Coverage for python/lsst/fgcmcal/fgcmOutputProducts.py: 15%

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2# 

3# This file is part of fgcmcal. 

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7# (https://www.lsst.org). 

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

9# for details of code ownership. 

10# 

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

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14# (at your option) any later version. 

15# 

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

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

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

23"""Make the final fgcmcal output products. 

24 

25This task takes the final output from fgcmFitCycle and produces the following 

26outputs for use in the DM stack: the FGCM standard stars in a reference 

27catalog format; the model atmospheres in "transmission_atmosphere_fgcm" 

28format; and the zeropoints in "fgcm_photoCalib" format. Optionally, the 

29task can transfer the 'absolute' calibration from a reference catalog 

30to put the fgcm standard stars in units of Jansky. This is accomplished 

31by matching stars in a sample of healpix pixels, and applying the median 

32offset per band. 

33""" 

34import copy 

35 

36import numpy as np 

37import healpy as hp 

38import esutil 

39from astropy import units 

40 

41import lsst.daf.base as dafBase 

42import lsst.pex.config as pexConfig 

43import lsst.pipe.base as pipeBase 

44from lsst.pipe.base import connectionTypes 

45from lsst.afw.image import TransmissionCurve 

46from lsst.meas.algorithms import LoadIndexedReferenceObjectsTask 

47from lsst.meas.algorithms import ReferenceObjectLoader, LoadReferenceObjectsConfig 

48from lsst.pipe.tasks.photoCal import PhotoCalTask 

49import lsst.geom 

50import lsst.afw.image as afwImage 

51import lsst.afw.math as afwMath 

52import lsst.afw.table as afwTable 

53from lsst.meas.algorithms import DatasetConfig 

54from lsst.meas.algorithms.ingestIndexReferenceTask import addRefCatMetadata 

55 

56from .utilities import computeApproxPixelAreaFields 

57from .utilities import lookupStaticCalibrations 

58from .utilities import FGCM_ILLEGAL_VALUE 

59 

60import fgcm 

61 

62__all__ = ['FgcmOutputProductsConfig', 'FgcmOutputProductsTask'] 

63 

64 

65class FgcmOutputProductsConnections(pipeBase.PipelineTaskConnections, 

66 dimensions=("instrument",), 

67 defaultTemplates={"cycleNumber": "0"}): 

68 camera = connectionTypes.PrerequisiteInput( 

69 doc="Camera instrument", 

70 name="camera", 

71 storageClass="Camera", 

72 dimensions=("instrument",), 

73 lookupFunction=lookupStaticCalibrations, 

74 isCalibration=True, 

75 ) 

76 

77 fgcmLookUpTable = connectionTypes.PrerequisiteInput( 

78 doc=("Atmosphere + instrument look-up-table for FGCM throughput and " 

79 "chromatic corrections."), 

80 name="fgcmLookUpTable", 

81 storageClass="Catalog", 

82 dimensions=("instrument",), 

83 deferLoad=True, 

84 ) 

85 

86 fgcmVisitCatalog = connectionTypes.Input( 

87 doc="Catalog of visit information for fgcm", 

88 name="fgcmVisitCatalog", 

89 storageClass="Catalog", 

90 dimensions=("instrument",), 

91 deferLoad=True, 

92 ) 

93 

94 fgcmStandardStars = connectionTypes.Input( 

95 doc="Catalog of standard star data from fgcm fit", 

96 name="fgcmStandardStars{cycleNumber}", 

97 storageClass="SimpleCatalog", 

98 dimensions=("instrument",), 

99 deferLoad=True, 

100 ) 

101 

102 fgcmZeropoints = connectionTypes.Input( 

103 doc="Catalog of zeropoints from fgcm fit", 

104 name="fgcmZeropoints{cycleNumber}", 

105 storageClass="Catalog", 

106 dimensions=("instrument",), 

107 deferLoad=True, 

108 ) 

109 

110 fgcmAtmosphereParameters = connectionTypes.Input( 

111 doc="Catalog of atmosphere parameters from fgcm fit", 

112 name="fgcmAtmosphereParameters{cycleNumber}", 

113 storageClass="Catalog", 

114 dimensions=("instrument",), 

115 deferLoad=True, 

116 ) 

117 

118 refCat = connectionTypes.PrerequisiteInput( 

119 doc="Reference catalog to use for photometric calibration", 

120 name="cal_ref_cat", 

121 storageClass="SimpleCatalog", 

122 dimensions=("skypix",), 

123 deferLoad=True, 

124 multiple=True, 

125 ) 

126 

127 fgcmPhotoCalib = connectionTypes.Output( 

128 doc=("Per-visit photometric calibrations derived from fgcm calibration. " 

129 "These catalogs use detector id for the id and are sorted for " 

130 "fast lookups of a detector."), 

131 name="fgcmPhotoCalibCatalog", 

132 storageClass="ExposureCatalog", 

133 dimensions=("instrument", "visit",), 

134 multiple=True, 

135 ) 

136 

137 fgcmTransmissionAtmosphere = connectionTypes.Output( 

138 doc="Per-visit atmosphere transmission files produced from fgcm calibration", 

139 name="transmission_atmosphere_fgcm", 

140 storageClass="TransmissionCurve", 

141 dimensions=("instrument", 

142 "visit",), 

143 multiple=True, 

144 ) 

145 

146 fgcmOffsets = connectionTypes.Output( 

147 doc="Per-band offsets computed from doReferenceCalibration", 

148 name="fgcmReferenceCalibrationOffsets", 

149 storageClass="Catalog", 

150 dimensions=("instrument",), 

151 multiple=False, 

152 ) 

153 

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

155 super().__init__(config=config) 

