lsst.ip.isr 23.0.0+4a35e5d482
ptcDataset.py
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2# LSST Data Management System
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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 noise.
93 ptcFitPars : `dict`, [`str`, `list`]
94 Dictionary keyed by amp names containing the fitted parameters of the
95 PTC model for ptcFitTye in ["POLYNOMIAL", "EXPAPPROXIMATION"].
96 ptcFitParsError : `dict`, [`str`, `list`]
97 Dictionary keyed by amp names containing the errors on the fitted
98 parameters of the PTC model for ptcFitTye in
99 ["POLYNOMIAL", "EXPAPPROXIMATION"].
100 ptcFitChiSq : `dict`, [`str`, `list`]
101 Dictionary keyed by amp names containing the reduced chi squared
102 of the fit for ptcFitTye in ["POLYNOMIAL", "EXPAPPROXIMATION"].
103 covariances : `dict`, [`str`, `list`]
104 Dictionary keyed by amp names containing a list of measured
105 covariances per mean flux.
106 covariancesModel : `dict`, [`str`, `list`]
107 Dictionary keyed by amp names containinging covariances model
108 (Eq. 20 of Astier+19) per mean flux.
109 covariancesSqrtWeights : `dict`, [`str`, `list`]
110 Dictionary keyed by amp names containinging sqrt. of covariances
111 weights.
112 aMatrix : `dict`, [`str`, `list`]
113 Dictionary keyed by amp names containing the "a" parameters from
114 the model in Eq. 20 of Astier+19.
115 bMatrix : `dict`, [`str`, `list`]
116 Dictionary keyed by amp names containing the "b" parameters from
117 the model in Eq. 20 of Astier+19.
118 covariancesModelNoB : `dict`, [`str`, `list`]
119 Dictionary keyed by amp names containing covariances model
120 (with 'b'=0 in Eq. 20 of Astier+19)
121 per mean flux.
122 aMatrixNoB : `dict`, [`str`, `list`]
123 Dictionary keyed by amp names containing the "a" parameters from the
124 model in Eq. 20 of Astier+19
125 (and 'b' = 0).
126 finalVars : `dict`, [`str`, `list`]
127 Dictionary keyed by amp names containing the masked variance of the
128 difference image of each flat
129 pair. If needed, each array will be right-padded with
130 np.nan to match the length of rawExpTimes.
131 finalModelVars : `dict`, [`str`, `list`]
132 Dictionary keyed by amp names containing the masked modeled
133 variance of the difference image of each flat pair. If needed, each
134 array will be right-padded with np.nan to match the length of
135 rawExpTimes.
136 finalMeans : `dict`, [`str`, `list`]
137 Dictionary keyed by amp names containing the masked average of the
138 means of the exposures in each flat pair. If needed, each array
139 will be right-padded with np.nan to match the length of
140 rawExpTimes.
141 photoCharge : `dict`, [`str`, `list`]
142 Dictionary keyed by amp names containing the integrated photocharge
143 for linearity calibration.
