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1# This file is part of cp_pipe. 

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 

23__all__ = ['MeasurePhotonTransferCurveTask', 

24 'MeasurePhotonTransferCurveTaskConfig', 

25 'PhotonTransferCurveDataset'] 

26 

27import numpy as np 

28import matplotlib.pyplot as plt 

29from sqlite3 import OperationalError 

30from collections import Counter 

31from dataclasses import dataclass 

32 

33import lsst.afw.math as afwMath 

34import lsst.pex.config as pexConfig 

35import lsst.pipe.base as pipeBase 

36from .utils import (NonexistentDatasetTaskDataIdContainer, PairedVisitListTaskRunner, 

37 checkExpLengthEqual, fitLeastSq, fitBootstrap, funcPolynomial, funcAstier) 

38from scipy.optimize import least_squares 

39 

40from lsst.ip.isr.linearize import Linearizer 

41import datetime 

42 

43from .astierCovPtcUtils import (fftSize, CovFft, computeCovDirect, fitData) 

44 

45 

46class MeasurePhotonTransferCurveTaskConfig(pexConfig.Config): 

47 """Config class for photon transfer curve measurement task""" 

48 ccdKey = pexConfig.Field( 

49 dtype=str, 

50 doc="The key by which to pull a detector from a dataId, e.g. 'ccd' or 'detector'.", 

51 default='ccd', 

52 ) 

53 ptcFitType = pexConfig.ChoiceField( 

54 dtype=str, 

55 doc="Fit PTC to approximation in Astier+19 (Equation 16) or to a polynomial.", 

56 default="POLYNOMIAL", 

57 allowed={ 

58 "POLYNOMIAL": "n-degree polynomial (use 'polynomialFitDegree' to set 'n').", 

59 "EXPAPPROXIMATION": "Approximation in Astier+19 (Eq. 16).", 

60 "FULLCOVARIANCE": "Full covariances model in Astier+19 (Eq. 20)" 

61 } 

62 ) 

63 sigmaClipFullFitCovariancesAstier = pexConfig.Field( 

64 dtype=float, 

65 doc="sigma clip for full model fit for FULLCOVARIANCE ptcFitType ", 

66 default=5.0, 

67 ) 

68 maxIterFullFitCovariancesAstier = pexConfig.Field( 

69 dtype=int, 

70 doc="Maximum number of iterations in full model fit for FULLCOVARIANCE ptcFitType", 

71 default=3, 

72 ) 

73 maximumRangeCovariancesAstier = pexConfig.Field( 

74 dtype=int, 

75 doc="Maximum range of covariances as in Astier+19", 

76 default=8, 

77 ) 

78 covAstierRealSpace = pexConfig.Field( 

79 dtype=bool, 

80 doc="Calculate covariances in real space or via FFT? (see appendix A of Astier+19).", 

81 default=False, 

82 ) 

83 polynomialFitDegree = pexConfig.Field( 

84 dtype=int, 

85 doc="Degree of polynomial to fit the PTC, when 'ptcFitType'=POLYNOMIAL.", 

86 default=3, 

87 ) 

88 doCreateLinearizer = pexConfig.Field( 

89 dtype=bool, 

90 doc="Calculate non-linearity and persist linearizer?", 

91 default=False, 

92 ) 

93 linearizerType = pexConfig.ChoiceField( 

94 dtype=str, 

95 doc="Linearizer type, if doCreateLinearizer=True", 

96 default="LINEARIZEPOLYNOMIAL", 

97 allowed={ 

98 "LINEARIZEPOLYNOMIAL": "n-degree polynomial (use 'polynomialFitDegreeNonLinearity' to set 'n').", 

99 "LINEARIZESQUARED": "c0 quadratic coefficient derived from coefficients of polynomiual fit", 

100 "LOOKUPTABLE": "Loouk table formed from linear part of polynomial fit." 

101 } 

102 ) 

103 polynomialFitDegreeNonLinearity = pexConfig.Field( 

104 dtype=int, 

105 doc="If doCreateLinearizer, degree of polynomial to fit the meanSignal vs exposureTime" + 

106 " curve to produce the table for LinearizeLookupTable.", 

107 default=3, 

108 ) 

109 binSize = pexConfig.Field( 

110 dtype=int, 

111 doc="Bin the image by this factor in both dimensions.", 

112 default=1, 

113 ) 

114 minMeanSignal = pexConfig.Field( 

115 dtype=float, 

116 doc="Minimum value (inclusive) of mean signal (in DN) above which to consider.", 

117 default=0, 

118 ) 

119 maxMeanSignal = pexConfig.Field( 

120 dtype=float, 

121 doc="Maximum value (inclusive) of mean signal (in DN) below which to consider.", 

122 default=9e6, 

123 ) 

124 initialNonLinearityExclusionThresholdPositive = pexConfig.RangeField( 

125 dtype=float, 

126 doc="Initially exclude data points with a variance that are more than a factor of this from being" 

127 " linear in the positive direction, from the PTC fit. Note that these points will also be" 

128 " excluded from the non-linearity fit. This is done before the iterative outlier rejection," 

129 " to allow an accurate determination of the sigmas for said iterative fit.", 

130 default=0.12, 

131 min=0.0, 

132 max=1.0, 

133 ) 

134 initialNonLinearityExclusionThresholdNegative = pexConfig.RangeField( 

135 dtype=float, 

136 doc="Initially exclude data points with a variance that are more than a factor of this from being" 

137 " linear in the negative direction, from the PTC fit. Note that these points will also be" 

138 " excluded from the non-linearity fit. This is done before the iterative outlier rejection," 

139 " to allow an accurate determination of the sigmas for said iterative fit.", 

140 default=0.25, 

141 min=0.0, 

142 max=1.0, 

143 ) 

144 sigmaCutPtcOutliers = pexConfig.Field( 

145 dtype=float, 

146 doc="Sigma cut for outlier rejection in PTC.", 

147 default=5.0, 

148 ) 

149 maskNameList = pexConfig.ListField( 

150 dtype=str, 

151 doc="Mask list to exclude from statistics calculations.", 

152 default=['SUSPECT', 'BAD', 'NO_DATA'], 

153 ) 

154 nSigmaClipPtc = pexConfig.Field( 

155 dtype=float, 

156 doc="Sigma cut for afwMath.StatisticsControl()", 

157 default=5.5, 

158 ) 

159 nIterSigmaClipPtc = pexConfig.Field( 

160 dtype=int, 

161 doc="Number of sigma-clipping iterations for afwMath.StatisticsControl()", 

162 default=1, 

163 ) 

164 maxIterationsPtcOutliers = pexConfig.Field( 

165 dtype=int, 

166 doc="Maximum number of iterations for outlier rejection in PTC.", 

167 default=2, 

168 ) 

169 doFitBootstrap = pexConfig.Field( 

170 dtype=bool, 

171 doc="Use bootstrap for the PTC fit parameters and errors?.", 

172 default=False, 

173 ) 

174 maxAduForLookupTableLinearizer = pexConfig.Field( 

175 dtype=int, 

176 doc="Maximum DN value for the LookupTable linearizer.", 

177 default=2**18, 

178 ) 

179 instrumentName = pexConfig.Field( 

180 dtype=str, 

181 doc="Instrument name.", 

182 default='', 

183 ) 

184 

185 

186@dataclass 

187class LinearityResidualsAndLinearizersDataset: 

188 """A simple class to hold the output from the 

189 `calculateLinearityResidualAndLinearizers` function. 

