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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# 

22import numpy as np 

23import matplotlib.pyplot as plt 

24from collections import Counter 

25 

26import lsst.afw.math as afwMath 

27import lsst.pex.config as pexConfig 

28import lsst.pipe.base as pipeBase 

29from .utils import (fitLeastSq, fitBootstrap, funcPolynomial, funcAstier) 

30from scipy.optimize import least_squares 

31 

32import datetime 

33 

34from .astierCovPtcUtils import (fftSize, CovFft, computeCovDirect, fitData) 

35from .linearity import LinearitySolveTask 

36from .photodiode import getBOTphotodiodeData 

37 

38from lsst.pipe.tasks.getRepositoryData import DataRefListRunner 

39from lsst.ip.isr import PhotonTransferCurveDataset 

40 

41__all__ = ['MeasurePhotonTransferCurveTask', 

42 'MeasurePhotonTransferCurveTaskConfig'] 

43 

44 

45class MeasurePhotonTransferCurveTaskConfig(pexConfig.Config): 

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

47 ccdKey = pexConfig.Field( 

48 dtype=str, 

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

50 default='ccd', 

51 ) 

52 ptcFitType = pexConfig.ChoiceField( 

53 dtype=str, 

54 doc="Fit PTC to Eq. 16, Eq. 20 in Astier+19, or to a polynomial.", 

55 default="POLYNOMIAL", 

56 allowed={ 

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

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

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

60 } 

61 ) 

62 sigmaClipFullFitCovariancesAstier = pexConfig.Field( 

63 dtype=float, 

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

65 default=5.0, 

66 ) 

67 maxIterFullFitCovariancesAstier = pexConfig.Field( 

68 dtype=int, 

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

70 default=3, 

71 ) 

72 maximumRangeCovariancesAstier = pexConfig.Field( 

73 dtype=int, 

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

75 default=8, 

76 ) 

77 covAstierRealSpace = pexConfig.Field( 

78 dtype=bool, 

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

80 default=False, 

81 ) 

82 polynomialFitDegree = pexConfig.Field( 

83 dtype=int, 

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

85 default=3, 

86 ) 

87 linearity = pexConfig.ConfigurableField( 

88 target=LinearitySolveTask, 

89 doc="Task to solve the linearity." 

90 ) 

91 

92 doCreateLinearizer = pexConfig.Field( 

93 dtype=bool, 

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

95 default=False, 

96 ) 

97 

98 binSize = pexConfig.Field( 

99 dtype=int, 

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

101 default=1, 

102 ) 

103 minMeanSignal = pexConfig.Field( 

104 dtype=float, 

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

106 default=0, 

107 ) 

108 maxMeanSignal = pexConfig.Field( 

109 dtype=float, 

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

111 default=9e6, 

112 ) 

113 initialNonLinearityExclusionThresholdPositive = pexConfig.RangeField( 

114 dtype=float, 

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

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

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

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

119 default=0.12, 

120 min=0.0, 

121 max=1.0, 

122 ) 

123 initialNonLinearityExclusionThresholdNegative = pexConfig.RangeField( 

124 dtype=float, 

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

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

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

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

129 default=0.25, 

130 min=0.0, 

131 max=1.0, 

132 ) 

133 sigmaCutPtcOutliers = pexConfig.Field( 

134 dtype=float, 

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

136 default=5.0, 

137 ) 

138 maskNameList = pexConfig.ListField( 

139 dtype=str, 

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

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

142 ) 

143 nSigmaClipPtc = pexConfig.Field( 

144 dtype=float, 

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

146 default=5.5, 

147 ) 

148 nIterSigmaClipPtc = pexConfig.Field( 

149 dtype=int, 

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

151 default=1, 

152 ) 

153 maxIterationsPtcOutliers = pexConfig.Field( 

154 dtype=int, 

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

156 default=2, 

157 ) 

158 doFitBootstrap = pexConfig.Field( 

159 dtype=bool, 

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

161 default=False, 

162 ) 

163 doPhotodiode = pexConfig.Field( 

164 dtype=bool, 

165 doc="Apply a correction based on the photodiode readings if available?", 

166 default=True, 

167 ) 

168 photodiodeDataPath = pexConfig.Field( 

169 dtype=str, 

170 doc="Gen2 only: path to locate the data photodiode data files.", 

171 default="" 

172 ) 

173 instrumentName = pexConfig.Field( 

174 dtype=str, 

175 doc="Instrument name.", 

176 default='', 

177 ) 

178 

179 

180class MeasurePhotonTransferCurveTask(pipeBase.CmdLineTask): 

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

182 

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

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

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

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

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

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

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

190 16 of Astier+19 ("The Shape of the Photon Transfer Curve of CCD sensors", 

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

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

193 

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

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

196 task this is not supported. 

