Coverage for python/lsst/pipe/tasks/calibrateImage.py: 24%

263 statements  

« prev     ^ index     » next       coverage.py v7.4.4, created at 2024-04-03 02:24 -0700

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 

22import collections.abc 

23 

24import numpy as np 

25 

26import lsst.afw.table as afwTable 

27import lsst.afw.image as afwImage 

28import lsst.meas.algorithms 

29import lsst.meas.algorithms.installGaussianPsf 

30import lsst.meas.algorithms.measureApCorr 

31import lsst.meas.algorithms.setPrimaryFlags 

32import lsst.meas.base 

33import lsst.meas.astrom 

34import lsst.meas.deblender 

35import lsst.meas.extensions.shapeHSM 

36import lsst.pex.config as pexConfig 

37import lsst.pipe.base as pipeBase 

38from lsst.pipe.base import connectionTypes 

39from lsst.utils.timer import timeMethod 

40 

41from . import measurePsf, repair, photoCal, computeExposureSummaryStats, snapCombine 

42 

43 

44class CalibrateImageConnections(pipeBase.PipelineTaskConnections, 

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

46 

47 astrometry_ref_cat = connectionTypes.PrerequisiteInput( 

48 doc="Reference catalog to use for astrometric calibration.", 

49 name="gaia_dr3_20230707", 

50 storageClass="SimpleCatalog", 

51 dimensions=("skypix",), 

52 deferLoad=True, 

53 multiple=True, 

54 ) 

55 photometry_ref_cat = connectionTypes.PrerequisiteInput( 

56 doc="Reference catalog to use for photometric calibration.", 

57 name="ps1_pv3_3pi_20170110", 

58 storageClass="SimpleCatalog", 

59 dimensions=("skypix",), 

60 deferLoad=True, 

61 multiple=True 

62 ) 

63 

64 exposures = connectionTypes.Input( 

65 doc="Exposure (or two snaps) to be calibrated, and detected and measured on.", 

66 name="postISRCCD", 

67 storageClass="Exposure", 

68 multiple=True, # to handle 1 exposure or 2 snaps 

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

70 ) 

71 

72 # outputs 

73 initial_stars_schema = connectionTypes.InitOutput( 

74 doc="Schema of the output initial stars catalog.", 

75 name="initial_stars_schema", 

76 storageClass="SourceCatalog", 

77 ) 

78 

79 # TODO DM-38732: We want some kind of flag on Exposures/Catalogs to make 

80 # it obvious which components had failed to be computed/persisted. 

81 exposure = connectionTypes.Output( 

82 doc="Photometrically calibrated exposure with fitted calibrations and summary statistics.", 

83 name="initial_pvi", 

84 storageClass="ExposureF", 

85 dimensions=("instrument", "visit", "detector"), 

86 ) 

87 stars = connectionTypes.Output( 

88 doc="Catalog of unresolved sources detected on the calibrated exposure.", 

89 name="initial_stars_detector", 

90 storageClass="ArrowAstropy", 

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

92 ) 

93 stars_footprints = connectionTypes.Output( 

94 doc="Catalog of unresolved sources detected on the calibrated exposure; " 

95 "includes source footprints.", 

96 name="initial_stars_footprints_detector", 

97 storageClass="SourceCatalog", 

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

99 ) 

100 applied_photo_calib = connectionTypes.Output( 

101 doc="Photometric calibration that was applied to exposure.", 

102 name="initial_photoCalib_detector", 

103 storageClass="PhotoCalib", 

104 dimensions=("instrument", "visit", "detector"), 

105 ) 

106 background = connectionTypes.Output( 

107 doc="Background models estimated during calibration task.", 

108 name="initial_pvi_background", 

109 storageClass="Background", 

110 dimensions=("instrument", "visit", "detector"), 

111 ) 

112 

113 # Optional outputs 

114 psf_stars_footprints = connectionTypes.Output( 

115 doc="Catalog of bright unresolved sources detected on the exposure used for PSF determination; " 

116 "includes source footprints.", 

117 name="initial_psf_stars_footprints_detector", 

118 storageClass="SourceCatalog", 

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

120 ) 

121 psf_stars = connectionTypes.Output( 

122 doc="Catalog of bright unresolved sources detected on the exposure used for PSF determination.", 

123 name="initial_psf_stars_detector", 

124 storageClass="ArrowAstropy", 

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

126 ) 

127 astrometry_matches = connectionTypes.Output( 

128 doc="Source to reference catalog matches from the astrometry solver.", 

129 name="initial_astrometry_match_detector", 

130 storageClass="Catalog", 

131 dimensions=("instrument", "visit", "detector"), 

132 ) 

133 photometry_matches = connectionTypes.Output( 

134 doc="Source to reference catalog matches from the photometry solver.", 

135 name="initial_photometry_match_detector", 

136 storageClass="Catalog", 

137 dimensions=("instrument", "visit", "detector"), 

138 ) 

139 

140 def __init__(self, *, config=None): 

141 super().__init__(config=config) 

142 if not config.optional_outputs: 

143 del self.psf_stars 

144 del self.psf_stars_footprints 

145 del self.astrometry_matches 

146 del self.photometry_matches 

147 

148 

149class CalibrateImageConfig(pipeBase.PipelineTaskConfig, pipelineConnections=CalibrateImageConnections): 

150 optional_outputs = pexConfig.ListField( 

151 doc="Which optional outputs to save (as their connection name)?", 

152 dtype=str, 

153 # TODO: note somewhere to disable this for benchmarking, but should 

154 # we always have it on for production runs? 

