{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis and Visualization\n",
    "\n",
    "Required files: \n",
    "* `numpy_data/decays.csv`\n",
    "* `numpy_data/many_decays.csv`\n",
    "\n",
    "## Steps of Data Preparation\n",
    "\n",
    "1. Load the data.\n",
    "    * possibly convert into different storage format\n",
    "    * Transform the data into an easy-access data structure (e.g. NumPy arrays, Pandas data frames)\n",
    "2. Clean the data.\n",
    "    * Handle missing values\n",
    "        - e.g. drop values or replace with defaults, etc. \n",
    "    * Fix erroneous entries\n",
    "        - e.g. detect errors, sort, standardize formats\n",
    "    * Handle stylistic issues\n",
    "        - e.g. rename fields, reformat irregular dates, times, numbers, strings, etc.\n",
    "3. Combine data with metadata.\n",
    "    * add additional/external data\n",
    "    * add identifying numbers, dates\n",
    "    * merge results from independent detectors/sensors, etc.\n",
    "    \n",
    "Then continue with analysis and visualization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A few  useful Python Packages\n",
    "### NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.00000000e+00,   1.00000000e+01],\n",
       "       [  1.00000000e+00,   1.35335283e+00],\n",
       "       [  2.00000000e+00,   1.83156389e-01],\n",
       "       [  3.00000000e+00,   2.47875220e-02],\n",
       "       [  4.00000000e+00,   3.35462600e-03],\n",
       "       [  5.00000000e+00,   4.53999000e-04],\n",
       "       [  6.00000000e+00,   6.14420000e-05],\n",
       "       [  7.00000000e+00,   8.31500000e-06],\n",
       "       [  8.00000000e+00,   1.12600000e-06],\n",
       "       [  9.00000000e+00,   1.52000000e-07]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "decays_arr = np.loadtxt('numpy_data/decays.csv', delimiter=\",\", skiprows=1)\n",
    "decays_arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time (s)</th>\n",
       "      <th>Decays (#)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.353353e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.831564e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.478752e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3.354626e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>4.539990e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>6.144200e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>8.315000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1.126000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1.520000e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time (s)    Decays (#)\n",
       "0         0  1.000000e+01\n",
       "1         1  1.353353e+00\n",
       "2         2  1.831564e-01\n",
       "3         3  2.478752e-02\n",
       "4         4  3.354626e-03\n",
       "5         5  4.539990e-04\n",
       "6         6  6.144200e-05\n",
       "7         7  8.315000e-06\n",
       "8         8  1.126000e-06\n",
       "9         9  1.520000e-07"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "decays_df = pd.read_csv('numpy_data/decays.csv') \n",
    "decays_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas can convert directly to HDF5 format (Chapter 10)\n",
    "\n",
    "```python\n",
    "decays_df.to_hdf('decays.h5', 'experimental')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Blaze\n",
    "\n",
    "The Blaze ecosystem is a set of libraries that help users store, describe, query and process data. \n",
    "It provides Python users high-level access to efficient computation on inconveniently large data. Blaze can refer to both a particular library as well as an ecosystem of related projects that have spun off of Blaze development.\n",
    "\n",
    "* <http://blaze.pydata.org/>\n",
    "* <http://blaze.readthedocs.io>\n",
    "\n",
    "```python\n",
    "import blaze as bz\n",
