python - 有人在 Matplotlib 中制作了 Parula 颜色图吗？

Question

我知道有一些 discussion of Matlab copyrighting their new default colormap ，但我想知道是否有勇敢的用户在 Matplotlib 中创建了颜色图。

Viridis很棒，但对于我要尝试做的事情来说有点暗。

Answer 1

如果@tom 提供的链接断开，这里是:

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()