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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 

22 

23"""Module defining Pipeline class and related methods. 

24""" 

25 

26__all__ = ["Pipeline", "TaskDef", "TaskDatasetTypes", "PipelineDatasetTypes", "LabelSpecifier"] 

27 

28# ------------------------------- 

29# Imports of standard modules -- 

30# ------------------------------- 

31from dataclasses import dataclass 

32from types import MappingProxyType 

33from typing import Mapping, Set, Union, Generator, TYPE_CHECKING, Optional 

34 

35import copy 

36import os 

37 

38# ----------------------------- 

39# Imports for other modules -- 

40from lsst.daf.butler import DatasetType, NamedValueSet, Registry, SkyPixDimension 

41from lsst.utils import doImport 

42from .configOverrides import ConfigOverrides 

43from .connections import iterConnections 

44from .pipelineTask import PipelineTask 

45 

46from . import pipelineIR 

47from . import pipeTools 

48 

49if TYPE_CHECKING: # Imports needed only for type annotations; may be circular. 49 ↛ 50line 49 didn't jump to line 50, because the condition on line 49 was never true

50 from lsst.obs.base.instrument import Instrument 

51 

52# ---------------------------------- 

53# Local non-exported definitions -- 

54# ---------------------------------- 

55 

56# ------------------------ 

57# Exported definitions -- 

58# ------------------------ 

59 

60 

61@dataclass 

62class LabelSpecifier: 

63 """A structure to specify a subset of labels to load 

64 

65 This structure may contain a set of labels to be used in subsetting a 

66 pipeline, or a beginning and end point. Beginning or end may be empty, 

67 in which case the range will be a half open interval. Unlike python 

68 iteration bounds, end bounds are *INCLUDED*. Note that range based 

69 selection is not well defined for pipelines that are not linear in nature, 

70 and correct behavior is not guaranteed, or may vary from run to run. 

71 """ 

72 labels: Optional[Set[str]] = None 

73 begin: Optional[str] = None 

74 end: Optional[str] = None 

75 

76 def __post_init__(self): 

77 if self.labels is not None and (self.begin or self.end): 

78 raise ValueError("This struct can only be initialized with a labels set or " 

79 "a begin (and/or) end specifier") 

80 

81 

82class TaskDef: 

83 """TaskDef is a collection of information about task needed by Pipeline. 

84 

85 The information includes task name, configuration object and optional 

86 task class. This class is just a collection of attributes and it exposes 

87 all of them so that attributes could potentially be modified in place 

88 (e.g. if configuration needs extra overrides). 

89 

90 Attributes 

91 ---------- 

92 taskName : `str` 

93 `PipelineTask` class name, currently it is not specified whether this 

94 is a fully-qualified name or partial name (e.g. ``module.TaskClass``). 

95 Framework should be prepared to handle all cases. 

96 config : `lsst.pex.config.Config` 

97 Instance of the configuration class corresponding to this task class, 

98 usually with all overrides applied. This config will be frozen. 

99 taskClass : `type` or ``None`` 

100 `PipelineTask` class object, can be ``None``. If ``None`` then 

101 framework will have to locate and load class. 

102 label : `str`, optional 

103 Task label, usually a short string unique in a pipeline. 

104 """ 

105 def __init__(self, taskName, config, taskClass=None, label=""): 

106 self.taskName = taskName 

107 config.freeze() 

108 self.config = config 

109 self.taskClass = taskClass 

110 self.label = label 

111 self.connections = config.connections.ConnectionsClass(config=config) 

112 

113 @property 

114 def configDatasetName(self): 

115 """Name of a dataset type for configuration of this task (`str`) 

116 """ 

117 return self.label + "_config" 

118 

119 @property 

120 def metadataDatasetName(self): 

121 """Name of a dataset type for metadata of this task, `None` if 

122 metadata is not to be saved (`str`) 

123 """ 

124 if self.config.saveMetadata: 

125 return self.label + "_metadata" 

126 else: 

127 return None 

128 

129 def __str__(self): 

130 rep = "TaskDef(" + self.taskName 

131 if self.label: 

132 rep += ", label=" + self.label 

133 rep += ")" 

134 return rep 

135 

136 def __eq__(self, other: object) -> bool: 

137 if not isinstance(other, TaskDef): 

138 return False 

139 return self.config == other.config and\ 

140 self.taskClass == other.taskClass and\ 

141 self.label == other.label 

142 

143 def __hash__(self): 

144 return hash((self.taskClass, self.label)) 

145 

146 

147class Pipeline: 

148 """A `Pipeline` is a representation of a series of tasks to run, and the 

149 configuration for those tasks. 

