contours_to_labels.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import contour_classification.contour_utils as cc\n",
      "import matplotlib.pyplot as plt\n",
      "import mir_eval\n",
      "%matplotlib inline\n",
      "import seaborn as sns\n",
      "sns.set()\n",
      "import mir_eval"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "contour_fpath = \\\n",
      "    \"test_data/LizNelson_Rainfall_MIX_vamp_melodia-contours_melodia-contours_contoursall.csv\"\n",
      "annotation_fpath = \\\n",
      "    \"test_data/LizNelson_Rainfall_MELODY1.csv\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "contour_data = cc.load_contour_data(contour_fpath)\n",
      "annot_data = cc.load_annotation(annotation_fpath)\n",
      "#annot_data = annot_data[annot_data['f0'] != 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "contour_data.iloc[0,13:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
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        "                ...    \n",
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        "3076                NaN\n",
        "3077                NaN\n",
        "3078                NaN\n",
        "3079                NaN\n",
        "overlap              -1\n",
        "labels               -1\n",
        "melodiness             \n",
        "mel prob             -1\n",
        "Name: 0, dtype: object"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "olap_contours = contour_data.copy()\n",
      "\n",
      "contour_times, contour_freqs, _ = cc.contours_from_contour_data(contour_data)\n",
      "\n",
      "mel_dat = pd.DataFrame(columns=['time', 'f0', 'annot_dist'])\n",
      "mel_dat['time'] = contour_times.values.ravel()\n",
      "mel_dat['f0'] = contour_freqs.values.ravel()\n",
      "mel_dat['annot_dist'] = -1\n",
      "\n",
      "mel_dat.dropna(inplace=True)\n",
      "mel_dat = mel_dat[mel_dat['f0'] != 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m_int = 128.0/44100.0\n",
      "\n",
      "#doing this the slow hacky way...\n",
      "annot_data['best est'] = -1\n",
      "annot_data.loc[annot_data['f0'] == 0, 'best est'] = 0\n",
      "niter = 0\n",
      "for row in annot_data[annot_data['f0'] != 0].iterrows():\n",
      "    idx = row[0]\n",
      "    dat = row[1]\n",
      "    t = dat['time']\n",
      "    f = dat['f0']\n",
      "    \n",
      "    cands = mel_dat[np.abs(mel_dat['time'] - t) <= m_int]\n",
      "    if len(cands) == 0:\n",
      "        annot_data.loc[idx, 'best est'] = 0\n",
      "    else:\n",
      "        f0_cands = cands['f0'].values\n",
      "        annot_data.loc[idx, 'best est'] = f0_cands[np.argmin(f0_cands - f)]\n",
      "#     if niter > 10000:\n",
      "#         break\n",
      "#     else:\n",
      "#        niter += 1\n",
      "\n",
      "annot_data[annot_data['f0'] != 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "      <th></th>\n",
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        "      <th>best est</th>\n",
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        "  </thead>\n",
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        "      <th>2519</th>\n",
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        "      <td>1922.492098</td>\n",
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        "      <th>2521</th>\n",
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        "      <td>96.3153</td>\n",
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        "      <td>1967.987804</td>\n",
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        "      <td>174.436</td>\n",
        "      <td>1998.233148</td>\n",
        "      <td>96.8732</td>\n",
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        "      <th>2525</th>\n",
        "      <td>14.657596</td>\n",
        "      <td>174.880</td>\n",
        "      <td>2002.634139</td>\n",
        "      <td>96.3153</td>\n",
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        "      <th>2526</th>\n",
        "      <td>14.663401</td>\n",
        "      <td>180.716</td>\n",
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        "      <td>2137.975007</td>\n",
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        "      <td>14.680816</td>\n",
        "      <td>192.670</td>\n",
        "      <td>2170.354114</td>\n",
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        "      <td>2216.642972</td>\n",
        "      <td>96.8732</td>\n",
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        "      <td>14.692426</td>\n",
        "      <td>198.956</td>\n",
        "      <td>2225.935061</td>\n",
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        "      <td>2261.001692</td>\n",
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        "      <th>2533</th>\n",
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        "      <td>2329.630840</td>\n",
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        "      <td>2381.654359</td>\n",
        "      <td>94.6607</td>\n",
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        "      <td>14.721451</td>\n",
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        "      <td>2437.812827</td>\n",
        "      <td>95.2090</td>\n",
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        "      <td>235.663</td>\n",
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        "      <td>236.553</td>\n",
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        "      <td>97.4344</td>\n",
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        "      <td>97.4344</td>\n",
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        "      <td>98.5666</td>\n",
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        "    <tr>\n",
        "      <th>47963</th>\n",
        "      <td>278.424671</td>\n",
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        "      <td>2252.462079</td>\n",
        "      <td>96.8732</td>\n",
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        "    <tr>\n",
        "      <th>47964</th>\n",
        "      <td>278.430476</td>\n",
        "      <td>202.307</td>\n",
        "      <td>2254.851259</td>\n",
        "      <td>96.8732</td>\n",
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        "    <tr>\n",
        "      <th>47965</th>\n",
        "      <td>278.436281</td>\n",
        "      <td>202.600</td>\n",
        "      <td>2257.356780</td>\n",
        "      <td>94.6607</td>\n",
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        "    <tr>\n",
        "      <th>47966</th>\n",
        "      <td>278.442086</td>\n",
        "      <td>202.643</td>\n",
        "      <td>2257.724180</td>\n",
        "      <td>95.7606</td>\n",
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        "    <tr>\n",
        "      <th>47967</th>\n",
        "      <td>278.447891</td>\n",
        "      <td>202.229</td>\n",
        "      <td>2254.183648</td>\n",
        "      <td>95.7606</td>\n",
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        "    <tr>\n",
        "      <th>47968</th>\n",
        "      <td>278.453696</td>\n",
        "      <td>201.426</td>\n",
        "      <td>2247.295673</td>\n",
        "      <td>98.5666</td>\n",
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        "    <tr>\n",
        "      <th>47969</th>\n",
        "      <td>278.459501</td>\n",
        "      <td>199.878</td>\n",
        "      <td>2233.939397</td>\n",
        "      <td>101.4550</td>\n",
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        "    <tr>\n",
        "      <th>47970</th>\n",
        "      <td>278.465306</td>\n",
        "      <td>198.674</td>\n",
        "      <td>2223.479471</td>\n",
        "      <td>110.6370</td>\n",
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        "    <tr>\n",
        "      <th>47971</th>\n",
        "      <td>278.471111</td>\n",
        "      <td>197.241</td>\n",
        "      <td>2210.947138</td>\n",
        "      <td>109.3660</td>\n",
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        "    <tr>\n",
        "      <th>47972</th>\n",
        "      <td>278.476916</td>\n",
        "      <td>195.989</td>\n",
        "      <td>2199.922993</td>\n",
        "      <td>110.6370</td>\n",
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        "    <tr>\n",
        "      <th>47973</th>\n",
        "      <td>278.482721</td>\n",
        "      <td>195.078</td>\n",
        "      <td>2191.857075</td>\n",
        "      <td>108.7370</td>\n",
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        "    <tr>\n",
        "      <th>47974</th>\n",
        "      <td>278.488526</td>\n",
        "      <td>194.432</td>\n",
        "      <td>2186.114588</td>\n",
        "      <td>105.6410</td>\n",
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        "    <tr>\n",
        "      <th>47975</th>\n",
        "      <td>278.494331</td>\n",
        "      <td>193.973</td>\n",
        "      <td>2182.022793</td>\n",
        "      <td>105.6410</td>\n",
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        "    <tr>\n",
        "      <th>47976</th>\n",
        "      <td>278.500136</td>\n",
        "      <td>192.911</td>\n",
        "      <td>2172.518263</td>\n",
        "      <td>106.2530</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47977</th>\n",
        "      <td>278.505941</td>\n",
        "      <td>191.698</td>\n",
        "      <td>2161.598114</td>\n",
        "      <td>109.3660</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47978</th>\n",
        "      <td>278.511746</td>\n",
        "      <td>190.549</td>\n",
        "      <td>2151.190216</td>\n",
        "      <td>108.1100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47979</th>\n",
        "      <td>278.517551</td>\n",
        "      <td>188.952</td>\n",
        "      <td>2136.619519</td>\n",
        "      <td>107.4880</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47980</th>\n",
        "      <td>278.523356</td>\n",
        "      <td>188.432</td>\n",
        "      <td>2131.848557</td>\n",
        "      <td>108.7370</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48006</th>\n",
        "      <td>278.674286</td>\n",
        "      <td>181.527</td>\n",
        "      <td>2067.216749</td>\n",
        "      <td>117.8950</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>27658 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "             time       f0        cents  best est\n",
        "2519    14.622766  167.490  1927.885726   96.3153\n",
        "2520    14.628571  166.969  1922.492098   96.3153\n",
        "2521    14.634376  167.174  1924.616356   96.3153\n",
        "2522    14.640181  167.229  1925.185836   95.7606\n",
        "2523    14.645986  171.415  1967.987804   96.8732\n",
        "2524    14.651791  174.436  1998.233148   96.8732\n",
        "2525    14.657596  174.880  2002.634139   96.3153\n",
        "2526    14.663401  180.716  2059.464863   96.3153\n",
        "2527    14.669206  185.733  2106.871973   96.3153\n",
        "2528    14.675011  189.100  2137.975007   96.3153\n",
        "2529    14.680816  192.670  2170.354114   96.3153\n",
        "2530    14.686621  197.891  2216.642972   96.8732\n",
        "2531    14.692426  198.956  2225.935061   97.4344\n",
        "2532    14.698231  203.027  2261.001692   97.4344\n",
        "2533    14.704036  206.930  2293.967152   97.4344\n",
        "2534    14.709841  211.237  2329.630840   96.3153\n",
        "2535    14.715646  217.681  2381.654359   94.6607\n",
        "2536    14.721451  224.858  2437.812827   95.2090\n",
        "2537    14.727256  228.237  2463.634998   96.3153\n",
        "2538    14.733061  229.583  2473.814753   96.3153\n",
        "2539    14.738866  229.699  2474.689263   95.7606\n",
        "2540    14.744671  230.055  2477.370346   95.7606\n",
        "2541    14.750476  232.794  2497.860421   96.3153\n",
        "2542    14.756281  234.156  2507.959779   96.3153\n",
        "2543    14.762086  234.845  2513.046425   95.7606\n",
        "2544    14.767891  235.797  2520.050207   96.3153\n",
        "2545    14.773696  235.500  2517.868243   96.3153\n",
        "2546    14.779501  235.663  2519.066093   95.7606\n",
        "2547    14.785306  236.553  2525.591920   96.3153\n",
        "2548    14.791111  238.086  2536.775119   96.3153\n",
        "...           ...      ...          ...       ...\n",
        "47952  278.360816  191.369  2158.624346   97.9989\n",
        "47953  278.366621  192.459  2168.457137   97.4344\n",
        "47954  278.372426  193.271  2175.745987   97.4344\n",
        "47955  278.378231  194.303  2184.965583   97.9989\n",
        "47956  278.384036  195.429  2194.969252   97.4344\n",
        "47957  278.389841  196.691  2206.112906   97.4344\n",
        "47958  278.395646  197.873  2216.485494   97.9989\n",
        "47959  278.401451  199.183  2227.909196   97.4344\n",
        "47960  278.407256  200.276  2237.383228   97.4344\n",
        "47961  278.413061  201.104  2244.525904   97.9989\n",
        "47962  278.418866  201.546  2248.326753   98.5666\n",
        "47963  278.424671  202.028  2252.462079   96.8732\n",
        "47964  278.430476  202.307  2254.851259   96.8732\n",
        "47965  278.436281  202.600  2257.356780   94.6607\n",
        "47966  278.442086  202.643  2257.724180   95.7606\n",
        "47967  278.447891  202.229  2254.183648   95.7606\n",
        "47968  278.453696  201.426  2247.295673   98.5666\n",
        "47969  278.459501  199.878  2233.939397  101.4550\n",
        "47970  278.465306  198.674  2223.479471  110.6370\n",
        "47971  278.471111  197.241  2210.947138  109.3660\n",
        "47972  278.476916  195.989  2199.922993  110.6370\n",
        "47973  278.482721  195.078  2191.857075  108.7370\n",
        "47974  278.488526  194.432  2186.114588  105.6410\n",
        "47975  278.494331  193.973  2182.022793  105.6410\n",
        "47976  278.500136  192.911  2172.518263  106.2530\n",
        "47977  278.505941  191.698  2161.598114  109.3660\n",
        "47978  278.511746  190.549  2151.190216  108.1100\n",
        "47979  278.517551  188.952  2136.619519  107.4880\n",
        "47980  278.523356  188.432  2131.848557  108.7370\n",
        "48006  278.674286  181.527  2067.216749  117.8950\n",
        "\n",
        "[27658 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mir_eval.melody.evaluate(annot_data['time'].values, annot_data['f0'].values, \n",
      "                         annot_data['time'].values, annot_data['best est'].values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "OrderedDict([('Voicing Recall', 0.957842215633813), ('Voicing False Alarm', 0.0055550368779759126), ('Raw Pitch Accuracy', 0.35317087280352882), ('Raw Chroma Accuracy', 0.53803601128064216), ('Overall Accuracy', 0.63306845965770175)])"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(np.abs(annot_data[annot_data['f0'] != 0]['f0'] - annot_data[annot_data['f0'] != 0]['best est'])).describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "count    27658.000000\n",
        "mean        80.510759\n",
        "std         73.604993\n",
        "min          0.000000\n",
        "25%          1.423250\n",
        "50%         87.702000\n",
        "75%        140.531650\n",
        "max        531.843000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# step_size = 256.0/44100.0  contour time stamp step size\n",
      "# mel_time_idx = np.arange(0, np.max(mel_dat['time'].values) + 1, step_size)\n",
      "\n",
      "# find index in evenly spaced grid of estimated time values\n",
      "annot_times = annot_data['time'].values\n",
      "old_times = mel_dat['time'].values\n",
      "reidx = np.searchsorted(annot_times, old_times)\n",
      "reidx[reidx >= len(annot_times)] = len(annot_times) - 1 \n",
      "\n",
      "shift_idx = (np.abs(old_times - annot_times[reidx - 1]) < \\\n",
      "             np.abs(old_`times - annot_times[reidx]))\n",
      "reidx[shift_idx] = reidx[shift_idx] - 1\n",
      "mel_dat.index = reidx\n",
      "mel_dat['reidx'] = reidx\n",
      "\n",
      "non_zero_annot = np.intersect1d(annot_data[annot_data['f0'] != 0].index, mel_dat.index)\n",
      "mel_dat = mel_dat.loc[non_zero_annot, :]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mel_dat['annot_dist'] = np.abs(mel_dat['f0'] - annot_data.loc[mel_dat.index, 'f0'])\n",
      "\n",
      "# test = annot_data.loc[mel_dat.index, :].dropna()#.drop_duplicates(subset='reidx')\n",
      "# mel_dat.loc[test.index, 'annot_dist'] = mel_dat.loc[test.index, :]['annot_f0'] - test['f0']\n",
      "mel_dat.sort(columns='annot_dist', inplace=True)\n",
      "mel_dat.sort(columns='time', inplace=True)\n",
      "mel_dat.drop_duplicates(subset='reidx', take_last=False, inplace=True)\n",
      "mel_dat"
     ],
     "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>time</th>\n",
        "      <th>f0</th>\n",
        "      <th>annot_dist</th>\n",
        "      <th>reidx</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2519</th>\n",
        "      <td>14.6199</td>\n",
        "      <td>96.3153</td>\n",
        "      <td>71.1747</td>\n",
        "      <td>2519</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2520</th>\n",
        "      <td>14.6257</td>\n",
        "      <td>96.8732</td>\n",
        "      <td>70.0958</td>\n",
        "      <td>2520</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2521</th>\n",
        "      <td>14.6315</td>\n",
        "      <td>96.3153</td>\n",
        "      <td>70.8587</td>\n",
        "      <td>2521</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2522</th>\n",
        "      <td>14.6373</td>\n",
        "      <td>95.7606</td>\n",
        "      <td>71.4684</td>\n",
        "      <td>2522</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2523</th>\n",
        "      <td>14.6431</td>\n",
        "      <td>96.8732</td>\n",
        "      <td>74.5418</td>\n",
        "      <td>2523</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2524</th>\n",
        "      <td>14.6489</td>\n",
        "      <td>96.8732</td>\n",
        "      <td>77.5628</td>\n",
        "      <td>2524</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2525</th>\n",
        "      <td>14.6547</td>\n",
        "      <td>97.4344</td>\n",
        "      <td>77.4456</td>\n",
        "      <td>2525</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2526</th>\n",
        "      <td>14.6605</td>\n",
        "      <td>96.3153</td>\n",
        "      <td>84.4007</td>\n",
        "      <td>2526</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2527</th>\n",
        "      <td>14.6692</td>\n",
        "      <td>96.3153</td>\n",
        "      <td>89.4177</td>\n",
        "      <td>2527</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2528</th>\n",
        "      <td>14.6750</td>\n",
        "      <td>96.8732</td>\n",
        "      <td>92.2268</td>\n",
        "      <td>2528</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2529</th>\n",
        "      <td>14.6808</td>\n",
        "      <td>96.3153</td>\n",
        "      <td>96.3547</td>\n",
        "      <td>2529</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2530</th>\n",
        "      <td>14.6866</td>\n",
        "      <td>96.8732</td>\n",
        "      <td>101.0178</td>\n",
        "      <td>2530</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2531</th>\n",
        "      <td>14.6924</td>\n",
        "      <td>97.4344</td>\n",
        "      <td>101.5216</td>\n",
        "      <td>2531</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2532</th>\n",
        "      <td>14.6982</td>\n",
        "      <td>97.9989</td>\n",
        "      <td>105.0281</td>\n",
        "      <td>2532</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2533</th>\n",
        "      <td>14.7040</td>\n",
        "      <td>200.5790</td>\n",
        "      <td>6.3510</td>\n",
        "      <td>2533</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2534</th>\n",
        "      <td>14.7098</td>\n",
        "      <td>206.4560</td>\n",
        "      <td>4.7810</td>\n",
        "      <td>2534</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2535</th>\n",
        "      <td>14.7156</td>\n",
        "      <td>218.7330</td>\n",
        "      <td>1.0520</td>\n",
        "      <td>2535</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2536</th>\n",
        "      <td>14.7215</td>\n",
        "      <td>95.2090</td>\n",
        "      <td>129.6490</td>\n",
        "      <td>2536</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2537</th>\n",
        "      <td>14.7244</td>\n",
        "      <td>225.1420</td>\n",
        "      <td>3.0950</td>\n",
        "      <td>2537</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2538</th>\n",
        "      <td>14.7302</td>\n",
        "      <td>96.8732</td>\n",
        "      <td>132.7098</td>\n",
        "      <td>2538</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2539</th>\n",
        "      <td>14.7360</td>\n",
        "      <td>229.0780</td>\n",
        "      <td>0.6210</td>\n",
        "      <td>2539</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2540</th>\n",
        "      <td>14.7418</td>\n",
        "      <td>95.7606</td>\n",
        "      <td>134.2944</td>\n",
        "      <td>2540</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2541</th>\n",
        "      <td>14.7476</td>\n",
        "      <td>96.3153</td>\n",
        "      <td>136.4787</td>\n",
        "      <td>2541</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2542</th>\n",
        "      <td>14.7534</td>\n",
        "      <td>96.3153</td>\n",
        "      <td>137.8407</td>\n",
        "      <td>2542</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2543</th>\n",
        "      <td>14.7592</td>\n",
        "      <td>235.7900</td>\n",
        "      <td>0.9450</td>\n",
        "      <td>2543</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2544</th>\n",
        "      <td>14.7650</td>\n",
        "      <td>96.3153</td>\n",
        "      <td>139.4817</td>\n",
        "      <td>2544</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2545</th>\n",
        "      <td>14.7708</td>\n",
        "      <td>96.8732</td>\n",
        "      <td>138.6268</td>\n",
        "      <td>2545</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2546</th>\n",
        "      <td>14.7766</td>\n",
        "      <td>95.7606</td>\n",
        "      <td>139.9024</td>\n",
        "      <td>2546</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2547</th>\n",
        "      <td>14.7853</td>\n",
        "      <td>237.1560</td>\n",
        "      <td>0.6030</td>\n",
        "      <td>2547</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2548</th>\n",
        "      <td>14.7911</td>\n",
        "      <td>96.3153</td>\n",
        "      <td>141.7707</td>\n",
        "      <td>2548</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47952</th>\n",
        "      <td>278.3580</td>\n",
        "      <td>99.1376</td>\n",
        "      <td>92.2314</td>\n",
        "      <td>47952</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47953</th>\n",
        "      <td>278.3640</td>\n",
        "      <td>97.4344</td>\n",
        "      <td>95.0246</td>\n",
        "      <td>47953</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47954</th>\n",
        "      <td>278.3700</td>\n",
        "      <td>97.4344</td>\n",
        "      <td>95.8366</td>\n",
        "      <td>47954</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47955</th>\n",
        "      <td>278.3780</td>\n",
        "      <td>195.9980</td>\n",
        "      <td>1.6950</td>\n",
        "      <td>47955</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47956</th>\n",
        "      <td>278.3840</td>\n",
        "      <td>97.4344</td>\n",
        "      <td>97.9946</td>\n",
        "      <td>47956</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47957</th>\n",
        "      <td>278.3870</td>\n",
        "      <td>97.4344</td>\n",
        "      <td>99.2566</td>\n",
        "      <td>47957</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47958</th>\n",
        "      <td>278.3930</td>\n",
        "      <td>197.1330</td>\n",
        "      <td>0.7400</td>\n",
        "      <td>47958</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47959</th>\n",
        "      <td>278.3990</td>\n",
        "      <td>197.1330</td>\n",
        "      <td>2.0500</td>\n",
        "      <td>47959</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47960</th>\n",
        "      <td>278.4070</td>\n",
        "      <td>194.8690</td>\n",
        "      <td>5.4070</td>\n",
        "      <td>47960</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47961</th>\n",
        "      <td>278.4130</td>\n",
        "      <td>197.1330</td>\n",
        "      <td>3.9710</td>\n",
        "      <td>47961</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47962</th>\n",
        "      <td>278.4160</td>\n",
        "      <td>197.1330</td>\n",
        "      <td>4.4130</td>\n",
        "      <td>47962</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47963</th>\n",
        "      <td>278.4220</td>\n",
        "      <td>97.9989</td>\n",
        "      <td>104.0291</td>\n",
        "      <td>47963</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47964</th>\n",
        "      <td>278.4280</td>\n",
        "      <td>193.7460</td>\n",
        "      <td>8.5610</td>\n",
        "      <td>47964</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47965</th>\n",
        "      <td>278.4360</td>\n",
        "      <td>95.2090</td>\n",
        "      <td>107.3910</td>\n",
        "      <td>47965</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47966</th>\n",
        "      <td>278.4420</td>\n",
        "      <td>190.4180</td>\n",
        "      <td>12.2250</td>\n",
        "      <td>47966</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47967</th>\n",
        "      <td>278.4450</td>\n",
        "      <td>95.7606</td>\n",
        "      <td>106.4684</td>\n",
        "      <td>47967</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47968</th>\n",
        "      <td>278.4510</td>\n",
        "      <td>98.5666</td>\n",
        "      <td>102.8594</td>\n",
        "      <td>47968</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47969</th>\n",
        "      <td>278.4570</td>\n",
        "      <td>202.9100</td>\n",
        "      <td>3.0320</td>\n",
        "      <td>47969</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47970</th>\n",
