tests/test.ipynb

{
 "metadata": {
  "name": "test"
 },
 "nbformat": 3,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "source": [
      "H1"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "source": [
      "H2"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "source": [
      "H3"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "source": [
      "H4"
     ]
    },
    {
     "cell_type": "heading",
     "level": 5,
     "source": [
      "H5"
     ]
    },
    {
     "cell_type": "heading",
     "level": 6,
     "source": [
      "H6"
     ]
    },
    {
     "cell_type": "markdown",
     "source": [
      "A section heading",
      "=================",
      "",
      "A bit of text, with *important things*:",
      "",
      "* and",
      "* more",
      "",
      "Using reST links to `ipython <http://ipython.org>`_."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = figure()",
      "plot([1])",
      "display(f)"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD3CAYAAAAUl4NyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABLEQAAYCkCAAAs9X/vh7DYicSaVQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x28f2750>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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DoRAlAAAeIhLXzpBTQF/P2VdUVKihoUGlpaVyuVzt1mlpadGdO3d0+/Ztbdq0SdOmTQuO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\nAABLEQAAYCkCAAAs9X/vh7DYicSaVQAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "# multiline input",
      "x = 1",
      "y = 2"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "1+2"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "3"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'hello world'"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "hello world"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "source": [
      "$e^{i\\pi} + 1 = 0$"
     ]
    },
    {
     "cell_type": "plaintext",
     "source": [
      "plain text"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [],
     "language": "python",
     "outputs": []
    }
   ]
  }
 ]
}