Coverage for python/lsst/pipe/tasks/calibrateImage.py: 26%

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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 

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 

31from lsst.meas.algorithms import sourceSelector 

32import lsst.meas.astrom 

33import lsst.meas.deblender 

34import lsst.meas.extensions.shapeHSM 

35import lsst.pex.config as pexConfig 

36import lsst.pipe.base as pipeBase 

37from lsst.pipe.base import connectionTypes 

38from lsst.utils.timer import timeMethod 

39 

40from . import measurePsf, repair, setPrimaryFlags, photoCal, \ 

41 computeExposureSummaryStats, maskStreaks, 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: We want some kind of flag on Exposures/Catalogs to make it obvious 

80 # which components had failed to be computed/persisted 

81 output_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 # TODO DM-40061: persist a parquet version of this! 

88 stars = connectionTypes.Output( 

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

90 "includes source footprints.", 

91 name="initial_stars_footprints_detector", 

92 storageClass="SourceCatalog", 

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

94 ) 

95 applied_photo_calib = connectionTypes.Output( 

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

97 name="initial_photoCalib_detector", 

98 storageClass="PhotoCalib", 

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

100 ) 

101 background = connectionTypes.Output( 

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

103 name="initial_pvi_background", 

104 storageClass="Background", 

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

106 ) 

107 

108 # Optional outputs 

109 

110 # TODO: We need to decide on what intermediate outputs we want to save, 

111 # and which to save by default. 

112 # TODO DM-40061: persist a parquet version of this! 

113 psf_stars = connectionTypes.Output( 

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

115 "includes source footprints.", 

116 name="initial_psf_stars_footprints", 

117 storageClass="SourceCatalog", 

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

119 ) 

120 astrometry_matches = connectionTypes.Output( 

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

122 name="initial_astrometry_match_detector", 

123 storageClass="Catalog", 

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

125 ) 

126 photometry_matches = connectionTypes.Output( 

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

128 name="initial_photometry_match_detector", 

129 storageClass="Catalog", 

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

131 ) 

132 

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

134 super().__init__(config=config) 

135 if not config.optional_outputs: 

136 self.outputs.remove("psf_stars") 

137 self.outputs.remove("astrometry_matches") 

138 self.outputs.remove("photometry_matches") 

139 

140 

141class CalibrateImageConfig(pipeBase.PipelineTaskConfig, pipelineConnections=CalibrateImageConnections): 

142 optional_outputs = pexConfig.ListField( 

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

144 dtype=str, 

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

146 # we always have it on for production runs? 

147 default=["psf_stars", "astrometry_matches", "photometry_matches"], 

148 optional=True 

149 ) 

150 

151 snap_combine = pexConfig.ConfigurableField( 

152 target=snapCombine.SnapCombineTask, 

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

154 ) 

155 

156 # subtasks used during psf characterization 

157 install_simple_psf = pexConfig.ConfigurableField( 

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

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

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

161 ) 

162 psf_repair = pexConfig.ConfigurableField( 

163 target=repair.RepairTask, 

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

165 ) 

166 psf_subtract_background = pexConfig.ConfigurableField( 

167 target=lsst.meas.algorithms.SubtractBackgroundTask, 

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

169 ) 

170 psf_detection = pexConfig.ConfigurableField( 

171 target=lsst.meas.algorithms.SourceDetectionTask, 

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

173 ) 

174 psf_source_measurement = pexConfig.ConfigurableField( 

175 target=lsst.meas.base.SingleFrameMeasurementTask, 

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

177 ) 

178 psf_measure_psf = pexConfig.ConfigurableField( 

179 target=measurePsf.MeasurePsfTask, 

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

181 ) 

182 

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

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

185 measure_aperture_correction = pexConfig.ConfigurableField( 

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

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

188 ) 

189 

190 # subtasks used during star measurement 

191 star_detection = pexConfig.ConfigurableField( 

192 target=lsst.meas.algorithms.SourceDetectionTask, 

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

194 ) 

