分布.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import tushare as ts\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "from ggplot import *\n",
      "%matplotlib inline\n",
      "import matplotlib\n",
      "matplotlib.style.use('ggplot')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 268
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "id = '002747'\n",
      "df = ts.get_h_data(id, autype='qfq', start='2013-06-10')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[Getting data:]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "#"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "#"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "#"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "#"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "#"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "#"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "#"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "#"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "#"
       ]
      }
     ],
     "prompt_number": 206
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ds = df.query('date > \"2015-06-10\"')\n",
      "ds.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>open</th>\n",
        "      <th>high</th>\n",
        "      <th>close</th>\n",
        "      <th>low</th>\n",
        "      <th>volume</th>\n",
        "      <th>amount</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>18.000000</td>\n",
        "      <td>18.000000</td>\n",
        "      <td>18.000000</td>\n",
        "      <td>18.000000</td>\n",
        "      <td>18.000000</td>\n",
        "      <td>1.800000e+01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>73.934444</td>\n",
        "      <td>76.625556</td>\n",
        "      <td>71.607778</td>\n",
        "      <td>69.512222</td>\n",
        "      <td>2499700.222222</td>\n",
        "      <td>1.793922e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>17.480090</td>\n",
        "      <td>17.489721</td>\n",
        "      <td>18.038825</td>\n",
        "      <td>17.289296</td>\n",
        "      <td>737439.729009</td>\n",
        "      <td>6.071824e+07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>39.500000</td>\n",
        "      <td>39.500000</td>\n",
        "      <td>38.460000</td>\n",
        "      <td>38.460000</td>\n",
        "      <td>1427064.000000</td>\n",
        "      <td>5.516722e+07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>60.697500</td>\n",
        "      <td>66.375000</td>\n",
        "      <td>59.652500</td>\n",
        "      <td>56.947500</td>\n",
        "      <td>1881261.250000</td>\n",
        "      <td>1.439116e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>76.550000</td>\n",
        "      <td>81.405000</td>\n",
        "      <td>75.880000</td>\n",
        "      <td>72.410000</td>\n",
        "      <td>2447812.000000</td>\n",
        "      <td>1.753058e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>86.022500</td>\n",
        "      <td>88.807500</td>\n",
        "      <td>85.397500</td>\n",
        "      <td>81.670000</td>\n",
        "      <td>2795925.000000</td>\n",
        "      <td>2.018335e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>99.200000</td>\n",
        "      <td>100.940000</td>\n",
        "      <td>98.650000</td>\n",
        "      <td>96.710000</td>\n",
        "      <td>4106260.000000</td>\n",
        "      <td>3.319090e+08</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 207,
       "text": [
        "            open        high      close        low          volume  \\\n",
        "count  18.000000   18.000000  18.000000  18.000000       18.000000   \n",
        "mean   73.934444   76.625556  71.607778  69.512222  2499700.222222   \n",
        "std    17.480090   17.489721  18.038825  17.289296   737439.729009   \n",
        "min    39.500000   39.500000  38.460000  38.460000  1427064.000000   \n",
        "25%    60.697500   66.375000  59.652500  56.947500  1881261.250000   \n",
        "50%    76.550000   81.405000  75.880000  72.410000  2447812.000000   \n",
        "75%    86.022500   88.807500  85.397500  81.670000  2795925.000000   \n",
        "max    99.200000  100.940000  98.650000  96.710000  4106260.000000   \n",
        "\n",
        "             amount  \n",
        "count  1.800000e+01  \n",
        "mean   1.793922e+08  \n",
        "std    6.071824e+07  \n",
        "min    5.516722e+07  \n",
        "25%    1.439116e+08  \n",
        "50%    1.753058e+08  \n",
        "75%    2.018335e+08  \n",
        "max    3.319090e+08  "
       ]
      }
     ],
     "prompt_number": 207
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ds = df.query('date > \"2015-05-10\"')\n",
      "ds.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>open</th>\n",
        "      <th>high</th>\n",
        "      <th>close</th>\n",
        "      <th>low</th>\n",
        "      <th>volume</th>\n",
        "      <th>amount</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>41.000000</td>\n",
        "      <td>41.000000</td>\n",
        "      <td>41.000000</td>\n",
        "      <td>41.000000</td>\n",
        "      <td>41.000000</td>\n",
        "      <td>4.100000e+01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>89.395122</td>\n",
        "      <td>93.562439</td>\n",
        "      <td>88.766098</td>\n",
        "      <td>84.699024</td>\n",
