21 from __future__
import annotations
23 """Module defining quantum graph classes and related methods. 25 There could be different representations of the quantum graph depending 26 on the client needs. Presently this module contains graph implementation 27 which is based on requirements of command-line environment. In the future 28 we could add other implementations and methods to convert between those 33 __all__ = [
"QuantumGraph",
"QuantumGraphTaskNodes",
"QuantumIterData"]
38 from itertools
import chain
39 from dataclasses
import dataclass
40 from typing
import List, FrozenSet, Mapping
45 from .pipeline
import Pipeline, TaskDef
46 from .pipeTools
import orderPipeline
47 from lsst.daf.butler
import Quantum, DatasetRef, DatasetType
48 from lsst.daf.butler.core.utils
import NamedKeyDict
61 """Helper class for iterating over quanta in a graph. 63 The `QuantumGraph.traverse` method needs to return topologically ordered 64 Quanta together with their dependencies. This class is used as a value 65 for the iterator, it contains enumerated Quantum and its dependencies. 68 __slots__ = [
"index",
"quantum",
"taskDef",
"dependencies"]
71 """Index of this Quantum, a unique but arbitrary integer.""" 74 """Quantum corresponding to a graph node.""" 77 """Task class to be run on this quantum, and corresponding label and 81 dependencies: FrozenSet(int)
82 """Possibly empty set of indices of dependencies for this Quantum. 83 Dependencies include other nodes in the graph; they do not reflect data 84 already in butler (there are no graph nodes for those). 90 """QuantumGraphTaskNodes represents a bunch of nodes in an quantum graph 91 corresponding to a single task. 93 The node in quantum graph is represented by the `PipelineTask` and a 94 single `~lsst.daf.butler.Quantum` instance. One possible representation 95 of the graph is just a list of nodes without edges (edges can be deduced 96 from nodes' quantum inputs and outputs if needed). That representation can 97 be reduced to the list of PipelineTasks (or their corresponding TaskDefs) 98 and the corresponding list of Quanta. This class is used in this reduced 99 representation for a single task, and full `QuantumGraph` is a sequence of 100 tinstances of this class for one or more tasks. 102 Different frameworks may use different graph representation, this 103 representation was based mostly on requirements of command-line 104 executor which does not need explicit edges information. 108 """Task defintion for this set of nodes.""" 110 quanta: List[Quantum]
111 """List of quanta corresponding to the task.""" 113 initInputs: Mapping[DatasetType, DatasetRef]
114 """Datasets that must be loaded or created to construct this task.""" 116 initOutputs: Mapping[DatasetType, DatasetRef]
117 """Datasets that may be written after constructing this task.""" 121 """QuantumGraph is a sequence of `QuantumGraphTaskNodes` objects. 123 Typically the order of the tasks in the list will be the same as the 124 order of tasks in a pipeline (obviously depends on the code which 129 iterable : iterable of `QuantumGraphTaskNodes`, optional 130 Initial sequence of per-task nodes. 133 list.__init__(self, iterable
or [])
138 initInputs: NamedKeyDict
139 """Datasets that must be provided to construct one or more Tasks in this 140 graph, and must be obtained from the data repository. 142 This is disjoint with both `initIntermediates` and `initOutputs`. 145 initIntermediates: NamedKeyDict
146 """Datasets that must be provided to construct one or more Tasks in this 147 graph, but are also produced after constructing a Task in this graph. 149 This is disjoint with both `initInputs` and `initOutputs`. 152 initOutputs: NamedKeyDict
153 """Datasets that are produced after constructing a Task in this graph, 154 and are not used to construct any other Task in this graph. 156 This is disjoint from both `initInputs` and `initIntermediates`. 160 """Iterator over quanta in a graph. 162 Quanta are returned in unspecified order. 167 Task definition for a Quantum. 168 quantum : `~lsst.daf.butler.Quantum` 171 for taskNodes
in self:
172 taskDef = taskNodes.taskDef
173 for quantum
in taskNodes.quanta:
174 yield taskDef, quantum
177 """Return total count of quanta in a graph. 182 Number of quanta in a graph. 184 return sum(len(taskNodes.quanta)
for taskNodes
in self)
187 """Return topologically ordered Quanta and their dependencies. 189 This method iterates over all Quanta in topological order, enumerating 190 them during iteration. Returned `QuantumIterData` object contains 191 Quantum instance, its ``index`` and the ``index`` of all its 192 prerequsites (Quanta that produce inputs for this Quantum): 193 - the ``index`` values are generated by an iteration of a 194 QuantumGraph, and are not intrinsic to the QuantumGraph 195 - during iteration, each ID will appear in index before it ever 196 appears in dependencies. 200 quantumData : `QuantumIterData` 203 def orderedTaskNodes(graph):
204 """Return topologically ordered task nodes. 208 nodes : `QuantumGraphTaskNodes` 213 nodesMap = {id(item.taskDef): item
for item
in graph}
215 for taskDef
in pipeline:
216 yield nodesMap[id(taskDef)]
220 for nodes
in orderedTaskNodes(self):
221 for quantum
in nodes.quanta:
225 for dataRef
in chain.from_iterable(quantum.predictedInputs.values()):
227 if dataRef.id
is None:
229 name, component = dataRef.datasetType.nameAndComponent()
230 key = (name, dataRef.dataId)
232 prereq.append(outputs[key])
240 if not (len(self) == 1
and len(self[0].quanta) == 1):
244 for dataRef
in chain.from_iterable(quantum.outputs.values()):
245 key = (dataRef.datasetType.name, dataRef.dataId)
248 yield QuantumIterData(index=index, quantum=quantum, taskDef=nodes.taskDef,
249 dependencies=frozenset(prereq))
def __init__(self, iterable=None)