When it comes to plot colors in matplotlib, it's why I start with<p>import seaborn as sns<p>to get a nice plot theme, not "good old 90s" style. See: <a href="https://speakerdeck.com/pmigdal/teaching-machine-learning?slide=5" rel="nofollow">https://speakerdeck.com/pmigdal/teaching-machine-learning?sl...</a>
This will be the default in Matplotlib 2.0, and a beta was just tagged: <a href="http://article.gmane.org/gmane.comp.python.matplotlib.announce/9" rel="nofollow">http://article.gmane.org/gmane.comp.python.matplotlib.announ...</a>
In the talk when he showed the 3 similar maps getting equal score, and then the green one getting a much higher score, that could easily be because people couldn't decide between the 3 similar maps, to they went with the one different map. To do a proper comparison the test should be 1-1 or 3-3, not 3-1.
OP here: really worth watching the embedded video on this topic. Viridis is born out of an awesome, intense deep-dive into research on human vision and perception.
I'm mostly using seaborn (<a href="https://web.stanford.edu/~mwaskom/software/seaborn/" rel="nofollow">https://web.stanford.edu/~mwaskom/software/seaborn/</a>) to wrap matplotlib graphs but great to see a direct alternative into the matplotlib library.
If you're interested in colour maps, cubehelix [1] is a useful scheme to make intensity maps which display well when converted to greyscale.<p>[1] <a href="https://www.mrao.cam.ac.uk/~dag/CUBEHELIX/" rel="nofollow">https://www.mrao.cam.ac.uk/~dag/CUBEHELIX/</a>
I ported these color scales to d3-scale and D3 4.0:<p><a href="https://github.com/d3/d3-scale/blob/master/README.md#sequential-scales" rel="nofollow">https://github.com/d3/d3-scale/blob/master/README.md#sequent...</a>
Is it strange that I find the old "JET" the best? It uses the most hues (green, yellow, red and blue are all there), so things are the most distinguishable. The others are either only blue/green/yellow, or only purple/red/yellow.
If you use gnuplot, which I do exclusively, you may like this repository:<p><pre><code> https://github.com/Gnuplotting/gnuplot-palettes</code></pre>