        "      <td>4057248.024390</td>\n",
        "      <td>3.837122e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>19.217424</td>\n",
        "      <td>20.431722</td>\n",
        "      <td>20.383966</td>\n",
        "      <td>18.405163</td>\n",
        "      <td>1787692.336699</td>\n",
        "      <td>2.247652e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>39.500000</td>\n",
        "      <td>39.500000</td>\n",
        "      <td>38.460000</td>\n",
        "      <td>38.460000</td>\n",
        "      <td>1427064.000000</td>\n",
        "      <td>5.516722e+07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>80.000000</td>\n",
        "      <td>82.990000</td>\n",
        "      <td>80.810000</td>\n",
        "      <td>76.020000</td>\n",
        "      <td>2480510.000000</td>\n",
        "      <td>1.911126e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>92.910000</td>\n",
        "      <td>97.990000</td>\n",
        "      <td>94.910000</td>\n",
        "      <td>91.410000</td>\n",
        "      <td>4015823.000000</td>\n",
        "      <td>3.771764e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>100.700000</td>\n",
        "      <td>105.000000</td>\n",
        "      <td>99.700000</td>\n",
        "      <td>96.410000</td>\n",
        "      <td>5341596.000000</td>\n",
        "      <td>5.647159e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>121.380000</td>\n",
        "      <td>129.690000</td>\n",
        "      <td>120.480000</td>\n",
        "      <td>111.490000</td>\n",
        "      <td>8214316.000000</td>\n",
        "      <td>1.004553e+09</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 208,
       "text": [
        "             open        high       close         low          volume  \\\n",
        "count   41.000000   41.000000   41.000000   41.000000       41.000000   \n",
        "mean    89.395122   93.562439   88.766098   84.699024  4057248.024390   \n",
        "std     19.217424   20.431722   20.383966   18.405163  1787692.336699   \n",
        "min     39.500000   39.500000   38.460000   38.460000  1427064.000000   \n",
        "25%     80.000000   82.990000   80.810000   76.020000  2480510.000000   \n",
        "50%     92.910000   97.990000   94.910000   91.410000  4015823.000000   \n",
        "75%    100.700000  105.000000   99.700000   96.410000  5341596.000000   \n",
        "max    121.380000  129.690000  120.480000  111.490000  8214316.000000   \n",
        "\n",
        "             amount  \n",
        "count  4.100000e+01  \n",
        "mean   3.837122e+08  \n",
        "std    2.247652e+08  \n",
        "min    5.516722e+07  \n",
        "25%    1.911126e+08  \n",
        "50%    3.771764e+08  \n",
        "75%    5.647159e+08  \n",
        "max    1.004553e+09  "
       ]
      }
     ],
     "prompt_number": 208
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ds = df.query('date > \"2015-03-10\"')\n",
      "ds.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>open</th>\n",
        "      <th>high</th>\n",
        "      <th>close</th>\n",
        "      <th>low</th>\n",
        "      <th>volume</th>\n",
        "      <th>amount</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>75.000000</td>\n",
        "      <td>75.000000</td>\n",
        "      <td>75.000000</td>\n",
        "      <td>75.000000</td>\n",
        "      <td>75.00000</td>\n",
        "      <td>7.500000e+01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>69.502267</td>\n",
        "      <td>72.619733</td>\n",
        "      <td>69.475200</td>\n",
        "      <td>66.510933</td>\n",
        "      <td>3831914.64000</td>\n",
        "      <td>3.219657e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>30.827059</td>\n",
        "      <td>32.796638</td>\n",
        "      <td>31.061161</td>\n",
        "      <td>28.895629</td>\n",
        "      <td>3079132.70105</td>\n",
        "      <td>2.647129e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>8.150000</td>\n",
        "      <td>9.780000</td>\n",
        "      <td>9.780000</td>\n",
        "      <td>8.150000</td>\n",
        "      <td>3900.00000</td>\n",
        "      <td>4.200300e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>49.235000</td>\n",
        "      <td>50.850000</td>\n",
        "      <td>46.230000</td>\n",
        "      <td>46.230000</td>\n",
        "      <td>1554158.50000</td>\n",
        "      <td>8.770203e+07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>73.010000</td>\n",
        "      <td>77.380000</td>\n",
        "      <td>73.430000</td>\n",
        "      <td>70.350000</td>\n",
        "      <td>3745346.00000</td>\n",
        "      <td>3.319090e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>93.520000</td>\n",
        "      <td>99.660000</td>\n",
        "      <td>96.425000</td>\n",
        "      <td>91.860000</td>\n",
        "      <td>5999366.00000</td>\n",
        "      <td>5.242804e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>121.380000</td>\n",
        "      <td>129.690000</td>\n",
        "      <td>120.480000</td>\n",
        "      <td>111.490000</td>\n",
        "      <td>15430822.00000</td>\n",
        "      <td>1.025160e+09</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 209,
       "text": [
        "             open        high       close         low          volume  \\\n",
        "count   75.000000   75.000000   75.000000   75.000000        75.00000   \n",
