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1# This file is part of pipe_base.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (http://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <http://www.gnu.org/licenses/>.
21from __future__ import annotations
23"""Module defining GraphBuilder class and related methods.
24"""
26__all__ = ['GraphBuilder']
28# -------------------------------
29# Imports of standard modules --
30# -------------------------------
31import itertools
32from collections import ChainMap
33from dataclasses import dataclass
34from typing import Set, List, Dict, Optional, Iterable
35import logging
37# -----------------------------
38# Imports for other modules --
39# -----------------------------
40from .pipeline import PipelineDatasetTypes, TaskDatasetTypes, TaskDef, Pipeline
41from .graph import QuantumGraph, QuantumGraphTaskNodes
42from lsst.daf.butler import (
43 DatasetRef,
44 DatasetType,
45 DimensionGraph,
46 DimensionUniverse,
47 ExpandedDataCoordinate,
48 Quantum,
49)
50from lsst.daf.butler.core.utils import NamedKeyDict
52# ----------------------------------
53# Local non-exported definitions --
54# ----------------------------------
56_LOG = logging.getLogger(__name__.partition(".")[2])
59@dataclass
60class _DatasetScaffolding:
61 """Helper class aggregating information about a `DatasetType`, used when
62 constructing a `QuantumGraph`.
64 `_DatasetScaffolding` does not hold the `DatasetType` instance itself
65 because it is usually used as the value type in `_DatasetScaffoldingDict`,
66 which uses `DatasetType` instances as keys.
68 See `_PipelineScaffolding` for a top-down description of the full
69 scaffolding data structure.
71 Parameters
72 ----------
73 dimensions : `DimensionGraph`
74 Dimensions of the `DatasetType`.
75 """
76 def __init__(self, dimensions: DimensionGraph):
77 self.dimensions = dimensions
78 self.producer = None
79 self.consumers = {}
80 self.dataIds = set()
81 self.refs = []
83 __slots__ = ("dimensions", "producer", "consumers", "dataIds", "refs")
85 dimensions: DimensionGraph
86 """The dimensions of the dataset type (`DimensionGraph`).
88 Set during `_PipelineScaffolding` construction.
89 """
91 producer: Optional[_TaskScaffolding]
92 """The scaffolding objects for the Task that produces this dataset.
94 Set during `_PipelineScaffolding` construction.
95 """
97 consumers: Dict[str, _TaskScaffolding]
98 """The scaffolding objects for the Tasks that consume this dataset,
99 keyed by their label in the `Pipeline`.
101 Set during `_PipelineScaffolding` construction.
102 """
104 dataIds: Set[ExpandedDataCoordinate]
105 """Data IDs for all instances of this dataset type in the graph.
107 Populated after construction by `_PipelineScaffolding.fillDataIds`.
108 """
110 refs: List[DatasetRef]
111 """References for all instances of this dataset type in the graph.
113 Populated after construction by `_PipelineScaffolding.fillDatasetRefs`.
114 """
117class _DatasetScaffoldingDict(NamedKeyDict):
118 """Custom dictionary that maps `DatasetType` to `_DatasetScaffolding`.
120 See `_PipelineScaffolding` for a top-down description of the full
121 scaffolding data structure.
123 Parameters
124 ----------
125 args
126 Positional arguments are forwarded to the `dict` constructor.
127 universe : `DimensionUniverse`
128 Universe of all possible dimensions.
129 """
130 def __init__(self, *args, universe: DimensionGraph):
131 super().__init__(*args)
132 self.universe = universe
134 @classmethod
135 def fromDatasetTypes(cls, datasetTypes: Iterable[DatasetType], *,
136 universe: DimensionUniverse) -> _DatasetScaffoldingDict:
137 """Construct a a dictionary from a flat iterable of `DatasetType` keys.
139 Parameters
140 ----------
141 datasetTypes : `iterable` of `DatasetType`
142 DatasetTypes to use as keys for the dict. Values will be
143 constructed from the dimensions of the keys.
