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/5731#issuecomment-977725089,https://api.github.com/repos/pydata/xarray/issues/5731,977725089,IC_kwDOAMm_X846Ruah,9466648,2021-11-24T10:08:04Z,2021-11-24T10:08:04Z,CONTRIBUTOR,Sorry for the unresponsiveness. I confirm that using the `asfloat` conversion also works for me. Should I close the issue or should I let you do it ?,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,976790237
https://github.com/pydata/xarray/pull/5731#issuecomment-904403621,https://api.github.com/repos/pydata/xarray/issues/5731,904403621,IC_kwDOAMm_X8416Bql,9466648,2021-08-24T07:43:28Z,2021-08-31T14:41:01Z,CONTRIBUTOR,"I included the proposed changes, thanks for the help.

However the new test I implemented does not go through and I fail to understand why. Would you be able to check ? The idea of the test is to ensure that lazy computations give the same results than normal ones. I tracked down the problem to line 1377 of `computation.py`:
```python
demeaned_da_a = da_a - da_a.mean(dim=dim)  # <-- this one returns nan upon computation (.compute())
demeaned_da_b = da_b - da_b.mean(dim=dim)  # <-- this one returns the correct value although the input is masked with nans in the same way
```

The values before the `mean` call are:
```
da_a.compute() = <xarray.DataArray <this-array> (x: 2, time: 2)>
array([[ 1.,  2.],
       [ 2., nan]])
da_b.compute() = <xarray.DataArray <this-array> (x: 2, time: 2)>
array([[ 1.,  2.],
       [ 1., nan]])
```

And for the means (dim is None):
```
da_a.mean(dim=dim).compute()=<xarray.DataArray <this-array> ()>
array(nan)
da_b.mean(dim=dim).compute()=<xarray.DataArray <this-array> ()>
array(1.33333333)
```

For non lazy computations everything seems fine.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,976790237
https://github.com/pydata/xarray/pull/5731#issuecomment-909300955,https://api.github.com/repos/pydata/xarray/issues/5731,909300955,IC_kwDOAMm_X842MtTb,9466648,2021-08-31T14:40:24Z,2021-08-31T14:40:24Z,CONTRIBUTOR,"I was referring to:
https://github.com/pydata/xarray/pull/5731#issuecomment-904403621

Otherwise I am working on the conflict with the main branch. Should be done soon.

","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,976790237
https://github.com/pydata/xarray/pull/5731#issuecomment-909095076,https://api.github.com/repos/pydata/xarray/issues/5731,909095076,IC_kwDOAMm_X842L7Ck,9466648,2021-08-31T10:10:15Z,2021-08-31T10:10:15Z,CONTRIBUTOR,"@keewis indeed this was the problem. I removed it and corrected one test which did not have the @require_dask decorator.

There is still a test error with the test arrays 5 and 6: the lazy array version do not return the same value than the usual corr. I consider this a different bug and just removed the tests with a TODO in the code.

The last commit pass the tests on my machine and I added the whats-new text to version 0.19.1.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,976790237
https://github.com/pydata/xarray/pull/5731#issuecomment-903574187,https://api.github.com/repos/pydata/xarray/issues/5731,903574187,IC_kwDOAMm_X84123Kr,9466648,2021-08-23T08:57:36Z,2021-08-23T08:57:36Z,CONTRIBUTOR,"Concerning the tests I added a test to check that lazy correlations have identical results to non-lazy correlations. But this is not directly related to the bug fix. Indeed there is a need to have a test that checks if the non-lazy correlation works without dask installed. The already existing test function `test_corr` does not have a `@requires_dask` decorator. But it did not fail as it should have. Not sure why.

About documenting the changes, I am not sure of the whats-new.rst format and I thus did not add it. Should I just add a bullet item on the top of the file ?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,976790237
https://github.com/pydata/xarray/issues/5715#issuecomment-902657635,https://api.github.com/repos/pydata/xarray/issues/5715,902657635,IC_kwDOAMm_X841zXZj,9466648,2021-08-20T12:30:19Z,2021-08-20T12:30:33Z,CONTRIBUTOR,"I had a look to it this morning and I think I managed to solve the issue by replacing the calls to `dask.is_dask_collection` by `is_duck_dask_array` from the `pycompat` module.

For (successful) testing I used the same code as above plus the following:
```python
ds_dask = ds.chunk({""t"": 10})

yy = xr.corr(ds['y'], ds['y']).to_numpy()
yy_dask = xr.corr(ds_dask['y'], ds_dask['y']).to_numpy()
yx = xr.corr(ds['y'], ds['x']).to_numpy()
yx_dask = xr.corr(ds_dask['y'], ds_dask['x']).to_numpy()
np.testing.assert_allclose(yy, yy_dask), ""YY: {} is different from {}"".format(yy, yy_dask)
np.testing.assert_allclose(yx, yx_dask), ""YX: {} is different from {}"".format(yx, yx_dask)
```
The results are not exactly identical but almost which is probably due to numerical approximations of multiple computations in the dask case.

I also tested the correlation of simple DataArrays without dask installed and the result seem coherent (close to 0 for uncorrelated data and very close to 1 when correlating identical variables).

Should I make a pull request ? Should I implement this test ? Any others ?
","{""total_count"": 1, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 1, ""rocket"": 0, ""eyes"": 0}",,974488736
https://github.com/pydata/xarray/issues/5715#issuecomment-902516397,https://api.github.com/repos/pydata/xarray/issues/5715,902516397,IC_kwDOAMm_X841y06t,9466648,2021-08-20T08:09:46Z,2021-08-20T08:10:50Z,CONTRIBUTOR,"The responsible code for the error originally comes from the call to `da_a = da_a.map_blocks(_get_valid_values, args=[da_b])`, which aim is to remove nan values from both DataArrays. I am confused by this given that the code lines below seems to accumplish something similar (despite of the comment saying it should not):
```python
# 4. Compute covariance along the given dim
# N.B. `skipna=False` is required or there is a bug when computing
# auto-covariance. E.g. Try xr.cov(da,da) for
# da = xr.DataArray([[1, 2], [1, np.nan]], dims=[""x"", ""time""])
cov = (demeaned_da_a * demeaned_da_b).sum(dim=dim, skipna=True, min_count=1) / (
    valid_count
)
```

In any case, the parrallel module imports dask in a try catch block to ignore the import error. So this is not a surprise that when using dask latter there is an error if it was not imported. I can see two possibilities:
- encapsulate all dask calls in a similar try/catch block
- set a boolean in the first place and do the tests only if dask is correctly imported

Now I do not have any big picure there so there are probably better solutions.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,974488736