lsst.ip.isr g7aa544c432+fabaea33e9
ptcDataset.py
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2# LSST Data Management System
3# Copyright 2008-2017 AURA/LSST.
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22"""
23Define dataset class for MeasurePhotonTransferCurve task
24"""
25import numpy as np
26from astropy.table import Table
27
28from lsst.ip.isr import IsrCalib
29
30__all__ = ['PhotonTransferCurveDataset']
31
32
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.
48
49 Parameters
50 ----------
51 ampNames : `list`
52 List with the names of the amplifiers of the detector at hand.
53
54 ptcFitType : `str`
55 Type of model fitted to the PTC: "POLYNOMIAL", "EXPAPPROXIMATION",
56 or "FULLCOVARIANCE".
57
58 covMatrixSide : `int`
59 Maximum lag of covariances (size of square covariance matrices).
60
61 kwargs : `dict`, optional
62 Other keyword arguments to pass to the parent init.
63
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 covariances : `dict`, [`str`, `list`]
105 Dictionary keyed by amp names containing a list of measured
106 covariances per mean flux.
107 covariancesModel : `dict`, [`str`, `list`]
108 Dictionary keyed by amp names containinging covariances model
109 (Eq. 20 of Astier+19) per mean flux.
110 covariancesSqrtWeights : `dict`, [`str`, `list`]
111 Dictionary keyed by amp names containinging sqrt. of covariances
112 weights.
113 aMatrix : `dict`, [`str`, `list`]
114 Dictionary keyed by amp names containing the "a" parameters from
115 the model in Eq. 20 of Astier+19.
116 bMatrix : `dict`, [`str`, `list`]
117 Dictionary keyed by amp names containing the "b" parameters from
118 the model in Eq. 20 of Astier+19.
119 covariancesModelNoB : `dict`, [`str`, `list`]
120 Dictionary keyed by amp names containing covariances model
121 (with 'b'=0 in Eq. 20 of Astier+19)
122 per mean flux.
123 aMatrixNoB : `dict`, [`str`, `list`]
124 Dictionary keyed by amp names containing the "a" parameters from the
125 model in Eq. 20 of Astier+19
126 (and 'b' = 0).
127 finalVars : `dict`, [`str`, `list`]
128 Dictionary keyed by amp names containing the masked variance of the
129 difference image of each flat
130 pair. If needed, each array will be right-padded with
131 np.nan to match the length of rawExpTimes.
132 finalModelVars : `dict`, [`str`, `list`]
133 Dictionary keyed by amp names containing the masked modeled
134 variance of the difference image of each flat pair. If needed, each
135 array will be right-padded with np.nan to match the length of
136 rawExpTimes.
137 finalMeans : `dict`, [`str`, `list`]
138 Dictionary keyed by amp names containing the masked average of the
139 means of the exposures in each flat pair. If needed, each array
140 will be right-padded with np.nan to match the length of
141 rawExpTimes.
142 photoCharge : `dict`, [`str`, `list`]
143 Dictionary keyed by amp names containing the integrated photocharge
144 for linearity calibration.
