{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [NTDS'17] demo 7: data exploration and visualisation\n",
    "[ntds'17]: https://github.com/mdeff/ntds_2017\n",
    "\n",
    "Michael Defferrard and Hermina Petric Maretic, based on [exercises from the 2016 edition of NTDS](https://github.com/mdeff/ntds_2016)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import sys\n",
    "import os\n",
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Organising and looking at data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename = os.path.join('..', 'data', 'credit_card_defaults.csv')\n",
    "data = pd.read_csv(filename, index_col = 0)\n",
    "attributes = data.columns.tolist()\n",
    "\n",
    "# Tansform from numerical to categorical variable.\n",
    "data['SEX'] = data['SEX'].astype('category')\n",
    "data['SEX'].cat.categories = ['MALE', 'FEMALE']\n",
    "data['MARRIAGE'] = data['MARRIAGE'].astype('category')\n",
    "data['MARRIAGE'].cat.categories = ['UNK', 'MARRIED', 'SINGLE', 'OTHERS']\n",
    "data['EDUCATION'] = data['EDUCATION'].astype('category')\n",
    "data['EDUCATION'].cat.categories = ['UNK', 'GRAD SCHOOL', 'UNIVERSITY', 'HIGH SCHOOL', 'OTHERS', 'UNK1', 'UNK2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>LIMIT</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DEFAULT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20000</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>UNIVERSITY</td>\n",
       "      <td>MARRIED</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120000</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>UNIVERSITY</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90000</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>UNIVERSITY</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>UNIVERSITY</td>\n",
       "      <td>MARRIED</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50000</td>\n",
       "      <td>MALE</td>\n",
       "      <td>UNIVERSITY</td>\n",
       "      <td>MARRIED</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50000</td>\n",
       "      <td>MALE</td>\n",
       "      <td>GRAD SCHOOL</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     LIMIT     SEX    EDUCATION MARRIAGE  AGE  DEFAULT\n",
       "ID                                                    \n",
       "1    20000  FEMALE   UNIVERSITY  MARRIED   24        1\n",
       "2   120000  FEMALE   UNIVERSITY   SINGLE   26        1\n",
       "3    90000  FEMALE   UNIVERSITY   SINGLE   34        0\n",
       "4    50000  FEMALE   UNIVERSITY  MARRIED   37        0\n",
       "5    50000    MALE   UNIVERSITY  MARRIED   57        0\n",
       "6    50000    MALE  GRAD SCHOOL   SINGLE   37        0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[:6, ['LIMIT', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'DEFAULT']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>AGE</th>\n",
       "      <th>DELAY1</th>\n",
       "      <th>DELAY2</th>\n",
       "      <th>DELAY3</th>\n",
       "      <th>DELAY4</th>\n",
       "      <th>DELAY5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34</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>37</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>5</th>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AGE  DELAY1  DELAY2  DELAY3  DELAY4  DELAY5\n",
       "ID                                             \n",
       "1    24       2       2      -1      -1      -2\n",
       "2    26      -1       2       0       0       0\n",
       "3    34       0       0       0       0       0\n",
       "4    37       0       0       0       0       0\n",
       "5    57      -1       0      -1       0       0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:5, 4:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>BILL1</th>\n",
       "      <th>BILL2</th>\n",
       "      <th>BILL3</th>\n",
       "      <th>BILL4</th>\n",
       "      <th>BILL5</th>\n",
       "      <th>BILL6</th>\n",
       "      <th>PAY1</th>\n",
       "      <th>PAY2</th>\n",
       "      <th>PAY3</th>\n",
       "      <th>PAY4</th>\n",
       "      <th>PAY5</th>\n",
       "      <th>PAY6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3913</td>\n",
       "      <td>3102</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>689</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>2682</td>\n",
       "      <td>1725</td>\n",
       "      <td>2682</td>\n",
       "      <td>3272</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29239</td>\n",
       "      <td>14027</td>\n",
       "      <td>13559</td>\n",
       "      <td>14331</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46990</td>\n",
       "      <td>48233</td>\n",
       "      <td>49291</td>\n",
       "      <td>28314</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8617</td>\n",
