DiaPipelineConnections

class lsst.ap.association.DiaPipelineConnections(*, config=None)

Bases: lsst.pipe.base.PipelineTaskConnections

Butler connections for DiaPipelineTask.

Attributes Summary

allConnections
apdbMarker
associatedDiaSources
defaultTemplates
diaSourceTable
diffIm
dimensions
exposure
initInputs
initOutputs
inputs
outputs
prerequisiteInputs
warpedExposure

Methods Summary

adjustQuantum(inputs, outputs, label, dataId)	Override to make adjustments to lsst.daf.butler.DatasetRef objects in the lsst.daf.butler.core.Quantum during the graph generation stage of the activator.
buildDatasetRefs(quantum)	Builds QuantizedConnections corresponding to input Quantum

Attributes Documentation

allConnections = {'apdbMarker': Output(name='apdb_marker', storageClass='Config', doc='Marker dataset storing the configuration of the Apdb for each visit/detector. Used to signal the completion of the pipeline.', multiple=False, dimensions=('instrument', 'visit', 'detector'), isCalibration=False), 'associatedDiaSources': Output(name='{fakesType}{coaddName}Diff_assocDiaSrc', storageClass='DataFrame', doc='Optional output storing the DiaSource catalog after matching, calibration, and standardization for insertation into the Apdb.', multiple=False, dimensions=('instrument', 'visit', 'detector'), isCalibration=False), 'diaSourceTable': Input(name='{fakesType}{coaddName}Diff_diaSrcTable', storageClass='DataFrame', doc='Catalog of calibrated DiaSources.', multiple=False, dimensions=('instrument', 'visit', 'detector'), isCalibration=False, deferLoad=False, minimum=1), 'diffIm': Input(name='{fakesType}{coaddName}Diff_differenceExp', storageClass='ExposureF', doc='Difference image on which the DiaSources were detected.', multiple=False, dimensions=('instrument', 'visit', 'detector'), isCalibration=False, deferLoad=False, minimum=1), 'exposure': Input(name='calexp', storageClass='ExposureF', doc='Calibrated exposure differenced with a template image during image differencing.', multiple=False, dimensions=('instrument', 'visit', 'detector'), isCalibration=False, deferLoad=False, minimum=1), 'warpedExposure': Input(name='{fakesType}{coaddName}Diff_warpedExp', storageClass='ExposureF', doc='Warped template used to create `subtractedExposure`. Not PSF matched.', multiple=False, dimensions=('instrument', 'visit', 'detector'), isCalibration=False, deferLoad=False, minimum=1)}

apdbMarker

associatedDiaSources

defaultTemplates = {'coaddName': 'deep', 'fakesType': ''}

diaSourceTable

diffIm

dimensions = {'visit', 'instrument', 'detector'}

exposure

initInputs = frozenset()

initOutputs = frozenset()

inputs = frozenset({'warpedExposure', 'exposure', 'diffIm', 'diaSourceTable'})

outputs = frozenset({'apdbMarker', 'associatedDiaSources'})

prerequisiteInputs = frozenset()

warpedExposure

Methods Documentation

adjustQuantum(inputs, outputs, label, dataId)

Override to make adjustments to lsst.daf.butler.DatasetRef objects in the lsst.daf.butler.core.Quantum during the graph generation stage of the activator.

This implementation checks to make sure that the filters in the dataset are compatible with AP processing as set by the Apdb/DPDD schema.

Parameters:

inputs : dict

Dictionary whose keys are an input (regular or prerequisite) connection name and whose values are a tuple of the connection instance and a collection of associated DatasetRef objects. The exact type of the nested collections is unspecified; it can be assumed to be multi-pass iterable and support len and in, but it should not be mutated in place. In contrast, the outer dictionaries are guaranteed to be temporary copies that are true dict instances, and hence may be modified and even returned; this is especially useful for delegating to super (see notes below).

outputs : dict

Dict of output datasets, with the same structure as inputs.

label : str

Label for this task in the pipeline (should be used in all diagnostic messages).

data_id : lsst.daf.butler.DataCoordinate

Data ID for this quantum in the pipeline (should be used in all diagnostic messages).

Returns:

adjusted_inputs : dict

Dict of the same form as inputs with updated containers of input DatasetRef objects. Connections that are not changed should not be returned at all. Datasets may only be removed, not added. Nested collections may be of any multi-pass iterable type, and the order of iteration will set the order of iteration within PipelineTask.runQuantum.

adjusted_outputs : dict

Dict of updated output datasets, with the same structure and interpretation as adjusted_inputs.

Raises:

ScalarError

Raised if any Input or PrerequisiteInput connection has multiple set to False, but multiple datasets.

NoWorkFound

Raised to indicate that this quantum should not be run; not enough datasets were found for a regular Input connection, and the quantum should be pruned or skipped.

FileNotFoundError

Raised to cause QuantumGraph generation to fail (with the message included in this exception); not enough datasets were found for a PrerequisiteInput connection.

buildDatasetRefs(quantum: lsst.daf.butler.core.quantum.Quantum) → Tuple[lsst.pipe.base.connections.InputQuantizedConnection, lsst.pipe.base.connections.OutputQuantizedConnection]

Builds QuantizedConnections corresponding to input Quantum

Parameters:

quantum : lsst.daf.butler.Quantum

Quantum object which defines the inputs and outputs for a given unit of processing

Returns:

retVal : tuple of (InputQuantizedConnection,

OutputQuantizedConnection) Namespaces mapping attribute names (identifiers of connections) to butler references defined in the input lsst.daf.butler.Quantum