SI_5_6_Individual_RT_distributions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from os.path import join\n",
    "import os\n",
    "\n",
    "from plotting_functions import cm2inch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and process data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <th>gaze_2</th>\n",
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       "      <th>dataset</th>\n",
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      ],
      "text/plain": [
       "   subject  trial      rt  choice  item_value_0  item_value_1  item_value_2  \\\n",
       "0        0      1  1913.0     0.0           3.0           2.0           NaN   \n",
       "1        0      3  2051.0     1.0           6.0           6.0           NaN   \n",
       "2        0      5  2504.0     0.0           6.0           2.0           NaN   \n",
       "3        0      7  1426.0     0.0           6.0           5.0           NaN   \n",
       "4        0      9  1209.0     0.0           6.0           8.0           NaN   \n",
       "\n",
       "     gaze_0    gaze_1  gaze_2  n_items       dataset  \n",
       "0  0.466578  0.533422     NaN        2  krajbich2010  \n",
       "1  0.464375  0.535625     NaN        2  krajbich2010  \n",
       "2  0.524205  0.475795     NaN        2  krajbich2010  \n",
       "3  1.000000  0.000000     NaN        2  krajbich2010  \n",
       "4  0.429714  0.570286     NaN        2  krajbich2010  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(join('data', 'data_aggregate.csv'))\n",
    "odd = data[(data['trial'] % 2) == 1].reset_index(drop=True)\n",
    "odd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/armin/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:14: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "/Users/armin/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:16: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prediction_multiplicative_list = []\n",
    "prediction_nobias_list = []\n",
    "\n",
    "# join prediction dataframes\n",
    "for subject in odd['subject'].unique():\n",
    "    subject_pred_multiplicative = pd.read_csv(os.path.join('results', 'predictions', 'out_of_sample', 'multiplicative', 'prediction_{}_multiplicative_oos.csv'.format(subject)), index_col=0)\n",
    "    subject_pred_nobias = pd.read_csv(os.path.join('results', 'predictions', 'out_of_sample', 'nobias', 'prediction_{}_nobias_oos.csv'.format(subject)), index_col=0)\n",
    "    subject_pred_multiplicative['subject'] = subject\n",
    "    subject_pred_nobias['subject'] = subject\n",
    "    \n",
    "    prediction_multiplicative_list.append(subject_pred_multiplicative)\n",
    "    prediction_nobias_list.append(subject_pred_nobias)\n",
    "\n",
    "prediction_multiplicative = pd.concat(prediction_multiplicative_list)\n",
    "del prediction_multiplicative_list\n",
    "prediction_nobias = pd.concat(prediction_nobias_list)\n",
    "del prediction_nobias_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# discard trials with negative predicted RT\n",
    "prediction_multiplicative = prediction_multiplicative[prediction_multiplicative['rt'] > 0].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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   "outputs": [
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      "text/plain": [
       "   choice       dataset  gamma    gaze_0    gaze_1  gaze_2  item_value_0  \\\n",
       "0     1.0  krajbich2010   0.65  0.466578  0.533422     NaN           3.0   \n",
       "1     0.0  krajbich2010   0.65  0.466578  0.533422     NaN           3.0   \n",
       "2     0.0  krajbich2010   0.65  0.466578  0.533422     NaN           3.0   \n",
       "3     0.0  krajbich2010   0.65  0.466578  0.533422     NaN           3.0   \n",
       "4     0.0  krajbich2010   0.65  0.466578  0.533422     NaN           3.0   \n",
       "\n",
       "   item_value_1  item_value_2  n_items  repeat      rt         s  subject  \\\n",
       "0           2.0           NaN      2.0     0.0  1247.0  0.008368        0   \n",
       "1           2.0           NaN      2.0     1.0  3079.0  0.008368        0   \n",
       "2           2.0           NaN      2.0     2.0  2398.0  0.008368        0   \n",
