Coverage for python/lsst/pipe/base/pipeline.py: 20%

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 

28import copy 

29import logging 

30import os 

31import re 

32import urllib.parse 

33import warnings 

34 

35# ------------------------------- 

36# Imports of standard modules -- 

37# ------------------------------- 

38from dataclasses import dataclass 

39from types import MappingProxyType 

40from typing import ( 

41 TYPE_CHECKING, 

42 AbstractSet, 

43 Callable, 

44 ClassVar, 

45 Dict, 

46 Generator, 

47 Iterable, 

48 Iterator, 

49 Mapping, 

50 Optional, 

51 Set, 

52 Tuple, 

53 Type, 

54 Union, 

55 cast, 

56) 

57 

58# ----------------------------- 

59# Imports for other modules -- 

60from lsst.daf.butler import DatasetType, NamedValueSet, Registry, SkyPixDimension 

61from lsst.resources import ResourcePath, ResourcePathExpression 

62from lsst.utils import doImportType 

63from lsst.utils.introspection import get_full_type_name 

64 

65from . import pipelineIR, pipeTools 

66from ._task_metadata import TaskMetadata 

67from .config import PipelineTaskConfig 

68from .configOverrides import ConfigOverrides 

69from .connections import iterConnections 

70from .pipelineTask import PipelineTask 

71from .task import _TASK_METADATA_TYPE 

72 

73if TYPE_CHECKING: # Imports needed only for type annotations; may be circular. 73 ↛ 74line 73 didn't jump to line 74, because the condition on line 73 was never true

74 from lsst.obs.base import Instrument 

75 from lsst.pex.config import Config 

76 

77# ---------------------------------- 

78# Local non-exported definitions -- 

79# ---------------------------------- 

80 

81_LOG = logging.getLogger(__name__) 

82 

83# ------------------------ 

84# Exported definitions -- 

85# ------------------------ 

86 

87 

88@dataclass 

89class LabelSpecifier: 

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

91 

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

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

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

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

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

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

98 """ 

99 

100 labels: Optional[Set[str]] = None 

101 begin: Optional[str] = None 

102 end: Optional[str] = None 

103 

104 def __post_init__(self) -> None: 

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

106 raise ValueError( 

107 "This struct can only be initialized with a labels set or a begin (and/or) end specifier" 

108 ) 

109 

110 

111class TaskDef: 

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

113 

114 The information includes task name, configuration object and optional 

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

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

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

118 

119 Attributes 

120 ---------- 

121 taskName : `str`, optional 

122 The fully-qualified `PipelineTask` class name. If not provided, 

123 ``taskClass`` must be. 

124 config : `lsst.pipe.base.config.PipelineTaskConfig`, optional 

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

126 usually with all overrides applied. This config will be frozen. If 

127 not provided, ``taskClass`` must be provided and 

128 ``taskClass.ConfigClass()`` will be used. 

129 taskClass : `type`, optional 

130 `PipelineTask` class object; if provided and ``taskName`` is as well, 

131 the caller guarantees that they are consistent. If not provided, 

132 ``taskName`` is used to import the type. 

133 label : `str`, optional 

134 Task label, usually a short string unique in a pipeline. If not 

135 provided, ``taskClass`` must be, and ``taskClass._DefaultName`` will 

136 be used. 

137 """ 

138 

139 def __init__( 

140 self, 

141 taskName: Optional[str] = None, 

142 config: Optional[PipelineTaskConfig] = None, 

143 taskClass: Optional[Type[PipelineTask]] = None, 

144 label: Optional[str] = None, 

145 ): 

146 if taskName is None: 

147 if taskClass is None: 

148 raise ValueError("At least one of `taskName` and `taskClass` must be provided.") 

149 taskName = get_full_type_name(taskClass) 

150 elif taskClass is None: 

151 taskClass = doImportType(taskName) 

152 if config is None: 

153 if taskClass is None: 

154 raise ValueError("`taskClass` must be provided if `config` is not.") 

155 config = taskClass.ConfigClass() 

156 if label is None: 

157 if taskClass is None: 

158 raise ValueError("`taskClass` must be provided if `label` is not.") 

