Hide keyboard shortcuts

Hot-keys on this page

r m x p   toggle line displays

j k   next/prev highlighted chunk

0   (zero) top of page

1   (one) first highlighted chunk

1# This file is part of obs_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 <http://www.gnu.org/licenses/>. 

21 

22from __future__ import annotations 

23 

24__all__ = [ 

25 "DefineVisitsConfig", 

26 "DefineVisitsTask", 

27 "GroupExposuresConfig", 

28 "GroupExposuresTask", 

29 "VisitDefinitionData", 

30] 

31 

32from abc import ABCMeta, abstractmethod 

33from collections import defaultdict 

34import itertools 

35import dataclasses 

36from typing import Any, Dict, Iterable, List, Optional, Tuple 

37from multiprocessing import Pool 

38 

39from lsst.daf.butler import ( 

40 Butler, 

41 DataCoordinate, 

42 DataId, 

43 DimensionGraph, 

44 DimensionRecord, 

45 Timespan, 

46) 

47 

48import lsst.geom 

49from lsst.geom import Box2D 

50from lsst.pex.config import Config, Field, makeRegistry, registerConfigurable 

51from lsst.afw.cameraGeom import FOCAL_PLANE, PIXELS 

52from lsst.pipe.base import Task 

53from lsst.sphgeom import ConvexPolygon, Region, UnitVector3d 

54from ._instrument import loadCamera, Instrument 

55 

56 

57@dataclasses.dataclass 

58class VisitDefinitionData: 

59 """Struct representing a group of exposures that will be used to define a 

60 visit. 

61 """ 

62 

63 instrument: str 

64 """Name of the instrument this visit will be associated with. 

65 """ 

66 

67 id: int 

68 """Integer ID of the visit. 

69 

70 This must be unique across all visit systems for the instrument. 

71 """ 

72 

73 name: str 

74 """String name for the visit. 

75 

76 This must be unique across all visit systems for the instrument. 

77 """ 

78 

79 exposures: List[DimensionRecord] = dataclasses.field(default_factory=list) 

80 """Dimension records for the exposures that are part of this visit. 

81 """ 

82 

83 

84@dataclasses.dataclass 

85class _VisitRecords: 

86 """Struct containing the dimension records associated with a visit. 

87 """ 

88 

89 visit: DimensionRecord 

90 """Record for the 'visit' dimension itself. 

91 """ 

92 

93 visit_definition: List[DimensionRecord] 

94 """Records for 'visit_definition', which relates 'visit' to 'exposure'. 

95 """ 

96 

97 visit_detector_region: List[DimensionRecord] 

98 """Records for 'visit_detector_region', which associates the combination 

99 of a 'visit' and a 'detector' with a region on the sky. 

100 """ 

101 

102 

103class GroupExposuresConfig(Config): 

104 pass 

105 

106 

107class GroupExposuresTask(Task, metaclass=ABCMeta): 

108 """Abstract base class for the subtask of `DefineVisitsTask` that is 

109 responsible for grouping exposures into visits. 

110 

111 Subclasses should be registered with `GroupExposuresTask.registry` to 

112 enable use by `DefineVisitsTask`, and should generally correspond to a 

113 particular 'visit_system' dimension value. They are also responsible for 

114 defining visit IDs and names that are unique across all visit systems in 

115 use by an instrument. 

116 

117 Parameters 

118 ---------- 

119 config : `GroupExposuresConfig` 

120 Configuration information. 

121 **kwargs 

122 Additional keyword arguments forwarded to the `Task` constructor. 

123 """ 

124 def __init__(self, config: GroupExposuresConfig, **kwargs: Any): 

125 Task.__init__(self, config=config, **kwargs) 

126 

127 ConfigClass = GroupExposuresConfig 

128 

129 _DefaultName = "groupExposures" 

130 

131 registry = makeRegistry( 

132 doc="Registry of algorithms for grouping exposures into visits.", 

133 configBaseType=GroupExposuresConfig, 

134 ) 

135 

136 @abstractmethod 

137 def group(self, exposures: List[DimensionRecord]) -> Iterable[VisitDefinitionData]: 

138 """Group the given exposures into visits. 

