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1# This file is part of fgcmcal. 

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"""Base class for running fgcmcal on a single tract using src tables 

22or sourceTable_visit tables. 

23""" 

24 

25import sys 

26import traceback 

27import abc 

28 

29import numpy as np 

30 

31import lsst.daf.persistence as dafPersist 

32import lsst.pex.config as pexConfig 

33import lsst.pipe.base as pipeBase 

34 

35from .fgcmBuildStars import FgcmBuildStarsTask, FgcmBuildStarsConfig 

36from .fgcmFitCycle import FgcmFitCycleConfig 

37from .fgcmOutputProducts import FgcmOutputProductsTask 

38from .utilities import makeConfigDict, translateFgcmLut, translateVisitCatalog 

39from .utilities import computeCcdOffsets, computeApertureRadiusFromDataRef, extractReferenceMags 

40from .utilities import makeZptSchema, makeZptCat 

41from .utilities import makeAtmSchema, makeAtmCat 

42from .utilities import makeStdSchema, makeStdCat 

43 

44import fgcm 

45 

46__all__ = ['FgcmCalibrateTractConfigBase', 'FgcmCalibrateTractBaseTask', 'FgcmCalibrateTractRunner'] 

47 

48 

49class FgcmCalibrateTractConfigBase(pexConfig.Config): 

50 """Config for FgcmCalibrateTract""" 

51 

52 fgcmBuildStars = pexConfig.ConfigurableField( 

53 target=FgcmBuildStarsTask, 

54 doc="Task to load and match stars for fgcm", 

55 ) 

56 fgcmFitCycle = pexConfig.ConfigField( 

57 dtype=FgcmFitCycleConfig, 

58 doc="Config to run a single fgcm fit cycle", 

59 ) 

60 fgcmOutputProducts = pexConfig.ConfigurableField( 

61 target=FgcmOutputProductsTask, 

62 doc="Task to output fgcm products", 

63 ) 

64 convergenceTolerance = pexConfig.Field( 

65 doc="Tolerance on repeatability convergence (per band)", 

66 dtype=float, 

67 default=0.005, 

68 ) 

69 maxFitCycles = pexConfig.Field( 

70 doc="Maximum number of fit cycles", 

71 dtype=int, 

72 default=5, 

73 ) 

74 doDebuggingPlots = pexConfig.Field( 

75 doc="Make plots for debugging purposes?", 

76 dtype=bool, 

77 default=False, 

78 ) 

79 

80 def setDefaults(self): 

81 pexConfig.Config.setDefaults(self) 

82 

83 self.fgcmFitCycle.quietMode = True 

84 self.fgcmOutputProducts.doReferenceCalibration = False 

85 self.fgcmOutputProducts.doRefcatOutput = False 

86 self.fgcmOutputProducts.cycleNumber = 0 

87 self.fgcmOutputProducts.photoCal.applyColorTerms = False 

88 

89 def validate(self): 

90 super().validate() 

91 

92 for band in self.fgcmFitCycle.bands: 

93 if not self.fgcmFitCycle.useRepeatabilityForExpGrayCutsDict[band]: 

94 msg = 'Must set useRepeatabilityForExpGrayCutsDict[band]=True for all bands' 

95 raise pexConfig.FieldValidationError(FgcmFitCycleConfig.useRepeatabilityForExpGrayCutsDict, 

96 self, msg) 

97 

98 

99class FgcmCalibrateTractRunner(pipeBase.ButlerInitializedTaskRunner): 

100 """Subclass of TaskRunner for FgcmCalibrateTractTask 

101 

102 fgcmCalibrateTractTask.run() takes a number of arguments, one of which is 

103 the butler (for persistence and mapper data), and a list of dataRefs 

104 extracted from the command line. This task runs on a constrained set 

105 of dataRefs, typically a single tract. 

106 This class transforms the process arguments generated by the ArgumentParser 

107 into the arguments expected by FgcmCalibrateTractTask.run(). 