190 """ 

191 # Normalized coefficients for polynomial NL correction 

192 polynomialLinearizerCoefficients: list 

193 # Normalized coefficient for quadratic polynomial NL correction (c0) 

194 quadraticPolynomialLinearizerCoefficient: float 

195 # LUT array row for the amplifier at hand 

196 linearizerTableRow: list 

197 meanSignalVsTimePolyFitPars: list 

198 meanSignalVsTimePolyFitParsErr: list 

199 meanSignalVsTimePolyFitReducedChiSq: float 

200 

201 

202class PhotonTransferCurveDataset: 

203 """A simple class to hold the output data from the PTC task. 

204 

205 The dataset is made up of a dictionary for each item, keyed by the 

206 amplifiers' names, which much be supplied at construction time. 

207 

208 New items cannot be added to the class to save accidentally saving to the 

209 wrong property, and the class can be frozen if desired. 

210 

211 inputVisitPairs records the visits used to produce the data. 

212 When fitPtc() or fitCovariancesAstier() is run, a mask is built up, which is by definition 

213 always the same length as inputVisitPairs, rawExpTimes, rawMeans 

214 and rawVars, and is a list of bools, which are incrementally set to False 

215 as points are discarded from the fits. 

216 

217 PTC fit parameters for polynomials are stored in a list in ascending order 

218 of polynomial term, i.e. par[0]*x^0 + par[1]*x + par[2]*x^2 etc 

219 with the length of the list corresponding to the order of the polynomial 

220 plus one. 

221 

222 Parameters 

223 ---------- 

224 ampNames : `list` 

225 List with the names of the amplifiers of the detector at hand. 

226 

227 ptcFitType : `str` 

228 Type of model fitted to the PTC: "POLYNOMIAL", "EXPAPPROXIMATION", or "FULLCOVARIANCE". 

229 

230 Returns 

231 ------- 

232 `lsst.cp.pipe.ptc.PhotonTransferCurveDataset` 

233 Output dataset from MeasurePhotonTransferCurveTask. 

234 """ 

235 

236 def __init__(self, ampNames, ptcFitType): 

237 # add items to __dict__ directly because __setattr__ is overridden 

238 

239 # instance variables 

240 self.__dict__["ptcFitType"] = ptcFitType 

241 self.__dict__["ampNames"] = ampNames 

242 self.__dict__["badAmps"] = [] 

243 

244 # raw data variables 

245 # visitMask is the mask produced after outlier rejection. The mask produced by "FULLCOVARIANCE" 

246 # may differ from the one produced in the other two PTC fit types. 

247 self.__dict__["inputVisitPairs"] = {ampName: [] for ampName in ampNames} 

248 self.__dict__["visitMask"] = {ampName: [] for ampName in ampNames} 

249 self.__dict__["rawExpTimes"] = {ampName: [] for ampName in ampNames} 

250 self.__dict__["rawMeans"] = {ampName: [] for ampName in ampNames} 

251 self.__dict__["rawVars"] = {ampName: [] for ampName in ampNames} 

252 

253 # Gain and noise 

254 self.__dict__["gain"] = {ampName: -1. for ampName in ampNames} 

255 self.__dict__["gainErr"] = {ampName: -1. for ampName in ampNames} 

256 self.__dict__["noise"] = {ampName: -1. for ampName in ampNames} 

257 self.__dict__["noiseErr"] = {ampName: -1. for ampName in ampNames} 

258 

259 # if ptcFitTye in ["POLYNOMIAL", "EXPAPPROXIMATION"] 

260 # fit information 

261 self.__dict__["ptcFitPars"] = {ampName: [] for ampName in ampNames} 

262 self.__dict__["ptcFitParsError"] = {ampName: [] for ampName in ampNames} 

263 self.__dict__["ptcFitReducedChiSquared"] = {ampName: [] for ampName in ampNames} 

264 

265 # if ptcFitTye in ["FULLCOVARIANCE"] 

266 # "covariancesTuple" is a numpy recarray with entries of the form 

267 # ['mu', 'i', 'j', 'var', 'cov', 'npix', 'ext', 'expTime', 'ampName'] 

268 # "covariancesFits" has CovFit objects that fit the measured covariances to Eq. 20 of Astier+19. 

269 # In "covariancesFitsWithNoB", "b"=0 in the model described by Eq. 20 of Astier+19. 

270 self.__dict__["covariancesTuple"] = {ampName: [] for ampName in ampNames} 

271 self.__dict__["covariancesFitsWithNoB"] = {ampName: [] for ampName in ampNames} 

272 self.__dict__["covariancesFits"] = {ampName: [] for ampName in ampNames} 

273 self.__dict__["aMatrix"] = {ampName: [] for ampName in ampNames} 

274 self.__dict__["bMatrix"] = {ampName: [] for ampName in ampNames} 

275 

276 # "final" means that the "raw" vectors above had "visitMask" applied. 

277 self.__dict__["finalVars"] = {ampName: [] for ampName in ampNames} 

278 self.__dict__["finalModelVars"] = {ampName: [] for ampName in ampNames} 

279 self.__dict__["finalMeans"] = {ampName: [] for ampName in ampNames} 

280 

281 def __setattr__(self, attribute, value): 

282 """Protect class attributes""" 

283 if attribute not in self.__dict__: 

284 raise AttributeError(f"{attribute} is not already a member of PhotonTransferCurveDataset, which" 

285 " does not support setting of new attributes.") 

286 else: 

287 self.__dict__[attribute] = value 

288 

289 def getVisitsUsed(self, ampName): 

290 """Get the visits used, i.e. not discarded, for a given amp. 

291 

292 If no mask has been created yet, all visits are returned. 

293 """ 

294 if len(self.visitMask[ampName]) == 0: 

295 return self.inputVisitPairs[ampName] 

296 

297 # if the mask exists it had better be the same length as the visitPairs 

298 assert len(self.visitMask[ampName]) == len(self.inputVisitPairs[ampName]) 

299 

300 pairs = self.inputVisitPairs[ampName] 

301 mask = self.visitMask[ampName] 

302 # cast to bool required because numpy 

303 return [(v1, v2) for ((v1, v2), m) in zip(pairs, mask) if bool(m) is True] 

304 

305 def getGoodAmps(self): 

306 return [amp for amp in self.ampNames if amp not in self.badAmps] 

307 

308 

309class MeasurePhotonTransferCurveTask(pipeBase.CmdLineTask): 

310 """A class to calculate, fit, and plot a PTC from a set of flat pairs. 

311 

312 The Photon Transfer Curve (var(signal) vs mean(signal)) is a standard tool 

313 used in astronomical detectors characterization (e.g., Janesick 2001, 

314 Janesick 2007). If ptcFitType is "EXPAPPROXIMATION" or "POLYNOMIAL", this task calculates the 

315 PTC from a series of pairs of flat-field images; each pair taken at identical exposure 

316 times. The difference image of each pair is formed to eliminate fixed pattern noise, 

317 and then the variance of the difference image and the mean of the average image 

318 are used to produce the PTC. An n-degree polynomial or the approximation in Equation 

319 16 of Astier+19 ("The Shape of the Photon Transfer Curve of CCD sensors", 

320 arXiv:1905.08677) can be fitted to the PTC curve. These models include 

321 parameters such as the gain (e/DN) and readout noise. 

322 

323 Linearizers to correct for signal-chain non-linearity are also calculated. 

324 The `Linearizer` class, in general, can support per-amp linearizers, but in this 

325 task this is not supported. 

326 

327 If ptcFitType is "FULLCOVARIANCE", the covariances of the difference images are calculated via the 

328 DFT methods described in Astier+19 and the variances for the PTC are given by the cov[0,0] elements 

329 at each signal level. The full model in Equation 20 of Astier+19 is fit to the PTC to get the gain 

330 and the noise. 

331 

332 Parameters 

333 ---------- 

334 

335 *args: `list` 

336 Positional arguments passed to the Task constructor. None used at this 

337 time. 

338 **kwargs: `dict` 

339 Keyword arguments passed on to the Task constructor. None used at this 

340 time. 

