CatalogMeasurementBaseConnections

class lsst.faro.base.CatalogMeasurementBaseConnections(*, config: PipelineTaskConfig | None = None)

Bases: MetricConnections

Attributes Summary

allConnections	Mapping holding all connection attributes.
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.
inputs	Set with the names of all connectionTypes.Input connection attributes.
measurement	Connection for output dataset.
outputs	Set with the names of all Output connection attributes.
prerequisiteInputs	Set with the names of all PrerequisiteInput connection attributes.
refCat	Class used for declaring PipelineTask prerequisite connections.

Attributes Documentation

allConnections: Mapping[str, BaseConnection] = {'measurement': Output(name='metricvalue_{package}_{metric}', storageClass='MetricValue', doc='The metric value computed by this task.', multiple=False, deprecated=None, _deprecation_context='', dimensions={'instrument', 'detector', 'visit'}, isCalibration=False), 'refCat': PrerequisiteInput(name='{refDataset}', storageClass='SimpleCatalog', doc='Reference catalog', multiple=True, deprecated=None, _deprecation_context='', dimensions=('skypix',), isCalibration=False, deferLoad=True, minimum=1, lookupFunction=None)}

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.

defaultTemplates = {'metric': None, 'package': None, 'refDataset': ''}

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.

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({})

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.

measurement

Connection for output dataset.

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

Set with the names of all Output connection attributes.

See inputs for additional information.

prerequisiteInputs: set[str] = frozenset({'refCat'})

Set with the names of all PrerequisiteInput connection attributes.

See inputs for additional information.

refCat

Class used for declaring PipelineTask prerequisite connections.

Attributes

namestr

The default name used to identify the dataset type.

storageClassstr

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

multiplebool

Indicates if this connection should expect to contain multiple objects of the given dataset type. Tasks with more than one connection with multiple=True with the same dimensions may want to implement PipelineTaskConnections.adjustQuantum to ensure those datasets are consistent (i.e. zip-iterable) in PipelineTask.runQuantum and notify the execution system as early as possible of outputs that will not be produced because the corresponding input is missing.

dimensionsiterable of str

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

minimumbool

Minimum number of datasets required for this connection, per quantum. This is checked in the base implementation of PipelineTaskConnections.adjustQuantum, which raises FileNotFoundError (causing QuantumGraph generation to fail). PipelineTask implementations may provide custom adjustQuantum implementations for more fine-grained or configuration-driven constraints, as long as they are compatible with this minium.

lookupFunctiontyping.Callable, optional

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.

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).