Coverage for python/lsst/pipe/base/testUtils.py: 12%

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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# (https://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 <https://www.gnu.org/licenses/>. 

21 

22from __future__ import annotations 

23 

24__all__ = [ 

25 "assertValidInitOutput", 

26 "assertValidOutput", 

27 "getInitInputs", 

28 "lintConnections", 

29 "makeQuantum", 

30 "runTestQuantum", 

31] 

32 

33 

34import collections.abc 

35import itertools 

36import unittest.mock 

37from collections import defaultdict 

38from typing import TYPE_CHECKING, AbstractSet, Any, Dict, Mapping, Optional, Sequence, Set, Union 

39 

40from lsst.daf.butler import ( 

41 Butler, 

42 DataCoordinate, 

43 DataId, 

44 DatasetRef, 

45 DatasetType, 

46 Dimension, 

47 DimensionUniverse, 

48 Quantum, 

49 SkyPixDimension, 

50 StorageClassFactory, 

51) 

52from lsst.pipe.base.connectionTypes import BaseConnection, DimensionedConnection 

53 

54from .butlerQuantumContext import ButlerQuantumContext 

55 

56if TYPE_CHECKING: 56 ↛ 57line 56 didn't jump to line 57, because the condition on line 56 was never true

57 from .config import PipelineTaskConfig 

58 from .connections import PipelineTaskConnections 

59 from .pipelineTask import PipelineTask 

60 from .struct import Struct 

61 

62 

63def makeQuantum( 

64 task: PipelineTask, 

65 butler: Butler, 

66 dataId: DataId, 

67 ioDataIds: Mapping[str, Union[DataId, Sequence[DataId]]], 

68) -> Quantum: 

69 """Create a Quantum for a particular data ID(s). 

70 

71 Parameters 

72 ---------- 

73 task : `lsst.pipe.base.PipelineTask` 

74 The task whose processing the quantum represents. 

75 butler : `lsst.daf.butler.Butler` 

76 The collection the quantum refers to. 

77 dataId: any data ID type 

78 The data ID of the quantum. Must have the same dimensions as 

79 ``task``'s connections class. 

80 ioDataIds : `collections.abc.Mapping` [`str`] 

81 A mapping keyed by input/output names. Values must be data IDs for 

82 single connections and sequences of data IDs for multiple connections. 

83 

84 Returns 

85 ------- 

86 quantum : `lsst.daf.butler.Quantum` 

87 A quantum for ``task``, when called with ``dataIds``. 

88 """ 

89 # This is a type ignore, because `connections` is a dynamic class, but 

90 # it for sure will have this property 

91 connections = task.config.ConnectionsClass(config=task.config) # type: ignore 

92 

93 try: 

94 _checkDimensionsMatch(butler.registry.dimensions, connections.dimensions, dataId.keys()) 

95 except ValueError as e: 

96 raise ValueError("Error in quantum dimensions.") from e 

97 

98 inputs = defaultdict(list) 

99 outputs = defaultdict(list) 

100 for name in itertools.chain(connections.inputs, connections.prerequisiteInputs): 

101 try: 

102 connection = connections.__getattribute__(name) 

103 _checkDataIdMultiplicity(name, ioDataIds[name], connection.multiple) 

104 ids = _normalizeDataIds(ioDataIds[name]) 

105 for id in ids: 

106 ref = _refFromConnection(butler, connection, id) 

107 inputs[ref.datasetType].append(ref) 

108 except (ValueError, KeyError) as e: 

109 raise ValueError(f"Error in connection {name}.") from e 

110 for name in connections.outputs: 

111 try: 

112 connection = connections.__getattribute__(name) 

113 _checkDataIdMultiplicity(name, ioDataIds[name], connection.multiple) 

114 ids = _normalizeDataIds(ioDataIds[name]) 

115 for id in ids: 

116 ref = _refFromConnection(butler, connection, id) 

117 outputs[ref.datasetType].append(ref) 

118 except (ValueError, KeyError) as e: 

119 raise ValueError(f"Error in connection {name}.") from e 

120 quantum = Quantum( 

121 taskClass=type(task), 

122 dataId=DataCoordinate.standardize(dataId, universe=butler.registry.dimensions), 

123 inputs=inputs, 

124 outputs=outputs, 

125 ) 

126 return quantum 

127 

128 

129def _checkDimensionsMatch( 

130 universe: DimensionUniverse, 

131 expected: Union[AbstractSet[str], AbstractSet[Dimension]], 

132 actual: Union[AbstractSet[str], AbstractSet[Dimension]], 

133) -> None: 

134 """Test whether two sets of dimensions agree after conversions. 