    "csv_data = bz.CSV('numpy_data/decays.csv')\n",
    "decays_bz = bz.data(csv_data)\n",
    "print(decays_bz.peek())\n",
    "```\n",
    "\n",
    "```\n",
    "   Time (s)    Decays (#)\n",
    "0         0  1.000000e+01\n",
    "1         1  1.353353e+00\n",
    "2         2  1.831564e-01\n",
    "3         3  2.478752e-02\n",
    "4         4  3.354626e-03\n",
    "5         5  4.539990e-04\n",
    "6         6  6.144200e-05\n",
    "7         7  8.315000e-06\n",
    "8         8  1.126000e-06\n",
    "9         9  1.520000e-07\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In this chapter will work primarily with NumPy and Pandas for handling data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working With Pandas\n",
    "\n",
    "* Pandas Homepage: <http://pandas.pydata.org/>\n",
    "* Pandas Documentation: <http://pandas.pydata.org/pandas-docs/stable/>\n",
    "* [Pandas Cheat Sheet.pdf](https://github.com/pandas-dev/pandas/raw/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf)\n",
    "\n",
    "### A first look at Pandas\n",
    "\n",
    "The file `many_decays.csv` contains 51 lines (one header, 50 lines of data) as comma-separated values:\n",
    "\n",
    "```\n",
    "Time,Decays\n",
    "2014-11-08T05:19:31.561782,10.0\n",
    "2014-11-08T05:19:32.561782,1.35335283237\n",
    "2014-11-08T05:19:33.561782,0.183156388887\n",
    "[...]\n",
    "2014-11-08T05:19:45.561782,6.91440010694e-12\n",
    "2014-11-08T05:19:46.561782,9.35762296884e-13\n",
    "2014-11-08T05:19:47.561782,2\n",
    "2014-11-08T05:19:49.561782,2\n",
    "[...]\n",
    "2014-11-08T05:19:55.561782,2\n",
    "2014-11-08T05:19:56.561782,1.92874984796e-21\n",
    "2014-11-08T05:19:57.561782,2.61027906967e-22\n",
    "2014-11-08T05:19:58.561782,3.5326285722e-23\n",
    "[...]\n",
    "2014-11-08T05:20:10.561782,1.3336148155e-33\n",
    "2014-11-08T05:20:11.561782,1.80485138785e-34\n",
    "2014-11-08T05:20:12.561782,NaN\n",
    "2014-11-08T05:20:13.561782,NaN\n",
    "2014-11-08T05:20:14.561782,NaN\n",
    "2014-11-08T05:20:15.561782,NaN\n",
    "2014-11-08T05:20:16.561782,8.19401262399e-39\n",
    "[...]\n",
    "```\n",
    "\n",
    "#### Loading data into pandas.DataFrame\n",
    "\n",
    "We can load this data into a **DataFrame** using `pandas.read_csv()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "decay_df = pd.read_csv(\"numpy_data/many_decays.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inspecting and summarizing the data\n",
    "\n",
    "The `DataFrame.head()` function displays the first entries of the data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Decays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11-08T05:19:31.561782</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-11-08T05:19:32.561782</td>\n",
       "      <td>1.353353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-11-08T05:19:33.561782</td>\n",
       "      <td>0.183156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-11-08T05:19:34.561782</td>\n",
       "      <td>0.024788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-11-08T05:19:35.561782</td>\n",
       "      <td>0.003355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Time     Decays\n",
       "0  2014-11-08T05:19:31.561782  10.000000\n",
       "1  2014-11-08T05:19:32.561782   1.353353\n",
       "2  2014-11-08T05:19:33.561782   0.183156\n",
       "3  2014-11-08T05:19:34.561782   0.024788\n",
       "4  2014-11-08T05:19:35.561782   0.003355"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decay_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas has found two columns and interprets the first line as column headers. Pandas also automatically creates row-indices.\n",
    "\n",