       "      <td>5670</td>\n",
       "      <td>35835</td>\n",
       "      <td>20940</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    BILL1  BILL2  BILL3  BILL4  BILL5  BILL6  PAY1   PAY2   PAY3  PAY4  PAY5  \\\n",
       "ID                                                                             \n",
       "1    3913   3102    689      0      0      0     0    689      0     0     0   \n",
       "2    2682   1725   2682   3272   3455   3261     0   1000   1000  1000     0   \n",
       "3   29239  14027  13559  14331  14948  15549  1518   1500   1000  1000  1000   \n",
       "4   46990  48233  49291  28314  28959  29547  2000   2019   1200  1100  1069   \n",
       "5    8617   5670  35835  20940  19146  19131  2000  36681  10000  9000   689   \n",
       "\n",
       "    PAY6  \n",
       "ID        \n",
       "1      0  \n",
       "2   2000  \n",
       "3   5000  \n",
       "4   1000  \n",
       "5    679  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:5, 11:23]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export as an [HTML table](./subset.html) for manual inspection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[:1000].to_html('subset.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Data Cleaning\n",
    "\n",
    "Problems come in two flavours:\n",
    "\n",
    "1. Missing data, i.e. unknown values.\n",
    "1. Errors in data, i.e. wrong values.\n",
    "\n",
    "The actions to be taken in each case is highly **data and problem specific**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example: marital status\n",
    "1. According to dataset description, it should either be 1 (married), 2 (single) or 3 (others).\n",
    "1. But we find some 0 (previously transformed to `UNK`).\n",
    "1. Let's *assume* that 0 represents errors when collecting the data and that we should remove those clients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SINGLE     15964\n",
      "MARRIED    13659\n",
      "OTHERS       323\n",
      "UNK           54\n",
      "Name: MARRIAGE, dtype: int64\n",
      "\n",
      "We are left with (29946, 24) clients\n",
      "\n",
      "[MARRIED, SINGLE, OTHERS]\n",
      "Categories (3, object): [MARRIED, SINGLE, OTHERS]\n"
     ]
    }
   ],
   "source": [
    "print(data['MARRIAGE'].value_counts())\n",
    "data = data[data['MARRIAGE'] != 'UNK']\n",
    "data['MARRIAGE'] = data['MARRIAGE'].cat.remove_unused_categories()\n",
    "print('\\nWe are left with {} clients\\n'.format(data.shape))\n",
    "print(data['MARRIAGE'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example: education\n",
    "1. It should either be 1 (graduate school), 2 (university), 3 (high school) or 4 (others).\n",
    "1. But we find some 0, 5 and 6 (previously transformed to `UNK`, `UNK1` and `UNK2`).\n",
    "1. Let's *assume* these values are dubious, but do not invalidate the data and keep them as they may have some predictive power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNIVERSITY     14024\n",
      "GRAD SCHOOL    10581\n",
      "HIGH SCHOOL     4873\n",
      "UNK1             280\n",
      "OTHERS           123\n",
      "UNK2              51\n",
      "UNK               14\n",
      "Name: EDUCATION, dtype: int64\n",
      "UNIVERSITY     14024\n",
      "GRAD SCHOOL    10581\n",
      "HIGH SCHOOL     4873\n",
      "UNK              345\n",
      "OTHERS           123\n",
      "Name: EDUCATION, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data['EDUCATION'].value_counts())\n",
    "data.loc[data['EDUCATION'] == 'UNK1', 'EDUCATION'] = 'UNK'\n",
    "data.loc[data['EDUCATION'] == 'UNK2', 'EDUCATION'] = 'UNK'\n",
    "data['EDUCATION'] = data['EDUCATION'].cat.remove_unused_categories()\n",
    "print(data['EDUCATION'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Data statistics\n",
    "\n",
    "* Get descriptive statistics.\n",
    "* Plot informative figures.\n",
    "* Verify some intuitive correlations.\n",
    "\n",
    "### 3.1 Descriptive statistics\n",
    "Let's get first some descriptive statistics of our numerical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>LIMIT</th>\n",
       "      <th>AGE</th>\n",
       "      <th>BILL1</th>\n",
       "      <th>BILL2</th>\n",
       "      <th>BILL3</th>\n",
       "      <th>BILL4</th>\n",
       "      <th>BILL5</th>\n",
       "      <th>BILL6</th>\n",
       "      <th>PAY1</th>\n",
       "      <th>PAY2</th>\n",
       "      <th>PAY3</th>\n",
       "      <th>PAY4</th>\n",
       "      <th>PAY5</th>\n",
       "      <th>PAY6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29946</td>\n",
       "      <td>29946</td>\n",
       "      <td>29946</td>\n",
       "      <td>29946</td>\n",
       "      <td>29946</td>\n",
       "      <td>29946</td>\n",
       "      <td>29946</td>\n",
       "      <td>29946</td>\n",