159 label = taskClass._DefaultName 

160 self.taskName = taskName 

161 try: 

162 config.validate() 

163 except Exception: 

164 _LOG.error("Configuration validation failed for task %s (%s)", label, taskName) 

165 raise 

166 config.freeze() 

167 self.config = config 

168 self.taskClass = taskClass 

169 self.label = label 

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

171 

172 @property 

173 def configDatasetName(self) -> str: 

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

175 return self.label + "_config" 

176 

177 @property 

178 def metadataDatasetName(self) -> Optional[str]: 

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

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

181 """ 

182 if self.config.saveMetadata: 

183 return self.makeMetadataDatasetName(self.label) 

184 else: 

185 return None 

186 

187 @classmethod 

188 def makeMetadataDatasetName(cls, label: str) -> str: 

189 """Construct the name of the dataset type for metadata for a task. 

190 

191 Parameters 

192 ---------- 

193 label : `str` 

194 Label for the task within its pipeline. 

195 

196 Returns 

197 ------- 

198 name : `str` 

199 Name of the task's metadata dataset type. 

200 """ 

201 return f"{label}_metadata" 

202 

203 @property 

204 def logOutputDatasetName(self) -> Optional[str]: 

205 """Name of a dataset type for log output from this task, `None` if 

206 logs are not to be saved (`str`) 

207 """ 

208 if cast(PipelineTaskConfig, self.config).saveLogOutput: 

209 return self.label + "_log" 

210 else: 

211 return None 

212 

213 def __str__(self) -> str: 

214 rep = "TaskDef(" + self.taskName 

215 if self.label: 

216 rep += ", label=" + self.label 

217 rep += ")" 

218 return rep 

219 

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

221 if not isinstance(other, TaskDef): 

222 return False 

223 # This does not consider equality of configs when determining equality 

224 # as config equality is a difficult thing to define. Should be updated 

225 # after DM-27847 

226 return self.taskClass == other.taskClass and self.label == other.label 

227 

228 def __hash__(self) -> int: 

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

230 

231 @classmethod 

232 def _unreduce(cls, taskName: str, config: PipelineTaskConfig, label: str) -> TaskDef: 

233 """Custom callable for unpickling. 

234 

235 All arguments are forwarded directly to the constructor; this 

236 trampoline is only needed because ``__reduce__`` callables can't be 

237 called with keyword arguments. 

238 """ 

239 return cls(taskName=taskName, config=config, label=label) 

240 

241 def __reduce__(self) -> Tuple[Callable[[str, PipelineTaskConfig, str], TaskDef], Tuple[str, Config, str]]: 

242 return (self._unreduce, (self.taskName, self.config, self.label)) 

243 

244 

245class Pipeline: 

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

247 configuration for those tasks. 

248 

249 Parameters 

250 ---------- 

251 description : `str` 

252 A description of that this pipeline does. 

253 """ 

254 

255 def __init__(self, description: str): 

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

257 self._pipelineIR = pipelineIR.PipelineIR(pipeline_dict) 

258 

259 @classmethod 

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

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

262 

263 Parameters 

264 ---------- 

265 filename: `str` 

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

267 filename may also supply additional labels to be used in 

268 subsetting the loaded Pipeline. These labels are separated from 

269 the path by a \\#, and may be specified as a comma separated 

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

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

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

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

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

275 or may vary from run to run. 

276 

277 Returns 

278 ------- 

279 pipeline: `Pipeline` 

280 The pipeline loaded from specified location with appropriate (if 

281 any) subsetting 

282 

283 Notes 

284 ----- 

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

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

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

288 than it should. 

289 """ 

290 return cls.from_uri(filename) 

291 

292 @classmethod 

293 def from_uri(cls, uri: ResourcePathExpression) -> Pipeline: 

294 """Load a pipeline defined in a pipeline yaml file at a location 

295 specified by a URI. 

