id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type 904149592,MDExOlB1bGxSZXF1ZXN0NjU1MzIyNTU1,5389,ignore this,56925856,closed,0,,,0,2021-05-27T20:16:22Z,2021-05-27T20:22:43Z,2021-05-27T20:16:45Z,CONTRIBUTOR,,0,pydata/xarray/pulls/5389,"ignore this, I did the branching wrong...","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5389/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 882876804,MDExOlB1bGxSZXF1ZXN0NjM2Mzg1Mjc2,5284,Dask-friendly nan check in xr.corr() and xr.cov(),56925856,closed,0,,,8,2021-05-09T22:06:03Z,2021-05-27T17:31:07Z,2021-05-27T17:31:07Z,CONTRIBUTOR,,0,pydata/xarray/pulls/5284,"Was reading the discussion [here](https://github.com/pydata/xarray/issues/4804) and thought I'd draft a PR to implement some of the changes people suggested. It seems like the main problem is that currently in `computation.py`, `if not valid_values.all()` is not a lazy operation, and so can be a bottleneck for very large dataarrays. To get around this, I've lifted some neat tricks from #4559 so that `valid_values` remains a dask array. --- - [x] Closes #4804 - [x] Tests added - [x] Passes `pre-commit run --all-files` - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5284/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 786839234,MDU6SXNzdWU3ODY4MzkyMzQ=,4816,Possible bug with da.interp_like(),56925856,closed,0,,,1,2021-01-15T11:54:50Z,2021-01-15T12:17:18Z,2021-01-15T12:17:18Z,CONTRIBUTOR,,,," **What happened**: When interpolating a multidimensional array, I used the `fill_value=None` kwarg, as [in the scipy docs](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interpn.html) it says that `If None, values outside the domain are extrapolated.` **What you expected to happen**: However, this did not work and the array was filled with `nan`s instead of being extrapolated. I had to explicitly add `fill_value='extrapolate'` in order to get extrapolation to work. However, as far as I can tell, in this context `interp_like()` **should** be calling `scipy.interpolate.interpn()`, which doesn't seem to require `fill_value='extrapolate'`? I might be misunderstanding something here, but if not then I think there might be a bug? Happy to be corrected if I've misunderstood the docs! **Minimal Complete Verifiable Example**: ```python # Put your MCVE code here import xarray as xr import numpy as np da = xr.DataArray( np.array(([1,2,3,4,0],[1,2,3,9,0])), dims=[""x"", ""y""], coords={""x"": [0,1],""y"": [0,1,2,3,4]} ) da2 = xr.DataArray( np.array(([2,3,0,4,5,6],[2,3,0,4,5,6],[2,3,0,4,5,6])), dims=[""x"", ""y""], coords={""x"": [0,1,2],""y"": [0,1,2,3,4,5]} ) print(da.interp_like(da2)) array([[ 1., 2., 3., 4., 0., nan], [ 1., 2., 3., 9., 0., nan], [nan, nan, nan, nan, nan, nan]]) Coordinates: * x (x) int64 0 1 2 * y (y) int64 0 1 2 3 4 5 print(da.interp_like(da2, kwargs={'fill_value':None})) array([[ 1., 2., 3., 4., 0., nan], [ 1., 2., 3., 9., 0., nan], [nan, nan, nan, nan, nan, nan]]) Coordinates: * x (x) int64 0 1 2 * y (y) int64 0 1 2 3 4 5 print(da.interp_like(da2, kwargs={'fill_value':'extrapolate'})) array([[ 1., 2., 3., 4., 0., -4.], [ 1., 2., 3., 9., 0., -9.], [ 1., 2., 3., 14., 0., -14.]]) Coordinates: * x (x) int64 0 1 2 * y (y) int64 0 1 2 3 4 5 ``` **Environment**:
Output of xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.6.12 |Anaconda, Inc.| (default, Sep 8 2020, 23:10:56) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 3.10.0-1127.19.1.el7.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.6.2 xarray: 0.16.1 pandas: 1.1.5 numpy: 1.19.2 scipy: 1.5.2 netCDF4: 1.5.1.2 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.3.0 nc_time_axis: None PseudoNetCDF: None rasterio: 1.1.8 cfgrib: None iris: None bottleneck: 1.3.2 dask: 2.30.0 distributed: 2.30.1 matplotlib: 3.3.3 cartopy: 0.18.0 seaborn: None numbagg: None pint: None setuptools: 51.0.0.post20201207 pip: 20.3.3 conda: None pytest: None IPython: 7.16.1 sphinx: None
