Coverage for python/lsst/pipe/tasks/characterizeImage.py: 30%

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1# This file is part of pipe_tasks. 

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 

22__all__ = ["CharacterizeImageConfig", "CharacterizeImageTask"] 

23 

24import numpy as np 

25 

26from lsstDebug import getDebugFrame 

27import lsst.afw.table as afwTable 

28import lsst.pex.config as pexConfig 

29import lsst.pipe.base as pipeBase 

30import lsst.daf.base as dafBase 

31import lsst.pipe.base.connectionTypes as cT 

32from lsst.afw.math import BackgroundList 

33from lsst.afw.table import SourceTable 

34from lsst.meas.algorithms import ( 

35 SubtractBackgroundTask, 

36 SourceDetectionTask, 

37 MeasureApCorrTask, 

38 MeasureApCorrError, 

39) 

40from lsst.meas.algorithms.installGaussianPsf import InstallGaussianPsfTask 

41from lsst.meas.astrom import displayAstrometry 

42from lsst.meas.base import ( 

43 SingleFrameMeasurementTask, 

44 ApplyApCorrTask, 

45 CatalogCalculationTask, 

46 IdGenerator, 

47 DetectorVisitIdGeneratorConfig, 

48) 

49from lsst.meas.deblender import SourceDeblendTask 

50import lsst.meas.extensions.shapeHSM # noqa: F401 needed for default shape plugin 

51from .measurePsf import MeasurePsfTask 

52from .repair import RepairTask 

53from .maskStreaks import MaskStreaksTask 

54from .computeExposureSummaryStats import ComputeExposureSummaryStatsTask 

55from lsst.pex.exceptions import LengthError 

56from lsst.utils.timer import timeMethod 

57 

58 

59class CharacterizeImageConnections(pipeBase.PipelineTaskConnections, 

60 dimensions=("instrument", "visit", "detector")): 

61 exposure = cT.Input( 

62 doc="Input exposure data", 

63 name="postISRCCD", 

64 storageClass="Exposure", 

65 dimensions=["instrument", "exposure", "detector"], 

66 ) 

67 characterized = cT.Output( 

68 doc="Output characterized data.", 

69 name="icExp", 

70 storageClass="ExposureF", 

71 dimensions=["instrument", "visit", "detector"], 

72 ) 

73 sourceCat = cT.Output( 

74 doc="Output source catalog.", 

75 name="icSrc", 

76 storageClass="SourceCatalog", 

77 dimensions=["instrument", "visit", "detector"], 

78 ) 

79 backgroundModel = cT.Output( 

80 doc="Output background model.", 

81 name="icExpBackground", 

82 storageClass="Background", 

83 dimensions=["instrument", "visit", "detector"], 

84 ) 

85 outputSchema = cT.InitOutput( 

86 doc="Schema of the catalog produced by CharacterizeImage", 

87 name="icSrc_schema", 

88 storageClass="SourceCatalog", 

89 ) 

90 

91 def adjustQuantum(self, inputs, outputs, label, dataId): 

92 # Docstring inherited from PipelineTaskConnections 

93 try: 

94 return super().adjustQuantum(inputs, outputs, label, dataId) 

95 except pipeBase.ScalarError as err: 

96 raise pipeBase.ScalarError( 

97 "CharacterizeImageTask can at present only be run on visits that are associated with " 

98 "exactly one exposure. Either this is not a valid exposure for this pipeline, or the " 

99 "snap-combination step you probably want hasn't been configured to run between ISR and " 

100 "this task (as of this writing, that would be because it hasn't been implemented yet)." 

101 ) from err 

102 

103 

104class CharacterizeImageConfig(pipeBase.PipelineTaskConfig, 

105 pipelineConnections=CharacterizeImageConnections): 

106 """Config for CharacterizeImageTask.""" 

