Output#

class lsst.pipe.base.connectionTypes.Output(name: str, storageClass: str, doc: str = '', multiple: bool = False, _deprecation_context: str = '', dimensions: Iterable[str] = (), isCalibration: bool = False, *, deprecated: str | None = None)#

Bases: DimensionedConnection

Connection for output dataset.

Attributes Summary

`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.
`multiple`	Indicates if this connection should expect to contain multiple objects of the given dataset type.
`name`	The name used to identify the dataset type.
`storageClass`	The storage class used when (un)/persisting the dataset type.

Attributes Documentation

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.

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.

name: str = <dataclasses._MISSING_TYPE object>#: The name used to identify the dataset type.

storageClass: str = <dataclasses._MISSING_TYPE object>#: The storage class used when (un)/persisting the dataset type.