197 

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

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

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

201 and the noise. 

202 

203 Parameters 

204 ---------- 

205 

206 *args: `list` 

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

208 time. 

209 **kwargs: `dict` 

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

211 time. 

212 

213 """ 

214 

215 RunnerClass = DataRefListRunner 

216 ConfigClass = MeasurePhotonTransferCurveTaskConfig 

217 _DefaultName = "measurePhotonTransferCurve" 

218 

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

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

221 self.makeSubtask("linearity") 

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

223 self.config.validate() 

224 self.config.freeze() 

225 

226 @pipeBase.timeMethod 

227 def runDataRef(self, dataRefList): 

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

229 

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

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

232 measure the PTC. 

233 

234 Parameters 

235 ---------- 

236 dataRefList : `list` [`lsst.daf.peristence.ButlerDataRef`] 

237 Data references for exposures for detectors to process. 

238 """ 

239 if len(dataRefList) < 2: 

240 raise RuntimeError("Insufficient inputs to combine.") 

241 

242 # setup necessary objects 

243 dataRef = dataRefList[0] 

244 

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

246 camera = dataRef.get('camera') 

247 detector = camera[dataRef.dataId[self.config.ccdKey]] 

248 

249 amps = detector.getAmplifiers() 

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

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

252 

253 # Get the pairs of flat indexed by expTime 

254 expPairs = self.makePairs(dataRefList) 

255 expIds = [] 

256 for (exp1, exp2) in expPairs.values(): 

257 id1 = exp1.getInfo().getVisitInfo().getExposureId() 

258 id2 = exp2.getInfo().getVisitInfo().getExposureId() 

259 expIds.append((id1, id2)) 

260 self.log.info(f"Measuring PTC using {expIds} exposures for detector {detector.getId()}") 

261 

262 # get photodiode data early so that logic can be put in to only use the 

263 # data if all files are found, as partial corrections are not possible 

264 # or at least require significant logic to deal with 

265 if self.config.doPhotodiode: 

266 for (expId1, expId2) in expIds: 

267 charges = [-1, -1] # necessary to have a not-found value to keep lists in step 

268 for i, expId in enumerate([expId1, expId2]): 

269 # //1000 is a Gen2 only hack, working around the fact an 

270 # exposure's ID is not the same as the expId in the 

271 # registry. Currently expId is concatenated with the 

272 # zero-padded detector ID. This will all go away in Gen3. 

273 dataRef.dataId['expId'] = expId//1000 

274 if self.config.photodiodeDataPath: 

275 photodiodeData = getBOTphotodiodeData(dataRef, self.config.photodiodeDataPath) 

276 else: 

277 photodiodeData = getBOTphotodiodeData(dataRef) 

278 if photodiodeData: # default path stored in function def to keep task clean 

279 charges[i] = photodiodeData.getCharge() 

280 else: 

281 # full expId (not //1000) here, as that encodes the 

282 # the detector number as so is fully qualifying 

283 self.log.warn(f"No photodiode data found for {expId}") 

284 

285 for ampName in ampNames: 

286 datasetPtc.photoCharge[ampName].append((charges[0], charges[1])) 

287 else: 

288 # Can't be an empty list, as initialized, because astropy.Table won't allow it 

289 # when saving as fits 

290 for ampName in ampNames: 

291 datasetPtc.photoCharge[ampName] = np.repeat(np.nan, len(expIds)) 

292 

293 for ampName in ampNames: 

294 datasetPtc.inputExpIdPairs[ampName] = expIds 

295 

296 tupleRecords = [] 

297 allTags = [] 

298 for expTime, (exp1, exp2) in expPairs.items(): 

299 expId1 = exp1.getInfo().getVisitInfo().getExposureId() 

300 expId2 = exp2.getInfo().getVisitInfo().getExposureId() 

301 tupleRows = [] 

302 nAmpsNan = 0 

303 for ampNumber, amp in enumerate(detector): 

304 ampName = amp.getName() 

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

306 doRealSpace = self.config.covAstierRealSpace 

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

308 covAstierRealSpace=doRealSpace) 

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

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

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

312 

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

314 msg = (f"NaN mean or var, or None cov in amp {ampName} in exposure pair {expId1}," 

315 f" {expId2} of detector {detNum}.") 

