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Preface

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This book is open-access (i.e. it's free to read at this address) because I believe knowledge should be free. However, if you think the book is worth a few dollars, you can give me a few euros (5€ or 10€). This money will help me to travel to Python conferences and to write other books as well. If you don't have money, it's fine. Just enjoy the book and spread the word about it. The teaser image above comes from the artwork section of my website. It has been made some years ago using the Povray (Persistence of Vision) raytracer. I like it very much because it is a kind of résumé of my research.

About the author

I am a full-time research scientist at Inria which is the French national institute for research in computer science and control. This is a public scientific and technological establishment (EPST) under the double supervision of the Research & Education Ministry, and the Ministry of Economy Finance and Industry. I'm working within the Mnemosyne project which lies at the frontier between integrative and computational neuroscience in association with the Institute of Neurodegenerative Diseases, the Bordeaux laboratory for research in computer science (LaBRI), the University of Bordeaux and the national center for scientific research (CNRS).

I've been using Python for more than 15 years and numpy for more than 10 years for modeling in neuroscience, machine learning and for advanced visualization (OpenGL). I'm the author of several online resources and tutorials (Matplotlib, numpy, OpenGL) and I've been teaching Python, numpy and scientific visualization at the University of Bordeaux and in various conferences and schools worldwide (SciPy, EuroScipy, etc). I'm also the author of the popular article Ten Simple Rules for Better Figures , a popular matplotlib tutorial and an open access book From Python To Numpy.

About this book

This book has been written in restructured text_ format and generated using a customized version of the docutils rst2html.py command line (available from the docutils python package) and a custom template.

If you want to rebuild the html output, from the top directory, type:

$ ./rst2html.py --link-stylesheet            \
                --cloak-email-addresses      \
                --toc-top-backlinks          \
                --stylesheet book.css        \
                --stylesheet-dirs .          \
                book.rst book.html

Or you use the provided make.sh shell script.

The sources are available from https://github.com/rougier/python-opengl.

Last point, I wrote the book in a kind of modern Kerouac's style such that you can download it once and continue reading it offline. Initial loading may be slow though.

Prerequisites

This is not a Python nor a NumpPy beginner guide and you should have an intermediate level in both Python and NumPy. No prior knowledge of OpenGL is necessary because I'll explain everything.

Conventions

I will use usual naming conventions. If not stated explicitly, each script should import numpy, scipy and glumpy as:

import numpy as np

We'll use up-to-date versions (at the date of writing, i.e. August, 2017) of the different packages:

Packages Version
Python 3.6.0
----------- ---------
Numpy 1.12.0
----------- ---------
Scipy 0.18.1
----------- ---------
Cython 0.25.2
----------- ---------
Triangle 20170106
----------- ---------
Glumpy 1.0.6

How to contribute

If you want to contribute to this book, you can:

Publishing

If you're an editor interested in publishing this book, you can contact me if you agree to have this version and all subsequent versions open access (i.e. online at this address), you know how to deal with restructured text (Word is not an option), you provide a real added-value as well as supporting services, and more importantly, you have a truly amazing latex book template (and be warned that I'm a bit picky about typography & design: Edward Tufte is my hero). Still here?

License

Book

This work is licensed under a Creative Commons Attribution-Non Commercial-Share Alike 4.0 International License. You are free to:

  • Share — copy and redistribute the material in any medium or format
  • Adapt — remix, transform, and build upon the material

The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • NonCommercial — You may not use the material for commercial purposes.
  • ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

Code

The code is licensed under the OSI-approved BSD 2-Clause License.