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 Returns
150 -------
151 `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
152 Output dataset from MeasurePhotonTransferCurveTask.
154 Notes
155 -----
156 Version 1.1 adds the `ptcTurnoff` attribute.
157 """
159 _OBSTYPE = 'PTC'
160 _SCHEMA = 'Gen3 Photon Transfer Curve'
161 _VERSION = 1.1
163 def __init__(self, ampNames=[], ptcFitType=None, covMatrixSide=1, **kwargs):
164 self.ptcFitType = ptcFitType
165 self.ampNames = ampNames
166 self.covMatrixSide = covMatrixSide
168 self.badAmps = [np.nan]
170 self.inputExpIdPairs = {ampName: [] for ampName in ampNames}
171 self.expIdMask = {ampName: [] for ampName in ampNames}
172 self.rawExpTimes = {ampName: [] for ampName in ampNames}
173 self.rawMeans = {ampName: [] for ampName in ampNames}
174 self.rawVars = {ampName: [] for ampName in ampNames}
175 self.photoCharge = {ampName: [] for ampName in ampNames}
177 self.gain = {ampName: np.nan for ampName in ampNames}
178 self.gainErr = {ampName: np.nan for ampName in ampNames}
179 self.noise = {ampName: np.nan for ampName in ampNames}
180 self.noiseErr = {ampName: np.nan for ampName in ampNames}
182 self.ptcFitPars = {ampName: [] for ampName in ampNames}
183 self.ptcFitParsError = {ampName: [] for ampName in ampNames}
184 self.ptcFitChiSq = {ampName: np.nan for ampName in ampNames}
185 self.ptcTurnoff = {ampName: np.nan for ampName in ampNames}
187 self.covariances = {ampName: [] for ampName in ampNames}
188 self.covariancesModel = {ampName: [] for ampName in ampNames}
189 self.covariancesSqrtWeights = {ampName: [] for ampName in ampNames}
190 self.aMatrix = {ampName: np.nan for ampName in ampNames}
191 self.bMatrix = {ampName: np.nan for ampName in ampNames}
192 self.covariancesModelNoB = {ampName: [] for ampName in ampNames}
193 self.aMatrixNoB = {ampName: np.nan for ampName in ampNames}
195 self.finalVars = {ampName: [] for ampName in ampNames}
196 self.finalModelVars = {ampName: [] for ampName in ampNames}
197 self.finalMeans = {ampName: [] for ampName in ampNames}
199 super().__init__(**kwargs)
200 self.requiredAttributes.update(['badAmps', 'inputExpIdPairs', 'expIdMask', 'rawExpTimes',
201 'rawMeans', 'rawVars', 'gain', 'gainErr', 'noise', 'noiseErr',
202 'ptcFitPars', 'ptcFitParsError', 'ptcFitChiSq', 'ptcTurnoff',
203 'aMatrixNoB', 'covariances', 'covariancesModel',
204 'covariancesSqrtWeights', 'covariancesModelNoB',
205 'aMatrix', 'bMatrix', 'finalVars', 'finalModelVars', 'finalMeans',
206 'photoCharge'])
208 self.updateMetadata(setCalibInfo=True, setCalibId=True, **kwargs)
210 def setAmpValues(self, ampName, inputExpIdPair=[(np.nan, np.nan)], expIdMask=[np.nan],
211 rawExpTime=[np.nan], rawMean=[np.nan], rawVar=[np.nan], photoCharge=[np.nan],
212 gain=np.nan, gainErr=np.nan, noise=np.nan, noiseErr=np.nan, ptcFitPars=[np.nan],
213 ptcFitParsError=[np.nan], ptcFitChiSq=np.nan, ptcTurnoff=np.nan, covArray=[],
214 covArrayModel=[], covSqrtWeights=[], aMatrix=[], bMatrix=[], covArrayModelNoB=[],
215 aMatrixNoB=[], finalVar=[np.nan], finalModelVar=[np.nan], finalMean=[np.nan]):
216 """Function to initialize an amp of a PhotonTransferCurveDataset.