316 self.log.warn(msg) 

317 nAmpsNan += 1 

318 continue 

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

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

321 continue 

322 

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

324 if nAmpsNan == len(ampNames): 

325 msg = f"NaN mean in all amps of exposure pair {expId1}, {expId2} of detector {detNum}." 

326 self.log.warn(msg) 

327 continue 

328 allTags += tags 

329 tupleRecords += tupleRows 

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

331 

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

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

334 datasetPtc = self.fitCovariancesAstier(datasetPtc, covariancesWithTags) 

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

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

337 # Fill up PhotonTransferCurveDataset object. 

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

339 

340 detName = detector.getName() 

341 now = datetime.datetime.utcnow() 

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

343 butler = dataRef.getButler() 

344 

345 datasetPtc.updateMetadata(setDate=True, camera=camera, detector=detector) 

346 

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

348 if self.config.doCreateLinearizer: 

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

350 # Fill up PhotonTransferCurveDataset object. 

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

352 # Produce coefficients for Polynomial and Squared linearizers. 

353 # Build linearizer objects. 

354 dimensions = {'camera': camera.getName(), 'detector': detector.getId()} 

355 linearityResults = self.linearity.run(datasetPtc, camera, dimensions) 

356 linearizer = linearityResults.outputLinearizer 

357 

358 self.log.info("Writing linearizer:") 

359 butler.put(linearizer, datasetType='Linearizer', dataId={'detector': detNum, 

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

361 

362 self.log.info(f"Writing PTC data.") 

363 butler.put(datasetPtc, datasetType='photonTransferCurveDataset', dataId={'detector': detNum, 

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

365 

366 return pipeBase.Struct(exitStatus=0) 

367 

368 def makePairs(self, dataRefList): 

369 """Produce a list of flat pairs indexed by exposure time. 

370 

371 Parameters 

372 ---------- 

373 dataRefList : `list` [`lsst.daf.peristence.ButlerDataRef`] 

374 Data references for exposures for detectors to process. 

375 

376 Return 

377 ------ 

378 flatPairs : `dict` [`float`, `lsst.afw.image.exposure.exposure.ExposureF`] 

379 Dictionary that groups flat-field exposures that have the same exposure time (seconds). 

380 

381 Notes 

382 ----- 

383 We use the difference of one pair of flat-field images taken at the same exposure time when 

384 calculating the PTC to reduce Fixed Pattern Noise. If there are > 2 flat-field images with the 

385 same exposure time, the first two are kept and the rest discarded. 

386 """ 

387 

388 # Organize exposures by observation date. 

389 expDict = {} 

390 for dataRef in dataRefList: 

391 try: 

392 tempFlat = dataRef.get("postISRCCD") 

393 except RuntimeError: 

394 self.log.warn("postISR exposure could not be retrieved. Ignoring flat.") 

395 continue 

396 expDate = tempFlat.getInfo().getVisitInfo().getDate().get() 

397 expDict.setdefault(expDate, tempFlat) 

398 sortedExps = {k: expDict[k] for k in sorted(expDict)} 

399 

400 flatPairs = {} 

401 for exp in sortedExps: 

402 tempFlat = sortedExps[exp] 

403 expTime = tempFlat.getInfo().getVisitInfo().getExposureTime() 

404 listAtExpTime = flatPairs.setdefault(expTime, []) 

405 if len(listAtExpTime) >= 2: 

406 self.log.warn(f"Already found 2 exposures at expTime {expTime}. " 

407 f"Ignoring exposure {tempFlat.getInfo().getVisitInfo().getExposureId()}") 

408 else: 

409 listAtExpTime.append(tempFlat) 

410 

411 keysToDrop = [] 

412 for (key, value) in flatPairs.items(): 

413 if len(value) < 2: 

414 keysToDrop.append(key) 

415 

416 if len(keysToDrop): 

417 for key in keysToDrop: 

418 self.log.warn(f"Only one exposure found at expTime {key}. Dropping exposure " 

419 f"{flatPairs[key][0].getInfo().getVisitInfo().getExposureId()}.") 

420 flatPairs.pop(key) 

421 return flatPairs 

422 

423 def fitCovariancesAstier(self, dataset, covariancesWithTagsArray): 

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

425 

426 Parameters 

427 ---------- 

428 dataset : `lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset` 

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

430 

431 covariancesWithTagsArray : `numpy.recarray` 

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

433 mu : 0.5*(m1 + m2), where: 

434 mu1: mean value of flat1 

435 mu2: mean value of flat2 

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

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

438 i: lag dimension 

439 j: lag dimension 

440 npix: number of pixels used for covariance calculation. 

