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

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] [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: [VIDEO]

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: [VIDEO] I'd love to see Turbo analyzed in the same fashion.

> 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: [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 seen. This is a valuable feature, since it greatly enhances detail when color can be used to disambiguate the low and high ends. Why would anyone want low-to-high-to-low colormap? > Viridis is a linear color map that is generally recommended when false color is needed because it is pleasant to the eye and it fixes most issues with Jet. Inferno has the same linear properties of Viridis, but is higher contrast, making it better for picking out detail. However, some feel that it can be harsh on the eyes. While this isn’t a concern for publishing, it does affect people’s choice when they must spend extended periods examining visualizations. Personal taste vs scientific accuracy - I will take the latter unless we are doing plots for marketing materials. In my view this is not substantiated reason

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 build perceptually uniform colormaps with accessibility considerations, here is an amazing presentation of Matplotlib's new colormaps: [VIDEO]

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 [VIDEO] (A Better Default Colormap for Matplotlib, SciPy 2015, Nathaniel Smith and Stéfan van der Walt). It is about perceptually uniform colormaps. [LINK]

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: [VIDEO]

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] [VIDEO]