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/4703#issuecomment-747596156,https://api.github.com/repos/pydata/xarray/issues/4703,747596156,MDEyOklzc3VlQ29tbWVudDc0NzU5NjE1Ng==,2448579,2020-12-17T17:49:15Z,2020-12-18T00:59:47Z,MEMBER,"All 4 test failures are from dask.

```
ind = ('all-aggregate-766249a946efdef1b82b2fbb43c52b35',), coll = {}

    def nested_get(ind, coll):
        """"""Get nested index from collection
    
        Examples
        --------
    
        >>> nested_get(1, 'abc')
        'b'
        >>> nested_get([1, 0], 'abc')
        ('b', 'a')
        >>> nested_get([[1, 0], [0, 1]], 'abc')
        (('b', 'a'), ('a', 'b'))
        """"""
        if isinstance(ind, list):
            return tuple([nested_get(i, coll) for i in ind])
        else:
>           return coll[ind]
E           KeyError: ('all-aggregate-766249a946efdef1b82b2fbb43c52b35',)
```

and
```
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/blockwise.py:1456: in fuse_roots
    and not any(dependencies[dep] for dep in deps)  # no need to fuse if 0 or 1
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

.0 = <set_iterator object at 0x7fd894067500>

>       and not any(dependencies[dep] for dep in deps)  # no need to fuse if 0 or 1
        and all(len(dependents[dep]) == 1 for dep in deps)
        and all(layer.annotations == graph.layers[dep].annotations for dep in deps)
    ):
E   KeyError: 'blockwise-create-zeros-83b321c493c9e89ba5406bf312d2ccc3'
```

Full error log:

<details>

```

=================================== FAILURES ===================================
_________________ test_argmin_max[x-True-min-True-False-str-1] _________________

dim_num = 1, dtype = <class 'str'>, contains_nan = False, dask = True
func = 'min', skipna = True, aggdim = 'x'

    @pytest.mark.parametrize(""dim_num"", [1, 2])
    @pytest.mark.parametrize(""dtype"", [float, int, np.float32, np.bool_, str])
    @pytest.mark.parametrize(""contains_nan"", [True, False])
    @pytest.mark.parametrize(""dask"", [False, True])
    @pytest.mark.parametrize(""func"", [""min"", ""max""])
    @pytest.mark.parametrize(""skipna"", [False, True])
    @pytest.mark.parametrize(""aggdim"", [""x"", ""y""])
    def test_argmin_max(dim_num, dtype, contains_nan, dask, func, skipna, aggdim):
        # pandas-dev/pandas#16830, we do not check consistency with pandas but
        # just make sure da[da.argmin()] == da.min()
    
        if aggdim == ""y"" and dim_num < 2:
            pytest.skip(""dim not in this test"")
    
        if dask and not has_dask:
            pytest.skip(""requires dask"")
    
        if contains_nan:
            if not skipna:
                pytest.skip(""numpy's argmin (not nanargmin) does not handle object-dtype"")
            if skipna and np.dtype(dtype).kind in ""iufc"":
                pytest.skip(""numpy's nanargmin raises ValueError for all nan axis"")
        da = construct_dataarray(dim_num, dtype, contains_nan=contains_nan, dask=dask)
    
        with warnings.catch_warnings():
            warnings.filterwarnings(""ignore"", ""All-NaN slice"")
    
            actual = da.isel(
                **{aggdim: getattr(da, ""arg"" + func)(dim=aggdim, skipna=skipna).compute()}
            )
            expected = getattr(da, func)(dim=aggdim, skipna=skipna)
>           assert_allclose(
                actual.drop_vars(list(actual.coords)),
                expected.drop_vars(list(expected.coords)),
            )

/home/runner/work/xarray/xarray/xarray/tests/test_duck_array_ops.py:497: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/home/runner/work/xarray/xarray/xarray/testing.py:139: in compat_variable
    return a.dims == b.dims and (a._data is b._data or equiv(a.data, b.data))
/home/runner/work/xarray/xarray/xarray/testing.py:36: in _data_allclose_or_equiv
    return duck_array_ops.array_equiv(arr1, arr2)
/home/runner/work/xarray/xarray/xarray/core/duck_array_ops.py:249: in array_equiv
    return bool(flag_array.all())
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/array/core.py:1555: in __bool__
    return bool(self.compute())
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/base.py:279: in compute
    (result,) = compute(self, traverse=False, **kwargs)
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/base.py:567: in compute
    results = schedule(dsk, keys, **kwargs)
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:527: in get_sync
    return get_async(apply_sync, 1, dsk, keys, **kwargs)
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:503: in get_async
    return nested_get(result, state[""cache""])
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:299: in nested_get
    return tuple([nested_get(i, coll) for i in ind])
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:299: in <listcomp>
    return tuple([nested_get(i, coll) for i in ind])
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:299: in nested_get
    return tuple([nested_get(i, coll) for i in ind])
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:299: in <listcomp>
