DM-47335: Fix dataset query missing implied dimensions

python/lsst/daf/butler/dimensions/_coordinate.py

@@ -709,7 +709,7 @@ def to_simple(self, minimal: bool = False) -> SerializedDataCoordinate:
         else:
             records = None
 
-        return SerializedDataCoordinate(dataId=dict(self.required), records=records)
+        return SerializedDataCoordinate(dataId=dict(self.mapping), records=records)
 
     @classmethod
     def from_simple(

python/lsst/daf/butler/tests/butler_queries.py

@@ -303,6 +303,24 @@ def test_simple_dataset_query(self) -> None:
             butler.query_datasets("bias", "*", detector=100, instrument="Unknown", find_first=False)
         self.assertIn("doomed", str(cm2.exception))
 
+        # Test for a regression of an issue where "band" was not being included
+        # in the data ID, despite being one of the dimensions in the "flat"
+        # dataset type.
+        #
+        # "band" is implied by "physical_filter", so it's technically not a
+        # 'required' dimension.  However, the contract of query_datasets is
+        # that hasFull() should be true, so implied dimensions must be
+        # included.
+        refs = butler.query_datasets("flat", "imported_r", where="detector = 2", instrument="Cam1")
+        self.assertEqual(len(refs), 1)
+        flat = refs[0]
+        self.assertTrue(flat.dataId.hasFull())
+        self.assertEqual(flat.datasetType.name, "flat")
+        self.assertEqual(flat.dataId["instrument"], "Cam1")
+        self.assertEqual(flat.dataId["detector"], 2)
+        self.assertEqual(flat.dataId["physical_filter"], "Cam1-R1")
+        self.assertEqual(flat.dataId["band"], "r")
+
     def test_general_query(self) -> None:
         """Test Query.general and its result."""
         butler = self.make_butler("base.yaml", "datasets.yaml")

tests/test_dimensions.py

@@ -42,6 +42,7 @@
     DataCoordinate,
     DataCoordinateSequence,
     DataCoordinateSet,
+    DatasetType,
     Dimension,
     DimensionConfig,
     DimensionElement,
@@ -411,6 +412,19 @@ def testPickling(self):
             group2 = pickle.loads(pickle.dumps(group1))
             self.assertIs(group1, group2)
 
+    def testSerialization(self):
+        # Check that dataset types round-trip correctly through serialization.
+        dataset_type = DatasetType(
+            "flat",
+            dimensions=["instrument", "detector", "physical_filter", "band"],
+            isCalibration=True,
+            universe=self.universe,
+            storageClass="int",
+        )
+        roundtripped = DatasetType.from_simple(dataset_type.to_simple(), universe=self.universe)
+
+        self.assertEqual(dataset_type, roundtripped)
+
 
 @dataclass
 class SplitByStateFlags: