Coverage for python/lsst/pipe/base/graphBuilder.py: 17%
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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
32import logging
33from collections import ChainMap
34from contextlib import contextmanager
35from dataclasses import dataclass
36from typing import Any, Collection, Dict, Iterable, Iterator, List, Mapping, Optional, Set, Tuple, Union
38from lsst.daf.butler import (
39 CollectionType,
40 DataCoordinate,
41 DatasetIdGenEnum,
42 DatasetRef,
43 DatasetType,
44 Datastore,
45 DatastoreRecordData,
46 DimensionGraph,
47 DimensionUniverse,
48 NamedKeyDict,
49 Quantum,
50 Registry,
51)
52from lsst.daf.butler.registry import MissingDatasetTypeError
53from lsst.daf.butler.registry.queries import DataCoordinateQueryResults
54from lsst.daf.butler.registry.wildcards import CollectionWildcard
55from lsst.utils import doImportType
57from ._datasetQueryConstraints import DatasetQueryConstraintVariant
58from ._status import NoWorkFound
60# -----------------------------
61# Imports for other modules --
62# -----------------------------
63from .connections import AdjustQuantumHelper, iterConnections
64from .graph import QuantumGraph
65from .pipeline import Pipeline, PipelineDatasetTypes, TaskDatasetTypes, TaskDef
67# ----------------------------------
68# Local non-exported definitions --
69# ----------------------------------
71_LOG = logging.getLogger(__name__)
74class _DatasetDict(NamedKeyDict[DatasetType, Dict[DataCoordinate, DatasetRef]]):
75 """A custom dictionary that maps `DatasetType` to a nested dictionary of
76 the known `DatasetRef` instances of that type.
78 Parameters
79 ----------
80 args
81 Positional arguments are forwarded to the `dict` constructor.
82 universe : `DimensionUniverse`
83 Universe of all possible dimensions.
84 """
86 def __init__(self, *args: Any, universe: DimensionUniverse):
87 super().__init__(*args)
88 self.universe = universe
90 @classmethod
91 def fromDatasetTypes(
92 cls, datasetTypes: Iterable[DatasetType], *, universe: DimensionUniverse
93 ) -> _DatasetDict:
94 """Construct a dictionary from a flat iterable of `DatasetType` keys.
96 Parameters
97 ----------
98 datasetTypes : `iterable` of `DatasetType`
99 DatasetTypes to use as keys for the dict. Values will be empty
100 dictionaries.
101 universe : `DimensionUniverse`
102 Universe of all possible dimensions.
104 Returns
105 -------
106 dictionary : `_DatasetDict`
107 A new `_DatasetDict` instance.
108 """
109 return cls({datasetType: {} for datasetType in datasetTypes}, universe=universe)
111 @classmethod
112 def fromSubset(
113 cls, datasetTypes: Collection[DatasetType], first: _DatasetDict, *rest: _DatasetDict
114 ) -> _DatasetDict:
115 """Return a new dictionary by extracting items corresponding to the
116 given keys from one or more existing dictionaries.
118 Parameters
119 ----------
120 datasetTypes : `iterable` of `DatasetType`
121 DatasetTypes to use as keys for the dict. Values will be obtained
122 by lookups against ``first`` and ``rest``.
123 first : `_DatasetDict`
124 Another dictionary from which to extract values.
125 rest
126 Additional dictionaries from which to extract values.
128 Returns
129 -------
130 dictionary : `_DatasetDict`
131 A new dictionary instance.
132 """
133 combined = ChainMap(first, *rest)
135 # Dataset types known to match immediately can be processed
136 # without checks.
137 matches = combined.keys() & set(datasetTypes)
138 _dict = {k: combined[k] for k in matches}
140 if len(_dict) < len(datasetTypes):
141 # Work out which ones are missing.
142 missing_datasetTypes = set(datasetTypes) - _dict.keys()
144 # Get the known names for comparison.
145 combined_by_name = {k.name: k for k in combined}
147 missing = set()
148 incompatible = {}
149 for datasetType in missing_datasetTypes:
150 # The dataset type is not found. It may not be listed
151 # or it may be that it is there with the same name
152 # but different definition.
153 if datasetType.name in combined_by_name:
154 # This implies some inconsistency in definitions
155 # for connections. If there is support for storage
156 # class conversion we can let it slide.
157 # At this point we do not know
158 # where the inconsistency is but trust that down
159 # stream code will be more explicit about input
160 # vs output incompatibilities.
161 existing = combined_by_name[datasetType.name]
162 if existing.is_compatible_with(datasetType) or datasetType.is_compatible_with(existing):
163 _LOG.warning(
164 "Dataset type mismatch (%s != %s) but continuing since they are compatible",
165 datasetType,
166 existing,
167 )
168 _dict[datasetType] = combined[existing]
169 else:
170 incompatible[datasetType] = existing
171 else:
172 missing.add(datasetType)
174 if missing or incompatible:
175 reasons = []
176 if missing:
177 reasons.append(
178 "DatasetTypes {'.'.join(missing)} not present in list of known types: "
179 + ", ".join(d.name for d in combined)
180 )
181 if incompatible:
182 for x, y in incompatible.items():
183 reasons.append(f"{x} incompatible with {y}")
184 raise KeyError("Errors matching dataset types: " + " & ".join(reasons))
186 return cls(_dict, universe=first.universe)
188 @property
189 def dimensions(self) -> DimensionGraph:
190 """The union of all dimensions used by all dataset types in this
191 dictionary, including implied dependencies (`DimensionGraph`).
192 """
193 base = self.universe.empty
194 if len(self) == 0:
195 return base
196 return base.union(*[datasetType.dimensions for datasetType in self.keys()])
198 def unpackSingleRefs(self) -> NamedKeyDict[DatasetType, DatasetRef]:
199 """Unpack nested single-element `DatasetRef` dicts into a new
200 mapping with `DatasetType` keys and `DatasetRef` values.
202 This method assumes that each nest contains exactly one item, as is the
203 case for all "init" datasets.
205 Returns
206 -------
207 dictionary : `NamedKeyDict`
208 Dictionary mapping `DatasetType` to `DatasetRef`, with both
209 `DatasetType` instances and string names usable as keys.
210 """
212 def getOne(refs: Dict[DataCoordinate, DatasetRef]) -> DatasetRef:
213 (ref,) = refs.values()
214 return ref
216 return NamedKeyDict({datasetType: getOne(refs) for datasetType, refs in self.items()})
218 def unpackMultiRefs(self) -> NamedKeyDict[DatasetType, List[DatasetRef]]:
219 """Unpack nested multi-element `DatasetRef` dicts into a new
220 mapping with `DatasetType` keys and `set` of `DatasetRef` values.
