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  • conditionally disable bottleneck · 6 ✖

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897697868 https://github.com/pydata/xarray/pull/5560#issuecomment-897697868 https://api.github.com/repos/pydata/xarray/issues/5560 IC_kwDOAMm_X841gchM dcherian 2448579 2021-08-12T14:41:30Z 2021-08-12T14:41:30Z MEMBER

Thanks @keewis. We can extend the tests for rolling later.

 
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   conditionally disable bottleneck 935279688
897161325 https://github.com/pydata/xarray/pull/5560#issuecomment-897161325 https://api.github.com/repos/pydata/xarray/issues/5560 IC_kwDOAMm_X841eZht keewis 14808389 2021-08-11T21:18:05Z 2021-08-11T21:18:18Z MEMBER

I'm not sure how to test rolling: monkeypatching bottleneck.move_sum does not work because Rolling only accesses that on import, i.e. before the test is executed.

Everything else ~is~ should be done, though.

 
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   conditionally disable bottleneck 935279688
884366726 https://github.com/pydata/xarray/pull/5560#issuecomment-884366726 https://api.github.com/repos/pydata/xarray/issues/5560 IC_kwDOAMm_X840tl2G dcherian 2448579 2021-07-21T17:35:36Z 2021-07-21T17:35:36Z MEMBER

so disabling bottleneck would fail the function. Should we just silently use bottleneck, or raise an error?

Ah this is a good test!

python with xr.set_options(use_bottleneck=False): with pytest.raises(...): dataarray.ffill()

IMO it should raise an error so that use_bottleneck is a "global" control on whether xarray uses bottleneck or not. The context manager gives the user some flexibility to opt-in to using bottleneck where they want to.

 
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   conditionally disable bottleneck 935279688
884359642 https://github.com/pydata/xarray/pull/5560#issuecomment-884359642 https://api.github.com/repos/pydata/xarray/issues/5560 IC_kwDOAMm_X840tkHa keewis 14808389 2021-07-21T17:23:45Z 2021-07-21T17:30:26Z MEMBER

as discussed in the meeting, we will keep use_bottleneck for now. I still don't know how to test this, but otherwise this should be ready for reviews and merging.

Edit: we also don't have alternatives for functions like duck_array_ops.push (used in ffill etc.) so disabling bottleneck would fail the function. Should we just silently use bottleneck, or raise an error?

 
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   conditionally disable bottleneck 935279688
872627259 https://github.com/pydata/xarray/pull/5560#issuecomment-872627259 https://api.github.com/repos/pydata/xarray/issues/5560 MDEyOklzc3VlQ29tbWVudDg3MjYyNzI1OQ== max-sixty 5635139 2021-07-02T00:20:32Z 2021-07-02T00:20:32Z MEMBER

Nice!

pandas uses use_bottleneck — and same for numba / numexpr.

To what extent would bottleneck & numba be mutually exclusive? Until everything is implemented in numbagg, I guess they won't be, and we might want separate options. Having a single option would be nicer otherwise.

 
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   conditionally disable bottleneck 935279688
872623510 https://github.com/pydata/xarray/pull/5560#issuecomment-872623510 https://api.github.com/repos/pydata/xarray/issues/5560 MDEyOklzc3VlQ29tbWVudDg3MjYyMzUxMA== dcherian 2448579 2021-07-02T00:09:36Z 2021-07-02T00:09:51Z MEMBER

We use it in other places too (for e.g.): https://github.com/pydata/xarray/blob/c472f8a4c79f872edb9dcd7825f786ecb9aff5c0/xarray/core/nputils.py#L139-L147

xr.set_options(accelerate_with="bottleneck")

I like this idea but we should wait for more input.

 
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   conditionally disable bottleneck 935279688

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