Coverage for python/lsst/ip/isr/ptcDataset.py: 6%
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22"""
23Define dataset class for MeasurePhotonTransferCurve task
24"""
25import numpy as np
26from astropy.table import Table
28from lsst.ip.isr import IsrCalib
30__all__ = ['PhotonTransferCurveDataset']
33class PhotonTransferCurveDataset(IsrCalib):
34 """A simple class to hold the output data from the PTC task.
35 The dataset is made up of a dictionary for each item, keyed by the
36 amplifiers' names, which much be supplied at construction time.
37 New items cannot be added to the class to save accidentally saving to the
38 wrong property, and the class can be frozen if desired.
39 inputExpIdPairs records the exposures used to produce the data.
40 When fitPtc() or fitCovariancesAstier() is run, a mask is built up, which
41 is by definition always the same length as inputExpIdPairs, rawExpTimes,
42 rawMeans and rawVars, and is a list of bools, which are incrementally set
43 to False as points are discarded from the fits.
44 PTC fit parameters for polynomials are stored in a list in ascending order
45 of polynomial term, i.e. par[0]*x^0 + par[1]*x + par[2]*x^2 etc
46 with the length of the list corresponding to the order of the polynomial
47 plus one.
49 Parameters
50 ----------
51 ampNames : `list`
52 List with the names of the amplifiers of the detector at hand.
54 ptcFitType : `str`
55 Type of model fitted to the PTC: "POLYNOMIAL", "EXPAPPROXIMATION",
56 or "FULLCOVARIANCE".
58 covMatrixSide : `int`
59 Maximum lag of covariances (size of square covariance matrices).
61 kwargs : `dict`, optional
62 Other keyword arguments to pass to the parent init.
64 Notes
65 -----
66 The stored attributes are:
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.
149 Version 1.1 adds the `ptcTurnoff` attribute.
150 """
152 _OBSTYPE = 'PTC'
153 _SCHEMA = 'Gen3 Photon Transfer Curve'
154 _VERSION = 1.1
156 def __init__(self, ampNames=[], ptcFitType=None, covMatrixSide=1, **kwargs):
158 self.ptcFitType = ptcFitType
159 self.ampNames = ampNames
160 self.covMatrixSide = covMatrixSide
162 self.badAmps = [np.nan]
164 self.inputExpIdPairs = {ampName: [] for ampName in ampNames}
165 self.expIdMask = {ampName: [] for ampName in ampNames}
166 self.rawExpTimes = {ampName: [] for ampName in ampNames}
167 self.rawMeans = {ampName: [] for ampName in ampNames}
168 self.rawVars = {ampName: [] for ampName in ampNames}
169 self.photoCharge = {ampName: [] for ampName in ampNames}
171 self.gain = {ampName: np.nan for ampName in ampNames}
172 self.gainErr = {ampName: np.nan for ampName in ampNames}
173 self.noise = {ampName: np.nan for ampName in ampNames}
174 self.noiseErr = {ampName: np.nan for ampName in ampNames}
176 self.ptcFitPars = {ampName: [] for ampName in ampNames}
177 self.ptcFitParsError = {ampName: [] for ampName in ampNames}
178 self.ptcFitChiSq = {ampName: np.nan for ampName in ampNames}
179 self.ptcTurnoff = {ampName: np.nan for ampName in ampNames}
181 self.covariances = {ampName: [] for ampName in ampNames}
182 self.covariancesModel = {ampName: [] for ampName in ampNames}
183 self.covariancesSqrtWeights = {ampName: [] for ampName in ampNames}
184 self.aMatrix = {ampName: np.nan for ampName in ampNames}
185 self.bMatrix = {ampName: np.nan for ampName in ampNames}
186 self.covariancesModelNoB = {ampName: [] for ampName in ampNames}
187 self.aMatrixNoB = {ampName: np.nan for ampName in ampNames}
189 self.finalVars = {ampName: [] for ampName in ampNames}
190 self.finalModelVars = {ampName: [] for ampName in ampNames}
191 self.finalMeans = {ampName: [] for ampName in ampNames}
193 super().__init__(**kwargs)
194 self.requiredAttributes.update(['badAmps', 'inputExpIdPairs', 'expIdMask', 'rawExpTimes',
195 'rawMeans', 'rawVars', 'gain', 'gainErr', 'noise', 'noiseErr',
196 'ptcFitPars', 'ptcFitParsError', 'ptcFitChiSq', 'ptcTurnoff',
197 'aMatrixNoB', 'covariances', 'covariancesModel',
198 'covariancesSqrtWeights', 'covariancesModelNoB',
199 'aMatrix', 'bMatrix', 'finalVars', 'finalModelVars', 'finalMeans',
200 'photoCharge'])
202 def setAmpValues(self, ampName, inputExpIdPair=[(np.nan, np.nan)], expIdMask=[np.nan],
203 rawExpTime=[np.nan], rawMean=[np.nan], rawVar=[np.nan], photoCharge=[np.nan],
204 gain=np.nan, gainErr=np.nan, noise=np.nan, noiseErr=np.nan, ptcFitPars=[np.nan],
205 ptcFitParsError=[np.nan], ptcFitChiSq=np.nan, ptcTurnoff=np.nan, covArray=[],
206 covArrayModel=[], covSqrtWeights=[], aMatrix=[], bMatrix=[], covArrayModelNoB=[],
207 aMatrixNoB=[], finalVar=[np.nan], finalModelVar=[np.nan], finalMean=[np.nan]):
208 """Function to initialize an amp of a PhotonTransferCurveDataset.
