Has someone made the Parula colormap in Matplotlib?

Question

I know that there's been some discussion of Matlab copyrighting their new default colormap, but I'm wondering if any intrepid user has created the colormap in Matplotlib.

Viridis is great, but it's a bit dark for what I'm trying to do.

Answer 1

In case the link that @tom provided breaks, here it is:

from matplotlib.colors import LinearSegmentedColormap

cm_data = [[0.2081, 0.1663, 0.5292], [0.2116238095, 0.1897809524, 0.5776761905], 
 [0.212252381, 0.2137714286, 0.6269714286], [0.2081, 0.2386, 0.6770857143], 
 [0.1959047619, 0.2644571429, 0.7279], [0.1707285714, 0.2919380952, 
  0.779247619], [0.1252714286, 0.3242428571, 0.8302714286], 
 [0.0591333333, 0.3598333333, 0.8683333333], [0.0116952381, 0.3875095238, 
  0.8819571429], [0.0059571429, 0.4086142857, 0.8828428571], 
 [0.0165142857, 0.4266, 0.8786333333], [0.032852381, 0.4430428571, 
  0.8719571429], [0.0498142857, 0.4585714286, 0.8640571429], 
 [0.0629333333, 0.4736904762, 0.8554380952], [0.0722666667, 0.4886666667, 
  0.8467], [0.0779428571, 0.5039857143, 0.8383714286], 
 [0.079347619, 0.5200238095, 0.8311809524], [0.0749428571, 0.5375428571, 
  0.8262714286], [0.0640571429, 0.5569857143, 0.8239571429], 
 [0.0487714286, 0.5772238095, 0.8228285714], [0.0343428571, 0.5965809524, 
  0.819852381], [0.0265, 0.6137, 0.8135], [0.0238904762, 0.6286619048, 
  0.8037619048], [0.0230904762, 0.6417857143, 0.7912666667], 
 [0.0227714286, 0.6534857143, 0.7767571429], [0.0266619048, 0.6641952381, 
  0.7607190476], [0.0383714286, 0.6742714286, 0.743552381], 
 [0.0589714286, 0.6837571429, 0.7253857143], 
 [0.0843, 0.6928333333, 0.7061666667], [0.1132952381, 0.7015, 0.6858571429], 
 [0.1452714286, 0.7097571429, 0.6646285714], [0.1801333333, 0.7176571429, 
  0.6424333333], [0.2178285714, 0.7250428571, 0.6192619048], 
 [0.2586428571, 0.7317142857, 0.5954285714], [0.3021714286, 0.7376047619, 
  0.5711857143], [0.3481666667, 0.7424333333, 0.5472666667], 
 [0.3952571429, 0.7459, 0.5244428571], [0.4420095238, 0.7480809524, 
  0.5033142857], [0.4871238095, 0.7490619048, 0.4839761905], 
 [0.5300285714, 0.7491142857, 0.4661142857], [0.5708571429, 0.7485190476, 
  0.4493904762], [0.609852381, 0.7473142857, 0.4336857143], 
 [0.6473, 0.7456, 0.4188], [0.6834190476, 0.7434761905, 0.4044333333], 
 [0.7184095238, 0.7411333333, 0.3904761905], 
 [0.7524857143, 0.7384, 0.3768142857], [0.7858428571, 0.7355666667, 
  0.3632714286], [0.8185047619, 0.7327333333, 0.3497904762], 
 [0.8506571429, 0.7299, 0.3360285714], [0.8824333333, 0.7274333333, 0.3217], 
 [0.9139333333, 0.7257857143, 0.3062761905], [0.9449571429, 0.7261142857, 
  0.2886428571], [0.9738952381, 0.7313952381, 0.266647619], 
 [0.9937714286, 0.7454571429, 0.240347619], [0.9990428571, 0.7653142857, 
  0.2164142857], [0.9955333333, 0.7860571429, 0.196652381], 
 [0.988, 0.8066, 0.1793666667], [0.9788571429, 0.8271428571, 0.1633142857], 
 [0.9697, 0.8481380952, 0.147452381], [0.9625857143, 0.8705142857, 0.1309], 
 [0.9588714286, 0.8949, 0.1132428571], [0.9598238095, 0.9218333333, 
  0.0948380952], [0.9661, 0.9514428571, 0.0755333333], 
 [0.9763, 0.9831, 0.0538]]

parula_map = LinearSegmentedColormap.from_list('parula', cm_data)
# For use of "viscm view"
test_cm = parula_map

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

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