Test and fix calibration dataset type registration bug.

Without this fix, calibration dataset types that were registered in
data repositories where the only existing dataset types with those
dimensions were not calibrations would incorrectly be registered as
non-calibrations.

python/lsst/daf/butler/registry/datasets/byDimensions/_manager.py

@@ -327,6 +327,8 @@ def register_dataset_type(self, dataset_type: DatasetType) -> bool:
                     dataset_type.dimensions, dimensions_key, dataset_type.isCalibration()
                 )
                 dynamic_tables.create(self._db, type(self._collections))
+            elif dataset_type.isCalibration() and dynamic_tables.calibs_name is None:
+                dynamic_tables.add_calibs(self._db, type(self._collections))
             row, inserted = self._db.sync(
                 self._static.dataset_type,
                 keys={"name": dataset_type.name},

python/lsst/daf/butler/registry/datasets/byDimensions/tables.py

@@ -531,6 +531,24 @@ def create(self, db: Database, collections: type[CollectionManager]) -> None:
                 makeCalibTableSpec(self._dimensions, collections, db.getTimespanRepresentation()),
             )
 
+    def add_calibs(self, db: Database, collections: type[CollectionManager]) -> None:
+        """Create a calibs table for a dataset type whose dimensions already
+        have a tags table.
+
+        Parameters
+        ----------
+        db : `Database`
+            Database interface.
+        collections : `type` [ `CollectionManager` ]
+            Manager class for collections; used to create foreign key columns
+            for collections.
+        """
+        self.calibs_name = makeCalibTableName(self.dimensions_key)
+        self._calibs_table = db.ensureTableExists(
+            self.calibs_name,
+            makeCalibTableSpec(self._dimensions, collections, db.getTimespanRepresentation()),
+        )
+
     def tags(self, db: Database, collections: type[CollectionManager]) -> sqlalchemy.Table:
         """Return the "tags" table that associates datasets with data IDs in
         TAGGED and RUN collections.

tests/test_simpleButler.py

@@ -823,6 +823,35 @@ def test_clone(self):
         self.assertCountEqual(clone5.registry.defaults.collections, ["imported_r"])
         self.assertEqual(clone5.run, "imported_r")
 
+    def test_calibration_dataset_type_registration(self) -> None:
+        # Register two dataset types that should share the same tags table,
+        # but only one is a calibration and hence needs a calibs table.
+        butler1 = self.makeButler(writeable=True)
+        a = DatasetType("a", ["instrument"], universe=butler1.dimensions, storageClass="StructuredDataDict")
+        b = DatasetType(
+            "b",
+            ["instrument"],
+            universe=butler1.dimensions,
+            storageClass="StructuredDataDict",
+            isCalibration=True,
+        )
+        butler1.registry.registerDatasetType(a)
+        butler1.registry.registerDatasetType(b)
+        self.assertEqual(butler1.get_dataset_type("a"), a)
+        self.assertEqual(butler1.get_dataset_type("b"), b)
+        butler1.registry.refresh()
+        self.assertEqual(butler1.get_dataset_type("a"), a)
+        self.assertEqual(butler1.get_dataset_type("b"), b)
+        # Register them in the opposite order in a new repo.
+        butler2 = self.makeButler(writeable=True)
+        butler2.registry.registerDatasetType(b)
+        butler2.registry.registerDatasetType(a)
+        self.assertEqual(butler2.get_dataset_type("a"), a)
+        self.assertEqual(butler2.get_dataset_type("b"), b)
+        butler2.registry.refresh()
+        self.assertEqual(butler2.get_dataset_type("a"), a)
+        self.assertEqual(butler2.get_dataset_type("b"), b)
+
 
 class DirectSimpleButlerTestCase(SimpleButlerTests, unittest.TestCase):
     """Run tests against DirectButler implementation."""