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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"""
25import sys
26import traceback
27import abc
29import numpy as np
31import lsst.daf.persistence as dafPersist
32import lsst.pex.config as pexConfig
33import lsst.pipe.base as pipeBase
34from lsst.utils.timer import timeMethod
36from .fgcmBuildStars import FgcmBuildStarsTask, FgcmBuildStarsConfig
37from .fgcmFitCycle import FgcmFitCycleConfig
38from .fgcmOutputProducts import FgcmOutputProductsTask
39from .utilities import makeConfigDict, translateFgcmLut, translateVisitCatalog
40from .utilities import computeApertureRadiusFromDataRef, extractReferenceMags
41from .utilities import makeZptSchema, makeZptCat
42from .utilities import makeAtmSchema, makeAtmCat
43from .utilities import makeStdSchema, makeStdCat
44from .focalPlaneProjector import FocalPlaneProjector
46import fgcm
48__all__ = ['FgcmCalibrateTractConfigBase', 'FgcmCalibrateTractBaseTask', 'FgcmCalibrateTractRunner']
51class FgcmCalibrateTractConfigBase(pexConfig.Config):
52 """Config for FgcmCalibrateTract"""
54 fgcmBuildStars = pexConfig.ConfigurableField(
55 target=FgcmBuildStarsTask,
56 doc="Task to load and match stars for fgcm",
57 )
58 fgcmFitCycle = pexConfig.ConfigField(
59 dtype=FgcmFitCycleConfig,
60 doc="Config to run a single fgcm fit cycle",
61 )
62 fgcmOutputProducts = pexConfig.ConfigurableField(
63 target=FgcmOutputProductsTask,
64 doc="Task to output fgcm products",
65 )
66 convergenceTolerance = pexConfig.Field(
67 doc="Tolerance on repeatability convergence (per band)",
68 dtype=float,
69 default=0.005,
70 )
71 maxFitCycles = pexConfig.Field(
72 doc="Maximum number of fit cycles",
73 dtype=int,
74 default=5,
75 )
76 doDebuggingPlots = pexConfig.Field(
77 doc="Make plots for debugging purposes?",
78 dtype=bool,
79 default=False,
80 )
82 def setDefaults(self):
83 pexConfig.Config.setDefaults(self)
85 self.fgcmFitCycle.quietMode = True
86 self.fgcmFitCycle.doPlots = False
87 self.fgcmOutputProducts.doReferenceCalibration = False
88 self.fgcmOutputProducts.doRefcatOutput = False
89 self.fgcmOutputProducts.cycleNumber = 0
90 self.fgcmOutputProducts.photoCal.applyColorTerms = False
92 def validate(self):
93 super().validate()
95 for band in self.fgcmFitCycle.bands:
96 if not self.fgcmFitCycle.useRepeatabilityForExpGrayCutsDict[band]:
97 msg = 'Must set useRepeatabilityForExpGrayCutsDict[band]=True for all bands'
98 raise pexConfig.FieldValidationError(FgcmFitCycleConfig.useRepeatabilityForExpGrayCutsDict,
99 self, msg)
102class FgcmCalibrateTractRunner(pipeBase.ButlerInitializedTaskRunner):
103 """Subclass of TaskRunner for FgcmCalibrateTractTask
105 fgcmCalibrateTractTask.run() takes a number of arguments, one of which is
106 the butler (for persistence and mapper data), and a list of dataRefs
107 extracted from the command line. This task runs on a constrained set
108 of dataRefs, typically a single tract.
109 This class transforms the process arguments generated by the ArgumentParser
110 into the arguments expected by FgcmCalibrateTractTask.run().
111 This runner does not use any parallelization.
112 """
114 @staticmethod
115 def getTargetList(parsedCmd):
116 """
117 Return a list with one element: a tuple with the butler and
118 list of dataRefs.
119 """
120 return [(parsedCmd.butler, parsedCmd.id.refList)]
122 def __call__(self, args):
123 """
124 Parameters
125 ----------
126 args: `tuple` with (butler, dataRefList)
128 Returns
129 -------
130 exitStatus: `list` with `lsst.pipe.base.Struct`
131 exitStatus (0: success; 1: failure)
132 May also contain results if `self.doReturnResults` is `True`.
133 """
134 butler, dataRefList = args
136 task = self.TaskClass(config=self.config, log=self.log)
138 exitStatus = 0
139 if self.doRaise:
140 results = task.runDataRef(butler, dataRefList)
141 else:
142 try:
143 results = task.runDataRef(butler, dataRefList)
144 except Exception as e:
145 exitStatus = 1
146 task.log.fatal("Failed: %s" % e)
147 if not isinstance(e, pipeBase.TaskError):
148 traceback.print_exc(file=sys.stderr)
150 task.writeMetadata(butler)
152 if self.doReturnResults:
153 return [pipeBase.Struct(exitStatus=exitStatus,
154 results=results)]
155 else:
156 return [pipeBase.Struct(exitStatus=exitStatus)]
158 def run(self, parsedCmd):
159 """
160 Run the task, with no multiprocessing
162 Parameters
163 ----------
164 parsedCmd: `lsst.pipe.base.ArgumentParser` parsed command line
165 """
167 resultList = []
169 if self.precall(parsedCmd):
170 targetList = self.getTargetList(parsedCmd)
171 resultList = self(targetList[0])
173 return resultList
176class FgcmCalibrateTractBaseTask(pipeBase.PipelineTask, pipeBase.CmdLineTask, abc.ABC):
177 """Base class to calibrate a single tract using fgcmcal
178 """
179 def __init__(self, initInputs=None, butler=None, **kwargs):
180 """
181 Instantiate an `FgcmCalibrateTractTask`.
183 Parameters
184 ----------
185 butler : `lsst.daf.persistence.Butler`, optional
186 """
187 super().__init__(**kwargs)
188 self.makeSubtask("fgcmBuildStars", initInputs=initInputs, butler=butler)
189 self.makeSubtask("fgcmOutputProducts", butler=butler)
191 # no saving of metadata for now
192 def _getMetadataName(self):
193 return None
195 @timeMethod
196 def runDataRef(self, butler, dataRefs):
197 """
198 Run full FGCM calibration on a single tract, including building star list,
199 fitting multiple cycles, and making outputs.
201 Parameters
202 ----------
203 butler: `lsst.daf.persistence.Butler`
204 dataRefs: `list` of `lsst.daf.persistence.ButlerDataRef`
205 Data references for the input visits.
206 These may be either per-ccd "src" or per-visit"sourceTable_visit"
207 references.
209 Raises
210 ------
211 RuntimeError: Raised if `config.fgcmBuildStars.doReferenceMatches` is
212 not True, or if fgcmLookUpTable is not available, or if
213 doSubtractLocalBackground is True and aperture radius cannot be
214 determined.
215 """
216 datasetType = dataRefs[0].butlerSubset.datasetType
217 self.log.info("Running with %d %s dataRefs" % (len(dataRefs), datasetType))
219 if not butler.datasetExists('fgcmLookUpTable'):
220 raise RuntimeError("Must run FgcmCalibrateTract with an fgcmLookUpTable")
222 if not self.config.fgcmBuildStars.doReferenceMatches:
223 raise RuntimeError("Must run FgcmCalibrateTract with fgcmBuildStars.doReferenceMatches")
224 if isinstance(self.config.fgcmBuildStars, FgcmBuildStarsConfig):
225 if self.config.fgcmBuildStars.checkAllCcds:
226 raise RuntimeError("Cannot run FgcmCalibrateTract with "
227 "fgcmBuildStars.checkAllCcds set to True")
229 tract = int(dataRefs[0].dataId['tract'])
230 camera = butler.get('camera')
232 dataRefDict = {}
233 dataRefDict['camera'] = camera
234 dataRefDict['source_catalogs'] = dataRefs
235 dataRefDict['sourceSchema'] = butler.get('src_schema', immediate=True).schema
236 dataRefDict['fgcmLookUpTable'] = butler.dataRef('fgcmLookUpTable')
238 struct = self.run(dataRefDict, tract, butler=butler, returnCatalogs=False)
240 visitDataRefName = self.config.fgcmBuildStars.visitDataRefName
241 ccdDataRefName = self.config.fgcmBuildStars.ccdDataRefName
243 if struct.photoCalibs is not None:
244 self.log.info("Outputting photoCalib files.")