195 star_mask_streaks = pexConfig.ConfigurableField( 

196 target=maskStreaks.MaskStreaksTask, 

197 doc="Task for masking streaks. Adds a STREAK mask plane to an exposure.", 

198 ) 

199 star_deblend = pexConfig.ConfigurableField( 

200 target=lsst.meas.deblender.SourceDeblendTask, 

201 doc="Split blended sources into their components" 

202 ) 

203 star_measurement = pexConfig.ConfigurableField( 

204 target=lsst.meas.base.SingleFrameMeasurementTask, 

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

206 ) 

207 star_apply_aperture_correction = pexConfig.ConfigurableField( 

208 target=lsst.meas.base.ApplyApCorrTask, 

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

210 ) 

211 star_catalog_calculation = pexConfig.ConfigurableField( 

212 target=lsst.meas.base.CatalogCalculationTask, 

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

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

215 ) 

216 star_set_primary_flags = pexConfig.ConfigurableField( 

217 target=setPrimaryFlags.SetPrimaryFlagsTask, 

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

219 ) 

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

221 default="science", 

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

223 ) 

224 

225 # final calibrations and statistics 

226 astrometry = pexConfig.ConfigurableField( 

227 target=lsst.meas.astrom.AstrometryTask, 

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

229 ) 

230 astrometry_ref_loader = pexConfig.ConfigField( 

231 dtype=lsst.meas.algorithms.LoadReferenceObjectsConfig, 

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

233 ) 

234 photometry = pexConfig.ConfigurableField( 

235 target=photoCal.PhotoCalTask, 

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

237 ) 

238 photometry_ref_loader = pexConfig.ConfigField( 

239 dtype=lsst.meas.algorithms.LoadReferenceObjectsConfig, 

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

241 ) 

242 

243 compute_summary_stats = pexConfig.ConfigurableField( 

244 target=computeExposureSummaryStats.ComputeExposureSummaryStatsTask, 

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

246 ) 

247 

248 def setDefaults(self): 

249 super().setDefaults() 

250 

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

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

253 # like CRs for very good seeing images. 

254 self.install_simple_psf.fwhm = 4 

255 

256 # Only use high S/N sources for PSF determination. 

257 self.psf_detection.thresholdValue = 50.0 

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

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

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

261 self.psf_detection.doTempLocalBackground = False 

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

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

264 

265 # Minimal measurement plugins for PSF determination. 

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

267 # shapeHSM/moments for star/galaxy separation. 

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

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

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

271 "base_SdssCentroid", 

272 "ext_shapeHSM_HsmSourceMoments", 

273 "base_CircularApertureFlux", 

274 "base_GaussianFlux", 

275 "base_PsfFlux", 

276 ] 

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

278 # Only measure apertures we need for PSF measurement. 

279 # TODO DM-40064: psfex has a hard-coded value of 9 in a psfex-config 

280 # file: make that configurable and/or change it to 12 to be consistent 

281 # with our other uses? 

282 # https://github.com/lsst/meas_extensions_psfex/blob/main/config/default-lsst.psfex#L14 

283 self.psf_source_measurement.plugins["base_CircularApertureFlux"].radii = [9.0, 12.0] 

284 

285 # No extendeness information available: we need the aperture 

286 # corrections to determine that. 

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

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

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

290 

291 # TODO investigation: how faint do we have to detect, to be able to 

292 # deblend, etc? We may need star_selector to have a separate value, 

293 # and do initial detection at S/N>5.0? 

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

295 # downstream tasks. 