144 universe : `DimensionUniverse`
145 Universe of all possible dimensions.
147 Returns
148 -------
149 dictionary : `_DatasetScaffoldingDict`
150 A new dictionary instance.
151 """
152 return cls(((datasetType, _DatasetScaffolding(datasetType.dimensions))
153 for datasetType in datasetTypes),
154 universe=universe)
156 @classmethod
157 def fromSubset(cls, datasetTypes: Iterable[DatasetType], first: _DatasetScaffoldingDict,
158 *rest) -> _DatasetScaffoldingDict:
159 """Return a new dictionary by extracting items corresponding to the
160 given keys from one or more existing dictionaries.
162 Parameters
163 ----------
164 datasetTypes : `iterable` of `DatasetType`
165 DatasetTypes to use as keys for the dict. Values will be obtained
166 by lookups against ``first`` and ``rest``.
167 first : `_DatasetScaffoldingDict`
168 Another dictionary from which to extract values.
169 rest
170 Additional dictionaries from which to extract values.
172 Returns
173 -------
174 dictionary : `_DatasetScaffoldingDict`
175 A new dictionary instance.
176 """
177 combined = ChainMap(first, *rest)
178 return cls(((datasetType, combined[datasetType]) for datasetType in datasetTypes),
179 universe=first.universe)
181 @property
182 def dimensions(self) -> DimensionGraph:
183 """The union of all dimensions used by all dataset types in this
184 dictionary, including implied dependencies (`DimensionGraph`).
185 """
186 base = self.universe.empty
187 if len(self) == 0:
188 return base
189 return base.union(*[scaffolding.dimensions for scaffolding in self.values()])
191 def unpackRefs(self) -> NamedKeyDict:
192 """Unpack nested single-element `DatasetRef` lists into a new
193 dictionary.
195 This method assumes that each `_DatasetScaffolding.refs` list contains
196 exactly one `DatasetRef`, as is the case for all "init" datasets.
198 Returns
199 -------
200 dictionary : `NamedKeyDict`
201 Dictionary mapping `DatasetType` to `DatasetRef`, with both
202 `DatasetType` instances and string names usable as keys.
203 """
204 return NamedKeyDict((datasetType, scaffolding.refs[0]) for datasetType, scaffolding in self.items())
207@dataclass
208class _TaskScaffolding:
209 """Helper class aggregating information about a `PipelineTask`, used when
210 constructing a `QuantumGraph`.
212 See `_PipelineScaffolding` for a top-down description of the full
213 scaffolding data structure.
215 Parameters
216 ----------
217 taskDef : `TaskDef`
218 Data structure that identifies the task class and its config.
219 parent : `_PipelineScaffolding`
220 The parent data structure that will hold the instance being
221 constructed.
222 datasetTypes : `TaskDatasetTypes`
223 Data structure that categorizes the dataset types used by this task.
225 Raises
226 ------
227 GraphBuilderError
228 Raised if the task's dimensions are not a subset of the union of the
229 pipeline's dataset dimensions.
230 """
231 def __init__(self, taskDef: TaskDef, parent: _PipelineScaffolding, datasetTypes: TaskDatasetTypes):
232 universe = parent.dimensions.universe
233 self.taskDef = taskDef
234 self.dimensions = DimensionGraph(universe, names=taskDef.connections.dimensions)
235 if not self.dimensions.issubset(parent.dimensions):
236 raise GraphBuilderError(f"Task with label '{taskDef.label}' has dimensions "
237 f"{self.dimensions} that are not a subset of "
238 f"the pipeline dimensions {parent.dimensions}.")
240 # Initialize _DatasetScaffoldingDicts as subsets of the one or two
241 # corresponding dicts in the parent _PipelineScaffolding.
242 self.initInputs = _DatasetScaffoldingDict.fromSubset(datasetTypes.initInputs,
243 parent.initInputs, parent.initIntermediates)
244 self.initOutputs = _DatasetScaffoldingDict.fromSubset(datasetTypes.initOutputs,
245 parent.initIntermediates, parent.initOutputs)
246 self.inputs = _DatasetScaffoldingDict.fromSubset(datasetTypes.inputs,
247 parent.inputs, parent.intermediates)
248 self.outputs = _DatasetScaffoldingDict.fromSubset(datasetTypes.outputs,
249 parent.intermediates, parent.outputs)
250 self.prerequisites = _DatasetScaffoldingDict.fromSubset(datasetTypes.prerequisites,
251 parent.prerequisites)
252 # Add backreferences to the _DatasetScaffolding objects that point to
253 # this Task.
