draft descriptive analysis attention networks

bipartite_cc_attention.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Descriptive analysis and simple regression modeling of weighted bipartite clustering measures in attention networks"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The descriptive analysis and the simple regression modeling is, for now, for only one year (2011). Note that we compute the dependent variable quarter by quarter, so in one year there are 4 quarter-years, and thus we generate 4 attention networks to compute the independent variables related to the bipartite clustering approach. The data for these networks ends just before the begining of the quarter in which the DV is caluclated, and goes one year backwards from there."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "# Adjust the full path to your system!!\n",
      "root = \"/home/jtorrents/projects/analysts/attention\"\n",
      "os.chdir(root)\n",
      "import networkx as nx\n",
      "import pandas as pd\n",
      "import statsmodels.formula.api as smf\n",
      "# Load our code\n",
      "import attention_networks as an"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "def plot_histogram(measure, bins=20,  mname='no name'):\n",
      "    fig = figure()\n",
      "    ax = fig.add_subplot(111)\n",
      "    ax.hist([v for v in measure.values() if not pd.isnull(v)], bins=bins)\n",
      "    ax.set_title(\"histogram of {}\".format(mname))\n",
      "    ax.set_xlabel(mname)\n",
      "    #ax.set_xlim(0, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "With the new implementation we compute both DV and IVs at the same tame. In my machine it takes about 13 minutes to analyze the four quarters of year 2011. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%time an.compute_all(start_year=2011, end_year=2011, start=20101231, end=20111231, fname='all_results_test_2011.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Computing DV for quarter 1 of year 2011 (with observations from 20110102 to 20110330), with attention network for computing IVs from 20091231 to 20101231.\n",
        "Computing DV for quarter 2 of year 2011 (with observations from 20110401 to 20110629), with attention network for computing IVs from 20100331 to 20110331."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Computing DV for quarter 3 of year 2011 (with observations from 20110701 to 20110929), with attention network for computing IVs from 20100630 to 20110630."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Computing DV for quarter 4 of year 2011 (with observations from 20111001 to 20111230), with attention network for computing IVs from 20100930 to 20110930."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CPU times: user 13min 12s, sys: 4.07 s, total: 13min 16s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Wall time: 13min 15s\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Load the results in a pandas dataframe\n",
      "df = pd.read_csv('all_results_test_2011.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py:1070: DtypeWarning: Columns (14,15) have mixed types. Specify dtype option on import or set low_memory=False.\n",
        "  data = self._reader.read(nrows)\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Dependent variable: forecast similarity between analysts"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We are exploring several normalizations for the similarity between analysts predictions:\n",
      "\n",
      "1. **all_period_norm_similarity**: Where $EPSF_{i,s}$ is the earnings per share forecasts made by analyst i on stock s in a given period, and $EPSF_{j,s}$ the same for analyst j. $max(DIFFEPSF_{s})$ is the maximum difference of any two forecast in the dataset on stock s on a given period and $min(DIFFEPSF_{s})$ is the minimum value. The maximum and the minimum are computed for all forecast in the focal quarter.\n",
      "$$ 1 - \\frac{|EPSF_{i,s} - EPSF_{j,s}| - min(DIFFEPSF_{s})}{max(DIFFEPSF_{s}) - min(DIFFEPSF_{s})} $$\n",
      "\n",
      "2. **all_period_norm_similarity_zscore**: Where $EPSF_{i,s}$ is the earnings per share forecasts made by analyst i on stock s in a given period, and $EPSF_{j,s}$ the same for analyst j. $\\overline{DIFFEPSF_{s}}$ is the mean of the differences between any two forecast in the dataset on stock s on a given period and $\\sigma_{DIFFEPSF_{s}}$ is the standard deviation. The mean and the standard deviation are computed for all forecast in the focal quarter.\n",
      "$$ - \\frac{|EPSF_{i,s} - EPSF_{j,s}| - \\overline{DIFFEPSF_{s}}}{\\sigma_{DIFFEPSF_{s}}} $$\n",
      "\n",
      "\n",
      "1. **norm_similarity**: Where $EPSF_{i,s}$ is the earnings per share forecasts made by analyst i on stock s in a given period, and $EPSF_{j,s}$ the same for analyst j. $max(DIFFEPSF_{s})$ is the maximum difference of any two forecast in the dataset on stock s **made before the forecast of analyst i** on a given period and $min(DIFFEPSF_{s})$ is the minimum value. The maximum and the minimum are computed only for forecast issued before the focal forecast.\n",
      "$$ 1 - \\frac{|EPSF_{i,s} - EPSF_{j,s}| - min(DIFFEPSF_{s})}{max(DIFFEPSF_{s}) - min(DIFFEPSF_{s})} $$\n",
      "\n",
      "2. **norm_similarity_zscore**: Where $EPSF_{i,s}$ is the earnings per share forecasts made by analyst i on stock s in a given period, and $EPSF_{j,s}$ the same for analyst j. $\\overline{DIFFEPSF_{s}}$ is the mean of the differences between any two forecast in the dataset on stock s **made before the forecast of analyst i** on a given period and $\\sigma_{DIFFEPSF_{s}}$ is the standard deviation. The mean and the standard deviation are computed only for forecast issued before the focal forecast.\n",
      "$$ - \\frac{|EPSF_{i,s} - EPSF_{j,s}| - \\overline{DIFFEPSF_{s}}}{\\sigma_{DIFFEPSF_{s}}} $$\n",
      "\n",
      "Now let's look at the relation between different normalization of similarity for year 2011:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dep_vars = ['norm_similarity','all_period_norm_similarity', 'norm_similarity_zscore','all_period_norm_similarity_zscore']\n",
      "df[dep_vars].describe()"
     ],
     "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>norm_similarity</th>\n",
        "      <th>all_period_norm_similarity</th>\n",
        "      <th>norm_similarity_zscore</th>\n",
        "      <th>all_period_norm_similarity_zscore</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 2249785.000000</td>\n",
        "      <td> 2255017.000000</td>\n",
        "      <td> 2.249785e+06</td>\n",
        "      <td> 2.255017e+06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>       0.677748</td>\n",
        "      <td>       0.757420</td>\n",
        "      <td> 4.235009e-08</td>\n",
        "      <td> 7.343485e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>       0.290289</td>\n",
        "      <td>       0.227114</td>\n",
        "      <td> 1.000000e+00</td>\n",
        "      <td> 1.000000e+00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>-1.399286e+01</td>\n",
        "      <td>-9.364178e+00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>       0.500000</td>\n",
        "      <td>       0.645833</td>\n",
        "      <td>-5.549967e-01</td>\n",
        "      <td>-5.140061e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>       0.760000</td>\n",
        "      <td>       0.827586</td>\n",
        "      <td> 2.286800e-01</td>\n",
        "      <td> 2.674934e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>       0.916667</td>\n",
        "      <td>       0.933333</td>\n",
        "      <td> 7.158600e-01</td>\n",
        "      <td> 7.270850e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>       1.000000</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td> 6.822597e+00</td>\n",
        "      <td> 3.076387e+00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "       norm_similarity  all_period_norm_similarity  norm_similarity_zscore  \\\n",
        "count   2249785.000000              2255017.000000            2.249785e+06   \n",
        "mean          0.677748                    0.757420            4.235009e-08   \n",
        "std           0.290289                    0.227114            1.000000e+00   \n",
        "min           0.000000                    0.000000           -1.399286e+01   \n",
        "25%           0.500000                    0.645833           -5.549967e-01   \n",
        "50%           0.760000                    0.827586            2.286800e-01   \n",
        "75%           0.916667                    0.933333            7.158600e-01   \n",
        "max           1.000000                    1.000000            6.822597e+00   \n",
        "\n",
        "       all_period_norm_similarity_zscore  \n",
        "count                       2.255017e+06  \n",
        "mean                        7.343485e-08  \n",
        "std                         1.000000e+00  \n",
        "min                        -9.364178e+00  \n",
        "25%                        -5.140061e-01  \n",
        "50%                         2.674934e-01  \n",
        "75%                         7.270850e-01  \n",
        "max                         3.076387e+00  \n",
        "\n",
        "[8 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[dep_vars].corr()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>norm_similarity</th>\n",
        "      <th>all_period_norm_similarity</th>\n",
        "      <th>norm_similarity_zscore</th>\n",
        "      <th>all_period_norm_similarity_zscore</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>norm_similarity</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.890107</td>\n",
        "      <td> 0.893379</td>\n",
        "      <td> 0.850015</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>all_period_norm_similarity</th>\n",
        "      <td> 0.890107</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.786723</td>\n",
        "      <td> 0.918500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>norm_similarity_zscore</th>\n",
        "      <td> 0.893379</td>\n",
        "      <td> 0.786723</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.856097</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>all_period_norm_similarity_zscore</th>\n",
        "      <td> 0.850015</td>\n",
        "      <td> 0.918500</td>\n",
        "      <td> 0.856097</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>4 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "                                   norm_similarity  \\\n",
        "norm_similarity                           1.000000   \n",
        "all_period_norm_similarity                0.890107   \n",
        "norm_similarity_zscore                    0.893379   \n",
        "all_period_norm_similarity_zscore         0.850015   \n",
        "\n",
        "                                   all_period_norm_similarity  \\\n",
        "norm_similarity                                      0.890107   \n",
        "all_period_norm_similarity                           1.000000   \n",
        "norm_similarity_zscore                               0.786723   \n",
        "all_period_norm_similarity_zscore                    0.918500   \n",
        "\n",
        "                                   norm_similarity_zscore  \\\n",
        "norm_similarity                                  0.893379   \n",
        "all_period_norm_similarity                       0.786723   \n",
        "norm_similarity_zscore                           1.000000   \n",
        "all_period_norm_similarity_zscore                0.856097   \n",
        "\n",
        "                                   all_period_norm_similarity_zscore  \n",
        "norm_similarity                                             0.850015  \n",
        "all_period_norm_similarity                                  0.918500  \n",
        "norm_similarity_zscore                                      0.856097  \n",
        "all_period_norm_similarity_zscore                           1.000000  \n",
        "\n",
        "[4 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for m in dep_vars:\n",
      "    plot_histogram(df[m].to_dict(), bins=40, mname=m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEaCAYAAADtxAsqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npNr/A8cI7sRxdFnPAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f53a3bb3950>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEaCAYAAADtxAsqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtUVOX+P/D3GKSmXAQVZAYdZQaRi0gKoqaNIqSWppES\nxxQvddK+p2OXZZa/PFLnW2p9beUxXd+OB0WtpLvaKpAkKbuIeemidjHlOiBfhQEBEUQ+vz+IHchl\nNveL79das9bw7P08+7OfGffHvZ9n79GIiICIiEiFHh0dABERdR1MGkREpBqTBhERqcakQUREqjFp\nEBGRakwaRESkGpNGJ6DX65GUlFTvssOHD8PLy6udI+p8Fi9eDCcnJwQHBze5rl6vx+effw4AiI6O\nxoIFC1o7vEZlZGTAzs4OzZndnpycDHd39zaIquO0pD8AwM7ODmlpaQCARYsWYc2aNc2OZcaMGdi9\ne3ez69+MbDo6AAI0Gg00Gk29yyZOnIhffvnFahvR0dE4d+5ct/wHcPjwYRw8eBDZ2dno1atXk+vX\n7NuG+rktDR48GEVFRe2+3c6qpf1Rs25j/3bU+PTTT5X3sbGxiImJweHDh5vd3s2AZxpkVUVFRYdu\nPz09HXq9vlkJ40btfS9rR/ddU3W1eIHmfaYi0u7fhe6CSaOTOHnyJPz9/eHo6IgHHngAZWVlAOpe\nntiwYQN0Oh3s7e3h5eWFzz//HAkJCVi3bh3eeecd2NnZISAgAACQnZ2NWbNmwdnZGUajEf/5z3+U\ndkpLSxEVFQUnJyd4e3vj5ZdfrrUdvV6Pl19+GSNHjoSdnR2uX7+O9evXw2AwwN7eHj4+Pti7d6+y\nfmxsLCZMmIAnn3wS/fr1g8FgwDfffIMdO3Zg8ODBcHFxwa5duxrc/4ZijYmJwcMPP4xvv/0WdnZ2\neP755+vUPXfuHKZMmYL+/ftjwIABePDBB1FYWNjMTwJIS0tDjx49sG3bNmi1Wri5uWHjxo3KchFR\n+qJ///6IiIiAxWKpVXf79u0YMmQIpk6divT0dPTo0QOVlZWN7itQ9bksWrQITk5O8PHxwXfffacq\nZr1ej40bN9b7HQKAbdu2wWg0wtnZGffeey9ycnKUZT169MDWrVthNBoxfPhwfPHFF9DpdHjllVcw\ncOBAuLm5Ye/evfj000/h6ekJZ2dnrF+/3mpMR48exZgxY+Dg4ABXV1c89dRTtfqouj9MJhPWrFmD\nCRMmwM7ODrNmzcKlS5cwf/58ODg4ICgoCOnp6bXiPX/+fJ3tWSwW3HPPPRg4cCCcnJwwc+ZMmM1m\nZbnJZMJzzz2HCRMmoG/fvjh//jxMJhNiYmLwyy+/YNmyZcr3zMnJCceOHYOLi0ut5PLhhx9i1KhR\nqj6Tbkuoww0ZMkTGjh0rOTk5kp+fLyNGjJD//d//FRGRQ4cOiU6nExGRX375Rdzd3SUnJ0dERNLT\n0+XcuXMiIhIdHS0LFiyo1e7EiRPlv/7rv6SsrEy+//57GTBggHz++eciIrJq1SoxmUxSUFAgWVlZ\n4ufnJ+7u7rViCggIkKysLLl69aqIiLz33nvKtt955x3p06ePXLhwQUREduzYITY2NhIbGyuVlZXy\n3HPPiVarlb/97W9SXl4uiYmJYmdnJyUlJfX2QWOxxsbGyh133NFg//3+++9y8OBBKS8vl4sXL8qk\nSZPk8ccfV5br9XpJSkoSEZG1a9fKgw8+2OjnkZqaKhqNRv7yl7/IlStX5KeffpIBAwbIwYMHRUTk\ntddek3HjxonZbJby8nJ55JFHJDIyslbdqKgouXLlily9elUpu379uqrPZdKkSWKxWCQzM1N8fHxq\nfS4N0ev1DX6HkpKSpH///nLy5EkpKyuTxx57TCZNmqTU1Wg0EhYWJhaLRa5evSqHDh0SGxsb+ec/\n/ykVFRWybds2cXZ2lr/85S9SXFwsp0+flt69e0taWlqjMQUHB8ubb74pIiIlJSVy5MiRWn1U3R93\n3nmnGI1GOX/+vBQWFoq3t7cYDAZJSkqSiooKWbhwoSxevLhWvNXf+0WLFslzzz0nIiJ5eXny4Ycf\nSmlpqRQVFcncuXNl9uzZSr0777xThgwZImfOnJHr16/LtWvXxGQySUxMjIjU/z3z9vaW+Ph45e/Z\ns2fLq6++avXz6M6YNDoBvV4vb731lvL3008/LcuWLROR2knj7NmzMnDgQOUAWdONB8OMjAy55ZZb\npLi4WCl79tlnZdGiRSIiMmzYMElMTFSW/ec//1G2Ux3Tjh07Go171KhRsm/fPhGpShpGo1FZ9uOP\nP4pGo5H/+7//U8qcnZ3lhx9+qNOOtVh37NjRaNK40UcffSQBAQG19qU5SePXX39Vyp5++mlZunSp\niIh4eXkp7YmIZGdni62trVy/fl2pm5qaWqe969evq/pcDhw4oCz797//XetzaUhj36ElS5bIqlWr\nlGXFxcVia2sr6enpIlJ1ED506JCy/NChQ9K7d2+prKwUEZHLly+LRqORo0ePKuuMHj1a9u7d22hM\nkyZNkrVr18rFixdrld+YNEwmk7z00kvK8qeeekpmzJih/P3xxx/LqFGjlL8bSho3OnnypPTr10/5\n22Qyydq1a2utUzNp1Pc9W79+vcyfP19EqpLSbbfdpvxH6WbFy1OdhKurq/K+d+/eKC4urrOOwWDA\na6+9hujoaLi4uCAyMrLWZYaasrOz4eTkhD59+ihlgwcPRnZ2trK85uUonU5Xp40bZ+3s2rULAQEB\n6NevH/r164dTp04hLy9PWe7i4lJrHwBgwIABVveroVhrXlpoTG5uLh544AHodDo4ODhgwYIFteJq\nrpr7X7Pv0tPTMWfOHKUfvL29YWNjg9zc3Hrr1tTUz2Xw4MGq473xO1RSUgIAyMnJwZAhQ5Rlffr0\ngbOzc63+vTFeZ2dnZYC5+rO88fOtbr8hMTEx+O233zBixAgEBQXhk08+aXDdmm336tULAwcOrPV3\nfd+bG125cgWPPPII9Ho9HBwccOedd6KwsLDW5aWmzkSbP38+Pv74Y1y5cgXvvvsuJk2aVCvWmxGT\nRhcTGRmJw4cPIz09HRqNBqtWrQJQd1aQm5sb8vPza/1jy8jIgFarBQAMGjQImZmZyrKa76vVbDM9\nPR1//etfsWXLFuTn58NiscDX17dVBhMbirW+RFaf1atX45ZbbsGpU6dQWFiI3bt3K9fLWyIjI6PW\n++q+Gzx4MBISEmCxWJTXlStXMGjQIGX9hmb0qPlcbtxuS7m5uSlTVAGgpKQEeXl5yjYbi7clDAYD\n3n77bVy8eBGrVq3C/fffj9LSUqv1mhpL9fobN27Eb7/9hqNHj6KwsBBffPFFnQHvxtqub5lOp0Nw\ncDA+/PBDvPnmm+0+XbszYtLoQn777Td8/vnnKCsrQ8+ePdGrVy/ccsstAKr+l5mWlqb8A3F3d8f4\n8ePx7LPPoqysDD/++CO2b9+OBx98EAAwb948rFu3DgUFBTCbzXj99dcb/QdVUlICjUaD/v37o7Ky\nEjt27MCpU6daZb+sxWpNcXEx+vTpA3t7e5jNZrzyyiutEtd///d/o7S0FKdPn0ZsbCwiIiIAAMuW\nLcPq1auVA/rFixexf/9+VW025XPJysrC5s2bmx1/9XchMjISO3bswA8//ICysjKsXr0awcHBTTqL\naY4333wTFy9eBAA4ODhAo9GgR4/6Dzk1D+xN+Y9IzaRQXFyM3r17w8HBAfn5+fVOmmisbRcXF2Rl\nZeHatWu1yhcuXIgNGzbg1KlTuO+++1TH1l0xaXRCN849r35fVlaGZ599FgMGDMCgQYNw6dIlrFu3\nDgAwd+5cAFWXFcaMGQMA2LNnD9LS0uDm5ob77rsPL7zwAqZMmQIA+Mc//gGdToehQ4ciLCwMc+fO\nxa233tpgTN7e3njqqacwbtw4uLq64tSpU7jjjjsajLlm3Go0Fqu1ufhr167FiRMn4ODggJkzZyI8\nPLzB9Zsyr//OO++EwWDA1KlTsXLlSkydOhUAsGLFCsyaNQthYWGwt7fHuHHjcPTo0VrbqG+7avZ1\n7dq1GDJkCIYOHYpp06Zh4cKFzToLqLmfISEh+Oc//4nw8HC4ubkhNTUVcXFxquNtaB1rDhw4AF9f\nX9jZ2eGJJ55AXFwcevbsabV9a9+lhtZ9/PHHUVpaiv79+2P8+PGYPn16k/YjJCQEPj4+cHV1rXV5\n7L777kNGRgbmzJnTKtO+u7zGBjxKS0slKChI/P39ZcSIEfLMM8+ISNWA0NSpU8VoNEpoaKhYLBal\nzksvvSQGg0GGDx9ea0Dv2LFj4uvrKwaDQf7+978r5VevXpV58+aJwWCQsWPH1pqRERsbK0ajUYxG\no+zcubNlozfUqK1bt4rJZOroMDqFGwdqiapnc5GK2VPVUySvXbsmY8eOlcOHD8vKlStlw4YNIlI1\nu6B6Zsbp06fF399fysvLJTU1VTw8PJQZGIGBgZKSkiIiItOnT1emsW3ZskWWL18uIiJxcXESEREh\nIlWJadiwYWKxWMRisSjvqXXk5OTIV199JdevX5dffvlFDAaDbNq0qaPD6hSYNKimDz74oNbMwJud\n1ctTt912GwCgvLwc169fR79+/bB//35ERUUBAKKiopSbvPbt24fIyEjY2tpCr9fDYDAgJSUFOTk5\nKCoqQlBQEICqa4TVdWq2FR4erjyD6cCBAwgLC4OjoyMcHR0RGhqKhISEVj7PunmVl5dj2bJlsLe3\nR0hICGbPno1HH320o8NqN2+99Rbs7OzqvPz8/Fr8aIq2UP28phtf9vb2yMrK6rC4pk+fXm9cam7+\n6wpMJhMeffRRbNmypaND6TSsPnuqsrISt99+O86dO4fly5fDx8cHubm5yrQzFxcXZaphdnZ2rQfK\n6XQ6mM1m2Nra1poJo9Vqlel+ZrNZmQZnY2MDBwcH5OXlITs7u1ad6raodQwePBg//fRTR4fRYebP\nn4/58+c3uPz69evtGI11nfX5VfHx8R0dQptKTk7u6BA6HatJo0ePHvj+++9RWFiIu+66C4cOHaq1\nvDP+r4yIiNqG6qfcOjg44O6778bx48fh4uKCCxcuwNXVFTk5OcpMA61WW2u+f1ZWFnQ6HbRaba1T\n6Ory6joZGRlwc3NDRUUFCgsL4ezsDK1WWyvLZ2ZmKjNMajIYDDh37lyTd5yI6Gbm4eGB33//vekV\nGxvwuHjxojL4fOXKFZk4caIcPHhQVq5cKevXrxcRkXXr1tUZCC8rK5Pz58/LsGHDlIHwoKAgOXLk\niFRWVtYZCK9+3MGePXtqDYQPHTpULBaL5OfnK+9vZGUXbio3PiLhZsa++BP74k/siz8199jZ6JlG\nTk4OoqKiUFlZicrKSixYsAAhISEICAjAvHnzEBMTA71ej3fffRdA1Vz+efPmKY9V2Lp1q3LpauvW\nrVi0aBFKS0sxY8YMTJs2DQCwdOlSLFiwQHkCZ/X8cScnJ6xZswaBgYEAquavOzo6Nj0rEhFRq2k0\nafj5+eHEiRN1yp2cnHDw4MF666xevRqrV6+uUz569Oh6B1579uypJJ0bLV68GIsXL24sRCIiake8\nI7wbMZlMHR1Cp8G++BP74k/si5bT/HFtq8vSaDT8BS4ioiZq7rGTZxpERKQakwYREanGpEFERKox\naRARkWpMGkREpBqTBhERqcakQUREqjFpEBGRakwaRESkGpMGERGpxqRBRESqMWkQEZFqTBpERKQa\nkwYREanGpEFERKoxaRARkWpMGkREpBqTBhERqcakQUREqjFpEBGRakwaRESkGpMGERGpxqRBRNTN\n2Ns7QaPRNPpqLo2ISCvG2u40Gg26+C4QEbWqqqRg7bjYvGMnzzSIiEg1Jg0iIlKt0aSRmZmJyZMn\nw8fHB76+vvjXv/4FAIiOjoZOp0NAQAACAgIQHx+v1Fm3bh2MRiO8vLyQmJiolB8/fhx+fn4wGo1Y\nsWKFUl5WVoaIiAgYjUYEBwcjPT1dWbZz5054enrC09MTu3btarWdJiKiZpJG5OTkyMmTJ0VEpKio\nSDw9PeXMmTMSHR0tGzdurLP+6dOnxd/fX8rLyyU1NVU8PDyksrJSREQCAwMlJSVFRESmT58u8fHx\nIiKyZcsWWb58uYiIxMXFSUREhIiI5OXlybBhw8RisYjFYlHe38jKLhAR3XQACCBWXs07djZ6puHq\n6opRo0YBAPr27YsRI0bAbDZXJ5s66+/btw+RkZGwtbWFXq+HwWBASkoKcnJyUFRUhKCgIADAwoUL\nsXfvXgDA/v37ERUVBQAIDw9HUlISAODAgQMICwuDo6MjHB0dERoaioSEhNbIk0RE1EyqxzTS0tJw\n8uRJBAcHAwA2b94Mf39/LF26FAUFBQCA7Oxs6HQ6pY5Op4PZbK5TrtVqleRjNpvh7u4OALCxsYGD\ngwPy8vIabIuIiDqOqqRRXFyM+++/H5s2bULfvn2xfPlypKam4vvvv8egQYPw1FNPtXWcRETUCdhY\nW+HatWsIDw/Hgw8+iNmzZwMABg4cqCx/6KGHMHPmTABVZxCZmZnKsqysLOh0Omi1WmRlZdUpr66T\nkZEBNzc3VFRUoLCwEM7OztBqtUhOTlbqZGZmYsqUKfXGGB0drbw3mUwwmUzW95yI6KaS/MerhRob\n8KisrJQFCxbI448/Xqs8Oztbef/qq69KZGSkiPw5EF5WVibnz5+XYcOGKQPhQUFBcuTIEamsrKwz\nEL5s2TIREdmzZ0+tgfChQ4eKxWKR/Px85X19Az5ERPQntOFAeKNnGl9//TXefPNNjBw5EgEBAQCA\nl156CXv27MH3338PjUaDoUOH4o033gAAeHt7Y968efD29oaNjQ22bt2q3K6+detWLFq0CKWlpZgx\nYwamTZsGAFi6dCkWLFgAo9EIZ2dnxMXFAQCcnJywZs0aBAYGAgDWrl0LR0fHlmdJIiJqNj5GhIio\nm+FjRIiIqFNg0iAiItWYNIiISDUmDSIiUo1Jg4iIVGPSICIi1Zg0iIhINSYNIiJSjUmDiIhUY9Ig\nIiLVmDSIiEg1Jg0iIlKNSYOIiFRj0iAiItWYNIiISDUmDSIiUo1Jg4iIVGPSICIi1Zg0iIhINSYN\nIiJSjUmDiIhUY9IgIiLVmDSIiEg1Jg0iIlKNSYOIiFRj0iAiItWYNIiISDUmDSIiUo1Jg4iIVGs0\naWRmZmLy5Mnw8fGBr68v/vWvfwEA8vPzERoaCk9PT4SFhaGgoECps27dOhiNRnh5eSExMVEpP378\nOPz8/GA0GrFixQqlvKysDBERETAajQgODkZ6erqybOfOnfD09ISnpyd27drVajtNRETNJI3IycmR\nkydPiohIUVGReHp6ypkzZ2TlypWyYcMGERFZv369rFq1SkRETp8+Lf7+/lJeXi6pqani4eEhlZWV\nIiISGBgoKSkpIiIyffp0iY+PFxGRLVu2yPLly0VEJC4uTiIiIkREJC8vT4YNGyYWi0UsFovy/kZW\ndoGI6KYDQACx8mresbPRMw1XV1eMGjUKANC3b1+MGDECZrMZ+/fvR1RUFAAgKioKe/fuBQDs27cP\nkZGRsLW1hV6vh8FgQEpKCnJyclBUVISgoCAAwMKFC5U6NdsKDw9HUlISAODAgQMICwuDo6MjHB0d\nERoaioSEhNbOmURE1ASqxzTS0tJw8uRJjB07Frm5uXBxcQEAuLi4IDc3FwCQnZ0NnU6n1NHpdDCb\nzXXKtVotzGYzAMBsNsPd3R0AYGNjAwcHB+Tl5TXYFhERdRwbNSsVFxcjPDwcmzZtgp2dXa1lGo0G\nGo2mTYJTKzo6WnlvMplgMpk6LBYios4p+Y9Xy1hNGteuXUN4eDgWLFiA2bNnA6g6u7hw4QJcXV2R\nk5ODgQMHAqg6g8jMzFTqZmVlQafTQavVIisrq055dZ2MjAy4ubmhoqIChYWFcHZ2hlarRXLynzuY\nmZmJKVOm1BtjzaRBRET1Mf3xqvZ8s1pp9PKUiGDp0qXw9vbG448/rpTPmjULO3fuBFA1w6k6mcya\nNQtxcXEoLy9Hamoqzp49i6CgILi6usLe3h4pKSkQEezevRv33ntvnbbef/99hISEAADCwsKQmJiI\ngoICWCwWfPbZZ7jrrruatZNERNRKGhslP3z4sGg0GvH395dRo0bJqFGjJD4+XvLy8iQkJESMRqOE\nhobWmtX04osvioeHhwwfPlwSEhKU8mPHjomvr694eHjIY489ppRfvXpV5s6dKwaDQcaOHSupqanK\nsu3bt4vBYBCDwSCxsbENzhIgIqI/oQ1nT2n+2ECXpdFo0MV3gYioVVWNM1s7Ljbv2Mk7womISDUm\nDSIiUo1Jg4iIVGPSICIi1Zg0iIhINSYNIiJSjUmDiIhUY9IgIiLVmDSIiEg1Jg0ioi7G3t5JecJ4\nfa+2xMeIEBF1MdYfE8LHiBARUSfApEFERKoxaRARkWpMGkREpBqTBhERqcakQUTUyXTklFprOOWW\niKiTafmUWk65JSKiToBJg4iIVGPSICIi1Zg0iIhINSYNIiJSjUmDiIhUY9IgIiLVmDSIiEg1Jg0i\nIlKNSYOIiFSzmjSWLFkCFxcX+Pn5KWXR0dHQ6XQICAhAQEAA4uPjlWXr1q2D0WiEl5cXEhMTlfLj\nx4/Dz88PRqMRK1asUMrLysoQEREBo9GI4OBgpKenK8t27twJT09PeHp6YteuXS3eWSIiaiGx4ssv\nv5QTJ06Ir6+vUhYdHS0bN26ss+7p06fF399fysvLJTU1VTw8PKSyslJERAIDAyUlJUVERKZPny7x\n8fEiIrJlyxZZvny5iIjExcVJRESEiIjk5eXJsGHDxGKxiMViUd7fSMUuEBF1KQAEkEZeLV3e/GOn\n1TONiRMnol+/fvUlmzpl+/btQ2RkJGxtbaHX62EwGJCSkoKcnBwUFRUhKCgIALBw4ULs3bsXALB/\n/35ERUUBAMLDw5GUlAQAOHDgAMLCwuDo6AhHR0eEhoYiISGhWYmRiIhaR7PHNDZv3gx/f38sXboU\nBQUFAIDs7GzodDplHZ1OB7PZXKdcq9XCbDYDAMxmM9zd3QEANjY2cHBwQF5eXoNtERFRx7FpTqXl\ny5fjH//4BwBgzZo1eOqppxATE9OqgTVFdHS08t5kMsFkMnVYLEREjbG3d0JRkaUDtpz8x6tlmpU0\nBg4cqLx/6KGHMHPmTABVZxCZmZnKsqysLOh0Omi1WmRlZdUpr66TkZEBNzc3VFRUoLCwEM7OztBq\ntUhOTlbqZGZmYsqUKfXGUzNpEBF1ZlUJw/pvXbQ+0x+vas83q5VmXZ7KyclR3n/00UfKzKpZs2Yh\nLi4O5eXlSE1NxdmzZxEUFARXV1fY29sjJSUFIoLdu3fj3nvvVers3LkTAPD+++8jJCQEABAWFobE\nxEQUFBTAYrHgs88+w1133dWsnSQiotZh9UwjMjISX3zxBS5dugR3d3c8//zzSE5Oxvfffw+NRoOh\nQ4fijTfeAAB4e3tj3rx58Pb2ho2NDbZu3ar8NOHWrVuxaNEilJaWYsaMGZg2bRoAYOnSpViwYAGM\nRiOcnZ0RFxcHAHBycsKaNWsQGBgIAFi7di0cHR3bpBOIiEgd/twrEVE7sv5TrkDLf86VP/dKRESd\nAJMGERGpxqRBRESqMWkQEZFqTBpERK3I3t4JGo2mwVdXx9lTREStyPrsKM6eIiKimwSTBhERqcak\nQUREqjFpEBGRakwaRESkGpMGERGpxqRBRESqMWkQEZFqTBpERE3Q3e/4toZ3hBMRNUHL7/jmHeFE\nRHSTYNIgIiLVmDSIiEg1Jg0iIlKNSYOIiFRj0iAiItWYNIiISDUmDSIiUo1Jg4joD9bu9r4Z7vi2\nhneEExH9wfrd3kB73a3NO8KJiKjLY9IgIiLVrCaNJUuWwMXFBX5+fkpZfn4+QkND4enpibCwMBQU\nFCjL1q1bB6PRCC8vLyQmJirlx48fh5+fH4xGI1asWKGUl5WVISIiAkajEcHBwUhPT1eW7dy5E56e\nnvD09MSuXbtavLNERNRCYsWXX34pJ06cEF9fX6Vs5cqVsmHDBhERWb9+vaxatUpERE6fPi3+/v5S\nXl4uqamp4uHhIZWVlSIiEhgYKCkpKSIiMn36dImPjxcRkS1btsjy5ctFRCQuLk4iIiJERCQvL0+G\nDRsmFotFLBaL8v5GKnaBiEgVAAKIlZe1dVq6vP220RxWzzQmTpyIfv361Srbv38/oqKiAABRUVHY\nu3cvAGDfvn2IjIyEra0t9Ho9DAYDUlJSkJOTg6KiIgQFBQEAFi5cqNSp2VZ4eDiSkpIAAAcOHEBY\nWBgcHR3h6OiI0NBQJCQktCxDEhFRizRrTCM3NxcuLi4AABcXF+Tm5gIAsrOzodPplPV0Oh3MZnOd\ncq1WC7PZDAAwm81wd3cHANjY2MDBwQF5eXkNtkVERB3HpqUNdIa5y9HR0cp7k8kEk8nUYbEQUedl\nb++EoiJLR4fRQZL/eLVMs5KGi4sLLly4AFdXV+Tk5GDgwIEAqs4gMjMzlfWysrKg0+mg1WqRlZVV\np7y6TkZGBtzc3FBRUYHCwkI4OztDq9UiOTlZqZOZmYkpU6bUG0/NpEFE1JCqhGHt/obuyvTHq9rz\nzWqlWZenZs2ahZ07dwKomuE0e/ZspTwuLg7l5eVITU3F2bNnERQUBFdXV9jb2yMlJQUigt27d+Pe\ne++t09b777+PkJAQAEBYWBgSExNRUFAAi8WCzz77DHfddVezdpKIiFqJtZHyBx54QAYNGiS2trai\n0+lk+/btkpeXJyEhIWI0GiU0NLTWrKYXX3xRPDw8ZPjw4ZKQkKCUHzt2THx9fcXDw0Mee+wxpfzq\n1asyd+5cMRgMMnbsWElNTVWWbd++XQwGgxgMBomNja03PhW7QEQkIl1rZlNnnT3Fx4gQ0U3D+mNC\nOs8jPtpjG805dvKOcCIiUo1Jg4iIVGPSIKJugY81bx8c0yCibqF9HmveGm10nm1wTIOIiNoUkwYR\nEanGpEFERKoxaRBRl2BtoJvaBwfCiahL6Bw35rVGG51nGxwIJyKiNsWkQUREqjFpEBGRakwaRESk\nGpMGERHh01CAAAARs0lEQVSpxqRBRJ0Cp9R2DZxyS0SdQsun1HLKbVO3wSm3RETUppg0iIhINSYN\nIiJSjUmDiNocfyCp++BAOBG1ufb5gSQOhDd1GxwIJyKiNsWkQUREqjFpEBGRakwaRNRivJv75mHT\n0QEQUednb++EoiKLlbWsDcxSd8DZU0RkVdd4xAdnTzV1G5w9RUREbapFSUOv12PkyJEICAhAUFAQ\nACA/Px+hoaHw9PREWFgYCgoKlPXXrVsHo9EILy8vJCYmKuXHjx+Hn58fjEYjVqxYoZSXlZUhIiIC\nRqMRwcHBSE9Pb0m4RETUQi1KGhqNBsnJyTh58iSOHj0KAFi/fj1CQ0Px22+/ISQkBOvXrwcAnDlz\nBu+88w7OnDmDhIQEPProo8qp0fLlyxETE4OzZ8/i7NmzSEhIAADExMTA2dkZZ8+exRNPPIFVq1a1\nJFwiqgfv1qamaPHlqRuvie3fvx9RUVEAgKioKOzduxcAsG/fPkRGRsLW1hZ6vR4GgwEpKSnIyclB\nUVGRcqaycOFCpU7NtsLDw5GUlNTScInoBlUD3GLlRVSlxWcaU6dOxZgxY7Bt2zYAQG5uLlxcXAAA\nLi4uyM3NBQBkZ2dDp9MpdXU6Hcxmc51yrVYLs9kMADCbzXB3dwcA2NjYwMHBAfn5+S0JmYiIWqBF\nU26//vprDBo0CBcvXkRoaCi8vLxqLeepLRFR99KipDFo0CAAwIABAzBnzhwcPXoULi4uuHDhAlxd\nXZGTk4OBAwcCqDqDyMzMVOpmZWVBp9NBq9UiKyurTnl1nYyMDLi5uaGiogKFhYVwcnKqE0d0dLTy\n3mQywWQytWS3iIi6oeQ/Xi0kzVRSUiKXL18WEZHi4mIZP368HDhwQFauXCnr168XEZF169bJqlWr\nRETk9OnT4u/vL2VlZXL+/HkZNmyYVFZWiohIUFCQHDlyRCorK2X69OkSHx8vIiJbtmyRZcuWiYjI\nnj17JCIiok4cLdgFIhL5Y9BCrLysrdPS5V1lG10lTnXbaI5mn2nk5uZizpw5AICKigrMnz8fYWFh\nGDNmDObNm4eYmBjo9Xq8++67AABvb2/MmzcP3t7esLGxwdatW5VLV1u3bsWiRYtQWlqKGTNmYNq0\naQCApUuXYsGCBTAajXB2dkZcXFxzwyUiolbAO8KJujB1j/ewBXDNyjrW/g11n7ugO76NzrON5hw7\n+ewpoi7sz+myjVFzgCFSh48RISIi1Zg0iIhINSYNok6Mv1NBnQ2TBlEbUfNMJ3v7uvcd1WT9ER9E\n7Yuzp4jaiPXfoACszWBp+e9YqFmn88zm6fzbaI02Os82mnPs5JkGERGpxim3RB3KhmMT1KUwaRB1\nqArwHgrqSnh5ioiIVGPSICIi1Zg0iIhINSYNogZYv8/iVt54RzcdDoTTTUnd02GBls+VJ+pemDTo\npqT+6bBEVBMvT1G3xGc2EbWNbnGm8fPPPze4zNnZWfmdcrp5WD+TYOIgao5u8ewpOzuvepdVVpbB\nzc0ev/32fTtHRW1N3ZhE13j+T+ffRmu00V220RptdJ5t3LS/3FdU1NCZxhlcuXJ/u8ZC1lk/4Kv5\neVKAZxJE7a9bJA3qWtRdOuIgNVFnxIFwIiJSjUmDWpWaHx4ioq6Ll6eoltYZb+ClJaLuikmDamn5\neAMTAlF3xstT3UhLn5XES0dEZA3PNLoRzkoiorbGpNFOWmesQO39C0REbYNJoxW0zxNT1azDswQi\naludfkwjISEBXl5eMBqN2LBhQ5tso6VjAX9eFmrsRUTU9XXqZ09dv34dw4cPx8GDB6HVahEYGIg9\ne/ZgxIgRyjpVg7cN7cIZaDQBEClXsbXO/iyZ1miju2yjNdrgNtq3je6yjdZoo/NsozmH/059pnH0\n6FEYDAbo9XrY2trigQcewL59+5rURlXC4FkAEVFr6NRJw2w2w93dXflbp9PBbDZ3YERERDe3Tj0Q\nrva+gdtue7je8spKC65ebc2IiIhubp06aWi1WmRmZip/Z2ZmQqfT1VrHw8MD5879x0pLapKPtXVa\nuryztNFdttEabXAb7dtGd9lGa7TR8dvw8PBQEUM9rXbmgfCKigoMHz4cSUlJcHNzQ1BQUJ2BcCIi\naj+d+kzDxsYGr7/+Ou666y5cv34dS5cuZcIgIupAnfpMg4iIOpdOPXuqJjU3+f3973+H0WiEv78/\nTp482c4Rth9rffHWW2/B398fI0eOxIQJE/Djjz92QJTtQ+3Nn9999x1sbGzw4YcftmN07UdNPyQn\nJyMgIAC+vr4wmUztG2A7stYXly5dwrRp0zBq1Cj4+voiNja2/YNsJ0uWLIGLiwv8/PwaXKfJx03p\nAioqKsTDw0NSU1OlvLxc/P395cyZM7XW+eSTT2T69OkiInLkyBEZO3ZsR4Ta5tT0xTfffCMFBQUi\nIhIfH39T90X1epMnT5a7775b3n///Q6ItG2p6QeLxSLe3t6SmZkpIiIXL17siFDbnJq+WLt2rTzz\nzDMiUtUPTk5Ocu3atY4It819+eWXcuLECfH19a13eXOOm13iTEPNTX779+9HVFQUAGDs2LEoKChA\nbm5uR4TbptT0xbhx4+Dg4ACgqi+ysrI6ItQ2p/bmz82bN+P+++/HgAEDOiDKtqemH95++22Eh4cr\nsw/79+/fEaG2OTV9MWjQIFy+fBkAcPnyZTg7O8PGplMP7zbbxIkT0a9fvwaXN+e42SWShpqb/Opb\npzseLJt6w2NMTAxmzJjRHqG1O7Xfi3379mH58uUA1N/705Wo6YezZ88iPz8fkydPxpgxY7B79+72\nDrNdqOmLhx9+GKdPn4abmxv8/f2xadOm9g6z02jOcbNLpFe1/9DlhjH97niAaMo+HTp0CNu3b8fX\nX3/dhhF1HDV98fjjj2P9+vXQaKqes3Pjd6Q7UNMP165dw4kTJ5CUlIQrV65g3LhxCA4OhtFobIcI\n24+avnjppZcwatQoJCcn49y5cwgNDcUPP/wAOzu7doiw82nqcbNLJA01N/nduE5WVha0Wm27xdhe\n1PQFAPz44494+OGHkZCQ0OjpaVempi+OHz+OBx54AEDVAGh8fDxsbW0xa9asdo21LanpB3d3d/Tv\n3x+9e/dG7969MWnSJPzwww/dLmmo6YtvvvkG/+///T8AVTe4DR06FL/++ivGjBnTrrF2Bs06brba\niEsbunbtmgwbNkxSU1OlrKzM6kD4t99+220Hf9X0RXp6unh4eMi3337bQVG2DzV9UdOiRYvkgw8+\naMcI24eafvj5558lJCREKioqpKSkRHx9feX06dMdFHHbUdMXTzzxhERHR4uIyIULF0Sr1UpeXl5H\nhNsuUlNTVQ2Eqz1udokzjYZu8nvjjTcAAI888ghmzJiBTz/9FAaDAX369MGOHTs6OOq2oaYvXnjh\nBVgsFuU6vq2tLY4ePdqRYbcJNX1xM1DTD15eXpg2bRpGjhyJHj164OGHH4a3t3cHR9761PTF6tWr\nsXjxYvj7+6OyshIvv/wynJycOjjythEZGYkvvvgCly5dgru7O55//nlcu1b165/NPW7y5j4iIlKt\nS8yeIiKizoFJg4iIVGPSICIi1Zg0iIhINSYNIiJSjUmDiIhUY9IgIiLVmDSoXen1euTn5wMA+vbt\n26bbWrt2LZKSklSvn5ycjJkzZ7ZhRK0rOzsbc+fObVKdtWvX4vPPPwcAmEwmHD9+vNn1X3vtNZSW\nljapPnV9XeKOcOo+aj4MrS0fKFlZWYnnn3++zdpvqup7aFtzn93c3PDee+81qU7NPtFoNE2K58Y+\n3bRpExYsWIDevXs3KQbq2nimQW1mzpw5GDNmDHx9fbFt27Ym1U1OTsakSZNwzz33wMvLC8uXL1cO\nvImJiRg/fjxGjx6NefPmoaSkBEDVWcwzzzyD0aNH47333sOiRYvwwQcfAACSkpJw++23Y+TIkVi6\ndCnKy8sBVP3K24gRIzB69Gh89NFHjcYUHR2NJUuWYPLkyfDw8MDmzZuVZa+++ir8/Pzg5+enPGo7\nLS0Nw4cPR1RUFPz8/HD48GF4eXlh8eLFGD58OObPn4/ExERMmDABnp6e+O677xrc9hdffIGAgAAE\nBATg9ttvR0lJCdLS0pRfZIuNjcXs2bMRFhaGoUOH4vXXX8f//M//4Pbbb8e4ceNgsVgAoFaf1PTo\no48iMDAQvr6+iI6OVspv7NPFixfjgw8+wObNm5GdnY3JkydjypQp2LFjB5544gml3rZt2/Dkk082\n2p/URbXaU7GIbpCfny8iIleuXBFfX1/Jy8sTvV6vPByub9++DdY9dOiQ9OrVS1JTU+X69esSGhoq\n77//vly8eFEmTZokV65cERGR9evXywsvvCAiInq9Xl555RWljeoHFJaWloq7u7ucPXtWREQWLlwo\nr732mlL++++/i4jIvHnzZObMmQ3GtHbtWpkwYYKUl5fLpUuXxNnZWSoqKuTYsWPi5+cnV65ckeLi\nYvHx8ZGTJ09Kamqq9OjRQ1JSUkSk6sFxNjY2curUKamsrJTRo0fLkiVLRERk3759Mnv27Aa3PXPm\nTPnmm29ERKSkpEQqKipqPYhux44dYjAYpLi4WC5evCj29vbyxhtviEjVA/pee+21Wn0iImIymeT4\n8eO1PquKigoxmUzy008/Ndqn1cuqP8vi4mLx8PCQiooKEREZP368nDp1qsH9oa6LZxrUZjZt2oRR\no0Zh3LhxyMrKwtmzZ5tUPygoCHq9Hj169EBkZCS++uorpKSk4MyZMxg/fjwCAgKwa9cuZGRkKHUi\nIiJqtSEi+PXXXzF06FAYDAYAQFRUFL788kul3MPDAwDw4IMPNvp7GxqNBnfffTdsbW3h7OyMgQMH\n4sKFC/jqq69w3333oXfv3ujTpw/uu+8+HD58GBqNBkOGDEFQUJDSxtChQ+Hj4wONRgMfHx9MnToV\nAODr64u0tLQGtz1hwgQ88cQT2Lx5MywWC2655ZY660yePBl9+vRB//794ejoqIzP+Pn5Ndo2ALzz\nzjsYPXo0br/9dpw+fRpnzpxpsE/r06dPH0yZMgUff/wxfvnlF1y7dg0+Pj5W61HXwzENahPJyclI\nSkrCkSNH0KtXL0yePBlXr15tUhs1r7eLiPJDSqGhoXj77bfrrdOnT59G26luqz6NJYxqt956q/L+\nlltuQUVFhRLXjbHWF0/Pnj2V9z169FDa69GjByoqKhrc7qpVq3DPPffgk08+wYQJE3DgwIFabdXX\ndvXf1tpOTU3Fxo0bcezYMTg4OGDx4sW1Pqv6+rQ+Dz30EF588UWMGDECS5YsUVWHuh6eaVCbuHz5\nMvr164devXrh559/xpEjR5rcxtGjR5GWlobKykq8++67mDhxIoKDg/H111/j3LlzAICSkpJGz2A0\nGg2GDx+OtLQ0pc7u3bthMpng5eWFtLQ0nD9/HgCwZ8+eJseo0WgwceJE7N27F6WlpSgpKcHevXsx\nceLEVv2VwHPnzsHHxwdPP/00AgMD8euvv6quay2Oy5cvo0+fPrC3t0dubi7i4+NVtWtnZ6f81jZQ\ndWaYlZWFt99+G5GRkarjo66FSYPaxLRp01BRUQFvb2+sXr0a48aNA6B+9pRGo0FgYCD+9re/wdvb\nG8OGDcOcOXPQv39/xMbGIjIyEv7+/hg/frzVA2jPnj2xY8cOzJ07FyNHjoSNjQ2WLVuGnj174t//\n/jfuvvtujB49Gi4uLlZnE9W3PCAgAIsWLUJQUBCCg4Px8MMPw9/fv971G/u7sW1v2rQJfn5+8Pf3\nx6233orp06fXqnPjTKgb3zfWtr+/PwICAuDl5YX58+fjjjvuaHDdmv76179i2rRpCAkJUcrmzZuH\nO+64Aw4ODqraoK6Hv6dBnVJycjI2btyIjz/+uKNDoSaYOXMmnnzySUyePLmjQ6E2wjMN6pSaeg8B\ndayCggIMHz4ct912GxNGN8czDepQP/30ExYuXFirrFevXvj22287KKKqex6q77Wodscdd9S6L6M7\nbptIDSYNIiJSjZeniIhINSYNIiJSjUmDiIhUY9IgIiLVmDSIiEi1/w9GTqGQYBZL5gAAAABJRU5E\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f5362b1df50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEaCAYAAADg2nttAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f5362a2fdd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEaCAYAAAAL7cBuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f53629196d0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Description of the edge weights for attention networks"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "1. **Accuracy** Given a portfolio of stocks we expect that analysts will allocate more attention to the stocks they have not been accurate at. Consistently with the behavioral theory of the firm, we posit that domains (stocks) on which analysts make inaccurate predictions grab the attention of analysts that try to recover from their mistakes. Paradoxically therefore we expect that analysts that have been  inaccurate on the same stocks will converge more on their estimates of another stock than analysts that have been accurate on them.\n",