107 

108 doMeasurePsf = pexConfig.Field( 

109 dtype=bool, 

110 default=True, 

111 doc="Measure PSF? If False then for all subsequent operations use either existing PSF " 

112 "model when present, or install simple PSF model when not (see installSimplePsf " 

113 "config options)" 

114 ) 

115 doWrite = pexConfig.Field( 

116 dtype=bool, 

117 default=True, 

118 doc="Persist results?", 

119 ) 

120 doWriteExposure = pexConfig.Field( 

121 dtype=bool, 

122 default=True, 

123 doc="Write icExp and icExpBackground in addition to icSrc? Ignored if doWrite False.", 

124 ) 

125 psfIterations = pexConfig.RangeField( 

126 dtype=int, 

127 default=2, 

128 min=1, 

129 doc="Number of iterations of detect sources, measure sources, " 

130 "estimate PSF. If useSimplePsf is True then 2 should be plenty; " 

131 "otherwise more may be wanted.", 

132 ) 

133 background = pexConfig.ConfigurableField( 

134 target=SubtractBackgroundTask, 

135 doc="Configuration for initial background estimation", 

136 ) 

137 detection = pexConfig.ConfigurableField( 

138 target=SourceDetectionTask, 

139 doc="Detect sources" 

140 ) 

141 doDeblend = pexConfig.Field( 

142 dtype=bool, 

143 default=True, 

144 doc="Run deblender input exposure" 

145 ) 

146 deblend = pexConfig.ConfigurableField( 

147 target=SourceDeblendTask, 

148 doc="Split blended source into their components" 

149 ) 

150 measurement = pexConfig.ConfigurableField( 

151 target=SingleFrameMeasurementTask, 

152 doc="Measure sources" 

153 ) 

154 doApCorr = pexConfig.Field( 

155 dtype=bool, 

156 default=True, 

157 doc="Run subtasks to measure and apply aperture corrections" 

158 ) 

159 measureApCorr = pexConfig.ConfigurableField( 

160 target=MeasureApCorrTask, 

161 doc="Subtask to measure aperture corrections" 

162 ) 

163 applyApCorr = pexConfig.ConfigurableField( 

164 target=ApplyApCorrTask, 

165 doc="Subtask to apply aperture corrections" 

166 ) 

167 # If doApCorr is False, and the exposure does not have apcorrections already applied, the 

168 # active plugins in catalogCalculation almost certainly should not contain the characterization plugin 

169 catalogCalculation = pexConfig.ConfigurableField( 

170 target=CatalogCalculationTask, 

171 doc="Subtask to run catalogCalculation plugins on catalog" 

172 ) 

173 doComputeSummaryStats = pexConfig.Field( 

174 dtype=bool, 

175 default=True, 

176 doc="Run subtask to measure exposure summary statistics", 

177 deprecated=("This subtask has been moved to CalibrateTask " 

178 "with DM-30701.") 

179 ) 

180 computeSummaryStats = pexConfig.ConfigurableField( 

181 target=ComputeExposureSummaryStatsTask, 

182 doc="Subtask to run computeSummaryStats on exposure", 

183 deprecated=("This subtask has been moved to CalibrateTask " 

184 "with DM-30701.") 

185 ) 

186 useSimplePsf = pexConfig.Field( 

187 dtype=bool, 

188 default=True, 

189 doc="Replace the existing PSF model with a simplified version that has the same sigma " 

190 "at the start of each PSF determination iteration? Doing so makes PSF determination " 

191 "converge more robustly and quickly.", 

192 ) 

193 installSimplePsf = pexConfig.ConfigurableField( 

194 target=InstallGaussianPsfTask, 

195 doc="Install a simple PSF model", 

196 ) 

197 measurePsf = pexConfig.ConfigurableField( 

198 target=MeasurePsfTask, 

199 doc="Measure PSF", 

200 ) 

201 repair = pexConfig.ConfigurableField( 

202 target=RepairTask, 

203 doc="Remove cosmic rays", 

204 ) 

205 requireCrForPsf = pexConfig.Field( 

206 dtype=bool, 

207 default=True, 

208 doc="Require cosmic ray detection and masking to run successfully before measuring the PSF." 

