colorbar 分享

起因

• 本人在绘制论文所需要的图时，对色带（colorbar）的选择上出现了疑惑；尝试了一些配色的包及部分经典的配色进行分享

CMAPS

#pip install cmaps
import cmaps 

• 这个包好像是对NCL中的配色的移植

help(cmaps)  #查看配色

• 习惯NCL配色的朋友可能比较喜欢这个，我用的不多，
• 调用该包中的一种配色，matplotlib可以直接使用
  

palettable

#pip install palettable
import palettable

• 这个包里面颜色非常多。但是时间有限，我没筛选到合适的颜色
  
• 上面的PACKAGE CONTENTS中就包含了很多的已有包，可以去调用其中的colorbar
• 查看其中的一种配色（使用时需要写成类似这种：cmap=CubeYF_8.mpl_colormap）

配色

MATLAB中的parula

• matlab中输入以下表达式，可以获得rgb数组；以下是我获得的连续数组
  
• arr=np.array([[0.2081,0.1663,0.5292], [0.2116,0.1898,0.5777], [0.2123,0.2138,0.6270], [0.2081,0.2386,0.6771], [0.1959,0.2645,0.7279], [0.1707,0.2919,0.7792], [0.1253,0.3242,0.8303], [0.0591,0.3598,0.8683], [0.0117,0.3875,0.8820], [0.0060,0.4086,0.8828], [0.0165,0.4266,0.8786], [0.0329,0.4430,0.8720], [0.0498,0.4586,0.8641], [0.0629,0.4737,0.8554], [0.0723,0.4887,0.8467], [0.0779,0.5040,0.8384], [0.0793,0.5200,0.8312], [0.0749,0.5375,0.8263], [0.0641,0.5570,0.8240], [0.0488,0.5772,0.8228], [0.0343,0.5966,0.8199], [0.0265,0.6137,0.8135], [0.0239,0.6287,0.8038], [0.0231,0.6418,0.7913], [0.0228,0.6535,0.7768], [0.0267,0.6642,0.7607], [0.0384,0.6743,0.7436], [0.0590,0.6838,0.7254], [0.0843,0.6928,0.7062], [0.1133,0.7015,0.6859], [0.1453,0.7098,0.6646], [0.1801,0.7177,0.6424], [0.2178,0.7250,0.6193], [0.2586,0.7317,0.5954], [0.3022,0.7376,0.5712], [0.3482,0.7424,0.5473], [0.3953,0.7459,0.5244], [0.4420,0.7481,0.5033], [0.4871,0.7491,0.4840], [0.5300,0.7491,0.4661], [0.5709,0.7485,0.4494], [0.6099,0.7473,0.4337], [0.6473,0.7456,0.4188], [0.6834,0.7435,0.4044], [0.7184,0.7411,0.3905], [0.7525,0.7384,0.3768], [0.7858,0.7356,0.3633], [0.8185,0.7327,0.3498], [0.8507,0.7299,0.3360], [0.8824,0.7274,0.3217], [0.9139,0.7258,0.3063], [0.9450,0.7261,0.2886], [0.9739,0.7314,0.2666], [0.9938,0.7455,0.2403], [0.9990,0.7653,0.2164], [0.9955,0.7861,0.1967], [0.9880,0.8066,0.1794], [0.9789,0.8271,0.1633], [0.9697,0.8481,0.1475], [0.9626,0.8705,0.1309], [0.9589,0.8949,0.1132], [0.9598,0.9218,0.0948], [0.9661,0.9514,0.0755], [0.9763,0.9831,0.0538]])
• 使用matplotlib转换出的颜色如下
  - 这里是matlab中的色带，可以看到两者是比较接近的
  
• 使用我自己的数据绘图效果是这样的
  

Gist_rain_bow_r

• matplotlib内置
• 该色带的初始位置是粉红色，颜色感觉太不搭了，我给移除了；代码如下

new=plt.cm.get_cmap('gist_rainbow_r',lut=8)
new1=[]
for i in range(1,8):
    new1.append(list(new(i)))
newcmp = LinearSegmentedColormap.from_list('chaos',new1)

• 更改后的效果如下
  
• 使用我自己的数据绘图后，效果如下
  

Rainbow

• 比较经典的配色，matplotlib内置
• 绘图效果如下
  

• JET

• 也是很经典的配色，效果如下，matplotlib内置