theory/python/matplotlib/8.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Condition 1</th>\n",
       "      <th>Condition 2</th>\n",
       "      <th>Condition 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.548814</td>\n",
       "      <td>0.880757</td>\n",
       "      <td>0.395459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.715189</td>\n",
       "      <td>0.719243</td>\n",
       "      <td>0.480735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.602763</td>\n",
       "      <td>0.415331</td>\n",
       "      <td>0.767394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.544883</td>\n",
       "      <td>0.702476</td>\n",
       "      <td>0.066248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.423655</td>\n",
       "      <td>0.106447</td>\n",
       "      <td>0.733443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Condition 1  Condition 2  Condition 3\n",
       "0     0.548814     0.880757     0.395459\n",
       "1     0.715189     0.719243     0.480735\n",
       "2     0.602763     0.415331     0.767394\n",
       "3     0.544883     0.702476     0.066248\n",
       "4     0.423655     0.106447     0.733443"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "np.random.seed(0)\n",
    "df = pd.DataFrame({'Condition 1': np.random.rand(20),\n",
    "                   'Condition 2': np.random.rand(20)*0.9,\n",
    "                   'Condition 3': np.random.rand(20)*1.1})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAFoCAYAAAAhN0NQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a586fb588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "df.plot.bar(ax=ax,stacked=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAFoCAYAAAACbBKFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a5992a898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "df_ratio = df.div(df.sum(axis=1),axis=0)\n",
    "fig,ax = plt.subplots()\n",
    "df_ratio.plot.bar(ax=ax,stacked=True)\n",
    "ax.yaxis.set_major_formatter(FuncFormatter(lambda y,_:'{:.0%}'.format(y)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Number of sexual partners</th>\n",
       "      <th>First sexual intercourse</th>\n",
       "      <th>Num of pregnancies</th>\n",
       "      <th>Smokes</th>\n",
       "      <th>Smokes (years)</th>\n",
       "      <th>Smokes (packs/year)</th>\n",
       "      <th>Hormonal Contraceptives</th>\n",
       "      <th>Hormonal Contraceptives (years)</th>\n",
       "      <th>IUD</th>\n",
       "      <th>...</th>\n",
       "      <th>STDs: Time since first diagnosis</th>\n",
       "      <th>STDs: Time since last diagnosis</th>\n",
       "      <th>Dx:Cancer</th>\n",
       "      <th>Dx:CIN</th>\n",
       "      <th>Dx:HPV</th>\n",
       "      <th>Dx</th>\n",
       "      <th>Hinselmann</th>\n",
       "      <th>Schiller</th>\n",
       "      <th>Citology</th>\n",
       "      <th>Biopsy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Number of sexual partners  First sexual intercourse  \\\n",
       "0   18                        4.0                      15.0   \n",
       "1   15                        1.0                      14.0   \n",
       "2   34                        1.0                       NaN   \n",
       "3   52                        5.0                      16.0   \n",
       "4   46                        3.0                      21.0   \n",
       "\n",
       "   Num of pregnancies  Smokes  Smokes (years)  Smokes (packs/year)  \\\n",
       "0                 1.0     0.0             0.0                  0.0   \n",
       "1                 1.0     0.0             0.0                  0.0   \n",
       "2                 1.0     0.0             0.0                  0.0   \n",
       "3                 4.0     1.0            37.0                 37.0   \n",
       "4                 4.0     0.0             0.0                  0.0   \n",
       "\n",
       "   Hormonal Contraceptives  Hormonal Contraceptives (years)  IUD   ...    \\\n",
       "0                      0.0                              0.0  0.0   ...     \n",
       "1                      0.0                              0.0  0.0   ...     \n",
       "2                      0.0                              0.0  0.0   ...     \n",
       "3                      1.0                              3.0  0.0   ...     \n",
       "4                      1.0                             15.0  0.0   ...     \n",
       "\n",
       "   STDs: Time since first diagnosis  STDs: Time since last diagnosis  \\\n",
       "0                               NaN                              NaN   \n",
       "1                               NaN                              NaN   \n",
       "2                               NaN                              NaN   \n",
       "3                               NaN                              NaN   \n",
       "4                               NaN                              NaN   \n",
       "\n",
       "   Dx:Cancer  Dx:CIN  Dx:HPV  Dx  Hinselmann  Schiller  Citology  Biopsy  \n",
       "0          0       0       0   0           0         0         0       0  \n",
       "1          0       0       0   0           0         0         0       0  \n",
       "2          0       0       0   0           0         0         0       0  \n",
       "3          1       0       1   0           0         0         0       0  \n",