108 This runner does not use any parallelization. 

109 """ 

110 

111 @staticmethod 

112 def getTargetList(parsedCmd): 

113 """ 

114 Return a list with one element: a tuple with the butler and 

115 list of dataRefs. 

116 """ 

117 return [(parsedCmd.butler, parsedCmd.id.refList)] 

118 

119 def __call__(self, args): 

120 """ 

121 Parameters 

122 ---------- 

123 args: `tuple` with (butler, dataRefList) 

124 

125 Returns 

126 ------- 

127 exitStatus: `list` with `lsst.pipe.base.Struct` 

128 exitStatus (0: success; 1: failure) 

129 May also contain results if `self.doReturnResults` is `True`. 

130 """ 

131 butler, dataRefList = args 

132 

133 task = self.TaskClass(config=self.config, log=self.log) 

134 

135 exitStatus = 0 

136 if self.doRaise: 

137 results = task.runDataRef(butler, dataRefList) 

138 else: 

139 try: 

140 results = task.runDataRef(butler, dataRefList) 

141 except Exception as e: 

142 exitStatus = 1 

143 task.log.fatal("Failed: %s" % e) 

144 if not isinstance(e, pipeBase.TaskError): 

145 traceback.print_exc(file=sys.stderr) 

146 

147 task.writeMetadata(butler) 

148 

149 if self.doReturnResults: 

150 return [pipeBase.Struct(exitStatus=exitStatus, 

151 results=results)] 

152 else: 

153 return [pipeBase.Struct(exitStatus=exitStatus)] 

154 

155 def run(self, parsedCmd): 

156 """ 

157 Run the task, with no multiprocessing 

158 

159 Parameters 

160 ---------- 

161 parsedCmd: `lsst.pipe.base.ArgumentParser` parsed command line 

162 """ 

163 

164 resultList = [] 

165 

166 if self.precall(parsedCmd): 

167 targetList = self.getTargetList(parsedCmd) 

168 resultList = self(targetList[0]) 

169 

170 return resultList 

171 

172 

173class FgcmCalibrateTractBaseTask(pipeBase.PipelineTask, pipeBase.CmdLineTask, abc.ABC): 

174 """Base class to calibrate a single tract using fgcmcal 

175 """ 

176 def __init__(self, butler=None, **kwargs): 

177 """ 

178 Instantiate an `FgcmCalibrateTractTask`. 

179 

180 Parameters 

181 ---------- 

182 butler : `lsst.daf.persistence.Butler`, optional 

183 """ 

184 super().__init__(**kwargs) 

185 self.makeSubtask("fgcmBuildStars", butler=butler) 

186 self.makeSubtask("fgcmOutputProducts", butler=butler) 

187 

188 # no saving of metadata for now 

189 def _getMetadataName(self): 

190 return None 

191 

192 @pipeBase.timeMethod 

193 def runDataRef(self, butler, dataRefs): 

194 """ 

195 Run full FGCM calibration on a single tract, including building star list, 

196 fitting multiple cycles, and making outputs. 

197 

198 Parameters 

199 ---------- 

200 butler: `lsst.daf.persistence.Butler` 

201 dataRefs: `list` of `lsst.daf.persistence.ButlerDataRef` 

202 Data references for the input visits. 

203 These may be either per-ccd "src" or per-visit"sourceTable_visit" 

204 references. 

205 

206 Raises 

207 ------ 

208 RuntimeError: Raised if `config.fgcmBuildStars.doReferenceMatches` is 

209 not True, or if fgcmLookUpTable is not available, or if 

210 doSubtractLocalBackground is True and aperture radius cannot be 

211 determined. 