139 

140 Parameters 

141 ---------- 

142 exposures : `list` [ `DimensionRecord` ] 

143 DimensionRecords (for the 'exposure' dimension) describing the 

144 exposures to group. 

145 

146 Returns 

147 ------- 

148 visits : `Iterable` [ `VisitDefinitionData` ] 

149 Structs identifying the visits and the exposures associated with 

150 them. This may be an iterator or a container. 

151 """ 

152 raise NotImplementedError() 

153 

154 @abstractmethod 

155 def getVisitSystem(self) -> Tuple[int, str]: 

156 """Return identifiers for the 'visit_system' dimension this 

157 algorithm implements. 

158 

159 Returns 

160 ------- 

161 id : `int` 

162 Integer ID for the visit system (given an instrument). 

163 name : `str` 

164 Unique string identifier for the visit system (given an 

165 instrument). 

166 """ 

167 raise NotImplementedError() 

168 

169 

170class ComputeVisitRegionsConfig(Config): 

171 padding = Field( 

172 dtype=int, 

173 default=0, 

174 doc=("Pad raw image bounding boxes with specified number of pixels " 

175 "when calculating their (conservatively large) region on the " 

176 "sky."), 

177 ) 

178 

179 

180class ComputeVisitRegionsTask(Task, metaclass=ABCMeta): 

181 """Abstract base class for the subtask of `DefineVisitsTask` that is 

182 responsible for extracting spatial regions for visits and visit+detector 

183 combinations. 

184 

185 Subclasses should be registered with `ComputeVisitRegionsTask.registry` to 

186 enable use by `DefineVisitsTask`. 

187 

188 Parameters 

189 ---------- 

190 config : `ComputeVisitRegionsConfig` 

191 Configuration information. 

192 butler : `lsst.daf.butler.Butler` 

193 The butler to use. 

194 **kwargs 

195 Additional keyword arguments forwarded to the `Task` constructor. 

196 """ 

197 def __init__(self, config: ComputeVisitRegionsConfig, *, butler: Butler, **kwargs: Any): 

198 Task.__init__(self, config=config, **kwargs) 

199 self.butler = butler 

200 self.instrumentMap = {} 

201 

202 ConfigClass = ComputeVisitRegionsConfig 

203 

204 _DefaultName = "computeVisitRegions" 

205 

206 registry = makeRegistry( 

207 doc=("Registry of algorithms for computing on-sky regions for visits " 

208 "and visit+detector combinations."), 

209 configBaseType=ComputeVisitRegionsConfig, 

210 ) 

211 

212 def getInstrument(self, instrumentName) -> Instrument: 

213 """Retrieve an `~lsst.obs.base.Instrument` associated with this 

214 instrument name. 

215 

216 Parameters 

217 ---------- 

218 instrumentName : `str` 

219 The name of the instrument. 

220 

221 Returns 

222 ------- 

223 instrument : `~lsst.obs.base.Instrument` 

224 The associated instrument object. 

225 

226 Notes 

227 ----- 

228 The result is cached. 

229 """ 

230 instrument = self.instrumentMap.get(instrumentName) 

231 if instrument is None: 

232 instrument = Instrument.fromName(instrumentName, self.butler.registry) 

233 self.instrumentMap[instrumentName] = instrument 

234 return instrument 

235 

236 @abstractmethod 

237 def compute(self, visit: VisitDefinitionData, *, collections: Any = None 

238 ) -> Tuple[Region, Dict[int, Region]]: 

239 """Compute regions for the given visit and all detectors in that visit. 

240 

241 Parameters 

242 ---------- 

243 visit : `VisitDefinitionData` 

244 Struct describing the visit and the exposures associated with it. 

245 collections : Any, optional 

246 Collections to be searched for raws and camera geometry, overriding 

247 ``self.butler.collections``. 

248 Can be any of the types supported by the ``collections`` argument 

249 to butler construction. 

250 

251 Returns 

252 ------- 

253 visitRegion : `lsst.sphgeom.Region` 

254 Region for the full visit. 

255 visitDetectorRegions : `dict` [ `int`, `lsst.sphgeom.Region` ] 

256 Dictionary mapping detector ID to the region for that detector. 

257 Should include all detectors in the visit. 

258 """ 

259 raise NotImplementedError() 

260 

261 

262class DefineVisitsConfig(Config): 

263 groupExposures = GroupExposuresTask.registry.makeField( 

264 doc="Algorithm for grouping exposures into visits.", 

265 default="one-to-one", 

266 ) 

267 computeVisitRegions = ComputeVisitRegionsTask.registry.makeField( 

268 doc="Algorithm from computing visit and visit+detector regions.", 

269 default="single-raw-wcs", 

270 ) 

271 ignoreNonScienceExposures = Field( 

272 doc=("If True, silently ignore input exposures that do not have " 

273 "observation_type=SCIENCE. If False, raise an exception if one " 

274 "encountered."), 

275 dtype=bool, 

276 optional=False, 

277 default=True, 

278 ) 

279 

280 

281class DefineVisitsTask(Task): 

282 """Driver Task for defining visits (and their spatial regions) in Gen3 

283 Butler repositories. 

284 

285 Parameters 

286 ---------- 

287 config : `DefineVisitsConfig` 

288 Configuration for the task. 

289 butler : `~lsst.daf.butler.Butler` 

290 Writeable butler instance. Will be used to read `raw.wcs` and `camera` 

291 datasets and insert/sync dimension data. 

292 **kwargs 

293 Additional keyword arguments are forwarded to the `lsst.pipe.base.Task` 

294 constructor. 

295 

296 Notes 

297 ----- 

298 Each instance of `DefineVisitsTask` reads from / writes to the same Butler. 

299 Each invocation of `DefineVisitsTask.run` processes an independent group of 

300 exposures into one or more new vists, all belonging to the same visit 

301 system and instrument. 

302 

303 The actual work of grouping exposures and computing regions is delegated 

304 to pluggable subtasks (`GroupExposuresTask` and `ComputeVisitRegionsTask`), 

305 respectively. The defaults are to create one visit for every exposure, 

306 and to use exactly one (arbitrary) detector-level raw dataset's WCS along 

307 with camera geometry to compute regions for all detectors. Other 

308 implementations can be created and configured for instruments for which 

309 these choices are unsuitable (e.g. because visits and exposures are not 

310 one-to-one, or because ``raw.wcs`` datasets for different detectors may not 

311 be consistent with camera geomery). 

312 

313 It is not necessary in general to ingest all raws for an exposure before 

314 defining a visit that includes the exposure; this depends entirely on the 

315 `ComputeVisitRegionTask` subclass used. For the default configuration, 

316 a single raw for each exposure is sufficient. 

317 """ 

318 def __init__(self, config: Optional[DefineVisitsConfig] = None, *, butler: Butler, **kwargs: Any): 

319 config.validate() # Not a CmdlineTask nor PipelineTask, so have to validate the config here. 

320 super().__init__(config, **kwargs) 

321 self.butler = butler 

322 self.universe = self.butler.registry.dimensions 

323 self.makeSubtask("groupExposures") 

324 self.makeSubtask("computeVisitRegions", butler=self.butler) 

325 

326 def _reduce_kwargs(self): 

327 # Add extra parameters to pickle 

328 return dict(**super()._reduce_kwargs(), butler=self.butler) 

329 

330 ConfigClass = DefineVisitsConfig 

331 

332 _DefaultName = "defineVisits" 

333 

334 def _buildVisitRecords(self, definition: VisitDefinitionData, *, 

335 collections: Any = None) -> _VisitRecords: 

336 """Build the DimensionRecords associated with a visit. 