       "4          0       0       0   0           0         0         0       0  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00383/risk_factors_cervical_cancer.csv'\n",
    "df = pd.read_csv(url, na_values=\"?\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Number of sexual partners</th>\n",
       "      <th>First sexual intercourse</th>\n",
       "      <th>Num of pregnancies</th>\n",
       "      <th>Smokes</th>\n",
       "      <th>Smokes (years)</th>\n",
       "      <th>Smokes (packs/year)</th>\n",
       "      <th>Hormonal Contraceptives</th>\n",
       "      <th>Hormonal Contraceptives (years)</th>\n",
       "      <th>IUD</th>\n",
       "      <th>...</th>\n",
       "      <th>STDs: Time since first diagnosis</th>\n",
       "      <th>STDs: Time since last diagnosis</th>\n",
       "      <th>Dx:Cancer</th>\n",
       "      <th>Dx:CIN</th>\n",
       "      <th>Dx:HPV</th>\n",
       "      <th>Dx</th>\n",
       "      <th>Hinselmann</th>\n",
       "      <th>Schiller</th>\n",
       "      <th>Citology</th>\n",
       "      <th>Biopsy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.140845</td>\n",
       "      <td>5.816901</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.140845</td>\n",
       "      <td>5.816901</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.9953</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.140845</td>\n",
       "      <td>5.816901</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.140845</td>\n",
       "      <td>5.816901</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.140845</td>\n",
       "      <td>5.816901</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Number of sexual partners  First sexual intercourse  \\\n",
       "0  18.0                        4.0                   15.0000   \n",
       "1  15.0                        1.0                   14.0000   \n",
       "2  34.0                        1.0                   16.9953   \n",
       "3  52.0                        5.0                   16.0000   \n",
       "4  46.0                        3.0                   21.0000   \n",
       "\n",
       "   Num of pregnancies  Smokes  Smokes (years)  Smokes (packs/year)  \\\n",
       "0                 1.0     0.0             0.0                  0.0   \n",
       "1                 1.0     0.0             0.0                  0.0   \n",
       "2                 1.0     0.0             0.0                  0.0   \n",
       "3                 4.0     1.0            37.0                 37.0   \n",
       "4                 4.0     0.0             0.0                  0.0   \n",
       "\n",
       "   Hormonal Contraceptives  Hormonal Contraceptives (years)  IUD   ...    \\\n",
       "0                      0.0                              0.0  0.0   ...     \n",
       "1                      0.0                              0.0  0.0   ...     \n",
       "2                      0.0                              0.0  0.0   ...     \n",
       "3                      1.0                              3.0  0.0   ...     \n",
       "4                      1.0                             15.0  0.0   ...     \n",
       "\n",
       "   STDs: Time since first diagnosis  STDs: Time since last diagnosis  \\\n",
       "0                          6.140845                         5.816901   \n",
       "1                          6.140845                         5.816901   \n",
       "2                          6.140845                         5.816901   \n",
       "3                          6.140845                         5.816901   \n",
       "4                          6.140845                         5.816901   \n",
       "\n",
       "   Dx:Cancer  Dx:CIN  Dx:HPV   Dx  Hinselmann  Schiller  Citology  Biopsy  \n",
       "0        0.0     0.0     0.0  0.0         0.0       0.0       0.0     0.0  \n",
       "1        0.0     0.0     0.0  0.0         0.0       0.0       0.0     0.0  \n",
       "2        0.0     0.0     0.0  0.0         0.0       0.0       0.0     0.0  \n",
       "3        1.0     0.0     1.0  0.0         0.0       0.0       0.0     0.0  \n",
       "4        0.0     0.0     0.0  0.0         0.0       0.0       0.0     0.0  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "impute =  pd.DataFrame(Imputer().fit_transform(df))\n",
    "impute.columns = df.columns\n",
    "impute.index = df.index\n",
    "\n",
    "impute.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained variance:\n",
      "PC1 59.41%\n",
      "PC2 14.59%\n",
      "PC3 9.02%\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = impute.drop('Dx:Cancer', axis=1)\n",
    "y = impute[\"Dx:Cancer\"]\n",
    "\n",
    "pca = PCA(n_components=3)\n",
    "X_r = pca.fit_transform(features)\n",
    "\n",
    "print(\"Explained variance:\\nPC1 {:.2%}\\nPC2 {:.2%}\\nPC3 {:.2%}\"\n",
    "      .format(pca.explained_variance_ratio_[0],\n",
    "              pca.explained_variance_ratio_[1],\n",
    "              pca.explained_variance_ratio_[2]))\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "\n",
    "ax.scatter(X_r[:, 0], X_r[:, 1], X_r[:, 2], c=y, cmap=plt.cm.coolwarm)\n",
    "\n",
    "# Label the axes\n",
    "ax.set_xlabel('PC1')\n",
    "ax.set_ylabel('PC2')\n",
    "ax.set_zlabel('PC3')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "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": 1
}