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I'm trying to plot an image on top of a Figure, but imshow seems to always
distort the size of the axes. What I want is that the lower part of the top
image stay always in the same position, for any image height
This minimal example shows my issue
import matplotlib.pyplot as plt 
import numpy as np
fig = plt.figure()
ax = fig.add_axes([0.1, 0, 1, 1])
# Top figure aligned with the bottom figure
# keeping the same width (?)
ax2 = fig.add_axes([0.1, 1, 1, 1])
ax2.set_xticks([])
# Depending on the number of rows or columns
# the top image will be moved further to the top
# or will be stretched if rows > columns
# I dont know how to control this to stay always
# with the same separation with respect
# to the bottom figure and keeping the same width
# (so the frame is the same width than the bottom figure)
im = np.random.rand(10, 30)
ax2.imshow(im)
plt.plot()
If it is possible to
I would prefer to avoid using subplots or grid, since I have already
specified a lot of things using the add_axes method.
--
View this message in context: http://matplotlib.1069221.n5.nabble.com/Fixing-axes-for-imshow-plot-on-top-of-a-figure-tp45579.html
Sent from the matplotlib - users mailing list archive at Nabble.com.

What are you plotting? How big is this list that the loops are taking
appreciable amounts of time?!? Are we talking seconds here or ms?
Have you done enough profiling to know exactly which line in here are
slow? I don't quite understand the `np.ravel` calls.
You might do better either with one (or many?) collection artists.
You might also look into just updating the artists you have.
Without some context of what these patches are it is really hard to help
(or even really understand why this is slow).
Tom
On Sat, May 16, 2015 at 6:44 PM bmer <bhm...@gm...> wrote:
> This is what my animation function (i.e. the one that gets called by
> `FuncAnimation`) looks like:
>
> import numpy as np
> ...
> def mpl_animation_function(n):
> print "animating timestep: ", n
>
> if n > 0:
> previous_relevant_patch_indices =
> np.ravel(patch_indices_per_timestep[n-1])
> for index in previous_relevant_patch_indices:
> (patches[index]).set_visible(False)
>
> relevant_patch_indices =
> np.ravel(patch_indices_per_timestep[n])
>
> for index in relevant_patch_indices:
> (patches[index]).set_visible(True)
>
> return patches,
>
> `patches` is a pre-generated list of patches (possibly large), that have
> already been added to an `axes` instance.
>
>
> This function is awfully time-consuming as the number of patches becomes
> large.
>
> One idea I had was to parallelize the `for` loop, but likely that won't
> work
> because of issues with the `axes` instance being accessed and modified in
> parallel -- so I am afraid of fruitlessly spending time there. Do I have
> any
> other options, or is parallelization possible?
>
>
>
> --
> View this message in context:
> http://matplotlib.1069221.n5.nabble.com/What-are-my-options-for-speeding-up-a-custom-function-called-by-FuncAnimation-tp45562.html
> Sent from the matplotlib - users mailing list archive at Nabble.com.
>
>
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