LatissMonolithTaskConnections

class lsst.ts.wep.task.LatissMonolithTaskConnections(*, config=None)

Bases: PipelineTaskConnections

Connections for LatissMonolithTask.

One quantum per CWFS pair, keyed on the extra-focal visit, as the ts_wep paired tasks and donutBlitzMonolith are. The pairing happens at graph-build time in adjust_all_quanta: the default graph gives every exposure its own quantum holding its own raw; the intra-focal raw is then moved into its partner’s quantum and the intra quantum dropped. Keying on visit is what makes _log/_metadata per pair – with a per-night or per-run quantum every pair in a long-lived output run (rapid analysis reuses LATISS/runs/quickLook/N) would write the same dataId and the second pair would fail on the provenance datasets. visit also implies day_obs, so the calibration lookup is time-bounded.

Parameters:

config (Any | None, default: None)

Attributes Summary

allConnections	Mapping holding all connection attributes.
bias	Class used for declaring PipelineTask prerequisite connections.
camera	Class used for declaring PipelineTask prerequisite connections.
crosstalk	Class used for declaring PipelineTask prerequisite connections.
dark	Class used for declaring PipelineTask prerequisite connections.
defaultTemplates
defects	Class used for declaring PipelineTask prerequisite connections.
deprecatedTemplates
dimensions	Set of dimension names that define the unit of work for this task.
donutStampsExtra	Connection for output dataset.
donutStampsIntra	Connection for output dataset.
flat	Class used for declaring PipelineTask prerequisite connections.
initInputs	Set with the names of all InitInput connection attributes.
initOutputs	Set with the names of all InitOutput connection attributes.
inputs	Set with the names of all connectionTypes.Input connection attributes.
linearizer	Class used for declaring PipelineTask prerequisite connections.
outputs	Set with the names of all Output connection attributes.
prerequisiteInputs	Set with the names of all PrerequisiteInput connection attributes.
ptc	Class used for declaring PipelineTask prerequisite connections.
raws	Class used for declaring PipelineTask input connections.
zernikes	Connection for output dataset.

Methods Summary

adjust_all_quanta(adjuster)

Turn one-quantum-per-exposure into one-quantum-per-pair.

Attributes Documentation

allConnections: Mapping[str, BaseConnection] = {'bias': PrerequisiteInput(name='bias', storageClass='ExposureF', doc='Combined bias calibration frame.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'detector'), isCalibration=True, deferLoad=False, minimum=0, lookupFunction=None), 'camera': PrerequisiteInput(name='camera', storageClass='Camera', doc='Input camera geometry.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument',), isCalibration=True, deferLoad=False, minimum=1, lookupFunction=None), 'crosstalk': PrerequisiteInput(name='crosstalk', storageClass='CrosstalkCalib', doc='Intra-detector crosstalk coefficients.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'detector'), isCalibration=True, deferLoad=False, minimum=0, lookupFunction=None), 'dark': PrerequisiteInput(name='dark', storageClass='ExposureF', doc='Combined dark calibration frame.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'detector'), isCalibration=True, deferLoad=False, minimum=0, lookupFunction=None), 'defects': PrerequisiteInput(name='defects', storageClass='Defects', doc='Defect list; masks the bad LATISS column that otherwise outranks the donut.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'detector'), isCalibration=True, deferLoad=False, minimum=0, lookupFunction=None), 'donutStampsExtra': Output(name='donutStampsExtra', storageClass='StampsBase', doc='Extra-focal donut postage stamps.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('visit', 'detector', 'instrument'), isCalibration=False), 'donutStampsIntra': Output(name='donutStampsIntra', storageClass='StampsBase', doc='Intra-focal donut postage stamps, stored under the extra-focal visit.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('visit', 'detector', 'instrument'), isCalibration=False), 'flat': PrerequisiteInput(name='flat', storageClass='ExposureF', doc='Combined flat calibration frames, one per physical_filter.', multiple=True, deprecated=None, _deprecation_context='', dimensions=('instrument', 'detector', 'physical_filter'), isCalibration=True, deferLoad=False, minimum=0, lookupFunction=None), 'linearizer': PrerequisiteInput(name='linearizer', storageClass='Linearizer', doc='Linearity correction calibration.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'detector'), isCalibration=True, deferLoad=False, minimum=0, lookupFunction=None), 'ptc': PrerequisiteInput(name='ptc', storageClass='PhotonTransferCurveDataset', doc='Photon transfer curve dataset. Required by IsrTaskLSST.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'detector'), isCalibration=True, deferLoad=False, minimum=0, lookupFunction=None), 'raws': Input(name='raw', storageClass='Exposure', doc="Raw LATISS exposures of one CWFS pair: the extra-focal raw of this quantum's visit plus its intra-focal partner, attached by adjust_all_quanta.", multiple=True, deprecated=None, _deprecation_context='', dimensions=('instrument', 'exposure', 'detector'), isCalibration=False, deferLoad=True, minimum=1, deferGraphConstraint=False, deferBinding=False), 'zernikes': Output(name='zernikes', storageClass='AstropyQTable', doc='Zernike coefficients for the pair and an average row, with fit quality columns.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('visit', 'detector', 'instrument'), 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.

bias

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).

camera

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).

crosstalk

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).

dark

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).

defaultTemplates = {}

defects

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).

deprecatedTemplates = {}

dimensions: set[str] = {'detector', 'instrument', 'visit'}

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.

donutStampsExtra

Connection for output dataset.

donutStampsIntra

Connection for output dataset.

flat

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).

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.

inputs: set[str] = frozenset({'raws'})

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.

linearizer

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).

outputs: set[str] = frozenset({'donutStampsExtra', 'donutStampsIntra', 'zernikes'})

Set with the names of all Output connection attributes.

See inputs for additional information.

prerequisiteInputs: set[str] = frozenset({'bias', 'camera', 'crosstalk', 'dark', 'defects', 'flat', 'linearizer', 'ptc'})

Set with the names of all PrerequisiteInput connection attributes.

See inputs for additional information.

ptc

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).

raws

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.

zernikes

Connection for output dataset.

Methods Documentation

adjust_all_quanta(adjuster)

Turn one-quantum-per-exposure into one-quantum-per-pair.

Every exposure in the data query starts with its own quantum. An exposure whose observation_reason contains extra keeps its quantum and receives the raw of the intra-focal exposure taken immediately before it (seq_num - 1 on the same night, which is how latiss_wep_align takes the pair). All other quanta are removed: intra exposures once their raw has been re-homed, and unpaired extras (whose partner was not in the query or is not marked intra), with a warning. This mirrors ReassignCwfsCutoutsFamTask for the LSSTCam FAM pipeline. A data query naming both exposures of the pair, e.g. exposure in (17, 18), is therefore all that is required; nothing needs to be said about which is which.

Parameters:

adjuster (QuantaAdjuster)

Return type:

None