150 

151 Parameters 

152 ---------- 

153 description : `str` 

154 A description of that this pipeline does. 

155 """ 

156 def __init__(self, description: str): 

157 pipeline_dict = {"description": description, "tasks": {}} 

158 self._pipelineIR = pipelineIR.PipelineIR(pipeline_dict) 

159 

160 @classmethod 

161 def fromFile(cls, filename: str) -> Pipeline: 

162 """Load a pipeline defined in a pipeline yaml file. 

163 

164 Parameters 

165 ---------- 

166 filename: `str` 

167 A path that points to a pipeline defined in yaml format. This 

168 filename may also supply additional labels to be used in 

169 subsetting the loaded Pipeline. These labels are separated from 

170 the path by a colon, and may be specified as a comma separated 

171 list, or a range denoted as beginning..end. Beginning or end may 

172 be empty, in which case the range will be a half open interval. 

173 Unlike python iteration bounds, end bounds are *INCLUDED*. Note 

174 that range based selection is not well defined for pipelines that 

175 are not linear in nature, and correct behavior is not guaranteed, 

176 or may vary from run to run. 

177 

178 Returns 

179 ------- 

180 pipeline: `Pipeline` 

181 The pipeline loaded from specified location with appropriate (if 

182 any) subsetting 

183 

184 Notes 

185 ----- 

186 This method attempts to prune any contracts that contain labels which 

187 are not in the declared subset of labels. This pruning is done using a 

188 string based matching due to the nature of contracts and may prune more 

189 than it should. 

190 """ 

191 # Split up the filename and any labels that were supplied 

192 filename, labelSpecifier = cls._parseFileSpecifier(filename) 

193 pipeline: Pipeline = cls.fromIR(pipelineIR.PipelineIR.from_file(filename)) 

194 

195 # If there are labels supplied, only keep those 

196 if labelSpecifier is not None: 

197 pipeline = pipeline.subsetFromLabels(labelSpecifier) 

198 return pipeline 

199 

200 def subsetFromLabels(self, labelSpecifier: LabelSpecifier) -> Pipeline: 

201 """Subset a pipeline to contain only labels specified in labelSpecifier 

202 

203 Parameters 

204 ---------- 

205 labelSpecifier : `labelSpecifier` 

206 Object containing labels that describes how to subset a pipeline. 

207 

208 Returns 

209 ------- 

210 pipeline : `Pipeline` 

211 A new pipeline object that is a subset of the old pipeline 

212 

213 Raises 

214 ------ 

215 ValueError 

216 Raised if there is an issue with specified labels 

217 

218 Notes 

219 ----- 

220 This method attempts to prune any contracts that contain labels which 

221 are not in the declared subset of labels. This pruning is done using a 

222 string based matching due to the nature of contracts and may prune more 

223 than it should. 

224 """ 

225 # Labels supplied as a set 

226 if labelSpecifier.labels: 

227 labelSet = labelSpecifier.labels 

228 # Labels supplied as a range, first create a list of all the labels 

229 # in the pipeline sorted according to task dependency. Then only 

230 # keep labels that lie between the supplied bounds 

231 else: 

232 # Create a copy of the pipeline to use when assessing the label 

233 # ordering. Use a dict for fast searching while preserving order. 