441 

442 Returns 

443 ------- 

444 dataset: `lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset` 

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

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

447 the class `PhotonTransferCurveDatase`. 

448 """ 

449 

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

451 r=self.config.maximumRangeCovariancesAstier, 

452 nSigmaFullFit=self.config.sigmaClipFullFitCovariancesAstier, 

453 maxIterFullFit=self.config.maxIterFullFitCovariancesAstier) 

454 

455 dataset = self.getOutputPtcDataCovAstier(dataset, covFits, covFitsNoB) 

456 

457 return dataset 

458 

459 def getOutputPtcDataCovAstier(self, dataset, covFits, covFitsNoB): 

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

461 

462 Parameters 

463 ---------- 

464 dataset : `lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset` 

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

466 

467 covFits: `dict` 

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

469 

470 covFitsNoB : `dict` 

471 Dictionary of CovFit objects, with amp names as keys, and 'b=0' in Eq. 20 of Astier+19. 

472 

473 Returns 

474 ------- 

475 dataset : `lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset` 

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

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

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

479 See the class `PhotonTransferCurveDatase`. 

480 """ 

481 assert(len(covFits) == len(covFitsNoB)) 

482 

483 for i, amp in enumerate(dataset.ampNames): 

484 lenInputTimes = len(dataset.rawExpTimes[amp]) 

485 # Not used when ptcFitType is 'FULLCOVARIANCE' 

486 dataset.ptcFitPars[amp] = np.nan 

487 dataset.ptcFitParsError[amp] = np.nan 

488 dataset.ptcFitChiSq[amp] = np.nan 

489 if amp in covFits: 

490 fit = covFits[amp] 

491 fitNoB = covFitsNoB[amp] 

492 # Save full covariances, covariances models, and their weights 

493 dataset.covariances[amp] = fit.cov 

494 dataset.covariancesModel[amp] = fit.evalCovModel() 

495 dataset.covariancesSqrtWeights[amp] = fit.sqrtW 

496 dataset.aMatrix[amp] = fit.getA() 

497 dataset.bMatrix[amp] = fit.getB() 

498 dataset.covariancesNoB[amp] = fitNoB.cov 

499 dataset.covariancesModelNoB[amp] = fitNoB.evalCovModel() 

500 dataset.covariancesSqrtWeightsNoB[amp] = fitNoB.sqrtW 

501 dataset.aMatrixNoB[amp] = fitNoB.getA() 

502 

503 (meanVecFinal, varVecFinal, varVecModel, 

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

505 gain = fit.getGain() 

506 dataset.expIdMask[amp] = varMask 

507 dataset.gain[amp] = gain 

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

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

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

511 

512 padLength = lenInputTimes - len(varVecFinal) 

513 dataset.finalVars[amp] = np.pad(varVecFinal/(gain**2), (0, padLength), 'constant', 

514 constant_values=np.nan) 

515 dataset.finalModelVars[amp] = np.pad(varVecModel/(gain**2), (0, padLength), 'constant', 

516 constant_values=np.nan) 

517 dataset.finalMeans[amp] = np.pad(meanVecFinal/gain, (0, padLength), 'constant', 

518 constant_values=np.nan) 

519 else: 

520 # Bad amp 

521 # Entries need to have proper dimensions so read/write with astropy.Table works. 

522 matrixSide = self.config.maximumRangeCovariancesAstier 

523 nanMatrix = np.full((matrixSide, matrixSide), np.nan) 

524 listNanMatrix = np.full((lenInputTimes, matrixSide, matrixSide), np.nan) 

525 

526 dataset.covariances[amp] = listNanMatrix 

527 dataset.covariancesModel[amp] = listNanMatrix 

528 dataset.covariancesSqrtWeights[amp] = listNanMatrix 

529 dataset.aMatrix[amp] = nanMatrix 

530 dataset.bMatrix[amp] = nanMatrix 

531 dataset.covariancesNoB[amp] = listNanMatrix 

532 dataset.covariancesModelNoB[amp] = listNanMatrix 

533 dataset.covariancesSqrtWeightsNoB[amp] = listNanMatrix 

534 dataset.aMatrixNoB[amp] = nanMatrix 

535 

536 dataset.expIdMask[amp] = np.repeat(np.nan, lenInputTimes) 

537 dataset.gain[amp] = np.nan 

538 dataset.gainErr[amp] = np.nan 

539 dataset.noise[amp] = np.nan 

540 dataset.noiseErr[amp] = np.nan 

541 dataset.finalVars[amp] = np.repeat(np.nan, lenInputTimes) 

542 dataset.finalModelVars[amp] = np.repeat(np.nan, lenInputTimes) 

543 dataset.finalMeans[amp] = np.repeat(np.nan, lenInputTimes) 

544 

545 return dataset 

546 

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

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

549 

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

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

552 keep one (covariance). 