      "\n",
      "    * Measure at the stock level:\n",
      "        * **raw\\_accuracy** For all predictions made on each stock we compute the absolute difference between the actual value and the prediction, then for each prediction on each stock substract the minimum raw\\_accuracy for the focal stock and divide by the maximum raw\\_accuracy minus the minimum raw\\_accuracy. Note that this normalization procedure ranks all predictions on each stock from 0 (most accurate prediction) to 1 (less accurate prediction). This explains the distribution shown in the next section.\n",
      "    * Normalizations at the analyst level:\n",
      "        * **accuracy_maxmin** For each prediction of each analyst substract the minimum raw\\_accuracy achieved by the focal analyst, and divide it by the maximum raw\\_accuracy minus the minimum raw\\_accuracies achieved by the focal analyst.\n",
      "\n",
      "        * **accuracy_sum** For each prediction of each analyst divide raw\\_accuracy by the sum all raw\\_accuracies of the focal analyst.\n",
      "\n",
      "2. **Experience** Given a portfolio of stocks we expect that analysts will allocate more attention to the stocks they have less experience on.  The analysis of stocks that have been in the portfolio of attention of the analyst for a long period of time becomes routinized. Conversely, analysts put more efforts in trying to make sense of the peculiarities of the novel stocks the cover. Thus we expect that two analysts who have been focusing on the same stocks for a lower period of time will converge more in their estimates than analysts who have been focusing on the same stocks for longer.\n",
      "\n",
      "    * Measure at the stock level:\n",
      "        * **experience_days** Number of days between the announcement of the actual value of the prediction made by the focal analyst and the announcement date of the first time that the focal analyst rated the focal stock.\n",
      "    * Normalizations at the analyst level:\n",
      "        * **experience_maxmin** Normalization at the analyst level: For each prediction of each analyst substract the minimum experience\\_days of the focal analyst in respect to all her stocks, and divide it by her maximum experience\\_days minus her minimum experience\\_days of all her stocks.\n",
      "\n",
      "        * **experience_sum** Normalization at the analyst level:  For each prediction of each analyst divide experience\\_days by the sum all experience\\_days of the stocks of the focal analyst.\n",
      "\n",
      "3. **timing**  Given a portfolio of stocks, we expect that the sequence by which analysts pay attention to them matters. The idea is that stocks to which analysts have paid more attention recently are more salient in the cognitive memory of analysts. To capture this sequence, we leverage the sequence by which firms announced their earning in the focal year so that firms that announced earnings many months back in the past will have less influence on the evaluations that are made now than stocks that have just announced earnings. Notice that this time difference lend itself to interesting interactions. We might expect for example that inaccuracy driven attention (see point 1) loose momentum if the inaccuracy is realized many months before the focal estimate (our DV) whereas recent inaccuracies gain prominence in the attention of the analyst. Thus analysts that have been recently shown inaccurate in the same stocks will converge more than analysts that have been shown recently accurate or that have been inaccurate in the past.  The sequence of attention may also be affected by the similarity across stocks (see point 4).\n",
      "\n",
      "    * Measure at the stock level:\n",
      "        * **days\\_to\\_end** For each prediction, the number of days between the announcement of the earnings for the stock and the end of the period that represents the focal network. Total time is one year at a quarter by quarter frequency, thus the maximum value is 365 days. Note that the DV refers to the next quarter after the end of the period represented by the focal network.\n",
      "    * Normalizations at the analyst level:\n",
      "        * **timing_sum** Normalization at the analyst level:  For each prediction of each analyst divide days_to_end by the sum all days_to_end of the stocks of the focal analyst.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Description of clustering coefficents "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "1. **CC_plain**: It is the classical pairwise bipartite clustering coefficient as defined in Latapy (2008): for a pair of analysts we divide the size of the intersection of the stocks they are covering by the size of the union of their stocks. It is the numerical proxy for the structural equivalence effect. \n",
      ".$$ CC_{i, j} = \\frac{N(i) \\cap N(j)}{N(i) \\cup N(j)}  $$\n",
      "\n",
      "2. **WCC**: Analogous to the CC_plain in that the denominator is the sum of the weight between each analyst (i and j) and each one of the stocks in the union of their portfolio.\n",
      ".$$ WCC_{i,j} = \\frac{\\sum\\limits_{p \\in N(i) \\cap N(j)} W(i, p) + W(j, p)}{\\sum\\limits_{p \\in N(i)} W(i, p) + \\sum\\limits_{p \\in N(j)} W(j, p)} $$\n",
      ".Turns out it is very correlated with **CC_plain**.\n",
      "\n",
      "3. **NWCC**: I also tried normalizing both the intersections and the union of **WCC**, and it seem to work better. At least is less correlated and has an interesteng distribution. See below.\n",
      ".$$ NWCC_{i,j} = \\frac{\\frac{\\sum\\limits_{p \\in N(i) \\cap N(j)} W(i, p) + W(j, p)}{|N(i) \\cap N(j)|}}{\\frac{\\sum\\limits_{p \\in N(i)} W(i, p) + \\sum\\limits_{p \\in N(j)} W(j, p)}{|N(i) \\cup N(j)|}} $$\n",
      "\n",
      "4. **WCC_intersection**: In the case of correlations we have also used as a clustering measure the weight of all stocks in the intersection divided by the number of stocks in the intersection.\n",
      ".$$ WCCI_{i,j} = \\frac{\\sum\\limits_{p \\in N(i) \\cap N(j)} W(i, p) + W(j, p)}{|N(i) \\cap N(j)|} $$\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Descriptive analysis and simple regression modeling"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that we compute each one of the clustering coefficients desribed above for each kind of weight separately. Here we explore experience, timing, and accuracy based edge weights.\n",
      "\n",
      "The variable `delta` is the number of days between the two predictions made for `analyst_i` and `analyst_j` used in computing the dependent variable: normalized similarity.\n",
      "\n",
      "From this explorative analysis, seems that the most simple measure (bipartite clustering number 2) is the one that works better. With this simple OLS regression modeling, it seems that the effect of the bipartite clustering coefficients (both weighted and unweighted) is small. The correlations between the dependent variable and the independent variables are also quite small.\n",
      "\n",
      "We reproduce here descriptive analysis and simple regression modeling for each one of the bipartite clustering coefficients discussed above."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Bipartite clustering number 2"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$$ WCC_{i,j} = \\frac{\\sum\\limits_{p \\in N(i) \\cap N(j)} W(i, p) + W(j, p)}{\\sum\\limits_{p \\in N(i)} W(i, p) + \\sum\\limits_{p \\in N(j)} W(j, p)} $$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "measures_2 = [u'norm_similarity', u'norm_similarity_zscore', u'previous_norm_similarity', u'previous_norm_similarity_zscore', \n",
      "              u'previous_average_accuracy_maxmin_mean', u'previous_accuracy_maxmin_mean_on_s',\n",
      "              u'previous_maximum_experience_mean', u'previous_exprience_mean_on_s', 'days_to_end',\n",
      "              u'days_between_estimates', u'degree_s', u'CC_unweighted', \n",
      "              u'WCC_accuracy_sum', u'WCC_accuracy_maxmin', u'WCC_experience_sum', u'WCC_experience_maxmin', u'WCC_timing_sum']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[measures_2].describe()"
     ],
     "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>norm_similarity</th>\n",
        "      <th>norm_similarity_zscore</th>\n",
        "      <th>previous_norm_similarity</th>\n",
        "      <th>previous_norm_similarity_zscore</th>\n",
        "      <th>previous_average_accuracy_maxmin_mean</th>\n",
        "      <th>previous_accuracy_maxmin_mean_on_s</th>\n",
        "      <th>previous_maximum_experience_mean</th>\n",
        "      <th>previous_exprience_mean_on_s</th>\n",
        "      <th>days_to_end</th>\n",
        "      <th>days_between_estimates</th>\n",
        "      <th>degree_s</th>\n",
        "      <th>CC_unweighted</th>\n",
        "      <th>WCC_accuracy_sum</th>\n",
        "      <th>WCC_accuracy_maxmin</th>\n",
        "      <th>WCC_experience_sum</th>\n",
        "      <th>WCC_experience_maxmin</th>\n",
        "      <th>WCC_timing_sum</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 2249785.000000</td>\n",
        "      <td> 2.249785e+06</td>\n",
        "      <td> 1119186.000000</td>\n",
        "      <td> 1349479.000000</td>\n",
        "      <td> 1303960.000000</td>\n",
        "      <td> 1303041.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 2256436.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>       0.677748</td>\n",
        "      <td> 4.235009e-08</td>\n",
        "      <td>       0.661184</td>\n",
        "      <td>       0.000004</td>\n",
        "      <td>       0.627896</td>\n",
        "      <td>       0.649600</td>\n",
        "      <td>    2482.559792</td>\n",
        "      <td>    1425.853661</td>\n",
        "      <td>     161.497271</td>\n",
        "      <td>      16.735975</td>\n",
        "      <td>      22.738920</td>\n",
        "      <td>       0.237576</td>\n",
        "      <td>       0.361586</td>\n",
        "      <td>       0.368233</td>\n",
        "      <td>       0.398772</td>\n",
        "      <td>       0.379470</td>\n",
        "      <td>       0.389037</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>       0.290289</td>\n",
        "      <td> 1.000000e+00</td>\n",
        "      <td>       0.330791</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>       0.074572</td>\n",
        "      <td>       0.254604</td>\n",
        "      <td>    1222.919958</td>\n",
        "      <td>    1080.627715</td>\n",
        "      <td>      95.218089</td>\n",
        "      <td>      20.180034</td>\n",
        "      <td>       9.781033</td>\n",
        "      <td>       0.156833</td>\n",
        "      <td>       0.203401</td>\n",
        "      <td>       0.202477</td>\n",
        "      <td>       0.242208</td>\n",
        "      <td>       0.240675</td>\n",
        "      <td>       0.205857</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>       0.000000</td>\n",
        "      <td>-1.399286e+01</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>     -10.877379</td>\n",
        "      <td>       0.202870</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       2.000000</td>\n",
        "      <td>       0.011364</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>       0.500000</td>\n",
        "      <td>-5.549967e-01</td>\n",
        "      <td>       0.457143</td>\n",
        "      <td>      -0.506558</td>\n",
        "      <td>       0.581287</td>\n",
        "      <td>       0.473997</td>\n",
        "      <td>    1641.000000</td>\n",
        "      <td>     549.500000</td>\n",
        "      <td>      65.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>      16.000000</td>\n",
        "      <td>       0.115385</td>\n",
        "      <td>       0.200208</td>\n",
        "      <td>       0.206988</td>\n",
        "      <td>       0.198732</td>\n",
        "      <td>       0.182927</td>\n",
        "      <td>       0.223632</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>       0.760000</td>\n",
        "      <td> 2.286800e-01</td>\n",
        "      <td>       0.769231</td>\n",
        "      <td>       0.238952</td>\n",
        "      <td>       0.632968</td>\n",
        "      <td>       0.689781</td>\n",
        "      <td>    2380.000000</td>\n",
        "      <td>    1273.000000</td>\n",
        "      <td>     150.000000</td>\n",
        "      <td>       8.000000</td>\n",
        "      <td>      22.000000</td>\n",
        "      <td>       0.208333</td>\n",
        "      <td>       0.347772</td>\n",
        "      <td>       0.355934</td>\n",
        "      <td>       0.385002</td>\n",
        "      <td>       0.355483</td>\n",
        "      <td>       0.381235</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>       0.916667</td>\n",
        "      <td> 7.158600e-01</td>\n",
        "      <td>       0.944444</td>\n",
        "      <td>       0.654007</td>\n",
        "      <td>       0.680485</td>\n",
        "      <td>       0.870968</td>\n",
        "      <td>    3292.500000</td>\n",
        "      <td>    2010.500000</td>\n",
        "      <td>     239.000000</td>\n",
        "      <td>      28.000000</td>\n",
        "      <td>      29.000000</td>\n",
        "      <td>       0.325581</td>\n",
        "      <td>       0.499271</td>\n",
        "      <td>       0.508610</td>\n",
        "      <td>       0.584658</td>\n",
        "      <td>       0.550625</td>\n",
        "      <td>       0.544919</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>       1.000000</td>\n",
        "      <td> 6.822597e+00</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>       6.422174</td>\n",
        "      <td>       0.877786</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>    6616.000000</td>\n",
        "      <td>    6593.000000</td>\n",
        "      <td>     364.000000</td>\n",
        "      <td>      90.000000</td>\n",
        "      <td>      49.000000</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>       1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 17 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "       norm_similarity  norm_similarity_zscore  previous_norm_similarity  \\\n",
        "count   2249785.000000            2.249785e+06            1119186.000000   \n",
        "mean          0.677748            4.235009e-08                  0.661184   \n",
        "std           0.290289            1.000000e+00                  0.330791   \n",
        "min           0.000000           -1.399286e+01                  0.000000   \n",
        "25%           0.500000           -5.549967e-01                  0.457143   \n",
        "50%           0.760000            2.286800e-01                  0.769231   \n",
        "75%           0.916667            7.158600e-01                  0.944444   \n",
        "max           1.000000            6.822597e+00                  1.000000   \n",
        "\n",
        "       previous_norm_similarity_zscore  previous_average_accuracy_maxmin_mean  \\\n",
        "count                   1349479.000000                         1303960.000000   \n",
        "mean                          0.000004                               0.627896   \n",
        "std                           1.000000                               0.074572   \n",
        "min                         -10.877379                               0.202870   \n",
        "25%                          -0.506558                               0.581287   \n",
        "50%                           0.238952                               0.632968   \n",
        "75%                           0.654007                               0.680485   \n",
        "max                           6.422174                               0.877786   \n",
        "\n",
        "       previous_accuracy_maxmin_mean_on_s  previous_maximum_experience_mean  \\\n",
        "count                      1303041.000000                    1359624.000000   \n",
        "mean                             0.649600                       2482.559792   \n",
        "std                              0.254604                       1222.919958   \n",
        "min                              0.000000                          0.000000   \n",
        "25%                              0.473997                       1641.000000   \n",
        "50%                              0.689781                       2380.000000   \n",
        "75%                              0.870968                       3292.500000   \n",
        "max                              1.000000                       6616.000000   \n",
        "\n",
        "       previous_exprience_mean_on_s     days_to_end  days_between_estimates  \\\n",
        "count                1359624.000000  1359624.000000          2256436.000000   \n",
        "mean                    1425.853661      161.497271               16.735975   \n",
        "std                     1080.627715       95.218089               20.180034   \n",
        "min                        0.000000        0.000000                0.000000   \n",
        "25%                      549.500000       65.000000                0.000000   \n",
        "50%                     1273.000000      150.000000                8.000000   \n",
        "75%                     2010.500000      239.000000               28.000000   \n",
        "max                     6593.000000      364.000000               90.000000   \n",
        "\n",
        "             degree_s   CC_unweighted  WCC_accuracy_sum  WCC_accuracy_maxmin  \\\n",
        "count  1359624.000000  1359624.000000    1359624.000000       1359624.000000   \n",
        "mean        22.738920        0.237576          0.361586             0.368233   \n",
        "std          9.781033        0.156833          0.203401             0.202477   \n",
        "min          2.000000        0.011364          0.000000             0.000000   \n",
        "25%         16.000000        0.115385          0.200208             0.206988   \n",
        "50%         22.000000        0.208333          0.347772             0.355934   \n",
        "75%         29.000000        0.325581          0.499271             0.508610   \n",
        "max         49.000000        1.000000          1.000000             1.000000   \n",
        "\n",
        "       WCC_experience_sum  WCC_experience_maxmin  WCC_timing_sum  \n",
        "count      1359624.000000         1359624.000000  1359624.000000  \n",
        "mean             0.398772               0.379470        0.389037  \n",
        "std              0.242208               0.240675        0.205857  \n",
        "min              0.000000               0.000000        0.000000  \n",
        "25%              0.198732               0.182927        0.223632  \n",
        "50%              0.385002               0.355483        0.381235  \n",
        "75%              0.584658               0.550625        0.544919  \n",
        "max              1.000000               1.000000        1.000000  \n",
        "\n",
        "[8 rows x 17 columns]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[measures_2].corr()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>norm_similarity</th>\n",
        "      <th>norm_similarity_zscore</th>\n",
        "      <th>previous_norm_similarity</th>\n",
        "      <th>previous_norm_similarity_zscore</th>\n",
        "      <th>previous_average_accuracy_maxmin_mean</th>\n",
        "      <th>previous_accuracy_maxmin_mean_on_s</th>\n",
        "      <th>previous_maximum_experience_mean</th>\n",
        "      <th>previous_exprience_mean_on_s</th>\n",
        "      <th>days_to_end</th>\n",
        "      <th>days_between_estimates</th>\n",
        "      <th>degree_s</th>\n",
        "      <th>CC_unweighted</th>\n",
        "      <th>WCC_accuracy_sum</th>\n",
        "      <th>WCC_accuracy_maxmin</th>\n",
        "      <th>WCC_experience_sum</th>\n",
        "      <th>WCC_experience_maxmin</th>\n",
        "      <th>WCC_timing_sum</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>norm_similarity</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.893379</td>\n",
        "      <td> 0.098772</td>\n",
        "      <td> 0.068987</td>\n",
        "      <td> 0.087003</td>\n",
        "      <td> 0.074040</td>\n",
        "      <td> 0.025517</td>\n",
        "      <td> 0.028985</td>\n",
        "      <td> 0.035313</td>\n",
        "      <td>-0.081718</td>\n",
        "      <td> 0.114371</td>\n",
        "      <td> 0.004464</td>\n",
        "      <td> 0.010289</td>\n",
        "      <td> 0.016396</td>\n",
        "      <td> 0.013104</td>\n",
        "      <td> 0.013896</td>\n",
        "      <td> 0.011752</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>norm_similarity_zscore</th>\n",
        "      <td> 0.893379</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.063969</td>\n",
        "      <td> 0.078692</td>\n",
        "      <td> 0.034640</td>\n",
        "      <td> 0.027339</td>\n",
        "      <td> 0.013434</td>\n",
        "      <td> 0.008407</td>\n",
        "      <td> 0.003493</td>\n",
        "      <td>-0.115809</td>\n",
        "      <td>-0.001362</td>\n",
        "      <td> 0.001212</td>\n",
        "      <td> 0.003156</td>\n",
        "      <td> 0.006591</td>\n",
        "      <td> 0.004544</td>\n",
        "      <td> 0.005896</td>\n",
        "      <td> 0.002100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_norm_similarity</th>\n",
        "      <td> 0.098772</td>\n",
        "      <td> 0.063969</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.863147</td>\n",
        "      <td> 0.164571</td>\n",
        "      <td> 0.313350</td>\n",
        "      <td> 0.025374</td>\n",
        "      <td> 0.017655</td>\n",
        "      <td>-0.018570</td>\n",
        "      <td> 0.010396</td>\n",
        "      <td> 0.113048</td>\n",
        "      <td> 0.015198</td>\n",
        "      <td> 0.024085</td>\n",
        "      <td> 0.054992</td>\n",
        "      <td> 0.020258</td>\n",
        "      <td> 0.021844</td>\n",
        "      <td> 0.014937</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_norm_similarity_zscore</th>\n",
        "      <td> 0.068987</td>\n",
        "      <td> 0.078692</td>\n",
        "      <td> 0.863147</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.086193</td>\n",
        "      <td> 0.177144</td>\n",
        "      <td> 0.015029</td>\n",
        "      <td> 0.004695</td>\n",
        "      <td> 0.000019</td>\n",
        "      <td>-0.002093</td>\n",
        "      <td>-0.000011</td>\n",
        "      <td> 0.015693</td>\n",
        "      <td> 0.019237</td>\n",
        "      <td> 0.037983</td>\n",
        "      <td> 0.012888</td>\n",
        "      <td> 0.015945</td>\n",
        "      <td> 0.014109</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_average_accuracy_maxmin_mean</th>\n",
        "      <td> 0.087003</td>\n",
        "      <td> 0.034640</td>\n",
        "      <td> 0.164571</td>\n",
        "      <td> 0.086193</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.313899</td>\n",
        "      <td> 0.112178</td>\n",
        "      <td> 0.080880</td>\n",
        "      <td> 0.022509</td>\n",
        "      <td>-0.003162</td>\n",
        "      <td> 0.205374</td>\n",
        "      <td> 0.040976</td>\n",
        "      <td> 0.048181</td>\n",
        "      <td> 0.070663</td>\n",
        "      <td> 0.068708</td>\n",
        "      <td> 0.078428</td>\n",
        "      <td> 0.021396</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_accuracy_maxmin_mean_on_s</th>\n",
        "      <td> 0.074040</td>\n",
        "      <td> 0.027339</td>\n",
        "      <td> 0.313350</td>\n",
        "      <td> 0.177144</td>\n",
        "      <td> 0.313899</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.023110</td>\n",
        "      <td> 0.027458</td>\n",
        "      <td> 0.011199</td>\n",
        "      <td>-0.010087</td>\n",
        "      <td> 0.139764</td>\n",
        "      <td>-0.000051</td>\n",
        "      <td> 0.028382</td>\n",
        "      <td> 0.122965</td>\n",
        "      <td> 0.011098</td>\n",
        "      <td> 0.015338</td>\n",
        "      <td>-0.006212</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_maximum_experience_mean</th>\n",
        "      <td> 0.025517</td>\n",
        "      <td> 0.013434</td>\n",
        "      <td> 0.025374</td>\n",
        "      <td> 0.015029</td>\n",
        "      <td> 0.112178</td>\n",
        "      <td> 0.023110</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.666666</td>\n",
        "      <td> 0.026392</td>\n",
        "      <td> 0.011223</td>\n",
        "      <td> 0.027199</td>\n",
        "      <td> 0.046416</td>\n",
        "      <td> 0.060694</td>\n",
        "      <td> 0.068446</td>\n",
        "      <td> 0.145344</td>\n",
        "      <td> 0.142607</td>\n",
        "      <td> 0.041147</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_exprience_mean_on_s</th>\n",
        "      <td> 0.028985</td>\n",
        "      <td> 0.008407</td>\n",
        "      <td> 0.017655</td>\n",
        "      <td> 0.004695</td>\n",
        "      <td> 0.080880</td>\n",
        "      <td> 0.027458</td>\n",
        "      <td> 0.666666</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.013001</td>\n",
        "      <td> 0.048318</td>\n",
        "      <td> 0.141046</td>\n",
        "      <td> 0.153741</td>\n",
        "      <td> 0.157316</td>\n",
        "      <td> 0.158936</td>\n",
        "      <td> 0.320047</td>\n",
        "      <td> 0.317728</td>\n",
        "      <td> 0.152761</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>days_to_end</th>\n",
        "      <td> 0.035313</td>\n",
        "      <td> 0.003493</td>\n",
        "      <td>-0.018570</td>\n",
        "      <td> 0.000019</td>\n",
        "      <td> 0.022509</td>\n",
        "      <td> 0.011199</td>\n",
        "      <td> 0.026392</td>\n",
        "      <td> 0.013001</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.072604</td>\n",
        "      <td> 0.007146</td>\n",
        "      <td> 0.010488</td>\n",
        "      <td> 0.013053</td>\n",
        "      <td> 0.010565</td>\n",
        "      <td> 0.010522</td>\n",
        "      <td> 0.013991</td>\n",
        "      <td> 0.074412</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>days_between_estimates</th>\n",
        "      <td>-0.081718</td>\n",
        "      <td>-0.115809</td>\n",
        "      <td> 0.010396</td>\n",
        "      <td>-0.002093</td>\n",
        "      <td>-0.003162</td>\n",
        "      <td>-0.010087</td>\n",
        "      <td> 0.011223</td>\n",
        "      <td> 0.048318</td>\n",
        "      <td>-0.072604</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.062358</td>\n",
        "      <td> 0.070071</td>\n",
        "      <td> 0.071705</td>\n",
        "      <td> 0.065022</td>\n",
        "      <td> 0.072027</td>\n",
        "      <td> 0.066284</td>\n",
        "      <td> 0.075918</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>degree_s</th>\n",
        "      <td> 0.114371</td>\n",
        "      <td>-0.001362</td>\n",
        "      <td> 0.113048</td>\n",
        "      <td>-0.000011</td>\n",
        "      <td> 0.205374</td>\n",
        "      <td> 0.139764</td>\n",
        "      <td> 0.027199</td>\n",
        "      <td> 0.141046</td>\n",
        "      <td> 0.007146</td>\n",
        "      <td> 0.062358</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.058965</td>\n",
        "      <td> 0.078479</td>\n",
        "      <td> 0.082698</td>\n",
        "      <td> 0.089403</td>\n",
        "      <td> 0.087635</td>\n",
        "      <td> 0.087192</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CC_unweighted</th>\n",
        "      <td> 0.004464</td>\n",
        "      <td> 0.001212</td>\n",
        "      <td> 0.015198</td>\n",
        "      <td> 0.015693</td>\n",
        "      <td> 0.040976</td>\n",
        "      <td>-0.000051</td>\n",
        "      <td> 0.046416</td>\n",
        "      <td> 0.153741</td>\n",
        "      <td> 0.010488</td>\n",
        "      <td> 0.070071</td>\n",
        "      <td> 0.058965</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.878334</td>\n",
        "      <td> 0.902844</td>\n",
        "      <td> 0.807407</td>\n",
        "      <td> 0.831961</td>\n",
        "      <td> 0.883684</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>WCC_accuracy_sum</th>\n",
        "      <td> 0.010289</td>\n",
        "      <td> 0.003156</td>\n",
        "      <td> 0.024085</td>\n",
        "      <td> 0.019237</td>\n",
        "      <td> 0.048181</td>\n",
        "      <td> 0.028382</td>\n",
        "      <td> 0.060694</td>\n",
        "      <td> 0.157316</td>\n",
        "      <td> 0.013053</td>\n",
        "      <td> 0.071705</td>\n",
        "      <td> 0.078479</td>\n",
        "      <td> 0.878334</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.906223</td>\n",
        "      <td> 0.792430</td>\n",
        "      <td> 0.804262</td>\n",
        "      <td> 0.873793</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>WCC_accuracy_maxmin</th>\n",
        "      <td> 0.016396</td>\n",
        "      <td> 0.006591</td>\n",
