Coverage for python/lsst/pipe/tasks/finalizeCharacterization.py: 14%

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1# This file is part of pipe_tasks. 

2# 

3# Developed for the LSST Data Management System. 

4# This product includes software developed by the LSST Project 

5# (https://www.lsst.org). 

6# See the COPYRIGHT file at the top-level directory of this distribution 

7# for details of code ownership. 

8# 

9# This program is free software: you can redistribute it and/or modify 

10# it under the terms of the GNU General Public License as published by 

11# the Free Software Foundation, either version 3 of the License, or 

12# (at your option) any later version. 

13# 

14# This program is distributed in the hope that it will be useful, 

15# but WITHOUT ANY WARRANTY; without even the implied warranty of 

16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

17# GNU General Public License for more details. 

18# 

19# You should have received a copy of the GNU General Public License 

20# along with this program. If not, see <https://www.gnu.org/licenses/>. 

21 

22"""Task to run a finalized image characterization, using additional data. 

23""" 

24 

25__all__ = ['FinalizeCharacterizationConnections', 

26 'FinalizeCharacterizationConfig', 

27 'FinalizeCharacterizationTask'] 

28 

29import numpy as np 

30import esutil 

31import pandas as pd 

32 

33import lsst.pex.config as pexConfig 

34import lsst.pipe.base as pipeBase 

35import lsst.daf.base as dafBase 

36import lsst.afw.table as afwTable 

37import lsst.meas.algorithms as measAlg 

38import lsst.meas.extensions.piff.piffPsfDeterminer # noqa: F401 

39from lsst.meas.algorithms import MeasureApCorrTask 

40from lsst.meas.base import SingleFrameMeasurementTask, ApplyApCorrTask 

41from lsst.meas.algorithms.sourceSelector import sourceSelectorRegistry 

42 

43from .reserveIsolatedStars import ReserveIsolatedStarsTask 

44 

45 

46class FinalizeCharacterizationConnections(pipeBase.PipelineTaskConnections, 

47 dimensions=('instrument', 'visit',), 

48 defaultTemplates={}): 

49 src_schema = pipeBase.connectionTypes.InitInput( 

50 doc='Input schema used for src catalogs.', 

51 name='src_schema', 

52 storageClass='SourceCatalog', 

53 ) 

54 srcs = pipeBase.connectionTypes.Input( 

55 doc='Source catalogs for the visit', 

56 name='src', 

57 storageClass='SourceCatalog', 

58 dimensions=('instrument', 'visit', 'detector'), 

59 deferLoad=True, 

60 multiple=True, 

61 ) 

62 calexps = pipeBase.connectionTypes.Input( 

63 doc='Calexps for the visit', 

64 name='calexp', 

65 storageClass='ExposureF', 

66 dimensions=('instrument', 'visit', 'detector'), 

67 deferLoad=True, 

68 multiple=True, 

69 ) 

70 isolated_star_cats = pipeBase.connectionTypes.Input( 

71 doc=('Catalog of isolated stars with average positions, number of associated ' 

72 'sources, and indexes to the isolated_star_sources catalogs.'), 

73 name='isolated_star_cat', 

74 storageClass='DataFrame', 

75 dimensions=('instrument', 'tract', 'skymap'), 

76 deferLoad=True, 

77 multiple=True, 

78 ) 

79 isolated_star_sources = pipeBase.connectionTypes.Input( 

80 doc=('Catalog of isolated star sources with sourceIds, and indexes to the ' 

81 'isolated_star_cats catalogs.'), 

82 name='isolated_star_sources', 

83 storageClass='DataFrame', 

84 dimensions=('instrument', 'tract', 'skymap'), 

85 deferLoad=True, 

86 multiple=True, 

87 ) 

88 finalized_psf_ap_corr_cat = pipeBase.connectionTypes.Output( 

89 doc=('Per-visit finalized psf models and aperture corrections. This ' 