296 

297 Parameters 

298 ---------- 

299 uri: convertible to `ResourcePath` 

300 If a string is supplied this should be a URI path that points to a 

301 pipeline defined in yaml format, either as a direct path to the 

302 yaml file, or as a directory containing a "pipeline.yaml" file (the 

303 form used by `write_to_uri` with ``expand=True``). This uri may 

304 also supply additional labels to be used in subsetting the loaded 

305 Pipeline. These labels are separated from the path by a \\#, and 

306 may be specified as a comma separated list, or a range denoted as 

307 beginning..end. Beginning or end may be empty, in which case the 

308 range will be a half open interval. Unlike python iteration bounds, 

309 end bounds are *INCLUDED*. Note that range based selection is not 

310 well defined for pipelines that are not linear in nature, and 

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

312 The same specifiers can be used with a `ResourcePath` object, by 

313 being the sole contents in the fragments attribute. 

314 

315 Returns 

316 ------- 

317 pipeline: `Pipeline` 

318 The pipeline loaded from specified location with appropriate (if 

319 any) subsetting 

320 

321 Notes 

322 ----- 

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

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

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

326 than it should. 

327 """ 

328 # Split up the uri and any labels that were supplied 

329 uri, label_specifier = cls._parse_file_specifier(uri) 

330 pipeline: Pipeline = cls.fromIR(pipelineIR.PipelineIR.from_uri(uri)) 

331 

332 # If there are labels supplied, only keep those 

333 if label_specifier is not None: 

334 pipeline = pipeline.subsetFromLabels(label_specifier) 

335 return pipeline 

336 

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

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

339 

340 Parameters 

341 ---------- 

342 labelSpecifier : `labelSpecifier` 

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

344 

345 Returns 

346 ------- 

347 pipeline : `Pipeline` 

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

349 

350 Raises 

351 ------ 

352 ValueError 

353 Raised if there is an issue with specified labels 

354 

355 Notes 

356 ----- 

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

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

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

360 than it should. 

361 """ 

362 # Labels supplied as a set 

363 if labelSpecifier.labels: 

364 labelSet = labelSpecifier.labels 

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

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

367 # keep labels that lie between the supplied bounds 

368 else: 

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

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

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

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

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

374 # be dropped 

375 pipeline = copy.deepcopy(self) 

376 pipeline._pipelineIR.contracts = [] 

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

378 

379 # Verify the bounds are in the labels 

380 if labelSpecifier.begin is not None: 

381 if labelSpecifier.begin not in labels: 

382 raise ValueError( 

383 f"Beginning of range subset, {labelSpecifier.begin}, not found in " 

384 "pipeline definition" 

385 ) 

386 if labelSpecifier.end is not None: 

387 if labelSpecifier.end not in labels: 

388 raise ValueError( 

389 f"End of range subset, {labelSpecifier.end}, not found in pipeline definition" 

390 ) 

391 

392 labelSet = set() 

393 for label in labels: 

394 if labelSpecifier.begin is not None: 

395 if label != labelSpecifier.begin: 

396 continue 

397 else: 

398 labelSpecifier.begin = None 

399 labelSet.add(label) 

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

401 break 

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

403 

404 @staticmethod 

405 def _parse_file_specifier(uri: ResourcePathExpression) -> Tuple[ResourcePath, Optional[LabelSpecifier]]: 

406 """Split appart a uri and any possible label subsets""" 

407 if isinstance(uri, str): 

408 # This is to support legacy pipelines during transition 

409 uri, num_replace = re.subn("[:](?!\\/\\/)", "#", uri) 

410 if num_replace: 

411 warnings.warn( 

412 f"The pipeline file {uri} seems to use the legacy : to separate " 

413 "labels, this is deprecated and will be removed after June 2021, please use " 

414 "# instead.", 

415 category=FutureWarning, 

416 ) 

417 if uri.count("#") > 1: 

418 raise ValueError("Only one set of labels is allowed when specifying a pipeline to load") 

419 # Everything else can be converted directly to ResourcePath. 

420 uri = ResourcePath(uri) 

421 label_subset = uri.fragment or None 

422 

423 specifier: Optional[LabelSpecifier] 

424 if label_subset is not None: 

425 label_subset = urllib.parse.unquote(label_subset) 

426 args: Dict[str, Union[Set[str], str, None]] 

427 # labels supplied as a list 

428 if "," in label_subset: 

429 if ".." in label_subset: 

430 raise ValueError( 

431 "Can only specify a list of labels or a rangewhen loading a Pipline not both" 

432 ) 

433 args = {"labels": set(label_subset.split(","))} 

434 # labels supplied as a range 

435 elif ".." in label_subset: 

436 # Try to de-structure the labelSubset, this will fail if more 

437 # than one range is specified 

438 begin, end, *rest = label_subset.split("..") 