222 Returns
223 -------
224 dictionary : `NamedKeyDict`
225 Dictionary mapping `DatasetType` to `list` of `DatasetRef`, with
226 both `DatasetType` instances and string names usable as keys.
227 """
228 return NamedKeyDict({datasetType: list(refs.values()) for datasetType, refs in self.items()})
230 def extract(self, datasetType: DatasetType, dataIds: Iterable[DataCoordinate]) -> Iterator[DatasetRef]:
231 """Iterate over the contained `DatasetRef` instances that match the
232 given `DatasetType` and data IDs.
234 Parameters
235 ----------
236 datasetType : `DatasetType`
237 Dataset type to match.
238 dataIds : `Iterable` [ `DataCoordinate` ]
239 Data IDs to match.
241 Returns
242 -------
243 refs : `Iterator` [ `DatasetRef` ]
244 DatasetRef instances for which ``ref.datasetType == datasetType``
245 and ``ref.dataId`` is in ``dataIds``.
246 """
247 refs = self[datasetType]
248 return (refs[dataId] for dataId in dataIds)
251class _QuantumScaffolding:
252 """Helper class aggregating information about a `Quantum`, used when
253 constructing a `QuantumGraph`.
255 See `_PipelineScaffolding` for a top-down description of the full
256 scaffolding data structure.
258 Parameters
259 ----------
260 task : _TaskScaffolding
261 Back-reference to the helper object for the `PipelineTask` this quantum
262 represents an execution of.
263 dataId : `DataCoordinate`
264 Data ID for this quantum.
265 """
267 def __init__(self, task: _TaskScaffolding, dataId: DataCoordinate):
268 self.task = task
269 self.dataId = dataId
270 self.inputs = _DatasetDict.fromDatasetTypes(task.inputs.keys(), universe=dataId.universe)
271 self.outputs = _DatasetDict.fromDatasetTypes(task.outputs.keys(), universe=dataId.universe)
272 self.prerequisites = _DatasetDict.fromDatasetTypes(
273 task.prerequisites.keys(), universe=dataId.universe
274 )
276 __slots__ = ("task", "dataId", "inputs", "outputs", "prerequisites")
278 def __repr__(self) -> str:
279 return f"_QuantumScaffolding(taskDef={self.task.taskDef}, dataId={self.dataId}, ...)"
281 task: _TaskScaffolding
282 """Back-reference to the helper object for the `PipelineTask` this quantum
283 represents an execution of.
284 """
286 dataId: DataCoordinate
287 """Data ID for this quantum.
288 """
290 inputs: _DatasetDict
291 """Nested dictionary containing `DatasetRef` inputs to this quantum.
293 This is initialized to map each `DatasetType` to an empty dictionary at
294 construction. Those nested dictionaries are populated (with data IDs as
295 keys) with unresolved `DatasetRef` instances in
296 `_PipelineScaffolding.connectDataIds`.
297 """
299 outputs: _DatasetDict
300 """Nested dictionary containing `DatasetRef` outputs this quantum.
301 """
303 prerequisites: _DatasetDict
304 """Nested dictionary containing `DatasetRef` prerequisite inputs to this
305 quantum.
306 """
308 def makeQuantum(self, datastore_records: Optional[Mapping[str, DatastoreRecordData]] = None) -> Quantum:
309 """Transform the scaffolding object into a true `Quantum` instance.
311 Parameters
312 ----------
313 datastore_records : `dict` [ `str`, `DatastoreRecordData` ], optional
314 If not `None` then fill datastore records in each generated Quantum
315 using the records from this structure.
317 Returns
318 -------
319 quantum : `Quantum`
320 An actual `Quantum` instance.
321 """
322 allInputs = self.inputs.unpackMultiRefs()
323 allInputs.update(self.prerequisites.unpackMultiRefs())
324 # Give the task's Connections class an opportunity to remove some
325 # inputs, or complain if they are unacceptable.
326 # This will raise if one of the check conditions is not met, which is
327 # the intended behavior.
328 # If it raises NotWorkFound, there is a bug in the QG algorithm
329 # or the adjustQuantum is incorrectly trying to make a prerequisite
330 # input behave like a regular input; adjustQuantum should only raise
331 # NoWorkFound if a regular input is missing, and it shouldn't be
332 # possible for us to have generated ``self`` if that's true.
333 helper = AdjustQuantumHelper(inputs=allInputs, outputs=self.outputs.unpackMultiRefs())
334 helper.adjust_in_place(self.task.taskDef.connections, self.task.taskDef.label, self.dataId)
335 initInputs = self.task.initInputs.unpackSingleRefs()
336 quantum_records: Optional[Mapping[str, DatastoreRecordData]] = None
337 if datastore_records is not None:
338 quantum_records = {}
339 input_refs = list(itertools.chain.from_iterable(helper.inputs.values()))
340 input_refs += list(initInputs.values())
341 input_ids = set(ref.id for ref in input_refs if ref.id is not None)
342 for datastore_name, records in datastore_records.items():
343 matching_records = records.subset(input_ids)
344 if matching_records is not None:
345 quantum_records[datastore_name] = matching_records
346 return Quantum(
347 taskName=self.task.taskDef.taskName,
348 taskClass=self.task.taskDef.taskClass,
349 dataId=self.dataId,
350 initInputs=initInputs,
351 inputs=helper.inputs,
352 outputs=helper.outputs,
353 datastore_records=quantum_records,
354 )
357@dataclass
358class _TaskScaffolding:
359 """Helper class aggregating information about a `PipelineTask`, used when
360 constructing a `QuantumGraph`.
362 See `_PipelineScaffolding` for a top-down description of the full
363 scaffolding data structure.
365 Parameters
366 ----------
367 taskDef : `TaskDef`
368 Data structure that identifies the task class and its config.
369 parent : `_PipelineScaffolding`
370 The parent data structure that will hold the instance being
371 constructed.
372 datasetTypes : `TaskDatasetTypes`
373 Data structure that categorizes the dataset types used by this task.
374 """
376 def __init__(self, taskDef: TaskDef, parent: _PipelineScaffolding, datasetTypes: TaskDatasetTypes):
377 universe = parent.dimensions.universe
378 self.taskDef = taskDef
379 self.dimensions = DimensionGraph(universe, names=taskDef.connections.dimensions)
380 assert self.dimensions.issubset(parent.dimensions)
381 # Initialize _DatasetDicts as subsets of the one or two
382 # corresponding dicts in the parent _PipelineScaffolding.
