来自 tab10 的 matplotlib 通用颜色图 [英] matplotlib generic colormap from tab10

问题描述

这个问题与 SO (

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}}

解决方案

您可以使用 HSV 系统为相同的色调获得不同的饱和度和发光颜色.假设您最多有10个类别，那么可以使用tab10映射来获取一定数量的基色.您可以从中为子类别选择几个较浅的色调.

以下将是一个函数 categorical_cmap，它将类别数 (nc) 和子类别数 (nsc) 并返回一个带有 nc*nsc 不同颜色的颜色图，其中每个类别都有 nsc 相同色调的颜色.

将 numpy 导入为 np导入 matplotlib.pyplot 作为 plt导入 matplotlib.colorsdef categorical_cmap(nc, nsc, cmap="tab10", 连续=假):如果 nc >plt.get_cmap(cmap).N:raise ValueError("颜色图的类别太多.")如果连续:ccolors = plt.get_cmap(cmap)(np.linspace(0,1,nc))别的:ccolors = plt.get_cmap(cmap)(np.arange(nc, dtype=int))cols = np.zeros((nc*nsc, 3))对于 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,:] = rgbcmap = matplotlib.colors.ListedColormap(cols)返回地图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()

This question is related to this one from SO (matplotlib-change-colormap-tab20-to-have-three-colors)

I would like to tweak the tab10 colormap in a way that I can change the alpha level of each color in as many steps as I would like to. Below is an example (for 9 color with 3 alpha levels) which does not yield the expected output. Furthermore, it is not generic enough (because of the if elif staements).

Any ideas how I could do that ?

In this example, I do have 3 groups with 3 subgroups:

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()

This is the data:

{'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}}

解决方案

You may use the HSV system to obtain differently saturated and luminated colors for the same hue. Suppose you have at most 10 categories, then the tab10 map can be used to get a certain number of base colors. From those you can choose a couple of lighter shades for the subcategories.

The following would be a function categorical_cmap, which takes as input the number of categories (nc) and the number of subcategories (nsc) and returns a colormap with nc*nsc different colors, where for each category there are nsc colors of same hue.

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()

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