How to force same size between matplotlib subplot image axes

Question

Suppose I have the following code to create three side-by-side images:

    n=10
    x = np.random.rand(n,1)
    y = np.random.rand(1,n)
    z = np.random.rand(n,n)

    fig, ax = plt.subplots(1, 3)
    ax[0].imshow(x)
    ax[1].imshow(z)
    ax[2].imshow(y)

However, the axes autoscale so that the vertical axis in the first image is larger than the vertical axis in the second.

Is there a way to programmatically force all image dimensions of size n to look the same in the three plots, regardless of window size? I'm looking for a way to either link the axes or the images so that the vertical axis of the first plot is the same size as the vertical axis of the second plot, and the horizontal axis of the third plot is the same size as the horizontal axis of the second plot, regardless of window size. i.e. something like this:

Answer 1

I think one easiest way is to use aspect='auto' with ax[1].imshow(z). But this will distort the image in a way that may be not the same as what you've shown in the question. And it may not work for cases where there is no single n. I'm not sure if I got you 100%, but let me try this method. The key idea here are:

1. Change the aspect ratio of your fig. The exact ratio comes from both your image data and your subplot layout.
2. Use tight layout to eliminate unnecessary between axes which may offset your graph a little bit.

Here is my example code and figure:

import matplotlib.pyplot as plt
from matplotlib.figure import figaspect
import numpy as np

n = 10
x = np.random.rand(n,1)
y = np.random.rand(1,n)
z = np.random.rand(n,n)

width_max = max(s.shape[0] for s in [x, y, z])
height_max = max(s.shape[1] for s in [x, y, z])

row = 1
col = 3
fig, ax = plt.subplots(row, col)
w, h = figaspect(row*width_max/(col*height_max))
fig.set_size_inches(w, h)

ax[0].imshow(x)
ax[1].imshow(z)
ax[2].imshow(y)

plt.tight_layout()
plt.show()

I hope this solves your real problem. I think this also works for a case like:

x = np.random.rand(3,1)
y = np.random.rand(1,10)
z = np.random.rand(7,6)