90 'catalog uses detector id for the id and are sorted for fast ' 

91 'lookups of a detector.'), 

92 name='finalized_psf_ap_corr_catalog', 

93 storageClass='ExposureCatalog', 

94 dimensions=('instrument', 'visit'), 

95 ) 

96 finalized_src_table = pipeBase.connectionTypes.Output( 

97 doc=('Per-visit catalog of measurements for psf/flag/etc.'), 

98 name='finalized_src_table', 

99 storageClass='DataFrame', 

100 dimensions=('instrument', 'visit'), 

101 ) 

102 

103 

104class FinalizeCharacterizationConfig(pipeBase.PipelineTaskConfig, 

105 pipelineConnections=FinalizeCharacterizationConnections): 

106 """Configuration for FinalizeCharacterizationTask.""" 

107 source_selector = sourceSelectorRegistry.makeField( 

108 doc="How to select sources", 

109 default="science" 

110 ) 

111 id_column = pexConfig.Field( 

112 doc='Name of column in isolated_star_sources with source id.', 

113 dtype=str, 

114 default='sourceId', 

115 ) 

116 reserve_selection = pexConfig.ConfigurableField( 

117 target=ReserveIsolatedStarsTask, 

118 doc='Task to select reserved stars', 

119 ) 

120 make_psf_candidates = pexConfig.ConfigurableField( 

121 target=measAlg.MakePsfCandidatesTask, 

122 doc='Task to make psf candidates from selected stars.', 

123 ) 

124 psf_determiner = measAlg.psfDeterminerRegistry.makeField( 

125 'PSF Determination algorithm', 

126 default='piff' 

127 ) 

128 measurement = pexConfig.ConfigurableField( 

129 target=SingleFrameMeasurementTask, 

130 doc='Measure sources for aperture corrections' 

131 ) 

132 measure_ap_corr = pexConfig.ConfigurableField( 

133 target=MeasureApCorrTask, 

134 doc="Subtask to measure aperture corrections" 

135 ) 

136 apply_ap_corr = pexConfig.ConfigurableField( 

137 target=ApplyApCorrTask, 

138 doc="Subtask to apply aperture corrections" 

139 ) 

140 

141 def setDefaults(self): 

142 super().setDefaults() 

143 

144 source_selector = self.source_selector['science'] 

145 source_selector.setDefaults() 

146 

147 # We use the source selector only to select out flagged objects 

148 # and signal-to-noise. Isolated, unresolved sources are handled 

149 # by the isolated star catalog. 

150 

151 source_selector.doFlags = True 

152 source_selector.doSignalToNoise = True 

153 source_selector.doFluxLimit = False 

154 source_selector.doUnresolved = False 

155 source_selector.doIsolated = False 

156 

157 source_selector.signalToNoise.minimum = 20.0 

158 source_selector.signalToNoise.maximum = 1000.0 

159 

160 source_selector.signalToNoise.fluxField = 'base_GaussianFlux_instFlux' 

161 source_selector.signalToNoise.errField = 'base_GaussianFlux_instFluxErr' 

162 

163 source_selector.flags.bad = ['base_PixelFlags_flag_edge', 

164 'base_PixelFlags_flag_interpolatedCenter', 

165 'base_PixelFlags_flag_saturatedCenter', 

166 'base_PixelFlags_flag_crCenter', 

167 'base_PixelFlags_flag_bad', 

168 'base_PixelFlags_flag_interpolated', 

169 'base_PixelFlags_flag_saturated', 

170 'slot_Centroid_flag', 

171 'base_GaussianFlux_flag'] 

172 

173 # Configure aperture correction to select only high s/n sources (that 

174 # were used in the psf modeling) to avoid background problems when 

175 # computing the aperture correction map. 

