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dcherian avatar dcherian commented on June 3, 2024

Note that (2) is worse because we always accumulate count with xarray because min_count=1 by default. Potentially this could be optimized (I don't remember if I did)

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ml31415 avatar ml31415 commented on June 3, 2024

About ml31415/numpy-groupies#3 I'm not categorically against adding multiple aggregations in one go. It's mainly, that so far I considered the setup overhead of aggregate as small enough to not be worth making the API more complicated. I'd argue this is still true for the 1D case, as it doesn't do more than the most necessary type and size checks. I didn't do any benchmarks, but if the raveling/unraveling should turn out to be a bottleneck, sure, we should try to find a better solution.

As you mentioned bincount, there is still a 2x-4x speed up to be gained by using the numba version compared to the bincount-depending numpy-only version (1D case).

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dcherian avatar dcherian commented on June 3, 2024

if the raveling/unraveling should turn out to be a bottleneck, sure, we should try to find a better solution.

In my benchmarks this was ~25-30% of the time for nd array, 1D group_idx though ml31415/numpy-groupies#77 should reduce that

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