    return tuple([nested_get(i, coll) for i in ind])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

ind = ('all-aggregate-16e252575441a0847c7885d6f7da3342',), coll = {}

    def nested_get(ind, coll):
        """"""Get nested index from collection
    
        Examples
        --------
    
        >>> nested_get(1, 'abc')
        'b'
        >>> nested_get([1, 0], 'abc')
        ('b', 'a')
        >>> nested_get([[1, 0], [0, 1]], 'abc')
        (('b', 'a'), ('a', 'b'))
        """"""
        if isinstance(ind, list):
            return tuple([nested_get(i, coll) for i in ind])
        else:
>           return coll[ind]
E           KeyError: ('all-aggregate-16e252575441a0847c7885d6f7da3342',)

/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:301: KeyError
_________________ test_argmin_max[x-True-max-True-False-str-1] _________________

dim_num = 1, dtype = <class 'str'>, contains_nan = False, dask = True
func = 'max', skipna = True, aggdim = 'x'

    @pytest.mark.parametrize(""dim_num"", [1, 2])
    @pytest.mark.parametrize(""dtype"", [float, int, np.float32, np.bool_, str])
    @pytest.mark.parametrize(""contains_nan"", [True, False])
    @pytest.mark.parametrize(""dask"", [False, True])
    @pytest.mark.parametrize(""func"", [""min"", ""max""])
    @pytest.mark.parametrize(""skipna"", [False, True])
    @pytest.mark.parametrize(""aggdim"", [""x"", ""y""])
    def test_argmin_max(dim_num, dtype, contains_nan, dask, func, skipna, aggdim):
        # pandas-dev/pandas#16830, we do not check consistency with pandas but
        # just make sure da[da.argmin()] == da.min()
    
        if aggdim == ""y"" and dim_num < 2:
            pytest.skip(""dim not in this test"")
    
        if dask and not has_dask:
            pytest.skip(""requires dask"")
    
        if contains_nan:
            if not skipna:
                pytest.skip(""numpy's argmin (not nanargmin) does not handle object-dtype"")
            if skipna and np.dtype(dtype).kind in ""iufc"":
                pytest.skip(""numpy's nanargmin raises ValueError for all nan axis"")
        da = construct_dataarray(dim_num, dtype, contains_nan=contains_nan, dask=dask)
    
        with warnings.catch_warnings():
            warnings.filterwarnings(""ignore"", ""All-NaN slice"")
    
            actual = da.isel(
                **{aggdim: getattr(da, ""arg"" + func)(dim=aggdim, skipna=skipna).compute()}
            )
            expected = getattr(da, func)(dim=aggdim, skipna=skipna)
>           assert_allclose(
                actual.drop_vars(list(actual.coords)),
                expected.drop_vars(list(expected.coords)),
            )

/home/runner/work/xarray/xarray/xarray/tests/test_duck_array_ops.py:497: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/home/runner/work/xarray/xarray/xarray/testing.py:139: in compat_variable
    return a.dims == b.dims and (a._data is b._data or equiv(a.data, b.data))
/home/runner/work/xarray/xarray/xarray/testing.py:36: in _data_allclose_or_equiv
    return duck_array_ops.array_equiv(arr1, arr2)
/home/runner/work/xarray/xarray/xarray/core/duck_array_ops.py:249: in array_equiv
    return bool(flag_array.all())
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/array/core.py:1555: in __bool__
    return bool(self.compute())
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/base.py:279: in compute
    (result,) = compute(self, traverse=False, **kwargs)
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/base.py:567: in compute
    results = schedule(dsk, keys, **kwargs)
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:527: in get_sync
    return get_async(apply_sync, 1, dsk, keys, **kwargs)
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:503: in get_async
    return nested_get(result, state[""cache""])
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:299: in nested_get
    return tuple([nested_get(i, coll) for i in ind])
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:299: in <listcomp>
    return tuple([nested_get(i, coll) for i in ind])
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:299: in nested_get
    return tuple([nested_get(i, coll) for i in ind])
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:299: in <listcomp>
    return tuple([nested_get(i, coll) for i in ind])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

ind = ('all-aggregate-766249a946efdef1b82b2fbb43c52b35',), coll = {}

    def nested_get(ind, coll):
        """"""Get nested index from collection
    
        Examples
        --------
    
        >>> nested_get(1, 'abc')
        'b'
        >>> nested_get([1, 0], 'abc')
        ('b', 'a')
        >>> nested_get([[1, 0], [0, 1]], 'abc')
        (('b', 'a'), ('a', 'b'))
        """"""
        if isinstance(ind, list):
            return tuple([nested_get(i, coll) for i in ind])
        else:
>           return coll[ind]
E           KeyError: ('all-aggregate-766249a946efdef1b82b2fbb43c52b35',)

/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:301: KeyError
