Merge pull request #724 from lsst/tickets/DM-35937

DM-35937: Get the composite dataset type from the component dataset type
lsst · Aug 18, 2022 · 6ee339d · 6ee339d
2 parents bf1ffe9 + d961432
commit 6ee339d
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Showing 2 changed files with 43 additions and 6 deletions.
diff --git a/python/lsst/daf/butler/registry/queries/_results.py b/python/lsst/daf/butler/registry/queries/_results.py
@@ -411,8 +411,8 @@ def findDatasets(
                 return ChainedDatasetQueryResults(
                     [],
                     doomed_by=[
-                        f"No registered dataset type {datasetType!r} found, so no instances can "
-                        "exist in any collection."
+                        f"Dataset type {datasetType!r} is not registered, so no instances of it can exist in "
+                        "any collection."
                     ],
                 )
             else:
@@ -426,10 +426,8 @@ def findDatasets(
             )
         if datasetType.isComponent():
             # We were given a true DatasetType instance, but it's a component.
-            parentName, componentName = datasetType.nameAndComponent()
-            storage = self._query.managers.datasets[parentName]
-            datasetType = storage.datasetType
-            components = [componentName]
+            components = [datasetType.component()]
+            datasetType = datasetType.makeCompositeDatasetType()
         else:
             components = [None]
         summary = QuerySummary(self.graph, whereRegion=self._query.whereRegion, datasets=[datasetType])

diff --git a/python/lsst/daf/butler/registry/tests/_registry.py b/python/lsst/daf/butler/registry/tests/_registry.py
@@ -1433,6 +1433,45 @@ def testQueryResults(self):
             list(subsetDataIds.findDatasets(bias, collections=["imported_r", "imported_g"], findFirst=True)),
             expectedDeduplicatedBiases,
         )
+
+        # Check dimensions match.
+        with self.assertRaises(ValueError):
+            subsetDataIds.findDatasets("flat", collections=["imported_r", "imported_g"], findFirst=True)
+
+        # Use a component dataset type.
+        self.assertCountEqual(
+            list(
+                subsetDataIds.findDatasets(
+                    bias.makeComponentDatasetType("image"),
+                    collections=["imported_r", "imported_g"],
+                    findFirst=False,
+                )
+            ),
+            [ref.makeComponentRef("image") for ref in expectedAllBiases],
+        )
+
+        # Use a named dataset type that does not exist and a dataset type
+        # object that does not exist.
+        unknown_type = DatasetType("not_known", dimensions=bias.dimensions, storageClass="Exposure")
+        unknown_component_type = unknown_type.makeComponentDatasetType("image")
+
+        # Four combinations of unknown dataset type need to be tested.
+        # Composite and component and string name vs dataset type object.
+        test_type: Union[str, DatasetType]
+        for test_type, test_type_name in (
+            (unknown_type, unknown_type.name),
+            (unknown_type.name, unknown_type.name),
+            (unknown_component_type, unknown_type.name),
+            (unknown_component_type.name, unknown_component_type.name),
+        ):
+            result = subsetDataIds.findDatasets(
+                test_type, collections=["imported_r", "imported_g"], findFirst=True
+            )
+            self.assertEqual(result.count(), 0)
+            self.assertIn(
+                f"Dataset type '{test_type_name}' is not registered", "\n".join(result.explain_no_results())
+            )
+
         # Materialize the bias dataset queries (only) by putting the results
         # into temporary tables, then repeat those tests.
         with subsetDataIds.findDatasets(