337 

338 Parameters 

339 ---------- 

340 definition : `VisitDefinition` 

341 Struct with identifiers for the visit and records for its 

342 constituent exposures. 

343 collections : Any, optional 

344 Collections to be searched for raws and camera geometry, overriding 

345 ``self.butler.collections``. 

346 Can be any of the types supported by the ``collections`` argument 

347 to butler construction. 

348 

349 Results 

350 ------- 

351 records : `_VisitRecords` 

352 Struct containing DimensionRecords for the visit, including 

353 associated dimension elements. 

354 """ 

355 # Compute all regions. 

356 visitRegion, visitDetectorRegions = self.computeVisitRegions.compute(definition, 

357 collections=collections) 

358 # Aggregate other exposure quantities. 

359 timespan = Timespan( 

360 begin=_reduceOrNone(min, (e.timespan.begin for e in definition.exposures)), 

361 end=_reduceOrNone(max, (e.timespan.end for e in definition.exposures)), 

362 ) 

363 exposure_time = _reduceOrNone(sum, (e.exposure_time for e in definition.exposures)) 

364 physical_filter = _reduceOrNone(lambda a, b: a if a == b else None, 

365 (e.physical_filter for e in definition.exposures)) 

366 target_name = _reduceOrNone(lambda a, b: a if a == b else None, 

367 (e.target_name for e in definition.exposures)) 

368 science_program = _reduceOrNone(lambda a, b: a if a == b else None, 

369 (e.science_program for e in definition.exposures)) 

370 observation_reason = _reduceOrNone(lambda a, b: a if a == b else None, 

371 (e.observation_reason for e in definition.exposures)) 

372 if observation_reason is None: 

373 # Be explicit about there being multiple reasons 

374 observation_reason = "various" 

375 

376 # Use the mean zenith angle as an approximation 

377 zenith_angle = _reduceOrNone(sum, (e.zenith_angle for e in definition.exposures)) 

378 if zenith_angle is not None: 

379 zenith_angle /= len(definition.exposures) 

380 

381 # Construct the actual DimensionRecords. 

382 return _VisitRecords( 

383 visit=self.universe["visit"].RecordClass( 

384 instrument=definition.instrument, 

385 id=definition.id, 

386 name=definition.name, 

387 physical_filter=physical_filter, 

388 target_name=target_name, 

389 science_program=science_program, 

390 observation_reason=observation_reason, 

391 zenith_angle=zenith_angle, 

392 visit_system=self.groupExposures.getVisitSystem()[0], 

393 exposure_time=exposure_time, 

394 timespan=timespan, 

395 region=visitRegion, 

396 # TODO: no seeing value in exposure dimension records, so we 

397 # can't set that here. But there are many other columns that 

398 # both dimensions should probably have as well. 

399 ), 

400 visit_definition=[ 

401 self.universe["visit_definition"].RecordClass( 

402 instrument=definition.instrument, 

403 visit=definition.id, 

404 exposure=exposure.id, 

405 visit_system=self.groupExposures.getVisitSystem()[0], 

406 ) 

407 for exposure in definition.exposures 

408 ], 

409 visit_detector_region=[ 

410 self.universe["visit_detector_region"].RecordClass( 

411 instrument=definition.instrument, 

412 visit=definition.id, 

413 detector=detectorId, 

414 region=detectorRegion, 

415 ) 

416 for detectorId, detectorRegion in visitDetectorRegions.items() 

417 ] 

418 ) 

419 

420 def _expandExposureId(self, dataId: DataId) -> DataCoordinate: 

421 """Return the expanded version of an exposure ID. 

422 

423 A private method to allow ID expansion in a pool without resorting 

424 to local callables. 

