{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Welcome to exa! Let's get started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import exa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You might be familiar with 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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.906796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.448143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.105326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.010824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.244684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x         y\n",
       "0  0.0  0.906796\n",
       "1  1.0  0.448143\n",
       "2  2.0  0.105326\n",
       "3  3.0  0.010824\n",
       "4  4.0  0.244684"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 11)\n",
    "y = np.random.rand(11)\n",
    "df1 = pd.DataFrame.from_dict({'x': x, 'y': y})\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrames are great! Usually you can find a way to represent your data in a single dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>zi</th>\n",
       "      <th>zj</th>\n",
       "      <th>zk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.906796</td>\n",
       "      <td>0.733138</td>\n",
       "      <td>0.396978</td>\n",
       "      <td>0.448463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.448143</td>\n",
       "      <td>0.413719</td>\n",
       "      <td>0.755660</td>\n",
       "      <td>0.719040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.105326</td>\n",
       "      <td>0.173512</td>\n",
       "      <td>0.154888</td>\n",
       "      <td>0.757688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.010824</td>\n",
       "      <td>0.730557</td>\n",
       "      <td>0.718124</td>\n",
       "      <td>0.229439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.244684</td>\n",
       "      <td>0.055820</td>\n",
       "      <td>0.428332</td>\n",
       "      <td>0.083746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x         y        zi        zj        zk\n",
       "0  0.0  0.906796  0.733138  0.396978  0.448463\n",
       "1  1.0  0.448143  0.413719  0.755660  0.719040\n",
       "2  2.0  0.105326  0.173512  0.154888  0.757688\n",
       "3  3.0  0.010824  0.730557  0.718124  0.229439\n",
       "4  4.0  0.244684  0.055820  0.428332  0.083746"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame.from_dict({'x': x, 'y': y})\n",
    "b = pd.DataFrame(np.random.rand(11, 3))\n",
    "b.columns = ['zi', 'zj', 'zk']\n",
    "pd.concat([df1, b], axis=1).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sometimes data doesn't fit nicely into a single dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   dxi  dyj  nx  ny   ox   oy\n",
      "0  0.4  0.4  15  15 -3.0 -3.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.126503</td>\n",
       "      <td>0.827317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.389927</td>\n",
       "      <td>0.768678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.388104</td>\n",
       "      <td>0.187729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.408751</td>\n",
       "      <td>0.614016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.011289</td>\n",
       "      <td>0.499478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         f1        f2\n",
       "0  0.126503  0.827317\n",
       "1  0.389927  0.768678\n",
       "2  0.388104  0.187729\n",
       "3  0.408751  0.614016\n",
       "4  0.011289  0.499478"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_params = pd.DataFrame.from_dict({'ox': [-3.0], 'oy': [-3.0], \n",
    "                                       'nx': [15],   'ny': [15],\n",
    "                                      'dxi': [0.4], 'dyj': [0.4]})\n",
    "array_values = pd.DataFrame.from_dict({'f1': (np.random.rand(15 * 15)),\n",
    "                                       'f2': (np.random.rand(15 * 15))})\n",
    "print(array_params)\n",
    "array_values.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "array_params stores the data to generate (x, y) coordinates\n",
    "array_values stores the data of some functions f1(x, y), f2(x, y) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The exa Container links dataframes together with minimal effort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Param(exa.DataFrame):\n",
    "    _indices = ['param_index'] # Set as the dataframe's index\n",
    "    \n",
    "class Value(exa.DataFrame):\n",
    "    _columns = ['param_index'] # Set as a REQUIRED column in the dataframe's construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "array_params.index.name = 'param_index'\n",