        "      <td> 0.054992</td>\n",
        "      <td> 0.037983</td>\n",
        "      <td> 0.070663</td>\n",
        "      <td> 0.122965</td>\n",
        "      <td> 0.068446</td>\n",
        "      <td> 0.158936</td>\n",
        "      <td> 0.010565</td>\n",
        "      <td> 0.065022</td>\n",
        "      <td> 0.082698</td>\n",
        "      <td> 0.902844</td>\n",
        "      <td> 0.906223</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.811115</td>\n",
        "      <td> 0.830729</td>\n",
        "      <td> 0.880070</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>WCC_experience_sum</th>\n",
        "      <td> 0.013104</td>\n",
        "      <td> 0.004544</td>\n",
        "      <td> 0.020258</td>\n",
        "      <td> 0.012888</td>\n",
        "      <td> 0.068708</td>\n",
        "      <td> 0.011098</td>\n",
        "      <td> 0.145344</td>\n",
        "      <td> 0.320047</td>\n",
        "      <td> 0.010522</td>\n",
        "      <td> 0.072027</td>\n",
        "      <td> 0.089403</td>\n",
        "      <td> 0.807407</td>\n",
        "      <td> 0.792430</td>\n",
        "      <td> 0.811115</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.961937</td>\n",
        "      <td> 0.819415</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>WCC_experience_maxmin</th>\n",
        "      <td> 0.013896</td>\n",
        "      <td> 0.005896</td>\n",
        "      <td> 0.021844</td>\n",
        "      <td> 0.015945</td>\n",
        "      <td> 0.078428</td>\n",
        "      <td> 0.015338</td>\n",
        "      <td> 0.142607</td>\n",
        "      <td> 0.317728</td>\n",
        "      <td> 0.013991</td>\n",
        "      <td> 0.066284</td>\n",
        "      <td> 0.087635</td>\n",
        "      <td> 0.831961</td>\n",
        "      <td> 0.804262</td>\n",
        "      <td> 0.830729</td>\n",
        "      <td> 0.961937</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.809477</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>WCC_timing_sum</th>\n",
        "      <td> 0.011752</td>\n",
        "      <td> 0.002100</td>\n",
        "      <td> 0.014937</td>\n",
        "      <td> 0.014109</td>\n",
        "      <td> 0.021396</td>\n",
        "      <td>-0.006212</td>\n",
        "      <td> 0.041147</td>\n",
        "      <td> 0.152761</td>\n",
        "      <td> 0.074412</td>\n",
        "      <td> 0.075918</td>\n",
        "      <td> 0.087192</td>\n",
        "      <td> 0.883684</td>\n",
        "      <td> 0.873793</td>\n",
        "      <td> 0.880070</td>\n",
        "      <td> 0.819415</td>\n",
        "      <td> 0.809477</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>17 rows \u00d7 17 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "                                       norm_similarity  \\\n",
        "norm_similarity                               1.000000   \n",
        "norm_similarity_zscore                        0.893379   \n",
        "previous_norm_similarity                      0.098772   \n",
        "previous_norm_similarity_zscore               0.068987   \n",
        "previous_average_accuracy_maxmin_mean         0.087003   \n",
        "previous_accuracy_maxmin_mean_on_s            0.074040   \n",
        "previous_maximum_experience_mean              0.025517   \n",
        "previous_exprience_mean_on_s                  0.028985   \n",
        "days_to_end                                   0.035313   \n",
        "days_between_estimates                       -0.081718   \n",
        "degree_s                                      0.114371   \n",
        "CC_unweighted                                 0.004464   \n",
        "WCC_accuracy_sum                              0.010289   \n",
        "WCC_accuracy_maxmin                           0.016396   \n",
        "WCC_experience_sum                            0.013104   \n",
        "WCC_experience_maxmin                         0.013896   \n",
        "WCC_timing_sum                                0.011752   \n",
        "\n",
        "                                       norm_similarity_zscore  \\\n",
        "norm_similarity                                      0.893379   \n",
        "norm_similarity_zscore                               1.000000   \n",
        "previous_norm_similarity                             0.063969   \n",
        "previous_norm_similarity_zscore                      0.078692   \n",
        "previous_average_accuracy_maxmin_mean                0.034640   \n",
        "previous_accuracy_maxmin_mean_on_s                   0.027339   \n",
        "previous_maximum_experience_mean                     0.013434   \n",
        "previous_exprience_mean_on_s                         0.008407   \n",
        "days_to_end                                          0.003493   \n",
        "days_between_estimates                              -0.115809   \n",
        "degree_s                                            -0.001362   \n",
        "CC_unweighted                                        0.001212   \n",
        "WCC_accuracy_sum                                     0.003156   \n",
        "WCC_accuracy_maxmin                                  0.006591   \n",
        "WCC_experience_sum                                   0.004544   \n",
        "WCC_experience_maxmin                                0.005896   \n",
        "WCC_timing_sum                                       0.002100   \n",
        "\n",
        "                                       previous_norm_similarity  \\\n",
        "norm_similarity                                        0.098772   \n",
        "norm_similarity_zscore                                 0.063969   \n",
        "previous_norm_similarity                               1.000000   \n",
        "previous_norm_similarity_zscore                        0.863147   \n",
        "previous_average_accuracy_maxmin_mean                  0.164571   \n",
        "previous_accuracy_maxmin_mean_on_s                     0.313350   \n",
        "previous_maximum_experience_mean                       0.025374   \n",
        "previous_exprience_mean_on_s                           0.017655   \n",
        "days_to_end                                           -0.018570   \n",
        "days_between_estimates                                 0.010396   \n",
        "degree_s                                               0.113048   \n",
        "CC_unweighted                                          0.015198   \n",
        "WCC_accuracy_sum                                       0.024085   \n",
        "WCC_accuracy_maxmin                                    0.054992   \n",
        "WCC_experience_sum                                     0.020258   \n",
        "WCC_experience_maxmin                                  0.021844   \n",
        "WCC_timing_sum                                         0.014937   \n",
        "\n",
        "                                       previous_norm_similarity_zscore  \\\n",
        "norm_similarity                                               0.068987   \n",
        "norm_similarity_zscore                                        0.078692   \n",
        "previous_norm_similarity                                      0.863147   \n",
        "previous_norm_similarity_zscore                               1.000000   \n",
        "previous_average_accuracy_maxmin_mean                         0.086193   \n",
        "previous_accuracy_maxmin_mean_on_s                            0.177144   \n",
        "previous_maximum_experience_mean                              0.015029   \n",
        "previous_exprience_mean_on_s                                  0.004695   \n",
        "days_to_end                                                   0.000019   \n",
        "days_between_estimates                                       -0.002093   \n",
        "degree_s                                                     -0.000011   \n",
        "CC_unweighted                                                 0.015693   \n",
        "WCC_accuracy_sum                                              0.019237   \n",
        "WCC_accuracy_maxmin                                           0.037983   \n",
        "WCC_experience_sum                                            0.012888   \n",
        "WCC_experience_maxmin                                         0.015945   \n",
        "WCC_timing_sum                                                0.014109   \n",
        "\n",
        "                                       previous_average_accuracy_maxmin_mean  \\\n",
        "norm_similarity                                                     0.087003   \n",
        "norm_similarity_zscore                                              0.034640   \n",
        "previous_norm_similarity                                            0.164571   \n",
        "previous_norm_similarity_zscore                                     0.086193   \n",
        "previous_average_accuracy_maxmin_mean                               1.000000   \n",
        "previous_accuracy_maxmin_mean_on_s                                  0.313899   \n",
        "previous_maximum_experience_mean                                    0.112178   \n",
        "previous_exprience_mean_on_s                                        0.080880   \n",
        "days_to_end                                                         0.022509   \n",
        "days_between_estimates                                             -0.003162   \n",
        "degree_s                                                            0.205374   \n",
        "CC_unweighted                                                       0.040976   \n",
        "WCC_accuracy_sum                                                    0.048181   \n",
        "WCC_accuracy_maxmin                                                 0.070663   \n",
        "WCC_experience_sum                                                  0.068708   \n",
        "WCC_experience_maxmin                                               0.078428   \n",
        "WCC_timing_sum                                                      0.021396   \n",
        "\n",
        "                                       previous_accuracy_maxmin_mean_on_s  \\\n",
        "norm_similarity                                                  0.074040   \n",
        "norm_similarity_zscore                                           0.027339   \n",
        "previous_norm_similarity                                         0.313350   \n",
        "previous_norm_similarity_zscore                                  0.177144   \n",
        "previous_average_accuracy_maxmin_mean                            0.313899   \n",
        "previous_accuracy_maxmin_mean_on_s                               1.000000   \n",
        "previous_maximum_experience_mean                                 0.023110   \n",
        "previous_exprience_mean_on_s                                     0.027458   \n",
        "days_to_end                                                      0.011199   \n",
        "days_between_estimates                                          -0.010087   \n",
        "degree_s                                                         0.139764   \n",
        "CC_unweighted                                                   -0.000051   \n",
        "WCC_accuracy_sum                                                 0.028382   \n",
        "WCC_accuracy_maxmin                                              0.122965   \n",
        "WCC_experience_sum                                               0.011098   \n",
        "WCC_experience_maxmin                                            0.015338   \n",
        "WCC_timing_sum                                                  -0.006212   \n",
        "\n",
        "                                       previous_maximum_experience_mean  \\\n",
        "norm_similarity                                                0.025517   \n",
        "norm_similarity_zscore                                         0.013434   \n",
        "previous_norm_similarity                                       0.025374   \n",
        "previous_norm_similarity_zscore                                0.015029   \n",
        "previous_average_accuracy_maxmin_mean                          0.112178   \n",
        "previous_accuracy_maxmin_mean_on_s                             0.023110   \n",
        "previous_maximum_experience_mean                               1.000000   \n",
        "previous_exprience_mean_on_s                                   0.666666   \n",
        "days_to_end                                                    0.026392   \n",
        "days_between_estimates                                         0.011223   \n",
        "degree_s                                                       0.027199   \n",
        "CC_unweighted                                                  0.046416   \n",
        "WCC_accuracy_sum                                               0.060694   \n",
        "WCC_accuracy_maxmin                                            0.068446   \n",
        "WCC_experience_sum                                             0.145344   \n",
        "WCC_experience_maxmin                                          0.142607   \n",
        "WCC_timing_sum                                                 0.041147   \n",
        "\n",
        "                                       previous_exprience_mean_on_s  \\\n",
        "norm_similarity                                            0.028985   \n",
        "norm_similarity_zscore                                     0.008407   \n",
        "previous_norm_similarity                                   0.017655   \n",
        "previous_norm_similarity_zscore                            0.004695   \n",
        "previous_average_accuracy_maxmin_mean                      0.080880   \n",
        "previous_accuracy_maxmin_mean_on_s                         0.027458   \n",
        "previous_maximum_experience_mean                           0.666666   \n",
        "previous_exprience_mean_on_s                               1.000000   \n",
        "days_to_end                                                0.013001   \n",
        "days_between_estimates                                     0.048318   \n",
        "degree_s                                                   0.141046   \n",
        "CC_unweighted                                              0.153741   \n",
        "WCC_accuracy_sum                                           0.157316   \n",
        "WCC_accuracy_maxmin                                        0.158936   \n",
        "WCC_experience_sum                                         0.320047   \n",
        "WCC_experience_maxmin                                      0.317728   \n",
        "WCC_timing_sum                                             0.152761   \n",
        "\n",
        "                                       days_to_end  days_between_estimates  \\\n",
        "norm_similarity                           0.035313               -0.081718   \n",
        "norm_similarity_zscore                    0.003493               -0.115809   \n",
        "previous_norm_similarity                 -0.018570                0.010396   \n",
        "previous_norm_similarity_zscore           0.000019               -0.002093   \n",
        "previous_average_accuracy_maxmin_mean     0.022509               -0.003162   \n",
        "previous_accuracy_maxmin_mean_on_s        0.011199               -0.010087   \n",
        "previous_maximum_experience_mean          0.026392                0.011223   \n",
        "previous_exprience_mean_on_s              0.013001                0.048318   \n",
        "days_to_end                               1.000000               -0.072604   \n",
        "days_between_estimates                   -0.072604                1.000000   \n",
        "degree_s                                  0.007146                0.062358   \n",
        "CC_unweighted                             0.010488                0.070071   \n",
        "WCC_accuracy_sum                          0.013053                0.071705   \n",
        "WCC_accuracy_maxmin                       0.010565                0.065022   \n",
        "WCC_experience_sum                        0.010522                0.072027   \n",
        "WCC_experience_maxmin                     0.013991                0.066284   \n",
        "WCC_timing_sum                            0.074412                0.075918   \n",
        "\n",
        "                                       degree_s  CC_unweighted  \\\n",
        "norm_similarity                        0.114371       0.004464   \n",
        "norm_similarity_zscore                -0.001362       0.001212   \n",
        "previous_norm_similarity               0.113048       0.015198   \n",
        "previous_norm_similarity_zscore       -0.000011       0.015693   \n",
        "previous_average_accuracy_maxmin_mean  0.205374       0.040976   \n",
        "previous_accuracy_maxmin_mean_on_s     0.139764      -0.000051   \n",
        "previous_maximum_experience_mean       0.027199       0.046416   \n",
        "previous_exprience_mean_on_s           0.141046       0.153741   \n",
        "days_to_end                            0.007146       0.010488   \n",
        "days_between_estimates                 0.062358       0.070071   \n",
        "degree_s                               1.000000       0.058965   \n",
        "CC_unweighted                          0.058965       1.000000   \n",
        "WCC_accuracy_sum                       0.078479       0.878334   \n",
        "WCC_accuracy_maxmin                    0.082698       0.902844   \n",
        "WCC_experience_sum                     0.089403       0.807407   \n",
        "WCC_experience_maxmin                  0.087635       0.831961   \n",
        "WCC_timing_sum                         0.087192       0.883684   \n",
        "\n",
        "                                       WCC_accuracy_sum  WCC_accuracy_maxmin  \\\n",
        "norm_similarity                                0.010289             0.016396   \n",
        "norm_similarity_zscore                         0.003156             0.006591   \n",
        "previous_norm_similarity                       0.024085             0.054992   \n",
        "previous_norm_similarity_zscore                0.019237             0.037983   \n",
        "previous_average_accuracy_maxmin_mean          0.048181             0.070663   \n",
        "previous_accuracy_maxmin_mean_on_s             0.028382             0.122965   \n",
        "previous_maximum_experience_mean               0.060694             0.068446   \n",
        "previous_exprience_mean_on_s                   0.157316             0.158936   \n",
        "days_to_end                                    0.013053             0.010565   \n",
        "days_between_estimates                         0.071705             0.065022   \n",
        "degree_s                                       0.078479             0.082698   \n",
        "CC_unweighted                                  0.878334             0.902844   \n",
        "WCC_accuracy_sum                               1.000000             0.906223   \n",
        "WCC_accuracy_maxmin                            0.906223             1.000000   \n",
        "WCC_experience_sum                             0.792430             0.811115   \n",
        "WCC_experience_maxmin                          0.804262             0.830729   \n",
        "WCC_timing_sum                                 0.873793             0.880070   \n",
        "\n",
        "                                       WCC_experience_sum  \\\n",
        "norm_similarity                                  0.013104   \n",
        "norm_similarity_zscore                           0.004544   \n",
        "previous_norm_similarity                         0.020258   \n",
        "previous_norm_similarity_zscore                  0.012888   \n",
        "previous_average_accuracy_maxmin_mean            0.068708   \n",
        "previous_accuracy_maxmin_mean_on_s               0.011098   \n",
        "previous_maximum_experience_mean                 0.145344   \n",
        "previous_exprience_mean_on_s                     0.320047   \n",
        "days_to_end                                      0.010522   \n",
        "days_between_estimates                           0.072027   \n",
        "degree_s                                         0.089403   \n",
        "CC_unweighted                                    0.807407   \n",
        "WCC_accuracy_sum                                 0.792430   \n",
        "WCC_accuracy_maxmin                              0.811115   \n",
        "WCC_experience_sum                               1.000000   \n",
        "WCC_experience_maxmin                            0.961937   \n",
        "WCC_timing_sum                                   0.819415   \n",
        "\n",
        "                                       WCC_experience_maxmin  WCC_timing_sum  \n",
        "norm_similarity                                     0.013896        0.011752  \n",
        "norm_similarity_zscore                              0.005896        0.002100  \n",
        "previous_norm_similarity                            0.021844        0.014937  \n",
        "previous_norm_similarity_zscore                     0.015945        0.014109  \n",
        "previous_average_accuracy_maxmin_mean               0.078428        0.021396  \n",
        "previous_accuracy_maxmin_mean_on_s                  0.015338       -0.006212  \n",
        "previous_maximum_experience_mean                    0.142607        0.041147  \n",
        "previous_exprience_mean_on_s                        0.317728        0.152761  \n",
        "days_to_end                                         0.013991        0.074412  \n",
        "days_between_estimates                              0.066284        0.075918  \n",
        "degree_s                                            0.087635        0.087192  \n",
        "CC_unweighted                                       0.831961        0.883684  \n",
        "WCC_accuracy_sum                                    0.804262        0.873793  \n",
        "WCC_accuracy_maxmin                                 0.830729        0.880070  \n",
        "WCC_experience_sum                                  0.961937        0.819415  \n",
        "WCC_experience_maxmin                               1.000000        0.809477  \n",
        "WCC_timing_sum                                      0.809477        1.000000  \n",
        "\n",
        "[17 rows x 17 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Using the dependent variable norm_similarity"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = smf.ols(formula='norm_similarity ~ previous_norm_similarity + previous_average_accuracy_maxmin_mean + previous_accuracy_maxmin_mean_on_s + days_between_estimates + degree_s_dv + CC_unweighted + WCC_accuracy_maxmin', data=df)\n",
      "result = model.fit()\n",
      "print(result.summary())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:        norm_similarity   R-squared:                       0.033\n",
        "Model:                            OLS   Adj. R-squared:                  0.033\n",
        "Method:                 Least Squares   F-statistic:                     5259.\n",
        "Date:                Thu, 18 Dec 2014   Prob (F-statistic):               0.00\n",
        "Time:                        20:47:01   Log-Likelihood:            -1.4413e+05\n",
        "No. Observations:             1074566   AIC:                         2.883e+05\n",
        "Df Residuals:                 1074558   BIC:                         2.884e+05\n",
        "Df Model:                           7                                         \n",
        "=========================================================================================================\n",
        "                                            coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "---------------------------------------------------------------------------------------------------------\n",
        "Intercept                                 0.4767      0.002    205.621      0.000         0.472     0.481\n",
        "previous_norm_similarity                  0.0614      0.001     71.493      0.000         0.060     0.063\n",
        "previous_average_accuracy_maxmin_mean     0.1625      0.004     42.171      0.000         0.155     0.170\n",
        "previous_accuracy_maxmin_mean_on_s        0.0253      0.001     20.447      0.000         0.023     0.028\n",
        "days_between_estimates                   -0.0013   1.32e-05   -100.210      0.000        -0.001    -0.001\n",
        "degree_s_dv                               0.0029   2.68e-05    107.314      0.000         0.003     0.003\n",
        "CC_unweighted                            -0.0113      0.006     -1.976      0.048        -0.023 -8.97e-05\n",
        "WCC_accuracy_maxmin                       0.0130      0.005      2.879      0.004         0.004     0.022\n",
        "==============================================================================\n",
        "Omnibus:                   102901.110   Durbin-Watson:                   1.615\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           135802.200\n",
        "Skew:                          -0.870   Prob(JB):                         0.00\n",
        "Kurtosis:                       2.915   Cond. No.                         891.\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Using the dependent variable norm_similarity"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = smf.ols(formula='norm_similarity_zscore ~ previous_norm_similarity_zscore + previous_average_accuracy_maxmin_mean + previous_accuracy_maxmin_mean_on_s + days_between_estimates + degree_s_dv + CC_unweighted + WCC_accuracy_maxmin', data=df)\n",
      "result = model.fit()\n",
      "print(result.summary())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                              OLS Regression Results                              \n",
        "==================================================================================\n",
        "Dep. Variable:     norm_similarity_zscore   R-squared:                       0.021\n",
        "Model:                                OLS   Adj. R-squared:                  0.021\n",
        "Method:                     Least Squares   F-statistic:                     3928.\n",
        "Date:                    Thu, 18 Dec 2014   Prob (F-statistic):               0.00\n",
        "Time:                            20:46:47   Log-Likelihood:            -1.8102e+06\n",
        "No. Observations:                 1294364   AIC:                         3.620e+06\n",
        "Df Residuals:                     1294356   BIC:                         3.620e+06\n",
        "Df Model:                               7                                         \n",
        "=========================================================================================================\n",
        "                                            coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "---------------------------------------------------------------------------------------------------------\n",
        "Intercept                                -0.1345      0.007    -17.995      0.000        -0.149    -0.120\n",
        "previous_norm_similarity_zscore           0.0715      0.001     81.354      0.000         0.070     0.073\n",
        "previous_average_accuracy_maxmin_mean     0.3449      0.012     27.818      0.000         0.321     0.369\n",
        "previous_accuracy_maxmin_mean_on_s        0.0172      0.004      4.428      0.000         0.010     0.025\n",
        "days_between_estimates                   -0.0059   4.28e-05   -137.074      0.000        -0.006    -0.006\n",
        "degree_s_dv                           -4.543e-05   8.75e-05     -0.519      0.604        -0.000     0.000\n",
        "CC_unweighted                             0.0056      0.019      0.304      0.761        -0.031     0.042\n",
        "WCC_accuracy_maxmin                       0.0357      0.015      2.453      0.014         0.007     0.064\n",
        "==============================================================================\n",
        "Omnibus:                   219882.785   Durbin-Watson:                   1.784\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           422432.226\n",
        "Skew:                          -1.054   Prob(JB):                         0.00\n",
        "Kurtosis:                       4.842   Cond. No.                         890.\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Bipartite clustering number 3"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$$ NWCC_{i,j} = \\frac{\\frac{\\sum\\limits_{p \\in N(i) \\cap N(j)} W(i, p) + W(j, p)}{|N(i) \\cap N(j)|}}{\\frac{\\sum\\limits_{p \\in N(i)} W(i, p) + \\sum\\limits_{p \\in N(j)} W(j, p)}{|N(i) \\cup N(j)|}} $$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "measures_3 = [u'norm_similarity', u'norm_similarity_zscore', u'previous_norm_similarity', u'previous_norm_similarity_zscore',\n",
      "              u'previous_average_accuracy_maxmin_mean', u'previous_accuracy_maxmin_mean_on_s',\n",
      "              u'previous_maximum_experience_mean', u'previous_exprience_mean_on_s',\n",
      "              u'days_between_estimates', u'degree_s', u'CC_unweighted', 'days_to_end', \n",
      "              u'NWCC_accuracy_sum', u'NWCC_accuracy_maxmin', u'NWCC_experience_sum', u'NWCC_experience_maxmin', u'NWCC_timing_sum']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[measures_3].describe()"
     ],
     "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>norm_similarity</th>\n",
        "      <th>norm_similarity_zscore</th>\n",
        "      <th>previous_norm_similarity</th>\n",
        "      <th>previous_norm_similarity_zscore</th>\n",
        "      <th>previous_average_accuracy_maxmin_mean</th>\n",
