HighResolutionHipsQuantumGraphBuilder

class lsst.pipe.tasks.hips.HighResolutionHipsQuantumGraphBuilder(pipeline_graph, butler, *, input_collections=None, output_run=None, constraint_order, constraint_ranges, where='')

Bases: QuantumGraphBuilder

A custom a lsst.pipe.base.QuantumGraphBuilder for running HighResolutionHipsTask only.

This is a workaround for incomplete butler query support for HEALPix dimensions.

Parameters:

pipeline_graphlsst.pipe.base.PipelineGraph

Pipeline graph with exactly one task, which must be a configuration of HighResolutionHipsTask.

butlerlsst.daf.butler.Butler

Client for the butler data repository. May be read-only.

input_collectionsstr or Iterable [ str ], optional

Collection or collections to search for input datasets, in order. If not provided, butler.collections will be searched.

output_runstr, optional

Name of the output collection. If not provided, butler.run will be used.

constraint_orderint

HEALPix order used to constrain which quanta are generated, via constraint_indices. This should be a coarser grid (smaller order) than the order used for the task’s quantum and output data IDs, and ideally something between the spatial scale of a patch or the data repository’s “common skypix” system (usually htm7).

constraint_rangeslsst.sphgeom.RangeSet

RangeSet that describes constraint pixels (HEALPix NEST, with order constraint_order) to constrain generated quanta.

wherestr, optional

A boolean str expression of the form accepted by lsst.daf.butler.Butler to constrain input datasets. This may contain a constraint on tracts, patches, or bands, but not HEALPix indices. Constraints on tracts and patches should usually be unnecessary, however - existing coadds that overlap the given HEALpix indices will be selected without such a constraint, and providing one may reject some that should normally be included.

Attributes Summary

universe

Definitions of all data dimensions.

Methods Summary

build([metadata, attach_datastore_records])	Build the quantum graph, returning an old QuantumGraph instance.
finish([output, metadata, ...])	Return quantum graph components that can be used to save or construct a PredictedQuantumGraph instance.
process_subgraph(subgraph)	Build the rough structure for an independent subset of the QuantumGraph and query for relevant existing datasets.

Attributes Documentation

universe

Definitions of all data dimensions.

Methods Documentation

build(metadata: Mapping[str, Any] | None = None, attach_datastore_records: bool = True) → QuantumGraph

Build the quantum graph, returning an old QuantumGraph instance.

Parameters:

metadataMapping, optional

Flexible metadata to add to the quantum graph.

attach_datastore_recordsbool, optional

Whether to include datastore records in the graph. Required for lsst.daf.butler.QuantumBackedButler execution.

Returns:

quantum_graphQuantumGraph

DAG describing processing to be performed.

Notes

External code is expected to construct a QuantumGraphBuilder and then call this method exactly once. See class documentation for details on what it does.

finish(output: str | None = None, metadata: Mapping[str, Any] | None = None, attach_datastore_records: bool = True) → PredictedQuantumGraphComponents

Return quantum graph components that can be used to save or construct a PredictedQuantumGraph instance.

Parameters:

outputstr or None, optional

Output CHAINED collection that combines the input and output collections.

metadataMapping, optional

Mapping of JSON-friendly metadata. Collection information, the current user, and the current timestamp are automatically included.

attach_datastore_recordsbool, optional

Whether to include datastore records for overall inputs for QuantumBackedButler.

Returns:

componentsquantum_graph.PredictedQuantumGraphComponents

Components that can be used to construct a graph object and/or save it to disk.

process_subgraph(subgraph)

Build the rough structure for an independent subset of the QuantumGraph and query for relevant existing datasets.

Parameters:

subgraphpipeline_graph.PipelineGraph

Subset of the pipeline graph that should be processed by this call. This is always resolved and topologically sorted. It should not be modified.

Returns:

skeletonquantum_graph_skeleton.QuantumGraphSkeleton

Class representing an initial quantum graph. See quantum_graph_skeleton.QuantumGraphSkeleton docs for details. After this is returned, the object may be modified in-place in unspecified ways.

Notes

The quantum_graph_skeleton.QuantumGraphSkeleton should associate lsst.daf.butler.DatasetRef objects with nodes for existing datasets. In particular:

• quantum_graph_skeleton.QuantumGraphSkeleton.set_dataset_ref must be used to associate existing datasets with all overall-input dataset nodes in the skeleton by querying input_collections. This includes all standard input nodes and any prerequisite nodes added by the method (prerequisite nodes may also be left out entirely, as the base class can add them later, albeit possibly less efficiently).

• quantum_graph_skeleton.QuantumGraphSkeleton.set_output_for_skip must be used to associate existing datasets with output dataset nodes by querying skip_existing_in.

• quantum_graph_skeleton.QuantumGraphSkeleton.add_output_in_the_way must be used to associated existing outputs with output dataset nodes by querying output_run if output_run_exists is True. Note that the presence of such datasets is not automatically an error, even if clobber is False, as these may be quanta that will be skipped.

lsst.daf.butler.DatasetRef objects for existing datasets with empty data IDs in all of the above categories may be found in the empty_dimensions_datasets attribute, as these are queried for prior to this call by the base class, but associating them with graph nodes is still this method’s responsibility.

Dataset types should never be components and should always use the “common” storage class definition in pipeline_graph.DatasetTypeNode (which is the data repository definition when the dataset type is registered).