156 

157 if str(int(config.connections.cycleNumber)) != config.connections.cycleNumber: 

158 raise ValueError("cycleNumber must be of integer format") 

159 

160 if not config.doReferenceCalibration: 

161 self.prerequisiteInputs.remove("refCat") 

162 if not config.doAtmosphereOutput: 

163 self.inputs.remove("fgcmAtmosphereParameters") 

164 if not config.doZeropointOutput: 

165 self.inputs.remove("fgcmZeropoints") 

166 if not config.doReferenceCalibration: 

167 self.outputs.remove("fgcmOffsets") 

168 

169 

170class FgcmOutputProductsConfig(pipeBase.PipelineTaskConfig, 

171 pipelineConnections=FgcmOutputProductsConnections): 

172 """Config for FgcmOutputProductsTask""" 

173 

174 cycleNumber = pexConfig.Field( 

175 doc="Final fit cycle from FGCM fit", 

176 dtype=int, 

177 default=None, 

178 ) 

179 physicalFilterMap = pexConfig.DictField( 

180 doc="Mapping from 'physicalFilter' to band.", 

181 keytype=str, 

182 itemtype=str, 

183 default={}, 

184 ) 

185 # The following fields refer to calibrating from a reference 

186 # catalog, but in the future this might need to be expanded 

187 doReferenceCalibration = pexConfig.Field( 

188 doc=("Transfer 'absolute' calibration from reference catalog? " 

189 "This afterburner step is unnecessary if reference stars " 

190 "were used in the full fit in FgcmFitCycleTask."), 

191 dtype=bool, 

192 default=False, 

193 ) 

194 doRefcatOutput = pexConfig.Field( 

195 doc="Output standard stars in reference catalog format", 

196 dtype=bool, 

197 default=False, 

198 deprecated="doRefcatOutput is no longer supported; this config will be removed after v24" 

199 ) 

200 doAtmosphereOutput = pexConfig.Field( 

201 doc="Output atmospheres in transmission_atmosphere_fgcm format", 

202 dtype=bool, 

203 default=True, 

204 ) 

205 doZeropointOutput = pexConfig.Field( 

206 doc="Output zeropoints in fgcm_photoCalib format", 

207 dtype=bool, 

208 default=True, 

209 ) 

210 doComposeWcsJacobian = pexConfig.Field( 

211 doc="Compose Jacobian of WCS with fgcm calibration for output photoCalib?", 

212 dtype=bool, 

213 default=True, 

214 ) 

215 doApplyMeanChromaticCorrection = pexConfig.Field( 

216 doc="Apply the mean chromatic correction to the zeropoints?", 

217 dtype=bool, 

218 default=True, 

219 ) 

220 refObjLoader = pexConfig.ConfigurableField( 

221 target=LoadIndexedReferenceObjectsTask, 

222 doc="reference object loader for 'absolute' photometric calibration", 

223 deprecated="refObjLoader is deprecated, and will be removed after v24", 

224 ) 

225 photoCal = pexConfig.ConfigurableField( 

226 target=PhotoCalTask, 

227 doc="task to perform 'absolute' calibration", 

228 ) 

229 referencePixelizationNside = pexConfig.Field( 

230 doc="Healpix nside to pixelize catalog to compare to reference catalog", 

231 dtype=int, 

232 default=64, 

233 ) 

234 referencePixelizationMinStars = pexConfig.Field( 

235 doc=("Minimum number of stars per healpix pixel to select for comparison" 

236 "to the specified reference catalog"), 

237 dtype=int, 

238 default=200, 

239 ) 

240 referenceMinMatch = pexConfig.Field( 

241 doc="Minimum number of stars matched to reference catalog to be used in statistics", 

242 dtype=int, 

243 default=50, 

244 ) 

245 referencePixelizationNPixels = pexConfig.Field( 

246 doc=("Number of healpix pixels to sample to do comparison. " 

247 "Doing too many will take a long time and not yield any more " 

248 "precise results because the final number is the median offset " 

249 "(per band) from the set of pixels."), 

250 dtype=int, 

251 default=100, 

252 ) 

253 datasetConfig = pexConfig.ConfigField( 

254 dtype=DatasetConfig, 

255 doc="Configuration for writing/reading ingested catalog", 

256 deprecated="The datasetConfig was only used for gen2; this config will be removed after v24.", 

257 ) 

258 

259 def setDefaults(self): 

260 pexConfig.Config.setDefaults(self) 

261 

262 # In order to transfer the "absolute" calibration from a reference 

263 # catalog to the relatively calibrated FGCM standard stars (one number 

264 # per band), we use the PhotoCalTask to match stars in a sample of healpix 

265 # pixels. These basic settings ensure that only well-measured, good stars 

266 # from the source and reference catalogs are used for the matching. 

267 

268 # applyColorTerms needs to be False if doReferenceCalibration is False, 

269 # as is the new default after DM-16702 

270 self.photoCal.applyColorTerms = False 

271 self.photoCal.fluxField = 'instFlux' 

272 self.photoCal.magErrFloor = 0.003 

273 self.photoCal.match.referenceSelection.doSignalToNoise = True 

274 self.photoCal.match.referenceSelection.signalToNoise.minimum = 10.0 

275 self.photoCal.match.sourceSelection.doSignalToNoise = True 

276 self.photoCal.match.sourceSelection.signalToNoise.minimum = 10.0 

277 self.photoCal.match.sourceSelection.signalToNoise.fluxField = 'instFlux' 

278 self.photoCal.match.sourceSelection.signalToNoise.errField = 'instFluxErr' 

279 self.photoCal.match.sourceSelection.doFlags = True 

280 self.photoCal.match.sourceSelection.flags.good = [] 

