Update unweighted temporal averages to not require bounds (#579)

- Time bounds are required for generating weights for weighted averages

Co-authored-by: tomvothecoder <[email protected]>

xcdat/temporal.py

@@ -757,21 +757,17 @@ def _averager(
         # Preprocess the dataset based on method argument values.
         ds = self._preprocess_dataset(ds)
 
-        # Get the data variable and the required time axis metadata.
-        dv = _get_data_var(ds, data_var)
-        time_bounds = ds.bounds.get_bounds("T", var_key=dv.name)
-
         if self._mode == "average":
-            dv = self._average(dv, time_bounds)
+            dv_avg = self._average(ds, data_var)
         elif self._mode in ["group_average", "climatology", "departures"]:
-            dv = self._group_average(dv, time_bounds)
+            dv_avg = self._group_average(ds, data_var)
 
         # The original time dimension is dropped from the dataset because
         # it becomes obsolete after the data variable is averaged. When the
         # averaged data variable is added to the dataset, the new time dimension
         # and its associated coordinates are also added.
         ds = ds.drop_dims(self.dim)  # type: ignore
-        ds[dv.name] = dv
+        ds[dv_avg.name] = dv_avg
 
         if keep_weights:
             ds = self._keep_weights(ds)
@@ -1075,28 +1071,28 @@ def _drop_leap_days(self, ds: xr.Dataset):
         )
         return ds
 
-    def _average(
-        self, data_var: xr.DataArray, time_bounds: xr.DataArray
-    ) -> xr.DataArray:
+    def _average(self, ds: xr.Dataset, data_var: str) -> xr.DataArray:
         """Averages a data variable with the time dimension removed.
 
         Parameters
         ----------
-        data_var : xr.DataArray
-            The data variable.
-        time_bounds : xr.DataArray
-            The time bounds.
+        ds : xr.Dataset
+            The dataset.
+        data_var : str
+            The key of the data variable.
 
         Returns
         -------
         xr.DataArray
-            The averages for a data variable with the time dimension removed.
+            The data variable averaged with the time dimension removed.
         """
-        dv = data_var.copy()
+        dv = _get_data_var(ds, data_var)
 
         with xr.set_options(keep_attrs=True):
             if self._weighted:
+                time_bounds = ds.bounds.get_bounds("T", var_key=data_var)
                 self._weights = self._get_weights(time_bounds)
+
                 dv = dv.weighted(self._weights).mean(dim=self.dim)  # type: ignore
             else:
                 dv = dv.mean(dim=self.dim)  # type: ignore
@@ -1105,31 +1101,31 @@ def _average(
 
         return dv
 
-    def _group_average(
-        self, data_var: xr.DataArray, time_bounds: xr.DataArray
-    ) -> xr.DataArray:
+    def _group_average(self, ds: xr.Dataset, data_var: str) -> xr.DataArray:
         """Averages a data variable by time group.
 
         Parameters
         ----------
-        data_var : xr.DataArray
-            The data variable.
-        time_bounds : xr.DataArray
-            The time bounds.
+        ds : xr.Dataset
+            The dataset.
+        data_var : str
+            The key of the data variable.
 
         Returns
         -------
         xr.DataArray
             The data variable averaged by time group.
         """
-        dv = data_var.copy()
+        dv = _get_data_var(ds, data_var)
 
         # Label the time coordinates for grouping weights and the data variable
         # values.
         self._labeled_time = self._label_time_coords(dv[self.dim])
 
         if self._weighted:
+            time_bounds = ds.bounds.get_bounds("T", var_key=data_var)
             self._weights = self._get_weights(time_bounds)
+
             # Weight the data variable.
             dv *= self._weights
 
@@ -1145,8 +1141,9 @@ def _group_average(
             # included to take into account zero weight for missing data.
             with xr.set_options(keep_attrs=True):
                 dv = self._group_data(dv).sum() / self._group_data(weights).sum()
+
             # Restore the data variable's name.
-            dv.name = data_var.name
+            dv.name = data_var
         else:
             dv = self._group_data(dv).mean()