        "      <td>278.4650</td>\n",
        "      <td>111.2780</td>\n",
        "      <td>87.3960</td>\n",
        "      <td>47970</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47971</th>\n",
        "      <td>278.4710</td>\n",
        "      <td>109.3660</td>\n",
        "      <td>87.8750</td>\n",
        "      <td>47971</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47972</th>\n",
        "      <td>278.4770</td>\n",
        "      <td>110.6370</td>\n",
        "      <td>85.3520</td>\n",
        "      <td>47972</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47973</th>\n",
        "      <td>278.4800</td>\n",
        "      <td>222.5560</td>\n",
        "      <td>27.4780</td>\n",
        "      <td>47973</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47974</th>\n",
        "      <td>278.4860</td>\n",
        "      <td>108.1100</td>\n",
        "      <td>86.3220</td>\n",
        "      <td>47974</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47975</th>\n",
        "      <td>278.4940</td>\n",
        "      <td>210.0650</td>\n",
        "      <td>16.0920</td>\n",
        "      <td>47975</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47976</th>\n",
        "      <td>278.5000</td>\n",
        "      <td>210.0650</td>\n",
        "      <td>17.1540</td>\n",
        "      <td>47976</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47977</th>\n",
        "      <td>278.5060</td>\n",
        "      <td>109.3660</td>\n",
        "      <td>82.3320</td>\n",
        "      <td>47977</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47978</th>\n",
        "      <td>278.5090</td>\n",
        "      <td>108.1100</td>\n",
        "      <td>82.4390</td>\n",
        "      <td>47978</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47979</th>\n",
        "      <td>278.5150</td>\n",
        "      <td>107.4880</td>\n",
        "      <td>81.4640</td>\n",
        "      <td>47979</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47980</th>\n",
        "      <td>278.5230</td>\n",
        "      <td>108.7370</td>\n",
        "      <td>79.6950</td>\n",
        "      <td>47980</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48006</th>\n",
        "      <td>278.6740</td>\n",
        "      <td>117.8950</td>\n",
        "      <td>63.6320</td>\n",
        "      <td>48006</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>26608 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "           time        f0  annot_dist  reidx\n",
        "2519    14.6199   96.3153     71.1747   2519\n",
        "2520    14.6257   96.8732     70.0958   2520\n",
        "2521    14.6315   96.3153     70.8587   2521\n",
        "2522    14.6373   95.7606     71.4684   2522\n",
        "2523    14.6431   96.8732     74.5418   2523\n",
        "2524    14.6489   96.8732     77.5628   2524\n",
        "2525    14.6547   97.4344     77.4456   2525\n",
        "2526    14.6605   96.3153     84.4007   2526\n",
        "2527    14.6692   96.3153     89.4177   2527\n",
        "2528    14.6750   96.8732     92.2268   2528\n",
        "2529    14.6808   96.3153     96.3547   2529\n",
        "2530    14.6866   96.8732    101.0178   2530\n",
        "2531    14.6924   97.4344    101.5216   2531\n",
        "2532    14.6982   97.9989    105.0281   2532\n",
        "2533    14.7040  200.5790      6.3510   2533\n",
        "2534    14.7098  206.4560      4.7810   2534\n",
        "2535    14.7156  218.7330      1.0520   2535\n",
        "2536    14.7215   95.2090    129.6490   2536\n",
        "2537    14.7244  225.1420      3.0950   2537\n",
        "2538    14.7302   96.8732    132.7098   2538\n",
        "2539    14.7360  229.0780      0.6210   2539\n",
        "2540    14.7418   95.7606    134.2944   2540\n",
        "2541    14.7476   96.3153    136.4787   2541\n",
        "2542    14.7534   96.3153    137.8407   2542\n",
        "2543    14.7592  235.7900      0.9450   2543\n",
        "2544    14.7650   96.3153    139.4817   2544\n",
        "2545    14.7708   96.8732    138.6268   2545\n",
        "2546    14.7766   95.7606    139.9024   2546\n",
        "2547    14.7853  237.1560      0.6030   2547\n",
        "2548    14.7911   96.3153    141.7707   2548\n",
        "...         ...       ...         ...    ...\n",
        "47952  278.3580   99.1376     92.2314  47952\n",
        "47953  278.3640   97.4344     95.0246  47953\n",
        "47954  278.3700   97.4344     95.8366  47954\n",
        "47955  278.3780  195.9980      1.6950  47955\n",
        "47956  278.3840   97.4344     97.9946  47956\n",
        "47957  278.3870   97.4344     99.2566  47957\n",
        "47958  278.3930  197.1330      0.7400  47958\n",
        "47959  278.3990  197.1330      2.0500  47959\n",
        "47960  278.4070  194.8690      5.4070  47960\n",
        "47961  278.4130  197.1330      3.9710  47961\n",
        "47962  278.4160  197.1330      4.4130  47962\n",
        "47963  278.4220   97.9989    104.0291  47963\n",
        "47964  278.4280  193.7460      8.5610  47964\n",
        "47965  278.4360   95.2090    107.3910  47965\n",
        "47966  278.4420  190.4180     12.2250  47966\n",
        "47967  278.4450   95.7606    106.4684  47967\n",
        "47968  278.4510   98.5666    102.8594  47968\n",
        "47969  278.4570  202.9100      3.0320  47969\n",
        "47970  278.4650  111.2780     87.3960  47970\n",
        "47971  278.4710  109.3660     87.8750  47971\n",
        "47972  278.4770  110.6370     85.3520  47972\n",
        "47973  278.4800  222.5560     27.4780  47973\n",
        "47974  278.4860  108.1100     86.3220  47974\n",
        "47975  278.4940  210.0650     16.0920  47975\n",
        "47976  278.5000  210.0650     17.1540  47976\n",
        "47977  278.5060  109.3660     82.3320  47977\n",
        "47978  278.5090  108.1100     82.4390  47978\n",
        "47979  278.5150  107.4880     81.4640  47979\n",
        "47980  278.5230  108.7370     79.6950  47980\n",
        "48006  278.6740  117.8950     63.6320  48006\n",
        "\n",
        "[26608 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "step_size = 128.0/44100.0  # contour time stamp step size\n",
      "mel_time_idx = np.arange(0, np.max(mel_dat['time'].values) + 1, step_size)\n",
      "\n",
      "# find index in evenly spaced grid of estimated time values\n",
      "old_times = mel_dat['time'].values\n",
      "reidx = np.searchsorted(mel_time_idx, old_times)\n",
      "shift_idx = (np.abs(old_times - mel_time_idx[reidx - 1]) < \\\n",
      "             np.abs(old_times - mel_time_idx[reidx]))\n",
      "reidx[shift_idx] = reidx[shift_idx] - 1\n",
      "\n",
      "# find duplicate time values\n",
      "mel_dat['reidx2'] = reidx\n",
      "\n",
      "mel_dat.drop_duplicates(subset='reidx', take_last=True, inplace=True)\n",
      "\n",
      "mel_output = pd.Series(np.zeros(mel_time_idx.shape), index=mel_time_idx)\n",
      "mel_output.iloc[mel_dat['reidx2']] = mel_dat['f0'].values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mel_output"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "0.000000      0\n",
        "0.002902      0\n",
        "0.005805      0\n",
        "0.008707      0\n",
        "0.011610      0\n",
        "0.014512      0\n",
        "0.017415      0\n",
        "0.020317      0\n",
        "0.023220      0\n",
        "0.026122      0\n",
        "0.029025      0\n",
        "0.031927      0\n",
        "0.034830      0\n",
        "0.037732      0\n",
        "0.040635      0\n",
        "0.043537      0\n",
        "0.046440      0\n",
        "0.049342      0\n",
        "0.052245      0\n",
        "0.055147      0\n",
        "0.058050      0\n",
        "0.060952      0\n",
        "0.063855      0\n",
        "0.066757      0\n",
        "0.069660      0\n",
        "0.072562      0\n",
        "0.075465      0\n",
        "0.078367      0\n",
        "0.081270      0\n",
        "0.084172      0\n",
        "             ..\n",
        "279.588571    0\n",
        "279.591474    0\n",
        "279.594376    0\n",
        "279.597279    0\n",
        "279.600181    0\n",
        "279.603084    0\n",
        "279.605986    0\n",
        "279.608889    0\n",
        "279.611791    0\n",
        "279.614694    0\n",
        "279.617596    0\n",
        "279.620499    0\n",
        "279.623401    0\n",
        "279.626304    0\n",
        "279.629206    0\n",
        "279.632109    0\n",
        "279.635011    0\n",
        "279.637914    0\n",
        "279.640816    0\n",
        "279.643719    0\n",
        "279.646621    0\n",
        "279.649524    0\n",
        "279.652426    0\n",
        "279.655329    0\n",
        "279.658231    0\n",
        "279.661134    0\n",
        "279.664036    0\n",
        "279.666939    0\n",
        "279.669841    0\n",
        "279.672744    0\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.semilogy(annot_data['time'], annot_data['f0'], 'o', basey=2)\n",
      "plt.semilogy(mel_output.index, mel_output.values, 'or', basey=2)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VFjCX9ZSksLW7xwJN00geeFBWmVG9vgHz0b8xOHeuZyeoQlvSbhqw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TAAAg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\nQBC5rFsuYt1M0WRTd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9lk/B3V6hZDRdxW9KoT1DpmtW7d2YDlb2z9WdHsBY83upnEJacf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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1088d4a90>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test = annot_data.loc[mel_dat['reidx']].dropna()\n",
      "test2 = test[test['f0'] != 0].drop_duplicates(subset='reidx')\n",
      "mel_dat[mel_dat['f0'] != 0]['f0'] - test2['f0']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "0         NaN\n",
        "1         NaN\n",
        "2         NaN\n",
        "3         NaN\n",
        "4         NaN\n",
        "5         NaN\n",
        "6         NaN\n",
        "7         NaN\n",
        "8         NaN\n",
        "9         NaN\n",
        "10        NaN\n",
        "11        NaN\n",
        "12        NaN\n",
        "13        NaN\n",
        "14        NaN\n",
        "15        NaN\n",
        "16        NaN\n",
        "17        NaN\n",
        "18        NaN\n",
        "19        NaN\n",
        "20        NaN\n",
        "21        NaN\n",
        "22        NaN\n",
        "23        NaN\n",
        "24        NaN\n",
        "25        NaN\n",
        "26        NaN\n",
        "27        NaN\n",
        "28        NaN\n",
        "29        NaN\n",
        "           ..\n",
        "1075202   NaN\n",
        "1075203   NaN\n",
        "1075204   NaN\n",
        "1075205   NaN\n",
        "1075206   NaN\n",
        "1075207   NaN\n",
        "1075208   NaN\n",
        "1075209   NaN\n",
        "1075210   NaN\n",
        "1075211   NaN\n",
        "1075212   NaN\n",
        "1075213   NaN\n",
        "1075214   NaN\n",
        "1075215   NaN\n",
        "1075216   NaN\n",
        "1075217   NaN\n",
        "1075218   NaN\n",
        "1075219   NaN\n",
        "1075220   NaN\n",
        "1075221   NaN\n",
        "1075222   NaN\n",
        "1075223   NaN\n",
        "1075224   NaN\n",
        "1075225   NaN\n",
        "1075226   NaN\n",
        "1075227   NaN\n",
        "1075228   NaN\n",
        "1075229   NaN\n",
        "1075230   NaN\n",
        "1075231   NaN\n",
        "Name: f0, dtype: float64"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "contours_s.iloc[1:10]"
     ],
     "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>16</th>\n",
        "      <th>19</th>\n",
        "      <th>22</th>\n",
        "      <th>25</th>\n",
        "      <th>28</th>\n",
        "      <th>31</th>\n",
        "      <th>34</th>\n",
        "      <th>37</th>\n",
        "      <th>40</th>\n",
        "      <th>43</th>\n",
        "      <th>...</th>\n",
        "      <th>3052</th>\n",
        "      <th>3055</th>\n",
        "      <th>3058</th>\n",
        "      <th>3061</th>\n",
        "      <th>3064</th>\n",
        "      <th>3067</th>\n",
        "      <th>3070</th>\n",
        "      <th>3073</th>\n",
        "      <th>3076</th>\n",
        "      <th>3079</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>0.011312</td>\n",
        "      <td>0.013001</td>\n",
        "      <td>0.014549</td>\n",
        "      <td>0.015595</td>\n",
        "      <td>0.016396</td>\n",
        "      <td>0.016584</td>\n",
        "      <td>0.017574</td>\n",
        "      <td>0.017265</td>\n",
        "      <td>0.017151</td>\n",
        "      <td>0.016752</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>0.012592</td>\n",
        "      <td>0.015366</td>\n",
        "      <td>0.017771</td>\n",
        "      <td>0.019807</td>\n",
        "      <td>0.021417</td>\n",
        "      <td>0.022747</td>\n",
        "      <td>0.023383</td>\n",
        "      <td>0.023807</td>\n",
        "      <td>0.023748</td>\n",
        "      <td>0.023086</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0.021969</td>\n",
        "      <td>0.022496</td>\n",
        "      <td>0.021048</td>\n",
        "      <td>0.021892</td>\n",
        "      <td>0.021848</td>\n",
        "      <td>0.021581</td>\n",
        "      <td>0.019097</td>\n",
