QuantumGraph¶

class lsst.pipe.base.QuantumGraph(quanta: Mapping[TaskDef, set[lsst.daf.butler._quantum.Quantum]], metadata: Mapping[str, Any] | None = None, universe: DimensionUniverse | None = None, initInputs: Mapping[TaskDef, Iterable[DatasetRef]] | None = None, initOutputs: Mapping[TaskDef, Iterable[DatasetRef]] | None = None, globalInitOutputs: Iterable[DatasetRef] | None = None, registryDatasetTypes: Iterable[DatasetType] | None = None)¶

Bases: object

QuantumGraph is a directed acyclic graph of QuantumNode objects.

This data structure represents a concrete workflow generated from a Pipeline.

Parameters:

quantaMapping [ TaskDef, set [ Quantum ] ]: This maps tasks (and their configs) to the sets of data they are to process.
metadataOptional Mapping of str to primitives: This is an optional parameter of extra data to carry with the graph. Entries in this mapping should be able to be serialized in JSON.
universeDimensionUniverse, optional: The dimensions in which quanta can be defined. Need only be provided if no quanta have data IDs.
initInputsMapping, optional: Maps tasks to their InitInput dataset refs. Dataset refs can be either resolved or non-resolved. Presently the same dataset refs are included in each Quantum for the same task.
initOutputsMapping, optional: Maps tasks to their InitOutput dataset refs. Dataset refs can be either resolved or non-resolved. For intermediate resolved refs their dataset ID must match initInputs and Quantum initInputs.
globalInitOutputsiterable [ DatasetRef ], optional: Dataset refs for some global objects produced by pipeline. These objects include task configurations and package versions. Typically they have an empty DataId, but there is no real restriction on what can appear here.
registryDatasetTypesiterable [ DatasetType ], optional: Dataset types which are used by this graph, their definitions must match registry. If registry does not define dataset type yet, then it should match one that will be created later.

Raises:

ValueError: Raised if the graph is pruned such that some tasks no longer have nodes associated with them.

Attributes Summary

`allDatasetTypes`	All the data set type names that are present in the graph (`tuple` [`str`]).
`graph`	A graph representing the relations between all the `QuantumNode` objects (`networkx.DiGraph`).
`graphID`	The ID generated by the graph at construction time (`str`).
`inputQuanta`	The nodes that are inputs to the graph (iterable [`QuantumNode`]).
`isConnected`	Whether all of the nodes in the graph are connected, ignoring directionality of connections (`bool`).
`metadata`	Extra data carried with the graph (mapping [`str`] or `None`).
`outputQuanta`	The nodes that are outputs of the graph (iterable [`QuantumNode`]).
`pipeline_graph`	A graph representation of the tasks and dataset types in the quantum graph.
`taskGraph`	A graph representing the relations between the tasks inside the quantum graph (`networkx.DiGraph`).
`universe`	Dimension universe associated with this graph (`DimensionUniverse`).

Methods Summary

`buildAndPrintHeader`()	Create a header that would be used in a save of this object and prints it out to standard out.
`checkQuantumInGraph`(quantum)	Check if specified quantum appears in the graph as part of a node.
`determineAncestorsOfQuantumNode`(node)	Return a graph of the specified node and all the ancestor nodes directly reachable by walking edges.
`determineConnectionsOfQuantumNode`(node)	Return a graph of `QuantumNode` that are direct inputs and outputs of a specified node.
`determineInputsToQuantumNode`(node)	Return a set of `QuantumNode` that are direct inputs to a specified node.
`determineOutputsOfQuantumNode`(node)	Return a set of `QuantumNode` that are direct outputs of a specified node.
`findCycle`()	Check a graph for the presense of cycles and returns the edges of any cycles found, or an empty list if there is no cycle.
`findQuantaWithDSType`(datasetTypeName)	Return all the `Quantum` that contain a specified `DatasetTypeName`.
`findTaskDefByLabel`(label)	Determine which `TaskDef` objects in this graph are associated with a `str` representing a tasks label.
`findTaskDefByName`(taskName)	Determine which `TaskDef` objects in this graph are associated with a `str` representing a task name (looks at the `taskName` property of `TaskDef` objects).
`findTaskWithOutput`(datasetTypeName)	Find all tasks that have the specified dataset type name as an output.
`findTasksWithInput`(datasetTypeName)	Find all tasks that have the specified dataset type name as an input.
`getNodesForTask`(taskDef)	Return all the `QuantumNode`s associated with a `TaskDef`.
`getNumberOfQuantaForTask`(taskDef)	Return the number of `Quantum` associated with a `TaskDef`.
`getQuantaForTask`(taskDef)	Return all the `Quantum` associated with a `TaskDef`.
`getQuantumNodeByNodeId`(nodeId)	Lookup a `QuantumNode` from an id associated with the node.
`getSummary`()	Create summary of graph.
`get_init_input_refs`(task_label)	Return the DatasetRefs for the given task's init inputs.
`get_init_output_refs`(task_label)	Return the DatasetRefs for the given task's init outputs.
`get_refs`(*[, include_init_inputs, ...])	Get the requested dataset refs from the graph.
`get_task_quanta`(label)	Return the quanta associated with the given task label.
`globalInitOutputRefs`()	Return DatasetRefs for global InitOutputs.
`initInputRefs`(taskDef)	Return DatasetRefs for a given task InitInputs.
`initOutputRefs`(taskDef)	Return DatasetRefs for a given task InitOutputs.
`init_output_run`(butler[, existing])	Initialize a new output RUN collection by writing init-output datasets (including configs and packages).
`iterTaskGraph`()	Iterate over the `taskGraph` attribute in topological order.
`load`(file[, universe, nodes, graphID, ...])	Read `QuantumGraph` from a file that was made by `save`.
`loadUri`(uri[, universe, nodes, graphID, ...])	Read `QuantumGraph` from a URI.
`make_init_qbb`(butler_config, *[, ...])	Construct an quantum-backed butler suitable for reading and writing init input and init output datasets, respectively.
`readHeader`(uri[, minimumVersion])	Read the header of a `QuantumGraph` pointed to by the uri parameter and return it as a string.
`registryDatasetTypes`()	Return dataset types used by this graph, their definitions match dataset types from registry.
`save`(file)	Save QuantumGraph to a file.
`saveUri`(uri)	Save `QuantumGraph` to the specified URI.
`subset`(nodes)	Create a new graph object that contains the subset of the nodes specified as input.
`subsetToConnected`()	Generate a list of subgraphs where each is connected.
`tasksWithDSType`(datasetTypeName)	Find all tasks that are associated with the specified dataset type name.
`updateRun`(run, *[, metadata_key, ...])	Change output run and dataset ID for each output dataset.
`writeDotGraph`(output)	Write out the graph as a dot graph.
`write_configs`(butler[, compare_existing])	Write the config datasets for all tasks in the quantum graph.
`write_init_outputs`(butler[, skip_existing])	Write the init-output datasets for all tasks in the quantum graph.
`write_packages`(butler[, compare_existing])	Write the 'packages' dataset for the currently-active software versions.

Attributes Documentation

allDatasetTypes¶

All the data set type names that are present in the graph (tuple [str]).

These types do not include global init-outputs.

graph¶

A graph representing the relations between all the QuantumNode objects (networkx.DiGraph).

The graph should usually be iterated over, or passed to methods of this class, but sometimes direct access to the networkx object may be helpful.

graphID¶: The ID generated by the graph at construction time (str).

inputQuanta¶

The nodes that are inputs to the graph (iterable [QuantumNode]).

These are the nodes that do not depend on any other nodes in the graph.

isConnected¶: Whether all of the nodes in the graph are connected, ignoring directionality of connections (bool).

metadata¶

Extra data carried with the graph (mapping [str] or None).

The mapping is a dynamic view of this object’s metadata. Values should be able to be serialized in JSON.

outputQuanta¶

The nodes that are outputs of the graph (iterable [QuantumNode]).

These are the nodes that have no nodes that depend on them in the graph.

pipeline_graph¶: A graph representation of the tasks and dataset types in the quantum graph.

taskGraph¶: A graph representing the relations between the tasks inside the quantum graph (networkx.DiGraph).

universe¶: Dimension universe associated with this graph (DimensionUniverse).

Methods Documentation

buildAndPrintHeader() → None¶: Create a header that would be used in a save of this object and prints it out to standard out.

checkQuantumInGraph(quantum: Quantum) → bool¶

Check if specified quantum appears in the graph as part of a node.

Parameters:

quantumlsst.daf.butler.Quantum: The quantum to search for.