246 for visit, detector, physicalFilter, photoCalib in struct.photoCalibs:
247 butler.put(photoCalib, 'fgcm_tract_photoCalib',
248 dataId={visitDataRefName: visit,
249 ccdDataRefName: detector,
250 'filter': physicalFilter,
251 'tract': tract})
253 self.log.info("Done outputting photoCalib files.")
255 if struct.atmospheres is not None:
256 self.log.info("Outputting atmosphere files.")
257 for visit, atm in struct.atmospheres:
258 butler.put(atm, "transmission_atmosphere_fgcm_tract",
259 dataId={visitDataRefName: visit,
260 'tract': tract})
261 self.log.info("Done outputting atmosphere transmissions.")
263 return pipeBase.Struct(repeatability=struct.repeatability)
265 def run(self, dataRefDict, tract,
266 buildStarsRefObjLoader=None, returnCatalogs=True, butler=None):
267 """Run the calibrations for a single tract with fgcm.
269 Parameters
270 ----------
271 dataRefDict : `dict`
272 All dataRefs are `lsst.daf.persistence.ButlerDataRef` (gen2) or
273 `lsst.daf.butler.DeferredDatasetHandle` (gen3)
274 dataRef dictionary with the following keys. Note that all
275 keys need not be set based on config parameters.
277 ``"camera"``
278 Camera object (`lsst.afw.cameraGeom.Camera`)
279 ``"source_catalogs"``
280 `list` of dataRefs for input source catalogs.
281 ``"sourceSchema"``
282 Schema for the source catalogs.
283 ``"fgcmLookUpTable"``
284 dataRef for the FGCM look-up table.
285 ``"calexps"``
286 `list` of dataRefs for the input calexps (Gen3 only)
287 ``"fgcmPhotoCalibs"``
288 `dict` of output photoCalib dataRefs. Key is
289 (tract, visit, detector). (Gen3 only)
290 Present if doZeropointOutput is True.
291 ``"fgcmTransmissionAtmospheres"``
292 `dict` of output atmosphere transmission dataRefs.
293 Key is (tract, visit). (Gen3 only)
294 Present if doAtmosphereOutput is True.
295 tract : `int`
296 Tract number
297 buildStarsRefObjLoader : `lsst.meas.algorithms.ReferenceObjectLoader`, optional
298 Reference object loader object for fgcmBuildStars.
299 returnCatalogs : `bool`, optional
300 Return photoCalibs as per-visit exposure catalogs.
301 butler : `lsst.daf.persistence.Butler`, optional
302 Gen2 butler used for reference star outputs
304 Returns
305 -------
306 outstruct : `lsst.pipe.base.Struct`
307 Output structure with keys:
309 offsets : `np.ndarray`
310 Final reference offsets, per band.
311 repeatability : `np.ndarray`
312 Raw fgcm repeatability for bright stars, per band.
313 atmospheres : `generator` [(`int`, `lsst.afw.image.TransmissionCurve`)]
314 Generator that returns (visit, transmissionCurve) tuples.
315 photoCalibs : `generator` [(`int`, `int`, `str`, `lsst.afw.image.PhotoCalib`)]
316 Generator that returns (visit, ccd, filtername, photoCalib) tuples.
317 (returned if returnCatalogs is False).
318 photoCalibCatalogs : `generator` [(`int`, `lsst.afw.table.ExposureCatalog`)]
319 Generator that returns (visit, exposureCatalog) tuples.
320 (returned if returnCatalogs is True).
321 """
322 self.log.info("Running on tract %d", (tract))
324 # Compute the aperture radius if necessary. This is useful to do now before
325 # any heavy lifting has happened (fail early).