        "      <td>0.021013</td>\n",
        "      <td>0.021053</td>\n",
        "      <td>0.018812</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>0.011602</td>\n",
        "      <td>0.013697</td>\n",
        "      <td>0.016035</td>\n",
        "      <td>0.019074</td>\n",
        "      <td>0.021045</td>\n",
        "      <td>0.022134</td>\n",
        "      <td>0.023356</td>\n",
        "      <td>0.023704</td>\n",
        "      <td>0.024441</td>\n",
        "      <td>0.024226</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>0.018049</td>\n",
        "      <td>0.017928</td>\n",
        "      <td>0.017774</td>\n",
        "      <td>0.021623</td>\n",
        "      <td>0.020774</td>\n",
        "      <td>0.018783</td>\n",
        "      <td>0.022218</td>\n",
        "      <td>0.018639</td>\n",
        "      <td>0.018936</td>\n",
        "      <td>0.018949</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>0.018303</td>\n",
        "      <td>0.020011</td>\n",
        "      <td>0.021411</td>\n",
        "      <td>0.021769</td>\n",
        "      <td>0.021779</td>\n",
        "      <td>0.022317</td>\n",
        "      <td>0.023400</td>\n",
        "      <td>0.023056</td>\n",
        "      <td>0.022428</td>\n",
        "      <td>0.022760</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>0.011278</td>\n",
        "      <td>0.013308</td>\n",
        "      <td>0.014151</td>\n",
        "      <td>0.011613</td>\n",
        "      <td>0.011170</td>\n",
        "      <td>0.013470</td>\n",
        "      <td>0.015963</td>\n",
        "      <td>0.015477</td>\n",
        "      <td>0.014734</td>\n",
        "      <td>0.014241</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>0.013490</td>\n",
        "      <td>0.016013</td>\n",
        "      <td>0.018399</td>\n",
        "      <td>0.020705</td>\n",
        "      <td>0.022265</td>\n",
        "      <td>0.023172</td>\n",
        "      <td>0.023963</td>\n",
        "      <td>0.024436</td>\n",
        "      <td>0.024541</td>\n",
        "      <td>0.024258</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>0.011835</td>\n",
        "      <td>0.015216</td>\n",
        "      <td>0.018106</td>\n",
        "      <td>0.020802</td>\n",
        "      <td>0.022834</td>\n",
        "      <td>0.025176</td>\n",
        "      <td>0.026641</td>\n",
        "      <td>0.028049</td>\n",
        "      <td>0.028955</td>\n",
        "      <td>0.028619</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>9 rows \u00d7 1022 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "         16        19        22        25        28        31        34  \\\n",
        "1  0.011312  0.013001  0.014549  0.015595  0.016396  0.016584  0.017574   \n",
        "2  0.012592  0.015366  0.017771  0.019807  0.021417  0.022747  0.023383   \n",
        "3  0.021969  0.022496  0.021048  0.021892  0.021848  0.021581  0.019097   \n",
        "4  0.011602  0.013697  0.016035  0.019074  0.021045  0.022134  0.023356   \n",
        "5  0.018049  0.017928  0.017774  0.021623  0.020774  0.018783  0.022218   \n",
        "6  0.018303  0.020011  0.021411  0.021769  0.021779  0.022317  0.023400   \n",
        "7  0.011278  0.013308  0.014151  0.011613  0.011170  0.013470  0.015963   \n",
        "8  0.013490  0.016013  0.018399  0.020705  0.022265  0.023172  0.023963   \n",
        "9  0.011835  0.015216  0.018106  0.020802  0.022834  0.025176  0.026641   \n",
        "\n",
        "         37        40        43  ...   3052  3055  3058  3061  3064  3067  \\\n",
        "1  0.017265  0.017151  0.016752  ...    NaN   NaN   NaN   NaN   NaN   NaN   \n",
        "2  0.023807  0.023748  0.023086  ...    NaN   NaN   NaN   NaN   NaN   NaN   \n",
        "3  0.021013  0.021053  0.018812  ...    NaN   NaN   NaN   NaN   NaN   NaN   \n",
        "4  0.023704  0.024441  0.024226  ...    NaN   NaN   NaN   NaN   NaN   NaN   \n",
        "5  0.018639  0.018936  0.018949  ...    NaN   NaN   NaN   NaN   NaN   NaN   \n",
        "6  0.023056  0.022428  0.022760  ...    NaN   NaN   NaN   NaN   NaN   NaN   \n",
        "7  0.015477  0.014734  0.014241  ...    NaN   NaN   NaN   NaN   NaN   NaN   \n",
        "8  0.024436  0.024541  0.024258  ...    NaN   NaN   NaN   NaN   NaN   NaN   \n",
        "9  0.028049  0.028955  0.028619  ...    NaN   NaN   NaN   NaN   NaN   NaN   \n",
        "\n",
        "   3070  3073  3076  3079  \n",
        "1   NaN   NaN   NaN   NaN  \n",
        "2   NaN   NaN   NaN   NaN  \n",
        "3   NaN   NaN   NaN   NaN  \n",
        "4   NaN   NaN   NaN   NaN  \n",
        "5   NaN   NaN   NaN   NaN  \n",
        "6   NaN   NaN   NaN   NaN  \n",
        "7   NaN   NaN   NaN   NaN  \n",
        "8   NaN   NaN   NaN   NaN  \n",
        "9   NaN   NaN   NaN   NaN  \n",
        "\n",
        "[9 rows x 1022 columns]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Ground Truth Annotation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "annot_data = cc.load_annotation(annotation_fpath)\n",
      "annot_data.iloc[1:10]"
     ],
     "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>time</th>\n",
        "      <th>f0</th>\n",
        "      <th>cents</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>0.005805</td>\n",
        "      <td>0</td>\n",
        "      <td>-inf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>0.011610</td>\n",
        "      <td>0</td>\n",
        "      <td>-inf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0.017415</td>\n",
        "      <td>0</td>\n",
        "      <td>-inf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>0.023220</td>\n",
        "      <td>0</td>\n",
        "      <td>-inf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>0.029025</td>\n",
        "      <td>0</td>\n",
        "      <td>-inf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>0.034830</td>\n",
        "      <td>0</td>\n",
        "      <td>-inf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>0.040635</td>\n",
        "      <td>0</td>\n",
        "      <td>-inf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>0.046440</td>\n",
        "      <td>0</td>\n",
        "      <td>-inf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>0.052245</td>\n",
        "      <td>0</td>\n",
        "      <td>-inf</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "       time  f0  cents\n",
        "1  0.005805   0   -inf\n",
        "2  0.011610   0   -inf\n",
        "3  0.017415   0   -inf\n",
        "4  0.023220   0   -inf\n",
        "5  0.029025   0   -inf\n",
        "6  0.034830   0   -inf\n",
        "7  0.040635   0   -inf\n",
        "8  0.046440   0   -inf\n",
        "9  0.052245   0   -inf"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Plot contours with Annotation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cc.make_coverage_plot(contour_data, annot_data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QWlpKcXExpaWlTJ06Na5tVFSox96RgoLcxM2nai9uCD9eMgR4PF4IdE+lndB5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\ncODAwWmK/w9SgfbKIEbgegAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112210dd0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Select annotation within time range of each contour"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Compute percentage overlap"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "feature_data = cc.contour_overlap(contour_data, annot_data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Create Features + Labels"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cc.label_contours(feature_data, 0.2)"
     ],
     "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>duration</th>\n",
        "      <th>pitch mean</th>\n",
        "      <th>pitch std</th>\n",
        "      <th>salience mean</th>\n",
        "      <th>salience std</th>\n",
        "      <th>salience tot</th>\n",
        "      <th>vibrato</th>\n",
        "      <th>vib rate</th>\n",
        "      <th>vib extent</th>\n",
        "      <th>vib coverage</th>\n",
        "      <th>labels</th>\n",
        "      <th>overlap</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>0.030395</td>\n",
        "      <td>977.538</td>\n",
        "      <td>5.55553</td>\n",
        "      <td>0.096894</td>\n",
        "      <td>0.006296</td>\n",
        "      <td>1.014678</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>0.073961</td>\n",
        "      <td>2196.570</td>\n",
        "      <td>15.28510</td>\n",
        "      <td>0.104677</td>\n",
        "      <td>0.013368</td>\n",
        "      <td>2.667378</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>0.183384</td>\n",
        "      <td>2198.560</td>\n",
        "      <td>23.15960</td>\n",
        "      <td>0.132872</td>\n",
        "      <td>0.035348</td>\n",
        "      <td>8.395048</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0.075988</td>\n",
        "      <td>1000.090</td>\n",
        "      <td>6.10446</td>\n",
        "      <td>0.111118</td>\n",
        "      <td>0.023826</td>\n",
        "      <td>2.909074</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>0.154002</td>\n",
        "      <td>2193.050</td>\n",
        "      <td>32.33870</td>\n",
        "      <td>0.131749</td>\n",
        "      <td>0.029850</td>\n",
        "      <td>6.990401</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>0.059777</td>\n",
        "      <td>1001.060</td>\n",
        "      <td>8.18250</td>\n",
        "      <td>0.127094</td>\n",
        "      <td>0.014193</td>\n",
        "      <td>2.617514</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>0.106383</td>\n",
        "      <td>1717.360</td>\n",
        "      <td>49.50630</td>\n",
        "      <td>0.125644</td>\n",
        "      <td>0.024296</td>\n",
        "      <td>4.605135</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>0.051672</td>\n",
        "      <td>1498.600</td>\n",
        "      <td>24.35980</td>\n",
        "      <td>0.094476</td>\n",
        "      <td>0.008903</td>\n",
        "      <td>1.681911</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>0.139818</td>\n",
        "      <td>2001.680</td>\n",
        "      <td>141.79300</td>\n",
        "      <td>0.153242</td>\n",
        "      <td>0.058430</td>\n",
        "      <td>7.381905</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>0.029382</td>\n",
        "      <td>2187.810</td>\n",
        "      <td>25.15380</td>\n",
        "      <td>0.175644</td>\n",
        "      <td>0.022059</td>\n",
        "      <td>1.778055</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>0.068896</td>\n",
        "      <td>1907.860</td>\n",
        "      <td>38.40640</td>\n",
        "      <td>0.144376</td>\n",
        "      <td>0.064651</td>\n",
        "      <td>3.427014</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>0.041540</td>\n",
        "      <td>2192.500</td>\n",
        "      <td>25.19010</td>\n",
        "      <td>0.098769</td>\n",
        "      <td>0.008425</td>\n",
        "      <td>1.413574</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>0.046606</td>\n",
        "      <td>2124.200</td>\n",
        "      <td>121.82600</td>\n",
        "      <td>0.178820</td>\n",
        "      <td>0.019906</td>\n",
        "      <td>2.871353</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>0.071935</td>\n",
        "      <td>2191.420</td>\n",
        "      <td>34.24220</td>\n",
        "      <td>0.103293</td>\n",
        "      <td>0.029740</td>\n",
        "      <td>2.560010</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>0.009119</td>\n",
        "      <td>1886.140</td>\n",
        "      <td>24.97420</td>\n",
        "      <td>0.059844</td>\n",
        "      <td>0.041578</td>\n",
        "      <td>0.188011</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>0.003040</td>\n",
        "      <td>1148.680</td>\n",
        "      <td>34.88110</td>\n",
        "      <td>0.127048</td>\n",
        "      <td>0.035709</td>\n",
        "      <td>0.133052</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>0.057751</td>\n",
        "      <td>2194.890</td>\n",
        "      <td>21.99280</td>\n",
        "      <td>0.132319</td>\n",
        "      <td>0.019634</td>\n",
        "      <td>2.632753</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>0.003040</td>\n",
        "      <td>1002.110</td>\n",
        "      <td>11.95560</td>\n",
        "      <td>0.115763</td>\n",
        "      <td>0.009933</td>\n",
        "      <td>0.121236</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>0.064843</td>\n",
        "      <td>1809.700</td>\n",
        "      <td>89.79720</td>\n",
        "      <td>0.140229</td>\n",
        "      <td>0.026903</td>\n",
        "      <td>3.132775</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>0.120567</td>\n",
        "      <td>1184.610</td>\n",
        "      <td>27.01930</td>\n",
        "      <td>0.115062</td>\n",
        "      <td>0.025272</td>\n",
        "      <td>4.779586</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>0.234042</td>\n",
        "      <td>1899.060</td>\n",
        "      <td>14.44010</td>\n",
        "      <td>0.143801</td>\n",
        "      <td>0.041644</td>\n",
        "      <td>11.595368</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>0.091186</td>\n",
