Figure featured in PRA Kaleidoscopes

3d matplotlib image

Q-function figure (without labels) as presented in PRA Kaleidoscope

This figure, generated from our data and visualized using matplotlib, was selected to be featured in PRA’s Kaleidoscope listings. Figures are selected for aesthetics so we are naturally pleased to have this recognition.

For more on how this figure was created, see my earlier post on publication-ready 3D figures from Matplotlib. Finally, the source for this figure is available as an Ipython notebook in our github repository:

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Publication-ready 3D figures from Matplotlib

Screen Shot 2014-06-27 at 9.23.25 AMI’m a big fan of scripting my plot creation. I also use python for all of my data acquisition and analysis. This naturally put me in a spot to dive into matplotlib when it came time to create figures for a paper I’m working on. It took a bit of digging, but I worked through the kinks and put together a 3D surface plot (with contours) that is PDF and publication ready. I addressed the several issues by overriding the defaults. I want to clarify that I like the defaults for on-screen display, presentations, and posters, but they weren’t quite right for a tiny two-pane figure in a two-column manuscript. Things I “fixed” include:

  • Too many ticks and labels
  • Small fonts (when reduced to publication dimensions)
  • Tick labels misaligned relative to ticks (problem seems to come from using larger fonts to fix the previous issue)
  • Odd axes label alignment, rotation, and placement
  • Busy background (removed the gray panes with gridlines)

Some of the solutions were easy, some are hackish and only one uses the lightly-documented _axinfo dict. I’ll highlight a few snippets that were useful in this process (full code linked below).
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