I sit a few buildings down from Cindy&#x27;s office and have also been studying colormaps quite a bit in the last 3 years.  Its interesting how many plotting packages get this wrong but are finally catching up.<p>I switched from Matlab to Python years ago and was sad to see pyplot using the default rainbow palette still. However, there was some good work done by Chris Beaumont to improve the plot quality. See: <a href="http:&#x2F;&#x2F;plotornot.chrisbeaumont.org&#x2F;" rel="nofollow">http:&#x2F;&#x2F;plotornot.chrisbeaumont.org&#x2F;</a>  You can easily import these styles into matplotlib using rcparams.<p>Matlab is using a roughly perceptually linearly luminant colormap they call Parula now.  Good job Matlab.<p>Paulo Penteado has also done some good work in this area. See: <a href="http:&#x2F;&#x2F;www.ppenteado.net&#x2F;ast&#x2F;csbc2012_pfp_2_pres.pdf" rel="nofollow">http:&#x2F;&#x2F;www.ppenteado.net&#x2F;ast&#x2F;csbc2012_pfp_2_pres.pdf</a><p>I want to talk about the Luv Lab colorspace.  There are several places on the net (even in the literature) that are wrong about these colorspaces saying Lab is for emissive displays and Luv is for reflected light.  This is actually not true.  (If anything it is reversed).  See: <a href="https:&#x2F;&#x2F;groups.google.com&#x2F;d&#x2F;msg&#x2F;scikit-image&#x2F;DIRaSXJoEes&#x2F;2jDwuwmxRTYJ" rel="nofollow">https:&#x2F;&#x2F;groups.google.com&#x2F;d&#x2F;msg&#x2F;scikit-image&#x2F;DIRaSXJoEes&#x2F;2jD...</a> and  Berns reference.<p>The interesting with colorspaces (and colormaps thereof) is that working in a perceptual space like Luv&#x2F;Lab is yields a non-linear (and non-convex) gamut in the sRGB space used by most monitors.  There is more &quot;headroom&quot; in the magenta hue of colors than say green.  However, you have to then look at monitor output as a function of hue and human sensitivity -- with a red object and blue object with the same reflectance under the same illumination, the red object will appear darker to humans.  So there are many transfer functions at work here which makes the problem challenging in picking the right colormap that is perceptually uniform, has the maximum number of perceived differences, and has the appropriate number of hues for best represeting your dataset.<p>Finally, it would not be complete of me to not mention this article: <a href="http:&#x2F;&#x2F;ieeexplore.ieee.org&#x2F;xpl&#x2F;login.jsp?tp=&amp;arnumber=4118486&amp;url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D4118486" rel="nofollow">http:&#x2F;&#x2F;ieeexplore.ieee.org&#x2F;xpl&#x2F;login.jsp?tp=&amp;arnumber=411848...</a><p>Slides: <a href="http:&#x2F;&#x2F;www.cs.odu.edu&#x2F;~mweigle&#x2F;cs725s15&#x2F;presentations&#x2F;nam-presentation.pdf" rel="nofollow">http:&#x2F;&#x2F;www.cs.odu.edu&#x2F;~mweigle&#x2F;cs725s15&#x2F;presentations&#x2F;nam-pr...</a>