281 self.photoCal.match.sourceSelection.flags.bad = ['flag_badStar'] 

282 self.photoCal.match.sourceSelection.doUnresolved = False 

283 

284 def validate(self): 

285 super().validate() 

286 

287 # Force the connections to conform with cycleNumber 

288 self.connections.cycleNumber = str(self.cycleNumber) 

289 

290 

291class FgcmOutputProductsTask(pipeBase.PipelineTask): 

292 """ 

293 Output products from FGCM global calibration. 

294 """ 

295 

296 ConfigClass = FgcmOutputProductsConfig 

297 _DefaultName = "fgcmOutputProducts" 

298 

299 def __init__(self, **kwargs): 

300 super().__init__(**kwargs) 

301 

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

303 handleDict = {} 

304 handleDict['camera'] = butlerQC.get(inputRefs.camera) 

305 handleDict['fgcmLookUpTable'] = butlerQC.get(inputRefs.fgcmLookUpTable) 

306 handleDict['fgcmVisitCatalog'] = butlerQC.get(inputRefs.fgcmVisitCatalog) 

307 handleDict['fgcmStandardStars'] = butlerQC.get(inputRefs.fgcmStandardStars) 

308 

309 if self.config.doZeropointOutput: 

310 handleDict['fgcmZeropoints'] = butlerQC.get(inputRefs.fgcmZeropoints) 

311 photoCalibRefDict = {photoCalibRef.dataId.byName()['visit']: 

312 photoCalibRef for photoCalibRef in outputRefs.fgcmPhotoCalib} 

313 

314 if self.config.doAtmosphereOutput: 

315 handleDict['fgcmAtmosphereParameters'] = butlerQC.get(inputRefs.fgcmAtmosphereParameters) 

316 atmRefDict = {atmRef.dataId.byName()['visit']: atmRef for 

317 atmRef in outputRefs.fgcmTransmissionAtmosphere} 

318 

319 if self.config.doReferenceCalibration: 

320 refConfig = LoadReferenceObjectsConfig() 

321 self.refObjLoader = ReferenceObjectLoader(dataIds=[ref.datasetRef.dataId 

322 for ref in inputRefs.refCat], 

323 refCats=butlerQC.get(inputRefs.refCat), 

324 log=self.log, 

325 config=refConfig) 

326 else: 

327 self.refObjLoader = None 

328 

329 struct = self.run(handleDict, self.config.physicalFilterMap) 

330 

331 # Output the photoCalib exposure catalogs 

332 if struct.photoCalibCatalogs is not None: 

333 self.log.info("Outputting photoCalib catalogs.") 

334 for visit, expCatalog in struct.photoCalibCatalogs: 

335 butlerQC.put(expCatalog, photoCalibRefDict[visit]) 

336 self.log.info("Done outputting photoCalib catalogs.") 

337 

338 # Output the atmospheres 

339 if struct.atmospheres is not None: 

340 self.log.info("Outputting atmosphere transmission files.") 

341 for visit, atm in struct.atmospheres: 

342 butlerQC.put(atm, atmRefDict[visit]) 

343 self.log.info("Done outputting atmosphere files.") 

344 

345 if self.config.doReferenceCalibration: 

346 # Turn offset into simple catalog for persistence if necessary 

347 schema = afwTable.Schema() 

348 schema.addField('offset', type=np.float64, 

349 doc="Post-process calibration offset (mag)") 

350 offsetCat = afwTable.BaseCatalog(schema) 

351 offsetCat.resize(len(struct.offsets)) 

352 offsetCat['offset'][:] = struct.offsets 

353 

354 butlerQC.put(offsetCat, outputRefs.fgcmOffsets) 

355 

356 return 

357 

358 def run(self, handleDict, physicalFilterMap): 

359 """Run the output products task. 

360 

361 Parameters 

362 ---------- 

363 handleDict : `dict` 

364 All handles are `lsst.daf.butler.DeferredDatasetHandle` 

365 handle dictionary with keys: 

366 

367 ``"camera"`` 

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

369 ``"fgcmLookUpTable"`` 

370 handle for the FGCM look-up table. 

371 ``"fgcmVisitCatalog"`` 

372 handle for visit summary catalog. 

373 ``"fgcmStandardStars"`` 

374 handle for the output standard star catalog. 

375 ``"fgcmZeropoints"`` 

376 handle for the zeropoint data catalog. 

377 ``"fgcmAtmosphereParameters"`` 

378 handle for the atmosphere parameter catalog. 

379 ``"fgcmBuildStarsTableConfig"`` 

380 Config for `lsst.fgcmcal.fgcmBuildStarsTableTask`. 

381 physicalFilterMap : `dict` 

382 Dictionary of mappings from physical filter to FGCM band. 

383 

384 Returns 

385 ------- 

386 retStruct : `lsst.pipe.base.Struct` 

387 Output structure with keys: 

388 

389 offsets : `np.ndarray` 

390 Final reference offsets, per band. 

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

392 Generator that returns (visit, transmissionCurve) tuples. 

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

394 Generator that returns (visit, exposureCatalog) tuples. 

395 """ 

396 stdCat = handleDict['fgcmStandardStars'].get() 

397 md = stdCat.getMetadata() 

398 bands = md.getArray('BANDS') 

399 

400 if self.config.doReferenceCalibration: 

401 lutCat = handleDict['fgcmLookUpTable'].get() 

402 offsets = self._computeReferenceOffsets(stdCat, lutCat, physicalFilterMap, bands) 

403 else: 

404 offsets = np.zeros(len(bands)) 

405 

406 del stdCat 

407 

408 if self.config.doZeropointOutput: 

409 zptCat = handleDict['fgcmZeropoints'].get() 

410 visitCat = handleDict['fgcmVisitCatalog'].get() 