296 self.star_detection.thresholdValue = 5.0 

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

298 "base_SdssCentroid", 

299 "ext_shapeHSM_HsmSourceMoments", 

300 'ext_shapeHSM_HsmPsfMoments', 

301 "base_GaussianFlux", 

302 "base_PsfFlux", 

303 "base_CircularApertureFlux", 

304 ] 

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

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

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

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

309 

310 # Keep track of which footprints contain streaks 

311 self.star_measurement.plugins['base_PixelFlags'].masksFpAnywhere = ['STREAK'] 

312 self.star_measurement.plugins['base_PixelFlags'].masksFpCenter = ['STREAK'] 

313 

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

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

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

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

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

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

320 

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

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

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

324 self.astrometry_ref_loader.anyFilterMapsToThis = "phot_g_mean" 

325 

326 # Do not subselect during fitting; we already selected good stars. 

327 self.astrometry.sourceSelector = "null" 

328 self.photometry.match.sourceSelection.retarget(sourceSelector.NullSourceSelectorTask) 

329 

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

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

332 self.compute_summary_stats.starSelection = "calib_photometry_used" 

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

334 

335 

336class CalibrateImageTask(pipeBase.PipelineTask): 

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

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

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

340 

341 Parameters 

342 ---------- 

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

344 Schema of the initial_stars output catalog. 

345 """ 

346 _DefaultName = "calibrateImage" 

347 ConfigClass = CalibrateImageConfig 

348 

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

350 super().__init__(**kwargs) 

351 

352 self.makeSubtask("snap_combine") 

353 

354 # PSF determination subtasks 

355 self.makeSubtask("install_simple_psf") 

356 self.makeSubtask("psf_repair") 

357 self.makeSubtask("psf_subtract_background") 

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

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

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

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

362 

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

364 

365 # star measurement subtasks 

366 if initial_stars_schema is None: 

367 initial_stars_schema = afwTable.SourceTable.makeMinimalSchema() 

368 

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

370 # aperture correction calculations. 

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

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

373 "apcorr_slot_CalibFlux_used", "apcorr_base_GaussianFlux_used", 

374 "apcorr_base_PsfFlux_used") 

375 for field in self.psf_fields: 

376 item = self.psf_schema.find(field) 

377 initial_stars_schema.addField(item.getField()) 

378 

379 afwTable.CoordKey.addErrorFields(initial_stars_schema) 

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

381 self.makeSubtask("star_mask_streaks") 

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

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

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

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

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

387 self.makeSubtask("star_selector") 

388 

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

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

391 

392 self.makeSubtask("compute_summary_stats") 

393 

394 # For the butler to persist it. 

395 self.initial_stars_schema = afwTable.SourceCatalog(initial_stars_schema) 

396 

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

398 inputs = butlerQC.get(inputRefs) 

399 

400 astrometry_loader = lsst.meas.algorithms.ReferenceObjectLoader( 

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

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

403 name=self.config.connections.astrometry_ref_cat, 

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

405 self.astrometry.setRefObjLoader(astrometry_loader) 

406 

407 photometry_loader = lsst.meas.algorithms.ReferenceObjectLoader( 

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

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

410 name=self.config.connections.photometry_ref_cat, 

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

412 self.photometry.match.setRefObjLoader(photometry_loader) 

413 

414 outputs = self.run(**inputs) 

415 

416 butlerQC.put(outputs, outputRefs) 

417 

418 @timeMethod 

419 def run(self, *, exposures): 

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

421 and measurement and calibrate astrometry and photometry from that. 

422 

423 Parameters 

424 ---------- 

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

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

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

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

429 before doing further processing. 

430 

431 Returns 

432 ------- 

433 result : `lsst.pipe.base.Struct` 

434 Results as a struct with attributes: 

435 

436 ``output_exposure`` 

437 Calibrated exposure, with pixels in nJy units. 

438 (`lsst.afw.image.Exposure`) 

439 ``stars`` 

440 Stars that were used to calibrate the exposure, with 

441 calibrated fluxes and magnitudes. 