        "mean    69.502267   72.619733   69.475200   66.510933   3831914.64000   \n",
        "std     30.827059   32.796638   31.061161   28.895629   3079132.70105   \n",
        "min      8.150000    9.780000    9.780000    8.150000      3900.00000   \n",
        "25%     49.235000   50.850000   46.230000   46.230000   1554158.50000   \n",
        "50%     73.010000   77.380000   73.430000   70.350000   3745346.00000   \n",
        "75%     93.520000   99.660000   96.425000   91.860000   5999366.00000   \n",
        "max    121.380000  129.690000  120.480000  111.490000  15430822.00000   \n",
        "\n",
        "             amount  \n",
        "count  7.500000e+01  \n",
        "mean   3.219657e+08  \n",
        "std    2.647129e+08  \n",
        "min    4.200300e+04  \n",
        "25%    8.770203e+07  \n",
        "50%    3.319090e+08  \n",
        "75%    5.242804e+08  \n",
        "max    1.025160e+09  "
       ]
      }
     ],
     "prompt_number": 209
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ds = df.query('date > \"2014-07-10\"')\n",
      "ds.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>open</th>\n",
        "      <th>high</th>\n",
        "      <th>close</th>\n",
        "      <th>low</th>\n",
        "      <th>volume</th>\n",
        "      <th>amount</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>75.000000</td>\n",
        "      <td>75.000000</td>\n",
        "      <td>75.000000</td>\n",
        "      <td>75.000000</td>\n",
        "      <td>75.00000</td>\n",
        "      <td>7.500000e+01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>69.502267</td>\n",
        "      <td>72.619733</td>\n",
        "      <td>69.475200</td>\n",
        "      <td>66.510933</td>\n",
        "      <td>3831914.64000</td>\n",
        "      <td>3.219657e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>30.827059</td>\n",
        "      <td>32.796638</td>\n",
        "      <td>31.061161</td>\n",
        "      <td>28.895629</td>\n",
        "      <td>3079132.70105</td>\n",
        "      <td>2.647129e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>8.150000</td>\n",
        "      <td>9.780000</td>\n",
        "      <td>9.780000</td>\n",
        "      <td>8.150000</td>\n",
        "      <td>3900.00000</td>\n",
        "      <td>4.200300e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>49.235000</td>\n",
        "      <td>50.850000</td>\n",
        "      <td>46.230000</td>\n",
        "      <td>46.230000</td>\n",
        "      <td>1554158.50000</td>\n",
        "      <td>8.770203e+07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>73.010000</td>\n",
        "      <td>77.380000</td>\n",
        "      <td>73.430000</td>\n",
        "      <td>70.350000</td>\n",
        "      <td>3745346.00000</td>\n",
        "      <td>3.319090e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>93.520000</td>\n",
        "      <td>99.660000</td>\n",
        "      <td>96.425000</td>\n",
        "      <td>91.860000</td>\n",
        "      <td>5999366.00000</td>\n",
        "      <td>5.242804e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>121.380000</td>\n",
        "      <td>129.690000</td>\n",
        "      <td>120.480000</td>\n",
        "      <td>111.490000</td>\n",
        "      <td>15430822.00000</td>\n",
        "      <td>1.025160e+09</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 210,
       "text": [
        "             open        high       close         low          volume  \\\n",
        "count   75.000000   75.000000   75.000000   75.000000        75.00000   \n",
        "mean    69.502267   72.619733   69.475200   66.510933   3831914.64000   \n",
        "std     30.827059   32.796638   31.061161   28.895629   3079132.70105   \n",
        "min      8.150000    9.780000    9.780000    8.150000      3900.00000   \n",
        "25%     49.235000   50.850000   46.230000   46.230000   1554158.50000   \n",
        "50%     73.010000   77.380000   73.430000   70.350000   3745346.00000   \n",
        "75%     93.520000   99.660000   96.425000   91.860000   5999366.00000   \n",
        "max    121.380000  129.690000  120.480000  111.490000  15430822.00000   \n",
        "\n",
        "             amount  \n",
        "count  7.500000e+01  \n",
        "mean   3.219657e+08  \n",
        "std    2.647129e+08  \n",
        "min    4.200300e+04  \n",
        "25%    8.770203e+07  \n",
        "50%    3.319090e+08  \n",
        "75%    5.242804e+08  \n",
        "max    1.025160e+09  "
       ]
      }
     ],
     "prompt_number": 210
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "close = pd.DataFrame(df['close'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 227
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(close)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 228,
       "text": [
        "pandas.core.frame.DataFrame"
       ]
      }
     ],
     "prompt_number": 228
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m1m = pd.rolling_mean(close, window=30, min_periods=1, center=True)\n",
      "m2m = pd.rolling_mean(close, window=60, min_periods=1, center=True)\n",
      "m4m = pd.rolling_mean(close, window=120, min_periods=1, center=True)\n",
      "wdf = close.join(m1m, how='left', rsuffix='_m1') \\\n",
      ".join(m2m, how='left',  rsuffix='_m2') \\\n",
      ".join(m4m, how='left', rsuffix='_m4') \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 261
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wdf.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>close</th>\n",
        "      <th>close_m1</th>\n",
        "      <th>close_m2</th>\n",
        "      <th>close_m4</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2015-07-07</th>\n",
        "      <td>38.46</td>\n",
        "      <td>67.198000</td>\n",
        "      <td>85.749667</td>\n",
        "      <td>81.661500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-07-06</th>\n",
        "      <td>42.73</td>\n",
        "      <td>68.395625</td>\n",
        "      <td>86.126774</td>\n",
        "      <td>80.932131</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-07-03</th>\n",
        "      <td>47.48</td>\n",
        "      <td>70.017059</td>\n",
        "      <td>86.490000</td>\n",
        "      <td>80.171935</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-07-02</th>\n",
        "      <td>52.75</td>\n",
        "      <td>71.607778</td>\n",
        "      <td>86.832424</td>\n",
        "      <td>79.386984</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-07-01</th>\n",
        "      <td>58.61</td>\n",
        "      <td>73.086316</td>\n",
        "      <td>87.210000</td>\n",
        "      <td>78.582813</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 262,
       "text": [
        "            close   close_m1   close_m2   close_m4\n",
        "date                                              \n",
        "2015-07-07  38.46  67.198000  85.749667  81.661500\n",
        "2015-07-06  42.73  68.395625  86.126774  80.932131\n",
        "2015-07-03  47.48  70.017059  86.490000  80.171935\n",
        "2015-07-02  52.75  71.607778  86.832424  79.386984\n",
        "2015-07-01  58.61  73.086316  87.210000  78.582813"
       ]
      }
     ],
     "prompt_number": 262
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print wdf.plot(title=id)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Axes(0.125,0.2;0.775x0.7)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RzJ8/n+TkZL799ttQh9Ik5O6sQB/838w85ToGXr/knxvz+MeGPGaNSuJ3gxOJ\nt/68MI8wmdB+cxfa7bMQ9tBMNd8aiMuuCUyp8YuBb4OSInnu0hTWH3bwt2+O4PT66y7jhKqms2xM\ncLNQCUI5YwcOHGDFihW0b1+zu+fZQDor0F+fF/i2f4oGz1KXjwe+OESOw8u8y1JJi699oJzo2RfR\nrXdThHvWEJoB7drp1abUOK5dhIknL+pMlMXIPZ8eJLus7k6/qqrp9KkEEWKtecnR4x588EFmz56N\nqTmXr2xG8u2/IzIGI/oPOel+h8rc3PfZQfonRjB7dNIpRzgrZ8Zk0JhxXiJXpMUy+4tDbMutfeBg\nfauapJR4PToVDj9FBT5yj3ir3fKOeinM81JU4KO0yEd5qR9HuR9nhR9XlY7brePzSnS/PGuuVlQv\nJuDFF19slHLuuOOOBu1/Niw5unz5ciwWC+PGjWvQsbcWsiAXuXMb2t9eq3Mfvy75IaeCl9flcvO5\nCYzrGtWMESoXdY+mfaSJZ9fkcPM5NT9/XZf4ErviPX8S3nffQ7/mt3jcUFnhp9Kh46zUqXIGbkKU\nY7YILBaB2SJ+Uc6xm18e+1n+4vGxbX6QEkxmQVi4ICxcw2wWmMwCo0kcK1/DbAk8FoITbic81gL3\nRpPAbA7Nd3mVIGj4ib2xtPYlRysqKnjqqadYvHhxo382LYXcsRXRZwDCXH2CuYJKL5uPVrLlaCXb\nciuJjTBx38gk+rav/9xLypnzeSUV5X5iqkzMSO5A5kYn/9lcRLhmwIjA75NICUajwGC8CKM1D+M3\n+ZjjorBGatjsBtp3NBEWLgmnitj4WCp8dbdp1JfUJR6PxFWl46oK/OzzSrweSZVTUlbsxe2WwfgC\nN4nUOeHx8asaiWYQ2OwakXYD9ujALcyiN8IneHIqQYRQYy05+sorr3DPPfcwaNAg5syZQ/fu3YNL\njj766KPVXpubm3vKBHGyJUctFguFhYUAPPfcc1x11VXVyjtbLq2DdmyFvgNx+/TAzKRHK9l8tJIy\nt58BiREM7Ghl+sAE4iLOzuq1lsTt0ikt9lNa7KOkyI+jzI/HI4m0BU70cVFGLhgSxa6yKrbmV5JZ\n5KRzrIUhyZGc0ymS9pFm5MEC9BfuR5v1DPLAHuT/VsOuHwOz3IZFUB4Whnj8FYTJfEaxCk1gCRNY\nwjSiznB+RCklbpfEUebHUa5TVuzn8H4P61ZXYrYI7FEatigDNruBSLtGpM2AwXjqVTrrQyWIEGqN\nS46euGBowhujAAAgAElEQVTQmjVrOHr0KG+99RYQWPnv97//PX/84x+5/fbbG/V9Q0HqOgV7D/BK\nylVk/XcP3WItDOhg5a7hHegWG4amuqeeNikl3mPfqH1eidfLsfvA4+M/ez2SygodR5kfXZdExxiJ\njjPQpZuZqGgD4VatxokwBQvjicbl09l6tJL12RX8J7OI6HAj5yVHMWjEFXR98Ha03v0QQ0Yjbvoj\nWG0ITUMsfAL/ulWIkReH6JOpSYifq6pObK+3WiPJyy0LtIWU6eTleNm7009FhR6o4jIJjMZANZbJ\nFKiqCvwcuLdGatijDUTa624rUwkihFrrkqPHHy9evBi/3x/cftlllzF37txg8mrtjvy0n7lpN3Nx\nUhT3joshwqQanRtCSomzUqe8pIqiQjdVTh1nhU5lhY6z0g/H6ulNJoHR/POJy2gM1NebzIIIq0aH\nTiZsdgNh4aJB34rDjBrndbJxXicbfl3yU2EV67MreN7UH/f4foxMiWJkFzvdIi3Bci0TplD5+vPI\nEReesjtzqGmaINJmINJmgE7Vn/P7qydan1fi8xFIyL5A4i3M97H/JzcVFTq/vaP2WgWVIEKotS85\nGhNT/drZYDAQFRVFRETrr4ffU+Ti8U1VTDXncFHfk/deaquklPh94PEEev+4qgLtAY5y/Vh1iB+T\nSRAd68USphNu1eiQbMIaqRERqWG2NN8J2KAJ0hIiSEuI4OZzEzhY6uabA+U88+0RLEaNmwbEM7Cj\nFWP6OWA0wo8bIWNws8XX2AwGgcEgAkuAnIKun2SwoVpRTmkuLf33cDy+zHwnT319hN/nr2TY0H6I\nc4eFOrQamuOz9PkkpUU+igr8VFb48XoC3zw9x+4DjaeBqwCzSWAJ1wLtAVGBagt7lIbJrLXo37uU\nkg1HKnhzcwEx4Ub+cH4K7bd8hfz2fxjueSLU4Z2UWlFOUZrZttxKnv02h7uHJtDvqS9h2vWhDqnZ\nSCkpK/aTn+ejINdLWYkfe5SBuHgj8e1NmC0/V/2Yj1UJaYbW3Q4jhGBIso2BHSP5395SHvhkN6nR\nXbm6Anof2ovo3LYnUVQJoo1RS47WbcPhMp79Nof7RiaRXroPPTEZYY0MdVhNyueVFOQFBoLlH/Vh\ntgjiE030SAsjNt6I0di6E0B9GTTBJT1imJSRzNKt2TzX61qSVuUwbWJStTUr2poWkSCWLl3KN998\ngxCCzp07M2PGDNxuN/Pnz6ewsJD4+HhmzpyJ1WoNdaitnlpytHbbcit5bs1RZo1KIi0hAv27LYi0\njFCH1SBSSvzHBmkhOf7PiT8iAd0PhXk+jmZ7KMr3ER1nJDHJRK++YURY23ZDvNmocVnPGC7saOR/\n81/h0ZWJDOhgZWr/+GrzaZ1NPB5Pnc+FPEHk5+fz5ZdfMn/+fEwmE/Pnz2fNmjVkZ2eTkZHBpEmT\nWLZsGcuWLWPq1JMvaK4op+NgqZtnv81hzsXd6W4PfGOWO7ehTWp5f29ul05ZiZ/D+8opKa4KjAKu\n1IPtAsdH3wo49k+AQAQfCwEx7Qx06GTmnPMiMIVolG5LZo60cYmpgDG9nCz1RfPHj/ZjtxjoaDfT\n0Waio80cuNnNJFhNGLSaV1rS54XcbGTOYUT6OQjrqRc7ai5er5f9+/eze/duDh8+XGO81HEhTxAR\nEREYDAbcbjeapuF2u4mNjWXZsmXMnTsXgDFjxjB37lyVIJRGV+T08tjKw/xmYALnJNlxOBzIkiLI\nPgghnkzveDfRwjwfhfk+igt8+H0QFWMgLkFgiwqMAo6wBqZtOBvaBFoS0SuD8N3buOGK/vw6ox35\nFV5yHB5yHB6OlHv44UgFOQ4PJVV+EiJNwcTRweClw7cfkrZjFcbYdhAeAVvWI353b7PF7na7cTgc\nlJeX43A4qKioCP7scDioqqoiKSmJnj17csEFF9RZTsgTRGRkJJdffjkzZszAbDbTv39/MjIyKCsr\nIzo6MBVyVFQUZWVlZ/Q+NtvJs7fBYAj26W/pVKyNw+n189iqbC