210 Notes
211 -----
212 The parameters are all documented in `init`.
213 """
214 nanMatrix = np.full((self.covMatrixSide, self.covMatrixSide), np.nan)
215 if len(covArray) == 0:
216 covArray = [nanMatrix]
217 if len(covArrayModel) == 0:
218 covArrayModel = [nanMatrix]
219 if len(covSqrtWeights) == 0:
220 covSqrtWeights = [nanMatrix]
221 if len(covArrayModelNoB) == 0:
222 covArrayModelNoB = [nanMatrix]
223 if len(aMatrix) == 0:
224 aMatrix = nanMatrix
225 if len(bMatrix) == 0:
226 bMatrix = nanMatrix
227 if len(aMatrixNoB) == 0:
228 aMatrixNoB = nanMatrix
230 self.inputExpIdPairs[ampName] = inputExpIdPair
231 self.expIdMask[ampName] = expIdMask
232 self.rawExpTimes[ampName] = rawExpTime
233 self.rawMeans[ampName] = rawMean
234 self.rawVars[ampName] = rawVar
235 self.photoCharge[ampName] = photoCharge
236 self.gain[ampName] = gain
237 self.gainErr[ampName] = gainErr
238 self.noise[ampName] = noise
239 self.noiseErr[ampName] = noiseErr
240 self.ptcFitPars[ampName] = ptcFitPars
241 self.ptcFitParsError[ampName] = ptcFitParsError
242 self.ptcFitChiSq[ampName] = ptcFitChiSq
243 self.ptcTurnoff[ampName] = ptcTurnoff
244 self.covariances[ampName] = covArray
245 self.covariancesSqrtWeights[ampName] = covSqrtWeights
246 self.covariancesModel[ampName] = covArrayModel
247 self.covariancesModelNoB[ampName] = covArrayModelNoB
248 self.aMatrix[ampName] = aMatrix
249 self.bMatrix[ampName] = bMatrix
250 self.aMatrixNoB[ampName] = aMatrixNoB
251 self.ptcFitPars[ampName] = ptcFitPars
252 self.ptcFitParsError[ampName] = ptcFitParsError
253 self.ptcFitChiSq[ampName] = ptcFitChiSq
254 self.finalVars[ampName] = finalVar
255 self.finalModelVars[ampName] = finalModelVar
256 self.finalMeans[ampName] = finalMean
258 def updateMetadata(self, setDate=False, **kwargs):
259 """Update calibration metadata.
260 This calls the base class's method after ensuring the required
261 calibration keywords will be saved.
262 Parameters
263 ----------
264 setDate : `bool`, optional
265 Update the CALIBDATE fields in the metadata to the current
266 time. Defaults to False.
267 kwargs :
268 Other keyword parameters to set in the metadata.
269 """
270 kwargs['PTC_FIT_TYPE'] = self.ptcFitType
272 super().updateMetadata(setDate=setDate, **kwargs)
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 Parameters
279 ----------
280 dictionary : `dict`
281 Dictionary of properties.
282 Returns
283 -------
284 calib : `lsst.ip.isr.CalibType`
285 Constructed calibration.
286 Raises
287 ------
288 RuntimeError :
289 Raised if the supplied dictionary is for a different
290 calibration.