411 

412 pcgen = self._outputZeropoints(handleDict['camera'], zptCat, visitCat, offsets, bands, 

413 physicalFilterMap) 

414 else: 

415 pcgen = None 

416 

417 if self.config.doAtmosphereOutput: 

418 atmCat = handleDict['fgcmAtmosphereParameters'].get() 

419 atmgen = self._outputAtmospheres(handleDict, atmCat) 

420 else: 

421 atmgen = None 

422 

423 retStruct = pipeBase.Struct(offsets=offsets, 

424 atmospheres=atmgen) 

425 retStruct.photoCalibCatalogs = pcgen 

426 

427 return retStruct 

428 

429 def generateTractOutputProducts(self, handleDict, tract, 

430 visitCat, zptCat, atmCat, stdCat, 

431 fgcmBuildStarsConfig): 

432 """ 

433 Generate the output products for a given tract, as specified in the config. 

434 

435 This method is here to have an alternate entry-point for 

436 FgcmCalibrateTract. 

437 

438 Parameters 

439 ---------- 

440 handleDict : `dict` 

441 All handles are `lsst.daf.butler.DeferredDatasetHandle` 

442 handle dictionary with keys: 

443 

444 ``"camera"`` 

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

446 ``"fgcmLookUpTable"`` 

447 handle for the FGCM look-up table. 

448 tract : `int` 

449 Tract number 

450 visitCat : `lsst.afw.table.BaseCatalog` 

451 FGCM visitCat from `FgcmBuildStarsTask` 

452 zptCat : `lsst.afw.table.BaseCatalog` 

453 FGCM zeropoint catalog from `FgcmFitCycleTask` 

454 atmCat : `lsst.afw.table.BaseCatalog` 

455 FGCM atmosphere parameter catalog from `FgcmFitCycleTask` 

456 stdCat : `lsst.afw.table.SimpleCatalog` 

457 FGCM standard star catalog from `FgcmFitCycleTask` 

458 fgcmBuildStarsConfig : `lsst.fgcmcal.FgcmBuildStarsConfig` 

459 Configuration object from `FgcmBuildStarsTask` 

460 

461 Returns 

462 ------- 

463 retStruct : `lsst.pipe.base.Struct` 

464 Output structure with keys: 

465 

466 offsets : `np.ndarray` 

467 Final reference offsets, per band. 

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

469 Generator that returns (visit, transmissionCurve) tuples. 

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

471 Generator that returns (visit, exposureCatalog) tuples. 

472 """ 

473 physicalFilterMap = fgcmBuildStarsConfig.physicalFilterMap 

474 

475 md = stdCat.getMetadata() 

476 bands = md.getArray('BANDS') 

477 

478 if self.config.doComposeWcsJacobian and not fgcmBuildStarsConfig.doApplyWcsJacobian: 

479 raise RuntimeError("Cannot compose the WCS jacobian if it hasn't been applied " 

480 "in fgcmBuildStarsTask.") 

481 

482 if not self.config.doComposeWcsJacobian and fgcmBuildStarsConfig.doApplyWcsJacobian: 

483 self.log.warning("Jacobian was applied in build-stars but doComposeWcsJacobian is not set.") 

484 

485 if self.config.doReferenceCalibration: 

486 lutCat = handleDict['fgcmLookUpTable'].get() 

487 offsets = self._computeReferenceOffsets(stdCat, lutCat, bands, physicalFilterMap) 

488 else: 

489 offsets = np.zeros(len(bands)) 

490 

491 if self.config.doZeropointOutput: 

492 pcgen = self._outputZeropoints(handleDict['camera'], zptCat, visitCat, offsets, bands, 

493 physicalFilterMap) 

494 else: 

495 pcgen = None 

496 

497 if self.config.doAtmosphereOutput: 

498 atmgen = self._outputAtmospheres(handleDict, atmCat) 

499 else: 

500 atmgen = None 

501 

502 retStruct = pipeBase.Struct(offsets=offsets, 

503 atmospheres=atmgen) 

504 retStruct.photoCalibCatalogs = pcgen 

505 

506 return retStruct 

507 

508 def _computeReferenceOffsets(self, stdCat, lutCat, physicalFilterMap, bands): 

509 """ 

510 Compute offsets relative to a reference catalog. 

511 

512 This method splits the star catalog into healpix pixels 

513 and computes the calibration transfer for a sample of 

514 these pixels to approximate the 'absolute' calibration 

515 values (on for each band) to apply to transfer the 

516 absolute scale. 

517 

518 Parameters 

519 ---------- 

520 stdCat : `lsst.afw.table.SimpleCatalog` 

521 FGCM standard stars 

522 lutCat : `lsst.afw.table.SimpleCatalog` 

523 FGCM Look-up table 

524 physicalFilterMap : `dict` 

525 Dictionary of mappings from physical filter to FGCM band. 

526 bands : `list` [`str`] 

527 List of band names from FGCM output 

528 Returns 

529 ------- 

530 offsets : `numpy.array` of floats 

531 Per band zeropoint offsets 

532 """ 

533 

534 # Only use stars that are observed in all the bands that were actually used 

535 # This will ensure that we use the same healpix pixels for the absolute 

536 # calibration of each band. 

