我知道有一些discussion of Matlab copywriting their new default colormap，但我想知道是否有任何强悍的用户在Matplotlib中创建了色彩映射。
Viridis很棒，但我想要做的事情有点暗。
答案 0 :(得分：11)
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()
答案 1 :(得分：1)
自2016年得到解答以来，Matlab的值似乎已略有更改。这是最新版本的Matlab（R2019b Update 3）中的Parula值：
cm_data = [[0.2422, 0.1504, 0.6603],
[0.2444, 0.1534, 0.6728],
[0.2464, 0.1569, 0.6847],
[0.2484, 0.1607, 0.6961],
[0.2503, 0.1648, 0.7071],
[0.2522, 0.1689, 0.7179],
[0.254, 0.1732, 0.7286],
[0.2558, 0.1773, 0.7393],
[0.2576, 0.1814, 0.7501],
[0.2594, 0.1854, 0.761],
[0.2611, 0.1893, 0.7719],
[0.2628, 0.1932, 0.7828],
[0.2645, 0.1972, 0.7937],
[0.2661, 0.2011, 0.8043],
[0.2676, 0.2052, 0.8148],
[0.2691, 0.2094, 0.8249],
[0.2704, 0.2138, 0.8346],
[0.2717, 0.2184, 0.8439],
[0.2729, 0.2231, 0.8528],
[0.274, 0.228, 0.8612],
[0.2749, 0.233, 0.8692],
[0.2758, 0.2382, 0.8767],
[0.2766, 0.2435, 0.884],
[0.2774, 0.2489, 0.8908],
[0.2781, 0.2543, 0.8973],
[0.2788, 0.2598, 0.9035],
[0.2794, 0.2653, 0.9094],
[0.2798, 0.2708, 0.915],
[0.2802, 0.2764, 0.9204],
[0.2806, 0.2819, 0.9255],
[0.2809, 0.2875, 0.9305],
[0.2811, 0.293, 0.9352],
[0.2813, 0.2985, 0.9397],
[0.2814, 0.304, 0.9441],
[0.2814, 0.3095, 0.9483],
[0.2813, 0.315, 0.9524],
[0.2811, 0.3204, 0.9563],
[0.2809, 0.3259, 0.96],
[0.2807, 0.3313, 0.9636],
[0.2803, 0.3367, 0.967],
[0.2798, 0.3421, 0.9702],
[0.2791, 0.3475, 0.9733],
[0.2784, 0.3529, 0.9763],
[0.2776, 0.3583, 0.9791],
[0.2766, 0.3638, 0.9817],
[0.2754, 0.3693, 0.984],
[0.2741, 0.3748, 0.9862],
[0.2726, 0.3804, 0.9881],
[0.271, 0.386, 0.9898],
[0.2691, 0.3916, 0.9912],
[0.267, 0.3973, 0.9924],
[0.2647, 0.403, 0.9935],
[0.2621, 0.4088, 0.9946],
[0.2591, 0.4145, 0.9955],
[0.2556, 0.4203, 0.9965],
[0.2517, 0.4261, 0.9974],
[0.2473, 0.4319, 0.9983],
[0.2424, 0.4378, 0.9991],
[0.2369, 0.4437, 0.9996],
[0.2311, 0.4497, 0.9995],
[0.225, 0.4559, 0.9985],
[0.2189, 0.462, 0.9968],
[0.2128, 0.4682, 0.9948],
[0.2066, 0.4743, 0.9926],
[0.2006, 0.4803, 0.9906],
[0.195, 0.4861, 0.9887],
[0.1903, 0.4919, 0.9867],
[0.1869, 0.4975, 0.9844],
[0.1847, 0.503, 0.9819],
[0.1831, 0.5084, 0.9793],
[0.1818, 0.5138, 0.9766],
[0.1806, 0.5191, 0.9738],
[0.1795, 0.5244, 0.9709],
[0.1785, 0.5296, 0.9677],
[0.1778, 0.5349, 0.9641],
[0.1773, 0.5401, 0.9602],
[0.1768, 0.5452, 0.956],
[0.1764, 0.5504, 0.9516],
[0.1755, 0.5554, 0.9473],