291 """
292 calib = cls()
293 if calib._OBSTYPE != dictionary['metadata']['OBSTYPE']:
294 raise RuntimeError(f"Incorrect Photon Transfer Curve dataset supplied. "
295 f"Expected {calib._OBSTYPE}, found {dictionary['metadata']['OBSTYPE']}")
296 calib.setMetadata(dictionary['metadata'])
297 calib.ptcFitType = dictionary['ptcFitType']
298 calib.covMatrixSide = dictionary['covMatrixSide']
299 calib.badAmps = np.array(dictionary['badAmps'], 'str').tolist()
300 # The cov matrices are square
301 covMatrixSide = calib.covMatrixSide
302 # Number of final signal levels
303 covDimensionsProduct = len(np.array(list(dictionary['covariances'].values())[0]).ravel())
304 nSignalPoints = int(covDimensionsProduct/(covMatrixSide*covMatrixSide))
306 for ampName in dictionary['ampNames']:
307 covsAmp = np.array(dictionary['covariances'][ampName]).reshape((nSignalPoints, covMatrixSide,
308 covMatrixSide))
310 # After cpPtcExtract runs in the PTC pipeline, the datasets
311 # created ('PARTIAL' and 'DUMMY') have a single measurement.
312 # Apply the maskign to the final ptcDataset, after running
313 # cpPtcSolve.
314 if len(covsAmp) > 1:
315 # Masks for covariances padding in `toTable`
316 maskCovsAmp = np.array([~np.isnan(entry).all() for entry in covsAmp])
317 maskAmp = ~np.isnan(np.array(dictionary['finalMeans'][ampName]))
318 else:
319 maskCovsAmp = np.array([True])
320 maskAmp = np.array([True])
322 calib.ampNames.append(ampName)
323 calib.inputExpIdPairs[ampName] = np.array(dictionary['inputExpIdPairs'][ampName]).tolist()
324 calib.expIdMask[ampName] = np.array(dictionary['expIdMask'][ampName]).tolist()
325 calib.rawExpTimes[ampName] = np.array(dictionary['rawExpTimes'][ampName]).tolist()
326 calib.rawMeans[ampName] = np.array(dictionary['rawMeans'][ampName]).tolist()
327 calib.rawVars[ampName] = np.array(dictionary['rawVars'][ampName]).tolist()
328 calib.gain[ampName] = np.array(dictionary['gain'][ampName]).tolist()
329 calib.gainErr[ampName] = np.array(dictionary['gainErr'][ampName]).tolist()
330 calib.noise[ampName] = np.array(dictionary['noise'][ampName]).tolist()
331 calib.noiseErr[ampName] = np.array(dictionary['noiseErr'][ampName]).tolist()
332 calib.ptcFitPars[ampName] = np.array(dictionary['ptcFitPars'][ampName]).tolist()
333 calib.ptcFitParsError[ampName] = np.array(dictionary['ptcFitParsError'][ampName]).tolist()
334 calib.ptcFitChiSq[ampName] = np.array(dictionary['ptcFitChiSq'][ampName]).tolist()
335 calib.ptcTurnoff[ampName] = np.array(dictionary['ptcTurnoff'][ampName]).tolist()
336 calib.covariances[ampName] = covsAmp[maskCovsAmp].tolist()
337 calib.covariancesModel[ampName] = np.array(
338 dictionary['covariancesModel'][ampName]).reshape(
339 (nSignalPoints, covMatrixSide, covMatrixSide))[maskCovsAmp].tolist()
340 calib.covariancesSqrtWeights[ampName] = np.array(
341 dictionary['covariancesSqrtWeights'][ampName]).reshape(
342 (nSignalPoints, covMatrixSide, covMatrixSide))[maskCovsAmp].tolist()
343 calib.aMatrix[ampName] = np.array(dictionary['aMatrix'][ampName]).reshape(
344 (covMatrixSide, covMatrixSide)).tolist()
345 calib.bMatrix[ampName] = np.array(dictionary['bMatrix'][ampName]).reshape(
346 (covMatrixSide, covMatrixSide)).tolist()
347 calib.covariancesModelNoB[ampName] = np.array(
348 dictionary['covariancesModelNoB'][ampName]).reshape(
349 (nSignalPoints, covMatrixSide, covMatrixSide))[maskCovsAmp].tolist()
350 calib.aMatrixNoB[ampName] = np.array(
351 dictionary['aMatrixNoB'][ampName]).reshape((covMatrixSide, covMatrixSide)).tolist()
352 calib.finalVars[ampName] = np.array(dictionary['finalVars'][ampName])[maskAmp].tolist()
353 calib.finalModelVars[ampName] = np.array(dictionary['finalModelVars'][ampName])[maskAmp].tolist()
354 calib.finalMeans[ampName] = np.array(dictionary['finalMeans'][ampName])[maskAmp].tolist()
355 calib.photoCharge[ampName] = np.array(dictionary['photoCharge'][ampName]).tolist()
356 calib.updateMetadata()
357 return calib
359 def toDict(self):
360 """Return a dictionary containing the calibration properties.
