Question

I would use imshow for this, so I will use it to describe my problem.
I have several matrices which I would like to plot on the same axis. Something like this:

import matplotlib.pyplot as plt
import numpy as np

a = np.array([[0,1,2],[0,1,2]])
x = np.array([0,1,2])
y = np.array([0,1])


a2 = np.array([[10,11,12],[10,11,12]])
x2 = np.array([10,11,12])
y2 = np.array([0,1])


plt.imshow(a,extent=[x.min(),x.max(),y.min(),y.max()])
plt.imshow(a2,extent=[x2.min(),x2.max(),y2.min(),y2.max()])

plt.show()

(With this code the first imshow is overwritten by the second)

The reason why I can’t combine them into a single matrix with one set of x and y axes (by filling the gaps with zeros) is that the combined matrix would be huge and there are large spaces in between the strips.

Answer 1

It’s not overwritten, the axes limits are just reset to the extents of the last image each time.

Just call plt.autoscale().

As a quick example of what you’re seeing:

import numpy as np
import matplotlib.pyplot as plt

data1, data2 = np.random.random((2,10,10))

fig, ax = plt.subplots()
ax.imshow(data1, extent=[-10, 0, -10, 0])
ax.imshow(data2, extent=[10, 20, 10, 20])

plt.show()

Now, if we just call autoscale:

import numpy as np
import matplotlib.pyplot as plt

data1, data2 = np.random.random((2,10,10))

fig, ax = plt.subplots()
ax.imshow(data1, extent=[-10, 0, -10, 0])
ax.imshow(data2, extent=[10, 20, 10, 20])

ax.autoscale()

plt.show()