218 Notes
219 -----
220 The parameters are all documented in `init`.
221 """
222 nanMatrix = np.full((self.covMatrixSide, self.covMatrixSide), np.nan)
223 if len(covArray) == 0:
224 covArray = [nanMatrix]
225 if len(covArrayModel) == 0:
226 covArrayModel = [nanMatrix]
227 if len(covSqrtWeights) == 0:
228 covSqrtWeights = [nanMatrix]
229 if len(covArrayModelNoB) == 0:
230 covArrayModelNoB = [nanMatrix]
231 if len(aMatrix) == 0:
232 aMatrix = nanMatrix
233 if len(bMatrix) == 0:
234 bMatrix = nanMatrix
235 if len(aMatrixNoB) == 0:
236 aMatrixNoB = nanMatrix
238 self.inputExpIdPairs[ampName] = inputExpIdPair
239 self.expIdMask[ampName] = expIdMask
240 self.rawExpTimes[ampName] = rawExpTime
241 self.rawMeans[ampName] = rawMean
242 self.rawVars[ampName] = rawVar
243 self.photoCharge[ampName] = photoCharge
244 self.gain[ampName] = gain
245 self.gainErr[ampName] = gainErr
246 self.noise[ampName] = noise
247 self.noiseErr[ampName] = noiseErr
248 self.ptcFitPars[ampName] = ptcFitPars
249 self.ptcFitParsError[ampName] = ptcFitParsError
250 self.ptcFitChiSq[ampName] = ptcFitChiSq
251 self.ptcTurnoff[ampName] = ptcTurnoff
252 self.covariances[ampName] = covArray
253 self.covariancesSqrtWeights[ampName] = covSqrtWeights
254 self.covariancesModel[ampName] = covArrayModel
255 self.covariancesModelNoB[ampName] = covArrayModelNoB
256 self.aMatrix[ampName] = aMatrix
257 self.bMatrix[ampName] = bMatrix
258 self.aMatrixNoB[ampName] = aMatrixNoB
259 self.ptcFitPars[ampName] = ptcFitPars
260 self.ptcFitParsError[ampName] = ptcFitParsError
261 self.ptcFitChiSq[ampName] = ptcFitChiSq
262 self.finalVars[ampName] = finalVar
263 self.finalModelVars[ampName] = finalModelVar
264 self.finalMeans[ampName] = finalMean
266 def updateMetadata(self, **kwargs):
267 """Update calibration metadata.
268 This calls the base class's method after ensuring the required
269 calibration keywords will be saved.
270 Parameters
271 ----------
272 setDate : `bool`, optional
273 Update the CALIBDATE fields in the metadata to the current
274 time. Defaults to False.
275 kwargs :
276 Other keyword parameters to set in the metadata.
277 """
278 super().updateMetadata(PTC_FIT_TYPE=self.ptcFitType, **kwargs)
280 @classmethod
281 def fromDict(cls, dictionary):
282 """Construct a calibration from a dictionary of properties.
283 Must be implemented by the specific calibration subclasses.
284 Parameters
285 ----------
286 dictionary : `dict`
287 Dictionary of properties.
288 Returns
289 -------
290 calib : `lsst.ip.isr.CalibType`
291 Constructed calibration.
292 Raises
293 ------
294 RuntimeError :
295 Raised if the supplied dictionary is for a different
296 calibration.
297 """
298 calib = cls()
299 if calib._OBSTYPE != dictionary['metadata']['OBSTYPE']:
300 raise RuntimeError(f"Incorrect Photon Transfer Curve dataset supplied. "
301 f"Expected {calib._OBSTYPE}, found {dictionary['metadata']['OBSTYPE']}")
302 calib.setMetadata(dictionary['metadata'])
303 calib.ptcFitType = dictionary['ptcFitType']
304 calib.covMatrixSide = dictionary['covMatrixSide']
305 calib.badAmps = np.array(dictionary['badAmps'], 'str').tolist()
307 # The cov matrices are square
308 covMatrixSide = calib.covMatrixSide
309 # Number of final signal levels
310 covDimensionsProduct = len(np.array(list(dictionary['covariances'].values())[0]).ravel())
311 nSignalPoints = int(covDimensionsProduct/(covMatrixSide*covMatrixSide))
313 for ampName in dictionary['ampNames']:
314 covsAmp = np.array(dictionary['covariances'][ampName]).reshape((nSignalPoints, covMatrixSide,
315 covMatrixSide))
317 # After cpPtcExtract runs in the PTC pipeline, the datasets
318 # created ('PARTIAL' and 'DUMMY') have a single measurement.