209 ) 

210 checkUnitsParseStrict = pexConfig.Field( 

211 doc="Strictness of Astropy unit compatibility check, can be 'raise', 'warn' or 'silent'", 

212 dtype=str, 

213 default="raise", 

214 ) 

215 doMaskStreaks = pexConfig.Field( 

216 doc="Mask streaks", 

217 default=True, 

218 dtype=bool, 

219 ) 

220 maskStreaks = pexConfig.ConfigurableField( 

221 target=MaskStreaksTask, 

222 doc="Subtask for masking streaks. Only used if doMaskStreaks is True. " 

223 "Adds a mask plane to an exposure, with the mask plane name set by streakMaskName.", 

224 ) 

225 idGenerator = DetectorVisitIdGeneratorConfig.make_field() 

226 

227 def setDefaults(self): 

228 super().setDefaults() 

229 # just detect bright stars; includeThresholdMultipler=10 seems large, 

230 # but these are the values we have been using 

231 self.detection.thresholdValue = 5.0 

232 self.detection.includeThresholdMultiplier = 10.0 

233 # do not deblend, as it makes a mess 

234 self.doDeblend = False 

235 # measure and apply aperture correction; note: measuring and applying aperture 

236 # correction are disabled until the final measurement, after PSF is measured 

237 self.doApCorr = True 

238 # During characterization, we don't have full source measurement information, 

239 # so must do the aperture correction with only psf stars, combined with the 

240 # default signal-to-noise cuts in MeasureApCorrTask. 

241 selector = self.measureApCorr.sourceSelector["science"] 

242 selector.doUnresolved = False 

243 selector.flags.good = ["calib_psf_used"] 

244 selector.flags.bad = [] 

245 

246 # minimal set of measurements needed to determine PSF 

247 self.measurement.plugins.names = [ 

248 "base_PixelFlags", 

249 "base_SdssCentroid", 

250 "ext_shapeHSM_HsmSourceMoments", 

251 "base_GaussianFlux", 

252 "base_PsfFlux", 

253 "base_CircularApertureFlux", 

254 ] 

255 self.measurement.slots.shape = "ext_shapeHSM_HsmSourceMoments" 

256 

257 def validate(self): 

258 if self.doApCorr and not self.measurePsf: 

259 raise RuntimeError("Must measure PSF to measure aperture correction, " 

260 "because flags determined by PSF measurement are used to identify " 

261 "sources used to measure aperture correction") 

262 

263 

264class CharacterizeImageTask(pipeBase.PipelineTask): 

265 """Measure bright sources and use this to estimate background and PSF of 

266 an exposure. 

267 

268 Given an exposure with defects repaired (masked and interpolated over, 

269 e.g. as output by `~lsst.ip.isr.IsrTask`): 

270 - detect and measure bright sources 

271 - repair cosmic rays 

272 - detect and mask streaks 

273 - measure and subtract background 

274 - measure PSF 

275 

276 Parameters 

277 ---------- 

278 schema : `lsst.afw.table.Schema`, optional 

279 Initial schema for icSrc catalog. 

280 **kwargs 

281 Additional keyword arguments. 

282 

283 Notes 

284 ----- 

285 Debugging: 

286 CharacterizeImageTask has a debug dictionary with the following keys: 

287 

288 frame 

289 int: if specified, the frame of first debug image displayed (defaults to 1) 

290 repair_iter 

291 bool; if True display image after each repair in the measure PSF loop 

292 background_iter 

293 bool; if True display image after each background subtraction in the measure PSF loop 

294 measure_iter 

295 bool; if True display image and sources at the end of each iteration of the measure PSF loop 

296 See `~lsst.meas.astrom.displayAstrometry` for the meaning of the various symbols. 

297 psf 

298 bool; if True display image and sources after PSF is measured; 

299 this will be identical to the final image displayed by measure_iter if measure_iter is true 

300 repair 

301 bool; if True display image and sources after final repair 

302 measure 

303 bool; if True display image and sources after final measurement 

304 """ 