       "3           2.0           NaN      2.0     3.0   960.0  0.008368        0   \n",
       "4           2.0           NaN      2.0     4.0  1515.0  0.008368        0   \n",
       "\n",
       "    t0   tau  trial         v  \n",
       "0  0.0  0.44    1.0  0.000098  \n",
       "1  0.0  0.44    1.0  0.000098  \n",
       "2  0.0  0.44    1.0  0.000098  \n",
       "3  0.0  0.44    1.0  0.000098  \n",
       "4  0.0  0.44    1.0  0.000098  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_multiplicative.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "def add_best_chosen(df):\n",
    "    df = df.copy()\n",
    "    value_cols = [col for col in df.columns\n",
    "                  if col.startswith('item_value_')]\n",
    "    values = df[value_cols].values\n",
    "    df['best_chosen'] = df['choice'] == np.nanargmax(values, axis=1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction_multiplicative = add_best_chosen(prediction_multiplicative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
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      "text/plain": [
       "   subject  best_chosen   count         mean          std    min      25%  \\\n",
       "0        0        False   290.0  1748.165517   642.029002  653.0  1310.25   \n",
       "1        0         True  1960.0  1387.736735   505.208074  419.0  1035.00   \n",
       "2        1        False  1012.0  1481.964427   773.630710  311.0   946.75   \n",
       "3        1         True  1438.0  1416.004172   748.509518  259.0   897.25   \n",
       "4        2        False   547.0  4576.968921  2839.395363  602.0  2550.50   \n",
       "\n",
       "      50%      75%      max  \n",
       "0  1641.0  2032.25   4065.0  \n",
       "1  1287.0  1620.25   4090.0  \n",
       "2  1289.5  1809.50   5570.0  \n",
       "3  1226.0  1708.75   5175.0  \n",
       "4  3902.0  5713.00  20673.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_multiplicative_summary = prediction_multiplicative.groupby(['subject', 'best_chosen'])['rt'].describe().reset_index()\n",
    "pred_multiplicative_summary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_multiplicative_summary['dataset'] = [odd.loc[odd['subject'] == subject, 'dataset'].head(1).values[0]\n",
    "                                          for subject in pred_multiplicative_summary['subject']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "odd = add_best_chosen(odd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>best_chosen</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1926.636364</td>\n",
       "      <td>892.757892</td>\n",
       "      <td>898.0</td>\n",
       "      <td>1296.50</td>\n",
       "      <td>1713.0</td>\n",
       "      <td>2153.50</td>\n",
       "      <td>3582.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1632.205882</td>\n",
       "      <td>892.802967</td>\n",
       "      <td>682.0</td>\n",
       "      <td>1059.50</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>1866.50</td>\n",
       "      <td>4142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1642.117647</td>\n",
       "      <td>897.868858</td>\n",
       "      <td>743.0</td>\n",
       "      <td>1128.00</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>1847.00</td>\n",
       "      <td>4602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1774.375000</td>\n",
       "      <td>859.613407</td>\n",
       "      <td>878.0</td>\n",
       "      <td>1192.25</td>\n",
       "      <td>1494.0</td>\n",
       "      <td>2167.75</td>\n",
       "      <td>4422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4756.636364</td>\n",
       "      <td>2437.472473</td>\n",
       "      <td>1917.0</td>\n",
       "      <td>2750.00</td>\n",
       "      <td>5164.0</td>\n",
       "      <td>5720.00</td>\n",
       "      <td>9120.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject  best_chosen  count         mean          std     min      25%  \\\n",
       "0        0        False   11.0  1926.636364   892.757892   898.0  1296.50   \n",
       "1        0         True   34.0  1632.205882   892.802967   682.0  1059.50   \n",
       "2        1        False   17.0  1642.117647   897.868858   743.0  1128.00   \n",
       "3        1         True   32.0  1774.375000   859.613407   878.0  1192.25   \n",
