Matplotlib's new default colormap

The contents of that talk are also in our documentation http://matplotlib.org/users/colormaps.html

Tom

···

On 22 November 2014 at 09:53, Eric Firing <efiring@…229…> wrote:

On 2014/11/21, 4:42 PM, Nathaniel Smith wrote:

On Fri, Nov 21, 2014 at 5:46 PM, Darren Dale <dsdale24@…149…> wrote:

On Fri, Nov 21, 2014 at 12:32 PM, Phil Elson <pelson.pub@…149…> wrote:

Please use this thread to discuss the best choice for a new default

matplotlib colormap.

This follows on from a discussion on the matplotlib-devel mailing list

entitled “How to move beyond JET as the default matplotlib colormap”.

I remember reading a (peer-reviewed, I think) article about how “jet” was a

very unfortunate choice of default. I can’t find the exact article now, but

I did find some other useful ones:

http://cresspahl.blogspot.com/2012/03/expanded-control-of-octaves-colormap.html

http://www.sandia.gov/~kmorel/documents/ColorMaps/

http://www.sandia.gov/~kmorel/documents/ColorMaps/ColorMapsExpanded.pdf

Those are good articles. There’s a lot of literature on the problems

with “jet”, and lots of links in the matplotlib issue [1]. For those

trying to get up to speed quickly, MathWorks recently put together a

nice review of the literature [2]. One particularly striking paper

they cite studied a group of medical students and found that (a) they

were used to/practiced at using jet, (b) when given a choice of

colormaps they said that they preferred jet, © they nonetheless made

more medical diagnostic errors when using jet than with better

designed colormaps (Borkin et al, 2011).

I won’t suggest a specific colormap, but I do propose that whatever we

chose satisfy the following criteria:

  • it should be a sequential colormap, because diverging colormaps are

really misleading unless you know where the “center” of the data is,

and for a default colormap we generally won’t.

  • it should be perceptually uniform, i.e., human subjective judgements

of how far apart nearby colors are should correspond as linearly as

possible to the difference between the numerical values they

represent, at least locally. There’s lots of research on how to

measure perceptual distance – a colleague and I happen to have

recently implemented a state-of-the-art model of this for another

project, in case anyone wants to play with it [3], or just using

good-old-Lab* is a reasonable quick-and-dirty approximation.

  • it should have a perceptually uniform luminance ramp, i.e. if you

convert to greyscale it should still be uniform. This is useful both

in practical terms (greyscale printers are still a thing!) and because

luminance is a very strong and natural cue to magnitude.

  • it should also have some kind of variation in hue, because hue

variation is a really helpful additional cue to perception, having two

cues is better than one, and there’s no reason not to do it.

  • the hue variation should be chosen to produce reasonable results

even for viewers with the more common types of colorblindness. (Which

rules out things like red-to-green.)

And, for bonus points, it would be nice to choose a hue ramp that

still works if you throw away the luminance variation, because then we

could use the version with varying luminance for 2d plots, and the

version with just hue variation for 3d plots. (In 3d plots you really

want to reserve the luminance channel for lighting/shading, because

your brain is really good at extracting 3d shape from luminance

variation. If the 3d surface itself has massively varying luminance

then this screws up the ability to see shape.)

Do these seem like good requirements?

Goals, yes, though I wouldn’t put much weight on the “bonus” criterion.

I would add that it should be aesthetically pleasing, or at least

comfortable, to most people. Perfection might not be attainable, and

some tradeoffs may be required. Is anyone set up to produce test images

and/or metrics for judging existing colormaps, or newly designed ones,

on all of these criteria?

Eric

-n

[1] https://github.com/matplotlib/matplotlib/issues/875

[2] http://uk.mathworks.com/company/newsletters/articles/rainbow-color-map-critiques-an-overview-and-annotated-bibliography.html

[3] https://github.com/njsmith/pycam02ucs ; install (or just run out

of the source tree) and then use pycam02ucs.deltaEp_sRGB to compute

the perceptual distance between two RGB colors. It’s also possible to

use the underlying color model stuff to do things like generate colors

with evenly spaced luminance and hues, or draw 3d plots of the shape

of the human color space. It’s pretty fun to play with :slight_smile:


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