Software – Brushing Up Science

Non-scientific software that helps me get science done

This is a shout out to all the software that helps my science happen despite not necessarily being developed for scientific purposes.

Fair warning, the list skews toward Linux programs since that’s what I use in my day-to-day work.

Tmux

I spend a lot of time at the command line. Or rather, command lines (note the plural). I often have four open at once. And I want to see all four at once, and jump back and forth between them all. Separate terminal windows or tabs don’t cut it. But Tmux does.

Here’s a pared-down example of how I might typically use Tmux: two panes, with one for editing text and the other exploring exploring directories.

Not gonna lie, Tmux is awkward to start with. The default keyboard shortcuts aren’t intuitive, simple things like copy/paste functionality don’t necessary work as you’d expect them to, and many online resources are outdated because older versions of Tmux used configuration commands that are no longer compatible.

But Tmux is well worth the learning curve.

Elaborate Matplotlib animations

Matplotlib, Python’s primary scientific plotting library, provides tools to make many elaborate plots, graphs, and diagrams. Many of these can be animated, but the process isn’t always intuitive. The hardest part is learning how to animate a simple line plot (here’s my easy way). Beyond that, the steps to creating most animations tend to be similar.

The examples below demonstrate the particular methods needed to animate common types of plot. Here I focus on the key components needed for updating each frame of the animation. The full code for the examples is here. It includes liberal and arbitrary use of sines and cosines so as to produce looping gifs.

Scatter with variable colour, position, and size

scatter

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Python spoilt me; returning to Matlab is hard

Using Python daily for more than three years as part of my scientific workflow and then abruptly returning to regular Matlab use has made me realise how much better Matlab could be and how evident its idiosyncrasies are. Conversely, while I was aware and noticed that Python makes things simple, it is Matlab’s comparative flaws that really made me come to appreciate just how much has been achieved in the past decade by the community in making Python an indispensable scientific tool.

An aim of this post is to recognize Python’s impressive convenience and versatility. Unfortunately, however, this post more naturally develops by taking the pessimistic approach of highlighting Matlab’s flaws. What follows are several minor, and a few major, annoyances that I’ve noticed on returning to Matlab.

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Organise scripts and figures easily with Jupyter Notebooks

Keeping track of scripts used to generate figures is difficult. Before realising that Jupyter Notebooks could solve most of my problems, I would have directories with dozens of scripts with filenames of varying levels of ambiguity. Names that probably meant something to me at the time, but are hardly descriptive months or years later. Names like ISW_plume_plots.m, new_ISW_model_plots.m, and plot_model_behaviour.m. A certain PhD comic springs to mind.

Regardless of whether its Python, R, Julia, Matlab, or pretty much any other type of code, Jupyter Notebooks solve the problem. For example, I use a single notebook to archive the code for all figures in a paper and, more importantly, I can associate each set of code with the figure it generates. Rather than trying to remember what file I want, I need only remember which figure I want. (I say archive because I much prefer to do the bulk of my exploratory analysis in an editor. Alternatively, JupyterLab may work better for you.)

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This is the best decade to be a grad student

Catching up on the literature is a daunting aspect of graduate studies. As a physical oceanographer, I regularly cite work from 30 to 40 years ago. In that time, and all the way back to the turn of the 20th century, the scientists before me got to answer all the low-hanging-fruit problems and write the papers that will be cited thousands of time. They leave behind the messy, complex, and esoteric questions for the current grad students. Surely, then, I would think the 60s or 70s or even earlier would have been the best time to be a grad student?

LyX: a middle-ground to LaTeX and Word

LyX is a document processor that provides the power and professional-looking typesetting of LaTeX with the familiarity of an easy-to-use graphical interface à la MS Word. Effectively, it provides the best of both worlds. For someone without knowledge of LaTeX, LyX is less imposing and has a smaller learning curve. But even seasoned LaTeX users who have no desire to leave their favourite text editor can take advantage of some of LyX’s features.

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Why LaTeX-typeset documents look professional

Documents typeset using LaTeX just look better than than their MS Word (or equivalent) counterparts. LaTeX has many well-known features to make document creation easy. However, it is some of its lesser-known features that together produce a professional-looking document.

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