383 self.initInputs = _DatasetDict.fromSubset(
384 datasetTypes.initInputs, parent.initInputs, parent.initIntermediates
385 )
386 self.initOutputs = _DatasetDict.fromSubset(
387 datasetTypes.initOutputs, parent.initIntermediates, parent.initOutputs
388 )
389 self.inputs = _DatasetDict.fromSubset(datasetTypes.inputs, parent.inputs, parent.intermediates)
390 self.outputs = _DatasetDict.fromSubset(datasetTypes.outputs, parent.intermediates, parent.outputs)
391 self.prerequisites = _DatasetDict.fromSubset(datasetTypes.prerequisites, parent.prerequisites)
392 self.dataIds: Set[DataCoordinate] = set()
393 self.quanta = {}
395 def __repr__(self) -> str:
396 # Default dataclass-injected __repr__ gets caught in an infinite loop
397 # because of back-references.
398 return f"_TaskScaffolding(taskDef={self.taskDef}, ...)"
400 taskDef: TaskDef
401 """Data structure that identifies the task class and its config
402 (`TaskDef`).
403 """
405 dimensions: DimensionGraph
406 """The dimensions of a single `Quantum` of this task (`DimensionGraph`).
407 """
409 initInputs: _DatasetDict
410 """Dictionary containing information about datasets used to construct this
411 task (`_DatasetDict`).
412 """
414 initOutputs: _DatasetDict
415 """Dictionary containing information about datasets produced as a
416 side-effect of constructing this task (`_DatasetDict`).
417 """
419 inputs: _DatasetDict
420 """Dictionary containing information about datasets used as regular,
421 graph-constraining inputs to this task (`_DatasetDict`).
422 """
424 outputs: _DatasetDict
425 """Dictionary containing information about datasets produced by this task
426 (`_DatasetDict`).
427 """
429 prerequisites: _DatasetDict
430 """Dictionary containing information about input datasets that must be
431 present in the repository before any Pipeline containing this task is run
432 (`_DatasetDict`).
433 """
435 quanta: Dict[DataCoordinate, _QuantumScaffolding]
436 """Dictionary mapping data ID to a scaffolding object for the Quantum of
437 this task with that data ID.
438 """
440 def makeQuantumSet(
441 self,
442 unresolvedRefs: Optional[Set[DatasetRef]] = None,
443 datastore_records: Optional[Mapping[str, DatastoreRecordData]] = None,
444 ) -> Set[Quantum]:
445 """Create a `set` of `Quantum` from the information in ``self``.
447 Parameters
448 ----------
449 unresolvedRefs : `set` [ `DatasetRef` ], optional
450 Input dataset refs that have not been found.
451 datastore_records : `dict`
454 Returns
455 -------
456 nodes : `set` of `Quantum`
457 The `Quantum` elements corresponding to this task.
458 """
459 if unresolvedRefs is None:
460 unresolvedRefs = set()
461 outputs = set()
462 for q in self.quanta.values():
463 try:
464 tmpQuanta = q.makeQuantum(datastore_records)
465 outputs.add(tmpQuanta)
466 except (NoWorkFound, FileNotFoundError) as exc:
467 refs = itertools.chain.from_iterable(self.inputs.unpackMultiRefs().values())
468 if unresolvedRefs.intersection(refs):
469 # This means it is a node that is Known to be pruned
470 # later and should be left in even though some follow up
471 # queries fail. This allows the pruning to start from this
472 # quantum with known issues, and prune other nodes it
473 # touches
474 inputs = q.inputs.unpackMultiRefs()
475 inputs.update(q.prerequisites.unpackMultiRefs())
476 tmpQuantum = Quantum(
477 taskName=q.task.taskDef.taskName,
478 taskClass=q.task.taskDef.taskClass,
479 dataId=q.dataId,
480 initInputs=q.task.initInputs.unpackSingleRefs(),
481 inputs=inputs,
482 outputs=q.outputs.unpackMultiRefs(),
483 )
484 outputs.add(tmpQuantum)
485 else:
486 raise exc
487 return outputs
490class _DatasetIdMaker:
491 """Helper class which generates random dataset UUIDs for unresolved
492 datasets.
493 """
495 def __init__(self, registry: Registry, run: str):
496 self.datasetIdFactory = registry.datasetIdFactory
497 self.run = run
498 # Dataset IDs generated so far
499 self.resolved: Dict[Tuple[DatasetType, DataCoordinate], DatasetRef] = {}
501 def resolveRef(self, ref: DatasetRef) -> DatasetRef:
502 if ref.id is not None:
503 return ref
504 key = ref.datasetType, ref.dataId
505 if (resolved := self.resolved.get(key)) is None:
506 datasetId = self.datasetIdFactory.makeDatasetId(
507 self.run, ref.datasetType, ref.dataId, DatasetIdGenEnum.UNIQUE
508 )
509 resolved = ref.resolved(datasetId, self.run)
510 self.resolved[key] = resolved
511 return resolved
513 def resolveDict(self, refs: Dict[DataCoordinate, DatasetRef]) -> Dict[DataCoordinate, DatasetRef]:
514 """Resolve all unresolved references in the provided dictionary."""
515 return {dataId: self.resolveRef(ref) for dataId, ref in refs.items()}
518@dataclass
519class _PipelineScaffolding:
520 """A helper data structure that organizes the information involved in
521 constructing a `QuantumGraph` for a `Pipeline`.
523 Parameters
524 ----------
525 pipeline : `Pipeline` or `Iterable` [ `TaskDef` ]
526 Sequence of tasks from which a graph is to be constructed. Must
527 have nested task classes already imported.
528 universe : `DimensionUniverse`
529 Universe of all possible dimensions.
531 Notes
532 -----
533 The scaffolding data structure contains nested data structures for both
534 tasks (`_TaskScaffolding`) and datasets (`_DatasetDict`). The dataset
535 data structures are shared between the pipeline-level structure (which
536 aggregates all datasets and categorizes them from the perspective of the
537 complete pipeline) and the individual tasks that use them as inputs and
538 outputs.
540 `QuantumGraph` construction proceeds in four steps, with each corresponding
541 to a different `_PipelineScaffolding` method:
543 1. When `_PipelineScaffolding` is constructed, we extract and categorize
544 the DatasetTypes used by the pipeline (delegating to
545 `PipelineDatasetTypes.fromPipeline`), then use these to construct the
546 nested `_TaskScaffolding` and `_DatasetDict` objects.