442 (`lsst.afw.table.SourceCatalog`) 

443 ``psf_stars`` 

444 Stars that were used to determine the image PSF. 

445 (`lsst.afw.table.SourceCatalog`) 

446 ``background`` 

447 Background that was fit to the exposure when detecting 

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

449 ``applied_photo_calib`` 

450 Photometric calibration that was fit to the star catalog and 

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

452 ``astrometry_matches`` 

453 Reference catalog stars matches used in the astrometric fit. 

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

455 ``photometry_matches`` 

456 Reference catalog stars matches used in the photometric fit. 

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

458 """ 

459 exposure = self._handle_snaps(exposures) 

460 

461 psf_stars, background, candidates = self._compute_psf(exposure) 

462 

463 self._measure_aperture_correction(exposure, psf_stars) 

464 

465 stars = self._find_stars(exposure, background) 

466 

467 astrometry_matches, astrometry_meta = self._fit_astrometry(exposure, stars) 

468 stars, photometry_matches, photometry_meta, photo_calib = self._fit_photometry(exposure, stars) 

469 

470 self._summarize(exposure, stars, background) 

471 

472 if self.config.optional_outputs: 

473 astrometry_matches = lsst.meas.astrom.denormalizeMatches(astrometry_matches, astrometry_meta) 

474 photometry_matches = lsst.meas.astrom.denormalizeMatches(photometry_matches, photometry_meta) 

475 

476 return pipeBase.Struct(output_exposure=exposure, 

477 stars=stars, 

478 psf_stars=psf_stars, 

479 background=background, 

480 applied_photo_calib=photo_calib, 

481 astrometry_matches=astrometry_matches, 

482 photometry_matches=photometry_matches) 

483 

484 def _handle_snaps(self, exposure): 

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

486 

487 Parameters 

488 ---------- 

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

490 One or two exposures to combine as snaps. 

491 

492 Returns 

493 ------- 

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

495 A single exposure to continue processing. 

496 

497 Raises 

498 ------ 

499 RuntimeError 

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

501 """ 

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

503 return exposure 

504 

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

506 match len(exposure): 

507 case 1: 

508 return exposure[0] 

509 case 2: 

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

511 case n: 

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

513 

514 def _compute_psf(self, exposure, guess_psf=True): 

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

516 them, repairing likely cosmic rays before detection. 

517 

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

519 model does not include contributions from cosmic rays. 

520 

521 Parameters 

522 ---------- 

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

524 Exposure to detect and measure bright stars on. 

525 

526 Returns 

527 ------- 

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

529 Catalog of detected bright sources. 

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

531 Background that was fit to the exposure during detection. 

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

533 PSF candidates returned by the psf determiner. 

534 """ 

535 def log_psf(msg): 

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

537 message. 

538 """ 

539 position = exposure.psf.getAveragePosition() 

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

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

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

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

544 msg, sigma, dimensions, median_background) 

545 

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

547 self.config.install_simple_psf.fwhm) 

548 self.install_simple_psf.run(exposure=exposure) 

549 

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

551 log_psf("Initial PSF:") 

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

553 

554 table = afwTable.SourceTable.make(self.psf_schema) 

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

556 # measurement uses the most accurate background-subtraction. 

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

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

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

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

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

562 self.install_simple_psf.run(exposure=exposure) 

563 

564 log_psf("Rerunning with simple PSF:") 

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

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

567 # use the fitted PSF? 

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

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

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

571 # once DM-39203 is merged? 

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

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

574 # measurement uses the most accurate background-subtraction. 

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

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

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

578 

579 log_psf("Final PSF:") 

580 

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

582 self.psf_repair.run(exposure=exposure) 

583 # Final measurement with the CRs removed. 

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

585 

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

587 return detections.sources, background, psf_result.cellSet 

588 

589 def _measure_aperture_correction(self, exposure, bright_sources): 

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

591 previously-measured bright sources. 

592 

593 Parameters 

594 ---------- 

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

596 Exposure to set the ApCorrMap on. 

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

598 Catalog of detected bright sources; modified to include columns 

599 necessary for point source determination for the aperture correction 

600 calculation. 

