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 bit of text, with *important things*:",
      "",
      "* and",
      "* more that **are boldface**, as well as `verbatim`.",
      "",
      "Using markdown hyperlinks for [ipython](http://ipython.org)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = figure()",
      "plot([1,2,3])",
      "display(f)"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD3CAYAAAAJxX+sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nz6ixi4j4GTV2ERE/8/9mUozJaDh2nQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x981bccc>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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CABISEoiOjmbYsGFERUXRvn17EhMTPV+1SD2lppqUHhZm9h0NDra7IhHPqXUpxm0n0lKM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\nz6ixi4j4GTV2ERE/8/9mUozJaDh2nQAAAABJRU5ErkJggg==\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": "raw",
     "source": [
      "plain text"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys",
      "m = 'A message'",
      "print m, 'to stdout'",
      "print >> sys.stderr, m, 'to stderr'",
      "m"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "A message to stdout",
        ""
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "A message to stderr",
        ""
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "'A message'"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# a traceback",
      "1/0"
     ],
     "language": "python",
     "outputs": [
      {
       "ename": "ZeroDivisionError",
       "evalue": "integer division or modulo by zero",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
        "\u001b[1;32m/home/fperez/ipython/nbconvert/tests/<ipython-input-6-03412a6702b7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# a traceback\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero"
       ]
      }
     ],
     "prompt_number": 6
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "# This is intended to be a cell with missing prompt number, so don't execute it!"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
    }
   ]
  }
 ]
}