4wzp3uZmJjhwge3GZiYZWBiljafk3DQRyTkw2LeeUJaR+cgjA5c\nZnTg8kPdt5EibQp4sKcODuArbBdoBuANYKvaOo5W/rx67xP84is/sf+z1Dl0HiUPrN7ZYfVgt7yz\n/C6uQcnlQj3VA4AluPv/Avm5hV2HA46sQLsHUZLwhPx4Qn5yq13MTyVZmbjLxHs3mHjvBr7jXYQ7\nI0S6I1jsB58MfRgEScLg9iK7PCj5HOWM5q7l7gyTuzOMKRzDFIlgCkeRzOYHP+GeL3aPu6Zogwbq\n2iKwiGBpqbprlgO5a2aDgNkg4jBJ5EsKydXDddcsBoljQQe9ATtnIk4mM3kuji2SXC6QrLprXpsR\nr+1we9ceBUEQCAW9hIJe+s/1MTk1S3wyyfWhKSZTKzA4zolWP5GQH7/XpQfl6tSN7lPn6T51vvb1\n9/7q/9r1vs1W7jSixW78AZoIE9DiOF4H/hD4U7QypwL8BPD/ouekHTkeJQ+s3tlh9WC3vLMT/c/x\n69cubzv+cxYLyxW1dg2H0Xd2VLE6bHSc7KHc205qZp5UYo7U8Bip4TFuAu3PnyHSHcUX8yMdpE/s\nIdHcNRuS1YZaDlNezlDKpFlPTrOenEYArMdOYgpHMXg89XHXpJ3ctWXtNrtXGzSQDuauOUwSdmNj\n3DVREAg6zAQdZs6EnUxl8kxmctysumtji1mCDs1dc5qbx10zm0309bbT29NG/9ljTEwmeeu9IYYm\n5xmanEc2yDzTEyMS8mGz6pOhOo2j2UTaHwH/HPjXW45dBH4XGAX+HfB1YBqt9PmrjT5BHZ298s6u\ntbbzKzcuszA+RkEUkWPtePyBx3zGjWGvlVt7lWtlg4FQe5RgW4S1pVVSM3OsxO8Sf/8m8fdvIssS\nvZ/4UN0mQx8GQZYxeP3IHh9KLkspk4b8Mtnbg2RvD2KKtGKORDGFI4imA5bEdnHXWEvBGgjWqrtm\nsMI+heHjcNfMBonegJ0ev42z0RYm0zneHltkdqXA7EqBFosBr1Vz14xyc7hUgiDg97vx+92cO9PL\n1PQcE/EZrg7EuTQ0AUMTHIv5iYT9BHzuHcOedXTqSXP8GlNf9Jy0JudR8sDqnR22Fw+Kz3hY9jrn\nf/2bv0Els/MOz8dVvt2gmUrL5VKZ9GyK1Mwc6wubbafRZ7TJ0HpuNXhY1HKZ0lKaciaNoGo5ZYIg\nbLprbnedc9cq1a0Gm7lrOLxgtGkL3g8ophRVZb264H0+u1mQqLe7tpVCqUJiKU88k+dGQvv/QBAg\n5DDjtxuxN1Hv2gaqqpLJrBCfTPLGO4MoFS0oWTbInO9rJRoOYDY1doWWzpPFUcpJ09F5LOwWqwHU\ndTKzkllu2r6zZiotywaZQGuIQGuI3Eo3qZl5lifukLg6ROLqEJIo0v2x80T7YrR4G7P+R5BljL4A\nBq+fSnaNcmYRCqtkhwfIDg9girRiikQwh6N1ctdkUNUt7poJVlNAququ2bTeNWF/wlAUtM0B5i07\nQ2dXizV3TayuoKq3u9btt9Pls3E24iSeyfP2aIrkSoHkSgG31YjPZsRjMyA3iUslCAIeTwseTwun\nT/WQmJlnbDzBlVsTvDcwjjA4wenOMJGQD4/L2XQiU+doo4s0HR12j9V4auMzmgir006b047S205m\nPk1qZo5ccpKRNy8x8uYlos+cINbXSqAt2LDeNdnuQLY7UEolyssZypk06zNTrM9MsSoIWI+fwhyN\nIbe4Dr7gvda7Vtl013JArtq75vBpMR7y/twcQRAwSAIGaTPGo1BWWMiWUKohueJhToaGHExmckym\n89xKrpDJFRFSEHGa8dqay10zGGQ62iO0t4U5e6aXsfEEP35viJtjM9wcm6HN7yQc8BEJ+rBY9MlQ\nnYOjizSdp46dypq7xWpsjc+oVzlUZ3+IkoQ37Mcb9lPIdpNKzJEZu11z1wwGieOvfJhoTxRzg9b+\niAbD/e5afoXs0C2yQ7cwt3VijsYwBkOIB90budVdK5c2F7yvLmg3W11Vd818IHfNapCwbMld2+qu\nSdX1U/V21/oCDnp8di3KI5PjnYk0ieUCiSaeDPV5Xfi8Ls6c6mZiMsnk1Cw3h6eZXFiBgTGOt/oJ\nh/wEvK6G/PKg82SiizSdhvMoDeb7bUbfjd3KmknTztEK5WrcRqPKoToPh9lmIdbXQaS7lfRsivnp\nWdYXEtz89tvcEgQ6X+qnta8VV8DdEBdmu7tWpJxJU15KU5gcpzA5jiCK2E+fwxSJIdvtD37CvV9M\nGzRAqrlrqsGMmluC3BI1d81U7V3b5/UYJQHjVnetpLCQK2kikfq7a6IoEG4xE24xcybiZHopz1Qm\nv20yNOQw47M3V+6axWLmxLFOjvd18Oy5JeJTSd58d6i21UCSJJ7pjRELB7BaDxjhovPU0Rzv8vqi\nDw7o7Mof/9Hv8Tt37m+A/68iMbyFwn2xGqe++GX6j5/e9XFf7T3OV/7VL2871kwN+I9CI4c06o2q\nqmSXV5mfmmVlchRVVQEInTlGrK+1LiuoHv2cFCqrK5TSi1DM1o5bOnu13DV/AOGg7trmi1VjPKru\nWpV6uGubL6FSUlQKJYXZtaIWFwJVd02oq7u2gaKqpNaKTGW0ydDqjxW31YDPZsJjMyKLzfcxViyV\nSCTmiU8m+eDGeO34ifYgrZEAXndL04hMncePPjigc2gctRLgbmVNj8nEyVd/YsdYjb0et9M2gfyN\n4V1/+3ncwwFPKoIgYHc5sbucFPs6SCXmWJieZfbmbWZv3kaSRHpefo5IdwSntzEfkIIgIjtdyE4X\nSqFAKbOIms2QH79DfvwOgihiO3VOW/DucBy8d63mrpVrYm27u+av5q7VwV0zNchdEwQCDhMBh7bV\nYCqTJ57JcWtmhUyuhJCCmMuCz2bCamyekqLRYKCzI0pnR5T+s8cYm0jwxsUBhuJzDMXn6Ai0EIto\nO0MN9RLqOk8k+rtDZ98cxRLgbtsCyrKB/uOndz3vvR53mBxVV+5xYjQZiXS1EuqIsjSfZn46SX52\nittvvM/tNyB0po9wV4RwZwSjuTHRCaLZjCkcRVVClFeWta0GpRxrN6+ydvMq5vYubcF7MIRoOOB7\nShC1FVTl8nZ3bXUeVkGwulFNVjBY9h3jsa13zSTV3LXD7l3rDdjp9ts4FXYSX8zx7kSaqYxWFvXb\nTfjsRlwWQ1Pt3nS5HDzbf5xTJ7qITyYZn0hw83aCifllRGmM/u4IsUgQe4P6KHWOFs3zTq4fermz\nQTxKCbBZ2BCWu5U16/24g9LIEuSTLAjzazkWk/MsJhdQVqqN9tXetVhvK+5gY3rXtqIUCpSW0pDN\n1LK3BEHAdvIs5mgU2VnHaJGtYm2jZihU3TXj/t21rSiqVgotlKvuGtRCcgWBQ8ldW1svM5HO8cOR\nBRRFuy6nWcZj06I8THLzuGsbKIrC7NwiY+MJ3r1yp3b8+JYVVPqgwdOFXu7UORQepQTYLOy1LeAw\nHneUeJLLtBa7lVhvB5HuNlZSGVIz86wlJhi7cJWxC1cfS++aaDZjCkVQlZDWu7aURl1fY23gOmsD\n17F0dGOKxurTu7abu7Yyr91sc2u9awbzwdw1o4TFIGKrDhvMrW1ZQXUI7prdJHM67OR4wE5iucBE\nOsfVqSVWCmUmFnMEHCZ8NiMtTeSuiaJIJKxtLTh7uoexiRl+eOHWtkGD/p4okZAfR4P21+o0L7pI\n09k3j6sEeFD2KmsexuOOGk+yoyaKIq6AF1fAS/FE9469a8c+9QLRnii2lgNOYT4kgigit7iQW1wo\nxXVKmTTqWpr8xCj5idHqZGg/pkgE2X7AtVg79a7JJtRsBrIZTczV3LX9fTwIgoBJFjDJ23PXUlt6\n1+rtrsmSSLvHSpvbwplqjMebd1PMr64zv7qOJAq0uix4bUbMhuZxqZxOO/1n+zh5onPboMHl25Nc\nvj1Jb9RLJOQn6Pcg6+7aU4ku0nT2zW6Lxs+99PJjPCudg9JMmwcOk9161wa/9w6D34PoMyerK6iC\njXPXjCZMwTCqP0h5ZanWu7Z64wqrN65g6ezFHI1i9AcQDvqhveGuVe511+a0m20erXdN3r+7JokC\nNqOE1bAp2A7TXRMEAY/NiMdm5FTYSWKpQDyjuWsT6RwT6RwBuwlvk/Wu3TtoMDGZ5IcXbnEnscid\nxCKiJHGuO0K06q7pk6FPD7pI09k3T0MJUOfJRxRFPCEfnpCP3EonC