        "      <th>previous_accuracy_maxmin_mean_on_s</th>\n",
        "      <th>previous_maximum_experience_mean</th>\n",
        "      <th>previous_exprience_mean_on_s</th>\n",
        "      <th>days_between_estimates</th>\n",
        "      <th>degree_s</th>\n",
        "      <th>CC_unweighted</th>\n",
        "      <th>days_to_end</th>\n",
        "      <th>NWCC_accuracy_sum</th>\n",
        "      <th>NWCC_accuracy_maxmin</th>\n",
        "      <th>NWCC_experience_sum</th>\n",
        "      <th>NWCC_experience_maxmin</th>\n",
        "      <th>NWCC_timing_sum</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 2249785.000000</td>\n",
        "      <td> 2.249785e+06</td>\n",
        "      <td> 1119186.000000</td>\n",
        "      <td> 1349479.000000</td>\n",
        "      <td> 1303960.000000</td>\n",
        "      <td> 1303041.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 2256436.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>       0.677748</td>\n",
        "      <td> 4.235009e-08</td>\n",
        "      <td>       0.661184</td>\n",
        "      <td>       0.000004</td>\n",
        "      <td>       0.627896</td>\n",
        "      <td>       0.649600</td>\n",
        "      <td>    2482.559792</td>\n",
        "      <td>    1425.853661</td>\n",
        "      <td>      16.735975</td>\n",
        "      <td>      22.738920</td>\n",
        "      <td>       0.237576</td>\n",
        "      <td>     161.497271</td>\n",
        "      <td>       1.668734</td>\n",
        "      <td>       1.690748</td>\n",
        "      <td>       1.825510</td>\n",
        "      <td>       1.685921</td>\n",
        "      <td>       1.822606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>       0.290289</td>\n",
        "      <td> 1.000000e+00</td>\n",
        "      <td>       0.330791</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>       0.074572</td>\n",
        "      <td>       0.254604</td>\n",
        "      <td>    1222.919958</td>\n",
        "      <td>    1080.627715</td>\n",
        "      <td>      20.180034</td>\n",
        "      <td>       9.781033</td>\n",
        "      <td>       0.156833</td>\n",
        "      <td>      95.218089</td>\n",
        "      <td>       0.704120</td>\n",
        "      <td>       0.597611</td>\n",
        "      <td>       0.967077</td>\n",
        "      <td>       0.750097</td>\n",
        "      <td>       0.722578</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>       0.000000</td>\n",
        "      <td>-1.399286e+01</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>     -10.877379</td>\n",
        "      <td>       0.202870</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       2.000000</td>\n",
        "      <td>       0.011364</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>       0.500000</td>\n",
        "      <td>-5.549967e-01</td>\n",
        "      <td>       0.457143</td>\n",
        "      <td>      -0.506558</td>\n",
        "      <td>       0.581287</td>\n",
        "      <td>       0.473997</td>\n",
        "      <td>    1641.000000</td>\n",
        "      <td>     549.500000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>      16.000000</td>\n",
        "      <td>       0.115385</td>\n",
        "      <td>      65.000000</td>\n",
        "      <td>       1.456397</td>\n",
        "      <td>       1.401791</td>\n",
        "      <td>       1.358230</td>\n",
        "      <td>       1.324620</td>\n",
        "      <td>       1.465741</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>       0.760000</td>\n",
        "      <td> 2.286800e-01</td>\n",
        "      <td>       0.769231</td>\n",
        "      <td>       0.238952</td>\n",
        "      <td>       0.632968</td>\n",
        "      <td>       0.689781</td>\n",
        "      <td>    2380.000000</td>\n",
        "      <td>    1273.000000</td>\n",
        "      <td>       8.000000</td>\n",
        "      <td>      22.000000</td>\n",
        "      <td>       0.208333</td>\n",
        "      <td>     150.000000</td>\n",
        "      <td>       1.615271</td>\n",
        "      <td>       1.622073</td>\n",
        "      <td>       1.709112</td>\n",
        "      <td>       1.639938</td>\n",
        "      <td>       1.704544</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>       0.916667</td>\n",
        "      <td> 7.158600e-01</td>\n",
        "      <td>       0.944444</td>\n",
        "      <td>       0.654007</td>\n",
        "      <td>       0.680485</td>\n",
        "      <td>       0.870968</td>\n",
        "      <td>    3292.500000</td>\n",
        "      <td>    2010.500000</td>\n",
        "      <td>      28.000000</td>\n",
        "      <td>      29.000000</td>\n",
        "      <td>       0.325581</td>\n",
        "      <td>     239.000000</td>\n",
        "      <td>       1.786393</td>\n",
        "      <td>       1.899979</td>\n",
        "      <td>       2.146571</td>\n",
        "      <td>       2.017077</td>\n",
        "      <td>       1.996974</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>       1.000000</td>\n",
        "      <td> 6.822597e+00</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>       6.422174</td>\n",
        "      <td>       0.877786</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>    6616.000000</td>\n",
        "      <td>    6593.000000</td>\n",
        "      <td>      90.000000</td>\n",
        "      <td>      49.000000</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>     364.000000</td>\n",
        "      <td>      31.500000</td>\n",
        "      <td>      24.000000</td>\n",
        "      <td>      26.000000</td>\n",
        "      <td>      26.000000</td>\n",
        "      <td>      18.097772</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 17 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "       norm_similarity  norm_similarity_zscore  previous_norm_similarity  \\\n",
        "count   2249785.000000            2.249785e+06            1119186.000000   \n",
        "mean          0.677748            4.235009e-08                  0.661184   \n",
        "std           0.290289            1.000000e+00                  0.330791   \n",
        "min           0.000000           -1.399286e+01                  0.000000   \n",
        "25%           0.500000           -5.549967e-01                  0.457143   \n",
        "50%           0.760000            2.286800e-01                  0.769231   \n",
        "75%           0.916667            7.158600e-01                  0.944444   \n",
        "max           1.000000            6.822597e+00                  1.000000   \n",
        "\n",
        "       previous_norm_similarity_zscore  previous_average_accuracy_maxmin_mean  \\\n",
        "count                   1349479.000000                         1303960.000000   \n",
        "mean                          0.000004                               0.627896   \n",
        "std                           1.000000                               0.074572   \n",
        "min                         -10.877379                               0.202870   \n",
        "25%                          -0.506558                               0.581287   \n",
        "50%                           0.238952                               0.632968   \n",
        "75%                           0.654007                               0.680485   \n",
        "max                           6.422174                               0.877786   \n",
        "\n",
        "       previous_accuracy_maxmin_mean_on_s  previous_maximum_experience_mean  \\\n",
        "count                      1303041.000000                    1359624.000000   \n",
        "mean                             0.649600                       2482.559792   \n",
        "std                              0.254604                       1222.919958   \n",
        "min                              0.000000                          0.000000   \n",
        "25%                              0.473997                       1641.000000   \n",
        "50%                              0.689781                       2380.000000   \n",
        "75%                              0.870968                       3292.500000   \n",
        "max                              1.000000                       6616.000000   \n",
        "\n",
        "       previous_exprience_mean_on_s  days_between_estimates        degree_s  \\\n",
        "count                1359624.000000          2256436.000000  1359624.000000   \n",
        "mean                    1425.853661               16.735975       22.738920   \n",
        "std                     1080.627715               20.180034        9.781033   \n",
        "min                        0.000000                0.000000        2.000000   \n",
        "25%                      549.500000                0.000000       16.000000   \n",
        "50%                     1273.000000                8.000000       22.000000   \n",
        "75%                     2010.500000               28.000000       29.000000   \n",
        "max                     6593.000000               90.000000       49.000000   \n",
        "\n",
        "        CC_unweighted     days_to_end  NWCC_accuracy_sum  \\\n",
        "count  1359624.000000  1359624.000000     1359624.000000   \n",
        "mean         0.237576      161.497271           1.668734   \n",
        "std          0.156833       95.218089           0.704120   \n",
        "min          0.011364        0.000000           0.000000   \n",
        "25%          0.115385       65.000000           1.456397   \n",
        "50%          0.208333      150.000000           1.615271   \n",
        "75%          0.325581      239.000000           1.786393   \n",
        "max          1.000000      364.000000          31.500000   \n",
        "\n",
        "       NWCC_accuracy_maxmin  NWCC_experience_sum  NWCC_experience_maxmin  \\\n",
        "count        1359624.000000       1359624.000000          1359624.000000   \n",
        "mean               1.690748             1.825510                1.685921   \n",
        "std                0.597611             0.967077                0.750097   \n",
        "min                0.000000             0.000000                0.000000   \n",
        "25%                1.401791             1.358230                1.324620   \n",
        "50%                1.622073             1.709112                1.639938   \n",
        "75%                1.899979             2.146571                2.017077   \n",
        "max               24.000000            26.000000               26.000000   \n",
        "\n",
        "       NWCC_timing_sum  \n",
        "count   1359624.000000  \n",
        "mean          1.822606  \n",
        "std           0.722578  \n",
        "min           0.000000  \n",
        "25%           1.465741  \n",
        "50%           1.704544  \n",
        "75%           1.996974  \n",
        "max          18.097772  \n",
        "\n",
        "[8 rows x 17 columns]"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[measures_3].corr()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>norm_similarity</th>\n",
        "      <th>norm_similarity_zscore</th>\n",
        "      <th>previous_norm_similarity</th>\n",
        "      <th>previous_norm_similarity_zscore</th>\n",
        "      <th>previous_average_accuracy_maxmin_mean</th>\n",
        "      <th>previous_accuracy_maxmin_mean_on_s</th>\n",
        "      <th>previous_maximum_experience_mean</th>\n",
        "      <th>previous_exprience_mean_on_s</th>\n",
        "      <th>days_between_estimates</th>\n",
        "      <th>degree_s</th>\n",
        "      <th>CC_unweighted</th>\n",
        "      <th>days_to_end</th>\n",
        "      <th>NWCC_accuracy_sum</th>\n",
        "      <th>NWCC_accuracy_maxmin</th>\n",
        "      <th>NWCC_experience_sum</th>\n",
        "      <th>NWCC_experience_maxmin</th>\n",
        "      <th>NWCC_timing_sum</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>norm_similarity</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.893379</td>\n",
        "      <td> 0.098772</td>\n",
        "      <td> 0.068987</td>\n",
        "      <td> 0.087003</td>\n",
        "      <td> 0.074040</td>\n",
        "      <td> 0.025517</td>\n",
        "      <td> 0.028985</td>\n",
        "      <td>-0.081718</td>\n",
        "      <td> 0.114371</td>\n",
        "      <td> 0.004464</td>\n",
        "      <td> 0.035313</td>\n",
        "      <td>-0.000989</td>\n",
        "      <td> 0.017199</td>\n",
        "      <td> 0.004276</td>\n",
        "      <td> 0.009342</td>\n",
        "      <td> 0.000174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>norm_similarity_zscore</th>\n",
        "      <td> 0.893379</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.063969</td>\n",
        "      <td> 0.078692</td>\n",
        "      <td> 0.034640</td>\n",
        "      <td> 0.027339</td>\n",
        "      <td> 0.013434</td>\n",
        "      <td> 0.008407</td>\n",
        "      <td>-0.115809</td>\n",
        "      <td>-0.001362</td>\n",
        "      <td> 0.001212</td>\n",
        "      <td> 0.003493</td>\n",
        "      <td>-0.000147</td>\n",
        "      <td> 0.009467</td>\n",
        "      <td>-0.000256</td>\n",
        "      <td> 0.004222</td>\n",
        "      <td>-0.003923</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_norm_similarity</th>\n",
        "      <td> 0.098772</td>\n",
        "      <td> 0.063969</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.863147</td>\n",
        "      <td> 0.164571</td>\n",
        "      <td> 0.313350</td>\n",
        "      <td> 0.025374</td>\n",
        "      <td> 0.017655</td>\n",
        "      <td> 0.010396</td>\n",
        "      <td> 0.113048</td>\n",
        "      <td> 0.015198</td>\n",
        "      <td>-0.018570</td>\n",
        "      <td> 0.000043</td>\n",
        "      <td> 0.081527</td>\n",
        "      <td>-0.004922</td>\n",
        "      <td> 0.004439</td>\n",
        "      <td>-0.018356</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_norm_similarity_zscore</th>\n",
        "      <td> 0.068987</td>\n",
        "      <td> 0.078692</td>\n",
        "      <td> 0.863147</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.086193</td>\n",
        "      <td> 0.177144</td>\n",
        "      <td> 0.015029</td>\n",
        "      <td> 0.004695</td>\n",
        "      <td>-0.002093</td>\n",
        "      <td>-0.000011</td>\n",
        "      <td> 0.015693</td>\n",
        "      <td> 0.000019</td>\n",
        "      <td> 0.000480</td>\n",
        "      <td> 0.049549</td>\n",
        "      <td>-0.013203</td>\n",
        "      <td>-0.004255</td>\n",
        "      <td>-0.012080</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_average_accuracy_maxmin_mean</th>\n",
        "      <td> 0.087003</td>\n",
        "      <td> 0.034640</td>\n",
        "      <td> 0.164571</td>\n",
        "      <td> 0.086193</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.313899</td>\n",
        "      <td> 0.112178</td>\n",
        "      <td> 0.080880</td>\n",
        "      <td>-0.003162</td>\n",
        "      <td> 0.205374</td>\n",
        "      <td> 0.040976</td>\n",
        "      <td> 0.022509</td>\n",
        "      <td>-0.051758</td>\n",
        "      <td>-0.013320</td>\n",
        "      <td>-0.009157</td>\n",
        "      <td> 0.034775</td>\n",
        "      <td>-0.096058</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_accuracy_maxmin_mean_on_s</th>\n",
        "      <td> 0.074040</td>\n",
        "      <td> 0.027339</td>\n",
        "      <td> 0.313350</td>\n",
        "      <td> 0.177144</td>\n",
        "      <td> 0.313899</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.023110</td>\n",
        "      <td> 0.027458</td>\n",
        "      <td>-0.010087</td>\n",
        "      <td> 0.139764</td>\n",
        "      <td>-0.000051</td>\n",
        "      <td> 0.011199</td>\n",
        "      <td> 0.061727</td>\n",
        "      <td> 0.420254</td>\n",
        "      <td> 0.003354</td>\n",
        "      <td> 0.018080</td>\n",
        "      <td>-0.028235</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_maximum_experience_mean</th>\n",
        "      <td> 0.025517</td>\n",
        "      <td> 0.013434</td>\n",
        "      <td> 0.025374</td>\n",
        "      <td> 0.015029</td>\n",
        "      <td> 0.112178</td>\n",
        "      <td> 0.023110</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.666666</td>\n",
        "      <td> 0.011223</td>\n",
        "      <td> 0.027199</td>\n",
        "      <td> 0.046416</td>\n",
        "      <td> 0.026392</td>\n",
        "      <td>-0.022224</td>\n",
        "      <td>-0.008521</td>\n",
        "      <td> 0.090129</td>\n",
        "      <td> 0.120996</td>\n",
        "      <td>-0.065768</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_exprience_mean_on_s</th>\n",
        "      <td> 0.028985</td>\n",
        "      <td> 0.008407</td>\n",
        "      <td> 0.017655</td>\n",
        "      <td> 0.004695</td>\n",
        "      <td> 0.080880</td>\n",
        "      <td> 0.027458</td>\n",
        "      <td> 0.666666</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.048318</td>\n",
        "      <td> 0.141046</td>\n",
        "      <td> 0.153741</td>\n",
        "      <td> 0.013001</td>\n",
        "      <td>-0.051003</td>\n",
        "      <td>-0.052557</td>\n",
        "      <td> 0.259029</td>\n",
        "      <td> 0.333793</td>\n",
        "      <td>-0.078916</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>days_between_estimates</th>\n",
        "      <td>-0.081718</td>\n",
        "      <td>-0.115809</td>\n",
        "      <td> 0.010396</td>\n",
        "      <td>-0.002093</td>\n",
        "      <td>-0.003162</td>\n",
        "      <td>-0.010087</td>\n",
        "      <td> 0.011223</td>\n",
        "      <td> 0.048318</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.062358</td>\n",
        "      <td> 0.070071</td>\n",
        "      <td>-0.072604</td>\n",
        "      <td>-0.012760</td>\n",
        "      <td>-0.025878</td>\n",
        "      <td> 0.003317</td>\n",
        "      <td>-0.005186</td>\n",
        "      <td>-0.014014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>degree_s</th>\n",
        "      <td> 0.114371</td>\n",
        "      <td>-0.001362</td>\n",
        "      <td> 0.113048</td>\n",
        "      <td>-0.000011</td>\n",
        "      <td> 0.205374</td>\n",
        "      <td> 0.139764</td>\n",
        "      <td> 0.027199</td>\n",
        "      <td> 0.141046</td>\n",
        "      <td> 0.062358</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.058965</td>\n",
        "      <td> 0.007146</td>\n",
        "      <td>-0.024119</td>\n",
        "      <td>-0.013691</td>\n",
        "      <td> 0.023223</td>\n",
        "      <td> 0.031628</td>\n",
        "      <td>-0.011391</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CC_unweighted</th>\n",
        "      <td> 0.004464</td>\n",
        "      <td> 0.001212</td>\n",
        "      <td> 0.015198</td>\n",
        "      <td> 0.015693</td>\n",
        "      <td> 0.040976</td>\n",
        "      <td>-0.000051</td>\n",
        "      <td> 0.046416</td>\n",
        "      <td> 0.153741</td>\n",
        "      <td> 0.070071</td>\n",
        "      <td> 0.058965</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.010488</td>\n",
        "      <td>-0.315725</td>\n",
        "      <td>-0.356882</td>\n",
        "      <td>-0.230275</td>\n",
        "      <td>-0.179066</td>\n",
        "      <td>-0.388013</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>days_to_end</th>\n",
        "      <td> 0.035313</td>\n",
        "      <td> 0.003493</td>\n",
        "      <td>-0.018570</td>\n",
        "      <td> 0.000019</td>\n",
        "      <td> 0.022509</td>\n",
        "      <td> 0.011199</td>\n",
        "      <td> 0.026392</td>\n",
        "      <td> 0.013001</td>\n",
        "      <td>-0.072604</td>\n",
        "      <td> 0.007146</td>\n",
        "      <td> 0.010488</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.008858</td>\n",
        "      <td>-0.011427</td>\n",
        "      <td>-0.017369</td>\n",
        "      <td>-0.005703</td>\n",
        "      <td> 0.163517</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NWCC_accuracy_sum</th>\n",
        "      <td>-0.000989</td>\n",
        "      <td>-0.000147</td>\n",
        "      <td> 0.000043</td>\n",
        "      <td> 0.000480</td>\n",
        "      <td>-0.051758</td>\n",
        "      <td> 0.061727</td>\n",
        "      <td>-0.022224</td>\n",
        "      <td>-0.051003</td>\n",
        "      <td>-0.012760</td>\n",
        "      <td>-0.024119</td>\n",
        "      <td>-0.315725</td>\n",
        "      <td>-0.008858</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.473129</td>\n",
        "      <td> 0.172005</td>\n",
        "      <td> 0.076512</td>\n",
        "      <td> 0.313163</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NWCC_accuracy_maxmin</th>\n",
        "      <td> 0.017199</td>\n",
        "      <td> 0.009467</td>\n",
        "      <td> 0.081527</td>\n",
        "      <td> 0.049549</td>\n",
        "      <td>-0.013320</td>\n",
        "      <td> 0.420254</td>\n",
        "      <td>-0.008521</td>\n",
        "      <td>-0.052557</td>\n",
        "      <td>-0.025878</td>\n",
        "      <td>-0.013691</td>\n",
        "      <td>-0.356882</td>\n",
        "      <td>-0.011427</td>\n",
        "      <td> 0.473129</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.145199</td>\n",
        "      <td> 0.100184</td>\n",
        "      <td> 0.205087</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NWCC_experience_sum</th>\n",
        "      <td> 0.004276</td>\n",
        "      <td>-0.000256</td>\n",
        "      <td>-0.004922</td>\n",
        "      <td>-0.013203</td>\n",
        "      <td>-0.009157</td>\n",
        "      <td> 0.003354</td>\n",
        "      <td> 0.090129</td>\n",
        "      <td> 0.259029</td>\n",
        "      <td> 0.003317</td>\n",
        "      <td> 0.023223</td>\n",
        "      <td>-0.230275</td>\n",
        "      <td>-0.017369</td>\n",
        "      <td> 0.172005</td>\n",
        "      <td> 0.145199</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.809452</td>\n",
        "      <td> 0.237559</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NWCC_experience_maxmin</th>\n",
        "      <td> 0.009342</td>\n",
        "      <td> 0.004222</td>\n",
        "      <td> 0.004439</td>\n",
        "      <td>-0.004255</td>\n",
        "      <td> 0.034775</td>\n",
        "      <td> 0.018080</td>\n",
        "      <td> 0.120996</td>\n",
        "      <td> 0.333793</td>\n",
        "      <td>-0.005186</td>\n",
        "      <td> 0.031628</td>\n",
        "      <td>-0.179066</td>\n",
        "      <td>-0.005703</td>\n",
        "      <td> 0.076512</td>\n",
        "      <td> 0.100184</td>\n",
        "      <td> 0.809452</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.045162</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NWCC_timing_sum</th>\n",
        "      <td> 0.000174</td>\n",
        "      <td>-0.003923</td>\n",
        "      <td>-0.018356</td>\n",
        "      <td>-0.012080</td>\n",
        "      <td>-0.096058</td>\n",
        "      <td>-0.028235</td>\n",
        "      <td>-0.065768</td>\n",
        "      <td>-0.078916</td>\n",
        "      <td>-0.014014</td>\n",
        "      <td>-0.011391</td>\n",
        "      <td>-0.388013</td>\n",
        "      <td> 0.163517</td>\n",
        "      <td> 0.313163</td>\n",
        "      <td> 0.205087</td>\n",
        "      <td> 0.237559</td>\n",
        "      <td> 0.045162</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>17 rows \u00d7 17 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "                                       norm_similarity  \\\n",
        "norm_similarity                               1.000000   \n",
        "norm_similarity_zscore                        0.893379   \n",
        "previous_norm_similarity                      0.098772   \n",
        "previous_norm_similarity_zscore               0.068987   \n",
        "previous_average_accuracy_maxmin_mean         0.087003   \n",
        "previous_accuracy_maxmin_mean_on_s            0.074040   \n",
        "previous_maximum_experience_mean              0.025517   \n",
        "previous_exprience_mean_on_s                  0.028985   \n",
        "days_between_estimates                       -0.081718   \n",
        "degree_s                                      0.114371   \n",
        "CC_unweighted                                 0.004464   \n",
        "days_to_end                                   0.035313   \n",
        "NWCC_accuracy_sum                            -0.000989   \n",
        "NWCC_accuracy_maxmin                          0.017199   \n",
        "NWCC_experience_sum                           0.004276   \n",
        "NWCC_experience_maxmin                        0.009342   \n",
        "NWCC_timing_sum                               0.000174   \n",
        "\n",
        "                                       norm_similarity_zscore  \\\n",
        "norm_similarity                                      0.893379   \n",
        "norm_similarity_zscore                               1.000000   \n",
        "previous_norm_similarity                             0.063969   \n",
        "previous_norm_similarity_zscore                      0.078692   \n",
        "previous_average_accuracy_maxmin_mean                0.034640   \n",
        "previous_accuracy_maxmin_mean_on_s                   0.027339   \n",
        "previous_maximum_experience_mean                     0.013434   \n",
        "previous_exprience_mean_on_s                         0.008407   \n",
        "days_between_estimates                              -0.115809   \n",
        "degree_s                                            -0.001362   \n",
        "CC_unweighted                                        0.001212   \n",
        "days_to_end                                          0.003493   \n",
        "NWCC_accuracy_sum                                   -0.000147   \n",
        "NWCC_accuracy_maxmin                                 0.009467   \n",
        "NWCC_experience_sum                                 -0.000256   \n",
        "NWCC_experience_maxmin                               0.004222   \n",
        "NWCC_timing_sum                                     -0.003923   \n",
        "\n",
        "                                       previous_norm_similarity  \\\n",
        "norm_similarity                                        0.098772   \n",
        "norm_similarity_zscore                                 0.063969   \n",
        "previous_norm_similarity                               1.000000   \n",
        "previous_norm_similarity_zscore                        0.863147   \n",
        "previous_average_accuracy_maxmin_mean                  0.164571   \n",
        "previous_accuracy_maxmin_mean_on_s                     0.313350   \n",
        "previous_maximum_experience_mean                       0.025374   \n",
        "previous_exprience_mean_on_s                           0.017655   \n",
        "days_between_estimates                                 0.010396   \n",
        "degree_s                                               0.113048   \n",
        "CC_unweighted                                          0.015198   \n",
        "days_to_end                                           -0.018570   \n",
        "NWCC_accuracy_sum                                      0.000043   \n",
        "NWCC_accuracy_maxmin                                   0.081527   \n",
        "NWCC_experience_sum                                   -0.004922   \n",
        "NWCC_experience_maxmin                                 0.004439   \n",
        "NWCC_timing_sum                                       -0.018356   \n",
        "\n",
        "                                       previous_norm_similarity_zscore  \\\n",
        "norm_similarity                                               0.068987   \n",
        "norm_similarity_zscore                                        0.078692   \n",
        "previous_norm_similarity                                      0.863147   \n",
        "previous_norm_similarity_zscore                               1.000000   \n",
        "previous_average_accuracy_maxmin_mean                         0.086193   \n",
        "previous_accuracy_maxmin_mean_on_s                            0.177144   \n",
        "previous_maximum_experience_mean                              0.015029   \n",
        "previous_exprience_mean_on_s                                  0.004695   \n",
        "days_between_estimates                                       -0.002093   \n",
        "degree_s                                                     -0.000011   \n",
        "CC_unweighted                                                 0.015693   \n",
        "days_to_end                                                   0.000019   \n",
        "NWCC_accuracy_sum                                             0.000480   \n",
        "NWCC_accuracy_maxmin                                          0.049549   \n",
        "NWCC_experience_sum                                          -0.013203   \n",
        "NWCC_experience_maxmin                                       -0.004255   \n",
        "NWCC_timing_sum                                              -0.012080   \n",
        "\n",
        "                                       previous_average_accuracy_maxmin_mean  \\\n",
        "norm_similarity                                                     0.087003   \n",
        "norm_similarity_zscore                                              0.034640   \n",
        "previous_norm_similarity                                            0.164571   \n",