        "      <td>1895.760</td>\n",
        "      <td>13.40230</td>\n",
        "      <td>0.134563</td>\n",
        "      <td>0.047846</td>\n",
        "      <td>4.227459</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>0.030395</td>\n",
        "      <td>2698.310</td>\n",
        "      <td>3.74956</td>\n",
        "      <td>0.092430</td>\n",
        "      <td>0.003825</td>\n",
        "      <td>0.967937</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>0.024316</td>\n",
        "      <td>2394.920</td>\n",
        "      <td>30.27410</td>\n",
        "      <td>0.101163</td>\n",
        "      <td>0.007833</td>\n",
        "      <td>0.847515</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>0.017224</td>\n",
        "      <td>1195.000</td>\n",
        "      <td>22.14460</td>\n",
        "      <td>0.092113</td>\n",
        "      <td>0.009307</td>\n",
        "      <td>0.546622</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>0.200608</td>\n",
        "      <td>1897.040</td>\n",
        "      <td>11.09010</td>\n",
        "      <td>0.121504</td>\n",
        "      <td>0.039916</td>\n",
        "      <td>8.397855</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>0.168186</td>\n",
        "      <td>2197.660</td>\n",
        "      <td>18.71510</td>\n",
        "      <td>0.136131</td>\n",
        "      <td>0.029320</td>\n",
        "      <td>7.888197</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>0.504559</td>\n",
        "      <td>992.383</td>\n",
        "      <td>15.48910</td>\n",
        "      <td>0.141290</td>\n",
        "      <td>0.050274</td>\n",
        "      <td>24.561203</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>0.189463</td>\n",
        "      <td>2191.260</td>\n",
        "      <td>42.53490</td>\n",
        "      <td>0.150530</td>\n",
        "      <td>0.043326</td>\n",
        "      <td>9.825997</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>0.359675</td>\n",
        "      <td>2195.330</td>\n",
        "      <td>20.61280</td>\n",
        "      <td>0.156396</td>\n",
        "      <td>0.056911</td>\n",
        "      <td>19.380522</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1023</th>\n",
        "      <td>0.524823</td>\n",
        "      <td>2695.570</td>\n",
        "      <td>74.82640</td>\n",
        "      <td>0.352373</td>\n",
        "      <td>0.091557</td>\n",
        "      <td>63.715222</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1</td>\n",
        "      <td>0.945848</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1024</th>\n",
        "      <td>0.073961</td>\n",
        "      <td>1891.390</td>\n",
        "      <td>18.92770</td>\n",
        "      <td>0.252083</td>\n",
        "      <td>0.062832</td>\n",
        "      <td>6.423596</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1025</th>\n",
        "      <td>0.001013</td>\n",
        "      <td>1451.940</td>\n",
        "      <td>39.00280</td>\n",
        "      <td>0.150689</td>\n",
        "      <td>0.045965</td>\n",
        "      <td>0.052610</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1026</th>\n",
        "      <td>0.060790</td>\n",
        "      <td>1605.370</td>\n",
        "      <td>84.55220</td>\n",
        "      <td>0.260300</td>\n",
        "      <td>0.062295</td>\n",
        "      <td>5.451750</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1027</th>\n",
        "      <td>0.285714</td>\n",
        "      <td>2478.580</td>\n",
        "      <td>100.82700</td>\n",
        "      <td>0.182582</td>\n",
        "      <td>0.053553</td>\n",
        "      <td>17.972951</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1</td>\n",
        "      <td>0.635220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1028</th>\n",
        "      <td>0.056738</td>\n",
        "      <td>709.231</td>\n",
        "      <td>15.91400</td>\n",
        "      <td>0.236396</td>\n",
        "      <td>0.022412</td>\n",
        "      <td>4.621058</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1029</th>\n",
        "      <td>0.084093</td>\n",
        "      <td>1902.120</td>\n",
        "      <td>18.07950</td>\n",
        "      <td>0.220612</td>\n",
        "      <td>0.040918</td>\n",
        "      <td>6.391738</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1030</th>\n",
        "      <td>0.023303</td>\n",
        "      <td>1897.760</td>\n",
        "      <td>16.81980</td>\n",
        "      <td>0.161947</td>\n",
        "      <td>0.055557</td>\n",
        "      <td>1.300219</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1031</th>\n",
        "      <td>0.010132</td>\n",
        "      <td>1183.110</td>\n",
        "      <td>11.89250</td>\n",
        "      <td>0.164383</td>\n",
        "      <td>0.024455</td>\n",
        "      <td>0.573814</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1032</th>\n",
        "      <td>0.002026</td>\n",
        "      <td>3592.160</td>\n",
        "      <td>6.21034</td>\n",
        "      <td>0.157785</td>\n",
        "      <td>0.010671</td>\n",
        "      <td>0.110156</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1033</th>\n",
        "      <td>0.667679</td>\n",
        "      <td>3502.160</td>\n",
        "      <td>97.72160</td>\n",
        "      <td>0.124988</td>\n",
        "      <td>0.032587</td>\n",
        "      <td>28.751831</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1034</th>\n",
        "      <td>0.179331</td>\n",
        "      <td>1492.640</td>\n",
        "      <td>7.74620</td>\n",
        "      <td>0.141491</td>\n",
        "      <td>0.024698</td>\n",
        "      <td>8.742058</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1035</th>\n",
        "      <td>0.040527</td>\n",
        "      <td>2620.130</td>\n",
        "      <td>119.61600</td>\n",
        "      <td>0.135203</td>\n",
        "      <td>0.024116</td>\n",
        "      <td>1.887808</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1</td>\n",
        "      <td>0.263158</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1036</th>\n",
        "      <td>0.246200</td>\n",
        "      <td>2387.410</td>\n",
        "      <td>12.36950</td>\n",
        "      <td>0.109807</td>\n",
        "      <td>0.030795</td>\n",
        "      <td>9.314248</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1</td>\n",
        "      <td>0.321429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1037</th>\n",
        "      <td>0.015198</td>\n",
        "      <td>2389.800</td>\n",
        "      <td>5.82752</td>\n",
        "      <td>0.079058</td>\n",
        "      <td>0.006794</td>\n",
        "      <td>0.413955</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1038</th>\n",
        "      <td>0.256332</td>\n",
        "      <td>2348.230</td>\n",
        "      <td>107.77100</td>\n",
        "      <td>0.277331</td>\n",
        "      <td>0.084160</td>\n",
        "      <td>24.492265</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1</td>\n",
        "      <td>0.937931</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1039</th>\n",
        "      <td>0.030395</td>\n",
        "      <td>1388.770</td>\n",
        "      <td>75.70200</td>\n",
        "      <td>0.231673</td>\n",
        "      <td>0.040961</td>\n",
        "      <td>2.426099</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1040</th>\n",
        "      <td>0.433636</td>\n",
        "      <td>2634.670</td>\n",
        "      <td>63.70020</td>\n",
        "      <td>0.241621</td>\n",
        "      <td>0.082773</td>\n",
        "      <td>36.098517</td>\n",
        "      <td>1</td>\n",
        "      <td>5.72649</td>\n",
        "      <td>83.7389</td>\n",
        "      <td>0.046512</td>\n",
        "      <td>1</td>\n",
        "      <td>0.978355</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1041</th>\n",
        "      <td>0.031409</td>\n",
        "      <td>1382.880</td>\n",
        "      <td>20.79960</td>\n",
        "      <td>0.217976</td>\n",
        "      <td>0.040070</td>\n",
        "      <td>2.358768</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1042</th>\n",
        "      <td>0.419453</td>\n",
        "      <td>2499.820</td>\n",
        "      <td>172.71100</td>\n",
        "      <td>0.149613</td>\n",
        "      <td>0.034690</td>\n",
        "      <td>21.621231</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1043</th>\n",
        "      <td>0.120567</td>\n",
        "      <td>969.351</td>\n",
        "      <td>16.38320</td>\n",
        "      <td>0.136587</td>\n",
        "      <td>0.037994</td>\n",
        "      <td>5.673711</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1044</th>\n",
        "      <td>0.034448</td>\n",
        "      <td>2166.520</td>\n",
        "      <td>18.00370</td>\n",
        "      <td>0.110441</td>\n",
        "      <td>0.021489</td>\n",
        "      <td>1.310742</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1</td>\n",
        "      <td>0.857143</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1045</th>\n",
        "      <td>0.163121</td>\n",
        "      <td>1701.070</td>\n",
        "      <td>9.16821</td>\n",
        "      <td>0.135167</td>\n",
        "      <td>0.022531</td>\n",
        "      <td>7.596388</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1046</th>\n",
        "      <td>0.038501</td>\n",
        "      <td>2199.450</td>\n",
        "      <td>11.09000</td>\n",
        "      <td>0.101150</td>\n",
        "      <td>0.017358</td>\n",
        "      <td>1.341712</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1047</th>\n",
        "      <td>0.397165</td>\n",
        "      <td>1002.580</td>\n",
        "      <td>45.01800</td>\n",
        "      <td>0.154778</td>\n",
        "      <td>0.043357</td>\n",
        "      <td>21.179052</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1048</th>\n",
        "      <td>0.319148</td>\n",
        "      <td>2209.910</td>\n",
        "      <td>43.69690</td>\n",
        "      <td>0.164749</td>\n",
        "      <td>0.063480</td>\n",
        "      <td>18.115304</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1</td>\n",
        "      <td>0.548571</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1049</th>\n",
        "      <td>0.011145</td>\n",
        "      <td>1426.090</td>\n",
        "      <td>65.05780</td>\n",
        "      <td>0.081688</td>\n",
        "      <td>0.012601</td>\n",
        "      <td>0.313668</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1050</th>\n",
        "      <td>0.301925</td>\n",
        "      <td>2197.360</td>\n",
        "      <td>17.00330</td>\n",
        "      <td>0.176143</td>\n",
        "      <td>0.070319</td>\n",
        "      <td>18.322900</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1051</th>\n",
        "      <td>0.127660</td>\n",
        "      <td>998.944</td>\n",
        "      <td>8.00640</td>\n",
        "      <td>0.104889</td>\n",
        "      <td>0.022412</td>\n",
        "      <td>4.613270</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1052</th>\n",
        "      <td>0.053698</td>\n",
        "      <td>991.818</td>\n",
        "      <td>27.82030</td>\n",
        "      <td>0.109184</td>\n",
        "      <td>0.025723</td>\n",
        "      <td>2.019984</td>\n",
        "      <td>0</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>1053 rows \u00d7 12 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "      duration  pitch mean  pitch std  salience mean  salience std  \\\n",
        "0     0.030395     977.538    5.55553       0.096894      0.006296   \n",
        "1     0.073961    2196.570   15.28510       0.104677      0.013368   \n",
        "2     0.183384    2198.560   23.15960       0.132872      0.035348   \n",
        "3     0.075988    1000.090    6.10446       0.111118      0.023826   \n",
        "4     0.154002    2193.050   32.33870       0.131749      0.029850   \n",
        "5     0.059777    1001.060    8.18250       0.127094      0.014193   \n",
        "6     0.106383    1717.360   49.50630       0.125644      0.024296   \n",
        "7     0.051672    1498.600   24.35980       0.094476      0.008903   \n",
        "8     0.139818    2001.680  141.79300       0.153242      0.058430   \n",
        "9     0.029382    2187.810   25.15380       0.175644      0.022059   \n",
        "10    0.068896    1907.860   38.40640       0.144376      0.064651   \n",
        "11    0.041540    2192.500   25.19010       0.098769      0.008425   \n",
        "12    0.046606    2124.200  121.82600       0.178820      0.019906   \n",
        "13    0.071935    2191.420   34.24220       0.103293      0.029740   \n",
        "14    0.009119    1886.140   24.97420       0.059844      0.041578   \n",
        "15    0.003040    1148.680   34.88110       0.127048      0.035709   \n",
        "16    0.057751    2194.890   21.99280       0.132319      0.019634   \n",
        "17    0.003040    1002.110   11.95560       0.115763      0.009933   \n",
        "18    0.064843    1809.700   89.79720       0.140229      0.026903   \n",
        "19    0.120567    1184.610   27.01930       0.115062      0.025272   \n",
        "20    0.234042    1899.060   14.44010       0.143801      0.041644   \n",
