Monthly mean (#39)

* add monthly weighted

* typo

c3s_eqc_automatic_quality_control/diagnostics.py

@@ -30,6 +30,8 @@
     "annual_weighted_mean",
     "annual_weighted_std",
     "grid_cell_area",
+    "monthly_weighted_mean",
+    "monthly_weighted_std",
     "regrid",
     "rolling_weighted_filter",
     "seasonal_weighted_mean",
@@ -239,6 +241,37 @@ def attrs_func(attrs: dict[str, Any]) -> dict[str, Any]:
     return _apply_attrs_func(coverage, obj, attrs_func)
 
 
+def monthly_weighted_mean(
+    obj: xr.DataArray | xr.Dataset,
+    time_name: Hashable | None = None,
+    weights: xr.DataArray | bool = True,
+    **kwargs: Any,
+) -> xr.DataArray | xr.Dataset:
+    """
+    Calculate monthly weighted mean.
+
+    Parameters
+    ----------
+    obj: DataArray or Dataset
+        Input data
+    time_name: str, optional
+        Name of time coordinate
+    weights: DataArray, bool, default: True
+        Weights to apply:
+        - True: weights are the number of days in each month
+        - False: unweighted
+        - DataArray: custom weights
+
+    Returns
+    -------
+    DataArray or Dataset
+        Reduced object
+    """
+    return _time_weighted.TimeWeighted(obj, time_name, weights).reduce(
+        "mean", "month", **kwargs
+    )
+
+
 def seasonal_weighted_mean(
     obj: xr.DataArray | xr.Dataset,
     time_name: Hashable | None = None,
@@ -270,6 +303,37 @@ def seasonal_weighted_mean(
     )
 
 
+def monthly_weighted_std(
+    obj: xr.DataArray | xr.Dataset,
+    time_name: Hashable | None = None,
+    weights: xr.DataArray | bool = True,
+    **kwargs: Any,
+) -> xr.DataArray | xr.Dataset:
+    """
+    Calculate monthly weighted std.
+
+    Parameters
+    ----------
+    obj: DataArray or Dataset
+        Input data
+    time_name: str, optional
+        Name of time coordinate
+    weights: DataArray, bool, default: True
+        Weights to apply:
+        - True: weights are the number of days in each month
+        - False: unweighted
+        - DataArray: custom weights
+
+    Returns
+    -------
+    DataArray or Dataset
+        Reduced object
+    """
+    return _time_weighted.TimeWeighted(obj, time_name, weights).reduce(
+        "std", "month", **kwargs
+    )
+
+
 def seasonal_weighted_std(
     obj: xr.DataArray | xr.Dataset,
     time_name: Hashable | None = None,

tests/test_21_time_diagnostics.py

@@ -74,6 +74,17 @@ def test_time_weighted_std(
         actual = diagnostics.time_weighted_std(obj, weights=weights)
         xr.testing.assert_equal(expected, actual)
 
+    def test_monthly_weighted_mean(
+        self, obj: xr.DataArray | xr.Dataset, weights: bool
+    ) -> None:
+        if weights:
+            expected = (obj).groupby("time.month").map(weighted_mean)
+            expected = expected.where(expected != 0)
+        else:
+            expected = obj.groupby("time.month").mean("time")
+        actual = diagnostics.monthly_weighted_mean(obj, weights=weights)
+        xr.testing.assert_equal(expected, actual)
+
     def test_seasonal_weighted_mean(
         self, obj: xr.DataArray | xr.Dataset, weights: bool
     ) -> None:
@@ -85,6 +96,17 @@ def test_seasonal_weighted_mean(
         actual = diagnostics.seasonal_weighted_mean(obj, weights=weights)
         xr.testing.assert_equal(expected, actual)
 
+    def test_monthly_weighted_std(
+        self, obj: xr.DataArray | xr.Dataset, weights: bool
+    ) -> None:
+        if weights:
+            expected = obj.groupby("time.month").map(weighted_std)
+            expected = expected.where(expected != 0)
+        else:
+            expected = obj.groupby("time.month").std("time")
+        actual = diagnostics.monthly_weighted_std(obj, weights=weights)
+        xr.testing.assert_equal(expected, actual)
+
     def test_seasonal_weighted_std(
         self, obj: xr.DataArray | xr.Dataset, weights: bool
     ) -> None: