SimplePipelineExecutor¶

class lsst.ctrl.mpexec.SimplePipelineExecutor(quantum_graph: lsst.pipe.base.graph.graph.QuantumGraph, butler: lsst.daf.butler._butler.Butler)¶

Bases: object

A simple, high-level executor for pipelines.

Parameters:	quantum_graph : `QuantumGraph` Graph to be executed. butler : `Butler` Object that manages all I/O. Must be initialized with `collections` and `run` properties that correspond to the input and output collections, which must be consistent with those used to create `quantum_graph`.

Notes

Most callers should use one of the classmethod factory functions (from_pipeline_filename, from_task_class, from_pipeline) instead of invoking the constructor directly; these guarantee that the Butler and QuantumGraph are created consistently.

This class is intended primarily to support unit testing and small-scale integration testing of PipelineTask classes. It deliberately lacks many features present in the command-line-only pipetask tool in order to keep the implementation simple. Python callers that need more sophistication should call lower-level tools like GraphBuilder, PreExecInit, and SingleQuantumExecutor directly.

Methods Summary

`as_generator`(register_dataset_types)	Yield quanta in the `QuantumGraph` in topological order.
`from_pipeline`(pipeline, …)	Create an executor by building a QuantumGraph from an in-memory pipeline.
`from_pipeline_filename`(pipeline_filename, *, …)	Create an executor by building a QuantumGraph from an on-disk pipeline YAML file.
`from_task_class`(task_class, config, label, …)	Create an executor by building a QuantumGraph from a pipeline containing a single task.
`prep_butler`(root, inputs, output, output_run)	Helper method for creating `Butler` instances with collections appropriate for processing.
`run`(register_dataset_types)	Run all the quanta in the `QuantumGraph` in topological order.

Methods Documentation

as_generator(register_dataset_types: bool = False) → Iterator[lsst.daf.butler.core.quantum.Quantum]¶

Yield quanta in the QuantumGraph in topological order.

These quanta will be run as the returned generator is iterated over. Use this method to run the quanta one at a time. Use run to run all quanta in the graph.

Parameters:	register_dataset_types : `bool`, optional If `True`, register all output dataset types before executing any quanta.
Returns:	quanta : `Iterator` [ `Quantum` ] Executed quanta. At present, these will contain only unresolved `DatasetRef` instances for output datasets, reflecting the state of the quantum just before it was run (but after any adjustments for predicted but now missing inputs). This may change in the future to include resolved output `DatasetRef` objects.

Notes

Global initialization steps (see PreExecInit) are performed immediately when this method is called, but individual quanta are not actually executed until the returned iterator is iterated over.

A topological ordering is not in general unique, but no other guarantees are made about the order in which quanta are processed.

classmethod from_pipeline(pipeline: Union[lsst.pipe.base.pipeline.Pipeline, Iterable[lsst.pipe.base.pipeline.TaskDef]], *, where: str = '', butler: lsst.daf.butler._butler.Butler, **kwargs) → lsst.ctrl.mpexec.simple_pipeline_executor.SimplePipelineExecutor¶

Create an executor by building a QuantumGraph from an in-memory pipeline.

Parameters:	pipeline : `Pipeline` or `Iterable` [ `TaskDef` ] A Python object describing the tasks to run, along with their labels and configuration. where : `str`, optional Data ID query expression that constraints the quanta generated. butler : `Butler` Butler that manages all I/O. `prep_butler` can be used to create one.
Returns:	executor : `SimplePipelineExecutor` An executor instance containing the constructed `QuantumGraph` and `Butler`, ready for `run` to be called.

classmethod from_pipeline_filename(pipeline_filename: str, *, where: str = '', butler: lsst.daf.butler._butler.Butler) → lsst.ctrl.mpexec.simple_pipeline_executor.SimplePipelineExecutor¶

Create an executor by building a QuantumGraph from an on-disk pipeline YAML file.

Parameters:	pipeline_filename : `str` Name of the YAML file to load the pipeline definition from. where : `str`, optional Data ID query expression that constraints the quanta generated. butler : `Butler` Butler that manages all I/O. `prep_butler` can be used to create one.
Returns:	executor : `SimplePipelineExecutor` An executor instance containing the constructed `QuantumGraph` and `Butler`, ready for `run` to be called.

classmethod from_task_class(task_class: Type[lsst.pipe.base.pipelineTask.PipelineTask], config: Optional[lsst.pex.config.config.Config] = None, label: Optional[str] = None, *, where: str = '', butler: lsst.daf.butler._butler.Butler) → lsst.ctrl.mpexec.simple_pipeline_executor.SimplePipelineExecutor¶

Create an executor by building a QuantumGraph from a pipeline containing a single task.

Parameters:

task_class : type: A concrete PipelineTask subclass.
config : Config, optional: Configuration for the task. If not provided, task-level defaults will be used (no per-instrument overrides).
label : str, optional: Label for the task in its pipeline; defaults to task_class._DefaultName.
where : str, optional: Data ID query expression that constraints the quanta generated.
butler : Butler: Butler that manages all I/O. prep_butler can be used to create one.

Returns:

executor : SimplePipelineExecutor: An executor instance containing the constructed QuantumGraph and Butler, ready for run to be called.

classmethod prep_butler(root: str, inputs: Iterable[str], output: str, output_run: Optional[str] = None) → lsst.daf.butler._butler.Butler¶

Helper method for creating Butler instances with collections appropriate for processing.

Parameters:

root : str: Root of the butler data repository; must already exist, with all necessary input data.
inputs : Iterable [ str ]: Collections to search for all input datasets, in search order.
output : str: Name of a new output CHAINED collection to create that will combine both inputs and outputs.
output_run : str, optional: Name of the output RUN that will directly hold all output datasets. If not provided, a name will be created from output and a timestamp.

Returns:

butler : Butler: Butler client instance compatible with all classmethod factories. Always writeable.

run(register_dataset_types: bool = False) → List[lsst.daf.butler.core.quantum.Quantum]¶

Run all the quanta in the QuantumGraph in topological order.

Use this method to run all quanta in the graph. Use as_generator to get a generator to run the quanta one at a time.

Parameters:	register_dataset_types : `bool`, optional If `True`, register all output dataset types before executing any quanta.
Returns:	quanta : `List` [ `Quantum` ] Executed quanta. At present, these will contain only unresolved `DatasetRef` instances for output datasets, reflecting the state of the quantum just before it was run (but after any adjustments for predicted but now missing inputs). This may change in the future to include resolved output `DatasetRef` objects.

Notes

A topological ordering is not in general unique, but no other guarantees are made about the order in which quanta are processed.

Navigation

SimplePipelineExecutor¶