341 

342 """ 

343 

344 RunnerClass = PairedVisitListTaskRunner 

345 ConfigClass = MeasurePhotonTransferCurveTaskConfig 

346 _DefaultName = "measurePhotonTransferCurve" 

347 

348 def __init__(self, *args, **kwargs): 

349 pipeBase.CmdLineTask.__init__(self, *args, **kwargs) 

350 plt.interactive(False) # stop windows popping up when plotting. When headless, use 'agg' backend too 

351 self.config.validate() 

352 self.config.freeze() 

353 

354 @classmethod 

355 def _makeArgumentParser(cls): 

356 """Augment argument parser for the MeasurePhotonTransferCurveTask.""" 

357 parser = pipeBase.ArgumentParser(name=cls._DefaultName) 

358 parser.add_argument("--visit-pairs", dest="visitPairs", nargs="*", 

359 help="Visit pairs to use. Each pair must be of the form INT,INT e.g. 123,456") 

360 parser.add_id_argument("--id", datasetType="photonTransferCurveDataset", 

361 ContainerClass=NonexistentDatasetTaskDataIdContainer, 

362 help="The ccds to use, e.g. --id ccd=0..100") 

363 return parser 

364 

365 @pipeBase.timeMethod 

366 def runDataRef(self, dataRef, visitPairs): 

367 """Run the Photon Transfer Curve (PTC) measurement task. 

368 

369 For a dataRef (which is each detector here), 

370 and given a list of visit pairs (postISR) at different exposure times, 

371 measure the PTC. 

372 

373 Parameters 

374 ---------- 

375 dataRef : list of lsst.daf.persistence.ButlerDataRef 

376 dataRef for the detector for the visits to be fit. 

377 

378 visitPairs : `iterable` of `tuple` of `int` 

379 Pairs of visit numbers to be processed together 

380 """ 

381 

382 # setup necessary objects 

383 detNum = dataRef.dataId[self.config.ccdKey] 

384 detector = dataRef.get('camera')[dataRef.dataId[self.config.ccdKey]] 

385 # expand some missing fields that we need for lsstCam. This is a work-around 

386 # for Gen2 problems that I (RHL) don't feel like solving. The calibs pipelines 

387 # (which inherit from CalibTask) use addMissingKeys() to do basically the same thing 

388 # 

389 # Basically, the butler's trying to look up the fields in `raw_visit` which won't work 

390 for name in dataRef.getButler().getKeys('bias'): 

391 if name not in dataRef.dataId: 

392 try: 

393 dataRef.dataId[name] = \ 

394 dataRef.getButler().queryMetadata('raw', [name], detector=detNum)[0] 

395 except OperationalError: 

396 pass 

397 

398 amps = detector.getAmplifiers() 

399 ampNames = [amp.getName() for amp in amps] 

400 datasetPtc = PhotonTransferCurveDataset(ampNames, self.config.ptcFitType) 

401 self.log.info('Measuring PTC using %s visits for detector %s' % (visitPairs, detector.getId())) 

402 

403 tupleRecords = [] 

404 allTags = [] 

405 for (v1, v2) in visitPairs: 

406 # Get postISR exposures. 

407 dataRef.dataId['expId'] = v1 

408 exp1 = dataRef.get("postISRCCD", immediate=True) 

409 dataRef.dataId['expId'] = v2 

410 exp2 = dataRef.get("postISRCCD", immediate=True) 

411 del dataRef.dataId['expId'] 

412 

413 checkExpLengthEqual(exp1, exp2, v1, v2, raiseWithMessage=True) 

414 expTime = exp1.getInfo().getVisitInfo().getExposureTime() 

415 tupleRows = [] 

416 nAmpsNan = 0 

417 for ampNumber, amp in enumerate(detector): 

418 ampName = amp.getName() 

419 # covAstier: (i, j, var (cov[0,0]), cov, npix) 

420 doRealSpace = self.config.covAstierRealSpace 

421 muDiff, varDiff, covAstier = self.measureMeanVarCov(exp1, exp2, region=amp.getBBox(), 

422 covAstierRealSpace=doRealSpace) 

423 if np.isnan(muDiff) or np.isnan(varDiff) or (covAstier is None): 

424 msg = (f"NaN mean or var, or None cov in amp {ampNumber} in visit pair {v1}, {v2} " 

425 "of detector {detNum}.") 

426 self.log.warn(msg) 

427 nAmpsNan += 1 

428 continue 

429 tags = ['mu', 'i', 'j', 'var', 'cov', 'npix', 'ext', 'expTime', 'ampName'] 

430 if (muDiff <= self.config.minMeanSignal) or (muDiff >= self.config.maxMeanSignal): 

431 continue 

432 datasetPtc.rawExpTimes[ampName].append(expTime) 

433 datasetPtc.rawMeans[ampName].append(muDiff) 

434 datasetPtc.rawVars[ampName].append(varDiff) 

435 datasetPtc.inputVisitPairs[ampName].append((v1, v2)) 

436 

437 tupleRows += [(muDiff, ) + covRow + (ampNumber, expTime, ampName) for covRow in covAstier] 

438 if nAmpsNan == len(ampNames): 

439 msg = f"NaN mean in all amps of visit pair {v1}, {v2} of detector {detNum}." 

440 self.log.warn(msg) 

441 continue 

442 allTags += tags 

443 tupleRecords += tupleRows 

444 covariancesWithTags = np.core.records.fromrecords(tupleRecords, names=allTags) 

445 

446 if self.config.ptcFitType in ["FULLCOVARIANCE", ]: 

447 # Calculate covariances and fit them, including the PTC, to Astier+19 full model (Eq. 20) 

448 datasetPtc = self.fitCovariancesAstier(datasetPtc, covariancesWithTags) 

449 elif self.config.ptcFitType in ["EXPAPPROXIMATION", "POLYNOMIAL"]: 

450 # Fit the PTC to a polynomial or to Astier+19 exponential approximation (Eq. 16) 

451 # Fill up PhotonTransferCurveDataset object. 

452 datasetPtc = self.fitPtc(datasetPtc, self.config.ptcFitType) 

453 

454 # Fit a poynomial to calculate non-linearity and persist linearizer. 

455 if self.config.doCreateLinearizer: 

456 numberAmps = len(amps) 

457 numberAduValues = self.config.maxAduForLookupTableLinearizer 

458 lookupTableArray = np.zeros((numberAmps, numberAduValues), dtype=np.float32) 

459 

460 # Fit (non)linearity of signal vs time curve. 

461 # Fill up PhotonTransferCurveDataset object. 

462 # Fill up array for LUT linearizer (tableArray). 

463 # Produce coefficients for Polynomial ans Squared linearizers. 

464 # Build linearizer objects. 

465 linearizer = self.fitNonLinearityAndBuildLinearizers(datasetPtc, detector, 

466 tableArray=lookupTableArray, 

467 log=self.log) 

468 

469 if self.config.linearizerType == "LINEARIZEPOLYNOMIAL": 

470 linDataType = 'linearizePolynomial' 

471 linMsg = "polynomial (coefficients for a polynomial correction)." 

472 elif self.config.linearizerType == "LINEARIZESQUARED": 

473 linDataType = 'linearizePolynomial' 

474 linMsg = "squared (c0, derived from k_i coefficients of a polynomial fit)." 

475 elif self.config.linearizerType == "LOOKUPTABLE": 

476 linDataType = 'linearizePolynomial' 

477 linMsg = "lookup table (linear component of polynomial fit)." 