145
146 Returns
147 -------
148 `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
149 Output dataset from MeasurePhotonTransferCurveTask.
150 """
151
152 _OBSTYPE = 'PTC'
153 _SCHEMA = 'Gen3 Photon Transfer Curve'
154 _VERSION = 1.0
155
156 def __init__(self, ampNames=[], ptcFitType=None, covMatrixSide=1, **kwargs):
157
158 self.ptcFitTypeptcFitType = ptcFitType
159 self.ampNamesampNames = ampNames
160 self.covMatrixSidecovMatrixSide = covMatrixSide
161
162 self.badAmpsbadAmps = [np.nan]
163
164 self.inputExpIdPairsinputExpIdPairs = {ampName: [] for ampName in ampNames}
165 self.expIdMaskexpIdMask = {ampName: [] for ampName in ampNames}
166 self.rawExpTimesrawExpTimes = {ampName: [] for ampName in ampNames}
167 self.rawMeansrawMeans = {ampName: [] for ampName in ampNames}
168 self.rawVarsrawVars = {ampName: [] for ampName in ampNames}
169 self.photoChargephotoCharge = {ampName: [] for ampName in ampNames}
170
171 self.gaingain = {ampName: np.nan for ampName in ampNames}
172 self.gainErrgainErr = {ampName: np.nan for ampName in ampNames}
173 self.noisenoise = {ampName: np.nan for ampName in ampNames}
174 self.noiseErrnoiseErr = {ampName: np.nan for ampName in ampNames}
175
176 self.ptcFitParsptcFitPars = {ampName: [] for ampName in ampNames}
177 self.ptcFitParsErrorptcFitParsError = {ampName: [] for ampName in ampNames}
178 self.ptcFitChiSqptcFitChiSq = {ampName: np.nan for ampName in ampNames}
179
180 self.covariancescovariances = {ampName: [] for ampName in ampNames}
181 self.covariancesModelcovariancesModel = {ampName: [] for ampName in ampNames}
182 self.covariancesSqrtWeightscovariancesSqrtWeights = {ampName: [] for ampName in ampNames}
183 self.aMatrixaMatrix = {ampName: np.nan for ampName in ampNames}
184 self.bMatrixbMatrix = {ampName: np.nan for ampName in ampNames}
185 self.covariancesModelNoBcovariancesModelNoB = {ampName: [] for ampName in ampNames}
186 self.aMatrixNoBaMatrixNoB = {ampName: np.nan for ampName in ampNames}
187
188 self.finalVarsfinalVars = {ampName: [] for ampName in ampNames}
189 self.finalModelVarsfinalModelVars = {ampName: [] for ampName in ampNames}
190 self.finalMeansfinalMeans = {ampName: [] for ampName in ampNames}
191
192 super().__init__(**kwargs)
193 self.requiredAttributesrequiredAttributesrequiredAttributesrequiredAttributes.update(['badAmps', 'inputExpIdPairs', 'expIdMask', 'rawExpTimes',
194 'rawMeans', 'rawVars', 'gain', 'gainErr', 'noise', 'noiseErr',
195 'ptcFitPars', 'ptcFitParsError', 'ptcFitChiSq', 'aMatrixNoB',
196 'covariances', 'covariancesModel', 'covariancesSqrtWeights',
197 'covariancesModelNoB',
198 'aMatrix', 'bMatrix', 'finalVars', 'finalModelVars', 'finalMeans',
199 'photoCharge'])
200
201 def setAmpValues(self, ampName, inputExpIdPair=[(np.nan, np.nan)], expIdMask=[np.nan],
202 rawExpTime=[np.nan], rawMean=[np.nan], rawVar=[np.nan], photoCharge=[np.nan],
203 gain=np.nan, gainErr=np.nan, noise=np.nan, noiseErr=np.nan, ptcFitPars=[np.nan],
204 ptcFitParsError=[np.nan], ptcFitChiSq=np.nan, covArray=[], covArrayModel=[],
205 covSqrtWeights=[], aMatrix=[], bMatrix=[], covArrayModelNoB=[], aMatrixNoB=[],
206 finalVar=[np.nan], finalModelVar=[np.nan], finalMean=[np.nan]):
207 """Function to initialize an amp of a PhotonTransferCurveDataset.
