Quantum

class lsst.daf.butler.Quantum(*, taskName: Optional[str] = None, taskClass: Optional[Type[CT_co]] = None, dataId: Optional[lsst.daf.butler.core.dimensions._coordinate.DataCoordinate] = None, initInputs: Union[Mapping[lsst.daf.butler.core.datasets.type.DatasetType, lsst.daf.butler.core.datasets.ref.DatasetRef], Iterable[lsst.daf.butler.core.datasets.ref.DatasetRef], None] = None, inputs: Optional[Mapping[lsst.daf.butler.core.datasets.type.DatasetType, List[lsst.daf.butler.core.datasets.ref.DatasetRef]]] = None, outputs: Optional[Mapping[lsst.daf.butler.core.datasets.type.DatasetType, List[lsst.daf.butler.core.datasets.ref.DatasetRef]]] = None, datastore_records: Optional[lsst.daf.butler.core.datastore.DatastoreRecordData] = None)

Bases: object

Class representing a discrete unit of work.

A Quantum may depend on one or more datasets and produce one or more datasets.

Most Quanta will be executions of a particular PipelineTask’s runQuantum method, but they can also be used to represent discrete units of work performed manually by human operators or other software agents.

Parameters:

taskName : str, optional

Fully-qualified name of the Task class that executed or will execute this Quantum. If not provided, taskClass must be.

taskClass : type, optional

The Task class that executed or will execute this Quantum. If not provided, taskName must be. Overrides taskName if both are provided.

dataId : DataId, optional

The dimension values that identify this Quantum.

initInputs : collection of DatasetRef, optional

Datasets that are needed to construct an instance of the Task. May be a flat iterable of DatasetRef instances or a mapping from DatasetType to DatasetRef.

inputs : Mapping, optional

Inputs identified prior to execution, organized as a mapping from DatasetType to a list of DatasetRef.

outputs : Mapping, optional

Outputs from executing this quantum of work, organized as a mapping from DatasetType to a list of DatasetRef.

datastore_records : DatastoreRecordData, optional

Datastore record data for input or initInput datasets that already exist.

Attributes Summary

dataId	Return dimension values of the unit of processing (DataId).
datastore_records	Tabular data stored with this quantum (dict).
initInputs	Return mapping of datasets used to construct the Task.
inputs	Return mapping of input datasets that were expected to be used.
outputs	Return mapping of output datasets (to be) generated by this quantum.
taskClass	Task class associated with this Quantum (type).
taskName	Return Fully-qualified name of the task associated with Quantum.

Methods Summary

from_simple(simple, universe, …)	Construct a new object from a simplified form.
to_simple(accumulator)	Convert this class to a simple python type.

Attributes Documentation

dataId

Return dimension values of the unit of processing (DataId).

datastore_records

Tabular data stored with this quantum (dict).

This attribute may be modified in place, but not assigned to.

initInputs

Return mapping of datasets used to construct the Task.

Has DatasetType instances as keys (names can also be used for lookups) and DatasetRef instances as values.

inputs

Return mapping of input datasets that were expected to be used.

Has DatasetType instances as keys (names can also be used for lookups) and a list of DatasetRef instances as values.

Notes

We cannot use set instead of list for the nested container because DatasetRef instances cannot be compared reliably when some have integers IDs and others do not.

outputs

Return mapping of output datasets (to be) generated by this quantum.

Has the same form as predictedInputs.

Notes

We cannot use set instead of list for the nested container because DatasetRef instances cannot be compared reliably when some have integers IDs and others do not.

taskClass

Task class associated with this Quantum (type).

taskName

Return Fully-qualified name of the task associated with Quantum.

(str).

Methods Documentation

classmethod from_simple(simple: lsst.daf.butler.core.quantum.SerializedQuantum, universe: lsst.daf.butler.core.dimensions._universe.DimensionUniverse, reconstitutedDimensions: Optional[Dict[int, Tuple[str, lsst.daf.butler.core.dimensions._records.DimensionRecord]]] = None) → lsst.daf.butler.core.quantum.Quantum

Construct a new object from a simplified form.

Generally this is data returned from the to_simple method.

Parameters:

simple : SerializedQuantum

The value returned by a call to to_simple

universe : DimensionUniverse

The special graph of all known dimensions.

reconstitutedDimensions : dict of int to DimensionRecord or None

A mapping of ids to dimension records to be used when populating dimensions for this Quantum. If supplied it will be used in place of the dimension Records stored with the SerializedQuantum, if a required dimension has already been loaded. Otherwise the record will be unpersisted from the SerializedQuatnum and added to the reconstitutedDimensions dict (if not None). Defaults to None.

to_simple(accumulator: Optional[lsst.daf.butler.core.quantum.DimensionRecordsAccumulator] = None) → lsst.daf.butler.core.quantum.SerializedQuantum

Convert this class to a simple python type.

This makes it suitable for serialization.

Parameters:

accumulator : DimensionRecordsAccumulator, optional

This accumulator can be used to aggregate dimension records accross multiple Quanta. If this is None, the default, dimension records are serialized with this Quantum. If an accumulator is supplied it is assumed something else is responsible for serializing the records, and they will not be stored with the SerializedQuantum.

Returns:

simple : SerializedQuantum

This object converted to a serializable representation.