144
145 Returns
146 -------
147 `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
148 Output dataset from MeasurePhotonTransferCurveTask.
149 """
150
151 _OBSTYPE = 'PTC'
152 _SCHEMA = 'Gen3 Photon Transfer Curve'
153 _VERSION = 1.0
154
155 def __init__(self, ampNames=[], ptcFitType=None, covMatrixSide=1, **kwargs):
156
157 self.ptcFitTypeptcFitType = ptcFitType
158 self.ampNamesampNames = ampNames
159 self.covMatrixSidecovMatrixSide = covMatrixSide
160
161 self.badAmpsbadAmps = [np.nan]
162
163 self.inputExpIdPairsinputExpIdPairs = {ampName: [] for ampName in ampNames}
164 self.expIdMaskexpIdMask = {ampName: [] for ampName in ampNames}
165 self.rawExpTimesrawExpTimes = {ampName: [] for ampName in ampNames}
166 self.rawMeansrawMeans = {ampName: [] for ampName in ampNames}
167 self.rawVarsrawVars = {ampName: [] for ampName in ampNames}
168 self.photoChargephotoCharge = {ampName: [] for ampName in ampNames}
169
170 self.gaingain = {ampName: np.nan for ampName in ampNames}
171 self.gainErrgainErr = {ampName: np.nan for ampName in ampNames}
172 self.noisenoise = {ampName: np.nan for ampName in ampNames}
173 self.noiseErrnoiseErr = {ampName: np.nan for ampName in ampNames}
174
175 self.ptcFitParsptcFitPars = {ampName: [] for ampName in ampNames}
176 self.ptcFitParsErrorptcFitParsError = {ampName: [] for ampName in ampNames}
177 self.ptcFitChiSqptcFitChiSq = {ampName: np.nan for ampName in ampNames}
178
179 self.covariancescovariances = {ampName: [] for ampName in ampNames}
180 self.covariancesModelcovariancesModel = {ampName: [] for ampName in ampNames}
181 self.covariancesSqrtWeightscovariancesSqrtWeights = {ampName: [] for ampName in ampNames}
182 self.aMatrixaMatrix = {ampName: np.nan for ampName in ampNames}
183 self.bMatrixbMatrix = {ampName: np.nan for ampName in ampNames}
184 self.covariancesModelNoBcovariancesModelNoB = {ampName: [] for ampName in ampNames}
185 self.aMatrixNoBaMatrixNoB = {ampName: np.nan for ampName in ampNames}
186
187 self.finalVarsfinalVars = {ampName: [] for ampName in ampNames}
188 self.finalModelVarsfinalModelVars = {ampName: [] for ampName in ampNames}
189 self.finalMeansfinalMeans = {ampName: [] for ampName in ampNames}
190
191 super().__init__(**kwargs)
192 self.requiredAttributesrequiredAttributesrequiredAttributesrequiredAttributes.update(['badAmps', 'inputExpIdPairs', 'expIdMask', 'rawExpTimes',
193 'rawMeans', 'rawVars', 'gain', 'gainErr', 'noise', 'noiseErr',
194 'ptcFitPars', 'ptcFitParsError', 'ptcFitChiSq', 'aMatrixNoB',
195 'covariances', 'covariancesModel', 'covariancesSqrtWeights',
196 'covariancesModelNoB',
197 'aMatrix', 'bMatrix', 'finalVars', 'finalModelVars', 'finalMeans',
198 'photoCharge'])
199
200 def setAmpValues(self, ampName, inputExpIdPair=[(np.nan, np.nan)], expIdMask=[np.nan],
201 rawExpTime=[np.nan], rawMean=[np.nan], rawVar=[np.nan], photoCharge=[np.nan],
202 gain=np.nan, gainErr=np.nan, noise=np.nan, noiseErr=np.nan, ptcFitPars=[np.nan],
203 ptcFitParsError=[np.nan], ptcFitChiSq=np.nan, covArray=[], covArrayModel=[],
204 covSqrtWeights=[], aMatrix=[], bMatrix=[], covArrayModelNoB=[], aMatrixNoB=[],
205 finalVar=[np.nan], finalModelVar=[np.nan], finalMean=[np.nan]):
206 """Function to initialize an amp of a PhotonTransferCurveDataset.
