Re: [matplotlib-devel] Matplotlib's new default colormap

[Thomas C. <tca...@gm...>]

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

Tom

On Sat Nov 22 2014 at 9:33:11 AM gary ruben <gar...@gm...> wrote:

> There was a talk by Kristen Thyng at scipy2014 that might be a good
> backgrounder for this:
> http://pyvideo.org/video/2769/perceptions-of-matplotlib-colormaps
>
> At the end she references this site http://mycarta.wordpress.com/ of
> Matteo Niccoli which is full of good content. I wonder if it's worth
> contacting Kristen or Matteo to let them know there's a discussion going on
> here that they might be interested in?
>
>
> On 22 November 2014 at 09:53, Eric Firing <ef...@ha...> wrote:
>
>> On 2014/11/21, 4:42 PM, Nathaniel Smith wrote:
>> > On Fri, Nov 21, 2014 at 5:46 PM, Darren Dale <dsd...@gm...>
>> wrote:
>> >> On Fri, Nov 21, 2014 at 12:32 PM, Phil Elson <pel...@gm...>
>> 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, (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-L*a*b* 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 :-)
>> >
>>
>>
>>
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