4m56gqqQRJXB5EkbQVVpDtK\ni69Bk6GiiMHlweDyoBTymru2dTJUkjbdNVudctdU8R53LQ3ZNILFiWqwaJOh4uG6axtpGvX4Hhsk\nkQ6vlXaPhXPVFVRv3k0xv7bO/JrmrsWq7pqlidw1l8tBv8vB6ZNdJGYWmIjPcOn6GFdHprg6MkV3\n2EM05CcY8OiToU8B+k9Y50A8LSVAnacDq9NOu9NOpbeDzPwiizPz5GYnGfnRJUZ+dIng6T4i3Y2e\nDLVsToYuL1Neqrpr1y+zev0ylu4+zJEoRp//YO7axgqqbe5aRctDy68Agta7ZrKBbKqruzZ7j7sm\nivUrhQqCgMtqxGU1cjLkYGa5wGQmx+XJJeLpHPF0TpsMtRlxWZvHXZNlmfa2MO1tYZ45p7lrP3j7\nFqPJNKPJNKIocrYrQiwSwOmwPe7T1TkkmuPdWF/06U6d+2hkn1U9X+tx9Icd5VDbw6CQzZNKzrM4\nM3/fZGikO4on5EFs8DLwSj5PObOImltCrYobUZKwnenHFK6Du7aBqgJqzV2jGmErWJyoRqvWuyYe\n3IWqKCr5ssLMynotJHeru3YY7uVSvsRUJscbd1K1yVBJFGhza7lrRrk5VlBtpVKpMJNMEZ9MbpsM\n7Yv6iEUCBHzuhr8XdQ7OXtOdukjTeSpopPA46iLnqJ//YaEoyrbJ0I2tBr7jXUR7YkS6o5gavFRb\nrVSqvWuLUC7UjtfcNX8AoV4f2rWtBtoaKg0Bwe7RBNsB3LXNl1BZr2hRHnPZYu3ofIwAACAASURB\nVO24VOeQ3K2UKwqJ5QLxdI4rU0vaQQFtBZXNiNPcPCuotrK2lmN8Yobv//gGlWqES6vXQSjoJRL0\nYbPquWtHBT2CQ0fnCHHUtjg8Ldw7GZpOzpNKzJMaHiM1PMZNQaD7Y+eJ9cUat9VAkjC4vcjV3rVy\nJo2aWyI/OkJ+dARTOIYpHMEUiSKZD7g3crfetbVFYLHqrtnAaNm3uyYIAmZZwFwNyc2XFJKr67WQ\n3MNw17ZOhp4OO5lI5/jxaIrZlQKzKwWcZgNem7bkvZly1+x2K2dO93DieAdT03OMjSe4OhBnanEV\nBifoi/oIh3wEfR7kJjpvnUdDF2k6Ok1EM2xxqPdS+ycRo8lIqCNGsD3KamaZ+alZ1qbGuPPWB9x5\n6wMi/SeI9bUSbA82JJhUEAQkixXJYkWthCmvLFFKL7KenGY9OY1wBazHTmKOtiK7Dxjc+xC9a9i9\nWilUNu7bXZNFAYdJwm60UCirFMoK89ktvWt1dte2T4Y6mF4qMJXJcW16mZVCifFq7prX1lyTobIs\n09kRpaM9Qv+5Y0xOJnnjnUFGEilGEqla71o46MXVcsD1YzoNRxdpOjpNxLWLb20TaABfS6f46sW3\nGibSnuRQ23ojCAJOjwunx8V6XwcLiTkyd4eZuTbEzLUhDAaJY59+kWhPDIu9MeWnbe5aLkspk4b8\nMtnbg2RvD2KOtWshucEQovGAww/3umtKtRS6pr2HBUtLtXftYO6axSBgMYjYjSKFskJytXio7ppJ\nluj22ejyWjkbadk1d81nbx53TRAEvJ4WvJ4WzpzuITGzwOTULO9dvcu1u9NcuztNZ9BNayRAKOjV\nc9eOCLpI09FpIo7iFgcdDZPFTKynnUhnjPTcIvNTSdYXEtx6/QIDoK2g6mvFHfQ0aMG7gGSzI9ns\nKOUS5UyaciZNYTpOYTqOIAhYT5zGHI4gu+rkrkkb7lpFK4cC5JfZr7umqiqZTAaPxwNosRoGSdto\nsF5WKZQrzGdLDXLXnNpkaDrH5Y3ctUyOkMOEz2Zqqt61bZOh/ceYnJwlPpVkYGSG8bkMoiTxbG+M\nWDiA1XrAMrjOoaKLNB2dJuKobnHQ2USUJHyRAN6wn+xKFwtTs6zE7zJ28RpjF68RONlDuDNCuCuM\nuUFLtUXZgNEfxOALUFldobyUhsIq2cGbZAdvau5aOIIpFEY0HXD4QRBAkLX8s93cNZvngRsNrly5\nwvz8PJIk4XK58Hq9dHV1adezzV2TGuKuGaq9a+0eK2ciWu/am3dTzK6sM7uyjstiwGc34rUakaXm\nmbC026ycPNHF8WMdnD2VYnR8mvevjfLBcJwPhuOcbA8SiwTwuhvTR6nzaOgiTUenzhykp0vf4vDk\nIAgC9hYH9hYHpd52FhJzLM7MMz94l/nBu9wA2l84S7grgi/mb1jvmuxsQXa2oJSKlJcylJfucdf6\nTmCKRDG4PQebDN3NXRNEFFW7XXT6d3zo2NgY3/nOd/jSl77E8ePHmZ+f5/r167S1tSFJ0jYxsd1d\nU6q9a4fnrgG4rEb6rUZOhpxMZbRNBjdnVljKlxgVskRbtFKozdg8H7GiKBKNBohGA5w93cv4RIIf\nXhxgMD7HYHyOjkALsUiAcNCnh+Q2EU+ibNYjOHTu4yjto7w2fIvr+haHJxJVVVnNLJNKzLM6OYpS\njfEwGCSOv/Jhor0xzA0uP6mqSmVtteaubUSLmCKtmKNRTOHowXvXNl9M+68gULF5aoe3irVCocBf\n//Vfo6oqP/MzPwNo8Sdf//rX+aVf+iUcjr33l6qqSllRa+7aRu6aKAjVP/WP8VBUlYW1deKLOS5O\npDfi5PDajHhtRtxWI7LYfB+36+tFJqdmGZtIcHN4GtDE3JmuMJGgTx80aBB6TpqOjo5Ok1EulUjP\npkgl5lhPzQCaeOiq9q65AgfsE9sHSrlUc9cEpVg7J+uJ05gjUeQW16Gc01bBNjQ1z5tvvskrr7xC\nX18fAHfu3OFHP/oRX/7yl2v9aRvMzc1x6dIl+vv7icVi269HVVkvK+TLCgtZra9TAMRDzF3LrpeJ\nZ3L84PYClWpIriCguWs2I1aj1HTCR1EUZucWGR9P8M6WkNyOgIto2E844MVo1FsuDgtdpOkcKfSc\nMJ2nCVVVyS6vMj81y8rkaM3JCp89RqQnRqg9hMHU2A9IVVWpZNcop1Oau1Y9bm7twBSJar1rhvqf\nU9nq5lvf/g6ra2v8lz/7SzUx88YbbzA9Pc1nP/tZ/P5N1y0ej3P79m3S6TR37tzB7XbzhS98gY6O\njvuup1zdajDbIHetoqjMrmgrqN6byNSOe6xGfPbmddfWsrltgwYAgihwuiNMNOTH7dLdtXqjh9ke\nAY5SOe4waYacMB2dRiIIAnaXE7vLSbGvg1RijoXpWZI3bpO8cRtRFOj6yDOEu7QVVI2aDJXtDmS7\nA6VYpLyU1nrXpiYoTE0gCAK2k2cwhaPILfVrOK9k5liaTdBx7CTqagoVWF1dY3Z2FovFsk2gJZNJ\n3n33Xdra2njttddYWFggmUzicrl2vB6DJGCQtEGDre6aUl3wXm93TRIFoi4LUZeFM5EWpjJ5vn97\nnnSuSDpXrLlrXpsRWxO5a1sHDfrPppmIz3DhgxFujs1wc2yGjkALkZCfcNCHSXfXDh1dpDUJuUp5\n71U8DT6fx0Uz5ITp6DwujCYjka5WQh1RlhbSpBLz5GYmuPvjK9z98RX8J7oJd2kL3huVuyYajRgD\nIQz+AJXVzd61tYEbrA3cwNzWqa2gCoYO7K7JssTcQoovvOJHyqYBGLk7xWpqlvP95wCtNCcIAuVy\nmUKhwIULF7DZbJw9e3abiNv1egQBi0HCLIs4jFLNXduYDJUEAUGgru6a3SRzIuSgL2BnbrVAPJ3n\nvYk000t5ppfyeKxa75rHZkBukt2boigSDmlbC86d6SM+lSQeT3JrJMHE/DKCMMqpjhDRkB+P29k0\nIvNJQxdpOk2FnhOmo6N9QHqCPjxBH8WT3aRmtAXvC0OjLAyNchNoe/4ske5GToaKm5OhxfXNydDJ\ncQqT41V37SymSATZuT93LZvLE4uEsNusgLZQ/Mbl94j6ffT1dKOsLGjlYEGgtbWVr3zlK3zzm9/k\nBz/4Ae3t7bS0tDzC9Wx31wplhUJJYSFX0uJDOBx3LdJiIdJi4UzEeb+7ltLcNb/diLWJJkOtVjMn\njnVyrLed/nMZzV27dJtb40lujSdp9zuJhQNEQn4MhuY57ycB/bup01ToOWE6Otsxmk1EuloJd8a0\nydAZbTI0/v4N4u/fQJYlej/xIaLdUezuvScf64VoNG1z10qZRdT1NdYGrrM2cB1zW+dm7tojuGtO\nh52utlau3Byiqy3GzeERVFXlpeeewUkBstoS+bLVTXlpDlEUefnll7l+/ToTExOcO3cOVVUfWVCJ\ngoDVIGGp7gwtlBRm17a7a5qzVn937VjAzuxqgcl0nne3uGs+mxGf3YTbYkBskt41URQJBb2Egl7O\nne1jcjLJxGSSm8PTxBdWEG6N0d8dIRYJ4rBbH/fpPhE8iXshfvMXX/nc4z6HRyfg4fJ77+140/kX\nX0BYSDf4hB4TFgt/MhXntXyudujXPF7OvPI5Qr7AYzwxHZ3HiyAImCxm3AEvvo4ODPYWiopEaW2Z\n1Ng0Y5eHWMuXkGQJq8OK0IAPdkEQEE1mDC43ksMFqkilUKSykmY9MUXuzjAIIqLR9NAhua2REBaz\niYHbd/F63Lz6sZe4OzHJajaL1631m4mlAmIpj2q0IlWKjAwP0RbyEYy1H0hECYKAJAqYZBGXWcZq\nkDBKItlSBUVVq5Epwrb774WiKCxl0lgsuwsWQRBwmA3E3Jq75rMZGU/nyBYrpLJFkisFVBWM1Ty4\nZsEgy/i8Lro7Y3S3B7FbjUwlUsymVxiJJ8mtraGqKlazGamJzrsZ+d5ffgPg3+10my7SmgVdpAEQ\n8gWoeL38cS7L91ta+Ed/kDOvfE7vR9PR2YIoSdhaHPhjIVpCYTBYKC6nWU7MM33rLouLq5TLZawO\nG3KDyk+CJCPZHRi8XgSjBaVUgfI6xfk5cqN3qOQKmgiyWh8YkmuzWuhsixEO+FEUhe++eRGv20Uo\n4OP64G1cDgcGg4xYyhMfu0t+bYVQtBW3zYS6nkMw2Q58PaIgYJRELLKIwyRjlkXWippYU1UVSRT2\nFGnXr3zAyNAAiwsLZBZTFItFWlzuPV/TKIv4HSaea3MTc1mwmiQSSwWWCyWSKwXyxQqg7RZtlgXv\ngiBgt1tpjYU4c6IDr8vGxPQCiys5JmcXGRqfgXIJWZYwm01679oO7CXSnsTv1pGM4FBP9ew9ODBw\nt8FnpKOjc5SolMssJlMsTCcpLiYB7QO0+6PPEOtro8XX+LU/yvq6VgrNZlArmsAQJAn72Wcwh6NI\n1ocviRWLJYqlEn/8F39DayTEZz7+UcxmE3/596/T2dbKyd4urBbLziG5SmXfC963UqmG5IoCzKyu\n145L9zz35PgY//C3/5nP/uRP0dndS2p+nuGBm7z4sY/ftzHhQSznS0xl8vzwzgLKPblrzTYZukGl\nUiE5myI+OavlrlVjZdr8TsIBH5GQD4v5gOvHniCeupy0D775j/cdbPYYCz2CQ0dHpx5oWw1WWJhO\nsjo5VssEi5w7TqyvlWB7CElubBFFVRTKK0uU04tQ1hajCYCl9zjmSBSD1/fQK6jmU4sMjowiigJW\nq5VCocBHnz+/430rNg8oFcT1NQSbG9VoBYP5oRe8Pwyj6c3WDEmUWC8UeP1bf4soinzxy/8UgHK5\nzB/+3n/iX/zsL2G1bbp8iqIgPuR1VxSV5EqBqXty17w2I77qVgOpSXrXtpLLFZicmmVyKsmN6lYD\nBIFT7UFaI0E9d42nUKT9wqc/e99B3Y3S0dF52igW1lmYniUzOkyppDlZsixx7FMvEOmOYms5eFnw\nUVBVFaWQp5xeRM0tba6gCscwhcKYwpFHctfK5TJydc/krgMDSgVVNrKxq0kwOzSxZrQ+cMn7ozKa\nzjEyNMili2/z8qdfpaOrB4Dx0Tu8d+HHfO4nv7RjyXPgxjUkSUZRFNo6O7Hb9x4AWVsv1yZDN7Ya\niKJAq0vbamA2NF8nk6qqpBaXmIgneeu9QdTqeXcG3bRGAoSCXuQGTCk3I3qYrY6Ojs5TiNFsItrT\nTrizlcx8ivmpWQrz0wx89yIDQOz8KSJdEQJtwYb0rgmCgGSxIkWtqOUw5eUMpUya9eQ068lpuArW\nnmOYwhGMPj/CAz605S2LwHd1Y0SpuuBdAbWiSbXCKiBo7prJCnJ93LUut4WbC9PYnU5aOzqpKBUk\nUSKZSGAymalUS74b3B4cYODGVWTZwInTZ2lt7yCfyz1QpG3NXUuuFIinc1yKZ4inc8TTOYIOEz67\niRaz3DQulSAI+H1u/D43Z051MxFPMh5PMDAyw/hcBlGSeLY3RiwcwNrg/bXNzJMoW3/zfPW3l608\nTc33Ojo6OlsRRAGrw4Y/GsQZDKHIZkorGZYT8yQGxxh9/xaKbMBoMTZswbsgikhWG7Lbi2h1oFaA\nSpHSYorC5AS5O8MIsgHJbD74gndBAEEEQURQFQSlDKIMpTzkV7SvFQUkSbvfPikWi1y6dIkzx3rp\nP95DJl9idWWZgRvXMJnMnH1msyx77YP3ufDmD+k//zwvf+pVvD4/RqNxWzn0QYiCgNNsoM1t5UTI\ngdtqJJ7OsbZeYWFtnZX1MoqqYpLFpiqFyrKMz6dNhrbH/FjNMtPJRWZSywxPzFApriNLEmbL0zFo\n8NRNd+oiTUdHR2dnjGYTLr8HX2cnRqeLkiJTXF1mcSLBxJVhVrNFJEnE4rQ+dL/UQRAEAdFgRHa2\nILV4QTKiFEtQKVGcS5K7exulWEKQJESL5WAf2oJQE2yCoiAoFYRKCVWpwHoWcsuaoSYI2qDBI76W\nIAh897vf5bXXXsNms+GxGJi+O0x6boZozzG8/oBW9puf50ffex2ny4XD6WRtdRWvz3+gazMbJEJO\nM8/GXIRbzJgMEpPpHJlciZmVAqWKiiho8SLNInwEQcDptNPeFuZkXxsup4WJ6RQLS2uMTs+znMlQ\nKpUxm41PdEiuLtJoTpF2bfgWr3/zrxi4dJEr16+AxVLXLLDDfn4dHZ2jiyiKWB12fNEgrkgUjFYt\nxmNmnumBUcYvDSDZbFid1sbFeIgiksWK7PIg2pyoClBep7SYIh8fp7y8glouIVqsiPIBz2k3d62Y\n23TXVFU79pDu2traGslkkmeeeQZZlimXy7z++uuEw2E+9ZEXCTqtZPIl3vz+d1GBlz/9KqFwlPcv\n/pjFhQXaO7sOdk1oWw3cViMdHivdPjstFgNTmTyr62Xmq+5aRVUxSc3lrpnNJsIhH+fP9hAOuDAb\nREYmF0gsLDE8PkNpvaC9Z59Ad00XaTSfSNtYJP7biUk+s5Thc+kUfzIVp+L11kVIHfbz6+joPDkY\njAZavC78nZ0YHS7WywLFtRXm704y+v4ARUXLZrPYD+hkPSSau2ZAdjg1d000UFmvhuQmE+RHhlHK\nFc1dMx+mu7YGuaWquyY+0F0zmUxks1mSySSSJPHuu++yurrKq6++itPpBMAhwztv/pDPffqTuMJt\nmMxmBEFgdOQ2bR2dmMz1KTcLgoDNJBNpsdAfbSHkNGPewV2Tmsxdk2UJr6eFrs4ox3uiuFusxBMp\nUktrjCcWWMpkKJUqT5S79tTlpB2FCI4//qPf43fu3H8+X+09zlf+1S83/fPr6Og8uaiqytrSKgvT\nSVYmx2pTmN6+ztqC98cyGZrLUsqkIb+8ORkaacUUDmMKR5HqJHC0XC9Vmww1mKlNhlpaNidD9ygF\nLywscOvWLdxuNydPnmRgYACbzUZfXx+jo6NcuHCBV155hUgkgqqq/OCDm7z+zb/hZ3/5VzCaTPfl\nrtULVVVJZYtMZvK8PZraiC+jxWLAazPitRoxys23HaBYLDE1PcdEfIarA/Ha8eNtAaIhP36vqyGl\n+cPiqZvu3ClqI/8YzmMvDnuRuL6ovDHo+XY6TyKCIOBwO3G4nZT6OllMLmhL3kfGWRwZ5xaPaTLU\nZkey2VHL5c3J0Jkp1memtNy1vhNa7prH+9C5a7u8GCCAJN4/GZpf1m6ze8FkBcl4n7vm9/v55Cc/\nCWgxITdu3KjtFW1paUFVVYzVYQhBEMjOjHOqq40TYTd3F7NUFG0KtN5iTRAE/HYTfruJUyEH00t5\nJjN5biSWWc6XGBeyhBxmvDYjziaaDDUaDXR3xejqjNJ/7hgT8Rl+9M4gw5PzDE/OE/PaiQT9REI+\nrJYnazL0iRRpR4HDXiSuLypvDLlKee9NEQ0+Hx2demMwGQl1RAm2R8gud5OamWclfpfpywNMXx5A\nkkR6Xn6OcFekYVsNBFnG4PUje3zb3LXcyBC5kaGquxbRctcO6q4JAggSqFXBtuGuraVgrequmaxg\n2Nldk2WZr3zlKxQK2losq9WK2WzGVp3iTCQS3L17ly9+8YsA9Hi146PpXE2sqSoIglhd8l6f76/Z\nINHjt9Pts3Em4mQynePtsUWSKwWSKwVcVXfNZzMiN8nuTUEQ8Hpa8HpaOHOqZ5u7Nr24BoPjnGgP\n0hoO4PU0fsPGYfBE9qQdid2duywSP/9zP