Colormap selection is surprisingly fascinating.
"A Better Default Colormap for Matplotlib (SciPy 2015) Nathaniel Smith and Stéfan van der Walt"
I understand why some maps are awful, but have had a hard time finding a color map which I like perceptually for interpreting LIDAR forest canopy maps.
I've tried using viridis and a few others, but exceptionally tall trees get lost in the noise (not enough contrast), especially when using transparency to overlay canopy height on the LIDAR intensity data.
Is this just a case of not clamping the ends of the range properly?
Say, if you have a bimodal underlying distribution and you're interested in coloring according to quantiles, you want to have a similarly bimodally distributed color map.
The easiest way to achieve this is to compute the quantiles for your data set, and color by that (rather than height).
Everyone’s eyes/brain interpret colors by taking signals from the 3 types of cone cell detectors and combining them into 2 color-difference signals (red–green and blue–yellow) and 1 brightness signal. The latter of these has the best spatial/temporal resolution and is used for interpreting textures/fine details, edges of shapes, motion, depth, etc.
In a sense, everyone’s vision is primarily grayscale, with lower-resolution color information layered over the top.
Since most folks (typically students) simply use the default this should help end the tyranny of jet.
I do still like jet for on screen visualisation, the extra contrast can be useful.
Each color map has a specific type of story it is best at telling and using program defaults is a really bad practice. For the type of phenomena you are trying to highlight and explain there is a color map for that problem and I think this is something that is understudied and under discussed for its impact