212 """ 

213 datasetType = dataRefs[0].butlerSubset.datasetType 

214 self.log.info("Running with %d %s dataRefs" % (len(dataRefs), datasetType)) 

215 

216 if not butler.datasetExists('fgcmLookUpTable'): 

217 raise RuntimeError("Must run FgcmCalibrateTract with an fgcmLookUpTable") 

218 

219 if not self.config.fgcmBuildStars.doReferenceMatches: 

220 raise RuntimeError("Must run FgcmCalibrateTract with fgcmBuildStars.doReferenceMatches") 

221 if isinstance(self.config.fgcmBuildStars, FgcmBuildStarsConfig): 

222 if self.config.fgcmBuildStars.checkAllCcds: 

223 raise RuntimeError("Cannot run FgcmCalibrateTract with " 

224 "fgcmBuildStars.checkAllCcds set to True") 

225 

226 tract = int(dataRefs[0].dataId['tract']) 

227 camera = butler.get('camera') 

228 

229 dataRefDict = {} 

230 dataRefDict['camera'] = camera 

231 dataRefDict['source_catalogs'] = dataRefs 

232 dataRefDict['sourceSchema'] = butler.dataRef('src_schema') 

233 dataRefDict['fgcmLookUpTable'] = butler.dataRef('fgcmLookUpTable') 

234 

235 struct = self.run(dataRefDict, tract, butler=butler, returnCatalogs=False) 

236 

237 visitDataRefName = self.config.fgcmBuildStars.visitDataRefName 

238 ccdDataRefName = self.config.fgcmBuildStars.ccdDataRefName 

239 

240 if struct.photoCalibs is not None: 

241 self.log.info("Outputting photoCalib files.") 

242 

243 for visit, detector, physicalFilter, photoCalib in struct.photoCalibs: 

244 butler.put(photoCalib, 'fgcm_tract_photoCalib', 

245 dataId={visitDataRefName: visit, 

246 ccdDataRefName: detector, 

247 'filter': physicalFilter, 

248 'tract': tract}) 

249 

250 self.log.info("Done outputting photoCalib files.") 

251 

252 if struct.atmospheres is not None: 

253 self.log.info("Outputting atmosphere files.") 

254 for visit, atm in struct.atmospheres: 

255 butler.put(atm, "transmission_atmosphere_fgcm_tract", 

256 dataId={visitDataRefName: visit, 

257 'tract': tract}) 

258 self.log.info("Done outputting atmosphere transmissions.") 

259 

260 return pipeBase.Struct(repeatability=struct.repeatability) 

261 

262 def run(self, dataRefDict, tract, 

263 buildStarsRefObjLoader=None, returnCatalogs=True, butler=None): 

264 """Run the calibrations for a single tract with fgcm. 

265 

266 Parameters 

267 ---------- 

268 dataRefDict : `dict` 

269 All dataRefs are `lsst.daf.persistence.ButlerDataRef` (gen2) or 

270 `lsst.daf.butler.DeferredDatasetHandle` (gen3) 

271 dataRef dictionary with the following keys. Note that all 

272 keys need not be set based on config parameters. 

273 

274 ``"camera"`` 

275 Camera object (`lsst.afw.cameraGeom.Camera`) 

276 ``"source_catalogs"`` 

277 `list` of dataRefs for input source catalogs. 

278 ``"sourceSchema"`` 

279 Schema for the source catalogs. 

280 ``"fgcmLookUpTable"`` 

281 dataRef for the FGCM look-up table. 

282 ``"calexps"`` 

283 `list` of dataRefs for the input calexps (Gen3 only) 

284 ``"fgcmPhotoCalibs"`` 

285 `dict` of output photoCalib dataRefs. Key is 

286 (tract, visit, detector). (Gen3 only) 

287 Present if doZeropointOutput is True. 

288 ``"fgcmTransmissionAtmospheres"`` 

289 `dict` of output atmosphere transmission dataRefs. 