       "4        2        False   11.0  4756.636364  2437.472473  1917.0  2750.00   \n",
       "\n",
       "      50%      75%     max  \n",
       "0  1713.0  2153.50  3582.0  \n",
       "1  1330.0  1866.50  4142.0  \n",
       "2  1332.0  1847.00  4602.0  \n",
       "3  1494.0  2167.75  4422.0  \n",
       "4  5164.0  5720.00  9120.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odd_summary = odd.groupby(['subject', 'best_chosen'])['rt'].describe().reset_index()\n",
    "odd_summary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall number of trials in which best item chosen: 16182\n",
      "Overall number of trials in which other item chosen: 4831\n"
     ]
    }
   ],
   "source": [
    "# count N trials in which best item chosen\n",
    "N_best_chosen = odd_summary[odd_summary['best_chosen']==True]['count'].sum()\n",
    "N_other_chosen = odd_summary[odd_summary['best_chosen']==False]['count'].sum()\n",
    "print('Overall number of trials in which best item chosen: {}'.format(np.int(N_best_chosen)))\n",
    "print('Overall number of trials in which other item chosen: {}'.format(np.int(N_other_chosen)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def rt_dists_individual(prediction, data, figsize=cm2inch(18, 22), fontsize=7):\n",
    "\n",
    "    fig, axs = plt.subplots(1, 6, figsize=figsize, sharey=True, sharex=True)\n",
    "    \n",
    "    subjects = prediction['subject'].unique()\n",
    "    # remove subjects for which we don't have predictions yet\n",
    "    data = data[data['subject'].isin(subjects)].copy()\n",
    "    \n",
    "    # colors\n",
    "    dataset_idx = pd.Categorical(prediction['dataset'],\n",
    "                                 categories=['krajbich2010', 'krajbich2011', 'folke2016', 'tavares2017'],\n",
    "                                 ordered=True).codes\n",
    "    colors = np.array(['C0', 'C1', 'C2', 'C3'])[dataset_idx][::2]\n",
    "    \n",
    "    i = 0\n",
    "    for best_chosen in [True, False]:\n",
    "        for q in ['25%', '50%', '75%']:\n",
    "            rel_diff = ((prediction[prediction['best_chosen'] == best_chosen][q].values -\n",
    "                         data[data['best_chosen'] == best_chosen][q].values) /\n",
    "                        data[data['best_chosen'] == best_chosen][q].values)\n",
    "            axs[i].scatter(rel_diff,\n",
    "                           data[data['best_chosen'] == best_chosen]['subject'],\n",
    "                           marker='o',\n",
    "                           color='none',\n",
    "                           edgecolor=colors,\n",
    "                           linewidth=0.5,\n",
    "                           s=30)\n",
    "            axs[i].scatter(rel_diff,\n",
    "                           data[data['best_chosen'] == best_chosen]['subject'],\n",
    "                           marker='o',\n",
    "                           color=colors,\n",
    "                           alpha=0.5,\n",
    "                           linewidth=0,\n",
    "                           s=30)\n",
    "            if best_chosen is False:\n",
    "                axs[i].axvline(0, color='red', alpha=0.5, zorder=-1)\n",
    "            else:\n",
    "                axs[i].axvline(0, color='black', alpha=0.5, zorder=-1)\n",
    "            axs[i].spines['left'].set_visible(False)\n",
    "            axs[i].spines['right'].set_visible(False)\n",
    "            axs[i].spines['top'].set_visible(False)\n",
    "            axs[i].set_xlim(-1*np.max(np.abs(rel_diff))*1.1, np.max(np.abs(rel_diff))*1.1)\n",
    "            axs[i].tick_params(axis='both', which='major', labelsize=fontsize)\n",
    "            i += 1\n",
    "\n",
    "    axs[0].set_title('25%', fontsize=fontsize)\n",
    "    axs[1].set_title('Best chosen\\n\\n50%', fontsize=fontsize)\n",
    "    axs[2].set_title('75%', fontsize=fontsize)\n",
    "    \n",
    "    axs[3].set_title('25%', fontsize=fontsize, color='red')\n",
    "    axs[4].set_title('Not best chosen\\n\\n50%', fontsize=fontsize, color='red')\n",
    "    axs[5].set_title('75%', fontsize=fontsize, color='red')\n",
    "    \n",
    "            \n",
    "    axs[0].spines['left'].set_visible(True)\n",
    "    # axs[3].spines['left'].set_visible(True)\n",
    "\n",