537 minObs = stdCat['ngood'].min(axis=1) 

538 

539 goodStars = (minObs >= 1) 

540 stdCat = stdCat[goodStars] 

541 

542 self.log.info("Found %d stars with at least 1 good observation in each band" % 

543 (len(stdCat))) 

544 

545 # Associate each band with the appropriate physicalFilter and make 

546 # filterLabels 

547 filterLabels = [] 

548 

549 lutPhysicalFilters = lutCat[0]['physicalFilters'].split(',') 

550 lutStdPhysicalFilters = lutCat[0]['stdPhysicalFilters'].split(',') 

551 physicalFilterMapBands = list(physicalFilterMap.values()) 

552 physicalFilterMapFilters = list(physicalFilterMap.keys()) 

553 for band in bands: 

554 # Find a physical filter associated from the band by doing 

555 # a reverse lookup on the physicalFilterMap dict 

556 physicalFilterMapIndex = physicalFilterMapBands.index(band) 

557 physicalFilter = physicalFilterMapFilters[physicalFilterMapIndex] 

558 # Find the appropriate fgcm standard physicalFilter 

559 lutPhysicalFilterIndex = lutPhysicalFilters.index(physicalFilter) 

560 stdPhysicalFilter = lutStdPhysicalFilters[lutPhysicalFilterIndex] 

561 filterLabels.append(afwImage.FilterLabel(band=band, 

562 physical=stdPhysicalFilter)) 

563 

564 # We have to make a table for each pixel with flux/fluxErr 

565 # This is a temporary table generated for input to the photoCal task. 

566 # These fluxes are not instFlux (they are top-of-the-atmosphere approximate and 

567 # have had chromatic corrections applied to get to the standard system 

568 # specified by the atmosphere/instrumental parameters), nor are they 

569 # in Jansky (since they don't have a proper absolute calibration: the overall 

570 # zeropoint is estimated from the telescope size, etc.) 

571 sourceMapper = afwTable.SchemaMapper(stdCat.schema) 

572 sourceMapper.addMinimalSchema(afwTable.SimpleTable.makeMinimalSchema()) 

573 sourceMapper.editOutputSchema().addField('instFlux', type=np.float64, 

574 doc="instrumental flux (counts)") 

575 sourceMapper.editOutputSchema().addField('instFluxErr', type=np.float64, 

576 doc="instrumental flux error (counts)") 

577 badStarKey = sourceMapper.editOutputSchema().addField('flag_badStar', 

578 type='Flag', 

579 doc="bad flag") 

580 

581 # Split up the stars 

582 # Note that there is an assumption here that the ra/dec coords stored 

583 # on-disk are in radians, and therefore that starObs['coord_ra'] / 

584 # starObs['coord_dec'] return radians when used as an array of numpy float64s. 

585 theta = np.pi/2. - stdCat['coord_dec'] 

586 phi = stdCat['coord_ra'] 

587 

588 ipring = hp.ang2pix(self.config.referencePixelizationNside, theta, phi) 

589 h, rev = esutil.stat.histogram(ipring, rev=True) 

590 

591 gdpix, = np.where(h >= self.config.referencePixelizationMinStars) 

592 

593 self.log.info("Found %d pixels (nside=%d) with at least %d good stars" % 

594 (gdpix.size, 

595 self.config.referencePixelizationNside, 

596 self.config.referencePixelizationMinStars)) 

597 

598 if gdpix.size < self.config.referencePixelizationNPixels: 

599 self.log.warning("Found fewer good pixels (%d) than preferred in configuration (%d)" % 

600 (gdpix.size, self.config.referencePixelizationNPixels)) 

601 else: 

602 # Sample out the pixels we want to use 

603 gdpix = np.random.choice(gdpix, size=self.config.referencePixelizationNPixels, replace=False) 

604 

605 results = np.zeros(gdpix.size, dtype=[('hpix', 'i4'), 

606 ('nstar', 'i4', len(bands)), 

607 ('nmatch', 'i4', len(bands)), 

608 ('zp', 'f4', len(bands)), 

609 ('zpErr', 'f4', len(bands))]) 

610 results['hpix'] = ipring[rev[rev[gdpix]]] 

611 

612 # We need a boolean index to deal with catalogs... 

613 selected = np.zeros(len(stdCat), dtype=bool) 

614 

615 refFluxFields = [None]*len(bands) 

616 

617 for p_index, pix in enumerate(gdpix): 

618 i1a = rev[rev[pix]: rev[pix + 1]] 

619 

620 # the stdCat afwTable can only be indexed with boolean arrays, 

621 # and not numpy index arrays (see DM-16497). This little trick 

622 # converts the index array into a boolean array 

623 selected[:] = False 

624 selected[i1a] = True 

625 

626 for b_index, filterLabel in enumerate(filterLabels): 

627 struct = self._computeOffsetOneBand(sourceMapper, badStarKey, b_index, 

628 filterLabel, stdCat, 

629 selected, refFluxFields) 

630 results['nstar'][p_index, b_index] = len(i1a) 

631 results['nmatch'][p_index, b_index] = len(struct.arrays.refMag) 

632 results['zp'][p_index, b_index] = struct.zp 

633 results['zpErr'][p_index, b_index] = struct.sigma 

634 

635 # And compute the summary statistics 

636 offsets = np.zeros(len(bands)) 

637 

638 for b_index, band in enumerate(bands): 

639 # make configurable 

640 ok, = np.where(results['nmatch'][:, b_index] >= self.config.referenceMinMatch) 

641 offsets[b_index] = np.median(results['zp'][ok, b_index]) 

642 # use median absolute deviation to estimate Normal sigma 

643 # see https://en.wikipedia.org/wiki/Median_absolute_deviation 

644 madSigma = 1.4826*np.median(np.abs(results['zp'][ok, b_index] - offsets[b_index])) 

645 self.log.info("Reference catalog offset for %s band: %.12f +/- %.12f", 

646 band, offsets[b_index], madSigma) 

647 

648 return offsets 

649 

650 def _computeOffsetOneBand(self, sourceMapper, badStarKey, 

651 b_index, filterLabel, stdCat, selected, refFluxFields): 

652 """ 