290 Key is (tract, visit). (Gen3 only) 

291 Present if doAtmosphereOutput is True. 

292 tract : `int` 

293 Tract number 

294 buildStarsRefObjLoader : `lsst.meas.algorithms.ReferenceObjectLoader`, optional 

295 Reference object loader object for fgcmBuildStars. 

296 returnCatalogs : `bool`, optional 

297 Return photoCalibs as per-visit exposure catalogs. 

298 butler : `lsst.daf.persistence.Butler`, optional 

299 Gen2 butler used for reference star outputs 

300 

301 Returns 

302 ------- 

303 outstruct : `lsst.pipe.base.Struct` 

304 Output structure with keys: 

305 

306 offsets : `np.ndarray` 

307 Final reference offsets, per band. 

308 repeatability : `np.ndarray` 

309 Raw fgcm repeatability for bright stars, per band. 

310 atmospheres : `generator` [(`int`, `lsst.afw.image.TransmissionCurve`)] 

311 Generator that returns (visit, transmissionCurve) tuples. 

312 photoCalibs : `generator` [(`int`, `int`, `str`, `lsst.afw.image.PhotoCalib`)] 

313 Generator that returns (visit, ccd, filtername, photoCalib) tuples. 

314 (returned if returnCatalogs is False). 

315 photoCalibCatalogs : `generator` [(`int`, `lsst.afw.table.ExposureCatalog`)] 

316 Generator that returns (visit, exposureCatalog) tuples. 

317 (returned if returnCatalogs is True). 

318 """ 

319 self.log.info("Running on tract %d", (tract)) 

320 

321 # Compute the aperture radius if necessary. This is useful to do now before 

322 # any heavy lifting has happened (fail early). 

323 calibFluxApertureRadius = None 

324 if self.config.fgcmBuildStars.doSubtractLocalBackground: 

325 try: 

326 field = self.config.fgcmBuildStars.instFluxField 

327 calibFluxApertureRadius = computeApertureRadiusFromDataRef(dataRefDict['source_catalogs'][0], 

328 field) 

329 except RuntimeError: 

330 raise RuntimeError("Could not determine aperture radius from %s. " 

331 "Cannot use doSubtractLocalBackground." % 

332 (field)) 

333 

334 # Run the build stars tasks 

335 

336 # Note that we will need visitCat at the end of the procedure for the outputs 

337 if isinstance(butler, dafPersist.Butler): 

338 # Gen2 

339 groupedDataRefs = self.fgcmBuildStars._findAndGroupDataRefsGen2(butler, dataRefDict['camera'], 

340 dataRefDict['source_catalogs']) 

341 else: 

342 # Gen3 

343 groupedDataRefs = self.fgcmBuildStars._groupDataRefs(dataRefDict['sourceTableDataRefDict'], 

344 dataRefDict['visitSummaryDataRefDict']) 

345 visitCat = self.fgcmBuildStars.fgcmMakeVisitCatalog(dataRefDict['camera'], groupedDataRefs) 

346 rad = calibFluxApertureRadius 

347 fgcmStarObservationCat = self.fgcmBuildStars.fgcmMakeAllStarObservations(groupedDataRefs, 

348 visitCat, 

349 dataRefDict['sourceSchema'], 

350 dataRefDict['camera'], 

351 calibFluxApertureRadius=rad) 

352 

353 if self.fgcmBuildStars.config.doReferenceMatches: 

354 lutDataRef = dataRefDict['fgcmLookUpTable'] 

355 if buildStarsRefObjLoader is not None: 

356 self.fgcmBuildStars.makeSubtask("fgcmLoadReferenceCatalog", 

357 refObjLoader=buildStarsRefObjLoader) 

358 else: 

359 self.fgcmBuildStars.makeSubtask("fgcmLoadReferenceCatalog", butler=butler) 

360 else: 

361 lutDataRef = None 

362 

363 fgcmStarIdCat, fgcmStarIndicesCat, fgcmRefCat = \ 

364 self.fgcmBuildStars.fgcmMatchStars(visitCat, 

365 fgcmStarObservationCat, 

366 lutDataRef=lutDataRef) 