439 if rest: 

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

441 args = {"begin": begin if begin else None, "end": end if end else None} 

442 # Assume anything else is a single label 

443 else: 

444 args = {"labels": {label_subset}} 

445 

446 # MyPy doesn't like how cavalier kwarg construction is with types. 

447 specifier = LabelSpecifier(**args) # type: ignore 

448 else: 

449 specifier = None 

450 

451 return uri, specifier 

452 

453 @classmethod 

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

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

456 

457 Parameters 

458 ---------- 

459 pipeline_string : `str` 

460 A string that is formatted according like a pipeline document 

461 

462 Returns 

463 ------- 

464 pipeline: `Pipeline` 

465 """ 

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

467 return pipeline 

468 

469 @classmethod 

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

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

472 

473 Parameters 

474 ---------- 

475 deserialized_pipeline: `PipelineIR` 

476 An already created pipeline intermediate representation object 

477 

478 Returns 

479 ------- 

480 pipeline: `Pipeline` 

481 """ 

482 pipeline = cls.__new__(cls) 

483 pipeline._pipelineIR = deserialized_pipeline 

484 return pipeline 

485 

486 @classmethod 

487 def fromPipeline(cls, pipeline: Pipeline) -> Pipeline: 

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

489 

490 Parameters 

491 ---------- 

492 pipeline: `Pipeline` 

493 An already created pipeline intermediate representation object 

494 

495 Returns 

496 ------- 

497 pipeline: `Pipeline` 

498 """ 

499 return cls.fromIR(copy.deepcopy(pipeline._pipelineIR)) 

500 

501 def __str__(self) -> str: 

502 return str(self._pipelineIR) 

503 

504 def addInstrument(self, instrument: Union[Instrument, str]) -> None: 

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

506 already defined. 

507 

508 Parameters 

509 ---------- 

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

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

512 a string corresponding to a fully qualified 

513 `lsst.daf.butler.instrument` name. 

514 """ 

515 if isinstance(instrument, str): 

516 pass 

517 else: 

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

519 # checking 

520 instrument = get_full_type_name(instrument) 

521 self._pipelineIR.instrument = instrument 

522 

523 def getInstrument(self) -> Optional[str]: 

524 """Get the instrument from the pipeline. 

525 

526 Returns 

527 ------- 

528 instrument : `str`, or None 

529 The fully qualified name of a `lsst.obs.base.Instrument` subclass, 

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

531 """ 

532 return self._pipelineIR.instrument 

533 

534 def addTask(self, task: Union[Type[PipelineTask], str], label: str) -> None: 

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

536 associated with the supplied label. 

537 

538 Parameters 

539 ---------- 

540 task: `PipelineTask` or `str` 

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

542 corresponding to a fully qualified `PipelineTask` name. 

543 label: `str` 

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

545 """ 

546 if isinstance(task, str): 

547 taskName = task 

548 elif issubclass(task, PipelineTask): 

549 taskName = get_full_type_name(task) 

550 else: 

551 raise ValueError( 

552 "task must be either a child class of PipelineTask or a string containing" 

553 " a fully qualified name to one" 

554 ) 

555 if not label: 

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

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

558 # _DefaultName in that case 

559 if isinstance(task, str): 

560 task_class = doImportType(task) 

561 label = task_class._DefaultName 

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

563 

564 def removeTask(self, label: str) -> None: 

565 """Remove a task from the pipeline. 

566 

567 Parameters 

568 ---------- 

569 label : `str` 

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

571 

572 Raises 

573 ------ 

574 KeyError 

575 If no task with that label exists in the pipeline 

576 

577 """ 

578 self._pipelineIR.tasks.pop(label) 

579 

580 def addConfigOverride(self, label: str, key: str, value: object) -> None: 

581 """Apply single config override. 