4/v3GkIeO77Yw0HKM+lDvT8+qLyOqPvXNV5ShAE4f7J\nUHXLZOjVYZaW86iKgtVha8hWA0EQEI2bk6GqIKGsF6msZA6vd02Uqr1r5c3etYLWu6apqGqMxz2v\ntZHpJkkSsiwzMDDAzMwMiUSCtrY2zp/fvjXBYzHU/izmS1SqC97VLfephwgRBAGb8f7etfiW3rWK\nCrIoYJD23lfaSCRJwuN20tkR4XhvDLfTQjyxORm6tryMoqpYzKamD8k9qj1pp4C/qP53g58CXgTS\nQCvwq8C99bsjs7vz2vAtrl98C7lcoiwbOPfSy5z78k/VbYfnTs+vLyqvL/rOVZ2nnUI2p5VCkwso\nKwsASKJIz8efI9bXisO9dzBrvdm1dy3ahjkSwRSOHjx3bfPF2LF3zeqq9q5Z9pwMnZqawmKx4PP5\n9nyZUkUhX1aYXS3WRJooCNU/9XPXNlBVlYW1IhPpHBfHFzcuC7dVc9c8ViOGJnHXtrK+XmRyapax\niQQ3qyuoBEHgZHuQSMiH193SlL1rR3EtlAX4/4CzwMZM8nngz4E+QAH+A1AEfuOexx4ZkbYT+of+\n0UL/eenoaCiKwkoqw/z0HNmZidrx2PlTtPa14o8FEBv8wa6Wy5SW0pQzaQS1CFSdoxOnMUVjyM46\nlsRUFdQKqEpVsAEI4PCB0QZ1aDVRVG3Be6GssJAtbbwCoqgJNi3irb4f69n1MpOZPNNLeQaSK9pr\nChBymvHZjDhMzdO7toGiKMzOLTI5OcuFD27XhHqr10Eo6CUc8GG3WR7zWW5yFAcH/hvgj4D/tOXY\nrwI/QhNoAH8LfBP4LTSxpqOjo6PzmBBFEVfAiyvgpXCsg/npOZbGbtd617x9nUR7Y0S7Y5ispoac\nkyDLGH0BDF4/lewa5cwi5FdYG7zJ2uBNzO1dmKMxTIEgQl1y12RQVYRyqSrWTLC6ACxU3TUbGM37\n3mogCgJWg4RFFrEbJQpVd62iqFTQ4jQEgbq6a7bqCqpjAbvWu5bJc3F8keRygeRyQZsMtRrx2ppn\nMlQURSJhP5Gwn/5zfUxNzxGfTHJtcJKpxVUYnKAv6iMS8hH0e5CauBzajCLtS8APgHu37L4E/O6W\nr+8AXjS37YPGnJqOjo6OzoMw26y0Hesk2t1GenaB+anZ2mToTUGg6yPPEOtrxeV3NSx3TbY7kO0O\nlGKR8lIadS1NIT5GIT6GIIrYT/djCkeQHQcsz24MGiAhVCraVKjBjJpb0vrWNtw1kw2k/blrgiBg\nlASM0qZYK5QUFnIlTSRSf3dNFAVCTjMhp5kzESfTS3mmtk6GprOEnGb8NhN2k9Q07prZbKK3p42e\n7lae6T/O5NQsb1wcYCSRYiSRQpJlnu3TdoZazI355eFRaI7v4iadwGeA/xP4BPB/V4+BlqLxK8A3\nql/LaA7aP0Fz1TbQy506DUOP4NDReTBa7toK81OzrE5t5q6Fzx4j0hMj1B7CYGrs5LmqKFRWlyml\nF6G0OWBl6ejGFIliDAQPvtWg9mJqNcaj6q5VqYe7tvkSKiVFpVBSmF3b7F3T3LXD613bKXfNbTXi\nt2u9a8201WCDcrlMYmaB8YkEH9wY1w4KAqfag7RGgrhdjoaKzKNS7jQAvwD8D7vcXmb7kMDGO/q+\nC/v972+G2Z7v6uW5rt46naKOznbyN4Z3/U2n2bL5dHQeF1ruWgsOdwvFvg4WEnNk7g6RvHGb5I3b\niKLmrkW6o7iDnsa4a6KI3OJGbnGjFPKUltKo2SXyE6PkJ0YRRBHbyTOYwlFkp7MOk6Eb7lq5Jta2\nu2v+au5aHdw1k0ShVO1dO0R37d7ctclMjsl0nlvJFTK5IqIoEGsx47ObsBiap6QoyzLtbWHaWkOc\nPd3L2ESCN98dZGBiloGJWTqDblojAUJB76FMho4OXGZ04PJD3beRErcVuLLH7TfRSpq14RU04VYA\n/inwv6GVOzf61ALALNq05/tbnudIO2m6M6Ojo/M0oFQUlhbSpGbmyc1M1P7h95/oJtoTI9IdxWiu\n0xTmQ1Jz1zJpKGZrx81tnZgjUYyhcGPcNZMNDJZHXvB+/0s03l1TFJXZ1QLxdI73JjK14wG7Ca/N\niMtiQGxCd61QWGcinmQ8nmBgZAbQVqE90xMlHPLhtB/eho2jON0J8HHgj9ksd/4+mpP2b6tfvwz8\nHZpY2+qwHWmRpqOjo/O0sZ4vsJhcYHFmnvLSHKC5Pj0f02I8nF5nw89JWS9QWspANoNS1n5x1nrX\nzmGKxJDt9vq92FaxtlEzFKrumnH/7tpWKsrmZGgqtzkZKomiZvRRf8G2UigRT+d4404KRdGuSxIF\noi0WfHZjU7lrGyiKwkxygbHxBO9fG60d7w57iIb8hAJe5DpnAB5VkfYJtvekPQ/8KZsRHP8esAH/\n9T2P00Wajo6OzhFEVVVWFpdYmJ5lbXq85vxEnzmpxXi0BRo+iaeqCpWVFUqZxW3umqWrV3PX/AGE\nep3Tbu6aza31rhnMdXHXihVNsM01yF0rVRRmlgtMZnJcnlyqHfdX3TV3k7prS8urTE7O8oMLt6hU\nhbooSZzrChMNB3A66uOuHWWR9kds5qQB/AvgWWAa6EGL5bi39UcXaTo6OjpHnEIuT2p6jszYbcpl\nbT2SwSDR+4nniXRFsDc4JBfQetcyi6jZJVRFS4MSJElz18LRw3HXZBO1LiBB3OKuHbzs+jjcteV8\nicnM/e5azGXBZzNibkJ3rVKpkJxNMT4xw3tXN4f3usMeYuEAQb/nQO7aURVp++U+kfbB2J1DHR64\nNnyLaxffwlAuUZIN9OvJ/o/EYf98dA6O/jNqfp7Un5FSqbA4m2JhKsl6aqZ2PPrMCcJdUYLtQQzG\nRk+GVigvL1NeSm+fDO3sxRzd2V17851LfPzDH9rHi+3mrnm0naHy4bprYjV7rd5irVxRSOzirvns\n1d61Bsd4DF5+h5PnP7znfVZXs0zEk3z/7Zvb3LX+7giRsH9fvWtHZbrz0Lh8iP94XRu+xeC3/prf\nSW/u1Pz1Re3vulB7OA7z56NTH/SfUfPzpP6MREnCHw3iiwTIrfawODPP8sQdEleHSFwdQhQFuj96\nnnBXBHfQ3aDJUAmD24PB7aGSz2u5a9kM+fE75Mfv7OiuvfnuPkXavQveq+6amk1DNo1gcaIaLNpk\nqLi/j3RBEP7/9u49OMrqjOP4d7NZQgghkQgkAYyGiwS5DGKlSItIvaBtrVbasbZWO0K1IpQZL62X\nXiy1aKvVemt1tLVUrb1ZO1qnyjggWqyCkWgVSCJNoG4gASK3QLiE/vGclJfwwQAACnNJREFUZd9s\nNpeGZN834feZyex7zr67e3bPvNlnn3Pe95CRHiLDc5HcWHbtkDszNJzWtQu8p4fTKBrYj6KB/Rhf\nmMPG7Q0sr9xK3e5G6nY3Ek4LMTw3k7wUZtfWdiBIy87OYvy4kYwtOYloTR1V1TW8+U4lpeWbKC3f\nxKjCPAoLBll2rQuGwY+JIK07rVm5olmABnD39q18e+UKBWkiIl0kFAqRNaA/WQP60zSqiPra7WyL\n1rKnppqKFaupWAGDx46koLiQguJC+vbr2/6TdoFwZibhzKEcbso/kl07fKCBXWWl7CortexaYeGR\n4dFOC4WAEITiwRqHD1nWa+9OIGRz1zKyID2j09m1cFqIrD5h+kU8qxrs3s9B1/5wNyxBlZsZIXdo\nDiX52UR32JmhpZs+pmp7A1XbGxiSncHx/TPI6RucJajC4TDDh+UzfFg+EyeMpqo6yiuvvUdFdBsV\n0W2Ew2EmjR7G8IIhZGZ2/iK5CtKOUuRg4vruJr2VehEROTpp4TB5BYPIKxhE494RbIvWsrWmjtoP\nKqn9oJJ1GRFmLbwspV/ozbJr+/ZysL55dm1f9X+68MXazq6RO9Qya0f1EvHsWpYL1qI7G48sQZUW\ngkgXn8QRcdm1E47LZELhAKrr97K8oo4tuxrZsquRkiHZDMxK7WVZOmJAdhYTxo1i7JhiojV2kdxV\nZRtYvbaaNZVRZp8zpdNDt8EISbvWcuzyHSIiIiJB9yp2sqSIiIiIiIiIiIiIiIiISG8RvKvGiYiI\niKTGicA3gVHAR0BDm3tLtzgFeD+h7iJsaambgAewxdwl9RYBNcBmt+2lPvLfUOBh4Brgt9ixJP46\nEygDdgIvAcNdvfoqeNKAZcRPZlMfBcuXgZXEl58E9VHKZQLPARs8dZOBSuwAAriLlgGCdL852IFQ\nggViTcBX3X3qI/+FgLeBs125BDuOlIH3z2Dsi2MccB5QBSx196mvgmcesA2Yjo6noJkB1AKFnjr1\nkQ9uBi4EvBepeQp4zFOeCtQBwbsAS+92dUJ5OfYLBtRHQXAOlvr3Xk9xPXCJP80R4FLAu2jlldj6\nxWejvgqaTwEXYN8909HxFCQhYC1wW0J94Poorf1derSLgVewYQGvM4B1nnIFkAdMSFG7xDySUN4C\nbHTb01Af+W0a9ivyoKeuHJjpT3MEeAbY5SnHjplpWDCgvgqGPOx75kVXDqE+CpKpwMnYfLQ/YwHb\nPALYR705SDsJGAK8leS+fGCHpxxb3XVYdzdK2jQaWOK2h6A+8ls+LX/g7EB9ECSnAr+k5f80UF/5\naSFwX0Jd4v80UB/5ZTL2Y+e7wGxsms0vgCkErI96a5AWwc7WSMzUxBwEvOs2xT6H3rgCQ09xIfAo\nEHVl9ZH/EvsAeu//jJ4oCxiPnVRzCPVVUMzFpmvsT6hXHwVHf2wYM7bwdimwGpsHHag+6qlrdw7H\nPtTWvIelmhe6choWuDVgZ3PUADme/XPd7Udd28xjWnt99DfsxAGws2nGA3d47lcf+S+KzavxysUm\nq4v/bgDmY1/+6qvgmAvc7ylnAC9jPzATrzKgPvLHZuxHjtd/sSHPsoR69VEKnEnzEwceAR70lKcD\n9egSD37IBm5JqIugPgqCqbQc7vwQ+6Ej/poLjPCUp6O+CqrYiQM6noJjDDbc6f0+eR74PuojX8yg\neZB2Os0v73AnNmQgqdUHeAiYiB00JdgvmRGoj4IghGWlz3LlMViGM9O3FgnYGZ1fw/pjDPYj9Erg\nXdRXQRQL0nQ8Bcty7ORCsO+iamxup/rIBzNofp00gMuBe4HrsYm36oTUexK7Npr373XP/eoj/xUD\nTwDXutvJfjZGmIXNmfEeM4eAkaivgioWpIH6KEiGAX/ATh54EDjX1auPRERERERERERERERERERE\nREREREREREREREREREREREREREREREREROSIYUCR340QEWmPr6u7i0iPNwNbkPgQ8Di2MsQLwGPA\nWP+a1arpwDvYUkqdMR9bumwjMKWrGiUiIiLSHRbRfG1cgDnAHmB26pvTrmXA1zvxuLOB37nti7Cl\nmEREuk263w0QkR7vUJK6x4AR2Np3rwObU9mgbnI6cNBtP+dnQ0Tk2KDhThHpLvcD/YAvufKngZ9g\nixr/FcgCBgMPYRmq24DXgNXYnLEHgHLgVx18vUuwrN484CniP0LTgZ8DNwM3YnPSAAYAzwMfYwEl\nwFeAl127vS4EPgNMAG4FxgNXA6XA+UAtcAEwEFgMXOPasMA9vhh4ErgXuAdYBfwDOBEbJq5yz5so\n7NpdBnwR+Ds21HoacBPwJvAG0N/znn7s3u8qYJqn/kHgKmAJcI6rn+Uef7n7LDYDn03SDhEREemB\nfkjL4c6YWizYysKClpj3gNvd9nVAJVDoym+4fUNADrAXKOhAO6JY8BJ7js+77VuB73j2W098uHMk\nlh0b6spfAD7ZyvP/APi1pzwJaALOwoKeIuBFYKa7vw8WUF3myj9z7crBgq9NWEAHMAbY7eoTnYJl\nK89w5cXAv4HjXPlfWHAJFvDGgtAbsOAPLKB8wW3PAt72PH+UeIB4HbAiSRtExAca7hSR7tTk/j4H\n5BMPlsqAiNvejQUsUVcuBz4EDgM7sECvCKhp57XOA97HArUcINfVL8CyXDFRz3YlsBwL2hYDn8Ay\nesmE3F9Mvbtd5m4LsQAoljncD/wem5/3NDZHb517TwAbgLVuuxzL3g2i5dDwHve6K125AqjzvP56\n4merXowFhgDHu+c9zrVjKZCNDdvGAjyARiyDCfb5DUVEAkFBmoh0lxwsUFiLBRFvAXd14HFNNA+G\nmrCsVHsagZ9iw3lb3HPkYYHP3jYe9zjwPSwLVd/Gfofbef1YBqsfFlgBVGPZuWSakmx35H02JSn3\nwYaOQ7T+Gc/BAuK3gG+0ss9hNA1GJDB0MIpId7kMCwr+AmzDLtfhNbGNx7YXECXKxDJaDwDveur3\nuOca08Zjn8WyfIuBP/2fr+tV5W5He+oysKxgKtRjQbH3vWa69izA5tM9CuxLUXtE5CgpSBORoxVJ\nUnc+8CPgCmxo7iVsDtcibFhwJjY0CM2zZrFyWkI5cZ9EY7F5axEse1aMDXcewIK367GAqS82nBfL\nOoFl4J4GSogPFSaTTvM5Y4ltqsUC0qs8dTOwwDHZ/t73GWpln47UpbnyAeykhyXAOOAkbB7cJmzO\nXGzk5DRs2DOS8HhIPidOREREeqAZwBos0HkYuA/4DTZ0WJyw72wsq1QPPIIFCYOBZ7BhwUlY1qcU\neNU9/lws83MP8TMYk8kgfqmPO7GsWDk26b4IC9Q2Ymc4PgvcgWWdYiYRn+CfzATgn9h8sPPc6y3C\nJvRfi50YAXYW5RLXhtuBb7n6WBtWA6OAU7Hg6Y9YcHmFe64baT4NJQLc4u6b7dr8BDacOxU4GfgA\nm292AjbkuhTYhc1hG++e51JsLtyr2Py8LcDdWP81Yp/vQOzSKQ3YRX9FREREREQkUXtDCCIiQXE/\nlvFKZj6W0RMRERERERERERERERERERERERERERERERERERERERERERERkd7hf1It9ZCSzjuFAAAA\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xdbd44e0>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Question 3: Trying to catch Silver: Poll Aggregation\n",
      "\n",
      "In the previous section, we tried to use heterogeneous side-information to build predictions of the election outcome. In this section, we switch gears to bringing together homogeneous information about the election, by aggregating different polling result together.\n",
      "\n",
      "This approach -- used by the professional poll analysists -- involves combining many polls about the election itself. One advantage of this approach is that it addresses the problem of bias in individual polls, a problem we found difficult to deal with in problem 1. If we assume that the polls are all attempting to estimate the same quantity, any individual biases should cancel out when averaging many polls (pollsters also try to correct for known biases). This is often a better assumption than assuming constant bias between election cycles, as we did above."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following table aggregates many of the pre-election polls available as of October 2, 2012. We are most interested in the column \"obama_spread\". We will clean the data for you:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "today "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "datetime.datetime(2012, 10, 2, 0, 0)"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "multipoll = pd.read_csv('cleaned-state_data2012.csv',index_col =0)\n",
