TractMultiBandTableMeasurementConnections¶

class lsst.faro.measurement.TractMultiBandTableMeasurementConnections(*, config=None)¶

Bases: lsst.faro.measurement.TractTableMeasurementConnections

Attributes Summary

`allConnections`
`catalog`
`defaultTemplates`
`dimensions`
`initInputs`
`initOutputs`
`inputs`
`measurement`
`outputs`
`prerequisiteInputs`
`refCat`	Class used for declaring PipelineTask prerequisite connections

Methods Summary

`adjustQuantum`(inputs, …)	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 = {'catalog': Input(name='objectTable_tract', storageClass='DataFrame', doc='Object table in parquet format, per tract.', multiple=False, dimensions=('tract', 'skymap'), isCalibration=False, deferLoad=True, minimum=1), 'measurement': Output(name='metricvalue_{package}_{metric}', storageClass='MetricValue', doc='Per-tract measurement.', multiple=False, dimensions=('tract', 'skymap'), isCalibration=False), 'refCat': PrerequisiteInput(name='{refDataset}', storageClass='SimpleCatalog', doc='Reference catalog', multiple=True, dimensions=('skypix',), isCalibration=False, deferLoad=True, minimum=1, lookupFunction=None)}¶

catalog¶

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

dimensions = {'skymap', 'tract'}¶

initInputs = frozenset()¶

initOutputs = frozenset()¶

inputs = frozenset({'catalog'})¶

measurement¶

outputs = frozenset({'measurement'})¶

prerequisiteInputs = frozenset({'refCat'})¶

refCat¶

Class used for declaring PipelineTask prerequisite connections

Parameters:

name : str: The default name used to identify the dataset type
storageClass : str: The storage class used when (un)/persisting the dataset type
multiple : bool: 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.
dimensions : iterable of str: The lsst.daf.butler.Butler lsst.daf.butler.Registry dimensions used to identify the dataset type identified by the specified name
minimum : bool: 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.
lookupFunction: `typing.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).

Methods Documentation

adjustQuantum(inputs: Dict[str, Tuple[lsst.pipe.base.connectionTypes.BaseInput, Collection[lsst.daf.butler.core.datasets.ref.DatasetRef]]], outputs: Dict[str, Tuple[lsst.pipe.base.connectionTypes.Output, Collection[lsst.daf.butler.core.datasets.ref.DatasetRef]]], label: str, data_id: lsst.daf.butler.core.dimensions._coordinate.DataCoordinate) → Tuple[Mapping[str, Tuple[lsst.pipe.base.connectionTypes.BaseInput, Collection[lsst.daf.butler.core.datasets.ref.DatasetRef]]], Mapping[str, Tuple[lsst.pipe.base.connectionTypes.Output, Collection[lsst.daf.butler.core.datasets.ref.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:	inputs : `dict` 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). outputs : `Mapping` Mapping of output datasets, with the same structure as `inputs`. label : `str` Label for this task in the pipeline (should be used in all diagnostic messages). data_id : `lsst.daf.butler.DataCoordinate` Data ID for this quantum in the pipeline (should be used in all diagnostic messages).
Returns:	adjusted_inputs : `Mapping` 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_outputs : `Mapping` 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: lsst.daf.butler.core.quantum.Quantum) → Tuple[lsst.pipe.base.connections.InputQuantizedConnection, lsst.pipe.base.connections.OutputQuantizedConnection]¶

Builds QuantizedConnections corresponding to input Quantum

Parameters:	quantum : `lsst.daf.butler.Quantum` Quantum object which defines the inputs and outputs for a given unit of processing
Returns:	retVal : `tuple` of (`InputQuantizedConnection`, `OutputQuantizedConnection`) Namespaces mapping attribute names (identifiers of connections) to butler references defined in the input `lsst.daf.butler.Quantum`

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TractMultiBandTableMeasurementConnections¶