SingleMetadataMetricConnections

class lsst.verify.tasks.SingleMetadataMetricConnections(*, config: PipelineTaskConfig | None = None)

Bases: MetricConnections

An abstract connections class defining a metadata input.

Notes

SingleMetadataMetricConnections defines the following dataset templates:

package

Name of the metric’s namespace. By verify_metrics convention, this is the name of the package the metric is most closely associated with.

metric

Name of the metric, excluding any namespace.

labelName

Pipeline label of the PipelineTask whose metadata are being read.

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.
metadata	Class used for declaring PipelineTask input connections.
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] = {'measurement': Output(name='metricvalue_{package}_{metric}', storageClass='MetricValue', doc='The metric value computed by this task.', multiple=False, deprecated=None, _deprecation_context='', dimensions={'instrument', 'visit', 'detector'}, isCalibration=False), 'metadata': Input(name='{labelName}_metadata', storageClass='TaskMetadata', doc="The target top-level task's metadata. The name must be set to the metadata's butler type, such as 'processCcd_metadata'.", multiple=False, deprecated=None, _deprecation_context='', dimensions={'instrument', 'visit', 'detector'}, isCalibration=False, deferLoad=False, minimum=1, deferGraphConstraint=False, deferBinding=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.

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

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({'metadata'})

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.

metadata

Class used for declaring PipelineTask input 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.

deferLoadbool

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.

minimumbool

Minimum number of datasets required for this connection, per quantum. This is checked in the base implementation of PipelineTaskConnections.adjustQuantum, which raises NoWorkFound if the minimum is not met for Input connections (causing the quantum to be pruned, skipped, or never created, depending on the context), and FileNotFoundError for PrerequisiteInput connections (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.

deferGraphConstraintbool, optional

If True, do not include this dataset type’s existence in the initial query that starts the QuantumGraph generation process. This can be used to make QuantumGraph generation faster by avoiding redundant datasets, and in certain cases it can (along with careful attention to which tasks are included in the same QuantumGraph) be used to work around the QuantumGraph generation algorithm’s inflexible handling of spatial overlaps. This option has no effect when the connection is not an overall input of the pipeline (or subset thereof) for which a graph is being created, and it never affects the ordering of quanta.

deferBindingbool, optional

If True, the dataset will not be automatically included in the pipeline graph, deferGraphConstraint is implied. The custom QuantumGraphBuilder is required to bind it and add a corresponding edge to the pipeline graph. This option allows to have the same dataset type as both input and output of a quantum.

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.

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

Set with the names of all Output connection attributes.

See inputs for additional information.

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

Set with the names of all PrerequisiteInput connection attributes.

See inputs for additional information.