_________________ TestVariableWithDask.test_getitem_with_mask __________________

self = <xarray.tests.test_variable.TestVariableWithDask object at 0x7fd86402ac10>

    def test_getitem_with_mask(self):
        v = self.cls([""x""], [0, 1, 2])
        assert_identical(v._getitem_with_mask(-1), Variable((), np.nan))
        assert_identical(
            v._getitem_with_mask([0, -1, 1]), self.cls([""x""], [0, np.nan, 1])
        )
>       assert_identical(v._getitem_with_mask(slice(2)), self.cls([""x""], [0, 1]))

/home/runner/work/xarray/xarray/xarray/tests/test_variable.py:132: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/home/runner/work/xarray/xarray/xarray/core/variable.py:1799: in identical
    return utils.dict_equiv(self.attrs, other.attrs) and self.equals(
/home/runner/work/xarray/xarray/xarray/core/variable.py:1778: in equals
    self._data is other._data or equiv(self.data, other.data)
/home/runner/work/xarray/xarray/xarray/core/duck_array_ops.py:249: in array_equiv
    return bool(flag_array.all())
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/array/core.py:1555: in __bool__
    return bool(self.compute())
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/base.py:279: in compute
    (result,) = compute(self, traverse=False, **kwargs)
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/base.py:567: in compute
    results = schedule(dsk, keys, **kwargs)
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:527: in get_sync
    return get_async(apply_sync, 1, dsk, keys, **kwargs)
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:503: in get_async
    return nested_get(result, state[""cache""])
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:299: in nested_get
    return tuple([nested_get(i, coll) for i in ind])
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:299: in <listcomp>
    return tuple([nested_get(i, coll) for i in ind])
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:299: in nested_get
    return tuple([nested_get(i, coll) for i in ind])
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:299: in <listcomp>
    return tuple([nested_get(i, coll) for i in ind])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

ind = ('all-aggregate-513dc24870cea02fa21755f960f05d30',), coll = {}

    def nested_get(ind, coll):
        """"""Get nested index from collection
    
        Examples
        --------
    
        >>> nested_get(1, 'abc')
        'b'
        >>> nested_get([1, 0], 'abc')
        ('b', 'a')
        >>> nested_get([[1, 0], [0, 1]], 'abc')
        (('b', 'a'), ('a', 'b'))
        """"""
        if isinstance(ind, list):
            return tuple([nested_get(i, coll) for i in ind])
        else:
>           return coll[ind]
E           KeyError: ('all-aggregate-513dc24870cea02fa21755f960f05d30',)

/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/local.py:301: KeyError
___________________ TestVariableWithDask.test_real_and_imag ____________________

self = <xarray.tests.test_variable.TestVariableWithDask object at 0x7fd856b4a8b0>

    def test_real_and_imag(self):
        v = self.cls(""x"", np.arange(3) - 1j * np.arange(3), {""foo"": ""bar""})
        expected_re = self.cls(""x"", np.arange(3), {""foo"": ""bar""})
>       assert_identical(v.real, expected_re)

/home/runner/work/xarray/xarray/xarray/tests/test_variable.py:580: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/home/runner/work/xarray/xarray/xarray/core/variable.py:1799: in identical
    return utils.dict_equiv(self.attrs, other.attrs) and self.equals(
/home/runner/work/xarray/xarray/xarray/core/variable.py:1778: in equals
    self._data is other._data or equiv(self.data, other.data)
/home/runner/work/xarray/xarray/xarray/core/duck_array_ops.py:249: in array_equiv
    return bool(flag_array.all())
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/array/core.py:1555: in __bool__
    return bool(self.compute())
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/base.py:279: in compute
    (result,) = compute(self, traverse=False, **kwargs)
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/base.py:561: in compute
    dsk = collections_to_dsk(collections, optimize_graph, **kwargs)
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/base.py:332: in collections_to_dsk
    _opt = opt(dsk, keys, **kwargs)
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/array/optimization.py:47: in optimize
    dsk = fuse_roots(dsk, keys=keys)
/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/blockwise.py:1456: in fuse_roots
    and not any(dependencies[dep] for dep in deps)  # no need to fuse if 0 or 1
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

.0 = <set_iterator object at 0x7fd894067500>

>       and not any(dependencies[dep] for dep in deps)  # no need to fuse if 0 or 1
        and all(len(dependents[dep]) == 1 for dep in deps)
        and all(layer.annotations == graph.layers[dep].annotations for dep in deps)
    ):
E   KeyError: 'blockwise-create-zeros-83b321c493c9e89ba5406bf312d2ccc3'
```
</details>

PS: Great work with this feature!","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,769382950