361 The dictionary should be able to be round-tripped through
362 `fromDict`.
363 Returns
364 -------
365 dictionary : `dict`
366 Dictionary of properties.
367 """
368 self.updateMetadata()
370 outDict = dict()
371 metadata = self.getMetadata()
372 outDict['metadata'] = metadata
374 outDict['ptcFitType'] = self.ptcFitType
375 outDict['covMatrixSide'] = self.covMatrixSide
376 outDict['ampNames'] = self.ampNames
377 outDict['badAmps'] = self.badAmps
378 outDict['inputExpIdPairs'] = self.inputExpIdPairs
379 outDict['expIdMask'] = self.expIdMask
380 outDict['rawExpTimes'] = self.rawExpTimes
381 outDict['rawMeans'] = self.rawMeans
382 outDict['rawVars'] = self.rawVars
383 outDict['gain'] = self.gain
384 outDict['gainErr'] = self.gainErr
385 outDict['noise'] = self.noise
386 outDict['noiseErr'] = self.noiseErr
387 outDict['ptcFitPars'] = self.ptcFitPars
388 outDict['ptcFitParsError'] = self.ptcFitParsError
389 outDict['ptcFitChiSq'] = self.ptcFitChiSq
390 outDict['ptcTurnoff'] = self.ptcTurnoff
391 outDict['covariances'] = self.covariances
392 outDict['covariancesModel'] = self.covariancesModel
393 outDict['covariancesSqrtWeights'] = self.covariancesSqrtWeights
394 outDict['aMatrix'] = self.aMatrix
395 outDict['bMatrix'] = self.bMatrix
396 outDict['covariancesModelNoB'] = self.covariancesModelNoB
397 outDict['aMatrixNoB'] = self.aMatrixNoB
398 outDict['finalVars'] = self.finalVars
399 outDict['finalModelVars'] = self.finalModelVars
400 outDict['finalMeans'] = self.finalMeans
401 outDict['photoCharge'] = self.photoCharge
403 return outDict
405 @classmethod
406 def fromTable(cls, tableList):
407 """Construct calibration from a list of tables.
408 This method uses the `fromDict` method to create the
409 calibration, after constructing an appropriate dictionary from
410 the input tables.
411 Parameters
412 ----------
413 tableList : `list` [`lsst.afw.table.Table`]
414 List of tables to use to construct the datasetPtc.
415 Returns
416 -------
417 calib : `lsst.cp.pipe.`
418 The calibration defined in the tables.
419 """
420 ptcTable = tableList[0]
422 metadata = ptcTable.meta
423 inDict = dict()
424 inDict['metadata'] = metadata
425 inDict['ampNames'] = []
426 inDict['ptcFitType'] = []
427 inDict['covMatrixSide'] = []
428 inDict['inputExpIdPairs'] = dict()
429 inDict['expIdMask'] = dict()
430 inDict['rawExpTimes'] = dict()
431 inDict['rawMeans'] = dict()
432 inDict['rawVars'] = dict()
433 inDict['gain'] = dict()
434 inDict['gainErr'] = dict()
435 inDict['noise'] = dict()
436 inDict['noiseErr'] = dict()
437 inDict['ptcFitPars'] = dict()
438 inDict['ptcFitParsError'] = dict()
439 inDict['ptcFitChiSq'] = dict()
440 inDict['ptcTurnoff'] = dict()
441 inDict['covariances'] = dict()
442 inDict['covariancesModel'] = dict()
443 inDict['covariancesSqrtWeights'] = dict()
444 inDict['aMatrix'] = dict()
445 inDict['bMatrix'] = dict()
446 inDict['covariancesModelNoB'] = dict()
447 inDict['aMatrixNoB'] = dict()
448 inDict['finalVars'] = dict()
449 inDict['finalModelVars'] = dict()
450 inDict['finalMeans'] = dict()
451 inDict['badAmps'] = []
452 inDict['photoCharge'] = dict()
454 calibVersion = metadata['PTC_VERSION']
455 if calibVersion == 1.0:
456 cls().log.warning(f"Previous version found for PTC dataset: {calibVersion}. "
457 f"Setting 'ptcTurnoff' in all amps to last value in 'finalMeans'.")