548 2. In `connectDataIds`, we construct and run the "Big Join Query", which
549 returns related tuples of all dimensions used to identify any regular
550 input, output, and intermediate datasets (not prerequisites). We then
551 iterate over these tuples of related dimensions, identifying the subsets
552 that correspond to distinct data IDs for each task and dataset type,
553 and then create `_QuantumScaffolding` objects.
555 3. In `resolveDatasetRefs`, we run follow-up queries against all of the
556 dataset data IDs previously identified, transforming unresolved
557 DatasetRefs into resolved DatasetRefs where appropriate. We then look
558 up prerequisite datasets for all quanta.
560 4. In `makeQuantumGraph`, we construct a `QuantumGraph` from the lists of
561 per-task `_QuantumScaffolding` objects.
562 """
564 def __init__(self, pipeline: Union[Pipeline, Iterable[TaskDef]], *, registry: Registry):
565 _LOG.debug("Initializing data structures for QuantumGraph generation.")
566 self.tasks = []
567 # Aggregate and categorize the DatasetTypes in the Pipeline.
568 datasetTypes = PipelineDatasetTypes.fromPipeline(pipeline, registry=registry)
569 # Construct dictionaries that map those DatasetTypes to structures
570 # that will (later) hold addiitonal information about them.
571 for attr in (
572 "initInputs",
573 "initIntermediates",
574 "initOutputs",
575 "inputs",
576 "intermediates",
577 "outputs",
578 "prerequisites",
579 ):
580 setattr(
581 self,
582 attr,
583 _DatasetDict.fromDatasetTypes(getattr(datasetTypes, attr), universe=registry.dimensions),
584 )
585 # Aggregate all dimensions for all non-init, non-prerequisite
586 # DatasetTypes. These are the ones we'll include in the big join
587 # query.
588 self.dimensions = self.inputs.dimensions.union(self.intermediates.dimensions, self.outputs.dimensions)
589 # Construct scaffolding nodes for each Task, and add backreferences
590 # to the Task from each DatasetScaffolding node.
591 # Note that there's only one scaffolding node for each DatasetType,
592 # shared by _PipelineScaffolding and all _TaskScaffoldings that
593 # reference it.
594 if isinstance(pipeline, Pipeline):
595 pipeline = pipeline.toExpandedPipeline()
596 self.tasks = [
597 _TaskScaffolding(taskDef=taskDef, parent=self, datasetTypes=taskDatasetTypes)
598 for taskDef, taskDatasetTypes in zip(pipeline, datasetTypes.byTask.values())
599 ]
601 def __repr__(self) -> str:
602 # Default dataclass-injected __repr__ gets caught in an infinite loop
603 # because of back-references.
604 return f"_PipelineScaffolding(tasks={self.tasks}, ...)"
606 tasks: List[_TaskScaffolding]
607 """Scaffolding data structures for each task in the pipeline
608 (`list` of `_TaskScaffolding`).
609 """
611 initInputs: _DatasetDict
612 """Datasets consumed but not produced when constructing the tasks in this
613 pipeline (`_DatasetDict`).
614 """
616 initIntermediates: _DatasetDict
617 """Datasets that are both consumed and produced when constructing the tasks
618 in this pipeline (`_DatasetDict`).
619 """
621 initOutputs: _DatasetDict
622 """Datasets produced but not consumed when constructing the tasks in this
623 pipeline (`_DatasetDict`).
624 """
626 inputs: _DatasetDict
627 """Datasets that are consumed but not produced when running this pipeline
628 (`_DatasetDict`).
629 """
631 intermediates: _DatasetDict
632 """Datasets that are both produced and consumed when running this pipeline
633 (`_DatasetDict`).
634 """
636 outputs: _DatasetDict
637 """Datasets produced but not consumed when when running this pipeline
638 (`_DatasetDict`).
639 """
641 prerequisites: _DatasetDict
642 """Datasets that are consumed when running this pipeline and looked up
643 per-Quantum when generating the graph (`_DatasetDict`).
644 """
646 dimensions: DimensionGraph
647 """All dimensions used by any regular input, intermediate, or output
648 (not prerequisite) dataset; the set of dimension used in the "Big Join
649 Query" (`DimensionGraph`).
651 This is required to be a superset of all task quantum dimensions.
652 """
654 @contextmanager
655 def connectDataIds(
656 self,
657 registry: Registry,
658 collections: Any,
659 userQuery: Optional[str],
660 externalDataId: DataCoordinate,
661 datasetQueryConstraint: DatasetQueryConstraintVariant = DatasetQueryConstraintVariant.ALL,
662 ) -> Iterator[DataCoordinateQueryResults]:
663 """Query for the data IDs that connect nodes in the `QuantumGraph`.
665 This method populates `_TaskScaffolding.dataIds` and
666 `_DatasetScaffolding.dataIds` (except for those in `prerequisites`).
668 Parameters
669 ----------
670 registry : `lsst.daf.butler.Registry`
671 Registry for the data repository; used for all data ID queries.
672 collections
673 Expressions representing the collections to search for input
674 datasets. See :ref:`daf_butler_ordered_collection_searches`.
675 userQuery : `str` or `None`
676 User-provided expression to limit the data IDs processed.
677 externalDataId : `DataCoordinate`
678 Externally-provided data ID that should be used to restrict the
679 results, just as if these constraints had been included via ``AND``
680 in ``userQuery``. This includes (at least) any instrument named
681 in the pipeline definition.
682 datasetQueryConstraint : `DatasetQueryConstraintVariant`, optional
683 The query constraint variant that should be used to constraint the
684 query based on dataset existance, defaults to
685 `DatasetQueryConstraintVariant.ALL`.
687 Returns
688 -------
689 commonDataIds : \
690 `lsst.daf.butler.registry.queries.DataCoordinateQueryResults`
691 An interface to a database temporary table containing all data IDs
692 that will appear in this `QuantumGraph`. Returned inside a
693 context manager, which will drop the temporary table at the end of
694 the `with` block in which this method is called.
695 """
696 _LOG.debug("Building query for data IDs.")
697 # Initialization datasets always have empty data IDs.
698 emptyDataId = DataCoordinate.makeEmpty(registry.dimensions)
699 for datasetType, refs in itertools.chain(
700 self.initInputs.items(), self.initIntermediates.items(), self.initOutputs.items()
701 ):
702 refs[emptyDataId] = DatasetRef(datasetType, emptyDataId)
703 # Run one big query for the data IDs for task dimensions and regular
704 # inputs and outputs. We limit the query to only dimensions that are
705 # associated with the input dataset types, but don't (yet) try to
706 # obtain the dataset_ids for those inputs.