        "21    0.091186    1895.760   13.40230       0.134563      0.047846   \n",
        "22    0.030395    2698.310    3.74956       0.092430      0.003825   \n",
        "23    0.024316    2394.920   30.27410       0.101163      0.007833   \n",
        "24    0.017224    1195.000   22.14460       0.092113      0.009307   \n",
        "25    0.200608    1897.040   11.09010       0.121504      0.039916   \n",
        "26    0.168186    2197.660   18.71510       0.136131      0.029320   \n",
        "27    0.504559     992.383   15.48910       0.141290      0.050274   \n",
        "28    0.189463    2191.260   42.53490       0.150530      0.043326   \n",
        "29    0.359675    2195.330   20.61280       0.156396      0.056911   \n",
        "...        ...         ...        ...            ...           ...   \n",
        "1023  0.524823    2695.570   74.82640       0.352373      0.091557   \n",
        "1024  0.073961    1891.390   18.92770       0.252083      0.062832   \n",
        "1025  0.001013    1451.940   39.00280       0.150689      0.045965   \n",
        "1026  0.060790    1605.370   84.55220       0.260300      0.062295   \n",
        "1027  0.285714    2478.580  100.82700       0.182582      0.053553   \n",
        "1028  0.056738     709.231   15.91400       0.236396      0.022412   \n",
        "1029  0.084093    1902.120   18.07950       0.220612      0.040918   \n",
        "1030  0.023303    1897.760   16.81980       0.161947      0.055557   \n",
        "1031  0.010132    1183.110   11.89250       0.164383      0.024455   \n",
        "1032  0.002026    3592.160    6.21034       0.157785      0.010671   \n",
        "1033  0.667679    3502.160   97.72160       0.124988      0.032587   \n",
        "1034  0.179331    1492.640    7.74620       0.141491      0.024698   \n",
        "1035  0.040527    2620.130  119.61600       0.135203      0.024116   \n",
        "1036  0.246200    2387.410   12.36950       0.109807      0.030795   \n",
        "1037  0.015198    2389.800    5.82752       0.079058      0.006794   \n",
        "1038  0.256332    2348.230  107.77100       0.277331      0.084160   \n",
        "1039  0.030395    1388.770   75.70200       0.231673      0.040961   \n",
        "1040  0.433636    2634.670   63.70020       0.241621      0.082773   \n",
        "1041  0.031409    1382.880   20.79960       0.217976      0.040070   \n",
        "1042  0.419453    2499.820  172.71100       0.149613      0.034690   \n",
        "1043  0.120567     969.351   16.38320       0.136587      0.037994   \n",
        "1044  0.034448    2166.520   18.00370       0.110441      0.021489   \n",
        "1045  0.163121    1701.070    9.16821       0.135167      0.022531   \n",
        "1046  0.038501    2199.450   11.09000       0.101150      0.017358   \n",
        "1047  0.397165    1002.580   45.01800       0.154778      0.043357   \n",
        "1048  0.319148    2209.910   43.69690       0.164749      0.063480   \n",
        "1049  0.011145    1426.090   65.05780       0.081688      0.012601   \n",
        "1050  0.301925    2197.360   17.00330       0.176143      0.070319   \n",
        "1051  0.127660     998.944    8.00640       0.104889      0.022412   \n",
        "1052  0.053698     991.818   27.82030       0.109184      0.025723   \n",
        "\n",
        "      salience tot  vibrato  vib rate  vib extent  vib coverage  labels  \\\n",
        "0         1.014678        0   0.00000      0.0000      0.000000       0   \n",
        "1         2.667378        0   0.00000      0.0000      0.000000       0   \n",
        "2         8.395048        0   0.00000      0.0000      0.000000       0   \n",
        "3         2.909074        0   0.00000      0.0000      0.000000       0   \n",
        "4         6.990401        0   0.00000      0.0000      0.000000       0   \n",
        "5         2.617514        0   0.00000      0.0000      0.000000       0   \n",
        "6         4.605135        0   0.00000      0.0000      0.000000       0   \n",
        "7         1.681911        0   0.00000      0.0000      0.000000       0   \n",
        "8         7.381905        0   0.00000      0.0000      0.000000       0   \n",
        "9         1.778055        0   0.00000      0.0000      0.000000       0   \n",
        "10        3.427014        0   0.00000      0.0000      0.000000       0   \n",
        "11        1.413574        0   0.00000      0.0000      0.000000       0   \n",
        "12        2.871353        0   0.00000      0.0000      0.000000       0   \n",
        "13        2.560010        0   0.00000      0.0000      0.000000       0   \n",
        "14        0.188011        0   0.00000      0.0000      0.000000       0   \n",
        "15        0.133052        0   0.00000      0.0000      0.000000       0   \n",
        "16        2.632753        0   0.00000      0.0000      0.000000       0   \n",
        "17        0.121236        0   0.00000      0.0000      0.000000       0   \n",
        "18        3.132775        0   0.00000      0.0000      0.000000       0   \n",
        "19        4.779586        0   0.00000      0.0000      0.000000       0   \n",
        "20       11.595368        0   0.00000      0.0000      0.000000       0   \n",
        "21        4.227459        0   0.00000      0.0000      0.000000       0   \n",
        "22        0.967937        0   0.00000      0.0000      0.000000       0   \n",
        "23        0.847515        0   0.00000      0.0000      0.000000       0   \n",
        "24        0.546622        0   0.00000      0.0000      0.000000       0   \n",
        "25        8.397855        0   0.00000      0.0000      0.000000       0   \n",
        "26        7.888197        0   0.00000      0.0000      0.000000       0   \n",
        "27       24.561203        0   0.00000      0.0000      0.000000       0   \n",
        "28        9.825997        0   0.00000      0.0000      0.000000       0   \n",
        "29       19.380522        0   0.00000      0.0000      0.000000       0   \n",
        "...            ...      ...       ...         ...           ...     ...   \n",
        "1023     63.715222        0   0.00000      0.0000      0.000000       1   \n",
        "1024      6.423596        0   0.00000      0.0000      0.000000       0   \n",
        "1025      0.052610        0   0.00000      0.0000      0.000000       0   \n",
        "1026      5.451750        0   0.00000      0.0000      0.000000       0   \n",
        "1027     17.972951        0   0.00000      0.0000      0.000000       1   \n",
        "1028      4.621058        0   0.00000      0.0000      0.000000       0   \n",
        "1029      6.391738        0   0.00000      0.0000      0.000000       0   \n",
        "1030      1.300219        0   0.00000      0.0000      0.000000       0   \n",
        "1031      0.573814        0   0.00000      0.0000      0.000000       0   \n",
        "1032      0.110156        0   0.00000      0.0000      0.000000       0   \n",
        "1033     28.751831        0   0.00000      0.0000      0.000000       0   \n",
        "1034      8.742058        0   0.00000      0.0000      0.000000       0   \n",
        "1035      1.887808        0   0.00000      0.0000      0.000000       1   \n",
        "1036      9.314248        0   0.00000      0.0000      0.000000       1   \n",
        "1037      0.413955        0   0.00000      0.0000      0.000000       0   \n",
        "1038     24.492265        0   0.00000      0.0000      0.000000       1   \n",
        "1039      2.426099        0   0.00000      0.0000      0.000000       0   \n",
        "1040     36.098517        1   5.72649     83.7389      0.046512       1   \n",
        "1041      2.358768        0   0.00000      0.0000      0.000000       0   \n",
        "1042     21.621231        0   0.00000      0.0000      0.000000       1   \n",
        "1043      5.673711        0   0.00000      0.0000      0.000000       0   \n",
        "1044      1.310742        0   0.00000      0.0000      0.000000       1   \n",
        "1045      7.596388        0   0.00000      0.0000      0.000000       0   \n",
        "1046      1.341712        0   0.00000      0.0000      0.000000       1   \n",
        "1047     21.179052        0   0.00000      0.0000      0.000000       0   \n",
        "1048     18.115304        0   0.00000      0.0000      0.000000       1   \n",
        "1049      0.313668        0   0.00000      0.0000      0.000000       0   \n",
        "1050     18.322900        0   0.00000      0.0000      0.000000       0   \n",
        "1051      4.613270        0   0.00000      0.0000      0.000000       0   \n",
        "1052      2.019984        0   0.00000      0.0000      0.000000       0   \n",
        "\n",
        "       overlap  \n",
        "0     0.000000  \n",
        "1     0.000000  \n",
        "2     0.000000  \n",
        "3     0.000000  \n",
        "4     0.000000  \n",
        "5     0.000000  \n",
        "6     0.000000  \n",
        "7     0.000000  \n",
        "8     0.000000  \n",
        "9     0.000000  \n",
        "10    0.000000  \n",
        "11    0.000000  \n",
        "12    0.000000  \n",
        "13    0.000000  \n",
        "14    0.000000  \n",
        "15    0.000000  \n",
        "16    0.000000  \n",
        "17    0.000000  \n",
        "18    0.000000  \n",
        "19    0.000000  \n",
        "20    0.000000  \n",
        "21    0.000000  \n",
        "22    0.000000  \n",
        "23    0.000000  \n",
        "24    0.000000  \n",
        "25    0.000000  \n",
        "26    0.000000  \n",
        "27    0.000000  \n",
        "28    0.000000  \n",
        "29    0.000000  \n",
        "...        ...  \n",
        "1023  0.945848  \n",
        "1024  0.000000  \n",
        "1025  0.000000  \n",
        "1026  0.000000  \n",
        "1027  0.635220  \n",
        "1028  0.000000  \n",
        "1029  0.000000  \n",
        "1030  0.000000  \n",
        "1031  0.000000  \n",
        "1032  0.000000  \n",
        "1033  0.000000  \n",
        "1034  0.000000  \n",
        "1035  0.263158  \n",
        "1036  0.321429  \n",
        "1037  0.000000  \n",
        "1038  0.937931  \n",
        "1039  0.000000  \n",
        "1040  0.978355  \n",
        "1041  0.000000  \n",
        "1042  1.000000  \n",
        "1043  0.000000  \n",
        "1044  0.857143  \n",
        "1045  0.000000  \n",
        "1046  1.000000  \n",
        "1047  0.000000  \n",
        "1048  0.548571  \n",
        "1049  0.000000  \n",
        "1050  0.000000  \n",
        "1051  0.000000  \n",
        "1052  0.000000  \n",
        "\n",
        "[1053 rows x 12 columns]"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print feature_data[feature_data['labels'] == 1].describe()\n",
      "print feature_data[feature_data['labels'] == 0].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         duration   pitch mean   pitch std  salience mean  salience std  \\\n",
        "count  214.000000   214.000000  214.000000     214.000000    214.000000   \n",
        "mean     0.158012  2629.755327   64.239431       0.212441      0.056830   \n",
        "std      0.140551   301.761543   46.030254       0.100791      0.034875   \n",
        "min      0.001013  2136.960000    9.419420       0.085697      0.010842   \n",
        "25%      0.050912  2401.510000   27.805475       0.148208      0.035171   \n",
        "50%      0.122087  2602.080000   52.025850       0.182762      0.048336   \n",
        "75%      0.211500  2829.652500   87.965325       0.243192      0.067621   \n",
        "max      1.000000  3413.140000  222.988000       0.620939      0.244426   \n",
        "\n",
        "       salience tot     vibrato    vib rate  vib extent  vib coverage  labels  \\\n",
        "count    214.000000  214.000000  214.000000  214.000000    214.000000     214   \n",
        "mean      13.619039    0.074766    0.448275    4.499864      0.024032       1   \n",
        "std       17.873876    0.263631    1.584836   16.717063      0.128346       0   \n",
        "min        0.039957    0.000000    0.000000    0.000000      0.000000       1   \n",
        "25%        2.655458    0.000000    0.000000    0.000000      0.000000       1   \n",
        "50%        8.647457    0.000000    0.000000    0.000000      0.000000       1   \n",
        "75%       16.553908    0.000000    0.000000    0.000000      0.000000       1   \n",
        "max      153.136680    1.000000    6.735750   98.707600      1.000000       1   \n",
        "\n",
        "          overlap  \n",
        "count  214.000000  \n",
        "mean     0.839638  \n",
        "std      0.206770  \n",
        "min      0.222222  \n",
        "25%      0.757549  \n",
        "50%      0.940424  \n",
        "75%      1.000000  \n",
        "max      1.000000  \n",
        "         duration   pitch mean   pitch std  salience mean  salience std  \\\n",
        "count  839.000000   839.000000  839.000000     839.000000    839.000000   \n",
        "mean     0.083176  1940.239746   28.731739       0.162542      0.032808   \n",
        "std      0.093916   900.685085   27.754233       0.071203      0.019779   \n",
        "min      0.000000   694.125000    0.000000       0.059844      0.002412   \n",
        "25%      0.021277  1193.655000   12.343550       0.118025      0.018659   \n",
