custom_cmap.py

from matplotlib.colors import ListedColormap
from numpy import nan, inf

# Used to reconstruct the colormap in viscm
parameters = {'xp': [24.397622053872112, 22.329064134250359, -3.5279098610215271, -7.3202660469947318, -3.5279098610215271],
              'yp': [10.224586288416106, 29.531126871552431, 21.60165484633572, 1.6055949566588197, -1.4972419227738101],
              'min_Jp': 18.730158730158728,
              'max_Jp': 99.6190476190476}

cm_data = [[  3.19188006e-01,  5.08411011e-04,  4.27847166e-02],
           [  3.23452429e-01,  2.94004376e-03,  4.29498405e-02],
           [  3.27697959e-01,  5.48550076e-03,  4.30937800e-02],
           [  3.31925014e-01,  8.14729136e-03,  4.32108710e-02],
           [  3.36134026e-01,  1.09282090e-02,  4.32932080e-02],
           [  3.40324261e-01,  1.38309580e-02,  4.33544174e-02],
           [  3.44496109e-01,  1.68585499e-02,  4.33863092e-02],
           [  3.48649544e-01,  2.00141344e-02,  4.33873393e-02],
           [  3.52784075e-01,  2.33004930e-02,  4.33674127e-02],
           [  3.56899901e-01,  2.67211674e-02,  4.33188700e-02],
           [  3.60996892e-01,  3.02796868e-02,  4.32405671e-02],
           [  3.65074618e-01,  3.39789376e-02,  4.31416825e-02],
           [  3.69133039e-01,  3.78227502e-02,  4.30181093e-02],
           [  3.73172062e-01,  4.17796063e-02,  4.28631975e-02],
           [  3.77191242e-01,  4.56665666e-02,  4.26883879e-02],
           [  3.81190385e-01,  4.94889072e-02,  4.24937807e-02],
           [  3.85169297e-01,  5.32578705e-02,  4.22682848e-02],
           [  3.89127602e-01,  5.69807657e-02,  4.20217552e-02],
           [  3.93065023e-01,  6.06644181e-02,  4.17567112e-02],
           [  3.96981266e-01,  6.43150594e-02,  4.14725462e-02],
           [  4.00875841e-01,  6.79394110e-02,  4.11600480e-02],
           [  4.04748512e-01,  7.15406015e-02,  4.08309528e-02],
           [  4.08598937e-01,  7.51227174e-02,  4.04858381e-02],
           [  4.12426752e-01,  7.86893890e-02,  4.01206455e-02],
           [  4.16231263e-01,  8.22452517e-02,  3.97338038e-02],
           [  4.20012268e-01,  8.57920330e-02,  3.93348179e-02],
           [  4.23769406e-01,  8.93321242e-02,  3.89262925e-02],
           [  4.27502256e-01,  9.28678889e-02,  3.85095772e-02],
           [  4.31210387e-01,  9.64014725e-02,  3.80861373e-02],
           [  4.34892901e-01,  9.99361677e-02,  3.76509851e-02],
           [  4.38549738e-01,  1.03472432e-01,  3.72127744e-02],
           [  4.42180470e-01,  1.07011798e-01,  3.67737224e-02],
           [  4.45784643e-01,  1.10555736e-01,  3.63357812e-02],
           [  4.49361798e-01,  1.14105584e-01,  3.59010342e-02],
           [  4.52911477e-01,  1.17662562e-01,  3.54716989e-02],
           [  4.56432847e-01,  1.21228614e-01,  3.50467949e-02],
           [  4.59925736e-01,  1.24804054e-01,  3.46317940e-02],
           [  4.63389808e-01,  1.28389545e-01,  3.42302550e-02],
           [  4.66824637e-01,  1.31985895e-01,  3.38449160e-02],
           [  4.70229810e-01,  1.35593819e-01,  3.34786526e-02],
           [  4.73604927e-01,  1.39213951e-01,  3.31344783e-02],
           [  4.76949608e-01,  1.42846843e-01,  3.28155440e-02],
           [  4.80263491e-01,  1.46492970e-01,  3.25251366e-02],
           [  4.83546237e-01,  1.50152732e-01,  3.22666779e-02],
           [  4.86797340e-01,  1.53826781e-01,  3.20427670e-02],
           [  4.90016620e-01,  1.57515177e-01,  3.18577613e-02],
           [  4.93203933e-01,  1.61217925e-01,  3.17160407e-02],
           [  4.96359052e-01,  1.64935164e-01,  3.16215131e-02],