    "The `DataFrame.describe()` function calculates basic descriptive statistics for each column containing numeric data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Decays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.600000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.427212e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.625242e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.748785e-42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.629417e-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.900265e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.435642e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Decays\n",
       "count  4.600000e+01\n",
       "mean   6.427212e-01\n",
       "std    1.625242e+00\n",
       "min    2.748785e-42\n",
       "25%    7.629417e-29\n",
       "50%    2.900265e-11\n",
       "75%    1.435642e-01\n",
       "max    1.000000e+01"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decay_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrames can show us some more useful information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 2 columns):\n",
      "Time      50 non-null object\n",
      "Decays    46 non-null float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 880.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "decay_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      50\n",
       "Decays    46\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decay_df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning and Munging Data\n",
    "\n",
    "Reald world data often contains gaps (i.e. missing values) or out of range values that can be attributed to external factors and for some reason should be excluded from further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      50\n",
       "Decays    46\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decay_df = pd.read_csv(\"numpy_data/many_decays.csv\")\n",
    "decay_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          Time        Decays\n",
      "0   2014-11-08T05:19:31.561782  1.000000e+01\n",
      "1   2014-11-08T05:19:32.561782  1.353353e+00\n",
      "2   2014-11-08T05:19:33.561782  1.831564e-01\n",
      "[...]\n",
      "14  2014-11-08T05:19:45.561782  6.914400e-12\n",
      "15  2014-11-08T05:19:46.561782  9.357623e-13\n",
      "16  2014-11-08T05:19:47.561782  2.000000e+00\n",
      "17  2014-11-08T05:19:48.561782  2.000000e+00\n",
      "18  2014-11-08T05:19:49.561782  2.000000e+00\n",
      "19  2014-11-08T05:19:50.561782  2.000000e+00\n",
      "20  2014-11-08T05:19:51.561782  2.000000e+00\n",
      "21  2014-11-08T05:19:52.561782  2.000000e+00\n",
      "22  2014-11-08T05:19:53.561782  2.000000e+00\n",
      "23  2014-11-08T05:19:54.561782  2.000000e+00\n",
      "24  2014-11-08T05:19:55.561782  2.000000e+00\n",
      "25  2014-11-08T05:19:56.561782  1.928750e-21\n",
      "26  2014-11-08T05:19:57.561782  2.610279e-22\n",
      "[...]\n",
      "39  2014-11-08T05:20:10.561782  1.333615e-33\n",
      "40  2014-11-08T05:20:11.561782  1.804851e-34\n",
      "41  2014-11-08T05:20:12.561782           NaN\n",
      "42  2014-11-08T05:20:13.561782           NaN\n",
      "43  2014-11-08T05:20:14.561782           NaN\n",
      "44  2014-11-08T05:20:15.561782           NaN\n",
      "45  2014-11-08T05:20:16.561782  8.194013e-39\n",
      "46  2014-11-08T05:20:17.561782  1.108939e-39\n",
      "47  2014-11-08T05:20:18.561782  1.500786e-40\n",
      "48  2014-11-08T05:20:19.561782  2.031093e-41\n",
      "49  2014-11-08T05:20:20.561782  2.748785e-42\n"
     ]
    }
   ],
   "source": [
    "print(decay_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb609695b70>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6096df400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# allow matplotlib to embed plots in the notebook:\n",
    "%matplotlib inline\n",
    "#plot the dataframe\n",
    "decay_df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file \"many_decays.csv\" has four missing values (**NaN** - Not a Number) and nine outliers with value \"2.000000e+00\".\n",
