Thanks for all the contributions so far. Looks like matplotlib is blessed
with people who are far more knowledgeable than I on the subject, and I'd
say we were pretty much at a consensus on requirements.
Given these requirements, what we need is some proposed colormaps - Max's
approach of generating an optimal solution in LAB space sounds interesting,
as do the other approaches of minimising some parameters of existing
colormaps.
To facilitate this discussion, do we need a repository of proposed
colormaps so that we can compare like with like? I notice that Mike Bostock
has an interesting post to show various color spaces in d3.js which may be
of interest http://bl.ocks.org/mbostock/3014589.
On 4 December 2014 at 14:38, Maximilian Albert <maximilian.alb...@gmail.com>
wrote:
> Hi all,
>
> I had a discussion with Phil Elson about this last weekend during the
> Bloomberg Open Source Day. I don't consider myself an expert on colormaps
> by any means, but I started digging into them a while ago when I was
> looking for a way of generating a perceptually linear *cyclic* colormap in
> order to represent phase angle values. (I've been meaning to discuss this
> issue on this list for a while but will do so in a different thread once I
> get around to tidying up my results so far.) Phil encouraged me to reply to
> this thread because he said that even non-expert views would be very
> welcome, so here you go.
>
> Basically, I agree with most of what Nathaniel Smith suggested in his
> email from November 21. I'm going to comment on some of his points inline
> below and will finally suggest a way of designing a new colormap at the end.
>
>
> Nathaniel Smith wrote:
>
> > it should be a sequential colormap [...]
>
> Agreed.
>
> > it should be perceptually uniform
>
> Agreed.
>
> > 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].
>
> I haven't had time to check this out in detail yet, but it looks pretty
> interesting and will certainly be very useful to assess the quality of any
> suggestions. However, can this help to actually *design* a new colormap?
> The answer might be hidden in the referenced paper [Luo2006], but I haven't
> read it yet.
>
> > or just using good-old-L*a*b* is a reasonable quick-and-dirty
> approximation.
>
> Can you elaborate how "dirty" you think using L*a*b* would be? (See my
> suggestion for finding a new colormap below.)
>
> >- it should have a perceptually uniform luminance ramp, i.e. if you
> > convert to greyscale it should still be uniform.
>
> Agreed. What's unclear to me is how large this luminance ramp should be.
> We certainly can't go all the way to full luminance because this won't be
> visible on a white background. This probably needs experimenting (again see
> below).
>
> > - 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.
>
> Agreed.
>
> > - 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.)
>
> Agreed. Are you aware of any simple ways of avoiding the most common
> issues? Are there any blog posts or papers on designing colormaps that are
> suitable for colorblind people?
>
> > 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.)
>
> Just out of interest, is there currently an easy way in matplotlib of
> producing a 3d plot where luminance is used for lighting/shading as you
> suggest?
>
>
> Now the question is: how do we actually *design* a colormap with these
> requirements? Leon Krischer's notebook [1] looks totally awesome, but if I
> understand correctly the optimisation he uses "only" takes care of
> linearising the luminance value, but this does not necessarily guarantee
> that the hue values are also linear, right? It also feels somewhat clumsy
> to me to start out with a colormap that's "wrong" (w.r.t. our requirements
> above) and then "fix" it. However, the notebook looks like a great guidance
> for finding suitable candidates and assessing their quality.
>
> It appears to me that a simple yet effective way of coming up with a good
> colormap would be to pick two points in the L*a*b* color space that can be
> represented by RGB values, connect them by a line and use the interpolated
> values for the resulting colormap. Since L*a*b* space is (close to)
> perceptually linear, this would pretty much guarantee all the requirements
> above.
>
> What's missing is an easy way of doing this. I'm envisaging a simply GUI
> which allows the user to easily pick two points in L*a*b* space, generates
> a colormap from them as described above and also generates a few sample
> plots to evaluate the quality of the colormap (along the lines of [1] or
> the numerous blog posts linked to in the discussion). I am close to having
> a prototype for such a GUI which should allow to do this relatively
> painlessly. I'll try to finish it up over the weekend and will post here
> once it's ready. Btw, if anyone has suggestions for sample datasets that
> can help in assessing the quality of colormaps they would be much
> appreciated.
>
> Any comments or clarifications of points that I misunderstood are very
> welcome.
>
> Best wishes,
> Max
>
> [1] http://nbviewer.ipython.org/gist/krischer/d35096a9d3b6da5846a5
>
>
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