matplotlib.pyplot.imshow: removing white space wit

问题:

I have a problem which is similar to the one posted here. The difference is that I get unwanted white spaces inside the plot area when I plot two subplots which share axes via the sharex and sharey attributes. The white spaces persist even after setting autoscale(False). For example, using similar code as in the answer to the post mentioned above:

import matplotlib.pyplot as plt
import numpy as np

fig = plt.figure()
ax = fig.add_subplot(2, 1, 1)
ax.imshow(np.random.random((10,10)))
ax.autoscale(False)
ax2 = fig.add_subplot(2, 1, 2, sharex=ax, sharey=ax)   # adding sharex and sharey
ax2.imshow(np.random.random((10,10)))
ax2.autoscale(False)
plt.show()

results in this image.

I have also tried ax.set_xlim(0, 10) and ax.set_xbound(0, 10) as per suggestions here, but to no avail. How can I get rid of the extra white spaces? Any ideas would be appreciated.

回答1:

As suggested here, adding:

ax.set_adjustable('box-forced')
ax2.set_adjustable('box-forced')

solves the problem.

(documentation)

回答2:

Using plt.subplots as:

fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, sharey=False)
ax[0].imshow(np.random.random((10,10)))
ax[0].autoscale(False)
ax[1].imshow(np.random.random((10,10)))
ax[1].autoscale(False)

I get with no white spaces within axes. Using figsize within plt.subplots or fig.subplots_adjust you can get better axis ratios.

回答3:

The issue is the helpful machinery from using add_subplot. Notice that the amount of white space changes if you resize the figure.

The following seems to work (until you re-size the figure)

import matplotlib.pyplot as plt
import numpy as np

fig = plt.figure(figsize=(5, 5))
ax = fig.add_axes([.3, .55, .35, .35]) 
ax.imshow(np.random.random((10,10)))
ax.autoscale(False)
ax2 = fig.add_axes([.3,  .05, .35, .35], sharex=ax, sharey=ax ) 
ax2.imshow(np.random.random((10,10)))
ax2.autoscale(False)

plt.show()

This looks like a bad interaction between the size/location of the axes object, the shared axes, and the equal aspect ratio from imshow.

If you can live with out the ticks, you can do

ax.set_axis_off()
ax2.set_axis_off()

I think it is worth opening an issue on the matplotlib github for this.