135 

136 Parameters 

137 ---------- 

138 universe : `lsst.daf.butler.DimensionUniverse` 

139 The set of all known dimensions. 

140 expected : `Set` [`str`] or `Set` [`~lsst.daf.butler.Dimension`] 

141 The dimensions expected from a task specification. 

142 actual : `Set` [`str`] or `Set` [`~lsst.daf.butler.Dimension`] 

143 The dimensions provided by input. 

144 

145 Raises 

146 ------ 

147 ValueError 

148 Raised if ``expected`` and ``actual`` cannot be reconciled. 

149 """ 

150 if _simplify(universe, expected) != _simplify(universe, actual): 

151 raise ValueError(f"Mismatch in dimensions; expected {expected} but got {actual}.") 

152 

153 

154def _simplify( 

155 universe: DimensionUniverse, dimensions: Union[AbstractSet[str], AbstractSet[Dimension]] 

156) -> Set[str]: 

157 """Reduce a set of dimensions to a string-only form. 

158 

159 Parameters 

160 ---------- 

161 universe : `lsst.daf.butler.DimensionUniverse` 

162 The set of all known dimensions. 

163 dimensions : `Set` [`str`] or `Set` [`~lsst.daf.butler.Dimension`] 

164 A set of dimensions to simplify. 

165 

166 Returns 

167 ------- 

168 dimensions : `Set` [`str`] 

169 A copy of ``dimensions`` reduced to string form, with all spatial 

170 dimensions simplified to ``skypix``. 

171 """ 

172 simplified: Set[str] = set() 

173 for dimension in dimensions: 

174 # skypix not a real Dimension, handle it first 

175 if dimension == "skypix": 

176 simplified.add(dimension) # type: ignore 

177 else: 

178 # Need a Dimension to test spatialness 

179 fullDimension = universe[dimension] if isinstance(dimension, str) else dimension 

180 if isinstance(fullDimension, SkyPixDimension): 

181 simplified.add("skypix") 

182 else: 

183 simplified.add(fullDimension.name) 

184 return simplified 

185 

186 

187def _checkDataIdMultiplicity(name: str, dataIds: Union[DataId, Sequence[DataId]], multiple: bool) -> None: 

188 """Test whether data IDs are scalars for scalar connections and sequences 

189 for multiple connections. 

190 

191 Parameters 

192 ---------- 

193 name : `str` 

194 The name of the connection being tested. 

195 dataIds : any data ID type or `~collections.abc.Sequence` [data ID] 

196 The data ID(s) provided for the connection. 

197 multiple : `bool` 

198 The ``multiple`` field of the connection. 

199 

200 Raises 

201 ------ 

202 ValueError 

203 Raised if ``dataIds`` and ``multiple`` do not match. 

204 """ 

205 if multiple: 

206 if not isinstance(dataIds, collections.abc.Sequence): 

207 raise ValueError(f"Expected multiple data IDs for {name}, got {dataIds}.") 

208 else: 

209 # DataCoordinate is a Mapping 

210 if not isinstance(dataIds, collections.abc.Mapping): 

211 raise ValueError(f"Expected single data ID for {name}, got {dataIds}.") 

212 

213 

214def _normalizeDataIds(dataIds: Union[DataId, Sequence[DataId]]) -> Sequence[DataId]: 

215 """Represent both single and multiple data IDs as a list. 

216 

217 Parameters 

218 ---------- 

219 dataIds : any data ID type or `~collections.abc.Sequence` thereof 

220 The data ID(s) provided for a particular input or output connection. 

221 

222 Returns 

223 ------- 

224 normalizedIds : `~collections.abc.Sequence` [data ID] 

225 A sequence equal to ``dataIds`` if it was already a sequence, or 

226 ``[dataIds]`` if it was a single ID. 

227 """ 

228 if isinstance(dataIds, collections.abc.Sequence): 

229 return dataIds 

230 else: 

231 return [dataIds] 

232 

233 

234def _refFromConnection( 

235 butler: Butler, connection: DimensionedConnection, dataId: DataId, **kwargs: Any 

236) -> DatasetRef: 

237 """Create a DatasetRef for a connection in a collection. 

238 

239 Parameters 

240 ---------- 

241 butler : `lsst.daf.butler.Butler` 

242 The collection to point to. 

243 connection : `lsst.pipe.base.connectionTypes.DimensionedConnection` 

244 The connection defining the dataset type to point to. 

245 dataId 

246 The data ID for the dataset to point to. 

247 **kwargs 

248 Additional keyword arguments used to augment or construct 

249 a `~lsst.daf.butler.DataCoordinate`. 

250 

251 Returns 

252 ------- 

253 ref : `lsst.daf.butler.DatasetRef` 

254 A reference to a dataset compatible with ``connection``, with ID 

255 ``dataId``, in the collection pointed to by ``butler``. 

