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 temporary 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 Registry.queryDatasets 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.
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.

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.

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

In addition to returning a quantum_graph_skeleton.QuantumGraphSkeleton, this method should populate the existing_datasets structure by querying for all relevant datasets with non-empty data IDs (those with empty data IDs will already be present). In particular:

• inputs must always be populated with all overall-input datasets (but not prerequisites), by querying input_collections;

• outputs_for_skip must be populated with any intermediate our output datasets present in skip_existing_in (it can be ignored if skip_existing_in is empty);

• outputs_in_the_way must be populated with any intermediate or output datasets present in output_run, if output_run_exists (it can be ignored if output_run_exists is False). 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.

• inputs must be populated with all prerequisite-input datasets that were included in the skeleton, by querying input_collections (not all prerequisite inputs need to be included in the skeleton, but the base class can only use per-quantum queries to find them, and that can be slow when there are many quanta).

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).