176 self.measure_ap_corr.sourceSelector = 'science' 

177 

178 ap_selector = self.measure_ap_corr.sourceSelector['science'] 

179 # We do not need to filter flags or unresolved because we have used 

180 # the filtered isolated stars as an input 

181 ap_selector.doFlags = False 

182 ap_selector.doUnresolved = False 

183 

184 import lsst.meas.modelfit # noqa: F401 

185 import lsst.meas.extensions.photometryKron # noqa: F401 

186 import lsst.meas.extensions.convolved # noqa: F401 

187 import lsst.meas.extensions.gaap # noqa: F401 

188 import lsst.meas.extensions.shapeHSM # noqa: F401 

189 

190 # Set up measurement defaults 

191 self.measurement.plugins.names = [ 

192 'base_PsfFlux', 

193 'base_GaussianFlux', 

194 'modelfit_DoubleShapeletPsfApprox', 

195 'modelfit_CModel', 

196 'ext_photometryKron_KronFlux', 

197 'ext_convolved_ConvolvedFlux', 

198 'ext_gaap_GaapFlux', 

199 'ext_shapeHSM_HsmShapeRegauss', 

200 'ext_shapeHSM_HsmSourceMoments', 

201 'ext_shapeHSM_HsmPsfMoments', 

202 'ext_shapeHSM_HsmSourceMomentsRound', 

203 ] 

204 self.measurement.slots.modelFlux = 'modelfit_CModel' 

205 self.measurement.plugins['ext_convolved_ConvolvedFlux'].seeing.append(8.0) 

206 self.measurement.plugins['ext_gaap_GaapFlux'].sigmas = [ 

207 0.5, 

208 0.7, 

209 1.0, 

210 1.5, 

211 2.5, 

212 3.0 

213 ] 

214 self.measurement.plugins['ext_gaap_GaapFlux'].doPsfPhotometry = True 

215 self.measurement.slots.shape = 'ext_shapeHSM_HsmSourceMoments' 

216 self.measurement.slots.psfShape = 'ext_shapeHSM_HsmPsfMoments' 

217 self.measurement.plugins['ext_shapeHSM_HsmShapeRegauss'].deblendNChild = "" 

218 

219 # Keep track of which footprints contain streaks 

220 self.measurement.plugins['base_PixelFlags'].masksFpAnywhere = ['STREAK'] 

221 self.measurement.plugins['base_PixelFlags'].masksFpCenter = ['STREAK'] 

222 

223 # Turn off slot setting for measurement for centroid and shape 

224 # (for which we use the input src catalog measurements) 

225 self.measurement.slots.centroid = None 

226 self.measurement.slots.apFlux = None 

227 self.measurement.slots.calibFlux = None 

228 

229 names = self.measurement.plugins['ext_convolved_ConvolvedFlux'].getAllResultNames() 

230 self.measure_ap_corr.allowFailure += names 

231 names = self.measurement.plugins["ext_gaap_GaapFlux"].getAllGaapResultNames() 

232 self.measure_ap_corr.allowFailure += names 

233 

234 

235class FinalizeCharacterizationTask(pipeBase.PipelineTask): 

236 """Run final characterization on exposures.""" 

237 ConfigClass = FinalizeCharacterizationConfig 

238 _DefaultName = 'finalize_characterization' 

239 

240 def __init__(self, initInputs=None, **kwargs): 

241 super().__init__(initInputs=initInputs, **kwargs) 

242 

243 self.schema_mapper, self.schema = self._make_output_schema_mapper( 

244 initInputs['src_schema'].schema 

245 ) 

246 

247 self.makeSubtask('reserve_selection') 

248 self.makeSubtask('source_selector') 

249 self.makeSubtask('make_psf_candidates') 

250 self.makeSubtask('psf_determiner') 

251 self.makeSubtask('measurement', schema=self.schema) 

252 self.makeSubtask('measure_ap_corr', schema=self.schema) 

253 self.makeSubtask('apply_ap_corr', schema=self.schema) 

254 

255 # Only log warning and fatal errors from the source_selector 

256 self.source_selector.log.setLevel(self.source_selector.log.WARN) 

