I’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).
Continue reading
In a 
Andrew Dawes 