155 default=["psf_stars", "psf_stars_footprints", "astrometry_matches", "photometry_matches"], 

156 optional=True 

157 ) 

158 

159 # To generate catalog ids consistently across subtasks. 

160 id_generator = lsst.meas.base.DetectorVisitIdGeneratorConfig.make_field() 

161 

162 snap_combine = pexConfig.ConfigurableField( 

163 target=snapCombine.SnapCombineTask, 

164 doc="Task to combine two snaps to make one exposure.", 

165 ) 

166 

167 # subtasks used during psf characterization 

168 install_simple_psf = pexConfig.ConfigurableField( 

169 target=lsst.meas.algorithms.installGaussianPsf.InstallGaussianPsfTask, 

170 doc="Task to install a simple PSF model into the input exposure to use " 

171 "when detecting bright sources for PSF estimation.", 

172 ) 

173 psf_repair = pexConfig.ConfigurableField( 

174 target=repair.RepairTask, 

175 doc="Task to repair cosmic rays on the exposure before PSF determination.", 

176 ) 

177 psf_subtract_background = pexConfig.ConfigurableField( 

178 target=lsst.meas.algorithms.SubtractBackgroundTask, 

179 doc="Task to perform intial background subtraction, before first detection pass.", 

180 ) 

181 psf_detection = pexConfig.ConfigurableField( 

182 target=lsst.meas.algorithms.SourceDetectionTask, 

183 doc="Task to detect sources for PSF determination." 

184 ) 

185 psf_source_measurement = pexConfig.ConfigurableField( 

186 target=lsst.meas.base.SingleFrameMeasurementTask, 

187 doc="Task to measure sources to be used for psf estimation." 

188 ) 

189 psf_measure_psf = pexConfig.ConfigurableField( 

190 target=measurePsf.MeasurePsfTask, 

191 doc="Task to measure the psf on bright sources." 

192 ) 

193 

194 # TODO DM-39203: we can remove aperture correction from this task once we are 

195 # using the shape-based star/galaxy code. 

196 measure_aperture_correction = pexConfig.ConfigurableField( 

197 target=lsst.meas.algorithms.measureApCorr.MeasureApCorrTask, 

198 doc="Task to compute the aperture correction from the bright stars." 

199 ) 

200 

201 # subtasks used during star measurement 

202 star_detection = pexConfig.ConfigurableField( 

203 target=lsst.meas.algorithms.SourceDetectionTask, 

204 doc="Task to detect stars to return in the output catalog." 

205 ) 

206 star_sky_sources = pexConfig.ConfigurableField( 

207 target=lsst.meas.algorithms.SkyObjectsTask, 

208 doc="Task to generate sky sources ('empty' regions where there are no detections).", 

209 ) 

210 star_deblend = pexConfig.ConfigurableField( 

211 target=lsst.meas.deblender.SourceDeblendTask, 

212 doc="Split blended sources into their components." 

213 ) 

214 star_measurement = pexConfig.ConfigurableField( 

215 target=lsst.meas.base.SingleFrameMeasurementTask, 

216 doc="Task to measure stars to return in the output catalog." 

217 ) 

218 star_apply_aperture_correction = pexConfig.ConfigurableField( 

219 target=lsst.meas.base.ApplyApCorrTask, 

220 doc="Task to apply aperture corrections to the selected stars." 

221 ) 

222 star_catalog_calculation = pexConfig.ConfigurableField( 

223 target=lsst.meas.base.CatalogCalculationTask, 

224 doc="Task to compute extendedness values on the star catalog, " 

225 "for the star selector to remove extended sources." 

226 ) 

227 star_set_primary_flags = pexConfig.ConfigurableField( 

228 target=lsst.meas.algorithms.setPrimaryFlags.SetPrimaryFlagsTask, 

229 doc="Task to add isPrimary to the catalog." 

230 ) 

231 star_selector = lsst.meas.algorithms.sourceSelectorRegistry.makeField( 

232 default="science", 

233 doc="Task to select isolated stars to use for calibration." 

