ISciNumPy.dev

I recently got an M1 mac, and I’ll be cataloging my experience with using it for scientific software development. I’ll be returning to update this page periodically, and will eventually have a focused recommendation for Apple Silicon setup, similar to my Intel setup.

cibuildwheel has just had two back-to-back releases, two weeks apart, representing several months of hard work and some exciting few features! I will be covering both releases at once, so we will discuss Apple Silicon support, architecture emulation on Linux, integrated PEP 621 Requires-Python support, the native GitHub Action, extended build and test controls, and more!

If you are following the releases, 1.7.0 came out last November (2020), and included the fantastic output folding feature, which makes logs much easier to read on CI systems that support folding, and makes it much easier to see how long each step takes. The 1.7.x series also included the addition of the working examples section of the documentation, which tracks some known projects using cibuildwheel, such as scikit-learn, Matlotlib, and MyPy; it is a great place to go to look into how other projects have integrated cibuildwheel into their workflow.

I have an general overview post as well. Now let’s look at what’s new! Update: cibuildwheel is now an official package of the PyPA!

This is the first of two posts on cibuildwheel, a fantastic project I joined after switching to it from my own azure-wheel-helpers, which I’ve blogged about here before. It is the best wheelbuilding system available for Python today, and can make something that is normally a pain to setup and a headache to maintain a breeze (by forcing all the headaches on us, of course, as maintainers, but it’s better to solve issues centrally! Obviously we rather like solving these problems. Or we are just crazy, which is also possible ;) ).

Be sure to checkout the followup post over new features in 1.8.0 and 1.9.0, too! Also, cibuildwheel was recently accepted into the PyPA!

I am pleased to announce the release of pybind11 2.6.0! This is the largest release since 2.2 (released over three years ago). I would like to highlight some of the key changes below; be sure to check out the changelog and upgrade guide for more information! The focus of this release was stability, packaging, and supporting more platforms, though there are a lot of small features and useful additions, covered by newly expanded docs.

Now that SciPy 2020 is over, I would like to share the process I used to create the talk video. The effect was designed to recreate the feeling of watching an actual in-person talk. I will first cover parts, detailing what I got and some general suggestions, then I’ll discuss the filming process, and finally, I will cover the post-process procedure and software. The entire process took about a day and a half, with an overnight render, and cost about $200 (best compared to the cost of registration of a live conference).

COVID-19 is ravaging the globe. Let’s look at the excellent Johns Hopkins dataset using Pandas. This will serve both as a guideline for getting the data and exploring on your own, as well as an example of Pandas multi-indexing in an easy to understand situation. I am currently involved in science-responds.

The foundational histogramming package for Python, boost-histogram, hit beta status with version 0.6! This is a major update to the new Boost.Histogram bindings. Since I have not written about boost-histogram yet here, I will introduce the library in its current state. Version 0.6.2 was based on the recently released Boost C++ Libraries 1.72 Histogram package. Feel free to visit the docs, or keep reading this post.

This Python library is part of a larger picture in the Scikit-HEP ecosystem of tools for Particle Physics and is funded by DIANA/HEP and IRIS-HEP. It is the core library for making and manipulating histograms. Other packages are under development to provide a complete set of tools to work with and visualize histograms. The Aghast package is designed to convert between popular histogram formats, and the Hist package will be designed to make common analysis tasks simple, like plotting via tools such as the mplhep package. Hist and Aghast will be initially driven by HEP (High Energy Physics and Particle Physics) needs, but outside issues and contributions are welcome and encouraged.

Python 3.8 is out, with new features and changes. The themes for this release have been performance, ABI/internals, and static typing, along with a smattering of new syntax. Given the recent community statement on Python support, we should be staying up to date with the current changes in Python. As Python 2 sunsets, we are finally in an era where we can hope to someday use the features we see coming out of Python release again!