234 # Remove contracts so they do not fail in the expansion step. This 

235 # is needed because a user may only configure the tasks they intend 

236 # to run, which may cause some contracts to fail if they will later 

237 # be dropped 

238 pipeline = copy.deepcopy(self) 

239 pipeline._pipelineIR.contracts = [] 

240 labels = {taskdef.label: True for taskdef in pipeline.toExpandedPipeline()} 

241 

242 # Verify the bounds are in the labels 

243 if labelSpecifier.begin is not None: 

244 if labelSpecifier.begin not in labels: 

245 raise ValueError(f"Beginning of range subset, {labelSpecifier.begin}, not found in " 

246 "pipeline definition") 

247 if labelSpecifier.end is not None: 

248 if labelSpecifier.end not in labels: 

249 raise ValueError(f"End of range subset, {labelSpecifier.end}, not found in pipeline " 

250 "definition") 

251 

252 labelSet = set() 

253 for label in labels: 

254 if labelSpecifier.begin is not None: 

255 if label != labelSpecifier.begin: 

256 continue 

257 else: 

258 labelSpecifier.begin = None 

259 labelSet.add(label) 

260 if labelSpecifier.end is not None and label == labelSpecifier.end: 

261 break 

262 return Pipeline.fromIR(self._pipelineIR.subset_from_labels(labelSet)) 

263 

264 @staticmethod 

265 def _parseFileSpecifier(fileSpecifer): 

266 """Split appart a filename path from label subsets 

267 """ 

268 split = fileSpecifer.split(':') 

269 # There is only a filename, return just that 

270 if len(split) == 1: 

271 return fileSpecifer, None 

272 # More than one specifier provided, bail out 

273 if len(split) > 2: 

274 raise ValueError("Only one : is allowed when specifying a pipeline to load") 

275 else: 

276 labelSubset: str 

277 filename: str 

278 filename, labelSubset = split[0], split[1] 

279 # labels supplied as a list 

280 if ',' in labelSubset: 

281 if '..' in labelSubset: 

282 raise ValueError("Can only specify a list of labels or a range" 

283 "when loading a Pipline not both") 

284 labels = set(labelSubset.split(",")) 

285 specifier = LabelSpecifier(labels=labels) 

286 # labels supplied as a range 

287 elif '..' in labelSubset: 

288 # Try to destructure the labelSubset, this will fail if more 

289 # than one range is specified 

290 try: 

291 begin, end = labelSubset.split("..") 

292 except ValueError: 

293 raise ValueError("Only one range can be specified when loading a pipeline") 

294 specifier = LabelSpecifier(begin=begin if begin else None, end=end if end else None) 

295 # Assume anything else is a single label 

296 else: 

297 labels = {labelSubset} 

298 specifier = LabelSpecifier(labels=labels) 

299 

300 return filename, specifier 

301 

302 @classmethod 

303 def fromString(cls, pipeline_string: str) -> Pipeline: 

304 """Create a pipeline from string formatted as a pipeline document. 

305 

306 Parameters 

307 ---------- 

308 pipeline_string : `str` 

309 A string that is formatted according like a pipeline document 

310 

311 Returns 

312 ------- 

313 pipeline: `Pipeline` 

314 """ 

315 pipeline = cls.fromIR(pipelineIR.PipelineIR.from_string(pipeline_string)) 

316 return pipeline 

317 

318 @classmethod 

319 def fromIR(cls, deserialized_pipeline: pipelineIR.PipelineIR) -> Pipeline: 

320 """Create a pipeline from an already created `PipelineIR` object. 

321 

322 Parameters 

323 ---------- 

324 deserialized_pipeline: `PipelineIR` 

325 An already created pipeline intermediate representation object 

326 

327 Returns 

328 ------- 

329 pipeline: `Pipeline` 

330 """ 

331 pipeline = cls.__new__(cls) 

332 pipeline._pipelineIR = deserialized_pipeline 

333 return pipeline 

334 

335 @classmethod 

336 def fromPipeline(cls, pipeline: pipelineIR.PipelineIR) -> Pipeline: 

337 """Create a new pipeline by copying an already existing `Pipeline`. 

338 

339 Parameters 

340 ---------- 

341 pipeline: `Pipeline` 

342 An already created pipeline intermediate representation object 

343 

344 Returns 

345 ------- 

346 pipeline: `Pipeline` 

347 """ 

348 return cls.fromIR(copy.deep_copy(pipeline._pipelineIR)) 

349 

350 def __str__(self) -> str: 

351 return str(self._pipelineIR) 

352 

353 def addInstrument(self, instrument: Union[Instrument, str]): 

354 """Add an instrument to the pipeline, or replace an instrument that is 

355 already defined. 

