Coverage for python/lsst/ip/isr/ptcDataset.py: 6%

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1# 

2# LSST Data Management System 

3# Copyright 2008-2017 AURA/LSST. 

4# 

5# This product includes software developed by the 

6# LSST Project (http://www.lsst.org/). 

7# 

8# This program is free software: you can redistribute it and/or modify 

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

10# the Free Software Foundation, either version 3 of the License, or 

11# (at your option) any later version. 

12# 

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

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

15# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

16# GNU General Public License for more details. 

17# 

18# You should have received a copy of the LSST License Statement and 

19# the GNU General Public License along with this program. If not, 

20# see <https://www.lsstcorp.org/LegalNotices/>. 

21# 

22""" 

23Define dataset class for MeasurePhotonTransferCurve task 

24""" 

25 

26__all__ = ['PhotonTransferCurveDataset'] 

27 

28import numpy as np 

29from astropy.table import Table 

30 

31from lsst.ip.isr import IsrCalib 

32 

33 

34class PhotonTransferCurveDataset(IsrCalib): 

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

36 

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

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

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

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

41 inputExpIdPairs records the exposures used to produce the data. 

42 When fitPtc() or fitCovariancesAstier() is run, a mask is built up, which 

43 is by definition always the same length as inputExpIdPairs, rawExpTimes, 

44 rawMeans and rawVars, and is a list of bools, which are incrementally set 

45 to False as points are discarded from the fits. 

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

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

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

49 plus one. 

50 

51 Parameters 

52 ---------- 

53 ampNames : `list` 

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

55 ptcFitType : `str` 

56 Type of model fitted to the PTC: "POLYNOMIAL", "EXPAPPROXIMATION", 

57 or "FULLCOVARIANCE". 

58 covMatrixSide : `int` 

59 Maximum lag of covariances (size of square covariance matrices). 

60 kwargs : `dict`, optional 

61 Other keyword arguments to pass to the parent init. 

62 

63 Notes 

64 ----- 

65 The stored attributes are: 

66 

67 badAmps : `list` 

68 List with bad amplifiers names. 

69 inputExpIdPairs : `dict`, [`str`, `list`] 

70 Dictionary keyed by amp names containing the input exposures IDs. 

71 expIdMask : `dict`, [`str`, `list`] 

72 Dictionary keyed by amp names containing the mask produced after 

73 outlier rejection. The mask produced by the "FULLCOVARIANCE" 

74 option may differ from the one produced in the other two PTC 

75 fit types. 

76 rawExpTimes : `dict`, [`str`, `list`] 

77 Dictionary keyed by amp names containing the unmasked exposure times. 

78 rawMeans : `dict`, [`str`, `list`] 

79 Dictionary keyed by amp namescontaining the unmasked average of the 

80 means of the exposures in each flat pair. 

81 rawVars : `dict`, [`str`, `list`] 

82 Dictionary keyed by amp names containing the variance of the 

83 difference image of the exposures in each flat pair. 

84 gain : `dict`, [`str`, `list`] 

85 Dictionary keyed by amp names containing the fitted gains. 

86 gainErr : `dict`, [`str`, `list`] 

87 Dictionary keyed by amp names containing the errors on the 

88 fitted gains. 

89 noise : `dict`, [`str`, `list`] 

90 Dictionary keyed by amp names containing the fitted noise. 

91 noiseErr : `dict`, [`str`, `list`] 

92 Dictionary keyed by amp names containing the errors on the fitted 

93 noise. 

94 ptcFitPars : `dict`, [`str`, `list`] 

95 Dictionary keyed by amp names containing the fitted parameters of the 

96 PTC model for ptcFitTye in ["POLYNOMIAL", "EXPAPPROXIMATION"]. 

97 ptcFitParsError : `dict`, [`str`, `list`] 

98 Dictionary keyed by amp names containing the errors on the fitted 

99 parameters of the PTC model for ptcFitTye in 

100 ["POLYNOMIAL", "EXPAPPROXIMATION"]. 

101 ptcFitChiSq : `dict`, [`str`, `list`] 

102 Dictionary keyed by amp names containing the reduced chi squared 

103 of the fit for ptcFitTye in ["POLYNOMIAL", "EXPAPPROXIMATION"]. 

