{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "IPython Basics"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "1+2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "3"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "foo = [10, 3, 3, 77]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Some plotting!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(foo)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "[<matplotlib.lines.Line2D at 0x105f08610>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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F14dWlixZQllZGZmZmfzlL39hyZIlXT6xiEhXrFgB8+cnf5iHQ0v/RSTunTgB3/qW+WtK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\nFxFJEgp0EZEkoUAXEUkSCnQRkSTx/wIt5mZ/L7uwAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105626490>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    }
   ],
   "metadata": {}
  }
 ]
}