425 

426 Parameters 

427 ---------- 

428 dataId : `dict` or `DataCoordinate` 

429 Exposure-level data ID. 

430 

431 Returns 

432 ------- 

433 expanded : `DataCoordinate` 

434 A data ID that includes full metadata for all exposure dimensions. 

435 """ 

436 dimensions = DimensionGraph(self.universe, names=["exposure"]) 

437 return self.butler.registry.expandDataId(dataId, graph=dimensions) 

438 

439 def _buildVisitRecordsSingle(self, args) -> _VisitRecords: 

440 """Build the DimensionRecords associated with a visit and collection. 

441 

442 A wrapper for `_buildVisitRecords` to allow it to be run as part of 

443 a pool without resorting to local callables. 

444 

445 Parameters 

446 ---------- 

447 args : `tuple` [`VisitDefinition`, any] 

448 A tuple consisting of the ``definition`` and ``collections`` 

449 arguments to `_buildVisitRecords`, in that order. 

450 

451 Results 

452 ------- 

453 records : `_VisitRecords` 

454 Struct containing DimensionRecords for the visit, including 

455 associated dimension elements. 

456 """ 

457 return self._buildVisitRecords(args[0], collections=args[1]) 

458 

459 def run(self, dataIds: Iterable[DataId], *, 

460 pool: Optional[Pool] = None, 

461 processes: int = 1, 

462 collections: Optional[str] = None): 

463 """Add visit definitions to the registry for the given exposures. 

464 

465 Parameters 

466 ---------- 

467 dataIds : `Iterable` [ `dict` or `DataCoordinate` ] 

468 Exposure-level data IDs. These must all correspond to the same 

469 instrument, and are expected to be on-sky science exposures. 

470 pool : `multiprocessing.Pool`, optional 

471 If not `None`, a process pool with which to parallelize some 

472 operations. 

473 processes : `int`, optional 

474 The number of processes to use. Ignored if ``pool`` is not `None`. 

475 collections : Any, optional 

476 Collections to be searched for raws and camera geometry, overriding 

477 ``self.butler.collections``. 

478 Can be any of the types supported by the ``collections`` argument 

479 to butler construction. 

480 """ 

481 # Set up multiprocessing, if desired. 

482 if pool is None and processes > 1: 

483 pool = Pool(processes) 

484 mapFunc = map if pool is None else pool.imap_unordered 

485 # Normalize, expand, and deduplicate data IDs. 

486 self.log.info("Preprocessing data IDs.") 

487 dataIds = set(mapFunc(self._expandExposureId, dataIds)) 

488 if not dataIds: 

489 raise RuntimeError("No exposures given.") 

490 # Extract exposure DimensionRecords, check that there's only one 

491 # instrument in play, and check for non-science exposures. 

492 exposures = [] 

493 instruments = set() 

494 for dataId in dataIds: 

495 record = dataId.records["exposure"] 

496 if record.observation_type != "science": 

497 if self.config.ignoreNonScienceExposures: 

498 continue 

499 else: 

500 raise RuntimeError(f"Input exposure {dataId} has observation_type " 

501 f"{record.observation_type}, not 'science'.") 

502 instruments.add(dataId["instrument"]) 

503 exposures.append(record) 

504 if not exposures: 

505 self.log.info("No science exposures found after filtering.") 

506 return 

507 if len(instruments) > 1: 

508 raise RuntimeError( 

509 f"All data IDs passed to DefineVisitsTask.run must be " 

510 f"from the same instrument; got {instruments}." 

511 ) 

512 instrument, = instruments 

513 # Ensure the visit_system our grouping algorithm uses is in the 

514 # registry, if it wasn't already. 

515 visitSystemId, visitSystemName = self.groupExposures.getVisitSystem() 

516 self.log.info("Registering visit_system %d: %s.", visitSystemId, visitSystemName) 

517 self.butler.registry.syncDimensionData( 

518 "visit_system", 

519 {"instrument": instrument, "id": visitSystemId, "name": visitSystemName} 

520 ) 

521 # Group exposures into visits, delegating to subtask. 