208
209 Notes
210 -----
211 The parameters are all documented in `init`.
212 """
213 nanMatrix = np.full((self.covMatrixSidecovMatrixSide, self.covMatrixSidecovMatrixSide), np.nan)
214 if len(covArray) == 0:
215 covArray = [nanMatrix]
216 if len(covArrayModel) == 0:
217 covArrayModel = [nanMatrix]
218 if len(covSqrtWeights) == 0:
219 covSqrtWeights = [nanMatrix]
220 if len(covArrayModelNoB) == 0:
221 covArrayModelNoB = [nanMatrix]
222 if len(aMatrix) == 0:
223 aMatrix = nanMatrix
224 if len(bMatrix) == 0:
225 bMatrix = nanMatrix
226 if len(aMatrixNoB) == 0:
227 aMatrixNoB = nanMatrix
228
229 self.inputExpIdPairsinputExpIdPairs[ampName] = inputExpIdPair
230 self.expIdMaskexpIdMask[ampName] = expIdMask
231 self.rawExpTimesrawExpTimes[ampName] = rawExpTime
232 self.rawMeansrawMeans[ampName] = rawMean
233 self.rawVarsrawVars[ampName] = rawVar
234 self.photoChargephotoCharge[ampName] = photoCharge
235 self.gaingain[ampName] = gain
236 self.gainErrgainErr[ampName] = gainErr
237 self.noisenoise[ampName] = noise
238 self.noiseErrnoiseErr[ampName] = noiseErr
239 self.ptcFitParsptcFitPars[ampName] = ptcFitPars
240 self.ptcFitParsErrorptcFitParsError[ampName] = ptcFitParsError
241 self.ptcFitChiSqptcFitChiSq[ampName]
242 self.covariancescovariances[ampName] = covArray
243 self.covariancesSqrtWeightscovariancesSqrtWeights[ampName] = covSqrtWeights
244 self.covariancesModelcovariancesModel[ampName] = covArrayModel
245 self.covariancesModelNoBcovariancesModelNoB[ampName] = covArrayModelNoB
246 self.aMatrixaMatrix[ampName] = aMatrix
247 self.bMatrixbMatrix[ampName] = bMatrix
248 self.aMatrixNoBaMatrixNoB[ampName] = aMatrixNoB
249 self.ptcFitParsptcFitPars[ampName] = ptcFitPars
250 self.ptcFitParsErrorptcFitParsError[ampName] = ptcFitParsError
251 self.ptcFitChiSqptcFitChiSq[ampName] = ptcFitChiSq
252 self.finalVarsfinalVars[ampName] = finalVar
253 self.finalModelVarsfinalModelVars[ampName] = finalModelVar
254 self.finalMeansfinalMeans[ampName] = finalMean
255
256 def updateMetadata(self, setDate=False, **kwargs):
257 """Update calibration metadata.
258 This calls the base class's method after ensuring the required
259 calibration keywords will be saved.
260 Parameters
261 ----------
262 setDate : `bool`, optional
263 Update the CALIBDATE fields in the metadata to the current
264 time. Defaults to False.
265 kwargs :
266 Other keyword parameters to set in the metadata.
267 """
268 kwargs['PTC_FIT_TYPE'] = self.ptcFitTypeptcFitType
269
270 super().updateMetadata(setDate=setDate, **kwargs)
271
272 @classmethod
273 def fromDict(cls, dictionary):
274 """Construct a calibration from a dictionary of properties.
275 Must be implemented by the specific calibration subclasses.
276 Parameters
277 ----------
278 dictionary : `dict`
279 Dictionary of properties.
280 Returns
281 -------
282 calib : `lsst.ip.isr.CalibType`
283 Constructed calibration.
284 Raises
285 ------
286 RuntimeError :
287 Raised if the supplied dictionary is for a different
288 calibration.
289 """
290 calib = cls()
291 if calib._OBSTYPE != dictionary['metadata']['OBSTYPE']:
292 raise RuntimeError(f"Incorrect Photon Transfer Curve dataset supplied. "
293 f"Expected {calib._OBSTYPE}, found {dictionary['metadata']['OBSTYPE']}")
294 calib.setMetadata(dictionary['metadata'])
295 calib.ptcFitType = dictionary['ptcFitType']
296 calib.covMatrixSide = dictionary['covMatrixSide']
297 calib.badAmps = np.array(dictionary['badAmps'], 'str').tolist()
298 # The cov matrices are square
299 covMatrixSide = calib.covMatrixSide
300 # Number of final signal levels
301 covDimensionsProduct = len(np.array(list(dictionary['covariances'].values())[0]).ravel())
302 nSignalPoints = int(covDimensionsProduct/(covMatrixSide*covMatrixSide))
303
304 for ampName in dictionary['ampNames']:
305 calib.ampNames.append(ampName)
306 calib.inputExpIdPairs[ampName] = np.array(dictionary['inputExpIdPairs'][ampName]).tolist()
307 calib.expIdMask[ampName] = np.array(dictionary['expIdMask'][ampName]).tolist()
308 calib.rawExpTimes[ampName] = np.array(dictionary['rawExpTimes'][ampName]).tolist()
309 calib.rawMeans[ampName] = np.array(dictionary['rawMeans'][ampName]).tolist()
