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/3847#issuecomment-634811793,https://api.github.com/repos/pydata/xarray/issues/3847,634811793,MDEyOklzc3VlQ29tbWVudDYzNDgxMTc5Mw==,2448579,2020-05-27T17:14:17Z,2020-05-27T17:14:17Z,MEMBER,"How about we call `compute` in `assert_allclose` for boolean dask arrays when `dask < 2.9.1`? This bit can then be removed in a couple of months.

This compute was happening in previous versions anyway (because `assert_allclose` was using `.values`) so this  would not be a regression.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,577425749
https://github.com/pydata/xarray/pull/3847#issuecomment-628198836,https://api.github.com/repos/pydata/xarray/issues/3847,628198836,MDEyOklzc3VlQ29tbWVudDYyODE5ODgzNg==,2448579,2020-05-13T19:28:42Z,2020-05-13T19:28:42Z,MEMBER,"> Would it make sense to wait until we can bump the dask version to 2.9?

Sounds good to me. It's only 1.5 months away","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,577425749
https://github.com/pydata/xarray/pull/3847#issuecomment-628090382,https://api.github.com/repos/pydata/xarray/issues/3847,628090382,MDEyOklzc3VlQ29tbWVudDYyODA5MDM4Mg==,2448579,2020-05-13T16:07:03Z,2020-05-13T16:07:03Z,MEMBER,this comment: `# Numpy < 1.13 does not handle object-type array.`  suggests that this code needs to be updated for our new `numpy>=1.15` requirement,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,577425749
https://github.com/pydata/xarray/pull/3847#issuecomment-628076804,https://api.github.com/repos/pydata/xarray/issues/3847,628076804,MDEyOklzc3VlQ29tbWVudDYyODA3NjgwNA==,2448579,2020-05-13T15:44:10Z,2020-05-13T15:44:10Z,MEMBER,"example failure:
```
___________________ test_reduce[None-True-var-True-bool_-1] ____________________

dim_num = 1, dtype = <class 'numpy.bool_'>, dask = True, func = 'var'
skipna = True, aggdim = None

    @pytest.mark.parametrize(""dim_num"", [1, 2])
    @pytest.mark.parametrize(""dtype"", [float, int, np.float32, np.bool_])
    @pytest.mark.parametrize(""dask"", [False, True])
    @pytest.mark.parametrize(""func"", [""sum"", ""min"", ""max"", ""mean"", ""var""])
    # TODO test cumsum, cumprod
    @pytest.mark.parametrize(""skipna"", [False, True])
    @pytest.mark.parametrize(""aggdim"", [None, ""x""])
    def test_reduce(dim_num, dtype, dask, func, skipna, aggdim):
    
        if aggdim == ""y"" and dim_num < 2:
            pytest.skip(""dim not in this test"")
    
        if dtype == np.bool_ and func == ""mean"":
            pytest.skip(""numpy does not support this"")
    
        if dask and not has_dask:
            pytest.skip(""requires dask"")
    
        if dask and skipna is False and dtype in [np.bool_]:
            pytest.skip(""dask does not compute object-typed array"")
    
        rtol = 1e-04 if dtype == np.float32 else 1e-05
    
        da = construct_dataarray(dim_num, dtype, contains_nan=True, dask=dask)
        axis = None if aggdim is None else da.get_axis_num(aggdim)
    
        # TODO: remove these after resolving
        # https://github.com/dask/dask/issues/3245
        with warnings.catch_warnings():
            warnings.filterwarnings(""ignore"", ""Mean of empty slice"")
            warnings.filterwarnings(""ignore"", ""All-NaN slice"")
            warnings.filterwarnings(""ignore"", ""invalid value encountered in"")
    
            if da.dtype.kind == ""O"" and skipna:
                # Numpy < 1.13 does not handle object-type array.
                try:
                    if skipna:
                        expected = getattr(np, f""nan{func}"")(da.values, axis=axis)
                    else:
                        expected = getattr(np, func)(da.values, axis=axis)
    
                    actual = getattr(da, func)(skipna=skipna, dim=aggdim)
                    assert_dask_array(actual, dask)
                    assert np.allclose(
                        actual.values, np.array(expected), rtol=1.0e-4, equal_nan=True
                    )
                except (TypeError, AttributeError, ZeroDivisionError):
                    # TODO currently, numpy does not support some methods such as
                    ""casting='same_kind'""
>                   % (funcname(function), str(dtype), str(result.dtype))
                )
E               ValueError: Inferred dtype from function 'sub' was 'int64' but got 'float64', which can't be cast using casting='same_kind'
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,577425749
https://github.com/pydata/xarray/pull/3847#issuecomment-625885180,https://api.github.com/repos/pydata/xarray/issues/3847,625885180,MDEyOklzc3VlQ29tbWVudDYyNTg4NTE4MA==,2448579,2020-05-08T15:58:59Z,2020-05-13T15:36:41Z,MEMBER,"I'm confused here. What did you change to trigger this error?

ALL WRONG: ~We could call `arr1.compute(), arr2.compute()` in 
https://github.com/pydata/xarray/blob/69548df9826cde9df6cbdae9c033c9fb1e62d493/xarray/core/duck_array_ops.py#L203-L204~

~At that point, we need to compare actual values. I'm not sure that dask exits `all()` early if one chunk evaluates to False ( in which case this change would be a performance regression)~

EDIT: ignore this. We can't compute because it might blow memory.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,577425749
https://github.com/pydata/xarray/pull/3847#issuecomment-601321231,https://api.github.com/repos/pydata/xarray/issues/3847,601321231,MDEyOklzc3VlQ29tbWVudDYwMTMyMTIzMQ==,2448579,2020-03-19T17:43:36Z,2020-03-19T17:43:36Z,MEMBER,"Our minimum versions policy allows us to bump dask. So let's do that?
```
dask          2.2     (2019-08-01) 2.5     (2019-09-27) <
distributed   2.2     (2019-08-01) 2.5     (2019-09-27) <
```

> Also, where should I put the whats-new.rst entry?

New features? It is public API and is a very nice enhancement.","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,577425749
https://github.com/pydata/xarray/pull/3847#issuecomment-596374204,https://api.github.com/repos/pydata/xarray/issues/3847,596374204,MDEyOklzc3VlQ29tbWVudDU5NjM3NDIwNA==,2448579,2020-03-09T07:39:31Z,2020-03-09T07:39:31Z,MEMBER,Let's mark those as xfail then?,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,577425749