553 

554 Parameters 

555 ---------- 

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

557 First exposure of flat field pair. 

558 

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

560 Second exposure of flat field pair. 

561 

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

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

564 

565 covAstierRealSpace : `bool`, optional 

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

567 See Appendix A of Astier+19. 

568 

569 Returns 

570 ------- 

571 mu : `float` or `NaN` 

572 0.5*(mu1 + mu2), where mu1, and mu2 are the clipped means of the regions in 

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

574 

575 varDiff : `float` or `NaN` 

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

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

578 

579 covDiffAstier : `list` or `NaN` 

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

581 dx : `int` 

582 Lag in x 

583 dy : `int` 

584 Lag in y 

585 var : `float` 

586 Variance at (dx, dy). 

587 cov : `float` 

588 Covariance at (dx, dy). 

589 nPix : `int` 

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

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

592 """ 

593 

594 if region is not None: 

595 im1Area = exposure1.maskedImage[region] 

596 im2Area = exposure2.maskedImage[region] 

597 else: 

598 im1Area = exposure1.maskedImage 

599 im2Area = exposure2.maskedImage 

600 

601 if self.config.binSize > 1: 

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

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

604 

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

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

607 self.config.nIterSigmaClipPtc, 

608 im1MaskVal) 

609 im1StatsCtrl.setNanSafe(True) 

610 im1StatsCtrl.setAndMask(im1MaskVal) 

611 

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

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

614 self.config.nIterSigmaClipPtc, 

615 im2MaskVal) 

616 im2StatsCtrl.setNanSafe(True) 

617 im2StatsCtrl.setAndMask(im2MaskVal) 

618 

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

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

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

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

623 return np.nan, np.nan, None 

624 mu = 0.5*(mu1 + mu2) 

625 

626 # Take difference of pairs 

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

628 temp = im2Area.clone() 

629 temp *= mu1 

630 diffIm = im1Area.clone() 

631 diffIm *= mu2 

632 diffIm -= temp 

633 diffIm /= mu 

634 

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

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

637 self.config.nIterSigmaClipPtc, 

638 diffImMaskVal) 

639 diffImStatsCtrl.setNanSafe(True) 

640 diffImStatsCtrl.setAndMask(diffImMaskVal) 

641 

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

643 

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

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

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

647 

648 w12 = w1*w2 

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

650 w = w12*wDiff 

651 

652 maxRangeCov = self.config.maximumRangeCovariancesAstier 

653 if covAstierRealSpace: 

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

655 else: 

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

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

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

659 covDiffAstier = c.reportCovFft(maxRangeCov) 

660 

661 return mu, varDiff, covDiffAstier 

662 

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

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

665 

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

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

668 

669 Parameters 

670 ---------- 

671 diffImage : `numpy.array` 

672 Image to compute the covariance of. 

673 

674 weightImage : `numpy.array` 

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

676 

677 maxRange : `int` 

678 Last index of the covariance to be computed. 

679 

680 Returns 

681 ------- 

682 outList : `list` 

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

684 dx : `int` 

685 Lag in x 

686 dy : `int` 

687 Lag in y 

688 var : `float` 

689 Variance at (dx, dy). 

690 cov : `float` 

691 Covariance at (dx, dy). 

692 nPix : `int` 

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

694 """ 

695 outList = [] 

696 var = 0 

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

698 for dy in range(maxRange + 1): 

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

700 if (dx*dy > 0): 

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

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

703 cov = 0.5*(cov1 + cov2) 

704 nPix = nPix1 + nPix2 

705 else: 

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

707 if (dx == 0 and dy == 0): 

708 var = cov 

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

710 

711 return outList 

712 

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

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

715 

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

717 

718 Parameters 

719 ---------- 

720 diffImage : `numpy.array` 

721 Image to compute the covariance of. 

722 

723 weightImage : `numpy.array` 

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

725 

726 dx : `int` 

727 Lag in x. 

728 

729 dy : `int` 

730 Lag in y. 

731 

732 Returns 

733 ------- 

734 cov : `float` 

735 Covariance at (dx, dy) 

736 

737 nPix : `int` 

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

739 """ 