257 

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

259 input_handle_dict = butlerQC.get(inputRefs) 

260 

261 band = butlerQC.quantum.dataId['band'] 

262 visit = butlerQC.quantum.dataId['visit'] 

263 

264 src_dict_temp = {handle.dataId['detector']: handle 

265 for handle in input_handle_dict['srcs']} 

266 calexp_dict_temp = {handle.dataId['detector']: handle 

267 for handle in input_handle_dict['calexps']} 

268 isolated_star_cat_dict_temp = {handle.dataId['tract']: handle 

269 for handle in input_handle_dict['isolated_star_cats']} 

270 isolated_star_source_dict_temp = {handle.dataId['tract']: handle 

271 for handle in input_handle_dict['isolated_star_sources']} 

272 # TODO: Sort until DM-31701 is done and we have deterministic 

273 # dataset ordering. 

274 src_dict = {detector: src_dict_temp[detector] for 

275 detector in sorted(src_dict_temp.keys())} 

276 calexp_dict = {detector: calexp_dict_temp[detector] for 

277 detector in sorted(calexp_dict_temp.keys())} 

278 isolated_star_cat_dict = {tract: isolated_star_cat_dict_temp[tract] for 

279 tract in sorted(isolated_star_cat_dict_temp.keys())} 

280 isolated_star_source_dict = {tract: isolated_star_source_dict_temp[tract] for 

281 tract in sorted(isolated_star_source_dict_temp.keys())} 

282 

283 struct = self.run(visit, 

284 band, 

285 isolated_star_cat_dict, 

286 isolated_star_source_dict, 

287 src_dict, 

288 calexp_dict) 

289 

290 butlerQC.put(struct.psf_ap_corr_cat, 

291 outputRefs.finalized_psf_ap_corr_cat) 

292 butlerQC.put(pd.DataFrame(struct.output_table), 

293 outputRefs.finalized_src_table) 

294 

295 def run(self, visit, band, isolated_star_cat_dict, isolated_star_source_dict, src_dict, calexp_dict): 

296 """ 

297 Run the FinalizeCharacterizationTask. 

298 

299 Parameters 

300 ---------- 

301 visit : `int` 

302 Visit number. Used in the output catalogs. 

303 band : `str` 

304 Band name. Used to select reserved stars. 

305 isolated_star_cat_dict : `dict` 

306 Per-tract dict of isolated star catalog handles. 

307 isolated_star_source_dict : `dict` 

308 Per-tract dict of isolated star source catalog handles. 

309 src_dict : `dict` 

310 Per-detector dict of src catalog handles. 

311 calexp_dict : `dict` 

312 Per-detector dict of calibrated exposure handles. 

313 

314 Returns 

315 ------- 

316 struct : `lsst.pipe.base.struct` 

317 Struct with outputs for persistence. 

318 

319 Raises 

320 ------ 

321 NoWorkFound 

322 Raised if the selector returns no good sources. 

323 """ 

324 # We do not need the isolated star table in this task. 

325 # However, it is used in tests to confirm consistency of indexes. 

326 _, isolated_source_table = self.concat_isolated_star_cats( 

327 band, 

328 isolated_star_cat_dict, 

329 isolated_star_source_dict 

330 ) 

331 

332 exposure_cat_schema = afwTable.ExposureTable.makeMinimalSchema() 

333 exposure_cat_schema.addField('visit', type='L', doc='Visit number') 

334 

335 metadata = dafBase.PropertyList() 

336 metadata.add("COMMENT", "Catalog id is detector id, sorted.") 

337 metadata.add("COMMENT", "Only detectors with data have entries.") 