104 ptcTurnoff : `float` 

105 Flux value (in ADU) where the variance of the PTC curve starts 

106 decreasing consistently. 

107 covariances : `dict`, [`str`, `list`] 

108 Dictionary keyed by amp names containing a list of measured 

109 covariances per mean flux. 

110 covariancesModel : `dict`, [`str`, `list`] 

111 Dictionary keyed by amp names containinging covariances model 

112 (Eq. 20 of Astier+19) per mean flux. 

113 covariancesSqrtWeights : `dict`, [`str`, `list`] 

114 Dictionary keyed by amp names containinging sqrt. of covariances 

115 weights. 

116 aMatrix : `dict`, [`str`, `list`] 

117 Dictionary keyed by amp names containing the "a" parameters from 

118 the model in Eq. 20 of Astier+19. 

119 bMatrix : `dict`, [`str`, `list`] 

120 Dictionary keyed by amp names containing the "b" parameters from 

121 the model in Eq. 20 of Astier+19. 

122 covariancesModelNoB : `dict`, [`str`, `list`] 

123 Dictionary keyed by amp names containing covariances model 

124 (with 'b'=0 in Eq. 20 of Astier+19) 

125 per mean flux. 

126 aMatrixNoB : `dict`, [`str`, `list`] 

127 Dictionary keyed by amp names containing the "a" parameters from the 

128 model in Eq. 20 of Astier+19 

129 (and 'b' = 0). 

130 finalVars : `dict`, [`str`, `list`] 

131 Dictionary keyed by amp names containing the masked variance of the 

132 difference image of each flat 

133 pair. If needed, each array will be right-padded with 

134 np.nan to match the length of rawExpTimes. 

135 finalModelVars : `dict`, [`str`, `list`] 

136 Dictionary keyed by amp names containing the masked modeled 

137 variance of the difference image of each flat pair. If needed, each 

138 array will be right-padded with np.nan to match the length of 

139 rawExpTimes. 

140 finalMeans : `dict`, [`str`, `list`] 

141 Dictionary keyed by amp names containing the masked average of the 

142 means of the exposures in each flat pair. If needed, each array 

143 will be right-padded with np.nan to match the length of 

144 rawExpTimes. 

145 photoCharge : `dict`, [`str`, `list`] 

146 Dictionary keyed by amp names containing the integrated photocharge 

147 for linearity calibration. 

148 

149 Version 1.1 adds the `ptcTurnoff` attribute. 

150 """ 

151 

152 _OBSTYPE = 'PTC' 

153 _SCHEMA = 'Gen3 Photon Transfer Curve' 

154 _VERSION = 1.1 

155 

156 def __init__(self, ampNames=[], ptcFitType=None, covMatrixSide=1, **kwargs): 

157 self.ptcFitType = ptcFitType 

158 self.ampNames = ampNames 

159 self.covMatrixSide = covMatrixSide 

160 

161 self.badAmps = [np.nan] 