      "multipoll.State.replace(states_abbrev, inplace = True)\n",
      "multipoll.start_date = multipoll.start_date.apply(pd.datetools.parse)\n",
      "multipoll.end_date = multipoll.end_date.apply(pd.datetools.parse)\n",
      "multipoll['poll_date'] = multipoll.start_date + (multipoll.end_date - multipoll.start_date).values/2\n",
      "multipoll['age_days'] = (today - multipoll['poll_date']).values/ np.timedelta64(1, 'D')\n",
      "multipoll = multipoll[multipoll.age_days > 0]\n",
      "multipoll = multipoll.drop(['Date', 'start_date', 'end_date', 'Spread'], axis=1)\n",
      "multipoll = multipoll.join(electoral_votes, on = 'State')\n",
      "multipoll.dropna()\n",
      "multipoll.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>Pollster</th>\n",
        "      <th>State</th>\n",
        "      <th>MoE</th>\n",
        "      <th>Obama (D)</th>\n",
        "      <th>Romney (R)</th>\n",
        "      <th>Sample</th>\n",
        "      <th>obama_spread</th>\n",
        "      <th>poll_date</th>\n",
        "      <th>age_days</th>\n",
        "      <th>Votes</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> Rasmussen Reports</td>\n",
        "      <td> Washington</td>\n",
        "      <td> 4.5</td>\n",
        "      <td> 52</td>\n",
        "      <td> 41</td>\n",
        "      <td> 500</td>\n",
        "      <td> 11</td>\n",
        "      <td>2012-09-26 00:00:00</td>\n",
        "      <td>  6.0</td>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  Gravis Marketing</td>\n",
        "      <td> Washington</td>\n",
        "      <td> 4.6</td>\n",
        "      <td> 56</td>\n",
        "      <td> 39</td>\n",
        "      <td> 625</td>\n",
        "      <td> 17</td>\n",
        "      <td>2012-09-21 12:00:00</td>\n",
        "      <td> 10.5</td>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>        Elway Poll</td>\n",
        "      <td> Washington</td>\n",
        "      <td> 5.0</td>\n",
        "      <td> 53</td>\n",
        "      <td> 36</td>\n",
        "      <td> 405</td>\n",
        "      <td> 17</td>\n",
        "      <td>2012-09-10 12:00:00</td>\n",
        "      <td> 21.5</td>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>         SurveyUSA</td>\n",
        "      <td> Washington</td>\n",
        "      <td> 4.4</td>\n",
        "      <td> 54</td>\n",
        "      <td> 38</td>\n",
        "      <td> 524</td>\n",
        "      <td> 16</td>\n",
        "      <td>2012-09-08 00:00:00</td>\n",
        "      <td> 24.0</td>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>         SurveyUSA</td>\n",
        "      <td> Washington</td>\n",
        "      <td> 4.4</td>\n",
        "      <td> 54</td>\n",
        "      <td> 37</td>\n",
        "      <td> 524</td>\n",
        "      <td> 17</td>\n",
        "      <td>2012-08-01 12:00:00</td>\n",
        "      <td> 61.5</td>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "            Pollster       State  MoE  Obama (D)  Romney (R)  Sample  obama_spread           poll_date  age_days  Votes\n",
        "0  Rasmussen Reports  Washington  4.5         52          41     500            11 2012-09-26 00:00:00       6.0     12\n",
        "1   Gravis Marketing  Washington  4.6         56          39     625            17 2012-09-21 12:00:00      10.5     12\n",
        "2         Elway Poll  Washington  5.0         53          36     405            17 2012-09-10 12:00:00      21.5     12\n",
        "3          SurveyUSA  Washington  4.4         54          38     524            16 2012-09-08 00:00:00      24.0     12\n",
        "4          SurveyUSA  Washington  4.4         54          37     524            17 2012-08-01 12:00:00      61.5     12"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**3.1** Using this data, compute a new data frame that averages the obama_spread for each state. Also compute the standard deviation of the obama_spread in each state, and the number of polls for each state.\n",
      "\n",
      "*Define a function `state_average` which returns this dataframe*\n",
      "\n",
      "**Hint**\n",
      "\n",
      "[pd.GroupBy](http://pandas.pydata.org/pandas-docs/dev/groupby.html) could come in handy"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Function\n",
      "--------\n",
      "state_average\n",