601 """ 

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

603 exposure.setApCorrMap(result.apCorrMap) 

604 

605 def _find_stars(self, exposure, background): 

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

607 PSF, circular aperture, compensated gaussian fluxes. 

608 

609 Parameters 

610 ---------- 

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

612 Exposure to set the ApCorrMap on. 

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

614 Background that was fit to the exposure during detection; 

615 modified in-place during subsequent detection. 

616 

617 Returns 

618 ------- 

619 stars : `SourceCatalog` 

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

621 measurements performed on them. 

622 """ 

623 table = afwTable.SourceTable.make(self.initial_stars_schema.schema) 

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

625 # measurement uses the most accurate background-subtraction. 

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

627 sources = detections.sources 

628 

629 # Mask streaks 

630 self.star_mask_streaks.run(exposure) 

631 

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

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

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

635 # contiguity for subsequent tasks. 

636 if not sources.isContiguous(): 

637 sources = sources.copy(deep=True) 

638 

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

640 self.star_measurement.run(sources, exposure) 

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

642 self.star_catalog_calculation.run(sources) 

643 self.star_set_primary_flags.run(sources) 

644 

645 result = self.star_selector.run(sources) 

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

647 if not result.sourceCat.isContiguous(): 

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

649 else: 

650 return result.sourceCat 

651 

652 def _match_psf_stars(self, psf_stars, stars): 

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

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

655 

656 Parameters 

657 ---------- 

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

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

660 populate psf-related flag fields. 

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

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

663 be updated in-place. 

664 

665 Notes 

666 ----- 

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

668 """ 

669 control = afwTable.MatchControl() 

670 # Return all matched objects, to separate blends. 

671 control.findOnlyClosest = False 

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

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

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

675 

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

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

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

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

680 best = {} 

681 for match0, match1, d in matches: 

682 id0 = match0.getId() 

683 match = best.get(id0) 

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

685 best[id0] = (match0, match1, d) 

686 matches = list(best.values()) 

687 ids = np.array([(match0.getId(), match1.getId()) for match0, match1, d in matches]).T 

688 

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

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

691 # in best above. 

692 n_matches = len(matches) 

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

694 if n_unique != n_matches: 

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

696 n_matches, n_unique) 

697 

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

699 idx0 = np.searchsorted(psf_stars["id"], ids[0]) 

700 idx1 = np.searchsorted(stars["id"], ids[1]) 

701 for field in self.psf_fields: 

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

703 result[idx0] = psf_stars[field][idx1] 

704 stars[field] = result 

705 

706 def _fit_astrometry(self, exposure, stars): 

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

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

709 

710 Parameters 

711 ---------- 

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

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

714 Modified to add the fitted skyWcs. 

715 stars : `SourceCatalog` 

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

717 computed from the pixel positions and fitted WCS. 

718 

719 Returns 

720 ------- 

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

722 Reference/stars matches used in the fit. 

723 """ 

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

725 return result.matches, result.matchMeta 

726 

727 def _fit_photometry(self, exposure, stars): 

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

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

730 

731 Parameters 

732 ---------- 

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

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

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

736 identically 1. 

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

738 Good stars selected for use in calibration. 

739 

740 Returns 

741 ------- 

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

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

744 photoCalib. 

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

746 Reference/stars matches used in the fit. 

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

748 Photometric calibration that was fit to the star catalog. 

749 """ 

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

751 calibrated_stars = result.photoCalib.calibrateCatalog(stars) 

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

753 identity = afwImage.PhotoCalib(1.0, 

754 result.photoCalib.getCalibrationErr(), 

755 bbox=exposure.getBBox()) 

756 exposure.setPhotoCalib(identity) 

757 

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

759 

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

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

762 calibrations attached to it. 

763 

764 Parameters 

765 ---------- 

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

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

768 Modified to contain the computed summary statistics. 

769 stars : `SourceCatalog` 

770 Good stars selected used in calibration. 

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

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

773 above stars. 

774 """ 

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

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

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

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

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

780 exposure.info.setSummaryStats(summary)