Obtain RGB indexes from colormap based on array value on Python

Question

I have an array storing pressure coefficient values for each triangular face from a 3D geometry, and I am trying to obtain the RGB index values from a jet colormap based on the min and max from this array.

I have done this on Matlab like this

 cmap = jet(40) * 255;

 cvars_map = (cvar - min(cvar))/(max(cvar)-min(cvar)) * (40 - 1) + 1;
 cvars_map = round(cvars_map);

 cmap = cmap(cvars_map(1:end), :);

where cvar is the array storing the values.

How can I achieve this on Python? Ultimately I will write a PLY file for the geometry with the RGB values for each face.

Answer 1

We may use NumPy, the syntax is almost the same as MATLAB:

import numpy as np

# MATLAB code:
# cvar = sin(-pi:0.1:pi)*10 + 17; % Fill cvar with arbitrary data (for example).
# cmap = jet(40) * 255;
# cvars_map = (cvar - min(cvar))/(max(cvar)-min(cvar)) * (40 - 1) + 1;
# cvars_map = round(cvars_map);
# cmap = cmap(cvars_map(1:end), :);
# writematrix(cmap, 'matlab_cmap.txt') % Write to text file for testing

cvar = np.sin(np.arange(-np.pi, np.pi, 0.1))*10 + 17  # Fill cvar with arbitrary data (for example).

# NumPy array that has the same content as MATLAB jet(40) 
jet40 = np.array([[     0,         0,    0.6000],
                  [     0,         0,    0.7000],
                  [     0,         0,    0.8000],
                  [     0,         0,    0.9000],
                  [     0,         0,    1.0000],
                  [     0,    0.1000,    1.0000],
                  [     0,    0.2000,    1.0000],
                  [     0,    0.3000,    1.0000],
                  [     0,    0.4000,    1.0000],
                  [     0,    0.5000,    1.0000],
                  [     0,    0.6000,    1.0000],
                  [     0,    0.7000,    1.0000],
                  [     0,    0.8000,    1.0000],
                  [     0,    0.9000,    1.0000],
                  [     0,    1.0000,    1.0000],
                  [0.1000,    1.0000,    0.9000],
                  [0.2000,    1.0000,    0.8000],
                  [0.3000,    1.0000,    0.7000],
                  [0.4000,    1.0000,    0.6000],
                  [0.5000,    1.0000,    0.5000],
                  [0.6000,    1.0000,    0.4000],
                  [0.7000,    1.0000,    0.3000],
                  [0.8000,    1.0000,    0.2000],
                  [0.9000,    1.0000,    0.1000],
                  [1.0000,    1.0000,         0],
                  [1.0000,    0.9000,         0],
                  [1.0000,    0.8000,         0],
                  [1.0000,    0.7000,         0],
                  [1.0000,    0.6000,         0],
                  [1.0000,    0.5000,         0],
                  [1.0000,    0.4000,         0],
                  [1.0000,    0.3000,         0],
                  [1.0000,    0.2000,         0],
                  [1.0000,    0.1000,         0],
                  [1.0000,         0,         0],
                  [0.9000,         0,         0],
                  [0.8000,         0,         0],
                  [0.7000,         0,         0],
                  [0.6000,         0,         0],
                  [0.5000,         0,         0]])

# cmap = jet(40) * 255;
cmap = jet40*255

# cvars_map = (cvar - min(cvar))/(max(cvar)-min(cvar)) * (40 - 1) + 1;
cvars_map = (cvar - np.min(cvar))/(np.max(cvar)-np.min(cvar)) * (40 - 1);  # No need to add 1 (NumPy arrays first index is 0)

# cvars_map = round(cvars_map);
cvars_map = np.round(cvars_map).astype(np.int32)  # Round and convert to int32 type (NumPy indices must be integers)

# cmap = cmap(cvars_map(1:end), :);
cmap = cmap[cvars_map]

np.savetxt('python_cmap.txt', cmap, fmt='%.1f')  # Save to text file for testing