Quantum¶
-
class
lsst.daf.butler.Quantum(*, taskName: Optional[str] = None, taskClass: Optional[Type] = None, dataId: Optional[lsst.daf.butler.DataCoordinate] = None, initInputs: Optional[Union[Mapping[lsst.daf.butler.DatasetType,lsst.daf.butler.DatasetRef], Iterable[lsst.daf.butler.DatasetRef]]] = None, inputs: Optional[Mapping[lsst.daf.butler.DatasetType, List[lsst.daf.butler.DatasetRef]]] = None, outputs: Optional[Mapping[lsst.daf.butler.DatasetType, List[lsst.daf.butler.DatasetRef]]] = None)¶ Bases:
objectClass 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’srunQuantummethod, 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,
taskClassmust be.- taskClass
type, optional The Task class that executed or will execute this Quantum. If not provided,
taskNamemust be. OverridestaskNameif both are provided.- dataId
DataId, optional The dimension values that identify this
Quantum.- initInputscollection of
DatasetRef, optional Datasets that are needed to construct an instance of the Task. May be a flat iterable of
DatasetRefinstances or a mapping fromDatasetTypetoDatasetRef.- inputs
Mapping, optional Inputs identified prior to execution, organized as a mapping from
DatasetTypeto a list ofDatasetRef.- outputs
Mapping, optional Outputs from executing this quantum of work, organized as a mapping from
DatasetTypeto a list ofDatasetRef.
- taskName
Attributes Summary
Return dimension values of the unit of processing (
DataId).Return mapping of datasets used to construct the Task.
Return mapping of input datasets that were expected to be used.
Return mapping of output datasets (to be) generated by this quantum.
Return Fully-qualified name of the task associated with
Quantum.Attributes Documentation
-
dataId¶ Return dimension values of the unit of processing (
DataId).
-
initInputs¶ Return mapping of datasets used to construct the Task.
Has
DatasetTypeinstances as keys (names can also be used for lookups) andDatasetRefinstances as values.
-
inputs¶ Return mapping of input datasets that were expected to be used.
Has
DatasetTypeinstances as keys (names can also be used for lookups) and a list ofDatasetRefinstances as values.Notes
We cannot use
setinstead oflistfor the nested container becauseDatasetRefinstances 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
setinstead oflistfor the nested container becauseDatasetRefinstances cannot be compared reliably when some have integers IDs and others do not.