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[Bug]: Memory leak with colorbar.make_axes #22085
Description
Bug summary
When using matplotlib.colorbar.make_axes function, plt.close() does not clear the new colorbar axes from memory. Tested for the Qt5 backend only. Maybe related to #22002, at least in the sense that I see no leak for Qt5 when no colorbar axes are added.
Code for reproduction
%pylab qt5
from matplotlib import colorbar
import psutil
import gc
p = psutil.Process()
for i in range(5):
fig, ax = plt.subplots(1,1)
ax.imshow(random.normal(size=(100,100)))
cax = colorbar.make_axes(ax)
plt.savefig("test.png")
plt.pause(0.1)
plt.close(fig)
del fig
del ax
del cax
gc.collect()
print(p.memory_full_info().uss/1e6)Actual outcome
250.744832
255.209472
259.514368
264.13056
268.099584
Expected outcome
Constant memory usage, as is when the line
cax = colorbar.make_axes(ax)
is commented out.
Additional information
No response
Operating system
Windows
Matplotlib Version
3.4.2
Matplotlib Backend
Qt5Agg (PyQt)
Python version
3.7.6
Jupyter version
No response
Installation
from source (.tar.gz)
Activity
vdrhtc commentedon Jan 2, 2022
Oh, also just found #22040, probably mine is a duplicate. The difference is that I do not yet know, how to workaround the problem in my example.
jklymak commentedon Jan 2, 2022
Perhaps also test the latest matplotlib. There were substantial changes to colorbar for 3.5. FWIW, I don;t know what make_axes is supposed to do in your code.
jklymak commentedon Jan 3, 2022
OK, I can confirm this when
make_axesis used. Whenmake_axes_gridspecis used, there is no memory leak.jklymak commentedon Jan 3, 2022
If I insert
... del ax del cax._colorbar_info del cax ...then the memory leak goes away.
This is defined in
make_axes:matplotlib/lib/matplotlib/colorbar.py
Lines 1463 to 1471 in 0c1aa86
and used by constrained_layout. I am not clear why adding a dictionary to an object would leave the dictionary orphaned when the parent is deleted?
vdrhtc commentedon Jan 3, 2022
Yes, I found that grid spec helps, too, thank you!
jklymak commentedon Jan 3, 2022
The culprit is
parents = np.atleast_1d(parents).ravel(). If I get rid of this, no memory leak. However, this is needed, and I am not sure why it leaks.vdrhtc commentedon Jan 3, 2022
@jklymak Thank you very much!