Drop wildcards from dataset column references.

It's just an unnecessary pain right now.  We can add it back in the
string-parsing layer in the future.

python/lsst/daf/butler/queries/expression_factory.py

@@ -30,7 +30,6 @@
 __all__ = ("ExpressionFactory", "ExpressionProxy", "ScalarExpressionProxy", "TimespanProxy", "RegionProxy")
 
 from collections.abc import Iterable
-from types import EllipsisType
 from typing import TYPE_CHECKING, get_args
 
 from lsst.sphgeom import Region
@@ -294,7 +293,7 @@ class DatasetTypeProxy:
 
     Parameters
     ----------
-    dataset_type : `str` or ``...``
+    dataset_type : `str`
         Dataset type name or wildcard.  Wildcards are usable only when the
         query contains exactly one dataset type or a wildcard.
 
@@ -304,11 +303,11 @@ class DatasetTypeProxy:
     associated with datasets rather than their dimensions.
     """
 
-    def __init__(self, dataset_type: rt.StringOrWildcard):
+    def __init__(self, dataset_type: str):
         self._dataset_type = dataset_type
 
     def __repr__(self) -> str:
-        return self._dataset_type if self._dataset_type is not ... else "(...)"
+        return self._dataset_type
 
     # Attributes are actually fixed, but we implement them with __getattr__
     # and __dir__ to avoid repeating the list.  And someday they might expand
@@ -351,7 +350,7 @@ def __getattr__(self, name: str) -> DimensionElementProxy:
             return DimensionProxy(element)
         return DimensionElementProxy(element)
 
-    def __getitem__(self, name: str | EllipsisType) -> DatasetTypeProxy:
+    def __getitem__(self, name: str) -> DatasetTypeProxy:
         return DatasetTypeProxy(name)
 
     def not_(self, operand: rt.Predicate) -> rt.Predicate:

python/lsst/daf/butler/queries/relation_tree/_base.py

@@ -32,14 +32,12 @@
     "RootRelationBase",
     "ColumnExpressionBase",
     "PredicateBase",
-    "StringOrWildcard",
     "DatasetFieldName",
     "InvalidRelationError",
 )
 
 from abc import ABC, abstractmethod
-from types import EllipsisType
-from typing import TYPE_CHECKING, Annotated, Any, Literal, TypeAlias, cast
+from typing import TYPE_CHECKING, Any, Literal, TypeAlias, cast
 
 import pydantic
 
@@ -52,14 +50,6 @@
     from ._relation import Relation, RootRelation
 
 
-StringOrWildcard = Annotated[
-    str | EllipsisType,
-    pydantic.PlainSerializer(lambda x: "..." if x is ... else x, return_type=str),
-    pydantic.BeforeValidator(lambda x: ... if x == "..." else x),
-    pydantic.GetPydanticSchema(lambda _s, h: h(str)),
-]
-
-
 DatasetFieldName: TypeAlias = Literal["dataset_id", "ingest_date", "run", "collection", "rank", "timespan"]
 
 

python/lsst/daf/butler/queries/relation_tree/_column_reference.py

@@ -35,7 +35,7 @@
 
 from ...column_spec import ColumnType
 from ...dimensions import Dimension, DimensionElement
-from ._base import ColumnExpressionBase, DatasetFieldName, InvalidRelationError, StringOrWildcard
+from ._base import ColumnExpressionBase, DatasetFieldName, InvalidRelationError
 
 
 @final
@@ -121,8 +121,8 @@ class DatasetFieldReference(ColumnExpressionBase):
 
     expression_type: Literal["dataset_field"] = "dataset_field"
 
-    dataset_type: StringOrWildcard
-    """Name of the dataset type, or ``...`` to match any dataset type."""
+    dataset_type: str
+    """Name of the dataset type to match any dataset type."""
 
     field: DatasetFieldName
     """Name of the field (i.e. column) in the dataset's logical table."""
@@ -160,10 +160,7 @@ def column_type(self) -> ColumnType:
         raise AssertionError(f"Invalid field {self.field!r} for dataset.")
 
     def __str__(self) -> str:
-        if self.dataset_type is ...:
-            return self.field
-        else:
-            return f"{self.dataset_type}.{self.field}"
+        return f"{self.dataset_type}.{self.field}"
 
 
 # Union without Pydantic annotation for the discriminator, for use in nesting