FlagGrouper: Encapsulate instead of subclassing UniqueGrouper

cf_xarray/groupers.py

@@ -2,11 +2,12 @@
 
 import numpy as np
 import pandas as pd
-from xarray.groupers import EncodedGroups, UniqueGrouper
+from xarray import Variable
+from xarray.groupers import EncodedGroups, Grouper, UniqueGrouper
 
 
 @dataclass
-class FlagGrouper(UniqueGrouper):
+class FlagGrouper(Grouper):
     """
     Grouper object that allows convenient categorical grouping by a CF flag variable.
 
@@ -23,18 +24,24 @@ def factorize(self, group) -> EncodedGroups:
         values = np.array(group.attrs["flag_values"])
         full_index = pd.Index(group.attrs["flag_meanings"].split(" "))
 
-        self.labels = values
+        grouper = UniqueGrouper(labels=values)
 
         # TODO: we could optimize here, since `group` is already factorized,
         # but there are subtleties. For example, the attrs must be up to date,
         # any value that is not in flag_values will cause an error, etc.
-        ret = super().factorize(group)
+        ret = grouper.factorize(group)
 
         ret.codes.attrs.pop("flag_values")
         ret.codes.attrs.pop("flag_meanings")
 
         return EncodedGroups(
             codes=ret.codes,
             full_index=full_index,
+            unique_coord=Variable(
+                dims=ret.codes.name, data=np.array(full_index), attrs=ret.codes.attrs
+            ),
             group_indices=ret.group_indices,
         )
+
+    def reset(self):
+        raise NotImplementedError()