[0.174, 0.5605, 0.9432],
[0.1716, 0.5655, 0.9393],
[0.1686, 0.5705, 0.9357],
[0.1649, 0.5755, 0.9323],
[0.161, 0.5805, 0.9289],
[0.1573, 0.5854, 0.9254],
[0.154, 0.5902, 0.9218],
[0.1513, 0.595, 0.9182],
[0.1492, 0.5997, 0.9147],
[0.1475, 0.6043, 0.9113],
[0.1461, 0.6089, 0.908],
[0.1446, 0.6135, 0.905],
[0.1429, 0.618, 0.9022],
[0.1408, 0.6226, 0.8998],
[0.1383, 0.6272, 0.8975],
[0.1354, 0.6317, 0.8953],
[0.1321, 0.6363, 0.8932],
[0.1288, 0.6408, 0.891],
[0.1253, 0.6453, 0.8887],
[0.1219, 0.6497, 0.8862],
[0.1185, 0.6541, 0.8834],
[0.1152, 0.6584, 0.8804],
[0.1119, 0.6627, 0.877],
[0.1085, 0.6669, 0.8734],
[0.1048, 0.671, 0.8695],
[0.1009, 0.675, 0.8653],
[0.0964, 0.6789, 0.8609],
[0.0914, 0.6828, 0.8562],
[0.0855, 0.6865, 0.8513],
[0.0789, 0.6902, 0.8462],
[0.0713, 0.6938, 0.8409],
[0.0628, 0.6972, 0.8355],
[0.0535, 0.7006, 0.8299],
[0.0433, 0.7039, 0.8242],
[0.0328, 0.7071, 0.8183],
[0.0234, 0.7103, 0.8124],
[0.0155, 0.7133, 0.8064],
[0.0091, 0.7163, 0.8003],
[0.0046, 0.7192, 0.7941],
[0.0019, 0.722, 0.7878],
[0.0009, 0.7248, 0.7815],
[0.0018, 0.7275, 0.7752],
[0.0046, 0.7301, 0.7688],
[0.0094, 0.7327, 0.7623],
[0.0162, 0.7352, 0.7558],
[0.0253, 0.7376, 0.7492],
[0.0369, 0.74, 0.7426],
[0.0504, 0.7423, 0.7359],
[0.0638, 0.7446, 0.7292],
[0.077, 0.7468, 0.7224],
[0.0899, 0.7489, 0.7156],
[0.1023, 0.751, 0.7088],
[0.1141, 0.7531, 0.7019],
[0.1252, 0.7552, 0.695],
[0.1354, 0.7572, 0.6881],
[0.1448, 0.7593, 0.6812],
[0.1532, 0.7614, 0.6741],
[0.1609, 0.7635, 0.6671],
[0.1678, 0.7656, 0.6599],
[0.1741, 0.7678, 0.6527],
[0.1799, 0.7699, 0.6454],
[0.1853, 0.7721, 0.6379],
[0.1905, 0.7743, 0.6303],
[0.1954, 0.7765, 0.6225],
[0.2003, 0.7787, 0.6146],
[0.2061, 0.7808, 0.6065],
[0.2118, 0.7828, 0.5983],
[0.2178, 0.7849, 0.5899],
[0.2244, 0.7869, 0.5813],
[0.2318, 0.7887, 0.5725],
[0.2401, 0.7905, 0.5636],
[0.2491, 0.7922, 0.5546],
[0.2589, 0.7937, 0.5454],
[0.2695, 0.7951, 0.536],
[0.2809, 0.7964, 0.5266],
[0.2929, 0.7975, 0.517],
[0.3052, 0.7985, 0.5074],
[0.3176, 0.7994, 0.4975],
[0.3301, 0.8002, 0.4876],
[0.3424, 0.8009, 0.4774],
[0.3548, 0.8016, 0.4669],
[0.3671, 0.8021, 0.4563],
[0.3795, 0.8026, 0.4454],
[0.3921, 0.8029, 0.4344],
[0.405, 0.8031, 0.4233],
[0.4184, 0.803, 0.4122],
[0.4322, 0.8028, 0.4013],
[0.4463, 0.8024, 0.3904],
[0.4608, 0.8018, 0.3797],
[0.4753, 0.8011, 0.3691],
