GbdesAstrometricMultibandFitConnections

class lsst.drp.tasks.gbdesAstrometricFit.GbdesAstrometricMultibandFitConnections(*, config: PipelineTaskConfig | None = None)

Bases: GbdesAstrometricFitConnections

Attributes Summary

allConnections	Mapping holding all connection attributes.
camera	Connection for output dataset.
colorCatalog	Class used for declaring PipelineTask input connections.
dcrCoefficients	Connection for output dataset.
defaultTemplates
deprecatedTemplates
dimensions	Set of dimension names that define the unit of work for this task.
initInputs	Set with the names of all InitInput connection attributes.
initOutputs	Set with the names of all InitOutput connection attributes.
inputCamera	Class used for declaring PipelineTask prerequisite connections.
inputCameraModel	Class used for declaring PipelineTask prerequisite connections.
inputCatalogRefs	Class used for declaring PipelineTask input connections.
inputVisitSummaries	Class used for declaring PipelineTask input connections.
inputs	Set with the names of all connectionTypes.Input connection attributes.
modelParams	Connection for output dataset.
outputCameraModel	Connection for output dataset.
outputCatalog	Connection for output dataset.
outputWcs	Connection for output dataset.
outputs	Set with the names of all Output connection attributes.
prerequisiteInputs	Set with the names of all PrerequisiteInput connection attributes.
referenceCatalog	Class used for declaring PipelineTask prerequisite connections.
starCatalog	Connection for output dataset.

Attributes Documentation

allConnections: Mapping[str, BaseConnection] = {'camera': Output(name='{outputName}Camera', storageClass='Camera', doc='Camera object constructed using the per-detector part of the astrometric model', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'skymap', 'tract', 'physical_filter'), isCalibration=False), 'colorCatalog': Input(name='fgcm_Cycle4_StandardStars', storageClass='SimpleCatalog', doc='The catalog of magnitudes to match to input sources.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument',), isCalibration=False, deferLoad=False, minimum=1, deferGraphConstraint=False, deferBinding=False), 'dcrCoefficients': Output(name='{outputName}_dcrCoefficients', storageClass='ArrowNumpyDict', doc='Per-visit coefficients for DCR correction.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'skymap', 'tract', 'physical_filter'), isCalibration=False), 'inputCamera': PrerequisiteInput(name='camera', storageClass='Camera', doc='Input camera to use when constructing camera from astrometric model.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument',), isCalibration=True, deferLoad=False, minimum=1, lookupFunction=None), 'inputCameraModel': PrerequisiteInput(name='gbdesAstrometricFit_cameraModel', storageClass='ArrowNumpyDict', doc="Camera parameters to use for 'device' part of model", multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'physical_filter'), isCalibration=False, deferLoad=False, minimum=1, lookupFunction=None), 'inputCatalogRefs': Input(name='preSourceTable_visit', storageClass='DataFrame', doc='Source table in parquet format, per visit.', multiple=True, deprecated=None, _deprecation_context='', dimensions=('instrument', 'visit'), isCalibration=False, deferLoad=True, minimum=1, deferGraphConstraint=False, deferBinding=False), 'inputVisitSummaries': Input(name='visitSummary', storageClass='ExposureCatalog', doc='Per-visit consolidated exposure metadata built from calexps. These catalogs use detector id for the id and must be sorted for fast lookups of a detector.', multiple=True, deprecated=None, _deprecation_context='', dimensions=('instrument', 'visit'), isCalibration=False, deferLoad=False, minimum=1, deferGraphConstraint=False, deferBinding=False), 'modelParams': Output(name='gbdesAstrometricMultibandFit_modelParams', storageClass='ArrowNumpyDict', doc='WCS parameters and covariance.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'skymap', 'tract'), isCalibration=False), 'outputCameraModel': Output(name='{outputName}_cameraModel', storageClass='ArrowNumpyDict', doc="Camera parameters to use for 'device' part of model", multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'skymap', 'tract', 'physical_filter'), isCalibration=False), 'outputCatalog': Output(name='gbdesAstrometricMultibandFit_fitStars', storageClass='ArrowNumpyDict', doc='Catalog of sources used in fit, along with residuals in pixel coordinates and tangent plane coordinates and chisq values.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'skymap', 'tract'), isCalibration=False), 'outputWcs': Output(name='{outputName}SkyWcsCatalog', storageClass='ExposureCatalog', doc='Per-tract, per-visit world coordinate systems derived from the fitted model. These catalogs only contain entries for detectors with an output, and use the detector id for the catalog id, sorted on id for fast lookups of a detector.', multiple=True, deprecated=None, _deprecation_context='', dimensions=('instrument', 'visit', 'skymap', 'tract'), isCalibration=False), 'referenceCatalog': PrerequisiteInput(name='gaia_dr3_20230707', storageClass='SimpleCatalog', doc='The astrometry reference catalog to match to loaded input catalog sources.', multiple=True, deprecated=None, _deprecation_context='', dimensions=('skypix',), isCalibration=False, deferLoad=True, minimum=1, lookupFunction=None), 'starCatalog': Output(name='gbdesAstrometricMultibandFit_starCatalog', storageClass='ArrowNumpyDict', doc='Catalog of best-fit object positions. Also includes the fit proper motion and parallax if fitProperMotion is True.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'skymap', 'tract'), isCalibration=False)}

Mapping holding all connection attributes.

