Favorite posts and series

C++ 11 14 17 20macOS SetupAzure DevOps (Python Wheels) • ROOT Conda-ForgeCLI11GooFitPython BindingsPython 3SSH

My books and workshops

Modern CMakeCMake WorkshopComputational Physics Class

My projects



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.

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Setup a New Mac

I recently set up a new mac. Here is a list of my favorite things to do to properly setup macOS for scientific work.

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Azure DevOps: Python Wheels

This is the third post in a series about Azure DevOps. This one is about making Python wheels. If you want to play nice with Python users, or you have a complex build, this will make your package far more accessible to users. They are faster to install and to use and more secure. We will quickly cover making universal wheels, then we will move on to fully compiled binaries, including C++14, manylinux2010, and other hot topics. This series was developed to update the testing and releasing of Python packages for Scikit-HEP. The results of this tutorial can be seen in the boost-histogram repository, under the .ci folder.

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Azure DevOps: Releases

This is the second post in a series about Azure DevOps. This one is about release pipelines; if you use Azure to build packages (like binaries, etc.), how do you push them to a final endpoint? In this example, we will be building a simple pure Python package, and pushing the result to Test-PyPI. You can adapt it to your situation, however. The third post will cover building Python binaries. This series was developed to update the testing and releasing of Python packages for Scikit-HEP. Several of the projects in SciKit-HEP are using release pipelines, include boost-histogram and Particle.

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Azure DevOps: Introduction

Continuous Integration (CI) is fantastic for software development and deployment. One of the newest entries into the CI market1 is Microsoft’s Azure DevOps. Their Open Source support is impressive; it is likely part of the recent push2 by Microsoft to be more Open Source friendly. Open Source projects get 10 parallel builds, unlimited build minutes, 6 hour job timeouts, and incredibly fast jobs on macOS, Linux, and Windows, all via a single platform. Quite a few major projects3 have been moving to Azure since the initial release in December 2018. The configuration of DevOps is second only to GitLab CI in ease of use and possibly the most expressive system available. The multiple pipeline support also scales well to complicated procedures.

This is the first in a series of posts covering an introduction to setting up projects in Azure DevOps, developed to update the testing and releasing of Python packages for Scikit-HEP, a project for a coherent High Energy Physics Python analysis toolset. The second post covers release pipelines, and the third covers building binary Python packages using DevOps.

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ROOT on Conda Forge

Linux and macOS packages for Python 2.7, 3.6, and 3.7

For High Energy Physics, the go-to framework for big data analysis has been CERN’s ROOT framework. ROOT is a massive C++ library that even predates the STL in some areas. It is1 also a JIT C++ interpreter called Cling, probably the best in the business. If you have heard of the Xeus C++ Kernel for Jupyter, that is built on top of Cling. ROOT has everything a HEP physicist could want: math, plotting, histograms, tuple and tree structures, a very powerful file format for IO, machine learning, Python bindings, and more. It also does things like dictionary generation and arbitrary class serialization (other large frameworks like Qt have similar generation tools).

You may already be guessing one of the most common problems for ROOT. It is huge and difficult to install – if you build from source, that’s a several hour task on a single core. It has gotten much better in the last 5 years, and there are several places you can find ROOT, but there are still areas where it is challenging. This is especially true for Python; ROOT is linked to just one version of Python, and the one you get with pre-built ROOT can often be the wrong one. And, if you use the Anaconda Python distribution, which is the most popular scientific distribution of Python and massively successful for ML frameworks, the general rule even for people who build ROOT themselves has been: don’t. But now, you can get a fully featured ROOT binary package for macOS or Linux, Python 2.7, 3.6, or 3.7, from Conda-Forge, the most popular Anacanda community channel!

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