      "\n",
      "Inputs\n",
      "------\n",
      "multipoll : DataFrame\n",
      "   The multipoll data above\n",
      "   \n",
      "Returns\n",
      "-------\n",
      "averages : DataFrame\n",
      "  A dataframe, indexed by State, with the following columns:\n",
      "     N: Number of polls averaged together\n",
      "     poll_mean: The average value for obama_spread for all polls in this state\n",
      "     poll_std: The standard deviation of obama_spread\n",
      "     \n",
      "Notes\n",
      "-----\n",
      "For states where poll_std isn't finite (because N is too small), estimate the\n",
      "poll_std value as .05 * poll_mean\n",
      "\"\"\"\n",
      "#your code here\n",
      "def state_average(multipoll):\n",
      "    state_wise_group = multipoll.groupby('State')\n",
      "    n = state_wise_group.size()\n",
      "    mean = state_wise_group.obama_spread.mean()\n",
      "    std = state_wise_group.obama_spread.std()\n",
      "    std[std.isnull()]= .05 * mean[std.isnull()] \n",
      "    return pd.DataFrame(dict(N=n, poll_mean = mean, poll_std = std))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#x = state_average(multipoll)\n",
      "#mean =groups.obama_spread.mean()\n",
      "#std =groups.obama_spread.std()\n",
      "#std[std.isnull()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 113
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Lets call the function on the `multipoll` data frame, and join it with the `electoral_votes` frame."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "avg = state_average(multipoll).join(electoral_votes,how='outer')\n",
      "avg.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>N</th>\n",
        "      <th>poll_mean</th>\n",
        "      <th>poll_std</th>\n",
        "      <th>Votes</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>State</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Alabama</th>\n",
        "      <td>NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alaska</th>\n",
        "      <td>NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arizona</th>\n",
        "      <td> 20</td>\n",
        "      <td> -5.500000</td>\n",
        "      <td> 4.559548</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arkansas</th>\n",
        "      <td>  3</td>\n",
        "      <td>-20.333333</td>\n",
        "      <td> 4.041452</td>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td> 20</td>\n",
        "      <td> 18.950000</td>\n",
        "      <td> 5.548589</td>\n",
        "      <td> 55</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 139,
       "text": [
        "             N  poll_mean  poll_std  Votes\n",
        "State                                     \n",
        "Alabama    NaN        NaN       NaN      9\n",
        "Alaska     NaN        NaN       NaN      3\n",
        "Arizona     20  -5.500000  4.559548     11\n",
        "Arkansas     3 -20.333333  4.041452      6\n",
        "California  20  18.950000  5.548589     55"
       ]
      }
     ],
     "prompt_number": 139
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some of the reddest and bluest states are not present in this data (people don't bother polling there as much). The `default_missing` function gives them strong Democratic/Republican advantages"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def default_missing(results):\n",
      "    red_states = [\"Alabama\", \"Alaska\", \"Arkansas\", \"Idaho\", \"Wyoming\"]\n",
      "    blue_states = [\"Delaware\", \"District of Columbia\", \"Hawaii\"]\n",
      "    results.ix[red_states, [\"poll_mean\"]] = -100.0\n",
      "    results.ix[red_states, [\"poll_std\"]] = 0.1\n",
      "    results.ix[blue_states, [\"poll_mean\"]] = 100.0\n",
      "    results.ix[blue_states, [\"poll_std\"]] = 0.1\n",
      "default_missing(avg)\n",
      "avg.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>N</th>\n",
        "      <th>poll_mean</th>\n",
        "      <th>poll_std</th>\n",
        "      <th>Votes</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>State</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Alabama</th>\n",
        "      <td>NaN</td>\n",
        "      <td>-100.00</td>\n",
        "      <td> 0.100000</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alaska</th>\n",