    "    axs[0].set_ylabel('Participant', fontsize=fontsize)\n",
    "    axs[0].set_yticks([])\n",
    "    \n",
    "    fig.tight_layout()\n",
    "    fig.text(0.5, -0.025, 'Relative error\\nin RT quantile predictions', ha='center', fontsize=fontsize)\n",
    "    return fig, axs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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vf0Nq1euYAxEijzxCkWGQKisj295O5Tecveb03iSlpO+ee8i2t5HesAGRTOIq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L2c2u5apiRO0dAyfWA2mfnUnE/he09cdUUpRdy/5X6K3EUD9yiKQo4rh9eqXuPS4ku1MjVu2Fi7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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127e46be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rt_dists_individual(pred_multiplicative_summary, odd_summary);\n",
    "plt.savefig(join('results', 'figures', 'si_figure_5_individual_rt_distributions.png'), dpi=330, bbox_inches='tight')\n",
    "plt.savefig(join('results', 'figures', 'si_figure_5_individual_rt_distributions.pdf'), dpi=330, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rt_dists_aggregate(data, predictions, fontsize=7, bins=np.linspace(0, 15000, 100), figsize=cm2inch(18, 20)):\n",
    "\n",
    "    fig, axs = plt.subplots(4, 2, figsize=figsize, sharey=True, sharex=True)\n",
    "\n",
    "    for d, dataset in enumerate(data['dataset'].unique()):\n",
    "\n",
    "        for i, best_chosen in enumerate([True, False]):\n",
    "\n",
    "            weights_data = np.empty_like(data['rt'][(data['best_chosen'] == best_chosen) & (data['dataset'] == dataset)].values)\n",
    "            weights_data.fill((data[data['dataset'] == dataset]['best_chosen'] == best_chosen).mean() * (len(bins) - 1) / (bins[-1] - bins[0]) / weights_data.size)\n",
    "            \n",
    "            weights_pred = np.empty_like(predictions['rt'][(predictions['best_chosen'] == best_chosen) & (predictions['dataset'] == dataset)].values)\n",
    "            weights_pred.fill((predictions[predictions['dataset'] == dataset]['best_chosen'] == best_chosen).mean() * (len(bins) - 1) / (bins[-1] - bins[0]) / weights_pred.size)\n",
    "\n",
    "            axs[d, i].hist(data['rt'][(data['best_chosen'] == best_chosen) & (data['dataset'] == dataset)],\n",
    "                           bins=bins, histtype='step', color='black', weights=weights_data, label='Data');\n",
    "            axs[d, i].hist(predictions['rt'][(predictions['best_chosen'] == best_chosen) & (predictions['dataset'] == dataset)],\n",
    "                           bins=bins, histtype='step', color='red', weights=weights_pred, label='Prediction');\n",
    "            axs[d, i].tick_params(axis='both', which='major', labelsize=fontsize)\n",
    "            axs[d, i].spines['right'].set_visible(False)\n",
    "            axs[d, i].spines['top'].set_visible(False)\n",
    "\n",
    "    \n",
    "    axs[0, 0].legend(loc='upper right', frameon=False, fontsize=fontsize)\n",
    "    axs[0, 0].set_title('Best chosen', fontsize=fontsize)\n",
    "    axs[0, 1].set_title('Not best chosen', fontsize=fontsize)\n",
    "    axs[0, 0].set_ylabel('Krajbich 2010\\n\\nDensity', fontsize=fontsize)\n",
    "    axs[1, 0].set_ylabel('Krajbich 2011\\n\\nDensity', fontsize=fontsize)\n",
    "    axs[2, 0].set_ylabel('Folke 2016\\n\\nDensity', fontsize=fontsize)\n",
    "    axs[3, 0].set_ylabel('Tavares 2017\\n\\nDensity', fontsize=fontsize)\n",
    "    \n",
    "    axs[3, 0].set_xlabel('Response time (ms)', fontsize=fontsize)\n",
    "    axs[3, 1].set_xlabel('Response time (ms)', fontsize=fontsize)\n",
    "    \n",
    "    fig.tight_layout()\n",
    "    \n",
    "    return fig, axs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SBBjQpckauM31u4a+9qNHXAg0O2/bysxfH8exZlq7DZk0y7+Dt7NJksalb0CP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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1267531d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rt_dists_aggregate(odd, prediction_multiplicative);\n",
    "plt.savefig(join('results', 'figures', 'si_figure_4_aggregate_rt_distributions.png'), dpi=330, bbox_inches='tight')\n",
    "plt.savefig(join('results', 'figures', 'si_figure_4_aggregate_rt_distributions.pdf'), dpi=330, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}