582 

583 Parameters 

584 ---------- 

585 label : `str` 

586 Label of the task. 

587 key: `str` 

588 Fully-qualified field name. 

589 value : object 

590 Value to be given to a field. 

591 """ 

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

593 

594 def addConfigFile(self, label: str, filename: str) -> None: 

595 """Add overrides from a specified file. 

596 

597 Parameters 

598 ---------- 

599 label : `str` 

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

601 modify 

602 filename : `str` 

603 Path to the override file. 

604 """ 

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

606 

607 def addConfigPython(self, label: str, pythonString: str) -> None: 

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

609 

610 Parameters 

611 ---------- 

612 label : `str` 

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

614 modify. 

615 pythonString: `str` 

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

617 with config as the only local accessible value. 

618 """ 

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

620 

621 def _addConfigImpl(self, label: str, newConfig: pipelineIR.ConfigIR) -> None: 

622 if label == "parameters": 

623 if newConfig.rest.keys() - self._pipelineIR.parameters.mapping.keys(): 

624 raise ValueError("Cannot override parameters that are not defined in pipeline") 

625 self._pipelineIR.parameters.mapping.update(newConfig.rest) 

626 if newConfig.file: 

627 raise ValueError("Setting parameters section with config file is not supported") 

628 if newConfig.python: 

629 raise ValueError("Setting parameters section using python block in unsupported") 

630 return 

631 if label not in self._pipelineIR.tasks: 

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

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

634 

635 def toFile(self, filename: str) -> None: 

636 self._pipelineIR.to_file(filename) 

637 

638 def write_to_uri(self, uri: ResourcePathExpression) -> None: 

639 """Write the pipeline to a file or directory. 

640 

641 Parameters 

642 ---------- 

643 uri : convertible to `ResourcePath` 

644 URI to write to; may have any scheme with `ResourcePath` write 

645 support or no scheme for a local file/directory. Should have a 

646 ``.yaml``. 

647 """ 

648 self._pipelineIR.write_to_uri(uri) 

649 

650 def toExpandedPipeline(self) -> Generator[TaskDef, None, None]: 

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

652 graphs. 

653 

654 Returns 

655 ------- 

656 generator : generator of `TaskDef` 

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

658 are to be used in constructing a quantum graph. 

659 

660 Raises 

661 ------ 

662 NotImplementedError 

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

664 future use 

665 """ 

666 taskDefs = [] 

667 for label in self._pipelineIR.tasks: 

668 taskDefs.append(self._buildTaskDef(label)) 

669 

670 # lets evaluate the contracts 

671 if self._pipelineIR.contracts is not None: 

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

673 for contract in self._pipelineIR.contracts: 

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

675 # message if there was problems with the eval 

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

677 if not success: 

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

679 raise pipelineIR.ContractError( 

680 f"Contract(s) '{contract.contract}' were not satisfied{extra_info}" 

681 ) 

682 

683 taskDefs = sorted(taskDefs, key=lambda x: x.label) 

684 yield from pipeTools.orderPipeline(taskDefs) 

685 

686 def _buildTaskDef(self, label: str) -> TaskDef: 

687 if (taskIR := self._pipelineIR.tasks.get(label)) is None: 

688 raise NameError(f"Label {label} does not appear in this pipeline") 

689 taskClass: Type[PipelineTask] = doImportType(taskIR.klass) 

690 taskName = get_full_type_name(taskClass) 

691 config = taskClass.ConfigClass() 

692 overrides = ConfigOverrides() 

693 if self._pipelineIR.instrument is not None: 

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

695 if taskIR.config is not None: 

696 for configIR in (configIr.formatted(self._pipelineIR.parameters) for configIr in taskIR.config): 

697 if configIR.dataId is not None: 

698 raise NotImplementedError( 

699 "Specializing a config on a partial data id is not yet " 

700 "supported in Pipeline definition" 

701 ) 

702 # only apply override if it applies to everything 

703 if configIR.dataId is None: 

704 if configIR.file: 

705 for configFile in configIR.file: 

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

707 if configIR.python is not None: 

708 overrides.addPythonOverride(configIR.python) 

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

710 overrides.addValueOverride(key, value) 

711 overrides.applyTo(config) 

