我试图用六个单独的地块制作一个数字,组成两排三个地块.每行图应具有其自己的颜色条,其对应于水平组中的三个图中所示的图像.从视觉上看,图形看起来像：

image_type1 | image_type1 | image_type1 | colorbar_for_type1_images

image_type2 | image_type2 | image_type2 | colorbar_for_type2_images

上面表示中的垂直线只是为了分离图中的不同组件.我的图中实际上并不需要垂直线条.

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尝试失败.

我以前能够成功地完成这项工作,其代码类似于下面的内容,当我使用我自己的彩色地图绘制一系列绘制的线条时,而不是像我在下面尝试的那样使用图像.

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()

最佳答案 我建议采用
this question中概述的方法.

除了简单地添加颜色条并且不依赖于第三个图像(应该是i == 2)之外,使用ImageGrid还无需明确(痛苦地)定义所有6个轴并变得更加灵活如果您的图像数量发生变化

更新：我添加了第三行,以显示可以使用vmin和vmax参数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()