        "50%      0.053698  1893.330000   19.463400       0.150530      0.029784   \n",
        "75%      0.117528  2197.520000   31.703100       0.187380      0.042707   \n",
        "max      0.667679  7316.490000  206.208000       0.850759      0.155489   \n",
        "\n",
        "       salience tot     vibrato    vib rate  vib extent  vib coverage  labels  \\\n",
        "count    839.000000  839.000000  839.000000  839.000000    839.000000     839   \n",
        "mean       4.741810    0.001192    0.007233    0.057276      0.000341       0   \n",
        "std        5.679116    0.034524    0.209502    1.659022      0.009864       0   \n",
        "min        0.000000    0.000000    0.000000    0.000000      0.000000       0   \n",
        "25%        0.997177    0.000000    0.000000    0.000000      0.000000       0   \n",
        "50%        2.837483    0.000000    0.000000    0.000000      0.000000       0   \n",
        "75%        6.293131    0.000000    0.000000    0.000000      0.000000       0   \n",
        "max       39.994277    1.000000    6.068340   48.054400      0.285714       0   \n",
        "\n",
        "          overlap  \n",
        "count  839.000000  \n",
        "mean     0.003105  \n",
        "std      0.019580  \n",
        "min      0.000000  \n",
        "25%      0.000000  \n",
        "50%      0.000000  \n",
        "75%      0.000000  \n",
        "max      0.190083  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cc.make_coverage_plot(contour_data[feature_data['labels'] == 1], annot_data, \\\n",
      "                      contour_data[feature_data['labels'] == 0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6UmfVU6CDun37Srr0KG1tU8FRPQXnXA50gt5rem9ht2TJEh588EGcTiejRo1i\n9uzZWCwW5s+fz5w5c3C5XCxatIioqCiys7NZvHgxc+bMISoqipUrV7Z6QaX78D8l01yZ9AyB5gVv\nzVzhIj2Rxd0F9546cmtZKB/hHjt2jPQjBxqU7Rw4nL59+7bL+4VyXXWkzqqnjt4e2oK2qeConoJz\nLi1xTbsqHS7Q2Ihgx0tI9xPo5h6tveGHSE+jEJcOFxkZSXFqhu95cWqGppfswWJjY5kYEeV7PjEi\nSiEuEqSuPfxTui3/IFeA92wWi4W3Uydw5e4vAHg7dYJvDI6INE8hLp1G4S1e3iD3PhaR4CjERaRL\nUHiLnD2dExcREQlRCnEREZEQpRAXEREJUQrxbqCqqoqqqqrOXgwREelgGtgW4qqqqhi+fycAn/cZ\nxODBgzt5iURap6amBoCYmJhOXpKuYf/+/QCMGDGi0c9sNnP/gfj4+A5dJul61BIPYf4BDjDpRCkl\nJSWduEQirVNTU8N5RQWcV1TgC/OebP/+/VxUdZyLqo77wtzLZrMx8mAhIw8W+sJcei6FeAgrLS1t\nVDbpRCknT57shKURaR1vgHv19CD3BriXf5B7A9xLQS4K8RDmdDoDlldW6oYDEjoqKiqCKuspdvkF\n+Jlln/kFuFegMuk5FOLdUHR0dGcvgkjQArUke3Lrsl8zZYFmlNcs8z2bQjyE9erVK2C5bh4hocRq\ntQZV1lOMGJDSZFmm342DvAKVSc+hEA9hgwYNIi+xf6NyhbiEkn79Grc9A5X1FM3dmjXQ1LSarrZn\nU4iHuGHDhjUI8pK0iURE6MpBCR0RERGUpE30Pe/p23B4eHiTZWFhYcRxOrTjsBAWpt14T9Zzvynd\nyLBhwyipGwTQo3d+Err8g7ynb8Nut7vJsrCwMPalT2Lkzs8B2Jc+SSHew/Xsb0s30tN3fBL6tA0b\ngerBv8wb5N7H0rNpCxAR6UIiIyMp9husVpyaQWRkZIPXhIWFKcAFUEtcpEO8VZAHwDfGT+7kJZFQ\n4B/kZwa4iD8dyom0s7cK8pgHzON0mIu0JDIyUgEuLVKIi7Qjb4B7KchFpC0pxEVEREKUQlykHX1j\n/GRW+T1fhc6Li0jbUYiLtDNvkLdVgB88eJCDBw+e899pDx8W5PGhTheIR1FREZ8W5HH8eOObukjb\n0Oh0kQ7QVq3vgwcPMtVWDsCnByElpfE8253lw4I8rvc8zinI41L1OPRoRUVFTKvx3Ba5dD+7gKSk\npE5dpu7hHk4cAAAgAElEQVRILXGREOEf4ABTbeVdpkXuH+AA13vKpGdqEOAeY0v3q0XeDhTiImfh\n6NGjHD16tFPe+6TtZFBlItJzKMRFWuBwOHA4HBw9epTzyw9zfvnhTgnylCGNu84DlXWGi9Mmstrv\neQ6cc3e6t94l9IwaNYpNMb0blO0aNELd6e1A58RFmuFwOBi6Z3uj8vPLD7MdGDBgwDm/h9PpZPPu\nbUQCFzYTfL1792Y3o0gtLgJg95BR9O7du8nXd5S6ujoGF271Pf8bbRPg3no/POZ8oqKizunvtRUd\nVARv1KhR7D31FYVffskoBXi7aXWIf/vb3yY+Ph4wd9G66667WLJkCWFhYYwZM4Zly5ZhsVhYu3Yt\na9asISIiggULFjBz5sy2Wnbpwbx3dWrPeyk3FeBeNTU1rf7bTqfT93jI7m2+x/8oyAsqyL2PO9uZ\nAQ5wI1BaXx/wlppNsdvtAFit1kb1PnTP9i4R5P7LVZs8vVOXpavzfp6jRo0i0e9WydL2WhXitbW1\nAKxadfoK2B/84AcsWrSIqVOnsmzZMtavX8/EiRNZtWoVOTk51NbWkp2dzfTp0zv9yyid69ixY9TU\n1NC3b19iYmLO+vcrKiq46vBewoHccZmtDvKvvvqKw2WH6d+rP0OHDvWVe7fvU6dONfv73hB1Op08\n42lJLwiiBep0OhsEt79vEVyQd5atBSawJ443tw3dVhh4PYqLiznvvPOC+pt2u50RB3YBsH/4WL78\n8stGr3ltz3bGEsX5489vzWKflRzPgLzrPZ/BwYMHcTgcXOI4vT1Eb9zYJQ4suiL/z9OWnBDU73zm\nqfMpnjqvr68HAt9bHeDkyZNsLC6iNzC9h18F0aoQ37VrF9XV1dx+++3U1dXxk5/8hB07djB16lQA\nsrKy+OijjwgLCyMzM9M3B3BKSgqFhYWcf377fxFDhbc115owC0XHjh0j/cgB8+TkEQ6NGt/kuntb\nq/7zR1dUVDDm8F7f86wd+a0K8q+++ooJZZ6wqDhKPjB06FBqa2sZtvcLAD6O7dPs3zh69ChxcXEN\nA7kgr9kgby7AvQ4DFwazEh1sa8FWZlEHwDsFW5k4fiJv0fje1xD4ntiB+O/wAUYc2MV6GgfjDwFw\nsL5ge7sGeU5BHj/wPinIY3J8vwZXBPg7deoU/fr1a7dlCUVnfp7xn37K/uFjsVqtTf7OZwV5XO15\nvK4gjwvGTmLQLnO/9NKxkxoF+cmTJ32nlABeLcjr0UHeqhCPjY3l9ttv58Ybb+TAgQPccccdDX5u\ntVqprKzEZrORkJDQoNxms7X495ODPHoLdTU1NfT3HIFWX3zxWQd5qNVTgwD3OK+oIOC6O51Ooj4y\ndeO45BIiIyM5depUgwAHKAT27dvBxRdf3Ox7+9dVgwD3yKw4yt7EKEb7tQIvrj7R7N/s1y++USAv\nA5IL8ljQxGmjioqKZv8mwDdHj+60z7ap99269XSAA8yijl8X5PFEE39n0qSxQd0fvNhvZ+yV1DuO\nosSBjDp0qNHPrsDBtiMH2qUhkLNhw+kAB/O4iQAHyDt6kFvSR7T5coSyEydKG5U5HKcYPnxgwNd/\n9tlnvgAHzGNPgAMM2vU5dTNm+IL8zAAHuA74aM92pk/vmac4WhXiw4cP900yMXz4cHr37s3OnTt9\nP7fZbCQmJhIfH+87NwLmKC0xMbHFv19WVtmaxQopNTU1nFdU4Hse+/HHzbZKz5ScnBBy9XT0aOBQ\nPHjwaINBL2e2VqM++oji1AxKSxvvIABKa2qarYsz62rv3sMBX/dFgG7c5tTVBf76HKHpbbiwcE+z\nfzO/1wCio3t1ymfb3Da1+0Tjz253E3+nJG0iJ05UB/WeJScbXyJXcvIkQ4aM4uPYSrZXn+D7Z/y8\n7Fhlu9TPV2f5+kp6xr7KX0tjUcrLGzfSystt9OkTuJ5KgmjUHTr0lW/81cmTgf9OmcMR0p/FuRy0\nt+oSs5ycHFasWAGYLkW73c4ll1zC5s2bAcjNzWXKlClkZGTw2Wef4XA4qKyspKioiDFjxrR6YbuT\n7X4B7uU/2Km1du3axasFeezatatB+aCCPPoX5DGoIK9N3qc14uLiApb7H7h8XvA5nwTobq6trW3y\ntoxj+g0NWN4U/94hr7cIY0BM8+ea/d+9dOykJruMF4yd1OTfCNQ63WCJZVNMb9ZyboPl2tO45GGN\nyn6cPKzBBC/PYQL8zHX8pCCP/yvIo6SkxFf2TkEe7xTkMazvkEZ/Nx/YsWMHI0eO5Nrxkxt0r69v\nx/Pi14/LxL9d3beF1//HmJ51WtDtdjN77w5m790RcNv/qCCPPY7G40iGDGn8GQP8sSCP5k8sGW8e\nLPQ1Bnv37s3uIaMa/PxVTp8X37NnD3v27OE6z/5uag+YcKhVLfHvfOc7/OxnP2Pu3LkAPProo/Tu\n3ZsHH3wQp9PJqFGjmD17NhaLhfnz5zNnzhxcLheLFi3SQBBM0H4zQPnZjOZt6u9m1Xt6Purt5O7a\nxdixYxlUkEe95zX1mNHQxakZHX6v4kABtnPgcF+4f17wOVf6lrQhl8tlXhdgwqfzyw+zKzIyqEtY\n6uvryThW3Kh8xJARJkBrAk+eEgMcSL/At/MKDw8PuD57ho5u9nMcOHAgHGj4HikpKZSWlnITQG0F\nG/fuZfTo0S2uS0eKjY1tVPavsi/J8Ty+Hrg2wHnJTwryuMb75EQpnwMFJ0qZ6yn6nwCfxYMA7mo2\n7NjBuHHjOH/8+awvMKPC2/N8uMVi4eNxmVy8Ix+A9SlpjDxYGPC1tdOnU1FR227L0tV4A3yLwxxk\nzt67g3+NHudrkX9UkMe3A/yehcDjff5YkMcSz+NH8HzmTfghwIFdvnPr3is0zhzYtmfPngaDDwEO\nAlML8vi0G58zt7iDHYHSgUK5W6QlDYL2DAdGpPsCze0ZnWlpIhD8uz5dLhe7du1ipju4Lkyvzgjy\n6upqUvbtAEyA9+1r2jvNBTjAS0DmwOGNzqn7a2oyCW9d1dfX+wbMnCkvsT9ut5splWUNyv8KDA23\nkpqaSljY6Y4r/wFw/j6N79fsfOaBBrb9OyyOy1xVDco2Rvdi9OjRuFwugAbv3V6a6k53u91M25HP\nvhZ+/1lOj+iGMwK8lTZYYhk3btw5/pWz490l2u32RiG+a9AI4uPjGTKkb0jsp1ye69rDzrHxdOaA\nNcAXqk0FOEAssD/9ggbbr3+An62mBskFCnB/KdClg7zDu9OlffjOM7lcfDWoD8WD+uAMcM7Qn8vl\n4vj27Xz51ltwOPC53qZUV59d6AejrqqKuqqqJn8eGxvLwZHjODhynC/AgxHN6ctOmtLSz5sbVBkX\nFxewu3/SgBTGjh3bKESbOvZtqTclMjKS4tQM33P/x/68pzyKX3mFw3//e9CjvR0VFTiCGDx3Nurq\n6loMcKDROPVKgPJy8y+EWCwWLBZLwDoPDw8Pnd7Eujoih/bDMbQf9n3BfIJN8152GajszWZ+b+eI\nEQ0Pfo8fp1bzp7cphXgHGzt2LBssjbsmwXwp3PX1JA3szVHgF0BO6nm4bbaAOxSXy4Vz507emjWD\ndx9dxqZ58+DECfC03nLDrfyymWXxH3TYFjZMm8azwwfy2vCBFP7ud02+LjY2tlH37KTxk3ib0wH4\ngN/P/g5kjZ8c8Fy2v5aunz527FjAcm+PQFJSEv4RHE7Td12Kjo5mgqVhYOclNrzevCneIPf2hAS6\nnnro0KG4Nm4k9oe38+HCO3hrQC9cLpevZR7IV+vW8cqYYXwwZhi04QFaoEFM4wPsOq4flwnAgQMH\nOHDgAOWXX86iG29k9Y03wltvAabXITrI9+2MVri/6OjGS5pxeG+zn8HHBXl83M7nYauLi/lt/0R+\n37/pQcIuh4OkwUmcAp4HXrh4Egf/53+aXfbmBNr/eMua2uLP7JV67brrWD12OMNuuIGffOc7vkbH\nfUEuw3egyUvVxowZw0dRgeujL127FX6uFOKdYNy4cfwjQHlERAQ7f/c7/gy8DOQCW4CSkYPZ9skn\nDQakuVwujm/ezLHLp7EF2A5sA4quv56lX3zBP6tcfFBvZ2kzyxHoPGdr1d13H5aiAj7CTLtZ9uB9\n1L37LnV1dS39qo83yN8mnHvHT+bvnA5w7/IeHNn0Tr2la8W9I1z9/TssztcjEBYWRnH6Bb4gz+sz\nqFELfFNBHpsK8rBYLKxPn8gESzi9MQE+bFjjwV9N8c6d0JTiV16h5rrZrAEz4A04fN55HD58OOAO\n1bFxI47vfZcSwAEkpgwwrfI2mCY0IiKCkrSJvucTLOG8N24SRz2hDXDUc63+gQMHuNB+jAuvzMIK\nbMKcCvnXihWs3bSJ5ORkDqZfQDSmd6V07CQmRkQxHDON7EdRiTxH5wc4QFRUFIfPGLxWDYzYuSVg\nGH5ckMd/AP/hedwujh+n5IJ0DgNJAFOmBHxZxfvvUwL8E7Mv2QXU/OIBSp57rlUH74G+WxaLBbvd\nzs8DvP5NGt4mt+CnP+X8je+xw7M8Fx87xql581heVMQ3m2jUnOnvwB+aqdem9mdbRo0P6u+HKp0T\n70SnTp1i9JfmkqN9KWnEx8fzfv9EfoS5TCkKSAdGA+XA5Q+t4O677wagd7Sb3/XqxVOYL2g4cAUw\nEKggBsuvH+ZVz+Q7gbTlvNsbLruML3Zu4f8B3rNS8cB0YMZV32bm/feTlpbWJu8F5jRASUlJo1sd\nNhWk/ud6/a8R/yJ5GP37N54SsqSkhEme61239R1iBqNhAvxaz2teA6aNn9wm07+Wl5cz7mjDW4q+\n/vxLHHrxD/zJ83wKcD5mOxj94t+58sorfa/96v33WX/ztfwdsAHjgame1/b/3+e4+eabg1qOli5b\nrKurw+12ExER4Vtf//X3BjiXX84izDly74mVYcBlwItXXEHuskdJTU0FzIFTR0yh21qFhYXMqGt8\nGubkBRfgcJw+wPMGuL/XgYvbsAVYXVxMzgXp/C/moO4iIAvzOcf+4jHuvPNO32sPP/cc/1j6X/ya\n09vERZj9RMzUGXzv2WfP6qAz0BiQL0dPwOl0Bhz8d8RzHjw5OYE/XPkfnHjnDf6FufLgJNAL+Cbw\ndSD6sd8yd3Lg00qB/Ddwxxn1evjwYTIrAt+U6D2imTB+QtB/vzPonHiISkxMZF9Kmi/A8+++Gxcm\nwMG0qGqAMsxO0H8n5zh+HBfgvXrXhRl57gQs1PBqE+eH59P2N86o2PkFH3M6wMHsON4GXn/z/9iz\np/lro89WbGxswG61yae+avG8eP/+/fkieViTAX7kyBFfgANkHCvmyJEjDQIc4FrwtcjPNXwCtcgP\nv/oquzAHaHswO74Dnn9nvl95bi4VwDHMeehaoMLzry0HLkZERBAZGdng/f3Xf7399OmKzzgd4ABf\nYlrkrF9P1vtvcvLkSV8vR1vUYUfriIGGZ9p2++18jhlxXQKUAp97/p3ZM7Dv//6P3ZjvIZiLOvZh\nru2vq6o662716Ohovhx9Ogi/HD2B6OhorFYrY894benYSQ3qp85m40vgPcx2DGbb/CvwF6A473No\nx8teI8K7932+FOKdLD4+3tfNW334MAcwR6fe+Y2GAT8CluQVNJgZr3DlSoZ7XgvmvE8G5lKfooce\ngrFnfrWMx8ZPbrMAr/7qK478+9+c/5vH+Qkw2O9nScDFQCbAvn2cCDBZSEvneJuTG+DSJIA3mxh9\n7q9///4BAxzgeIBBN4HK2lKvXr3YM/T0JWV7ho6m76BBDALGYQ7OhmNa17NeeJnLL720we8feftt\nxmNaWwcx28KlwAWPPcmVLcxk15bKtm+H7dvhRz/ivwD/YYJj4fRBkMvF2NL9IXFHsLS0ND6IaHga\npui81EbjMy4eP5nX/Z63dSscIC9/IwDeExtlwAXARUsf4fvfbzglTslnHzIA+JrneR/MjWnuAX74\n5z+fVSvcyxvk3gAHcwD273GZjAVSaTxN6sH33uP8uXO5GxqMYB8HjMSMXrccP857EY1PdW1NGszR\ncZkNxvUEaoWDGUOS36vxHQVzw62MbWJf2F0oxLuQUZddxkhMq7oXZiOfBEwGKt97j5K//9332rrS\nUgowR+NgdvRJmC/r3x56CN55p9HfD3YASVAqK0mcMJrwG68h7r57yOD0lzQMOA+wepZ9THg4nxTk\n8Ynf+SyXy8WgnVsY1MT5xdY61w3a23V+Ztm08ZN5za/M250erH8V5PGvZs7neYN8z9DRRNfUMH7P\ndkqB/ZjtIA0Y/afVXPK979IrZcDplktdHUP3bOdN4AvM+icAiYDlpwuxTRoLLdzIpU2UlHDtvffy\n9r33sv+3v2Ui+CaCGYa5zj4T4O67ITUV3O4mZ+DrarxBvo7GAf7Hgjz+6JlAaXJqBq/TPgFOdTXT\nML0b3rH+YzDbReyGDez70598L3Vt385ETK9NGeaU3HTMgd1Q4FhOTqt7EqKjoxsN+PMG+QfjMhsE\n+Duvr2XHFVdweOEdDMJMqToQMzlJFNAPs01c/NZrRBw/zmZrX67HdLNvTRrMoEGDsFgsfH/8ZP6b\npgPc68wg7wkBDrqf+Fmp8ex0YgYNavO/XbBmDeW/Ws5GTLeXExPMnwKH/vtxku+7B4D6oUOp6NOH\nfq+8QhymOz0WmID5gg7BdKHtefppxiQnwyWXQGQk9wH3BfgC1Hp28NF+0+HWey7FCg8wEAxMCzxy\nwmiOYFp+kZgNyfvVdmF2NGHAUWDS8p8xf8ECuOwy3iCPqekXMGjnFt8lSYN2bqH0jGtJW3LT+Mm4\nC/K4x69sFHCV32Cr1khKSmIXMLZ0P9Dw2vNp4yfzmieIzzbA53se/6Ugj9lN/G6vXr0A+Pimm3x1\n68J0PW4Dkm7NJgnTXR4zpC/ln32BdcoEwjB1fQIT4N7daC1mwGO/0UOp3VoIiYnN3ojC6XTidDob\ndcG3dM762Pbt9LviEo56lnUQZpvwRl0F5kDEAaaHqLIS7HamjrGwtTSKQe3wfWprgcZ0+F/vbN+y\nidvGT2778Aacp05xaPRQjmG6o70zyldiutXHfbSe6o/WU1FTQ2lSEid//AM2YfYdtZjBeFswod4f\niPnvn1OblgZfM+30QKPwz9aZ28Yf/vc3fPeRR3gd8/mneZbdAdRh7nkwGDPV7XGg/M9/ZsBdd/Hs\nhYFv/dNcePsbOnQoXwVxhUh3ooFtwTpxAkeaGW0ZVXgQ+jR/h6uztenmm6l4/03+BawDkoG7gJ8C\nv8bsjCswrd01mCPZU5gR7CMwA1zeB2Zgul7jgUuAS++4g7mXX84TswJMu1Fbi31YMgDWL8sgOhqq\nqzmRYo5m+xw8CgFGfFZs347jikvYjTnvfQTTdT4AeAPIw3xRe+PpYsOMUiY9HZ5+mseBRQCbNkFN\nDUybxkeJ/Vs1Je+agjwe8NTNauCKAF/21swz7+1CD3SJ2dncec4/wL3+Ak0G+cmtW3l91gx2AgXA\nx5hWVBgmnH+MGScxENPz8i5mZ70bc250HFCMCdB5mG1pNjDroRWQlcWECafPa3pbwt4Qde7bweED\nRxh80UVEe8J+6+LFnCwuZuKvfkXvJqbP/OCaa+j1yb9Z51mGZMxgzHhM70ApZnt9Z/Zs6v/1r9O/\n+M47EBHha3UB7PRM55y+xMSj97akren+bU9vFORxO5zuEfn0U24A/ud7p2+h0lbjEez79/PGRRPZ\nD3wEbMb0tFRirs2/BBOQ38IcSL+KOXiuwXwv+mMOqjMwl2lFYU7DVS57FL72NdLT05t872rPPBWx\nfqfg8pYsoaqqiskrVgScW+GdgjzmXn45qzD7qn2YXoB4To/rKMb01I3FbMcO4IaXXuHyWbPOtnp8\nbHvNzZHiu9hsh8HQwLZ2VlNaSnxaCjWYL0Z8Wgo1paVNz3PtdJ71QI20K69kJiaEr8HcDevbmIFo\n2zFf0q8wl26sxwwUisJ8aftjWqFJmBb8F5ggrQT+be3LT/wuD/KqPXWKhGHJFAFFQMKwZKpKSohL\nGUC+5/d7pwyg3mZrNFgsPCqKBMyX0Hsg8TGmR+DbwG2Y7vQIzLSLRZidzis7d8KGDewF2L2b0vvv\nZ8/DD3PqqqsIa2WX+s3jJ/Os53E28F4bXdqTlJTU5DXi+/btY985Tp7RlIrPPmME5sAsHHNwloX5\nTCsxg9w2AH/GBLQNc+olCdMCTse0yA9hBrodxXTJ1z20hCNbtlBZaQ5mSktLmViyj4kl+zh06BDO\nffs4fvHFnPrudbhHDMJZXY3jhRdw/ukZdr39OicvSPfNV3DmcX/i0KF4D2cOA//AHID0w1xuNee3\nv6Pvd2/h7oIiX8/JNKBw1iyoq2Pfcc+c6q+9Rvzj/0384/8Nr73Gl19+yeRTXzH51FcB7zHe3mqO\nH6empfEQDof5d+wYR48dw+Fw+P41p/bkSWpbmMjJy7Z3L+dhZh3ri/kuzeL05DqHMPX9Z8yBvAtz\nQA0muH+AOaVRi9l+3gN2AGHl5c33ftXWcjD1PA6mngfeyV7+9jesf3yaIy+/QNG3A8/T5q6vZwPm\nYC4e01uQjzmwn4mZRvVSzJiJrzDb8zqgcu4NcBaXpDZw5AjR0zOJnp4JR460/PpuRN3pQOm771JV\nVcWgr3+d8hwzG/SQ7GzKDhwgPDycirfeYicmSEdgzu8d//xzmDiRwYMHN/hbNfv2EXXxJGKA0g8/\ng6go+g4f7vv5qS1bOHb4MIkXXECMZ0IO65gxWCZOxIoJxr6YnXMRZkSvf4R+7Pl/O+Z8eRimG70P\nZke+G7PjjsdcIzr7t7+ictIk4uLi6NevH/We7vMjn3xCHadHwh8BNi1bRj9gJ+ZgZTxg+8c/YPLk\nBq1k26FDHMHc8OIVzE7jPcxOYgimNXgJ8BtMN3AkplX+DvCL5cvZsHw5mzzvc9KzfsNnTOXolp30\nHTAgqFtYer1XkEe23/PvAi8X5PG1s+jWrNqzB5xOYkeMoOLQISwRETgOHKD60CGSr7iCKpeLulOn\nsA4cyMmPPybvH2/QL+syRo4c6fsbZ7bKD731Fsf37GFcWhr/70QpPx4+HKxWXqit5crxk/nyH/8g\n77XXGPatbzH52tPj3iP79OEizI76M8znedxTT/mYVow3VgZiBiv15vTnOBzT+7EbfAc3BZ7fLf+v\nH1IWEcHMmTOZWLIP11VXARD20EMUPfQQhzm97ZSmDOAkp7tho4ALRw6m/IWXmTh9Or1796a6upq6\nujqSr7mGslde4kNO39ksz7N8g4CRvXuT9/KLFGGCHUzPzJ3Af86axRcPraDP628T+8SjbPS8f8z3\n5zH5G9+Aa66B2lomh4WRl3Vlh7XIS/76VyJ//AMGA84DRyAujq9eN0PXolNSuMYazcMnTvDz3bvh\nhRcY+uWXTP3enRQWFvqCcdKkSb5ZzaKjoyl54w1K1q6l3yWX0O/n/x9RQM0nW6mxWIgZOBD3sWPU\n1dURO2QIh1euBGD44sWEx8aSifl+52MO2v0PLbyHN0c8/87z/KvAfG7jMPsKJ+Zqh1OY7+W4p1ZS\ne8MN1NbWNupSrz55kn6p5+GdMsg6LJm3rp9DXM5f+RSz/6nbsomTF10Ejz3G5MmTfa3yyVUuKjA9\nRBsw2+5OzH6gDtPgSMbsnwZg9mFOzHY6bHASrjfeYVCvXkRERFDnuZHT4G99yyyI92DCb3ltn39O\nryuz2O15j/SMVMo35sPAgQ3mhjj65pvse+01Blx1FSOv9b/WJLSFfIjv2bOHw45TjOk1IKjZso7s\n2GHO/W3dygs/XUgsZoM/BXzjP+8i9s+/oxr4x6K72Y4Z3TkMszEeBuyYI8jovn2xV1VhP3ECV0UF\nxcXFJERFUf3NK3gb0yodcekUJgI7vvcDNr/wLOMGjaB36X5fF2kMJsBiPvyMsLg437nNPpgvWzE0\nOZt4HWYnj2fZwzGtNe/OPBWzQy4A3Dk5DDz/fLDbiRpt6uirG+bi4vTtF78C1r+2hijMObSTwAdA\n/3vv5NhTzzN69GgOebpCrVYrRzCTunh5v7QHPOvl7Rxyef7FeNan2LPsj3veZySmtT4BsB04QOHy\n5Rzfvp3Mxx/nvGnTmlj70z5souxrAcrrbDbqbDYi4uOp93R1RpeXs/OSyeRgTglcgmnZHsN0W47D\nfCbeOxjnedZh2KtrsFqt9POcKx03bhwVnlZbr6NHOTLvRnI9fzMZ04Ny