           [  4.99481779e-01,  1.68666973e-01,  3.15782102e-02],
           [  5.02571949e-01,  1.72413379e-01,  3.15902848e-02],
           [  5.05629430e-01,  1.76174354e-01,  3.16620080e-02],
           [  5.08654123e-01,  1.79949823e-01,  3.17977662e-02],
           [  5.11645963e-01,  1.83739667e-01,  3.20020585e-02],
           [  5.14604920e-01,  1.87543723e-01,  3.22794932e-02],
           [  5.17530997e-01,  1.91361790e-01,  3.26347858e-02],
           [  5.20424232e-01,  1.95193632e-01,  3.30727557e-02],
           [  5.23284694e-01,  1.99038983e-01,  3.35983244e-02],
           [  5.26112485e-01,  2.02897548e-01,  3.42165124e-02],
           [  5.28907738e-01,  2.06769007e-01,  3.49324380e-02],
           [  5.31670618e-01,  2.10653019e-01,  3.57513149e-02],
           [  5.34401314e-01,  2.14549225e-01,  3.66784508e-02],
           [  5.37100046e-01,  2.18457250e-01,  3.77192462e-02],
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           [  8.45370193e-01,  8.75310323e-01,  8.11973366e-01],
           [  8.48740412e-01,  8.79222758e-01,  8.18175322e-01],
           [  8.52163579e-01,  8.83132479e-01,  8.24362799e-01],
           [  8.55639953e-01,  8.87039686e-01,  8.30534495e-01],
           [  8.59169726e-01,  8.90944601e-01,  8.36689115e-01],
           [  8.62752326e-01,  8.94847329e-01,  8.42829467e-01],
           [  8.66388535e-01,  8.98748189e-01,  8.48950924e-01],
           [  8.70078463e-01,  9.02647473e-01,  8.55051709e-01],
           [  8.73821857e-01,  9.06545384e-01,  8.61132327e-01],
           [  8.77618473e-01,  9.10442054e-01,  8.67193908e-01],
           [  8.81468712e-01,  9.14337976e-01,  8.73231456e-01],
           [  8.85372253e-01,  9.18233284e-01,  8.79246333e-01],
           [  8.89328979e-01,  9.22128095e-01,  8.85239267e-01],
           [  8.93339074e-01,  9.26022993e-01,  8.91205135e-01],
           [  8.97402366e-01,  9.29917617e-01,  8.97149605e-01],
           [  9.01519093e-01,  9.33812678e-01,  9.03066039e-01],
           [  9.05689518e-01,  9.37707793e-01,  9.08958623e-01],
           [  9.09913938e-01,  9.41603448e-01,  9.14822730e-01],
           [  9.14193252e-01,  9.45499026e-01,  9.20662416e-01],
           [  9.18527956e-01,  9.49395047e-01,  9.26471928e-01],
           [  9.22919852e-01,  9.53290490e-01,  9.32255874e-01],
           [  9.27370222e-01,  9.57185424e-01,  9.38009990e-01],
           [  9.31881046e-01,  9.61079371e-01,  9.43732754e-01],
           [  9.36455353e-01,  9.64971274e-01,  9.49424410e-01],
           [  9.41096680e-01,  9.68860078e-01,  9.55083193e-01],
           [  9.45808721e-01,  9.72744936e-01,  9.60704680e-01],
           [  9.50596398e-01,  9.76624444e-01,  9.66285224e-01],
           [  9.55465459e-01,  9.80497024e-01,  9.71819696e-01],
           [  9.60422407e-01,  9.84360987e-01,  9.77301129e-01],
           [  9.65474320e-01,  9.88214632e-01,  9.82720362e-01],
           [  9.70629792e-01,  9.92055757e-01,  9.88067257e-01],
           [  9.75895168e-01,  9.95883564e-01,  9.93326172e-01],
           [  9.81275361e-01,  9.99697973e-01,  9.98479623e-01]]

cm_data = [x for i,x in enumerate(cm_data) for _ in range((len(cm_data)-i))]

test_cm = ListedColormap(cm_data, name=__file__)


if __name__ == "__main__":
    import matplotlib.pyplot as plt
    import numpy as np

    try:
        from viscm import viscm
        viscm(test_cm)
    except ImportError:
        print("viscm not found, falling back on simple display")
        plt.imshow(np.linspace(0, 100, 256)[None, :], aspect='auto',
                   cmap=test_cm)
    plt.show()