478 else: 

479 raise RuntimeError("Invalid config.linearizerType {selg.config.linearizerType}. " 

480 "Supported: 'LOOKUPTABLE', 'LINEARIZESQUARED', or 'LINEARIZEPOLYNOMIAL'") 

481 

482 butler = dataRef.getButler() 

483 self.log.info(f"Writing linearizer: \n {linMsg}") 

484 

485 detName = detector.getName() 

486 now = datetime.datetime.utcnow() 

487 calibDate = now.strftime("%Y-%m-%d") 

488 

489 butler.put(linearizer, datasetType=linDataType, dataId={'detector': detNum, 

490 'detectorName': detName, 'calibDate': calibDate}) 

491 

492 self.log.info(f"Writing PTC data to {dataRef.getUri(write=True)}") 

493 dataRef.put(datasetPtc, datasetType="photonTransferCurveDataset") 

494 

495 return pipeBase.Struct(exitStatus=0) 

496 

497 def fitCovariancesAstier(self, dataset, covariancesWithTagsArray): 

498 """Fit measured flat covariances to full model in Astier+19. 

499 

500 Parameters 

501 ---------- 

502 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset` 

503 The dataset containing information such as the means, variances and exposure times. 

504 

505 covariancesWithTagsArray : `numpy.recarray` 

506 Tuple with at least (mu, cov, var, i, j, npix), where: 

507 mu : 0.5*(m1 + m2), where: 

508 mu1: mean value of flat1 

509 mu2: mean value of flat2 

510 cov: covariance value at lag(i, j) 

511 var: variance(covariance value at lag(0, 0)) 

512 i: lag dimension 

513 j: lag dimension 

514 npix: number of pixels used for covariance calculation. 

515 

516 Returns 

517 ------- 

518 dataset: `lsst.cp.pipe.ptc.PhotonTransferCurveDataset` 

519 This is the same dataset as the input paramter, however, it has been modified 

520 to include information such as the fit vectors and the fit parameters. See 

521 the class `PhotonTransferCurveDatase`. 

522 """ 

523 

524 covFits, covFitsNoB = fitData(covariancesWithTagsArray, maxMu=self.config.maxMeanSignal, 

525 r=self.config.maximumRangeCovariancesAstier, 

526 nSigmaFullFit=self.config.sigmaClipFullFitCovariancesAstier, 

527 maxIterFullFit=self.config.maxIterFullFitCovariancesAstier) 

528 

529 dataset.covariancesTuple = covariancesWithTagsArray 

530 dataset.covariancesFits = covFits 

531 dataset.covariancesFitsWithNoB = covFitsNoB 

532 dataset = self.getOutputPtcDataCovAstier(dataset, covFits) 

533 

534 return dataset 

535 

536 def getOutputPtcDataCovAstier(self, dataset, covFits): 

537 """Get output data for PhotonTransferCurveCovAstierDataset from CovFit objects. 

538 

539 Parameters 

540 ---------- 

541 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset` 

542 The dataset containing information such as the means, variances and exposure times. 

543 

544 covFits: `dict` 

545 Dictionary of CovFit objects, with amp names as keys. 

546 

547 Returns 

548 ------- 

549 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset` 

550 This is the same dataset as the input paramter, however, it has been modified 

551 to include extra information such as the mask 1D array, gains, reoudout noise, measured signal, 

552 measured variance, modeled variance, a, and b coefficient matrices (see Astier+19) per amplifier. 

553 See the class `PhotonTransferCurveDatase`. 

554 """ 

555 

556 for i, amp in enumerate(covFits): 

557 fit = covFits[amp] 

558 (meanVecFinal, varVecFinal, varVecModel, 

559 wc, varMask) = fit.getFitData(0, 0, divideByMu=False, returnMasked=True) 

560 gain = fit.getGain() 

561 dataset.visitMask[amp] = varMask 

562 dataset.gain[amp] = gain 

563 dataset.gainErr[amp] = fit.getGainErr() 

564 dataset.noise[amp] = np.sqrt(np.fabs(fit.getRon())) 

565 dataset.noiseErr[amp] = fit.getRonErr() 

566 dataset.finalVars[amp].append(varVecFinal/(gain**2)) 

567 dataset.finalModelVars[amp].append(varVecModel/(gain**2)) 

568 dataset.finalMeans[amp].append(meanVecFinal/gain) 

569 dataset.aMatrix[amp].append(fit.getA()) 

570 dataset.bMatrix[amp].append(fit.getB()) 

571 

572 return dataset 

573 

574 def measureMeanVarCov(self, exposure1, exposure2, region=None, covAstierRealSpace=False): 

575 """Calculate the mean of each of two exposures and the variance and covariance of their difference. 

576 

577 The variance is calculated via afwMath, and the covariance via the methods in Astier+19 (appendix A). 

578 In theory, var = covariance[0,0]. This should be validated, and in the future, we may decide to just 

579 keep one (covariance). 

580 

581 Parameters 

582 ---------- 

583 exposure1 : `lsst.afw.image.exposure.exposure.ExposureF` 

584 First exposure of flat field pair. 

585 

586 exposure2 : `lsst.afw.image.exposure.exposure.ExposureF` 

587 Second exposure of flat field pair. 

588 

589 region : `lsst.geom.Box2I`, optional 

590 Region of each exposure where to perform the calculations (e.g, an amplifier). 

591 

592 covAstierRealSpace : `bool`, optional 

593 Should the covariannces in Astier+19 be calculated in real space or via FFT? 

594 See Appendix A of Astier+19. 

595 

596 Returns 

597 ------- 

598 mu : `float` or `NaN` 

599 0.5*(mu1 + mu2), where mu1, and mu2 are the clipped means of the regions in 

600 both exposures. If either mu1 or m2 are NaN's, the returned value is NaN. 

601 

602 varDiff : `float` or `NaN` 

603 Half of the clipped variance of the difference of the regions inthe two input 

604 exposures. If either mu1 or m2 are NaN's, the returned value is NaN. 

605 

606 covDiffAstier : `list` or `NaN` 

607 List with tuples of the form (dx, dy, var, cov, npix), where: 

608 dx : `int` 

609 Lag in x 

610 dy : `int` 

611 Lag in y 

612 var : `float` 

613 Variance at (dx, dy). 

614 cov : `float` 

615 Covariance at (dx, dy). 

616 nPix : `int` 

617 Number of pixel pairs used to evaluate var and cov. 

618 If either mu1 or m2 are NaN's, the returned value is NaN. 

619 """ 

620 

621 if region is not None: 

622 im1Area = exposure1.maskedImage[region] 

623 im2Area = exposure2.maskedImage[region] 

624 else: 

625 im1Area = exposure1.maskedImage 

626 im2Area = exposure2.maskedImage 

627 

628 im1Area = afwMath.binImage(im1Area, self.config.binSize) 

629 im2Area = afwMath.binImage(im2Area, self.config.binSize) 

630 

631 im1MaskVal = exposure1.getMask().getPlaneBitMask(self.config.maskNameList) 

632 im1StatsCtrl = afwMath.StatisticsControl(self.config.nSigmaClipPtc, 

633 self.config.nIterSigmaClipPtc, 

634 im1MaskVal) 

635 im1StatsCtrl.setNanSafe(True) 

636 im1StatsCtrl.setAndMask(im1MaskVal) 

637 

638 im2MaskVal = exposure2.getMask().getPlaneBitMask(self.config.maskNameList) 

639 im2StatsCtrl = afwMath.StatisticsControl(self.config.nSigmaClipPtc, 

640 self.config.nIterSigmaClipPtc, 

641 im2MaskVal) 

642 im2StatsCtrl.setNanSafe(True) 

643 im2StatsCtrl.setAndMask(im2MaskVal) 

644 

645 # Clipped mean of images; then average of mean. 

