Scientific colour maps

Mentioned in the text is IBM, which did research back in the 90s on perceptually-based colormaps and how to best represent various types of data within the color dimensions of luminescence, saturation and hue [1]. For example, they found that,

(1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad.

(2) The mechanisms in human vision responsible for high spatial frequency information processing are luminance channels. If the data to be represented have high spatial frequency, use a color map which has a strong luminance variation across the data range.

(3) For interval and ratio data, both luminance- and saturation-varying color maps should produce the effect of having equal steps in data value correspond to equal perceptual steps, but the first will be most effective for high spatial frequency data variations and the second will be most effective for low spatial frequency variations.

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[1] the original link got removed from IBMs website. Back in the day it was under

https://www.research.ibm.com/people/l/lloydt/color/color.HTM

A pdf copy is here:

https://github.com/frankMilde/interesting-reads/blob/master/...

I've tried using these before and it always progresses as follows:

> Why are you using such ugly colors? This is weird, just use a rainbow plot, everyone knows what a rainbow plot is. We're not approving these reports until you change it to a rainbow plot.

About 90% of the time rainbow plots just encode "blue=no problem; red=problem; everything else=kinda interesting" so it works out fine anyway.

Sometimes I can sneak a red/blue gradient through, but its rare.

Also see https://matplotlib.org/cmocean/

On this note, is anyone aware of any two-dimensional colourmaps that are perceptually-uniform (or close to it), or perhaps periodic?

I find I often have to hand-roll these myself.

They claim that "[t]he colour gradients are perceptually uniform and ordered to represent data both fairly, without visual distortion, and intuitively", however on my calibrated screen many gradients are visibly biased with non-uniform gradients, especially near the darker end. Most noticeable examples include "batlowK", "lipari", "navia" and even "grayC" which is a grayscale gradient.

edit: missed a part of the quote

Relevant:

http://jakevdp.github.io/blog/2014/10/16/how-bad-is-your-col...

https://matplotlib.org/2.0.2/users/colormaps.html

Relevant:

https://personal.sron.nl/~pault/

The one they call Batlow seems quite similar to the color scheme Matplotlib calls Cubehelix. I quite like it, particularly reversed (cubehelix_r); it goes to white near zero, which is nice when plotting sparse data, is sequential (which is of course table-stakes for accessibility/black and white printing reasons), but the different colors make it easy eyeball “tiny/medium/huge” points.

what a coincidence, just been going thrubour grafana dashboards and setting heatmaps to at least perceptually uniform colour maps. this is great

on my way out the door... Can these be used in R?

No