A Better Default Colormap for Matplotlib | SciPy 2015 | Nathaniel Smith and Stéfan van der Walt

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Strong rec vitalique · 2018-08 · on “New Map Scale Is More Readable by People Who Are Color Blind”

I am not colorblind, but work with data visualization in Matlab a lot. This hilarious SciPy2015 talk[1] convinced me to switch to the Parula color scheme. There's no way I'm going back to Jet. [1] [this video]

Strong rec Mizza · 2019-04 · on “Unveiling the first-ever image of a black hole [video]”

Grrr, why aren't scientists all using perceptually uniforms color schemes! This would still look awesome in `plasma`. Is there any where I can get the data and do it myself? One of my favorite talks ever is on this subject: [this video]

Strong rec kbd · 2019-08 · on “Turbo, an Improved Rainbow Colormap for Visualization”

If you have any interest in this topic, I’d strongly recommend watching this presentation at SciPy 2015 where Viridis and Inferno were first presented, when the developers of Matplotlib were coming up with a new default colormap to replace Jet: [this video] I'd love to see Turbo analyzed in the same fashion.

Strong rec spectramax · 2019-08 · on “Turbo, an Improved Rainbow Colormap for Visualization”

> Today we are happy to introduce Turbo, a new colormap that has the desirable properties of Jet while also addressing some of its shortcomings, such as false detail, banding and color blindness ambiguity. What are the desirable properties of Jet? Why not use Viridis or Magma or any of the recent linear colormaps from matplotlib 2.0? These shortcomings were the exact reason why Viridis was created as exemplified in this brilliant talk: [this video] I don't understand the point of Turbo. > Turbo mimics the lightness profile of Jet, going from low to high back down to low, without banding. As such, its lightness slope is generally double that of Viridis, allowing subtle changes to be more easily se

Strong rec neilpanchal · 2019-10 · on “Designing Accessible Color Systems”

I built similar perceptually uniform color library: [LINK] The sad part is that we have all these colors available in the physical world but representing in sRGB (or even Adobe RGB or DCI P3) space takes away all the fun. We have a narrow space to work within and it poses limits to what you can do. Vast majority of monitors in the world are barely capable of representing sRGB space, and even if they are, calibration is another issue. You can employ all kinds of science in representing proper colors but if the end user's calibration is not good, all bets are off. Colors are a problem for interface design (and accessibility), it becomes a real challenge when dealing with quantitative data and

Strong rec azepoi · 2020-06 · on “How to Avoid Equidistant HSV Colors (2011)”

I'm impressed by the other mentioned picker, for HCL this time [LINK] This tool is also useful [LINK] I have to mention this fantastic presentation on colormaps [this video] (A Better Default Colormap for Matplotlib, SciPy 2015, Nathaniel Smith and Stéfan van der Walt). It is about perceptually uniform colormaps. [LINK]

Recommends elsherbini · 2016-04 · on “HCL: a color model that actually matches our perceptions (2011)”

This talk from the matplotlib people on making a better color map goes into some details about physical perception and what it means for picking colors: [this video]

Recommends vanderZwan · 2017-10 · on “Sorting Algorithms Revisualized”

There is the cube-helix approach, but I suspect you're thinking about something else. The linked talk about the creation of the viridis colour map[0] actually spends a good ten minutes on explaining colour theory, and makes it look very simple in the process. About ten minutes in it shows the "perceptual colour space" of a typical non-colourblind person. A few minutes later it shows the color spaces for people with various types of colourblindness. Perhaps one could project one perceptual space into the other in a way that more-or-less preserves the original information, but essentially with more banding? [0] [this video]