522 self.log.info("Grouping %d exposure(s) into visits.", len(exposures)) 

523 definitions = list(self.groupExposures.group(exposures)) 

524 # Compute regions and build DimensionRecords for each visit. 

525 # This is the only parallel step, but it _should_ be the most expensive 

526 # one (unless DB operations are slow). 

527 self.log.info("Computing regions and other metadata for %d visit(s).", len(definitions)) 

528 allRecords = mapFunc(self._buildVisitRecordsSingle, 

529 zip(definitions, itertools.repeat(collections))) 

530 # Iterate over visits and insert dimension data, one transaction per 

531 # visit. 

532 for visitRecords in allRecords: 

533 with self.butler.registry.transaction(): 

534 self.butler.registry.insertDimensionData("visit", visitRecords.visit) 

535 self.butler.registry.insertDimensionData("visit_definition", 

536 *visitRecords.visit_definition) 

537 self.butler.registry.insertDimensionData("visit_detector_region", 

538 *visitRecords.visit_detector_region) 

539 

540 

541def _reduceOrNone(func, iterable): 

542 """Apply a binary function to pairs of elements in an iterable until a 

543 single value is returned, but return `None` if any element is `None` or 

544 there are no elements. 

545 """ 

546 r = None 

547 for v in iterable: 

548 if v is None: 

549 return None 

550 if r is None: 

551 r = v 

552 else: 

553 r = func(r, v) 

554 return r 

555 

556 

557class _GroupExposuresOneToOneConfig(GroupExposuresConfig): 

558 visitSystemId = Field( 

559 doc=("Integer ID of the visit_system implemented by this grouping " 

560 "algorithm."), 

561 dtype=int, 

562 default=0, 

563 ) 

564 visitSystemName = Field( 

565 doc=("String name of the visit_system implemented by this grouping " 

566 "algorithm."), 

567 dtype=str, 

568 default="one-to-one", 

569 ) 

570 

571 

572@registerConfigurable("one-to-one", GroupExposuresTask.registry) 

573class _GroupExposuresOneToOneTask(GroupExposuresTask, metaclass=ABCMeta): 

574 """An exposure grouping algorithm that simply defines one visit for each 

575 exposure, reusing the exposures identifiers for the visit. 

576 """ 

577 

578 ConfigClass = _GroupExposuresOneToOneConfig 

579 

580 def group(self, exposures: List[DimensionRecord]) -> Iterable[VisitDefinitionData]: 

581 # Docstring inherited from GroupExposuresTask. 

582 for exposure in exposures: 

583 yield VisitDefinitionData( 

584 instrument=exposure.instrument, 

585 id=exposure.id, 

586 name=exposure.name, 

587 exposures=[exposure], 

588 ) 

589 

590 def getVisitSystem(self) -> Tuple[int, str]: 

591 # Docstring inherited from GroupExposuresTask. 

592 return (self.config.visitSystemId, self.config.visitSystemName) 

593 

594 

595class _GroupExposuresByGroupMetadataConfig(GroupExposuresConfig): 

596 visitSystemId = Field( 

597 doc=("Integer ID of the visit_system implemented by this grouping " 

598 "algorithm."), 

599 dtype=int, 

600 default=1, 

601 ) 

602 visitSystemName = Field( 

603 doc=("String name of the visit_system implemented by this grouping " 

604 "algorithm."), 

605 dtype=str, 

606 default="by-group-metadata", 

607 ) 

608 

609 

610@registerConfigurable("by-group-metadata", GroupExposuresTask.registry) 

611class _GroupExposuresByGroupMetadataTask(GroupExposuresTask, metaclass=ABCMeta): 

612 """An exposure grouping algorithm that uses exposure.group_name and 

613 exposure.group_id. 

