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/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, (c) 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
[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
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