707 _LOG.debug("Submitting data ID query and materializing results.")
708 queryArgs: Dict[str, Any] = {
709 "dimensions": self.dimensions,
710 "where": userQuery,
711 "dataId": externalDataId,
712 }
713 if datasetQueryConstraint == DatasetQueryConstraintVariant.ALL:
714 _LOG.debug("Constraining graph query using all datasets in pipeline.")
715 queryArgs["datasets"] = list(self.inputs)
716 queryArgs["collections"] = collections
717 elif datasetQueryConstraint == DatasetQueryConstraintVariant.OFF:
718 _LOG.debug("Not using dataset existence to constrain query.")
719 elif datasetQueryConstraint == DatasetQueryConstraintVariant.LIST:
720 constraint = set(datasetQueryConstraint)
721 inputs = {k.name: k for k in self.inputs.keys()}
722 if remainder := constraint.difference(inputs.keys()):
723 raise ValueError(
724 f"{remainder} dataset type(s) specified as a graph constraint, but"
725 f" do not appear as an input to the specified pipeline: {inputs.keys()}"
726 )
727 _LOG.debug(f"Constraining graph query using {constraint}")
728 queryArgs["datasets"] = [typ for name, typ in inputs.items() if name in constraint]
729 queryArgs["collections"] = collections
730 else:
731 raise ValueError(
732 f"Unable to handle type {datasetQueryConstraint} given as datasetQueryConstraint."
733 )
735 with registry.queryDataIds(**queryArgs).materialize() as commonDataIds:
736 _LOG.debug("Expanding data IDs.")
737 commonDataIds = commonDataIds.expanded()
738 _LOG.debug("Iterating over query results to associate quanta with datasets.")
739 # Iterate over query results, populating data IDs for datasets and
740 # quanta and then connecting them to each other.
741 n = -1
742 for n, commonDataId in enumerate(commonDataIds):
743 _LOG.debug("Next DataID = %s", commonDataId)
744 # Create DatasetRefs for all DatasetTypes from this result row,
745 # noting that we might have created some already.
746 # We remember both those that already existed and those that we
747 # create now.
748 refsForRow = {}
749 dataIdCacheForRow: Dict[DimensionGraph, DataCoordinate] = {}
750 for datasetType, refs in itertools.chain(
751 self.inputs.items(), self.intermediates.items(), self.outputs.items()
752 ):
753 datasetDataId: Optional[DataCoordinate]
754 if (datasetDataId := dataIdCacheForRow.get(datasetType.dimensions)) is None:
755 datasetDataId = commonDataId.subset(datasetType.dimensions)
756 dataIdCacheForRow[datasetType.dimensions] = datasetDataId
757 ref = refs.get(datasetDataId)
758 if ref is None:
759 ref = DatasetRef(datasetType, datasetDataId)
760 _LOG.debug("Made new ref = %s", ref)
761 refs[datasetDataId] = ref
762 refsForRow[datasetType.name] = ref
763 # Create _QuantumScaffolding objects for all tasks from this
764 # result row, noting that we might have created some already.
765 for task in self.tasks:
766 quantumDataId = commonDataId.subset(task.dimensions)
767 quantum = task.quanta.get(quantumDataId)
768 if quantum is None:
769 quantum = _QuantumScaffolding(task=task, dataId=quantumDataId)
770 task.quanta[quantumDataId] = quantum
771 # Whether this is a new quantum or an existing one, we can
772 # now associate the DatasetRefs for this row with it. The
773 # fact that a Quantum data ID and a dataset data ID both
774 # came from the same result row is what tells us they
775 # should be associated.
776 # Many of these associates will be duplicates (because
777 # another query row that differed from this one only in
778 # irrelevant dimensions already added them), and we use
779 # sets to skip.
780 for datasetType in task.inputs:
781 ref = refsForRow[datasetType.name]
782 quantum.inputs[datasetType.name][ref.dataId] = ref
783 for datasetType in task.outputs:
784 ref = refsForRow[datasetType.name]
785 quantum.outputs[datasetType.name][ref.dataId] = ref
786 if n < 0:
787 _LOG.critical("Initial data ID query returned no rows, so QuantumGraph will be empty.")
788 emptiness_explained = False
789 for message in commonDataIds.explain_no_results():
790 _LOG.critical(message)
791 emptiness_explained = True
792 if not emptiness_explained:
793 _LOG.critical(
794 "To reproduce this query for debugging purposes, run "
795 "Registry.queryDataIds with these arguments:"
796 )
797 # We could just repr() the queryArgs dict to get something
798 # the user could make sense of, but it's friendlier to
799 # put these args in an easier-to-construct equivalent form
800 # so they can read it more easily and copy and paste into
801 # a Python terminal.
802 _LOG.critical(" dimensions=%s,", list(queryArgs["dimensions"].names))
803 _LOG.critical(" dataId=%s,", queryArgs["dataId"].byName())
804 if queryArgs["where"]:
805 _LOG.critical(" where=%s,", repr(queryArgs["where"]))
806 if "datasets" in queryArgs:
807 _LOG.critical(" datasets=%s,", [t.name for t in queryArgs["datasets"]])
808 if "collections" in queryArgs:
809 _LOG.critical(" collections=%s,", list(queryArgs["collections"]))
810 _LOG.debug("Finished processing %d rows from data ID query.", n)
811 yield commonDataIds
813 def resolveDatasetRefs(
814 self,
815 registry: Registry,
816 collections: Any,
817 run: Optional[str],
818 commonDataIds: DataCoordinateQueryResults,
819 *,
820 skipExistingIn: Any = None,
821 clobberOutputs: bool = True,
822 constrainedByAllDatasets: bool = True,
823 resolveRefs: bool = False,
824 ) -> None:
825 """Perform follow up queries for each dataset data ID produced in
826 `fillDataIds`.
828 This method populates `_DatasetScaffolding.refs` (except for those in
829 `prerequisites`).
831 Parameters
832 ----------
833 registry : `lsst.daf.butler.Registry`
834 Registry for the data repository; used for all data ID queries.
835 collections
836 Expressions representing the collections to search for input
837 datasets. See :ref:`daf_butler_ordered_collection_searches`.
838 run : `str`, optional
839 Name of the `~lsst.daf.butler.CollectionType.RUN` collection for
840 output datasets, if it already exists.
