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Python particle tracking toolkit
http://soft-matter.github.io/trackpy
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Comment: numba performance!!! #222

Closed apiszcz closed 9 years ago

apiszcz commented 9 years ago

69,686 input detections NUMPY 31,008 seconds NUMBA 77 seconds

caspervdw commented 9 years ago

Is this feature finding? That is extremely slow for numpy... did you compare the outputs?

Could you profile it for one frame and post the output? (%prun locate(frame, diameter, engine='python'))

apiszcz commented 9 years ago

Yes, the outputs are the same count wise, # of trajectories and detections per trajectory. I will probably do more tests. I'm not using python notebook(I think that is what you are showing). Is there another type of profile of interest?

On Fri, Mar 6, 2015 at 9:32 AM, Casper van der Wel <notifications@github.com

wrote:

Is this feature finding? That is extremely slow for numpy... did you compare the outputs?

Could you profile it for one frame and post the output? (%prun locate(frame, diameter, engine='python'))

— Reply to this email directly or view it on GitHub https://github.com/soft-matter/trackpy/issues/222#issuecomment-77567382.

caspervdw commented 9 years ago

I think you should do the following:

import cProfile
cProfile.runctx('tp.locate(frame, diam, engine="python")', {'frame': frame, 'diam': diameter}, {})

Or use profile if there is no cProfile

apiszcz commented 9 years ago

I am not using the image processing capability, only link_df. FYI

On Fri, Mar 6, 2015 at 10:41 AM, Casper van der Wel < notifications@github.com> wrote:

I think you should do the following:

import cProfile cProfile.runctx('tp.locate(frame, diam, engine="python")', {'frame': frame, 'diam': diameter}, {})

Or use profile if there is no cProfile

— Reply to this email directly or view it on GitHub https://github.com/soft-matter/trackpy/issues/222#issuecomment-77579380.

nkeim commented 9 years ago

That kind of speedup for linking is uncommon, but not impossible. The algorithm is such that the pure-Python version can't really use the array-based operations of numpy. So it is an ideal case for numba. That's not true of the feature-finding algorithm, so the speedup there tends to be much less impressive. Anyhow, we're glad trackpy is working so well for you!

apiszcz commented 9 years ago

Please find the attached cProfile output of the run. See Frame 577 , in this case it was 6 minutes

http://apvm1.mitre.org/p/deepsearch/tools/deepsearch_v0033_20150214_164403.7z

On Fri, Mar 6, 2015 at 10:41 AM, Casper van der Wel < notifications@github.com> wrote:

I think you should do the following:

import cProfile cProfile.runctx('tp.locate(frame, diam, engine="python")', {'frame': frame, 'diam': diameter}, {})

Or use profile if there is no cProfile

— Reply to this email directly or view it on GitHub https://github.com/soft-matter/trackpy/issues/222#issuecomment-77579380.

nkeim commented 9 years ago

@apiszcz , the server seems unavailable. Perhaps you could create a gist?

apiszcz commented 9 years ago

I'll try it inline:

Yes, but could it be faster? FAST: numba is available and enabled (fast subnets and feature-finding). FAST: Using pyfftw for image preprocessing.

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nkeim commented 9 years ago

OK, now I understand what you mean. trackpy encountered an usually large subnet in that frame. The tutorial on subnets may help you understand what is going on. This may be an appropriate movie for using adaptive search.

apiszcz commented 9 years ago

I plan to try adaptive and velocity next/soon. Thank you for the review.

On Fri, Mar 6, 2015 at 5:10 PM, Nathan Keim notifications@github.com wrote:

OK, now I understand what you mean. trackpy encountered an usually large subnet in that frame. The tutorial on subnets may help you understand what is going on. This may be an appropriate movie for using adaptive search.

— Reply to this email directly or view it on GitHub https://github.com/soft-matter/trackpy/issues/222#issuecomment-77645560.

apiszcz commented 9 years ago

Adaptive search is about 10-20x faster, and not pausing ... Thank you for adding this to 0.3 candidate

On Fri, Mar 6, 2015 at 5:10 PM, Nathan Keim notifications@github.com wrote:

OK, now I understand what you mean. trackpy encountered an usually large subnet in that frame. The tutorial on subnets may help you understand what is going on. This may be an appropriate movie for using adaptive search.

— Reply to this email directly or view it on GitHub https://github.com/soft-matter/trackpy/issues/222#issuecomment-77645560.

tacaswell commented 9 years ago

@apiszcz Be aware that adaptive search is getting faster by truncating the search range. This can lead to different results than if you ran with a fixed search range. My guess is that this will artificially shorten tracks and in the worst case introduce erroneous linking. If you only have one frame with significant sub-nets my knee-jerk reaction is that some thing caused your whole frame to jump (ex someone kicked your optical table)

apiszcz commented 9 years ago

Thanks, concur on limits/threshold producing unexpected truncation. I understand nothing in this topic area is likely to be 'perfect' or even as good as tired human :).. However scale is an issue, and getting some of the process automated is a step in the right direction.

I reviewed some of the differences and am still learning. However in my case differences are nominal. I am also trying to compare non adaptive, adaptive and the velocity prediction model.

On Sun, Mar 8, 2015 at 2:35 PM, Thomas A Caswell notifications@github.com wrote:

@apiszcz https://github.com/apiszcz Be aware that adaptive search is getting faster by truncating the search range. This can lead to different results than if you ran with a fixed search range. My guess is that this will artificially shorten tracks and in the worst case introduce erroneous linking. If you only have one frame with significant sub-nets my knee-jerk reaction is that some thing caused your whole frame to jump (ex someone kicked your optical table)

— Reply to this email directly or view it on GitHub https://github.com/soft-matter/trackpy/issues/222#issuecomment-77766420.

nkeim commented 9 years ago

I agree with both @tacaswell and @apiszcz . The "glitch" frame should get special attention. I encounter vibrational glitches in my own data, but thankfully we are able to discard the fraction of the movie that's affected by the glitches and still do our science. Adaptive search lets me track the entire movie first without a lot of fiddling, and then identify the glitches and figure out how to deal with them. But I still have to deal with them :). Whatever your situation is, and whatever trackpy features you end up using, you're absolutely doing the right thing by inspecting the results yourself.

I'd also like to point out that our linking diagnostics feature may help you here, by showing you which particles are pushing trackpy to its limits.

p.s. If you're curious about how I deal with my glitches: if there are just 1 or 2 bad frames in a glitch, I actually cut out the bad frames and use prediction to skip over them, because I am analyzing channel flow.