162 

163 self.inputExpIdPairs = {ampName: [] for ampName in ampNames} 

164 self.expIdMask = {ampName: [] for ampName in ampNames} 

165 self.rawExpTimes = {ampName: [] for ampName in ampNames} 

166 self.rawMeans = {ampName: [] for ampName in ampNames} 

167 self.rawVars = {ampName: [] for ampName in ampNames} 

168 self.photoCharge = {ampName: [] for ampName in ampNames} 

169 

170 self.gain = {ampName: np.nan for ampName in ampNames} 

171 self.gainErr = {ampName: np.nan for ampName in ampNames} 

172 self.noise = {ampName: np.nan for ampName in ampNames} 

173 self.noiseErr = {ampName: np.nan for ampName in ampNames} 

174 

175 self.ptcFitPars = {ampName: [] for ampName in ampNames} 

176 self.ptcFitParsError = {ampName: [] for ampName in ampNames} 

177 self.ptcFitChiSq = {ampName: np.nan for ampName in ampNames} 

178 self.ptcTurnoff = {ampName: np.nan for ampName in ampNames} 

179 

180 self.covariances = {ampName: [] for ampName in ampNames} 

181 self.covariancesModel = {ampName: [] for ampName in ampNames} 

182 self.covariancesSqrtWeights = {ampName: [] for ampName in ampNames} 

183 self.aMatrix = {ampName: np.nan for ampName in ampNames} 

184 self.bMatrix = {ampName: np.nan for ampName in ampNames} 

185 self.covariancesModelNoB = {ampName: [] for ampName in ampNames} 

186 self.aMatrixNoB = {ampName: np.nan for ampName in ampNames} 

187 

188 self.finalVars = {ampName: [] for ampName in ampNames} 

189 self.finalModelVars = {ampName: [] for ampName in ampNames} 

190 self.finalMeans = {ampName: [] for ampName in ampNames} 

191 

192 super().__init__(**kwargs) 

193 self.requiredAttributes.update(['badAmps', 'inputExpIdPairs', 'expIdMask', 'rawExpTimes', 

194 'rawMeans', 'rawVars', 'gain', 'gainErr', 'noise', 'noiseErr', 

195 'ptcFitPars', 'ptcFitParsError', 'ptcFitChiSq', 'ptcTurnoff', 

196 'aMatrixNoB', 'covariances', 'covariancesModel', 

197 'covariancesSqrtWeights', 'covariancesModelNoB', 

198 'aMatrix', 'bMatrix', 'finalVars', 'finalModelVars', 'finalMeans', 

199 'photoCharge']) 

200 

201 self.updateMetadata(setCalibInfo=True, setCalibId=True, **kwargs) 

202 

203 def setAmpValues(self, ampName, inputExpIdPair=[(np.nan, np.nan)], expIdMask=[np.nan], 

204 rawExpTime=[np.nan], rawMean=[np.nan], rawVar=[np.nan], photoCharge=[np.nan], 

205 gain=np.nan, gainErr=np.nan, noise=np.nan, noiseErr=np.nan, ptcFitPars=[np.nan], 

206 ptcFitParsError=[np.nan], ptcFitChiSq=np.nan, ptcTurnoff=np.nan, covArray=[], 

207 covArrayModel=[], covSqrtWeights=[], aMatrix=[], bMatrix=[], covArrayModelNoB=[], 

208 aMatrixNoB=[], finalVar=[np.nan], finalModelVar=[np.nan], finalMean=[np.nan]): 

209 """Function to initialize an amp of a PhotonTransferCurveDataset. 

210 

211 Notes 

212 ----- 

213 The parameters are all documented in `init`. 

214 """ 

215 nanMatrix = np.full((self.covMatrixSide, self.covMatrixSide), np.nan) 

216 if len(covArray) == 0: 

217 covArray = [nanMatrix] 

218 if len(covArrayModel) == 0: 

219 covArrayModel = [nanMatrix] 

220 if len(covSqrtWeights) == 0: 

221 covSqrtWeights = [nanMatrix] 

222 if len(covArrayModelNoB) == 0: 

223 covArrayModelNoB = [nanMatrix] 

224 if len(aMatrix) == 0: 

225 aMatrix = nanMatrix 

226 if len(bMatrix) == 0: 

227 bMatrix = nanMatrix 

228 if len(aMatrixNoB) == 0: 

229 aMatrixNoB = nanMatrix 

230 

231 self.inputExpIdPairs[ampName] = inputExpIdPair 

232 self.expIdMask[ampName] = expIdMask 

233 self.rawExpTimes[ampName] = rawExpTime 

234 self.rawMeans[ampName] = rawMean 

235 self.rawVars[ampName] = rawVar 

236 self.photoCharge[ampName] = photoCharge 

237 self.gain[ampName] = gain 

238 self.gainErr[ampName] = gainErr 

239 self.noise[ampName] = noise 

240 self.noiseErr[ampName] = noiseErr 

241 self.ptcFitPars[ampName] = ptcFitPars 

242 self.ptcFitParsError[ampName] = ptcFitParsError 

243 self.ptcFitChiSq[ampName] = ptcFitChiSq 

244 self.ptcTurnoff[ampName] = ptcTurnoff 

245 self.covariances[ampName] = covArray 

246 self.covariancesSqrtWeights[ampName] = covSqrtWeights 

247 self.covariancesModel[ampName] = covArrayModel 

248 self.covariancesModelNoB[ampName] = covArrayModelNoB 

249 self.aMatrix[ampName] = aMatrix 

250 self.bMatrix[ampName] = bMatrix 

251 self.aMatrixNoB[ampName] = aMatrixNoB 

252 self.ptcFitPars[ampName] = ptcFitPars 

253 self.ptcFitParsError[ampName] = ptcFitParsError 

254 self.ptcFitChiSq[ampName] = ptcFitChiSq 

255 self.finalVars[ampName] = finalVar 

256 self.finalModelVars[ampName] = finalModelVar 

257 self.finalMeans[ampName] = finalMean 

258 

259 def updateMetadata(self, **kwargs): 

