Support more dtypes for CFTimeIndex resampling

xarray/coding/cftime_offsets.py

@@ -772,11 +772,18 @@ def _emit_freq_deprecation_warning(deprecated_freq):
     emit_user_level_warning(message, FutureWarning)
 
 
-def to_offset(freq: BaseCFTimeOffset | str, warn: bool = True) -> BaseCFTimeOffset:
+def to_offset(
+    freq: BaseCFTimeOffset | str | timedelta | pd.Timedelta | pd.DateOffset,
+    warn: bool = True,
+) -> BaseCFTimeOffset:
     """Convert a frequency string to the appropriate subclass of
     BaseCFTimeOffset."""
     if isinstance(freq, BaseCFTimeOffset):
         return freq
+    if isinstance(freq, timedelta | pd.Timedelta):
+        return delta_to_tick(freq)
+    if isinstance(freq, pd.DateOffset):
+        freq = freq.freqstr
 
     match = re.match(_PATTERN, freq)
     if match is None:
@@ -791,6 +798,34 @@ def to_offset(freq: BaseCFTimeOffset | str, warn: bool = True) -> BaseCFTimeOffs
     return _FREQUENCIES[freq](n=multiples)
 
 
+def delta_to_tick(delta: timedelta | pd.Timedelta) -> Tick:
+    """Adapted from pandas.tslib.delta_to_tick"""
+    if isinstance(delta, pd.Timedelta) and delta.nanoseconds != 0:
+        # pandas.Timedelta has nanoseconds, but these are not supported
+        raise ValueError(
+            "Unable to convert 'pandas.Timedelta' object with non-zero "
+            "nanoseconds to 'CFTimeOffset' object"
+        )
+    if delta.microseconds == 0:
+        if delta.seconds == 0:
+            return Day(n=delta.days)
+        else:
+            seconds = delta.days * 86400 + delta.seconds
+            if seconds % 3600 == 0:
+                return Hour(n=seconds // 3600)
+            elif seconds % 60 == 0:
+                return Minute(n=seconds // 60)
+            else:
+                return Second(n=seconds)
+    else:
+        # Regardless of the days and seconds this will always be a Millsecond
+        # or Microsecond object
+        if delta.microseconds % 1_000 == 0:
+            return Millisecond(n=delta.microseconds // 1_000)
+        else:
+            return Microsecond(n=delta.microseconds)
+
+
 def to_cftime_datetime(date_str_or_date, calendar=None):
     if cftime is None:
         raise ModuleNotFoundError("No module named 'cftime'")

xarray/core/dataarray.py

@@ -7252,7 +7252,11 @@ def resample(
         offset: pd.Timedelta | datetime.timedelta | str | None = None,
         origin: str | DatetimeLike = "start_day",
         restore_coord_dims: bool | None = None,
-        **indexer_kwargs: str | Resampler,
+        **indexer_kwargs: str
+        | datetime.timedelta
+        | pd.Timedelta
+        | pd.DateOffset
+        | Resampler,
     ) -> DataArrayResample:
         """Returns a Resample object for performing resampling operations.
 

xarray/core/dataset.py

@@ -10693,7 +10693,11 @@ def resample(
         offset: pd.Timedelta | datetime.timedelta | str | None = None,
         origin: str | DatetimeLike = "start_day",
         restore_coord_dims: bool | None = None,
-        **indexer_kwargs: str | Resampler,
+        **indexer_kwargs: str
+        | datetime.timedelta
+        | pd.Timedelta
+        | pd.DateOffset
+        | Resampler,
     ) -> DatasetResample:
         """Returns a Resample object for performing resampling operations.
 

xarray/core/resample_cftime.py

@@ -75,7 +75,11 @@ class CFTimeGrouper:
 
     def __init__(
         self,
-        freq: str | BaseCFTimeOffset,
+        freq: str
+        | datetime.timedelta
+        | pd.Timedelta
+        | pd.DateOffset
+        | BaseCFTimeOffset,
         closed: SideOptions | None = None,
         label: SideOptions | None = None,
         origin: str | CFTimeDatetime = "start_day",

xarray/groupers.py

@@ -380,13 +380,6 @@ def _init_properties(self, group: T_Group) -> None:
         if isinstance(group_as_index, CFTimeIndex):
             from xarray.core.resample_cftime import CFTimeGrouper
 
-            if not isinstance(self.freq, str | BaseCFTimeOffset):
-                raise ValueError(
-                    "Resample frequency must be a string or 'BaseCFTimeOffset' "
-                    "object when resampling a 'CFTimeIndex'. Received "
-                    f"{type(self.freq)} instead."
-                )
-
             self.index_grouper = CFTimeGrouper(
                 freq=self.freq,
                 closed=self.closed,

xarray/tests/test_groupby.py

@@ -758,7 +758,6 @@ def test_groupby_none_group_name() -> None:
 
 
 def test_groupby_getitem(dataset) -> None:
-
     assert_identical(dataset.sel(x=["a"]), dataset.groupby("x")["a"])
     assert_identical(dataset.sel(z=[1]), dataset.groupby("z")[1])
     assert_identical(dataset.foo.sel(x=["a"]), dataset.foo.groupby("x")["a"])
@@ -1829,13 +1828,12 @@ def test_resample_dtype(self, use_cftime: bool) -> None:
                 )
             ],
         )
-        test_resample_freqs = ["10min"]
-        if not use_cftime:
-            test_resample_freqs += [
-                pd.Timedelta(hours=2),
-                pd.offsets.MonthBegin(),
-                datetime.timedelta(days=1, hours=6),
-            ]
+        test_resample_freqs = (
+            "10min",
+            pd.Timedelta(hours=2),
+            pd.offsets.MonthBegin(),
+            datetime.timedelta(days=1, hours=6),
+        )
         for freq in test_resample_freqs:
             array.resample(time=freq)
 
@@ -2258,6 +2256,29 @@ def test_resample_and_first(self) -> None:
             result = actual.reduce(method)
             assert_equal(expected, result)
 
+    @pytest.mark.parametrize("use_cftime", [True, False])
+    def test_resample_dtype(self, use_cftime: bool) -> None:
+        if use_cftime and not has_cftime:
+            pytest.skip()
+        times = xr.date_range(
+            "2000-01-01", freq="6h", periods=10, use_cftime=use_cftime
+        )
+        ds = Dataset(
+            {
+                "foo": (["time", "x", "y"], np.random.randn(10, 5, 3)),
+                "bar": ("time", np.random.randn(10), {"meta": "data"}),
+                "time": times,
+            }
+        )
+        test_resample_freqs = [
+            "10min",
+            pd.Timedelta(hours=2),
+            pd.offsets.MonthBegin(),
+            datetime.timedelta(days=1, hours=6),
+        ]
+        for freq in test_resample_freqs:
+            ds.resample(time=freq)
+
     def test_resample_min_count(self) -> None:
         times = pd.date_range("2000-01-01", freq="6h", periods=10)
         ds = Dataset(