    "\n",
    "If we wish to just drop these entries, we can do so using Panda's `DataFrame.dropna()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Decays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11-08T05:19:31.561782</td>\n",
       "      <td>1.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-11-08T05:19:32.561782</td>\n",
       "      <td>1.353353e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-11-08T05:19:33.561782</td>\n",
       "      <td>1.831564e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-11-08T05:19:34.561782</td>\n",
       "      <td>2.478752e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[...]</th>\n",
       "      <td>[...]</td>\n",
       "      <td>[...]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2014-11-08T05:20:09.561782</td>\n",
       "      <td>9.854155e-33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2014-11-08T05:20:10.561782</td>\n",
       "      <td>1.333615e-33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2014-11-08T05:20:11.561782</td>\n",
       "      <td>1.804851e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2014-11-08T05:20:16.561782</td>\n",
       "      <td>8.194013e-39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2014-11-08T05:20:17.561782</td>\n",
       "      <td>1.108939e-39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2014-11-08T05:20:18.561782</td>\n",
       "      <td>1.500786e-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2014-11-08T05:20:19.561782</td>\n",
       "      <td>2.031093e-41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2014-11-08T05:20:20.561782</td>\n",
       "      <td>2.748785e-42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Time        Decays\n",
       "0   2014-11-08T05:19:31.561782  1.000000e+01\n",
       "1   2014-11-08T05:19:32.561782  1.353353e+00\n",
       "2   2014-11-08T05:19:33.561782  1.831564e-01\n",
       "3   2014-11-08T05:19:34.561782  2.478752e-02\n",
       "[...]\n",
       "38  2014-11-08T05:20:09.561782  9.854155e-33\n",
       "39  2014-11-08T05:20:10.561782  1.333615e-33\n",
       "40  2014-11-08T05:20:11.561782  1.804851e-34\n",
       "45  2014-11-08T05:20:16.561782  8.194013e-39\n",
       "46  2014-11-08T05:20:17.561782  1.108939e-39\n",
       "47  2014-11-08T05:20:18.561782  1.500786e-40\n",
       "48  2014-11-08T05:20:19.561782  2.031093e-41\n",
       "49  2014-11-08T05:20:20.561782  2.748785e-42"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decay_df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How to deal with the outliers?\n",
    "\n",
    "In this example the outliers were caused by some external radiation source that moved though the room.\n",
    "We decided to remove them.\n",
    "\n",
    "#### Pandas can also do \"Fancy indexing\"\n",
    "In this case we want to identify all datapoints with the value `2.0` and replace them with `NaN` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Decays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-11-08T05:19:47.561782</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-11-08T05:19:48.561782</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-11-08T05:19:49.561782</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-11-08T05:19:50.561782</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-11-08T05:19:51.561782</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-11-08T05:19:52.561782</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-11-08T05:19:53.561782</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-11-08T05:19:54.561782</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-11-08T05:19:55.561782</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Time  Decays\n",
       "16  2014-11-08T05:19:47.561782     2.0\n",
       "17  2014-11-08T05:19:48.561782     2.0\n",
       "18  2014-11-08T05:19:49.561782     2.0\n",
       "19  2014-11-08T05:19:50.561782     2.0\n",