234 ) 

235 

236 # final calibrations and statistics 

237 astrometry = pexConfig.ConfigurableField( 

238 target=lsst.meas.astrom.AstrometryTask, 

239 doc="Task to perform astrometric calibration to fit a WCS.", 

240 ) 

241 astrometry_ref_loader = pexConfig.ConfigField( 

242 dtype=lsst.meas.algorithms.LoadReferenceObjectsConfig, 

243 doc="Configuration of reference object loader for astrometric fit.", 

244 ) 

245 photometry = pexConfig.ConfigurableField( 

246 target=photoCal.PhotoCalTask, 

247 doc="Task to perform photometric calibration to fit a PhotoCalib.", 

248 ) 

249 photometry_ref_loader = pexConfig.ConfigField( 

250 dtype=lsst.meas.algorithms.LoadReferenceObjectsConfig, 

251 doc="Configuration of reference object loader for photometric fit.", 

252 ) 

253 

254 compute_summary_stats = pexConfig.ConfigurableField( 

255 target=computeExposureSummaryStats.ComputeExposureSummaryStatsTask, 

256 doc="Task to to compute summary statistics on the calibrated exposure." 

257 ) 

258 

259 def setDefaults(self): 

260 super().setDefaults() 

261 

262 # Use a very broad PSF here, to throughly reject CRs. 

263 # TODO investigation: a large initial psf guess may make stars look 

264 # like CRs for very good seeing images. 

265 self.install_simple_psf.fwhm = 4 

266 

267 # S/N>=50 sources for PSF determination, but detection to S/N=5. 

268 self.psf_detection.thresholdValue = 5.0 

269 self.psf_detection.includeThresholdMultiplier = 10.0 

270 # TODO investigation: Probably want False here, but that may require 

271 # tweaking the background spatial scale, to make it small enough to 

272 # prevent extra peaks in the wings of bright objects. 

273 self.psf_detection.doTempLocalBackground = False 

274 # NOTE: we do want reEstimateBackground=True in psf_detection, so that 

275 # each measurement step is done with the best background available. 

276 

277 # Minimal measurement plugins for PSF determination. 

278 # TODO DM-39203: We can drop GaussianFlux and PsfFlux, if we use 

279 # shapeHSM/moments for star/galaxy separation. 

280 # TODO DM-39203: we can remove aperture correction from this task once 

281 # we are using the shape-based star/galaxy code. 

282 self.psf_source_measurement.plugins = ["base_PixelFlags", 

283 "base_SdssCentroid", 

284 "ext_shapeHSM_HsmSourceMoments", 

285 "base_CircularApertureFlux", 

286 "base_GaussianFlux", 

287 "base_PsfFlux", 

288 "base_ClassificationSizeExtendedness", 

289 ] 

290 self.psf_source_measurement.slots.shape = "ext_shapeHSM_HsmSourceMoments" 

291 # Only measure apertures we need for PSF measurement. 

292 self.psf_source_measurement.plugins["base_CircularApertureFlux"].radii = [12.0] 

293 # TODO DM-40843: Remove this line once this is the psfex default. 

294 self.psf_measure_psf.psfDeterminer["psfex"].photometricFluxField = \ 

295 "base_CircularApertureFlux_12_0_instFlux" 

296 

297 # No extendeness information available: we need the aperture 

298 # corrections to determine that. 

299 self.measure_aperture_correction.sourceSelector["science"].doUnresolved = False 

300 self.measure_aperture_correction.sourceSelector["science"].flags.good = ["calib_psf_used"] 

301 self.measure_aperture_correction.sourceSelector["science"].flags.bad = [] 

302 

303 # Detection for good S/N for astrometry/photometry and other 

304 # downstream tasks; detection mask to S/N>=5, but S/N>=10 peaks. 

305 self.star_detection.thresholdValue = 5.0 

306 self.star_detection.includeThresholdMultiplier = 2.0 

307 self.star_measurement.plugins = ["base_PixelFlags", 

308 "base_SdssCentroid", 

309 "ext_shapeHSM_HsmSourceMoments", 

310 'ext_shapeHSM_HsmPsfMoments', 

311 "base_GaussianFlux", 

312 "base_PsfFlux", 

313 "base_CircularApertureFlux", 

314 "base_ClassificationSizeExtendedness", 

315 ] 

316 self.star_measurement.slots.psfShape = "ext_shapeHSM_HsmPsfMoments" 

317 self.star_measurement.slots.shape = "ext_shapeHSM_HsmSourceMoments" 

318 # Only measure the apertures we need for star selection. 

319 self.star_measurement.plugins["base_CircularApertureFlux"].radii = [12.0] 

320 

321 # Select isolated stars with reliable measurements and no bad flags. 

322 self.star_selector["science"].doFlags = True 

323 self.star_selector["science"].doUnresolved = True 

324 self.star_selector["science"].doSignalToNoise = True 

325 self.star_selector["science"].doIsolated = True 

326 self.star_selector["science"].signalToNoise.minimum = 10.0 

327 # Keep sky sources in the output catalog, even though they aren't 

328 # wanted for calibration. 

329 self.star_selector["science"].doSkySources = True 

330 

331 # Use the affine WCS fitter (assumes we have a good camera geometry). 