254 for dataset in itertools.chain(self.initInputs.values(), self.inputs.values(),
255 self.prerequisites.values()):
256 dataset.consumers[self.taskDef.label] = self
257 for dataset in itertools.chain(self.initOutputs.values(), self.outputs.values()):
258 assert dataset.producer is None
259 dataset.producer = self
260 self.dataIds = set()
261 self.quanta = []
263 taskDef: TaskDef
264 """Data structure that identifies the task class and its config
265 (`TaskDef`).
266 """
268 dimensions: DimensionGraph
269 """The dimensions of a single `Quantum` of this task (`DimensionGraph`).
270 """
272 initInputs: _DatasetScaffoldingDict
273 """Dictionary containing information about datasets used to construct this
274 task (`_DatasetScaffoldingDict`).
275 """
277 initOutputs: _DatasetScaffoldingDict
278 """Dictionary containing information about datasets produced as a
279 side-effect of constructing this task (`_DatasetScaffoldingDict`).
280 """
282 inputs: _DatasetScaffoldingDict
283 """Dictionary containing information about datasets used as regular,
284 graph-constraining inputs to this task (`_DatasetScaffoldingDict`).
285 """
287 outputs: _DatasetScaffoldingDict
288 """Dictionary containing information about datasets produced by this task
289 (`_DatasetScaffoldingDict`).
290 """
292 prerequisites: _DatasetScaffoldingDict
293 """Dictionary containing information about input datasets that must be
294 present in the repository before any Pipeline containing this task is run
295 (`_DatasetScaffoldingDict`).
296 """
298 dataIds: Set[ExpandedDataCoordinate]
299 """Data IDs for all quanta for this task in the graph (`set` of
300 `ExpandedDataCoordinate`).
302 Populated after construction by `_PipelineScaffolding.fillDataIds`.
303 """
305 quanta: List[Quantum]
306 """All quanta for this task in the graph (`list` of `Quantum`).
308 Populated after construction by `_PipelineScaffolding.fillQuanta`.
309 """
311 def addQuantum(self, quantum: Quantum):
312 config = self.taskDef.config
313 connectionClass = config.connections.ConnectionsClass
314 connectionInstance = connectionClass(config=config)
315 # This will raise if one of the check conditions is not met, which is the intended
316 # behavior
317 result = connectionInstance.adjustQuantum(quantum.predictedInputs)
318 quantum._predictedInputs = NamedKeyDict(result)
320 # If this function has reached this far add the quantum
321 self.quanta.append(quantum)
323 def makeQuantumGraphTaskNodes(self) -> QuantumGraphTaskNodes:
324 """Create a `QuantumGraphTaskNodes` instance from the information in
325 ``self``.
327 Returns
328 -------
329 nodes : `QuantumGraphTaskNodes`
330 The `QuantumGraph` elements corresponding to this task.
331 """
332 return QuantumGraphTaskNodes(
333 taskDef=self.taskDef,
334 quanta=self.quanta,
335 initInputs=self.initInputs.unpackRefs(),
336 initOutputs=self.initOutputs.unpackRefs(),
337 )
340@dataclass
341class _PipelineScaffolding:
342 """A helper data structure that organizes the information involved in
343 constructing a `QuantumGraph` for a `Pipeline`.
345 Parameters
346 ----------
347 pipeline : `Pipeline`
348 Sequence of tasks from which a graph is to be constructed. Must
349 have nested task classes already imported.
350 universe : `DimensionUniverse`
351 Universe of all possible dimensions.
353 Raises
354 ------
355 GraphBuilderError
356 Raised if the task's dimensions are not a subset of the union of the
357 pipeline's dataset dimensions.
359 Notes
360 -----
361 The scaffolding data structure contains nested data structures for both
362 tasks (`_TaskScaffolding`) and datasets (`_DatasetScaffolding`), with the
363 latter held by `_DatasetScaffoldingDict`. The dataset data structures are
364 shared between the pipeline-level structure (which aggregates all datasets
365 and categorizes them from the perspective of the complete pipeline) and the
366 individual tasks that use them as inputs and outputs.
368 `QuantumGraph` construction proceeds in five steps, with each corresponding
369 to a different `_PipelineScaffolding` method:
371 1. When `_PipelineScaffolding` is constructed, we extract and categorize
372 the DatasetTypes used by the pipeline (delegating to
373 `PipelineDatasetTypes.fromPipeline`), then use these to construct the
374 nested `_TaskScaffolding` and `_DatasetScaffolding` objects.
