started to add docs using sphinx for readthedocs (ongoing)

This commit is contained in:
Brian Delhaisse
2019-06-21 03:30:03 +02:00
parent af8af82926
commit 2598e973ab
14 changed files with 486 additions and 21 deletions
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# Sphinx documentation
docs/_build/
docs/build/
docs/source/docstring/
# PyBuilder
target/
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# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line.
SPHINXOPTS =
SPHINXBUILD = sphinx-build
SOURCEDIR = source
BUILDDIR = build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
.PHONY: help Makefile
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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@ECHO OFF
pushd %~dp0
REM Command file for Sphinx documentation
if "%SPHINXBUILD%" == "" (
set SPHINXBUILD=sphinx-build
)
set SOURCEDIR=source
set BUILDDIR=build
if "%1" == "" goto help
%SPHINXBUILD% >NUL 2>NUL
if errorlevel 9009 (
echo.
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
echo.installed, then set the SPHINXBUILD environment variable to point
echo.to the full path of the 'sphinx-build' executable. Alternatively you
echo.may add the Sphinx directory to PATH.
echo.
echo.If you don't have Sphinx installed, grab it from
echo.http://sphinx-doc.org/
exit /b 1
)
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
goto end
:help
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
:end
popd
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#!/usr/bin/env bash
# . venv/bin/activate
rm -Rf build
rm -Rf source/docstring
sphinx-apidoc -f -o source/docstring/ ../pyrobolearn
make html
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# -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import os
import sys
sys.path.insert(0, os.path.abspath('../..'))
# -- Project information -----------------------------------------------------
project = u'PyRoboLearn'
copyright = u'2019, Brian Delhaisse'
author = u'Brian Delhaisse'
# The short X.Y version
version = u''
# The full version, including alpha/beta/rc tags
release = u'0.0.1'
# -- General configuration ---------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#
# needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.intersphinx',
'sphinx.ext.coverage',
'sphinx.ext.mathjax',
'sphinx.ext.viewcode',
'sphinx.ext.githubpages',
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'
# The master toctree document.
master_doc = 'index'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = []
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = None
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = 'sphinx_rtd_theme' # 'alabaster'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#
# html_theme_options = {}
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# Custom sidebar templates, must be a dictionary that maps document names
# to template names.
#
# The default sidebars (for documents that don't match any pattern) are
# defined by theme itself. Builtin themes are using these templates by
# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
# 'searchbox.html']``.
#
# html_sidebars = {}
# -- Options for HTMLHelp output ---------------------------------------------
# Output file base name for HTML help builder.
htmlhelp_basename = 'PyRoboLearndoc'
# -- Options for LaTeX output ------------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#
# 'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#
# 'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#
# 'preamble': '',
# Latex figure (float) alignment
#
# 'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, 'PyRoboLearn.tex', u'PyRoboLearn Documentation',
u'Brian Delhaisse', 'manual'),
]
# -- Options for manual page output ------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(master_doc, 'pyrobolearn', u'PyRoboLearn Documentation',
[author], 1)
]
# -- Options for Texinfo output ----------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, 'PyRoboLearn', u'PyRoboLearn Documentation',
author, 'PyRoboLearn', 'One line description of project.',
'Miscellaneous'),
]
# -- Options for Epub output -------------------------------------------------
# Bibliographic Dublin Core info.
epub_title = project
# The unique identifier of the text. This can be a ISBN number
# or the project homepage.
#
# epub_identifier = ''
# A unique identification for the text.
#
# epub_uid = ''
# A list of files that should not be packed into the epub file.
epub_exclude_files = ['search.html']
# -- Extension configuration -------------------------------------------------
# -- Options for intersphinx extension ---------------------------------------
# Example configuration for intersphinx: refer to the Python standard library.
intersphinx_mapping = {'https://docs.python.org/': None}
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.. include:: ../../examples/README.rst
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.. PyRoboLearn documentation master file, created by
sphinx-quickstart on Thu Jun 20 20:28:11 2019.
You can adapt this file completely to your liking, but it should at least
contain the root toctree directive.
Welcome to PyRoboLearn's documentation!
=======================================
.. toctree::
:maxdepth: 2
:caption: PyRoboLearn
readme
.. toctree::
:maxdepth: 3
:caption: Installation
installation
.. toctree::
:maxdepth: 2
:caption: Examples
examples
.. toctree::
:maxdepth: 3
:caption: Package Reference
docstring/modules
.. toctree::
:maxdepth: 2
:caption: Index
indices
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Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
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Installation
============
There are 2 ways to install the PyRoboLearn framework.
1. via :ref:`Docker`
2. using a :ref:`Virtual Environment`
.. _Docker:
Docker
-------
At the moment the docker is a self contained Ubuntu image with all the libraries installed. When launched we have access to a Python3.6 interpreter and we can import pyrobolearn directly.
In the future, ROS may be splitted in another container and linked to this one.
1. Install Docker and nvidia-docker
.. code-block:: bash
sudo apt-get update
sudo apt install apt-transport-https ca-certificates curl software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu bionic stable # you should replace bionic by your version
sudo apt update
sudo apt install docker-ce
sudo systemctl status docker # check that docker is active
2. Build the image
.. code-block:: bash
docker build -t pyrobolearn .