310 calib.rawVars[ampName] = np.array(dictionary['rawVars'][ampName]).tolist()
311 calib.gain[ampName] = np.array(dictionary['gain'][ampName]).tolist()
312 calib.gainErr[ampName] = np.array(dictionary['gainErr'][ampName]).tolist()
313 calib.noise[ampName] = np.array(dictionary['noise'][ampName]).tolist()
314 calib.noiseErr[ampName] = np.array(dictionary['noiseErr'][ampName]).tolist()
315 calib.ptcFitPars[ampName] = np.array(dictionary['ptcFitPars'][ampName]).tolist()
316 calib.ptcFitParsError[ampName] = np.array(dictionary['ptcFitParsError'][ampName]).tolist()
317 calib.ptcFitChiSq[ampName] = np.array(dictionary['ptcFitChiSq'][ampName]).tolist()
318 calib.covariances[ampName] = np.array(dictionary['covariances'][ampName]).reshape(
319 (nSignalPoints, covMatrixSide, covMatrixSide)).tolist()
320 calib.covariancesModel[ampName] = np.array(
321 dictionary['covariancesModel'][ampName]).reshape(
322 (nSignalPoints, covMatrixSide, covMatrixSide)).tolist()
323 calib.covariancesSqrtWeights[ampName] = np.array(
324 dictionary['covariancesSqrtWeights'][ampName]).reshape(
325 (nSignalPoints, covMatrixSide, covMatrixSide)).tolist()
326 calib.aMatrix[ampName] = np.array(dictionary['aMatrix'][ampName]).reshape(
327 (covMatrixSide, covMatrixSide)).tolist()
328 calib.bMatrix[ampName] = np.array(dictionary['bMatrix'][ampName]).reshape(
329 (covMatrixSide, covMatrixSide)).tolist()
330 calib.covariancesModelNoB[ampName] = np.array(
331 dictionary['covariancesModelNoB'][ampName]).reshape(
332 (nSignalPoints, covMatrixSide, covMatrixSide)).tolist()
333 calib.aMatrixNoB[ampName] = np.array(
334 dictionary['aMatrixNoB'][ampName]).reshape((covMatrixSide, covMatrixSide)).tolist()
335 calib.finalVars[ampName] = np.array(dictionary['finalVars'][ampName]).tolist()
336 calib.finalModelVars[ampName] = np.array(dictionary['finalModelVars'][ampName]).tolist()
337 calib.finalMeans[ampName] = np.array(dictionary['finalMeans'][ampName]).tolist()
338 calib.photoCharge[ampName] = np.array(dictionary['photoCharge'][ampName]).tolist()
339 calib.updateMetadata()
340 return calib
341
342 def toDict(self):
343 """Return a dictionary containing the calibration properties.
344 The dictionary should be able to be round-tripped through
345 `fromDict`.
346 Returns
347 -------
348 dictionary : `dict`
349 Dictionary of properties.
350 """
351 self.updateMetadataupdateMetadataupdateMetadata()
352
353 outDict = dict()
354 metadata = self.getMetadatagetMetadata()
355 outDict['metadata'] = metadata
356
357 outDict['ptcFitType'] = self.ptcFitTypeptcFitType
358 outDict['covMatrixSide'] = self.covMatrixSidecovMatrixSide
359 outDict['ampNames'] = self.ampNamesampNames
360 outDict['badAmps'] = self.badAmpsbadAmps
361 outDict['inputExpIdPairs'] = self.inputExpIdPairsinputExpIdPairs
362 outDict['expIdMask'] = self.expIdMaskexpIdMask
363 outDict['rawExpTimes'] = self.rawExpTimesrawExpTimes
364 outDict['rawMeans'] = self.rawMeansrawMeans
365 outDict['rawVars'] = self.rawVarsrawVars
366 outDict['gain'] = self.gaingain
367 outDict['gainErr'] = self.gainErrgainErr
368 outDict['noise'] = self.noisenoise
369 outDict['noiseErr'] = self.noiseErrnoiseErr
370 outDict['ptcFitPars'] = self.ptcFitParsptcFitPars
371 outDict['ptcFitParsError'] = self.ptcFitParsErrorptcFitParsError
372 outDict['ptcFitChiSq'] = self.ptcFitChiSqptcFitChiSq
373 outDict['covariances'] = self.covariancescovariances
374 outDict['covariancesModel'] = self.covariancesModelcovariancesModel
375 outDict['covariancesSqrtWeights'] = self.covariancesSqrtWeightscovariancesSqrtWeights
376 outDict['aMatrix'] = self.aMatrixaMatrix
377 outDict['bMatrix'] = self.bMatrixbMatrix
378 outDict['covariancesModelNoB'] = self.covariancesModelNoBcovariancesModelNoB
379 outDict['aMatrixNoB'] = self.aMatrixNoBaMatrixNoB
380 outDict['finalVars'] = self.finalVarsfinalVars
381 outDict['finalModelVars'] = self.finalModelVarsfinalModelVars
382 outDict['finalMeans'] = self.finalMeansfinalMeans
383 outDict['photoCharge'] = self.photoChargephotoCharge
384
385 return outDict
386
387 @classmethod
388 def fromTable(cls, tableList):
389 """Construct calibration from a list of tables.
