Matplotlib：如何在2个或更多的颜色中切割一个颜色条？

Question

我有3组值的数组：0和1（绿色梯度）

值等于2（红色）之间

• 值
• 值等于3（灰）

多亏了这个帖子（Define a colormap for each set of values in an array）我能得到这个代码：

from random import random 
import numpy as np 
import matplotlib.pyplot as plt 
import matplotlib.colors as mcolors 
from matplotlib import cm 

n=11 
tab = np.array([[random() for i in range(n)] for j in range(n)]) 
tab[1,2] = 2. 
tab[3,4] = 2. 
tab[5,6] = 3. 
tab[7,8] = 3. 

values1 = np.ma.masked_array(tab, tab > 1.) 
values2 = np.ma.masked_array(tab, tab != 2.) 
values3 = np.ma.masked_array(tab, tab != 3.) 

# 50 values for later use from 0 to 1 
greens = cm.Greens(np.linspace(0,1, num=50)) 
# 25 values for later use from 1 to 1.5 
greensfill = cm.Greens(np.ones(25)) 
# 50 values red for later use from 1.5 to 2.5 
red = [(1,0,0,1)]*len(greens) 
# 50 values gray for later use from 2.5 to 3.5 
gray = [(.5,.5,.5,1)]*len(greens) 

colors = np.vstack((greens, greensfill, red, gray)) 
# in total we now have 175 colors in the colormap 
mycmap = mcolors.LinearSegmentedColormap.from_list('my_colormap', colors) 

#we now map those 175 colors to the range between 0 and 3.5 
im = plt.imshow(tab, cmap = mycmap, interpolation="none", vmin=0, vmax=3.5) 
cb = plt.colorbar(im) 
cb.set_ticks([0,1,2,3]) 

plt.show() 

下面是结果：

我的问题是：可以matplotlib获得通过削减现有的颜色映射成3个独立的颜色表或者相近的东西像下面的图片（编辑用Photoshop做）？

编辑：

greens = cm.Greens(np.linspace(0,1, num=75)) 
red = [(1,0,0,1)]*(len(greens)/2) 
white = [(1,1,1,1)]*3 
black = [(0,0,0,1)]*1 
gray = [(.5,.5,.5,1)]*(len(greens)/2) 

colors = np.vstack((greens, black, white, black, red, black, white, black, gray)) 
mycmap = mcolors.LinearSegmentedColormap.from_list('my_colormap', colors) 

我修改了代码，并获得内容，但它并不好看和索引是在错误的地方：

Answer 1

首先：不要更改颜色表！这是用于指定数据的映射，因此如果更改它，数据表示形式与您最初的目标不同。

你可以做的是创建3个不同的颜色条，如下所示。注意区别：我们有一个单一的色彩映射，但有三个色彩条，它们都显示了一个色彩映射的一部分。

from random import random 
import numpy as np 
import matplotlib.pyplot as plt 
import matplotlib.colors as mcolors 
from matplotlib import cm 

n=11 
tab = np.array([[random() for i in range(n)] for j in range(n)]) 
tab[1,2] = 2. 
tab[3,4] = 2. 
tab[5,6] = 3. 
tab[7,8] = 3. 

values1 = np.ma.masked_array(tab, tab > 1.) 
values2 = np.ma.masked_array(tab, tab != 2.) 
values3 = np.ma.masked_array(tab, tab != 3.) 

# 50 values for later use from 0 to 1 
greens = cm.Greens(np.linspace(0,1, num=50)) 
# 25 values for later use from 1 to 1.5 
greensfill = cm.Greens(np.ones(25)) 
# 50 values red for later use from 1.5 to 2.5 
red = [(1,0,0,1)]*len(greens) 
# 50 values gray for later use from 2.5 to 3.5 
gray = [(.5,.5,.5,1)]*len(greens) 

colors = np.vstack((greens, greensfill, red, gray)) 
# in total we now have 175 colors in the colormap 
mycmap = mcolors.LinearSegmentedColormap.from_list('my_colormap', colors) 

#we now map those 175 colors to the range between 0 and 3.5 
im = plt.imshow(tab, cmap = mycmap, interpolation="none", vmin=0, vmax=3.5) 

plt.subplots_adjust(top=0.90, bottom=0.1) 
cb1ax = plt.gcf().add_axes([0.85, 0.8, 0.035, 0.1]) 
cb2ax = plt.gcf().add_axes([0.85, 0.67, 0.035, 0.1]) 
cb3ax = plt.gcf().add_axes([0.85, 0.1, 0.035, 0.54]) 

plt.gcf().colorbar(im, cax=cb1ax, boundaries=[2.5,3.5], ticks=[3]) 
plt.gcf().colorbar(im, cax=cb2ax, boundaries=[1.5,2.5], ticks=[2]) 
plt.gcf().colorbar(im, cax=cb3ax, boundaries=np.linspace(0,1, num=100), ticks=[0,1]) 

plt.show() 

---

编辑：现在，它可能发生在彩条不坐间隔紧密的图像，甚至重叠的。也可能发生这样的情况，三个条的高度大于图像的高度。为了防止这种情况发生，我们可以调整栏杆大小并调整它们的位置，使它们始终看起来不错。因此脚本的下半部分可能会被下面的代码替代。

plt.subplots_adjust(top=0.90, bottom=0.1) 
plt.gcf().canvas.draw() 
ax = plt.gca() 

plt.subplots_adjust(top=0.90, bottom=0.1) 
cb1ax = plt.gcf().add_axes([0.85, 0.8, 0.035, 0.1]) 
cb2ax = plt.gcf().add_axes([0.85, 0.67, 0.035, 0.1]) 
cb3ax = plt.gcf().add_axes([0.85, 0.1, 0.035, 0.54]) 

plt.gcf().colorbar(im, cax=cb1ax, boundaries=[2.5,3.5], ticks=[3]) 
plt.gcf().colorbar(im, cax=cb2ax, boundaries=[1.5,2.5], ticks=[2]) 
plt.gcf().colorbar(im, cax=cb3ax, boundaries=np.linspace(0,1, num=100), ticks=[0,1]) 

def resize(event=None): 
    s = 0.03 
    pos = ax.get_position() 
    smh = pos.height/8.; loh = pos.height*0.675 
    w = 0.06*pos.width; 
    x0 = pos.x1+0.065*pos.width 
    cb1ax.set_position([x0, pos.y0+loh+smh+2*s, w, smh]) 
    cb2ax.set_position([x0, pos.y0+loh+s,  w, smh]) 
    cb3ax.set_position([x0, pos.y0,    w, loh]) 

resize() 
plt.connect("resize_event", resize) 

plt.show()