#!/usr/bin/env python
# coding: utf-8

# > 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.
# 

# # 3.2. Converting an IPython notebook to other formats with nbconvert

# 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).

# 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).

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with open('data/test.ipynb', 'r') as f:
    contents = f.read()
print(len(contents))


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print(contents[:345] + '...' + contents[-33:])


# 2. Now that we have loaded the notebook as a string, let's parse it with the `json` module.

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import json
nb = json.loads(contents)


# 3. Let's have a look at the keys in the notebook dictionary.

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print(nb.keys())
print('nbformat ' + str(nb['nbformat']) + 
      '.' + str(nb['nbformat_minor']))


# The version of the notebook format is indicated in `nbformat` and `nbformat_minor`.

# 3. The main field is `worksheets`: there is only one by default. A worksheet contains a list of cells, and some metadata.

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nb['worksheets'][0].keys()


# 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.

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nb['worksheets'][0]['cells'][1]


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nb['worksheets'][0]['cells'][2]


# 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.

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cells = nb['worksheets'][0]['cells']
nm = len([cell for cell in cells
          if cell['cell_type'] == 'markdown'])
nc = len([cell for cell in cells
          if cell['cell_type'] == 'code'])
print(("There are {nm} Markdown cells and "
       "{nc} code cells.").format(
        nm=nm, nc=nc))


# 6. Let's have a closer look at the image output of the cell with the matplotlib figure.

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png = cells[2]['outputs'][0]['png']
cells[2]['outputs'][0]['png'] = png[:20] + '...' + png[-20:]
cells[2]['outputs'][0]


# 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).

# 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.

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get_ipython().system('jupyter nbconvert --to html data/test.ipynb')


# 8. Let's display this document in an `<iframe>` (a small window showing an external HTML document within the notebook).

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from IPython.display import IFrame
IFrame('test.html', 600, 200)


# 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.

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get_ipython().run_cell_magic('writefile', 'mytemplate.tplx', "((*- 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 *))\n")


# 10. Then, we can run nbconvert by specifying our custom template.

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get_ipython().system('jupyter nbconvert --to latex --template mytemplate data/test.ipynb')
get_ipython().system('pdflatex test.tex')


# > You'll find all the explanations, figures, references, and much more in the book (to be released later this summer).
# 
# > [IPython Cookbook](http://ipython-books.github.io/), by [Cyrille Rossant](http://cyrille.rossant.net), Packt Publishing, 2014 (500 pages).