        "previous_norm_similarity_zscore                                     0.086193   \n",
        "previous_average_accuracy_maxmin_mean                               1.000000   \n",
        "previous_accuracy_maxmin_mean_on_s                                  0.313899   \n",
        "previous_maximum_experience_mean                                    0.112178   \n",
        "previous_exprience_mean_on_s                                        0.080880   \n",
        "days_between_estimates                                             -0.003162   \n",
        "degree_s                                                            0.205374   \n",
        "CC_unweighted                                                       0.040976   \n",
        "days_to_end                                                         0.022509   \n",
        "NWCC_accuracy_sum                                                  -0.051758   \n",
        "NWCC_accuracy_maxmin                                               -0.013320   \n",
        "NWCC_experience_sum                                                -0.009157   \n",
        "NWCC_experience_maxmin                                              0.034775   \n",
        "NWCC_timing_sum                                                    -0.096058   \n",
        "\n",
        "                                       previous_accuracy_maxmin_mean_on_s  \\\n",
        "norm_similarity                                                  0.074040   \n",
        "norm_similarity_zscore                                           0.027339   \n",
        "previous_norm_similarity                                         0.313350   \n",
        "previous_norm_similarity_zscore                                  0.177144   \n",
        "previous_average_accuracy_maxmin_mean                            0.313899   \n",
        "previous_accuracy_maxmin_mean_on_s                               1.000000   \n",
        "previous_maximum_experience_mean                                 0.023110   \n",
        "previous_exprience_mean_on_s                                     0.027458   \n",
        "days_between_estimates                                          -0.010087   \n",
        "degree_s                                                         0.139764   \n",
        "CC_unweighted                                                   -0.000051   \n",
        "days_to_end                                                      0.011199   \n",
        "NWCC_accuracy_sum                                                0.061727   \n",
        "NWCC_accuracy_maxmin                                             0.420254   \n",
        "NWCC_experience_sum                                              0.003354   \n",
        "NWCC_experience_maxmin                                           0.018080   \n",
        "NWCC_timing_sum                                                 -0.028235   \n",
        "\n",
        "                                       previous_maximum_experience_mean  \\\n",
        "norm_similarity                                                0.025517   \n",
        "norm_similarity_zscore                                         0.013434   \n",
        "previous_norm_similarity                                       0.025374   \n",
        "previous_norm_similarity_zscore                                0.015029   \n",
        "previous_average_accuracy_maxmin_mean                          0.112178   \n",
        "previous_accuracy_maxmin_mean_on_s                             0.023110   \n",
        "previous_maximum_experience_mean                               1.000000   \n",
        "previous_exprience_mean_on_s                                   0.666666   \n",
        "days_between_estimates                                         0.011223   \n",
        "degree_s                                                       0.027199   \n",
        "CC_unweighted                                                  0.046416   \n",
        "days_to_end                                                    0.026392   \n",
        "NWCC_accuracy_sum                                             -0.022224   \n",
        "NWCC_accuracy_maxmin                                          -0.008521   \n",
        "NWCC_experience_sum                                            0.090129   \n",
        "NWCC_experience_maxmin                                         0.120996   \n",
        "NWCC_timing_sum                                               -0.065768   \n",
        "\n",
        "                                       previous_exprience_mean_on_s  \\\n",
        "norm_similarity                                            0.028985   \n",
        "norm_similarity_zscore                                     0.008407   \n",
        "previous_norm_similarity                                   0.017655   \n",
        "previous_norm_similarity_zscore                            0.004695   \n",
        "previous_average_accuracy_maxmin_mean                      0.080880   \n",
        "previous_accuracy_maxmin_mean_on_s                         0.027458   \n",
        "previous_maximum_experience_mean                           0.666666   \n",
        "previous_exprience_mean_on_s                               1.000000   \n",
        "days_between_estimates                                     0.048318   \n",
        "degree_s                                                   0.141046   \n",
        "CC_unweighted                                              0.153741   \n",
        "days_to_end                                                0.013001   \n",
        "NWCC_accuracy_sum                                         -0.051003   \n",
        "NWCC_accuracy_maxmin                                      -0.052557   \n",
        "NWCC_experience_sum                                        0.259029   \n",
        "NWCC_experience_maxmin                                     0.333793   \n",
        "NWCC_timing_sum                                           -0.078916   \n",
        "\n",
        "                                       days_between_estimates  degree_s  \\\n",
        "norm_similarity                                     -0.081718  0.114371   \n",
        "norm_similarity_zscore                              -0.115809 -0.001362   \n",
        "previous_norm_similarity                             0.010396  0.113048   \n",
        "previous_norm_similarity_zscore                     -0.002093 -0.000011   \n",
        "previous_average_accuracy_maxmin_mean               -0.003162  0.205374   \n",
        "previous_accuracy_maxmin_mean_on_s                  -0.010087  0.139764   \n",
        "previous_maximum_experience_mean                     0.011223  0.027199   \n",
        "previous_exprience_mean_on_s                         0.048318  0.141046   \n",
        "days_between_estimates                               1.000000  0.062358   \n",
        "degree_s                                             0.062358  1.000000   \n",
        "CC_unweighted                                        0.070071  0.058965   \n",
        "days_to_end                                         -0.072604  0.007146   \n",
        "NWCC_accuracy_sum                                   -0.012760 -0.024119   \n",
        "NWCC_accuracy_maxmin                                -0.025878 -0.013691   \n",
        "NWCC_experience_sum                                  0.003317  0.023223   \n",
        "NWCC_experience_maxmin                              -0.005186  0.031628   \n",
        "NWCC_timing_sum                                     -0.014014 -0.011391   \n",
        "\n",
        "                                       CC_unweighted  days_to_end  \\\n",
        "norm_similarity                             0.004464     0.035313   \n",
        "norm_similarity_zscore                      0.001212     0.003493   \n",
        "previous_norm_similarity                    0.015198    -0.018570   \n",
        "previous_norm_similarity_zscore             0.015693     0.000019   \n",
        "previous_average_accuracy_maxmin_mean       0.040976     0.022509   \n",
        "previous_accuracy_maxmin_mean_on_s         -0.000051     0.011199   \n",
        "previous_maximum_experience_mean            0.046416     0.026392   \n",
        "previous_exprience_mean_on_s                0.153741     0.013001   \n",
        "days_between_estimates                      0.070071    -0.072604   \n",
        "degree_s                                    0.058965     0.007146   \n",
        "CC_unweighted                               1.000000     0.010488   \n",
        "days_to_end                                 0.010488     1.000000   \n",
        "NWCC_accuracy_sum                          -0.315725    -0.008858   \n",
        "NWCC_accuracy_maxmin                       -0.356882    -0.011427   \n",
        "NWCC_experience_sum                        -0.230275    -0.017369   \n",
        "NWCC_experience_maxmin                     -0.179066    -0.005703   \n",
        "NWCC_timing_sum                            -0.388013     0.163517   \n",
        "\n",
        "                                       NWCC_accuracy_sum  \\\n",
        "norm_similarity                                -0.000989   \n",
        "norm_similarity_zscore                         -0.000147   \n",
        "previous_norm_similarity                        0.000043   \n",
        "previous_norm_similarity_zscore                 0.000480   \n",
        "previous_average_accuracy_maxmin_mean          -0.051758   \n",
        "previous_accuracy_maxmin_mean_on_s              0.061727   \n",
        "previous_maximum_experience_mean               -0.022224   \n",
        "previous_exprience_mean_on_s                   -0.051003   \n",
        "days_between_estimates                         -0.012760   \n",
        "degree_s                                       -0.024119   \n",
        "CC_unweighted                                  -0.315725   \n",
        "days_to_end                                    -0.008858   \n",
        "NWCC_accuracy_sum                               1.000000   \n",
        "NWCC_accuracy_maxmin                            0.473129   \n",
        "NWCC_experience_sum                             0.172005   \n",
        "NWCC_experience_maxmin                          0.076512   \n",
        "NWCC_timing_sum                                 0.313163   \n",
        "\n",
        "                                       NWCC_accuracy_maxmin  \\\n",
        "norm_similarity                                    0.017199   \n",
        "norm_similarity_zscore                             0.009467   \n",
        "previous_norm_similarity                           0.081527   \n",
        "previous_norm_similarity_zscore                    0.049549   \n",
        "previous_average_accuracy_maxmin_mean             -0.013320   \n",
        "previous_accuracy_maxmin_mean_on_s                 0.420254   \n",
        "previous_maximum_experience_mean                  -0.008521   \n",
        "previous_exprience_mean_on_s                      -0.052557   \n",
        "days_between_estimates                            -0.025878   \n",
        "degree_s                                          -0.013691   \n",
        "CC_unweighted                                     -0.356882   \n",
        "days_to_end                                       -0.011427   \n",
        "NWCC_accuracy_sum                                  0.473129   \n",
        "NWCC_accuracy_maxmin                               1.000000   \n",
        "NWCC_experience_sum                                0.145199   \n",
        "NWCC_experience_maxmin                             0.100184   \n",
        "NWCC_timing_sum                                    0.205087   \n",
        "\n",
        "                                       NWCC_experience_sum  \\\n",
        "norm_similarity                                   0.004276   \n",
        "norm_similarity_zscore                           -0.000256   \n",
        "previous_norm_similarity                         -0.004922   \n",
        "previous_norm_similarity_zscore                  -0.013203   \n",
        "previous_average_accuracy_maxmin_mean            -0.009157   \n",
        "previous_accuracy_maxmin_mean_on_s                0.003354   \n",
        "previous_maximum_experience_mean                  0.090129   \n",
        "previous_exprience_mean_on_s                      0.259029   \n",
        "days_between_estimates                            0.003317   \n",
        "degree_s                                          0.023223   \n",
        "CC_unweighted                                    -0.230275   \n",
        "days_to_end                                      -0.017369   \n",
        "NWCC_accuracy_sum                                 0.172005   \n",
        "NWCC_accuracy_maxmin                              0.145199   \n",
        "NWCC_experience_sum                               1.000000   \n",
        "NWCC_experience_maxmin                            0.809452   \n",
        "NWCC_timing_sum                                   0.237559   \n",
        "\n",
        "                                       NWCC_experience_maxmin  NWCC_timing_sum  \n",
        "norm_similarity                                      0.009342         0.000174  \n",
        "norm_similarity_zscore                               0.004222        -0.003923  \n",
        "previous_norm_similarity                             0.004439        -0.018356  \n",
        "previous_norm_similarity_zscore                     -0.004255        -0.012080  \n",
        "previous_average_accuracy_maxmin_mean                0.034775        -0.096058  \n",
        "previous_accuracy_maxmin_mean_on_s                   0.018080        -0.028235  \n",
        "previous_maximum_experience_mean                     0.120996        -0.065768  \n",
        "previous_exprience_mean_on_s                         0.333793        -0.078916  \n",
        "days_between_estimates                              -0.005186        -0.014014  \n",
        "degree_s                                             0.031628        -0.011391  \n",
        "CC_unweighted                                       -0.179066        -0.388013  \n",
        "days_to_end                                         -0.005703         0.163517  \n",
        "NWCC_accuracy_sum                                    0.076512         0.313163  \n",
        "NWCC_accuracy_maxmin                                 0.100184         0.205087  \n",
        "NWCC_experience_sum                                  0.809452         0.237559  \n",
        "NWCC_experience_maxmin                               1.000000         0.045162  \n",
        "NWCC_timing_sum                                      0.045162         1.000000  \n",
        "\n",
        "[17 rows x 17 columns]"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Using the dependent variable norm_similarity"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = smf.ols(formula='norm_similarity ~ previous_norm_similarity + previous_average_accuracy_maxmin_mean + previous_accuracy_maxmin_mean_on_s + days_between_estimates + degree_s_dv + degree_i + CC_unweighted + NWCC_accuracy_maxmin', data=df)\n",
      "result = model.fit()\n",
      "print(result.summary())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:        norm_similarity   R-squared:                       0.033\n",
        "Model:                            OLS   Adj. R-squared:                  0.033\n",
        "Method:                 Least Squares   F-statistic:                     4603.\n",
        "Date:                Thu, 18 Dec 2014   Prob (F-statistic):               0.00\n",
        "Time:                        20:49:05   Log-Likelihood:            -1.4413e+05\n",
        "No. Observations:             1074566   AIC:                         2.883e+05\n",
        "Df Residuals:                 1074557   BIC:                         2.884e+05\n",
        "Df Model:                           8                                         \n",
        "=========================================================================================================\n",
        "                                            coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "---------------------------------------------------------------------------------------------------------\n",
        "Intercept                                 0.4734      0.003    171.418      0.000         0.468     0.479\n",
        "previous_norm_similarity                  0.0614      0.001     71.448      0.000         0.060     0.063\n",
        "previous_average_accuracy_maxmin_mean     0.1657      0.004     41.984      0.000         0.158     0.173\n",
        "previous_accuracy_maxmin_mean_on_s        0.0247      0.001     18.736      0.000         0.022     0.027\n",
        "days_between_estimates                   -0.0013   1.32e-05   -100.100      0.000        -0.001    -0.001\n",
        "degree_s_dv                               0.0029   2.68e-05    107.308      0.000         0.003     0.003\n",
        "degree_i                                 -0.0001   4.17e-05     -2.859      0.004        -0.000 -3.75e-05\n",
        "CC_unweighted                             0.0072      0.002      3.624      0.000         0.003     0.011\n",
        "NWCC_accuracy_maxmin                      0.0022      0.001      2.859      0.004         0.001     0.004\n",
        "==============================================================================\n",
        "Omnibus:                   102921.835   Durbin-Watson:                   1.615\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           135838.979\n",
        "Skew:                          -0.870   Prob(JB):                         0.00\n",
        "Kurtosis:                       2.915   Cond. No.                         616.\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Using the dependent variable norm_similarity_zscore"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = smf.ols(formula='norm_similarity_zscore ~ previous_norm_similarity_zscore + previous_average_accuracy_maxmin_mean + previous_accuracy_maxmin_mean_on_s + days_between_estimates + degree_s_dv + degree_i + CC_unweighted + WCC_accuracy_maxmin', data=df)\n",
      "result = model.fit()\n",
      "print(result.summary())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                              OLS Regression Results                              \n",
        "==================================================================================\n",
        "Dep. Variable:     norm_similarity_zscore   R-squared:                       0.021\n",
        "Model:                                OLS   Adj. R-squared:                  0.021\n",
        "Method:                     Least Squares   F-statistic:                     3439.\n",
        "Date:                    Thu, 18 Dec 2014   Prob (F-statistic):               0.00\n",
        "Time:                            20:49:55   Log-Likelihood:            -1.8101e+06\n",
        "No. Observations:                 1294364   AIC:                         3.620e+06\n",
        "Df Residuals:                     1294355   BIC:                         3.620e+06\n",
        "Df Model:                               8                                         \n",
        "=========================================================================================================\n",
        "                                            coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "---------------------------------------------------------------------------------------------------------\n",
        "Intercept                                -0.1321      0.008    -17.592      0.000        -0.147    -0.117\n",
        "previous_norm_similarity_zscore           0.0715      0.001     81.282      0.000         0.070     0.073\n",
        "previous_average_accuracy_maxmin_mean     0.3518      0.013     28.040      0.000         0.327     0.376\n",
        "previous_accuracy_maxmin_mean_on_s        0.0171      0.004      4.393      0.000         0.009     0.025\n",
        "days_between_estimates                   -0.0059   4.28e-05   -136.939      0.000        -0.006    -0.006\n",
        "degree_s_dv                           -5.244e-05   8.75e-05     -0.599      0.549        -0.000     0.000\n",
        "degree_i                                 -0.0005      0.000     -3.597      0.000        -0.001    -0.000\n",
        "CC_unweighted                             0.0019      0.019      0.102      0.919        -0.035     0.038\n",
        "WCC_accuracy_maxmin                       0.0387      0.015      2.656      0.008         0.010     0.067\n",
        "==============================================================================\n",
        "Omnibus:                   219853.016   Durbin-Watson:                   1.784\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           422349.732\n",
        "Skew:                          -1.053   Prob(JB):                         0.00\n",
        "Kurtosis:                       4.842   Cond. No.                         958.\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Bipartite clustering number 4"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$$ WCCI_{i,j} = \\frac{\\sum\\limits_{p \\in N(i) \\cap N(j)} W(i, p) + W(j, p)}{|N(i) \\cap N(j)|} $$\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "measures_4 = [u'norm_similarity', u'norm_similarity_zscore', u'previous_norm_similarity', u'previous_norm_similarity_zscore', \n",
      "              u'previous_average_accuracy_maxmin_mean', u'previous_accuracy_maxmin_mean_on_s',\n",
      "              u'previous_maximum_experience_mean', u'previous_exprience_mean_on_s',\n",
      "              u'days_between_estimates', u'degree_s', u'CC_unweighted', 'days_to_end',\n",
      "              u'WCC_intersection_accuracy_sum', u'WCC_intersection_accuracy_maxmin', u'WCC_intersection_experience_sum', \n",
      "              u'WCC_intersection_experience_maxmin', u'WCC_intersection_timing_sum']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[measures_4].describe()"
     ],
     "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>norm_similarity</th>\n",
        "      <th>norm_similarity_zscore</th>\n",
        "      <th>previous_norm_similarity</th>\n",
        "      <th>previous_norm_similarity_zscore</th>\n",
        "      <th>previous_average_accuracy_maxmin_mean</th>\n",
        "      <th>previous_accuracy_maxmin_mean_on_s</th>\n",
        "      <th>previous_maximum_experience_mean</th>\n",
        "      <th>previous_exprience_mean_on_s</th>\n",
        "      <th>days_between_estimates</th>\n",
        "      <th>degree_s</th>\n",
        "      <th>CC_unweighted</th>\n",
        "      <th>days_to_end</th>\n",
        "      <th>WCC_intersection_accuracy_sum</th>\n",
        "      <th>WCC_intersection_accuracy_maxmin</th>\n",
        "      <th>WCC_intersection_experience_sum</th>\n",
        "      <th>WCC_intersection_experience_maxmin</th>\n",
        "      <th>WCC_intersection_timing_sum</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 2249785.000000</td>\n",
        "      <td> 2.249785e+06</td>\n",
        "      <td> 1119186.000000</td>\n",
        "      <td> 1349479.000000</td>\n",
        "      <td> 1303960.000000</td>\n",
        "      <td> 1303041.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 2256436.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "      <td> 1359624.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>       0.677748</td>\n",
        "      <td> 4.235009e-08</td>\n",
        "      <td>       0.661184</td>\n",
        "      <td>       0.000004</td>\n",
        "      <td>       0.627896</td>\n",
        "      <td>       0.649600</td>\n",
        "      <td>    2482.559792</td>\n",
        "      <td>    1425.853661</td>\n",
        "      <td>      16.735975</td>\n",
        "      <td>      22.738920</td>\n",
        "      <td>       0.237576</td>\n",
        "      <td>     161.497271</td>\n",
        "      <td>       1.620217</td>\n",
        "      <td>       1.266337</td>\n",
        "      <td>       0.167940</td>\n",
        "      <td>       0.991705</td>\n",
        "      <td>       0.179694</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>       0.290289</td>\n",
        "      <td> 1.000000e+00</td>\n",
        "      <td>       0.330791</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>       0.074572</td>\n",
        "      <td>       0.254604</td>\n",
        "      <td>    1222.919958</td>\n",
        "      <td>    1080.627715</td>\n",
        "      <td>      20.180034</td>\n",
        "      <td>       9.781033</td>\n",
        "      <td>       0.156833</td>\n",
        "      <td>      95.218089</td>\n",
        "      <td>       0.438955</td>\n",
        "      <td>       0.334042</td>\n",
        "      <td>       0.119985</td>\n",
        "      <td>       0.438721</td>\n",
        "      <td>       0.106835</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>       0.000000</td>\n",
        "      <td>-1.399286e+01</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>     -10.877379</td>\n",
        "      <td>       0.202870</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       2.000000</td>\n",
        "      <td>       0.011364</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>       0.500000</td>\n",
        "      <td>-5.549967e-01</td>\n",
        "      <td>       0.457143</td>\n",
        "      <td>      -0.506558</td>\n",
        "      <td>       0.581287</td>\n",
        "      <td>       0.473997</td>\n",
        "      <td>    1641.000000</td>\n",
        "      <td>     549.500000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>      16.000000</td>\n",
        "      <td>       0.115385</td>\n",
        "      <td>      65.000000</td>\n",
        "      <td>       1.620767</td>\n",
        "      <td>       1.094566</td>\n",
        "      <td>       0.102841</td>\n",
        "      <td>       0.713503</td>\n",
        "      <td>       0.119027</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>       0.760000</td>\n",
        "      <td> 2.286800e-01</td>\n",
        "      <td>       0.769231</td>\n",
        "      <td>       0.238952</td>\n",
        "      <td>       0.632968</td>\n",
        "      <td>       0.689781</td>\n",
        "      <td>    2380.000000</td>\n",
        "      <td>    1273.000000</td>\n",
        "      <td>       8.000000</td>\n",
        "      <td>      22.000000</td>\n",
        "      <td>       0.208333</td>\n",
        "      <td>     150.000000</td>\n",
        "      <td>       1.831239</td>\n",
        "      <td>       1.296879</td>\n",
        "      <td>       0.145540</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>       0.152077</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>       0.916667</td>\n",
        "      <td> 7.158600e-01</td>\n",
        "      <td>       0.944444</td>\n",
        "      <td>       0.654007</td>\n",
        "      <td>       0.680485</td>\n",
        "      <td>       0.870968</td>\n",
        "      <td>    3292.500000</td>\n",
        "      <td>    2010.500000</td>\n",
        "      <td>      28.000000</td>\n",
        "      <td>      29.000000</td>\n",
        "      <td>       0.325581</td>\n",
        "      <td>     239.000000</td>\n",
        "      <td>       1.884496</td>\n",
        "      <td>       1.475555</td>\n",
        "      <td>       0.200464</td>\n",
        "      <td>       1.298461</td>\n",
        "      <td>       0.203747</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>       1.000000</td>\n",
        "      <td> 6.822597e+00</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>       6.422174</td>\n",
        "      <td>       0.877786</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>    6616.000000</td>\n",
        "      <td>    6593.000000</td>\n",
        "      <td>      90.000000</td>\n",
        "      <td>      49.000000</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>     364.000000</td>\n",
        "      <td>       2.000000</td>\n",
        "      <td>       2.000000</td>\n",
        "      <td>       2.000000</td>\n",
        "      <td>       2.000000</td>\n",
        "      <td>       1.701073</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 17 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "       norm_similarity  norm_similarity_zscore  previous_norm_similarity  \\\n",
        "count   2249785.000000            2.249785e+06            1119186.000000   \n",
        "mean          0.677748            4.235009e-08                  0.661184   \n",
        "std           0.290289            1.000000e+00                  0.330791   \n",
        "min           0.000000           -1.399286e+01                  0.000000   \n",
        "25%           0.500000           -5.549967e-01                  0.457143   \n",
        "50%           0.760000            2.286800e-01                  0.769231   \n",
        "75%           0.916667            7.158600e-01                  0.944444   \n",
        "max           1.000000            6.822597e+00                  1.000000   \n",
        "\n",
        "       previous_norm_similarity_zscore  previous_average_accuracy_maxmin_mean  \\\n",
        "count                   1349479.000000                         1303960.000000   \n",
        "mean                          0.000004                               0.627896   \n",
        "std                           1.000000                               0.074572   \n",
        "min                         -10.877379                               0.202870   \n",
        "25%                          -0.506558                               0.581287   \n",
        "50%                           0.238952                               0.632968   \n",