       "      <td>29946</td>\n",
       "      <td>29946</td>\n",
       "      <td>29946</td>\n",
       "      <td>29946</td>\n",
       "      <td>29946</td>\n",
       "      <td>29946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>167546</td>\n",
       "      <td>35</td>\n",
       "      <td>51278</td>\n",
       "      <td>49224</td>\n",
       "      <td>47063</td>\n",
       "      <td>43306</td>\n",
       "      <td>40352</td>\n",
       "      <td>38911</td>\n",
       "      <td>5659</td>\n",
       "      <td>5926</td>\n",
       "      <td>5227</td>\n",
       "      <td>4829</td>\n",
       "      <td>4804</td>\n",
       "      <td>5220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>129807</td>\n",
       "      <td>9</td>\n",
       "      <td>73682</td>\n",
       "      <td>71219</td>\n",
       "      <td>69393</td>\n",
       "      <td>64374</td>\n",
       "      <td>60836</td>\n",
       "      <td>59592</td>\n",
       "      <td>16552</td>\n",
       "      <td>23060</td>\n",
       "      <td>17618</td>\n",
       "      <td>15677</td>\n",
       "      <td>15290</td>\n",
       "      <td>17791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10000</td>\n",
       "      <td>21</td>\n",
       "      <td>-165580</td>\n",
       "      <td>-69777</td>\n",
       "      <td>-157264</td>\n",
       "      <td>-170000</td>\n",
       "      <td>-81334</td>\n",
       "      <td>-339603</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>25%</th>\n",
       "      <td>50000</td>\n",
       "      <td>28</td>\n",
       "      <td>3570</td>\n",
       "      <td>2988</td>\n",
       "      <td>2684</td>\n",
       "      <td>2335</td>\n",
       "      <td>1770</td>\n",
       "      <td>1261</td>\n",
       "      <td>1000</td>\n",
       "      <td>836</td>\n",
       "      <td>390</td>\n",
       "      <td>298</td>\n",
       "      <td>255</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>140000</td>\n",
       "      <td>34</td>\n",
       "      <td>22400</td>\n",
       "      <td>21221</td>\n",
       "      <td>20108</td>\n",
       "      <td>19066</td>\n",
       "      <td>18121</td>\n",
       "      <td>17098</td>\n",
       "      <td>2100</td>\n",
       "      <td>2010</td>\n",
       "      <td>1800</td>\n",
       "      <td>1500</td>\n",
       "      <td>1500</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>240000</td>\n",
       "      <td>41</td>\n",
       "      <td>67263</td>\n",
       "      <td>64108</td>\n",
       "      <td>60240</td>\n",
       "      <td>54601</td>\n",
       "      <td>50244</td>\n",
       "      <td>49248</td>\n",
       "      <td>5007</td>\n",
       "      <td>5000</td>\n",
       "      <td>4511</td>\n",
       "      <td>4015</td>\n",
       "      <td>4040</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1000000</td>\n",
       "      <td>79</td>\n",
       "      <td>964511</td>\n",
       "      <td>983931</td>\n",
       "      <td>1664089</td>\n",
       "      <td>891586</td>\n",
       "      <td>927171</td>\n",
       "      <td>961664</td>\n",
       "      <td>873552</td>\n",
       "      <td>1684259</td>\n",
       "      <td>896040</td>\n",
       "      <td>621000</td>\n",
       "      <td>426529</td>\n",
       "      <td>528666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         LIMIT    AGE   BILL1   BILL2    BILL3   BILL4   BILL5   BILL6  \\\n",
       "count    29946  29946   29946   29946    29946   29946   29946   29946   \n",
       "mean    167546     35   51278   49224    47063   43306   40352   38911   \n",
       "std     129807      9   73682   71219    69393   64374   60836   59592   \n",
       "min      10000     21 -165580  -69777  -157264 -170000  -81334 -339603   \n",
       "25%      50000     28    3570    2988     2684    2335    1770    1261   \n",
       "50%     140000     34   22400   21221    20108   19066   18121   17098   \n",
       "75%     240000     41   67263   64108    60240   54601   50244   49248   \n",
       "max    1000000     79  964511  983931  1664089  891586  927171  961664   \n",
       "\n",
       "         PAY1     PAY2    PAY3    PAY4    PAY5    PAY6  \n",
       "count   29946    29946   29946   29946   29946   29946  \n",
       "mean     5659     5926    5227    4829    4804    5220  \n",
       "std     16552    23060   17618   15677   15290   17791  \n",
       "min         0        0       0       0       0       0  \n",
       "25%      1000      836     390     298     255     122  \n",
       "50%      2100     2010    1800    1500    1500    1500  \n",
       "75%      5007     5000    4511    4015    4040    4000  \n",
       "max    873552  1684259  896040  621000  426529  528666  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attributes_numerical = ['LIMIT', 'AGE']\n",