390 This method uses the `fromDict` method to create the
391 calibration, after constructing an appropriate dictionary from
392 the input tables.
393 Parameters
394 ----------
395 tableList : `list` [`lsst.afw.table.Table`]
396 List of tables to use to construct the datasetPtc.
397 Returns
398 -------
399 calib : `lsst.cp.pipe.`
400 The calibration defined in the tables.
401 """
402 ptcTable = tableList[0]
403
404 metadata = ptcTable.meta
405 inDict = dict()
406 inDict['metadata'] = metadata
407 inDict['ampNames'] = []
408 inDict['ptcFitType'] = []
409 inDict['covMatrixSide'] = []
410 inDict['inputExpIdPairs'] = dict()
411 inDict['expIdMask'] = dict()
412 inDict['rawExpTimes'] = dict()
413 inDict['rawMeans'] = dict()
414 inDict['rawVars'] = dict()
415 inDict['gain'] = dict()
416 inDict['gainErr'] = dict()
417 inDict['noise'] = dict()
418 inDict['noiseErr'] = dict()
419 inDict['ptcFitPars'] = dict()
420 inDict['ptcFitParsError'] = dict()
421 inDict['ptcFitChiSq'] = dict()
422 inDict['covariances'] = dict()
423 inDict['covariancesModel'] = dict()
424 inDict['covariancesSqrtWeights'] = dict()
425 inDict['aMatrix'] = dict()
426 inDict['bMatrix'] = dict()
427 inDict['covariancesModelNoB'] = dict()
428 inDict['aMatrixNoB'] = dict()
429 inDict['finalVars'] = dict()
430 inDict['finalModelVars'] = dict()
431 inDict['finalMeans'] = dict()
432 inDict['badAmps'] = []
433 inDict['photoCharge'] = dict()
434
435 for record in ptcTable:
436 ampName = record['AMPLIFIER_NAME']
437
438 inDict['ptcFitType'] = record['PTC_FIT_TYPE']
439 inDict['covMatrixSide'] = record['COV_MATRIX_SIDE']
440 inDict['ampNames'].append(ampName)
441 inDict['inputExpIdPairs'][ampName] = record['INPUT_EXP_ID_PAIRS']
442 inDict['expIdMask'][ampName] = record['EXP_ID_MASK']
443 inDict['rawExpTimes'][ampName] = record['RAW_EXP_TIMES']
444 inDict['rawMeans'][ampName] = record['RAW_MEANS']
445 inDict['rawVars'][ampName] = record['RAW_VARS']
446 inDict['gain'][ampName] = record['GAIN']
447 inDict['gainErr'][ampName] = record['GAIN_ERR']
448 inDict['noise'][ampName] = record['NOISE']
449 inDict['noiseErr'][ampName] = record['NOISE_ERR']
450 inDict['ptcFitPars'][ampName] = record['PTC_FIT_PARS']
451 inDict['ptcFitParsError'][ampName] = record['PTC_FIT_PARS_ERROR']
452 inDict['ptcFitChiSq'][ampName] = record['PTC_FIT_CHI_SQ']
453 inDict['covariances'][ampName] = record['COVARIANCES']
454 inDict['covariancesModel'][ampName] = record['COVARIANCES_MODEL']
455 inDict['covariancesSqrtWeights'][ampName] = record['COVARIANCES_SQRT_WEIGHTS']
456 inDict['aMatrix'][ampName] = record['A_MATRIX']
457 inDict['bMatrix'][ampName] = record['B_MATRIX']
458 inDict['covariancesModelNoB'][ampName] = record['COVARIANCES_MODEL_NO_B']
459 inDict['aMatrixNoB'][ampName] = record['A_MATRIX_NO_B']
460 inDict['finalVars'][ampName] = record['FINAL_VARS']
461 inDict['finalModelVars'][ampName] = record['FINAL_MODEL_VARS']
462 inDict['finalMeans'][ampName] = record['FINAL_MEANS']
463 inDict['badAmps'] = record['BAD_AMPS']
464 inDict['photoCharge'][ampName] = record['PHOTO_CHARGE']
465 return cls().fromDict(inDict)
466
467 def toTable(self):
468 """Construct a list of tables containing the information in this
469 calibration.
