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 

22 

23__all__ = ("RawIngestTask", "RawIngestConfig", "makeTransferChoiceField") 

24 

25import os.path 

26from dataclasses import dataclass, InitVar 

27from typing import List, Iterator, Iterable, Type, Optional, Any 

28from collections import defaultdict 

29from multiprocessing import Pool 

30 

31from astro_metadata_translator import ObservationInfo, fix_header, merge_headers 

32from lsst.afw.fits import readMetadata 

33from lsst.daf.butler import ( 

34 Butler, 

35 DataCoordinate, 

36 DatasetRef, 

37 DatasetType, 

38 DimensionRecord, 

39 DimensionUniverse, 

40 FileDataset, 

41) 

42from lsst.pex.config import Config, ChoiceField 

43from lsst.pipe.base import Task 

44 

45from ._instrument import Instrument, makeExposureRecordFromObsInfo 

46from ._fitsRawFormatterBase import FitsRawFormatterBase 

47 

48 

49@dataclass 

50class RawFileDatasetInfo: 

51 """Structure that holds information about a single dataset within a 

52 raw file. 

53 """ 

54 

55 dataId: DataCoordinate 

56 """Data ID for this file (`lsst.daf.butler.DataCoordinate`). 

57 

58 This may be a minimal `~lsst.daf.butler.DataCoordinate` base instance, or 

59 a complete `~lsst.daf.butler.ExpandedDataCoordinate`. 

60 """ 

61 

62 obsInfo: ObservationInfo 

63 """Standardized observation metadata extracted directly from the file 

64 headers (`astro_metadata_translator.ObservationInfo`). 

65 """ 

66 

67 

68@dataclass 

69class RawFileData: 

70 """Structure that holds information about a single raw file, used during 

71 ingest. 

72 """ 

73 

74 datasets: List[RawFileDatasetInfo] 

75 """The information describing each dataset within this raw file. 

76 (`list` of `RawFileDatasetInfo`) 

77 """ 

78 

79 filename: str 

80 """Name of the file this information was extracted from (`str`). 

81 

82 This is the path prior to ingest, not the path after ingest. 

83 """ 

84 

85 FormatterClass: Type[FitsRawFormatterBase] 

86 """Formatter class that should be used to ingest this file (`type`; as 

87 subclass of `FitsRawFormatterBase`). 

88 """ 

89 

90 

91@dataclass 

92class RawExposureData: 

93 """Structure that holds information about a complete raw exposure, used 

94 during ingest. 

95 """ 

96 

97 dataId: DataCoordinate 

98 """Data ID for this exposure (`lsst.daf.butler.DataCoordinate`). 

99 

100 This may be a minimal `~lsst.daf.butler.DataCoordinate` base instance, or 

101 a complete `~lsst.daf.butler.ExpandedDataCoordinate`. 

102 """ 

103 

104 files: List[RawFileData] 

105 """List of structures containing file-level information. 

106 """ 

107 

108 universe: InitVar[DimensionUniverse] 

109 """Set of all known dimensions. 

110 """ 

111 

112 record: Optional[DimensionRecord] = None 

113 """The exposure `DimensionRecord` that must be inserted into the 

114 `~lsst.daf.butler.Registry` prior to file-level ingest (`DimensionRecord`). 

115 """ 

116 

117 def __post_init__(self, universe: DimensionUniverse): 

118 # We don't care which file or dataset we read metadata from, because 

119 # we're assuming they'll all be the same; just use the first ones. 

120 self.record = makeExposureRecordFromObsInfo(self.files[0].datasets[0].obsInfo, universe) 

121 

122 

123def makeTransferChoiceField(doc="How to transfer files (None for no transfer).", default=None): 

124 """Create a Config field with options for how to transfer files between 

125 data repositories. 

126 

127 The allowed options for the field are exactly those supported by 

128 `lsst.daf.butler.Datastore.ingest`. 

129 

130 Parameters 

131 ---------- 

132 doc : `str` 

133 Documentation for the configuration field. 

134 

135 Returns 

136 ------- 

137 field : `lsst.pex.config.ChoiceField` 

138 Configuration field. 

139 """ 

140 return ChoiceField( 

141 doc=doc, 

142 dtype=str, 

143 allowed={"move": "move", 

144 "copy": "copy", 

145 "auto": "choice will depend on datastore", 

146 "link": "hard link falling back to symbolic link", 

147 "hardlink": "hard link", 

148 "symlink": "symbolic (soft) link", 

149 "relsymlink": "relative symbolic link", 

150 }, 

151 optional=True, 

152 default=default 

153 ) 

154 

155 

156class RawIngestConfig(Config): 

157 transfer = makeTransferChoiceField() 

158 

159 

160class RawIngestTask(Task): 

161 """Driver Task for ingesting raw data into Gen3 Butler repositories. 

162 

163 Parameters 

164 ---------- 

165 config : `RawIngestConfig` 

166 Configuration for the task. 

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

168 Writeable butler instance, with ``butler.run`` set to the appropriate 

169 `~lsst.daf.butler.CollectionType.RUN` collection for these raw 

170 datasets. 

171 **kwargs 

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

173 constructor. 

174 

175 Notes 

176 ----- 

177 Each instance of `RawIngestTask` writes to the same Butler. Each 

178 invocation of `RawIngestTask.run` ingests a list of files. 

179 """ 

180 

181 ConfigClass = RawIngestConfig 

182 

183 _DefaultName = "ingest" 

184 

185 def getDatasetType(self): 

186 """Return the DatasetType of the datasets ingested by this Task. 

187 """ 

188 return DatasetType("raw", ("instrument", "detector", "exposure"), "Exposure", 

189 universe=self.butler.registry.dimensions) 

190 

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

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

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

194 self.butler = butler 

195 self.universe = self.butler.registry.dimensions 

196 self.datasetType = self.getDatasetType() 

197 

198 # Import all the instrument classes so that we ensure that we 

199 # have all the relevant metadata translators loaded. 

200 Instrument.importAll(self.butler.registry) 

201 

202 def extractMetadata(self, filename: str) -> RawFileData: 

203 """Extract and process metadata from a single raw file. 

204 

205 Parameters 

206 ---------- 

207 filename : `str` 

208 Path to the file. 

209 

210 Returns 

211 ------- 

212 data : `RawFileData` 

213 A structure containing the metadata extracted from the file, 

214 as well as the original filename. All fields will be populated, 

215 but the `RawFileData.dataId` attribute will be a minimal 

216 (unexpanded) `DataCoordinate` instance. 

217 

218 Notes 

219 ----- 

220 Assumes that there is a single dataset associated with the given 

221 file. Instruments using a single file to store multiple datasets 

222 must implement their own version of this method. 

223 """ 

224 # Manually merge the primary and "first data" headers here because we 

225 # do not know in general if an input file has set INHERIT=T. 

226 phdu = readMetadata(filename, 0) 

227 header = merge_headers([phdu, readMetadata(filename)], mode="overwrite") 

228 fix_header(header) 

229 datasets = [self._calculate_dataset_info(header, filename)] 

230 

231 # The data model currently assumes that whilst multiple datasets 

232 # can be associated with a single file, they must all share the 

233 # same formatter. 

234 instrument = Instrument.fromName(datasets[0].dataId["instrument"], self.butler.registry) 

235 FormatterClass = instrument.getRawFormatter(datasets[0].dataId) 

236 

237 return RawFileData(datasets=datasets, filename=filename, 

238 FormatterClass=FormatterClass) 

239 

240 def _calculate_dataset_info(self, header, filename): 

241 """Calculate a RawFileDatasetInfo from the supplied information. 

242 

243 Parameters 

244 ---------- 

245 header : `Mapping` 

246 Header from the dataset. 

247 filename : `str` 

248 Filename to use for error messages. 

249 

250 Returns 

251 ------- 

252 dataset : `RawFileDatasetInfo` 

253 The dataId, and observation information associated with this 

254 dataset. 

