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).[Read More]
Favorite posts and series
My books and workshops
pybind11 (python_example, cmake_example, scikit_build_example) • cibuildwheel • build • scikit-build (cmake, ninja) • boost-histogram • Hist • UHI • Scikit-HEP/cookie • Vector • CLI11 • Plumbum • GooFit • Particle • DecayLanguage • Conda-Forge ROOT • POVM • Jekyll-Indico • PyTest GHA annotate-failures
Johns Hopkins COVID-19 Dataset in Pandas
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.[Read More]
The boost-histogram beta release
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.[Read More]
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![Read More]
The final meeting for new features in C++ is over, so let’s explore the new features in C++, from a data science point of view. This is the largest release of C++ since C++11, and when you consider C++14 and C++17 to be interim releases, the entire 9 year cycle is possibly the largest yet! It may not feel quite as massive as C++11, since we didn’t have interim releases for C++11 and because C++11 is a much more complete, useful language than C++03, but this is still a really impactful release!
Let’s look at the major new features, as well as collections of smaller ones.[Read More]