Bug summary

Using plt.imshow(B, alpha=alphas) where B has dimensions NxMx3 and alphas NxM ignores the the alpha parameter is silently ignored and B displayed as is.

Code for reproduction

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

A=np.arange(5*5*3).reshape((5,5,3)) / (5*5*3)

plt.imshow(A)
plt.title("A")
plt.show()

B = np.zeros(5*5*3).reshape((5,3,5))
B[2:4, 2:4] = 1
B.shape = (5,5,3) # do some shifting so its not grayscale
plt.imshow(B)
plt.title("B")
plt.show()

alphas = np.zeros(5*5).reshape((5,5))
alphas[B[:,:,1] > 0] = 0.66
plt.imshow(alphas)
plt.title("Alpha mask")
plt.show()

plt.imshow(A)
plt.imshow(B, alpha=alphas) # gives wrong result
plt.title("wrong result")
plt.show()

Actual outcome

Expected outcome

Additional information

Providing just a single alpha value or the alpha instead as alpha channel in B NxMx4 works.
But there is no warning to apply this fix or automatically merge it to apply the alpha channel as expected.

Operating system

linux and windows

Matplotlib Version

3.5.2 and 3.6.0

Matplotlib Backend

module://matplotlib_inline.backend_inline

Python version

'3.9.7' and 3.10.6

Jupyter version

3.4.4

Installation

conda