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/issues/1524#issuecomment-514002753,https://api.github.com/repos/pydata/xarray/issues/1524,514002753,MDEyOklzc3VlQ29tbWVudDUxNDAwMjc1Mw==,1217238,2019-07-23T00:18:05Z,2019-07-23T00:18:05Z,MEMBER,"> @shoyer does [dask/dask#4677](https://github.com/dask/dask/pull/4677) solve those accuracy concerns?
Yes, to some degree. I'm still troubled by that the ""default"" algorithm (which is selected by default) has no error bounds. It seems a little backwards to me to default to a fast algorithm with unknown accuracy.
Also, it still only works on 1D arrays, which would not be terribly useful for us.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,252548859
https://github.com/pydata/xarray/issues/1524#issuecomment-513996346,https://api.github.com/repos/pydata/xarray/issues/1524,513996346,MDEyOklzc3VlQ29tbWVudDUxMzk5NjM0Ng==,7799184,2019-07-22T23:47:13Z,2019-07-22T23:47:13Z,CONTRIBUTOR,@shoyer does https://github.com/dask/dask/pull/4677 solve those accuracy concerns?,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,252548859
https://github.com/pydata/xarray/issues/1524#issuecomment-405990579,https://api.github.com/repos/pydata/xarray/issues/1524,405990579,MDEyOklzc3VlQ29tbWVudDQwNTk5MDU3OQ==,865212,2018-07-18T16:20:38Z,2018-07-18T16:20:38Z,NONE,Thanks @shoyer I had forgotten that the dask implementation has its own problems anyway.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,252548859
https://github.com/pydata/xarray/issues/1524#issuecomment-404733610,https://api.github.com/repos/pydata/xarray/issues/1524,404733610,MDEyOklzc3VlQ29tbWVudDQwNDczMzYxMA==,1217238,2018-07-13T05:55:14Z,2018-07-13T05:55:14Z,MEMBER,See also https://github.com/dask/dask/issues/3099,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,252548859
https://github.com/pydata/xarray/issues/1524#issuecomment-404733398,https://api.github.com/repos/pydata/xarray/issues/1524,404733398,MDEyOklzc3VlQ29tbWVudDQwNDczMzM5OA==,1217238,2018-07-13T05:53:58Z,2018-07-13T05:53:58Z,MEMBER,"@acrosby if you're at SciPy, I'd be happy to chat about this tomorrow or over the weekend if you're staying for the sprints. This is not an immediate priority for me, but it would be straightforward to make quantile work over non-chunked dimensions by rewriting it to use `apply_ufunc`.
Approximate quantiles over chunked dimensions *could* be done by leveraging dask.array.percentile, but that algorithm has some accuracy concerns. See https://github.com/dask/dask/issues/1225 for discussion.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,252548859
https://github.com/pydata/xarray/issues/1524#issuecomment-404613718,https://api.github.com/repos/pydata/xarray/issues/1524,404613718,MDEyOklzc3VlQ29tbWVudDQwNDYxMzcxOA==,865212,2018-07-12T18:51:42Z,2018-07-12T18:55:11Z,NONE,"Now that SciPy is going on, is there any momentum here for trying to add the dask implementation in someway? This is an issue for some of our workloads, would be great if someone was looking into it or could point me in the direction to start adapting the current source to support it.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,252548859
https://github.com/pydata/xarray/issues/1524#issuecomment-325252313,https://api.github.com/repos/pydata/xarray/issues/1524,325252313,MDEyOklzc3VlQ29tbWVudDMyNTI1MjMxMw==,2443309,2017-08-28T03:33:39Z,2017-08-28T03:33:39Z,MEMBER,"@crusaderky - thanks for this report. I just opened #1529 which takes care of the trivial part of this issue. If you want to tackle bringing dask.percentile in, that would be awesome.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,252548859
https://github.com/pydata/xarray/issues/1524#issuecomment-324616689,https://api.github.com/repos/pydata/xarray/issues/1524,324616689,MDEyOklzc3VlQ29tbWVudDMyNDYxNjY4OQ==,6213168,2017-08-24T12:07:53Z,2017-08-24T12:07:53Z,MEMBER,Dask only supports 1d. One would first need to expand dask to support N-dimensional arrays like numpy does. I plan to di it if/when I have the time ,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,252548859
https://github.com/pydata/xarray/issues/1524#issuecomment-324607199,https://api.github.com/repos/pydata/xarray/issues/1524,324607199,MDEyOklzc3VlQ29tbWVudDMyNDYwNzE5OQ==,1197350,2017-08-24T11:18:34Z,2017-08-24T11:18:34Z,MEMBER,"Dask implements percentile now
http://dask.pydata.org/en/latest/array-api.html#dask.array.percentile
So perhaps our version of quantile can be refactored to accommodate actual lazy computation on dask arrays, rather than simply erroring.
In any case, I agree that automatic silent eager evaluation of dask arrays is bad.
Sent from my iPhone
> On Aug 24, 2017, at 11:54 AM, crusaderky wrote:
>
> In variable.py, line 1116, you're missing a raise statement:
>
> if isinstance(self.data, dask_array_type):
> TypeError(""quantile does not work for arrays stored as dask ""
> ""arrays. Load the data via .compute() or .load() prior ""
> ""to calling this method."")
> Currently looking into extending dask.percentile() to support more than 1D arrays, and then use it in xarray too.
>
> —
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> Reply to this email directly, view it on GitHub, or mute the thread.
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