Clean up logic in various private methods

- Methods include `_subset_coords_for_custom_seasons()` and `_shift_custom_season_years()`

xcdat/temporal.py

@@ -1099,11 +1099,12 @@ def _preprocess_dataset(self, ds: xr.Dataset) -> xr.Dataset:
         """Preprocess the dataset based on averaging settings.
 
         Operations include:
-          1. Drop leap days for daily climatologies.
-          2. Subset the dataset based on the reference period.
-          3. Shift years for custom seasons spanning the calendar year.
-          4. Shift Decembers for "DJF" mode and drop incomplete "DJF" seasons.
-          5. Drop incomplete seasons if specified.
+            1. Drop leap days for daily climatologies.
+            2. Subset the dataset based on the reference period.
+            3. Shift years for custom seasons spanning the calendar year.
+            4. Shift Decembers for "DJF" mode and drop incomplete "DJF" seasons,
+               if specified.
+            5. Drop incomplete seasons if specified.
 
         Parameters
         ----------
@@ -1141,6 +1142,9 @@ def _preprocess_dataset(self, ds: xr.Dataset) -> xr.Dataset:
             if len(months) != 12:
                 ds = self._subset_coords_for_custom_seasons(ds, months)
 
+            # The years for time coordinates needs to be shifted by 1 for months
+            # that span the calendar because Xarray groups seasons by months
+            # in the same year, rather than the previous year.
             ds = self._shift_custom_season_years(ds)
 
         if self._freq == "season" and self._season_config.get("dec_mode") == "DJF":
@@ -1180,16 +1184,8 @@ def _subset_coords_for_custom_seasons(
             The dataset with time coordinate subsetted to months used in
             custom seasons.
         """
-        month_ints = sorted([MONTH_STR_TO_INT[month] for month in months])
-
-        coords_by_month = ds[self.dim].groupby(f"{self.dim}.month").groups
-        month_to_time_idx = {
-            k: coords_by_month[k] for k in month_ints if k in coords_by_month
-        }
-        month_to_time_idx = sorted(
-            list(chain.from_iterable(month_to_time_idx.values()))  # type: ignore
-        )
-        ds_new = ds.isel({f"{self.dim}": month_to_time_idx})
+        month_ints = [MONTH_STR_TO_INT[month] for month in months]
+        ds_new = ds.sel({self.dim: ds[self.dim].dt.month.isin(month_ints)})
 
         return ds_new
 
@@ -1231,34 +1227,31 @@ def _shift_custom_season_years(self, ds: xr.Dataset) -> xr.Dataset:
         ds_new = ds.copy()
         custom_seasons = self._season_config["custom_seasons"]
 
+        # Identify months that span across years in custom seasons by getting
+        # the months before "Jan" if "Jan" is not the first month of the season.
+        # Note: Only one custom season can span the calendar year.
         span_months: List[int] = []
-
-        # Identify the months that span across years in custom seasons.
-        # This is done by checking if "Jan" is not the first month in the
-        # custom season and getting all months before "Jan".
         for months in custom_seasons.values():  # type: ignore
-            month_nums = [MONTH_STR_TO_INT[month] for month in months]
-            if 1 in month_nums:
-                jan_index = month_nums.index(1)
+            month_ints = [MONTH_STR_TO_INT[month] for month in months]
 
-                if jan_index != 0:
-                    span_months.extend(month_nums[:jan_index])
+            if 1 in month_ints and month_ints.index(1) != 0:
+                span_months.extend(month_ints[: month_ints.index(1)])
             break
 
         if span_months:
             time_coords = ds_new[self.dim].copy()
-            idxs = np.where(time_coords.dt.month.isin(span_months))[0]
+            indexes = time_coords.dt.month.isin(span_months)
 
             if isinstance(time_coords.values[0], cftime.datetime):
-                for idx in idxs:
-                    time_coords.values[idx] = time_coords.values[idx].replace(
-                        year=time_coords.values[idx].year + 1
-                    )
+                time_coords.values[indexes] = [
+                    time.replace(year=time.year + 1)
+                    for time in time_coords.values[indexes]
+                ]
             else:
-                for idx in idxs:
-                    time_coords.values[idx] = pd.Timestamp(
-                        time_coords.values[idx]
-                    ) + pd.DateOffset(years=1)
+                time_coords.values[indexes] = [
+                    pd.Timestamp(time) + pd.DateOffset(years=1)
+                    for time in time_coords.values[indexes]
+                ]
 
             ds_new = ds_new.assign_coords({self.dim: time_coords})
 
@@ -1298,9 +1291,16 @@ def _shift_djf_decembers(self, ds: xr.Dataset) -> xr.Dataset:
         time_coords = ds_new[self.dim].copy()
         dec_indexes = time_coords.dt.month == 12
 
-        time_coords.values[dec_indexes] = [
-            time.replace(year=time.year + 1) for time in time_coords.values[dec_indexes]
-        ]
+        if isinstance(time_coords.values[0], cftime.datetime):
+            time_coords.values[dec_indexes] = [
+                time.replace(year=time.year + 1)
+                for time in time_coords.values[dec_indexes]
+            ]
+        else:
+            time_coords.values[dec_indexes] = [
+                pd.Timestamp(time) + pd.DateOffset(years=1)
+                for time in time_coords.values[dec_indexes]
+            ]
 
         ds_new = ds_new.assign_coords({self.dim: time_coords})