Support "subsampled" seasons

xarray/groupers.py

@@ -7,7 +7,9 @@
 from __future__ import annotations
 
 import datetime
+import functools
 import itertools
+import operator
 from abc import ABC, abstractmethod
 from collections import defaultdict
 from collections.abc import Mapping, Sequence
@@ -670,7 +672,12 @@ class SeasonResampler(Resampler):
 
     def __post_init__(self):
         self.season_inds = season_to_month_tuple(self.seasons)
-        self.season_tuples = dict(zip(self.seasons, self.season_inds, strict=False))
+        all_inds = functools.reduce(operator.add, self.season_inds)
+        if len(all_inds) > len(set(all_inds)):
+            raise ValueError(
+                f"Overlapping seasons are not allowed. Received {self.seasons!r}"
+            )
+        self.season_tuples = dict(zip(self.seasons, self.season_inds, strict=True))
 
     def factorize(self, group):
         if group.ndim != 1:
@@ -696,12 +703,22 @@ def factorize(self, group):
             season_label[month.isin(season_ind)] = season_str
             if "DJ" in season_str:
                 after_dec = season_ind[season_str.index("D") + 1 :]
+                # important this is assuming non-overlapping seasons
                 year[month.isin(after_dec)] -= 1
 
+        # Allow users to skip one or more months?
+        # present_seasons is a mask that is True for months that are requestsed in the output
+        present_seasons = season_label != ""
+        if present_seasons.all():
+            present_seasons = slice(None)
         frame = pd.DataFrame(
-            data={"index": np.arange(group.size), "month": month},
+            data={
+                "index": np.arange(group[present_seasons].size),
+                "month": month[present_seasons],
+            },
             index=pd.MultiIndex.from_arrays(
-                [year.data, season_label], names=["year", "season"]
+                [year.data[present_seasons], season_label[present_seasons]],
+                names=["year", "season"],
             ),
         )
 
@@ -727,19 +744,19 @@ def factorize(self, group):
 
         sbins = first_items.values.astype(int)
         group_indices = [
-            slice(i, j) for i, j in zip(sbins[:-1], sbins[1:], strict=False)
+            slice(i, j) for i, j in zip(sbins[:-1], sbins[1:], strict=True)
         ]
         group_indices += [slice(sbins[-1], None)]
 
         # Make sure the first and last timestamps
         # are for the correct months,if not we have incomplete seasons
         unique_codes = np.arange(len(unique_coord))
         if self.drop_incomplete:
-            for idx, slicer in zip([0, -1], (slice(1, None), slice(-1)), strict=False):
+            for idx, slicer in zip([0, -1], (slice(1, None), slice(-1)), strict=True):
                 stamp_year, stamp_season = frame.index[idx]
                 code = seasons.index(stamp_season)
                 stamp_month = season_inds[code][idx]
-                if stamp_month != month[idx].item():
+                if stamp_month != month[present_seasons][idx].item():
                     # we have an incomplete season!
                     group_indices = group_indices[slicer]
                     unique_coord = unique_coord[slicer]
@@ -769,7 +786,9 @@ def factorize(self, group):
         if not full_index.equals(unique_coord):
             raise ValueError("Are there seasons missing in the middle of the dataset?")
 
-        codes = group.copy(data=np.repeat(unique_codes, counts), deep=False)
+        final_codes = np.full(group.data.size, -1)
+        final_codes[present_seasons] = np.repeat(unique_codes, counts)
+        codes = group.copy(data=final_codes, deep=False)
         unique_coord_var = Variable(group.name, unique_coord, group.attrs)
 
         return EncodedGroups(

xarray/tests/test_groupby.py

@@ -2958,6 +2958,9 @@ def test_season_resampler():
     # skip september
     da.groupby(time=SeasonResampler(["DJF", "MAM", "JJA", "ON"])).sum()
 
+    # "subsampling"
+    da.groupby(time=SeasonResampler(["JJAS"])).sum()
+
     # overlapping
     with pytest.raises(ValueError):
         da.groupby(time=SeasonResampler(["DJFM", "MAMJ", "JJAS", "SOND"])).sum()