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4816/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 625064501,MDExOlB1bGxSZXF1ZXN0NDIzMzU0MjIw,4096,Corrcov typo fix,56925856,closed,0,,,4,2020-05-26T17:44:07Z,2020-05-27T03:09:37Z,2020-05-26T19:03:25Z,CONTRIBUTOR,,0,pydata/xarray/pulls/4096," Fixing typo in recent PR #4089 - [x] Closes https://github.com/pydata/xarray/pull/4089#issuecomment-634157768 - [x] Passes `isort -rc . && black . && mypy . && flake8`","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4096/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 623751213,MDExOlB1bGxSZXF1ZXN0NDIyMzMzMTg0,4089,xr.cov() and xr.corr(),56925856,closed,0,,,20,2020-05-23T22:09:07Z,2020-05-26T18:31:31Z,2020-05-25T16:55:33Z,CONTRIBUTOR,,0,pydata/xarray/pulls/4089,"**PR** for the `xr.cov()` and `xr.corr()` functionality which others have been working on. Most code adapted from @r-beer in PR #3550. TODO: - [x] ~Write a reasonable set of tests, maybe not using `pandas` as a benchmark? (See https://github.com/pydata/xarray/issues/3784#issuecomment-633145887) Will probably need some help with this~ CHECKLIST: - [x] Closes #3784, #3550, #2652, #1115 - [x] Tests added - [x] Passes `isort -rc . && black . && mypy . && flake8` ~(something wrong with docs though??)~ - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4089/reactions"", ""total_count"": 3, ""+1"": 3, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 618828102,MDExOlB1bGxSZXF1ZXN0NDE4NDc4MjY5,4064,Auto chunk,56925856,closed,0,,,23,2020-05-15T09:25:13Z,2020-05-25T20:38:53Z,2020-05-25T19:23:45Z,CONTRIBUTOR,,0,pydata/xarray/pulls/4064,"Adding `chunks='auto'` option to `Dataset.chunk()`. - [x] Closes #4055 - [x] Tests added in `test_dask.py` - [x] Passes `isort -rc . && black . && mypy . && flake8` - [x] Updated `whats-new.rst` for changes ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4064/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 617476316,MDU6SXNzdWU2MTc0NzYzMTY=,4055,Automatic chunking of arrays ?,56925856,closed,0,,,11,2020-05-13T14:02:41Z,2020-05-25T19:23:45Z,2020-05-25T19:23:45Z,CONTRIBUTOR,,,,"Hi there, Hopefully this turns out to be a basic issue, but I was wondering why the `chunks='auto'` that dask seems to provide (https://docs.dask.org/en/latest/array-chunks.html#automatic-chunking) isn't an option for xarray? I'm not 100% sure of how dask decides how to automatically chunk its arrays, so maybe there's a compatibility issue? I get the impression that the dask method automatically tries to prevent the issues of ""too many chunks"" or ""too few chunks"" which can sometimes happen when choosing chunk sizes automatically. If so, it would maybe be a useful thing to include in future versions? Happy to be corrected if I've misunderstood something here though, still getting my head around how the dask/xarray compatibility really works... Cheers!","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4055/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 568378007,MDU6SXNzdWU1NjgzNzgwMDc=,3784,Function for regressing/correlating multiple fields?,56925856,closed,0,,,6,2020-02-20T15:24:14Z,2020-05-25T16:55:33Z,2020-05-25T16:55:33Z,CONTRIBUTOR,,,,"I came across this StackOverflow thread about applying linear regression across multi-dimensional arrays and there were some very efficient, xarray-based solutions suggested to the OP which leveraged vectorized operations. I was wondering if there was any interest in adding a function like this to future releases of xarray? It seems like a very neat and useful functionality to have, and not just for meteorological applications! https://stackoverflow.com/questions/52108417/how-to-apply-linear-regression-to-every-pixel-in-a-large-multi-dimensional-array Most answers draw from this blog post: https://hrishichandanpurkar.blogspot.com/2017/09/vectorized-functions-for-correlation.html","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3784/reactions"", ""total_count"": 1, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 1, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue