Color maps portray the spatial variations of a scalar value. In PIV analysis, the scalar value may be a spatial derivative of the vector field, or an invariant or a principal component.
In many cases, the plotted scalar value varies between 0 and a maximum value, or between a negative minimum value and 0. In these cases, a perceptually uniform color map allows to faithfully report the small and large variations of the scalar value.
I have now included in the colormap folder of TecPIV the common colormaps from python matplotlib: viridis, magma, plasma, and inferno. viridis is a yellow-green-blue colormap while the others are yellow-orange-pink-purple.
I also created three new color maps following the same pattern: C1, C2, and C3. They are perceptually uniform varying between orange or pale yellow and dark purple.
The perceptual uniformity is obtained by sampling regularly in the L-a-b space instead of RGB space and enforcing that the luminance L (blue curve in figures below) is varying linearly throughout the color map like in the grayscale map.
The perceptual uniformity means that the human eye should be able to perceive the small variations equally across the entire colormap. The figure below shows a small-scale periodic variation that can be seen correctly from one side to the other.
The new colormaps and the inverses are available in the TecPIV repository, in the colormap folder.