        "75%                           0.654007                               0.680485   \n",
        "max                           6.422174                               0.877786   \n",
        "\n",
        "       previous_accuracy_maxmin_mean_on_s  previous_maximum_experience_mean  \\\n",
        "count                      1303041.000000                    1359624.000000   \n",
        "mean                             0.649600                       2482.559792   \n",
        "std                              0.254604                       1222.919958   \n",
        "min                              0.000000                          0.000000   \n",
        "25%                              0.473997                       1641.000000   \n",
        "50%                              0.689781                       2380.000000   \n",
        "75%                              0.870968                       3292.500000   \n",
        "max                              1.000000                       6616.000000   \n",
        "\n",
        "       previous_exprience_mean_on_s  days_between_estimates        degree_s  \\\n",
        "count                1359624.000000          2256436.000000  1359624.000000   \n",
        "mean                    1425.853661               16.735975       22.738920   \n",
        "std                     1080.627715               20.180034        9.781033   \n",
        "min                        0.000000                0.000000        2.000000   \n",
        "25%                      549.500000                0.000000       16.000000   \n",
        "50%                     1273.000000                8.000000       22.000000   \n",
        "75%                     2010.500000               28.000000       29.000000   \n",
        "max                     6593.000000               90.000000       49.000000   \n",
        "\n",
        "        CC_unweighted     days_to_end  WCC_intersection_accuracy_sum  \\\n",
        "count  1359624.000000  1359624.000000                 1359624.000000   \n",
        "mean         0.237576      161.497271                       1.620217   \n",
        "std          0.156833       95.218089                       0.438955   \n",
        "min          0.011364        0.000000                       0.000000   \n",
        "25%          0.115385       65.000000                       1.620767   \n",
        "50%          0.208333      150.000000                       1.831239   \n",
        "75%          0.325581      239.000000                       1.884496   \n",
        "max          1.000000      364.000000                       2.000000   \n",
        "\n",
        "       WCC_intersection_accuracy_maxmin  WCC_intersection_experience_sum  \\\n",
        "count                    1359624.000000                   1359624.000000   \n",
        "mean                           1.266337                         0.167940   \n",
        "std                            0.334042                         0.119985   \n",
        "min                            0.000000                         0.000000   \n",
        "25%                            1.094566                         0.102841   \n",
        "50%                            1.296879                         0.145540   \n",
        "75%                            1.475555                         0.200464   \n",
        "max                            2.000000                         2.000000   \n",
        "\n",
        "       WCC_intersection_experience_maxmin  WCC_intersection_timing_sum  \n",
        "count                      1359624.000000               1359624.000000  \n",
        "mean                             0.991705                     0.179694  \n",
        "std                              0.438721                     0.106835  \n",
        "min                              0.000000                     0.000000  \n",
        "25%                              0.713503                     0.119027  \n",
        "50%                              1.000000                     0.152077  \n",
        "75%                              1.298461                     0.203747  \n",
        "max                              2.000000                     1.701073  \n",
        "\n",
        "[8 rows x 17 columns]"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[measures_4].corr()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>norm_similarity</th>\n",
        "      <th>norm_similarity_zscore</th>\n",
        "      <th>previous_norm_similarity</th>\n",
        "      <th>previous_norm_similarity_zscore</th>\n",
        "      <th>previous_average_accuracy_maxmin_mean</th>\n",
        "      <th>previous_accuracy_maxmin_mean_on_s</th>\n",
        "      <th>previous_maximum_experience_mean</th>\n",
        "      <th>previous_exprience_mean_on_s</th>\n",
        "      <th>days_between_estimates</th>\n",
        "      <th>degree_s</th>\n",
        "      <th>CC_unweighted</th>\n",
        "      <th>days_to_end</th>\n",
        "      <th>WCC_intersection_accuracy_sum</th>\n",
        "      <th>WCC_intersection_accuracy_maxmin</th>\n",
        "      <th>WCC_intersection_experience_sum</th>\n",
        "      <th>WCC_intersection_experience_maxmin</th>\n",
        "      <th>WCC_intersection_timing_sum</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>norm_similarity</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.893379</td>\n",
        "      <td> 0.098772</td>\n",
        "      <td> 0.068987</td>\n",
        "      <td> 0.087003</td>\n",
        "      <td> 0.074040</td>\n",
        "      <td> 0.025517</td>\n",
        "      <td> 0.028985</td>\n",
        "      <td>-0.081718</td>\n",
        "      <td> 0.114371</td>\n",
        "      <td> 0.004464</td>\n",
        "      <td> 0.035313</td>\n",
        "      <td> 0.026182</td>\n",
        "      <td> 0.076318</td>\n",
        "      <td>-0.001685</td>\n",
        "      <td> 0.011283</td>\n",
        "      <td>-0.010128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>norm_similarity_zscore</th>\n",
        "      <td> 0.893379</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.063969</td>\n",
        "      <td> 0.078692</td>\n",
        "      <td> 0.034640</td>\n",
        "      <td> 0.027339</td>\n",
        "      <td> 0.013434</td>\n",
        "      <td> 0.008407</td>\n",
        "      <td>-0.115809</td>\n",
        "      <td>-0.001362</td>\n",
        "      <td> 0.001212</td>\n",
        "      <td> 0.003493</td>\n",
        "      <td>-0.000357</td>\n",
        "      <td> 0.029394</td>\n",
        "      <td>-0.001455</td>\n",
        "      <td> 0.002590</td>\n",
        "      <td>-0.006921</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_norm_similarity</th>\n",
        "      <td> 0.098772</td>\n",
        "      <td> 0.063969</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.863147</td>\n",
        "      <td> 0.164571</td>\n",
        "      <td> 0.313350</td>\n",
        "      <td> 0.025374</td>\n",
        "      <td> 0.017655</td>\n",
        "      <td> 0.010396</td>\n",
        "      <td> 0.113048</td>\n",
        "      <td> 0.015198</td>\n",
        "      <td>-0.018570</td>\n",
        "      <td> 0.042112</td>\n",
        "      <td> 0.199636</td>\n",
        "      <td>-0.012011</td>\n",
        "      <td> 0.011016</td>\n",
        "      <td>-0.025183</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_norm_similarity_zscore</th>\n",
        "      <td> 0.068987</td>\n",
        "      <td> 0.078692</td>\n",
        "      <td> 0.863147</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.086193</td>\n",
        "      <td> 0.177144</td>\n",
        "      <td> 0.015029</td>\n",
        "      <td> 0.004695</td>\n",
        "      <td>-0.002093</td>\n",
        "      <td>-0.000011</td>\n",
        "      <td> 0.015693</td>\n",
        "      <td> 0.000019</td>\n",
        "      <td> 0.013728</td>\n",
        "      <td> 0.107246</td>\n",
        "      <td>-0.015934</td>\n",
        "      <td> 0.001558</td>\n",
        "      <td>-0.017189</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_average_accuracy_maxmin_mean</th>\n",
        "      <td> 0.087003</td>\n",
        "      <td> 0.034640</td>\n",
        "      <td> 0.164571</td>\n",
        "      <td> 0.086193</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.313899</td>\n",
        "      <td> 0.112178</td>\n",
        "      <td> 0.080880</td>\n",
        "      <td>-0.003162</td>\n",
        "      <td> 0.205374</td>\n",
        "      <td> 0.040976</td>\n",
        "      <td> 0.022509</td>\n",
        "      <td> 0.106835</td>\n",
        "      <td> 0.527336</td>\n",
        "      <td>-0.112432</td>\n",
        "      <td> 0.092622</td>\n",
        "      <td>-0.236252</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_accuracy_maxmin_mean_on_s</th>\n",
        "      <td> 0.074040</td>\n",
        "      <td> 0.027339</td>\n",
        "      <td> 0.313350</td>\n",
        "      <td> 0.177144</td>\n",
        "      <td> 0.313899</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.023110</td>\n",
        "      <td> 0.027458</td>\n",
        "      <td>-0.010087</td>\n",
        "      <td> 0.139764</td>\n",
        "      <td>-0.000051</td>\n",
        "      <td> 0.011199</td>\n",
        "      <td> 0.102707</td>\n",
        "      <td> 0.592557</td>\n",
        "      <td>-0.036772</td>\n",
        "      <td> 0.024499</td>\n",
        "      <td>-0.080377</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_maximum_experience_mean</th>\n",
        "      <td> 0.025517</td>\n",
        "      <td> 0.013434</td>\n",
        "      <td> 0.025374</td>\n",
        "      <td> 0.015029</td>\n",
        "      <td> 0.112178</td>\n",
        "      <td> 0.023110</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.666666</td>\n",
        "      <td> 0.011223</td>\n",
        "      <td> 0.027199</td>\n",
        "      <td> 0.046416</td>\n",
        "      <td> 0.026392</td>\n",
        "      <td> 0.103264</td>\n",
        "      <td> 0.093389</td>\n",
        "      <td> 0.019888</td>\n",
        "      <td> 0.218425</td>\n",
        "      <td>-0.220197</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>previous_exprience_mean_on_s</th>\n",
        "      <td> 0.028985</td>\n",
        "      <td> 0.008407</td>\n",
        "      <td> 0.017655</td>\n",
        "      <td> 0.004695</td>\n",
        "      <td> 0.080880</td>\n",
        "      <td> 0.027458</td>\n",
        "      <td> 0.666666</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.048318</td>\n",
        "      <td> 0.141046</td>\n",
        "      <td> 0.153741</td>\n",
        "      <td> 0.013001</td>\n",
        "      <td> 0.098808</td>\n",
        "      <td> 0.062860</td>\n",
        "      <td> 0.188515</td>\n",
        "      <td> 0.518773</td>\n",
        "      <td>-0.131480</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>days_between_estimates</th>\n",
        "      <td>-0.081718</td>\n",
        "      <td>-0.115809</td>\n",
        "      <td> 0.010396</td>\n",
        "      <td>-0.002093</td>\n",
        "      <td>-0.003162</td>\n",
        "      <td>-0.010087</td>\n",
        "      <td> 0.011223</td>\n",
        "      <td> 0.048318</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.062358</td>\n",
        "      <td> 0.070071</td>\n",
        "      <td>-0.072604</td>\n",
        "      <td> 0.040732</td>\n",
        "      <td>-0.006476</td>\n",
        "      <td>-0.000629</td>\n",
        "      <td> 0.028516</td>\n",
        "      <td>-0.006605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>degree_s</th>\n",
        "      <td> 0.114371</td>\n",
        "      <td>-0.001362</td>\n",
        "      <td> 0.113048</td>\n",
        "      <td>-0.000011</td>\n",
        "      <td> 0.205374</td>\n",
        "      <td> 0.139764</td>\n",
        "      <td> 0.027199</td>\n",
        "      <td> 0.141046</td>\n",
        "      <td> 0.062358</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.058965</td>\n",
        "      <td> 0.007146</td>\n",
        "      <td> 0.167538</td>\n",
        "      <td> 0.178961</td>\n",
        "      <td>-0.007444</td>\n",
        "      <td> 0.072137</td>\n",
        "      <td>-0.028886</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CC_unweighted</th>\n",
        "      <td> 0.004464</td>\n",
        "      <td> 0.001212</td>\n",
        "      <td> 0.015198</td>\n",
        "      <td> 0.015693</td>\n",
        "      <td> 0.040976</td>\n",
        "      <td>-0.000051</td>\n",
        "      <td> 0.046416</td>\n",
        "      <td> 0.153741</td>\n",
        "      <td> 0.070071</td>\n",
        "      <td> 0.058965</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.010488</td>\n",
        "      <td> 0.160370</td>\n",
        "      <td> 0.013824</td>\n",
        "      <td>-0.009370</td>\n",
        "      <td> 0.164355</td>\n",
        "      <td>-0.049207</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>days_to_end</th>\n",
        "      <td> 0.035313</td>\n",
        "      <td> 0.003493</td>\n",
        "      <td>-0.018570</td>\n",
        "      <td> 0.000019</td>\n",
        "      <td> 0.022509</td>\n",
        "      <td> 0.011199</td>\n",
        "      <td> 0.026392</td>\n",
        "      <td> 0.013001</td>\n",
        "      <td>-0.072604</td>\n",
        "      <td> 0.007146</td>\n",
        "      <td> 0.010488</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.029690</td>\n",
        "      <td> 0.016316</td>\n",
        "      <td>-0.014792</td>\n",
        "      <td> 0.002218</td>\n",
        "      <td> 0.088181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>WCC_intersection_accuracy_sum</th>\n",
        "      <td> 0.026182</td>\n",
        "      <td>-0.000357</td>\n",
        "      <td> 0.042112</td>\n",
        "      <td> 0.013728</td>\n",
        "      <td> 0.106835</td>\n",
        "      <td> 0.102707</td>\n",
        "      <td> 0.103264</td>\n",
        "      <td> 0.098808</td>\n",
        "      <td> 0.040732</td>\n",
        "      <td> 0.167538</td>\n",
        "      <td> 0.160370</td>\n",
        "      <td> 0.029690</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.335527</td>\n",
        "      <td>-0.043182</td>\n",
        "      <td> 0.097731</td>\n",
        "      <td>-0.087628</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>WCC_intersection_accuracy_maxmin</th>\n",
        "      <td> 0.076318</td>\n",
        "      <td> 0.029394</td>\n",
        "      <td> 0.199636</td>\n",
        "      <td> 0.107246</td>\n",
        "      <td> 0.527336</td>\n",
        "      <td> 0.592557</td>\n",
        "      <td> 0.093389</td>\n",
        "      <td> 0.062860</td>\n",
        "      <td>-0.006476</td>\n",
        "      <td> 0.178961</td>\n",
        "      <td> 0.013824</td>\n",
        "      <td> 0.016316</td>\n",
        "      <td> 0.335527</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.034586</td>\n",
        "      <td> 0.095163</td>\n",
        "      <td>-0.094947</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>WCC_intersection_experience_sum</th>\n",
        "      <td>-0.001685</td>\n",
        "      <td>-0.001455</td>\n",
        "      <td>-0.012011</td>\n",
        "      <td>-0.015934</td>\n",
        "      <td>-0.112432</td>\n",
        "      <td>-0.036772</td>\n",
        "      <td> 0.019888</td>\n",
        "      <td> 0.188515</td>\n",
        "      <td>-0.000629</td>\n",
        "      <td>-0.007444</td>\n",
        "      <td>-0.009370</td>\n",
        "      <td>-0.014792</td>\n",
        "      <td>-0.043182</td>\n",
        "      <td>-0.034586</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.469363</td>\n",
        "      <td> 0.428016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>WCC_intersection_experience_maxmin</th>\n",
        "      <td> 0.011283</td>\n",
        "      <td> 0.002590</td>\n",
        "      <td> 0.011016</td>\n",
        "      <td> 0.001558</td>\n",
        "      <td> 0.092622</td>\n",
        "      <td> 0.024499</td>\n",
        "      <td> 0.218425</td>\n",
        "      <td> 0.518773</td>\n",
        "      <td> 0.028516</td>\n",
        "      <td> 0.072137</td>\n",
        "      <td> 0.164355</td>\n",
        "      <td> 0.002218</td>\n",
        "      <td> 0.097731</td>\n",
        "      <td> 0.095163</td>\n",
        "      <td> 0.469363</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.129682</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>WCC_intersection_timing_sum</th>\n",
        "      <td>-0.010128</td>\n",
        "      <td>-0.006921</td>\n",
        "      <td>-0.025183</td>\n",
        "      <td>-0.017189</td>\n",
        "      <td>-0.236252</td>\n",
        "      <td>-0.080377</td>\n",
        "      <td>-0.220197</td>\n",
        "      <td>-0.131480</td>\n",
        "      <td>-0.006605</td>\n",
        "      <td>-0.028886</td>\n",
        "      <td>-0.049207</td>\n",
        "      <td> 0.088181</td>\n",
        "      <td>-0.087628</td>\n",
        "      <td>-0.094947</td>\n",
        "      <td> 0.428016</td>\n",
        "      <td>-0.129682</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>17 rows \u00d7 17 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "                                       norm_similarity  \\\n",
        "norm_similarity                               1.000000   \n",
        "norm_similarity_zscore                        0.893379   \n",
        "previous_norm_similarity                      0.098772   \n",
        "previous_norm_similarity_zscore               0.068987   \n",
        "previous_average_accuracy_maxmin_mean         0.087003   \n",
        "previous_accuracy_maxmin_mean_on_s            0.074040   \n",
        "previous_maximum_experience_mean              0.025517   \n",
        "previous_exprience_mean_on_s                  0.028985   \n",
        "days_between_estimates                       -0.081718   \n",
        "degree_s                                      0.114371   \n",
        "CC_unweighted                                 0.004464   \n",
        "days_to_end                                   0.035313   \n",
        "WCC_intersection_accuracy_sum                 0.026182   \n",
        "WCC_intersection_accuracy_maxmin              0.076318   \n",
        "WCC_intersection_experience_sum              -0.001685   \n",
        "WCC_intersection_experience_maxmin            0.011283   \n",
        "WCC_intersection_timing_sum                  -0.010128   \n",
        "\n",
        "                                       norm_similarity_zscore  \\\n",
        "norm_similarity                                      0.893379   \n",
        "norm_similarity_zscore                               1.000000   \n",
        "previous_norm_similarity                             0.063969   \n",
        "previous_norm_similarity_zscore                      0.078692   \n",
        "previous_average_accuracy_maxmin_mean                0.034640   \n",
        "previous_accuracy_maxmin_mean_on_s                   0.027339   \n",
        "previous_maximum_experience_mean                     0.013434   \n",
        "previous_exprience_mean_on_s                         0.008407   \n",
        "days_between_estimates                              -0.115809   \n",
        "degree_s                                            -0.001362   \n",
        "CC_unweighted                                        0.001212   \n",
        "days_to_end                                          0.003493   \n",
        "WCC_intersection_accuracy_sum                       -0.000357   \n",
        "WCC_intersection_accuracy_maxmin                     0.029394   \n",
        "WCC_intersection_experience_sum                     -0.001455   \n",
        "WCC_intersection_experience_maxmin                   0.002590   \n",
        "WCC_intersection_timing_sum                         -0.006921   \n",
        "\n",
        "                                       previous_norm_similarity  \\\n",
        "norm_similarity                                        0.098772   \n",
        "norm_similarity_zscore                                 0.063969   \n",
        "previous_norm_similarity                               1.000000   \n",
        "previous_norm_similarity_zscore                        0.863147   \n",
        "previous_average_accuracy_maxmin_mean                  0.164571   \n",
        "previous_accuracy_maxmin_mean_on_s                     0.313350   \n",
        "previous_maximum_experience_mean                       0.025374   \n",
        "previous_exprience_mean_on_s                           0.017655   \n",
        "days_between_estimates                                 0.010396   \n",
        "degree_s                                               0.113048   \n",
        "CC_unweighted                                          0.015198   \n",
        "days_to_end                                           -0.018570   \n",
        "WCC_intersection_accuracy_sum                          0.042112   \n",
        "WCC_intersection_accuracy_maxmin                       0.199636   \n",
        "WCC_intersection_experience_sum                       -0.012011   \n",
        "WCC_intersection_experience_maxmin                     0.011016   \n",
        "WCC_intersection_timing_sum                           -0.025183   \n",
        "\n",
        "                                       previous_norm_similarity_zscore  \\\n",
        "norm_similarity                                               0.068987   \n",
        "norm_similarity_zscore                                        0.078692   \n",
        "previous_norm_similarity                                      0.863147   \n",
        "previous_norm_similarity_zscore                               1.000000   \n",
        "previous_average_accuracy_maxmin_mean                         0.086193   \n",
        "previous_accuracy_maxmin_mean_on_s                            0.177144   \n",
        "previous_maximum_experience_mean                              0.015029   \n",
        "previous_exprience_mean_on_s                                  0.004695   \n",
        "days_between_estimates                                       -0.002093   \n",
        "degree_s                                                     -0.000011   \n",
        "CC_unweighted                                                 0.015693   \n",
        "days_to_end                                                   0.000019   \n",
        "WCC_intersection_accuracy_sum                                 0.013728   \n",
        "WCC_intersection_accuracy_maxmin                              0.107246   \n",
        "WCC_intersection_experience_sum                              -0.015934   \n",
        "WCC_intersection_experience_maxmin                            0.001558   \n",
        "WCC_intersection_timing_sum                                  -0.017189   \n",
        "\n",
        "                                       previous_average_accuracy_maxmin_mean  \\\n",
        "norm_similarity                                                     0.087003   \n",
        "norm_similarity_zscore                                              0.034640   \n",
        "previous_norm_similarity                                            0.164571   \n",
        "previous_norm_similarity_zscore                                     0.086193   \n",
        "previous_average_accuracy_maxmin_mean                               1.000000   \n",
        "previous_accuracy_maxmin_mean_on_s                                  0.313899   \n",
        "previous_maximum_experience_mean                                    0.112178   \n",
        "previous_exprience_mean_on_s                                        0.080880   \n",
        "days_between_estimates                                             -0.003162   \n",
        "degree_s                                                            0.205374   \n",
        "CC_unweighted                                                       0.040976   \n",
        "days_to_end                                                         0.022509   \n",
        "WCC_intersection_accuracy_sum                                       0.106835   \n",
        "WCC_intersection_accuracy_maxmin                                    0.527336   \n",
        "WCC_intersection_experience_sum                                    -0.112432   \n",
        "WCC_intersection_experience_maxmin                                  0.092622   \n",
        "WCC_intersection_timing_sum                                        -0.236252   \n",
        "\n",
        "                                       previous_accuracy_maxmin_mean_on_s  \\\n",
        "norm_similarity                                                  0.074040   \n",
        "norm_similarity_zscore                                           0.027339   \n",
        "previous_norm_similarity                                         0.313350   \n",
        "previous_norm_similarity_zscore                                  0.177144   \n",
        "previous_average_accuracy_maxmin_mean                            0.313899   \n",
        "previous_accuracy_maxmin_mean_on_s                               1.000000   \n",
        "previous_maximum_experience_mean                                 0.023110   \n",
        "previous_exprience_mean_on_s                                     0.027458   \n",
        "days_between_estimates                                          -0.010087   \n",
        "degree_s                                                         0.139764   \n",
        "CC_unweighted                                                   -0.000051   \n",
        "days_to_end                                                      0.011199   \n",
        "WCC_intersection_accuracy_sum                                    0.102707   \n",
        "WCC_intersection_accuracy_maxmin                                 0.592557   \n",
        "WCC_intersection_experience_sum                                 -0.036772   \n",
        "WCC_intersection_experience_maxmin                               0.024499   \n",
        "WCC_intersection_timing_sum                                     -0.080377   \n",
        "\n",
        "                                       previous_maximum_experience_mean  \\\n",
        "norm_similarity                                                0.025517   \n",
        "norm_similarity_zscore                                         0.013434   \n",
        "previous_norm_similarity                                       0.025374   \n",
        "previous_norm_similarity_zscore                                0.015029   \n",
        "previous_average_accuracy_maxmin_mean                          0.112178   \n",
        "previous_accuracy_maxmin_mean_on_s                             0.023110   \n",
        "previous_maximum_experience_mean                               1.000000   \n",
        "previous_exprience_mean_on_s                                   0.666666   \n",
        "days_between_estimates                                         0.011223   \n",
        "degree_s                                                       0.027199   \n",
        "CC_unweighted                                                  0.046416   \n",
        "days_to_end                                                    0.026392   \n",
        "WCC_intersection_accuracy_sum                                  0.103264   \n",
        "WCC_intersection_accuracy_maxmin                               0.093389   \n",
        "WCC_intersection_experience_sum                                0.019888   \n",
        "WCC_intersection_experience_maxmin                             0.218425   \n",
        "WCC_intersection_timing_sum                                   -0.220197   \n",
        "\n",
        "                                       previous_exprience_mean_on_s  \\\n",
        "norm_similarity                                            0.028985   \n",
        "norm_similarity_zscore                                     0.008407   \n",
        "previous_norm_similarity                                   0.017655   \n",
        "previous_norm_similarity_zscore                            0.004695   \n",
        "previous_average_accuracy_maxmin_mean                      0.080880   \n",
        "previous_accuracy_maxmin_mean_on_s                         0.027458   \n",
        "previous_maximum_experience_mean                           0.666666   \n",
        "previous_exprience_mean_on_s                               1.000000   \n",
        "days_between_estimates                                     0.048318   \n",
        "degree_s                                                   0.141046   \n",
        "CC_unweighted                                              0.153741   \n",
        "days_to_end                                                0.013001   \n",
        "WCC_intersection_accuracy_sum                              0.098808   \n",
        "WCC_intersection_accuracy_maxmin                           0.062860   \n",
        "WCC_intersection_experience_sum                            0.188515   \n",
        "WCC_intersection_experience_maxmin                         0.518773   \n",
        "WCC_intersection_timing_sum                               -0.131480   \n",
        "\n",
        "                                       days_between_estimates  degree_s  \\\n",
        "norm_similarity                                     -0.081718  0.114371   \n",
        "norm_similarity_zscore                              -0.115809 -0.001362   \n",
        "previous_norm_similarity                             0.010396  0.113048   \n",
        "previous_norm_similarity_zscore                     -0.002093 -0.000011   \n",
        "previous_average_accuracy_maxmin_mean               -0.003162  0.205374   \n",
        "previous_accuracy_maxmin_mean_on_s                  -0.010087  0.139764   \n",
