Coverage for python/lsst/pipe/tasks/calibrateImage.py: 32%

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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 numpy as np 

23 

24import lsst.afw.table as afwTable 

25import lsst.afw.image as afwImage 

26import lsst.meas.algorithms 

27import lsst.meas.algorithms.installGaussianPsf 

28import lsst.meas.algorithms.measureApCorr 

29from lsst.meas.algorithms import sourceSelector 

30import lsst.meas.astrom 

31import lsst.meas.deblender 

32import lsst.meas.extensions.shapeHSM 

33import lsst.pex.config as pexConfig 

34import lsst.pipe.base as pipeBase 

35from lsst.pipe.base import connectionTypes 

36from lsst.utils.timer import timeMethod 

37 

38from . import measurePsf, repair, setPrimaryFlags, photoCal, computeExposureSummaryStats, maskStreaks 

39 

40 

41class CalibrateImageConnections(pipeBase.PipelineTaskConnections, 

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

43 

44 astrometry_ref_cat = connectionTypes.PrerequisiteInput( 

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

46 name="gaia_dr3_20230707", 

47 storageClass="SimpleCatalog", 

48 dimensions=("skypix",), 

49 deferLoad=True, 

50 multiple=True, 

51 ) 

52 photometry_ref_cat = connectionTypes.PrerequisiteInput( 

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

54 name="ps1_pv3_3pi_20170110", 

55 storageClass="SimpleCatalog", 

56 dimensions=("skypix",), 

57 deferLoad=True, 

58 multiple=True 

59 ) 

60 

61 exposure = connectionTypes.Input( 

62 doc="Exposure to be calibrated, and detected and measured on.", 

63 name="postISRCCD", 

64 storageClass="Exposure", 

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

66 ) 

67 

68 # outputs 

69 initial_stars_schema = connectionTypes.InitOutput( 

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

71 name="initial_stars_schema", 

72 storageClass="SourceCatalog", 

73 ) 

74 

75 # TODO: We want some kind of flag on Exposures/Catalogs to make it obvious 

76 # which components had failed to be computed/persisted 

77 output_exposure = connectionTypes.Output( 

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

79 name="initial_pvi", 

80 storageClass="ExposureF", 

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

82 ) 

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

84 stars = connectionTypes.Output( 

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

86 "includes source footprints.", 

87 name="initial_stars_footprints_detector", 

88 storageClass="SourceCatalog", 

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

90 ) 

91 applied_photo_calib = connectionTypes.Output( 

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

93 name="initial_photoCalib_detector", 

94 storageClass="PhotoCalib", 

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

96 ) 

97 background = connectionTypes.Output( 

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

99 name="initial_pvi_background", 

100 storageClass="Background", 

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

102 ) 

103 

104 # Optional outputs 

105 

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

107 # and which to save by default. 

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

109 psf_stars = connectionTypes.Output( 

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

111 "includes source footprints.", 

112 name="initial_psf_stars_footprints", 

113 storageClass="SourceCatalog", 

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

115 ) 

116 astrometry_matches = connectionTypes.Output( 

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

118 name="initial_astrometry_match_detector", 

119 storageClass="Catalog", 

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

121 ) 

122 photometry_matches = connectionTypes.Output( 

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

124 name="initial_photometry_match_detector", 

125 storageClass="Catalog", 

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

127 ) 

128 

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

130 super().__init__(config=config) 

131 if not config.optional_outputs: 

132 self.outputs.remove("psf_stars") 

133 self.outputs.remove("astrometry_matches") 

134 self.outputs.remove("photometry_matches") 

135 

136 

137class CalibrateImageConfig(pipeBase.PipelineTaskConfig, pipelineConnections=CalibrateImageConnections): 

138 optional_outputs = pexConfig.ListField( 

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

140 dtype=str, 

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

142 # we always have it on for production runs? 