    "array_values['param_index'] = 0\n",
    "t = exa.Container(params=Param(array_params), values=Value(array_values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The network() method shows you a network graph of the relationships in your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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5Q48nM54e7ThJkiRJkroog5jUAyyvW8ctFU+wvG4dAP2zCrm69DQ+V3IU8SAW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19837a189b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t.network()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are idx-col and col-col relationships, as well as other class attributes\n",
    "\n",
    "#### Categories are efficient if your data has a small number of possible values but normally require special attention\n",
    "#### with the _categories attribute, they require no special attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class CatDF(exa.DataFrame):\n",
    "    _categories = {'cat1': str, 'cat2': np.int64}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat1    category\n",
      "cat2    category\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cat1</th>\n",
       "      <th>cat2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cat1 cat2\n",
       "0  foo    0\n",
       "1  foo    0\n",
       "2  foo    0\n",
       "3  foo    0\n",
       "4  foo    0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note that the pandas API remains intact\n",
    "c = CatDF.from_dict({'cat1': ['foo'] * 30 + ['bar'] * 40, 'cat2': [0] * 18 + [1] * 13 + [2] * 39})\n",
    "print(c.dtypes)\n",
    "c.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are attributes for _groupbys, _traits, _precision allowing for automatic passing to JS widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class TraitDF(exa.DataFrame):\n",
    "    _traits = ['f1', 'f2']\n",
    "    _precision = {'f1': 8, 'f2': 3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tr = exa.Container(values=TraitDF(array_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<exa.container.Container at 0x19837cb9588>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr._traits_need_update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[0.827,0.769,0.188,0.614,0.499,0.942,0.252,0.173,0.374,0.207,0.317,0.549,0.169,0.607,0.351,0.854,0.048,0.446,0.617,0.891,0.029,0.556,0.621,0.812,0.36,0.154,0.902,0.821,0.876,0.497,0.479,0.839,0.643,0.222,0.472,0.586,0.115,0.821,0.232,0.959,0.237,0.722,0.679,0.496,0.865,0.893,0.529,0.856,0.272,0.66,0.935,0.275,0.604,0.399,0.334,0.656,0.771,0.044,0.758,0.725,0.508,0.408,0.129,0.102,0.687,0.735,0.221,0.119,0.19,0.049,0.2,0.217,0.716,0.297,0.085,0.025,0.163,0.537,0.628,0.815,0.58,0.154,0.24,0.93,0.275,0.935,0.669,0.125,0.925,0.357,0.314,0.449,0.556,0.062,0.37,0.472,0.969,0.867,0.291,0.233,0.09,0.826,0.157,0.411,0.228,0.758,0.235,0.419,0.033,0.019,0.605,0.358,0.58,0.255,0.066,0.719,0.425,0.406,0.796,0.304,0.858,0.972,0.817,0.063,0.789,0.635,0.369,0.192,0.464,0.002,0.088,0.804,0.427,0.882,0.933,0.942,0.628,0.326,0.488,0.523,0.335,0.852,0.222,0.38,0.233,0.457,0.866,0.409,0.03,0.342,0.558,0.991,0.148,0.987,0.646,0.544,0.553,0.827,0.467,0.654,0.477,0.101,0.35,0.364,0.193,0.265,0.875,0.925,0.219,0.936,0.098,0.771,0.797,0.994,0.919,0.9,0.518,0.167,0.937,0.186,0.019,0.273,0.239,0.893,0.283,0.343,0.806,0.207,0.514,0.027,0.818,0.51,0.911,0.406,0.973,0.679,0.289,0.622,0.782,0.699,0.258,0.329,0.733,0.409,0.751,0.258,0.651,0.398,0.941,0.255,0.396,0.054,0.936,0.782,0.041,0.394,0.566,0.529,0.784,0.854,0.561,0.583,0.379,0.344,0.308]'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr._widget.traitdf_f2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class GroupDF(exa.DataFrame):\n",
    "    _columns = ['f1', 'f2']\n",
    "    _traits = ['f1', 'f2']\n",
    "    _precision = {'f1': 2, 'f2': 2}\n",
    "    _groupbys = ['grp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "array_values['grp'] = np.repeat(range(15), 15)\n",
    "gr = exa.Container(values=GroupDF(array_values))\n",
