PredictedQuantumGraphReader

class lsst.pipe.base.quantum_graph.PredictedQuantumGraphReader(header: HeaderModel, pipeline_graph: PipelineGraph, zf: zipfile.ZipFile, decompressor: Decompressor, address_reader: AddressReader, page_size: int)

Bases: BaseQuantumGraphReader

A helper class for reading predicted quantum graphs.

Methods Summary

finish()	Construct a PredictedQuantumGraph instance from this reader.
open(uri, *[, page_size, import_mode])	Construct a reader from a URI.
read_all()	Read all components in full.
read_dimension_data()	Read all dimension records.
read_execution_quanta([quantum_ids])	Read all information needed to execute the given quanta.
read_init_quanta()	Read the list of special quanta that represent init-inputs and init-outputs.
read_quantum_datasets([quantum_ids])	Read information about all datasets produced and consumed by the given quantum IDs.
read_thin_graph()	Read the thin graph.

Methods Documentation

finish() → PredictedQuantumGraph

Construct a PredictedQuantumGraph instance from this reader.

classmethod open(uri: str | ParseResult | ResourcePath | Path, *, page_size: int = 5000000, import_mode: TaskImportMode = TaskImportMode.ASSUME_CONSISTENT_EDGES) → Iterator[PredictedQuantumGraphReader]

Construct a reader from a URI.

Parameters:

uriconvertible to lsst.resources.ResourcePath

URI to open. Should have a .qg extension.

page_sizeint, optional

Approximate number of bytes to read at once from address files. Note that this does not set a page size for all reads, but it does affect the smallest, most numerous reads.

import_modepipeline_graph.TaskImportMode, optional

How to handle importing the task classes referenced in the pipeline graph.

Returns:

readercontextlib.AbstractContextManager [ PredictedQuantumGraphReader ]

A context manager that returns the reader when entered.

read_all() → PredictedQuantumGraphReader

Read all components in full.

read_dimension_data() → PredictedQuantumGraphReader

Read all dimension records.

Record data IDs will be immediately deserialized, while other fields will be left in serialized form until they are needed.

read_execution_quanta(quantum_ids: Iterable[UUID] | None = None) → PredictedQuantumGraphReader

Read all information needed to execute the given quanta.

Parameters:

quantum_idsIterable [ uuid.UUID ], optional

Iterable of quantum IDs to load. If not provided, all quanta will be loaded. The UUIDs of special init quanta will be ignored.

read_init_quanta() → PredictedQuantumGraphReader

Read the list of special quanta that represent init-inputs and init-outputs.

read_quantum_datasets(quantum_ids: Iterable[UUID] | None = None) → PredictedQuantumGraphReader

Read information about all datasets produced and consumed by the given quantum IDs.

Parameters:

quantum_idsIterable [ uuid.UUID ], optional

Iterable of quantum IDs to load. If not provided, all quanta will be loaded. The UUIDs of special init quanta will be ignored.

read_thin_graph() → PredictedQuantumGraphReader

Read the thin graph.

The thin graph is a quantum-quantum DAG with internal integer IDs for nodes and just task labels and data IDs as node attributes. It always includes all regular quanta, and does not include init-input or init-output information.