740 (nCols, nRows) = diffImage.shape 

741 # switching both signs does not change anything: 

742 # it just swaps im1 and im2 below 

743 if (dx < 0): 

744 (dx, dy) = (-dx, -dy) 

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

746 # depending on the sign of dy 

747 if dy >= 0: 

748 im1 = diffImage[dy:, dx:] 

749 w1 = weightImage[dy:, dx:] 

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

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

752 else: 

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

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

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

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

757 # use the same mask for all 3 calculations 

758 wAll = w1*w2 

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

760 nPix = wAll.sum() 

761 im1TimesW = im1*wAll 

762 s1 = im1TimesW.sum()/nPix 

763 s2 = (im2*wAll).sum()/nPix 

764 p = (im1TimesW*im2).sum()/nPix 

765 cov = p - s1*s2 

766 

767 return cov, nPix 

768 

769 @staticmethod 

770 def _initialParsForPolynomial(order): 

771 assert(order >= 2) 

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

773 pars[0] = 10 

774 pars[1] = 1 

775 pars[2:] = 0.0001 

776 return pars 

777 

778 @staticmethod 

779 def _boundsForPolynomial(initialPars): 

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

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

782 lowers[1] = 0 # no negative gains 

783 return (lowers, uppers) 

784 

785 @staticmethod 

786 def _boundsForAstier(initialPars): 

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

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

789 return (lowers, uppers) 

790 

791 @staticmethod 

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

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

794 

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

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

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

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

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

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

801 is wise to be stricter about restricting positive outliers than 

802 negative ones. 

803 

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

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

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

807 medianRatio = np.median(ratios) 

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

809 

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

811 maxDeviationPositive = abs(maxDeviationPositive) 

812 maxDeviationNegative = -1. * abs(maxDeviationNegative) 

813 

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

815 else False for r in ratioDeviations]) 

816 return goodPoints 

817 

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

819 """""" 

820 nBad = Counter(array)[0] 

821 if nBad == 0: 

822 return array 

823 

824 if warn: 

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

826 self.log.warn(msg) 

827 

828 array[array == 0] = substituteValue 

829 return array 

830 

831 def fitPtc(self, dataset, ptcFitType): 

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

833 

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

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

836 

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

838 initial clipping of data points that are more than 

839 config.initialNonLinearityExclusionThreshold away from lying on a 

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

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

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

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

844 sigma-clipping. 

845 

846 Parameters 

847 ---------- 

848 dataset : `lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset` 

849 The dataset containing the means, variances and exposure times 

850 

851 ptcFitType : `str` 

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

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

854 

855 Returns 

856 ------- 

857 dataset: `lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset` 

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

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

860 the class `PhotonTransferCurveDatase`. 

861 """ 

862 

863 matrixSide = self.config.maximumRangeCovariancesAstier 

864 nanMatrix = np.empty((matrixSide, matrixSide)) 

865 nanMatrix[:] = np.nan 

866 

867 for amp in dataset.ampNames: 

868 lenInputTimes = len(dataset.rawExpTimes[amp]) 

869 listNanMatrix = np.empty((lenInputTimes, matrixSide, matrixSide)) 

870 listNanMatrix[:] = np.nan 

871 

872 dataset.covariances[amp] = listNanMatrix 

873 dataset.covariancesModel[amp] = listNanMatrix 

874 dataset.covariancesSqrtWeights[amp] = listNanMatrix 

875 dataset.aMatrix[amp] = nanMatrix 

876 dataset.bMatrix[amp] = nanMatrix 

877 dataset.covariancesNoB[amp] = listNanMatrix 

878 dataset.covariancesModelNoB[amp] = listNanMatrix 

879 dataset.covariancesSqrtWeightsNoB[amp] = listNanMatrix 

880 dataset.aMatrixNoB[amp] = nanMatrix 

881 

882 def errFunc(p, x, y): 

883 return ptcFunc(p, x) - y 

884 

885 sigmaCutPtcOutliers = self.config.sigmaCutPtcOutliers 

886 maxIterationsPtcOutliers = self.config.maxIterationsPtcOutliers 

887 

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

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

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

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

892 varVecOriginal = self._makeZeroSafe(varVecOriginal) 

893 

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

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

896 

897 goodPoints = self._getInitialGoodPoints(meanVecOriginal, varVecOriginal, 

898 self.config.initialNonLinearityExclusionThresholdPositive, 

899 self.config.initialNonLinearityExclusionThresholdNegative) 

900 if not (mask.any() and goodPoints.any()): 

901 msg = (f"\nSERIOUS: All points in either mask: {mask} or goodPoints: {goodPoints} are bad." 

