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6 rows where author_association = "CONTRIBUTOR", issue = 1076265104 and user = 81219 sorted by updated_at descending

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user 1

  • huard · 6 ✖

issue 1

  • Weighted quantile · 6 ✖

author_association 1

  • CONTRIBUTOR · 6 ✖
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1047254700 https://github.com/pydata/xarray/pull/6059#issuecomment-1047254700 https://api.github.com/repos/pydata/xarray/issues/6059 IC_kwDOAMm_X84-a9as huard 81219 2022-02-21T21:59:22Z 2022-02-21T21:59:22Z CONTRIBUTOR

The alignment of data and weights is not done automatically. So I agree this would be ideal, but I'll need some guidance to make it happen without the align call.

This is the error I get python ValueError: operand to apply_ufunc has required core dimensions ['dim_0', 'dim_1'], but some of these dimensions are absent on an input variable: ['dim_1']

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  Weighted quantile 1076265104
1043394963 https://github.com/pydata/xarray/pull/6059#issuecomment-1043394963 https://api.github.com/repos/pydata/xarray/issues/6059 IC_kwDOAMm_X84-MPGT huard 81219 2022-02-17T20:26:50Z 2022-02-17T20:26:50Z CONTRIBUTOR

@mathause I'm not 100% confident the methods other than linear work as expected, so I suggest we do not expose method until a robust test suite can confirm results are valid.

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1035083891 https://github.com/pydata/xarray/pull/6059#issuecomment-1035083891 https://api.github.com/repos/pydata/xarray/issues/6059 IC_kwDOAMm_X849siBz huard 81219 2022-02-10T15:52:34Z 2022-02-10T15:52:34Z CONTRIBUTOR

We have local changes that expose method in the public API, and compare results against numpy for the case with uniform weights to confirm results are identical.

Do you want us to push those changes here ?

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  Weighted quantile 1076265104
1032858964 https://github.com/pydata/xarray/pull/6059#issuecomment-1032858964 https://api.github.com/repos/pydata/xarray/issues/6059 IC_kwDOAMm_X849kC1U huard 81219 2022-02-08T17:13:14Z 2022-02-08T17:13:14Z CONTRIBUTOR

Correct, it's not exposed yet because I don't have the bandwidth to create tests for all the different methods.

The equal weight case could be tested against numpy fairly easily though.

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  Weighted quantile 1076265104
1032829729 https://github.com/pydata/xarray/pull/6059#issuecomment-1032829729 https://api.github.com/repos/pydata/xarray/issues/6059 IC_kwDOAMm_X849j7sh huard 81219 2022-02-08T16:44:21Z 2022-02-08T16:44:21Z CONTRIBUTOR

@mathause Should this PR be amended to account for #6108 ?

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1011450968 https://github.com/pydata/xarray/pull/6059#issuecomment-1011450968 https://api.github.com/repos/pydata/xarray/issues/6059 IC_kwDOAMm_X848SYRY huard 81219 2022-01-12T21:06:01Z 2022-01-12T21:06:01Z CONTRIBUTOR

I had the same interrogation. My guess is that the DataArray.quantile and DataArrayWeighted.quantile should have similar signatures.

The way I see it is that another PR would bring the DataArray.quantile signature up to date with numpy 1.22, which includes the 9 h types, and harmonize DataArrayWeighted.quantile with it.

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  Weighted quantile 1076265104

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