numba 0.12: extremely slow `_refine_numba()` about trackpy HOT 10 CLOSED

Thanks for investigating. I will leave the README as it is, recommending numba 0.11 with numpy 1.7.1, until numba rights itself.

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I see now how to do the "major rewrite" of _refine_numba(): pass in a big results array (along with its length) to be changed in-place; split make all of the little length-2 vectors into _x and _y components.

It might be less work to do it in Cython. But that's only if Cython makes it easy to overload the function to accept images with a variety of dtypes.

I'm concerned about the "out-of-the-box" experience now for someone who downloads the latest Anaconda and then trackpy. Is there an easy conditional way to just keep numba's mitts off of _refine_numba() altogether?

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From what I've read, conda doesn't make cython as painless as it does numba. (For example, if a Windows machine doesn't have the right C compiler, conda can't help it.) But am I no expert on that -- cython might be a viable choice.

The slow import time is bad. Maybe numba is compiling everything on import now. I can't find detailed release notes. Have you come across any? I pinged ContinuumIO's twitter account about it too. It might not be in the open.

See #83 for one approach that could serve in the meantime. Not sure if it passes local tests yet -- they are running really slow -- but import trackpy is back to normal speed.

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Take a look at this: https://twitter.com/teoliphant/status/433993752625942530

This prompts the question, "Why is this the new default?!"

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Better question: why is this being announced via twitter?

also, get off my lawn.

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Get off my lawn, indeed! :)

https://discussions.apple.com/thread/5472161?start=0&tstart=0

On Feb 13, 2014, at 3:07 PM, Thomas A Caswell [email protected] wrote:

Better question: why is this being announced via twitter?

also, get off my lawn.

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Hmm...

https://twitter.com/teoliphant/status/434074669616869376

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Travis himself invites us to post the broken code for priority attention: https://twitter.com/teoliphant/status/434076107738865664

@nkeim seems to understand the problem well enough, but I guess it doesn't hurt to get expert advice as it's offered. Hard to reduce _refine_numba to a self-contained concise question...

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@nkeim probably could have said it better, but I wanted to jump at Travis's offer while it was fresh in mind. Feel free to chime in. https://groups.google.com/a/continuum.io/forum/#!topic/numba-users/-lNug_ZI2xs

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Thanks! I added a link to a stripped-down, standalone version of feature.py that still causes the problem --- one of my failed experiments.

On Feb 13, 2014, at 4:46 PM, Dan Allan [email protected] wrote:

@nkeim probably could have said it better, but I wanted to jump at Travis's offer while it was fresh in mind. Feel free to chime in. https://groups.google.com/a/continuum.io/forum/#!topic/numba-users/-lNug_ZI2xs

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