AssembleCoaddConnections

class lsst.pipe.tasks.assembleCoadd.AssembleCoaddConnections(*, config=None)

Bases: lsst.pipe.base.PipelineTaskConnections

Attributes Summary

allConnections
brightObjectMask	Class used for declaring PipelineTask prerequisite connections
coaddExposure
defaultTemplates
dimensions
initInputs
initOutputs
inputWarps
inputs
nImage
outputs
prerequisiteInputs
skyMap

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 = {'brightObjectMask': PrerequisiteInput(name='brightObjectMask', storageClass='ObjectMaskCatalog', doc='Input Bright Object Mask mask produced with external catalogs to be applied to the mask plane BRIGHT_OBJECT.', multiple=False, dimensions=('tract', 'patch', 'skymap', 'abstract_filter'), deferLoad=False, lookupFunction=None), 'coaddExposure': Output(name='{fakesType}{outputCoaddName}Coadd{warpTypeSuffix}', storageClass='ExposureF', doc='Output coadded exposure, produced by stacking input warps', multiple=False, dimensions=('tract', 'patch', 'skymap', 'abstract_filter')), 'inputWarps': Input(name='{inputCoaddName}Coadd_{warpType}Warp', storageClass='ExposureF', doc='Input list of warps to be assemebled i.e. stacked.WarpType (e.g. direct, psfMatched) is controlled by the warpType config parameter', multiple=True, dimensions=('tract', 'patch', 'skymap', 'visit', 'instrument'), deferLoad=True), 'nImage': Output(name='{outputCoaddName}Coadd_nImage', storageClass='ImageU', doc='Output image of number of input images per pixel', multiple=False, dimensions=('tract', 'patch', 'skymap', 'abstract_filter')), 'skyMap': Input(name='{inputCoaddName}Coadd_skyMap', storageClass='SkyMap', doc='Input definition of geometry/bbox and projection/wcs for coadded exposures', multiple=False, dimensions=('skymap',), deferLoad=False)}

brightObjectMask

Class used for declaring PipelineTask prerequisite connections

Parameters:

name : str

The default name used to identify the dataset type

storageClass : str

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

multiple : bool

Indicates if this connection should expect to contain multiple objects of the given dataset type

dimensions : iterable of str

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

deferLoad : bool

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.

lookupFunction: `typing.Callable`

An optional callable function that will look up PrerequisiteInputs using the DatasetType, registry, quantum dataId, and input collections passed to it. If no function is specified, the default temporal spatial lookup will be used.

coaddExposure

defaultTemplates = {'fakesType': '', 'inputCoaddName': 'deep', 'outputCoaddName': 'deep', 'warpType': 'direct', 'warpTypeSuffix': ''}

dimensions = {'patch', 'skymap', 'tract', 'abstract_filter'}

initInputs = frozenset()

initOutputs = frozenset()

inputWarps

inputs = frozenset({'inputWarps', 'skyMap'})

nImage

outputs = frozenset({'nImage', 'coaddExposure'})

prerequisiteInputs = frozenset({'brightObjectMask'})

skyMap

Methods Documentation

adjustQuantum(datasetRefMap: lsst.pipe.base.connections.InputQuantizedConnection)

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.

Parameters:

datasetRefMap : dict

Mapping with keys of dataset type name to list of lsst.daf.butler.DatasetRef objects

Returns:

datasetRefMap : dict

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

Raises:

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