712 return TaskDef(taskName=taskName, config=config, taskClass=taskClass, label=label) 

713 

714 def __iter__(self) -> Generator[TaskDef, None, None]: 

715 return self.toExpandedPipeline() 

716 

717 def __getitem__(self, item: str) -> TaskDef: 

718 return self._buildTaskDef(item) 

719 

720 def __len__(self) -> int: 

721 return len(self._pipelineIR.tasks) 

722 

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

724 if not isinstance(other, Pipeline): 

725 return False 

726 elif self._pipelineIR == other._pipelineIR: 

727 # Shortcut: if the IR is the same, the expanded pipeline must be 

728 # the same as well. But the converse is not true. 

729 return True 

730 else: 

731 self_expanded = {td.label: (td.taskClass,) for td in self} 

732 other_expanded = {td.label: (td.taskClass,) for td in other} 

733 if self_expanded != other_expanded: 

734 return False 

735 # After DM-27847, we should compare configuration here, or better, 

736 # delegated to TaskDef.__eq__ after making that compare configurations. 

737 raise NotImplementedError( 

738 "Pipelines cannot be compared because config instances cannot be compared; see DM-27847." 

739 ) 

740 

741 

742@dataclass(frozen=True) 

743class TaskDatasetTypes: 

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

745 by a `PipelineTask` 

746 """ 

747 

748 initInputs: NamedValueSet[DatasetType] 

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

750 

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

752 `~PipelineDatasetTypes.initInputs` or 

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

754 """ 

755 

756 initOutputs: NamedValueSet[DatasetType] 

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

758 

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

760 `~PipelineDatasetTypes.initOutputs` or 

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

762 """ 

763 

764 inputs: NamedValueSet[DatasetType] 

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

766 

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

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

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

770 

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

772 `~PipelineDatasetTypes.inputs` or `~PipelineDatasetTypes.intermediates` 

773 at the Pipeline level. 

774 """ 

775 

776 prerequisites: NamedValueSet[DatasetType] 

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

778 

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

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

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

782 

783 Prerequisite inputs are not resolved until the second stage of 

784 QuantumGraph generation. 

785 """ 

786 

787 outputs: NamedValueSet[DatasetType] 

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

789 

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

791 `~PipelineDatasetTypes.outputs` or `~PipelineDatasetTypes.intermediates` 

792 at the Pipeline level. 

793 """ 

794 

795 @classmethod 

796 def fromTaskDef( 

797 cls, 

798 taskDef: TaskDef, 

799 *, 

800 registry: Registry, 

801 include_configs: bool = True, 

802 storage_class_mapping: Optional[Mapping[str, str]] = None, 

803 ) -> TaskDatasetTypes: 

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

805 

806 Parameters 

807 ---------- 

808 taskDef: `TaskDef` 

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

810 registry: `Registry` 

811 Registry used to construct normalized `DatasetType` objects and 

812 retrieve those that are incomplete. 

813 include_configs : `bool`, optional 

814 If `True` (default) include config dataset types as 

815 ``initOutputs``. 

816 storage_class_mapping : `Mapping` of `str` to `StorageClass`, optional 

817 If a taskdef contains a component dataset type that is unknown 

818 to the registry, its parent StorageClass will be looked up in this 

819 mapping if it is supplied. If the mapping does not contain the 

820 composite dataset type, or the mapping is not supplied an exception 

821 will be raised. 

822 

823 Returns 

824 ------- 

825 types: `TaskDatasetTypes` 

826 The dataset types used by this task. 

827 

828 Raises 

829 ------ 

830 ValueError 

831 Raised if dataset type connection definition differs from 

832 registry definition. 

833 LookupError 

834 Raised if component parent StorageClass could not be determined 

835 and storage_class_mapping does not contain the composite type, or 

836 is set to None. 

837 """ 

838 

839 def makeDatasetTypesSet( 

840 connectionType: str, 

841 is_input: bool, 

842 freeze: bool = True, 

843 ) -> NamedValueSet[DatasetType]: 

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

845 

846 Parameters 

847 ---------- 

848 connectionType : `str` 

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

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

851 is_input : `bool` 

852 These are input dataset types, else they are output dataset 

853 types. 

