Matplotlib：如何在使用 plt.figure() 和 figure.addaxes() 时显示图像颜色条

Question

我正在尝试制作一个包含六个独立地块的图形，以两排三个地块组织。每行图都应该有自己的颜色条，对应于水平组中三个图中显示的图像。从视觉上看，该图应如下所示：

image_type1 | image_type1 | image_type1 | colorbar_for_type1_images

image_type2 | image_type2 | image_type2 | colorbar_for_type2_images

上图中的竖线只是为了分隔图形的不同组成部分。我的图中实际上不需要垂直线。

下面显示了我正在尝试做的一个示例，以及我尝试使用每行中的第三个图像绘制颜色条的失败尝试。

过去，当我将自己的颜色图用于一系列绘制线时，我已经能够使用类似于下面显示的代码成功地做到这一点，而不是像我一样用于图像正在尝试在下面做。

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.cbook import get_sample_data

#Make 6 plotting areas of the same dimensions
figuresizex = 9.0
figuresizey = 6.1
lowerx = .07
lowery = .09
upperx = .92
uppery = .97
xspace = .05
yspace = .11
xwidth = (upperx-lowerx-2*xspace)/3.
ywidth = (uppery-lowery-yspace)/2.

fig = plt.figure(figsize=(figuresizex,figuresizey))
ax1 = fig.add_axes([lowerx,lowery+ywidth+yspace,xwidth,ywidth])
ax2 = fig.add_axes([lowerx+xwidth+xspace,lowery+ywidth+yspace,xwidth,ywidth])
ax3 = fig.add_axes([lowerx+2*xwidth+2*xspace,lowery+ywidth+yspace,xwidth,ywidth])
ax4 = fig.add_axes([lowerx,lowery,xwidth,ywidth])
ax5 = fig.add_axes([lowerx+xwidth+xspace,lowery,xwidth,ywidth])
ax6 = fig.add_axes([lowerx+2*xwidth+2*xspace,lowery,xwidth,ywidth])
axlist = [ax1,ax2,ax3,ax4,ax5,ax6]

#Start plotting images
image = np.identity(5)

for i in range(0,3):
    vmin, vmax = image.min(),image.max()
    axuse = axlist[i]
    im = axuse.imshow(image, vmin=vmin, vmax=vmax)
    if i == 3:
        cbar = axuse.colorbar(im)
        cbar = plt.colorbar(im)

image_2 = np.arange(16).reshape((4,4))

for i in range(0,3):
    vmin, vmax = image_2.min(),image_2.max()
    axuse = axlist[i+3]
    axuse.imshow(image_2,vmin=vmin, vmax=vmax)
    if i == 3:
        cbar = axuse.colorbar()
        cbar = plt.colorbar()

plt.show()

Answer 1

我建议采用this question 中概述的方法。

除了使添加颜色条变得简单且不依赖于您的第三张图像（应该是 i==2）之外，ImageGrid 的使用消除了显式（痛苦地？）定义所有 6 个轴和如果您的图像数量发生变化，它会变得更加灵活。

更新：我添加了第三行，以表明使用 vmin 和 vmax 参数可以将相同的比例应用于每行中的所有图像。

import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid

figuresizex = 9.0
figuresizey = 6.1

# generate images
image1 = np.identity(5)
image2 = np.arange(16).reshape((4,4))



fig = plt.figure(figsize=(figuresizex,figuresizey))

# create your grid objects
top_row = ImageGrid(fig, 311, nrows_ncols = (1,3), axes_pad = .25,
                    cbar_location = "right", cbar_mode="single")
middle_row = ImageGrid(fig, 312, nrows_ncols = (1,3), axes_pad = .25,
                       cbar_location = "right", cbar_mode="single")
bottom_row = ImageGrid(fig, 313, nrows_ncols = (1,3), axes_pad = .25,
                       cbar_location = "right", cbar_mode="single")

# plot the images            
for i in range(3):
    vmin, vmax = image1.min(),image1.max()
    ax = top_row[i]
    im1 = ax.imshow(image1, vmin=vmin, vmax=vmax)

for i in range(3):
    vmin, vmax = image2.min(),image2.max()
    ax =middle_row[i]
    im2 = ax.imshow(image2, vmin=vmin, vmax=vmax)

# Update showing how to use identical scale across all 3 images
# make some slightly different images and get their bounds
image2s = [image2,image2 + 5,image2 - 5]

# inelegant way to get the absolute upper and lower bounds from the three images
i_max, i_min = 0,0
for im in image2s:
    if im.max() > i_max: 
        i_max= im.max()
    if im.min() < i_min: 
        i_min = im.min()
# plot these as you would the others, but use identical vmin and vmax for all three plots
for i,im in enumerate(image2s):
    ax = bottom_row[i]
    im2_scaled = ax.imshow(im, vmin = i_min, vmax = i_max)

# add your colorbars
cbar1 = top_row.cbar_axes[0].colorbar(im1)
middle_row.cbar_axes[0].colorbar(im2)       
bottom_row.cbar_axes[0].colorbar(im2_scaled)

# example of titling colorbar1
cbar1.set_label_text("label"))

# readjust figure margins after adding colorbars, 
# left and right are unequal because of how
# colorbar labels don't appear to factor in to the adjustment
plt.subplots_adjust(left=0.075, right=0.9)

plt.show()