376 2. In `fillDataIds`, we construct and run the "Big Join Query", which
377 returns related tuples of all dimensions used to identify any regular
378 input, output, and intermediate datasets (not prerequisites). We then
379 iterate over these tuples of related dimensions, identifying the subsets
380 that correspond to distinct data IDs for each task and dataset type.
382 3. In `fillDatasetRefs`, we run follow-up queries against all of the
383 dataset data IDs previously identified, populating the
384 `_DatasetScaffolding.refs` lists - except for those for prerequisite
385 datasets, which cannot be resolved until distinct quanta are
386 identified.
388 4. In `fillQuanta`, we extract subsets from the lists of `DatasetRef` into
389 the inputs and outputs for each `Quantum` and search for prerequisite
390 datasets, populating `_TaskScaffolding.quanta`.
392 5. In `makeQuantumGraph`, we construct a `QuantumGraph` from the lists of
393 per-task quanta identified in the previous step.
394 """
395 def __init__(self, pipeline, *, registry):
396 self.tasks = []
397 # Aggregate and categorize the DatasetTypes in the Pipeline.
398 datasetTypes = PipelineDatasetTypes.fromPipeline(pipeline, registry=registry)
399 # Construct dictionaries that map those DatasetTypes to structures
400 # that will (later) hold addiitonal information about them.
401 for attr in ("initInputs", "initIntermediates", "initOutputs",
402 "inputs", "intermediates", "outputs", "prerequisites"):
403 setattr(self, attr, _DatasetScaffoldingDict.fromDatasetTypes(getattr(datasetTypes, attr),
404 universe=registry.dimensions))
405 # Aggregate all dimensions for all non-init, non-prerequisite
406 # DatasetTypes. These are the ones we'll include in the big join query.
407 self.dimensions = self.inputs.dimensions.union(self.intermediates.dimensions,
408 self.outputs.dimensions)
409 # Construct scaffolding nodes for each Task, and add backreferences
410 # to the Task from each DatasetScaffolding node.
411 # Note that there's only one scaffolding node for each DatasetType, shared by
412 # _PipelineScaffolding and all _TaskScaffoldings that reference it.
413 if isinstance(pipeline, Pipeline):
414 pipeline = pipeline.toExpandedPipeline()
415 self.tasks = [_TaskScaffolding(taskDef=taskDef, parent=self, datasetTypes=taskDatasetTypes)
416 for taskDef, taskDatasetTypes in zip(pipeline,
417 datasetTypes.byTask.values())]
419 tasks: List[_TaskScaffolding]
420 """Scaffolding data structures for each task in the pipeline
421 (`list` of `_TaskScaffolding`).
422 """
424 initInputs: _DatasetScaffoldingDict
425 """Datasets consumed but not produced when constructing the tasks in this
426 pipeline (`_DatasetScaffoldingDict`).
427 """
429 initIntermediates: _DatasetScaffoldingDict
430 """Datasets that are both consumed and produced when constructing the tasks
431 in this pipeline (`_DatasetScaffoldingDict`).
432 """
434 initOutputs: _DatasetScaffoldingDict
435 """Datasets produced but not consumed when constructing the tasks in this
436 pipeline (`_DatasetScaffoldingDict`).
437 """
439 inputs: _DatasetScaffoldingDict
440 """Datasets that are consumed but not produced when running this pipeline
441 (`_DatasetScaffoldingDict`).
442 """
444 intermediates: _DatasetScaffoldingDict
445 """Datasets that are both produced and consumed when running this pipeline
446 (`_DatasetScaffoldingDict`).
447 """
449 outputs: _DatasetScaffoldingDict
450 """Datasets produced but not consumed when when running this pipeline
451 (`_DatasetScaffoldingDict`).
452 """
454 prerequisites: _DatasetScaffoldingDict
455 """Datasets that are consumed when running this pipeline and looked up
456 per-Quantum when generating the graph (`_DatasetScaffoldingDict`).
