Input

class lsst.pipe.base.connectionTypes.Input(name: str, storageClass: str, doc: str = '', multiple: bool = False, _deprecation_context: str = '', dimensions: Iterable[str] = (), isCalibration: bool = False, deferLoad: bool = False, minimum: int = 1, deferGraphConstraint: bool = False, *, deprecated: str | None = None)

Bases: BaseInput

Class used for declaring PipelineTask input connections

Parameters:

namestr

The default name used to identify the dataset type

storageClassstr

The storage class used when (un)/persisting the dataset type

multiplebool

Indicates if this connection should expect to contain multiple objects of the given dataset type. Tasks with more than one connection with multiple=True with the same dimensions may want to implement PipelineTaskConnections.adjustQuantum to ensure those datasets are consistent (i.e. zip-iterable) in PipelineTask.runQuantum and notify the execution system as early as possible of outputs that will not be produced because the corresponding input is missing.

dimensionsiterable of str

The lsst.daf.butler.Butler lsst.daf.butler.Registry dimensions used to identify the dataset type identified by the specified name

deferLoadbool

Indicates that this dataset type will be loaded as a lsst.daf.butler.DeferredDatasetHandle. PipelineTasks can use this object to load the object at a later time.

minimumbool

Minimum number of datasets required for this connection, per quantum. This is checked in the base implementation of PipelineTaskConnections.adjustQuantum, which raises NoWorkFound if the minimum is not met for Input connections (causing the quantum to be pruned, skipped, or never created, depending on the context), and FileNotFoundError for PrerequisiteInput connections (causing QuantumGraph generation to fail). PipelineTask implementations may provide custom adjustQuantum implementations for more fine-grained or configuration-driven constraints, as long as they are compatible with this minium.

deferGraphConstraint: `bool`, optional

If True, do not include this dataset type’s existence in the initial query that starts the QuantumGraph generation process. This can be used to make QuantumGraph generation faster by avoiding redundant datasets, and in certain cases it can (along with careful attention to which tasks are included in the same QuantumGraph) be used to work around the QuantumGraph generation algorithm’s inflexible handling of spatial overlaps. This option has no effect when the connection is not an overall input of the pipeline (or subset thereof) for which a graph is being created, and it never affects the ordering of quanta.

Raises:

TypeError

Raised if minimum is greater than one but multiple=False.

NotImplementedError

Raised if minimum is zero for a regular Input connection; this is not currently supported by our QuantumGraph generation algorithm.

Attributes Summary

deferGraphConstraint
deferLoad
deprecated
dimensions
doc
isCalibration
minimum
multiple

Methods Summary

makeDatasetType(universe[, parentStorageClass])

Construct a true DatasetType instance with normalized dimensions.

Attributes Documentation

deferGraphConstraint: bool = False

deferLoad: bool = False

deprecated: str | None = None

dimensions: Iterable[str] = ()

doc: str = ''

isCalibration: bool = False

minimum: int = 1

multiple: bool = False

Methods Documentation

makeDatasetType(universe: DimensionUniverse, parentStorageClass: StorageClass | str | None = None) → DatasetType

Construct a true DatasetType instance with normalized dimensions.

Parameters:

universelsst.daf.butler.DimensionUniverse

Set of all known dimensions to be used to normalize the dimension names specified in config.

parentStorageClasslsst.daf.butler.StorageClass or str, optional

Parent storage class for component datasets; None otherwise.

Returns:

datasetTypeDatasetType

The DatasetType defined by this connection.