Returns:

in_graphbool: The result of searching for the quantum.

determineAncestorsOfQuantumNode(node: QuantumNode) → _T¶

Return a graph of the specified node and all the ancestor nodes directly reachable by walking edges.

Parameters:

nodeQuantumNode: The node for which all ancestors are to be determined.

Returns:

ancestorsgraph of QuantumNode: Graph of node and all of its ancestors.

determineConnectionsOfQuantumNode(node: QuantumNode) → _T¶

Return a graph of QuantumNode that are direct inputs and outputs of a specified node.

Parameters:

nodeQuantumNode: The node of the graph for which connected nodes are to be determined.

Returns:

graphgraph of QuantumNode: All the nodes that are directly connected to specified node.

determineInputsToQuantumNode(node: QuantumNode) → set[lsst.pipe.base.graph.quantumNode.QuantumNode]¶

Return a set of QuantumNode that are direct inputs to a specified node.

Parameters:

nodeQuantumNode: The node of the graph for which inputs are to be determined.

Returns:

inputsset of QuantumNode: All the nodes that are direct inputs to specified node.

determineOutputsOfQuantumNode(node: QuantumNode) → set[lsst.pipe.base.graph.quantumNode.QuantumNode]¶

Return a set of QuantumNode that are direct outputs of a specified node.

Parameters:

nodeQuantumNode: The node of the graph for which outputs are to be determined.

Returns:

outputsset of QuantumNode: All the nodes that are direct outputs to specified node.

findCycle() → list[tuple[lsst.pipe.base.graph.quantumNode.QuantumNode, lsst.pipe.base.graph.quantumNode.QuantumNode]]¶

Check a graph for the presense of cycles and returns the edges of any cycles found, or an empty list if there is no cycle.

Returns:

resultlist of tuple of [ QuantumNode, QuantumNode ]: A list of any graph edges that form a cycle, or an empty list if there is no cycle. Empty list to so support if graph.find_cycle() syntax as an empty list is falsy.

findQuantaWithDSType(datasetTypeName: DatasetTypeName) → set[lsst.daf.butler._quantum.Quantum]¶

Return all the Quantum that contain a specified DatasetTypeName.

Parameters:

datasetTypeNamestr: The name of the dataset type to search for as a string, can also accept a DatasetTypeName which is a NewType of str for type safety in static type checking.

Returns:

resultset of QuantumNode objects: A set of QuantumNodes that contain specified DatasetTypeName.

Raises:

KeyError: Raised if the DatasetTypeName is not part of the QuantumGraph.

findTaskDefByLabel(label: str) → TaskDef | None¶

Determine which TaskDef objects in this graph are associated with a str representing a tasks label.

Parameters:

labelstr: Name of a task to search for.

Returns:

resultTaskDef: TaskDef objects that has the specified label.

findTaskDefByName(taskName: str) → list[lsst.pipe.base.pipeline.TaskDef]¶

Determine which TaskDef objects in this graph are associated with a str representing a task name (looks at the taskName property of TaskDef objects).

Returns a list of TaskDef objects as a PipelineTask may appear multiple times in a graph with different labels.

Parameters:

taskNamestr: Name of a task to search for.

Returns:

resultlist of TaskDef: List of the TaskDef objects that have the name specified. Multiple values are returned in the case that a task is used multiple times with different labels.

findTaskWithOutput(datasetTypeName: DatasetTypeName) → TaskDef | None¶

Find all tasks that have the specified dataset type name as an output.

Parameters:

datasetTypeNamestr: A string representing the name of a dataset type to be queried, can also accept a DatasetTypeName which is a NewType of str for type safety in static type checking.

Returns:

resultTaskDef or None: TaskDef that outputs DatasetTypeName as an output or None if none of the tasks produce this DatasetTypeName.

Raises:

KeyError: Raised if the DatasetTypeName is not part of the QuantumGraph.

findTasksWithInput(datasetTypeName: DatasetTypeName) → Iterable[TaskDef]¶

Find all tasks that have the specified dataset type name as an input.

Parameters:

datasetTypeNamestr: A string representing the name of a dataset type to be queried, can also accept a DatasetTypeName which is a NewType of str for type safety in static type checking.

Returns:

tasksiterable of TaskDef: TaskDef objects that have the specified DatasetTypeName as an input, list will be empty if no tasks use specified DatasetTypeName as an input.

