{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> This is one of the 100 recipes of the [IPython Cookbook](http://ipython-books.github.io/), the definitive guide to high-performance scientific computing and data science in Python.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.2. Converting an IPython notebook to other formats with nbconvert"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You need pandoc, a LateX distribution, and the Notebook dataset on the book's website. On Windows, you also need pywin32 (`conda install pywin32` if you use Anaconda)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Let's open the test notebook in the `data` folder. A notebook is just a plain text file (JSON), so we open it in text mode (`r` mode)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('data/test.ipynb', 'r') as f:\n",
    "    contents = f.read()\n",
    "print(len(contents))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "strip_output": [
     10,
     10
    ]
   },
   "outputs": [],
   "source": [
    "print(contents[:345] + '...' + contents[-33:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Now that we have loaded the notebook as a string, let's parse it with the `json` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import json\n",
    "nb = json.loads(contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Let's have a look at the keys in the notebook dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(nb.keys())\n",
    "print('nbformat ' + str(nb['nbformat']) + \n",
    "      '.' + str(nb['nbformat_minor']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The version of the notebook format is indicated in `nbformat` and `nbformat_minor`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. The main field is `worksheets`: there is only one by default. A worksheet contains a list of cells, and some metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nb['worksheets'][0].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Each cell has a type, optional metadata, some contents (text or code), possibly one or several outputs, and other information. Let's look at a Markdown cell and a code cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nb['worksheets'][0]['cells'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nb['worksheets'][0]['cells'][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Once parsed, the notebook is represented as a Python dictionary. Manipulating it is therefore quite convenient in Python. Here, we count the number of Markdown and code cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cells = nb['worksheets'][0]['cells']\n",
    "nm = len([cell for cell in cells\n",
    "          if cell['cell_type'] == 'markdown'])\n",
    "nc = len([cell for cell in cells\n",
    "          if cell['cell_type'] == 'code'])\n",
    "print((\"There are {nm} Markdown cells and \"\n",
    "       \"{nc} code cells.\").format(\n",
    "        nm=nm, nc=nc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Let's have a closer look at the image output of the cell with the matplotlib figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "png = cells[2]['outputs'][0]['png']\n",
    "cells[2]['outputs'][0]['png'] = png[:20] + '...' + png[-20:]\n",
    "cells[2]['outputs'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, there can be zero, one, or multiple outputs. Besides, each output can have multiple representations. Here, the matplotlib figure has a PNG representation (the base64-encoded image) and a text representation (the internal representation of the figure)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Now, we are going to use nbconvert to convert our text notebook to other formats. This tool can be used from the command-line (if you are using IPython < 4.x, replace command `jupyter` with `ipython`). Here, we convert the notebook to an HTML document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!jupyter nbconvert --to html data/test.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. Let's display this document in an `<iframe>` (a small window showing an external HTML document within the notebook)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame('test.html', 600, 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9. We can also convert the notebook to LaTeX and PDF. In order to specify the title and author of the document, we need to *extend* the default LaTeX template. First, we create a file `mytemplate.tplx` that extends the default `article.tplx` template provided by nbconvert. We precise the contents of the author and title blocks here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%writefile mytemplate.tplx\n",
    "((*- extends 'article.tplx' -*))\n",
    "\n",
    "((* block author *))\n",
    "\\author{Cyrille Rossant}\n",
    "((* endblock author *))\n",
    "\n",
    "((* block title *))\n",
    "\\title{My document}\n",
    "((* endblock title *))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10. Then, we can run nbconvert by specifying our custom template."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!jupyter nbconvert --to latex --template mytemplate data/test.ipynb\n",
    "!pdflatex test.tex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> You'll find all the explanations, figures, references, and much more in the book (to be released later this summer).\n",
    "\n",
    "> [IPython Cookbook](http://ipython-books.github.io/), by [Cyrille Rossant](http://cyrille.rossant.net), Packt Publishing, 2014 (500 pages)."
   ]
  }
 ],
 "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
}