332 self.astrometry.wcsFitter.retarget(lsst.meas.astrom.FitAffineWcsTask) 

333 # phot_g_mean is the primary Gaia band for all input bands. 

334 self.astrometry_ref_loader.anyFilterMapsToThis = "phot_g_mean" 

335 

336 # Only reject sky sources; we already selected good stars. 

337 self.astrometry.sourceSelector["science"].doFlags = True 

338 self.astrometry.sourceSelector["science"].flags.bad = ["sky_source"] 

339 self.photometry.match.sourceSelection.doFlags = True 

340 self.photometry.match.sourceSelection.flags.bad = ["sky_source"] 

341 

342 # All sources should be good for PSF summary statistics. 

343 # TODO: These should both be changed to calib_psf_used with DM-41640. 

344 self.compute_summary_stats.starSelection = "calib_photometry_used" 

345 self.compute_summary_stats.starSelector.flags.good = ["calib_photometry_used"] 

346 

347 

348class CalibrateImageTask(pipeBase.PipelineTask): 

349 """Compute the PSF, aperture corrections, astrometric and photometric 

350 calibrations, and summary statistics for a single science exposure, and 

351 produce a catalog of brighter stars that were used to calibrate it. 

352 

353 Parameters 

354 ---------- 

355 initial_stars_schema : `lsst.afw.table.Schema` 

356 Schema of the initial_stars output catalog. 

357 """ 

358 _DefaultName = "calibrateImage" 

359 ConfigClass = CalibrateImageConfig 

360 

361 def __init__(self, initial_stars_schema=None, **kwargs): 

362 super().__init__(**kwargs) 

363 

364 self.makeSubtask("snap_combine") 

365 

366 # PSF determination subtasks 

367 self.makeSubtask("install_simple_psf") 

368 self.makeSubtask("psf_repair") 

369 self.makeSubtask("psf_subtract_background") 

370 self.psf_schema = afwTable.SourceTable.makeMinimalSchema() 

371 self.makeSubtask("psf_detection", schema=self.psf_schema) 

372 self.makeSubtask("psf_source_measurement", schema=self.psf_schema) 

373 self.makeSubtask("psf_measure_psf", schema=self.psf_schema) 

374 

375 self.makeSubtask("measure_aperture_correction", schema=self.psf_schema) 

376 

377 # star measurement subtasks 

378 if initial_stars_schema is None: 

379 initial_stars_schema = afwTable.SourceTable.makeMinimalSchema() 

380 

381 # These fields let us track which sources were used for psf and 

382 # aperture correction calculations. 

383 self.psf_fields = ("calib_psf_candidate", "calib_psf_used", "calib_psf_reserved", 

384 # TODO DM-39203: these can be removed once apcorr is gone. 

385 "apcorr_slot_CalibFlux_used", "apcorr_base_GaussianFlux_used", 

386 "apcorr_base_PsfFlux_used") 

387 for field in self.psf_fields: 

388 item = self.psf_schema.find(field) 

389 initial_stars_schema.addField(item.getField()) 

390 

391 afwTable.CoordKey.addErrorFields(initial_stars_schema) 

392 self.makeSubtask("star_detection", schema=initial_stars_schema) 

393 self.makeSubtask("star_sky_sources", schema=initial_stars_schema) 

394 self.makeSubtask("star_deblend", schema=initial_stars_schema) 

395 self.makeSubtask("star_measurement", schema=initial_stars_schema) 

396 self.makeSubtask("star_apply_aperture_correction", schema=initial_stars_schema) 

397 self.makeSubtask("star_catalog_calculation", schema=initial_stars_schema) 

398 self.makeSubtask("star_set_primary_flags", schema=initial_stars_schema, isSingleFrame=True) 

399 self.makeSubtask("star_selector") 

400 

401 self.makeSubtask("astrometry", schema=initial_stars_schema) 

402 self.makeSubtask("photometry", schema=initial_stars_schema) 

403 

404 self.makeSubtask("compute_summary_stats") 

405 

406 # For the butler to persist it. 

407 self.initial_stars_schema = afwTable.SourceCatalog(initial_stars_schema) 

408 

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

410 inputs = butlerQC.get(inputRefs) 

411 exposures = inputs.pop("exposures") 

412 

413 id_generator = self.config.id_generator.apply(butlerQC.quantum.dataId) 

414 

415 astrometry_loader = lsst.meas.algorithms.ReferenceObjectLoader( 

416 dataIds=[ref.datasetRef.dataId for ref in inputRefs.astrometry_ref_cat], 

417 refCats=inputs.pop("astrometry_ref_cat"), 

418 name=self.config.connections.astrometry_ref_cat, 

419 config=self.config.astrometry_ref_loader, log=self.log) 

420 self.astrometry.setRefObjLoader(astrometry_loader) 

421 

422 photometry_loader = lsst.meas.algorithms.ReferenceObjectLoader( 

423 dataIds=[ref.datasetRef.dataId for ref in inputRefs.photometry_ref_cat], 

424 refCats=inputs.pop("photometry_ref_cat"), 

425 name=self.config.connections.photometry_ref_cat, 

426 config=self.config.photometry_ref_loader, log=self.log) 

427 self.photometry.match.setRefObjLoader(photometry_loader) 

428 

429 # This should not happen with a properly configured execution context. 