207
208 Notes
209 -----
210 The parameters are all documented in `init`.
211 """
212 nanMatrix = np.full((self.covMatrixSidecovMatrixSide, self.covMatrixSidecovMatrixSide), np.nan)
213 if len(covArray) == 0:
214 covArray = [nanMatrix]
215 if len(covArrayModel) == 0:
216 covArrayModel = [nanMatrix]
217 if len(covSqrtWeights) == 0:
218 covSqrtWeights = [nanMatrix]
219 if len(covArrayModelNoB) == 0:
220 covArrayModelNoB = [nanMatrix]
221 if len(aMatrix) == 0:
222 aMatrix = nanMatrix
223 if len(bMatrix) == 0:
224 bMatrix = nanMatrix
225 if len(aMatrixNoB) == 0:
226 aMatrixNoB = nanMatrix
227
228 self.inputExpIdPairsinputExpIdPairs[ampName] = inputExpIdPair
229 self.expIdMaskexpIdMask[ampName] = expIdMask
230 self.rawExpTimesrawExpTimes[ampName] = rawExpTime
231 self.rawMeansrawMeans[ampName] = rawMean
232 self.rawVarsrawVars[ampName] = rawVar
233 self.photoChargephotoCharge[ampName] = photoCharge
234 self.gaingain[ampName] = gain
235 self.gainErrgainErr[ampName] = gainErr
236 self.noisenoise[ampName] = noise
237 self.noiseErrnoiseErr[ampName] = noiseErr
238 self.ptcFitParsptcFitPars[ampName] = ptcFitPars
239 self.ptcFitParsErrorptcFitParsError[ampName] = ptcFitParsError
240 self.ptcFitChiSqptcFitChiSq[ampName]
241 self.covariancescovariances[ampName] = covArray
242 self.covariancesSqrtWeightscovariancesSqrtWeights[ampName] = covSqrtWeights
243 self.covariancesModelcovariancesModel[ampName] = covArrayModel
244 self.covariancesModelNoBcovariancesModelNoB[ampName] = covArrayModelNoB
245 self.aMatrixaMatrix[ampName] = aMatrix
246 self.bMatrixbMatrix[ampName] = bMatrix
247 self.aMatrixNoBaMatrixNoB[ampName] = aMatrixNoB
248 self.ptcFitParsptcFitPars[ampName] = ptcFitPars
249 self.ptcFitParsErrorptcFitParsError[ampName] = ptcFitParsError
250 self.ptcFitChiSqptcFitChiSq[ampName] = ptcFitChiSq
251 self.finalVarsfinalVars[ampName] = finalVar
252 self.finalModelVarsfinalModelVars[ampName] = finalModelVar
253 self.finalMeansfinalMeans[ampName] = finalMean
254
255 def updateMetadata(self, setDate=False, **kwargs):
256 """Update calibration metadata.
257 This calls the base class's method after ensuring the required
258 calibration keywords will be saved.
259 Parameters
260 ----------
261 setDate : `bool`, optional
262 Update the CALIBDATE fields in the metadata to the current
263 time. Defaults to False.
264 kwargs :
265 Other keyword parameters to set in the metadata.
266 """
267 kwargs['PTC_FIT_TYPE'] = self.ptcFitTypeptcFitType
268
269 super().updateMetadata(setDate=setDate, **kwargs)
270
271 @classmethod
272 def fromDict(cls, dictionary):
273 """Construct a calibration from a dictionary of properties.
274 Must be implemented by the specific calibration subclasses.
275 Parameters
276 ----------
277 dictionary : `dict`
278 Dictionary of properties.
279 Returns
280 -------
281 calib : `lsst.ip.isr.CalibType`
282 Constructed calibration.
283 Raises
284 ------
285 RuntimeError :
286 Raised if the supplied dictionary is for a different
287 calibration.
288 """
289 calib = cls()
290 if calib._OBSTYPE != dictionary['metadata']['OBSTYPE']:
291 raise RuntimeError(f"Incorrect Photon Transfer Curve dataset supplied. "
292 f"Expected {calib._OBSTYPE}, found {dictionary['metadata']['OBSTYPE']}")
293 calib.setMetadata(dictionary['metadata'])
294 calib.ptcFitType = dictionary['ptcFitType']
295 calib.covMatrixSide = dictionary['covMatrixSide']
296 calib.badAmps = np.array(dictionary['badAmps'], 'str').tolist()
297 # The cov matrices are square
298 covMatrixSide = calib.covMatrixSide
299 # Number of final signal levels
300 covDimensionsProduct = len(np.array(list(dictionary['covariances'].values())[0]).ravel())
301 nSignalPoints = int(covDimensionsProduct/(covMatrixSide*covMatrixSide))
302
303 for ampName in dictionary['ampNames']:
304 calib.ampNames.append(ampName)
305 calib.inputExpIdPairs[ampName] = np.array(dictionary['inputExpIdPairs'][ampName]).tolist()
306 calib.expIdMask[ampName] = np.array(dictionary['expIdMask'][ampName]).tolist()
307 calib.rawExpTimes[ampName] = np.array(dictionary['rawExpTimes'][ampName]).tolist()
308 calib.rawMeans[ampName] = np.array(dictionary['rawMeans'][ampName]).tolist()
309 calib.rawVars[ampName] = np.array(dictionary['rawVars'][ampName]).tolist()
310 calib.gain[ampName] = np.array(dictionary['gain'][ampName]).tolist()
311 calib.gainErr[ampName] = np.array(dictionary['gainErr'][ampName]).tolist()
312 calib.noise[ampName] = np.array(dictionary['noise'][ampName]).tolist()
313 calib.noiseErr[ampName] = np.array(dictionary['noiseErr'][ampName]).tolist()
314 calib.ptcFitPars[ampName] = np.array(dictionary['ptcFitPars'][ampName]).tolist()
315 calib.ptcFitParsError[ampName] = np.array(dictionary['ptcFitParsError'][ampName]).tolist()
316 calib.ptcFitChiSq[ampName] = np.array(dictionary['ptcFitChiSq'][ampName]).tolist()
317 calib.covariances[ampName] = np.array(dictionary['covariances'][ampName]).reshape(
318 (nSignalPoints, covMatrixSide, covMatrixSide)).tolist()
319 calib.covariancesModel[ampName] = np.array(
320 dictionary['covariancesModel'][ampName]).reshape(
321 (nSignalPoints, covMatrixSide, covMatrixSide)).tolist()
322 calib.covariancesSqrtWeights[ampName] = np.array(
323 dictionary['covariancesSqrtWeights'][ampName]).reshape(
324 (nSignalPoints, covMatrixSide, covMatrixSide)).tolist()
325 calib.aMatrix[ampName] = np.array(dictionary['aMatrix'][ampName]).reshape(
326 (covMatrixSide, covMatrixSide)).tolist()
327 calib.bMatrix[ampName] = np.array(dictionary['bMatrix'][ampName]).reshape(
328 (covMatrixSide, covMatrixSide)).tolist()
329 calib.covariancesModelNoB[ampName] = np.array(
330 dictionary['covariancesModelNoB'][ampName]).reshape(
331 (nSignalPoints, covMatrixSide, covMatrixSide)).tolist()
332 calib.aMatrixNoB[ampName] = np.array(
333 dictionary['aMatrixNoB'][ampName]).reshape((covMatrixSide, covMatrixSide)).tolist()
334 calib.finalVars[ampName] = np.array(dictionary['finalVars'][ampName]).tolist()
335 calib.finalModelVars[ampName] = np.array(dictionary['finalModelVars'][ampName]).tolist()
336 calib.finalMeans[ampName] = np.array(dictionary['finalMeans'][ampName]).tolist()
337 calib.photoCharge[ampName] = np.array(dictionary['photoCharge'][ampName]).tolist()
338 calib.updateMetadata()
339 return calib
340
341 def toDict(self):
342 """Return a dictionary containing the calibration properties.
343 The dictionary should be able to be round-tripped through
344 `fromDict`.