305 

306 ConfigClass = CharacterizeImageConfig 

307 _DefaultName = "characterizeImage" 

308 

309 def __init__(self, schema=None, **kwargs): 

310 super().__init__(**kwargs) 

311 

312 if schema is None: 

313 schema = SourceTable.makeMinimalSchema() 

314 self.schema = schema 

315 self.makeSubtask("background") 

316 self.makeSubtask("installSimplePsf") 

317 self.makeSubtask("repair") 

318 if self.config.doMaskStreaks: 

319 self.makeSubtask("maskStreaks") 

320 self.makeSubtask("measurePsf", schema=self.schema) 

321 self.algMetadata = dafBase.PropertyList() 

322 self.makeSubtask('detection', schema=self.schema) 

323 if self.config.doDeblend: 

324 self.makeSubtask("deblend", schema=self.schema) 

325 self.makeSubtask('measurement', schema=self.schema, algMetadata=self.algMetadata) 

326 if self.config.doApCorr: 

327 self.makeSubtask('measureApCorr', schema=self.schema) 

328 self.makeSubtask('applyApCorr', schema=self.schema) 

329 self.makeSubtask('catalogCalculation', schema=self.schema) 

330 self._initialFrame = getDebugFrame(self._display, "frame") or 1 

331 self._frame = self._initialFrame 

332 self.schema.checkUnits(parse_strict=self.config.checkUnitsParseStrict) 

333 self.outputSchema = afwTable.SourceCatalog(self.schema) 

334 

335 def runQuantum(self, butlerQC, inputRefs, outputRefs): 

336 inputs = butlerQC.get(inputRefs) 

337 if 'idGenerator' not in inputs.keys(): 

338 inputs['idGenerator'] = self.config.idGenerator.apply(butlerQC.quantum.dataId) 

339 outputs = self.run(**inputs) 

340 butlerQC.put(outputs, outputRefs) 

341 

342 @timeMethod 

343 def run(self, exposure, exposureIdInfo=None, background=None, idGenerator=None): 

344 """Characterize a science image. 

345 

346 Peforms the following operations: 

347 - Iterate the following config.psfIterations times, or once if config.doMeasurePsf false: 

348 - detect and measure sources and estimate PSF (see detectMeasureAndEstimatePsf for details) 

349 - interpolate over cosmic rays 

350 - perform final measurement 

351 

352 Parameters 

353 ---------- 

354 exposure : `lsst.afw.image.ExposureF` 

355 Exposure to characterize. 

356 exposureIdInfo : `lsst.obs.base.ExposureIdInfo`, optional 

357 Exposure ID info. Deprecated in favor of ``idGenerator``, and 

358 ignored if that is provided. 

359 background : `lsst.afw.math.BackgroundList`, optional 

360 Initial model of background already subtracted from exposure. 

361 idGenerator : `lsst.meas.base.IdGenerator`, optional 

362 Object that generates source IDs and provides RNG seeds. 

363 

364 Returns 

365 ------- 

366 result : `lsst.pipe.base.Struct` 

367 Results as a struct with attributes: 

368 

369 ``exposure`` 

370 Characterized exposure (`lsst.afw.image.ExposureF`). 

371 ``sourceCat`` 

372 Detected sources (`lsst.afw.table.SourceCatalog`). 

373 ``background`` 

374 Model of subtracted background (`lsst.afw.math.BackgroundList`). 

375 ``psfCellSet`` 

376 Spatial cells of PSF candidates (`lsst.afw.math.SpatialCellSet`). 

377 ``characterized`` 

378 Another reference to ``exposure`` for compatibility. 

379 ``backgroundModel`` 

380 Another reference to ``background`` for compatibility. 

381 

382 Raises 

383 ------ 

384 RuntimeError 

385 Raised if PSF sigma is NaN. 

386 """ 

387 self._frame = self._initialFrame # reset debug display frame 

388 

389 if not self.config.doMeasurePsf and not exposure.hasPsf(): 

390 self.log.info("CharacterizeImageTask initialized with 'simple' PSF.") 