841 commonDataIds : \
842 `lsst.daf.butler.registry.queries.DataCoordinateQueryResults`
843 Result of a previous call to `connectDataIds`.
844 skipExistingIn
845 Expressions representing the collections to search for existing
846 output datasets that should be skipped. See
847 :ref:`daf_butler_ordered_collection_searches` for allowed types.
848 `None` or empty string/sequence disables skipping.
849 clobberOutputs : `bool`, optional
850 If `True` (default), allow quanta to created even if outputs exist;
851 this requires the same behavior behavior to be enabled when
852 executing. If ``skipExistingIn`` is not `None`, completed quanta
853 (those with metadata, or all outputs if there is no metadata
854 dataset configured) will be skipped rather than clobbered.
855 constrainedByAllDatasets : `bool`, optional
856 Indicates if the commonDataIds were generated with a constraint on
857 all dataset types.
858 resolveRefs : `bool`, optional
859 If `True` then resolve all input references and generate random
860 dataset IDs for all output and intermediate datasets. True value
861 requires ``run`` collection to be specified.
863 Raises
864 ------
865 OutputExistsError
866 Raised if an output dataset already exists in the output run
867 and ``skipExistingIn`` does not include output run, or if only
868 some outputs are present and ``clobberOutputs`` is `False`.
869 """
870 skip_collections_wildcard: CollectionWildcard | None = None
871 skipExistingInRun = False
872 if skipExistingIn:
873 skip_collections_wildcard = CollectionWildcard.from_expression(skipExistingIn)
874 if run:
875 # as optimization check in the explicit list of names first
876 skipExistingInRun = run in skip_collections_wildcard.strings
877 if not skipExistingInRun:
878 # need to flatten it and check again
879 skipExistingInRun = run in registry.queryCollections(
880 skipExistingIn,
881 collectionTypes=CollectionType.RUN,
882 )
884 idMaker: Optional[_DatasetIdMaker] = None
885 if resolveRefs:
886 assert run is not None, "run cannot be None when resolveRefs is True"
887 idMaker = _DatasetIdMaker(registry, run)
889 resolvedRefQueryResults: Iterable[DatasetRef]
891 # Look up [init] intermediate and output datasets in the output
892 # collection, if there is an output collection.
893 if run is not None or skip_collections_wildcard is not None:
894 for datasetType, refs in itertools.chain(
895 self.initIntermediates.items(),
896 self.initOutputs.items(),
897 self.intermediates.items(),
898 self.outputs.items(),
899 ):
900 _LOG.debug(
901 "Resolving %d datasets for intermediate and/or output dataset %s.",
902 len(refs),
903 datasetType.name,
904 )
905 isInit = datasetType in self.initIntermediates or datasetType in self.initOutputs
906 subset = commonDataIds.subset(datasetType.dimensions, unique=True)
908 # look at RUN collection first
909 if run is not None:
910 try:
911 resolvedRefQueryResults = subset.findDatasets(
912 datasetType, collections=run, findFirst=True
913 )
914 except MissingDatasetTypeError:
915 resolvedRefQueryResults = []
916 for resolvedRef in resolvedRefQueryResults:
917 # TODO: we could easily support per-DatasetType
918 # skipExisting and I could imagine that being useful -
919 # it's probably required in order to support writing
920 # initOutputs before QuantumGraph generation.
921 assert resolvedRef.dataId in refs
922 if not (skipExistingInRun or isInit or clobberOutputs):
923 raise OutputExistsError(
924 f"Output dataset {datasetType.name} already exists in "
925 f"output RUN collection '{run}' with data ID"
926 f" {resolvedRef.dataId}."
927 )
928 # If we are going to resolve all outputs then we have
929 # to remember existing ones to avoid generating new
930 # dataset IDs for them.
931 if resolveRefs:
932 refs[resolvedRef.dataId] = resolvedRef
934 # And check skipExistingIn too, if RUN collection is in
935 # it is handled above
936 if skip_collections_wildcard is not None:
937 try:
938 resolvedRefQueryResults = subset.findDatasets(
939 datasetType, collections=skip_collections_wildcard, findFirst=True
940 )
941 except MissingDatasetTypeError:
942 resolvedRefQueryResults = []
943 for resolvedRef in resolvedRefQueryResults:
944 assert resolvedRef.dataId in refs
945 refs[resolvedRef.dataId] = resolvedRef
947 # Look up input and initInput datasets in the input collection(s).
948 # container to accumulate unfound refs, if the common dataIs were not
949 # constrained on dataset type existence.
950 self.unfoundRefs = set()
951 for datasetType, refs in itertools.chain(self.initInputs.items(), self.inputs.items()):
952 _LOG.debug("Resolving %d datasets for input dataset %s.", len(refs), datasetType.name)
953 try:
954 resolvedRefQueryResults = commonDataIds.subset(
955 datasetType.dimensions, unique=True
956 ).findDatasets(datasetType, collections=collections, findFirst=True)
957 except MissingDatasetTypeError:
958 resolvedRefQueryResults = []
959 dataIdsNotFoundYet = set(refs.keys())
960 for resolvedRef in resolvedRefQueryResults:
961 dataIdsNotFoundYet.discard(resolvedRef.dataId)
962 refs[resolvedRef.dataId] = resolvedRef
963 if dataIdsNotFoundYet:
964 if constrainedByAllDatasets:
965 raise RuntimeError(
966 f"{len(dataIdsNotFoundYet)} dataset(s) of type "
967 f"'{datasetType.name}' was/were present in a previous "
968 f"query, but could not be found now."
969 f"This is either a logic bug in QuantumGraph generation "
970 f"or the input collections have been modified since "
971 f"QuantumGraph generation began."
972 )
973 else:
974 # if the common dataIds were not constrained using all the
975 # input dataset types, it is possible that some data ids
976 # found dont correspond to existing dataset types and they
977 # will be un-resolved. Mark these for later pruning from
978 # the quantum graph.
979 for k in dataIdsNotFoundYet:
980 self.unfoundRefs.add(refs[k])
982 # Copy the resolved DatasetRefs to the _QuantumScaffolding objects,
983 # replacing the unresolved refs there, and then look up prerequisites.
984 for task in self.tasks:
985 _LOG.debug(
986 "Applying resolutions and finding prerequisites for %d quanta of task with label '%s'.",
987 len(task.quanta),
988 task.taskDef.label,
989 )
990 # The way iterConnections is designed makes it impossible to
991 # annotate precisely enough to satisfy MyPy here.