    "attributes_numerical.extend(attributes[11:23])\n",
    "data.loc[:, attributes_numerical].describe().astype(np.int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot an histogram of the ages, so that we get a better impression of who our clients are. That may even be an end goal, e.g. if your marketing team asks which customer groups to target.\n",
    "\n",
    "Then a boxplot of the bills, which may serve as a verification of the quality of the acquired data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A4OCwtv3yVZewqV07tq26BAA4qFkpBAAAGJhQCAAAMDChEAAAYGBCIQAAwMCEQgAAgIEJ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\nYGD/H8cJlTiSXrSOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbd9ecb8710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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f5fgjSR4ZakUAAADMazFTKAEAAFhGnQY4a+AAAAAG12mAswYOAABgcKZQAgAA9IQABwAA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\nAAD0xP8HrILUS2kVxmcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbd9cbf9cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.loc[:, 'AGE'].plot.hist(bins=20, figsize=(15,5))\n",
    "ax = data.iloc[:, 11:17].plot.box(logy=True, figsize=(15,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Check a hypotesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple **question**: which proportion of our clients default ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of defaults: 22.14%\n"
     ]
    }
   ],
   "source": [
    "percentage = data['DEFAULT'].value_counts()[1] / data.shape[0] * 100\n",
    "print('Percentage of defaults: {:.2f}%'.format(percentage))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another **question**: who's more susceptible to default, males or females ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "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>DEFAULT</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MALE</th>\n",
       "      <td>9003</td>\n",
       "      <td>2871</td>\n",
       "      <td>11874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE</th>\n",
       "      <td>14312</td>\n",
       "      <td>3760</td>\n",
       "      <td>18072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>23315</td>\n",
       "      <td>6631</td>\n",
       "      <td>29946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "DEFAULT      0     1    All\n",
       "SEX                        \n",
       "MALE      9003  2871  11874\n",
       "FEMALE   14312  3760  18072\n",
       "All      23315  6631  29946"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observed = pd.crosstab(data['SEX'], data['DEFAULT'], margins=True)\n",
    "observed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems like females are better risk. Let's verify with a Chi-Squared test of independance, using [scipy.stats](http://docs.scipy.org/doc/scipy/reference/stats.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value = 6.75e-12\n",
      "expected values:\n",
      "[[  9244.71749148   2629.28250852]\n",
      " [ 14070.28250852   4001.71749148]]\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "_, p, _, expected = stats.chi2_contingency(observed.iloc[:2,:2])\n",
    "print('p-value = {:.2e}'.format(p))\n",
    "print('expected values:\\n{}'.format(expected))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Intuition**: people who pay late present a higher risk of defaulting. Let's verify!\n",
    "Verifying some intuitions will also help you to identify mistakes. E.g. it would be suspicious if that intuition is not verified in the data: did we select the right column, or did we miss-compute a result?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aMeEMHD2DN35cja74IxWZc477P5zPjyu38dRlbTmtYQ2vI/mliNBgXhmYRvdm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbd95617160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group = data.groupby('DELAY1').mean()\n",
    "corr = data['DEFAULT'].corr(data['DELAY1'], method='pearson')\n",
    "group['DEFAULT'].plot(grid=True, title='Pearson correlation: {:.4f}'.format(corr), figsize=(15,5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SHxUa7HD6lZJBEREREREZtl7dXszxRjc3Lhgd7FD6nZJBEREREREZtlasd5GdGMWicSOD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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbd9caf1470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group = data.groupby('AGE').mean()\n",
    "group['LIMIT'].plot(grid=True, title='Mean credit card limit per age', figsize=(15,5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29    1604\n",
      "27    1475\n",
      "28    1408\n",
      "30    1393\n",
      "26    1253\n",
      "31    1213\n",
      "25    1186\n",
      "34    1160\n",
      "32    1157\n",
      "33    1145\n",
      "24    1125\n",
      "35    1111\n",
      "36    1106\n",
      "37    1035\n",
