Fix direct reductions of Xarray objects

Closes pydata/xarray#8819

flox/xarray.py

@@ -303,14 +303,16 @@ def xarray_reduce(
         # reducing along a dimension along which groups do not vary
         # This is really just a normal reduction.
         # This is not right when binning so we exclude.
-        if isinstance(func, str):
-            dsfunc = func[3:] if skipna else func
-        else:
+        if isinstance(func, str) and func.startswith("nan"):
+            raise ValueError(f"Specify func={func[3:]}, skipna=True instead of func={func}")
+        elif isinstance(func, Aggregation):
             raise NotImplementedError(
                 "func must be a string when reducing along a dimension not present in `by`"
             )
-        # TODO: skipna needs test
-        result = getattr(ds_broad, dsfunc)(dim=dim_tuple, skipna=skipna)
+        # skipna is not supported for all reductions
+        # https://github.com/pydata/xarray/issues/8819
+        kwargs = {"skipna": skipna} if skipna is not None else {}
+        result = getattr(ds_broad, func)(dim=dim_tuple, **kwargs)
         if isinstance(obj, xr.DataArray):
             return obj._from_temp_dataset(result)
         else:

tests/__init__.py

@@ -124,3 +124,35 @@ def assert_equal_tuple(a, b):
             np.testing.assert_array_equal(a_, b_)
         else:
             assert a_ == b_
+
+
+SCIPY_STATS_FUNCS = ("mode", "nanmode")
+BLOCKWISE_FUNCS = ("median", "nanmedian", "quantile", "nanquantile") + SCIPY_STATS_FUNCS
+ALL_FUNCS = (
+    "sum",
+    "nansum",
+    "argmax",
+    "nanfirst",
+    "nanargmax",
+    "prod",
+    "nanprod",
+    "mean",
+    "nanmean",
+    "var",
+    "nanvar",
+    "std",
+    "nanstd",
+    "max",
+    "nanmax",
+    "min",
+    "nanmin",
+    "argmin",
+    "nanargmin",
+    "any",
+    "all",
+    "nanlast",
+    "median",
+    "nanmedian",
+    "quantile",
+    "nanquantile",
+) + tuple(pytest.param(func, marks=requires_scipy) for func in SCIPY_STATS_FUNCS)

tests/test_core.py

@@ -31,12 +31,14 @@
 )
 
 from . import (
+    ALL_FUNCS,
+    BLOCKWISE_FUNCS,
+    SCIPY_STATS_FUNCS,
     assert_equal,
     assert_equal_tuple,
     has_dask,
     raise_if_dask_computes,
     requires_dask,
-    requires_scipy,
 )
 
 logger = logging.getLogger("flox")
@@ -60,36 +62,6 @@ def dask_array_ones(*args):
 
 
 DEFAULT_QUANTILE = 0.9
-SCIPY_STATS_FUNCS = ("mode", "nanmode")
-BLOCKWISE_FUNCS = ("median", "nanmedian", "quantile", "nanquantile") + SCIPY_STATS_FUNCS
-ALL_FUNCS = (
-    "sum",
-    "nansum",
-    "argmax",
-    "nanfirst",
-    "nanargmax",
-    "prod",
-    "nanprod",
-    "mean",
-    "nanmean",
-    "var",
-    "nanvar",
-    "std",
-    "nanstd",
-    "max",
-    "nanmax",
-    "min",
-    "nanmin",
-    "argmin",
-    "nanargmin",
-    "any",
-    "all",
-    "nanlast",
-    "median",
-    "nanmedian",
-    "quantile",
-    "nanquantile",
-) + tuple(pytest.param(func, marks=requires_scipy) for func in SCIPY_STATS_FUNCS)
 
 if TYPE_CHECKING:
     from flox.core import T_Agg, T_Engine, T_ExpectedGroupsOpt, T_Method

tests/test_xarray.py

@@ -9,6 +9,7 @@
 from flox.xarray import rechunk_for_blockwise, xarray_reduce
 
 from . import (
+    ALL_FUNCS,
     assert_equal,
     has_dask,
     raise_if_dask_computes,
@@ -710,3 +711,32 @@ def test_multiple_quantiles(q, chunk, by_ndim, skipna):
     with xr.set_options(use_flox=False):
         expected = da.groupby(by).quantile(q, skipna=skipna)
     xr.testing.assert_allclose(expected, actual)
+
+
+@pytest.mark.parametrize("func", ALL_FUNCS)
+def test_direct_reduction(func):
+    if "arg" in func or "mode" in func:
+        pytest.skip()
+    # regression test for https://github.com/pydata/xarray/issues/8819
+    rand = np.random.choice([True, False], size=(2, 3))
+    if func not in ["any", "all"]:
+        rand = rand.astype(float)
+
+    if "nan" in func:
+        func = func[3:]
+        kwargs = {"skipna": True}
+    else:
+        kwargs = {}
+
+    if "first" not in func and "last" not in func:
+        kwargs["dim"] = "y"
+
+    if "quantile" in func:
+        kwargs["q"] = 0.9
+
+    data = xr.DataArray(rand, dims=("x", "y"), coords={"x": [10, 20], "y": [0, 1, 2]})
+    with xr.set_options(use_flox=True):
+        actual = getattr(data.groupby("x"), func)(**kwargs)
+    with xr.set_options(use_flox=False):
+        expected = getattr(data.groupby("x"), func)(**kwargs)
+    xr.testing.assert_identical(expected, actual)