PatchSummaryConnections¶

class lsst.faro.summary.PatchSummaryConnections(*, config: 'PipelineTaskConfig' | None = None)¶

Bases: CatalogSummaryBaseConnections

Attributes Summary

`allConnections`
`defaultTemplates`
`dimensions`
`initInputs`
`initOutputs`
`inputs`
`measurement`
`measurements`	Class used for declaring PipelineTask input connections
`outputs`
`prerequisiteInputs`

Methods Summary

`adjustQuantum`(inputs, outputs, label, data_id)	Override to make adjustments to `lsst.daf.butler.DatasetRef` objects in the `lsst.daf.butler.core.Quantum` during the graph generation stage of the activator.
`buildDatasetRefs`(quantum)	Builds QuantizedConnections corresponding to input Quantum

Attributes Documentation

allConnections: Dict[str, BaseConnection] = {'measurement': Output(name='metricvalue_{agg_name}_{package}_{metric}', storageClass='MetricValue', doc='{agg_name} {package}_{metric}.', multiple=False, dimensions=('instrument', 'tract', 'band'), isCalibration=False), 'measurements': Input(name='metricvalue_{package}_{metric}', storageClass='MetricValue', doc='{package}_{metric}.', multiple=True, dimensions=('tract', 'patch', 'skymap', 'band'), isCalibration=False, deferLoad=False, minimum=1, deferGraphConstraint=False)}¶

defaultTemplates = {'agg_name': None, 'metric': None, 'package': None}¶

dimensions: ClassVar[Set[str]] = {'band', 'instrument', 'skymap', 'tract'}¶

initInputs: Set[str] = frozenset({})¶

initOutputs: Set[str] = frozenset({})¶

inputs: Set[str] = frozenset({'measurements'})¶

measurement¶

measurements¶

Class used for declaring PipelineTask input connections

Parameters:

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.
deferGraphConstraint: `bool`, 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.

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'})¶

prerequisiteInputs: Set[str] = frozenset({})¶

Methods Documentation

adjustQuantum(inputs: Dict[str, Tuple[BaseInput, Collection[DatasetRef]]], outputs: Dict[str, Tuple[Output, Collection[DatasetRef]]], label: str, data_id: DataCoordinate) → Tuple[Mapping[str, Tuple[BaseInput, Collection[DatasetRef]]], Mapping[str, Tuple[Output, Collection[DatasetRef]]]]¶

Override to make adjustments to lsst.daf.butler.DatasetRef objects in the lsst.daf.butler.core.Quantum during the graph generation stage of the activator.

Parameters:

inputsdict: Dictionary whose keys are an input (regular or prerequisite) connection name and whose values are a tuple of the connection instance and a collection of associated DatasetRef objects. The exact type of the nested collections is unspecified; it can be assumed to be multi-pass iterable and support len and in, but it should not be mutated in place. In contrast, the outer dictionaries are guaranteed to be temporary copies that are true dict instances, and hence may be modified and even returned; this is especially useful for delegating to super (see notes below).
outputsMapping: Mapping of output datasets, with the same structure as inputs.
labelstr: Label for this task in the pipeline (should be used in all diagnostic messages).
data_idlsst.daf.butler.DataCoordinate: Data ID for this quantum in the pipeline (should be used in all diagnostic messages).

Returns:

adjusted_inputsMapping: Mapping of the same form as inputs with updated containers of input DatasetRef objects. Connections that are not changed should not be returned at all. Datasets may only be removed, not added. Nested collections may be of any multi-pass iterable type, and the order of iteration will set the order of iteration within PipelineTask.runQuantum.
adjusted_outputsMapping: Mapping of updated output datasets, with the same structure and interpretation as adjusted_inputs.

Raises:

ScalarError: Raised if any Input or PrerequisiteInput connection has multiple set to False, but multiple datasets.
NoWorkFound: Raised to indicate that this quantum should not be run; not enough datasets were found for a regular Input connection, and the quantum should be pruned or skipped.
FileNotFoundError: Raised to cause QuantumGraph generation to fail (with the message included in this exception); not enough datasets were found for a PrerequisiteInput connection.

Notes

The base class implementation performs important checks. It always returns an empty mapping (i.e. makes no adjustments). It should always called be via super by custom implementations, ideally at the end of the custom implementation with already-adjusted mappings when any datasets are actually dropped, e.g.:

def adjustQuantum(self, inputs, outputs, label, data_id):
    # Filter out some dataset refs for one connection.
    connection, old_refs = inputs["my_input"]
    new_refs = [ref for ref in old_refs if ...]
    adjusted_inputs = {"my_input", (connection, new_refs)}
    # Update the original inputs so we can pass them to super.
    inputs.update(adjusted_inputs)
    # Can ignore outputs from super because they are guaranteed
    # to be empty.
    super().adjustQuantum(inputs, outputs, label_data_id)
    # Return only the connections we modified.
    return adjusted_inputs, {}

Removing outputs here is guaranteed to affect what is actually passed to PipelineTask.runQuantum, but its effect on the larger graph may be deferred to execution, depending on the context in which adjustQuantum is being run: if one quantum removes an output that is needed by a second quantum as input, the second quantum may not be adjusted (and hence pruned or skipped) until that output is actually found to be missing at execution time.

Tasks that desire zip-iteration consistency between any combinations of connections that have the same data ID should generally implement adjustQuantum to achieve this, even if they could also run that logic during execution; this allows the system to see outputs that will not be produced because the corresponding input is missing as early as possible.

buildDatasetRefs(quantum: Quantum) → Tuple[InputQuantizedConnection, OutputQuantizedConnection]¶

Builds QuantizedConnections corresponding to input Quantum

Parameters:

quantumlsst.daf.butler.Quantum: Quantum object which defines the inputs and outputs for a given unit of processing

Returns:

retValtuple of (InputQuantizedConnection,: OutputQuantizedConnection) Namespaces mapping attribute names (identifiers of connections) to butler references defined in the input lsst.daf.butler.Quantum

Navigation

PatchSummaryConnections¶