scientific-visualization-book README

The project's README from github.com/rougier/scientific-visualization-book. As one markdown file: README.md.

Scientific Visualization: Python + Matplotlib

Nicolas P. Rougier, Bordeaux, November 2021.

Front cover

The Python scientific visualisation landscape is huge. It is composed of a myriad of tools, ranging from the most versatile and widely used down to the more specialised and confidential. Some of these tools are community based while others are developed by companies. Some are made specifically for the web, others are for the desktop only, some deal with 3D and large data, while others target flawless 2D rendering. In this landscape, Matplotlib has a very special place. It is a versatile and powerful library that allows you to design very high quality figures, suitable for scientific publishing. It also offers a simple and intuitive interface as well as an object oriented architecture that allows you to tweak anything within a figure. Finally, it can be used as a regular graphic library in order to design non‐scientific figures. This book is organized into four parts. The first part considers the fundamental principles of the Matplotlib library. This includes reviewing the different parts that constitute a figure, the different coordinate systems, the available scales and projections, and we’ll also introduce a few concepts related to typography and colors. The second part is dedicated to the actual design of a figure. After introducing some simple rules for generating better figures, we’ll then go on to explain the Matplotlib defaults and styling system before diving on into figure layout organization. We’ll then explore the different types of plot available and see how a figure can be ornamented with different elements. The third part is dedicated to more advanced concepts, namely 3D figures, optimization & animation. The fourth and final part is a collection of showcases.

Read the book

You can read the book PDF (95Mo, preferred site) that is open access and hosted on HAL which is a French open archive for academics. Up to date version is also available on GitHub here. Sources for the book (including code examples) are available at github.com/rougier/scientific-visualization-book.

Buy the book

If you want to buy the book, you can order a printed edition at amazon.com for 49$. If you want to support or sponsor my future work on Python (and Emacs), you can use paypal, github or liberapay.

If you don't want to spend money, you can simply nominate me for the GitHub stars program if you find my work useful for the community.

Build the book

Ubuntu

See also

scientific-visualization-book (rougier/scientific-visualization-book)

Searching Database…

11.6K ·GitHub
Scientific-visualization-book
What is this?This is an open access book that teaches scientific visualization in Python using Matplotlib, covering everything from core principles to 3D figures, animation, and a showcase gallery.
Is this a book or a code library?It is a book (with accompanying source code and figure examples), not a library you import into your own project.
How do I read it?Read the free PDF hosted on HAL (the open access archive), or get the up-to-date version from the GitHub repo; a printed edition can also be bought on Amazon for $49.
Do I need anything beyond Python and Matplotlib to follow along?The book is built around the standard Python scientific stack: Python, Matplotlib, and NumPy are the core tools used throughout the examples.
Is this only for 2D plots, or does it cover 3D and animation too?It covers more than basic 2D plotting: a dedicated part is devoted to advanced topics including 3D figures, optimization, and animation, plus a final showcase section.
Who is this book written for?It's aimed at scientists and researchers who want to produce publication-quality figures with Matplotlib, from beginners learning the basics to those wanting advanced techniques.
How is the book organized?It has four parts: fundamental Matplotlib principles (figure anatomy, coordinate systems, typography, color), actual figure design (defaults, styling, layout, plot types, ornaments), advanced concepts (3D, optimization, animation), and a final showcase gallery.
Why does the author highlight Matplotlib specifically?The author notes that among the huge Python visualization landscape, Matplotlib stands out for being versatile, powerful, offering an intuitive object-oriented interface that lets you tweak any part of a figure, and suitable for both scientific and non-scientific graphics.
Can I support the author instead of just reading it free?Yes, you can buy a printed edition on Amazon for $49, or sponsor the author's future Python/Emacs work via PayPal, GitHub, or Liberapay.
What are similar projects?pydata-book, word_cloud, data-science-ipython-notebooks, matplotlib, 100-Days-Of-ML-Code, mlcourse.ai, abu, PythonDataScienceHandbook
Which topics is it in?dataviz, plotting, matplotlib, numpy, visualization, data-visualization, data-science, book, learning, tutorial, research, education
Creator
rougier
Size
282.9 MB
Stars
11.6K
Updated
CommitJan 04
Age
7y
Commits · 90 Days