Scientific Visualization Book
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
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| Statistical data visualization in Python | ||||||||||
| Scientific Visualization Book | 9,131 | 4 days ago | 17 | other | Python | |||||
| An open access book on scientific visualization using python and matplotlib | ||||||||||
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| Visual analysis and diagnostic tools to facilitate machine learning model selection. | ||||||||||
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| High-level geospatial data visualization library for Python. | ||||||||||
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Scientific Visualization: Python + Matplotlib
Nicolas P. Rougier, Bordeaux, November 2021.
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
- Python & OpenGL for Scientific Visualization
- From Python to Numpy (Scientific Python Volume I)
- 100 Numpy exercices
- Matplotlib cheat sheets
Book gallery