345 Returns
346 -------
347 dictionary : `dict`
348 Dictionary of properties.
349 """
350 self.updateMetadataupdateMetadataupdateMetadata()
351
352 outDict = dict()
353 metadata = self.getMetadatagetMetadata()
354 outDict['metadata'] = metadata
355
356 outDict['ptcFitType'] = self.ptcFitTypeptcFitType
357 outDict['covMatrixSide'] = self.covMatrixSidecovMatrixSide
358 outDict['ampNames'] = self.ampNamesampNames
359 outDict['badAmps'] = self.badAmpsbadAmps
360 outDict['inputExpIdPairs'] = self.inputExpIdPairsinputExpIdPairs
361 outDict['expIdMask'] = self.expIdMaskexpIdMask
362 outDict['rawExpTimes'] = self.rawExpTimesrawExpTimes
363 outDict['rawMeans'] = self.rawMeansrawMeans
364 outDict['rawVars'] = self.rawVarsrawVars
365 outDict['gain'] = self.gaingain
366 outDict['gainErr'] = self.gainErrgainErr
367 outDict['noise'] = self.noisenoise
368 outDict['noiseErr'] = self.noiseErrnoiseErr
369 outDict['ptcFitPars'] = self.ptcFitParsptcFitPars
370 outDict['ptcFitParsError'] = self.ptcFitParsErrorptcFitParsError
371 outDict['ptcFitChiSq'] = self.ptcFitChiSqptcFitChiSq
372 outDict['covariances'] = self.covariancescovariances
373 outDict['covariancesModel'] = self.covariancesModelcovariancesModel
374 outDict['covariancesSqrtWeights'] = self.covariancesSqrtWeightscovariancesSqrtWeights
375 outDict['aMatrix'] = self.aMatrixaMatrix
376 outDict['bMatrix'] = self.bMatrixbMatrix
377 outDict['covariancesModelNoB'] = self.covariancesModelNoBcovariancesModelNoB
378 outDict['aMatrixNoB'] = self.aMatrixNoBaMatrixNoB
379 outDict['finalVars'] = self.finalVarsfinalVars
380 outDict['finalModelVars'] = self.finalModelVarsfinalModelVars
381 outDict['finalMeans'] = self.finalMeansfinalMeans
382 outDict['photoCharge'] = self.photoChargephotoCharge
383
384 return outDict
385
386 @classmethod
387 def fromTable(cls, tableList):
388 """Construct calibration from a list of tables.
389 This method uses the `fromDict` method to create the
390 calibration, after constructing an appropriate dictionary from
391 the input tables.
392 Parameters
393 ----------
394 tableList : `list` [`lsst.afw.table.Table`]
395 List of tables to use to construct the datasetPtc.
396 Returns
397 -------
398 calib : `lsst.cp.pipe.`
399 The calibration defined in the tables.
400 """
401 ptcTable = tableList[0]
402
403 metadata = ptcTable.meta
404 inDict = dict()
405 inDict['metadata'] = metadata
406 inDict['ampNames'] = []
407 inDict['ptcFitType'] = []
408 inDict['covMatrixSide'] = []
409 inDict['inputExpIdPairs'] = dict()
410 inDict['expIdMask'] = dict()
411 inDict['rawExpTimes'] = dict()
412 inDict['rawMeans'] = dict()
413 inDict['rawVars'] = dict()
414 inDict['gain'] = dict()
415 inDict['gainErr'] = dict()
416 inDict['noise'] = dict()
417 inDict['noiseErr'] = dict()
418 inDict['ptcFitPars'] = dict()
419 inDict['ptcFitParsError'] = dict()
420 inDict['ptcFitChiSq'] = dict()
421 inDict['covariances'] = dict()
422 inDict['covariancesModel'] = dict()
423 inDict['covariancesSqrtWeights'] = dict()
424 inDict['aMatrix'] = dict()
425 inDict['bMatrix'] = dict()
426 inDict['covariancesModelNoB'] = dict()
427 inDict['aMatrixNoB'] = dict()
428 inDict['finalVars'] = dict()
429 inDict['finalModelVars'] = dict()
430 inDict['finalMeans'] = dict()
431 inDict['badAmps'] = []
432 inDict['photoCharge'] = dict()
433
434 for record in ptcTable:
435 ampName = record['AMPLIFIER_NAME']
436