This is a read-only view that is automatically updated when connection attributes are added, removed, or replaced in __init__. It is also updated after __init__ completes to reflect changes in inputs, prerequisiteInputs, outputs, initInputs, and initOutputs.

camera

Connection for output dataset.

colorCatalog

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.

dcrCoefficients

Connection for output dataset.

defaultTemplates = {'outputName': 'gbdesAstrometricFit'}

deprecatedTemplates = {}

dimensions: set[str] = {'instrument', 'skymap', 'tract'}

Set of dimension names that define the unit of work for this task.

Required and implied dependencies will automatically be expanded later and need not be provided.

This may be replaced or modified in __init__ to change the dimensions of the task. After __init__ it will be a frozenset and may not be replaced.

initInputs: set[str] = frozenset({})

Set with the names of all InitInput connection attributes.

See inputs for additional information.

initOutputs: set[str] = frozenset({})

Set with the names of all InitOutput connection attributes.

See inputs for additional information.

inputCamera

Class used for declaring PipelineTask prerequisite connections.

Raises

TypeError

Raised if minimum is greater than one but multiple=False.

Notes

Prerequisite inputs are used for datasets that must exist in the data repository before a pipeline including this is run; they cannot be produced by another task in the same pipeline.

In exchange for this limitation, they have a number of advantages relative to regular Input connections:

• The query used to find them then during QuantumGraph generation can be fully customized by providing a lookupFunction.

• Failed searches for prerequisites during QuantumGraph generation will usually generate more helpful diagnostics than those for regular Input connections.

• The default query for prerequisite inputs relates the quantum dimensions directly to the dimensions of its dataset type, without being constrained by any of the other dimensions in the pipeline. This allows them to be used for temporal calibration lookups (which regular Input connections cannot do at present) and to work around QuantumGraph generation limitations involving cases where naive spatial overlap relationships between dimensions are not desired (e.g. a task that wants all detectors in each visit for which the visit overlaps a tract, not just those where that detector+visit combination overlaps the tract).

• Prerequisite inputs may be optional (regular inputs are never optional).

inputCameraModel

Class used for declaring PipelineTask prerequisite connections.

Raises

TypeError

Raised if minimum is greater than one but multiple=False.

Notes

Prerequisite inputs are used for datasets that must exist in the data repository before a pipeline including this is run; they cannot be produced by another task in the same pipeline.

In exchange for this limitation, they have a number of advantages relative to regular Input connections:

• The query used to find them then during QuantumGraph generation can be fully customized by providing a lookupFunction.

• Failed searches for prerequisites during QuantumGraph generation will usually generate more helpful diagnostics than those for regular Input connections.

• The default query for prerequisite inputs relates the quantum dimensions directly to the dimensions of its dataset type, without being constrained by any of the other dimensions in the pipeline. This allows them to be used for temporal calibration lookups (which regular Input connections cannot do at present) and to work around QuantumGraph generation limitations involving cases where naive spatial overlap relationships between dimensions are not desired (e.g. a task that wants all detectors in each visit for which the visit overlaps a tract, not just those where that detector+visit combination overlaps the tract).

• Prerequisite inputs may be optional (regular inputs are never optional).

inputCatalogRefs

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.

inputVisitSummaries

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.

inputs: set[str] = frozenset({'colorCatalog', 'inputCatalogRefs', 'inputVisitSummaries'})

Set with the names of all connectionTypes.Input connection attributes.

This is updated automatically as class attributes are added, removed, or replaced in __init__. Removing entries from this set will cause those connections to be removed after __init__ completes, but this is supported only for backwards compatibility; new code should instead just delete the collection attributed directly. After __init__ this will be a frozenset and may not be replaced.

modelParams

Connection for output dataset.

outputCameraModel

Connection for output dataset.

outputCatalog

Connection for output dataset.

outputWcs

Connection for output dataset.

outputs: set[str] = frozenset({'camera', 'dcrCoefficients', 'modelParams', 'outputCameraModel', 'outputCatalog', 'outputWcs', 'starCatalog'})

Set with the names of all Output connection attributes.

See inputs for additional information.

prerequisiteInputs: set[str] = frozenset({'inputCamera', 'inputCameraModel', 'referenceCatalog'})

Set with the names of all PrerequisiteInput connection attributes.

See inputs for additional information.

referenceCatalog

Class used for declaring PipelineTask prerequisite connections.

Raises

TypeError

Raised if minimum is greater than one but multiple=False.

Notes

Prerequisite inputs are used for datasets that must exist in the data repository before a pipeline including this is run; they cannot be produced by another task in the same pipeline.

In exchange for this limitation, they have a number of advantages relative to regular Input connections:

• The query used to find them then during QuantumGraph generation can be fully customized by providing a lookupFunction.

• Failed searches for prerequisites during QuantumGraph generation will usually generate more helpful diagnostics than those for regular Input connections.

• The default query for prerequisite inputs relates the quantum dimensions directly to the dimensions of its dataset type, without being constrained by any of the other dimensions in the pipeline. This allows them to be used for temporal calibration lookups (which regular Input connections cannot do at present) and to work around QuantumGraph generation limitations involving cases where naive spatial overlap relationships between dimensions are not desired (e.g. a task that wants all detectors in each visit for which the visit overlaps a tract, not just those where that detector+visit combination overlaps the tract).

• Prerequisite inputs may be optional (regular inputs are never optional).

starCatalog

Connection for output dataset.