[0.4899, 0.8002, 0.3586],
[0.5044, 0.7993, 0.348],
[0.5187, 0.7982, 0.3374],
[0.5329, 0.797, 0.3267],
[0.547, 0.7957, 0.3159],
[0.5609, 0.7943, 0.305],
[0.5748, 0.7929, 0.2941],
[0.5886, 0.7913, 0.2833],
[0.6024, 0.7896, 0.2726],
[0.6161, 0.7878, 0.2622],
[0.6297, 0.7859, 0.2521],
[0.6433, 0.7839, 0.2423],
[0.6567, 0.7818, 0.2329],
[0.6701, 0.7796, 0.2239],
[0.6833, 0.7773, 0.2155],
[0.6963, 0.775, 0.2075],
[0.7091, 0.7727, 0.1998],
[0.7218, 0.7703, 0.1924],
[0.7344, 0.7679, 0.1852],
[0.7468, 0.7654, 0.1782],
[0.759, 0.7629, 0.1717],
[0.771, 0.7604, 0.1658],
[0.7829, 0.7579, 0.1608],
[0.7945, 0.7554, 0.157],
[0.806, 0.7529, 0.1546],
[0.8172, 0.7505, 0.1535],
[0.8281, 0.7481, 0.1536],
[0.8389, 0.7457, 0.1546],
[0.8495, 0.7435, 0.1564],
[0.86, 0.7413, 0.1587],
[0.8703, 0.7392, 0.1615],
[0.8804, 0.7372, 0.165],
[0.8903, 0.7353, 0.1695],
[0.9, 0.7336, 0.1749],
[0.9093, 0.7321, 0.1815],
[0.9184, 0.7308, 0.189],
[0.9272, 0.7298, 0.1973],
[0.9357, 0.729, 0.2061],
[0.944, 0.7285, 0.2151],
[0.9523, 0.7284, 0.2237],
[0.9606, 0.7285, 0.2312],
[0.9689, 0.7292, 0.2373],
[0.977, 0.7304, 0.2418],
[0.9842, 0.733, 0.2446],
[0.99, 0.7365, 0.2429],
[0.9946, 0.7407, 0.2394],
[0.9966, 0.7458, 0.2351],
[0.9971, 0.7513, 0.2309],
[0.9972, 0.7569, 0.2267],
[0.9971, 0.7626, 0.2224],
[0.9969, 0.7683, 0.2181],
[0.9966, 0.774, 0.2138],
[0.9962, 0.7798, 0.2095],
[0.9957, 0.7856, 0.2053],
[0.9949, 0.7915, 0.2012],
[0.9938, 0.7974, 0.1974],
[0.9923, 0.8034, 0.1939],
[0.9906, 0.8095, 0.1906],
[0.9885, 0.8156, 0.1875],
[0.9861, 0.8218, 0.1846],
[0.9835, 0.828, 0.1817],
[0.9807, 0.8342, 0.1787],
[0.9778, 0.8404, 0.1757],
[0.9748, 0.8467, 0.1726],
[0.972, 0.8529, 0.1695],
[0.9694, 0.8591, 0.1665],
[0.9671, 0.8654, 0.1636],
[0.9651, 0.8716, 0.1608],
[0.9634, 0.8778, 0.1582],
[0.9619, 0.884, 0.1557],
[0.9608, 0.8902, 0.1532],
[0.9601, 0.8963, 0.1507],
[0.9596, 0.9023, 0.148],
[0.9595, 0.9084, 0.145],
[0.9597, 0.9143, 0.1418],
[0.9601, 0.9203, 0.1382],
[0.9608, 0.9262, 0.1344],
[0.9618, 0.932, 0.1304],
[0.9629, 0.9379, 0.1261],
[0.9642, 0.9437, 0.1216],
[0.9657, 0.9494, 0.1168],
[0.9674, 0.9552, 0.1116],
[0.9692, 0.9609, 0.1061],
[0.9711, 0.9667, 0.1001],
[0.973, 0.9724, 0.0938],
[0.9749, 0.9782, 0.0872],
[0.9769, 0.9839, 0.0805]]
注意：这是MATLAB的“ parula”的副本。我没有要求对此数据拥有任何权利，但是Mathworks拥有。如果要使用它们，请咨询他们和/或律师。