Add target_sharding_type to match/difference tasks

Author: taranu

python/lsst/pipe/tasks/diff_matched_tract_catalog.py

@@ -106,14 +106,27 @@ def __init__(self, *, config=None):
         if config.refcat_sharding_type != "tract":
             if config.refcat_sharding_type == "none":
                 old = self.cat_ref
-                del self.cat_ref
                 self.cat_ref = cT.Input(
                     doc=old.doc,
                     name=old.name,
                     storageClass=old.storageClass,
                     dimensions=(),
                     deferLoad=old.deferLoad,
                 )
+            else:
+                raise NotImplementedError(f"{config.refcat_sharding_type=} not implemented")
+        if config.target_sharding_type != "tract":
+            if config.target_sharding_type == "none":
+                old = self.cat_target
+                self.cat_target = cT.Input(
+                    doc=old.doc,
+                    name=old.name,
+                    storageClass=old.storageClass,
+                    dimensions=(),
+                    deferLoad=old.deferLoad,
+                )
+            else:
+                raise NotImplementedError(f"{config.target_sharding_type=} not implemented")
 
 
 class MatchedCatalogFluxesConfig(pexConfig.Config):
@@ -265,6 +278,11 @@ def columns_in_target(self) -> list[str]:
         allowed={"tract": "Tract-based shards", "none": "No sharding at all"},
         default="tract",
     )
+    target_sharding_type = pexConfig.ChoiceField[str](
+        doc="The type of sharding (spatial splitting) for the target catalog",
+        allowed={"tract": "Tract-based shards", "none": "No sharding at all"},
+        default="tract",
+    )
 
     def validate(self):
         super().validate()

python/lsst/pipe/tasks/match_tract_catalog.py

@@ -85,14 +85,27 @@ def __init__(self, *, config=None):
         if config.refcat_sharding_type != "tract":
             if config.refcat_sharding_type == "none":
                 old = self.cat_ref
-                del self.cat_ref
                 self.cat_ref = cT.Input(
                     doc=old.doc,
                     name=old.name,
                     storageClass=old.storageClass,
                     dimensions=(),
                     deferLoad=old.deferLoad,
                 )
+            else:
+                raise NotImplementedError(f"{config.refcat_sharding_type=} not implemented")
+        if config.target_sharding_type != "tract":
+            if config.target_sharding_type == "none":
+                old = self.cat_target
+                self.cat_target = cT.Input(
+                    doc=old.doc,
+                    name=old.name,
+                    storageClass=old.storageClass,
+                    dimensions=(),
+                    deferLoad=old.deferLoad,
+                )
+            else:
+                raise NotImplementedError(f"{config.target_sharding_type=} not implemented")
 
 
 class MatchTractCatalogSubConfig(pexConfig.Config):
@@ -167,6 +180,11 @@ class MatchTractCatalogConfig(
         allowed={"tract": "Tract-based shards", "none": "No sharding at all"},
         default="tract",
     )
+    target_sharding_type = pexConfig.ChoiceField[str](
+        doc="The type of sharding (spatial splitting) for the target catalog",
+        allowed={"tract": "Tract-based shards", "none": "No sharding at all"},
+        default="tract",
+    )
 
     def get_columns_in(self) -> Tuple[Set, Set]:
         """Get the set of input columns required for matching.