992 lookupFunctions = {
993 c.name: c.lookupFunction # type: ignore
994 for c in iterConnections(task.taskDef.connections, "prerequisiteInputs")
995 if c.lookupFunction is not None # type: ignore
996 }
997 dataIdsFailed = []
998 dataIdsSucceeded = []
999 for quantum in task.quanta.values():
1000 # Process outputs datasets only if skipExistingIn is not None
1001 # or there is a run to look for outputs in and clobberOutputs
1002 # is True. Note that if skipExistingIn is None, any output
1003 # datasets that already exist would have already caused an
1004 # exception to be raised. We never update the DatasetRefs in
1005 # the quantum because those should never be resolved.
1006 if skip_collections_wildcard is not None or (run is not None and clobberOutputs):
1007 resolvedRefs = []
1008 unresolvedRefs = []
1009 haveMetadata = False
1010 for datasetType, originalRefs in quantum.outputs.items():
1011 for ref in task.outputs.extract(datasetType, originalRefs.keys()):
1012 if ref.id is not None:
1013 resolvedRefs.append(ref)
1014 if datasetType.name == task.taskDef.metadataDatasetName:
1015 haveMetadata = True
1016 else:
1017 unresolvedRefs.append(ref)
1018 if resolvedRefs:
1019 if haveMetadata or not unresolvedRefs:
1020 dataIdsSucceeded.append(quantum.dataId)
1021 if skip_collections_wildcard is not None:
1022 continue
1023 else:
1024 dataIdsFailed.append(quantum.dataId)
1025 if not clobberOutputs:
1026 raise OutputExistsError(
1027 f"Quantum {quantum.dataId} of task with label "
1028 f"'{quantum.task.taskDef.label}' has some outputs that exist "
1029 f"({resolvedRefs}) "
1030 f"and others that don't ({unresolvedRefs}), with no metadata output, "
1031 "and clobbering outputs was not enabled."
1032 )
1033 # Update the input DatasetRefs to the resolved ones we already
1034 # searched for.
1035 for datasetType, input_refs in quantum.inputs.items():
1036 for ref in task.inputs.extract(datasetType, input_refs.keys()):
1037 input_refs[ref.dataId] = ref
1038 # Look up prerequisite datasets in the input collection(s).
1039 # These may have dimensions that extend beyond those we queried
1040 # for originally, because we want to permit those data ID
1041 # values to differ across quanta and dataset types.
1042 for datasetType in task.prerequisites:
1043 lookupFunction = lookupFunctions.get(datasetType.name)
1044 if lookupFunction is not None:
1045 # PipelineTask has provided its own function to do the
1046 # lookup. This always takes precedence.
1047 prereq_refs = list(lookupFunction(datasetType, registry, quantum.dataId, collections))
1048 elif (
1049 datasetType.isCalibration()
1050 and datasetType.dimensions <= quantum.dataId.graph
1051 and quantum.dataId.graph.temporal
1052 ):
1053 # This is a master calibration lookup, which we have to
1054 # handle specially because the query system can't do a
1055 # temporal join on a non-dimension-based timespan yet.
1056 timespan = quantum.dataId.timespan
1057 try:
1058 prereq_refs = [
1059 registry.findDataset(
1060 datasetType, quantum.dataId, collections=collections, timespan=timespan
1061 )
1062 ]
1063 except KeyError:
1064 # This dataset type is not present in the registry,
1065 # which just means there are no datasets here.
1066 prereq_refs = []
1067 else:
1068 # Most general case.
1069 prereq_refs = list(
1070 registry.queryDatasets(
1071 datasetType, collections=collections, dataId=quantum.dataId, findFirst=True
1072 ).expanded()
1073 )
1074 quantum.prerequisites[datasetType].update(
1075 {ref.dataId: ref for ref in prereq_refs if ref is not None}
1076 )
1078 # Resolve all quantum inputs and outputs.
1079 if idMaker:
1080 for datasetDict in (quantum.inputs, quantum.outputs):
1081 for refDict in datasetDict.values():
1082 refDict.update(idMaker.resolveDict(refDict))
1084 # Resolve task initInputs and initOutputs.
1085 if idMaker:
1086 for datasetDict in (task.initInputs, task.initOutputs):
1087 for refDict in datasetDict.values():
1088 refDict.update(idMaker.resolveDict(refDict))
1090 # Actually remove any quanta that we decided to skip above.
1091 if dataIdsSucceeded:
1092 if skip_collections_wildcard is not None:
1093 _LOG.debug(
1094 "Pruning successful %d quanta for task with label '%s' because all of their "
1095 "outputs exist or metadata was written successfully.",
1096 len(dataIdsSucceeded),
1097 task.taskDef.label,
1098 )
1099 for dataId in dataIdsSucceeded:
1100 del task.quanta[dataId]
1101 elif clobberOutputs:
1102 _LOG.info(
1103 "Found %d successful quanta for task with label '%s' "
1104 "that will need to be clobbered during execution.",
1105 len(dataIdsSucceeded),
1106 task.taskDef.label,
1107 )
1108 else:
1109 raise AssertionError("OutputExistsError should have already been raised.")
1110 if dataIdsFailed:
1111 if clobberOutputs:
1112 _LOG.info(
1113 "Found %d failed/incomplete quanta for task with label '%s' "
1114 "that will need to be clobbered during execution.",
1115 len(dataIdsFailed),
1116 task.taskDef.label,
1117 )
1118 else:
1119 raise AssertionError("OutputExistsError should have already been raised.")
1121 def makeQuantumGraph(
1122 self, metadata: Optional[Mapping[str, Any]] = None, datastore: Optional[Datastore] = None
1123 ) -> QuantumGraph:
1124 """Create a `QuantumGraph` from the quanta already present in
1125 the scaffolding data structure.