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

144 optional=True 

145 ) 

146 

147 # subtasks used during psf characterization 

148 install_simple_psf = pexConfig.ConfigurableField( 

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

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

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

152 ) 

153 psf_repair = pexConfig.ConfigurableField( 

154 target=repair.RepairTask, 

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

156 ) 

157 psf_subtract_background = pexConfig.ConfigurableField( 

158 target=lsst.meas.algorithms.SubtractBackgroundTask, 

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

160 ) 

161 psf_detection = pexConfig.ConfigurableField( 

162 target=lsst.meas.algorithms.SourceDetectionTask, 

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

164 ) 

165 psf_source_measurement = pexConfig.ConfigurableField( 

166 target=lsst.meas.base.SingleFrameMeasurementTask, 

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

168 ) 

169 psf_measure_psf = pexConfig.ConfigurableField( 

170 target=measurePsf.MeasurePsfTask, 

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

172 ) 

173 

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

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

176 measure_aperture_correction = pexConfig.ConfigurableField( 

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

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

179 ) 

180 

181 # subtasks used during star measurement 

182 star_detection = pexConfig.ConfigurableField( 

183 target=lsst.meas.algorithms.SourceDetectionTask, 

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

185 ) 

186 star_mask_streaks = pexConfig.ConfigurableField( 

187 target=maskStreaks.MaskStreaksTask, 

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

189 ) 

190 star_deblend = pexConfig.ConfigurableField( 

191 target=lsst.meas.deblender.SourceDeblendTask, 

192 doc="Split blended sources into their components" 

193 ) 

194 star_measurement = pexConfig.ConfigurableField( 

195 target=lsst.meas.base.SingleFrameMeasurementTask, 

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

197 ) 

198 star_apply_aperture_correction = pexConfig.ConfigurableField( 

199 target=lsst.meas.base.ApplyApCorrTask, 

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

201 ) 

202 star_catalog_calculation = pexConfig.ConfigurableField( 

203 target=lsst.meas.base.CatalogCalculationTask, 

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

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

206 ) 

207 star_set_primary_flags = pexConfig.ConfigurableField( 

208 target=setPrimaryFlags.SetPrimaryFlagsTask, 

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

210 ) 

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

212 default="science", 

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

214 ) 

215 

216 # final calibrations and statistics 

217 astrometry = pexConfig.ConfigurableField( 

218 target=lsst.meas.astrom.AstrometryTask, 

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

220 ) 

221 astrometry_ref_loader = pexConfig.ConfigField( 

222 dtype=lsst.meas.algorithms.LoadReferenceObjectsConfig, 

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

224 ) 

225 photometry = pexConfig.ConfigurableField( 

226 target=photoCal.PhotoCalTask, 

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

228 ) 

229 photometry_ref_loader = pexConfig.ConfigField( 

230 dtype=lsst.meas.algorithms.LoadReferenceObjectsConfig, 

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

232 ) 

233 

234 compute_summary_stats = pexConfig.ConfigurableField( 

235 target=computeExposureSummaryStats.ComputeExposureSummaryStatsTask, 

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

237 ) 

238 

239 def setDefaults(self): 

240 super().setDefaults() 

241 

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

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

244 # like CRs for very good seeing images. 

245 self.install_simple_psf.fwhm = 4 

246 

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

248 self.psf_detection.thresholdValue = 50.0 

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

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

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

252 self.psf_detection.doTempLocalBackground = False 

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

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

255 

256 # Minimal measurement plugins for PSF determination. 

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

258 # shapeHSM/moments for star/galaxy separation. 

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

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

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

262 "base_SdssCentroid", 

263 "ext_shapeHSM_HsmSourceMoments", 

264 "base_CircularApertureFlux", 

265 "base_GaussianFlux", 

266 "base_PsfFlux", 

267 ] 

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

269 # Only measure apertures we need for PSF measurement. 

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

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

272 # with our other uses? 

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

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

275 

276 # No extendeness information available: we need the aperture 

277 # corrections to determine that. 

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

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

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

281 

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

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

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

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

286 # downstream tasks. 

