3 ms·

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

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 5y ago
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.
- an1sotropy 5y ago
  We may be approaching this with different levels of expertise. No, Viridis is not strictly better. You are assuming a particular (simple) tasks, i.e., what is the question about the data that you want the visualization to help answer. Here is a task for which Viridis is not strictly better: show me (1) all places in the data where there the gradient magnitude exceeds some small threshold, while also permitting (2) some appreciation of the over-all form of things. What these requirements ask of the colormap, considered as a path through perceptual colorspace, respectively: (1) always have a high velocity, and be as long a path as possible, while (2) not overlapping on itself in an ambiguous way. The "rainbow" colormap is better at (1) than Viridis because it is simply a longer path through colorspace. It gets that extra length in part by not caring about being monotonic in luminance. If (2) was the only thing that mattered, Viridis might be better, but for lots of technical settings, the over-all form of things are already known, and it is the small fluctuations that are interesting to show, so "rainbow" can be better. Now, you could take things to an extreme and say, okay, if (1) is the task, than here's your colormap: a high-frequency sinusoid. Then the visualization is essentially a huge mess of isocontours. Which is terrible for (2). But "rainbow" is more legible than that, and is going to be better than Viridis for some weightings of (1) and (2). My points are: - blanket statements like "Viridis is better" are both simplistic and arrogant, because neither you nor I are in a position to know what exactly the task is in a given visualization, and different tasks merit consideration of different colormaps - the popularity of blanket statements like "Viridis is better" give people who haven't done as much research in data visualization the impression that vis is an adhoc collection of simple strategies, when it is far from that.

echelon 5y ago

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.

an1sotropy 5y ago
We may be approaching this with different levels of expertise. No, Viridis is not strictly better. You are assuming a particular (simple) tasks, i.e., what is the question about the data that you want the visualization to help answer. Here is a task for which Viridis is not strictly better: show me (1) all places in the data where there the gradient magnitude exceeds some small threshold, while also permitting (2) some appreciation of the over-all form of things. What these requirements ask of the colormap, considered as a path through perceptual colorspace, respectively: (1) always have a high velocity, and be as long a path as possible, while (2) not overlapping on itself in an ambiguous way. The "rainbow" colormap is better at (1) than Viridis because it is simply a longer path through colorspace. It gets that extra length in part by not caring about being monotonic in luminance. If (2) was the only thing that mattered, Viridis might be better, but for lots of technical settings, the over-all form of things are already known, and it is the small fluctuations that are interesting to show, so "rainbow" can be better. Now, you could take things to an extreme and say, okay, if (1) is the task, than here's your colormap: a high-frequency sinusoid. Then the visualization is essentially a huge mess of isocontours. Which is terrible for (2). But "rainbow" is more legible than that, and is going to be better than Viridis for some weightings of (1) and (2). My points are: - blanket statements like "Viridis is better" are both simplistic and arrogant, because neither you nor I are in a position to know what exactly the task is in a given visualization, and different tasks merit consideration of different colormaps - the popularity of blanket statements like "Viridis is better" give people who haven't done as much research in data visualization the impression that vis is an adhoc collection of simple strategies, when it is far from that.