255 """ 

256 obsInfo = ObservationInfo(header) 

257 dataId = DataCoordinate.standardize(instrument=obsInfo.instrument, 

258 exposure=obsInfo.exposure_id, 

259 detector=obsInfo.detector_num, 

260 universe=self.universe) 

261 return RawFileDatasetInfo(obsInfo=obsInfo, dataId=dataId) 

262 

263 def groupByExposure(self, files: Iterable[RawFileData]) -> List[RawExposureData]: 

264 """Group an iterable of `RawFileData` by exposure. 

265 

266 Parameters 

267 ---------- 

268 files : iterable of `RawFileData` 

269 File-level information to group. 

270 

271 Returns 

272 ------- 

273 exposures : `list` of `RawExposureData` 

274 A list of structures that group the file-level information by 

275 exposure. All fields will be populated. The 

276 `RawExposureData.dataId` attributes will be minimal (unexpanded) 

277 `DataCoordinate` instances. 

278 """ 

279 exposureDimensions = self.universe["exposure"].graph 

280 byExposure = defaultdict(list) 

281 for f in files: 

282 # Assume that the first dataset is representative for the file 

283 byExposure[f.datasets[0].dataId.subset(exposureDimensions)].append(f) 

284 

285 return [RawExposureData(dataId=dataId, files=exposureFiles, universe=self.universe) 

286 for dataId, exposureFiles in byExposure.items()] 

287 

288 def expandDataIds(self, data: RawExposureData) -> RawExposureData: 

289 """Expand the data IDs associated with a raw exposure to include 

290 additional metadata records. 

291 

292 Parameters 

293 ---------- 

294 exposure : `RawExposureData` 

295 A structure containing information about the exposure to be 

296 ingested. Must have `RawExposureData.records` populated. Should 

297 be considered consumed upon return. 

298 

299 Returns 

300 ------- 

301 exposure : `RawExposureData` 

302 An updated version of the input structure, with 

303 `RawExposureData.dataId` and nested `RawFileData.dataId` attributes 

304 containing `~lsst.daf.butler.ExpandedDataCoordinate` instances. 

305 """ 

306 # We start by expanded the exposure-level data ID; we won't use that 

307 # directly in file ingest, but this lets us do some database lookups 

308 # once per exposure instead of once per file later. 

309 data.dataId = self.butler.registry.expandDataId( 

310 data.dataId, 

311 # We pass in the records we'll be inserting shortly so they aren't 

312 # looked up from the database. We do expect instrument and filter 

313 # records to be retrieved from the database here (though the 

314 # Registry may cache them so there isn't a lookup every time). 

315 records={ 

316 self.butler.registry.dimensions["exposure"]: data.record, 

317 } 

318 ) 

319 # Now we expand the per-file (exposure+detector) data IDs. This time 

320 # we pass in the records we just retrieved from the exposure data ID 

321 # expansion. 

322 for file in data.files: 

323 for dataset in file.datasets: 

324 dataset.dataId = self.butler.registry.expandDataId( 

325 dataset.dataId, 

326 records=dict(data.dataId.records) 

327 ) 

328 return data 

329 

330 def prep(self, files, *, pool: Optional[Pool] = None, processes: int = 1) -> Iterator[RawExposureData]: 

331 """Perform all ingest preprocessing steps that do not involve actually 

332 modifying the database. 

333 

334 Parameters 

335 ---------- 

336 files : iterable over `str` or path-like objects 

337 Paths to the files to be ingested. Will be made absolute 

338 if they are not already. 

339 pool : `multiprocessing.Pool`, optional 

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

341 operations. 

342 processes : `int`, optional 

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

344 

345 Yields 

346 ------ 

347 exposure : `RawExposureData` 

348 Data structures containing dimension records, filenames, and data 

349 IDs to be ingested (one structure for each exposure). 

350 """ 

351 if pool is None and processes > 1: 

352 pool = Pool(processes) 

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

354 

355 # Extract metadata and build per-detector regions. 

356 fileData: Iterator[RawFileData] = mapFunc(self.extractMetadata, files) 

357 

358 # Use that metadata to group files (and extracted metadata) by 

359 # exposure. Never parallelized because it's intrinsically a gather 

360 # step. 