3. You can now start the python interpreter with every library already installed
.. code-block:: bash
docker run -p 11311:11311 -v catkin_ws:/pyrobolearn/catkin_ws/ -ti pyrobolearn python3
To open an interactive terminal in the docker image use:
.. code-block:: bash
docker run -p 11311:11311 -v catkin_ws:/pyrobolearn/catkin_ws/ -ti pyrobolearn /bin/bash
4. If the GPU is not recognized in the interpreter, you can install ``nvidia-docker``
.. code-block:: bash
curl -sL https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -sL https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
sudo apt-get install nvidia-docker2
sudo pkill -SIGHUP dockerd
And use:
.. code-block:: bash
nvidia-docker run -p 11311:11311 -v catkin_ws:/pyrobolearn/catkin_ws/ -ti pyrobolearn
.. _Virtual Environment:
Virtual Environment
-------------------
0. Prerequisites: install the following packages on your Ubuntu system
.. code-block:: bash
sudo apt-get install cmake gfortran
1. First download the ``pip`` Python package manager and create a virtual environment for Python as described in the following link: https://packaging.python.org/guides/installing-using-pip-and-virtualenv/
On Ubuntu, you can install ``pip`` and ``virtualenv`` by typing in the terminal:
- In Python 2.7:
.. code-block:: bash
sudo apt install python-pip
sudo pip install virtualenv
- In Python 3.5:
.. code-block:: bash
sudo apt install python3-pip
sudo pip install virtualenv
You can then create the virtual environment by typing:
.. code-block:: bash
virtualenv -p /usr/bin/python<version> <virtualenv_name>
# activate the virtual environment
source <virtualenv_name>/bin/activate
where ``<version>`` is the python version you want to use (select between ``2.7`` or ``3.5``), and ``<virtualenv_name>`` is a name of your choice for the virtual environment. For instance, it can be ``py2.7`` or ``py3.5``.
To deactivate the virtual environment, just type:
.. code-block:: bash
deactivate
2. clone this repository and install the requirements by executing the ``setup.py``
In Python 2.7:
.. code-block:: bash
git clone https://github.com/robotlearn/pyrobolearn
cd pyrobolearn
pip install numpy cython
pip install http://github.com/cornellius-gp/gpytorch/archive/alpha.zip # this is for Python 2.7
pip install -e . # this will install pyrobolearn as well as the required packages (so no need for: pip install -r requirements.txt)
In Python 3.5:
.. code-block:: bash
git clone https://github.com/robotlearn/pyrobolearn
cd pyrobolearn
pip install numpy cython
pip install gpytorch # this is for Python 3.5
pip install -e . # this will install pyrobolearn as well as the required packages (so no need for: pip install -r requirements.txt)
Depending on your computer configuration and the python version you use, you might need to install also the following packages through ``apt-get``:
.. code-block:: bash
sudo apt install python-tk # if python 2.7
sudo apt install python3-tk # if python 3.5
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PyRoboLearn
===========
.. include:: ../../README.md
Design Decisions
================
.. image:: ../UML/pyrobolearn_uml.png
Citation
========
.. code-block:: latex
@misc{delhaisse2019pyrobolearn,
author = {Delhaisse, Brian and Xin, Songyan and Rozo, Leonel, and Caldwell, Darwin},
title = {PyRoboLearn: A Python Framework for Robot Learning Practitioners},
howpublished = {\url{https://github.com/robotlearn/pyrobolearn}},
year=2019,
}
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## Examples
In this folder, you will find different examples on how to use the framework.
Warning: this folder is currently being updated; few files might still have some bugs or not
implemented completely. Some other folders will be added in the upcoming days.
You can check the following folders:
- `worlds`: how to create a world in the simulator, load various objects inside and interact with
them, use the camera, and load or generate terrains.
- `robots`: check how to load a specific robot (biped, quadruped, wheeled, etc) into the world.
- `interfaces`: the various interfaces (game controllers, webcam, etc) and bridges that you can use.
- `kinematics`: check how to use forward and inverse kinematics as well as position and velocity control.
- `manipulability`: check how to use the velocity and dynamic manipulability ellipsoids.
- `states`: how to query the states / observations.
- `models`: the different learning models that you can use.
- `imitation`: how to use imitation learning with the framework.
- `gym/cartpole`: policies are trained with different algorithms on the gym Cartpole environment.
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Examples
========
In this folder, you will find different examples on how to use the framework.
Warning: this folder is currently being updated; few files might still have some bugs or not
implemented completely. Some other folders will be added in the upcoming days.
You can check the following folders:
- ``worlds``: how to create a world in the simulator, load various objects inside and interact with them, use the camera, and load or generate terrains.
- ``robots``: check how to load a specific robot (biped, quadruped, wheeled, etc) into the world.
- ``interfaces``: the various interfaces (game controllers, webcam, etc) and bridges that you can use.
- ``kinematics``: check how to use forward and inverse kinematics as well as position and velocity control.
- ``manipulability``: check how to use the velocity and dynamic manipulability ellipsoids.
- ``states``: how to query the states / observations.
- ``models``: the different learning models that you can use.
- ``imitation``: how to use imitation learning with the framework.
- ``gym/cartpole``: policies are trained with different algorithms on the gym Cartpole environment.