319 # Apply the maskign to the final ptcDataset, after running
320 # cpPtcSolve.
321 if len(covsAmp) > 1:
322 # Masks for covariances padding in `toTable`
323 maskCovsAmp = np.array([~np.isnan(entry).all() for entry in covsAmp])
324 maskAmp = ~np.isnan(np.array(dictionary['finalMeans'][ampName]))
325 else:
326 maskCovsAmp = np.array([True])
327 maskAmp = np.array([True])
329 calib.ampNames.append(ampName)
330 calib.inputExpIdPairs[ampName] = np.array(dictionary['inputExpIdPairs'][ampName]).tolist()
331 calib.expIdMask[ampName] = np.array(dictionary['expIdMask'][ampName]).tolist()
332 calib.rawExpTimes[ampName] = np.array(dictionary['rawExpTimes'][ampName]).tolist()
333 calib.rawMeans[ampName] = np.array(dictionary['rawMeans'][ampName]).tolist()
334 calib.rawVars[ampName] = np.array(dictionary['rawVars'][ampName]).tolist()
335 calib.gain[ampName] = np.array(dictionary['gain'][ampName]).tolist()
336 calib.gainErr[ampName] = np.array(dictionary['gainErr'][ampName]).tolist()
337 calib.noise[ampName] = np.array(dictionary['noise'][ampName]).tolist()
338 calib.noiseErr[ampName] = np.array(dictionary['noiseErr'][ampName]).tolist()
339 calib.ptcFitPars[ampName] = np.array(dictionary['ptcFitPars'][ampName]).tolist()
340 calib.ptcFitParsError[ampName] = np.array(dictionary['ptcFitParsError'][ampName]).tolist()
341 calib.ptcFitChiSq[ampName] = np.array(dictionary['ptcFitChiSq'][ampName]).tolist()
342 calib.ptcTurnoff[ampName] = np.array(dictionary['ptcTurnoff'][ampName]).tolist()
343 calib.covariances[ampName] = covsAmp[maskCovsAmp].tolist()
344 calib.covariancesModel[ampName] = np.array(
345 dictionary['covariancesModel'][ampName]).reshape(
346 (nSignalPoints, covMatrixSide, covMatrixSide))[maskCovsAmp].tolist()
347 calib.covariancesSqrtWeights[ampName] = np.array(
348 dictionary['covariancesSqrtWeights'][ampName]).reshape(
349 (nSignalPoints, covMatrixSide, covMatrixSide))[maskCovsAmp].tolist()
350 calib.aMatrix[ampName] = np.array(dictionary['aMatrix'][ampName]).reshape(
351 (covMatrixSide, covMatrixSide)).tolist()
352 calib.bMatrix[ampName] = np.array(dictionary['bMatrix'][ampName]).reshape(
353 (covMatrixSide, covMatrixSide)).tolist()
354 calib.covariancesModelNoB[ampName] = np.array(
355 dictionary['covariancesModelNoB'][ampName]).reshape(
356 (nSignalPoints, covMatrixSide, covMatrixSide))[maskCovsAmp].tolist()
357 calib.aMatrixNoB[ampName] = np.array(
358 dictionary['aMatrixNoB'][ampName]).reshape((covMatrixSide, covMatrixSide)).tolist()
359 calib.finalVars[ampName] = np.array(dictionary['finalVars'][ampName])[maskAmp].tolist()
360 calib.finalModelVars[ampName] = np.array(dictionary['finalModelVars'][ampName])[maskAmp].tolist()
361 calib.finalMeans[ampName] = np.array(dictionary['finalMeans'][ampName])[maskAmp].tolist()
362 calib.photoCharge[ampName] = np.array(dictionary['photoCharge'][ampName]).tolist()
363 calib.updateMetadata()
364 return calib
366 def toDict(self):
367 """Return a dictionary containing the calibration properties.
368 The dictionary should be able to be round-tripped through
369 `fromDict`.