Raises:

KeyError: Raised if the DatasetTypeName is not part of the QuantumGraph.

getNodesForTask(taskDef: TaskDef) → frozenset[lsst.pipe.base.graph.quantumNode.QuantumNode]¶

Return all the QuantumNodes associated with a TaskDef.

Parameters:

taskDefTaskDef: The TaskDef for which Quantum are to be queried.

Returns:

nodesfrozenset [ QuantumNode ]: A frozenset of QuantumNode that is associated with the specified TaskDef.

getNumberOfQuantaForTask(taskDef: TaskDef) → int¶

Return the number of Quantum associated with a TaskDef.

Parameters:

taskDefTaskDef: The TaskDef for which Quantum are to be queried.

Returns:

countint: The number of Quantum that are associated with the specified TaskDef.

getQuantaForTask(taskDef: TaskDef) → frozenset[lsst.daf.butler._quantum.Quantum]¶

Return all the Quantum associated with a TaskDef.

Parameters:

taskDefTaskDef: The TaskDef for which Quantum are to be queried.

Returns:

quantafrozenset of Quantum: The set of Quantum that is associated with the specified TaskDef.

getQuantumNodeByNodeId(nodeId: UUID) → QuantumNode¶

Lookup a QuantumNode from an id associated with the node.

Parameters:

nodeIdNodeId: The number associated with a node.

Returns:

nodeQuantumNode: The node corresponding with input number.

Raises:

KeyError: Raised if the requested nodeId is not in the graph.

getSummary() → QgraphSummary¶

Create summary of graph.

Returns:

summaryQgraphSummary: Summary of QuantumGraph.

get_init_input_refs(task_label: str) → list[lsst.daf.butler._dataset_ref.DatasetRef]¶

Return the DatasetRefs for the given task’s init inputs.

Parameters:

task_labelstr: Label of the task.

Returns:

refslist [ lsst.daf.butler.DatasetRef ]: Dataset references. Guaranteed to be a new list, not internal state.

get_init_output_refs(task_label: str) → list[lsst.daf.butler._dataset_ref.DatasetRef]¶

Return the DatasetRefs for the given task’s init outputs.

Parameters:

task_labelstr: Label of the task.

Returns:

refslist [ lsst.daf.butler.DatasetRef ]: Dataset references. Guaranteed to be a new list, not internal state.

get_refs(*, include_init_inputs: bool = False, include_inputs: bool = False, include_intermediates: bool | None = None, include_init_outputs: bool = False, include_outputs: bool = False, conform_outputs: bool = True) → tuple[set[lsst.daf.butler._dataset_ref.DatasetRef], dict[str, lsst.daf.butler.datastore.record_data.DatastoreRecordData]]¶

Get the requested dataset refs from the graph.

Parameters:

include_init_inputsbool, optional: Include init inputs.
include_inputsbool, optional: Include inputs.
include_intermediatesbool or None, optional: If None, no special handling for intermediates is performed. If True intermediates are calculated even if other flags do not request datasets. If False intermediates will be removed from any results.
include_init_outputsbool, optional: Include init outpus.
include_outputsbool, optional: Include outputs.
conform_outputsbool, optional: Whether any outputs found should have their dataset types conformed with the registry dataset types.

Returns:

refsset [ lsst.daf.butler.DatasetRef ]: The requested dataset refs found in the graph.
datastore_recordsdict [ str, lsst.daf.butler.datastore.record_data.DatastoreRecordData ]: Any datastore records found.

Notes

Conforming and requesting inputs and outputs can result in the same dataset appearing in the results twice with differing storage classes.

get_task_quanta(label: str) → Mapping[UUID, Quantum]¶

Return the quanta associated with the given task label.

Parameters:

labelstr: Task label.

Returns:

quantaMapping [ uuid.UUID, Quantum ]: Mapping from quantum ID to quantum. Empty if label does not correspond to a task in this graph.

globalInitOutputRefs() → list[lsst.daf.butler._dataset_ref.DatasetRef]¶

Return DatasetRefs for global InitOutputs.

Returns:

refslist [ DatasetRef ]: DatasetRefs for global InitOutputs.

initInputRefs(taskDef: TaskDef) → list[lsst.daf.butler._dataset_ref.DatasetRef] | None¶

Return DatasetRefs for a given task InitInputs.

Parameters:

taskDefTaskDef: Task definition structure.

