TractMatchedPreparationConnections¶

class lsst.faro.preparation.TractMatchedPreparationConnections(*, config=None)¶

Bases: lsst.faro.base.MatchedBaseConnections

Attributes Summary

`allConnections`
`astromCalibs`
`defaultTemplates`
`dimensions`
`externalPhotoCalibGlobalCatalog`
`externalPhotoCalibTractCatalog`
`externalSkyWcsGlobalCatalog`
`externalSkyWcsTractCatalog`
`initInputs`
`initOutputs`
`inputs`
`outputCatalog`
`outputs`
`photoCalibs`
`prerequisiteInputs`
`skyMap`
`sourceCatalogs`

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 = {'astromCalibs': Input(name='{wcsName}', storageClass='Wcs', doc='WCS for the catalog.', multiple=True, dimensions=('instrument', 'visit', 'detector', 'band'), isCalibration=False, deferLoad=False, minimum=1), 'externalPhotoCalibGlobalCatalog': Input(name='{externalPhotoCalibName}PhotoCalibCatalog', storageClass='ExposureCatalog', doc='Per-visit photometric calibrations computed globally (with no tract information). These catalogs use the detector id for the catalog id, sorted on id for fast lookup.', multiple=True, dimensions=('instrument', 'visit', 'band'), isCalibration=False, deferLoad=False, minimum=1), 'externalPhotoCalibTractCatalog': Input(name='{externalPhotoCalibName}PhotoCalibCatalog', storageClass='ExposureCatalog', doc='Per-tract, per-visit photometric calibrations. These catalogs use the detector id for the catalog id, sorted on id for fast lookup.', multiple=True, dimensions=('instrument', 'visit', 'tract', 'band'), isCalibration=False, deferLoad=False, minimum=1), 'externalSkyWcsGlobalCatalog': Input(name='{externalWcsName}SkyWcsCatalog', storageClass='ExposureCatalog', doc='Per-visit wcs calibrations computed globally (with no tract information). These catalogs use the detector id for the catalog id, sorted on id for fast lookup.', multiple=True, dimensions=('instrument', 'visit', 'band'), isCalibration=False, deferLoad=False, minimum=1), 'externalSkyWcsTractCatalog': Input(name='{externalWcsName}SkyWcsCatalog', storageClass='ExposureCatalog', doc='Per-tract, per-visit wcs calibrations. These catalogs use the detector id for the catalog id, sorted on id for fast lookup.', multiple=True, dimensions=('instrument', 'visit', 'tract', 'band'), isCalibration=False, deferLoad=False, minimum=1), 'outputCatalog': Output(name='matchedCatalogTract', storageClass='SimpleCatalog', doc='Resulting matched catalog.', multiple=False, dimensions=('tract', 'instrument', 'band'), isCalibration=False), 'photoCalibs': Input(name='{photoCalibName}', storageClass='PhotoCalib', doc='Photometric calibration object.', multiple=True, dimensions=('instrument', 'visit', 'detector', 'band'), isCalibration=False, deferLoad=False, minimum=1), 'skyMap': Input(name='skyMap', storageClass='SkyMap', doc='Input definition of geometry/bbox and projection/wcs for warped exposures', multiple=False, dimensions=('skymap',), isCalibration=False, deferLoad=False, minimum=1), 'sourceCatalogs': Input(name='src', storageClass='SourceCatalog', doc='Source catalogs to match up.', multiple=True, dimensions=('instrument', 'visit', 'detector', 'band'), isCalibration=False, deferLoad=False, minimum=1)}¶

astromCalibs¶

defaultTemplates = {'coaddName': 'deep', 'externalPhotoCalibName': 'fgcm', 'externalWcsName': 'jointcal', 'photoCalibName': 'calexp.photoCalib', 'wcsName': 'calexp.wcs'}¶

dimensions = {'band', 'instrument', 'skymap', 'tract'}¶

externalPhotoCalibGlobalCatalog¶

externalPhotoCalibTractCatalog¶

externalSkyWcsGlobalCatalog¶

externalSkyWcsTractCatalog¶

initInputs = frozenset()¶

initOutputs = frozenset()¶

inputs = frozenset({'externalSkyWcsGlobalCatalog', 'sourceCatalogs', 'skyMap', 'astromCalibs', 'externalSkyWcsTractCatalog', 'photoCalibs', 'externalPhotoCalibTractCatalog', 'externalPhotoCalibGlobalCatalog'})¶

outputCatalog¶

outputs = frozenset({'outputCatalog'})¶

photoCalibs¶

prerequisiteInputs = frozenset()¶

skyMap¶

sourceCatalogs¶

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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TractMatchedPreparationConnections¶