458 for record in ptcTable:
459 ampName = record['AMPLIFIER_NAME']
461 inDict['ptcFitType'] = record['PTC_FIT_TYPE']
462 inDict['covMatrixSide'] = record['COV_MATRIX_SIDE']
463 inDict['ampNames'].append(ampName)
464 inDict['inputExpIdPairs'][ampName] = record['INPUT_EXP_ID_PAIRS']
465 inDict['expIdMask'][ampName] = record['EXP_ID_MASK']
466 inDict['rawExpTimes'][ampName] = record['RAW_EXP_TIMES']
467 inDict['rawMeans'][ampName] = record['RAW_MEANS']
468 inDict['rawVars'][ampName] = record['RAW_VARS']
469 inDict['gain'][ampName] = record['GAIN']
470 inDict['gainErr'][ampName] = record['GAIN_ERR']
471 inDict['noise'][ampName] = record['NOISE']
472 inDict['noiseErr'][ampName] = record['NOISE_ERR']
473 inDict['ptcFitPars'][ampName] = record['PTC_FIT_PARS']
474 inDict['ptcFitParsError'][ampName] = record['PTC_FIT_PARS_ERROR']
475 inDict['ptcFitChiSq'][ampName] = record['PTC_FIT_CHI_SQ']
476 inDict['covariances'][ampName] = record['COVARIANCES']
477 inDict['covariancesModel'][ampName] = record['COVARIANCES_MODEL']
478 inDict['covariancesSqrtWeights'][ampName] = record['COVARIANCES_SQRT_WEIGHTS']
479 inDict['aMatrix'][ampName] = record['A_MATRIX']
480 inDict['bMatrix'][ampName] = record['B_MATRIX']
481 inDict['covariancesModelNoB'][ampName] = record['COVARIANCES_MODEL_NO_B']
482 inDict['aMatrixNoB'][ampName] = record['A_MATRIX_NO_B']
483 inDict['finalVars'][ampName] = record['FINAL_VARS']
484 inDict['finalModelVars'][ampName] = record['FINAL_MODEL_VARS']
485 inDict['finalMeans'][ampName] = record['FINAL_MEANS']
486 inDict['badAmps'] = record['BAD_AMPS']
487 inDict['photoCharge'][ampName] = record['PHOTO_CHARGE']
488 if calibVersion == 1.0:
489 mask = record['FINAL_MEANS'].mask
490 inDict['ptcTurnoff'][ampName] = record['FINAL_MEANS'][~mask][-1]
491 else:
492 inDict['ptcTurnoff'][ampName] = record['PTC_TURNOFF']
493 return cls().fromDict(inDict)
495 def toTable(self):
496 """Construct a list of tables containing the information in this
497 calibration.
499 The list of tables should create an identical calibration
500 after being passed to this class's fromTable method.
501 Returns
502 -------
503 tableList : `list` [`astropy.table.Table`]
504 List of tables containing the linearity calibration
505 information.