       "20  2014-11-08T05:19:51.561782     2.0\n",
       "21  2014-11-08T05:19:52.561782     2.0\n",
       "22  2014-11-08T05:19:53.561782     2.0\n",
       "23  2014-11-08T05:19:54.561782     2.0\n",
       "24  2014-11-08T05:19:55.561782     2.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decay_df[ decay_df['Decays'] == 2.0 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "decay_df[ decay_df['Decays'] == 2.0 ] = np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb6093060b8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J9vRtQ/oV1Gg8ACRNAj4E/LBgcs3GYyDKkdC39TDp3cuw3+FgYkS8mr5+DZiY\nZ2PyImkycDjwCDUck7S88CSwHLg3Imo6HsB3gMuBLQXTajke2+VB0QoRyfmjNXcOqaQxwM+Bv4uI\nNYXzai0mEdEVEYeRPJ/33ZIO6TW/ZuIh6WRgeUTM72uZWorHQJUjofth0n17XdKuAOn35Tm3p6wk\nNZAk8zkR8Yt0ck3HBCAiVgEPkoy51Go8ZgAfkbSUpEx7jKRbqN14DEg5ErofJt23O4Dz0tfnAfNy\nbEtZSRJwA7AwIr5VMKsmYyKpRdL49PUo4HhgETUaj4j4QkRMiojJJDnjgYg4hxqNx0CV5UpRSSeR\n1MO6Hyb99SHfaYWR9DOgleT2n68DXwZuB+YCewIvAP8rInoPnFYlSe8HfgM8xVs10i+S1NFrLiaS\nppIM8tWRdLTmRsRXJO1MDcajkKRW4P9ExMmOR/986b+ZWZXwoKiZWZVwQjczqxJO6GZmVcIJ3cys\nSjihm5lVCSd0M7Mq4YRuZlYl/j8b+0oZNzo3nQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb609389320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# drop NaN values from DataFrame and plot with title and grid:\n",
    "decay_df.dropna().plot(title=\"Many Decays, dropped NaNs\", grid=True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting with Matplotlib / Pylab\n",
    "\n",
    "Pandas uses Matplotlib as it's underlying plotting library.\n",
    "Matplotlib contains the `pylab` package, which provides commands that are very similar to the plotting functionality present in MATLAB.\n",
    "\n",
    "* Matplotlib Homepage:    <http://matplotlib.org/>\n",
    "* Matplotlib Gallery:     <http://matplotlib.org/gallery.html>\n",
    "* Pylab commands summary: <http://matplotlib.org/api/pyplot_summary.html#>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Sc8CbJE0o/N6cRQoGzhN5ZnEpRESfpI8Ct5Mf+f9ORKxKuKykzAMuAlZIeqSw\n7tOFZ0ebAXwMuL7wR9Na4JKE60lERNwvaSnwEPmr7R4mBVNNeIoJM7OUq+auITMzGwEHgZlZyjkI\nzMxSzkFgZpZyDgIzs5RzEFiqSDpE0iOFrxckrR+0/LsiHfONkq7dz/vTJN1WjGObjUTV3kdgNpyI\n2AycBCDpc0B3RHylyIf9NPCv+6lpo6QNkuZFxL1FrsXsNXxGYFYgqbvwfb6kuyX9TNJaSV+S9CFJ\nf5C0QtJRhe2mSfqxpAcKX/OG+cyJwBsi4tHC8lsHnYE8XHgf4KfAh0r0o5oN4SAwG96JwGXACeTv\nyj42Ik4jP231xwrbXANcHRGnAu9j+Cmt24GVg5b/Hrg8Ik4CzgB2FNY/WFg2Kzl3DZkN74GI2AAg\n6WlgYCriFcCCwuu3A3PyU9IA0CypKSK6B33OdPJTPA+4F7hK0vXAjRGxrrD+JfKzXZqVnIPAbHg9\ng173D1ruZ8/vTQZ4U0Ts3M/n7ABefdRhRHxJ0i+A84B7JZ0dEY8Xttmxj88wKyp3DZmN3h3s6SZC\n0knDbLMGOHrQNkdFxIqI+DfyM+QeX3jrWIZ2IZmVjIPAbPSuANolPSZpNfkxhSEKf+1nBw0Kf7zw\nUPTHgF3ArYX1C4BflKJos7159lGzIpN0JdAVEft8PrKke4ALIuKV0lVmluczArPi+wZDxxyGkDQN\nuMohYEnxGYGZWcr5jMDMLOUcBGZmKecgMDNLOQeBmVnKOQjMzFLu/wPCDhT1VIo9FAAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6390d7a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# as in the previous example, load decays.csv into a NumPy array\n",
    "decaydata = np.loadtxt('numpy_data/decays.csv', delimiter=\",\", skiprows=1)\n",
    "\n",
    "# provide handles for the x and y columns\n",
    "time = decaydata[:,0]\n",
    "decays = decaydata[:,1]\n",
    "\n",
    "# import the matplotlib plotting functionality\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import pylab as plt\n",
    "\n",
    "plt.plot(time, decays)\n",
    "\n",
    "plt.xlabel('Time (s)')\n",
    "plt.ylabel('Decays')\n",
    "plt.title('Decays')\n",
    "plt.grid(True)\n",
    "#plt.savefig(\"decays_matplotlib.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib is much more powerful that the above example. \n",
    "\n",