902 f"Setting {ampName} to BAD.") 

903 self.log.warn(msg) 

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

905 # for the final NL coefficients 

906 dataset.badAmps.append(ampName) 

907 dataset.expIdMask[ampName] = np.repeat(np.nan, len(dataset.rawExpTimes[ampName])) 

908 dataset.gain[ampName] = np.nan 

909 dataset.gainErr[ampName] = np.nan 

910 dataset.noise[ampName] = np.nan 

911 dataset.noiseErr[ampName] = np.nan 

912 dataset.ptcFitPars[ampName] = (np.repeat(np.nan, self.config.polynomialFitDegree + 1) if 

913 ptcFitType in ["POLYNOMIAL", ] else np.repeat(np.nan, 3)) 

914 dataset.ptcFitParsError[ampName] = (np.repeat(np.nan, self.config.polynomialFitDegree + 1) if 

915 ptcFitType in ["POLYNOMIAL", ] else np.repeat(np.nan, 3)) 

916 dataset.ptcFitChiSq[ampName] = np.nan 

917 dataset.finalVars[ampName] = np.repeat(np.nan, len(dataset.rawExpTimes[ampName])) 

918 dataset.finalModelVars[ampName] = np.repeat(np.nan, len(dataset.rawExpTimes[ampName])) 

919 dataset.finalMeans[ampName] = np.repeat(np.nan, len(dataset.rawExpTimes[ampName])) 

920 continue 

921 

922 mask = mask & goodPoints 

923 

924 if ptcFitType == 'EXPAPPROXIMATION': 

925 ptcFunc = funcAstier 

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

927 bounds = self._boundsForAstier(parsIniPtc) 

928 if ptcFitType == 'POLYNOMIAL': 

929 ptcFunc = funcPolynomial 

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

931 bounds = self._boundsForPolynomial(parsIniPtc) 

932 

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

934 count = 1 

935 while count <= maxIterationsPtcOutliers: 

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

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

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

939 meanTempVec = meanVecOriginal[mask] 

940 varTempVec = varVecOriginal[mask] 

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

942 pars = res.x 

943 

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

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

946 # always be the same length for broadcasting 

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

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

949 mask = mask & newMask 

950 if not (mask.any() and newMask.any()): 

951 msg = (f"\nSERIOUS: All points in either mask: {mask} or newMask: {newMask} are bad. " 

952 f"Setting {ampName} to BAD.") 

953 self.log.warn(msg) 

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

955 # for the final NL coefficients 

956 dataset.badAmps.append(ampName) 

957 dataset.expIdMask[ampName] = np.repeat(np.nan, len(dataset.rawExpTimes[ampName])) 

958 dataset.gain[ampName] = np.nan 

959 dataset.gainErr[ampName] = np.nan 

960 dataset.noise[ampName] = np.nan 

961 dataset.noiseErr[ampName] = np.nan 

962 dataset.ptcFitPars[ampName] = (np.repeat(np.nan, self.config.polynomialFitDegree + 1) 

963 if ptcFitType in ["POLYNOMIAL", ] else 

964 np.repeat(np.nan, 3)) 

965 dataset.ptcFitParsError[ampName] = (np.repeat(np.nan, self.config.polynomialFitDegree + 1) 

966 if ptcFitType in ["POLYNOMIAL", ] else 

967 np.repeat(np.nan, 3)) 

968 dataset.ptcFitChiSq[ampName] = np.nan 

969 dataset.finalVars[ampName] = np.repeat(np.nan, len(dataset.rawExpTimes[ampName])) 

970 dataset.finalModelVars[ampName] = np.repeat(np.nan, len(dataset.rawExpTimes[ampName])) 

971 dataset.finalMeans[ampName] = np.repeat(np.nan, len(dataset.rawExpTimes[ampName])) 

972 break 

973 nDroppedTotal = Counter(mask)[False] 

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

975 count += 1 

976 # objects should never shrink 

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

978 

979 if not (mask.any() and newMask.any()): 

980 continue 

981 dataset.expIdMask[ampName] = mask # store the final mask 

982 parsIniPtc = pars 

983 meanVecFinal = meanVecOriginal[mask] 

984 varVecFinal = varVecOriginal[mask] 

985 

986 if Counter(mask)[False] > 0: 