370 Returns
371 -------
372 dictionary : `dict`
373 Dictionary of properties.
374 """
375 self.updateMetadata()
377 outDict = dict()
378 metadata = self.getMetadata()
379 outDict['metadata'] = metadata
381 outDict['ptcFitType'] = self.ptcFitType
382 outDict['covMatrixSide'] = self.covMatrixSide
383 outDict['ampNames'] = self.ampNames
384 outDict['badAmps'] = self.badAmps
385 outDict['inputExpIdPairs'] = self.inputExpIdPairs
386 outDict['expIdMask'] = self.expIdMask
387 outDict['rawExpTimes'] = self.rawExpTimes
388 outDict['rawMeans'] = self.rawMeans
389 outDict['rawVars'] = self.rawVars
390 outDict['gain'] = self.gain
391 outDict['gainErr'] = self.gainErr
392 outDict['noise'] = self.noise
393 outDict['noiseErr'] = self.noiseErr
394 outDict['ptcFitPars'] = self.ptcFitPars
395 outDict['ptcFitParsError'] = self.ptcFitParsError
396 outDict['ptcFitChiSq'] = self.ptcFitChiSq
397 outDict['ptcTurnoff'] = self.ptcTurnoff
398 outDict['covariances'] = self.covariances
399 outDict['covariancesModel'] = self.covariancesModel
400 outDict['covariancesSqrtWeights'] = self.covariancesSqrtWeights
401 outDict['aMatrix'] = self.aMatrix
402 outDict['bMatrix'] = self.bMatrix
403 outDict['covariancesModelNoB'] = self.covariancesModelNoB
404 outDict['aMatrixNoB'] = self.aMatrixNoB
405 outDict['finalVars'] = self.finalVars
406 outDict['finalModelVars'] = self.finalModelVars
407 outDict['finalMeans'] = self.finalMeans
408 outDict['photoCharge'] = self.photoCharge
410 return outDict
412 @classmethod
413 def fromTable(cls, tableList):
414 """Construct calibration from a list of tables.
415 This method uses the `fromDict` method to create the
416 calibration, after constructing an appropriate dictionary from
417 the input tables.
418 Parameters
419 ----------
420 tableList : `list` [`lsst.afw.table.Table`]
421 List of tables to use to construct the datasetPtc.
422 Returns
423 -------
424 calib : `lsst.cp.pipe.`
425 The calibration defined in the tables.
426 """
427 ptcTable = tableList[0]
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()
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']
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)
506 def toTable(self):
507 """Construct a list of tables containing the information in this
508 calibration.
510 The list of tables should create an identical calibration
511 after being passed to this class's fromTable method.
512 Returns
513 -------
514 tableList : `list` [`astropy.table.Table`]
515 List of tables containing the linearity calibration
516 information.
517 """
518 tableList = []
519 self.updateMetadata()
520 nPoints = []
521 for i, ampName in enumerate(self.ampNames):
522 nPoints.append(len(list(self.covariances.values())[i]))
523 nSignalPoints = max(nPoints)
524 nPadPoints = {}
525 for i, ampName in enumerate(self.ampNames):
526 nPadPoints[ampName] = nSignalPoints - len(list(self.covariances.values())[i])
527 covMatrixSide = self.covMatrixSide
529 catalog = Table([{'AMPLIFIER_NAME': ampName,
530 'PTC_FIT_TYPE': self.ptcFitType,
531 'COV_MATRIX_SIDE': self.covMatrixSide,
532 'INPUT_EXP_ID_PAIRS': self.inputExpIdPairs[ampName]
533 if len(self.expIdMask[ampName]) else np.nan,
534 'EXP_ID_MASK': self.expIdMask[ampName]
535 if len(self.expIdMask[ampName]) else np.nan,
536 'RAW_EXP_TIMES': np.array(self.rawExpTimes[ampName]).tolist()
537 if len(self.rawExpTimes[ampName]) else np.nan,
538 'RAW_MEANS': np.array(self.rawMeans[ampName]).tolist()
539 if len(self.rawMeans[ampName]) else np.nan,
540 'RAW_VARS': np.array(self.rawVars[ampName]).tolist()
