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| id ▼ | node_id | number | state | locked | title | user | body | created_at | updated_at | closed_at | merged_at | merge_commit_sha | assignee | milestone | draft | head | base | author_association | auto_merge | repo | url | merged_by |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 759368405 | PR_kwDOAMm_X84tQwrV | 5870 | closed | 0 | Add var and std to weighted computations | cjauvin 488992 | - [x] Tests added - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [x] New functions/methods are listed in `api.rst` This follows https://github.com/pydata/xarray/pull/2922 to add `var`, `std` and `sum_of_squares` to `DataArray.weighted` and `Dataset.weighted`. I would also like to add weighted quantile, eventually. | 2021-10-15T17:13:31Z | 2022-01-04T21:20:58Z | 2021-10-28T11:02:54Z | 2021-10-28T11:02:54Z | b3b77f5f98077202516a568183236bd0cb457d82 | 0 | 4b7d866ab2711e4532095d81e4081f376c6b16b3 | 07de257c5884df49335496ee6347fb633a7c302c | CONTRIBUTOR | xarray 13221727 | https://github.com/pydata/xarray/pull/5870 | ||||
| 799424119 | PR_kwDOAMm_X84vpj53 | 6059 | closed | 0 | Weighted quantile | cjauvin 488992 | - [x] Tests added - [x] Passes `pre-commit run --all-files` - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [x] New functions/methods are listed in `api.rst` This is a follow-up to https://github.com/pydata/xarray/pull/5870/, which adds a weighted `quantile` function. The question of how to precisely define the weighted quantile function is surprisingly complex, and this implementation offers a compromise in terms of simplicity and compatibility: * The only interpolation method supported is the so-called "Type 7", as explained in https://aakinshin.net/posts/weighted-quantiles/, which proposes an R implementation, that I have adapted * It turns out that Type 7 is apparently the most "popular" one, at least in the Python world: it corresponds to the default `linear` interpolation option of `numpy.quantile` (https://numpy.org/doc/stable/reference/generated/numpy.quantile.html) which is also the basis of xarray's already existing non-weighted quantile function * I have taken care in making sure that the results of this new function, with equal weights, are equivalent to the ones of the already existing, non-weighted function (when used with its default interporlation option) The interpolation question is so complex and confusing that entire articles have been written about it, as mentioned in the blog post above, in particular this one, which establishes the "nine types" taxoxomy, used, implicitly or not, by many software packages: https://doi.org/10.2307/2684934. The situation seems even more complex in the NumPy world, where many discussions and suggestions are aimed toward trying to improve the consistency of the API. The current non-weighted situation has the 9 options, as well as 4 extra legacy ones: https://github.com/numpy/numpy/blob/376ad691fe4df77e502108d279872f56b30376dc/numpy/lib/function_base.py#L4177-L4203 This PR cuts the Gordian knot by offering only one interpolation option, but.. given that its implementation is based on `apply_ufunc… | 2021-12-10T01:11:36Z | 2022-03-27T20:36:22Z | 2022-03-27T20:36:22Z | 2022-03-27T20:36:22Z | 8a2fbb89ea6880ef43362281792dce2005b6d08a | 0 | c298bd0b63e8502e06ed219a39e4f1eebd14dba4 | 8f42bfd3a5fd0b1a351b535be207ed4771b02c8b | CONTRIBUTOR | xarray 13221727 | https://github.com/pydata/xarray/pull/6059 |
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