rolling_exp: keep_attrs and typing

doc/whats-new.rst

@@ -45,6 +45,10 @@ New Features
   By `Michal Baumgartner <https://github.com/m1so>`_.
 - :py:meth:`Dataset.weighted` and :py:meth:`DataArray.weighted` are now executing value checks lazily if weights are provided as dask arrays (:issue:`4541`, :pull:`4559`).
   By `Julius Busecke <https://github.com/jbusecke>`_.
+- Added the ``keep_attrs`` keyword to :py:meth:`~xarray.DataArray.rolling_exp.mean` and :py:meth:`~xarray.Dataset.rolling_exp.mean`.
+  The attributes are now kept per default.
+  By `Mathias Hauser <https://github.com/mathause>`_.
+
 
 Bug fixes
 ~~~~~~~~~

xarray/core/rolling_exp.py

@@ -1,8 +1,17 @@
+from typing import TYPE_CHECKING, Generic, Hashable, Mapping, Optional, TypeVar
+
 import numpy as np
 
+from .options import _get_keep_attrs
 from .pdcompat import count_not_none
 from .pycompat import is_duck_dask_array
 
+if TYPE_CHECKING:
+    from .dataarray import DataArray  # noqa: F401
+    from .dataset import Dataset  # noqa: F401
+
+T_DSorDA = TypeVar("T_DSorDA", "DataArray", "Dataset")
+
 
 def _get_alpha(com=None, span=None, halflife=None, alpha=None):
     # pandas defines in terms of com (converting to alpha in the algo)
@@ -56,7 +65,7 @@ def _get_center_of_mass(comass, span, halflife, alpha):
     return float(comass)
 
 
-class RollingExp:
+class RollingExp(Generic[T_DSorDA]):
     """
     Exponentially-weighted moving window object.
     Similar to EWM in pandas
@@ -78,16 +87,28 @@ class RollingExp:
     RollingExp : type of input argument
     """
 
-    def __init__(self, obj, windows, window_type="span"):
-        self.obj = obj
+    def __init__(
+        self,
+        obj: T_DSorDA,
+        windows: Mapping[Hashable, int],
+        window_type: str = "span",
+    ):
+        self.obj: T_DSorDA = obj
         dim, window = next(iter(windows.items()))
         self.dim = dim
         self.alpha = _get_alpha(**{window_type: window})
 
-    def mean(self):
+    def mean(self, keep_attrs: Optional[bool] = None) -> T_DSorDA:
         """
         Exponentially weighted moving average
 
+        Parameters
+        ----------
+        keep_attrs : bool, default: None
+            If True, the attributes (``attrs``) will be copied from the original
+            object to the new one. If False, the new object will be returned
+            without attributes. If None uses the global default.
+
         Examples
         --------
         >>> da = xr.DataArray([1, 1, 2, 2, 2], dims="x")
@@ -97,4 +118,9 @@ def mean(self):
         Dimensions without coordinates: x
         """
 
-        return self.obj.reduce(move_exp_nanmean, dim=self.dim, alpha=self.alpha)
+        if keep_attrs is None:
+            keep_attrs = _get_keep_attrs(default=True)
+
+        return self.obj.reduce(
+            move_exp_nanmean, dim=self.dim, alpha=self.alpha, keep_attrs=keep_attrs
+        )

xarray/tests/test_dataarray.py

@@ -6927,6 +6927,35 @@ def test_rolling_exp(da, dim, window_type, window):
     assert_allclose(expected.variable, result.variable)
 
 
+@requires_numbagg
+def test_rolling_exp_keep_attrs(da):
+
+    attrs = {"attrs": "da"}
+    da.attrs = attrs
+
+    # attrs are kept per default
+    result = da.rolling_exp(time=10).mean()
+    assert result.attrs == attrs
+
+    # discard attrs
+    result = da.rolling_exp(time=10).mean(keep_attrs=False)
+    assert result.attrs == {}
+
+    # test discard attrs using global option
+    with set_options(keep_attrs=False):
+        result = da.rolling_exp(time=10).mean()
+    assert result.attrs == {}
+
+    # keyword takes precedence over global option
+    with set_options(keep_attrs=False):
+        result = da.rolling_exp(time=10).mean(keep_attrs=True)
+    assert result.attrs == attrs
+
+    with set_options(keep_attrs=True):
+        result = da.rolling_exp(time=10).mean(keep_attrs=False)
+    assert result.attrs == {}
+
+
 def test_no_dict():
     d = DataArray()
     with pytest.raises(AttributeError):

xarray/tests/test_dataset.py

@@ -6145,6 +6145,43 @@ def test_rolling_exp(ds):
     assert isinstance(result, Dataset)
 
 
+@requires_numbagg
+def test_rolling_exp_keep_attrs(ds):
+
+    attrs_global = {"attrs": "global"}
+    attrs_z1 = {"attr": "z1"}
+
+    ds.attrs = attrs_global
+    ds.z1.attrs = attrs_z1
+
+    # attrs are kept per default
+    result = ds.rolling_exp(time=10).mean()
+    assert result.attrs == attrs_global
+    assert result.z1.attrs == attrs_z1
+
+    # discard attrs
+    result = ds.rolling_exp(time=10).mean(keep_attrs=False)
+    assert result.attrs == {}
+    assert result.z1.attrs == {}
+
+    # test discard attrs using global option
+    with set_options(keep_attrs=False):
+        result = ds.rolling_exp(time=10).mean()
+    assert result.attrs == {}
+    assert result.z1.attrs == {}
+
+    # keyword takes precedence over global option
+    with set_options(keep_attrs=False):
+        result = ds.rolling_exp(time=10).mean(keep_attrs=True)
+    assert result.attrs == attrs_global
+    assert result.z1.attrs == attrs_z1
+
+    with set_options(keep_attrs=True):
+        result = ds.rolling_exp(time=10).mean(keep_attrs=False)
+    assert result.attrs == {}
+    assert result.z1.attrs == {}
+
+
 @pytest.mark.parametrize("center", (True, False))
 @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
 @pytest.mark.parametrize("window", (1, 2, 3, 4))