326 calibFluxApertureRadius = None
327 if self.config.fgcmBuildStars.doSubtractLocalBackground:
328 try:
329 field = self.config.fgcmBuildStars.instFluxField
330 calibFluxApertureRadius = computeApertureRadiusFromDataRef(dataRefDict['source_catalogs'][0],
331 field)
332 except RuntimeError:
333 raise RuntimeError("Could not determine aperture radius from %s. "
334 "Cannot use doSubtractLocalBackground." %
335 (field))
337 # Run the build stars tasks
339 # Note that we will need visitCat at the end of the procedure for the outputs
340 if isinstance(butler, dafPersist.Butler):
341 # Gen2
342 groupedDataRefs = self.fgcmBuildStars._findAndGroupDataRefsGen2(butler, dataRefDict['camera'],
343 dataRefDict['source_catalogs'])
344 else:
345 # Gen3
346 groupedDataRefs = self.fgcmBuildStars._groupDataRefs(dataRefDict['sourceTableDataRefDict'],
347 dataRefDict['visitSummaryDataRefDict'])
348 visitCat = self.fgcmBuildStars.fgcmMakeVisitCatalog(dataRefDict['camera'], groupedDataRefs)
349 rad = calibFluxApertureRadius
350 fgcmStarObservationCat = self.fgcmBuildStars.fgcmMakeAllStarObservations(groupedDataRefs,
351 visitCat,
352 dataRefDict['sourceSchema'],
353 dataRefDict['camera'],
354 calibFluxApertureRadius=rad)
356 if self.fgcmBuildStars.config.doReferenceMatches:
357 lutDataRef = dataRefDict['fgcmLookUpTable']
358 if buildStarsRefObjLoader is not None:
359 self.fgcmBuildStars.makeSubtask("fgcmLoadReferenceCatalog",
360 refObjLoader=buildStarsRefObjLoader)
361 else:
362 self.fgcmBuildStars.makeSubtask("fgcmLoadReferenceCatalog", butler=butler)
363 else:
364 lutDataRef = None
366 fgcmStarIdCat, fgcmStarIndicesCat, fgcmRefCat = \
367 self.fgcmBuildStars.fgcmMatchStars(visitCat,
368 fgcmStarObservationCat,
369 lutDataRef=lutDataRef)
371 # Load the LUT
372 lutCat = dataRefDict['fgcmLookUpTable'].get()
373 fgcmLut, lutIndexVals, lutStd = translateFgcmLut(lutCat,
374 dict(self.config.fgcmFitCycle.physicalFilterMap))
375 del lutCat
377 # Translate the visit catalog into fgcm format
378 fgcmExpInfo = translateVisitCatalog(visitCat)
380 configDict = makeConfigDict(self.config.fgcmFitCycle, self.log, dataRefDict['camera'],
381 self.config.fgcmFitCycle.maxIterBeforeFinalCycle,
382 True, False, lutIndexVals[0]['FILTERNAMES'],
383 tract=tract)
385 focalPlaneProjector = FocalPlaneProjector(dataRefDict['camera'],
386 self.config.fgcmFitCycle.defaultCameraOrientation)
388 # Set up the fit cycle task
390 noFitsDict = {'lutIndex': lutIndexVals,
391 'lutStd': lutStd,
392 'expInfo': fgcmExpInfo,
393 'focalPlaneProjector': focalPlaneProjector}
395 fgcmFitCycle = fgcm.FgcmFitCycle(configDict, useFits=False,
396 noFitsDict=noFitsDict, noOutput=True)
398 # We determine the conversion from the native units (typically radians) to
399 # degrees for the first star. This allows us to treat coord_ra/coord_dec as
400 # numpy arrays rather than Angles, which would we approximately 600x slower.
401 conv = fgcmStarObservationCat[0]['ra'].asDegrees() / float(fgcmStarObservationCat[0]['ra'])
403 # To load the stars, we need an initial parameter object
404 fgcmPars = fgcm.FgcmParameters.newParsWithArrays(fgcmFitCycle.fgcmConfig,
405 fgcmLut,
406 fgcmExpInfo)
408 # Match star observations to visits
409 # Only those star observations that match visits from fgcmExpInfo['VISIT'] will
410 # actually be transferred into fgcm using the indexing below.