256 """ 

257 universe = butler.registry.dimensions 

258 # DatasetRef only tests if required dimension is missing, but not extras 

259 _checkDimensionsMatch(universe, set(connection.dimensions), dataId.keys()) 

260 dataId = DataCoordinate.standardize(dataId, **kwargs, universe=universe) 

261 

262 datasetType = butler.registry.getDatasetType(connection.name) 

263 

264 try: 

265 butler.registry.getDatasetType(datasetType.name) 

266 except KeyError: 

267 raise ValueError(f"Invalid dataset type {connection.name}.") 

268 if not butler.run: 

269 raise ValueError("Can not create a resolved DatasetRef since the butler has no default run defined.") 

270 try: 

271 registry_ref = butler.registry.findDataset(datasetType, dataId, collections=[butler.run]) 

272 if registry_ref: 

273 ref = registry_ref 

274 else: 

275 ref = DatasetRef(datasetType=datasetType, dataId=dataId, run=butler.run) 

276 butler.registry._importDatasets([ref]) 

277 return ref 

278 except KeyError as e: 

279 raise ValueError(f"Dataset type ({connection.name}) and ID {dataId.byName()} not compatible.") from e 

280 

281 

282def runTestQuantum( 

283 task: PipelineTask, butler: Butler, quantum: Quantum, mockRun: bool = True 

284) -> Optional[unittest.mock.Mock]: 

285 """Run a PipelineTask on a Quantum. 

286 

287 Parameters 

288 ---------- 

289 task : `lsst.pipe.base.PipelineTask` 

290 The task to run on the quantum. 

291 butler : `lsst.daf.butler.Butler` 

292 The collection to run on. 

293 quantum : `lsst.daf.butler.Quantum` 

294 The quantum to run. 

295 mockRun : `bool` 

296 Whether or not to replace ``task``'s ``run`` method. The default of 

297 `True` is recommended unless ``run`` needs to do real work (e.g., 

298 because the test needs real output datasets). 

299 

300 Returns 

301 ------- 

302 run : `unittest.mock.Mock` or `None` 

303 If ``mockRun`` is set, the mock that replaced ``run``. This object can 

304 be queried for the arguments ``runQuantum`` passed to ``run``. 

305 """ 

306 butlerQc = ButlerQuantumContext(butler, quantum) 

307 # This is a type ignore, because `connections` is a dynamic class, but 

308 # it for sure will have this property 

309 connections = task.config.ConnectionsClass(config=task.config) # type: ignore 

310 inputRefs, outputRefs = connections.buildDatasetRefs(quantum) 

311 if mockRun: 

312 with unittest.mock.patch.object(task, "run") as mock, unittest.mock.patch( 

313 "lsst.pipe.base.ButlerQuantumContext.put" 

314 ): 

315 task.runQuantum(butlerQc, inputRefs, outputRefs) 

316 return mock 

317 else: 

318 task.runQuantum(butlerQc, inputRefs, outputRefs) 

319 return None 

320 

321 

322def _assertAttributeMatchesConnection(obj: Any, attrName: str, connection: BaseConnection) -> None: 

323 """Test that an attribute on an object matches the specification given in 

324 a connection. 

325 

326 Parameters 

327 ---------- 

328 obj 

329 An object expected to contain the attribute ``attrName``. 

330 attrName : `str` 

331 The name of the attribute to be tested. 

332 connection : `lsst.pipe.base.connectionTypes.BaseConnection` 

333 The connection, usually some type of output, specifying ``attrName``. 

334 

335 Raises 

336 ------ 

337 AssertionError: 

338 Raised if ``obj.attrName`` does not match what's expected 

339 from ``connection``. 

340 """ 

341 # name 

342 try: 

343 attrValue = obj.__getattribute__(attrName) 

344 except AttributeError: 

345 raise AssertionError(f"No such attribute on {obj!r}: {attrName}") 

346 # multiple 

347 if connection.multiple: 

348 if not isinstance(attrValue, collections.abc.Sequence): 

349 raise AssertionError(f"Expected {attrName} to be a sequence, got {attrValue!r} instead.") 

350 else: 

351 # use lazy evaluation to not use StorageClassFactory unless 

352 # necessary 

353 if isinstance(attrValue, collections.abc.Sequence) and not issubclass( 

354 StorageClassFactory().getStorageClass(connection.storageClass).pytype, collections.abc.Sequence 

355 ): 

356 raise AssertionError(f"Expected {attrName} to be a single value, got {attrValue!r} instead.") 

357 # no test for storageClass, as I'm not sure how much persistence 

358 # depends on duck-typing 

359 

360 

361def assertValidOutput(task: PipelineTask, result: Struct) -> None: 

362 """Test that the output of a call to ``run`` conforms to its own 

363 connections. 

364 

365 Parameters 

366 ---------- 

367 task : `lsst.pipe.base.PipelineTask` 

368 The task whose connections need validation. This is a fully-configured 

369 task object to support features such as optional outputs. 