437 inDict['ptcFitType'] = record['PTC_FIT_TYPE']
438 inDict['covMatrixSide'] = record['COV_MATRIX_SIDE']
439 inDict['ampNames'].append(ampName)
440 inDict['inputExpIdPairs'][ampName] = record['INPUT_EXP_ID_PAIRS']
441 inDict['expIdMask'][ampName] = record['EXP_ID_MASK']
442 inDict['rawExpTimes'][ampName] = record['RAW_EXP_TIMES']
443 inDict['rawMeans'][ampName] = record['RAW_MEANS']
444 inDict['rawVars'][ampName] = record['RAW_VARS']
445 inDict['gain'][ampName] = record['GAIN']
446 inDict['gainErr'][ampName] = record['GAIN_ERR']
447 inDict['noise'][ampName] = record['NOISE']
448 inDict['noiseErr'][ampName] = record['NOISE_ERR']
449 inDict['ptcFitPars'][ampName] = record['PTC_FIT_PARS']
450 inDict['ptcFitParsError'][ampName] = record['PTC_FIT_PARS_ERROR']
451 inDict['ptcFitChiSq'][ampName] = record['PTC_FIT_CHI_SQ']
452 inDict['covariances'][ampName] = record['COVARIANCES']
453 inDict['covariancesModel'][ampName] = record['COVARIANCES_MODEL']
454 inDict['covariancesSqrtWeights'][ampName] = record['COVARIANCES_SQRT_WEIGHTS']
455 inDict['aMatrix'][ampName] = record['A_MATRIX']
456 inDict['bMatrix'][ampName] = record['B_MATRIX']
457 inDict['covariancesModelNoB'][ampName] = record['COVARIANCES_MODEL_NO_B']
458 inDict['aMatrixNoB'][ampName] = record['A_MATRIX_NO_B']
459 inDict['finalVars'][ampName] = record['FINAL_VARS']
460 inDict['finalModelVars'][ampName] = record['FINAL_MODEL_VARS']
461 inDict['finalMeans'][ampName] = record['FINAL_MEANS']
462 inDict['badAmps'] = record['BAD_AMPS']
463 inDict['photoCharge'][ampName] = record['PHOTO_CHARGE']
464 return cls().fromDict(inDict)
465
466 def toTable(self):
467 """Construct a list of tables containing the information in this
468 calibration.
469
470 The list of tables should create an identical calibration
471 after being passed to this class's fromTable method.
472 Returns
473 -------
474 tableList : `list` [`astropy.table.Table`]
475 List of tables containing the linearity calibration
476 information.
477 """
478 tableList = []
479 self.updateMetadataupdateMetadataupdateMetadata()
480 nPoints = []
481 for i, ampName in enumerate(self.ampNamesampNames):
482 nPoints.append(len(list(self.covariancescovariances.values())[i]))
483 nSignalPoints = max(nPoints)
484 nPadPoints = {}
485 for i, ampName in enumerate(self.ampNamesampNames):
486 nPadPoints[ampName] = nSignalPoints - len(list(self.covariancescovariances.values())[i])
487 covMatrixSide = self.covMatrixSidecovMatrixSide
488
489 catalog = Table([{'AMPLIFIER_NAME': ampName,
490 'PTC_FIT_TYPE': self.ptcFitTypeptcFitType,
491 'COV_MATRIX_SIDE': self.covMatrixSidecovMatrixSide,
492 'INPUT_EXP_ID_PAIRS': self.inputExpIdPairsinputExpIdPairs[ampName]
493 if len(self.expIdMaskexpIdMask[ampName]) else np.nan,
494 'EXP_ID_MASK': self.expIdMaskexpIdMask[ampName]
495 if len(self.expIdMaskexpIdMask[ampName]) else np.nan,
496 'RAW_EXP_TIMES': np.array(self.rawExpTimesrawExpTimes[ampName]).tolist()
497 if len(self.rawExpTimesrawExpTimes[ampName]) else np.nan,
498 'RAW_MEANS': np.array(self.rawMeansrawMeans[ampName]).tolist()
499 if len(self.rawMeansrawMeans[ampName]) else np.nan,
500 'RAW_VARS': np.array(self.rawVarsrawVars[ampName]).tolist()
501 if len(self.rawVarsrawVars[ampName]) else np.nan,
502 'GAIN': self.gaingain[ampName],
503 'GAIN_ERR': self.gainErrgainErr[ampName],
504 'NOISE': self.noisenoise[ampName],
505 'NOISE_ERR': self.noiseErrnoiseErr[ampName],