412 obsIndex = fgcmStarIndicesCat['obsIndex']
413 visitIndex = np.searchsorted(fgcmExpInfo['VISIT'],
414 fgcmStarObservationCat['visit'][obsIndex])
416 refMag, refMagErr = extractReferenceMags(fgcmRefCat,
417 self.config.fgcmFitCycle.bands,
418 self.config.fgcmFitCycle.physicalFilterMap)
419 refId = fgcmRefCat['fgcm_id'][:]
421 fgcmStars = fgcm.FgcmStars(fgcmFitCycle.fgcmConfig)
422 fgcmStars.loadStars(fgcmPars,
423 fgcmStarObservationCat['visit'][obsIndex],
424 fgcmStarObservationCat['ccd'][obsIndex],
425 fgcmStarObservationCat['ra'][obsIndex] * conv,
426 fgcmStarObservationCat['dec'][obsIndex] * conv,
427 fgcmStarObservationCat['instMag'][obsIndex],
428 fgcmStarObservationCat['instMagErr'][obsIndex],
429 fgcmExpInfo['FILTERNAME'][visitIndex],
430 fgcmStarIdCat['fgcm_id'][:],
431 fgcmStarIdCat['ra'][:],
432 fgcmStarIdCat['dec'][:],
433 fgcmStarIdCat['obsArrIndex'][:],
434 fgcmStarIdCat['nObs'][:],
435 obsX=fgcmStarObservationCat['x'][obsIndex],
436 obsY=fgcmStarObservationCat['y'][obsIndex],
437 obsDeltaMagBkg=fgcmStarObservationCat['deltaMagBkg'][obsIndex],
438 obsDeltaAper=fgcmStarObservationCat['deltaMagAper'][obsIndex],
439 psfCandidate=fgcmStarObservationCat['psf_candidate'][obsIndex],
440 refID=refId,
441 refMag=refMag,
442 refMagErr=refMagErr,
443 flagID=None,
444 flagFlag=None,
445 computeNobs=True)
447 # Clear out some memory
448 del fgcmStarIdCat
449 del fgcmStarIndicesCat
450 del fgcmRefCat
452 fgcmFitCycle.setLUT(fgcmLut)
453 fgcmFitCycle.setStars(fgcmStars, fgcmPars)
455 converged = False
456 cycleNumber = 0
458 previousReservedRawRepeatability = np.zeros(fgcmPars.nBands) + 1000.0
459 previousParInfo = None
460 previousParams = None
461 previousSuperStar = None
463 while (not converged and cycleNumber < self.config.maxFitCycles):
465 fgcmFitCycle.fgcmConfig.updateCycleNumber(cycleNumber)
467 if cycleNumber > 0:
468 # Use parameters from previous cycle
469 fgcmPars = fgcm.FgcmParameters.loadParsWithArrays(fgcmFitCycle.fgcmConfig,
470 fgcmExpInfo,
471 previousParInfo,
472 previousParams,
473 previousSuperStar)
474 # We need to reset the star magnitudes and errors for the next
475 # cycle
476 fgcmFitCycle.fgcmStars.reloadStarMagnitudes(fgcmStarObservationCat['instMag'][obsIndex],
477 fgcmStarObservationCat['instMagErr'][obsIndex])
478 fgcmFitCycle.initialCycle = False
480 fgcmFitCycle.setPars(fgcmPars)
481 fgcmFitCycle.finishSetup()
483 fgcmFitCycle.run()
485 # Grab the parameters for the next cycle
486 previousParInfo, previousParams = fgcmFitCycle.fgcmPars.parsToArrays()
487 previousSuperStar = fgcmFitCycle.fgcmPars.parSuperStarFlat.copy()
489 self.log.info("Raw repeatability after cycle number %d is:" % (cycleNumber))
490 for i, band in enumerate(fgcmFitCycle.fgcmPars.bands):
491 if not fgcmFitCycle.fgcmPars.hasExposuresInBand[i]:
492 continue
493 rep = fgcmFitCycle.fgcmPars.compReservedRawRepeatability[i] * 1000.0
494 self.log.info(" Band %s, repeatability: %.2f mmag" % (band, rep))
496 # Check for convergence
497 if np.alltrue((previousReservedRawRepeatability
498 - fgcmFitCycle.fgcmPars.compReservedRawRepeatability)
499 < self.config.convergenceTolerance):
500 self.log.info("Raw repeatability has converged after cycle number %d." % (cycleNumber))