      "39     953\n",
      "38     943\n",
      "23     930\n",
      "40     868\n",
      "41     821\n",
      "42     793\n",
      "44     699\n",
      "43     670\n",
      "45     613\n",
      "46     569\n",
      "22     560\n",
      "47     499\n",
      "48     466\n",
      "49     451\n",
      "50     409\n",
      "51     338\n",
      "53     324\n",
      "52     304\n",
      "54     247\n",
      "55     208\n",
      "56     177\n",
      "58     122\n",
      "57     122\n",
      "59      83\n",
      "60      67\n",
      "21      67\n",
      "61      56\n",
      "62      44\n",
      "63      31\n",
      "64      31\n",
      "66      25\n",
      "65      24\n",
      "67      16\n",
      "69      15\n",
      "70      10\n",
      "68       5\n",
      "73       4\n",
      "71       3\n",
      "72       3\n",
      "75       3\n",
      "74       1\n",
      "79       1\n",
      "Name: AGE, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data['AGE'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Statistical modelling\n",
    "\n",
    "[Statsmodels](http://statsmodels.sourceforge.net/) is similar to scikit-learn, with much stronger emphasis on parameter estimation and (statistical) testing. It is similar in spirit to other statistical packages such as [R](https://www.r-project.org), [SPSS](http://www.ibm.com/analytics/us/en/technology/spss), [SAS](http://www.sas.com/de_ch/home.html) and [Stata](http://www.stata.com). That split reflects the [two statistical modeling cultures](http://projecteuclid.org/euclid.ss/1009213726): (1) Statistics, which want to know how well a given model fits the data, and what variables \"explain\" or affect the outcome, and (2) Machine Learning, where the main supported task is chosing the \"best\" model for prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Back to numeric values.\n",
    "data['SEX'].cat.categories = [-1, 1]\n",
    "data['SEX'] = data['SEX'].astype(np.int)\n",
    "data['MARRIAGE'].cat.categories = [-1, 1, 0]\n",
    "data['MARRIAGE'] = data['MARRIAGE'].astype(np.int)\n",
    "data['EDUCATION'].cat.categories = [-2, 2, 1, 0, -1]\n",
    "data['EDUCATION'] = data['EDUCATION'].astype(np.int)\n",
    "\n",
    "data['DEFAULT'] = data['DEFAULT'] * 2 - 1  # [0,1] --> [-1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data is a <class 'numpy.ndarray'> with 29946 samples of dimensionality 23.\n"
     ]
    }
   ],
   "source": [
    "# Observations and targets.\n",
    "X = data.values[:,:23]\n",
    "y = data.values[:,23]\n",
    "n, d = X.shape\n",
    "print('The data is a {} with {} samples of dimensionality {}.'.format(type(X), n, d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.384\n",
      "Model:                            OLS   Adj. R-squared:                  0.383\n",
      "Method:                 Least Squares   F-statistic:                     809.4\n",
      "Date:                Mon, 27 Nov 2017   Prob (F-statistic):               0.00\n",
      "Time:                        15:09:14   Log-Likelihood:                -35248.\n",
      "No. Observations:               29946   AIC:                         7.054e+04\n",
      "Df Residuals:                   29923   BIC:                         7.073e+04\n",
      "Df Model:                          23                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1         -2.364e-07   4.35e-08     -5.428      0.000   -3.22e-07   -1.51e-07\n",
      "x2            -0.0337      0.005     -7.280      0.000      -0.043      -0.025\n",
      "x3            -0.0265      0.006     -4.656      0.000      -0.038      -0.015\n",
      "x4            -0.0801      0.005    -16.708      0.000      -0.090      -0.071\n",
      "x5            -0.0103      0.000    -42.750      0.000      -0.011      -0.010\n",
      "x6             0.1853      0.006     33.159      0.000       0.174       0.196\n",
      "x7             0.0352      0.007      5.221      0.000       0.022       0.048\n",
      "x8             0.0196      0.007      2.695      0.007       0.005       0.034\n",
      "x9             0.0060      0.008      0.747      0.455      -0.010       0.022\n",
      "x10            0.0114      0.009      1.314      0.189      -0.006       0.029\n",
      "x11            0.0068      0.007      0.959      0.338      -0.007       0.021\n",
      "x12        -1.372e-06   2.31e-07     -5.946      0.000   -1.82e-06    -9.2e-07\n",
      "x13         3.537e-07   3.24e-07      1.091      0.275   -2.82e-07    9.89e-07\n",
      "x14         3.364e-08   3.05e-07      0.110      0.912   -5.65e-07    6.32e-07\n",
      "x15        -1.465e-07   3.18e-07     -0.461      0.645    -7.7e-07    4.77e-07\n",
      "x16         1.527e-08   3.73e-07      0.041      0.967   -7.16e-07    7.46e-07\n",
      "x17         2.117e-07   2.95e-07      0.717      0.473   -3.67e-07     7.9e-07\n",