356 

357 Parameters 

358 ---------- 

359 instrument : `~lsst.daf.butler.instrument.Instrument` or `str` 

360 Either a derived class object of a `lsst.daf.butler.instrument` or 

361 a string corresponding to a fully qualified 

362 `lsst.daf.butler.instrument` name. 

363 """ 

364 if isinstance(instrument, str): 

365 pass 

366 else: 

367 # TODO: assume that this is a subclass of Instrument, no type 

368 # checking 

369 instrument = f"{instrument.__module__}.{instrument.__qualname__}" 

370 self._pipelineIR.instrument = instrument 

371 

372 def getInstrument(self): 

373 """Get the instrument from the pipeline. 

374 

375 Returns 

376 ------- 

377 instrument : `~lsst.daf.butler.instrument.Instrument`, `str`, or None 

378 A derived class object of a `lsst.daf.butler.instrument`, a string 

379 corresponding to a fully qualified `lsst.daf.butler.instrument` 

380 name, or None if the pipeline does not have an instrument. 

381 """ 

382 return self._pipelineIR.instrument 

383 

384 def addTask(self, task: Union[PipelineTask, str], label: str): 

385 """Add a new task to the pipeline, or replace a task that is already 

386 associated with the supplied label. 

387 

388 Parameters 

389 ---------- 

390 task: `PipelineTask` or `str` 

391 Either a derived class object of a `PipelineTask` or a string 

392 corresponding to a fully qualified `PipelineTask` name. 

393 label: `str` 

394 A label that is used to identify the `PipelineTask` being added 

395 """ 

396 if isinstance(task, str): 

397 taskName = task 

398 elif issubclass(task, PipelineTask): 

399 taskName = f"{task.__module__}.{task.__qualname__}" 

400 else: 

401 raise ValueError("task must be either a child class of PipelineTask or a string containing" 

402 " a fully qualified name to one") 

403 if not label: 

404 # in some cases (with command line-generated pipeline) tasks can 

405 # be defined without label which is not acceptable, use task 

406 # _DefaultName in that case 

407 if isinstance(task, str): 

408 task = doImport(task) 

409 label = task._DefaultName 

410 self._pipelineIR.tasks[label] = pipelineIR.TaskIR(label, taskName) 

411 

412 def removeTask(self, label: str): 

413 """Remove a task from the pipeline. 

414 

415 Parameters 

416 ---------- 

417 label : `str` 

418 The label used to identify the task that is to be removed 

419 

420 Raises 

421 ------ 

422 KeyError 

423 If no task with that label exists in the pipeline 

424 

425 """ 

426 self._pipelineIR.tasks.pop(label) 

427 

428 def addConfigOverride(self, label: str, key: str, value: object): 

429 """Apply single config override. 

430 

431 Parameters 

432 ---------- 

433 label : `str` 

434 Label of the task. 

435 key: `str` 

436 Fully-qualified field name. 

437 value : object 

438 Value to be given to a field. 

439 """ 

440 self._addConfigImpl(label, pipelineIR.ConfigIR(rest={key: value})) 

441 

442 def addConfigFile(self, label: str, filename: str): 

443 """Add overrides from a specified file. 

444 

445 Parameters 

446 ---------- 

447 label : `str` 

448 The label used to identify the task associated with config to 

449 modify 

450 filename : `str` 

451 Path to the override file. 

452 """ 

453 self._addConfigImpl(label, pipelineIR.ConfigIR(file=[filename])) 

454 

455 def addConfigPython(self, label: str, pythonString: str): 

456 """Add Overrides by running a snippet of python code against a config. 

457 

458 Parameters 

459 ---------- 

460 label : `str` 

461 The label used to identity the task associated with config to 

462 modify. 

463 pythonString: `str` 

464 A string which is valid python code to be executed. This is done 

465 with config as the only local accessible value. 

466 """ 

467 self._addConfigImpl(label, pipelineIR.ConfigIR(python=pythonString)) 

468 

469 def _addConfigImpl(self, label: str, newConfig: pipelineIR.ConfigIR): 

470 if label not in self._pipelineIR.tasks: 

471 raise LookupError(f"There are no tasks labeled '{label}' in the pipeline") 

472 self._pipelineIR.tasks[label].add_or_update_config(newConfig) 

473 

474 def toFile(self, filename: str): 

475 self._pipelineIR.to_file(filename) 

476 

477 def toExpandedPipeline(self) -> Generator[TaskDef]: 

478 """Returns a generator of TaskDefs which can be used to create quantum 

479 graphs. 