391 self.installSimplePsf.run(exposure=exposure) 

392 

393 if idGenerator is None: 

394 if exposureIdInfo is not None: 

395 idGenerator = IdGenerator._from_exposure_id_info(exposureIdInfo) 

396 else: 

397 idGenerator = IdGenerator() 

398 

399 del exposureIdInfo 

400 

401 # subtract an initial estimate of background level 

402 background = self.background.run(exposure).background 

403 

404 psfIterations = self.config.psfIterations if self.config.doMeasurePsf else 1 

405 for i in range(psfIterations): 

406 dmeRes = self.detectMeasureAndEstimatePsf( 

407 exposure=exposure, 

408 idGenerator=idGenerator, 

409 background=background, 

410 ) 

411 

412 psf = dmeRes.exposure.getPsf() 

413 # Just need a rough estimate; average positions are fine 

414 psfAvgPos = psf.getAveragePosition() 

415 psfSigma = psf.computeShape(psfAvgPos).getDeterminantRadius() 

416 psfDimensions = psf.computeImage(psfAvgPos).getDimensions() 

417 medBackground = np.median(dmeRes.background.getImage().getArray()) 

418 self.log.info("iter %s; PSF sigma=%0.4f, dimensions=%s; median background=%0.2f", 

419 i + 1, psfSigma, psfDimensions, medBackground) 

420 if np.isnan(psfSigma): 

421 raise RuntimeError("PSF sigma is NaN, cannot continue PSF determination.") 

422 

423 self.display("psf", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat) 

424 

425 # perform final repair with final PSF 

426 self.repair.run(exposure=dmeRes.exposure) 

427 self.display("repair", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat) 

428 

429 # mask streaks 

430 if self.config.doMaskStreaks: 

431 _ = self.maskStreaks.run(dmeRes.exposure) 

432 

433 # perform final measurement with final PSF, including measuring and applying aperture correction, 

434 # if wanted 

435 self.measurement.run(measCat=dmeRes.sourceCat, exposure=dmeRes.exposure, 

436 exposureId=idGenerator.catalog_id) 

437 if self.config.doApCorr: 

438 try: 

439 apCorrMap = self.measureApCorr.run( 

440 exposure=dmeRes.exposure, 

441 catalog=dmeRes.sourceCat, 

442 ).apCorrMap 

443 except MeasureApCorrError: 

444 # We have failed to get a valid aperture correction map. 

445 # Proceed with processing, and image will be filtered 

446 # downstream. 

447 dmeRes.exposure.info.setApCorrMap(None) 

448 else: 

449 dmeRes.exposure.info.setApCorrMap(apCorrMap) 

450 self.applyApCorr.run(catalog=dmeRes.sourceCat, apCorrMap=exposure.getInfo().getApCorrMap()) 

451 

452 self.catalogCalculation.run(dmeRes.sourceCat) 

453 

454 self.display("measure", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat) 

455 

456 return pipeBase.Struct( 

457 exposure=dmeRes.exposure, 

458 sourceCat=dmeRes.sourceCat, 

459 background=dmeRes.background, 

460 psfCellSet=dmeRes.psfCellSet, 

461 

462 characterized=dmeRes.exposure, 

463 backgroundModel=dmeRes.background 

464 ) 

465 

466 @timeMethod 

467 def detectMeasureAndEstimatePsf(self, exposure, idGenerator, background): 

468 """Perform one iteration of detect, measure, and estimate PSF. 