Returns:

refslist [ DatasetRef ] or None: DatasetRef for the task InitInput, can be None. This can return either resolved or non-resolved reference.

initOutputRefs(taskDef: TaskDef) → list[lsst.daf.butler._dataset_ref.DatasetRef] | None¶

Return DatasetRefs for a given task InitOutputs.

Parameters:

taskDefTaskDef: Task definition structure.

Returns:

refslist [ DatasetRef ] or None: DatasetRefs for the task InitOutput, can be None. This can return either resolved or non-resolved reference. Resolved reference will match Quantum’s initInputs if this is an intermediate dataset type.

init_output_run(butler: LimitedButler, existing: bool = True) → None¶

Initialize a new output RUN collection by writing init-output datasets (including configs and packages).

Parameters:

butlerlsst.daf.butler.LimitedButler: A limited butler data repository client.
existingbool, optional: If True check or ignore outputs that already exist. If False, always raise if an output dataset already exists.

Raises:

lsst.daf.butler.registry.ConflictingDefinitionError: Raised if there are existing init output datasets, and either existing=False or their contents are not compatible with this graph.

iterTaskGraph() → Generator[TaskDef, None, None]¶

Iterate over the taskGraph attribute in topological order.

Yields:

taskDefTaskDef: TaskDef objects in topological order.

classmethod load(file: BinaryIO, universe: DimensionUniverse | None = None, nodes: Iterable[UUID] | None = None, graphID: BuildId | None = None, minimumVersion: int = 3) → QuantumGraph¶

Read QuantumGraph from a file that was made by save.

Parameters:

fileio.IO of bytes: File with data open in binary mode.
universeDimensionUniverse, optional: If None it is loaded from the QuantumGraph saved structure. If supplied, the DimensionUniverse from the loaded QuantumGraph will be validated against the supplied argument for compatibility.
nodesiterable of uuid.UUID or None: UUIDs that correspond to nodes in the graph. If specified, only these nodes will be loaded. Defaults to None, in which case all nodes will be loaded.
graphIDstr or None: If specified this ID is verified against the loaded graph prior to loading any Nodes. This defaults to None in which case no validation is done.
minimumVersionint: Minimum version of a save file to load. Set to -1 to load all versions. Older versions may need to be loaded, and re-saved to upgrade them to the latest format before they can be used in production.

Returns:

graphQuantumGraph: Resulting QuantumGraph instance.

Raises:

TypeError: Raised if data contains instance of a type other than QuantumGraph.
ValueError: Raised if one or more of the nodes requested is not in the QuantumGraph or if graphID parameter does not match the graph being loaded or if the supplied uri does not point at a valid QuantumGraph save file.

Read QuantumGraph from a URI.

Parameters:

uriconvertible to ResourcePath: URI from where to load the graph.
universeDimensionUniverse, optional: If None it is loaded from the QuantumGraph saved structure. If supplied, the DimensionUniverse from the loaded QuantumGraph will be validated against the supplied argument for compatibility.
nodesiterable of [ uuid.UUID | str ] or None: UUIDs that correspond to nodes in the graph. If specified, only these nodes will be loaded. Defaults to None, in which case all nodes will be loaded.
graphIDstr or None: If specified this ID is verified against the loaded graph prior to loading any Nodes. This defaults to None in which case no validation is done.
minimumVersionint: Minimum version of a save file to load. Set to -1 to load all versions. Older versions may need to be loaded, and re-saved to upgrade them to the latest format before they can be used in production.

Returns:

graphQuantumGraph: Resulting QuantumGraph instance.

Raises:

TypeError: Raised if file contains instance of a type other than QuantumGraph.
ValueError: Raised if one or more of the nodes requested is not in the QuantumGraph or if graphID parameter does not match the graph being loaded or if the supplied uri does not point at a valid QuantumGraph save file.
RuntimeError: Raise if Supplied DimensionUniverse is not compatible with the DimensionUniverse saved in the graph.

Construct an quantum-backed butler suitable for reading and writing init input and init output datasets, respectively.

This requires the full graph to have been loaded.

Parameters:

butler_configConfig or ResourcePathExpression: A butler repository root, configuration filename, or configuration instance.
config_search_pathsIterable [ str ], optional: Additional search paths for butler configuration.

Returns:

qbbQuantumBackedButler: A limited butler that can get init-input datasets and put init-output datasets.

classmethod readHeader(uri: str | ParseResult | ResourcePath | Path, minimumVersion: int = 3) → str | None¶

Read the header of a QuantumGraph pointed to by the uri parameter and return it as a string.