      "x18        -1.583e-06   3.58e-07     -4.421      0.000   -2.29e-06   -8.81e-07\n",
      "x19        -4.521e-07   2.94e-07     -1.536      0.125   -1.03e-06    1.25e-07\n",
      "x20        -3.437e-08   3.41e-07     -0.101      0.920   -7.04e-07    6.35e-07\n",
      "x21        -6.357e-07   3.72e-07     -1.710      0.087   -1.36e-06    9.28e-08\n",
      "x22        -6.479e-07   3.86e-07     -1.680      0.093    -1.4e-06    1.08e-07\n",
      "x23        -1.959e-07   2.76e-07     -0.710      0.478   -7.37e-07    3.45e-07\n",
      "==============================================================================\n",
      "Omnibus:                     4479.171   Durbin-Watson:                   1.999\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             6851.842\n",
      "Skew:                           1.169   Prob(JB):                         0.00\n",
      "Kurtosis:                       3.160   Cond. No.                     6.14e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 6.14e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Fit the Ordinary Least Square regression model.\n",
    "results = sm.OLS(y, X).fit()\n",
    "\n",
    "# Inspect the results.\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Data visualisation\n",
    "Data visualization is a key aspect of exploratory data analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Time series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To start slowly, let's make a static line plot from some time series. Reproduce the plots below using:\n",
    "1. The procedural API of [matplotlib](http://matplotlib.org), the main data visualization library for Python. Its procedural API is similar to matlab and convenient for interactive work.\n",
    "2. [Pandas](http://pandas.pydata.org), which wraps matplotlib around his DataFrame format and makes many standard plots easy to code. It offers many [helpers for data visualization](http://pandas.pydata.org/pandas-docs/version/0.19.1/visualization.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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161YaNGhQJvHefPPNwNkqt1F1fhnObLPGhb/erpWwzQ3J/4K40HYoovSqTAIY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\ncUVG4oqMxBWZ4sa1YsUKpWm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4XPt8I+H8+7tAwf/+GzQwCpc89dRTIZVBMzIyuPHGG7n7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\ndZSYh2Kcr95F6fKh3LOVwu47PoFlM+D+PdJj44KgiUGEXBxC+bpyhE2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W3XaQ3iwwccOgOYBzwFZACPAWNkWX5HluUXZFnuh0J8Hz58/GrJ/Bbq8g+/Xfl2SHtc+D89Fg85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\nqIV3OsPmD1p3v/oqmDdU+r9oP74Ve3n++eftTxuNRh6a+jAvvPACycnJT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s0AACAASURB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbd90aa5390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Random time series.\n",
    "n = 1000\n",
    "rs = np.random.RandomState(42)\n",
    "data = rs.randn(n, 4).cumsum(axis=0)\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(data[:, 0], label='A')\n",
    "plt.plot(data[:, 1], '.-k', label='B')\n",
    "plt.plot(data[:, 2], '--m', label='C')\n",
    "plt.plot(data[:, 3], ':', label='D')\n",
    "plt.legend(loc='upper left')\n",
    "plt.xticks(range(0, 1000, 50))\n",
    "plt.ylabel('Value')\n",
    "plt.xlabel('Day')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EckIIIYTofZXZEDbC/dpgcK5h0iBvh1rbBKDb1bq5qjyVkQtpp6F3fzJhCUxaCrtXgcPR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\nv7sh57DBYbdq3N4vYPBl7fsYS0BrAKsRDi7G1iR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ursdpqvTNae45vsOpfLilkOW1UQxS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\nbBwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbd90a7a320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = pd.date_range('1/1/2000', periods=n)\n",
    "df = pd.DataFrame(data, index=idx, columns=list('ABCD'))\n",
    "df.plot(figsize=(15,5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Frequency\n",
    "\n",
    "A frequency plot is a graph that shows the pattern in a set of data by plotting how often particular values of a measure occur. They often take the form of an [histogram](https://en.wikipedia.org/wiki/Histogram) or a [box plot](https://en.wikipedia.org/wiki/Box_plot).\n",
    "\n",
    "[Seaborn](http://seaborn.pydata.org/) is a statistical visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics. Its advantage is that you can modify the produced plots with matplotlib, so you loose nothing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "df = sns.load_dataset('iris', data_home=os.path.join('..', 'data'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EJImpaBMREREREUli6UEHEBERERGR2M2aNYuysrKgY8TF5s2bAZg5c2bASdqvpKQkYT+H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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbd8a9121d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "g = sns.distplot(df['petal_width'], kde=True, rug=False, ax=axes[0])\n",