430 assert not inputs, "runQuantum got more inputs than expected" 

431 

432 # Specify the fields that `annotate` needs below, to ensure they 

433 # exist, even as None. 

434 result = pipeBase.Struct(exposure=None, 

435 stars_footprints=None, 

436 psf_stars_footprints=None, 

437 ) 

438 try: 

439 self.run(exposures=exposures, result=result, id_generator=id_generator) 

440 except pipeBase.AlgorithmError as e: 

441 error = pipeBase.AnnotatedPartialOutputsError.annotate( 

442 e, 

443 self, 

444 result.exposure, 

445 result.psf_stars_footprints, 

446 result.stars_footprints, 

447 log=self.log 

448 ) 

449 butlerQC.put(result, outputRefs) 

450 raise error from e 

451 

452 butlerQC.put(result, outputRefs) 

453 

454 @timeMethod 

455 def run(self, *, exposures, id_generator=None, result=None): 

456 """Find stars and perform psf measurement, then do a deeper detection 

457 and measurement and calibrate astrometry and photometry from that. 

458 

459 Parameters 

460 ---------- 

461 exposures : `lsst.afw.image.Exposure` or `list` [`lsst.afw.image.Exposure`] 

462 Post-ISR exposure(s), with an initial WCS, VisitInfo, and Filter. 

463 Modified in-place during processing if only one is passed. 

464 If two exposures are passed, treat them as snaps and combine 

465 before doing further processing. 

466 id_generator : `lsst.meas.base.IdGenerator`, optional 

467 Object that generates source IDs and provides random seeds. 

468 result : `lsst.pipe.base.Struct`, optional 

469 Result struct that is modified to allow saving of partial outputs 

470 for some failure conditions. If the task completes successfully, 

471 this is also returned. 

472 

473 Returns 

474 ------- 

475 result : `lsst.pipe.base.Struct` 

476 Results as a struct with attributes: 

477 

478 ``exposure`` 

479 Calibrated exposure, with pixels in nJy units. 

480 (`lsst.afw.image.Exposure`) 

481 ``stars`` 

482 Stars that were used to calibrate the exposure, with 

483 calibrated fluxes and magnitudes. 

484 (`astropy.table.Table`) 

485 ``stars_footprints`` 

486 Footprints of stars that were used to calibrate the exposure. 

487 (`lsst.afw.table.SourceCatalog`) 

488 ``psf_stars`` 

489 Stars that were used to determine the image PSF. 

490 (`astropy.table.Table`) 

491 ``psf_stars_footprints`` 

492 Footprints of stars that were used to determine the image PSF. 

493 (`lsst.afw.table.SourceCatalog`) 

494 ``background`` 

495 Background that was fit to the exposure when detecting 

496 ``stars``. (`lsst.afw.math.BackgroundList`) 

497 ``applied_photo_calib`` 

498 Photometric calibration that was fit to the star catalog and 

499 applied to the exposure. (`lsst.afw.image.PhotoCalib`) 

500 ``astrometry_matches`` 

501 Reference catalog stars matches used in the astrometric fit. 

502 (`list` [`lsst.afw.table.ReferenceMatch`] or `lsst.afw.table.BaseCatalog`) 

503 ``photometry_matches`` 

504 Reference catalog stars matches used in the photometric fit. 

505 (`list` [`lsst.afw.table.ReferenceMatch`] or `lsst.afw.table.BaseCatalog`) 

506 """ 

507 if result is None: 

508 result = pipeBase.Struct() 

509 if id_generator is None: 

510 id_generator = lsst.meas.base.IdGenerator() 

511 

512 result.exposure = self._handle_snaps(exposures) 

513 

514 # TODO remove on DM-43083: work around the fact that we don't want 

515 # to run streak detection in this task in production. 

516 result.exposure.mask.addMaskPlane("STREAK") 

517 

518 result.psf_stars_footprints, result.background, candidates = self._compute_psf(result.exposure, 

519 id_generator) 

520 result.psf_stars = result.psf_stars_footprints.asAstropy() 

521 

522 self._measure_aperture_correction(result.exposure, result.psf_stars) 

523 

524 result.stars_footprints = self._find_stars(result.exposure, result.background, id_generator) 

525 self._match_psf_stars(result.psf_stars_footprints, result.stars_footprints) 

526 result.stars = result.stars_footprints.asAstropy() 

527 

528 astrometry_matches, astrometry_meta = self._fit_astrometry(result.exposure, result.stars_footprints) 

529 if self.config.optional_outputs: 

530 result.astrometry_matches = lsst.meas.astrom.denormalizeMatches(astrometry_matches, 

531 astrometry_meta) 

532 

533 result.stars_footprints, photometry_matches, \ 

534 photometry_meta, result.applied_photo_calib = self._fit_photometry(result.exposure, 

535 result.stars_footprints) 

536 # fit_photometry returns a new catalog, so we need a new astropy table view. 