Parameters:

uriconvertible to ResourcePath: The location of the QuantumGraph to load. If the argument is a string, it must correspond to a valid ResourcePath path.
minimumVersionint: Minimum version of a save file to load. Set to -1 to load all versions. Older versions may need to be loaded, and re-saved to upgrade them to the latest format before they can be used in production.

Returns:

headerstr or None: The header associated with the specified QuantumGraph it there is one, else None.

Raises:

ValueError: Raised if the extension of the file specified by uri is not a QuantumGraph extension.

registryDatasetTypes() → list[lsst.daf.butler._dataset_type.DatasetType]¶

Return dataset types used by this graph, their definitions match dataset types from registry.

Returns:

refslist [ DatasetType ]: Dataset types for this graph.

save(file: BinaryIO) → None¶

Save QuantumGraph to a file.

Parameters:

fileio.BufferedIOBase: File to write data open in binary mode.

saveUri(uri: str | ParseResult | ResourcePath | Path) → None¶

Save QuantumGraph to the specified URI.

Parameters:

uriconvertible to ResourcePath: URI to where the graph should be saved.

subset(nodes: QuantumNode | Iterable[QuantumNode]) → _T¶

Create a new graph object that contains the subset of the nodes specified as input. Node number is preserved.

Parameters:

nodesQuantumNode or iterable of QuantumNode: Nodes from which to create subset.

Returns:

graphinstance of graph type: An instance of the type from which the subset was created.

subsetToConnected() → tuple[_T, ...]¶

Generate a list of subgraphs where each is connected.

Returns:

resultlist of QuantumGraph: A list of graphs that are each connected.

tasksWithDSType(datasetTypeName: DatasetTypeName) → Iterable[TaskDef]¶

Find all tasks that are associated with the specified dataset type name.

Parameters:

datasetTypeNamestr: A string representing the name of a dataset type to be queried, can also accept a DatasetTypeName which is a NewType of str for type safety in static type checking.

Returns:

resultiterable of TaskDef: TaskDef objects that are associated with the specified DatasetTypeName.

Raises:

KeyError: Raised if the DatasetTypeName is not part of the QuantumGraph.

updateRun(run: str, *, metadata_key: str | None = None, update_graph_id: bool = False) → None¶

Change output run and dataset ID for each output dataset.

Parameters:

runstr: New output run name.
metadata_keystr or None: Specifies matadata key corresponding to output run name to update with new run name. If None or if metadata is missing it is not updated. If metadata is present but key is missing, it will be added.
update_graph_idbool, optional: If True then also update graph ID with a new unique value.

writeDotGraph(output: str | BufferedIOBase) → None¶

Write out the graph as a dot graph.

Parameters:

outputstr or io.BufferedIOBase: Either a filesystem path to write to, or a file handle object.

write_configs(butler: LimitedButler, compare_existing: bool = True) → None¶

Write the config datasets for all tasks in the quantum graph.

Parameters:

butlerlsst.daf.butler.LimitedButler: A limited butler data repository client.
compare_existingbool, optional: If True check configs that already exist for consistency. If False, always raise if configs already exist.

Raises:

lsst.daf.butler.registry.ConflictingDefinitionError: Raised if an config dataset already exists and compare_existing=False, or if the existing config is not consistent with the config in the quantum graph.

write_init_outputs(butler: LimitedButler, skip_existing: bool = True) → None¶

Write the init-output datasets for all tasks in the quantum graph.

Parameters:

butlerlsst.daf.butler.LimitedButler: A limited butler data repository client.
skip_existingbool, optional: If True (default) ignore init-outputs that already exist. If False, raise.

Raises:

lsst.daf.butler.registry.ConflictingDefinitionError: Raised if an init-output dataset already exists and skip_existing=False.

write_packages(butler: LimitedButler, compare_existing: bool = True) → None¶

Write the ‘packages’ dataset for the currently-active software versions.

Parameters:

butlerlsst.daf.butler.LimitedButler: A limited butler data repository client.
compare_existingbool, optional: If True check packages that already exist for consistency. If False, always raise if the packages dataset already exists.

Raises:

lsst.daf.butler.registry.ConflictingDefinitionError: Raised if the packages dataset already exists and is not consistent with the current packages.

Navigation

QuantumGraph¶