    "gr._update_traits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[[0.83,0.77,0.19,0.61,0.5,0.94,0.25,0.17,0.37,0.21,0.32,0.55,0.17,0.61,0.35],[0.85,0.05,0.45,0.62,0.89,0.03,0.56,0.62,0.81,0.36,0.15,0.9,0.82,0.88,0.5],[0.48,0.84,0.64,0.22,0.47,0.59,0.11,0.82,0.23,0.96,0.24,0.72,0.68,0.5,0.87],[0.89,0.53,0.86,0.27,0.66,0.93,0.28,0.6,0.4,0.33,0.66,0.77,0.04,0.76,0.72],[0.51,0.41,0.13,0.1,0.69,0.74,0.22,0.12,0.19,0.05,0.2,0.22,0.72,0.3,0.09],[0.03,0.16,0.54,0.63,0.82,0.58,0.15,0.24,0.93,0.28,0.93,0.67,0.13,0.92,0.36],[0.31,0.45,0.56,0.06,0.37,0.47,0.97,0.87,0.29,0.23,0.09,0.83,0.16,0.41,0.23],[0.76,0.23,0.42,0.03,0.02,0.6,0.36,0.58,0.26,0.07,0.72,0.43,0.41,0.8,0.3],[0.86,0.97,0.82,0.06,0.79,0.63,0.37,0.19,0.46,0.0,0.09,0.8,0.43,0.88,0.93],[0.94,0.63,0.33,0.49,0.52,0.33,0.85,0.22,0.38,0.23,0.46,0.87,0.41,0.03,0.34],[0.56,0.99,0.15,0.99,0.65,0.54,0.55,0.83,0.47,0.65,0.48,0.1,0.35,0.36,0.19],[0.27,0.88,0.93,0.22,0.94,0.1,0.77,0.8,0.99,0.92,0.9,0.52,0.17,0.94,0.19],[0.02,0.27,0.24,0.89,0.28,0.34,0.81,0.21,0.51,0.03,0.82,0.51,0.91,0.41,0.97],[0.68,0.29,0.62,0.78,0.7,0.26,0.33,0.73,0.41,0.75,0.26,0.65,0.4,0.94,0.25],[0.4,0.05,0.94,0.78,0.04,0.39,0.57,0.53,0.78,0.85,0.56,0.58,0.38,0.34,0.31]]'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr._widget.groupdf_f2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trait data is passed to JS for viewing in a widget with a rather capable app utilizing Three.JS and WebGL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<exa.container.Container at 0x1983b0aa978>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['type', 'app', 'content', 'data'])\n"
     ]
    }
   ],
   "source": [
    "exa.Container()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The real power is in crafting a container specific to the needs of a data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### There is also an Editor class that makes repetitive file I/O tasks simpler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  0: {\n",
       "  1:  \"cells\": [\n",
       "  2:   {\n",
       "  3:    \"cell_type\": \"markdown\",\n",
       "  4:    \"metadata\": {},\n",
       "  5:    \"source\": [\n",
       "  6:     \"### Welcome to exa! Let's get started\"\n",
       "  7:    ]\n",
       "  8:   },\n",
       "  9:   {\n",
       " 10:    \"cell_type\": \"code\",\n",
       " 11:    \"execution_count\": 1,\n",
       " 12:    \"metadata\": {\n",
       " 13:     \"collapsed\": false\n",
       " 14:    },\n",
       " 15:    \"outputs\": [],\n",
       " 16:    \"source\": [\n",
       " 17:     \"import pandas as pd\\n\",\n",
       " 18:     \"import numpy as np\\n\",\n",
       " 19:     \"import exa\"\n",
       " 20:    ]\n",
       " 21:   },\n",
       " 22:   {\n",
       " 23:    \"cell_type\": \"markdown\",\n",
       " 24:    \"metadata\": {},\n",
       " 25:    \"source\": [\n",
       " 26:     \"### You might be familiar with pandas\"\n",
       " 27:    ]\n",
       " 28:   },\n",
       " 29:   {\n",
       "...\n",
       "764:    \"execution_count\": null,\n",
       "765:    \"metadata\": {\n",
       "766:     \"collapsed\": true\n",
       "767:    },\n",
       "768:    \"outputs\": [],\n",
       "769:    \"source\": []\n",
       "770:   }\n",
       "771:  ],\n",
       "772:  \"metadata\": {\n",
       "773:   \"kernelspec\": {\n",
       "774:    \"display_name\": \"Python 3\",\n",
       "775:    \"language\": \"python\",\n",
       "776:    \"name\": \"python3\"\n",
       "777:   },\n",
       "778:   \"language_info\": {\n",
       "779:    \"codemirror_mode\": {\n",
       "780:     \"name\": \"ipython\",\n",
       "781:     \"version\": 3\n",
       "782:    },\n",
       "783:    \"file_extension\": \".py\",\n",
       "784:    \"mimetype\": \"text/x-python\",\n",
       "785:    \"name\": \"python\",\n",
       "786:    \"nbconvert_exporter\": \"python\",\n",
       "787:    \"pygments_lexer\": \"ipython3\",\n",
       "788:    \"version\": \"3.5.1\"\n",
       "789:   }\n",
       "790:  },\n",
       "791:  \"nbformat\": 4,\n",
       "792:  \"nbformat_minor\": 0\n",
       "793: }"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = exa.Editor('exa_demo.ipynb')    # Before you run this cell, save the notebook!\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'exa_demo.ipynb': OrderedDict([(733,\n",
       "               '    \"s = exa.Editor(\\'exa_demo.ipynb\\')    # Before you run this cell, save the notebook!\\\\n\",'),\n",
       "              (747, '       \"{\\'exa_demo.ipynb\\': OrderedDict([(733,\\\\n\",'),\n",
       "              (748,\n",
       "               '       \"               \\'    \\\\\"s = exa.Editor(\\\\\\\\\\'exa_demo.ipynb\\\\\\\\\\')\\\\\\\\\\\\\\\\n\\\\\",\\'),\\\\n\",'),\n",
       "              (749,\n",
       "               '       \"              (747, \\'       \\\\\"{\\\\\\\\\\'exa_demo.ipynb\\\\\\\\\\': OrderedDict()}\\\\\"\\'),\\\\n\",'),\n",
       "              (750,\n",
       "               '       \"              (756, \\'    \\\\\"s.find(\\\\\\\\\\'exa_demo.ipynb\\\\\\\\\\')\\\\\"\\')])}\"'),\n",
       "              (759, '    \"s.find(\\'exa_demo.ipynb\\')\"')])}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.find('exa_demo.ipynb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}