Python – Brushing Up Science

Jupyter Notebooks are gone from my scientific workflow

TL;DR: I’ve just learned that the text editor Sublime Text can display images within Markdown files. Gone therefore is my need to use Jupyter Notebooks.

I was never a true convert to Jupyter Notebooks. I used them for several years, and saw their appeal, but they just didn’t quite feel right to me.

Most complaints against Notebooks are technical ones: they’re awkward to version control, they’re hard to debug, and they promote poor programming practices. But these issues are tangential to my complaints against Notebooks, which are are less concrete:

I’m always scrolling. It’s inefficient.
I don’t want to do work in a browser. Maybe it’s a weak reason, but I like keeping my scientific and programming tools separate from the browser.
Editing and navigating Notebooks feels clumsy. Maybe it’s a lack of practice, but I’d rather leverage the time I’ve invested in learning and setting up my text editor than spend time learning a bunch of new shortcuts specific to Notebooks.

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, Part 2

In a previous post, I listed a range of Matlab’s idiosyncrasies and flaws that seemed so much more blatant once I returned from several years of Python use. This post is a continuation, except this time highlighting ways in which Python makes life simpler rather than Matlab making life more difficult.

If you haven’t tried Python and you’re on the fence about whether it’s worth learning, let the points below convince you.

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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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An improved Matlab command line (plus Jupyter Notebooks for Matlab)

The command line is a large part of any Matlab user’s workflow. This vital tool, however, isn’t as user friendly as it should be: it’s cumbersome to recall multi-line commands from the history, there’s no support for Vim key bindings, and there’s no syntax highlighting if using the nodesktop option (on a remote computer, say). Fortunately, there’s an alternative that avoids these problems: IMatlab. This kernel also lets you run Matlab code in Jupyter Notebooks (originally known as IPython Notebooks).

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Invest in a good text editor

Scientists should invest time in a good text editor: pay the upfront cost of learning to use and customising a single editor for all of your text needs. This may be obvious to programmers, but less so to scientists who may have yet to recognise the benefits of a good editor.

Much scientific analysis and documentation can be achieved with plain text files (e.g., .py, .m, .f, .r, .tex, or .md). The default method to work with multiple file types is to use multiple IDEs (Integrated Development Environments): Matlab for m-files, Spyder or IPython notebooks for python scripts, TexStudio or TeXnicCenter for latex files, RStudio for R, or one of the countless editors for Markdown currently available.

Using a single editor has many benefits over using a range of editors within each IDE:

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Vector and raster in one with Matplotlib

Vector images are great, except when they shouldn’t be vector. Figures with intricate detail can actually benefit from being rasterized. This can reduce file size and help the figure load more quickly. Python’s Matplotlib has an option to rasterize certain elements, but it doesn’t always work as simply as expected.

This post describes a function that (i) lets you rasterize any chosen elements when you export the figure and (ii) overcomes problems with the current implementation of rasterizing objects with Matplotlib.

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Matplotlib animations the easy way

Creating animations with Python’s Matplotlib is quick and easy once you know how to do it. However, when learning I found the tutorials and examples online either daunting, overly sophisticated, or lacking explanation. In many cases all I need is a quick-and-dirty script that works, rather than longer code that adheres to best practices.

See here for a follow up post with more elaborate animation examples.

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