506 """
507 tableList = []
508 self.updateMetadata()
509 nPoints = []
510 for i, ampName in enumerate(self.ampNames):
511 nPoints.append(len(list(self.covariances.values())[i]))
512 nSignalPoints = max(nPoints)
513 nPadPoints = {}
514 for i, ampName in enumerate(self.ampNames):
515 nPadPoints[ampName] = nSignalPoints - len(list(self.covariances.values())[i])
516 covMatrixSide = self.covMatrixSide
518 catalog = Table([{'AMPLIFIER_NAME': ampName,
519 'PTC_FIT_TYPE': self.ptcFitType,
520 'COV_MATRIX_SIDE': self.covMatrixSide,
521 'INPUT_EXP_ID_PAIRS': self.inputExpIdPairs[ampName]
522 if len(self.expIdMask[ampName]) else np.nan,
523 'EXP_ID_MASK': self.expIdMask[ampName]
524 if len(self.expIdMask[ampName]) else np.nan,
525 'RAW_EXP_TIMES': np.array(self.rawExpTimes[ampName]).tolist()
526 if len(self.rawExpTimes[ampName]) else np.nan,
527 'RAW_MEANS': np.array(self.rawMeans[ampName]).tolist()
528 if len(self.rawMeans[ampName]) else np.nan,
529 'RAW_VARS': np.array(self.rawVars[ampName]).tolist()
530 if len(self.rawVars[ampName]) else np.nan,
531 'GAIN': self.gain[ampName],
532 'GAIN_ERR': self.gainErr[ampName],
533 'NOISE': self.noise[ampName],
534 'NOISE_ERR': self.noiseErr[ampName],
535 'PTC_FIT_PARS': np.array(self.ptcFitPars[ampName]).tolist(),
536 'PTC_FIT_PARS_ERROR': np.array(self.ptcFitParsError[ampName]).tolist(),
537 'PTC_FIT_CHI_SQ': self.ptcFitChiSq[ampName],
538 'PTC_TURNOFF': self.ptcTurnoff[ampName],
539 'COVARIANCES': np.pad(np.array(self.covariances[ampName]),
540 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
541 'constant', constant_values=np.nan).reshape(
542 nSignalPoints*covMatrixSide**2).tolist(),
543 'COVARIANCES_MODEL': np.pad(np.array(self.covariancesModel[ampName]),
544 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
545 'constant', constant_values=np.nan).reshape(
546 nSignalPoints*covMatrixSide**2).tolist(),
547 'COVARIANCES_SQRT_WEIGHTS': np.pad(np.array(self.covariancesSqrtWeights[ampName]),
548 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
549 'constant', constant_values=0.0).reshape(
550 nSignalPoints*covMatrixSide**2).tolist(),
551 'A_MATRIX': np.array(self.aMatrix[ampName]).reshape(covMatrixSide**2).tolist(),
552 'B_MATRIX': np.array(self.bMatrix[ampName]).reshape(covMatrixSide**2).tolist(),
553 'COVARIANCES_MODEL_NO_B':
554 np.pad(np.array(self.covariancesModelNoB[ampName]),
555 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
556 'constant', constant_values=np.nan).reshape(
557 nSignalPoints*covMatrixSide**2).tolist(),
558 'A_MATRIX_NO_B': np.array(self.aMatrixNoB[ampName]).reshape(
559 covMatrixSide**2).tolist(),
560 'FINAL_VARS': np.pad(np.array(self.finalVars[ampName]), (0, nPadPoints[ampName]),
561 'constant', constant_values=np.nan).tolist(),
562 'FINAL_MODEL_VARS': np.pad(np.array(self.finalModelVars[ampName]),
563 (0, nPadPoints[ampName]),
564 'constant', constant_values=np.nan).tolist(),
565 'FINAL_MEANS': np.pad(np.array(self.finalMeans[ampName]),
566 (0, nPadPoints[ampName]),
567 'constant', constant_values=np.nan).tolist(),
568 'BAD_AMPS': np.array(self.badAmps).tolist() if len(self.badAmps) else np.nan,
569 'PHOTO_CHARGE': np.array(self.photoCharge[ampName]).tolist(),
570 } for ampName in self.ampNames])
571 inMeta = self.getMetadata().toDict()
572 outMeta = {k: v for k, v in inMeta.items() if v is not None}
573 outMeta.update({k: "" for k, v in inMeta.items() if v is None})
574 catalog.meta = outMeta
575 tableList.append(catalog)
577 return(tableList)
579 def getExpIdsUsed(self, ampName):
580 """Get the exposures used, i.e. not discarded, for a given amp.
581 If no mask has been created yet, all exposures are returned.
582 """
583 if len(self.expIdMask[ampName]) == 0:
584 return self.inputExpIdPairs[ampName]
586 # if the mask exists it had better be the same length as the expIdPairs
587 assert len(self.expIdMask[ampName]) == len(self.inputExpIdPairs[ampName])
589 pairs = self.inputExpIdPairs[ampName]
590 mask = self.expIdMask[ampName]
591 # cast to bool required because numpy
592 return [(exp1, exp2) for ((exp1, exp2), m) in zip(pairs, mask) if bool(m) is True]
594 def getGoodAmps(self):
595 return [amp for amp in self.ampNames if amp not in self.badAmps]