646 mu1 = afwMath.makeStatistics(im1Area, afwMath.MEANCLIP, im1StatsCtrl).getValue() 

647 mu2 = afwMath.makeStatistics(im2Area, afwMath.MEANCLIP, im2StatsCtrl).getValue() 

648 if np.isnan(mu1) or np.isnan(mu2): 

649 return np.nan, np.nan, None 

650 mu = 0.5*(mu1 + mu2) 

651 

652 # Take difference of pairs 

653 # symmetric formula: diff = (mu2*im1-mu1*im2)/(0.5*(mu1+mu2)) 

654 temp = im2Area.clone() 

655 temp *= mu1 

656 diffIm = im1Area.clone() 

657 diffIm *= mu2 

658 diffIm -= temp 

659 diffIm /= mu 

660 

661 diffImMaskVal = diffIm.getMask().getPlaneBitMask(self.config.maskNameList) 

662 diffImStatsCtrl = afwMath.StatisticsControl(self.config.nSigmaClipPtc, 

663 self.config.nIterSigmaClipPtc, 

664 diffImMaskVal) 

665 diffImStatsCtrl.setNanSafe(True) 

666 diffImStatsCtrl.setAndMask(diffImMaskVal) 

667 

668 varDiff = 0.5*(afwMath.makeStatistics(diffIm, afwMath.VARIANCECLIP, diffImStatsCtrl).getValue()) 

669 

670 # Get the mask and identify good pixels as '1', and the rest as '0'. 

671 w1 = np.where(im1Area.getMask().getArray() == 0, 1, 0) 

672 w2 = np.where(im2Area.getMask().getArray() == 0, 1, 0) 

673 

674 w12 = w1*w2 

675 wDiff = np.where(diffIm.getMask().getArray() == 0, 1, 0) 

676 w = w12*wDiff 

677 

678 maxRangeCov = self.config.maximumRangeCovariancesAstier 

679 if covAstierRealSpace: 

680 covDiffAstier = computeCovDirect(diffIm.getImage().getArray(), w, maxRangeCov) 

681 else: 

682 shapeDiff = diffIm.getImage().getArray().shape 

683 fftShape = (fftSize(shapeDiff[0] + maxRangeCov), fftSize(shapeDiff[1]+maxRangeCov)) 

684 c = CovFft(diffIm.getImage().getArray(), w, fftShape, maxRangeCov) 

685 covDiffAstier = c.reportCovFft(maxRangeCov) 

686 

687 return mu, varDiff, covDiffAstier 

688 

689 def computeCovDirect(self, diffImage, weightImage, maxRange): 

690 """Compute covariances of diffImage in real space. 

691 

692 For lags larger than ~25, it is slower than the FFT way. 

693 Taken from https://github.com/PierreAstier/bfptc/ 

694 

695 Parameters 

696 ---------- 

697 diffImage : `numpy.array` 

698 Image to compute the covariance of. 

699 

700 weightImage : `numpy.array` 

701 Weight image of diffImage (1's and 0's for good and bad pixels, respectively). 

702 

703 maxRange : `int` 

704 Last index of the covariance to be computed. 

705 

706 Returns 

707 ------- 

708 outList : `list` 

709 List with tuples of the form (dx, dy, var, cov, npix), where: 

710 dx : `int` 

711 Lag in x 

712 dy : `int` 

713 Lag in y 

714 var : `float` 

715 Variance at (dx, dy). 

716 cov : `float` 

717 Covariance at (dx, dy). 

718 nPix : `int` 

719 Number of pixel pairs used to evaluate var and cov. 

720 """ 

721 outList = [] 

722 var = 0 

723 # (dy,dx) = (0,0) has to be first 

724 for dy in range(maxRange + 1): 

725 for dx in range(0, maxRange + 1): 

726 if (dx*dy > 0): 

727 cov1, nPix1 = self.covDirectValue(diffImage, weightImage, dx, dy) 

728 cov2, nPix2 = self.covDirectValue(diffImage, weightImage, dx, -dy) 

729 cov = 0.5*(cov1 + cov2) 

730 nPix = nPix1 + nPix2 

731 else: 

732 cov, nPix = self.covDirectValue(diffImage, weightImage, dx, dy) 

733 if (dx == 0 and dy == 0): 

734 var = cov 

735 outList.append((dx, dy, var, cov, nPix)) 

736 

737 return outList 

738 

739 def covDirectValue(self, diffImage, weightImage, dx, dy): 

740 """Compute covariances of diffImage in real space at lag (dx, dy). 

741 

742 Taken from https://github.com/PierreAstier/bfptc/ (c.f., appendix of Astier+19). 

743 

744 Parameters 

745 ---------- 

746 diffImage : `numpy.array` 

747 Image to compute the covariance of. 

748 

749 weightImage : `numpy.array` 

750 Weight image of diffImage (1's and 0's for good and bad pixels, respectively). 

751 

752 dx : `int` 

753 Lag in x. 

754 

755 dy : `int` 

756 Lag in y. 

757 

758 Returns 

759 ------- 

760 cov : `float` 

761 Covariance at (dx, dy) 

762 

763 nPix : `int` 

764 Number of pixel pairs used to evaluate var and cov. 

765 """ 

766 (nCols, nRows) = diffImage.shape 

767 # switching both signs does not change anything: 

768 # it just swaps im1 and im2 below 

769 if (dx < 0): 

770 (dx, dy) = (-dx, -dy) 

771 # now, we have dx >0. We have to distinguish two cases 

772 # depending on the sign of dy 

773 if dy >= 0: 

774 im1 = diffImage[dy:, dx:] 

775 w1 = weightImage[dy:, dx:] 

776 im2 = diffImage[:nCols - dy, :nRows - dx] 

777 w2 = weightImage[:nCols - dy, :nRows - dx] 

778 else: 

779 im1 = diffImage[:nCols + dy, dx:] 

780 w1 = weightImage[:nCols + dy, dx:] 

781 im2 = diffImage[-dy:, :nRows - dx] 

782 w2 = weightImage[-dy:, :nRows - dx] 

783 # use the same mask for all 3 calculations 

784 wAll = w1*w2 

785 # do not use mean() because weightImage=0 pixels would then count 

786 nPix = wAll.sum() 

787 im1TimesW = im1*wAll 

788 s1 = im1TimesW.sum()/nPix 

789 s2 = (im2*wAll).sum()/nPix 

790 p = (im1TimesW*im2).sum()/nPix 

791 cov = p - s1*s2 

792 

793 return cov, nPix 

794 

795 def fitNonLinearityAndBuildLinearizers(self, datasetPtc, detector, tableArray=None, log=None): 

796 """Fit non-linearity function and build linearizer objects. 

797 

798 Parameters 

799 ---------- 

800 datasePtct : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset` 

801 The dataset containing information such as the means, variances and exposure times. 

802 nLinearity 

803 

804 detector : `lsst.afw.cameraGeom.Detector` 

805 Detector object. 

806 

807 tableArray : `np.array`, optional 

808 Optional. Look-up table array with size rows=nAmps and columns=DN values. 

809 It will be modified in-place if supplied. 

810 

811 log : `lsst.log.Log`, optional 

812 Logger to handle messages. 

813 

814 Returns 

815 ------- 

816 linearizer : `lsst.ip.isr.Linearizer` 

817 Linearizer object 

818 """ 

819 

820 # Fit NonLinearity 

821 datasetNonLinearity = self.fitNonLinearity(datasetPtc, tableArray=tableArray) 

822 

823 # Produce linearizer 

824 now = datetime.datetime.utcnow() 

825 calibDate = now.strftime("%Y-%m-%d") 

826 linType = self.config.linearizerType 

827 

828 if linType == "LOOKUPTABLE": 

829 tableArray = tableArray 

830 else: 

831 tableArray = None 

832 

833 linearizer = self.buildLinearizerObject(datasetNonLinearity, detector, calibDate, linType, 

834 instruName=self.config.instrumentName, 

835 tableArray=tableArray, 

836 log=log) 

837 

838 return linearizer 

839 

840 def fitNonLinearity(self, datasetPtc, tableArray=None): 

841 """Fit a polynomial to signal vs effective time curve to calculate linearity and residuals. 