480 

481 Returns 

482 ------- 

483 generator : generator of `TaskDef` 

484 The generator returned will be the sorted iterator of tasks which 

485 are to be used in constructing a quantum graph. 

486 

487 Raises 

488 ------ 

489 NotImplementedError 

490 If a dataId is supplied in a config block. This is in place for 

491 future use 

492 """ 

493 taskDefs = [] 

494 for label, taskIR in self._pipelineIR.tasks.items(): 

495 taskClass = doImport(taskIR.klass) 

496 taskName = taskClass.__qualname__ 

497 config = taskClass.ConfigClass() 

498 overrides = ConfigOverrides() 

499 if self._pipelineIR.instrument is not None: 

500 overrides.addInstrumentOverride(self._pipelineIR.instrument, taskClass._DefaultName) 

501 if taskIR.config is not None: 

502 for configIR in taskIR.config: 

503 if configIR.dataId is not None: 

504 raise NotImplementedError("Specializing a config on a partial data id is not yet " 

505 "supported in Pipeline definition") 

506 # only apply override if it applies to everything 

507 if configIR.dataId is None: 

508 if configIR.file: 

509 for configFile in configIR.file: 

510 overrides.addFileOverride(os.path.expandvars(configFile)) 

511 if configIR.python is not None: 

512 overrides.addPythonOverride(configIR.python) 

513 for key, value in configIR.rest.items(): 

514 overrides.addValueOverride(key, value) 

515 overrides.applyTo(config) 

516 # This may need to be revisited 

517 config.validate() 

518 taskDefs.append(TaskDef(taskName=taskName, config=config, taskClass=taskClass, label=label)) 

519 

520 # lets evaluate the contracts 

521 if self._pipelineIR.contracts is not None: 

522 label_to_config = {x.label: x.config for x in taskDefs} 

523 for contract in self._pipelineIR.contracts: 

524 # execute this in its own line so it can raise a good error 

525 # message if there was problems with the eval 

526 success = eval(contract.contract, None, label_to_config) 

527 if not success: 

528 extra_info = f": {contract.msg}" if contract.msg is not None else "" 

529 raise pipelineIR.ContractError(f"Contract(s) '{contract.contract}' were not " 

530 f"satisfied{extra_info}") 

531 

532 yield from pipeTools.orderPipeline(taskDefs) 

533 

534 def __len__(self): 

535 return len(self._pipelineIR.tasks) 

536 

537 def __eq__(self, other: "Pipeline"): 

538 if not isinstance(other, Pipeline): 

539 return False 

540 return self._pipelineIR == other._pipelineIR 

541 

542 

543@dataclass(frozen=True) 

544class TaskDatasetTypes: 

545 """An immutable struct that extracts and classifies the dataset types used 

546 by a `PipelineTask` 

547 """ 

548 

549 initInputs: NamedValueSet[DatasetType] 

550 """Dataset types that are needed as inputs in order to construct this Task. 

551 

552 Task-level `initInputs` may be classified as either 

553 `~PipelineDatasetTypes.initInputs` or 

554 `~PipelineDatasetTypes.initIntermediates` at the Pipeline level. 

555 """ 

556 

557 initOutputs: NamedValueSet[DatasetType] 

558 """Dataset types that may be written after constructing this Task. 

559 

560 Task-level `initOutputs` may be classified as either 

561 `~PipelineDatasetTypes.initOutputs` or 

562 `~PipelineDatasetTypes.initIntermediates` at the Pipeline level. 

563 """ 

564 

565 inputs: NamedValueSet[DatasetType] 

566 """Dataset types that are regular inputs to this Task. 

567 

568 If an input dataset needed for a Quantum cannot be found in the input 

569 collection(s) or produced by another Task in the Pipeline, that Quantum 

570 (and all dependent Quanta) will not be produced. 

571 

572 Task-level `inputs` may be classified as either 

573 `~PipelineDatasetTypes.inputs` or `~PipelineDatasetTypes.intermediates` 

574 at the Pipeline level. 