367 

368 # Load the LUT 

369 lutCat = dataRefDict['fgcmLookUpTable'].get() 

370 fgcmLut, lutIndexVals, lutStd = translateFgcmLut(lutCat, 

371 dict(self.config.fgcmFitCycle.physicalFilterMap)) 

372 del lutCat 

373 

374 # Translate the visit catalog into fgcm format 

375 fgcmExpInfo = translateVisitCatalog(visitCat) 

376 

377 configDict = makeConfigDict(self.config.fgcmFitCycle, self.log, dataRefDict['camera'], 

378 self.config.fgcmFitCycle.maxIterBeforeFinalCycle, 

379 True, False, tract=tract) 

380 # Turn off plotting in tract mode 

381 configDict['doPlots'] = False 

382 

383 # Use the first orientation. 

384 # TODO: DM-21215 will generalize to arbitrary camera orientations 

385 ccdOffsets = computeCcdOffsets(dataRefDict['camera'], fgcmExpInfo['TELROT'][0]) 

386 

387 # Set up the fit cycle task 

388 

389 noFitsDict = {'lutIndex': lutIndexVals, 

390 'lutStd': lutStd, 

391 'expInfo': fgcmExpInfo, 

392 'ccdOffsets': ccdOffsets} 

393 

394 fgcmFitCycle = fgcm.FgcmFitCycle(configDict, useFits=False, 

395 noFitsDict=noFitsDict, noOutput=True) 

396 

397 # We determine the conversion from the native units (typically radians) to 

398 # degrees for the first star. This allows us to treat coord_ra/coord_dec as 

399 # numpy arrays rather than Angles, which would we approximately 600x slower. 

400 conv = fgcmStarObservationCat[0]['ra'].asDegrees() / float(fgcmStarObservationCat[0]['ra']) 

401 

402 # To load the stars, we need an initial parameter object 

403 fgcmPars = fgcm.FgcmParameters.newParsWithArrays(fgcmFitCycle.fgcmConfig, 

404 fgcmLut, 

405 fgcmExpInfo) 

406 

407 # Match star observations to visits 

408 # Only those star observations that match visits from fgcmExpInfo['VISIT'] will 

409 # actually be transferred into fgcm using the indexing below. 

410 

411 obsIndex = fgcmStarIndicesCat['obsIndex'] 

412 visitIndex = np.searchsorted(fgcmExpInfo['VISIT'], 

413 fgcmStarObservationCat['visit'][obsIndex]) 

414 

415 refMag, refMagErr = extractReferenceMags(fgcmRefCat, 

416 self.config.fgcmFitCycle.bands, 

417 self.config.fgcmFitCycle.physicalFilterMap) 

418 refId = fgcmRefCat['fgcm_id'][:] 

419 

420 fgcmStars = fgcm.FgcmStars(fgcmFitCycle.fgcmConfig) 

421 fgcmStars.loadStars(fgcmPars, 

422 fgcmStarObservationCat['visit'][obsIndex], 

423 fgcmStarObservationCat['ccd'][obsIndex], 

424 fgcmStarObservationCat['ra'][obsIndex] * conv, 

425 fgcmStarObservationCat['dec'][obsIndex] * conv, 

426 fgcmStarObservationCat['instMag'][obsIndex], 

427 fgcmStarObservationCat['instMagErr'][obsIndex], 

428 fgcmExpInfo['FILTERNAME'][visitIndex], 

429 fgcmStarIdCat['fgcm_id'][:], 

430 fgcmStarIdCat['ra'][:], 

431 fgcmStarIdCat['dec'][:], 

432 fgcmStarIdCat['obsArrIndex'][:], 

433 fgcmStarIdCat['nObs'][:], 

434 obsX=fgcmStarObservationCat['x'][obsIndex], 

435 obsY=fgcmStarObservationCat['y'][obsIndex], 

436 obsDeltaMagBkg=fgcmStarObservationCat['deltaMagBkg'][obsIndex], 

437 psfCandidate=fgcmStarObservationCat['psf_candidate'][obsIndex], 

438 refID=refId, 

439 refMag=refMag, 

440 refMagErr=refMagErr, 

441 flagID=None, 

442 flagFlag=None, 

443 computeNobs=True) 

