[Matplotlib-users] What are my options for speeding up a custom function called by `FuncAnimation`?

[bmer]

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?



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[Thomas Caswell]

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

Post by bmer
This is what my animation function (i.e. the one that gets called by
import numpy as np
...
print "animating timestep: ", n
previous_relevant_patch_indices =
np.ravel(patch_indices_per_timestep[n-1])
(patches[index]).set_visible(False)
relevant_patch_indices =
np.ravel(patch_indices_per_timestep[n])
(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?
--
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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