+E9/Iv7WW/kUs3P9AJj1\n2hqqc7/PP1c9Ryww87Mv6OVZBgemxeTPf+d9BHjBU3feK3g/5PRlRV4HPe/Xn2jCw8PZ5wlw73C3\nvQ895AuAo573/hwTGMWYwVTRmM+tPCeHhIEDSU9PJ8VzoxC+3MNTnvryOuFZlmjgqj17iMDsqP2H\nsv3b82/mY49RZCunDBNQEzEHkRQX89jChSRitp/yp59l2LA5NGdbwTacOMlIzeAvu7exDbidSDLG\nB74OucozRbB/t7AtJ4ecH/+ALZjBWDOGDyTxhZfZdsctODEHqgcwvQy7MAckA4H8V/5G//4DGTly\nJPX19Zw6dYojR44QERHBkCNHOHb7XJ4HRr71GpdiWtbRF03kUyD+pw8w5rFfcBQYcPciip9+nJ3A\nJX/4A6M//ZR+mO9LEmbf0tyO+6jntWA+uzLMwZX3ZtHhmBZ7JJBw551M/N//ZfLkyeB2Q309WCzk\n3XEH/TDbO573/DDnr5RieoS881hE7N9J+fPP07t3b0YNHUpRYSH27dvpjbmEzLs/smP2EYWe5YvE\nfMb+syOsBp4A+l0zi8We5Y0Fc9riXxtg/HiODkvmI2BAagaXfPgh2Gy8f2UWj2EGyc31LF/lmjVw\n222+EK/4y18o+ulCXgem5PyVAe6/YL3uumZqMXSEdIg3mPS+4ij5EDDIN15/Pf/68F0mcPqyl0mY\nHcohTOu3DLD++Xc4gacwOw0bZidzNyYwD2KCKg+YFxODfXompzDh/iHmS12KaXG6MS1ZOxD2wrO8\nDaLPLKIAACAASURBVHxZup9emNGrVZigexf49qVTuPz1twnD7PQzMF+6esw5rLdpeA12NKZbqgTz\nhbgRmLr453zrVw/zDmaH4sZckpQFVFx2Gb0jI+kzYhDeK7bTX3kJMAET7vmdKzBfugJgIyYw5gP9\n+vShdssWkv7TTBoS++c1uDE7s1OeehqLOSeX4ikvOX8qd23/lCpMF14UppVYiuk5OOV5fBDTkpgA\nHLrjDrYcK8YGZFz7Ddiyk1dOmt3ADU20rL8GPHlGWcBO8Joa6kcOZo1n+cLvvJdt77/Pz/Zs5ytM\nD0cd5nMZgNkelnmWdQLmM4311InL83jbXf9JXNZlnH/oKH92VjK/uNi3Lh8AOZiWbAxmYM+1t97K\nw5hW0FhPvTiB+lXP8ZTnNVfeey/VO/cTlz6C6ZjR/RGYqTO9A5q8UjHbwVeYc9DeUzH/66nfgZjP\nMxWzg6t7/o+MvuIKDueb8PXuQIdjdqpVnv+HYraLFEw350HMdhoLXJWVRf6BXcyOdp/utjx0iAOe\nvxWF2Z7SMNuD+9vZjElLY/lfcyidcz1rMadfvoY5WK0B/mkrZxEmeLZ5Pr844E9ffEFf4EVPPXzn\n7h9QPiqdfHc10dHRTD5jm9hWsI2v4zmN5XeTmdU4ebdgW8AgL/QcmPadMgWn00nExx/j/skCijEH\nbp94Xjf2e98lF3Nwk+b52QZPPXkHm40+tI+Tr75KzP33E3bgAPl//zslMTFY8vMZ/5ff8zam9ZmI\nOcUxCLMtvgeEPfYL0jzl1z39OPsx+57kiqMcSz2PDMx2NB1zoDnMU09rzlifCZjR3VWYA7vLMKcu\n9mIOCi2ev3MAs22MGzzYzAXvdrN/QC9qMZetxVx9PTGe1+L5/+KR4zi+bwdfYvZPH2K2q4vGjyca\n6J2WQgpmO9nL6X1tL886nw9cDpy8cR7uI0eo/+AdPvAsRyJmuzvC6QG9H2MOoj8ALp49kyGe9/wc\nuGb3Nu6deTF/3rGD/Zh9SBVmmxoCxP32V1Skp8P113P097/n2Qd+youY/fGrQNGd85n+0V0MWbCg\nQY9aKArZgW1N3bUm/8wW+WOPkf/rh3kWE6qfYHZI4TTdyg3GfZgjSztmx/fCWf6+93zn+8BVwMOe\n8vMwXzQ3ZkDIU5iuqxOYkDmB6T79AeYL1B8I+/hzap1O/m/GVPLx9BRgvsBXA5E79lGzaRPJt89l\ni+d9LgDK77yXXb//H2KAXnf9iP6/+y37MTvN1zHB8F9A+AXT6LdlE9We9T2K2cEcxLRSd2N2+D/D\n7LhdwAfLHiVs+c/YC/weEyhTMLcdTKThQUkC5gt4mae8FnPObBZw/Xe+A9dcwzPnnRcwyPMK8rgq\nQP2+TTgjh5kvZ8qAXjgTEtiD+ZxSMTuWtzAh+ppnvfzF07hF25QbMDvzCzm90/8n5uDOn/dzbc7/\nwwThiUX3c/XVVzN4yBD2po/gMcyBRC/Mzq4Ec8B3H6d3gjP/+U8oLGTxokVswtRnqed9s4EvFi9j\nSO/euHbs4OurnuM9z3t+DegzZw4f//Wv2DEh5cAMhsrBfNbFmAOG84Bf/OUvkJwMe/dCTg4fvv8+\n/8AclFgxYzamAUuBiE+3E261EhUVRXhdHf8aO5ylmNb2KOAPnv+9V/17R5l8E/OZxGCCbRRwHfBb\noOD22+GWW3gTfEHeIMCb8K5fi7yqqgr272fv5aa3x/KHF6mIj+eD717H5v+/vfOOj7LK+vg305mZ\n9A4EgkAoCb0XRTpIYG2rtJVV4VVexLKwisK64IqiL2BZsYsLoiILIkWliaAUaVIkdJASQgghpNfJ\nzPvHeZ6ZSZiEUISgz+/zmU8myczz3Ofec8/vnHPPPRdJILtS9ItrTqtDe1gHBLToQM7uLZzFE82p\nCmoiY1wPGZ/6CIHXQ2Tt8BPPsOCNV5iHjL8N0SlNgMeAnFFjsSYk0LVPH4Kb1GM14gX7A7sR3dUZ\nyWHRHUkmuE0z1mddYBeij5oD6f+Yyv5/TaQEaDLxX2S1b0/Jn/ryJbLMl43UsGiOiUYUY0NkYKty\nj0PI+BUhXvVIJKE1c9cB/PR6zn35JcX/fIZXEcOuLzJvykOdi3546iY0QebSVxX0XyLwDlD46psk\nPf04vnzuAUCHB0fzl4kTZdfGVR4FfTW4msS2m9oTvxQ23X032zesYSES9vIe8KshcJD13qtBKULg\nIErqG+V9PDJpLYBz3kICht9LNLIntAjPNo2ETz+lfvP2GI1GUs+dY9u2bWzt2pPdG74jB1H264DZ\nQP/ERD7NOcdaxCAAId0Ms5lD9wwjxGIh7b03eNarTSBhqfeA2J2byUKMh+V4QmQxyERKV17qNrOS\n1m2pmZBAqHKNPYhHqoa+y5teqsmmkooZseZPAyxcCO3aMbpOHUjaUaFHXh6plNJHKWk7+Y0v3Qpm\ntnLtpngI1xeqSuAgeQEgoe8CRHn5OkfsUgRuQLyPGCArOxtbnTr4BQWx/s7BJH01n2IkQmBFDKlT\nQPu+faV+dlQUWK2Ql8crSDThh6gouqSmcgxRkh1fmcJ/EU+lIeKpFSq/s2MHmx5/nHCHgVpvz+Qb\nxEBQ50ltJBrlAHjAk28fgBhcIYhMFSDjtg3QbdlNhNfhLn5+fmS/8CoZzz/N93hkzRe5eStzo/Ja\nj0SJUGp/9we+TdpxkUdeFRxcsYKwR//KF4hn13bkcEq692MfHoPiSnHw0B5OIs/VfPcW8vDIeFWh\npPpxCjEGGyEGZzQiJ5Y//YmQN14hAskzsCEyoSZFlvbsiaVxY8yhoWQdPoWhYQwFyBjuR/JuvkJk\n9UyD2jRHIjpJyPzLAdLDwsjt1J2tW7dy0G5n75/6uvMsVHwOHKAYPSLfG/GMpxqBSkbmkw0wnj5P\nhF6PTqfj7G1dOHj/cEq+mIeDiuejOhddSvsCEEOmsvm7HJHL7U8/zsIKPvM1EPTxO+z4+B0GAZln\nLtxQIr9SVGsSVzNp1eIKpaWlFBcXU1paSt26ddl44kQZb/xtJJyuZlPnHjlCOh7SqQyhiAdZzMVe\nWVs868+tECuzLkK+lSEWsagNiBKtCHpEwC0IwQQiVmfdW2+l5eRpxE6eQCKQsWw1m7dtI6+4mAYJ\nCQSFhuLn54cpKwu73U6Llq1ITztD4aF9hCGKNANwKiGws0jYz4Ioh7SSEpyNGmF46XnO4VlHA5nI\nRmQCGZW+2YsYEkF4ktXiEcVe2iuR5r/soLhWTTKWr8KYmkqo0gePIeE7NSTbBFlvLQ8r4pEXIaTQ\nCKBmTfepbFlIvXl1nSs3N5e4OnEsO3noonOrvbd1Tc7N5WWlnU5EQf2KEHk0osTPI2MbpozHT3hK\nRFYVGxEDrBniAR9VrnlE6QcHnnVJX6infKYUaBkSQl5eHgUFBeQ5ndyFGGAWhFA3Km1k2DCw2+UF\nYLfz3HMvcOTIAU4fO0aP1FTOIl6Yd4XreEQhliBGAwcPUm/aNM7vPUIWMg+MeEi8EBk7bwMshLJr\n9HbleYOB3NjG2MPC8PPzw+l04nK5cDqd3DN0KAs2rWXHihUQFgbp6QxEjFi1YFEEYnSDJ0nPolz7\n07p1oVUr9z0diEzcUvcWVp04UuGZ9M9BmXC6Pj8fvfKcpxBPV/f9CgYg4/8sQhoNkQhEHLJeq5Kr\nBc8OAjUfYQBCDK0RbzQYkSF/DNyNw50QdrvSV3WQOVre6/fHQ/pxyBg58RiITYHghg25T32v/H+b\n0neuBUuJ6dIFo9Eoxw2bTET/sJVGt7UnTWlbMRIdO6a0X00KK1aeYQlQ8sQj9H7+JVYcO0bps0+V\nKaMbhMhCpjIGLuU73savFcl5KQTCIuoQ98oroNOh0+k8kZM/3UGPRQup7yh0x1FqI/pPTdiLQPSo\nHyKz2YiR+iqip97AIy/emI9EoypCM8SA3Y8sRYVHB5N+ExJ5tQynT1+3jsNIGNb9N2TPtDcWImvG\nFSIpiQ2PPcbryme9UQfPGmMAQiwjEcJfg1jjR4D7kXXGBchEVxVhDcTa8z5ltxviKRUiZNBC+e53\nwJvIWpavRCwV4xHySQCsa9ZQ6OcH58/D3r0QGgoREbKv2mwGiwV7cDBHm7ampKSE+VvW8/wvv5C/\nfDlDt29nJUJMKsKQddhiRBHlIKT4OBJirqiml1Vpz358exNxQKfwGOa8OAkaNAA/P3kVF+Po29e9\nJ/QMYmRlIOtiexFPTF3WUA0YtThrDUSZdUAyzoctW+YhqstFairZQ4bwM/AxYlBEKde+Fzn29FOl\nXc2A1+Lj+UtSEp+Uu0wE4q2EI2u0aYgSPYTITSbiHXqvU0Yrz/4S4pG8zcXLCUF4DIYgRFm/ATz6\n0Udwyy2QkkLesGHuinHtgQY9e8KIEeCVrb0WMxscebzmcHBh0yb417+Ygqzt+4JapCde6Y/lI0bA\nnDnoEFKoCLciXpCvTVSPIsQ3buZMiI29uJ6CywX79zNqzBg2ItGLFxACWlbuWiFIP6mbjiKRvnsS\nGPPJJ3A1RT2WL2fljBnMR8bnAiLLHZX37yP6oTXwc82afJ+SwgdIPoxaOa0vQqD/QvpwXtOm0LAh\ni5YsYbPybBeAzW3bQqNGjPtUMvi3AC8jy2kblPfBSjsKgaFIUhiInO1GDJhBiMH1AJC+6wCHsrMJ\nu02Ko7y0cCFzf/xR5ki3bnQyGvkgPMZ92M+LwKQ5c/jlP/9hOUJw0QhhZiFz7RtEn5UpkNy4MRzw\nRZEe+CPLar6iT0bECOHJJ6W4jNnMTJNJ6kSAyMPy5XwxcyYfIktGtyFyudbrOgbl2b0dJzMSns9A\nIoaXC285nww8D0T+978kdet7BVe7Ovyu9onPWbeOv1OWwOFiAodLEDhA7doEIxNfreWtQwitKULk\nnRFF0QQR6LbIelCQ8hnVW+uCKHiH8v0o5ad3eRI7MuluRyy8D1q3JhYhwlrKNVX4qpH0GeIV6ubO\npVDdZ+rvL8owNFRetWpBdDQEB5ML1N/3MysP/8L44GDyg4MhKAgLZQkcJNy9AbHCk5EJF4JMisqK\ncqrWd2AF/28EzDE5pW06nRA4gMmEYfVqauDZz25GLPZIJPIB0u8gZO5N0QUISR5BJmnKwIFw5syV\n7SONiiJg6VJOIUaWESHdfMQTMePxeF7r0AGefJJPlHaqZSP0iBHXAqk/3R2RnT8jpBan/N4G4LXX\nYNQoaNGCMy1bQsOG1EcUcQPEGGiKRF96InKlIhdRpEnA2ocfhq1bwWLBYTRiV/qoFOChh8oQOEAP\ninjeYOCCxQLKlreLK9Z7UKK8LiBRmnfnzAEqJ3CQRKOKdkFbUPYoZ2ZK2Nvl8rwALlxg8Zgx3I8k\nlyYiRmICnr3NKtTqXirOIksiPwG7/vIX2L8fsnxRRxVgt5ONzAsH0g+qkWpFIiptgZ/tdmjbFivi\nDdZFxmcBMm+ylLY3A6hbF5o1455vv2UwYiRubt8eevWC++5jxtNPs6V5c+jUiWe7d6cuoj+6Ix5r\nKOII9ETqzKvmj0O5TwoyF44CW9etpWvBBRovXEjjhQuZa7NBhw7Qpg0YjWwGN4GDGKp060YMIsP1\nEL0Yiei2Xoj+u0heLkHg4HEIfMFdxLhxY8l8z8/3EDiIvjh6lKYoyzpIX6onwqkSro6RN4rAnVB5\nuaiLR98YkPbnAkf//GfqrVtR4feqI6qdJ+63bt21u1huLgUDBzIX8eQbId7zY0gVqxCE7AYOH87O\nefOoiZDM54hFrCoWdV9mFFCzYUMICGDzjh2sRMLCycpP/ud/+Pr99wkAbv2//wODAaKjOTl4MAcR\nxazWMk5D1tXLV49Ww4eMGSOWa0iIO5zMpcqS/vqrHEBRUMDf3n2XDxFF0xBRmD/je/3RiAh0iNKu\nxxCy/075fxfE+1yDWN11kDXuj4D7APPq1VLwAmRd1huTJ7N+/XqSkCSmFCQRbFZiIuh0fL10KR8g\nRkIpklTnC3VRsqknTYKePSvvh/JQi9X06kUrxCg7hyjMbojh1ApIfOIJCAyUNeaffuLDl15yl43s\niITEoxEiPgjcExsLnTvzyGefcUB5hqX9+8Mdd8iYlZZCUhIEB+OcPp00xDv7Gok09EMU6Rokw967\nfEwX5V4L/fzg2WehZ09WKs/dd8kSkYXKIhO7dsHBg3z/7rs8jxCvijrK8yxQftcjMt4cSfb7X8Sw\nmY0YKX2Ap/BU2QrEE1b2xhRgOEoN/xo1xJjxrqPg7w/du/MDEolZjBDmXUgkQvVGv0XIOgrfyy7g\niYowahQMrXzbmU/MnEnysmXMQHZi1ETmSS9EBwQjxmfLUaOgZUvOjBnDScRTNAFPP/AAGAzsnT0b\nExA3bZqMSUgI1K8v83DdOjGmbr8dGjaUPc5OJ5w+LcWgSkvJGDuWo0giaTKSzDl0xAg2zpnDdCRa\nlYkYR10RfVQPePHOO+GeezzRCLW41KWqxGVksPeee/gaMdYaAuZnJhL9ylTaUXneRhhi9HSg8vVo\nFX8G/tuqFShV+FCL23gfwHTsGGtHjyYDiYZtU773GrINtQOiJyOQflhAWUQhxk+Sj/v7IwbSasQp\n6ILI76pK2pwA7O3ShfAXXyTpNyihWxG0xLaKYDBQY+lSDgwaRChwb5Mm0K4dd8ydSylCXq0BevSg\n1Z//TOGf/oQDeOqZZ8TCj4/HMXasO9wUCPDCC2Cz0WntWjhxAhYvBpMJpk+H5GQGTJsm5UVV4g0K\nos4nn0BREatHjuQCcN/8+cwcPJjmXEzibkW+ZQu0bi1KwRd5u1yQny+WrEqctWpJ+NJkYmaDBkwZ\nP54vlXafmDiRv0+dyqNel4hArN8MhJhMiEBEA989/LAYBWvXokOsXtUSNiAKpSsKgRsMHhIvj9RU\ndzJeM2TdMAGYFRcHXbowoFMn/vXssxQgIeeK4N5PeiWV3Qo8q/2TkES7IiT0bQb+0by5hPvUQhIW\nC/Tqxcj4eD4dNoxUYNyECaRPm0YwoO/WDfr3F8/rxAneO3NGPJZbb4XmzaU/QBRsjRru+9dAPPVi\nxOrvOn8+FBcz64EHaIsoUDVc6PZA1aIWOh19v1bSvXyUurwI8fHQoAHd27fn64ceIgLZz5yNEG33\nGbPQjxvDt8rHQxCFvhJPXkYQQrI24PR998GCBcQjsrIWiWa0RAiwC2L8XUCJ3tSpA7m58lKh5LYE\nKS/15KsExMuuiezzbY5kFjdGPOJFXJx/otY38B7by4LBQLDSbpfyzO3wrL2C4qnVqgV2O/tiYmh9\n6hQhwE8mE9x/P5jNJPTqJWuogYESeVDHKzJSZMFslmUw8PxPrRVvsxHyzjvgcDBj7FiygaEzZ0J4\nOEFz5hCLR0GXKq98FJ1x4ULZqpBVLfEaEkLCm2/KfN28majUVBb0H4julal0wFNMSoVqPFsRA+db\nFAKfNIlRL77IfKXP6iHGqAPRq37Af+vUEb2oHsCj7MnHZCpzD7U+hh0h7RYAjRtTcuAA8cjyxV4k\nzJ5C2SXJYrwiiR06iN5UMBLPoUxmJJq7uX48h44mVejBOwH8/S+qSlidUe088f+sW8eD1+pi6gQ/\ne1a2w+Tny6SMimLr8OH4Ae0++ECUdmioEKLLJUo4Nxd0OrLuugu90kWlfn4ErlkjpPrrrxLa3bpV\nhPT+++H4cflbbKyHbKKi4KSy+q7U9qVHD/jpJ1i5konr1vFfxPvugEyUqXfeCc2aCYlXdGyoLxJX\n/w6ylj53LixbRvfb+/GvF18k0mZjy5tvsu3jd/gRIbEHESv1ArI2Phvgr3+Fu+6CU6dg6VJeW7WK\nQERx1wemdOsGNptkKasnOqkT1LstViscOsThRx4hGbGWWwJdP/pIjBO7XbzV11+XsercWX6+/jq9\nEQ/JiFT2erVmTZgyRcihnBK4JNRjUI8fZ6+y/aoE8TAbDB8uiVLBwR4lGxrqea+EmBkxwhPKNxg8\nIeLsbHn2I0ckX6FlS48s6XSQkQFOJ1n33UehMjb7gO4vvAAdOwoBbNwIR4+yaM4c3kKSnx4CXh01\nSsi4bt2K5eBSUNYcefNNcDjwA2aOn8TgceM4d+AAh7t34n0kSvXmoEEwaBCfjBlDjaIipiP9/+Mn\nn8gc2bKFp6ZPJwExhLYgMpSFGACPIKHVwLZtQT02t7wnXlpKUa9e7EeiIc2QKE8hQqb2OXNE7j78\nUMivWzdYuZLhe/bwOUJkdyv9k/j882Iw+zh57pLIzYUPPmDT0qVsQAzYrkDbHj3IWSursf4LFggx\n+/uD2czykSPJyspi2Ntvyz1NpsqToEpLRWb0eo9hB7695sOH5WCYhg3lfkeP8svIkWxA9v1H4tli\nFgfc9s47spxSyYE2FULVizk5orNCQtgU25ic3reyEYkK7EKKrHz0wAOQliZjkpsrjsvAgTBmDB/2\n6ydLXUqb1GpwR4ANffrAvffK86i6wZcnbrXC0aOcHjmStYhX3QOwLFkCZjOrBgzg69JS3hg3TiIY\nixfzcVER3yIGR2NgNHDXyJHQrp2076uv4PhxPsjPZxASha0B/HXZamzt23NhwQLWjR3Fv5GIjxFZ\nIvtfoM6HH2KtVYtfW3e+rmfcX40nXu1IHOD/1q3j6WtxIXWyqMJTo4YQqtnsWetp5rUiqa4x+vl5\nvqvXc04JY4Z/953HK1Y9z/x8SE4WQjp+XMi7Th2ZtBkZ4o2pRTEilVU/o1Esd4B9+3ju8cd5Dwml\nzX/6aVk/Cg723MvrhDE3efsice/3DoesIe/ZAzodq3ol0qhRI5KTk3l17BiW/PyTKMD/+R9SnnoK\nOxDQsSOMV7IPQpVV62XLwOlk0euvA3CPdx+oz+/9U71/To68T0+HoiLWjR5NGnDfq6/KZFPhcEBK\nivSX2j+nT8OIEdRBogXbAwJgifdBoJcJtca90palI0eSCwwdPBj69hWSt9s9SVh2u3tNGfW4zPIn\nbeXmyhioPzMz5Tq1a8v4eyuAkhJwOjnQrx8AjVesKJM74Ja1c+d4etgwvge29ewpSwdXC1VO9u0T\nwzMoiJmNGzOkzyD0ej0716xhxtC72QDkL1ok7XG52PjMMwB0mTpV2hoUBEePwrff8vWiRQQg2cNN\nEa+93urV8JISS3n+eRlTKEviKkpL+blXL7KV75tRMtNnz4Z69eS7u3fLd9Vyv08/jTEpiTDEo+rY\nsCFd3nuvbD9fDlSZmDnTc2xvs2bw6qsiuyB74dVzEAoLZc6ra/CRkUKg1+iQk4tQUiL9cOYMo599\nliwk4a0+EHoshUUnDjL6WtwnP19k12RilcFO6l39mY1sTwXgm29EdrKzRV8YDDKvrVYoLubpvn3Z\nhuzhr4cQ+dOffXbxfAHfJK5i1y5WPPUUoUC7b7/1zD+nUwwInc5zGNPmzcyaOZPPkOWwl954Q6Ie\nAMeOiUF0/jx9b7mFuc8+y0ng54kTSevVizZIlKD/5Mmwfn3ZNnzxBSERERy4jmF0Fb87Ej93Lod/\nJO24ooxDn1Anpb+/eOXg8Yq9T7xRSaQ8vD2wcjAWFFCSnS2T2dsTNxiE3AMCPCTunU3r/X7GDPGW\nQkPlvOXyWb3e76tK4uXavsRgoH3jluTl5dFnxlSOLV4sEYH775e2G40+ny929WpebNoanE6Gnzos\n3rIvlCfx9PSyP48ckXHo2FEUdWU4c0b2h3/5pYzZ2LFwFacbuaG2JTVVQudNmggJ5+QIcatRBbP5\n0ttMlL3KZUg8L0/GtbwcqfdNU4LAangVZJuV9+e++AJ27pRxuRbPrJbSdDrlmUpKiC4t5ec2XdDr\n9Zw8eZLPPvsMf39/BgzoSXtV0aqyoBqrZjOkptJ182Y2BAXJmeDqurzJVKaWNeA2fvyio32vs544\nIdGiunU98q0ah945IOr7H3+Ef/8bzp/nIWD22rWXTeDeO1LcUKM0+fnST7VqeYg6MFDI2+n0kLk6\nV/R6IRofS10Lga5NPNvgnE4nNQ/uvqy2up87Lw/mzZO+atGCV2JjeXDwX/ln0g7eubwrVozcXHA6\nmZSZScMCF09Mm0ymGl28805PWywWT6RP7fudO2HbNggMpHvbtnx/yy0Vj0tlJH7+POzYITqwTTkS\nVe+pGrt+fjBrlhj2depIJM9bRzoc7ojqN3mlHEs5xmNhYRL9QxL4krdsgTfe8Bjpf/kLPPggW+1h\nxHqddXG98Lsi8e/XrSM0pCa7MlIYca0uqoaPvI/VPH5cfl7LAStP4iqxJSu1u7yJ25tss7NlIjid\nQnBBQWWF0hehVBROrwALgNvj2+ByuWi36ydO+vlVzYtQ17PMZinjWdHZw+VJXF1fVgmsRg3pG5vt\n0iFAp1MmV3KyTOorCaH7gqqw1YQwo1EUWH6+/K0qa83loZK41eoJoZeHSgpqX3gTtxqRAemfEyfk\nmlcTQq8Cngcei29DZmYm3Q7tIQiYGxnJ5txcZlC2prUbJSXyLH5+YpwaDBUrbMVw/l9/fx6Pruc+\nn/11ZIsYUPWETZD7Hj0qY1W/vm9P70qgrtlbLHIPg6EsiatJkWazPL/F4pk312s/8fHjIrvh4RAc\nzAKbDT2SIHrN4HTyemYmHWvV542jSXweEuLxhitDcbH0kclEd72+TLGoi+Brya38taBqc/3sWTh0\nSAzmuLiKP1fe6PBGTo7c02SSMTWbwc+PccgOgbY3SWJbtSNxNTv9YST7+ZrA1xqUuk7tVVHqqpGc\nLIq4du2yXq0vEi8PVaGpCuRS55VfJokDbLWF8nHe+WtnwXujognqi7huFNQQqrdyys2Vv3uH0C8H\nKonb7Zf2DH/jvuhD5Zm35fE4Ur/gt0Q7pPyqWvbyK2TN1NeW0RsCb5lQCd2bxFUYDFBSwvrASBo3\nbszf9v3Mp9e3pWXwFpJIeCUYgRxb6o3/OJ10rFWfFft38uRvaDzeTPiG60fkv0sS/82hhtUrCqFf\nCbKzhcRVL0VFVUhcheot/gYk3oOyBRSuC6oTiftCUZF4ElUJofuCGlavSoJRde8LDQJfJK6Ed8X6\nzAAAFPVJREFU1edZLMSaA+lacrmFVKsXHkQKHwHMBfrFt2HBlvViGFxpUaVqiJXo6HvJygcV43oR\n+R9mi9nlehqV4lqStwrvBDRvhPg8V+vyrlEefn6XnZl6jQKQlwf/KxfO64KKlgeqissYg4lGI1Ov\n7m4abhSUKF5AVUL/l0BlVfSuF+4CXilHTrbfEXkDfI+F+Ph4jmRn0+DU4Ut/wQeqlYdbAapdxTYV\nD5f7fTowL77NRcVAvrzK+3wMHK7d4JKfuxy8Wv4PajZnVaDXX7ZHOKoKnznVIIFpDRIu67pXijp4\nHTZjNl89UV4npDZpxRsV/G+D8eqNkVEtO3LYS3Eert2AxVd91SvD/qhY9kXW9fm/a7aMhewuKI+K\n9hk0usJ7VGQYPUrZKoBVhq85qPytbaMWxMXFcdBX1n0V8BUwOr4Nq334T/8p9/vKCtTzet0V5G54\nYSnQ0Yd3eUfT1tS/qitfYX9XgGfK/f5LWO0KTy0rj5eB+Ph4AAICAjgS05CfAyNJiqj68mkI0LZp\n6yp//kah2oXT1cS26Oho3kzawVxk7e4BL6FblbSDvUAfzCTEJ1BaWorT6aSW1xnCNsRC8Q56PYVU\nAlLxMTBAuW5WVhbfJh+hEOgdFEVkZCTFxcX8+Ot+DiAFLqqyf/1d4O74NmRX0fqbS9nDOu4GZjVu\nSfSBXYAI49t4DgMoj32RdQkLC+O1pB287OP/h2s3wGKxYFaItKioiJgje318UjAOmHHJVpfFF0iY\n/n2kTOK2pq3x8/Njc9IOfgU6WUOoV68e9ZJ2kIco14dsobTPK1scdiFSQe8RH/cwAScat8ThcFTa\nfpDKcCO9fm+FFIXwVdVJRWqTVugUL2t+0g52AYPRE2ST9cHY2FiKi4tJS0ujddbZMt/thBS8OAbu\no17v5mIDM6VRCwwGA1lKuDZQCdduTNoBSKEJ72SlfwN/btKKz/fv5KlKn1iKowyKa15mDnRCaks3\nCIriy8xU9EA3LERFRRGqbCFMT0+n6VlJY9tssXChsJC28W04c+YMJzJSeBGp3ucLbwMtTAGkF2dj\nw8g+SqgJ3BbfhgHKMy1v2prO+352VwqsD2xq2pozZ87Q8oJkBtuQMZ8Q34avk3ZwBtl3rp5J7cIr\nGc4LQcAHQOdyzw0eL8zlcnHq1Cna5pxz/+8OPKcGeiNAaYd6QmFKoxY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       "text": [
        "<matplotlib.figure.Figure at 0x115540e90>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}