Delete resample_reduce (#246)

Closes #245

flox/xarray.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import warnings
 from typing import TYPE_CHECKING, Any, Hashable, Iterable, Sequence, Union
 
 import numpy as np
@@ -21,7 +20,6 @@
 from .xrutils import _contains_cftime_datetimes, _to_pytimedelta, datetime_to_numeric
 
 if TYPE_CHECKING:
-    from xarray.core.resample import Resample
     from xarray.core.types import T_DataArray, T_Dataset
 
     Dims = Union[str, Iterable[Hashable], None]
@@ -450,7 +448,11 @@ def wrapper(array, *by, func, skipna, core_dims, **kwargs):
             actual = actual.drop_vars(name)
         # When grouping by MultiIndex, expect is an pd.Index wrapping
         # an object array of tuples
-        if name in ds_broad.indexes and isinstance(ds_broad.indexes[name], pd.MultiIndex):
+        if (
+            name in ds_broad.indexes
+            and isinstance(ds_broad.indexes[name], pd.MultiIndex)
+            and not isinstance(expect, pd.RangeIndex)
+        ):
             levelnames = ds_broad.indexes[name].names
             expect = pd.MultiIndex.from_tuples(expect.values, names=levelnames)
             actual[name] = expect
@@ -582,43 +584,3 @@ def _rechunk(func, obj, dim, labels, **kwargs):
             )
 
     return obj
-
-
-def resample_reduce(
-    resampler: Resample,
-    func: str | Aggregation,
-    keep_attrs: bool = True,
-    **kwargs,
-):
-    warnings.warn(
-        "flox.xarray.resample_reduce is now deprecated. Please use Xarray's resample method directly.",
-        DeprecationWarning,
-    )
-
-    obj = resampler._obj
-    dim = resampler._group_dim
-
-    # this creates a label DataArray since resample doesn't do that somehow
-    tostack = []
-    for idx, slicer in enumerate(resampler._group_indices):
-        if slicer.stop is None:
-            stop = resampler._obj.sizes[dim]
-        else:
-            stop = slicer.stop
-        tostack.append(idx * np.ones((stop - slicer.start,), dtype=np.int32))
-    by = xr.DataArray(np.hstack(tostack), dims=(dim,), name="__resample_dim__")
-
-    result = (
-        xarray_reduce(
-            obj,
-            by,
-            func=func,
-            method="blockwise",
-            keep_attrs=keep_attrs,
-            **kwargs,
-        )
-        .rename({"__resample_dim__": dim})
-        .transpose(dim, ...)
-    )
-    result[dim] = resampler._unique_coord.data
-    return result

tests/test_xarray.py

@@ -6,7 +6,7 @@
 xr = pytest.importorskip("xarray")
 # isort: on
 
-from flox.xarray import rechunk_for_blockwise, resample_reduce, xarray_reduce
+from flox.xarray import rechunk_for_blockwise, xarray_reduce
 
 from . import assert_equal, has_dask, raise_if_dask_computes, requires_dask
 
@@ -245,48 +245,6 @@ def test_xarray_reduce_errors():
             xarray_reduce(da, by.chunk(), func="mean")
 
 
-@pytest.mark.parametrize("isdask", [True, False])
-@pytest.mark.parametrize("dataarray", [True, False])
-@pytest.mark.parametrize("chunklen", [27, 4 * 31 + 1, 4 * 31 + 20])
-def test_xarray_resample(chunklen, isdask, dataarray, engine):
-    if isdask:
-        if not has_dask:
-            pytest.skip()
-        ds = xr.tutorial.open_dataset("air_temperature", chunks={"time": chunklen})
-    else:
-        ds = xr.tutorial.open_dataset("air_temperature")
-
-    if dataarray:
-        ds = ds.air
-
-    resampler = ds.resample(time="M")
-    with pytest.warns(DeprecationWarning):
-        actual = resample_reduce(resampler, "mean", engine=engine)
-    expected = resampler.mean()
-    xr.testing.assert_allclose(actual, expected)
-
-    with xr.set_options(use_flox=True):
-        actual = resampler.mean()
-    xr.testing.assert_allclose(actual, expected)
-
-
-@requires_dask
-def test_xarray_resample_dataset_multiple_arrays(engine):
-    # regression test for #35
-    times = pd.date_range("2000", periods=5)
-    foo = xr.DataArray(range(5), dims=["time"], coords=[times], name="foo")
-    bar = xr.DataArray(range(1, 6), dims=["time"], coords=[times], name="bar")
-    ds = xr.merge([foo, bar]).chunk({"time": 4})
-
-    resampler = ds.resample(time="4D")
-    # The separate computes are necessary here to force xarray
-    # to compute all variables in result at the same time.
-    expected = resampler.mean().compute()
-    with pytest.warns(DeprecationWarning):
-        result = resample_reduce(resampler, "mean", engine=engine).compute()
-    xr.testing.assert_allclose(expected, result)
-
-
 @requires_dask
 @pytest.mark.parametrize(
     "inchunks, expected",
@@ -439,20 +397,6 @@ def test_cache():
     assert len(cache.data) == 2
 
 
-@pytest.mark.parametrize("use_cftime", [True, False])
-@pytest.mark.parametrize("func", ["count", "mean"])
-def test_datetime_array_reduce(use_cftime, func, engine):
-    time = xr.DataArray(
-        xr.date_range("2009-01-01", "2012-12-31", use_cftime=use_cftime),
-        dims=("time",),
-        name="time",
-    )
-    expected = getattr(time.resample(time="YS"), func)()
-    with pytest.warns(DeprecationWarning):
-        actual = resample_reduce(time.resample(time="YS"), func=func, engine=engine)
-    assert_equal(expected, actual)
-
-
 @requires_dask
 @pytest.mark.parametrize("method", ["cohorts", "map-reduce"])
 def test_groupby_bins_indexed_coordinate(method):