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

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

989 

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

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

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

993 self.log.warn(msg) 

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

995 # for the final NL coefficients 

996 dataset.badAmps.append(ampName) 

997 dataset.expIdMask[ampName] = np.repeat(np.nan, len(dataset.rawExpTimes[ampName])) 

998 dataset.gain[ampName] = np.nan 

999 dataset.gainErr[ampName] = np.nan 

1000 dataset.noise[ampName] = np.nan 

1001 dataset.noiseErr[ampName] = np.nan 

1002 dataset.ptcFitPars[ampName] = (np.repeat(np.nan, self.config.polynomialFitDegree + 1) if 

1003 ptcFitType in ["POLYNOMIAL", ] else np.repeat(np.nan, 3)) 

1004 dataset.ptcFitParsError[ampName] = (np.repeat(np.nan, self.config.polynomialFitDegree + 1) if 

1005 ptcFitType in ["POLYNOMIAL", ] else np.repeat(np.nan, 3)) 

1006 dataset.ptcFitChiSq[ampName] = np.nan 

1007 dataset.finalVars[ampName] = np.repeat(np.nan, len(dataset.rawExpTimes[ampName])) 

1008 dataset.finalModelVars[ampName] = np.repeat(np.nan, len(dataset.rawExpTimes[ampName])) 

1009 dataset.finalMeans[ampName] = np.repeat(np.nan, len(dataset.rawExpTimes[ampName])) 

1010 continue 

1011 

1012 # Fit the PTC 

1013 if self.config.doFitBootstrap: 

1014 parsFit, parsFitErr, reducedChiSqPtc = fitBootstrap(parsIniPtc, meanVecFinal, 

1015 varVecFinal, ptcFunc, 

1016 weightsY=1./np.sqrt(varVecFinal)) 

1017 else: 

1018 parsFit, parsFitErr, reducedChiSqPtc = fitLeastSq(parsIniPtc, meanVecFinal, 

1019 varVecFinal, ptcFunc, 

1020 weightsY=1./np.sqrt(varVecFinal)) 

1021 dataset.ptcFitPars[ampName] = parsFit 

1022 dataset.ptcFitParsError[ampName] = parsFitErr 

1023 dataset.ptcFitChiSq[ampName] = reducedChiSqPtc 

1024 # Masked variances (measured and modeled) and means. Need to pad the array so astropy.Table does 

1025 # not crash (the mask may vary per amp). 

1026 padLength = len(dataset.rawExpTimes[ampName]) - len(varVecFinal) 

1027 dataset.finalVars[ampName] = np.pad(varVecFinal, (0, padLength), 'constant', 

1028 constant_values=np.nan) 

1029 dataset.finalModelVars[ampName] = np.pad(ptcFunc(parsFit, meanVecFinal), (0, padLength), 

1030 'constant', constant_values=np.nan) 

1031 dataset.finalMeans[ampName] = np.pad(meanVecFinal, (0, padLength), 'constant', 

1032 constant_values=np.nan) 

1033 

1034 if ptcFitType == 'EXPAPPROXIMATION': 

1035 ptcGain = parsFit[1] 

1036 ptcGainErr = parsFitErr[1] 

1037 ptcNoise = np.sqrt(np.fabs(parsFit[2])) 

1038 ptcNoiseErr = 0.5*(parsFitErr[2]/np.fabs(parsFit[2]))*np.sqrt(np.fabs(parsFit[2])) 

1039 if ptcFitType == 'POLYNOMIAL': 

1040 ptcGain = 1./parsFit[1] 

1041 ptcGainErr = np.fabs(1./parsFit[1])*(parsFitErr[1]/parsFit[1]) 

1042 ptcNoise = np.sqrt(np.fabs(parsFit[0]))*ptcGain 

1043 ptcNoiseErr = (0.5*(parsFitErr[0]/np.fabs(parsFit[0]))*(np.sqrt(np.fabs(parsFit[0]))))*ptcGain 

1044 dataset.gain[ampName] = ptcGain 

1045 dataset.gainErr[ampName] = ptcGainErr 

1046 dataset.noise[ampName] = ptcNoise 

1047 dataset.noiseErr[ampName] = ptcNoiseErr 

1048 if not len(dataset.ptcFitType) == 0: 

1049 dataset.ptcFitType = ptcFitType 

1050 if len(dataset.badAmps) == 0: 

1051 dataset.badAmps = np.repeat(np.nan, len(list(dataset.rawExpTimes.values())[0])) 

1052 

1053 return dataset