338 

339 psf_ap_corr_cat = afwTable.ExposureCatalog(exposure_cat_schema) 

340 psf_ap_corr_cat.setMetadata(metadata) 

341 

342 measured_src_tables = [] 

343 measured_src_table = None 

344 

345 for detector in src_dict: 

346 src = src_dict[detector].get() 

347 exposure = calexp_dict[detector].get() 

348 

349 psf, ap_corr_map, measured_src = self.compute_psf_and_ap_corr_map( 

350 visit, 

351 detector, 

352 exposure, 

353 src, 

354 isolated_source_table 

355 ) 

356 

357 # And now we package it together... 

358 if measured_src is not None: 

359 record = psf_ap_corr_cat.addNew() 

360 record['id'] = int(detector) 

361 record['visit'] = visit 

362 if psf is not None: 

363 record.setPsf(psf) 

364 if ap_corr_map is not None: 

365 record.setApCorrMap(ap_corr_map) 

366 

367 measured_src['visit'][:] = visit 

368 measured_src['detector'][:] = detector 

369 

370 measured_src_tables.append(measured_src.asAstropy().as_array()) 

371 

372 if len(measured_src_tables) > 0: 

373 measured_src_table = np.concatenate(measured_src_tables) 

374 

375 if measured_src_table is None: 

376 raise pipeBase.NoWorkFound(f'No good sources found for any detectors in visit {visit}') 

377 

378 return pipeBase.Struct(psf_ap_corr_cat=psf_ap_corr_cat, 

379 output_table=measured_src_table) 

380 

381 def _make_output_schema_mapper(self, input_schema): 

382 """Make the schema mapper from the input schema to the output schema. 

383 

384 Parameters 

385 ---------- 

386 input_schema : `lsst.afw.table.Schema` 

387 Input schema. 

388 

389 Returns 

390 ------- 

391 mapper : `lsst.afw.table.SchemaMapper` 

392 Schema mapper 

393 output_schema : `lsst.afw.table.Schema` 

394 Output schema (with alias map) 

395 """ 

396 mapper = afwTable.SchemaMapper(input_schema) 

397 mapper.addMinimalSchema(afwTable.SourceTable.makeMinimalSchema()) 

398 mapper.addMapping(input_schema['slot_Centroid_x'].asKey()) 

399 mapper.addMapping(input_schema['slot_Centroid_y'].asKey()) 

400 

401 # The aperture fields may be used by the psf determiner. 

402 aper_fields = input_schema.extract('base_CircularApertureFlux_*') 

403 for field, item in aper_fields.items(): 

404 mapper.addMapping(item.key) 

405 

406 # The following two may be redundant, but then the mapping is a no-op. 

407 apflux_fields = input_schema.extract('slot_ApFlux_*') 

408 for field, item in apflux_fields.items(): 

409 mapper.addMapping(item.key) 

410 

411 calibflux_fields = input_schema.extract('slot_CalibFlux_*') 

412 for field, item in calibflux_fields.items(): 

413 mapper.addMapping(item.key) 

414 

415 mapper.addMapping( 

416 input_schema[self.config.source_selector.active.signalToNoise.fluxField].asKey(), 

417 'calib_psf_selection_flux') 

418 mapper.addMapping( 

419 input_schema[self.config.source_selector.active.signalToNoise.errField].asKey(), 

420 'calib_psf_selection_flux_err') 

421 

422 output_schema = mapper.getOutputSchema() 

423 

424 output_schema.addField( 

425 'calib_psf_candidate', 

426 type='Flag', 

427 doc=('set if the source was a candidate for PSF determination, ' 

428 'as determined from FinalizeCharacterizationTask.'), 

429 ) 

430 output_schema.addField( 

431 'calib_psf_reserved', 

432 type='Flag', 

433 doc=('set if source was reserved from PSF determination by ' 

434 'FinalizeCharacterizationTask.'), 

435 ) 

436 output_schema.addField( 

437 'calib_psf_used', 

438 type='Flag', 

439 doc=('set if source was used in the PSF determination by ' 

440 'FinalizeCharacterizationTask.'), 

441 ) 