        "      <td>NaN</td>\n",
        "      <td>-100.00</td>\n",
        "      <td> 0.100000</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arizona</th>\n",
        "      <td> 20</td>\n",
        "      <td>  -5.50</td>\n",
        "      <td> 4.559548</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arkansas</th>\n",
        "      <td>  3</td>\n",
        "      <td>-100.00</td>\n",
        "      <td> 0.100000</td>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td> 20</td>\n",
        "      <td>  18.95</td>\n",
        "      <td> 5.548589</td>\n",
        "      <td> 55</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 140,
       "text": [
        "             N  poll_mean  poll_std  Votes\n",
        "State                                     \n",
        "Alabama    NaN    -100.00  0.100000      9\n",
        "Alaska     NaN    -100.00  0.100000      3\n",
        "Arizona     20      -5.50  4.559548     11\n",
        "Arkansas     3    -100.00  0.100000      6\n",
        "California  20      18.95  5.548589     55"
       ]
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Unweighted aggregation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**3.2** *Build an `aggregated_poll_model` function that takes the `avg` DataFrame as input, and returns a forecast DataFrame*\n",
      "in the format you've been using to simulate elections. Assume that the probability that Obama wins a state\n",
      "is given by the probability that a draw from a Gaussian with $\\mu=$poll_mean and $\\sigma=$poll_std is positive."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Function\n",
      "--------\n",
      "aggregated_poll_model\n",
      "\n",
      "Inputs\n",
      "------\n",
      "polls : DataFrame\n",
      "   DataFrame indexed by State, with the following columns:\n",
      "      poll_mean\n",
      "      poll_std\n",
      "      Votes\n",
      "\n",
      "Returns\n",
      "-------\n",
      "A DataFrame indexed by State, with the following columns:\n",
      "   Votes: Electoral votes for that state\n",
      "   Obama: Estimated probability that Obama wins the state\n",
      "\"\"\"\n",
      "#your code here\n",
      "def aggregated_poll_model(polls):\n",
      "    sigma = polls.poll_std\n",
      "    mean = polls.mean\n",
      "    prob =  .5 * (1 + erf(polls.poll_mean / np.sqrt(2 * sigma ** 2)))\n",
      "    return pd.DataFrame(dict(Obama=prob, Votes=polls.Votes))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 143
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**3.3** *Run 10,000 simulations with this model, and plot the results. Describe the results in a paragraph -- compare the methodology and the simulation outcome to the Gallup poll. Also plot the usual map of the probabilities*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "model = aggregated_poll_model(avg)\n",
      "sims= simulate_election(model, 10000)\n",
      "plot_simulation(sims)\n",
      "plt.xlim(250,400)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 144,
       "text": [
        "(250, 400)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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0bNmSV155hc2bN/PFF19QsWJFRo8ejcvlYuXKlbz22mv8+uuvVKlShbp169Ky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\n/h+zeu5aGhCMLgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbb0b9b0>"
       ]
      }
     ],
     "prompt_number": 144
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*Your summary here*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "make_map(model.Obama, \"P(Obama): Poll Aggregation\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 90,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xdd8f5f8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8/vnnQOYKAzVq1LjhmqZZy21dmYxmsdvtAPTp04e5c+eSkpKCy+Xi/PnzXsMw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\nO+xMO2I0VZWVzJg5kxXLl5OZm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K1csVxDhgyxehIAAOjjeLKKHjP1hmwZ8aP0r6JjGpMx\nVr9f/JDVkwAAgEMRqwha4Z4imZFJ6kieqLZheVq+6q969tnnrJ4FAAAciFhFUDZs2KAjR8tkxKRL\nkozwSLUMyNT651+weBkAAHAiYhVB2bj5VTVHD5fhCvvqZ0ZkvIr27FZxcbGFywAAgBMRqwjK9265\nWdEdn5/3MyNigFqSrtasG2frxIkTFi0DAABORKwiKLm5uTpzqkJmR9v5f+COkGma8nq91gwDAACO\n5LZ6APqWuLg4