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
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.)
Continue reading “Organise scripts and figures easily with Jupyter Notebooks”
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.
Continue reading “An improved Matlab command line”
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.,
.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 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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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.
Continue reading “Matplotlib animations the easy way”