python - Matplotlib:如何将 colorbar 切割成 2 个或更多个？

Question

我有一个包含 3 组值的数组:

• 0 到 1 之间的值(绿色渐变)
• 值等于2(红色)
• 值等于3(灰色)

感谢这篇文章 ( Define a colormap for each set of values in an array ) 我能够得到这段代码:

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()

结果如下:

我的问题是:ma​​tplotlib 能否通过将现有的颜色图切割成 3 个单独的颜色图或接近的颜色图来获得类似下图的效果(使用 photoshop 完成编辑)？

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)

我修改了代码并得到了以下内容，但它看起来不太好，索引位置错误:

Answer 1

首先:不要更改颜色图!这是指定您的数据的 map ，因此如果您更改它，数据表示将不同于您最初的目标。

您可以做的是创建 3 个不同的颜色条，如下所示。注意区别:我们有一个单一的颜色图，但是有 3 个颜色条，它们都显示了一个颜色图的一部分。

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:现在可能会出现颜色条与图像的间距不紧密甚至与图像重叠的情况。这三个条的高度也可能比图像大。为了防止这种情况，我们可以调整条形的大小并调整它们的位置，这样它们看起来总是很漂亮。因此，脚本的下半部分可能会被以下代码替换。

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()