501 converged = True
502 else:
503 fgcmFitCycle.fgcmConfig.expGrayPhotometricCut[:] = fgcmFitCycle.updatedPhotometricCut
504 fgcmFitCycle.fgcmConfig.expGrayHighCut[:] = fgcmFitCycle.updatedHighCut
505 fgcmFitCycle.fgcmConfig.precomputeSuperStarInitialCycle = False
506 fgcmFitCycle.fgcmConfig.freezeStdAtmosphere = False
507 previousReservedRawRepeatability[:] = fgcmFitCycle.fgcmPars.compReservedRawRepeatability
508 self.log.info("Setting exposure gray photometricity cuts to:")
509 for i, band in enumerate(fgcmFitCycle.fgcmPars.bands):
510 if not fgcmFitCycle.fgcmPars.hasExposuresInBand[i]:
511 continue
512 cut = fgcmFitCycle.updatedPhotometricCut[i] * 1000.0
513 self.log.info(" Band %s, photometricity cut: %.2f mmag" % (band, cut))
515 cycleNumber += 1
517 # Log warning if not converged
518 if not converged:
519 self.log.warning("Maximum number of fit cycles exceeded (%d) without convergence.", cycleNumber)
521 # Do final clean-up iteration
522 fgcmFitCycle.fgcmConfig.freezeStdAtmosphere = False
523 fgcmFitCycle.fgcmConfig.resetParameters = False
524 fgcmFitCycle.fgcmConfig.maxIter = 0
525 fgcmFitCycle.fgcmConfig.outputZeropoints = True
526 fgcmFitCycle.fgcmConfig.outputStandards = True
527 fgcmFitCycle.fgcmConfig.doPlots = self.config.doDebuggingPlots
528 fgcmFitCycle.fgcmConfig.updateCycleNumber(cycleNumber)
529 fgcmFitCycle.initialCycle = False
531 fgcmPars = fgcm.FgcmParameters.loadParsWithArrays(fgcmFitCycle.fgcmConfig,
532 fgcmExpInfo,
533 previousParInfo,
534 previousParams,
535 previousSuperStar)
536 fgcmFitCycle.fgcmStars.reloadStarMagnitudes(fgcmStarObservationCat['instMag'][obsIndex],
537 fgcmStarObservationCat['instMagErr'][obsIndex])
538 fgcmFitCycle.setPars(fgcmPars)
539 fgcmFitCycle.finishSetup()
541 self.log.info("Running final clean-up fit cycle...")
542 fgcmFitCycle.run()
544 self.log.info("Raw repeatability after clean-up cycle is:")
545 for i, band in enumerate(fgcmFitCycle.fgcmPars.bands):
546 if not fgcmFitCycle.fgcmPars.hasExposuresInBand[i]:
547 continue
548 rep = fgcmFitCycle.fgcmPars.compReservedRawRepeatability[i] * 1000.0
549 self.log.info(" Band %s, repeatability: %.2f mmag" % (band, rep))
551 # Do the outputs. Need to keep track of tract.
553 superStarChebSize = fgcmFitCycle.fgcmZpts.zpStruct['FGCM_FZPT_SSTAR_CHEB'].shape[1]
554 zptChebSize = fgcmFitCycle.fgcmZpts.zpStruct['FGCM_FZPT_CHEB'].shape[1]
556 zptSchema = makeZptSchema(superStarChebSize, zptChebSize)
557 zptCat = makeZptCat(zptSchema, fgcmFitCycle.fgcmZpts.zpStruct)
559 atmSchema = makeAtmSchema()
560 atmCat = makeAtmCat(atmSchema, fgcmFitCycle.fgcmZpts.atmStruct)
562 stdStruct, goodBands = fgcmFitCycle.fgcmStars.retrieveStdStarCatalog(fgcmFitCycle.fgcmPars)
563 stdSchema = makeStdSchema(len(goodBands))
564 stdCat = makeStdCat(stdSchema, stdStruct, goodBands)
566 outStruct = self.fgcmOutputProducts.generateTractOutputProducts(dataRefDict,
567 tract,
568 visitCat,
569 zptCat, atmCat, stdCat,
570 self.config.fgcmBuildStars,
571 returnCatalogs=returnCatalogs,
572 butler=butler)
574 outStruct.repeatability = fgcmFitCycle.fgcmPars.compReservedRawRepeatability
576 fgcmFitCycle.freeSharedMemory()
578 return outStruct