260 """Update calibration metadata. 

261 This calls the base class's method after ensuring the required 

262 calibration keywords will be saved. 

263 

264 Parameters 

265 ---------- 

266 setDate : `bool`, optional 

267 Update the CALIBDATE fields in the metadata to the current 

268 time. Defaults to False. 

269 kwargs : 

270 Other keyword parameters to set in the metadata. 

271 """ 

272 super().updateMetadata(PTC_FIT_TYPE=self.ptcFitType, **kwargs) 

273 

274 @classmethod 

275 def fromDict(cls, dictionary): 

276 """Construct a calibration from a dictionary of properties. 

277 Must be implemented by the specific calibration subclasses. 

278 

279 Parameters 

280 ---------- 

281 dictionary : `dict` 

282 Dictionary of properties. 

283 

284 Returns 

285 ------- 

286 calib : `lsst.ip.isr.PhotonTransferCurveDataset` 

287 Constructed calibration. 

288 

289 Raises 

290 ------ 

291 RuntimeError 

292 Raised if the supplied dictionary is for a different 

293 calibration. 

294 """ 

295 calib = cls() 

296 if calib._OBSTYPE != dictionary['metadata']['OBSTYPE']: 

297 raise RuntimeError(f"Incorrect Photon Transfer Curve dataset supplied. " 

298 f"Expected {calib._OBSTYPE}, found {dictionary['metadata']['OBSTYPE']}") 

299 calib.setMetadata(dictionary['metadata']) 

300 calib.ptcFitType = dictionary['ptcFitType'] 

301 calib.covMatrixSide = dictionary['covMatrixSide'] 

302 calib.badAmps = np.array(dictionary['badAmps'], 'str').tolist() 

303 

304 # The cov matrices are square 

305 covMatrixSide = calib.covMatrixSide 

306 # Number of final signal levels 

307 covDimensionsProduct = len(np.array(list(dictionary['covariances'].values())[0]).ravel()) 

308 nSignalPoints = int(covDimensionsProduct/(covMatrixSide*covMatrixSide)) 

309 

310 for ampName in dictionary['ampNames']: 

311 covsAmp = np.array(dictionary['covariances'][ampName]).reshape((nSignalPoints, covMatrixSide, 

312 covMatrixSide)) 

313 

314 # After cpPtcExtract runs in the PTC pipeline, the datasets 

315 # created ('PARTIAL' and 'DUMMY') have a single measurement. 

316 # Apply the maskign to the final ptcDataset, after running 

317 # cpPtcSolve. 

318 if len(covsAmp) > 1: 

319 # Masks for covariances padding in `toTable` 

320 maskCovsAmp = np.array([~np.isnan(entry).all() for entry in covsAmp]) 

321 maskAmp = ~np.isnan(np.array(dictionary['finalMeans'][ampName])) 

322 else: 

323 maskCovsAmp = np.array([True]) 

324 maskAmp = np.array([True]) 

325 

326 calib.ampNames.append(ampName) 

327 calib.inputExpIdPairs[ampName] = np.array(dictionary['inputExpIdPairs'][ampName]).tolist() 

328 calib.expIdMask[ampName] = np.array(dictionary['expIdMask'][ampName]).tolist() 

329 calib.rawExpTimes[ampName] = np.array(dictionary['rawExpTimes'][ampName]).tolist() 

330 calib.rawMeans[ampName] = np.array(dictionary['rawMeans'][ampName]).tolist() 

331 calib.rawVars[ampName] = np.array(dictionary['rawVars'][ampName]).tolist() 

332 calib.gain[ampName] = np.array(dictionary['gain'][ampName]).tolist() 

