TractMatchedPreparationTaskConnections

class lsst.faro.preparation.TractMatchedPreparationTaskConnections(*, config=None)

Bases: lsst.faro.base.MatchedBaseTaskConnections

Attributes Summary

allConnections
astromCalibs
defaultTemplates
dimensions
externalPhotoCalibGlobalCatalog
externalPhotoCalibTractCatalog
externalSkyWcsGlobalCatalog
externalSkyWcsTractCatalog
initInputs
initOutputs
inputs
outputCatalog
outputs
photoCalibs
prerequisiteInputs
skyMap
sourceCatalogs

Methods Summary

adjustQuantum(datasetRefMap, …)	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 = {'astromCalibs': Input(name='{wcsName}', storageClass='Wcs', doc='WCS for the catalog.', multiple=True, dimensions=('instrument', 'visit', 'detector', 'band'), isCalibration=False, deferLoad=False), 'externalPhotoCalibGlobalCatalog': Input(name='{externalPhotoCalibName}PhotoCalibCatalog', storageClass='ExposureCatalog', doc='Per-visit photometric calibrations computed globally (with no tract information). These catalogs use the detector id for the catalog id, sorted on id for fast lookup.', multiple=True, dimensions=('instrument', 'visit', 'band'), isCalibration=False, deferLoad=False), 'externalPhotoCalibTractCatalog': Input(name='{externalPhotoCalibName}PhotoCalibCatalog', storageClass='ExposureCatalog', doc='Per-tract, per-visit photometric calibrations. These catalogs use the detector id for the catalog id, sorted on id for fast lookup.', multiple=True, dimensions=('instrument', 'visit', 'tract', 'band'), isCalibration=False, deferLoad=False), 'externalSkyWcsGlobalCatalog': Input(name='{externalWcsName}SkyWcsCatalog', storageClass='ExposureCatalog', doc='Per-visit wcs calibrations computed globally (with no tract information). These catalogs use the detector id for the catalog id, sorted on id for fast lookup.', multiple=True, dimensions=('instrument', 'visit', 'band'), isCalibration=False, deferLoad=False), 'externalSkyWcsTractCatalog': Input(name='{externalWcsName}SkyWcsCatalog', storageClass='ExposureCatalog', doc='Per-tract, per-visit wcs calibrations. These catalogs use the detector id for the catalog id, sorted on id for fast lookup.', multiple=True, dimensions=('instrument', 'visit', 'tract', 'band'), isCalibration=False, deferLoad=False), 'outputCatalog': Output(name='matchedCatalogTract', storageClass='SimpleCatalog', doc='Resulting matched catalog.', multiple=False, dimensions=('tract', 'instrument', 'band'), isCalibration=False), 'photoCalibs': Input(name='{photoCalibName}', storageClass='PhotoCalib', doc='Photometric calibration object.', multiple=True, dimensions=('instrument', 'visit', 'detector', 'band'), isCalibration=False, deferLoad=False), 'skyMap': Input(name='skyMap', storageClass='SkyMap', doc='Input definition of geometry/bbox and projection/wcs for warped exposures', multiple=False, dimensions=('skymap',), isCalibration=False, deferLoad=False), 'sourceCatalogs': Input(name='src', storageClass='SourceCatalog', doc='Source catalogs to match up.', multiple=True, dimensions=('instrument', 'visit', 'detector', 'band'), isCalibration=False, deferLoad=False)}

astromCalibs

defaultTemplates = {'coaddName': 'deep', 'externalPhotoCalibName': 'fgcm', 'externalWcsName': 'jointcal', 'photoCalibName': 'calexp.photoCalib', 'wcsName': 'calexp.wcs'}

dimensions = {'tract', 'skymap', 'instrument', 'band'}

externalPhotoCalibGlobalCatalog

externalPhotoCalibTractCatalog

externalSkyWcsGlobalCatalog

externalSkyWcsTractCatalog

initInputs = frozenset()

initOutputs = frozenset()

inputs = frozenset({'externalSkyWcsGlobalCatalog', 'astromCalibs', 'skyMap', 'externalSkyWcsTractCatalog', 'externalPhotoCalibTractCatalog', 'externalPhotoCalibGlobalCatalog', 'sourceCatalogs', 'photoCalibs'})

outputCatalog

outputs = frozenset({'outputCatalog'})

photoCalibs

prerequisiteInputs = frozenset()

skyMap

sourceCatalogs

Methods Documentation

adjustQuantum(datasetRefMap: lsst.daf.butler.core.named.NamedKeyDict[lsst.daf.butler.core.datasets.type.DatasetType, typing.Set[lsst.daf.butler.core.datasets.ref.DatasetRef]][lsst.daf.butler.core.datasets.type.DatasetType, Set[lsst.daf.butler.core.datasets.ref.DatasetRef]]) → lsst.daf.butler.core.named.NamedKeyDict[lsst.daf.butler.core.datasets.type.DatasetType, typing.Set[lsst.daf.butler.core.datasets.ref.DatasetRef]][lsst.daf.butler.core.datasets.type.DatasetType, Set[lsst.daf.butler.core.datasets.ref.DatasetRef]]

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.

The base class implementation simply checks that input connections with multiple set to False have no more than one dataset.

Parameters:

datasetRefMap : NamedKeyDict

Mapping from dataset type to a set of lsst.daf.butler.DatasetRef objects

Returns:

datasetRefMap : NamedKeyDict

Modified mapping of input with possibly adjusted lsst.daf.butler.DatasetRef objects.

Raises:

ScalarError

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

Exception

Overrides of this function have the option of raising an Exception if a field in the input does not satisfy a need for a corresponding pipelineTask, i.e. no reference catalogs are found.

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