653 Compute the zeropoint offset between the fgcm stdCat and the reference 

654 stars for one pixel in one band 

655 

656 Parameters 

657 ---------- 

658 sourceMapper : `lsst.afw.table.SchemaMapper` 

659 Mapper to go from stdCat to calibratable catalog 

660 badStarKey : `lsst.afw.table.Key` 

661 Key for the field with bad stars 

662 b_index : `int` 

663 Index of the band in the star catalog 

664 filterLabel : `lsst.afw.image.FilterLabel` 

665 filterLabel with band and physical filter 

666 stdCat : `lsst.afw.table.SimpleCatalog` 

667 FGCM standard stars 

668 selected : `numpy.array(dtype=bool)` 

669 Boolean array of which stars are in the pixel 

670 refFluxFields : `list` 

671 List of names of flux fields for reference catalog 

672 """ 

673 

674 sourceCat = afwTable.SimpleCatalog(sourceMapper.getOutputSchema()) 

675 sourceCat.reserve(selected.sum()) 

676 sourceCat.extend(stdCat[selected], mapper=sourceMapper) 

677 sourceCat['instFlux'] = 10.**(stdCat['mag_std_noabs'][selected, b_index]/(-2.5)) 

678 sourceCat['instFluxErr'] = (np.log(10.)/2.5)*(stdCat['magErr_std'][selected, b_index] 

679 * sourceCat['instFlux']) 

680 # Make sure we only use stars that have valid measurements 

681 # (This is perhaps redundant with requirements above that the 

682 # stars be observed in all bands, but it can't hurt) 

683 badStar = (stdCat['mag_std_noabs'][selected, b_index] > 90.0) 

684 for rec in sourceCat[badStar]: 

685 rec.set(badStarKey, True) 

686 

687 exposure = afwImage.ExposureF() 

688 exposure.setFilterLabel(filterLabel) 

689 

690 if refFluxFields[b_index] is None: 

691 # Need to find the flux field in the reference catalog 

692 # to work around limitations of DirectMatch in PhotoCal 

693 ctr = stdCat[0].getCoord() 

694 rad = 0.05*lsst.geom.degrees 

695 refDataTest = self.refObjLoader.loadSkyCircle(ctr, rad, filterLabel.bandLabel) 

696 refFluxFields[b_index] = refDataTest.fluxField 

697 

698 # Make a copy of the config so that we can modify it 

699 calConfig = copy.copy(self.config.photoCal.value) 

700 calConfig.match.referenceSelection.signalToNoise.fluxField = refFluxFields[b_index] 

701 calConfig.match.referenceSelection.signalToNoise.errField = refFluxFields[b_index] + 'Err' 

702 calTask = self.config.photoCal.target(refObjLoader=self.refObjLoader, 

703 config=calConfig, 

704 schema=sourceCat.getSchema()) 

705 

706 struct = calTask.run(exposure, sourceCat) 

707 

708 return struct 

709 

710 def _formatCatalog(self, fgcmStarCat, offsets, bands): 

711 """ 

712 Turn an FGCM-formatted star catalog, applying zeropoint offsets. 

713 

714 Parameters 

715 ---------- 

716 fgcmStarCat : `lsst.afw.Table.SimpleCatalog` 

717 SimpleCatalog as output by fgcmcal 

718 offsets : `list` with len(self.bands) entries 

719 Zeropoint offsets to apply 

720 bands : `list` [`str`] 

721 List of band names from FGCM output 

722 

723 Returns 

724 ------- 

725 formattedCat: `lsst.afw.table.SimpleCatalog` 

726 SimpleCatalog suitable for using as a reference catalog 

727 """ 

728 

729 sourceMapper = afwTable.SchemaMapper(fgcmStarCat.schema) 

730 minSchema = LoadIndexedReferenceObjectsTask.makeMinimalSchema(bands, 

731 addCentroid=False, 

732 addIsResolved=True, 

733 coordErrDim=0) 

734 sourceMapper.addMinimalSchema(minSchema) 

735 for band in bands: 

736 sourceMapper.editOutputSchema().addField('%s_nGood' % (band), type=np.int32) 

737 sourceMapper.editOutputSchema().addField('%s_nTotal' % (band), type=np.int32) 

738 sourceMapper.editOutputSchema().addField('%s_nPsfCandidate' % (band), type=np.int32) 

739 

740 formattedCat = afwTable.SimpleCatalog(sourceMapper.getOutputSchema()) 

741 formattedCat.reserve(len(fgcmStarCat)) 

742 formattedCat.extend(fgcmStarCat, mapper=sourceMapper) 

743 

744 # Note that we don't have to set `resolved` because the default is False 

745 

746 for b, band in enumerate(bands): 

747 mag = fgcmStarCat['mag_std_noabs'][:, b].astype(np.float64) + offsets[b] 

748 # We want fluxes in nJy from calibrated AB magnitudes 

749 # (after applying offset). Updated after RFC-549 and RFC-575. 

750 flux = (mag*units.ABmag).to_value(units.nJy) 

751 fluxErr = (np.log(10.)/2.5)*flux*fgcmStarCat['magErr_std'][:, b].astype(np.float64) 

752 

753 formattedCat['%s_flux' % (band)][:] = flux 

754 formattedCat['%s_fluxErr' % (band)][:] = fluxErr 

755 formattedCat['%s_nGood' % (band)][:] = fgcmStarCat['ngood'][:, b] 

756 formattedCat['%s_nTotal' % (band)][:] = fgcmStarCat['ntotal'][:, b] 

757 formattedCat['%s_nPsfCandidate' % (band)][:] = fgcmStarCat['npsfcand'][:, b] 

758 

759 addRefCatMetadata(formattedCat) 

760 

761 return formattedCat 

762 

763 def _outputZeropoints(self, camera, zptCat, visitCat, offsets, bands, 

764 physicalFilterMap, tract=None): 

765 """Output the zeropoints in fgcm_photoCalib format. 