506 'PTC_FIT_PARS': np.array(self.ptcFitParsptcFitPars[ampName]).tolist(),
507 'PTC_FIT_PARS_ERROR': np.array(self.ptcFitParsErrorptcFitParsError[ampName]).tolist(),
508 'PTC_FIT_CHI_SQ': self.ptcFitChiSqptcFitChiSq[ampName],
509 'COVARIANCES': np.pad(np.array(self.covariancescovariances[ampName]),
510 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
511 'constant', constant_values=np.nan).reshape(
512 nSignalPoints*covMatrixSide**2).tolist(),
513 'COVARIANCES_MODEL': np.pad(np.array(self.covariancesModelcovariancesModel[ampName]),
514 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
515 'constant', constant_values=np.nan).reshape(
516 nSignalPoints*covMatrixSide**2).tolist(),
517 'COVARIANCES_SQRT_WEIGHTS': np.pad(np.array(self.covariancesSqrtWeightscovariancesSqrtWeights[ampName]),
518 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
519 'constant', constant_values=0.0).reshape(
520 nSignalPoints*covMatrixSide**2).tolist(),
521 'A_MATRIX': np.array(self.aMatrixaMatrix[ampName]).reshape(covMatrixSide**2).tolist(),
522 'B_MATRIX': np.array(self.bMatrixbMatrix[ampName]).reshape(covMatrixSide**2).tolist(),
523 'COVARIANCES_MODEL_NO_B':
524 np.pad(np.array(self.covariancesModelNoBcovariancesModelNoB[ampName]),
525 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
526 'constant', constant_values=np.nan).reshape(
527 nSignalPoints*covMatrixSide**2).tolist(),
528 'A_MATRIX_NO_B': np.array(self.aMatrixNoBaMatrixNoB[ampName]).reshape(
529 covMatrixSide**2).tolist(),
530 'FINAL_VARS': np.pad(np.array(self.finalVarsfinalVars[ampName]), (0, nPadPoints[ampName]),
531 'constant', constant_values=np.nan).tolist(),
532 'FINAL_MODEL_VARS': np.pad(np.array(self.finalModelVarsfinalModelVars[ampName]),
533 (0, nPadPoints[ampName]),
534 'constant', constant_values=np.nan).tolist(),
535 'FINAL_MEANS': np.pad(np.array(self.finalMeansfinalMeans[ampName]),
536 (0, nPadPoints[ampName]),
537 'constant', constant_values=np.nan).tolist(),
538 'BAD_AMPS': np.array(self.badAmpsbadAmps).tolist() if len(self.badAmpsbadAmps) else np.nan,
539 'PHOTO_CHARGE': np.array(self.photoChargephotoCharge[ampName]).tolist(),
540 } for ampName in self.ampNamesampNames])
541 inMeta = self.getMetadatagetMetadata().toDict()
542 outMeta = {k: v for k, v in inMeta.items() if v is not None}
543 outMeta.update({k: "" for k, v in inMeta.items() if v is None})
544 catalog.meta = outMeta
545 tableList.append(catalog)
546
547 return(tableList)
548
549 def getExpIdsUsed(self, ampName):
550 """Get the exposures used, i.e. not discarded, for a given amp.
551 If no mask has been created yet, all exposures are returned.
552 """
553 if len(self.expIdMaskexpIdMask[ampName]) == 0:
554 return self.inputExpIdPairsinputExpIdPairs[ampName]
555
556 # if the mask exists it had better be the same length as the expIdPairs
557 assert len(self.expIdMaskexpIdMask[ampName]) == len(self.inputExpIdPairsinputExpIdPairs[ampName])
558
559 pairs = self.inputExpIdPairsinputExpIdPairs[ampName]
560 mask = self.expIdMaskexpIdMask[ampName]
561 # cast to bool required because numpy
562 return [(exp1, exp2) for ((exp1, exp2), m) in zip(pairs, mask) if bool(m) is True]
563
564 def getGoodAmps(self):
565 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:155
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:205
def updateMetadata(self, setDate=False, **kwargs)
Definition: ptcDataset.py:255