来自tab10的matplotlib通用色彩映射

Question

这个问题与SO中的这个问题有关(matplotlib-change-colormap-tab20-to-have-three-colors)

我想以一种方式调整tab10色彩图,我可以按照我想要的步骤更改每种颜色的alpha级别.下面是一个示例(对于具有3个alpha级别的9种颜色),它不会产生预期的输出.此外,它不够通用(因为if elif staements).

任何想法我怎么能这样做？

在这个例子中,我有3个组,有3个子组:

import pandas as pd
from matplotlib import pyplot as plt
import numpy as np

n_feature = 3
sub_feature = 3
col = []
for index in range(n_feature*sub_feature):
# loop over colors and change the last entry in descending order 3 times
        col.append(list(plt.cm.tab10(index)))

i = 0        
for item in col:
# loop over colors and change the last entry in descending order 3 times
    if i == 0:
        item[-1] = 0.9
        i+=1
    elif i == 1:
        item[-1] = 0.7
        i+=1
    elif i == 2:
        item[-1] = 0.5
        i = 0

gr = df.groupby(['a', 'a1'])

for index, item in enumerate(gr):
    name, val = item
    y = val.iloc[0,2:].values
    x = np.arange(len(y))
    plt.plot(x, y, '.-', color=col[index])

plt.show()

这是数据:

{'a':{0:'A',1:'A',2:'A',3:'B',4:'B',5:'B',6:'C',7: 'C',8:'C'},'a1':{0:1,1:2,2:3,3:1,4:2,5:3,6:1,7:2,8: 3},'b':{0:1.0,1:5.0,2:9.0,3:1.5,4:5.5,5:9.5,6:1.75,7:5.75,8:9.75},'c':{ 0:2.0,1:6.0,2:10.0,3:2.5,4:6.5,5:10.5,6:2.75,7:6.75,8:10.75},'d':{0:3.0,1:7.0, 2:11.0,3:3.5,4:7.5,5:11.5,6:3.75,7:7.75,8:11.75},'e':{0:4.0,1:8.0,2:12.0,3:4.5, 4:8.5,5:12.5,6:4.75,7:8.75,8:12.75}}

Answer 1

您可以使用HSV系统获得相同色调的不同饱和度和发光颜色.假设您最多有10个类别,则tab10可以使用地图获取一定数量的基色.从那些你可以为子类别选择几个较浅的色调.

以下是一个函数categorical_cmap,它将categories(nc)的数量和子类别()的数量作为输入,nsc并返回具有nc*nsc不同颜色的colormap ,其中每个类别都有nsc相同色调的颜色.

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors

def categorical_cmap(nc, nsc, cmap="tab10", continuous=False):
    if nc > plt.get_cmap(cmap).N:
        raise ValueError("Too many categories for colormap.")
    if continuous:
        ccolors = plt.get_cmap(cmap)(np.linspace(0,1,nc))
    else:
        ccolors = plt.get_cmap(cmap)(np.arange(nc, dtype=int))
    cols = np.zeros((nc*nsc, 3))
    for i, c in enumerate(ccolors):
        chsv = matplotlib.colors.rgb_to_hsv(c[:3])
        arhsv = np.tile(chsv,nsc).reshape(nsc,3)
        arhsv[:,1] = np.linspace(chsv[1],0.25,nsc)
        arhsv[:,2] = np.linspace(chsv[2],1,nsc)
        rgb = matplotlib.colors.hsv_to_rgb(arhsv)
        cols[i*nsc:(i+1)*nsc,:] = rgb       
    cmap = matplotlib.colors.ListedColormap(cols)
    return cmap

c1 = categorical_cmap(4, 3, cmap="tab10")
plt.scatter(np.arange(4*3),np.ones(4*3)+1, c=np.arange(4*3), s=180, cmap=c1)

c2 = categorical_cmap(2, 5, cmap="tab10")
plt.scatter(np.arange(10),np.ones(10), c=np.arange(10), s=180, cmap=c2)

c3 = categorical_cmap(5, 4, cmap="tab10")
plt.scatter(np.arange(20),np.ones(20)-1, c=np.arange(20), s=180, cmap=c3)    

plt.margins(y=0.3)
plt.xticks([])
plt.yticks([0,1,2],["(5, 4)", "(2, 5)", "(4, 3)"])
plt.show()