575 """ 

576 

577 prerequisites: NamedValueSet[DatasetType] 

578 """Dataset types that are prerequisite inputs to this Task. 

579 

580 Prerequisite inputs must exist in the input collection(s) before the 

581 pipeline is run, but do not constrain the graph - if a prerequisite is 

582 missing for a Quantum, `PrerequisiteMissingError` is raised. 

583 

584 Prerequisite inputs are not resolved until the second stage of 

585 QuantumGraph generation. 

586 """ 

587 

588 outputs: NamedValueSet[DatasetType] 

589 """Dataset types that are produced by this Task. 

590 

591 Task-level `outputs` may be classified as either 

592 `~PipelineDatasetTypes.outputs` or `~PipelineDatasetTypes.intermediates` 

593 at the Pipeline level. 

594 """ 

595 

596 @classmethod 

597 def fromTaskDef(cls, taskDef: TaskDef, *, registry: Registry) -> TaskDatasetTypes: 

598 """Extract and classify the dataset types from a single `PipelineTask`. 

599 

600 Parameters 

601 ---------- 

602 taskDef: `TaskDef` 

603 An instance of a `TaskDef` class for a particular `PipelineTask`. 

604 registry: `Registry` 

605 Registry used to construct normalized `DatasetType` objects and 

606 retrieve those that are incomplete. 

607 

608 Returns 

609 ------- 

610 types: `TaskDatasetTypes` 

611 The dataset types used by this task. 

612 """ 

613 def makeDatasetTypesSet(connectionType, freeze=True): 

614 """Constructs a set of true `DatasetType` objects 

615 

616 Parameters 

617 ---------- 

618 connectionType : `str` 

619 Name of the connection type to produce a set for, corresponds 

620 to an attribute of type `list` on the connection class instance 

621 freeze : `bool`, optional 

622 If `True`, call `NamedValueSet.freeze` on the object returned. 

623 

624 Returns 

625 ------- 

626 datasetTypes : `NamedValueSet` 

627 A set of all datasetTypes which correspond to the input 

628 connection type specified in the connection class of this 

629 `PipelineTask` 

630 

631 Notes 

632 ----- 

633 This function is a closure over the variables ``registry`` and 

634 ``taskDef``. 

635 """ 

636 datasetTypes = NamedValueSet() 

637 for c in iterConnections(taskDef.connections, connectionType): 

638 dimensions = set(getattr(c, 'dimensions', set())) 

639 if "skypix" in dimensions: 

640 try: 

641 datasetType = registry.getDatasetType(c.name) 

642 except LookupError as err: 

643 raise LookupError( 

644 f"DatasetType '{c.name}' referenced by " 

645 f"{type(taskDef.connections).__name__} uses 'skypix' as a dimension " 

646 f"placeholder, but does not already exist in the registry. " 

647 f"Note that reference catalog names are now used as the dataset " 

648 f"type name instead of 'ref_cat'." 

649 ) from err 

650 rest1 = set(registry.dimensions.extract(dimensions - set(["skypix"])).names) 

651 rest2 = set(dim.name for dim in datasetType.dimensions 

652 if not isinstance(dim, SkyPixDimension)) 

653 if rest1 != rest2: 

654 raise ValueError(f"Non-skypix dimensions for dataset type {c.name} declared in " 

655 f"connections ({rest1}) are inconsistent with those in " 

656 f"registry's version of this dataset ({rest2}).") 

657 else: 

658 # Component dataset types are not explicitly in the 

659 # registry. This complicates consistency checks with 

660 # registry and requires we work out the composite storage 

661 # class. 

662 registryDatasetType = None 

663 try: 

664 registryDatasetType = registry.getDatasetType(c.name) 

665 except KeyError: 

666 compositeName, componentName = DatasetType.splitDatasetTypeName(c.name) 

667 parentStorageClass = DatasetType.PlaceholderParentStorageClass \ 

668 if componentName else None 

669 datasetType = c.makeDatasetType( 

670 registry.dimensions, 

671 parentStorageClass=parentStorageClass 

672 ) 

673 registryDatasetType = datasetType 

674 else: 

675 datasetType = c.makeDatasetType( 

676 registry.dimensions, 

677 parentStorageClass=registryDatasetType.parentStorageClass 

678 ) 

679 

680 if registryDatasetType and datasetType != registryDatasetType: 

681 raise ValueError(f"Supplied dataset type ({datasetType}) inconsistent with " 

682 f"registry definition ({registryDatasetType}) " 

683 f"for {taskDef.label}.") 

