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id	html_url	issue_url	node_id	user	created_at	updated_at ▲	author_association	body	reactions	performed_via_github_app	issue
748535256	https://github.com/pydata/xarray/issues/4703#issuecomment-748535256	https://api.github.com/repos/pydata/xarray/issues/4703	MDEyOklzc3VlQ29tbWVudDc0ODUzNTI1Ng==	keewis 14808389	2020-12-19T22:44:07Z	2020-12-19T23:16:21Z	MEMBER	it seems the changes to `dask` that caused this were temporarily reverted. The PR upstream-dev CI fails to run the `to_dask_dataframe` tests (see pipelines overview), so it might be cleaner to have the scheduled CI open a new issue for that.	{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }		⚠️ Nightly upstream-dev CI failed ⚠️ 769382950
747596156	https://github.com/pydata/xarray/issues/4703#issuecomment-747596156	https://api.github.com/repos/pydata/xarray/issues/4703	MDEyOklzc3VlQ29tbWVudDc0NzU5NjE1Ng==	dcherian 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: ``` =================================== 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' ``` 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 }		⚠️ Nightly upstream-dev CI failed ⚠️ 769382950

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