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, deferBinding: bool = False, *, deprecated: str | None = None)

Bases: BaseInput

Class used for declaring PipelineTask input connections.

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

deferBinding	If True, the dataset will not be automatically included in the pipeline graph (deferGraphConstraint=True is implied).
deferGraphConstraint	If True, do not include this dataset type's existence in the initial query that starts the QuantumGraph generation process.
deferLoad	Whether this dataset type will be loaded as a lsst.daf.butler.DeferredDatasetHandle.
deprecated	A description of why this connection is deprecated, including the version after which it may be removed.
dimensions	The keys of the butler data coordinates for this dataset type.
doc	Documentation for this connection.
isCalibration	True if this dataset type may be included in CALIBRATION collections to associate it with a validity range, False (default) otherwise.
minimum	Minimum number of datasets required for this connection, per quantum.
multiple	Indicates if this connection should expect to contain multiple objects of the given dataset type.

Attributes Documentation

deferBinding: bool = False

If True, the dataset will not be automatically included in the pipeline graph (deferGraphConstraint=True is implied).

A custom QuantumGraphBuilder is required to bind it and add a corresponding edge to the pipeline graph. This option allows the same dataset type to be used as both an input and an output of a quantum.

deferGraphConstraint: bool = False

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.

deferLoad: bool = False

Whether 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.

deprecated: str | None = None

A description of why this connection is deprecated, including the version after which it may be removed.

If not None, the string is appended to the docstring for this connection and the corresponding config Field.

dimensions: Iterable[str] = ()

The keys of the butler data coordinates for this dataset type.

doc: str = ''

Documentation for this connection.

isCalibration: bool = False

True if this dataset type may be included in CALIBRATION collections to associate it with a validity range, False (default) otherwise.

minimum: int = 1

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 minimum.

multiple: bool = False

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.