Blog

• Trigger Azure DevOps PR build validation pipelines with az cli

Branch policies in Azure DevOps (ADO) can be used to protect important branches. With branch policies, it’s possible to enforce, for example, that all pull requests into your main branch are given a minimum number of approvals before being allowed to complete.
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• In Defence of the Humble Pie Chart

If you’re active in any Data Science communities—whether on Twitter, Reddit, LinkedIn, Discord, or wherever else—you’ve probably already encountered a member of the pie chart hate-brigade. In fact, in reading this post, there is a good chance that you’re already a member of this group, rallying to heap further criticism on your least favourite graphic!
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• Posterior predictives for AR(p) models in Stan

Autoregressive (AR) models represent a popular type of statistical model. They are used to describe processes which evolve through time. Often then, a statistician is interested in fitting such a model to real data, with the intention of using the fitted model to make predictions about the future.
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• Matplotlib plots for LaTeX with PGF

Matplotlib’s pgf backend is pretty great, allowing plots to be exported directly from python to pgf drawing commands. These drawing commands can be inserted directly into a LaTeX .tex document, and so the generated plot will be realised at compile time. This method of embedding plots into a LaTeX document allows a quick and easy method to ensure that fonts between your document body and plots match.
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• Speed-up numpy with Intel's Math Kernel Library (MKL)

The numpy package is at the core of scientific computing in python. It is the go-to tool for implementing any numerically intensive tasks. The popular pandas package is also built on top of the capabilities of numpy.
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• Matplotlib graphics for the metropolis beamer theme

Beamer is a great tool to make presentations with, and is indispensable to those who need to typeset mathematics within their slides. Beamer is actually just a LaTeX document class, so its syntax and setup is familiar to those who have experience working with TeX and friends.
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• Matplotlib boxplots with custom percentiles

This post was inspired by a question I answered on stack overflow. In the question a user asked if it was possible to make a boxplot with box boundaries at arbitrary percentiles, using matplotlib. Of course, with matplotlib anything is possible and so I set to work…
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• Speed up Python code with Numpy: an example case

In this post I shall introduce the definition of the effective sample size (ESS) as given by Gelman et. al in their book Bayesian Data Analysis 3. Afterwards I shall review PyMC’s computation of the ESS. PyMC’s implementation provides a perfect example case of how we can speed up code with Numpy. I show how we can do so and compute the ESS over 500x faster than PyMC. I’ve posted the full example code and speed comparison used in this post here.
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• Plot circular data with matplotlib

Circular data arises very naturally in many different situations. Meterologists regularly encounter directional data when considering wind directions, ecologists may come across angular data when looking at the directions of motion of animals, and we all come into contact with at least one type of circular data every day: the time.
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• Plot publication-quality figures with matplotlib and LaTeX

Figures are an incredibly important aspect of effectively communicating research and ideas. Bad figures are bad communicators: difficult to understand and interpret. They rear their ugly heads only to nauseate the reader and detract from the accompanying text. Good plots, however, are clear and concise. They seamlessly blend with their accompanied text and complement its narrative. Well executed figures should leave our readers informed, soothed, and certainly not nauseated.
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