442 output_schema.addField( 

443 'visit', 

444 type=np.int64, 

445 doc='Visit number for the sources.', 

446 ) 

447 output_schema.addField( 

448 'detector', 

449 type=np.int32, 

450 doc='Detector number for the sources.', 

451 ) 

452 

453 alias_map = input_schema.getAliasMap() 

454 alias_map_output = afwTable.AliasMap() 

455 alias_map_output.set('slot_Centroid', alias_map.get('slot_Centroid')) 

456 alias_map_output.set('slot_ApFlux', alias_map.get('slot_ApFlux')) 

457 alias_map_output.set('slot_CalibFlux', alias_map.get('slot_CalibFlux')) 

458 

459 output_schema.setAliasMap(alias_map_output) 

460 

461 return mapper, output_schema 

462 

463 def _make_selection_schema_mapper(self, input_schema): 

464 """Make the schema mapper from the input schema to the selection schema. 

465 

466 Parameters 

467 ---------- 

468 input_schema : `lsst.afw.table.Schema` 

469 Input schema. 

470 

471 Returns 

472 ------- 

473 mapper : `lsst.afw.table.SchemaMapper` 

474 Schema mapper 

475 selection_schema : `lsst.afw.table.Schema` 

476 Selection schema (with alias map) 

477 """ 

478 mapper = afwTable.SchemaMapper(input_schema) 

479 mapper.addMinimalSchema(input_schema) 

480 

481 selection_schema = mapper.getOutputSchema() 

482 

483 selection_schema.setAliasMap(input_schema.getAliasMap()) 

484 

485 return mapper, selection_schema 

486 

487 def concat_isolated_star_cats(self, band, isolated_star_cat_dict, isolated_star_source_dict): 

488 """ 

489 Concatenate isolated star catalogs and make reserve selection. 

490 

491 Parameters 

492 ---------- 

493 band : `str` 

494 Band name. Used to select reserved stars. 

495 isolated_star_cat_dict : `dict` 

496 Per-tract dict of isolated star catalog handles. 

497 isolated_star_source_dict : `dict` 

498 Per-tract dict of isolated star source catalog handles. 

499 

500 Returns 

501 ------- 

502 isolated_table : `np.ndarray` (N,) 

503 Table of isolated stars, with indexes to isolated sources. 

504 isolated_source_table : `np.ndarray` (M,) 

505 Table of isolated sources, with indexes to isolated stars. 

506 """ 

507 isolated_tables = [] 

508 isolated_sources = [] 

509 merge_cat_counter = 0 

510 merge_source_counter = 0 

511 

512 for tract in isolated_star_cat_dict: 

513 df_cat = isolated_star_cat_dict[tract].get() 

514 table_cat = df_cat.to_records() 

515 

516 df_source = isolated_star_source_dict[tract].get( 

517 parameters={'columns': [self.config.id_column, 

518 'obj_index']} 

519 ) 

520 table_source = df_source.to_records() 

521 

522 # Cut isolated star table to those observed in this band, and adjust indexes 

523 (use_band,) = (table_cat[f'nsource_{band}'] > 0).nonzero() 

524 

525 if len(use_band) == 0: 

526 # There are no sources in this band in this tract. 

527 self.log.info("No sources found in %s band in tract %d.", band, tract) 

528 continue 

529 

530 # With the following matching: 

531 # table_source[b] <-> table_cat[use_band[a]] 

532 obj_index = table_source['obj_index'][:] 

533 a, b = esutil.numpy_util.match(use_band, obj_index) 

534 

535 # Update indexes and cut to band-selected stars/sources 

536 table_source['obj_index'][b] = a 

537 _, index_new = np.unique(a, return_index=True) 

538 table_cat[f'source_cat_index_{band}'][use_band] = index_new 

539 

540 # After the following cuts, the catalogs have the following properties: 

541 # - table_cat only contains isolated stars that have at least one source 

542 # in ``band``. 

543 # - table_source only contains ``band`` sources. 