470
471 The list of tables should create an identical calibration
472 after being passed to this class's fromTable method.
473 Returns
474 -------
475 tableList : `list` [`astropy.table.Table`]
476 List of tables containing the linearity calibration
477 information.
478 """
479 tableList = []
480 self.updateMetadataupdateMetadataupdateMetadata()
481 nPoints = []
482 for i, ampName in enumerate(self.ampNamesampNames):
483 nPoints.append(len(list(self.covariancescovariances.values())[i]))
484 nSignalPoints = max(nPoints)
485 nPadPoints = {}
486 for i, ampName in enumerate(self.ampNamesampNames):
487 nPadPoints[ampName] = nSignalPoints - len(list(self.covariancescovariances.values())[i])
488 covMatrixSide = self.covMatrixSidecovMatrixSide
489
490 catalog = Table([{'AMPLIFIER_NAME': ampName,
491 'PTC_FIT_TYPE': self.ptcFitTypeptcFitType,
492 'COV_MATRIX_SIDE': self.covMatrixSidecovMatrixSide,
493 'INPUT_EXP_ID_PAIRS': self.inputExpIdPairsinputExpIdPairs[ampName]
494 if len(self.expIdMaskexpIdMask[ampName]) else np.nan,
495 'EXP_ID_MASK': self.expIdMaskexpIdMask[ampName]
496 if len(self.expIdMaskexpIdMask[ampName]) else np.nan,
497 'RAW_EXP_TIMES': np.array(self.rawExpTimesrawExpTimes[ampName]).tolist()
498 if len(self.rawExpTimesrawExpTimes[ampName]) else np.nan,
499 'RAW_MEANS': np.array(self.rawMeansrawMeans[ampName]).tolist()
500 if len(self.rawMeansrawMeans[ampName]) else np.nan,
501 'RAW_VARS': np.array(self.rawVarsrawVars[ampName]).tolist()
502 if len(self.rawVarsrawVars[ampName]) else np.nan,
503 'GAIN': self.gaingain[ampName],
504 'GAIN_ERR': self.gainErrgainErr[ampName],
505 'NOISE': self.noisenoise[ampName],
506 'NOISE_ERR': self.noiseErrnoiseErr[ampName],
507 'PTC_FIT_PARS': np.array(self.ptcFitParsptcFitPars[ampName]).tolist(),
508 'PTC_FIT_PARS_ERROR': np.array(self.ptcFitParsErrorptcFitParsError[ampName]).tolist(),
509 'PTC_FIT_CHI_SQ': self.ptcFitChiSqptcFitChiSq[ampName],
510 'COVARIANCES': np.pad(np.array(self.covariancescovariances[ampName]),
511 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
512 'constant', constant_values=np.nan).reshape(
513 nSignalPoints*covMatrixSide**2).tolist(),
514 'COVARIANCES_MODEL': np.pad(np.array(self.covariancesModelcovariancesModel[ampName]),
515 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
516 'constant', constant_values=np.nan).reshape(
517 nSignalPoints*covMatrixSide**2).tolist(),
518 'COVARIANCES_SQRT_WEIGHTS': np.pad(np.array(self.covariancesSqrtWeightscovariancesSqrtWeights[ampName]),
519 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
520 'constant', constant_values=0.0).reshape(
521 nSignalPoints*covMatrixSide**2).tolist(),
522 'A_MATRIX': np.array(self.aMatrixaMatrix[ampName]).reshape(covMatrixSide**2).tolist(),
523 'B_MATRIX': np.array(self.bMatrixbMatrix[ampName]).reshape(covMatrixSide**2).tolist(),
524 'COVARIANCES_MODEL_NO_B':
525 np.pad(np.array(self.covariancesModelNoBcovariancesModelNoB[ampName]),