457 """
459 dimensions: DimensionGraph
460 """All dimensions used by any regular input, intermediate, or output
461 (not prerequisite) dataset; the set of dimension used in the "Big Join
462 Query" (`DimensionGraph`).
464 This is required to be a superset of all task quantum dimensions.
465 """
467 def fillDataIds(self, registry, inputCollections, userQuery):
468 """Query for the data IDs that connect nodes in the `QuantumGraph`.
470 This method populates `_TaskScaffolding.dataIds` and
471 `_DatasetScaffolding.dataIds` (except for those in `prerequisites`).
473 Parameters
474 ----------
475 registry : `lsst.daf.butler.Registry`
476 Registry for the data repository; used for all data ID queries.
477 inputCollections : `~collections.abc.Mapping`
478 Mapping from dataset type name to an ordered sequence of
479 collections to search for that dataset. A `defaultdict` is
480 recommended for the case where the same collections should be
481 used for most datasets.
482 userQuery : `str`, optional
483 User-provided expression to limit the data IDs processed.
484 """
485 # Initialization datasets always have empty data IDs.
486 emptyDataId = ExpandedDataCoordinate(registry.dimensions.empty, (), records={})
487 for scaffolding in itertools.chain(self.initInputs.values(),
488 self.initIntermediates.values(),
489 self.initOutputs.values()):
490 scaffolding.dataIds.add(emptyDataId)
491 # Run one big query for the data IDs for task dimensions and regular
492 # inputs and outputs. We limit the query to only dimensions that are
493 # associated with the input dataset types, but don't (yet) try to
494 # obtain the dataset_ids for those inputs.
495 resultIter = registry.queryDimensions(
496 self.dimensions,
497 datasets={
498 datasetType: inputCollections[datasetType.name]
499 for datasetType in self.inputs
500 },
501 where=userQuery,
502 )
503 # Iterate over query results and populate the data IDs in
504 # self._TaskScaffolding.refs, extracting the subsets of the common data
505 # ID from the query corresponding to the dimensions of each. By using
506 # sets, we remove duplicates caused by query rows in which the
507 # dimensions that change are not relevant for that task or dataset
508 # type. For example, if the Big Join Query involves the dimensions
509 # (instrument, visit, detector, skymap, tract, patch), we extract
510 # "calexp" data IDs from the instrument, visit, and detector values
511 # only, and rely on `set.add` to avoid duplications due to result rows
512 # in which only skymap, tract, and patch are varying. The Big Join
513 # Query is defined such that only visit+detector and tract+patch
514 # combinations that represent spatial overlaps are included in the
515 # results.
516 for commonDataId in resultIter:
517 for taskScaffolding in self.tasks:
518 taskScaffolding.dataIds.add(commonDataId.subset(taskScaffolding.dimensions))
519 for datasetType, scaffolding in itertools.chain(self.inputs.items(),
520 self.intermediates.items(),
521 self.outputs.items()):
522 scaffolding.dataIds.add(commonDataId.subset(scaffolding.dimensions))
524 def fillDatasetRefs(self, registry, inputCollections, outputCollection, *,
525 skipExisting=True, clobberExisting=False):
526 """Perform follow up queries for each dataset data ID produced in
527 `fillDataIds`.
529 This method populates `_DatasetScaffolding.refs` (except for those in
530 `prerequisites`).
532 Parameters
533 ----------
534 registry : `lsst.daf.butler.Registry`
535 Registry for the data repository; used for all data ID queries.
536 inputCollections : `~collections.abc.Mapping`
537 Mapping from dataset type name to an ordered sequence of
538 collections to search for that dataset. A `defaultdict` is
539 recommended for the case where the same collections should be
540 used for most datasets.
541 outputCollection : `str`
542 Collection for all output datasets.
543 skipExisting : `bool`, optional
544 If `True` (default), a Quantum is not created if all its outputs
545 already exist.
546 clobberExisting : `bool`, optional
547 If `True`, overwrite any outputs that already exist. Cannot be
548 `True` if ``skipExisting`` is.
550 Raises
551 ------
552 ValueError
553 Raised if both `skipExisting` and `clobberExisting` are `True`.
554 OutputExistsError
555 Raised if an output dataset already exists in the output collection
556 and both ``skipExisting`` and ``clobberExisting`` are `False`. The
557 case where some but not all of a quantum's outputs are present and
558 ``skipExisting`` is `True` cannot be identified at this stage, and
559 is handled by `fillQuanta` instead.
560 """
561 if clobberExisting and skipExisting:
562 raise ValueError("clobberExisting and skipExisting cannot both be true.")
563 # Look up input and initInput datasets in the input collection(s).
564 for datasetType, scaffolding in itertools.chain(self.initInputs.items(), self.inputs.items()):
565 for dataId in scaffolding.dataIds:
566 refs = list(
567 registry.queryDatasets(
568 datasetType,
569 collections=inputCollections[datasetType.name],
570 dataId=dataId,
571 deduplicate=True,
572 expand=True,
573 )
574 )
575 assert len(refs) == 1, "BJQ guarantees exactly one input for each data ID."
576 scaffolding.refs.extend(refs)
577 # Look up [init] intermediate and output datasets in the output collection,
578 # unless clobberExisting is True (in which case we don't care if these
579 # already exist).
580 for datasetType, scaffolding in itertools.chain(self.initIntermediates.items(),
581 self.initOutputs.items(),
582 self.intermediates.items(),
583 self.outputs.items()):
584 for dataId in scaffolding.dataIds:
585 # TODO: we could easily support per-DatasetType clobberExisting
586 # and skipExisting (it might make sense to put them in
587 # originInfo), and I could imagine that being useful - it's
588 # probably required in order to support writing initOutputs
589 # before QuantumGraph generation.
590 if clobberExisting:
591 ref = None
592 else:
593 ref = registry.find(collection=outputCollection, datasetType=datasetType, dataId=dataId)
594 if ref is None:
595 ref = DatasetRef(datasetType, dataId)
596 elif not skipExisting:
597 raise OutputExistsError(f"Output dataset {datasetType.name} already exists in "
598 f"output collection {outputCollection} with data ID {dataId}.")
599 scaffolding.refs.append(ref)
600 # Prerequisite dataset lookups are deferred until fillQuanta.
602 def fillQuanta(self, registry, inputCollections, *, skipExisting=True):
603 """Define quanta for each task by splitting up the datasets associated
604 with each task data ID.
606 This method populates `_TaskScaffolding.quanta`.
608 Parameters
609 ----------
610 registry : `lsst.daf.butler.Registry`
611 Registry for the data repository; used for all data ID queries.
612 inputCollections : `~collections.abc.Mapping`
613 Mapping from dataset type name to an ordered sequence of
614 collections to search for that dataset. A `defaultdict` is
615 recommended for the case where the same collections should be
616 used for most datasets.
617 skipExisting : `bool`, optional
618 If `True` (default), a Quantum is not created if all its outputs
619 already exist.
620 """
621 for task in self.tasks:
622 for quantumDataId in task.dataIds:
623 # Identify the (regular) inputs that correspond to the Quantum
624 # with this data ID. These are those whose data IDs have the
625 # same values for all dimensions they have in common.
626 # We do this data IDs expanded to include implied dimensions,
627 # which is why _DatasetScaffolding.dimensions is thus expanded
628 # even though DatasetType.dimensions is not.
629 inputs = NamedKeyDict()
630 for datasetType, scaffolding in task.inputs.items():
631 inputs[datasetType] = [ref for ref, dataId in zip(scaffolding.refs, scaffolding.dataIds)
632 if quantumDataId.matches(dataId)]
633 # Same for outputs.
634 outputs = NamedKeyDict()
635 allOutputsPresent = True
636 for datasetType, scaffolding in task.outputs.items():
637 outputs[datasetType] = []
638 for ref, dataId in zip(scaffolding.refs, scaffolding.dataIds):
639 if quantumDataId.matches(dataId):
640 if ref.id is None:
641 allOutputsPresent = False
642 else:
643 assert skipExisting, "Existing outputs should have already been identified."
644 if not allOutputsPresent:
645 raise OutputExistsError(f"Output {datasetType.name} with data ID "
646 f"{dataId} already exists, but other outputs "
647 f"for task with label {task.taskDef.label} "
648 f"and data ID {quantumDataId} do not.")
649 outputs[datasetType].append(ref)
650 if allOutputsPresent and skipExisting:
651 continue
653 # Look up prerequisite datasets in the input collection(s).
654 # These may have dimensions that extend beyond those we queried
655 # for originally, because we want to permit those data ID
656 # values to differ across quanta and dataset types.