    "The example below is based on a polar plot with 20 angular wedges and annotations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Annotation at 0x7fb60593aeb8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rnAt5F9jw8wbNq3971ZZtzU4rHl38I8HOCaSTSFxCKhH8yDqlWHFKynvnvXpZ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\nKxVsKIqiKIrSrv4fifJuFv3xfsgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6059a3780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import various necessary Python and matplotlib packages\n",
    "import numpy as np\n",
    "import matplotlib.cm as cm\n",
    "from matplotlib.pyplot import figure, show, rc\n",
    "from matplotlib.patches import Ellipse\n",
    "\n",
    "# Create a square figure on which to place the plot\n",
    "fig = figure(figsize=(8,8))\n",
    "\n",
    "# Create square axes to hold the circular polar plot\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True)\n",
    "\n",
    "# Generate 20 colored, angular wedges for the polar plot\n",
    "N = 20\n",
    "theta = np.arange(0.0, 2*np.pi, 2*np.pi/N)\n",
    "radii = 10*np.random.rand(N)\n",
    "width = np.pi/4*np.random.rand(N)\n",
    "bars = ax.bar(theta, radii, width=width, bottom=0.0)\n",
    "for r,bar in zip(radii, bars):\n",
    "    bar.set_facecolor(cm.jet(r/10.))\n",
    "    bar.set_alpha(0.5)\n",
    "\n",
    "# Using dictionaries, create a color scheme for the text boxes\n",
    "bbox_args = dict(boxstyle=\"round, pad=0.9\", fc=\"green\", alpha=0.5)\n",
    "bbox_white = dict(boxstyle=\"round, pad=0.9\", fc=\"1\", alpha=0.9)\n",
    "patch_white = dict(boxstyle=\"round, pad=1\", fc=\"1\", ec=\"1\")\n",
    "\n",
    "# Create various boxes with text annotations in them at specific\n",
    "# x and y coordinates\n",
    "ax.annotate('Matplotlib and the Python Ecosystem for Scientific Computing',\n",
    "    xy=(.5,.95),\n",
    "    xycoords='figure fraction',\n",
    "    xytext=(0, 0), textcoords='offset points',\n",
    "    size=15,\n",
    "    ha=\"center\", va=\"center\",\n",
    "    bbox=bbox_args)\n",
    "\n",
    "ax.annotate('Author and Lead Developer \\n of Matplotlib ',\n",
    "    xy=(.5,.82),\n",
    "    xycoords='figure fraction',\n",
    "    xytext=(0, 0), textcoords='offset points',\n",
    "    ha=\"center\", va=\"center\",\n",
    "    bbox=bbox_args)\n",
    "\n",
    "ax.annotate('John D. Hunter',\n",
    "    xy=(.5,.89),\n",
    "    xycoords='figure fraction',\n",
    "    xytext=(0, 0), textcoords='offset points',\n",
    "    size=15,\n",
    "    ha=\"center\", va=\"center\",\n",
    "    bbox=bbox_white)\n",
    "\n",
    "ax.annotate('Friday November 5th  \\n 2:00 pm \\n1106ME ',\n",
    "    xy=(.5,.25),\n",
    "    xycoords='figure fraction',\n",
    "    xytext=(0, 0), textcoords='offset points',\n",
    "    size=15,\n",
    "    ha=\"center\", va=\"center\",\n",
    "    bbox=bbox_args)\n",
    "\n",
    "ax.annotate('Sponsored by: \\n The Hacker Within, \\n'\n",
    "    'The University Lectures Committee, \\n The Department of '\n",
    "    'Medical Physics\\n and \\n The American Nuclear Society',\n",
    "    xy=(.78,.1),\n",
    "    xycoords='figure fraction',\n",
    "    xytext=(0, 0), textcoords='offset points',\n",
    "    size=9,\n",
    "    ha=\"center\", va=\"center\",\n",
    "    bbox=bbox_args)\n",
    "\n",
    "#fig.savefig(\"plot.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting with Bokeh\n",
    "\n",
    "* Bokeh Homepage/Documentation: <http://bokeh.pydata.org/en/latest/>\n",
    "* Bokeh Gallery: <http://bokeh.pydata.org/en/latest/docs/gallery.html#gallery>\n",
    "* Bokeh in Notebooks (Gallery): <http://nbviewer.jupyter.org/github/bokeh/bokeh-notebooks/blob/master/index.ipynb>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# import the Bokeh plotting tools\n",
    "from bokeh import plotting as bp\n",
    "\n",
    "# as in the matplotlib example, load decays.csv into a NumPy array\n",
    "decaydata = np.loadtxt('numpy_data/decays.csv',delimiter=\",\",skiprows=1)\n",
    "\n",
    "# provide handles for the x and y columns\n",
    "time = decaydata[:,0]\n",
    "decays = decaydata[:,1]\n",
