html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/pull/6059#issuecomment-1043334868,https://api.github.com/repos/pydata/xarray/issues/6059,1043334868,IC_kwDOAMm_X84-MAbU,488992,2022-02-17T19:27:23Z,2022-02-17T19:27:23Z,CONTRIBUTOR,"I have added a test to verify that using equal weights with the different interpolation methods that this PR supports _would_ work (i.e. would yield the same results as `np.quantile`, with the corresponding methods). It is skipped however because the method argument is not currently exposed in the API (it would be in future work, ideally).","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1076265104
https://github.com/pydata/xarray/pull/6059#issuecomment-1011136209,https://api.github.com/repos/pydata/xarray/issues/6059,1011136209,IC_kwDOAMm_X848RLbR,488992,2022-01-12T15:03:31Z,2022-01-12T15:03:31Z,CONTRIBUTOR,"@huard's latest commit modifies the algorithm so that it uses Kish's effective sample size, as described in the blog where the algorithm comes from: https://aakinshin.net/posts/kish-ess-weighted-quantiles/, which seems to solve the problem mentioned by @mathause.

Also he adds support for the interpolation types 4 to 9 (those that share a common way of computing `Qp`, as described here:  https://en.wikipedia.org/wiki/Quantile#Estimating_quantiles_from_a_sample","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1076265104
https://github.com/pydata/xarray/pull/6059#issuecomment-992713850,https://api.github.com/repos/pydata/xarray/issues/6059,992713850,IC_kwDOAMm_X847K5x6,488992,2021-12-13T17:39:03Z,2021-12-13T17:39:03Z,CONTRIBUTOR,"@mathause About this:

> I did some tries and got an unexpected result:
> 
> ```python
> data = xr.DataArray([0, 1, 2, 3])
> weights = xr.DataArray([1, 0, 1, 0])
> data.weighted(weights).quantile([0.75])
> 
> np.quantile([0, 2], 0.75)
> ```
> 
> Can you double-check? Or do I misunderstand something?

My latest [commit](https://github.com/pydata/xarray/pull/6059/commits/15e3834d05c8a34be062a1647e9854eb7f1ebbd5) should fix (and test) this.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1076265104
https://github.com/pydata/xarray/pull/6059#issuecomment-991714854,https://api.github.com/repos/pydata/xarray/issues/6059,991714854,IC_kwDOAMm_X847HF4m,488992,2021-12-11T17:08:55Z,2021-12-11T17:08:55Z,CONTRIBUTOR,"@mathause Thanks for the many excellent suggestions! After having removed the `for` loop the way you suggested, I tried to address this:

> The algorithm is quite clever but it multiplies all elements (except 2) with 0 - this could maybe be sped up by only using the relevant elements.

At first I thought that something like this could work:

```python
w = np.diff(v)
nz = np.nonzero(w)
d2 = np.tile(data, (n, 1))
r = w[nz] * d2[nz]
r = r[::2] + r[1::2]
```

The problem however is that it turns out that `w`'s rows sometimes have one element only, instead of two (which is when an `h` coincides exactly with a weight value, instead of lying between two). Given that difficulty, my impression is that it's not really solvable, or at least not in a way that would result in a more efficient version.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1076265104