[HN Gopher] Rainbow Color Map Still Considered Harmful (2007) [pdf]
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Rainbow Color Map Still Considered Harmful (2007) [pdf]
Author : Tomte
Score : 38 points
Date : 2021-06-28 07:14 UTC (1 days ago)
(HTM) web link (raw.githubusercontent.com)
(TXT) w3m dump (raw.githubusercontent.com)
| joannajohn wrote:
| Fine
| chrismorgan wrote:
| The integrity of the argument may be harmed somewhat by the use
| of an inferior gradient blending technique. Eyeballing Figure 3,
| the gradient interpolation is linear, which is a common but bad
| technique. https://larsenwork.com/easing-gradients/ shows some
| nice examples of the problems and the proper easing technique.
| Moreover, looking at the green-red gradient especially, I think
| the interpolation may have been done in sRGB space, which is
| awful for these sorts of things (on the other hand, they do call
| it _isoluminant green-red_ , so I may be missing the mark in my
| sRGB guess--it's hard to be sure by just eyeballing). On the
| matter of such colour spaces,
| https://raphlinus.github.io/color/2021/01/18/oklab-critique....
| has the best tool for demonstration and comparison that I know
| of.
|
| The techniques employed harm the rainbow gradients significantly,
| making them _far_ less smooth than they would be with better
| interpolation, even with the same colour stops. On that point,
| the first row of that diagram feels more like a straw man than an
| honest comparison--it shows massive and uneven colour bands which
| I don't _think_ is entirely artefacts of the gradient
| interpolation.
|
| I will admit that users of rainbow colour maps are more likely to
| interpolate poorly and not space colours evenly, but still, I
| wish the article had done as good a job as possible on the
| rainbow colour map, to show that even then it's still
| problematic.
| [deleted]
| echelon wrote:
| Viridis is my favorite and solves so many problems.
|
| Magma/plasma/inferno are nice too.
|
| https://cran.r-project.org/web/packages/viridis/vignettes/in...
|
| Here's plenty of other people advocating for viridis as well:
|
| https://stats.stackexchange.com/questions/223315/why-use-col...
|
| https://medvis.org/2016/02/23/better-than-the-rainbow-the-ma...
|
| Use viridis.
| an1sotropy wrote:
| but you know that this simplistic "use viridis" advice is about
| as sound as saying "use eslint" or "use python" or "use double
| precision [floating point]". All of these things may be helpful
| advice in many circumstances, when the audience doesn't know
| any better.
|
| But, all of it lives within a one-size-fits-all mentality that
| more experienced users will be annoyed with (who made you the
| expert to know that viridis was best for my problem?).
|
| And, it decreases the chances that new users will appreciate
| that this is a domain (whether it be how to code with JS, or
| what language to pick, or how to do numeric computations) in
| which the path you take depends on the where you want to go,
| and there are lots of places to go. Colormap choice depends on
| the questions you want the visualization to help answer about
| your data.
|
| So, implied by "use viridis", or any ther one-size-fits-all
| advice, is either "you are simple", "your needs are simple", or
| "this domain is simple", which implies a lot more hubris than
| was probably intended.
| echelon wrote:
| Viridis is strictly better for human perception, and I cited
| a lot of arguments and evidence for this.
|
| I guess I don't understand your take here.
| sega_sai wrote:
| I certainly understand some arguments against rainbow, but in the
| same time the new default colour map in matplotlib (viridis) is
| (IMO) worse for many applications. It doesn't give enough
| contrast/doesn't define contour-lines so well as rainbow. It'
| true that I could probably find some other colour map, but I
| still often switch to jet.
| NavinF wrote:
| Turbo has all the contrast, but none of the false contours of
| Jet: https://ai.googleblog.com/2019/08/turbo-improved-rainbow-
| col...
| bluenose69 wrote:
| I suggest turbo to lots of people who love jet and they find
| it a useful replacement for those cases where the
| distinctness of colours is helpful, e.g. noisy fields for
| which contours are too much of a mess to understand.
|
| As others have said, the choice of colour scheme depends on
| the application. And it can even make sense to use a
| distinctly suboptimal scheme, if the goal is to produce
| graphs that can be compared easily with existing works. For
| example, annual reports of datasets are a lot easier to
| compare if the changes to the colour scheme are avoided.
| beojan wrote:
| Part of the point of viridis is to not have random contours
| that stick out because of the colour map.
|
| If you want to highlight certain contours, plot the contour
| lines.
| aurelwu wrote:
| it's rather strange they suggest red-green as an alternative
| given that this combination is affected by the most common form
| of color-weakness/blindness.
| an1sotropy wrote:
| In the tradition of "considered harmful" papers, I think this is
| one of the less convincing. Or rather, it doesn't meaningfully
| answer the question: if the rainbow colormap is so awful, why is
| it still so common? Is it really "due to inertia", as the authors
| suggest? Or is there some other virtue?
|
| How about: I want to maximize the number of distinctions between
| values that are possible via comparisons of colors. Something
| that takes a long path through colorspace is best for this. Short
| simple paths do better enable making ordinal judgements between
| values (which the authors care a lot about), but they're not as
| good for maximizing distinctions.
|
| Were data vis a more conceptually mature discipline, we wouldn't
| rely on this kind of simple prescriptive guidance.
| vlmutolo wrote:
| The problem is that people will naturally interpret colors on
| certain scales whether that was intended by the authors or not.
| The goal of visualization is to make the natural reaction the
| correct one.
|
| https://jakevdp.github.io/blog/2014/10/16/how-bad-is-your-co...
|
| The link above goes over some interesting plots that have very
| misleading color gradients.
| an1sotropy wrote:
| The implicit assumption there is that all data visualizations
| should be fast or "natural" to comprehend. Some people think
| that all user interfaces should be simple and easy for
| novices. Those people are not airplane pilots, or train
| engineers, or art historians for that matter.
|
| Not all visual things are simple, and not all simple visual
| things are effective for their intended purpose.
|
| If non-monotonic luminance variations are an acceptable cost
| for a colormap that otherwise offers superior
| discriminability, than that's a colormap that someone may
| have a good reason to choose.
| touisteur wrote:
| I always thought that it was a quick metaphor for heat(red,
| white) and cold (blue, black). I have a hard time relating
| other colormaps in the same way.
|
| Maybe the difference is not 'conceptual' but some atavic or
| cultural reaction?
| timy2shoes wrote:
| > if the rainbow colormap is so awful, why is it still so
| common?
|
| Because it's the default for a lot of matplotlib
| visualizations. Per Goethe: "Misunderstandings and lethargy
| perhaps produce more wrong in the world than deceit and malice
| do."
| hprotagonist wrote:
| https://jakevdp.github.io/blog/2014/10/16/how-bad-is-your-co...
|
| a decade or so later, people are still using jet and it still
| sucks!
| ridaj wrote:
| See also https://ai.googleblog.com/2019/08/turbo-improved-
| rainbow-col...
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