684 datasetTypes.add(datasetType) 

685 if freeze: 

686 datasetTypes.freeze() 

687 return datasetTypes 

688 

689 # optionally add output dataset for metadata 

690 outputs = makeDatasetTypesSet("outputs", freeze=False) 

691 if taskDef.metadataDatasetName is not None: 

692 # Metadata is supposed to be of the PropertySet type, its 

693 # dimensions correspond to a task quantum 

694 dimensions = registry.dimensions.extract(taskDef.connections.dimensions) 

695 outputs |= {DatasetType(taskDef.metadataDatasetName, dimensions, "PropertySet")} 

696 outputs.freeze() 

697 

698 return cls( 

699 initInputs=makeDatasetTypesSet("initInputs"), 

700 initOutputs=makeDatasetTypesSet("initOutputs"), 

701 inputs=makeDatasetTypesSet("inputs"), 

702 prerequisites=makeDatasetTypesSet("prerequisiteInputs"), 

703 outputs=outputs, 

704 ) 

705 

706 

707@dataclass(frozen=True) 

708class PipelineDatasetTypes: 

709 """An immutable struct that classifies the dataset types used in a 

710 `Pipeline`. 

711 """ 

712 

713 initInputs: NamedValueSet[DatasetType] 

714 """Dataset types that are needed as inputs in order to construct the Tasks 

715 in this Pipeline. 

716 

717 This does not include dataset types that are produced when constructing 

718 other Tasks in the Pipeline (these are classified as `initIntermediates`). 

719 """ 

720 

721 initOutputs: NamedValueSet[DatasetType] 

722 """Dataset types that may be written after constructing the Tasks in this 

723 Pipeline. 

724 

725 This does not include dataset types that are also used as inputs when 

726 constructing other Tasks in the Pipeline (these are classified as 

727 `initIntermediates`). 

728 """ 

729 

730 initIntermediates: NamedValueSet[DatasetType] 

731 """Dataset types that are both used when constructing one or more Tasks 

732 in the Pipeline and produced as a side-effect of constructing another 

733 Task in the Pipeline. 

734 """ 

735 

736 inputs: NamedValueSet[DatasetType] 

737 """Dataset types that are regular inputs for the full pipeline. 

738 

739 If an input dataset needed for a Quantum cannot be found in the input 

740 collection(s), that Quantum (and all dependent Quanta) will not be 

741 produced. 

742 """ 

743 

744 prerequisites: NamedValueSet[DatasetType] 

745 """Dataset types that are prerequisite inputs for the full Pipeline. 

746 

747 Prerequisite inputs must exist in the input collection(s) before the 

748 pipeline is run, but do not constrain the graph - if a prerequisite is 

749 missing for a Quantum, `PrerequisiteMissingError` is raised. 

750 

751 Prerequisite inputs are not resolved until the second stage of 

752 QuantumGraph generation. 

753 """ 

754 

755 intermediates: NamedValueSet[DatasetType] 

756 """Dataset types that are output by one Task in the Pipeline and consumed 

757 as inputs by one or more other Tasks in the Pipeline. 

758 """ 

759 

760 outputs: NamedValueSet[DatasetType] 

761 """Dataset types that are output by a Task in the Pipeline and not consumed 

762 by any other Task in the Pipeline. 

763 """ 

764 

765 byTask: Mapping[str, TaskDatasetTypes] 

766 """Per-Task dataset types, keyed by label in the `Pipeline`. 

767 

768 This is guaranteed to be zip-iterable with the `Pipeline` itself (assuming 

769 neither has been modified since the dataset types were extracted, of 

770 course). 

771 """ 

772 

773 @classmethod 

774 def fromPipeline(cls, pipeline, *, registry: Registry) -> PipelineDatasetTypes: 

775 """Extract and classify the dataset types from all tasks in a 

776 `Pipeline`. 

