vibespatial.predicates.binary¶

Attributes¶

`PredicateInput`
`PREDICATE_SPECS`

Classes¶

`NullBehavior`	Enum where members are also (and must be) strings
`CoarseRelation`	Enum where members are also (and must be) strings
`BinaryPredicateSpec`
`BinaryPredicateResult`

Functions¶

`supports_binary_predicate`(→ bool)
`binary_predicate_expression`(predicate, left, right, *)	Return row-aligned binary predicate results as a private expression.
`binary_predicate_expressions`(predicates, left, right, *)	Return multiple row-aligned predicate vectors as native expressions.
`evaluate_binary_predicate`(→ BinaryPredicateResult)
`evaluate_geopandas_binary_predicate`(→ numpy.ndarray \| None)
`benchmark_binary_predicate`(→ dict[str, int])

Module Contents¶

vibespatial.predicates.binary.PredicateInput¶

class vibespatial.predicates.binary.NullBehavior¶

Enum where members are also (and must be) strings

PROPAGATE = 'propagate'¶

FALSE = 'false'¶

class vibespatial.predicates.binary.CoarseRelation¶

Enum where members are also (and must be) strings

INTERSECTS = 'intersects'¶

CONTAINS = 'contains'¶

WITHIN = 'within'¶

DISJOINT = 'disjoint'¶

class vibespatial.predicates.binary.BinaryPredicateSpec¶

name: str¶

coarse_relation: CoarseRelation¶

shapely_op: str¶

class vibespatial.predicates.binary.BinaryPredicateResult¶

predicate: str¶

values: numpy.ndarray¶

row_count: int¶

candidate_rows: numpy.ndarray¶

coarse_true_rows: numpy.ndarray¶

coarse_false_rows: numpy.ndarray¶

runtime_selection: vibespatial.runtime.RuntimeSelection¶

precision_plan: vibespatial.runtime.precision.PrecisionPlan¶

robustness_plan: vibespatial.runtime.robustness.RobustnessPlan¶

vibespatial.predicates.binary.PREDICATE_SPECS: dict[str, BinaryPredicateSpec]¶

vibespatial.predicates.binary.supports_binary_predicate(name: str) → bool¶

vibespatial.predicates.binary.binary_predicate_expression(predicate: str, left: PredicateInput, right: PredicateInput, *, dispatch_mode: vibespatial.runtime.ExecutionMode | str = ExecutionMode.GPU, precision: vibespatial.runtime.precision.PrecisionMode | str = PrecisionMode.AUTO, source_token: str | None = None, operation: str | None = None)¶

Return row-aligned binary predicate results as a private expression.

This is the Native* consumer path for admitted binary predicates. It keeps the bool vector on device so sanctioned callers can lower directly to a NativeRowSet instead of exporting a public bool Series.

vibespatial.predicates.binary.binary_predicate_expressions(predicates: collections.abc.Sequence[str], left: PredicateInput, right: PredicateInput, *, dispatch_mode: vibespatial.runtime.ExecutionMode | str = ExecutionMode.GPU, precision: vibespatial.runtime.precision.PrecisionMode | str = PrecisionMode.AUTO, source_token: str | None = None, operation_prefix: str = 'binary_predicate')¶

Return multiple row-aligned predicate vectors as native expressions.

Physical shape: aligned pairwise predicate expression. For all-DE-9IM, non-point family pairs, one candidate/refine pass computes all requested predicates and scatters full-size device vectors. Other admitted native shapes reuse the single-predicate expression producer per predicate.

vibespatial.predicates.binary.evaluate_binary_predicate(predicate: str, left: PredicateInput, right: object | PredicateInput, *, dispatch_mode: vibespatial.runtime.ExecutionMode | str = ExecutionMode.AUTO, precision: vibespatial.runtime.precision.PrecisionMode | str = PrecisionMode.AUTO, null_behavior: NullBehavior | str = NullBehavior.PROPAGATE, **kwargs: Any) → BinaryPredicateResult¶

vibespatial.predicates.binary.evaluate_geopandas_binary_predicate(predicate: str, left: numpy.ndarray | vibespatial.geometry.owned.OwnedGeometryArray, right: object | numpy.ndarray | vibespatial.geometry.owned.OwnedGeometryArray, **kwargs: Any) → numpy.ndarray | None¶

vibespatial.predicates.binary.benchmark_binary_predicate(predicate: str, left: PredicateInput, right: object | PredicateInput, **kwargs: Any) → dict[str, int]¶