7pEcOY1MD8PdLnRV/0FOKSK2tM\nr9GYjo+OdTl1qqp0XE557F7HVaVTVRWYpsFsFsQlGIlvb6R3vzAijg0Ma8k9g84WIi0Dfem/ADBq\nInD1YK85wtrj18l1BJLHkT0H2bPtRz5LGEb4ry7lvlGdiNb86HNuR/6UieiZ3iSxSinJyclh586d\n7N27F7/fj81mq3ZLTU3Fbrdjs9mwWq0nncHhuJAniNzcXFasWMHLL79MREQE8+bN4+uvv662T12D\nYzIzM8nMzAw+njJlyikTQV1a8onsl1pzrKf7+2lsVV4/T375E/062Ll5aBeEEJjNZozL3kaPjsV6\n7W8afaCUrkvyc90c3l/F4QNOkBARaSA8wkCE1UhkpJmE9gbCrQYirAYiIgwYTbXHYDabW8xneSqt\nNVbZfzBlLz9BpEFDRJy8/TPWrtPtg3/j/mwpEX+YjaHvufxrYw73fHaQORd2o+fU3+P+v38S+cQr\njfZ3ZTab8fv9bNu2je3bt2M0GsnIyGDs2LHY7fYGT7WxZMmS4M/p6emkp6eHPkHs27ePXr16BX8p\nxxtRo6OjKS0tJTo6mpKSEqKias6QefwgTnS636pa0zcyFeuZcft0Hl+dTXyEiVsGxFJRUQGAZcs6\nPJvXoT3wHBWVtU8dfTqcFX4O7vNweL+HsPDAyOBhY62BEbA16MduXqpcgKv2Mlvi51qXVh1rSg8c\nm9Yj+tc9aE5WOtBfnw9VlWizn8MVEwcVFVzZy07nSI2HPtvN5N7dmKgZKf9sGdr5F51RjF6vlz17\n9vDTTz+Rl5dHz549GT9+PAkJCcGkcPxvur5sNhtTpkypsT3kCaJjx47897//xePxYDKZ2LZtG927\ndycsLIxVq1YxefJkVq9ezeDBrXdUo9JyVHr8PLcmhyiLgT+elxicT0nmZlP11stof34cEXHmXVul\nlBQV+Nm700VpsZ+kLmaGjYnEFtW2ewm1NqJ3BnLntjoThDywG/2VpxDnDkNcOQ1hrH5KHZQUybPj\nU3j+uxx+SL+FP614mcSBIxDhDZttQEpJXl4eWVlZ7N69m8TERAYOHEhiYiJGY9OdxkOeIFJSUhg1\nahT3338/QghSU1O58MILcblczJ8/n5UrVwa7uSpKQxU5vWTlV7GjwElWQRVHHR6Gd7bzh/MSgz1P\npJTo/15E+BU34EnqckbvJ6Uk/6iP3VkuPG5J9zQLg0ZYMajG41ZJ9M5Af/vvNbZLKZGrP0F+8G+0\nG2YgBg6vs4yESBOPXdCZD3YUc1/ZrVz7+feMnzga0yn+JtxuN2VlZeTk5JCZmYnX6yU9PZ3rr78+\n2K7Q1FdmbWKqjfpo1ZfBLVhzxiqlJLvcQ1Z+FVkFTnYUVOH0+ANz8MSHk54QQdeYsBr/MfUN3yA/\n/j+innqVCqfztN+7INfHrh9d6H5Jj/QwOiSZELV0f2wM6m+gafwyVunzod99A9oT/0DY7IFtbhfy\nXy8jsw8E1hVvX/s0FbXZ/933vLGjgty4LkztF0ffGImjvJzy8nLKysqq3eu6jt1uJz4+nrS0NJKT\nk6u1K6ipNhTlFHIdHtYedrCjoIodBVVEmDTS4sPpEx/B1elxJNnNJ52WW7qcyCX/RLvtPkQdU67X\nxu0KTEhXXha4Ly3yoUvo1TeMDsmmRpmLXwk9YTRC9z6waxsMOh95NBv9739FpPRAm/UswtKwnm4p\n/dO5Y8kMvh4/jR8++YJ1aAiLlfBIGzFRUSTGd2JovwG0j4smPDw85H9HKkEordb+EhdzvzrMsE42\nRnWx8/vB7Ru8cI/88F1E+gBE97Tan5eSshI/ZSWBBWocZTrlZX6kBFuUhj3KQFSMgeQUM7Fxhia7\nYlBC53g7hJQg//0K4sqbEOdf1KCTt8/nY+/evWRmZlKUMoDeVSWMvu4atAg7h0rdHDx221rs5uC+\nCqwmJ52jLXSJttCrXRiDkiIxG5p/QKNKEEqrdDw5/G5we0Z0tjf49dLrQX77BXLdKrRHXqr5vJTk\nHvGyO8uNzyuJbWfEFqWR0NGEPcqAJaxhaxMorZfonYG+7G3I2oI285EGTeBXVFREZmYmO3fuJD4+\nnr59+5JqcmOoKkWLjQUgOtFIRuLP3Wh1KSmo9HKo1MPBUjef7C5l4fpchne2c1H3KHrEhTf6MdZF\nJQil1TmT5CBdTuS3/0N+thQ6dUW7cy7C9nMX6uOJYed2FwaDoEcfC4lJqsqoTUtOQUyeGrhqqKOH\nm8/no7KyMnhzOBzs2bMHh8NBnz59uPbaa4Nd9aUJ9DderPPtNCFoH2mmfaSZwcmRXN03joJKL6v2\nl/G3r4/QOcrCdRntGNgMY0tUglBalUOl7gYnB+nzwY4tyO9WIn/cCGkD0P74EKJL9W+Chfluflhb\ngdcr6TMgnIREo0oMChLI7XsejsNHggnA6XRWSwh+v5+IiAisVitWq5WIiAgGDRpESkpKzRHLXbpB\nWQmypAgRE1evGOKtJq7p247JabF8sbeMp745Qo+dZfz23LgmXQ9dJQil1Tjq8DD3q8Pccm7CKZOD\nlBIO7EGuX4X8/muIT0QMHYt23W3B3ijHuV06WVurKMp30DPdTKcUs2pLUCgrKyMrK4usrCzCw8OJ\niYkJJoB27doFf7ZarVgslnp/mRCaAZHWH5m1GTHiwgbFZDJoXNozhgu7RbF8TwUzPz7ArYPaMyql\n4dWs9aESRAMdX5NZ1/Xgva7rwaU4hRAYjcZ6zXNytpOVDqo+eg85bBwiLuGMyiqo9DLny0Nc269d\ncN6kWt+zIBe5fjVy/Srw+xFDx6Dd/xQioWY3Pl2XHNjjYXeWi06pZiZenYDL3XgjqJXWx+v1smvX\nLjIzMyksLKR3795MnjyZuLj6fdOvtz4DIHMzNDBBHGcyaEwblES/dibmrz3KhuwKfj+kPVZz4w7E\nPOsTRGCOfD9erxev14vH48HpdOJ0OnG73fj9fvx+P5qmUV5ejsvlCt7cbjcul6taMpBSIoRA07Rq\n90Dweb/fj9FoxGw2B+b3MRoxmUxYLBbCw8MJDw/HbDZjMBgwGo0YDIbgra7HZrOZiIiIVlXlITd8\ni3f91+hfLEcMGYm47BpEdMP/o5W7fMz58jATe8Uyvkc0+tsLkUX5iGHjEAPOA68XufFb5HerIDcb\nMeh8tJvvhK696vy8igt8bN/oxBymMXxcJDa7AZNZw+U+w4NWWh0pJfn5+WRlZbFnz55gY3LXrl2b\nbJSySD8H/f03kbofoZ3+Sb1HXDjzL03h9Y353P3JAe49P4nucY23wNFZlyDeeecdvF4vPp8veK9p\nWvAkffxEGxERgcViCZ6AIyIiiI2NJSwsrNrNYrFgMBiqJYNTnaSllHg8HjweTzAxeb1e3G43VVVV\nVFVV4fF4gsnp+M3n89X6s9/vx+Vy4ff7iY6OJiEhAbvdTlxcHO3atcNms7XIxCE3rSX8+t/iSu6K\n/PS/6I/ehfbgfERsu3qXcXzepKGdIpmUFot0VSHXr0ZMmY5c8wXynb8HKon7DEBefBX+Xv2Rwojf\nL9HL9cC9n8C9HrjPy/FSmOcjfUA4HTqpBui2yul0smvXLrKysvB6vfTp04fp06c3y9W/iI0HWzQc\n3AepPc6oLItRY8Z5iXxzoJxHVh7m6vQ4xqbasYed+en9rBtJvXXrVkwmEyaTKZgU6vMLbw2jPV0u\nF6WlpVRVVXHkyBEKCwspKirC5/ORkJBAXFwchgYM9mpK0uOGrz/FMv4KPH49sG3PDigtgoEj6nVS\nllLy3eEKTJpgUJIVIQR6Tjau3ELcXfrirpK4qvzBVdQCi+UIhABNVH98fCEdTQhMFkFMnAHtF+0M\nZrP5pKtrtSQq1jNTUlJCdnY2Xbt2rTZKuTnPA/p//gmOMsQtd53Wl5TaYj3q8PDPTfn8mOckwWqi\nX/sI+raPoG9CBJGWus8NdY2kPusSRFucaqOyspKCggKKi4vRdT2Ekf1M7t8N+TmEjboYtztQbyN1\nHbnqE0RKD0TXnid9fXmlmy05DtzCyND2NlyVOs5KHVeFD7PBT3hMOOERGmERgYVyhKh7Wvj6slgs\nwVhbOhXrmYmIiKBbt25YfjESulmnhqlyoj/7AKLfQLTJNzT49SeL1adL9ha72J7rZHu+k10FVXSw\nBRJGv/ZW+iSEV2uvUFNtnMWO96RISUkJdShB/jUfIYZfSNTw4dXntklORH9mFr4RY/HYEvC4dDzF\nZXjyinAWlXGwwkC2sOM1hNPF5yY8LBqjz0xKZyNxMZKoebdjfnRBjZ5IjaE1f0loyVpTrM1JhEeg\n3fkw+tOz0G1RaBdc3mhlGzVBr3bh9GoXztXE4fVL9hRVsT3PyYe7inlujYtRKXauz2hHdHjdaUAl\nCKXRycoK2LsTcdtfgEBvodIiP4X5PkqLYykdNg//ah2LfhCTqxSf9JEXZuewsQO2cB892pnp0SWW\nCEcuke8+iOWR+QhLGHLjGvROnZskOShKKAh7NNrMR9Cfuh/dakMbOqZJ3sdkEIFJKxMimAJUuP38\nJ7OIP67Yz6TeMdypriCU5iK3rsfbexCFuQa2bisk94iL8AiN+EQjySlm0gdYkFu+5Vs9lo8ddkq8\ncGG3KKZ1jSYh8sRBP6no61KRX3yImDAFuXEtYtCIkB2XojQFEZeAdudc9OceQFptiH4Dm/w9Iy0G\nbjk3gUt6RPPWloI691MJQjlj0uWEvBz0pFTyjvo5tCeOkna3EHfYQ0o3G737mQgL/7mjwL5iFw/n\nJZOeEM5150QzoIM1uDbDL4krbkT/6z3IoWOQP25C+/Vvm+uwFKXZiKTOaH94AP2lx9H+8ECdk0c2\ntg42M38ZmVTn8ypBKGfEX1ZK+SsLKTB25mCCgXCzn84HvmDgXb/HZI/EZousVv9cUuXjydXZ/H5w\ne0Z0OXVVkUjogDhvDPoLj0CXbgh7dFMejqKEjOjWG236TPSFTwZWNjzDxasag0oQSr1IKXFW6sHp\nrh2lfhzFHiodPsJTbiI2JYZB0cVE7VgD/Tui2WtOaubx6zy5OpsLu0XXKzkcJyZMQa79EjFuQmMe\nkqK0OKLvQMS1t6K/8AjafX9FtGsffE7u2Ir8KRNt0vXNFo9KEEqd3C6d/bvdFOT6cJT7MZsFtigD\ntigDCdYKuv7vZSLPOxfTJZOPvcIKvWrvriel5KV1ucRbTVzbr2GjqYUtCu3ev0Ji3ZfCinK20M4b\njV7hQJ//MNpf/oawRyM3r0N/6yXweZHjJjZbRw2VIJQaKiv87N3pJuewl6TOJtIHhGOLMmAyH1vD\nOXs/+vOPIH51Hdqo8fUq8+2theQ4PDxxYefTGq8gOqU2+DWK0lppF0xEryhDf2EuYuTFyI8Wo935\nMPLzZciNaxBjLm2WOFpEgqisrOSVV14hOzsbgBkzZtChQwfmz59PYWEh8fHxzJw5E6vVeoqSlDNR\n4fCzO8tFXo6PLt3MjL3UhiWs+ih0uXcn+stPIK77HdrgkfUq94MdxXx32MFfL+qMxagmMVSU+hC/\nuh4qypEr/hNok+jQCQaPRP/iQ2imBNEiRlK/9NJL9OnTh3HjxuH3+3G73bz//vvYbDYmTZrEsmXL\nqKysZOrUqacsqy2OpD5TFeV+fspyUZDrI7WHhdQeZkzmmidymbUZ/dXn0H4zs95d8dYddfPqusP8\n7eIuxFubbt76xtCW/waakor19EkpwedFmMyBx14v+j3T0OYuwN45pdFirWskdci/zjmdTnbu3Mm4\nceMAghPn/fDDD4wePRqAMWPGsGHDhlCGeVaqKPez6btK1nxVQaTdwLgJdnqmh9WeHDZ9h/7aPLTb\nZ9U7OWw5Wskr3x3m4XGdWnxyUJSWSAgRTA4AwmRCnHMecsM3zfL+Ia9iys/Px263s3DhQg4ePEhq\naio333wzZWVlREcHujRGRUVRVlYW4kjPHh6Pzk8/ujhyyEvXnhb6DYrAZKq7XUBf8yVy6VuB5Tm7\n1G893sNlbuatyWHu+O50tqnZUhWlsYjBowJrZF91Y5O/V8gThN/vZ//+/fzmN7+he/fuvPHGGyxb\ntqzaPmo65jPncQe6qJYU+9i7001ikokxl9RsY/glff1q5IfvoP35CUSH5Hq9V7nLx+Orspl2Tjz9\nO9pb1CW7orR6vTOgKB9/7hGwNm1vppAniLi4OGJjY+nevTsAQ4cOZenSpURHR1NaWkp0dDQlJSXB\nBb9PlJmZSWZmZvDxlClTsJ3mQt5ms/m0X9vcTharq8pPWYmXslIvZSU+ykq9lJd48fslUTEmomJM\njLs0gdh25lpffyIpJY5P/o+IPzyAqWf9RnZ6/DpPf7WLsT3imDyg01nzubY0Ktam0VpidQ4bg75+\nNbZGHBOxZMmS4M/p6emkp6eHPkFER0fTrl07cnJy6NixI9u2baNTp0506tSJVatWMXnyZFavXs3g\nwYNrvPb4QZzodL+ttrTGqZOpMd23w8+BvR6OHPSg+yEySsNmD4xX6NrTSKTdQlj4iQsduXE4Tj39\nstyzA93no6pTV1z1+GyklLy47ihWI0xJi8LhcLTqz7UlU7E2jdYSqxwwDN87f6dq7MRGqWGx2WxM\nmTKlxvaQJwiAW265hQULFuDz+Wjfvj0zZsxA13Xmz5/PypUrg91clZ9JKck/6mP/bjdlJX46pZoZ\nPi4Sa6TWaFVy8pvPESMvqnd572cVc6DEzV8v7oKmqgUVpel06w0eNxzaB/VsFzwdLSJBpKSk8Ne/\n/rXG9oceeigE0bRsPq/kpywHO7Y5MJoEqT0sDD7fhMHQuCdk6axEbl6HdtVN9dr/u8MOVuwq4ZlL\nuhCmxjooSpMSmobx/IvwrFtZ744jp0P9T24l3C6dndur+HJFOXk5bvoPiWDkRZF0SjU3enIAAt3o\n0voj7DEn3c/j1/libykL1+cya3QScRGqO6uiNAfzyIuQ33+N9Pub7D1axBWEEuDzBibEc1bqVB27\nP/Fxx84mzr8gksSO0U1eTyq/+Rxtct0DE/MrvHy6u4Qv9pbRPS6M2aOS6BEX3qQxKYryM0PHThCX\nAFlboInWkFAJIgTKS/0U5fsCJ3/nz8lA90vCrRoRx27hVo2YdiYiIjSstp/nQmpq8tBecJRBnwHV\nt0vJ9jwnH+0qISvfydiuUfzt4i50tJ+6R5SiKI1PDBuLXLeyyRYZUgmiGTkr/eza7qIgz0dikomI\nyJ8TQLhVw2wRLWLMh/zmc8SICxFaYFFzp9fPqv3lrNhVgkEILusVzczhHQk3qRpKRQklMWgk+tJ/\nIauciPCIRi9fJYhm4HHr7M5yc/iAh9QeZsYOtJ905HIoSWcF8vuv0R55CYDP95Ty5uZ8MhKt3D4k\nkfSE8BaRxBRFITDtd8++yE1rESMubPTyVYJoQrpfsn+Pmz073HRINtU6O2pLI9d8GVi0JDqOzHwn\nb28t4OnxKSSpaiRFaZG0YePQV66AJkgQLfts1Ur5fZL9u9189XE5hXk+ho+NJGNQRMtPDrof+dVH\niAsup7TKx3Pf5nDn0A4qOShKS5YxGHIOIfOPNnrR6gqiEfl9gSuGfbvcRMcaGDjMSky7VvQRb/sB\nbFHoKT159qvDXNAtioFJNZcOVRSl5RAmU6Cx+uvPEFff3Khlt+yvtK2ElJLDBzys/KSckkI/Q0dH\nMmRkZOtKDoD+1UeIcRN5e2sBmoBf92sX6pAURakHMXI8cu2XSK+3UcttXWewFsLj1ikv9VNW6qe8\n1E9JoR+zRXDOUCtx8a3zI5VHDkLOYb6K68farFKeGd8Fg6YaoxWlNRCJSZCcgtz8HWLIqEYrt3We\nzZqRs1IPzIpa4g8mBZ9HYo82YI82ENvOSGoPC1Exhlbdu0d+uZys4Vfy1tZinrioM/Yw9aehKK2J\nNmo8+sqPQSWIpuf1SHZsq+JotpeYuEAySE4xkx5jIMLaeBPitQSytIicH3fw7JCLuXtERzpFWUId\nkqIoDTXgPHj3H8ijhwPrVzcClSBqcTTbw4+bqmjf0cS4y2y1LsF5Nin5/GMe7z+dX2fEM6CDNdTh\nKIpyGoTRhBhxYaCx+tpbG6XMs/vM10Aej86mdZXs2OrinKFWMgZFnPXJwVFSxqOlyYzuHselPU8+\nMZ+iKC2bGHkxcu1XyJ9+bJTyzu6zXwMczXax+lMHJpNg1Hgb7RLO/osrt0/nic920yfMw6+HdA51\nOIqinCERn4h2693oi55G/2gxUj+zmV7bfIJwu3S2rHey/pti+g+JoN/ACIzGs6d9oS5ev+Sp1YdJ\nyNvH9Iv6nFVtKorSlol+g9AemIfcsQX9+bnIspLTLqvNJgipSw7scbPqUwcms2DCVYkkJLaNtQz8\numTe2hyMxfn80XIAQyM1aCmK0jKI2HZodz+O6JaG/thMZNaW0yrn7K9H+QUpJblHvOzc7sJsEQwb\nE4k92oDJrOE69TLNrZ4uJS+tP4rT7WXW969g+tPsUIekKEoTEAYDYtL1yJ7p6P+cjxh+IeJX1yEM\nhnqXcdYnCCklripJeZkfR5mfnENepIQ+A8JJSDS2maoVKSUHSt0szSqmoNLLHN8mzN16Ijo33XKF\niqKEnkjrj/bQfPTX5yOfewDt1nsQsfWbJeGsSxCF+V4cZTqOMn8wKRgMAluUAVuUgR59LCQmmdpE\nYpBScrDUzZpDDr496MCnS87vYuO2vpGY576PuO9voQ5RUZRmIOwxaHfORX76X/Qn7kab9idExuBT\nvq5FJAhd17n//vuJjY3l/vvvp6Kigvnz51NYWEh8fDwzZ87Eaq1f//yd213YjyWDjp1N2OyGFj+L\namM7VOZmzcFyvj3owO3TGdHFzt0jOtA9NgwhBPoH70DGYESH5FCHqihKMxGahrjsGmSPdPRFT6NN\nn4lI63/S17SIBPHxxx+TnJxMVVUVAMuWLSMjI4NJkyaxbNkyli1bxtSpda+PfKLzL7A1ZagtVrnb\nzxqSdAAAACAASURBVKc/lfDNwXIqPTojuti4Y1gHesaFVbtako4y5MqP0R6cF8JoFUUJFdGjD+Lq\nm9E/fBetd8ZJa1NC/tW6qKiIzZs3M27cOKSUAPzwww+MHj0agDFjxrBhw4ZQhtiiVXr8vLutgBnL\n95FX6WXGkEReu6Ib0we2p1e76qu/SSmRH76LGDIS0a59CKNWFCWUxJCRgXXnd2476X4hv4J48803\nueGGG4JXDwBlZWVER0cDEBUVRVlZWajCa7GcXj8f7Szho10lDEyy8uz4LiTa6l7YRx7ai/7eq+Cq\nQrtrbvMFqihKiyM0A2LitcGriLqENEFs3LgRu91OamoqmZmZte5zssufzMzMaq+bMmUKNtvpVTGZ\nzebTfm1zcnr8/HtLHv/ZksOQzlG8eEUfOkWH1bm/Xl6Ka/HreH9YQ/g1t2AedxlCq383tzPVWj5X\nULE2FRVr0zjTWOUFl+H4+D+EH9wNSUksWbIk+Fx6ejrp6emhTRC7du1i48aN/7+9M4+Psjr7/vee\nzEwy2TNZScgKIQkJWVhCFlkMiIi0Kq3YwqO2LtVaafWpb327fFr7tFqXV+2jgtqColjZFAPKvkYQ\nEhIIkAVCFrKRjewJIcvM3O8fcW4TCIrKzCTkfD+ffJKZyT33b87c97nOdZ3rXIfc3Fz6+vq4dOkS\nr7/+Om5ubrS2tuLu7k5LSwtubm5DHm/+EAPp6Oj4TlpcXFy+87HWwGCS2V3Syvq8RqYEuvGPW4K+\n3Aq0j46OKzcJkQ0G5ANbkbduRJo+C+mvy+l1cqb3YpdVdQ/3dh2I0GoZhFbLcD20ygvu5uL6d9Cn\nzGbx4sVXvG5TA7FkyRKWLFkCQGFhIVu2bGHZsmV88MEHHDhwgDvvvJOMjAymTfvmdKwbBZMs09Zt\n5MLFvv6frj4uXDSQW3sRT0c1f745kLhg76+9MOSCXEzrV4KHJ6r/8xySv6izJBAIrkRKnIH82fqr\nvm7zOYiBmMNJd955J6+++ir79+9X0lxvJDp7jZQ2d39lBC4avjQEfTReNOCoUeHlpMHHSf3lbw2P\nJfoR7aP72pCb3FCLacMqqKlEtfhBiEscFes9BALBd0NS2aH6779d/XXZnDp0g1BTU/OdjrOGa2k0\nyewqaWVtXiNjXbX4OGnwHvSjxttRg73665PLhtIq11ZjevFppHl3Ic29A0kzPOpKjTaX3VoIrZZh\ntGr19/cf8vlh5UHcyBQ0dPHvnHqcNCr+Jy2QEI+rTyx/W2STCdOaN5B+8FNUaQuv2/sKBILRjTAQ\nFqat28Dq3AucrLvIzxN8uCnY5bqHfeRDu8FgQJp923V9X4FAMLoRBsJCyLLMvrI23jtxgVkhrryx\nMBRHzfVPL5Vbm5E/WYPqt3+zavqqQCC48REGwgJ09hpZkVVHdXsvz9wcSJj++oWTLkdevxJpxjyk\nsaEWO4dAIBid2LzUxo1GUeMlntxWjpuDHf9vfrBljcPxw8iVZUgL77HYOQQCwehFeBDXkd0lraw5\ncYHHpvuRFGjZ1ZhyWwum/7yF6rE/IGntLXougUAwOhEG4jpgNMmsOt5Abs1FnpsXxFhXy3bYsixj\neu/1/tDSuEiLnksgEIxehIH4jhhMMqXN3eTVd3GksgNXeztemh+Ms9byE8W9ez+DthakhT+x+LkE\nAsHoRRiIa2SgQciv7+LMhUv4uWiI8XVkcYwnUwOcsVNZftWyXHee7vUrUT31HJJafH0CgcByiB7m\na+gzymw720Ju7cWvDIKPI/PD3fltqj8u9tZNK5V7ejC99Ty6xQ/SK+orCQQCCyMMxFVo7zbw/MHz\nOKhVzA93579T/XG1skG4HHntW0gBIWjn/oDezk6bahEIBDc+wkAMQXVbD387UE1KkAv3xnujGgYF\n70xf7EEuO4vqD/9PFOATCARWQRiIy8g538lrR2q5L8GbuePcbapFlmWoKkM+lY2897P+0t0OOptq\nEggEowdhIL7EJMusO9XIzpJW/u/MACb6ONpEh9zTDadPIuflIJ/KBq0DUuw0VE/8VezrIBAIrIow\nEEBHj5F/HCym/VIvL98Wgl5n3WaRmxqQT31pEEoKISS83yjccieSX4BVtQgEAoGZUW8gChq6ePWL\nGmaN9+SnqX6orZGqajJCWdFXRqGtBWnSFFSpc+Dhp5AcnSyuQSAQCL6JUWsgjCaZjQVNbD/bwrKk\nMdwcOcYqG4XIfX2YXv8faG1GSkhCde+vIDRcVGIVCATDjlFpIGrae/nnkVrs7SReuS0ET0fr7L4m\nm4yYVr4MOkdUv3kGyU4YBYFAMHwZVQbCaJLZUdy/5edPJnmyYIKH1VJYZVlG/uBNuHQR1bI/C+Mg\nEAiGPTesgZBlmQsXDRQ3XeJsUzfFTZcobe4hzMOeF+YFE+CqtaoW+aPVyFXn+jf2GSb7RQsEAsHX\nYXMD0djYyPLly2lra0OSJObMmcOCBQvo7Ozk1VdfpbGxEW9vb5588kmcnL558nZ9XqNiFFTABC8d\n4Z4OLI7xYryng1WK6Q1ElmXkje8gnzmF6r//huRgm/RZgUAg+LbY3ECo1Wruv/9+QkJC6O7u5umn\nnyY2NpYDBw4QGxvLHXfcQXp6Ounp6SxduvQb36/bYCItzI1Hpvnh5ai26apjWZaR169ELjmN6rd/\nR3Ky7B4RAoFAcD2x+Y5y7u7uhISEAODg4EBAQADNzc3k5OQwa9YsAGbPnk12dvY1vd/9CT6kBLni\n7aSxrXEwGZH/8yZyWRGq//4fYRwEAsGIw+YexEAaGhooLy8nPDyctrY23N37S124ubnR1tZmY3XX\njtzX25+tdLGzfwW0WNcgEAhGIMPGQHR3d/Pyyy/zs5/9DJ1ucL2hq3kCBQUFFBQUKI8XL16Mi8t3\nG6lrtdrvfOxATJ0dXHztf9B4eOL45DNImus/GX69tFoDodUyCK2WYTRr3bBhg/J3dHQ00dHRw8NA\nGAwGXn75ZWbOnEliYiLQ7zW0trbi7u5OS0sLbm5uVxxn/hAD+a6L3VxcXL73Qjm5vgbT8meRohOQ\n736Azu4e6O75Xu85FNdDq7UQWi2D0GoZRqtWFxcXFi9efMXzNp+DkGWZt956i4CAAG6//Xbl+alT\np3LgwAEAMjIymDZtmo0UXhtyYS6mF55GSluI6p6HkFQ2b1qBQCD4XtjcgygqKuLgwYMEBQXxu9/9\nDoAlS5Zw55138uqrr7J//34lzXW4Icsy1FQhH/sC+fMdqB59GmlCjK1lCQQCwXVBkmVZtrWI60lN\nTc13Ou5a3DXZZIKaCuSiAuSz+VBcAPYOSBExSD/4KZKnz3c6tyW0DheEVssgtFqG0arV399/yOdt\n7kEMd+TqcuQzJ5GL8qG4EJyckSbEIMVPR1r8IJKnt60lCgQCgUUQBuIqyBc7+xe5nT6BFDsNaepN\nSEseRfLwtLU0gUAgsArCQAyBfCob05oVSPHTUf3tTbHNp0AgGJUIAzEAuaWp32uoKEH14JNIkbG2\nliQQCAQ2QxgIQDYY6N66EdMna5Bm3Ybq508g2dvbWpZAIBDYlFFtIGRZhpNHMX28GoPPGFRPv4Dk\nN9bWsgQCgWBYMGoNhFx6BtMna6CjDdU9D+GUNIvOzk5byxIIBIJhw6gzEPK5YkxbPoSaCqTb70FK\nnYtkZ2fTyq8CgUAwHLkhDYRsMkFLE9RXI9ee7/9ddx7qz4MM0oIfIz32B7Gzm0AgEHwNN5yBMP71\nN9BQAzon8AtA8gsAvwBUk6aBXwB4eiOpxH7QAoFA8E3ccAZCdf/j4BuApBNbewoEAsH34YYzEFJI\nuK0lCAQCwQ2BqEktEAgEgiERBkIgEAgEQyIMhEAgEAiGRBgIgUAgEAyJMBACgUAgGBJhIAQCgUAw\nJMJACAQCgWBIhIEQCAQCwZAM64VyJ06cYPXq1ZhMJtLS0rjzzjttLUkgEAhGDcPWgzCZTKxatYo/\n/OEPvPLKK3zxxRdUV1fbWpZAIBCMGoatgSgpKcHPzw8fHx/UajWpqank5OTYWpZAIBCMGoatgWhu\nbsbT01N5rNfraW5utqEigUAgGF0M6zmIb6KgoICCggLl8eLFi/H39//O7+fi4nI9ZFkFodUyCK2W\nQWi1DNdT64YNG5S/o6OjiY6OHr4ehF6vp6mpSXnc1NSEXq8f9D/R0dEsXrxY+fk+DGyc4Y7QahmE\nVssgtFqG6611YF8aHR0NDOMQ07hx46irq6OhoQGDwcDhw4eZOnWqrWUJBALBqGHYhpjs7Ox44IEH\nePbZZ5U017Fjx9palkAgEIwahq2BAEhISCAhIcEq5zK7VCMBodUyCK2WQWi1DNbQKsmyLFv8LAKB\nQCAYcQzbOQiBQCAQ2BZhIAQCgUAwJMJACASC68pIilqPJK22YFQZiJ07d3Ly5Elby7gm+vr6yMjI\n4MKFC7aW8o2MJK0AFRUVtpZwzYwUrQcOHOCjjz4CQJIkG6v5ekaSVoDu7m6bnXtYZzFdLwoLC9m8\neTMGg4H777/f1nK+kT179rBnzx7Cw8NJSUmxtZyvZSRpra2tZf369fT29vLwww/j4eFha0lXZaRo\nLSwsZOPGjeh0uu+9WNXSjCSt0L84+L333mP8+PEsXLgQlcr64/kb3kB0dnbywgsvcNdddynlwk0m\nk00a+1rIyMhg8+bN3HvvvSQmJtpaztcykrRmZmby6quv8sgjj5CWlmZrOV/LSNK6fft2ent7+ctf\n/gIM73trJGndtm0bO3bsYNasWfzwhz9Unpdl2apej90zzzzzjNXOZkXMDanVarl48SLt7e1MnjyZ\n9PR0qqqq0Gg0ODs7D4sLxGQyKV+6yWTCZDIRFBSESqVi9+7dSpzUycnJljKBkaV1ICqViry8PB54\n4AHUajXFxcXY29uj1WptLe0KhrvWgddAUFAQx48fZ/z48Rw4cIC8vDzq6+sJDQ21scp+RpJWM729\nvWzbto34+Hh+9KMfAdDa2oqDg4PVQ2I3nIHYs2cP6enpuLu74+3tDUBMTAyrVq1ix44dODk50dPT\nw5EjR1CpVAQFBdlU7wcffMDx48eVBYEeHh7U19ezY8cOtm7dioeHByUlJRw8eJBZs2YJrddIYWEh\n7e3tSv0uFxcXent7WblyJcePH+fMmTPs2rULBwcHxo4da9NY9EjRun//fl555RVCQ0Px8vJCkiRc\nXV2pq6vj9ddfx9vbm5iYGNavX4+joyMhISE20TnStEJ/SFGj0aBWq7Gzs0Oj0VBeXk5DQwMbN25U\njJmjoyNubm5W8yRuGAMhyzInT55k48aNuLi4IMsyY8eORavVIkkSgYGBxMbG8oMf/IC4uDiamppo\nbGwkKirKJl5Eb28vb731FtXV1VRVVTFmzBh8fX0BcHd3R6VScd9995GSkkJCQgLZ2dn09PTYZLQz\nkrT29fWxceNG3nrrLdrb24mJicHe3h6AgIAA8vLymDFjBkuXLsXZ2Zm9e/cyefJk5X+E1qEpLi7m\niy++QK1W09nZSXh4uOLRhIaGEhcXx4IFC/D390ev17N582ZuueUWq+scaVq7u7tZvnw5a9euxWAw\nEBMTA4C/vz8nTpwgNzeXefPmER8fT1VVFceOHWP69OlWGySMeAPR29uLnZ0dkiTh6OjIjBkz8PX1\npaSkBEmSCAgIAMDX13dQKfCamhqam5uZPHmyVfWaXV47OzucnJz4wQ9+gLOzMzt27GD27NkAODo6\nEhQUhLOzM9AfcigoKCA2NnbQHhlC65V0dXXR09PD3XffTX5+Pvb29sqoW6vVMn36dMaNGwfA2LFj\n2bFjB4GBgYrBE1q/oqenh76+PmVkO2nSJGbPns3WrVtxd3dnzJgxSJKEWq0epEmSJGpqaoiPj0et\nts4050jSOpCGhgYKCwu58847ycvLw9/fH3d3dwCCg4NJSUlhwoQJeHp6otVqKSkpITIyEnt7e+FB\nfBMfffQRH330Ed3d3djb2+Pj44NOp8PLy4vKykoaGhrw9fXFyclJccn6+vrYunUrO3fuJC0tTTEg\nlqa9vZ0VK1ZQUlJCc3Oz4vpqtVr8/PzIysri0qVLjB8/HqPRiEajAfpjjx988AHV1dXMnDnTKrH9\nkaQVICsri4qKCnx8fHByckKv1+Pl5YUsyxw+fJiIiAhFy8BOwLyN7bx583BwcBBaB/Dhhx/y8ccf\nc/r0aQIDA/H09MTR0RG1Wo3JZCIrK4vIyEh0Oh0qlQqj0YjRaGTz5s1s2LCB6dOnExERYXGdI00r\nQFFREY6OjqhUKtzc3IiKimLMmDFcuHCB/Px8pkyZAoBOp0On0ynHHT16lM7OTm666SbhQXwT+/bt\n4+jRoyxdupSKigq++OILwsPDcXJyQpIkNBoNpaWl9PT0EBYWphiHNWvWUFVVxeOPP86ECROsorWr\nq4u3334bHx8fpk2bxgcffICHhweBgYFAf0fg6urK5s2bmTFjhuIOl5WV8cYbbxAQEMCvfvUrq2xk\nMpK0GgwGXnvtNY4dO0ZbWxvHjx/HxcVFMfpBQUEcPXqUixcvEhwcjFqtxmAwUF1dzRtvvEF5eTk/\n/vGPrVIleCRpLS4u5vDhw/z2t7+lrq6OwsJCurq6CA4OBvrDNIcOHcJgMBAeHq4ct2/fPkpKSvjl\nL39JbGysxXWONK3Nzc387//+LxkZGdTW1lJWVkZMTAwODg5oNBocHR3Jzc1Fo9EQEBCgDGrz8/N5\n9913KSsr484778TT01PMQXwdJpOJnJwcJk+eTHx8POPHj6empobMzEySkpKA/g2H2traaG5upqGh\ngRMnThAdHc2ECROYPXu2VbNsZFnm0KFDLFq0iNDQUDw9PdmzZw+hoaHKhJOvry9VVVWUl5ejVqup\nqakhMjKSadOmMXXqVFQq1aCMDKEVGhsbycnJ4S9/+QtJSUm0tLRQUFCAj48Prq6uQP8cyf79+5ky\nZQq9vb309fUpntDdd9+tjN5Hu9aB31dmZiZtbW3MmjWL0NBQenp6KCwsJCAgQDH83t7efP7556hU\nKtLT04mKiiI8PJzU1FScnJwwmUyAZRaijSStAykoKKC6upq//vWvBAYGsmrVKkJCQpSQl06no6en\nh+