361 exposureData: List[RawExposureData] = self.groupByExposure(fileData) 

362 

363 # The next operation operates on RawExposureData instances (one at 

364 # a time) in-place and then returns the modified instance. We call it 

365 # as a pass-through instead of relying on the arguments we pass in to 

366 # have been modified because in the parallel case those arguments are 

367 # going to be pickled and unpickled, and I'm not certain 

368 # multiprocessing is careful enough with that for output arguments to 

369 # work. 

370 

371 # Expand the data IDs to include all dimension metadata; we need this 

372 # because we may need to generate path templates that rely on that 

373 # metadata. 

374 # This is the first step that involves actual database calls (but just 

375 # SELECTs), so if there's going to be a problem with connections vs. 

376 # multiple processes, or lock contention (in SQLite) slowing things 

377 # down, it'll happen here. 

378 return mapFunc(self.expandDataIds, exposureData) 

379 

380 def ingestExposureDatasets(self, exposure: RawExposureData, *, run: Optional[str] = None 

381 ) -> List[DatasetRef]: 

382 """Ingest all raw files in one exposure. 

383 

384 Parameters 

385 ---------- 

386 exposure : `RawExposureData` 

387 A structure containing information about the exposure to be 

388 ingested. Must have `RawExposureData.records` populated and all 

389 data ID attributes expanded. 

390 run : `str`, optional 

391 Name of a RUN-type collection to write to, overriding 

392 ``self.butler.run``. 

393 

394 Returns 

395 ------- 

396 refs : `list` of `lsst.daf.butler.DatasetRef` 

397 Dataset references for ingested raws. 

398 """ 

399 datasets = [FileDataset(path=os.path.abspath(file.filename), 

400 refs=[DatasetRef(self.datasetType, d.dataId) for d in file.datasets], 

401 formatter=file.FormatterClass) 

402 for file in exposure.files] 

403 self.butler.ingest(*datasets, transfer=self.config.transfer, run=run) 

404 return [ref for dataset in datasets for ref in dataset.refs] 

405 

406 def run(self, files, *, pool: Optional[Pool] = None, processes: int = 1, run: Optional[str] = None): 

407 """Ingest files into a Butler data repository. 

408 

409 This creates any new exposure or visit Dimension entries needed to 

410 identify the ingested files, creates new Dataset entries in the 

411 Registry and finally ingests the files themselves into the Datastore. 

412 Any needed instrument, detector, and physical_filter Dimension entries 

413 must exist in the Registry before `run` is called. 

414 

415 Parameters 

416 ---------- 

417 files : iterable over `str` or path-like objects 

418 Paths to the files to be ingested. Will be made absolute 

419 if they are not already. 

420 pool : `multiprocessing.Pool`, optional 

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

422 operations. 

423 processes : `int`, optional 

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

425 run : `str`, optional 

426 Name of a RUN-type collection to write to, overriding 

427 ``self.butler.run``. 

428 

429 Returns 

430 ------- 

431 refs : `list` of `lsst.daf.butler.DatasetRef` 

432 Dataset references for ingested raws. 

433 

434 Notes 

435 ----- 

436 This method inserts all datasets for an exposure within a transaction, 

437 guaranteeing that partial exposures are never ingested. The exposure 

438 dimension record is inserted with `Registry.syncDimensionData` first 

439 (in its own transaction), which inserts only if a record with the same 

440 primary key does not already exist. This allows different files within 

441 the same exposure to be incremented in different runs. 

442 """ 

443 exposureData = self.prep(files, pool=pool, processes=processes) 

444 # Up to this point, we haven't modified the data repository at all. 

445 # Now we finally do that, with one transaction per exposure. This is 

446 # not parallelized at present because the performance of this step is 

447 # limited by the database server. That may or may not change in the 

448 # future once we increase our usage of bulk inserts and reduce our 

449 # usage of savepoints; we've tried to get everything but the database 

450 # operations done in advance to reduce the time spent inside 

451 # transactions. 

452 self.butler.registry.registerDatasetType(self.datasetType) 

453 refs = [] 

454 for exposure in exposureData: 

455 self.butler.registry.syncDimensionData("exposure", exposure.record) 

456 with self.butler.transaction(): 

457 refs.extend(self.ingestExposureDatasets(exposure, run=run)) 

458 return refs