614 

615 This algorithm _assumes_ exposure.group_id (generally populated from 

616 `astro_metadata_translator.ObservationInfo.visit_id`) is not just unique, 

617 but disjoint from all `ObservationInfo.exposure_id` values - if it isn't, 

618 it will be impossible to ever use both this grouping algorithm and the 

619 one-to-one algorithm for a particular camera in the same data repository. 

620 """ 

621 

622 ConfigClass = _GroupExposuresByGroupMetadataConfig 

623 

624 def group(self, exposures: List[DimensionRecord]) -> Iterable[VisitDefinitionData]: 

625 # Docstring inherited from GroupExposuresTask. 

626 groups = defaultdict(list) 

627 for exposure in exposures: 

628 groups[exposure.group_name].append(exposure) 

629 for visitName, exposuresInGroup in groups.items(): 

630 instrument = exposuresInGroup[0].instrument 

631 visitId = exposuresInGroup[0].group_id 

632 assert all(e.group_id == visitId for e in exposuresInGroup), \ 

633 "Grouping by exposure.group_name does not yield consistent group IDs" 

634 yield VisitDefinitionData(instrument=instrument, id=visitId, name=visitName, 

635 exposures=exposuresInGroup) 

636 

637 def getVisitSystem(self) -> Tuple[int, str]: 

638 # Docstring inherited from GroupExposuresTask. 

639 return (self.config.visitSystemId, self.config.visitSystemName) 

640 

641 

642class _ComputeVisitRegionsFromSingleRawWcsConfig(ComputeVisitRegionsConfig): 

643 mergeExposures = Field( 

644 doc=("If True, merge per-detector regions over all exposures in a " 

645 "visit (via convex hull) instead of using the first exposure and " 

646 "assuming its regions are valid for all others."), 

647 dtype=bool, 

648 default=False, 

649 ) 

650 detectorId = Field( 

651 doc=("Load the WCS for the detector with this ID. If None, use an " 

652 "arbitrary detector (the first found in a query of the data " 

653 "repository for each exposure (or all exposures, if " 

654 "mergeExposures is True)."), 

655 dtype=int, 

656 optional=True, 

657 default=None 

658 ) 

659 requireVersionedCamera = Field( 

660 doc=("If True, raise LookupError if version camera geometry cannot be " 

661 "loaded for an exposure. If False, use the nominal camera from " 

662 "the Instrument class instead."), 

663 dtype=bool, 

664 optional=False, 

665 default=False, 

666 ) 

667 

668 

669@registerConfigurable("single-raw-wcs", ComputeVisitRegionsTask.registry) 

670class _ComputeVisitRegionsFromSingleRawWcsTask(ComputeVisitRegionsTask): 

671 """A visit region calculator that uses a single raw WCS and a camera to 

672 project the bounding boxes of all detectors onto the sky, relating 

673 different detectors by their positions in focal plane coordinates. 

674 

675 Notes 

676 ----- 

677 Most instruments should have their raw WCSs determined from a combination 

678 of boresight angle, rotator angle, and camera geometry, and hence this 

679 algorithm should produce stable results regardless of which detector the 

680 raw corresponds to. If this is not the case (e.g. because a per-file FITS 

681 WCS is used instead), either the ID of the detector should be fixed (see 

682 the ``detectorId`` config parameter) or a different algorithm used. 

683 """ 

684 

685 ConfigClass = _ComputeVisitRegionsFromSingleRawWcsConfig 

686 

687 def computeExposureBounds(self, exposure: DimensionRecord, *, collections: Any = None 

688 ) -> Dict[int, List[UnitVector3d]]: 

689 """Compute the lists of unit vectors on the sphere that correspond to 

690 the sky positions of detector corners. 

691 

692 Parameters 

693 ---------- 

694 exposure : `DimensionRecord` 

695 Dimension record for the exposure. 

696 collections : Any, optional 

697 Collections to be searched for raws and camera geometry, overriding 

698 ``self.butler.collections``. 

699 Can be any of the types supported by the ``collections`` argument 

700 to butler construction. 