1127 Parameters
1128 ---------
1129 metadata : Optional Mapping of `str` to primitives
1130 This is an optional parameter of extra data to carry with the
1131 graph. Entries in this mapping should be able to be serialized in
1132 JSON.
1133 datastore : `Datastore`, optional
1134 If not `None` then fill datastore records in each generated
1135 Quantum.
1137 Returns
1138 -------
1139 graph : `QuantumGraph`
1140 The full `QuantumGraph`.
1141 """
1143 def _make_refs(dataset_dict: _DatasetDict) -> Iterable[DatasetRef]:
1144 """Extract all DatasetRefs from the dictionaries"""
1145 for ref_dict in dataset_dict.values():
1146 yield from ref_dict.values()
1148 datastore_records: Optional[Mapping[str, DatastoreRecordData]] = None
1149 if datastore is not None:
1150 datastore_records = datastore.export_records(
1151 itertools.chain(
1152 _make_refs(self.inputs), _make_refs(self.initInputs), _make_refs(self.prerequisites)
1153 )
1154 )
1156 graphInput: Dict[TaskDef, Set[Quantum]] = {}
1157 for task in self.tasks:
1158 qset = task.makeQuantumSet(unresolvedRefs=self.unfoundRefs, datastore_records=datastore_records)
1159 graphInput[task.taskDef] = qset
1161 taskInitInputs = {task.taskDef: task.initInputs.unpackSingleRefs().values() for task in self.tasks}
1162 taskInitOutputs = {task.taskDef: task.initOutputs.unpackSingleRefs().values() for task in self.tasks}
1164 graph = QuantumGraph(
1165 graphInput,
1166 metadata=metadata,
1167 pruneRefs=self.unfoundRefs,
1168 universe=self.dimensions.universe,
1169 initInputs=taskInitInputs,
1170 initOutputs=taskInitOutputs,
1171 )
1172 return graph
1175# ------------------------
1176# Exported definitions --
1177# ------------------------
1180class GraphBuilderError(Exception):
1181 """Base class for exceptions generated by graph builder."""
1183 pass
1186class OutputExistsError(GraphBuilderError):
1187 """Exception generated when output datasets already exist."""
1189 pass
1192class PrerequisiteMissingError(GraphBuilderError):
1193 """Exception generated when a prerequisite dataset does not exist."""
1195 pass
1198class GraphBuilder:
1199 """GraphBuilder class is responsible for building task execution graph from
1200 a Pipeline.
1202 Parameters
1203 ----------
1204 registry : `~lsst.daf.butler.Registry`
1205 Data butler instance.
1206 skipExistingIn
1207 Expressions representing the collections to search for existing
1208 output datasets that should be skipped. See
1209 :ref:`daf_butler_ordered_collection_searches`.
1210 clobberOutputs : `bool`, optional
1211 If `True` (default), allow quanta to created even if partial outputs
1212 exist; this requires the same behavior behavior to be enabled when
1213 executing.
1214 datastore : `Datastore`, optional
1215 If not `None` then fill datastore records in each generated Quantum.
1216 """
1218 def __init__(
1219 self,
1220 registry: Registry,
1221 skipExistingIn: Any = None,
1222 clobberOutputs: bool = True,
1223 datastore: Optional[Datastore] = None,
1224 ):
1225 self.registry = registry
1226 self.dimensions = registry.dimensions
1227 self.skipExistingIn = skipExistingIn
1228 self.clobberOutputs = clobberOutputs
1229 self.datastore = datastore
1231 def makeGraph(
1232 self,
1233 pipeline: Union[Pipeline, Iterable[TaskDef]],
1234 collections: Any,
1235 run: Optional[str],
1236 userQuery: Optional[str],
1237 datasetQueryConstraint: DatasetQueryConstraintVariant = DatasetQueryConstraintVariant.ALL,
1238 metadata: Optional[Mapping[str, Any]] = None,
1239 resolveRefs: bool = False,
1240 ) -> QuantumGraph:
1241 """Create execution graph for a pipeline.
1243 Parameters
1244 ----------
1245 pipeline : `Pipeline` or `Iterable` [ `TaskDef` ]
1246 Pipeline definition, task names/classes and their configs.
1247 collections
1248 Expressions representing the collections to search for input
1249 datasets. See :ref:`daf_butler_ordered_collection_searches`.
1250 run : `str`, optional
1251 Name of the `~lsst.daf.butler.CollectionType.RUN` collection for
1252 output datasets, if it already exists.
1253 userQuery : `str`
1254 String which defines user-defined selection for registry, should be
1255 empty or `None` if there is no restrictions on data selection.
1256 datasetQueryConstraint : `DatasetQueryConstraintVariant`, optional
1257 The query constraint variant that should be used to constraint the
1258 query based on dataset existance, defaults to
1259 `DatasetQueryConstraintVariant.ALL`.
1260 metadata : Optional Mapping of `str` to primitives
1261 This is an optional parameter of extra data to carry with the
1262 graph. Entries in this mapping should be able to be serialized in
1263 JSON.
1264 resolveRefs : `bool`, optional
1265 If `True` then resolve all input references and generate random
1266 dataset IDs for all output and intermediate datasets. True value
1267 requires ``run`` collection to be specified.
1269 Returns
1270 -------
1271 graph : `QuantumGraph`
1273 Raises
1274 ------
1275 UserExpressionError
1276 Raised when user expression cannot be parsed.
1277 OutputExistsError
1278 Raised when output datasets already exist.
1279 Exception
1280 Other exceptions types may be raised by underlying registry
1281 classes.
1282 """
1283 if resolveRefs and run is None:
1284 raise ValueError("`resolveRefs` requires `run` parameter.")
1285 scaffolding = _PipelineScaffolding(pipeline, registry=self.registry)
1286 if not collections and (scaffolding.initInputs or scaffolding.inputs or scaffolding.prerequisites):
1287 raise ValueError("Pipeline requires input datasets but no input collections provided.")
1288 instrument_class: Optional[Any] = None
1289 if isinstance(pipeline, Pipeline):
1290 instrument_class_name = pipeline.getInstrument()
1291 if instrument_class_name is not None:
1292 instrument_class = doImportType(instrument_class_name)
1293 pipeline = list(pipeline.toExpandedPipeline())
1294 if instrument_class is not None:
1295 dataId = DataCoordinate.standardize(
1296 instrument=instrument_class.getName(), universe=self.registry.dimensions
1297 )
1298 else:
1299 dataId = DataCoordinate.makeEmpty(self.registry.dimensions)
1300 with scaffolding.connectDataIds(
1301 self.registry, collections, userQuery, dataId, datasetQueryConstraint
1302 ) as commonDataIds:
1303 condition = datasetQueryConstraint == DatasetQueryConstraintVariant.ALL
1304 scaffolding.resolveDatasetRefs(
1305 self.registry,
1306 collections,
1307 run,
1308 commonDataIds,
1309 skipExistingIn=self.skipExistingIn,
1310 clobberOutputs=self.clobberOutputs,
1311 constrainedByAllDatasets=condition,
1312 resolveRefs=resolveRefs,
1313 )
1314 return scaffolding.makeQuantumGraph(metadata=metadata, datastore=self.datastore)