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)

Bases: object

A discrete unit of work that may depend on one or more datasets and produces 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.

Attributes Summary

dataId	The dimension values of the unit of processing (DataId).
initInputs	A mapping of datasets used to construct the Task, with DatasetType instances as keys (names can also be used for lookups) and DatasetRef instances as values.
inputs	A mapping of input datasets that were expected to be used, with DatasetType instances as keys (names can also be used for lookups) and a list of DatasetRef instances as values.
outputs	A mapping of output datasets (to be) generated for this quantum, with the same form as predictedInputs.
taskClass	Task class associated with this Quantum (type).
taskName	Fully-qualified name of the task associated with Quantum (str).

Attributes Documentation

dataId

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

initInputs

A mapping of datasets used to construct the Task, with DatasetType instances as keys (names can also be used for lookups) and DatasetRef instances as values.

inputs

A mapping of input datasets that were expected to be used, with 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

A mapping of output datasets (to be) generated for this quantum, with 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

Fully-qualified name of the task associated with Quantum (str).