    "\n",
    "# define some output file metadata\n",
    "bp.output_file(\"decays.html\", title=\"Experiment 1 Radioactivity\")\n",
    "\n",
    "# create a figure with fun Internet-friendly features (optional)\n",
    "p = bp.figure(tools=\"pan,wheel_zoom,box_zoom,reset,previewsave\",\n",
    "              title=\"Decays\", x_axis_label=\"Time (s)\", y_axis_label=\"Decays (#)\" )\n",
    "\n",
    "# on that figure, create a line plot\n",
    "p.line(time, decays, color='#1F78B4', legend='Decays per second' )\n",
    "\n",
    "# additional customization to the figure can be specified separately\n",
    "p.grid.grid_line_alpha=0.3\n",
    "\n",
    "# open a browser\n",
    "bp.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bokeh can also embed the plot in the notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"0f17a3ec-c3fd-4e72-b3e4-f7069bc49710\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(global) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    window._bokeh_onload_callbacks = [];\n",
       "    window._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "\n",
       "  \n",
       "  if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    window._bokeh_timeout = Date.now() + 5000;\n",
       "    window._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    if (window.Bokeh !== undefined) {\n",
       "      var el = document.getElementById(\"0f17a3ec-c3fd-4e72-b3e4-f7069bc49710\");\n",
       "      el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n",
       "    } else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    delete window._bokeh_onload_callbacks\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        window._bokeh_is_loading--;\n",
       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"0f17a3ec-c3fd-4e72-b3e4-f7069bc49710\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '0f17a3ec-c3fd-4e72-b3e4-f7069bc49710' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "      document.getElementById(\"0f17a3ec-c3fd-4e72-b3e4-f7069bc49710\").textContent = \"BokehJS is loading...\";\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!window._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      window._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"0f17a3ec-c3fd-4e72-b3e4-f7069bc49710\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp.reset_output()    # disable previous output_file()\n",
    "bp.output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"378f0a93-a6ce-4b04-842e-3d191215b509\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    var force = false;\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "  \n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
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       "        el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n",
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       "        setTimeout(display_loaded, 100)\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
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       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
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       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
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       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
