TL;DR: If you have GPU code in your project, setup a GitHub hosted GPU runner today. It is fairly quick to do and will free you from having to run tests manually. Writing automated tests for your code base and certainly for the more complex parts of it has become as normal as brushing your teeth in the morning. Having a system that automatically runs a project’s tests for every Pull Request is completely normal. Read more...

In November 2022, the Chan Zuckerberg Initiative (CZI) awarded the Scientific Python project with a grant to improve communications infrastructure and accessibility. This proposal involves several key areas to improve sustainability, inclusivity and accessibility of the Scientific Python ecosystem. One of these areas is making software documentation and user interfaces available in multiple languages. We are happy to announce that we have organized the necessary infrastructure and processes to allow volunteers to start translating multiple project websites. Read more...

Eighteen years since the release of NumPy 1.0, we are thrilled to announce the launch of NumPy 2.0! This major release marks a significant milestone in the evolution of NumPy, bringing a wealth of enhancements and improvements to users, and setting the stage for future feature development. NumPy has improved and evolved over the past 18 years, with many old releases bringing significant performance, usability, and consistency improvements. That said, our approach for a long time has been to make only incremental changes while carefully managing backward compatibility. Read more...

Given the practical challenges of achieving true randomness, deterministic algorithms, known as Pseudo Random Number Generators (RNGs), are employed in science to create sequences that mimic randomness. These generators are used for simulations, experiments, and analysis where it is essential to have numbers that appear unpredictable. I want to share here what I have learned about best practices with pseudo RNGs and especially the ones available in NumPy. A pseudo RNG works by updating an internal state through a deterministic algorithm. Read more...

One outcome of the 2023 Scientific Python Developer Summit was the Scientific Python Development Guide, a comprehensive guide to modern Python package development, complete with a new project template supporting 10+ build backends and a WebAssembly-powered checker with checks linked to the guide. The guide covers topics like modern, compiled, and classic packaging, style checks, type checking, docs, task runners, CI, tests, and much more! There also are sections of tutorials, principles, and some common patterns.

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In mid-2018 I started learning Python by reading textbooks and watching online tutorials. I had absolutely zero background in computer science, but it seemed interesting so I continued to try. At some point, I decided I wanted to do a master’s degree in statistics, so I began to work on more statistics-based programming. That’s when I found SciPy. I became (and still am) fascinated by the idea of open-source software that is completely free to use and supported by a community of diligent programmers. Read more...

The first Scientific Python Developer Summit (May 22-26, 2023) brought together 34 developers at the eScience Institute at the University of Washington to develop shared infrastructure, documentation, tools, and recommendations for libraries in the Scientific Python ecosystem. Pre-summit planning# Prior to the summit we held several hour-long planning meetings: General (2023-02-27) May 15, Package metrics, DevStats May 15, SPECs May 18, Community & Documentation May 19, Build Systems & CI Infrastructure May 19, PyTest plugins & Sphinx extensions Summit execution# At the summit, we had a brief check-in and then split into several groups based on each developers time and interests. Read more...

Scientific-Python Developer Summit# (May 22-26, 2023, Seattle WA) – The first Scientific Python Developer Summit provided an opportunity for core developers from the scientific Python ecosystem to come together to: improve joint infrastructure better coordinate core projects work on a shared strategic plan Related notes/sites: Worklog. Planning Meeting Notes and Info. One of the focuses of the summit was Sparse Arrays, and specifically their implementation in SciPy. This post attempts to recap what happened with “sparse” at the summit and a glimpse of plans for our continuing work. Read more...