CpCtiSolveConnections#

class lsst.cp.pipe.CpCtiSolveConnections(*, config: PipelineTaskConfig | None = None)#

Bases: PipelineTaskConnections

Attributes Summary

`allConnections`	Mapping holding all connection attributes.
`camera`	Class used for declaring PipelineTask prerequisite connections.
`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.
`inputMeasurements`	Class used for declaring PipelineTask input connections.
`inputs`	Set with the names of all `connectionTypes.Input` connection attributes.
`linearizer`	Class used for declaring PipelineTask prerequisite connections.
`outputCalib`	Connection for output dataset.
`outputs`	Set with the names of all `Output` connection attributes.
`prerequisiteInputs`	Set with the names of all `PrerequisiteInput` connection attributes.

Attributes Documentation

allConnections: Mapping[str, BaseConnection] = {'camera': PrerequisiteInput(name='camera', storageClass='Camera', doc='Camera geometry to use.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument',), isCalibration=True, deferLoad=False, minimum=1, lookupFunction=None), 'inputMeasurements': Input(name='cpCtiMeas', storageClass='StructuredDataDict', doc='Input overscan measurements to fit.', multiple=True, deprecated=None, _deprecation_context='', dimensions=('instrument', 'exposure', 'detector'), isCalibration=False, deferLoad=False, minimum=1, deferGraphConstraint=False, deferBinding=False), 'linearizer': PrerequisiteInput(name='linearizer', storageClass='Linearizer', doc='Input linearizer for metadata inheritance.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'detector'), isCalibration=True, deferLoad=False, minimum=1, lookupFunction=None), 'outputCalib': Output(name='cti', storageClass='IsrCalib', doc='Output CTI calibration.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'detector'), isCalibration=True)}#

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#

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 = {}#

deprecatedTemplates = {}#

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

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.

inputMeasurements#

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({'inputMeasurements'})#

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).

outputCalib#: Connection for output dataset.

outputs: set[str] = frozenset({'outputCalib'})#

Set with the names of all Output connection attributes.

See inputs for additional information.

prerequisiteInputs: set[str] = frozenset({'camera', 'linearizer'})#

Set with the names of all PrerequisiteInput connection attributes.

See inputs for additional information.