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A common criticism about the Python programming language is that it is slow, often with reference to a benchmark comparing a range of tasks. This criticism is widely addressed with articles by Jake van der Plass and Anthony Shaw being two excellent examples. While I don’t disagree with any of the points raised in these articles, I think they miss an important aspect of performance—specificity. Python is a general purpose language, used for nearly everything from embedded devices with uPython to distributed processing of petabytes of data.

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A guide to using continuous integration with LaTeX projects.

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Abstract One of the key features of computational experiments is being able to run the experiment over a large variable space. However, in my experience there aren’t tools available to assist with this, particularly in the realm of High Performance Computing (HPC), where bash arrays and loops are commonplace. Using the current toolset, I made lots of errors in the specification of files, turning a ‘quick edit’ into a tedious process of find the bug.

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A piece of software I have been using in my reasearch is Hoomd, a ‘relatively’ new package for running Molecular Dynamics (MD) simulations. These MD simulations have the basic premise of throwing hundreds of balls into a box and shaking it to find out what happens. The relative newness of Hoomd is in comparison to other software packages like LAMMPS and GROMACS which have been around for decades, while the initial release of Hoomd was in 2012.

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Competition is a strange thing making you suddenly interested in the most unusual of problems It has become a tradition of The Lancer Band, of which I am a member, to produce a video as part of our ANZAC Day commemorations. These videos have been highly successful garnering millions of views on Facebook and with some choice communications throughout the rest of the year have resulted in a commendable social media following.

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