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       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = 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(#)\",\"formatter\":{\"id\":\"2afb799e-23ff-4f99-a0f6-3f2c38ef48f7\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"f899818e-4ecd-43d2-b3f1-11561baa2e38\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"051911fb-858f-4f0e-a7da-51857d7ee192\",\"type\":\"BasicTicker\"}},\"id\":\"2e22d221-d399-4410-9d67-f8a10dd464f7\",\"type\":\"LinearAxis\"},{\"attributes\":{\"plot\":{\"id\":\"f899818e-4ecd-43d2-b3f1-11561baa2e38\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"0fed7beb-20de-4c9e-a4d6-8d6d96a8c4fa\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"plot\":{\"id\":\"f899818e-4ecd-43d2-b3f1-11561baa2e38\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"a50fc1a3-438e-4047-8d13-8e0948eb6fdb\",\"type\":\"SaveTool\"},{\"attributes\":{\"label\":{\"value\":\"Decays per second\"},\"renderers\":[{\"id\":\"b725d976-ada2-4fe4-ab50-002b992aa390\",\"type\":\"GlyphRenderer\"}]},\"id\":\"93f63f0f-5ee6-452e-9cc9-b9cf15638b8c\",\"type\":\"LegendItem\"},{\"attributes\":{\"line_color\":{\"value\":\"#1F78B4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"ae1dd7f0-cd0a-4e6c-b1c3-f1897e1bf19e\",\"type\":\"Line\"},{\"attributes\":{\"plot\":null,\"text\":\"Decays\"},\"id\":\"386ce934-1823-4a41-988f-1ce561e49b94\",\"type\":\"Title\"},{\"attributes\":{\"dimension\":1,\"grid_line_alpha\":{\"value\":0.3},\"plot\":{\"id\":\"f899818e-4ecd-43d2-b3f1-11561baa2e38\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"051911fb-858f-4f0e-a7da-51857d7ee192\",\"type\":\"BasicTicker\"}},\"id\":\"d7312fe6-9838-4053-9af5-293d41f8b286\",\"type\":\"Grid\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1a9f6241-84a3-4fed-bca3-0eb607d5bd35\",\"type\":\"PanTool\"},{\"id\":\"0fed7beb-20de-4c9e-a4d6-8d6d96a8c4fa\",\"type\":\"WheelZoomTool\"},{\"id\":\"cf810cdc-00cf-4830-b837-1b6e8a792992\",\"type\":\"BoxZoomTool\"},{\"id\":\"46ed9a53-debf-40d5-b2e3-5b8bef1b8f40\",\"type\":\"ResetTool\"},{\"id\":\"a50fc1a3-438e-4047-8d13-8e0948eb6fdb\",\"type\":\"SaveTool\"}]},\"id\":\"704cbdc5-5d40-4941-99b7-6bc994321003\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"3a139e96-c934-45ae-ae60-b65e48df1120\",\"type\":\"ToolEvents\"},{\"attributes\":{},\"id\":\"2afb799e-23ff-4f99-a0f6-3f2c38ef48f7\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"items\":[{\"id\":\"93f63f0f-5ee6-452e-9cc9-b9cf15638b8c\",\"type\":\"LegendItem\"}],\"plot\":{\"id\":\"f899818e-4ecd-43d2-b3f1-11561baa2e38\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"dbd112b6-c143-49a5-988d-9540a0ae5b05\",\"type\":\"Legend\"},{\"attributes\":{\"overlay\":{\"id\":\"3787ab1f-39c3-477b-91bd-30f56911159c\",\"type\":\"BoxAnnotation\"},\"plot\":{\"id\":\"f899818e-4ecd-43d2-b3f1-11561baa2e38\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"cf810cdc-00cf-4830-b837-1b6e8a792992\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"callback\":null},\"id\":\"58cf0a64-e98f-43f3-95e3-817a1e408eb7\",\"type\":\"DataRange1d\"},{\"attributes\":{\"plot\":{\"id\":\"f899818e-4ecd-43d2-b3f1-11561baa2e38\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"1a9f6241-84a3-4fed-bca3-0eb607d5bd35\",\"type\":\"PanTool\"},{\"attributes\":{\"axis_label\":\"Time (s)\",\"formatter\":{\"id\":\"cbfd5dd8-0fbf-47d8-bd9f-788ccee492b9\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"f899818e-4ecd-43d2-b3f1-11561baa2e38\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"a154d45d-2d7e-40e3-a030-9e9649bb5cac\",\"type\":\"BasicTicker\"}},\"id\":\"0e50c718-c21f-46c9-84f8-8e438cf9d2f6\",\"type\":\"LinearAxis\"}],\"root_ids\":[\"f899818e-4ecd-43d2-b3f1-11561baa2e38\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.5\"}};\n",
       "            var render_items = [{\"docid\":\"e0400620-1626-499c-9d51-facffe2ce253\",\"elementid\":\"378f0a93-a6ce-4b04-842e-3d191215b509\",\"modelid\":\"f899818e-4ecd-43d2-b3f1-11561baa2e38\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"378f0a93-a6ce-4b04-842e-3d191215b509\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Same plot as before\n",
    "p = bp.figure( tools=\"pan,wheel_zoom,box_zoom,reset,previewsave\", title=\"Decays\", \n",
    "               x_axis_label=\"Time (s)\", y_axis_label=\"Decays (#)\" )\n",
    "p.line(time, decays, color='#1F78B4', legend='Decays per second' )\n",
    "p.grid.grid_line_alpha=0.3\n",
    "bp.show(p)"
   ]
  },
  {
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