pull_requests: 683067959

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id node_id number state locked title user body created_at updated_at closed_at merged_at merge_commit_sha assignee milestone draft head base author_association auto_merge repo url merged_by
683067959 MDExOlB1bGxSZXF1ZXN0NjgzMDY3OTU5 5571 closed 0 Rely on NEP-18 to dispatch to dask in duck_array_ops 35968931 Removes special-casing for dask in `duck_array_ops.py`, instead relying on NEP-18 to call it when the input is a dask array. Probably actually don't need the `_dask_or_eager_func()` (now `_module_func()`) helper function at all, because all remaining instances look like `pandas_isnull = _module_func("isnull", module=pd)`, which could just be `pandas_isnull = pd.isnull`. Only problem is that I seem to have broken one (parameterized) test: `test_duck_array_ops.py::test_min_count[True-True-None-sum-True-bool_-1]` fails with ```python @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", "prod"]) @pytest.mark.parametrize("aggdim", [None, "x"]) @pytest.mark.parametrize("contains_nan", [True, False]) @pytest.mark.parametrize("skipna", [True, False, None]) def test_min_count(dim_num, dtype, dask, func, aggdim, contains_nan, skipna): if dask and not has_dask: pytest.skip("requires dask") da = construct_dataarray(dim_num, dtype, contains_nan=contains_nan, dask=dask) min_count = 3 # If using Dask, the function call should be lazy. with raise_if_dask_computes(): > actual = getattr(da, func)(dim=aggdim, skipna=skipna, min_count=min_count) /home/tegn500/Documents/Work/Code/xarray/xarray/tests/test_duck_array_ops.py:578: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /home/tegn500/Documents/Work/Code/xarray/xarray/core/common.py:56: in wrapped_func return self.reduce(func, dim, axis, skipna=skipna, **kwargs) /home/tegn500/Documents/Work/Code/xarray/xarray/core/dataarray.py:2638: in reduce var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs) /home/tegn500/Documents/Work/Code/xarray/xarray/core/variable.py:1725: in reduce data = func(self.data, **kwargs) /home/tegn500/Documents/Work/Code/xarray/xarray/core/duck_array_ops.py:328: in f return func(values, axis=axis, **kwargs) /home/tegn500/Documents/Work/Code/xarray/xarray/core/nanops.py:106: in nansum a, mask = _replace_nan(a, 0) /home/tegn500/Documents/Work/Code/xarray/xarray/core/nanops.py:23: in _replace_nan mask = isnull(a) /home/tegn500/Documents/Work/Code/xarray/xarray/core/duck_array_ops.py:83: in isnull return pandas_isnull(data) /home/tegn500/Documents/Work/Code/xarray/xarray/core/duck_array_ops.py:40: in f return getattr(module, name)(*args, **kwargs) /home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/pandas/core/dtypes/missing.py:127: in isna return _isna(obj) /home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/pandas/core/dtypes/missing.py:166: in _isna return _isna_ndarraylike(np.asarray(obj), inf_as_na=inf_as_na) /home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/numpy/core/_asarray.py:102: in asarray return array(a, dtype, copy=False, order=order) /home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/dask/array/core.py:1502: in __array__ x = self.compute() /home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/dask/base.py:285: in compute (result,) = compute(self, traverse=False, **kwargs) /home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/dask/base.py:567: in compute results = schedule(dsk, keys, **kwargs) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <xarray.tests.CountingScheduler object at 0x7f0804db2310> dsk = {('xarray-<this-array>-29953318277423606f95b509ad1a9aa7', 0): array([False, False, False, False], dtype=object), ('xar...pe=object), ('xarray-<this-array>-29953318277423606f95b509ad1a9aa7', 3): array([nan, False, False, nan], dtype=object)} keys = [[('xarray-<this-array>-29953318277423606f95b509ad1a9aa7', 0), ('xarray-<this-array>-29953318277423606f95b509ad1a9aa7'...array-<this-array>-29953318277423606f95b509ad1a9aa7', 2), ('xarray-<this-array>-29953318277423606f95b509ad1a9aa7', 3)]] kwargs = {} def __call__(self, dsk, keys, **kwargs): self.total_computes += 1 if self.total_computes > self.max_computes: > raise RuntimeError( "Too many computes. Total: %d > max: %d." % (self.total_computes, self.max_computes) ) E RuntimeError: Too many computes. Total: 1 > max: 0. /home/tegn500/Documents/Work/Code/xarray/xarray/tests/__init__.py:118: RuntimeError ``` - [x] Closes #5559 - [x] Tests added - [x] Passes `pre-commit run --all-files` - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst` 2021-07-03T19:24:33Z 2022-07-09T18:12:05Z 2021-09-29T17:48:40Z 2021-09-29T17:48:39Z cd6065e98d99c8f749050172d5e2fc5f8f36a768     0 a349538c82cadf5d89da6486d335ced1f2e319ff 434090909dfad3e77df2488ab957ceb650045285 MEMBER   13221727 https://github.com/pydata/xarray/pull/5571  

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