333 calib.gainErr[ampName] = np.array(dictionary['gainErr'][ampName]).tolist() 

334 calib.noise[ampName] = np.array(dictionary['noise'][ampName]).tolist() 

335 calib.noiseErr[ampName] = np.array(dictionary['noiseErr'][ampName]).tolist() 

336 calib.ptcFitPars[ampName] = np.array(dictionary['ptcFitPars'][ampName]).tolist() 

337 calib.ptcFitParsError[ampName] = np.array(dictionary['ptcFitParsError'][ampName]).tolist() 

338 calib.ptcFitChiSq[ampName] = np.array(dictionary['ptcFitChiSq'][ampName]).tolist() 

339 calib.ptcTurnoff[ampName] = np.array(dictionary['ptcTurnoff'][ampName]).tolist() 

340 calib.covariances[ampName] = covsAmp[maskCovsAmp].tolist() 

341 calib.covariancesModel[ampName] = np.array( 

342 dictionary['covariancesModel'][ampName]).reshape( 

343 (nSignalPoints, covMatrixSide, covMatrixSide))[maskCovsAmp].tolist() 

344 calib.covariancesSqrtWeights[ampName] = np.array( 

345 dictionary['covariancesSqrtWeights'][ampName]).reshape( 

346 (nSignalPoints, covMatrixSide, covMatrixSide))[maskCovsAmp].tolist() 

347 calib.aMatrix[ampName] = np.array(dictionary['aMatrix'][ampName]).reshape( 

348 (covMatrixSide, covMatrixSide)).tolist() 

349 calib.bMatrix[ampName] = np.array(dictionary['bMatrix'][ampName]).reshape( 

350 (covMatrixSide, covMatrixSide)).tolist() 

351 calib.covariancesModelNoB[ampName] = np.array( 

352 dictionary['covariancesModelNoB'][ampName]).reshape( 

353 (nSignalPoints, covMatrixSide, covMatrixSide))[maskCovsAmp].tolist() 

354 calib.aMatrixNoB[ampName] = np.array( 

355 dictionary['aMatrixNoB'][ampName]).reshape((covMatrixSide, covMatrixSide)).tolist() 

356 calib.finalVars[ampName] = np.array(dictionary['finalVars'][ampName])[maskAmp].tolist() 

357 calib.finalModelVars[ampName] = np.array(dictionary['finalModelVars'][ampName])[maskAmp].tolist() 

358 calib.finalMeans[ampName] = np.array(dictionary['finalMeans'][ampName])[maskAmp].tolist() 

359 calib.photoCharge[ampName] = np.array(dictionary['photoCharge'][ampName]).tolist() 

360 calib.updateMetadata() 

361 return calib 

362 

363 def toDict(self): 

364 """Return a dictionary containing the calibration properties. 

365 The dictionary should be able to be round-tripped through 

366 `fromDict`. 

367 

368 Returns 

369 ------- 

370 dictionary : `dict` 

371 Dictionary of properties. 

372 """ 

373 self.updateMetadata() 

374 

375 outDict = dict() 

376 metadata = self.getMetadata() 

377 outDict['metadata'] = metadata 

378 

379 outDict['ptcFitType'] = self.ptcFitType 

380 outDict['covMatrixSide'] = self.covMatrixSide 

381 outDict['ampNames'] = self.ampNames 

382 outDict['badAmps'] = self.badAmps 

383 outDict['inputExpIdPairs'] = self.inputExpIdPairs 

384 outDict['expIdMask'] = self.expIdMask 

385 outDict['rawExpTimes'] = self.rawExpTimes 

386 outDict['rawMeans'] = self.rawMeans 

387 outDict['rawVars'] = self.rawVars 

388 outDict['gain'] = self.gain 

389 outDict['gainErr'] = self.gainErr 

390 outDict['noise'] = self.noise 

391 outDict['noiseErr'] = self.noiseErr 

392 outDict['ptcFitPars'] = self.ptcFitPars 

393 outDict['ptcFitParsError'] = self.ptcFitParsError 

394 outDict['ptcFitChiSq'] = self.ptcFitChiSq 

395 outDict['ptcTurnoff'] = self.ptcTurnoff 

396 outDict['covariances'] = self.covariances 

397 outDict['covariancesModel'] = self.covariancesModel 

398 outDict['covariancesSqrtWeights'] = self.covariancesSqrtWeights 

399 outDict['aMatrix'] = self.aMatrix 

400 outDict['bMatrix'] = self.bMatrix 

401 outDict['covariancesModelNoB'] = self.covariancesModelNoB 

402 outDict['aMatrixNoB'] = self.aMatrixNoB 

403 outDict['finalVars'] = self.finalVars 

404 outDict['finalModelVars'] = self.finalModelVars 

405 outDict['finalMeans'] = self.finalMeans 

406 outDict['photoCharge'] = self.photoCharge 

407 

408 return outDict 

409 

410 @classmethod 

411 def fromTable(cls, tableList): 

412 """Construct calibration from a list of tables. 

413 This method uses the `fromDict` method to create the 

414 calibration, after constructing an appropriate dictionary from 

415 the input tables. 

416 

417 Parameters 

418 ---------- 

419 tableList : `list` [`lsst.afw.table.Table`] 

420 List of tables to use to construct the datasetPtc. 

421 

422 Returns 

423 ------- 

424 calib : `lsst.ip.isr.PhotonTransferCurveDataset` 

425 The calibration defined in the tables. 

426 """ 

427 ptcTable = tableList[0] 

428 

429 metadata = ptcTable.meta 

430 inDict = dict() 

431 inDict['metadata'] = metadata 

432 inDict['ampNames'] = [] 

433 inDict['ptcFitType'] = [] 

434 inDict['covMatrixSide'] = [] 

435 inDict['inputExpIdPairs'] = dict() 

436 inDict['expIdMask'] = dict() 

437 inDict['rawExpTimes'] = dict() 

438 inDict['rawMeans'] = dict() 

439 inDict['rawVars'] = dict() 

440 inDict['gain'] = dict() 

441 inDict['gainErr'] = dict() 

442 inDict['noise'] = dict() 

443 inDict['noiseErr'] = dict() 

444 inDict['ptcFitPars'] = dict() 

445 inDict['ptcFitParsError'] = dict() 

446 inDict['ptcFitChiSq'] = dict() 

447 inDict['ptcTurnoff'] = dict() 

448 inDict['covariances'] = dict() 

449 inDict['covariancesModel'] = dict() 

450 inDict['covariancesSqrtWeights'] = dict() 

451 inDict['aMatrix'] = dict() 

452 inDict['bMatrix'] = dict() 

453 inDict['covariancesModelNoB'] = dict() 

454 inDict['aMatrixNoB'] = dict() 

455 inDict['finalVars'] = dict() 

456 inDict['finalModelVars'] = dict() 

457 inDict['finalMeans'] = dict() 

458 inDict['badAmps'] = [] 

459 inDict['photoCharge'] = dict() 

460 

461 calibVersion = metadata['PTC_VERSION'] 

462 if calibVersion == 1.0: 

463 cls().log.warning(f"Previous version found for PTC dataset: {calibVersion}. " 

464 f"Setting 'ptcTurnoff' in all amps to last value in 'finalMeans'.") 

465 for record in ptcTable: 

466 ampName = record['AMPLIFIER_NAME'] 

467 

468 inDict['ptcFitType'] = record['PTC_FIT_TYPE'] 

469 inDict['covMatrixSide'] = record['COV_MATRIX_SIDE'] 

470 inDict['ampNames'].append(ampName) 

471 inDict['inputExpIdPairs'][ampName] = record['INPUT_EXP_ID_PAIRS'] 

472 inDict['expIdMask'][ampName] = record['EXP_ID_MASK'] 

473 inDict['rawExpTimes'][ampName] = record['RAW_EXP_TIMES'] 

474 inDict['rawMeans'][ampName] = record['RAW_MEANS'] 

475 inDict['rawVars'][ampName] = record['RAW_VARS'] 

476 inDict['gain'][ampName] = record['GAIN'] 

477 inDict['gainErr'][ampName] = record['GAIN_ERR'] 

478 inDict['noise'][ampName] = record['NOISE'] 

479 inDict['noiseErr'][ampName] = record['NOISE_ERR'] 

480 inDict['ptcFitPars'][ampName] = record['PTC_FIT_PARS'] 

