Call for code nominations for Elegant SciPy!

Update: See the also the clarifications to this post, and submit code by creating an issue in our GitHub repo!

It’s official! Harriet Dashnow, Stéfan van der Walt, and I will be writing an O’Reilly book about the SciPy library and the surrounding ecosystem. The book is called Elegant SciPy, and is intended to teach SciPy to fledgling Pythonistas, guided by the most elegant SciPy code examples we can find.

So, if you recently came across scientific Python code that made you go “Wow!” with its elegance, simplicity, cleverness, or power, please point us to it! As an example, have a look at Vighnesh Birodkar’s code to build a region adjacency graph from an n-dimensional image, which I highlighted previously here.

Each chapter will have one or two snippets that we will work towards. Each of these will be credited as “written by/nominated by”, and needs to be published under a permissive license such as MIT, BSD, or public domain to be considered for inclusion. We would especially like nominations in the following categories:

  • statistics (using scipy.stats)
  • image processing and computer vision
  • Fourier transforms
  • sparse matrix operations
  • eigendecomposition and linear algebra
  • optimization
  • streaming data analysis
  • spatial and geophysical data analysis

We’ll also consider other parts of the SciPy library and ecosystem.

We invite you to submit code snippets for inclusion in the book. We’d also appreciate a small description of about one paragraph explaining what the code is used for and why you think it’s elegant, even though this is often self-evident. =)

How to submit

Thank you,

Juan, Harriet, and Stéfan.

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