469 

470 Performs the following operations: 

471 

472 - if config.doMeasurePsf or not exposure.hasPsf(): 

473 

474 - install a simple PSF model (replacing the existing one, if need be) 

475 

476 - interpolate over cosmic rays with keepCRs=True 

477 - estimate background and subtract it from the exposure 

478 - detect, deblend and measure sources, and subtract a refined background model; 

479 - if config.doMeasurePsf: 

480 - measure PSF 

481 

482 Parameters 

483 ---------- 

484 exposure : `lsst.afw.image.ExposureF` 

485 Exposure to characterize. 

486 idGenerator : `lsst.meas.base.IdGenerator` 

487 Object that generates source IDs and provides RNG seeds. 

488 background : `lsst.afw.math.BackgroundList`, optional 

489 Initial model of background already subtracted from exposure. 

490 

491 Returns 

492 ------- 

493 result : `lsst.pipe.base.Struct` 

494 Results as a struct with attributes: 

495 

496 ``exposure`` 

497 Characterized exposure (`lsst.afw.image.ExposureF`). 

498 ``sourceCat`` 

499 Detected sources (`lsst.afw.table.SourceCatalog`). 

500 ``background`` 

501 Model of subtracted background (`lsst.afw.math.BackgroundList`). 

502 ``psfCellSet`` 

503 Spatial cells of PSF candidates (`lsst.afw.math.SpatialCellSet`). 

504 

505 Raises 

506 ------ 

507 LengthError 

508 Raised if there are too many CR pixels. 

509 """ 

510 # install a simple PSF model, if needed or wanted 

511 if not exposure.hasPsf() or (self.config.doMeasurePsf and self.config.useSimplePsf): 

512 self.log.info("PSF estimation initialized with 'simple' PSF") 

513 self.installSimplePsf.run(exposure=exposure) 

514 

515 # run repair, but do not interpolate over cosmic rays (do that elsewhere, with the final PSF model) 

516 if self.config.requireCrForPsf: 

517 self.repair.run(exposure=exposure, keepCRs=True) 

518 else: 

519 try: 

520 self.repair.run(exposure=exposure, keepCRs=True) 

521 except LengthError: 

522 self.log.warning("Skipping cosmic ray detection: Too many CR pixels (max %0.f)", 

523 self.config.repair.cosmicray.nCrPixelMax) 

524 

525 self.display("repair_iter", exposure=exposure) 

526 

527 if background is None: 

528 background = BackgroundList() 

529 

530 sourceIdFactory = idGenerator.make_table_id_factory() 

531 table = SourceTable.make(self.schema, sourceIdFactory) 

532 table.setMetadata(self.algMetadata) 

533 

534 detRes = self.detection.run(table=table, exposure=exposure, doSmooth=True) 

535 sourceCat = detRes.sources 

536 if detRes.background: 

537 for bg in detRes.background: 

538 background.append(bg) 

539 

540 if self.config.doDeblend: 

541 self.deblend.run(exposure=exposure, sources=sourceCat) 

542 # We need the output catalog to be contiguous for further processing. 

543 if not sourceCat.isContiguous(): 

544 sourceCat = sourceCat.copy(deep=True) 

545 

546 self.measurement.run(measCat=sourceCat, exposure=exposure, exposureId=idGenerator.catalog_id) 

547 

548 measPsfRes = pipeBase.Struct(cellSet=None) 

549 if self.config.doMeasurePsf: 

550 measPsfRes = self.measurePsf.run(exposure=exposure, sources=sourceCat, 

551 expId=idGenerator.catalog_id) 

552 self.display("measure_iter", exposure=exposure, sourceCat=sourceCat) 

553 

554 return pipeBase.Struct( 

555 exposure=exposure, 

556 sourceCat=sourceCat, 

557 background=background, 

558 psfCellSet=measPsfRes.cellSet, 

559 ) 

560 

561 def display(self, itemName, exposure, sourceCat=None): 

562 """Display exposure and sources on next frame (for debugging). 

563 

564 Parameters 

565 ---------- 

566 itemName : `str` 

567 Name of item in ``debugInfo``. 

568 exposure : `lsst.afw.image.ExposureF` 

569 Exposure to display. 

570 sourceCat : `lsst.afw.table.SourceCatalog`, optional 

571 Catalog of sources detected on the exposure. 

572 """ 

573 val = getDebugFrame(self._display, itemName) 

574 if not val: 

575 return 

576 

577 displayAstrometry(exposure=exposure, sourceCat=sourceCat, frame=self._frame, pause=False) 

578 self._frame += 1