537 result.stars = result.stars_footprints.asAstropy() 

538 if self.config.optional_outputs: 

539 result.photometry_matches = lsst.meas.astrom.denormalizeMatches(photometry_matches, 

540 photometry_meta) 

541 

542 self._summarize(result.exposure, result.stars_footprints, result.background) 

543 

544 return result 

545 

546 def _handle_snaps(self, exposure): 

547 """Combine two snaps into one exposure, or return a single exposure. 

548 

549 Parameters 

550 ---------- 

551 exposure : `lsst.afw.image.Exposure` or `list` [`lsst.afw.image.Exposure]` 

552 One or two exposures to combine as snaps. 

553 

554 Returns 

555 ------- 

556 exposure : `lsst.afw.image.Exposure` 

557 A single exposure to continue processing. 

558 

559 Raises 

560 ------ 

561 RuntimeError 

562 Raised if input does not contain either 1 or 2 exposures. 

563 """ 

564 if isinstance(exposure, lsst.afw.image.Exposure): 

565 return exposure 

566 

567 if isinstance(exposure, collections.abc.Sequence): 

568 match len(exposure): 

569 case 1: 

570 return exposure[0] 

571 case 2: 

572 return self.snap_combine.run(exposure[0], exposure[1]).exposure 

573 case n: 

574 raise RuntimeError(f"Can only process 1 or 2 snaps, not {n}.") 

575 

576 def _compute_psf(self, exposure, id_generator): 

577 """Find bright sources detected on an exposure and fit a PSF model to 

578 them, repairing likely cosmic rays before detection. 

579 

580 Repair, detect, measure, and compute PSF twice, to ensure the PSF 

581 model does not include contributions from cosmic rays. 

582 

583 Parameters 

584 ---------- 

585 exposure : `lsst.afw.image.Exposure` 

586 Exposure to detect and measure bright stars on. 

587 id_generator : `lsst.meas.base.IdGenerator`, optional 

588 Object that generates source IDs and provides random seeds. 

589 

590 Returns 

591 ------- 

592 sources : `lsst.afw.table.SourceCatalog` 

593 Catalog of detected bright sources. 

594 background : `lsst.afw.math.BackgroundList` 

595 Background that was fit to the exposure during detection. 

596 cell_set : `lsst.afw.math.SpatialCellSet` 

597 PSF candidates returned by the psf determiner. 

598 """ 

599 def log_psf(msg): 

600 """Log the parameters of the psf and background, with a prepended 

601 message. 

602 """ 

603 position = exposure.psf.getAveragePosition() 

604 sigma = exposure.psf.computeShape(position).getDeterminantRadius() 

605 dimensions = exposure.psf.computeImage(position).getDimensions() 

606 median_background = np.median(background.getImage().array) 

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

608 msg, sigma, dimensions, median_background) 

609 

610 self.log.info("First pass detection with Guassian PSF FWHM=%s pixels", 

611 self.config.install_simple_psf.fwhm) 

612 self.install_simple_psf.run(exposure=exposure) 

613 

614 background = self.psf_subtract_background.run(exposure=exposure).background 

615 log_psf("Initial PSF:") 

616 self.psf_repair.run(exposure=exposure, keepCRs=True) 

617 

618 table = afwTable.SourceTable.make(self.psf_schema, id_generator.make_table_id_factory()) 

619 # Re-estimate the background during this detection step, so that 

620 # measurement uses the most accurate background-subtraction. 

621 detections = self.psf_detection.run(table=table, exposure=exposure, background=background) 

622 self.psf_source_measurement.run(detections.sources, exposure) 

623 psf_result = self.psf_measure_psf.run(exposure=exposure, sources=detections.sources) 

624 # Replace the initial PSF with something simpler for the second 

625 # repair/detect/measure/measure_psf step: this can help it converge. 

626 self.install_simple_psf.run(exposure=exposure) 

627 

628 log_psf("Rerunning with simple PSF:") 

629 # TODO investigation: Should we only re-run repair here, to use the 

630 # new PSF? Maybe we *do* need to re-run measurement with PsfFlux, to 

631 # use the fitted PSF? 

632 # TODO investigation: do we need a separate measurement task here 

633 # for the post-psf_measure_psf step, since we only want to do PsfFlux 

634 # and GaussianFlux *after* we have a PSF? Maybe that's not relevant 

635 # once DM-39203 is merged? 

636 self.psf_repair.run(exposure=exposure, keepCRs=True) 

637 # Re-estimate the background during this detection step, so that 

638 # measurement uses the most accurate background-subtraction. 