481 inDict['ptcFitParsError'][ampName] = record['PTC_FIT_PARS_ERROR'] 

482 inDict['ptcFitChiSq'][ampName] = record['PTC_FIT_CHI_SQ'] 

483 inDict['covariances'][ampName] = record['COVARIANCES'] 

484 inDict['covariancesModel'][ampName] = record['COVARIANCES_MODEL'] 

485 inDict['covariancesSqrtWeights'][ampName] = record['COVARIANCES_SQRT_WEIGHTS'] 

486 inDict['aMatrix'][ampName] = record['A_MATRIX'] 

487 inDict['bMatrix'][ampName] = record['B_MATRIX'] 

488 inDict['covariancesModelNoB'][ampName] = record['COVARIANCES_MODEL_NO_B'] 

489 inDict['aMatrixNoB'][ampName] = record['A_MATRIX_NO_B'] 

490 inDict['finalVars'][ampName] = record['FINAL_VARS'] 

491 inDict['finalModelVars'][ampName] = record['FINAL_MODEL_VARS'] 

492 inDict['finalMeans'][ampName] = record['FINAL_MEANS'] 

493 inDict['badAmps'] = record['BAD_AMPS'] 

494 inDict['photoCharge'][ampName] = record['PHOTO_CHARGE'] 

495 if calibVersion == 1.0: 

496 mask = record['FINAL_MEANS'].mask 

497 array = record['FINAL_MEANS'][~mask] 

498 if len(array) > 0: 

499 inDict['ptcTurnoff'][ampName] = record['FINAL_MEANS'][~mask][-1] 

500 else: 

501 inDict['ptcTurnoff'][ampName] = np.nan 

502 else: 

503 inDict['ptcTurnoff'][ampName] = record['PTC_TURNOFF'] 

504 return cls().fromDict(inDict) 

505 

506 def toTable(self): 

507 """Construct a list of tables containing the information in this 

508 calibration. 

509 

510 The list of tables should create an identical calibration 

511 after being passed to this class's fromTable method. 

512 

513 Returns 

514 ------- 

515 tableList : `list` [`astropy.table.Table`] 

516 List of tables containing the linearity calibration 

517 information. 

518 """ 

519 tableList = [] 

520 self.updateMetadata() 

521 nPoints = [] 

522 for i, ampName in enumerate(self.ampNames): 

523 nPoints.append(len(list(self.covariances.values())[i])) 

524 nSignalPoints = max(nPoints) 

525 nPadPoints = {} 

526 for i, ampName in enumerate(self.ampNames): 

527 nPadPoints[ampName] = nSignalPoints - len(list(self.covariances.values())[i]) 

528 covMatrixSide = self.covMatrixSide 

529 

530 catalog = Table([{'AMPLIFIER_NAME': ampName, 

531 'PTC_FIT_TYPE': self.ptcFitType, 

532 'COV_MATRIX_SIDE': self.covMatrixSide, 

533 'INPUT_EXP_ID_PAIRS': self.inputExpIdPairs[ampName] 

534 if len(self.expIdMask[ampName]) else np.nan, 

535 'EXP_ID_MASK': self.expIdMask[ampName] 

536 if len(self.expIdMask[ampName]) else np.nan, 

537 'RAW_EXP_TIMES': np.array(self.rawExpTimes[ampName]).tolist() 

538 if len(self.rawExpTimes[ampName]) else np.nan, 

539 'RAW_MEANS': np.array(self.rawMeans[ampName]).tolist() 

540 if len(self.rawMeans[ampName]) else np.nan, 

541 'RAW_VARS': np.array(self.rawVars[ampName]).tolist() 