444 

445 # Clear out some memory 

446 del fgcmStarIdCat 

447 del fgcmStarIndicesCat 

448 del fgcmRefCat 

449 

450 fgcmFitCycle.setLUT(fgcmLut) 

451 fgcmFitCycle.setStars(fgcmStars, fgcmPars) 

452 

453 converged = False 

454 cycleNumber = 0 

455 

456 previousReservedRawRepeatability = np.zeros(fgcmPars.nBands) + 1000.0 

457 previousParInfo = None 

458 previousParams = None 

459 previousSuperStar = None 

460 

461 while (not converged and cycleNumber < self.config.maxFitCycles): 

462 

463 fgcmFitCycle.fgcmConfig.updateCycleNumber(cycleNumber) 

464 

465 if cycleNumber > 0: 

466 # Use parameters from previous cycle 

467 fgcmPars = fgcm.FgcmParameters.loadParsWithArrays(fgcmFitCycle.fgcmConfig, 

468 fgcmExpInfo, 

469 previousParInfo, 

470 previousParams, 

471 previousSuperStar) 

472 # We need to reset the star magnitudes and errors for the next 

473 # cycle 

474 fgcmFitCycle.fgcmStars.reloadStarMagnitudes(fgcmStarObservationCat['instMag'][obsIndex], 

475 fgcmStarObservationCat['instMagErr'][obsIndex]) 

476 fgcmFitCycle.initialCycle = False 

477 

478 fgcmFitCycle.setPars(fgcmPars) 

479 fgcmFitCycle.finishSetup() 

480 

481 fgcmFitCycle.run() 

482 

483 # Grab the parameters for the next cycle 

484 previousParInfo, previousParams = fgcmFitCycle.fgcmPars.parsToArrays() 

485 previousSuperStar = fgcmFitCycle.fgcmPars.parSuperStarFlat.copy() 

486 

487 self.log.info("Raw repeatability after cycle number %d is:" % (cycleNumber)) 

488 for i, band in enumerate(fgcmFitCycle.fgcmPars.bands): 

489 if not fgcmFitCycle.fgcmPars.hasExposuresInBand[i]: 

490 continue 

491 rep = fgcmFitCycle.fgcmPars.compReservedRawRepeatability[i] * 1000.0 

492 self.log.info(" Band %s, repeatability: %.2f mmag" % (band, rep)) 

493 

494 # Check for convergence 

495 if np.alltrue((previousReservedRawRepeatability 

496 - fgcmFitCycle.fgcmPars.compReservedRawRepeatability) 

497 < self.config.convergenceTolerance): 

498 self.log.info("Raw repeatability has converged after cycle number %d." % (cycleNumber)) 

499 converged = True 

500 else: 

501 fgcmFitCycle.fgcmConfig.expGrayPhotometricCut[:] = fgcmFitCycle.updatedPhotometricCut 

502 fgcmFitCycle.fgcmConfig.expGrayHighCut[:] = fgcmFitCycle.updatedHighCut 

503 fgcmFitCycle.fgcmConfig.precomputeSuperStarInitialCycle = False 

504 fgcmFitCycle.fgcmConfig.freezeStdAtmosphere = False 

505 previousReservedRawRepeatability[:] = fgcmFitCycle.fgcmPars.compReservedRawRepeatability 

506 self.log.info("Setting exposure gray photometricity cuts to:") 

507 for i, band in enumerate(fgcmFitCycle.fgcmPars.bands): 

