{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Notebook generation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This module contains the scripts and API to auto-generate or update a documentation notebook skeleton from a given .py file or a full package. It is not expected you'd use this skeleton as your final docs - you should add markdown, examples, etc to it. The skeleton just has a minimal list of exported symbols.\n", "\n", "[`tools/sgen_notebooks`](https://github.com/fastai/fastai/blob/master/docs_src/tools/sgen_notebooks.py) contains a command line tool that transforms a given module into a notebook skeleton. It's essentially a wrapper around [`gen_notebooks.update_notebooks`](/gen_doc.gen_notebooks.html#update_notebooks) The usage is\n", "\n", "```\n", "python -m sgen_notebooks package path_to_result [--update]\n", "```\n", " - **package** is the package you want to write the documentation for. Note that if the package isn't installed in your environment, you need to execute the script in a place where package is a directory (or make a simlink to it). The script will search thourgh all the subdirectories to create all the relevant notebooks.\n", " - **path_to_result** is a directory where you want those notebooks. The script will auto-execute them, so this directory should contain the file nbdoc.py from this package. If the module you are documenting isn't installed, you will also need to have a simlink to it in your path_to_result folder.\n", " - if the flag **--update** is added, the script will update the notebooks (to reflect the addition of new functions or new arguments).\n", "\n", "Alternatively, you can access the same functionality through the module API, documented below.\n", "\n", "**Important note:** The notebooks automatically generated or updated need to be trusted before you can see the results in the output cells. To trust a notebook, click on File, then Trust notebook.\n", "\n", "This module also contains the scripts and API to convert the documentation notebooks into HTML, which is the format used for the final documentation site." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "hide_input": true }, "outputs": [], "source": [ "from fastai import gen_doc\n", "from fastai.gen_doc import nbdoc\n", "from fastai.gen_doc.nbdoc import *\n", "from fastai.gen_doc.gen_notebooks import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Installation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This package requires:\n", "- [nbconvert](https://github.com/jupyter/nbconvert): conda install nbconvert\n", "- [nb_extensions](https://github.com/ipython-contrib/jupyter_contrib_nbextensions): conda install -c conda-forge jupyter_contrib_nbextensions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once nbextensions is installed, your home page of jupyter notebook will look like this:\n", "\n", "![Homepage with nbextension](imgs/nbext.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Click on the Nbextensions tab then make sure the hide inputs extension is activated:\n", "\n", "![Activate hidden input](imgs/hide_input.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As its name suggests, this will allow you to hide input cells and only show their results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convert modules into notebook skeleton" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first (optional) step is to create a notebook \"skeleton\" - i.e. a notebook containing all the classes, methods, functions, and other symbols you wish to document. You can create this manually if you prefer, however using the automatic approach can save you some time and ensure you don't miss anything. For the initial skelton, use [`create_module_page`](/gen_doc.gen_notebooks.html#create_module_page), which creates a new module from scratch. To update it later with any newly-added symbols, use [`update_module_page`](/gen_doc.gen_notebooks.html#update_module_page)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "
create_module_page
[source]create_module_page
(`mod`, `dest_path`, `force`=`False`)\n",
"\n",
"Create the documentation notebook for module `mod_name` in path `dest_path` \n",
"\n",
"- *mod*: the module\n",
"- *dest_path*: the folder in which to generate the notebook\n",
"- *force*: if False, will raise an exception if the notebook is already present "
],
"text/plain": [
"link_nb
[source]link_nb
(`nb_path`)"
],
"text/plain": [
"update_module_page
[source]update_module_page
(`mod`, `dest_path`=`'.'`)\n",
"\n",
"Update the documentation notebook of a given module. \n",
"\n",
"- *mod*: the module\n",
"- *dest_path*: the folder in which to generate the notebook "
],
"text/plain": [
"generate_missing_metadata
[source]generate_missing_metadata
(`dest_file`)"
],
"text/plain": [
"update_nb_metadata
[source]update_nb_metadata
(`nb_path`=`None`, `title`=`None`, `summary`=`None`, `keywords`=`'fastai'`, `overwrite`=`True`, `kwargs`)\n",
"\n",
"Creates jekyll metadata for given notebook path. "
],
"text/plain": [
"update_notebooks
[source]update_notebooks
(`source_path`, `dest_path`=`None`, `update_html`=`True`, `update_nb`=`False`, `update_nb_links`=`True`, `do_execute`=`False`, `update_line_num`=`True`, `html_path`=`None`)\n",
"\n",
"`source_path` can be a directory or a file. Assume all modules reside in the fastai directory. "
],
"text/plain": [
"