https://research.google/blog/turbo-an-improved-rainbow-colormap-for-visualization/
Turbo colormap is a modern scientific color map developed by researchers at Google as an improved alternative to the traditional Jet colormap. It was designed to retain the intuitive rainbow-like appearance while fixing many of the perceptual problems that make Jet misleading for scientific visualization.
Key characteristics:
Continuous rainbow-like gradient
Smooth transitions between colors
Approximately monotonic luminance increase
Accentuates detail in the background
Rainbow colormaps (e.g., Turbo) emphasize visual contrast and feature detection.
Perceptually uniform colormaps (e.g., Viridis) emphasize accurate representation of numerical differences.
It has been used in Matplotlib.
You can find the color map data and usage instructions for Python here and for C/C++ here. There is also a polynomial approximation here, for cases where a look-up table may not be desirable.
https://research.google/blog/turbo-an-improved-rainbow-colormap-for-visualization/
Turbo colormap is a modern scientific color map developed by researchers at Google as an improved alternative to the traditional Jet colormap. It was designed to retain the intuitive rainbow-like appearance while fixing many of the perceptual problems that make Jet misleading for scientific visualization.
Key characteristics:
Continuous rainbow-like gradient
Smooth transitions between colors
Approximately monotonic luminance increase
Accentuates detail in the background
Rainbow colormaps (e.g., Turbo) emphasize visual contrast and feature detection.
Perceptually uniform colormaps (e.g., Viridis) emphasize accurate representation of numerical differences.
It has been used in Matplotlib.
You can find the color map data and usage instructions for Python here and for C/C++ here. There is also a polynomial approximation here, for cases where a look-up table may not be desirable.