508 if not fgcmFitCycle.fgcmPars.hasExposuresInBand[i]: 

509 continue 

510 cut = fgcmFitCycle.updatedPhotometricCut[i] * 1000.0 

511 self.log.info(" Band %s, photometricity cut: %.2f mmag" % (band, cut)) 

512 

513 cycleNumber += 1 

514 

515 # Log warning if not converged 

516 if not converged: 

517 self.log.warn("Maximum number of fit cycles exceeded (%d) without convergence." % (cycleNumber)) 

518 

519 # Do final clean-up iteration 

520 fgcmFitCycle.fgcmConfig.freezeStdAtmosphere = False 

521 fgcmFitCycle.fgcmConfig.resetParameters = False 

522 fgcmFitCycle.fgcmConfig.maxIter = 0 

523 fgcmFitCycle.fgcmConfig.outputZeropoints = True 

524 fgcmFitCycle.fgcmConfig.outputStandards = True 

525 fgcmFitCycle.fgcmConfig.doPlots = self.config.doDebuggingPlots 

526 fgcmFitCycle.fgcmConfig.updateCycleNumber(cycleNumber) 

527 fgcmFitCycle.initialCycle = False 

528 

529 fgcmPars = fgcm.FgcmParameters.loadParsWithArrays(fgcmFitCycle.fgcmConfig, 

530 fgcmExpInfo, 

531 previousParInfo, 

532 previousParams, 

533 previousSuperStar) 

534 fgcmFitCycle.fgcmStars.reloadStarMagnitudes(fgcmStarObservationCat['instMag'][obsIndex], 

535 fgcmStarObservationCat['instMagErr'][obsIndex]) 

536 fgcmFitCycle.setPars(fgcmPars) 

537 fgcmFitCycle.finishSetup() 

538 

539 self.log.info("Running final clean-up fit cycle...") 

540 fgcmFitCycle.run() 

541 

542 self.log.info("Raw repeatability after clean-up cycle is:") 

543 for i, band in enumerate(fgcmFitCycle.fgcmPars.bands): 

544 if not fgcmFitCycle.fgcmPars.hasExposuresInBand[i]: 

545 continue 

546 rep = fgcmFitCycle.fgcmPars.compReservedRawRepeatability[i] * 1000.0 

547 self.log.info(" Band %s, repeatability: %.2f mmag" % (band, rep)) 

548 

549 # Do the outputs. Need to keep track of tract. 

550 

551 superStarChebSize = fgcmFitCycle.fgcmZpts.zpStruct['FGCM_FZPT_SSTAR_CHEB'].shape[1] 

552 zptChebSize = fgcmFitCycle.fgcmZpts.zpStruct['FGCM_FZPT_CHEB'].shape[1] 

553 

554 zptSchema = makeZptSchema(superStarChebSize, zptChebSize) 

555 zptCat = makeZptCat(zptSchema, fgcmFitCycle.fgcmZpts.zpStruct) 

556 

557 atmSchema = makeAtmSchema() 

558 atmCat = makeAtmCat(atmSchema, fgcmFitCycle.fgcmZpts.atmStruct) 

559 

560 stdStruct, goodBands = fgcmFitCycle.fgcmStars.retrieveStdStarCatalog(fgcmFitCycle.fgcmPars) 

561 stdSchema = makeStdSchema(len(goodBands)) 

562 stdCat = makeStdCat(stdSchema, stdStruct, goodBands) 

563 

564 outStruct = self.fgcmOutputProducts.generateTractOutputProducts(dataRefDict, 

565 tract, 

566 visitCat, 

567 zptCat, atmCat, stdCat, 

568 self.config.fgcmBuildStars, 

569 returnCatalogs=returnCatalogs, 

570 butler=butler) 

571 

572 outStruct.repeatability = fgcmFitCycle.fgcmPars.compReservedRawRepeatability 

573 

574 return outStruct