544 # - The slice table_cat["source_cat_index_{band}"]: table_cat["source_cat_index_{band}"] 

545 # + table_cat["nsource_{band}] 

546 # applied to table_source will give all the sources associated with the star. 

547 # - For each source, table_source["obj_index"] points to the index of the associated 

548 # isolated star. 

549 table_source = table_source[b] 

550 table_cat = table_cat[use_band] 

551 

552 # Add reserved flag column to tables 

553 table_cat = np.lib.recfunctions.append_fields( 

554 table_cat, 

555 'reserved', 

556 np.zeros(table_cat.size, dtype=bool), 

557 usemask=False 

558 ) 

559 table_source = np.lib.recfunctions.append_fields( 

560 table_source, 

561 'reserved', 

562 np.zeros(table_source.size, dtype=bool), 

563 usemask=False 

564 ) 

565 

566 # Get reserve star flags 

567 table_cat['reserved'][:] = self.reserve_selection.run( 

568 len(table_cat), 

569 extra=f'{band}_{tract}', 

570 ) 

571 table_source['reserved'][:] = table_cat['reserved'][table_source['obj_index']] 

572 

573 # Offset indexes to account for tract merging 

574 table_cat[f'source_cat_index_{band}'] += merge_source_counter 

575 table_source['obj_index'] += merge_cat_counter 

576 

577 isolated_tables.append(table_cat) 

578 isolated_sources.append(table_source) 

579 

580 merge_cat_counter += len(table_cat) 

581 merge_source_counter += len(table_source) 

582 

583 isolated_table = np.concatenate(isolated_tables) 

584 isolated_source_table = np.concatenate(isolated_sources) 

585 

586 return isolated_table, isolated_source_table 

587 

588 def compute_psf_and_ap_corr_map(self, visit, detector, exposure, src, isolated_source_table): 

589 """Compute psf model and aperture correction map for a single exposure. 

590 

591 Parameters 

592 ---------- 

593 visit : `int` 

594 Visit number (for logging). 

595 detector : `int` 

596 Detector number (for logging). 

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

598 src : `lsst.afw.table.SourceCatalog` 

599 isolated_source_table : `np.ndarray` 

600 

601 Returns 

602 ------- 

603 psf : `lsst.meas.algorithms.ImagePsf` 

604 PSF Model 

605 ap_corr_map : `lsst.afw.image.ApCorrMap` 

606 Aperture correction map. 

607 measured_src : `lsst.afw.table.SourceCatalog` 

608 Updated source catalog with measurements, flags and aperture corrections. 

609 """ 

610 # Apply source selector (s/n, flags, etc.) 

611 good_src = self.source_selector.selectSources(src) 

612 if sum(good_src.selected) == 0: 

613 self.log.warning('No good sources remain after cuts for visit %d, detector %d', 

614 visit, detector) 

615 return None, None, None 

616 

617 # Cut down input src to the selected sources 

618 # We use a separate schema/mapper here than for the output/measurement catalog because of 

619 # clashes between fields that were previously run and those that need to be rerun with 

620 # the new psf model. This may be slightly inefficient but keeps input 

621 # and output values cleanly separated. 

622 selection_mapper, selection_schema = self._make_selection_schema_mapper(src.schema) 

623 

624 selected_src = afwTable.SourceCatalog(selection_schema) 

625 selected_src.reserve(good_src.selected.sum()) 

626 selected_src.extend(src[good_src.selected], mapper=selection_mapper) 

627 

628 # The calib flags have been copied from the input table, 

629 # and we reset them here just to ensure they aren't propagated. 