639 detections = self.psf_detection.run(table=table, exposure=exposure, background=background) 

640 self.psf_source_measurement.run(detections.sources, exposure) 

641 psf_result = self.psf_measure_psf.run(exposure=exposure, sources=detections.sources) 

642 

643 log_psf("Final PSF:") 

644 

645 # Final repair with final PSF, removing cosmic rays this time. 

646 self.psf_repair.run(exposure=exposure) 

647 # Final measurement with the CRs removed. 

648 self.psf_source_measurement.run(detections.sources, exposure) 

649 

650 # PSF is set on exposure; only return candidates for optional saving. 

651 return detections.sources, background, psf_result.cellSet 

652 

653 def _measure_aperture_correction(self, exposure, bright_sources): 

654 """Measure and set the ApCorrMap on the Exposure, using 

655 previously-measured bright sources. 

656 

657 Parameters 

658 ---------- 

659 exposure : `lsst.afw.image.Exposure` 

660 Exposure to set the ApCorrMap on. 

661 bright_sources : `lsst.afw.table.SourceCatalog` 

662 Catalog of detected bright sources; modified to include columns 

663 necessary for point source determination for the aperture correction 

664 calculation. 

665 """ 

666 result = self.measure_aperture_correction.run(exposure, bright_sources) 

667 exposure.setApCorrMap(result.apCorrMap) 

668 

669 def _find_stars(self, exposure, background, id_generator): 

670 """Detect stars on an exposure that has a PSF model, and measure their 

671 PSF, circular aperture, compensated gaussian fluxes. 

672 

673 Parameters 

674 ---------- 

675 exposure : `lsst.afw.image.Exposure` 

676 Exposure to set the ApCorrMap on. 

677 background : `lsst.afw.math.BackgroundList` 

678 Background that was fit to the exposure during detection; 

679 modified in-place during subsequent detection. 

680 id_generator : `lsst.meas.base.IdGenerator` 

681 Object that generates source IDs and provides random seeds. 

682 

683 Returns 

684 ------- 

685 stars : `SourceCatalog` 

686 Sources that are very likely to be stars, with a limited set of 

687 measurements performed on them. 

688 """ 

689 table = afwTable.SourceTable.make(self.initial_stars_schema.schema, 

690 id_generator.make_table_id_factory()) 

691 # Re-estimate the background during this detection step, so that 

692 # measurement uses the most accurate background-subtraction. 

693 detections = self.star_detection.run(table=table, exposure=exposure, background=background) 

694 sources = detections.sources 

695 self.star_sky_sources.run(exposure.mask, id_generator.catalog_id, sources) 

696 

697 # TODO investigation: Could this deblender throw away blends of non-PSF sources? 

698 self.star_deblend.run(exposure=exposure, sources=sources) 

699 # The deblender may not produce a contiguous catalog; ensure 

700 # contiguity for subsequent tasks. 

701 if not sources.isContiguous(): 

702 sources = sources.copy(deep=True) 

703 

704 # Measure everything, and use those results to select only stars. 

705 self.star_measurement.run(sources, exposure) 

706 self.star_apply_aperture_correction.run(sources, exposure.info.getApCorrMap()) 

707 self.star_catalog_calculation.run(sources) 

708 self.star_set_primary_flags.run(sources) 

709 

710 result = self.star_selector.run(sources) 

711 # The star selector may not produce a contiguous catalog. 

712 if not result.sourceCat.isContiguous(): 

713 return result.sourceCat.copy(deep=True) 

714 else: 

715 return result.sourceCat 

716 

717 def _match_psf_stars(self, psf_stars, stars): 

718 """Match calibration stars to psf stars, to identify which were psf 

719 candidates, and which were used or reserved during psf measurement. 

720 

721 Parameters 

722 ---------- 

723 psf_stars : `lsst.afw.table.SourceCatalog` 

724 PSF candidate stars that were sent to the psf determiner. Used to 

725 populate psf-related flag fields. 

726 stars : `lsst.afw.table.SourceCatalog` 

727 Stars that will be used for calibration; psf-related fields will 

728 be updated in-place. 

729 

730 Notes 

731 ----- 

732 This code was adapted from CalibrateTask.copyIcSourceFields(). 

733 """ 

734 control = afwTable.MatchControl() 

735 # Return all matched objects, to separate blends. 

736 control.findOnlyClosest = False 

737 matches = afwTable.matchXy(psf_stars, stars, 3.0, control) 

738 deblend_key = stars.schema["deblend_nChild"].asKey() 

739 matches = [m for m in matches if m[1].get(deblend_key) == 0] 

740 

741 # Because we had to allow multiple matches to handle parents, we now 

742 # need to prune to the best (closest) matches. 

743 # Closest matches is a dict of psf_stars source ID to Match record 

744 # (psf_stars source, sourceCat source, distance in pixels). 