766 

767 Parameters 

768 ---------- 

769 camera : `lsst.afw.cameraGeom.Camera` 

770 Camera from the butler. 

771 zptCat : `lsst.afw.table.BaseCatalog` 

772 FGCM zeropoint catalog from `FgcmFitCycleTask`. 

773 visitCat : `lsst.afw.table.BaseCatalog` 

774 FGCM visitCat from `FgcmBuildStarsTask`. 

775 offsets : `numpy.array` 

776 Float array of absolute calibration offsets, one for each filter. 

777 bands : `list` [`str`] 

778 List of band names from FGCM output. 

779 physicalFilterMap : `dict` 

780 Dictionary of mappings from physical filter to FGCM band. 

781 tract: `int`, optional 

782 Tract number to output. Default is None (global calibration) 

783 

784 Returns 

785 ------- 

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

787 Generator that returns (visit, exposureCatalog) tuples. 

788 """ 

789 # Select visit/ccds where we have a calibration 

790 # This includes ccds where we were able to interpolate from neighboring 

791 # ccds. 

792 cannot_compute = fgcm.fgcmUtilities.zpFlagDict['CANNOT_COMPUTE_ZEROPOINT'] 

793 selected = (((zptCat['fgcmFlag'] & cannot_compute) == 0) 

794 & (zptCat['fgcmZptVar'] > 0.0) 

795 & (zptCat['fgcmZpt'] > FGCM_ILLEGAL_VALUE)) 

796 

797 # Log warnings for any visit which has no valid zeropoints 

798 badVisits = np.unique(zptCat['visit'][~selected]) 

799 goodVisits = np.unique(zptCat['visit'][selected]) 

800 allBadVisits = badVisits[~np.isin(badVisits, goodVisits)] 

801 for allBadVisit in allBadVisits: 

802 self.log.warning(f'No suitable photoCalib for visit {allBadVisit}') 

803 

804 # Get a mapping from filtername to the offsets 

805 offsetMapping = {} 

806 for f in physicalFilterMap: 

807 # Not every filter in the map will necesarily have a band. 

808 if physicalFilterMap[f] in bands: 

809 offsetMapping[f] = offsets[bands.index(physicalFilterMap[f])] 

810 

811 # Get a mapping from "ccd" to the ccd index used for the scaling 

812 ccdMapping = {} 

813 for ccdIndex, detector in enumerate(camera): 

814 ccdMapping[detector.getId()] = ccdIndex 

815 

816 # And a mapping to get the flat-field scaling values 

817 scalingMapping = {} 

818 for rec in visitCat: 

819 scalingMapping[rec['visit']] = rec['scaling'] 

820 

821 if self.config.doComposeWcsJacobian: 

822 approxPixelAreaFields = computeApproxPixelAreaFields(camera) 

823 

824 # The zptCat is sorted by visit, which is useful 

825 lastVisit = -1 

826 zptVisitCatalog = None 

827 

828 metadata = dafBase.PropertyList() 

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

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

831 

832 for rec in zptCat[selected]: 

833 # Retrieve overall scaling 

834 scaling = scalingMapping[rec['visit']][ccdMapping[rec['detector']]] 

835 

836 # The postCalibrationOffset describe any zeropoint offsets 

837 # to apply after the fgcm calibration. The first part comes 

838 # from the reference catalog match (used in testing). The 

839 # second part comes from the mean chromatic correction 

840 # (if configured). 

841 postCalibrationOffset = offsetMapping[rec['filtername']] 

842 if self.config.doApplyMeanChromaticCorrection: 

843 postCalibrationOffset += rec['fgcmDeltaChrom'] 

844 

845 fgcmSuperStarField = self._getChebyshevBoundedField(rec['fgcmfZptSstarCheb'], 

846 rec['fgcmfZptChebXyMax']) 

847 # Convert from FGCM AB to nJy 

848 fgcmZptField = self._getChebyshevBoundedField((rec['fgcmfZptCheb']*units.AB).to_value(units.nJy), 

849 rec['fgcmfZptChebXyMax'], 

850 offset=postCalibrationOffset, 

851 scaling=scaling) 

852 

853 if self.config.doComposeWcsJacobian: 

854 

855 fgcmField = afwMath.ProductBoundedField([approxPixelAreaFields[rec['detector']], 

856 fgcmSuperStarField, 

857 fgcmZptField]) 

858 else: 

859 # The photoCalib is just the product of the fgcmSuperStarField and the 

860 # fgcmZptField 

861 fgcmField = afwMath.ProductBoundedField([fgcmSuperStarField, fgcmZptField]) 

862 

863 # The "mean" calibration will be set to the center of the ccd for reference 

864 calibCenter = fgcmField.evaluate(fgcmField.getBBox().getCenter()) 

865 calibErr = (np.log(10.0)/2.5)*calibCenter*np.sqrt(rec['fgcmZptVar']) 

866 photoCalib = afwImage.PhotoCalib(calibrationMean=calibCenter, 

867 calibrationErr=calibErr, 

868 calibration=fgcmField, 

869 isConstant=False) 

870 

871 # Return full per-visit exposure catalogs 

872 if rec['visit'] != lastVisit: 

873 # This is a new visit. If the last visit was not -1, yield 

874 # the ExposureCatalog 

875 if lastVisit > -1: 

876 # ensure that the detectors are in sorted order, for fast lookups 

877 zptVisitCatalog.sort() 

878 yield (int(lastVisit), zptVisitCatalog) 

879 else: 