630 selected_src['calib_psf_candidate'] = np.zeros(len(selected_src), dtype=bool) 

631 selected_src['calib_psf_used'] = np.zeros(len(selected_src), dtype=bool) 

632 selected_src['calib_psf_reserved'] = np.zeros(len(selected_src), dtype=bool) 

633 

634 # Find the isolated sources and set flags 

635 matched_src, matched_iso = esutil.numpy_util.match( 

636 selected_src['id'], 

637 isolated_source_table[self.config.id_column] 

638 ) 

639 

640 matched_arr = np.zeros(len(selected_src), dtype=bool) 

641 matched_arr[matched_src] = True 

642 selected_src['calib_psf_candidate'] = matched_arr 

643 

644 reserved_arr = np.zeros(len(selected_src), dtype=bool) 

645 reserved_arr[matched_src] = isolated_source_table['reserved'][matched_iso] 

646 selected_src['calib_psf_reserved'] = reserved_arr 

647 

648 selected_src = selected_src[selected_src['calib_psf_candidate']].copy(deep=True) 

649 

650 # Make the measured source catalog as well, based on the selected catalog. 

651 measured_src = afwTable.SourceCatalog(self.schema) 

652 measured_src.reserve(len(selected_src)) 

653 measured_src.extend(selected_src, mapper=self.schema_mapper) 

654 

655 # We need to copy over the calib_psf flags because they were not in the mapper 

656 measured_src['calib_psf_candidate'] = selected_src['calib_psf_candidate'] 

657 measured_src['calib_psf_reserved'] = selected_src['calib_psf_reserved'] 

658 

659 # Select the psf candidates from the selection catalog 

660 try: 

661 psf_selection_result = self.make_psf_candidates.run(selected_src, exposure=exposure) 

662 except Exception as e: 

663 self.log.warning('Failed to make psf candidates for visit %d, detector %d: %s', 

664 visit, detector, e) 

665 return None, None, measured_src 

666 

667 psf_cand_cat = psf_selection_result.goodStarCat 

668 

669 # Make list of psf candidates to send to the determiner 

670 # (omitting those marked as reserved) 

671 psf_determiner_list = [cand for cand, use 

672 in zip(psf_selection_result.psfCandidates, 

673 ~psf_cand_cat['calib_psf_reserved']) if use] 

674 flag_key = psf_cand_cat.schema['calib_psf_used'].asKey() 

675 try: 

676 psf, cell_set = self.psf_determiner.determinePsf(exposure, 

677 psf_determiner_list, 

678 self.metadata, 

679 flagKey=flag_key) 

680 except Exception as e: 

681 self.log.warning('Failed to determine psf for visit %d, detector %d: %s', 

682 visit, detector, e) 

683 return None, None, measured_src 

684 

685 # Set the psf in the exposure for measurement/aperture corrections. 

686 exposure.setPsf(psf) 

687 

688 # At this point, we need to transfer the psf used flag from the selection 

689 # catalog to the measurement catalog. 

690 matched_selected, matched_measured = esutil.numpy_util.match( 

691 selected_src['id'], 

692 measured_src['id'] 

693 ) 

694 measured_used = np.zeros(len(measured_src), dtype=bool) 

695 measured_used[matched_measured] = selected_src['calib_psf_used'][matched_selected] 

696 measured_src['calib_psf_used'] = measured_used 

697 

698 # Next, we do the measurement on all the psf candidate, used, and reserved stars. 

699 try: 

700 self.measurement.run(measCat=measured_src, exposure=exposure) 

701 except Exception as e: 

702 self.log.warning('Failed to make measurements for visit %d, detector %d: %s', 

703 visit, detector, e) 

704 return psf, None, measured_src 

705 

706 # And finally the ap corr map. 

707 try: 

708 ap_corr_map = self.measure_ap_corr.run(exposure=exposure, 

709 catalog=measured_src).apCorrMap 

710 except Exception as e: 

711 self.log.warning('Failed to compute aperture corrections for visit %d, detector %d: %s', 

712 visit, detector, e) 

713 return psf, None, measured_src 

714 

715 self.apply_ap_corr.run(catalog=measured_src, apCorrMap=ap_corr_map) 

716 

717 return psf, ap_corr_map, measured_src