541 if len(self.rawVars[ampName]) else np.nan,
542 'GAIN': self.gain[ampName],
543 'GAIN_ERR': self.gainErr[ampName],
544 'NOISE': self.noise[ampName],
545 'NOISE_ERR': self.noiseErr[ampName],
546 'PTC_FIT_PARS': np.array(self.ptcFitPars[ampName]).tolist(),
547 'PTC_FIT_PARS_ERROR': np.array(self.ptcFitParsError[ampName]).tolist(),
548 'PTC_FIT_CHI_SQ': self.ptcFitChiSq[ampName],
549 'PTC_TURNOFF': self.ptcTurnoff[ampName],
550 'COVARIANCES': np.pad(np.array(self.covariances[ampName]),
551 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
552 'constant', constant_values=np.nan).reshape(
553 nSignalPoints*covMatrixSide**2).tolist(),
554 'COVARIANCES_MODEL': np.pad(np.array(self.covariancesModel[ampName]),
555 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
556 'constant', constant_values=np.nan).reshape(
557 nSignalPoints*covMatrixSide**2).tolist(),
558 'COVARIANCES_SQRT_WEIGHTS': np.pad(np.array(self.covariancesSqrtWeights[ampName]),
559 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
560 'constant', constant_values=0.0).reshape(
561 nSignalPoints*covMatrixSide**2).tolist(),
562 'A_MATRIX': np.array(self.aMatrix[ampName]).reshape(covMatrixSide**2).tolist(),
563 'B_MATRIX': np.array(self.bMatrix[ampName]).reshape(covMatrixSide**2).tolist(),
564 'COVARIANCES_MODEL_NO_B':
565 np.pad(np.array(self.covariancesModelNoB[ampName]),
566 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
567 'constant', constant_values=np.nan).reshape(
568 nSignalPoints*covMatrixSide**2).tolist(),
569 'A_MATRIX_NO_B': np.array(self.aMatrixNoB[ampName]).reshape(
570 covMatrixSide**2).tolist(),
571 'FINAL_VARS': np.pad(np.array(self.finalVars[ampName]), (0, nPadPoints[ampName]),
572 'constant', constant_values=np.nan).tolist(),
573 'FINAL_MODEL_VARS': np.pad(np.array(self.finalModelVars[ampName]),
574 (0, nPadPoints[ampName]),
575 'constant', constant_values=np.nan).tolist(),
576 'FINAL_MEANS': np.pad(np.array(self.finalMeans[ampName]),
577 (0, nPadPoints[ampName]),
578 'constant', constant_values=np.nan).tolist(),
579 'BAD_AMPS': np.array(self.badAmps).tolist() if len(self.badAmps) else np.nan,
580 'PHOTO_CHARGE': np.array(self.photoCharge[ampName]).tolist(),
581 } for ampName in self.ampNames])
582 inMeta = self.getMetadata().toDict()
583 outMeta = {k: v for k, v in inMeta.items() if v is not None}
584 outMeta.update({k: "" for k, v in inMeta.items() if v is None})
585 catalog.meta = outMeta
586 tableList.append(catalog)
588 return(tableList)
590 def fromDetector(self, detector):
591 """Read metadata parameters from a detector.
593 Parameters
594 ----------
595 detector : `lsst.afw.cameraGeom.detector`
596 Input detector with parameters to use.
598 Returns
599 -------
600 calib : `lsst.ip.isr.Linearizer`
601 The calibration constructed from the detector.
602 """
604 pass
606 def getExpIdsUsed(self, ampName):
607 """Get the exposures used, i.e. not discarded, for a given amp.
608 If no mask has been created yet, all exposures are returned.
609 """
610 if len(self.expIdMask[ampName]) == 0:
611 return self.inputExpIdPairs[ampName]
613 # if the mask exists it had better be the same length as the expIdPairs
614 assert len(self.expIdMask[ampName]) == len(self.inputExpIdPairs[ampName])
616 pairs = self.inputExpIdPairs[ampName]
617 mask = self.expIdMask[ampName]
618 # cast to bool required because numpy
619 return [(exp1, exp2) for ((exp1, exp2), m) in zip(pairs, mask) if bool(m) is True]
621 def getGoodAmps(self):
622 return [amp for amp in self.ampNames if amp not in self.badAmps]