370 result : `lsst.pipe.base.Struct` 

371 A result object produced by calling ``task.run``. 

372 

373 Raises 

374 ------ 

375 AssertionError: 

376 Raised if ``result`` does not match what's expected from ``task's`` 

377 connections. 

378 """ 

379 # This is a type ignore, because `connections` is a dynamic class, but 

380 # it for sure will have this property 

381 connections = task.config.ConnectionsClass(config=task.config) # type: ignore 

382 

383 for name in connections.outputs: 

384 connection = connections.__getattribute__(name) 

385 _assertAttributeMatchesConnection(result, name, connection) 

386 

387 

388def assertValidInitOutput(task: PipelineTask) -> None: 

389 """Test that a constructed task conforms to its own init-connections. 

390 

391 Parameters 

392 ---------- 

393 task : `lsst.pipe.base.PipelineTask` 

394 The task whose connections need validation. 

395 

396 Raises 

397 ------ 

398 AssertionError: 

399 Raised if ``task`` does not have the state expected from ``task's`` 

400 connections. 

401 """ 

402 # This is a type ignore, because `connections` is a dynamic class, but 

403 # it for sure will have this property 

404 connections = task.config.ConnectionsClass(config=task.config) # type: ignore 

405 

406 for name in connections.initOutputs: 

407 connection = connections.__getattribute__(name) 

408 _assertAttributeMatchesConnection(task, name, connection) 

409 

410 

411def getInitInputs(butler: Butler, config: PipelineTaskConfig) -> Dict[str, Any]: 

412 """Return the initInputs object that would have been passed to a 

413 `~lsst.pipe.base.PipelineTask` constructor. 

414 

415 Parameters 

416 ---------- 

417 butler : `lsst.daf.butler.Butler` 

418 The repository to search for input datasets. Must have 

419 pre-configured collections. 

420 config : `lsst.pipe.base.PipelineTaskConfig` 

421 The config for the task to be constructed. 

422 

423 Returns 

424 ------- 

425 initInputs : `dict` [`str`] 

426 A dictionary of objects in the format of the ``initInputs`` parameter 

427 to `lsst.pipe.base.PipelineTask`. 

428 """ 

429 connections = config.connections.ConnectionsClass(config=config) 

430 initInputs = {} 

431 for name in connections.initInputs: 

432 attribute = getattr(connections, name) 

433 # Get full dataset type to check for consistency problems 

434 dsType = DatasetType( 

435 attribute.name, butler.registry.dimensions.extract(set()), attribute.storageClass 

436 ) 

437 # All initInputs have empty data IDs 

438 initInputs[name] = butler.get(dsType) 

439 

440 return initInputs 

441 

442 

443def lintConnections( 

444 connections: PipelineTaskConnections, 

445 *, 

446 checkMissingMultiple: bool = True, 

447 checkUnnecessaryMultiple: bool = True, 

448) -> None: 

449 """Inspect a connections class for common errors. 

450 

451 These tests are designed to detect misuse of connections features in 

452 standard designs. An unusually designed connections class may trigger 

453 alerts despite being correctly written; specific checks can be turned off 

454 using keywords. 

455 

456 Parameters 

457 ---------- 

458 connections : `lsst.pipe.base.PipelineTaskConnections`-type 

459 The connections class to test. 

460 checkMissingMultiple : `bool` 

461 Whether to test for single connections that would match multiple 

462 datasets at run time. 

463 checkUnnecessaryMultiple : `bool` 

464 Whether to test for multiple connections that would only match 

465 one dataset. 

466 

467 Raises 

468 ------ 

469 AssertionError 

470 Raised if any of the selected checks fail for any connection. 

471 """ 

472 # Since all comparisons are inside the class, don't bother 

473 # normalizing skypix. 

474 quantumDimensions = connections.dimensions 

475 

476 errors = "" 

477 # connectionTypes.DimensionedConnection is implementation detail, 

478 # don't use it. 

479 for name in itertools.chain(connections.inputs, connections.prerequisiteInputs, connections.outputs): 

480 connection: DimensionedConnection = connections.allConnections[name] # type: ignore 

481 connDimensions = set(connection.dimensions) 

482 if checkMissingMultiple and not connection.multiple and connDimensions > quantumDimensions: 

483 errors += ( 

484 f"Connection {name} may be called with multiple values of " 

485 f"{connDimensions - quantumDimensions} but has multiple=False.\n" 

486 ) 

487 if checkUnnecessaryMultiple and connection.multiple and connDimensions <= quantumDimensions: 

488 errors += ( 

489 f"Connection {name} has multiple=True but can only be called with one " 

490 f"value of {connDimensions} for each {quantumDimensions}.\n" 

491 ) 

492 if errors: 

493 raise AssertionError(errors)