287 self.star_detection.thresholdValue = 5.0 

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

289 "base_SdssCentroid", 

290 "ext_shapeHSM_HsmSourceMoments", 

291 'ext_shapeHSM_HsmPsfMoments', 

292 "base_GaussianFlux", 

293 "base_PsfFlux", 

294 "base_CircularApertureFlux", 

295 ] 

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

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

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

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

300 

301 # Keep track of which footprints contain streaks 

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

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

304 

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

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

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

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

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

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

311 

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

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

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

315 self.astrometry_ref_loader.anyFilterMapsToThis = "phot_g_mean" 

316 

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

318 self.astrometry.sourceSelector = "null" 

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

320 

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

322 self.compute_summary_stats.starSelection = "calib_photometry_used" 

323 

324 

325class CalibrateImageTask(pipeBase.PipelineTask): 

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

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

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

329 

330 Parameters 

331 ---------- 

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

333 Schema of the initial_stars output catalog. 

334 """ 

335 _DefaultName = "calibrateImage" 

336 ConfigClass = CalibrateImageConfig 

337 

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

339 super().__init__(**kwargs) 

340 

341 # PSF determination subtasks 

342 self.makeSubtask("install_simple_psf") 

343 self.makeSubtask("psf_repair") 

344 self.makeSubtask("psf_subtract_background") 

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

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

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

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

349 

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

351 

352 # star measurement subtasks 

353 if initial_stars_schema is None: 

354 initial_stars_schema = afwTable.SourceTable.makeMinimalSchema() 

355 afwTable.CoordKey.addErrorFields(initial_stars_schema) 

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

357 self.makeSubtask("star_mask_streaks") 

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

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

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

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

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

363 self.makeSubtask("star_selector") 

364 

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

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

367 

368 self.makeSubtask("compute_summary_stats") 

369 

370 # For the butler to persist it. 

371 self.initial_stars_schema = afwTable.SourceCatalog(initial_stars_schema) 

372 

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

374 inputs = butlerQC.get(inputRefs) 

375 

376 astrometry_loader = lsst.meas.algorithms.ReferenceObjectLoader( 

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

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

379 name=self.config.connections.astrometry_ref_cat, 

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

381 self.astrometry.setRefObjLoader(astrometry_loader) 

382 

383 photometry_loader = lsst.meas.algorithms.ReferenceObjectLoader( 

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

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

386 name=self.config.connections.photometry_ref_cat, 

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

388 self.photometry.match.setRefObjLoader(photometry_loader) 

389 

390 outputs = self.run(**inputs) 

391 

392 butlerQC.put(outputs, outputRefs) 

393 

394 @timeMethod 

395 def run(self, *, exposure): 

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

397 and measurement and calibrate astrometry and photometry from that. 

398 

399 Parameters 

400 ---------- 

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

402 Post-ISR exposure, with an initial WCS, VisitInfo, and Filter. 

403 Modified in-place during processing. 

404 

405 Returns 

406 ------- 

407 result : `lsst.pipe.base.Struct` 

408 Results as a struct with attributes: 

409 

410 ``output_exposure`` 

411 Calibrated exposure, with pixels in nJy units. 

412 (`lsst.afw.image.Exposure`) 

413 ``stars`` 

414 Stars that were used to calibrate the exposure, with 

415 calibrated fluxes and magnitudes. 

416 (`lsst.afw.table.SourceCatalog`) 

417 ``psf_stars`` 

418 Stars that were used to determine the image PSF. 

419 (`lsst.afw.table.SourceCatalog`) 

420 ``background`` 

421 Background that was fit to the exposure when detecting 

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

423 ``applied_photo_calib`` 

424 Photometric calibration that was fit to the star catalog and 

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

426 ``astrometry_matches`` 

427 Reference catalog stars matches used in the astrometric fit. 

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

429 ``photometry_matches`` 

430 Reference catalog stars matches used in the photometric fit. 

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

432 """ 

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

434 

435 self._measure_aperture_correction(exposure, psf_stars) 

436 

437 stars = self._find_stars(exposure, background) 

438 

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

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

441 

442 self._summarize(exposure, stars, background) 

443 

444 if self.config.optional_outputs: 

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

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

447 

448 return pipeBase.Struct(output_exposure=exposure, 

449 stars=stars, 

450 psf_stars=psf_stars, 

451 background=background, 

452 applied_photo_calib=photo_calib, 

453 astrometry_matches=astrometry_matches, 

454 photometry_matches=photometry_matches) 

455 

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

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

458 them, repairing likely cosmic rays before detection. 

459 

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

461 model does not include contributions from cosmic rays. 