842 

843 Parameters 

844 ---------- 

845 datasetPtc : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset` 

846 The dataset containing the means, variances and exposure times. 

847 

848 tableArray : `np.array` 

849 Optional. Look-up table array with size rows=nAmps and columns=DN values. 

850 It will be modified in-place if supplied. 

851 

852 Returns 

853 ------- 

854 datasetNonLinearity : `dict` 

855 Dictionary of `lsst.cp.pipe.ptc.LinearityResidualsAndLinearizersDataset` 

856 dataclasses. Each one holds the output of `calculateLinearityResidualAndLinearizers` per 

857 amplifier. 

858 """ 

859 datasetNonLinearity = {ampName: [] for ampName in datasetPtc.ampNames} 

860 for i, ampName in enumerate(datasetPtc.ampNames): 

861 # If a mask is not found, use all points. 

862 if (len(datasetPtc.visitMask[ampName]) == 0): 

863 self.log.warn(f"Mask not found for {ampName} in non-linearity fit. Using all points.") 

864 mask = np.repeat(True, len(datasetPtc.rawExpTimes[ampName])) 

865 else: 

866 mask = datasetPtc.visitMask[ampName] 

867 

868 timeVecFinal = np.array(datasetPtc.rawExpTimes[ampName])[mask] 

869 meanVecFinal = np.array(datasetPtc.rawMeans[ampName])[mask] 

870 

871 # Non-linearity residuals (NL of mean vs time curve): percentage, and fit to a quadratic function 

872 # In this case, len(parsIniNonLinearity) = 3 indicates that we want a quadratic fit 

873 datasetLinRes = self.calculateLinearityResidualAndLinearizers(timeVecFinal, meanVecFinal) 

874 

875 # LinearizerLookupTable 

876 if tableArray is not None: 

877 tableArray[i, :] = datasetLinRes.linearizerTableRow 

878 

879 datasetNonLinearity[ampName] = datasetLinRes 

880 

881 return datasetNonLinearity 

882 

883 def calculateLinearityResidualAndLinearizers(self, exposureTimeVector, meanSignalVector): 

884 """Calculate linearity residual and fit an n-order polynomial to the mean vs time curve 

885 to produce corrections (deviation from linear part of polynomial) for a particular amplifier 

886 to populate LinearizeLookupTable. 

887 Use the coefficients of this fit to calculate the correction coefficients for LinearizePolynomial 

888 and LinearizeSquared." 

889 

890 Parameters 

891 --------- 

892 

893 exposureTimeVector: `list` of `float` 

894 List of exposure times for each flat pair 

895 

896 meanSignalVector: `list` of `float` 

897 List of mean signal from diference image of flat pairs 

898 

899 Returns 

900 ------- 

901 dataset : `lsst.cp.pipe.ptc.LinearityResidualsAndLinearizersDataset` 

902 The dataset containing the fit parameters, the NL correction coefficients, and the 

903 LUT row for the amplifier at hand. 

904 

905 Notes 

906 ----- 

907 datase members: 

908 

909 dataset.polynomialLinearizerCoefficients : `list` of `float` 

910 Coefficients for LinearizePolynomial, where corrImage = uncorrImage + sum_i c_i uncorrImage^(2 + 

911 i). 

912 c_(j-2) = -k_j/(k_1^j) with units DN^(1-j) (c.f., Eq. 37 of 2003.05978). The units of k_j are 

913 DN/t^j, and they are fit from meanSignalVector = k0 + k1*exposureTimeVector + 

914 k2*exposureTimeVector^2 + ... + kn*exposureTimeVector^n, with 

915 n = "polynomialFitDegreeNonLinearity". k_0 and k_1 and degenerate with bias level and gain, 

916 and are not used by the non-linearity correction. Therefore, j = 2...n in the above expression 

917 (see `LinearizePolynomial` class in `linearize.py`.) 

918 

919 dataset.quadraticPolynomialLinearizerCoefficient : `float` 

920 Coefficient for LinearizeSquared, where corrImage = uncorrImage + c0*uncorrImage^2. 

921 c0 = -k2/(k1^2), where k1 and k2 are fit from 

922 meanSignalVector = k0 + k1*exposureTimeVector + k2*exposureTimeVector^2 +... 

923 + kn*exposureTimeVector^n, with n = "polynomialFitDegreeNonLinearity". 

924 

925 dataset.linearizerTableRow : `list` of `float` 

926 One dimensional array with deviation from linear part of n-order polynomial fit 

927 to mean vs time curve. This array will be one row (for the particular amplifier at hand) 

928 of the table array for LinearizeLookupTable. 

929 

930 dataset.meanSignalVsTimePolyFitPars : `list` of `float` 

931 Parameters from n-order polynomial fit to meanSignalVector vs exposureTimeVector. 

932 

933 dataset.meanSignalVsTimePolyFitParsErr : `list` of `float` 

934 Parameters from n-order polynomial fit to meanSignalVector vs exposureTimeVector. 

935 

936 dataset.meanSignalVsTimePolyFitReducedChiSq : `float` 

937 Reduced unweighted chi squared from polynomial fit to meanSignalVector vs exposureTimeVector. 

938 """ 

939 

940 # Lookup table linearizer 

941 parsIniNonLinearity = self._initialParsForPolynomial(self.config.polynomialFitDegreeNonLinearity + 1) 

942 if self.config.doFitBootstrap: 

943 parsFit, parsFitErr, reducedChiSquaredNonLinearityFit = fitBootstrap(parsIniNonLinearity, 

944 exposureTimeVector, 

945 meanSignalVector, 

946 funcPolynomial) 

947 else: 

948 parsFit, parsFitErr, reducedChiSquaredNonLinearityFit = fitLeastSq(parsIniNonLinearity, 

949 exposureTimeVector, 

950 meanSignalVector, 

951 funcPolynomial) 

952 

953 # LinearizeLookupTable: 

954 # Use linear part to get time at wich signal is maxAduForLookupTableLinearizer DN 

955 tMax = (self.config.maxAduForLookupTableLinearizer - parsFit[0])/parsFit[1] 

956 timeRange = np.linspace(0, tMax, self.config.maxAduForLookupTableLinearizer) 

957 signalIdeal = parsFit[0] + parsFit[1]*timeRange 

958 signalUncorrected = funcPolynomial(parsFit, timeRange) 

959 linearizerTableRow = signalIdeal - signalUncorrected # LinearizerLookupTable has corrections 

960 # LinearizePolynomial and LinearizeSquared: 

961 # Check that magnitude of higher order (>= 3) coefficents of the polyFit are small, 

962 # i.e., less than threshold = 1e-10 (typical quadratic and cubic coefficents are ~1e-6 

963 # and ~1e-12). 

964 k1 = parsFit[1] 

965 polynomialLinearizerCoefficients = [] 

966 for i, coefficient in enumerate(parsFit): 

967 c = -coefficient/(k1**i) 

968 polynomialLinearizerCoefficients.append(c) 

969 if np.fabs(c) > 1e-10: 

970 msg = f"Coefficient {c} in polynomial fit larger than threshold 1e-10." 

971 self.log.warn(msg) 

972 # Coefficient for LinearizedSquared. Called "c0" in linearize.py 

973 c0 = polynomialLinearizerCoefficients[2] 

974 

975 dataset = LinearityResidualsAndLinearizersDataset([], None, [], [], [], None) 

976 dataset.polynomialLinearizerCoefficients = polynomialLinearizerCoefficients 

977 dataset.quadraticPolynomialLinearizerCoefficient = c0 

978 dataset.linearizerTableRow = linearizerTableRow 

979 dataset.meanSignalVsTimePolyFitPars = parsFit 

980 dataset.meanSignalVsTimePolyFitParsErr = parsFitErr 

981 dataset.meanSignalVsTimePolyFitReducedChiSq = reducedChiSquaredNonLinearityFit 

982 

983 return dataset 

984 

985 def buildLinearizerObject(self, datasetNonLinearity, detector, calibDate, linearizerType, instruName='', 

986 tableArray=None, log=None): 

987 """Build linearizer object to persist. 