657 # For example, the same quantum may have a flat and bias with
658 # a different calibration_label, or a refcat with a skypix
659 # value that overlaps the quantum's data ID's region, but not
660 # the user expression used for the initial query.
661 connections = task.taskDef.connections
662 for con_name in connections.prerequisiteInputs:
663 con = getattr(connections, con_name)
664 for datasetType in task.prerequisites:
665 if datasetType.name == con.name:
666 break
667 if con.lookupFunction is not None:
668 refs = list(con.lookupFunction(datasetType, registry,
669 quantumDataId, inputCollections))
670 else:
671 refs = list(
672 registry.queryDatasets(
673 datasetType,
674 collections=inputCollections[con.name],
675 dataId=quantumDataId,
676 deduplicate=True,
677 expand=True,
678 )
679 )
680 inputs[datasetType] = refs
682 task.addQuantum(
683 Quantum(
684 taskName=task.taskDef.taskName,
685 taskClass=task.taskDef.taskClass,
686 dataId=quantumDataId,
687 initInputs=task.initInputs.unpackRefs(),
688 predictedInputs=inputs,
689 outputs=outputs,
690 )
691 )
693 def makeQuantumGraph(self):
694 """Create a `QuantumGraph` from the quanta already present in
695 the scaffolding data structure.
696 """
697 graph = QuantumGraph(task.makeQuantumGraphTaskNodes() for task in self.tasks)
698 graph.initInputs = self.initInputs.unpackRefs()
699 graph.initOutputs = self.initOutputs.unpackRefs()
700 graph.initIntermediates = self.initIntermediates.unpackRefs()
701 return graph
704# ------------------------
705# Exported definitions --
706# ------------------------
709class GraphBuilderError(Exception):
710 """Base class for exceptions generated by graph builder.
711 """
712 pass
715class OutputExistsError(GraphBuilderError):
716 """Exception generated when output datasets already exist.
717 """
718 pass
721class PrerequisiteMissingError(GraphBuilderError):
722 """Exception generated when a prerequisite dataset does not exist.
723 """
724 pass
727class GraphBuilder(object):
728 """GraphBuilder class is responsible for building task execution graph from
729 a Pipeline.
731 Parameters
732 ----------
733 registry : `~lsst.daf.butler.Registry`
734 Data butler instance.
735 skipExisting : `bool`, optional
736 If `True` (default), a Quantum is not created if all its outputs
737 already exist.
738 clobberExisting : `bool`, optional
739 If `True`, overwrite any outputs that already exist. Cannot be
740 `True` if ``skipExisting`` is.
741 """
743 def __init__(self, registry, skipExisting=True, clobberExisting=False):
744 self.registry = registry
745 self.dimensions = registry.dimensions
746 self.skipExisting = skipExisting
747 self.clobberExisting = clobberExisting
749 def makeGraph(self, pipeline, inputCollections, outputCollection, userQuery):
750 """Create execution graph for a pipeline.
752 Parameters
753 ----------
754 pipeline : `Pipeline`
755 Pipeline definition, task names/classes and their configs.
756 inputCollections : `~collections.abc.Mapping`
757 Mapping from dataset type name to an ordered sequence of
758 collections to search for that dataset. A `defaultdict` is
759 recommended for the case where the same collections should be
760 used for most datasets.
761 outputCollection : `str`
762 Collection for all output datasets.
763 userQuery : `str`
764 String which defunes user-defined selection for registry, should be
765 empty or `None` if there is no restrictions on data selection.
767 Returns
768 -------
769 graph : `QuantumGraph`
771 Raises
772 ------
773 UserExpressionError
774 Raised when user expression cannot be parsed.
775 OutputExistsError
776 Raised when output datasets already exist.
777 Exception
778 Other exceptions types may be raised by underlying registry
779 classes.
780 """
781 scaffolding = _PipelineScaffolding(pipeline, registry=self.registry)
783 scaffolding.fillDataIds(self.registry, inputCollections, userQuery)
784 scaffolding.fillDatasetRefs(self.registry, inputCollections, outputCollection,
785 skipExisting=self.skipExisting,
786 clobberExisting=self.clobberExisting)
787 scaffolding.fillQuanta(self.registry, inputCollections,
788 skipExisting=self.skipExisting)
790 return scaffolding.makeQuantumGraph()