745 best = {} 

746 for match_psf, match_stars, d in matches: 

747 match = best.get(match_psf.getId()) 

748 if match is None or d <= match[2]: 

749 best[match_psf.getId()] = (match_psf, match_stars, d) 

750 matches = list(best.values()) 

751 # We'll use this to construct index arrays into each catalog. 

752 ids = np.array([(match_psf.getId(), match_stars.getId()) for match_psf, match_stars, d in matches]).T 

753 

754 # Check that no stars sources are listed twice; we already know 

755 # that each match has a unique psf_stars id, due to using as the key 

756 # in best above. 

757 n_matches = len(matches) 

758 n_unique = len(set(m[1].getId() for m in matches)) 

759 if n_unique != n_matches: 

760 self.log.warning("%d psf_stars matched only %d stars; ", 

761 n_matches, n_unique) 

762 if n_matches == 0: 

763 msg = (f"0 psf_stars out of {len(psf_stars)} matched {len(stars)} calib stars." 

764 " Downstream processes probably won't have useful stars in this case." 

765 " Is `star_source_selector` too strict?") 

766 # TODO DM-39842: Turn this into an AlgorithmicError. 

767 raise RuntimeError(msg) 

768 

769 # The indices of the IDs, so we can update the flag fields as arrays. 

770 idx_psf_stars = np.searchsorted(psf_stars["id"], ids[0]) 

771 idx_stars = np.searchsorted(stars["id"], ids[1]) 

772 for field in self.psf_fields: 

773 result = np.zeros(len(stars), dtype=bool) 

774 result[idx_stars] = psf_stars[field][idx_psf_stars] 

775 stars[field] = result 

776 

777 def _fit_astrometry(self, exposure, stars): 

778 """Fit an astrometric model to the data and return the reference 

779 matches used in the fit, and the fitted WCS. 

780 

781 Parameters 

782 ---------- 

783 exposure : `lsst.afw.image.Exposure` 

784 Exposure that is being fit, to get PSF and other metadata from. 

785 Modified to add the fitted skyWcs. 

786 stars : `SourceCatalog` 

787 Good stars selected for use in calibration, with RA/Dec coordinates 

788 computed from the pixel positions and fitted WCS. 

789 

790 Returns 

791 ------- 

792 matches : `list` [`lsst.afw.table.ReferenceMatch`] 

793 Reference/stars matches used in the fit. 

794 """ 

795 result = self.astrometry.run(stars, exposure) 

796 return result.matches, result.matchMeta 

797 

798 def _fit_photometry(self, exposure, stars): 

799 """Fit a photometric model to the data and return the reference 

800 matches used in the fit, and the fitted PhotoCalib. 

801 

802 Parameters 

803 ---------- 

804 exposure : `lsst.afw.image.Exposure` 

805 Exposure that is being fit, to get PSF and other metadata from. 

806 Modified to be in nanojanksy units, with an assigned photoCalib 

807 identically 1. 

808 stars : `lsst.afw.table.SourceCatalog` 

809 Good stars selected for use in calibration. 

810 

811 Returns 

812 ------- 

813 calibrated_stars : `lsst.afw.table.SourceCatalog` 

814 Star catalog with flux/magnitude columns computed from the fitted 

815 photoCalib. 

816 matches : `list` [`lsst.afw.table.ReferenceMatch`] 

817 Reference/stars matches used in the fit. 

818 photoCalib : `lsst.afw.image.PhotoCalib` 

819 Photometric calibration that was fit to the star catalog. 

820 """ 

821 result = self.photometry.run(exposure, stars) 

822 calibrated_stars = result.photoCalib.calibrateCatalog(stars) 

823 exposure.maskedImage = result.photoCalib.calibrateImage(exposure.maskedImage) 

824 identity = afwImage.PhotoCalib(1.0, 

825 result.photoCalib.getCalibrationErr(), 

826 bbox=exposure.getBBox()) 

827 exposure.setPhotoCalib(identity) 

828 

829 return calibrated_stars, result.matches, result.matchMeta, result.photoCalib 

830 

831 def _summarize(self, exposure, stars, background): 

832 """Compute summary statistics on the exposure and update in-place the 

833 calibrations attached to it. 

834 

835 Parameters 

836 ---------- 

837 exposure : `lsst.afw.image.Exposure` 

838 Exposure that was calibrated, to get PSF and other metadata from. 

839 Modified to contain the computed summary statistics. 

840 stars : `SourceCatalog` 

841 Good stars selected used in calibration. 

842 background : `lsst.afw.math.BackgroundList` 

843 Background that was fit to the exposure during detection of the 

844 above stars. 

845 """ 

846 # TODO investigation: because this takes the photoCalib from the 

847 # exposure, photometric summary values may be "incorrect" (i.e. they 

848 # will reflect the ==1 nJy calibration on the exposure, not the 

849 # applied calibration). This needs to be checked. 

850 summary = self.compute_summary_stats.run(exposure, stars, background) 

851 exposure.info.setSummaryStats(summary)