issues

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

Whilst trying to fix #5559 I noticed that xarray.testing.assert_equal (and xarray.testing.assert_equal) don't behave well with wrapped duck-typed arrays.

Firstly, they can give unhelpful AssertionError messages:

```python In [5]: a = np.array([1,2,3])

In [6]: q = pint.Quantity([1,2,3], units='m')

In [7]: da_np = xr.DataArray(a, dims='x')

In [8]: da_p = xr.DataArray(q, dims='x')

In [9]: da_np Out[9]: <xarray.DataArray (x: 3)> array([1, 2, 3]) Dimensions without coordinates: x

In [10]: da_p Out[10]: <xarray.DataArray (x: 3)> <Quantity([1 2 3], 'meter')> Dimensions without coordinates: x

In [11]: from xarray.testing import assert_equal

In [12]: assert_equal(da_np, da_p) /home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/xarray/core/duck_array_ops.py:265: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray. flag_array = (arr1 == arr2) | (isnull(arr1) & isnull(arr2)) /home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/xarray/core/duck_array_ops.py:265: DeprecationWarning: elementwise comparison failed; this will raise an error in the future. flag_array = (arr1 == arr2) | (isnull(arr1) & isnull(arr2)) /home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/xarray/core/duck_array_ops.py:265: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray. flag_array = (arr1 == arr2) | (isnull(arr1) & isnull(arr2)) /home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/xarray/core/duck_array_ops.py:265: DeprecationWarning: elementwise comparison failed; this will raise an error in the future. flag_array = (arr1 == arr2) | (isnull(arr1) & isnull(arr2)) /home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/numpy/core/_asarray.py:102: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray. return array(a, dtype, copy=False, order=order)

AssertionError Traceback (most recent call last) <ipython-input-12-33b16d6b79ed> in <module> ----> 1 assert_equal(da_np, da_p)

[... skipping hidden 1 frame]

~/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/xarray/testing.py in assert_equal(a, b) 79 assert type(a) == type(b) 80 if isinstance(a, (Variable, DataArray)): ---> 81 assert a.equals(b), formatting.diff_array_repr(a, b, "equals") 82 elif isinstance(a, Dataset): 83 assert a.equals(b), formatting.diff_dataset_repr(a, b, "equals")

AssertionError: Left and right DataArray objects are not equal

Differing values: L array([1, 2, 3]) R array([1, 2, 3]) `` These are different, but not because the array values are different. At the moment.valuesis converting the wrapped array type by stripping the units too - it might be better to check the type of the wrapped array first, then use.valuesto compare. Or could we even do duck-typed testing by delegating viaexpected.data.equals(actual.data)? (EDIT: I don't think a.equals()method exists in the numpy API, but you could do the equivalent ofassert all(expected.data == actual.data)`

Secondly, given that we coerce before comparison, I think it's possible that assert_equal could say two different wrapped duck-type arrays are equal when they are not, just because np.asarray() coerces them to the same values.

EDIT2: Looks like there is some discussion here