988 

989 Parameters 

990 ---------- 

991 datasetNonLinearity : `dict` 

992 Dictionary of `lsst.cp.pipe.ptc.LinearityResidualsAndLinearizersDataset` objects. 

993 

994 detector : `lsst.afw.cameraGeom.Detector` 

995 Detector object 

996 

997 calibDate : `datetime.datetime` 

998 Calibration date 

999 

1000 linearizerType : `str` 

1001 'LOOKUPTABLE', 'LINEARIZESQUARED', or 'LINEARIZEPOLYNOMIAL' 

1002 

1003 instruName : `str`, optional 

1004 Instrument name 

1005 

1006 tableArray : `np.array`, optional 

1007 Look-up table array with size rows=nAmps and columns=DN values 

1008 

1009 log : `lsst.log.Log`, optional 

1010 Logger to handle messages 

1011 

1012 Returns 

1013 ------- 

1014 linearizer : `lsst.ip.isr.Linearizer` 

1015 Linearizer object 

1016 """ 

1017 detName = detector.getName() 

1018 detNum = detector.getId() 

1019 if linearizerType == "LOOKUPTABLE": 

1020 if tableArray is not None: 

1021 linearizer = Linearizer(detector=detector, table=tableArray, log=log) 

1022 else: 

1023 raise RuntimeError("tableArray must be provided when creating a LookupTable linearizer") 

1024 elif linearizerType in ("LINEARIZESQUARED", "LINEARIZEPOLYNOMIAL"): 

1025 linearizer = Linearizer(log=log) 

1026 else: 

1027 raise RuntimeError("Invalid linearizerType {linearizerType} to build a Linearizer object. " 

1028 "Supported: 'LOOKUPTABLE', 'LINEARIZESQUARED', or 'LINEARIZEPOLYNOMIAL'") 

1029 for i, amp in enumerate(detector.getAmplifiers()): 

1030 ampName = amp.getName() 

1031 datasetNonLinAmp = datasetNonLinearity[ampName] 

1032 if linearizerType == "LOOKUPTABLE": 

1033 linearizer.linearityCoeffs[ampName] = [i, 0] 

1034 linearizer.linearityType[ampName] = "LookupTable" 

1035 elif linearizerType == "LINEARIZESQUARED": 

1036 linearizer.fitParams[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitPars 

1037 linearizer.fitParamsErr[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitParsErr 

1038 linearizer.linearityFitReducedChiSquared[ampName] = ( 

1039 datasetNonLinAmp.meanSignalVsTimePolyFitReducedChiSq) 

1040 linearizer.linearityCoeffs[ampName] = [ 

1041 datasetNonLinAmp.quadraticPolynomialLinearizerCoefficient] 

1042 linearizer.linearityType[ampName] = "Squared" 

1043 elif linearizerType == "LINEARIZEPOLYNOMIAL": 

1044 linearizer.fitParams[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitPars 

1045 linearizer.fitParamsErr[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitParsErr 

1046 linearizer.linearityFitReducedChiSquared[ampName] = ( 

1047 datasetNonLinAmp.meanSignalVsTimePolyFitReducedChiSq) 

1048 # Slice correction coefficients (starting at 2) for polynomial linearizer 

1049 # (and squared linearizer above). The first and second are reduntant with 

1050 # the bias and gain, respectively, and are not used by LinearizerPolynomial. 

1051 polyLinCoeffs = np.array(datasetNonLinAmp.polynomialLinearizerCoefficients[2:]) 

1052 linearizer.linearityCoeffs[ampName] = polyLinCoeffs 

1053 linearizer.linearityType[ampName] = "Polynomial" 

1054 linearizer.linearityBBox[ampName] = amp.getBBox() 

1055 linearizer.validate() 

1056 calibId = f"detectorName={detName} detector={detNum} calibDate={calibDate} ccd={detNum} filter=NONE" 

1057 

1058 try: 

1059 raftName = detName.split("_")[0] 

1060 calibId += f" raftName={raftName}" 

1061 except Exception: 

1062 raftname = "NONE" 

1063 calibId += f" raftName={raftname}" 

1064 

1065 serial = detector.getSerial() 

1066 linearizer.updateMetadata(instrumentName=instruName, detectorId=f"{detNum}", 

1067 calibId=calibId, serial=serial, detectorName=f"{detName}") 

1068 

1069 return linearizer 

1070 

1071 @staticmethod 

1072 def _initialParsForPolynomial(order): 

1073 assert(order >= 2) 

1074 pars = np.zeros(order, dtype=np.float) 

1075 pars[0] = 10 

1076 pars[1] = 1 

1077 pars[2:] = 0.0001 

1078 return pars 

1079 

1080 @staticmethod 

1081 def _boundsForPolynomial(initialPars): 

1082 lowers = [np.NINF for p in initialPars] 

1083 uppers = [np.inf for p in initialPars] 

1084 lowers[1] = 0 # no negative gains 

1085 return (lowers, uppers) 

1086 

1087 @staticmethod 

1088 def _boundsForAstier(initialPars): 

1089 lowers = [np.NINF for p in initialPars] 

1090 uppers = [np.inf for p in initialPars] 

1091 return (lowers, uppers) 

1092 

1093 @staticmethod 

1094 def _getInitialGoodPoints(means, variances, maxDeviationPositive, maxDeviationNegative): 

1095 """Return a boolean array to mask bad points. 

1096 

1097 A linear function has a constant ratio, so find the median 

1098 value of the ratios, and exclude the points that deviate 

1099 from that by more than a factor of maxDeviationPositive/negative. 

1100 Asymmetric deviations are supported as we expect the PTC to turn 

1101 down as the flux increases, but sometimes it anomalously turns 

1102 upwards just before turning over, which ruins the fits, so it 

1103 is wise to be stricter about restricting positive outliers than 

1104 negative ones. 

1105 

1106 Too high and points that are so bad that fit will fail will be included 

1107 Too low and the non-linear points will be excluded, biasing the NL fit.""" 

1108 ratios = [b/a for (a, b) in zip(means, variances)] 

1109 medianRatio = np.median(ratios) 

1110 ratioDeviations = [(r/medianRatio)-1 for r in ratios] 

1111 

1112 # so that it doesn't matter if the deviation is expressed as positive or negative 

1113 maxDeviationPositive = abs(maxDeviationPositive) 

1114 maxDeviationNegative = -1. * abs(maxDeviationNegative) 

1115 

1116 goodPoints = np.array([True if (r < maxDeviationPositive and r > maxDeviationNegative) 

1117 else False for r in ratioDeviations]) 

1118 return goodPoints 

1119 

1120 def _makeZeroSafe(self, array, warn=True, substituteValue=1e-9): 

1121 """""" 

1122 nBad = Counter(array)[0] 

1123 if nBad == 0: 

1124 return array 

1125 

1126 if warn: 

1127 msg = f"Found {nBad} zeros in array at elements {[x for x in np.where(array==0)[0]]}" 

1128 self.log.warn(msg) 

1129 

1130 array[array == 0] = substituteValue 

1131 return array 

1132 

1133 def fitPtc(self, dataset, ptcFitType): 

1134 """Fit the photon transfer curve to a polynimial or to Astier+19 approximation. 

1135 

1136 Fit the photon transfer curve with either a polynomial of the order 

1137 specified in the task config, or using the Astier approximation. 