880 # We need to create a new schema 

881 zptExpCatSchema = afwTable.ExposureTable.makeMinimalSchema() 

882 zptExpCatSchema.addField('visit', type='L', doc='Visit number') 

883 

884 # And start a new one 

885 zptVisitCatalog = afwTable.ExposureCatalog(zptExpCatSchema) 

886 zptVisitCatalog.setMetadata(metadata) 

887 

888 lastVisit = int(rec['visit']) 

889 

890 catRecord = zptVisitCatalog.addNew() 

891 catRecord['id'] = int(rec['detector']) 

892 catRecord['visit'] = rec['visit'] 

893 catRecord.setPhotoCalib(photoCalib) 

894 

895 # Final output of last exposure catalog 

896 # ensure that the detectors are in sorted order, for fast lookups 

897 zptVisitCatalog.sort() 

898 yield (int(lastVisit), zptVisitCatalog) 

899 

900 def _getChebyshevBoundedField(self, coefficients, xyMax, offset=0.0, scaling=1.0): 

901 """ 

902 Make a ChebyshevBoundedField from fgcm coefficients, with optional offset 

903 and scaling. 

904 

905 Parameters 

906 ---------- 

907 coefficients: `numpy.array` 

908 Flattened array of chebyshev coefficients 

909 xyMax: `list` of length 2 

910 Maximum x and y of the chebyshev bounding box 

911 offset: `float`, optional 

912 Absolute calibration offset. Default is 0.0 

913 scaling: `float`, optional 

914 Flat scaling value from fgcmBuildStars. Default is 1.0 

915 

916 Returns 

917 ------- 

918 boundedField: `lsst.afw.math.ChebyshevBoundedField` 

919 """ 

920 

921 orderPlus1 = int(np.sqrt(coefficients.size)) 

922 pars = np.zeros((orderPlus1, orderPlus1)) 

923 

924 bbox = lsst.geom.Box2I(lsst.geom.Point2I(0.0, 0.0), 

925 lsst.geom.Point2I(*xyMax)) 

926 

927 pars[:, :] = (coefficients.reshape(orderPlus1, orderPlus1) 

928 * (10.**(offset/-2.5))*scaling) 

929 

930 boundedField = afwMath.ChebyshevBoundedField(bbox, pars) 

931 

932 return boundedField 

933 

934 def _outputAtmospheres(self, handleDict, atmCat): 

935 """ 

936 Output the atmospheres. 

937 

938 Parameters 

939 ---------- 

940 handleDict : `dict` 

941 All data handles are `lsst.daf.butler.DeferredDatasetHandle` 

942 The handleDict has the follownig keys: 

943 

944 ``"fgcmLookUpTable"`` 

945 handle for the FGCM look-up table. 

946 atmCat : `lsst.afw.table.BaseCatalog` 

947 FGCM atmosphere parameter catalog from fgcmFitCycleTask. 

948 

949 Returns 

950 ------- 

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

952 Generator that returns (visit, transmissionCurve) tuples. 

953 """ 

954 # First, we need to grab the look-up table and key info 

955 lutCat = handleDict['fgcmLookUpTable'].get() 

956 

957 atmosphereTableName = lutCat[0]['tablename'] 

958 elevation = lutCat[0]['elevation'] 

959 atmLambda = lutCat[0]['atmLambda'] 

960 lutCat = None 

961 

962 # Make the atmosphere table if possible 

963 try: 

964 atmTable = fgcm.FgcmAtmosphereTable.initWithTableName(atmosphereTableName) 

965 atmTable.loadTable() 

966 except IOError: 

967 atmTable = None 

968 

969 if atmTable is None: 

970 # Try to use MODTRAN instead 

971 try: 

972 modGen = fgcm.ModtranGenerator(elevation) 

973 lambdaRange = np.array([atmLambda[0], atmLambda[-1]])/10. 

974 lambdaStep = (atmLambda[1] - atmLambda[0])/10. 

975 except (ValueError, IOError) as e: 

976 raise RuntimeError("FGCM look-up-table generated with modtran, " 

977 "but modtran not configured to run.") from e 

978 

979 zenith = np.degrees(np.arccos(1./atmCat['secZenith'])) 

980 

981 for i, visit in enumerate(atmCat['visit']): 

982 if atmTable is not None: 

983 # Interpolate the atmosphere table 

984 atmVals = atmTable.interpolateAtmosphere(pmb=atmCat[i]['pmb'], 

985 pwv=atmCat[i]['pwv'], 

986 o3=atmCat[i]['o3'], 

987 tau=atmCat[i]['tau'], 

988 alpha=atmCat[i]['alpha'], 

989 zenith=zenith[i], 

990 ctranslamstd=[atmCat[i]['cTrans'], 

991 atmCat[i]['lamStd']]) 

992 else: 

993 # Run modtran 

994 modAtm = modGen(pmb=atmCat[i]['pmb'], 

995 pwv=atmCat[i]['pwv'], 

996 o3=atmCat[i]['o3'], 

997 tau=atmCat[i]['tau'], 

998 alpha=atmCat[i]['alpha'], 

999 zenith=zenith[i], 

1000 lambdaRange=lambdaRange, 

1001 lambdaStep=lambdaStep, 

1002 ctranslamstd=[atmCat[i]['cTrans'], 

1003 atmCat[i]['lamStd']]) 

1004 atmVals = modAtm['COMBINED'] 

1005 

1006 # Now need to create something to persist... 

1007 curve = TransmissionCurve.makeSpatiallyConstant(throughput=atmVals, 

1008 wavelengths=atmLambda, 

1009 throughputAtMin=atmVals[0], 

1010 throughputAtMax=atmVals[-1]) 

1011 

1012 yield (int(visit), curve)