Matplotlib：如何在2個或更多的顏色中切割一個顏色條？

Question

我有3組值的數組：0和1（綠色梯度）

值等於2（紅色）之間

• 值
• 值等於3（灰）

多虧了這個帖子（Define a colormap for each set of values in an array）我能得到這個代碼：

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() 

下面是結果：

我的問題是：可以matplotlib獲得通過削減現有的顏色映射成3個獨立的顏色表或者相近的東西像下面的圖片（編輯用Photoshop做）？

編輯：

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) 

我修改了代碼，並獲得內容，但它並不好看和索引是在錯誤的地方：

Answer 1

首先：不要更改顏色表！這是用於指定數據的映射，因此如果更改它，數據表示形式與您最初的目標不同。

你可以做的是創建3個不同的顏色條，如下所示。注意區別：我們有一個單一的色彩映射，但有三個色彩條，它們都顯示了一個色彩映射的一部分。

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() 

---

編輯：現在，它可能發生在彩條不坐間隔緊密的圖像，甚至重疊的。也可能發生這樣的情況，三個條的高度大於圖像的高度。爲了防止這種情況發生，我們可以調整欄杆大小並調整它們的位置，使它們始終看起來不錯。因此腳本的下半部分可能會被下面的代碼替代。

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()