777 

778 Parameters 

779 ---------- 

780 pipeline: `Pipeline` 

781 An ordered collection of tasks that can be run together. 

782 registry: `Registry` 

783 Registry used to construct normalized `DatasetType` objects and 

784 retrieve those that are incomplete. 

785 

786 Returns 

787 ------- 

788 types: `PipelineDatasetTypes` 

789 The dataset types used by this `Pipeline`. 

790 

791 Raises 

792 ------ 

793 ValueError 

794 Raised if Tasks are inconsistent about which datasets are marked 

795 prerequisite. This indicates that the Tasks cannot be run as part 

796 of the same `Pipeline`. 

797 """ 

798 allInputs = NamedValueSet() 

799 allOutputs = NamedValueSet() 

800 allInitInputs = NamedValueSet() 

801 allInitOutputs = NamedValueSet() 

802 prerequisites = NamedValueSet() 

803 byTask = dict() 

804 if isinstance(pipeline, Pipeline): 

805 pipeline = pipeline.toExpandedPipeline() 

806 for taskDef in pipeline: 

807 thisTask = TaskDatasetTypes.fromTaskDef(taskDef, registry=registry) 

808 allInitInputs |= thisTask.initInputs 

809 allInitOutputs |= thisTask.initOutputs 

810 allInputs |= thisTask.inputs 

811 prerequisites |= thisTask.prerequisites 

812 allOutputs |= thisTask.outputs 

813 byTask[taskDef.label] = thisTask 

814 if not prerequisites.isdisjoint(allInputs): 

815 raise ValueError("{} marked as both prerequisites and regular inputs".format( 

816 {dt.name for dt in allInputs & prerequisites} 

817 )) 

818 if not prerequisites.isdisjoint(allOutputs): 

819 raise ValueError("{} marked as both prerequisites and outputs".format( 

820 {dt.name for dt in allOutputs & prerequisites} 

821 )) 

822 # Make sure that components which are marked as inputs get treated as 

823 # intermediates if there is an output which produces the composite 

824 # containing the component 

825 intermediateComponents = NamedValueSet() 

826 intermediateComposites = NamedValueSet() 

827 outputNameMapping = {dsType.name: dsType for dsType in allOutputs} 

828 for dsType in allInputs: 

829 # get the name of a possible component 

830 name, component = dsType.nameAndComponent() 

831 # if there is a component name, that means this is a component 

832 # DatasetType, if there is an output which produces the parent of 

833 # this component, treat this input as an intermediate 

834 if component is not None: 

835 if name in outputNameMapping: 

836 if outputNameMapping[name].dimensions != dsType.dimensions: 

837 raise ValueError(f"Component dataset type {dsType.name} has different " 

838 f"dimensions ({dsType.dimensions}) than its parent " 

839 f"({outputNameMapping[name].dimensions}).") 

840 composite = DatasetType(name, dsType.dimensions, outputNameMapping[name].storageClass, 

841 universe=registry.dimensions) 

842 intermediateComponents.add(dsType) 

843 intermediateComposites.add(composite) 

844 

845 def checkConsistency(a: NamedValueSet, b: NamedValueSet): 

846 common = a.names & b.names 

847 for name in common: 

848 if a[name] != b[name]: 

849 raise ValueError(f"Conflicting definitions for dataset type: {a[name]} != {b[name]}.") 

850 

851 checkConsistency(allInitInputs, allInitOutputs) 

852 checkConsistency(allInputs, allOutputs) 

853 checkConsistency(allInputs, intermediateComposites) 

854 checkConsistency(allOutputs, intermediateComposites) 

855 

856 def frozen(s: NamedValueSet) -> NamedValueSet: 

857 s.freeze() 

858 return s 

859 

860 return cls( 

861 initInputs=frozen(allInitInputs - allInitOutputs), 

862 initIntermediates=frozen(allInitInputs & allInitOutputs), 

863 initOutputs=frozen(allInitOutputs - allInitInputs), 

864 inputs=frozen(allInputs - allOutputs - intermediateComponents), 

865 intermediates=frozen(allInputs & allOutputs | intermediateComponents), 

866 outputs=frozen(allOutputs - allInputs - intermediateComposites), 

867 prerequisites=frozen(prerequisites), 

868 byTask=MappingProxyType(byTask), # MappingProxyType -> frozen view of dict for immutability 

869 )