1138 

1139 Sigma clipping is performed iteratively for the fit, as well as an 

1140 initial clipping of data points that are more than 

1141 config.initialNonLinearityExclusionThreshold away from lying on a 

1142 straight line. This other step is necessary because the photon transfer 

1143 curve turns over catastrophically at very high flux (because saturation 

1144 drops the variance to ~0) and these far outliers cause the initial fit 

1145 to fail, meaning the sigma cannot be calculated to perform the 

1146 sigma-clipping. 

1147 

1148 Parameters 

1149 ---------- 

1150 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset` 

1151 The dataset containing the means, variances and exposure times 

1152 

1153 ptcFitType : `str` 

1154 Fit a 'POLYNOMIAL' (degree: 'polynomialFitDegree') or 

1155 'EXPAPPROXIMATION' (Eq. 16 of Astier+19) to the PTC 

1156 

1157 Returns 

1158 ------- 

1159 dataset: `lsst.cp.pipe.ptc.PhotonTransferCurveDataset` 

1160 This is the same dataset as the input paramter, however, it has been modified 

1161 to include information such as the fit vectors and the fit parameters. See 

1162 the class `PhotonTransferCurveDatase`. 

1163 """ 

1164 

1165 def errFunc(p, x, y): 

1166 return ptcFunc(p, x) - y 

1167 

1168 sigmaCutPtcOutliers = self.config.sigmaCutPtcOutliers 

1169 maxIterationsPtcOutliers = self.config.maxIterationsPtcOutliers 

1170 

1171 for i, ampName in enumerate(dataset.ampNames): 

1172 timeVecOriginal = np.array(dataset.rawExpTimes[ampName]) 

1173 meanVecOriginal = np.array(dataset.rawMeans[ampName]) 

1174 varVecOriginal = np.array(dataset.rawVars[ampName]) 

1175 varVecOriginal = self._makeZeroSafe(varVecOriginal) 

1176 

1177 mask = ((meanVecOriginal >= self.config.minMeanSignal) & 

1178 (meanVecOriginal <= self.config.maxMeanSignal)) 

1179 

1180 goodPoints = self._getInitialGoodPoints(meanVecOriginal, varVecOriginal, 

1181 self.config.initialNonLinearityExclusionThresholdPositive, 

1182 self.config.initialNonLinearityExclusionThresholdNegative) 

1183 mask = mask & goodPoints 

1184 

1185 if ptcFitType == 'EXPAPPROXIMATION': 

1186 ptcFunc = funcAstier 

1187 parsIniPtc = [-1e-9, 1.0, 10.] # a00, gain, noise 

1188 bounds = self._boundsForAstier(parsIniPtc) 

1189 if ptcFitType == 'POLYNOMIAL': 

1190 ptcFunc = funcPolynomial 

1191 parsIniPtc = self._initialParsForPolynomial(self.config.polynomialFitDegree + 1) 

1192 bounds = self._boundsForPolynomial(parsIniPtc) 

1193 

1194 # Before bootstrap fit, do an iterative fit to get rid of outliers 

1195 count = 1 

1196 while count <= maxIterationsPtcOutliers: 

1197 # Note that application of the mask actually shrinks the array 

1198 # to size rather than setting elements to zero (as we want) so 

1199 # always update mask itself and re-apply to the original data 

1200 meanTempVec = meanVecOriginal[mask] 

1201 varTempVec = varVecOriginal[mask] 

1202 res = least_squares(errFunc, parsIniPtc, bounds=bounds, args=(meanTempVec, varTempVec)) 

1203 pars = res.x 

1204 

1205 # change this to the original from the temp because the masks are ANDed 

1206 # meaning once a point is masked it's always masked, and the masks must 

1207 # always be the same length for broadcasting 

1208 sigResids = (varVecOriginal - ptcFunc(pars, meanVecOriginal))/np.sqrt(varVecOriginal) 

1209 newMask = np.array([True if np.abs(r) < sigmaCutPtcOutliers else False for r in sigResids]) 

1210 mask = mask & newMask 

1211 

1212 nDroppedTotal = Counter(mask)[False] 

1213 self.log.debug(f"Iteration {count}: discarded {nDroppedTotal} points in total for {ampName}") 

1214 count += 1 

1215 # objects should never shrink 

1216 assert (len(mask) == len(timeVecOriginal) == len(meanVecOriginal) == len(varVecOriginal)) 

1217 

1218 dataset.visitMask[ampName] = mask # store the final mask 

1219 parsIniPtc = pars 

1220 meanVecFinal = meanVecOriginal[mask] 

1221 varVecFinal = varVecOriginal[mask] 

1222 

1223 if Counter(mask)[False] > 0: 

1224 self.log.info((f"Number of points discarded in PTC of amplifier {ampName}:" + 

1225 f" {Counter(mask)[False]} out of {len(meanVecOriginal)}")) 

1226 

1227 if (len(meanVecFinal) < len(parsIniPtc)): 

1228 msg = (f"\nSERIOUS: Not enough data points ({len(meanVecFinal)}) compared to the number of" 

1229 f"parameters of the PTC model({len(parsIniPtc)}). Setting {ampName} to BAD.") 

1230 self.log.warn(msg) 

1231 # The first and second parameters of initial fit are discarded (bias and gain) 

1232 # for the final NL coefficients 

1233 dataset.badAmps.append(ampName) 

1234 dataset.gain[ampName] = np.nan 

1235 dataset.gainErr[ampName] = np.nan 

1236 dataset.noise[ampName] = np.nan 

1237 dataset.noiseErr[ampName] = np.nan 

1238 dataset.ptcFitPars[ampName] = np.nan 

1239 dataset.ptcFitParsError[ampName] = np.nan 

1240 dataset.ptcFitReducedChiSquared[ampName] = np.nan 

1241 continue 

1242 

1243 # Fit the PTC 

1244 if self.config.doFitBootstrap: 

1245 parsFit, parsFitErr, reducedChiSqPtc = fitBootstrap(parsIniPtc, meanVecFinal, 

1246 varVecFinal, ptcFunc) 

1247 else: 

1248 parsFit, parsFitErr, reducedChiSqPtc = fitLeastSq(parsIniPtc, meanVecFinal, 

1249 varVecFinal, ptcFunc) 

1250 dataset.ptcFitPars[ampName] = parsFit 

1251 dataset.ptcFitParsError[ampName] = parsFitErr 

1252 dataset.ptcFitReducedChiSquared[ampName] = reducedChiSqPtc 

1253 

1254 if ptcFitType == 'EXPAPPROXIMATION': 

1255 ptcGain = parsFit[1] 

1256 ptcGainErr = parsFitErr[1] 

1257 ptcNoise = np.sqrt(np.fabs(parsFit[2])) 

1258 ptcNoiseErr = 0.5*(parsFitErr[2]/np.fabs(parsFit[2]))*np.sqrt(np.fabs(parsFit[2])) 

1259 if ptcFitType == 'POLYNOMIAL': 

1260 ptcGain = 1./parsFit[1] 

1261 ptcGainErr = np.fabs(1./parsFit[1])*(parsFitErr[1]/parsFit[1]) 

1262 ptcNoise = np.sqrt(np.fabs(parsFit[0]))*ptcGain 

1263 ptcNoiseErr = (0.5*(parsFitErr[0]/np.fabs(parsFit[0]))*(np.sqrt(np.fabs(parsFit[0]))))*ptcGain 

1264 dataset.gain[ampName] = ptcGain 

1265 dataset.gainErr[ampName] = ptcGainErr 

1266 dataset.noise[ampName] = ptcNoise 

1267 dataset.noiseErr[ampName] = ptcNoiseErr 

1268 if not len(dataset.ptcFitType) == 0: 

1269 dataset.ptcFitType = ptcFitType 

1270 

1271 return dataset