lightweight_distribution

Lightweight distribution snapshot for transformation pipelines.

The snapshot keeps only metadata required by transformations and strategies, while avoiding strong references to full parent distribution objects.

class pysatl_core.transformations.lightweight_distribution.LightweightDistribution(*, distribution_type, analytical_computations, support=None, sampling_strategy=None, computation_strategy=None, bases=None, loop_analytical_flags=None)

Bases: Distribution

Lightweight Distribution implementation for transformation internals.

Parameters:

• distribution_type (DistributionType) – Type descriptor of the distribution.

• analytical_computations (Mapping[) –

  GenericCharacteristicName, (

  AnalyticalComputation[Any, Any] | Mapping[LabelName, AnalyticalComputation[Any, Any]]

  ),

• ] – Labeled characteristic methods exposed by the snapshot.

• support (Support | None, optional) – Support metadata copied from the source distribution.

• sampling_strategy (SamplingStrategy | None, optional) – Sampling strategy attached to the snapshot.

• computation_strategy (ComputationStrategy | None, optional) – Computation strategy attached to the snapshot.

• bases (Mapping[ParentRole, LightweightDistribution] | None, optional) – Lightweight base snapshots for chained transformations.

• loop_analytical_flags (Mapping[) – GenericCharacteristicName, Mapping[LabelName, bool],

• None (] |) – Optional analytical flags for loop variants.

• optional – Optional analytical flags for loop variants.

__init__(*, distribution_type, analytical_computations, support=None, sampling_strategy=None, computation_strategy=None, bases=None, loop_analytical_flags=None)

Initialize common distribution state.

Parameters:

• distribution_type (DistributionType) – Type information about the distribution (kind, dimension, etc.).

• analytical_computations (Mapping) –

  Distribution-provided characteristic methods. For non-transformed distributions these methods are fully analytical.

  Note

  Each characteristic callable should accept and return NumPy arrays (array semantics). Scalar-only callables are wrapped automatically via numpy.vectorize, but at a significant per-element overhead cost.

• support (Support or None, default None) – Support of the distribution.

• sampling_strategy (SamplingStrategy or None, default None) – Sampling strategy instance. If omitted, univariate default is used.

• computation_strategy (ComputationStrategy or None, default None) – Computation strategy instance. If omitted, default strategy is used.

• bases (Mapping[ParentRole, LightweightDistribution] | None)

• loop_analytical_flags (Mapping[GenericCharacteristicName, Mapping[LabelName, bool]] | None)

Return type:

None

classmethod from_distribution(distribution)

Build a lightweight snapshot from an arbitrary distribution.

The method copies only strategy-relevant fields and recursively snapshots known bases when the source distribution exposes them.

Parameters:

distribution (Distribution) – Source distribution.

Returns:

Lightweight snapshot compatible with Distribution.

Return type:

LightweightDistribution

property bases: Mapping[ParentRole, pysatl_core.distributions.distribution.Distribution]

Get lightweight base snapshots grouped by role.

loop_is_analytical(characteristic_name, label_name)

Return preserved loop analytical flag for the snapshot.

Return type:

bool

Parameters:

• characteristic_name (GenericCharacteristicName)

• label_name (LabelName)