    "g.set(title='Distribution of petal width')\n",
    "\n",
    "g = sns.boxplot('species', 'petal_width', data=df, ax=axes[1])\n",
    "g.set(title='Distribution of petal width by species');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Correlation\n",
    "\n",
    "[Scatter plots](https://en.wikipedia.org/wiki/Scatter_plot) are very much used to assess the correlation between 2 variables. Pair plots are then a useful way of displaying the pairwise relations between variables in a dataset.\n",
    "\n",
    "Use the seaborn `pairplot()` function to analyze how separable is the iris dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eK4S43873IKp6VmYIz6qSE46FDC9Zh2PGlXRSzCrqGKVJZZ9RMpwxVvGgLpmC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\n53GteM3pXG9W8hDPfUHrWNjVvSttGR8P5kdPytxYLQ4bgDVCiLcSH4X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fIpEIrvnbNUm6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\niqIoSl3RSYeiKIqiKIqiKHXl/wFIlKf2s+r7kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbd8aff2978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, hue=\"species\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Dimensionality reduction\n",
    "\n",
    "Humans can only comprehend up to 3 dimensions (in space, then there is e.g. color or size), so [dimensionality reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction) is often needed to explore high dimensional datasets. Analyze how separable is the iris dataset by visualizing it in a 2D scatter plot after reduction from 4 to 2 dimensions with two popular methods:\n",
    "1. The classical [principal componant analysis (PCA)](https://en.wikipedia.org/wiki/Principal_component_analysis).\n",
    "2. [t-distributed stochastic neighbor embedding (t-SNE)](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding).\n",
    "\n",
    "**Hints**:\n",
    "* t-SNE is a stochastic method, so you may want to run it multiple times.\n",
    "* The easiest way to create the scatter plot is to add columns to the pandas DataFrame, then use the Seaborn `swarmplot()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)\n",
    "X = pca.fit_transform(df.values[:, :4])\n",
    "df['pca1'] = X[:, 0]\n",
    "df['pca2'] = X[:, 1]\n",
    "\n",
    "tsne = TSNE(n_components=2)\n",
    "X = tsne.fit_transform(df.values[:, :4])\n",
    "df['tsne1'] = X[:, 0]\n",
    "df['tsne2'] = X[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jX+1Jp5ckIhI3He2zkgj5g8eH6uD+al+werbV90lgsNTe3s9QwVc0WcFggVJgcOwPohsb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\n3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbd819f67b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "sns.swarmplot(x='pca1', y='pca2', data=df, hue='species', ax=axes[0])\n",
    "sns.swarmplot(x='tsne1', y='tsne2', data=df, hue='species', ax=axes[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 Graph visualisation\n",
    "You've already worked with Networkx to make basic graph plots. Other useful resources are [Gephi](https://gephi.org/), which allows for a nicer and more artistic approach, and [Plotly](https://plot.ly/python/network-graphs/), useful for data visualisation as well as graph drawing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import time\n",
    "import networkx as nx\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\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",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\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",
       "        fig.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 backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\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 * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\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",
       "    var width = fig.canvas.width/mpl.ratio\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 + '\" width=\"' + width + '\">');\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 width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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": [
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 20\n",
    "T = 50\n",
    "er = nx.erdos_renyi_graph(20,0.3)\n",
    "er.remove_nodes_from(nx.isolates(er))  # removing isolated nodes\n",
    "n = len(er)\n",
    "nx.draw(er)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time series data can also live on the nodes of our graph (e.g. temperatures in cities). Create random time series data for our graph and plot them on a graph with node size representing the signal value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also experiment with different layouts for drawing the graph. The default layout positions nodes using the Fruchterman-Reingold [force-directed algorithm](https://en.wikipedia.org/wiki/Force-directed_graph_drawing) (spring layout). Draw the graph using the spectral layout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sigma_eps = 1;\n",