        "previous_maximum_experience_mean                     0.011223  0.027199   \n",
        "previous_exprience_mean_on_s                         0.048318  0.141046   \n",
        "days_between_estimates                               1.000000  0.062358   \n",
        "degree_s                                             0.062358  1.000000   \n",
        "CC_unweighted                                        0.070071  0.058965   \n",
        "days_to_end                                         -0.072604  0.007146   \n",
        "WCC_intersection_accuracy_sum                        0.040732  0.167538   \n",
        "WCC_intersection_accuracy_maxmin                    -0.006476  0.178961   \n",
        "WCC_intersection_experience_sum                     -0.000629 -0.007444   \n",
        "WCC_intersection_experience_maxmin                   0.028516  0.072137   \n",
        "WCC_intersection_timing_sum                         -0.006605 -0.028886   \n",
        "\n",
        "                                       CC_unweighted  days_to_end  \\\n",
        "norm_similarity                             0.004464     0.035313   \n",
        "norm_similarity_zscore                      0.001212     0.003493   \n",
        "previous_norm_similarity                    0.015198    -0.018570   \n",
        "previous_norm_similarity_zscore             0.015693     0.000019   \n",
        "previous_average_accuracy_maxmin_mean       0.040976     0.022509   \n",
        "previous_accuracy_maxmin_mean_on_s         -0.000051     0.011199   \n",
        "previous_maximum_experience_mean            0.046416     0.026392   \n",
        "previous_exprience_mean_on_s                0.153741     0.013001   \n",
        "days_between_estimates                      0.070071    -0.072604   \n",
        "degree_s                                    0.058965     0.007146   \n",
        "CC_unweighted                               1.000000     0.010488   \n",
        "days_to_end                                 0.010488     1.000000   \n",
        "WCC_intersection_accuracy_sum               0.160370     0.029690   \n",
        "WCC_intersection_accuracy_maxmin            0.013824     0.016316   \n",
        "WCC_intersection_experience_sum            -0.009370    -0.014792   \n",
        "WCC_intersection_experience_maxmin          0.164355     0.002218   \n",
        "WCC_intersection_timing_sum                -0.049207     0.088181   \n",
        "\n",
        "                                       WCC_intersection_accuracy_sum  \\\n",
        "norm_similarity                                             0.026182   \n",
        "norm_similarity_zscore                                     -0.000357   \n",
        "previous_norm_similarity                                    0.042112   \n",
        "previous_norm_similarity_zscore                             0.013728   \n",
        "previous_average_accuracy_maxmin_mean                       0.106835   \n",
        "previous_accuracy_maxmin_mean_on_s                          0.102707   \n",
        "previous_maximum_experience_mean                            0.103264   \n",
        "previous_exprience_mean_on_s                                0.098808   \n",
        "days_between_estimates                                      0.040732   \n",
        "degree_s                                                    0.167538   \n",
        "CC_unweighted                                               0.160370   \n",
        "days_to_end                                                 0.029690   \n",
        "WCC_intersection_accuracy_sum                               1.000000   \n",
        "WCC_intersection_accuracy_maxmin                            0.335527   \n",
        "WCC_intersection_experience_sum                            -0.043182   \n",
        "WCC_intersection_experience_maxmin                          0.097731   \n",
        "WCC_intersection_timing_sum                                -0.087628   \n",
        "\n",
        "                                       WCC_intersection_accuracy_maxmin  \\\n",
        "norm_similarity                                                0.076318   \n",
        "norm_similarity_zscore                                         0.029394   \n",
        "previous_norm_similarity                                       0.199636   \n",
        "previous_norm_similarity_zscore                                0.107246   \n",
        "previous_average_accuracy_maxmin_mean                          0.527336   \n",
        "previous_accuracy_maxmin_mean_on_s                             0.592557   \n",
        "previous_maximum_experience_mean                               0.093389   \n",
        "previous_exprience_mean_on_s                                   0.062860   \n",
        "days_between_estimates                                        -0.006476   \n",
        "degree_s                                                       0.178961   \n",
        "CC_unweighted                                                  0.013824   \n",
        "days_to_end                                                    0.016316   \n",
        "WCC_intersection_accuracy_sum                                  0.335527   \n",
        "WCC_intersection_accuracy_maxmin                               1.000000   \n",
        "WCC_intersection_experience_sum                               -0.034586   \n",
        "WCC_intersection_experience_maxmin                             0.095163   \n",
        "WCC_intersection_timing_sum                                   -0.094947   \n",
        "\n",
        "                                       WCC_intersection_experience_sum  \\\n",
        "norm_similarity                                              -0.001685   \n",
        "norm_similarity_zscore                                       -0.001455   \n",
        "previous_norm_similarity                                     -0.012011   \n",
        "previous_norm_similarity_zscore                              -0.015934   \n",
        "previous_average_accuracy_maxmin_mean                        -0.112432   \n",
        "previous_accuracy_maxmin_mean_on_s                           -0.036772   \n",
        "previous_maximum_experience_mean                              0.019888   \n",
        "previous_exprience_mean_on_s                                  0.188515   \n",
        "days_between_estimates                                       -0.000629   \n",
        "degree_s                                                     -0.007444   \n",
        "CC_unweighted                                                -0.009370   \n",
        "days_to_end                                                  -0.014792   \n",
        "WCC_intersection_accuracy_sum                                -0.043182   \n",
        "WCC_intersection_accuracy_maxmin                             -0.034586   \n",
        "WCC_intersection_experience_sum                               1.000000   \n",
        "WCC_intersection_experience_maxmin                            0.469363   \n",
        "WCC_intersection_timing_sum                                   0.428016   \n",
        "\n",
        "                                       WCC_intersection_experience_maxmin  \\\n",
        "norm_similarity                                                  0.011283   \n",
        "norm_similarity_zscore                                           0.002590   \n",
        "previous_norm_similarity                                         0.011016   \n",
        "previous_norm_similarity_zscore                                  0.001558   \n",
        "previous_average_accuracy_maxmin_mean                            0.092622   \n",
        "previous_accuracy_maxmin_mean_on_s                               0.024499   \n",
        "previous_maximum_experience_mean                                 0.218425   \n",
        "previous_exprience_mean_on_s                                     0.518773   \n",
        "days_between_estimates                                           0.028516   \n",
        "degree_s                                                         0.072137   \n",
        "CC_unweighted                                                    0.164355   \n",
        "days_to_end                                                      0.002218   \n",
        "WCC_intersection_accuracy_sum                                    0.097731   \n",
        "WCC_intersection_accuracy_maxmin                                 0.095163   \n",
        "WCC_intersection_experience_sum                                  0.469363   \n",
        "WCC_intersection_experience_maxmin                               1.000000   \n",
        "WCC_intersection_timing_sum                                     -0.129682   \n",
        "\n",
        "                                       WCC_intersection_timing_sum  \n",
        "norm_similarity                                          -0.010128  \n",
        "norm_similarity_zscore                                   -0.006921  \n",
        "previous_norm_similarity                                 -0.025183  \n",
        "previous_norm_similarity_zscore                          -0.017189  \n",
        "previous_average_accuracy_maxmin_mean                    -0.236252  \n",
        "previous_accuracy_maxmin_mean_on_s                       -0.080377  \n",
        "previous_maximum_experience_mean                         -0.220197  \n",
        "previous_exprience_mean_on_s                             -0.131480  \n",
        "days_between_estimates                                   -0.006605  \n",
        "degree_s                                                 -0.028886  \n",
        "CC_unweighted                                            -0.049207  \n",
        "days_to_end                                               0.088181  \n",
        "WCC_intersection_accuracy_sum                            -0.087628  \n",
        "WCC_intersection_accuracy_maxmin                         -0.094947  \n",
        "WCC_intersection_experience_sum                           0.428016  \n",
        "WCC_intersection_experience_maxmin                       -0.129682  \n",
        "WCC_intersection_timing_sum                               1.000000  \n",
        "\n",
        "[17 rows x 17 columns]"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Using the dependent variable norm_similarity"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = smf.ols(formula='norm_similarity ~ previous_norm_similarity + previous_average_accuracy_maxmin_mean + previous_accuracy_maxmin_mean_on_s + days_between_estimates + degree_s_dv + degree_i + CC_unweighted + WCC_intersection_accuracy_maxmin', data=df)\n",
      "result = model.fit()\n",
      "print(result.summary())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:        norm_similarity   R-squared:                       0.033\n",
        "Model:                            OLS   Adj. R-squared:                  0.033\n",
        "Method:                 Least Squares   F-statistic:                     4615.\n",
        "Date:                Thu, 18 Dec 2014   Prob (F-statistic):               0.00\n",
        "Time:                        20:52:46   Log-Likelihood:            -1.4408e+05\n",
        "No. Observations:             1074566   AIC:                         2.882e+05\n",
        "Df Residuals:                 1074557   BIC:                         2.883e+05\n",
        "Df Model:                           8                                         \n",
        "=========================================================================================================\n",
        "                                            coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "---------------------------------------------------------------------------------------------------------\n",
        "Intercept                                 0.4782      0.002    205.569      0.000         0.474     0.483\n",
        "previous_norm_similarity                  0.0613      0.001     71.419      0.000         0.060     0.063\n",
        "previous_average_accuracy_maxmin_mean     0.1442      0.004     33.209      0.000         0.136     0.153\n",
        "previous_accuracy_maxmin_mean_on_s        0.0195      0.001     14.573      0.000         0.017     0.022\n",
        "days_between_estimates                   -0.0013   1.32e-05   -100.117      0.000        -0.001    -0.001\n",
        "degree_s_dv                               0.0029   2.68e-05    107.195      0.000         0.003     0.003\n",
        "degree_i                                 -0.0001   4.17e-05     -2.795      0.005        -0.000 -3.48e-05\n",
        "CC_unweighted                             0.0049      0.002      2.854      0.004         0.002     0.008\n",
        "WCC_intersection_accuracy_maxmin          0.0127      0.001     10.321      0.000         0.010     0.015\n",
        "==============================================================================\n",
        "Omnibus:                   102879.455   Durbin-Watson:                   1.615\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           135767.524\n",
        "Skew:                          -0.870   Prob(JB):                         0.00\n",
        "Kurtosis:                       2.915   Cond. No.                         632.\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Using the dependent variable norm_similarity_zscore"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = smf.ols(formula='norm_similarity_zscore ~ previous_norm_similarity_zscore + previous_average_accuracy_maxmin_mean + previous_accuracy_maxmin_mean_on_s + days_between_estimates + degree_s_dv + degree_i + CC_unweighted + WCC_intersection_accuracy_maxmin', data=df)\n",
      "result = model.fit()\n",
      "print(result.summary())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                              OLS Regression Results                              \n",
        "==================================================================================\n",
        "Dep. Variable:     norm_similarity_zscore   R-squared:                       0.021\n",
        "Model:                                OLS   Adj. R-squared:                  0.021\n",
        "Method:                     Least Squares   F-statistic:                     3441.\n",
        "Date:                    Thu, 18 Dec 2014   Prob (F-statistic):               0.00\n",
        "Time:                            20:53:08   Log-Likelihood:            -1.8101e+06\n",
        "No. Observations:                 1294364   AIC:                         3.620e+06\n",
        "Df Residuals:                     1294355   BIC:                         3.620e+06\n",
        "Df Model:                               8                                         \n",
        "=========================================================================================================\n",
        "                                            coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "---------------------------------------------------------------------------------------------------------\n",
        "Intercept                                -0.1303      0.007    -17.378      0.000        -0.145    -0.116\n",
        "previous_norm_similarity_zscore           0.0715      0.001     81.287      0.000         0.070     0.073\n",
        "previous_average_accuracy_maxmin_mean     0.3199      0.014     22.951      0.000         0.293     0.347\n",
        "previous_accuracy_maxmin_mean_on_s        0.0096      0.004      2.272      0.023         0.001     0.018\n",
        "days_between_estimates                   -0.0059   4.28e-05   -136.942      0.000        -0.006    -0.006\n",
        "degree_s_dv                           -5.458e-05   8.75e-05     -0.623      0.533        -0.000     0.000\n",
        "degree_i                                 -0.0005      0.000     -3.462      0.001        -0.001    -0.000\n",
        "CC_unweighted                             0.0500      0.006      8.933      0.000         0.039     0.061\n",
        "WCC_intersection_accuracy_maxmin          0.0199      0.004      5.060      0.000         0.012     0.028\n",
        "==============================================================================\n",
        "Omnibus:                   219825.890   Durbin-Watson:                   1.784\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           422267.630\n",
        "Skew:                          -1.053   Prob(JB):                         0.00\n",
        "Kurtosis:                       4.842   Cond. No.                         627.\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Distribution plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for m in sorted(set(measures_2) | set(measures_3) | set(measures_4)):\n",
      "    plot_histogram(df[m].to_dict(), bins=20, mname=m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEaCAYAAADtxAsqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nESljaBARkbL/B2yiB1maLDM9AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f5362a491d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEaCAYAAADQVmpMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npBpDhIiIVGOIEBGRagwRIiJS7f8DplsSt4pcvd8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f534c15a2d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEaCAYAAADg2nttAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/xP/rdDhAjaHAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f535ba8ed90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEaCAYAAADQVmpMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSM+fSRiBgQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f5362a2fcd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEaCAYAAADg2nttAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f534beda250>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEaCAYAAADg2nttAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiJSxbAgIiJVDAsiIlL1fydkqVMXW1SDAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f534bec01d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEaCAYAAAACBmAUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMDCIiEiV/w/mvTvGZd2YPQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f535b9d6b90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEaCAYAAAACBmAUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERGp8v8AOKpfGGApaeMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f535c4edbd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEaCAYAAAACBmAUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtYVOXePvB7EEy3gggKyAwGwgAiiEcQTaUIT2WeNd0p\n4mmru7ZpB7O2PzG3Quey9K3X8JC71DJTMyXSJH094BZPKRWogDAgKQdBQXCY7+8PYm0J0MWIAnp/\nrmuui3nW+q55ngHWPes4GhEREBER3YZFfXeAiIgaBwYGERGpwsAgIiJVGBhERKQKA4OIiFRhYBAR\nkSoMjAbC1dUVe/bsqXba/v374e3tfY971PCEh4fDzs4OvXr1qu+uNAqRkZGYPn16fXej0bhw4QKs\nra3BKw1qxsBoIDQaDTQaTbXT+vbti19//fW2y4iIiMDEiRPrumsNwv79+7F7925kZmbi8OHDlaYZ\njUa0bNkSR44cUdo+//xzWFhYVGnr2LGj8jwpKQljxoxB27ZtYWtrC39/f7z33nswmUwAgNLSUkRE\nRMDT0xMtW7aEm5sbpk6dirS0tLs82rqxYMECrFq1qr670Wi0b98ehYWFNf4fEgODVDIajfX6+mlp\naXB1dUWzZs2qTLO0tETv3r2xb98+pW3fvn3o2LFjlbb+/fsDAM6dO4fAwEA8/PDDOH36NPLz8/HV\nV18hISEBV69eBQCMHj0aO3bswIYNG1BQUICTJ0+iR48eNW4JNiRlZWX13QW6Hwk1CK6urvL2229L\n586dpVWrVjJu3Di5fv26iIjs3btXdDqdMm9UVJRotVqxtrYWLy8v2bNnj+zatUuaNm0qVlZW0rJl\nS+nSpYuIiBgMBhk6dKjY2dmJh4eHrFq1SllOUVGRTJo0SVq3bi0dO3aUN954o9LrPPzww/LGG2+I\nn5+fNGvWTIxGo0RGRoq7u7tYW1uLj4+PfPPNN8r8a9askd69e8vcuXPF1tZW3N3d5cCBA7J69Wpx\ncXERBwcHWbduXY3vQU19/fTTT6VZs2bSpEkTadmypURERFSpXbJkiQwdOlR57uPjI+vWravU1rFj\nR/n8889FROSvf/2rPPnkkzX25YcffpDmzZtLRkZGjfPUJD8/X6ZMmSLt2rUTrVYr//znP6WsrExK\nSkqkS5cu8uGHH4qIiNFolN69e8uSJUtERGTRokUyatQoGTdunFhbW0u3bt3k5MmTld6fkSNHStu2\nbcXNzU2WL1+uTKuofeaZZ8TGxkY+/fRTWbRokTzzzDPKPIcOHZKgoCCxtbUVf39/iYuLU6b1799f\nFi5cKH369BFra2sZMGCAXL58WZm+f/9+pdbFxUXWrl0rIiLXr1+XF154Qdq3by+Ojo4yc+ZMKS4u\nvuX7s3fvXtFqtfLmm29K27ZtpV27dvLNN9/Id999J3q9Xuzs7CQyMlKZPz4+Xnr16iW2trbSrl07\nefbZZ6W0tFRERA4cOCBt2rSR9PR0ERE5ceKEtG7dWn777TcRKf8bfuutt8TPz09atmwpU6ZMkYsX\nL8qgQYPExsZGHn/8ccnLyxMRkZSUFNFoNFJWVqbqPXkQMTAaiIcfflgCAwMlKytLcnNzpWPHjvLx\nxx+LSOXA+PXXX8XFxUWysrJERCQtLU3OnTsnIiIREREyceLESsvt27ev/P3vf5eSkhI5ceKEtG3b\nVn788UcREZk/f74EBwdLfn6+ZGRkiJ+fn7i4uFTqU9euXSUjI0MJr6+++kp57U2bNkmLFi3k4sWL\nIlIeGJaWlrJ27VoxmUzyz3/+U7RarfIPHhsbK9bW1nLt2rVq34Nb9XXt2rXyyCOP1Pj+/fTTT2Jn\nZyciIpcuXZKHH35YioqKxNHRUWnTaDTKisXJyUlZ6VWn4r0xx/Dhw2XmzJlSVFQkv//+uwQEBMgn\nn3wiIiKnT5+W1q1byy+//CL/+te/JCgoSEwmk4iUr/StrKzk66+/FqPRKG+//ba4ubmJ0WiUsrIy\n6datmyxZskRu3Lgh58+flw4dOsj3339fqXbbtm0iIlJcXCwRERFKYGRkZIi9vb3s2rVLRMoD0d7e\nXlkB9u/fXzw8PCQ5OVmKi4slODhYXnnlFRERSU1NFWtra9m4caMYjUbJycmREydOiIjI888/L8OG\nDZO8vDwpLCyUoUOHyoIFC275/uzdu1csLS1lyZIlYjQaZdWqVWJvby8TJkyQq1evypkzZ6R58+aS\nmpoqIiIJCQkSHx8vZWVlkpqaKh07dpT3339fWd5rr70mjz32mBQVFYmvr6+sWLFCmebq6ipBQUHy\n+++/i8FgEAcHB+nataucOHFCrl+/Lo899pgsXrxYRKoPjJrekwcVA6OBcHV1VT79ioi8/PLLMnPm\nTBGpHBjJycni4OAgu3fvVj5lVfjzJ8oLFy5IkyZN5OrVq0rbggULZPLkySIi0qFDB4mNjVWmffrp\np5W2MFxdXWXNmjW37HeXLl2UldSaNWtEr9cr006dOiUajUZ+//13pc3e3r7Sp2a1fV2zZs0tA6O4\nuFiaNWsmJ0+elC1btijvQ69evZQ2Nzc3ZX4rKytlZVudadOmydNPP33LsVfn4sWL8tBDD1X6lP3F\nF1/Io48+qjx/5513xNPTU+zs7OTs2bNK+6JFiyQoKEh5bjKZpF27drJ//345fPiwtG/fvtJrLVu2\nTMLDw5Xa/v37V5p+899DVFRUlQ8TAwcOVLb4goODZenSpcq0lStXyqBBg5TXGTlyZJWxmkwmadGi\nhfKBRUTk4MGDld7n6uzdu1eaN2+uBGVBQYFoNBo5cuSIMk/37t1l69at1da/9957MmLECOX5jRs3\npHv37uLr6yuDBw+uNK+rq6t88cUXyvNRo0bJ7NmzlecffvihDB8+XESqBsat3pMHlWV97xKj/3Jy\nclJ+bt68OTIzM6vM4+Hhgffffx8RERE4c+YMBg4ciHfffRft2rWrMm9mZibs7OzQokULpa19+/ZI\nSEhQpru4uCjTdDpdlWXcPB0APvvsM7z33ntITU0FAFy9ehU5OTnKdEdHx0pjAIC2bdtWaqs4RqCm\nr0ePHq0yb3WaNWuGgIAA7Nu3D+fPn0ffvn0BAI888ojSVnH8AgDs7e2rfX8rtGnTBsnJyape+2Zp\naWm4ceNGpd+HyWRC+/btleeTJk3Ca6+9htGjR8Pd3b1S/c2/A41GA51Oh8zMTGg0GmRmZqJ169bK\n9LKyMvTr16/a2ur69dVXX+Hbb79V2oxGIx577DHl+Z///ip+T+np6ejQoUOVZV66dAlFRUXo3r27\n0iYiykkDt2Jvb68cXK74O/nz3861a9cAlJ+cMG/ePCQkJKCoqAhGoxE9evRQ5rW0tERYWBjmzJmD\n999/v8pr/Xm5Nz9v1qxZtX+PFWp6Tx5UPOjdCI0fPx779+9HWloaNBoN5s+fDwBVzu5wdnZGbm5u\npT/yCxcuQKvVAgDatWuH9PR0ZdrNP1e4eZlpaWmYMWMGVqxYgdzcXOTl5cHX17dOTkOsqa+3Wgn+\nWb9+/bBv3z7s379fCYy+ffvip59+wv79+yutXB9//HF8/fXXNS7r8ccfx5EjR2AwGGo1DhcXFzz0\n0EPIyclBXl4e8vLycOXKFfz888/KPLNnz8aTTz6JmJgYHDhwoFL9zb8Dk8mEjIwMaLVauLi4wM3N\nTVlmXl4eCgoKsGPHDgC3PssOKA/fiRMnVqovLCzEyy+/fNsxtW/fHufOnavS3qZNGzRv3hyJiYnK\nMvPz81FQUHDbZdbGrFmz4OPjg7Nnz+LKlStYunRppVAyGAx4/fXXMWXKFMybNw+lpaW3XF5d/L0+\nqBgYjUxSUhJ+/PFHlJSU4KGHHkKzZs3QpEkTAOWfhlJTU5V/CBcXF/Tu3RsLFixASUkJTp06hdWr\nV+OZZ54BAIwdOxaRkZHIz8+HwWDARx99dMuVzrVr16DRaNCmTRuYTCasWbMGp0+frpNx3a6vavTr\n1w8//vgjMjIylNNn+/Tpg7i4OJw4caJSYCxevBgHDx7Eyy+/jOzsbADA2bNnMXHiRBQUFCAkJASh\noaEYMWIEjh07BqPRiMLCQnz88cdYs2ZNjX1o164dBgwYgHnz5qGwsBAmkwnnzp1TztZav349jh8/\njnXr1mH58uUICwtTPkkDQEJCAr755hsYjUa8//77aNasGXr16oWePXvC2toab775JoqLi1FWVobT\np08rW2C3Wwk+88wz+PbbbxEbG4uysjJcv34dcXFxlQKxpmVMmDABu3fvxldffQWj0YicnBycPHkS\nFhYWmD59Op5//nlcunQJQPnKOzY29pZ9qa2rV6/C2toaf/nLX/Drr7/if/7nfyr1efLkyZg2bRo+\n/fRTtGvXDgsXLqyz12a4VMbAaKD+/Imx4ueSkhIsWLAAbdu2Rbt27XD58mVERkYCAMaMGQOgfHO/\nYpN9w4YNSE1NhbOzM0aOHInXX39d2Q3x//7f/4NOp4ObmxsGDBiAMWPGoGnTpjX2ycfHBy+88AKC\ngoLg5OSE06dP45FHHqmxzzf3W41b9fV2n6ABICgoCAUFBQgMDFTa7O3t4eDgAEdHx0q7fzp06IBD\nhw4hNTUVnTp1gq2tLUaPHo2ePXuiZcuWAIDNmzdjyJAhGDduHGxtbeHn54djx44hNDT0lv347LPP\nUFpaCh8fH9jZ2WHMmDG4ePEiLly4gLlz5+Kzzz7DX/7yF4wfPx49evTAvHnzlNphw4Zh06ZNsLOz\nw+eff44tW7agSZMmaNKkCXbs2IETJ06gQ4cOaNu2LWbMmKF8mq/pva9o0+l02LZtG5YtWwYHBwe0\nb98e77zzTqUV4p//3iqet2/fHjt37sQ777wDe3t7dO3aFadOnQIAvPHGG/Dw8ECvXr3QqlUrhIaG\nIikp6Zbvz59fq7rnN3v77bfxxRdfwMbGBjNmzMDTTz+tzL98+XJcvnwZS5YsAQCsWbMGa9asqbLl\nVtNr1fR/pmbeB9KtDnCEh4eLg4OD+Pr6Km0vvviieHt7S+fOnWXEiBGSn5+vTFu2bJl4eHiIl5dX\npQOKR48eFV9fX/Hw8JB//OMfSvv169dl7Nix4uHhIYGBgcpZESLlZ8Xo9XrR6/W3PBWT6s7KlSvN\nPjOI7tzNZzURNUS33MIIDw9HTExMpbYBAwbgzJkzOHnyJDw9PZVPt4mJidi0aRMSExMRExOD2bNn\nK59eZs2ahejoaCQnJyM5OVlZZnR0NOzt7ZGcnIy5c+cq++Jzc3Px+uuv48iRIzhy5AgWL16M/Pz8\nOg/LB93Fixdx4MABmEwm/Pbbb3j33XcxYsSI+u7WA0u4+4MauFsGRt++fSudlQEAoaGhsLAoLwsM\nDERGRgYAYNu2bRg/fjysrKzg6uoKDw8PxMfHIysrC4WFhQgICABQfobI1q1bAQDbt29HWFgYAGDU\nqFHKFbTff/89BgwYAFtbW9ja2iI0NLRKcNGdKy0txcyZM2FjY4OQkBAMHz4cs2fPru9uNQotW7aE\ntbV1lcetdoXczv20y2PZsmXVvj9PPPFEfXeN7sAdnVa7evVqjB8/HkD5aZE33xROp9PBYDDAysqq\n0pkuWq1WOdBmMBiU0zYtLS3RqlUr5OTkIDMzs1JNxbKobrVv377S2Tuk3t04vXLRokV1vsz68uqr\nr+LVV1+t725QHTP7oPfSpUvRtGlTTJgwoS77Q0REDZRZWxhr167Fzp07K92ETavVVjqHPCMjAzqd\nDlqtVtltdXN7Rc2FCxfg7OwMo9GIK1euwN7eHlqtFnFxcUpNenp6pQuMbubh4VHtOeJERFQ9d3d3\nnD17tvaFtzsqnpKSUuksqV27domPj49cunSp0nxnzpwRf39/KSkpUe5zU3Hpf0BAgBw+fFhMJpMM\nHjxYuZ/NihUrlNtfbNiwQcaNGyciIjk5OeLm5iZ5eXmSm5ur/FwdFUNotBYtWlTfXbirOL7GjeNr\nvMxdb95yC2P8+PH46aefcPnyZbi4uGDx4sWIjIxEaWmpci56UFAQVq5cCR8fH4wdOxY+Pj6wtLTE\nypUrlQN4K1euxOTJk1FcXIwhQ4Zg0KBBAICpU6di4sSJ0Ov1sLe3x8aNGwEAdnZ2WLhwIXr27Amg\nfN+ura1t7dOQiIjqzC0DY8OGDVXapkyZUuP8NR3o6t69e7UHVx966CF8+eWX1S4rPDwc4eHht+oe\nERHdQ7zSuwELDg6u7y7cVRxf48bxPXg0f+zParQ0Gg0veCIiqgVz15vcwiAiIlUYGEREpAoDg4iI\nVGFgEBGRKgwMIiJShYFBRESqMDCIiEgVBgYREanCwCAiIlUYGEREpAoDg4iIVGFgEBGRKgwMIiJS\nhYFBRESqMDCIiEgVBgYREanCwCAiIlUYGEREpAoDg4iIVGFgEBGRKgwMIiJShYFBRESqMDCIiEgV\nBgYREanCwCAiIlUYGEREpMotA2PKlClwdHSEn5+f0pabm4vQ0FB4enpiwIAByM/PV6ZFRkZCr9fD\n29sbsbGxSntCQgL8/Pyg1+sxZ84cpb2kpATjxo2DXq9Hr169kJaWpkxbt24dPD094enpic8++6xO\nBktEROa7ZWCEh4cjJiamUltUVBRCQ0ORlJSEkJAQREVFAQASExOxadMmJCYmIiYmBrNnz4aIAABm\nzZqF6OhoJCcnIzk5WVlmdHQ07O3tkZycjLlz52L+/PkAykPp9ddfx5EjR3DkyBEsXry4UjAREdG9\nd8vA6Nu3L1q3bl2pbfv27QgLCwMAhIWFYevWrQCAbdu2Yfz48bCysoKrqys8PDwQHx+PrKwsFBYW\nIiAgAAAwadIkpebmZY0aNQp79uwBAHz//fcYMGAAbG1tYWtri9DQ0CrBRURE91atj2FkZ2fD0dER\nAODo6Ijs7GwAQGZmJnQ6nTKfTqeDwWCo0q7VamEwGAAABoMBLi4uAABLS0u0atUKOTk5NS6LiIjq\nzx0d9NZoNNBoNHXVF/oTGxs75T2uzcPGxq6+u05E9yHL2hY4Ojri4sWLcHJyQlZWFhwcHACUbzmk\np6cr82VkZECn00Gr1SIjI6NKe0XNhQsX4OzsDKPRiCtXrsDe3h5arRZxcXFKTXp6Oh577LEa+xQR\nEaH8HBwcjODg4NoOq0EqLMwDIGbUMcSJ6L/i4uIqrVPNJreRkpIivr6+yvOXXnpJoqKiREQkMjJS\n5s+fLyIiZ86cEX9/fykpKZHz589Lhw4dxGQyiYhIQECAHD58WEwmkwwePFh27dolIiIrVqyQmTNn\niojIhg0bZNy4cSIikpOTI25ubpKXlye5ubnKz9VRMYRGC4AAYsbj/n1PiOjOmbuOuGXV008/Le3a\ntRMrKyvR6XSyevVqycnJkZCQENHr9RIaGlppRb506VJxd3cXLy8viYmJUdqPHj0qvr6+4u7uLs89\n95zSfv36dRkzZox4eHhIYGCgpKSkKNNWr14tHh4e4uHhIWvXrq15APfxypGBQUR3g7nrCM0fxY2W\nRqNBIx9CjcqPD5kzNvPeExsbuz92g9WOtXVrFBTk1rqOiOqHuetNBkYDdq8D416/HhHVD3PXm7w1\nyD1g7tlOREQNCbcw7oE7+eRuXp0VAKMZdTDz9Rr+74CI/svc9WatT6ulxsAI8wOKiKh63CVFRESq\nMDCIiEiV+2KX1G+//Var+Vu0aFHpXlVqmXvaKRHR/eC+OOhtbe1Zq5qiovMoLi6ClZVVrV/r3h68\nbjx1jfzPiOiB8kAf9C4srN0WhoVFU67giIhqiccwqA5Y8q66RA+A+2ILg+qbeafx8q66RI0LtzCI\niEgVBgYREanCwCAiIlUYGEREpAoDg+oRz64iakx4lhTVI55dRdSYcAuDiIhUYWAQEZEqD2xgtGnj\nxG/AIyKqhQf2GEb5XWdru/+coUFED64HdguDiIhqh4FBRESqMDCIiEgVBgY1Qrzgj6g+PLAHvakx\n4wV/RPWBWxhERKQKA4OIiFRhYBARkSpmB0ZkZCQ6deoEPz8/TJgwASUlJcjNzUVoaCg8PT0xYMAA\n5OfnV5pfr9fD29sbsbGxSntCQgL8/Pyg1+sxZ84cpb2kpATjxo2DXq9Hr169kJaWZm5XiYioDpgV\nGKmpqVi1ahWOHTuGn3/+GWVlZdi4cSOioqIQGhqKpKQkhISEICoqCgCQmJiITZs2ITExETExMZg9\nezZEyg9azpo1C9HR0UhOTkZycjJiYmIAANHR0bC3t0dycjLmzp2L+fPn19GQ6cHFs6uI7oRZgWFj\nYwMrKysUFRXBaDSiqKgIzs7O2L59O8LCwgAAYWFh2Lp1KwBg27ZtGD9+PKysrODq6goPDw/Ex8cj\nKysLhYWFCAgIAABMmjRJqbl5WaNGjcKePXvueLD0oKs4u6p2j/LbyBCRWYFhZ2eHF154Ae3bt4ez\nszNsbW0RGhqK7OxsODo6AgAcHR2RnZ0NAMjMzIROp1PqdTodDAZDlXatVguDwQAAMBgMcHFxAQBY\nWlqiVatWyM3NNW+URER0x8y6DuPcuXN4//33kZqailatWmHMmDH497//XWmee3uH14ibfg7+40FE\nRAAQFxeHuLi4O16OWYFx9OhR9O7dG/b29gCAkSNH4tChQ3BycsLFixfh5OSErKwsODg4ACjfckhP\nT1fqMzIyoNPpoNVqkZGRUaW9oubChQtwdnaG0WjElStXYGdX077kCHOGQUT0QAgODkZwcLDyfPHi\nxWYtx6xdUt7e3jh8+DCKi4shIti9ezd8fHwwdOhQrFu3DgCwbt06DB8+HADw1FNPYePGjSgtLUVK\nSgqSk5MREBAAJycn2NjYID4+HiKC9evXY9iwYUpNxbI2b96MkJAQswZIRER1w6wtDH9/f0yaNAk9\nevSAhYUFunXrhhkzZqCwsBBjx45FdHQ0XF1d8eWXXwIAfHx8MHbsWPj4+MDS0hIrV65UdletXLkS\nkydPRnFxMYYMGYJBgwYBAKZOnYqJEydCr9fD3t4eGzdurKMhExGROTRScX5rI1UePLUbgoVFU5hM\nN2pdV/4FSua8Xaxr7HWN/N+EqBKNxry/aV7pTUREqjAwiIhIFQYGERGpwsAgIiJVGBhERKQKA4OI\niFRhYBARkSoMDCIiUoWBQUREqjAwiIhIFQYGERGpwsAgIiJVGBhERKQKA4OIiFRhYBARkSoMDCIi\nUoWBQUREqjAwiIhIFQYG0W1ZQqPR1PphY2NX3x0nqlOW9d0BoobPCHO+C7ywUFP3XSGqR9zCICIi\nVRgYRESkCgODiIhUYWAQEZEqDAwiIlKFgUFERKowMIiISBUGBhERqWJ2YOTn52P06NHo2LEjfHx8\nEB8fj9zcXISGhsLT0xMDBgxAfn6+Mn9kZCT0ej28vb0RGxurtCckJMDPzw96vR5z5sxR2ktKSjBu\n3Djo9Xr06tULaWlp5naViIjqgNmBMWfOHAwZMgS//PILTp06BW9vb0RFRSE0NBRJSUkICQlBVFQU\nACAxMRGbNm1CYmIiYmJiMHv2bIiUXzk7a9YsREdHIzk5GcnJyYiJiQEAREdHw97eHsnJyZg7dy7m\nz59fB8MlIiKziRny8/PFzc2tSruXl5dcvHhRRESysrLEy8tLRESWLVsmUVFRynwDBw6UQ4cOSWZm\npnh7eyvtGzZskL/97W/KPIcPHxYRkRs3bkibNm2q7QsAAaRWDwsLK7PqzKth3YNcR9QQmfu3adYW\nRkpKCtq2bYvw8HB069YN06dPx7Vr15CdnQ1HR0cAgKOjI7KzswEAmZmZ0Ol0Sr1Op4PBYKjSrtVq\nYTAYAAAGgwEuLi4AAEtLS7Rq1Qq5ubnmdJeIiOqAWTcfNBqNOHbsGD766CP07NkTzz//vLL7qULF\nHTvvjYibfg7+40FU3yzN+h+wtm6NggJ+OKK6ExcXh7i4uDtejlmBodPpoNPp0LNnTwDA6NGjERkZ\nCScnJ1y8eBFOTk7IysqCg4MDgPIth/T0dKU+IyMDOp0OWq0WGRkZVdorai5cuABnZ2cYjUZcuXIF\ndnY13S46wpxhEN1lvMstNQzBwcEIDg5Wni9evNis5Zi1S8rJyQkuLi5ISkoCAOzevRudOnXC0KFD\nsW7dOgDAunXrMHz4cADAU089hY0bN6K0tBQpKSlITk5GQEAAnJycYGNjg/j4eIgI1q9fj2HDhik1\nFcvavHkzQkJCzBogERHVEXMPmpw4cUJ69OghnTt3lhEjRkh+fr7k5ORISEiI6PV6CQ0Nlby8PGX+\npUuXiru7u3h5eUlMTIzSfvToUfH19RV3d3d57rnnlPbr16/LmDFjxMPDQwIDAyUlJaXafsCMA5I8\n6M26hl5HdDeZ+zem+aO40SrfR1y7IVhYNIXJdKPWdUDtX4t1rDOnrpH/W1IDp9GY9zfGK72JiEgV\nBgYREanCwCAiIlUYGEREpAoDg4iIVGFgEDU4lsqdEmrzsLGp6cJWorph1pXeRHQ38Qpxapi4hUFE\nRKowMIiISBUGBhERqcLAICIiVRgYRESkCgODiIhUYWAQEZEqDAwiIlKFgUFERKowMIiISBUGBhER\nqcLAILpv8KaFdHfx5oNE9w3etJDuLm5hEBGRKgwMIiJShYFBRESqMDCIiEgVBgYREanCwCAiIlUY\nGEREpAoDg4iIVLmjwCgrK0PXrl0xdOhQAEBubi5CQ0Ph6emJAQMGID8/X5k3MjISer0e3t7eiI2N\nVdoTEhLg5+cHvV6POXPmKO0lJSUYN24c9Ho9evXqhbS0tDvpKhER3aE7CowPPvgAPj4+0GjKrxSN\niopCaGgokpKSEBISgqioKABAYmIiNm3ahMTERMTExGD27NkQKb8iddasWYiOjkZycjKSk5MRExMD\nAIiOjoa9vT2Sk5Mxd+5czJ8//066SkREd8jswMjIyMDOnTsxbdo0ZeW/fft2hIWFAQDCwsKwdetW\nAMC2bdswfvx4WFlZwdXVFR4eHoiPj0dWVhYKCwsREBAAAJg0aZJSc/OyRo0ahT179pg/SiIiumNm\nB8bcuXPx1ltvwcLiv4vIzs6Go6MjAMDR0RHZ2dkAgMzMTOh0OmU+nU4Hg8FQpV2r1cJgMAAADAYD\nXFxcAACYlXhJAAATXElEQVSWlpZo1aoVcnNzze0uERHdIbNuPrhjxw44ODiga9euiIuLq3aeijth\n3hsRN/0c/MeDiIgAIC4ursZ1dW2YFRgHDx7E9u3bsXPnTly/fh0FBQWYOHEiHB0dcfHiRTg5OSEr\nKwsODg4Ayrcc0tPTlfqMjAzodDpotVpkZGRUaa+ouXDhApydnWE0GnHlyhXY2dV0G+YIc4ZBRPRA\nCA4ORnBwsPJ88eLFZi3HrF1Sy5YtQ3p6OlJSUrBx40Y89thjWL9+PZ566imsW7cOALBu3ToMHz4c\nAPDUU09h48aNKC0tRUpKCpKTkxEQEAAnJyfY2NggPj4eIoL169dj2LBhSk3FsjZv3oyQkBCzBkhE\nRHWjTr4Po2LX0yuvvIKxY8ciOjoarq6u+PLLLwEAPj4+GDt2LHx8fGBpaYmVK1cqNStXrsTkyZNR\nXFyMIUOGYNCgQQCAqVOnYuLEidDr9bC3t8fGjRvroqtERGQmjVSc4tRIlQdP7YZgYdEUJtONWtcB\ntX8t1rGuMdQ18tUA1ZJGY97vnFd6ExGRKgwMIiJShYFBRESqMDCIHniWynVTtXnY2NR0mjvdr+rk\nLCkiasyMMOdgeWHhvbowlxoKbmEQEZEqDAwiIlKFgUFERKowMIiISBUGBhERqcLAICIiVRgYRESk\nCgODiIhUYWAQEZEqDAwiIlKFgUFERKowMIjITLxp4YOGNx8kIjPxpoUPGm5hEBGRKgwMIiJShYFB\nRESqMDCIiEgVBgYREanCwCAiIlUYGEREpAoDg4iIVGFgEBGRKgwMIiJSxazASE9Px6OPPopOnTrB\n19cXy5cvBwDk5uYiNDQUnp6eGDBgAPLz85WayMhI6PV6eHt7IzY2VmlPSEiAn58f9Ho95syZo7SX\nlJRg3Lhx0Ov16NWrF9LS0swdIxER1QUxQ1ZWlhw/flxERAoLC8XT01MSExPlpZdekjfeeENERKKi\nomT+/PkiInLmzBnx9/eX0tJSSUlJEXd3dzGZTCIi0rNnT4mPjxcRkcGDB8uuXbtERGTFihUya9Ys\nERHZuHGjjBs3rtq+ABBAavWwsLAyq868GtaxjnV/rqP6Ze7vwKwtDCcnJ3Tp0gUA0LJlS3Ts2BEG\ngwHbt29HWFgYACAsLAxbt24FAGzbtg3jx4+HlZUVXF1d4eHhgfj4eGRlZaGwsBABAQEAgEmTJik1\nNy9r1KhR2LNnjzldJSKiOnLHxzBSU1Nx/PhxBAYGIjs7G46OjgAAR0dHZGdnAwAyMzOh0+mUGp1O\nB4PBUKVdq9XCYDAAAAwGA1xcXAAAlpaWaNWqFXJzc++0u0REZKY7CoyrV69i1KhR+OCDD2BtbV1p\nWsW974mI6P5g9vdh3LhxA6NGjcLEiRMxfPhwAOVbFRcvXoSTkxOysrLg4OAAoHzLIT09XanNyMiA\nTqeDVqtFRkZGlfaKmgsXLsDZ2RlGoxFXrlyBnV1NX7wScdPPwX88iIgIAOLi4hAXF3fnCzLnwIfJ\nZJKJEyfK888/X6n9pZdekqioKBERiYyMrHLQu6SkRM6fPy8dOnRQDnoHBATI4cOHxWQyVTnoPXPm\nTBER2bBhAw96s451902d5R+1tXtYW7c2Z3VF1TBz1S9mVe3fv180Go34+/tLly5dpEuXLrJr1y7J\nycmRkJAQ0ev1EhoaKnl5eUrN0qVLxd3dXby8vCQmJkZpP3r0qPj6+oq7u7s899xzSvv169dlzJgx\n4uHhIYGBgZKSklL9AMz4o2VgsI51jbOO6oa576Xmj+JGq/w4Se2GYGHRFCbTjVrXAbV/LdaxjnV1\nV9fIV1cNhkZj3nvJK72JiEgVBgYREanCwCAiIlUYGEREpAoDg4iIVGFgEBGRKgwMIiJShYFBRESq\nMDCI6L5mY2On3Ay1Ng8bm5ruXffgMvvmg0RE95blHdwBu/ZXNRcW8m7bf8bAIKJGwgjzb0VCdYG7\npIiISBUGBhERqcLAICIiVRgYRESkCgODiIhUYWAQEZEqDAwiIlKFgUFERKowMIiISBUGBhERqcLA\nICIiVRgYRESkCgODiIhUYWAQEZEqDAwiIlKFgUFERKowMIiISJUGHxgxMTHw9vaGXq/HG2+8Ud/d\nISJ6YGlExJzvPLwnysrK4OXlhd27d0Or1aJnz57YsGEDOnbsqMxT/h2/tRuChUVTmEw3al1X/lWP\n5n5FJOtYx7rGVteAV493RKMxb2wNegvjyJEj8PDwgKurK6ysrPD0009j27Zt9d0tIqIHUoMODIPB\nABcXF+W5TqeDwWCoxx4RET24LOu7A7dSvrvp9v7yl+m1Wm5xsdGc7hARPdAadGBotVqkp6crz9PT\n06HT6SrN4+7ujnPnPjXzFdQF0p3XsI51rGuMdWo/tDY27u7uZtU16IPeRqMRXl5e2LNnD5ydnREQ\nEFDloDcREd0bDXoLw9LSEh999BEGDhyIsrIyTJ06lWFBRFRPGvQWBhERNRwN+iypCmou3vvHP/4B\nvV4Pf39/HD9+/B738M7cbnyff/45/P390blzZ/Tp0wenTp2qh16aT+3Fl//5z39gaWmJLVu23MPe\n3Tk144uLi0PXrl3h6+uL4ODge9vBO3C7sV2+fBmDBg1Cly5d4Ovri7Vr1977Tt6BKVOmwNHREX5+\nfjXO01jXLbcbm1nrFWngjEajuLu7S0pKipSWloq/v78kJiZWmue7776TwYMHi4jI4cOHJTAwsD66\nahY14zt48KDk5+eLiMiuXbvuu/FVzPfoo4/KE088IZs3b66HnppHzfjy8vLEx8dH0tPTRUTk0qVL\n9dHVWlMztkWLFskrr7wiIuXjsrOzkxs3btRHd82yb98+OXbsmPj6+lY7vTGvW243NnPWKw1+C0PN\nxXvbt29HWFgYACAwMBD5+fnIzs6uj+7WmprxBQUFoVWrVgDKx5eRkVEfXTWL2osvP/zwQ4wePRpt\n27ath16aT834vvjiC4waNUo5w69Nmzb10dVaUzO2du3aoaCgAABQUFAAe3t7WFo26EOjlfTt2xet\nW7eucXpjXrfcbmzmrFcafGCouXivunkay0q1thcnRkdHY8iQIfeia3VC7e9v27ZtmDVrFoDGdSqj\nmvElJycjNzcXjz76KHr06IH169ff626aRc3Ypk+fjjNnzsDZ2Rn+/v744IMP7nU376rGvG6pDbXr\nlQb/UUDtykP+dOy+sax0atPPvXv3YvXq1Thw4MBd7FHdUjO+559/HlFRUcr9bf78u2zI1Izvxo0b\nOHbsGPbs2YOioiIEBQWhV69e0Ov196CH5lMztmXLlqFLly6Ii4vDuXPnEBoaipMnT8La2voe9PDe\naKzrFrVqs15p8IGh5uK9P8+TkZEBrVZ7z/p4J9SMDwBOnTqF6dOnIyYm5pabmQ2NmvElJCTg6aef\nBlB+EHXXrl2wsrLCU089dU/7ag4143NxcUGbNm3QvHlzNG/eHP369cPJkycbfGCoGdvBgwfx2muv\nASi/GMzNzQ2//fYbevTocU/7erc05nWLGrVer9TZEZa75MaNG9KhQwdJSUmRkpKS2x70PnToUKM6\nMKVmfGlpaeLu7i6HDh2qp16aT834bjZ58mT5+uuv72EP74ya8f3yyy8SEhIiRqNRrl27Jr6+vnLm\nzJl66rF6asY2d+5ciYiIEBGRixcvilarlZycnPrortlSUlJUHfRubOsWkVuPzZz1SoPfwqjp4r1P\nPvkEAPC3v/0NQ4YMwc6dO+Hh4YEWLVpgzZo19dxr9dSM7/XXX0deXp6yj9/KygpHjhypz26rpmZ8\njZma8Xl7e2PQoEHo3LkzLCwsMH36dPj4+NRzz29PzdheffVVhIeHw9/fHyaTCW+++Sbs7Ozquefq\njR8/Hj/99BMuX74MFxcXLF68GDdu3ADQ+NcttxubOesVXrhHRESqNPizpIiIqGFgYBARkSoMDCIi\nUoWBQUREqjAwiIhIFQYGERGpwsAgIiJVGBh018ydO7fSzegGDhyI6dOnK89feOEFvPfee0hKSsKQ\nIUPg6emJ7t27Y9y4cfj9998BlN8xtV+/fvD29ka3bt0wffp0FBcX3/Ox1KRPnz713YV6M336dPzy\nyy/13Q26hxgYdNc88sgjOHjwIADAZDIhJycHiYmJyvRDhw6hd+/eePLJJ/H3v/8dSUlJSEhIwOzZ\ns3Hp0iVkZ2dj7NixeOutt/Drr7/i2LFjGDRoEAoLC+trSAqj0QgAjepGkHVt1apV/MrkBwwDg+6a\noKAgHDp0CABw5swZ+Pr6wtraGvn5+SgpKcEvv/yCU6dOoXfv3njiiSeUuv79+6NTp05YsWIFJk+e\njMDAQGXaqFGj4ODgUO3rXbt2DVOmTEFgYCC6deuG7du3Ayi/G+6SJUsAAN9//z369+8PEcHkyZMx\nc+ZM9OzZE15eXvjuu+8AAGVlZXjppZcQEBAAf39//O///i+A8m/N69u3L4YNGwZfX18AQMuWLZXX\nf+utt5SaiIgIAEBqaio6duyIGTNmwNfXFwMHDsT169cBAGfPnsXjjz+OLl26oHv37khJSalxOdVJ\nTU2Ft7c3wsPD4eXlhb/+9a+IjY1Fnz594Onpif/85z8AyrfSevfujW7duqFPnz5ISkoCALz33nuY\nOnUqAODnn3+Gn58fiouLERERgbCwMPTr1w+urq7YsmULXnzxRXTu3BmDBw9WwjI4OBjHjh1T3od/\n/vOf6NKlC4KCgpQtRLrP1Mkdrohq4ObmJhcuXJBPPvlEPv74Y1m4cKHs3LlT/u///k/69u0r8+bN\nkw8++KDa2pEjR8r27dtVv9aCBQvk3//+t4iUf8udp6enFBUVSVFRkXTq1El+/PFH8fLykvPnz4uI\nSFhYmHJjueTkZNHpdHL9+nX55JNP5F//+peIiFy/fl169OghKSkpsnfvXmnRooWkpqYqr9myZUsR\nEfn+++9lxowZIiJSVlYmTz75pOzbt09SUlLE0tJSTp48KSIiY8eOVfoYEBAgW7duFRGRkpISKSoq\nqnE51alY9unTp8VkMkn37t1lypQpIiKybds2GT58uIiIFBQUiNFoFBGRH374QUaNGiUiIiaTSfr1\n6ydbtmyRHj16yMGDB0Wk/Fv0+vbtK0ajUU6ePCnNmzeXmJgYEREZMWKE0ufg4GBJSEgQERGNRiM7\nduwQEZGXX35Zef/o/tLgbz5IjVvv3r1x8OBBHDx4EPPmzYPBYMDBgwfRqlUr9OnTB6Wlpbesl1rc\n6iw2Nhbffvst3n77bQBASUkJLly4AC8vL6xatQp9+/bFBx98ADc3NwDl32swduxYAICHhwc6dOiA\nX3/9FbGxsfj555+xefNmAOXfJHf27FlYWloiICAADz/8cLWvHRsbi65duwIo39o5e/YsXFxc4Obm\nhs6dOwMAunfvjtTUVFy9ehWZmZkYNmwYAKBp06a3XE7fvn2rHbObmxs6deoEAOjUqRMef/xxAICv\nry9SU1MBAPn5+Zg0aRLOnj0LjUaj3IBOo9Fg7dq18PPzw6xZsxAUFKS0Dx48GE2aNIGvry9MJhMG\nDhwIAPDz81OWe7OmTZsqW4ndu3fHDz/8cJvfFjVGDAy6q/r06YMDBw4ouzxcXFzw9ttvo1WrVggP\nD8elS5fw008/VVvbqVMnJCQk1Op7MbZs2VLt90ycOnUKbdu2veW3GQL//XKcjz76CKGhoZWmxcXF\noUWLFjXWLliwADNmzKjUlpqaioceekh53qRJE2WXVG2WU5Obl21hYaEEj4WFhbLraOHChQgJCcE3\n33yDtLQ0BAcHKzVJSUmwtrau8r7cvBwrK6tKr1FWVlalH3+ep+K16f7CYxh0V/Xu3Rs7duyAvb09\nNBoNWrdujfz8fBw6dAh9+vTBhAkTcPDgQezcuVOp2bdvH86cOYNnn30W69atq3TL5S1bttS4f3zg\nwIFYvny58vz48eMAgLS0NLz77rs4fvw4du3apSxPRPDVV19BRHDu3DmcP38e3t7eGDhwIFauXKms\n9JKSklBUVHTLcQ4cOBCrV6/GtWvXAJR/teelS5eqnVdE0LJlS+h0OuU7sktKSlBcXFyr5ahVUFAA\nZ2dnAKh0e+4rV65gzpw52L9/P3JycvD111+rWl5ttvro/sLAoLvK19cXOTk56NWrl9LWuXNn2Nra\nws7ODs2aNcOOHTvw4YcfwtPTE506dcLHH38MBwcHODg4YOPGjXjxxRfh7e0NHx8f/PDDDzV+/efC\nhQtx48YNdO7cGb6+vli0aBEAYNq0aXjnnXfg5OSE6OhoTJs2DSUlJdBoNGjfvj0CAgIwZMgQfPLJ\nJ2jatCmmTZsGHx8fdOvWTdldYzQaodFoqnw9Z8Xz0NBQTJgwAUFBQejcuTPGjh2Lq1evVprnzzXr\n16/H8uXL4e/vjz59+iA7O/uWy6lOTcu++eeXX34ZCxYsQLdu3VBWVqa0z5s3D88++yw8PDwQHR2N\nV155RQmn6pZT0/Pq5r/fvsaUyvH7MOiBFR4ejqFDh2LkyJH13RWiRoFbGEREpAq3MKjRWbt2baUr\nyIHyiwQ//PDDeurR3ZWTk6Oc/XSzPXv2NKqvQ6XGj4FBRESqcJcUERGpwsAgIiJVGBhERKQKA4OI\niFRhYBARkSr/Hw89+sPXN4djAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f535c1bc350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEaCAYAAAACBmAUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqvw/psR4CKl8ZlEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f535c1bcd10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEaCAYAAADtxAsqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEZFqDA0iIlKNoUFERKoxNIiISLX/B+ewD6e8iEpMAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f534da39350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEaCAYAAADtxAsqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f534d941390>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEaCAYAAAACBmAUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGEREpAoTBhERqcKEQUREqjBhEBGRKv8fFxnZ0IbOmasAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f535c270ad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEaCAYAAADtxAsqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f534d687c90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEaCAYAAADtxAsqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtcVHX+P/DXKHhZFUaQi8ygCAwigoDKRc1CEVB3vZQK\ny5ague7mbqb9yrTaFq1vAdt+v1ttum5litbKmm1qloixkq4XzHthprkoMFyqYUC8cJP37w+/nC/I\n7Thc3V7Px2Mej5lzzuec9+dw5rw4c86c0YiIgIiISIUeXV0AERHdOxgaRESkGkODiIhUY2gQEZFq\nDA0iIlKNoUFERKoxNDqIm5sbMjIymhx38OBBeHt7d3JF3c/ChQthZ2eH0NDQri6lRdOnT8eWLVu6\nuox2tWTJEvzXf/3XPbPMxMRELF68uJ0rIosIdQg3NzfJyMho0zwSEhLkkUceaaeKupcDBw6IXq+X\nmzdvNhpXXV0t/fr1k6ysLGXYe++9JxqNptEwb29v5fU333wjc+fOlUGDBomtra2MGjVK/ud//kdu\n3bolIiKVlZWSkJAgBoNB+vXrJ25ubvLoo4/K5cuX261fDzzwgLzzzjvtNr/2sHHjRrnvvvv+45dJ\nnYNHGj9SNTU1Xbr8K1euwM3NDX369Gk0zsrKCuPHj8eBAweUYQcOHMCIESMaDXvggQcAAJcuXUJI\nSAiGDh2Kr776CqWlpfjggw9w4sQJXLt2DQAwd+5c7N69G1u3bsXVq1dx5swZjB07ttkjQktoNJo2\nta+trW2nSog6SFen1n8qNzc3+eMf/yijRo0SW1tbiYmJkYqKChER2b9/v+j1emXapKQk0el0MmDA\nABk+fLhkZGTInj17pFevXmJtbS39+/eXgIAAERExGo0yY8YMsbOzE09PT3n77beV+dy4cUPi4uJk\n4MCBMmLECElOTm6wnKFDh0pycrL4+flJnz59pKamRhITE8XDw0MGDBggPj4+8tFHHynTb9y4UcaP\nHy9PPvmkaLVa8fDwkEOHDsm7774rrq6u4ujoKCkpKc2ug+Zqfeedd6RPnz7Ss2dP6d+/v6xevbpR\n25deeklmzJihvPbx8ZGUlJQGw0aMGCHvv/++iIg8/PDD8rOf/azZWvbt2yd9+/aV/Pz8ZqdpTv2j\nh40bN8qECRPk6aefloEDB8qwYcNkz549IiLy3HPPSc+ePaVPnz7Sv39/Wbp0qYiIfP311zJlyhSx\ns7OT4cOHy7Zt25R5x8fHy2OPPSbTpk2Tfv36SUZGhnzyyScyYsQIGTBggOh0OvnjH/+oTP/xxx+L\nv7+/aLVaGT9+vJw9e1YZl5ubKw8++KA4ODiIvb29PP744/L1119L7969lXU9cOBAZbm/+93vlLZv\nvfWWeHp6ip2dncycOVMKCgqUcRqNRtavXy8Gg0G0Wq389re/bXF9nTt3rsHft6ll7t+/X3Q6nfzh\nD38QBwcHGTx4sHz00UfyySefiMFgEDs7O0lMTFTmWf+oOycnRzQajaSkpMiQIUNk0KBB8vLLLyvT\ntvY+aM6d78N//vOfTa6rO9+/Q4cOlVdffVX8/Pykf//+8uijj0pRUZFMnTpVbGxsZMqUKWI2m1td\n/r2CodFBhg4dKiEhIVJYWCglJSUyYsQIWb9+vYg03OjOnz8vrq6uUlhYKCIiV65ckUuXLomIyOrV\nq2X+/PkN5jtx4kT57W9/K5WVlXL69GlxcHBQNu6VK1dKWFiYlJaWSn5+vvj5+Ymrq2uDmgIDAyU/\nP18JsA8++EBZ9t///nfp16+fFBUVicjtHaSVlZVs2rRJamtr5Xe/+53odDp5/PHHpaqqStLT02XA\ngAFy/fr1JtdBS7Vu2rSpxY8vPv/8c7GzsxMRke+//16GDh0qN27cECcnJ2WYRqORvLw8ERFxdnaW\nTZs2NTu/unVjibCwMNmwYYOI3F4n1tbW8s4770htba385S9/ERcXlyanFRG5du2a6PV62bRpk9y6\ndUtOnTolgwYNknPnzonI7R2Sra2tHD58WEREbt68Kc7OzvKvf/1LRERKS0vl5MmTIiJy8uRJcXR0\nlGPHjkltba2kpKSIm5ubVFVVSU1NjYwaNUr+3//7f3Ljxg2pqKiQQ4cOiUjT63rBggXywgsviIhI\nRkaGDBo0SE6dOiWVlZWydOlSuf/++5VpNRqNzJgxQ8rKyiQ3N1ccHBwkLS2txXXW2jL3798vVlZW\n8tJLL0lNTY28/fbbYm9vL7/4xS/k2rVrkp2dLX379lU+Oly9enWj0PjVr34lFRUVcubMGendu7ec\nP39eRFp/HzSlpfdh/brraq8fGm5ubjJu3Dj57rvvxGg0iqOjowQGBsrp06eloqJCJk+eLGvWrGlx\n+fcSfjzVQTQaDZ544gk4Oztj4MCBmDFjBk6fPt1oup49e6KyshLZ2dmorq7GkCFD4O7uDgCQ26Gu\nTJuXl4fDhw8jOTkZvXr1gr+/P375y19i8+bNAIAPPvgAzz33HGxtbaHT6bBs2bIG7etq0ul06N27\nN4DbH9k4OzsDAKKjo2EwGJCVlaW0GTZsGOLj46HRaBAdHY2CggL8/ve/h7W1NSIiItCrVy98++23\njfrVWq3Syi3PgoODcePGDZw9exYHDx7ExIkT0bdvXwwbNkwZ5ubmBr1eDwAwmUwYPHhws/MzmUxK\nP9tq6NChWLRoETQaDeLi4lBYWIjvvvtOGV+/b7t371bWYY8ePRAQEICHHnoIH3zwgTLN7NmzMW7c\nOABAnz590KtXL2RnZ+Pq1auwtbVFYGAgAOCtt97Cr3/9awQFBSnL7t27N44cOYJjx46hsLAQr776\nKvr27YvevXtj/Pjxjeppyvvvv49FixYhICAAvXr1QmJiIo4cOYLc3FxlmlWrVsHGxgaurq6YNGlS\nk9tyfc0ts/5wa2trPP/88+jZsydiYmJQUlKC5cuXo1+/fvDx8YGPjw/OnDnT7PwSEhLQu3dvjBo1\nCv7+/sq0rb0PmtLS+7Cl/tRZunQpHBwc4OLigokTJ2LcuHHw9/dH79698eCDD+LUqVMttr+XMDQ6\nUP2dVN++fZXP1uvz9PTEa6+9htWrV8PJyQmxsbEoLCxscn4FBQWws7NDv379lGFDhgxBQUGBMt7V\n1VUZV7dDra/+eADYvHkzAgMDMXDgQAwcOBBfffUVTCaTMt7JyalBHwDAwcGh1X41V6vRaGyyb3fq\n06cPgoODceDAASU0AOC+++5ThtWdzwAAe3t7ZT00ZdCgQc2u17tV/+/6k5/8BAAarIP65zWuXLmC\nrKwsZf0OHDgQf/vb31BcXKxMe+ff5MMPP8Snn34KNzc3hIWF4ejRo8q8/vu//7vBvPLz81FYWIi8\nvDwMHToUPXrc/Vu6sLAQQ4cOVV7369cP9vb2Df5Wd/a5qb/53bK3t1fWVd22def21tJymqtJzfvg\nTnfzPmzKnXXXf92nT592WV/dBUOjG4iNjcXBgwdx5coVaDQarFy5EkDjk6ouLi4oKSlpsAHm5uZC\np9MBAAYPHoy8vDxlXP3nde7cof3qV7/C2rVrUVJSArPZDF9f31b/q1KjuVrVvIHr3H///Y1CY+LE\nifj8889x8OBB3H///cq0U6ZMwYcfftjsvKZMmYJjx46pDi1L3fk3GzJkCB544AGYzWblUV5ejrVr\n1zY7j7Fjx2LHjh34/vvvMXv2bERHRyvzev755xvM69q1a4iJiYGrqytyc3Nx69atVmu6k4uLCy5f\nvqy8vn79Okwmk7JdWaK5Zbb1QgE11LwPmtLc+7Bfv364ceOGMl1RUVGr82qP91B3xdDoYhcuXMA/\n//lPVFZWonfv3ujTpw969uwJ4PZ/UpcvX1Y2QFdXV4wfPx7PPvssKisrcfbsWbz77rt45JFHANz+\neCkxMRGlpaUwGo148803W3yTXr9+HRqNBoMGDUJtbS02btyIr776ql361Vqtatx///345z//ifz8\nfIwYMQIAMGHCBGRmZuL06dMNQmPNmjU4fPgwnnnmGeW/+G+//Rbz58/H1atXER4ejoiICDz44IM4\nefIkampqUF5ejvXr12Pjxo3t0mfg9n+cly5dUl7/7Gc/w4ULF/Dee++huroa1dXV+OKLL3D+/HkA\njXcu1dXVeP/991FWVoaePXtiwIAByvawePFirF+/HseOHYOI4Pr16/jkk09w7do1hISEYPDgwVi1\nahVu3LiBiooKHD58WKkpPz8f1dXVynLqf/QZGxuLjRs34syZM6isrMRzzz2H0NBQDBkypMk+qtkh\nOjs7t7jMjnS37wOg5fdhQEAAPv30U5jNZhQVFeG1117r8D50ZwyNTqLRaBpsuHXPKysr8eyzz8LB\nwQGDBw/GDz/8gMTERADAvHnzANw+jB87diwAYOvWrbh8+TJcXFzw0EMP4cUXX8TkyZMBAL///e+h\n1+sxbNgwREZGYt68eejVq1ezNfn4+OCpp57CuHHj4OzsjK+++gr33XdfszXXr1uNlmptat53Gjdu\nHK5evYqQkBBlmL29PRwdHeHk5AQPDw9luLu7O44cOYLLly9j5MiR0Gq1mDt3LoKCgtC/f38AwPbt\n2zF9+nTExMRAq9XCz88PJ0+eREREhOo+tbZOli1bhu3bt8POzg7Lly9H//79kZ6ejtTUVOh0Ogwe\nPBjPPvssqqqqmp3fe++9h2HDhsHW1hZvvfUW3n//fQDAmDFj8Pbbb+Pxxx+HnZ0dDAaDco6oR48e\n+Pjjj/Htt99iyJAhcHV1xbZt2wAA4eHhGDlyJJydneHo6NhoueHh4XjppZcwZ84cuLi4ICcnB6mp\nqU32r7ma7zR58uQWl9ncfJvTWtv67vZ9ALT8Ppw/fz78/f3h5uaGqVOn4uc//3mr/b+z1s44wuo0\nrZ0pf+WVV8THx0d8fX0lNjZWKioqxGQyyZQpU8RgMEhERESDy8leeeUV8fT0lOHDh8vevXuV4ceP\nHxdfX1/x9PSUJ554QhleUVEh0dHR4unpKSEhIQ2+aLVp0yYxGAxiMBhavLSTmrZu3TqLrxgi+k/B\n90H7ajE0cnJyZNiwYcrlmdHR0bJp0yZZsWKFJCcni8jta5tXrlwpIiLZ2dni7+8vVVVVkpOTIx4e\nHlJbWysiIkFBQcq3eadNm6Zc27527VpZsmSJiIikpqZKTEyMiIiYTCZxd3cXs9ksZrNZeU7NKyws\nlH/9619y69YtOX/+vHh6esrrr7/e1WURdSq+DzpWix9P2djYwNraGjdu3EBNTQ1u3LgBFxcX7Nq1\nC/Hx8QCA+Ph47NixAwCwc+dOxMbGwtraGm5ubvD09ERWVhYKCwtRXl6O4OBgAEBcXJzSpv685syZ\no3w7d+/evYiMjIRWq4VWq0VERATS0tI65nDrP0RVVRUee+wx2NjYIDw8HLNnz8ZvfvObri7rntC/\nf38MGDCg0ePQoUNdXVq39dhjjzW5zrp6m2vufZCbm9tkvTY2NsjPz+/Smu8lVi2NtLOzw1NPPYUh\nQ4agb9++iIqKQkREBIqLi5VLypycnJQTjwUFBQ1uPqfX62E0GmFtbd3gqhmdTqdcxWI0GpXL46ys\nrGBrawuTyYSCgoIGbermRc0bMmQIvvzyy64u4570n3RJZGdZv3491q9f39VlNNLc+2DIkCEoLy/v\ngor+s7R4pHHp0iW89tpruHz5MgoKCnDt2jW89957Dab5jzvJQ0REzWrxSOP48eMYP3487O3tAQAP\nPfQQjhw5AmdnZxQVFcHZ2RmFhYXK1RE6na7BNdH5+fnQ6/XQ6XQNDv/qhte1yc3NhYuLC2pqalBW\nVgZ7e3vodDpkZmYqbfLy8pQrb+rz9PRscIkjERG1zsPDo8m7ObSmxSMNb29vHD16FDdv3oSI4LPP\nPoOPjw9mzJiBlJQUAEBKSgpmz54NAJg5cyZSU1NRVVWFnJwcXLx4EcHBwXB2doaNjQ2ysrIgItiy\nZQtmzZqltKmb1/bt2xEeHg4AiIyMRHp6OkpLS2E2m7Fv3z5ERUU1qvHSpUvK9d/d5ZGQkNDlNdwr\ndbEm1vRjqKs71mTpP9stHmn4+/sjLi4OY8eORY8ePTB69Gj86le/Qnl5OaKjo7Fhwwa4ubkp14P7\n+PggOjoaPj4+sLKywrp165SPrtatW4cFCxbg5s2bmD59OqZOnQoAWLRoEebPnw+DwQB7e3vl+nA7\nOzu88MILCAoKAnD7PjNardaiThIRUftoMTQA4JlnnsEzzzzTYJidnR0+++yzJqd/7rnn8NxzzzUa\nPmbMmCZPTvXu3VsJnTstXLgQCxcubK1EIiLqJPxGeAcICwvr6hKa1B3rYk3qsCb1umNd3bEmS2lE\n5J6+s5ZGo8E93gUiok5n6b6TRxpERKQaQ4OIiFRjaBARkWoMDSIiUo2hQUREqjE0iIhINYYGERGp\nxtAgIiLVGBpERKTajz40bGzslN8EseRhY2PX1V0gIuo0P/rbiNy+C29bVgFvY0JE9x7eRoSIiDoc\nQ4OIiFRjaBARkWoMDSIiUo2hQUREqjE0iIhItVZD45tvvkFgYKDysLW1xRtvvIGSkhJERETAy8sL\nkZGRKC0tVdokJibCYDDA29sb6enpyvATJ07Az88PBoMBy5YtU4ZXVlYiJiYGBoMBoaGhuHLlijIu\nJSUFXl5e8PLywubNm9ur30REZAm5C7du3RJnZ2fJzc2VFStWSHJysoiIJCUlycqVK0VEJDs7W/z9\n/aWqqkpycnLEw8NDamtrRUQkKChIsrKyRERk2rRpsmfPHhERWbt2rSxZskRERFJTUyUmJkZEREwm\nk7i7u4vZbBaz2aw8r+8uu9AIAAGkDY+2LZ+IqCtYuu+6q4+nPvvsM3h6esLV1RW7du1CfHw8ACA+\nPh47duwAAOzcuROxsbGwtraGm5sbPD09kZWVhcLCQpSXlyM4OBgAEBcXp7SpP685c+YgIyMDALB3\n715ERkZCq9VCq9UiIiICaWlpbU9KIiKyyF2FRmpqKmJjYwEAxcXFcHJyAgA4OTmhuLgYAFBQUAC9\nXq+00ev1MBqNjYbrdDoYjUYAgNFohKurKwDAysoKtra2MJlMzc6LiIi6hurQqKqqwscff4x58+Y1\nGld3HyYiIvrPZqV2wj179mDMmDFwcHAAcPvooqioCM7OzigsLISjoyOA20cQeXl5Srv8/Hzo9Xro\ndDrk5+c3Gl7XJjc3Fy4uLqipqUFZWRns7e2h0+mQmZmptMnLy8PkyZMb1bZ69WrleVhYGMLCwtR2\ni4joRyEzM7PB/tRiak9+xMTEyKZNm5TXK1askKSkJBERSUxMbHQivLKyUv7973+Lu7u7ciI8ODhY\njh49KrW1tY1OhD/22GMiIrJ169YGJ8KHDRsmZrNZSkpKlOftcTKnfnueCCeiHxtL912qWl27dk3s\n7e3l6tWryjCTySTh4eFiMBgkIiKiwc785ZdfFg8PDxk+fLikpaUpw48fPy6+vr7i4eEhS5cuVYZX\nVFTIvHnzxNPTU0JCQiQnJ0cZ9+6774qnp6d4eno2CC2lAwwNIqK7Zum+i7dG563RiehHiLdGJyKi\nDsfQICIi1RgaRESkGkODiIhUY2gQEZFqDA0iIlKNoUFERKoxNIiISDWGBhERqcbQICIi1RgaRESk\nGkODiIhUY2gQEZFqDA0iIlKNoUFERKoxNIiISDWGBhERqcbQICIi1RgaRESkmqrQKC0txdy5czFi\nxAj4+PggKysLJSUliIiIgJeXFyIjI1FaWqpMn5iYCIPBAG9vb6SnpyvDT5w4AT8/PxgMBixbtkwZ\nXllZiZiYGBgMBoSGhuLKlSvKuJSUFHh5ecHLywubN29ujz4TEZGlRIW4uDjZsGGDiIhUV1dLaWmp\nrFixQpKTk0VEJCkpSVauXCkiItnZ2eLv7y9VVVWSk5MjHh4eUltbKyIiQUFBkpWVJSIi06ZNkz17\n9oiIyNq1a2XJkiUiIpKamioxMTEiImIymcTd3V3MZrOYzWbleX0qu9AsAAJIGx5tWz4RUVewdN/V\n6pFGWVkZDh48iEcffRQAYGVlBVtbW+zatQvx8fEAgPj4eOzYsQMAsHPnTsTGxsLa2hpubm7w9PRE\nVlYWCgsLUV5ejuDgYABAXFyc0qb+vObMmYOMjAwAwN69exEZGQmtVgutVouIiAikpaW1Y2QSEdHd\naDU0cnJy4ODggIULF2L06NFYvHgxrl+/juLiYjg5OQEAnJycUFxcDAAoKCiAXq9X2uv1ehiNxkbD\ndTodjEYjAMBoNMLV1RXA/4WSyWRqdl5ERNQ1rFqboKamBidPnsSbb76JoKAgLF++HElJSQ2m0Wg0\n0Gg0HVZka1avXq08DwsLQ1hYWJfVQkTUHWVmZiIzM7PN82k1NPR6PfR6PYKCggAAc+fORWJiIpyd\nnVFUVARnZ2cUFhbC0dERwO0jiLy8PKV9fn4+9Ho9dDod8vPzGw2va5ObmwsXFxfU1NSgrKwM9vb2\n0Ol0DTqZl5eHyZMnN6qxfmgQEVFjd/5DvWbNGovm0+rHU87OznB1dcWFCxcAAJ999hlGjhyJGTNm\nICUlBcDtK5xmz54NAJg5cyZSU1NRVVWFnJwcXLx4EcHBwXB2doaNjQ2ysrIgItiyZQtmzZqltKmb\n1/bt2xEeHg4AiIyMRHp6OkpLS2E2m7Fv3z5ERUVZ1FEiImoHas6Wnz59WsaOHSujRo2SBx98UEpL\nS8VkMkl4eLgYDAaJiIhocFXTyy+/LB4eHjJ8+HBJS0tThh8/flx8fX3Fw8NDli5dqgyvqKiQefPm\niaenp4SEhEhOTo4y7t133xVPT0/x9PSUTZs2NapNZReaBV49RUQ/QpbuuzT/2/iepdFo0JYu3D4X\n05ZV0LblExF1BUv3nfxGOBERqcbQICIi1RgaRESkGkODiIhUY2gQEZFqDA0iIlKNoUFERKoxNIiI\nSDWGBhERqcbQICIi1RgaRESkGkODiIhUY2gQEZFqDA0iIlKNoUFERKoxNIiISDWGBhERqcbQICIi\n1RgaRESkmqrQcHNzw6hRoxAYGIjg4GAAQElJCSIiIuDl5YXIyEiUlpYq0ycmJsJgMMDb2xvp6enK\n8BMnTsDPzw8GgwHLli1ThldWViImJgYGgwGhoaG4cuWKMi4lJQVeXl7w8vLC5s2b29xhIiJqA1HB\nzc1NTCZTg2ErVqyQ5ORkERFJSkqSlStXiohIdna2+Pv7S1VVleTk5IiHh4fU1taKiEhQUJBkZWWJ\niMi0adNkz549IiKydu1aWbJkiYiIpKamSkxMjIiImEwmcXd3F7PZLGazWXlen8ouNAuAANKGR9uW\nT0TUFSzdd6n+eOr2Mv7Prl27EB8fDwCIj4/Hjh07AAA7d+5EbGwsrK2t4ebmBk9PT2RlZaGwsBDl\n5eXKkUpcXJzSpv685syZg4yMDADA3r17ERkZCa1WC61Wi4iICKSlpbUhIomIqC1UhYZGo8GUKVMw\nduxYvP322wCA4uJiODk5AQCcnJxQXFwMACgoKIBer1fa6vV6GI3GRsN1Oh2MRiMAwGg0wtXVFQBg\nZWUFW1tbmEymZudFRERdw0rNRIcOHcLgwYPx/fffIyIiAt7e3g3GazQaaDSaDilQjdWrVyvPw8LC\nEBYW1mW1EBF1R5mZmcjMzGzzfFSFxuDBgwEADg4OePDBB3Hs2DE4OTmhqKgIzs7OKCwshKOjI4Db\nRxB5eXlK2/z8fOj1euh0OuTn5zcaXtcmNzcXLi4uqKmpQVlZGezt7aHT6Rp0Mi8vD5MnT25UX/3Q\nICKixu78h3rNmjUWzafVj6du3LiB8vJyAMD169eRnp4OPz8/zJw5EykpKQBuX+E0e/ZsAMDMmTOR\nmpqKqqoq5OTk4OLFiwgODoazszNsbGyQlZUFEcGWLVswa9YspU3dvLZv347w8HAAQGRkJNLT01Fa\nWgqz2Yx9+/YhKirKoo4SEVHbtXqkUVxcjAcffBAAUFNTg4cffhiRkZEYO3YsoqOjsWHDBri5uWHb\ntm0AAB8fH0RHR8PHxwdWVlZYt26d8tHVunXrsGDBAty8eRPTp0/H1KlTAQCLFi3C/PnzYTAYYG9v\nj9TUVACAnZ0dXnjhBQQFBQEAEhISoNVq238tEBGRKhq587Koe4xGo2l0Zdfdtgfasgratnwioq5g\n6b6T3wgnIiLVGBpERKQaQ4OIiFRjaBARkWoMDSIiUo2hQUREqjE0iIhINYYGERGpxtAgIiLVGBpE\nRKQaQ4OIiFRjaBARkWoMDSIiUo2hQUREqjE0iIhINYYGERGpxtAgIiLVGBpERKQaQ4OIiFRTFRq3\nbt1CYGAgZsyYAQAoKSlBREQEvLy8EBkZidLSUmXaxMREGAwGeHt7Iz09XRl+4sQJ+Pn5wWAwYNmy\nZcrwyspKxMTEwGAwIDQ0FFeuXFHGpaSkwMvLC15eXti8eXObO0tERG2jKjRef/11+Pj4QKPRAACS\nkpIQERGBCxcuIDw8HElJSQCAc+fO4e9//zvOnTuHtLQ0/OY3v1F+uHzJkiXYsGEDLl68iIsXLyIt\nLQ0AsGHDBtjb2+PixYt48sknsXLlSgC3g+nFF1/EsWPHcOzYMaxZs6ZBOBERUedrNTTy8/Px6aef\n4pe//KUSALt27UJ8fDwAID4+Hjt27AAA7Ny5E7GxsbC2toabmxs8PT2RlZWFwsJClJeXIzg4GAAQ\nFxentKk/rzlz5iAjIwMAsHfvXkRGRkKr1UKr1SIiIkIJGiIi6hqthsaTTz6JV199FT16/N+kxcXF\ncHJyAgA4OTmhuLgYAFBQUAC9Xq9Mp9frYTQaGw3X6XQwGo0AAKPRCFdXVwCAlZUVbG1tYTKZmp0X\nERF1HauWRu7evRuOjo4IDAxEZmZmk9NoNBrlY6uusnr1auV5WFgYwsLCuqwWIqLuKDMzs9n9+N1o\nMTQOHz6MXbt24dNPP0VFRQWuXr2K+fPnw8nJCUVFRXB2dkZhYSEcHR0B3D6CyMvLU9rn5+dDr9dD\np9MhPz+/0fC6Nrm5uXBxcUFNTQ3Kyspgb28PnU7XoIN5eXmYPHlyk3XWDw0iImrszn+o16xZY9F8\nWvx46pXRqm79AAAbIUlEQVRXXkFeXh5ycnKQmpqKyZMnY8uWLZg5cyZSUlIA3L7Cafbs2QCAmTNn\nIjU1FVVVVcjJycHFixcRHBwMZ2dn2NjYICsrCyKCLVu2YNasWUqbunlt374d4eHhAIDIyEikp6ej\ntLQUZrMZ+/btQ1RUlEWdJCKi9tHikcad6j6GWrVqFaKjo7Fhwwa4ublh27ZtAAAfHx9ER0fDx8cH\nVlZWWLdundJm3bp1WLBgAW7evInp06dj6tSpAIBFixZh/vz5MBgMsLe3R2pqKgDAzs4OL7zwAoKC\nggAACQkJ0Gq17dNrIiKyiEbqLom6R2k0GrSlC7dDrS2roG3LJyLqCpbuO/mNcCIiUo2hQUREqjE0\niIhINYYGERGpxtAgIiLVGBpERKQaQ4OIiFRjaBARkWoMDSIiUo2hQUREqjE0iIhINYYGERGpxtAg\nIiLVGBptZqX8eqElDxsbu67uABGRarw1ejvcGp23Vieiew1vjU5ERB2OoUFERKoxNIiISDWGBhER\nqdZiaFRUVCAkJAQBAQHw8fHBs88+CwAoKSlBREQEvLy8EBkZidLSUqVNYmIiDAYDvL29kZ6ergw/\nceIE/Pz8YDAYsGzZMmV4ZWUlYmJiYDAYEBoaiitXrijjUlJS4OXlBS8vL2zevLndOk1ERBaSVly/\nfl1ERKqrqyUkJEQOHjwoK1askOTkZBERSUpKkpUrV4qISHZ2tvj7+0tVVZXk5OSIh4eH1NbWiohI\nUFCQZGVliYjItGnTZM+ePSIisnbtWlmyZImIiKSmpkpMTIyIiJhMJnF3dxez2Sxms1l5ficVXWgR\nAAGkDY+2tyci6myW7nta/XjqJz/5CQCgqqoKt27dwsCBA7Fr1y7Ex8cDAOLj47Fjxw4AwM6dOxEb\nGwtra2u4ubnB09MTWVlZKCwsRHl5OYKDgwEAcXFxSpv685ozZw4yMjIAAHv37kVkZCS0Wi20Wi0i\nIiKQlpbWfmlJRER3rdXQqK2tRUBAAJycnDBp0iSMHDkSxcXFcHJyAgA4OTmhuLgYAFBQUAC9Xq+0\n1ev1MBqNjYbrdDoYjUYAgNFohKurKwDAysoKtra2MJlMzc6LiIi6jlVrE/To0QOnT59GWVkZoqKi\nsH///gbj677Z3JVWr16tPA8LC0NYWFiX1UJE1B1lZmYiMzOzzfNpNTTq2Nra4qc//SlOnDgBJycn\nFBUVwdnZGYWFhXB0dARw+wgiLy9PaZOfnw+9Xg+dTof8/PxGw+va5ObmwsXFBTU1NSgrK4O9vT10\nOl2DDubl5WHy5MlN1lY/NIiIqLE7/6Fes2aNRfNp8eOpH374Qbky6ubNm9i3bx8CAwMxc+ZMpKSk\nALh9hdPs2bMBADNnzkRqaiqqqqqQk5ODixcvIjg4GM7OzrCxsUFWVhZEBFu2bMGsWbOUNnXz2r59\nO8LDwwEAkZGRSE9PR2lpKcxmM/bt24eoqCiLOklERO2jxSONwsJCxMfHo7a2FrW1tZg/fz7Cw8MR\nGBiI6OhobNiwAW5ubti2bRsAwMfHB9HR0fDx8YGVlRXWrVunfHS1bt06LFiwADdv3sT06dMxdepU\nAMCiRYswf/58GAwG2NvbIzU1FQBgZ2eHF154AUFBQQCAhIQEaLXaDlsRRETUOt6wkDcsJKIfId6w\nkIiIOhxDg4iIVGNoEBGRagwNIiJSjaFBRESqMTSIiEg1hgYREanG0CAiItUYGkREpBpDg4iIVGNo\nEBGRagwNIiJSjaFBRESqMTSIiEg1hgYREanG0CAiItUYGkREpBpDg4iIVGNoEBGRaq2GRl5eHiZN\nmoSRI0fC19cXb7zxBgCgpKQEERER8PLyQmRkJEpLS5U2iYmJMBgM8Pb2Rnp6ujL8xIkT8PPzg8Fg\nwLJly5ThlZWViImJgcFgQGhoKK5cuaKMS0lJgZeXF7y8vLB58+Z26TQREVlIWlFYWCinTp0SEZHy\n8nLx8vKSc+fOyYoVKyQ5OVlERJKSkmTlypUiIpKdnS3+/v5SVVUlOTk54uHhIbW1tSIiEhQUJFlZ\nWSIiMm3aNNmzZ4+IiKxdu1aWLFkiIiKpqakSExMjIiImk0nc3d3FbDaL2WxWntenogstAiCAtOHR\n9vZERJ3N0n1Pq0cazs7OCAgIAAD0798fI0aMgNFoxK5duxAfHw8AiI+Px44dOwAAO3fuRGxsLKyt\nreHm5gZPT09kZWWhsLAQ5eXlCA4OBgDExcUpberPa86cOcjIyAAA7N27F5GRkdBqtdBqtYiIiEBa\nWlr7JSYREd2VuzqncfnyZZw6dQohISEoLi6Gk5MTAMDJyQnFxcUAgIKCAuj1eqWNXq+H0WhsNFyn\n08FoNAIAjEYjXF1dAQBWVlawtbWFyWRqdl5ERNQ1rNROeO3aNcyZMwevv/46BgwY0GCcRqOBRqNp\n9+LUWr16tfI8LCwMYWFhXVYLEVF3lJmZiczMzDbPR1VoVFdXY86cOZg/fz5mz54N4PbRRVFREZyd\nnVFYWAhHR0cAt48g8vLylLb5+fnQ6/XQ6XTIz89vNLyuTW5uLlxcXFBTU4OysjLY29tDp9M16GRe\nXh4mT57cqL76oUFERI3d+Q/1mjVrLJpPqx9PiQgWLVoEHx8fLF++XBk+c+ZMpKSkALh9hVNdmMyc\nOROpqamoqqpCTk4OLl68iODgYDg7O8PGxgZZWVkQEWzZsgWzZs1qNK/t27cjPDwcABAZGYn09HSU\nlpbCbDZj3759iIqKsqijRETUDlo7U37w4EHRaDTi7+8vAQEBEhAQIHv27BGTySTh4eFiMBgkIiKi\nwVVNL7/8snh4eMjw4cMlLS1NGX78+HHx9fUVDw8PWbp0qTK8oqJC5s2bJ56enhISEiI5OTnKuHff\nfVc8PT3F09NTNm3a1G5XANRvz6uniOjHxtJ9j+Z/G9+zNBoN2tKF2+di2rIK2t7+Hv8TENE9yNJ9\nJ78RTkREqjE0iIhINYYGERGpxtAgIiLVGBpERKQaQ4OIiFRjaBARkWoMDSIiUo2hQUREqjE0iIhI\nNYYGERGpxtAgIiLVGBpERKQaQ4OIiFRjaBARkWoMDSIiUo2hQUREqjE0iIhItVZD49FHH4WTkxP8\n/PyUYSUlJYiIiICXlxciIyNRWlqqjEtMTITBYIC3tzfS09OV4SdOnICfnx8MBgOWLVumDK+srERM\nTAwMBgNCQ0Nx5coVZVxKSgq8vLzg5eWFzZs3t7mzRETURq39iPiBAwfk5MmT4uvrqwxbsWKFJCcn\ni4hIUlKSrFy5UkREsrOzxd/fX6qqqiQnJ0c8PDyktrZWRESCgoIkKytLRESmTZsme/bsERGRtWvX\nypIlS0REJDU1VWJiYkRExGQyibu7u5jNZjGbzcrzO6noQosACCBteLS9PRFRZ7N039PqkcbEiRMx\ncODABsN27dqF+Ph4AEB8fDx27NgBANi5cydiY2NhbW0NNzc3eHp6IisrC4WFhSgvL0dwcDAAIC4u\nTmlTf15z5sxBRkYGAGDv3r2IjIyEVquFVqtFREQE0tLS2p6SRERkMYvOaRQXF8PJyQkA4OTkhOLi\nYgBAQUEB9Hq9Mp1er4fRaGw0XKfTwWg0AgCMRiNcXV0BAFZWVrC1tYXJZGp2XkRE1HXafCJco9FA\no9G0Ry1ERNTNWVnSyMnJCUVFRXB2dkZhYSEcHR0B3D6CyMvLU6bLz8+HXq+HTqdDfn5+o+F1bXJz\nc+Hi4oKamhqUlZXB3t4eOp0OmZmZSpu8vDxMnjy5yXpWr16tPA8LC0NYWJgl3SIi+o+VmZnZYJ9q\nMTUnPnJychqdCE9KShIRkcTExEYnwisrK+Xf//63uLu7KyfCg4OD5ejRo1JbW9voRPhjjz0mIiJb\nt25tcCJ82LBhYjabpaSkRHneXidz6rfv2hPhVv87D8seAwYMbFP/iejHydJ9Z6tHGrGxsfj888/x\nww8/wNXVFS+++CJWrVqF6OhobNiwAW5ubti2bRsAwMfHB9HR0fDx8YGVlRXWrVunfHS1bt06LFiw\nADdv3sT06dMxdepUAMCiRYswf/58GAwG2NvbIzU1FQBgZ2eHF154AUFBQQCAhIQEaLXaJmusf5nu\nvacGt/f/likv50eDRNR5NP+bOPcsjUaDfv2GWNS2urocVVVmtGWnDWi6vP09/ickoi6g0Vi27/iP\nCA3Ld7prATzehvYAQ4OI7kWWhgZvI0JERKoxNIiISDWGBhERqcbQICIi1RgaRESkGkODiIhUY2gQ\nEZFqDA0iIlKNoUFERKoxNIiISDWGBhERqcbQICIi1Rga9zwr5dcT7/ZhY2PX1cUT0T3Gol/uo+7E\n8t/j4G9xENHd4pEGERGpxtAgIiLVGBpERKQaQ4MsZmNjZ/FJeJ6IJ7o3dfvQSEtLg7e3NwwGA5KT\nk7u6nP8wll95pdFoUF5e9/vqlj1utyeie0m3Do1bt27h8ccfR1paGs6dO4etW7fi66+/7uqyVMjs\n6gKakXnH67orryx9tENFmXfW1PVYkzrdsSage9bVHWuyVLcOjWPHjsHT0xNubm6wtrbGz3/+c+zc\nubOry1Ihs6sLaEZmVxfQSHd8M7EmdbpjTUD3rKs71mSpbh0aRqMRrq6uymu9Xg+j0diFFVH7ssKa\nNWt4ToToHtKtv9yn0aj78tlPfrLYovlXV2ejutqiptQuagAkAFhtUevycmvV20jTrAE0vQGsWbOm\nxZYDBgzE1aslbVg20T1KurEjR45IVFSU8vqVV16RpKSkBtN4eHi05UN5Pvjgg48f5cPDw8Oi/bJG\nRATdVE1NDYYPH46MjAy4uLggODgYW7duxYgRI7q6NCKiH6Vu/fGUlZUV3nzzTURFReHWrVtYtGgR\nA4OIqAt16yMNIiLqXrr11VP1qfmS3xNPPAGDwQB/f3+cOnWqy2t6//334e/vj1GjRmHChAk4e/Zs\nl9dU54svvoCVlRX+8Y9/dIuaMjMzERgYCF9fX4SFhXV4TWrq+uGHHzB16lQEBATA19cXmzZt6tB6\nHn30UTg5OcHPz6/ZaTp7G1dTV1ds52rWFdC527mamjp7O2+tJou2cYvPUneimpoa8fDwkJycHKmq\nqhJ/f385d+5cg2k++eQTmTZtmoiIHD16VEJCQrq8psOHD0tpaamIiOzZs6db1FQ33aRJk+SnP/2p\nbN++vctrMpvN4uPjI3l5eSIi8v3333doTWrrSkhIkFWrVik12dnZSXV1dYfVdODAATl58qT4+vo2\nOb6zt3G1dXX2dq6mJpHO3c7V1NQV23lrNVmyjd8TRxpqvuS3a9cuxMfHAwBCQkJQWlqK4uLiLq1p\n3LhxsLW1VWrKz8/vsHrU1gQAf/7znzF37lw4ODh0aD1qa/rb3/6GOXPmQK/XAwAGDRrULeoaPHgw\nrl69CgC4evUq7O3tYWXVcacBJ06ciIEDBzY7vrO3cbV1dfZ2rqYmoHO3czU1dcV23lpNlmzj90Ro\nqPmSX1PTdOTGe7dfPNywYQOmT5/eYfWorcloNGLnzp1YsmQJAPXfhenImi5evIiSkhJMmjQJY8eO\nxZYtWzq0JrV1LV68GNnZ2XBxcYG/vz9ef/31Dq+rJZ29jVuiM7ZzNTp7O1ejK7bz1liyjXfrq6fq\nqP2Dyx3n9DtyQ7mbee/fvx/vvvsuDh061GH1AOpqWr58OZKSkqDRaCAijdZZV9RUXV2NkydPIiMj\nAzdu3MC4ceMQGhoKg8HQpXW98sorCAgIQGZmJi5duoSIiAicOXMGAwYM6LC6WtOZ2/jd6qztXI3O\n3s7V6IrtvDWWbOP3RGjodDrk5eUpr/Py8pRDvOamyc/Ph06n69KaAODs2bNYvHgx0tLSWj2c7oya\nTpw4gZ///OcAbp8E27NnD6ytrTFz5swuq8nV1RWDBg1C37590bdvX9x///04c+ZMh76Z1NR1+PBh\nPP/88wAADw8PDBs2DN988w3Gjh3bYXW1pLO38bvRmdu5Gp29navRFdt5ayzaxtvxnEuHqa6uFnd3\nd8nJyZHKyspWT4QfOXKkw0/GqanpypUr4uHhIUeOHOnQWu6mpvoWLFggH374YZfX9PXXX0t4eLjU\n1NTI9evXxdfXV7Kzs7u8rieffFJWr14tIiJFRUWi0+nEZDJ1aF05OTmqToR3xjautq7O3s7V1FRf\nZ2zndVqqqSu289ZqsmQbvyeONJr7kt9f//pXAMCvf/1rTJ8+HZ9++ik8PT3Rr18/bNy4sctrevHF\nF2E2m5XPVa2trXHs2LEuramzqanJ29sbU6dOxahRo9CjRw8sXrwYPj4+XV7Xc889h4ULF8Lf3x+1\ntbX4wx/+ADu7jrtJYmxsLD7//HP88MMPcHV1xZo1a1D9vzdH64ptXG1dnb2dq6mpK7RWU1ds563V\nZMk2zi/3ERGRavfE1VNERNQ9MDSIiEg1hgYREanG0CAiItUYGkREpBpDg4iIVGNoEBGRagwNumtP\nPvlkgxubRUVFYfHixcrrp556Cn/6059w4cIFTJ8+HV5eXhgzZgxiYmLw3XffAbh9l9n7778f3t7e\nGD16NBYvXoybN282ubyPP/64xd8GAYArV65g69at7dC7u3fnsk+cOIFly5a12/zLysrwl7/8RXld\nUFCAefPm3dU8EhISkJGR0W410Y9YO39jnX4Etm/fLtHR0SIicuvWLRkzZoyMHz9eGT9u3Dg5evSo\nGAwG2b17tzI8MzNTvvrqKykqKpKhQ4fK0aNHG8yzuLjY4pr2798vP/vZz+6qTXv9NoYly74bam+X\nQdQZGBp014xGo7i6uoqIyNmzZyU+Pl6ioqLEbDZLRUWFaLVaeeuttyQ+Pr7J9i+88IIkJCSoXt7G\njRvl8ccfFxGR+Ph4eeKJJ2T8+PHi7u6u/LhOSEiI2NraSkBAgLz22mty69YtefrppyUoKEhGjRol\nf/3rX0Xk9g7+vvvuk5kzZ8rw4cPl+vXrMn36dPH39xdfX1/5+9//LiIix48flwceeEDGjBkjUVFR\nUlhYKCIiFy9elPDwcPH395cxY8bIpUuXGiz7T3/6U4MQMZlMMmvWLBk1apSEhobK2bNnReT2j98s\nXLhQwsLCxN3dXd54441m+x8TEyN9+/aVgIAAeeaZZ+Ty5ctKiGzcuFFmzZolERER4ubmJn/+85/l\n1VdflcDAQAkNDZWSkhJlvdWtq6FDh0pCQoKMHj1a/Pz85Pz58yIi8t1338mUKVNk5MiR8stf/lKG\nDh3a7H2Irl271mC9bdu2TZl3XZsvvvhCwsLClP7GxcXJxIkTZejQofLhhx/KU089JX5+fjJ16tQO\n/XEral/8eIrumouLC6ysrJCXl4cjR45g3LhxCA4OxpEjR3D8+HH4+fnh/PnzGD16dJPts7OzMWbM\nGNXLu/P230VFRTh06BB2796NVatWAQCSk5MxceJEnDp1CsuWLcM777wDrVaLY8eO4dixY3j77bdx\n+fJlAMCpU6fwxhtv4Pz589izZw90Oh1Onz6NL7/8ElOnTkV1dTWWLl2KDz/8EMePH8fChQuVO4E+\n/PDDWLp0KU6fPo3Dhw9j8ODBDZa9fPnyBrUmJCRgzJgxOHPmDF555RXExcUp4y5cuID09HQcO3YM\na9aswa1bt5rsf3JyMjw8PHDq1CkkJyc3us13dnY2PvroI3zxxRd4/vnnYWNjg5MnT2LcuHHYvHmz\nsg7r1qNGo4GDgwNOnDiBJUuW4I9//CMAYM2aNZgyZQq++uorzJ07F7m5uc3+TdLS0hqtt6b+VvXl\n5ORg//792LVrFx555BFERETg7Nmz6Nu3Lz755JNm21H3wtAgi4wfPx6HDx/G4cOHMW7cOIwbNw6H\nDx/GkSNHMGHChFbb37njU0uj0WD27NkAgBEjRii/XHfn/NLT07F582YEBgYiNDQUJSUl+PbbbwEA\nwcHBGDp0KABg1KhR2LdvH1atWoV//etfsLGxwTfffIPs7GxMmTIFgYGBePnll2E0GnHt2jUUFBRg\n1qxZAIBevXqhb9++Lfbl0KFDmD9/PgBg0qRJMJlMKC8vh0ajwU9/+lNYW1vD3t4ejo6Ozf4KX2vr\natKkSejXrx8GDRoErVaLGTNmAAD8/PyUoLzTQw89BAAYPXq0Ms2hQ4eU24lHRUW1eIvzO9dba78x\notFoMG3aNPTs2RO+vr6ora1FVFRUq3VS98PQIItMmDABhw4dwpdffgk/Pz+EhoYqITJ+/HiMHDkS\nJ06caLJtS+PU6NWrl/K8pR3qm2++iVOnTuHUqVO4dOkSpkyZAgDo16+fMo3BYMCpU6fg5+eH3/3u\nd3jppZeUGuvanj17FmlpaRYHXXPt6vejZ8+eqKmpsWj+vXv3Vp736NFDed2jR49m51k3zZ3LVdvH\n5tablZUVamtrAQAVFRUN2tT1t0ePHrC2tm5Qs6V9p87H0CCLjB8/Hrt374a9vT00Gg0GDhyI0tJS\n5UjjF7/4BQ4fPoxPP/1UaXPgwAFkZ2fj8ccfR0pKSoPbZ//jH/9Qrqy6k5od2YABA1BeXq68joqK\nwrp165Sd0YULF3Djxo1G7QoLC9GnTx88/PDDePrpp3Hq1CkMHz4c33//PY4ePQrg9i+unTt3DgMG\nDIBer1d+S7yyshI3b96EjY1Ng2XXN3HiRLz//vsAgMzMTDg4OGDAgAF3FUB39k2tuw25CRMmYNu2\nbQBuH6mZzeZmp21qvQGAm5sbjh8/DgD48MMPLa6Fuq974vc0qPvx9fWFyWTCI488ogwbNWoUbty4\nodyPf/fu3Vi+fDmWL18Oa2tr5TeIHRwckJqaiqeffhrfffcdevTogQceeADTpk1rcln1P4+ve33n\nc39/f/Ts2RMBAQFYuHAhnnjiCVy+fBmjR4+GiMDR0REfffRRo3l9+eWXWLFihfLf7/r162FtbY3t\n27fjiSeeQFlZGWpqavDkk0/Cx8cHW7Zswa9//Wv8/ve/V6YbNWqUsuwFCxYgMDBQWcbq1avx6KOP\nwt/fH/369UNKSkqTfWqJvb09JkyYAD8/P0yfPh2/+c1vGpyfaGndtLaM+tMkJCQgNjYWW7Zswbhx\n4+Ds7Nzsx05Nrbe6eSxatAg2NjYICwtTVWdTr6n74u9pEBEAoKqqCj179kTPnj1x5MgR/Pa3v8XJ\nkye7uizqZnikQUQAgNzcXERHR6O2tha9evXC22+/3dUlUTfEIw3qNjZt2tTgm+YAcN999+HPf/5z\nF1XUuUwmk3Kyvr6MjIwO/ZnZlnTHmqhrMTSIiEg1Xj1FRESqMTSIiEg1hgYREanG0CAiItUYGkRE\npNr/B8nK9MhPJPlWAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f5358b2d550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEaCAYAAAACBmAUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f535bb776d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEaCAYAAADQVmpMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f5358c78d50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEaCAYAAAAG87ApAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIhUYXAQEZEq/wukoxR+W5CmWwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f534d392090>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEaCAYAAADg2nttAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRKSKYUFERKoYFkREpOr/AQ2bEhr8ipSbAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f534d170a50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEaCAYAAADtxAsqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f535bbed650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEaCAYAAADQVmpMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f535cb873d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEaCAYAAAACBmAUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJh53AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f534cdd43d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEaCAYAAADtxAsqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nICIi1Zg0iIhItf8PCOS36vC5OEwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f534d248510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEaCAYAAADQVmpMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niEi1/we0/VT7+GUeJgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f534cd7f310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEaCAYAAADQVmpMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f534ca864d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEaCAYAAADtxAsqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f534ca79250>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEaCAYAAADg2nttAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f534ce75e10>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}