462 

463 Parameters 

464 ---------- 

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

466 Exposure to detect and measure bright stars on. 

467 

468 Returns 

469 ------- 

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

471 Catalog of detected bright sources. 

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

473 Background that was fit to the exposure during detection. 

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

475 PSF candidates returned by the psf determiner. 

476 """ 

477 def log_psf(msg): 

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

479 message. 

480 """ 

481 position = exposure.psf.getAveragePosition() 

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

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

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

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

486 msg, sigma, dimensions, median_background) 

487 

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

489 self.config.install_simple_psf.fwhm) 

490 self.install_simple_psf.run(exposure=exposure) 

491 

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

493 log_psf("Initial PSF:") 

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

495 

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

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

498 # measurement uses the most accurate background-subtraction. 

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

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

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

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

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

504 self.install_simple_psf.run(exposure=exposure) 

505 

506 log_psf("Rerunning with simple PSF:") 

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

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

509 # use the fitted PSF? 

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

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

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

513 # once DM-39203 is merged? 

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

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

516 # measurement uses the most accurate background-subtraction. 

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

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

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

520 

521 log_psf("Final PSF:") 

522 

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

524 self.psf_repair.run(exposure=exposure) 

525 # Final measurement with the CRs removed. 

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

527 

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

529 return detections.sources, background, psf_result.cellSet 

530 

531 def _measure_aperture_correction(self, exposure, bright_sources): 

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

533 previously-measured bright sources. 

534 

535 Parameters 

536 ---------- 

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

538 Exposure to set the ApCorrMap on. 

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

540 Catalog of detected bright sources; modified to include columns 

541 necessary for point source determination for the aperture correction 

542 calculation. 

543 """ 

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

545 exposure.setApCorrMap(result.apCorrMap) 

546 

547 def _find_stars(self, exposure, background): 

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

549 PSF, circular aperture, compensated gaussian fluxes. 

550 

551 Parameters 

552 ---------- 

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

554 Exposure to set the ApCorrMap on. 

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

556 Background that was fit to the exposure during detection; 

557 modified in-place during subsequent detection. 

558 

559 Returns 

560 ------- 

561 stars : `SourceCatalog` 

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

563 measurements performed on them. 

564 """ 

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

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

567 # measurement uses the most accurate background-subtraction. 

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

569 sources = detections.sources 

570 

571 # Mask streaks 

572 self.star_mask_streaks.run(exposure) 

573 

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

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

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

577 # contiguity for subsequent tasks. 

578 if not sources.isContiguous(): 

579 sources = sources.copy(deep=True) 

580 

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

582 self.star_measurement.run(sources, exposure) 

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

584 self.star_catalog_calculation.run(sources) 

585 self.star_set_primary_flags.run(sources) 

586 

587 result = self.star_selector.run(sources) 

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

589 if not result.sourceCat.isContiguous(): 

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

591 else: 

592 return result.sourceCat 

593 

594 def _fit_astrometry(self, exposure, stars): 

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

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

597 

598 Parameters 

599 ---------- 

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

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

602 Modified to add the fitted skyWcs. 

603 stars : `SourceCatalog` 

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

605 computed from the pixel positions and fitted WCS. 

606 

607 Returns 

608 ------- 

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

610 Reference/stars matches used in the fit. 

611 """ 

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

613 return result.matches, result.matchMeta 

614 

615 def _fit_photometry(self, exposure, stars): 

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

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

618 

619 Parameters 

620 ---------- 

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

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

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

624 identically 1. 

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

626 Good stars selected for use in calibration. 

627 

628 Returns 

629 ------- 

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

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

632 photoCalib. 

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

634 Reference/stars matches used in the fit. 

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

636 Photometric calibration that was fit to the star catalog. 

637 """ 

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

639 calibrated_stars = result.photoCalib.calibrateCatalog(stars) 

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

641 identity = afwImage.PhotoCalib(1.0, 

642 result.photoCalib.getCalibrationErr(), 

643 bbox=exposure.getBBox()) 

644 exposure.setPhotoCalib(identity) 

645 

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

647 

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

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

650 calibrations attached to it. 

651 

652 Parameters 

653 ---------- 

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

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

656 Modified to contain the computed summary statistics. 

657 stars : `SourceCatalog` 

658 Good stars selected used in calibration. 

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

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

661 above stars. 

662 """ 

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

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

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

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

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

668 exposure.info.setSummaryStats(summary)