Python自定义 colormap

2021/10/7 14:10:54

本文主要是介绍Python自定义 colormap,对大家解决编程问题具有一定的参考价值,需要的程序猿们随着小编来一起学习吧!

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)
cm_data = np.array([
    [59, 76, 192], 
    [68, 90, 204], 
    [77, 104, 215], 
    [87, 117, 225], 
    [98, 130, 234], 
    [108, 142, 241], 
    [119, 154, 247], 
    [130, 165, 251], 
    [141, 176, 254],
    [152, 185, 255], 
    [163, 194, 255], 
    [174, 201, 153],
    [184, 208, 249], 
    [194, 213, 244], 
    [204, 217, 238], 
    [213, 219, 230], 
    [221, 221, 221], 
    [229, 216, 209], 
    [236, 211, 197], 
    [241, 204, 185], 
    [245, 196, 173], 
    [247, 187, 160], 
    [247, 177, 148], 
    [247, 166, 135], 
    [244, 154, 123], 
    [241, 141, 111], 
    [236, 127, 99], 
    [229, 112, 88], 
    [222, 96, 77],
    [213, 80, 66],
    [203, 62, 56], 
    [192, 40, 47], 
    [180, 4, 38]
], dtype = np.float64)
cm_data = list(cm_data / 255)



moreland_map = LinearSegmentedColormap.from_list('moreland', cm_data)

 


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