zsbJKTkxU9eXl5eHt78/DDDythW+FBDKC7u5t169ZRXV2Nvb097u7uFBQUkJuby4wZM1Cr1QQH\nB7Njxw78/Pzw8fEB+htx7dq15ObmMmHCBCIjI62SKlhZWcm2bduQZRlHR0ckSaK4uBhfX188PT0Z\nO3YsZ86cobm5mYkTJyJJEpIk0dPTw7vvvktRUREzZ87Ew8MDe3t7TCYTsixbZDJ9JGkFyM/PR6fT\nYW9vj5OTEx999BHh4eHo9XqcnJyor6+nurpaKYXs7e1NRUUFGzduZOvWrYwZM4bw8HDlprRkLvxI\n0Nrd3c0HH3zAqVOnlHCis7MzOTk5jB8/Hr1ej0ajob6+nqamJsXr9vT05J133uHkyZMkJSURGxuL\nnZ3dIJ3XuxMbSVrNNDc3K2GisrIyjEYjERERuLq64ujoyI4dO5g5c6YyPxIQEEBhYSGfffYZhw8f\nJiYmhokTJzJ+/HhFr1gHMYAjR44oaWkmk4lNmzYxa9YsJk6cSHp6OkFBQXh7e6PRaOjr66OiooJJ\nkybR19fH66+/TmBgIH/+85+tUjvdaDTy4YcfsmnTJsaMGcOpU6eoq6sjISGBvLw8Ojs7CQ4ORqvV\n4u3tzfr165k5cyYajYajR4+yYcMGFi1axLJlywatnDV3yqNVK/TH7t98801KS0vJzs7GZDIREhJC\ne3s758+fJyYmBhcXF7q7u6mursbf3x8nJydqampYs2YNQUFBPPHEE0RFRQ1639GstaCggJdeegk/\nPz/i4uJYt24der2eiIgIqqqqqK+vJyoqSjFmnZ2dREVFcenSJT755BPc3Nz44x//yMSJEy2qc6Rp\nBcjNzWXFihWcOnWKjo4OAgMDaW9v59ixYyQnJ2NnZ0dYWBi7d+8GICwsDIATJ06wdetW9Ho9//Vf\n/4Wfn5/ynpYceF2NYW0gjEYjFRUVJCYmMn/+fGJiYjh69CiAYlE/++wz5syZgyRJlJaWolariYiI\nwM7OjsTERFJTU5XRgqVpb2/n+PHjPPHEE0ydOhVHR0dKSkpISEhAr9eTkZGBn58fbm5u6PV68vPz\ncXFxYcyYMbi7u3PrrbcSGRmpfHZLXgwjSWt5eTk7d+7kjjvu4O6770ar1fLJJ58wb948TCaTspDM\nPNLes2cPM2fOxN7engsXLpCYmMgdd9yBs7OzxcMJI0lrVVUVU6ZM4dZbb2XMmDHY29uTn59PYmIi\nRqORgoICHBwc8PPzo7Ozk6ysLGbMmIFGo2HcuHGkpKSgVqsxGo0WGxiMRK27d+9m8+bN3HfffURH\nR5OdnY1arWbq1Kns3r0bSZIUg+Dm5sbevXuVrMD9+/eTlpbGkiVLcHd3H+Qx2GL9y7A2ECqVChcX\nF4KDg5VOqK6uDnd3d4KDgxk/fjz79++nrq4OjUbDnj178PX1VTISzNk11kCWZXQ6HSEhIbi5uSFJ\nEjqdji1btpCcnIyvry+tra0UFBTQ1NRET08POTk53Hbbbeh0OiU9z9wpWLLDHSlazfF2lUpFcHAw\nkZGRivaKigri4+Nxdnamq6uLjIwMUlNTcXV15dChQ8oo3cPDQ4ndm0dglrzRhrtW80p388IxHx8f\n5TwnT57EycmJqKgoXFxc6OvrY+3atQQEBLBt2zZCQkKIjIxEpVKhVqsVneY08+utU5IkpYMczlov\n1+zg4EBMTAyRkZF4enpSUFBAR0cHkyZNwsPDg+3btxMUFIRer6ejo4Oenh6io6ORJInY2FglEcFo\nNFptcHs1hk0tJqPRSGVlJUFBQYMaxby61NwJ5efnKyNXgGXLlnHy5EnWrl1LXFzcoLollqSrqwuN\nRqMYIfNF5+XlpfyPOc3WnJY4b948zp07x86dO8nKyuL2229XPp/5eEt0tgaDQUmZHBjHHo5aod+7\ncXV1Vc7j6uqqTDhKkkRTU5MyH+Xg4MDcuXMpLi7mlVde4cyZM6SlpQ1yzc3HWbpjGK5aTSYTTU1N\neHt7D3pf89oVs6Hv7e1VnnN2dubmm29GlmVycnIICQnhJz/5iUV1Qn8/sG3bNnx8fJg+fbpyjQ1H\nrZdjfn/zeitzB2++z2RZJj4+nsrKSnbt2sWuXbs4ffo0c+fOHXQvma8nWxsHGCYGYt++fWzdupU5\nc+Yo6WmXYzKZaG9vx2QyERcXB3zVqc2bN4+0tDSrLHQxGo28//77VFRU4Ovry3333XdFRpS5E66v\nr0etVqPVaunr66Orq4uIiAgCAwNxdHS0itYPP/yQtrY2/Pz8+PGPf3xFpz5ctAIcP36cjz/+mHnz\n5pGcnDwooUCSJOXGqaurUybMod9QPfLIIzQ3N2Myma7ocC3BiRMnKC8vZ/r06fj6+iqjXfMId7ho\n3bdvHzt37kSv15OQkKBk7gzEfE2YQ47QX/5j4sSJ3HzzzcBXnZ8lJ/UPHDjA9u3bqamp4cEHHxzy\nfMNFK0BLSwtubm6oVKpBg4TLf5eUlJCYmKg8vv3222ltbeWLL75g/vz5SrjJzHAqQW7TEFN3dzfv\nv/8++/fv5ze/+c2gRrw8nU+SJFpbW7lw4QJqtZo33niDjo4OpVSGNSZvent7eeONN1CpVCxbtoxP\nP/2Unp4eJZxw+YVx9OhRxo4dS2trKytXrsTLywt/f3/FzbVkRkJlZSX/+Mc/cHNz4/bbb2fdunUY\nDAYiIiKGjGvaUiv0T+p9/PHH3HXXXaSkpFx1xCdJErm5uYSHh6NSqXjzzTcJDAzEw8MDJycnJXZv\nydTVjRs3smXLFvR6PVlZWXR0dDB+/PhBhsHWWmVZZuXKlRQVFfHoo4/i5+fH4cOHSUhIGHIVbmtr\nq5Ie+vbbb1NUVERSUtKg7x8s5+G+8sorlJaW8uSTT+Lh4UFeXh7JycnAlR2mLbUCdHR08Morr/D5\n55/j6+s7KGtyIJIk0dXVRWZmJj/60Y9ob28nMzMTvV6vTLB7eHhY/Hr9PtjEgzBbdgcHBzw8PJQc\n5q6uLkpKSggPD1dSwwY2XGFhIbt376ampob58+eTmppqde0ajYakpCTs7e2ZNm0abm5uQ8YKDQYD\nRUVFlJWVMWHCBO69916lbIL5wrWkUTOZTCxevJipU6cC8NBDD7F27doh68rbUqv5Wujo6CA5OZmp\nU6diMBjo6upS1gdcnoqYlZXFsWPHkGWZyZMnX1Fw0ZJzIn19fbS0tPCnP/0Jb29vTp06xYEDB3B3\ndycpKUm5Xm2l1WAwKAOm22+/fVB5GaPRSFdXF87Ozsockvn8ly5d4tixYzQ3N3PHHXconbOldMJX\n97ZarWbJkiWK1sjISHJzc2lublbCmgOxhVYzJpOJgoIC7O3tCQ4OpqSkhODgYKX+2+WdvLmcyn/+\n8x9OnTrFvHnzlFIa5vcbDhWlr4bVPYgNGzaQm5tLX18f/v7+uLq6cubMGTZv3szOnTu5cOECWVlZ\nGI1GgoODBzV6c3MzISEh/OIXv7BKFdbm5mbWrFlDU1MTOp0ONzc3amtrOXr0KBkZGWRkZGAymTh5\n8uSgekTQf5Hm5uYyf/58li5dil6vt2hWSnNzMxs3bqSxsRF7e3uCgoJwd3dHo9FgMpmora2lq6uL\nqVOnXuENWFsroHSabm5uAOzduxdnZ2fs7Ox44YUXKC8v58yZM0RFRSmfAeDixYtkZmYSFhbGI488\nooQbLTkCO3HiBLIs4+Ligp2dHenp6eh0OsLCwpQBwuHDh5k2bZoyIWoLratXr2bPnj1UVVUxadIk\nZS7k+PHjvPTSS/j6+pKbm0tjYyMRERGDQiO1tbWEhoby6KOPKqvmLek1btmyhfr6ekK+rKI6cOV9\nfX095eXlTJ06dcjChNbWClBdXa3Mi+n1elJSUrCzs6OkpATgqlV2z549y6ZNm5gwYQIPP/zwRsgB\nNwAAFtZJREFUFau2h6PXMBCrGYji4mJefPFFtFotEyZM4JNPPsHX15fw8HDa2toAePzxx5kxYwb2\n9vakp6cza9Ys5YaTJAk/P79BE9SWpLa2lhdeeIGQkBCMRiM7d+4kLCyM6dOno9FoqKys5OWXX2ba\ntGmcOHECo9GoxBLNGVdJSUnKnIolF+Ts2rWLf/3rXwQGBtLW1sbRo0cZP368kianUqkoLi6mpaWF\nyZMnD9Jgba2tra28+OKL5OfnU1hYSGlpKfHx8Tg5ObF69Wp6e3tZsmQJkyZN4vjx45SVlREXF6dk\n9Wi1WuLi4khJSUGr1VrUkBUVFbFixQpOnz7NyZMnKS4uZvLkyTg4OHDo0CFFg06no7KyEpVKhb+/\nv+JBWEurLMts376dqqoq7r//fvbu3UtFRQX+/v5Keeg5c+aQlpaGu7s7p0+fxtPTEw8PD0WLp6en\nkjpuviYs0aadnZ08//zz5OXlKXMwbm5ug9rGy8uLtWvXMmHCBGX900At1tIK/em1r7zyCllZWdTX\n1wP9xkClUuHt7U1VVRUNDQ14eXnh4uJyhVYvLy+mTZvGzTffjE6ns9qq7euFVX2bW2+9lccee4yZ\nM2cSFxenrGmYPn06S5YsUVbyjh07lqCgIHp6egaNtqzpivX09BAVFcWSJUv40Y9+RHx8PMuXL1d0\nuLm50dnZiUajYezYscpIAhgUbrJ0PNRgMNDa2spTTz3F0qVLWbhwIR4eHtTV1Q06b2ZmplK5Nj8/\nXxnlWlMr9O+xrNPpePbZZ3nsscdoaGhg//79hIWFMXHiREpKSggKCmLMmDGkpaXR0dFBX1/fIE16\nvR5Zli1qyNrb2zl48CCpqak888wzPProoxw5coTm5mbi4+OVdEXoz2U3VxWGr1JJraXVPBk+btw4\nvL29+cUvfkFrayv5+fl0d3fj6OiohDXCwsJoa2u76ham5lRQS+Hs7MyiRYv47W9/i7+/P5mZmQBK\n25ivwaSkJAoLC5XXbKEVIDs7m4iICJ555hm8vb3ZtGkTNTU1AEpaak9PD0VFRYO0mj+HWq0mLCxM\n2VzL0mnW1xur9biBgYHcdNNNSsOZV4maTCalWBX0TwRv2rQJSZJwcXGxWWM2Nzdz4cIF5fEPf/hD\nuru7OXLkCF5eXhgMBjIzM6muriY3N1eJ2V+OpeOharWauXPnKvFbvV5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       "text": [
        "<matplotlib.figure.Figure at 0x10cb7f410>"
       ]
      }
     ],
     "prompt_number": 269
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 214
    }
   ],
   "metadata": {}
  }
 ]
}