542 if len(self.rawVars[ampName]) else np.nan, 

543 'GAIN': self.gain[ampName], 

544 'GAIN_ERR': self.gainErr[ampName], 

545 'NOISE': self.noise[ampName], 

546 'NOISE_ERR': self.noiseErr[ampName], 

547 'PTC_FIT_PARS': np.array(self.ptcFitPars[ampName]).tolist(), 

548 'PTC_FIT_PARS_ERROR': np.array(self.ptcFitParsError[ampName]).tolist(), 

549 'PTC_FIT_CHI_SQ': self.ptcFitChiSq[ampName], 

550 'PTC_TURNOFF': self.ptcTurnoff[ampName], 

551 'COVARIANCES': np.pad(np.array(self.covariances[ampName]), 

552 ((0, nPadPoints[ampName]), (0, 0), (0, 0)), 

553 'constant', constant_values=np.nan).reshape( 

554 nSignalPoints*covMatrixSide**2).tolist(), 

555 'COVARIANCES_MODEL': np.pad(np.array(self.covariancesModel[ampName]), 

556 ((0, nPadPoints[ampName]), (0, 0), (0, 0)), 

557 'constant', constant_values=np.nan).reshape( 

558 nSignalPoints*covMatrixSide**2).tolist(), 

559 'COVARIANCES_SQRT_WEIGHTS': np.pad(np.array(self.covariancesSqrtWeights[ampName]), 

560 ((0, nPadPoints[ampName]), (0, 0), (0, 0)), 

561 'constant', constant_values=0.0).reshape( 

562 nSignalPoints*covMatrixSide**2).tolist(), 

563 'A_MATRIX': np.array(self.aMatrix[ampName]).reshape(covMatrixSide**2).tolist(), 

564 'B_MATRIX': np.array(self.bMatrix[ampName]).reshape(covMatrixSide**2).tolist(), 

565 'COVARIANCES_MODEL_NO_B': 

566 np.pad(np.array(self.covariancesModelNoB[ampName]), 

567 ((0, nPadPoints[ampName]), (0, 0), (0, 0)), 

568 'constant', constant_values=np.nan).reshape( 

569 nSignalPoints*covMatrixSide**2).tolist(), 

570 'A_MATRIX_NO_B': np.array(self.aMatrixNoB[ampName]).reshape( 

571 covMatrixSide**2).tolist(), 

572 'FINAL_VARS': np.pad(np.array(self.finalVars[ampName]), (0, nPadPoints[ampName]), 

573 'constant', constant_values=np.nan).tolist(), 

574 'FINAL_MODEL_VARS': np.pad(np.array(self.finalModelVars[ampName]), 

575 (0, nPadPoints[ampName]), 

576 'constant', constant_values=np.nan).tolist(), 

577 'FINAL_MEANS': np.pad(np.array(self.finalMeans[ampName]), 

578 (0, nPadPoints[ampName]), 

579 'constant', constant_values=np.nan).tolist(), 

580 'BAD_AMPS': np.array(self.badAmps).tolist() if len(self.badAmps) else np.nan, 

581 'PHOTO_CHARGE': np.array(self.photoCharge[ampName]).tolist(), 

582 } for ampName in self.ampNames]) 

583 inMeta = self.getMetadata().toDict() 

584 outMeta = {k: v for k, v in inMeta.items() if v is not None} 

585 outMeta.update({k: "" for k, v in inMeta.items() if v is None}) 

586 catalog.meta = outMeta 

587 tableList.append(catalog) 

588 

589 return(tableList) 

590 

591 def fromDetector(self, detector): 

592 """Read metadata parameters from a detector. 

593 

594 Parameters 

595 ---------- 

596 detector : `lsst.afw.cameraGeom.detector` 

597 Input detector with parameters to use. 

598 

599 Returns 

600 ------- 

601 calib : `lsst.ip.isr.PhotonTransferCurveDataset` 

602 The calibration constructed from the detector. 

603 """ 

604 

605 pass 

606 

607 def getExpIdsUsed(self, ampName): 

608 """Get the exposures used, i.e. not discarded, for a given amp. 

609 If no mask has been created yet, all exposures are returned. 

610 """ 

611 if len(self.expIdMask[ampName]) == 0: 

612 return self.inputExpIdPairs[ampName] 

613 

614 # if the mask exists it had better be the same length as the expIdPairs 

615 assert len(self.expIdMask[ampName]) == len(self.inputExpIdPairs[ampName]) 

616 

617 pairs = self.inputExpIdPairs[ampName] 

618 mask = self.expIdMask[ampName] 

619 # cast to bool required because numpy 

620 return [(exp1, exp2) for ((exp1, exp2), m) in zip(pairs, mask) if bool(m) is True] 

621 

622 def getGoodAmps(self): 

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