526 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
527 'constant', constant_values=np.nan).reshape(
528 nSignalPoints*covMatrixSide**2).tolist(),
529 'A_MATRIX_NO_B': np.array(self.aMatrixNoBaMatrixNoB[ampName]).reshape(
530 covMatrixSide**2).tolist(),
531 'FINAL_VARS': np.pad(np.array(self.finalVarsfinalVars[ampName]), (0, nPadPoints[ampName]),
532 'constant', constant_values=np.nan).tolist(),
533 'FINAL_MODEL_VARS': np.pad(np.array(self.finalModelVarsfinalModelVars[ampName]),
534 (0, nPadPoints[ampName]),
535 'constant', constant_values=np.nan).tolist(),
536 'FINAL_MEANS': np.pad(np.array(self.finalMeansfinalMeans[ampName]),
537 (0, nPadPoints[ampName]),
538 'constant', constant_values=np.nan).tolist(),
539 'BAD_AMPS': np.array(self.badAmpsbadAmps).tolist() if len(self.badAmpsbadAmps) else np.nan,
540 'PHOTO_CHARGE': np.array(self.photoChargephotoCharge[ampName]).tolist(),
541 } for ampName in self.ampNamesampNames])
542 inMeta = self.getMetadatagetMetadata().toDict()
543 outMeta = {k: v for k, v in inMeta.items() if v is not None}
544 outMeta.update({k: "" for k, v in inMeta.items() if v is None})
545 catalog.meta = outMeta
546 tableList.append(catalog)
547
548 return(tableList)
549
550 def getExpIdsUsed(self, ampName):
551 """Get the exposures used, i.e. not discarded, for a given amp.
552 If no mask has been created yet, all exposures are returned.
553 """
554 if len(self.expIdMaskexpIdMask[ampName]) == 0:
555 return self.inputExpIdPairsinputExpIdPairs[ampName]
556
557 # if the mask exists it had better be the same length as the expIdPairs
558 assert len(self.expIdMaskexpIdMask[ampName]) == len(self.inputExpIdPairsinputExpIdPairs[ampName])
559
560 pairs = self.inputExpIdPairsinputExpIdPairs[ampName]
561 mask = self.expIdMaskexpIdMask[ampName]
562 # cast to bool required because numpy
563 return [(exp1, exp2) for ((exp1, exp2), m) in zip(pairs, mask) if bool(m) is True]
564
565 def getGoodAmps(self):
566 return [amp for amp in self.ampNamesampNames if amp not in self.badAmpsbadAmps]
def requiredAttributes(self, value)
Definition: calibType.py:142
def updateMetadata(self, camera=None, detector=None, filterName=None, setCalibId=False, setCalibInfo=False, setDate=False, **kwargs)
Definition: calibType.py:181
def __init__(self, ampNames=[], ptcFitType=None, covMatrixSide=1, **kwargs)
Definition: ptcDataset.py:156
def setAmpValues(self, ampName, inputExpIdPair=[(np.nan, np.nan)], expIdMask=[np.nan], rawExpTime=[np.nan], rawMean=[np.nan], rawVar=[np.nan], photoCharge=[np.nan], gain=np.nan, gainErr=np.nan, noise=np.nan, noiseErr=np.nan, ptcFitPars=[np.nan], ptcFitParsError=[np.nan], ptcFitChiSq=np.nan, covArray=[], covArrayModel=[], covSqrtWeights=[], aMatrix=[], bMatrix=[], covArrayModelNoB=[], aMatrixNoB=[], finalVar=[np.nan], finalModelVar=[np.nan], finalMean=[np.nan])
Definition: ptcDataset.py:206
def updateMetadata(self, setDate=False, **kwargs)
Definition: ptcDataset.py:256