How to add an image as the background to a matplotlib figure (not to plots, but to the "whitesp-CodePudding

I am trying to add an image to the "whitespace" behind the various subplots of a matplotlib figure.

Most discussions similar to this topic are to add images to the plots themselves, however I have not yet come across a means to change the background of the overall "canvas".

The most similar function I have found is set_facecolor(), however this only allows a single color to be set as the background.

fig, ax = plt.subplots(2,2)
fig.patch.set_facecolor('xkcd:mint green')
plt.show()

However, I am seeking a solution to import an image behind the plots, similar to this (manually made):

I have googled, searched SO, and looked through the matplotlib docs but I only get results for either plt.imshow(image) or set_facecolor() or similar.

CodePudding user response：

You can use a dummy subplot, with the same size as the figure, and plot the background onto that subplot.

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

image = plt.imread('test.jpg') 

# make ticks white, for readability on colored background
mpl.rcParams.update({'xtick.color': "white",
                     'ytick.color': "white",
                     'axes.labelcolor': "white"}) 
# create a figure with 4 subplots, with the same aspect ratio as the image
width = 8
fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(width, width * image.shape[0] / image.shape[1]))
for ax in np.ravel(axs):
    ax.patch.set_alpha(0.7) # make subplots semi-transparent

background_ax = plt.axes([0, 0, 1, 1]) # create a dummy subplot for the background
background_ax.set_zorder(-1) # set the background subplot behind the others
background_ax.imshow(image, aspect='auto') # show the backgroud image

# plot something onto the subplots
t = np.linspace(0, 8 * np.pi, 2000)
for i in range(2):
    for j in range(2):
        axs[i, j].plot(np.sin(t * (i   2)), np.sin(t * (j   4)))

# plt.tight_layout() gives a warning, as the background ax won't be taken into account,
# but normally the other subplots will be rearranged to nicely fill the figure
plt.tight_layout() 
plt.show()