701 

702 Returns 

703 ------- 

704 bounds : `dict` 

705 Dictionary mapping detector ID to a list of unit vectors on the 

706 sphere representing that detector's corners projected onto the sky. 

707 """ 

708 if collections is None: 

709 collections = self.butler.collections 

710 camera, versioned = loadCamera(self.butler, exposure.dataId, collections=collections) 

711 if not versioned and self.config.requireVersionedCamera: 

712 raise LookupError(f"No versioned camera found for exposure {exposure.dataId}.") 

713 

714 # Derive WCS from boresight information -- if available in registry 

715 use_registry = True 

716 try: 

717 orientation = lsst.geom.Angle(exposure.sky_angle, lsst.geom.degrees) 

718 radec = lsst.geom.SpherePoint(lsst.geom.Angle(exposure.tracking_ra, lsst.geom.degrees), 

719 lsst.geom.Angle(exposure.tracking_dec, lsst.geom.degrees)) 

720 except AttributeError: 

721 use_registry = False 

722 

723 if use_registry: 

724 if self.config.detectorId is None: 

725 detectorId = next(camera.getIdIter()) 

726 else: 

727 detectorId = self.config.detectorId 

728 wcsDetector = camera[detectorId] 

729 

730 # Ask the raw formatter to create the relevant WCS 

731 # This allows flips to be taken into account 

732 instrument = self.getInstrument(exposure.instrument) 

733 rawFormatter = instrument.getRawFormatter({"detector": detectorId}) 

734 wcs = rawFormatter.makeRawSkyWcsFromBoresight(radec, orientation, wcsDetector) 

735 

736 else: 

737 if self.config.detectorId is None: 

738 wcsRefsIter = self.butler.registry.queryDatasets("raw.wcs", dataId=exposure.dataId, 

739 collections=collections) 

740 if not wcsRefsIter: 

741 raise LookupError(f"No raw.wcs datasets found for data ID {exposure.dataId} " 

742 f"in collections {collections}.") 

743 wcsRef = next(iter(wcsRefsIter)) 

744 wcsDetector = camera[wcsRef.dataId["detector"]] 

745 wcs = self.butler.getDirect(wcsRef) 

746 else: 

747 wcsDetector = camera[self.config.detectorId] 

748 wcs = self.butler.get("raw.wcs", dataId=exposure.dataId, detector=self.config.detectorId, 

749 collections=collections) 

750 fpToSky = wcsDetector.getTransform(FOCAL_PLANE, PIXELS).then(wcs.getTransform()) 

751 bounds = {} 

752 for detector in camera: 

753 pixelsToSky = detector.getTransform(PIXELS, FOCAL_PLANE).then(fpToSky) 

754 pixCorners = Box2D(detector.getBBox().dilatedBy(self.config.padding)).getCorners() 

755 bounds[detector.getId()] = [ 

756 skyCorner.getVector() for skyCorner in pixelsToSky.applyForward(pixCorners) 

757 ] 

758 return bounds 

759 

760 def compute(self, visit: VisitDefinitionData, *, collections: Any = None 

761 ) -> Tuple[Region, Dict[int, Region]]: 

762 # Docstring inherited from ComputeVisitRegionsTask. 

763 if self.config.mergeExposures: 

764 detectorBounds = defaultdict(list) 

765 for exposure in visit.exposures: 

766 exposureDetectorBounds = self.computeExposureBounds(exposure, collections=collections) 

767 for detectorId, bounds in exposureDetectorBounds.items(): 

768 detectorBounds[detectorId].extend(bounds) 

769 else: 

770 detectorBounds = self.computeExposureBounds(visit.exposures[0], collections=collections) 

771 visitBounds = [] 

772 detectorRegions = {} 

773 for detectorId, bounds in detectorBounds.items(): 

774 detectorRegions[detectorId] = ConvexPolygon.convexHull(bounds) 

775 visitBounds.extend(bounds) 

776 return ConvexPolygon.convexHull(visitBounds), detectorRegions