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imshow() subplots with shared axes generate unwanted white spaces #1789
Description
When plotting two (or more) subplots which share the same axes, I see large areas of white spaces within the plots as seen here:
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)
ax2.imshow(np.random.random((10,10)))
ax2.autoscale(False)
plt.show()These white spaces persist even after autoscale(False), ax.set_xlim(0, 10), and/or ax.set_xbound(0, 10). One way to get rid of the white spaces is to set aspect=auto in imshow(), but this is not always ideal.
One could also set a figsize and manually adjust the axes to get the image contained exactly within the figure without white spaces, but resizing the figure would make the white spaces appear again.
Is there a way to correct for this behaviour?
Activity
pelson commented on Feb 27, 2013
Hi @mlynn1 - it looks like sharing forces the aspect ratio to be equal. As you say, setting the aspect to 'auto' stretches the image. The only other behaviour I can come up with is being able to reduce the size of the actual Axes itself. Is that what you had in mind? As far as I'm aware, there is no such functionality to do that automatically.
Thanks,
rycmak commented on Feb 27, 2013
I guess what I don't understand is why imshow() has to show all that white space in the figure above. The equal aspect ratio of the images would remain even if the white spaces were not there, wouldn't it?
Yes, I could manually set axes positions and sizes with fig.add_axes() etc., but I was hoping that there was some way for fig.add_subplot() to take care of automatically resizing the shared axes and display everything perfectly ;-).
pelson commented on Feb 27, 2013
Only if the black y axis lines moved inwards to be tight against the image boundary. (unless I've misunderstood you). That was the bit that I don't know whether it is possible - I've never seen it done, though I'm no expert in this.
I believe these are your options:
I'm struggling to see any other way of arranging the plot (be it in matplotlib, or by pen and paper). Sorry if I'm missing something obvious.
Cheers,
rycmak commented on Feb 27, 2013
I tried your first three suggestions above, and while the amount of white space is reduced, it is still there. And there are times when one would like an unstretched image...
I don't know why this is not done automatically by imshow()?
Basically, if I do not add the sharex and sharey options in the code above, I get exactly the plots I want. I can resize the figure, and each subplot would resize accordingly. The reason I would like the axes to be shared is that when I zoom in on one subplot, I would like the other subplot to zoom in correspondingly.
WeatherGod commented on Feb 27, 2013
On Wed, Feb 27, 2013 at 4:46 PM, mlynn1 notifications@github.com wrote:
pelson commented on Feb 27, 2013
@mlynn1 - thanks I now know what you're after (the behaviour before vs after of the share definition was the key).
I've just learnt something new about mpl in the process of digging into this one. There is an adjustable attribute (if you look in matplotlib.axes.Axes.__init__ you will see that this is set when sharing) which controls whether the axis is squeezed up to the data. To force the behaviour which you see when not sharing, set this to 'bbox-forced':
ax.set_adjustable('box-forced') ax2.set_adjustable('box-forced')When I do this, I get the same behaviour as when not sharing, only with the nice functionality that sharing adds.
HTH
tacaswell commented on Feb 27, 2013
related: http://stackoverflow.com/questions/15077364/matplotlib-pyplot-imshow-removing-white-space-within-plots-when-using-attribute/15078156#comment21255021_15078156
[had a long comment about what I thought was going on, that is now irrelevant]
efiring commented on Feb 27, 2013
@WeatherGod, no it is not a bug. @pelson has the right solution. Sharing and adjustable 'box' are not always compatible.
rycmak commented on Feb 27, 2013
@pelson: Thank you for your solution and time!
philippos86 commented on Jan 9, 2018
I had a similar issue and been searching first about the reason (really couldn't know why), and got here while searching for a solution. However, the solutions was by using the matplotlib.axes.Axes.set_xlim to set the dimensions of the plot, since I know beforehand the dimensions of my graph.
matplotlib.axes.Axes.set_xlim
efiring commented on Jan 9, 2018
This is fixed by #10033.
