Bug summary

Using imshow to visualize a 2x2x1 array with nans scattered throughout works as expected. With the default "bad" color of black with alpha = 0, you'll see an axis' facecolor where the nans are.

With a 2x2x3 array, the nans are always black.

With a 2x2x4 array, and the alpha manually set to 0 where the nans are, you get the correct behavior (i.e. you can see the axis where the nans are).

Code for reproduction

import numpy
from matplotlib import pyplot

img = numpy.ones((2, 2))  # single channel 2x2
img_nan = img.copy()
img_nan[0, 0] = float('nan')

img3 = numpy.ones((2, 2, 3))  # RGB 2x2
img3_nan = img3.copy()
img3_nan[0, 0, :] = float('nan')

img4 = numpy.ones((2, 2, 4))  # RGBA 2x2
img4_nan = img4.copy()
img4_nan[0, 0, :] = float('nan')
img4_nan[0, 0, 3] = 0  # alpha


def imshow(a):
    pyplot.figure()
    im = pyplot.imshow(a)
    # im.get_cmap().set_bad(color='y', alpha=1) # only works for single channel images
    im.get_cmap().set_bad(alpha=0)
    pyplot.gca().set_facecolor('y')


imshow(img_nan)   # nan shows up as 'y'
imshow(img3_nan)  # nan shows up as black
imshow(img4_nan)  # nan shows up as 'y'


pyplot.show()

Actual outcome

Expected outcome

Figures 2 and 3 above should look identical.

Additional information

I traced through imshow and cannot see any difference between the single- and 3-channel image; the mask is there and it's correct in both cases. So I wonder if this is a backend issue?

Operating system

Windows

Matplotlib Version

3.7.3

Matplotlib Backend

QtAgg

Python version

3.8.10

Jupyter version

No response

Installation

pip