854 freeze : `bool`, optional 

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

856 

857 Returns 

858 ------- 

859 datasetTypes : `NamedValueSet` 

860 A set of all datasetTypes which correspond to the input 

861 connection type specified in the connection class of this 

862 `PipelineTask` 

863 

864 Raises 

865 ------ 

866 ValueError 

867 Raised if dataset type connection definition differs from 

868 registry definition. 

869 LookupError 

870 Raised if component parent StorageClass could not be determined 

871 and storage_class_mapping does not contain the composite type, 

872 or is set to None. 

873 

874 Notes 

875 ----- 

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

877 ``taskDef``, and ``storage_class_mapping``. 

878 """ 

879 datasetTypes = NamedValueSet[DatasetType]() 

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

881 dimensions = set(getattr(c, "dimensions", set())) 

882 if "skypix" in dimensions: 

883 try: 

884 datasetType = registry.getDatasetType(c.name) 

885 except LookupError as err: 

886 raise LookupError( 

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

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

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

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

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

892 ) from err 

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

894 rest2 = set( 

895 dim.name for dim in datasetType.dimensions if not isinstance(dim, SkyPixDimension) 

896 ) 

897 if rest1 != rest2: 

898 raise ValueError( 

899 f"Non-skypix dimensions for dataset type {c.name} declared in " 

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

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

902 ) 

903 else: 

904 # Component dataset types are not explicitly in the 

905 # registry. This complicates consistency checks with 

906 # registry and requires we work out the composite storage 

907 # class. 

908 registryDatasetType = None 

909 try: 

910 registryDatasetType = registry.getDatasetType(c.name) 

911 except KeyError: 

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

913 if componentName: 

914 if storage_class_mapping is None or compositeName not in storage_class_mapping: 

915 raise LookupError( 

916 "Component parent class cannot be determined, and " 

917 "composite name was not in storage class mapping, or no " 

918 "storage_class_mapping was supplied" 

919 ) 

920 else: 

921 parentStorageClass = storage_class_mapping[compositeName] 

922 else: 

923 parentStorageClass = None 

924 datasetType = c.makeDatasetType( 

925 registry.dimensions, parentStorageClass=parentStorageClass 

926 ) 

927 registryDatasetType = datasetType 

928 else: 

929 datasetType = c.makeDatasetType( 

930 registry.dimensions, parentStorageClass=registryDatasetType.parentStorageClass 

931 ) 

932 

933 if registryDatasetType and datasetType != registryDatasetType: 

934 # The dataset types differ but first check to see if 

935 # they are compatible before raising. 

936 if is_input: 

937 # This DatasetType must be compatible on get. 

938 is_compatible = datasetType.is_compatible_with(registryDatasetType) 

939 else: 

940 # Has to be able to be converted to expect type 

941 # on put. 

942 is_compatible = registryDatasetType.is_compatible_with(datasetType) 

943 if is_compatible: 

944 # For inputs we want the pipeline to use the 

945 # pipeline definition, for outputs it should use 

946 # the registry definition. 

947 if not is_input: 

948 datasetType = registryDatasetType 

949 _LOG.debug( 

950 "Dataset types differ (task %s != registry %s) but are compatible" 

951 " for %s in %s.", 

952 datasetType, 

953 registryDatasetType, 

954 "input" if is_input else "output", 

955 taskDef.label, 

956 ) 

957 else: 

958 try: 

959 # Explicitly check for storage class just to 

960 # make more specific message. 

961 _ = datasetType.storageClass 

962 except KeyError: 

963 raise ValueError( 

964 "Storage class does not exist for supplied dataset type " 

965 f"{datasetType} for {taskDef.label}." 

966 ) from None 

967 raise ValueError( 

968 f"Supplied dataset type ({datasetType}) inconsistent with " 

969 f"registry definition ({registryDatasetType}) " 

970 f"for {taskDef.label}." 

971 ) 

972 datasetTypes.add(datasetType) 

973 if freeze: 

974 datasetTypes.freeze() 

975 return datasetTypes 

976 

977 # optionally add initOutput dataset for config 

978 initOutputs = makeDatasetTypesSet("initOutputs", is_input=False, freeze=False)