    "epsilon = stats.norm.rvs(scale = sigma_eps, size=[n,T])  # noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def draw_graph_time_series(x):\n",
    "    pos = nx.spectral_layout(er)  # we have to fix the position of nodes\n",
    "\n",
    "    l = 0\n",
    "    for i in range(0,T):\n",
    "        ax.clear()\n",
    "        nx.draw_networkx(er, ax=ax, pos=pos, node_size=x[:,i]*100)\n",
    "        ax.text(0,0,i)\n",
    "        fig.canvas.draw()\n",
    "        time.sleep(0.03)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\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",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\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",
       "        fig.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 backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\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 * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\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",
       "    var width = fig.canvas.width/mpl.ratio\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 + '\" width=\"' + width + '\">');\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 width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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": {
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.ion()\n",
    "fig.show()\n",
    "fig.canvas.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "draw_graph_time_series(epsilon)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the last experiment we plotted noise. Create two events on your graph at times 10 and 30. Each event should be centered in a randomly selected node and increase the signal value of that node by 50. Both events should then propagate through the graph with the heat kernel model. When an event occurs, color the source node of the event to a different color to help visualise it better. Draw the graph using the spring layout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_x(Lapl):\n",
    "    x = epsilon.copy()\n",
    "    peak = np.zeros((n,2))\n",
    "    for i in range(1,T):\n",
    "        l = 0\n",
    "        while (t[l] < i):\n",
    "            peak[source[l]][l] = 1\n",
    "            event = np.dot(scipy.linalg.expm((t[l]-i)*Lapl), peak[:,l])\n",
    "            x[:,i] = x[:,i] + 50*event\n",
    "            l += 1\n",
    "            if l >= 2:\n",
    "                break\n",
    "    return x\n",
    "\n",
    "t = [10, 30]\n",
    "source = stats.randint.rvs(low=0, high=n, size=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Lapl = nx.normalized_laplacian_matrix(er)\n",
    "Lapl = Lapl.todense()\n",
    "x = create_x(Lapl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def draw_graph_diffusion(x):\n",
    "    pos = nx.spring_layout(er)  # we have to fix the position of nodes\n",
    "    node_col = ['r'] * T\n",
    "    l = 0\n",
    "    for i in range(0,T):\n",
    "        ax.clear()\n",
    "        if l<2:\n",
    "            if i == t[l]:\n",
    "                node_col[source[l]] = 'b'\n",
    "                l = l+1\n",
    "        nx.draw_networkx(er, ax=ax, node_color = node_col, pos=pos, node_size=x[:,i]*100)\n",
    "        ax.text(0,0,i)\n",
    "        fig.canvas.draw()\n",
    "        time.sleep(0.03)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\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",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\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",
       "        fig.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 backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\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 * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\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",
       "    var width = fig.canvas.width/mpl.ratio\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 + '\" width=\"' + width + '\">');\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 width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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>"
      ]
     },
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     "output_type": "display_data"
    },
    {
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "plt.ion()\n",
    "fig.show()\n",
    "fig.canvas.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "draw_graph_diffusion(x)"
   ]
  }
 ],
 "metadata": {
  "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}